diff --git a/CODEOWNERS b/CODEOWNERS deleted file mode 100644 index c8acd66d5e3eef1884cadcaf9db0e510a472ed5c..0000000000000000000000000000000000000000 --- a/CODEOWNERS +++ /dev/null @@ -1,25 +0,0 @@ -# Admins -* @comfyanonymous - -# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org. -# Inlined the team members for now. - -# Maintainers -*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill -/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill - -# Python web server -/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill -/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill -/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill - -# Node developers -/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill -/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill -/comfy_api_nodes/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 048f127e72ddc351451a9612d4c715f603393a84..0000000000000000000000000000000000000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,41 +0,0 @@ -# Contributing to ComfyUI - -Welcome, and thank you for your interest in contributing to ComfyUI! - -There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved. - -## Asking Questions - -Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you. - -## Providing Feedback - -Your comments and feedback are welcome, and the development team is available via a handful of different channels. - -See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord. - -## Reporting Issues - -Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible. - - -### Look For an Existing Issue - -Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed. - -If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment: - -* 👍 - upvote -* 👎 - downvote - -If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues. - - -### Creating Pull Requests - -* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project. - - -## Thank You - -Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index f288702d2fa16d3cdf0035b15a9fcbc552cd88e7..0000000000000000000000000000000000000000 --- a/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. By contrast, -the GNU General Public License is intended to guarantee your freedom to -share and change all versions of a program--to make sure it remains free -software for all its users. 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Limitation of Liability. - - IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING -WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS -THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY -GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE -USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF -DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD -PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), -EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF -SUCH DAMAGES. - - 17. Interpretation of Sections 15 and 16. - - If the disclaimer of warranty and limitation of liability provided -above cannot be given local legal effect according to their terms, -reviewing courts shall apply local law that most closely approximates -an absolute waiver of all civil liability in connection with the -Program, unless a warranty or assumption of liability accompanies a -copy of the Program in return for a fee. - - END OF TERMS AND CONDITIONS - - How to Apply These Terms to Your New Programs - - If you develop a new program, and you want it to be of the greatest -possible use to the public, the best way to achieve this is to make it -free software which everyone can redistribute and change under these terms. - - To do so, attach the following notices to the program. It is safest -to attach them to the start of each source file to most effectively -state the exclusion of warranty; and each file should have at least -the "copyright" line and a pointer to where the full notice is found. - - - Copyright (C) - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If the program does terminal interaction, make it output a short -notice like this when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type `show c' for details. - -The hypothetical commands `show w' and `show c' should show the appropriate -parts of the General Public License. Of course, your program's commands -might be different; for a GUI interface, you would use an "about box". - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU GPL, see -. - - The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. diff --git a/__init__.py b/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/_output_images_will_be_put_here b/_output_images_will_be_put_here deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/alembic.ini b/alembic.ini deleted file mode 100644 index 12f18712f4306f1b76e6b291956797b01469cd80..0000000000000000000000000000000000000000 --- a/alembic.ini +++ /dev/null @@ -1,84 +0,0 @@ -# A generic, single database configuration. - -[alembic] -# path to migration scripts -# Use forward slashes (/) also on windows to provide an os agnostic path -script_location = alembic_db - -# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s -# Uncomment the line below if you want the files to be prepended with date and time -# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file -# for all available tokens -# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s - -# sys.path path, will be prepended to sys.path if present. -# defaults to the current working directory. -prepend_sys_path = . - -# timezone to use when rendering the date within the migration file -# as well as the filename. -# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library. -# Any required deps can installed by adding `alembic[tz]` to the pip requirements -# string value is passed to ZoneInfo() -# leave blank for localtime -# timezone = - -# max length of characters to apply to the "slug" field -# truncate_slug_length = 40 - -# set to 'true' to run the environment during -# the 'revision' command, regardless of autogenerate -# revision_environment = false - -# set to 'true' to allow .pyc and .pyo files without -# a source .py file to be detected as revisions in the -# versions/ directory -# sourceless = false - -# version location specification; This defaults -# to alembic_db/versions. When using multiple version -# directories, initial revisions must be specified with --version-path. -# The path separator used here should be the separator specified by "version_path_separator" below. -# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions - -# version path separator; As mentioned above, this is the character used to split -# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep. -# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas. -# Valid values for version_path_separator are: -# -# version_path_separator = : -# version_path_separator = ; -# version_path_separator = space -# version_path_separator = newline -# -# Use os.pathsep. Default configuration used for new projects. -version_path_separator = os - -# set to 'true' to search source files recursively -# in each "version_locations" directory -# new in Alembic version 1.10 -# recursive_version_locations = false - -# the output encoding used when revision files -# are written from script.py.mako -# output_encoding = utf-8 - -sqlalchemy.url = sqlite:///user/comfyui.db - - -[post_write_hooks] -# post_write_hooks defines scripts or Python functions that are run -# on newly generated revision scripts. See the documentation for further -# detail and examples - -# format using "black" - use the console_scripts runner, against the "black" entrypoint -# hooks = black -# black.type = console_scripts -# black.entrypoint = black -# black.options = -l 79 REVISION_SCRIPT_FILENAME - -# lint with attempts to fix using "ruff" - use the exec runner, execute a binary -# hooks = ruff -# ruff.type = exec -# ruff.executable = %(here)s/.venv/bin/ruff -# ruff.options = check --fix REVISION_SCRIPT_FILENAME diff --git a/alembic_db/README.md b/alembic_db/README.md deleted file mode 100644 index 3b808c7cab30eaab91c9ff5c1b2d5cc960904e14..0000000000000000000000000000000000000000 --- a/alembic_db/README.md +++ /dev/null @@ -1,4 +0,0 @@ -## Generate new revision - -1. Update models in `/app/database/models.py` -2. Run `alembic revision --autogenerate -m "{your message}"` diff --git a/alembic_db/env.py b/alembic_db/env.py deleted file mode 100644 index 4d7770679875e2fad065fba0c9222758a40d266b..0000000000000000000000000000000000000000 --- a/alembic_db/env.py +++ /dev/null @@ -1,64 +0,0 @@ -from sqlalchemy import engine_from_config -from sqlalchemy import pool - -from alembic import context - -# this is the Alembic Config object, which provides -# access to the values within the .ini file in use. -config = context.config - - -from app.database.models import Base -target_metadata = Base.metadata - -# other values from the config, defined by the needs of env.py, -# can be acquired: -# my_important_option = config.get_main_option("my_important_option") -# ... etc. - - -def run_migrations_offline() -> None: - """Run migrations in 'offline' mode. - This configures the context with just a URL - and not an Engine, though an Engine is acceptable - here as well. By skipping the Engine creation - we don't even need a DBAPI to be available. - Calls to context.execute() here emit the given string to the - script output. - """ - url = config.get_main_option("sqlalchemy.url") - context.configure( - url=url, - target_metadata=target_metadata, - literal_binds=True, - dialect_opts={"paramstyle": "named"}, - ) - - with context.begin_transaction(): - context.run_migrations() - - -def run_migrations_online() -> None: - """Run migrations in 'online' mode. - In this scenario we need to create an Engine - and associate a connection with the context. - """ - connectable = engine_from_config( - config.get_section(config.config_ini_section, {}), - prefix="sqlalchemy.", - poolclass=pool.NullPool, - ) - - with connectable.connect() as connection: - context.configure( - connection=connection, target_metadata=target_metadata - ) - - with context.begin_transaction(): - context.run_migrations() - - -if context.is_offline_mode(): - run_migrations_offline() -else: - run_migrations_online() diff --git a/alembic_db/script.py.mako b/alembic_db/script.py.mako deleted file mode 100644 index 480b130d632ca677c11f23d9fe82cf4014d15e0c..0000000000000000000000000000000000000000 --- a/alembic_db/script.py.mako +++ /dev/null @@ -1,28 +0,0 @@ -"""${message} - -Revision ID: ${up_revision} -Revises: ${down_revision | comma,n} -Create Date: ${create_date} - -""" -from typing import Sequence, Union - -from alembic import op -import sqlalchemy as sa -${imports if imports else ""} - -# revision identifiers, used by Alembic. -revision: str = ${repr(up_revision)} -down_revision: Union[str, None] = ${repr(down_revision)} -branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} -depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} - - -def upgrade() -> None: - """Upgrade schema.""" - ${upgrades if upgrades else "pass"} - - -def downgrade() -> None: - """Downgrade schema.""" - ${downgrades if downgrades else "pass"} diff --git a/api_server/.DS_Store b/api_server/.DS_Store deleted file mode 100644 index 38734ca2de71d90578b12a191d5ff30a57f26d5c..0000000000000000000000000000000000000000 Binary files a/api_server/.DS_Store and /dev/null differ diff --git a/api_server/__init__.py b/api_server/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/api_server/routes/.DS_Store b/api_server/routes/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/api_server/routes/.DS_Store and /dev/null differ diff --git a/api_server/routes/__init__.py b/api_server/routes/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/api_server/routes/internal/.DS_Store b/api_server/routes/internal/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/api_server/routes/internal/.DS_Store and /dev/null differ diff --git a/api_server/routes/internal/README.md b/api_server/routes/internal/README.md deleted file mode 100644 index 35330c36f83962385b4afe4653c5967f2bdb73c1..0000000000000000000000000000000000000000 --- a/api_server/routes/internal/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# ComfyUI Internal Routes - -All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice. diff --git a/api_server/routes/internal/__init__.py b/api_server/routes/internal/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/api_server/routes/internal/internal_routes.py b/api_server/routes/internal/internal_routes.py deleted file mode 100644 index 613b0f7c7cf1cdbb24aae3e880e749839ac1110e..0000000000000000000000000000000000000000 --- a/api_server/routes/internal/internal_routes.py +++ /dev/null @@ -1,73 +0,0 @@ -from aiohttp import web -from typing import Optional -from folder_paths import folder_names_and_paths, get_directory_by_type -from api_server.services.terminal_service import TerminalService -import app.logger -import os - -class InternalRoutes: - ''' - The top level web router for internal routes: /internal/* - The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only. - Check README.md for more information. - ''' - - def __init__(self, prompt_server): - self.routes: web.RouteTableDef = web.RouteTableDef() - self._app: Optional[web.Application] = None - self.prompt_server = prompt_server - self.terminal_service = TerminalService(prompt_server) - - def setup_routes(self): - @self.routes.get('/logs') - async def get_logs(request): - return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()])) - - @self.routes.get('/logs/raw') - async def get_raw_logs(request): - self.terminal_service.update_size() - return web.json_response({ - "entries": list(app.logger.get_logs()), - "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows} - }) - - @self.routes.patch('/logs/subscribe') - async def subscribe_logs(request): - json_data = await request.json() - client_id = json_data["clientId"] - enabled = json_data["enabled"] - if enabled: - self.terminal_service.subscribe(client_id) - else: - self.terminal_service.unsubscribe(client_id) - - return web.Response(status=200) - - - @self.routes.get('/folder_paths') - async def get_folder_paths(request): - response = {} - for key in folder_names_and_paths: - response[key] = folder_names_and_paths[key][0] - return web.json_response(response) - - @self.routes.get('/files/{directory_type}') - async def get_files(request: web.Request) -> web.Response: - directory_type = request.match_info['directory_type'] - if directory_type not in ("output", "input", "temp"): - return web.json_response({"error": "Invalid directory type"}, status=400) - - directory = get_directory_by_type(directory_type) - sorted_files = sorted( - (entry for entry in os.scandir(directory) if entry.is_file()), - key=lambda entry: -entry.stat().st_mtime - ) - return web.json_response([entry.name for entry in sorted_files], status=200) - - - def get_app(self): - if self._app is None: - self._app = web.Application() - self.setup_routes() - self._app.add_routes(self.routes) - return self._app diff --git a/api_server/services/.DS_Store b/api_server/services/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/api_server/services/.DS_Store and /dev/null differ diff --git a/api_server/services/__init__.py b/api_server/services/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/api_server/services/terminal_service.py b/api_server/services/terminal_service.py deleted file mode 100644 index ab4371f4f855c6d347744f751fd1bc5cc0f5a173..0000000000000000000000000000000000000000 --- a/api_server/services/terminal_service.py +++ /dev/null @@ -1,60 +0,0 @@ -from app.logger import on_flush -import os -import shutil - - -class TerminalService: - def __init__(self, server): - self.server = server - self.cols = None - self.rows = None - self.subscriptions = set() - on_flush(self.send_messages) - - def get_terminal_size(self): - try: - size = os.get_terminal_size() - return (size.columns, size.lines) - except OSError: - try: - size = shutil.get_terminal_size() - return (size.columns, size.lines) - except OSError: - return (80, 24) # fallback to 80x24 - - def update_size(self): - columns, lines = self.get_terminal_size() - changed = False - - if columns != self.cols: - self.cols = columns - changed = True - - if lines != self.rows: - self.rows = lines - changed = True - - if changed: - return {"cols": self.cols, "rows": self.rows} - - return None - - def subscribe(self, client_id): - self.subscriptions.add(client_id) - - def unsubscribe(self, client_id): - self.subscriptions.discard(client_id) - - def send_messages(self, entries): - if not len(entries) or not len(self.subscriptions): - return - - new_size = self.update_size() - - for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration - if client_id not in self.server.sockets: - # Automatically unsub if the socket has disconnected - self.unsubscribe(client_id) - continue - - self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id) diff --git a/api_server/utils/file_operations.py b/api_server/utils/file_operations.py deleted file mode 100644 index 32d6e047a5da09d0f382bb3fa93ce8d52b699aa7..0000000000000000000000000000000000000000 --- a/api_server/utils/file_operations.py +++ /dev/null @@ -1,42 +0,0 @@ -import os -from typing import List, Union, TypedDict, Literal -from typing_extensions import TypeGuard -class FileInfo(TypedDict): - name: str - path: str - type: Literal["file"] - size: int - -class DirectoryInfo(TypedDict): - name: str - path: str - type: Literal["directory"] - -FileSystemItem = Union[FileInfo, DirectoryInfo] - -def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]: - return item["type"] == "file" - -class FileSystemOperations: - @staticmethod - def walk_directory(directory: str) -> List[FileSystemItem]: - file_list: List[FileSystemItem] = [] - for root, dirs, files in os.walk(directory): - for name in files: - file_path = os.path.join(root, name) - relative_path = os.path.relpath(file_path, directory) - file_list.append({ - "name": name, - "path": relative_path, - "type": "file", - "size": os.path.getsize(file_path) - }) - for name in dirs: - dir_path = os.path.join(root, name) - relative_path = os.path.relpath(dir_path, directory) - file_list.append({ - "name": name, - "path": relative_path, - "type": "directory" - }) - return file_list diff --git a/app/.DS_Store b/app/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/app/.DS_Store and /dev/null differ diff --git a/app/__init__.py b/app/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/app/app_settings.py b/app/app_settings.py deleted file mode 100644 index c7ac73bf6a59d3e89647aed13b70ca9428e16f2a..0000000000000000000000000000000000000000 --- a/app/app_settings.py +++ /dev/null @@ -1,65 +0,0 @@ -import os -import json -from aiohttp import web -import logging - - -class AppSettings(): - def __init__(self, user_manager): - self.user_manager = user_manager - - def get_settings(self, request): - try: - file = self.user_manager.get_request_user_filepath( - request, - "comfy.settings.json" - ) - except KeyError as e: - logging.error("User settings not found.") - raise web.HTTPUnauthorized() from e - if os.path.isfile(file): - try: - with open(file) as f: - return json.load(f) - except: - logging.error(f"The user settings file is corrupted: {file}") - return {} - else: - return {} - - def save_settings(self, request, settings): - file = self.user_manager.get_request_user_filepath( - request, "comfy.settings.json") - with open(file, "w") as f: - f.write(json.dumps(settings, indent=4)) - - def add_routes(self, routes): - @routes.get("/settings") - async def get_settings(request): - return web.json_response(self.get_settings(request)) - - @routes.get("/settings/{id}") - async def get_setting(request): - value = None - settings = self.get_settings(request) - setting_id = request.match_info.get("id", None) - if setting_id and setting_id in settings: - value = settings[setting_id] - return web.json_response(value) - - @routes.post("/settings") - async def post_settings(request): - settings = self.get_settings(request) - new_settings = await request.json() - self.save_settings(request, {**settings, **new_settings}) - return web.Response(status=200) - - @routes.post("/settings/{id}") - async def post_setting(request): - setting_id = request.match_info.get("id", None) - if not setting_id: - return web.Response(status=400) - settings = self.get_settings(request) - settings[setting_id] = await request.json() - self.save_settings(request, settings) - return web.Response(status=200) diff --git a/app/custom_node_manager.py b/app/custom_node_manager.py deleted file mode 100644 index 281febca952363659eb3925280fab00f07c986d0..0000000000000000000000000000000000000000 --- a/app/custom_node_manager.py +++ /dev/null @@ -1,145 +0,0 @@ -from __future__ import annotations - -import os -import folder_paths -import glob -from aiohttp import web -import json -import logging -from functools import lru_cache - -from utils.json_util import merge_json_recursive - - -# Extra locale files to load into main.json -EXTRA_LOCALE_FILES = [ - "nodeDefs.json", - "commands.json", - "settings.json", -] - - -def safe_load_json_file(file_path: str) -> dict: - if not os.path.exists(file_path): - return {} - - try: - with open(file_path, "r", encoding="utf-8") as f: - return json.load(f) - except json.JSONDecodeError: - logging.error(f"Error loading {file_path}") - return {} - - -class CustomNodeManager: - @lru_cache(maxsize=1) - def build_translations(self): - """Load all custom nodes translations during initialization. Translations are - expected to be loaded from `locales/` folder. - - The folder structure is expected to be the following: - - custom_nodes/ - - custom_node_1/ - - locales/ - - en/ - - main.json - - commands.json - - settings.json - - returned translations are expected to be in the following format: - { - "en": { - "nodeDefs": {...}, - "commands": {...}, - "settings": {...}, - ...{other main.json keys} - } - } - """ - - translations = {} - - for folder in folder_paths.get_folder_paths("custom_nodes"): - # Sort glob results for deterministic ordering - for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))): - locales_dir = os.path.join(custom_node_dir, "locales") - if not os.path.exists(locales_dir): - continue - - for lang_dir in glob.glob(os.path.join(locales_dir, "*/")): - lang_code = os.path.basename(os.path.dirname(lang_dir)) - - if lang_code not in translations: - translations[lang_code] = {} - - # Load main.json - main_file = os.path.join(lang_dir, "main.json") - node_translations = safe_load_json_file(main_file) - - # Load extra locale files - for extra_file in EXTRA_LOCALE_FILES: - extra_file_path = os.path.join(lang_dir, extra_file) - key = extra_file.split(".")[0] - json_data = safe_load_json_file(extra_file_path) - if json_data: - node_translations[key] = json_data - - if node_translations: - translations[lang_code] = merge_json_recursive( - translations[lang_code], node_translations - ) - - return translations - - def add_routes(self, routes, webapp, loadedModules): - - example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"] - - @routes.get("/workflow_templates") - async def get_workflow_templates(request): - """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.""" - - files = [] - - for folder in folder_paths.get_folder_paths("custom_nodes"): - for folder_name in example_workflow_folder_names: - pattern = os.path.join(folder, f"*/{folder_name}/*.json") - matched_files = glob.glob(pattern) - files.extend(matched_files) - - workflow_templates_dict = ( - {} - ) # custom_nodes folder name -> example workflow names - for file in files: - custom_nodes_name = os.path.basename( - os.path.dirname(os.path.dirname(file)) - ) - workflow_name = os.path.splitext(os.path.basename(file))[0] - workflow_templates_dict.setdefault(custom_nodes_name, []).append( - workflow_name - ) - return web.json_response(workflow_templates_dict) - - # Serve workflow templates from custom nodes. - for module_name, module_dir in loadedModules: - for folder_name in example_workflow_folder_names: - workflows_dir = os.path.join(module_dir, folder_name) - - if os.path.exists(workflows_dir): - if folder_name != "example_workflows": - logging.debug( - "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", - folder_name, module_name) - - webapp.add_routes( - [ - web.static( - "/api/workflow_templates/" + module_name, workflows_dir - ) - ] - ) - - @routes.get("/i18n") - async def get_i18n(request): - """Returns translations from all custom nodes' locales folders.""" - return web.json_response(self.build_translations()) diff --git a/app/database/.DS_Store b/app/database/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/app/database/.DS_Store and /dev/null differ diff --git a/app/database/db.py b/app/database/db.py deleted file mode 100644 index 1de8b80edd8a5761039c7314c6387d2184a15e14..0000000000000000000000000000000000000000 --- a/app/database/db.py +++ /dev/null @@ -1,112 +0,0 @@ -import logging -import os -import shutil -from app.logger import log_startup_warning -from utils.install_util import get_missing_requirements_message -from comfy.cli_args import args - -_DB_AVAILABLE = False -Session = None - - -try: - from alembic import command - from alembic.config import Config - from alembic.runtime.migration import MigrationContext - from alembic.script import ScriptDirectory - from sqlalchemy import create_engine - from sqlalchemy.orm import sessionmaker - - _DB_AVAILABLE = True -except ImportError as e: - log_startup_warning( - f""" ------------------------------------------------------------------------- -Error importing dependencies: {e} -{get_missing_requirements_message()} -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -""".strip() - ) - - -def dependencies_available(): - """ - Temporary function to check if the dependencies are available - """ - return _DB_AVAILABLE - - -def can_create_session(): - """ - Temporary function to check if the database is available to create a session - During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created - """ - return dependencies_available() and Session is not None - - -def get_alembic_config(): - root_path = os.path.join(os.path.dirname(__file__), "../..") - config_path = os.path.abspath(os.path.join(root_path, "alembic.ini")) - scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db")) - - config = Config(config_path) - config.set_main_option("script_location", scripts_path) - config.set_main_option("sqlalchemy.url", args.database_url) - - return config - - -def get_db_path(): - url = args.database_url - if url.startswith("sqlite:///"): - return url.split("///")[1] - else: - raise ValueError(f"Unsupported database URL '{url}'.") - - -def init_db(): - db_url = args.database_url - logging.debug(f"Database URL: {db_url}") - db_path = get_db_path() - db_exists = os.path.exists(db_path) - - config = get_alembic_config() - - # Check if we need to upgrade - engine = create_engine(db_url) - conn = engine.connect() - - context = MigrationContext.configure(conn) - current_rev = context.get_current_revision() - - script = ScriptDirectory.from_config(config) - target_rev = script.get_current_head() - - if target_rev is None: - logging.warning("No target revision found.") - elif current_rev != target_rev: - # Backup the database pre upgrade - backup_path = db_path + ".bkp" - if db_exists: - shutil.copy(db_path, backup_path) - else: - backup_path = None - - try: - command.upgrade(config, target_rev) - logging.info(f"Database upgraded from {current_rev} to {target_rev}") - except Exception as e: - if backup_path: - # Restore the database from backup if upgrade fails - shutil.copy(backup_path, db_path) - os.remove(backup_path) - logging.exception("Error upgrading database: ") - raise e - - global Session - Session = sessionmaker(bind=engine) - - -def create_session(): - return Session() diff --git a/app/database/models.py b/app/database/models.py deleted file mode 100644 index 6facfb8f2b5e7382274021579a453ad3b7853bf7..0000000000000000000000000000000000000000 --- a/app/database/models.py +++ /dev/null @@ -1,14 +0,0 @@ -from sqlalchemy.orm import declarative_base - -Base = declarative_base() - - -def to_dict(obj): - fields = obj.__table__.columns.keys() - return { - field: (val.to_dict() if hasattr(val, "to_dict") else val) - for field in fields - if (val := getattr(obj, field)) - } - -# TODO: Define models here diff --git a/app/frontend_management.py b/app/frontend_management.py deleted file mode 100644 index 0bee73685b931bc1eddef79c7acafc2d0958098b..0000000000000000000000000000000000000000 --- a/app/frontend_management.py +++ /dev/null @@ -1,361 +0,0 @@ -from __future__ import annotations -import argparse -import logging -import os -import re -import sys -import tempfile -import zipfile -import importlib -from dataclasses import dataclass -from functools import cached_property -from pathlib import Path -from typing import TypedDict, Optional -from importlib.metadata import version - -import requests -from typing_extensions import NotRequired - -from utils.install_util import get_missing_requirements_message, requirements_path - -from comfy.cli_args import DEFAULT_VERSION_STRING -import app.logger - - -def frontend_install_warning_message(): - return f""" -{get_missing_requirements_message()} - -This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead. -""".strip() - -def parse_version(version: str) -> tuple[int, int, int]: - return tuple(map(int, version.split("."))) - -def is_valid_version(version: str) -> bool: - """Validate if a string is a valid semantic version (X.Y.Z format).""" - pattern = r"^(\d+)\.(\d+)\.(\d+)$" - return bool(re.match(pattern, version)) - -def get_installed_frontend_version(): - """Get the currently installed frontend package version.""" - frontend_version_str = version("comfyui-frontend-package") - return frontend_version_str - -def get_required_frontend_version(): - """Get the required frontend version from requirements.txt.""" - try: - with open(requirements_path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if line.startswith("comfyui-frontend-package=="): - version_str = line.split("==")[-1] - if not is_valid_version(version_str): - logging.error(f"Invalid version format in requirements.txt: {version_str}") - return None - return version_str - logging.error("comfyui-frontend-package not found in requirements.txt") - return None - except FileNotFoundError: - logging.error("requirements.txt not found. Cannot determine required frontend version.") - return None - except Exception as e: - logging.error(f"Error reading requirements.txt: {e}") - return None - -def check_frontend_version(): - """Check if the frontend version is up to date.""" - - try: - frontend_version_str = get_installed_frontend_version() - frontend_version = parse_version(frontend_version_str) - required_frontend_str = get_required_frontend_version() - required_frontend = parse_version(required_frontend_str) - if frontend_version < required_frontend: - app.logger.log_startup_warning( - f""" -________________________________________________________________________ -WARNING WARNING WARNING WARNING WARNING - -Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}. - -{frontend_install_warning_message()} -________________________________________________________________________ -""".strip() - ) - else: - logging.info("ComfyUI frontend version: {}".format(frontend_version_str)) - except Exception as e: - logging.error(f"Failed to check frontend version: {e}") - - -REQUEST_TIMEOUT = 10 # seconds - - -class Asset(TypedDict): - url: str - - -class Release(TypedDict): - id: int - tag_name: str - name: str - prerelease: bool - created_at: str - published_at: str - body: str - assets: NotRequired[list[Asset]] - - -@dataclass -class FrontEndProvider: - owner: str - repo: str - - @property - def folder_name(self) -> str: - return f"{self.owner}_{self.repo}" - - @property - def release_url(self) -> str: - return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases" - - @cached_property - def all_releases(self) -> list[Release]: - releases = [] - api_url = self.release_url - while api_url: - response = requests.get(api_url, timeout=REQUEST_TIMEOUT) - response.raise_for_status() # Raises an HTTPError if the response was an error - releases.extend(response.json()) - # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly. - if "next" in response.links: - api_url = response.links["next"]["url"] - else: - api_url = None - return releases - - @cached_property - def latest_release(self) -> Release: - latest_release_url = f"{self.release_url}/latest" - response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT) - response.raise_for_status() # Raises an HTTPError if the response was an error - return response.json() - - @cached_property - def latest_prerelease(self) -> Release: - """Get the latest pre-release version - even if it's older than the latest release""" - release = [release for release in self.all_releases if release["prerelease"]] - - if not release: - raise ValueError("No pre-releases found") - - # GitHub returns releases in reverse chronological order, so first is latest - return release[0] - - def get_release(self, version: str) -> Release: - if version == "latest": - return self.latest_release - elif version == "prerelease": - return self.latest_prerelease - else: - for release in self.all_releases: - if release["tag_name"] in [version, f"v{version}"]: - return release - raise ValueError(f"Version {version} not found in releases") - - -def download_release_asset_zip(release: Release, destination_path: str) -> None: - """Download dist.zip from github release.""" - asset_url = None - for asset in release.get("assets", []): - if asset["name"] == "dist.zip": - asset_url = asset["url"] - break - - if not asset_url: - raise ValueError("dist.zip not found in the release assets") - - # Use a temporary file to download the zip content - with tempfile.TemporaryFile() as tmp_file: - headers = {"Accept": "application/octet-stream"} - response = requests.get( - asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT - ) - response.raise_for_status() # Ensure we got a successful response - - # Write the content to the temporary file - tmp_file.write(response.content) - - # Go back to the beginning of the temporary file - tmp_file.seek(0) - - # Extract the zip file content to the destination path - with zipfile.ZipFile(tmp_file, "r") as zip_ref: - zip_ref.extractall(destination_path) - - -class FrontendManager: - CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions") - - @classmethod - def get_required_frontend_version(cls) -> str: - """Get the required frontend package version.""" - return get_required_frontend_version() - - @classmethod - def default_frontend_path(cls) -> str: - try: - import comfyui_frontend_package - - return str(importlib.resources.files(comfyui_frontend_package) / "static") - except ImportError: - logging.error( - f""" -********** ERROR *********** - -comfyui-frontend-package is not installed. - -{frontend_install_warning_message()} - -********** ERROR *********** -""".strip() - ) - sys.exit(-1) - - @classmethod - def templates_path(cls) -> str: - try: - import comfyui_workflow_templates - - return str( - importlib.resources.files(comfyui_workflow_templates) / "templates" - ) - except ImportError: - logging.error( - f""" -********** ERROR *********** - -comfyui-workflow-templates is not installed. - -{frontend_install_warning_message()} - -********** ERROR *********** -""".strip() - ) - - @classmethod - def embedded_docs_path(cls) -> str: - """Get the path to embedded documentation""" - try: - import comfyui_embedded_docs - - return str( - importlib.resources.files(comfyui_embedded_docs) / "docs" - ) - except ImportError: - logging.info("comfyui-embedded-docs package not found") - return None - - @classmethod - def parse_version_string(cls, value: str) -> tuple[str, str, str]: - """ - Args: - value (str): The version string to parse. - - Returns: - tuple[str, str]: A tuple containing provider name and version. - - Raises: - argparse.ArgumentTypeError: If the version string is invalid. - """ - VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$" - match_result = re.match(VERSION_PATTERN, value) - if match_result is None: - raise argparse.ArgumentTypeError(f"Invalid version string: {value}") - - return match_result.group(1), match_result.group(2), match_result.group(3) - - @classmethod - def init_frontend_unsafe( - cls, version_string: str, provider: Optional[FrontEndProvider] = None - ) -> str: - """ - Initializes the frontend for the specified version. - - Args: - version_string (str): The version string. - provider (FrontEndProvider, optional): The provider to use. Defaults to None. - - Returns: - str: The path to the initialized frontend. - - Raises: - Exception: If there is an error during the initialization process. - main error source might be request timeout or invalid URL. - """ - if version_string == DEFAULT_VERSION_STRING: - check_frontend_version() - return cls.default_frontend_path() - - repo_owner, repo_name, version = cls.parse_version_string(version_string) - - if version.startswith("v"): - expected_path = str( - Path(cls.CUSTOM_FRONTENDS_ROOT) - / f"{repo_owner}_{repo_name}" - / version.lstrip("v") - ) - if os.path.exists(expected_path): - logging.info( - f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}" - ) - return expected_path - - logging.info( - f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..." - ) - - provider = provider or FrontEndProvider(repo_owner, repo_name) - release = provider.get_release(version) - - semantic_version = release["tag_name"].lstrip("v") - web_root = str( - Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version - ) - if not os.path.exists(web_root): - try: - os.makedirs(web_root, exist_ok=True) - logging.info( - "Downloading frontend(%s) version(%s) to (%s)", - provider.folder_name, - semantic_version, - web_root, - ) - logging.debug(release) - download_release_asset_zip(release, destination_path=web_root) - finally: - # Clean up the directory if it is empty, i.e. the download failed - if not os.listdir(web_root): - os.rmdir(web_root) - - return web_root - - @classmethod - def init_frontend(cls, version_string: str) -> str: - """ - Initializes the frontend with the specified version string. - - Args: - version_string (str): The version string to initialize the frontend with. - - Returns: - str: The path of the initialized frontend. - """ - try: - return cls.init_frontend_unsafe(version_string) - except Exception as e: - logging.error("Failed to initialize frontend: %s", e) - logging.info("Falling back to the default frontend.") - check_frontend_version() - return cls.default_frontend_path() diff --git a/app/logger.py b/app/logger.py deleted file mode 100644 index 3d26d98fe28005a3ed8b6deb7da34823b98b5696..0000000000000000000000000000000000000000 --- a/app/logger.py +++ /dev/null @@ -1,98 +0,0 @@ -from collections import deque -from datetime import datetime -import io -import logging -import sys -import threading - -logs = None -stdout_interceptor = None -stderr_interceptor = None - - -class LogInterceptor(io.TextIOWrapper): - def __init__(self, stream, *args, **kwargs): - buffer = stream.buffer - encoding = stream.encoding - super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering) - self._lock = threading.Lock() - self._flush_callbacks = [] - self._logs_since_flush = [] - - def write(self, data): - entry = {"t": datetime.now().isoformat(), "m": data} - with self._lock: - self._logs_since_flush.append(entry) - - # Simple handling for cr to overwrite the last output if it isnt a full line - # else logs just get full of progress messages - if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"): - logs.pop() - logs.append(entry) - super().write(data) - - def flush(self): - super().flush() - for cb in self._flush_callbacks: - cb(self._logs_since_flush) - self._logs_since_flush = [] - - def on_flush(self, callback): - self._flush_callbacks.append(callback) - - -def get_logs(): - return logs - - -def on_flush(callback): - if stdout_interceptor is not None: - stdout_interceptor.on_flush(callback) - if stderr_interceptor is not None: - stderr_interceptor.on_flush(callback) - -def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False): - global logs - if logs: - return - - # Override output streams and log to buffer - logs = deque(maxlen=capacity) - - global stdout_interceptor - global stderr_interceptor - stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout) - stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr) - - # Setup default global logger - logger = logging.getLogger() - logger.setLevel(log_level) - - stream_handler = logging.StreamHandler() - stream_handler.setFormatter(logging.Formatter("%(message)s")) - - if use_stdout: - # Only errors and critical to stderr - stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR) - - # Lesser to stdout - stdout_handler = logging.StreamHandler(sys.stdout) - stdout_handler.setFormatter(logging.Formatter("%(message)s")) - stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR) - logger.addHandler(stdout_handler) - - logger.addHandler(stream_handler) - - -STARTUP_WARNINGS = [] - - -def log_startup_warning(msg): - logging.warning(msg) - STARTUP_WARNINGS.append(msg) - - -def print_startup_warnings(): - for s in STARTUP_WARNINGS: - logging.warning(s) - STARTUP_WARNINGS.clear() diff --git a/app/model_manager.py b/app/model_manager.py deleted file mode 100644 index ab36bca7441468ef3683f39c16fc6cec85d9740a..0000000000000000000000000000000000000000 --- a/app/model_manager.py +++ /dev/null @@ -1,195 +0,0 @@ -from __future__ import annotations - -import os -import base64 -import json -import time -import logging -import folder_paths -import glob -import comfy.utils -from aiohttp import web -from PIL import Image -from io import BytesIO -from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types - - -class ModelFileManager: - def __init__(self) -> None: - self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {} - - def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None: - return self.cache.get(key, default) - - def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]): - self.cache[key] = value - - def clear_cache(self): - self.cache.clear() - - def add_routes(self, routes): - # NOTE: This is an experiment to replace `/models` - @routes.get("/experiment/models") - async def get_model_folders(request): - model_types = list(folder_paths.folder_names_and_paths.keys()) - folder_black_list = ["configs", "custom_nodes"] - output_folders: list[dict] = [] - for folder in model_types: - if folder in folder_black_list: - continue - output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)}) - return web.json_response(output_folders) - - # NOTE: This is an experiment to replace `/models/{folder}` - @routes.get("/experiment/models/{folder}") - async def get_all_models(request): - folder = request.match_info.get("folder", None) - if not folder in folder_paths.folder_names_and_paths: - return web.Response(status=404) - files = self.get_model_file_list(folder) - return web.json_response(files) - - @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}") - async def get_model_preview(request): - folder_name = request.match_info.get("folder", None) - path_index = int(request.match_info.get("path_index", None)) - filename = request.match_info.get("filename", None) - - if not folder_name in folder_paths.folder_names_and_paths: - return web.Response(status=404) - - folders = folder_paths.folder_names_and_paths[folder_name] - folder = folders[0][path_index] - full_filename = os.path.join(folder, filename) - - previews = self.get_model_previews(full_filename) - default_preview = previews[0] if len(previews) > 0 else None - if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)): - return web.Response(status=404) - - try: - with Image.open(default_preview) as img: - img_bytes = BytesIO() - img.save(img_bytes, format="WEBP") - img_bytes.seek(0) - return web.Response(body=img_bytes.getvalue(), content_type="image/webp") - except: - return web.Response(status=404) - - def get_model_file_list(self, folder_name: str): - folder_name = map_legacy(folder_name) - folders = folder_paths.folder_names_and_paths[folder_name] - output_list: list[dict] = [] - - for index, folder in enumerate(folders[0]): - if not os.path.isdir(folder): - continue - out = self.cache_model_file_list_(folder) - if out is None: - out = self.recursive_search_models_(folder, index) - self.set_cache(folder, out) - output_list.extend(out[0]) - - return output_list - - def cache_model_file_list_(self, folder: str): - model_file_list_cache = self.get_cache(folder) - - if model_file_list_cache is None: - return None - if not os.path.isdir(folder): - return None - if os.path.getmtime(folder) != model_file_list_cache[1]: - return None - for x in model_file_list_cache[1]: - time_modified = model_file_list_cache[1][x] - folder = x - if os.path.getmtime(folder) != time_modified: - return None - - return model_file_list_cache - - def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]: - if not os.path.isdir(directory): - return [], {}, time.perf_counter() - - excluded_dir_names = [".git"] - # TODO use settings - include_hidden_files = False - - result: list[str] = [] - dirs: dict[str, float] = {} - - for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): - subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] - if not include_hidden_files: - subdirs[:] = [d for d in subdirs if not d.startswith(".")] - filenames = [f for f in filenames if not f.startswith(".")] - - filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions) - - for file_name in filenames: - try: - full_path = os.path.join(dirpath, file_name) - relative_path = os.path.relpath(full_path, directory) - - # Get file metadata - file_info = { - "name": relative_path, - "pathIndex": pathIndex, - "modified": os.path.getmtime(full_path), # Add modification time - "created": os.path.getctime(full_path), # Add creation time - "size": os.path.getsize(full_path) # Add file size - } - result.append(file_info) - - except Exception as e: - logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.") - continue - - for d in subdirs: - path: str = os.path.join(dirpath, d) - try: - dirs[path] = os.path.getmtime(path) - except FileNotFoundError: - logging.warning(f"Warning: Unable to access {path}. Skipping this path.") - continue - - return result, dirs, time.perf_counter() - - def get_model_previews(self, filepath: str) -> list[str | BytesIO]: - dirname = os.path.dirname(filepath) - - if not os.path.exists(dirname): - return [] - - basename = os.path.splitext(filepath)[0] - match_files = glob.glob(f"{basename}.*", recursive=False) - image_files = filter_files_content_types(match_files, "image") - safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None) - safetensors_metadata = {} - - result: list[str | BytesIO] = [] - - for filename in image_files: - _basename = os.path.splitext(filename)[0] - if _basename == basename: - result.append(filename) - if _basename == f"{basename}.preview": - result.append(filename) - - if safetensors_file: - safetensors_filepath = os.path.join(dirname, safetensors_file) - header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024) - if header: - safetensors_metadata = json.loads(header) - safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None) - if safetensors_images: - safetensors_images = json.loads(safetensors_images) - for image in safetensors_images: - result.append(BytesIO(base64.b64decode(image))) - - return result - - def __exit__(self, exc_type, exc_value, traceback): - self.clear_cache() diff --git a/app/user_manager.py b/app/user_manager.py deleted file mode 100644 index a2d376c0c4766b750fe6a1fd1558da4739f364dd..0000000000000000000000000000000000000000 --- a/app/user_manager.py +++ /dev/null @@ -1,445 +0,0 @@ -from __future__ import annotations -import json -import os -import re -import uuid -import glob -import shutil -import logging -from aiohttp import web -from urllib import parse -from comfy.cli_args import args -import folder_paths -from .app_settings import AppSettings -from typing import TypedDict - -default_user = "default" - - -class FileInfo(TypedDict): - path: str - size: int - modified: int - created: int - - -def get_file_info(path: str, relative_to: str) -> FileInfo: - return { - "path": os.path.relpath(path, relative_to).replace(os.sep, '/'), - "size": os.path.getsize(path), - "modified": os.path.getmtime(path), - "created": os.path.getctime(path) - } - - -class UserManager(): - def __init__(self): - user_directory = folder_paths.get_user_directory() - - self.settings = AppSettings(self) - if not os.path.exists(user_directory): - os.makedirs(user_directory, exist_ok=True) - if not args.multi_user: - logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******") - logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******") - - if args.multi_user: - if os.path.isfile(self.get_users_file()): - with open(self.get_users_file()) as f: - self.users = json.load(f) - else: - self.users = {} - else: - self.users = {"default": "default"} - - def get_users_file(self): - return os.path.join(folder_paths.get_user_directory(), "users.json") - - def get_request_user_id(self, request): - user = "default" - if args.multi_user and "comfy-user" in request.headers: - user = request.headers["comfy-user"] - - if user not in self.users: - raise KeyError("Unknown user: " + user) - - return user - - def get_request_user_filepath(self, request, file, type="userdata", create_dir=True): - user_directory = folder_paths.get_user_directory() - - if type == "userdata": - root_dir = user_directory - else: - raise KeyError("Unknown filepath type:" + type) - - user = self.get_request_user_id(request) - path = user_root = os.path.abspath(os.path.join(root_dir, user)) - - # prevent leaving /{type} - if os.path.commonpath((root_dir, user_root)) != root_dir: - return None - - if file is not None: - # Check if filename is url encoded - if "%" in file: - file = parse.unquote(file) - - # prevent leaving /{type}/{user} - path = os.path.abspath(os.path.join(user_root, file)) - if os.path.commonpath((user_root, path)) != user_root: - return None - - parent = os.path.split(path)[0] - - if create_dir and not os.path.exists(parent): - os.makedirs(parent, exist_ok=True) - - return path - - def add_user(self, name): - name = name.strip() - if not name: - raise ValueError("username not provided") - user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name) - user_id = user_id + "_" + str(uuid.uuid4()) - - self.users[user_id] = name - - with open(self.get_users_file(), "w") as f: - json.dump(self.users, f) - - return user_id - - def add_routes(self, routes): - self.settings.add_routes(routes) - - @routes.get("/users") - async def get_users(request): - if args.multi_user: - return web.json_response({"storage": "server", "users": self.users}) - else: - user_dir = self.get_request_user_filepath(request, None, create_dir=False) - return web.json_response({ - "storage": "server", - "migrated": os.path.exists(user_dir) - }) - - @routes.post("/users") - async def post_users(request): - body = await request.json() - username = body["username"] - if username in self.users.values(): - return web.json_response({"error": "Duplicate username."}, status=400) - - user_id = self.add_user(username) - return web.json_response(user_id) - - @routes.get("/userdata") - async def listuserdata(request): - """ - List user data files in a specified directory. - - This endpoint allows listing files in a user's data directory, with options for recursion, - full file information, and path splitting. - - Query Parameters: - - dir (required): The directory to list files from. - - recurse (optional): If "true", recursively list files in subdirectories. - - full_info (optional): If "true", return detailed file information (path, size, modified time). - - split (optional): If "true", split file paths into components (only applies when full_info is false). - - Returns: - - 400: If 'dir' parameter is missing. - - 403: If the requested path is not allowed. - - 404: If the requested directory does not exist. - - 200: JSON response with the list of files or file information. - - The response format depends on the query parameters: - - Default: List of relative file paths. - - full_info=true: List of dictionaries with file details. - - split=true (and full_info=false): List of lists, each containing path components. - """ - directory = request.rel_url.query.get('dir', '') - if not directory: - return web.Response(status=400, text="Directory not provided") - - path = self.get_request_user_filepath(request, directory) - if not path: - return web.Response(status=403, text="Invalid directory") - - if not os.path.exists(path): - return web.Response(status=404, text="Directory not found") - - recurse = request.rel_url.query.get('recurse', '').lower() == "true" - full_info = request.rel_url.query.get('full_info', '').lower() == "true" - split_path = request.rel_url.query.get('split', '').lower() == "true" - - # Use different patterns based on whether we're recursing or not - if recurse: - pattern = os.path.join(glob.escape(path), '**', '*') - else: - pattern = os.path.join(glob.escape(path), '*') - - def process_full_path(full_path: str) -> FileInfo | str | list[str]: - if full_info: - return get_file_info(full_path, path) - - rel_path = os.path.relpath(full_path, path).replace(os.sep, '/') - if split_path: - return [rel_path] + rel_path.split('/') - - return rel_path - - results = [ - process_full_path(full_path) - for full_path in glob.glob(pattern, recursive=recurse) - if os.path.isfile(full_path) - ] - - return web.json_response(results) - - @routes.get("/v2/userdata") - async def list_userdata_v2(request): - """ - List files and directories in a user's data directory. - - This endpoint provides a structured listing of contents within a specified - subdirectory of the user's data storage. - - Query Parameters: - - path (optional): The relative path within the user's data directory - to list. Defaults to the root (''). - - Returns: - - 400: If the requested path is invalid, outside the user's data directory, or is not a directory. - - 404: If the requested path does not exist. - - 403: If the user is invalid. - - 500: If there is an error reading the directory contents. - - 200: JSON response containing a list of file and directory objects. - Each object includes: - - name: The name of the file or directory. - - type: 'file' or 'directory'. - - path: The relative path from the user's data root. - - size (for files): The size in bytes. - - modified (for files): The last modified timestamp (Unix epoch). - """ - requested_rel_path = request.rel_url.query.get('path', '') - - # URL-decode the path parameter - try: - requested_rel_path = parse.unquote(requested_rel_path) - except Exception as e: - logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}") - return web.Response(status=400, text="Invalid characters in path parameter") - - - # Check user validity and get the absolute path for the requested directory - try: - base_user_path = self.get_request_user_filepath(request, None, create_dir=False) - - if requested_rel_path: - target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False) - else: - target_abs_path = base_user_path - - except KeyError as e: - # Invalid user detected by get_request_user_id inside get_request_user_filepath - logging.warning(f"Access denied for user: {e}") - return web.Response(status=403, text="Invalid user specified in request") - - - if not target_abs_path: - # Path traversal or other issue detected by get_request_user_filepath - return web.Response(status=400, text="Invalid path requested") - - # Handle cases where the user directory or target path doesn't exist - if not os.path.exists(target_abs_path): - # Check if it's the base user directory that's missing (new user case) - if target_abs_path == base_user_path: - # It's okay if the base user directory doesn't exist yet, return empty list - return web.json_response([]) - else: - # A specific subdirectory was requested but doesn't exist - return web.Response(status=404, text="Requested path not found") - - if not os.path.isdir(target_abs_path): - return web.Response(status=400, text="Requested path is not a directory") - - results = [] - try: - for root, dirs, files in os.walk(target_abs_path, topdown=True): - # Process directories - for dir_name in dirs: - dir_path = os.path.join(root, dir_name) - rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/') - results.append({ - "name": dir_name, - "path": rel_path, - "type": "directory" - }) - - # Process files - for file_name in files: - file_path = os.path.join(root, file_name) - rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/') - entry_info = { - "name": file_name, - "path": rel_path, - "type": "file" - } - try: - stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk - entry_info["size"] = stats.st_size - entry_info["modified"] = stats.st_mtime - except OSError as stat_error: - logging.warning(f"Could not stat file {file_path}: {stat_error}") - pass # Include file with available info - results.append(entry_info) - except OSError as e: - logging.error(f"Error listing directory {target_abs_path}: {e}") - return web.Response(status=500, text="Error reading directory contents") - - # Sort results alphabetically, directories first then files - results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower())) - - return web.json_response(results) - - def get_user_data_path(request, check_exists = False, param = "file"): - file = request.match_info.get(param, None) - if not file: - return web.Response(status=400) - - path = self.get_request_user_filepath(request, file) - if not path: - return web.Response(status=403) - - if check_exists and not os.path.exists(path): - return web.Response(status=404) - - return path - - @routes.get("/userdata/{file}") - async def getuserdata(request): - path = get_user_data_path(request, check_exists=True) - if not isinstance(path, str): - return path - - return web.FileResponse(path) - - @routes.post("/userdata/{file}") - async def post_userdata(request): - """ - Upload or update a user data file. - - This endpoint handles file uploads to a user's data directory, with options for - controlling overwrite behavior and response format. - - Query Parameters: - - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". - - full_info (optional): If "true", returns detailed file information (path, size, modified time). - If "false", returns only the relative file path. - - Path Parameters: - - file: The target file path (URL encoded if necessary). - - Returns: - - 400: If 'file' parameter is missing. - - 403: If the requested path is not allowed. - - 409: If overwrite=false and the file already exists. - - 200: JSON response with either: - - Full file information (if full_info=true) - - Relative file path (if full_info=false) - - The request body should contain the raw file content to be written. - """ - path = get_user_data_path(request) - if not isinstance(path, str): - return path - - overwrite = request.query.get("overwrite", 'true') != "false" - full_info = request.query.get('full_info', 'false').lower() == "true" - - if not overwrite and os.path.exists(path): - return web.Response(status=409, text="File already exists") - - try: - body = await request.read() - - with open(path, "wb") as f: - f.write(body) - except OSError as e: - logging.warning(f"Error saving file '{path}': {e}") - return web.Response( - status=400, - reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|" - ) - - user_path = self.get_request_user_filepath(request, None) - if full_info: - resp = get_file_info(path, user_path) - else: - resp = os.path.relpath(path, user_path) - - return web.json_response(resp) - - @routes.delete("/userdata/{file}") - async def delete_userdata(request): - path = get_user_data_path(request, check_exists=True) - if not isinstance(path, str): - return path - - os.remove(path) - - return web.Response(status=204) - - @routes.post("/userdata/{file}/move/{dest}") - async def move_userdata(request): - """ - Move or rename a user data file. - - This endpoint handles moving or renaming files within a user's data directory, with options for - controlling overwrite behavior and response format. - - Path Parameters: - - file: The source file path (URL encoded if necessary) - - dest: The destination file path (URL encoded if necessary) - - Query Parameters: - - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". - - full_info (optional): If "true", returns detailed file information (path, size, modified time). - If "false", returns only the relative file path. - - Returns: - - 400: If either 'file' or 'dest' parameter is missing - - 403: If either requested path is not allowed - - 404: If the source file does not exist - - 409: If overwrite=false and the destination file already exists - - 200: JSON response with either: - - Full file information (if full_info=true) - - Relative file path (if full_info=false) - """ - source = get_user_data_path(request, check_exists=True) - if not isinstance(source, str): - return source - - dest = get_user_data_path(request, check_exists=False, param="dest") - if not isinstance(source, str): - return dest - - overwrite = request.query.get("overwrite", 'true') != "false" - full_info = request.query.get('full_info', 'false').lower() == "true" - - if not overwrite and os.path.exists(dest): - return web.Response(status=409, text="File already exists") - - logging.info(f"moving '{source}' -> '{dest}'") - shutil.move(source, dest) - - user_path = self.get_request_user_filepath(request, None) - if full_info: - resp = get_file_info(dest, user_path) - else: - resp = os.path.relpath(dest, user_path) - - return web.json_response(resp) diff --git a/app_settings.py b/app_settings.py deleted file mode 100644 index c7ac73bf6a59d3e89647aed13b70ca9428e16f2a..0000000000000000000000000000000000000000 --- a/app_settings.py +++ /dev/null @@ -1,65 +0,0 @@ -import os -import json -from aiohttp import web -import logging - - -class AppSettings(): - def __init__(self, user_manager): - self.user_manager = user_manager - - def get_settings(self, request): - try: - file = self.user_manager.get_request_user_filepath( - request, - "comfy.settings.json" - ) - except KeyError as e: - logging.error("User settings not found.") - raise web.HTTPUnauthorized() from e - if os.path.isfile(file): - try: - with open(file) as f: - return json.load(f) - except: - logging.error(f"The user settings file is corrupted: {file}") - return {} - else: - return {} - - def save_settings(self, request, settings): - file = self.user_manager.get_request_user_filepath( - request, "comfy.settings.json") - with open(file, "w") as f: - f.write(json.dumps(settings, indent=4)) - - def add_routes(self, routes): - @routes.get("/settings") - async def get_settings(request): - return web.json_response(self.get_settings(request)) - - @routes.get("/settings/{id}") - async def get_setting(request): - value = None - settings = self.get_settings(request) - setting_id = request.match_info.get("id", None) - if setting_id and setting_id in settings: - value = settings[setting_id] - return web.json_response(value) - - @routes.post("/settings") - async def post_settings(request): - settings = self.get_settings(request) - new_settings = await request.json() - self.save_settings(request, {**settings, **new_settings}) - return web.Response(status=200) - - @routes.post("/settings/{id}") - async def post_setting(request): - setting_id = request.match_info.get("id", None) - if not setting_id: - return web.Response(status=400) - settings = self.get_settings(request) - settings[setting_id] = await request.json() - self.save_settings(request, settings) - return web.Response(status=200) diff --git a/capyabara_zoomed.png b/capyabara_zoomed.png deleted file mode 100644 index e1c4ed1b1c274d0f93a80d5f54eaa726f181199e..0000000000000000000000000000000000000000 --- a/capyabara_zoomed.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:37c27e972f09ab9b1c7df8aaa4b7c2cdbb702466e5bb0fecf5cb502ee531a26c -size 1583644 diff --git a/capybara.webp b/capybara.webp deleted file mode 100644 index 840da4e01fa74e876697a6c998f9d3569dff63da..0000000000000000000000000000000000000000 --- a/capybara.webp +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:26f8ee938a1f453a81e85c2035e3787b1e5ddbb9a92acb01688b39abd987c1e8 -size 467126 diff --git a/comfy/.DS_Store b/comfy/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/.DS_Store and /dev/null differ diff --git a/comfy/audio_encoders/.DS_Store b/comfy/audio_encoders/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/audio_encoders/.DS_Store and /dev/null differ diff --git a/comfy/audio_encoders/audio_encoders.py b/comfy/audio_encoders/audio_encoders.py deleted file mode 100644 index 538c21bd5cf085b5a93ff6516d809be7647b4c7c..0000000000000000000000000000000000000000 --- a/comfy/audio_encoders/audio_encoders.py +++ /dev/null @@ -1,42 +0,0 @@ -from .wav2vec2 import Wav2Vec2Model -import comfy.model_management -import comfy.ops -import comfy.utils -import logging -import torchaudio - - -class AudioEncoderModel(): - def __init__(self, config): - self.load_device = comfy.model_management.text_encoder_device() - offload_device = comfy.model_management.text_encoder_offload_device() - self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) - self.model = Wav2Vec2Model(dtype=self.dtype, device=offload_device, operations=comfy.ops.manual_cast) - self.model.eval() - self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) - self.model_sample_rate = 16000 - - def load_sd(self, sd): - return self.model.load_state_dict(sd, strict=False) - - def get_sd(self): - return self.model.state_dict() - - def encode_audio(self, audio, sample_rate): - comfy.model_management.load_model_gpu(self.patcher) - audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate) - out, all_layers = self.model(audio.to(self.load_device)) - outputs = {} - outputs["encoded_audio"] = out - outputs["encoded_audio_all_layers"] = all_layers - return outputs - - -def load_audio_encoder_from_sd(sd, prefix=""): - audio_encoder = AudioEncoderModel(None) - sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""}) - m, u = audio_encoder.load_sd(sd) - if len(m) > 0: - logging.warning("missing audio encoder: {}".format(m)) - - return audio_encoder diff --git a/comfy/audio_encoders/wav2vec2.py b/comfy/audio_encoders/wav2vec2.py deleted file mode 100644 index de906622aa2d726a6a36b86ffb8ad1bc1b8d63d6..0000000000000000000000000000000000000000 --- a/comfy/audio_encoders/wav2vec2.py +++ /dev/null @@ -1,207 +0,0 @@ -import torch -import torch.nn as nn -from comfy.ldm.modules.attention import optimized_attention_masked - - -class LayerNormConv(nn.Module): - def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): - super().__init__() - self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) - self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype) - - def forward(self, x): - x = self.conv(x) - return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1)) - - -class ConvFeatureEncoder(nn.Module): - def __init__(self, conv_dim, dtype=None, device=None, operations=None): - super().__init__() - self.conv_layers = nn.ModuleList([ - LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations), - LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations), - LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations), - LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations), - LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations), - LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations), - LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations), - ]) - - def forward(self, x): - x = x.unsqueeze(1) - - for conv in self.conv_layers: - x = conv(x) - - return x.transpose(1, 2) - - -class FeatureProjection(nn.Module): - def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None): - super().__init__() - self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype) - self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype) - - def forward(self, x): - x = self.layer_norm(x) - x = self.projection(x) - return x - - -class PositionalConvEmbedding(nn.Module): - def __init__(self, embed_dim=768, kernel_size=128, groups=16): - super().__init__() - self.conv = nn.Conv1d( - embed_dim, - embed_dim, - kernel_size=kernel_size, - padding=kernel_size // 2, - groups=groups, - ) - self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2) - self.activation = nn.GELU() - - def forward(self, x): - x = x.transpose(1, 2) - x = self.conv(x)[:, :, :-1] - x = self.activation(x) - x = x.transpose(1, 2) - return x - - -class TransformerEncoder(nn.Module): - def __init__( - self, - embed_dim=768, - num_heads=12, - num_layers=12, - mlp_ratio=4.0, - dtype=None, device=None, operations=None - ): - super().__init__() - - self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim) - self.layers = nn.ModuleList([ - TransformerEncoderLayer( - embed_dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - device=device, dtype=dtype, operations=operations - ) - for _ in range(num_layers) - ]) - - self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype) - - def forward(self, x, mask=None): - x = x + self.pos_conv_embed(x) - all_x = () - for layer in self.layers: - all_x += (x,) - x = layer(x, mask) - x = self.layer_norm(x) - all_x += (x,) - return x, all_x - - -class Attention(nn.Module): - def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None): - super().__init__() - self.embed_dim = embed_dim - self.num_heads = num_heads - self.head_dim = embed_dim // num_heads - - self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) - self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) - self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) - self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) - - def forward(self, x, mask=None): - assert (mask is None) # TODO? - q = self.q_proj(x) - k = self.k_proj(x) - v = self.v_proj(x) - - out = optimized_attention_masked(q, k, v, self.num_heads) - return self.out_proj(out) - - -class FeedForward(nn.Module): - def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None): - super().__init__() - self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype) - self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype) - - def forward(self, x): - x = self.intermediate_dense(x) - x = torch.nn.functional.gelu(x) - x = self.output_dense(x) - return x - - -class TransformerEncoderLayer(nn.Module): - def __init__( - self, - embed_dim=768, - num_heads=12, - mlp_ratio=4.0, - dtype=None, device=None, operations=None - ): - super().__init__() - - self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations) - - self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype) - self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations) - self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype) - - def forward(self, x, mask=None): - residual = x - x = self.layer_norm(x) - x = self.attention(x, mask=mask) - x = residual + x - - x = x + self.feed_forward(self.final_layer_norm(x)) - return x - - -class Wav2Vec2Model(nn.Module): - """Complete Wav2Vec 2.0 model.""" - - def __init__( - self, - embed_dim=1024, - final_dim=256, - num_heads=16, - num_layers=24, - dtype=None, device=None, operations=None - ): - super().__init__() - - conv_dim = 512 - self.feature_extractor = ConvFeatureEncoder(conv_dim, device=device, dtype=dtype, operations=operations) - self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations) - - self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype)) - - self.encoder = TransformerEncoder( - embed_dim=embed_dim, - num_heads=num_heads, - num_layers=num_layers, - device=device, dtype=dtype, operations=operations - ) - - def forward(self, x, mask_time_indices=None, return_dict=False): - - x = torch.mean(x, dim=1) - - x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7) - - features = self.feature_extractor(x) - features = self.feature_projection(features) - - batch_size, seq_len, _ = features.shape - - x, all_x = self.encoder(features) - - return x, all_x diff --git a/comfy/checkpoint_pickle.py b/comfy/checkpoint_pickle.py deleted file mode 100644 index 206551d3c1cf0d654c907534629a800196ba138b..0000000000000000000000000000000000000000 --- a/comfy/checkpoint_pickle.py +++ /dev/null @@ -1,13 +0,0 @@ -import pickle - -load = pickle.load - -class Empty: - pass - -class Unpickler(pickle.Unpickler): - def find_class(self, module, name): - #TODO: safe unpickle - if module.startswith("pytorch_lightning"): - return Empty - return super().find_class(module, name) diff --git a/comfy/cldm/.DS_Store b/comfy/cldm/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/cldm/.DS_Store and /dev/null differ diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py deleted file mode 100644 index ec01665e2181d4aea1c9cbb5b95bdc7715a3e8a8..0000000000000000000000000000000000000000 --- a/comfy/cldm/cldm.py +++ /dev/null @@ -1,433 +0,0 @@ -#taken from: https://github.com/lllyasviel/ControlNet -#and modified - -import torch -import torch.nn as nn - -from ..ldm.modules.diffusionmodules.util import ( - timestep_embedding, -) - -from ..ldm.modules.attention import SpatialTransformer -from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample -from ..ldm.util import exists -from .control_types import UNION_CONTROLNET_TYPES -from collections import OrderedDict -import comfy.ops -from comfy.ldm.modules.attention import optimized_attention - -class OptimizedAttention(nn.Module): - def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): - super().__init__() - self.heads = nhead - self.c = c - - self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device) - self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - - def forward(self, x): - x = self.in_proj(x) - q, k, v = x.split(self.c, dim=2) - out = optimized_attention(q, k, v, self.heads) - return self.out_proj(out) - -class QuickGELU(nn.Module): - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - -class ResBlockUnionControlnet(nn.Module): - def __init__(self, dim, nhead, dtype=None, device=None, operations=None): - super().__init__() - self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations) - self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device) - self.mlp = nn.Sequential( - OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()), - ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))])) - self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device) - - def attention(self, x: torch.Tensor): - return self.attn(x) - - def forward(self, x: torch.Tensor): - x = x + self.attention(self.ln_1(x)) - x = x + self.mlp(self.ln_2(x)) - return x - -class ControlledUnetModel(UNetModel): - #implemented in the ldm unet - pass - -class ControlNet(nn.Module): - def __init__( - self, - image_size, - in_channels, - model_channels, - hint_channels, - num_res_blocks, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=2, - num_classes=None, - use_checkpoint=False, - dtype=torch.float32, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - use_new_attention_order=False, - use_spatial_transformer=False, # custom transformer support - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy=True, - disable_self_attentions=None, - num_attention_blocks=None, - disable_middle_self_attn=False, - use_linear_in_transformer=False, - adm_in_channels=None, - transformer_depth_middle=None, - transformer_depth_output=None, - attn_precision=None, - union_controlnet_num_control_type=None, - device=None, - operations=comfy.ops.disable_weight_init, - **kwargs, - ): - super().__init__() - assert use_spatial_transformer == True, "use_spatial_transformer has to be true" - if use_spatial_transformer: - assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' - - if context_dim is not None: - assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' - # from omegaconf.listconfig import ListConfig - # if type(context_dim) == ListConfig: - # context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.dims = dims - self.image_size = image_size - self.in_channels = in_channels - self.model_channels = model_channels - - if isinstance(num_res_blocks, int): - self.num_res_blocks = len(channel_mult) * [num_res_blocks] - else: - if len(num_res_blocks) != len(channel_mult): - raise ValueError("provide num_res_blocks either as an int (globally constant) or " - "as a list/tuple (per-level) with the same length as channel_mult") - self.num_res_blocks = num_res_blocks - - if disable_self_attentions is not None: - # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not - assert len(disable_self_attentions) == len(channel_mult) - if num_attention_blocks is not None: - assert len(num_attention_blocks) == len(self.num_res_blocks) - assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) - - transformer_depth = transformer_depth[:] - - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = dtype - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.predict_codebook_ids = n_embed is not None - - time_embed_dim = model_channels * 4 - self.time_embed = nn.Sequential( - operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), - nn.SiLU(), - operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), - ) - - if self.num_classes is not None: - if isinstance(self.num_classes, int): - self.label_emb = nn.Embedding(num_classes, time_embed_dim) - elif self.num_classes == "continuous": - self.label_emb = nn.Linear(1, time_embed_dim) - elif self.num_classes == "sequential": - assert adm_in_channels is not None - self.label_emb = nn.Sequential( - nn.Sequential( - operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), - nn.SiLU(), - operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), - ) - ) - else: - raise ValueError() - - self.input_blocks = nn.ModuleList( - [ - TimestepEmbedSequential( - operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) - ) - ] - ) - self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) - - self.input_hint_block = TimestepEmbedSequential( - operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) - ) - - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - for level, mult in enumerate(channel_mult): - for nr in range(self.num_res_blocks[level]): - layers = [ - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations, - ) - ] - ch = mult * model_channels - num_transformers = transformer_depth.pop(0) - if num_transformers > 0: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - if exists(disable_self_attentions): - disabled_sa = disable_self_attentions[level] - else: - disabled_sa = False - - if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: - layers.append( - SpatialTransformer( - ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, - disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations - ) - ) - self.input_blocks.append(TimestepEmbedSequential(*layers)) - self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - TimestepEmbedSequential( - ResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - dtype=self.dtype, - device=device, - operations=operations - ) - if resblock_updown - else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - mid_block = [ - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations - )] - if transformer_depth_middle >= 0: - mid_block += [SpatialTransformer( # always uses a self-attn - ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations - ), - ResBlock( - ch, - time_embed_dim, - dropout, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations - )] - self.middle_block = TimestepEmbedSequential(*mid_block) - self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) - self._feature_size += ch - - if union_controlnet_num_control_type is not None: - self.num_control_type = union_controlnet_num_control_type - num_trans_channel = 320 - num_trans_head = 8 - num_trans_layer = 1 - num_proj_channel = 320 - # task_scale_factor = num_trans_channel ** 0.5 - self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device)) - - self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)]) - self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device) - #----------------------------------------------------------------------------------------------------- - - control_add_embed_dim = 256 - class ControlAddEmbedding(nn.Module): - def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None): - super().__init__() - self.num_control_type = num_control_type - self.in_dim = in_dim - self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device) - self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device) - def forward(self, control_type, dtype, device): - c_type = torch.zeros((self.num_control_type,), device=device) - c_type[control_type] = 1.0 - c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim)) - return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type))) - - self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations) - else: - self.task_embedding = None - self.control_add_embedding = None - - def union_controlnet_merge(self, hint, control_type, emb, context): - # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main - inputs = [] - condition_list = [] - - for idx in range(min(1, len(control_type))): - controlnet_cond = self.input_hint_block(hint[idx], emb, context) - feat_seq = torch.mean(controlnet_cond, dim=(2, 3)) - if idx < len(control_type): - feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device) - - inputs.append(feat_seq.unsqueeze(1)) - condition_list.append(controlnet_cond) - - x = torch.cat(inputs, dim=1) - x = self.transformer_layes(x) - controlnet_cond_fuser = None - for idx in range(len(control_type)): - alpha = self.spatial_ch_projs(x[:, idx]) - alpha = alpha.unsqueeze(-1).unsqueeze(-1) - o = condition_list[idx] + alpha - if controlnet_cond_fuser is None: - controlnet_cond_fuser = o - else: - controlnet_cond_fuser += o - return controlnet_cond_fuser - - def make_zero_conv(self, channels, operations=None, dtype=None, device=None): - return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) - - def forward(self, x, hint, timesteps, context, y=None, **kwargs): - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) - emb = self.time_embed(t_emb) - - guided_hint = None - if self.control_add_embedding is not None: #Union Controlnet - control_type = kwargs.get("control_type", []) - - if any([c >= self.num_control_type for c in control_type]): - max_type = max(control_type) - max_type_name = { - v: k for k, v in UNION_CONTROLNET_TYPES.items() - }[max_type] - raise ValueError( - f"Control type {max_type_name}({max_type}) is out of range for the number of control types" + - f"({self.num_control_type}) supported.\n" + - "Please consider using the ProMax ControlNet Union model.\n" + - "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main" - ) - - emb += self.control_add_embedding(control_type, emb.dtype, emb.device) - if len(control_type) > 0: - if len(hint.shape) < 5: - hint = hint.unsqueeze(dim=0) - guided_hint = self.union_controlnet_merge(hint, control_type, emb, context) - - if guided_hint is None: - guided_hint = self.input_hint_block(hint, emb, context) - - out_output = [] - out_middle = [] - - if self.num_classes is not None: - assert y.shape[0] == x.shape[0] - emb = emb + self.label_emb(y) - - h = x - for module, zero_conv in zip(self.input_blocks, self.zero_convs): - if guided_hint is not None: - h = module(h, emb, context) - h += guided_hint - guided_hint = None - else: - h = module(h, emb, context) - out_output.append(zero_conv(h, emb, context)) - - h = self.middle_block(h, emb, context) - out_middle.append(self.middle_block_out(h, emb, context)) - - return {"middle": out_middle, "output": out_output} - diff --git a/comfy/cldm/control_types.py b/comfy/cldm/control_types.py deleted file mode 100644 index 4128631a305a13d65c3c37ced17179d23fbbdcff..0000000000000000000000000000000000000000 --- a/comfy/cldm/control_types.py +++ /dev/null @@ -1,10 +0,0 @@ -UNION_CONTROLNET_TYPES = { - "openpose": 0, - "depth": 1, - "hed/pidi/scribble/ted": 2, - "canny/lineart/anime_lineart/mlsd": 3, - "normal": 4, - "segment": 5, - "tile": 6, - "repaint": 7, -} diff --git a/comfy/cldm/dit_embedder.py b/comfy/cldm/dit_embedder.py deleted file mode 100644 index f9bf31012b1319e929cab381c9a3b32be62fc589..0000000000000000000000000000000000000000 --- a/comfy/cldm/dit_embedder.py +++ /dev/null @@ -1,120 +0,0 @@ -import math -from typing import List, Optional, Tuple - -import torch -import torch.nn as nn -from torch import Tensor - -from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch - - -class ControlNetEmbedder(nn.Module): - - def __init__( - self, - img_size: int, - patch_size: int, - in_chans: int, - attention_head_dim: int, - num_attention_heads: int, - adm_in_channels: int, - num_layers: int, - main_model_double: int, - double_y_emb: bool, - device: torch.device, - dtype: torch.dtype, - pos_embed_max_size: Optional[int] = None, - operations = None, - ): - super().__init__() - self.main_model_double = main_model_double - self.dtype = dtype - self.hidden_size = num_attention_heads * attention_head_dim - self.patch_size = patch_size - self.x_embedder = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=in_chans, - embed_dim=self.hidden_size, - strict_img_size=pos_embed_max_size is None, - device=device, - dtype=dtype, - operations=operations, - ) - - self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) - - self.double_y_emb = double_y_emb - if self.double_y_emb: - self.orig_y_embedder = VectorEmbedder( - adm_in_channels, self.hidden_size, dtype, device, operations=operations - ) - self.y_embedder = VectorEmbedder( - self.hidden_size, self.hidden_size, dtype, device, operations=operations - ) - else: - self.y_embedder = VectorEmbedder( - adm_in_channels, self.hidden_size, dtype, device, operations=operations - ) - - self.transformer_blocks = nn.ModuleList( - DismantledBlock( - hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True, - dtype=dtype, device=device, operations=operations - ) - for _ in range(num_layers) - ) - - # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features - # TODO double check this logic when 8b - self.use_y_embedder = True - - self.controlnet_blocks = nn.ModuleList([]) - for _ in range(len(self.transformer_blocks)): - controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) - self.controlnet_blocks.append(controlnet_block) - - self.pos_embed_input = PatchEmbed( - img_size=img_size, - patch_size=patch_size, - in_chans=in_chans, - embed_dim=self.hidden_size, - strict_img_size=False, - device=device, - dtype=dtype, - operations=operations, - ) - - def forward( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - y: Optional[torch.Tensor] = None, - context: Optional[torch.Tensor] = None, - hint = None, - ) -> Tuple[Tensor, List[Tensor]]: - x_shape = list(x.shape) - x = self.x_embedder(x) - if not self.double_y_emb: - h = (x_shape[-2] + 1) // self.patch_size - w = (x_shape[-1] + 1) // self.patch_size - x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device) - c = self.t_embedder(timesteps, dtype=x.dtype) - if y is not None and self.y_embedder is not None: - if self.double_y_emb: - y = self.orig_y_embedder(y) - y = self.y_embedder(y) - c = c + y - - x = x + self.pos_embed_input(hint) - - block_out = () - - repeat = math.ceil(self.main_model_double / len(self.transformer_blocks)) - for i in range(len(self.transformer_blocks)): - out = self.transformer_blocks[i](x, c) - if not self.double_y_emb: - x = out - block_out += (self.controlnet_blocks[i](out),) * repeat - - return {"output": block_out} diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py deleted file mode 100644 index b7764085e9431259d43701654af897effe0799da..0000000000000000000000000000000000000000 --- a/comfy/cldm/mmdit.py +++ /dev/null @@ -1,81 +0,0 @@ -import torch -from typing import Optional -import comfy.ldm.modules.diffusionmodules.mmdit - -class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT): - def __init__( - self, - num_blocks = None, - control_latent_channels = None, - dtype = None, - device = None, - operations = None, - **kwargs, - ): - super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs) - # controlnet_blocks - self.controlnet_blocks = torch.nn.ModuleList([]) - for _ in range(len(self.joint_blocks)): - self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype)) - - if control_latent_channels is None: - control_latent_channels = self.in_channels - - self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed( - None, - self.patch_size, - control_latent_channels, - self.hidden_size, - bias=True, - strict_img_size=False, - dtype=dtype, - device=device, - operations=operations - ) - - def forward( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - y: Optional[torch.Tensor] = None, - context: Optional[torch.Tensor] = None, - hint = None, - ) -> torch.Tensor: - - #weird sd3 controlnet specific stuff - y = torch.zeros_like(y) - - if self.context_processor is not None: - context = self.context_processor(context) - - hw = x.shape[-2:] - x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device) - x += self.pos_embed_input(hint) - - c = self.t_embedder(timesteps, dtype=x.dtype) - if y is not None and self.y_embedder is not None: - y = self.y_embedder(y) - c = c + y - - if context is not None: - context = self.context_embedder(context) - - output = [] - - blocks = len(self.joint_blocks) - for i in range(blocks): - context, x = self.joint_blocks[i]( - context, - x, - c=c, - use_checkpoint=self.use_checkpoint, - ) - - out = self.controlnet_blocks[i](x) - count = self.depth // blocks - if i == blocks - 1: - count -= 1 - for j in range(count): - output.append(out) - - return {"output": output} diff --git a/comfy/cli_args.py b/comfy/cli_args.py deleted file mode 100644 index de3e85c08d8c3bea609d84ed8276581345681aa9..0000000000000000000000000000000000000000 --- a/comfy/cli_args.py +++ /dev/null @@ -1,239 +0,0 @@ -import argparse -import enum -import os -import comfy.options - - -class EnumAction(argparse.Action): - """ - Argparse action for handling Enums - """ - def __init__(self, **kwargs): - # Pop off the type value - enum_type = kwargs.pop("type", None) - - # Ensure an Enum subclass is provided - if enum_type is None: - raise ValueError("type must be assigned an Enum when using EnumAction") - if not issubclass(enum_type, enum.Enum): - raise TypeError("type must be an Enum when using EnumAction") - - # Generate choices from the Enum - choices = tuple(e.value for e in enum_type) - kwargs.setdefault("choices", choices) - kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") - - super(EnumAction, self).__init__(**kwargs) - - self._enum = enum_type - - def __call__(self, parser, namespace, values, option_string=None): - # Convert value back into an Enum - value = self._enum(values) - setattr(namespace, self.dest, value) - - -parser = argparse.ArgumentParser() - -parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)") -parser.add_argument("--port", type=int, default=8188, help="Set the listen port.") -parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function") -parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function") -parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") -parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") - -parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.") -parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") -parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.") -parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.") -parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.") -parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") -parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") -parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.") -parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.") -cm_group = parser.add_mutually_exclusive_group() -cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") -cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") - - -fp_group = parser.add_mutually_exclusive_group() -fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).") -fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.") - -fpunet_group = parser.add_mutually_exclusive_group() -fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.") -fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.") -fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.") -fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16") -fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") -fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") -fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.") - -fpvae_group = parser.add_mutually_exclusive_group() -fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") -fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.") -fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.") - -parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.") - -fpte_group = parser.add_mutually_exclusive_group() -fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).") -fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") -fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") -fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") -fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.") - -parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") - -parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") - -parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.") -parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.") -parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.") - -class LatentPreviewMethod(enum.Enum): - NoPreviews = "none" - Auto = "auto" - Latent2RGB = "latent2rgb" - TAESD = "taesd" - -parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction) - -parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.") - -cache_group = parser.add_mutually_exclusive_group() -cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.") -cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.") -cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.") - -attn_group = parser.add_mutually_exclusive_group() -attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") -attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") -attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") -attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.") -attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.") - -parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") - -upcast = parser.add_mutually_exclusive_group() -upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.") -upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.") - - -vram_group = parser.add_mutually_exclusive_group() -vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).") -vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.") -vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.") -vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.") -vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.") -vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") - -parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.") - -parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.") - -parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.") - -parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.") - -parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") -parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.") - -class PerformanceFeature(enum.Enum): - Fp16Accumulation = "fp16_accumulation" - Fp8MatrixMultiplication = "fp8_matrix_mult" - CublasOps = "cublas_ops" - -parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops") - -parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.") -parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.") - -parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") -parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") -parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).") - -parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") -parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") -parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.") -parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.") - -parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") - -parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level') -parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).") - -# The default built-in provider hosted under web/ -DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest" - -parser.add_argument( - "--front-end-version", - type=str, - default=DEFAULT_VERSION_STRING, - help=""" - Specifies the version of the frontend to be used. This command needs internet connectivity to query and - download available frontend implementations from GitHub releases. - - The version string should be in the format of: - [repoOwner]/[repoName]@[version] - where version is one of: "latest" or a valid version number (e.g. "1.0.0") - """, -) - -def is_valid_directory(path: str) -> str: - """Validate if the given path is a directory, and check permissions.""" - if not os.path.exists(path): - raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.") - if not os.path.isdir(path): - raise argparse.ArgumentTypeError(f"'{path}' is not a directory.") - if not os.access(path, os.R_OK): - raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.") - return path - -parser.add_argument( - "--front-end-root", - type=is_valid_directory, - default=None, - help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.", -) - -parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.") - -parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.") - -parser.add_argument( - "--comfy-api-base", - type=str, - default="https://api.comfy.org", - help="Set the base URL for the ComfyUI API. (default: https://api.comfy.org)", -) - -database_default_path = os.path.abspath( - os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db") -) -parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.") - -if comfy.options.args_parsing: - args = parser.parse_args() -else: - args = parser.parse_args([]) - -if args.windows_standalone_build: - args.auto_launch = True - -if args.disable_auto_launch: - args.auto_launch = False - -if args.force_fp16: - args.fp16_unet = True - - -# '--fast' is not provided, use an empty set -if args.fast is None: - args.fast = set() -# '--fast' is provided with an empty list, enable all optimizations -elif args.fast == []: - args.fast = set(PerformanceFeature) -# '--fast' is provided with a list of performance features, use that list -else: - args.fast = set(args.fast) diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json deleted file mode 100644 index 35261deef14a68fcc6c5b1fc32914b5c102781a9..0000000000000000000000000000000000000000 --- a/comfy/clip_config_bigg.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "gelu", - "hidden_size": 1280, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 5120, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 77, - "model_type": "clip_text_model", - "num_attention_heads": 20, - "num_hidden_layers": 32, - "pad_token_id": 1, - "projection_dim": 1280, - "torch_dtype": "float32", - "vocab_size": 49408 -} diff --git a/comfy/clip_model.py b/comfy/clip_model.py deleted file mode 100644 index 7e47d8a55b0984891e43fbb2c75a276ac38bccc0..0000000000000000000000000000000000000000 --- a/comfy/clip_model.py +++ /dev/null @@ -1,244 +0,0 @@ -import torch -from comfy.ldm.modules.attention import optimized_attention_for_device -import comfy.ops - -class CLIPAttention(torch.nn.Module): - def __init__(self, embed_dim, heads, dtype, device, operations): - super().__init__() - - self.heads = heads - self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - - self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - - def forward(self, x, mask=None, optimized_attention=None): - q = self.q_proj(x) - k = self.k_proj(x) - v = self.v_proj(x) - - out = optimized_attention(q, k, v, self.heads, mask) - return self.out_proj(out) - -ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), - "gelu": torch.nn.functional.gelu, - "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), -} - -class CLIPMLP(torch.nn.Module): - def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): - super().__init__() - self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) - self.activation = ACTIVATIONS[activation] - self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) - - def forward(self, x): - x = self.fc1(x) - x = self.activation(x) - x = self.fc2(x) - return x - -class CLIPLayer(torch.nn.Module): - def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): - super().__init__() - self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) - self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) - - def forward(self, x, mask=None, optimized_attention=None): - x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) - x += self.mlp(self.layer_norm2(x)) - return x - - -class CLIPEncoder(torch.nn.Module): - def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): - super().__init__() - self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) - - def forward(self, x, mask=None, intermediate_output=None): - optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) - - if intermediate_output is not None: - if intermediate_output < 0: - intermediate_output = len(self.layers) + intermediate_output - - intermediate = None - for i, l in enumerate(self.layers): - x = l(x, mask, optimized_attention) - if i == intermediate_output: - intermediate = x.clone() - return x, intermediate - -class CLIPEmbeddings(torch.nn.Module): - def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None): - super().__init__() - self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) - self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device) - - def forward(self, input_tokens, dtype=torch.float32): - return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device) - - -class CLIPTextModel_(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - num_layers = config_dict["num_hidden_layers"] - embed_dim = config_dict["hidden_size"] - heads = config_dict["num_attention_heads"] - intermediate_size = config_dict["intermediate_size"] - intermediate_activation = config_dict["hidden_act"] - num_positions = config_dict["max_position_embeddings"] - self.eos_token_id = config_dict["eos_token_id"] - - super().__init__() - self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations) - self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) - self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - - def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]): - if embeds is not None: - x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device) - else: - x = self.embeddings(input_tokens, dtype=dtype) - - mask = None - if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) - - causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1) - - if mask is not None: - mask += causal_mask - else: - mask = causal_mask - - x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) - x = self.final_layer_norm(x) - if i is not None and final_layer_norm_intermediate: - i = self.final_layer_norm(i) - - if num_tokens is not None: - pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))] - else: - pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] - return x, i, pooled_output - -class CLIPTextModel(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - self.num_layers = config_dict["num_hidden_layers"] - self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) - embed_dim = config_dict["hidden_size"] - self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) - self.dtype = dtype - - def get_input_embeddings(self): - return self.text_model.embeddings.token_embedding - - def set_input_embeddings(self, embeddings): - self.text_model.embeddings.token_embedding = embeddings - - def forward(self, *args, **kwargs): - x = self.text_model(*args, **kwargs) - out = self.text_projection(x[2]) - return (x[0], x[1], out, x[2]) - - -class CLIPVisionEmbeddings(torch.nn.Module): - def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None): - super().__init__() - - num_patches = (image_size // patch_size) ** 2 - if model_type == "siglip_vision_model": - self.class_embedding = None - patch_bias = True - else: - num_patches = num_patches + 1 - self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) - patch_bias = False - - self.patch_embedding = operations.Conv2d( - in_channels=num_channels, - out_channels=embed_dim, - kernel_size=patch_size, - stride=patch_size, - bias=patch_bias, - dtype=dtype, - device=device - ) - - self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device) - - def forward(self, pixel_values): - embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) - if self.class_embedding is not None: - embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) - return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds) - - -class CLIPVision(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - num_layers = config_dict["num_hidden_layers"] - embed_dim = config_dict["hidden_size"] - heads = config_dict["num_attention_heads"] - intermediate_size = config_dict["intermediate_size"] - intermediate_activation = config_dict["hidden_act"] - model_type = config_dict["model_type"] - - self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations) - if model_type == "siglip_vision_model": - self.pre_layrnorm = lambda a: a - self.output_layernorm = True - else: - self.pre_layrnorm = operations.LayerNorm(embed_dim) - self.output_layernorm = False - self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) - self.post_layernorm = operations.LayerNorm(embed_dim) - - def forward(self, pixel_values, attention_mask=None, intermediate_output=None): - x = self.embeddings(pixel_values) - x = self.pre_layrnorm(x) - #TODO: attention_mask? - x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) - if self.output_layernorm: - x = self.post_layernorm(x) - pooled_output = x - else: - pooled_output = self.post_layernorm(x[:, 0, :]) - return x, i, pooled_output - -class LlavaProjector(torch.nn.Module): - def __init__(self, in_dim, out_dim, dtype, device, operations): - super().__init__() - self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype) - self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype) - - def forward(self, x): - return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:]))) - -class CLIPVisionModelProjection(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - self.vision_model = CLIPVision(config_dict, dtype, device, operations) - if "projection_dim" in config_dict: - self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) - else: - self.visual_projection = lambda a: a - - if "llava3" == config_dict.get("projector_type", None): - self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations) - else: - self.multi_modal_projector = None - - def forward(self, *args, **kwargs): - x = self.vision_model(*args, **kwargs) - out = self.visual_projection(x[2]) - projected = None - if self.multi_modal_projector is not None: - projected = self.multi_modal_projector(x[1]) - - return (x[0], x[1], out, projected) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py deleted file mode 100644 index 00aab9164e5ec2060e34f22df9d1098cfe7b7e47..0000000000000000000000000000000000000000 --- a/comfy/clip_vision.py +++ /dev/null @@ -1,148 +0,0 @@ -from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace -import os -import torch -import json -import logging - -import comfy.ops -import comfy.model_patcher -import comfy.model_management -import comfy.utils -import comfy.clip_model -import comfy.image_encoders.dino2 - -class Output: - def __getitem__(self, key): - return getattr(self, key) - def __setitem__(self, key, item): - setattr(self, key, item) - -def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True): - image = image[:, :, :, :3] if image.shape[3] > 3 else image - mean = torch.tensor(mean, device=image.device, dtype=image.dtype) - std = torch.tensor(std, device=image.device, dtype=image.dtype) - image = image.movedim(-1, 1) - if not (image.shape[2] == size and image.shape[3] == size): - if crop: - scale = (size / min(image.shape[2], image.shape[3])) - scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3])) - else: - scale_size = (size, size) - - image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True) - h = (image.shape[2] - size)//2 - w = (image.shape[3] - size)//2 - image = image[:,:,h:h+size,w:w+size] - image = torch.clip((255. * image), 0, 255).round() / 255.0 - return (image - mean.view([3,1,1])) / std.view([3,1,1]) - -IMAGE_ENCODERS = { - "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection, - "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection, - "dinov2": comfy.image_encoders.dino2.Dinov2Model, -} - -class ClipVisionModel(): - def __init__(self, json_config): - with open(json_config) as f: - config = json.load(f) - - self.image_size = config.get("image_size", 224) - self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073]) - self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711]) - model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model")) - self.load_device = comfy.model_management.text_encoder_device() - offload_device = comfy.model_management.text_encoder_offload_device() - self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) - self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast) - self.model.eval() - - self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) - - def load_sd(self, sd): - return self.model.load_state_dict(sd, strict=False) - - def get_sd(self): - return self.model.state_dict() - - def encode_image(self, image, crop=True): - comfy.model_management.load_model_gpu(self.patcher) - pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float() - out = self.model(pixel_values=pixel_values, intermediate_output=-2) - - outputs = Output() - outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) - outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) - outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) - outputs["mm_projected"] = out[3] - return outputs - -def convert_to_transformers(sd, prefix): - sd_k = sd.keys() - if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: - keys_to_replace = { - "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", - "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", - "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", - "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", - "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", - "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", - "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", - } - - for x in keys_to_replace: - if x in sd_k: - sd[keys_to_replace[x]] = sd.pop(x) - - if "{}proj".format(prefix) in sd_k: - sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) - - sd = transformers_convert(sd, prefix, "vision_model.", 48) - else: - replace_prefix = {prefix: ""} - sd = state_dict_prefix_replace(sd, replace_prefix) - return sd - -def load_clipvision_from_sd(sd, prefix="", convert_keys=False): - if convert_keys: - sd = convert_to_transformers(sd, prefix) - if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") - elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") - elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: - embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0] - if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152: - if embed_shape == 729: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json") - elif embed_shape == 1024: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json") - elif embed_shape == 577: - if "multi_modal_projector.linear_1.bias" in sd: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json") - else: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") - else: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") - elif "embeddings.patch_embeddings.projection.weight" in sd: - json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json") - else: - return None - - clip = ClipVisionModel(json_config) - m, u = clip.load_sd(sd) - if len(m) > 0: - logging.warning("missing clip vision: {}".format(m)) - u = set(u) - keys = list(sd.keys()) - for k in keys: - if k not in u: - sd.pop(k) - return clip - -def load(ckpt_path): - sd = load_torch_file(ckpt_path) - if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: - return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) - else: - return load_clipvision_from_sd(sd) diff --git a/comfy/clip_vision_config_g.json b/comfy/clip_vision_config_g.json deleted file mode 100644 index 708e7e21ac3513a719d6a49e88e756f5ef7e2c8d..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_config_g.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "attention_dropout": 0.0, - "dropout": 0.0, - "hidden_act": "gelu", - "hidden_size": 1664, - "image_size": 224, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 8192, - "layer_norm_eps": 1e-05, - "model_type": "clip_vision_model", - "num_attention_heads": 16, - "num_channels": 3, - "num_hidden_layers": 48, - "patch_size": 14, - "projection_dim": 1280, - "torch_dtype": "float32" -} diff --git a/comfy/clip_vision_config_h.json b/comfy/clip_vision_config_h.json deleted file mode 100644 index bb71be419a4be0ad5c8c157850de032a65593cb9..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_config_h.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "attention_dropout": 0.0, - "dropout": 0.0, - "hidden_act": "gelu", - "hidden_size": 1280, - "image_size": 224, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 5120, - "layer_norm_eps": 1e-05, - "model_type": "clip_vision_model", - "num_attention_heads": 16, - "num_channels": 3, - "num_hidden_layers": 32, - "patch_size": 14, - "projection_dim": 1024, - "torch_dtype": "float32" -} diff --git a/comfy/clip_vision_config_vitl.json b/comfy/clip_vision_config_vitl.json deleted file mode 100644 index c59b8ed5a4c1f41fbcc9e6811d2c7dfe44273de7..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_config_vitl.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "attention_dropout": 0.0, - "dropout": 0.0, - "hidden_act": "quick_gelu", - "hidden_size": 1024, - "image_size": 224, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-05, - "model_type": "clip_vision_model", - "num_attention_heads": 16, - "num_channels": 3, - "num_hidden_layers": 24, - "patch_size": 14, - "projection_dim": 768, - "torch_dtype": "float32" -} diff --git a/comfy/clip_vision_config_vitl_336.json b/comfy/clip_vision_config_vitl_336.json deleted file mode 100644 index f26945273d99e88f207d64dcec78feee63b4b625..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_config_vitl_336.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "attention_dropout": 0.0, - "dropout": 0.0, - "hidden_act": "quick_gelu", - "hidden_size": 1024, - "image_size": 336, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-5, - "model_type": "clip_vision_model", - "num_attention_heads": 16, - "num_channels": 3, - "num_hidden_layers": 24, - "patch_size": 14, - "projection_dim": 768, - "torch_dtype": "float32" -} diff --git a/comfy/clip_vision_config_vitl_336_llava.json b/comfy/clip_vision_config_vitl_336_llava.json deleted file mode 100644 index f23a50d8b77fa29de2af621fb50c5825cf9b1a86..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_config_vitl_336_llava.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "attention_dropout": 0.0, - "dropout": 0.0, - "hidden_act": "quick_gelu", - "hidden_size": 1024, - "image_size": 336, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-5, - "model_type": "clip_vision_model", - "num_attention_heads": 16, - "num_channels": 3, - "num_hidden_layers": 24, - "patch_size": 14, - "projection_dim": 768, - "projector_type": "llava3", - "torch_dtype": "float32" -} diff --git a/comfy/clip_vision_siglip_384.json b/comfy/clip_vision_siglip_384.json deleted file mode 100644 index 532e03ac181d8849a7202445d42565f01441177b..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_siglip_384.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "num_channels": 3, - "hidden_act": "gelu_pytorch_tanh", - "hidden_size": 1152, - "image_size": 384, - "intermediate_size": 4304, - "model_type": "siglip_vision_model", - "num_attention_heads": 16, - "num_hidden_layers": 27, - "patch_size": 14, - "image_mean": [0.5, 0.5, 0.5], - "image_std": [0.5, 0.5, 0.5] -} diff --git a/comfy/clip_vision_siglip_512.json b/comfy/clip_vision_siglip_512.json deleted file mode 100644 index 7fb93ce15e6da3ce7653a7a91ab278d707a096b4..0000000000000000000000000000000000000000 --- a/comfy/clip_vision_siglip_512.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "num_channels": 3, - "hidden_act": "gelu_pytorch_tanh", - "hidden_size": 1152, - "image_size": 512, - "intermediate_size": 4304, - "model_type": "siglip_vision_model", - "num_attention_heads": 16, - "num_hidden_layers": 27, - "patch_size": 16, - "image_mean": [0.5, 0.5, 0.5], - "image_std": [0.5, 0.5, 0.5] -} diff --git a/comfy/comfy_types/.DS_Store b/comfy/comfy_types/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/comfy_types/.DS_Store and /dev/null differ diff --git a/comfy/comfy_types/README.md b/comfy/comfy_types/README.md deleted file mode 100644 index 20a786a5eac805a0b3f41c4fa42be558819b89c8..0000000000000000000000000000000000000000 --- a/comfy/comfy_types/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# Comfy Typing -## Type hinting for ComfyUI Node development - -This module provides type hinting and concrete convenience types for node developers. -If cloned to the custom_nodes directory of ComfyUI, types can be imported using: - -```python -from comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin - -class ExampleNode(ComfyNodeABC): - @classmethod - def INPUT_TYPES(s) -> InputTypeDict: - return {"required": {}} -``` - -Full example is in [examples/example_nodes.py](examples/example_nodes.py). - -# Types -A few primary types are documented below. More complete information is available via the docstrings on each type. - -## `IO` - -A string enum of built-in and a few custom data types. Includes the following special types and their requisite plumbing: - -- `ANY`: `"*"` -- `NUMBER`: `"FLOAT,INT"` -- `PRIMITIVE`: `"STRING,FLOAT,INT,BOOLEAN"` - -## `ComfyNodeABC` - -An abstract base class for nodes, offering type-hinting / autocomplete, and somewhat-alright docstrings. - -### Type hinting for `INPUT_TYPES` - -![INPUT_TYPES auto-completion in Visual Studio Code](examples/input_types.png) - -### `INPUT_TYPES` return dict - -![INPUT_TYPES return value type hinting in Visual Studio Code](examples/required_hint.png) - -### Options for individual inputs - -![INPUT_TYPES return value option auto-completion in Visual Studio Code](examples/input_options.png) diff --git a/comfy/comfy_types/__init__.py b/comfy/comfy_types/__init__.py deleted file mode 100644 index 7640fbe3f827bdaac2384e0dcc96d243ec257f2f..0000000000000000000000000000000000000000 --- a/comfy/comfy_types/__init__.py +++ /dev/null @@ -1,46 +0,0 @@ -import torch -from typing import Callable, Protocol, TypedDict, Optional, List -from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator - - -class UnetApplyFunction(Protocol): - """Function signature protocol on comfy.model_base.BaseModel.apply_model""" - - def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor: - pass - - -class UnetApplyConds(TypedDict): - """Optional conditions for unet apply function.""" - - c_concat: Optional[torch.Tensor] - c_crossattn: Optional[torch.Tensor] - control: Optional[torch.Tensor] - transformer_options: Optional[dict] - - -class UnetParams(TypedDict): - # Tensor of shape [B, C, H, W] - input: torch.Tensor - # Tensor of shape [B] - timestep: torch.Tensor - c: UnetApplyConds - # List of [0, 1], [0], [1], ... - # 0 means conditional, 1 means conditional unconditional - cond_or_uncond: List[int] - - -UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor] - - -__all__ = [ - "UnetWrapperFunction", - UnetApplyConds.__name__, - UnetParams.__name__, - UnetApplyFunction.__name__, - IO.__name__, - InputTypeDict.__name__, - ComfyNodeABC.__name__, - CheckLazyMixin.__name__, - FileLocator.__name__, -] diff --git a/comfy/comfy_types/examples/example_nodes.py b/comfy/comfy_types/examples/example_nodes.py deleted file mode 100644 index 6e19c545153db36b2c2e7481e5c8bc96ccbbe1f1..0000000000000000000000000000000000000000 --- a/comfy/comfy_types/examples/example_nodes.py +++ /dev/null @@ -1,28 +0,0 @@ -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict -from inspect import cleandoc - - -class ExampleNode(ComfyNodeABC): - """An example node that just adds 1 to an input integer. - - * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc). - * This node is intended as an example for developers only. - """ - - DESCRIPTION = cleandoc(__doc__) - CATEGORY = "examples" - - @classmethod - def INPUT_TYPES(s) -> InputTypeDict: - return { - "required": { - "input_int": (IO.INT, {"defaultInput": True}), - } - } - - RETURN_TYPES = (IO.INT,) - RETURN_NAMES = ("input_plus_one",) - FUNCTION = "execute" - - def execute(self, input_int: int): - return (input_int + 1,) diff --git a/comfy/comfy_types/examples/input_options.png b/comfy/comfy_types/examples/input_options.png deleted file mode 100644 index ac859bbc0c15728e6f5464f9388f44d5e38dd650..0000000000000000000000000000000000000000 Binary files a/comfy/comfy_types/examples/input_options.png and /dev/null differ diff --git a/comfy/comfy_types/examples/input_types.png b/comfy/comfy_types/examples/input_types.png deleted file mode 100644 index 27e031ccf9c32958da0d41164b567e252c1b0c5c..0000000000000000000000000000000000000000 Binary files a/comfy/comfy_types/examples/input_types.png and /dev/null differ diff --git a/comfy/comfy_types/examples/required_hint.png b/comfy/comfy_types/examples/required_hint.png deleted file mode 100644 index 22c0182a0aed3245201f2ffac86a6d3ad5b7e2ea..0000000000000000000000000000000000000000 Binary files a/comfy/comfy_types/examples/required_hint.png and /dev/null differ diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py deleted file mode 100644 index 071b98332ee2a588448fde472f9c0f104e72d13d..0000000000000000000000000000000000000000 --- a/comfy/comfy_types/node_typing.py +++ /dev/null @@ -1,350 +0,0 @@ -"""Comfy-specific type hinting""" - -from __future__ import annotations -from typing import Literal, TypedDict, Optional -from typing_extensions import NotRequired -from abc import ABC, abstractmethod -from enum import Enum - - -class StrEnum(str, Enum): - """Base class for string enums. Python's StrEnum is not available until 3.11.""" - - def __str__(self) -> str: - return self.value - - -class IO(StrEnum): - """Node input/output data types. - - Includes functionality for ``"*"`` (`ANY`) and ``"MULTI,TYPES"``. - """ - - STRING = "STRING" - IMAGE = "IMAGE" - MASK = "MASK" - LATENT = "LATENT" - BOOLEAN = "BOOLEAN" - INT = "INT" - FLOAT = "FLOAT" - COMBO = "COMBO" - CONDITIONING = "CONDITIONING" - SAMPLER = "SAMPLER" - SIGMAS = "SIGMAS" - GUIDER = "GUIDER" - NOISE = "NOISE" - CLIP = "CLIP" - CONTROL_NET = "CONTROL_NET" - VAE = "VAE" - MODEL = "MODEL" - LORA_MODEL = "LORA_MODEL" - LOSS_MAP = "LOSS_MAP" - CLIP_VISION = "CLIP_VISION" - CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT" - STYLE_MODEL = "STYLE_MODEL" - GLIGEN = "GLIGEN" - UPSCALE_MODEL = "UPSCALE_MODEL" - AUDIO = "AUDIO" - WEBCAM = "WEBCAM" - POINT = "POINT" - FACE_ANALYSIS = "FACE_ANALYSIS" - BBOX = "BBOX" - SEGS = "SEGS" - VIDEO = "VIDEO" - - ANY = "*" - """Always matches any type, but at a price. - - Causes some functionality issues (e.g. reroutes, link types), and should be avoided whenever possible. - """ - NUMBER = "FLOAT,INT" - """A float or an int - could be either""" - PRIMITIVE = "STRING,FLOAT,INT,BOOLEAN" - """Could be any of: string, float, int, or bool""" - - def __ne__(self, value: object) -> bool: - if self == "*" or value == "*": - return False - if not isinstance(value, str): - return True - a = frozenset(self.split(",")) - b = frozenset(value.split(",")) - return not (b.issubset(a) or a.issubset(b)) - - -class RemoteInputOptions(TypedDict): - route: str - """The route to the remote source.""" - refresh_button: bool - """Specifies whether to show a refresh button in the UI below the widget.""" - control_after_refresh: Literal["first", "last"] - """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on.""" - timeout: int - """The maximum amount of time to wait for a response from the remote source in milliseconds.""" - max_retries: int - """The maximum number of retries before aborting the request.""" - refresh: int - """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.""" - - -class MultiSelectOptions(TypedDict): - placeholder: NotRequired[str] - """The placeholder text to display in the multi-select widget when no items are selected.""" - chip: NotRequired[bool] - """Specifies whether to use chips instead of comma separated values for the multi-select widget.""" - - -class InputTypeOptions(TypedDict): - """Provides type hinting for the return type of the INPUT_TYPES node function. - - Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`). - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes - """ - - default: NotRequired[bool | str | float | int | list | tuple] - """The default value of the widget""" - defaultInput: NotRequired[bool] - """@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist. - - defaultInput on required inputs should be dropped. - - defaultInput on optional inputs should be replaced with forceInput. - Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364 - """ - forceInput: NotRequired[bool] - """Forces the input to be an input slot rather than a widget even a widget is available for the input type.""" - lazy: NotRequired[bool] - """Declares that this input uses lazy evaluation""" - rawLink: NotRequired[bool] - """When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `["nodeId", ]`). Designed for node expansion.""" - tooltip: NotRequired[str] - """Tooltip for the input (or widget), shown on pointer hover""" - socketless: NotRequired[bool] - """All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created. - Available from frontend v1.17.5 - Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548 - """ - widgetType: NotRequired[str] - """Specifies a type to be used for widget initialization if different from the input type. - Available from frontend v1.18.0 - https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550""" - # class InputTypeNumber(InputTypeOptions): - # default: float | int - min: NotRequired[float] - """The minimum value of a number (``FLOAT`` | ``INT``)""" - max: NotRequired[float] - """The maximum value of a number (``FLOAT`` | ``INT``)""" - step: NotRequired[float] - """The amount to increment or decrement a widget by when stepping up/down (``FLOAT`` | ``INT``)""" - round: NotRequired[float] - """Floats are rounded by this value (``FLOAT``)""" - # class InputTypeBoolean(InputTypeOptions): - # default: bool - label_on: NotRequired[str] - """The label to use in the UI when the bool is True (``BOOLEAN``)""" - label_off: NotRequired[str] - """The label to use in the UI when the bool is False (``BOOLEAN``)""" - # class InputTypeString(InputTypeOptions): - # default: str - multiline: NotRequired[bool] - """Use a multiline text box (``STRING``)""" - placeholder: NotRequired[str] - """Placeholder text to display in the UI when empty (``STRING``)""" - # Deprecated: - # defaultVal: str - dynamicPrompts: NotRequired[bool] - """Causes the front-end to evaluate dynamic prompts (``STRING``)""" - # class InputTypeCombo(InputTypeOptions): - image_upload: NotRequired[bool] - """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`.""" - image_folder: NotRequired[Literal["input", "output", "temp"]] - """Specifies which folder to get preview images from if the input has the ``image_upload`` flag. - """ - remote: NotRequired[RemoteInputOptions] - """Specifies the configuration for a remote input. - Available after ComfyUI frontend v1.9.7 - https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422""" - control_after_generate: NotRequired[bool] - """Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types.""" - options: NotRequired[list[str | int | float]] - """COMBO type only. Specifies the selectable options for the combo widget. - Prefer: - ["COMBO", {"options": ["Option 1", "Option 2", "Option 3"]}] - Over: - [["Option 1", "Option 2", "Option 3"]] - """ - multi_select: NotRequired[MultiSelectOptions] - """COMBO type only. Specifies the configuration for a multi-select widget. - Available after ComfyUI frontend v1.13.4 - https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987""" - - -class HiddenInputTypeDict(TypedDict): - """Provides type hinting for the hidden entry of node INPUT_TYPES.""" - - node_id: NotRequired[Literal["UNIQUE_ID"]] - """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" - unique_id: NotRequired[Literal["UNIQUE_ID"]] - """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" - prompt: NotRequired[Literal["PROMPT"]] - """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" - extra_pnginfo: NotRequired[Literal["EXTRA_PNGINFO"]] - """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" - dynprompt: NotRequired[Literal["DYNPROMPT"]] - """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" - - -class InputTypeDict(TypedDict): - """Provides type hinting for node INPUT_TYPES. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs - """ - - required: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] - """Describes all inputs that must be connected for the node to execute.""" - optional: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] - """Describes inputs which do not need to be connected.""" - hidden: NotRequired[HiddenInputTypeDict] - """Offers advanced functionality and server-client communication. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs - """ - - -class ComfyNodeABC(ABC): - """Abstract base class for Comfy nodes. Includes the names and expected types of attributes. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview - """ - - DESCRIPTION: str - """Node description, shown as a tooltip when hovering over the node. - - Usage:: - - # Explicitly define the description - DESCRIPTION = "Example description here." - - # Use the docstring of the node class. - DESCRIPTION = cleandoc(__doc__) - """ - CATEGORY: str - """The category of the node, as per the "Add Node" menu. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category - """ - EXPERIMENTAL: bool - """Flags a node as experimental, informing users that it may change or not work as expected.""" - DEPRECATED: bool - """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" - API_NODE: Optional[bool] - """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" - - @classmethod - @abstractmethod - def INPUT_TYPES(s) -> InputTypeDict: - """Defines node inputs. - - * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute. - * The ``optional`` key can be added to describe inputs which do not need to be connected. - * The ``hidden`` key offers some advanced functionality. More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types - """ - return {"required": {}} - - OUTPUT_NODE: bool - """Flags this node as an output node, causing any inputs it requires to be executed. - - If a node is not connected to any output nodes, that node will not be executed. Usage:: - - OUTPUT_NODE = True - - From the docs: - - By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node - """ - INPUT_IS_LIST: bool - """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes. - - All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in. This also affects ``check_lazy_status``. - - From the docs: - - A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing - """ - OUTPUT_IS_LIST: tuple[bool, ...] - """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items. - - Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list. - - A ``tuple[bool]``, where the items match those in `RETURN_TYPES`:: - - RETURN_TYPES = (IO.INT, IO.INT, IO.STRING) - OUTPUT_IS_LIST = (True, True, False) # The string output will be handled normally - - From the docs: - - In order to tell Comfy that the list being returned should not be wrapped, but treated as a series of data for sequential processing, - the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`, - specifying which outputs which should be so treated. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing - """ - - RETURN_TYPES: tuple[IO, ...] - """A tuple representing the outputs of this node. - - Usage:: - - RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE") - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types - """ - RETURN_NAMES: tuple[str, ...] - """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")`` - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names - """ - OUTPUT_TOOLTIPS: tuple[str, ...] - """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`.""" - FUNCTION: str - """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"` - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function - """ - - -class CheckLazyMixin: - """Provides a basic check_lazy_status implementation and type hinting for nodes that use lazy inputs.""" - - def check_lazy_status(self, **kwargs) -> list[str]: - """Returns a list of input names that should be evaluated. - - This basic mixin impl. requires all inputs. - - :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ - When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. - - Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). - Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status - """ - - need = [name for name in kwargs if kwargs[name] is None] - return need - - -class FileLocator(TypedDict): - """Provides type hinting for the file location""" - - filename: str - """The filename of the file.""" - subfolder: str - """The subfolder of the file.""" - type: Literal["input", "output", "temp"] - """The root folder of the file.""" diff --git a/comfy/conds.py b/comfy/conds.py deleted file mode 100644 index 5af3e93eaeecdef08d9e07901b33a70bda60fb43..0000000000000000000000000000000000000000 --- a/comfy/conds.py +++ /dev/null @@ -1,137 +0,0 @@ -import torch -import math -import comfy.utils -import logging - - -class CONDRegular: - def __init__(self, cond): - self.cond = cond - - def _copy_with(self, cond): - return self.__class__(cond) - - def process_cond(self, batch_size, **kwargs): - return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size)) - - def can_concat(self, other): - if self.cond.shape != other.cond.shape: - return False - if self.cond.device != other.cond.device: - logging.warning("WARNING: conds not on same device, skipping concat.") - return False - return True - - def concat(self, others): - conds = [self.cond] - for x in others: - conds.append(x.cond) - return torch.cat(conds) - - def size(self): - return list(self.cond.size()) - - -class CONDNoiseShape(CONDRegular): - def process_cond(self, batch_size, area, **kwargs): - data = self.cond - if area is not None: - dims = len(area) // 2 - for i in range(dims): - data = data.narrow(i + 2, area[i + dims], area[i]) - - return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size)) - - -class CONDCrossAttn(CONDRegular): - def can_concat(self, other): - s1 = self.cond.shape - s2 = other.cond.shape - if s1 != s2: - if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen - return False - - mult_min = math.lcm(s1[1], s2[1]) - diff = mult_min // min(s1[1], s2[1]) - if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much - return False - if self.cond.device != other.cond.device: - logging.warning("WARNING: conds not on same device: skipping concat.") - return False - return True - - def concat(self, others): - conds = [self.cond] - crossattn_max_len = self.cond.shape[1] - for x in others: - c = x.cond - crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1]) - conds.append(c) - - out = [] - for c in conds: - if c.shape[1] < crossattn_max_len: - c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result - out.append(c) - return torch.cat(out) - - -class CONDConstant(CONDRegular): - def __init__(self, cond): - self.cond = cond - - def process_cond(self, batch_size, **kwargs): - return self._copy_with(self.cond) - - def can_concat(self, other): - if self.cond != other.cond: - return False - return True - - def concat(self, others): - return self.cond - - def size(self): - return [1] - - -class CONDList(CONDRegular): - def __init__(self, cond): - self.cond = cond - - def process_cond(self, batch_size, **kwargs): - out = [] - for c in self.cond: - out.append(comfy.utils.repeat_to_batch_size(c, batch_size)) - - return self._copy_with(out) - - def can_concat(self, other): - if len(self.cond) != len(other.cond): - return False - for i in range(len(self.cond)): - if self.cond[i].shape != other.cond[i].shape: - return False - - return True - - def concat(self, others): - out = [] - for i in range(len(self.cond)): - o = [self.cond[i]] - for x in others: - o.append(x.cond[i]) - out.append(torch.cat(o)) - - return out - - def size(self): # hackish implementation to make the mem estimation work - o = 0 - c = 1 - for c in self.cond: - size = c.size() - o += math.prod(size) - if len(size) > 1: - c = size[1] - - return [1, c, o // c] diff --git a/comfy/context_windows.py b/comfy/context_windows.py deleted file mode 100644 index 041f380f9140b9e41d94399dd8ce781189699efd..0000000000000000000000000000000000000000 --- a/comfy/context_windows.py +++ /dev/null @@ -1,540 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING, Callable -import torch -import numpy as np -import collections -from dataclasses import dataclass -from abc import ABC, abstractmethod -import logging -import comfy.model_management -import comfy.patcher_extension -if TYPE_CHECKING: - from comfy.model_base import BaseModel - from comfy.model_patcher import ModelPatcher - from comfy.controlnet import ControlBase - - -class ContextWindowABC(ABC): - def __init__(self): - ... - - @abstractmethod - def get_tensor(self, full: torch.Tensor) -> torch.Tensor: - """ - Get torch.Tensor applicable to current window. - """ - raise NotImplementedError("Not implemented.") - - @abstractmethod - def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torch.Tensor: - """ - Apply torch.Tensor of window to the full tensor, in place. Returns reference to updated full tensor, not a copy. - """ - raise NotImplementedError("Not implemented.") - -class ContextHandlerABC(ABC): - def __init__(self): - ... - - @abstractmethod - def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: - raise NotImplementedError("Not implemented.") - - @abstractmethod - def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: ContextWindowABC, device=None) -> list: - raise NotImplementedError("Not implemented.") - - @abstractmethod - def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): - raise NotImplementedError("Not implemented.") - - - -class IndexListContextWindow(ContextWindowABC): - def __init__(self, index_list: list[int], dim: int=0): - self.index_list = index_list - self.context_length = len(index_list) - self.dim = dim - - def get_tensor(self, full: torch.Tensor, device=None, dim=None) -> torch.Tensor: - if dim is None: - dim = self.dim - if dim == 0 and full.shape[dim] == 1: - return full - idx = [slice(None)] * dim + [self.index_list] - return full[idx].to(device) - - def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor: - if dim is None: - dim = self.dim - idx = [slice(None)] * dim + [self.index_list] - full[idx] += to_add - return full - - -class IndexListCallbacks: - EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows" - COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results" - EXECUTE_START = "execute_start" - EXECUTE_CLEANUP = "execute_cleanup" - - def init_callbacks(self): - return {} - - -@dataclass -class ContextSchedule: - name: str - func: Callable - -@dataclass -class ContextFuseMethod: - name: str - func: Callable - -ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window']) -class IndexListContextHandler(ContextHandlerABC): - def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop=False, dim=0): - self.context_schedule = context_schedule - self.fuse_method = fuse_method - self.context_length = context_length - self.context_overlap = context_overlap - self.context_stride = context_stride - self.closed_loop = closed_loop - self.dim = dim - self._step = 0 - - self.callbacks = {} - - def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: - # for now, assume first dim is batch - should have stored on BaseModel in actual implementation - if x_in.size(self.dim) > self.context_length: - logging.info(f"Using context windows {self.context_length} for {x_in.size(self.dim)} frames.") - return True - return False - - def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase: - if control.previous_controlnet is not None: - self.prepare_control_objects(control.previous_controlnet, device) - return control - - def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list: - if cond_in is None: - return None - # reuse or resize cond items to match context requirements - resized_cond = [] - # cond object is a list containing a dict - outer list is irrelevant, so just loop through it - for actual_cond in cond_in: - resized_actual_cond = actual_cond.copy() - # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary - for key in actual_cond: - try: - cond_item = actual_cond[key] - if isinstance(cond_item, torch.Tensor): - # check that tensor is the expected length - x.size(0) - if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim): - # if so, it's subsetting time - tell controls the expected indeces so they can handle them - actual_cond_item = window.get_tensor(cond_item) - resized_actual_cond[key] = actual_cond_item.to(device) - else: - resized_actual_cond[key] = cond_item.to(device) - # look for control - elif key == "control": - resized_actual_cond[key] = self.prepare_control_objects(cond_item, device) - elif isinstance(cond_item, dict): - new_cond_item = cond_item.copy() - # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor) - for cond_key, cond_value in new_cond_item.items(): - if isinstance(cond_value, torch.Tensor): - if cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim): - new_cond_item[cond_key] = window.get_tensor(cond_value, device) - # if has cond that is a Tensor, check if needs to be subset - elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): - if cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim): - new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device)) - elif cond_key == "num_video_frames": # for SVD - new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond) - new_cond_item[cond_key].cond = window.context_length - resized_actual_cond[key] = new_cond_item - else: - resized_actual_cond[key] = cond_item - finally: - del cond_item # just in case to prevent VRAM issues - resized_cond.append(resized_actual_cond) - return resized_cond - - def set_step(self, timestep: torch.Tensor, model_options: dict[str]): - mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001) - matches = torch.nonzero(mask) - if torch.numel(matches) == 0: - raise Exception("No sample_sigmas matched current timestep; something went wrong.") - self._step = int(matches[0].item()) - - def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]: - full_length = x_in.size(self.dim) # TODO: choose dim based on model - context_windows = self.context_schedule.func(full_length, self, model_options) - context_windows = [IndexListContextWindow(window, dim=self.dim) for window in context_windows] - return context_windows - - def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): - self.set_step(timestep, model_options) - context_windows = self.get_context_windows(model, x_in, model_options) - enumerated_context_windows = list(enumerate(context_windows)) - - conds_final = [torch.zeros_like(x_in) for _ in conds] - if self.fuse_method.name == ContextFuseMethods.RELATIVE: - counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] - else: - counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] - biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds] - - for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks): - callback(self, model, x_in, conds, timestep, model_options) - - for enum_window in enumerated_context_windows: - results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options) - for result in results: - self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep, - conds_final, counts_final, biases_final) - try: - # finalize conds - if self.fuse_method.name == ContextFuseMethods.RELATIVE: - # relative is already normalized, so return as is - del counts_final - return conds_final - else: - # normalize conds via division by context usage counts - for i in range(len(conds_final)): - conds_final[i] /= counts_final[i] - del counts_final - return conds_final - finally: - for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks): - callback(self, model, x_in, conds, timestep, model_options) - - def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]], - model_options, device=None, first_device=None): - results: list[ContextResults] = [] - for window_idx, window in enumerated_context_windows: - # allow processing to end between context window executions for faster Cancel - comfy.model_management.throw_exception_if_processing_interrupted() - - for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks): - callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device) - - # update exposed params - model_options["transformer_options"]["context_window"] = window - # get subsections of x, timestep, conds - sub_x = window.get_tensor(x_in, device) - sub_timestep = window.get_tensor(timestep, device, dim=0) - sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds] - - sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options) - if device is not None: - for i in range(len(sub_conds_out)): - sub_conds_out[i] = sub_conds_out[i].to(x_in.device) - results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window)) - return results - - - def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor, - conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]): - if self.fuse_method.name == ContextFuseMethods.RELATIVE: - for pos, idx in enumerate(window.index_list): - # bias is the influence of a specific index in relation to the whole context window - bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2) - bias = max(1e-2, bias) - # take weighted average relative to total bias of current idx - for i in range(len(sub_conds_out)): - bias_total = biases_final[i][idx] - prev_weight = (bias_total / (bias_total + bias)) - new_weight = (bias / (bias_total + bias)) - # account for dims of tensors - idx_window = [slice(None)] * self.dim + [idx] - pos_window = [slice(None)] * self.dim + [pos] - # apply new values - conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight - biases_final[i][idx] = bias_total + bias - else: - # add conds and counts based on weights of fuse method - weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep) - weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device) - for i in range(len(sub_conds_out)): - window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor) - window.add_window(counts_final[i], weights_tensor) - - for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks): - callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final) - - -def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs): - # limit noise_shape length to context_length for more accurate vram use estimation - model_options = kwargs.get("model_options", None) - if model_options is None: - raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.") - handler: IndexListContextHandler = model_options.get("context_handler", None) - if handler is not None: - noise_shape = list(noise_shape) - noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length) - return executor(model, noise_shape, *args, **kwargs) - - -def create_prepare_sampling_wrapper(model: ModelPatcher): - model.add_wrapper_with_key( - comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, - "ContextWindows_prepare_sampling", - _prepare_sampling_wrapper - ) - - -def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor: - total_dims = len(x_in.shape) - weights_tensor = torch.Tensor(weights).to(device=device) - for _ in range(dim): - weights_tensor = weights_tensor.unsqueeze(0) - for _ in range(total_dims - dim - 1): - weights_tensor = weights_tensor.unsqueeze(-1) - return weights_tensor - -def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]: - total_dims = len(x_in.shape) - shape = [] - for _ in range(dim): - shape.append(1) - shape.append(x_in.shape[dim]) - for _ in range(total_dims - dim - 1): - shape.append(1) - return shape - -class ContextSchedules: - UNIFORM_LOOPED = "looped_uniform" - UNIFORM_STANDARD = "standard_uniform" - STATIC_STANDARD = "standard_static" - BATCHED = "batched" - - -# from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py -def create_windows_uniform_looped(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): - windows = [] - if num_frames < handler.context_length: - windows.append(list(range(num_frames))) - return windows - - context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1) - # obtain uniform windows as normal, looping and all - for context_step in 1 << np.arange(context_stride): - pad = int(round(num_frames * ordered_halving(handler._step))) - for j in range( - int(ordered_halving(handler._step) * context_step) + pad, - num_frames + pad + (0 if handler.closed_loop else -handler.context_overlap), - (handler.context_length * context_step - handler.context_overlap), - ): - windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)]) - - return windows - -def create_windows_uniform_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): - # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning; - # instead, they get shifted to the corresponding end of the frames. - # in the case that a window (shifted or not) is identical to the previous one, it gets skipped. - windows = [] - if num_frames <= handler.context_length: - windows.append(list(range(num_frames))) - return windows - - context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1) - # first, obtain uniform windows as normal, looping and all - for context_step in 1 << np.arange(context_stride): - pad = int(round(num_frames * ordered_halving(handler._step))) - for j in range( - int(ordered_halving(handler._step) * context_step) + pad, - num_frames + pad + (-handler.context_overlap), - (handler.context_length * context_step - handler.context_overlap), - ): - windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)]) - - # now that windows are created, shift any windows that loop, and delete duplicate windows - delete_idxs = [] - win_i = 0 - while win_i < len(windows): - # if window is rolls over itself, need to shift it - is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames) - if is_roll: - roll_val = windows[win_i][roll_idx] # roll_val might not be 0 for windows of higher strides - shift_window_to_end(windows[win_i], num_frames=num_frames) - # check if next window (cyclical) is missing roll_val - if roll_val not in windows[(win_i+1) % len(windows)]: - # need to insert new window here - just insert window starting at roll_val - windows.insert(win_i+1, list(range(roll_val, roll_val + handler.context_length))) - # delete window if it's not unique - for pre_i in range(0, win_i): - if windows[win_i] == windows[pre_i]: - delete_idxs.append(win_i) - break - win_i += 1 - - # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation - delete_idxs.reverse() - for i in delete_idxs: - windows.pop(i) - - return windows - - -def create_windows_static_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): - windows = [] - if num_frames <= handler.context_length: - windows.append(list(range(num_frames))) - return windows - # always return the same set of windows - delta = handler.context_length - handler.context_overlap - for start_idx in range(0, num_frames, delta): - # if past the end of frames, move start_idx back to allow same context_length - ending = start_idx + handler.context_length - if ending >= num_frames: - final_delta = ending - num_frames - final_start_idx = start_idx - final_delta - windows.append(list(range(final_start_idx, final_start_idx + handler.context_length))) - break - windows.append(list(range(start_idx, start_idx + handler.context_length))) - return windows - - -def create_windows_batched(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): - windows = [] - if num_frames <= handler.context_length: - windows.append(list(range(num_frames))) - return windows - # always return the same set of windows; - # no overlap, just cut up based on context_length; - # last window size will be different if num_frames % opts.context_length != 0 - for start_idx in range(0, num_frames, handler.context_length): - windows.append(list(range(start_idx, min(start_idx + handler.context_length, num_frames)))) - return windows - - -def create_windows_default(num_frames: int, handler: IndexListContextHandler): - return [list(range(num_frames))] - - -CONTEXT_MAPPING = { - ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped, - ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard, - ContextSchedules.STATIC_STANDARD: create_windows_static_standard, - ContextSchedules.BATCHED: create_windows_batched, -} - - -def get_matching_context_schedule(context_schedule: str) -> ContextSchedule: - func = CONTEXT_MAPPING.get(context_schedule, None) - if func is None: - raise ValueError(f"Unknown context_schedule '{context_schedule}'.") - return ContextSchedule(context_schedule, func) - - -def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None): - return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs) - - -def create_weights_flat(length: int, **kwargs) -> list[float]: - # weight is the same for all - return [1.0] * length - -def create_weights_pyramid(length: int, **kwargs) -> list[float]: - # weight is based on the distance away from the edge of the context window; - # based on weighted average concept in FreeNoise paper - if length % 2 == 0: - max_weight = length // 2 - weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1)) - else: - max_weight = (length + 1) // 2 - weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1)) - return weight_sequence - -def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs): - # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302 - # only expected overlap is given different weights - weights_torch = torch.ones((length)) - # blend left-side on all except first window - if min(idxs) > 0: - ramp_up = torch.linspace(1e-37, 1, handler.context_overlap) - weights_torch[:handler.context_overlap] = ramp_up - # blend right-side on all except last window - if max(idxs) < full_length-1: - ramp_down = torch.linspace(1, 1e-37, handler.context_overlap) - weights_torch[-handler.context_overlap:] = ramp_down - return weights_torch - -class ContextFuseMethods: - FLAT = "flat" - PYRAMID = "pyramid" - RELATIVE = "relative" - OVERLAP_LINEAR = "overlap-linear" - - LIST = [PYRAMID, FLAT, OVERLAP_LINEAR] - LIST_STATIC = [PYRAMID, RELATIVE, FLAT, OVERLAP_LINEAR] - - -FUSE_MAPPING = { - ContextFuseMethods.FLAT: create_weights_flat, - ContextFuseMethods.PYRAMID: create_weights_pyramid, - ContextFuseMethods.RELATIVE: create_weights_pyramid, - ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear, -} - -def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod: - func = FUSE_MAPPING.get(fuse_method, None) - if func is None: - raise ValueError(f"Unknown fuse_method '{fuse_method}'.") - return ContextFuseMethod(fuse_method, func) - -# Returns fraction that has denominator that is a power of 2 -def ordered_halving(val): - # get binary value, padded with 0s for 64 bits - bin_str = f"{val:064b}" - # flip binary value, padding included - bin_flip = bin_str[::-1] - # convert binary to int - as_int = int(bin_flip, 2) - # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616, - # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's) - return as_int / (1 << 64) - - -def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]: - all_indexes = list(range(num_frames)) - for w in windows: - for val in w: - try: - all_indexes.remove(val) - except ValueError: - pass - return all_indexes - - -def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]: - prev_val = -1 - for i, val in enumerate(window): - val = val % num_frames - if val < prev_val: - return True, i - prev_val = val - return False, -1 - - -def shift_window_to_start(window: list[int], num_frames: int): - start_val = window[0] - for i in range(len(window)): - # 1) subtract each element by start_val to move vals relative to the start of all frames - # 2) add num_frames and take modulus to get adjusted vals - window[i] = ((window[i] - start_val) + num_frames) % num_frames - - -def shift_window_to_end(window: list[int], num_frames: int): - # 1) shift window to start - shift_window_to_start(window, num_frames) - end_val = window[-1] - end_delta = num_frames - end_val - 1 - for i in range(len(window)): - # 2) add end_delta to each val to slide windows to end - window[i] = window[i] + end_delta diff --git a/comfy/controlnet.py b/comfy/controlnet.py deleted file mode 100644 index e3dfedf554c761ea17b04a3c42e92331cc1e7124..0000000000000000000000000000000000000000 --- a/comfy/controlnet.py +++ /dev/null @@ -1,871 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - - -import torch -from enum import Enum -import math -import os -import logging -import comfy.utils -import comfy.model_management -import comfy.model_detection -import comfy.model_patcher -import comfy.ops -import comfy.latent_formats -import comfy.model_base - -import comfy.cldm.cldm -import comfy.t2i_adapter.adapter -import comfy.ldm.cascade.controlnet -import comfy.cldm.mmdit -import comfy.ldm.hydit.controlnet -import comfy.ldm.flux.controlnet -import comfy.ldm.qwen_image.controlnet -import comfy.cldm.dit_embedder -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from comfy.hooks import HookGroup - - -def broadcast_image_to(tensor, target_batch_size, batched_number): - current_batch_size = tensor.shape[0] - if current_batch_size == 1: - return tensor - - per_batch = target_batch_size // batched_number - tensor = tensor[:per_batch] - - if per_batch > tensor.shape[0]: - tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) - - current_batch_size = tensor.shape[0] - if current_batch_size == target_batch_size: - return tensor - else: - return torch.cat([tensor] * batched_number, dim=0) - -class StrengthType(Enum): - CONSTANT = 1 - LINEAR_UP = 2 - -class ControlBase: - def __init__(self): - self.cond_hint_original = None - self.cond_hint = None - self.strength = 1.0 - self.timestep_percent_range = (0.0, 1.0) - self.latent_format = None - self.vae = None - self.global_average_pooling = False - self.timestep_range = None - self.compression_ratio = 8 - self.upscale_algorithm = 'nearest-exact' - self.extra_args = {} - self.previous_controlnet = None - self.extra_conds = [] - self.strength_type = StrengthType.CONSTANT - self.concat_mask = False - self.extra_concat_orig = [] - self.extra_concat = None - self.extra_hooks: HookGroup = None - self.preprocess_image = lambda a: a - - def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]): - self.cond_hint_original = cond_hint - self.strength = strength - self.timestep_percent_range = timestep_percent_range - if self.latent_format is not None: - if vae is None: - logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.") - self.vae = vae - self.extra_concat_orig = extra_concat.copy() - if self.concat_mask and len(self.extra_concat_orig) == 0: - self.extra_concat_orig.append(torch.tensor([[[[1.0]]]])) - return self - - def pre_run(self, model, percent_to_timestep_function): - self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) - if self.previous_controlnet is not None: - self.previous_controlnet.pre_run(model, percent_to_timestep_function) - - def set_previous_controlnet(self, controlnet): - self.previous_controlnet = controlnet - return self - - def cleanup(self): - if self.previous_controlnet is not None: - self.previous_controlnet.cleanup() - - self.cond_hint = None - self.extra_concat = None - self.timestep_range = None - - def get_models(self): - out = [] - if self.previous_controlnet is not None: - out += self.previous_controlnet.get_models() - return out - - def get_extra_hooks(self): - out = [] - if self.extra_hooks is not None: - out.append(self.extra_hooks) - if self.previous_controlnet is not None: - out += self.previous_controlnet.get_extra_hooks() - return out - - def copy_to(self, c): - c.cond_hint_original = self.cond_hint_original - c.strength = self.strength - c.timestep_percent_range = self.timestep_percent_range - c.global_average_pooling = self.global_average_pooling - c.compression_ratio = self.compression_ratio - c.upscale_algorithm = self.upscale_algorithm - c.latent_format = self.latent_format - c.extra_args = self.extra_args.copy() - c.vae = self.vae - c.extra_conds = self.extra_conds.copy() - c.strength_type = self.strength_type - c.concat_mask = self.concat_mask - c.extra_concat_orig = self.extra_concat_orig.copy() - c.extra_hooks = self.extra_hooks.clone() if self.extra_hooks else None - c.preprocess_image = self.preprocess_image - - def inference_memory_requirements(self, dtype): - if self.previous_controlnet is not None: - return self.previous_controlnet.inference_memory_requirements(dtype) - return 0 - - def control_merge(self, control, control_prev, output_dtype): - out = {'input':[], 'middle':[], 'output': []} - - for key in control: - control_output = control[key] - applied_to = set() - for i in range(len(control_output)): - x = control_output[i] - if x is not None: - if self.global_average_pooling: - x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) - - if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once - applied_to.add(x) - if self.strength_type == StrengthType.CONSTANT: - x *= self.strength - elif self.strength_type == StrengthType.LINEAR_UP: - x *= (self.strength ** float(len(control_output) - i)) - - if output_dtype is not None and x.dtype != output_dtype: - x = x.to(output_dtype) - - out[key].append(x) - - if control_prev is not None: - for x in ['input', 'middle', 'output']: - o = out[x] - for i in range(len(control_prev[x])): - prev_val = control_prev[x][i] - if i >= len(o): - o.append(prev_val) - elif prev_val is not None: - if o[i] is None: - o[i] = prev_val - else: - if o[i].shape[0] < prev_val.shape[0]: - o[i] = prev_val + o[i] - else: - o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue - return out - - def set_extra_arg(self, argument, value=None): - self.extra_args[argument] = value - - -class ControlNet(ControlBase): - def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a): - super().__init__() - self.control_model = control_model - self.load_device = load_device - if control_model is not None: - self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device()) - - self.compression_ratio = compression_ratio - self.global_average_pooling = global_average_pooling - self.model_sampling_current = None - self.manual_cast_dtype = manual_cast_dtype - self.latent_format = latent_format - self.extra_conds += extra_conds - self.strength_type = strength_type - self.concat_mask = concat_mask - self.preprocess_image = preprocess_image - - def get_control(self, x_noisy, t, cond, batched_number, transformer_options): - control_prev = None - if self.previous_controlnet is not None: - control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) - - if self.timestep_range is not None: - if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: - if control_prev is not None: - return control_prev - else: - return None - - dtype = self.control_model.dtype - if self.manual_cast_dtype is not None: - dtype = self.manual_cast_dtype - - if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: - if self.cond_hint is not None: - del self.cond_hint - self.cond_hint = None - compression_ratio = self.compression_ratio - if self.vae is not None: - compression_ratio *= self.vae.spacial_compression_encode() - else: - if self.latent_format is not None: - raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.") - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center") - self.cond_hint = self.preprocess_image(self.cond_hint) - if self.vae is not None: - loaded_models = comfy.model_management.loaded_models(only_currently_used=True) - self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1)) - comfy.model_management.load_models_gpu(loaded_models) - if self.latent_format is not None: - self.cond_hint = self.latent_format.process_in(self.cond_hint) - if len(self.extra_concat_orig) > 0: - to_concat = [] - for c in self.extra_concat_orig: - c = c.to(self.cond_hint.device) - c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center") - to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0])) - self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1) - - self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype) - if x_noisy.shape[0] != self.cond_hint.shape[0]: - self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) - - context = cond.get('crossattn_controlnet', cond['c_crossattn']) - extra = self.extra_args.copy() - for c in self.extra_conds: - temp = cond.get(c, None) - if temp is not None: - extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device) - - timestep = self.model_sampling_current.timestep(t) - x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) - - control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra) - return self.control_merge(control, control_prev, output_dtype=None) - - def copy(self): - c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) - c.control_model = self.control_model - c.control_model_wrapped = self.control_model_wrapped - self.copy_to(c) - return c - - def get_models(self): - out = super().get_models() - out.append(self.control_model_wrapped) - return out - - def pre_run(self, model, percent_to_timestep_function): - super().pre_run(model, percent_to_timestep_function) - self.model_sampling_current = model.model_sampling - - def cleanup(self): - self.model_sampling_current = None - super().cleanup() - -class ControlLoraOps: - class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp): - def __init__(self, in_features: int, out_features: int, bias: bool = True, - device=None, dtype=None) -> None: - super().__init__() - self.in_features = in_features - self.out_features = out_features - self.weight = None - self.up = None - self.down = None - self.bias = None - - def forward(self, input): - weight, bias = comfy.ops.cast_bias_weight(self, input) - if self.up is not None: - return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) - else: - return torch.nn.functional.linear(input, weight, bias) - - class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True, - padding_mode='zeros', - device=None, - dtype=None - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - self.dilation = dilation - self.transposed = False - self.output_padding = 0 - self.groups = groups - self.padding_mode = padding_mode - - self.weight = None - self.bias = None - self.up = None - self.down = None - - - def forward(self, input): - weight, bias = comfy.ops.cast_bias_weight(self, input) - if self.up is not None: - return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) - else: - return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) - - -class ControlLora(ControlNet): - def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options - ControlBase.__init__(self) - self.control_weights = control_weights - self.global_average_pooling = global_average_pooling - self.extra_conds += ["y"] - - def pre_run(self, model, percent_to_timestep_function): - super().pre_run(model, percent_to_timestep_function) - controlnet_config = model.model_config.unet_config.copy() - controlnet_config.pop("out_channels") - controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] - self.manual_cast_dtype = model.manual_cast_dtype - dtype = model.get_dtype() - if self.manual_cast_dtype is None: - class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init): - pass - else: - class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast): - pass - dtype = self.manual_cast_dtype - - controlnet_config["operations"] = control_lora_ops - controlnet_config["dtype"] = dtype - self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) - self.control_model.to(comfy.model_management.get_torch_device()) - diffusion_model = model.diffusion_model - sd = diffusion_model.state_dict() - - for k in sd: - weight = sd[k] - try: - comfy.utils.set_attr_param(self.control_model, k, weight) - except: - pass - - for k in self.control_weights: - if (k not in {"lora_controlnet"}): - if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k): - comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) - - def copy(self): - c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) - self.copy_to(c) - return c - - def cleanup(self): - del self.control_model - self.control_model = None - super().cleanup() - - def get_models(self): - out = ControlBase.get_models(self) - return out - - def inference_memory_requirements(self, dtype): - return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) - -def controlnet_config(sd, model_options={}): - model_config = comfy.model_detection.model_config_from_unet(sd, "", True) - - unet_dtype = model_options.get("dtype", None) - if unet_dtype is None: - weight_dtype = comfy.utils.weight_dtype(sd) - - supported_inference_dtypes = list(model_config.supported_inference_dtypes) - unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) - - load_device = comfy.model_management.get_torch_device() - manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) - - operations = model_options.get("custom_operations", None) - if operations is None: - operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) - - offload_device = comfy.model_management.unet_offload_device() - return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device - -def controlnet_load_state_dict(control_model, sd): - missing, unexpected = control_model.load_state_dict(sd, strict=False) - - if len(missing) > 0: - logging.warning("missing controlnet keys: {}".format(missing)) - - if len(unexpected) > 0: - logging.debug("unexpected controlnet keys: {}".format(unexpected)) - return control_model - - -def load_controlnet_mmdit(sd, model_options={}): - new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") - model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) - num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.') - for k in sd: - new_sd[k] = sd[k] - - concat_mask = False - control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1] - if control_latent_channels == 17: #inpaint controlnet - concat_mask = True - - control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) - control_model = controlnet_load_state_dict(control_model, new_sd) - - latent_format = comfy.latent_formats.SD3() - latent_format.shift_factor = 0 #SD3 controlnet weirdness - control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype) - return control - - -class ControlNetSD35(ControlNet): - def pre_run(self, model, percent_to_timestep_function): - if self.control_model.double_y_emb: - missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False) - else: - missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False) - super().pre_run(model, percent_to_timestep_function) - - def copy(self): - c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) - c.control_model = self.control_model - c.control_model_wrapped = self.control_model_wrapped - self.copy_to(c) - return c - -def load_controlnet_sd35(sd, model_options={}): - control_type = -1 - if "control_type" in sd: - control_type = round(sd.pop("control_type").item()) - - # blur_cnet = control_type == 0 - canny_cnet = control_type == 1 - depth_cnet = control_type == 2 - - new_sd = {} - for k in comfy.utils.MMDIT_MAP_BASIC: - if k[1] in sd: - new_sd[k[0]] = sd.pop(k[1]) - for k in sd: - new_sd[k] = sd[k] - sd = new_sd - - y_emb_shape = sd["y_embedder.mlp.0.weight"].shape - depth = y_emb_shape[0] // 64 - hidden_size = 64 * depth - num_heads = depth - head_dim = hidden_size // num_heads - num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.') - - load_device = comfy.model_management.get_torch_device() - offload_device = comfy.model_management.unet_offload_device() - unet_dtype = comfy.model_management.unet_dtype(model_params=-1) - - manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) - - operations = model_options.get("custom_operations", None) - if operations is None: - operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True) - - control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None, - patch_size=2, - in_chans=16, - num_layers=num_blocks, - main_model_double=depth, - double_y_emb=y_emb_shape[0] == y_emb_shape[1], - attention_head_dim=head_dim, - num_attention_heads=num_heads, - adm_in_channels=2048, - device=offload_device, - dtype=unet_dtype, - operations=operations) - - control_model = controlnet_load_state_dict(control_model, sd) - - latent_format = comfy.latent_formats.SD3() - preprocess_image = lambda a: a - if canny_cnet: - preprocess_image = lambda a: (a * 255 * 0.5 + 0.5) - elif depth_cnet: - preprocess_image = lambda a: 1.0 - a - - control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image) - return control - - - -def load_controlnet_hunyuandit(controlnet_data, model_options={}): - model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options) - - control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype) - control_model = controlnet_load_state_dict(control_model, controlnet_data) - - latent_format = comfy.latent_formats.SDXL() - extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img'] - control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT) - return control - -def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}): - model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) - control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) - control_model = controlnet_load_state_dict(control_model, sd) - extra_conds = ['y', 'guidance'] - control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) - return control - -def load_controlnet_flux_instantx(sd, model_options={}): - new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "") - model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options) - for k in sd: - new_sd[k] = sd[k] - - num_union_modes = 0 - union_cnet = "controlnet_mode_embedder.weight" - if union_cnet in new_sd: - num_union_modes = new_sd[union_cnet].shape[0] - - control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4 - concat_mask = False - if control_latent_channels == 17: - concat_mask = True - - control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) - control_model = controlnet_load_state_dict(control_model, new_sd) - - latent_format = comfy.latent_formats.Flux() - extra_conds = ['y', 'guidance'] - control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) - return control - -def load_controlnet_qwen_instantx(sd, model_options={}): - model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) - control_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) - control_model = controlnet_load_state_dict(control_model, sd) - latent_format = comfy.latent_formats.Wan21() - extra_conds = [] - control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) - return control - -def convert_mistoline(sd): - return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."}) - - -def load_controlnet_state_dict(state_dict, model=None, model_options={}): - controlnet_data = state_dict - if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT - return load_controlnet_hunyuandit(controlnet_data, model_options=model_options) - - if "lora_controlnet" in controlnet_data: - return ControlLora(controlnet_data, model_options=model_options) - - controlnet_config = None - supported_inference_dtypes = None - - if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format - controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data) - diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config) - diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" - diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" - - count = 0 - loop = True - while loop: - suffix = [".weight", ".bias"] - for s in suffix: - k_in = "controlnet_down_blocks.{}{}".format(count, s) - k_out = "zero_convs.{}.0{}".format(count, s) - if k_in not in controlnet_data: - loop = False - break - diffusers_keys[k_in] = k_out - count += 1 - - count = 0 - loop = True - while loop: - suffix = [".weight", ".bias"] - for s in suffix: - if count == 0: - k_in = "controlnet_cond_embedding.conv_in{}".format(s) - else: - k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) - k_out = "input_hint_block.{}{}".format(count * 2, s) - if k_in not in controlnet_data: - k_in = "controlnet_cond_embedding.conv_out{}".format(s) - loop = False - diffusers_keys[k_in] = k_out - count += 1 - - new_sd = {} - for k in diffusers_keys: - if k in controlnet_data: - new_sd[diffusers_keys[k]] = controlnet_data.pop(k) - - if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet - controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0] - for k in list(controlnet_data.keys()): - new_k = k.replace('.attn.in_proj_', '.attn.in_proj.') - new_sd[new_k] = controlnet_data.pop(k) - - leftover_keys = controlnet_data.keys() - if len(leftover_keys) > 0: - logging.warning("leftover keys: {}".format(leftover_keys)) - controlnet_data = new_sd - elif "controlnet_blocks.0.weight" in controlnet_data: - if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data: - return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options) - elif "pos_embed_input.proj.weight" in controlnet_data: - if "transformer_blocks.0.adaLN_modulation.1.bias" in controlnet_data: - return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format - else: - return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet - elif "transformer_blocks.0.img_mlp.net.0.proj.weight" in controlnet_data: - return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options) - elif "controlnet_x_embedder.weight" in controlnet_data: - return load_controlnet_flux_instantx(controlnet_data, model_options=model_options) - - elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux - return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options) - - pth_key = 'control_model.zero_convs.0.0.weight' - pth = False - key = 'zero_convs.0.0.weight' - if pth_key in controlnet_data: - pth = True - key = pth_key - prefix = "control_model." - elif key in controlnet_data: - prefix = "" - else: - net = load_t2i_adapter(controlnet_data, model_options=model_options) - if net is None: - logging.error("error could not detect control model type.") - return net - - if controlnet_config is None: - model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True) - supported_inference_dtypes = list(model_config.supported_inference_dtypes) - controlnet_config = model_config.unet_config - - unet_dtype = model_options.get("dtype", None) - if unet_dtype is None: - weight_dtype = comfy.utils.weight_dtype(controlnet_data) - - if supported_inference_dtypes is None: - supported_inference_dtypes = [comfy.model_management.unet_dtype()] - - unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) - - load_device = comfy.model_management.get_torch_device() - - manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) - operations = model_options.get("custom_operations", None) - if operations is None: - operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype) - - controlnet_config["operations"] = operations - controlnet_config["dtype"] = unet_dtype - controlnet_config["device"] = comfy.model_management.unet_offload_device() - controlnet_config.pop("out_channels") - controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] - control_model = comfy.cldm.cldm.ControlNet(**controlnet_config) - - if pth: - if 'difference' in controlnet_data: - if model is not None: - comfy.model_management.load_models_gpu([model]) - model_sd = model.model_state_dict() - for x in controlnet_data: - c_m = "control_model." - if x.startswith(c_m): - sd_key = "diffusion_model.{}".format(x[len(c_m):]) - if sd_key in model_sd: - cd = controlnet_data[x] - cd += model_sd[sd_key].type(cd.dtype).to(cd.device) - else: - logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") - - class WeightsLoader(torch.nn.Module): - pass - w = WeightsLoader() - w.control_model = control_model - missing, unexpected = w.load_state_dict(controlnet_data, strict=False) - else: - missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) - - if len(missing) > 0: - logging.warning("missing controlnet keys: {}".format(missing)) - - if len(unexpected) > 0: - logging.debug("unexpected controlnet keys: {}".format(unexpected)) - - global_average_pooling = model_options.get("global_average_pooling", False) - control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) - return control - -def load_controlnet(ckpt_path, model=None, model_options={}): - model_options = model_options.copy() - if "global_average_pooling" not in model_options: - filename = os.path.splitext(ckpt_path)[0] - if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling - model_options["global_average_pooling"] = True - - cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options) - if cnet is None: - logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path)) - return cnet - -class T2IAdapter(ControlBase): - def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None): - super().__init__() - self.t2i_model = t2i_model - self.channels_in = channels_in - self.control_input = None - self.compression_ratio = compression_ratio - self.upscale_algorithm = upscale_algorithm - if device is None: - device = comfy.model_management.get_torch_device() - self.device = device - - def scale_image_to(self, width, height): - unshuffle_amount = self.t2i_model.unshuffle_amount - width = math.ceil(width / unshuffle_amount) * unshuffle_amount - height = math.ceil(height / unshuffle_amount) * unshuffle_amount - return width, height - - def get_control(self, x_noisy, t, cond, batched_number, transformer_options): - control_prev = None - if self.previous_controlnet is not None: - control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options) - - if self.timestep_range is not None: - if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: - if control_prev is not None: - return control_prev - else: - return None - - if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]: - if self.cond_hint is not None: - del self.cond_hint - self.control_input = None - self.cond_hint = None - width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio) - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device) - if self.channels_in == 1 and self.cond_hint.shape[1] > 1: - self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) - if x_noisy.shape[0] != self.cond_hint.shape[0]: - self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) - if self.control_input is None: - self.t2i_model.to(x_noisy.dtype) - self.t2i_model.to(self.device) - self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) - self.t2i_model.cpu() - - control_input = {} - for k in self.control_input: - control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k])) - - return self.control_merge(control_input, control_prev, x_noisy.dtype) - - def copy(self): - c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm) - self.copy_to(c) - return c - -def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options - compression_ratio = 8 - upscale_algorithm = 'nearest-exact' - - if 'adapter' in t2i_data: - t2i_data = t2i_data['adapter'] - if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format - prefix_replace = {} - for i in range(4): - for j in range(2): - prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) - prefix_replace["adapter.body.{}.".format(i, )] = "body.{}.".format(i * 2) - prefix_replace["adapter."] = "" - t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace) - keys = t2i_data.keys() - - if "body.0.in_conv.weight" in keys: - cin = t2i_data['body.0.in_conv.weight'].shape[1] - model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) - elif 'conv_in.weight' in keys: - cin = t2i_data['conv_in.weight'].shape[1] - channel = t2i_data['conv_in.weight'].shape[0] - ksize = t2i_data['body.0.block2.weight'].shape[2] - use_conv = False - down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) - if len(down_opts) > 0: - use_conv = True - xl = False - if cin == 256 or cin == 768: - xl = True - model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) - elif "backbone.0.0.weight" in keys: - model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) - compression_ratio = 32 - upscale_algorithm = 'bilinear' - elif "backbone.10.blocks.0.weight" in keys: - model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63]) - compression_ratio = 1 - upscale_algorithm = 'nearest-exact' - else: - return None - - missing, unexpected = model_ad.load_state_dict(t2i_data) - if len(missing) > 0: - logging.warning("t2i missing {}".format(missing)) - - if len(unexpected) > 0: - logging.debug("t2i unexpected {}".format(unexpected)) - - return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm) diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py deleted file mode 100644 index fb9495348704c4f58b537f524ff6194d572d9fad..0000000000000000000000000000000000000000 --- a/comfy/diffusers_convert.py +++ /dev/null @@ -1,189 +0,0 @@ -import re -import torch -import logging - -# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py - -# ================# -# VAE Conversion # -# ================# - -vae_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("nin_shortcut", "conv_shortcut"), - ("norm_out", "conv_norm_out"), - ("mid.attn_1.", "mid_block.attentions.0."), -] - -for i in range(4): - # down_blocks have two resnets - for j in range(2): - hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." - sd_down_prefix = f"encoder.down.{i}.block.{j}." - vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) - - if i < 3: - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." - sd_downsample_prefix = f"down.{i}.downsample." - vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) - - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"up.{3 - i}.upsample." - vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) - - # up_blocks have three resnets - # also, up blocks in hf are numbered in reverse from sd - for j in range(3): - hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." - sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." - vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) - -# this part accounts for mid blocks in both the encoder and the decoder -for i in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{i}." - sd_mid_res_prefix = f"mid.block_{i + 1}." - vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) - -vae_conversion_map_attn = [ - # (stable-diffusion, HF Diffusers) - ("norm.", "group_norm."), - ("q.", "query."), - ("k.", "key."), - ("v.", "value."), - ("q.", "to_q."), - ("k.", "to_k."), - ("v.", "to_v."), - ("proj_out.", "to_out.0."), - ("proj_out.", "proj_attn."), -] - - -def reshape_weight_for_sd(w, conv3d=False): - # convert HF linear weights to SD conv2d weights - if conv3d: - return w.reshape(*w.shape, 1, 1, 1) - else: - return w.reshape(*w.shape, 1, 1) - - -def convert_vae_state_dict(vae_state_dict): - mapping = {k: k for k in vae_state_dict.keys()} - conv3d = False - for k, v in mapping.items(): - for sd_part, hf_part in vae_conversion_map: - v = v.replace(hf_part, sd_part) - if v.endswith(".conv.weight"): - if not conv3d and vae_state_dict[k].ndim == 5: - conv3d = True - mapping[k] = v - for k, v in mapping.items(): - if "attentions" in k: - for sd_part, hf_part in vae_conversion_map_attn: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} - weights_to_convert = ["q", "k", "v", "proj_out"] - for k, v in new_state_dict.items(): - for weight_name in weights_to_convert: - if f"mid.attn_1.{weight_name}.weight" in k: - logging.debug(f"Reshaping {k} for SD format") - new_state_dict[k] = reshape_weight_for_sd(v, conv3d=conv3d) - return new_state_dict - - -# =========================# -# Text Encoder Conversion # -# =========================# - - -textenc_conversion_lst = [ - # (stable-diffusion, HF Diffusers) - ("resblocks.", "text_model.encoder.layers."), - ("ln_1", "layer_norm1"), - ("ln_2", "layer_norm2"), - (".c_fc.", ".fc1."), - (".c_proj.", ".fc2."), - (".attn", ".self_attn"), - ("ln_final.", "transformer.text_model.final_layer_norm."), - ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), - ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), -] -protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} -textenc_pattern = re.compile("|".join(protected.keys())) - -# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp -code2idx = {"q": 0, "k": 1, "v": 2} - - -# This function exists because at the time of writing torch.cat can't do fp8 with cuda -def cat_tensors(tensors): - x = 0 - for t in tensors: - x += t.shape[0] - - shape = [x] + list(tensors[0].shape)[1:] - out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) - - x = 0 - for t in tensors: - out[x:x + t.shape[0]] = t - x += t.shape[0] - - return out - - -def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): - new_state_dict = {} - capture_qkv_weight = {} - capture_qkv_bias = {} - for k, v in text_enc_dict.items(): - if not k.startswith(prefix): - continue - if ( - k.endswith(".self_attn.q_proj.weight") - or k.endswith(".self_attn.k_proj.weight") - or k.endswith(".self_attn.v_proj.weight") - ): - k_pre = k[: -len(".q_proj.weight")] - k_code = k[-len("q_proj.weight")] - if k_pre not in capture_qkv_weight: - capture_qkv_weight[k_pre] = [None, None, None] - capture_qkv_weight[k_pre][code2idx[k_code]] = v - continue - - if ( - k.endswith(".self_attn.q_proj.bias") - or k.endswith(".self_attn.k_proj.bias") - or k.endswith(".self_attn.v_proj.bias") - ): - k_pre = k[: -len(".q_proj.bias")] - k_code = k[-len("q_proj.bias")] - if k_pre not in capture_qkv_bias: - capture_qkv_bias[k_pre] = [None, None, None] - capture_qkv_bias[k_pre][code2idx[k_code]] = v - continue - - text_proj = "transformer.text_projection.weight" - if k.endswith(text_proj): - new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous() - else: - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) - new_state_dict[relabelled_key] = v - - for k_pre, tensors in capture_qkv_weight.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors) - - for k_pre, tensors in capture_qkv_bias.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors) - - return new_state_dict - - -def convert_text_enc_state_dict(text_enc_dict): - return text_enc_dict diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py deleted file mode 100644 index 56e63a7565f083eb4e3bc484a3a9f90103306a2f..0000000000000000000000000000000000000000 --- a/comfy/diffusers_load.py +++ /dev/null @@ -1,36 +0,0 @@ -import os - -import comfy.sd - -def first_file(path, filenames): - for f in filenames: - p = os.path.join(path, f) - if os.path.exists(p): - return p - return None - -def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): - diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] - unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) - vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) - - text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] - text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) - text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) - - text_encoder_paths = [text_encoder1_path] - if text_encoder2_path is not None: - text_encoder_paths.append(text_encoder2_path) - - unet = comfy.sd.load_diffusion_model(unet_path) - - clip = None - if output_clip: - clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) - - vae = None - if output_vae: - sd = comfy.utils.load_torch_file(vae_path) - vae = comfy.sd.VAE(sd=sd) - - return (unet, clip, vae) diff --git a/comfy/extra_samplers/.DS_Store b/comfy/extra_samplers/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/extra_samplers/.DS_Store and /dev/null differ diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py deleted file mode 100644 index c57e081e45cfaa694e3e78528798f62a4698c056..0000000000000000000000000000000000000000 --- a/comfy/extra_samplers/uni_pc.py +++ /dev/null @@ -1,873 +0,0 @@ -#code taken from: https://github.com/wl-zhao/UniPC and modified - -import torch -import math -import logging - -from tqdm.auto import trange - - -class NoiseScheduleVP: - def __init__( - self, - schedule='discrete', - betas=None, - alphas_cumprod=None, - continuous_beta_0=0.1, - continuous_beta_1=20., - ): - r"""Create a wrapper class for the forward SDE (VP type). - - *** - Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. - We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. - *** - - The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). - We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). - Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: - - log_alpha_t = self.marginal_log_mean_coeff(t) - sigma_t = self.marginal_std(t) - lambda_t = self.marginal_lambda(t) - - Moreover, as lambda(t) is an invertible function, we also support its inverse function: - - t = self.inverse_lambda(lambda_t) - - =============================================================== - - We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). - - 1. For discrete-time DPMs: - - For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: - t_i = (i + 1) / N - e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. - We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. - - Args: - betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) - alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) - - Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. - - **Important**: Please pay special attention for the args for `alphas_cumprod`: - The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that - q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). - Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have - alpha_{t_n} = \sqrt{\hat{alpha_n}}, - and - log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). - - - 2. For continuous-time DPMs: - - We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise - schedule are the default settings in DDPM and improved-DDPM: - - Args: - beta_min: A `float` number. The smallest beta for the linear schedule. - beta_max: A `float` number. The largest beta for the linear schedule. - cosine_s: A `float` number. The hyperparameter in the cosine schedule. - cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. - T: A `float` number. The ending time of the forward process. - - =============================================================== - - Args: - schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, - 'linear' or 'cosine' for continuous-time DPMs. - Returns: - A wrapper object of the forward SDE (VP type). - - =============================================================== - - Example: - - # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): - >>> ns = NoiseScheduleVP('discrete', betas=betas) - - # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): - >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) - - # For continuous-time DPMs (VPSDE), linear schedule: - >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) - - """ - - if schedule not in ['discrete', 'linear', 'cosine']: - raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) - - self.schedule = schedule - if schedule == 'discrete': - if betas is not None: - log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) - else: - assert alphas_cumprod is not None - log_alphas = 0.5 * torch.log(alphas_cumprod) - self.total_N = len(log_alphas) - self.T = 1. - self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) - self.log_alpha_array = log_alphas.reshape((1, -1,)) - else: - self.total_N = 1000 - self.beta_0 = continuous_beta_0 - self.beta_1 = continuous_beta_1 - self.cosine_s = 0.008 - self.cosine_beta_max = 999. - self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s - self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) - self.schedule = schedule - if schedule == 'cosine': - # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. - # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. - self.T = 0.9946 - else: - self.T = 1. - - def marginal_log_mean_coeff(self, t): - """ - Compute log(alpha_t) of a given continuous-time label t in [0, T]. - """ - if self.schedule == 'discrete': - return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) - elif self.schedule == 'linear': - return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 - elif self.schedule == 'cosine': - log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) - log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 - return log_alpha_t - - def marginal_alpha(self, t): - """ - Compute alpha_t of a given continuous-time label t in [0, T]. - """ - return torch.exp(self.marginal_log_mean_coeff(t)) - - def marginal_std(self, t): - """ - Compute sigma_t of a given continuous-time label t in [0, T]. - """ - return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) - - def marginal_lambda(self, t): - """ - Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. - """ - log_mean_coeff = self.marginal_log_mean_coeff(t) - log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) - return log_mean_coeff - log_std - - def inverse_lambda(self, lamb): - """ - Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. - """ - if self.schedule == 'linear': - tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) - Delta = self.beta_0**2 + tmp - return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) - elif self.schedule == 'discrete': - log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) - t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) - return t.reshape((-1,)) - else: - log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) - t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s - t = t_fn(log_alpha) - return t - - -def model_wrapper( - model, - noise_schedule, - model_type="noise", - model_kwargs={}, - guidance_type="uncond", - condition=None, - unconditional_condition=None, - guidance_scale=1., - classifier_fn=None, - classifier_kwargs={}, -): - """Create a wrapper function for the noise prediction model. - - DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to - firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. - - We support four types of the diffusion model by setting `model_type`: - - 1. "noise": noise prediction model. (Trained by predicting noise). - - 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). - - 3. "v": velocity prediction model. (Trained by predicting the velocity). - The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. - - [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." - arXiv preprint arXiv:2202.00512 (2022). - [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." - arXiv preprint arXiv:2210.02303 (2022). - - 4. "score": marginal score function. (Trained by denoising score matching). - Note that the score function and the noise prediction model follows a simple relationship: - ``` - noise(x_t, t) = -sigma_t * score(x_t, t) - ``` - - We support three types of guided sampling by DPMs by setting `guidance_type`: - 1. "uncond": unconditional sampling by DPMs. - The input `model` has the following format: - `` - model(x, t_input, **model_kwargs) -> noise | x_start | v | score - `` - - 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. - The input `model` has the following format: - `` - model(x, t_input, **model_kwargs) -> noise | x_start | v | score - `` - - The input `classifier_fn` has the following format: - `` - classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) - `` - - [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," - in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. - - 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. - The input `model` has the following format: - `` - model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score - `` - And if cond == `unconditional_condition`, the model output is the unconditional DPM output. - - [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." - arXiv preprint arXiv:2207.12598 (2022). - - - The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) - or continuous-time labels (i.e. epsilon to T). - - We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: - `` - def model_fn(x, t_continuous) -> noise: - t_input = get_model_input_time(t_continuous) - return noise_pred(model, x, t_input, **model_kwargs) - `` - where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. - - =============================================================== - - Args: - model: A diffusion model with the corresponding format described above. - noise_schedule: A noise schedule object, such as NoiseScheduleVP. - model_type: A `str`. The parameterization type of the diffusion model. - "noise" or "x_start" or "v" or "score". - model_kwargs: A `dict`. A dict for the other inputs of the model function. - guidance_type: A `str`. The type of the guidance for sampling. - "uncond" or "classifier" or "classifier-free". - condition: A pytorch tensor. The condition for the guided sampling. - Only used for "classifier" or "classifier-free" guidance type. - unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. - Only used for "classifier-free" guidance type. - guidance_scale: A `float`. The scale for the guided sampling. - classifier_fn: A classifier function. Only used for the classifier guidance. - classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. - Returns: - A noise prediction model that accepts the noised data and the continuous time as the inputs. - """ - - def get_model_input_time(t_continuous): - """ - Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. - For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. - For continuous-time DPMs, we just use `t_continuous`. - """ - if noise_schedule.schedule == 'discrete': - return (t_continuous - 1. / noise_schedule.total_N) * 1000. - else: - return t_continuous - - def noise_pred_fn(x, t_continuous, cond=None): - if t_continuous.reshape((-1,)).shape[0] == 1: - t_continuous = t_continuous.expand((x.shape[0])) - t_input = get_model_input_time(t_continuous) - output = model(x, t_input, **model_kwargs) - if model_type == "noise": - return output - elif model_type == "x_start": - alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) - dims = x.dim() - return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) - elif model_type == "v": - alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) - dims = x.dim() - return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x - elif model_type == "score": - sigma_t = noise_schedule.marginal_std(t_continuous) - dims = x.dim() - return -expand_dims(sigma_t, dims) * output - - def cond_grad_fn(x, t_input): - """ - Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). - """ - with torch.enable_grad(): - x_in = x.detach().requires_grad_(True) - log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) - return torch.autograd.grad(log_prob.sum(), x_in)[0] - - def model_fn(x, t_continuous): - """ - The noise predicition model function that is used for DPM-Solver. - """ - if t_continuous.reshape((-1,)).shape[0] == 1: - t_continuous = t_continuous.expand((x.shape[0])) - if guidance_type == "uncond": - return noise_pred_fn(x, t_continuous) - elif guidance_type == "classifier": - assert classifier_fn is not None - t_input = get_model_input_time(t_continuous) - cond_grad = cond_grad_fn(x, t_input) - sigma_t = noise_schedule.marginal_std(t_continuous) - noise = noise_pred_fn(x, t_continuous) - return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad - elif guidance_type == "classifier-free": - if guidance_scale == 1. or unconditional_condition is None: - return noise_pred_fn(x, t_continuous, cond=condition) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t_continuous] * 2) - c_in = torch.cat([unconditional_condition, condition]) - noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) - return noise_uncond + guidance_scale * (noise - noise_uncond) - - assert model_type in ["noise", "x_start", "v"] - assert guidance_type in ["uncond", "classifier", "classifier-free"] - return model_fn - - -class UniPC: - def __init__( - self, - model_fn, - noise_schedule, - predict_x0=True, - thresholding=False, - max_val=1., - variant='bh1', - ): - """Construct a UniPC. - - We support both data_prediction and noise_prediction. - """ - self.model = model_fn - self.noise_schedule = noise_schedule - self.variant = variant - self.predict_x0 = predict_x0 - self.thresholding = thresholding - self.max_val = max_val - - def dynamic_thresholding_fn(self, x0, t=None): - """ - The dynamic thresholding method. - """ - dims = x0.dim() - p = self.dynamic_thresholding_ratio - s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) - s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) - x0 = torch.clamp(x0, -s, s) / s - return x0 - - def noise_prediction_fn(self, x, t): - """ - Return the noise prediction model. - """ - return self.model(x, t) - - def data_prediction_fn(self, x, t): - """ - Return the data prediction model (with thresholding). - """ - noise = self.noise_prediction_fn(x, t) - dims = x.dim() - alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) - x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) - if self.thresholding: - p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. - s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) - s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) - x0 = torch.clamp(x0, -s, s) / s - return x0 - - def model_fn(self, x, t): - """ - Convert the model to the noise prediction model or the data prediction model. - """ - if self.predict_x0: - return self.data_prediction_fn(x, t) - else: - return self.noise_prediction_fn(x, t) - - def get_time_steps(self, skip_type, t_T, t_0, N, device): - """Compute the intermediate time steps for sampling. - """ - if skip_type == 'logSNR': - lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) - lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) - logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) - return self.noise_schedule.inverse_lambda(logSNR_steps) - elif skip_type == 'time_uniform': - return torch.linspace(t_T, t_0, N + 1).to(device) - elif skip_type == 'time_quadratic': - t_order = 2 - t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) - return t - else: - raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) - - def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): - """ - Get the order of each step for sampling by the singlestep DPM-Solver. - """ - if order == 3: - K = steps // 3 + 1 - if steps % 3 == 0: - orders = [3,] * (K - 2) + [2, 1] - elif steps % 3 == 1: - orders = [3,] * (K - 1) + [1] - else: - orders = [3,] * (K - 1) + [2] - elif order == 2: - if steps % 2 == 0: - K = steps // 2 - orders = [2,] * K - else: - K = steps // 2 + 1 - orders = [2,] * (K - 1) + [1] - elif order == 1: - K = steps - orders = [1,] * steps - else: - raise ValueError("'order' must be '1' or '2' or '3'.") - if skip_type == 'logSNR': - # To reproduce the results in DPM-Solver paper - timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) - else: - timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] - return timesteps_outer, orders - - def denoise_to_zero_fn(self, x, s): - """ - Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. - """ - return self.data_prediction_fn(x, s) - - def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): - if len(t.shape) == 0: - t = t.view(-1) - if 'bh' in self.variant: - return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) - else: - assert self.variant == 'vary_coeff' - return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) - - def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): - logging.info(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') - ns = self.noise_schedule - assert order <= len(model_prev_list) - - # first compute rks - t_prev_0 = t_prev_list[-1] - lambda_prev_0 = ns.marginal_lambda(t_prev_0) - lambda_t = ns.marginal_lambda(t) - model_prev_0 = model_prev_list[-1] - sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) - log_alpha_t = ns.marginal_log_mean_coeff(t) - alpha_t = torch.exp(log_alpha_t) - - h = lambda_t - lambda_prev_0 - - rks = [] - D1s = [] - for i in range(1, order): - t_prev_i = t_prev_list[-(i + 1)] - model_prev_i = model_prev_list[-(i + 1)] - lambda_prev_i = ns.marginal_lambda(t_prev_i) - rk = (lambda_prev_i - lambda_prev_0) / h - rks.append(rk) - D1s.append((model_prev_i - model_prev_0) / rk) - - rks.append(1.) - rks = torch.tensor(rks, device=x.device) - - K = len(rks) - # build C matrix - C = [] - - col = torch.ones_like(rks) - for k in range(1, K + 1): - C.append(col) - col = col * rks / (k + 1) - C = torch.stack(C, dim=1) - - if len(D1s) > 0: - D1s = torch.stack(D1s, dim=1) # (B, K) - C_inv_p = torch.linalg.inv(C[:-1, :-1]) - A_p = C_inv_p - - if use_corrector: - C_inv = torch.linalg.inv(C) - A_c = C_inv - - hh = -h if self.predict_x0 else h - h_phi_1 = torch.expm1(hh) - h_phi_ks = [] - factorial_k = 1 - h_phi_k = h_phi_1 - for k in range(1, K + 2): - h_phi_ks.append(h_phi_k) - h_phi_k = h_phi_k / hh - 1 / factorial_k - factorial_k *= (k + 1) - - model_t = None - if self.predict_x0: - x_t_ = ( - sigma_t / sigma_prev_0 * x - - alpha_t * h_phi_1 * model_prev_0 - ) - # now predictor - x_t = x_t_ - if len(D1s) > 0: - # compute the residuals for predictor - for k in range(K - 1): - x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) - # now corrector - if use_corrector: - model_t = self.model_fn(x_t, t) - D1_t = (model_t - model_prev_0) - x_t = x_t_ - k = 0 - for k in range(K - 1): - x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) - x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) - else: - log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) - x_t_ = ( - (torch.exp(log_alpha_t - log_alpha_prev_0)) * x - - (sigma_t * h_phi_1) * model_prev_0 - ) - # now predictor - x_t = x_t_ - if len(D1s) > 0: - # compute the residuals for predictor - for k in range(K - 1): - x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) - # now corrector - if use_corrector: - model_t = self.model_fn(x_t, t) - D1_t = (model_t - model_prev_0) - x_t = x_t_ - k = 0 - for k in range(K - 1): - x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) - x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) - return x_t, model_t - - def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): - # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') - ns = self.noise_schedule - assert order <= len(model_prev_list) - dims = x.dim() - - # first compute rks - t_prev_0 = t_prev_list[-1] - lambda_prev_0 = ns.marginal_lambda(t_prev_0) - lambda_t = ns.marginal_lambda(t) - model_prev_0 = model_prev_list[-1] - sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) - log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) - alpha_t = torch.exp(log_alpha_t) - - h = lambda_t - lambda_prev_0 - - rks = [] - D1s = [] - for i in range(1, order): - t_prev_i = t_prev_list[-(i + 1)] - model_prev_i = model_prev_list[-(i + 1)] - lambda_prev_i = ns.marginal_lambda(t_prev_i) - rk = ((lambda_prev_i - lambda_prev_0) / h)[0] - rks.append(rk) - D1s.append((model_prev_i - model_prev_0) / rk) - - rks.append(1.) - rks = torch.tensor(rks, device=x.device) - - R = [] - b = [] - - hh = -h[0] if self.predict_x0 else h[0] - h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 - h_phi_k = h_phi_1 / hh - 1 - - factorial_i = 1 - - if self.variant == 'bh1': - B_h = hh - elif self.variant == 'bh2': - B_h = torch.expm1(hh) - else: - raise NotImplementedError() - - for i in range(1, order + 1): - R.append(torch.pow(rks, i - 1)) - b.append(h_phi_k * factorial_i / B_h) - factorial_i *= (i + 1) - h_phi_k = h_phi_k / hh - 1 / factorial_i - - R = torch.stack(R) - b = torch.tensor(b, device=x.device) - - # now predictor - use_predictor = len(D1s) > 0 and x_t is None - if len(D1s) > 0: - D1s = torch.stack(D1s, dim=1) # (B, K) - if x_t is None: - # for order 2, we use a simplified version - if order == 2: - rhos_p = torch.tensor([0.5], device=b.device) - else: - rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) - else: - D1s = None - - if use_corrector: - # print('using corrector') - # for order 1, we use a simplified version - if order == 1: - rhos_c = torch.tensor([0.5], device=b.device) - else: - rhos_c = torch.linalg.solve(R, b) - - model_t = None - if self.predict_x0: - x_t_ = ( - expand_dims(sigma_t / sigma_prev_0, dims) * x - - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 - ) - - if x_t is None: - if use_predictor: - pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_p, D1s) - else: - pred_res = 0 - x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res - - if use_corrector: - model_t = self.model_fn(x_t, t) - if D1s is not None: - corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) - else: - corr_res = 0 - D1_t = (model_t - model_prev_0) - x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) - else: - x_t_ = ( - expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x - - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 - ) - if x_t is None: - if use_predictor: - pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) - else: - pred_res = 0 - x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res - - if use_corrector: - model_t = self.model_fn(x_t, t) - if D1s is not None: - corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) - else: - corr_res = 0 - D1_t = (model_t - model_prev_0) - x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) - return x_t, model_t - - - def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', - method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', - atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False - ): - # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end - # t_T = self.noise_schedule.T if t_start is None else t_start - steps = len(timesteps) - 1 - if method == 'multistep': - assert steps >= order - # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) - assert timesteps.shape[0] - 1 == steps - # with torch.no_grad(): - for step_index in trange(steps, disable=disable_pbar): - if step_index == 0: - vec_t = timesteps[0].expand((x.shape[0])) - model_prev_list = [self.model_fn(x, vec_t)] - t_prev_list = [vec_t] - elif step_index < order: - init_order = step_index - # Init the first `order` values by lower order multistep DPM-Solver. - # for init_order in range(1, order): - vec_t = timesteps[init_order].expand(x.shape[0]) - x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) - if model_x is None: - model_x = self.model_fn(x, vec_t) - model_prev_list.append(model_x) - t_prev_list.append(vec_t) - else: - extra_final_step = 0 - if step_index == (steps - 1): - extra_final_step = 1 - for step in range(step_index, step_index + 1 + extra_final_step): - vec_t = timesteps[step].expand(x.shape[0]) - if lower_order_final: - step_order = min(order, steps + 1 - step) - else: - step_order = order - # print('this step order:', step_order) - if step == steps: - # print('do not run corrector at the last step') - use_corrector = False - else: - use_corrector = True - x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) - for i in range(order - 1): - t_prev_list[i] = t_prev_list[i + 1] - model_prev_list[i] = model_prev_list[i + 1] - t_prev_list[-1] = vec_t - # We do not need to evaluate the final model value. - if step < steps: - if model_x is None: - model_x = self.model_fn(x, vec_t) - model_prev_list[-1] = model_x - if callback is not None: - callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]}) - else: - raise NotImplementedError() - # if denoise_to_zero: - # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) - return x - - -############################################################# -# other utility functions -############################################################# - -def interpolate_fn(x, xp, yp): - """ - A piecewise linear function y = f(x), using xp and yp as keypoints. - We implement f(x) in a differentiable way (i.e. applicable for autograd). - The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) - - Args: - x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). - xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. - yp: PyTorch tensor with shape [C, K]. - Returns: - The function values f(x), with shape [N, C]. - """ - N, K = x.shape[0], xp.shape[1] - all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) - sorted_all_x, x_indices = torch.sort(all_x, dim=2) - x_idx = torch.argmin(x_indices, dim=2) - cand_start_idx = x_idx - 1 - start_idx = torch.where( - torch.eq(x_idx, 0), - torch.tensor(1, device=x.device), - torch.where( - torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, - ), - ) - end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) - start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) - end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) - start_idx2 = torch.where( - torch.eq(x_idx, 0), - torch.tensor(0, device=x.device), - torch.where( - torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, - ), - ) - y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) - start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) - end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) - cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) - return cand - - -def expand_dims(v, dims): - """ - Expand the tensor `v` to the dim `dims`. - - Args: - `v`: a PyTorch tensor with shape [N]. - `dim`: a `int`. - Returns: - a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. - """ - return v[(...,) + (None,)*(dims - 1)] - - -class SigmaConvert: - schedule = "" - def marginal_log_mean_coeff(self, sigma): - return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) - - def marginal_alpha(self, t): - return torch.exp(self.marginal_log_mean_coeff(t)) - - def marginal_std(self, t): - return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) - - def marginal_lambda(self, t): - """ - Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. - """ - log_mean_coeff = self.marginal_log_mean_coeff(t) - log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) - return log_mean_coeff - log_std - -def predict_eps_sigma(model, input, sigma_in, **kwargs): - sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) - input = input * ((sigma ** 2 + 1.0) ** 0.5) - return (input - model(input, sigma_in, **kwargs)) / sigma - - -def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'): - timesteps = sigmas.clone() - if sigmas[-1] == 0: - timesteps = sigmas[:] - timesteps[-1] = 0.001 - else: - timesteps = sigmas.clone() - ns = SigmaConvert() - - noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0) - model_type = "noise" - - model_fn = model_wrapper( - lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), - ns, - model_type=model_type, - guidance_type="uncond", - model_kwargs=extra_args, - ) - - order = min(3, len(timesteps) - 2) - uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant) - x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) - x /= ns.marginal_alpha(timesteps[-1]) - return x - -def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False): - return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2') diff --git a/comfy/float.py b/comfy/float.py deleted file mode 100644 index 1a982ec5d779a65b7cd23f97a49f7cfcd85b35fd..0000000000000000000000000000000000000000 --- a/comfy/float.py +++ /dev/null @@ -1,70 +0,0 @@ -import torch - -def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None): - mantissa_scaled = torch.where( - normal_mask, - (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS), - (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS))) - ) - - mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator) - return mantissa_scaled.floor() / (2**MANTISSA_BITS) - -#Not 100% sure about this -def manual_stochastic_round_to_float8(x, dtype, generator=None): - if dtype == torch.float8_e4m3fn: - EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7 - elif dtype == torch.float8_e5m2: - EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 5, 2, 15 - else: - raise ValueError("Unsupported dtype") - - x = x.half() - sign = torch.sign(x) - abs_x = x.abs() - sign = torch.where(abs_x == 0, 0, sign) - - # Combine exponent calculation and clamping - exponent = torch.clamp( - torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS, - 0, 2**EXPONENT_BITS - 1 - ) - - # Combine mantissa calculation and rounding - normal_mask = ~(exponent == 0) - - abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator) - - sign *= torch.where( - normal_mask, - (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x), - (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x - ) - - inf = torch.finfo(dtype) - torch.clamp(sign, min=inf.min, max=inf.max, out=sign) - return sign - - - -def stochastic_rounding(value, dtype, seed=0): - if dtype == torch.float32: - return value.to(dtype=torch.float32) - if dtype == torch.float16: - return value.to(dtype=torch.float16) - if dtype == torch.bfloat16: - return value.to(dtype=torch.bfloat16) - if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2: - #generator = torch.Generator(device='cuda' if torch.cuda.is_available() else 'cpu') - torch.manual_seed(seed) - if(torch.cuda.is_available()): - torch.cuda.manual_seed(seed) - output = torch.empty_like(value, dtype=dtype) - num_slices = max(1, (value.numel() / (4096 * 4096))) - slice_size = max(1, round(value.shape[0] / num_slices)) - with torch.no_grad(): - for i in range(0, value.shape[0], slice_size): - output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype)) - return output - - return value.to(dtype=dtype) diff --git a/comfy/gligen.py b/comfy/gligen.py deleted file mode 100644 index 1d7b6c2f4cd1009ac3c52bc5f68442a91b417048..0000000000000000000000000000000000000000 --- a/comfy/gligen.py +++ /dev/null @@ -1,299 +0,0 @@ -import math -import torch -from torch import nn -from .ldm.modules.attention import CrossAttention, FeedForward -import comfy.ops -ops = comfy.ops.manual_cast - - -class GatedCrossAttentionDense(nn.Module): - def __init__(self, query_dim, context_dim, n_heads, d_head): - super().__init__() - - self.attn = CrossAttention( - query_dim=query_dim, - context_dim=context_dim, - heads=n_heads, - dim_head=d_head, - operations=ops) - self.ff = FeedForward(query_dim, glu=True) - - self.norm1 = ops.LayerNorm(query_dim) - self.norm2 = ops.LayerNorm(query_dim) - - self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) - self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) - - # this can be useful: we can externally change magnitude of tanh(alpha) - # for example, when it is set to 0, then the entire model is same as - # original one - self.scale = 1 - - def forward(self, x, objs): - - x = x + self.scale * \ - torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) - x = x + self.scale * \ - torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) - - return x - - -class GatedSelfAttentionDense(nn.Module): - def __init__(self, query_dim, context_dim, n_heads, d_head): - super().__init__() - - # we need a linear projection since we need cat visual feature and obj - # feature - self.linear = ops.Linear(context_dim, query_dim) - - self.attn = CrossAttention( - query_dim=query_dim, - context_dim=query_dim, - heads=n_heads, - dim_head=d_head, - operations=ops) - self.ff = FeedForward(query_dim, glu=True) - - self.norm1 = ops.LayerNorm(query_dim) - self.norm2 = ops.LayerNorm(query_dim) - - self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) - self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) - - # this can be useful: we can externally change magnitude of tanh(alpha) - # for example, when it is set to 0, then the entire model is same as - # original one - self.scale = 1 - - def forward(self, x, objs): - - N_visual = x.shape[1] - objs = self.linear(objs) - - x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( - self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] - x = x + self.scale * \ - torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) - - return x - - -class GatedSelfAttentionDense2(nn.Module): - def __init__(self, query_dim, context_dim, n_heads, d_head): - super().__init__() - - # we need a linear projection since we need cat visual feature and obj - # feature - self.linear = ops.Linear(context_dim, query_dim) - - self.attn = CrossAttention( - query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops) - self.ff = FeedForward(query_dim, glu=True) - - self.norm1 = ops.LayerNorm(query_dim) - self.norm2 = ops.LayerNorm(query_dim) - - self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) - self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) - - # this can be useful: we can externally change magnitude of tanh(alpha) - # for example, when it is set to 0, then the entire model is same as - # original one - self.scale = 1 - - def forward(self, x, objs): - - B, N_visual, _ = x.shape - B, N_ground, _ = objs.shape - - objs = self.linear(objs) - - # sanity check - size_v = math.sqrt(N_visual) - size_g = math.sqrt(N_ground) - assert int(size_v) == size_v, "Visual tokens must be square rootable" - assert int(size_g) == size_g, "Grounding tokens must be square rootable" - size_v = int(size_v) - size_g = int(size_g) - - # select grounding token and resize it to visual token size as residual - out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ - :, N_visual:, :] - out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) - out = torch.nn.functional.interpolate( - out, (size_v, size_v), mode='bicubic') - residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) - - # add residual to visual feature - x = x + self.scale * torch.tanh(self.alpha_attn) * residual - x = x + self.scale * \ - torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) - - return x - - -class FourierEmbedder(): - def __init__(self, num_freqs=64, temperature=100): - - self.num_freqs = num_freqs - self.temperature = temperature - self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) - - @torch.no_grad() - def __call__(self, x, cat_dim=-1): - "x: arbitrary shape of tensor. dim: cat dim" - out = [] - for freq in self.freq_bands: - out.append(torch.sin(freq * x)) - out.append(torch.cos(freq * x)) - return torch.cat(out, cat_dim) - - -class PositionNet(nn.Module): - def __init__(self, in_dim, out_dim, fourier_freqs=8): - super().__init__() - self.in_dim = in_dim - self.out_dim = out_dim - - self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) - self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy - - self.linears = nn.Sequential( - ops.Linear(self.in_dim + self.position_dim, 512), - nn.SiLU(), - ops.Linear(512, 512), - nn.SiLU(), - ops.Linear(512, out_dim), - ) - - self.null_positive_feature = torch.nn.Parameter( - torch.zeros([self.in_dim])) - self.null_position_feature = torch.nn.Parameter( - torch.zeros([self.position_dim])) - - def forward(self, boxes, masks, positive_embeddings): - B, N, _ = boxes.shape - masks = masks.unsqueeze(-1) - positive_embeddings = positive_embeddings - - # embedding position (it may includes padding as placeholder) - xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C - - # learnable null embedding - positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) - xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1) - - # replace padding with learnable null embedding - positive_embeddings = positive_embeddings * \ - masks + (1 - masks) * positive_null - xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null - - objs = self.linears( - torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) - assert objs.shape == torch.Size([B, N, self.out_dim]) - return objs - - -class Gligen(nn.Module): - def __init__(self, modules, position_net, key_dim): - super().__init__() - self.module_list = nn.ModuleList(modules) - self.position_net = position_net - self.key_dim = key_dim - self.max_objs = 30 - self.current_device = torch.device("cpu") - - def _set_position(self, boxes, masks, positive_embeddings): - objs = self.position_net(boxes, masks, positive_embeddings) - def func(x, extra_options): - key = extra_options["transformer_index"] - module = self.module_list[key] - return module(x, objs.to(device=x.device, dtype=x.dtype)) - return func - - def set_position(self, latent_image_shape, position_params, device): - batch, c, h, w = latent_image_shape - masks = torch.zeros([self.max_objs], device="cpu") - boxes = [] - positive_embeddings = [] - for p in position_params: - x1 = (p[4]) / w - y1 = (p[3]) / h - x2 = (p[4] + p[2]) / w - y2 = (p[3] + p[1]) / h - masks[len(boxes)] = 1.0 - boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] - positive_embeddings += [p[0]] - append_boxes = [] - append_conds = [] - if len(boxes) < self.max_objs: - append_boxes = [torch.zeros( - [self.max_objs - len(boxes), 4], device="cpu")] - append_conds = [torch.zeros( - [self.max_objs - len(boxes), self.key_dim], device="cpu")] - - box_out = torch.cat( - boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) - masks = masks.unsqueeze(0).repeat(batch, 1) - conds = torch.cat(positive_embeddings + - append_conds).unsqueeze(0).repeat(batch, 1, 1) - return self._set_position( - box_out.to(device), - masks.to(device), - conds.to(device)) - - def set_empty(self, latent_image_shape, device): - batch, c, h, w = latent_image_shape - masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) - box_out = torch.zeros([self.max_objs, 4], - device="cpu").repeat(batch, 1, 1) - conds = torch.zeros([self.max_objs, self.key_dim], - device="cpu").repeat(batch, 1, 1) - return self._set_position( - box_out.to(device), - masks.to(device), - conds.to(device)) - - -def load_gligen(sd): - sd_k = sd.keys() - output_list = [] - key_dim = 768 - for a in ["input_blocks", "middle_block", "output_blocks"]: - for b in range(20): - k_temp = filter(lambda k: "{}.{}.".format(a, b) - in k and ".fuser." in k, sd_k) - k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) - - n_sd = {} - for k in k_temp: - n_sd[k[1]] = sd[k[0]] - if len(n_sd) > 0: - query_dim = n_sd["linear.weight"].shape[0] - key_dim = n_sd["linear.weight"].shape[1] - - if key_dim == 768: # SD1.x - n_heads = 8 - d_head = query_dim // n_heads - else: - d_head = 64 - n_heads = query_dim // d_head - - gated = GatedSelfAttentionDense( - query_dim, key_dim, n_heads, d_head) - gated.load_state_dict(n_sd, strict=False) - output_list.append(gated) - - if "position_net.null_positive_feature" in sd_k: - in_dim = sd["position_net.null_positive_feature"].shape[0] - out_dim = sd["position_net.linears.4.weight"].shape[0] - - class WeightsLoader(torch.nn.Module): - pass - w = WeightsLoader() - w.position_net = PositionNet(in_dim, out_dim) - w.load_state_dict(sd, strict=False) - - gligen = Gligen(output_list, w.position_net, key_dim) - return gligen diff --git a/comfy/hooks.py b/comfy/hooks.py deleted file mode 100644 index 9d07310729020a033f705395433c318152432935..0000000000000000000000000000000000000000 --- a/comfy/hooks.py +++ /dev/null @@ -1,785 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING, Callable -import enum -import math -import torch -import numpy as np -import itertools -import logging - -if TYPE_CHECKING: - from comfy.model_patcher import ModelPatcher, PatcherInjection - from comfy.model_base import BaseModel - from comfy.sd import CLIP -import comfy.lora -import comfy.model_management -import comfy.patcher_extension -from node_helpers import conditioning_set_values - -# ####################################################################################################### -# Hooks explanation -# ------------------- -# The purpose of hooks is to allow conds to influence sampling without the need for ComfyUI core code to -# make explicit special cases like it does for ControlNet and GLIGEN. -# -# This is necessary for nodes/features that are intended for use with masked or scheduled conds, or those -# that should run special code when a 'marked' cond is used in sampling. -# ####################################################################################################### - -class EnumHookMode(enum.Enum): - ''' - Priority of hook memory optimization vs. speed, mostly related to WeightHooks. - - MinVram: No caching will occur for any operations related to hooks. - MaxSpeed: Excess VRAM (and RAM, once VRAM is sufficiently depleted) will be used to cache hook weights when switching hook groups. - ''' - MinVram = "minvram" - MaxSpeed = "maxspeed" - -class EnumHookType(enum.Enum): - ''' - Hook types, each of which has different expected behavior. - ''' - Weight = "weight" - ObjectPatch = "object_patch" - AdditionalModels = "add_models" - TransformerOptions = "transformer_options" - Injections = "add_injections" - -class EnumWeightTarget(enum.Enum): - Model = "model" - Clip = "clip" - -class EnumHookScope(enum.Enum): - ''' - Determines if hook should be limited in its influence over sampling. - - AllConditioning: hook will affect all conds used in sampling. - HookedOnly: hook will only affect the conds it was attached to. - ''' - AllConditioning = "all_conditioning" - HookedOnly = "hooked_only" - - -class _HookRef: - pass - - -def default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - '''Example for how custom_should_register function can look like.''' - return True - - -def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[str]: - '''Creates base dictionary for use with Hooks' target param.''' - d = {} - if target is not None: - d['target'] = target - d.update(kwargs) - return d - - -class Hook: - def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None, hook_id: str=None, - hook_keyframe: HookKeyframeGroup=None, hook_scope=EnumHookScope.AllConditioning): - self.hook_type = hook_type - '''Enum identifying the general class of this hook.''' - self.hook_ref = hook_ref if hook_ref else _HookRef() - '''Reference shared between hook clones that have the same value. Should NOT be modified.''' - self.hook_id = hook_id - '''Optional string ID to identify hook; useful if need to consolidate duplicates at registration time.''' - self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup() - '''Keyframe storage that can be referenced to get strength for current sampling step.''' - self.hook_scope = hook_scope - '''Scope of where this hook should apply in terms of the conds used in sampling run.''' - self.custom_should_register = default_should_register - '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register''' - - @property - def strength(self): - return self.hook_keyframe.strength - - def initialize_timesteps(self, model: BaseModel): - self.reset() - self.hook_keyframe.initialize_timesteps(model) - - def reset(self): - self.hook_keyframe.reset() - - def clone(self): - c: Hook = self.__class__() - c.hook_type = self.hook_type - c.hook_ref = self.hook_ref - c.hook_id = self.hook_id - c.hook_keyframe = self.hook_keyframe - c.hook_scope = self.hook_scope - c.custom_should_register = self.custom_should_register - return c - - def should_register(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - return self.custom_should_register(self, model, model_options, target_dict, registered) - - def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - raise NotImplementedError("add_hook_patches should be defined for Hook subclasses") - - def __eq__(self, other: Hook): - return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref - - def __hash__(self): - return hash(self.hook_ref) - -class WeightHook(Hook): - ''' - Hook responsible for tracking weights to be applied to some model/clip. - - Note, value of hook_scope is ignored and is treated as HookedOnly. - ''' - def __init__(self, strength_model=1.0, strength_clip=1.0): - super().__init__(hook_type=EnumHookType.Weight, hook_scope=EnumHookScope.HookedOnly) - self.weights: dict = None - self.weights_clip: dict = None - self.need_weight_init = True - self._strength_model = strength_model - self._strength_clip = strength_clip - self.hook_scope = EnumHookScope.HookedOnly # this value does not matter for WeightHooks, just for docs - - @property - def strength_model(self): - return self._strength_model * self.strength - - @property - def strength_clip(self): - return self._strength_clip * self.strength - - def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - if not self.should_register(model, model_options, target_dict, registered): - return False - weights = None - - target = target_dict.get('target', None) - if target == EnumWeightTarget.Clip: - strength = self._strength_clip - else: - strength = self._strength_model - - if self.need_weight_init: - key_map = {} - if target == EnumWeightTarget.Clip: - key_map = comfy.lora.model_lora_keys_clip(model.model, key_map) - else: - key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) - weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False) - else: - if target == EnumWeightTarget.Clip: - weights = self.weights_clip - else: - weights = self.weights - model.add_hook_patches(hook=self, patches=weights, strength_patch=strength) - registered.add(self) - return True - # TODO: add logs about any keys that were not applied - - def clone(self): - c: WeightHook = super().clone() - c.weights = self.weights - c.weights_clip = self.weights_clip - c.need_weight_init = self.need_weight_init - c._strength_model = self._strength_model - c._strength_clip = self._strength_clip - return c - -class ObjectPatchHook(Hook): - def __init__(self, object_patches: dict[str]=None, - hook_scope=EnumHookScope.AllConditioning): - super().__init__(hook_type=EnumHookType.ObjectPatch) - self.object_patches = object_patches - self.hook_scope = hook_scope - - def clone(self): - c: ObjectPatchHook = super().clone() - c.object_patches = self.object_patches - return c - - def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - raise NotImplementedError("ObjectPatchHook is not supported yet in ComfyUI.") - -class AdditionalModelsHook(Hook): - ''' - Hook responsible for telling model management any additional models that should be loaded. - - Note, value of hook_scope is ignored and is treated as AllConditioning. - ''' - def __init__(self, models: list[ModelPatcher]=None, key: str=None): - super().__init__(hook_type=EnumHookType.AdditionalModels) - self.models = models - self.key = key - - def clone(self): - c: AdditionalModelsHook = super().clone() - c.models = self.models.copy() if self.models else self.models - c.key = self.key - return c - - def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - if not self.should_register(model, model_options, target_dict, registered): - return False - registered.add(self) - return True - -class TransformerOptionsHook(Hook): - ''' - Hook responsible for adding wrappers, callbacks, patches, or anything else related to transformer_options. - ''' - def __init__(self, transformers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None, - hook_scope=EnumHookScope.AllConditioning): - super().__init__(hook_type=EnumHookType.TransformerOptions) - self.transformers_dict = transformers_dict - self.hook_scope = hook_scope - self._skip_adding = False - '''Internal value used to avoid double load of transformer_options when hook_scope is AllConditioning.''' - - def clone(self): - c: TransformerOptionsHook = super().clone() - c.transformers_dict = self.transformers_dict - c._skip_adding = self._skip_adding - return c - - def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - if not self.should_register(model, model_options, target_dict, registered): - return False - # NOTE: to_load_options will be used to manually load patches/wrappers/callbacks from hooks - self._skip_adding = False - if self.hook_scope == EnumHookScope.AllConditioning: - add_model_options = {"transformer_options": self.transformers_dict, - "to_load_options": self.transformers_dict} - # skip_adding if included in AllConditioning to avoid double loading - self._skip_adding = True - else: - add_model_options = {"to_load_options": self.transformers_dict} - registered.add(self) - comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False) - return True - - def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str]): - if not self._skip_adding: - comfy.patcher_extension.merge_nested_dicts(transformer_options, self.transformers_dict, copy_dict1=False) - -WrapperHook = TransformerOptionsHook -'''Only here for backwards compatibility, WrapperHook is identical to TransformerOptionsHook.''' - -class InjectionsHook(Hook): - def __init__(self, key: str=None, injections: list[PatcherInjection]=None, - hook_scope=EnumHookScope.AllConditioning): - super().__init__(hook_type=EnumHookType.Injections) - self.key = key - self.injections = injections - self.hook_scope = hook_scope - - def clone(self): - c: InjectionsHook = super().clone() - c.key = self.key - c.injections = self.injections.copy() if self.injections else self.injections - return c - - def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): - raise NotImplementedError("InjectionsHook is not supported yet in ComfyUI.") - -class HookGroup: - ''' - Stores groups of hooks, and allows them to be queried by type. - - To prevent breaking their functionality, never modify the underlying self.hooks or self._hook_dict vars directly; - always use the provided functions on HookGroup. - ''' - def __init__(self): - self.hooks: list[Hook] = [] - self._hook_dict: dict[EnumHookType, list[Hook]] = {} - - def __len__(self): - return len(self.hooks) - - def add(self, hook: Hook): - if hook not in self.hooks: - self.hooks.append(hook) - self._hook_dict.setdefault(hook.hook_type, []).append(hook) - - def remove(self, hook: Hook): - if hook in self.hooks: - self.hooks.remove(hook) - self._hook_dict[hook.hook_type].remove(hook) - - def get_type(self, hook_type: EnumHookType): - return self._hook_dict.get(hook_type, []) - - def contains(self, hook: Hook): - return hook in self.hooks - - def is_subset_of(self, other: HookGroup): - self_hooks = set(self.hooks) - other_hooks = set(other.hooks) - return self_hooks.issubset(other_hooks) - - def new_with_common_hooks(self, other: HookGroup): - c = HookGroup() - for hook in self.hooks: - if other.contains(hook): - c.add(hook.clone()) - return c - - def clone(self): - c = HookGroup() - for hook in self.hooks: - c.add(hook.clone()) - return c - - def clone_and_combine(self, other: HookGroup): - c = self.clone() - if other is not None: - for hook in other.hooks: - c.add(hook.clone()) - return c - - def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup): - if hook_kf is None: - hook_kf = HookKeyframeGroup() - else: - hook_kf = hook_kf.clone() - for hook in self.hooks: - hook.hook_keyframe = hook_kf - - def get_hooks_for_clip_schedule(self): - scheduled_hooks: dict[WeightHook, list[tuple[tuple[float,float], HookKeyframe]]] = {} - # only care about WeightHooks, for now - for hook in self.get_type(EnumHookType.Weight): - hook: WeightHook - hook_schedule = [] - # if no hook keyframes, assign default value - if len(hook.hook_keyframe.keyframes) == 0: - hook_schedule.append(((0.0, 1.0), None)) - scheduled_hooks[hook] = hook_schedule - continue - # find ranges of values - prev_keyframe = hook.hook_keyframe.keyframes[0] - for keyframe in hook.hook_keyframe.keyframes: - if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength): - hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe)) - prev_keyframe = keyframe - elif keyframe.start_percent == prev_keyframe.start_percent: - prev_keyframe = keyframe - # create final range, assuming last start_percent was not 1.0 - if not math.isclose(prev_keyframe.start_percent, 1.0): - hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe)) - scheduled_hooks[hook] = hook_schedule - # hooks should not have their schedules in a list of tuples - all_ranges: list[tuple[float, float]] = [] - for range_kfs in scheduled_hooks.values(): - for t_range, keyframe in range_kfs: - all_ranges.append(t_range) - # turn list of ranges into boundaries - boundaries_set = set(itertools.chain.from_iterable(all_ranges)) - boundaries_set.add(0.0) - boundaries = sorted(boundaries_set) - real_ranges = [(boundaries[i], boundaries[i + 1]) for i in range(len(boundaries) - 1)] - # with real ranges defined, give appropriate hooks w/ keyframes for each range - scheduled_keyframes: list[tuple[tuple[float,float], list[tuple[WeightHook, HookKeyframe]]]] = [] - for t_range in real_ranges: - hooks_schedule = [] - for hook, val in scheduled_hooks.items(): - keyframe = None - # check if is a keyframe that works for the current t_range - for stored_range, stored_kf in val: - # if stored start is less than current end, then fits - give it assigned keyframe - if stored_range[0] < t_range[1] and stored_range[1] > t_range[0]: - keyframe = stored_kf - break - hooks_schedule.append((hook, keyframe)) - scheduled_keyframes.append((t_range, hooks_schedule)) - return scheduled_keyframes - - def reset(self): - for hook in self.hooks: - hook.reset() - - @staticmethod - def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) -> HookGroup: - actual: list[HookGroup] = [] - for group in hooks_list: - if group is not None: - actual.append(group) - if len(actual) < require_count: - raise Exception(f"Need at least {require_count} hooks to combine, but only had {len(actual)}.") - # if no hooks, then return None - if len(actual) == 0: - return None - # if only 1 hook, just return itself without cloning - elif len(actual) == 1: - return actual[0] - final_hook: HookGroup = None - for hook in actual: - if final_hook is None: - final_hook = hook.clone() - else: - final_hook = final_hook.clone_and_combine(hook) - return final_hook - - -class HookKeyframe: - def __init__(self, strength: float, start_percent=0.0, guarantee_steps=1): - self.strength = strength - # scheduling - self.start_percent = float(start_percent) - self.start_t = 999999999.9 - self.guarantee_steps = guarantee_steps - - def get_effective_guarantee_steps(self, max_sigma: torch.Tensor): - '''If keyframe starts before current sampling range (max_sigma), treat as 0.''' - if self.start_t > max_sigma: - return 0 - return self.guarantee_steps - - def clone(self): - c = HookKeyframe(strength=self.strength, - start_percent=self.start_percent, guarantee_steps=self.guarantee_steps) - c.start_t = self.start_t - return c - -class HookKeyframeGroup: - def __init__(self): - self.keyframes: list[HookKeyframe] = [] - self._current_keyframe: HookKeyframe = None - self._current_used_steps = 0 - self._current_index = 0 - self._current_strength = None - self._curr_t = -1. - - # properties shadow those of HookWeightsKeyframe - @property - def strength(self): - if self._current_keyframe is not None: - return self._current_keyframe.strength - return 1.0 - - def reset(self): - self._current_keyframe = None - self._current_used_steps = 0 - self._current_index = 0 - self._current_strength = None - self.curr_t = -1. - self._set_first_as_current() - - def add(self, keyframe: HookKeyframe): - # add to end of list, then sort - self.keyframes.append(keyframe) - self.keyframes = get_sorted_list_via_attr(self.keyframes, "start_percent") - self._set_first_as_current() - - def _set_first_as_current(self): - if len(self.keyframes) > 0: - self._current_keyframe = self.keyframes[0] - else: - self._current_keyframe = None - - def has_guarantee_steps(self): - for kf in self.keyframes: - if kf.guarantee_steps > 0: - return True - return False - - def has_index(self, index: int): - return index >= 0 and index < len(self.keyframes) - - def is_empty(self): - return len(self.keyframes) == 0 - - def clone(self): - c = HookKeyframeGroup() - for keyframe in self.keyframes: - c.keyframes.append(keyframe.clone()) - c._set_first_as_current() - return c - - def initialize_timesteps(self, model: BaseModel): - for keyframe in self.keyframes: - keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent) - - def prepare_current_keyframe(self, curr_t: float, transformer_options: dict[str, torch.Tensor]) -> bool: - if self.is_empty(): - return False - if curr_t == self._curr_t: - return False - max_sigma = torch.max(transformer_options["sample_sigmas"]) - prev_index = self._current_index - prev_strength = self._current_strength - # if met guaranteed steps, look for next keyframe in case need to switch - if self._current_used_steps >= self._current_keyframe.get_effective_guarantee_steps(max_sigma): - # if has next index, loop through and see if need to switch - if self.has_index(self._current_index+1): - for i in range(self._current_index+1, len(self.keyframes)): - eval_c = self.keyframes[i] - # check if start_t is greater or equal to curr_t - # NOTE: t is in terms of sigmas, not percent, so bigger number = earlier step in sampling - if eval_c.start_t >= curr_t: - self._current_index = i - self._current_strength = eval_c.strength - self._current_keyframe = eval_c - self._current_used_steps = 0 - # if guarantee_steps greater than zero, stop searching for other keyframes - if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0: - break - # if eval_c is outside the percent range, stop looking further - else: break - # update steps current context is used - self._current_used_steps += 1 - # update current timestep this was performed on - self._curr_t = curr_t - # return True if keyframe changed, False if no change - return prev_index != self._current_index and prev_strength != self._current_strength - - -class InterpolationMethod: - LINEAR = "linear" - EASE_IN = "ease_in" - EASE_OUT = "ease_out" - EASE_IN_OUT = "ease_in_out" - - _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT] - - @classmethod - def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False): - diff = num_to - num_from - if method == cls.LINEAR: - weights = torch.linspace(num_from, num_to, length) - elif method == cls.EASE_IN: - index = torch.linspace(0, 1, length) - weights = diff * np.power(index, 2) + num_from - elif method == cls.EASE_OUT: - index = torch.linspace(0, 1, length) - weights = diff * (1 - np.power(1 - index, 2)) + num_from - elif method == cls.EASE_IN_OUT: - index = torch.linspace(0, 1, length) - weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from - else: - raise ValueError(f"Unrecognized interpolation method '{method}'.") - if reverse: - weights = weights.flip(dims=(0,)) - return weights - -def get_sorted_list_via_attr(objects: list, attr: str) -> list: - if not objects: - return objects - elif len(objects) <= 1: - return [x for x in objects] - # now that we know we have to sort, do it following these rules: - # a) if objects have same value of attribute, maintain their relative order - # b) perform sorting of the groups of objects with same attributes - unique_attrs = {} - for o in objects: - val_attr = getattr(o, attr) - attr_list: list = unique_attrs.get(val_attr, list()) - attr_list.append(o) - if val_attr not in unique_attrs: - unique_attrs[val_attr] = attr_list - # now that we have the unique attr values grouped together in relative order, sort them by key - sorted_attrs = dict(sorted(unique_attrs.items())) - # now flatten out the dict into a list to return - sorted_list = [] - for object_list in sorted_attrs.values(): - sorted_list.extend(object_list) - return sorted_list - -def create_transformer_options_from_hooks(model: ModelPatcher, hooks: HookGroup, transformer_options: dict[str]=None): - # if no hooks or is not a ModelPatcher for sampling, return empty dict - if hooks is None or model.is_clip: - return {} - if transformer_options is None: - transformer_options = {} - for hook in hooks.get_type(EnumHookType.TransformerOptions): - hook: TransformerOptionsHook - hook.on_apply_hooks(model, transformer_options) - return transformer_options - -def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float): - hook_group = HookGroup() - hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) - hook_group.add(hook) - hook.weights = lora - return hook_group - -def create_hook_model_as_lora(weights_model, weights_clip, strength_model: float, strength_clip: float): - hook_group = HookGroup() - hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) - hook_group.add(hook) - patches_model = None - patches_clip = None - if weights_model is not None: - patches_model = {} - for key in weights_model: - patches_model[key] = ("model_as_lora", (weights_model[key],)) - if weights_clip is not None: - patches_clip = {} - for key in weights_clip: - patches_clip[key] = ("model_as_lora", (weights_clip[key],)) - hook.weights = patches_model - hook.weights_clip = patches_clip - hook.need_weight_init = False - return hook_group - -def get_patch_weights_from_model(model: ModelPatcher, discard_model_sampling=True): - if model is None: - return None - patches_model: dict[str, torch.Tensor] = model.model.state_dict() - if discard_model_sampling: - # do not include ANY model_sampling components of the model that should act as a patch - for key in list(patches_model.keys()): - if key.startswith("model_sampling"): - patches_model.pop(key, None) - return patches_model - -# NOTE: this function shows how to register weight hooks directly on the ModelPatchers -def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dict[str, torch.Tensor], - strength_model: float, strength_clip: float): - key_map = {} - if model is not None: - key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) - if clip is not None: - key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) - - hook_group = HookGroup() - hook = WeightHook() - hook_group.add(hook) - loaded: dict[str] = comfy.lora.load_lora(lora, key_map) - if model is not None: - new_modelpatcher = model.clone() - k = new_modelpatcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_model) - else: - k = () - new_modelpatcher = None - - if clip is not None: - new_clip = clip.clone() - k1 = new_clip.patcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_clip) - else: - k1 = () - new_clip = None - k = set(k) - k1 = set(k1) - for x in loaded: - if (x not in k) and (x not in k1): - logging.warning(f"NOT LOADED {x}") - return (new_modelpatcher, new_clip, hook_group) - -def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, HookGroup], cache: dict[tuple[HookGroup, HookGroup], HookGroup]): - hooks_key = 'hooks' - # if hooks only exist in one dict, do what's needed so that it ends up in c_dict - if hooks_key not in values: - return - if hooks_key not in c_dict: - hooks_value = values.get(hooks_key, None) - if hooks_value is not None: - c_dict[hooks_key] = hooks_value - return - # otherwise, need to combine with minimum duplication via cache - hooks_tuple = (c_dict[hooks_key], values[hooks_key]) - cached_hooks = cache.get(hooks_tuple, None) - if cached_hooks is None: - new_hooks = hooks_tuple[0].clone_and_combine(hooks_tuple[1]) - cache[hooks_tuple] = new_hooks - c_dict[hooks_key] = new_hooks - else: - c_dict[hooks_key] = cache[hooks_tuple] - -def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True, - cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): - c = [] - if cache is None: - cache = {} - for t in conditioning: - n = [t[0], t[1].copy()] - for k in values: - if append_hooks and k == 'hooks': - _combine_hooks_from_values(n[1], values, cache) - else: - n[1][k] = values[k] - c.append(n) - - return c - -def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): - if hooks is None: - return cond - return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks, cache=cache) - -def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,float]): - if timestep_range is None: - return cond - return conditioning_set_values(cond, {"start_percent": timestep_range[0], - "end_percent": timestep_range[1]}) - -def set_mask_for_conditioning(cond, mask: torch.Tensor, set_cond_area: str, strength: float): - if mask is None: - return cond - set_area_to_bounds = False - if set_cond_area != 'default': - set_area_to_bounds = True - if len(mask.shape) < 3: - mask = mask.unsqueeze(0) - return conditioning_set_values(cond, {'mask': mask, - 'set_area_to_bounds': set_area_to_bounds, - 'mask_strength': strength}) - -def combine_conditioning(conds: list): - combined_conds = [] - for cond in conds: - combined_conds.extend(cond) - return combined_conds - -def combine_with_new_conds(conds: list, new_conds: list): - combined_conds = [] - for c, new_c in zip(conds, new_conds): - combined_conds.append(combine_conditioning([c, new_c])) - return combined_conds - -def set_conds_props(conds: list, strength: float, set_cond_area: str, - mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): - final_conds = [] - cache = {} - for c in conds: - # first, apply lora_hook to conditioning, if provided - c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks, cache=cache) - # next, apply mask to conditioning - c = set_mask_for_conditioning(cond=c, mask=mask, strength=strength, set_cond_area=set_cond_area) - # apply timesteps, if present - c = set_timesteps_for_conditioning(cond=c, timestep_range=timesteps_range) - # finally, apply mask to conditioning and store - final_conds.append(c) - return final_conds - -def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.0, set_cond_area: str="default", - mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): - combined_conds = [] - cache = {} - for c, masked_c in zip(conds, new_conds): - # first, apply lora_hook to new conditioning, if provided - masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks, cache=cache) - # next, apply mask to new conditioning, if provided - masked_c = set_mask_for_conditioning(cond=masked_c, mask=mask, set_cond_area=set_cond_area, strength=strength) - # apply timesteps, if present - masked_c = set_timesteps_for_conditioning(cond=masked_c, timestep_range=timesteps_range) - # finally, combine with existing conditioning and store - combined_conds.append(combine_conditioning([c, masked_c])) - return combined_conds - -def set_default_conds_and_combine(conds: list, new_conds: list, - hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): - combined_conds = [] - cache = {} - for c, new_c in zip(conds, new_conds): - # first, apply lora_hook to new conditioning, if provided - new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks, cache=cache) - # next, add default_cond key to cond so that during sampling, it can be identified - new_c = conditioning_set_values(new_c, {'default': True}) - # apply timesteps, if present - new_c = set_timesteps_for_conditioning(cond=new_c, timestep_range=timesteps_range) - # finally, combine with existing conditioning and store - combined_conds.append(combine_conditioning([c, new_c])) - return combined_conds diff --git a/comfy/image_encoders/.DS_Store b/comfy/image_encoders/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/image_encoders/.DS_Store and /dev/null differ diff --git a/comfy/image_encoders/dino2.py b/comfy/image_encoders/dino2.py deleted file mode 100644 index 976f98c656a6b55eb6ceb8d2c585a2713ac6dddb..0000000000000000000000000000000000000000 --- a/comfy/image_encoders/dino2.py +++ /dev/null @@ -1,141 +0,0 @@ -import torch -from comfy.text_encoders.bert import BertAttention -import comfy.model_management -from comfy.ldm.modules.attention import optimized_attention_for_device - - -class Dino2AttentionOutput(torch.nn.Module): - def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): - super().__init__() - self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) - - def forward(self, x): - return self.dense(x) - - -class Dino2AttentionBlock(torch.nn.Module): - def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): - super().__init__() - self.attention = BertAttention(embed_dim, heads, dtype, device, operations) - self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) - - def forward(self, x, mask, optimized_attention): - return self.output(self.attention(x, mask, optimized_attention)) - - -class LayerScale(torch.nn.Module): - def __init__(self, dim, dtype, device, operations): - super().__init__() - self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) - - def forward(self, x): - return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype) - - -class SwiGLUFFN(torch.nn.Module): - def __init__(self, dim, dtype, device, operations): - super().__init__() - in_features = out_features = dim - hidden_features = int(dim * 4) - hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 - - self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype) - self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype) - - def forward(self, x): - x = self.weights_in(x) - x1, x2 = x.chunk(2, dim=-1) - x = torch.nn.functional.silu(x1) * x2 - return self.weights_out(x) - - -class Dino2Block(torch.nn.Module): - def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations): - super().__init__() - self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations) - self.layer_scale1 = LayerScale(dim, dtype, device, operations) - self.layer_scale2 = LayerScale(dim, dtype, device, operations) - self.mlp = SwiGLUFFN(dim, dtype, device, operations) - self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) - self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) - - def forward(self, x, optimized_attention): - x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention)) - x = x + self.layer_scale2(self.mlp(self.norm2(x))) - return x - - -class Dino2Encoder(torch.nn.Module): - def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations): - super().__init__() - self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)]) - - def forward(self, x, intermediate_output=None): - optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) - - if intermediate_output is not None: - if intermediate_output < 0: - intermediate_output = len(self.layer) + intermediate_output - - intermediate = None - for i, l in enumerate(self.layer): - x = l(x, optimized_attention) - if i == intermediate_output: - intermediate = x.clone() - return x, intermediate - - -class Dino2PatchEmbeddings(torch.nn.Module): - def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None): - super().__init__() - self.projection = operations.Conv2d( - in_channels=num_channels, - out_channels=dim, - kernel_size=patch_size, - stride=patch_size, - bias=True, - dtype=dtype, - device=device - ) - - def forward(self, pixel_values): - return self.projection(pixel_values).flatten(2).transpose(1, 2) - - -class Dino2Embeddings(torch.nn.Module): - def __init__(self, dim, dtype, device, operations): - super().__init__() - patch_size = 14 - image_size = 518 - - self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations) - self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device)) - self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) - self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) - - def forward(self, pixel_values): - x = self.patch_embeddings(pixel_values) - # TODO: mask_token? - x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1) - x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) - return x - - -class Dinov2Model(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - num_layers = config_dict["num_hidden_layers"] - dim = config_dict["hidden_size"] - heads = config_dict["num_attention_heads"] - layer_norm_eps = config_dict["layer_norm_eps"] - - self.embeddings = Dino2Embeddings(dim, dtype, device, operations) - self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations) - self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) - - def forward(self, pixel_values, attention_mask=None, intermediate_output=None): - x = self.embeddings(pixel_values) - x, i = self.encoder(x, intermediate_output=intermediate_output) - x = self.layernorm(x) - pooled_output = x[:, 0, :] - return x, i, pooled_output, None diff --git a/comfy/image_encoders/dino2_giant.json b/comfy/image_encoders/dino2_giant.json deleted file mode 100644 index f6076a4dc98dfee6c200294397bfdfe3d2c688e1..0000000000000000000000000000000000000000 --- a/comfy/image_encoders/dino2_giant.json +++ /dev/null @@ -1,21 +0,0 @@ -{ - "attention_probs_dropout_prob": 0.0, - "drop_path_rate": 0.0, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.0, - "hidden_size": 1536, - "image_size": 518, - "initializer_range": 0.02, - "layer_norm_eps": 1e-06, - "layerscale_value": 1.0, - "mlp_ratio": 4, - "model_type": "dinov2", - "num_attention_heads": 24, - "num_channels": 3, - "num_hidden_layers": 40, - "patch_size": 14, - "qkv_bias": true, - "use_swiglu_ffn": true, - "image_mean": [0.485, 0.456, 0.406], - "image_std": [0.229, 0.224, 0.225] -} diff --git a/comfy/k_diffusion/.DS_Store b/comfy/k_diffusion/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/k_diffusion/.DS_Store and /dev/null differ diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py deleted file mode 100644 index a1167a4a36c42b22168ea8b3d80b1d83b2505db5..0000000000000000000000000000000000000000 --- a/comfy/k_diffusion/deis.py +++ /dev/null @@ -1,120 +0,0 @@ -#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py -#under Apache 2 license -import torch -import numpy as np - -# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis). -############################# -### Utils for DEIS solver ### -############################# -#---------------------------------------------------------------------------- -# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS. - -def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80): - vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d - vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1) - vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d - t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu()) - return t_steps, vp_beta_min, vp_beta_d + vp_beta_min - -#---------------------------------------------------------------------------- - -def cal_poly(prev_t, j, taus): - poly = 1 - for k in range(prev_t.shape[0]): - if k == j: - continue - poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k]) - return poly - -#---------------------------------------------------------------------------- -# Transfer from t to alpha_t. - -def t2alpha_fn(beta_0, beta_1, t): - return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0) - -#---------------------------------------------------------------------------- - -def cal_intergrand(beta_0, beta_1, taus): - with torch.inference_mode(mode=False): - taus = taus.clone() - beta_0 = beta_0.clone() - beta_1 = beta_1.clone() - with torch.enable_grad(): - taus.requires_grad_(True) - alpha = t2alpha_fn(beta_0, beta_1, taus) - log_alpha = alpha.log() - log_alpha.sum().backward() - d_log_alpha_dtau = taus.grad - integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha)) - return integrand - -#---------------------------------------------------------------------------- - -def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'): - """ - Get the coefficient list for DEIS sampling. - - Args: - t_steps: A pytorch tensor. The time steps for sampling. - max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4 - N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'. - deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS. - Returns: - A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True. - """ - if deis_mode == 'tab': - t_steps, beta_0, beta_1 = edm2t(t_steps) - C = [] - for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): - order = min(i+1, max_order) - if order == 1: - C.append([]) - else: - taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation - dtau = (t_next - t_cur) / N - prev_t = t_steps[[i - k for k in range(order)]] - coeff_temp = [] - integrand = cal_intergrand(beta_0, beta_1, taus) - for j in range(order): - poly = cal_poly(prev_t, j, taus) - coeff_temp.append(torch.sum(integrand * poly) * dtau) - C.append(coeff_temp) - - elif deis_mode == 'rhoab': - # Analytical solution, second order - def get_def_intergral_2(a, b, start, end, c): - coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b - return coeff / ((c - a) * (c - b)) - - # Analytical solution, third order - def get_def_intergral_3(a, b, c, start, end, d): - coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \ - + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c - return coeff / ((d - a) * (d - b) * (d - c)) - - C = [] - for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): - order = min(i, max_order) - if order == 0: - C.append([]) - else: - prev_t = t_steps[[i - k for k in range(order+1)]] - if order == 1: - coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1])) - coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur)) - coeff_temp = [coeff_cur, coeff_prev1] - elif order == 2: - coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur) - coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1]) - coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2]) - coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2] - elif order == 3: - coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur) - coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1]) - coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2]) - coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3]) - coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3] - C.append(coeff_temp) - return C - diff --git a/comfy/k_diffusion/sa_solver.py b/comfy/k_diffusion/sa_solver.py deleted file mode 100644 index 0c6821b605d4b44ac81414f2c635e28ce5d9feb5..0000000000000000000000000000000000000000 --- a/comfy/k_diffusion/sa_solver.py +++ /dev/null @@ -1,121 +0,0 @@ -# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019) -# Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf -# Codebase ref: https://github.com/scxue/SA-Solver - -import math -from typing import Union, Callable -import torch - - -def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor: - """Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts. - - Integral of exp((1 + tau^2) * x) * x^p dx - = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx, - with base case p=0 where integral equals product_terms[0]. - - where - product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2). - - Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1). - Return coefficients used by the SA-Solver in data prediction mode. - - Args: - s: Start time s. - t: End time t. - solver_order: Current order of the solver. - tau_t: Stochastic strength parameter in the SDE. - - Returns: - Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,). - """ - tau_mul = 1 + tau_t ** 2 - h = t - s - p = torch.arange(solver_order, dtype=s.dtype, device=s.device) - - # product_terms after factoring out exp((1 + tau^2) * t) - # Includes (1 + tau^2) factor from outside the integral - product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp()) - - # Lower triangular recursive coefficient matrix - # Accumulates recursive coefficients based on p / (1 + tau^2) - recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0) - log_factorial = (p + 1).lgamma() - recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0) - if tau_t > 0: - recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul)) - signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0) - recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril() - - return recursive_coeff_mat @ product_terms_factored - - -def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor: - """Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details).""" - tau_mul = 1 + tau_t ** 2 - h = lambda_t - lambda_s - alpha_t = sigma_next * lambda_t.exp() - if is_corrector_step: - # Simplified 1-step (order-2) corrector - b_1 = alpha_t * (0.5 * tau_mul * h) - b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1 - else: - # Simplified 2-step predictor - b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s) - b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2 - return torch.stack([b_2, b_1]) - - -def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor: - """Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18). - - The solver order corresponds to the number of input lambdas (half-logSNR points). - - Args: - sigma_next: Sigma at end time t. - curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,). - lambda_s: Lambda at start time s. - lambda_t: Lambda at end time t. - tau_t: Stochastic strength parameter in the SDE. - simple_order_2: Whether to enable the simple order-2 scheme. - is_corrector_step: Flag for corrector step in simple order-2 mode. - - Returns: - b_i coefficients for the SA-Solver, shape (N,), where N is the solver order. - """ - num_timesteps = curr_lambdas.shape[0] - - if simple_order_2 and num_timesteps == 2: - return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step) - - # Compute coefficients by solving a linear system from Lagrange basis interpolation - exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t) - vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T - lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs) - - # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t) - # = sigma_t * exp(lambda_t) = alpha_t - # exp((1 + tau^2) * lambda_t) is extracted from the integral - alpha_t = sigma_next * lambda_t.exp() - return alpha_t * lagrange_integrals - - -def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]: - """Return a function that controls the stochasticity of SA-Solver. - - When eta = 0, SA-Solver runs as ODE. The official approach uses - time t to determine the SDE interval, while here we use sigma instead. - - See: - https://github.com/scxue/SA-Solver/blob/main/README.md - """ - - def tau_func(sigma: Union[torch.Tensor, float]) -> float: - if eta <= 0: - return 0.0 # ODE - - if isinstance(sigma, torch.Tensor): - sigma = sigma.item() - return eta if start_sigma >= sigma >= end_sigma else 0.0 - - return tau_func diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py deleted file mode 100644 index fe6844b17efefc23d4140d36c653151ffb35a4de..0000000000000000000000000000000000000000 --- a/comfy/k_diffusion/sampling.py +++ /dev/null @@ -1,1776 +0,0 @@ -import math -from functools import partial - -from scipy import integrate -import torch -from torch import nn -import torchsde -from tqdm.auto import trange, tqdm - -from . import utils -from . import deis -from . import sa_solver -import comfy.model_patcher -import comfy.model_sampling - -def append_zero(x): - return torch.cat([x, x.new_zeros([1])]) - - -def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): - """Constructs the noise schedule of Karras et al. (2022).""" - ramp = torch.linspace(0, 1, n, device=device) - min_inv_rho = sigma_min ** (1 / rho) - max_inv_rho = sigma_max ** (1 / rho) - sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho - return append_zero(sigmas).to(device) - - -def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): - """Constructs an exponential noise schedule.""" - sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() - return append_zero(sigmas) - - -def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): - """Constructs an polynomial in log sigma noise schedule.""" - ramp = torch.linspace(1, 0, n, device=device) ** rho - sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) - return append_zero(sigmas) - - -def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): - """Constructs a continuous VP noise schedule.""" - t = torch.linspace(1, eps_s, n, device=device) - sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t)) - return append_zero(sigmas) - - -def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'): - """Constructs the noise schedule proposed by Tiankai et al. (2024). """ - epsilon = 1e-5 # avoid log(0) - x = torch.linspace(0, 1, n, device=device) - clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max) - lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon) - sigmas = clamp(torch.exp(lmb)) - return sigmas - - - -def to_d(x, sigma, denoised): - """Converts a denoiser output to a Karras ODE derivative.""" - return (x - denoised) / utils.append_dims(sigma, x.ndim) - - -def get_ancestral_step(sigma_from, sigma_to, eta=1.): - """Calculates the noise level (sigma_down) to step down to and the amount - of noise to add (sigma_up) when doing an ancestral sampling step.""" - if not eta: - return sigma_to, 0. - sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) - sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 - return sigma_down, sigma_up - - -def default_noise_sampler(x, seed=None): - if seed is not None: - generator = torch.Generator(device=x.device) - generator.manual_seed(seed) - else: - generator = None - - return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator) - - -class BatchedBrownianTree: - """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" - - def __init__(self, x, t0, t1, seed=None, **kwargs): - self.cpu_tree = True - if "cpu" in kwargs: - self.cpu_tree = kwargs.pop("cpu") - t0, t1, self.sign = self.sort(t0, t1) - w0 = kwargs.get('w0', torch.zeros_like(x)) - if seed is None: - seed = torch.randint(0, 2 ** 63 - 1, []).item() - self.batched = True - try: - assert len(seed) == x.shape[0] - w0 = w0[0] - except TypeError: - seed = [seed] - self.batched = False - if self.cpu_tree: - self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] - else: - self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] - - @staticmethod - def sort(a, b): - return (a, b, 1) if a < b else (b, a, -1) - - def __call__(self, t0, t1): - t0, t1, sign = self.sort(t0, t1) - if self.cpu_tree: - w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) - else: - w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) - - return w if self.batched else w[0] - - -class BrownianTreeNoiseSampler: - """A noise sampler backed by a torchsde.BrownianTree. - - Args: - x (Tensor): The tensor whose shape, device and dtype to use to generate - random samples. - sigma_min (float): The low end of the valid interval. - sigma_max (float): The high end of the valid interval. - seed (int or List[int]): The random seed. If a list of seeds is - supplied instead of a single integer, then the noise sampler will - use one BrownianTree per batch item, each with its own seed. - transform (callable): A function that maps sigma to the sampler's - internal timestep. - """ - - def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): - self.transform = transform - t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) - self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) - - def __call__(self, sigma, sigma_next): - t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) - return self.tree(t0, t1) / (t1 - t0).abs().sqrt() - - -def sigma_to_half_log_snr(sigma, model_sampling): - """Convert sigma to half-logSNR log(alpha_t / sigma_t).""" - if isinstance(model_sampling, comfy.model_sampling.CONST): - # log((1 - t) / t) = log((1 - sigma) / sigma) - return sigma.logit().neg() - return sigma.log().neg() - - -def half_log_snr_to_sigma(half_log_snr, model_sampling): - """Convert half-logSNR log(alpha_t / sigma_t) to sigma.""" - if isinstance(model_sampling, comfy.model_sampling.CONST): - # 1 / (1 + exp(half_log_snr)) - return half_log_snr.neg().sigmoid() - return half_log_snr.neg().exp() - - -def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4): - """Adjust the first sigma to avoid invalid logSNR.""" - if len(sigmas) <= 1: - return sigmas - if isinstance(model_sampling, comfy.model_sampling.CONST): - if sigmas[0] >= 1: - sigmas = sigmas.clone() - sigmas[0] = model_sampling.percent_to_sigma(percent_offset) - return sigmas - - -@torch.no_grad() -def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - if s_churn > 0: - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - # Euler method - x = x + d * dt - return x - - -@torch.no_grad() -def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): - return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) - """Ancestral sampling with Euler method steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - if sigma_down == 0: - x = denoised - else: - d = to_d(x, sigmas[i], denoised) - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - -@torch.no_grad() -def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None): - """Ancestral sampling with Euler method steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - if sigmas[i + 1] == 0: - x = denoised - else: - downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta - sigma_down = sigmas[i + 1] * downstep_ratio - alpha_ip1 = 1 - sigmas[i + 1] - alpha_down = 1 - sigma_down - renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5 - # Euler method - sigma_down_i_ratio = sigma_down / sigmas[i] - x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised - if eta > 0: - x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff - return x - -@torch.no_grad() -def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - if s_churn > 0: - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - if sigmas[i + 1] == 0: - # Euler method - x = x + d * dt - else: - # Heun's method - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - d_prime = (d + d_2) / 2 - x = x + d_prime * dt - return x - - -@torch.no_grad() -def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - if s_churn > 0: - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - sigma_hat = sigmas[i] * (gamma + 1) - else: - gamma = 0 - sigma_hat = sigmas[i] - - if gamma > 0: - eps = torch.randn_like(x) * s_noise - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Euler method - dt = sigmas[i + 1] - sigma_hat - x = x + d * dt - else: - # DPM-Solver-2 - sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() - dt_1 = sigma_mid - sigma_hat - dt_2 = sigmas[i + 1] - sigma_hat - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - return x - - -@torch.no_grad() -def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): - return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) - - """Ancestral sampling with DPM-Solver second-order steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x, sigmas[i], denoised) - if sigma_down == 0: - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver-2 - sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() - dt_1 = sigma_mid - sigmas[i] - dt_2 = sigma_down - sigmas[i] - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - -@torch.no_grad() -def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """Ancestral sampling with DPM-Solver second-order steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta - sigma_down = sigmas[i+1] * downstep_ratio - alpha_ip1 = 1 - sigmas[i+1] - alpha_down = 1 - sigma_down - renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 - - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x, sigmas[i], denoised) - if sigma_down == 0: - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver-2 - sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() - dt_1 = sigma_mid - sigmas[i] - dt_2 = sigma_down - sigmas[i] - x_2 = x + d * dt_1 - denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff - return x - -def linear_multistep_coeff(order, t, i, j): - if order - 1 > i: - raise ValueError(f'Order {order} too high for step {i}') - def fn(tau): - prod = 1. - for k in range(order): - if j == k: - continue - prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) - return prod - return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] - - -@torch.no_grad() -def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - sigmas_cpu = sigmas.detach().cpu().numpy() - ds = [] - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - d = to_d(x, sigmas[i], denoised) - ds.append(d) - if len(ds) > order: - ds.pop(0) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - cur_order = min(i + 1, order) - coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] - x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) - return x - - -class PIDStepSizeController: - """A PID controller for ODE adaptive step size control.""" - def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): - self.h = h - self.b1 = (pcoeff + icoeff + dcoeff) / order - self.b2 = -(pcoeff + 2 * dcoeff) / order - self.b3 = dcoeff / order - self.accept_safety = accept_safety - self.eps = eps - self.errs = [] - - def limiter(self, x): - return 1 + math.atan(x - 1) - - def propose_step(self, error): - inv_error = 1 / (float(error) + self.eps) - if not self.errs: - self.errs = [inv_error, inv_error, inv_error] - self.errs[0] = inv_error - factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 - factor = self.limiter(factor) - accept = factor >= self.accept_safety - if accept: - self.errs[2] = self.errs[1] - self.errs[1] = self.errs[0] - self.h *= factor - return accept - - -class DPMSolver(nn.Module): - """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" - - def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): - super().__init__() - self.model = model - self.extra_args = {} if extra_args is None else extra_args - self.eps_callback = eps_callback - self.info_callback = info_callback - - def t(self, sigma): - return -sigma.log() - - def sigma(self, t): - return t.neg().exp() - - def eps(self, eps_cache, key, x, t, *args, **kwargs): - if key in eps_cache: - return eps_cache[key], eps_cache - sigma = self.sigma(t) * x.new_ones([x.shape[0]]) - eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) - if self.eps_callback is not None: - self.eps_callback() - return eps, {key: eps, **eps_cache} - - def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): - eps_cache = {} if eps_cache is None else eps_cache - h = t_next - t - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - x_1 = x - self.sigma(t_next) * h.expm1() * eps - return x_1, eps_cache - - def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): - eps_cache = {} if eps_cache is None else eps_cache - h = t_next - t - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - s1 = t + r1 * h - u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps - eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) - x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) - return x_2, eps_cache - - def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): - eps_cache = {} if eps_cache is None else eps_cache - h = t_next - t - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - s1 = t + r1 * h - s2 = t + r2 * h - u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps - eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) - u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) - eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) - x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) - return x_3, eps_cache - - def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): - noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler - if not t_end > t_start and eta: - raise ValueError('eta must be 0 for reverse sampling') - - m = math.floor(nfe / 3) + 1 - ts = torch.linspace(t_start, t_end, m + 1, device=x.device) - - if nfe % 3 == 0: - orders = [3] * (m - 2) + [2, 1] - else: - orders = [3] * (m - 1) + [nfe % 3] - - for i in range(len(orders)): - eps_cache = {} - t, t_next = ts[i], ts[i + 1] - if eta: - sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) - t_next_ = torch.minimum(t_end, self.t(sd)) - su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 - else: - t_next_, su = t_next, 0. - - eps, eps_cache = self.eps(eps_cache, 'eps', x, t) - denoised = x - self.sigma(t) * eps - if self.info_callback is not None: - self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) - - if orders[i] == 1: - x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) - elif orders[i] == 2: - x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) - else: - x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) - - x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) - - return x - - def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): - noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler - if order not in {2, 3}: - raise ValueError('order should be 2 or 3') - forward = t_end > t_start - if not forward and eta: - raise ValueError('eta must be 0 for reverse sampling') - h_init = abs(h_init) * (1 if forward else -1) - atol = torch.tensor(atol) - rtol = torch.tensor(rtol) - s = t_start - x_prev = x - accept = True - pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) - info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} - - while s < t_end - 1e-5 if forward else s > t_end + 1e-5: - eps_cache = {} - t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) - if eta: - sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) - t_ = torch.minimum(t_end, self.t(sd)) - su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 - else: - t_, su = t, 0. - - eps, eps_cache = self.eps(eps_cache, 'eps', x, s) - denoised = x - self.sigma(s) * eps - - if order == 2: - x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) - x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) - else: - x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) - x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) - delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) - error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 - accept = pid.propose_step(error) - if accept: - x_prev = x_low - x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) - s = t - info['n_accept'] += 1 - else: - info['n_reject'] += 1 - info['nfe'] += order - info['steps'] += 1 - - if self.info_callback is not None: - self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) - - return x, info - - -@torch.no_grad() -def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): - """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" - if sigma_min <= 0 or sigma_max <= 0: - raise ValueError('sigma_min and sigma_max must not be 0') - with tqdm(total=n, disable=disable) as pbar: - dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) - if callback is not None: - dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) - return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) - - -@torch.no_grad() -def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): - """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" - if sigma_min <= 0 or sigma_max <= 0: - raise ValueError('sigma_min and sigma_max must not be 0') - with tqdm(disable=disable) as pbar: - dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) - if callback is not None: - dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) - x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) - if return_info: - return x, info - return x - - -@torch.no_grad() -def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST): - return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler) - - """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigma_down == 0: - # Euler method - d = to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver++(2S) - t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) - r = 1 / 2 - h = t_next - t - s = t + r * h - x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised - denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 - # Noise addition - if sigmas[i + 1] > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - - -@torch.no_grad() -def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1 - lambda_fn = lambda sigma: ((1-sigma)/sigma).log() - - # logged_x = x.unsqueeze(0) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta - sigma_down = sigmas[i+1] * downstep_ratio - alpha_ip1 = 1 - sigmas[i+1] - alpha_down = 1 - sigma_down - renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5 - # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Euler method - d = to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # DPM-Solver++(2S) - if sigmas[i] == 1.0: - sigma_s = 0.9999 - else: - t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down) - r = 1 / 2 - h = t_down - t_i - s = t_i + r * h - sigma_s = sigma_fn(s) - # sigma_s = sigmas[i+1] - sigma_s_i_ratio = sigma_s / sigmas[i] - u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised - D_i = model(u, sigma_s * s_in, **extra_args) - sigma_down_i_ratio = sigma_down / sigmas[i] - x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i - # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff) - # Noise addition - if sigmas[i + 1] > 0 and eta > 0: - x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff - # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0) - return x - - -@torch.no_grad() -def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): - """DPM-Solver++ (stochastic).""" - if len(sigmas) <= 1: - return x - - extra_args = {} if extra_args is None else extra_args - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - seed = extra_args.get("seed", None) - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - # DPM-Solver++ - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - lambda_s_1 = lambda_s + r * h - fac = 1 / (2 * r) - - sigma_s_1 = sigma_fn(lambda_s_1) - - alpha_s = sigmas[i] * lambda_s.exp() - alpha_s_1 = sigma_s_1 * lambda_s_1.exp() - alpha_t = sigmas[i + 1] * lambda_t.exp() - - # Step 1 - sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta) - lambda_s_1_ = sd.log().neg() - h_ = lambda_s_1_ - lambda_s - x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised - if eta > 0 and s_noise > 0: - x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su - denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) - - # Step 2 - sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta) - lambda_t_ = sd.log().neg() - h_ = lambda_t_ - lambda_s - denoised_d = (1 - fac) * denoised + fac * denoised_2 - x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d - if eta > 0 and s_noise > 0: - x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su - return x - - -@torch.no_grad() -def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): - """DPM-Solver++(2M).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - old_denoised = None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - if old_denoised is None or sigmas[i + 1] == 0: - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised - else: - h_last = t - t_fn(sigmas[i - 1]) - r = h_last / h - denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised - x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d - old_denoised = denoised - return x - - -@torch.no_grad() -def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): - """DPM-Solver++(2M) SDE.""" - if len(sigmas) <= 1: - return x - - if solver_type not in {'heun', 'midpoint'}: - raise ValueError('solver_type must be \'heun\' or \'midpoint\'') - - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - old_denoised = None - h, h_last = None, None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - # DPM-Solver++(2M) SDE - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - - alpha_t = sigmas[i + 1] * lambda_t.exp() - - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised - - if old_denoised is not None: - r = h_last / h - if solver_type == 'heun': - x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised) - elif solver_type == 'midpoint': - x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised) - - if eta > 0 and s_noise > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise - - old_denoised = denoised - h_last = h - return x - - -@torch.no_grad() -def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'): - return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) - - -@torch.no_grad() -def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """DPM-Solver++(3M) SDE.""" - - if len(sigmas) <= 1: - return x - - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - denoised_1, denoised_2 = None, None - h, h_1, h_2 = None, None, None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - - alpha_t = sigmas[i + 1] * lambda_t.exp() - - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised - - if h_2 is not None: - # DPM-Solver++(3M) SDE - r0 = h_1 / h - r1 = h_2 / h - d1_0 = (denoised - denoised_1) / r0 - d1_1 = (denoised_1 - denoised_2) / r1 - d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) - d2 = (d1_0 - d1_1) / (r0 + r1) - phi_2 = h_eta.neg().expm1() / h_eta + 1 - phi_3 = phi_2 / h_eta - 0.5 - x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2 - elif h_1 is not None: - # DPM-Solver++(2M) SDE - r = h_1 / h - d = (denoised - denoised_1) / r - phi_2 = h_eta.neg().expm1() / h_eta + 1 - x = x + (alpha_t * phi_2) * d - - if eta > 0 and s_noise > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise - - denoised_1, denoised_2 = denoised, denoised_1 - h_1, h_2 = h, h_1 - return x - - -@torch.no_grad() -def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - if len(sigmas) <= 1: - return x - extra_args = {} if extra_args is None else extra_args - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler - return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) - - -@torch.no_grad() -def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'): - if len(sigmas) <= 1: - return x - extra_args = {} if extra_args is None else extra_args - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler - return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) - - -@torch.no_grad() -def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): - if len(sigmas) <= 1: - return x - extra_args = {} if extra_args is None else extra_args - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler - return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) - - -@torch.no_grad() -def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): - if len(sigmas) <= 1: - return x - extra_args = {} if extra_args is None else extra_args - sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() - noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler - return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) - - -def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): - alpha_cumprod = 1 / ((sigma * sigma) + 1) - alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) - alpha = (alpha_cumprod / alpha_cumprod_prev) - - mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) - if sigma_prev > 0: - mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) - return mu - -def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) - if sigmas[i + 1] != 0: - x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) - return x - - -@torch.no_grad() -def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): - return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) - -@torch.no_grad() -def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - x = denoised - if sigmas[i + 1] > 0: - x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x) - return x - - - -@torch.no_grad() -def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): - # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - s_end = sigmas[-1] - for i in trange(len(sigmas) - 1, disable=disable): - gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. - eps = torch.randn_like(x) * s_noise - sigma_hat = sigmas[i] * (gamma + 1) - if gamma > 0: - x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - if sigmas[i + 1] == s_end: - # Euler method - x = x + d * dt - elif sigmas[i + 2] == s_end: - - # Heun's method - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - - w = 2 * sigmas[0] - w2 = sigmas[i+1]/w - w1 = 1 - w2 - - d_prime = d * w1 + d_2 * w2 - - - x = x + d_prime * dt - - else: - # Heun++ - x_2 = x + d * dt - denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - dt_2 = sigmas[i + 2] - sigmas[i + 1] - - x_3 = x_2 + d_2 * dt_2 - denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) - d_3 = to_d(x_3, sigmas[i + 2], denoised_3) - - w = 3 * sigmas[0] - w2 = sigmas[i + 1] / w - w3 = sigmas[i + 2] / w - w1 = 1 - w2 - w3 - - d_prime = w1 * d + w2 * d_2 + w3 * d_3 - x = x + d_prime * dt - return x - - -#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py -#under Apache 2 license -def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - - x_next = x - - buffer_model = [] - for i in trange(len(sigmas) - 1, disable=disable): - t_cur = sigmas[i] - t_next = sigmas[i + 1] - - x_cur = x_next - - denoised = model(x_cur, t_cur * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - d_cur = (x_cur - denoised) / t_cur - - order = min(max_order, i+1) - if t_next == 0: # Denoising step - x_next = denoised - elif order == 1: # First Euler step. - x_next = x_cur + (t_next - t_cur) * d_cur - elif order == 2: # Use one history point. - x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 - elif order == 3: # Use two history points. - x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12 - elif order == 4: # Use three history points. - x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24 - - if len(buffer_model) == max_order - 1: - for k in range(max_order - 2): - buffer_model[k] = buffer_model[k+1] - buffer_model[-1] = d_cur - else: - buffer_model.append(d_cur) - - return x_next - - -#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py -#under Apache 2 license -def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - - x_next = x - t_steps = sigmas - - buffer_model = [] - for i in trange(len(sigmas) - 1, disable=disable): - t_cur = sigmas[i] - t_next = sigmas[i + 1] - - x_cur = x_next - - denoised = model(x_cur, t_cur * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - d_cur = (x_cur - denoised) / t_cur - - order = min(max_order, i+1) - if t_next == 0: # Denoising step - x_next = denoised - elif order == 1: # First Euler step. - x_next = x_cur + (t_next - t_cur) * d_cur - elif order == 2: # Use one history point. - h_n = (t_next - t_cur) - h_n_1 = (t_cur - t_steps[i-1]) - coeff1 = (2 + (h_n / h_n_1)) / 2 - coeff2 = -(h_n / h_n_1) / 2 - x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1]) - elif order == 3: # Use two history points. - h_n = (t_next - t_cur) - h_n_1 = (t_cur - t_steps[i-1]) - h_n_2 = (t_steps[i-1] - t_steps[i-2]) - temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 - coeff1 = (2 + (h_n / h_n_1)) / 2 + temp - coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp - coeff3 = temp * h_n_1 / h_n_2 - x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2]) - elif order == 4: # Use three history points. - h_n = (t_next - t_cur) - h_n_1 = (t_cur - t_steps[i-1]) - h_n_2 = (t_steps[i-1] - t_steps[i-2]) - h_n_3 = (t_steps[i-2] - t_steps[i-3]) - temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2 - temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \ - * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3)) - coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2 - coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2 - coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2 - coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2 - x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3]) - - if len(buffer_model) == max_order - 1: - for k in range(max_order - 2): - buffer_model[k] = buffer_model[k+1] - buffer_model[-1] = d_cur.detach() - else: - buffer_model.append(d_cur.detach()) - - return x_next - - -#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py -#under Apache 2 license -@torch.no_grad() -def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'): - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - - x_next = x - t_steps = sigmas - - coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode) - - buffer_model = [] - for i in trange(len(sigmas) - 1, disable=disable): - t_cur = sigmas[i] - t_next = sigmas[i + 1] - - x_cur = x_next - - denoised = model(x_cur, t_cur * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - d_cur = (x_cur - denoised) / t_cur - - order = min(max_order, i+1) - if t_next <= 0: - order = 1 - - if order == 1: # First Euler step. - x_next = x_cur + (t_next - t_cur) * d_cur - elif order == 2: # Use one history point. - coeff_cur, coeff_prev1 = coeff_list[i] - x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] - elif order == 3: # Use two history points. - coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i] - x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] - elif order == 4: # Use three history points. - coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i] - x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3] - - if len(buffer_model) == max_order - 1: - for k in range(max_order - 2): - buffer_model[k] = buffer_model[k+1] - buffer_model[-1] = d_cur.detach() - else: - buffer_model.append(d_cur.detach()) - - return x_next - - -@torch.no_grad() -def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """Ancestral sampling with Euler method steps (CFG++).""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - - model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - - uncond_denoised = None - - def post_cfg_function(args): - nonlocal uncond_denoised - uncond_denoised = args["uncond_denoised"] - return args["denoised"] - - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp() - alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp() - d = to_d(x, sigmas[i], alpha_s * uncond_denoised) # to noise - - # DDIM stochastic sampling - sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta) - sigma_down = alpha_t * sigma_down - - # Euler method - x = alpha_t * denoised + sigma_down * d - if eta > 0 and s_noise > 0: - x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - - -@torch.no_grad() -def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): - """Euler method steps (CFG++).""" - return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None) - - -@torch.no_grad() -def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - - temp = [0] - def post_cfg_function(args): - temp[0] = args["uncond_denoised"] - return args["denoised"] - - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigma_down == 0: - # Euler method - d = to_d(x, sigmas[i], temp[0]) - x = denoised + d * sigma_down - else: - # DPM-Solver++(2S) - t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) - # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird - r = 1 / 2 - h = t_next - t - s = t + r * h - x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised - denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) - x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2 - # Noise addition - if sigmas[i + 1] > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - return x - -@torch.no_grad() -def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): - """DPM-Solver++(2M).""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - t_fn = lambda sigma: sigma.log().neg() - - old_uncond_denoised = None - uncond_denoised = None - def post_cfg_function(args): - nonlocal uncond_denoised - uncond_denoised = args["uncond_denoised"] - return args["denoised"] - - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - if old_uncond_denoised is None or sigmas[i + 1] == 0: - denoised_mix = -torch.exp(-h) * uncond_denoised - else: - h_last = t - t_fn(sigmas[i - 1]) - r = h_last / h - denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised) - x = denoised + denoised_mix + torch.exp(-h) * x - old_uncond_denoised = uncond_denoised - return x - -@torch.no_grad() -def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False): - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() - phi1_fn = lambda t: torch.expm1(t) / t - phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t - - old_sigma_down = None - old_denoised = None - uncond_denoised = None - def post_cfg_function(args): - nonlocal uncond_denoised - uncond_denoised = args["uncond_denoised"] - return args["denoised"] - - if cfg_pp: - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) - if callback is not None: - callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) - if sigma_down == 0 or old_denoised is None: - # Euler method - if cfg_pp: - d = to_d(x, sigmas[i], uncond_denoised) - x = denoised + d * sigma_down - else: - d = to_d(x, sigmas[i], denoised) - dt = sigma_down - sigmas[i] - x = x + d * dt - else: - # Second order multistep method in https://arxiv.org/pdf/2308.02157 - t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1]) - h = t_next - t - c2 = (t_prev - t_old) / h - - phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) - b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0) - b2 = torch.nan_to_num(phi2_val / c2, nan=0.0) - - if cfg_pp: - x = x + (denoised - uncond_denoised) - x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised) - else: - x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised) - - # Noise addition - if sigmas[i + 1] > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up - - if cfg_pp: - old_denoised = uncond_denoised - else: - old_denoised = denoised - old_sigma_down = sigma_down - return x - -@torch.no_grad() -def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): - return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False) - -@torch.no_grad() -def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): - return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True) - -@torch.no_grad() -def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False) - -@torch.no_grad() -def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True) - - -@torch.no_grad() -def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False): - """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK""" - extra_args = {} if extra_args is None else extra_args - s_in = x.new_ones([x.shape[0]]) - old_d = None - - uncond_denoised = None - def post_cfg_function(args): - nonlocal uncond_denoised - uncond_denoised = args["uncond_denoised"] - return args["denoised"] - - if cfg_pp: - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if cfg_pp: - d = to_d(x, sigmas[i], uncond_denoised) - else: - d = to_d(x, sigmas[i], denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - dt = sigmas[i + 1] - sigmas[i] - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - # Euler method - if cfg_pp: - x = denoised + d * sigmas[i + 1] - else: - x = x + d * dt - - if i >= 1: - # Gradient estimation - d_bar = (ge_gamma - 1) * (d - old_d) - x = x + d_bar * dt - old_d = d - return x - - -@torch.no_grad() -def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): - return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True) - - -@torch.no_grad() -def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3): - """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169. - Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py. - """ - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - def default_er_sde_noise_scaler(x): - return x * ((x ** 0.3).exp() + 10.0) - - noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler - num_integration_points = 200.0 - point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device) - - model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling) - er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t - - old_denoised = None - old_denoised_d = None - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - stage_used = min(max_stage, i + 1) - if sigmas[i + 1] == 0: - x = denoised - else: - er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1] - alpha_s = sigmas[i] / er_lambda_s - alpha_t = sigmas[i + 1] / er_lambda_t - r_alpha = alpha_t / alpha_s - r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s) - - # Stage 1 Euler - x = r_alpha * r * x + alpha_t * (1 - r) * denoised - - if stage_used >= 2: - dt = er_lambda_t - er_lambda_s - lambda_step_size = -dt / num_integration_points - lambda_pos = er_lambda_t + point_indice * lambda_step_size - scaled_pos = noise_scaler(lambda_pos) - - # Stage 2 - s = torch.sum(1 / scaled_pos) * lambda_step_size - denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1]) - x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d - - if stage_used >= 3: - # Stage 3 - s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size - denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2) - x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u - old_denoised_d = denoised_d - - if s_noise > 0: - x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0) - old_denoised = denoised - return x - - -@torch.no_grad() -def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5): - """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2. - arXiv: https://arxiv.org/abs/2305.14267 - """ - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - inject_noise = eta > 0 and s_noise > 0 - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - x = denoised - else: - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - lambda_s_1 = lambda_s + r * h - fac = 1 / (2 * r) - sigma_s_1 = sigma_fn(lambda_s_1) - - # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t) - alpha_s_1 = sigma_s_1 * lambda_s_1.exp() - alpha_t = sigmas[i + 1] * lambda_t.exp() - - coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1() - if inject_noise: - # 0 < r < 1 - noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt() - noise_coeff_2 = (-r * h * eta).exp() * (-2 * (1 - r) * h * eta).expm1().neg().sqrt() - noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigmas[i + 1]) - - # Step 1 - x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised - if inject_noise: - x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise - denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) - - # Step 2 - denoised_d = (1 - fac) * denoised + fac * denoised_2 - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_2 * denoised_d - if inject_noise: - x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise - return x - - -@torch.no_grad() -def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3): - """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3. - arXiv: https://arxiv.org/abs/2305.14267 - """ - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - inject_noise = eta > 0 and s_noise > 0 - - model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') - sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) - lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - if sigmas[i + 1] == 0: - x = denoised - else: - lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) - h = lambda_t - lambda_s - h_eta = h * (eta + 1) - lambda_s_1 = lambda_s + r_1 * h - lambda_s_2 = lambda_s + r_2 * h - sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2) - - # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t) - alpha_s_1 = sigma_s_1 * lambda_s_1.exp() - alpha_s_2 = sigma_s_2 * lambda_s_2.exp() - alpha_t = sigmas[i + 1] * lambda_t.exp() - - coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1() - if inject_noise: - # 0 < r_1 < r_2 < 1 - noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt() - noise_coeff_2 = (-r_1 * h * eta).exp() * (-2 * (r_2 - r_1) * h * eta).expm1().neg().sqrt() - noise_coeff_3 = (-r_2 * h * eta).exp() * (-2 * (1 - r_2) * h * eta).expm1().neg().sqrt() - noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1]) - - # Step 1 - x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised - if inject_noise: - x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise - denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) - - # Step 2 - x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * coeff_2 * denoised + (r_2 / r_1) * alpha_s_2 * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised) - if inject_noise: - x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise - denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args) - - # Step 3 - x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_3 * denoised + (1. / r_2) * alpha_t * (coeff_3 / h_eta + 1) * (denoised_3 - denoised) - if inject_noise: - x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise - return x - - -@torch.no_grad() -def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False): - """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023).""" - if len(sigmas) <= 1: - return x - extra_args = {} if extra_args is None else extra_args - seed = extra_args.get("seed", None) - noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - s_in = x.new_ones([x.shape[0]]) - - model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") - sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) - lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling) - - if tau_func is None: - # Use default interval for stochastic sampling - start_sigma = model_sampling.percent_to_sigma(0.2) - end_sigma = model_sampling.percent_to_sigma(0.8) - tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0) - - max_used_order = max(predictor_order, corrector_order) - x_pred = x # x: current state, x_pred: predicted next state - - h = 0.0 - tau_t = 0.0 - noise = 0.0 - pred_list = [] - - # Lower order near the end to improve stability - lower_order_to_end = sigmas[-1].item() == 0 - - for i in trange(len(sigmas) - 1, disable=disable): - # Evaluation - denoised = model(x_pred, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) - pred_list.append(denoised) - pred_list = pred_list[-max_used_order:] - - predictor_order_used = min(predictor_order, len(pred_list)) - if i == 0 or (sigmas[i + 1] == 0 and not use_pece): - corrector_order_used = 0 - else: - corrector_order_used = min(corrector_order, len(pred_list)) - - if lower_order_to_end: - predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i) - corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i) - - # Corrector - if corrector_order_used == 0: - # Update by the predicted state - x = x_pred - else: - curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1] - b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( - sigmas[i], - curr_lambdas, - lambdas[i - 1], - lambdas[i], - tau_t, - simple_order_2, - is_corrector_step=True, - ) - pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...) - corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) - x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res - - if tau_t > 0 and s_noise > 0: - # The noise from the previous predictor step - x = x + noise - - if use_pece: - # Evaluate the corrected state - denoised = model(x, sigmas[i] * s_in, **extra_args) - pred_list[-1] = denoised - - # Predictor - if sigmas[i + 1] == 0: - # Denoising step - x = denoised - else: - tau_t = tau_func(sigmas[i + 1]) - curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1] - b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( - sigmas[i + 1], - curr_lambdas, - lambdas[i], - lambdas[i + 1], - tau_t, - simple_order_2, - is_corrector_step=False, - ) - pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...) - pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) - h = lambdas[i + 1] - lambdas[i] - x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res - - if tau_t > 0 and s_noise > 0: - noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise - x_pred = x_pred + noise - return x - - -@torch.no_grad() -def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False): - """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023).""" - return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2) diff --git a/comfy/k_diffusion/utils.py b/comfy/k_diffusion/utils.py deleted file mode 100644 index a644df2f3cf82b32ac6e9bf2cb7bfc70c95e05f9..0000000000000000000000000000000000000000 --- a/comfy/k_diffusion/utils.py +++ /dev/null @@ -1,313 +0,0 @@ -from contextlib import contextmanager -import hashlib -import math -from pathlib import Path -import shutil -import urllib -import warnings - -from PIL import Image -import torch -from torch import nn, optim -from torch.utils import data - - -def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): - """Apply passed in transforms for HuggingFace Datasets.""" - images = [transform(image.convert(mode)) for image in examples[image_key]] - return {image_key: images} - - -def append_dims(x, target_dims): - """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') - expanded = x[(...,) + (None,) * dims_to_append] - # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. - # https://github.com/pytorch/pytorch/issues/84364 - return expanded.detach().clone() if expanded.device.type == 'mps' else expanded - - -def n_params(module): - """Returns the number of trainable parameters in a module.""" - return sum(p.numel() for p in module.parameters()) - - -def download_file(path, url, digest=None): - """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" - path = Path(path) - path.parent.mkdir(parents=True, exist_ok=True) - if not path.exists(): - with urllib.request.urlopen(url) as response, open(path, 'wb') as f: - shutil.copyfileobj(response, f) - if digest is not None: - file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() - if digest != file_digest: - raise OSError(f'hash of {path} (url: {url}) failed to validate') - return path - - -@contextmanager -def train_mode(model, mode=True): - """A context manager that places a model into training mode and restores - the previous mode on exit.""" - modes = [module.training for module in model.modules()] - try: - yield model.train(mode) - finally: - for i, module in enumerate(model.modules()): - module.training = modes[i] - - -def eval_mode(model): - """A context manager that places a model into evaluation mode and restores - the previous mode on exit.""" - return train_mode(model, False) - - -@torch.no_grad() -def ema_update(model, averaged_model, decay): - """Incorporates updated model parameters into an exponential moving averaged - version of a model. It should be called after each optimizer step.""" - model_params = dict(model.named_parameters()) - averaged_params = dict(averaged_model.named_parameters()) - assert model_params.keys() == averaged_params.keys() - - for name, param in model_params.items(): - averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) - - model_buffers = dict(model.named_buffers()) - averaged_buffers = dict(averaged_model.named_buffers()) - assert model_buffers.keys() == averaged_buffers.keys() - - for name, buf in model_buffers.items(): - averaged_buffers[name].copy_(buf) - - -class EMAWarmup: - """Implements an EMA warmup using an inverse decay schedule. - If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are - good values for models you plan to train for a million or more steps (reaches decay - factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models - you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at - 215.4k steps). - Args: - inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. - power (float): Exponential factor of EMA warmup. Default: 1. - min_value (float): The minimum EMA decay rate. Default: 0. - max_value (float): The maximum EMA decay rate. Default: 1. - start_at (int): The epoch to start averaging at. Default: 0. - last_epoch (int): The index of last epoch. Default: 0. - """ - - def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, - last_epoch=0): - self.inv_gamma = inv_gamma - self.power = power - self.min_value = min_value - self.max_value = max_value - self.start_at = start_at - self.last_epoch = last_epoch - - def state_dict(self): - """Returns the state of the class as a :class:`dict`.""" - return dict(self.__dict__.items()) - - def load_state_dict(self, state_dict): - """Loads the class's state. - Args: - state_dict (dict): scaler state. Should be an object returned - from a call to :meth:`state_dict`. - """ - self.__dict__.update(state_dict) - - def get_value(self): - """Gets the current EMA decay rate.""" - epoch = max(0, self.last_epoch - self.start_at) - value = 1 - (1 + epoch / self.inv_gamma) ** -self.power - return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) - - def step(self): - """Updates the step count.""" - self.last_epoch += 1 - - -class InverseLR(optim.lr_scheduler._LRScheduler): - """Implements an inverse decay learning rate schedule with an optional exponential - warmup. When last_epoch=-1, sets initial lr as lr. - inv_gamma is the number of steps/epochs required for the learning rate to decay to - (1 / 2)**power of its original value. - Args: - optimizer (Optimizer): Wrapped optimizer. - inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. - power (float): Exponential factor of learning rate decay. Default: 1. - warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) - Default: 0. - min_lr (float): The minimum learning rate. Default: 0. - last_epoch (int): The index of last epoch. Default: -1. - verbose (bool): If ``True``, prints a message to stdout for - each update. Default: ``False``. - """ - - def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., - last_epoch=-1, verbose=False): - self.inv_gamma = inv_gamma - self.power = power - if not 0. <= warmup < 1: - raise ValueError('Invalid value for warmup') - self.warmup = warmup - self.min_lr = min_lr - super().__init__(optimizer, last_epoch, verbose) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn("To get the last learning rate computed by the scheduler, " - "please use `get_last_lr()`.") - - return self._get_closed_form_lr() - - def _get_closed_form_lr(self): - warmup = 1 - self.warmup ** (self.last_epoch + 1) - lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power - return [warmup * max(self.min_lr, base_lr * lr_mult) - for base_lr in self.base_lrs] - - -class ExponentialLR(optim.lr_scheduler._LRScheduler): - """Implements an exponential learning rate schedule with an optional exponential - warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate - continuously by decay (default 0.5) every num_steps steps. - Args: - optimizer (Optimizer): Wrapped optimizer. - num_steps (float): The number of steps to decay the learning rate by decay in. - decay (float): The factor by which to decay the learning rate every num_steps - steps. Default: 0.5. - warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) - Default: 0. - min_lr (float): The minimum learning rate. Default: 0. - last_epoch (int): The index of last epoch. Default: -1. - verbose (bool): If ``True``, prints a message to stdout for - each update. Default: ``False``. - """ - - def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., - last_epoch=-1, verbose=False): - self.num_steps = num_steps - self.decay = decay - if not 0. <= warmup < 1: - raise ValueError('Invalid value for warmup') - self.warmup = warmup - self.min_lr = min_lr - super().__init__(optimizer, last_epoch, verbose) - - def get_lr(self): - if not self._get_lr_called_within_step: - warnings.warn("To get the last learning rate computed by the scheduler, " - "please use `get_last_lr()`.") - - return self._get_closed_form_lr() - - def _get_closed_form_lr(self): - warmup = 1 - self.warmup ** (self.last_epoch + 1) - lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch - return [warmup * max(self.min_lr, base_lr * lr_mult) - for base_lr in self.base_lrs] - - -def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): - """Draws samples from an lognormal distribution.""" - return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() - - -def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): - """Draws samples from an optionally truncated log-logistic distribution.""" - min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) - max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) - min_cdf = min_value.log().sub(loc).div(scale).sigmoid() - max_cdf = max_value.log().sub(loc).div(scale).sigmoid() - u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf - return u.logit().mul(scale).add(loc).exp().to(dtype) - - -def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): - """Draws samples from an log-uniform distribution.""" - min_value = math.log(min_value) - max_value = math.log(max_value) - return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() - - -def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): - """Draws samples from a truncated v-diffusion training timestep distribution.""" - min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi - max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi - u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf - return torch.tan(u * math.pi / 2) * sigma_data - - -def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): - """Draws samples from a split lognormal distribution.""" - n = torch.randn(shape, device=device, dtype=dtype).abs() - u = torch.rand(shape, device=device, dtype=dtype) - n_left = n * -scale_1 + loc - n_right = n * scale_2 + loc - ratio = scale_1 / (scale_1 + scale_2) - return torch.where(u < ratio, n_left, n_right).exp() - - -class FolderOfImages(data.Dataset): - """Recursively finds all images in a directory. It does not support - classes/targets.""" - - IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} - - def __init__(self, root, transform=None): - super().__init__() - self.root = Path(root) - self.transform = nn.Identity() if transform is None else transform - self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) - - def __repr__(self): - return f'FolderOfImages(root="{self.root}", len: {len(self)})' - - def __len__(self): - return len(self.paths) - - def __getitem__(self, key): - path = self.paths[key] - with open(path, 'rb') as f: - image = Image.open(f).convert('RGB') - image = self.transform(image) - return image, - - -class CSVLogger: - def __init__(self, filename, columns): - self.filename = Path(filename) - self.columns = columns - if self.filename.exists(): - self.file = open(self.filename, 'a') - else: - self.file = open(self.filename, 'w') - self.write(*self.columns) - - def write(self, *args): - print(*args, sep=',', file=self.file, flush=True) - - -@contextmanager -def tf32_mode(cudnn=None, matmul=None): - """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" - cudnn_old = torch.backends.cudnn.allow_tf32 - matmul_old = torch.backends.cuda.matmul.allow_tf32 - try: - if cudnn is not None: - torch.backends.cudnn.allow_tf32 = cudnn - if matmul is not None: - torch.backends.cuda.matmul.allow_tf32 = matmul - yield - finally: - if cudnn is not None: - torch.backends.cudnn.allow_tf32 = cudnn_old - if matmul is not None: - torch.backends.cuda.matmul.allow_tf32 = matmul_old diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py deleted file mode 100644 index caf4991fcab1ef2ad1c6f32effae0e23846aaf0a..0000000000000000000000000000000000000000 --- a/comfy/latent_formats.py +++ /dev/null @@ -1,548 +0,0 @@ -import torch - -class LatentFormat: - scale_factor = 1.0 - latent_channels = 4 - latent_dimensions = 2 - latent_rgb_factors = None - latent_rgb_factors_bias = None - taesd_decoder_name = None - - def process_in(self, latent): - return latent * self.scale_factor - - def process_out(self, latent): - return latent / self.scale_factor - -class SD15(LatentFormat): - def __init__(self, scale_factor=0.18215): - self.scale_factor = scale_factor - self.latent_rgb_factors = [ - # R G B - [ 0.3512, 0.2297, 0.3227], - [ 0.3250, 0.4974, 0.2350], - [-0.2829, 0.1762, 0.2721], - [-0.2120, -0.2616, -0.7177] - ] - self.taesd_decoder_name = "taesd_decoder" - -class SDXL(LatentFormat): - scale_factor = 0.13025 - - def __init__(self): - self.latent_rgb_factors = [ - # R G B - [ 0.3651, 0.4232, 0.4341], - [-0.2533, -0.0042, 0.1068], - [ 0.1076, 0.1111, -0.0362], - [-0.3165, -0.2492, -0.2188] - ] - self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011] - - self.taesd_decoder_name = "taesdxl_decoder" - -class SDXL_Playground_2_5(LatentFormat): - def __init__(self): - self.scale_factor = 0.5 - self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1) - self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1) - - self.latent_rgb_factors = [ - # R G B - [ 0.3920, 0.4054, 0.4549], - [-0.2634, -0.0196, 0.0653], - [ 0.0568, 0.1687, -0.0755], - [-0.3112, -0.2359, -0.2076] - ] - self.taesd_decoder_name = "taesdxl_decoder" - - def process_in(self, latent): - latents_mean = self.latents_mean.to(latent.device, latent.dtype) - latents_std = self.latents_std.to(latent.device, latent.dtype) - return (latent - latents_mean) * self.scale_factor / latents_std - - def process_out(self, latent): - latents_mean = self.latents_mean.to(latent.device, latent.dtype) - latents_std = self.latents_std.to(latent.device, latent.dtype) - return latent * latents_std / self.scale_factor + latents_mean - - -class SD_X4(LatentFormat): - def __init__(self): - self.scale_factor = 0.08333 - self.latent_rgb_factors = [ - [-0.2340, -0.3863, -0.3257], - [ 0.0994, 0.0885, -0.0908], - [-0.2833, -0.2349, -0.3741], - [ 0.2523, -0.0055, -0.1651] - ] - -class SC_Prior(LatentFormat): - latent_channels = 16 - def __init__(self): - self.scale_factor = 1.0 - self.latent_rgb_factors = [ - [-0.0326, -0.0204, -0.0127], - [-0.1592, -0.0427, 0.0216], - [ 0.0873, 0.0638, -0.0020], - [-0.0602, 0.0442, 0.1304], - [ 0.0800, -0.0313, -0.1796], - [-0.0810, -0.0638, -0.1581], - [ 0.1791, 0.1180, 0.0967], - [ 0.0740, 0.1416, 0.0432], - [-0.1745, -0.1888, -0.1373], - [ 0.2412, 0.1577, 0.0928], - [ 0.1908, 0.0998, 0.0682], - [ 0.0209, 0.0365, -0.0092], - [ 0.0448, -0.0650, -0.1728], - [-0.1658, -0.1045, -0.1308], - [ 0.0542, 0.1545, 0.1325], - [-0.0352, -0.1672, -0.2541] - ] - -class SC_B(LatentFormat): - def __init__(self): - self.scale_factor = 1.0 / 0.43 - self.latent_rgb_factors = [ - [ 0.1121, 0.2006, 0.1023], - [-0.2093, -0.0222, -0.0195], - [-0.3087, -0.1535, 0.0366], - [ 0.0290, -0.1574, -0.4078] - ] - -class SD3(LatentFormat): - latent_channels = 16 - def __init__(self): - self.scale_factor = 1.5305 - self.shift_factor = 0.0609 - self.latent_rgb_factors = [ - [-0.0922, -0.0175, 0.0749], - [ 0.0311, 0.0633, 0.0954], - [ 0.1994, 0.0927, 0.0458], - [ 0.0856, 0.0339, 0.0902], - [ 0.0587, 0.0272, -0.0496], - [-0.0006, 0.1104, 0.0309], - [ 0.0978, 0.0306, 0.0427], - [-0.0042, 0.1038, 0.1358], - [-0.0194, 0.0020, 0.0669], - [-0.0488, 0.0130, -0.0268], - [ 0.0922, 0.0988, 0.0951], - [-0.0278, 0.0524, -0.0542], - [ 0.0332, 0.0456, 0.0895], - [-0.0069, -0.0030, -0.0810], - [-0.0596, -0.0465, -0.0293], - [-0.1448, -0.1463, -0.1189] - ] - self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925] - self.taesd_decoder_name = "taesd3_decoder" - - def process_in(self, latent): - return (latent - self.shift_factor) * self.scale_factor - - def process_out(self, latent): - return (latent / self.scale_factor) + self.shift_factor - -class StableAudio1(LatentFormat): - latent_channels = 64 - latent_dimensions = 1 - -class Flux(SD3): - latent_channels = 16 - def __init__(self): - self.scale_factor = 0.3611 - self.shift_factor = 0.1159 - self.latent_rgb_factors =[ - [-0.0346, 0.0244, 0.0681], - [ 0.0034, 0.0210, 0.0687], - [ 0.0275, -0.0668, -0.0433], - [-0.0174, 0.0160, 0.0617], - [ 0.0859, 0.0721, 0.0329], - [ 0.0004, 0.0383, 0.0115], - [ 0.0405, 0.0861, 0.0915], - [-0.0236, -0.0185, -0.0259], - [-0.0245, 0.0250, 0.1180], - [ 0.1008, 0.0755, -0.0421], - [-0.0515, 0.0201, 0.0011], - [ 0.0428, -0.0012, -0.0036], - [ 0.0817, 0.0765, 0.0749], - [-0.1264, -0.0522, -0.1103], - [-0.0280, -0.0881, -0.0499], - [-0.1262, -0.0982, -0.0778] - ] - self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] - self.taesd_decoder_name = "taef1_decoder" - - def process_in(self, latent): - return (latent - self.shift_factor) * self.scale_factor - - def process_out(self, latent): - return (latent / self.scale_factor) + self.shift_factor - -class Mochi(LatentFormat): - latent_channels = 12 - latent_dimensions = 3 - - def __init__(self): - self.scale_factor = 1.0 - self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141, - -0.05565264470995561, 0.012767231469026969, -0.04703542746246419, - 0.043896967884726704, -0.09346305707025976, -0.09918314763016893, - -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1) - self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617, - 0.959253732819592, 0.8244560132752793, 0.917259975397747, - 0.9294154431013696, 1.3720942357788521, 0.881393668867029, - 0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1) - - self.latent_rgb_factors =[ - [-0.0069, -0.0045, 0.0018], - [ 0.0154, -0.0692, -0.0274], - [ 0.0333, 0.0019, 0.0206], - [-0.1390, 0.0628, 0.1678], - [-0.0725, 0.0134, -0.1898], - [ 0.0074, -0.0270, -0.0209], - [-0.0176, -0.0277, -0.0221], - [ 0.5294, 0.5204, 0.3852], - [-0.0326, -0.0446, -0.0143], - [-0.0659, 0.0153, -0.0153], - [ 0.0185, -0.0217, 0.0014], - [-0.0396, -0.0495, -0.0281] - ] - self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453] - self.taesd_decoder_name = None #TODO - - def process_in(self, latent): - latents_mean = self.latents_mean.to(latent.device, latent.dtype) - latents_std = self.latents_std.to(latent.device, latent.dtype) - return (latent - latents_mean) * self.scale_factor / latents_std - - def process_out(self, latent): - latents_mean = self.latents_mean.to(latent.device, latent.dtype) - latents_std = self.latents_std.to(latent.device, latent.dtype) - return latent * latents_std / self.scale_factor + latents_mean - -class LTXV(LatentFormat): - latent_channels = 128 - latent_dimensions = 3 - - def __init__(self): - self.latent_rgb_factors = [ - [ 1.1202e-02, -6.3815e-04, -1.0021e-02], - [ 8.6031e-02, 6.5813e-02, 9.5409e-04], - [-1.2576e-02, -7.5734e-03, -4.0528e-03], - [ 9.4063e-03, -2.1688e-03, 2.6093e-03], - [ 3.7636e-03, 1.2765e-02, 9.1548e-03], - [ 2.1024e-02, -5.2973e-03, 3.4373e-03], - [-8.8896e-03, -1.9703e-02, -1.8761e-02], - [-1.3160e-02, -1.0523e-02, 1.9709e-03], - [-1.5152e-03, -6.9891e-03, -7.5810e-03], - [-1.7247e-03, 4.6560e-04, -3.3839e-03], - [ 1.3617e-02, 4.7077e-03, -2.0045e-03], - [ 1.0256e-02, 7.7318e-03, 1.3948e-02], - [-1.6108e-02, -6.2151e-03, 1.1561e-03], - [ 7.3407e-03, 1.5628e-02, 4.4865e-04], - [ 9.5357e-04, -2.9518e-03, -1.4760e-02], - [ 1.9143e-02, 1.0868e-02, 1.2264e-02], - [ 4.4575e-03, 3.6682e-05, -6.8508e-03], - [-4.5681e-04, 3.2570e-03, 7.7929e-03], - [ 3.3902e-02, 3.3405e-02, 3.7454e-02], - [-2.3001e-02, -2.4877e-03, -3.1033e-03], - [ 5.0265e-02, 3.8841e-02, 3.3539e-02], - [-4.1018e-03, -1.1095e-03, 1.5859e-03], - [-1.2689e-01, -1.3107e-01, -2.1005e-01], - [ 2.6276e-02, 1.4189e-02, -3.5963e-03], - [-4.8679e-03, 8.8486e-03, 7.8029e-03], - [-1.6610e-03, -4.8597e-03, -5.2060e-03], - [-2.1010e-03, 2.3610e-03, 9.3796e-03], - [-2.2482e-02, -2.1305e-02, -1.5087e-02], - [-1.5753e-02, -1.0646e-02, -6.5083e-03], - [-4.6975e-03, 5.0288e-03, -6.7390e-03], - [ 1.1951e-02, 2.0712e-02, 1.6191e-02], - [-6.3704e-03, -8.4827e-03, -9.5483e-03], - [ 7.2610e-03, -9.9326e-03, -2.2978e-02], - [-9.1904e-04, 6.2882e-03, 9.5720e-03], - [-3.7178e-02, -3.7123e-02, -5.6713e-02], - [-1.3373e-01, -1.0720e-01, -5.3801e-02], - [-5.3702e-03, 8.1256e-03, 8.8397e-03], - [-1.5247e-01, -2.1437e-01, -2.1843e-01], - [ 3.1441e-02, 7.0335e-03, -9.7541e-03], - [ 2.1528e-03, -8.9817e-03, -2.1023e-02], - [ 3.8461e-03, -5.8957e-03, -1.5014e-02], - [-4.3470e-03, -1.2940e-02, -1.5972e-02], - [-5.4781e-03, -1.0842e-02, -3.0204e-03], - [-6.5347e-03, 3.0806e-03, -1.0163e-02], - [-5.0414e-03, -7.1503e-03, -8.9686e-04], - [-8.5851e-03, -2.4351e-03, 1.0674e-03], - [-9.0016e-03, -9.6493e-03, 1.5692e-03], - [ 5.0914e-03, 1.2099e-02, 1.9968e-02], - [ 1.3758e-02, 1.1669e-02, 8.1958e-03], - [-1.0518e-02, -1.1575e-02, -4.1307e-03], - [-2.8410e-02, -3.1266e-02, -2.2149e-02], - [ 2.9336e-03, 3.6511e-02, 1.8717e-02], - [-1.6703e-02, -1.6696e-02, -4.4529e-03], - [ 4.8818e-02, 4.0063e-02, 8.7410e-03], - [-1.5066e-02, -5.7328e-04, 2.9785e-03], - [-1.7613e-02, -8.1034e-03, 1.3086e-02], - [-9.2633e-03, 1.0803e-02, -6.3489e-03], - [ 3.0851e-03, 4.7750e-04, 1.2347e-02], - [-2.2785e-02, -2.3043e-02, -2.6005e-02], - [-2.4787e-02, -1.5389e-02, -2.2104e-02], - [-2.3572e-02, 1.0544e-03, 1.2361e-02], - [-7.8915e-03, -1.2271e-03, -6.0968e-03], - [-1.1478e-02, -1.2543e-03, 6.2679e-03], - [-5.4229e-02, 2.6644e-02, 6.3394e-03], - [ 4.4216e-03, -7.3338e-03, -1.0464e-02], - [-4.5013e-03, 1.6082e-03, 1.4420e-02], - [ 1.3673e-02, 8.8877e-03, 4.1253e-03], - [-1.0145e-02, 9.0072e-03, 1.5695e-02], - [-5.6234e-03, 1.1847e-03, 8.1261e-03], - [-3.7171e-03, -5.3538e-03, 1.2590e-03], - [ 2.9476e-02, 2.1424e-02, 3.0424e-02], - [-3.4925e-02, -2.4340e-02, -2.5316e-02], - [-3.4127e-02, -2.2406e-02, -1.0589e-02], - [-1.7342e-02, -1.3249e-02, -1.0719e-02], - [-2.1478e-03, -8.6051e-03, -2.9878e-03], - [ 1.2089e-03, -4.2391e-03, -6.8569e-03], - [ 9.0411e-04, -6.6886e-03, -6.7547e-05], - [ 1.6048e-02, -1.0057e-02, -2.8929e-02], - [ 1.2290e-03, 1.0163e-02, 1.8861e-02], - [ 1.7264e-02, 2.7257e-04, 1.3785e-02], - [-1.3482e-02, -3.6427e-03, 6.7481e-04], - [ 4.6782e-03, -5.2423e-03, 2.4467e-03], - [-5.9113e-03, -6.2244e-03, -1.8162e-03], - [ 1.5496e-02, 1.4582e-02, 1.9514e-03], - [ 7.4958e-03, 1.5886e-03, -8.2305e-03], - [ 1.9086e-02, 1.6360e-03, -3.9674e-03], - [-5.7021e-03, -2.7307e-03, -4.1066e-03], - [ 1.7450e-03, 1.4602e-02, 2.5794e-02], - [-8.2788e-04, 2.2902e-03, 4.5161e-03], - [ 1.1632e-02, 8.9193e-03, -7.2813e-03], - [ 7.5721e-03, 2.6784e-03, 1.1393e-02], - [ 5.1939e-03, 3.6903e-03, 1.4049e-02], - [-1.8383e-02, -2.2529e-02, -2.4477e-02], - [ 5.8842e-04, -5.7874e-03, -1.4770e-02], - [-1.6125e-02, -8.6101e-03, -1.4533e-02], - [ 2.0540e-02, 2.0729e-02, 6.4338e-03], - [ 3.3587e-03, -1.1226e-02, -1.6444e-02], - [-1.4742e-03, -1.0489e-02, 1.7097e-03], - [ 2.8130e-02, 2.3546e-02, 3.2791e-02], - [-1.8532e-02, -1.2842e-02, -8.7756e-03], - [-8.0533e-03, -1.0771e-02, -1.7536e-02], - [-3.9009e-03, 1.6150e-02, 3.3359e-02], - [-7.4554e-03, -1.4154e-02, -6.1910e-03], - [ 3.4734e-03, -1.1370e-02, -1.0581e-02], - [ 1.1476e-02, 3.9281e-03, 2.8231e-03], - [ 7.1639e-03, -1.4741e-03, -3.8066e-03], - [ 2.2250e-03, -8.7552e-03, -9.5719e-03], - [ 2.4146e-02, 2.1696e-02, 2.8056e-02], - [-5.4365e-03, -2.4291e-02, -1.7802e-02], - [ 7.4263e-03, 1.0510e-02, 1.2705e-02], - [ 6.2669e-03, 6.2658e-03, 1.9211e-02], - [ 1.6378e-02, 9.4933e-03, 6.6971e-03], - [ 1.7173e-02, 2.3601e-02, 2.3296e-02], - [-1.4568e-02, -9.8279e-03, -1.1556e-02], - [ 1.4431e-02, 1.4430e-02, 6.6362e-03], - [-6.8230e-03, 1.8863e-02, 1.4555e-02], - [ 6.1156e-03, 3.4700e-03, -2.6662e-03], - [-2.6983e-03, -5.9402e-03, -9.2276e-03], - [ 1.0235e-02, 7.4173e-03, -7.6243e-03], - [-1.3255e-02, 1.9322e-02, -9.2153e-04], - [ 2.4222e-03, -4.8039e-03, -1.5759e-02], - [ 2.6244e-02, 2.5951e-02, 2.0249e-02], - [ 1.5711e-02, 1.8498e-02, 2.7407e-03], - [-2.1714e-03, 4.7214e-03, -2.2443e-02], - [-7.4747e-03, 7.4166e-03, 1.4430e-02], - [-8.3906e-03, -7.9776e-03, 9.7927e-03], - [ 3.8321e-02, 9.6622e-03, -1.9268e-02], - [-1.4605e-02, -6.7032e-03, 3.9675e-03] - ] - - self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512] - -class HunyuanVideo(LatentFormat): - latent_channels = 16 - latent_dimensions = 3 - scale_factor = 0.476986 - latent_rgb_factors = [ - [-0.0395, -0.0331, 0.0445], - [ 0.0696, 0.0795, 0.0518], - [ 0.0135, -0.0945, -0.0282], - [ 0.0108, -0.0250, -0.0765], - [-0.0209, 0.0032, 0.0224], - [-0.0804, -0.0254, -0.0639], - [-0.0991, 0.0271, -0.0669], - [-0.0646, -0.0422, -0.0400], - [-0.0696, -0.0595, -0.0894], - [-0.0799, -0.0208, -0.0375], - [ 0.1166, 0.1627, 0.0962], - [ 0.1165, 0.0432, 0.0407], - [-0.2315, -0.1920, -0.1355], - [-0.0270, 0.0401, -0.0821], - [-0.0616, -0.0997, -0.0727], - [ 0.0249, -0.0469, -0.1703] - ] - - latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761] - -class Cosmos1CV8x8x8(LatentFormat): - latent_channels = 16 - latent_dimensions = 3 - - latent_rgb_factors = [ - [ 0.1817, 0.2284, 0.2423], - [-0.0586, -0.0862, -0.3108], - [-0.4703, -0.4255, -0.3995], - [ 0.0803, 0.1963, 0.1001], - [-0.0820, -0.1050, 0.0400], - [ 0.2511, 0.3098, 0.2787], - [-0.1830, -0.2117, -0.0040], - [-0.0621, -0.2187, -0.0939], - [ 0.3619, 0.1082, 0.1455], - [ 0.3164, 0.3922, 0.2575], - [ 0.1152, 0.0231, -0.0462], - [-0.1434, -0.3609, -0.3665], - [ 0.0635, 0.1471, 0.1680], - [-0.3635, -0.1963, -0.3248], - [-0.1865, 0.0365, 0.2346], - [ 0.0447, 0.0994, 0.0881] - ] - - latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976] - -class Wan21(LatentFormat): - latent_channels = 16 - latent_dimensions = 3 - - latent_rgb_factors = [ - [-0.1299, -0.1692, 0.2932], - [ 0.0671, 0.0406, 0.0442], - [ 0.3568, 0.2548, 0.1747], - [ 0.0372, 0.2344, 0.1420], - [ 0.0313, 0.0189, -0.0328], - [ 0.0296, -0.0956, -0.0665], - [-0.3477, -0.4059, -0.2925], - [ 0.0166, 0.1902, 0.1975], - [-0.0412, 0.0267, -0.1364], - [-0.1293, 0.0740, 0.1636], - [ 0.0680, 0.3019, 0.1128], - [ 0.0032, 0.0581, 0.0639], - [-0.1251, 0.0927, 0.1699], - [ 0.0060, -0.0633, 0.0005], - [ 0.3477, 0.2275, 0.2950], - [ 0.1984, 0.0913, 0.1861] - ] - - latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360] - - def __init__(self): - self.scale_factor = 1.0 - self.latents_mean = torch.tensor([ - -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, - 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 - ]).view(1, self.latent_channels, 1, 1, 1) - self.latents_std = torch.tensor([ - 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, - 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 - ]).view(1, self.latent_channels, 1, 1, 1) - - - self.taesd_decoder_name = None #TODO - - def process_in(self, latent): - latents_mean = self.latents_mean.to(latent.device, latent.dtype) - latents_std = self.latents_std.to(latent.device, latent.dtype) - return (latent - latents_mean) * self.scale_factor / latents_std - - def process_out(self, latent): - latents_mean = self.latents_mean.to(latent.device, latent.dtype) - latents_std = self.latents_std.to(latent.device, latent.dtype) - return latent * latents_std / self.scale_factor + latents_mean - -class Wan22(Wan21): - latent_channels = 48 - latent_dimensions = 3 - - latent_rgb_factors = [ - [ 0.0119, 0.0103, 0.0046], - [-0.1062, -0.0504, 0.0165], - [ 0.0140, 0.0409, 0.0491], - [-0.0813, -0.0677, 0.0607], - [ 0.0656, 0.0851, 0.0808], - [ 0.0264, 0.0463, 0.0912], - [ 0.0295, 0.0326, 0.0590], - [-0.0244, -0.0270, 0.0025], - [ 0.0443, -0.0102, 0.0288], - [-0.0465, -0.0090, -0.0205], - [ 0.0359, 0.0236, 0.0082], - [-0.0776, 0.0854, 0.1048], - [ 0.0564, 0.0264, 0.0561], - [ 0.0006, 0.0594, 0.0418], - [-0.0319, -0.0542, -0.0637], - [-0.0268, 0.0024, 0.0260], - [ 0.0539, 0.0265, 0.0358], - [-0.0359, -0.0312, -0.0287], - [-0.0285, -0.1032, -0.1237], - [ 0.1041, 0.0537, 0.0622], - [-0.0086, -0.0374, -0.0051], - [ 0.0390, 0.0670, 0.2863], - [ 0.0069, 0.0144, 0.0082], - [ 0.0006, -0.0167, 0.0079], - [ 0.0313, -0.0574, -0.0232], - [-0.1454, -0.0902, -0.0481], - [ 0.0714, 0.0827, 0.0447], - [-0.0304, -0.0574, -0.0196], - [ 0.0401, 0.0384, 0.0204], - [-0.0758, -0.0297, -0.0014], - [ 0.0568, 0.1307, 0.1372], - [-0.0055, -0.0310, -0.0380], - [ 0.0239, -0.0305, 0.0325], - [-0.0663, -0.0673, -0.0140], - [-0.0416, -0.0047, -0.0023], - [ 0.0166, 0.0112, -0.0093], - [-0.0211, 0.0011, 0.0331], - [ 0.1833, 0.1466, 0.2250], - [-0.0368, 0.0370, 0.0295], - [-0.3441, -0.3543, -0.2008], - [-0.0479, -0.0489, -0.0420], - [-0.0660, -0.0153, 0.0800], - [-0.0101, 0.0068, 0.0156], - [-0.0690, -0.0452, -0.0927], - [-0.0145, 0.0041, 0.0015], - [ 0.0421, 0.0451, 0.0373], - [ 0.0504, -0.0483, -0.0356], - [-0.0837, 0.0168, 0.0055] - ] - - latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388] - - def __init__(self): - self.scale_factor = 1.0 - self.latents_mean = torch.tensor([ - -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, - -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825, - -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, - -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230, - -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748, - 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667, - ]).view(1, self.latent_channels, 1, 1, 1) - self.latents_std = torch.tensor([ - 0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013, - 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, - 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, - 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, - 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, - 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744 - ]).view(1, self.latent_channels, 1, 1, 1) - -class Hunyuan3Dv2(LatentFormat): - latent_channels = 64 - latent_dimensions = 1 - scale_factor = 0.9990943042622529 - -class Hunyuan3Dv2mini(LatentFormat): - latent_channels = 64 - latent_dimensions = 1 - scale_factor = 1.0188137142395404 - -class ACEAudio(LatentFormat): - latent_channels = 8 - latent_dimensions = 2 diff --git a/comfy/ldm/.DS_Store b/comfy/ldm/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/.DS_Store and /dev/null differ diff --git a/comfy/ldm/ace/.DS_Store b/comfy/ldm/ace/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/ace/.DS_Store and /dev/null differ diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py deleted file mode 100644 index f20a016691453789b6608af2b54dc1e18a1e9a3e..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/attention.py +++ /dev/null @@ -1,761 +0,0 @@ -# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Tuple, Union, Optional - -import torch -import torch.nn.functional as F -from torch import nn - -import comfy.model_management -from comfy.ldm.modules.attention import optimized_attention - -class Attention(nn.Module): - def __init__( - self, - query_dim: int, - cross_attention_dim: Optional[int] = None, - heads: int = 8, - kv_heads: Optional[int] = None, - dim_head: int = 64, - dropout: float = 0.0, - bias: bool = False, - qk_norm: Optional[str] = None, - added_kv_proj_dim: Optional[int] = None, - added_proj_bias: Optional[bool] = True, - out_bias: bool = True, - scale_qk: bool = True, - only_cross_attention: bool = False, - eps: float = 1e-5, - rescale_output_factor: float = 1.0, - residual_connection: bool = False, - processor=None, - out_dim: int = None, - out_context_dim: int = None, - context_pre_only=None, - pre_only=False, - elementwise_affine: bool = True, - is_causal: bool = False, - dtype=None, device=None, operations=None - ): - super().__init__() - - self.inner_dim = out_dim if out_dim is not None else dim_head * heads - self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads - self.query_dim = query_dim - self.use_bias = bias - self.is_cross_attention = cross_attention_dim is not None - self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim - self.rescale_output_factor = rescale_output_factor - self.residual_connection = residual_connection - self.dropout = dropout - self.fused_projections = False - self.out_dim = out_dim if out_dim is not None else query_dim - self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim - self.context_pre_only = context_pre_only - self.pre_only = pre_only - self.is_causal = is_causal - - self.scale_qk = scale_qk - self.scale = dim_head**-0.5 if self.scale_qk else 1.0 - - self.heads = out_dim // dim_head if out_dim is not None else heads - # for slice_size > 0 the attention score computation - # is split across the batch axis to save memory - # You can set slice_size with `set_attention_slice` - self.sliceable_head_dim = heads - - self.added_kv_proj_dim = added_kv_proj_dim - self.only_cross_attention = only_cross_attention - - if self.added_kv_proj_dim is None and self.only_cross_attention: - raise ValueError( - "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." - ) - - self.group_norm = None - self.spatial_norm = None - - self.norm_q = None - self.norm_k = None - - self.norm_cross = None - self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) - - if not self.only_cross_attention: - # only relevant for the `AddedKVProcessor` classes - self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) - self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) - else: - self.to_k = None - self.to_v = None - - self.added_proj_bias = added_proj_bias - if self.added_kv_proj_dim is not None: - self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) - self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) - if self.context_pre_only is not None: - self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device) - else: - self.add_q_proj = None - self.add_k_proj = None - self.add_v_proj = None - - if not self.pre_only: - self.to_out = nn.ModuleList([]) - self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device)) - self.to_out.append(nn.Dropout(dropout)) - else: - self.to_out = None - - if self.context_pre_only is not None and not self.context_pre_only: - self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) - else: - self.to_add_out = None - - self.norm_added_q = None - self.norm_added_k = None - self.processor = processor - - def forward( - self, - hidden_states: torch.Tensor, - encoder_hidden_states: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - **cross_attention_kwargs, - ) -> torch.Tensor: - return self.processor( - self, - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - - -class CustomLiteLAProcessor2_0: - """Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE""" - - def __init__(self): - self.kernel_func = nn.ReLU(inplace=False) - self.eps = 1e-15 - self.pad_val = 1.0 - - def apply_rotary_emb( - self, - x: torch.Tensor, - freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings - to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are - reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting - tensors contain rotary embeddings and are returned as real tensors. - - Args: - x (`torch.Tensor`): - Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply - freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. - """ - cos, sin = freqs_cis # [S, D] - cos = cos[None, None] - sin = sin[None, None] - cos, sin = cos.to(x.device), sin.to(x.device) - - x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] - x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) - out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) - - return out - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: torch.FloatTensor = None, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, - rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - hidden_states_len = hidden_states.shape[1] - - input_ndim = hidden_states.ndim - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - if encoder_hidden_states is not None: - context_input_ndim = encoder_hidden_states.ndim - if context_input_ndim == 4: - batch_size, channel, height, width = encoder_hidden_states.shape - encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size = hidden_states.shape[0] - - # `sample` projections. - dtype = hidden_states.dtype - query = attn.to_q(hidden_states) - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) - - # `context` projections. - has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") - if encoder_hidden_states is not None and has_encoder_hidden_state_proj: - encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states) - encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) - encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) - - # attention - if not attn.is_cross_attention: - query = torch.cat([query, encoder_hidden_states_query_proj], dim=1) - key = torch.cat([key, encoder_hidden_states_key_proj], dim=1) - value = torch.cat([value, encoder_hidden_states_value_proj], dim=1) - else: - query = hidden_states - key = encoder_hidden_states - value = encoder_hidden_states - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) - key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2) - value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) - - # RoPE需要 [B, H, S, D] 输入 - # 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE - query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S]) - - # Apply query and key normalization if needed - if attn.norm_q is not None: - query = attn.norm_q(query) - if attn.norm_k is not None: - key = attn.norm_k(key) - - # Apply RoPE if needed - if rotary_freqs_cis is not None: - query = self.apply_rotary_emb(query, rotary_freqs_cis) - if not attn.is_cross_attention: - key = self.apply_rotary_emb(key, rotary_freqs_cis) - elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: - key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) - - # 此时 query是 [B, H, S, D]，需要还原成 [B, H, D, S] - query = query.permute(0, 1, 3, 2) # [B, H, D, S] - - if attention_mask is not None: - # attention_mask: [B, S] -> [B, 1, S, 1] - attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1] - query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1] - if not attn.is_cross_attention: - key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘 - value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S]，那么需调整mask以匹配S维度 - - if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: - encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1] - # 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc] - key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1] - value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc] - - query = self.kernel_func(query) - key = self.kernel_func(key) - - query, key, value = query.float(), key.float(), value.float() - - value = F.pad(value, (0, 0, 0, 1), mode="constant", value=self.pad_val) - - vk = torch.matmul(value, key) - - hidden_states = torch.matmul(vk, query) - - if hidden_states.dtype in [torch.float16, torch.bfloat16]: - hidden_states = hidden_states.float() - - hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) - - hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1) - - hidden_states = hidden_states.to(dtype) - if encoder_hidden_states is not None: - encoder_hidden_states = encoder_hidden_states.to(dtype) - - # Split the attention outputs. - if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj: - hidden_states, encoder_hidden_states = ( - hidden_states[:, : hidden_states_len], - hidden_states[:, hidden_states_len:], - ) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, "to_add_out"): - encoder_hidden_states = attn.to_add_out(encoder_hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - if encoder_hidden_states is not None and context_input_ndim == 4: - encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if torch.get_autocast_gpu_dtype() == torch.float16: - hidden_states = hidden_states.clip(-65504, 65504) - if encoder_hidden_states is not None: - encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504) - - return hidden_states, encoder_hidden_states - - -class CustomerAttnProcessor2_0: - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def apply_rotary_emb( - self, - x: torch.Tensor, - freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings - to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are - reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting - tensors contain rotary embeddings and are returned as real tensors. - - Args: - x (`torch.Tensor`): - Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply - freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. - """ - cos, sin = freqs_cis # [S, D] - cos = cos[None, None] - sin = sin[None, None] - cos, sin = cos.to(x.device), sin.to(x.device) - - x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] - x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) - out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) - - return out - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: torch.FloatTensor = None, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, - rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, - *args, - **kwargs, - ) -> torch.Tensor: - - residual = hidden_states - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - if attn.norm_q is not None: - query = attn.norm_q(query) - if attn.norm_k is not None: - key = attn.norm_k(key) - - # Apply RoPE if needed - if rotary_freqs_cis is not None: - query = self.apply_rotary_emb(query, rotary_freqs_cis) - if not attn.is_cross_attention: - key = self.apply_rotary_emb(key, rotary_freqs_cis) - elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: - key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) - - if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: - # attention_mask: N x S1 - # encoder_attention_mask: N x S2 - # cross attention 整合attention_mask和encoder_attention_mask - combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :] - attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf) - attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype) - - elif not attn.is_cross_attention and attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - hidden_states = optimized_attention( - query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, - ).to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - -def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore - """Repeat `val` for `repeat_time` times and return the list or val if list/tuple.""" - if isinstance(x, (list, tuple)): - return list(x) - return [x for _ in range(repeat_time)] - - -def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore - """Return tuple with min_len by repeating element at idx_repeat.""" - # convert to list first - x = val2list(x) - - # repeat elements if necessary - if len(x) > 0: - x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))] - - return tuple(x) - - -def t2i_modulate(x, shift, scale): - return x * (1 + scale) + shift - - -def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]: - if isinstance(kernel_size, tuple): - return tuple([get_same_padding(ks) for ks in kernel_size]) - else: - assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number" - return kernel_size // 2 - -class ConvLayer(nn.Module): - def __init__( - self, - in_dim: int, - out_dim: int, - kernel_size=3, - stride=1, - dilation=1, - groups=1, - padding: Union[int, None] = None, - use_bias=False, - norm=None, - act=None, - dtype=None, device=None, operations=None - ): - super().__init__() - if padding is None: - padding = get_same_padding(kernel_size) - padding *= dilation - - self.in_dim = in_dim - self.out_dim = out_dim - self.kernel_size = kernel_size - self.stride = stride - self.dilation = dilation - self.groups = groups - self.padding = padding - self.use_bias = use_bias - - self.conv = operations.Conv1d( - in_dim, - out_dim, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=use_bias, - device=device, - dtype=dtype - ) - if norm is not None: - self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device) - else: - self.norm = None - if act is not None: - self.act = nn.SiLU(inplace=True) - else: - self.act = None - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.conv(x) - if self.norm: - x = self.norm(x) - if self.act: - x = self.act(x) - return x - - -class GLUMBConv(nn.Module): - def __init__( - self, - in_features: int, - hidden_features: int, - out_feature=None, - kernel_size=3, - stride=1, - padding: Union[int, None] = None, - use_bias=False, - norm=(None, None, None), - act=("silu", "silu", None), - dilation=1, - dtype=None, device=None, operations=None - ): - out_feature = out_feature or in_features - super().__init__() - use_bias = val2tuple(use_bias, 3) - norm = val2tuple(norm, 3) - act = val2tuple(act, 3) - - self.glu_act = nn.SiLU(inplace=False) - self.inverted_conv = ConvLayer( - in_features, - hidden_features * 2, - 1, - use_bias=use_bias[0], - norm=norm[0], - act=act[0], - dtype=dtype, - device=device, - operations=operations, - ) - self.depth_conv = ConvLayer( - hidden_features * 2, - hidden_features * 2, - kernel_size, - stride=stride, - groups=hidden_features * 2, - padding=padding, - use_bias=use_bias[1], - norm=norm[1], - act=None, - dilation=dilation, - dtype=dtype, - device=device, - operations=operations, - ) - self.point_conv = ConvLayer( - hidden_features, - out_feature, - 1, - use_bias=use_bias[2], - norm=norm[2], - act=act[2], - dtype=dtype, - device=device, - operations=operations, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = x.transpose(1, 2) - x = self.inverted_conv(x) - x = self.depth_conv(x) - - x, gate = torch.chunk(x, 2, dim=1) - gate = self.glu_act(gate) - x = x * gate - - x = self.point_conv(x) - x = x.transpose(1, 2) - - return x - - -class LinearTransformerBlock(nn.Module): - """ - A Sana block with global shared adaptive layer norm (adaLN-single) conditioning. - """ - def __init__( - self, - dim, - num_attention_heads, - attention_head_dim, - use_adaln_single=True, - cross_attention_dim=None, - added_kv_proj_dim=None, - context_pre_only=False, - mlp_ratio=4.0, - add_cross_attention=False, - add_cross_attention_dim=None, - qk_norm=None, - dtype=None, device=None, operations=None - ): - super().__init__() - - self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6) - self.attn = Attention( - query_dim=dim, - cross_attention_dim=cross_attention_dim, - added_kv_proj_dim=added_kv_proj_dim, - dim_head=attention_head_dim, - heads=num_attention_heads, - out_dim=dim, - bias=True, - qk_norm=qk_norm, - processor=CustomLiteLAProcessor2_0(), - dtype=dtype, - device=device, - operations=operations, - ) - - self.add_cross_attention = add_cross_attention - self.context_pre_only = context_pre_only - - if add_cross_attention and add_cross_attention_dim is not None: - self.cross_attn = Attention( - query_dim=dim, - cross_attention_dim=add_cross_attention_dim, - added_kv_proj_dim=add_cross_attention_dim, - dim_head=attention_head_dim, - heads=num_attention_heads, - out_dim=dim, - context_pre_only=context_pre_only, - bias=True, - qk_norm=qk_norm, - processor=CustomerAttnProcessor2_0(), - dtype=dtype, - device=device, - operations=operations, - ) - - self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False) - - self.ff = GLUMBConv( - in_features=dim, - hidden_features=int(dim * mlp_ratio), - use_bias=(True, True, False), - norm=(None, None, None), - act=("silu", "silu", None), - dtype=dtype, - device=device, - operations=operations, - ) - self.use_adaln_single = use_adaln_single - if use_adaln_single: - self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device)) - - def forward( - self, - hidden_states: torch.FloatTensor, - encoder_hidden_states: torch.FloatTensor = None, - attention_mask: torch.FloatTensor = None, - encoder_attention_mask: torch.FloatTensor = None, - rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, - rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, - temb: torch.FloatTensor = None, - ): - - N = hidden_states.shape[0] - - # step 1: AdaLN single - if self.use_adaln_single: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1) - ).chunk(6, dim=1) - - norm_hidden_states = self.norm1(hidden_states) - if self.use_adaln_single: - norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa - - # step 2: attention - if not self.add_cross_attention: - attn_output, encoder_hidden_states = self.attn( - hidden_states=norm_hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - rotary_freqs_cis=rotary_freqs_cis, - rotary_freqs_cis_cross=rotary_freqs_cis_cross, - ) - else: - attn_output, _ = self.attn( - hidden_states=norm_hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=None, - encoder_attention_mask=None, - rotary_freqs_cis=rotary_freqs_cis, - rotary_freqs_cis_cross=None, - ) - - if self.use_adaln_single: - attn_output = gate_msa * attn_output - hidden_states = attn_output + hidden_states - - if self.add_cross_attention: - attn_output = self.cross_attn( - hidden_states=hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - rotary_freqs_cis=rotary_freqs_cis, - rotary_freqs_cis_cross=rotary_freqs_cis_cross, - ) - hidden_states = attn_output + hidden_states - - # step 3: add norm - norm_hidden_states = self.norm2(hidden_states) - if self.use_adaln_single: - norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp - - # step 4: feed forward - ff_output = self.ff(norm_hidden_states) - if self.use_adaln_single: - ff_output = gate_mlp * ff_output - - hidden_states = hidden_states + ff_output - - return hidden_states diff --git a/comfy/ldm/ace/lyric_encoder.py b/comfy/ldm/ace/lyric_encoder.py deleted file mode 100644 index ff4359b26e8d27591d6fa59d6d931ab13596efe1..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/lyric_encoder.py +++ /dev/null @@ -1,1067 +0,0 @@ -# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py -from typing import Optional, Tuple, Union -import math -import torch -from torch import nn - -import comfy.model_management - -class ConvolutionModule(nn.Module): - """ConvolutionModule in Conformer model.""" - - def __init__(self, - channels: int, - kernel_size: int = 15, - activation: nn.Module = nn.ReLU(), - norm: str = "batch_norm", - causal: bool = False, - bias: bool = True, - dtype=None, device=None, operations=None): - """Construct an ConvolutionModule object. - Args: - channels (int): The number of channels of conv layers. - kernel_size (int): Kernel size of conv layers. - causal (int): Whether use causal convolution or not - """ - super().__init__() - - self.pointwise_conv1 = operations.Conv1d( - channels, - 2 * channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - dtype=dtype, device=device - ) - # self.lorder is used to distinguish if it's a causal convolution, - # if self.lorder > 0: it's a causal convolution, the input will be - # padded with self.lorder frames on the left in forward. - # else: it's a symmetrical convolution - if causal: - padding = 0 - self.lorder = kernel_size - 1 - else: - # kernel_size should be an odd number for none causal convolution - assert (kernel_size - 1) % 2 == 0 - padding = (kernel_size - 1) // 2 - self.lorder = 0 - self.depthwise_conv = operations.Conv1d( - channels, - channels, - kernel_size, - stride=1, - padding=padding, - groups=channels, - bias=bias, - dtype=dtype, device=device - ) - - assert norm in ['batch_norm', 'layer_norm'] - if norm == "batch_norm": - self.use_layer_norm = False - self.norm = nn.BatchNorm1d(channels) - else: - self.use_layer_norm = True - self.norm = operations.LayerNorm(channels, dtype=dtype, device=device) - - self.pointwise_conv2 = operations.Conv1d( - channels, - channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias, - dtype=dtype, device=device - ) - self.activation = activation - - def forward( - self, - x: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - cache: torch.Tensor = torch.zeros((0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute convolution module. - Args: - x (torch.Tensor): Input tensor (#batch, time, channels). - mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), - (0, 0, 0) means fake mask. - cache (torch.Tensor): left context cache, it is only - used in causal convolution (#batch, channels, cache_t), - (0, 0, 0) meas fake cache. - Returns: - torch.Tensor: Output tensor (#batch, time, channels). - """ - # exchange the temporal dimension and the feature dimension - x = x.transpose(1, 2) # (#batch, channels, time) - - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - if self.lorder > 0: - if cache.size(2) == 0: # cache_t == 0 - x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) - else: - assert cache.size(0) == x.size(0) # equal batch - assert cache.size(1) == x.size(1) # equal channel - x = torch.cat((cache, x), dim=2) - assert (x.size(2) > self.lorder) - new_cache = x[:, :, -self.lorder:] - else: - # It's better we just return None if no cache is required, - # However, for JIT export, here we just fake one tensor instead of - # None. - new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - - # GLU mechanism - x = self.pointwise_conv1(x) # (batch, 2*channel, dim) - x = nn.functional.glu(x, dim=1) # (batch, channel, dim) - - # 1D Depthwise Conv - x = self.depthwise_conv(x) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.activation(self.norm(x)) - if self.use_layer_norm: - x = x.transpose(1, 2) - x = self.pointwise_conv2(x) - # mask batch padding - if mask_pad.size(2) > 0: # time > 0 - x.masked_fill_(~mask_pad, 0.0) - - return x.transpose(1, 2), new_cache - -class PositionwiseFeedForward(torch.nn.Module): - """Positionwise feed forward layer. - - FeedForward are appied on each position of the sequence. - The output dim is same with the input dim. - - Args: - idim (int): Input dimenstion. - hidden_units (int): The number of hidden units. - dropout_rate (float): Dropout rate. - activation (torch.nn.Module): Activation function - """ - - def __init__( - self, - idim: int, - hidden_units: int, - dropout_rate: float, - activation: torch.nn.Module = torch.nn.ReLU(), - dtype=None, device=None, operations=None - ): - """Construct a PositionwiseFeedForward object.""" - super(PositionwiseFeedForward, self).__init__() - self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device) - self.activation = activation - self.dropout = torch.nn.Dropout(dropout_rate) - self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device) - - def forward(self, xs: torch.Tensor) -> torch.Tensor: - """Forward function. - - Args: - xs: input tensor (B, L, D) - Returns: - output tensor, (B, L, D) - """ - return self.w_2(self.dropout(self.activation(self.w_1(xs)))) - -class Swish(torch.nn.Module): - """Construct an Swish object.""" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Return Swish activation function.""" - return x * torch.sigmoid(x) - -class MultiHeadedAttention(nn.Module): - """Multi-Head Attention layer. - - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - - """ - - def __init__(self, - n_head: int, - n_feat: int, - dropout_rate: float, - key_bias: bool = True, - dtype=None, device=None, operations=None): - """Construct an MultiHeadedAttention object.""" - super().__init__() - assert n_feat % n_head == 0 - # We assume d_v always equals d_k - self.d_k = n_feat // n_head - self.h = n_head - self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) - self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device) - self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) - self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) - self.dropout = nn.Dropout(p=dropout_rate) - - def forward_qkv( - self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Transform query, key and value. - - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - - Returns: - torch.Tensor: Transformed query tensor, size - (#batch, n_head, time1, d_k). - torch.Tensor: Transformed key tensor, size - (#batch, n_head, time2, d_k). - torch.Tensor: Transformed value tensor, size - (#batch, n_head, time2, d_k). - - """ - n_batch = query.size(0) - q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) - k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) - v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) - q = q.transpose(1, 2) # (batch, head, time1, d_k) - k = k.transpose(1, 2) # (batch, head, time2, d_k) - v = v.transpose(1, 2) # (batch, head, time2, d_k) - return q, k, v - - def forward_attention( - self, - value: torch.Tensor, - scores: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) - ) -> torch.Tensor: - """Compute attention context vector. - - Args: - value (torch.Tensor): Transformed value, size - (#batch, n_head, time2, d_k). - scores (torch.Tensor): Attention score, size - (#batch, n_head, time1, time2). - mask (torch.Tensor): Mask, size (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - - Returns: - torch.Tensor: Transformed value (#batch, time1, d_model) - weighted by the attention score (#batch, time1, time2). - - """ - n_batch = value.size(0) - - if mask is not None and mask.size(2) > 0: # time2 > 0 - mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) - # For last chunk, time2 might be larger than scores.size(-1) - mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) - scores = scores.masked_fill(mask, -float('inf')) - attn = torch.softmax(scores, dim=-1).masked_fill( - mask, 0.0) # (batch, head, time1, time2) - - else: - attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - - p_attn = self.dropout(attn) - x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) - x = (x.transpose(1, 2).contiguous().view(n_batch, -1, - self.h * self.d_k) - ) # (batch, time1, d_model) - - return self.linear_out(x) # (batch, time1, d_model) - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - pos_emb: torch.Tensor = torch.empty(0), - cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute scaled dot product attention. - - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2). - 1.When applying cross attention between decoder and encoder, - the batch padding mask for input is in (#batch, 1, T) shape. - 2.When applying self attention of encoder, - the mask is in (#batch, T, T) shape. - 3.When applying self attention of decoder, - the mask is in (#batch, L, L) shape. - 4.If the different position in decoder see different block - of the encoder, such as Mocha, the passed in mask could be - in (#batch, L, T) shape. But there is no such case in current - CosyVoice. - cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - - - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - - """ - q, k, v = self.forward_qkv(query, key, value) - if cache.size(0) > 0: - key_cache, value_cache = torch.split(cache, - cache.size(-1) // 2, - dim=-1) - k = torch.cat([key_cache, k], dim=2) - v = torch.cat([value_cache, v], dim=2) - new_cache = torch.cat((k, v), dim=-1) - - scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) - return self.forward_attention(v, scores, mask), new_cache - - -class RelPositionMultiHeadedAttention(MultiHeadedAttention): - """Multi-Head Attention layer with relative position encoding. - Paper: https://arxiv.org/abs/1901.02860 - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - """ - - def __init__(self, - n_head: int, - n_feat: int, - dropout_rate: float, - key_bias: bool = True, - dtype=None, device=None, operations=None): - """Construct an RelPositionMultiHeadedAttention object.""" - super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations) - # linear transformation for positional encoding - self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device) - # these two learnable bias are used in matrix c and matrix d - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) - self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) - # torch.nn.init.xavier_uniform_(self.pos_bias_u) - # torch.nn.init.xavier_uniform_(self.pos_bias_v) - - def rel_shift(self, x: torch.Tensor) -> torch.Tensor: - """Compute relative positional encoding. - - Args: - x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). - time1 means the length of query vector. - - Returns: - torch.Tensor: Output tensor. - - """ - zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), - device=x.device, - dtype=x.dtype) - x_padded = torch.cat([zero_pad, x], dim=-1) - - x_padded = x_padded.view(x.size()[0], - x.size()[1], - x.size(3) + 1, x.size(2)) - x = x_padded[:, :, 1:].view_as(x)[ - :, :, :, : x.size(-1) // 2 + 1 - ] # only keep the positions from 0 to time2 - return x - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - pos_emb: torch.Tensor = torch.empty(0), - cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Compute 'Scaled Dot Product Attention' with rel. positional encoding. - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2), (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): Positional embedding tensor - (#batch, time2, size). - cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) - where `cache_t == chunk_size * num_decoding_left_chunks` - and `head * d_k == size` - """ - q, k, v = self.forward_qkv(query, key, value) - q = q.transpose(1, 2) # (batch, time1, head, d_k) - - if cache.size(0) > 0: - key_cache, value_cache = torch.split(cache, - cache.size(-1) // 2, - dim=-1) - k = torch.cat([key_cache, k], dim=2) - v = torch.cat([value_cache, v], dim=2) - # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's - # non-trivial to calculate `next_cache_start` here. - new_cache = torch.cat((k, v), dim=-1) - - n_batch_pos = pos_emb.size(0) - p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) - p = p.transpose(1, 2) # (batch, head, time1, d_k) - - # (batch, head, time1, d_k) - q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2) - # (batch, head, time1, d_k) - q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2) - - # compute attention score - # first compute matrix a and matrix c - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - # (batch, head, time1, time2) - matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) - - # compute matrix b and matrix d - # (batch, head, time1, time2) - matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) - # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used - if matrix_ac.shape != matrix_bd.shape: - matrix_bd = self.rel_shift(matrix_bd) - - scores = (matrix_ac + matrix_bd) / math.sqrt( - self.d_k) # (batch, head, time1, time2) - - return self.forward_attention(v, scores, mask), new_cache - - - -def subsequent_mask( - size: int, - device: torch.device = torch.device("cpu"), -) -> torch.Tensor: - """Create mask for subsequent steps (size, size). - - This mask is used only in decoder which works in an auto-regressive mode. - This means the current step could only do attention with its left steps. - - In encoder, fully attention is used when streaming is not necessary and - the sequence is not long. In this case, no attention mask is needed. - - When streaming is need, chunk-based attention is used in encoder. See - subsequent_chunk_mask for the chunk-based attention mask. - - Args: - size (int): size of mask - str device (str): "cpu" or "cuda" or torch.Tensor.device - dtype (torch.device): result dtype - - Returns: - torch.Tensor: mask - - Examples: - >>> subsequent_mask(3) - [[1, 0, 0], - [1, 1, 0], - [1, 1, 1]] - """ - arange = torch.arange(size, device=device) - mask = arange.expand(size, size) - arange = arange.unsqueeze(-1) - mask = mask <= arange - return mask - - -def subsequent_chunk_mask( - size: int, - chunk_size: int, - num_left_chunks: int = -1, - device: torch.device = torch.device("cpu"), - ) -> torch.Tensor: - """Create mask for subsequent steps (size, size) with chunk size, - this is for streaming encoder - - Args: - size (int): size of mask - chunk_size (int): size of chunk - num_left_chunks (int): number of left chunks - <0: use full chunk - >=0: use num_left_chunks - device (torch.device): "cpu" or "cuda" or torch.Tensor.device - - Returns: - torch.Tensor: mask - - Examples: - >>> subsequent_chunk_mask(4, 2) - [[1, 1, 0, 0], - [1, 1, 0, 0], - [1, 1, 1, 1], - [1, 1, 1, 1]] - """ - ret = torch.zeros(size, size, device=device, dtype=torch.bool) - for i in range(size): - if num_left_chunks < 0: - start = 0 - else: - start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) - ending = min((i // chunk_size + 1) * chunk_size, size) - ret[i, start:ending] = True - return ret - -def add_optional_chunk_mask(xs: torch.Tensor, - masks: torch.Tensor, - use_dynamic_chunk: bool, - use_dynamic_left_chunk: bool, - decoding_chunk_size: int, - static_chunk_size: int, - num_decoding_left_chunks: int, - enable_full_context: bool = True): - """ Apply optional mask for encoder. - - Args: - xs (torch.Tensor): padded input, (B, L, D), L for max length - mask (torch.Tensor): mask for xs, (B, 1, L) - use_dynamic_chunk (bool): whether to use dynamic chunk or not - use_dynamic_left_chunk (bool): whether to use dynamic left chunk for - training. - decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's - 0: default for training, use random dynamic chunk. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - static_chunk_size (int): chunk size for static chunk training/decoding - if it's greater than 0, if use_dynamic_chunk is true, - this parameter will be ignored - num_decoding_left_chunks: number of left chunks, this is for decoding, - the chunk size is decoding_chunk_size. - >=0: use num_decoding_left_chunks - <0: use all left chunks - enable_full_context (bool): - True: chunk size is either [1, 25] or full context(max_len) - False: chunk size ~ U[1, 25] - - Returns: - torch.Tensor: chunk mask of the input xs. - """ - # Whether to use chunk mask or not - if use_dynamic_chunk: - max_len = xs.size(1) - if decoding_chunk_size < 0: - chunk_size = max_len - num_left_chunks = -1 - elif decoding_chunk_size > 0: - chunk_size = decoding_chunk_size - num_left_chunks = num_decoding_left_chunks - else: - # chunk size is either [1, 25] or full context(max_len). - # Since we use 4 times subsampling and allow up to 1s(100 frames) - # delay, the maximum frame is 100 / 4 = 25. - chunk_size = torch.randint(1, max_len, (1, )).item() - num_left_chunks = -1 - if chunk_size > max_len // 2 and enable_full_context: - chunk_size = max_len - else: - chunk_size = chunk_size % 25 + 1 - if use_dynamic_left_chunk: - max_left_chunks = (max_len - 1) // chunk_size - num_left_chunks = torch.randint(0, max_left_chunks, - (1, )).item() - chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, - num_left_chunks, - xs.device) # (L, L) - chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) - chunk_masks = masks & chunk_masks # (B, L, L) - elif static_chunk_size > 0: - num_left_chunks = num_decoding_left_chunks - chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, - num_left_chunks, - xs.device) # (L, L) - chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) - chunk_masks = masks & chunk_masks # (B, L, L) - else: - chunk_masks = masks - return chunk_masks - - -class ConformerEncoderLayer(nn.Module): - """Encoder layer module. - Args: - size (int): Input dimension. - self_attn (torch.nn.Module): Self-attention module instance. - `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` - instance can be used as the argument. - feed_forward (torch.nn.Module): Feed-forward module instance. - `PositionwiseFeedForward` instance can be used as the argument. - feed_forward_macaron (torch.nn.Module): Additional feed-forward module - instance. - `PositionwiseFeedForward` instance can be used as the argument. - conv_module (torch.nn.Module): Convolution module instance. - `ConvlutionModule` instance can be used as the argument. - dropout_rate (float): Dropout rate. - normalize_before (bool): - True: use layer_norm before each sub-block. - False: use layer_norm after each sub-block. - """ - - def __init__( - self, - size: int, - self_attn: torch.nn.Module, - feed_forward: Optional[nn.Module] = None, - feed_forward_macaron: Optional[nn.Module] = None, - conv_module: Optional[nn.Module] = None, - dropout_rate: float = 0.1, - normalize_before: bool = True, - dtype=None, device=None, operations=None - ): - """Construct an EncoderLayer object.""" - super().__init__() - self.self_attn = self_attn - self.feed_forward = feed_forward - self.feed_forward_macaron = feed_forward_macaron - self.conv_module = conv_module - self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module - self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module - if feed_forward_macaron is not None: - self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) - self.ff_scale = 0.5 - else: - self.ff_scale = 1.0 - if self.conv_module is not None: - self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module - self.norm_final = operations.LayerNorm( - size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block - self.dropout = nn.Dropout(dropout_rate) - self.size = size - self.normalize_before = normalize_before - - def forward( - self, - x: torch.Tensor, - mask: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), - att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - """Compute encoded features. - - Args: - x (torch.Tensor): (#batch, time, size) - mask (torch.Tensor): Mask tensor for the input (#batch, time，time), - (0, 0, 0) means fake mask. - pos_emb (torch.Tensor): positional encoding, must not be None - for ConformerEncoderLayer. - mask_pad (torch.Tensor): batch padding mask used for conv module. - (#batch, 1，time), (0, 0, 0) means fake mask. - att_cache (torch.Tensor): Cache tensor of the KEY & VALUE - (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. - cnn_cache (torch.Tensor): Convolution cache in conformer layer - (#batch=1, size, cache_t2) - Returns: - torch.Tensor: Output tensor (#batch, time, size). - torch.Tensor: Mask tensor (#batch, time, time). - torch.Tensor: att_cache tensor, - (#batch=1, head, cache_t1 + time, d_k * 2). - torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). - """ - - # whether to use macaron style - if self.feed_forward_macaron is not None: - residual = x - if self.normalize_before: - x = self.norm_ff_macaron(x) - x = residual + self.ff_scale * self.dropout( - self.feed_forward_macaron(x)) - if not self.normalize_before: - x = self.norm_ff_macaron(x) - - # multi-headed self-attention module - residual = x - if self.normalize_before: - x = self.norm_mha(x) - x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, - att_cache) - x = residual + self.dropout(x_att) - if not self.normalize_before: - x = self.norm_mha(x) - - # convolution module - # Fake new cnn cache here, and then change it in conv_module - new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) - if self.conv_module is not None: - residual = x - if self.normalize_before: - x = self.norm_conv(x) - x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) - x = residual + self.dropout(x) - - if not self.normalize_before: - x = self.norm_conv(x) - - # feed forward module - residual = x - if self.normalize_before: - x = self.norm_ff(x) - - x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) - if not self.normalize_before: - x = self.norm_ff(x) - - if self.conv_module is not None: - x = self.norm_final(x) - - return x, mask, new_att_cache, new_cnn_cache - - - -class EspnetRelPositionalEncoding(torch.nn.Module): - """Relative positional encoding module (new implementation). - - Details can be found in https://github.com/espnet/espnet/pull/2816. - - See : Appendix B in https://arxiv.org/abs/1901.02860 - - Args: - d_model (int): Embedding dimension. - dropout_rate (float): Dropout rate. - max_len (int): Maximum input length. - - """ - - def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): - """Construct an PositionalEncoding object.""" - super(EspnetRelPositionalEncoding, self).__init__() - self.d_model = d_model - self.xscale = math.sqrt(self.d_model) - self.dropout = torch.nn.Dropout(p=dropout_rate) - self.pe = None - self.extend_pe(torch.tensor(0.0).expand(1, max_len)) - - def extend_pe(self, x: torch.Tensor): - """Reset the positional encodings.""" - if self.pe is not None: - # self.pe contains both positive and negative parts - # the length of self.pe is 2 * input_len - 1 - if self.pe.size(1) >= x.size(1) * 2 - 1: - if self.pe.dtype != x.dtype or self.pe.device != x.device: - self.pe = self.pe.to(dtype=x.dtype, device=x.device) - return - # Suppose `i` means to the position of query vecotr and `j` means the - # position of key vector. We use position relative positions when keys - # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: - """Add positional encoding. - - Args: - x (torch.Tensor): Input tensor (batch, time, `*`). - - Returns: - torch.Tensor: Encoded tensor (batch, time, `*`). - - """ - self.extend_pe(x) - x = x * self.xscale - pos_emb = self.position_encoding(size=x.size(1), offset=offset) - return self.dropout(x), self.dropout(pos_emb) - - def position_encoding(self, - offset: Union[int, torch.Tensor], - size: int) -> torch.Tensor: - """ For getting encoding in a streaming fashion - - Attention!!!!! - we apply dropout only once at the whole utterance level in a none - streaming way, but will call this function several times with - increasing input size in a streaming scenario, so the dropout will - be applied several times. - - Args: - offset (int or torch.tensor): start offset - size (int): required size of position encoding - - Returns: - torch.Tensor: Corresponding encoding - """ - pos_emb = self.pe[ - :, - self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size, - ] - return pos_emb - - - -class LinearEmbed(torch.nn.Module): - """Linear transform the input without subsampling - - Args: - idim (int): Input dimension. - odim (int): Output dimension. - dropout_rate (float): Dropout rate. - - """ - - def __init__(self, idim: int, odim: int, dropout_rate: float, - pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None): - """Construct an linear object.""" - super().__init__() - self.out = torch.nn.Sequential( - operations.Linear(idim, odim, dtype=dtype, device=device), - operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device), - torch.nn.Dropout(dropout_rate), - ) - self.pos_enc = pos_enc_class #rel_pos_espnet - - def position_encoding(self, offset: Union[int, torch.Tensor], - size: int) -> torch.Tensor: - return self.pos_enc.position_encoding(offset, size) - - def forward( - self, - x: torch.Tensor, - offset: Union[int, torch.Tensor] = 0 - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """Input x. - - Args: - x (torch.Tensor): Input tensor (#batch, time, idim). - x_mask (torch.Tensor): Input mask (#batch, 1, time). - - Returns: - torch.Tensor: linear input tensor (#batch, time', odim), - where time' = time . - torch.Tensor: linear input mask (#batch, 1, time'), - where time' = time . - - """ - x = self.out(x) - x, pos_emb = self.pos_enc(x, offset) - return x, pos_emb - - -ATTENTION_CLASSES = { - "selfattn": MultiHeadedAttention, - "rel_selfattn": RelPositionMultiHeadedAttention, -} - -ACTIVATION_CLASSES = { - "hardtanh": torch.nn.Hardtanh, - "tanh": torch.nn.Tanh, - "relu": torch.nn.ReLU, - "selu": torch.nn.SELU, - "swish": getattr(torch.nn, "SiLU", Swish), - "gelu": torch.nn.GELU, -} - - -def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: - """Make mask tensor containing indices of padded part. - - See description of make_non_pad_mask. - - Args: - lengths (torch.Tensor): Batch of lengths (B,). - Returns: - torch.Tensor: Mask tensor containing indices of padded part. - - Examples: - >>> lengths = [5, 3, 2] - >>> make_pad_mask(lengths) - masks = [[0, 0, 0, 0 ,0], - [0, 0, 0, 1, 1], - [0, 0, 1, 1, 1]] - """ - batch_size = lengths.size(0) - max_len = max_len if max_len > 0 else lengths.max().item() - seq_range = torch.arange(0, - max_len, - dtype=torch.int64, - device=lengths.device) - seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) - seq_length_expand = lengths.unsqueeze(-1) - mask = seq_range_expand >= seq_length_expand - return mask - -#https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml -class ConformerEncoder(torch.nn.Module): - """Conformer encoder module.""" - - def __init__( - self, - input_size: int, - output_size: int = 1024, - attention_heads: int = 16, - linear_units: int = 4096, - num_blocks: int = 6, - dropout_rate: float = 0.1, - positional_dropout_rate: float = 0.1, - attention_dropout_rate: float = 0.0, - input_layer: str = 'linear', - pos_enc_layer_type: str = 'rel_pos_espnet', - normalize_before: bool = True, - static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask - use_dynamic_chunk: bool = False, - use_dynamic_left_chunk: bool = False, - positionwise_conv_kernel_size: int = 1, - macaron_style: bool =False, - selfattention_layer_type: str = "rel_selfattn", - activation_type: str = "swish", - use_cnn_module: bool = False, - cnn_module_kernel: int = 15, - causal: bool = False, - cnn_module_norm: str = "batch_norm", - key_bias: bool = True, - dtype=None, device=None, operations=None - ): - """Construct ConformerEncoder - - Args: - input_size to use_dynamic_chunk, see in BaseEncoder - positionwise_conv_kernel_size (int): Kernel size of positionwise - conv1d layer. - macaron_style (bool): Whether to use macaron style for - positionwise layer. - selfattention_layer_type (str): Encoder attention layer type, - the parameter has no effect now, it's just for configure - compatibility. #'rel_selfattn' - activation_type (str): Encoder activation function type. - use_cnn_module (bool): Whether to use convolution module. - cnn_module_kernel (int): Kernel size of convolution module. - causal (bool): whether to use causal convolution or not. - key_bias: whether use bias in attention.linear_k, False for whisper models. - """ - super().__init__() - self.output_size = output_size - self.embed = LinearEmbed(input_size, output_size, dropout_rate, - EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations) - self.normalize_before = normalize_before - self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device) - self.use_dynamic_chunk = use_dynamic_chunk - - self.static_chunk_size = static_chunk_size - self.use_dynamic_chunk = use_dynamic_chunk - self.use_dynamic_left_chunk = use_dynamic_left_chunk - activation = ACTIVATION_CLASSES[activation_type]() - - # self-attention module definition - encoder_selfattn_layer_args = ( - attention_heads, - output_size, - attention_dropout_rate, - key_bias, - ) - # feed-forward module definition - positionwise_layer_args = ( - output_size, - linear_units, - dropout_rate, - activation, - ) - # convolution module definition - convolution_layer_args = (output_size, cnn_module_kernel, activation, - cnn_module_norm, causal) - - self.encoders = torch.nn.ModuleList([ - ConformerEncoderLayer( - output_size, - RelPositionMultiHeadedAttention( - *encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations), - PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations), - PositionwiseFeedForward( - *positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None, - ConvolutionModule( - *convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None, - dropout_rate, - normalize_before, dtype=dtype, device=device, operations=operations - ) for _ in range(num_blocks) - ]) - - def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor) -> torch.Tensor: - for layer in self.encoders: - xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) - return xs - - def forward( - self, - xs: torch.Tensor, - pad_mask: torch.Tensor, - decoding_chunk_size: int = 0, - num_decoding_left_chunks: int = -1, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Embed positions in tensor. - - Args: - xs: padded input tensor (B, T, D) - xs_lens: input length (B) - decoding_chunk_size: decoding chunk size for dynamic chunk - 0: default for training, use random dynamic chunk. - <0: for decoding, use full chunk. - >0: for decoding, use fixed chunk size as set. - num_decoding_left_chunks: number of left chunks, this is for decoding, - the chunk size is decoding_chunk_size. - >=0: use num_decoding_left_chunks - <0: use all left chunks - Returns: - encoder output tensor xs, and subsampled masks - xs: padded output tensor (B, T' ~= T/subsample_rate, D) - masks: torch.Tensor batch padding mask after subsample - (B, 1, T' ~= T/subsample_rate) - NOTE(xcsong): - We pass the `__call__` method of the modules instead of `forward` to the - checkpointing API because `__call__` attaches all the hooks of the module. - https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 - """ - masks = None - if pad_mask is not None: - masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T) - xs, pos_emb = self.embed(xs) - mask_pad = masks # (B, 1, T/subsample_rate) - chunk_masks = add_optional_chunk_mask(xs, masks, - self.use_dynamic_chunk, - self.use_dynamic_left_chunk, - decoding_chunk_size, - self.static_chunk_size, - num_decoding_left_chunks) - - xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) - if self.normalize_before: - xs = self.after_norm(xs) - # Here we assume the mask is not changed in encoder layers, so just - # return the masks before encoder layers, and the masks will be used - # for cross attention with decoder later - return xs, masks - diff --git a/comfy/ldm/ace/model.py b/comfy/ldm/ace/model.py deleted file mode 100644 index 41d85eeb571b08431c62d0a6d2ff35d9b56b44da..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/model.py +++ /dev/null @@ -1,407 +0,0 @@ -# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.py - -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Optional, List, Union - -import torch -from torch import nn - -import comfy.model_management -import comfy.patcher_extension - -from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps -from .attention import LinearTransformerBlock, t2i_modulate -from .lyric_encoder import ConformerEncoder as LyricEncoder - - -def cross_norm(hidden_states, controlnet_input): - # input N x T x c - mean_hidden_states, std_hidden_states = hidden_states.mean(dim=(1,2), keepdim=True), hidden_states.std(dim=(1,2), keepdim=True) - mean_controlnet_input, std_controlnet_input = controlnet_input.mean(dim=(1,2), keepdim=True), controlnet_input.std(dim=(1,2), keepdim=True) - controlnet_input = (controlnet_input - mean_controlnet_input) * (std_hidden_states / (std_controlnet_input + 1e-12)) + mean_hidden_states - return controlnet_input - - -# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2 -class Qwen2RotaryEmbedding(nn.Module): - def __init__(self, dim, max_position_embeddings=2048, base=10000, dtype=None, device=None): - super().__init__() - - self.dim = dim - self.max_position_embeddings = max_position_embeddings - self.base = base - inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim)) - self.register_buffer("inv_freq", inv_freq, persistent=False) - - # Build here to make `torch.jit.trace` work. - self._set_cos_sin_cache( - seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 - ) - - def _set_cos_sin_cache(self, seq_len, device, dtype): - self.max_seq_len_cached = seq_len - t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) - - freqs = torch.outer(t, self.inv_freq) - # Different from paper, but it uses a different permutation in order to obtain the same calculation - emb = torch.cat((freqs, freqs), dim=-1) - self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) - self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) - - def forward(self, x, seq_len=None): - # x: [bs, num_attention_heads, seq_len, head_size] - if seq_len > self.max_seq_len_cached: - self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) - - return ( - self.cos_cached[:seq_len].to(dtype=x.dtype), - self.sin_cached[:seq_len].to(dtype=x.dtype), - ) - - -class T2IFinalLayer(nn.Module): - """ - The final layer of Sana. - """ - - def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True, dtype=dtype, device=device) - self.scale_shift_table = nn.Parameter(torch.empty(2, hidden_size, dtype=dtype, device=device)) - self.out_channels = out_channels - self.patch_size = patch_size - - def unpatchfy( - self, - hidden_states: torch.Tensor, - width: int, - ): - # 4 unpatchify - new_height, new_width = 1, hidden_states.size(1) - hidden_states = hidden_states.reshape( - shape=(hidden_states.shape[0], new_height, new_width, self.patch_size[0], self.patch_size[1], self.out_channels) - ).contiguous() - hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) - output = hidden_states.reshape( - shape=(hidden_states.shape[0], self.out_channels, new_height * self.patch_size[0], new_width * self.patch_size[1]) - ).contiguous() - if width > new_width: - output = torch.nn.functional.pad(output, (0, width - new_width, 0, 0), 'constant', 0) - elif width < new_width: - output = output[:, :, :, :width] - return output - - def forward(self, x, t, output_length): - shift, scale = (comfy.model_management.cast_to(self.scale_shift_table[None], device=t.device, dtype=t.dtype) + t[:, None]).chunk(2, dim=1) - x = t2i_modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - # unpatchify - output = self.unpatchfy(x, output_length) - return output - - -class PatchEmbed(nn.Module): - """2D Image to Patch Embedding""" - - def __init__( - self, - height=16, - width=4096, - patch_size=(16, 1), - in_channels=8, - embed_dim=1152, - bias=True, - dtype=None, device=None, operations=None - ): - super().__init__() - patch_size_h, patch_size_w = patch_size - self.early_conv_layers = nn.Sequential( - operations.Conv2d(in_channels, in_channels*256, kernel_size=patch_size, stride=patch_size, padding=0, bias=bias, dtype=dtype, device=device), - operations.GroupNorm(num_groups=32, num_channels=in_channels*256, eps=1e-6, affine=True, dtype=dtype, device=device), - operations.Conv2d(in_channels*256, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device) - ) - self.patch_size = patch_size - self.height, self.width = height // patch_size_h, width // patch_size_w - self.base_size = self.width - - def forward(self, latent): - # early convolutions, N x C x H x W -> N x 256 * sqrt(patch_size) x H/patch_size x W/patch_size - latent = self.early_conv_layers(latent) - latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC - return latent - - -class ACEStepTransformer2DModel(nn.Module): - # _supports_gradient_checkpointing = True - - def __init__( - self, - in_channels: Optional[int] = 8, - num_layers: int = 28, - inner_dim: int = 1536, - attention_head_dim: int = 64, - num_attention_heads: int = 24, - mlp_ratio: float = 4.0, - out_channels: int = 8, - max_position: int = 32768, - rope_theta: float = 1000000.0, - speaker_embedding_dim: int = 512, - text_embedding_dim: int = 768, - ssl_encoder_depths: List[int] = [9, 9], - ssl_names: List[str] = ["mert", "m-hubert"], - ssl_latent_dims: List[int] = [1024, 768], - lyric_encoder_vocab_size: int = 6681, - lyric_hidden_size: int = 1024, - patch_size: List[int] = [16, 1], - max_height: int = 16, - max_width: int = 4096, - audio_model=None, - dtype=None, device=None, operations=None - - ): - super().__init__() - - self.dtype = dtype - self.num_attention_heads = num_attention_heads - self.attention_head_dim = attention_head_dim - inner_dim = num_attention_heads * attention_head_dim - self.inner_dim = inner_dim - self.out_channels = out_channels - self.max_position = max_position - self.patch_size = patch_size - - self.rope_theta = rope_theta - - self.rotary_emb = Qwen2RotaryEmbedding( - dim=self.attention_head_dim, - max_position_embeddings=self.max_position, - base=self.rope_theta, - dtype=dtype, - device=device, - ) - - # 2. Define input layers - self.in_channels = in_channels - - self.num_layers = num_layers - # 3. Define transformers blocks - self.transformer_blocks = nn.ModuleList( - [ - LinearTransformerBlock( - dim=self.inner_dim, - num_attention_heads=self.num_attention_heads, - attention_head_dim=attention_head_dim, - mlp_ratio=mlp_ratio, - add_cross_attention=True, - add_cross_attention_dim=self.inner_dim, - dtype=dtype, - device=device, - operations=operations, - ) - for i in range(self.num_layers) - ] - ) - - self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) - self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim, dtype=dtype, device=device, operations=operations) - self.t_block = nn.Sequential(nn.SiLU(), operations.Linear(self.inner_dim, 6 * self.inner_dim, bias=True, dtype=dtype, device=device)) - - # speaker - self.speaker_embedder = operations.Linear(speaker_embedding_dim, self.inner_dim, dtype=dtype, device=device) - - # genre - self.genre_embedder = operations.Linear(text_embedding_dim, self.inner_dim, dtype=dtype, device=device) - - # lyric - self.lyric_embs = operations.Embedding(lyric_encoder_vocab_size, lyric_hidden_size, dtype=dtype, device=device) - self.lyric_encoder = LyricEncoder(input_size=lyric_hidden_size, static_chunk_size=0, dtype=dtype, device=device, operations=operations) - self.lyric_proj = operations.Linear(lyric_hidden_size, self.inner_dim, dtype=dtype, device=device) - - projector_dim = 2 * self.inner_dim - - self.projectors = nn.ModuleList([ - nn.Sequential( - operations.Linear(self.inner_dim, projector_dim, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(projector_dim, projector_dim, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(projector_dim, ssl_dim, dtype=dtype, device=device), - ) for ssl_dim in ssl_latent_dims - ]) - - self.proj_in = PatchEmbed( - height=max_height, - width=max_width, - patch_size=patch_size, - embed_dim=self.inner_dim, - bias=True, - dtype=dtype, - device=device, - operations=operations, - ) - - self.final_layer = T2IFinalLayer(self.inner_dim, patch_size=patch_size, out_channels=out_channels, dtype=dtype, device=device, operations=operations) - - def forward_lyric_encoder( - self, - lyric_token_idx: Optional[torch.LongTensor] = None, - lyric_mask: Optional[torch.LongTensor] = None, - out_dtype=None, - ): - # N x T x D - lyric_embs = self.lyric_embs(lyric_token_idx, out_dtype=out_dtype) - prompt_prenet_out, _mask = self.lyric_encoder(lyric_embs, lyric_mask, decoding_chunk_size=1, num_decoding_left_chunks=-1) - prompt_prenet_out = self.lyric_proj(prompt_prenet_out) - return prompt_prenet_out - - def encode( - self, - encoder_text_hidden_states: Optional[torch.Tensor] = None, - text_attention_mask: Optional[torch.LongTensor] = None, - speaker_embeds: Optional[torch.FloatTensor] = None, - lyric_token_idx: Optional[torch.LongTensor] = None, - lyric_mask: Optional[torch.LongTensor] = None, - lyrics_strength=1.0, - ): - - bs = encoder_text_hidden_states.shape[0] - device = encoder_text_hidden_states.device - - # speaker embedding - encoder_spk_hidden_states = self.speaker_embedder(speaker_embeds).unsqueeze(1) - - # genre embedding - encoder_text_hidden_states = self.genre_embedder(encoder_text_hidden_states) - - # lyric - encoder_lyric_hidden_states = self.forward_lyric_encoder( - lyric_token_idx=lyric_token_idx, - lyric_mask=lyric_mask, - out_dtype=encoder_text_hidden_states.dtype, - ) - - encoder_lyric_hidden_states *= lyrics_strength - - encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1) - - encoder_hidden_mask = None - if text_attention_mask is not None: - speaker_mask = torch.ones(bs, 1, device=device) - encoder_hidden_mask = torch.cat([speaker_mask, text_attention_mask, lyric_mask], dim=1) - - return encoder_hidden_states, encoder_hidden_mask - - def decode( - self, - hidden_states: torch.Tensor, - attention_mask: torch.Tensor, - encoder_hidden_states: torch.Tensor, - encoder_hidden_mask: torch.Tensor, - timestep: Optional[torch.Tensor], - output_length: int = 0, - block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, - controlnet_scale: Union[float, torch.Tensor] = 1.0, - ): - embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) - temb = self.t_block(embedded_timestep) - - hidden_states = self.proj_in(hidden_states) - - # controlnet logic - if block_controlnet_hidden_states is not None: - control_condi = cross_norm(hidden_states, block_controlnet_hidden_states) - hidden_states = hidden_states + control_condi * controlnet_scale - - # inner_hidden_states = [] - - rotary_freqs_cis = self.rotary_emb(hidden_states, seq_len=hidden_states.shape[1]) - encoder_rotary_freqs_cis = self.rotary_emb(encoder_hidden_states, seq_len=encoder_hidden_states.shape[1]) - - for index_block, block in enumerate(self.transformer_blocks): - hidden_states = block( - hidden_states=hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_hidden_mask, - rotary_freqs_cis=rotary_freqs_cis, - rotary_freqs_cis_cross=encoder_rotary_freqs_cis, - temb=temb, - ) - - output = self.final_layer(hidden_states, embedded_timestep, output_length) - return output - - def forward(self, - x, - timestep, - attention_mask=None, - context: Optional[torch.Tensor] = None, - text_attention_mask: Optional[torch.LongTensor] = None, - speaker_embeds: Optional[torch.FloatTensor] = None, - lyric_token_idx: Optional[torch.LongTensor] = None, - lyric_mask: Optional[torch.LongTensor] = None, - block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, - controlnet_scale: Union[float, torch.Tensor] = 1.0, - lyrics_strength=1.0, - **kwargs - ): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) - ).execute(x, timestep, attention_mask, context, text_attention_mask, speaker_embeds, lyric_token_idx, lyric_mask, block_controlnet_hidden_states, - controlnet_scale, lyrics_strength, **kwargs) - - def _forward( - self, - x, - timestep, - attention_mask=None, - context: Optional[torch.Tensor] = None, - text_attention_mask: Optional[torch.LongTensor] = None, - speaker_embeds: Optional[torch.FloatTensor] = None, - lyric_token_idx: Optional[torch.LongTensor] = None, - lyric_mask: Optional[torch.LongTensor] = None, - block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, - controlnet_scale: Union[float, torch.Tensor] = 1.0, - lyrics_strength=1.0, - **kwargs - ): - hidden_states = x - encoder_text_hidden_states = context - encoder_hidden_states, encoder_hidden_mask = self.encode( - encoder_text_hidden_states=encoder_text_hidden_states, - text_attention_mask=text_attention_mask, - speaker_embeds=speaker_embeds, - lyric_token_idx=lyric_token_idx, - lyric_mask=lyric_mask, - lyrics_strength=lyrics_strength, - ) - - output_length = hidden_states.shape[-1] - - output = self.decode( - hidden_states=hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_hidden_mask=encoder_hidden_mask, - timestep=timestep, - output_length=output_length, - block_controlnet_hidden_states=block_controlnet_hidden_states, - controlnet_scale=controlnet_scale, - ) - - return output diff --git a/comfy/ldm/ace/vae/.DS_Store b/comfy/ldm/ace/vae/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/ace/vae/.DS_Store and /dev/null differ diff --git a/comfy/ldm/ace/vae/autoencoder_dc.py b/comfy/ldm/ace/vae/autoencoder_dc.py deleted file mode 100644 index e7b1d4801e7b3f47f29cb90f41adff4d415868cd..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/vae/autoencoder_dc.py +++ /dev/null @@ -1,644 +0,0 @@ -# Rewritten from diffusers -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Tuple, Union - -import comfy.model_management -import comfy.ops -ops = comfy.ops.disable_weight_init - - -class RMSNorm(ops.RMSNorm): - def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False): - super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine) - if elementwise_affine: - self.bias = nn.Parameter(torch.empty(dim)) if bias else None - - def forward(self, x): - x = super().forward(x) - if self.elementwise_affine: - if self.bias is not None: - x = x + comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device) - return x - - -def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5): - if norm_type == "batch_norm": - return nn.BatchNorm2d(num_features) - elif norm_type == "group_norm": - return ops.GroupNorm(num_groups, num_features) - elif norm_type == "layer_norm": - return ops.LayerNorm(num_features) - elif norm_type == "rms_norm": - return RMSNorm(num_features, eps=eps, elementwise_affine=True, bias=True) - else: - raise ValueError(f"Unknown normalization type: {norm_type}") - - -def get_activation(activation_type): - if activation_type == "relu": - return nn.ReLU() - elif activation_type == "relu6": - return nn.ReLU6() - elif activation_type == "silu": - return nn.SiLU() - elif activation_type == "leaky_relu": - return nn.LeakyReLU(0.2) - else: - raise ValueError(f"Unknown activation type: {activation_type}") - - -class ResBlock(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - norm_type: str = "batch_norm", - act_fn: str = "relu6", - ) -> None: - super().__init__() - - self.norm_type = norm_type - self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity() - self.conv1 = ops.Conv2d(in_channels, in_channels, 3, 1, 1) - self.conv2 = ops.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False) - self.norm = get_normalization(norm_type, out_channels) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - residual = hidden_states - hidden_states = self.conv1(hidden_states) - hidden_states = self.nonlinearity(hidden_states) - hidden_states = self.conv2(hidden_states) - - if self.norm_type == "rms_norm": - # move channel to the last dimension so we apply RMSnorm across channel dimension - hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) - else: - hidden_states = self.norm(hidden_states) - - return hidden_states + residual - -class SanaMultiscaleAttentionProjection(nn.Module): - def __init__( - self, - in_channels: int, - num_attention_heads: int, - kernel_size: int, - ) -> None: - super().__init__() - - channels = 3 * in_channels - self.proj_in = ops.Conv2d( - channels, - channels, - kernel_size, - padding=kernel_size // 2, - groups=channels, - bias=False, - ) - self.proj_out = ops.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - hidden_states = self.proj_in(hidden_states) - hidden_states = self.proj_out(hidden_states) - return hidden_states - -class SanaMultiscaleLinearAttention(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - num_attention_heads: int = None, - attention_head_dim: int = 8, - mult: float = 1.0, - norm_type: str = "batch_norm", - kernel_sizes: tuple = (5,), - eps: float = 1e-15, - residual_connection: bool = False, - ): - super().__init__() - - self.eps = eps - self.attention_head_dim = attention_head_dim - self.norm_type = norm_type - self.residual_connection = residual_connection - - num_attention_heads = ( - int(in_channels // attention_head_dim * mult) - if num_attention_heads is None - else num_attention_heads - ) - inner_dim = num_attention_heads * attention_head_dim - - self.to_q = ops.Linear(in_channels, inner_dim, bias=False) - self.to_k = ops.Linear(in_channels, inner_dim, bias=False) - self.to_v = ops.Linear(in_channels, inner_dim, bias=False) - - self.to_qkv_multiscale = nn.ModuleList() - for kernel_size in kernel_sizes: - self.to_qkv_multiscale.append( - SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size) - ) - - self.nonlinearity = nn.ReLU() - self.to_out = ops.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False) - self.norm_out = get_normalization(norm_type, out_channels) - - def apply_linear_attention(self, query, key, value): - value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) - scores = torch.matmul(value, key.transpose(-1, -2)) - hidden_states = torch.matmul(scores, query) - - hidden_states = hidden_states.to(dtype=torch.float32) - hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) - return hidden_states - - def apply_quadratic_attention(self, query, key, value): - scores = torch.matmul(key.transpose(-1, -2), query) - scores = scores.to(dtype=torch.float32) - scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps) - hidden_states = torch.matmul(value, scores.to(value.dtype)) - return hidden_states - - def forward(self, hidden_states): - height, width = hidden_states.shape[-2:] - if height * width > self.attention_head_dim: - use_linear_attention = True - else: - use_linear_attention = False - - residual = hidden_states - - batch_size, _, height, width = list(hidden_states.size()) - original_dtype = hidden_states.dtype - - hidden_states = hidden_states.movedim(1, -1) - query = self.to_q(hidden_states) - key = self.to_k(hidden_states) - value = self.to_v(hidden_states) - hidden_states = torch.cat([query, key, value], dim=3) - hidden_states = hidden_states.movedim(-1, 1) - - multi_scale_qkv = [hidden_states] - for block in self.to_qkv_multiscale: - multi_scale_qkv.append(block(hidden_states)) - - hidden_states = torch.cat(multi_scale_qkv, dim=1) - - if use_linear_attention: - # for linear attention upcast hidden_states to float32 - hidden_states = hidden_states.to(dtype=torch.float32) - - hidden_states = hidden_states.reshape(batch_size, -1, 3 * self.attention_head_dim, height * width) - - query, key, value = hidden_states.chunk(3, dim=2) - query = self.nonlinearity(query) - key = self.nonlinearity(key) - - if use_linear_attention: - hidden_states = self.apply_linear_attention(query, key, value) - hidden_states = hidden_states.to(dtype=original_dtype) - else: - hidden_states = self.apply_quadratic_attention(query, key, value) - - hidden_states = torch.reshape(hidden_states, (batch_size, -1, height, width)) - hidden_states = self.to_out(hidden_states.movedim(1, -1)).movedim(-1, 1) - - if self.norm_type == "rms_norm": - hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) - else: - hidden_states = self.norm_out(hidden_states) - - if self.residual_connection: - hidden_states = hidden_states + residual - - return hidden_states - - -class EfficientViTBlock(nn.Module): - def __init__( - self, - in_channels: int, - mult: float = 1.0, - attention_head_dim: int = 32, - qkv_multiscales: tuple = (5,), - norm_type: str = "batch_norm", - ) -> None: - super().__init__() - - self.attn = SanaMultiscaleLinearAttention( - in_channels=in_channels, - out_channels=in_channels, - mult=mult, - attention_head_dim=attention_head_dim, - norm_type=norm_type, - kernel_sizes=qkv_multiscales, - residual_connection=True, - ) - - self.conv_out = GLUMBConv( - in_channels=in_channels, - out_channels=in_channels, - norm_type="rms_norm", - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.attn(x) - x = self.conv_out(x) - return x - - -class GLUMBConv(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - expand_ratio: float = 4, - norm_type: str = None, - residual_connection: bool = True, - ) -> None: - super().__init__() - - hidden_channels = int(expand_ratio * in_channels) - self.norm_type = norm_type - self.residual_connection = residual_connection - - self.nonlinearity = nn.SiLU() - self.conv_inverted = ops.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0) - self.conv_depth = ops.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2) - self.conv_point = ops.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False) - - self.norm = None - if norm_type == "rms_norm": - self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - if self.residual_connection: - residual = hidden_states - - hidden_states = self.conv_inverted(hidden_states) - hidden_states = self.nonlinearity(hidden_states) - - hidden_states = self.conv_depth(hidden_states) - hidden_states, gate = torch.chunk(hidden_states, 2, dim=1) - hidden_states = hidden_states * self.nonlinearity(gate) - - hidden_states = self.conv_point(hidden_states) - - if self.norm_type == "rms_norm": - # move channel to the last dimension so we apply RMSnorm across channel dimension - hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) - - if self.residual_connection: - hidden_states = hidden_states + residual - - return hidden_states - - -def get_block( - block_type: str, - in_channels: int, - out_channels: int, - attention_head_dim: int, - norm_type: str, - act_fn: str, - qkv_mutliscales: tuple = (), -): - if block_type == "ResBlock": - block = ResBlock(in_channels, out_channels, norm_type, act_fn) - elif block_type == "EfficientViTBlock": - block = EfficientViTBlock( - in_channels, - attention_head_dim=attention_head_dim, - norm_type=norm_type, - qkv_multiscales=qkv_mutliscales - ) - else: - raise ValueError(f"Block with {block_type=} is not supported.") - - return block - - -class DCDownBlock2d(nn.Module): - def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None: - super().__init__() - - self.downsample = downsample - self.factor = 2 - self.stride = 1 if downsample else 2 - self.group_size = in_channels * self.factor**2 // out_channels - self.shortcut = shortcut - - out_ratio = self.factor**2 - if downsample: - assert out_channels % out_ratio == 0 - out_channels = out_channels // out_ratio - - self.conv = ops.Conv2d( - in_channels, - out_channels, - kernel_size=3, - stride=self.stride, - padding=1, - ) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - x = self.conv(hidden_states) - if self.downsample: - x = F.pixel_unshuffle(x, self.factor) - - if self.shortcut: - y = F.pixel_unshuffle(hidden_states, self.factor) - y = y.unflatten(1, (-1, self.group_size)) - y = y.mean(dim=2) - hidden_states = x + y - else: - hidden_states = x - - return hidden_states - - -class DCUpBlock2d(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - interpolate: bool = False, - shortcut: bool = True, - interpolation_mode: str = "nearest", - ) -> None: - super().__init__() - - self.interpolate = interpolate - self.interpolation_mode = interpolation_mode - self.shortcut = shortcut - self.factor = 2 - self.repeats = out_channels * self.factor**2 // in_channels - - out_ratio = self.factor**2 - if not interpolate: - out_channels = out_channels * out_ratio - - self.conv = ops.Conv2d(in_channels, out_channels, 3, 1, 1) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - if self.interpolate: - x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode) - x = self.conv(x) - else: - x = self.conv(hidden_states) - x = F.pixel_shuffle(x, self.factor) - - if self.shortcut: - y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats) - y = F.pixel_shuffle(y, self.factor) - hidden_states = x + y - else: - hidden_states = x - - return hidden_states - - -class Encoder(nn.Module): - def __init__( - self, - in_channels: int, - latent_channels: int, - attention_head_dim: int = 32, - block_type: str or tuple = "ResBlock", - block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), - layers_per_block: tuple = (2, 2, 2, 2, 2, 2), - qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), - downsample_block_type: str = "pixel_unshuffle", - out_shortcut: bool = True, - ): - super().__init__() - - num_blocks = len(block_out_channels) - - if isinstance(block_type, str): - block_type = (block_type,) * num_blocks - - if layers_per_block[0] > 0: - self.conv_in = ops.Conv2d( - in_channels, - block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], - kernel_size=3, - stride=1, - padding=1, - ) - else: - self.conv_in = DCDownBlock2d( - in_channels=in_channels, - out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], - downsample=downsample_block_type == "pixel_unshuffle", - shortcut=False, - ) - - down_blocks = [] - for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)): - down_block_list = [] - - for _ in range(num_layers): - block = get_block( - block_type[i], - out_channel, - out_channel, - attention_head_dim=attention_head_dim, - norm_type="rms_norm", - act_fn="silu", - qkv_mutliscales=qkv_multiscales[i], - ) - down_block_list.append(block) - - if i < num_blocks - 1 and num_layers > 0: - downsample_block = DCDownBlock2d( - in_channels=out_channel, - out_channels=block_out_channels[i + 1], - downsample=downsample_block_type == "pixel_unshuffle", - shortcut=True, - ) - down_block_list.append(downsample_block) - - down_blocks.append(nn.Sequential(*down_block_list)) - - self.down_blocks = nn.ModuleList(down_blocks) - - self.conv_out = ops.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1) - - self.out_shortcut = out_shortcut - if out_shortcut: - self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - hidden_states = self.conv_in(hidden_states) - for down_block in self.down_blocks: - hidden_states = down_block(hidden_states) - - if self.out_shortcut: - x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size)) - x = x.mean(dim=2) - hidden_states = self.conv_out(hidden_states) + x - else: - hidden_states = self.conv_out(hidden_states) - - return hidden_states - - -class Decoder(nn.Module): - def __init__( - self, - in_channels: int, - latent_channels: int, - attention_head_dim: int = 32, - block_type: str or tuple = "ResBlock", - block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), - layers_per_block: tuple = (2, 2, 2, 2, 2, 2), - qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), - norm_type: str or tuple = "rms_norm", - act_fn: str or tuple = "silu", - upsample_block_type: str = "pixel_shuffle", - in_shortcut: bool = True, - ): - super().__init__() - - num_blocks = len(block_out_channels) - - if isinstance(block_type, str): - block_type = (block_type,) * num_blocks - if isinstance(norm_type, str): - norm_type = (norm_type,) * num_blocks - if isinstance(act_fn, str): - act_fn = (act_fn,) * num_blocks - - self.conv_in = ops.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1) - - self.in_shortcut = in_shortcut - if in_shortcut: - self.in_shortcut_repeats = block_out_channels[-1] // latent_channels - - up_blocks = [] - for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))): - up_block_list = [] - - if i < num_blocks - 1 and num_layers > 0: - upsample_block = DCUpBlock2d( - block_out_channels[i + 1], - out_channel, - interpolate=upsample_block_type == "interpolate", - shortcut=True, - ) - up_block_list.append(upsample_block) - - for _ in range(num_layers): - block = get_block( - block_type[i], - out_channel, - out_channel, - attention_head_dim=attention_head_dim, - norm_type=norm_type[i], - act_fn=act_fn[i], - qkv_mutliscales=qkv_multiscales[i], - ) - up_block_list.append(block) - - up_blocks.insert(0, nn.Sequential(*up_block_list)) - - self.up_blocks = nn.ModuleList(up_blocks) - - channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1] - - self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True) - self.conv_act = nn.ReLU() - self.conv_out = None - - if layers_per_block[0] > 0: - self.conv_out = ops.Conv2d(channels, in_channels, 3, 1, 1) - else: - self.conv_out = DCUpBlock2d( - channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False - ) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - if self.in_shortcut: - x = hidden_states.repeat_interleave( - self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats - ) - hidden_states = self.conv_in(hidden_states) + x - else: - hidden_states = self.conv_in(hidden_states) - - for up_block in reversed(self.up_blocks): - hidden_states = up_block(hidden_states) - - hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) - hidden_states = self.conv_act(hidden_states) - hidden_states = self.conv_out(hidden_states) - return hidden_states - - -class AutoencoderDC(nn.Module): - def __init__( - self, - in_channels: int = 2, - latent_channels: int = 8, - attention_head_dim: int = 32, - encoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], - decoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], - encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), - decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), - encoder_layers_per_block: Tuple[int] = (2, 2, 3, 3), - decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3), - encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), - decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), - upsample_block_type: str = "interpolate", - downsample_block_type: str = "Conv", - decoder_norm_types: Union[str, Tuple[str]] = "rms_norm", - decoder_act_fns: Union[str, Tuple[str]] = "silu", - scaling_factor: float = 0.41407, - ) -> None: - super().__init__() - - self.encoder = Encoder( - in_channels=in_channels, - latent_channels=latent_channels, - attention_head_dim=attention_head_dim, - block_type=encoder_block_types, - block_out_channels=encoder_block_out_channels, - layers_per_block=encoder_layers_per_block, - qkv_multiscales=encoder_qkv_multiscales, - downsample_block_type=downsample_block_type, - ) - - self.decoder = Decoder( - in_channels=in_channels, - latent_channels=latent_channels, - attention_head_dim=attention_head_dim, - block_type=decoder_block_types, - block_out_channels=decoder_block_out_channels, - layers_per_block=decoder_layers_per_block, - qkv_multiscales=decoder_qkv_multiscales, - norm_type=decoder_norm_types, - act_fn=decoder_act_fns, - upsample_block_type=upsample_block_type, - ) - - self.scaling_factor = scaling_factor - self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1) - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Internal encoding function.""" - encoded = self.encoder(x) - return encoded * self.scaling_factor - - def decode(self, z: torch.Tensor) -> torch.Tensor: - # Scale the latents back - z = z / self.scaling_factor - decoded = self.decoder(z) - return decoded - - def forward(self, x: torch.Tensor) -> torch.Tensor: - z = self.encode(x) - return self.decode(z) - diff --git a/comfy/ldm/ace/vae/music_dcae_pipeline.py b/comfy/ldm/ace/vae/music_dcae_pipeline.py deleted file mode 100644 index af81280eb0dd5fce49c0aa4b86a4f0feb841b3fd..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/vae/music_dcae_pipeline.py +++ /dev/null @@ -1,109 +0,0 @@ -# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py -import torch -from .autoencoder_dc import AutoencoderDC -import logging -try: - import torchaudio -except: - logging.warning("torchaudio missing, ACE model will be broken") - -import torchvision.transforms as transforms -from .music_vocoder import ADaMoSHiFiGANV1 - - -class MusicDCAE(torch.nn.Module): - def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}): - super(MusicDCAE, self).__init__() - - self.dcae = AutoencoderDC(**dcae_config) - self.vocoder = ADaMoSHiFiGANV1(**vocoder_config) - - if source_sample_rate is None: - self.source_sample_rate = 48000 - else: - self.source_sample_rate = source_sample_rate - - # self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100) - - self.transform = transforms.Compose([ - transforms.Normalize(0.5, 0.5), - ]) - self.min_mel_value = -11.0 - self.max_mel_value = 3.0 - self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000))) - self.mel_chunk_size = 1024 - self.time_dimention_multiple = 8 - self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple - self.scale_factor = 0.1786 - self.shift_factor = -1.9091 - - def load_audio(self, audio_path): - audio, sr = torchaudio.load(audio_path) - return audio, sr - - def forward_mel(self, audios): - mels = [] - for i in range(len(audios)): - image = self.vocoder.mel_transform(audios[i]) - mels.append(image) - mels = torch.stack(mels) - return mels - - @torch.no_grad() - def encode(self, audios, audio_lengths=None, sr=None): - if audio_lengths is None: - audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0]) - audio_lengths = audio_lengths.to(audios.device) - - if sr is None: - sr = self.source_sample_rate - - if sr != 44100: - audios = torchaudio.functional.resample(audios, sr, 44100) - - max_audio_len = audios.shape[-1] - if max_audio_len % (8 * 512) != 0: - audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512))) - - mels = self.forward_mel(audios) - mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value) - mels = self.transform(mels) - latents = [] - for mel in mels: - latent = self.dcae.encoder(mel.unsqueeze(0)) - latents.append(latent) - latents = torch.cat(latents, dim=0) - # latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long() - latents = (latents - self.shift_factor) * self.scale_factor - return latents - # return latents, latent_lengths - - @torch.no_grad() - def decode(self, latents, audio_lengths=None, sr=None): - latents = latents / self.scale_factor + self.shift_factor - - pred_wavs = [] - - for latent in latents: - mels = self.dcae.decoder(latent.unsqueeze(0)) - mels = mels * 0.5 + 0.5 - mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value - wav = self.vocoder.decode(mels[0]).squeeze(1) - - if sr is not None: - # resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype) - wav = torchaudio.functional.resample(wav, 44100, sr) - # wav = resampler(wav) - else: - sr = 44100 - pred_wavs.append(wav) - - if audio_lengths is not None: - pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)] - return torch.stack(pred_wavs) - # return sr, pred_wavs - - def forward(self, audios, audio_lengths=None, sr=None): - latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr) - sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr) - return sr, pred_wavs, latents, latent_lengths diff --git a/comfy/ldm/ace/vae/music_log_mel.py b/comfy/ldm/ace/vae/music_log_mel.py deleted file mode 100644 index 9c584eb7fa75ffaab1c3a42184ea75171b2a1d8f..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/vae/music_log_mel.py +++ /dev/null @@ -1,113 +0,0 @@ -# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py -import torch -import torch.nn as nn -from torch import Tensor -import logging -try: - from torchaudio.transforms import MelScale -except: - logging.warning("torchaudio missing, ACE model will be broken") - -import comfy.model_management - -class LinearSpectrogram(nn.Module): - def __init__( - self, - n_fft=2048, - win_length=2048, - hop_length=512, - center=False, - mode="pow2_sqrt", - ): - super().__init__() - - self.n_fft = n_fft - self.win_length = win_length - self.hop_length = hop_length - self.center = center - self.mode = mode - - self.register_buffer("window", torch.hann_window(win_length)) - - def forward(self, y: Tensor) -> Tensor: - if y.ndim == 3: - y = y.squeeze(1) - - y = torch.nn.functional.pad( - y.unsqueeze(1), - ( - (self.win_length - self.hop_length) // 2, - (self.win_length - self.hop_length + 1) // 2, - ), - mode="reflect", - ).squeeze(1) - dtype = y.dtype - spec = torch.stft( - y.float(), - self.n_fft, - hop_length=self.hop_length, - win_length=self.win_length, - window=comfy.model_management.cast_to(self.window, dtype=torch.float32, device=y.device), - center=self.center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) - spec = torch.view_as_real(spec) - - if self.mode == "pow2_sqrt": - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - spec = spec.to(dtype) - return spec - - -class LogMelSpectrogram(nn.Module): - def __init__( - self, - sample_rate=44100, - n_fft=2048, - win_length=2048, - hop_length=512, - n_mels=128, - center=False, - f_min=0.0, - f_max=None, - ): - super().__init__() - - self.sample_rate = sample_rate - self.n_fft = n_fft - self.win_length = win_length - self.hop_length = hop_length - self.center = center - self.n_mels = n_mels - self.f_min = f_min - self.f_max = f_max or sample_rate // 2 - - self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center) - self.mel_scale = MelScale( - self.n_mels, - self.sample_rate, - self.f_min, - self.f_max, - self.n_fft // 2 + 1, - "slaney", - "slaney", - ) - - def compress(self, x: Tensor) -> Tensor: - return torch.log(torch.clamp(x, min=1e-5)) - - def decompress(self, x: Tensor) -> Tensor: - return torch.exp(x) - - def forward(self, x: Tensor, return_linear: bool = False) -> Tensor: - linear = self.spectrogram(x) - x = self.mel_scale(linear) - x = self.compress(x) - # print(x.shape) - if return_linear: - return x, self.compress(linear) - - return x diff --git a/comfy/ldm/ace/vae/music_vocoder.py b/comfy/ldm/ace/vae/music_vocoder.py deleted file mode 100644 index 2f989fa86e8c964994886c6a22ed53e742279ec4..0000000000000000000000000000000000000000 --- a/comfy/ldm/ace/vae/music_vocoder.py +++ /dev/null @@ -1,538 +0,0 @@ -# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py -import torch -from torch import nn - -from functools import partial -from math import prod -from typing import Callable, Tuple, List - -import numpy as np -import torch.nn.functional as F -from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm - -from .music_log_mel import LogMelSpectrogram - -import comfy.model_management -import comfy.ops -ops = comfy.ops.disable_weight_init - - -def drop_path( - x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True -): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, - the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... - See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for - changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use - 'survival rate' as the argument. - - """ # noqa: E501 - - if drop_prob == 0.0 or not training: - return x - keep_prob = 1 - drop_prob - shape = (x.shape[0],) + (1,) * ( - x.ndim - 1 - ) # work with diff dim tensors, not just 2D ConvNets - random_tensor = x.new_empty(shape).bernoulli_(keep_prob) - if keep_prob > 0.0 and scale_by_keep: - random_tensor.div_(keep_prob) - return x * random_tensor - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501 - - def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - self.scale_by_keep = scale_by_keep - - def forward(self, x): - return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) - - def extra_repr(self): - return f"drop_prob={round(self.drop_prob,3):0.3f}" - - -class LayerNorm(nn.Module): - r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. - The ordering of the dimensions in the inputs. channels_last corresponds to inputs with - shape (batch_size, height, width, channels) while channels_first corresponds to inputs - with shape (batch_size, channels, height, width). - """ # noqa: E501 - - def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): - super().__init__() - self.weight = nn.Parameter(torch.ones(normalized_shape)) - self.bias = nn.Parameter(torch.zeros(normalized_shape)) - self.eps = eps - self.data_format = data_format - if self.data_format not in ["channels_last", "channels_first"]: - raise NotImplementedError - self.normalized_shape = (normalized_shape,) - - def forward(self, x): - if self.data_format == "channels_last": - return F.layer_norm( - x, self.normalized_shape, comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device), comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device), self.eps - ) - elif self.data_format == "channels_first": - u = x.mean(1, keepdim=True) - s = (x - u).pow(2).mean(1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.eps) - x = comfy.model_management.cast_to(self.weight[:, None], dtype=x.dtype, device=x.device) * x + comfy.model_management.cast_to(self.bias[:, None], dtype=x.dtype, device=x.device) - return x - - -class ConvNeXtBlock(nn.Module): - r"""ConvNeXt Block. There are two equivalent implementations: - (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) - (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back - We use (2) as we find it slightly faster in PyTorch - - Args: - dim (int): Number of input channels. - drop_path (float): Stochastic depth rate. Default: 0.0 - layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. - kernel_size (int): Kernel size for depthwise conv. Default: 7. - dilation (int): Dilation for depthwise conv. Default: 1. - """ # noqa: E501 - - def __init__( - self, - dim: int, - drop_path: float = 0.0, - layer_scale_init_value: float = 1e-6, - mlp_ratio: float = 4.0, - kernel_size: int = 7, - dilation: int = 1, - ): - super().__init__() - - self.dwconv = ops.Conv1d( - dim, - dim, - kernel_size=kernel_size, - padding=int(dilation * (kernel_size - 1) / 2), - groups=dim, - ) # depthwise conv - self.norm = LayerNorm(dim, eps=1e-6) - self.pwconv1 = ops.Linear( - dim, int(mlp_ratio * dim) - ) # pointwise/1x1 convs, implemented with linear layers - self.act = nn.GELU() - self.pwconv2 = ops.Linear(int(mlp_ratio * dim), dim) - self.gamma = ( - nn.Parameter(torch.empty((dim)), requires_grad=False) - if layer_scale_init_value > 0 - else None - ) - self.drop_path = DropPath( - drop_path) if drop_path > 0.0 else nn.Identity() - - def forward(self, x, apply_residual: bool = True): - input = x - - x = self.dwconv(x) - x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C) - x = self.norm(x) - x = self.pwconv1(x) - x = self.act(x) - x = self.pwconv2(x) - - if self.gamma is not None: - x = comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) * x - - x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L) - x = self.drop_path(x) - - if apply_residual: - x = input + x - - return x - - -class ParallelConvNeXtBlock(nn.Module): - def __init__(self, kernel_sizes: List[int], *args, **kwargs): - super().__init__() - self.blocks = nn.ModuleList( - [ - ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs) - for kernel_size in kernel_sizes - ] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return torch.stack( - [block(x, apply_residual=False) for block in self.blocks] + [x], - dim=1, - ).sum(dim=1) - - -class ConvNeXtEncoder(nn.Module): - def __init__( - self, - input_channels=3, - depths=[3, 3, 9, 3], - dims=[96, 192, 384, 768], - drop_path_rate=0.0, - layer_scale_init_value=1e-6, - kernel_sizes: Tuple[int] = (7,), - ): - super().__init__() - assert len(depths) == len(dims) - - self.channel_layers = nn.ModuleList() - stem = nn.Sequential( - ops.Conv1d( - input_channels, - dims[0], - kernel_size=7, - padding=3, - padding_mode="replicate", - ), - LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), - ) - self.channel_layers.append(stem) - - for i in range(len(depths) - 1): - mid_layer = nn.Sequential( - LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), - ops.Conv1d(dims[i], dims[i + 1], kernel_size=1), - ) - self.channel_layers.append(mid_layer) - - block_fn = ( - partial(ConvNeXtBlock, kernel_size=kernel_sizes[0]) - if len(kernel_sizes) == 1 - else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes) - ) - - self.stages = nn.ModuleList() - drop_path_rates = [ - x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) - ] - - cur = 0 - for i in range(len(depths)): - stage = nn.Sequential( - *[ - block_fn( - dim=dims[i], - drop_path=drop_path_rates[cur + j], - layer_scale_init_value=layer_scale_init_value, - ) - for j in range(depths[i]) - ] - ) - self.stages.append(stage) - cur += depths[i] - - self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first") - - def forward( - self, - x: torch.Tensor, - ) -> torch.Tensor: - for channel_layer, stage in zip(self.channel_layers, self.stages): - x = channel_layer(x) - x = stage(x) - - return self.norm(x) - - -def get_padding(kernel_size, dilation=1): - return (kernel_size * dilation - dilation) // 2 - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super().__init__() - - self.convs1 = nn.ModuleList( - [ - torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]), - ) - ), - torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]), - ) - ), - torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]), - ) - ), - ] - ) - - self.convs2 = nn.ModuleList( - [ - torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - channels, - channels, - kernel_size, - 1, - dilation=1, - padding=get_padding(kernel_size, 1), - ) - ), - ] - ) - - def forward(self, x): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.silu(x) - xt = c1(xt) - xt = F.silu(xt) - xt = c2(xt) - x = xt + x - return x - - def remove_weight_norm(self): - for conv in self.convs1: - remove_weight_norm(conv) - for conv in self.convs2: - remove_weight_norm(conv) - - -class HiFiGANGenerator(nn.Module): - def __init__( - self, - *, - hop_length: int = 512, - upsample_rates: Tuple[int] = (8, 8, 2, 2, 2), - upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2), - resblock_kernel_sizes: Tuple[int] = (3, 7, 11), - resblock_dilation_sizes: Tuple[Tuple[int]] = ( - (1, 3, 5), (1, 3, 5), (1, 3, 5)), - num_mels: int = 128, - upsample_initial_channel: int = 512, - use_template: bool = True, - pre_conv_kernel_size: int = 7, - post_conv_kernel_size: int = 7, - post_activation: Callable = partial(nn.SiLU, inplace=True), - ): - super().__init__() - - assert ( - prod(upsample_rates) == hop_length - ), f"hop_length must be {prod(upsample_rates)}" - - self.conv_pre = torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - num_mels, - upsample_initial_channel, - pre_conv_kernel_size, - 1, - padding=get_padding(pre_conv_kernel_size), - ) - ) - - self.num_upsamples = len(upsample_rates) - self.num_kernels = len(resblock_kernel_sizes) - - self.noise_convs = nn.ModuleList() - self.use_template = use_template - self.ups = nn.ModuleList() - - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - torch.nn.utils.parametrizations.weight_norm( - ops.ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - if not use_template: - continue - - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1:]) - self.noise_convs.append( - ops.Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(ops.Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): - self.resblocks.append(ResBlock1(ch, k, d)) - - self.activation_post = post_activation() - self.conv_post = torch.nn.utils.parametrizations.weight_norm( - ops.Conv1d( - ch, - 1, - post_conv_kernel_size, - 1, - padding=get_padding(post_conv_kernel_size), - ) - ) - - def forward(self, x, template=None): - x = self.conv_pre(x) - - for i in range(self.num_upsamples): - x = F.silu(x, inplace=True) - x = self.ups[i](x) - - if self.use_template: - x = x + self.noise_convs[i](template) - - xs = None - - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - - x = xs / self.num_kernels - - x = self.activation_post(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for up in self.ups: - remove_weight_norm(up) - for block in self.resblocks: - block.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class ADaMoSHiFiGANV1(nn.Module): - def __init__( - self, - input_channels: int = 128, - depths: List[int] = [3, 3, 9, 3], - dims: List[int] = [128, 256, 384, 512], - drop_path_rate: float = 0.0, - kernel_sizes: Tuple[int] = (7,), - upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2), - upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4), - resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13), - resblock_dilation_sizes: Tuple[Tuple[int]] = ( - (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)), - num_mels: int = 512, - upsample_initial_channel: int = 1024, - use_template: bool = False, - pre_conv_kernel_size: int = 13, - post_conv_kernel_size: int = 13, - sampling_rate: int = 44100, - n_fft: int = 2048, - win_length: int = 2048, - hop_length: int = 512, - f_min: int = 40, - f_max: int = 16000, - n_mels: int = 128, - ): - super().__init__() - - self.backbone = ConvNeXtEncoder( - input_channels=input_channels, - depths=depths, - dims=dims, - drop_path_rate=drop_path_rate, - kernel_sizes=kernel_sizes, - ) - - self.head = HiFiGANGenerator( - hop_length=hop_length, - upsample_rates=upsample_rates, - upsample_kernel_sizes=upsample_kernel_sizes, - resblock_kernel_sizes=resblock_kernel_sizes, - resblock_dilation_sizes=resblock_dilation_sizes, - num_mels=num_mels, - upsample_initial_channel=upsample_initial_channel, - use_template=use_template, - pre_conv_kernel_size=pre_conv_kernel_size, - post_conv_kernel_size=post_conv_kernel_size, - ) - self.sampling_rate = sampling_rate - self.mel_transform = LogMelSpectrogram( - sample_rate=sampling_rate, - n_fft=n_fft, - win_length=win_length, - hop_length=hop_length, - f_min=f_min, - f_max=f_max, - n_mels=n_mels, - ) - self.eval() - - @torch.no_grad() - def decode(self, mel): - y = self.backbone(mel) - y = self.head(y) - return y - - @torch.no_grad() - def encode(self, x): - return self.mel_transform(x) - - def forward(self, mel): - y = self.backbone(mel) - y = self.head(y) - return y diff --git a/comfy/ldm/audio/.DS_Store b/comfy/ldm/audio/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/audio/.DS_Store and /dev/null differ diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py deleted file mode 100644 index 78ed6ffa63ab9fca66ba009709bb262a18830dcd..0000000000000000000000000000000000000000 --- a/comfy/ldm/audio/autoencoder.py +++ /dev/null @@ -1,276 +0,0 @@ -# code adapted from: https://github.com/Stability-AI/stable-audio-tools - -import torch -from torch import nn -from typing import Literal -import math -import comfy.ops -ops = comfy.ops.disable_weight_init - -def vae_sample(mean, scale): - stdev = nn.functional.softplus(scale) + 1e-4 - var = stdev * stdev - logvar = torch.log(var) - latents = torch.randn_like(mean) * stdev + mean - - kl = (mean * mean + var - logvar - 1).sum(1).mean() - - return latents, kl - -class VAEBottleneck(nn.Module): - def __init__(self): - super().__init__() - self.is_discrete = False - - def encode(self, x, return_info=False, **kwargs): - info = {} - - mean, scale = x.chunk(2, dim=1) - - x, kl = vae_sample(mean, scale) - - info["kl"] = kl - - if return_info: - return x, info - else: - return x - - def decode(self, x): - return x - - -def snake_beta(x, alpha, beta): - return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2) - -# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license -class SnakeBeta(nn.Module): - - def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): - super(SnakeBeta, self).__init__() - self.in_features = in_features - - # initialize alpha - self.alpha_logscale = alpha_logscale - if self.alpha_logscale: # log scale alphas initialized to zeros - self.alpha = nn.Parameter(torch.zeros(in_features) * alpha) - self.beta = nn.Parameter(torch.zeros(in_features) * alpha) - else: # linear scale alphas initialized to ones - self.alpha = nn.Parameter(torch.ones(in_features) * alpha) - self.beta = nn.Parameter(torch.ones(in_features) * alpha) - - # self.alpha.requires_grad = alpha_trainable - # self.beta.requires_grad = alpha_trainable - - self.no_div_by_zero = 0.000000001 - - def forward(self, x): - alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T] - beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device) - if self.alpha_logscale: - alpha = torch.exp(alpha) - beta = torch.exp(beta) - x = snake_beta(x, alpha, beta) - - return x - -def WNConv1d(*args, **kwargs): - return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) - -def WNConvTranspose1d(*args, **kwargs): - return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) - -def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: - if activation == "elu": - act = torch.nn.ELU() - elif activation == "snake": - act = SnakeBeta(channels) - elif activation == "none": - act = torch.nn.Identity() - else: - raise ValueError(f"Unknown activation {activation}") - - if antialias: - act = Activation1d(act) # noqa: F821 Activation1d is not defined - - return act - - -class ResidualUnit(nn.Module): - def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False): - super().__init__() - - self.dilation = dilation - - padding = (dilation * (7-1)) // 2 - - self.layers = nn.Sequential( - get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), - WNConv1d(in_channels=in_channels, out_channels=out_channels, - kernel_size=7, dilation=dilation, padding=padding), - get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels), - WNConv1d(in_channels=out_channels, out_channels=out_channels, - kernel_size=1) - ) - - def forward(self, x): - res = x - - #x = checkpoint(self.layers, x) - x = self.layers(x) - - return x + res - -class EncoderBlock(nn.Module): - def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False): - super().__init__() - - self.layers = nn.Sequential( - ResidualUnit(in_channels=in_channels, - out_channels=in_channels, dilation=1, use_snake=use_snake), - ResidualUnit(in_channels=in_channels, - out_channels=in_channels, dilation=3, use_snake=use_snake), - ResidualUnit(in_channels=in_channels, - out_channels=in_channels, dilation=9, use_snake=use_snake), - get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), - WNConv1d(in_channels=in_channels, out_channels=out_channels, - kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)), - ) - - def forward(self, x): - return self.layers(x) - -class DecoderBlock(nn.Module): - def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False): - super().__init__() - - if use_nearest_upsample: - upsample_layer = nn.Sequential( - nn.Upsample(scale_factor=stride, mode="nearest"), - WNConv1d(in_channels=in_channels, - out_channels=out_channels, - kernel_size=2*stride, - stride=1, - bias=False, - padding='same') - ) - else: - upsample_layer = WNConvTranspose1d(in_channels=in_channels, - out_channels=out_channels, - kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)) - - self.layers = nn.Sequential( - get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels), - upsample_layer, - ResidualUnit(in_channels=out_channels, out_channels=out_channels, - dilation=1, use_snake=use_snake), - ResidualUnit(in_channels=out_channels, out_channels=out_channels, - dilation=3, use_snake=use_snake), - ResidualUnit(in_channels=out_channels, out_channels=out_channels, - dilation=9, use_snake=use_snake), - ) - - def forward(self, x): - return self.layers(x) - -class OobleckEncoder(nn.Module): - def __init__(self, - in_channels=2, - channels=128, - latent_dim=32, - c_mults = [1, 2, 4, 8], - strides = [2, 4, 8, 8], - use_snake=False, - antialias_activation=False - ): - super().__init__() - - c_mults = [1] + c_mults - - self.depth = len(c_mults) - - layers = [ - WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3) - ] - - for i in range(self.depth-1): - layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)] - - layers += [ - get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels), - WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1) - ] - - self.layers = nn.Sequential(*layers) - - def forward(self, x): - return self.layers(x) - - -class OobleckDecoder(nn.Module): - def __init__(self, - out_channels=2, - channels=128, - latent_dim=32, - c_mults = [1, 2, 4, 8], - strides = [2, 4, 8, 8], - use_snake=False, - antialias_activation=False, - use_nearest_upsample=False, - final_tanh=True): - super().__init__() - - c_mults = [1] + c_mults - - self.depth = len(c_mults) - - layers = [ - WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3), - ] - - for i in range(self.depth-1, 0, -1): - layers += [DecoderBlock( - in_channels=c_mults[i]*channels, - out_channels=c_mults[i-1]*channels, - stride=strides[i-1], - use_snake=use_snake, - antialias_activation=antialias_activation, - use_nearest_upsample=use_nearest_upsample - ) - ] - - layers += [ - get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels), - WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False), - nn.Tanh() if final_tanh else nn.Identity() - ] - - self.layers = nn.Sequential(*layers) - - def forward(self, x): - return self.layers(x) - - -class AudioOobleckVAE(nn.Module): - def __init__(self, - in_channels=2, - channels=128, - latent_dim=64, - c_mults = [1, 2, 4, 8, 16], - strides = [2, 4, 4, 8, 8], - use_snake=True, - antialias_activation=False, - use_nearest_upsample=False, - final_tanh=False): - super().__init__() - self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation) - self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation, - use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh) - self.bottleneck = VAEBottleneck() - - def encode(self, x): - return self.bottleneck.encode(self.encoder(x)) - - def decode(self, x): - return self.decoder(self.bottleneck.decode(x)) - diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py deleted file mode 100644 index 179c5b67eac40a5c907f46d821c057c5de18e8d9..0000000000000000000000000000000000000000 --- a/comfy/ldm/audio/dit.py +++ /dev/null @@ -1,896 +0,0 @@ -# code adapted from: https://github.com/Stability-AI/stable-audio-tools - -from comfy.ldm.modules.attention import optimized_attention -import typing as tp - -import torch - -from einops import rearrange -from torch import nn -from torch.nn import functional as F -import math -import comfy.ops - -class FourierFeatures(nn.Module): - def __init__(self, in_features, out_features, std=1., dtype=None, device=None): - super().__init__() - assert out_features % 2 == 0 - self.weight = nn.Parameter(torch.empty( - [out_features // 2, in_features], dtype=dtype, device=device)) - - def forward(self, input): - f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input) - return torch.cat([f.cos(), f.sin()], dim=-1) - -# norms -class LayerNorm(nn.Module): - def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None): - """ - bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less - """ - super().__init__() - - self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) - - if bias: - self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device)) - else: - self.beta = None - - def forward(self, x): - beta = self.beta - if beta is not None: - beta = comfy.ops.cast_to_input(beta, x) - return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta) - -class GLU(nn.Module): - def __init__( - self, - dim_in, - dim_out, - activation, - use_conv = False, - conv_kernel_size = 3, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.act = activation - self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device) - self.use_conv = use_conv - - def forward(self, x): - if self.use_conv: - x = rearrange(x, 'b n d -> b d n') - x = self.proj(x) - x = rearrange(x, 'b d n -> b n d') - else: - x = self.proj(x) - - x, gate = x.chunk(2, dim = -1) - return x * self.act(gate) - -class AbsolutePositionalEmbedding(nn.Module): - def __init__(self, dim, max_seq_len): - super().__init__() - self.scale = dim ** -0.5 - self.max_seq_len = max_seq_len - self.emb = nn.Embedding(max_seq_len, dim) - - def forward(self, x, pos = None, seq_start_pos = None): - seq_len, device = x.shape[1], x.device - assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}' - - if pos is None: - pos = torch.arange(seq_len, device = device) - - if seq_start_pos is not None: - pos = (pos - seq_start_pos[..., None]).clamp(min = 0) - - pos_emb = self.emb(pos) - pos_emb = pos_emb * self.scale - return pos_emb - -class ScaledSinusoidalEmbedding(nn.Module): - def __init__(self, dim, theta = 10000): - super().__init__() - assert (dim % 2) == 0, 'dimension must be divisible by 2' - self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5) - - half_dim = dim // 2 - freq_seq = torch.arange(half_dim).float() / half_dim - inv_freq = theta ** -freq_seq - self.register_buffer('inv_freq', inv_freq, persistent = False) - - def forward(self, x, pos = None, seq_start_pos = None): - seq_len, device = x.shape[1], x.device - - if pos is None: - pos = torch.arange(seq_len, device = device) - - if seq_start_pos is not None: - pos = pos - seq_start_pos[..., None] - - emb = torch.einsum('i, j -> i j', pos, self.inv_freq) - emb = torch.cat((emb.sin(), emb.cos()), dim = -1) - return emb * self.scale - -class RotaryEmbedding(nn.Module): - def __init__( - self, - dim, - use_xpos = False, - scale_base = 512, - interpolation_factor = 1., - base = 10000, - base_rescale_factor = 1., - dtype=None, - device=None, - ): - super().__init__() - # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning - # has some connection to NTK literature - # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ - base *= base_rescale_factor ** (dim / (dim - 2)) - - # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) - self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype)) - - assert interpolation_factor >= 1. - self.interpolation_factor = interpolation_factor - - if not use_xpos: - self.register_buffer('scale', None) - return - - scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim) - - self.scale_base = scale_base - self.register_buffer('scale', scale) - - def forward_from_seq_len(self, seq_len, device, dtype): - # device = self.inv_freq.device - - t = torch.arange(seq_len, device=device, dtype=dtype) - return self.forward(t) - - def forward(self, t): - # device = self.inv_freq.device - device = t.device - - # t = t.to(torch.float32) - - t = t / self.interpolation_factor - - freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t)) - freqs = torch.cat((freqs, freqs), dim = -1) - - if self.scale is None: - return freqs, 1. - - power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base # noqa: F821 seq_len is not defined - scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1') - scale = torch.cat((scale, scale), dim = -1) - - return freqs, scale - -def rotate_half(x): - x = rearrange(x, '... (j d) -> ... j d', j = 2) - x1, x2 = x.unbind(dim = -2) - return torch.cat((-x2, x1), dim = -1) - -def apply_rotary_pos_emb(t, freqs, scale = 1): - out_dtype = t.dtype - - # cast to float32 if necessary for numerical stability - dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32)) - rot_dim, seq_len = freqs.shape[-1], t.shape[-2] - freqs, t = freqs.to(dtype), t.to(dtype) - freqs = freqs[-seq_len:, :] - - if t.ndim == 4 and freqs.ndim == 3: - freqs = rearrange(freqs, 'b n d -> b 1 n d') - - # partial rotary embeddings, Wang et al. GPT-J - t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:] - t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale) - - t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype) - - return torch.cat((t, t_unrotated), dim = -1) - -class FeedForward(nn.Module): - def __init__( - self, - dim, - dim_out = None, - mult = 4, - no_bias = False, - glu = True, - use_conv = False, - conv_kernel_size = 3, - zero_init_output = True, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - inner_dim = int(dim * mult) - - # Default to SwiGLU - - activation = nn.SiLU() - - dim_out = dim if dim_out is None else dim_out - - if glu: - linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations) - else: - linear_in = nn.Sequential( - rearrange('b n d -> b d n') if use_conv else nn.Identity(), - operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device), - rearrange('b n d -> b d n') if use_conv else nn.Identity(), - activation - ) - - linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device) - - # # init last linear layer to 0 - # if zero_init_output: - # nn.init.zeros_(linear_out.weight) - # if not no_bias: - # nn.init.zeros_(linear_out.bias) - - - self.ff = nn.Sequential( - linear_in, - rearrange('b d n -> b n d') if use_conv else nn.Identity(), - linear_out, - rearrange('b n d -> b d n') if use_conv else nn.Identity(), - ) - - def forward(self, x): - return self.ff(x) - -class Attention(nn.Module): - def __init__( - self, - dim, - dim_heads = 64, - dim_context = None, - causal = False, - zero_init_output=True, - qk_norm = False, - natten_kernel_size = None, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.dim = dim - self.dim_heads = dim_heads - self.causal = causal - - dim_kv = dim_context if dim_context is not None else dim - - self.num_heads = dim // dim_heads - self.kv_heads = dim_kv // dim_heads - - if dim_context is not None: - self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device) - else: - self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device) - - self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - - # if zero_init_output: - # nn.init.zeros_(self.to_out.weight) - - self.qk_norm = qk_norm - - - def forward( - self, - x, - context = None, - mask = None, - context_mask = None, - rotary_pos_emb = None, - causal = None - ): - h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None - - kv_input = context if has_context else x - - if hasattr(self, 'to_q'): - # Use separate linear projections for q and k/v - q = self.to_q(x) - q = rearrange(q, 'b n (h d) -> b h n d', h = h) - - k, v = self.to_kv(kv_input).chunk(2, dim=-1) - - k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v)) - else: - # Use fused linear projection - q, k, v = self.to_qkv(x).chunk(3, dim=-1) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v)) - - # Normalize q and k for cosine sim attention - if self.qk_norm: - q = F.normalize(q, dim=-1) - k = F.normalize(k, dim=-1) - - if rotary_pos_emb is not None and not has_context: - freqs, _ = rotary_pos_emb - - q_dtype = q.dtype - k_dtype = k.dtype - - q = q.to(torch.float32) - k = k.to(torch.float32) - freqs = freqs.to(torch.float32) - - q = apply_rotary_pos_emb(q, freqs) - k = apply_rotary_pos_emb(k, freqs) - - q = q.to(q_dtype) - k = k.to(k_dtype) - - input_mask = context_mask - - if input_mask is None and not has_context: - input_mask = mask - - # determine masking - masks = [] - - if input_mask is not None: - input_mask = rearrange(input_mask, 'b j -> b 1 1 j') - masks.append(~input_mask) - - # Other masks will be added here later - n = q.shape[-2] - - causal = self.causal if causal is None else causal - - if n == 1 and causal: - causal = False - - if h != kv_h: - # Repeat interleave kv_heads to match q_heads - heads_per_kv_head = h // kv_h - k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) - - out = optimized_attention(q, k, v, h, skip_reshape=True) - out = self.to_out(out) - - if mask is not None: - mask = rearrange(mask, 'b n -> b n 1') - out = out.masked_fill(~mask, 0.) - - return out - -class ConformerModule(nn.Module): - def __init__( - self, - dim, - norm_kwargs = {}, - ): - - super().__init__() - - self.dim = dim - - self.in_norm = LayerNorm(dim, **norm_kwargs) - self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False) - self.glu = GLU(dim, dim, nn.SiLU()) - self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False) - self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm - self.swish = nn.SiLU() - self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False) - - def forward(self, x): - x = self.in_norm(x) - x = rearrange(x, 'b n d -> b d n') - x = self.pointwise_conv(x) - x = rearrange(x, 'b d n -> b n d') - x = self.glu(x) - x = rearrange(x, 'b n d -> b d n') - x = self.depthwise_conv(x) - x = rearrange(x, 'b d n -> b n d') - x = self.mid_norm(x) - x = self.swish(x) - x = rearrange(x, 'b n d -> b d n') - x = self.pointwise_conv_2(x) - x = rearrange(x, 'b d n -> b n d') - - return x - -class TransformerBlock(nn.Module): - def __init__( - self, - dim, - dim_heads = 64, - cross_attend = False, - dim_context = None, - global_cond_dim = None, - causal = False, - zero_init_branch_outputs = True, - conformer = False, - layer_ix = -1, - remove_norms = False, - attn_kwargs = {}, - ff_kwargs = {}, - norm_kwargs = {}, - dtype=None, - device=None, - operations=None, - ): - - super().__init__() - self.dim = dim - self.dim_heads = dim_heads - self.cross_attend = cross_attend - self.dim_context = dim_context - self.causal = causal - - self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() - - self.self_attn = Attention( - dim, - dim_heads = dim_heads, - causal = causal, - zero_init_output=zero_init_branch_outputs, - dtype=dtype, - device=device, - operations=operations, - **attn_kwargs - ) - - if cross_attend: - self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() - self.cross_attn = Attention( - dim, - dim_heads = dim_heads, - dim_context=dim_context, - causal = causal, - zero_init_output=zero_init_branch_outputs, - dtype=dtype, - device=device, - operations=operations, - **attn_kwargs - ) - - self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity() - self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs) - - self.layer_ix = layer_ix - - self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None - - self.global_cond_dim = global_cond_dim - - if global_cond_dim is not None: - self.to_scale_shift_gate = nn.Sequential( - nn.SiLU(), - nn.Linear(global_cond_dim, dim * 6, bias=False) - ) - - nn.init.zeros_(self.to_scale_shift_gate[1].weight) - #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias) - - def forward( - self, - x, - context = None, - global_cond=None, - mask = None, - context_mask = None, - rotary_pos_emb = None - ): - if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: - - scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1) - - # self-attention with adaLN - residual = x - x = self.pre_norm(x) - x = x * (1 + scale_self) + shift_self - x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) - x = x * torch.sigmoid(1 - gate_self) - x = x + residual - - if context is not None: - x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) - - if self.conformer is not None: - x = x + self.conformer(x) - - # feedforward with adaLN - residual = x - x = self.ff_norm(x) - x = x * (1 + scale_ff) + shift_ff - x = self.ff(x) - x = x * torch.sigmoid(1 - gate_ff) - x = x + residual - - else: - x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) - - if context is not None: - x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) - - if self.conformer is not None: - x = x + self.conformer(x) - - x = x + self.ff(self.ff_norm(x)) - - return x - -class ContinuousTransformer(nn.Module): - def __init__( - self, - dim, - depth, - *, - dim_in = None, - dim_out = None, - dim_heads = 64, - cross_attend=False, - cond_token_dim=None, - global_cond_dim=None, - causal=False, - rotary_pos_emb=True, - zero_init_branch_outputs=True, - conformer=False, - use_sinusoidal_emb=False, - use_abs_pos_emb=False, - abs_pos_emb_max_length=10000, - dtype=None, - device=None, - operations=None, - **kwargs - ): - - super().__init__() - - self.dim = dim - self.depth = depth - self.causal = causal - self.layers = nn.ModuleList([]) - - self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity() - self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity() - - if rotary_pos_emb: - self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype) - else: - self.rotary_pos_emb = None - - self.use_sinusoidal_emb = use_sinusoidal_emb - if use_sinusoidal_emb: - self.pos_emb = ScaledSinusoidalEmbedding(dim) - - self.use_abs_pos_emb = use_abs_pos_emb - if use_abs_pos_emb: - self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length) - - for i in range(depth): - self.layers.append( - TransformerBlock( - dim, - dim_heads = dim_heads, - cross_attend = cross_attend, - dim_context = cond_token_dim, - global_cond_dim = global_cond_dim, - causal = causal, - zero_init_branch_outputs = zero_init_branch_outputs, - conformer=conformer, - layer_ix=i, - dtype=dtype, - device=device, - operations=operations, - **kwargs - ) - ) - - def forward( - self, - x, - mask = None, - prepend_embeds = None, - prepend_mask = None, - global_cond = None, - return_info = False, - **kwargs - ): - patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {}) - batch, seq, device = *x.shape[:2], x.device - context = kwargs["context"] - - info = { - "hidden_states": [], - } - - x = self.project_in(x) - - if prepend_embeds is not None: - prepend_length, prepend_dim = prepend_embeds.shape[1:] - - assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension' - - x = torch.cat((prepend_embeds, x), dim = -2) - - if prepend_mask is not None or mask is not None: - mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool) - prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool) - - mask = torch.cat((prepend_mask, mask), dim = -1) - - # Attention layers - - if self.rotary_pos_emb is not None: - rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device) - else: - rotary_pos_emb = None - - if self.use_sinusoidal_emb or self.use_abs_pos_emb: - x = x + self.pos_emb(x) - - blocks_replace = patches_replace.get("dit", {}) - # Iterate over the transformer layers - for i, layer in enumerate(self.layers): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"]) - return out - - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap}) - x = out["img"] - else: - x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context) - # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) - - if return_info: - info["hidden_states"].append(x) - - x = self.project_out(x) - - if return_info: - return x, info - - return x - -class AudioDiffusionTransformer(nn.Module): - def __init__(self, - io_channels=64, - patch_size=1, - embed_dim=1536, - cond_token_dim=768, - project_cond_tokens=False, - global_cond_dim=1536, - project_global_cond=True, - input_concat_dim=0, - prepend_cond_dim=0, - depth=24, - num_heads=24, - transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer", - global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend", - audio_model="", - dtype=None, - device=None, - operations=None, - **kwargs): - - super().__init__() - - self.dtype = dtype - self.cond_token_dim = cond_token_dim - - # Timestep embeddings - timestep_features_dim = 256 - - self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device) - - self.to_timestep_embed = nn.Sequential( - operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device), - ) - - if cond_token_dim > 0: - # Conditioning tokens - - cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim - self.to_cond_embed = nn.Sequential( - operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device) - ) - else: - cond_embed_dim = 0 - - if global_cond_dim > 0: - # Global conditioning - global_embed_dim = global_cond_dim if not project_global_cond else embed_dim - self.to_global_embed = nn.Sequential( - operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device) - ) - - if prepend_cond_dim > 0: - # Prepend conditioning - self.to_prepend_embed = nn.Sequential( - operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device) - ) - - self.input_concat_dim = input_concat_dim - - dim_in = io_channels + self.input_concat_dim - - self.patch_size = patch_size - - # Transformer - - self.transformer_type = transformer_type - - self.global_cond_type = global_cond_type - - if self.transformer_type == "continuous_transformer": - - global_dim = None - - if self.global_cond_type == "adaLN": - # The global conditioning is projected to the embed_dim already at this point - global_dim = embed_dim - - self.transformer = ContinuousTransformer( - dim=embed_dim, - depth=depth, - dim_heads=embed_dim // num_heads, - dim_in=dim_in * patch_size, - dim_out=io_channels * patch_size, - cross_attend = cond_token_dim > 0, - cond_token_dim = cond_embed_dim, - global_cond_dim=global_dim, - dtype=dtype, - device=device, - operations=operations, - **kwargs - ) - else: - raise ValueError(f"Unknown transformer type: {self.transformer_type}") - - self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device) - self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device) - - def _forward( - self, - x, - t, - mask=None, - cross_attn_cond=None, - cross_attn_cond_mask=None, - input_concat_cond=None, - global_embed=None, - prepend_cond=None, - prepend_cond_mask=None, - return_info=False, - **kwargs): - - if cross_attn_cond is not None: - cross_attn_cond = self.to_cond_embed(cross_attn_cond) - - if global_embed is not None: - # Project the global conditioning to the embedding dimension - global_embed = self.to_global_embed(global_embed) - - prepend_inputs = None - prepend_mask = None - prepend_length = 0 - if prepend_cond is not None: - # Project the prepend conditioning to the embedding dimension - prepend_cond = self.to_prepend_embed(prepend_cond) - - prepend_inputs = prepend_cond - if prepend_cond_mask is not None: - prepend_mask = prepend_cond_mask - - if input_concat_cond is not None: - - # Interpolate input_concat_cond to the same length as x - if input_concat_cond.shape[2] != x.shape[2]: - input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest') - - x = torch.cat([x, input_concat_cond], dim=1) - - # Get the batch of timestep embeddings - timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim) - - # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists - if global_embed is not None: - global_embed = global_embed + timestep_embed - else: - global_embed = timestep_embed - - # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer - if self.global_cond_type == "prepend": - if prepend_inputs is None: - # Prepend inputs are just the global embed, and the mask is all ones - prepend_inputs = global_embed.unsqueeze(1) - prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool) - else: - # Prepend inputs are the prepend conditioning + the global embed - prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1) - prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1) - - prepend_length = prepend_inputs.shape[1] - - x = self.preprocess_conv(x) + x - - x = rearrange(x, "b c t -> b t c") - - extra_args = {} - - if self.global_cond_type == "adaLN": - extra_args["global_cond"] = global_embed - - if self.patch_size > 1: - x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size) - - if self.transformer_type == "x-transformers": - output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs) - elif self.transformer_type == "continuous_transformer": - output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs) - - if return_info: - output, info = output - elif self.transformer_type == "mm_transformer": - output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs) - - output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:] - - if self.patch_size > 1: - output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size) - - output = self.postprocess_conv(output) + output - - if return_info: - return output, info - - return output - - def forward( - self, - x, - timestep, - context=None, - context_mask=None, - input_concat_cond=None, - global_embed=None, - negative_global_embed=None, - prepend_cond=None, - prepend_cond_mask=None, - mask=None, - return_info=False, - control=None, - **kwargs): - return self._forward( - x, - timestep, - cross_attn_cond=context, - cross_attn_cond_mask=context_mask, - input_concat_cond=input_concat_cond, - global_embed=global_embed, - prepend_cond=prepend_cond, - prepend_cond_mask=prepend_cond_mask, - mask=mask, - return_info=return_info, - **kwargs - ) diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py deleted file mode 100644 index 20edb365aaafe7f344a2616a53470c837d0a8a6e..0000000000000000000000000000000000000000 --- a/comfy/ldm/audio/embedders.py +++ /dev/null @@ -1,108 +0,0 @@ -# code adapted from: https://github.com/Stability-AI/stable-audio-tools - -import torch -import torch.nn as nn -from torch import Tensor -from typing import List, Union -from einops import rearrange -import math -import comfy.ops - -class LearnedPositionalEmbedding(nn.Module): - """Used for continuous time""" - - def __init__(self, dim: int): - super().__init__() - assert (dim % 2) == 0 - half_dim = dim // 2 - self.weights = nn.Parameter(torch.empty(half_dim)) - - def forward(self, x: Tensor) -> Tensor: - x = rearrange(x, "b -> b 1") - freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi - fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1) - fouriered = torch.cat((x, fouriered), dim=-1) - return fouriered - -def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module: - return nn.Sequential( - LearnedPositionalEmbedding(dim), - comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features), - ) - - -class NumberEmbedder(nn.Module): - def __init__( - self, - features: int, - dim: int = 256, - ): - super().__init__() - self.features = features - self.embedding = TimePositionalEmbedding(dim=dim, out_features=features) - - def forward(self, x: Union[List[float], Tensor]) -> Tensor: - if not torch.is_tensor(x): - device = next(self.embedding.parameters()).device - x = torch.tensor(x, device=device) - assert isinstance(x, Tensor) - shape = x.shape - x = rearrange(x, "... -> (...)") - embedding = self.embedding(x) - x = embedding.view(*shape, self.features) - return x # type: ignore - - -class Conditioner(nn.Module): - def __init__( - self, - dim: int, - output_dim: int, - project_out: bool = False - ): - - super().__init__() - - self.dim = dim - self.output_dim = output_dim - self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity() - - def forward(self, x): - raise NotImplementedError() - -class NumberConditioner(Conditioner): - ''' - Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings - ''' - def __init__(self, - output_dim: int, - min_val: float=0, - max_val: float=1 - ): - super().__init__(output_dim, output_dim) - - self.min_val = min_val - self.max_val = max_val - - self.embedder = NumberEmbedder(features=output_dim) - - def forward(self, floats, device=None): - # Cast the inputs to floats - floats = [float(x) for x in floats] - - if device is None: - device = next(self.embedder.parameters()).device - - floats = torch.tensor(floats).to(device) - - floats = floats.clamp(self.min_val, self.max_val) - - normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val) - - # Cast floats to same type as embedder - embedder_dtype = next(self.embedder.parameters()).dtype - normalized_floats = normalized_floats.to(embedder_dtype) - - float_embeds = self.embedder(normalized_floats).unsqueeze(1) - - return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)] diff --git a/comfy/ldm/aura/.DS_Store b/comfy/ldm/aura/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/aura/.DS_Store and /dev/null differ diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py deleted file mode 100644 index d7f32b5e82f768b357edcaf515be06931054d600..0000000000000000000000000000000000000000 --- a/comfy/ldm/aura/mmdit.py +++ /dev/null @@ -1,506 +0,0 @@ -#AuraFlow MMDiT -#Originally written by the AuraFlow Authors - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from comfy.ldm.modules.attention import optimized_attention -import comfy.ops -import comfy.patcher_extension -import comfy.ldm.common_dit - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -def find_multiple(n: int, k: int) -> int: - if n % k == 0: - return n - return n + k - (n % k) - - -class MLP(nn.Module): - def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None: - super().__init__() - if hidden_dim is None: - hidden_dim = 4 * dim - - n_hidden = int(2 * hidden_dim / 3) - n_hidden = find_multiple(n_hidden, 256) - - self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) - self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device) - self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = F.silu(self.c_fc1(x)) * self.c_fc2(x) - x = self.c_proj(x) - return x - - -class MultiHeadLayerNorm(nn.Module): - def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None): - # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78 - - super().__init__() - self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) - self.variance_epsilon = eps - - def forward(self, hidden_states): - input_dtype = hidden_states.dtype - hidden_states = hidden_states.to(torch.float32) - mean = hidden_states.mean(-1, keepdim=True) - variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True) - hidden_states = (hidden_states - mean) * torch.rsqrt( - variance + self.variance_epsilon - ) - hidden_states = self.weight.to(torch.float32) * hidden_states - return hidden_states.to(input_dtype) - -class SingleAttention(nn.Module): - def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): - super().__init__() - - self.n_heads = n_heads - self.head_dim = dim // n_heads - - # this is for cond - self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - - self.q_norm1 = ( - MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) - if mh_qknorm - else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) - ) - self.k_norm1 = ( - MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) - if mh_qknorm - else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) - ) - - #@torch.compile() - def forward(self, c): - - bsz, seqlen1, _ = c.shape - - q, k, v = self.w1q(c), self.w1k(c), self.w1v(c) - q = q.view(bsz, seqlen1, self.n_heads, self.head_dim) - k = k.view(bsz, seqlen1, self.n_heads, self.head_dim) - v = v.view(bsz, seqlen1, self.n_heads, self.head_dim) - q, k = self.q_norm1(q), self.k_norm1(k) - - output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) - c = self.w1o(output) - return c - - - -class DoubleAttention(nn.Module): - def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None): - super().__init__() - - self.n_heads = n_heads - self.head_dim = dim // n_heads - - # this is for cond - self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - - # this is for x - self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device) - - self.q_norm1 = ( - MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) - if mh_qknorm - else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) - ) - self.k_norm1 = ( - MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) - if mh_qknorm - else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) - ) - - self.q_norm2 = ( - MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) - if mh_qknorm - else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) - ) - self.k_norm2 = ( - MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device) - if mh_qknorm - else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device) - ) - - - #@torch.compile() - def forward(self, c, x): - - bsz, seqlen1, _ = c.shape - bsz, seqlen2, _ = x.shape - - cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c) - cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim) - ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim) - cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim) - cq, ck = self.q_norm1(cq), self.k_norm1(ck) - - xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x) - xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim) - xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim) - xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim) - xq, xk = self.q_norm2(xq), self.k_norm2(xk) - - # concat all - q, k, v = ( - torch.cat([cq, xq], dim=1), - torch.cat([ck, xk], dim=1), - torch.cat([cv, xv], dim=1), - ) - - output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) - - c, x = output.split([seqlen1, seqlen2], dim=1) - c = self.w1o(c) - x = self.w2o(x) - - return c, x - - -class MMDiTBlock(nn.Module): - def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None): - super().__init__() - - self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) - self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) - if not is_last: - self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) - self.modC = nn.Sequential( - nn.SiLU(), - operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), - ) - else: - self.modC = nn.Sequential( - nn.SiLU(), - operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), - ) - - self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) - self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) - self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) - self.modX = nn.Sequential( - nn.SiLU(), - operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), - ) - - self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations) - self.is_last = is_last - - #@torch.compile() - def forward(self, c, x, global_cond, **kwargs): - - cres, xres = c, x - - cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = ( - self.modC(global_cond).chunk(6, dim=1) - ) - - c = modulate(self.normC1(c), cshift_msa, cscale_msa) - - # xpath - xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = ( - self.modX(global_cond).chunk(6, dim=1) - ) - - x = modulate(self.normX1(x), xshift_msa, xscale_msa) - - # attention - c, x = self.attn(c, x) - - - c = self.normC2(cres + cgate_msa.unsqueeze(1) * c) - c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp)) - c = cres + c - - x = self.normX2(xres + xgate_msa.unsqueeze(1) * x) - x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp)) - x = xres + x - - return c, x - -class DiTBlock(nn.Module): - # like MMDiTBlock, but it only has X - def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None): - super().__init__() - - self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) - self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device) - - self.modCX = nn.Sequential( - nn.SiLU(), - operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device), - ) - - self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations) - self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) - - #@torch.compile() - def forward(self, cx, global_cond, **kwargs): - cxres = cx - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX( - global_cond - ).chunk(6, dim=1) - cx = modulate(self.norm1(cx), shift_msa, scale_msa) - cx = self.attn(cx) - cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx) - mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp)) - cx = gate_mlp.unsqueeze(1) * mlpout - - cx = cxres + cx - - return cx - - - -class TimestepEmbedder(nn.Module): - def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): - super().__init__() - self.mlp = nn.Sequential( - operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device), - ) - self.frequency_embedding_size = frequency_embedding_size - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - half = dim // 2 - freqs = 1000 * torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half) / half - ).to(t.device) - args = t[:, None] * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return embedding - - #@torch.compile() - def forward(self, t, dtype): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype) - t_emb = self.mlp(t_freq) - return t_emb - - -class MMDiT(nn.Module): - def __init__( - self, - in_channels=4, - out_channels=4, - patch_size=2, - dim=3072, - n_layers=36, - n_double_layers=4, - n_heads=12, - global_conddim=3072, - cond_seq_dim=2048, - max_seq=32 * 32, - device=None, - dtype=None, - operations=None, - ): - super().__init__() - self.dtype = dtype - - self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations) - - self.cond_seq_linear = operations.Linear( - cond_seq_dim, dim, bias=False, dtype=dtype, device=device - ) # linear for something like text sequence. - self.init_x_linear = operations.Linear( - patch_size * patch_size * in_channels, dim, dtype=dtype, device=device - ) # init linear for patchified image. - - self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device)) - self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device)) - - self.double_layers = nn.ModuleList([]) - self.single_layers = nn.ModuleList([]) - - - for idx in range(n_double_layers): - self.double_layers.append( - MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations) - ) - - for idx in range(n_double_layers, n_layers): - self.single_layers.append( - DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations) - ) - - - self.final_linear = operations.Linear( - dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device - ) - - self.modF = nn.Sequential( - nn.SiLU(), - operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device), - ) - - self.out_channels = out_channels - self.patch_size = patch_size - self.n_double_layers = n_double_layers - self.n_layers = n_layers - - self.h_max = round(max_seq**0.5) - self.w_max = round(max_seq**0.5) - - @torch.no_grad() - def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)): - # extend pe - pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]] - - pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1) - - # now we need to extend this to target_dim. for this we will use interpolation. - # we will use torch.nn.functional.interpolate - pe_as_2d = F.interpolate( - pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear" - ) - pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1) - self.positional_encoding.data = pe_new.unsqueeze(0).contiguous() - self.h_max, self.w_max = target_dim - - def pe_selection_index_based_on_dim(self, h, w): - h_p, w_p = h // self.patch_size, w // self.patch_size - original_pe_indexes = torch.arange(self.positional_encoding.shape[1]) - original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max) - starth = self.h_max // 2 - h_p // 2 - endh =starth + h_p - startw = self.w_max // 2 - w_p // 2 - endw = startw + w_p - original_pe_indexes = original_pe_indexes[ - starth:endh, startw:endw - ] - return original_pe_indexes.flatten() - - def unpatchify(self, x, h, w): - c = self.out_channels - p = self.patch_size - - x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) - x = torch.einsum("nhwpqc->nchpwq", x) - imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) - return imgs - - def patchify(self, x): - B, C, H, W = x.size() - x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) - x = x.view( - B, - C, - (H + 1) // self.patch_size, - self.patch_size, - (W + 1) // self.patch_size, - self.patch_size, - ) - x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2) - return x - - def apply_pos_embeds(self, x, h, w): - h = (h + 1) // self.patch_size - w = (w + 1) // self.patch_size - max_dim = max(h, w) - - cur_dim = self.h_max - pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x) - - if max_dim > cur_dim: - pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1) - cur_dim = max_dim - - from_h = (cur_dim - h) // 2 - from_w = (cur_dim - w) // 2 - pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w] - return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1]) - - def forward(self, x, timestep, context, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, transformer_options, **kwargs) - - def _forward(self, x, timestep, context, transformer_options={}, **kwargs): - patches_replace = transformer_options.get("patches_replace", {}) - # patchify x, add PE - b, c, h, w = x.shape - - # pe_indexes = self.pe_selection_index_based_on_dim(h, w) - # print(pe_indexes, pe_indexes.shape) - - x = self.init_x_linear(self.patchify(x)) # B, T_x, D - x = self.apply_pos_embeds(x, h, w) - # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype) - # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype) - - # process conditions for MMDiT Blocks - c_seq = context # B, T_c, D_c - t = timestep - - c = self.cond_seq_linear(c_seq) # B, T_c, D - c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1) - - global_cond = self.t_embedder(t, x.dtype) # B, D - - blocks_replace = patches_replace.get("dit", {}) - if len(self.double_layers) > 0: - for i, layer in enumerate(self.double_layers): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["txt"], out["img"] = layer(args["txt"], - args["img"], - args["vec"]) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap}) - c = out["txt"] - x = out["img"] - else: - c, x = layer(c, x, global_cond, **kwargs) - - if len(self.single_layers) > 0: - c_len = c.size(1) - cx = torch.cat([c, x], dim=1) - for i, layer in enumerate(self.single_layers): - if ("single_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = layer(args["img"], args["vec"]) - return out - - out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap}) - cx = out["img"] - else: - cx = layer(cx, global_cond, **kwargs) - - x = cx[:, c_len:] - - fshift, fscale = self.modF(global_cond).chunk(2, dim=1) - - x = modulate(x, fshift, fscale) - x = self.final_linear(x) - x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w] - return x diff --git a/comfy/ldm/cascade/.DS_Store b/comfy/ldm/cascade/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/cascade/.DS_Store and /dev/null differ diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py deleted file mode 100644 index 3eaa0c821cccddbe891ac8a705d702c509c85582..0000000000000000000000000000000000000000 --- a/comfy/ldm/cascade/common.py +++ /dev/null @@ -1,154 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -import torch.nn as nn -from comfy.ldm.modules.attention import optimized_attention -import comfy.ops - -class OptimizedAttention(nn.Module): - def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): - super().__init__() - self.heads = nhead - - self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - - self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - - def forward(self, q, k, v): - q = self.to_q(q) - k = self.to_k(k) - v = self.to_v(v) - - out = optimized_attention(q, k, v, self.heads) - - return self.out_proj(out) - -class Attention2D(nn.Module): - def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None): - super().__init__() - self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) - # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) - - def forward(self, x, kv, self_attn=False): - orig_shape = x.shape - x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 - if self_attn: - kv = torch.cat([x, kv], dim=1) - # x = self.attn(x, kv, kv, need_weights=False)[0] - x = self.attn(x, kv, kv) - x = x.permute(0, 2, 1).view(*orig_shape) - return x - - -def LayerNorm2d_op(operations): - class LayerNorm2d(operations.LayerNorm): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def forward(self, x): - return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) - return LayerNorm2d - -class GlobalResponseNorm(nn.Module): - "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105" - def __init__(self, dim, dtype=None, device=None): - super().__init__() - self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) - self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device)) - - def forward(self, x): - Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True) - Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6) - return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x - - -class ResBlock(nn.Module): - def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2): - super().__init__() - self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device) - # self.depthwise = SAMBlock(c, num_heads, expansion) - self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.channelwise = nn.Sequential( - operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device), - nn.GELU(), - GlobalResponseNorm(c * 4, dtype=dtype, device=device), - nn.Dropout(dropout), - operations.Linear(c * 4, c, dtype=dtype, device=device) - ) - - def forward(self, x, x_skip=None): - x_res = x - x = self.norm(self.depthwise(x)) - if x_skip is not None: - x = torch.cat([x, x_skip], dim=1) - x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) - return x + x_res - - -class AttnBlock(nn.Module): - def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None): - super().__init__() - self.self_attn = self_attn - self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations) - self.kv_mapper = nn.Sequential( - nn.SiLU(), - operations.Linear(c_cond, c, dtype=dtype, device=device) - ) - - def forward(self, x, kv): - kv = self.kv_mapper(kv) - x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) - return x - - -class FeedForwardBlock(nn.Module): - def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None): - super().__init__() - self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.channelwise = nn.Sequential( - operations.Linear(c, c * 4, dtype=dtype, device=device), - nn.GELU(), - GlobalResponseNorm(c * 4, dtype=dtype, device=device), - nn.Dropout(dropout), - operations.Linear(c * 4, c, dtype=dtype, device=device) - ) - - def forward(self, x): - x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2) - return x - - -class TimestepBlock(nn.Module): - def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None): - super().__init__() - self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device) - self.conds = conds - for cname in conds: - setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)) - - def forward(self, x, t): - t = t.chunk(len(self.conds) + 1, dim=1) - a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1) - for i, c in enumerate(self.conds): - ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1) - a, b = a + ac, b + bc - return x * (1 + a) + b diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py deleted file mode 100644 index 90473481a078323be11e2c5530de378b1ac6425c..0000000000000000000000000000000000000000 --- a/comfy/ldm/cascade/controlnet.py +++ /dev/null @@ -1,92 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torchvision -from torch import nn -from .common import LayerNorm2d_op - - -class CNetResBlock(nn.Module): - def __init__(self, c, dtype=None, device=None, operations=None): - super().__init__() - self.blocks = nn.Sequential( - LayerNorm2d_op(operations)(c, dtype=dtype, device=device), - nn.GELU(), - operations.Conv2d(c, c, kernel_size=3, padding=1), - LayerNorm2d_op(operations)(c, dtype=dtype, device=device), - nn.GELU(), - operations.Conv2d(c, c, kernel_size=3, padding=1), - ) - - def forward(self, x): - return x + self.blocks(x) - - -class ControlNet(nn.Module): - def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): - super().__init__() - if bottleneck_mode is None: - bottleneck_mode = 'effnet' - self.proj_blocks = proj_blocks - if bottleneck_mode == 'effnet': - embd_channels = 1280 - self.backbone = torchvision.models.efficientnet_v2_s().features.eval() - if c_in != 3: - in_weights = self.backbone[0][0].weight.data - self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) - if c_in > 3: - # nn.init.constant_(self.backbone[0][0].weight, 0) - self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() - else: - self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() - elif bottleneck_mode == 'simple': - embd_channels = c_in - self.backbone = nn.Sequential( - operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), - nn.LeakyReLU(0.2, inplace=True), - operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), - ) - elif bottleneck_mode == 'large': - self.backbone = nn.Sequential( - operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), - nn.LeakyReLU(0.2, inplace=True), - operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), - *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], - operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), - ) - embd_channels = 1280 - else: - raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') - self.projections = nn.ModuleList() - for _ in range(len(proj_blocks)): - self.projections.append(nn.Sequential( - operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), - nn.LeakyReLU(0.2, inplace=True), - operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), - )) - # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection - self.xl = False - self.input_channels = c_in - self.unshuffle_amount = 8 - - def forward(self, x): - x = self.backbone(x) - proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] - for i, idx in enumerate(self.proj_blocks): - proj_outputs[idx] = self.projections[i](x) - return {"input": proj_outputs[::-1]} diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py deleted file mode 100644 index 145e6e69a7c56c0b983cd79d43f1c6d7d26fd349..0000000000000000000000000000000000000000 --- a/comfy/ldm/cascade/stage_a.py +++ /dev/null @@ -1,259 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -from torch import nn -from torch.autograd import Function -import comfy.ops - -ops = comfy.ops.disable_weight_init - - -class vector_quantize(Function): - @staticmethod - def forward(ctx, x, codebook): - with torch.no_grad(): - codebook_sqr = torch.sum(codebook ** 2, dim=1) - x_sqr = torch.sum(x ** 2, dim=1, keepdim=True) - - dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0) - _, indices = dist.min(dim=1) - - ctx.save_for_backward(indices, codebook) - ctx.mark_non_differentiable(indices) - - nn = torch.index_select(codebook, 0, indices) - return nn, indices - - @staticmethod - def backward(ctx, grad_output, grad_indices): - grad_inputs, grad_codebook = None, None - - if ctx.needs_input_grad[0]: - grad_inputs = grad_output.clone() - if ctx.needs_input_grad[1]: - # Gradient wrt. the codebook - indices, codebook = ctx.saved_tensors - - grad_codebook = torch.zeros_like(codebook) - grad_codebook.index_add_(0, indices, grad_output) - - return (grad_inputs, grad_codebook) - - -class VectorQuantize(nn.Module): - def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False): - """ - Takes an input of variable size (as long as the last dimension matches the embedding size). - Returns one tensor containing the nearest neigbour embeddings to each of the inputs, - with the same size as the input, vq and commitment components for the loss as a touple - in the second output and the indices of the quantized vectors in the third: - quantized, (vq_loss, commit_loss), indices - """ - super(VectorQuantize, self).__init__() - - self.codebook = nn.Embedding(k, embedding_size) - self.codebook.weight.data.uniform_(-1./k, 1./k) - self.vq = vector_quantize.apply - - self.ema_decay = ema_decay - self.ema_loss = ema_loss - if ema_loss: - self.register_buffer('ema_element_count', torch.ones(k)) - self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight)) - - def _laplace_smoothing(self, x, epsilon): - n = torch.sum(x) - return ((x + epsilon) / (n + x.size(0) * epsilon) * n) - - def _updateEMA(self, z_e_x, indices): - mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float() - elem_count = mask.sum(dim=0) - weight_sum = torch.mm(mask.t(), z_e_x) - - self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count) - self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5) - self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum) - - self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1) - - def idx2vq(self, idx, dim=-1): - q_idx = self.codebook(idx) - if dim != -1: - q_idx = q_idx.movedim(-1, dim) - return q_idx - - def forward(self, x, get_losses=True, dim=-1): - if dim != -1: - x = x.movedim(dim, -1) - z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x - z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach()) - vq_loss, commit_loss = None, None - if self.ema_loss and self.training: - self._updateEMA(z_e_x.detach(), indices.detach()) - # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss - z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices) - if get_losses: - vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean() - commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean() - - z_q_x = z_q_x.view(x.shape) - if dim != -1: - z_q_x = z_q_x.movedim(-1, dim) - return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1]) - - -class ResBlock(nn.Module): - def __init__(self, c, c_hidden): - super().__init__() - # depthwise/attention - self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) - self.depthwise = nn.Sequential( - nn.ReplicationPad2d(1), - ops.Conv2d(c, c, kernel_size=3, groups=c) - ) - - # channelwise - self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) - self.channelwise = nn.Sequential( - ops.Linear(c, c_hidden), - nn.GELU(), - ops.Linear(c_hidden, c), - ) - - self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) - - # Init weights - def _basic_init(module): - if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - - self.apply(_basic_init) - - def _norm(self, x, norm): - return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) - - def forward(self, x): - mods = self.gammas - - x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1] - try: - x = x + self.depthwise(x_temp) * mods[2] - except: #operation not implemented for bf16 - x_temp = self.depthwise[0](x_temp.float()).to(x.dtype) - x = x + self.depthwise[1](x_temp) * mods[2] - - x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4] - x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5] - - return x - - -class StageA(nn.Module): - def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192): - super().__init__() - self.c_latent = c_latent - c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))] - - # Encoder blocks - self.in_block = nn.Sequential( - nn.PixelUnshuffle(2), - ops.Conv2d(3 * 4, c_levels[0], kernel_size=1) - ) - down_blocks = [] - for i in range(levels): - if i > 0: - down_blocks.append(ops.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) - block = ResBlock(c_levels[i], c_levels[i] * 4) - down_blocks.append(block) - down_blocks.append(nn.Sequential( - ops.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), - nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 - )) - self.down_blocks = nn.Sequential(*down_blocks) - self.down_blocks[0] - - self.codebook_size = codebook_size - self.vquantizer = VectorQuantize(c_latent, k=codebook_size) - - # Decoder blocks - up_blocks = [nn.Sequential( - ops.Conv2d(c_latent, c_levels[-1], kernel_size=1) - )] - for i in range(levels): - for j in range(bottleneck_blocks if i == 0 else 1): - block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4) - up_blocks.append(block) - if i < levels - 1: - up_blocks.append( - ops.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, - padding=1)) - self.up_blocks = nn.Sequential(*up_blocks) - self.out_block = nn.Sequential( - ops.Conv2d(c_levels[0], 3 * 4, kernel_size=1), - nn.PixelShuffle(2), - ) - - def encode(self, x, quantize=False): - x = self.in_block(x) - x = self.down_blocks(x) - if quantize: - qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1) - return qe, x, indices, vq_loss + commit_loss * 0.25 - else: - return x - - def decode(self, x): - x = self.up_blocks(x) - x = self.out_block(x) - return x - - def forward(self, x, quantize=False): - qe, x, _, vq_loss = self.encode(x, quantize) - x = self.decode(qe) - return x, vq_loss - - -class Discriminator(nn.Module): - def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6): - super().__init__() - d = max(depth - 3, 3) - layers = [ - nn.utils.spectral_norm(ops.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), - nn.LeakyReLU(0.2), - ] - for i in range(depth - 1): - c_in = c_hidden // (2 ** max((d - i), 0)) - c_out = c_hidden // (2 ** max((d - 1 - i), 0)) - layers.append(nn.utils.spectral_norm(ops.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) - layers.append(nn.InstanceNorm2d(c_out)) - layers.append(nn.LeakyReLU(0.2)) - self.encoder = nn.Sequential(*layers) - self.shuffle = ops.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) - self.logits = nn.Sigmoid() - - def forward(self, x, cond=None): - x = self.encoder(x) - if cond is not None: - cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1)) - x = torch.cat([x, cond], dim=1) - x = self.shuffle(x) - x = self.logits(x) - return x diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py deleted file mode 100644 index 77383095681d994dcf94e52a106cfe93dcbb6a50..0000000000000000000000000000000000000000 --- a/comfy/ldm/cascade/stage_b.py +++ /dev/null @@ -1,256 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import math -import torch -from torch import nn -from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock - -class StageB(nn.Module): - def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280], - nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]], - block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280, - c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True, - t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None): - super().__init__() - self.dtype = dtype - self.c_r = c_r - self.t_conds = t_conds - self.c_clip_seq = c_clip_seq - if not isinstance(dropout, list): - dropout = [dropout] * len(c_hidden) - if not isinstance(self_attn, list): - self_attn = [self_attn] * len(c_hidden) - - # CONDITIONING - self.effnet_mapper = nn.Sequential( - operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), - nn.GELU(), - operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - ) - self.pixels_mapper = nn.Sequential( - operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device), - nn.GELU(), - operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device), - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - ) - self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device) - self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - self.embedding = nn.Sequential( - nn.PixelUnshuffle(patch_size), - operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - ) - - def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): - if block_type == 'C': - return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) - elif block_type == 'A': - return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) - elif block_type == 'F': - return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) - elif block_type == 'T': - return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) - else: - raise Exception(f'Block type {block_type} not supported') - - # BLOCKS - # -- down blocks - self.down_blocks = nn.ModuleList() - self.down_downscalers = nn.ModuleList() - self.down_repeat_mappers = nn.ModuleList() - for i in range(len(c_hidden)): - if i > 0: - self.down_downscalers.append(nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), - operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device), - )) - else: - self.down_downscalers.append(nn.Identity()) - down_block = nn.ModuleList() - for _ in range(blocks[0][i]): - for block_type in level_config[i]: - block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) - down_block.append(block) - self.down_blocks.append(down_block) - if block_repeat is not None: - block_repeat_mappers = nn.ModuleList() - for _ in range(block_repeat[0][i] - 1): - block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) - self.down_repeat_mappers.append(block_repeat_mappers) - - # -- up blocks - self.up_blocks = nn.ModuleList() - self.up_upscalers = nn.ModuleList() - self.up_repeat_mappers = nn.ModuleList() - for i in reversed(range(len(c_hidden))): - if i > 0: - self.up_upscalers.append(nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), - operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device), - )) - else: - self.up_upscalers.append(nn.Identity()) - up_block = nn.ModuleList() - for j in range(blocks[1][::-1][i]): - for k, block_type in enumerate(level_config[i]): - c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 - block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], - self_attn=self_attn[i]) - up_block.append(block) - self.up_blocks.append(up_block) - if block_repeat is not None: - block_repeat_mappers = nn.ModuleList() - for _ in range(block_repeat[1][::-1][i] - 1): - block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) - self.up_repeat_mappers.append(block_repeat_mappers) - - # OUTPUT - self.clf = nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), - operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), - nn.PixelShuffle(patch_size), - ) - - # --- WEIGHT INIT --- - # self.apply(self._init_weights) # General init - # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings - # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings - # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings - # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings - # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings - # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs - # nn.init.constant_(self.clf[1].weight, 0) # outputs - # - # # blocks - # for level_block in self.down_blocks + self.up_blocks: - # for block in level_block: - # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): - # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) - # elif isinstance(block, TimestepBlock): - # for layer in block.modules(): - # if isinstance(layer, nn.Linear): - # nn.init.constant_(layer.weight, 0) - # - # def _init_weights(self, m): - # if isinstance(m, (nn.Conv2d, nn.Linear)): - # torch.nn.init.xavier_uniform_(m.weight) - # if m.bias is not None: - # nn.init.constant_(m.bias, 0) - - def gen_r_embedding(self, r, max_positions=10000): - r = r * max_positions - half_dim = self.c_r // 2 - emb = math.log(max_positions) / (half_dim - 1) - emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() - emb = r[:, None] * emb[None, :] - emb = torch.cat([emb.sin(), emb.cos()], dim=1) - if self.c_r % 2 == 1: # zero pad - emb = nn.functional.pad(emb, (0, 1), mode='constant') - return emb - - def gen_c_embeddings(self, clip): - if len(clip.shape) == 2: - clip = clip.unsqueeze(1) - clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1) - clip = self.clip_norm(clip) - return clip - - def _down_encode(self, x, r_embed, clip): - level_outputs = [] - block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) - for down_block, downscaler, repmap in block_group: - x = downscaler(x) - for i in range(len(repmap) + 1): - for block in down_block: - if isinstance(block, ResBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - ResBlock)): - x = block(x) - elif isinstance(block, AttnBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - AttnBlock)): - x = block(x, clip) - elif isinstance(block, TimestepBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - TimestepBlock)): - x = block(x, r_embed) - else: - x = block(x) - if i < len(repmap): - x = repmap[i](x) - level_outputs.insert(0, x) - return level_outputs - - def _up_decode(self, level_outputs, r_embed, clip): - x = level_outputs[0] - block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) - for i, (up_block, upscaler, repmap) in enumerate(block_group): - for j in range(len(repmap) + 1): - for k, block in enumerate(up_block): - if isinstance(block, ResBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - ResBlock)): - skip = level_outputs[i] if k == 0 and i > 0 else None - if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): - x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', - align_corners=True) - x = block(x, skip) - elif isinstance(block, AttnBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - AttnBlock)): - x = block(x, clip) - elif isinstance(block, TimestepBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - TimestepBlock)): - x = block(x, r_embed) - else: - x = block(x) - if j < len(repmap): - x = repmap[j](x) - x = upscaler(x) - return x - - def forward(self, x, r, effnet, clip, pixels=None, **kwargs): - if pixels is None: - pixels = x.new_zeros(x.size(0), 3, 8, 8) - - # Process the conditioning embeddings - r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) - for c in self.t_conds: - t_cond = kwargs.get(c, torch.zeros_like(r)) - r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) - clip = self.gen_c_embeddings(clip) - - # Model Blocks - x = self.embedding(x) - x = x + self.effnet_mapper( - nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) - x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', - align_corners=True) - level_outputs = self._down_encode(x, r_embed, clip) - x = self._up_decode(level_outputs, r_embed, clip) - return self.clf(x) - - def update_weights_ema(self, src_model, beta=0.999): - for self_params, src_params in zip(self.parameters(), src_model.parameters()): - self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) - for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): - self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py deleted file mode 100644 index b952d03490578840deadfb4623225819903bf09d..0000000000000000000000000000000000000000 --- a/comfy/ldm/cascade/stage_c.py +++ /dev/null @@ -1,273 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -from torch import nn -import math -from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock -# from .controlnet import ControlNetDeliverer - -class UpDownBlock2d(nn.Module): - def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None): - super().__init__() - assert mode in ['up', 'down'] - interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear', - align_corners=True) if enabled else nn.Identity() - mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device) - self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation]) - - def forward(self, x): - for block in self.blocks: - x = block(x) - return x - - -class StageC(nn.Module): - def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32], - blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'], - c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3, - dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None, - dtype=None, device=None, operations=None): - super().__init__() - self.dtype = dtype - self.c_r = c_r - self.t_conds = t_conds - self.c_clip_seq = c_clip_seq - if not isinstance(dropout, list): - dropout = [dropout] * len(c_hidden) - if not isinstance(self_attn, list): - self_attn = [self_attn] * len(c_hidden) - - # CONDITIONING - self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device) - self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device) - self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device) - self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - self.embedding = nn.Sequential( - nn.PixelUnshuffle(patch_size), - operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device), - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6) - ) - - def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True): - if block_type == 'C': - return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations) - elif block_type == 'A': - return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations) - elif block_type == 'F': - return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations) - elif block_type == 'T': - return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations) - else: - raise Exception(f'Block type {block_type} not supported') - - # BLOCKS - # -- down blocks - self.down_blocks = nn.ModuleList() - self.down_downscalers = nn.ModuleList() - self.down_repeat_mappers = nn.ModuleList() - for i in range(len(c_hidden)): - if i > 0: - self.down_downscalers.append(nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6), - UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) - )) - else: - self.down_downscalers.append(nn.Identity()) - down_block = nn.ModuleList() - for _ in range(blocks[0][i]): - for block_type in level_config[i]: - block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i]) - down_block.append(block) - self.down_blocks.append(down_block) - if block_repeat is not None: - block_repeat_mappers = nn.ModuleList() - for _ in range(block_repeat[0][i] - 1): - block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) - self.down_repeat_mappers.append(block_repeat_mappers) - - # -- up blocks - self.up_blocks = nn.ModuleList() - self.up_upscalers = nn.ModuleList() - self.up_repeat_mappers = nn.ModuleList() - for i in reversed(range(len(c_hidden))): - if i > 0: - self.up_upscalers.append(nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6), - UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations) - )) - else: - self.up_upscalers.append(nn.Identity()) - up_block = nn.ModuleList() - for j in range(blocks[1][::-1][i]): - for k, block_type in enumerate(level_config[i]): - c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0 - block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i], - self_attn=self_attn[i]) - up_block.append(block) - self.up_blocks.append(up_block) - if block_repeat is not None: - block_repeat_mappers = nn.ModuleList() - for _ in range(block_repeat[1][::-1][i] - 1): - block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device)) - self.up_repeat_mappers.append(block_repeat_mappers) - - # OUTPUT - self.clf = nn.Sequential( - LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device), - operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device), - nn.PixelShuffle(patch_size), - ) - - # --- WEIGHT INIT --- - # self.apply(self._init_weights) # General init - # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings - # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings - # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings - # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs - # nn.init.constant_(self.clf[1].weight, 0) # outputs - # - # # blocks - # for level_block in self.down_blocks + self.up_blocks: - # for block in level_block: - # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock): - # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0])) - # elif isinstance(block, TimestepBlock): - # for layer in block.modules(): - # if isinstance(layer, nn.Linear): - # nn.init.constant_(layer.weight, 0) - # - # def _init_weights(self, m): - # if isinstance(m, (nn.Conv2d, nn.Linear)): - # torch.nn.init.xavier_uniform_(m.weight) - # if m.bias is not None: - # nn.init.constant_(m.bias, 0) - - def gen_r_embedding(self, r, max_positions=10000): - r = r * max_positions - half_dim = self.c_r // 2 - emb = math.log(max_positions) / (half_dim - 1) - emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp() - emb = r[:, None] * emb[None, :] - emb = torch.cat([emb.sin(), emb.cos()], dim=1) - if self.c_r % 2 == 1: # zero pad - emb = nn.functional.pad(emb, (0, 1), mode='constant') - return emb - - def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img): - clip_txt = self.clip_txt_mapper(clip_txt) - if len(clip_txt_pooled.shape) == 2: - clip_txt_pooled = clip_txt_pooled.unsqueeze(1) - if len(clip_img.shape) == 2: - clip_img = clip_img.unsqueeze(1) - clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1) - clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1) - clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1) - clip = self.clip_norm(clip) - return clip - - def _down_encode(self, x, r_embed, clip, cnet=None): - level_outputs = [] - block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) - for down_block, downscaler, repmap in block_group: - x = downscaler(x) - for i in range(len(repmap) + 1): - for block in down_block: - if isinstance(block, ResBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - ResBlock)): - if cnet is not None: - next_cnet = cnet.pop() - if next_cnet is not None: - x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', - align_corners=True).to(x.dtype) - x = block(x) - elif isinstance(block, AttnBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - AttnBlock)): - x = block(x, clip) - elif isinstance(block, TimestepBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - TimestepBlock)): - x = block(x, r_embed) - else: - x = block(x) - if i < len(repmap): - x = repmap[i](x) - level_outputs.insert(0, x) - return level_outputs - - def _up_decode(self, level_outputs, r_embed, clip, cnet=None): - x = level_outputs[0] - block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) - for i, (up_block, upscaler, repmap) in enumerate(block_group): - for j in range(len(repmap) + 1): - for k, block in enumerate(up_block): - if isinstance(block, ResBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - ResBlock)): - skip = level_outputs[i] if k == 0 and i > 0 else None - if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)): - x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear', - align_corners=True) - if cnet is not None: - next_cnet = cnet.pop() - if next_cnet is not None: - x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear', - align_corners=True).to(x.dtype) - x = block(x, skip) - elif isinstance(block, AttnBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - AttnBlock)): - x = block(x, clip) - elif isinstance(block, TimestepBlock) or ( - hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, - TimestepBlock)): - x = block(x, r_embed) - else: - x = block(x) - if j < len(repmap): - x = repmap[j](x) - x = upscaler(x) - return x - - def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): - # Process the conditioning embeddings - r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) - for c in self.t_conds: - t_cond = kwargs.get(c, torch.zeros_like(r)) - r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1) - clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img) - - if control is not None: - cnet = control.get("input") - else: - cnet = None - - # Model Blocks - x = self.embedding(x) - level_outputs = self._down_encode(x, r_embed, clip, cnet) - x = self._up_decode(level_outputs, r_embed, clip, cnet) - return self.clf(x) - - def update_weights_ema(self, src_model, beta=0.999): - for self_params, src_params in zip(self.parameters(), src_model.parameters()): - self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta) - for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()): - self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta) diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py deleted file mode 100644 index b467a70a848edeef57d7911ac844868b251f8e45..0000000000000000000000000000000000000000 --- a/comfy/ldm/cascade/stage_c_coder.py +++ /dev/null @@ -1,98 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" -import torch -import torchvision -from torch import nn - -import comfy.ops - -ops = comfy.ops.disable_weight_init - -# EfficientNet -class EfficientNetEncoder(nn.Module): - def __init__(self, c_latent=16): - super().__init__() - self.backbone = torchvision.models.efficientnet_v2_s().features.eval() - self.mapper = nn.Sequential( - ops.Conv2d(1280, c_latent, kernel_size=1, bias=False), - nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1 - ) - self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) - self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225])) - - def forward(self, x): - x = x * 0.5 + 0.5 - x = (x - self.mean.view([3,1,1]).to(device=x.device, dtype=x.dtype)) / self.std.view([3,1,1]).to(device=x.device, dtype=x.dtype) - o = self.mapper(self.backbone(x)) - return o - - -# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192 -class Previewer(nn.Module): - def __init__(self, c_in=16, c_hidden=512, c_out=3): - super().__init__() - self.blocks = nn.Sequential( - ops.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels - nn.GELU(), - nn.BatchNorm2d(c_hidden), - - ops.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), - nn.GELU(), - nn.BatchNorm2d(c_hidden), - - ops.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 - nn.GELU(), - nn.BatchNorm2d(c_hidden // 2), - - ops.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), - nn.GELU(), - nn.BatchNorm2d(c_hidden // 2), - - ops.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 - nn.GELU(), - nn.BatchNorm2d(c_hidden // 4), - - ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), - nn.GELU(), - nn.BatchNorm2d(c_hidden // 4), - - ops.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 - nn.GELU(), - nn.BatchNorm2d(c_hidden // 4), - - ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), - nn.GELU(), - nn.BatchNorm2d(c_hidden // 4), - - ops.Conv2d(c_hidden // 4, c_out, kernel_size=1), - ) - - def forward(self, x): - return (self.blocks(x) - 0.5) * 2.0 - -class StageC_coder(nn.Module): - def __init__(self): - super().__init__() - self.previewer = Previewer() - self.encoder = EfficientNetEncoder() - - def encode(self, x): - return self.encoder(x) - - def decode(self, x): - return self.previewer(x) diff --git a/comfy/ldm/chroma/.DS_Store b/comfy/ldm/chroma/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/chroma/.DS_Store and /dev/null differ diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py deleted file mode 100644 index 2a0dec606239cdc21ee21192172a812a53e0107c..0000000000000000000000000000000000000000 --- a/comfy/ldm/chroma/layers.py +++ /dev/null @@ -1,181 +0,0 @@ -import torch -from torch import Tensor, nn - -from comfy.ldm.flux.math import attention -from comfy.ldm.flux.layers import ( - MLPEmbedder, - RMSNorm, - QKNorm, - SelfAttention, - ModulationOut, -) - - - -class ChromaModulationOut(ModulationOut): - @classmethod - def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: - return cls( - shift=tensor[:, offset : offset + 1, :], - scale=tensor[:, offset + 1 : offset + 2, :], - gate=tensor[:, offset + 2 : offset + 3, :], - ) - - - - -class Approximator(nn.Module): - def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): - super().__init__() - self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) - self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) - self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) - self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) - - @property - def device(self): - # Get the device of the module (assumes all parameters are on the same device) - return next(self.parameters()).device - - def forward(self, x: Tensor) -> Tensor: - x = self.in_proj(x) - - for layer, norms in zip(self.layers, self.norms): - x = x + layer(norms(x)) - - x = self.out_proj(x) - - return x - - -class DoubleStreamBlock(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): - super().__init__() - - mlp_hidden_dim = int(hidden_size * mlp_ratio) - self.num_heads = num_heads - self.hidden_size = hidden_size - self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - - self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - - self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - self.flipped_img_txt = flipped_img_txt - - def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None): - (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec - - # prepare image for attention - img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img)) - img_qkv = self.img_attn.qkv(img_modulated) - img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) - - # prepare txt for attention - txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt)) - txt_qkv = self.txt_attn.qkv(txt_modulated) - txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) - - # run actual attention - attn = attention(torch.cat((txt_q, img_q), dim=2), - torch.cat((txt_k, img_k), dim=2), - torch.cat((txt_v, img_v), dim=2), - pe=pe, mask=attn_mask) - - txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] - - # calculate the img bloks - img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn)) - img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img)))) - - # calculate the txt bloks - txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn)) - txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt)))) - - if txt.dtype == torch.float16: - txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) - - return img, txt - - -class SingleStreamBlock(nn.Module): - """ - A DiT block with parallel linear layers as described in - https://arxiv.org/abs/2302.05442 and adapted modulation interface. - """ - - def __init__( - self, - hidden_size: int, - num_heads: int, - mlp_ratio: float = 4.0, - qk_scale: float = None, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.hidden_dim = hidden_size - self.num_heads = num_heads - head_dim = hidden_size // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.mlp_hidden_dim = int(hidden_size * mlp_ratio) - # qkv and mlp_in - self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) - # proj and mlp_out - self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) - - self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - - self.hidden_size = hidden_size - self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - self.mlp_act = nn.GELU(approximate="tanh") - - def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor: - mod = vec - x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x)) - qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) - - q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k = self.norm(q, k, v) - - # compute attention - attn = attention(q, k, v, pe=pe, mask=attn_mask) - # compute activation in mlp stream, cat again and run second linear layer - output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) - x.addcmul_(mod.gate, output) - if x.dtype == torch.float16: - x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) - return x - - -class LastLayer(nn.Module): - def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) - - def forward(self, x: Tensor, vec: Tensor) -> Tensor: - shift, scale = vec - shift = shift.squeeze(1) - scale = scale.squeeze(1) - x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x)) - x = self.linear(x) - return x diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py deleted file mode 100644 index 5cff44dc8a6f7759868b0a33b009224169714b0a..0000000000000000000000000000000000000000 --- a/comfy/ldm/chroma/model.py +++ /dev/null @@ -1,278 +0,0 @@ -#Original code can be found on: https://github.com/black-forest-labs/flux - -from dataclasses import dataclass - -import torch -from torch import Tensor, nn -from einops import rearrange, repeat -import comfy.patcher_extension -import comfy.ldm.common_dit - -from comfy.ldm.flux.layers import ( - EmbedND, - timestep_embedding, -) - -from .layers import ( - DoubleStreamBlock, - LastLayer, - SingleStreamBlock, - Approximator, - ChromaModulationOut, -) - - -@dataclass -class ChromaParams: - in_channels: int - out_channels: int - context_in_dim: int - hidden_size: int - mlp_ratio: float - num_heads: int - depth: int - depth_single_blocks: int - axes_dim: list - theta: int - patch_size: int - qkv_bias: bool - in_dim: int - out_dim: int - hidden_dim: int - n_layers: int - - - - -class Chroma(nn.Module): - """ - Transformer model for flow matching on sequences. - """ - - def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): - super().__init__() - self.dtype = dtype - params = ChromaParams(**kwargs) - self.params = params - self.patch_size = params.patch_size - self.in_channels = params.in_channels - self.out_channels = params.out_channels - if params.hidden_size % params.num_heads != 0: - raise ValueError( - f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" - ) - pe_dim = params.hidden_size // params.num_heads - if sum(params.axes_dim) != pe_dim: - raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") - self.hidden_size = params.hidden_size - self.num_heads = params.num_heads - self.in_dim = params.in_dim - self.out_dim = params.out_dim - self.hidden_dim = params.hidden_dim - self.n_layers = params.n_layers - self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) - self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) - self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) - # set as nn identity for now, will overwrite it later. - self.distilled_guidance_layer = Approximator( - in_dim=self.in_dim, - hidden_dim=self.hidden_dim, - out_dim=self.out_dim, - n_layers=self.n_layers, - dtype=dtype, device=device, operations=operations - ) - - - self.double_blocks = nn.ModuleList( - [ - DoubleStreamBlock( - self.hidden_size, - self.num_heads, - mlp_ratio=params.mlp_ratio, - qkv_bias=params.qkv_bias, - dtype=dtype, device=device, operations=operations - ) - for _ in range(params.depth) - ] - ) - - self.single_blocks = nn.ModuleList( - [ - SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) - for _ in range(params.depth_single_blocks) - ] - ) - - if final_layer: - self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) - - self.skip_mmdit = [] - self.skip_dit = [] - self.lite = False - - def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): - # This function slices up the modulations tensor which has the following layout: - # single : num_single_blocks * 3 elements - # double_img : num_double_blocks * 6 elements - # double_txt : num_double_blocks * 6 elements - # final : 2 elements - if block_type == "final": - return (tensor[:, -2:-1, :], tensor[:, -1:, :]) - single_block_count = self.params.depth_single_blocks - double_block_count = self.params.depth - offset = 3 * idx - if block_type == "single": - return ChromaModulationOut.from_offset(tensor, offset) - # Double block modulations are 6 elements so we double 3 * idx. - offset *= 2 - if block_type in {"double_img", "double_txt"}: - # Advance past the single block modulations. - offset += 3 * single_block_count - if block_type == "double_txt": - # Advance past the double block img modulations. - offset += 6 * double_block_count - return ( - ChromaModulationOut.from_offset(tensor, offset), - ChromaModulationOut.from_offset(tensor, offset + 3), - ) - raise ValueError("Bad block_type") - - - def forward_orig( - self, - img: Tensor, - img_ids: Tensor, - txt: Tensor, - txt_ids: Tensor, - timesteps: Tensor, - guidance: Tensor = None, - control = None, - transformer_options={}, - attn_mask: Tensor = None, - ) -> Tensor: - patches_replace = transformer_options.get("patches_replace", {}) - if img.ndim != 3 or txt.ndim != 3: - raise ValueError("Input img and txt tensors must have 3 dimensions.") - - # running on sequences img - img = self.img_in(img) - - # distilled vector guidance - mod_index_length = 344 - distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) - # guidance = guidance * - distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) - - # get all modulation index - modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) - # we need to broadcast the modulation index here so each batch has all of the index - modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) - # and we need to broadcast timestep and guidance along too - timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) - # then and only then we could concatenate it together - input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) - - mod_vectors = self.distilled_guidance_layer(input_vec) - - txt = self.txt_in(txt) - - ids = torch.cat((txt_ids, img_ids), dim=1) - pe = self.pe_embedder(ids) - - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.double_blocks): - if i not in self.skip_mmdit: - double_mod = ( - self.get_modulations(mod_vectors, "double_img", idx=i), - self.get_modulations(mod_vectors, "double_txt", idx=i), - ) - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"], out["txt"] = block(img=args["img"], - txt=args["txt"], - vec=args["vec"], - pe=args["pe"], - attn_mask=args.get("attn_mask")) - return out - - out = blocks_replace[("double_block", i)]({"img": img, - "txt": txt, - "vec": double_mod, - "pe": pe, - "attn_mask": attn_mask}, - {"original_block": block_wrap}) - txt = out["txt"] - img = out["img"] - else: - img, txt = block(img=img, - txt=txt, - vec=double_mod, - pe=pe, - attn_mask=attn_mask) - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - img += add - - img = torch.cat((txt, img), 1) - - for i, block in enumerate(self.single_blocks): - if i not in self.skip_dit: - single_mod = self.get_modulations(mod_vectors, "single", idx=i) - if ("single_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], - vec=args["vec"], - pe=args["pe"], - attn_mask=args.get("attn_mask")) - return out - - out = blocks_replace[("single_block", i)]({"img": img, - "vec": single_mod, - "pe": pe, - "attn_mask": attn_mask}, - {"original_block": block_wrap}) - img = out["img"] - else: - img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask) - - if control is not None: # Controlnet - control_o = control.get("output") - if i < len(control_o): - add = control_o[i] - if add is not None: - img[:, txt.shape[1] :, ...] += add - - img = img[:, txt.shape[1] :, ...] - final_mod = self.get_modulations(mod_vectors, "final") - img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) - return img - - def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs) - - def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): - bs, c, h, w = x.shape - x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) - - img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size) - - h_len = ((h + (self.patch_size // 2)) // self.patch_size) - w_len = ((w + (self.patch_size // 2)) // self.patch_size) - img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) - img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) - - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) - return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w] diff --git a/comfy/ldm/common_dit.py b/comfy/ldm/common_dit.py deleted file mode 100644 index f7f56b72ca6fcd3ba2831b8881a7124d84490bee..0000000000000000000000000000000000000000 --- a/comfy/ldm/common_dit.py +++ /dev/null @@ -1,16 +0,0 @@ -import torch -import comfy.rmsnorm - - -def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): - if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()): - padding_mode = "reflect" - - pad = () - for i in range(img.ndim - 2): - pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad - - return torch.nn.functional.pad(img, pad, mode=padding_mode) - - -rms_norm = comfy.rmsnorm.rms_norm diff --git a/comfy/ldm/cosmos/.DS_Store b/comfy/ldm/cosmos/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/cosmos/.DS_Store and /dev/null differ diff --git a/comfy/ldm/cosmos/blocks.py b/comfy/ldm/cosmos/blocks.py deleted file mode 100644 index 5c4356a3ff9d97c182de8afeb1f130dca5bd2756..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/blocks.py +++ /dev/null @@ -1,797 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import math -from typing import Optional -import logging - -import numpy as np -import torch -from einops import rearrange, repeat -from einops.layers.torch import Rearrange -from torch import nn - -from comfy.ldm.modules.attention import optimized_attention - - -def get_normalization(name: str, channels: int, weight_args={}, operations=None): - if name == "I": - return nn.Identity() - elif name == "R": - return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args) - else: - raise ValueError(f"Normalization {name} not found") - - -class BaseAttentionOp(nn.Module): - def __init__(self): - super().__init__() - - -class Attention(nn.Module): - """ - Generalized attention impl. - - Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided. - If `context_dim` is None, self-attention is assumed. - - Parameters: - query_dim (int): Dimension of each query vector. - context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed. - heads (int, optional): Number of attention heads. Defaults to 8. - dim_head (int, optional): Dimension of each head. Defaults to 64. - dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0. - attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default. - qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False. - out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False. - qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections. - Defaults to "SSI". - qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections. - Defaults to 'per_head'. Only support 'per_head'. - - Examples: - >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1) - >>> query = torch.randn(10, 128) # Batch size of 10 - >>> context = torch.randn(10, 256) # Batch size of 10 - >>> output = attn(query, context) # Perform the attention operation - - Note: - https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 - """ - - def __init__( - self, - query_dim: int, - context_dim=None, - heads=8, - dim_head=64, - dropout=0.0, - attn_op: Optional[BaseAttentionOp] = None, - qkv_bias: bool = False, - out_bias: bool = False, - qkv_norm: str = "SSI", - qkv_norm_mode: str = "per_head", - backend: str = "transformer_engine", - qkv_format: str = "bshd", - weight_args={}, - operations=None, - ) -> None: - super().__init__() - - self.is_selfattn = context_dim is None # self attention - - inner_dim = dim_head * heads - context_dim = query_dim if context_dim is None else context_dim - - self.heads = heads - self.dim_head = dim_head - self.qkv_norm_mode = qkv_norm_mode - self.qkv_format = qkv_format - - if self.qkv_norm_mode == "per_head": - norm_dim = dim_head - else: - raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") - - self.backend = backend - - self.to_q = nn.Sequential( - operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args), - get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations), - ) - self.to_k = nn.Sequential( - operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), - get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations), - ) - self.to_v = nn.Sequential( - operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), - get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations), - ) - - self.to_out = nn.Sequential( - operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args), - nn.Dropout(dropout), - ) - - def cal_qkv( - self, x, context=None, mask=None, rope_emb=None, **kwargs - ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - del kwargs - - - """ - self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers. - Before 07/24/2024, these modules normalize across all heads. - After 07/24/2024, to support tensor parallelism and follow the common practice in the community, - we support to normalize per head. - To keep the checkpoint copatibility with the previous code, - we keep the nn.Sequential but call the projection and the normalization layers separately. - We use a flag `self.qkv_norm_mode` to control the normalization behavior. - The default value of `self.qkv_norm_mode` is "per_head", which means we normalize per head. - """ - if self.qkv_norm_mode == "per_head": - q = self.to_q[0](x) - context = x if context is None else context - k = self.to_k[0](context) - v = self.to_v[0](context) - q, k, v = map( - lambda t: rearrange(t, "s b (n c) -> b n s c", n=self.heads, c=self.dim_head), - (q, k, v), - ) - else: - raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") - - q = self.to_q[1](q) - k = self.to_k[1](k) - v = self.to_v[1](v) - if self.is_selfattn and rope_emb is not None: # only apply to self-attention! - # apply_rotary_pos_emb inlined - q_shape = q.shape - q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) - q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1] - q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype) - - # apply_rotary_pos_emb inlined - k_shape = k.shape - k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) - k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1] - k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype) - return q, k, v - - def forward( - self, - x, - context=None, - mask=None, - rope_emb=None, - **kwargs, - ): - """ - Args: - x (Tensor): The query tensor of shape [B, Mq, K] - context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None - """ - q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs) - out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True) - del q, k, v - out = rearrange(out, " b n s c -> s b (n c)") - return self.to_out(out) - - -class FeedForward(nn.Module): - """ - Transformer FFN with optional gating - - Parameters: - d_model (int): Dimensionality of input features. - d_ff (int): Dimensionality of the hidden layer. - dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1. - activation (callable, optional): The activation function applied after the first linear layer. - Defaults to nn.ReLU(). - is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer. - Defaults to False. - bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True. - - Example: - >>> ff = FeedForward(d_model=512, d_ff=2048) - >>> x = torch.randn(64, 10, 512) # Example input tensor - >>> output = ff(x) - >>> print(output.shape) # Expected shape: (64, 10, 512) - """ - - def __init__( - self, - d_model: int, - d_ff: int, - dropout: float = 0.1, - activation=nn.ReLU(), - is_gated: bool = False, - bias: bool = False, - weight_args={}, - operations=None, - ) -> None: - super().__init__() - - self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args) - self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args) - - self.dropout = nn.Dropout(dropout) - self.activation = activation - self.is_gated = is_gated - if is_gated: - self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args) - - def forward(self, x: torch.Tensor): - g = self.activation(self.layer1(x)) - if self.is_gated: - x = g * self.linear_gate(x) - else: - x = g - assert self.dropout.p == 0.0, "we skip dropout" - return self.layer2(x) - - -class GPT2FeedForward(FeedForward): - def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None): - super().__init__( - d_model=d_model, - d_ff=d_ff, - dropout=dropout, - activation=nn.GELU(), - is_gated=False, - bias=bias, - weight_args=weight_args, - operations=operations, - ) - - def forward(self, x: torch.Tensor): - assert self.dropout.p == 0.0, "we skip dropout" - - x = self.layer1(x) - x = self.activation(x) - x = self.layer2(x) - - return x - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class Timesteps(nn.Module): - def __init__(self, num_channels): - super().__init__() - self.num_channels = num_channels - - def forward(self, timesteps): - half_dim = self.num_channels // 2 - exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) - exponent = exponent / (half_dim - 0.0) - - emb = torch.exp(exponent) - emb = timesteps[:, None].float() * emb[None, :] - - sin_emb = torch.sin(emb) - cos_emb = torch.cos(emb) - emb = torch.cat([cos_emb, sin_emb], dim=-1) - - return emb - - -class TimestepEmbedding(nn.Module): - def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None): - super().__init__() - logging.debug( - f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." - ) - self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args) - self.activation = nn.SiLU() - self.use_adaln_lora = use_adaln_lora - if use_adaln_lora: - self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args) - else: - self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args) - - def forward(self, sample: torch.Tensor) -> torch.Tensor: - emb = self.linear_1(sample) - emb = self.activation(emb) - emb = self.linear_2(emb) - - if self.use_adaln_lora: - adaln_lora_B_3D = emb - emb_B_D = sample - else: - emb_B_D = emb - adaln_lora_B_3D = None - - return emb_B_D, adaln_lora_B_3D - - -class FourierFeatures(nn.Module): - """ - Implements a layer that generates Fourier features from input tensors, based on randomly sampled - frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems. - - [B] -> [B, D] - - Parameters: - num_channels (int): The number of Fourier features to generate. - bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1. - normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize - the variance of the features. Defaults to False. - - Example: - >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True) - >>> x = torch.randn(10, 256) # Example input tensor - >>> output = layer(x) - >>> print(output.shape) # Expected shape: (10, 256) - """ - - def __init__(self, num_channels, bandwidth=1, normalize=False): - super().__init__() - self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True) - self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True) - self.gain = np.sqrt(2) if normalize else 1 - - def forward(self, x, gain: float = 1.0): - """ - Apply the Fourier feature transformation to the input tensor. - - Args: - x (torch.Tensor): The input tensor. - gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1. - - Returns: - torch.Tensor: The transformed tensor, with Fourier features applied. - """ - in_dtype = x.dtype - x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32)) - x = x.cos().mul(self.gain * gain).to(in_dtype) - return x - - -class PatchEmbed(nn.Module): - """ - PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, - depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, - making it suitable for video and image processing tasks. It supports dividing the input into patches - and embedding each patch into a vector of size `out_channels`. - - Parameters: - - spatial_patch_size (int): The size of each spatial patch. - - temporal_patch_size (int): The size of each temporal patch. - - in_channels (int): Number of input channels. Default: 3. - - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. - - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. - """ - - def __init__( - self, - spatial_patch_size, - temporal_patch_size, - in_channels=3, - out_channels=768, - bias=True, - weight_args={}, - operations=None, - ): - super().__init__() - self.spatial_patch_size = spatial_patch_size - self.temporal_patch_size = temporal_patch_size - - self.proj = nn.Sequential( - Rearrange( - "b c (t r) (h m) (w n) -> b t h w (c r m n)", - r=temporal_patch_size, - m=spatial_patch_size, - n=spatial_patch_size, - ), - operations.Linear( - in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args - ), - ) - self.out = nn.Identity() - - def forward(self, x): - """ - Forward pass of the PatchEmbed module. - - Parameters: - - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where - B is the batch size, - C is the number of channels, - T is the temporal dimension, - H is the height, and - W is the width of the input. - - Returns: - - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. - """ - assert x.dim() == 5 - _, _, T, H, W = x.shape - assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 - assert T % self.temporal_patch_size == 0 - x = self.proj(x) - return self.out(x) - - -class FinalLayer(nn.Module): - """ - The final layer of video DiT. - """ - - def __init__( - self, - hidden_size, - spatial_patch_size, - temporal_patch_size, - out_channels, - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - weight_args={}, - operations=None, - ): - super().__init__() - self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args) - self.linear = operations.Linear( - hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args - ) - self.hidden_size = hidden_size - self.n_adaln_chunks = 2 - self.use_adaln_lora = use_adaln_lora - if use_adaln_lora: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args), - operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args), - ) - else: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args) - ) - - def forward( - self, - x_BT_HW_D, - emb_B_D, - adaln_lora_B_3D: Optional[torch.Tensor] = None, - ): - if self.use_adaln_lora: - assert adaln_lora_B_3D is not None - shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk( - 2, dim=1 - ) - else: - shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1) - - B = emb_B_D.shape[0] - T = x_BT_HW_D.shape[0] // B - shift_BT_D, scale_BT_D = repeat(shift_B_D, "b d -> (b t) d", t=T), repeat(scale_B_D, "b d -> (b t) d", t=T) - x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D) - - x_BT_HW_D = self.linear(x_BT_HW_D) - return x_BT_HW_D - - -class VideoAttn(nn.Module): - """ - Implements video attention with optional cross-attention capabilities. - - This module processes video features while maintaining their spatio-temporal structure. It can perform - self-attention within the video features or cross-attention with external context features. - - Parameters: - x_dim (int): Dimension of input feature vectors - context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention - num_heads (int): Number of attention heads - bias (bool): Whether to include bias in attention projections. Default: False - qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be "per_head". Default: "per_head" - x_format (str): Format of input tensor. Must be "BTHWD". Default: "BTHWD" - - Input shape: - - x: (T, H, W, B, D) video features - - context (optional): (M, B, D) context features for cross-attention - where: - T: temporal dimension - H: height - W: width - B: batch size - D: feature dimension - M: context sequence length - """ - - def __init__( - self, - x_dim: int, - context_dim: Optional[int], - num_heads: int, - bias: bool = False, - qkv_norm_mode: str = "per_head", - x_format: str = "BTHWD", - weight_args={}, - operations=None, - ) -> None: - super().__init__() - self.x_format = x_format - - self.attn = Attention( - x_dim, - context_dim, - num_heads, - x_dim // num_heads, - qkv_bias=bias, - qkv_norm="RRI", - out_bias=bias, - qkv_norm_mode=qkv_norm_mode, - qkv_format="sbhd", - weight_args=weight_args, - operations=operations, - ) - - def forward( - self, - x: torch.Tensor, - context: Optional[torch.Tensor] = None, - crossattn_mask: Optional[torch.Tensor] = None, - rope_emb_L_1_1_D: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """ - Forward pass for video attention. - - Args: - x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data. - context (Tensor): Context tensor of shape (B, M, D) or (M, B, D), - where M is the sequence length of the context. - crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms. - rope_emb_L_1_1_D (Optional[Tensor]): - Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. - - Returns: - Tensor: The output tensor with applied attention, maintaining the input shape. - """ - - x_T_H_W_B_D = x - context_M_B_D = context - T, H, W, B, D = x_T_H_W_B_D.shape - x_THW_B_D = rearrange(x_T_H_W_B_D, "t h w b d -> (t h w) b d") - x_THW_B_D = self.attn( - x_THW_B_D, - context_M_B_D, - crossattn_mask, - rope_emb=rope_emb_L_1_1_D, - ) - x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W) - return x_T_H_W_B_D - - -def adaln_norm_state(norm_state, x, scale, shift): - normalized = norm_state(x) - return normalized * (1 + scale) + shift - - -class DITBuildingBlock(nn.Module): - """ - A building block for the DiT (Diffusion Transformer) architecture that supports different types of - attention and MLP operations with adaptive layer normalization. - - Parameters: - block_type (str): Type of block - one of: - - "cross_attn"/"ca": Cross-attention - - "full_attn"/"fa": Full self-attention - - "mlp"/"ff": MLP/feedforward block - x_dim (int): Dimension of input features - context_dim (Optional[int]): Dimension of context features for cross-attention - num_heads (int): Number of attention heads - mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 - bias (bool): Whether to use bias in layers. Default: False - mlp_dropout (float): Dropout rate for MLP. Default: 0.0 - qkv_norm_mode (str): QKV normalization mode. Default: "per_head" - x_format (str): Input tensor format. Default: "BTHWD" - use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False - adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 - """ - - def __init__( - self, - block_type: str, - x_dim: int, - context_dim: Optional[int], - num_heads: int, - mlp_ratio: float = 4.0, - bias: bool = False, - mlp_dropout: float = 0.0, - qkv_norm_mode: str = "per_head", - x_format: str = "BTHWD", - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - weight_args={}, - operations=None - ) -> None: - block_type = block_type.lower() - - super().__init__() - self.x_format = x_format - if block_type in ["cross_attn", "ca"]: - self.block = VideoAttn( - x_dim, - context_dim, - num_heads, - bias=bias, - qkv_norm_mode=qkv_norm_mode, - x_format=self.x_format, - weight_args=weight_args, - operations=operations, - ) - elif block_type in ["full_attn", "fa"]: - self.block = VideoAttn( - x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations - ) - elif block_type in ["mlp", "ff"]: - self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations) - else: - raise ValueError(f"Unknown block type: {block_type}") - - self.block_type = block_type - self.use_adaln_lora = use_adaln_lora - - self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) - self.n_adaln_chunks = 3 - if use_adaln_lora: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args), - operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args), - ) - else: - self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args)) - - def forward( - self, - x: torch.Tensor, - emb_B_D: torch.Tensor, - crossattn_emb: torch.Tensor, - crossattn_mask: Optional[torch.Tensor] = None, - rope_emb_L_1_1_D: Optional[torch.Tensor] = None, - adaln_lora_B_3D: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """ - Forward pass for dynamically configured blocks with adaptive normalization. - - Args: - x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D). - emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation. - crossattn_emb (Tensor): Tensor for cross-attention blocks. - crossattn_mask (Optional[Tensor]): Optional mask for cross-attention. - rope_emb_L_1_1_D (Optional[Tensor]): - Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. - - Returns: - Tensor: The output tensor after processing through the configured block and adaptive normalization. - """ - if self.use_adaln_lora: - shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk( - self.n_adaln_chunks, dim=1 - ) - else: - shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1) - - shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = ( - shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), - scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), - gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), - ) - - if self.block_type in ["mlp", "ff"]: - x = x + gate_1_1_1_B_D * self.block( - adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), - ) - elif self.block_type in ["full_attn", "fa"]: - x = x + gate_1_1_1_B_D * self.block( - adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), - context=None, - rope_emb_L_1_1_D=rope_emb_L_1_1_D, - ) - elif self.block_type in ["cross_attn", "ca"]: - x = x + gate_1_1_1_B_D * self.block( - adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), - context=crossattn_emb, - crossattn_mask=crossattn_mask, - rope_emb_L_1_1_D=rope_emb_L_1_1_D, - ) - else: - raise ValueError(f"Unknown block type: {self.block_type}") - - return x - - -class GeneralDITTransformerBlock(nn.Module): - """ - A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer. - Each block in the sequence is specified by a block configuration string. - - Parameters: - x_dim (int): Dimension of input features - context_dim (int): Dimension of context features for cross-attention blocks - num_heads (int): Number of attention heads - block_config (str): String specifying block sequence (e.g. "ca-fa-mlp" for cross-attention, - full-attention, then MLP) - mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 - x_format (str): Input tensor format. Default: "BTHWD" - use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False - adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 - - The block_config string uses "-" to separate block types: - - "ca"/"cross_attn": Cross-attention block - - "fa"/"full_attn": Full self-attention block - - "mlp"/"ff": MLP/feedforward block - - Example: - block_config = "ca-fa-mlp" creates a sequence of: - 1. Cross-attention block - 2. Full self-attention block - 3. MLP block - """ - - def __init__( - self, - x_dim: int, - context_dim: int, - num_heads: int, - block_config: str, - mlp_ratio: float = 4.0, - x_format: str = "BTHWD", - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - weight_args={}, - operations=None - ): - super().__init__() - self.blocks = nn.ModuleList() - self.x_format = x_format - for block_type in block_config.split("-"): - self.blocks.append( - DITBuildingBlock( - block_type, - x_dim, - context_dim, - num_heads, - mlp_ratio, - x_format=self.x_format, - use_adaln_lora=use_adaln_lora, - adaln_lora_dim=adaln_lora_dim, - weight_args=weight_args, - operations=operations, - ) - ) - - def forward( - self, - x: torch.Tensor, - emb_B_D: torch.Tensor, - crossattn_emb: torch.Tensor, - crossattn_mask: Optional[torch.Tensor] = None, - rope_emb_L_1_1_D: Optional[torch.Tensor] = None, - adaln_lora_B_3D: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - for block in self.blocks: - x = block( - x, - emb_B_D, - crossattn_emb, - crossattn_mask, - rope_emb_L_1_1_D=rope_emb_L_1_1_D, - adaln_lora_B_3D=adaln_lora_B_3D, - ) - return x diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/.DS_Store b/comfy/ldm/cosmos/cosmos_tokenizer/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/cosmos/cosmos_tokenizer/.DS_Store and /dev/null differ diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py b/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py deleted file mode 100644 index 9a3ebed6aa5362c79aff91c088cce8ba7b6f3141..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py +++ /dev/null @@ -1,1041 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""The model definition for 3D layers - -Adapted from: https://github.com/lucidrains/magvit2-pytorch/blob/ -9f49074179c912736e617d61b32be367eb5f993a/magvit2_pytorch/magvit2_pytorch.py#L889 - -[MIT License Copyright (c) 2023 Phil Wang] -https://github.com/lucidrains/magvit2-pytorch/blob/ -9f49074179c912736e617d61b32be367eb5f993a/LICENSE -""" -import math -from typing import Tuple, Union - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import logging - -from comfy.ldm.modules.diffusionmodules.model import vae_attention - -from .patching import ( - Patcher, - Patcher3D, - UnPatcher, - UnPatcher3D, -) -from .utils import ( - CausalNormalize, - batch2space, - batch2time, - cast_tuple, - is_odd, - nonlinearity, - replication_pad, - space2batch, - time2batch, -) - -import comfy.ops -ops = comfy.ops.disable_weight_init - -_LEGACY_NUM_GROUPS = 32 - - -class CausalConv3d(nn.Module): - def __init__( - self, - chan_in: int = 1, - chan_out: int = 1, - kernel_size: Union[int, Tuple[int, int, int]] = 3, - pad_mode: str = "constant", - **kwargs, - ): - super().__init__() - kernel_size = cast_tuple(kernel_size, 3) - - time_kernel_size, height_kernel_size, width_kernel_size = kernel_size - - assert is_odd(height_kernel_size) and is_odd(width_kernel_size) - - dilation = kwargs.pop("dilation", 1) - stride = kwargs.pop("stride", 1) - time_stride = kwargs.pop("time_stride", 1) - time_dilation = kwargs.pop("time_dilation", 1) - padding = kwargs.pop("padding", 1) - - self.pad_mode = pad_mode - time_pad = time_dilation * (time_kernel_size - 1) + (1 - time_stride) - self.time_pad = time_pad - - self.spatial_pad = (padding, padding, padding, padding) - - stride = (time_stride, stride, stride) - dilation = (time_dilation, dilation, dilation) - self.conv3d = ops.Conv3d( - chan_in, - chan_out, - kernel_size, - stride=stride, - dilation=dilation, - **kwargs, - ) - - def _replication_pad(self, x: torch.Tensor) -> torch.Tensor: - x_prev = x[:, :, :1, ...].repeat(1, 1, self.time_pad, 1, 1) - x = torch.cat([x_prev, x], dim=2) - padding = self.spatial_pad + (0, 0) - return F.pad(x, padding, mode=self.pad_mode, value=0.0) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self._replication_pad(x) - return self.conv3d(x) - - -class CausalUpsample3d(nn.Module): - def __init__(self, in_channels: int) -> None: - super().__init__() - self.conv = CausalConv3d( - in_channels, in_channels, kernel_size=3, stride=1, padding=1 - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) - time_factor = 1.0 + 1.0 * (x.shape[2] > 1) - if isinstance(time_factor, torch.Tensor): - time_factor = time_factor.item() - x = x.repeat_interleave(int(time_factor), dim=2) - # TODO(freda): Check if this causes temporal inconsistency. - # Shoule reverse the order of the following two ops, - # better perf and better temporal smoothness. - x = self.conv(x) - return x[..., int(time_factor - 1) :, :, :] - - -class CausalDownsample3d(nn.Module): - def __init__(self, in_channels: int) -> None: - super().__init__() - self.conv = CausalConv3d( - in_channels, - in_channels, - kernel_size=3, - stride=2, - time_stride=2, - padding=0, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - pad = (0, 1, 0, 1, 0, 0) - x = F.pad(x, pad, mode="constant", value=0) - x = replication_pad(x) - x = self.conv(x) - return x - - -class CausalHybridUpsample3d(nn.Module): - def __init__( - self, - in_channels: int, - spatial_up: bool = True, - temporal_up: bool = True, - **kwargs, - ) -> None: - super().__init__() - self.spatial_up = spatial_up - self.temporal_up = temporal_up - if not self.spatial_up and not self.temporal_up: - return - - self.conv1 = CausalConv3d( - in_channels, - in_channels, - kernel_size=(3, 1, 1), - stride=1, - time_stride=1, - padding=0, - ) - self.conv2 = CausalConv3d( - in_channels, - in_channels, - kernel_size=(1, 3, 3), - stride=1, - time_stride=1, - padding=1, - ) - self.conv3 = CausalConv3d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - time_stride=1, - padding=0, - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if not self.spatial_up and not self.temporal_up: - return x - - # hybrid upsample temporally. - if self.temporal_up: - time_factor = 1.0 + 1.0 * (x.shape[2] > 1) - if isinstance(time_factor, torch.Tensor): - time_factor = time_factor.item() - x = x.repeat_interleave(int(time_factor), dim=2) - x = x[..., int(time_factor - 1) :, :, :] - x = self.conv1(x) + x - - # hybrid upsample spatially. - if self.spatial_up: - x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) - x = self.conv2(x) + x - - # final 1x1x1 conv. - x = self.conv3(x) - return x - - -class CausalHybridDownsample3d(nn.Module): - def __init__( - self, - in_channels: int, - spatial_down: bool = True, - temporal_down: bool = True, - **kwargs, - ) -> None: - super().__init__() - self.spatial_down = spatial_down - self.temporal_down = temporal_down - if not self.spatial_down and not self.temporal_down: - return - - self.conv1 = CausalConv3d( - in_channels, - in_channels, - kernel_size=(1, 3, 3), - stride=2, - time_stride=1, - padding=0, - ) - self.conv2 = CausalConv3d( - in_channels, - in_channels, - kernel_size=(3, 1, 1), - stride=1, - time_stride=2, - padding=0, - ) - self.conv3 = CausalConv3d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - time_stride=1, - padding=0, - ) - - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if not self.spatial_down and not self.temporal_down: - return x - - # hybrid downsample spatially. - if self.spatial_down: - pad = (0, 1, 0, 1, 0, 0) - x = F.pad(x, pad, mode="constant", value=0) - x1 = self.conv1(x) - x2 = F.avg_pool3d(x, kernel_size=(1, 2, 2), stride=(1, 2, 2)) - x = x1 + x2 - - # hybrid downsample temporally. - if self.temporal_down: - x = replication_pad(x) - x1 = self.conv2(x) - x2 = F.avg_pool3d(x, kernel_size=(2, 1, 1), stride=(2, 1, 1)) - x = x1 + x2 - - # final 1x1x1 conv. - x = self.conv3(x) - return x - - -class CausalResnetBlock3d(nn.Module): - def __init__( - self, - *, - in_channels: int, - out_channels: int = None, - dropout: float, - num_groups: int, - ) -> None: - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - - self.norm1 = CausalNormalize(in_channels, num_groups=num_groups) - self.conv1 = CausalConv3d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) - self.dropout = torch.nn.Dropout(dropout) - self.conv2 = CausalConv3d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - self.nin_shortcut = ( - CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - if in_channels != out_channels - else nn.Identity() - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h = x - h = self.norm1(h) - h = nonlinearity(h) - h = self.conv1(h) - - h = self.norm2(h) - h = nonlinearity(h) - h = self.dropout(h) - h = self.conv2(h) - x = self.nin_shortcut(x) - - return x + h - - -class CausalResnetBlockFactorized3d(nn.Module): - def __init__( - self, - *, - in_channels: int, - out_channels: int = None, - dropout: float, - num_groups: int, - ) -> None: - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - - self.norm1 = CausalNormalize(in_channels, num_groups=1) - self.conv1 = nn.Sequential( - CausalConv3d( - in_channels, - out_channels, - kernel_size=(1, 3, 3), - stride=1, - padding=1, - ), - CausalConv3d( - out_channels, - out_channels, - kernel_size=(3, 1, 1), - stride=1, - padding=0, - ), - ) - self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) - self.dropout = torch.nn.Dropout(dropout) - self.conv2 = nn.Sequential( - CausalConv3d( - out_channels, - out_channels, - kernel_size=(1, 3, 3), - stride=1, - padding=1, - ), - CausalConv3d( - out_channels, - out_channels, - kernel_size=(3, 1, 1), - stride=1, - padding=0, - ), - ) - self.nin_shortcut = ( - CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - if in_channels != out_channels - else nn.Identity() - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h = x - h = self.norm1(h) - h = nonlinearity(h) - h = self.conv1(h) - - h = self.norm2(h) - h = nonlinearity(h) - h = self.dropout(h) - h = self.conv2(h) - x = self.nin_shortcut(x) - - return x + h - - -class CausalAttnBlock(nn.Module): - def __init__(self, in_channels: int, num_groups: int) -> None: - super().__init__() - - self.norm = CausalNormalize(in_channels, num_groups=num_groups) - self.q = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.k = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.v = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.proj_out = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - - self.optimized_attention = vae_attention() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - q, batch_size = time2batch(q) - k, batch_size = time2batch(k) - v, batch_size = time2batch(v) - - b, c, h, w = q.shape - h_ = self.optimized_attention(q, k, v) - - h_ = batch2time(h_, batch_size) - h_ = self.proj_out(h_) - return x + h_ - - -class CausalTemporalAttnBlock(nn.Module): - def __init__(self, in_channels: int, num_groups: int) -> None: - super().__init__() - - self.norm = CausalNormalize(in_channels, num_groups=num_groups) - self.q = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.k = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.v = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - self.proj_out = CausalConv3d( - in_channels, in_channels, kernel_size=1, stride=1, padding=0 - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - q, batch_size, height = space2batch(q) - k, _, _ = space2batch(k) - v, _, _ = space2batch(v) - - bhw, c, t = q.shape - q = q.permute(0, 2, 1) # (bhw, t, c) - k = k.permute(0, 2, 1) # (bhw, t, c) - v = v.permute(0, 2, 1) # (bhw, t, c) - - w_ = torch.bmm(q, k.permute(0, 2, 1)) # (bhw, t, t) - w_ = w_ * (int(c) ** (-0.5)) - - # Apply causal mask - mask = torch.tril(torch.ones_like(w_)) - w_ = w_.masked_fill(mask == 0, float("-inf")) - w_ = F.softmax(w_, dim=2) - - # attend to values - h_ = torch.bmm(w_, v) # (bhw, t, c) - h_ = h_.permute(0, 2, 1).reshape(bhw, c, t) # (bhw, c, t) - - h_ = batch2space(h_, batch_size, height) - h_ = self.proj_out(h_) - return x + h_ - - -class EncoderBase(nn.Module): - def __init__( - self, - in_channels: int, - channels: int, - channels_mult: list[int], - num_res_blocks: int, - attn_resolutions: list[int], - dropout: float, - resolution: int, - z_channels: int, - **ignore_kwargs, - ) -> None: - super().__init__() - self.num_resolutions = len(channels_mult) - self.num_res_blocks = num_res_blocks - - # Patcher. - patch_size = ignore_kwargs.get("patch_size", 1) - self.patcher = Patcher( - patch_size, ignore_kwargs.get("patch_method", "rearrange") - ) - in_channels = in_channels * patch_size * patch_size - - # downsampling - self.conv_in = CausalConv3d( - in_channels, channels, kernel_size=3, stride=1, padding=1 - ) - - # num of groups for GroupNorm, num_groups=1 for LayerNorm. - num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) - curr_res = resolution // patch_size - in_ch_mult = (1,) + tuple(channels_mult) - self.in_ch_mult = in_ch_mult - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = channels * in_ch_mult[i_level] - block_out = channels * channels_mult[i_level] - for _ in range(self.num_res_blocks): - block.append( - CausalResnetBlock3d( - in_channels=block_in, - out_channels=block_out, - dropout=dropout, - num_groups=num_groups, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions - 1: - down.downsample = CausalDownsample3d(block_in) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = CausalResnetBlock3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=num_groups, - ) - self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) - self.mid.block_2 = CausalResnetBlock3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=num_groups, - ) - - # end - self.norm_out = CausalNormalize(block_in, num_groups=num_groups) - self.conv_out = CausalConv3d( - block_in, z_channels, kernel_size=3, stride=1, padding=1 - ) - - def patcher3d(self, x: torch.Tensor) -> torch.Tensor: - x, batch_size = time2batch(x) - x = self.patcher(x) - x = batch2time(x, batch_size) - return x - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.patcher3d(x) - - # downsampling - hs = [self.conv_in(x)] - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](hs[-1]) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - hs.append(h) - if i_level != self.num_resolutions - 1: - hs.append(self.down[i_level].downsample(hs[-1])) - else: - # temporal downsample (last level) - time_factor = 1 + 1 * (hs[-1].shape[2] > 1) - if isinstance(time_factor, torch.Tensor): - time_factor = time_factor.item() - hs[-1] = replication_pad(hs[-1]) - hs.append( - F.avg_pool3d( - hs[-1], - kernel_size=[time_factor, 1, 1], - stride=[2, 1, 1], - ) - ) - - # middle - h = hs[-1] - h = self.mid.block_1(h) - h = self.mid.attn_1(h) - h = self.mid.block_2(h) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class DecoderBase(nn.Module): - def __init__( - self, - out_channels: int, - channels: int, - channels_mult: list[int], - num_res_blocks: int, - attn_resolutions: list[int], - dropout: float, - resolution: int, - z_channels: int, - **ignore_kwargs, - ): - super().__init__() - self.num_resolutions = len(channels_mult) - self.num_res_blocks = num_res_blocks - - # UnPatcher. - patch_size = ignore_kwargs.get("patch_size", 1) - self.unpatcher = UnPatcher( - patch_size, ignore_kwargs.get("patch_method", "rearrange") - ) - out_ch = out_channels * patch_size * patch_size - - block_in = channels * channels_mult[self.num_resolutions - 1] - curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) - self.z_shape = (1, z_channels, curr_res, curr_res) - logging.debug( - "Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape) - ) - ) - - # z to block_in - self.conv_in = CausalConv3d( - z_channels, block_in, kernel_size=3, stride=1, padding=1 - ) - - # num of groups for GroupNorm, num_groups=1 for LayerNorm. - num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) - - # middle - self.mid = nn.Module() - self.mid.block_1 = CausalResnetBlock3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=num_groups, - ) - self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) - self.mid.block_2 = CausalResnetBlock3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=num_groups, - ) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = channels * channels_mult[i_level] - for _ in range(self.num_res_blocks + 1): - block.append( - CausalResnetBlock3d( - in_channels=block_in, - out_channels=block_out, - dropout=dropout, - num_groups=num_groups, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = CausalUpsample3d(block_in) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = CausalNormalize(block_in, num_groups=num_groups) - self.conv_out = CausalConv3d( - block_in, out_ch, kernel_size=3, stride=1, padding=1 - ) - - def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor: - x, batch_size = time2batch(x) - x = self.unpatcher(x) - x = batch2time(x, batch_size) - - return x - - def forward(self, z): - h = self.conv_in(z) - - # middle block. - h = self.mid.block_1(h) - h = self.mid.attn_1(h) - h = self.mid.block_2(h) - - # decoder blocks. - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.up[i_level].block[i_block](h) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h) - if i_level != 0: - h = self.up[i_level].upsample(h) - else: - # temporal upsample (last level) - time_factor = 1.0 + 1.0 * (h.shape[2] > 1) - if isinstance(time_factor, torch.Tensor): - time_factor = time_factor.item() - h = h.repeat_interleave(int(time_factor), dim=2) - h = h[..., int(time_factor - 1) :, :, :] - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - h = self.unpatcher3d(h) - return h - - -class EncoderFactorized(nn.Module): - def __init__( - self, - in_channels: int, - channels: int, - channels_mult: list[int], - num_res_blocks: int, - attn_resolutions: list[int], - dropout: float, - resolution: int, - z_channels: int, - spatial_compression: int = 8, - temporal_compression: int = 8, - **ignore_kwargs, - ) -> None: - super().__init__() - self.num_resolutions = len(channels_mult) - self.num_res_blocks = num_res_blocks - - # Patcher. - patch_size = ignore_kwargs.get("patch_size", 1) - self.patcher3d = Patcher3D( - patch_size, ignore_kwargs.get("patch_method", "haar") - ) - in_channels = in_channels * patch_size * patch_size * patch_size - - # calculate the number of downsample operations - self.num_spatial_downs = int(math.log2(spatial_compression)) - int( - math.log2(patch_size) - ) - assert ( - self.num_spatial_downs <= self.num_resolutions - ), f"Spatially downsample {self.num_resolutions} times at most" - - self.num_temporal_downs = int(math.log2(temporal_compression)) - int( - math.log2(patch_size) - ) - assert ( - self.num_temporal_downs <= self.num_resolutions - ), f"Temporally downsample {self.num_resolutions} times at most" - - # downsampling - self.conv_in = nn.Sequential( - CausalConv3d( - in_channels, - channels, - kernel_size=(1, 3, 3), - stride=1, - padding=1, - ), - CausalConv3d( - channels, channels, kernel_size=(3, 1, 1), stride=1, padding=0 - ), - ) - - curr_res = resolution // patch_size - in_ch_mult = (1,) + tuple(channels_mult) - self.in_ch_mult = in_ch_mult - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = channels * in_ch_mult[i_level] - block_out = channels * channels_mult[i_level] - for _ in range(self.num_res_blocks): - block.append( - CausalResnetBlockFactorized3d( - in_channels=block_in, - out_channels=block_out, - dropout=dropout, - num_groups=1, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append( - nn.Sequential( - CausalAttnBlock(block_in, num_groups=1), - CausalTemporalAttnBlock(block_in, num_groups=1), - ) - ) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions - 1: - spatial_down = i_level < self.num_spatial_downs - temporal_down = i_level < self.num_temporal_downs - down.downsample = CausalHybridDownsample3d( - block_in, - spatial_down=spatial_down, - temporal_down=temporal_down, - ) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = CausalResnetBlockFactorized3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=1, - ) - self.mid.attn_1 = nn.Sequential( - CausalAttnBlock(block_in, num_groups=1), - CausalTemporalAttnBlock(block_in, num_groups=1), - ) - self.mid.block_2 = CausalResnetBlockFactorized3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=1, - ) - - # end - self.norm_out = CausalNormalize(block_in, num_groups=1) - self.conv_out = nn.Sequential( - CausalConv3d( - block_in, z_channels, kernel_size=(1, 3, 3), stride=1, padding=1 - ), - CausalConv3d( - z_channels, - z_channels, - kernel_size=(3, 1, 1), - stride=1, - padding=0, - ), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.patcher3d(x) - - # downsampling - h = self.conv_in(x) - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](h) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - if i_level != self.num_resolutions - 1: - h = self.down[i_level].downsample(h) - - # middle - h = self.mid.block_1(h) - h = self.mid.attn_1(h) - h = self.mid.block_2(h) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class DecoderFactorized(nn.Module): - def __init__( - self, - out_channels: int, - channels: int, - channels_mult: list[int], - num_res_blocks: int, - attn_resolutions: list[int], - dropout: float, - resolution: int, - z_channels: int, - spatial_compression: int = 8, - temporal_compression: int = 8, - **ignore_kwargs, - ): - super().__init__() - self.num_resolutions = len(channels_mult) - self.num_res_blocks = num_res_blocks - - # UnPatcher. - patch_size = ignore_kwargs.get("patch_size", 1) - self.unpatcher3d = UnPatcher3D( - patch_size, ignore_kwargs.get("patch_method", "haar") - ) - out_ch = out_channels * patch_size * patch_size * patch_size - - # calculate the number of upsample operations - self.num_spatial_ups = int(math.log2(spatial_compression)) - int( - math.log2(patch_size) - ) - assert ( - self.num_spatial_ups <= self.num_resolutions - ), f"Spatially upsample {self.num_resolutions} times at most" - self.num_temporal_ups = int(math.log2(temporal_compression)) - int( - math.log2(patch_size) - ) - assert ( - self.num_temporal_ups <= self.num_resolutions - ), f"Temporally upsample {self.num_resolutions} times at most" - - block_in = channels * channels_mult[self.num_resolutions - 1] - curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) - self.z_shape = (1, z_channels, curr_res, curr_res) - logging.debug( - "Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape) - ) - ) - - # z to block_in - self.conv_in = nn.Sequential( - CausalConv3d( - z_channels, block_in, kernel_size=(1, 3, 3), stride=1, padding=1 - ), - CausalConv3d( - block_in, block_in, kernel_size=(3, 1, 1), stride=1, padding=0 - ), - ) - - # middle - self.mid = nn.Module() - self.mid.block_1 = CausalResnetBlockFactorized3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=1, - ) - self.mid.attn_1 = nn.Sequential( - CausalAttnBlock(block_in, num_groups=1), - CausalTemporalAttnBlock(block_in, num_groups=1), - ) - self.mid.block_2 = CausalResnetBlockFactorized3d( - in_channels=block_in, - out_channels=block_in, - dropout=dropout, - num_groups=1, - ) - - legacy_mode = ignore_kwargs.get("legacy_mode", False) - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = channels * channels_mult[i_level] - for _ in range(self.num_res_blocks + 1): - block.append( - CausalResnetBlockFactorized3d( - in_channels=block_in, - out_channels=block_out, - dropout=dropout, - num_groups=1, - ) - ) - block_in = block_out - if curr_res in attn_resolutions: - attn.append( - nn.Sequential( - CausalAttnBlock(block_in, num_groups=1), - CausalTemporalAttnBlock(block_in, num_groups=1), - ) - ) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - # The layer index for temporal/spatial downsampling performed - # in the encoder should correspond to the layer index in - # reverse order where upsampling is performed in the decoder. - # If you've a pre-trained model, you can simply finetune. - i_level_reverse = self.num_resolutions - i_level - 1 - if legacy_mode: - temporal_up = i_level_reverse < self.num_temporal_ups - else: - temporal_up = 0 < i_level_reverse < self.num_temporal_ups + 1 - spatial_up = temporal_up or ( - i_level_reverse < self.num_spatial_ups - and self.num_spatial_ups > self.num_temporal_ups - ) - up.upsample = CausalHybridUpsample3d( - block_in, spatial_up=spatial_up, temporal_up=temporal_up - ) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = CausalNormalize(block_in, num_groups=1) - self.conv_out = nn.Sequential( - CausalConv3d(block_in, out_ch, kernel_size=(1, 3, 3), stride=1, padding=1), - CausalConv3d(out_ch, out_ch, kernel_size=(3, 1, 1), stride=1, padding=0), - ) - - def forward(self, z): - h = self.conv_in(z) - - # middle block. - h = self.mid.block_1(h) - h = self.mid.attn_1(h) - h = self.mid.block_2(h) - - # decoder blocks. - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks + 1): - h = self.up[i_level].block[i_block](h) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h) - if i_level != 0: - h = self.up[i_level].upsample(h) - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - h = self.unpatcher3d(h) - return h diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/patching.py b/comfy/ldm/cosmos/cosmos_tokenizer/patching.py deleted file mode 100644 index 87a53a1d9f9ac4fa6bdbb1f8f682c298fdd4376c..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/cosmos_tokenizer/patching.py +++ /dev/null @@ -1,377 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""The patcher and unpatcher implementation for 2D and 3D data. - -The idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions. -One on the rows and one on the columns. -For example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2. -We can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component. -For H component, we can use a 1D convolution with kernel [1, -1] and stride 2. -Although in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all - as we need to support downsampling for more than 2x. -For example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be. - [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64] -""" - -import torch -import torch.nn.functional as F -from einops import rearrange - -_WAVELETS = { - "haar": torch.tensor([0.7071067811865476, 0.7071067811865476]), - "rearrange": torch.tensor([1.0, 1.0]), -} -_PERSISTENT = False - - -class Patcher(torch.nn.Module): - """A module to convert image tensors into patches using torch operations. - - The main difference from `class Patching` is that this module implements - all operations using torch, rather than python or numpy, for efficiency purpose. - - It's bit-wise identical to the Patching module outputs, with the added - benefit of being torch.jit scriptable. - """ - - def __init__(self, patch_size=1, patch_method="haar"): - super().__init__() - self.patch_size = patch_size - self.patch_method = patch_method - self.register_buffer( - "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT - ) - self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) - self.register_buffer( - "_arange", - torch.arange(_WAVELETS[patch_method].shape[0]), - persistent=_PERSISTENT, - ) - for param in self.parameters(): - param.requires_grad = False - - def forward(self, x): - if self.patch_method == "haar": - return self._haar(x) - elif self.patch_method == "rearrange": - return self._arrange(x) - else: - raise ValueError("Unknown patch method: " + self.patch_method) - - def _dwt(self, x, mode="reflect", rescale=False): - dtype = x.dtype - h = self.wavelets.to(device=x.device) - - n = h.shape[0] - g = x.shape[1] - hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) - hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) - hh = hh.to(dtype=dtype) - hl = hl.to(dtype=dtype) - - x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype) - xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2)) - xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2)) - xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1)) - xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1)) - xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1)) - xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1)) - - out = torch.cat([xll, xlh, xhl, xhh], dim=1) - if rescale: - out = out / 2 - return out - - def _haar(self, x): - for _ in self.range: - x = self._dwt(x, rescale=True) - return x - - def _arrange(self, x): - x = rearrange( - x, - "b c (h p1) (w p2) -> b (c p1 p2) h w", - p1=self.patch_size, - p2=self.patch_size, - ).contiguous() - return x - - -class Patcher3D(Patcher): - """A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos.""" - - def __init__(self, patch_size=1, patch_method="haar"): - super().__init__(patch_method=patch_method, patch_size=patch_size) - self.register_buffer( - "patch_size_buffer", - patch_size * torch.ones([1], dtype=torch.int32), - persistent=_PERSISTENT, - ) - - def _dwt(self, x, wavelet, mode="reflect", rescale=False): - dtype = x.dtype - h = self.wavelets.to(device=x.device) - - n = h.shape[0] - g = x.shape[1] - hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) - hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) - hh = hh.to(dtype=dtype) - hl = hl.to(dtype=dtype) - - # Handles temporal axis. - x = F.pad( - x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode - ).to(dtype) - xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) - xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) - - # Handles spatial axes. - xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) - xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) - xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) - xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) - - xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) - - out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1) - if rescale: - out = out / (2 * torch.sqrt(torch.tensor(2.0))) - return out - - def _haar(self, x): - xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) - x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) - for _ in self.range: - x = self._dwt(x, "haar", rescale=True) - return x - - def _arrange(self, x): - xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) - x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) - x = rearrange( - x, - "b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w", - p1=self.patch_size, - p2=self.patch_size, - p3=self.patch_size, - ).contiguous() - return x - - -class UnPatcher(torch.nn.Module): - """A module to convert patches into image tensorsusing torch operations. - - The main difference from `class Unpatching` is that this module implements - all operations using torch, rather than python or numpy, for efficiency purpose. - - It's bit-wise identical to the Unpatching module outputs, with the added - benefit of being torch.jit scriptable. - """ - - def __init__(self, patch_size=1, patch_method="haar"): - super().__init__() - self.patch_size = patch_size - self.patch_method = patch_method - self.register_buffer( - "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT - ) - self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) - self.register_buffer( - "_arange", - torch.arange(_WAVELETS[patch_method].shape[0]), - persistent=_PERSISTENT, - ) - for param in self.parameters(): - param.requires_grad = False - - def forward(self, x): - if self.patch_method == "haar": - return self._ihaar(x) - elif self.patch_method == "rearrange": - return self._iarrange(x) - else: - raise ValueError("Unknown patch method: " + self.patch_method) - - def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): - dtype = x.dtype - h = self.wavelets.to(device=x.device) - n = h.shape[0] - - g = x.shape[1] // 4 - hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) - hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) - hh = hh.to(dtype=dtype) - hl = hl.to(dtype=dtype) - - xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1) - - # Inverse transform. - yl = torch.nn.functional.conv_transpose2d( - xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) - ) - yl += torch.nn.functional.conv_transpose2d( - xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) - ) - yh = torch.nn.functional.conv_transpose2d( - xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) - ) - yh += torch.nn.functional.conv_transpose2d( - xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) - ) - y = torch.nn.functional.conv_transpose2d( - yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) - ) - y += torch.nn.functional.conv_transpose2d( - yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) - ) - - if rescale: - y = y * 2 - return y - - def _ihaar(self, x): - for _ in self.range: - x = self._idwt(x, "haar", rescale=True) - return x - - def _iarrange(self, x): - x = rearrange( - x, - "b (c p1 p2) h w -> b c (h p1) (w p2)", - p1=self.patch_size, - p2=self.patch_size, - ) - return x - - -class UnPatcher3D(UnPatcher): - """A 3D inverse discrete wavelet transform for video wavelet decompositions.""" - - def __init__(self, patch_size=1, patch_method="haar"): - super().__init__(patch_method=patch_method, patch_size=patch_size) - - def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): - dtype = x.dtype - h = self.wavelets.to(device=x.device) - - g = x.shape[1] // 8 # split into 8 spatio-temporal filtered tesnors. - hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) - hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) - hl = hl.to(dtype=dtype) - hh = hh.to(dtype=dtype) - - xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1) - del x - - # Height height transposed convolutions. - xll = F.conv_transpose3d( - xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xlll - - xll += F.conv_transpose3d( - xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xllh - - xlh = F.conv_transpose3d( - xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xlhl - - xlh += F.conv_transpose3d( - xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xlhh - - xhl = F.conv_transpose3d( - xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xhll - - xhl += F.conv_transpose3d( - xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xhlh - - xhh = F.conv_transpose3d( - xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xhhl - - xhh += F.conv_transpose3d( - xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) - ) - del xhhh - - # Handles width transposed convolutions. - xl = F.conv_transpose3d( - xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) - ) - del xll - - xl += F.conv_transpose3d( - xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) - ) - del xlh - - xh = F.conv_transpose3d( - xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) - ) - del xhl - - xh += F.conv_transpose3d( - xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) - ) - del xhh - - # Handles time axis transposed convolutions. - x = F.conv_transpose3d( - xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) - ) - del xl - - x += F.conv_transpose3d( - xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) - ) - - if rescale: - x = x * (2 * torch.sqrt(torch.tensor(2.0))) - return x - - def _ihaar(self, x): - for _ in self.range: - x = self._idwt(x, "haar", rescale=True) - x = x[:, :, self.patch_size - 1 :, ...] - return x - - def _iarrange(self, x): - x = rearrange( - x, - "b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)", - p1=self.patch_size, - p2=self.patch_size, - p3=self.patch_size, - ) - x = x[:, :, self.patch_size - 1 :, ...] - return x diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/utils.py b/comfy/ldm/cosmos/cosmos_tokenizer/utils.py deleted file mode 100644 index ca993006fc800db4cd016fe43e67ae7473ab1634..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/cosmos_tokenizer/utils.py +++ /dev/null @@ -1,113 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Shared utilities for the networks module.""" - -from typing import Any - -import torch -from einops import rearrange - - -import comfy.ops -ops = comfy.ops.disable_weight_init - -def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: - batch_size = x.shape[0] - return rearrange(x, "b c t h w -> (b t) c h w"), batch_size - - -def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor: - return rearrange(x, "(b t) c h w -> b c t h w", b=batch_size) - - -def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: - batch_size, height = x.shape[0], x.shape[-2] - return rearrange(x, "b c t h w -> (b h w) c t"), batch_size, height - - -def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor: - return rearrange(x, "(b h w) c t -> b c t h w", b=batch_size, h=height) - - -def cast_tuple(t: Any, length: int = 1) -> Any: - return t if isinstance(t, tuple) else ((t,) * length) - - -def replication_pad(x): - return torch.cat([x[:, :, :1, ...], x], dim=2) - - -def divisible_by(num: int, den: int) -> bool: - return (num % den) == 0 - - -def is_odd(n: int) -> bool: - return not divisible_by(n, 2) - - -def nonlinearity(x): - # x * sigmoid(x) - return torch.nn.functional.silu(x) - - -def Normalize(in_channels, num_groups=32): - return ops.GroupNorm( - num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True - ) - - -class CausalNormalize(torch.nn.Module): - def __init__(self, in_channels, num_groups=1): - super().__init__() - self.norm = ops.GroupNorm( - num_groups=num_groups, - num_channels=in_channels, - eps=1e-6, - affine=True, - ) - self.num_groups = num_groups - - def forward(self, x): - # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose. - # All new models should use num_groups=1, otherwise causality is not guaranteed. - if self.num_groups == 1: - x, batch_size = time2batch(x) - return batch2time(self.norm(x), batch_size) - return self.norm(x) - - -def exists(v): - return v is not None - - -def default(*args): - for arg in args: - if exists(arg): - return arg - return None - - -def round_ste(z: torch.Tensor) -> torch.Tensor: - """Round with straight through gradients.""" - zhat = z.round() - return z + (zhat - z).detach() - - -def log(t, eps=1e-5): - return t.clamp(min=eps).log() - - -def entropy(prob): - return (-prob * log(prob)).sum(dim=-1) diff --git a/comfy/ldm/cosmos/model.py b/comfy/ldm/cosmos/model.py deleted file mode 100644 index 53698b758b537445567aa8eb0a58edc825846fe7..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/model.py +++ /dev/null @@ -1,550 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. -""" - -from typing import Optional, Tuple - -import torch -from einops import rearrange -from torch import nn -from torchvision import transforms - -from enum import Enum -import logging - -import comfy.patcher_extension - -from .blocks import ( - FinalLayer, - GeneralDITTransformerBlock, - PatchEmbed, - TimestepEmbedding, - Timesteps, -) - -from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb - - -class DataType(Enum): - IMAGE = "image" - VIDEO = "video" - - -class GeneralDIT(nn.Module): - """ - A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. - - Args: - max_img_h (int): Maximum height of the input images. - max_img_w (int): Maximum width of the input images. - max_frames (int): Maximum number of frames in the video sequence. - in_channels (int): Number of input channels (e.g., RGB channels for color images). - out_channels (int): Number of output channels. - patch_spatial (tuple): Spatial resolution of patches for input processing. - patch_temporal (int): Temporal resolution of patches for input processing. - concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. - block_config (str): Configuration of the transformer block. See Notes for supported block types. - model_channels (int): Base number of channels used throughout the model. - num_blocks (int): Number of transformer blocks. - num_heads (int): Number of heads in the multi-head attention layers. - mlp_ratio (float): Expansion ratio for MLP blocks. - block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD'). - crossattn_emb_channels (int): Number of embedding channels for cross-attention. - use_cross_attn_mask (bool): Whether to use mask in cross-attention. - pos_emb_cls (str): Type of positional embeddings. - pos_emb_learnable (bool): Whether positional embeddings are learnable. - pos_emb_interpolation (str): Method for interpolating positional embeddings. - affline_emb_norm (bool): Whether to normalize affine embeddings. - use_adaln_lora (bool): Whether to use AdaLN-LoRA. - adaln_lora_dim (int): Dimension for AdaLN-LoRA. - rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. - rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. - rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. - extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. - extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings. - extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. - extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. - extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. - - Notes: - Supported block types in block_config: - * cross_attn, ca: Cross attention - * full_attn: Full attention on all flattened tokens - * mlp, ff: Feed forward block - """ - - def __init__( - self, - max_img_h: int, - max_img_w: int, - max_frames: int, - in_channels: int, - out_channels: int, - patch_spatial: tuple, - patch_temporal: int, - concat_padding_mask: bool = True, - # attention settings - block_config: str = "FA-CA-MLP", - model_channels: int = 768, - num_blocks: int = 10, - num_heads: int = 16, - mlp_ratio: float = 4.0, - block_x_format: str = "BTHWD", - # cross attention settings - crossattn_emb_channels: int = 1024, - use_cross_attn_mask: bool = False, - # positional embedding settings - pos_emb_cls: str = "sincos", - pos_emb_learnable: bool = False, - pos_emb_interpolation: str = "crop", - affline_emb_norm: bool = False, # whether or not to normalize the affine embedding - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - rope_h_extrapolation_ratio: float = 1.0, - rope_w_extrapolation_ratio: float = 1.0, - rope_t_extrapolation_ratio: float = 1.0, - extra_per_block_abs_pos_emb: bool = False, - extra_per_block_abs_pos_emb_type: str = "sincos", - extra_h_extrapolation_ratio: float = 1.0, - extra_w_extrapolation_ratio: float = 1.0, - extra_t_extrapolation_ratio: float = 1.0, - image_model=None, - device=None, - dtype=None, - operations=None, - ) -> None: - super().__init__() - self.max_img_h = max_img_h - self.max_img_w = max_img_w - self.max_frames = max_frames - self.in_channels = in_channels - self.out_channels = out_channels - self.patch_spatial = patch_spatial - self.patch_temporal = patch_temporal - self.num_heads = num_heads - self.num_blocks = num_blocks - self.model_channels = model_channels - self.use_cross_attn_mask = use_cross_attn_mask - self.concat_padding_mask = concat_padding_mask - # positional embedding settings - self.pos_emb_cls = pos_emb_cls - self.pos_emb_learnable = pos_emb_learnable - self.pos_emb_interpolation = pos_emb_interpolation - self.affline_emb_norm = affline_emb_norm - self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio - self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio - self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio - self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb - self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower() - self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio - self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio - self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio - self.dtype = dtype - weight_args = {"device": device, "dtype": dtype} - - in_channels = in_channels + 1 if concat_padding_mask else in_channels - self.x_embedder = PatchEmbed( - spatial_patch_size=patch_spatial, - temporal_patch_size=patch_temporal, - in_channels=in_channels, - out_channels=model_channels, - bias=False, - weight_args=weight_args, - operations=operations, - ) - - self.build_pos_embed(device=device, dtype=dtype) - self.block_x_format = block_x_format - self.use_adaln_lora = use_adaln_lora - self.adaln_lora_dim = adaln_lora_dim - self.t_embedder = nn.ModuleList( - [Timesteps(model_channels), - TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),] - ) - - self.blocks = nn.ModuleDict() - - for idx in range(num_blocks): - self.blocks[f"block{idx}"] = GeneralDITTransformerBlock( - x_dim=model_channels, - context_dim=crossattn_emb_channels, - num_heads=num_heads, - block_config=block_config, - mlp_ratio=mlp_ratio, - x_format=self.block_x_format, - use_adaln_lora=use_adaln_lora, - adaln_lora_dim=adaln_lora_dim, - weight_args=weight_args, - operations=operations, - ) - - if self.affline_emb_norm: - logging.debug("Building affine embedding normalization layer") - self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype) - else: - self.affline_norm = nn.Identity() - - self.final_layer = FinalLayer( - hidden_size=self.model_channels, - spatial_patch_size=self.patch_spatial, - temporal_patch_size=self.patch_temporal, - out_channels=self.out_channels, - use_adaln_lora=self.use_adaln_lora, - adaln_lora_dim=self.adaln_lora_dim, - weight_args=weight_args, - operations=operations, - ) - - def build_pos_embed(self, device=None, dtype=None): - if self.pos_emb_cls == "rope3d": - cls_type = VideoRopePosition3DEmb - else: - raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") - - logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") - kwargs = dict( - model_channels=self.model_channels, - len_h=self.max_img_h // self.patch_spatial, - len_w=self.max_img_w // self.patch_spatial, - len_t=self.max_frames // self.patch_temporal, - is_learnable=self.pos_emb_learnable, - interpolation=self.pos_emb_interpolation, - head_dim=self.model_channels // self.num_heads, - h_extrapolation_ratio=self.rope_h_extrapolation_ratio, - w_extrapolation_ratio=self.rope_w_extrapolation_ratio, - t_extrapolation_ratio=self.rope_t_extrapolation_ratio, - device=device, - ) - self.pos_embedder = cls_type( - **kwargs, - ) - - if self.extra_per_block_abs_pos_emb: - assert self.extra_per_block_abs_pos_emb_type in [ - "learnable", - ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}" - kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio - kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio - kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio - kwargs["device"] = device - kwargs["dtype"] = dtype - self.extra_pos_embedder = LearnablePosEmbAxis( - **kwargs, - ) - - def prepare_embedded_sequence( - self, - x_B_C_T_H_W: torch.Tensor, - fps: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - latent_condition: Optional[torch.Tensor] = None, - latent_condition_sigma: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - """ - Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. - - Args: - x_B_C_T_H_W (torch.Tensor): video - fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. - If None, a default value (`self.base_fps`) will be used. - padding_mask (Optional[torch.Tensor]): current it is not used - - Returns: - Tuple[torch.Tensor, Optional[torch.Tensor]]: - - A tensor of shape (B, T, H, W, D) with the embedded sequence. - - An optional positional embedding tensor, returned only if the positional embedding class - (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. - - Notes: - - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. - - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. - - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using - the `self.pos_embedder` with the shape [T, H, W]. - - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the - `self.pos_embedder` with the fps tensor. - - Otherwise, the positional embeddings are generated without considering fps. - """ - if self.concat_padding_mask: - if padding_mask is not None: - padding_mask = transforms.functional.resize( - padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST - ) - else: - padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) - - x_B_C_T_H_W = torch.cat( - [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 - ) - x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) - - if self.extra_per_block_abs_pos_emb: - extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) - else: - extra_pos_emb = None - - if "rope" in self.pos_emb_cls.lower(): - return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb - - if "fps_aware" in self.pos_emb_cls: - x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device) # [B, T, H, W, D] - else: - x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] - - return x_B_T_H_W_D, None, extra_pos_emb - - def decoder_head( - self, - x_B_T_H_W_D: torch.Tensor, - emb_B_D: torch.Tensor, - crossattn_emb: torch.Tensor, - origin_shape: Tuple[int, int, int, int, int], # [B, C, T, H, W] - crossattn_mask: Optional[torch.Tensor] = None, - adaln_lora_B_3D: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - del crossattn_emb, crossattn_mask - B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape - x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D") - x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D) - # This is to ensure x_BT_HW_D has the correct shape because - # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D). - x_BT_HW_D = x_BT_HW_D.view( - B * T_before_patchify // self.patch_temporal, - H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial, - -1, - ) - x_B_D_T_H_W = rearrange( - x_BT_HW_D, - "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)", - p1=self.patch_spatial, - p2=self.patch_spatial, - H=H_before_patchify // self.patch_spatial, - W=W_before_patchify // self.patch_spatial, - t=self.patch_temporal, - B=B, - ) - return x_B_D_T_H_W - - def forward_before_blocks( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - crossattn_emb: torch.Tensor, - crossattn_mask: Optional[torch.Tensor] = None, - fps: Optional[torch.Tensor] = None, - image_size: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - scalar_feature: Optional[torch.Tensor] = None, - data_type: Optional[DataType] = DataType.VIDEO, - latent_condition: Optional[torch.Tensor] = None, - latent_condition_sigma: Optional[torch.Tensor] = None, - **kwargs, - ) -> torch.Tensor: - """ - Args: - x: (B, C, T, H, W) tensor of spatial-temp inputs - timesteps: (B, ) tensor of timesteps - crossattn_emb: (B, N, D) tensor of cross-attention embeddings - crossattn_mask: (B, N) tensor of cross-attention masks - """ - del kwargs - assert isinstance( - data_type, DataType - ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later." - original_shape = x.shape - x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( - x, - fps=fps, - padding_mask=padding_mask, - latent_condition=latent_condition, - latent_condition_sigma=latent_condition_sigma, - ) - # logging affline scale information - affline_scale_log_info = {} - - timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype)) - affline_emb_B_D = timesteps_B_D - affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach() - - if scalar_feature is not None: - raise NotImplementedError("Scalar feature is not implemented yet.") - - affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach() - affline_emb_B_D = self.affline_norm(affline_emb_B_D) - - if self.use_cross_attn_mask: - if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask): - crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max - crossattn_mask = crossattn_mask[:, None, None, :] # .to(dtype=torch.bool) # [B, 1, 1, length] - else: - crossattn_mask = None - - if self.blocks["block0"].x_format == "THWBD": - x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D") - if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: - extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange( - extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D" - ) - crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D") - - if crossattn_mask: - crossattn_mask = rearrange(crossattn_mask, "B M -> M B") - - elif self.blocks["block0"].x_format == "BTHWD": - x = x_B_T_H_W_D - else: - raise ValueError(f"Unknown x_format {self.blocks[0].x_format}") - output = { - "x": x, - "affline_emb_B_D": affline_emb_B_D, - "crossattn_emb": crossattn_emb, - "crossattn_mask": crossattn_mask, - "rope_emb_L_1_1_D": rope_emb_L_1_1_D, - "adaln_lora_B_3D": adaln_lora_B_3D, - "original_shape": original_shape, - "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, - } - return output - - def forward( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - context: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - # crossattn_emb: torch.Tensor, - # crossattn_mask: Optional[torch.Tensor] = None, - fps: Optional[torch.Tensor] = None, - image_size: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - scalar_feature: Optional[torch.Tensor] = None, - data_type: Optional[DataType] = DataType.VIDEO, - latent_condition: Optional[torch.Tensor] = None, - latent_condition_sigma: Optional[torch.Tensor] = None, - condition_video_augment_sigma: Optional[torch.Tensor] = None, - **kwargs, - ): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) - ).execute(x, - timesteps, - context, - attention_mask, - fps, - image_size, - padding_mask, - scalar_feature, - data_type, - latent_condition, - latent_condition_sigma, - condition_video_augment_sigma, - **kwargs) - - def _forward( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - context: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - # crossattn_emb: torch.Tensor, - # crossattn_mask: Optional[torch.Tensor] = None, - fps: Optional[torch.Tensor] = None, - image_size: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - scalar_feature: Optional[torch.Tensor] = None, - data_type: Optional[DataType] = DataType.VIDEO, - latent_condition: Optional[torch.Tensor] = None, - latent_condition_sigma: Optional[torch.Tensor] = None, - condition_video_augment_sigma: Optional[torch.Tensor] = None, - **kwargs, - ): - """ - Args: - x: (B, C, T, H, W) tensor of spatial-temp inputs - timesteps: (B, ) tensor of timesteps - crossattn_emb: (B, N, D) tensor of cross-attention embeddings - crossattn_mask: (B, N) tensor of cross-attention masks - condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to - augment condition input, the lvg model will condition on the condition_video_augment_sigma value; - we need forward_before_blocks pass to the forward_before_blocks function. - """ - - crossattn_emb = context - crossattn_mask = attention_mask - - inputs = self.forward_before_blocks( - x=x, - timesteps=timesteps, - crossattn_emb=crossattn_emb, - crossattn_mask=crossattn_mask, - fps=fps, - image_size=image_size, - padding_mask=padding_mask, - scalar_feature=scalar_feature, - data_type=data_type, - latent_condition=latent_condition, - latent_condition_sigma=latent_condition_sigma, - condition_video_augment_sigma=condition_video_augment_sigma, - **kwargs, - ) - x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = ( - inputs["x"], - inputs["affline_emb_B_D"], - inputs["crossattn_emb"], - inputs["crossattn_mask"], - inputs["rope_emb_L_1_1_D"], - inputs["adaln_lora_B_3D"], - inputs["original_shape"], - ) - extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype) - del inputs - - if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: - assert ( - x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape - ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}" - - for _, block in self.blocks.items(): - assert ( - self.blocks["block0"].x_format == block.x_format - ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}" - - if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: - x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D - x = block( - x, - affline_emb_B_D, - crossattn_emb, - crossattn_mask, - rope_emb_L_1_1_D=rope_emb_L_1_1_D, - adaln_lora_B_3D=adaln_lora_B_3D, - ) - - x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D") - - x_B_D_T_H_W = self.decoder_head( - x_B_T_H_W_D=x_B_T_H_W_D, - emb_B_D=affline_emb_B_D, - crossattn_emb=None, - origin_shape=original_shape, - crossattn_mask=None, - adaln_lora_B_3D=adaln_lora_B_3D, - ) - - return x_B_D_T_H_W diff --git a/comfy/ldm/cosmos/position_embedding.py b/comfy/ldm/cosmos/position_embedding.py deleted file mode 100644 index c925811d4c2275699a030ef8bbcb299a65918754..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/position_embedding.py +++ /dev/null @@ -1,207 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from typing import List, Optional - -import torch -from einops import rearrange, repeat -from torch import nn -import math - - -def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0) -> torch.Tensor: - """ - Normalizes the input tensor along specified dimensions such that the average square norm of elements is adjusted. - - Args: - x (torch.Tensor): The input tensor to normalize. - dim (list, optional): The dimensions over which to normalize. If None, normalizes over all dimensions except the first. - eps (float, optional): A small constant to ensure numerical stability during division. - - Returns: - torch.Tensor: The normalized tensor. - """ - if dim is None: - dim = list(range(1, x.ndim)) - norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) - norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel())) - return x / norm.to(x.dtype) - - -class VideoPositionEmb(nn.Module): - def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: - """ - It delegates the embedding generation to generate_embeddings function. - """ - B_T_H_W_C = x_B_T_H_W_C.shape - embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype) - - return embeddings - - def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None): - raise NotImplementedError - - -class VideoRopePosition3DEmb(VideoPositionEmb): - def __init__( - self, - *, # enforce keyword arguments - head_dim: int, - len_h: int, - len_w: int, - len_t: int, - base_fps: int = 24, - h_extrapolation_ratio: float = 1.0, - w_extrapolation_ratio: float = 1.0, - t_extrapolation_ratio: float = 1.0, - enable_fps_modulation: bool = True, - device=None, - **kwargs, # used for compatibility with other positional embeddings; unused in this class - ): - del kwargs - super().__init__() - self.base_fps = base_fps - self.max_h = len_h - self.max_w = len_w - self.enable_fps_modulation = enable_fps_modulation - - dim = head_dim - dim_h = dim // 6 * 2 - dim_w = dim_h - dim_t = dim - 2 * dim_h - assert dim == dim_h + dim_w + dim_t, f"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}" - self.register_buffer( - "dim_spatial_range", - torch.arange(0, dim_h, 2, device=device)[: (dim_h // 2)].float() / dim_h, - persistent=False, - ) - self.register_buffer( - "dim_temporal_range", - torch.arange(0, dim_t, 2, device=device)[: (dim_t // 2)].float() / dim_t, - persistent=False, - ) - - self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2)) - self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2)) - self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2)) - - def generate_embeddings( - self, - B_T_H_W_C: torch.Size, - fps: Optional[torch.Tensor] = None, - h_ntk_factor: Optional[float] = None, - w_ntk_factor: Optional[float] = None, - t_ntk_factor: Optional[float] = None, - device=None, - dtype=None, - ): - """ - Generate embeddings for the given input size. - - Args: - B_T_H_W_C (torch.Size): Input tensor size (Batch, Time, Height, Width, Channels). - fps (Optional[torch.Tensor], optional): Frames per second. Defaults to None. - h_ntk_factor (Optional[float], optional): Height NTK factor. If None, uses self.h_ntk_factor. - w_ntk_factor (Optional[float], optional): Width NTK factor. If None, uses self.w_ntk_factor. - t_ntk_factor (Optional[float], optional): Time NTK factor. If None, uses self.t_ntk_factor. - - Returns: - Not specified in the original code snippet. - """ - h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor - w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor - t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor - - h_theta = 10000.0 * h_ntk_factor - w_theta = 10000.0 * w_ntk_factor - t_theta = 10000.0 * t_ntk_factor - - h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range.to(device=device)) - w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range.to(device=device)) - temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device)) - - B, T, H, W, _ = B_T_H_W_C - seq = torch.arange(max(H, W, T), dtype=torch.float, device=device) - uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max()) - assert ( - uniform_fps or B == 1 or T == 1 - ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1" - half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs) - half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs) - - # apply sequence scaling in temporal dimension - if fps is None or self.enable_fps_modulation is False: # image case - half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs) - else: - half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs) - - half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1) - half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1) - half_emb_t = torch.stack([torch.cos(half_emb_t), -torch.sin(half_emb_t), torch.sin(half_emb_t), torch.cos(half_emb_t)], dim=-1) - - em_T_H_W_D = torch.cat( - [ - repeat(half_emb_t, "t d x -> t h w d x", h=H, w=W), - repeat(half_emb_h, "h d x -> t h w d x", t=T, w=W), - repeat(half_emb_w, "w d x -> t h w d x", t=T, h=H), - ] - , dim=-2, - ) - - return rearrange(em_T_H_W_D, "t h w d (i j) -> (t h w) d i j", i=2, j=2).float() - - -class LearnablePosEmbAxis(VideoPositionEmb): - def __init__( - self, - *, # enforce keyword arguments - interpolation: str, - model_channels: int, - len_h: int, - len_w: int, - len_t: int, - device=None, - dtype=None, - **kwargs, - ): - """ - Args: - interpolation (str): we curretly only support "crop", ideally when we need extrapolation capacity, we should adjust frequency or other more advanced methods. they are not implemented yet. - """ - del kwargs # unused - super().__init__() - self.interpolation = interpolation - assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}" - - self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device, dtype=dtype)) - self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype)) - self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype)) - - def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: - B, T, H, W, _ = B_T_H_W_C - if self.interpolation == "crop": - emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype) - emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype) - emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype) - emb = ( - repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W) - + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W) - + repeat(emb_w_W, "w d-> b t h w d", b=B, t=T, h=H) - ) - assert list(emb.shape)[:4] == [B, T, H, W], f"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}" - else: - raise ValueError(f"Unknown interpolation method {self.interpolation}") - - return normalize(emb, dim=-1, eps=1e-6) diff --git a/comfy/ldm/cosmos/predict2.py b/comfy/ldm/cosmos/predict2.py deleted file mode 100644 index fcc83ba76d0d53ad1ea1fd7584616b7a9d1717e6..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/predict2.py +++ /dev/null @@ -1,879 +0,0 @@ -# original code from: https://github.com/nvidia-cosmos/cosmos-predict2 - -import torch -from torch import nn -from einops import rearrange -from einops.layers.torch import Rearrange -import logging -from typing import Callable, Optional, Tuple -import math - -from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis -from torchvision import transforms - -import comfy.patcher_extension -from comfy.ldm.modules.attention import optimized_attention - -def apply_rotary_pos_emb( - t: torch.Tensor, - freqs: torch.Tensor, -) -> torch.Tensor: - t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float() - t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1] - t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t) - return t_out - - -# ---------------------- Feed Forward Network ----------------------- -class GPT2FeedForward(nn.Module): - def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None: - super().__init__() - self.activation = nn.GELU() - self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype) - self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype) - - self._layer_id = None - self._dim = d_model - self._hidden_dim = d_ff - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.layer1(x) - - x = self.activation(x) - x = self.layer2(x) - return x - - -def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor: - """Computes multi-head attention using PyTorch's native implementation. - - This function provides a PyTorch backend alternative to Transformer Engine's attention operation. - It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product - attention, and rearranges the output back to the original format. - - The input tensor names use the following dimension conventions: - - - B: batch size - - S: sequence length - - H: number of attention heads - - D: head dimension - - Args: - q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim) - k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim) - v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim) - - Returns: - Attention output tensor with shape (batch, seq_len, n_heads * head_dim) - """ - in_q_shape = q_B_S_H_D.shape - in_k_shape = k_B_S_H_D.shape - q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1]) - k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) - v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) - return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True) - - -class Attention(nn.Module): - """ - A flexible attention module supporting both self-attention and cross-attention mechanisms. - - This module implements a multi-head attention layer that can operate in either self-attention - or cross-attention mode. The mode is determined by whether a context dimension is provided. - The implementation uses scaled dot-product attention and supports optional bias terms and - dropout regularization. - - Args: - query_dim (int): The dimensionality of the query vectors. - context_dim (int, optional): The dimensionality of the context (key/value) vectors. - If None, the module operates in self-attention mode using query_dim. Default: None - n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8 - head_dim (int, optional): The dimension of each attention head. Default: 64 - dropout (float, optional): Dropout probability applied to the output. Default: 0.0 - qkv_format (str, optional): Format specification for QKV tensors. Default: "bshd" - backend (str, optional): Backend to use for the attention operation. Default: "transformer_engine" - - Examples: - >>> # Self-attention with 512 dimensions and 8 heads - >>> self_attn = Attention(query_dim=512) - >>> x = torch.randn(32, 16, 512) # (batch_size, seq_len, dim) - >>> out = self_attn(x) # (32, 16, 512) - - >>> # Cross-attention - >>> cross_attn = Attention(query_dim=512, context_dim=256) - >>> query = torch.randn(32, 16, 512) - >>> context = torch.randn(32, 8, 256) - >>> out = cross_attn(query, context) # (32, 16, 512) - """ - - def __init__( - self, - query_dim: int, - context_dim: Optional[int] = None, - n_heads: int = 8, - head_dim: int = 64, - dropout: float = 0.0, - device=None, - dtype=None, - operations=None, - ) -> None: - super().__init__() - logging.debug( - f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " - f"{n_heads} heads with a dimension of {head_dim}." - ) - self.is_selfattn = context_dim is None # self attention - - context_dim = query_dim if context_dim is None else context_dim - inner_dim = head_dim * n_heads - - self.n_heads = n_heads - self.head_dim = head_dim - self.query_dim = query_dim - self.context_dim = context_dim - - self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype) - self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) - - self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) - self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) - - self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) - self.v_norm = nn.Identity() - - self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype) - self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity() - - self.attn_op = torch_attention_op - - self._query_dim = query_dim - self._context_dim = context_dim - self._inner_dim = inner_dim - - def compute_qkv( - self, - x: torch.Tensor, - context: Optional[torch.Tensor] = None, - rope_emb: Optional[torch.Tensor] = None, - ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - q = self.q_proj(x) - context = x if context is None else context - k = self.k_proj(context) - v = self.v_proj(context) - q, k, v = map( - lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim), - (q, k, v), - ) - - def apply_norm_and_rotary_pos_emb( - q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor] - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - q = self.q_norm(q) - k = self.k_norm(k) - v = self.v_norm(v) - if self.is_selfattn and rope_emb is not None: # only apply to self-attention! - q = apply_rotary_pos_emb(q, rope_emb) - k = apply_rotary_pos_emb(k, rope_emb) - return q, k, v - - q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb) - - return q, k, v - - def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor: - result = self.attn_op(q, k, v) # [B, S, H, D] - return self.output_dropout(self.output_proj(result)) - - def forward( - self, - x: torch.Tensor, - context: Optional[torch.Tensor] = None, - rope_emb: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - """ - Args: - x (Tensor): The query tensor of shape [B, Mq, K] - context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None - """ - q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb) - return self.compute_attention(q, k, v) - - -class Timesteps(nn.Module): - def __init__(self, num_channels: int): - super().__init__() - self.num_channels = num_channels - - def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor: - assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}" - timesteps = timesteps_B_T.flatten().float() - half_dim = self.num_channels // 2 - exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) - exponent = exponent / (half_dim - 0.0) - - emb = torch.exp(exponent) - emb = timesteps[:, None].float() * emb[None, :] - - sin_emb = torch.sin(emb) - cos_emb = torch.cos(emb) - emb = torch.cat([cos_emb, sin_emb], dim=-1) - - return rearrange(emb, "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1]) - - -class TimestepEmbedding(nn.Module): - def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None): - super().__init__() - logging.debug( - f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." - ) - self.in_dim = in_features - self.out_dim = out_features - self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype) - self.activation = nn.SiLU() - self.use_adaln_lora = use_adaln_lora - if use_adaln_lora: - self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype) - else: - self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype) - - def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - emb = self.linear_1(sample) - emb = self.activation(emb) - emb = self.linear_2(emb) - - if self.use_adaln_lora: - adaln_lora_B_T_3D = emb - emb_B_T_D = sample - else: - adaln_lora_B_T_3D = None - emb_B_T_D = emb - - return emb_B_T_D, adaln_lora_B_T_3D - - -class PatchEmbed(nn.Module): - """ - PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, - depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, - making it suitable for video and image processing tasks. It supports dividing the input into patches - and embedding each patch into a vector of size `out_channels`. - - Parameters: - - spatial_patch_size (int): The size of each spatial patch. - - temporal_patch_size (int): The size of each temporal patch. - - in_channels (int): Number of input channels. Default: 3. - - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. - - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. - """ - - def __init__( - self, - spatial_patch_size: int, - temporal_patch_size: int, - in_channels: int = 3, - out_channels: int = 768, - device=None, dtype=None, operations=None - ): - super().__init__() - self.spatial_patch_size = spatial_patch_size - self.temporal_patch_size = temporal_patch_size - - self.proj = nn.Sequential( - Rearrange( - "b c (t r) (h m) (w n) -> b t h w (c r m n)", - r=temporal_patch_size, - m=spatial_patch_size, - n=spatial_patch_size, - ), - operations.Linear( - in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype - ), - ) - self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ - Forward pass of the PatchEmbed module. - - Parameters: - - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where - B is the batch size, - C is the number of channels, - T is the temporal dimension, - H is the height, and - W is the width of the input. - - Returns: - - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. - """ - assert x.dim() == 5 - _, _, T, H, W = x.shape - assert ( - H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 - ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}" - assert T % self.temporal_patch_size == 0 - x = self.proj(x) - return x - - -class FinalLayer(nn.Module): - """ - The final layer of video DiT. - """ - - def __init__( - self, - hidden_size: int, - spatial_patch_size: int, - temporal_patch_size: int, - out_channels: int, - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - device=None, dtype=None, operations=None - ): - super().__init__() - self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = operations.Linear( - hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype - ) - self.hidden_size = hidden_size - self.n_adaln_chunks = 2 - self.use_adaln_lora = use_adaln_lora - self.adaln_lora_dim = adaln_lora_dim - if use_adaln_lora: - self.adaln_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype), - operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype), - ) - else: - self.adaln_modulation = nn.Sequential( - nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype) - ) - - def forward( - self, - x_B_T_H_W_D: torch.Tensor, - emb_B_T_D: torch.Tensor, - adaln_lora_B_T_3D: Optional[torch.Tensor] = None, - ): - if self.use_adaln_lora: - assert adaln_lora_B_T_3D is not None - shift_B_T_D, scale_B_T_D = ( - self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size] - ).chunk(2, dim=-1) - else: - shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1) - - shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d"), rearrange( - scale_B_T_D, "b t d -> b t 1 1 d" - ) - - def _fn( - _x_B_T_H_W_D: torch.Tensor, - _norm_layer: nn.Module, - _scale_B_T_1_1_D: torch.Tensor, - _shift_B_T_1_1_D: torch.Tensor, - ) -> torch.Tensor: - return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D - - x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D) - x_B_T_H_W_O = self.linear(x_B_T_H_W_D) - return x_B_T_H_W_O - - -class Block(nn.Module): - """ - A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation. - Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation. - - Parameters: - x_dim (int): Dimension of input features - context_dim (int): Dimension of context features for cross-attention - num_heads (int): Number of attention heads - mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0 - use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False - adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256 - - The block applies the following sequence: - 1. Self-attention with AdaLN modulation - 2. Cross-attention with AdaLN modulation - 3. MLP with AdaLN modulation - - Each component uses skip connections and layer normalization. - """ - - def __init__( - self, - x_dim: int, - context_dim: int, - num_heads: int, - mlp_ratio: float = 4.0, - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - device=None, - dtype=None, - operations=None, - ): - super().__init__() - self.x_dim = x_dim - self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) - self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations) - - self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) - self.cross_attn = Attention( - x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations - ) - - self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) - self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations) - - self.use_adaln_lora = use_adaln_lora - if self.use_adaln_lora: - self.adaln_modulation_self_attn = nn.Sequential( - nn.SiLU(), - operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), - operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), - ) - self.adaln_modulation_cross_attn = nn.Sequential( - nn.SiLU(), - operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), - operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), - ) - self.adaln_modulation_mlp = nn.Sequential( - nn.SiLU(), - operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), - operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), - ) - else: - self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) - self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) - self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) - - def forward( - self, - x_B_T_H_W_D: torch.Tensor, - emb_B_T_D: torch.Tensor, - crossattn_emb: torch.Tensor, - rope_emb_L_1_1_D: Optional[torch.Tensor] = None, - adaln_lora_B_T_3D: Optional[torch.Tensor] = None, - extra_per_block_pos_emb: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - if extra_per_block_pos_emb is not None: - x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb - - if self.use_adaln_lora: - shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = ( - self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D - ).chunk(3, dim=-1) - shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = ( - self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D - ).chunk(3, dim=-1) - shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = ( - self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D - ).chunk(3, dim=-1) - else: - shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn( - emb_B_T_D - ).chunk(3, dim=-1) - shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn( - emb_B_T_D - ).chunk(3, dim=-1) - shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1) - - # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting - shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d") - scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d") - gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d") - - shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d") - scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d") - gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d") - - shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d") - scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d") - gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d") - - B, T, H, W, D = x_B_T_H_W_D.shape - - def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D): - return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D - - normalized_x_B_T_H_W_D = _fn( - x_B_T_H_W_D, - self.layer_norm_self_attn, - scale_self_attn_B_T_1_1_D, - shift_self_attn_B_T_1_1_D, - ) - result_B_T_H_W_D = rearrange( - self.self_attn( - # normalized_x_B_T_HW_D, - rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), - None, - rope_emb=rope_emb_L_1_1_D, - ), - "b (t h w) d -> b t h w d", - t=T, - h=H, - w=W, - ) - x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D - - def _x_fn( - _x_B_T_H_W_D: torch.Tensor, - layer_norm_cross_attn: Callable, - _scale_cross_attn_B_T_1_1_D: torch.Tensor, - _shift_cross_attn_B_T_1_1_D: torch.Tensor, - ) -> torch.Tensor: - _normalized_x_B_T_H_W_D = _fn( - _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D - ) - _result_B_T_H_W_D = rearrange( - self.cross_attn( - rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), - crossattn_emb, - rope_emb=rope_emb_L_1_1_D, - ), - "b (t h w) d -> b t h w d", - t=T, - h=H, - w=W, - ) - return _result_B_T_H_W_D - - result_B_T_H_W_D = _x_fn( - x_B_T_H_W_D, - self.layer_norm_cross_attn, - scale_cross_attn_B_T_1_1_D, - shift_cross_attn_B_T_1_1_D, - ) - x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D - - normalized_x_B_T_H_W_D = _fn( - x_B_T_H_W_D, - self.layer_norm_mlp, - scale_mlp_B_T_1_1_D, - shift_mlp_B_T_1_1_D, - ) - result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D) - x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D - return x_B_T_H_W_D - - -class MiniTrainDIT(nn.Module): - """ - A clean impl of DIT that can load and reproduce the training results of the original DIT model in~(cosmos 1) - A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. - - Args: - max_img_h (int): Maximum height of the input images. - max_img_w (int): Maximum width of the input images. - max_frames (int): Maximum number of frames in the video sequence. - in_channels (int): Number of input channels (e.g., RGB channels for color images). - out_channels (int): Number of output channels. - patch_spatial (tuple): Spatial resolution of patches for input processing. - patch_temporal (int): Temporal resolution of patches for input processing. - concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. - model_channels (int): Base number of channels used throughout the model. - num_blocks (int): Number of transformer blocks. - num_heads (int): Number of heads in the multi-head attention layers. - mlp_ratio (float): Expansion ratio for MLP blocks. - crossattn_emb_channels (int): Number of embedding channels for cross-attention. - pos_emb_cls (str): Type of positional embeddings. - pos_emb_learnable (bool): Whether positional embeddings are learnable. - pos_emb_interpolation (str): Method for interpolating positional embeddings. - min_fps (int): Minimum frames per second. - max_fps (int): Maximum frames per second. - use_adaln_lora (bool): Whether to use AdaLN-LoRA. - adaln_lora_dim (int): Dimension for AdaLN-LoRA. - rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. - rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. - rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. - extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. - extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. - extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. - extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. - """ - - def __init__( - self, - max_img_h: int, - max_img_w: int, - max_frames: int, - in_channels: int, - out_channels: int, - patch_spatial: int, # tuple, - patch_temporal: int, - concat_padding_mask: bool = True, - # attention settings - model_channels: int = 768, - num_blocks: int = 10, - num_heads: int = 16, - mlp_ratio: float = 4.0, - # cross attention settings - crossattn_emb_channels: int = 1024, - # positional embedding settings - pos_emb_cls: str = "sincos", - pos_emb_learnable: bool = False, - pos_emb_interpolation: str = "crop", - min_fps: int = 1, - max_fps: int = 30, - use_adaln_lora: bool = False, - adaln_lora_dim: int = 256, - rope_h_extrapolation_ratio: float = 1.0, - rope_w_extrapolation_ratio: float = 1.0, - rope_t_extrapolation_ratio: float = 1.0, - extra_per_block_abs_pos_emb: bool = False, - extra_h_extrapolation_ratio: float = 1.0, - extra_w_extrapolation_ratio: float = 1.0, - extra_t_extrapolation_ratio: float = 1.0, - rope_enable_fps_modulation: bool = True, - image_model=None, - device=None, - dtype=None, - operations=None, - ) -> None: - super().__init__() - self.dtype = dtype - self.max_img_h = max_img_h - self.max_img_w = max_img_w - self.max_frames = max_frames - self.in_channels = in_channels - self.out_channels = out_channels - self.patch_spatial = patch_spatial - self.patch_temporal = patch_temporal - self.num_heads = num_heads - self.num_blocks = num_blocks - self.model_channels = model_channels - self.concat_padding_mask = concat_padding_mask - # positional embedding settings - self.pos_emb_cls = pos_emb_cls - self.pos_emb_learnable = pos_emb_learnable - self.pos_emb_interpolation = pos_emb_interpolation - self.min_fps = min_fps - self.max_fps = max_fps - self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio - self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio - self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio - self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb - self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio - self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio - self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio - self.rope_enable_fps_modulation = rope_enable_fps_modulation - - self.build_pos_embed(device=device, dtype=dtype) - self.use_adaln_lora = use_adaln_lora - self.adaln_lora_dim = adaln_lora_dim - self.t_embedder = nn.Sequential( - Timesteps(model_channels), - TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,), - ) - - in_channels = in_channels + 1 if concat_padding_mask else in_channels - self.x_embedder = PatchEmbed( - spatial_patch_size=patch_spatial, - temporal_patch_size=patch_temporal, - in_channels=in_channels, - out_channels=model_channels, - device=device, dtype=dtype, operations=operations, - ) - - self.blocks = nn.ModuleList( - [ - Block( - x_dim=model_channels, - context_dim=crossattn_emb_channels, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - use_adaln_lora=use_adaln_lora, - adaln_lora_dim=adaln_lora_dim, - device=device, dtype=dtype, operations=operations, - ) - for _ in range(num_blocks) - ] - ) - - self.final_layer = FinalLayer( - hidden_size=self.model_channels, - spatial_patch_size=self.patch_spatial, - temporal_patch_size=self.patch_temporal, - out_channels=self.out_channels, - use_adaln_lora=self.use_adaln_lora, - adaln_lora_dim=self.adaln_lora_dim, - device=device, dtype=dtype, operations=operations, - ) - - self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype) - - def build_pos_embed(self, device=None, dtype=None) -> None: - if self.pos_emb_cls == "rope3d": - cls_type = VideoRopePosition3DEmb - else: - raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") - - logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") - kwargs = dict( - model_channels=self.model_channels, - len_h=self.max_img_h // self.patch_spatial, - len_w=self.max_img_w // self.patch_spatial, - len_t=self.max_frames // self.patch_temporal, - max_fps=self.max_fps, - min_fps=self.min_fps, - is_learnable=self.pos_emb_learnable, - interpolation=self.pos_emb_interpolation, - head_dim=self.model_channels // self.num_heads, - h_extrapolation_ratio=self.rope_h_extrapolation_ratio, - w_extrapolation_ratio=self.rope_w_extrapolation_ratio, - t_extrapolation_ratio=self.rope_t_extrapolation_ratio, - enable_fps_modulation=self.rope_enable_fps_modulation, - device=device, - ) - self.pos_embedder = cls_type( - **kwargs, # type: ignore - ) - - if self.extra_per_block_abs_pos_emb: - kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio - kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio - kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio - kwargs["device"] = device - kwargs["dtype"] = dtype - self.extra_pos_embedder = LearnablePosEmbAxis( - **kwargs, # type: ignore - ) - - def prepare_embedded_sequence( - self, - x_B_C_T_H_W: torch.Tensor, - fps: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: - """ - Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. - - Args: - x_B_C_T_H_W (torch.Tensor): video - fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. - If None, a default value (`self.base_fps`) will be used. - padding_mask (Optional[torch.Tensor]): current it is not used - - Returns: - Tuple[torch.Tensor, Optional[torch.Tensor]]: - - A tensor of shape (B, T, H, W, D) with the embedded sequence. - - An optional positional embedding tensor, returned only if the positional embedding class - (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. - - Notes: - - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. - - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. - - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using - the `self.pos_embedder` with the shape [T, H, W]. - - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the - `self.pos_embedder` with the fps tensor. - - Otherwise, the positional embeddings are generated without considering fps. - """ - if self.concat_padding_mask: - if padding_mask is None: - padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) - else: - padding_mask = transforms.functional.resize( - padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST - ) - x_B_C_T_H_W = torch.cat( - [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 - ) - x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) - - if self.extra_per_block_abs_pos_emb: - extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) - else: - extra_pos_emb = None - - if "rope" in self.pos_emb_cls.lower(): - return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb - x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] - - return x_B_T_H_W_D, None, extra_pos_emb - - def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor: - x_B_C_Tt_Hp_Wp = rearrange( - x_B_T_H_W_M, - "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)", - p1=self.patch_spatial, - p2=self.patch_spatial, - t=self.patch_temporal, - ) - return x_B_C_Tt_Hp_Wp - - def forward(self, - x: torch.Tensor, - timesteps: torch.Tensor, - context: torch.Tensor, - fps: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - **kwargs, - ): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) - ).execute(x, timesteps, context, fps, padding_mask, **kwargs) - - def _forward( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - context: torch.Tensor, - fps: Optional[torch.Tensor] = None, - padding_mask: Optional[torch.Tensor] = None, - **kwargs, - ): - x_B_C_T_H_W = x - timesteps_B_T = timesteps - crossattn_emb = context - """ - Args: - x: (B, C, T, H, W) tensor of spatial-temp inputs - timesteps: (B, ) tensor of timesteps - crossattn_emb: (B, N, D) tensor of cross-attention embeddings - """ - x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( - x_B_C_T_H_W, - fps=fps, - padding_mask=padding_mask, - ) - - if timesteps_B_T.ndim == 1: - timesteps_B_T = timesteps_B_T.unsqueeze(1) - t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype)) - t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D) - - # for logging purpose - affline_scale_log_info = {} - affline_scale_log_info["t_embedding_B_T_D"] = t_embedding_B_T_D.detach() - self.affline_scale_log_info = affline_scale_log_info - self.affline_emb = t_embedding_B_T_D - self.crossattn_emb = crossattn_emb - - if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: - assert ( - x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape - ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}" - - block_kwargs = { - "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0), - "adaln_lora_B_T_3D": adaln_lora_B_T_3D, - "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, - } - for block in self.blocks: - x_B_T_H_W_D = block( - x_B_T_H_W_D, - t_embedding_B_T_D, - crossattn_emb, - **block_kwargs, - ) - - x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D) - x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O) - return x_B_C_Tt_Hp_Wp diff --git a/comfy/ldm/cosmos/vae.py b/comfy/ldm/cosmos/vae.py deleted file mode 100644 index d64f292de735258f43c07099bee03e3c871e9a26..0000000000000000000000000000000000000000 --- a/comfy/ldm/cosmos/vae.py +++ /dev/null @@ -1,131 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""The causal continuous video tokenizer with VAE or AE formulation for 3D data..""" - -import logging -import torch -from torch import nn -from enum import Enum -import math - -from .cosmos_tokenizer.layers3d import ( - EncoderFactorized, - DecoderFactorized, - CausalConv3d, -) - - -class IdentityDistribution(torch.nn.Module): - def __init__(self): - super().__init__() - - def forward(self, parameters): - return parameters, (torch.tensor([0.0]), torch.tensor([0.0])) - - -class GaussianDistribution(torch.nn.Module): - def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0): - super().__init__() - self.min_logvar = min_logvar - self.max_logvar = max_logvar - - def sample(self, mean, logvar): - std = torch.exp(0.5 * logvar) - return mean + std * torch.randn_like(mean) - - def forward(self, parameters): - mean, logvar = torch.chunk(parameters, 2, dim=1) - logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar) - return self.sample(mean, logvar), (mean, logvar) - - -class ContinuousFormulation(Enum): - VAE = GaussianDistribution - AE = IdentityDistribution - - -class CausalContinuousVideoTokenizer(nn.Module): - def __init__( - self, z_channels: int, z_factor: int, latent_channels: int, **kwargs - ) -> None: - super().__init__() - self.name = kwargs.get("name", "CausalContinuousVideoTokenizer") - self.latent_channels = latent_channels - self.sigma_data = 0.5 - - # encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name) - self.encoder = EncoderFactorized( - z_channels=z_factor * z_channels, **kwargs - ) - if kwargs.get("temporal_compression", 4) == 4: - kwargs["channels_mult"] = [2, 4] - # decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name) - self.decoder = DecoderFactorized( - z_channels=z_channels, **kwargs - ) - - self.quant_conv = CausalConv3d( - z_factor * z_channels, - z_factor * latent_channels, - kernel_size=1, - padding=0, - ) - self.post_quant_conv = CausalConv3d( - latent_channels, z_channels, kernel_size=1, padding=0 - ) - - # formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name) - self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value() - - num_parameters = sum(param.numel() for param in self.parameters()) - logging.debug(f"model={self.name}, num_parameters={num_parameters:,}") - logging.debug( - f"z_channels={z_channels}, latent_channels={self.latent_channels}." - ) - - latent_temporal_chunk = 16 - self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) - self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) - - - def encode(self, x): - h = self.encoder(x) - moments = self.quant_conv(h) - z, posteriors = self.distribution(moments) - latent_ch = z.shape[1] - latent_t = z.shape[2] - in_dtype = z.dtype - mean = self.latent_mean.view(latent_ch, -1) - std = self.latent_std.view(latent_ch, -1) - - mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) - std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) - return ((z - mean) / std) * self.sigma_data - - def decode(self, z): - in_dtype = z.dtype - latent_ch = z.shape[1] - latent_t = z.shape[2] - mean = self.latent_mean.view(latent_ch, -1) - std = self.latent_std.view(latent_ch, -1) - - mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) - std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) - - z = z / self.sigma_data - z = z * std + mean - z = self.post_quant_conv(z) - return self.decoder(z) - diff --git a/comfy/ldm/flux/.DS_Store b/comfy/ldm/flux/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/flux/.DS_Store and /dev/null differ diff --git a/comfy/ldm/flux/controlnet.py b/comfy/ldm/flux/controlnet.py deleted file mode 100644 index 7dcf82bbf3e8e9a369dda0df39f37f0653514747..0000000000000000000000000000000000000000 --- a/comfy/ldm/flux/controlnet.py +++ /dev/null @@ -1,208 +0,0 @@ -#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py -#modified to support different types of flux controlnets - -import torch -import math -from torch import Tensor, nn -from einops import rearrange, repeat - -from .layers import (timestep_embedding) - -from .model import Flux -import comfy.ldm.common_dit - -class MistolineCondDownsamplBlock(nn.Module): - def __init__(self, dtype=None, device=None, operations=None): - super().__init__() - self.encoder = nn.Sequential( - operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) - ) - - def forward(self, x): - return self.encoder(x) - -class MistolineControlnetBlock(nn.Module): - def __init__(self, hidden_size, dtype=None, device=None, operations=None): - super().__init__() - self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device) - self.act = nn.SiLU() - - def forward(self, x): - return self.act(self.linear(x)) - - -class ControlNetFlux(Flux): - def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs): - super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) - - self.main_model_double = 19 - self.main_model_single = 38 - - self.mistoline = mistoline - # add ControlNet blocks - if self.mistoline: - control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations) - else: - control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) - - self.controlnet_blocks = nn.ModuleList([]) - for _ in range(self.params.depth): - self.controlnet_blocks.append(control_block()) - - self.controlnet_single_blocks = nn.ModuleList([]) - for _ in range(self.params.depth_single_blocks): - self.controlnet_single_blocks.append(control_block()) - - self.num_union_modes = num_union_modes - self.controlnet_mode_embedder = None - if self.num_union_modes > 0: - self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device) - - self.gradient_checkpointing = False - self.latent_input = latent_input - if control_latent_channels is None: - control_latent_channels = self.in_channels - else: - control_latent_channels *= 2 * 2 #patch size - - self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device) - if not self.latent_input: - if self.mistoline: - self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations) - else: - self.input_hint_block = nn.Sequential( - operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device), - nn.SiLU(), - operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device) - ) - - def forward_orig( - self, - img: Tensor, - img_ids: Tensor, - controlnet_cond: Tensor, - txt: Tensor, - txt_ids: Tensor, - timesteps: Tensor, - y: Tensor, - guidance: Tensor = None, - control_type: Tensor = None, - ) -> Tensor: - if img.ndim != 3 or txt.ndim != 3: - raise ValueError("Input img and txt tensors must have 3 dimensions.") - - if y is None: - y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) - else: - y = y[:, :self.params.vec_in_dim] - - # running on sequences img - img = self.img_in(img) - - controlnet_cond = self.pos_embed_input(controlnet_cond) - img = img + controlnet_cond - vec = self.time_in(timestep_embedding(timesteps, 256)) - if self.params.guidance_embed: - vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) - vec = vec + self.vector_in(y) - txt = self.txt_in(txt) - - if self.controlnet_mode_embedder is not None and len(control_type) > 0: - control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1)) - txt = torch.cat([control_cond, txt], dim=1) - txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1) - - ids = torch.cat((txt_ids, img_ids), dim=1) - pe = self.pe_embedder(ids) - - controlnet_double = () - - for i in range(len(self.double_blocks)): - img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe) - controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),) - - img = torch.cat((txt, img), 1) - - controlnet_single = () - - for i in range(len(self.single_blocks)): - img = self.single_blocks[i](img, vec=vec, pe=pe) - controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),) - - repeat = math.ceil(self.main_model_double / len(controlnet_double)) - if self.latent_input: - out_input = () - for x in controlnet_double: - out_input += (x,) * repeat - else: - out_input = (controlnet_double * repeat) - - out = {"input": out_input[:self.main_model_double]} - if len(controlnet_single) > 0: - repeat = math.ceil(self.main_model_single / len(controlnet_single)) - out_output = () - if self.latent_input: - for x in controlnet_single: - out_output += (x,) * repeat - else: - out_output = (controlnet_single * repeat) - out["output"] = out_output[:self.main_model_single] - return out - - def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs): - patch_size = 2 - if self.latent_input: - hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) - elif self.mistoline: - hint = hint * 2.0 - 1.0 - hint = self.input_cond_block(hint) - else: - hint = hint * 2.0 - 1.0 - hint = self.input_hint_block(hint) - - hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) - - bs, c, h, w = x.shape - x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) - - img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) - - h_len = ((h + (patch_size // 2)) // patch_size) - w_len = ((w + (patch_size // 2)) // patch_size) - img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) - img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None] - img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :] - img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) - - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", [])) diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py deleted file mode 100644 index 113eb20962a2ead9237940bc8585901dd038ec61..0000000000000000000000000000000000000000 --- a/comfy/ldm/flux/layers.py +++ /dev/null @@ -1,278 +0,0 @@ -import math -from dataclasses import dataclass - -import torch -from torch import Tensor, nn - -from .math import attention, rope -import comfy.ops -import comfy.ldm.common_dit - - -class EmbedND(nn.Module): - def __init__(self, dim: int, theta: int, axes_dim: list): - super().__init__() - self.dim = dim - self.theta = theta - self.axes_dim = axes_dim - - def forward(self, ids: Tensor) -> Tensor: - n_axes = ids.shape[-1] - emb = torch.cat( - [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], - dim=-3, - ) - - return emb.unsqueeze(1) - - -def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): - """ - Create sinusoidal timestep embeddings. - :param t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an (N, D) Tensor of positional embeddings. - """ - t = time_factor * t - half = dim // 2 - freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half) - - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - if torch.is_floating_point(t): - embedding = embedding.to(t) - return embedding - -class MLPEmbedder(nn.Module): - def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None): - super().__init__() - self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) - self.silu = nn.SiLU() - self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device) - - def forward(self, x: Tensor) -> Tensor: - return self.out_layer(self.silu(self.in_layer(x))) - - -class RMSNorm(torch.nn.Module): - def __init__(self, dim: int, dtype=None, device=None, operations=None): - super().__init__() - self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device)) - - def forward(self, x: Tensor): - return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6) - - -class QKNorm(torch.nn.Module): - def __init__(self, dim: int, dtype=None, device=None, operations=None): - super().__init__() - self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) - self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations) - - def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple: - q = self.query_norm(q) - k = self.key_norm(k) - return q.to(v), k.to(v) - - -class SelfAttention(nn.Module): - def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None): - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - - self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) - self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) - - -@dataclass -class ModulationOut: - shift: Tensor - scale: Tensor - gate: Tensor - - -class Modulation(nn.Module): - def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None): - super().__init__() - self.is_double = double - self.multiplier = 6 if double else 3 - self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) - - def forward(self, vec: Tensor) -> tuple: - if vec.ndim == 2: - vec = vec[:, None, :] - out = self.lin(nn.functional.silu(vec)).chunk(self.multiplier, dim=-1) - - return ( - ModulationOut(*out[:3]), - ModulationOut(*out[3:]) if self.is_double else None, - ) - - -def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None): - if modulation_dims is None: - if m_add is not None: - return torch.addcmul(m_add, tensor, m_mult) - else: - return tensor * m_mult - else: - for d in modulation_dims: - tensor[:, d[0]:d[1]] *= m_mult[:, d[2]] - if m_add is not None: - tensor[:, d[0]:d[1]] += m_add[:, d[2]] - return tensor - - -class DoubleStreamBlock(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): - super().__init__() - - mlp_hidden_dim = int(hidden_size * mlp_ratio) - self.num_heads = num_heads - self.hidden_size = hidden_size - self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) - self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - - self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.img_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations) - self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) - - self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.txt_mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.GELU(approximate="tanh"), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - self.flipped_img_txt = flipped_img_txt - - def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None): - img_mod1, img_mod2 = self.img_mod(vec) - txt_mod1, txt_mod2 = self.txt_mod(vec) - - # prepare image for attention - img_modulated = self.img_norm1(img) - img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img) - img_qkv = self.img_attn.qkv(img_modulated) - img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) - - # prepare txt for attention - txt_modulated = self.txt_norm1(txt) - txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt) - txt_qkv = self.txt_attn.qkv(txt_modulated) - txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) - - if self.flipped_img_txt: - # run actual attention - attn = attention(torch.cat((img_q, txt_q), dim=2), - torch.cat((img_k, txt_k), dim=2), - torch.cat((img_v, txt_v), dim=2), - pe=pe, mask=attn_mask) - - img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:] - else: - # run actual attention - attn = attention(torch.cat((txt_q, img_q), dim=2), - torch.cat((txt_k, img_k), dim=2), - torch.cat((txt_v, img_v), dim=2), - pe=pe, mask=attn_mask) - - txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] - - # calculate the img bloks - img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img) - img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img) - - # calculate the txt bloks - txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt) - txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt) - - if txt.dtype == torch.float16: - txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) - - return img, txt - - -class SingleStreamBlock(nn.Module): - """ - A DiT block with parallel linear layers as described in - https://arxiv.org/abs/2302.05442 and adapted modulation interface. - """ - - def __init__( - self, - hidden_size: int, - num_heads: int, - mlp_ratio: float = 4.0, - qk_scale: float = None, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.hidden_dim = hidden_size - self.num_heads = num_heads - head_dim = hidden_size // num_heads - self.scale = qk_scale or head_dim**-0.5 - - self.mlp_hidden_dim = int(hidden_size * mlp_ratio) - # qkv and mlp_in - self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) - # proj and mlp_out - self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) - - self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) - - self.hidden_size = hidden_size - self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - self.mlp_act = nn.GELU(approximate="tanh") - self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) - - def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor: - mod, _ = self.modulation(vec) - qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) - - q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - q, k = self.norm(q, k, v) - - # compute attention - attn = attention(q, k, v, pe=pe, mask=attn_mask) - # compute activation in mlp stream, cat again and run second linear layer - output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) - x += apply_mod(output, mod.gate, None, modulation_dims) - if x.dtype == torch.float16: - x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) - return x - - -class LastLayer(nn.Module): - def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)) - - def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor: - if vec.ndim == 2: - vec = vec[:, None, :] - - shift, scale = self.adaLN_modulation(vec).chunk(2, dim=-1) - x = apply_mod(self.norm_final(x), (1 + scale), shift, modulation_dims) - x = self.linear(x) - return x diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py deleted file mode 100644 index 3e09781768a7b1fb1a72e18a62811b139f459a0b..0000000000000000000000000000000000000000 --- a/comfy/ldm/flux/math.py +++ /dev/null @@ -1,45 +0,0 @@ -import torch -from einops import rearrange -from torch import Tensor - -from comfy.ldm.modules.attention import optimized_attention -import comfy.model_management - - -def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: - q_shape = q.shape - k_shape = k.shape - - if pe is not None: - q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2) - k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2) - q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v) - k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v) - - heads = q.shape[1] - x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) - return x - - -def rope(pos: Tensor, dim: int, theta: int) -> Tensor: - assert dim % 2 == 0 - if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): - device = torch.device("cpu") - else: - device = pos.device - - scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) - omega = 1.0 / (theta**scale) - out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) - out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) - out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) - return out.to(dtype=torch.float32, device=pos.device) - - -def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): - xq_ = xq.to(dtype=freqs_cis.dtype).reshape(*xq.shape[:-1], -1, 1, 2) - xk_ = xk.to(dtype=freqs_cis.dtype).reshape(*xk.shape[:-1], -1, 1, 2) - xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] - xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] - return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) - diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py deleted file mode 100644 index 1344c3a57507391161e6c7067d080bf1868e84ea..0000000000000000000000000000000000000000 --- a/comfy/ldm/flux/model.py +++ /dev/null @@ -1,260 +0,0 @@ -#Original code can be found on: https://github.com/black-forest-labs/flux - -from dataclasses import dataclass - -import torch -from torch import Tensor, nn -from einops import rearrange, repeat -import comfy.ldm.common_dit -import comfy.patcher_extension - -from .layers import ( - DoubleStreamBlock, - EmbedND, - LastLayer, - MLPEmbedder, - SingleStreamBlock, - timestep_embedding, -) - -@dataclass -class FluxParams: - in_channels: int - out_channels: int - vec_in_dim: int - context_in_dim: int - hidden_size: int - mlp_ratio: float - num_heads: int - depth: int - depth_single_blocks: int - axes_dim: list - theta: int - patch_size: int - qkv_bias: bool - guidance_embed: bool - - -class Flux(nn.Module): - """ - Transformer model for flow matching on sequences. - """ - - def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): - super().__init__() - self.dtype = dtype - params = FluxParams(**kwargs) - self.params = params - self.patch_size = params.patch_size - self.in_channels = params.in_channels * params.patch_size * params.patch_size - self.out_channels = params.out_channels * params.patch_size * params.patch_size - if params.hidden_size % params.num_heads != 0: - raise ValueError( - f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" - ) - pe_dim = params.hidden_size // params.num_heads - if sum(params.axes_dim) != pe_dim: - raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") - self.hidden_size = params.hidden_size - self.num_heads = params.num_heads - self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) - self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) - self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) - self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) - self.guidance_in = ( - MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() - ) - self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) - - self.double_blocks = nn.ModuleList( - [ - DoubleStreamBlock( - self.hidden_size, - self.num_heads, - mlp_ratio=params.mlp_ratio, - qkv_bias=params.qkv_bias, - dtype=dtype, device=device, operations=operations - ) - for _ in range(params.depth) - ] - ) - - self.single_blocks = nn.ModuleList( - [ - SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) - for _ in range(params.depth_single_blocks) - ] - ) - - if final_layer: - self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) - - def forward_orig( - self, - img: Tensor, - img_ids: Tensor, - txt: Tensor, - txt_ids: Tensor, - timesteps: Tensor, - y: Tensor, - guidance: Tensor = None, - control = None, - transformer_options={}, - attn_mask: Tensor = None, - ) -> Tensor: - - if y is None: - y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) - - patches_replace = transformer_options.get("patches_replace", {}) - if img.ndim != 3 or txt.ndim != 3: - raise ValueError("Input img and txt tensors must have 3 dimensions.") - - # running on sequences img - img = self.img_in(img) - vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) - if self.params.guidance_embed: - if guidance is not None: - vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) - - vec = vec + self.vector_in(y[:,:self.params.vec_in_dim]) - txt = self.txt_in(txt) - - if img_ids is not None: - ids = torch.cat((txt_ids, img_ids), dim=1) - pe = self.pe_embedder(ids) - else: - pe = None - - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.double_blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"], out["txt"] = block(img=args["img"], - txt=args["txt"], - vec=args["vec"], - pe=args["pe"], - attn_mask=args.get("attn_mask")) - return out - - out = blocks_replace[("double_block", i)]({"img": img, - "txt": txt, - "vec": vec, - "pe": pe, - "attn_mask": attn_mask}, - {"original_block": block_wrap}) - txt = out["txt"] - img = out["img"] - else: - img, txt = block(img=img, - txt=txt, - vec=vec, - pe=pe, - attn_mask=attn_mask) - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - img[:, :add.shape[1]] += add - - if img.dtype == torch.float16: - img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504) - - img = torch.cat((txt, img), 1) - - for i, block in enumerate(self.single_blocks): - if ("single_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], - vec=args["vec"], - pe=args["pe"], - attn_mask=args.get("attn_mask")) - return out - - out = blocks_replace[("single_block", i)]({"img": img, - "vec": vec, - "pe": pe, - "attn_mask": attn_mask}, - {"original_block": block_wrap}) - img = out["img"] - else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) - - if control is not None: # Controlnet - control_o = control.get("output") - if i < len(control_o): - add = control_o[i] - if add is not None: - img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add - - img = img[:, txt.shape[1] :, ...] - - img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) - return img - - def process_img(self, x, index=0, h_offset=0, w_offset=0): - bs, c, h, w = x.shape - patch_size = self.patch_size - x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) - - img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) - h_len = ((h + (patch_size // 2)) // patch_size) - w_len = ((w + (patch_size // 2)) // patch_size) - - h_offset = ((h_offset + (patch_size // 2)) // patch_size) - w_offset = ((w_offset + (patch_size // 2)) // patch_size) - - img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) - img_ids[:, :, 0] = img_ids[:, :, 1] + index - img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - return img, repeat(img_ids, "h w c -> b (h w) c", b=bs) - - def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs) - - def _forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): - bs, c, h_orig, w_orig = x.shape - patch_size = self.patch_size - - h_len = ((h_orig + (patch_size // 2)) // patch_size) - w_len = ((w_orig + (patch_size // 2)) // patch_size) - img, img_ids = self.process_img(x) - img_tokens = img.shape[1] - if ref_latents is not None: - h = 0 - w = 0 - index = 0 - index_ref_method = kwargs.get("ref_latents_method", "offset") == "index" - for ref in ref_latents: - if index_ref_method: - index += 1 - h_offset = 0 - w_offset = 0 - else: - index = 1 - h_offset = 0 - w_offset = 0 - if ref.shape[-2] + h > ref.shape[-1] + w: - w_offset = w - else: - h_offset = h - h = max(h, ref.shape[-2] + h_offset) - w = max(w, ref.shape[-1] + w_offset) - - kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) - img = torch.cat([img, kontext], dim=1) - img_ids = torch.cat([img_ids, kontext_ids], dim=1) - - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) - out = out[:, :img_tokens] - return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig] diff --git a/comfy/ldm/flux/redux.py b/comfy/ldm/flux/redux.py deleted file mode 100644 index 527e83164ea2fd1bdf7431f3791ebede00895051..0000000000000000000000000000000000000000 --- a/comfy/ldm/flux/redux.py +++ /dev/null @@ -1,25 +0,0 @@ -import torch -import comfy.ops - -ops = comfy.ops.manual_cast - -class ReduxImageEncoder(torch.nn.Module): - def __init__( - self, - redux_dim: int = 1152, - txt_in_features: int = 4096, - device=None, - dtype=None, - ) -> None: - super().__init__() - - self.redux_dim = redux_dim - self.device = device - self.dtype = dtype - - self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype) - self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype) - - def forward(self, sigclip_embeds) -> torch.Tensor: - projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds))) - return projected_x diff --git a/comfy/ldm/genmo/joint_model/.DS_Store b/comfy/ldm/genmo/joint_model/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/genmo/joint_model/.DS_Store and /dev/null differ diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py deleted file mode 100644 index 366a8b7133cb665731148a2cdb2ce1013cf0ae92..0000000000000000000000000000000000000000 --- a/comfy/ldm/genmo/joint_model/asymm_models_joint.py +++ /dev/null @@ -1,556 +0,0 @@ -#original code from https://github.com/genmoai/models under apache 2.0 license -#adapted to ComfyUI - -from typing import Dict, List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -# from flash_attn import flash_attn_varlen_qkvpacked_func -from comfy.ldm.modules.attention import optimized_attention - -from .layers import ( - FeedForward, - PatchEmbed, - TimestepEmbedder, -) - -from .rope_mixed import ( - compute_mixed_rotation, - create_position_matrix, -) -from .temporal_rope import apply_rotary_emb_qk_real -from .utils import ( - AttentionPool, - modulate, -) - -import comfy.ldm.common_dit -import comfy.ops - - -def modulated_rmsnorm(x, scale, eps=1e-6): - # Normalize and modulate - x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps) - x_modulated = x_normed * (1 + scale.unsqueeze(1)) - - return x_modulated - - -def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6): - # Apply tanh to gate - tanh_gate = torch.tanh(gate).unsqueeze(1) - - # Normalize and apply gated scaling - x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate - - # Apply residual connection - output = x + x_normed - - return output - -class AsymmetricAttention(nn.Module): - def __init__( - self, - dim_x: int, - dim_y: int, - num_heads: int = 8, - qkv_bias: bool = True, - qk_norm: bool = False, - attn_drop: float = 0.0, - update_y: bool = True, - out_bias: bool = True, - attend_to_padding: bool = False, - softmax_scale: Optional[float] = None, - device: Optional[torch.device] = None, - dtype=None, - operations=None, - ): - super().__init__() - self.dim_x = dim_x - self.dim_y = dim_y - self.num_heads = num_heads - self.head_dim = dim_x // num_heads - self.attn_drop = attn_drop - self.update_y = update_y - self.attend_to_padding = attend_to_padding - self.softmax_scale = softmax_scale - if dim_x % num_heads != 0: - raise ValueError( - f"dim_x={dim_x} should be divisible by num_heads={num_heads}" - ) - - # Input layers. - self.qkv_bias = qkv_bias - self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) - # Project text features to match visual features (dim_y -> dim_x) - self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype) - - # Query and key normalization for stability. - assert qk_norm - self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) - self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) - self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) - self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) - - # Output layers. y features go back down from dim_x -> dim_y. - self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype) - self.proj_y = ( - operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype) - if update_y - else nn.Identity() - ) - - def forward( - self, - x: torch.Tensor, # (B, N, dim_x) - y: torch.Tensor, # (B, L, dim_y) - scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm. - scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm. - crop_y, - **rope_rotation, - ) -> Tuple[torch.Tensor, torch.Tensor]: - rope_cos = rope_rotation.get("rope_cos") - rope_sin = rope_rotation.get("rope_sin") - # Pre-norm for visual features - x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size - - # Process visual features - # qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x) - # assert qkv_x.dtype == torch.bfloat16 - # qkv_x = all_to_all_collect_tokens( - # qkv_x, self.num_heads - # ) # (3, B, N, local_h, head_dim) - - # Process text features - y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y) - q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) - - q_y = self.q_norm_y(q_y) - k_y = self.k_norm_y(k_y) - - # Split qkv_x into q, k, v - q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim) - q_x = self.q_norm_x(q_x) - q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin) - k_x = self.k_norm_x(k_x) - k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin) - - q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2) - k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2) - v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2) - - xy = optimized_attention(q, - k, - v, self.num_heads, skip_reshape=True) - - x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1) - x = self.proj_x(x) - o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype) - o[:, :y.shape[1]] = y - - y = self.proj_y(o) - # print("ox", x) - # print("oy", y) - return x, y - - -class AsymmetricJointBlock(nn.Module): - def __init__( - self, - hidden_size_x: int, - hidden_size_y: int, - num_heads: int, - *, - mlp_ratio_x: float = 8.0, # Ratio of hidden size to d_model for MLP for visual tokens. - mlp_ratio_y: float = 4.0, # Ratio of hidden size to d_model for MLP for text tokens. - update_y: bool = True, # Whether to update text tokens in this block. - device: Optional[torch.device] = None, - dtype=None, - operations=None, - **block_kwargs, - ): - super().__init__() - self.update_y = update_y - self.hidden_size_x = hidden_size_x - self.hidden_size_y = hidden_size_y - self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype) - if self.update_y: - self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype) - else: - self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype) - - # Self-attention: - self.attn = AsymmetricAttention( - hidden_size_x, - hidden_size_y, - num_heads=num_heads, - update_y=update_y, - device=device, - dtype=dtype, - operations=operations, - **block_kwargs, - ) - - # MLP. - mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x) - assert mlp_hidden_dim_x == int(1536 * 8) - self.mlp_x = FeedForward( - in_features=hidden_size_x, - hidden_size=mlp_hidden_dim_x, - multiple_of=256, - ffn_dim_multiplier=None, - device=device, - dtype=dtype, - operations=operations, - ) - - # MLP for text not needed in last block. - if self.update_y: - mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y) - self.mlp_y = FeedForward( - in_features=hidden_size_y, - hidden_size=mlp_hidden_dim_y, - multiple_of=256, - ffn_dim_multiplier=None, - device=device, - dtype=dtype, - operations=operations, - ) - - def forward( - self, - x: torch.Tensor, - c: torch.Tensor, - y: torch.Tensor, - **attn_kwargs, - ): - """Forward pass of a block. - - Args: - x: (B, N, dim) tensor of visual tokens - c: (B, dim) tensor of conditioned features - y: (B, L, dim) tensor of text tokens - num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens - - Returns: - x: (B, N, dim) tensor of visual tokens after block - y: (B, L, dim) tensor of text tokens after block - """ - N = x.size(1) - - c = F.silu(c) - mod_x = self.mod_x(c) - scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1) - - mod_y = self.mod_y(c) - if self.update_y: - scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1) - else: - scale_msa_y = mod_y - - # Self-attention block. - x_attn, y_attn = self.attn( - x, - y, - scale_x=scale_msa_x, - scale_y=scale_msa_y, - **attn_kwargs, - ) - - assert x_attn.size(1) == N - x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x) - if self.update_y: - y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y) - - # MLP block. - x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x) - if self.update_y: - y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y) - - return x, y - - def ff_block_x(self, x, scale_x, gate_x): - x_mod = modulated_rmsnorm(x, scale_x) - x_res = self.mlp_x(x_mod) - x = residual_tanh_gated_rmsnorm(x, x_res, gate_x) # Sandwich norm - return x - - def ff_block_y(self, y, scale_y, gate_y): - y_mod = modulated_rmsnorm(y, scale_y) - y_res = self.mlp_y(y_mod) - y = residual_tanh_gated_rmsnorm(y, y_res, gate_y) # Sandwich norm - return y - - -class FinalLayer(nn.Module): - """ - The final layer of DiT. - """ - - def __init__( - self, - hidden_size, - patch_size, - out_channels, - device: Optional[torch.device] = None, - dtype=None, - operations=None, - ): - super().__init__() - self.norm_final = operations.LayerNorm( - hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype - ) - self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype) - self.linear = operations.Linear( - hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype - ) - - def forward(self, x, c): - c = F.silu(c) - shift, scale = self.mod(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class AsymmDiTJoint(nn.Module): - """ - Diffusion model with a Transformer backbone. - - Ingests text embeddings instead of a label. - """ - - def __init__( - self, - *, - patch_size=2, - in_channels=4, - hidden_size_x=1152, - hidden_size_y=1152, - depth=48, - num_heads=16, - mlp_ratio_x=8.0, - mlp_ratio_y=4.0, - use_t5: bool = False, - t5_feat_dim: int = 4096, - t5_token_length: int = 256, - learn_sigma=True, - patch_embed_bias: bool = True, - timestep_mlp_bias: bool = True, - attend_to_padding: bool = False, - timestep_scale: Optional[float] = None, - use_extended_posenc: bool = False, - posenc_preserve_area: bool = False, - rope_theta: float = 10000.0, - image_model=None, - device: Optional[torch.device] = None, - dtype=None, - operations=None, - **block_kwargs, - ): - super().__init__() - - self.dtype = dtype - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - self.num_heads = num_heads - self.hidden_size_x = hidden_size_x - self.hidden_size_y = hidden_size_y - self.head_dim = ( - hidden_size_x // num_heads - ) # Head dimension and count is determined by visual. - self.attend_to_padding = attend_to_padding - self.use_extended_posenc = use_extended_posenc - self.posenc_preserve_area = posenc_preserve_area - self.use_t5 = use_t5 - self.t5_token_length = t5_token_length - self.t5_feat_dim = t5_feat_dim - self.rope_theta = ( - rope_theta # Scaling factor for frequency computation for temporal RoPE. - ) - - self.x_embedder = PatchEmbed( - patch_size=patch_size, - in_chans=in_channels, - embed_dim=hidden_size_x, - bias=patch_embed_bias, - dtype=dtype, - device=device, - operations=operations - ) - # Conditionings - # Timestep - self.t_embedder = TimestepEmbedder( - hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations - ) - - if self.use_t5: - # Caption Pooling (T5) - self.t5_y_embedder = AttentionPool( - t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations - ) - - # Dense Embedding Projection (T5) - self.t5_yproj = operations.Linear( - t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device - ) - - # Initialize pos_frequencies as an empty parameter. - self.pos_frequencies = nn.Parameter( - torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device) - ) - - assert not self.attend_to_padding - - # for depth 48: - # b = 0: AsymmetricJointBlock, update_y=True - # b = 1: AsymmetricJointBlock, update_y=True - # ... - # b = 46: AsymmetricJointBlock, update_y=True - # b = 47: AsymmetricJointBlock, update_y=False. No need to update text features. - blocks = [] - for b in range(depth): - # Joint multi-modal block - update_y = b < depth - 1 - block = AsymmetricJointBlock( - hidden_size_x, - hidden_size_y, - num_heads, - mlp_ratio_x=mlp_ratio_x, - mlp_ratio_y=mlp_ratio_y, - update_y=update_y, - attend_to_padding=attend_to_padding, - device=device, - dtype=dtype, - operations=operations, - **block_kwargs, - ) - - blocks.append(block) - self.blocks = nn.ModuleList(blocks) - - self.final_layer = FinalLayer( - hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations - ) - - def embed_x(self, x: torch.Tensor) -> torch.Tensor: - """ - Args: - x: (B, C=12, T, H, W) tensor of visual tokens - - Returns: - x: (B, C=3072, N) tensor of visual tokens with positional embedding. - """ - return self.x_embedder(x) # Convert BcTHW to BCN - - def prepare( - self, - x: torch.Tensor, - sigma: torch.Tensor, - t5_feat: torch.Tensor, - t5_mask: torch.Tensor, - ): - """Prepare input and conditioning embeddings.""" - # Visual patch embeddings with positional encoding. - T, H, W = x.shape[-3:] - pH, pW = H // self.patch_size, W // self.patch_size - x = self.embed_x(x) # (B, N, D), where N = T * H * W / patch_size ** 2 - assert x.ndim == 3 - - pH, pW = H // self.patch_size, W // self.patch_size - N = T * pH * pW - assert x.size(1) == N - pos = create_position_matrix( - T, pH=pH, pW=pW, device=x.device, dtype=torch.float32 - ) # (N, 3) - rope_cos, rope_sin = compute_mixed_rotation( - freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos - ) # Each are (N, num_heads, dim // 2) - - c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype) # (B, D) - - t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask) # (B, D) - - c = c_t + t5_y_pool - - y_feat = self.t5_yproj(t5_feat) # (B, L, t5_feat_dim) --> (B, L, D) - - return x, c, y_feat, rope_cos, rope_sin - - def forward( - self, - x: torch.Tensor, - timestep: torch.Tensor, - context: List[torch.Tensor], - attention_mask: List[torch.Tensor], - num_tokens=256, - packed_indices: Dict[str, torch.Tensor] = None, - rope_cos: torch.Tensor = None, - rope_sin: torch.Tensor = None, - control=None, transformer_options={}, **kwargs - ): - patches_replace = transformer_options.get("patches_replace", {}) - y_feat = context - y_mask = attention_mask - sigma = timestep - """Forward pass of DiT. - - Args: - x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images) - sigma: (B,) tensor of noise standard deviations - y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048) - y_mask: List((B, L) boolean tensor indicating which tokens are not padding) - packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices. - """ - B, _, T, H, W = x.shape - - x, c, y_feat, rope_cos, rope_sin = self.prepare( - x, sigma, y_feat, y_mask - ) - del y_mask - - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"], out["txt"] = block( - args["img"], - args["vec"], - args["txt"], - rope_cos=args["rope_cos"], - rope_sin=args["rope_sin"], - crop_y=args["num_tokens"] - ) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap}) - y_feat = out["txt"] - x = out["img"] - else: - x, y_feat = block( - x, - c, - y_feat, - rope_cos=rope_cos, - rope_sin=rope_sin, - crop_y=num_tokens, - ) # (B, M, D), (B, L, D) - del y_feat # Final layers don't use dense text features. - - x = self.final_layer(x, c) # (B, M, patch_size ** 2 * out_channels) - x = rearrange( - x, - "B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)", - T=T, - hp=H // self.patch_size, - wp=W // self.patch_size, - p1=self.patch_size, - p2=self.patch_size, - c=self.out_channels, - ) - - return -x diff --git a/comfy/ldm/genmo/joint_model/layers.py b/comfy/ldm/genmo/joint_model/layers.py deleted file mode 100644 index e310bd717833b29e3730894370a55cd49802de93..0000000000000000000000000000000000000000 --- a/comfy/ldm/genmo/joint_model/layers.py +++ /dev/null @@ -1,153 +0,0 @@ -#original code from https://github.com/genmoai/models under apache 2.0 license -#adapted to ComfyUI - -import collections.abc -import math -from itertools import repeat -from typing import Callable, Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -import comfy.ldm.common_dit - - -# From PyTorch internals -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): - return tuple(x) - return tuple(repeat(x, n)) - - return parse - - -to_2tuple = _ntuple(2) - - -class TimestepEmbedder(nn.Module): - def __init__( - self, - hidden_size: int, - frequency_embedding_size: int = 256, - *, - bias: bool = True, - timestep_scale: Optional[float] = None, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.mlp = nn.Sequential( - operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device), - ) - self.frequency_embedding_size = frequency_embedding_size - self.timestep_scale = timestep_scale - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - half = dim // 2 - freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) - freqs.mul_(-math.log(max_period) / half).exp_() - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return embedding - - def forward(self, t, out_dtype): - if self.timestep_scale is not None: - t = t * self.timestep_scale - t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype) - t_emb = self.mlp(t_freq) - return t_emb - - -class FeedForward(nn.Module): - def __init__( - self, - in_features: int, - hidden_size: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float], - device: Optional[torch.device] = None, - dtype=None, - operations=None, - ): - super().__init__() - # keep parameter count and computation constant compared to standard FFN - hidden_size = int(2 * hidden_size / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_size = int(ffn_dim_multiplier * hidden_size) - hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of) - - self.hidden_dim = hidden_size - self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype) - self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype) - - def forward(self, x): - x, gate = self.w1(x).chunk(2, dim=-1) - x = self.w2(F.silu(x) * gate) - return x - - -class PatchEmbed(nn.Module): - def __init__( - self, - patch_size: int = 16, - in_chans: int = 3, - embed_dim: int = 768, - norm_layer: Optional[Callable] = None, - flatten: bool = True, - bias: bool = True, - dynamic_img_pad: bool = False, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.patch_size = to_2tuple(patch_size) - self.flatten = flatten - self.dynamic_img_pad = dynamic_img_pad - - self.proj = operations.Conv2d( - in_chans, - embed_dim, - kernel_size=patch_size, - stride=patch_size, - bias=bias, - device=device, - dtype=dtype, - ) - assert norm_layer is None - self.norm = ( - norm_layer(embed_dim, device=device) if norm_layer else nn.Identity() - ) - - def forward(self, x): - B, _C, T, H, W = x.shape - if not self.dynamic_img_pad: - assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})." - assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})." - else: - pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0] - pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1] - x = F.pad(x, (0, pad_w, 0, pad_h)) - - x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T) - x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular') - x = self.proj(x) - - # Flatten temporal and spatial dimensions. - if not self.flatten: - raise NotImplementedError("Must flatten output.") - x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T) - - x = self.norm(x) - return x diff --git a/comfy/ldm/genmo/joint_model/rope_mixed.py b/comfy/ldm/genmo/joint_model/rope_mixed.py deleted file mode 100644 index dee3fa21f5318a610321fc9372553d618462f773..0000000000000000000000000000000000000000 --- a/comfy/ldm/genmo/joint_model/rope_mixed.py +++ /dev/null @@ -1,88 +0,0 @@ -#original code from https://github.com/genmoai/models under apache 2.0 license - -# import functools -import math - -import torch - - -def centers(start: float, stop, num, dtype=None, device=None): - """linspace through bin centers. - - Args: - start (float): Start of the range. - stop (float): End of the range. - num (int): Number of points. - dtype (torch.dtype): Data type of the points. - device (torch.device): Device of the points. - - Returns: - centers (Tensor): Centers of the bins. Shape: (num,). - """ - edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device) - return (edges[:-1] + edges[1:]) / 2 - - -# @functools.lru_cache(maxsize=1) -def create_position_matrix( - T: int, - pH: int, - pW: int, - device: torch.device, - dtype: torch.dtype, - *, - target_area: float = 36864, -): - """ - Args: - T: int - Temporal dimension - pH: int - Height dimension after patchify - pW: int - Width dimension after patchify - - Returns: - pos: [T * pH * pW, 3] - position matrix - """ - # Create 1D tensors for each dimension - t = torch.arange(T, dtype=dtype) - - # Positionally interpolate to area 36864. - # (3072x3072 frame with 16x16 patches = 192x192 latents). - # This automatically scales rope positions when the resolution changes. - # We use a large target area so the model is more sensitive - # to changes in the learned pos_frequencies matrix. - scale = math.sqrt(target_area / (pW * pH)) - w = centers(-pW * scale / 2, pW * scale / 2, pW) - h = centers(-pH * scale / 2, pH * scale / 2, pH) - - # Use meshgrid to create 3D grids - grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij") - - # Stack and reshape the grids. - pos = torch.stack([grid_t, grid_h, grid_w], dim=-1) # [T, pH, pW, 3] - pos = pos.view(-1, 3) # [T * pH * pW, 3] - pos = pos.to(dtype=dtype, device=device) - - return pos - - -def compute_mixed_rotation( - freqs: torch.Tensor, - pos: torch.Tensor, -): - """ - Project each 3-dim position into per-head, per-head-dim 1D frequencies. - - Args: - freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position - pos: [N, 3] - position of each token - num_heads: int - - Returns: - freqs_cos: [N, num_heads, num_freqs] - cosine components - freqs_sin: [N, num_heads, num_freqs] - sine components - """ - assert freqs.ndim == 3 - freqs_sum = torch.einsum("Nd,dhf->Nhf", pos.to(freqs), freqs) - freqs_cos = torch.cos(freqs_sum) - freqs_sin = torch.sin(freqs_sum) - return freqs_cos, freqs_sin diff --git a/comfy/ldm/genmo/joint_model/temporal_rope.py b/comfy/ldm/genmo/joint_model/temporal_rope.py deleted file mode 100644 index 88f5d6d26151db0c8ad0a89fcf748c45d4b89bc0..0000000000000000000000000000000000000000 --- a/comfy/ldm/genmo/joint_model/temporal_rope.py +++ /dev/null @@ -1,34 +0,0 @@ -#original code from https://github.com/genmoai/models under apache 2.0 license - -# Based on Llama3 Implementation. -import torch - - -def apply_rotary_emb_qk_real( - xqk: torch.Tensor, - freqs_cos: torch.Tensor, - freqs_sin: torch.Tensor, -) -> torch.Tensor: - """ - Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers. - - Args: - xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D) - Can be either just query or just key, or both stacked along some batch or * dim. - freqs_cos (torch.Tensor): Precomputed cosine frequency tensor. - freqs_sin (torch.Tensor): Precomputed sine frequency tensor. - - Returns: - torch.Tensor: The input tensor with rotary embeddings applied. - """ - # Split the last dimension into even and odd parts - xqk_even = xqk[..., 0::2] - xqk_odd = xqk[..., 1::2] - - # Apply rotation - cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk) - sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk) - - # Interleave the results back into the original shape - out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2) - return out diff --git a/comfy/ldm/genmo/joint_model/utils.py b/comfy/ldm/genmo/joint_model/utils.py deleted file mode 100644 index 1b399d5d2128b7f5c7be141c3d8e7047df93b273..0000000000000000000000000000000000000000 --- a/comfy/ldm/genmo/joint_model/utils.py +++ /dev/null @@ -1,102 +0,0 @@ -#original code from https://github.com/genmoai/models under apache 2.0 license -#adapted to ComfyUI - -from typing import Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor: - """ - Pool tokens in x using mask. - - NOTE: We assume x does not require gradients. - - Args: - x: (B, L, D) tensor of tokens. - mask: (B, L) boolean tensor indicating which tokens are not padding. - - Returns: - pooled: (B, D) tensor of pooled tokens. - """ - assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens. - assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens. - mask = mask[:, :, None].to(dtype=x.dtype) - mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1) - pooled = (x * mask).sum(dim=1, keepdim=keepdim) - return pooled - - -class AttentionPool(nn.Module): - def __init__( - self, - embed_dim: int, - num_heads: int, - output_dim: int = None, - device: Optional[torch.device] = None, - dtype=None, - operations=None, - ): - """ - Args: - spatial_dim (int): Number of tokens in sequence length. - embed_dim (int): Dimensionality of input tokens. - num_heads (int): Number of attention heads. - output_dim (int): Dimensionality of output tokens. Defaults to embed_dim. - """ - super().__init__() - self.num_heads = num_heads - self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype) - self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype) - self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype) - - def forward(self, x, mask): - """ - Args: - x (torch.Tensor): (B, L, D) tensor of input tokens. - mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding. - - NOTE: We assume x does not require gradients. - - Returns: - x (torch.Tensor): (B, D) tensor of pooled tokens. - """ - D = x.size(2) - - # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L). - attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L). - attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L). - - # Average non-padding token features. These will be used as the query. - x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D) - - # Concat pooled features to input sequence. - x = torch.cat([x_pool, x], dim=1) # (B, L+1, D) - - # Compute queries, keys, values. Only the mean token is used to create a query. - kv = self.to_kv(x) # (B, L+1, 2 * D) - q = self.to_q(x[:, 0]) # (B, D) - - # Extract heads. - head_dim = D // self.num_heads - kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim) - kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim) - k, v = kv.unbind(2) # (B, H, 1+L, head_dim) - q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim) - q = q.unsqueeze(2) # (B, H, 1, head_dim) - - # Compute attention. - x = F.scaled_dot_product_attention( - q, k, v, attn_mask=attn_mask, dropout_p=0.0 - ) # (B, H, 1, head_dim) - - # Concatenate heads and run output. - x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim) - x = self.to_out(x) - return x diff --git a/comfy/ldm/genmo/vae/.DS_Store b/comfy/ldm/genmo/vae/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/genmo/vae/.DS_Store and /dev/null differ diff --git a/comfy/ldm/genmo/vae/model.py b/comfy/ldm/genmo/vae/model.py deleted file mode 100644 index 1bde0c1ed7372be184e634777b9e27b67692b1f4..0000000000000000000000000000000000000000 --- a/comfy/ldm/genmo/vae/model.py +++ /dev/null @@ -1,711 +0,0 @@ -#original code from https://github.com/genmoai/models under apache 2.0 license -#adapted to ComfyUI - -from typing import List, Optional, Tuple, Union -from functools import partial -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange - -from comfy.ldm.modules.attention import optimized_attention - -import comfy.ops -ops = comfy.ops.disable_weight_init - -# import mochi_preview.dit.joint_model.context_parallel as cp -# from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames - - -def cast_tuple(t, length=1): - return t if isinstance(t, tuple) else ((t,) * length) - - -class GroupNormSpatial(ops.GroupNorm): - """ - GroupNorm applied per-frame. - """ - - def forward(self, x: torch.Tensor, *, chunk_size: int = 8): - B, C, T, H, W = x.shape - x = rearrange(x, "B C T H W -> (B T) C H W") - # Run group norm in chunks. - output = torch.empty_like(x) - for b in range(0, B * T, chunk_size): - output[b : b + chunk_size] = super().forward(x[b : b + chunk_size]) - return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T) - -class PConv3d(ops.Conv3d): - def __init__( - self, - in_channels, - out_channels, - kernel_size: Union[int, Tuple[int, int, int]], - stride: Union[int, Tuple[int, int, int]], - causal: bool = True, - context_parallel: bool = True, - **kwargs, - ): - self.causal = causal - self.context_parallel = context_parallel - kernel_size = cast_tuple(kernel_size, 3) - stride = cast_tuple(stride, 3) - height_pad = (kernel_size[1] - 1) // 2 - width_pad = (kernel_size[2] - 1) // 2 - - super().__init__( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - dilation=(1, 1, 1), - padding=(0, height_pad, width_pad), - **kwargs, - ) - - def forward(self, x: torch.Tensor): - # Compute padding amounts. - context_size = self.kernel_size[0] - 1 - if self.causal: - pad_front = context_size - pad_back = 0 - else: - pad_front = context_size // 2 - pad_back = context_size - pad_front - - # Apply padding. - assert self.padding_mode == "replicate" # DEBUG - mode = "constant" if self.padding_mode == "zeros" else self.padding_mode - x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode) - return super().forward(x) - - -class Conv1x1(ops.Linear): - """*1x1 Conv implemented with a linear layer.""" - - def __init__(self, in_features: int, out_features: int, *args, **kwargs): - super().__init__(in_features, out_features, *args, **kwargs) - - def forward(self, x: torch.Tensor): - """Forward pass. - - Args: - x: Input tensor. Shape: [B, C, *] or [B, *, C]. - - Returns: - x: Output tensor. Shape: [B, C', *] or [B, *, C']. - """ - x = x.movedim(1, -1) - x = super().forward(x) - x = x.movedim(-1, 1) - return x - - -class DepthToSpaceTime(nn.Module): - def __init__( - self, - temporal_expansion: int, - spatial_expansion: int, - ): - super().__init__() - self.temporal_expansion = temporal_expansion - self.spatial_expansion = spatial_expansion - - # When printed, this module should show the temporal and spatial expansion factors. - def extra_repr(self): - return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}" - - def forward(self, x: torch.Tensor): - """Forward pass. - - Args: - x: Input tensor. Shape: [B, C, T, H, W]. - - Returns: - x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s]. - """ - x = rearrange( - x, - "B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)", - st=self.temporal_expansion, - sh=self.spatial_expansion, - sw=self.spatial_expansion, - ) - - # cp_rank, _ = cp.get_cp_rank_size() - if self.temporal_expansion > 1: # and cp_rank == 0: - # Drop the first self.temporal_expansion - 1 frames. - # This is because we always want the 3x3x3 conv filter to only apply - # to the first frame, and the first frame doesn't need to be repeated. - assert all(x.shape) - x = x[:, :, self.temporal_expansion - 1 :] - assert all(x.shape) - - return x - - -def norm_fn( - in_channels: int, - affine: bool = True, -): - return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels) - - -class ResBlock(nn.Module): - """Residual block that preserves the spatial dimensions.""" - - def __init__( - self, - channels: int, - *, - affine: bool = True, - attn_block: Optional[nn.Module] = None, - causal: bool = True, - prune_bottleneck: bool = False, - padding_mode: str, - bias: bool = True, - ): - super().__init__() - self.channels = channels - - assert causal - self.stack = nn.Sequential( - norm_fn(channels, affine=affine), - nn.SiLU(inplace=True), - PConv3d( - in_channels=channels, - out_channels=channels // 2 if prune_bottleneck else channels, - kernel_size=(3, 3, 3), - stride=(1, 1, 1), - padding_mode=padding_mode, - bias=bias, - causal=causal, - ), - norm_fn(channels, affine=affine), - nn.SiLU(inplace=True), - PConv3d( - in_channels=channels // 2 if prune_bottleneck else channels, - out_channels=channels, - kernel_size=(3, 3, 3), - stride=(1, 1, 1), - padding_mode=padding_mode, - bias=bias, - causal=causal, - ), - ) - - self.attn_block = attn_block if attn_block else nn.Identity() - - def forward(self, x: torch.Tensor): - """Forward pass. - - Args: - x: Input tensor. Shape: [B, C, T, H, W]. - """ - residual = x - x = self.stack(x) - x = x + residual - del residual - - return self.attn_block(x) - - -class Attention(nn.Module): - def __init__( - self, - dim: int, - head_dim: int = 32, - qkv_bias: bool = False, - out_bias: bool = True, - qk_norm: bool = True, - ) -> None: - super().__init__() - self.head_dim = head_dim - self.num_heads = dim // head_dim - self.qk_norm = qk_norm - - self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias) - self.out = nn.Linear(dim, dim, bias=out_bias) - - def forward( - self, - x: torch.Tensor, - ) -> torch.Tensor: - """Compute temporal self-attention. - - Args: - x: Input tensor. Shape: [B, C, T, H, W]. - chunk_size: Chunk size for large tensors. - - Returns: - x: Output tensor. Shape: [B, C, T, H, W]. - """ - B, _, T, H, W = x.shape - - if T == 1: - # No attention for single frame. - x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C] - qkv = self.qkv(x) - _, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys. - x = self.out(x) - return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W] - - # 1D temporal attention. - x = rearrange(x, "B C t h w -> (B h w) t C") - qkv = self.qkv(x) - - # Input: qkv with shape [B, t, 3 * num_heads * head_dim] - # Output: x with shape [B, num_heads, t, head_dim] - q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2) - - if self.qk_norm: - q = F.normalize(q, p=2, dim=-1) - k = F.normalize(k, p=2, dim=-1) - - x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True) - - assert x.size(0) == q.size(0) - - x = self.out(x) - x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W) - return x - - -class AttentionBlock(nn.Module): - def __init__( - self, - dim: int, - **attn_kwargs, - ) -> None: - super().__init__() - self.norm = norm_fn(dim) - self.attn = Attention(dim, **attn_kwargs) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return x + self.attn(self.norm(x)) - - -class CausalUpsampleBlock(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - num_res_blocks: int, - *, - temporal_expansion: int = 2, - spatial_expansion: int = 2, - **block_kwargs, - ): - super().__init__() - - blocks = [] - for _ in range(num_res_blocks): - blocks.append(block_fn(in_channels, **block_kwargs)) - self.blocks = nn.Sequential(*blocks) - - self.temporal_expansion = temporal_expansion - self.spatial_expansion = spatial_expansion - - # Change channels in the final convolution layer. - self.proj = Conv1x1( - in_channels, - out_channels * temporal_expansion * (spatial_expansion**2), - ) - - self.d2st = DepthToSpaceTime( - temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion - ) - - def forward(self, x): - x = self.blocks(x) - x = self.proj(x) - x = self.d2st(x) - return x - - -def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs): - attn_block = AttentionBlock(channels) if has_attention else None - return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs) - - -class DownsampleBlock(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - num_res_blocks, - *, - temporal_reduction=2, - spatial_reduction=2, - **block_kwargs, - ): - """ - Downsample block for the VAE encoder. - - Args: - in_channels: Number of input channels. - out_channels: Number of output channels. - num_res_blocks: Number of residual blocks. - temporal_reduction: Temporal reduction factor. - spatial_reduction: Spatial reduction factor. - """ - super().__init__() - layers = [] - - # Change the channel count in the strided convolution. - # This lets the ResBlock have uniform channel count, - # as in ConvNeXt. - assert in_channels != out_channels - layers.append( - PConv3d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction), - stride=(temporal_reduction, spatial_reduction, spatial_reduction), - # First layer in each block always uses replicate padding - padding_mode="replicate", - bias=block_kwargs["bias"], - ) - ) - - for _ in range(num_res_blocks): - layers.append(block_fn(out_channels, **block_kwargs)) - - self.layers = nn.Sequential(*layers) - - def forward(self, x): - return self.layers(x) - - -def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1): - num_freqs = (stop - start) // step - assert inputs.ndim == 5 - C = inputs.size(1) - - # Create Base 2 Fourier features. - freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device) - assert num_freqs == len(freqs) - w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs] - C = inputs.shape[1] - w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1] - - # Interleaved repeat of input channels to match w. - h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W] - # Scale channels by frequency. - h = w * h - - return torch.cat( - [ - inputs, - torch.sin(h), - torch.cos(h), - ], - dim=1, - ) - - -class FourierFeatures(nn.Module): - def __init__(self, start: int = 6, stop: int = 8, step: int = 1): - super().__init__() - self.start = start - self.stop = stop - self.step = step - - def forward(self, inputs): - """Add Fourier features to inputs. - - Args: - inputs: Input tensor. Shape: [B, C, T, H, W] - - Returns: - h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W] - """ - return add_fourier_features(inputs, self.start, self.stop, self.step) - - -class Decoder(nn.Module): - def __init__( - self, - *, - out_channels: int = 3, - latent_dim: int, - base_channels: int, - channel_multipliers: List[int], - num_res_blocks: List[int], - temporal_expansions: Optional[List[int]] = None, - spatial_expansions: Optional[List[int]] = None, - has_attention: List[bool], - output_norm: bool = True, - nonlinearity: str = "silu", - output_nonlinearity: str = "silu", - causal: bool = True, - **block_kwargs, - ): - super().__init__() - self.input_channels = latent_dim - self.base_channels = base_channels - self.channel_multipliers = channel_multipliers - self.num_res_blocks = num_res_blocks - self.output_nonlinearity = output_nonlinearity - assert nonlinearity == "silu" - assert causal - - ch = [mult * base_channels for mult in channel_multipliers] - self.num_up_blocks = len(ch) - 1 - assert len(num_res_blocks) == self.num_up_blocks + 2 - - blocks = [] - - first_block = [ - ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1)) - ] # Input layer. - # First set of blocks preserve channel count. - for _ in range(num_res_blocks[-1]): - first_block.append( - block_fn( - ch[-1], - has_attention=has_attention[-1], - causal=causal, - **block_kwargs, - ) - ) - blocks.append(nn.Sequential(*first_block)) - - assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks - assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2 - - upsample_block_fn = CausalUpsampleBlock - - for i in range(self.num_up_blocks): - block = upsample_block_fn( - ch[-i - 1], - ch[-i - 2], - num_res_blocks=num_res_blocks[-i - 2], - has_attention=has_attention[-i - 2], - temporal_expansion=temporal_expansions[-i - 1], - spatial_expansion=spatial_expansions[-i - 1], - causal=causal, - **block_kwargs, - ) - blocks.append(block) - - assert not output_norm - - # Last block. Preserve channel count. - last_block = [] - for _ in range(num_res_blocks[0]): - last_block.append( - block_fn( - ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs - ) - ) - blocks.append(nn.Sequential(*last_block)) - - self.blocks = nn.ModuleList(blocks) - self.output_proj = Conv1x1(ch[0], out_channels) - - def forward(self, x): - """Forward pass. - - Args: - x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1]. - - Returns: - x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]. - T + 1 = (t - 1) * 4. - H = h * 16, W = w * 16. - """ - for block in self.blocks: - x = block(x) - - if self.output_nonlinearity == "silu": - x = F.silu(x, inplace=not self.training) - else: - assert ( - not self.output_nonlinearity - ) # StyleGAN3 omits the to-RGB nonlinearity. - - return self.output_proj(x).contiguous() - -class LatentDistribution: - def __init__(self, mean: torch.Tensor, logvar: torch.Tensor): - """Initialize latent distribution. - - Args: - mean: Mean of the distribution. Shape: [B, C, T, H, W]. - logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W]. - """ - assert mean.shape == logvar.shape - self.mean = mean - self.logvar = logvar - - def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None): - if temperature == 0.0: - return self.mean - - if noise is None: - noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator) - else: - assert noise.device == self.mean.device - noise = noise.to(self.mean.dtype) - - if temperature != 1.0: - raise NotImplementedError(f"Temperature {temperature} is not supported.") - - # Just Gaussian sample with no scaling of variance. - return noise * torch.exp(self.logvar * 0.5) + self.mean - - def mode(self): - return self.mean - -class Encoder(nn.Module): - def __init__( - self, - *, - in_channels: int, - base_channels: int, - channel_multipliers: List[int], - num_res_blocks: List[int], - latent_dim: int, - temporal_reductions: List[int], - spatial_reductions: List[int], - prune_bottlenecks: List[bool], - has_attentions: List[bool], - affine: bool = True, - bias: bool = True, - input_is_conv_1x1: bool = False, - padding_mode: str, - ): - super().__init__() - self.temporal_reductions = temporal_reductions - self.spatial_reductions = spatial_reductions - self.base_channels = base_channels - self.channel_multipliers = channel_multipliers - self.num_res_blocks = num_res_blocks - self.latent_dim = latent_dim - - self.fourier_features = FourierFeatures() - ch = [mult * base_channels for mult in channel_multipliers] - num_down_blocks = len(ch) - 1 - assert len(num_res_blocks) == num_down_blocks + 2 - - layers = ( - [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)] - if not input_is_conv_1x1 - else [Conv1x1(in_channels, ch[0])] - ) - - assert len(prune_bottlenecks) == num_down_blocks + 2 - assert len(has_attentions) == num_down_blocks + 2 - block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias) - - for _ in range(num_res_blocks[0]): - layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0])) - prune_bottlenecks = prune_bottlenecks[1:] - has_attentions = has_attentions[1:] - - assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1 - for i in range(num_down_blocks): - layer = DownsampleBlock( - ch[i], - ch[i + 1], - num_res_blocks=num_res_blocks[i + 1], - temporal_reduction=temporal_reductions[i], - spatial_reduction=spatial_reductions[i], - prune_bottleneck=prune_bottlenecks[i], - has_attention=has_attentions[i], - affine=affine, - bias=bias, - padding_mode=padding_mode, - ) - - layers.append(layer) - - # Additional blocks. - for _ in range(num_res_blocks[-1]): - layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1])) - - self.layers = nn.Sequential(*layers) - - # Output layers. - self.output_norm = norm_fn(ch[-1]) - self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False) - - @property - def temporal_downsample(self): - return math.prod(self.temporal_reductions) - - @property - def spatial_downsample(self): - return math.prod(self.spatial_reductions) - - def forward(self, x) -> LatentDistribution: - """Forward pass. - - Args: - x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1] - - Returns: - means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1]. - h = H // 8, w = W // 8, t - 1 = (T - 1) // 6 - logvar: Shape: [B, latent_dim, t, h, w]. - """ - assert x.ndim == 5, f"Expected 5D input, got {x.shape}" - x = self.fourier_features(x) - - x = self.layers(x) - - x = self.output_norm(x) - x = F.silu(x, inplace=True) - x = self.output_proj(x) - - means, logvar = torch.chunk(x, 2, dim=1) - - assert means.ndim == 5 - assert logvar.shape == means.shape - assert means.size(1) == self.latent_dim - - return LatentDistribution(means, logvar) - - -class VideoVAE(nn.Module): - def __init__(self): - super().__init__() - self.encoder = Encoder( - in_channels=15, - base_channels=64, - channel_multipliers=[1, 2, 4, 6], - num_res_blocks=[3, 3, 4, 6, 3], - latent_dim=12, - temporal_reductions=[1, 2, 3], - spatial_reductions=[2, 2, 2], - prune_bottlenecks=[False, False, False, False, False], - has_attentions=[False, True, True, True, True], - affine=True, - bias=True, - input_is_conv_1x1=True, - padding_mode="replicate" - ) - self.decoder = Decoder( - out_channels=3, - base_channels=128, - channel_multipliers=[1, 2, 4, 6], - temporal_expansions=[1, 2, 3], - spatial_expansions=[2, 2, 2], - num_res_blocks=[3, 3, 4, 6, 3], - latent_dim=12, - has_attention=[False, False, False, False, False], - padding_mode="replicate", - output_norm=False, - nonlinearity="silu", - output_nonlinearity="silu", - causal=True, - ) - - def encode(self, x): - return self.encoder(x).mode() - - def decode(self, x): - return self.decoder(x) diff --git a/comfy/ldm/hidream/.DS_Store b/comfy/ldm/hidream/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/hidream/.DS_Store and /dev/null differ diff --git a/comfy/ldm/hidream/model.py b/comfy/ldm/hidream/model.py deleted file mode 100644 index ae49cf94586b1f37cfb4fe71e3410a22d714eb66..0000000000000000000000000000000000000000 --- a/comfy/ldm/hidream/model.py +++ /dev/null @@ -1,819 +0,0 @@ -from typing import Optional, Tuple, List - -import torch -import torch.nn as nn -import einops -from einops import repeat - -from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps -import torch.nn.functional as F - -from comfy.ldm.flux.math import apply_rope, rope -from comfy.ldm.flux.layers import LastLayer - -from comfy.ldm.modules.attention import optimized_attention -import comfy.model_management -import comfy.patcher_extension -import comfy.ldm.common_dit - - -# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py -class EmbedND(nn.Module): - def __init__(self, theta: int, axes_dim: List[int]): - super().__init__() - self.theta = theta - self.axes_dim = axes_dim - - def forward(self, ids: torch.Tensor) -> torch.Tensor: - n_axes = ids.shape[-1] - emb = torch.cat( - [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], - dim=-3, - ) - return emb.unsqueeze(2) - - -class PatchEmbed(nn.Module): - def __init__( - self, - patch_size=2, - in_channels=4, - out_channels=1024, - dtype=None, device=None, operations=None - ): - super().__init__() - self.patch_size = patch_size - self.out_channels = out_channels - self.proj = operations.Linear(in_channels * patch_size * patch_size, out_channels, bias=True, dtype=dtype, device=device) - - def forward(self, latent): - latent = self.proj(latent) - return latent - - -class PooledEmbed(nn.Module): - def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None, operations=None): - super().__init__() - self.pooled_embedder = TimestepEmbedding(in_channels=text_emb_dim, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) - - def forward(self, pooled_embed): - return self.pooled_embedder(pooled_embed) - - -class TimestepEmbed(nn.Module): - def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): - super().__init__() - self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0) - self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) - - def forward(self, timesteps, wdtype): - t_emb = self.time_proj(timesteps).to(dtype=wdtype) - t_emb = self.timestep_embedder(t_emb) - return t_emb - - -def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor): - return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2]) - - -class HiDreamAttnProcessor_flashattn: - """Attention processor used typically in processing the SD3-like self-attention projections.""" - - def __call__( - self, - attn, - image_tokens: torch.FloatTensor, - image_tokens_masks: Optional[torch.FloatTensor] = None, - text_tokens: Optional[torch.FloatTensor] = None, - rope: torch.FloatTensor = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - dtype = image_tokens.dtype - batch_size = image_tokens.shape[0] - - query_i = attn.q_rms_norm(attn.to_q(image_tokens)).to(dtype=dtype) - key_i = attn.k_rms_norm(attn.to_k(image_tokens)).to(dtype=dtype) - value_i = attn.to_v(image_tokens) - - inner_dim = key_i.shape[-1] - head_dim = inner_dim // attn.heads - - query_i = query_i.view(batch_size, -1, attn.heads, head_dim) - key_i = key_i.view(batch_size, -1, attn.heads, head_dim) - value_i = value_i.view(batch_size, -1, attn.heads, head_dim) - if image_tokens_masks is not None: - key_i = key_i * image_tokens_masks.view(batch_size, -1, 1, 1) - - if not attn.single: - query_t = attn.q_rms_norm_t(attn.to_q_t(text_tokens)).to(dtype=dtype) - key_t = attn.k_rms_norm_t(attn.to_k_t(text_tokens)).to(dtype=dtype) - value_t = attn.to_v_t(text_tokens) - - query_t = query_t.view(batch_size, -1, attn.heads, head_dim) - key_t = key_t.view(batch_size, -1, attn.heads, head_dim) - value_t = value_t.view(batch_size, -1, attn.heads, head_dim) - - num_image_tokens = query_i.shape[1] - num_text_tokens = query_t.shape[1] - query = torch.cat([query_i, query_t], dim=1) - key = torch.cat([key_i, key_t], dim=1) - value = torch.cat([value_i, value_t], dim=1) - else: - query = query_i - key = key_i - value = value_i - - if query.shape[-1] == rope.shape[-3] * 2: - query, key = apply_rope(query, key, rope) - else: - query_1, query_2 = query.chunk(2, dim=-1) - key_1, key_2 = key.chunk(2, dim=-1) - query_1, key_1 = apply_rope(query_1, key_1, rope) - query = torch.cat([query_1, query_2], dim=-1) - key = torch.cat([key_1, key_2], dim=-1) - - hidden_states = attention(query, key, value) - - if not attn.single: - hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1) - hidden_states_i = attn.to_out(hidden_states_i) - hidden_states_t = attn.to_out_t(hidden_states_t) - return hidden_states_i, hidden_states_t - else: - hidden_states = attn.to_out(hidden_states) - return hidden_states - -class HiDreamAttention(nn.Module): - def __init__( - self, - query_dim: int, - heads: int = 8, - dim_head: int = 64, - upcast_attention: bool = False, - upcast_softmax: bool = False, - scale_qk: bool = True, - eps: float = 1e-5, - processor = None, - out_dim: int = None, - single: bool = False, - dtype=None, device=None, operations=None - ): - # super(Attention, self).__init__() - super().__init__() - self.inner_dim = out_dim if out_dim is not None else dim_head * heads - self.query_dim = query_dim - self.upcast_attention = upcast_attention - self.upcast_softmax = upcast_softmax - self.out_dim = out_dim if out_dim is not None else query_dim - - self.scale_qk = scale_qk - self.scale = dim_head**-0.5 if self.scale_qk else 1.0 - - self.heads = out_dim // dim_head if out_dim is not None else heads - self.sliceable_head_dim = heads - self.single = single - - linear_cls = operations.Linear - self.linear_cls = linear_cls - self.to_q = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) - self.to_k = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) - self.to_v = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) - self.to_out = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) - self.q_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) - self.k_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) - - if not single: - self.to_q_t = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) - self.to_k_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) - self.to_v_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) - self.to_out_t = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) - self.q_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) - self.k_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) - - self.processor = processor - - def forward( - self, - norm_image_tokens: torch.FloatTensor, - image_tokens_masks: torch.FloatTensor = None, - norm_text_tokens: torch.FloatTensor = None, - rope: torch.FloatTensor = None, - ) -> torch.Tensor: - return self.processor( - self, - image_tokens = norm_image_tokens, - image_tokens_masks = image_tokens_masks, - text_tokens = norm_text_tokens, - rope = rope, - ) - - -class FeedForwardSwiGLU(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - dtype=None, device=None, operations=None - ): - super().__init__() - hidden_dim = int(2 * hidden_dim / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ( - (hidden_dim + multiple_of - 1) // multiple_of - ) - - self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) - self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device) - self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) - - def forward(self, x): - return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x)) - - -# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py -class MoEGate(nn.Module): - def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01, dtype=None, device=None, operations=None): - super().__init__() - self.top_k = num_activated_experts - self.n_routed_experts = num_routed_experts - - self.scoring_func = 'softmax' - self.alpha = aux_loss_alpha - self.seq_aux = False - - # topk selection algorithm - self.norm_topk_prob = False - self.gating_dim = embed_dim - self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), dtype=dtype, device=device)) - self.reset_parameters() - - def reset_parameters(self) -> None: - pass - # import torch.nn.init as init - # init.kaiming_uniform_(self.weight, a=math.sqrt(5)) - - def forward(self, hidden_states): - bsz, seq_len, h = hidden_states.shape - - ### compute gating score - hidden_states = hidden_states.view(-1, h) - logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), None) - if self.scoring_func == 'softmax': - scores = logits.softmax(dim=-1) - else: - raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') - - ### select top-k experts - topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) - - ### norm gate to sum 1 - if self.top_k > 1 and self.norm_topk_prob: - denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 - topk_weight = topk_weight / denominator - - aux_loss = None - return topk_idx, topk_weight, aux_loss - - -# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py -class MOEFeedForwardSwiGLU(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - num_routed_experts: int, - num_activated_experts: int, - dtype=None, device=None, operations=None - ): - super().__init__() - self.shared_experts = FeedForwardSwiGLU(dim, hidden_dim // 2, dtype=dtype, device=device, operations=operations) - self.experts = nn.ModuleList([FeedForwardSwiGLU(dim, hidden_dim, dtype=dtype, device=device, operations=operations) for i in range(num_routed_experts)]) - self.gate = MoEGate( - embed_dim = dim, - num_routed_experts = num_routed_experts, - num_activated_experts = num_activated_experts, - dtype=dtype, device=device, operations=operations - ) - self.num_activated_experts = num_activated_experts - - def forward(self, x): - wtype = x.dtype - identity = x - orig_shape = x.shape - topk_idx, topk_weight, aux_loss = self.gate(x) - x = x.view(-1, x.shape[-1]) - flat_topk_idx = topk_idx.view(-1) - if True: # self.training: # TODO: check which branch performs faster - x = x.repeat_interleave(self.num_activated_experts, dim=0) - y = torch.empty_like(x, dtype=wtype) - for i, expert in enumerate(self.experts): - y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(dtype=wtype) - y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) - y = y.view(*orig_shape).to(dtype=wtype) - #y = AddAuxiliaryLoss.apply(y, aux_loss) - else: - y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) - y = y + self.shared_experts(identity) - return y - - @torch.no_grad() - def moe_infer(self, x, flat_expert_indices, flat_expert_weights): - expert_cache = torch.zeros_like(x) - idxs = flat_expert_indices.argsort() - tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) - token_idxs = idxs // self.num_activated_experts - for i, end_idx in enumerate(tokens_per_expert): - start_idx = 0 if i == 0 else tokens_per_expert[i-1] - if start_idx == end_idx: - continue - expert = self.experts[i] - exp_token_idx = token_idxs[start_idx:end_idx] - expert_tokens = x[exp_token_idx] - expert_out = expert(expert_tokens) - expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) - - # for fp16 and other dtype - expert_cache = expert_cache.to(expert_out.dtype) - expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum') - return expert_cache - - -class TextProjection(nn.Module): - def __init__(self, in_features, hidden_size, dtype=None, device=None, operations=None): - super().__init__() - self.linear = operations.Linear(in_features=in_features, out_features=hidden_size, bias=False, dtype=dtype, device=device) - - def forward(self, caption): - hidden_states = self.linear(caption) - return hidden_states - - -class BlockType: - TransformerBlock = 1 - SingleTransformerBlock = 2 - - -class HiDreamImageSingleTransformerBlock(nn.Module): - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - num_routed_experts: int = 4, - num_activated_experts: int = 2, - dtype=None, device=None, operations=None - ): - super().__init__() - self.num_attention_heads = num_attention_heads - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device) - ) - - # 1. Attention - self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) - self.attn1 = HiDreamAttention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - processor = HiDreamAttnProcessor_flashattn(), - single = True, - dtype=dtype, device=device, operations=operations - ) - - # 3. Feed-forward - self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) - if num_routed_experts > 0: - self.ff_i = MOEFeedForwardSwiGLU( - dim = dim, - hidden_dim = 4 * dim, - num_routed_experts = num_routed_experts, - num_activated_experts = num_activated_experts, - dtype=dtype, device=device, operations=operations - ) - else: - self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) - - def forward( - self, - image_tokens: torch.FloatTensor, - image_tokens_masks: Optional[torch.FloatTensor] = None, - text_tokens: Optional[torch.FloatTensor] = None, - adaln_input: Optional[torch.FloatTensor] = None, - rope: torch.FloatTensor = None, - - ) -> torch.FloatTensor: - wtype = image_tokens.dtype - shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \ - self.adaLN_modulation(adaln_input)[:,None].chunk(6, dim=-1) - - # 1. MM-Attention - norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) - norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i - attn_output_i = self.attn1( - norm_image_tokens, - image_tokens_masks, - rope = rope, - ) - image_tokens = gate_msa_i * attn_output_i + image_tokens - - # 2. Feed-forward - norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) - norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i - ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens.to(dtype=wtype)) - image_tokens = ff_output_i + image_tokens - return image_tokens - - -class HiDreamImageTransformerBlock(nn.Module): - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - num_routed_experts: int = 4, - num_activated_experts: int = 2, - dtype=None, device=None, operations=None - ): - super().__init__() - self.num_attention_heads = num_attention_heads - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(dim, 12 * dim, bias=True, dtype=dtype, device=device) - ) - # nn.init.zeros_(self.adaLN_modulation[1].weight) - # nn.init.zeros_(self.adaLN_modulation[1].bias) - - # 1. Attention - self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) - self.norm1_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) - self.attn1 = HiDreamAttention( - query_dim=dim, - heads=num_attention_heads, - dim_head=attention_head_dim, - processor = HiDreamAttnProcessor_flashattn(), - single = False, - dtype=dtype, device=device, operations=operations - ) - - # 3. Feed-forward - self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) - if num_routed_experts > 0: - self.ff_i = MOEFeedForwardSwiGLU( - dim = dim, - hidden_dim = 4 * dim, - num_routed_experts = num_routed_experts, - num_activated_experts = num_activated_experts, - dtype=dtype, device=device, operations=operations - ) - else: - self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) - self.norm3_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False) - self.ff_t = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) - - def forward( - self, - image_tokens: torch.FloatTensor, - image_tokens_masks: Optional[torch.FloatTensor] = None, - text_tokens: Optional[torch.FloatTensor] = None, - adaln_input: Optional[torch.FloatTensor] = None, - rope: torch.FloatTensor = None, - ) -> torch.FloatTensor: - wtype = image_tokens.dtype - shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \ - shift_msa_t, scale_msa_t, gate_msa_t, shift_mlp_t, scale_mlp_t, gate_mlp_t = \ - self.adaLN_modulation(adaln_input)[:,None].chunk(12, dim=-1) - - # 1. MM-Attention - norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) - norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i - norm_text_tokens = self.norm1_t(text_tokens).to(dtype=wtype) - norm_text_tokens = norm_text_tokens * (1 + scale_msa_t) + shift_msa_t - - attn_output_i, attn_output_t = self.attn1( - norm_image_tokens, - image_tokens_masks, - norm_text_tokens, - rope = rope, - ) - - image_tokens = gate_msa_i * attn_output_i + image_tokens - text_tokens = gate_msa_t * attn_output_t + text_tokens - - # 2. Feed-forward - norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) - norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i - norm_text_tokens = self.norm3_t(text_tokens).to(dtype=wtype) - norm_text_tokens = norm_text_tokens * (1 + scale_mlp_t) + shift_mlp_t - - ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens) - ff_output_t = gate_mlp_t * self.ff_t(norm_text_tokens) - image_tokens = ff_output_i + image_tokens - text_tokens = ff_output_t + text_tokens - return image_tokens, text_tokens - - -class HiDreamImageBlock(nn.Module): - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - num_routed_experts: int = 4, - num_activated_experts: int = 2, - block_type: BlockType = BlockType.TransformerBlock, - dtype=None, device=None, operations=None - ): - super().__init__() - block_classes = { - BlockType.TransformerBlock: HiDreamImageTransformerBlock, - BlockType.SingleTransformerBlock: HiDreamImageSingleTransformerBlock, - } - self.block = block_classes[block_type]( - dim, - num_attention_heads, - attention_head_dim, - num_routed_experts, - num_activated_experts, - dtype=dtype, device=device, operations=operations - ) - - def forward( - self, - image_tokens: torch.FloatTensor, - image_tokens_masks: Optional[torch.FloatTensor] = None, - text_tokens: Optional[torch.FloatTensor] = None, - adaln_input: torch.FloatTensor = None, - rope: torch.FloatTensor = None, - ) -> torch.FloatTensor: - return self.block( - image_tokens, - image_tokens_masks, - text_tokens, - adaln_input, - rope, - ) - - -class HiDreamImageTransformer2DModel(nn.Module): - def __init__( - self, - patch_size: Optional[int] = None, - in_channels: int = 64, - out_channels: Optional[int] = None, - num_layers: int = 16, - num_single_layers: int = 32, - attention_head_dim: int = 128, - num_attention_heads: int = 20, - caption_channels: List[int] = None, - text_emb_dim: int = 2048, - num_routed_experts: int = 4, - num_activated_experts: int = 2, - axes_dims_rope: Tuple[int, int] = (32, 32), - max_resolution: Tuple[int, int] = (128, 128), - llama_layers: List[int] = None, - image_model=None, - dtype=None, device=None, operations=None - ): - self.patch_size = patch_size - self.num_attention_heads = num_attention_heads - self.attention_head_dim = attention_head_dim - self.num_layers = num_layers - self.num_single_layers = num_single_layers - - self.gradient_checkpointing = False - - super().__init__() - self.dtype = dtype - self.out_channels = out_channels or in_channels - self.inner_dim = self.num_attention_heads * self.attention_head_dim - self.llama_layers = llama_layers - - self.t_embedder = TimestepEmbed(self.inner_dim, dtype=dtype, device=device, operations=operations) - self.p_embedder = PooledEmbed(text_emb_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) - self.x_embedder = PatchEmbed( - patch_size = patch_size, - in_channels = in_channels, - out_channels = self.inner_dim, - dtype=dtype, device=device, operations=operations - ) - self.pe_embedder = EmbedND(theta=10000, axes_dim=axes_dims_rope) - - self.double_stream_blocks = nn.ModuleList( - [ - HiDreamImageBlock( - dim = self.inner_dim, - num_attention_heads = self.num_attention_heads, - attention_head_dim = self.attention_head_dim, - num_routed_experts = num_routed_experts, - num_activated_experts = num_activated_experts, - block_type = BlockType.TransformerBlock, - dtype=dtype, device=device, operations=operations - ) - for i in range(self.num_layers) - ] - ) - - self.single_stream_blocks = nn.ModuleList( - [ - HiDreamImageBlock( - dim = self.inner_dim, - num_attention_heads = self.num_attention_heads, - attention_head_dim = self.attention_head_dim, - num_routed_experts = num_routed_experts, - num_activated_experts = num_activated_experts, - block_type = BlockType.SingleTransformerBlock, - dtype=dtype, device=device, operations=operations - ) - for i in range(self.num_single_layers) - ] - ) - - self.final_layer = LastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) - - caption_channels = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ] - caption_projection = [] - for caption_channel in caption_channels: - caption_projection.append(TextProjection(in_features=caption_channel, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations)) - self.caption_projection = nn.ModuleList(caption_projection) - self.max_seq = max_resolution[0] * max_resolution[1] // (patch_size * patch_size) - - def expand_timesteps(self, timesteps, batch_size, device): - if not torch.is_tensor(timesteps): - is_mps = device.type == "mps" - if isinstance(timesteps, float): - dtype = torch.float32 if is_mps else torch.float64 - else: - dtype = torch.int32 if is_mps else torch.int64 - timesteps = torch.tensor([timesteps], dtype=dtype, device=device) - elif len(timesteps.shape) == 0: - timesteps = timesteps[None].to(device) - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timesteps = timesteps.expand(batch_size) - return timesteps - - def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]) -> List[torch.Tensor]: - x_arr = [] - for i, img_size in enumerate(img_sizes): - pH, pW = img_size - x_arr.append( - einops.rearrange(x[i, :pH*pW].reshape(1, pH, pW, -1), 'B H W (p1 p2 C) -> B C (H p1) (W p2)', - p1=self.patch_size, p2=self.patch_size) - ) - x = torch.cat(x_arr, dim=0) - return x - - def patchify(self, x, max_seq, img_sizes=None): - pz2 = self.patch_size * self.patch_size - if isinstance(x, torch.Tensor): - B = x.shape[0] - device = x.device - dtype = x.dtype - else: - B = len(x) - device = x[0].device - dtype = x[0].dtype - x_masks = torch.zeros((B, max_seq), dtype=dtype, device=device) - - if img_sizes is not None: - for i, img_size in enumerate(img_sizes): - x_masks[i, 0:img_size[0] * img_size[1]] = 1 - x = einops.rearrange(x, 'B C S p -> B S (p C)', p=pz2) - elif isinstance(x, torch.Tensor): - pH, pW = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size - x = einops.rearrange(x, 'B C (H p1) (W p2) -> B (H W) (p1 p2 C)', p1=self.patch_size, p2=self.patch_size) - img_sizes = [[pH, pW]] * B - x_masks = None - else: - raise NotImplementedError - return x, x_masks, img_sizes - - def forward(self, - x: torch.Tensor, - t: torch.Tensor, - y: Optional[torch.Tensor] = None, - context: Optional[torch.Tensor] = None, - encoder_hidden_states_llama3=None, - image_cond=None, - control = None, - transformer_options = {}, - ): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, t, y, context, encoder_hidden_states_llama3, image_cond, control, transformer_options) - - def _forward( - self, - x: torch.Tensor, - t: torch.Tensor, - y: Optional[torch.Tensor] = None, - context: Optional[torch.Tensor] = None, - encoder_hidden_states_llama3=None, - image_cond=None, - control = None, - transformer_options = {}, - ) -> torch.Tensor: - bs, c, h, w = x.shape - if image_cond is not None: - x = torch.cat([x, image_cond], dim=-1) - hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) - timesteps = t - pooled_embeds = y - T5_encoder_hidden_states = context - - img_sizes = None - - # spatial forward - batch_size = hidden_states.shape[0] - hidden_states_type = hidden_states.dtype - - # 0. time - timesteps = self.expand_timesteps(timesteps, batch_size, hidden_states.device) - timesteps = self.t_embedder(timesteps, hidden_states_type) - p_embedder = self.p_embedder(pooled_embeds) - adaln_input = timesteps + p_embedder - - hidden_states, image_tokens_masks, img_sizes = self.patchify(hidden_states, self.max_seq, img_sizes) - if image_tokens_masks is None: - pH, pW = img_sizes[0] - img_ids = torch.zeros(pH, pW, 3, device=hidden_states.device) - img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH, device=hidden_states.device)[:, None] - img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW, device=hidden_states.device)[None, :] - img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size) - hidden_states = self.x_embedder(hidden_states) - - # T5_encoder_hidden_states = encoder_hidden_states[0] - encoder_hidden_states = encoder_hidden_states_llama3.movedim(1, 0) - encoder_hidden_states = [encoder_hidden_states[k] for k in self.llama_layers] - - if self.caption_projection is not None: - new_encoder_hidden_states = [] - for i, enc_hidden_state in enumerate(encoder_hidden_states): - enc_hidden_state = self.caption_projection[i](enc_hidden_state) - enc_hidden_state = enc_hidden_state.view(batch_size, -1, hidden_states.shape[-1]) - new_encoder_hidden_states.append(enc_hidden_state) - encoder_hidden_states = new_encoder_hidden_states - T5_encoder_hidden_states = self.caption_projection[-1](T5_encoder_hidden_states) - T5_encoder_hidden_states = T5_encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) - encoder_hidden_states.append(T5_encoder_hidden_states) - - txt_ids = torch.zeros( - batch_size, - encoder_hidden_states[-1].shape[1] + encoder_hidden_states[-2].shape[1] + encoder_hidden_states[0].shape[1], - 3, - device=img_ids.device, dtype=img_ids.dtype - ) - ids = torch.cat((img_ids, txt_ids), dim=1) - rope = self.pe_embedder(ids) - - # 2. Blocks - block_id = 0 - initial_encoder_hidden_states = torch.cat([encoder_hidden_states[-1], encoder_hidden_states[-2]], dim=1) - initial_encoder_hidden_states_seq_len = initial_encoder_hidden_states.shape[1] - for bid, block in enumerate(self.double_stream_blocks): - cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] - cur_encoder_hidden_states = torch.cat([initial_encoder_hidden_states, cur_llama31_encoder_hidden_states], dim=1) - hidden_states, initial_encoder_hidden_states = block( - image_tokens = hidden_states, - image_tokens_masks = image_tokens_masks, - text_tokens = cur_encoder_hidden_states, - adaln_input = adaln_input, - rope = rope, - ) - initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len] - block_id += 1 - - image_tokens_seq_len = hidden_states.shape[1] - hidden_states = torch.cat([hidden_states, initial_encoder_hidden_states], dim=1) - hidden_states_seq_len = hidden_states.shape[1] - if image_tokens_masks is not None: - encoder_attention_mask_ones = torch.ones( - (batch_size, initial_encoder_hidden_states.shape[1] + cur_llama31_encoder_hidden_states.shape[1]), - device=image_tokens_masks.device, dtype=image_tokens_masks.dtype - ) - image_tokens_masks = torch.cat([image_tokens_masks, encoder_attention_mask_ones], dim=1) - - for bid, block in enumerate(self.single_stream_blocks): - cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] - hidden_states = torch.cat([hidden_states, cur_llama31_encoder_hidden_states], dim=1) - hidden_states = block( - image_tokens=hidden_states, - image_tokens_masks=image_tokens_masks, - text_tokens=None, - adaln_input=adaln_input, - rope=rope, - ) - hidden_states = hidden_states[:, :hidden_states_seq_len] - block_id += 1 - - hidden_states = hidden_states[:, :image_tokens_seq_len, ...] - output = self.final_layer(hidden_states, adaln_input) - output = self.unpatchify(output, img_sizes) - return -output[:, :, :h, :w] diff --git a/comfy/ldm/hunyuan3d/.DS_Store b/comfy/ldm/hunyuan3d/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/hunyuan3d/.DS_Store and /dev/null differ diff --git a/comfy/ldm/hunyuan3d/model.py b/comfy/ldm/hunyuan3d/model.py deleted file mode 100644 index 0fa5e78c1594e63ea65b22dd6ad0026311fa9357..0000000000000000000000000000000000000000 --- a/comfy/ldm/hunyuan3d/model.py +++ /dev/null @@ -1,143 +0,0 @@ -import torch -from torch import nn -from comfy.ldm.flux.layers import ( - DoubleStreamBlock, - LastLayer, - MLPEmbedder, - SingleStreamBlock, - timestep_embedding, -) -import comfy.patcher_extension - - -class Hunyuan3Dv2(nn.Module): - def __init__( - self, - in_channels=64, - context_in_dim=1536, - hidden_size=1024, - mlp_ratio=4.0, - num_heads=16, - depth=16, - depth_single_blocks=32, - qkv_bias=True, - guidance_embed=False, - image_model=None, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.dtype = dtype - - if hidden_size % num_heads != 0: - raise ValueError( - f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}" - ) - - self.max_period = 1000 # While reimplementing the model I noticed that they messed up. This 1000 value was meant to be the time_factor but they set the max_period instead - self.latent_in = operations.Linear(in_channels, hidden_size, bias=True, dtype=dtype, device=device) - self.time_in = MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) - self.guidance_in = ( - MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) if guidance_embed else None - ) - self.cond_in = operations.Linear(context_in_dim, hidden_size, dtype=dtype, device=device) - self.double_blocks = nn.ModuleList( - [ - DoubleStreamBlock( - hidden_size, - num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - dtype=dtype, device=device, operations=operations - ) - for _ in range(depth) - ] - ) - self.single_blocks = nn.ModuleList( - [ - SingleStreamBlock( - hidden_size, - num_heads, - mlp_ratio=mlp_ratio, - dtype=dtype, device=device, operations=operations - ) - for _ in range(depth_single_blocks) - ] - ) - self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations) - - def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, guidance, transformer_options, **kwargs) - - def _forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): - x = x.movedim(-1, -2) - timestep = 1.0 - timestep - txt = context - img = self.latent_in(x) - - vec = self.time_in(timestep_embedding(timestep, 256, self.max_period).to(dtype=img.dtype)) - if self.guidance_in is not None: - if guidance is not None: - vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.max_period).to(img.dtype)) - - txt = self.cond_in(txt) - pe = None - attn_mask = None - - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.double_blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"], out["txt"] = block(img=args["img"], - txt=args["txt"], - vec=args["vec"], - pe=args["pe"], - attn_mask=args.get("attn_mask")) - return out - - out = blocks_replace[("double_block", i)]({"img": img, - "txt": txt, - "vec": vec, - "pe": pe, - "attn_mask": attn_mask}, - {"original_block": block_wrap}) - txt = out["txt"] - img = out["img"] - else: - img, txt = block(img=img, - txt=txt, - vec=vec, - pe=pe, - attn_mask=attn_mask) - - img = torch.cat((txt, img), 1) - - for i, block in enumerate(self.single_blocks): - if ("single_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], - vec=args["vec"], - pe=args["pe"], - attn_mask=args.get("attn_mask")) - return out - - out = blocks_replace[("single_block", i)]({"img": img, - "vec": vec, - "pe": pe, - "attn_mask": attn_mask}, - {"original_block": block_wrap}) - img = out["img"] - else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) - - img = img[:, txt.shape[1]:, ...] - img = self.final_layer(img, vec) - return img.movedim(-2, -1) * (-1.0) diff --git a/comfy/ldm/hunyuan3d/vae.py b/comfy/ldm/hunyuan3d/vae.py deleted file mode 100644 index 6e8cbf1d98a339941b6633f65c2a1a38af8a346b..0000000000000000000000000000000000000000 --- a/comfy/ldm/hunyuan3d/vae.py +++ /dev/null @@ -1,587 +0,0 @@ -# Original: https://github.com/Tencent/Hunyuan3D-2/blob/main/hy3dgen/shapegen/models/autoencoders/model.py -# Since the header on their VAE source file was a bit confusing we asked for permission to use this code from tencent under the GPL license used in ComfyUI. - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -from typing import Union, Tuple, List, Callable, Optional - -import numpy as np -from einops import repeat, rearrange -from tqdm import tqdm -import logging - -import comfy.ops -ops = comfy.ops.disable_weight_init - -def generate_dense_grid_points( - bbox_min: np.ndarray, - bbox_max: np.ndarray, - octree_resolution: int, - indexing: str = "ij", -): - length = bbox_max - bbox_min - num_cells = octree_resolution - - x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32) - y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32) - z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32) - [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing) - xyz = np.stack((xs, ys, zs), axis=-1) - grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1] - - return xyz, grid_size, length - - -class VanillaVolumeDecoder: - @torch.no_grad() - def __call__( - self, - latents: torch.FloatTensor, - geo_decoder: Callable, - bounds: Union[Tuple[float], List[float], float] = 1.01, - num_chunks: int = 10000, - octree_resolution: int = None, - enable_pbar: bool = True, - **kwargs, - ): - device = latents.device - dtype = latents.dtype - batch_size = latents.shape[0] - - # 1. generate query points - if isinstance(bounds, float): - bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] - - bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6]) - xyz_samples, grid_size, length = generate_dense_grid_points( - bbox_min=bbox_min, - bbox_max=bbox_max, - octree_resolution=octree_resolution, - indexing="ij" - ) - xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3) - - # 2. latents to 3d volume - batch_logits = [] - for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc="Volume Decoding", - disable=not enable_pbar): - chunk_queries = xyz_samples[start: start + num_chunks, :] - chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size) - logits = geo_decoder(queries=chunk_queries, latents=latents) - batch_logits.append(logits) - - grid_logits = torch.cat(batch_logits, dim=1) - grid_logits = grid_logits.view((batch_size, *grid_size)).float() - - return grid_logits - - -class FourierEmbedder(nn.Module): - """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts - each feature dimension of `x[..., i]` into: - [ - sin(x[..., i]), - sin(f_1*x[..., i]), - sin(f_2*x[..., i]), - ... - sin(f_N * x[..., i]), - cos(x[..., i]), - cos(f_1*x[..., i]), - cos(f_2*x[..., i]), - ... - cos(f_N * x[..., i]), - x[..., i] # only present if include_input is True. - ], here f_i is the frequency. - - Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs]. - If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...]; - Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]. - - Args: - num_freqs (int): the number of frequencies, default is 6; - logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], - otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]; - input_dim (int): the input dimension, default is 3; - include_input (bool): include the input tensor or not, default is True. - - Attributes: - frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], - otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1); - - out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1), - otherwise, it is input_dim * num_freqs * 2. - - """ - - def __init__(self, - num_freqs: int = 6, - logspace: bool = True, - input_dim: int = 3, - include_input: bool = True, - include_pi: bool = True) -> None: - - """The initialization""" - - super().__init__() - - if logspace: - frequencies = 2.0 ** torch.arange( - num_freqs, - dtype=torch.float32 - ) - else: - frequencies = torch.linspace( - 1.0, - 2.0 ** (num_freqs - 1), - num_freqs, - dtype=torch.float32 - ) - - if include_pi: - frequencies *= torch.pi - - self.register_buffer("frequencies", frequencies, persistent=False) - self.include_input = include_input - self.num_freqs = num_freqs - - self.out_dim = self.get_dims(input_dim) - - def get_dims(self, input_dim): - temp = 1 if self.include_input or self.num_freqs == 0 else 0 - out_dim = input_dim * (self.num_freqs * 2 + temp) - - return out_dim - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """ Forward process. - - Args: - x: tensor of shape [..., dim] - - Returns: - embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] - where temp is 1 if include_input is True and 0 otherwise. - """ - - if self.num_freqs > 0: - embed = (x[..., None].contiguous() * self.frequencies.to(device=x.device, dtype=x.dtype)).view(*x.shape[:-1], -1) - if self.include_input: - return torch.cat((x, embed.sin(), embed.cos()), dim=-1) - else: - return torch.cat((embed.sin(), embed.cos()), dim=-1) - else: - return x - - -class CrossAttentionProcessor: - def __call__(self, attn, q, k, v): - out = comfy.ops.scaled_dot_product_attention(q, k, v) - return out - - -class DropPath(nn.Module): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - """ - - def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): - super(DropPath, self).__init__() - self.drop_prob = drop_prob - self.scale_by_keep = scale_by_keep - - def forward(self, x): - """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). - - This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, - the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... - See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for - changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use - 'survival rate' as the argument. - - """ - if self.drop_prob == 0. or not self.training: - return x - keep_prob = 1 - self.drop_prob - shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets - random_tensor = x.new_empty(shape).bernoulli_(keep_prob) - if keep_prob > 0.0 and self.scale_by_keep: - random_tensor.div_(keep_prob) - return x * random_tensor - - def extra_repr(self): - return f'drop_prob={round(self.drop_prob, 3):0.3f}' - - -class MLP(nn.Module): - def __init__( - self, *, - width: int, - expand_ratio: int = 4, - output_width: int = None, - drop_path_rate: float = 0.0 - ): - super().__init__() - self.width = width - self.c_fc = ops.Linear(width, width * expand_ratio) - self.c_proj = ops.Linear(width * expand_ratio, output_width if output_width is not None else width) - self.gelu = nn.GELU() - self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() - - def forward(self, x): - return self.drop_path(self.c_proj(self.gelu(self.c_fc(x)))) - - -class QKVMultiheadCrossAttention(nn.Module): - def __init__( - self, - *, - heads: int, - width=None, - qk_norm=False, - norm_layer=ops.LayerNorm - ): - super().__init__() - self.heads = heads - self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - - self.attn_processor = CrossAttentionProcessor() - - def forward(self, q, kv): - _, n_ctx, _ = q.shape - bs, n_data, width = kv.shape - attn_ch = width // self.heads // 2 - q = q.view(bs, n_ctx, self.heads, -1) - kv = kv.view(bs, n_data, self.heads, -1) - k, v = torch.split(kv, attn_ch, dim=-1) - - q = self.q_norm(q) - k = self.k_norm(k) - q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) - out = self.attn_processor(self, q, k, v) - out = out.transpose(1, 2).reshape(bs, n_ctx, -1) - return out - - -class MultiheadCrossAttention(nn.Module): - def __init__( - self, - *, - width: int, - heads: int, - qkv_bias: bool = True, - data_width: Optional[int] = None, - norm_layer=ops.LayerNorm, - qk_norm: bool = False, - kv_cache: bool = False, - ): - super().__init__() - self.width = width - self.heads = heads - self.data_width = width if data_width is None else data_width - self.c_q = ops.Linear(width, width, bias=qkv_bias) - self.c_kv = ops.Linear(self.data_width, width * 2, bias=qkv_bias) - self.c_proj = ops.Linear(width, width) - self.attention = QKVMultiheadCrossAttention( - heads=heads, - width=width, - norm_layer=norm_layer, - qk_norm=qk_norm - ) - self.kv_cache = kv_cache - self.data = None - - def forward(self, x, data): - x = self.c_q(x) - if self.kv_cache: - if self.data is None: - self.data = self.c_kv(data) - logging.info('Save kv cache,this should be called only once for one mesh') - data = self.data - else: - data = self.c_kv(data) - x = self.attention(x, data) - x = self.c_proj(x) - return x - - -class ResidualCrossAttentionBlock(nn.Module): - def __init__( - self, - *, - width: int, - heads: int, - mlp_expand_ratio: int = 4, - data_width: Optional[int] = None, - qkv_bias: bool = True, - norm_layer=ops.LayerNorm, - qk_norm: bool = False - ): - super().__init__() - - if data_width is None: - data_width = width - - self.attn = MultiheadCrossAttention( - width=width, - heads=heads, - data_width=data_width, - qkv_bias=qkv_bias, - norm_layer=norm_layer, - qk_norm=qk_norm - ) - self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) - self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6) - self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6) - self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio) - - def forward(self, x: torch.Tensor, data: torch.Tensor): - x = x + self.attn(self.ln_1(x), self.ln_2(data)) - x = x + self.mlp(self.ln_3(x)) - return x - - -class QKVMultiheadAttention(nn.Module): - def __init__( - self, - *, - heads: int, - width=None, - qk_norm=False, - norm_layer=ops.LayerNorm - ): - super().__init__() - self.heads = heads - self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - - def forward(self, qkv): - bs, n_ctx, width = qkv.shape - attn_ch = width // self.heads // 3 - qkv = qkv.view(bs, n_ctx, self.heads, -1) - q, k, v = torch.split(qkv, attn_ch, dim=-1) - - q = self.q_norm(q) - k = self.k_norm(k) - - q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) - out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) - return out - - -class MultiheadAttention(nn.Module): - def __init__( - self, - *, - width: int, - heads: int, - qkv_bias: bool, - norm_layer=ops.LayerNorm, - qk_norm: bool = False, - drop_path_rate: float = 0.0 - ): - super().__init__() - self.width = width - self.heads = heads - self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias) - self.c_proj = ops.Linear(width, width) - self.attention = QKVMultiheadAttention( - heads=heads, - width=width, - norm_layer=norm_layer, - qk_norm=qk_norm - ) - self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() - - def forward(self, x): - x = self.c_qkv(x) - x = self.attention(x) - x = self.drop_path(self.c_proj(x)) - return x - - -class ResidualAttentionBlock(nn.Module): - def __init__( - self, - *, - width: int, - heads: int, - qkv_bias: bool = True, - norm_layer=ops.LayerNorm, - qk_norm: bool = False, - drop_path_rate: float = 0.0, - ): - super().__init__() - self.attn = MultiheadAttention( - width=width, - heads=heads, - qkv_bias=qkv_bias, - norm_layer=norm_layer, - qk_norm=qk_norm, - drop_path_rate=drop_path_rate - ) - self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) - self.mlp = MLP(width=width, drop_path_rate=drop_path_rate) - self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6) - - def forward(self, x: torch.Tensor): - x = x + self.attn(self.ln_1(x)) - x = x + self.mlp(self.ln_2(x)) - return x - - -class Transformer(nn.Module): - def __init__( - self, - *, - width: int, - layers: int, - heads: int, - qkv_bias: bool = True, - norm_layer=ops.LayerNorm, - qk_norm: bool = False, - drop_path_rate: float = 0.0 - ): - super().__init__() - self.width = width - self.layers = layers - self.resblocks = nn.ModuleList( - [ - ResidualAttentionBlock( - width=width, - heads=heads, - qkv_bias=qkv_bias, - norm_layer=norm_layer, - qk_norm=qk_norm, - drop_path_rate=drop_path_rate - ) - for _ in range(layers) - ] - ) - - def forward(self, x: torch.Tensor): - for block in self.resblocks: - x = block(x) - return x - - -class CrossAttentionDecoder(nn.Module): - - def __init__( - self, - *, - out_channels: int, - fourier_embedder: FourierEmbedder, - width: int, - heads: int, - mlp_expand_ratio: int = 4, - downsample_ratio: int = 1, - enable_ln_post: bool = True, - qkv_bias: bool = True, - qk_norm: bool = False, - label_type: str = "binary" - ): - super().__init__() - - self.enable_ln_post = enable_ln_post - self.fourier_embedder = fourier_embedder - self.downsample_ratio = downsample_ratio - self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width) - if self.downsample_ratio != 1: - self.latents_proj = ops.Linear(width * downsample_ratio, width) - if self.enable_ln_post == False: - qk_norm = False - self.cross_attn_decoder = ResidualCrossAttentionBlock( - width=width, - mlp_expand_ratio=mlp_expand_ratio, - heads=heads, - qkv_bias=qkv_bias, - qk_norm=qk_norm - ) - - if self.enable_ln_post: - self.ln_post = ops.LayerNorm(width) - self.output_proj = ops.Linear(width, out_channels) - self.label_type = label_type - self.count = 0 - - def forward(self, queries=None, query_embeddings=None, latents=None): - if query_embeddings is None: - query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) - self.count += query_embeddings.shape[1] - if self.downsample_ratio != 1: - latents = self.latents_proj(latents) - x = self.cross_attn_decoder(query_embeddings, latents) - if self.enable_ln_post: - x = self.ln_post(x) - occ = self.output_proj(x) - return occ - - -class ShapeVAE(nn.Module): - def __init__( - self, - *, - embed_dim: int, - width: int, - heads: int, - num_decoder_layers: int, - geo_decoder_downsample_ratio: int = 1, - geo_decoder_mlp_expand_ratio: int = 4, - geo_decoder_ln_post: bool = True, - num_freqs: int = 8, - include_pi: bool = True, - qkv_bias: bool = True, - qk_norm: bool = False, - label_type: str = "binary", - drop_path_rate: float = 0.0, - scale_factor: float = 1.0, - ): - super().__init__() - self.geo_decoder_ln_post = geo_decoder_ln_post - - self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) - - self.post_kl = ops.Linear(embed_dim, width) - - self.transformer = Transformer( - width=width, - layers=num_decoder_layers, - heads=heads, - qkv_bias=qkv_bias, - qk_norm=qk_norm, - drop_path_rate=drop_path_rate - ) - - self.geo_decoder = CrossAttentionDecoder( - fourier_embedder=self.fourier_embedder, - out_channels=1, - mlp_expand_ratio=geo_decoder_mlp_expand_ratio, - downsample_ratio=geo_decoder_downsample_ratio, - enable_ln_post=self.geo_decoder_ln_post, - width=width // geo_decoder_downsample_ratio, - heads=heads // geo_decoder_downsample_ratio, - qkv_bias=qkv_bias, - qk_norm=qk_norm, - label_type=label_type, - ) - - self.volume_decoder = VanillaVolumeDecoder() - self.scale_factor = scale_factor - - def decode(self, latents, **kwargs): - latents = self.post_kl(latents.movedim(-2, -1)) - latents = self.transformer(latents) - - bounds = kwargs.get("bounds", 1.01) - num_chunks = kwargs.get("num_chunks", 8000) - octree_resolution = kwargs.get("octree_resolution", 256) - enable_pbar = kwargs.get("enable_pbar", True) - - grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar) - return grid_logits.movedim(-2, -1) - - def encode(self, x): - return None diff --git a/comfy/ldm/hunyuan_video/.DS_Store b/comfy/ldm/hunyuan_video/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/hunyuan_video/.DS_Store and /dev/null differ diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py deleted file mode 100644 index da1011596b5cb688d67c1086f13105eb35e9bdb6..0000000000000000000000000000000000000000 --- a/comfy/ldm/hunyuan_video/model.py +++ /dev/null @@ -1,363 +0,0 @@ -#Based on Flux code because of weird hunyuan video code license. - -import torch -import comfy.patcher_extension -import comfy.ldm.flux.layers -import comfy.ldm.modules.diffusionmodules.mmdit -from comfy.ldm.modules.attention import optimized_attention - - -from dataclasses import dataclass -from einops import repeat - -from torch import Tensor, nn - -from comfy.ldm.flux.layers import ( - DoubleStreamBlock, - EmbedND, - LastLayer, - MLPEmbedder, - SingleStreamBlock, - timestep_embedding -) - -import comfy.ldm.common_dit - - -@dataclass -class HunyuanVideoParams: - in_channels: int - out_channels: int - vec_in_dim: int - context_in_dim: int - hidden_size: int - mlp_ratio: float - num_heads: int - depth: int - depth_single_blocks: int - axes_dim: list - theta: int - patch_size: list - qkv_bias: bool - guidance_embed: bool - - -class SelfAttentionRef(nn.Module): - def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, device=None, operations=None): - super().__init__() - self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) - self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) - - -class TokenRefinerBlock(nn.Module): - def __init__( - self, - hidden_size, - heads, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.heads = heads - mlp_hidden_dim = hidden_size * 4 - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device), - ) - - self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) - self.self_attn = SelfAttentionRef(hidden_size, True, dtype=dtype, device=device, operations=operations) - - self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) - - self.mlp = nn.Sequential( - operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - def forward(self, x, c, mask): - mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1) - - norm_x = self.norm1(x) - qkv = self.self_attn.qkv(norm_x) - q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4) - attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True) - - x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1) - x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1) - return x - - -class IndividualTokenRefiner(nn.Module): - def __init__( - self, - hidden_size, - heads, - num_blocks, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.blocks = nn.ModuleList( - [ - TokenRefinerBlock( - hidden_size=hidden_size, - heads=heads, - dtype=dtype, - device=device, - operations=operations - ) - for _ in range(num_blocks) - ] - ) - - def forward(self, x, c, mask): - m = None - if mask is not None: - m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1) - m = m + m.transpose(2, 3) - - for block in self.blocks: - x = block(x, c, m) - return x - - - -class TokenRefiner(nn.Module): - def __init__( - self, - text_dim, - hidden_size, - heads, - num_blocks, - dtype=None, - device=None, - operations=None - ): - super().__init__() - - self.input_embedder = operations.Linear(text_dim, hidden_size, bias=True, dtype=dtype, device=device) - self.t_embedder = MLPEmbedder(256, hidden_size, dtype=dtype, device=device, operations=operations) - self.c_embedder = MLPEmbedder(text_dim, hidden_size, dtype=dtype, device=device, operations=operations) - self.individual_token_refiner = IndividualTokenRefiner(hidden_size, heads, num_blocks, dtype=dtype, device=device, operations=operations) - - def forward( - self, - x, - timesteps, - mask, - ): - t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype)) - # m = mask.float().unsqueeze(-1) - # c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise - c = x.sum(dim=1) / x.shape[1] - - c = t + self.c_embedder(c.to(x.dtype)) - x = self.input_embedder(x) - x = self.individual_token_refiner(x, c, mask) - return x - -class HunyuanVideo(nn.Module): - """ - Transformer model for flow matching on sequences. - """ - - def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): - super().__init__() - self.dtype = dtype - params = HunyuanVideoParams(**kwargs) - self.params = params - self.patch_size = params.patch_size - self.in_channels = params.in_channels - self.out_channels = params.out_channels - if params.hidden_size % params.num_heads != 0: - raise ValueError( - f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" - ) - pe_dim = params.hidden_size // params.num_heads - if sum(params.axes_dim) != pe_dim: - raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") - self.hidden_size = params.hidden_size - self.num_heads = params.num_heads - self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) - - self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=True, dtype=dtype, device=device, operations=operations) - self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) - self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) - self.guidance_in = ( - MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() - ) - - self.txt_in = TokenRefiner(params.context_in_dim, self.hidden_size, self.num_heads, 2, dtype=dtype, device=device, operations=operations) - - self.double_blocks = nn.ModuleList( - [ - DoubleStreamBlock( - self.hidden_size, - self.num_heads, - mlp_ratio=params.mlp_ratio, - qkv_bias=params.qkv_bias, - flipped_img_txt=True, - dtype=dtype, device=device, operations=operations - ) - for _ in range(params.depth) - ] - ) - - self.single_blocks = nn.ModuleList( - [ - SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) - for _ in range(params.depth_single_blocks) - ] - ) - - if final_layer: - self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations) - - def forward_orig( - self, - img: Tensor, - img_ids: Tensor, - txt: Tensor, - txt_ids: Tensor, - txt_mask: Tensor, - timesteps: Tensor, - y: Tensor, - guidance: Tensor = None, - guiding_frame_index=None, - ref_latent=None, - control=None, - transformer_options={}, - ) -> Tensor: - patches_replace = transformer_options.get("patches_replace", {}) - - initial_shape = list(img.shape) - # running on sequences img - img = self.img_in(img) - vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) - - if ref_latent is not None: - ref_latent_ids = self.img_ids(ref_latent) - ref_latent = self.img_in(ref_latent) - img = torch.cat([ref_latent, img], dim=-2) - ref_latent_ids[..., 0] = -1 - ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1]) - img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2) - - if guiding_frame_index is not None: - token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0)) - vec_ = self.vector_in(y[:, :self.params.vec_in_dim]) - vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1) - frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2]) - modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)] - modulation_dims_txt = [(0, None, 1)] - else: - vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) - modulation_dims = None - modulation_dims_txt = None - - if self.params.guidance_embed: - if guidance is not None: - vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) - - if txt_mask is not None and not torch.is_floating_point(txt_mask): - txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max - - txt = self.txt_in(txt, timesteps, txt_mask) - - ids = torch.cat((img_ids, txt_ids), dim=1) - pe = self.pe_embedder(ids) - - img_len = img.shape[1] - if txt_mask is not None: - attn_mask_len = img_len + txt.shape[1] - attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device) - attn_mask[:, 0, img_len:] = txt_mask - else: - attn_mask = None - - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.double_blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"]) - return out - - out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap}) - txt = out["txt"] - img = out["img"] - else: - img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt) - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - img += add - - img = torch.cat((img, txt), 1) - - for i, block in enumerate(self.single_blocks): - if ("single_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"]) - return out - - out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap}) - img = out["img"] - else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims) - - if control is not None: # Controlnet - control_o = control.get("output") - if i < len(control_o): - add = control_o[i] - if add is not None: - img[:, : img_len] += add - - img = img[:, : img_len] - if ref_latent is not None: - img = img[:, ref_latent.shape[1]:] - - img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels) - - shape = initial_shape[-3:] - for i in range(len(shape)): - shape[i] = shape[i] // self.patch_size[i] - img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size) - img = img.permute(0, 4, 1, 5, 2, 6, 3, 7) - img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4]) - return img - - def img_ids(self, x): - bs, c, t, h, w = x.shape - patch_size = self.patch_size - t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) - h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) - w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) - img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype) - img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) - img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) - img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) - return repeat(img_ids, "t h w c -> b (t h w) c", b=bs) - - def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, y, guidance, attention_mask, guiding_frame_index, ref_latent, control, transformer_options, **kwargs) - - def _forward(self, x, timestep, context, y, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs): - bs, c, t, h, w = x.shape - img_ids = self.img_ids(x) - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options) - return out diff --git a/comfy/ldm/hydit/.DS_Store b/comfy/ldm/hydit/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/hydit/.DS_Store and /dev/null differ diff --git a/comfy/ldm/hydit/attn_layers.py b/comfy/ldm/hydit/attn_layers.py deleted file mode 100644 index 3ca25a5df175bcadd8e50ad34ee0bfcddcb37aa0..0000000000000000000000000000000000000000 --- a/comfy/ldm/hydit/attn_layers.py +++ /dev/null @@ -1,218 +0,0 @@ -import torch -import torch.nn as nn -from typing import Tuple, Union, Optional -from comfy.ldm.modules.attention import optimized_attention - - -def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as the target tensor 'x' - for the purpose of broadcasting the frequency tensor during element-wise operations. - - Args: - freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - head_first (bool): head dimension first (except batch dim) or not. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected shape. - AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - - if isinstance(freqs_cis, tuple): - # freqs_cis: (cos, sin) in real space - if head_first: - assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' - shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - else: - assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) - else: - # freqs_cis: values in complex space - if head_first: - assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' - shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - else: - assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - -def rotate_half(x): - x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] - return torch.stack([-x_imag, x_real], dim=-1).flatten(3) - - -def apply_rotary_emb( - xq: torch.Tensor, - xk: Optional[torch.Tensor], - freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], - head_first: bool = False, -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Apply rotary embeddings to input tensors using the given frequency tensor. - - This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided - frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are - returned as real tensors. - - Args: - xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] - xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] - freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. - head_first (bool): head dimension first (except batch dim) or not. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. - - """ - xk_out = None - if isinstance(freqs_cis, tuple): - cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] - xq_out = (xq * cos + rotate_half(xq) * sin) - if xk is not None: - xk_out = (xk * cos + rotate_half(xk) * sin) - else: - xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] - freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] - xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) - if xk is not None: - xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] - xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) - - return xq_out, xk_out - - - -class CrossAttention(nn.Module): - """ - Use QK Normalization. - """ - def __init__(self, - qdim, - kdim, - num_heads, - qkv_bias=True, - qk_norm=False, - attn_drop=0.0, - proj_drop=0.0, - attn_precision=None, - device=None, - dtype=None, - operations=None, - ): - factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__() - self.attn_precision = attn_precision - self.qdim = qdim - self.kdim = kdim - self.num_heads = num_heads - assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" - self.head_dim = self.qdim // num_heads - assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" - self.scale = self.head_dim ** -0.5 - - self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) - self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) - - # TODO: eps should be 1 / 65530 if using fp16 - self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() - self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() - self.attn_drop = nn.Dropout(attn_drop) - self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, y, freqs_cis_img=None): - """ - Parameters - ---------- - x: torch.Tensor - (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) - y: torch.Tensor - (batch, seqlen2, hidden_dim2) - freqs_cis_img: torch.Tensor - (batch, hidden_dim // 2), RoPE for image - """ - b, s1, c = x.shape # [b, s1, D] - _, s2, c = y.shape # [b, s2, 1024] - - q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] - kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] - k, v = kv.unbind(dim=2) # [b, s, h, d] - q = self.q_norm(q) - k = self.k_norm(k) - - # Apply RoPE if needed - if freqs_cis_img is not None: - qq, _ = apply_rotary_emb(q, None, freqs_cis_img) - assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' - q = qq - - q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C - k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2 - v = v.transpose(-2, -3).contiguous() - - context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) - - out = self.out_proj(context) # context.reshape - B, L1, -1 - out = self.proj_drop(out) - - out_tuple = (out,) - - return out_tuple - - -class Attention(nn.Module): - """ - We rename some layer names to align with flash attention - """ - def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None): - super().__init__() - self.attn_precision = attn_precision - self.dim = dim - self.num_heads = num_heads - assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' - self.head_dim = self.dim // num_heads - # This assertion is aligned with flash attention - assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" - self.scale = self.head_dim ** -0.5 - - # qkv --> Wqkv - self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) - # TODO: eps should be 1 / 65530 if using fp16 - self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() - self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity() - self.attn_drop = nn.Dropout(attn_drop) - self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, freqs_cis_img=None): - B, N, C = x.shape - qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] - q, k, v = qkv.unbind(0) # [b, h, s, d] - q = self.q_norm(q) # [b, h, s, d] - k = self.k_norm(k) # [b, h, s, d] - - # Apply RoPE if needed - if freqs_cis_img is not None: - qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) - assert qq.shape == q.shape and kk.shape == k.shape, \ - f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' - q, k = qq, kk - - x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision) - x = self.out_proj(x) - x = self.proj_drop(x) - - out_tuple = (x,) - - return out_tuple diff --git a/comfy/ldm/hydit/controlnet.py b/comfy/ldm/hydit/controlnet.py deleted file mode 100644 index 31a6bff9409bb5264e552f91197915a136c3964d..0000000000000000000000000000000000000000 --- a/comfy/ldm/hydit/controlnet.py +++ /dev/null @@ -1,311 +0,0 @@ - -import torch -import torch.nn as nn - - -from comfy.ldm.modules.diffusionmodules.mmdit import ( - TimestepEmbedder, - PatchEmbed, -) -from .poolers import AttentionPool - -import comfy.latent_formats -from .models import HunYuanDiTBlock, calc_rope - - - -class HunYuanControlNet(nn.Module): - """ - HunYuanDiT: Diffusion model with a Transformer backbone. - - Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. - - Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. - - Parameters - ---------- - args: argparse.Namespace - The arguments parsed by argparse. - input_size: tuple - The size of the input image. - patch_size: int - The size of the patch. - in_channels: int - The number of input channels. - hidden_size: int - The hidden size of the transformer backbone. - depth: int - The number of transformer blocks. - num_heads: int - The number of attention heads. - mlp_ratio: float - The ratio of the hidden size of the MLP in the transformer block. - log_fn: callable - The logging function. - """ - - def __init__( - self, - input_size: tuple = 128, - patch_size: int = 2, - in_channels: int = 4, - hidden_size: int = 1408, - depth: int = 40, - num_heads: int = 16, - mlp_ratio: float = 4.3637, - text_states_dim=1024, - text_states_dim_t5=2048, - text_len=77, - text_len_t5=256, - qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details. - size_cond=False, - use_style_cond=False, - learn_sigma=True, - norm="layer", - log_fn: callable = print, - attn_precision=None, - dtype=None, - device=None, - operations=None, - **kwargs, - ): - super().__init__() - self.log_fn = log_fn - self.depth = depth - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - self.num_heads = num_heads - self.hidden_size = hidden_size - self.text_states_dim = text_states_dim - self.text_states_dim_t5 = text_states_dim_t5 - self.text_len = text_len - self.text_len_t5 = text_len_t5 - self.size_cond = size_cond - self.use_style_cond = use_style_cond - self.norm = norm - self.dtype = dtype - self.latent_format = comfy.latent_formats.SDXL - - self.mlp_t5 = nn.Sequential( - nn.Linear( - self.text_states_dim_t5, - self.text_states_dim_t5 * 4, - bias=True, - dtype=dtype, - device=device, - ), - nn.SiLU(), - nn.Linear( - self.text_states_dim_t5 * 4, - self.text_states_dim, - bias=True, - dtype=dtype, - device=device, - ), - ) - # learnable replace - self.text_embedding_padding = nn.Parameter( - torch.randn( - self.text_len + self.text_len_t5, - self.text_states_dim, - dtype=dtype, - device=device, - ) - ) - - # Attention pooling - pooler_out_dim = 1024 - self.pooler = AttentionPool( - self.text_len_t5, - self.text_states_dim_t5, - num_heads=8, - output_dim=pooler_out_dim, - dtype=dtype, - device=device, - operations=operations, - ) - - # Dimension of the extra input vectors - self.extra_in_dim = pooler_out_dim - - if self.size_cond: - # Image size and crop size conditions - self.extra_in_dim += 6 * 256 - - if self.use_style_cond: - # Here we use a default learned embedder layer for future extension. - self.style_embedder = nn.Embedding( - 1, hidden_size, dtype=dtype, device=device - ) - self.extra_in_dim += hidden_size - - # Text embedding for `add` - self.x_embedder = PatchEmbed( - input_size, - patch_size, - in_channels, - hidden_size, - dtype=dtype, - device=device, - operations=operations, - ) - self.t_embedder = TimestepEmbedder( - hidden_size, dtype=dtype, device=device, operations=operations - ) - self.extra_embedder = nn.Sequential( - operations.Linear( - self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device - ), - nn.SiLU(), - operations.Linear( - hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device - ), - ) - - # HUnYuanDiT Blocks - self.blocks = nn.ModuleList( - [ - HunYuanDiTBlock( - hidden_size=hidden_size, - c_emb_size=hidden_size, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - text_states_dim=self.text_states_dim, - qk_norm=qk_norm, - norm_type=self.norm, - skip=False, - attn_precision=attn_precision, - dtype=dtype, - device=device, - operations=operations, - ) - for _ in range(19) - ] - ) - - # Input zero linear for the first block - self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device) - - - # Output zero linear for the every block - self.after_proj_list = nn.ModuleList( - [ - - operations.Linear( - self.hidden_size, self.hidden_size, dtype=dtype, device=device - ) - for _ in range(len(self.blocks)) - ] - ) - - def forward( - self, - x, - hint, - timesteps, - context,#encoder_hidden_states=None, - text_embedding_mask=None, - encoder_hidden_states_t5=None, - text_embedding_mask_t5=None, - image_meta_size=None, - style=None, - return_dict=False, - **kwarg, - ): - """ - Forward pass of the encoder. - - Parameters - ---------- - x: torch.Tensor - (B, D, H, W) - t: torch.Tensor - (B) - encoder_hidden_states: torch.Tensor - CLIP text embedding, (B, L_clip, D) - text_embedding_mask: torch.Tensor - CLIP text embedding mask, (B, L_clip) - encoder_hidden_states_t5: torch.Tensor - T5 text embedding, (B, L_t5, D) - text_embedding_mask_t5: torch.Tensor - T5 text embedding mask, (B, L_t5) - image_meta_size: torch.Tensor - (B, 6) - style: torch.Tensor - (B) - cos_cis_img: torch.Tensor - sin_cis_img: torch.Tensor - return_dict: bool - Whether to return a dictionary. - """ - condition = hint - if condition.shape[0] == 1: - condition = torch.repeat_interleave(condition, x.shape[0], dim=0) - - text_states = context # 2,77,1024 - text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 - text_states_mask = text_embedding_mask.bool() # 2,77 - text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 - b_t5, l_t5, c_t5 = text_states_t5.shape - text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) - - padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) - - text_states[:, -self.text_len :] = torch.where( - text_states_mask[:, -self.text_len :].unsqueeze(2), - text_states[:, -self.text_len :], - padding[: self.text_len], - ) - text_states_t5[:, -self.text_len_t5 :] = torch.where( - text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2), - text_states_t5[:, -self.text_len_t5 :], - padding[self.text_len :], - ) - - text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 - - # _, _, oh, ow = x.shape - # th, tw = oh // self.patch_size, ow // self.patch_size - - # Get image RoPE embedding according to `reso`lution. - freqs_cis_img = calc_rope( - x, self.patch_size, self.hidden_size // self.num_heads - ) # (cos_cis_img, sin_cis_img) - - # ========================= Build time and image embedding ========================= - t = self.t_embedder(timesteps, dtype=self.dtype) - x = self.x_embedder(x) - - # ========================= Concatenate all extra vectors ========================= - # Build text tokens with pooling - extra_vec = self.pooler(encoder_hidden_states_t5) - - # Build image meta size tokens if applicable - # if image_meta_size is not None: - # image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256] - # if image_meta_size.dtype != self.dtype: - # image_meta_size = image_meta_size.half() - # image_meta_size = image_meta_size.view(-1, 6 * 256) - # extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] - - # Build style tokens - if style is not None: - style_embedding = self.style_embedder(style) - extra_vec = torch.cat([extra_vec, style_embedding], dim=1) - - # Concatenate all extra vectors - c = t + self.extra_embedder(extra_vec) # [B, D] - - # ========================= Deal with Condition ========================= - condition = self.x_embedder(condition) - - # ========================= Forward pass through HunYuanDiT blocks ========================= - controls = [] - x = x + self.before_proj(condition) # add condition - for layer, block in enumerate(self.blocks): - x = block(x, c, text_states, freqs_cis_img) - controls.append(self.after_proj_list[layer](x)) # zero linear for output - - return {"output": controls} diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py deleted file mode 100644 index 5ba2b76e0cad3c611fce1e74b024a66a2255b814..0000000000000000000000000000000000000000 --- a/comfy/ldm/hydit/models.py +++ /dev/null @@ -1,417 +0,0 @@ - -import torch -import torch.nn as nn - -import comfy.ops -from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed -from comfy.ldm.modules.diffusionmodules.util import timestep_embedding -from torch.utils import checkpoint - -from .attn_layers import Attention, CrossAttention -from .poolers import AttentionPool -from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop - -def calc_rope(x, patch_size, head_size): - th = (x.shape[2] + (patch_size // 2)) // patch_size - tw = (x.shape[3] + (patch_size // 2)) // patch_size - base_size = 512 // 8 // patch_size - start, stop = get_fill_resize_and_crop((th, tw), base_size) - sub_args = [start, stop, (th, tw)] - # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads'] - rope = get_2d_rotary_pos_embed(head_size, *sub_args) - rope = (rope[0].to(x), rope[1].to(x)) - return rope - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class HunYuanDiTBlock(nn.Module): - """ - A HunYuanDiT block with `add` conditioning. - """ - def __init__(self, - hidden_size, - c_emb_size, - num_heads, - mlp_ratio=4.0, - text_states_dim=1024, - qk_norm=False, - norm_type="layer", - skip=False, - attn_precision=None, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - use_ele_affine = True - - if norm_type == "layer": - norm_layer = operations.LayerNorm - elif norm_type == "rms": - norm_layer = operations.RMSNorm - else: - raise ValueError(f"Unknown norm_type: {norm_type}") - - # ========================= Self-Attention ========================= - self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) - self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) - - # ========================= FFN ========================= - self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device) - mlp_hidden_dim = int(hidden_size * mlp_ratio) - approx_gelu = lambda: nn.GELU(approximate="tanh") - self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations) - - # ========================= Add ========================= - # Simply use add like SDXL. - self.default_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device) - ) - - # ========================= Cross-Attention ========================= - self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, - qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) - self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) - - # ========================= Skip Connection ========================= - if skip: - self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) - self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device) - else: - self.skip_linear = None - - self.gradient_checkpointing = False - - def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): - # Long Skip Connection - if self.skip_linear is not None: - cat = torch.cat([x, skip], dim=-1) - if cat.dtype != x.dtype: - cat = cat.to(x.dtype) - cat = self.skip_norm(cat) - x = self.skip_linear(cat) - - # Self-Attention - shift_msa = self.default_modulation(c).unsqueeze(dim=1) - attn_inputs = ( - self.norm1(x) + shift_msa, freq_cis_img, - ) - x = x + self.attn1(*attn_inputs)[0] - - # Cross-Attention - cross_inputs = ( - self.norm3(x), text_states, freq_cis_img - ) - x = x + self.attn2(*cross_inputs)[0] - - # FFN Layer - mlp_inputs = self.norm2(x) - x = x + self.mlp(mlp_inputs) - - return x - - def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): - if self.gradient_checkpointing and self.training: - return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip) - return self._forward(x, c, text_states, freq_cis_img, skip) - - -class FinalLayer(nn.Module): - """ - The final layer of HunYuanDiT. - """ - def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class HunYuanDiT(nn.Module): - """ - HunYuanDiT: Diffusion model with a Transformer backbone. - - Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. - - Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. - - Parameters - ---------- - args: argparse.Namespace - The arguments parsed by argparse. - input_size: tuple - The size of the input image. - patch_size: int - The size of the patch. - in_channels: int - The number of input channels. - hidden_size: int - The hidden size of the transformer backbone. - depth: int - The number of transformer blocks. - num_heads: int - The number of attention heads. - mlp_ratio: float - The ratio of the hidden size of the MLP in the transformer block. - log_fn: callable - The logging function. - """ - #@register_to_config - def __init__(self, - input_size: tuple = 32, - patch_size: int = 2, - in_channels: int = 4, - hidden_size: int = 1152, - depth: int = 28, - num_heads: int = 16, - mlp_ratio: float = 4.0, - text_states_dim = 1024, - text_states_dim_t5 = 2048, - text_len = 77, - text_len_t5 = 256, - qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details. - size_cond = False, - use_style_cond = False, - learn_sigma = True, - norm = "layer", - log_fn: callable = print, - attn_precision=None, - dtype=None, - device=None, - operations=None, - **kwargs, - ): - super().__init__() - self.log_fn = log_fn - self.depth = depth - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - self.num_heads = num_heads - self.hidden_size = hidden_size - self.text_states_dim = text_states_dim - self.text_states_dim_t5 = text_states_dim_t5 - self.text_len = text_len - self.text_len_t5 = text_len_t5 - self.size_cond = size_cond - self.use_style_cond = use_style_cond - self.norm = norm - self.dtype = dtype - #import pdb - #pdb.set_trace() - - self.mlp_t5 = nn.Sequential( - operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device), - ) - # learnable replace - self.text_embedding_padding = nn.Parameter( - torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device)) - - # Attention pooling - pooler_out_dim = 1024 - self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations) - - # Dimension of the extra input vectors - self.extra_in_dim = pooler_out_dim - - if self.size_cond: - # Image size and crop size conditions - self.extra_in_dim += 6 * 256 - - if self.use_style_cond: - # Here we use a default learned embedder layer for future extension. - self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device) - self.extra_in_dim += hidden_size - - # Text embedding for `add` - self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations) - self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations) - self.extra_embedder = nn.Sequential( - operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device), - ) - - # HUnYuanDiT Blocks - self.blocks = nn.ModuleList([ - HunYuanDiTBlock(hidden_size=hidden_size, - c_emb_size=hidden_size, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - text_states_dim=self.text_states_dim, - qk_norm=qk_norm, - norm_type=self.norm, - skip=layer > depth // 2, - attn_precision=attn_precision, - dtype=dtype, - device=device, - operations=operations, - ) - for layer in range(depth) - ]) - - self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) - self.unpatchify_channels = self.out_channels - - - - def forward(self, - x, - t, - context,#encoder_hidden_states=None, - text_embedding_mask=None, - encoder_hidden_states_t5=None, - text_embedding_mask_t5=None, - image_meta_size=None, - style=None, - return_dict=False, - control=None, - transformer_options={}, - ): - """ - Forward pass of the encoder. - - Parameters - ---------- - x: torch.Tensor - (B, D, H, W) - t: torch.Tensor - (B) - encoder_hidden_states: torch.Tensor - CLIP text embedding, (B, L_clip, D) - text_embedding_mask: torch.Tensor - CLIP text embedding mask, (B, L_clip) - encoder_hidden_states_t5: torch.Tensor - T5 text embedding, (B, L_t5, D) - text_embedding_mask_t5: torch.Tensor - T5 text embedding mask, (B, L_t5) - image_meta_size: torch.Tensor - (B, 6) - style: torch.Tensor - (B) - cos_cis_img: torch.Tensor - sin_cis_img: torch.Tensor - return_dict: bool - Whether to return a dictionary. - """ - patches_replace = transformer_options.get("patches_replace", {}) - encoder_hidden_states = context - text_states = encoder_hidden_states # 2,77,1024 - text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 - text_states_mask = text_embedding_mask.bool() # 2,77 - text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 - b_t5, l_t5, c_t5 = text_states_t5.shape - text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1) - - padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states) - - text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len]) - text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:]) - - text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205，1024 - # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1) - - _, _, oh, ow = x.shape - th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size - - - # Get image RoPE embedding according to `reso`lution. - freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img) - - # ========================= Build time and image embedding ========================= - t = self.t_embedder(t, dtype=x.dtype) - x = self.x_embedder(x) - - # ========================= Concatenate all extra vectors ========================= - # Build text tokens with pooling - extra_vec = self.pooler(encoder_hidden_states_t5) - - # Build image meta size tokens if applicable - if self.size_cond: - image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256] - image_meta_size = image_meta_size.view(-1, 6 * 256) - extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] - - # Build style tokens - if self.use_style_cond: - if style is None: - style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int) - style_embedding = self.style_embedder(style, out_dtype=x.dtype) - extra_vec = torch.cat([extra_vec, style_embedding], dim=1) - - # Concatenate all extra vectors - c = t + self.extra_embedder(extra_vec) # [B, D] - - blocks_replace = patches_replace.get("dit", {}) - - controls = None - if control: - controls = control.get("output", None) - # ========================= Forward pass through HunYuanDiT blocks ========================= - skips = [] - for layer, block in enumerate(self.blocks): - if layer > self.depth // 2: - if controls is not None: - skip = skips.pop() + controls.pop().to(dtype=x.dtype) - else: - skip = skips.pop() - else: - skip = None - - if ("double_block", layer) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], args["vec"], args["txt"], args["pe"], args["skip"]) - return out - - out = blocks_replace[("double_block", layer)]({"img": x, "txt": text_states, "vec": c, "pe": freqs_cis_img, "skip": skip}, {"original_block": block_wrap}) - x = out["img"] - else: - x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D) - - - if layer < (self.depth // 2 - 1): - skips.append(x) - if controls is not None and len(controls) != 0: - raise ValueError("The number of controls is not equal to the number of skip connections.") - - # ========================= Final layer ========================= - x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels) - x = self.unpatchify(x, th, tw) # (N, out_channels, H, W) - - if return_dict: - return {'x': x} - if self.learn_sigma: - return x[:,:self.out_channels // 2,:oh,:ow] - return x[:,:,:oh,:ow] - - def unpatchify(self, x, h, w): - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - c = self.unpatchify_channels - p = self.x_embedder.patch_size[0] - # h = w = int(x.shape[1] ** 0.5) - assert h * w == x.shape[1] - - x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) - x = torch.einsum('nhwpqc->nchpwq', x) - imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) - return imgs diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py deleted file mode 100644 index c1b878ed6b0541116794635c5c1b53a7ca13af47..0000000000000000000000000000000000000000 --- a/comfy/ldm/hydit/poolers.py +++ /dev/null @@ -1,36 +0,0 @@ -import torch -import torch.nn as nn -from comfy.ldm.modules.attention import optimized_attention -import comfy.ops - -class AttentionPool(nn.Module): - def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None): - super().__init__() - self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device)) - self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) - self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) - self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device) - self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device) - self.num_heads = num_heads - self.embed_dim = embed_dim - - def forward(self, x): - x = x[:,:self.positional_embedding.shape[0] - 1] - x = x.permute(1, 0, 2) # NLC -> LNC - x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC - x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC - - q = self.q_proj(x[:1]) - k = self.k_proj(x) - v = self.v_proj(x) - - batch_size = q.shape[1] - head_dim = self.embed_dim // self.num_heads - q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) - k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) - v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim) - - attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1) - - attn_output = self.c_proj(attn_output) - return attn_output.squeeze(0) diff --git a/comfy/ldm/hydit/posemb_layers.py b/comfy/ldm/hydit/posemb_layers.py deleted file mode 100644 index dcb41a713cd94ea8472ff26e8865066887b1e486..0000000000000000000000000000000000000000 --- a/comfy/ldm/hydit/posemb_layers.py +++ /dev/null @@ -1,224 +0,0 @@ -import torch -import numpy as np -from typing import Union - - -def _to_tuple(x): - if isinstance(x, int): - return x, x - else: - return x - - -def get_fill_resize_and_crop(src, tgt): - th, tw = _to_tuple(tgt) - h, w = _to_tuple(src) - - tr = th / tw # base resolution - r = h / w # target resolution - - # resize - if r > tr: - resize_height = th - resize_width = int(round(th / h * w)) - else: - resize_width = tw - resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution - - crop_top = int(round((th - resize_height) / 2.0)) - crop_left = int(round((tw - resize_width) / 2.0)) - - return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) - - -def get_meshgrid(start, *args): - if len(args) == 0: - # start is grid_size - num = _to_tuple(start) - start = (0, 0) - stop = num - elif len(args) == 1: - # start is start, args[0] is stop, step is 1 - start = _to_tuple(start) - stop = _to_tuple(args[0]) - num = (stop[0] - start[0], stop[1] - start[1]) - elif len(args) == 2: - # start is start, args[0] is stop, args[1] is num - start = _to_tuple(start) - stop = _to_tuple(args[0]) - num = _to_tuple(args[1]) - else: - raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") - - grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) - grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) - grid = np.meshgrid(grid_w, grid_h) # here w goes first - grid = np.stack(grid, axis=0) # [2, W, H] - return grid - -################################################################################# -# Sine/Cosine Positional Embedding Functions # -################################################################################# -# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py - -def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0): - """ - grid_size: int of the grid height and width - return: - pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) - """ - grid = get_meshgrid(start, *args) # [2, H, w] - # grid_h = np.arange(grid_size, dtype=np.float32) - # grid_w = np.arange(grid_size, dtype=np.float32) - # grid = np.meshgrid(grid_w, grid_h) # here w goes first - # grid = np.stack(grid, axis=0) # [2, W, H] - - grid = grid.reshape([2, 1, *grid.shape[1:]]) - pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) - if cls_token and extra_tokens > 0: - pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) - return pos_embed - - -def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): - assert embed_dim % 2 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) - emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) - - emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) - return emb - - -def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): - """ - embed_dim: output dimension for each position - pos: a list of positions to be encoded: size (W,H) - out: (M, D) - """ - assert embed_dim % 2 == 0 - omega = np.arange(embed_dim // 2, dtype=np.float64) - omega /= embed_dim / 2. - omega = 1. / 10000**omega # (D/2,) - - pos = pos.reshape(-1) # (M,) - out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product - - emb_sin = np.sin(out) # (M, D/2) - emb_cos = np.cos(out) # (M, D/2) - - emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) - return emb - - -################################################################################# -# Rotary Positional Embedding Functions # -################################################################################# -# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443 - -def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True): - """ - This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure. - - Parameters - ---------- - embed_dim: int - embedding dimension size - start: int or tuple of int - If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; - If len(args) == 2, start is start, args[0] is stop, args[1] is num. - use_real: bool - If True, return real part and imaginary part separately. Otherwise, return complex numbers. - - Returns - ------- - pos_embed: torch.Tensor - [HW, D/2] - """ - grid = get_meshgrid(start, *args) # [2, H, w] - grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution - pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) - return pos_embed - - -def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): - assert embed_dim % 4 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) - emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) - - if use_real: - cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) - sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) - return cos, sin - else: - emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) - return emb - - -def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): - """ - Precompute the frequency tensor for complex exponentials (cis) with given dimensions. - - This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' - and the end index 'end'. The 'theta' parameter scales the frequencies. - The returned tensor contains complex values in complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar - theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. - use_real (bool, optional): If True, return real part and imaginary part separately. - Otherwise, return complex numbers. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2] - - """ - if isinstance(pos, int): - pos = np.arange(pos) - freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] - t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] - freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] - if use_real: - freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] - freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] - return freqs_cos, freqs_sin - else: - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] - return freqs_cis - - - -def calc_sizes(rope_img, patch_size, th, tw): - if rope_img == 'extend': - # Expansion mode - sub_args = [(th, tw)] - elif rope_img.startswith('base'): - # Based on the specified dimensions, other dimensions are obtained through interpolation. - base_size = int(rope_img[4:]) // 8 // patch_size - start, stop = get_fill_resize_and_crop((th, tw), base_size) - sub_args = [start, stop, (th, tw)] - else: - raise ValueError(f"Unknown rope_img: {rope_img}") - return sub_args - - -def init_image_posemb(rope_img, - resolutions, - patch_size, - hidden_size, - num_heads, - log_fn, - rope_real=True, - ): - freqs_cis_img = {} - for reso in resolutions: - th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size - sub_args = calc_sizes(rope_img, patch_size, th, tw) - freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real) - log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) " - f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}") - return freqs_cis_img diff --git a/comfy/ldm/lightricks/.DS_Store b/comfy/ldm/lightricks/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/lightricks/.DS_Store and /dev/null differ diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py deleted file mode 100644 index aa2ea62b16a54359bf54afab25842b5659f7618a..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/model.py +++ /dev/null @@ -1,514 +0,0 @@ -import torch -from torch import nn -import comfy.patcher_extension -import comfy.ldm.modules.attention -import comfy.ldm.common_dit -from einops import rearrange -import math -from typing import Dict, Optional, Tuple - -from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords - - -def get_timestep_embedding( - timesteps: torch.Tensor, - embedding_dim: int, - flip_sin_to_cos: bool = False, - downscale_freq_shift: float = 1, - scale: float = 1, - max_period: int = 10000, -): - """ - This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. - - Args - timesteps (torch.Tensor): - a 1-D Tensor of N indices, one per batch element. These may be fractional. - embedding_dim (int): - the dimension of the output. - flip_sin_to_cos (bool): - Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False) - downscale_freq_shift (float): - Controls the delta between frequencies between dimensions - scale (float): - Scaling factor applied to the embeddings. - max_period (int): - Controls the maximum frequency of the embeddings - Returns - torch.Tensor: an [N x dim] Tensor of positional embeddings. - """ - assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" - - half_dim = embedding_dim // 2 - exponent = -math.log(max_period) * torch.arange( - start=0, end=half_dim, dtype=torch.float32, device=timesteps.device - ) - exponent = exponent / (half_dim - downscale_freq_shift) - - emb = torch.exp(exponent) - emb = timesteps[:, None].float() * emb[None, :] - - # scale embeddings - emb = scale * emb - - # concat sine and cosine embeddings - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) - - # flip sine and cosine embeddings - if flip_sin_to_cos: - emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1) - - # zero pad - if embedding_dim % 2 == 1: - emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) - return emb - - -class TimestepEmbedding(nn.Module): - def __init__( - self, - in_channels: int, - time_embed_dim: int, - act_fn: str = "silu", - out_dim: int = None, - post_act_fn: Optional[str] = None, - cond_proj_dim=None, - sample_proj_bias=True, - dtype=None, device=None, operations=None, - ): - super().__init__() - - self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device) - - if cond_proj_dim is not None: - self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device) - else: - self.cond_proj = None - - self.act = nn.SiLU() - - if out_dim is not None: - time_embed_dim_out = out_dim - else: - time_embed_dim_out = time_embed_dim - self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device) - - if post_act_fn is None: - self.post_act = None - # else: - # self.post_act = get_activation(post_act_fn) - - def forward(self, sample, condition=None): - if condition is not None: - sample = sample + self.cond_proj(condition) - sample = self.linear_1(sample) - - if self.act is not None: - sample = self.act(sample) - - sample = self.linear_2(sample) - - if self.post_act is not None: - sample = self.post_act(sample) - return sample - - -class Timesteps(nn.Module): - def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1): - super().__init__() - self.num_channels = num_channels - self.flip_sin_to_cos = flip_sin_to_cos - self.downscale_freq_shift = downscale_freq_shift - self.scale = scale - - def forward(self, timesteps): - t_emb = get_timestep_embedding( - timesteps, - self.num_channels, - flip_sin_to_cos=self.flip_sin_to_cos, - downscale_freq_shift=self.downscale_freq_shift, - scale=self.scale, - ) - return t_emb - - -class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): - """ - For PixArt-Alpha. - - Reference: - https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29 - """ - - def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None): - super().__init__() - - self.outdim = size_emb_dim - self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) - self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations) - - def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype): - timesteps_proj = self.time_proj(timestep) - timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) - return timesteps_emb - - -class AdaLayerNormSingle(nn.Module): - r""" - Norm layer adaptive layer norm single (adaLN-single). - - As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3). - - Parameters: - embedding_dim (`int`): The size of each embedding vector. - use_additional_conditions (`bool`): To use additional conditions for normalization or not. - """ - - def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None): - super().__init__() - - self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings( - embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations - ) - - self.silu = nn.SiLU() - self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device) - - def forward( - self, - timestep: torch.Tensor, - added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, - batch_size: Optional[int] = None, - hidden_dtype: Optional[torch.dtype] = None, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - # No modulation happening here. - added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None} - embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype) - return self.linear(self.silu(embedded_timestep)), embedded_timestep - -class PixArtAlphaTextProjection(nn.Module): - """ - Projects caption embeddings. Also handles dropout for classifier-free guidance. - - Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py - """ - - def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None): - super().__init__() - if out_features is None: - out_features = hidden_size - self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device) - if act_fn == "gelu_tanh": - self.act_1 = nn.GELU(approximate="tanh") - elif act_fn == "silu": - self.act_1 = nn.SiLU() - else: - raise ValueError(f"Unknown activation function: {act_fn}") - self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device) - - def forward(self, caption): - hidden_states = self.linear_1(caption) - hidden_states = self.act_1(hidden_states) - hidden_states = self.linear_2(hidden_states) - return hidden_states - - -class GELU_approx(nn.Module): - def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None): - super().__init__() - self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device) - - def forward(self, x): - return torch.nn.functional.gelu(self.proj(x), approximate="tanh") - - -class FeedForward(nn.Module): - def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None): - super().__init__() - inner_dim = int(dim * mult) - project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) - ) - - def forward(self, x): - return self.net(x) - - -def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one - cos_freqs = freqs_cis[0] - sin_freqs = freqs_cis[1] - - t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2) - t1, t2 = t_dup.unbind(dim=-1) - t_dup = torch.stack((-t2, t1), dim=-1) - input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)") - - out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs - - return out - - -class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None): - super().__init__() - inner_dim = dim_head * heads - context_dim = query_dim if context_dim is None else context_dim - self.attn_precision = attn_precision - - self.heads = heads - self.dim_head = dim_head - - self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) - self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) - - self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device) - self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) - self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) - - self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) - - def forward(self, x, context=None, mask=None, pe=None): - q = self.to_q(x) - context = x if context is None else context - k = self.to_k(context) - v = self.to_v(context) - - q = self.q_norm(q) - k = self.k_norm(k) - - if pe is not None: - q = apply_rotary_emb(q, pe) - k = apply_rotary_emb(k, pe) - - if mask is None: - out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) - else: - out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) - return self.to_out(out) - - -class BasicTransformerBlock(nn.Module): - def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None): - super().__init__() - - self.attn_precision = attn_precision - self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) - self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations) - - self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) - - self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype)) - - def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None): - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2) - - x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa - - x += self.attn2(x, context=context, mask=attention_mask) - - y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp - x += self.ff(y) * gate_mlp - - return x - -def get_fractional_positions(indices_grid, max_pos): - fractional_positions = torch.stack( - [ - indices_grid[:, i] / max_pos[i] - for i in range(3) - ], - dim=-1, - ) - return fractional_positions - - -def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]): - dtype = torch.float32 #self.dtype - - fractional_positions = get_fractional_positions(indices_grid, max_pos) - - start = 1 - end = theta - device = fractional_positions.device - - indices = theta ** ( - torch.linspace( - math.log(start, theta), - math.log(end, theta), - dim // 6, - device=device, - dtype=dtype, - ) - ) - indices = indices.to(dtype=dtype) - - indices = indices * math.pi / 2 - - freqs = ( - (indices * (fractional_positions.unsqueeze(-1) * 2 - 1)) - .transpose(-1, -2) - .flatten(2) - ) - - cos_freq = freqs.cos().repeat_interleave(2, dim=-1) - sin_freq = freqs.sin().repeat_interleave(2, dim=-1) - if dim % 6 != 0: - cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6]) - sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6]) - cos_freq = torch.cat([cos_padding, cos_freq], dim=-1) - sin_freq = torch.cat([sin_padding, sin_freq], dim=-1) - return cos_freq.to(out_dtype), sin_freq.to(out_dtype) - - -class LTXVModel(torch.nn.Module): - def __init__(self, - in_channels=128, - cross_attention_dim=2048, - attention_head_dim=64, - num_attention_heads=32, - - caption_channels=4096, - num_layers=28, - - - positional_embedding_theta=10000.0, - positional_embedding_max_pos=[20, 2048, 2048], - causal_temporal_positioning=False, - vae_scale_factors=(8, 32, 32), - dtype=None, device=None, operations=None, **kwargs): - super().__init__() - self.generator = None - self.vae_scale_factors = vae_scale_factors - self.dtype = dtype - self.out_channels = in_channels - self.inner_dim = num_attention_heads * attention_head_dim - self.causal_temporal_positioning = causal_temporal_positioning - - self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device) - - self.adaln_single = AdaLayerNormSingle( - self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations - ) - - # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device) - - self.caption_projection = PixArtAlphaTextProjection( - in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations - ) - - self.transformer_blocks = nn.ModuleList( - [ - BasicTransformerBlock( - self.inner_dim, - num_attention_heads, - attention_head_dim, - context_dim=cross_attention_dim, - # attn_precision=attn_precision, - dtype=dtype, device=device, operations=operations - ) - for d in range(num_layers) - ] - ) - - self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device)) - self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device) - - self.patchifier = SymmetricPatchifier(1) - - def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, **kwargs) - - def _forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs): - patches_replace = transformer_options.get("patches_replace", {}) - - orig_shape = list(x.shape) - - x, latent_coords = self.patchifier.patchify(x) - pixel_coords = latent_to_pixel_coords( - latent_coords=latent_coords, - scale_factors=self.vae_scale_factors, - causal_fix=self.causal_temporal_positioning, - ) - - if keyframe_idxs is not None: - pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs - - fractional_coords = pixel_coords.to(torch.float32) - fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate) - - x = self.patchify_proj(x) - timestep = timestep * 1000.0 - - if attention_mask is not None and not torch.is_floating_point(attention_mask): - attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max - - pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype) - - batch_size = x.shape[0] - timestep, embedded_timestep = self.adaln_single( - timestep.flatten(), - {"resolution": None, "aspect_ratio": None}, - batch_size=batch_size, - hidden_dtype=x.dtype, - ) - # Second dimension is 1 or number of tokens (if timestep_per_token) - timestep = timestep.view(batch_size, -1, timestep.shape[-1]) - embedded_timestep = embedded_timestep.view( - batch_size, -1, embedded_timestep.shape[-1] - ) - - # 2. Blocks - if self.caption_projection is not None: - batch_size = x.shape[0] - context = self.caption_projection(context) - context = context.view( - batch_size, -1, x.shape[-1] - ) - - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.transformer_blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"]) - return out - - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap}) - x = out["img"] - else: - x = block( - x, - context=context, - attention_mask=attention_mask, - timestep=timestep, - pe=pe - ) - - # 3. Output - scale_shift_values = ( - self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None] - ) - shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1] - x = self.norm_out(x) - # Modulation - x = x * (1 + scale) + shift - x = self.proj_out(x) - - x = self.patchifier.unpatchify( - latents=x, - output_height=orig_shape[3], - output_width=orig_shape[4], - output_num_frames=orig_shape[2], - out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size), - ) - - return x diff --git a/comfy/ldm/lightricks/symmetric_patchifier.py b/comfy/ldm/lightricks/symmetric_patchifier.py deleted file mode 100644 index 4b9972b9fb589bb5516c64dd557c0914dda45116..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/symmetric_patchifier.py +++ /dev/null @@ -1,117 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Tuple - -import torch -from einops import rearrange -from torch import Tensor - - -def latent_to_pixel_coords( - latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False -) -> Tensor: - """ - Converts latent coordinates to pixel coordinates by scaling them according to the VAE's - configuration. - Args: - latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents] - containing the latent corner coordinates of each token. - scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space. - causal_fix (bool): Whether to take into account the different temporal scale - of the first frame. Default = False for backwards compatibility. - Returns: - Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates. - """ - pixel_coords = ( - latent_coords - * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None] - ) - if causal_fix: - # Fix temporal scale for first frame to 1 due to causality - pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0) - return pixel_coords - - -class Patchifier(ABC): - def __init__(self, patch_size: int): - super().__init__() - self._patch_size = (1, patch_size, patch_size) - - @abstractmethod - def patchify( - self, latents: Tensor, frame_rates: Tensor, scale_grid: bool - ) -> Tuple[Tensor, Tensor]: - pass - - @abstractmethod - def unpatchify( - self, - latents: Tensor, - output_height: int, - output_width: int, - output_num_frames: int, - out_channels: int, - ) -> Tuple[Tensor, Tensor]: - pass - - @property - def patch_size(self): - return self._patch_size - - def get_latent_coords( - self, latent_num_frames, latent_height, latent_width, batch_size, device - ): - """ - Return a tensor of shape [batch_size, 3, num_patches] containing the - top-left corner latent coordinates of each latent patch. - The tensor is repeated for each batch element. - """ - latent_sample_coords = torch.meshgrid( - torch.arange(0, latent_num_frames, self._patch_size[0], device=device), - torch.arange(0, latent_height, self._patch_size[1], device=device), - torch.arange(0, latent_width, self._patch_size[2], device=device), - indexing="ij", - ) - latent_sample_coords = torch.stack(latent_sample_coords, dim=0) - latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) - latent_coords = rearrange( - latent_coords, "b c f h w -> b c (f h w)", b=batch_size - ) - return latent_coords - - -class SymmetricPatchifier(Patchifier): - def patchify( - self, - latents: Tensor, - ) -> Tuple[Tensor, Tensor]: - b, _, f, h, w = latents.shape - latent_coords = self.get_latent_coords(f, h, w, b, latents.device) - latents = rearrange( - latents, - "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)", - p1=self._patch_size[0], - p2=self._patch_size[1], - p3=self._patch_size[2], - ) - return latents, latent_coords - - def unpatchify( - self, - latents: Tensor, - output_height: int, - output_width: int, - output_num_frames: int, - out_channels: int, - ) -> Tuple[Tensor, Tensor]: - output_height = output_height // self._patch_size[1] - output_width = output_width // self._patch_size[2] - latents = rearrange( - latents, - "b (f h w) (c p q) -> b c f (h p) (w q) ", - f=output_num_frames, - h=output_height, - w=output_width, - p=self._patch_size[1], - q=self._patch_size[2], - ) - return latents diff --git a/comfy/ldm/lightricks/vae/.DS_Store b/comfy/ldm/lightricks/vae/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/lightricks/vae/.DS_Store and /dev/null differ diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py deleted file mode 100644 index 70d612e86376c86bd73a95df620a5434871ee2c7..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/vae/causal_conv3d.py +++ /dev/null @@ -1,65 +0,0 @@ -from typing import Tuple, Union - -import torch -import torch.nn as nn -import comfy.ops -ops = comfy.ops.disable_weight_init - - -class CausalConv3d(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size: int = 3, - stride: Union[int, Tuple[int]] = 1, - dilation: int = 1, - groups: int = 1, - spatial_padding_mode: str = "zeros", - **kwargs, - ): - super().__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - - kernel_size = (kernel_size, kernel_size, kernel_size) - self.time_kernel_size = kernel_size[0] - - dilation = (dilation, 1, 1) - - height_pad = kernel_size[1] // 2 - width_pad = kernel_size[2] // 2 - padding = (0, height_pad, width_pad) - - self.conv = ops.Conv3d( - in_channels, - out_channels, - kernel_size, - stride=stride, - dilation=dilation, - padding=padding, - padding_mode=spatial_padding_mode, - groups=groups, - ) - - def forward(self, x, causal: bool = True): - if causal: - first_frame_pad = x[:, :, :1, :, :].repeat( - (1, 1, self.time_kernel_size - 1, 1, 1) - ) - x = torch.concatenate((first_frame_pad, x), dim=2) - else: - first_frame_pad = x[:, :, :1, :, :].repeat( - (1, 1, (self.time_kernel_size - 1) // 2, 1, 1) - ) - last_frame_pad = x[:, :, -1:, :, :].repeat( - (1, 1, (self.time_kernel_size - 1) // 2, 1, 1) - ) - x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2) - x = self.conv(x) - return x - - @property - def weight(self): - return self.conv.weight diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py deleted file mode 100644 index 75ed069ad45dc2f915f0c749f1afc3536d292e33..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ /dev/null @@ -1,1092 +0,0 @@ -from __future__ import annotations -import torch -from torch import nn -from functools import partial -import math -from einops import rearrange -from typing import List, Optional, Tuple, Union -from .conv_nd_factory import make_conv_nd, make_linear_nd -from .pixel_norm import PixelNorm -from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings -import comfy.ops - -ops = comfy.ops.disable_weight_init - -class Encoder(nn.Module): - r""" - The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation. - - Args: - dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3): - The number of dimensions to use in convolutions. - in_channels (`int`, *optional*, defaults to 3): - The number of input channels. - out_channels (`int`, *optional*, defaults to 3): - The number of output channels. - blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`): - The blocks to use. Each block is a tuple of the block name and the number of layers. - base_channels (`int`, *optional*, defaults to 128): - The number of output channels for the first convolutional layer. - norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups for normalization. - patch_size (`int`, *optional*, defaults to 1): - The patch size to use. Should be a power of 2. - norm_layer (`str`, *optional*, defaults to `group_norm`): - The normalization layer to use. Can be either `group_norm` or `pixel_norm`. - latent_log_var (`str`, *optional*, defaults to `per_channel`): - The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`. - """ - - def __init__( - self, - dims: Union[int, Tuple[int, int]] = 3, - in_channels: int = 3, - out_channels: int = 3, - blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], - base_channels: int = 128, - norm_num_groups: int = 32, - patch_size: Union[int, Tuple[int]] = 1, - norm_layer: str = "group_norm", # group_norm, pixel_norm - latent_log_var: str = "per_channel", - spatial_padding_mode: str = "zeros", - ): - super().__init__() - self.patch_size = patch_size - self.norm_layer = norm_layer - self.latent_channels = out_channels - self.latent_log_var = latent_log_var - self.blocks_desc = blocks - - in_channels = in_channels * patch_size**2 - output_channel = base_channels - - self.conv_in = make_conv_nd( - dims=dims, - in_channels=in_channels, - out_channels=output_channel, - kernel_size=3, - stride=1, - padding=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - self.down_blocks = nn.ModuleList([]) - - for block_name, block_params in blocks: - input_channel = output_channel - if isinstance(block_params, int): - block_params = {"num_layers": block_params} - - if block_name == "res_x": - block = UNetMidBlock3D( - dims=dims, - in_channels=input_channel, - num_layers=block_params["num_layers"], - resnet_eps=1e-6, - resnet_groups=norm_num_groups, - norm_layer=norm_layer, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "res_x_y": - output_channel = block_params.get("multiplier", 2) * output_channel - block = ResnetBlock3D( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - eps=1e-6, - groups=norm_num_groups, - norm_layer=norm_layer, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_time": - block = make_conv_nd( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - kernel_size=3, - stride=(2, 1, 1), - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_space": - block = make_conv_nd( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - kernel_size=3, - stride=(1, 2, 2), - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_all": - block = make_conv_nd( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - kernel_size=3, - stride=(2, 2, 2), - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_all_x_y": - output_channel = block_params.get("multiplier", 2) * output_channel - block = make_conv_nd( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - kernel_size=3, - stride=(2, 2, 2), - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_all_res": - output_channel = block_params.get("multiplier", 2) * output_channel - block = SpaceToDepthDownsample( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - stride=(2, 2, 2), - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_space_res": - output_channel = block_params.get("multiplier", 2) * output_channel - block = SpaceToDepthDownsample( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - stride=(1, 2, 2), - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_time_res": - output_channel = block_params.get("multiplier", 2) * output_channel - block = SpaceToDepthDownsample( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - stride=(2, 1, 1), - spatial_padding_mode=spatial_padding_mode, - ) - else: - raise ValueError(f"unknown block: {block_name}") - - self.down_blocks.append(block) - - # out - if norm_layer == "group_norm": - self.conv_norm_out = nn.GroupNorm( - num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6 - ) - elif norm_layer == "pixel_norm": - self.conv_norm_out = PixelNorm() - elif norm_layer == "layer_norm": - self.conv_norm_out = LayerNorm(output_channel, eps=1e-6) - - self.conv_act = nn.SiLU() - - conv_out_channels = out_channels - if latent_log_var == "per_channel": - conv_out_channels *= 2 - elif latent_log_var == "uniform": - conv_out_channels += 1 - elif latent_log_var == "constant": - conv_out_channels += 1 - elif latent_log_var != "none": - raise ValueError(f"Invalid latent_log_var: {latent_log_var}") - self.conv_out = make_conv_nd( - dims, - output_channel, - conv_out_channels, - 3, - padding=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - self.gradient_checkpointing = False - - def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor: - r"""The forward method of the `Encoder` class.""" - - sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) - sample = self.conv_in(sample) - - checkpoint_fn = ( - partial(torch.utils.checkpoint.checkpoint, use_reentrant=False) - if self.gradient_checkpointing and self.training - else lambda x: x - ) - - for down_block in self.down_blocks: - sample = checkpoint_fn(down_block)(sample) - - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - if self.latent_log_var == "uniform": - last_channel = sample[:, -1:, ...] - num_dims = sample.dim() - - if num_dims == 4: - # For shape (B, C, H, W) - repeated_last_channel = last_channel.repeat( - 1, sample.shape[1] - 2, 1, 1 - ) - sample = torch.cat([sample, repeated_last_channel], dim=1) - elif num_dims == 5: - # For shape (B, C, F, H, W) - repeated_last_channel = last_channel.repeat( - 1, sample.shape[1] - 2, 1, 1, 1 - ) - sample = torch.cat([sample, repeated_last_channel], dim=1) - else: - raise ValueError(f"Invalid input shape: {sample.shape}") - elif self.latent_log_var == "constant": - sample = sample[:, :-1, ...] - approx_ln_0 = ( - -30 - ) # this is the minimal clamp value in DiagonalGaussianDistribution objects - sample = torch.cat( - [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0], - dim=1, - ) - - return sample - - -class Decoder(nn.Module): - r""" - The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample. - - Args: - dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3): - The number of dimensions to use in convolutions. - in_channels (`int`, *optional*, defaults to 3): - The number of input channels. - out_channels (`int`, *optional*, defaults to 3): - The number of output channels. - blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`): - The blocks to use. Each block is a tuple of the block name and the number of layers. - base_channels (`int`, *optional*, defaults to 128): - The number of output channels for the first convolutional layer. - norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups for normalization. - patch_size (`int`, *optional*, defaults to 1): - The patch size to use. Should be a power of 2. - norm_layer (`str`, *optional*, defaults to `group_norm`): - The normalization layer to use. Can be either `group_norm` or `pixel_norm`. - causal (`bool`, *optional*, defaults to `True`): - Whether to use causal convolutions or not. - """ - - def __init__( - self, - dims, - in_channels: int = 3, - out_channels: int = 3, - blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], - base_channels: int = 128, - layers_per_block: int = 2, - norm_num_groups: int = 32, - patch_size: int = 1, - norm_layer: str = "group_norm", - causal: bool = True, - timestep_conditioning: bool = False, - spatial_padding_mode: str = "zeros", - ): - super().__init__() - self.patch_size = patch_size - self.layers_per_block = layers_per_block - out_channels = out_channels * patch_size**2 - self.causal = causal - self.blocks_desc = blocks - - # Compute output channel to be product of all channel-multiplier blocks - output_channel = base_channels - for block_name, block_params in list(reversed(blocks)): - block_params = block_params if isinstance(block_params, dict) else {} - if block_name == "res_x_y": - output_channel = output_channel * block_params.get("multiplier", 2) - if block_name == "compress_all": - output_channel = output_channel * block_params.get("multiplier", 1) - - self.conv_in = make_conv_nd( - dims, - in_channels, - output_channel, - kernel_size=3, - stride=1, - padding=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - self.up_blocks = nn.ModuleList([]) - - for block_name, block_params in list(reversed(blocks)): - input_channel = output_channel - if isinstance(block_params, int): - block_params = {"num_layers": block_params} - - if block_name == "res_x": - block = UNetMidBlock3D( - dims=dims, - in_channels=input_channel, - num_layers=block_params["num_layers"], - resnet_eps=1e-6, - resnet_groups=norm_num_groups, - norm_layer=norm_layer, - inject_noise=block_params.get("inject_noise", False), - timestep_conditioning=timestep_conditioning, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "attn_res_x": - block = UNetMidBlock3D( - dims=dims, - in_channels=input_channel, - num_layers=block_params["num_layers"], - resnet_groups=norm_num_groups, - norm_layer=norm_layer, - inject_noise=block_params.get("inject_noise", False), - timestep_conditioning=timestep_conditioning, - attention_head_dim=block_params["attention_head_dim"], - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "res_x_y": - output_channel = output_channel // block_params.get("multiplier", 2) - block = ResnetBlock3D( - dims=dims, - in_channels=input_channel, - out_channels=output_channel, - eps=1e-6, - groups=norm_num_groups, - norm_layer=norm_layer, - inject_noise=block_params.get("inject_noise", False), - timestep_conditioning=False, - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_time": - block = DepthToSpaceUpsample( - dims=dims, - in_channels=input_channel, - stride=(2, 1, 1), - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_space": - block = DepthToSpaceUpsample( - dims=dims, - in_channels=input_channel, - stride=(1, 2, 2), - spatial_padding_mode=spatial_padding_mode, - ) - elif block_name == "compress_all": - output_channel = output_channel // block_params.get("multiplier", 1) - block = DepthToSpaceUpsample( - dims=dims, - in_channels=input_channel, - stride=(2, 2, 2), - residual=block_params.get("residual", False), - out_channels_reduction_factor=block_params.get("multiplier", 1), - spatial_padding_mode=spatial_padding_mode, - ) - else: - raise ValueError(f"unknown layer: {block_name}") - - self.up_blocks.append(block) - - if norm_layer == "group_norm": - self.conv_norm_out = nn.GroupNorm( - num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6 - ) - elif norm_layer == "pixel_norm": - self.conv_norm_out = PixelNorm() - elif norm_layer == "layer_norm": - self.conv_norm_out = LayerNorm(output_channel, eps=1e-6) - - self.conv_act = nn.SiLU() - self.conv_out = make_conv_nd( - dims, - output_channel, - out_channels, - 3, - padding=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - self.gradient_checkpointing = False - - self.timestep_conditioning = timestep_conditioning - - if timestep_conditioning: - self.timestep_scale_multiplier = nn.Parameter( - torch.tensor(1000.0, dtype=torch.float32) - ) - self.last_time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings( - output_channel * 2, 0, operations=ops, - ) - self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel)) - - # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor: - def forward( - self, - sample: torch.FloatTensor, - timestep: Optional[torch.Tensor] = None, - ) -> torch.FloatTensor: - r"""The forward method of the `Decoder` class.""" - batch_size = sample.shape[0] - - sample = self.conv_in(sample, causal=self.causal) - - checkpoint_fn = ( - partial(torch.utils.checkpoint.checkpoint, use_reentrant=False) - if self.gradient_checkpointing and self.training - else lambda x: x - ) - - scaled_timestep = None - if self.timestep_conditioning: - assert ( - timestep is not None - ), "should pass timestep with timestep_conditioning=True" - scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device) - - for up_block in self.up_blocks: - if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D): - sample = checkpoint_fn(up_block)( - sample, causal=self.causal, timestep=scaled_timestep - ) - else: - sample = checkpoint_fn(up_block)(sample, causal=self.causal) - - sample = self.conv_norm_out(sample) - - if self.timestep_conditioning: - embedded_timestep = self.last_time_embedder( - timestep=scaled_timestep.flatten(), - resolution=None, - aspect_ratio=None, - batch_size=sample.shape[0], - hidden_dtype=sample.dtype, - ) - embedded_timestep = embedded_timestep.view( - batch_size, embedded_timestep.shape[-1], 1, 1, 1 - ) - ada_values = self.last_scale_shift_table[ - None, ..., None, None, None - ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape( - batch_size, - 2, - -1, - embedded_timestep.shape[-3], - embedded_timestep.shape[-2], - embedded_timestep.shape[-1], - ) - shift, scale = ada_values.unbind(dim=1) - sample = sample * (1 + scale) + shift - - sample = self.conv_act(sample) - sample = self.conv_out(sample, causal=self.causal) - - sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1) - - return sample - - -class UNetMidBlock3D(nn.Module): - """ - A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks. - - Args: - in_channels (`int`): The number of input channels. - dropout (`float`, *optional*, defaults to 0.0): The dropout rate. - num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. - resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. - resnet_groups (`int`, *optional*, defaults to 32): - The number of groups to use in the group normalization layers of the resnet blocks. - norm_layer (`str`, *optional*, defaults to `group_norm`): - The normalization layer to use. Can be either `group_norm` or `pixel_norm`. - inject_noise (`bool`, *optional*, defaults to `False`): - Whether to inject noise into the hidden states. - timestep_conditioning (`bool`, *optional*, defaults to `False`): - Whether to condition the hidden states on the timestep. - - Returns: - `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, - in_channels, height, width)`. - - """ - - def __init__( - self, - dims: Union[int, Tuple[int, int]], - in_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_groups: int = 32, - norm_layer: str = "group_norm", - inject_noise: bool = False, - timestep_conditioning: bool = False, - spatial_padding_mode: str = "zeros", - ): - super().__init__() - resnet_groups = ( - resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - ) - - self.timestep_conditioning = timestep_conditioning - - if timestep_conditioning: - self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings( - in_channels * 4, 0, operations=ops, - ) - - self.res_blocks = nn.ModuleList( - [ - ResnetBlock3D( - dims=dims, - in_channels=in_channels, - out_channels=in_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - norm_layer=norm_layer, - inject_noise=inject_noise, - timestep_conditioning=timestep_conditioning, - spatial_padding_mode=spatial_padding_mode, - ) - for _ in range(num_layers) - ] - ) - - def forward( - self, - hidden_states: torch.FloatTensor, - causal: bool = True, - timestep: Optional[torch.Tensor] = None, - ) -> torch.FloatTensor: - timestep_embed = None - if self.timestep_conditioning: - assert ( - timestep is not None - ), "should pass timestep with timestep_conditioning=True" - batch_size = hidden_states.shape[0] - timestep_embed = self.time_embedder( - timestep=timestep.flatten(), - resolution=None, - aspect_ratio=None, - batch_size=batch_size, - hidden_dtype=hidden_states.dtype, - ) - timestep_embed = timestep_embed.view( - batch_size, timestep_embed.shape[-1], 1, 1, 1 - ) - - for resnet in self.res_blocks: - hidden_states = resnet(hidden_states, causal=causal, timestep=timestep_embed) - - return hidden_states - - -class SpaceToDepthDownsample(nn.Module): - def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode): - super().__init__() - self.stride = stride - self.group_size = in_channels * math.prod(stride) // out_channels - self.conv = make_conv_nd( - dims=dims, - in_channels=in_channels, - out_channels=out_channels // math.prod(stride), - kernel_size=3, - stride=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - def forward(self, x, causal: bool = True): - if self.stride[0] == 2: - x = torch.cat( - [x[:, :, :1, :, :], x], dim=2 - ) # duplicate first frames for padding - - # skip connection - x_in = rearrange( - x, - "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", - p1=self.stride[0], - p2=self.stride[1], - p3=self.stride[2], - ) - x_in = rearrange(x_in, "b (c g) d h w -> b c g d h w", g=self.group_size) - x_in = x_in.mean(dim=2) - - # conv - x = self.conv(x, causal=causal) - x = rearrange( - x, - "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", - p1=self.stride[0], - p2=self.stride[1], - p3=self.stride[2], - ) - - x = x + x_in - - return x - - -class DepthToSpaceUpsample(nn.Module): - def __init__( - self, - dims, - in_channels, - stride, - residual=False, - out_channels_reduction_factor=1, - spatial_padding_mode="zeros", - ): - super().__init__() - self.stride = stride - self.out_channels = ( - math.prod(stride) * in_channels // out_channels_reduction_factor - ) - self.conv = make_conv_nd( - dims=dims, - in_channels=in_channels, - out_channels=self.out_channels, - kernel_size=3, - stride=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - self.residual = residual - self.out_channels_reduction_factor = out_channels_reduction_factor - - def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None): - if self.residual: - # Reshape and duplicate the input to match the output shape - x_in = rearrange( - x, - "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", - p1=self.stride[0], - p2=self.stride[1], - p3=self.stride[2], - ) - num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor - x_in = x_in.repeat(1, num_repeat, 1, 1, 1) - if self.stride[0] == 2: - x_in = x_in[:, :, 1:, :, :] - x = self.conv(x, causal=causal) - x = rearrange( - x, - "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)", - p1=self.stride[0], - p2=self.stride[1], - p3=self.stride[2], - ) - if self.stride[0] == 2: - x = x[:, :, 1:, :, :] - if self.residual: - x = x + x_in - return x - -class LayerNorm(nn.Module): - def __init__(self, dim, eps, elementwise_affine=True) -> None: - super().__init__() - self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) - - def forward(self, x): - x = rearrange(x, "b c d h w -> b d h w c") - x = self.norm(x) - x = rearrange(x, "b d h w c -> b c d h w") - return x - - -class ResnetBlock3D(nn.Module): - r""" - A Resnet block. - - Parameters: - in_channels (`int`): The number of channels in the input. - out_channels (`int`, *optional*, default to be `None`): - The number of output channels for the first conv layer. If None, same as `in_channels`. - dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use. - groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer. - eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization. - """ - - def __init__( - self, - dims: Union[int, Tuple[int, int]], - in_channels: int, - out_channels: Optional[int] = None, - dropout: float = 0.0, - groups: int = 32, - eps: float = 1e-6, - norm_layer: str = "group_norm", - inject_noise: bool = False, - timestep_conditioning: bool = False, - spatial_padding_mode: str = "zeros", - ): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.inject_noise = inject_noise - - if norm_layer == "group_norm": - self.norm1 = nn.GroupNorm( - num_groups=groups, num_channels=in_channels, eps=eps, affine=True - ) - elif norm_layer == "pixel_norm": - self.norm1 = PixelNorm() - elif norm_layer == "layer_norm": - self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True) - - self.non_linearity = nn.SiLU() - - self.conv1 = make_conv_nd( - dims, - in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - if inject_noise: - self.per_channel_scale1 = nn.Parameter(torch.zeros((in_channels, 1, 1))) - - if norm_layer == "group_norm": - self.norm2 = nn.GroupNorm( - num_groups=groups, num_channels=out_channels, eps=eps, affine=True - ) - elif norm_layer == "pixel_norm": - self.norm2 = PixelNorm() - elif norm_layer == "layer_norm": - self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True) - - self.dropout = torch.nn.Dropout(dropout) - - self.conv2 = make_conv_nd( - dims, - out_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1, - causal=True, - spatial_padding_mode=spatial_padding_mode, - ) - - if inject_noise: - self.per_channel_scale2 = nn.Parameter(torch.zeros((in_channels, 1, 1))) - - self.conv_shortcut = ( - make_linear_nd( - dims=dims, in_channels=in_channels, out_channels=out_channels - ) - if in_channels != out_channels - else nn.Identity() - ) - - self.norm3 = ( - LayerNorm(in_channels, eps=eps, elementwise_affine=True) - if in_channels != out_channels - else nn.Identity() - ) - - self.timestep_conditioning = timestep_conditioning - - if timestep_conditioning: - self.scale_shift_table = nn.Parameter( - torch.randn(4, in_channels) / in_channels**0.5 - ) - - def _feed_spatial_noise( - self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor - ) -> torch.FloatTensor: - spatial_shape = hidden_states.shape[-2:] - device = hidden_states.device - dtype = hidden_states.dtype - - # similar to the "explicit noise inputs" method in style-gan - spatial_noise = torch.randn(spatial_shape, device=device, dtype=dtype)[None] - scaled_noise = (spatial_noise * per_channel_scale)[None, :, None, ...] - hidden_states = hidden_states + scaled_noise - - return hidden_states - - def forward( - self, - input_tensor: torch.FloatTensor, - causal: bool = True, - timestep: Optional[torch.Tensor] = None, - ) -> torch.FloatTensor: - hidden_states = input_tensor - batch_size = hidden_states.shape[0] - - hidden_states = self.norm1(hidden_states) - if self.timestep_conditioning: - assert ( - timestep is not None - ), "should pass timestep with timestep_conditioning=True" - ada_values = self.scale_shift_table[ - None, ..., None, None, None - ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape( - batch_size, - 4, - -1, - timestep.shape[-3], - timestep.shape[-2], - timestep.shape[-1], - ) - shift1, scale1, shift2, scale2 = ada_values.unbind(dim=1) - - hidden_states = hidden_states * (1 + scale1) + shift1 - - hidden_states = self.non_linearity(hidden_states) - - hidden_states = self.conv1(hidden_states, causal=causal) - - if self.inject_noise: - hidden_states = self._feed_spatial_noise( - hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype) - ) - - hidden_states = self.norm2(hidden_states) - - if self.timestep_conditioning: - hidden_states = hidden_states * (1 + scale2) + shift2 - - hidden_states = self.non_linearity(hidden_states) - - hidden_states = self.dropout(hidden_states) - - hidden_states = self.conv2(hidden_states, causal=causal) - - if self.inject_noise: - hidden_states = self._feed_spatial_noise( - hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype) - ) - - input_tensor = self.norm3(input_tensor) - - batch_size = input_tensor.shape[0] - - input_tensor = self.conv_shortcut(input_tensor) - - output_tensor = input_tensor + hidden_states - - return output_tensor - - -def patchify(x, patch_size_hw, patch_size_t=1): - if patch_size_hw == 1 and patch_size_t == 1: - return x - if x.dim() == 4: - x = rearrange( - x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size_hw, r=patch_size_hw - ) - elif x.dim() == 5: - x = rearrange( - x, - "b c (f p) (h q) (w r) -> b (c p r q) f h w", - p=patch_size_t, - q=patch_size_hw, - r=patch_size_hw, - ) - else: - raise ValueError(f"Invalid input shape: {x.shape}") - - return x - - -def unpatchify(x, patch_size_hw, patch_size_t=1): - if patch_size_hw == 1 and patch_size_t == 1: - return x - - if x.dim() == 4: - x = rearrange( - x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size_hw, r=patch_size_hw - ) - elif x.dim() == 5: - x = rearrange( - x, - "b (c p r q) f h w -> b c (f p) (h q) (w r)", - p=patch_size_t, - q=patch_size_hw, - r=patch_size_hw, - ) - - return x - -class processor(nn.Module): - def __init__(self): - super().__init__() - self.register_buffer("std-of-means", torch.empty(128)) - self.register_buffer("mean-of-means", torch.empty(128)) - self.register_buffer("mean-of-stds", torch.empty(128)) - self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128)) - self.register_buffer("channel", torch.empty(128)) - - def un_normalize(self, x): - return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x) - - def normalize(self, x): - return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x) - -class VideoVAE(nn.Module): - def __init__(self, version=0, config=None): - super().__init__() - - if config is None: - config = self.guess_config(version) - - self.timestep_conditioning = config.get("timestep_conditioning", False) - double_z = config.get("double_z", True) - latent_log_var = config.get( - "latent_log_var", "per_channel" if double_z else "none" - ) - - self.encoder = Encoder( - dims=config["dims"], - in_channels=config.get("in_channels", 3), - out_channels=config["latent_channels"], - blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), - patch_size=config.get("patch_size", 1), - latent_log_var=latent_log_var, - norm_layer=config.get("norm_layer", "group_norm"), - spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), - ) - - self.decoder = Decoder( - dims=config["dims"], - in_channels=config["latent_channels"], - out_channels=config.get("out_channels", 3), - blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), - patch_size=config.get("patch_size", 1), - norm_layer=config.get("norm_layer", "group_norm"), - causal=config.get("causal_decoder", False), - timestep_conditioning=self.timestep_conditioning, - spatial_padding_mode=config.get("spatial_padding_mode", "reflect"), - ) - - self.per_channel_statistics = processor() - - def guess_config(self, version): - if version == 0: - config = { - "_class_name": "CausalVideoAutoencoder", - "dims": 3, - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "blocks": [ - ["res_x", 4], - ["compress_all", 1], - ["res_x_y", 1], - ["res_x", 3], - ["compress_all", 1], - ["res_x_y", 1], - ["res_x", 3], - ["compress_all", 1], - ["res_x", 3], - ["res_x", 4], - ], - "scaling_factor": 1.0, - "norm_layer": "pixel_norm", - "patch_size": 4, - "latent_log_var": "uniform", - "use_quant_conv": False, - "causal_decoder": False, - } - elif version == 1: - config = { - "_class_name": "CausalVideoAutoencoder", - "dims": 3, - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "decoder_blocks": [ - ["res_x", {"num_layers": 5, "inject_noise": True}], - ["compress_all", {"residual": True, "multiplier": 2}], - ["res_x", {"num_layers": 6, "inject_noise": True}], - ["compress_all", {"residual": True, "multiplier": 2}], - ["res_x", {"num_layers": 7, "inject_noise": True}], - ["compress_all", {"residual": True, "multiplier": 2}], - ["res_x", {"num_layers": 8, "inject_noise": False}] - ], - "encoder_blocks": [ - ["res_x", {"num_layers": 4}], - ["compress_all", {}], - ["res_x_y", 1], - ["res_x", {"num_layers": 3}], - ["compress_all", {}], - ["res_x_y", 1], - ["res_x", {"num_layers": 3}], - ["compress_all", {}], - ["res_x", {"num_layers": 3}], - ["res_x", {"num_layers": 4}] - ], - "scaling_factor": 1.0, - "norm_layer": "pixel_norm", - "patch_size": 4, - "latent_log_var": "uniform", - "use_quant_conv": False, - "causal_decoder": False, - "timestep_conditioning": True, - } - else: - config = { - "_class_name": "CausalVideoAutoencoder", - "dims": 3, - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "encoder_blocks": [ - ["res_x", {"num_layers": 4}], - ["compress_space_res", {"multiplier": 2}], - ["res_x", {"num_layers": 6}], - ["compress_time_res", {"multiplier": 2}], - ["res_x", {"num_layers": 6}], - ["compress_all_res", {"multiplier": 2}], - ["res_x", {"num_layers": 2}], - ["compress_all_res", {"multiplier": 2}], - ["res_x", {"num_layers": 2}] - ], - "decoder_blocks": [ - ["res_x", {"num_layers": 5, "inject_noise": False}], - ["compress_all", {"residual": True, "multiplier": 2}], - ["res_x", {"num_layers": 5, "inject_noise": False}], - ["compress_all", {"residual": True, "multiplier": 2}], - ["res_x", {"num_layers": 5, "inject_noise": False}], - ["compress_all", {"residual": True, "multiplier": 2}], - ["res_x", {"num_layers": 5, "inject_noise": False}] - ], - "scaling_factor": 1.0, - "norm_layer": "pixel_norm", - "patch_size": 4, - "latent_log_var": "uniform", - "use_quant_conv": False, - "causal_decoder": False, - "timestep_conditioning": True - } - return config - - def encode(self, x): - frames_count = x.shape[2] - if ((frames_count - 1) % 8) != 0: - raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.") - means, logvar = torch.chunk(self.encoder(x), 2, dim=1) - return self.per_channel_statistics.normalize(means) - - def decode(self, x, timestep=0.05, noise_scale=0.025): - if self.timestep_conditioning: #TODO: seed - x = torch.randn_like(x) * noise_scale + (1.0 - noise_scale) * x - return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=timestep) - diff --git a/comfy/ldm/lightricks/vae/conv_nd_factory.py b/comfy/ldm/lightricks/vae/conv_nd_factory.py deleted file mode 100644 index b4026b14fae386850459b18fb06e8b5d7f79ab94..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/vae/conv_nd_factory.py +++ /dev/null @@ -1,90 +0,0 @@ -from typing import Tuple, Union - - -from .dual_conv3d import DualConv3d -from .causal_conv3d import CausalConv3d -import comfy.ops -ops = comfy.ops.disable_weight_init - -def make_conv_nd( - dims: Union[int, Tuple[int, int]], - in_channels: int, - out_channels: int, - kernel_size: int, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True, - causal=False, - spatial_padding_mode="zeros", - temporal_padding_mode="zeros", -): - if not (spatial_padding_mode == temporal_padding_mode or causal): - raise NotImplementedError("spatial and temporal padding modes must be equal") - if dims == 2: - return ops.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=bias, - padding_mode=spatial_padding_mode, - ) - elif dims == 3: - if causal: - return CausalConv3d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=bias, - spatial_padding_mode=spatial_padding_mode, - ) - return ops.Conv3d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - bias=bias, - padding_mode=spatial_padding_mode, - ) - elif dims == (2, 1): - return DualConv3d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=bias, - padding_mode=spatial_padding_mode, - ) - else: - raise ValueError(f"unsupported dimensions: {dims}") - - -def make_linear_nd( - dims: int, - in_channels: int, - out_channels: int, - bias=True, -): - if dims == 2: - return ops.Conv2d( - in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias - ) - elif dims == 3 or dims == (2, 1): - return ops.Conv3d( - in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias - ) - else: - raise ValueError(f"unsupported dimensions: {dims}") diff --git a/comfy/ldm/lightricks/vae/dual_conv3d.py b/comfy/ldm/lightricks/vae/dual_conv3d.py deleted file mode 100644 index dcf889296750d3d7e553af37ecf77d1b10245af3..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/vae/dual_conv3d.py +++ /dev/null @@ -1,217 +0,0 @@ -import math -from typing import Tuple, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange - - -class DualConv3d(nn.Module): - def __init__( - self, - in_channels, - out_channels, - kernel_size, - stride: Union[int, Tuple[int, int, int]] = 1, - padding: Union[int, Tuple[int, int, int]] = 0, - dilation: Union[int, Tuple[int, int, int]] = 1, - groups=1, - bias=True, - padding_mode="zeros", - ): - super(DualConv3d, self).__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.padding_mode = padding_mode - # Ensure kernel_size, stride, padding, and dilation are tuples of length 3 - if isinstance(kernel_size, int): - kernel_size = (kernel_size, kernel_size, kernel_size) - if kernel_size == (1, 1, 1): - raise ValueError( - "kernel_size must be greater than 1. Use make_linear_nd instead." - ) - if isinstance(stride, int): - stride = (stride, stride, stride) - if isinstance(padding, int): - padding = (padding, padding, padding) - if isinstance(dilation, int): - dilation = (dilation, dilation, dilation) - - # Set parameters for convolutions - self.groups = groups - self.bias = bias - - # Define the size of the channels after the first convolution - intermediate_channels = ( - out_channels if in_channels < out_channels else in_channels - ) - - # Define parameters for the first convolution - self.weight1 = nn.Parameter( - torch.Tensor( - intermediate_channels, - in_channels // groups, - 1, - kernel_size[1], - kernel_size[2], - ) - ) - self.stride1 = (1, stride[1], stride[2]) - self.padding1 = (0, padding[1], padding[2]) - self.dilation1 = (1, dilation[1], dilation[2]) - if bias: - self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels)) - else: - self.register_parameter("bias1", None) - - # Define parameters for the second convolution - self.weight2 = nn.Parameter( - torch.Tensor( - out_channels, intermediate_channels // groups, kernel_size[0], 1, 1 - ) - ) - self.stride2 = (stride[0], 1, 1) - self.padding2 = (padding[0], 0, 0) - self.dilation2 = (dilation[0], 1, 1) - if bias: - self.bias2 = nn.Parameter(torch.Tensor(out_channels)) - else: - self.register_parameter("bias2", None) - - # Initialize weights and biases - self.reset_parameters() - - def reset_parameters(self): - nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5)) - nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5)) - if self.bias: - fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1) - bound1 = 1 / math.sqrt(fan_in1) - nn.init.uniform_(self.bias1, -bound1, bound1) - fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2) - bound2 = 1 / math.sqrt(fan_in2) - nn.init.uniform_(self.bias2, -bound2, bound2) - - def forward(self, x, use_conv3d=False, skip_time_conv=False): - if use_conv3d: - return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv) - else: - return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv) - - def forward_with_3d(self, x, skip_time_conv): - # First convolution - x = F.conv3d( - x, - self.weight1, - self.bias1, - self.stride1, - self.padding1, - self.dilation1, - self.groups, - padding_mode=self.padding_mode, - ) - - if skip_time_conv: - return x - - # Second convolution - x = F.conv3d( - x, - self.weight2, - self.bias2, - self.stride2, - self.padding2, - self.dilation2, - self.groups, - padding_mode=self.padding_mode, - ) - - return x - - def forward_with_2d(self, x, skip_time_conv): - b, c, d, h, w = x.shape - - # First 2D convolution - x = rearrange(x, "b c d h w -> (b d) c h w") - # Squeeze the depth dimension out of weight1 since it's 1 - weight1 = self.weight1.squeeze(2) - # Select stride, padding, and dilation for the 2D convolution - stride1 = (self.stride1[1], self.stride1[2]) - padding1 = (self.padding1[1], self.padding1[2]) - dilation1 = (self.dilation1[1], self.dilation1[2]) - x = F.conv2d( - x, - weight1, - self.bias1, - stride1, - padding1, - dilation1, - self.groups, - padding_mode=self.padding_mode, - ) - - _, _, h, w = x.shape - - if skip_time_conv: - x = rearrange(x, "(b d) c h w -> b c d h w", b=b) - return x - - # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension - x = rearrange(x, "(b d) c h w -> (b h w) c d", b=b) - - # Reshape weight2 to match the expected dimensions for conv1d - weight2 = self.weight2.squeeze(-1).squeeze(-1) - # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution - stride2 = self.stride2[0] - padding2 = self.padding2[0] - dilation2 = self.dilation2[0] - x = F.conv1d( - x, - weight2, - self.bias2, - stride2, - padding2, - dilation2, - self.groups, - padding_mode=self.padding_mode, - ) - x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w) - - return x - - @property - def weight(self): - return self.weight2 - - -def test_dual_conv3d_consistency(): - # Initialize parameters - in_channels = 3 - out_channels = 5 - kernel_size = (3, 3, 3) - stride = (2, 2, 2) - padding = (1, 1, 1) - - # Create an instance of the DualConv3d class - dual_conv3d = DualConv3d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=True, - ) - - # Example input tensor - test_input = torch.randn(1, 3, 10, 10, 10) - - # Perform forward passes with both 3D and 2D settings - output_conv3d = dual_conv3d(test_input, use_conv3d=True) - output_2d = dual_conv3d(test_input, use_conv3d=False) - - # Assert that the outputs from both methods are sufficiently close - assert torch.allclose( - output_conv3d, output_2d, atol=1e-6 - ), "Outputs are not consistent between 3D and 2D convolutions." diff --git a/comfy/ldm/lightricks/vae/pixel_norm.py b/comfy/ldm/lightricks/vae/pixel_norm.py deleted file mode 100644 index 9bc3ea60e8a6453e7e12a7fb5aca4de3958a2567..0000000000000000000000000000000000000000 --- a/comfy/ldm/lightricks/vae/pixel_norm.py +++ /dev/null @@ -1,12 +0,0 @@ -import torch -from torch import nn - - -class PixelNorm(nn.Module): - def __init__(self, dim=1, eps=1e-8): - super(PixelNorm, self).__init__() - self.dim = dim - self.eps = eps - - def forward(self, x): - return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps) diff --git a/comfy/ldm/lumina/.DS_Store b/comfy/ldm/lumina/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/lumina/.DS_Store and /dev/null differ diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py deleted file mode 100644 index e08ed817de3938e8965423215b2d67daf8bebb12..0000000000000000000000000000000000000000 --- a/comfy/ldm/lumina/model.py +++ /dev/null @@ -1,630 +0,0 @@ -# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py -from __future__ import annotations - -from typing import List, Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -import comfy.ldm.common_dit - -from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder -from comfy.ldm.modules.attention import optimized_attention_masked -from comfy.ldm.flux.layers import EmbedND -import comfy.patcher_extension - - -def modulate(x, scale): - return x * (1 + scale.unsqueeze(1)) - -############################################################################# -# Core NextDiT Model # -############################################################################# - - -class JointAttention(nn.Module): - """Multi-head attention module.""" - - def __init__( - self, - dim: int, - n_heads: int, - n_kv_heads: Optional[int], - qk_norm: bool, - operation_settings={}, - ): - """ - Initialize the Attention module. - - Args: - dim (int): Number of input dimensions. - n_heads (int): Number of heads. - n_kv_heads (Optional[int]): Number of kv heads, if using GQA. - - """ - super().__init__() - self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.n_local_heads = n_heads - self.n_local_kv_heads = self.n_kv_heads - self.n_rep = self.n_local_heads // self.n_local_kv_heads - self.head_dim = dim // n_heads - - self.qkv = operation_settings.get("operations").Linear( - dim, - (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim, - bias=False, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - self.out = operation_settings.get("operations").Linear( - n_heads * self.head_dim, - dim, - bias=False, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - - if qk_norm: - self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - else: - self.q_norm = self.k_norm = nn.Identity() - - @staticmethod - def apply_rotary_emb( - x_in: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> torch.Tensor: - """ - Apply rotary embeddings to input tensors using the given frequency - tensor. - - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. - - Args: - x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - - t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2) - t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] - return t_out.reshape(*x_in.shape) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> torch.Tensor: - """ - - Args: - x: - x_mask: - freqs_cis: - - Returns: - - """ - bsz, seqlen, _ = x.shape - - xq, xk, xv = torch.split( - self.qkv(x), - [ - self.n_local_heads * self.head_dim, - self.n_local_kv_heads * self.head_dim, - self.n_local_kv_heads * self.head_dim, - ], - dim=-1, - ) - xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) - xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) - xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) - - xq = self.q_norm(xq) - xk = self.k_norm(xk) - - xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis) - xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis) - - n_rep = self.n_local_heads // self.n_local_kv_heads - if n_rep >= 1: - xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True) - - return self.out(output) - - -class FeedForward(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float], - operation_settings={}, - ): - """ - Initialize the FeedForward module. - - Args: - dim (int): Input dimension. - hidden_dim (int): Hidden dimension of the feedforward layer. - multiple_of (int): Value to ensure hidden dimension is a multiple - of this value. - ffn_dim_multiplier (float, optional): Custom multiplier for hidden - dimension. Defaults to None. - - """ - super().__init__() - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - self.w1 = operation_settings.get("operations").Linear( - dim, - hidden_dim, - bias=False, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - self.w2 = operation_settings.get("operations").Linear( - hidden_dim, - dim, - bias=False, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - self.w3 = operation_settings.get("operations").Linear( - dim, - hidden_dim, - bias=False, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - - # @torch.compile - def _forward_silu_gating(self, x1, x3): - return F.silu(x1) * x3 - - def forward(self, x): - return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) - - -class JointTransformerBlock(nn.Module): - def __init__( - self, - layer_id: int, - dim: int, - n_heads: int, - n_kv_heads: int, - multiple_of: int, - ffn_dim_multiplier: float, - norm_eps: float, - qk_norm: bool, - modulation=True, - operation_settings={}, - ) -> None: - """ - Initialize a TransformerBlock. - - Args: - layer_id (int): Identifier for the layer. - dim (int): Embedding dimension of the input features. - n_heads (int): Number of attention heads. - n_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as - query. - multiple_of (int): - ffn_dim_multiplier (float): - norm_eps (float): - - """ - super().__init__() - self.dim = dim - self.head_dim = dim // n_heads - self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings) - self.feed_forward = FeedForward( - dim=dim, - hidden_dim=4 * dim, - multiple_of=multiple_of, - ffn_dim_multiplier=ffn_dim_multiplier, - operation_settings=operation_settings, - ) - self.layer_id = layer_id - self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - - self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - - self.modulation = modulation - if modulation: - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operation_settings.get("operations").Linear( - min(dim, 1024), - 4 * dim, - bias=True, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ), - ) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - adaln_input: Optional[torch.Tensor]=None, - ): - """ - Perform a forward pass through the TransformerBlock. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. - - Returns: - torch.Tensor: Output tensor after applying attention and - feedforward layers. - - """ - if self.modulation: - assert adaln_input is not None - scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) - - x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( - self.attention( - modulate(self.attention_norm1(x), scale_msa), - x_mask, - freqs_cis, - ) - ) - x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( - self.feed_forward( - modulate(self.ffn_norm1(x), scale_mlp), - ) - ) - else: - assert adaln_input is None - x = x + self.attention_norm2( - self.attention( - self.attention_norm1(x), - x_mask, - freqs_cis, - ) - ) - x = x + self.ffn_norm2( - self.feed_forward( - self.ffn_norm1(x), - ) - ) - return x - - -class FinalLayer(nn.Module): - """ - The final layer of NextDiT. - """ - - def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}): - super().__init__() - self.norm_final = operation_settings.get("operations").LayerNorm( - hidden_size, - elementwise_affine=False, - eps=1e-6, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - self.linear = operation_settings.get("operations").Linear( - hidden_size, - patch_size * patch_size * out_channels, - bias=True, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operation_settings.get("operations").Linear( - min(hidden_size, 1024), - hidden_size, - bias=True, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ), - ) - - def forward(self, x, c): - scale = self.adaLN_modulation(c) - x = modulate(self.norm_final(x), scale) - x = self.linear(x) - return x - - -class NextDiT(nn.Module): - """ - Diffusion model with a Transformer backbone. - """ - - def __init__( - self, - patch_size: int = 2, - in_channels: int = 4, - dim: int = 4096, - n_layers: int = 32, - n_refiner_layers: int = 2, - n_heads: int = 32, - n_kv_heads: Optional[int] = None, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - norm_eps: float = 1e-5, - qk_norm: bool = False, - cap_feat_dim: int = 5120, - axes_dims: List[int] = (16, 56, 56), - axes_lens: List[int] = (1, 512, 512), - image_model=None, - device=None, - dtype=None, - operations=None, - ) -> None: - super().__init__() - self.dtype = dtype - operation_settings = {"operations": operations, "device": device, "dtype": dtype} - self.in_channels = in_channels - self.out_channels = in_channels - self.patch_size = patch_size - - self.x_embedder = operation_settings.get("operations").Linear( - in_features=patch_size * patch_size * in_channels, - out_features=dim, - bias=True, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ) - - self.noise_refiner = nn.ModuleList( - [ - JointTransformerBlock( - layer_id, - dim, - n_heads, - n_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - modulation=True, - operation_settings=operation_settings, - ) - for layer_id in range(n_refiner_layers) - ] - ) - self.context_refiner = nn.ModuleList( - [ - JointTransformerBlock( - layer_id, - dim, - n_heads, - n_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - modulation=False, - operation_settings=operation_settings, - ) - for layer_id in range(n_refiner_layers) - ] - ) - - self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings) - self.cap_embedder = nn.Sequential( - operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), - operation_settings.get("operations").Linear( - cap_feat_dim, - dim, - bias=True, - device=operation_settings.get("device"), - dtype=operation_settings.get("dtype"), - ), - ) - - self.layers = nn.ModuleList( - [ - JointTransformerBlock( - layer_id, - dim, - n_heads, - n_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - operation_settings=operation_settings, - ) - for layer_id in range(n_layers) - ] - ) - self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings) - - assert (dim // n_heads) == sum(axes_dims) - self.axes_dims = axes_dims - self.axes_lens = axes_lens - self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims) - self.dim = dim - self.n_heads = n_heads - - def unpatchify( - self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False - ) -> List[torch.Tensor]: - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - pH = pW = self.patch_size - imgs = [] - for i in range(x.size(0)): - H, W = img_size[i] - begin = cap_size[i] - end = begin + (H // pH) * (W // pW) - imgs.append( - x[i][begin:end] - .view(H // pH, W // pW, pH, pW, self.out_channels) - .permute(4, 0, 2, 1, 3) - .flatten(3, 4) - .flatten(1, 2) - ) - - if return_tensor: - imgs = torch.stack(imgs, dim=0) - return imgs - - def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens - ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]: - bsz = len(x) - pH = pW = self.patch_size - device = x[0].device - dtype = x[0].dtype - - if cap_mask is not None: - l_effective_cap_len = cap_mask.sum(dim=1).tolist() - else: - l_effective_cap_len = [num_tokens] * bsz - - if cap_mask is not None and not torch.is_floating_point(cap_mask): - cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max - - img_sizes = [(img.size(1), img.size(2)) for img in x] - l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes] - - max_seq_len = max( - (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len)) - ) - max_cap_len = max(l_effective_cap_len) - max_img_len = max(l_effective_img_len) - - position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device) - - for i in range(bsz): - cap_len = l_effective_cap_len[i] - img_len = l_effective_img_len[i] - H, W = img_sizes[i] - H_tokens, W_tokens = H // pH, W // pW - assert H_tokens * W_tokens == img_len - - position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device) - position_ids[i, cap_len:cap_len+img_len, 0] = cap_len - row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten() - col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten() - position_ids[i, cap_len:cap_len+img_len, 1] = row_ids - position_ids[i, cap_len:cap_len+img_len, 2] = col_ids - - freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype) - - # build freqs_cis for cap and image individually - cap_freqs_cis_shape = list(freqs_cis.shape) - # cap_freqs_cis_shape[1] = max_cap_len - cap_freqs_cis_shape[1] = cap_feats.shape[1] - cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - img_freqs_cis_shape = list(freqs_cis.shape) - img_freqs_cis_shape[1] = max_img_len - img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - for i in range(bsz): - cap_len = l_effective_cap_len[i] - img_len = l_effective_img_len[i] - cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len] - img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len] - - # refine context - for layer in self.context_refiner: - cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis) - - # refine image - flat_x = [] - for i in range(bsz): - img = x[i] - C, H, W = img.size() - img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1) - flat_x.append(img) - x = flat_x - padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype) - padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device) - for i in range(bsz): - padded_img_embed[i, :l_effective_img_len[i]] = x[i] - padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max - - padded_img_embed = self.x_embedder(padded_img_embed) - padded_img_mask = padded_img_mask.unsqueeze(1) - for layer in self.noise_refiner: - padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t) - - if cap_mask is not None: - mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device) - mask[:, :max_cap_len] = cap_mask[:, :max_cap_len] - else: - mask = None - - padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype) - for i in range(bsz): - cap_len = l_effective_cap_len[i] - img_len = l_effective_img_len[i] - - padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len] - padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len] - - return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis - - def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) - ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs) - - # def forward(self, x, t, cap_feats, cap_mask): - def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): - t = 1.0 - timesteps - cap_feats = context - cap_mask = attention_mask - bs, c, h, w = x.shape - x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) - """ - Forward pass of NextDiT. - t: (N,) tensor of diffusion timesteps - y: (N,) tensor of text tokens/features - """ - - t = self.t_embedder(t, dtype=x.dtype) # (N, D) - adaln_input = t - - cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute - - x_is_tensor = isinstance(x, torch.Tensor) - x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens) - freqs_cis = freqs_cis.to(x.device) - - for layer in self.layers: - x = layer(x, mask, freqs_cis, adaln_input) - - x = self.final_layer(x, adaln_input) - x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w] - - return -x - diff --git a/comfy/ldm/models/.DS_Store b/comfy/ldm/models/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/models/.DS_Store and /dev/null differ diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py deleted file mode 100644 index 13bd6e16b642e529ba57b4a8be981fb274924197..0000000000000000000000000000000000000000 --- a/comfy/ldm/models/autoencoder.py +++ /dev/null @@ -1,231 +0,0 @@ -import logging -import math -import torch -from contextlib import contextmanager -from typing import Any, Dict, Tuple, Union - -from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution - -from comfy.ldm.util import get_obj_from_str, instantiate_from_config -from comfy.ldm.modules.ema import LitEma -import comfy.ops - -class DiagonalGaussianRegularizer(torch.nn.Module): - def __init__(self, sample: bool = False): - super().__init__() - self.sample = sample - - def get_trainable_parameters(self) -> Any: - yield from () - - def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: - posterior = DiagonalGaussianDistribution(z) - if self.sample: - z = posterior.sample() - else: - z = posterior.mode() - return z, None - - -class AbstractAutoencoder(torch.nn.Module): - """ - This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, - unCLIP models, etc. Hence, it is fairly general, and specific features - (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. - """ - - def __init__( - self, - ema_decay: Union[None, float] = None, - monitor: Union[None, str] = None, - input_key: str = "jpg", - **kwargs, - ): - super().__init__() - - self.input_key = input_key - self.use_ema = ema_decay is not None - if monitor is not None: - self.monitor = monitor - - if self.use_ema: - self.model_ema = LitEma(self, decay=ema_decay) - logging.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - def get_input(self, batch) -> Any: - raise NotImplementedError() - - def on_train_batch_end(self, *args, **kwargs): - # for EMA computation - if self.use_ema: - self.model_ema(self) - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.parameters()) - self.model_ema.copy_to(self) - if context is not None: - logging.info(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.parameters()) - if context is not None: - logging.info(f"{context}: Restored training weights") - - def encode(self, *args, **kwargs) -> torch.Tensor: - raise NotImplementedError("encode()-method of abstract base class called") - - def decode(self, *args, **kwargs) -> torch.Tensor: - raise NotImplementedError("decode()-method of abstract base class called") - - def instantiate_optimizer_from_config(self, params, lr, cfg): - logging.info(f"loading >>> {cfg['target']} <<< optimizer from config") - return get_obj_from_str(cfg["target"])( - params, lr=lr, **cfg.get("params", dict()) - ) - - def configure_optimizers(self) -> Any: - raise NotImplementedError() - - -class AutoencodingEngine(AbstractAutoencoder): - """ - Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL - (we also restore them explicitly as special cases for legacy reasons). - Regularizations such as KL or VQ are moved to the regularizer class. - """ - - def __init__( - self, - *args, - encoder_config: Dict, - decoder_config: Dict, - regularizer_config: Dict, - **kwargs, - ): - super().__init__(*args, **kwargs) - - self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) - self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) - self.regularization = instantiate_from_config( - regularizer_config - ) - - def get_last_layer(self): - return self.decoder.get_last_layer() - - def encode( - self, - x: torch.Tensor, - return_reg_log: bool = False, - unregularized: bool = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: - z = self.encoder(x) - if unregularized: - return z, dict() - z, reg_log = self.regularization(z) - if return_reg_log: - return z, reg_log - return z - - def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: - x = self.decoder(z, **kwargs) - return x - - def forward( - self, x: torch.Tensor, **additional_decode_kwargs - ) -> Tuple[torch.Tensor, torch.Tensor, dict]: - z, reg_log = self.encode(x, return_reg_log=True) - dec = self.decode(z, **additional_decode_kwargs) - return z, dec, reg_log - - -class AutoencodingEngineLegacy(AutoencodingEngine): - def __init__(self, embed_dim: int, **kwargs): - self.max_batch_size = kwargs.pop("max_batch_size", None) - ddconfig = kwargs.pop("ddconfig") - super().__init__( - encoder_config={ - "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", - "params": ddconfig, - }, - decoder_config={ - "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", - "params": ddconfig, - }, - **kwargs, - ) - - if ddconfig.get("conv3d", False): - conv_op = comfy.ops.disable_weight_init.Conv3d - else: - conv_op = comfy.ops.disable_weight_init.Conv2d - - self.quant_conv = conv_op( - (1 + ddconfig["double_z"]) * ddconfig["z_channels"], - (1 + ddconfig["double_z"]) * embed_dim, - 1, - ) - - self.post_quant_conv = conv_op(embed_dim, ddconfig["z_channels"], 1) - self.embed_dim = embed_dim - - def get_autoencoder_params(self) -> list: - params = super().get_autoencoder_params() - return params - - def encode( - self, x: torch.Tensor, return_reg_log: bool = False - ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: - if self.max_batch_size is None: - z = self.encoder(x) - z = self.quant_conv(z) - else: - N = x.shape[0] - bs = self.max_batch_size - n_batches = int(math.ceil(N / bs)) - z = list() - for i_batch in range(n_batches): - z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) - z_batch = self.quant_conv(z_batch) - z.append(z_batch) - z = torch.cat(z, 0) - - z, reg_log = self.regularization(z) - if return_reg_log: - return z, reg_log - return z - - def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: - if self.max_batch_size is None: - dec = self.post_quant_conv(z) - dec = self.decoder(dec, **decoder_kwargs) - else: - N = z.shape[0] - bs = self.max_batch_size - n_batches = int(math.ceil(N / bs)) - dec = list() - for i_batch in range(n_batches): - dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) - dec_batch = self.decoder(dec_batch, **decoder_kwargs) - dec.append(dec_batch) - dec = torch.cat(dec, 0) - - return dec - - -class AutoencoderKL(AutoencodingEngineLegacy): - def __init__(self, **kwargs): - if "lossconfig" in kwargs: - kwargs["loss_config"] = kwargs.pop("lossconfig") - super().__init__( - regularizer_config={ - "target": ( - "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer" - ) - }, - **kwargs, - ) diff --git a/comfy/ldm/modules/.DS_Store b/comfy/ldm/modules/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/modules/.DS_Store and /dev/null differ diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py deleted file mode 100644 index 043df28dfdc827021827d3af3621445bdab45fe3..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/attention.py +++ /dev/null @@ -1,1035 +0,0 @@ -import math -import sys - -import torch -import torch.nn.functional as F -from torch import nn, einsum -from einops import rearrange, repeat -from typing import Optional -import logging - -from .diffusionmodules.util import AlphaBlender, timestep_embedding -from .sub_quadratic_attention import efficient_dot_product_attention - -from comfy import model_management - -if model_management.xformers_enabled(): - import xformers - import xformers.ops - -if model_management.sage_attention_enabled(): - try: - from sageattention import sageattn - except ModuleNotFoundError as e: - if e.name == "sageattention": - logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") - else: - raise e - exit(-1) - -if model_management.flash_attention_enabled(): - try: - from flash_attn import flash_attn_func - except ModuleNotFoundError: - logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn") - exit(-1) - -from comfy.cli_args import args -import comfy.ops -ops = comfy.ops.disable_weight_init - -FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() - -def get_attn_precision(attn_precision, current_dtype): - if args.dont_upcast_attention: - return None - - if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE: - return FORCE_UPCAST_ATTENTION_DTYPE[current_dtype] - return attn_precision - -def exists(val): - return val is not None - - -def default(val, d): - if exists(val): - return val - return d - - -# feedforward -class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): - super().__init__() - self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) - - def forward(self, x): - x, gate = self.proj(x).chunk(2, dim=-1) - return x * F.gelu(gate) - - -class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): - super().__init__() - inner_dim = int(dim * mult) - dim_out = default(dim_out, dim) - project_in = nn.Sequential( - operations.Linear(dim, inner_dim, dtype=dtype, device=device), - nn.GELU() - ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) - ) - - def forward(self, x): - return self.net(x) - -def Normalize(in_channels, dtype=None, device=None): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) - -def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - attn_precision = get_attn_precision(attn_precision, q.dtype) - - if skip_reshape: - b, _, _, dim_head = q.shape - else: - b, _, dim_head = q.shape - dim_head //= heads - - scale = dim_head ** -0.5 - - h = heads - if skip_reshape: - q, k, v = map( - lambda t: t.reshape(b * heads, -1, dim_head), - (q, k, v), - ) - else: - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), - (q, k, v), - ) - - # force cast to fp32 to avoid overflowing - if attn_precision == torch.float32: - sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale - else: - sim = einsum('b i d, b j d -> b i j', q, k) * scale - - del q, k - - if exists(mask): - if mask.dtype == torch.bool: - mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - else: - if len(mask.shape) == 2: - bs = 1 - else: - bs = mask.shape[0] - mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) - sim.add_(mask) - - # attention, what we cannot get enough of - sim = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) - - if skip_output_reshape: - out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - ) - else: - out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) - return out - - -def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - attn_precision = get_attn_precision(attn_precision, query.dtype) - - if skip_reshape: - b, _, _, dim_head = query.shape - else: - b, _, dim_head = query.shape - dim_head //= heads - - if skip_reshape: - query = query.reshape(b * heads, -1, dim_head) - value = value.reshape(b * heads, -1, dim_head) - key = key.reshape(b * heads, -1, dim_head).movedim(1, 2) - else: - query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) - value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) - key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) - - - dtype = query.dtype - upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32 - if upcast_attention: - bytes_per_token = torch.finfo(torch.float32).bits//8 - else: - bytes_per_token = torch.finfo(query.dtype).bits//8 - batch_x_heads, q_tokens, _ = query.shape - _, _, k_tokens = key.shape - - mem_free_total, _ = model_management.get_free_memory(query.device, True) - - kv_chunk_size_min = None - kv_chunk_size = None - query_chunk_size = None - - for x in [4096, 2048, 1024, 512, 256]: - count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) - if count >= k_tokens: - kv_chunk_size = k_tokens - query_chunk_size = x - break - - if query_chunk_size is None: - query_chunk_size = 512 - - if mask is not None: - if len(mask.shape) == 2: - bs = 1 - else: - bs = mask.shape[0] - mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) - - hidden_states = efficient_dot_product_attention( - query, - key, - value, - query_chunk_size=query_chunk_size, - kv_chunk_size=kv_chunk_size, - kv_chunk_size_min=kv_chunk_size_min, - use_checkpoint=False, - upcast_attention=upcast_attention, - mask=mask, - ) - - hidden_states = hidden_states.to(dtype) - if skip_output_reshape: - hidden_states = hidden_states.unflatten(0, (-1, heads)) - else: - hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) - return hidden_states - -def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - attn_precision = get_attn_precision(attn_precision, q.dtype) - - if skip_reshape: - b, _, _, dim_head = q.shape - else: - b, _, dim_head = q.shape - dim_head //= heads - - scale = dim_head ** -0.5 - - if skip_reshape: - q, k, v = map( - lambda t: t.reshape(b * heads, -1, dim_head), - (q, k, v), - ) - else: - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), - (q, k, v), - ) - - r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) - - mem_free_total = model_management.get_free_memory(q.device) - - if attn_precision == torch.float32: - element_size = 4 - upcast = True - else: - element_size = q.element_size() - upcast = False - - gb = 1024 ** 3 - tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size - modifier = 3 - mem_required = tensor_size * modifier - steps = 1 - - - if mem_required > mem_free_total: - steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) - # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " - # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") - - if steps > 64: - max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 - raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' - f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') - - if mask is not None: - if len(mask.shape) == 2: - bs = 1 - else: - bs = mask.shape[0] - mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1]) - - # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) - first_op_done = False - cleared_cache = False - while True: - try: - slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] - for i in range(0, q.shape[1], slice_size): - end = i + slice_size - if upcast: - with torch.autocast(enabled=False, device_type = 'cuda'): - s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale - else: - s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale - - if mask is not None: - if len(mask.shape) == 2: - s1 += mask[i:end] - else: - if mask.shape[1] == 1: - s1 += mask - else: - s1 += mask[:, i:end] - - s2 = s1.softmax(dim=-1).to(v.dtype) - del s1 - first_op_done = True - - r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) - del s2 - break - except model_management.OOM_EXCEPTION as e: - if first_op_done == False: - model_management.soft_empty_cache(True) - if cleared_cache == False: - cleared_cache = True - logging.warning("out of memory error, emptying cache and trying again") - continue - steps *= 2 - if steps > 64: - raise e - logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) - else: - raise e - - del q, k, v - - if skip_output_reshape: - r1 = ( - r1.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - ) - else: - r1 = ( - r1.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) - return r1 - -BROKEN_XFORMERS = False -try: - x_vers = xformers.__version__ - # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error) - BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20") -except: - pass - -def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - b = q.shape[0] - dim_head = q.shape[-1] - # check to make sure xformers isn't broken - disabled_xformers = False - - if BROKEN_XFORMERS: - if b * heads > 65535: - disabled_xformers = True - - if not disabled_xformers: - if torch.jit.is_tracing() or torch.jit.is_scripting(): - disabled_xformers = True - - if disabled_xformers: - return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) - - if skip_reshape: - # b h k d -> b k h d - q, k, v = map( - lambda t: t.permute(0, 2, 1, 3), - (q, k, v), - ) - # actually do the reshaping - else: - dim_head //= heads - q, k, v = map( - lambda t: t.reshape(b, -1, heads, dim_head), - (q, k, v), - ) - - if mask is not None: - # add a singleton batch dimension - if mask.ndim == 2: - mask = mask.unsqueeze(0) - # add a singleton heads dimension - if mask.ndim == 3: - mask = mask.unsqueeze(1) - # pad to a multiple of 8 - pad = 8 - mask.shape[-1] % 8 - # the xformers docs says that it's allowed to have a mask of shape (1, Nq, Nk) - # but when using separated heads, the shape has to be (B, H, Nq, Nk) - # in flux, this matrix ends up being over 1GB - # here, we create a mask with the same batch/head size as the input mask (potentially singleton or full) - mask_out = torch.empty([mask.shape[0], mask.shape[1], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device) - - mask_out[..., :mask.shape[-1]] = mask - # doesn't this remove the padding again?? - mask = mask_out[..., :mask.shape[-1]] - mask = mask.expand(b, heads, -1, -1) - - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) - - if skip_output_reshape: - out = out.permute(0, 2, 1, 3) - else: - out = ( - out.reshape(b, -1, heads * dim_head) - ) - - return out - -if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue. - SDP_BATCH_LIMIT = 2**15 -else: - #TODO: other GPUs ? - SDP_BATCH_LIMIT = 2**31 - - -def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - if skip_reshape: - b, _, _, dim_head = q.shape - else: - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), - (q, k, v), - ) - - if mask is not None: - # add a batch dimension if there isn't already one - if mask.ndim == 2: - mask = mask.unsqueeze(0) - # add a heads dimension if there isn't already one - if mask.ndim == 3: - mask = mask.unsqueeze(1) - - if SDP_BATCH_LIMIT >= b: - out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) - if not skip_output_reshape: - out = ( - out.transpose(1, 2).reshape(b, -1, heads * dim_head) - ) - else: - out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device) - for i in range(0, b, SDP_BATCH_LIMIT): - m = mask - if mask is not None: - if mask.shape[0] > 1: - m = mask[i : i + SDP_BATCH_LIMIT] - - out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention( - q[i : i + SDP_BATCH_LIMIT], - k[i : i + SDP_BATCH_LIMIT], - v[i : i + SDP_BATCH_LIMIT], - attn_mask=m, - dropout_p=0.0, is_causal=False - ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) - return out - - -def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - if skip_reshape: - b, _, _, dim_head = q.shape - tensor_layout = "HND" - else: - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head), - (q, k, v), - ) - tensor_layout = "NHD" - - if mask is not None: - # add a batch dimension if there isn't already one - if mask.ndim == 2: - mask = mask.unsqueeze(0) - # add a heads dimension if there isn't already one - if mask.ndim == 3: - mask = mask.unsqueeze(1) - - try: - out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) - except Exception as e: - logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e)) - if tensor_layout == "NHD": - q, k, v = map( - lambda t: t.transpose(1, 2), - (q, k, v), - ) - return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape) - - if tensor_layout == "HND": - if not skip_output_reshape: - out = ( - out.transpose(1, 2).reshape(b, -1, heads * dim_head) - ) - else: - if skip_output_reshape: - out = out.transpose(1, 2) - else: - out = out.reshape(b, -1, heads * dim_head) - return out - - -try: - @torch.library.custom_op("flash_attention::flash_attn", mutates_args=()) - def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, - dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: - return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal) - - - @flash_attn_wrapper.register_fake - def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False): - # Output shape is the same as q - return q.new_empty(q.shape) -except AttributeError as error: - FLASH_ATTN_ERROR = error - - def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, - dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: - assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}" - - -def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - if skip_reshape: - b, _, _, dim_head = q.shape - else: - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), - (q, k, v), - ) - - if mask is not None: - # add a batch dimension if there isn't already one - if mask.ndim == 2: - mask = mask.unsqueeze(0) - # add a heads dimension if there isn't already one - if mask.ndim == 3: - mask = mask.unsqueeze(1) - - try: - assert mask is None - out = flash_attn_wrapper( - q.transpose(1, 2), - k.transpose(1, 2), - v.transpose(1, 2), - dropout_p=0.0, - causal=False, - ).transpose(1, 2) - except Exception as e: - logging.warning(f"Flash Attention failed, using default SDPA: {e}") - out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) - if not skip_output_reshape: - out = ( - out.transpose(1, 2).reshape(b, -1, heads * dim_head) - ) - return out - - -optimized_attention = attention_basic - -if model_management.sage_attention_enabled(): - logging.info("Using sage attention") - optimized_attention = attention_sage -elif model_management.xformers_enabled(): - logging.info("Using xformers attention") - optimized_attention = attention_xformers -elif model_management.flash_attention_enabled(): - logging.info("Using Flash Attention") - optimized_attention = attention_flash -elif model_management.pytorch_attention_enabled(): - logging.info("Using pytorch attention") - optimized_attention = attention_pytorch -else: - if args.use_split_cross_attention: - logging.info("Using split optimization for attention") - optimized_attention = attention_split - else: - logging.info("Using sub quadratic optimization for attention, if you have memory or speed issues try using: --use-split-cross-attention") - optimized_attention = attention_sub_quad - -optimized_attention_masked = optimized_attention - -def optimized_attention_for_device(device, mask=False, small_input=False): - if small_input: - if model_management.pytorch_attention_enabled(): - return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases - else: - return attention_basic - - if device == torch.device("cpu"): - return attention_sub_quad - - if mask: - return optimized_attention_masked - - return optimized_attention - - -class CrossAttention(nn.Module): - def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops): - super().__init__() - inner_dim = dim_head * heads - context_dim = default(context_dim, query_dim) - self.attn_precision = attn_precision - - self.heads = heads - self.dim_head = dim_head - - self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) - - self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) - - def forward(self, x, context=None, value=None, mask=None): - q = self.to_q(x) - context = default(context, x) - k = self.to_k(context) - if value is not None: - v = self.to_v(value) - del value - else: - v = self.to_v(context) - - if mask is None: - out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) - else: - out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) - return self.to_out(out) - - -class BasicTransformerBlock(nn.Module): - def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, - disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops): - super().__init__() - - self.ff_in = ff_in or inner_dim is not None - if inner_dim is None: - inner_dim = dim - - self.is_res = inner_dim == dim - self.attn_precision = attn_precision - - if self.ff_in: - self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) - self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) - - self.disable_self_attn = disable_self_attn - self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, - context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn - self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) - - if disable_temporal_crossattention: - if switch_temporal_ca_to_sa: - raise ValueError - else: - self.attn2 = None - else: - context_dim_attn2 = None - if not switch_temporal_ca_to_sa: - context_dim_attn2 = context_dim - - self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, - heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none - self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - - self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) - self.n_heads = n_heads - self.d_head = d_head - self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa - - def forward(self, x, context=None, transformer_options={}): - extra_options = {} - block = transformer_options.get("block", None) - block_index = transformer_options.get("block_index", 0) - transformer_patches = {} - transformer_patches_replace = {} - - for k in transformer_options: - if k == "patches": - transformer_patches = transformer_options[k] - elif k == "patches_replace": - transformer_patches_replace = transformer_options[k] - else: - extra_options[k] = transformer_options[k] - - extra_options["n_heads"] = self.n_heads - extra_options["dim_head"] = self.d_head - extra_options["attn_precision"] = self.attn_precision - - if self.ff_in: - x_skip = x - x = self.ff_in(self.norm_in(x)) - if self.is_res: - x += x_skip - - n = self.norm1(x) - if self.disable_self_attn: - context_attn1 = context - else: - context_attn1 = None - value_attn1 = None - - if "attn1_patch" in transformer_patches: - patch = transformer_patches["attn1_patch"] - if context_attn1 is None: - context_attn1 = n - value_attn1 = context_attn1 - for p in patch: - n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) - - if block is not None: - transformer_block = (block[0], block[1], block_index) - else: - transformer_block = None - attn1_replace_patch = transformer_patches_replace.get("attn1", {}) - block_attn1 = transformer_block - if block_attn1 not in attn1_replace_patch: - block_attn1 = block - - if block_attn1 in attn1_replace_patch: - if context_attn1 is None: - context_attn1 = n - value_attn1 = n - n = self.attn1.to_q(n) - context_attn1 = self.attn1.to_k(context_attn1) - value_attn1 = self.attn1.to_v(value_attn1) - n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) - n = self.attn1.to_out(n) - else: - n = self.attn1(n, context=context_attn1, value=value_attn1) - - if "attn1_output_patch" in transformer_patches: - patch = transformer_patches["attn1_output_patch"] - for p in patch: - n = p(n, extra_options) - - x = n + x - if "middle_patch" in transformer_patches: - patch = transformer_patches["middle_patch"] - for p in patch: - x = p(x, extra_options) - - if self.attn2 is not None: - n = self.norm2(x) - if self.switch_temporal_ca_to_sa: - context_attn2 = n - else: - context_attn2 = context - value_attn2 = None - if "attn2_patch" in transformer_patches: - patch = transformer_patches["attn2_patch"] - value_attn2 = context_attn2 - for p in patch: - n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) - - attn2_replace_patch = transformer_patches_replace.get("attn2", {}) - block_attn2 = transformer_block - if block_attn2 not in attn2_replace_patch: - block_attn2 = block - - if block_attn2 in attn2_replace_patch: - if value_attn2 is None: - value_attn2 = context_attn2 - n = self.attn2.to_q(n) - context_attn2 = self.attn2.to_k(context_attn2) - value_attn2 = self.attn2.to_v(value_attn2) - n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) - n = self.attn2.to_out(n) - else: - n = self.attn2(n, context=context_attn2, value=value_attn2) - - if "attn2_output_patch" in transformer_patches: - patch = transformer_patches["attn2_output_patch"] - for p in patch: - n = p(n, extra_options) - - x = n + x - if self.is_res: - x_skip = x - x = self.ff(self.norm3(x)) - if self.is_res: - x = x_skip + x - - return x - - -class SpatialTransformer(nn.Module): - """ - Transformer block for image-like data. - First, project the input (aka embedding) - and reshape to b, t, d. - Then apply standard transformer action. - Finally, reshape to image - NEW: use_linear for more efficiency instead of the 1x1 convs - """ - def __init__(self, in_channels, n_heads, d_head, - depth=1, dropout=0., context_dim=None, - disable_self_attn=False, use_linear=False, - use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops): - super().__init__() - if exists(context_dim) and not isinstance(context_dim, list): - context_dim = [context_dim] * depth - self.in_channels = in_channels - inner_dim = n_heads * d_head - self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) - if not use_linear: - self.proj_in = operations.Conv2d(in_channels, - inner_dim, - kernel_size=1, - stride=1, - padding=0, dtype=dtype, device=device) - else: - self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) - - self.transformer_blocks = nn.ModuleList( - [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], - disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations) - for d in range(depth)] - ) - if not use_linear: - self.proj_out = operations.Conv2d(inner_dim,in_channels, - kernel_size=1, - stride=1, - padding=0, dtype=dtype, device=device) - else: - self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) - self.use_linear = use_linear - - def forward(self, x, context=None, transformer_options={}): - # note: if no context is given, cross-attention defaults to self-attention - if not isinstance(context, list): - context = [context] * len(self.transformer_blocks) - b, c, h, w = x.shape - transformer_options["activations_shape"] = list(x.shape) - x_in = x - x = self.norm(x) - if not self.use_linear: - x = self.proj_in(x) - x = x.movedim(1, 3).flatten(1, 2).contiguous() - if self.use_linear: - x = self.proj_in(x) - for i, block in enumerate(self.transformer_blocks): - transformer_options["block_index"] = i - x = block(x, context=context[i], transformer_options=transformer_options) - if self.use_linear: - x = self.proj_out(x) - x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous() - if not self.use_linear: - x = self.proj_out(x) - return x + x_in - - -class SpatialVideoTransformer(SpatialTransformer): - def __init__( - self, - in_channels, - n_heads, - d_head, - depth=1, - dropout=0.0, - use_linear=False, - context_dim=None, - use_spatial_context=False, - timesteps=None, - merge_strategy: str = "fixed", - merge_factor: float = 0.5, - time_context_dim=None, - ff_in=False, - checkpoint=False, - time_depth=1, - disable_self_attn=False, - disable_temporal_crossattention=False, - max_time_embed_period: int = 10000, - attn_precision=None, - dtype=None, device=None, operations=ops - ): - super().__init__( - in_channels, - n_heads, - d_head, - depth=depth, - dropout=dropout, - use_checkpoint=checkpoint, - context_dim=context_dim, - use_linear=use_linear, - disable_self_attn=disable_self_attn, - attn_precision=attn_precision, - dtype=dtype, device=device, operations=operations - ) - self.time_depth = time_depth - self.depth = depth - self.max_time_embed_period = max_time_embed_period - - time_mix_d_head = d_head - n_time_mix_heads = n_heads - - time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) - - inner_dim = n_heads * d_head - if use_spatial_context: - time_context_dim = context_dim - - self.time_stack = nn.ModuleList( - [ - BasicTransformerBlock( - inner_dim, - n_time_mix_heads, - time_mix_d_head, - dropout=dropout, - context_dim=time_context_dim, - # timesteps=timesteps, - checkpoint=checkpoint, - ff_in=ff_in, - inner_dim=time_mix_inner_dim, - disable_self_attn=disable_self_attn, - disable_temporal_crossattention=disable_temporal_crossattention, - attn_precision=attn_precision, - dtype=dtype, device=device, operations=operations - ) - for _ in range(self.depth) - ] - ) - - assert len(self.time_stack) == len(self.transformer_blocks) - - self.use_spatial_context = use_spatial_context - self.in_channels = in_channels - - time_embed_dim = self.in_channels * 4 - self.time_pos_embed = nn.Sequential( - operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), - ) - - self.time_mixer = AlphaBlender( - alpha=merge_factor, merge_strategy=merge_strategy - ) - - def forward( - self, - x: torch.Tensor, - context: Optional[torch.Tensor] = None, - time_context: Optional[torch.Tensor] = None, - timesteps: Optional[int] = None, - image_only_indicator: Optional[torch.Tensor] = None, - transformer_options={} - ) -> torch.Tensor: - _, _, h, w = x.shape - transformer_options["activations_shape"] = list(x.shape) - x_in = x - spatial_context = None - if exists(context): - spatial_context = context - - if self.use_spatial_context: - assert ( - context.ndim == 3 - ), f"n dims of spatial context should be 3 but are {context.ndim}" - - if time_context is None: - time_context = context - time_context_first_timestep = time_context[::timesteps] - time_context = repeat( - time_context_first_timestep, "b ... -> (b n) ...", n=h * w - ) - elif time_context is not None and not self.use_spatial_context: - time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) - if time_context.ndim == 2: - time_context = rearrange(time_context, "b c -> b 1 c") - - x = self.norm(x) - if not self.use_linear: - x = self.proj_in(x) - x = rearrange(x, "b c h w -> b (h w) c") - if self.use_linear: - x = self.proj_in(x) - - num_frames = torch.arange(timesteps, device=x.device) - num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) - num_frames = rearrange(num_frames, "b t -> (b t)") - t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) - emb = self.time_pos_embed(t_emb) - emb = emb[:, None, :] - - for it_, (block, mix_block) in enumerate( - zip(self.transformer_blocks, self.time_stack) - ): - transformer_options["block_index"] = it_ - x = block( - x, - context=spatial_context, - transformer_options=transformer_options, - ) - - x_mix = x - x_mix = x_mix + emb - - B, S, C = x_mix.shape - x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) - x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options - x_mix = rearrange( - x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps - ) - - x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) - - if self.use_linear: - x = self.proj_out(x) - x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) - if not self.use_linear: - x = self.proj_out(x) - out = x + x_in - return out - - diff --git a/comfy/ldm/modules/diffusionmodules/.DS_Store b/comfy/ldm/modules/diffusionmodules/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/modules/diffusionmodules/.DS_Store and /dev/null differ diff --git a/comfy/ldm/modules/diffusionmodules/__init__.py b/comfy/ldm/modules/diffusionmodules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py deleted file mode 100644 index 4d6beba2d762e54bfc7bb3a3a405c0711971a897..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ /dev/null @@ -1,1031 +0,0 @@ -from functools import partial -from typing import Dict, Optional, List - -import numpy as np -import torch -import torch.nn as nn -from ..attention import optimized_attention -from einops import rearrange, repeat -from .util import timestep_embedding -import comfy.ops -import comfy.ldm.common_dit - -def default(x, y): - if x is not None: - return x - return y - -class Mlp(nn.Module): - """ MLP as used in Vision Transformer, MLP-Mixer and related networks - """ - def __init__( - self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - norm_layer=None, - bias=True, - drop=0., - use_conv=False, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - drop_probs = drop - linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear - - self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device) - self.act = act_layer() - self.drop1 = nn.Dropout(drop_probs) - self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() - self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device) - self.drop2 = nn.Dropout(drop_probs) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop1(x) - x = self.norm(x) - x = self.fc2(x) - x = self.drop2(x) - return x - -class PatchEmbed(nn.Module): - """ 2D Image to Patch Embedding - """ - dynamic_img_pad: torch.jit.Final[bool] - - def __init__( - self, - img_size: Optional[int] = 224, - patch_size: int = 16, - in_chans: int = 3, - embed_dim: int = 768, - norm_layer = None, - flatten: bool = True, - bias: bool = True, - strict_img_size: bool = True, - dynamic_img_pad: bool = True, - padding_mode='circular', - conv3d=False, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - try: - len(patch_size) - self.patch_size = patch_size - except: - if conv3d: - self.patch_size = (patch_size, patch_size, patch_size) - else: - self.patch_size = (patch_size, patch_size) - self.padding_mode = padding_mode - - # flatten spatial dim and transpose to channels last, kept for bwd compat - self.flatten = flatten - self.strict_img_size = strict_img_size - self.dynamic_img_pad = dynamic_img_pad - if conv3d: - self.proj = operations.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) - else: - self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) - self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() - - def forward(self, x): - if self.dynamic_img_pad: - x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode) - x = self.proj(x) - if self.flatten: - x = x.flatten(2).transpose(1, 2) # NCHW -> NLC - x = self.norm(x) - return x - -def modulate(x, shift, scale): - if shift is None: - shift = torch.zeros_like(scale) - return torch.addcmul(shift.unsqueeze(1), x, 1+ scale.unsqueeze(1)) - - -################################################################################# -# Sine/Cosine Positional Embedding Functions # -################################################################################# - - -def get_2d_sincos_pos_embed( - embed_dim, - grid_size, - cls_token=False, - extra_tokens=0, - scaling_factor=None, - offset=None, -): - """ - grid_size: int of the grid height and width - return: - pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) - """ - grid_h = np.arange(grid_size, dtype=np.float32) - grid_w = np.arange(grid_size, dtype=np.float32) - grid = np.meshgrid(grid_w, grid_h) # here w goes first - grid = np.stack(grid, axis=0) - if scaling_factor is not None: - grid = grid / scaling_factor - if offset is not None: - grid = grid - offset - - grid = grid.reshape([2, 1, grid_size, grid_size]) - pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) - if cls_token and extra_tokens > 0: - pos_embed = np.concatenate( - [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0 - ) - return pos_embed - - -def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): - assert embed_dim % 2 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) - emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) - - emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) - return emb - - -def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): - """ - embed_dim: output dimension for each position - pos: a list of positions to be encoded: size (M,) - out: (M, D) - """ - assert embed_dim % 2 == 0 - omega = np.arange(embed_dim // 2, dtype=np.float64) - omega /= embed_dim / 2.0 - omega = 1.0 / 10000**omega # (D/2,) - - pos = pos.reshape(-1) # (M,) - out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product - - emb_sin = np.sin(out) # (M, D/2) - emb_cos = np.cos(out) # (M, D/2) - - emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) - return emb - -def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32): - omega = torch.arange(embed_dim // 2, device=device, dtype=dtype) - omega /= embed_dim / 2.0 - omega = 1.0 / 10000**omega # (D/2,) - pos = pos.reshape(-1) # (M,) - out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product - emb_sin = torch.sin(out) # (M, D/2) - emb_cos = torch.cos(out) # (M, D/2) - emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) - return emb - -def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32): - small = min(h, w) - val_h = (h / small) * val_magnitude - val_w = (w / small) * val_magnitude - grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij') - emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) - emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) - emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) - return emb - - -################################################################################# -# Embedding Layers for Timesteps and Class Labels # -################################################################################# - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - - def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): - super().__init__() - self.mlp = nn.Sequential( - operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), - ) - self.frequency_embedding_size = frequency_embedding_size - - def forward(self, t, dtype, **kwargs): - t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype) - t_emb = self.mlp(t_freq) - return t_emb - - -class VectorEmbedder(nn.Module): - """ - Embeds a flat vector of dimension input_dim - """ - - def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None): - super().__init__() - self.mlp = nn.Sequential( - operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - emb = self.mlp(x) - return emb - - -################################################################################# -# Core DiT Model # -################################################################################# - - -def split_qkv(qkv, head_dim): - qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0) - return qkv[0], qkv[1], qkv[2] - - -class SelfAttention(nn.Module): - ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") - - def __init__( - self, - dim: int, - num_heads: int = 8, - qkv_bias: bool = False, - qk_scale: Optional[float] = None, - proj_drop: float = 0.0, - attn_mode: str = "xformers", - pre_only: bool = False, - qk_norm: Optional[str] = None, - rmsnorm: bool = False, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.num_heads = num_heads - self.head_dim = dim // num_heads - - self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) - if not pre_only: - self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) - self.proj_drop = nn.Dropout(proj_drop) - assert attn_mode in self.ATTENTION_MODES - self.attn_mode = attn_mode - self.pre_only = pre_only - - if qk_norm == "rms": - self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) - self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) - elif qk_norm == "ln": - self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) - self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device) - elif qk_norm is None: - self.ln_q = nn.Identity() - self.ln_k = nn.Identity() - else: - raise ValueError(qk_norm) - - def pre_attention(self, x: torch.Tensor) -> torch.Tensor: - B, L, C = x.shape - qkv = self.qkv(x) - q, k, v = split_qkv(qkv, self.head_dim) - q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1) - k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1) - return (q, k, v) - - def post_attention(self, x: torch.Tensor) -> torch.Tensor: - assert not self.pre_only - x = self.proj(x) - x = self.proj_drop(x) - return x - - def forward(self, x: torch.Tensor) -> torch.Tensor: - q, k, v = self.pre_attention(x) - x = optimized_attention( - q, k, v, heads=self.num_heads - ) - x = self.post_attention(x) - return x - - -class RMSNorm(torch.nn.Module): - def __init__( - self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs - ): - """ - Initialize the RMSNorm normalization layer. - Args: - dim (int): The dimension of the input tensor. - eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. - Attributes: - eps (float): A small value added to the denominator for numerical stability. - weight (nn.Parameter): Learnable scaling parameter. - """ - super().__init__() - self.eps = eps - self.learnable_scale = elementwise_affine - if self.learnable_scale: - self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) - else: - self.register_parameter("weight", None) - - def forward(self, x): - return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) - - - -class SwiGLUFeedForward(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float] = None, - ): - """ - Initialize the FeedForward module. - - Args: - dim (int): Input dimension. - hidden_dim (int): Hidden dimension of the feedforward layer. - multiple_of (int): Value to ensure hidden dimension is a multiple of this value. - ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. - - Attributes: - w1 (ColumnParallelLinear): Linear transformation for the first layer. - w2 (RowParallelLinear): Linear transformation for the second layer. - w3 (ColumnParallelLinear): Linear transformation for the third layer. - - """ - super().__init__() - hidden_dim = int(2 * hidden_dim / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - self.w1 = nn.Linear(dim, hidden_dim, bias=False) - self.w2 = nn.Linear(hidden_dim, dim, bias=False) - self.w3 = nn.Linear(dim, hidden_dim, bias=False) - - def forward(self, x): - return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x)) - - -class DismantledBlock(nn.Module): - """ - A DiT block with gated adaptive layer norm (adaLN) conditioning. - """ - - ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug") - - def __init__( - self, - hidden_size: int, - num_heads: int, - mlp_ratio: float = 4.0, - attn_mode: str = "xformers", - qkv_bias: bool = False, - pre_only: bool = False, - rmsnorm: bool = False, - scale_mod_only: bool = False, - swiglu: bool = False, - qk_norm: Optional[str] = None, - x_block_self_attn: bool = False, - dtype=None, - device=None, - operations=None, - **block_kwargs, - ): - super().__init__() - assert attn_mode in self.ATTENTION_MODES - if not rmsnorm: - self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - else: - self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.attn = SelfAttention( - dim=hidden_size, - num_heads=num_heads, - qkv_bias=qkv_bias, - attn_mode=attn_mode, - pre_only=pre_only, - qk_norm=qk_norm, - rmsnorm=rmsnorm, - dtype=dtype, - device=device, - operations=operations - ) - if x_block_self_attn: - assert not pre_only - assert not scale_mod_only - self.x_block_self_attn = True - self.attn2 = SelfAttention( - dim=hidden_size, - num_heads=num_heads, - qkv_bias=qkv_bias, - attn_mode=attn_mode, - pre_only=False, - qk_norm=qk_norm, - rmsnorm=rmsnorm, - dtype=dtype, - device=device, - operations=operations - ) - else: - self.x_block_self_attn = False - if not pre_only: - if not rmsnorm: - self.norm2 = operations.LayerNorm( - hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device - ) - else: - self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6) - mlp_hidden_dim = int(hidden_size * mlp_ratio) - if not pre_only: - if not swiglu: - self.mlp = Mlp( - in_features=hidden_size, - hidden_features=mlp_hidden_dim, - act_layer=lambda: nn.GELU(approximate="tanh"), - drop=0, - dtype=dtype, - device=device, - operations=operations - ) - else: - self.mlp = SwiGLUFeedForward( - dim=hidden_size, - hidden_dim=mlp_hidden_dim, - multiple_of=256, - ) - self.scale_mod_only = scale_mod_only - if x_block_self_attn: - assert not pre_only - assert not scale_mod_only - n_mods = 9 - elif not scale_mod_only: - n_mods = 6 if not pre_only else 2 - else: - n_mods = 4 if not pre_only else 1 - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device) - ) - self.pre_only = pre_only - - def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: - if not self.pre_only: - if not self.scale_mod_only: - ( - shift_msa, - scale_msa, - gate_msa, - shift_mlp, - scale_mlp, - gate_mlp, - ) = self.adaLN_modulation(c).chunk(6, dim=1) - else: - shift_msa = None - shift_mlp = None - ( - scale_msa, - gate_msa, - scale_mlp, - gate_mlp, - ) = self.adaLN_modulation( - c - ).chunk(4, dim=1) - qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) - return qkv, ( - x, - gate_msa, - shift_mlp, - scale_mlp, - gate_mlp, - ) - else: - if not self.scale_mod_only: - ( - shift_msa, - scale_msa, - ) = self.adaLN_modulation( - c - ).chunk(2, dim=1) - else: - shift_msa = None - scale_msa = self.adaLN_modulation(c) - qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa)) - return qkv, None - - def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp): - assert not self.pre_only - x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn) - x = x + gate_mlp.unsqueeze(1) * self.mlp( - modulate(self.norm2(x), shift_mlp, scale_mlp) - ) - return x - - def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: - assert self.x_block_self_attn - ( - shift_msa, - scale_msa, - gate_msa, - shift_mlp, - scale_mlp, - gate_mlp, - shift_msa2, - scale_msa2, - gate_msa2, - ) = self.adaLN_modulation(c).chunk(9, dim=1) - x_norm = self.norm1(x) - qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa)) - qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2)) - return qkv, qkv2, ( - x, - gate_msa, - shift_mlp, - scale_mlp, - gate_mlp, - gate_msa2, - ) - - def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2): - assert not self.pre_only - attn1 = self.attn.post_attention(attn) - attn2 = self.attn2.post_attention(attn2) - x = gate_cat(x, gate_msa, gate_msa2, attn1, attn2) - x = x + gate_mlp.unsqueeze(1) * self.mlp( - modulate(self.norm2(x), shift_mlp, scale_mlp) - ) - return x - - def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: - assert not self.pre_only - if self.x_block_self_attn: - qkv, qkv2, intermediates = self.pre_attention_x(x, c) - attn, _ = optimized_attention( - qkv[0], qkv[1], qkv[2], - num_heads=self.attn.num_heads, - ) - attn2, _ = optimized_attention( - qkv2[0], qkv2[1], qkv2[2], - num_heads=self.attn2.num_heads, - ) - return self.post_attention_x(attn, attn2, *intermediates) - else: - qkv, intermediates = self.pre_attention(x, c) - attn = optimized_attention( - qkv[0], qkv[1], qkv[2], - heads=self.attn.num_heads, - ) - return self.post_attention(attn, *intermediates) - -def gate_cat(x, gate_msa, gate_msa2, attn1, attn2): - out1 = gate_msa.unsqueeze(1) * attn1 - out2 = gate_msa2.unsqueeze(1) * attn2 - x = torch.stack([x, out1, out2], dim=0).sum(dim=0) - return x - -def block_mixing(*args, use_checkpoint=True, **kwargs): - if use_checkpoint: - return torch.utils.checkpoint.checkpoint( - _block_mixing, *args, use_reentrant=False, **kwargs - ) - else: - return _block_mixing(*args, **kwargs) - - -def _block_mixing(context, x, context_block, x_block, c): - context_qkv, context_intermediates = context_block.pre_attention(context, c) - - if x_block.x_block_self_attn: - x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c) - else: - x_qkv, x_intermediates = x_block.pre_attention(x, c) - - o = [] - for t in range(3): - o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1)) - qkv = tuple(o) - - attn = optimized_attention( - qkv[0], qkv[1], qkv[2], - heads=x_block.attn.num_heads, - ) - context_attn, x_attn = ( - attn[:, : context_qkv[0].shape[1]], - attn[:, context_qkv[0].shape[1] :], - ) - - if not context_block.pre_only: - context = context_block.post_attention(context_attn, *context_intermediates) - - else: - context = None - if x_block.x_block_self_attn: - attn2 = optimized_attention( - x_qkv2[0], x_qkv2[1], x_qkv2[2], - heads=x_block.attn2.num_heads, - ) - x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) - else: - x = x_block.post_attention(x_attn, *x_intermediates) - return context, x - - -class JointBlock(nn.Module): - """just a small wrapper to serve as a fsdp unit""" - - def __init__( - self, - *args, - **kwargs, - ): - super().__init__() - pre_only = kwargs.pop("pre_only") - qk_norm = kwargs.pop("qk_norm", None) - x_block_self_attn = kwargs.pop("x_block_self_attn", False) - self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs) - self.x_block = DismantledBlock(*args, - pre_only=False, - qk_norm=qk_norm, - x_block_self_attn=x_block_self_attn, - **kwargs) - - def forward(self, *args, **kwargs): - return block_mixing( - *args, context_block=self.context_block, x_block=self.x_block, **kwargs - ) - - -class FinalLayer(nn.Module): - """ - The final layer of DiT. - """ - - def __init__( - self, - hidden_size: int, - patch_size: int, - out_channels: int, - total_out_channels: Optional[int] = None, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = ( - operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - if (total_out_channels is None) - else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device) - ) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) - ) - - def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - -class SelfAttentionContext(nn.Module): - def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None): - super().__init__() - dim_head = dim // heads - inner_dim = dim - - self.heads = heads - self.dim_head = dim_head - - self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device) - - self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device) - - def forward(self, x): - qkv = self.qkv(x) - q, k, v = split_qkv(qkv, self.dim_head) - x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads) - return self.proj(x) - -class ContextProcessorBlock(nn.Module): - def __init__(self, context_size, dtype=None, device=None, operations=None): - super().__init__() - self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations) - self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations) - - def forward(self, x): - x += self.attn(self.norm1(x)) - x += self.mlp(self.norm2(x)) - return x - -class ContextProcessor(nn.Module): - def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None): - super().__init__() - self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)]) - self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - - def forward(self, x): - for i, l in enumerate(self.layers): - x = l(x) - return self.norm(x) - -class MMDiT(nn.Module): - """ - Diffusion model with a Transformer backbone. - """ - - def __init__( - self, - input_size: int = 32, - patch_size: int = 2, - in_channels: int = 4, - depth: int = 28, - # hidden_size: Optional[int] = None, - # num_heads: Optional[int] = None, - mlp_ratio: float = 4.0, - learn_sigma: bool = False, - adm_in_channels: Optional[int] = None, - context_embedder_config: Optional[Dict] = None, - compile_core: bool = False, - use_checkpoint: bool = False, - register_length: int = 0, - attn_mode: str = "torch", - rmsnorm: bool = False, - scale_mod_only: bool = False, - swiglu: bool = False, - out_channels: Optional[int] = None, - pos_embed_scaling_factor: Optional[float] = None, - pos_embed_offset: Optional[float] = None, - pos_embed_max_size: Optional[int] = None, - num_patches = None, - qk_norm: Optional[str] = None, - qkv_bias: bool = True, - context_processor_layers = None, - x_block_self_attn: bool = False, - x_block_self_attn_layers: Optional[List[int]] = [], - context_size = 4096, - num_blocks = None, - final_layer = True, - skip_blocks = False, - dtype = None, #TODO - device = None, - operations = None, - ): - super().__init__() - self.dtype = dtype - self.learn_sigma = learn_sigma - self.in_channels = in_channels - default_out_channels = in_channels * 2 if learn_sigma else in_channels - self.out_channels = default(out_channels, default_out_channels) - self.patch_size = patch_size - self.pos_embed_scaling_factor = pos_embed_scaling_factor - self.pos_embed_offset = pos_embed_offset - self.pos_embed_max_size = pos_embed_max_size - self.x_block_self_attn_layers = x_block_self_attn_layers - - # hidden_size = default(hidden_size, 64 * depth) - # num_heads = default(num_heads, hidden_size // 64) - - # apply magic --> this defines a head_size of 64 - self.hidden_size = 64 * depth - num_heads = depth - if num_blocks is None: - num_blocks = depth - - self.depth = depth - self.num_heads = num_heads - - self.x_embedder = PatchEmbed( - input_size, - patch_size, - in_channels, - self.hidden_size, - bias=True, - strict_img_size=self.pos_embed_max_size is None, - dtype=dtype, - device=device, - operations=operations - ) - self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations) - - self.y_embedder = None - if adm_in_channels is not None: - assert isinstance(adm_in_channels, int) - self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations) - - if context_processor_layers is not None: - self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations) - else: - self.context_processor = None - - self.context_embedder = nn.Identity() - if context_embedder_config is not None: - if context_embedder_config["target"] == "torch.nn.Linear": - self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device) - - self.register_length = register_length - if self.register_length > 0: - self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device)) - - # num_patches = self.x_embedder.num_patches - # Will use fixed sin-cos embedding: - # just use a buffer already - if num_patches is not None: - self.register_buffer( - "pos_embed", - torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device), - ) - else: - self.pos_embed = None - - self.use_checkpoint = use_checkpoint - if not skip_blocks: - self.joint_blocks = nn.ModuleList( - [ - JointBlock( - self.hidden_size, - num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - attn_mode=attn_mode, - pre_only=(i == num_blocks - 1) and final_layer, - rmsnorm=rmsnorm, - scale_mod_only=scale_mod_only, - swiglu=swiglu, - qk_norm=qk_norm, - x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn, - dtype=dtype, - device=device, - operations=operations, - ) - for i in range(num_blocks) - ] - ) - - if final_layer: - self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) - - if compile_core: - assert False - self.forward_core_with_concat = torch.compile(self.forward_core_with_concat) - - def cropped_pos_embed(self, hw, device=None): - p = self.x_embedder.patch_size[0] - h, w = hw - # patched size - h = (h + 1) // p - w = (w + 1) // p - if self.pos_embed is None: - return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device) - assert self.pos_embed_max_size is not None - assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size) - assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size) - top = (self.pos_embed_max_size - h) // 2 - left = (self.pos_embed_max_size - w) // 2 - spatial_pos_embed = rearrange( - self.pos_embed, - "1 (h w) c -> 1 h w c", - h=self.pos_embed_max_size, - w=self.pos_embed_max_size, - ) - spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :] - spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c") - # print(spatial_pos_embed, top, left, h, w) - # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res - # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better - # # print(t) - # return t - return spatial_pos_embed - - def unpatchify(self, x, hw=None): - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - c = self.out_channels - p = self.x_embedder.patch_size[0] - if hw is None: - h = w = int(x.shape[1] ** 0.5) - else: - h, w = hw - h = (h + 1) // p - w = (w + 1) // p - assert h * w == x.shape[1] - - x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) - x = torch.einsum("nhwpqc->nchpwq", x) - imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) - return imgs - - def forward_core_with_concat( - self, - x: torch.Tensor, - c_mod: torch.Tensor, - context: Optional[torch.Tensor] = None, - control = None, - transformer_options = {}, - ) -> torch.Tensor: - patches_replace = transformer_options.get("patches_replace", {}) - if self.register_length > 0: - context = torch.cat( - ( - repeat(self.register, "1 ... -> b ...", b=x.shape[0]), - default(context, torch.Tensor([]).type_as(x)), - ), - 1, - ) - - # context is B, L', D - # x is B, L, D - blocks_replace = patches_replace.get("dit", {}) - blocks = len(self.joint_blocks) - for i in range(blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"]) - return out - - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap}) - context = out["txt"] - x = out["img"] - else: - context, x = self.joint_blocks[i]( - context, - x, - c=c_mod, - use_checkpoint=self.use_checkpoint, - ) - if control is not None: - control_o = control.get("output") - if i < len(control_o): - add = control_o[i] - if add is not None: - x += add - - x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels) - return x - - def forward( - self, - x: torch.Tensor, - t: torch.Tensor, - y: Optional[torch.Tensor] = None, - context: Optional[torch.Tensor] = None, - control = None, - transformer_options = {}, - ) -> torch.Tensor: - """ - Forward pass of DiT. - x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) - t: (N,) tensor of diffusion timesteps - y: (N,) tensor of class labels - """ - - if self.context_processor is not None: - context = self.context_processor(context) - - hw = x.shape[-2:] - x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x) - c = self.t_embedder(t, dtype=x.dtype) # (N, D) - if y is not None and self.y_embedder is not None: - y = self.y_embedder(y) # (N, D) - c = c + y # (N, D) - - if context is not None: - context = self.context_embedder(context) - - x = self.forward_core_with_concat(x, c, context, control, transformer_options) - - x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W) - return x[:,:,:hw[-2],:hw[-1]] - - -class OpenAISignatureMMDITWrapper(MMDiT): - def forward( - self, - x: torch.Tensor, - timesteps: torch.Tensor, - context: Optional[torch.Tensor] = None, - y: Optional[torch.Tensor] = None, - control = None, - transformer_options = {}, - **kwargs, - ) -> torch.Tensor: - return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options) - diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py deleted file mode 100644 index 1fd12b35ab4d3b79b4f94697b5fc6a664517a90d..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ /dev/null @@ -1,734 +0,0 @@ -# pytorch_diffusion + derived encoder decoder -import math -import torch -import torch.nn as nn -import numpy as np -import logging - -from comfy import model_management -import comfy.ops -ops = comfy.ops.disable_weight_init - -if model_management.xformers_enabled_vae(): - import xformers - import xformers.ops - -def get_timestep_embedding(timesteps, embedding_dim): - """ - This matches the implementation in Denoising Diffusion Probabilistic Models: - From Fairseq. - Build sinusoidal embeddings. - This matches the implementation in tensor2tensor, but differs slightly - from the description in Section 3.5 of "Attention Is All You Need". - """ - assert len(timesteps.shape) == 1 - - half_dim = embedding_dim // 2 - emb = math.log(10000) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) - emb = emb.to(device=timesteps.device) - emb = timesteps.float()[:, None] * emb[None, :] - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) - if embedding_dim % 2 == 1: # zero pad - emb = torch.nn.functional.pad(emb, (0,1,0,0)) - return emb - - -def nonlinearity(x): - # swish - return torch.nn.functional.silu(x) - - -def Normalize(in_channels, num_groups=32): - return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) - - -class VideoConv3d(nn.Module): - def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs): - super().__init__() - - self.padding_mode = padding_mode - if padding != 0: - padding = (padding, padding, padding, padding, kernel_size - 1, 0) - else: - kwargs["padding"] = padding - - self.padding = padding - self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs) - - def forward(self, x): - if self.padding != 0: - x = torch.nn.functional.pad(x, self.padding, mode=self.padding_mode) - return self.conv(x) - -def interpolate_up(x, scale_factor): - try: - return torch.nn.functional.interpolate(x, scale_factor=scale_factor, mode="nearest") - except: #operation not implemented for bf16 - orig_shape = list(x.shape) - out_shape = orig_shape[:2] - for i in range(len(orig_shape) - 2): - out_shape.append(round(orig_shape[i + 2] * scale_factor[i])) - out = torch.empty(out_shape, dtype=x.dtype, layout=x.layout, device=x.device) - split = 8 - l = out.shape[1] // split - for i in range(0, out.shape[1], l): - out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=scale_factor, mode="nearest").to(x.dtype) - return out - -class Upsample(nn.Module): - def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_factor=2.0): - super().__init__() - self.with_conv = with_conv - self.scale_factor = scale_factor - - if self.with_conv: - self.conv = conv_op(in_channels, - in_channels, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x): - scale_factor = self.scale_factor - if isinstance(scale_factor, (int, float)): - scale_factor = (scale_factor,) * (x.ndim - 2) - - if x.ndim == 5 and scale_factor[0] > 1.0: - t = x.shape[2] - if t > 1: - a, b = x.split((1, t - 1), dim=2) - del x - b = interpolate_up(b, scale_factor) - else: - a = x - - a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2) - if t > 1: - x = torch.cat((a, b), dim=2) - else: - x = a - else: - x = interpolate_up(x, scale_factor) - if self.with_conv: - x = self.conv(x) - return x - - -class Downsample(nn.Module): - def __init__(self, in_channels, with_conv, stride=2, conv_op=ops.Conv2d): - super().__init__() - self.with_conv = with_conv - if self.with_conv: - # no asymmetric padding in torch conv, must do it ourselves - self.conv = conv_op(in_channels, - in_channels, - kernel_size=3, - stride=stride, - padding=0) - - def forward(self, x): - if self.with_conv: - if x.ndim == 4: - pad = (0, 1, 0, 1) - mode = "constant" - x = torch.nn.functional.pad(x, pad, mode=mode, value=0) - elif x.ndim == 5: - pad = (1, 1, 1, 1, 2, 0) - mode = "replicate" - x = torch.nn.functional.pad(x, pad, mode=mode) - x = self.conv(x) - else: - x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) - return x - - -class ResnetBlock(nn.Module): - def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, - dropout, temb_channels=512, conv_op=ops.Conv2d): - super().__init__() - self.in_channels = in_channels - out_channels = in_channels if out_channels is None else out_channels - self.out_channels = out_channels - self.use_conv_shortcut = conv_shortcut - - self.swish = torch.nn.SiLU(inplace=True) - self.norm1 = Normalize(in_channels) - self.conv1 = conv_op(in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - if temb_channels > 0: - self.temb_proj = ops.Linear(temb_channels, - out_channels) - self.norm2 = Normalize(out_channels) - self.dropout = torch.nn.Dropout(dropout, inplace=True) - self.conv2 = conv_op(out_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - if self.in_channels != self.out_channels: - if self.use_conv_shortcut: - self.conv_shortcut = conv_op(in_channels, - out_channels, - kernel_size=3, - stride=1, - padding=1) - else: - self.nin_shortcut = conv_op(in_channels, - out_channels, - kernel_size=1, - stride=1, - padding=0) - - def forward(self, x, temb): - h = x - h = self.norm1(h) - h = self.swish(h) - h = self.conv1(h) - - if temb is not None: - h = h + self.temb_proj(self.swish(temb))[:,:,None,None] - - h = self.norm2(h) - h = self.swish(h) - h = self.dropout(h) - h = self.conv2(h) - - if self.in_channels != self.out_channels: - if self.use_conv_shortcut: - x = self.conv_shortcut(x) - else: - x = self.nin_shortcut(x) - - return x+h - -def slice_attention(q, k, v): - r1 = torch.zeros_like(k, device=q.device) - scale = (int(q.shape[-1])**(-0.5)) - - mem_free_total = model_management.get_free_memory(q.device) - - tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() - modifier = 3 if q.element_size() == 2 else 2.5 - mem_required = tensor_size * modifier - steps = 1 - - if mem_required > mem_free_total: - steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) - - while True: - try: - slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] - for i in range(0, q.shape[1], slice_size): - end = i + slice_size - s1 = torch.bmm(q[:, i:end], k) * scale - - s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) - del s1 - - r1[:, :, i:end] = torch.bmm(v, s2) - del s2 - break - except model_management.OOM_EXCEPTION as e: - model_management.soft_empty_cache(True) - steps *= 2 - if steps > 128: - raise e - logging.warning("out of memory error, increasing steps and trying again {}".format(steps)) - - return r1 - -def normal_attention(q, k, v): - # compute attention - orig_shape = q.shape - b = orig_shape[0] - c = orig_shape[1] - - q = q.reshape(b, c, -1) - q = q.permute(0, 2, 1) # b,hw,c - k = k.reshape(b, c, -1) # b,c,hw - v = v.reshape(b, c, -1) - - r1 = slice_attention(q, k, v) - h_ = r1.reshape(orig_shape) - del r1 - return h_ - -def xformers_attention(q, k, v): - # compute attention - orig_shape = q.shape - B = orig_shape[0] - C = orig_shape[1] - q, k, v = map( - lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), - (q, k, v), - ) - - try: - out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) - out = out.transpose(1, 2).reshape(orig_shape) - except NotImplementedError: - out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape) - return out - -def pytorch_attention(q, k, v): - # compute attention - orig_shape = q.shape - B = orig_shape[0] - C = orig_shape[1] - q, k, v = map( - lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), - (q, k, v), - ) - - try: - out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) - out = out.transpose(2, 3).reshape(orig_shape) - except model_management.OOM_EXCEPTION: - logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") - out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape) - return out - - -def vae_attention(): - if model_management.xformers_enabled_vae(): - logging.info("Using xformers attention in VAE") - return xformers_attention - elif model_management.pytorch_attention_enabled_vae(): - logging.info("Using pytorch attention in VAE") - return pytorch_attention - else: - logging.info("Using split attention in VAE") - return normal_attention - -class AttnBlock(nn.Module): - def __init__(self, in_channels, conv_op=ops.Conv2d): - super().__init__() - self.in_channels = in_channels - - self.norm = Normalize(in_channels) - self.q = conv_op(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.k = conv_op(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.v = conv_op(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - self.proj_out = conv_op(in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0) - - self.optimized_attention = vae_attention() - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - h_ = self.optimized_attention(q, k, v) - - h_ = self.proj_out(h_) - - return x+h_ - - -def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None, conv_op=ops.Conv2d): - return AttnBlock(in_channels, conv_op=conv_op) - - -class Model(nn.Module): - def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, - resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): - super().__init__() - if use_linear_attn: attn_type = "linear" - self.ch = ch - self.temb_ch = self.ch*4 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - - self.use_timestep = use_timestep - if self.use_timestep: - # timestep embedding - self.temb = nn.Module() - self.temb.dense = nn.ModuleList([ - ops.Linear(self.ch, - self.temb_ch), - ops.Linear(self.temb_ch, - self.temb_ch), - ]) - - # downsampling - self.conv_in = ops.Conv2d(in_channels, - self.ch, - kernel_size=3, - stride=1, - padding=1) - - curr_res = resolution - in_ch_mult = (1,)+tuple(ch_mult) - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch*in_ch_mult[i_level] - block_out = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks): - block.append(ResnetBlock(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type)) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions-1: - down.downsample = Downsample(block_in, resamp_with_conv) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) - self.mid.block_2 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch*ch_mult[i_level] - skip_in = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks+1): - if i_block == self.num_res_blocks: - skip_in = ch*in_ch_mult[i_level] - block.append(ResnetBlock(in_channels=block_in+skip_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - up.upsample = Upsample(block_in, resamp_with_conv) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Normalize(block_in) - self.conv_out = ops.Conv2d(block_in, - out_ch, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x, t=None, context=None): - #assert x.shape[2] == x.shape[3] == self.resolution - if context is not None: - # assume aligned context, cat along channel axis - x = torch.cat((x, context), dim=1) - if self.use_timestep: - # timestep embedding - assert t is not None - temb = get_timestep_embedding(t, self.ch) - temb = self.temb.dense[0](temb) - temb = nonlinearity(temb) - temb = self.temb.dense[1](temb) - else: - temb = None - - # downsampling - hs = [self.conv_in(x)] - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](hs[-1], temb) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - hs.append(h) - if i_level != self.num_resolutions-1: - hs.append(self.down[i_level].downsample(hs[-1])) - - # middle - h = hs[-1] - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks+1): - h = self.up[i_level].block[i_block]( - torch.cat([h, hs.pop()], dim=1), temb) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h) - if i_level != 0: - h = self.up[i_level].upsample(h) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - def get_last_layer(self): - return self.conv_out.weight - - -class Encoder(nn.Module): - def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, - resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", - conv3d=False, time_compress=None, - **ignore_kwargs): - super().__init__() - if use_linear_attn: attn_type = "linear" - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - - if conv3d: - conv_op = VideoConv3d - mid_attn_conv_op = ops.Conv3d - else: - conv_op = ops.Conv2d - mid_attn_conv_op = ops.Conv2d - - # downsampling - self.conv_in = conv_op(in_channels, - self.ch, - kernel_size=3, - stride=1, - padding=1) - - curr_res = resolution - in_ch_mult = (1,)+tuple(ch_mult) - self.in_ch_mult = in_ch_mult - self.down = nn.ModuleList() - for i_level in range(self.num_resolutions): - block = nn.ModuleList() - attn = nn.ModuleList() - block_in = ch*in_ch_mult[i_level] - block_out = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks): - block.append(ResnetBlock(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - conv_op=conv_op)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(make_attn(block_in, attn_type=attn_type, conv_op=conv_op)) - down = nn.Module() - down.block = block - down.attn = attn - if i_level != self.num_resolutions-1: - stride = 2 - if time_compress is not None: - if (self.num_resolutions - 1 - i_level) > math.log2(time_compress): - stride = (1, 2, 2) - down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op) - curr_res = curr_res // 2 - self.down.append(down) - - # middle - self.mid = nn.Module() - self.mid.block_1 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - conv_op=conv_op) - self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, conv_op=mid_attn_conv_op) - self.mid.block_2 = ResnetBlock(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - conv_op=conv_op) - - # end - self.norm_out = Normalize(block_in) - self.conv_out = conv_op(block_in, - 2*z_channels if double_z else z_channels, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, x): - # timestep embedding - temb = None - # downsampling - h = self.conv_in(x) - for i_level in range(self.num_resolutions): - for i_block in range(self.num_res_blocks): - h = self.down[i_level].block[i_block](h, temb) - if len(self.down[i_level].attn) > 0: - h = self.down[i_level].attn[i_block](h) - if i_level != self.num_resolutions-1: - h = self.down[i_level].downsample(h) - - # middle - h = self.mid.block_1(h, temb) - h = self.mid.attn_1(h) - h = self.mid.block_2(h, temb) - - # end - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h) - return h - - -class Decoder(nn.Module): - def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, - attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, - resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, - conv_out_op=ops.Conv2d, - resnet_op=ResnetBlock, - attn_op=AttnBlock, - conv3d=False, - time_compress=None, - **ignorekwargs): - super().__init__() - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.in_channels = in_channels - self.give_pre_end = give_pre_end - self.tanh_out = tanh_out - - if conv3d: - conv_op = VideoConv3d - conv_out_op = VideoConv3d - mid_attn_conv_op = ops.Conv3d - else: - conv_op = ops.Conv2d - mid_attn_conv_op = ops.Conv2d - - # compute block_in and curr_res at lowest res - block_in = ch*ch_mult[self.num_resolutions-1] - curr_res = resolution // 2**(self.num_resolutions-1) - self.z_shape = (1,z_channels,curr_res,curr_res) - logging.debug("Working with z of shape {} = {} dimensions.".format( - self.z_shape, np.prod(self.z_shape))) - - # z to block_in - self.conv_in = conv_op(z_channels, - block_in, - kernel_size=3, - stride=1, - padding=1) - - # middle - self.mid = nn.Module() - self.mid.block_1 = resnet_op(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - conv_op=conv_op) - self.mid.attn_1 = attn_op(block_in, conv_op=mid_attn_conv_op) - self.mid.block_2 = resnet_op(in_channels=block_in, - out_channels=block_in, - temb_channels=self.temb_ch, - dropout=dropout, - conv_op=conv_op) - - # upsampling - self.up = nn.ModuleList() - for i_level in reversed(range(self.num_resolutions)): - block = nn.ModuleList() - attn = nn.ModuleList() - block_out = ch*ch_mult[i_level] - for i_block in range(self.num_res_blocks+1): - block.append(resnet_op(in_channels=block_in, - out_channels=block_out, - temb_channels=self.temb_ch, - dropout=dropout, - conv_op=conv_op)) - block_in = block_out - if curr_res in attn_resolutions: - attn.append(attn_op(block_in, conv_op=conv_op)) - up = nn.Module() - up.block = block - up.attn = attn - if i_level != 0: - scale_factor = 2.0 - if time_compress is not None: - if i_level > math.log2(time_compress): - scale_factor = (1.0, 2.0, 2.0) - - up.upsample = Upsample(block_in, resamp_with_conv, conv_op=conv_op, scale_factor=scale_factor) - curr_res = curr_res * 2 - self.up.insert(0, up) # prepend to get consistent order - - # end - self.norm_out = Normalize(block_in) - self.conv_out = conv_out_op(block_in, - out_ch, - kernel_size=3, - stride=1, - padding=1) - - def forward(self, z, **kwargs): - # timestep embedding - temb = None - - # z to block_in - h = self.conv_in(z) - - # middle - h = self.mid.block_1(h, temb, **kwargs) - h = self.mid.attn_1(h, **kwargs) - h = self.mid.block_2(h, temb, **kwargs) - - # upsampling - for i_level in reversed(range(self.num_resolutions)): - for i_block in range(self.num_res_blocks+1): - h = self.up[i_level].block[i_block](h, temb, **kwargs) - if len(self.up[i_level].attn) > 0: - h = self.up[i_level].attn[i_block](h, **kwargs) - if i_level != 0: - h = self.up[i_level].upsample(h) - - # end - if self.give_pre_end: - return h - - h = self.norm_out(h) - h = nonlinearity(h) - h = self.conv_out(h, **kwargs) - if self.tanh_out: - h = torch.tanh(h) - return h diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py deleted file mode 100644 index 4c8d53cac9c2631f6985a3c670128bb4316b11ca..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ /dev/null @@ -1,913 +0,0 @@ -from abc import abstractmethod - -import torch as th -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -import logging - -from .util import ( - checkpoint, - avg_pool_nd, - timestep_embedding, - AlphaBlender, -) -from ..attention import SpatialTransformer, SpatialVideoTransformer, default -from comfy.ldm.util import exists -import comfy.patcher_extension -import comfy.ops -ops = comfy.ops.disable_weight_init - -class TimestepBlock(nn.Module): - """ - Any module where forward() takes timestep embeddings as a second argument. - """ - - @abstractmethod - def forward(self, x, emb): - """ - Apply the module to `x` given `emb` timestep embeddings. - """ - -#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" -def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): - for layer in ts: - if isinstance(layer, VideoResBlock): - x = layer(x, emb, num_video_frames, image_only_indicator) - elif isinstance(layer, TimestepBlock): - x = layer(x, emb) - elif isinstance(layer, SpatialVideoTransformer): - x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) - if "transformer_index" in transformer_options: - transformer_options["transformer_index"] += 1 - elif isinstance(layer, SpatialTransformer): - x = layer(x, context, transformer_options) - if "transformer_index" in transformer_options: - transformer_options["transformer_index"] += 1 - elif isinstance(layer, Upsample): - x = layer(x, output_shape=output_shape) - else: - if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]: - found_patched = False - for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]: - if isinstance(layer, class_type): - x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator) - found_patched = True - break - if found_patched: - continue - x = layer(x) - return x - -class TimestepEmbedSequential(nn.Sequential, TimestepBlock): - """ - A sequential module that passes timestep embeddings to the children that - support it as an extra input. - """ - - def forward(self, *args, **kwargs): - return forward_timestep_embed(self, *args, **kwargs) - -class Upsample(nn.Module): - """ - An upsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - upsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - if use_conv: - self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) - - def forward(self, x, output_shape=None): - assert x.shape[1] == self.channels - if self.dims == 3: - shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] - if output_shape is not None: - shape[1] = output_shape[3] - shape[2] = output_shape[4] - else: - shape = [x.shape[2] * 2, x.shape[3] * 2] - if output_shape is not None: - shape[0] = output_shape[2] - shape[1] = output_shape[3] - - x = F.interpolate(x, size=shape, mode="nearest") - if self.use_conv: - x = self.conv(x) - return x - -class Downsample(nn.Module): - """ - A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - if use_conv: - self.op = operations.conv_nd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device - ) - else: - assert self.channels == self.out_channels - self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) - - def forward(self, x): - assert x.shape[1] == self.channels - return self.op(x) - - -class ResBlock(TimestepBlock): - """ - A residual block that can optionally change the number of channels. - :param channels: the number of input channels. - :param emb_channels: the number of timestep embedding channels. - :param dropout: the rate of dropout. - :param out_channels: if specified, the number of out channels. - :param use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param use_checkpoint: if True, use gradient checkpointing on this module. - :param up: if True, use this block for upsampling. - :param down: if True, use this block for downsampling. - """ - - def __init__( - self, - channels, - emb_channels, - dropout, - out_channels=None, - use_conv=False, - use_scale_shift_norm=False, - dims=2, - use_checkpoint=False, - up=False, - down=False, - kernel_size=3, - exchange_temb_dims=False, - skip_t_emb=False, - dtype=None, - device=None, - operations=ops - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_checkpoint = use_checkpoint - self.use_scale_shift_norm = use_scale_shift_norm - self.exchange_temb_dims = exchange_temb_dims - - if isinstance(kernel_size, list): - padding = [k // 2 for k in kernel_size] - else: - padding = kernel_size // 2 - - self.in_layers = nn.Sequential( - operations.GroupNorm(32, channels, dtype=dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), - ) - - self.updown = up or down - - if up: - self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) - self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) - elif down: - self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) - self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) - else: - self.h_upd = self.x_upd = nn.Identity() - - self.skip_t_emb = skip_t_emb - if self.skip_t_emb: - self.emb_layers = None - self.exchange_temb_dims = False - else: - self.emb_layers = nn.Sequential( - nn.SiLU(), - operations.Linear( - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device - ), - ) - self.out_layers = nn.Sequential( - operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), - nn.SiLU(), - nn.Dropout(p=dropout), - operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) - , - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - elif use_conv: - self.skip_connection = operations.conv_nd( - dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device - ) - else: - self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) - - def forward(self, x, emb): - """ - Apply the block to a Tensor, conditioned on a timestep embedding. - :param x: an [N x C x ...] Tensor of features. - :param emb: an [N x emb_channels] Tensor of timestep embeddings. - :return: an [N x C x ...] Tensor of outputs. - """ - return checkpoint( - self._forward, (x, emb), self.parameters(), self.use_checkpoint - ) - - - def _forward(self, x, emb): - if self.updown: - in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] - h = in_rest(x) - h = self.h_upd(h) - x = self.x_upd(x) - h = in_conv(h) - else: - h = self.in_layers(x) - - emb_out = None - if not self.skip_t_emb: - emb_out = self.emb_layers(emb).type(h.dtype) - while len(emb_out.shape) < len(h.shape): - emb_out = emb_out[..., None] - if self.use_scale_shift_norm: - out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - h = out_norm(h) - if emb_out is not None: - scale, shift = th.chunk(emb_out, 2, dim=1) - h *= (1 + scale) - h += shift - h = out_rest(h) - else: - if emb_out is not None: - if self.exchange_temb_dims: - emb_out = emb_out.movedim(1, 2) - h = h + emb_out - h = self.out_layers(h) - return self.skip_connection(x) + h - - -class VideoResBlock(ResBlock): - def __init__( - self, - channels: int, - emb_channels: int, - dropout: float, - video_kernel_size=3, - merge_strategy: str = "fixed", - merge_factor: float = 0.5, - out_channels=None, - use_conv: bool = False, - use_scale_shift_norm: bool = False, - dims: int = 2, - use_checkpoint: bool = False, - up: bool = False, - down: bool = False, - dtype=None, - device=None, - operations=ops - ): - super().__init__( - channels, - emb_channels, - dropout, - out_channels=out_channels, - use_conv=use_conv, - use_scale_shift_norm=use_scale_shift_norm, - dims=dims, - use_checkpoint=use_checkpoint, - up=up, - down=down, - dtype=dtype, - device=device, - operations=operations - ) - - self.time_stack = ResBlock( - default(out_channels, channels), - emb_channels, - dropout=dropout, - dims=3, - out_channels=default(out_channels, channels), - use_scale_shift_norm=False, - use_conv=False, - up=False, - down=False, - kernel_size=video_kernel_size, - use_checkpoint=use_checkpoint, - exchange_temb_dims=True, - dtype=dtype, - device=device, - operations=operations - ) - self.time_mixer = AlphaBlender( - alpha=merge_factor, - merge_strategy=merge_strategy, - rearrange_pattern="b t -> b 1 t 1 1", - ) - - def forward( - self, - x: th.Tensor, - emb: th.Tensor, - num_video_frames: int, - image_only_indicator = None, - ) -> th.Tensor: - x = super().forward(x, emb) - - x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) - x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) - - x = self.time_stack( - x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) - ) - x = self.time_mixer( - x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator - ) - x = rearrange(x, "b c t h w -> (b t) c h w") - return x - - -class Timestep(nn.Module): - def __init__(self, dim): - super().__init__() - self.dim = dim - - def forward(self, t): - return timestep_embedding(t, self.dim) - -def apply_control(h, control, name): - if control is not None and name in control and len(control[name]) > 0: - ctrl = control[name].pop() - if ctrl is not None: - try: - h += ctrl - except: - logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape)) - return h - -class UNetModel(nn.Module): - """ - The full UNet model with attention and timestep embedding. - :param in_channels: channels in the input Tensor. - :param model_channels: base channel count for the model. - :param out_channels: channels in the output Tensor. - :param num_res_blocks: number of residual blocks per downsample. - :param dropout: the dropout probability. - :param channel_mult: channel multiplier for each level of the UNet. - :param conv_resample: if True, use learned convolutions for upsampling and - downsampling. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param num_classes: if specified (as an int), then this model will be - class-conditional with `num_classes` classes. - :param use_checkpoint: use gradient checkpointing to reduce memory usage. - :param num_heads: the number of attention heads in each attention layer. - :param num_heads_channels: if specified, ignore num_heads and instead use - a fixed channel width per attention head. - :param num_heads_upsample: works with num_heads to set a different number - of heads for upsampling. Deprecated. - :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. - :param resblock_updown: use residual blocks for up/downsampling. - :param use_new_attention_order: use a different attention pattern for potentially - increased efficiency. - """ - - def __init__( - self, - image_size, - in_channels, - model_channels, - out_channels, - num_res_blocks, - dropout=0, - channel_mult=(1, 2, 4, 8), - conv_resample=True, - dims=2, - num_classes=None, - use_checkpoint=False, - dtype=th.float32, - num_heads=-1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - use_new_attention_order=False, - use_spatial_transformer=False, # custom transformer support - transformer_depth=1, # custom transformer support - context_dim=None, # custom transformer support - n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model - legacy=True, - disable_self_attentions=None, - num_attention_blocks=None, - disable_middle_self_attn=False, - use_linear_in_transformer=False, - adm_in_channels=None, - transformer_depth_middle=None, - transformer_depth_output=None, - use_temporal_resblock=False, - use_temporal_attention=False, - time_context_dim=None, - extra_ff_mix_layer=False, - use_spatial_context=False, - merge_strategy=None, - merge_factor=0.0, - video_kernel_size=None, - disable_temporal_crossattention=False, - max_ddpm_temb_period=10000, - attn_precision=None, - device=None, - operations=ops, - ): - super().__init__() - - if context_dim is not None: - assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' - # from omegaconf.listconfig import ListConfig - # if type(context_dim) == ListConfig: - # context_dim = list(context_dim) - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - if num_heads == -1: - assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' - - if num_head_channels == -1: - assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' - - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - - if isinstance(num_res_blocks, int): - self.num_res_blocks = len(channel_mult) * [num_res_blocks] - else: - if len(num_res_blocks) != len(channel_mult): - raise ValueError("provide num_res_blocks either as an int (globally constant) or " - "as a list/tuple (per-level) with the same length as channel_mult") - self.num_res_blocks = num_res_blocks - - if disable_self_attentions is not None: - # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not - assert len(disable_self_attentions) == len(channel_mult) - if num_attention_blocks is not None: - assert len(num_attention_blocks) == len(self.num_res_blocks) - - transformer_depth = transformer_depth[:] - transformer_depth_output = transformer_depth_output[:] - - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.num_classes = num_classes - self.use_checkpoint = use_checkpoint - self.dtype = dtype - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.use_temporal_resblocks = use_temporal_resblock - self.predict_codebook_ids = n_embed is not None - - self.default_num_video_frames = None - - time_embed_dim = model_channels * 4 - self.time_embed = nn.Sequential( - operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), - nn.SiLU(), - operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), - ) - - if self.num_classes is not None: - if isinstance(self.num_classes, int): - self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) - elif self.num_classes == "continuous": - logging.debug("setting up linear c_adm embedding layer") - self.label_emb = nn.Linear(1, time_embed_dim) - elif self.num_classes == "sequential": - assert adm_in_channels is not None - self.label_emb = nn.Sequential( - nn.Sequential( - operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), - nn.SiLU(), - operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), - ) - ) - else: - raise ValueError() - - self.input_blocks = nn.ModuleList( - [ - TimestepEmbedSequential( - operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) - ) - ] - ) - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - - def get_attention_layer( - ch, - num_heads, - dim_head, - depth=1, - context_dim=None, - use_checkpoint=False, - disable_self_attn=False, - ): - if use_temporal_attention: - return SpatialVideoTransformer( - ch, - num_heads, - dim_head, - depth=depth, - context_dim=context_dim, - time_context_dim=time_context_dim, - dropout=dropout, - ff_in=extra_ff_mix_layer, - use_spatial_context=use_spatial_context, - merge_strategy=merge_strategy, - merge_factor=merge_factor, - checkpoint=use_checkpoint, - use_linear=use_linear_in_transformer, - disable_self_attn=disable_self_attn, - disable_temporal_crossattention=disable_temporal_crossattention, - max_time_embed_period=max_ddpm_temb_period, - attn_precision=attn_precision, - dtype=self.dtype, device=device, operations=operations - ) - else: - return SpatialTransformer( - ch, num_heads, dim_head, depth=depth, context_dim=context_dim, - disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, - use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations - ) - - def get_resblock( - merge_factor, - merge_strategy, - video_kernel_size, - ch, - time_embed_dim, - dropout, - out_channels, - dims, - use_checkpoint, - use_scale_shift_norm, - down=False, - up=False, - dtype=None, - device=None, - operations=ops - ): - if self.use_temporal_resblocks: - return VideoResBlock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - channels=ch, - emb_channels=time_embed_dim, - dropout=dropout, - out_channels=out_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=down, - up=up, - dtype=dtype, - device=device, - operations=operations - ) - else: - return ResBlock( - channels=ch, - emb_channels=time_embed_dim, - dropout=dropout, - out_channels=out_channels, - use_checkpoint=use_checkpoint, - dims=dims, - use_scale_shift_norm=use_scale_shift_norm, - down=down, - up=up, - dtype=dtype, - device=device, - operations=operations - ) - - for level, mult in enumerate(channel_mult): - for nr in range(self.num_res_blocks[level]): - layers = [ - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=mult * model_channels, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations, - ) - ] - ch = mult * model_channels - num_transformers = transformer_depth.pop(0) - if num_transformers > 0: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - if exists(disable_self_attentions): - disabled_sa = disable_self_attentions[level] - else: - disabled_sa = False - - if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: - layers.append(get_attention_layer( - ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, - disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) - ) - self.input_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - self.input_blocks.append( - TimestepEmbedSequential( - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - dtype=self.dtype, - device=device, - operations=operations - ) - if resblock_updown - else Downsample( - ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations - ) - ) - ) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - mid_block = [ - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=None, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations - )] - - self.middle_block = None - if transformer_depth_middle >= -1: - if transformer_depth_middle >= 0: - mid_block += [get_attention_layer( # always uses a self-attn - ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, - disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint - ), - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=None, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations - )] - self.middle_block = TimestepEmbedSequential(*mid_block) - self._feature_size += ch - - self.output_blocks = nn.ModuleList([]) - for level, mult in list(enumerate(channel_mult))[::-1]: - for i in range(self.num_res_blocks[level] + 1): - ich = input_block_chans.pop() - layers = [ - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch + ich, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=model_channels * mult, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - dtype=self.dtype, - device=device, - operations=operations - ) - ] - ch = model_channels * mult - num_transformers = transformer_depth_output.pop() - if num_transformers > 0: - if num_head_channels == -1: - dim_head = ch // num_heads - else: - num_heads = ch // num_head_channels - dim_head = num_head_channels - if legacy: - #num_heads = 1 - dim_head = ch // num_heads if use_spatial_transformer else num_head_channels - if exists(disable_self_attentions): - disabled_sa = disable_self_attentions[level] - else: - disabled_sa = False - - if not exists(num_attention_blocks) or i < num_attention_blocks[level]: - layers.append( - get_attention_layer( - ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, - disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint - ) - ) - if level and i == self.num_res_blocks[level]: - out_ch = ch - layers.append( - get_resblock( - merge_factor=merge_factor, - merge_strategy=merge_strategy, - video_kernel_size=video_kernel_size, - ch=ch, - time_embed_dim=time_embed_dim, - dropout=dropout, - out_channels=out_ch, - dims=dims, - use_checkpoint=use_checkpoint, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - dtype=self.dtype, - device=device, - operations=operations - ) - if resblock_updown - else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) - ) - ds //= 2 - self.output_blocks.append(TimestepEmbedSequential(*layers)) - self._feature_size += ch - - self.out = nn.Sequential( - operations.GroupNorm(32, ch, dtype=self.dtype, device=device), - nn.SiLU(), - operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device), - ) - if self.predict_codebook_ids: - self.id_predictor = nn.Sequential( - operations.GroupNorm(32, ch, dtype=self.dtype, device=device), - operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), - #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits - ) - - def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timesteps, context, y, control, transformer_options, **kwargs) - - def _forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): - """ - Apply the model to an input batch. - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param context: conditioning plugged in via crossattn - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - transformer_options["original_shape"] = list(x.shape) - transformer_options["transformer_index"] = 0 - transformer_patches = transformer_options.get("patches", {}) - - num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) - image_only_indicator = kwargs.get("image_only_indicator", None) - time_context = kwargs.get("time_context", None) - - assert (y is not None) == ( - self.num_classes is not None - ), "must specify y if and only if the model is class-conditional" - hs = [] - t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) - emb = self.time_embed(t_emb) - - if "emb_patch" in transformer_patches: - patch = transformer_patches["emb_patch"] - for p in patch: - emb = p(emb, self.model_channels, transformer_options) - - if self.num_classes is not None: - assert y.shape[0] == x.shape[0] - emb = emb + self.label_emb(y) - - h = x - for id, module in enumerate(self.input_blocks): - transformer_options["block"] = ("input", id) - h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) - h = apply_control(h, control, 'input') - if "input_block_patch" in transformer_patches: - patch = transformer_patches["input_block_patch"] - for p in patch: - h = p(h, transformer_options) - - hs.append(h) - if "input_block_patch_after_skip" in transformer_patches: - patch = transformer_patches["input_block_patch_after_skip"] - for p in patch: - h = p(h, transformer_options) - - transformer_options["block"] = ("middle", 0) - if self.middle_block is not None: - h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) - h = apply_control(h, control, 'middle') - - - for id, module in enumerate(self.output_blocks): - transformer_options["block"] = ("output", id) - hsp = hs.pop() - hsp = apply_control(hsp, control, 'output') - - if "output_block_patch" in transformer_patches: - patch = transformer_patches["output_block_patch"] - for p in patch: - h, hsp = p(h, hsp, transformer_options) - - h = th.cat([h, hsp], dim=1) - del hsp - if len(hs) > 0: - output_shape = hs[-1].shape - else: - output_shape = None - h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) - h = h.type(x.dtype) - if self.predict_codebook_ids: - return self.id_predictor(h) - else: - return self.out(h) diff --git a/comfy/ldm/modules/diffusionmodules/upscaling.py b/comfy/ldm/modules/diffusionmodules/upscaling.py deleted file mode 100644 index 9dbf1fe7b93b0c7f1a9e8cbf8e89d0b33c282a08..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/diffusionmodules/upscaling.py +++ /dev/null @@ -1,84 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np -from functools import partial - -from .util import extract_into_tensor, make_beta_schedule - - -class AbstractLowScaleModel(nn.Module): - # for concatenating a downsampled image to the latent representation - def __init__(self, noise_schedule_config=None): - super(AbstractLowScaleModel, self).__init__() - if noise_schedule_config is not None: - self.register_schedule(**noise_schedule_config) - - def register_schedule(self, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, - cosine_s=cosine_s) - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - def q_sample(self, x_start, t, noise=None, seed=None): - if noise is None: - if seed is None: - noise = torch.randn_like(x_start) - else: - noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) - return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) - - def forward(self, x): - return x, None - - def decode(self, x): - return x - - -class SimpleImageConcat(AbstractLowScaleModel): - # no noise level conditioning - def __init__(self): - super(SimpleImageConcat, self).__init__(noise_schedule_config=None) - self.max_noise_level = 0 - - def forward(self, x): - # fix to constant noise level - return x, torch.zeros(x.shape[0], device=x.device).long() - - -class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): - def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): - super().__init__(noise_schedule_config=noise_schedule_config) - self.max_noise_level = max_noise_level - - def forward(self, x, noise_level=None, seed=None): - if noise_level is None: - noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() - else: - assert isinstance(noise_level, torch.Tensor) - z = self.q_sample(x, noise_level, seed=seed) - return z, noise_level - - - diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py deleted file mode 100644 index 233011dc9524c3d5258c69c673385aaf45329c81..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/diffusionmodules/util.py +++ /dev/null @@ -1,306 +0,0 @@ -# adopted from -# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py -# and -# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py -# and -# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py -# -# thanks! - - -import math -import logging -import torch -import torch.nn as nn -import numpy as np -from einops import repeat, rearrange - -from comfy.ldm.util import instantiate_from_config - -class AlphaBlender(nn.Module): - strategies = ["learned", "fixed", "learned_with_images"] - - def __init__( - self, - alpha: float, - merge_strategy: str = "learned_with_images", - rearrange_pattern: str = "b t -> (b t) 1 1", - ): - super().__init__() - self.merge_strategy = merge_strategy - self.rearrange_pattern = rearrange_pattern - - assert ( - merge_strategy in self.strategies - ), f"merge_strategy needs to be in {self.strategies}" - - if self.merge_strategy == "fixed": - self.register_buffer("mix_factor", torch.Tensor([alpha])) - elif ( - self.merge_strategy == "learned" - or self.merge_strategy == "learned_with_images" - ): - self.register_parameter( - "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) - ) - else: - raise ValueError(f"unknown merge strategy {self.merge_strategy}") - - def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor: - # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) - if self.merge_strategy == "fixed": - # make shape compatible - # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) - alpha = self.mix_factor.to(device) - elif self.merge_strategy == "learned": - alpha = torch.sigmoid(self.mix_factor.to(device)) - # make shape compatible - # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) - elif self.merge_strategy == "learned_with_images": - if image_only_indicator is None: - alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1") - else: - alpha = torch.where( - image_only_indicator.bool(), - torch.ones(1, 1, device=image_only_indicator.device), - rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), - ) - alpha = rearrange(alpha, self.rearrange_pattern) - # make shape compatible - # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) - else: - raise NotImplementedError() - return alpha - - def forward( - self, - x_spatial, - x_temporal, - image_only_indicator=None, - ) -> torch.Tensor: - alpha = self.get_alpha(image_only_indicator, x_spatial.device) - x = ( - alpha.to(x_spatial.dtype) * x_spatial - + (1.0 - alpha).to(x_spatial.dtype) * x_temporal - ) - return x - - -def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): - if schedule == "linear": - betas = ( - torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 - ) - - elif schedule == "cosine": - timesteps = ( - torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s - ) - alphas = timesteps / (1 + cosine_s) * np.pi / 2 - alphas = torch.cos(alphas).pow(2) - alphas = alphas / alphas[0] - betas = 1 - alphas[1:] / alphas[:-1] - betas = torch.clamp(betas, min=0, max=0.999) - - elif schedule == "squaredcos_cap_v2": # used for karlo prior - # return early - return betas_for_alpha_bar( - n_timestep, - lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, - ) - - elif schedule == "sqrt_linear": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) - elif schedule == "sqrt": - betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 - else: - raise ValueError(f"schedule '{schedule}' unknown.") - return betas - - -def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): - if ddim_discr_method == 'uniform': - c = num_ddpm_timesteps // num_ddim_timesteps - ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) - elif ddim_discr_method == 'quad': - ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) - else: - raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') - - # assert ddim_timesteps.shape[0] == num_ddim_timesteps - # add one to get the final alpha values right (the ones from first scale to data during sampling) - steps_out = ddim_timesteps + 1 - if verbose: - logging.info(f'Selected timesteps for ddim sampler: {steps_out}') - return steps_out - - -def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): - # select alphas for computing the variance schedule - alphas = alphacums[ddim_timesteps] - alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) - - # according the the formula provided in https://arxiv.org/abs/2010.02502 - sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) - if verbose: - logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') - logging.info(f'For the chosen value of eta, which is {eta}, ' - f'this results in the following sigma_t schedule for ddim sampler {sigmas}') - return sigmas, alphas, alphas_prev - - -def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, - which defines the cumulative product of (1-beta) over time from t = [0,1]. - :param num_diffusion_timesteps: the number of betas to produce. - :param alpha_bar: a lambda that takes an argument t from 0 to 1 and - produces the cumulative product of (1-beta) up to that - part of the diffusion process. - :param max_beta: the maximum beta to use; use values lower than 1 to - prevent singularities. - """ - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) - return np.array(betas) - - -def extract_into_tensor(a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - - -def checkpoint(func, inputs, params, flag): - """ - Evaluate a function without caching intermediate activations, allowing for - reduced memory at the expense of extra compute in the backward pass. - :param func: the function to evaluate. - :param inputs: the argument sequence to pass to `func`. - :param params: a sequence of parameters `func` depends on but does not - explicitly take as arguments. - :param flag: if False, disable gradient checkpointing. - """ - if flag: - args = tuple(inputs) + tuple(params) - return CheckpointFunction.apply(func, len(inputs), *args) - else: - return func(*inputs) - - -class CheckpointFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, run_function, length, *args): - ctx.run_function = run_function - ctx.input_tensors = list(args[:length]) - ctx.input_params = list(args[length:]) - ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), - "dtype": torch.get_autocast_gpu_dtype(), - "cache_enabled": torch.is_autocast_cache_enabled()} - with torch.no_grad(): - output_tensors = ctx.run_function(*ctx.input_tensors) - return output_tensors - - @staticmethod - def backward(ctx, *output_grads): - ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] - with torch.enable_grad(), \ - torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): - # Fixes a bug where the first op in run_function modifies the - # Tensor storage in place, which is not allowed for detach()'d - # Tensors. - shallow_copies = [x.view_as(x) for x in ctx.input_tensors] - output_tensors = ctx.run_function(*shallow_copies) - input_grads = torch.autograd.grad( - output_tensors, - ctx.input_tensors + ctx.input_params, - output_grads, - allow_unused=True, - ) - del ctx.input_tensors - del ctx.input_params - del output_tensors - return (None, None) + input_grads - - -def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): - """ - Create sinusoidal timestep embeddings. - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - if not repeat_only: - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half - ) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - else: - embedding = repeat(timesteps, 'b -> b d', d=dim) - return embedding - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def mean_flat(tensor): - """ - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -class HybridConditioner(nn.Module): - - def __init__(self, c_concat_config, c_crossattn_config): - super().__init__() - self.concat_conditioner = instantiate_from_config(c_concat_config) - self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) - - def forward(self, c_concat, c_crossattn): - c_concat = self.concat_conditioner(c_concat) - c_crossattn = self.crossattn_conditioner(c_crossattn) - return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} - - -def noise_like(shape, device, repeat=False): - repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) - noise = lambda: torch.randn(shape, device=device) - return repeat_noise() if repeat else noise() diff --git a/comfy/ldm/modules/distributions/.DS_Store b/comfy/ldm/modules/distributions/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/modules/distributions/.DS_Store and /dev/null differ diff --git a/comfy/ldm/modules/distributions/__init__.py b/comfy/ldm/modules/distributions/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/comfy/ldm/modules/distributions/distributions.py b/comfy/ldm/modules/distributions/distributions.py deleted file mode 100644 index df987c5ec3faaba7da2b02029ad413134bfa08ad..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/distributions/distributions.py +++ /dev/null @@ -1,92 +0,0 @@ -import torch -import numpy as np - - -class AbstractDistribution: - def sample(self): - raise NotImplementedError() - - def mode(self): - raise NotImplementedError() - - -class DiracDistribution(AbstractDistribution): - def __init__(self, value): - self.value = value - - def sample(self): - return self.value - - def mode(self): - return self.value - - -class DiagonalGaussianDistribution(object): - def __init__(self, parameters, deterministic=False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - if self.deterministic: - self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device) - - def sample(self): - x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device) - return x - - def kl(self, other=None): - if self.deterministic: - return torch.Tensor([0.]) - else: - if other is None: - return 0.5 * torch.sum(torch.pow(self.mean, 2) - + self.var - 1.0 - self.logvar, - dim=[1, 2, 3]) - else: - return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var - + self.var / other.var - 1.0 - self.logvar + other.logvar, - dim=[1, 2, 3]) - - def nll(self, sample, dims=[1,2,3]): - if self.deterministic: - return torch.Tensor([0.]) - logtwopi = np.log(2.0 * np.pi) - return 0.5 * torch.sum( - logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, - dim=dims) - - def mode(self): - return self.mean - - -def normal_kl(mean1, logvar1, mean2, logvar2): - """ - source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 - Compute the KL divergence between two gaussians. - Shapes are automatically broadcasted, so batches can be compared to - scalars, among other use cases. - """ - tensor = None - for obj in (mean1, logvar1, mean2, logvar2): - if isinstance(obj, torch.Tensor): - tensor = obj - break - assert tensor is not None, "at least one argument must be a Tensor" - - # Force variances to be Tensors. Broadcasting helps convert scalars to - # Tensors, but it does not work for torch.exp(). - logvar1, logvar2 = [ - x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) - for x in (logvar1, logvar2) - ] - - return 0.5 * ( - -1.0 - + logvar2 - - logvar1 - + torch.exp(logvar1 - logvar2) - + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) - ) diff --git a/comfy/ldm/modules/ema.py b/comfy/ldm/modules/ema.py deleted file mode 100644 index bded25019b9bcbcd0260f0b8185f8c7859ca58c4..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/ema.py +++ /dev/null @@ -1,80 +0,0 @@ -import torch -from torch import nn - - -class LitEma(nn.Module): - def __init__(self, model, decay=0.9999, use_num_upates=True): - super().__init__() - if decay < 0.0 or decay > 1.0: - raise ValueError('Decay must be between 0 and 1') - - self.m_name2s_name = {} - self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) - self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates - else torch.tensor(-1, dtype=torch.int)) - - for name, p in model.named_parameters(): - if p.requires_grad: - # remove as '.'-character is not allowed in buffers - s_name = name.replace('.', '') - self.m_name2s_name.update({name: s_name}) - self.register_buffer(s_name, p.clone().detach().data) - - self.collected_params = [] - - def reset_num_updates(self): - del self.num_updates - self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) - - def forward(self, model): - decay = self.decay - - if self.num_updates >= 0: - self.num_updates += 1 - decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) - - one_minus_decay = 1.0 - decay - - with torch.no_grad(): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - - for key in m_param: - if m_param[key].requires_grad: - sname = self.m_name2s_name[key] - shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) - shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) - else: - assert not key in self.m_name2s_name - - def copy_to(self, model): - m_param = dict(model.named_parameters()) - shadow_params = dict(self.named_buffers()) - for key in m_param: - if m_param[key].requires_grad: - m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) - else: - assert not key in self.m_name2s_name - - def store(self, parameters): - """ - Save the current parameters for restoring later. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - temporarily stored. - """ - self.collected_params = [param.clone() for param in parameters] - - def restore(self, parameters): - """ - Restore the parameters stored with the `store` method. - Useful to validate the model with EMA parameters without affecting the - original optimization process. Store the parameters before the - `copy_to` method. After validation (or model saving), use this to - restore the former parameters. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - updated with the stored parameters. - """ - for c_param, param in zip(self.collected_params, parameters): - param.data.copy_(c_param.data) diff --git a/comfy/ldm/modules/encoders/.DS_Store b/comfy/ldm/modules/encoders/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/modules/encoders/.DS_Store and /dev/null differ diff --git a/comfy/ldm/modules/encoders/__init__.py b/comfy/ldm/modules/encoders/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py deleted file mode 100644 index a5d8660301636fde75808cba50afa539cf1162e0..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/encoders/noise_aug_modules.py +++ /dev/null @@ -1,35 +0,0 @@ -from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation -from ..diffusionmodules.openaimodel import Timestep -import torch - -class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): - def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): - super().__init__(*args, **kwargs) - if clip_stats_path is None: - clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) - else: - clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") - self.register_buffer("data_mean", clip_mean[None, :], persistent=False) - self.register_buffer("data_std", clip_std[None, :], persistent=False) - self.time_embed = Timestep(timestep_dim) - - def scale(self, x): - # re-normalize to centered mean and unit variance - x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) - return x - - def unscale(self, x): - # back to original data stats - x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) - return x - - def forward(self, x, noise_level=None, seed=None): - if noise_level is None: - noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() - else: - assert isinstance(noise_level, torch.Tensor) - x = self.scale(x) - z = self.q_sample(x, noise_level, seed=seed) - z = self.unscale(z) - noise_level = self.time_embed(noise_level) - return z, noise_level diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py deleted file mode 100644 index fab145f1c208930f1d5bd385325e4e844b959aca..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/sub_quadratic_attention.py +++ /dev/null @@ -1,275 +0,0 @@ -# original source: -# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py -# license: -# MIT -# credit: -# Amin Rezaei (original author) -# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) -# implementation of: -# Self-attention Does Not Need O(n2) Memory": -# https://arxiv.org/abs/2112.05682v2 - -from functools import partial -import torch -from torch import Tensor -from torch.utils.checkpoint import checkpoint -import math -import logging - -try: - from typing import Optional, NamedTuple, List, Protocol -except ImportError: - from typing import Optional, NamedTuple, List - from typing_extensions import Protocol - -from typing import List - -from comfy import model_management - -def dynamic_slice( - x: Tensor, - starts: List[int], - sizes: List[int], -) -> Tensor: - slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes)) - return x[slicing] - -class AttnChunk(NamedTuple): - exp_values: Tensor - exp_weights_sum: Tensor - max_score: Tensor - -class SummarizeChunk(Protocol): - @staticmethod - def __call__( - query: Tensor, - key_t: Tensor, - value: Tensor, - ) -> AttnChunk: ... - -class ComputeQueryChunkAttn(Protocol): - @staticmethod - def __call__( - query: Tensor, - key_t: Tensor, - value: Tensor, - ) -> Tensor: ... - -def _summarize_chunk( - query: Tensor, - key_t: Tensor, - value: Tensor, - scale: float, - upcast_attention: bool, - mask, -) -> AttnChunk: - if upcast_attention: - with torch.autocast(enabled=False, device_type = 'cuda'): - query = query.float() - key_t = key_t.float() - attn_weights = torch.baddbmm( - torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), - query, - key_t, - alpha=scale, - beta=0, - ) - else: - attn_weights = torch.baddbmm( - torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), - query, - key_t, - alpha=scale, - beta=0, - ) - max_score, _ = torch.max(attn_weights, -1, keepdim=True) - max_score = max_score.detach() - attn_weights -= max_score - if mask is not None: - attn_weights += mask - torch.exp(attn_weights, out=attn_weights) - exp_weights = attn_weights.to(value.dtype) - exp_values = torch.bmm(exp_weights, value) - max_score = max_score.squeeze(-1) - return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) - -def _query_chunk_attention( - query: Tensor, - key_t: Tensor, - value: Tensor, - summarize_chunk: SummarizeChunk, - kv_chunk_size: int, - mask, -) -> Tensor: - batch_x_heads, k_channels_per_head, k_tokens = key_t.shape - _, _, v_channels_per_head = value.shape - - def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: - key_chunk = dynamic_slice( - key_t, - (0, 0, chunk_idx), - (batch_x_heads, k_channels_per_head, kv_chunk_size) - ) - value_chunk = dynamic_slice( - value, - (0, chunk_idx, 0), - (batch_x_heads, kv_chunk_size, v_channels_per_head) - ) - if mask is not None: - mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] - - return summarize_chunk(query, key_chunk, value_chunk, mask=mask) - - chunks: List[AttnChunk] = [ - chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) - ] - acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) - chunk_values, chunk_weights, chunk_max = acc_chunk - - global_max, _ = torch.max(chunk_max, 0, keepdim=True) - max_diffs = torch.exp(chunk_max - global_max) - chunk_values *= torch.unsqueeze(max_diffs, -1) - chunk_weights *= max_diffs - - all_values = chunk_values.sum(dim=0) - all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) - return all_values / all_weights - -# TODO: refactor CrossAttention#get_attention_scores to share code with this -def _get_attention_scores_no_kv_chunking( - query: Tensor, - key_t: Tensor, - value: Tensor, - scale: float, - upcast_attention: bool, - mask, -) -> Tensor: - if upcast_attention: - with torch.autocast(enabled=False, device_type = 'cuda'): - query = query.float() - key_t = key_t.float() - attn_scores = torch.baddbmm( - torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), - query, - key_t, - alpha=scale, - beta=0, - ) - else: - attn_scores = torch.baddbmm( - torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), - query, - key_t, - alpha=scale, - beta=0, - ) - - if mask is not None: - attn_scores += mask - try: - attn_probs = attn_scores.softmax(dim=-1) - del attn_scores - except model_management.OOM_EXCEPTION: - logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") - attn_scores -= attn_scores.max(dim=-1, keepdim=True).values # noqa: F821 attn_scores is not defined - torch.exp(attn_scores, out=attn_scores) - summed = torch.sum(attn_scores, dim=-1, keepdim=True) - attn_scores /= summed - attn_probs = attn_scores - - hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) - return hidden_states_slice - -class ScannedChunk(NamedTuple): - chunk_idx: int - attn_chunk: AttnChunk - -def efficient_dot_product_attention( - query: Tensor, - key_t: Tensor, - value: Tensor, - query_chunk_size=1024, - kv_chunk_size: Optional[int] = None, - kv_chunk_size_min: Optional[int] = None, - use_checkpoint=True, - upcast_attention=False, - mask = None, -): - """Computes efficient dot-product attention given query, transposed key, and value. - This is efficient version of attention presented in - https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. - Args: - query: queries for calculating attention with shape of - `[batch * num_heads, tokens, channels_per_head]`. - key_t: keys for calculating attention with shape of - `[batch * num_heads, channels_per_head, tokens]`. - value: values to be used in attention with shape of - `[batch * num_heads, tokens, channels_per_head]`. - query_chunk_size: int: query chunks size - kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) - kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). - use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) - Returns: - Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. - """ - batch_x_heads, q_tokens, q_channels_per_head = query.shape - _, _, k_tokens = key_t.shape - scale = q_channels_per_head ** -0.5 - - kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) - if kv_chunk_size_min is not None: - kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) - - if mask is not None and len(mask.shape) == 2: - mask = mask.unsqueeze(0) - - def get_query_chunk(chunk_idx: int) -> Tensor: - return dynamic_slice( - query, - (0, chunk_idx, 0), - (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) - ) - - def get_mask_chunk(chunk_idx: int) -> Tensor: - if mask is None: - return None - if mask.shape[1] == 1: - return mask - chunk = min(query_chunk_size, q_tokens) - return mask[:,chunk_idx:chunk_idx + chunk] - - summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) - summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk - compute_query_chunk_attn: ComputeQueryChunkAttn = partial( - _get_attention_scores_no_kv_chunking, - scale=scale, - upcast_attention=upcast_attention - ) if k_tokens <= kv_chunk_size else ( - # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) - partial( - _query_chunk_attention, - kv_chunk_size=kv_chunk_size, - summarize_chunk=summarize_chunk, - ) - ) - - if q_tokens <= query_chunk_size: - # fast-path for when there's just 1 query chunk - return compute_query_chunk_attn( - query=query, - key_t=key_t, - value=value, - mask=mask, - ) - - # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, - # and pass slices to be mutated, instead of torch.cat()ing the returned slices - res = torch.cat([ - compute_query_chunk_attn( - query=get_query_chunk(i * query_chunk_size), - key_t=key_t, - value=value, - mask=get_mask_chunk(i * query_chunk_size) - ) for i in range(math.ceil(q_tokens / query_chunk_size)) - ], dim=1) - return res diff --git a/comfy/ldm/modules/temporal_ae.py b/comfy/ldm/modules/temporal_ae.py deleted file mode 100644 index e0f78bf66b7b06b3f49f5add7a8a502384a9f445..0000000000000000000000000000000000000000 --- a/comfy/ldm/modules/temporal_ae.py +++ /dev/null @@ -1,246 +0,0 @@ -import functools -from typing import Iterable, Union - -import torch -from einops import rearrange, repeat - -import comfy.ops -ops = comfy.ops.disable_weight_init - -from .diffusionmodules.model import ( - AttnBlock, - Decoder, - ResnetBlock, -) -from .diffusionmodules.openaimodel import ResBlock, timestep_embedding -from .attention import BasicTransformerBlock - -def partialclass(cls, *args, **kwargs): - class NewCls(cls): - __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) - - return NewCls - - -class VideoResBlock(ResnetBlock): - def __init__( - self, - out_channels, - *args, - dropout=0.0, - video_kernel_size=3, - alpha=0.0, - merge_strategy="learned", - **kwargs, - ): - super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) - if video_kernel_size is None: - video_kernel_size = [3, 1, 1] - self.time_stack = ResBlock( - channels=out_channels, - emb_channels=0, - dropout=dropout, - dims=3, - use_scale_shift_norm=False, - use_conv=False, - up=False, - down=False, - kernel_size=video_kernel_size, - use_checkpoint=False, - skip_t_emb=True, - ) - - self.merge_strategy = merge_strategy - if self.merge_strategy == "fixed": - self.register_buffer("mix_factor", torch.Tensor([alpha])) - elif self.merge_strategy == "learned": - self.register_parameter( - "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) - ) - else: - raise ValueError(f"unknown merge strategy {self.merge_strategy}") - - def get_alpha(self, bs): - if self.merge_strategy == "fixed": - return self.mix_factor - elif self.merge_strategy == "learned": - return torch.sigmoid(self.mix_factor) - else: - raise NotImplementedError() - - def forward(self, x, temb, skip_video=False, timesteps=None): - b, c, h, w = x.shape - if timesteps is None: - timesteps = b - - x = super().forward(x, temb) - - if not skip_video: - x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) - - x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) - - x = self.time_stack(x, temb) - - alpha = self.get_alpha(bs=b // timesteps).to(x.device) - x = alpha * x + (1.0 - alpha) * x_mix - - x = rearrange(x, "b c t h w -> (b t) c h w") - return x - - -class AE3DConv(ops.Conv2d): - def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): - super().__init__(in_channels, out_channels, *args, **kwargs) - if isinstance(video_kernel_size, Iterable): - padding = [int(k // 2) for k in video_kernel_size] - else: - padding = int(video_kernel_size // 2) - - self.time_mix_conv = ops.Conv3d( - in_channels=out_channels, - out_channels=out_channels, - kernel_size=video_kernel_size, - padding=padding, - ) - - def forward(self, input, timesteps=None, skip_video=False): - if timesteps is None: - timesteps = input.shape[0] - x = super().forward(input) - if skip_video: - return x - x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) - x = self.time_mix_conv(x) - return rearrange(x, "b c t h w -> (b t) c h w") - - -class AttnVideoBlock(AttnBlock): - def __init__( - self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" - ): - super().__init__(in_channels) - # no context, single headed, as in base class - self.time_mix_block = BasicTransformerBlock( - dim=in_channels, - n_heads=1, - d_head=in_channels, - checkpoint=False, - ff_in=True, - ) - - time_embed_dim = self.in_channels * 4 - self.video_time_embed = torch.nn.Sequential( - ops.Linear(self.in_channels, time_embed_dim), - torch.nn.SiLU(), - ops.Linear(time_embed_dim, self.in_channels), - ) - - self.merge_strategy = merge_strategy - if self.merge_strategy == "fixed": - self.register_buffer("mix_factor", torch.Tensor([alpha])) - elif self.merge_strategy == "learned": - self.register_parameter( - "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) - ) - else: - raise ValueError(f"unknown merge strategy {self.merge_strategy}") - - def forward(self, x, timesteps=None, skip_time_block=False): - if skip_time_block: - return super().forward(x) - - if timesteps is None: - timesteps = x.shape[0] - - x_in = x - x = self.attention(x) - h, w = x.shape[2:] - x = rearrange(x, "b c h w -> b (h w) c") - - x_mix = x - num_frames = torch.arange(timesteps, device=x.device) - num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) - num_frames = rearrange(num_frames, "b t -> (b t)") - t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) - emb = self.video_time_embed(t_emb) # b, n_channels - emb = emb[:, None, :] - x_mix = x_mix + emb - - alpha = self.get_alpha().to(x.device) - x_mix = self.time_mix_block(x_mix, timesteps=timesteps) - x = alpha * x + (1.0 - alpha) * x_mix # alpha merge - - x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) - x = self.proj_out(x) - - return x_in + x - - def get_alpha( - self, - ): - if self.merge_strategy == "fixed": - return self.mix_factor - elif self.merge_strategy == "learned": - return torch.sigmoid(self.mix_factor) - else: - raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") - - - -def make_time_attn( - in_channels, - attn_type="vanilla", - attn_kwargs=None, - alpha: float = 0, - merge_strategy: str = "learned", - conv_op=ops.Conv2d, -): - return partialclass( - AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy - ) - - -class Conv2DWrapper(torch.nn.Conv2d): - def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: - return super().forward(input) - - -class VideoDecoder(Decoder): - available_time_modes = ["all", "conv-only", "attn-only"] - - def __init__( - self, - *args, - video_kernel_size: Union[int, list] = 3, - alpha: float = 0.0, - merge_strategy: str = "learned", - time_mode: str = "conv-only", - **kwargs, - ): - self.video_kernel_size = video_kernel_size - self.alpha = alpha - self.merge_strategy = merge_strategy - self.time_mode = time_mode - assert ( - self.time_mode in self.available_time_modes - ), f"time_mode parameter has to be in {self.available_time_modes}" - - if self.time_mode != "attn-only": - kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) - if self.time_mode not in ["conv-only", "only-last-conv"]: - kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) - if self.time_mode not in ["attn-only", "only-last-conv"]: - kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) - - super().__init__(*args, **kwargs) - - def get_last_layer(self, skip_time_mix=False, **kwargs): - if self.time_mode == "attn-only": - raise NotImplementedError("TODO") - else: - return ( - self.conv_out.time_mix_conv.weight - if not skip_time_mix - else self.conv_out.weight - ) diff --git a/comfy/ldm/omnigen/.DS_Store b/comfy/ldm/omnigen/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/omnigen/.DS_Store and /dev/null differ diff --git a/comfy/ldm/omnigen/omnigen2.py b/comfy/ldm/omnigen/omnigen2.py deleted file mode 100644 index 4884449f85ff9ca0565ba677bbb22f4f4d55f1d0..0000000000000000000000000000000000000000 --- a/comfy/ldm/omnigen/omnigen2.py +++ /dev/null @@ -1,469 +0,0 @@ -# Original code: https://github.com/VectorSpaceLab/OmniGen2 - -from typing import Optional, Tuple - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange, repeat -from comfy.ldm.lightricks.model import Timesteps -from comfy.ldm.flux.layers import EmbedND -from comfy.ldm.modules.attention import optimized_attention_masked -import comfy.model_management -import comfy.ldm.common_dit - - -def apply_rotary_emb(x, freqs_cis): - if x.shape[1] == 0: - return x - - t_ = x.reshape(*x.shape[:-1], -1, 1, 2) - t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] - return t_out.reshape(*x.shape).to(dtype=x.dtype) - - -def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: - return F.silu(x) * y - - -class TimestepEmbedding(nn.Module): - def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None): - super().__init__() - self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device) - self.act = nn.SiLU() - self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device) - - def forward(self, sample: torch.Tensor) -> torch.Tensor: - sample = self.linear_1(sample) - sample = self.act(sample) - sample = self.linear_2(sample) - return sample - - -class LuminaRMSNormZero(nn.Module): - def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None): - super().__init__() - self.silu = nn.SiLU() - self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device) - self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device) - - def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - emb = self.linear(self.silu(emb)) - scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1) - x = self.norm(x) * (1 + scale_msa[:, None]) - return x, gate_msa, scale_mlp, gate_mlp - - -class LuminaLayerNormContinuous(nn.Module): - def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None): - super().__init__() - self.silu = nn.SiLU() - self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device) - self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device) - self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None - - def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: - emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype)) - x = self.norm(x) * (1 + emb)[:, None, :] - if self.linear_2 is not None: - x = self.linear_2(x) - return x - - -class LuminaFeedForward(nn.Module): - def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None): - super().__init__() - inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of) - self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) - self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device) - self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - h1, h2 = self.linear_1(x), self.linear_3(x) - return self.linear_2(swiglu(h1, h2)) - - -class Lumina2CombinedTimestepCaptionEmbedding(nn.Module): - def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None): - super().__init__() - self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale) - self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations) - self.caption_embedder = nn.Sequential( - operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device), - operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device), - ) - - def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]: - timestep_proj = self.time_proj(timestep).to(dtype=dtype) - time_embed = self.timestep_embedder(timestep_proj) - caption_embed = self.caption_embedder(text_hidden_states) - return time_embed, caption_embed - - -class Attention(nn.Module): - def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None): - super().__init__() - self.heads = heads - self.kv_heads = kv_heads - self.dim_head = dim_head - self.scale = dim_head ** -0.5 - - self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device) - self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) - self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) - - self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) - self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) - - self.to_out = nn.Sequential( - operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device), - nn.Dropout(0.0) - ) - - def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor: - batch_size, sequence_length, _ = hidden_states.shape - - query = self.to_q(hidden_states) - key = self.to_k(encoder_hidden_states) - value = self.to_v(encoder_hidden_states) - - query = query.view(batch_size, -1, self.heads, self.dim_head) - key = key.view(batch_size, -1, self.kv_heads, self.dim_head) - value = value.view(batch_size, -1, self.kv_heads, self.dim_head) - - query = self.norm_q(query) - key = self.norm_k(key) - - if image_rotary_emb is not None: - query = apply_rotary_emb(query, image_rotary_emb) - key = apply_rotary_emb(key, image_rotary_emb) - - query = query.transpose(1, 2) - key = key.transpose(1, 2) - value = value.transpose(1, 2) - - if self.kv_heads < self.heads: - key = key.repeat_interleave(self.heads // self.kv_heads, dim=1) - value = value.repeat_interleave(self.heads // self.kv_heads, dim=1) - - hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True) - hidden_states = self.to_out[0](hidden_states) - return hidden_states - - -class OmniGen2TransformerBlock(nn.Module): - def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None): - super().__init__() - self.modulation = modulation - - self.attn = Attention( - query_dim=dim, - dim_head=dim // num_attention_heads, - heads=num_attention_heads, - kv_heads=num_kv_heads, - eps=1e-5, - bias=False, - dtype=dtype, device=device, operations=operations, - ) - - self.feed_forward = LuminaFeedForward( - dim=dim, - inner_dim=4 * dim, - multiple_of=multiple_of, - dtype=dtype, device=device, operations=operations - ) - - if modulation: - self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) - else: - self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) - - self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) - self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) - self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) - - def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor: - if self.modulation: - norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) - attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb) - hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output) - mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1))) - hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) - else: - norm_hidden_states = self.norm1(hidden_states) - attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb) - hidden_states = hidden_states + self.norm2(attn_output) - mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) - hidden_states = hidden_states + self.ffn_norm2(mlp_output) - return hidden_states - - -class OmniGen2RotaryPosEmbed(nn.Module): - def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2): - super().__init__() - self.theta = theta - self.axes_dim = axes_dim - self.axes_lens = axes_lens - self.patch_size = patch_size - self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim) - - def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device): - p = self.patch_size - - seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)] - - max_seq_len = max(seq_lengths) - max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len]) - max_img_len = max(l_effective_img_len) - - position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device) - - for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): - position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3") - - pe_shift = cap_seq_len - pe_shift_len = cap_seq_len - - if ref_img_sizes[i] is not None: - for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]): - H, W = ref_img_size - ref_H_tokens, ref_W_tokens = H // p, W // p - - row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten() - col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten() - position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift - position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids - position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids - - pe_shift += max(ref_H_tokens, ref_W_tokens) - pe_shift_len += ref_img_len - - H, W = img_sizes[i] - H_tokens, W_tokens = H // p, W // p - - row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten() - col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten() - - position_ids[i, pe_shift_len: seq_len, 0] = pe_shift - position_ids[i, pe_shift_len: seq_len, 1] = row_ids - position_ids[i, pe_shift_len: seq_len, 2] = col_ids - - freqs_cis = self.rope_embedder(position_ids).movedim(1, 2) - - cap_freqs_cis_shape = list(freqs_cis.shape) - cap_freqs_cis_shape[1] = encoder_seq_len - cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - ref_img_freqs_cis_shape = list(freqs_cis.shape) - ref_img_freqs_cis_shape[1] = max_ref_img_len - ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - img_freqs_cis_shape = list(freqs_cis.shape) - img_freqs_cis_shape[1] = max_img_len - img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) - - for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)): - cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len] - ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)] - img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len] - - return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths - - -class OmniGen2Transformer2DModel(nn.Module): - def __init__( - self, - patch_size: int = 2, - in_channels: int = 16, - out_channels: Optional[int] = None, - hidden_size: int = 2304, - num_layers: int = 26, - num_refiner_layers: int = 2, - num_attention_heads: int = 24, - num_kv_heads: int = 8, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - norm_eps: float = 1e-5, - axes_dim_rope: Tuple[int, int, int] = (32, 32, 32), - axes_lens: Tuple[int, int, int] = (300, 512, 512), - text_feat_dim: int = 1024, - timestep_scale: float = 1.0, - image_model=None, - device=None, - dtype=None, - operations=None, - ): - super().__init__() - - self.patch_size = patch_size - self.out_channels = out_channels or in_channels - self.hidden_size = hidden_size - self.dtype = dtype - - self.rope_embedder = OmniGen2RotaryPosEmbed( - theta=10000, - axes_dim=axes_dim_rope, - axes_lens=axes_lens, - patch_size=patch_size, - ) - - self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) - self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) - - self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding( - hidden_size=hidden_size, - text_feat_dim=text_feat_dim, - norm_eps=norm_eps, - timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations - ) - - self.noise_refiner = nn.ModuleList([ - OmniGen2TransformerBlock( - hidden_size, num_attention_heads, num_kv_heads, - multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations - ) for _ in range(num_refiner_layers) - ]) - - self.ref_image_refiner = nn.ModuleList([ - OmniGen2TransformerBlock( - hidden_size, num_attention_heads, num_kv_heads, - multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations - ) for _ in range(num_refiner_layers) - ]) - - self.context_refiner = nn.ModuleList([ - OmniGen2TransformerBlock( - hidden_size, num_attention_heads, num_kv_heads, - multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations - ) for _ in range(num_refiner_layers) - ]) - - self.layers = nn.ModuleList([ - OmniGen2TransformerBlock( - hidden_size, num_attention_heads, num_kv_heads, - multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations - ) for _ in range(num_layers) - ]) - - self.norm_out = LuminaLayerNormContinuous( - embedding_dim=hidden_size, - conditioning_embedding_dim=min(hidden_size, 1024), - elementwise_affine=False, - eps=1e-6, - out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations - ) - - self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype)) - - def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states): - batch_size = len(hidden_states) - p = self.patch_size - - img_sizes = [(img.size(1), img.size(2)) for img in hidden_states] - l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes] - - if ref_image_hidden_states is not None: - ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states)) - ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size - l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes] - else: - ref_img_sizes = [None for _ in range(batch_size)] - l_effective_ref_img_len = [[0] for _ in range(batch_size)] - - flat_ref_img_hidden_states = None - if ref_image_hidden_states is not None: - imgs = [] - for ref_img in ref_image_hidden_states: - B, C, H, W = ref_img.size() - ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) - imgs.append(ref_img) - flat_ref_img_hidden_states = torch.cat(imgs, dim=1) - - img = hidden_states - B, C, H, W = img.size() - flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) - - return ( - flat_hidden_states, flat_ref_img_hidden_states, - None, None, - l_effective_ref_img_len, l_effective_img_len, - ref_img_sizes, img_sizes, - ) - - def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb): - batch_size = len(hidden_states) - - hidden_states = self.x_embedder(hidden_states) - if ref_image_hidden_states is not None: - ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states) - image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device) - - for i in range(batch_size): - shift = 0 - for j, ref_img_len in enumerate(l_effective_ref_img_len[i]): - ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j] - shift += ref_img_len - - for layer in self.noise_refiner: - hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb) - - if ref_image_hidden_states is not None: - for layer in self.ref_image_refiner: - ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb) - - hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1) - - return hidden_states - - def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs): - B, C, H, W = x.shape - hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) - _, _, H_padded, W_padded = hidden_states.shape - timestep = 1.0 - timesteps - text_hidden_states = context - text_attention_mask = attention_mask - ref_image_hidden_states = ref_latents - device = hidden_states.device - - temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype) - - ( - hidden_states, ref_image_hidden_states, - img_mask, ref_img_mask, - l_effective_ref_img_len, l_effective_img_len, - ref_img_sizes, img_sizes, - ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states) - - ( - context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb, - rotary_emb, encoder_seq_lengths, seq_lengths, - ) = self.rope_embedder( - hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0], - l_effective_ref_img_len, l_effective_img_len, - ref_img_sizes, img_sizes, device, - ) - - for layer in self.context_refiner: - text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb) - - img_len = hidden_states.shape[1] - combined_img_hidden_states = self.img_patch_embed_and_refine( - hidden_states, ref_image_hidden_states, - img_mask, ref_img_mask, - noise_rotary_emb, ref_img_rotary_emb, - l_effective_ref_img_len, l_effective_img_len, - temb, - ) - - hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1) - attention_mask = None - - for layer in self.layers: - hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb) - - hidden_states = self.norm_out(hidden_states, temb) - - p = self.patch_size - output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W] - - return -output diff --git a/comfy/ldm/pixart/.DS_Store b/comfy/ldm/pixart/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/pixart/.DS_Store and /dev/null differ diff --git a/comfy/ldm/pixart/blocks.py b/comfy/ldm/pixart/blocks.py deleted file mode 100644 index 2225076e5754d08cd618014469c2464bad055334..0000000000000000000000000000000000000000 --- a/comfy/ldm/pixart/blocks.py +++ /dev/null @@ -1,380 +0,0 @@ -# Based on: -# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] -# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange - -from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, Mlp, timestep_embedding -from comfy.ldm.modules.attention import optimized_attention - -# if model_management.xformers_enabled(): -# import xformers.ops -# if int((xformers.__version__).split(".")[2].split("+")[0]) >= 28: -# block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens -# else: -# block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - -def t2i_modulate(x, shift, scale): - return x * (1 + scale) + shift - -class MultiHeadCrossAttention(nn.Module): - def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **kwargs): - super(MultiHeadCrossAttention, self).__init__() - assert d_model % num_heads == 0, "d_model must be divisible by num_heads" - - self.d_model = d_model - self.num_heads = num_heads - self.head_dim = d_model // num_heads - - self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device) - self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, cond, mask=None): - # query/value: img tokens; key: condition; mask: if padding tokens - B, N, C = x.shape - - q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim) - kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim) - k, v = kv.unbind(2) - - assert mask is None # TODO? - # # TODO: xformers needs separate mask logic here - # if model_management.xformers_enabled(): - # attn_bias = None - # if mask is not None: - # attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask) - # x = xformers.ops.memory_efficient_attention(q, k, v, p=0, attn_bias=attn_bias) - # else: - # q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),) - # attn_mask = None - # mask = torch.ones(()) - # if mask is not None and len(mask) > 1: - # # Create equivalent of xformer diagonal block mask, still only correct for square masks - # # But depth doesn't matter as tensors can expand in that dimension - # attn_mask_template = torch.ones( - # [q.shape[2] // B, mask[0]], - # dtype=torch.bool, - # device=q.device - # ) - # attn_mask = torch.block_diag(attn_mask_template) - # - # # create a mask on the diagonal for each mask in the batch - # for _ in range(B - 1): - # attn_mask = torch.block_diag(attn_mask, attn_mask_template) - # x = optimized_attention(q, k, v, self.num_heads, mask=attn_mask, skip_reshape=True) - - x = optimized_attention(q.view(B, -1, C), k.view(B, -1, C), v.view(B, -1, C), self.num_heads, mask=None) - x = self.proj(x) - x = self.proj_drop(x) - return x - - -class AttentionKVCompress(nn.Module): - """Multi-head Attention block with KV token compression and qk norm.""" - def __init__(self, dim, num_heads=8, qkv_bias=True, sampling='conv', sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **kwargs): - """ - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - qkv_bias (bool: If True, add a learnable bias to query, key, value. - """ - super().__init__() - assert dim % num_heads == 0, 'dim should be divisible by num_heads' - self.num_heads = num_heads - self.head_dim = dim // num_heads - self.scale = self.head_dim ** -0.5 - - self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) - self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) - - self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every'] - self.sr_ratio = sr_ratio - if sr_ratio > 1 and sampling == 'conv': - # Avg Conv Init. - self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device) - # self.sr.weight.data.fill_(1/sr_ratio**2) - # self.sr.bias.data.zero_() - self.norm = operations.LayerNorm(dim, dtype=dtype, device=device) - if qk_norm: - self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device) - self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device) - else: - self.q_norm = nn.Identity() - self.k_norm = nn.Identity() - - def downsample_2d(self, tensor, H, W, scale_factor, sampling=None): - if sampling is None or scale_factor == 1: - return tensor - B, N, C = tensor.shape - - if sampling == 'uniform_every': - return tensor[:, ::scale_factor], int(N // scale_factor) - - tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2) - new_H, new_W = int(H / scale_factor), int(W / scale_factor) - new_N = new_H * new_W - - if sampling == 'ave': - tensor = F.interpolate( - tensor, scale_factor=1 / scale_factor, mode='nearest' - ).permute(0, 2, 3, 1) - elif sampling == 'uniform': - tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1) - elif sampling == 'conv': - tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1) - tensor = self.norm(tensor) - else: - raise ValueError - - return tensor.reshape(B, new_N, C).contiguous(), new_N - - def forward(self, x, mask=None, HW=None, block_id=None): - B, N, C = x.shape # 2 4096 1152 - new_N = N - if HW is None: - H = W = int(N ** 0.5) - else: - H, W = HW - qkv = self.qkv(x).reshape(B, N, 3, C) - - q, k, v = qkv.unbind(2) - q = self.q_norm(q) - k = self.k_norm(k) - - # KV compression - if self.sr_ratio > 1: - k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling) - v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling) - - q = q.reshape(B, N, self.num_heads, C // self.num_heads) - k = k.reshape(B, new_N, self.num_heads, C // self.num_heads) - v = v.reshape(B, new_N, self.num_heads, C // self.num_heads) - - if mask is not None: - raise NotImplementedError("Attn mask logic not added for self attention") - - # This is never called at the moment - # attn_bias = None - # if mask is not None: - # attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device) - # attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf')) - - # attention 2 - q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),) - x = optimized_attention(q, k, v, self.num_heads, mask=None, skip_reshape=True) - - x = x.view(B, N, C) - x = self.proj(x) - return x - - -class FinalLayer(nn.Module): - """ - The final layer of PixArt. - """ - def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - -class T2IFinalLayer(nn.Module): - """ - The final layer of PixArt. - """ - def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5) - self.out_channels = out_channels - - def forward(self, x, t): - shift, scale = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t[:, None]).chunk(2, dim=1) - x = t2i_modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class MaskFinalLayer(nn.Module): - """ - The final layer of PixArt. - """ - def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): - super().__init__() - self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) - ) - def forward(self, x, t): - shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class DecoderLayer(nn.Module): - """ - The final layer of PixArt. - """ - def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None): - super().__init__() - self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) - ) - def forward(self, x, t): - shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) - x = modulate(self.norm_decoder(x), shift, scale) - x = self.linear(x) - return x - - -class SizeEmbedder(TimestepEmbedder): - """ - Embeds scalar timesteps into vector representations. - """ - def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): - super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations) - self.mlp = nn.Sequential( - operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), - nn.SiLU(), - operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), - ) - self.frequency_embedding_size = frequency_embedding_size - self.outdim = hidden_size - - def forward(self, s, bs): - if s.ndim == 1: - s = s[:, None] - assert s.ndim == 2 - if s.shape[0] != bs: - s = s.repeat(bs//s.shape[0], 1) - assert s.shape[0] == bs - b, dims = s.shape[0], s.shape[1] - s = rearrange(s, "b d -> (b d)") - s_freq = timestep_embedding(s, self.frequency_embedding_size) - s_emb = self.mlp(s_freq.to(s.dtype)) - s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim) - return s_emb - - -class LabelEmbedder(nn.Module): - """ - Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. - """ - def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None): - super().__init__() - use_cfg_embedding = dropout_prob > 0 - self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device), - self.num_classes = num_classes - self.dropout_prob = dropout_prob - - def token_drop(self, labels, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob - else: - drop_ids = force_drop_ids == 1 - labels = torch.where(drop_ids, self.num_classes, labels) - return labels - - def forward(self, labels, train, force_drop_ids=None): - use_dropout = self.dropout_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - labels = self.token_drop(labels, force_drop_ids) - embeddings = self.embedding_table(labels) - return embeddings - - -class CaptionEmbedder(nn.Module): - """ - Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. - """ - def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): - super().__init__() - self.y_proj = Mlp( - in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, - dtype=dtype, device=device, operations=operations, - ) - self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5)) - self.uncond_prob = uncond_prob - - def token_drop(self, caption, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob - else: - drop_ids = force_drop_ids == 1 - caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) - return caption - - def forward(self, caption, train, force_drop_ids=None): - if train: - assert caption.shape[2:] == self.y_embedding.shape - use_dropout = self.uncond_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - caption = self.token_drop(caption, force_drop_ids) - caption = self.y_proj(caption) - return caption - - -class CaptionEmbedderDoubleBr(nn.Module): - """ - Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. - """ - def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): - super().__init__() - self.proj = Mlp( - in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, - dtype=dtype, device=device, operations=operations, - ) - self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5) - self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5) - self.uncond_prob = uncond_prob - - def token_drop(self, global_caption, caption, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob - else: - drop_ids = force_drop_ids == 1 - global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption) - caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption) - return global_caption, caption - - def forward(self, caption, train, force_drop_ids=None): - assert caption.shape[2: ] == self.y_embedding.shape - global_caption = caption.mean(dim=2).squeeze() - use_dropout = self.uncond_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids) - y_embed = self.proj(global_caption) - return y_embed, caption diff --git a/comfy/ldm/pixart/pixartms.py b/comfy/ldm/pixart/pixartms.py deleted file mode 100644 index d1ac49d84c554f1531427dea251dd6d2ea3e0dc7..0000000000000000000000000000000000000000 --- a/comfy/ldm/pixart/pixartms.py +++ /dev/null @@ -1,256 +0,0 @@ -# Based on: -# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] -# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] -import torch -import torch.nn as nn - -from .blocks import ( - t2i_modulate, - CaptionEmbedder, - AttentionKVCompress, - MultiHeadCrossAttention, - T2IFinalLayer, - SizeEmbedder, -) -from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch - - -def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32): - grid_h, grid_w = torch.meshgrid( - torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation, - torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation, - indexing='ij' - ) - emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) - emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) - emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) - return emb - -class PixArtMSBlock(nn.Module): - """ - A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. - """ - def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, - sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): - super().__init__() - self.hidden_size = hidden_size - self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.attn = AttentionKVCompress( - hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, - qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs - ) - self.cross_attn = MultiHeadCrossAttention( - hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs - ) - self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - # to be compatible with lower version pytorch - approx_gelu = lambda: nn.GELU(approximate="tanh") - self.mlp = Mlp( - in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, - dtype=dtype, device=device, operations=operations - ) - self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) - - def forward(self, x, y, t, mask=None, HW=None, **kwargs): - B, N, C = x.shape - - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1) - x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) - x = x + self.cross_attn(x, y, mask) - x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) - - return x - - -### Core PixArt Model ### -class PixArtMS(nn.Module): - """ - Diffusion model with a Transformer backbone. - """ - def __init__( - self, - input_size=32, - patch_size=2, - in_channels=4, - hidden_size=1152, - depth=28, - num_heads=16, - mlp_ratio=4.0, - class_dropout_prob=0.1, - learn_sigma=True, - pred_sigma=True, - drop_path: float = 0., - caption_channels=4096, - pe_interpolation=None, - pe_precision=None, - config=None, - model_max_length=120, - micro_condition=True, - qk_norm=False, - kv_compress_config=None, - dtype=None, - device=None, - operations=None, - **kwargs, - ): - nn.Module.__init__(self) - self.dtype = dtype - self.pred_sigma = pred_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if pred_sigma else in_channels - self.patch_size = patch_size - self.num_heads = num_heads - self.pe_interpolation = pe_interpolation - self.pe_precision = pe_precision - self.hidden_size = hidden_size - self.depth = depth - - approx_gelu = lambda: nn.GELU(approximate="tanh") - self.t_block = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) - ) - self.x_embedder = PatchEmbed( - patch_size=patch_size, - in_chans=in_channels, - embed_dim=hidden_size, - bias=True, - dtype=dtype, - device=device, - operations=operations - ) - self.t_embedder = TimestepEmbedder( - hidden_size, dtype=dtype, device=device, operations=operations, - ) - self.y_embedder = CaptionEmbedder( - in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, - act_layer=approx_gelu, token_num=model_max_length, - dtype=dtype, device=device, operations=operations, - ) - - self.micro_conditioning = micro_condition - if self.micro_conditioning: - self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) - self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) - - # For fixed sin-cos embedding: - # num_patches = (input_size // patch_size) * (input_size // patch_size) - # self.base_size = input_size // self.patch_size - # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) - - drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule - if kv_compress_config is None: - kv_compress_config = { - 'sampling': None, - 'scale_factor': 1, - 'kv_compress_layer': [], - } - self.blocks = nn.ModuleList([ - PixArtMSBlock( - hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], - sampling=kv_compress_config['sampling'], - sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, - qk_norm=qk_norm, - dtype=dtype, - device=device, - operations=operations, - ) - for i in range(depth) - ]) - self.final_layer = T2IFinalLayer( - hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations - ) - - def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs): - """ - Original forward pass of PixArt. - x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) - t: (N,) tensor of diffusion timesteps - y: (N, 1, 120, C) conditioning - ar: (N, 1): aspect ratio - cs: (N ,2) size conditioning for height/width - """ - B, C, H, W = x.shape - c_res = (H + W) // 2 - pe_interpolation = self.pe_interpolation - if pe_interpolation is None or self.pe_precision is not None: - # calculate pe_interpolation on-the-fly - pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0) - - pos_embed = get_2d_sincos_pos_embed_torch( - self.hidden_size, - h=(H // self.patch_size), - w=(W // self.patch_size), - pe_interpolation=pe_interpolation, - base_size=((round(c_res / 64) * 64) // self.patch_size), - device=x.device, - dtype=x.dtype, - ).unsqueeze(0) - - x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 - t = self.t_embedder(timestep, x.dtype) # (N, D) - - if self.micro_conditioning and (c_size is not None and c_ar is not None): - bs = x.shape[0] - c_size = self.csize_embedder(c_size, bs) # (N, D) - c_ar = self.ar_embedder(c_ar, bs) # (N, D) - t = t + torch.cat([c_size, c_ar], dim=1) - - t0 = self.t_block(t) - y = self.y_embedder(y, self.training) # (N, D) - - if mask is not None: - if mask.shape[0] != y.shape[0]: - mask = mask.repeat(y.shape[0] // mask.shape[0], 1) - mask = mask.squeeze(1).squeeze(1) - y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) - y_lens = mask.sum(dim=1).tolist() - else: - y_lens = None - y = y.squeeze(1).view(1, -1, x.shape[-1]) - for block in self.blocks: - x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D) - - x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) - x = self.unpatchify(x, H, W) # (N, out_channels, H, W) - - return x - - def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs): - B, C, H, W = x.shape - - # Fallback for missing microconds - if self.micro_conditioning: - if c_size is None: - c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1) - - if c_ar is None: - c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1) - - ## Still accepts the input w/o that dim but returns garbage - if len(context.shape) == 3: - context = context.unsqueeze(1) - - ## run original forward pass - out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar) - - ## only return EPS - if self.pred_sigma: - return out[:, :self.in_channels] - return out - - def unpatchify(self, x, h, w): - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - c = self.out_channels - p = self.x_embedder.patch_size[0] - h = h // self.patch_size - w = w // self.patch_size - assert h * w == x.shape[1] - - x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) - x = torch.einsum('nhwpqc->nchpwq', x) - imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) - return imgs diff --git a/comfy/ldm/qwen_image/.DS_Store b/comfy/ldm/qwen_image/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/qwen_image/.DS_Store and /dev/null differ diff --git a/comfy/ldm/qwen_image/controlnet.py b/comfy/ldm/qwen_image/controlnet.py deleted file mode 100644 index 92ac3cf0afd6518df1106b1acb9b80a935412ef5..0000000000000000000000000000000000000000 --- a/comfy/ldm/qwen_image/controlnet.py +++ /dev/null @@ -1,77 +0,0 @@ -import torch -import math - -from .model import QwenImageTransformer2DModel - - -class QwenImageControlNetModel(QwenImageTransformer2DModel): - def __init__( - self, - extra_condition_channels=0, - dtype=None, - device=None, - operations=None, - **kwargs - ): - super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) - self.main_model_double = 60 - - # controlnet_blocks - self.controlnet_blocks = torch.nn.ModuleList([]) - for _ in range(len(self.transformer_blocks)): - self.controlnet_blocks.append(operations.Linear(self.inner_dim, self.inner_dim, device=device, dtype=dtype)) - self.controlnet_x_embedder = operations.Linear(self.in_channels + extra_condition_channels, self.inner_dim, device=device, dtype=dtype) - - def forward( - self, - x, - timesteps, - context, - attention_mask=None, - guidance: torch.Tensor = None, - ref_latents=None, - hint=None, - transformer_options={}, - **kwargs - ): - timestep = timesteps - encoder_hidden_states = context - encoder_hidden_states_mask = attention_mask - - hidden_states, img_ids, orig_shape = self.process_img(x) - hint, _, _ = self.process_img(hint) - - txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2)) - txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) - ids = torch.cat((txt_ids, img_ids), dim=1) - image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype) - del ids, txt_ids, img_ids - - hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint) - encoder_hidden_states = self.txt_norm(encoder_hidden_states) - encoder_hidden_states = self.txt_in(encoder_hidden_states) - - if guidance is not None: - guidance = guidance * 1000 - - temb = ( - self.time_text_embed(timestep, hidden_states) - if guidance is None - else self.time_text_embed(timestep, guidance, hidden_states) - ) - - repeat = math.ceil(self.main_model_double / len(self.controlnet_blocks)) - - controlnet_block_samples = () - for i, block in enumerate(self.transformer_blocks): - encoder_hidden_states, hidden_states = block( - hidden_states=hidden_states, - encoder_hidden_states=encoder_hidden_states, - encoder_hidden_states_mask=encoder_hidden_states_mask, - temb=temb, - image_rotary_emb=image_rotary_emb, - ) - - controlnet_block_samples = controlnet_block_samples + (self.controlnet_blocks[i](hidden_states),) * repeat - - return {"input": controlnet_block_samples[:self.main_model_double]} diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py deleted file mode 100644 index 04071f31ce59f31fce7739e80ec8cddc59ffa8e4..0000000000000000000000000000000000000000 --- a/comfy/ldm/qwen_image/model.py +++ /dev/null @@ -1,469 +0,0 @@ -# https://github.com/QwenLM/Qwen-Image (Apache 2.0) -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Optional, Tuple -from einops import repeat - -from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps -from comfy.ldm.modules.attention import optimized_attention_masked -from comfy.ldm.flux.layers import EmbedND -import comfy.ldm.common_dit -import comfy.patcher_extension - -class GELU(nn.Module): - def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None): - super().__init__() - self.proj = operations.Linear(dim_in, dim_out, bias=bias, dtype=dtype, device=device) - self.approximate = approximate - - def forward(self, hidden_states): - hidden_states = self.proj(hidden_states) - hidden_states = F.gelu(hidden_states, approximate=self.approximate) - return hidden_states - - -class FeedForward(nn.Module): - def __init__( - self, - dim: int, - dim_out: Optional[int] = None, - mult: int = 4, - dropout: float = 0.0, - inner_dim=None, - bias: bool = True, - dtype=None, device=None, operations=None - ): - super().__init__() - if inner_dim is None: - inner_dim = int(dim * mult) - dim_out = dim_out if dim_out is not None else dim - - self.net = nn.ModuleList([]) - self.net.append(GELU(dim, inner_dim, approximate="tanh", bias=bias, dtype=dtype, device=device, operations=operations)) - self.net.append(nn.Dropout(dropout)) - self.net.append(operations.Linear(inner_dim, dim_out, bias=bias, dtype=dtype, device=device)) - - def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: - for module in self.net: - hidden_states = module(hidden_states) - return hidden_states - - -def apply_rotary_emb(x, freqs_cis): - if x.shape[1] == 0: - return x - - t_ = x.reshape(*x.shape[:-1], -1, 1, 2) - t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] - return t_out.reshape(*x.shape) - - -class QwenTimestepProjEmbeddings(nn.Module): - def __init__(self, embedding_dim, pooled_projection_dim, dtype=None, device=None, operations=None): - super().__init__() - self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000) - self.timestep_embedder = TimestepEmbedding( - in_channels=256, - time_embed_dim=embedding_dim, - dtype=dtype, - device=device, - operations=operations - ) - - def forward(self, timestep, hidden_states): - timesteps_proj = self.time_proj(timestep) - timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype)) - return timesteps_emb - - -class Attention(nn.Module): - def __init__( - self, - query_dim: int, - dim_head: int = 64, - heads: int = 8, - dropout: float = 0.0, - bias: bool = False, - eps: float = 1e-5, - out_bias: bool = True, - out_dim: int = None, - out_context_dim: int = None, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.inner_dim = out_dim if out_dim is not None else dim_head * heads - self.inner_kv_dim = self.inner_dim - self.heads = heads - self.dim_head = dim_head - self.out_dim = out_dim if out_dim is not None else query_dim - self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim - self.dropout = dropout - - # Q/K normalization - self.norm_q = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device) - self.norm_k = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device) - self.norm_added_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) - self.norm_added_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) - - # Image stream projections - self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) - self.to_k = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) - self.to_v = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) - - # Text stream projections - self.add_q_proj = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) - self.add_k_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) - self.add_v_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) - - # Output projections - self.to_out = nn.ModuleList([ - operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device), - nn.Dropout(dropout) - ]) - self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) - - def forward( - self, - hidden_states: torch.FloatTensor, # Image stream - encoder_hidden_states: torch.FloatTensor = None, # Text stream - encoder_hidden_states_mask: torch.FloatTensor = None, - attention_mask: Optional[torch.FloatTensor] = None, - image_rotary_emb: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - seq_txt = encoder_hidden_states.shape[1] - - img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1)) - img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1)) - img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1)) - - txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) - txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) - txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) - - img_query = self.norm_q(img_query) - img_key = self.norm_k(img_key) - txt_query = self.norm_added_q(txt_query) - txt_key = self.norm_added_k(txt_key) - - joint_query = torch.cat([txt_query, img_query], dim=1) - joint_key = torch.cat([txt_key, img_key], dim=1) - joint_value = torch.cat([txt_value, img_value], dim=1) - - joint_query = apply_rotary_emb(joint_query, image_rotary_emb) - joint_key = apply_rotary_emb(joint_key, image_rotary_emb) - - joint_query = joint_query.flatten(start_dim=2) - joint_key = joint_key.flatten(start_dim=2) - joint_value = joint_value.flatten(start_dim=2) - - joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask) - - txt_attn_output = joint_hidden_states[:, :seq_txt, :] - img_attn_output = joint_hidden_states[:, seq_txt:, :] - - img_attn_output = self.to_out[0](img_attn_output) - img_attn_output = self.to_out[1](img_attn_output) - txt_attn_output = self.to_add_out(txt_attn_output) - - return img_attn_output, txt_attn_output - - -class QwenImageTransformerBlock(nn.Module): - def __init__( - self, - dim: int, - num_attention_heads: int, - attention_head_dim: int, - eps: float = 1e-6, - dtype=None, - device=None, - operations=None - ): - super().__init__() - self.dim = dim - self.num_attention_heads = num_attention_heads - self.attention_head_dim = attention_head_dim - - self.img_mod = nn.Sequential( - nn.SiLU(), - operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device), - ) - self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) - self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) - self.img_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations) - - self.txt_mod = nn.Sequential( - nn.SiLU(), - operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device), - ) - self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) - self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) - self.txt_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations) - - self.attn = Attention( - query_dim=dim, - dim_head=attention_head_dim, - heads=num_attention_heads, - out_dim=dim, - bias=True, - eps=eps, - dtype=dtype, - device=device, - operations=operations, - ) - - def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: - shift, scale, gate = torch.chunk(mod_params, 3, dim=-1) - return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1) - - def forward( - self, - hidden_states: torch.Tensor, - encoder_hidden_states: torch.Tensor, - encoder_hidden_states_mask: torch.Tensor, - temb: torch.Tensor, - image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - img_mod_params = self.img_mod(temb) - txt_mod_params = self.txt_mod(temb) - img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1) - txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1) - - img_normed = self.img_norm1(hidden_states) - img_modulated, img_gate1 = self._modulate(img_normed, img_mod1) - txt_normed = self.txt_norm1(encoder_hidden_states) - txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1) - - img_attn_output, txt_attn_output = self.attn( - hidden_states=img_modulated, - encoder_hidden_states=txt_modulated, - encoder_hidden_states_mask=encoder_hidden_states_mask, - image_rotary_emb=image_rotary_emb, - ) - - hidden_states = hidden_states + img_gate1 * img_attn_output - encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output - - img_normed2 = self.img_norm2(hidden_states) - img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2) - hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2)) - - txt_normed2 = self.txt_norm2(encoder_hidden_states) - txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2) - encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2)) - - return encoder_hidden_states, hidden_states - - -class LastLayer(nn.Module): - def __init__( - self, - embedding_dim: int, - conditioning_embedding_dim: int, - elementwise_affine=False, - eps=1e-6, - bias=True, - dtype=None, device=None, operations=None - ): - super().__init__() - self.silu = nn.SiLU() - self.linear = operations.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias, dtype=dtype, device=device) - self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine=False, bias=bias, dtype=dtype, device=device) - - def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: - emb = self.linear(self.silu(conditioning_embedding)) - scale, shift = torch.chunk(emb, 2, dim=1) - x = torch.addcmul(shift[:, None, :], self.norm(x), (1 + scale)[:, None, :]) - return x - - -class QwenImageTransformer2DModel(nn.Module): - def __init__( - self, - patch_size: int = 2, - in_channels: int = 64, - out_channels: Optional[int] = 16, - num_layers: int = 60, - attention_head_dim: int = 128, - num_attention_heads: int = 24, - joint_attention_dim: int = 3584, - pooled_projection_dim: int = 768, - guidance_embeds: bool = False, - axes_dims_rope: Tuple[int, int, int] = (16, 56, 56), - image_model=None, - final_layer=True, - dtype=None, - device=None, - operations=None, - ): - super().__init__() - self.dtype = dtype - self.patch_size = patch_size - self.in_channels = in_channels - self.out_channels = out_channels or in_channels - self.inner_dim = num_attention_heads * attention_head_dim - - self.pe_embedder = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope)) - - self.time_text_embed = QwenTimestepProjEmbeddings( - embedding_dim=self.inner_dim, - pooled_projection_dim=pooled_projection_dim, - dtype=dtype, - device=device, - operations=operations - ) - - self.txt_norm = operations.RMSNorm(joint_attention_dim, eps=1e-6, dtype=dtype, device=device) - self.img_in = operations.Linear(in_channels, self.inner_dim, dtype=dtype, device=device) - self.txt_in = operations.Linear(joint_attention_dim, self.inner_dim, dtype=dtype, device=device) - - self.transformer_blocks = nn.ModuleList([ - QwenImageTransformerBlock( - dim=self.inner_dim, - num_attention_heads=num_attention_heads, - attention_head_dim=attention_head_dim, - dtype=dtype, - device=device, - operations=operations - ) - for _ in range(num_layers) - ]) - - if final_layer: - self.norm_out = LastLayer(self.inner_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) - self.proj_out = operations.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True, dtype=dtype, device=device) - - def process_img(self, x, index=0, h_offset=0, w_offset=0): - bs, c, t, h, w = x.shape - patch_size = self.patch_size - hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size)) - orig_shape = hidden_states.shape - hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2) - hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5) - hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4) - h_len = ((h + (patch_size // 2)) // patch_size) - w_len = ((w + (patch_size // 2)) // patch_size) - - h_offset = ((h_offset + (patch_size // 2)) // patch_size) - w_offset = ((w_offset + (patch_size // 2)) // patch_size) - - img_ids = torch.zeros((h_len, w_len, 3), device=x.device) - img_ids[:, :, 0] = img_ids[:, :, 1] + index - img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - (h_len // 2) - img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - (w_len // 2) - return hidden_states, repeat(img_ids, "h w c -> b (h w) c", b=bs), orig_shape - - def forward(self, x, timestep, context, attention_mask=None, guidance=None, ref_latents=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, attention_mask, guidance, ref_latents, transformer_options, **kwargs) - - def _forward( - self, - x, - timesteps, - context, - attention_mask=None, - guidance: torch.Tensor = None, - ref_latents=None, - transformer_options={}, - control=None, - **kwargs - ): - timestep = timesteps - encoder_hidden_states = context - encoder_hidden_states_mask = attention_mask - - hidden_states, img_ids, orig_shape = self.process_img(x) - num_embeds = hidden_states.shape[1] - - if ref_latents is not None: - h = 0 - w = 0 - index = 0 - index_ref_method = kwargs.get("ref_latents_method", "index") == "index" - for ref in ref_latents: - if index_ref_method: - index += 1 - h_offset = 0 - w_offset = 0 - else: - index = 1 - h_offset = 0 - w_offset = 0 - if ref.shape[-2] + h > ref.shape[-1] + w: - w_offset = w - else: - h_offset = h - h = max(h, ref.shape[-2] + h_offset) - w = max(w, ref.shape[-1] + w_offset) - - kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) - hidden_states = torch.cat([hidden_states, kontext], dim=1) - img_ids = torch.cat([img_ids, kontext_ids], dim=1) - - txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2)) - txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) - ids = torch.cat((txt_ids, img_ids), dim=1) - image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype) - del ids, txt_ids, img_ids - - hidden_states = self.img_in(hidden_states) - encoder_hidden_states = self.txt_norm(encoder_hidden_states) - encoder_hidden_states = self.txt_in(encoder_hidden_states) - - if guidance is not None: - guidance = guidance * 1000 - - temb = ( - self.time_text_embed(timestep, hidden_states) - if guidance is None - else self.time_text_embed(timestep, guidance, hidden_states) - ) - - patches_replace = transformer_options.get("patches_replace", {}) - patches = transformer_options.get("patches", {}) - blocks_replace = patches_replace.get("dit", {}) - - for i, block in enumerate(self.transformer_blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"]) - return out - out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb}, {"original_block": block_wrap}) - hidden_states = out["img"] - encoder_hidden_states = out["txt"] - else: - encoder_hidden_states, hidden_states = block( - hidden_states=hidden_states, - encoder_hidden_states=encoder_hidden_states, - encoder_hidden_states_mask=encoder_hidden_states_mask, - temb=temb, - image_rotary_emb=image_rotary_emb, - ) - - if "double_block" in patches: - for p in patches["double_block"]: - out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i}) - hidden_states = out["img"] - encoder_hidden_states = out["txt"] - - if control is not None: # Controlnet - control_i = control.get("input") - if i < len(control_i): - add = control_i[i] - if add is not None: - hidden_states[:, :add.shape[1]] += add - - hidden_states = self.norm_out(hidden_states, temb) - hidden_states = self.proj_out(hidden_states) - - hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2) - hidden_states = hidden_states.permute(0, 3, 1, 4, 2, 5) - return hidden_states.reshape(orig_shape)[:, :, :, :x.shape[-2], :x.shape[-1]] diff --git a/comfy/ldm/util.py b/comfy/ldm/util.py deleted file mode 100644 index 30b4b4721056216ee817e7d54d0bca9e09b79098..0000000000000000000000000000000000000000 --- a/comfy/ldm/util.py +++ /dev/null @@ -1,197 +0,0 @@ -import importlib -import logging - -import torch -from torch import optim -import numpy as np - -from inspect import isfunction -from PIL import Image, ImageDraw, ImageFont - - -def log_txt_as_img(wh, xc, size=10): - # wh a tuple of (width, height) - # xc a list of captions to plot - b = len(xc) - txts = list() - for bi in range(b): - txt = Image.new("RGB", wh, color="white") - draw = ImageDraw.Draw(txt) - font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) - nc = int(40 * (wh[0] / 256)) - lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) - - try: - draw.text((0, 0), lines, fill="black", font=font) - except UnicodeEncodeError: - logging.warning("Cant encode string for logging. Skipping.") - - txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 - txts.append(txt) - txts = np.stack(txts) - txts = torch.tensor(txts) - return txts - - -def ismap(x): - if not isinstance(x, torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] > 3) - - -def isimage(x): - if not isinstance(x,torch.Tensor): - return False - return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) - - -def exists(x): - return x is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -def mean_flat(tensor): - """ - https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 - Take the mean over all non-batch dimensions. - """ - return tensor.mean(dim=list(range(1, len(tensor.shape)))) - - -def count_params(model, verbose=False): - total_params = sum(p.numel() for p in model.parameters()) - if verbose: - logging.info(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") - return total_params - - -def instantiate_from_config(config): - if not "target" in config: - if config == '__is_first_stage__': - return None - elif config == "__is_unconditional__": - return None - raise KeyError("Expected key `target` to instantiate.") - return get_obj_from_str(config["target"])(**config.get("params", dict())) - - -def get_obj_from_str(string, reload=False): - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - - -class AdamWwithEMAandWings(optim.Optimizer): - # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 - def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using - weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code - ema_power=1., param_names=()): - """AdamW that saves EMA versions of the parameters.""" - if not 0.0 <= lr: - raise ValueError("Invalid learning rate: {}".format(lr)) - if not 0.0 <= eps: - raise ValueError("Invalid epsilon value: {}".format(eps)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - if not 0.0 <= weight_decay: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - if not 0.0 <= ema_decay <= 1.0: - raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) - defaults = dict(lr=lr, betas=betas, eps=eps, - weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, - ema_power=ema_power, param_names=param_names) - super().__init__(params, defaults) - - def __setstate__(self, state): - super().__setstate__(state) - for group in self.param_groups: - group.setdefault('amsgrad', False) - - @torch.no_grad() - def step(self, closure=None): - """Performs a single optimization step. - Args: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - for group in self.param_groups: - params_with_grad = [] - grads = [] - exp_avgs = [] - exp_avg_sqs = [] - ema_params_with_grad = [] - max_exp_avg_sqs = [] - state_steps = [] - amsgrad = group['amsgrad'] - beta1, beta2 = group['betas'] - ema_decay = group['ema_decay'] - ema_power = group['ema_power'] - - for p in group['params']: - if p.grad is None: - continue - params_with_grad.append(p) - if p.grad.is_sparse: - raise RuntimeError('AdamW does not support sparse gradients') - grads.append(p.grad) - - state = self.state[p] - - # State initialization - if len(state) == 0: - state['step'] = 0 - # Exponential moving average of gradient values - state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) - # Exponential moving average of squared gradient values - state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) - if amsgrad: - # Maintains max of all exp. moving avg. of sq. grad. values - state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) - # Exponential moving average of parameter values - state['param_exp_avg'] = p.detach().float().clone() - - exp_avgs.append(state['exp_avg']) - exp_avg_sqs.append(state['exp_avg_sq']) - ema_params_with_grad.append(state['param_exp_avg']) - - if amsgrad: - max_exp_avg_sqs.append(state['max_exp_avg_sq']) - - # update the steps for each param group update - state['step'] += 1 - # record the step after step update - state_steps.append(state['step']) - - optim._functional.adamw(params_with_grad, - grads, - exp_avgs, - exp_avg_sqs, - max_exp_avg_sqs, - state_steps, - amsgrad=amsgrad, - beta1=beta1, - beta2=beta2, - lr=group['lr'], - weight_decay=group['weight_decay'], - eps=group['eps'], - maximize=False) - - cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) - for param, ema_param in zip(params_with_grad, ema_params_with_grad): - ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) - - return loss diff --git a/comfy/ldm/wan/.DS_Store b/comfy/ldm/wan/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/ldm/wan/.DS_Store and /dev/null differ diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py deleted file mode 100644 index 47857dc2b0079874f24eac9b922626d84200507a..0000000000000000000000000000000000000000 --- a/comfy/ldm/wan/model.py +++ /dev/null @@ -1,1321 +0,0 @@ -# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py -# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. -import math - -import torch -import torch.nn as nn -from einops import rearrange - -from comfy.ldm.modules.attention import optimized_attention -from comfy.ldm.flux.layers import EmbedND -from comfy.ldm.flux.math import apply_rope -import comfy.ldm.common_dit -import comfy.model_management -import comfy.patcher_extension - - -def sinusoidal_embedding_1d(dim, position): - # preprocess - assert dim % 2 == 0 - half = dim // 2 - position = position.type(torch.float32) - - # calculation - sinusoid = torch.outer( - position, torch.pow(10000, -torch.arange(half).to(position).div(half))) - x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) - return x - - -class WanSelfAttention(nn.Module): - - def __init__(self, - dim, - num_heads, - window_size=(-1, -1), - qk_norm=True, - eps=1e-6, operation_settings={}): - assert dim % num_heads == 0 - super().__init__() - self.dim = dim - self.num_heads = num_heads - self.head_dim = dim // num_heads - self.window_size = window_size - self.qk_norm = qk_norm - self.eps = eps - - # layers - self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() - self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() - - def forward(self, x, freqs): - r""" - Args: - x(Tensor): Shape [B, L, num_heads, C / num_heads] - freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] - """ - b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim - - # query, key, value function - def qkv_fn(x): - q = self.norm_q(self.q(x)).view(b, s, n, d) - k = self.norm_k(self.k(x)).view(b, s, n, d) - v = self.v(x).view(b, s, n * d) - return q, k, v - - q, k, v = qkv_fn(x) - q, k = apply_rope(q, k, freqs) - - x = optimized_attention( - q.view(b, s, n * d), - k.view(b, s, n * d), - v, - heads=self.num_heads, - ) - - x = self.o(x) - return x - - -class WanT2VCrossAttention(WanSelfAttention): - - def forward(self, x, context, **kwargs): - r""" - Args: - x(Tensor): Shape [B, L1, C] - context(Tensor): Shape [B, L2, C] - """ - # compute query, key, value - q = self.norm_q(self.q(x)) - k = self.norm_k(self.k(context)) - v = self.v(context) - - # compute attention - x = optimized_attention(q, k, v, heads=self.num_heads) - - x = self.o(x) - return x - - -class WanI2VCrossAttention(WanSelfAttention): - - def __init__(self, - dim, - num_heads, - window_size=(-1, -1), - qk_norm=True, - eps=1e-6, operation_settings={}): - super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings) - - self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - # self.alpha = nn.Parameter(torch.zeros((1, ))) - self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() - - def forward(self, x, context, context_img_len): - r""" - Args: - x(Tensor): Shape [B, L1, C] - context(Tensor): Shape [B, L2, C] - """ - context_img = context[:, :context_img_len] - context = context[:, context_img_len:] - - # compute query, key, value - q = self.norm_q(self.q(x)) - k = self.norm_k(self.k(context)) - v = self.v(context) - k_img = self.norm_k_img(self.k_img(context_img)) - v_img = self.v_img(context_img) - img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads) - # compute attention - x = optimized_attention(q, k, v, heads=self.num_heads) - - # output - x = x + img_x - x = self.o(x) - return x - - -WAN_CROSSATTENTION_CLASSES = { - 't2v_cross_attn': WanT2VCrossAttention, - 'i2v_cross_attn': WanI2VCrossAttention, -} - - -def repeat_e(e, x): - repeats = 1 - if e.size(1) > 1: - repeats = x.size(1) // e.size(1) - if repeats == 1: - return e - if repeats * e.size(1) == x.size(1): - return torch.repeat_interleave(e, repeats, dim=1) - else: - return torch.repeat_interleave(e, repeats + 1, dim=1)[:, :x.size(1)] - - -class WanAttentionBlock(nn.Module): - - def __init__(self, - cross_attn_type, - dim, - ffn_dim, - num_heads, - window_size=(-1, -1), - qk_norm=True, - cross_attn_norm=False, - eps=1e-6, operation_settings={}): - super().__init__() - self.dim = dim - self.ffn_dim = ffn_dim - self.num_heads = num_heads - self.window_size = window_size - self.qk_norm = qk_norm - self.cross_attn_norm = cross_attn_norm - self.eps = eps - - # layers - self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, - eps, operation_settings=operation_settings) - self.norm3 = operation_settings.get("operations").LayerNorm( - dim, eps, - elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity() - self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, - num_heads, - (-1, -1), - qk_norm, - eps, operation_settings=operation_settings) - self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.ffn = nn.Sequential( - operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), - operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - - # modulation - self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - - def forward( - self, - x, - e, - freqs, - context, - context_img_len=257, - ): - r""" - Args: - x(Tensor): Shape [B, L, C] - e(Tensor): Shape [B, 6, C] - freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] - """ - # assert e.dtype == torch.float32 - - if e.ndim < 4: - e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1) - else: - e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2) - # assert e[0].dtype == torch.float32 - - # self-attention - y = self.self_attn( - torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)), - freqs) - - x = torch.addcmul(x, y, repeat_e(e[2], x)) - - # cross-attention & ffn - x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len) - y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x))) - x = torch.addcmul(x, y, repeat_e(e[5], x)) - return x - - -class VaceWanAttentionBlock(WanAttentionBlock): - def __init__( - self, - cross_attn_type, - dim, - ffn_dim, - num_heads, - window_size=(-1, -1), - qk_norm=True, - cross_attn_norm=False, - eps=1e-6, - block_id=0, - operation_settings={} - ): - super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) - self.block_id = block_id - if block_id == 0: - self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - - def forward(self, c, x, **kwargs): - if self.block_id == 0: - c = self.before_proj(c) + x - c = super().forward(c, **kwargs) - c_skip = self.after_proj(c) - return c_skip, c - - -class WanCamAdapter(nn.Module): - def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}): - super(WanCamAdapter, self).__init__() - - # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 - self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) - - # Convolution: reduce spatial dimensions by a factor - # of 2 (without overlap) - self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - - # Residual blocks for feature extraction - self.residual_blocks = nn.Sequential( - *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)] - ) - - def forward(self, x): - # Reshape to merge the frame dimension into batch - bs, c, f, h, w = x.size() - x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) - - # Pixel Unshuffle operation - x_unshuffled = self.pixel_unshuffle(x) - - # Convolution operation - x_conv = self.conv(x_unshuffled) - - # Feature extraction with residual blocks - out = self.residual_blocks(x_conv) - - # Reshape to restore original bf dimension - out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) - - # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames - out = out.permute(0, 2, 1, 3, 4) - - return out - - -class WanCamResidualBlock(nn.Module): - def __init__(self, dim, operation_settings={}): - super(WanCamResidualBlock, self).__init__() - self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.relu = nn.ReLU(inplace=True) - self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - - def forward(self, x): - residual = x - out = self.relu(self.conv1(x)) - out = self.conv2(out) - out += residual - return out - - -class Head(nn.Module): - - def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}): - super().__init__() - self.dim = dim - self.out_dim = out_dim - self.patch_size = patch_size - self.eps = eps - - # layers - out_dim = math.prod(patch_size) * out_dim - self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - - # modulation - self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - - def forward(self, x, e): - r""" - Args: - x(Tensor): Shape [B, L1, C] - e(Tensor): Shape [B, C] - """ - # assert e.dtype == torch.float32 - if e.ndim < 3: - e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1) - else: - e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2) - - x = (self.head(torch.addcmul(repeat_e(e[0], x), self.norm(x), 1 + repeat_e(e[1], x)))) - return x - - -class MLPProj(torch.nn.Module): - - def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}): - super().__init__() - - self.proj = torch.nn.Sequential( - operation_settings.get("operations").LayerNorm(in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), - torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), - operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - - if flf_pos_embed_token_number is not None: - self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - else: - self.emb_pos = None - - def forward(self, image_embeds): - if self.emb_pos is not None: - image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device) - - clip_extra_context_tokens = self.proj(image_embeds) - return clip_extra_context_tokens - - -class WanModel(torch.nn.Module): - r""" - Wan diffusion backbone supporting both text-to-video and image-to-video. - """ - - def __init__(self, - model_type='t2v', - patch_size=(1, 2, 2), - text_len=512, - in_dim=16, - dim=2048, - ffn_dim=8192, - freq_dim=256, - text_dim=4096, - out_dim=16, - num_heads=16, - num_layers=32, - window_size=(-1, -1), - qk_norm=True, - cross_attn_norm=True, - eps=1e-6, - flf_pos_embed_token_number=None, - in_dim_ref_conv=None, - image_model=None, - device=None, - dtype=None, - operations=None, - ): - r""" - Initialize the diffusion model backbone. - - Args: - model_type (`str`, *optional*, defaults to 't2v'): - Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) - patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): - 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) - text_len (`int`, *optional*, defaults to 512): - Fixed length for text embeddings - in_dim (`int`, *optional*, defaults to 16): - Input video channels (C_in) - dim (`int`, *optional*, defaults to 2048): - Hidden dimension of the transformer - ffn_dim (`int`, *optional*, defaults to 8192): - Intermediate dimension in feed-forward network - freq_dim (`int`, *optional*, defaults to 256): - Dimension for sinusoidal time embeddings - text_dim (`int`, *optional*, defaults to 4096): - Input dimension for text embeddings - out_dim (`int`, *optional*, defaults to 16): - Output video channels (C_out) - num_heads (`int`, *optional*, defaults to 16): - Number of attention heads - num_layers (`int`, *optional*, defaults to 32): - Number of transformer blocks - window_size (`tuple`, *optional*, defaults to (-1, -1)): - Window size for local attention (-1 indicates global attention) - qk_norm (`bool`, *optional*, defaults to True): - Enable query/key normalization - cross_attn_norm (`bool`, *optional*, defaults to False): - Enable cross-attention normalization - eps (`float`, *optional*, defaults to 1e-6): - Epsilon value for normalization layers - """ - - super().__init__() - self.dtype = dtype - operation_settings = {"operations": operations, "device": device, "dtype": dtype} - - assert model_type in ['t2v', 'i2v'] - self.model_type = model_type - - self.patch_size = patch_size - self.text_len = text_len - self.in_dim = in_dim - self.dim = dim - self.ffn_dim = ffn_dim - self.freq_dim = freq_dim - self.text_dim = text_dim - self.out_dim = out_dim - self.num_heads = num_heads - self.num_layers = num_layers - self.window_size = window_size - self.qk_norm = qk_norm - self.cross_attn_norm = cross_attn_norm - self.eps = eps - - # embeddings - self.patch_embedding = operations.Conv3d( - in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32) - self.text_embedding = nn.Sequential( - operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), - operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - - self.time_embedding = nn.Sequential( - operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) - - # blocks - cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' - self.blocks = nn.ModuleList([ - WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, - window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) - for _ in range(num_layers) - ]) - - # head - self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings) - - d = dim // num_heads - self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]) - - if model_type == 'i2v': - self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings) - else: - self.img_emb = None - - if in_dim_ref_conv is not None: - self.ref_conv = operations.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:], device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) - else: - self.ref_conv = None - - def forward_orig( - self, - x, - t, - context, - clip_fea=None, - freqs=None, - transformer_options={}, - **kwargs, - ): - r""" - Forward pass through the diffusion model - - Args: - x (Tensor): - List of input video tensors with shape [B, C_in, F, H, W] - t (Tensor): - Diffusion timesteps tensor of shape [B] - context (List[Tensor]): - List of text embeddings each with shape [B, L, C] - seq_len (`int`): - Maximum sequence length for positional encoding - clip_fea (Tensor, *optional*): - CLIP image features for image-to-video mode - y (List[Tensor], *optional*): - Conditional video inputs for image-to-video mode, same shape as x - - Returns: - List[Tensor]: - List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] - """ - # embeddings - x = self.patch_embedding(x.float()).to(x.dtype) - grid_sizes = x.shape[2:] - x = x.flatten(2).transpose(1, 2) - - # time embeddings - e = self.time_embedding( - sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) - e = e.reshape(t.shape[0], -1, e.shape[-1]) - e0 = self.time_projection(e).unflatten(2, (6, self.dim)) - - full_ref = None - if self.ref_conv is not None: - full_ref = kwargs.get("reference_latent", None) - if full_ref is not None: - full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) - x = torch.concat((full_ref, x), dim=1) - - # context - context = self.text_embedding(context) - - context_img_len = None - if clip_fea is not None: - if self.img_emb is not None: - context_clip = self.img_emb(clip_fea) # bs x 257 x dim - context = torch.concat([context_clip, context], dim=1) - context_img_len = clip_fea.shape[-2] - - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) - x = out["img"] - else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - - # head - x = self.head(x, e) - - if full_ref is not None: - x = x[:, full_ref.shape[1]:] - - # unpatchify - x = self.unpatchify(x, grid_sizes) - return x - - def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None): - patch_size = self.patch_size - t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) - h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) - w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) - - if steps_t is None: - steps_t = t_len - if steps_h is None: - steps_h = h_len - if steps_w is None: - steps_w = w_len - - img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype) - img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1) - img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1) - img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1) - img_ids = img_ids.reshape(1, -1, img_ids.shape[-1]) - - freqs = self.rope_embedder(img_ids).movedim(1, 2) - return freqs - - def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._forward, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) - ).execute(x, timestep, context, clip_fea, time_dim_concat, transformer_options, **kwargs) - - def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): - bs, c, t, h, w = x.shape - x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) - - t_len = t - if time_dim_concat is not None: - time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) - x = torch.cat([x, time_dim_concat], dim=2) - t_len = x.shape[2] - - if self.ref_conv is not None and "reference_latent" in kwargs: - t_len += 1 - - freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype) - return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w] - - def unpatchify(self, x, grid_sizes): - r""" - Reconstruct video tensors from patch embeddings. - - Args: - x (List[Tensor]): - List of patchified features, each with shape [L, C_out * prod(patch_size)] - grid_sizes (Tensor): - Original spatial-temporal grid dimensions before patching, - shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) - - Returns: - List[Tensor]: - Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8] - """ - - c = self.out_dim - u = x - b = u.shape[0] - u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c) - u = torch.einsum('bfhwpqrc->bcfphqwr', u) - u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)]) - return u - - -class VaceWanModel(WanModel): - r""" - Wan diffusion backbone supporting both text-to-video and image-to-video. - """ - - def __init__(self, - model_type='vace', - patch_size=(1, 2, 2), - text_len=512, - in_dim=16, - dim=2048, - ffn_dim=8192, - freq_dim=256, - text_dim=4096, - out_dim=16, - num_heads=16, - num_layers=32, - window_size=(-1, -1), - qk_norm=True, - cross_attn_norm=True, - eps=1e-6, - flf_pos_embed_token_number=None, - image_model=None, - vace_layers=None, - vace_in_dim=None, - device=None, - dtype=None, - operations=None, - ): - - super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) - operation_settings = {"operations": operations, "device": device, "dtype": dtype} - - # Vace - if vace_layers is not None: - self.vace_layers = vace_layers - self.vace_in_dim = vace_in_dim - # vace blocks - self.vace_blocks = nn.ModuleList([ - VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=i, operation_settings=operation_settings) - for i in range(self.vace_layers) - ]) - - self.vace_layers_mapping = {i: n for n, i in enumerate(range(0, self.num_layers, self.num_layers // self.vace_layers))} - # vace patch embeddings - self.vace_patch_embedding = operations.Conv3d( - self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size, device=device, dtype=torch.float32 - ) - - def forward_orig( - self, - x, - t, - context, - vace_context, - vace_strength, - clip_fea=None, - freqs=None, - transformer_options={}, - **kwargs, - ): - # embeddings - x = self.patch_embedding(x.float()).to(x.dtype) - grid_sizes = x.shape[2:] - x = x.flatten(2).transpose(1, 2) - - # time embeddings - e = self.time_embedding( - sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) - e0 = self.time_projection(e).unflatten(1, (6, self.dim)) - - # context - context = self.text_embedding(context) - - context_img_len = None - if clip_fea is not None: - if self.img_emb is not None: - context_clip = self.img_emb(clip_fea) # bs x 257 x dim - context = torch.concat([context_clip, context], dim=1) - context_img_len = clip_fea.shape[-2] - - orig_shape = list(vace_context.shape) - vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) - c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) - c = c.flatten(2).transpose(1, 2) - c = list(c.split(orig_shape[0], dim=0)) - - # arguments - x_orig = x - - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) - x = out["img"] - else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - - ii = self.vace_layers_mapping.get(i, None) - if ii is not None: - for iii in range(len(c)): - c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - x += c_skip * vace_strength[iii] - del c_skip - # head - x = self.head(x, e) - - # unpatchify - x = self.unpatchify(x, grid_sizes) - return x - -class CameraWanModel(WanModel): - r""" - Wan diffusion backbone supporting both text-to-video and image-to-video. - """ - - def __init__(self, - model_type='camera', - patch_size=(1, 2, 2), - text_len=512, - in_dim=16, - dim=2048, - ffn_dim=8192, - freq_dim=256, - text_dim=4096, - out_dim=16, - num_heads=16, - num_layers=32, - window_size=(-1, -1), - qk_norm=True, - cross_attn_norm=True, - eps=1e-6, - flf_pos_embed_token_number=None, - image_model=None, - in_dim_control_adapter=24, - device=None, - dtype=None, - operations=None, - ): - - if model_type == 'camera': - model_type = 'i2v' - else: - model_type = 't2v' - - super().__init__(model_type=model_type, patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) - operation_settings = {"operations": operations, "device": device, "dtype": dtype} - - self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings) - - - def forward_orig( - self, - x, - t, - context, - clip_fea=None, - freqs=None, - camera_conditions = None, - transformer_options={}, - **kwargs, - ): - # embeddings - x = self.patch_embedding(x.float()).to(x.dtype) - if self.control_adapter is not None and camera_conditions is not None: - x = x + self.control_adapter(camera_conditions).to(x.dtype) - grid_sizes = x.shape[2:] - x = x.flatten(2).transpose(1, 2) - - # time embeddings - e = self.time_embedding( - sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) - e0 = self.time_projection(e).unflatten(1, (6, self.dim)) - - # context - context = self.text_embedding(context) - - context_img_len = None - if clip_fea is not None: - if self.img_emb is not None: - context_clip = self.img_emb(clip_fea) # bs x 257 x dim - context = torch.concat([context_clip, context], dim=1) - context_img_len = clip_fea.shape[-2] - - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) - x = out["img"] - else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - - # head - x = self.head(x, e) - - # unpatchify - x = self.unpatchify(x, grid_sizes) - return x - - -class CausalConv1d(nn.Module): - - def __init__(self, - chan_in, - chan_out, - kernel_size=3, - stride=1, - dilation=1, - pad_mode='replicate', - operations=None, - **kwargs): - super().__init__() - - self.pad_mode = pad_mode - padding = (kernel_size - 1, 0) # T - self.time_causal_padding = padding - - self.conv = operations.Conv1d( - chan_in, - chan_out, - kernel_size, - stride=stride, - dilation=dilation, - **kwargs) - - def forward(self, x): - x = torch.nn.functional.pad(x, self.time_causal_padding, mode=self.pad_mode) - return self.conv(x) - - -class MotionEncoder_tc(nn.Module): - - def __init__(self, - in_dim: int, - hidden_dim: int, - num_heads=int, - need_global=True, - dtype=None, - device=None, - operations=None,): - factory_kwargs = {"dtype": dtype, "device": device} - super().__init__() - - self.num_heads = num_heads - self.need_global = need_global - self.conv1_local = CausalConv1d(in_dim, hidden_dim // 4 * num_heads, 3, stride=1, operations=operations, **factory_kwargs) - if need_global: - self.conv1_global = CausalConv1d( - in_dim, hidden_dim // 4, 3, stride=1, operations=operations, **factory_kwargs) - self.norm1 = operations.LayerNorm( - hidden_dim // 4, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) - self.act = nn.SiLU() - self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2, operations=operations, **factory_kwargs) - self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2, operations=operations, **factory_kwargs) - - if need_global: - self.final_linear = operations.Linear(hidden_dim, hidden_dim, **factory_kwargs) - - self.norm1 = operations.LayerNorm( - hidden_dim // 4, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) - - self.norm2 = operations.LayerNorm( - hidden_dim // 2, - elementwise_affine=False, - eps=1e-6, - **factory_kwargs) - - self.norm3 = operations.LayerNorm( - hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs) - - self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs)) - - def forward(self, x): - x = rearrange(x, 'b t c -> b c t') - x_ori = x.clone() - b, c, t = x.shape - x = self.conv1_local(x) - x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads) - x = self.norm1(x) - x = self.act(x) - x = rearrange(x, 'b t c -> b c t') - x = self.conv2(x) - x = rearrange(x, 'b c t -> b t c') - x = self.norm2(x) - x = self.act(x) - x = rearrange(x, 'b t c -> b c t') - x = self.conv3(x) - x = rearrange(x, 'b c t -> b t c') - x = self.norm3(x) - x = self.act(x) - x = rearrange(x, '(b n) t c -> b t n c', b=b) - padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1) - x = torch.cat([x, padding], dim=-2) - x_local = x.clone() - - if not self.need_global: - return x_local - - x = self.conv1_global(x_ori) - x = rearrange(x, 'b c t -> b t c') - x = self.norm1(x) - x = self.act(x) - x = rearrange(x, 'b t c -> b c t') - x = self.conv2(x) - x = rearrange(x, 'b c t -> b t c') - x = self.norm2(x) - x = self.act(x) - x = rearrange(x, 'b t c -> b c t') - x = self.conv3(x) - x = rearrange(x, 'b c t -> b t c') - x = self.norm3(x) - x = self.act(x) - x = self.final_linear(x) - x = rearrange(x, '(b n) t c -> b t n c', b=b) - - return x, x_local - - -class CausalAudioEncoder(nn.Module): - - def __init__(self, - dim=5120, - num_layers=25, - out_dim=2048, - video_rate=8, - num_token=4, - need_global=False, - dtype=None, - device=None, - operations=None): - super().__init__() - self.encoder = MotionEncoder_tc( - in_dim=dim, - hidden_dim=out_dim, - num_heads=num_token, - need_global=need_global, dtype=dtype, device=device, operations=operations) - weight = torch.empty((1, num_layers, 1, 1), dtype=dtype, device=device) - - self.weights = torch.nn.Parameter(weight) - self.act = torch.nn.SiLU() - - def forward(self, features): - # features B * num_layers * dim * video_length - weights = self.act(comfy.model_management.cast_to(self.weights, dtype=features.dtype, device=features.device)) - weights_sum = weights.sum(dim=1, keepdims=True) - weighted_feat = ((features * weights) / weights_sum).sum( - dim=1) # b dim f - weighted_feat = weighted_feat.permute(0, 2, 1) # b f dim - res = self.encoder(weighted_feat) # b f n dim - return res # b f n dim - - -class AdaLayerNorm(nn.Module): - def __init__(self, embedding_dim, output_dim=None, norm_elementwise_affine=False, norm_eps=1e-5, dtype=None, device=None, operations=None): - super().__init__() - - output_dim = output_dim or embedding_dim * 2 - - self.silu = nn.SiLU() - self.linear = operations.Linear(embedding_dim, output_dim, dtype=dtype, device=device) - self.norm = operations.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine, dtype=dtype, device=device) - - def forward(self, x, temb): - temb = self.linear(self.silu(temb)) - shift, scale = temb.chunk(2, dim=1) - shift = shift[:, None, :] - scale = scale[:, None, :] - x = self.norm(x) * (1 + scale) + shift - return x - - -class AudioInjector_WAN(nn.Module): - - def __init__(self, - dim=2048, - num_heads=32, - inject_layer=[0, 27], - root_net=None, - enable_adain=False, - adain_dim=2048, - adain_mode=None, - dtype=None, - device=None, - operations=None): - super().__init__() - self.enable_adain = enable_adain - self.adain_mode = adain_mode - self.injected_block_id = {} - audio_injector_id = 0 - for inject_id in inject_layer: - self.injected_block_id[inject_id] = audio_injector_id - audio_injector_id += 1 - - self.injector = nn.ModuleList([ - WanT2VCrossAttention( - dim=dim, - num_heads=num_heads, - qk_norm=True, operation_settings={"operations": operations, "device": device, "dtype": dtype} - ) for _ in range(audio_injector_id) - ]) - self.injector_pre_norm_feat = nn.ModuleList([ - operations.LayerNorm( - dim, - elementwise_affine=False, - eps=1e-6, dtype=dtype, device=device - ) for _ in range(audio_injector_id) - ]) - self.injector_pre_norm_vec = nn.ModuleList([ - operations.LayerNorm( - dim, - elementwise_affine=False, - eps=1e-6, dtype=dtype, device=device - ) for _ in range(audio_injector_id) - ]) - if enable_adain: - self.injector_adain_layers = nn.ModuleList([ - AdaLayerNorm( - output_dim=dim * 2, embedding_dim=adain_dim, dtype=dtype, device=device, operations=operations) - for _ in range(audio_injector_id) - ]) - if adain_mode != "attn_norm": - self.injector_adain_output_layers = nn.ModuleList( - [operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)]) - - def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len): - audio_attn_id = self.injected_block_id.get(block_id, None) - if audio_attn_id is None: - return x - - num_frames = audio_emb.shape[1] - input_hidden_states = rearrange(x[:, :seq_len], "b (t n) c -> (b t) n c", t=num_frames) - if self.enable_adain and self.adain_mode == "attn_norm": - audio_emb_global = rearrange(audio_emb_global, "b t n c -> (b t) n c") - adain_hidden_states = self.injector_adain_layers[audio_attn_id](input_hidden_states, temb=audio_emb_global[:, 0]) - attn_hidden_states = adain_hidden_states - else: - attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states) - audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames) - attn_audio_emb = audio_emb - residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb) - residual_out = rearrange( - residual_out, "(b t) n c -> b (t n) c", t=num_frames) - x[:, :seq_len] = x[:, :seq_len] + residual_out - return x - - -class FramePackMotioner(nn.Module): - def __init__( - self, - inner_dim=1024, - num_heads=16, # Used to indicate the number of heads in the backbone network; unrelated to this module's design - zip_frame_buckets=[ - 1, 2, 16 - ], # Three numbers representing the number of frames sampled for patch operations from the nearest to the farthest frames - drop_mode="drop", # If not "drop", it will use "padd", meaning padding instead of deletion - dtype=None, - device=None, - operations=None): - super().__init__() - self.proj = operations.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2), dtype=dtype, device=device) - self.proj_2x = operations.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4), dtype=dtype, device=device) - self.proj_4x = operations.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8), dtype=dtype, device=device) - self.zip_frame_buckets = zip_frame_buckets - - self.inner_dim = inner_dim - self.num_heads = num_heads - - self.drop_mode = drop_mode - - def forward(self, motion_latents, rope_embedder, add_last_motion=2): - lat_height, lat_width = motion_latents.shape[3], motion_latents.shape[4] - padd_lat = torch.zeros(motion_latents.shape[0], 16, sum(self.zip_frame_buckets), lat_height, lat_width).to(device=motion_latents.device, dtype=motion_latents.dtype) - overlap_frame = min(padd_lat.shape[2], motion_latents.shape[2]) - if overlap_frame > 0: - padd_lat[:, :, -overlap_frame:] = motion_latents[:, :, -overlap_frame:] - - if add_last_motion < 2 and self.drop_mode != "drop": - zero_end_frame = sum(self.zip_frame_buckets[:len(self.zip_frame_buckets) - add_last_motion - 1]) - padd_lat[:, :, -zero_end_frame:] = 0 - - clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -sum(self.zip_frame_buckets):, :, :].split(self.zip_frame_buckets[::-1], dim=2) # 16, 2 ,1 - - # patchfy - clean_latents_post = self.proj(clean_latents_post).flatten(2).transpose(1, 2) - clean_latents_2x = self.proj_2x(clean_latents_2x) - l_2x_shape = clean_latents_2x.shape - clean_latents_2x = clean_latents_2x.flatten(2).transpose(1, 2) - clean_latents_4x = self.proj_4x(clean_latents_4x) - l_4x_shape = clean_latents_4x.shape - clean_latents_4x = clean_latents_4x.flatten(2).transpose(1, 2) - - if add_last_motion < 2 and self.drop_mode == "drop": - clean_latents_post = clean_latents_post[:, : - 0] if add_last_motion < 2 else clean_latents_post - clean_latents_2x = clean_latents_2x[:, : - 0] if add_last_motion < 1 else clean_latents_2x - - motion_lat = torch.cat([clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1) - - rope_post = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-1, device=motion_latents.device, dtype=motion_latents.dtype) - rope_2x = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-3, steps_h=l_2x_shape[-2], steps_w=l_2x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype) - rope_4x = rope_embedder.rope_encode(4, lat_height, lat_width, t_start=-19, steps_h=l_4x_shape[-2], steps_w=l_4x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype) - - rope = torch.cat([rope_post, rope_2x, rope_4x], dim=1) - return motion_lat, rope - - -class WanModel_S2V(WanModel): - def __init__(self, - model_type='s2v', - patch_size=(1, 2, 2), - text_len=512, - in_dim=16, - dim=2048, - ffn_dim=8192, - freq_dim=256, - text_dim=4096, - out_dim=16, - num_heads=16, - num_layers=32, - window_size=(-1, -1), - qk_norm=True, - cross_attn_norm=True, - eps=1e-6, - audio_dim=1024, - num_audio_token=4, - enable_adain=True, - cond_dim=16, - audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39], - adain_mode="attn_norm", - framepack_drop_mode="padd", - image_model=None, - device=None, - dtype=None, - operations=None, - ): - - super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, device=device, dtype=dtype, operations=operations) - - self.trainable_cond_mask = operations.Embedding(3, self.dim, device=device, dtype=dtype) - - self.casual_audio_encoder = CausalAudioEncoder( - dim=audio_dim, - out_dim=self.dim, - num_token=num_audio_token, - need_global=enable_adain, dtype=dtype, device=device, operations=operations) - - if cond_dim > 0: - self.cond_encoder = operations.Conv3d( - cond_dim, - self.dim, - kernel_size=self.patch_size, - stride=self.patch_size, device=device, dtype=dtype) - - self.audio_injector = AudioInjector_WAN( - dim=self.dim, - num_heads=self.num_heads, - inject_layer=audio_inject_layers, - root_net=self, - enable_adain=enable_adain, - adain_dim=self.dim, - adain_mode=adain_mode, - dtype=dtype, device=device, operations=operations - ) - - self.frame_packer = FramePackMotioner( - inner_dim=self.dim, - num_heads=self.num_heads, - zip_frame_buckets=[1, 2, 16], - drop_mode=framepack_drop_mode, - dtype=dtype, device=device, operations=operations) - - def forward_orig( - self, - x, - t, - context, - audio_embed=None, - reference_latent=None, - control_video=None, - reference_motion=None, - clip_fea=None, - freqs=None, - transformer_options={}, - **kwargs, - ): - if audio_embed is not None: - num_embeds = x.shape[-3] * 4 - audio_emb_global, audio_emb = self.casual_audio_encoder(audio_embed[:, :, :, :num_embeds]) - else: - audio_emb = None - - # embeddings - bs, _, time, height, width = x.shape - x = self.patch_embedding(x.float()).to(x.dtype) - if control_video is not None: - x = x + self.cond_encoder(control_video) - - if t.ndim == 1: - t = t.unsqueeze(1).repeat(1, x.shape[2]) - - grid_sizes = x.shape[2:] - x = x.flatten(2).transpose(1, 2) - seq_len = x.size(1) - - cond_mask_weight = comfy.model_management.cast_to(self.trainable_cond_mask.weight, dtype=x.dtype, device=x.device).unsqueeze(1).unsqueeze(1) - x = x + cond_mask_weight[0] - - if reference_latent is not None: - ref = self.patch_embedding(reference_latent.float()).to(x.dtype) - ref = ref.flatten(2).transpose(1, 2) - freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=max(30, time + 9), device=x.device, dtype=x.dtype) - ref = ref + cond_mask_weight[1] - x = torch.cat([x, ref], dim=1) - freqs = torch.cat([freqs, freqs_ref], dim=1) - t = torch.cat([t, torch.zeros((t.shape[0], reference_latent.shape[-3]), device=t.device, dtype=t.dtype)], dim=1) - del ref, freqs_ref - - if reference_motion is not None: - motion_encoded, freqs_motion = self.frame_packer(reference_motion, self) - motion_encoded = motion_encoded + cond_mask_weight[2] - x = torch.cat([x, motion_encoded], dim=1) - freqs = torch.cat([freqs, freqs_motion], dim=1) - - t = torch.repeat_interleave(t, 2, dim=1) - t = torch.cat([t, torch.zeros((t.shape[0], 3), device=t.device, dtype=t.dtype)], dim=1) - del motion_encoded, freqs_motion - - # time embeddings - e = self.time_embedding( - sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) - e = e.reshape(t.shape[0], -1, e.shape[-1]) - e0 = self.time_projection(e).unflatten(2, (6, self.dim)) - - # context - context = self.text_embedding(context) - - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"]) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) - x = out["img"] - else: - x = block(x, e=e0, freqs=freqs, context=context) - if audio_emb is not None: - x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len) - # head - x = self.head(x, e) - - # unpatchify - x = self.unpatchify(x, grid_sizes) - return x diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py deleted file mode 100644 index 791596938a26cf525e2e0513ca47ad217619c2e4..0000000000000000000000000000000000000000 --- a/comfy/ldm/wan/vae.py +++ /dev/null @@ -1,522 +0,0 @@ -# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py -# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -from comfy.ldm.modules.diffusionmodules.model import vae_attention - -import comfy.ops -ops = comfy.ops.disable_weight_init - -CACHE_T = 2 - - -class CausalConv3d(ops.Conv3d): - """ - Causal 3d convolusion. - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self._padding = (self.padding[2], self.padding[2], self.padding[1], - self.padding[1], 2 * self.padding[0], 0) - self.padding = (0, 0, 0) - - def forward(self, x, cache_x=None, cache_list=None, cache_idx=None): - if cache_list is not None: - cache_x = cache_list[cache_idx] - cache_list[cache_idx] = None - - padding = list(self._padding) - if cache_x is not None and self._padding[4] > 0: - cache_x = cache_x.to(x.device) - x = torch.cat([cache_x, x], dim=2) - padding[4] -= cache_x.shape[2] - del cache_x - x = F.pad(x, padding) - - return super().forward(x) - - -class RMS_norm(nn.Module): - - def __init__(self, dim, channel_first=True, images=True, bias=False): - super().__init__() - broadcastable_dims = (1, 1, 1) if not images else (1, 1) - shape = (dim, *broadcastable_dims) if channel_first else (dim,) - - self.channel_first = channel_first - self.scale = dim**0.5 - self.gamma = nn.Parameter(torch.ones(shape)) - self.bias = nn.Parameter(torch.zeros(shape)) if bias else None - - def forward(self, x): - return F.normalize( - x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0) - - -class Resample(nn.Module): - - def __init__(self, dim, mode): - assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', - 'downsample3d') - super().__init__() - self.dim = dim - self.mode = mode - - # layers - if mode == 'upsample2d': - self.resample = nn.Sequential( - nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), - ops.Conv2d(dim, dim // 2, 3, padding=1)) - elif mode == 'upsample3d': - self.resample = nn.Sequential( - nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), - ops.Conv2d(dim, dim // 2, 3, padding=1)) - self.time_conv = CausalConv3d( - dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) - - elif mode == 'downsample2d': - self.resample = nn.Sequential( - nn.ZeroPad2d((0, 1, 0, 1)), - ops.Conv2d(dim, dim, 3, stride=(2, 2))) - elif mode == 'downsample3d': - self.resample = nn.Sequential( - nn.ZeroPad2d((0, 1, 0, 1)), - ops.Conv2d(dim, dim, 3, stride=(2, 2))) - self.time_conv = CausalConv3d( - dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) - - else: - self.resample = nn.Identity() - - def forward(self, x, feat_cache=None, feat_idx=[0]): - b, c, t, h, w = x.size() - if self.mode == 'upsample3d': - if feat_cache is not None: - idx = feat_idx[0] - if feat_cache[idx] is None: - feat_cache[idx] = 'Rep' - feat_idx[0] += 1 - else: - - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[ - idx] is not None and feat_cache[idx] != 'Rep': - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - if cache_x.shape[2] < 2 and feat_cache[ - idx] is not None and feat_cache[idx] == 'Rep': - cache_x = torch.cat([ - torch.zeros_like(cache_x).to(cache_x.device), - cache_x - ], - dim=2) - if feat_cache[idx] == 'Rep': - x = self.time_conv(x) - else: - x = self.time_conv(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - - x = x.reshape(b, 2, c, t, h, w) - x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), - 3) - x = x.reshape(b, c, t * 2, h, w) - t = x.shape[2] - x = rearrange(x, 'b c t h w -> (b t) c h w') - x = self.resample(x) - x = rearrange(x, '(b t) c h w -> b c t h w', t=t) - - if self.mode == 'downsample3d': - if feat_cache is not None: - idx = feat_idx[0] - if feat_cache[idx] is None: - feat_cache[idx] = x.clone() - feat_idx[0] += 1 - else: - - cache_x = x[:, :, -1:, :, :].clone() - # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': - # # cache last frame of last two chunk - # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) - - x = self.time_conv( - torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - return x - - -class ResidualBlock(nn.Module): - - def __init__(self, in_dim, out_dim, dropout=0.0): - super().__init__() - self.in_dim = in_dim - self.out_dim = out_dim - - # layers - self.residual = nn.Sequential( - RMS_norm(in_dim, images=False), nn.SiLU(), - CausalConv3d(in_dim, out_dim, 3, padding=1), - RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), - CausalConv3d(out_dim, out_dim, 3, padding=1)) - self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ - if in_dim != out_dim else nn.Identity() - - def forward(self, x, feat_cache=None, feat_idx=[0]): - old_x = x - for layer in self.residual: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - x = layer(x, cache_list=feat_cache, cache_idx=idx) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - return x + self.shortcut(old_x) - - -class AttentionBlock(nn.Module): - """ - Causal self-attention with a single head. - """ - - def __init__(self, dim): - super().__init__() - self.dim = dim - - # layers - self.norm = RMS_norm(dim) - self.to_qkv = ops.Conv2d(dim, dim * 3, 1) - self.proj = ops.Conv2d(dim, dim, 1) - self.optimized_attention = vae_attention() - - def forward(self, x): - identity = x - b, c, t, h, w = x.size() - x = rearrange(x, 'b c t h w -> (b t) c h w') - x = self.norm(x) - # compute query, key, value - - q, k, v = self.to_qkv(x).chunk(3, dim=1) - x = self.optimized_attention(q, k, v) - - # output - x = self.proj(x) - x = rearrange(x, '(b t) c h w-> b c t h w', t=t) - return x + identity - - -class Encoder3d(nn.Module): - - def __init__(self, - dim=128, - z_dim=4, - dim_mult=[1, 2, 4, 4], - num_res_blocks=2, - attn_scales=[], - temperal_downsample=[True, True, False], - dropout=0.0): - super().__init__() - self.dim = dim - self.z_dim = z_dim - self.dim_mult = dim_mult - self.num_res_blocks = num_res_blocks - self.attn_scales = attn_scales - self.temperal_downsample = temperal_downsample - - # dimensions - dims = [dim * u for u in [1] + dim_mult] - scale = 1.0 - - # init block - self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) - - # downsample blocks - downsamples = [] - for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): - # residual (+attention) blocks - for _ in range(num_res_blocks): - downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) - if scale in attn_scales: - downsamples.append(AttentionBlock(out_dim)) - in_dim = out_dim - - # downsample block - if i != len(dim_mult) - 1: - mode = 'downsample3d' if temperal_downsample[ - i] else 'downsample2d' - downsamples.append(Resample(out_dim, mode=mode)) - scale /= 2.0 - self.downsamples = nn.Sequential(*downsamples) - - # middle blocks - self.middle = nn.Sequential( - ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), - ResidualBlock(out_dim, out_dim, dropout)) - - # output blocks - self.head = nn.Sequential( - RMS_norm(out_dim, images=False), nn.SiLU(), - CausalConv3d(out_dim, z_dim, 3, padding=1)) - - def forward(self, x, feat_cache=None, feat_idx=[0]): - if feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - x = self.conv1(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = self.conv1(x) - - ## downsamples - for layer in self.downsamples: - if feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## middle - for layer in self.middle: - if isinstance(layer, ResidualBlock) and feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## head - for layer in self.head: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - x = layer(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - return x - - -class Decoder3d(nn.Module): - - def __init__(self, - dim=128, - z_dim=4, - dim_mult=[1, 2, 4, 4], - num_res_blocks=2, - attn_scales=[], - temperal_upsample=[False, True, True], - dropout=0.0): - super().__init__() - self.dim = dim - self.z_dim = z_dim - self.dim_mult = dim_mult - self.num_res_blocks = num_res_blocks - self.attn_scales = attn_scales - self.temperal_upsample = temperal_upsample - - # dimensions - dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] - scale = 1.0 / 2**(len(dim_mult) - 2) - - # init block - self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) - - # middle blocks - self.middle = nn.Sequential( - ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), - ResidualBlock(dims[0], dims[0], dropout)) - - # upsample blocks - upsamples = [] - for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): - # residual (+attention) blocks - if i == 1 or i == 2 or i == 3: - in_dim = in_dim // 2 - for _ in range(num_res_blocks + 1): - upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) - if scale in attn_scales: - upsamples.append(AttentionBlock(out_dim)) - in_dim = out_dim - - # upsample block - if i != len(dim_mult) - 1: - mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' - upsamples.append(Resample(out_dim, mode=mode)) - scale *= 2.0 - self.upsamples = nn.Sequential(*upsamples) - - # output blocks - self.head = nn.Sequential( - RMS_norm(out_dim, images=False), nn.SiLU(), - CausalConv3d(out_dim, 3, 3, padding=1)) - - def forward(self, x, feat_cache=None, feat_idx=[0]): - ## conv1 - if feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - x = self.conv1(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = self.conv1(x) - - ## middle - for layer in self.middle: - if isinstance(layer, ResidualBlock) and feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## upsamples - for layer in self.upsamples: - if feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## head - for layer in self.head: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat([ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), cache_x - ], - dim=2) - x = layer(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - return x - - -def count_conv3d(model): - count = 0 - for m in model.modules(): - if isinstance(m, CausalConv3d): - count += 1 - return count - - -class WanVAE(nn.Module): - - def __init__(self, - dim=128, - z_dim=4, - dim_mult=[1, 2, 4, 4], - num_res_blocks=2, - attn_scales=[], - temperal_downsample=[True, True, False], - dropout=0.0): - super().__init__() - self.dim = dim - self.z_dim = z_dim - self.dim_mult = dim_mult - self.num_res_blocks = num_res_blocks - self.attn_scales = attn_scales - self.temperal_downsample = temperal_downsample - self.temperal_upsample = temperal_downsample[::-1] - - # modules - self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, - attn_scales, self.temperal_downsample, dropout) - self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) - self.conv2 = CausalConv3d(z_dim, z_dim, 1) - self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, - attn_scales, self.temperal_upsample, dropout) - - def encode(self, x): - self.clear_cache() - ## cache - t = x.shape[2] - iter_ = 1 + (t - 1) // 4 - ## 对encode输入的x，按时间拆分为1、4、4、4.... - for i in range(iter_): - self._enc_conv_idx = [0] - if i == 0: - out = self.encoder( - x[:, :, :1, :, :], - feat_cache=self._enc_feat_map, - feat_idx=self._enc_conv_idx) - else: - out_ = self.encoder( - x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], - feat_cache=self._enc_feat_map, - feat_idx=self._enc_conv_idx) - out = torch.cat([out, out_], 2) - mu, log_var = self.conv1(out).chunk(2, dim=1) - self.clear_cache() - return mu - - def decode(self, z): - self.clear_cache() - # z: [b,c,t,h,w] - - iter_ = z.shape[2] - x = self.conv2(z) - for i in range(iter_): - self._conv_idx = [0] - if i == 0: - out = self.decoder( - x[:, :, i:i + 1, :, :], - feat_cache=self._feat_map, - feat_idx=self._conv_idx) - else: - out_ = self.decoder( - x[:, :, i:i + 1, :, :], - feat_cache=self._feat_map, - feat_idx=self._conv_idx) - out = torch.cat([out, out_], 2) - self.clear_cache() - return out - - def clear_cache(self): - self._conv_num = count_conv3d(self.decoder) - self._conv_idx = [0] - self._feat_map = [None] * self._conv_num - #cache encode - self._enc_conv_num = count_conv3d(self.encoder) - self._enc_conv_idx = [0] - self._enc_feat_map = [None] * self._enc_conv_num diff --git a/comfy/ldm/wan/vae2_2.py b/comfy/ldm/wan/vae2_2.py deleted file mode 100644 index 1f6d584a22f3a888e290f233934f214f4a36fc55..0000000000000000000000000000000000000000 --- a/comfy/ldm/wan/vae2_2.py +++ /dev/null @@ -1,726 +0,0 @@ -# original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py -# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -from .vae import AttentionBlock, CausalConv3d, RMS_norm - -import comfy.ops -ops = comfy.ops.disable_weight_init - -CACHE_T = 2 - - -class Resample(nn.Module): - - def __init__(self, dim, mode): - assert mode in ( - "none", - "upsample2d", - "upsample3d", - "downsample2d", - "downsample3d", - ) - super().__init__() - self.dim = dim - self.mode = mode - - # layers - if mode == "upsample2d": - self.resample = nn.Sequential( - nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), - ops.Conv2d(dim, dim, 3, padding=1), - ) - elif mode == "upsample3d": - self.resample = nn.Sequential( - nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), - ops.Conv2d(dim, dim, 3, padding=1), - # ops.Conv2d(dim, dim//2, 3, padding=1) - ) - self.time_conv = CausalConv3d( - dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) - elif mode == "downsample2d": - self.resample = nn.Sequential( - nn.ZeroPad2d((0, 1, 0, 1)), - ops.Conv2d(dim, dim, 3, stride=(2, 2))) - elif mode == "downsample3d": - self.resample = nn.Sequential( - nn.ZeroPad2d((0, 1, 0, 1)), - ops.Conv2d(dim, dim, 3, stride=(2, 2))) - self.time_conv = CausalConv3d( - dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) - else: - self.resample = nn.Identity() - - def forward(self, x, feat_cache=None, feat_idx=[0]): - b, c, t, h, w = x.size() - if self.mode == "upsample3d": - if feat_cache is not None: - idx = feat_idx[0] - if feat_cache[idx] is None: - feat_cache[idx] = "Rep" - feat_idx[0] += 1 - else: - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and - feat_cache[idx] != "Rep"): - # cache last frame of last two chunk - cache_x = torch.cat( - [ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), - cache_x, - ], - dim=2, - ) - if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and - feat_cache[idx] == "Rep"): - cache_x = torch.cat( - [ - torch.zeros_like(cache_x).to(cache_x.device), - cache_x - ], - dim=2, - ) - if feat_cache[idx] == "Rep": - x = self.time_conv(x) - else: - x = self.time_conv(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - x = x.reshape(b, 2, c, t, h, w) - x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), - 3) - x = x.reshape(b, c, t * 2, h, w) - t = x.shape[2] - x = rearrange(x, "b c t h w -> (b t) c h w") - x = self.resample(x) - x = rearrange(x, "(b t) c h w -> b c t h w", t=t) - - if self.mode == "downsample3d": - if feat_cache is not None: - idx = feat_idx[0] - if feat_cache[idx] is None: - feat_cache[idx] = x.clone() - feat_idx[0] += 1 - else: - cache_x = x[:, :, -1:, :, :].clone() - x = self.time_conv( - torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - return x - - -class ResidualBlock(nn.Module): - - def __init__(self, in_dim, out_dim, dropout=0.0): - super().__init__() - self.in_dim = in_dim - self.out_dim = out_dim - - # layers - self.residual = nn.Sequential( - RMS_norm(in_dim, images=False), - nn.SiLU(), - CausalConv3d(in_dim, out_dim, 3, padding=1), - RMS_norm(out_dim, images=False), - nn.SiLU(), - nn.Dropout(dropout), - CausalConv3d(out_dim, out_dim, 3, padding=1), - ) - self.shortcut = ( - CausalConv3d(in_dim, out_dim, 1) - if in_dim != out_dim else nn.Identity()) - - def forward(self, x, feat_cache=None, feat_idx=[0]): - old_x = x - for layer in self.residual: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - # cache last frame of last two chunk - cache_x = torch.cat( - [ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), - cache_x, - ], - dim=2, - ) - x = layer(x, cache_list=feat_cache, cache_idx=idx) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - return x + self.shortcut(old_x) - - -def patchify(x, patch_size): - if patch_size == 1: - return x - if x.dim() == 4: - x = rearrange( - x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size) - elif x.dim() == 5: - x = rearrange( - x, - "b c f (h q) (w r) -> b (c r q) f h w", - q=patch_size, - r=patch_size, - ) - else: - raise ValueError(f"Invalid input shape: {x.shape}") - - return x - - -def unpatchify(x, patch_size): - if patch_size == 1: - return x - - if x.dim() == 4: - x = rearrange( - x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size) - elif x.dim() == 5: - x = rearrange( - x, - "b (c r q) f h w -> b c f (h q) (w r)", - q=patch_size, - r=patch_size, - ) - return x - - -class AvgDown3D(nn.Module): - - def __init__( - self, - in_channels, - out_channels, - factor_t, - factor_s=1, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.factor_t = factor_t - self.factor_s = factor_s - self.factor = self.factor_t * self.factor_s * self.factor_s - - assert in_channels * self.factor % out_channels == 0 - self.group_size = in_channels * self.factor // out_channels - - def forward(self, x: torch.Tensor) -> torch.Tensor: - pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t - pad = (0, 0, 0, 0, pad_t, 0) - x = F.pad(x, pad) - B, C, T, H, W = x.shape - x = x.view( - B, - C, - T // self.factor_t, - self.factor_t, - H // self.factor_s, - self.factor_s, - W // self.factor_s, - self.factor_s, - ) - x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() - x = x.view( - B, - C * self.factor, - T // self.factor_t, - H // self.factor_s, - W // self.factor_s, - ) - x = x.view( - B, - self.out_channels, - self.group_size, - T // self.factor_t, - H // self.factor_s, - W // self.factor_s, - ) - x = x.mean(dim=2) - return x - - -class DupUp3D(nn.Module): - - def __init__( - self, - in_channels: int, - out_channels: int, - factor_t, - factor_s=1, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - - self.factor_t = factor_t - self.factor_s = factor_s - self.factor = self.factor_t * self.factor_s * self.factor_s - - assert out_channels * self.factor % in_channels == 0 - self.repeats = out_channels * self.factor // in_channels - - def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor: - x = x.repeat_interleave(self.repeats, dim=1) - x = x.view( - x.size(0), - self.out_channels, - self.factor_t, - self.factor_s, - self.factor_s, - x.size(2), - x.size(3), - x.size(4), - ) - x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() - x = x.view( - x.size(0), - self.out_channels, - x.size(2) * self.factor_t, - x.size(4) * self.factor_s, - x.size(6) * self.factor_s, - ) - if first_chunk: - x = x[:, :, self.factor_t - 1:, :, :] - return x - - -class Down_ResidualBlock(nn.Module): - - def __init__(self, - in_dim, - out_dim, - dropout, - mult, - temperal_downsample=False, - down_flag=False): - super().__init__() - - # Shortcut path with downsample - self.avg_shortcut = AvgDown3D( - in_dim, - out_dim, - factor_t=2 if temperal_downsample else 1, - factor_s=2 if down_flag else 1, - ) - - # Main path with residual blocks and downsample - downsamples = [] - for _ in range(mult): - downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) - in_dim = out_dim - - # Add the final downsample block - if down_flag: - mode = "downsample3d" if temperal_downsample else "downsample2d" - downsamples.append(Resample(out_dim, mode=mode)) - - self.downsamples = nn.Sequential(*downsamples) - - def forward(self, x, feat_cache=None, feat_idx=[0]): - x_copy = x - for module in self.downsamples: - x = module(x, feat_cache, feat_idx) - - return x + self.avg_shortcut(x_copy) - - -class Up_ResidualBlock(nn.Module): - - def __init__(self, - in_dim, - out_dim, - dropout, - mult, - temperal_upsample=False, - up_flag=False): - super().__init__() - # Shortcut path with upsample - if up_flag: - self.avg_shortcut = DupUp3D( - in_dim, - out_dim, - factor_t=2 if temperal_upsample else 1, - factor_s=2 if up_flag else 1, - ) - else: - self.avg_shortcut = None - - # Main path with residual blocks and upsample - upsamples = [] - for _ in range(mult): - upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) - in_dim = out_dim - - # Add the final upsample block - if up_flag: - mode = "upsample3d" if temperal_upsample else "upsample2d" - upsamples.append(Resample(out_dim, mode=mode)) - - self.upsamples = nn.Sequential(*upsamples) - - def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): - x_main = x - for module in self.upsamples: - x_main = module(x_main, feat_cache, feat_idx) - if self.avg_shortcut is not None: - x_shortcut = self.avg_shortcut(x, first_chunk) - return x_main + x_shortcut - else: - return x_main - - -class Encoder3d(nn.Module): - - def __init__( - self, - dim=128, - z_dim=4, - dim_mult=[1, 2, 4, 4], - num_res_blocks=2, - attn_scales=[], - temperal_downsample=[True, True, False], - dropout=0.0, - ): - super().__init__() - self.dim = dim - self.z_dim = z_dim - self.dim_mult = dim_mult - self.num_res_blocks = num_res_blocks - self.attn_scales = attn_scales - self.temperal_downsample = temperal_downsample - - # dimensions - dims = [dim * u for u in [1] + dim_mult] - scale = 1.0 - - # init block - self.conv1 = CausalConv3d(12, dims[0], 3, padding=1) - - # downsample blocks - downsamples = [] - for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): - t_down_flag = ( - temperal_downsample[i] - if i < len(temperal_downsample) else False) - downsamples.append( - Down_ResidualBlock( - in_dim=in_dim, - out_dim=out_dim, - dropout=dropout, - mult=num_res_blocks, - temperal_downsample=t_down_flag, - down_flag=i != len(dim_mult) - 1, - )) - scale /= 2.0 - self.downsamples = nn.Sequential(*downsamples) - - # middle blocks - self.middle = nn.Sequential( - ResidualBlock(out_dim, out_dim, dropout), - AttentionBlock(out_dim), - ResidualBlock(out_dim, out_dim, dropout), - ) - - # # output blocks - self.head = nn.Sequential( - RMS_norm(out_dim, images=False), - nn.SiLU(), - CausalConv3d(out_dim, z_dim, 3, padding=1), - ) - - def forward(self, x, feat_cache=None, feat_idx=[0]): - - if feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - cache_x = torch.cat( - [ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), - cache_x, - ], - dim=2, - ) - x = self.conv1(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = self.conv1(x) - - ## downsamples - for layer in self.downsamples: - if feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## middle - for layer in self.middle: - if isinstance(layer, ResidualBlock) and feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## head - for layer in self.head: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - cache_x = torch.cat( - [ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), - cache_x, - ], - dim=2, - ) - x = layer(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - - return x - - -class Decoder3d(nn.Module): - - def __init__( - self, - dim=128, - z_dim=4, - dim_mult=[1, 2, 4, 4], - num_res_blocks=2, - attn_scales=[], - temperal_upsample=[False, True, True], - dropout=0.0, - ): - super().__init__() - self.dim = dim - self.z_dim = z_dim - self.dim_mult = dim_mult - self.num_res_blocks = num_res_blocks - self.attn_scales = attn_scales - self.temperal_upsample = temperal_upsample - - # dimensions - dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] - # init block - self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) - - # middle blocks - self.middle = nn.Sequential( - ResidualBlock(dims[0], dims[0], dropout), - AttentionBlock(dims[0]), - ResidualBlock(dims[0], dims[0], dropout), - ) - - # upsample blocks - upsamples = [] - for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): - t_up_flag = temperal_upsample[i] if i < len( - temperal_upsample) else False - upsamples.append( - Up_ResidualBlock( - in_dim=in_dim, - out_dim=out_dim, - dropout=dropout, - mult=num_res_blocks + 1, - temperal_upsample=t_up_flag, - up_flag=i != len(dim_mult) - 1, - )) - self.upsamples = nn.Sequential(*upsamples) - - # output blocks - self.head = nn.Sequential( - RMS_norm(out_dim, images=False), - nn.SiLU(), - CausalConv3d(out_dim, 12, 3, padding=1), - ) - - def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): - if feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - cache_x = torch.cat( - [ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), - cache_x, - ], - dim=2, - ) - x = self.conv1(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = self.conv1(x) - - for layer in self.middle: - if isinstance(layer, ResidualBlock) and feat_cache is not None: - x = layer(x, feat_cache, feat_idx) - else: - x = layer(x) - - ## upsamples - for layer in self.upsamples: - if feat_cache is not None: - x = layer(x, feat_cache, feat_idx, first_chunk) - else: - x = layer(x) - - ## head - for layer in self.head: - if isinstance(layer, CausalConv3d) and feat_cache is not None: - idx = feat_idx[0] - cache_x = x[:, :, -CACHE_T:, :, :].clone() - if cache_x.shape[2] < 2 and feat_cache[idx] is not None: - cache_x = torch.cat( - [ - feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( - cache_x.device), - cache_x, - ], - dim=2, - ) - x = layer(x, feat_cache[idx]) - feat_cache[idx] = cache_x - feat_idx[0] += 1 - else: - x = layer(x) - return x - - -def count_conv3d(model): - count = 0 - for m in model.modules(): - if isinstance(m, CausalConv3d): - count += 1 - return count - - -class WanVAE(nn.Module): - - def __init__( - self, - dim=160, - dec_dim=256, - z_dim=16, - dim_mult=[1, 2, 4, 4], - num_res_blocks=2, - attn_scales=[], - temperal_downsample=[True, True, False], - dropout=0.0, - ): - super().__init__() - self.dim = dim - self.z_dim = z_dim - self.dim_mult = dim_mult - self.num_res_blocks = num_res_blocks - self.attn_scales = attn_scales - self.temperal_downsample = temperal_downsample - self.temperal_upsample = temperal_downsample[::-1] - - # modules - self.encoder = Encoder3d( - dim, - z_dim * 2, - dim_mult, - num_res_blocks, - attn_scales, - self.temperal_downsample, - dropout, - ) - self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) - self.conv2 = CausalConv3d(z_dim, z_dim, 1) - self.decoder = Decoder3d( - dec_dim, - z_dim, - dim_mult, - num_res_blocks, - attn_scales, - self.temperal_upsample, - dropout, - ) - - def encode(self, x): - self.clear_cache() - x = patchify(x, patch_size=2) - t = x.shape[2] - iter_ = 1 + (t - 1) // 4 - for i in range(iter_): - self._enc_conv_idx = [0] - if i == 0: - out = self.encoder( - x[:, :, :1, :, :], - feat_cache=self._enc_feat_map, - feat_idx=self._enc_conv_idx, - ) - else: - out_ = self.encoder( - x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], - feat_cache=self._enc_feat_map, - feat_idx=self._enc_conv_idx, - ) - out = torch.cat([out, out_], 2) - mu, log_var = self.conv1(out).chunk(2, dim=1) - self.clear_cache() - return mu - - def decode(self, z): - self.clear_cache() - iter_ = z.shape[2] - x = self.conv2(z) - for i in range(iter_): - self._conv_idx = [0] - if i == 0: - out = self.decoder( - x[:, :, i:i + 1, :, :], - feat_cache=self._feat_map, - feat_idx=self._conv_idx, - first_chunk=True, - ) - else: - out_ = self.decoder( - x[:, :, i:i + 1, :, :], - feat_cache=self._feat_map, - feat_idx=self._conv_idx, - ) - out = torch.cat([out, out_], 2) - out = unpatchify(out, patch_size=2) - self.clear_cache() - return out - - def reparameterize(self, mu, log_var): - std = torch.exp(0.5 * log_var) - eps = torch.randn_like(std) - return eps * std + mu - - def sample(self, imgs, deterministic=False): - mu, log_var = self.encode(imgs) - if deterministic: - return mu - std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) - return mu + std * torch.randn_like(std) - - def clear_cache(self): - self._conv_num = count_conv3d(self.decoder) - self._conv_idx = [0] - self._feat_map = [None] * self._conv_num - # cache encode - self._enc_conv_num = count_conv3d(self.encoder) - self._enc_conv_idx = [0] - self._enc_feat_map = [None] * self._enc_conv_num diff --git a/comfy/lora.py b/comfy/lora.py deleted file mode 100644 index 00358884bed47d971f67860dfb1faa320d98495a..0000000000000000000000000000000000000000 --- a/comfy/lora.py +++ /dev/null @@ -1,405 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -from __future__ import annotations -import comfy.utils -import comfy.model_management -import comfy.model_base -import comfy.weight_adapter as weight_adapter -import logging -import torch - -LORA_CLIP_MAP = { - "mlp.fc1": "mlp_fc1", - "mlp.fc2": "mlp_fc2", - "self_attn.k_proj": "self_attn_k_proj", - "self_attn.q_proj": "self_attn_q_proj", - "self_attn.v_proj": "self_attn_v_proj", - "self_attn.out_proj": "self_attn_out_proj", -} - - -def load_lora(lora, to_load, log_missing=True): - patch_dict = {} - loaded_keys = set() - for x in to_load: - alpha_name = "{}.alpha".format(x) - alpha = None - if alpha_name in lora.keys(): - alpha = lora[alpha_name].item() - loaded_keys.add(alpha_name) - - dora_scale_name = "{}.dora_scale".format(x) - dora_scale = None - if dora_scale_name in lora.keys(): - dora_scale = lora[dora_scale_name] - loaded_keys.add(dora_scale_name) - - for adapter_cls in weight_adapter.adapters: - adapter = adapter_cls.load(x, lora, alpha, dora_scale, loaded_keys) - if adapter is not None: - patch_dict[to_load[x]] = adapter - loaded_keys.update(adapter.loaded_keys) - continue - - w_norm_name = "{}.w_norm".format(x) - b_norm_name = "{}.b_norm".format(x) - w_norm = lora.get(w_norm_name, None) - b_norm = lora.get(b_norm_name, None) - - if w_norm is not None: - loaded_keys.add(w_norm_name) - patch_dict[to_load[x]] = ("diff", (w_norm,)) - if b_norm is not None: - loaded_keys.add(b_norm_name) - patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) - - diff_name = "{}.diff".format(x) - diff_weight = lora.get(diff_name, None) - if diff_weight is not None: - patch_dict[to_load[x]] = ("diff", (diff_weight,)) - loaded_keys.add(diff_name) - - diff_bias_name = "{}.diff_b".format(x) - diff_bias = lora.get(diff_bias_name, None) - if diff_bias is not None: - patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) - loaded_keys.add(diff_bias_name) - - set_weight_name = "{}.set_weight".format(x) - set_weight = lora.get(set_weight_name, None) - if set_weight is not None: - patch_dict[to_load[x]] = ("set", (set_weight,)) - loaded_keys.add(set_weight_name) - - if log_missing: - for x in lora.keys(): - if x not in loaded_keys: - logging.warning("lora key not loaded: {}".format(x)) - - return patch_dict - -def model_lora_keys_clip(model, key_map={}): - sdk = model.state_dict().keys() - for k in sdk: - if k.endswith(".weight"): - key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names - - text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" - clip_l_present = False - clip_g_present = False - for b in range(32): #TODO: clean up - for c in LORA_CLIP_MAP: - k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) - key_map[lora_key] = k - lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) - key_map[lora_key] = k - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - - k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) - key_map[lora_key] = k - lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base - key_map[lora_key] = k - clip_l_present = True - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - - k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) - if k in sdk: - clip_g_present = True - if clip_l_present: - lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base - key_map[lora_key] = k - lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - else: - lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner - key_map[lora_key] = k - lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora - key_map[lora_key] = k - lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config - key_map[lora_key] = k - - for k in sdk: - if k.endswith(".weight"): - if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora - l_key = k[len("t5xxl.transformer."):-len(".weight")] - t5_index = 1 - if clip_g_present: - t5_index += 1 - if clip_l_present: - t5_index += 1 - if t5_index == 2: - key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux - t5_index += 1 - - key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k - elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora - l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")] - lora_key = "lora_te1_{}".format(l_key.replace(".", "_")) - key_map[lora_key] = k - - - k = "clip_g.transformer.text_projection.weight" - if k in sdk: - key_map["lora_prior_te_text_projection"] = k #cascade lora? - # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too - key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora - - k = "clip_l.transformer.text_projection.weight" - if k in sdk: - key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning - - return key_map - -def model_lora_keys_unet(model, key_map={}): - sd = model.state_dict() - sdk = sd.keys() - - for k in sdk: - if k.startswith("diffusion_model."): - if k.endswith(".weight"): - key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") - key_map["lora_unet_{}".format(key_lora)] = k - key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names - else: - key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names - - diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config) - for k in diffusers_keys: - if k.endswith(".weight"): - unet_key = "diffusion_model.{}".format(diffusers_keys[k]) - key_lora = k[:-len(".weight")].replace(".", "_") - key_map["lora_unet_{}".format(key_lora)] = unet_key - key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format - - diffusers_lora_prefix = ["", "unet."] - for p in diffusers_lora_prefix: - diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) - if diffusers_lora_key.endswith(".to_out.0"): - diffusers_lora_key = diffusers_lora_key[:-2] - key_map[diffusers_lora_key] = unet_key - - if isinstance(model, comfy.model_base.StableCascade_C): - for k in sdk: - if k.startswith("diffusion_model."): - if k.endswith(".weight"): - key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") - key_map["lora_prior_unet_{}".format(key_lora)] = k - - if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 - diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") - for k in diffusers_keys: - if k.endswith(".weight"): - to = diffusers_keys[k] - key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format - key_map[key_lora] = to - - key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others? - key_map[key_lora] = to - - key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora - key_map[key_lora] = to - - key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format - key_map[key_lora] = to - - if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow - diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") - for k in diffusers_keys: - if k.endswith(".weight"): - to = diffusers_keys[k] - key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format - key_map[key_lora] = to - - if isinstance(model, comfy.model_base.PixArt): - diffusers_keys = comfy.utils.pixart_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") - for k in diffusers_keys: - if k.endswith(".weight"): - to = diffusers_keys[k] - key_lora = "transformer.{}".format(k[:-len(".weight")]) #default format - key_map[key_lora] = to - - key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #diffusers training script - key_map[key_lora] = to - - key_lora = "unet.base_model.model.{}".format(k[:-len(".weight")]) #old reference peft script - key_map[key_lora] = to - - if isinstance(model, comfy.model_base.HunyuanDiT): - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): - key_lora = k[len("diffusion_model."):-len(".weight")] - key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format - - if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux - diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") - for k in diffusers_keys: - if k.endswith(".weight"): - to = diffusers_keys[k] - key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format - key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris - key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer - - if isinstance(model, comfy.model_base.GenmoMochi): - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official Mochi lora format - key_lora = k[len("diffusion_model."):-len(".weight")] - key_map["{}".format(key_lora)] = k - - if isinstance(model, comfy.model_base.HunyuanVideo): - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): - # diffusion-pipe lora format - key_lora = k - key_lora = key_lora.replace("_mod.lin.", "_mod.linear.").replace("_attn.qkv.", "_attn_qkv.").replace("_attn.proj.", "_attn_proj.") - key_lora = key_lora.replace("mlp.0.", "mlp.fc1.").replace("mlp.2.", "mlp.fc2.") - key_lora = key_lora.replace(".modulation.lin.", ".modulation.linear.") - key_lora = key_lora[len("diffusion_model."):-len(".weight")] - key_map["transformer.{}".format(key_lora)] = k - key_map["diffusion_model.{}".format(key_lora)] = k # Old loras - - if isinstance(model, comfy.model_base.HiDream): - for k in sdk: - if k.startswith("diffusion_model."): - if k.endswith(".weight"): - key_lora = k[len("diffusion_model."):-len(".weight")] - key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format - key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format - - if isinstance(model, comfy.model_base.ACEStep): - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format - key_lora = k[len("diffusion_model."):-len(".weight")] - key_map["{}".format(key_lora)] = k - - if isinstance(model, comfy.model_base.QwenImage): - for k in sdk: - if k.startswith("diffusion_model.") and k.endswith(".weight"): #QwenImage lora format - key_lora = k[len("diffusion_model."):-len(".weight")] - # Direct mapping for transformer_blocks format (QwenImage LoRA format) - key_map["{}".format(key_lora)] = k - # Support transformer prefix format - key_map["transformer.{}".format(key_lora)] = k - key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format - - return key_map - - -def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: - """ - Pad a tensor to a new shape with zeros. - - Args: - tensor (torch.Tensor): The original tensor to be padded. - new_shape (List[int]): The desired shape of the padded tensor. - - Returns: - torch.Tensor: A new tensor padded with zeros to the specified shape. - - Note: - If the new shape is smaller than the original tensor in any dimension, - the original tensor will be truncated in that dimension. - """ - if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): - raise ValueError("The new shape must be larger than the original tensor in all dimensions") - - if len(new_shape) != len(tensor.shape): - raise ValueError("The new shape must have the same number of dimensions as the original tensor") - - # Create a new tensor filled with zeros - padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) - - # Create slicing tuples for both tensors - orig_slices = tuple(slice(0, dim) for dim in tensor.shape) - new_slices = tuple(slice(0, dim) for dim in tensor.shape) - - # Copy the original tensor into the new tensor - padded_tensor[new_slices] = tensor[orig_slices] - - return padded_tensor - -def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, original_weights=None): - for p in patches: - strength = p[0] - v = p[1] - strength_model = p[2] - offset = p[3] - function = p[4] - if function is None: - function = lambda a: a - - old_weight = None - if offset is not None: - old_weight = weight - weight = weight.narrow(offset[0], offset[1], offset[2]) - - if strength_model != 1.0: - weight *= strength_model - - if isinstance(v, list): - v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), ) - - if isinstance(v, weight_adapter.WeightAdapterBase): - output = v.calculate_weight(weight, key, strength, strength_model, offset, function, intermediate_dtype, original_weights) - if output is None: - logging.warning("Calculate Weight Failed: {} {}".format(v.name, key)) - else: - weight = output - if old_weight is not None: - weight = old_weight - continue - - if len(v) == 1: - patch_type = "diff" - elif len(v) == 2: - patch_type = v[0] - v = v[1] - - if patch_type == "diff": - diff: torch.Tensor = v[0] - # An extra flag to pad the weight if the diff's shape is larger than the weight - do_pad_weight = len(v) > 1 and v[1]['pad_weight'] - if do_pad_weight and diff.shape != weight.shape: - logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape)) - weight = pad_tensor_to_shape(weight, diff.shape) - - if strength != 0.0: - if diff.shape != weight.shape: - logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape)) - else: - weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype)) - elif patch_type == "set": - weight.copy_(v[0]) - elif patch_type == "model_as_lora": - target_weight: torch.Tensor = v[0] - diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \ - comfy.model_management.cast_to_device(original_weights[key][0][0], weight.device, intermediate_dtype) - weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype)) - else: - logging.warning("patch type not recognized {} {}".format(patch_type, key)) - - if old_weight is not None: - weight = old_weight - - return weight diff --git a/comfy/lora_convert.py b/comfy/lora_convert.py deleted file mode 100644 index 3e00b63db94969e813b4a9244dce2b1c9d046a8d..0000000000000000000000000000000000000000 --- a/comfy/lora_convert.py +++ /dev/null @@ -1,24 +0,0 @@ -import torch -import comfy.utils - - -def convert_lora_bfl_control(sd): #BFL loras for Flux - sd_out = {} - for k in sd: - k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight")) - sd_out[k_to] = sd[k] - - sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]]) - return sd_out - - -def convert_lora_wan_fun(sd): #Wan Fun loras - return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"}) - - -def convert_lora(sd): - if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd: - return convert_lora_bfl_control(sd) - if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd: - return convert_lora_wan_fun(sd) - return sd diff --git a/comfy/model_base.py b/comfy/model_base.py deleted file mode 100644 index 56a6798be0ab298621fc210503200b60df6357cf..0000000000000000000000000000000000000000 --- a/comfy/model_base.py +++ /dev/null @@ -1,1393 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -import logging -from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep -from comfy.ldm.cascade.stage_c import StageC -from comfy.ldm.cascade.stage_b import StageB -from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation -from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation -from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper -import comfy.ldm.genmo.joint_model.asymm_models_joint -import comfy.ldm.aura.mmdit -import comfy.ldm.pixart.pixartms -import comfy.ldm.hydit.models -import comfy.ldm.audio.dit -import comfy.ldm.audio.embedders -import comfy.ldm.flux.model -import comfy.ldm.lightricks.model -import comfy.ldm.hunyuan_video.model -import comfy.ldm.cosmos.model -import comfy.ldm.cosmos.predict2 -import comfy.ldm.lumina.model -import comfy.ldm.wan.model -import comfy.ldm.hunyuan3d.model -import comfy.ldm.hidream.model -import comfy.ldm.chroma.model -import comfy.ldm.ace.model -import comfy.ldm.omnigen.omnigen2 -import comfy.ldm.qwen_image.model - -import comfy.model_management -import comfy.patcher_extension -import comfy.conds -import comfy.ops -from enum import Enum -from . import utils -import comfy.latent_formats -import comfy.model_sampling -import math -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from comfy.model_patcher import ModelPatcher - -class ModelType(Enum): - EPS = 1 - V_PREDICTION = 2 - V_PREDICTION_EDM = 3 - STABLE_CASCADE = 4 - EDM = 5 - FLOW = 6 - V_PREDICTION_CONTINUOUS = 7 - FLUX = 8 - IMG_TO_IMG = 9 - FLOW_COSMOS = 10 - - -def model_sampling(model_config, model_type): - s = comfy.model_sampling.ModelSamplingDiscrete - - if model_type == ModelType.EPS: - c = comfy.model_sampling.EPS - elif model_type == ModelType.V_PREDICTION: - c = comfy.model_sampling.V_PREDICTION - elif model_type == ModelType.V_PREDICTION_EDM: - c = comfy.model_sampling.V_PREDICTION - s = comfy.model_sampling.ModelSamplingContinuousEDM - elif model_type == ModelType.FLOW: - c = comfy.model_sampling.CONST - s = comfy.model_sampling.ModelSamplingDiscreteFlow - elif model_type == ModelType.STABLE_CASCADE: - c = comfy.model_sampling.EPS - s = comfy.model_sampling.StableCascadeSampling - elif model_type == ModelType.EDM: - c = comfy.model_sampling.EDM - s = comfy.model_sampling.ModelSamplingContinuousEDM - elif model_type == ModelType.V_PREDICTION_CONTINUOUS: - c = comfy.model_sampling.V_PREDICTION - s = comfy.model_sampling.ModelSamplingContinuousV - elif model_type == ModelType.FLUX: - c = comfy.model_sampling.CONST - s = comfy.model_sampling.ModelSamplingFlux - elif model_type == ModelType.IMG_TO_IMG: - c = comfy.model_sampling.IMG_TO_IMG - elif model_type == ModelType.FLOW_COSMOS: - c = comfy.model_sampling.COSMOS_RFLOW - s = comfy.model_sampling.ModelSamplingCosmosRFlow - - class ModelSampling(s, c): - pass - - return ModelSampling(model_config) - - -def convert_tensor(extra, dtype, device): - if hasattr(extra, "dtype"): - if extra.dtype != torch.int and extra.dtype != torch.long: - extra = comfy.model_management.cast_to_device(extra, device, dtype) - else: - extra = comfy.model_management.cast_to_device(extra, device, None) - return extra - - -class BaseModel(torch.nn.Module): - def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): - super().__init__() - - unet_config = model_config.unet_config - self.latent_format = model_config.latent_format - self.model_config = model_config - self.manual_cast_dtype = model_config.manual_cast_dtype - self.device = device - self.current_patcher: 'ModelPatcher' = None - - if not unet_config.get("disable_unet_model_creation", False): - if model_config.custom_operations is None: - fp8 = model_config.optimizations.get("fp8", False) - operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8) - else: - operations = model_config.custom_operations - self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) - if comfy.model_management.force_channels_last(): - self.diffusion_model.to(memory_format=torch.channels_last) - logging.debug("using channels last mode for diffusion model") - logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype)) - self.model_type = model_type - self.model_sampling = model_sampling(model_config, model_type) - - self.adm_channels = unet_config.get("adm_in_channels", None) - if self.adm_channels is None: - self.adm_channels = 0 - - self.concat_keys = () - logging.info("model_type {}".format(model_type.name)) - logging.debug("adm {}".format(self.adm_channels)) - self.memory_usage_factor = model_config.memory_usage_factor - self.memory_usage_factor_conds = () - self.memory_usage_shape_process = {} - - def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self._apply_model, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.APPLY_MODEL, transformer_options) - ).execute(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs) - - def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): - sigma = t - xc = self.model_sampling.calculate_input(sigma, x) - - if c_concat is not None: - xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1) - - context = c_crossattn - dtype = self.get_dtype() - - if self.manual_cast_dtype is not None: - dtype = self.manual_cast_dtype - - xc = xc.to(dtype) - device = xc.device - t = self.model_sampling.timestep(t).float() - if context is not None: - context = comfy.model_management.cast_to_device(context, device, dtype) - - extra_conds = {} - for o in kwargs: - extra = kwargs[o] - - if hasattr(extra, "dtype"): - extra = convert_tensor(extra, dtype, device) - elif isinstance(extra, list): - ex = [] - for ext in extra: - ex.append(convert_tensor(ext, dtype, device)) - extra = ex - extra_conds[o] = extra - - t = self.process_timestep(t, x=x, **extra_conds) - model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() - return self.model_sampling.calculate_denoised(sigma, model_output, x) - - def process_timestep(self, timestep, **kwargs): - return timestep - - def get_dtype(self): - return self.diffusion_model.dtype - - def encode_adm(self, **kwargs): - return None - - def concat_cond(self, **kwargs): - if len(self.concat_keys) > 0: - cond_concat = [] - denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) - concat_latent_image = kwargs.get("concat_latent_image", None) - if concat_latent_image is None: - concat_latent_image = kwargs.get("latent_image", None) - else: - concat_latent_image = self.process_latent_in(concat_latent_image) - - noise = kwargs.get("noise", None) - device = kwargs["device"] - - if concat_latent_image.shape[1:] != noise.shape[1:]: - concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") - if noise.ndim == 5: - if concat_latent_image.shape[-3] < noise.shape[-3]: - concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), "constant", 0) - else: - concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]] - - concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) - - if denoise_mask is not None: - if len(denoise_mask.shape) == len(noise.shape): - denoise_mask = denoise_mask[:, :1] - - num_dim = noise.ndim - 2 - denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:])) - if denoise_mask.shape[-2:] != noise.shape[-2:]: - denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") - denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) - - for ck in self.concat_keys: - if denoise_mask is not None: - if ck == "mask": - cond_concat.append(denoise_mask.to(device)) - elif ck == "masked_image": - cond_concat.append(concat_latent_image.to(device)) # NOTE: the latent_image should be masked by the mask in pixel space - elif ck == "mask_inverted": - cond_concat.append(1.0 - denoise_mask.to(device)) - else: - if ck == "mask": - cond_concat.append(torch.ones_like(noise)[:, :1]) - elif ck == "masked_image": - cond_concat.append(self.blank_inpaint_image_like(noise)) - elif ck == "mask_inverted": - cond_concat.append(torch.zeros_like(noise)[:, :1]) - if ck == "concat_image": - if concat_latent_image is not None: - cond_concat.append(concat_latent_image.to(device)) - else: - cond_concat.append(torch.zeros_like(noise)) - data = torch.cat(cond_concat, dim=1) - return data - return None - - def extra_conds(self, **kwargs): - out = {} - concat_cond = self.concat_cond(**kwargs) - if concat_cond is not None: - out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond) - - adm = self.encode_adm(**kwargs) - if adm is not None: - out['y'] = comfy.conds.CONDRegular(adm) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) - - cross_attn_cnet = kwargs.get("cross_attn_controlnet", None) - if cross_attn_cnet is not None: - out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet) - - c_concat = kwargs.get("noise_concat", None) - if c_concat is not None: - out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat) - - return out - - def load_model_weights(self, sd, unet_prefix=""): - to_load = {} - keys = list(sd.keys()) - for k in keys: - if k.startswith(unet_prefix): - to_load[k[len(unet_prefix):]] = sd.pop(k) - - to_load = self.model_config.process_unet_state_dict(to_load) - m, u = self.diffusion_model.load_state_dict(to_load, strict=False) - if len(m) > 0: - logging.warning("unet missing: {}".format(m)) - - if len(u) > 0: - logging.warning("unet unexpected: {}".format(u)) - del to_load - return self - - def process_latent_in(self, latent): - return self.latent_format.process_in(latent) - - def process_latent_out(self, latent): - return self.latent_format.process_out(latent) - - def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): - extra_sds = [] - if clip_state_dict is not None: - extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) - if vae_state_dict is not None: - extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) - if clip_vision_state_dict is not None: - extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) - - unet_state_dict = self.diffusion_model.state_dict() - - if self.model_config.scaled_fp8 is not None: - unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8) - - unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) - - if self.model_type == ModelType.V_PREDICTION: - unet_state_dict["v_pred"] = torch.tensor([]) - - for sd in extra_sds: - unet_state_dict.update(sd) - - return unet_state_dict - - def set_inpaint(self): - self.concat_keys = ("mask", "masked_image") - def blank_inpaint_image_like(latent_image): - blank_image = torch.ones_like(latent_image) - # these are the values for "zero" in pixel space translated to latent space - blank_image[:,0] *= 0.8223 - blank_image[:,1] *= -0.6876 - blank_image[:,2] *= 0.6364 - blank_image[:,3] *= 0.1380 - return blank_image - self.blank_inpaint_image_like = blank_inpaint_image_like - - def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): - return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image) - - def memory_required(self, input_shape, cond_shapes={}): - input_shapes = [input_shape] - for c in self.memory_usage_factor_conds: - shape = cond_shapes.get(c, None) - if shape is not None: - if c in self.memory_usage_shape_process: - out = [] - for s in shape: - out.append(self.memory_usage_shape_process[c](s)) - shape = out - - if len(shape) > 0: - input_shapes += shape - - if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): - dtype = self.get_dtype() - if self.manual_cast_dtype is not None: - dtype = self.manual_cast_dtype - #TODO: this needs to be tweaked - area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) - return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024) - else: - #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. - area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) - return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024) - - def extra_conds_shapes(self, **kwargs): - return {} - - -def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): - adm_inputs = [] - weights = [] - noise_aug = [] - for unclip_cond in unclip_conditioning: - for adm_cond in unclip_cond["clip_vision_output"].image_embeds: - weight = unclip_cond["strength"] - noise_augment = unclip_cond["noise_augmentation"] - noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) - c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) - adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight - weights.append(weight) - noise_aug.append(noise_augment) - adm_inputs.append(adm_out) - - if len(noise_aug) > 1: - adm_out = torch.stack(adm_inputs).sum(0) - noise_augment = noise_augment_merge - noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) - c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) - adm_out = torch.cat((c_adm, noise_level_emb), 1) - - return adm_out - -class SD21UNCLIP(BaseModel): - def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): - super().__init__(model_config, model_type, device=device) - self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) - - def encode_adm(self, **kwargs): - unclip_conditioning = kwargs.get("unclip_conditioning", None) - device = kwargs["device"] - if unclip_conditioning is None: - return torch.zeros((1, self.adm_channels), device=device) - else: - return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) - -def sdxl_pooled(args, noise_augmentor): - if "unclip_conditioning" in args: - return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] - else: - return args["pooled_output"] - -class SDXLRefiner(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): - super().__init__(model_config, model_type, device=device) - self.embedder = Timestep(256) - self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) - - def encode_adm(self, **kwargs): - clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) - width = kwargs.get("width", 768) - height = kwargs.get("height", 768) - crop_w = kwargs.get("crop_w", 0) - crop_h = kwargs.get("crop_h", 0) - - if kwargs.get("prompt_type", "") == "negative": - aesthetic_score = kwargs.get("aesthetic_score", 2.5) - else: - aesthetic_score = kwargs.get("aesthetic_score", 6) - - out = [] - out.append(self.embedder(torch.Tensor([height]))) - out.append(self.embedder(torch.Tensor([width]))) - out.append(self.embedder(torch.Tensor([crop_h]))) - out.append(self.embedder(torch.Tensor([crop_w]))) - out.append(self.embedder(torch.Tensor([aesthetic_score]))) - flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) - return torch.cat((clip_pooled.to(flat.device), flat), dim=1) - -class SDXL(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): - super().__init__(model_config, model_type, device=device) - self.embedder = Timestep(256) - self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) - - def encode_adm(self, **kwargs): - clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) - width = kwargs.get("width", 768) - height = kwargs.get("height", 768) - crop_w = kwargs.get("crop_w", 0) - crop_h = kwargs.get("crop_h", 0) - target_width = kwargs.get("target_width", width) - target_height = kwargs.get("target_height", height) - - out = [] - out.append(self.embedder(torch.Tensor([height]))) - out.append(self.embedder(torch.Tensor([width]))) - out.append(self.embedder(torch.Tensor([crop_h]))) - out.append(self.embedder(torch.Tensor([crop_w]))) - out.append(self.embedder(torch.Tensor([target_height]))) - out.append(self.embedder(torch.Tensor([target_width]))) - flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) - return torch.cat((clip_pooled.to(flat.device), flat), dim=1) - - -class SVD_img2vid(BaseModel): - def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): - super().__init__(model_config, model_type, device=device) - self.embedder = Timestep(256) - - def encode_adm(self, **kwargs): - fps_id = kwargs.get("fps", 6) - 1 - motion_bucket_id = kwargs.get("motion_bucket_id", 127) - augmentation = kwargs.get("augmentation_level", 0) - - out = [] - out.append(self.embedder(torch.Tensor([fps_id]))) - out.append(self.embedder(torch.Tensor([motion_bucket_id]))) - out.append(self.embedder(torch.Tensor([augmentation]))) - - flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) - return flat - - def extra_conds(self, **kwargs): - out = {} - adm = self.encode_adm(**kwargs) - if adm is not None: - out['y'] = comfy.conds.CONDRegular(adm) - - latent_image = kwargs.get("concat_latent_image", None) - noise = kwargs.get("noise", None) - - if latent_image is None: - latent_image = torch.zeros_like(noise) - - if latent_image.shape[1:] != noise.shape[1:]: - latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") - - latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) - - out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) - - if "time_conditioning" in kwargs: - out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"]) - - out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0]) - return out - -class SV3D_u(SVD_img2vid): - def encode_adm(self, **kwargs): - augmentation = kwargs.get("augmentation_level", 0) - - out = [] - out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) - - flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) - return flat - -class SV3D_p(SVD_img2vid): - def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): - super().__init__(model_config, model_type, device=device) - self.embedder_512 = Timestep(512) - - def encode_adm(self, **kwargs): - augmentation = kwargs.get("augmentation_level", 0) - elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here - azimuth = kwargs.get("azimuth", 0) - noise = kwargs.get("noise", None) - - out = [] - out.append(self.embedder(torch.flatten(torch.Tensor([augmentation])))) - out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0)))) - out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0)))) - - out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out)) - return torch.cat(out, dim=1) - - -class Stable_Zero123(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): - super().__init__(model_config, model_type, device=device) - self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) - self.cc_projection.weight.copy_(cc_projection_weight) - self.cc_projection.bias.copy_(cc_projection_bias) - - def extra_conds(self, **kwargs): - out = {} - - latent_image = kwargs.get("concat_latent_image", None) - noise = kwargs.get("noise", None) - - if latent_image is None: - latent_image = torch.zeros_like(noise) - - if latent_image.shape[1:] != noise.shape[1:]: - latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") - - latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) - - out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - if cross_attn.shape[-1] != 768: - cross_attn = self.cc_projection(cross_attn) - out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) - return out - -class SD_X4Upscaler(BaseModel): - def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): - super().__init__(model_config, model_type, device=device) - self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) - - def extra_conds(self, **kwargs): - out = {} - - image = kwargs.get("concat_image", None) - noise = kwargs.get("noise", None) - noise_augment = kwargs.get("noise_augmentation", 0.0) - device = kwargs["device"] - seed = kwargs["seed"] - 10 - - noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) - - if image is None: - image = torch.zeros_like(noise)[:,:3] - - if image.shape[1:] != noise.shape[1:]: - image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") - - noise_level = torch.tensor([noise_level], device=device) - if noise_augment > 0: - image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) - - image = utils.resize_to_batch_size(image, noise.shape[0]) - - out['c_concat'] = comfy.conds.CONDNoiseShape(image) - out['y'] = comfy.conds.CONDRegular(noise_level) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) - return out - -class IP2P: - def concat_cond(self, **kwargs): - image = kwargs.get("concat_latent_image", None) - noise = kwargs.get("noise", None) - device = kwargs["device"] - - if image is None: - image = torch.zeros_like(noise) - else: - image = image.to(device=device) - - if image.shape[1:] != noise.shape[1:]: - image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center") - - image = utils.resize_to_batch_size(image, noise.shape[0]) - return self.process_ip2p_image_in(image) - - -class SD15_instructpix2pix(IP2P, BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): - super().__init__(model_config, model_type, device=device) - self.process_ip2p_image_in = lambda image: image - - -class SDXL_instructpix2pix(IP2P, SDXL): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): - super().__init__(model_config, model_type, device=device) - if model_type == ModelType.V_PREDICTION_EDM: - self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p - else: - self.process_ip2p_image_in = lambda image: image #diffusers ip2p - -class Lotus(BaseModel): - def extra_conds(self, **kwargs): - out = {} - cross_attn = kwargs.get("cross_attn", None) - out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) - device = kwargs["device"] - task_emb = torch.tensor([1, 0]).float().to(device) - task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)]).unsqueeze(0) - out['y'] = comfy.conds.CONDRegular(task_emb) - return out - - def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, device=None): - super().__init__(model_config, model_type, device=device) - -class StableCascade_C(BaseModel): - def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): - super().__init__(model_config, model_type, device=device, unet_model=StageC) - self.diffusion_model.eval().requires_grad_(False) - - def extra_conds(self, **kwargs): - out = {} - clip_text_pooled = kwargs["pooled_output"] - if clip_text_pooled is not None: - out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled) - - if "unclip_conditioning" in kwargs: - embeds = [] - for unclip_cond in kwargs["unclip_conditioning"]: - weight = unclip_cond["strength"] - embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight) - clip_img = torch.cat(embeds, dim=1) - else: - clip_img = torch.zeros((1, 1, 768)) - out["clip_img"] = comfy.conds.CONDRegular(clip_img) - out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) - out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,))) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn) - return out - - -class StableCascade_B(BaseModel): - def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): - super().__init__(model_config, model_type, device=device, unet_model=StageB) - self.diffusion_model.eval().requires_grad_(False) - - def extra_conds(self, **kwargs): - out = {} - noise = kwargs.get("noise", None) - - clip_text_pooled = kwargs["pooled_output"] - if clip_text_pooled is not None: - out['clip'] = comfy.conds.CONDRegular(clip_text_pooled) - - #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched - prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) - - out["effnet"] = comfy.conds.CONDRegular(prior.to(device=noise.device)) - out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) - return out - - -class SD3(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper) - - def encode_adm(self, **kwargs): - return kwargs["pooled_output"] - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - return out - - -class AuraFlow(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - return out - - -class StableAudio1(BaseModel): - def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer) - self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) - self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512) - self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights) - self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights) - - def extra_conds(self, **kwargs): - out = {} - - noise = kwargs.get("noise", None) - device = kwargs["device"] - - seconds_start = kwargs.get("seconds_start", 0) - seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53)) - - seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device) - seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device) - - global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1)) - out['global_embed'] = comfy.conds.CONDRegular(global_embed) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1) - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - return out - - def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): - sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict) - d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()} - for k in d: - s = d[k] - for l in s: - sd["{}{}".format(k, l)] = s[l] - return sd - - -class HunyuanDiT(BaseModel): - def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask) - - conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None) - if conditioning_mt5xl is not None: - out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl) - - attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None) - if attention_mask_mt5xl is not None: - out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl) - - width = kwargs.get("width", 768) - height = kwargs.get("height", 768) - target_width = kwargs.get("target_width", width) - target_height = kwargs.get("target_height", height) - - out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]])) - return out - -class PixArt(BaseModel): - def __init__(self, model_config, model_type=ModelType.EPS, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - width = kwargs.get("width", None) - height = kwargs.get("height", None) - if width is not None and height is not None: - out["c_size"] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width]])) - out["c_ar"] = comfy.conds.CONDRegular(torch.FloatTensor([[kwargs.get("aspect_ratio", height/width)]])) - - return out - -class Flux(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux): - super().__init__(model_config, model_type, device=device, unet_model=unet_model) - self.memory_usage_factor_conds = ("ref_latents",) - - def concat_cond(self, **kwargs): - try: - #Handle Flux control loras dynamically changing the img_in weight. - num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size) - except: - #Some cases like tensorrt might not have the weights accessible - num_channels = self.model_config.unet_config["in_channels"] - - out_channels = self.model_config.unet_config["out_channels"] - - if num_channels <= out_channels: - return None - - image = kwargs.get("concat_latent_image", None) - noise = kwargs.get("noise", None) - device = kwargs["device"] - - if image is None: - image = torch.zeros_like(noise) - - image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") - image = utils.resize_to_batch_size(image, noise.shape[0]) - image = self.process_latent_in(image) - if num_channels <= out_channels * 2: - return image - - #inpaint model - mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) - if mask is None: - mask = torch.ones_like(noise)[:, :1] - - mask = torch.mean(mask, dim=1, keepdim=True) - mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center") - mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8) - mask = utils.resize_to_batch_size(mask, noise.shape[0]) - return torch.cat((image, mask), dim=1) - - def encode_adm(self, **kwargs): - return kwargs["pooled_output"] - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - # upscale the attention mask, since now we - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - shape = kwargs["noise"].shape - mask_ref_size = kwargs["attention_mask_img_shape"] - # the model will pad to the patch size, and then divide - # essentially dividing and rounding up - (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) - attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - - guidance = kwargs.get("guidance", 3.5) - if guidance is not None: - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) - - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - latents = [] - for lat in ref_latents: - latents.append(self.process_latent_in(lat)) - out['ref_latents'] = comfy.conds.CONDList(latents) - - ref_latents_method = kwargs.get("reference_latents_method", None) - if ref_latents_method is not None: - out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method) - return out - - def extra_conds_shapes(self, **kwargs): - out = {} - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) - return out - - -class GenmoMochi(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - return out - -class LTXV(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25)) - - denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) - if denoise_mask is not None: - out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) - - keyframe_idxs = kwargs.get("keyframe_idxs", None) - if keyframe_idxs is not None: - out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs) - - return out - - def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): - if denoise_mask is None: - return timestep - return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0] - - def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): - return latent_image - -class HunyuanVideo(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) - - def encode_adm(self, **kwargs): - return kwargs["pooled_output"] - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - guidance = kwargs.get("guidance", 6.0) - if guidance is not None: - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) - - guiding_frame_index = kwargs.get("guiding_frame_index", None) - if guiding_frame_index is not None: - out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index])) - - ref_latent = kwargs.get("ref_latent", None) - if ref_latent is not None: - out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent)) - - return out - - def scale_latent_inpaint(self, latent_image, **kwargs): - return latent_image - -class HunyuanVideoI2V(HunyuanVideo): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device) - self.concat_keys = ("concat_image", "mask_inverted") - - def scale_latent_inpaint(self, latent_image, **kwargs): - return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) - -class HunyuanVideoSkyreelsI2V(HunyuanVideo): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device) - self.concat_keys = ("concat_image",) - - def scale_latent_inpaint(self, latent_image, **kwargs): - return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) - -class CosmosVideo(BaseModel): - def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT) - self.image_to_video = image_to_video - if self.image_to_video: - self.concat_keys = ("mask_inverted",) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) - return out - - def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): - sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) - sigma_noise_augmentation = 0 #TODO - if sigma_noise_augmentation != 0: - latent_image = latent_image + noise - latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) - return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5) - -class CosmosPredict2(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, image_to_video=False, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.predict2.MiniTrainDIT) - self.image_to_video = image_to_video - if self.image_to_video: - self.concat_keys = ("mask_inverted",) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) - if denoise_mask is not None: - out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) - - out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) - return out - - def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): - if denoise_mask is None: - return timestep - if denoise_mask.ndim <= 4: - return timestep - condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True) - c_noise_B_1_T_1_1 = 0.0 * (1.0 - condition_video_mask_B_1_T_1_1) + timestep.reshape(timestep.shape[0], 1, 1, 1, 1) * condition_video_mask_B_1_T_1_1 - out = c_noise_B_1_T_1_1.squeeze(dim=[1, 3, 4]) - return out - - def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): - sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) - sigma_noise_augmentation = 0 #TODO - if sigma_noise_augmentation != 0: - latent_image = latent_image + noise - latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) - sigma = (sigma / (sigma + 1)) - return latent_image / (1.0 - sigma) - -class Lumina2(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - if torch.numel(attention_mask) != attention_mask.sum(): - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - return out - -class WAN21(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) - self.image_to_video = image_to_video - - def concat_cond(self, **kwargs): - noise = kwargs.get("noise", None) - extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1] - if extra_channels == 0: - return None - - image = kwargs.get("concat_latent_image", None) - device = kwargs["device"] - - if image is None: - shape_image = list(noise.shape) - shape_image[1] = extra_channels - image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) - else: - latent_dim = self.latent_format.latent_channels - image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") - for i in range(0, image.shape[1], latent_dim): - image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim]) - image = utils.resize_to_batch_size(image, noise.shape[0]) - - if extra_channels != image.shape[1] + 4: - if not self.image_to_video or extra_channels == image.shape[1]: - return image - - if image.shape[1] > (extra_channels - 4): - image = image[:, :(extra_channels - 4)] - - mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) - if mask is None: - mask = torch.zeros_like(noise)[:, :4] - else: - if mask.shape[1] != 4: - mask = torch.mean(mask, dim=1, keepdim=True) - mask = 1.0 - mask - mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") - if mask.shape[-3] < noise.shape[-3]: - mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) - if mask.shape[1] == 1: - mask = mask.repeat(1, 4, 1, 1, 1) - mask = utils.resize_to_batch_size(mask, noise.shape[0]) - - concat_mask_index = kwargs.get("concat_mask_index", 0) - if concat_mask_index != 0: - return torch.cat((image[:, :concat_mask_index], mask, image[:, concat_mask_index:]), dim=1) - else: - return torch.cat((mask, image), dim=1) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - clip_vision_output = kwargs.get("clip_vision_output", None) - if clip_vision_output is not None: - out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states) - - time_dim_concat = kwargs.get("time_dim_concat", None) - if time_dim_concat is not None: - out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat)) - - reference_latents = kwargs.get("reference_latents", None) - if reference_latents is not None: - out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0]) - - return out - - -class WAN21_Vace(WAN21): - def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): - super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel) - self.image_to_video = image_to_video - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - noise = kwargs.get("noise", None) - noise_shape = list(noise.shape) - vace_frames = kwargs.get("vace_frames", None) - if vace_frames is None: - noise_shape[1] = 32 - vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)] - - mask = kwargs.get("vace_mask", None) - if mask is None: - noise_shape[1] = 64 - mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames) - - vace_frames_out = [] - for j in range(len(vace_frames)): - vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True) - for i in range(0, vf.shape[1], 16): - vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16]) - vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1) - vace_frames_out.append(vf) - - vace_frames = torch.stack(vace_frames_out, dim=1) - out['vace_context'] = comfy.conds.CONDRegular(vace_frames) - - vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out)) - out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) - return out - -class WAN21_Camera(WAN21): - def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): - super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel) - self.image_to_video = image_to_video - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - camera_conditions = kwargs.get("camera_conditions", None) - if camera_conditions is not None: - out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions) - return out - -class WAN22_S2V(WAN21): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V) - self.memory_usage_factor_conds = ("reference_latent", "reference_motion") - self.memory_usage_shape_process = {"reference_motion": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]} - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - audio_embed = kwargs.get("audio_embed", None) - if audio_embed is not None: - out['audio_embed'] = comfy.conds.CONDRegular(audio_embed) - - reference_latents = kwargs.get("reference_latents", None) - if reference_latents is not None: - out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])) - - reference_motion = kwargs.get("reference_motion", None) - if reference_motion is not None: - out['reference_motion'] = comfy.conds.CONDRegular(self.process_latent_in(reference_motion)) - - control_video = kwargs.get("control_video", None) - if control_video is not None: - out['control_video'] = comfy.conds.CONDRegular(self.process_latent_in(control_video)) - return out - - def extra_conds_shapes(self, **kwargs): - out = {} - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - out['reference_latent'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) - - reference_motion = kwargs.get("reference_motion", None) - if reference_motion is not None: - out['reference_motion'] = reference_motion.shape - return out - -class WAN22(WAN21): - def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): - super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) - self.image_to_video = image_to_video - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - denoise_mask = kwargs.get("denoise_mask", None) - if denoise_mask is not None: - out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) - return out - - def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): - if denoise_mask is None: - return timestep - temp_ts = (torch.mean(denoise_mask[:, :, :, :, :], dim=(1, 3, 4), keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1) - return temp_ts - - def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): - return latent_image - -class Hunyuan3Dv2(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - guidance = kwargs.get("guidance", 5.0) - if guidance is not None: - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) - return out - -class HiDream(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel) - - def encode_adm(self, **kwargs): - return kwargs["pooled_output"] - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - conditioning_llama3 = kwargs.get("conditioning_llama3", None) - if conditioning_llama3 is not None: - out['encoder_hidden_states_llama3'] = comfy.conds.CONDRegular(conditioning_llama3) - image_cond = kwargs.get("concat_latent_image", None) - if image_cond is not None: - out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) - return out - -class Chroma(Flux): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - - guidance = kwargs.get("guidance", 0) - if guidance is not None: - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) - return out - -class ACEStep(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - noise = kwargs.get("noise", None) - - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - - conditioning_lyrics = kwargs.get("conditioning_lyrics", None) - if cross_attn is not None: - out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics) - out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype)) - out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0)) - return out - -class Omnigen2(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLOW, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel) - self.memory_usage_factor_conds = ("ref_latents",) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - attention_mask = kwargs.get("attention_mask", None) - if attention_mask is not None: - if torch.numel(attention_mask) != attention_mask.sum(): - out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - latents = [] - for lat in ref_latents: - latents.append(self.process_latent_in(lat)) - out['ref_latents'] = comfy.conds.CONDList(latents) - return out - - def extra_conds_shapes(self, **kwargs): - out = {} - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) - return out - -class QwenImage(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel) - self.memory_usage_factor_conds = ("ref_latents",) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - cross_attn = kwargs.get("cross_attn", None) - if cross_attn is not None: - out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - latents = [] - for lat in ref_latents: - latents.append(self.process_latent_in(lat)) - out['ref_latents'] = comfy.conds.CONDList(latents) - - ref_latents_method = kwargs.get("reference_latents_method", None) - if ref_latents_method is not None: - out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method) - return out - - def extra_conds_shapes(self, **kwargs): - out = {} - ref_latents = kwargs.get("reference_latents", None) - if ref_latents is not None: - out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) - return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py deleted file mode 100644 index 9f3ab64dfe40ee8af820c6f4f96625eca14b703a..0000000000000000000000000000000000000000 --- a/comfy/model_detection.py +++ /dev/null @@ -1,927 +0,0 @@ -import json -import comfy.supported_models -import comfy.supported_models_base -import comfy.utils -import math -import logging -import torch - -def count_blocks(state_dict_keys, prefix_string): - count = 0 - while True: - c = False - for k in state_dict_keys: - if k.startswith(prefix_string.format(count)): - c = True - break - if c == False: - break - count += 1 - return count - -def calculate_transformer_depth(prefix, state_dict_keys, state_dict): - context_dim = None - use_linear_in_transformer = False - - transformer_prefix = prefix + "1.transformer_blocks." - transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) - if len(transformer_keys) > 0: - last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') - context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] - use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 - time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict - time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict - return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross - return None - -def detect_unet_config(state_dict, key_prefix, metadata=None): - state_dict_keys = list(state_dict.keys()) - - if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model - unet_config = {} - unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1] - patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2] - unet_config["patch_size"] = patch_size - final_layer = '{}final_layer.linear.weight'.format(key_prefix) - if final_layer in state_dict: - unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size) - - unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64 - unet_config["input_size"] = None - y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix) - if y_key in state_dict_keys: - unet_config["adm_in_channels"] = state_dict[y_key].shape[1] - - context_key = '{}context_embedder.weight'.format(key_prefix) - if context_key in state_dict_keys: - in_features = state_dict[context_key].shape[1] - out_features = state_dict[context_key].shape[0] - unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}} - num_patches_key = '{}pos_embed'.format(key_prefix) - if num_patches_key in state_dict_keys: - num_patches = state_dict[num_patches_key].shape[1] - unet_config["num_patches"] = num_patches - unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches)) - - rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix) - if rms_qk in state_dict_keys: - unet_config["qk_norm"] = "rms" - - unet_config["pos_embed_scaling_factor"] = None #unused for inference - context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix) - if context_processor in state_dict_keys: - unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.') - unet_config["x_block_self_attn_layers"] = [] - for key in state_dict_keys: - if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'): - layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')] - unet_config["x_block_self_attn_layers"].append(int(layer)) - return unet_config - - if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade - unet_config = {} - text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix) - if text_mapper_name in state_dict_keys: - unet_config['stable_cascade_stage'] = 'c' - w = state_dict[text_mapper_name] - if w.shape[0] == 1536: #stage c lite - unet_config['c_cond'] = 1536 - unet_config['c_hidden'] = [1536, 1536] - unet_config['nhead'] = [24, 24] - unet_config['blocks'] = [[4, 12], [12, 4]] - elif w.shape[0] == 2048: #stage c full - unet_config['c_cond'] = 2048 - elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys: - unet_config['stable_cascade_stage'] = 'b' - w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)] - if w.shape[-1] == 640: - unet_config['c_hidden'] = [320, 640, 1280, 1280] - unet_config['nhead'] = [-1, -1, 20, 20] - unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]] - unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]] - elif w.shape[-1] == 576: #stage b lite - unet_config['c_hidden'] = [320, 576, 1152, 1152] - unet_config['nhead'] = [-1, 9, 18, 18] - unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]] - unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]] - return unet_config - - if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit - unet_config = {} - unet_config["audio_model"] = "dit1.0" - return unet_config - - if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit - unet_config = {} - unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1] - unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1] - double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.') - single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.') - unet_config["n_double_layers"] = double_layers - unet_config["n_layers"] = double_layers + single_layers - return unet_config - - if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT - unet_config = {} - unet_config["image_model"] = "hydit" - unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') - unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] - if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2 - unet_config["mlp_ratio"] = 4.3637 - if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968: - unet_config["size_cond"] = True - unet_config["use_style_cond"] = True - unet_config["image_model"] = "hydit1" - return unet_config - - if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video - dit_config = {} - dit_config["image_model"] = "hunyuan_video" - dit_config["in_channels"] = state_dict['{}img_in.proj.weight'.format(key_prefix)].shape[1] #SkyReels img2video has 32 input channels - dit_config["patch_size"] = [1, 2, 2] - dit_config["out_channels"] = 16 - dit_config["vec_in_dim"] = 768 - dit_config["context_in_dim"] = 4096 - dit_config["hidden_size"] = 3072 - dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 24 - dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') - dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') - dit_config["axes_dim"] = [16, 56, 56] - dit_config["theta"] = 256 - dit_config["qkv_bias"] = True - guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys)) - dit_config["guidance_embed"] = len(guidance_keys) > 0 - return dit_config - - if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux - dit_config = {} - dit_config["image_model"] = "flux" - dit_config["in_channels"] = 16 - patch_size = 2 - dit_config["patch_size"] = patch_size - in_key = "{}img_in.weight".format(key_prefix) - if in_key in state_dict_keys: - dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size) - dit_config["out_channels"] = 16 - vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix) - if vec_in_key in state_dict_keys: - dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1] - dit_config["context_in_dim"] = 4096 - dit_config["hidden_size"] = 3072 - dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 24 - dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') - dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') - dit_config["axes_dim"] = [16, 56, 56] - dit_config["theta"] = 10000 - dit_config["qkv_bias"] = True - if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma - dit_config["image_model"] = "chroma" - dit_config["in_channels"] = 64 - dit_config["out_channels"] = 64 - dit_config["in_dim"] = 64 - dit_config["out_dim"] = 3072 - dit_config["hidden_dim"] = 5120 - dit_config["n_layers"] = 5 - else: - dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys - return dit_config - - if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview - dit_config = {} - dit_config["image_model"] = "mochi_preview" - dit_config["depth"] = 48 - dit_config["patch_size"] = 2 - dit_config["num_heads"] = 24 - dit_config["hidden_size_x"] = 3072 - dit_config["hidden_size_y"] = 1536 - dit_config["mlp_ratio_x"] = 4.0 - dit_config["mlp_ratio_y"] = 4.0 - dit_config["learn_sigma"] = False - dit_config["in_channels"] = 12 - dit_config["qk_norm"] = True - dit_config["qkv_bias"] = False - dit_config["out_bias"] = True - dit_config["attn_drop"] = 0.0 - dit_config["patch_embed_bias"] = True - dit_config["posenc_preserve_area"] = True - dit_config["timestep_mlp_bias"] = True - dit_config["attend_to_padding"] = False - dit_config["timestep_scale"] = 1000.0 - dit_config["use_t5"] = True - dit_config["t5_feat_dim"] = 4096 - dit_config["t5_token_length"] = 256 - dit_config["rope_theta"] = 10000.0 - return dit_config - - if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys: - # PixArt diffusers - return None - - if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv - dit_config = {} - dit_config["image_model"] = "ltxv" - dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') - shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape - dit_config["attention_head_dim"] = shape[0] // 32 - dit_config["cross_attention_dim"] = shape[1] - if metadata is not None and "config" in metadata: - dit_config.update(json.loads(metadata["config"]).get("transformer", {})) - return dit_config - - if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model - dit_config = {} - dit_config["audio_model"] = "ace" - dit_config["attention_head_dim"] = 128 - dit_config["in_channels"] = 8 - dit_config["inner_dim"] = 2560 - dit_config["max_height"] = 16 - dit_config["max_position"] = 32768 - dit_config["max_width"] = 32768 - dit_config["mlp_ratio"] = 2.5 - dit_config["num_attention_heads"] = 20 - dit_config["num_layers"] = 24 - dit_config["out_channels"] = 8 - dit_config["patch_size"] = [16, 1] - dit_config["rope_theta"] = 1000000.0 - dit_config["speaker_embedding_dim"] = 512 - dit_config["text_embedding_dim"] = 768 - - dit_config["ssl_encoder_depths"] = [8, 8] - dit_config["ssl_latent_dims"] = [1024, 768] - dit_config["ssl_names"] = ["mert", "m-hubert"] - dit_config["lyric_encoder_vocab_size"] = 6693 - dit_config["lyric_hidden_size"] = 1024 - return dit_config - - if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt - patch_size = 2 - dit_config = {} - dit_config["num_heads"] = 16 - dit_config["patch_size"] = patch_size - dit_config["hidden_size"] = 1152 - dit_config["in_channels"] = 4 - dit_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') - - y_key = "{}y_embedder.y_embedding".format(key_prefix) - if y_key in state_dict_keys: - dit_config["model_max_length"] = state_dict[y_key].shape[0] - - pe_key = "{}pos_embed".format(key_prefix) - if pe_key in state_dict_keys: - dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size - dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess - - ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix) - if ar_key in state_dict_keys: - dit_config["image_model"] = "pixart_alpha" - dit_config["micro_condition"] = True - else: - dit_config["image_model"] = "pixart_sigma" - dit_config["micro_condition"] = False - return dit_config - - if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos - dit_config = {} - dit_config["image_model"] = "cosmos" - dit_config["max_img_h"] = 240 - dit_config["max_img_w"] = 240 - dit_config["max_frames"] = 128 - concat_padding_mask = True - dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) - dit_config["out_channels"] = 16 - dit_config["patch_spatial"] = 2 - dit_config["patch_temporal"] = 1 - dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0] - dit_config["block_config"] = "FA-CA-MLP" - dit_config["concat_padding_mask"] = concat_padding_mask - dit_config["pos_emb_cls"] = "rope3d" - dit_config["pos_emb_learnable"] = False - dit_config["pos_emb_interpolation"] = "crop" - dit_config["block_x_format"] = "THWBD" - dit_config["affline_emb_norm"] = True - dit_config["use_adaln_lora"] = True - dit_config["adaln_lora_dim"] = 256 - - if dit_config["model_channels"] == 4096: - # 7B - dit_config["num_blocks"] = 28 - dit_config["num_heads"] = 32 - dit_config["extra_per_block_abs_pos_emb"] = True - dit_config["rope_h_extrapolation_ratio"] = 1.0 - dit_config["rope_w_extrapolation_ratio"] = 1.0 - dit_config["rope_t_extrapolation_ratio"] = 2.0 - dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" - else: # 5120 - # 14B - dit_config["num_blocks"] = 36 - dit_config["num_heads"] = 40 - dit_config["extra_per_block_abs_pos_emb"] = True - dit_config["rope_h_extrapolation_ratio"] = 2.0 - dit_config["rope_w_extrapolation_ratio"] = 2.0 - dit_config["rope_t_extrapolation_ratio"] = 2.0 - dit_config["extra_h_extrapolation_ratio"] = 2.0 - dit_config["extra_w_extrapolation_ratio"] = 2.0 - dit_config["extra_t_extrapolation_ratio"] = 2.0 - dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" - return dit_config - - if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys: # Lumina 2 - dit_config = {} - dit_config["image_model"] = "lumina2" - dit_config["patch_size"] = 2 - dit_config["in_channels"] = 16 - dit_config["dim"] = 2304 - dit_config["cap_feat_dim"] = 2304 - dit_config["n_layers"] = 26 - dit_config["n_heads"] = 24 - dit_config["n_kv_heads"] = 8 - dit_config["qk_norm"] = True - dit_config["axes_dims"] = [32, 32, 32] - dit_config["axes_lens"] = [300, 512, 512] - return dit_config - - if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1 - dit_config = {} - dit_config["image_model"] = "wan2.1" - dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1] - out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4 - dit_config["dim"] = dim - dit_config["out_dim"] = out_dim - dit_config["num_heads"] = dim // 128 - dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0] - dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') - dit_config["patch_size"] = (1, 2, 2) - dit_config["freq_dim"] = 256 - dit_config["window_size"] = (-1, -1) - dit_config["qk_norm"] = True - dit_config["cross_attn_norm"] = True - dit_config["eps"] = 1e-6 - dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1] - if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys: - dit_config["model_type"] = "vace" - dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1] - dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.') - elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys: - if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: - dit_config["model_type"] = "camera" - else: - dit_config["model_type"] = "camera_2.2" - elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys: - dit_config["model_type"] = "s2v" - else: - if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: - dit_config["model_type"] = "i2v" - else: - dit_config["model_type"] = "t2v" - flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix)) - if flf_weight is not None: - dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1] - - ref_conv_weight = state_dict.get('{}ref_conv.weight'.format(key_prefix)) - if ref_conv_weight is not None: - dit_config["in_dim_ref_conv"] = ref_conv_weight.shape[1] - - return dit_config - - if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D - in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape - dit_config = {} - dit_config["image_model"] = "hunyuan3d2" - dit_config["in_channels"] = in_shape[1] - dit_config["context_in_dim"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1] - dit_config["hidden_size"] = in_shape[0] - dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 16 - dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') - dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') - dit_config["qkv_bias"] = True - dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys - return dit_config - - if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream - dit_config = {} - dit_config["image_model"] = "hidream" - dit_config["attention_head_dim"] = 128 - dit_config["axes_dims_rope"] = [64, 32, 32] - dit_config["caption_channels"] = [4096, 4096] - dit_config["max_resolution"] = [128, 128] - dit_config["in_channels"] = 16 - dit_config["llama_layers"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31] - dit_config["num_attention_heads"] = 20 - dit_config["num_routed_experts"] = 4 - dit_config["num_activated_experts"] = 2 - dit_config["num_layers"] = 16 - dit_config["num_single_layers"] = 32 - dit_config["out_channels"] = 16 - dit_config["patch_size"] = 2 - dit_config["text_emb_dim"] = 2048 - return dit_config - - if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2 - dit_config = {} - dit_config["image_model"] = "cosmos_predict2" - dit_config["max_img_h"] = 240 - dit_config["max_img_w"] = 240 - dit_config["max_frames"] = 128 - concat_padding_mask = True - dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) - dit_config["out_channels"] = 16 - dit_config["patch_spatial"] = 2 - dit_config["patch_temporal"] = 1 - dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0] - dit_config["concat_padding_mask"] = concat_padding_mask - dit_config["crossattn_emb_channels"] = 1024 - dit_config["pos_emb_cls"] = "rope3d" - dit_config["pos_emb_learnable"] = True - dit_config["pos_emb_interpolation"] = "crop" - dit_config["min_fps"] = 1 - dit_config["max_fps"] = 30 - - dit_config["use_adaln_lora"] = True - dit_config["adaln_lora_dim"] = 256 - if dit_config["model_channels"] == 2048: - dit_config["num_blocks"] = 28 - dit_config["num_heads"] = 16 - elif dit_config["model_channels"] == 5120: - dit_config["num_blocks"] = 36 - dit_config["num_heads"] = 40 - - if dit_config["in_channels"] == 16: - dit_config["extra_per_block_abs_pos_emb"] = False - dit_config["rope_h_extrapolation_ratio"] = 4.0 - dit_config["rope_w_extrapolation_ratio"] = 4.0 - dit_config["rope_t_extrapolation_ratio"] = 1.0 - elif dit_config["in_channels"] == 17: # img to video - if dit_config["model_channels"] == 2048: - dit_config["extra_per_block_abs_pos_emb"] = False - dit_config["rope_h_extrapolation_ratio"] = 3.0 - dit_config["rope_w_extrapolation_ratio"] = 3.0 - dit_config["rope_t_extrapolation_ratio"] = 1.0 - elif dit_config["model_channels"] == 5120: - dit_config["rope_h_extrapolation_ratio"] = 2.0 - dit_config["rope_w_extrapolation_ratio"] = 2.0 - dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334 - - dit_config["extra_h_extrapolation_ratio"] = 1.0 - dit_config["extra_w_extrapolation_ratio"] = 1.0 - dit_config["extra_t_extrapolation_ratio"] = 1.0 - dit_config["rope_enable_fps_modulation"] = False - - return dit_config - - if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2 - dit_config = {} - dit_config["image_model"] = "omnigen2" - dit_config["axes_dim_rope"] = [40, 40, 40] - dit_config["axes_lens"] = [1024, 1664, 1664] - dit_config["ffn_dim_multiplier"] = None - dit_config["hidden_size"] = 2520 - dit_config["in_channels"] = 16 - dit_config["multiple_of"] = 256 - dit_config["norm_eps"] = 1e-05 - dit_config["num_attention_heads"] = 21 - dit_config["num_kv_heads"] = 7 - dit_config["num_layers"] = 32 - dit_config["num_refiner_layers"] = 2 - dit_config["out_channels"] = None - dit_config["patch_size"] = 2 - dit_config["text_feat_dim"] = 2048 - dit_config["timestep_scale"] = 1000.0 - return dit_config - - if '{}txt_norm.weight'.format(key_prefix) in state_dict_keys: # Qwen Image - dit_config = {} - dit_config["image_model"] = "qwen_image" - dit_config["in_channels"] = state_dict['{}img_in.weight'.format(key_prefix)].shape[1] - dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') - return dit_config - - if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: - return None - - unet_config = { - "use_checkpoint": False, - "image_size": 32, - "use_spatial_transformer": True, - "legacy": False - } - - y_input = '{}label_emb.0.0.weight'.format(key_prefix) - if y_input in state_dict_keys: - unet_config["num_classes"] = "sequential" - unet_config["adm_in_channels"] = state_dict[y_input].shape[1] - else: - unet_config["adm_in_channels"] = None - - model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] - in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] - - out_key = '{}out.2.weight'.format(key_prefix) - if out_key in state_dict: - out_channels = state_dict[out_key].shape[0] - else: - out_channels = 4 - - num_res_blocks = [] - channel_mult = [] - transformer_depth = [] - transformer_depth_output = [] - context_dim = None - use_linear_in_transformer = False - - video_model = False - video_model_cross = False - - current_res = 1 - count = 0 - - last_res_blocks = 0 - last_channel_mult = 0 - - input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') - for count in range(input_block_count): - prefix = '{}input_blocks.{}.'.format(key_prefix, count) - prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) - - block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) - if len(block_keys) == 0: - break - - block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) - - if "{}0.op.weight".format(prefix) in block_keys: #new layer - num_res_blocks.append(last_res_blocks) - channel_mult.append(last_channel_mult) - - current_res *= 2 - last_res_blocks = 0 - last_channel_mult = 0 - out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) - if out is not None: - transformer_depth_output.append(out[0]) - else: - transformer_depth_output.append(0) - else: - res_block_prefix = "{}0.in_layers.0.weight".format(prefix) - if res_block_prefix in block_keys: - last_res_blocks += 1 - last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels - - out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) - if out is not None: - transformer_depth.append(out[0]) - if context_dim is None: - context_dim = out[1] - use_linear_in_transformer = out[2] - video_model = out[3] - video_model_cross = out[4] - else: - transformer_depth.append(0) - - res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) - if res_block_prefix in block_keys_output: - out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) - if out is not None: - transformer_depth_output.append(out[0]) - else: - transformer_depth_output.append(0) - - - num_res_blocks.append(last_res_blocks) - channel_mult.append(last_channel_mult) - if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: - transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') - elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys: - transformer_depth_middle = -1 - else: - transformer_depth_middle = -2 - - unet_config["in_channels"] = in_channels - unet_config["out_channels"] = out_channels - unet_config["model_channels"] = model_channels - unet_config["num_res_blocks"] = num_res_blocks - unet_config["transformer_depth"] = transformer_depth - unet_config["transformer_depth_output"] = transformer_depth_output - unet_config["channel_mult"] = channel_mult - unet_config["transformer_depth_middle"] = transformer_depth_middle - unet_config['use_linear_in_transformer'] = use_linear_in_transformer - unet_config["context_dim"] = context_dim - - if video_model: - unet_config["extra_ff_mix_layer"] = True - unet_config["use_spatial_context"] = True - unet_config["merge_strategy"] = "learned_with_images" - unet_config["merge_factor"] = 0.0 - unet_config["video_kernel_size"] = [3, 1, 1] - unet_config["use_temporal_resblock"] = True - unet_config["use_temporal_attention"] = True - unet_config["disable_temporal_crossattention"] = not video_model_cross - else: - unet_config["use_temporal_resblock"] = False - unet_config["use_temporal_attention"] = False - - return unet_config - -def model_config_from_unet_config(unet_config, state_dict=None): - for model_config in comfy.supported_models.models: - if model_config.matches(unet_config, state_dict): - return model_config(unet_config) - - logging.error("no match {}".format(unet_config)) - return None - -def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None): - unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata) - if unet_config is None: - return None - model_config = model_config_from_unet_config(unet_config, state_dict) - if model_config is None and use_base_if_no_match: - model_config = comfy.supported_models_base.BASE(unet_config) - - scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix) - if scaled_fp8_key in state_dict: - scaled_fp8_weight = state_dict.pop(scaled_fp8_key) - model_config.scaled_fp8 = scaled_fp8_weight.dtype - if model_config.scaled_fp8 == torch.float32: - model_config.scaled_fp8 = torch.float8_e4m3fn - if scaled_fp8_weight.nelement() == 2: - model_config.optimizations["fp8"] = False - else: - model_config.optimizations["fp8"] = True - - return model_config - -def unet_prefix_from_state_dict(state_dict): - candidates = ["model.diffusion_model.", #ldm/sgm models - "model.model.", #audio models - "net.", #cosmos - ] - counts = {k: 0 for k in candidates} - for k in state_dict: - for c in candidates: - if k.startswith(c): - counts[c] += 1 - break - - top = max(counts, key=counts.get) - if counts[top] > 5: - return top - else: - return "model." #aura flow and others - - -def convert_config(unet_config): - new_config = unet_config.copy() - num_res_blocks = new_config.get("num_res_blocks", None) - channel_mult = new_config.get("channel_mult", None) - - if isinstance(num_res_blocks, int): - num_res_blocks = len(channel_mult) * [num_res_blocks] - - if "attention_resolutions" in new_config: - attention_resolutions = new_config.pop("attention_resolutions") - transformer_depth = new_config.get("transformer_depth", None) - transformer_depth_middle = new_config.get("transformer_depth_middle", None) - - if isinstance(transformer_depth, int): - transformer_depth = len(channel_mult) * [transformer_depth] - if transformer_depth_middle is None: - transformer_depth_middle = transformer_depth[-1] - t_in = [] - t_out = [] - s = 1 - for i in range(len(num_res_blocks)): - res = num_res_blocks[i] - d = 0 - if s in attention_resolutions: - d = transformer_depth[i] - - t_in += [d] * res - t_out += [d] * (res + 1) - s *= 2 - transformer_depth = t_in - new_config["transformer_depth"] = t_in - new_config["transformer_depth_output"] = t_out - new_config["transformer_depth_middle"] = transformer_depth_middle - - new_config["num_res_blocks"] = num_res_blocks - return new_config - - -def unet_config_from_diffusers_unet(state_dict, dtype=None): - if "conv_in.weight" not in state_dict: - return None - - match = {} - transformer_depth = [] - - attn_res = 1 - down_blocks = count_blocks(state_dict, "down_blocks.{}") - for i in range(down_blocks): - attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') - res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}') - for ab in range(attn_blocks): - transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') - transformer_depth.append(transformer_count) - if transformer_count > 0: - match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] - - attn_res *= 2 - if attn_blocks == 0: - for i in range(res_blocks): - transformer_depth.append(0) - - match["transformer_depth"] = transformer_depth - - match["model_channels"] = state_dict["conv_in.weight"].shape[0] - match["in_channels"] = state_dict["conv_in.weight"].shape[1] - match["adm_in_channels"] = None - if "class_embedding.linear_1.weight" in state_dict: - match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] - elif "add_embedding.linear_1.weight" in state_dict: - match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] - - SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, - 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, - 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], - 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, - 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, - 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, - 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, - 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], - 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, - 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, - 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, - 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], - 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], - 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5], - 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, - 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6], - 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], - 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, - 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1], - 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]} - - SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, - 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1], - 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False, - 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, - 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], - 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, - 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4, - 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], - 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_heads': 8, - 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], - 'use_temporal_attention': False, 'use_temporal_resblock': False} - - supported_models = [LotusD, SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint] - - for unet_config in supported_models: - matches = True - for k in match: - if match[k] != unet_config[k]: - matches = False - break - if matches: - return convert_config(unet_config) - return None - -def model_config_from_diffusers_unet(state_dict): - unet_config = unet_config_from_diffusers_unet(state_dict) - if unet_config is not None: - return model_config_from_unet_config(unet_config) - return None - -def convert_diffusers_mmdit(state_dict, output_prefix=""): - out_sd = {} - - if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow - num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.') - num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.') - sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix) - elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt - num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') - sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix) - elif 'x_embedder.weight' in state_dict: #Flux - depth = count_blocks(state_dict, 'transformer_blocks.{}.') - depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.') - hidden_size = state_dict["x_embedder.bias"].shape[0] - sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix) - elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict and 'pos_embed.proj.weight' in state_dict: #SD3 - num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') - depth = state_dict["pos_embed.proj.weight"].shape[0] // 64 - sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix) - else: - return None - - for k in sd_map: - weight = state_dict.get(k, None) - if weight is not None: - t = sd_map[k] - - if not isinstance(t, str): - if len(t) > 2: - fun = t[2] - else: - fun = lambda a: a - offset = t[1] - if offset is not None: - old_weight = out_sd.get(t[0], None) - if old_weight is None: - old_weight = torch.empty_like(weight) - if old_weight.shape[offset[0]] < offset[1] + offset[2]: - exp = list(weight.shape) - exp[offset[0]] = offset[1] + offset[2] - new = torch.empty(exp, device=weight.device, dtype=weight.dtype) - new[:old_weight.shape[0]] = old_weight - old_weight = new - - w = old_weight.narrow(offset[0], offset[1], offset[2]) - else: - old_weight = weight - w = weight - w[:] = fun(weight) - t = t[0] - out_sd[t] = old_weight - else: - out_sd[t] = weight - state_dict.pop(k) - - return out_sd diff --git a/comfy/model_management.py b/comfy/model_management.py deleted file mode 100644 index d08aee1fe10acc4207d1feef083993374eb565bf..0000000000000000000000000000000000000000 --- a/comfy/model_management.py +++ /dev/null @@ -1,1408 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import psutil -import logging -from enum import Enum -from comfy.cli_args import args, PerformanceFeature -import torch -import sys -import platform -import weakref -import gc - -class VRAMState(Enum): - DISABLED = 0 #No vram present: no need to move models to vram - NO_VRAM = 1 #Very low vram: enable all the options to save vram - LOW_VRAM = 2 - NORMAL_VRAM = 3 - HIGH_VRAM = 4 - SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. - -class CPUState(Enum): - GPU = 0 - CPU = 1 - MPS = 2 - -# Determine VRAM State -vram_state = VRAMState.NORMAL_VRAM -set_vram_to = VRAMState.NORMAL_VRAM -cpu_state = CPUState.GPU - -total_vram = 0 - -def get_supported_float8_types(): - float8_types = [] - try: - float8_types.append(torch.float8_e4m3fn) - except: - pass - try: - float8_types.append(torch.float8_e4m3fnuz) - except: - pass - try: - float8_types.append(torch.float8_e5m2) - except: - pass - try: - float8_types.append(torch.float8_e5m2fnuz) - except: - pass - try: - float8_types.append(torch.float8_e8m0fnu) - except: - pass - return float8_types - -FLOAT8_TYPES = get_supported_float8_types() - -xpu_available = False -torch_version = "" -try: - torch_version = torch.version.__version__ - temp = torch_version.split(".") - torch_version_numeric = (int(temp[0]), int(temp[1])) -except: - pass - -lowvram_available = True -if args.deterministic: - logging.info("Using deterministic algorithms for pytorch") - torch.use_deterministic_algorithms(True, warn_only=True) - -directml_enabled = False -if args.directml is not None: - import torch_directml - directml_enabled = True - device_index = args.directml - if device_index < 0: - directml_device = torch_directml.device() - else: - directml_device = torch_directml.device(device_index) - logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index))) - # torch_directml.disable_tiled_resources(True) - lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. - -try: - import intel_extension_for_pytorch as ipex # noqa: F401 -except: - pass - -try: - _ = torch.xpu.device_count() - xpu_available = torch.xpu.is_available() -except: - xpu_available = False - -try: - if torch.backends.mps.is_available(): - cpu_state = CPUState.MPS - import torch.mps -except: - pass - -try: - import torch_npu # noqa: F401 - _ = torch.npu.device_count() - npu_available = torch.npu.is_available() -except: - npu_available = False - -try: - import torch_mlu # noqa: F401 - _ = torch.mlu.device_count() - mlu_available = torch.mlu.is_available() -except: - mlu_available = False - -try: - ixuca_available = hasattr(torch, "corex") -except: - ixuca_available = False - -if args.cpu: - cpu_state = CPUState.CPU - -def is_intel_xpu(): - global cpu_state - global xpu_available - if cpu_state == CPUState.GPU: - if xpu_available: - return True - return False - -def is_ascend_npu(): - global npu_available - if npu_available: - return True - return False - -def is_mlu(): - global mlu_available - if mlu_available: - return True - return False - -def is_ixuca(): - global ixuca_available - if ixuca_available: - return True - return False - -def get_torch_device(): - global directml_enabled - global cpu_state - if directml_enabled: - global directml_device - return directml_device - if cpu_state == CPUState.MPS: - return torch.device("mps") - if cpu_state == CPUState.CPU: - return torch.device("cpu") - else: - if is_intel_xpu(): - return torch.device("xpu", torch.xpu.current_device()) - elif is_ascend_npu(): - return torch.device("npu", torch.npu.current_device()) - elif is_mlu(): - return torch.device("mlu", torch.mlu.current_device()) - else: - return torch.device(torch.cuda.current_device()) - -def get_total_memory(dev=None, torch_total_too=False): - global directml_enabled - if dev is None: - dev = get_torch_device() - - if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): - mem_total = psutil.virtual_memory().total - mem_total_torch = mem_total - else: - if directml_enabled: - mem_total = 1024 * 1024 * 1024 #TODO - mem_total_torch = mem_total - elif is_intel_xpu(): - stats = torch.xpu.memory_stats(dev) - mem_reserved = stats['reserved_bytes.all.current'] - mem_total_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_total_torch = mem_reserved - mem_total = mem_total_xpu - elif is_ascend_npu(): - stats = torch.npu.memory_stats(dev) - mem_reserved = stats['reserved_bytes.all.current'] - _, mem_total_npu = torch.npu.mem_get_info(dev) - mem_total_torch = mem_reserved - mem_total = mem_total_npu - elif is_mlu(): - stats = torch.mlu.memory_stats(dev) - mem_reserved = stats['reserved_bytes.all.current'] - _, mem_total_mlu = torch.mlu.mem_get_info(dev) - mem_total_torch = mem_reserved - mem_total = mem_total_mlu - else: - stats = torch.cuda.memory_stats(dev) - mem_reserved = stats['reserved_bytes.all.current'] - _, mem_total_cuda = torch.cuda.mem_get_info(dev) - mem_total_torch = mem_reserved - mem_total = mem_total_cuda - - if torch_total_too: - return (mem_total, mem_total_torch) - else: - return mem_total - -def mac_version(): - try: - return tuple(int(n) for n in platform.mac_ver()[0].split(".")) - except: - return None - -total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) -total_ram = psutil.virtual_memory().total / (1024 * 1024) -logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) - -try: - logging.info("pytorch version: {}".format(torch_version)) - mac_ver = mac_version() - if mac_ver is not None: - logging.info("Mac Version {}".format(mac_ver)) -except: - pass - -try: - OOM_EXCEPTION = torch.cuda.OutOfMemoryError -except: - OOM_EXCEPTION = Exception - -XFORMERS_VERSION = "" -XFORMERS_ENABLED_VAE = True -if args.disable_xformers: - XFORMERS_IS_AVAILABLE = False -else: - try: - import xformers - import xformers.ops - XFORMERS_IS_AVAILABLE = True - try: - XFORMERS_IS_AVAILABLE = xformers._has_cpp_library - except: - pass - try: - XFORMERS_VERSION = xformers.version.__version__ - logging.info("xformers version: {}".format(XFORMERS_VERSION)) - if XFORMERS_VERSION.startswith("0.0.18"): - logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") - logging.warning("Please downgrade or upgrade xformers to a different version.\n") - XFORMERS_ENABLED_VAE = False - except: - pass - except: - XFORMERS_IS_AVAILABLE = False - -def is_nvidia(): - global cpu_state - if cpu_state == CPUState.GPU: - if torch.version.cuda: - return True - return False - -def is_amd(): - global cpu_state - if cpu_state == CPUState.GPU: - if torch.version.hip: - return True - return False - -MIN_WEIGHT_MEMORY_RATIO = 0.4 -if is_nvidia(): - MIN_WEIGHT_MEMORY_RATIO = 0.0 - -ENABLE_PYTORCH_ATTENTION = False -if args.use_pytorch_cross_attention: - ENABLE_PYTORCH_ATTENTION = True - XFORMERS_IS_AVAILABLE = False - -try: - if is_nvidia(): - if torch_version_numeric[0] >= 2: - if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: - ENABLE_PYTORCH_ATTENTION = True - if is_intel_xpu() or is_ascend_npu() or is_mlu() or is_ixuca(): - if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: - ENABLE_PYTORCH_ATTENTION = True -except: - pass - - -SUPPORT_FP8_OPS = args.supports_fp8_compute -try: - if is_amd(): - try: - rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2])) - except: - rocm_version = (6, -1) - arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName - logging.info("AMD arch: {}".format(arch)) - logging.info("ROCm version: {}".format(rocm_version)) - if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: - if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much - if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches, TODO: gfx950 - ENABLE_PYTORCH_ATTENTION = True -# if torch_version_numeric >= (2, 8): -# if any((a in arch) for a in ["gfx1201"]): -# ENABLE_PYTORCH_ATTENTION = True - if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4): - if any((a in arch) for a in ["gfx1201", "gfx942", "gfx950"]): # TODO: more arches - SUPPORT_FP8_OPS = True - -except: - pass - - -if ENABLE_PYTORCH_ATTENTION: - torch.backends.cuda.enable_math_sdp(True) - torch.backends.cuda.enable_flash_sdp(True) - torch.backends.cuda.enable_mem_efficient_sdp(True) - - -PRIORITIZE_FP16 = False # TODO: remove and replace with something that shows exactly which dtype is faster than the other -try: - if (is_nvidia() or is_amd()) and PerformanceFeature.Fp16Accumulation in args.fast: - torch.backends.cuda.matmul.allow_fp16_accumulation = True - PRIORITIZE_FP16 = True # TODO: limit to cards where it actually boosts performance - logging.info("Enabled fp16 accumulation.") -except: - pass - -try: - if torch_version_numeric >= (2, 5): - torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True) -except: - logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp") - -if args.lowvram: - set_vram_to = VRAMState.LOW_VRAM - lowvram_available = True -elif args.novram: - set_vram_to = VRAMState.NO_VRAM -elif args.highvram or args.gpu_only: - vram_state = VRAMState.HIGH_VRAM - -FORCE_FP32 = False -if args.force_fp32: - logging.info("Forcing FP32, if this improves things please report it.") - FORCE_FP32 = True - -if lowvram_available: - if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): - vram_state = set_vram_to - - -if cpu_state != CPUState.GPU: - vram_state = VRAMState.DISABLED - -if cpu_state == CPUState.MPS: - vram_state = VRAMState.SHARED - -logging.info(f"Set vram state to: {vram_state.name}") - -DISABLE_SMART_MEMORY = args.disable_smart_memory - -if DISABLE_SMART_MEMORY: - logging.info("Disabling smart memory management") - -def get_torch_device_name(device): - if hasattr(device, 'type'): - if device.type == "cuda": - try: - allocator_backend = torch.cuda.get_allocator_backend() - except: - allocator_backend = "" - return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) - elif device.type == "xpu": - return "{} {}".format(device, torch.xpu.get_device_name(device)) - else: - return "{}".format(device.type) - elif is_intel_xpu(): - return "{} {}".format(device, torch.xpu.get_device_name(device)) - elif is_ascend_npu(): - return "{} {}".format(device, torch.npu.get_device_name(device)) - elif is_mlu(): - return "{} {}".format(device, torch.mlu.get_device_name(device)) - else: - return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) - -try: - logging.info("Device: {}".format(get_torch_device_name(get_torch_device()))) -except: - logging.warning("Could not pick default device.") - - -current_loaded_models = [] - -def module_size(module): - module_mem = 0 - sd = module.state_dict() - for k in sd: - t = sd[k] - module_mem += t.nelement() * t.element_size() - return module_mem - -class LoadedModel: - def __init__(self, model): - self._set_model(model) - self.device = model.load_device - self.real_model = None - self.currently_used = True - self.model_finalizer = None - self._patcher_finalizer = None - - def _set_model(self, model): - self._model = weakref.ref(model) - if model.parent is not None: - self._parent_model = weakref.ref(model.parent) - self._patcher_finalizer = weakref.finalize(model, self._switch_parent) - - def _switch_parent(self): - model = self._parent_model() - if model is not None: - self._set_model(model) - - @property - def model(self): - return self._model() - - def model_memory(self): - return self.model.model_size() - - def model_loaded_memory(self): - return self.model.loaded_size() - - def model_offloaded_memory(self): - return self.model.model_size() - self.model.loaded_size() - - def model_memory_required(self, device): - if device == self.model.current_loaded_device(): - return self.model_offloaded_memory() - else: - return self.model_memory() - - def model_load(self, lowvram_model_memory=0, force_patch_weights=False): - self.model.model_patches_to(self.device) - self.model.model_patches_to(self.model.model_dtype()) - - # if self.model.loaded_size() > 0: - use_more_vram = lowvram_model_memory - if use_more_vram == 0: - use_more_vram = 1e32 - self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights) - real_model = self.model.model - - if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None: - with torch.no_grad(): - real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True) - - self.real_model = weakref.ref(real_model) - self.model_finalizer = weakref.finalize(real_model, cleanup_models) - return real_model - - def should_reload_model(self, force_patch_weights=False): - if force_patch_weights and self.model.lowvram_patch_counter() > 0: - return True - return False - - def model_unload(self, memory_to_free=None, unpatch_weights=True): - if memory_to_free is not None: - if memory_to_free < self.model.loaded_size(): - freed = self.model.partially_unload(self.model.offload_device, memory_to_free) - if freed >= memory_to_free: - return False - self.model.detach(unpatch_weights) - self.model_finalizer.detach() - self.model_finalizer = None - self.real_model = None - return True - - def model_use_more_vram(self, extra_memory, force_patch_weights=False): - return self.model.partially_load(self.device, extra_memory, force_patch_weights=force_patch_weights) - - def __eq__(self, other): - return self.model is other.model - - def __del__(self): - if self._patcher_finalizer is not None: - self._patcher_finalizer.detach() - - def is_dead(self): - return self.real_model() is not None and self.model is None - - -def use_more_memory(extra_memory, loaded_models, device): - for m in loaded_models: - if m.device == device: - extra_memory -= m.model_use_more_vram(extra_memory) - if extra_memory <= 0: - break - -def offloaded_memory(loaded_models, device): - offloaded_mem = 0 - for m in loaded_models: - if m.device == device: - offloaded_mem += m.model_offloaded_memory() - return offloaded_mem - -WINDOWS = any(platform.win32_ver()) - -EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 -if WINDOWS: - EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue - if total_vram > (15 * 1024): # more extra reserved vram on 16GB+ cards - EXTRA_RESERVED_VRAM += 100 * 1024 * 1024 - -if args.reserve_vram is not None: - EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024 - logging.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024))) - -def extra_reserved_memory(): - return EXTRA_RESERVED_VRAM - -def minimum_inference_memory(): - return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory() - -def free_memory(memory_required, device, keep_loaded=[]): - cleanup_models_gc() - unloaded_model = [] - can_unload = [] - unloaded_models = [] - - for i in range(len(current_loaded_models) -1, -1, -1): - shift_model = current_loaded_models[i] - if shift_model.device == device: - if shift_model not in keep_loaded and not shift_model.is_dead(): - can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) - shift_model.currently_used = False - - for x in sorted(can_unload): - i = x[-1] - memory_to_free = None - if not DISABLE_SMART_MEMORY: - free_mem = get_free_memory(device) - if free_mem > memory_required: - break - memory_to_free = memory_required - free_mem - logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}") - if current_loaded_models[i].model_unload(memory_to_free): - unloaded_model.append(i) - - for i in sorted(unloaded_model, reverse=True): - unloaded_models.append(current_loaded_models.pop(i)) - - if len(unloaded_model) > 0: - soft_empty_cache() - else: - if vram_state != VRAMState.HIGH_VRAM: - mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) - if mem_free_torch > mem_free_total * 0.25: - soft_empty_cache() - return unloaded_models - -def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False): - cleanup_models_gc() - global vram_state - - inference_memory = minimum_inference_memory() - extra_mem = max(inference_memory, memory_required + extra_reserved_memory()) - if minimum_memory_required is None: - minimum_memory_required = extra_mem - else: - minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory()) - - models_temp = set() - for m in models: - models_temp.add(m) - for mm in m.model_patches_models(): - models_temp.add(mm) - - models = models_temp - - models_to_load = [] - - for x in models: - loaded_model = LoadedModel(x) - try: - loaded_model_index = current_loaded_models.index(loaded_model) - except: - loaded_model_index = None - - if loaded_model_index is not None: - loaded = current_loaded_models[loaded_model_index] - loaded.currently_used = True - models_to_load.append(loaded) - else: - if hasattr(x, "model"): - logging.info(f"Requested to load {x.model.__class__.__name__}") - models_to_load.append(loaded_model) - - for loaded_model in models_to_load: - to_unload = [] - for i in range(len(current_loaded_models)): - if loaded_model.model.is_clone(current_loaded_models[i].model): - to_unload = [i] + to_unload - for i in to_unload: - current_loaded_models.pop(i).model.detach(unpatch_all=False) - - total_memory_required = {} - for loaded_model in models_to_load: - total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) - - for device in total_memory_required: - if device != torch.device("cpu"): - free_memory(total_memory_required[device] * 1.1 + extra_mem, device) - - for device in total_memory_required: - if device != torch.device("cpu"): - free_mem = get_free_memory(device) - if free_mem < minimum_memory_required: - models_l = free_memory(minimum_memory_required, device) - logging.info("{} models unloaded.".format(len(models_l))) - - for loaded_model in models_to_load: - model = loaded_model.model - torch_dev = model.load_device - if is_device_cpu(torch_dev): - vram_set_state = VRAMState.DISABLED - else: - vram_set_state = vram_state - lowvram_model_memory = 0 - if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load: - loaded_memory = loaded_model.model_loaded_memory() - current_free_mem = get_free_memory(torch_dev) + loaded_memory - - lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) - lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory) - - if vram_set_state == VRAMState.NO_VRAM: - lowvram_model_memory = 0.1 - - loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) - current_loaded_models.insert(0, loaded_model) - return - -def load_model_gpu(model): - return load_models_gpu([model]) - -def loaded_models(only_currently_used=False): - output = [] - for m in current_loaded_models: - if only_currently_used: - if not m.currently_used: - continue - - output.append(m.model) - return output - - -def cleanup_models_gc(): - do_gc = False - for i in range(len(current_loaded_models)): - cur = current_loaded_models[i] - if cur.is_dead(): - logging.info("Potential memory leak detected with model {}, doing a full garbage collect, for maximum performance avoid circular references in the model code.".format(cur.real_model().__class__.__name__)) - do_gc = True - break - - if do_gc: - gc.collect() - soft_empty_cache() - - for i in range(len(current_loaded_models)): - cur = current_loaded_models[i] - if cur.is_dead(): - logging.warning("WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.".format(cur.real_model().__class__.__name__)) - - - -def cleanup_models(): - to_delete = [] - for i in range(len(current_loaded_models)): - if current_loaded_models[i].real_model() is None: - to_delete = [i] + to_delete - - for i in to_delete: - x = current_loaded_models.pop(i) - del x - -def dtype_size(dtype): - dtype_size = 4 - if dtype == torch.float16 or dtype == torch.bfloat16: - dtype_size = 2 - elif dtype == torch.float32: - dtype_size = 4 - else: - try: - dtype_size = dtype.itemsize - except: #Old pytorch doesn't have .itemsize - pass - return dtype_size - -def unet_offload_device(): - if vram_state == VRAMState.HIGH_VRAM: - return get_torch_device() - else: - return torch.device("cpu") - -def unet_inital_load_device(parameters, dtype): - torch_dev = get_torch_device() - if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED: - return torch_dev - - cpu_dev = torch.device("cpu") - if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM: - return cpu_dev - - model_size = dtype_size(dtype) * parameters - - mem_dev = get_free_memory(torch_dev) - mem_cpu = get_free_memory(cpu_dev) - if mem_dev > mem_cpu and model_size < mem_dev: - return torch_dev - else: - return cpu_dev - -def maximum_vram_for_weights(device=None): - return (get_total_memory(device) * 0.88 - minimum_inference_memory()) - -def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None): - if model_params < 0: - model_params = 1000000000000000000000 - if args.fp32_unet: - return torch.float32 - if args.fp64_unet: - return torch.float64 - if args.bf16_unet: - return torch.bfloat16 - if args.fp16_unet: - return torch.float16 - if args.fp8_e4m3fn_unet: - return torch.float8_e4m3fn - if args.fp8_e5m2_unet: - return torch.float8_e5m2 - if args.fp8_e8m0fnu_unet: - return torch.float8_e8m0fnu - - fp8_dtype = None - if weight_dtype in FLOAT8_TYPES: - fp8_dtype = weight_dtype - - if fp8_dtype is not None: - if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive - return fp8_dtype - - free_model_memory = maximum_vram_for_weights(device) - if model_params * 2 > free_model_memory: - return fp8_dtype - - if PRIORITIZE_FP16 or weight_dtype == torch.float16: - if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params): - return torch.float16 - - for dt in supported_dtypes: - if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params): - if torch.float16 in supported_dtypes: - return torch.float16 - if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params): - if torch.bfloat16 in supported_dtypes: - return torch.bfloat16 - - for dt in supported_dtypes: - if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True): - if torch.float16 in supported_dtypes: - return torch.float16 - if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True): - if torch.bfloat16 in supported_dtypes: - return torch.bfloat16 - - return torch.float32 - -# None means no manual cast -def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): - if weight_dtype == torch.float32 or weight_dtype == torch.float64: - return None - - fp16_supported = should_use_fp16(inference_device, prioritize_performance=False) - if fp16_supported and weight_dtype == torch.float16: - return None - - bf16_supported = should_use_bf16(inference_device) - if bf16_supported and weight_dtype == torch.bfloat16: - return None - - fp16_supported = should_use_fp16(inference_device, prioritize_performance=True) - if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes: - return torch.float16 - - for dt in supported_dtypes: - if dt == torch.float16 and fp16_supported: - return torch.float16 - if dt == torch.bfloat16 and bf16_supported: - return torch.bfloat16 - - return torch.float32 - -def text_encoder_offload_device(): - if args.gpu_only: - return get_torch_device() - else: - return torch.device("cpu") - -def text_encoder_device(): - if args.gpu_only: - return get_torch_device() - elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: - if should_use_fp16(prioritize_performance=False): - return get_torch_device() - else: - return torch.device("cpu") - else: - return torch.device("cpu") - -def text_encoder_initial_device(load_device, offload_device, model_size=0): - if load_device == offload_device or model_size <= 1024 * 1024 * 1024: - return offload_device - - if is_device_mps(load_device): - return load_device - - mem_l = get_free_memory(load_device) - mem_o = get_free_memory(offload_device) - if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l: - return load_device - else: - return offload_device - -def text_encoder_dtype(device=None): - if args.fp8_e4m3fn_text_enc: - return torch.float8_e4m3fn - elif args.fp8_e5m2_text_enc: - return torch.float8_e5m2 - elif args.fp16_text_enc: - return torch.float16 - elif args.bf16_text_enc: - return torch.bfloat16 - elif args.fp32_text_enc: - return torch.float32 - - if is_device_cpu(device): - return torch.float16 - - return torch.float16 - - -def intermediate_device(): - if args.gpu_only: - return get_torch_device() - else: - return torch.device("cpu") - -def vae_device(): - if args.cpu_vae: - return torch.device("cpu") - return get_torch_device() - -def vae_offload_device(): - if args.gpu_only: - return get_torch_device() - else: - return torch.device("cpu") - -def vae_dtype(device=None, allowed_dtypes=[]): - if args.fp16_vae: - return torch.float16 - elif args.bf16_vae: - return torch.bfloat16 - elif args.fp32_vae: - return torch.float32 - - for d in allowed_dtypes: - if d == torch.float16 and should_use_fp16(device): - return d - - # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32 - # slowness still a problem on pytorch nightly 2.9.0.dev20250720+rocm6.4 tested on RDNA3 - if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device): - return d - - return torch.float32 - -def get_autocast_device(dev): - if hasattr(dev, 'type'): - return dev.type - return "cuda" - -def supports_dtype(device, dtype): #TODO - if dtype == torch.float32: - return True - if is_device_cpu(device): - return False - if dtype == torch.float16: - return True - if dtype == torch.bfloat16: - return True - return False - -def supports_cast(device, dtype): #TODO - if dtype == torch.float32: - return True - if dtype == torch.float16: - return True - if directml_enabled: #TODO: test this - return False - if dtype == torch.bfloat16: - return True - if is_device_mps(device): - return False - if dtype == torch.float8_e4m3fn: - return True - if dtype == torch.float8_e5m2: - return True - return False - -def pick_weight_dtype(dtype, fallback_dtype, device=None): - if dtype is None: - dtype = fallback_dtype - elif dtype_size(dtype) > dtype_size(fallback_dtype): - dtype = fallback_dtype - - if not supports_cast(device, dtype): - dtype = fallback_dtype - - return dtype - -def device_supports_non_blocking(device): - if args.force_non_blocking: - return True - if is_device_mps(device): - return False #pytorch bug? mps doesn't support non blocking - if is_intel_xpu(): #xpu does support non blocking but it is slower on iGPUs for some reason so disable by default until situation changes - return False - if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) - return False - if directml_enabled: - return False - return True - -def device_should_use_non_blocking(device): - if not device_supports_non_blocking(device): - return False - return False - # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others - -def force_channels_last(): - if args.force_channels_last: - return True - - #TODO - return False - - -STREAMS = {} -NUM_STREAMS = 1 -if args.async_offload: - NUM_STREAMS = 2 - logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) - -stream_counters = {} -def get_offload_stream(device): - stream_counter = stream_counters.get(device, 0) - if NUM_STREAMS <= 1: - return None - - if device in STREAMS: - ss = STREAMS[device] - s = ss[stream_counter] - stream_counter = (stream_counter + 1) % len(ss) - if is_device_cuda(device): - ss[stream_counter].wait_stream(torch.cuda.current_stream()) - elif is_device_xpu(device): - ss[stream_counter].wait_stream(torch.xpu.current_stream()) - stream_counters[device] = stream_counter - return s - elif is_device_cuda(device): - ss = [] - for k in range(NUM_STREAMS): - ss.append(torch.cuda.Stream(device=device, priority=0)) - STREAMS[device] = ss - s = ss[stream_counter] - stream_counter = (stream_counter + 1) % len(ss) - stream_counters[device] = stream_counter - return s - elif is_device_xpu(device): - ss = [] - for k in range(NUM_STREAMS): - ss.append(torch.xpu.Stream(device=device, priority=0)) - STREAMS[device] = ss - s = ss[stream_counter] - stream_counter = (stream_counter + 1) % len(ss) - stream_counters[device] = stream_counter - return s - return None - -def sync_stream(device, stream): - if stream is None: - return - if is_device_cuda(device): - torch.cuda.current_stream().wait_stream(stream) - elif is_device_xpu(device): - torch.xpu.current_stream().wait_stream(stream) - -def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None): - if device is None or weight.device == device: - if not copy: - if dtype is None or weight.dtype == dtype: - return weight - if stream is not None: - with stream: - return weight.to(dtype=dtype, copy=copy) - return weight.to(dtype=dtype, copy=copy) - - if stream is not None: - with stream: - r = torch.empty_like(weight, dtype=dtype, device=device) - r.copy_(weight, non_blocking=non_blocking) - else: - r = torch.empty_like(weight, dtype=dtype, device=device) - r.copy_(weight, non_blocking=non_blocking) - return r - -def cast_to_device(tensor, device, dtype, copy=False): - non_blocking = device_supports_non_blocking(device) - return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy) - -def sage_attention_enabled(): - return args.use_sage_attention - -def flash_attention_enabled(): - return args.use_flash_attention - -def xformers_enabled(): - global directml_enabled - global cpu_state - if cpu_state != CPUState.GPU: - return False - if is_intel_xpu(): - return False - if is_ascend_npu(): - return False - if is_mlu(): - return False - if is_ixuca(): - return False - if directml_enabled: - return False - return XFORMERS_IS_AVAILABLE - - -def xformers_enabled_vae(): - enabled = xformers_enabled() - if not enabled: - return False - - return XFORMERS_ENABLED_VAE - -def pytorch_attention_enabled(): - global ENABLE_PYTORCH_ATTENTION - return ENABLE_PYTORCH_ATTENTION - -def pytorch_attention_enabled_vae(): - if is_amd(): - return False # enabling pytorch attention on AMD currently causes crash when doing high res - return pytorch_attention_enabled() - -def pytorch_attention_flash_attention(): - global ENABLE_PYTORCH_ATTENTION - if ENABLE_PYTORCH_ATTENTION: - #TODO: more reliable way of checking for flash attention? - if is_nvidia(): - return True - if is_intel_xpu(): - return True - if is_ascend_npu(): - return True - if is_mlu(): - return True - if is_amd(): - return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention - if is_ixuca(): - return True - return False - -def force_upcast_attention_dtype(): - upcast = args.force_upcast_attention - - macos_version = mac_version() - if macos_version is not None and ((14, 5) <= macos_version): # black image bug on recent versions of macOS, I don't think it's ever getting fixed - upcast = True - - if upcast: - return {torch.float16: torch.float32} - else: - return None - -def get_free_memory(dev=None, torch_free_too=False): - global directml_enabled - if dev is None: - dev = get_torch_device() - - if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): - mem_free_total = psutil.virtual_memory().available - mem_free_torch = mem_free_total - else: - if directml_enabled: - mem_free_total = 1024 * 1024 * 1024 #TODO - mem_free_torch = mem_free_total - elif is_intel_xpu(): - stats = torch.xpu.memory_stats(dev) - mem_active = stats['active_bytes.all.current'] - mem_reserved = stats['reserved_bytes.all.current'] - mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_xpu + mem_free_torch - elif is_ascend_npu(): - stats = torch.npu.memory_stats(dev) - mem_active = stats['active_bytes.all.current'] - mem_reserved = stats['reserved_bytes.all.current'] - mem_free_npu, _ = torch.npu.mem_get_info(dev) - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_npu + mem_free_torch - elif is_mlu(): - stats = torch.mlu.memory_stats(dev) - mem_active = stats['active_bytes.all.current'] - mem_reserved = stats['reserved_bytes.all.current'] - mem_free_mlu, _ = torch.mlu.mem_get_info(dev) - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_mlu + mem_free_torch - else: - stats = torch.cuda.memory_stats(dev) - mem_active = stats['active_bytes.all.current'] - mem_reserved = stats['reserved_bytes.all.current'] - mem_free_cuda, _ = torch.cuda.mem_get_info(dev) - mem_free_torch = mem_reserved - mem_active - mem_free_total = mem_free_cuda + mem_free_torch - - if torch_free_too: - return (mem_free_total, mem_free_torch) - else: - return mem_free_total - -def cpu_mode(): - global cpu_state - return cpu_state == CPUState.CPU - -def mps_mode(): - global cpu_state - return cpu_state == CPUState.MPS - -def is_device_type(device, type): - if hasattr(device, 'type'): - if (device.type == type): - return True - return False - -def is_device_cpu(device): - return is_device_type(device, 'cpu') - -def is_device_mps(device): - return is_device_type(device, 'mps') - -def is_device_xpu(device): - return is_device_type(device, 'xpu') - -def is_device_cuda(device): - return is_device_type(device, 'cuda') - -def is_directml_enabled(): - global directml_enabled - if directml_enabled: - return True - - return False - -def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): - if device is not None: - if is_device_cpu(device): - return False - - if args.force_fp16: - return True - - if FORCE_FP32: - return False - - if is_directml_enabled(): - return True - - if (device is not None and is_device_mps(device)) or mps_mode(): - return True - - if cpu_mode(): - return False - - if is_intel_xpu(): - if torch_version_numeric < (2, 3): - return True - else: - return torch.xpu.get_device_properties(device).has_fp16 - - if is_ascend_npu(): - return True - - if is_mlu(): - return True - - if is_ixuca(): - return True - - if torch.version.hip: - return True - - props = torch.cuda.get_device_properties(device) - if props.major >= 8: - return True - - if props.major < 6: - return False - - #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32 - nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"] - for x in nvidia_10_series: - if x in props.name.lower(): - if WINDOWS or manual_cast: - return True - else: - return False #weird linux behavior where fp32 is faster - - if manual_cast: - free_model_memory = maximum_vram_for_weights(device) - if (not prioritize_performance) or model_params * 4 > free_model_memory: - return True - - if props.major < 7: - return False - - #FP16 is just broken on these cards - nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] - for x in nvidia_16_series: - if x in props.name: - return False - - return True - -def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): - if device is not None: - if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow - return False - - if FORCE_FP32: - return False - - if directml_enabled: - return False - - if (device is not None and is_device_mps(device)) or mps_mode(): - if mac_version() < (14,): - return False - return True - - if cpu_mode(): - return False - - if is_intel_xpu(): - if torch_version_numeric < (2, 3): - return True - else: - return torch.xpu.is_bf16_supported() - - if is_ascend_npu(): - return True - - if is_ixuca(): - return True - - if is_amd(): - arch = torch.cuda.get_device_properties(device).gcnArchName - if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]): # RDNA2 and older don't support bf16 - if manual_cast: - return True - return False - - props = torch.cuda.get_device_properties(device) - - if is_mlu(): - if props.major > 3: - return True - - if props.major >= 8: - return True - - bf16_works = torch.cuda.is_bf16_supported() - - if bf16_works and manual_cast: - free_model_memory = maximum_vram_for_weights(device) - if (not prioritize_performance) or model_params * 4 > free_model_memory: - return True - - return False - -def supports_fp8_compute(device=None): - if SUPPORT_FP8_OPS: - return True - - if not is_nvidia(): - return False - - props = torch.cuda.get_device_properties(device) - if props.major >= 9: - return True - if props.major < 8: - return False - if props.minor < 9: - return False - - if torch_version_numeric < (2, 3): - return False - - if WINDOWS: - if torch_version_numeric < (2, 4): - return False - - return True - -def extended_fp16_support(): - # TODO: check why some models work with fp16 on newer torch versions but not on older - if torch_version_numeric < (2, 7): - return False - - return True - -def soft_empty_cache(force=False): - global cpu_state - if cpu_state == CPUState.MPS: - torch.mps.empty_cache() - elif is_intel_xpu(): - torch.xpu.empty_cache() - elif is_ascend_npu(): - torch.npu.empty_cache() - elif is_mlu(): - torch.mlu.empty_cache() - elif torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.ipc_collect() - -def unload_all_models(): - free_memory(1e30, get_torch_device()) - - -#TODO: might be cleaner to put this somewhere else -import threading - -class InterruptProcessingException(Exception): - pass - -interrupt_processing_mutex = threading.RLock() - -interrupt_processing = False -def interrupt_current_processing(value=True): - global interrupt_processing - global interrupt_processing_mutex - with interrupt_processing_mutex: - interrupt_processing = value - -def processing_interrupted(): - global interrupt_processing - global interrupt_processing_mutex - with interrupt_processing_mutex: - return interrupt_processing - -def throw_exception_if_processing_interrupted(): - global interrupt_processing - global interrupt_processing_mutex - with interrupt_processing_mutex: - if interrupt_processing: - interrupt_processing = False - raise InterruptProcessingException() diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py deleted file mode 100644 index a944cb421842018be29a41562290d237801232f6..0000000000000000000000000000000000000000 --- a/comfy/model_patcher.py +++ /dev/null @@ -1,1242 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -from __future__ import annotations - -import collections -import copy -import inspect -import logging -import math -import uuid -from typing import Callable, Optional - -import torch - -import comfy.float -import comfy.hooks -import comfy.lora -import comfy.model_management -import comfy.patcher_extension -import comfy.utils -from comfy.comfy_types import UnetWrapperFunction -from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP - - -def string_to_seed(data): - crc = 0xFFFFFFFF - for byte in data: - if isinstance(byte, str): - byte = ord(byte) - crc ^= byte - for _ in range(8): - if crc & 1: - crc = (crc >> 1) ^ 0xEDB88320 - else: - crc >>= 1 - return crc ^ 0xFFFFFFFF - -def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None): - to = model_options["transformer_options"].copy() - - if "patches_replace" not in to: - to["patches_replace"] = {} - else: - to["patches_replace"] = to["patches_replace"].copy() - - if name not in to["patches_replace"]: - to["patches_replace"][name] = {} - else: - to["patches_replace"][name] = to["patches_replace"][name].copy() - - if transformer_index is not None: - block = (block_name, number, transformer_index) - else: - block = (block_name, number) - to["patches_replace"][name][block] = patch - model_options["transformer_options"] = to - return model_options - -def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False): - model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] - if disable_cfg1_optimization: - model_options["disable_cfg1_optimization"] = True - return model_options - -def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False): - model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function] - if disable_cfg1_optimization: - model_options["disable_cfg1_optimization"] = True - return model_options - -def create_model_options_clone(orig_model_options: dict): - return comfy.patcher_extension.copy_nested_dicts(orig_model_options) - -def create_hook_patches_clone(orig_hook_patches): - new_hook_patches = {} - for hook_ref in orig_hook_patches: - new_hook_patches[hook_ref] = {} - for k in orig_hook_patches[hook_ref]: - new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:] - return new_hook_patches - -def wipe_lowvram_weight(m): - if hasattr(m, "prev_comfy_cast_weights"): - m.comfy_cast_weights = m.prev_comfy_cast_weights - del m.prev_comfy_cast_weights - - if hasattr(m, "weight_function"): - m.weight_function = [] - - if hasattr(m, "bias_function"): - m.bias_function = [] - -def move_weight_functions(m, device): - if device is None: - return 0 - - memory = 0 - if hasattr(m, "weight_function"): - for f in m.weight_function: - if hasattr(f, "move_to"): - memory += f.move_to(device=device) - - if hasattr(m, "bias_function"): - for f in m.bias_function: - if hasattr(f, "move_to"): - memory += f.move_to(device=device) - return memory - -class LowVramPatch: - def __init__(self, key, patches): - self.key = key - self.patches = patches - def __call__(self, weight): - intermediate_dtype = weight.dtype - if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops - intermediate_dtype = torch.float32 - return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key)) - - return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype) - -def get_key_weight(model, key): - set_func = None - convert_func = None - op_keys = key.rsplit('.', 1) - if len(op_keys) < 2: - weight = comfy.utils.get_attr(model, key) - else: - op = comfy.utils.get_attr(model, op_keys[0]) - try: - set_func = getattr(op, "set_{}".format(op_keys[1])) - except AttributeError: - pass - - try: - convert_func = getattr(op, "convert_{}".format(op_keys[1])) - except AttributeError: - pass - - weight = getattr(op, op_keys[1]) - if convert_func is not None: - weight = comfy.utils.get_attr(model, key) - - return weight, set_func, convert_func - -class AutoPatcherEjector: - def __init__(self, model: 'ModelPatcher', skip_and_inject_on_exit_only=False): - self.model = model - self.was_injected = False - self.prev_skip_injection = False - self.skip_and_inject_on_exit_only = skip_and_inject_on_exit_only - - def __enter__(self): - self.was_injected = False - self.prev_skip_injection = self.model.skip_injection - if self.skip_and_inject_on_exit_only: - self.model.skip_injection = True - if self.model.is_injected: - self.model.eject_model() - self.was_injected = True - - def __exit__(self, *args): - if self.skip_and_inject_on_exit_only: - self.model.skip_injection = self.prev_skip_injection - self.model.inject_model() - if self.was_injected and not self.model.skip_injection: - self.model.inject_model() - self.model.skip_injection = self.prev_skip_injection - -class MemoryCounter: - def __init__(self, initial: int, minimum=0): - self.value = initial - self.minimum = minimum - # TODO: add a safe limit besides 0 - - def use(self, weight: torch.Tensor): - weight_size = weight.nelement() * weight.element_size() - if self.is_useable(weight_size): - self.decrement(weight_size) - return True - return False - - def is_useable(self, used: int): - return self.value - used > self.minimum - - def decrement(self, used: int): - self.value -= used - -class ModelPatcher: - def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False): - self.size = size - self.model = model - if not hasattr(self.model, 'device'): - logging.debug("Model doesn't have a device attribute.") - self.model.device = offload_device - elif self.model.device is None: - self.model.device = offload_device - - self.patches = {} - self.backup = {} - self.object_patches = {} - self.object_patches_backup = {} - self.weight_wrapper_patches = {} - self.model_options = {"transformer_options":{}} - self.model_size() - self.load_device = load_device - self.offload_device = offload_device - self.weight_inplace_update = weight_inplace_update - self.force_cast_weights = False - self.patches_uuid = uuid.uuid4() - self.parent = None - - self.attachments: dict[str] = {} - self.additional_models: dict[str, list[ModelPatcher]] = {} - self.callbacks: dict[str, dict[str, list[Callable]]] = CallbacksMP.init_callbacks() - self.wrappers: dict[str, dict[str, list[Callable]]] = WrappersMP.init_wrappers() - - self.is_injected = False - self.skip_injection = False - self.injections: dict[str, list[PatcherInjection]] = {} - - self.hook_patches: dict[comfy.hooks._HookRef] = {} - self.hook_patches_backup: dict[comfy.hooks._HookRef] = None - self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {} - self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {} - self.current_hooks: Optional[comfy.hooks.HookGroup] = None - self.forced_hooks: Optional[comfy.hooks.HookGroup] = None # NOTE: only used for CLIP at this time - self.is_clip = False - self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed - - if not hasattr(self.model, 'model_loaded_weight_memory'): - self.model.model_loaded_weight_memory = 0 - - if not hasattr(self.model, 'lowvram_patch_counter'): - self.model.lowvram_patch_counter = 0 - - if not hasattr(self.model, 'model_lowvram'): - self.model.model_lowvram = False - - if not hasattr(self.model, 'current_weight_patches_uuid'): - self.model.current_weight_patches_uuid = None - - def model_size(self): - if self.size > 0: - return self.size - self.size = comfy.model_management.module_size(self.model) - return self.size - - def loaded_size(self): - return self.model.model_loaded_weight_memory - - def lowvram_patch_counter(self): - return self.model.lowvram_patch_counter - - def clone(self): - n = self.__class__(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update) - n.patches = {} - for k in self.patches: - n.patches[k] = self.patches[k][:] - n.patches_uuid = self.patches_uuid - - n.object_patches = self.object_patches.copy() - n.weight_wrapper_patches = self.weight_wrapper_patches.copy() - n.model_options = copy.deepcopy(self.model_options) - n.backup = self.backup - n.object_patches_backup = self.object_patches_backup - n.parent = self - - n.force_cast_weights = self.force_cast_weights - - # attachments - n.attachments = {} - for k in self.attachments: - if hasattr(self.attachments[k], "on_model_patcher_clone"): - n.attachments[k] = self.attachments[k].on_model_patcher_clone() - else: - n.attachments[k] = self.attachments[k] - # additional models - for k, c in self.additional_models.items(): - n.additional_models[k] = [x.clone() for x in c] - # callbacks - for k, c in self.callbacks.items(): - n.callbacks[k] = {} - for k1, c1 in c.items(): - n.callbacks[k][k1] = c1.copy() - # sample wrappers - for k, w in self.wrappers.items(): - n.wrappers[k] = {} - for k1, w1 in w.items(): - n.wrappers[k][k1] = w1.copy() - # injection - n.is_injected = self.is_injected - n.skip_injection = self.skip_injection - for k, i in self.injections.items(): - n.injections[k] = i.copy() - # hooks - n.hook_patches = create_hook_patches_clone(self.hook_patches) - n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup - for group in self.cached_hook_patches: - n.cached_hook_patches[group] = {} - for k in self.cached_hook_patches[group]: - n.cached_hook_patches[group][k] = self.cached_hook_patches[group][k] - n.hook_backup = self.hook_backup - n.current_hooks = self.current_hooks.clone() if self.current_hooks else self.current_hooks - n.forced_hooks = self.forced_hooks.clone() if self.forced_hooks else self.forced_hooks - n.is_clip = self.is_clip - n.hook_mode = self.hook_mode - - for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE): - callback(self, n) - return n - - def is_clone(self, other): - if hasattr(other, 'model') and self.model is other.model: - return True - return False - - def clone_has_same_weights(self, clone: 'ModelPatcher'): - if not self.is_clone(clone): - return False - - if self.current_hooks != clone.current_hooks: - return False - if self.forced_hooks != clone.forced_hooks: - return False - if self.hook_patches.keys() != clone.hook_patches.keys(): - return False - if self.attachments.keys() != clone.attachments.keys(): - return False - if self.additional_models.keys() != clone.additional_models.keys(): - return False - for key in self.callbacks: - if len(self.callbacks[key]) != len(clone.callbacks[key]): - return False - for key in self.wrappers: - if len(self.wrappers[key]) != len(clone.wrappers[key]): - return False - if self.injections.keys() != clone.injections.keys(): - return False - - if len(self.patches) == 0 and len(clone.patches) == 0: - return True - - if self.patches_uuid == clone.patches_uuid: - if len(self.patches) != len(clone.patches): - logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.") - else: - return True - - def memory_required(self, input_shape): - return self.model.memory_required(input_shape=input_shape) - - def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): - if len(inspect.signature(sampler_cfg_function).parameters) == 3: - self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way - else: - self.model_options["sampler_cfg_function"] = sampler_cfg_function - if disable_cfg1_optimization: - self.model_options["disable_cfg1_optimization"] = True - - def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): - self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization) - - def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False): - self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization) - - def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond_batch_function): - self.model_options["sampler_calc_cond_batch_function"] = sampler_calc_cond_batch_function - - def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): - self.model_options["model_function_wrapper"] = unet_wrapper_function - - def set_model_denoise_mask_function(self, denoise_mask_function): - self.model_options["denoise_mask_function"] = denoise_mask_function - - def set_model_patch(self, patch, name): - to = self.model_options["transformer_options"] - if "patches" not in to: - to["patches"] = {} - to["patches"][name] = to["patches"].get(name, []) + [patch] - - def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): - self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index) - - def set_model_attn1_patch(self, patch): - self.set_model_patch(patch, "attn1_patch") - - def set_model_attn2_patch(self, patch): - self.set_model_patch(patch, "attn2_patch") - - def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): - self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) - - def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): - self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) - - def set_model_attn1_output_patch(self, patch): - self.set_model_patch(patch, "attn1_output_patch") - - def set_model_attn2_output_patch(self, patch): - self.set_model_patch(patch, "attn2_output_patch") - - def set_model_input_block_patch(self, patch): - self.set_model_patch(patch, "input_block_patch") - - def set_model_input_block_patch_after_skip(self, patch): - self.set_model_patch(patch, "input_block_patch_after_skip") - - def set_model_output_block_patch(self, patch): - self.set_model_patch(patch, "output_block_patch") - - def set_model_emb_patch(self, patch): - self.set_model_patch(patch, "emb_patch") - - def set_model_forward_timestep_embed_patch(self, patch): - self.set_model_patch(patch, "forward_timestep_embed_patch") - - def set_model_double_block_patch(self, patch): - self.set_model_patch(patch, "double_block") - - def add_object_patch(self, name, obj): - self.object_patches[name] = obj - - def set_model_compute_dtype(self, dtype): - self.add_object_patch("manual_cast_dtype", dtype) - if dtype is not None: - self.force_cast_weights = True - self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this - - def add_weight_wrapper(self, name, function): - self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function] - self.patches_uuid = uuid.uuid4() - - def get_model_object(self, name: str) -> torch.nn.Module: - """Retrieves a nested attribute from an object using dot notation considering - object patches. - - Args: - name (str): The attribute path using dot notation (e.g. "model.layer.weight") - - Returns: - The value of the requested attribute - - Example: - patcher = ModelPatcher() - weight = patcher.get_model_object("layer1.conv.weight") - """ - if name in self.object_patches: - return self.object_patches[name] - else: - if name in self.object_patches_backup: - return self.object_patches_backup[name] - else: - return comfy.utils.get_attr(self.model, name) - - def model_patches_to(self, device): - to = self.model_options["transformer_options"] - if "patches" in to: - patches = to["patches"] - for name in patches: - patch_list = patches[name] - for i in range(len(patch_list)): - if hasattr(patch_list[i], "to"): - patch_list[i] = patch_list[i].to(device) - if "patches_replace" in to: - patches = to["patches_replace"] - for name in patches: - patch_list = patches[name] - for k in patch_list: - if hasattr(patch_list[k], "to"): - patch_list[k] = patch_list[k].to(device) - if "model_function_wrapper" in self.model_options: - wrap_func = self.model_options["model_function_wrapper"] - if hasattr(wrap_func, "to"): - self.model_options["model_function_wrapper"] = wrap_func.to(device) - - def model_patches_models(self): - to = self.model_options["transformer_options"] - models = [] - if "patches" in to: - patches = to["patches"] - for name in patches: - patch_list = patches[name] - for i in range(len(patch_list)): - if hasattr(patch_list[i], "models"): - models += patch_list[i].models() - if "patches_replace" in to: - patches = to["patches_replace"] - for name in patches: - patch_list = patches[name] - for k in patch_list: - if hasattr(patch_list[k], "models"): - models += patch_list[k].models() - if "model_function_wrapper" in self.model_options: - wrap_func = self.model_options["model_function_wrapper"] - if hasattr(wrap_func, "models"): - models += wrap_func.models() - - return models - - def model_dtype(self): - if hasattr(self.model, "get_dtype"): - return self.model.get_dtype() - - def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): - with self.use_ejected(): - p = set() - model_sd = self.model.state_dict() - for k in patches: - offset = None - function = None - if isinstance(k, str): - key = k - else: - offset = k[1] - key = k[0] - if len(k) > 2: - function = k[2] - - if key in model_sd: - p.add(k) - current_patches = self.patches.get(key, []) - current_patches.append((strength_patch, patches[k], strength_model, offset, function)) - self.patches[key] = current_patches - - self.patches_uuid = uuid.uuid4() - return list(p) - - def get_key_patches(self, filter_prefix=None): - model_sd = self.model_state_dict() - p = {} - for k in model_sd: - if filter_prefix is not None: - if not k.startswith(filter_prefix): - continue - bk = self.backup.get(k, None) - hbk = self.hook_backup.get(k, None) - weight, set_func, convert_func = get_key_weight(self.model, k) - if bk is not None: - weight = bk.weight - if hbk is not None: - weight = hbk[0] - if convert_func is None: - convert_func = lambda a, **kwargs: a - - if k in self.patches: - p[k] = [(weight, convert_func)] + self.patches[k] - else: - p[k] = [(weight, convert_func)] - return p - - def model_state_dict(self, filter_prefix=None): - with self.use_ejected(): - sd = self.model.state_dict() - keys = list(sd.keys()) - if filter_prefix is not None: - for k in keys: - if not k.startswith(filter_prefix): - sd.pop(k) - return sd - - def patch_weight_to_device(self, key, device_to=None, inplace_update=False): - if key not in self.patches: - return - - weight, set_func, convert_func = get_key_weight(self.model, key) - inplace_update = self.weight_inplace_update or inplace_update - - if key not in self.backup: - self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) - - if device_to is not None: - temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) - else: - temp_weight = weight.to(torch.float32, copy=True) - if convert_func is not None: - temp_weight = convert_func(temp_weight, inplace=True) - - out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) - if set_func is None: - out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key)) - if inplace_update: - comfy.utils.copy_to_param(self.model, key, out_weight) - else: - comfy.utils.set_attr_param(self.model, key, out_weight) - else: - set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key)) - - def _load_list(self): - loading = [] - for n, m in self.model.named_modules(): - params = [] - skip = False - for name, param in m.named_parameters(recurse=False): - params.append(name) - for name, param in m.named_parameters(recurse=True): - if name not in params: - skip = True # skip random weights in non leaf modules - break - if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0): - loading.append((comfy.model_management.module_size(m), n, m, params)) - return loading - - def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False): - with self.use_ejected(): - self.unpatch_hooks() - mem_counter = 0 - patch_counter = 0 - lowvram_counter = 0 - loading = self._load_list() - - load_completely = [] - loading.sort(reverse=True) - for x in loading: - n = x[1] - m = x[2] - params = x[3] - module_mem = x[0] - - lowvram_weight = False - - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - - if not full_load and hasattr(m, "comfy_cast_weights"): - if mem_counter + module_mem >= lowvram_model_memory: - lowvram_weight = True - lowvram_counter += 1 - if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed - continue - - cast_weight = self.force_cast_weights - if lowvram_weight: - if hasattr(m, "comfy_cast_weights"): - m.weight_function = [] - m.bias_function = [] - - if weight_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(weight_key) - else: - m.weight_function = [LowVramPatch(weight_key, self.patches)] - patch_counter += 1 - if bias_key in self.patches: - if force_patch_weights: - self.patch_weight_to_device(bias_key) - else: - m.bias_function = [LowVramPatch(bias_key, self.patches)] - patch_counter += 1 - - cast_weight = True - else: - if hasattr(m, "comfy_cast_weights"): - wipe_lowvram_weight(m) - - if full_load or mem_counter + module_mem < lowvram_model_memory: - mem_counter += module_mem - load_completely.append((module_mem, n, m, params)) - - if cast_weight and hasattr(m, "comfy_cast_weights"): - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True - - if weight_key in self.weight_wrapper_patches: - m.weight_function.extend(self.weight_wrapper_patches[weight_key]) - - if bias_key in self.weight_wrapper_patches: - m.bias_function.extend(self.weight_wrapper_patches[bias_key]) - - mem_counter += move_weight_functions(m, device_to) - - load_completely.sort(reverse=True) - for x in load_completely: - n = x[1] - m = x[2] - params = x[3] - if hasattr(m, "comfy_patched_weights"): - if m.comfy_patched_weights == True: - continue - - for param in params: - self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to) - - logging.debug("lowvram: loaded module regularly {} {}".format(n, m)) - m.comfy_patched_weights = True - - for x in load_completely: - x[2].to(device_to) - - if lowvram_counter > 0: - logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter)) - self.model.model_lowvram = True - else: - logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load)) - self.model.model_lowvram = False - if full_load: - self.model.to(device_to) - mem_counter = self.model_size() - - self.model.lowvram_patch_counter += patch_counter - self.model.device = device_to - self.model.model_loaded_weight_memory = mem_counter - self.model.current_weight_patches_uuid = self.patches_uuid - - for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD): - callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load) - - self.apply_hooks(self.forced_hooks, force_apply=True) - - def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): - with self.use_ejected(): - for k in self.object_patches: - old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) - if k not in self.object_patches_backup: - self.object_patches_backup[k] = old - - if lowvram_model_memory == 0: - full_load = True - else: - full_load = False - - if load_weights: - self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load) - self.inject_model() - return self.model - - def unpatch_model(self, device_to=None, unpatch_weights=True): - self.eject_model() - if unpatch_weights: - self.unpatch_hooks() - if self.model.model_lowvram: - for m in self.model.modules(): - move_weight_functions(m, device_to) - wipe_lowvram_weight(m) - - self.model.model_lowvram = False - self.model.lowvram_patch_counter = 0 - - keys = list(self.backup.keys()) - - for k in keys: - bk = self.backup[k] - if bk.inplace_update: - comfy.utils.copy_to_param(self.model, k, bk.weight) - else: - comfy.utils.set_attr_param(self.model, k, bk.weight) - - self.model.current_weight_patches_uuid = None - self.backup.clear() - - if device_to is not None: - self.model.to(device_to) - self.model.device = device_to - self.model.model_loaded_weight_memory = 0 - - for m in self.model.modules(): - if hasattr(m, "comfy_patched_weights"): - del m.comfy_patched_weights - - keys = list(self.object_patches_backup.keys()) - for k in keys: - comfy.utils.set_attr(self.model, k, self.object_patches_backup[k]) - - self.object_patches_backup.clear() - - def partially_unload(self, device_to, memory_to_free=0): - with self.use_ejected(): - hooks_unpatched = False - memory_freed = 0 - patch_counter = 0 - unload_list = self._load_list() - unload_list.sort() - for unload in unload_list: - if memory_to_free < memory_freed: - break - module_mem = unload[0] - n = unload[1] - m = unload[2] - params = unload[3] - - lowvram_possible = hasattr(m, "comfy_cast_weights") - if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True: - move_weight = True - for param in params: - key = "{}.{}".format(n, param) - bk = self.backup.get(key, None) - if bk is not None: - if not lowvram_possible: - move_weight = False - break - - if not hooks_unpatched: - self.unpatch_hooks() - hooks_unpatched = True - - if bk.inplace_update: - comfy.utils.copy_to_param(self.model, key, bk.weight) - else: - comfy.utils.set_attr_param(self.model, key, bk.weight) - self.backup.pop(key) - - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - if move_weight: - cast_weight = self.force_cast_weights - m.to(device_to) - module_mem += move_weight_functions(m, device_to) - if lowvram_possible: - if weight_key in self.patches: - m.weight_function.append(LowVramPatch(weight_key, self.patches)) - patch_counter += 1 - if bias_key in self.patches: - m.bias_function.append(LowVramPatch(bias_key, self.patches)) - patch_counter += 1 - cast_weight = True - - if cast_weight: - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True - m.comfy_patched_weights = False - memory_freed += module_mem - logging.debug("freed {}".format(n)) - - self.model.model_lowvram = True - self.model.lowvram_patch_counter += patch_counter - self.model.model_loaded_weight_memory -= memory_freed - return memory_freed - - def partially_load(self, device_to, extra_memory=0, force_patch_weights=False): - with self.use_ejected(skip_and_inject_on_exit_only=True): - unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights) - # TODO: force_patch_weights should not unload + reload full model - used = self.model.model_loaded_weight_memory - self.unpatch_model(self.offload_device, unpatch_weights=unpatch_weights) - if unpatch_weights: - extra_memory += (used - self.model.model_loaded_weight_memory) - - self.patch_model(load_weights=False) - full_load = False - if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0: - self.apply_hooks(self.forced_hooks, force_apply=True) - return 0 - if self.model.model_loaded_weight_memory + extra_memory > self.model_size(): - full_load = True - current_used = self.model.model_loaded_weight_memory - try: - self.load(device_to, lowvram_model_memory=current_used + extra_memory, force_patch_weights=force_patch_weights, full_load=full_load) - except Exception as e: - self.detach() - raise e - - return self.model.model_loaded_weight_memory - current_used - - def detach(self, unpatch_all=True): - self.eject_model() - self.model_patches_to(self.offload_device) - if unpatch_all: - self.unpatch_model(self.offload_device, unpatch_weights=unpatch_all) - for callback in self.get_all_callbacks(CallbacksMP.ON_DETACH): - callback(self, unpatch_all) - return self.model - - def current_loaded_device(self): - return self.model.device - - def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32): - logging.warning("The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead") - return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype) - - def cleanup(self): - self.clean_hooks() - if hasattr(self.model, "current_patcher"): - self.model.current_patcher = None - for callback in self.get_all_callbacks(CallbacksMP.ON_CLEANUP): - callback(self) - - def add_callback(self, call_type: str, callback: Callable): - self.add_callback_with_key(call_type, None, callback) - - def add_callback_with_key(self, call_type: str, key: str, callback: Callable): - c = self.callbacks.setdefault(call_type, {}).setdefault(key, []) - c.append(callback) - - def remove_callbacks_with_key(self, call_type: str, key: str): - c = self.callbacks.get(call_type, {}) - if key in c: - c.pop(key) - - def get_callbacks(self, call_type: str, key: str): - return self.callbacks.get(call_type, {}).get(key, []) - - def get_all_callbacks(self, call_type: str): - c_list = [] - for c in self.callbacks.get(call_type, {}).values(): - c_list.extend(c) - return c_list - - def add_wrapper(self, wrapper_type: str, wrapper: Callable): - self.add_wrapper_with_key(wrapper_type, None, wrapper) - - def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable): - w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, []) - w.append(wrapper) - - def remove_wrappers_with_key(self, wrapper_type: str, key: str): - w = self.wrappers.get(wrapper_type, {}) - if key in w: - w.pop(key) - - def get_wrappers(self, wrapper_type: str, key: str): - return self.wrappers.get(wrapper_type, {}).get(key, []) - - def get_all_wrappers(self, wrapper_type: str): - w_list = [] - for w in self.wrappers.get(wrapper_type, {}).values(): - w_list.extend(w) - return w_list - - def set_attachments(self, key: str, attachment): - self.attachments[key] = attachment - - def remove_attachments(self, key: str): - if key in self.attachments: - self.attachments.pop(key) - - def get_attachment(self, key: str): - return self.attachments.get(key, None) - - def set_injections(self, key: str, injections: list[PatcherInjection]): - self.injections[key] = injections - - def remove_injections(self, key: str): - if key in self.injections: - self.injections.pop(key) - - def get_injections(self, key: str): - return self.injections.get(key, None) - - def set_additional_models(self, key: str, models: list['ModelPatcher']): - self.additional_models[key] = models - - def remove_additional_models(self, key: str): - if key in self.additional_models: - self.additional_models.pop(key) - - def get_additional_models_with_key(self, key: str): - return self.additional_models.get(key, []) - - def get_additional_models(self): - all_models = [] - for models in self.additional_models.values(): - all_models.extend(models) - return all_models - - def get_nested_additional_models(self): - def _evaluate_sub_additional_models(prev_models: list[ModelPatcher], cache_set: set[ModelPatcher]): - '''Make sure circular references do not cause infinite recursion.''' - next_models = [] - for model in prev_models: - candidates = model.get_additional_models() - for c in candidates: - if c not in cache_set: - next_models.append(c) - cache_set.add(c) - if len(next_models) == 0: - return prev_models - return prev_models + _evaluate_sub_additional_models(next_models, cache_set) - - all_models = self.get_additional_models() - models_set = set(all_models) - real_all_models = _evaluate_sub_additional_models(prev_models=all_models, cache_set=models_set) - return real_all_models - - def use_ejected(self, skip_and_inject_on_exit_only=False): - return AutoPatcherEjector(self, skip_and_inject_on_exit_only=skip_and_inject_on_exit_only) - - def inject_model(self): - if self.is_injected or self.skip_injection: - return - for injections in self.injections.values(): - for inj in injections: - inj.inject(self) - self.is_injected = True - if self.is_injected: - for callback in self.get_all_callbacks(CallbacksMP.ON_INJECT_MODEL): - callback(self) - - def eject_model(self): - if not self.is_injected: - return - for injections in self.injections.values(): - for inj in injections: - inj.eject(self) - self.is_injected = False - for callback in self.get_all_callbacks(CallbacksMP.ON_EJECT_MODEL): - callback(self) - - def pre_run(self): - if hasattr(self.model, "current_patcher"): - self.model.current_patcher = self - for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN): - callback(self) - - def prepare_state(self, timestep): - for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE): - callback(self, timestep) - - def restore_hook_patches(self): - if self.hook_patches_backup is not None: - self.hook_patches = self.hook_patches_backup - self.hook_patches_backup = None - - def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode): - self.hook_mode = hook_mode - - def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]): - curr_t = t[0] - reset_current_hooks = False - transformer_options = model_options.get("transformer_options", {}) - for hook in hook_group.hooks: - changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options) - # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref; - # this will cause the weights to be recalculated when sampling - if changed: - # reset current_hooks if contains hook that changed - if self.current_hooks is not None: - for current_hook in self.current_hooks.hooks: - if current_hook == hook: - reset_current_hooks = True - break - for cached_group in list(self.cached_hook_patches.keys()): - if cached_group.contains(hook): - self.cached_hook_patches.pop(cached_group) - if reset_current_hooks: - self.patch_hooks(None) - - def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None, - registered: comfy.hooks.HookGroup = None): - self.restore_hook_patches() - if registered is None: - registered = comfy.hooks.HookGroup() - # handle WeightHooks - weight_hooks_to_register: list[comfy.hooks.WeightHook] = [] - for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight): - if hook.hook_ref not in self.hook_patches: - weight_hooks_to_register.append(hook) - else: - registered.add(hook) - if len(weight_hooks_to_register) > 0: - # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state - self.hook_patches_backup = create_hook_patches_clone(self.hook_patches) - for hook in weight_hooks_to_register: - hook.add_hook_patches(self, model_options, target_dict, registered) - for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES): - callback(self, hooks, target_dict, model_options, registered) - return registered - - def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0): - with self.use_ejected(): - # NOTE: this mirrors behavior of add_patches func - current_hook_patches: dict[str,list] = self.hook_patches.get(hook.hook_ref, {}) - p = set() - model_sd = self.model.state_dict() - for k in patches: - offset = None - function = None - if isinstance(k, str): - key = k - else: - offset = k[1] - key = k[0] - if len(k) > 2: - function = k[2] - - if key in model_sd: - p.add(k) - current_patches: list[tuple] = current_hook_patches.get(key, []) - current_patches.append((strength_patch, patches[k], strength_model, offset, function)) - current_hook_patches[key] = current_patches - self.hook_patches[hook.hook_ref] = current_hook_patches - # since should care about these patches too to determine if same model, reroll patches_uuid - self.patches_uuid = uuid.uuid4() - return list(p) - - def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup): - # combined_patches will contain weights of all relevant hooks, per key - combined_patches = {} - if hooks is not None: - for hook in hooks.hooks: - hook_patches: dict = self.hook_patches.get(hook.hook_ref, {}) - for key in hook_patches.keys(): - current_patches: list[tuple] = combined_patches.get(key, []) - if math.isclose(hook.strength, 1.0): - current_patches.extend(hook_patches[key]) - else: - # patches are stored as tuples: (strength_patch, (tuple_with_weights,), strength_model) - for patch in hook_patches[key]: - new_patch = list(patch) - new_patch[0] *= hook.strength - current_patches.append(tuple(new_patch)) - combined_patches[key] = current_patches - return combined_patches - - def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False): - # TODO: return transformer_options dict with any additions from hooks - if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)): - return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) - self.patch_hooks(hooks=hooks) - for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS): - callback(self, hooks) - return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) - - def patch_hooks(self, hooks: comfy.hooks.HookGroup): - with self.use_ejected(): - if hooks is not None: - model_sd_keys = list(self.model_state_dict().keys()) - memory_counter = None - if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: - # TODO: minimum_counter should have a minimum that conforms to loaded model requirements - memory_counter = MemoryCounter(initial=comfy.model_management.get_free_memory(self.load_device), - minimum=comfy.model_management.minimum_inference_memory()*2) - # if have cached weights for hooks, use it - cached_weights = self.cached_hook_patches.get(hooks, None) - if cached_weights is not None: - model_sd_keys_set = set(model_sd_keys) - for key in cached_weights: - if key not in model_sd_keys: - logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}") - continue - self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter) - model_sd_keys_set.remove(key) - self.unpatch_hooks(model_sd_keys_set) - else: - self.unpatch_hooks() - relevant_patches = self.get_combined_hook_patches(hooks=hooks) - original_weights = None - if len(relevant_patches) > 0: - original_weights = self.get_key_patches() - for key in relevant_patches: - if key not in model_sd_keys: - logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}") - continue - self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights, - memory_counter=memory_counter) - else: - self.unpatch_hooks() - self.current_hooks = hooks - - def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter): - if key not in self.hook_backup: - weight: torch.Tensor = comfy.utils.get_attr(self.model, key) - target_device = self.offload_device - if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: - used = memory_counter.use(weight) - if used: - target_device = weight.device - self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device) - comfy.utils.copy_to_param(self.model, key, cached_weights[key][0].to(device=cached_weights[key][1])) - - def clear_cached_hook_weights(self): - self.cached_hook_patches.clear() - self.patch_hooks(None) - - def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter): - if key not in combined_patches: - return - - weight, set_func, convert_func = get_key_weight(self.model, key) - weight: torch.Tensor - if key not in self.hook_backup: - target_device = self.offload_device - if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: - used = memory_counter.use(weight) - if used: - target_device = weight.device - self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device) - # TODO: properly handle LowVramPatch, if it ends up an issue - temp_weight = comfy.model_management.cast_to_device(weight, weight.device, torch.float32, copy=True) - if convert_func is not None: - temp_weight = convert_func(temp_weight, inplace=True) - - out_weight = comfy.lora.calculate_weight(combined_patches[key], - temp_weight, - key, original_weights=original_weights) - del original_weights[key] - if set_func is None: - out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key)) - comfy.utils.copy_to_param(self.model, key, out_weight) - else: - set_func(out_weight, inplace_update=True, seed=string_to_seed(key)) - if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed: - # TODO: disable caching if not enough system RAM to do so - target_device = self.offload_device - used = memory_counter.use(weight) - if used: - target_device = weight.device - self.cached_hook_patches.setdefault(hooks, {}) - self.cached_hook_patches[hooks][key] = (out_weight.to(device=target_device, copy=False), weight.device) - del temp_weight - del out_weight - del weight - - def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None: - with self.use_ejected(): - if len(self.hook_backup) == 0: - self.current_hooks = None - return - keys = list(self.hook_backup.keys()) - if whitelist_keys_set: - for k in keys: - if k in whitelist_keys_set: - comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) - self.hook_backup.pop(k) - else: - for k in keys: - comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) - - self.hook_backup.clear() - self.current_hooks = None - - def clean_hooks(self): - self.unpatch_hooks() - self.clear_cached_hook_weights() - - def __del__(self): - self.detach(unpatch_all=False) - diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py deleted file mode 100644 index b240b7f291905c90d40718565a3fdecbf85cabb3..0000000000000000000000000000000000000000 --- a/comfy/model_sampling.py +++ /dev/null @@ -1,383 +0,0 @@ -import torch -from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule -import math - -def rescale_zero_terminal_snr_sigmas(sigmas): - alphas_cumprod = 1 / ((sigmas * sigmas) + 1) - alphas_bar_sqrt = alphas_cumprod.sqrt() - - # Store old values. - alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() - alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() - - # Shift so the last timestep is zero. - alphas_bar_sqrt -= (alphas_bar_sqrt_T) - - # Scale so the first timestep is back to the old value. - alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) - - # Convert alphas_bar_sqrt to betas - alphas_bar = alphas_bar_sqrt**2 # Revert sqrt - alphas_bar[-1] = 4.8973451890853435e-08 - return ((1 - alphas_bar) / alphas_bar) ** 0.5 - -class EPS: - def calculate_input(self, sigma, noise): - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) - return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 - - def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input - model_output * sigma - - def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) - if max_denoise: - noise = noise * torch.sqrt(1.0 + sigma ** 2.0) - else: - noise = noise * sigma - - noise += latent_image - return noise - - def inverse_noise_scaling(self, sigma, latent): - return latent - -class V_PREDICTION(EPS): - def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 - -class EDM(V_PREDICTION): - def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 - -class CONST: - def calculate_input(self, sigma, noise): - return noise - - def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input - model_output * sigma - - def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) - return sigma * noise + (1.0 - sigma) * latent_image - - def inverse_noise_scaling(self, sigma, latent): - sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1)) - return latent / (1.0 - sigma) - -class X0(EPS): - def calculate_denoised(self, sigma, model_output, model_input): - return model_output - -class IMG_TO_IMG(X0): - def calculate_input(self, sigma, noise): - return noise - -class COSMOS_RFLOW: - def calculate_input(self, sigma, noise): - sigma = (sigma / (sigma + 1)) - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) - return noise * (1.0 - sigma) - - def calculate_denoised(self, sigma, model_output, model_input): - sigma = (sigma / (sigma + 1)) - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - return model_input * (1.0 - sigma) - model_output * sigma - - def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) - noise = noise * sigma - noise += latent_image - return noise - - def inverse_noise_scaling(self, sigma, latent): - return latent - -class ModelSamplingDiscrete(torch.nn.Module): - def __init__(self, model_config=None, zsnr=None): - super().__init__() - - if model_config is not None: - sampling_settings = model_config.sampling_settings - else: - sampling_settings = {} - - beta_schedule = sampling_settings.get("beta_schedule", "linear") - linear_start = sampling_settings.get("linear_start", 0.00085) - linear_end = sampling_settings.get("linear_end", 0.012) - timesteps = sampling_settings.get("timesteps", 1000) - - if zsnr is None: - zsnr = sampling_settings.get("zsnr", False) - - self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr) - self.sigma_data = 1.0 - - def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, - linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False): - if given_betas is not None: - betas = given_betas - else: - betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) - alphas = 1. - betas - alphas_cumprod = torch.cumprod(alphas, dim=0) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.linear_start = linear_start - self.linear_end = linear_end - self.zsnr = zsnr - - # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) - # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) - # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) - - sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 - if self.zsnr: - sigmas = rescale_zero_terminal_snr_sigmas(sigmas) - - self.set_sigmas(sigmas) - - def set_sigmas(self, sigmas): - self.register_buffer('sigmas', sigmas.float()) - self.register_buffer('log_sigmas', sigmas.log().float()) - - @property - def sigma_min(self): - return self.sigmas[0] - - @property - def sigma_max(self): - return self.sigmas[-1] - - def timestep(self, sigma): - log_sigma = sigma.log() - dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] - return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) - - def sigma(self, timestep): - t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) - low_idx = t.floor().long() - high_idx = t.ceil().long() - w = t.frac() - log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] - return log_sigma.exp().to(timestep.device) - - def percent_to_sigma(self, percent): - if percent <= 0.0: - return 999999999.9 - if percent >= 1.0: - return 0.0 - percent = 1.0 - percent - return self.sigma(torch.tensor(percent * 999.0)).item() - -class ModelSamplingDiscreteEDM(ModelSamplingDiscrete): - def timestep(self, sigma): - return 0.25 * sigma.log() - - def sigma(self, timestep): - return (timestep / 0.25).exp() - -class ModelSamplingContinuousEDM(torch.nn.Module): - def __init__(self, model_config=None): - super().__init__() - if model_config is not None: - sampling_settings = model_config.sampling_settings - else: - sampling_settings = {} - - sigma_min = sampling_settings.get("sigma_min", 0.002) - sigma_max = sampling_settings.get("sigma_max", 120.0) - sigma_data = sampling_settings.get("sigma_data", 1.0) - self.set_parameters(sigma_min, sigma_max, sigma_data) - - def set_parameters(self, sigma_min, sigma_max, sigma_data): - self.sigma_data = sigma_data - sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() - - self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers - self.register_buffer('log_sigmas', sigmas.log()) - - @property - def sigma_min(self): - return self.sigmas[0] - - @property - def sigma_max(self): - return self.sigmas[-1] - - def timestep(self, sigma): - return 0.25 * sigma.log() - - def sigma(self, timestep): - return (timestep / 0.25).exp() - - def percent_to_sigma(self, percent): - if percent <= 0.0: - return 999999999.9 - if percent >= 1.0: - return 0.0 - percent = 1.0 - percent - - log_sigma_min = math.log(self.sigma_min) - return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) - - -class ModelSamplingContinuousV(ModelSamplingContinuousEDM): - def timestep(self, sigma): - return sigma.atan() / math.pi * 2 - - def sigma(self, timestep): - return (timestep * math.pi / 2).tan() - - -def time_snr_shift(alpha, t): - if alpha == 1.0: - return t - return alpha * t / (1 + (alpha - 1) * t) - -class ModelSamplingDiscreteFlow(torch.nn.Module): - def __init__(self, model_config=None): - super().__init__() - if model_config is not None: - sampling_settings = model_config.sampling_settings - else: - sampling_settings = {} - - self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000)) - - def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000): - self.shift = shift - self.multiplier = multiplier - ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier) - self.register_buffer('sigmas', ts) - - @property - def sigma_min(self): - return self.sigmas[0] - - @property - def sigma_max(self): - return self.sigmas[-1] - - def timestep(self, sigma): - return sigma * self.multiplier - - def sigma(self, timestep): - return time_snr_shift(self.shift, timestep / self.multiplier) - - def percent_to_sigma(self, percent): - if percent <= 0.0: - return 1.0 - if percent >= 1.0: - return 0.0 - return time_snr_shift(self.shift, 1.0 - percent) - -class StableCascadeSampling(ModelSamplingDiscrete): - def __init__(self, model_config=None): - super().__init__() - - if model_config is not None: - sampling_settings = model_config.sampling_settings - else: - sampling_settings = {} - - self.set_parameters(sampling_settings.get("shift", 1.0)) - - def set_parameters(self, shift=1.0, cosine_s=8e-3): - self.shift = shift - self.cosine_s = torch.tensor(cosine_s) - self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 - - #This part is just for compatibility with some schedulers in the codebase - self.num_timesteps = 10000 - sigmas = torch.empty((self.num_timesteps), dtype=torch.float32) - for x in range(self.num_timesteps): - t = (x + 1) / self.num_timesteps - sigmas[x] = self.sigma(t) - - self.set_sigmas(sigmas) - - def sigma(self, timestep): - alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod) - - if self.shift != 1.0: - var = alpha_cumprod - logSNR = (var/(1-var)).log() - logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift)) - alpha_cumprod = logSNR.sigmoid() - - alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999) - return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5 - - def timestep(self, sigma): - var = 1 / ((sigma * sigma) + 1) - var = var.clamp(0, 1.0) - s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device) - t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s - return t - - def percent_to_sigma(self, percent): - if percent <= 0.0: - return 999999999.9 - if percent >= 1.0: - return 0.0 - - percent = 1.0 - percent - return self.sigma(torch.tensor(percent)) - - -def flux_time_shift(mu: float, sigma: float, t): - return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) - -class ModelSamplingFlux(torch.nn.Module): - def __init__(self, model_config=None): - super().__init__() - if model_config is not None: - sampling_settings = model_config.sampling_settings - else: - sampling_settings = {} - - self.set_parameters(shift=sampling_settings.get("shift", 1.15)) - - def set_parameters(self, shift=1.15, timesteps=10000): - self.shift = shift - ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps)) - self.register_buffer('sigmas', ts) - - @property - def sigma_min(self): - return self.sigmas[0] - - @property - def sigma_max(self): - return self.sigmas[-1] - - def timestep(self, sigma): - return sigma - - def sigma(self, timestep): - return flux_time_shift(self.shift, 1.0, timestep) - - def percent_to_sigma(self, percent): - if percent <= 0.0: - return 1.0 - if percent >= 1.0: - return 0.0 - return flux_time_shift(self.shift, 1.0, 1.0 - percent) - - -class ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM): - def timestep(self, sigma): - return sigma / (sigma + 1) - - def sigma(self, timestep): - sigma_max = self.sigma_max - if timestep >= (sigma_max / (sigma_max + 1)): - return sigma_max - - return timestep / (1 - timestep) diff --git a/comfy/ops.py b/comfy/ops.py deleted file mode 100644 index 18e7db705af9ea17bd180485c2223528371d9475..0000000000000000000000000000000000000000 --- a/comfy/ops.py +++ /dev/null @@ -1,467 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -import logging -import comfy.model_management -from comfy.cli_args import args, PerformanceFeature -import comfy.float -import comfy.rmsnorm -import contextlib - - -def scaled_dot_product_attention(q, k, v, *args, **kwargs): - return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) - - -try: - if torch.cuda.is_available(): - from torch.nn.attention import SDPBackend, sdpa_kernel - import inspect - if "set_priority" in inspect.signature(sdpa_kernel).parameters: - SDPA_BACKEND_PRIORITY = [ - SDPBackend.FLASH_ATTENTION, - SDPBackend.EFFICIENT_ATTENTION, - SDPBackend.MATH, - ] - - SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION) - - def scaled_dot_product_attention(q, k, v, *args, **kwargs): - with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True): - return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) - else: - logging.warning("Torch version too old to set sdpa backend priority.") -except (ModuleNotFoundError, TypeError): - logging.warning("Could not set sdpa backend priority.") - -cast_to = comfy.model_management.cast_to #TODO: remove once no more references - -def cast_to_input(weight, input, non_blocking=False, copy=True): - return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) - -def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): - if input is not None: - if dtype is None: - dtype = input.dtype - if bias_dtype is None: - bias_dtype = dtype - if device is None: - device = input.device - - offload_stream = comfy.model_management.get_offload_stream(device) - if offload_stream is not None: - wf_context = offload_stream - else: - wf_context = contextlib.nullcontext() - - bias = None - non_blocking = comfy.model_management.device_supports_non_blocking(device) - if s.bias is not None: - has_function = len(s.bias_function) > 0 - bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) - - if has_function: - with wf_context: - for f in s.bias_function: - bias = f(bias) - - has_function = len(s.weight_function) > 0 - weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) - if has_function: - with wf_context: - for f in s.weight_function: - weight = f(weight) - - comfy.model_management.sync_stream(device, offload_stream) - return weight, bias - -class CastWeightBiasOp: - comfy_cast_weights = False - weight_function = [] - bias_function = [] - -class disable_weight_init: - class Linear(torch.nn.Linear, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Conv1d(torch.nn.Conv1d, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Conv2d(torch.nn.Conv2d, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Conv3d(torch.nn.Conv3d, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return self._conv_forward(input, weight, bias) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - if self.weight is not None: - weight, bias = cast_bias_weight(self, input) - else: - weight = None - bias = None - return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class RMSNorm(comfy.rmsnorm.RMSNorm, CastWeightBiasOp): - def reset_parameters(self): - self.bias = None - return None - - def forward_comfy_cast_weights(self, input): - if self.weight is not None: - weight, bias = cast_bias_weight(self, input) - else: - weight = None - return comfy.rmsnorm.rms_norm(input, weight, self.eps) # TODO: switch to commented out line when old torch is deprecated - # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input, output_size=None): - num_spatial_dims = 2 - output_padding = self._output_padding( - input, output_size, self.stride, self.padding, self.kernel_size, - num_spatial_dims, self.dilation) - - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.conv_transpose2d( - input, weight, bias, self.stride, self.padding, - output_padding, self.groups, self.dilation) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input, output_size=None): - num_spatial_dims = 1 - output_padding = self._output_padding( - input, output_size, self.stride, self.padding, self.kernel_size, - num_spatial_dims, self.dilation) - - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.conv_transpose1d( - input, weight, bias, self.stride, self.padding, - output_padding, self.groups, self.dilation) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - class Embedding(torch.nn.Embedding, CastWeightBiasOp): - def reset_parameters(self): - self.bias = None - return None - - def forward_comfy_cast_weights(self, input, out_dtype=None): - output_dtype = out_dtype - if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16: - out_dtype = None - weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype) - return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - if "out_dtype" in kwargs: - kwargs.pop("out_dtype") - return super().forward(*args, **kwargs) - - @classmethod - def conv_nd(s, dims, *args, **kwargs): - if dims == 2: - return s.Conv2d(*args, **kwargs) - elif dims == 3: - return s.Conv3d(*args, **kwargs) - else: - raise ValueError(f"unsupported dimensions: {dims}") - - -class manual_cast(disable_weight_init): - class Linear(disable_weight_init.Linear): - comfy_cast_weights = True - - class Conv1d(disable_weight_init.Conv1d): - comfy_cast_weights = True - - class Conv2d(disable_weight_init.Conv2d): - comfy_cast_weights = True - - class Conv3d(disable_weight_init.Conv3d): - comfy_cast_weights = True - - class GroupNorm(disable_weight_init.GroupNorm): - comfy_cast_weights = True - - class LayerNorm(disable_weight_init.LayerNorm): - comfy_cast_weights = True - - class ConvTranspose2d(disable_weight_init.ConvTranspose2d): - comfy_cast_weights = True - - class ConvTranspose1d(disable_weight_init.ConvTranspose1d): - comfy_cast_weights = True - - class RMSNorm(disable_weight_init.RMSNorm): - comfy_cast_weights = True - - class Embedding(disable_weight_init.Embedding): - comfy_cast_weights = True - - -def fp8_linear(self, input): - dtype = self.weight.dtype - if dtype not in [torch.float8_e4m3fn]: - return None - - tensor_2d = False - if len(input.shape) == 2: - tensor_2d = True - input = input.unsqueeze(1) - - input_shape = input.shape - input_dtype = input.dtype - if len(input.shape) == 3: - w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype) - w = w.t() - - scale_weight = self.scale_weight - scale_input = self.scale_input - if scale_weight is None: - scale_weight = torch.ones((), device=input.device, dtype=torch.float32) - else: - scale_weight = scale_weight.to(input.device) - - if scale_input is None: - scale_input = torch.ones((), device=input.device, dtype=torch.float32) - input = torch.clamp(input, min=-448, max=448, out=input) - input = input.reshape(-1, input_shape[2]).to(dtype).contiguous() - else: - scale_input = scale_input.to(input.device) - input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous() - - if bias is not None: - o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight) - else: - o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight) - - if isinstance(o, tuple): - o = o[0] - - if tensor_2d: - return o.reshape(input_shape[0], -1) - - return o.reshape((-1, input_shape[1], self.weight.shape[0])) - - return None - -class fp8_ops(manual_cast): - class Linear(manual_cast.Linear): - def reset_parameters(self): - self.scale_weight = None - self.scale_input = None - return None - - def forward_comfy_cast_weights(self, input): - try: - out = fp8_linear(self, input) - if out is not None: - return out - except Exception as e: - logging.info("Exception during fp8 op: {}".format(e)) - - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) - -def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None): - logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input)) - class scaled_fp8_op(manual_cast): - class Linear(manual_cast.Linear): - def __init__(self, *args, **kwargs): - if override_dtype is not None: - kwargs['dtype'] = override_dtype - super().__init__(*args, **kwargs) - - def reset_parameters(self): - if not hasattr(self, 'scale_weight'): - self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False) - - if not scale_input: - self.scale_input = None - - if not hasattr(self, 'scale_input'): - self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False) - return None - - def forward_comfy_cast_weights(self, input): - if fp8_matrix_mult: - out = fp8_linear(self, input) - if out is not None: - return out - - weight, bias = cast_bias_weight(self, input) - - if weight.numel() < input.numel(): #TODO: optimize - return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias) - else: - return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias) - - def convert_weight(self, weight, inplace=False, **kwargs): - if inplace: - weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype) - return weight - else: - return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype) - - def set_weight(self, weight, inplace_update=False, seed=None, **kwargs): - weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed) - if inplace_update: - self.weight.data.copy_(weight) - else: - self.weight = torch.nn.Parameter(weight, requires_grad=False) - - return scaled_fp8_op - -CUBLAS_IS_AVAILABLE = False -try: - from cublas_ops import CublasLinear - CUBLAS_IS_AVAILABLE = True -except ImportError: - pass - -if CUBLAS_IS_AVAILABLE: - class cublas_ops(disable_weight_init): - class Linear(CublasLinear, disable_weight_init.Linear): - def reset_parameters(self): - return None - - def forward_comfy_cast_weights(self, input): - return super().forward(input) - - def forward(self, *args, **kwargs): - return super().forward(*args, **kwargs) - -def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None): - fp8_compute = comfy.model_management.supports_fp8_compute(load_device) - if scaled_fp8 is not None: - return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8) - - if ( - fp8_compute and - (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and - not disable_fast_fp8 - ): - return fp8_ops - - if ( - PerformanceFeature.CublasOps in args.fast and - CUBLAS_IS_AVAILABLE and - weight_dtype == torch.float16 and - (compute_dtype == torch.float16 or compute_dtype is None) - ): - logging.info("Using cublas ops") - return cublas_ops - - if compute_dtype is None or weight_dtype == compute_dtype: - return disable_weight_init - - return manual_cast diff --git a/comfy/options.py b/comfy/options.py deleted file mode 100644 index f7f8af41ebd8b9669ef0ef21827ea6195bcb4752..0000000000000000000000000000000000000000 --- a/comfy/options.py +++ /dev/null @@ -1,6 +0,0 @@ - -args_parsing = False - -def enable_args_parsing(enable=True): - global args_parsing - args_parsing = enable diff --git a/comfy/patcher_extension.py b/comfy/patcher_extension.py deleted file mode 100644 index 46cc7b2a8858f3842c3fcf8711b0346f76e75e06..0000000000000000000000000000000000000000 --- a/comfy/patcher_extension.py +++ /dev/null @@ -1,158 +0,0 @@ -from __future__ import annotations -from typing import Callable - -class CallbacksMP: - ON_CLONE = "on_clone" - ON_LOAD = "on_load_after" - ON_DETACH = "on_detach_after" - ON_CLEANUP = "on_cleanup" - ON_PRE_RUN = "on_pre_run" - ON_PREPARE_STATE = "on_prepare_state" - ON_APPLY_HOOKS = "on_apply_hooks" - ON_REGISTER_ALL_HOOK_PATCHES = "on_register_all_hook_patches" - ON_INJECT_MODEL = "on_inject_model" - ON_EJECT_MODEL = "on_eject_model" - - # callbacks dict is in the format: - # {"call_type": {"key": [Callable1, Callable2, ...]} } - @classmethod - def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]: - return {} - -def add_callback(call_type: str, callback: Callable, transformer_options: dict, is_model_options=False): - add_callback_with_key(call_type, None, callback, transformer_options, is_model_options) - -def add_callback_with_key(call_type: str, key: str, callback: Callable, transformer_options: dict, is_model_options=False): - if is_model_options: - transformer_options = transformer_options.setdefault("transformer_options", {}) - callbacks: dict[str, dict[str, list]] = transformer_options.setdefault("callbacks", {}) - c = callbacks.setdefault(call_type, {}).setdefault(key, []) - c.append(callback) - -def get_callbacks_with_key(call_type: str, key: str, transformer_options: dict, is_model_options=False): - if is_model_options: - transformer_options = transformer_options.get("transformer_options", {}) - c_list = [] - callbacks: dict[str, list] = transformer_options.get("callbacks", {}) - c_list.extend(callbacks.get(call_type, {}).get(key, [])) - return c_list - -def get_all_callbacks(call_type: str, transformer_options: dict, is_model_options=False): - if is_model_options: - transformer_options = transformer_options.get("transformer_options", {}) - c_list = [] - callbacks: dict[str, list] = transformer_options.get("callbacks", {}) - for c in callbacks.get(call_type, {}).values(): - c_list.extend(c) - return c_list - -class WrappersMP: - OUTER_SAMPLE = "outer_sample" - PREPARE_SAMPLING = "prepare_sampling" - SAMPLER_SAMPLE = "sampler_sample" - PREDICT_NOISE = "predict_noise" - CALC_COND_BATCH = "calc_cond_batch" - APPLY_MODEL = "apply_model" - DIFFUSION_MODEL = "diffusion_model" - - # wrappers dict is in the format: - # {"wrapper_type": {"key": [Callable1, Callable2, ...]} } - @classmethod - def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]: - return {} - -def add_wrapper(wrapper_type: str, wrapper: Callable, transformer_options: dict, is_model_options=False): - add_wrapper_with_key(wrapper_type, None, wrapper, transformer_options, is_model_options) - -def add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable, transformer_options: dict, is_model_options=False): - if is_model_options: - transformer_options = transformer_options.setdefault("transformer_options", {}) - wrappers: dict[str, dict[str, list]] = transformer_options.setdefault("wrappers", {}) - w = wrappers.setdefault(wrapper_type, {}).setdefault(key, []) - w.append(wrapper) - -def get_wrappers_with_key(wrapper_type: str, key: str, transformer_options: dict, is_model_options=False): - if is_model_options: - transformer_options = transformer_options.get("transformer_options", {}) - w_list = [] - wrappers: dict[str, list] = transformer_options.get("wrappers", {}) - w_list.extend(wrappers.get(wrapper_type, {}).get(key, [])) - return w_list - -def get_all_wrappers(wrapper_type: str, transformer_options: dict, is_model_options=False): - if is_model_options: - transformer_options = transformer_options.get("transformer_options", {}) - w_list = [] - wrappers: dict[str, list] = transformer_options.get("wrappers", {}) - for w in wrappers.get(wrapper_type, {}).values(): - w_list.extend(w) - return w_list - -class WrapperExecutor: - """Handles call stack of wrappers around a function in an ordered manner.""" - def __init__(self, original: Callable, class_obj: object, wrappers: list[Callable], idx: int): - # NOTE: class_obj exists so that wrappers surrounding a class method can access - # the class instance at runtime via executor.class_obj - self.original = original - self.class_obj = class_obj - self.wrappers = wrappers.copy() - self.idx = idx - self.is_last = idx == len(wrappers) - - def __call__(self, *args, **kwargs): - """Calls the next wrapper or original function, whichever is appropriate.""" - new_executor = self._create_next_executor() - return new_executor.execute(*args, **kwargs) - - def execute(self, *args, **kwargs): - """Used to initiate executor internally - DO NOT use this if you received executor in wrapper.""" - args = list(args) - kwargs = dict(kwargs) - if self.is_last: - return self.original(*args, **kwargs) - return self.wrappers[self.idx](self, *args, **kwargs) - - def _create_next_executor(self) -> 'WrapperExecutor': - new_idx = self.idx + 1 - if new_idx > len(self.wrappers): - raise Exception("Wrapper idx exceeded available wrappers; something went very wrong.") - if self.class_obj is None: - return WrapperExecutor.new_executor(self.original, self.wrappers, new_idx) - return WrapperExecutor.new_class_executor(self.original, self.class_obj, self.wrappers, new_idx) - - @classmethod - def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0): - return cls(original, class_obj=None, wrappers=wrappers, idx=idx) - - @classmethod - def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0): - return cls(original, class_obj, wrappers, idx=idx) - -class PatcherInjection: - def __init__(self, inject: Callable, eject: Callable): - self.inject = inject - self.eject = eject - -def copy_nested_dicts(input_dict: dict): - new_dict = input_dict.copy() - for key, value in input_dict.items(): - if isinstance(value, dict): - new_dict[key] = copy_nested_dicts(value) - elif isinstance(value, list): - new_dict[key] = value.copy() - return new_dict - -def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True): - if copy_dict1: - merged_dict = copy_nested_dicts(dict1) - else: - merged_dict = dict1 - for key, value in dict2.items(): - if isinstance(value, dict): - curr_value = merged_dict.setdefault(key, {}) - merged_dict[key] = merge_nested_dicts(value, curr_value) - elif isinstance(value, list): - merged_dict.setdefault(key, []).extend(value) - else: - merged_dict[key] = value - return merged_dict diff --git a/comfy/rmsnorm.py b/comfy/rmsnorm.py deleted file mode 100644 index 555542a46b54b9821b394374147d7fc4f9bc74c1..0000000000000000000000000000000000000000 --- a/comfy/rmsnorm.py +++ /dev/null @@ -1,57 +0,0 @@ -import torch -import comfy.model_management -import numbers -import logging - -RMSNorm = None - -try: - rms_norm_torch = torch.nn.functional.rms_norm - RMSNorm = torch.nn.RMSNorm -except: - rms_norm_torch = None - logging.warning("Please update pytorch to use native RMSNorm") - - -def rms_norm(x, weight=None, eps=1e-6): - if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()): - if weight is None: - return rms_norm_torch(x, (x.shape[-1],), eps=eps) - else: - return rms_norm_torch(x, weight.shape, weight=comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) - else: - r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) - if weight is None: - return r - else: - return r * comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device) - - -if RMSNorm is None: - class RMSNorm(torch.nn.Module): - def __init__( - self, - normalized_shape, - eps=1e-6, - elementwise_affine=True, - device=None, - dtype=None, - ): - factory_kwargs = {"device": device, "dtype": dtype} - super().__init__() - if isinstance(normalized_shape, numbers.Integral): - # mypy error: incompatible types in assignment - normalized_shape = (normalized_shape,) # type: ignore[assignment] - self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type] - self.eps = eps - self.elementwise_affine = elementwise_affine - if self.elementwise_affine: - self.weight = torch.nn.Parameter( - torch.empty(self.normalized_shape, **factory_kwargs) - ) - else: - self.register_parameter("weight", None) - self.bias = None - - def forward(self, x): - return rms_norm(x, self.weight, self.eps) diff --git a/comfy/sample.py b/comfy/sample.py deleted file mode 100644 index be5a7e246fdf6168cd74d1bb1b550c6852a83212..0000000000000000000000000000000000000000 --- a/comfy/sample.py +++ /dev/null @@ -1,52 +0,0 @@ -import torch -import comfy.model_management -import comfy.samplers -import comfy.utils -import numpy as np -import logging - -def prepare_noise(latent_image, seed, noise_inds=None): - """ - creates random noise given a latent image and a seed. - optional arg skip can be used to skip and discard x number of noise generations for a given seed - """ - generator = torch.manual_seed(seed) - if noise_inds is None: - return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") - - unique_inds, inverse = np.unique(noise_inds, return_inverse=True) - noises = [] - for i in range(unique_inds[-1]+1): - noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") - if i in unique_inds: - noises.append(noise) - noises = [noises[i] for i in inverse] - noises = torch.cat(noises, axis=0) - return noises - -def fix_empty_latent_channels(model, latent_image): - latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels - if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: - latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1) - if latent_format.latent_dimensions == 3 and latent_image.ndim == 4: - latent_image = latent_image.unsqueeze(2) - return latent_image - -def prepare_sampling(model, noise_shape, positive, negative, noise_mask): - logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed") - return model, positive, negative, noise_mask, [] - -def cleanup_additional_models(models): - logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed") - -def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): - sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) - - samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) - samples = samples.to(comfy.model_management.intermediate_device()) - return samples - -def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): - samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) - samples = samples.to(comfy.model_management.intermediate_device()) - return samples diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py deleted file mode 100644 index e46971afb7785e40d02ed64ff6647c329f97cf5e..0000000000000000000000000000000000000000 --- a/comfy/sampler_helpers.py +++ /dev/null @@ -1,184 +0,0 @@ -from __future__ import annotations -import uuid -import math -import collections -import comfy.model_management -import comfy.conds -import comfy.utils -import comfy.hooks -import comfy.patcher_extension -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from comfy.model_patcher import ModelPatcher - from comfy.model_base import BaseModel - from comfy.controlnet import ControlBase - -def prepare_mask(noise_mask, shape, device): - return comfy.utils.reshape_mask(noise_mask, shape).to(device) - -def get_models_from_cond(cond, model_type): - models = [] - for c in cond: - if model_type in c: - if isinstance(c[model_type], list): - models += c[model_type] - else: - models += [c[model_type]] - return models - -def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup): - # get hooks from conds, and collect cnets so they can be checked for extra_hooks - cnets: list[ControlBase] = [] - for c in cond: - if 'hooks' in c: - for hook in c['hooks'].hooks: - full_hooks.add(hook) - if 'control' in c: - cnets.append(c['control']) - - def get_extra_hooks_from_cnet(cnet: ControlBase, _list: list): - if cnet.extra_hooks is not None: - _list.append(cnet.extra_hooks) - if cnet.previous_controlnet is None: - return _list - return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list) - - hooks_list = [] - cnets = set(cnets) - for base_cnet in cnets: - get_extra_hooks_from_cnet(base_cnet, hooks_list) - extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list) - if extra_hooks is not None: - for hook in extra_hooks.hooks: - full_hooks.add(hook) - - return full_hooks - -def convert_cond(cond): - out = [] - for c in cond: - temp = c[1].copy() - model_conds = temp.get("model_conds", {}) - if c[0] is not None: - temp["cross_attn"] = c[0] - temp["model_conds"] = model_conds - temp["uuid"] = uuid.uuid4() - out.append(temp) - return out - -def get_additional_models(conds, dtype): - """loads additional models in conditioning""" - cnets: list[ControlBase] = [] - gligen = [] - add_models = [] - - for k in conds: - cnets += get_models_from_cond(conds[k], "control") - gligen += get_models_from_cond(conds[k], "gligen") - add_models += get_models_from_cond(conds[k], "additional_models") - - control_nets = set(cnets) - - inference_memory = 0 - control_models = [] - for m in control_nets: - control_models += m.get_models() - inference_memory += m.inference_memory_requirements(dtype) - - gligen = [x[1] for x in gligen] - models = control_models + gligen + add_models - - return models, inference_memory - -def get_additional_models_from_model_options(model_options: dict[str]=None): - """loads additional models from registered AddModels hooks""" - models = [] - if model_options is not None and "registered_hooks" in model_options: - registered: comfy.hooks.HookGroup = model_options["registered_hooks"] - for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels): - hook: comfy.hooks.AdditionalModelsHook - models.extend(hook.models) - return models - -def cleanup_additional_models(models): - """cleanup additional models that were loaded""" - for m in models: - if hasattr(m, 'cleanup'): - m.cleanup() - -def estimate_memory(model, noise_shape, conds): - cond_shapes = collections.defaultdict(list) - cond_shapes_min = {} - for _, cs in conds.items(): - for cond in cs: - for k, v in model.model.extra_conds_shapes(**cond).items(): - cond_shapes[k].append(v) - if cond_shapes_min.get(k, None) is None: - cond_shapes_min[k] = [v] - elif math.prod(v) > math.prod(cond_shapes_min[k][0]): - cond_shapes_min[k] = [v] - - memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes) - minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min) - return memory_required, minimum_memory_required - -def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): - executor = comfy.patcher_extension.WrapperExecutor.new_executor( - _prepare_sampling, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True) - ) - return executor.execute(model, noise_shape, conds, model_options=model_options) - -def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): - real_model: BaseModel = None - models, inference_memory = get_additional_models(conds, model.model_dtype()) - models += get_additional_models_from_model_options(model_options) - models += model.get_nested_additional_models() # TODO: does this require inference_memory update? - memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds) - comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory) - real_model = model.model - - return real_model, conds, models - -def cleanup_models(conds, models): - cleanup_additional_models(models) - - control_cleanup = [] - for k in conds: - control_cleanup += get_models_from_cond(conds[k], "control") - - cleanup_additional_models(set(control_cleanup)) - -def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict): - ''' - Registers hooks from conds. - ''' - # check for hooks in conds - if not registered, see if can be applied - hooks = comfy.hooks.HookGroup() - for k in conds: - get_hooks_from_cond(conds[k], hooks) - # add wrappers and callbacks from ModelPatcher to transformer_options - comfy.patcher_extension.merge_nested_dicts(model_options["transformer_options"].setdefault("wrappers", {}), model.wrappers, copy_dict1=False) - comfy.patcher_extension.merge_nested_dicts(model_options["transformer_options"].setdefault("callbacks", {}), model.callbacks, copy_dict1=False) - # begin registering hooks - registered = comfy.hooks.HookGroup() - target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model) - # handle all TransformerOptionsHooks - for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions): - hook: comfy.hooks.TransformerOptionsHook - hook.add_hook_patches(model, model_options, target_dict, registered) - # handle all AddModelsHooks - for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels): - hook: comfy.hooks.AdditionalModelsHook - hook.add_hook_patches(model, model_options, target_dict, registered) - # handle all WeightHooks by registering on ModelPatcher - model.register_all_hook_patches(hooks, target_dict, model_options, registered) - # add registered_hooks onto model_options for further reference - if len(registered) > 0: - model_options["registered_hooks"] = registered - # merge original wrappers and callbacks with hooked wrappers and callbacks - to_load_options: dict[str] = model_options.setdefault("to_load_options", {}) - for wc_name in ["wrappers", "callbacks"]: - comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name], - copy_dict1=False) - return to_load_options diff --git a/comfy/samplers.py b/comfy/samplers.py deleted file mode 100644 index b3202cec6f2c6b7a0020b3ec43ef3817062fe14a..0000000000000000000000000000000000000000 --- a/comfy/samplers.py +++ /dev/null @@ -1,1161 +0,0 @@ -from __future__ import annotations -from .k_diffusion import sampling as k_diffusion_sampling -from .extra_samplers import uni_pc -from typing import TYPE_CHECKING, Callable, NamedTuple -if TYPE_CHECKING: - from comfy.model_patcher import ModelPatcher - from comfy.model_base import BaseModel - from comfy.controlnet import ControlBase -import torch -from functools import partial -import collections -from comfy import model_management -import math -import logging -import comfy.sampler_helpers -import comfy.model_patcher -import comfy.patcher_extension -import comfy.hooks -import comfy.context_windows -import comfy.utils -import scipy.stats -import numpy - - -def add_area_dims(area, num_dims): - while (len(area) // 2) < num_dims: - area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:] - return area - -def get_area_and_mult(conds, x_in, timestep_in): - dims = tuple(x_in.shape[2:]) - area = None - strength = 1.0 - - if 'timestep_start' in conds: - timestep_start = conds['timestep_start'] - if timestep_in[0] > timestep_start: - return None - if 'timestep_end' in conds: - timestep_end = conds['timestep_end'] - if timestep_in[0] < timestep_end: - return None - if 'area' in conds: - area = list(conds['area']) - area = add_area_dims(area, len(dims)) - if (len(area) // 2) > len(dims): - area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)] - - if 'strength' in conds: - strength = conds['strength'] - - input_x = x_in - if area is not None: - for i in range(len(dims)): - area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i]) - input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i]) - - if 'mask' in conds: - # Scale the mask to the size of the input - # The mask should have been resized as we began the sampling process - mask_strength = 1.0 - if "mask_strength" in conds: - mask_strength = conds["mask_strength"] - mask = conds['mask'] - # assert (mask.shape[1:] == x_in.shape[2:]) - - mask = mask[:input_x.shape[0]] - if area is not None: - for i in range(len(dims)): - mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) - - mask = mask * mask_strength - mask = mask.unsqueeze(1).repeat((input_x.shape[0] // mask.shape[0], input_x.shape[1]) + (1, ) * (mask.ndim - 1)) - else: - mask = torch.ones_like(input_x) - mult = mask * strength - - if 'mask' not in conds and area is not None: - fuzz = 8 - for i in range(len(dims)): - rr = min(fuzz, mult.shape[2 + i] // 4) - if area[len(dims) + i] != 0: - for t in range(rr): - m = mult.narrow(i + 2, t, 1) - m *= ((1.0 / rr) * (t + 1)) - if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: - for t in range(rr): - m = mult.narrow(i + 2, area[i] - 1 - t, 1) - m *= ((1.0 / rr) * (t + 1)) - - conditioning = {} - model_conds = conds["model_conds"] - for c in model_conds: - conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], area=area) - - hooks = conds.get('hooks', None) - control = conds.get('control', None) - - patches = None - if 'gligen' in conds: - gligen = conds['gligen'] - patches = {} - gligen_type = gligen[0] - gligen_model = gligen[1] - if gligen_type == "position": - gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) - else: - gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) - - patches['middle_patch'] = [gligen_patch] - - cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches', 'uuid', 'hooks']) - return cond_obj(input_x, mult, conditioning, area, control, patches, conds['uuid'], hooks) - -def cond_equal_size(c1, c2): - if c1 is c2: - return True - if c1.keys() != c2.keys(): - return False - for k in c1: - if not c1[k].can_concat(c2[k]): - return False - return True - -def can_concat_cond(c1, c2): - if c1.input_x.shape != c2.input_x.shape: - return False - - def objects_concatable(obj1, obj2): - if (obj1 is None) != (obj2 is None): - return False - if obj1 is not None: - if obj1 is not obj2: - return False - return True - - if not objects_concatable(c1.control, c2.control): - return False - - if not objects_concatable(c1.patches, c2.patches): - return False - - return cond_equal_size(c1.conditioning, c2.conditioning) - -def cond_cat(c_list): - temp = {} - for x in c_list: - for k in x: - cur = temp.get(k, []) - cur.append(x[k]) - temp[k] = cur - - out = {} - for k in temp: - conds = temp[k] - out[k] = conds[0].concat(conds[1:]) - - return out - -def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep, model_options): - # need to figure out remaining unmasked area for conds - default_mults = [] - for _ in default_conds: - default_mults.append(torch.ones_like(x_in)) - # look through each finalized cond in hooked_to_run for 'mult' and subtract it from each cond - for lora_hooks, to_run in hooked_to_run.items(): - for cond_obj, i in to_run: - # if no default_cond for cond_type, do nothing - if len(default_conds[i]) == 0: - continue - area: list[int] = cond_obj.area - if area is not None: - curr_default_mult: torch.Tensor = default_mults[i] - dims = len(area) // 2 - for i in range(dims): - curr_default_mult = curr_default_mult.narrow(i + 2, area[i + dims], area[i]) - curr_default_mult -= cond_obj.mult - else: - default_mults[i] -= cond_obj.mult - # for each default_mult, ReLU to make negatives=0, and then check for any nonzeros - for i, mult in enumerate(default_mults): - # if no default_cond for cond type, do nothing - if len(default_conds[i]) == 0: - continue - torch.nn.functional.relu(mult, inplace=True) - # if mult is all zeros, then don't add default_cond - if torch.max(mult) == 0.0: - continue - - cond = default_conds[i] - for x in cond: - # do get_area_and_mult to get all the expected values - p = get_area_and_mult(x, x_in, timestep) - if p is None: - continue - # replace p's mult with calculated mult - p = p._replace(mult=mult) - if p.hooks is not None: - model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options) - hooked_to_run.setdefault(p.hooks, list()) - hooked_to_run[p.hooks] += [(p, i)] - -def calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options: dict[str]): - handler: comfy.context_windows.ContextHandlerABC = model_options.get("context_handler", None) - if handler is None or not handler.should_use_context(model, conds, x_in, timestep, model_options): - return _calc_cond_batch_outer(model, conds, x_in, timestep, model_options) - return handler.execute(_calc_cond_batch_outer, model, conds, x_in, timestep, model_options) - -def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): - executor = comfy.patcher_extension.WrapperExecutor.new_executor( - _calc_cond_batch, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True) - ) - return executor.execute(model, conds, x_in, timestep, model_options) - -def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): - out_conds = [] - out_counts = [] - # separate conds by matching hooks - hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {} - default_conds = [] - has_default_conds = False - - for i in range(len(conds)): - out_conds.append(torch.zeros_like(x_in)) - out_counts.append(torch.ones_like(x_in) * 1e-37) - - cond = conds[i] - default_c = [] - if cond is not None: - for x in cond: - if 'default' in x: - default_c.append(x) - has_default_conds = True - continue - p = get_area_and_mult(x, x_in, timestep) - if p is None: - continue - if p.hooks is not None: - model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options) - hooked_to_run.setdefault(p.hooks, list()) - hooked_to_run[p.hooks] += [(p, i)] - default_conds.append(default_c) - - if has_default_conds: - finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options) - - model.current_patcher.prepare_state(timestep) - - # run every hooked_to_run separately - for hooks, to_run in hooked_to_run.items(): - while len(to_run) > 0: - first = to_run[0] - first_shape = first[0][0].shape - to_batch_temp = [] - for x in range(len(to_run)): - if can_concat_cond(to_run[x][0], first[0]): - to_batch_temp += [x] - - to_batch_temp.reverse() - to_batch = to_batch_temp[:1] - - free_memory = model_management.get_free_memory(x_in.device) - for i in range(1, len(to_batch_temp) + 1): - batch_amount = to_batch_temp[:len(to_batch_temp)//i] - input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] - cond_shapes = collections.defaultdict(list) - for tt in batch_amount: - cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()} - for k, v in to_run[tt][0].conditioning.items(): - cond_shapes[k].append(v.size()) - - if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory: - to_batch = batch_amount - break - - input_x = [] - mult = [] - c = [] - cond_or_uncond = [] - uuids = [] - area = [] - control = None - patches = None - for x in to_batch: - o = to_run.pop(x) - p = o[0] - input_x.append(p.input_x) - mult.append(p.mult) - c.append(p.conditioning) - area.append(p.area) - cond_or_uncond.append(o[1]) - uuids.append(p.uuid) - control = p.control - patches = p.patches - - batch_chunks = len(cond_or_uncond) - input_x = torch.cat(input_x) - c = cond_cat(c) - timestep_ = torch.cat([timestep] * batch_chunks) - - transformer_options = model.current_patcher.apply_hooks(hooks=hooks) - if 'transformer_options' in model_options: - transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options, - model_options['transformer_options'], - copy_dict1=False) - - if patches is not None: - # TODO: replace with merge_nested_dicts function - if "patches" in transformer_options: - cur_patches = transformer_options["patches"].copy() - for p in patches: - if p in cur_patches: - cur_patches[p] = cur_patches[p] + patches[p] - else: - cur_patches[p] = patches[p] - transformer_options["patches"] = cur_patches - else: - transformer_options["patches"] = patches - - transformer_options["cond_or_uncond"] = cond_or_uncond[:] - transformer_options["uuids"] = uuids[:] - transformer_options["sigmas"] = timestep - - c['transformer_options'] = transformer_options - - if control is not None: - c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options) - - if 'model_function_wrapper' in model_options: - output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) - else: - output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) - - for o in range(batch_chunks): - cond_index = cond_or_uncond[o] - a = area[o] - if a is None: - out_conds[cond_index] += output[o] * mult[o] - out_counts[cond_index] += mult[o] - else: - out_c = out_conds[cond_index] - out_cts = out_counts[cond_index] - dims = len(a) // 2 - for i in range(dims): - out_c = out_c.narrow(i + 2, a[i + dims], a[i]) - out_cts = out_cts.narrow(i + 2, a[i + dims], a[i]) - out_c += output[o] * mult[o] - out_cts += mult[o] - - for i in range(len(out_conds)): - out_conds[i] /= out_counts[i] - - return out_conds - -def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove - logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.") - return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options)) - -def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): - if "sampler_cfg_function" in model_options: - args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, - "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} - cfg_result = x - model_options["sampler_cfg_function"](args) - else: - cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale - - for fn in model_options.get("sampler_post_cfg_function", []): - args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "cond_scale": cond_scale, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, - "sigma": timestep, "model_options": model_options, "input": x} - cfg_result = fn(args) - - return cfg_result - -#The main sampling function shared by all the samplers -#Returns denoised -def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): - if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: - uncond_ = None - else: - uncond_ = uncond - - conds = [cond, uncond_] - if "sampler_calc_cond_batch_function" in model_options: - args = {"conds": conds, "input": x, "sigma": timestep, "model": model, "model_options": model_options} - out = model_options["sampler_calc_cond_batch_function"](args) - else: - out = calc_cond_batch(model, conds, x, timestep, model_options) - - for fn in model_options.get("sampler_pre_cfg_function", []): - args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep, - "input": x, "sigma": timestep, "model": model, "model_options": model_options} - out = fn(args) - - return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) - - -class KSamplerX0Inpaint: - def __init__(self, model, sigmas): - self.inner_model = model - self.sigmas = sigmas - def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None): - if denoise_mask is not None: - if "denoise_mask_function" in model_options: - denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) - latent_mask = 1. - denoise_mask - x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask - out = self.inner_model(x, sigma, model_options=model_options, seed=seed) - if denoise_mask is not None: - out = out * denoise_mask + self.latent_image * latent_mask - return out - -def simple_scheduler(model_sampling, steps): - s = model_sampling - sigs = [] - ss = len(s.sigmas) / steps - for x in range(steps): - sigs += [float(s.sigmas[-(1 + int(x * ss))])] - sigs += [0.0] - return torch.FloatTensor(sigs) - -def ddim_scheduler(model_sampling, steps): - s = model_sampling - sigs = [] - x = 1 - if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001): - steps += 1 - sigs = [] - else: - sigs = [0.0] - - ss = max(len(s.sigmas) // steps, 1) - while x < len(s.sigmas): - sigs += [float(s.sigmas[x])] - x += ss - sigs = sigs[::-1] - return torch.FloatTensor(sigs) - -def normal_scheduler(model_sampling, steps, sgm=False, floor=False): - s = model_sampling - start = s.timestep(s.sigma_max) - end = s.timestep(s.sigma_min) - - append_zero = True - if sgm: - timesteps = torch.linspace(start, end, steps + 1)[:-1] - else: - if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001): - steps += 1 - append_zero = False - timesteps = torch.linspace(start, end, steps) - - sigs = [] - for x in range(len(timesteps)): - ts = timesteps[x] - sigs.append(float(s.sigma(ts))) - - if append_zero: - sigs += [0.0] - - return torch.FloatTensor(sigs) - -# Implemented based on: https://arxiv.org/abs/2407.12173 -def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6): - total_timesteps = (len(model_sampling.sigmas) - 1) - ts = 1 - numpy.linspace(0, 1, steps, endpoint=False) - ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps) - - sigs = [] - last_t = -1 - for t in ts: - if t != last_t: - sigs += [float(model_sampling.sigmas[int(t)])] - last_t = t - sigs += [0.0] - return torch.FloatTensor(sigs) - -# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41 -def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None): - if steps == 1: - sigma_schedule = [1.0, 0.0] - else: - if linear_steps is None: - linear_steps = steps // 2 - linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)] - threshold_noise_step_diff = linear_steps - threshold_noise * steps - quadratic_steps = steps - linear_steps - quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2) - linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2) - const = quadratic_coef * (linear_steps ** 2) - quadratic_sigma_schedule = [ - quadratic_coef * (i ** 2) + linear_coef * i + const - for i in range(linear_steps, steps) - ] - sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0] - sigma_schedule = [1.0 - x for x in sigma_schedule] - return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu() - -# Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608 -def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor: - adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1) - sigmas = adj_idxs.new_zeros(n + 1) - sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_() - return sigmas - -def get_mask_aabb(masks): - if masks.numel() == 0: - return torch.zeros((0, 4), device=masks.device, dtype=torch.int) - - b = masks.shape[0] - - bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) - is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) - for i in range(b): - mask = masks[i] - if mask.numel() == 0: - continue - if torch.max(mask != 0) == False: - is_empty[i] = True - continue - y, x = torch.where(mask) - bounding_boxes[i, 0] = torch.min(x) - bounding_boxes[i, 1] = torch.min(y) - bounding_boxes[i, 2] = torch.max(x) - bounding_boxes[i, 3] = torch.max(y) - - return bounding_boxes, is_empty - -def resolve_areas_and_cond_masks_multidim(conditions, dims, device): - # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. - # While we're doing this, we can also resolve the mask device and scaling for performance reasons - for i in range(len(conditions)): - c = conditions[i] - if 'area' in c: - area = c['area'] - if area[0] == "percentage": - modified = c.copy() - a = area[1:] - a_len = len(a) // 2 - area = () - for d in range(len(dims)): - area += (max(1, round(a[d] * dims[d])),) - for d in range(len(dims)): - area += (round(a[d + a_len] * dims[d]),) - - modified['area'] = area - c = modified - conditions[i] = c - - if 'mask' in c: - mask = c['mask'] - mask = mask.to(device=device) - modified = c.copy() - if len(mask.shape) == len(dims): - mask = mask.unsqueeze(0) - if mask.shape[1:] != dims: - if mask.ndim < 4: - mask = comfy.utils.common_upscale(mask.unsqueeze(1), dims[-1], dims[-2], 'bilinear', 'none').squeeze(1) - else: - mask = comfy.utils.common_upscale(mask, dims[-1], dims[-2], 'bilinear', 'none') - - if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2 - bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) - boxes, is_empty = get_mask_aabb(bounds) - if is_empty[0]: - # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) - modified['area'] = (8, 8, 0, 0) - else: - box = boxes[0] - H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) - H = max(8, H) - W = max(8, W) - area = (int(H), int(W), int(Y), int(X)) - modified['area'] = area - - modified['mask'] = mask - conditions[i] = modified - -def resolve_areas_and_cond_masks(conditions, h, w, device): - logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") - return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) - -def create_cond_with_same_area_if_none(conds, c): - if 'area' not in c: - return - - def area_inside(a, area_cmp): - a = add_area_dims(a, len(area_cmp) // 2) - area_cmp = add_area_dims(area_cmp, len(a) // 2) - - a_l = len(a) // 2 - area_cmp_l = len(area_cmp) // 2 - for i in range(min(a_l, area_cmp_l)): - if a[a_l + i] < area_cmp[area_cmp_l + i]: - return False - for i in range(min(a_l, area_cmp_l)): - if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]): - return False - return True - - c_area = c['area'] - smallest = None - for x in conds: - if 'area' in x: - a = x['area'] - if area_inside(c_area, a): - if smallest is None: - smallest = x - elif 'area' not in smallest: - smallest = x - else: - if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]): - smallest = x - else: - if smallest is None: - smallest = x - if smallest is None: - return - if 'area' in smallest: - if smallest['area'] == c_area: - return - - out = c.copy() - out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? - conds += [out] - -def calculate_start_end_timesteps(model, conds): - s = model.model_sampling - for t in range(len(conds)): - x = conds[t] - - timestep_start = None - timestep_end = None - # handle clip hook schedule, if needed - if 'clip_start_percent' in x: - timestep_start = s.percent_to_sigma(max(x['clip_start_percent'], x.get('start_percent', 0.0))) - timestep_end = s.percent_to_sigma(min(x['clip_end_percent'], x.get('end_percent', 1.0))) - else: - if 'start_percent' in x: - timestep_start = s.percent_to_sigma(x['start_percent']) - if 'end_percent' in x: - timestep_end = s.percent_to_sigma(x['end_percent']) - - if (timestep_start is not None) or (timestep_end is not None): - n = x.copy() - if (timestep_start is not None): - n['timestep_start'] = timestep_start - if (timestep_end is not None): - n['timestep_end'] = timestep_end - conds[t] = n - -def pre_run_control(model, conds): - s = model.model_sampling - for t in range(len(conds)): - x = conds[t] - - percent_to_timestep_function = lambda a: s.percent_to_sigma(a) - if 'control' in x: - x['control'].pre_run(model, percent_to_timestep_function) - -def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): - cond_cnets = [] - cond_other = [] - uncond_cnets = [] - uncond_other = [] - for t in range(len(conds)): - x = conds[t] - if 'area' not in x: - if name in x and x[name] is not None: - cond_cnets.append(x[name]) - else: - cond_other.append((x, t)) - for t in range(len(uncond)): - x = uncond[t] - if 'area' not in x: - if name in x and x[name] is not None: - uncond_cnets.append(x[name]) - else: - uncond_other.append((x, t)) - - if len(uncond_cnets) > 0: - return - - for x in range(len(cond_cnets)): - temp = uncond_other[x % len(uncond_other)] - o = temp[0] - if name in o and o[name] is not None: - n = o.copy() - n[name] = uncond_fill_func(cond_cnets, x) - uncond += [n] - else: - n = o.copy() - n[name] = uncond_fill_func(cond_cnets, x) - uncond[temp[1]] = n - -def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): - for t in range(len(conds)): - x = conds[t] - params = x.copy() - params["device"] = device - params["noise"] = noise - default_width = None - if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model - default_width = noise.shape[3] * 8 - params["width"] = params.get("width", default_width) - params["height"] = params.get("height", noise.shape[2] * 8) - params["prompt_type"] = params.get("prompt_type", prompt_type) - for k in kwargs: - if k not in params: - params[k] = kwargs[k] - - out = model_function(**params) - x = x.copy() - model_conds = x['model_conds'].copy() - for k in out: - model_conds[k] = out[k] - x['model_conds'] = model_conds - conds[t] = x - return conds - -class Sampler: - def sample(self): - pass - - def max_denoise(self, model_wrap, sigmas): - max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) - sigma = float(sigmas[0]) - return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma - -KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", - "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", - "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", - "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp", - "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"] - -class KSAMPLER(Sampler): - def __init__(self, sampler_function, extra_options={}, inpaint_options={}): - self.sampler_function = sampler_function - self.extra_options = extra_options - self.inpaint_options = inpaint_options - - def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): - extra_args["denoise_mask"] = denoise_mask - model_k = KSamplerX0Inpaint(model_wrap, sigmas) - model_k.latent_image = latent_image - if self.inpaint_options.get("random", False): #TODO: Should this be the default? - generator = torch.manual_seed(extra_args.get("seed", 41) + 1) - model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) - else: - model_k.noise = noise - - noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas)) - - k_callback = None - total_steps = len(sigmas) - 1 - if callback is not None: - k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) - - samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) - samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples) - return samples - - -def ksampler(sampler_name, extra_options={}, inpaint_options={}): - if sampler_name == "dpm_fast": - def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): - if len(sigmas) <= 1: - return noise - - sigma_min = sigmas[-1] - if sigma_min == 0: - sigma_min = sigmas[-2] - total_steps = len(sigmas) - 1 - return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) - sampler_function = dpm_fast_function - elif sampler_name == "dpm_adaptive": - def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options): - if len(sigmas) <= 1: - return noise - - sigma_min = sigmas[-1] - if sigma_min == 0: - sigma_min = sigmas[-2] - return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options) - sampler_function = dpm_adaptive_function - else: - sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) - - return KSAMPLER(sampler_function, extra_options, inpaint_options) - - -def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): - for k in conds: - conds[k] = conds[k][:] - resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) - - for k in conds: - calculate_start_end_timesteps(model, conds[k]) - - if hasattr(model, 'extra_conds'): - for k in conds: - conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) - - #make sure each cond area has an opposite one with the same area - for k in conds: - for c in conds[k]: - for kk in conds: - if k != kk: - create_cond_with_same_area_if_none(conds[kk], c) - - for k in conds: - for c in conds[k]: - if 'hooks' in c: - for hook in c['hooks'].hooks: - hook.initialize_timesteps(model) - - for k in conds: - pre_run_control(model, conds[k]) - - if "positive" in conds: - positive = conds["positive"] - for k in conds: - if k != "positive": - apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x]) - apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x]) - - return conds - - -def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]): - # determine which ControlNets have extra_hooks that should be combined with normal hooks - hook_replacement: dict[tuple[ControlBase, comfy.hooks.HookGroup], list[dict]] = {} - for k in conds: - for kk in conds[k]: - if 'control' in kk: - control: 'ControlBase' = kk['control'] - extra_hooks = control.get_extra_hooks() - if len(extra_hooks) > 0: - hooks: comfy.hooks.HookGroup = kk.get('hooks', None) - to_replace = hook_replacement.setdefault((control, hooks), []) - to_replace.append(kk) - # if nothing to replace, do nothing - if len(hook_replacement) == 0: - return - - # for optimal sampling performance, common ControlNets + hook combos should have identical hooks - # on the cond dicts - for key, conds_to_modify in hook_replacement.items(): - control = key[0] - hooks = key[1] - hooks = comfy.hooks.HookGroup.combine_all_hooks(control.get_extra_hooks() + [hooks]) - # if combined hooks are not None, set as new hooks for all relevant conds - if hooks is not None: - for cond in conds_to_modify: - cond['hooks'] = hooks - -def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]): - '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for - HookGroups that have the same reference.''' - registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None) - # if None were registered, make sure all hooks are cleaned from conds - if registered is None: - for k in conds: - for kk in conds[k]: - kk.pop('hooks', None) - return - # find conds that contain hooks to be replaced - group by common HookGroup refs - hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {} - for k in conds: - for kk in conds[k]: - hooks: comfy.hooks.HookGroup = kk.get('hooks', None) - if hooks is not None: - if not hooks.is_subset_of(registered): - to_replace = hook_replacement.setdefault(hooks, []) - to_replace.append(kk) - # for each hook to replace, create a new proper HookGroup and assign to all common conds - for hooks, conds_to_modify in hook_replacement.items(): - new_hooks = hooks.new_with_common_hooks(registered) - if len(new_hooks) == 0: - new_hooks = None - for kk in conds_to_modify: - kk['hooks'] = new_hooks - - -def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]): - hooks_set = set() - for k in conds: - for kk in conds[k]: - hooks_set.add(kk.get('hooks', None)) - return len(hooks_set) - - -def cast_to_load_options(model_options: dict[str], device=None, dtype=None): - ''' - If any patches from hooks, wrappers, or callbacks have .to to be called, call it. - ''' - if model_options is None: - return - to_load_options = model_options.get("to_load_options", None) - if to_load_options is None: - return - - casts = [] - if device is not None: - casts.append(device) - if dtype is not None: - casts.append(dtype) - # if nothing to apply, do nothing - if len(casts) == 0: - return - - # try to call .to on patches - if "patches" in to_load_options: - patches = to_load_options["patches"] - for name in patches: - patch_list = patches[name] - for i in range(len(patch_list)): - if hasattr(patch_list[i], "to"): - for cast in casts: - patch_list[i] = patch_list[i].to(cast) - if "patches_replace" in to_load_options: - patches = to_load_options["patches_replace"] - for name in patches: - patch_list = patches[name] - for k in patch_list: - if hasattr(patch_list[k], "to"): - for cast in casts: - patch_list[k] = patch_list[k].to(cast) - # try to call .to on any wrappers/callbacks - wrappers_and_callbacks = ["wrappers", "callbacks"] - for wc_name in wrappers_and_callbacks: - if wc_name in to_load_options: - wc: dict[str, list] = to_load_options[wc_name] - for wc_dict in wc.values(): - for wc_list in wc_dict.values(): - for i in range(len(wc_list)): - if hasattr(wc_list[i], "to"): - for cast in casts: - wc_list[i] = wc_list[i].to(cast) - - -class CFGGuider: - def __init__(self, model_patcher: ModelPatcher): - self.model_patcher = model_patcher - self.model_options = model_patcher.model_options - self.original_conds = {} - self.cfg = 1.0 - - def set_conds(self, positive, negative): - self.inner_set_conds({"positive": positive, "negative": negative}) - - def set_cfg(self, cfg): - self.cfg = cfg - - def inner_set_conds(self, conds): - for k in conds: - self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) - - def __call__(self, *args, **kwargs): - return self.outer_predict_noise(*args, **kwargs) - - def outer_predict_noise(self, x, timestep, model_options={}, seed=None): - return comfy.patcher_extension.WrapperExecutor.new_class_executor( - self.predict_noise, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, self.model_options, is_model_options=True) - ).execute(x, timestep, model_options, seed) - - def predict_noise(self, x, timestep, model_options={}, seed=None): - return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) - - def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed): - if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. - latent_image = self.inner_model.process_latent_in(latent_image) - - self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) - - extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options) - extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas - extra_args = {"model_options": extra_model_options, "seed": seed} - - executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( - sampler.sample, - sampler, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, extra_args["model_options"], is_model_options=True) - ) - samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) - return self.inner_model.process_latent_out(samples.to(torch.float32)) - - def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) - device = self.model_patcher.load_device - - if denoise_mask is not None: - denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device) - - noise = noise.to(device) - latent_image = latent_image.to(device) - sigmas = sigmas.to(device) - cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) - - try: - self.model_patcher.pre_run() - output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) - finally: - self.model_patcher.cleanup() - - comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models) - del self.inner_model - del self.loaded_models - return output - - def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - if sigmas.shape[-1] == 0: - return latent_image - - self.conds = {} - for k in self.original_conds: - self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) - preprocess_conds_hooks(self.conds) - - try: - orig_model_options = self.model_options - self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options) - # if one hook type (or just None), then don't bother caching weights for hooks (will never change after first step) - orig_hook_mode = self.model_patcher.hook_mode - if get_total_hook_groups_in_conds(self.conds) <= 1: - self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram - comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options) - filter_registered_hooks_on_conds(self.conds, self.model_options) - executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( - self.outer_sample, - self, - comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True) - ) - output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) - finally: - cast_to_load_options(self.model_options, device=self.model_patcher.offload_device) - self.model_options = orig_model_options - self.model_patcher.hook_mode = orig_hook_mode - self.model_patcher.restore_hook_patches() - - del self.conds - return output - - -def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - cfg_guider = CFGGuider(model) - cfg_guider.set_conds(positive, negative) - cfg_guider.set_cfg(cfg) - return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) - - -SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] - -class SchedulerHandler(NamedTuple): - handler: Callable[..., torch.Tensor] - # Boolean indicates whether to call the handler like: - # scheduler_function(model_sampling, steps) or - # scheduler_function(n, sigma_min: float, sigma_max: float) - use_ms: bool = True - -SCHEDULER_HANDLERS = { - "simple": SchedulerHandler(simple_scheduler), - "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)), - "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False), - "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False), - "ddim_uniform": SchedulerHandler(ddim_scheduler), - "beta": SchedulerHandler(beta_scheduler), - "normal": SchedulerHandler(normal_scheduler), - "linear_quadratic": SchedulerHandler(linear_quadratic_schedule), - "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False), -} -SCHEDULER_NAMES = list(SCHEDULER_HANDLERS) - -def calculate_sigmas(model_sampling: object, scheduler_name: str, steps: int) -> torch.Tensor: - handler = SCHEDULER_HANDLERS.get(scheduler_name) - if handler is None: - err = f"error invalid scheduler {scheduler_name}" - logging.error(err) - raise ValueError(err) - if handler.use_ms: - return handler.handler(model_sampling, steps) - return handler.handler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max)) - -def sampler_object(name): - if name == "uni_pc": - sampler = KSAMPLER(uni_pc.sample_unipc) - elif name == "uni_pc_bh2": - sampler = KSAMPLER(uni_pc.sample_unipc_bh2) - elif name == "ddim": - sampler = ksampler("euler", inpaint_options={"random": True}) - else: - sampler = ksampler(name) - return sampler - -class KSampler: - SCHEDULERS = SCHEDULER_NAMES - SAMPLERS = SAMPLER_NAMES - DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2')) - - def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): - self.model = model - self.device = device - if scheduler not in self.SCHEDULERS: - scheduler = self.SCHEDULERS[0] - if sampler not in self.SAMPLERS: - sampler = self.SAMPLERS[0] - self.scheduler = scheduler - self.sampler = sampler - self.set_steps(steps, denoise) - self.denoise = denoise - self.model_options = model_options - - def calculate_sigmas(self, steps): - sigmas = None - - discard_penultimate_sigma = False - if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS: - steps += 1 - discard_penultimate_sigma = True - - sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps) - - if discard_penultimate_sigma: - sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) - return sigmas - - def set_steps(self, steps, denoise=None): - self.steps = steps - if denoise is None or denoise > 0.9999: - self.sigmas = self.calculate_sigmas(steps).to(self.device) - else: - if denoise <= 0.0: - self.sigmas = torch.FloatTensor([]) - else: - new_steps = int(steps/denoise) - sigmas = self.calculate_sigmas(new_steps).to(self.device) - self.sigmas = sigmas[-(steps + 1):] - - def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): - if sigmas is None: - sigmas = self.sigmas - - if last_step is not None and last_step < (len(sigmas) - 1): - sigmas = sigmas[:last_step + 1] - if force_full_denoise: - sigmas[-1] = 0 - - if start_step is not None: - if start_step < (len(sigmas) - 1): - sigmas = sigmas[start_step:] - else: - if latent_image is not None: - return latent_image - else: - return torch.zeros_like(noise) - - sampler = sampler_object(self.sampler) - - return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) diff --git a/comfy/sd.py b/comfy/sd.py deleted file mode 100644 index bb5d61fb33413c26e8a0adde05b36c3fc4439937..0000000000000000000000000000000000000000 --- a/comfy/sd.py +++ /dev/null @@ -1,1243 +0,0 @@ -from __future__ import annotations -import json -import torch -from enum import Enum -import logging - -from comfy import model_management -from comfy.utils import ProgressBar -from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine -from .ldm.cascade.stage_a import StageA -from .ldm.cascade.stage_c_coder import StageC_coder -from .ldm.audio.autoencoder import AudioOobleckVAE -import comfy.ldm.genmo.vae.model -import comfy.ldm.lightricks.vae.causal_video_autoencoder -import comfy.ldm.cosmos.vae -import comfy.ldm.wan.vae -import comfy.ldm.wan.vae2_2 -import comfy.ldm.hunyuan3d.vae -import comfy.ldm.ace.vae.music_dcae_pipeline -import yaml -import math -import os - -import comfy.utils - -from . import clip_vision -from . import gligen -from . import diffusers_convert -from . import model_detection - -from . import sd1_clip -from . import sdxl_clip -import comfy.text_encoders.sd2_clip -import comfy.text_encoders.sd3_clip -import comfy.text_encoders.sa_t5 -import comfy.text_encoders.aura_t5 -import comfy.text_encoders.pixart_t5 -import comfy.text_encoders.hydit -import comfy.text_encoders.flux -import comfy.text_encoders.long_clipl -import comfy.text_encoders.genmo -import comfy.text_encoders.lt -import comfy.text_encoders.hunyuan_video -import comfy.text_encoders.cosmos -import comfy.text_encoders.lumina2 -import comfy.text_encoders.wan -import comfy.text_encoders.hidream -import comfy.text_encoders.ace -import comfy.text_encoders.omnigen2 -import comfy.text_encoders.qwen_image - -import comfy.model_patcher -import comfy.lora -import comfy.lora_convert -import comfy.hooks -import comfy.t2i_adapter.adapter -import comfy.taesd.taesd - -import comfy.ldm.flux.redux - -def load_lora_for_models(model, clip, lora, strength_model, strength_clip): - key_map = {} - if model is not None: - key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) - if clip is not None: - key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) - - lora = comfy.lora_convert.convert_lora(lora) - loaded = comfy.lora.load_lora(lora, key_map) - if model is not None: - new_modelpatcher = model.clone() - k = new_modelpatcher.add_patches(loaded, strength_model) - else: - k = () - new_modelpatcher = None - - if clip is not None: - new_clip = clip.clone() - k1 = new_clip.add_patches(loaded, strength_clip) - else: - k1 = () - new_clip = None - k = set(k) - k1 = set(k1) - for x in loaded: - if (x not in k) and (x not in k1): - logging.warning("NOT LOADED {}".format(x)) - - return (new_modelpatcher, new_clip) - - -class CLIP: - def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}): - if no_init: - return - params = target.params.copy() - clip = target.clip - tokenizer = target.tokenizer - - load_device = model_options.get("load_device", model_management.text_encoder_device()) - offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device()) - dtype = model_options.get("dtype", None) - if dtype is None: - dtype = model_management.text_encoder_dtype(load_device) - - params['dtype'] = dtype - params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))) - params['model_options'] = model_options - - self.cond_stage_model = clip(**(params)) - - for dt in self.cond_stage_model.dtypes: - if not model_management.supports_cast(load_device, dt): - load_device = offload_device - if params['device'] != offload_device: - self.cond_stage_model.to(offload_device) - logging.warning("Had to shift TE back.") - - self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) - self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram - self.patcher.is_clip = True - self.apply_hooks_to_conds = None - if params['device'] == load_device: - model_management.load_models_gpu([self.patcher], force_full_load=True) - self.layer_idx = None - self.use_clip_schedule = False - logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype)) - self.tokenizer_options = {} - - def clone(self): - n = CLIP(no_init=True) - n.patcher = self.patcher.clone() - n.cond_stage_model = self.cond_stage_model - n.tokenizer = self.tokenizer - n.layer_idx = self.layer_idx - n.tokenizer_options = self.tokenizer_options.copy() - n.use_clip_schedule = self.use_clip_schedule - n.apply_hooks_to_conds = self.apply_hooks_to_conds - return n - - def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): - return self.patcher.add_patches(patches, strength_patch, strength_model) - - def set_tokenizer_option(self, option_name, value): - self.tokenizer_options[option_name] = value - - def clip_layer(self, layer_idx): - self.layer_idx = layer_idx - - def tokenize(self, text, return_word_ids=False, **kwargs): - tokenizer_options = kwargs.get("tokenizer_options", {}) - if len(self.tokenizer_options) > 0: - tokenizer_options = {**self.tokenizer_options, **tokenizer_options} - if len(tokenizer_options) > 0: - kwargs["tokenizer_options"] = tokenizer_options - return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) - - def add_hooks_to_dict(self, pooled_dict: dict[str]): - if self.apply_hooks_to_conds: - pooled_dict["hooks"] = self.apply_hooks_to_conds - return pooled_dict - - def encode_from_tokens_scheduled(self, tokens, unprojected=False, add_dict: dict[str]={}, show_pbar=True): - all_cond_pooled: list[tuple[torch.Tensor, dict[str]]] = [] - all_hooks = self.patcher.forced_hooks - if all_hooks is None or not self.use_clip_schedule: - # if no hooks or shouldn't use clip schedule, do unscheduled encode_from_tokens and perform add_dict - return_pooled = "unprojected" if unprojected else True - pooled_dict = self.encode_from_tokens(tokens, return_pooled=return_pooled, return_dict=True) - cond = pooled_dict.pop("cond") - # add/update any keys with the provided add_dict - pooled_dict.update(add_dict) - all_cond_pooled.append([cond, pooled_dict]) - else: - scheduled_keyframes = all_hooks.get_hooks_for_clip_schedule() - - self.cond_stage_model.reset_clip_options() - if self.layer_idx is not None: - self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) - if unprojected: - self.cond_stage_model.set_clip_options({"projected_pooled": False}) - - self.load_model() - all_hooks.reset() - self.patcher.patch_hooks(None) - if show_pbar: - pbar = ProgressBar(len(scheduled_keyframes)) - - for scheduled_opts in scheduled_keyframes: - t_range = scheduled_opts[0] - # don't bother encoding any conds outside of start_percent and end_percent bounds - if "start_percent" in add_dict: - if t_range[1] < add_dict["start_percent"]: - continue - if "end_percent" in add_dict: - if t_range[0] > add_dict["end_percent"]: - continue - hooks_keyframes = scheduled_opts[1] - for hook, keyframe in hooks_keyframes: - hook.hook_keyframe._current_keyframe = keyframe - # apply appropriate hooks with values that match new hook_keyframe - self.patcher.patch_hooks(all_hooks) - # perform encoding as normal - o = self.cond_stage_model.encode_token_weights(tokens) - cond, pooled = o[:2] - pooled_dict = {"pooled_output": pooled} - # add clip_start_percent and clip_end_percent in pooled - pooled_dict["clip_start_percent"] = t_range[0] - pooled_dict["clip_end_percent"] = t_range[1] - # add/update any keys with the provided add_dict - pooled_dict.update(add_dict) - # add hooks stored on clip - self.add_hooks_to_dict(pooled_dict) - all_cond_pooled.append([cond, pooled_dict]) - if show_pbar: - pbar.update(1) - model_management.throw_exception_if_processing_interrupted() - all_hooks.reset() - return all_cond_pooled - - def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False): - self.cond_stage_model.reset_clip_options() - - if self.layer_idx is not None: - self.cond_stage_model.set_clip_options({"layer": self.layer_idx}) - - if return_pooled == "unprojected": - self.cond_stage_model.set_clip_options({"projected_pooled": False}) - - self.load_model() - o = self.cond_stage_model.encode_token_weights(tokens) - cond, pooled = o[:2] - if return_dict: - out = {"cond": cond, "pooled_output": pooled} - if len(o) > 2: - for k in o[2]: - out[k] = o[2][k] - self.add_hooks_to_dict(out) - return out - - if return_pooled: - return cond, pooled - return cond - - def encode(self, text): - tokens = self.tokenize(text) - return self.encode_from_tokens(tokens) - - def load_sd(self, sd, full_model=False): - if full_model: - return self.cond_stage_model.load_state_dict(sd, strict=False) - else: - return self.cond_stage_model.load_sd(sd) - - def get_sd(self): - sd_clip = self.cond_stage_model.state_dict() - sd_tokenizer = self.tokenizer.state_dict() - for k in sd_tokenizer: - sd_clip[k] = sd_tokenizer[k] - return sd_clip - - def load_model(self): - model_management.load_model_gpu(self.patcher) - return self.patcher - - def get_key_patches(self): - return self.patcher.get_key_patches() - -class VAE: - def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None): - if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format - sd = diffusers_convert.convert_vae_state_dict(sd) - - self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) - self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) - self.downscale_ratio = 8 - self.upscale_ratio = 8 - self.latent_channels = 4 - self.latent_dim = 2 - self.output_channels = 3 - self.process_input = lambda image: image * 2.0 - 1.0 - self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) - self.working_dtypes = [torch.bfloat16, torch.float32] - self.disable_offload = False - - self.downscale_index_formula = None - self.upscale_index_formula = None - self.extra_1d_channel = None - - if config is None: - if "decoder.mid.block_1.mix_factor" in sd: - encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} - decoder_config = encoder_config.copy() - decoder_config["video_kernel_size"] = [3, 1, 1] - decoder_config["alpha"] = 0.0 - self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, - encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, - decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) - elif "taesd_decoder.1.weight" in sd: - self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] - self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) - elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade - self.first_stage_model = StageA() - self.downscale_ratio = 4 - self.upscale_ratio = 4 - #TODO - #self.memory_used_encode - #self.memory_used_decode - self.process_input = lambda image: image - self.process_output = lambda image: image - elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade - self.first_stage_model = StageC_coder() - self.downscale_ratio = 32 - self.latent_channels = 16 - new_sd = {} - for k in sd: - new_sd["encoder.{}".format(k)] = sd[k] - sd = new_sd - elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade - self.first_stage_model = StageC_coder() - self.latent_channels = 16 - new_sd = {} - for k in sd: - new_sd["previewer.{}".format(k)] = sd[k] - sd = new_sd - elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade - self.first_stage_model = StageC_coder() - self.downscale_ratio = 32 - self.latent_channels = 16 - elif "decoder.conv_in.weight" in sd: - #default SD1.x/SD2.x VAE parameters - ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} - - if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE - ddconfig['ch_mult'] = [1, 2, 4] - self.downscale_ratio = 4 - self.upscale_ratio = 4 - - self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] - if 'post_quant_conv.weight' in sd: - self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) - else: - self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, - encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, - decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) - elif "decoder.layers.1.layers.0.beta" in sd: - self.first_stage_model = AudioOobleckVAE() - self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) - self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype) - self.latent_channels = 64 - self.output_channels = 2 - self.upscale_ratio = 2048 - self.downscale_ratio = 2048 - self.latent_dim = 1 - self.process_output = lambda audio: audio - self.process_input = lambda audio: audio - self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] - self.disable_offload = True - elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae - if "blocks.2.blocks.3.stack.5.weight" in sd: - sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."}) - if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd: - sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."}) - self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE() - self.latent_channels = 12 - self.latent_dim = 3 - self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) - self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype) - self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8) - self.upscale_index_formula = (6, 8, 8) - self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8) - self.downscale_index_formula = (6, 8, 8) - self.working_dtypes = [torch.float16, torch.float32] - elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv - tensor_conv1 = sd["decoder.up_blocks.0.res_blocks.0.conv1.conv.weight"] - version = 0 - if tensor_conv1.shape[0] == 512: - version = 0 - elif tensor_conv1.shape[0] == 1024: - version = 1 - if "encoder.down_blocks.1.conv.conv.bias" in sd: - version = 2 - vae_config = None - if metadata is not None and "config" in metadata: - vae_config = json.loads(metadata["config"]).get("vae", None) - self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config) - self.latent_channels = 128 - self.latent_dim = 3 - self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) - self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype) - self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32) - self.upscale_index_formula = (8, 32, 32) - self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32) - self.downscale_index_formula = (8, 32, 32) - self.working_dtypes = [torch.bfloat16, torch.float32] - elif "decoder.conv_in.conv.weight" in sd: - ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} - ddconfig["conv3d"] = True - ddconfig["time_compress"] = 4 - self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) - self.upscale_index_formula = (4, 8, 8) - self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) - self.downscale_index_formula = (4, 8, 8) - self.latent_dim = 3 - self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] - self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) - self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype) - self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype) - self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] - elif "decoder.unpatcher3d.wavelets" in sd: - self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8) - self.upscale_index_formula = (8, 8, 8) - self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8) - self.downscale_index_formula = (8, 8, 8) - self.latent_dim = 3 - self.latent_channels = 16 - ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8} - self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig) - #TODO: these values are a bit off because this is not a standard VAE - self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) - self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype) - self.working_dtypes = [torch.bfloat16, torch.float32] - elif "decoder.middle.0.residual.0.gamma" in sd: - if "decoder.upsamples.0.upsamples.0.residual.2.weight" in sd: # Wan 2.2 VAE - self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) - self.upscale_index_formula = (4, 16, 16) - self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) - self.downscale_index_formula = (4, 16, 16) - self.latent_dim = 3 - self.latent_channels = 48 - ddconfig = {"dim": 160, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0} - self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig) - self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] - self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype) - self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype) - else: # Wan 2.1 VAE - self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) - self.upscale_index_formula = (4, 8, 8) - self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) - self.downscale_index_formula = (4, 8, 8) - self.latent_dim = 3 - self.latent_channels = 16 - ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0} - self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig) - self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] - self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype) - self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype) - elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd: - self.latent_dim = 1 - ln_post = "geo_decoder.ln_post.weight" in sd - inner_size = sd["geo_decoder.output_proj.weight"].shape[1] - downsample_ratio = sd["post_kl.weight"].shape[0] // inner_size - mlp_expand = sd["geo_decoder.cross_attn_decoder.mlp.c_fc.weight"].shape[0] // inner_size - self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) # TODO - self.memory_used_decode = lambda shape, dtype: (1024 * 1024 * 1024 * 2.0) * model_management.dtype_size(dtype) # TODO - ddconfig = {"embed_dim": 64, "num_freqs": 8, "include_pi": False, "heads": 16, "width": 1024, "num_decoder_layers": 16, "qkv_bias": False, "qk_norm": True, "geo_decoder_mlp_expand_ratio": mlp_expand, "geo_decoder_downsample_ratio": downsample_ratio, "geo_decoder_ln_post": ln_post} - self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE(**ddconfig) - self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] - elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio - self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) - self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) - self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) - self.latent_channels = 8 - self.output_channels = 2 - self.upscale_ratio = 4096 - self.downscale_ratio = 4096 - self.latent_dim = 2 - self.process_output = lambda audio: audio - self.process_input = lambda audio: audio - self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] - self.disable_offload = True - self.extra_1d_channel = 16 - else: - logging.warning("WARNING: No VAE weights detected, VAE not initalized.") - self.first_stage_model = None - return - else: - self.first_stage_model = AutoencoderKL(**(config['params'])) - self.first_stage_model = self.first_stage_model.eval() - - m, u = self.first_stage_model.load_state_dict(sd, strict=False) - if len(m) > 0: - logging.warning("Missing VAE keys {}".format(m)) - - if len(u) > 0: - logging.debug("Leftover VAE keys {}".format(u)) - - if device is None: - device = model_management.vae_device() - self.device = device - offload_device = model_management.vae_offload_device() - if dtype is None: - dtype = model_management.vae_dtype(self.device, self.working_dtypes) - self.vae_dtype = dtype - self.first_stage_model.to(self.vae_dtype) - self.output_device = model_management.intermediate_device() - - self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) - logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) - - def throw_exception_if_invalid(self): - if self.first_stage_model is None: - raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.") - - def vae_encode_crop_pixels(self, pixels): - downscale_ratio = self.spacial_compression_encode() - - dims = pixels.shape[1:-1] - for d in range(len(dims)): - x = (dims[d] // downscale_ratio) * downscale_ratio - x_offset = (dims[d] % downscale_ratio) // 2 - if x != dims[d]: - pixels = pixels.narrow(d + 1, x_offset, x) - return pixels - - def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): - steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) - steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) - steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) - pbar = comfy.utils.ProgressBar(steps) - - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() - output = self.process_output( - (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + - comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) + - comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar)) - / 3.0) - return output - - def decode_tiled_1d(self, samples, tile_x=128, overlap=32): - if samples.ndim == 3: - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() - else: - og_shape = samples.shape - samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1)) - decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float() - - return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) - - def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() - return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device)) - - def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): - steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) - steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) - steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) - pbar = comfy.utils.ProgressBar(steps) - - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() - samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) - samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) - samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) - samples /= 3.0 - return samples - - def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048): - if self.latent_dim == 1: - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() - out_channels = self.latent_channels - upscale_amount = 1 / self.downscale_ratio - else: - extra_channel_size = self.extra_1d_channel - out_channels = self.latent_channels * extra_channel_size - tile_x = tile_x // extra_channel_size - overlap = overlap // extra_channel_size - upscale_amount = 1 / self.downscale_ratio - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float() - - out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device) - if self.latent_dim == 1: - return out - else: - return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1) - - def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)): - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() - return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device) - - def decode(self, samples_in, vae_options={}): - self.throw_exception_if_invalid() - pixel_samples = None - try: - memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) - model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) - free_memory = model_management.get_free_memory(self.device) - batch_number = int(free_memory / memory_used) - batch_number = max(1, batch_number) - - for x in range(0, samples_in.shape[0], batch_number): - samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float()) - if pixel_samples is None: - pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) - pixel_samples[x:x+batch_number] = out - except model_management.OOM_EXCEPTION: - logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") - dims = samples_in.ndim - 2 - if dims == 1 or self.extra_1d_channel is not None: - pixel_samples = self.decode_tiled_1d(samples_in) - elif dims == 2: - pixel_samples = self.decode_tiled_(samples_in) - elif dims == 3: - tile = 256 // self.spacial_compression_decode() - overlap = tile // 4 - pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) - - pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) - return pixel_samples - - def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): - self.throw_exception_if_invalid() - memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile - model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) - dims = samples.ndim - 2 - args = {} - if tile_x is not None: - args["tile_x"] = tile_x - if tile_y is not None: - args["tile_y"] = tile_y - if overlap is not None: - args["overlap"] = overlap - - if dims == 1: - args.pop("tile_y") - output = self.decode_tiled_1d(samples, **args) - elif dims == 2: - output = self.decode_tiled_(samples, **args) - elif dims == 3: - if overlap_t is None: - args["overlap"] = (1, overlap, overlap) - else: - args["overlap"] = (max(1, overlap_t), overlap, overlap) - if tile_t is not None: - args["tile_t"] = max(2, tile_t) - - output = self.decode_tiled_3d(samples, **args) - return output.movedim(1, -1) - - def encode(self, pixel_samples): - self.throw_exception_if_invalid() - pixel_samples = self.vae_encode_crop_pixels(pixel_samples) - pixel_samples = pixel_samples.movedim(-1, 1) - if self.latent_dim == 3 and pixel_samples.ndim < 5: - pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) - try: - memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) - model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) - free_memory = model_management.get_free_memory(self.device) - batch_number = int(free_memory / max(1, memory_used)) - batch_number = max(1, batch_number) - samples = None - for x in range(0, pixel_samples.shape[0], batch_number): - pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device) - out = self.first_stage_model.encode(pixels_in).to(self.output_device).float() - if samples is None: - samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device) - samples[x:x + batch_number] = out - - except model_management.OOM_EXCEPTION: - logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") - if self.latent_dim == 3: - tile = 256 - overlap = tile // 4 - samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) - elif self.latent_dim == 1 or self.extra_1d_channel is not None: - samples = self.encode_tiled_1d(pixel_samples) - else: - samples = self.encode_tiled_(pixel_samples) - - return samples - - def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): - self.throw_exception_if_invalid() - pixel_samples = self.vae_encode_crop_pixels(pixel_samples) - dims = self.latent_dim - pixel_samples = pixel_samples.movedim(-1, 1) - if dims == 3: - pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) - - memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) # TODO: calculate mem required for tile - model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) - - args = {} - if tile_x is not None: - args["tile_x"] = tile_x - if tile_y is not None: - args["tile_y"] = tile_y - if overlap is not None: - args["overlap"] = overlap - - if dims == 1: - args.pop("tile_y") - samples = self.encode_tiled_1d(pixel_samples, **args) - elif dims == 2: - samples = self.encode_tiled_(pixel_samples, **args) - elif dims == 3: - if tile_t is not None: - tile_t_latent = max(2, self.downscale_ratio[0](tile_t)) - else: - tile_t_latent = 9999 - args["tile_t"] = self.upscale_ratio[0](tile_t_latent) - - if overlap_t is None: - args["overlap"] = (1, overlap, overlap) - else: - args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap) - maximum = pixel_samples.shape[2] - maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum)) - - samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args) - - return samples - - def get_sd(self): - return self.first_stage_model.state_dict() - - def spacial_compression_decode(self): - try: - return self.upscale_ratio[-1] - except: - return self.upscale_ratio - - def spacial_compression_encode(self): - try: - return self.downscale_ratio[-1] - except: - return self.downscale_ratio - - def temporal_compression_decode(self): - try: - return round(self.upscale_ratio[0](8192) / 8192) - except: - return None - -class StyleModel: - def __init__(self, model, device="cpu"): - self.model = model - - def get_cond(self, input): - return self.model(input.last_hidden_state) - - -def load_style_model(ckpt_path): - model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) - keys = model_data.keys() - if "style_embedding" in keys: - model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) - elif "redux_down.weight" in keys: - model = comfy.ldm.flux.redux.ReduxImageEncoder() - else: - raise Exception("invalid style model {}".format(ckpt_path)) - model.load_state_dict(model_data) - return StyleModel(model) - -class CLIPType(Enum): - STABLE_DIFFUSION = 1 - STABLE_CASCADE = 2 - SD3 = 3 - STABLE_AUDIO = 4 - HUNYUAN_DIT = 5 - FLUX = 6 - MOCHI = 7 - LTXV = 8 - HUNYUAN_VIDEO = 9 - PIXART = 10 - COSMOS = 11 - LUMINA2 = 12 - WAN = 13 - HIDREAM = 14 - CHROMA = 15 - ACE = 16 - OMNIGEN2 = 17 - QWEN_IMAGE = 18 - - -def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): - clip_data = [] - for p in ckpt_paths: - clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) - return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options) - - -class TEModel(Enum): - CLIP_L = 1 - CLIP_H = 2 - CLIP_G = 3 - T5_XXL = 4 - T5_XL = 5 - T5_BASE = 6 - LLAMA3_8 = 7 - T5_XXL_OLD = 8 - GEMMA_2_2B = 9 - QWEN25_3B = 10 - QWEN25_7B = 11 - -def detect_te_model(sd): - if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: - return TEModel.CLIP_G - if "text_model.encoder.layers.22.mlp.fc1.weight" in sd: - return TEModel.CLIP_H - if "text_model.encoder.layers.0.mlp.fc1.weight" in sd: - return TEModel.CLIP_L - if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: - weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"] - if weight.shape[-1] == 4096: - return TEModel.T5_XXL - elif weight.shape[-1] == 2048: - return TEModel.T5_XL - if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd: - return TEModel.T5_XXL_OLD - if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd: - return TEModel.T5_BASE - if 'model.layers.0.post_feedforward_layernorm.weight' in sd: - return TEModel.GEMMA_2_2B - if 'model.layers.0.self_attn.k_proj.bias' in sd: - weight = sd['model.layers.0.self_attn.k_proj.bias'] - if weight.shape[0] == 256: - return TEModel.QWEN25_3B - if weight.shape[0] == 512: - return TEModel.QWEN25_7B - if "model.layers.0.post_attention_layernorm.weight" in sd: - return TEModel.LLAMA3_8 - return None - - -def t5xxl_detect(clip_data): - weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" - weight_name_old = "encoder.block.23.layer.1.DenseReluDense.wi.weight" - - for sd in clip_data: - if weight_name in sd or weight_name_old in sd: - return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd) - - return {} - -def llama_detect(clip_data): - weight_name = "model.layers.0.self_attn.k_proj.weight" - - for sd in clip_data: - if weight_name in sd: - return comfy.text_encoders.hunyuan_video.llama_detect(sd) - - return {} - -def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): - clip_data = state_dicts - - class EmptyClass: - pass - - for i in range(len(clip_data)): - if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: - clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") - else: - if "text_projection" in clip_data[i]: - clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node - - tokenizer_data = {} - clip_target = EmptyClass() - clip_target.params = {} - if len(clip_data) == 1: - te_model = detect_te_model(clip_data[0]) - if te_model == TEModel.CLIP_G: - if clip_type == CLIPType.STABLE_CASCADE: - clip_target.clip = sdxl_clip.StableCascadeClipModel - clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer - elif clip_type == CLIPType.SD3: - clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False) - clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer - elif clip_type == CLIPType.HIDREAM: - clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) - clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - else: - clip_target.clip = sdxl_clip.SDXLRefinerClipModel - clip_target.tokenizer = sdxl_clip.SDXLTokenizer - elif te_model == TEModel.CLIP_H: - clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel - clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer - elif te_model == TEModel.T5_XXL: - if clip_type == CLIPType.SD3: - clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer - elif clip_type == CLIPType.LTXV: - clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer - elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA: - clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer - elif clip_type == CLIPType.WAN: - clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer - tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) - elif clip_type == CLIPType.HIDREAM: - clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), - clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None) - clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - else: #CLIPType.MOCHI - clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer - elif te_model == TEModel.T5_XXL_OLD: - clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer - elif te_model == TEModel.T5_XL: - clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model - clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer - elif te_model == TEModel.T5_BASE: - if clip_type == CLIPType.ACE or "spiece_model" in clip_data[0]: - clip_target.clip = comfy.text_encoders.ace.AceT5Model - clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer - tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) - else: - clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model - clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer - elif te_model == TEModel.GEMMA_2_2B: - clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer - tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) - elif te_model == TEModel.LLAMA3_8: - clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data), - clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None) - clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - elif te_model == TEModel.QWEN25_3B: - clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer - elif te_model == TEModel.QWEN25_7B: - clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer - else: - # clip_l - if clip_type == CLIPType.SD3: - clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) - clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer - elif clip_type == CLIPType.HIDREAM: - clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) - clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - else: - clip_target.clip = sd1_clip.SD1ClipModel - clip_target.tokenizer = sd1_clip.SD1Tokenizer - elif len(clip_data) == 2: - if clip_type == CLIPType.SD3: - te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])] - clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer - elif clip_type == CLIPType.HUNYUAN_DIT: - clip_target.clip = comfy.text_encoders.hydit.HyditModel - clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer - elif clip_type == CLIPType.FLUX: - clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer - elif clip_type == CLIPType.HUNYUAN_VIDEO: - clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer - elif clip_type == CLIPType.HIDREAM: - # Detect - hidream_dualclip_classes = [] - for hidream_te in clip_data: - te_model = detect_te_model(hidream_te) - hidream_dualclip_classes.append(te_model) - - clip_l = TEModel.CLIP_L in hidream_dualclip_classes - clip_g = TEModel.CLIP_G in hidream_dualclip_classes - t5 = TEModel.T5_XXL in hidream_dualclip_classes - llama = TEModel.LLAMA3_8 in hidream_dualclip_classes - - # Initialize t5xxl_detect and llama_detect kwargs if needed - t5_kwargs = t5xxl_detect(clip_data) if t5 else {} - llama_kwargs = llama_detect(clip_data) if llama else {} - - clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs) - clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - else: - clip_target.clip = sdxl_clip.SDXLClipModel - clip_target.tokenizer = sdxl_clip.SDXLTokenizer - elif len(clip_data) == 3: - clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer - elif len(clip_data) == 4: - clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), **llama_detect(clip_data)) - clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer - - parameters = 0 - for c in clip_data: - parameters += comfy.utils.calculate_parameters(c) - tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) - - clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options) - for c in clip_data: - m, u = clip.load_sd(c) - if len(m) > 0: - logging.warning("clip missing: {}".format(m)) - - if len(u) > 0: - logging.debug("clip unexpected: {}".format(u)) - return clip - -def load_gligen(ckpt_path): - data = comfy.utils.load_torch_file(ckpt_path, safe_load=True) - model = gligen.load_gligen(data) - if model_management.should_use_fp16(): - model = model.half() - return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) - -def model_detection_error_hint(path, state_dict): - filename = os.path.basename(path) - if 'lora' in filename.lower(): - return "\nHINT: This seems to be a Lora file and Lora files should be put in the lora folder and loaded with a lora loader node.." - return "" - -def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): - logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") - model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) - #TODO: this function is a mess and should be removed eventually - if config is None: - with open(config_path, 'r') as stream: - config = yaml.safe_load(stream) - model_config_params = config['model']['params'] - clip_config = model_config_params['cond_stage_config'] - - if "parameterization" in model_config_params: - if model_config_params["parameterization"] == "v": - m = model.clone() - class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION): - pass - m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config)) - model = m - - layer_idx = clip_config.get("params", {}).get("layer_idx", None) - if layer_idx is not None: - clip.clip_layer(layer_idx) - - return (model, clip, vae) - -def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): - sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) - out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata) - if out is None: - raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd))) - return out - -def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None): - clip = None - clipvision = None - vae = None - model = None - model_patcher = None - - diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) - parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) - weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) - load_device = model_management.get_torch_device() - - model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) - if model_config is None: - logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.") - diffusion_model = load_diffusion_model_state_dict(sd, model_options={}) - if diffusion_model is None: - return None - return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used' - - - unet_weight_dtype = list(model_config.supported_inference_dtypes) - if model_config.scaled_fp8 is not None: - weight_dtype = None - - model_config.custom_operations = model_options.get("custom_operations", None) - unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) - - if unet_dtype is None: - unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) - - manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) - model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) - - if model_config.clip_vision_prefix is not None: - if output_clipvision: - clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) - - if output_model: - inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) - model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) - model.load_model_weights(sd, diffusion_model_prefix) - - if output_vae: - vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) - vae_sd = model_config.process_vae_state_dict(vae_sd) - vae = VAE(sd=vae_sd, metadata=metadata) - - if output_clip: - clip_target = model_config.clip_target(state_dict=sd) - if clip_target is not None: - clip_sd = model_config.process_clip_state_dict(sd) - if len(clip_sd) > 0: - parameters = comfy.utils.calculate_parameters(clip_sd) - clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options) - m, u = clip.load_sd(clip_sd, full_model=True) - if len(m) > 0: - m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) - if len(m_filter) > 0: - logging.warning("clip missing: {}".format(m)) - else: - logging.debug("clip missing: {}".format(m)) - - if len(u) > 0: - logging.debug("clip unexpected {}:".format(u)) - else: - logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") - - left_over = sd.keys() - if len(left_over) > 0: - logging.debug("left over keys: {}".format(left_over)) - - if output_model: - model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device()) - if inital_load_device != torch.device("cpu"): - logging.info("loaded diffusion model directly to GPU") - model_management.load_models_gpu([model_patcher], force_full_load=True) - - return (model_patcher, clip, vae, clipvision) - - -def load_diffusion_model_state_dict(sd, model_options={}): - """ - Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats. - - Args: - sd (dict): State dictionary containing model weights and configuration - model_options (dict, optional): Additional options for model loading. Supports: - - dtype: Override model data type - - custom_operations: Custom model operations - - fp8_optimizations: Enable FP8 optimizations - - Returns: - ModelPatcher: A wrapped model instance that handles device management and weight loading. - Returns None if the model configuration cannot be detected. - - The function: - 1. Detects and handles different model formats (regular, diffusers, mmdit) - 2. Configures model dtype based on parameters and device capabilities - 3. Handles weight conversion and device placement - 4. Manages model optimization settings - 5. Loads weights and returns a device-managed model instance - """ - dtype = model_options.get("dtype", None) - - #Allow loading unets from checkpoint files - diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) - temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) - if len(temp_sd) > 0: - sd = temp_sd - - parameters = comfy.utils.calculate_parameters(sd) - weight_dtype = comfy.utils.weight_dtype(sd) - - load_device = model_management.get_torch_device() - model_config = model_detection.model_config_from_unet(sd, "") - - if model_config is not None: - new_sd = sd - else: - new_sd = model_detection.convert_diffusers_mmdit(sd, "") - if new_sd is not None: #diffusers mmdit - model_config = model_detection.model_config_from_unet(new_sd, "") - if model_config is None: - return None - else: #diffusers unet - model_config = model_detection.model_config_from_diffusers_unet(sd) - if model_config is None: - return None - - diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config) - - new_sd = {} - for k in diffusers_keys: - if k in sd: - new_sd[diffusers_keys[k]] = sd.pop(k) - else: - logging.warning("{} {}".format(diffusers_keys[k], k)) - - offload_device = model_management.unet_offload_device() - unet_weight_dtype = list(model_config.supported_inference_dtypes) - if model_config.scaled_fp8 is not None: - weight_dtype = None - - if dtype is None: - unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) - else: - unet_dtype = dtype - - manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) - model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) - model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations) - if model_options.get("fp8_optimizations", False): - model_config.optimizations["fp8"] = True - - model = model_config.get_model(new_sd, "") - model = model.to(offload_device) - model.load_model_weights(new_sd, "") - left_over = sd.keys() - if len(left_over) > 0: - logging.info("left over keys in diffusion model: {}".format(left_over)) - return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) - - -def load_diffusion_model(unet_path, model_options={}): - sd = comfy.utils.load_torch_file(unet_path) - model = load_diffusion_model_state_dict(sd, model_options=model_options) - if model is None: - logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path)) - raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd))) - return model - -def load_unet(unet_path, dtype=None): - logging.warning("The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model") - return load_diffusion_model(unet_path, model_options={"dtype": dtype}) - -def load_unet_state_dict(sd, dtype=None): - logging.warning("The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict") - return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype}) - -def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}): - clip_sd = None - load_models = [model] - if clip is not None: - load_models.append(clip.load_model()) - clip_sd = clip.get_sd() - vae_sd = None - if vae is not None: - vae_sd = vae.get_sd() - - model_management.load_models_gpu(load_models, force_patch_weights=True) - clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None - sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd) - for k in extra_keys: - sd[k] = extra_keys[k] - - for k in sd: - t = sd[k] - if not t.is_contiguous(): - sd[k] = t.contiguous() - - comfy.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py deleted file mode 100644 index f8a7c2a1b43c8e90645bfcef513b862b32076054..0000000000000000000000000000000000000000 --- a/comfy/sd1_clip.py +++ /dev/null @@ -1,693 +0,0 @@ -import os - -from transformers import CLIPTokenizer -import comfy.ops -import torch -import traceback -import zipfile -from . import model_management -import comfy.clip_model -import json -import logging -import numbers -import re - -def gen_empty_tokens(special_tokens, length): - start_token = special_tokens.get("start", None) - end_token = special_tokens.get("end", None) - pad_token = special_tokens.get("pad") - output = [] - if start_token is not None: - output.append(start_token) - if end_token is not None: - output.append(end_token) - output += [pad_token] * (length - len(output)) - return output - -class ClipTokenWeightEncoder: - def encode_token_weights(self, token_weight_pairs): - to_encode = list() - max_token_len = 0 - has_weights = False - for x in token_weight_pairs: - tokens = list(map(lambda a: a[0], x)) - max_token_len = max(len(tokens), max_token_len) - has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) - to_encode.append(tokens) - - sections = len(to_encode) - if has_weights or sections == 0: - if hasattr(self, "gen_empty_tokens"): - to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len)) - else: - to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) - - o = self.encode(to_encode) - out, pooled = o[:2] - - if pooled is not None: - first_pooled = pooled[0:1].to(model_management.intermediate_device()) - else: - first_pooled = pooled - - output = [] - for k in range(0, sections): - z = out[k:k+1] - if has_weights: - z_empty = out[-1] - for i in range(len(z)): - for j in range(len(z[i])): - weight = token_weight_pairs[k][j][1] - if weight != 1.0: - z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] - output.append(z) - - if (len(output) == 0): - r = (out[-1:].to(model_management.intermediate_device()), first_pooled) - else: - r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled) - - if len(o) > 2: - extra = {} - for k in o[2]: - v = o[2][k] - if k == "attention_mask": - v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()) - extra[k] = v - - r = r + (extra,) - return r - -class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): - LAYERS = [ - "last", - "pooled", - "hidden", - "all" - ] - def __init__(self, device="cpu", max_length=77, - freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, - special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False, - return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32 - super().__init__() - assert layer in self.LAYERS - - if textmodel_json_config is None: - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") - if "model_name" not in model_options: - model_options = {**model_options, "model_name": "clip_l"} - - if isinstance(textmodel_json_config, dict): - config = textmodel_json_config - else: - with open(textmodel_json_config) as f: - config = json.load(f) - - te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {}) - for k, v in te_model_options.items(): - config[k] = v - - operations = model_options.get("custom_operations", None) - scaled_fp8 = None - - if operations is None: - scaled_fp8 = model_options.get("scaled_fp8", None) - if scaled_fp8 is not None: - operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8) - else: - operations = comfy.ops.manual_cast - - self.operations = operations - self.transformer = model_class(config, dtype, device, self.operations) - if scaled_fp8 is not None: - self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8)) - - self.num_layers = self.transformer.num_layers - - self.max_length = max_length - if freeze: - self.freeze() - self.layer = layer - self.layer_idx = None - self.special_tokens = special_tokens - - self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) - self.enable_attention_masks = enable_attention_masks - self.zero_out_masked = zero_out_masked - - self.layer_norm_hidden_state = layer_norm_hidden_state - self.return_projected_pooled = return_projected_pooled - self.return_attention_masks = return_attention_masks - - if layer == "hidden": - assert layer_idx is not None - assert abs(layer_idx) < self.num_layers - self.set_clip_options({"layer": layer_idx}) - self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled) - - def freeze(self): - self.transformer = self.transformer.eval() - #self.train = disabled_train - for param in self.parameters(): - param.requires_grad = False - - def set_clip_options(self, options): - layer_idx = options.get("layer", self.layer_idx) - self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) - if self.layer == "all": - pass - elif layer_idx is None or abs(layer_idx) > self.num_layers: - self.layer = "last" - else: - self.layer = "hidden" - self.layer_idx = layer_idx - - def reset_clip_options(self): - self.layer = self.options_default[0] - self.layer_idx = self.options_default[1] - self.return_projected_pooled = self.options_default[2] - - def process_tokens(self, tokens, device): - end_token = self.special_tokens.get("end", None) - if end_token is None: - cmp_token = self.special_tokens.get("pad", -1) - else: - cmp_token = end_token - - embeds_out = [] - attention_masks = [] - num_tokens = [] - - for x in tokens: - attention_mask = [] - tokens_temp = [] - other_embeds = [] - eos = False - index = 0 - for y in x: - if isinstance(y, numbers.Integral): - if eos: - attention_mask.append(0) - else: - attention_mask.append(1) - token = int(y) - tokens_temp += [token] - if not eos and token == cmp_token: - if end_token is None: - attention_mask[-1] = 0 - eos = True - else: - other_embeds.append((index, y)) - index += 1 - - tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) - tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32) - index = 0 - pad_extra = 0 - embeds_info = [] - for o in other_embeds: - emb = o[1] - if torch.is_tensor(emb): - emb = {"type": "embedding", "data": emb} - - extra = None - emb_type = emb.get("type", None) - if emb_type == "embedding": - emb = emb.get("data", None) - else: - if hasattr(self.transformer, "preprocess_embed"): - emb, extra = self.transformer.preprocess_embed(emb, device=device) - else: - emb = None - - if emb is None: - index += -1 - continue - - ind = index + o[0] - emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) - emb_shape = emb.shape[1] - if emb.shape[-1] == tokens_embed.shape[-1]: - tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) - attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] - index += emb_shape - 1 - embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra}) - else: - index += -1 - pad_extra += emb_shape - logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1])) - - if pad_extra > 0: - padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32) - tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1) - attention_mask = attention_mask + [0] * pad_extra - - embeds_out.append(tokens_embed) - attention_masks.append(attention_mask) - num_tokens.append(sum(attention_mask)) - - return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info - - def forward(self, tokens): - device = self.transformer.get_input_embeddings().weight.device - embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device) - - attention_mask_model = None - if self.enable_attention_masks: - attention_mask_model = attention_mask - - if self.layer == "all": - intermediate_output = "all" - else: - intermediate_output = self.layer_idx - - outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info) - - if self.layer == "last": - z = outputs[0].float() - else: - z = outputs[1].float() - - if self.zero_out_masked: - z *= attention_mask.unsqueeze(-1).float() - - pooled_output = None - if len(outputs) >= 3: - if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None: - pooled_output = outputs[3].float() - elif outputs[2] is not None: - pooled_output = outputs[2].float() - - extra = {} - if self.return_attention_masks: - extra["attention_mask"] = attention_mask - - if len(extra) > 0: - return z, pooled_output, extra - - return z, pooled_output - - def encode(self, tokens): - return self(tokens) - - def load_sd(self, sd): - return self.transformer.load_state_dict(sd, strict=False) - -def parse_parentheses(string): - result = [] - current_item = "" - nesting_level = 0 - for char in string: - if char == "(": - if nesting_level == 0: - if current_item: - result.append(current_item) - current_item = "(" - else: - current_item = "(" - else: - current_item += char - nesting_level += 1 - elif char == ")": - nesting_level -= 1 - if nesting_level == 0: - result.append(current_item + ")") - current_item = "" - else: - current_item += char - else: - current_item += char - if current_item: - result.append(current_item) - return result - -def token_weights(string, current_weight): - a = parse_parentheses(string) - out = [] - for x in a: - weight = current_weight - if len(x) >= 2 and x[-1] == ')' and x[0] == '(': - x = x[1:-1] - xx = x.rfind(":") - weight *= 1.1 - if xx > 0: - try: - weight = float(x[xx+1:]) - x = x[:xx] - except: - pass - out += token_weights(x, weight) - else: - out += [(x, current_weight)] - return out - -def escape_important(text): - text = text.replace("\\)", "\0\1") - text = text.replace("\\(", "\0\2") - return text - -def unescape_important(text): - text = text.replace("\0\1", ")") - text = text.replace("\0\2", "(") - return text - -def safe_load_embed_zip(embed_path): - with zipfile.ZipFile(embed_path) as myzip: - names = list(filter(lambda a: "data/" in a, myzip.namelist())) - names.reverse() - for n in names: - with myzip.open(n) as myfile: - data = myfile.read() - number = len(data) // 4 - length_embed = 1024 #sd2.x - if number < 768: - continue - if number % 768 == 0: - length_embed = 768 #sd1.x - num_embeds = number // length_embed - embed = torch.frombuffer(data, dtype=torch.float) - out = embed.reshape((num_embeds, length_embed)).clone() - del embed - return out - -def expand_directory_list(directories): - dirs = set() - for x in directories: - dirs.add(x) - for root, subdir, file in os.walk(x, followlinks=True): - dirs.add(root) - return list(dirs) - -def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format - out_list = [] - for k in embed: - if k.startswith(prefix) and k.endswith(suffix): - out_list.append(embed[k]) - if len(out_list) == 0: - return None - - return torch.cat(out_list, dim=0) - -def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): - if isinstance(embedding_directory, str): - embedding_directory = [embedding_directory] - - embedding_directory = expand_directory_list(embedding_directory) - - valid_file = None - for embed_dir in embedding_directory: - embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) - embed_dir = os.path.abspath(embed_dir) - try: - if os.path.commonpath((embed_dir, embed_path)) != embed_dir: - continue - except: - continue - if not os.path.isfile(embed_path): - extensions = ['.safetensors', '.pt', '.bin'] - for x in extensions: - t = embed_path + x - if os.path.isfile(t): - valid_file = t - break - else: - valid_file = embed_path - if valid_file is not None: - break - - if valid_file is None: - return None - - embed_path = valid_file - - embed_out = None - - try: - if embed_path.lower().endswith(".safetensors"): - import safetensors.torch - embed = safetensors.torch.load_file(embed_path, device="cpu") - else: - try: - embed = torch.load(embed_path, weights_only=True, map_location="cpu") - except: - embed_out = safe_load_embed_zip(embed_path) - except Exception: - logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) - return None - - if embed_out is None: - if 'string_to_param' in embed: - values = embed['string_to_param'].values() - embed_out = next(iter(values)) - elif isinstance(embed, list): - out_list = [] - for x in range(len(embed)): - for k in embed[x]: - t = embed[x][k] - if t.shape[-1] != embedding_size: - continue - out_list.append(t.reshape(-1, t.shape[-1])) - embed_out = torch.cat(out_list, dim=0) - elif embed_key is not None and embed_key in embed: - embed_out = embed[embed_key] - else: - embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*') - if embed_out is None: - embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key)) - if embed_out is None: - values = embed.values() - embed_out = next(iter(values)) - return embed_out - -class SDTokenizer: - def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}): - if tokenizer_path is None: - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") - self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) - self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length) - self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length) - self.end_token = None - self.min_padding = min_padding - - empty = self.tokenizer('')["input_ids"] - self.tokenizer_adds_end_token = has_end_token - if has_start_token: - self.tokens_start = 1 - self.start_token = empty[0] - if end_token is not None: - self.end_token = end_token - else: - if has_end_token: - self.end_token = empty[1] - else: - self.tokens_start = 0 - self.start_token = None - if end_token is not None: - self.end_token = end_token - else: - if has_end_token: - self.end_token = empty[0] - - if pad_token is not None: - self.pad_token = pad_token - elif pad_with_end: - self.pad_token = self.end_token - else: - self.pad_token = 0 - - self.pad_with_end = pad_with_end - self.pad_to_max_length = pad_to_max_length - - vocab = self.tokenizer.get_vocab() - self.inv_vocab = {v: k for k, v in vocab.items()} - self.embedding_directory = embedding_directory - self.max_word_length = 8 - self.embedding_identifier = "embedding:" - self.embedding_size = embedding_size - self.embedding_key = embedding_key - - def _try_get_embedding(self, embedding_name:str): - ''' - Takes a potential embedding name and tries to retrieve it. - Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. - ''' - split_embed = embedding_name.split() - embedding_name = split_embed[0] - leftover = ' '.join(split_embed[1:]) - embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) - if embed is None: - stripped = embedding_name.strip(',') - if len(stripped) < len(embedding_name): - embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) - return (embed, "{} {}".format(embedding_name[len(stripped):], leftover)) - return (embed, leftover) - - - def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs): - ''' - Takes a prompt and converts it to a list of (token, weight, word id) elements. - Tokens can both be integer tokens and pre computed CLIP tensors. - Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. - Returned list has the dimensions NxM where M is the input size of CLIP - ''' - min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length) - min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding) - - text = escape_important(text) - if kwargs.get("disable_weights", False): - parsed_weights = [(text, 1.0)] - else: - parsed_weights = token_weights(text, 1.0) - - # tokenize words - tokens = [] - for weighted_segment, weight in parsed_weights: - to_tokenize = unescape_important(weighted_segment) - split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize) - to_tokenize = [split[0]] - for i in range(1, len(split)): - to_tokenize.append("{}{}".format(self.embedding_identifier, split[i])) - - to_tokenize = [x for x in to_tokenize if x != ""] - for word in to_tokenize: - # if we find an embedding, deal with the embedding - if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: - embedding_name = word[len(self.embedding_identifier):].strip('\n') - embed, leftover = self._try_get_embedding(embedding_name) - if embed is None: - logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring") - else: - if len(embed.shape) == 1: - tokens.append([(embed, weight)]) - else: - tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) - #if we accidentally have leftover text, continue parsing using leftover, else move on to next word - if leftover != "": - word = leftover - else: - continue - end = 999999999999 - if self.tokenizer_adds_end_token: - end = -1 - #parse word - tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:end]]) - - #reshape token array to CLIP input size - batched_tokens = [] - batch = [] - if self.start_token is not None: - batch.append((self.start_token, 1.0, 0)) - batched_tokens.append(batch) - for i, t_group in enumerate(tokens): - #determine if we're going to try and keep the tokens in a single batch - is_large = len(t_group) >= self.max_word_length - if self.end_token is not None: - has_end_token = 1 - else: - has_end_token = 0 - - while len(t_group) > 0: - if len(t_group) + len(batch) > self.max_length - has_end_token: - remaining_length = self.max_length - len(batch) - has_end_token - #break word in two and add end token - if is_large: - batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) - if self.end_token is not None: - batch.append((self.end_token, 1.0, 0)) - t_group = t_group[remaining_length:] - #add end token and pad - else: - if self.end_token is not None: - batch.append((self.end_token, 1.0, 0)) - if self.pad_to_max_length: - batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) - #start new batch - batch = [] - if self.start_token is not None: - batch.append((self.start_token, 1.0, 0)) - batched_tokens.append(batch) - else: - batch.extend([(t,w,i+1) for t,w in t_group]) - t_group = [] - - #fill last batch - if self.end_token is not None: - batch.append((self.end_token, 1.0, 0)) - if min_padding is not None: - batch.extend([(self.pad_token, 1.0, 0)] * min_padding) - if self.pad_to_max_length and len(batch) < self.max_length: - batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) - if min_length is not None and len(batch) < min_length: - batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch))) - - if not return_word_ids: - batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] - - return batched_tokens - - - def untokenize(self, token_weight_pair): - return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) - - def state_dict(self): - return {} - -class SD1Tokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None): - if name is not None: - self.clip_name = name - self.clip = "{}".format(self.clip_name) - else: - self.clip_name = clip_name - self.clip = "clip_{}".format(self.clip_name) - - tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) - setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return getattr(self, self.clip).untokenize(token_weight_pair) - - def state_dict(self): - return getattr(self, self.clip).state_dict() - -class SD1CheckpointClipModel(SDClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options) - -class SD1ClipModel(torch.nn.Module): - def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs): - super().__init__() - - if name is not None: - self.clip_name = name - self.clip = "{}".format(self.clip_name) - else: - self.clip_name = clip_name - self.clip = "clip_{}".format(self.clip_name) - - clip_model = model_options.get("{}_class".format(self.clip), clip_model) - model_options = {**model_options, "model_name": self.clip} - setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs)) - - self.dtypes = set() - if dtype is not None: - self.dtypes.add(dtype) - - def set_clip_options(self, options): - getattr(self, self.clip).set_clip_options(options) - - def reset_clip_options(self): - getattr(self, self.clip).reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs = token_weight_pairs[self.clip_name] - out = getattr(self, self.clip).encode_token_weights(token_weight_pairs) - return out - - def load_sd(self, sd): - return getattr(self, self.clip).load_sd(sd) diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json deleted file mode 100644 index 3ba8c6b5bc3d6389fb6c9e2c8231729ad9d663a4..0000000000000000000000000000000000000000 --- a/comfy/sd1_clip_config.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "_name_or_path": "openai/clip-vit-large-patch14", - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "quick_gelu", - "hidden_size": 768, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 77, - "model_type": "clip_text_model", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 1, - "projection_dim": 768, - "torch_dtype": "float32", - "transformers_version": "4.24.0", - "vocab_size": 49408 -} diff --git a/comfy/sd1_tokenizer/merges.txt b/comfy/sd1_tokenizer/merges.txt deleted file mode 100644 index 76e821f1b6f0a9709293c3b6b51ed90980b3166b..0000000000000000000000000000000000000000 --- a/comfy/sd1_tokenizer/merges.txt +++ /dev/null @@ -1,48895 +0,0 @@ -#version: 0.2 -i n -t h -a n -r e -a r -e r -th e -in g -o u -o n -s t -o r -e n -o n -a l -a t -e r -i t -i n -t o -r o -i s -l e -i c -a t -an d -e d -o f -c h -o r -e s -i l -e l -s t -a c -o m -a m -l o -a n -a y -s h -r i -l i -t i -f or -n e -ð Ł -r a -h a -d e -o l -v e -s i -u r -a l -s e -' s -u n -d i -b e -l a -w h -o o -d ay -e n -m a -n o -l e -t o -ou r -i r -g h -w it -i t -y o -a s -s p -th is -t s -at i -yo u -wit h -a d -i s -a b -l y -w e -th e -t e -a s -a g -v i -p p -s u -h o -m y -. . -b u -c om -s e -er s -m e -m e -al l -c on -m o -k e -g e -ou t -en t -c o -f e -v er -a r -f ro -a u -p o -c e -gh t -ar e -s s -fro m -c h -t r -ou n -on e -b y -d o -t h -w or -er e -k e -p ro -f or -d s -b o -t a -w e -g o -h e -t er -in g -d e -b e -ati on -m or -a y -e x -il l -p e -k s -s c -l u -f u -q u -v er -ðŁ ĺ -j u -m u -at e -an d -v e -k ing -m ar -o p -h i -.. . -p re -a d -r u -th at -j o -o f -c e -ne w -a m -a p -g re -s s -d u -no w -y e -t ing -y our -it y -n i -c i -p ar -g u -f i -a f -p er -t er -u p -s o -g i -on s -g r -g e -b r -p l -' t -m i -in e -we e -b i -u s -sh o -ha ve -to day -a v -m an -en t -ac k -ur e -ou r -â Ģ -c u -l d -lo o -i m -ic e -s om -f in -re d -re n -oo d -w as -ti on -p i -i r -th er -t y -p h -ar d -e c -! ! -m on -mor e -w ill -t ra -c an -c ol -p u -t e -w n -m b -s o -it i -ju st -n ing -h ere -t u -p a -p r -bu t -wh at -al ly -f ir -m in -c a -an t -s a -t ed -e v -m ent -f a -ge t -am e -ab out -g ra -no t -ha pp -ay s -m an -h is -ti me -li ke -g h -ha s -th an -lo ve -ar t -st e -d ing -h e -c re -w s -w at -d er -it e -s er -ac e -ag e -en d -st r -a w -st or -r e -c ar -el l -al l -p s -f ri -p ho -p or -d o -a k -w i -f re -wh o -sh i -b oo -s on -el l -wh en -il l -ho w -gre at -w in -e l -b l -s si -al i -som e -ðŁ Ĵ -t on -d er -le s -p la -ï ¸ -e d -s ch -h u -on g -d on -k i -s h -an n -c or -. . -oun d -a z -in e -ar y -fu l -st u -ou ld -st i -g o -se e -ab le -ar s -l l -m is -b er -c k -w a -en ts -n o -si g -f e -fir st -e t -sp e -ac k -i f -ou s -' m -st er -a pp -an g -an ce -an s -g ood -b re -e ver -the y -t ic -com e -of f -b ack -as e -ing s -ol d -i ght -f o -h er -happ y -p ic -it s -v ing -u s -m at -h om -d y -e m -s k -y ing -the ir -le d -r y -u l -h ar -c k -t on -on al -h el -r ic -b ir -vi e -w ay -t ri -d a -p le -b ro -st o -oo l -ni ght -tr u -b a -re ad -re s -ye ar -f r -t or -al s -c oun -c la -t ure -v el -at ed -le c -en d -th ing -v o -ic i -be st -c an -wor k -la st -af ter -en ce -p ri -p e -e s -i l -âĢ ¦ -d re -y s -o ver -i es -ðŁ ij -com m -t w -in k -s un -c l -li fe -t t -a ch -l and -s y -t re -t al -p ol -s m -du c -s al -f t -' re -ch e -w ar -t ur -ati ons -ac h -m s -il e -p m -ou gh -at e -st ar -wee k -! !! -c lu -th ere -n er -t om -s el -ï¸ ı -wor ld -v es -c am -go t -in ter -of f -u m -ton ight -o ther -h ou -loo k -j e -i d -si on -be au -at t -el i -or t -re c -f f -st er -su pp -g en -be en -il y -te am -m m -i c -pe op -it t -at s -on ly -mb er -en g -b ri -m p -k now -b ur -b ar -in s -lo w -sh e -ro w -â Ŀ -t ro -peop le -vi a -lo w -ag a -be t -x t -f ac -ch ar -e ar -w al -s en -f am -b le -n ati -is h -n or -g ame -li ve -s co -le y -d on -ic k -b all -ver y -the se -p an -i a -at ing -c r -a re -g ir -ma ke -st re -sho w -. 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"łï¸ı": 27899, - "łï¸ı": 12715, - "łĪ": 43364, - "Ń": 255, - "Ń": 511 -} diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py deleted file mode 100644 index c8cef14e4d6f81eb8ec8f4b8825c8afaa1d5caf3..0000000000000000000000000000000000000000 --- a/comfy/sdxl_clip.py +++ /dev/null @@ -1,95 +0,0 @@ -from comfy import sd1_clip -import torch -import os - -class SDXLClipG(sd1_clip.SDClipModel): - def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): - if layer == "penultimate": - layer="hidden" - layer_idx=-2 - - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") - model_options = {**model_options, "model_name": "clip_g"} - super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, - special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options) - - def load_sd(self, sd): - return super().load_sd(sd) - -class SDXLClipGTokenizer(sd1_clip.SDTokenizer): - def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): - super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) - - -class SDXLTokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return self.clip_g.untokenize(token_weight_pair) - - def state_dict(self): - return {} - -class SDXLClipModel(torch.nn.Module): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__() - self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options) - self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options) - self.dtypes = set([dtype]) - - def set_clip_options(self, options): - self.clip_l.set_clip_options(options) - self.clip_g.set_clip_options(options) - - def reset_clip_options(self): - self.clip_g.reset_clip_options() - self.clip_l.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs_g = token_weight_pairs["g"] - token_weight_pairs_l = token_weight_pairs["l"] - g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) - l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - cut_to = min(l_out.shape[1], g_out.shape[1]) - return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled - - def load_sd(self, sd): - if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: - return self.clip_g.load_sd(sd) - else: - return self.clip_l.load_sd(sd) - -class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG, model_options=model_options) - - -class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): - def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): - super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) - -class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer) - -class StableCascadeClipG(sd1_clip.SDClipModel): - def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") - model_options = {**model_options, "model_name": "clip_g"} - super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, - special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options) - - def load_sd(self, sd): - return super().load_sd(sd) - -class StableCascadeClipModel(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG, model_options=model_options) diff --git a/comfy/supported_models.py b/comfy/supported_models.py deleted file mode 100644 index 76260de003a946588dfa74380adfab4f1ee899d1..0000000000000000000000000000000000000000 --- a/comfy/supported_models.py +++ /dev/null @@ -1,1290 +0,0 @@ -import torch -from . import model_base -from . import utils - -from . import sd1_clip -from . import sdxl_clip -import comfy.text_encoders.sd2_clip -import comfy.text_encoders.sd3_clip -import comfy.text_encoders.sa_t5 -import comfy.text_encoders.aura_t5 -import comfy.text_encoders.pixart_t5 -import comfy.text_encoders.hydit -import comfy.text_encoders.flux -import comfy.text_encoders.genmo -import comfy.text_encoders.lt -import comfy.text_encoders.hunyuan_video -import comfy.text_encoders.cosmos -import comfy.text_encoders.lumina2 -import comfy.text_encoders.wan -import comfy.text_encoders.ace -import comfy.text_encoders.omnigen2 -import comfy.text_encoders.qwen_image - -from . import supported_models_base -from . import latent_formats - -from . import diffusers_convert - -class SD15(supported_models_base.BASE): - unet_config = { - "context_dim": 768, - "model_channels": 320, - "use_linear_in_transformer": False, - "adm_in_channels": None, - "use_temporal_attention": False, - } - - unet_extra_config = { - "num_heads": 8, - "num_head_channels": -1, - } - - latent_format = latent_formats.SD15 - memory_usage_factor = 1.0 - - def process_clip_state_dict(self, state_dict): - k = list(state_dict.keys()) - for x in k: - if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): - y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") - state_dict[y] = state_dict.pop(x) - - if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: - ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] - if ids.dtype == torch.float32: - state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() - - replace_prefix = {} - replace_prefix["cond_stage_model."] = "clip_l." - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - return state_dict - - def process_clip_state_dict_for_saving(self, state_dict): - pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] - for p in pop_keys: - if p in state_dict: - state_dict.pop(p) - - replace_prefix = {"clip_l.": "cond_stage_model."} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) - -class SD20(supported_models_base.BASE): - unet_config = { - "context_dim": 1024, - "model_channels": 320, - "use_linear_in_transformer": True, - "adm_in_channels": None, - "use_temporal_attention": False, - } - - unet_extra_config = { - "num_heads": -1, - "num_head_channels": 64, - "attn_precision": torch.float32, - } - - latent_format = latent_formats.SD15 - memory_usage_factor = 1.0 - - def model_type(self, state_dict, prefix=""): - if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction - k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) - out = state_dict.get(k, None) - if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. - return model_base.ModelType.V_PREDICTION - return model_base.ModelType.EPS - - def process_clip_state_dict(self, state_dict): - replace_prefix = {} - replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format - replace_prefix["cond_stage_model.model."] = "clip_h." - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.") - return state_dict - - def process_clip_state_dict_for_saving(self, state_dict): - replace_prefix = {} - replace_prefix["clip_h"] = "cond_stage_model.model" - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) - state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) - return state_dict - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel) - -class SD21UnclipL(SD20): - unet_config = { - "context_dim": 1024, - "model_channels": 320, - "use_linear_in_transformer": True, - "adm_in_channels": 1536, - "use_temporal_attention": False, - } - - clip_vision_prefix = "embedder.model.visual." - noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} - - -class SD21UnclipH(SD20): - unet_config = { - "context_dim": 1024, - "model_channels": 320, - "use_linear_in_transformer": True, - "adm_in_channels": 2048, - "use_temporal_attention": False, - } - - clip_vision_prefix = "embedder.model.visual." - noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} - -class SDXLRefiner(supported_models_base.BASE): - unet_config = { - "model_channels": 384, - "use_linear_in_transformer": True, - "context_dim": 1280, - "adm_in_channels": 2560, - "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], - "use_temporal_attention": False, - } - - latent_format = latent_formats.SDXL - memory_usage_factor = 1.0 - - def get_model(self, state_dict, prefix="", device=None): - return model_base.SDXLRefiner(self, device=device) - - def process_clip_state_dict(self, state_dict): - keys_to_replace = {} - replace_prefix = {} - replace_prefix["conditioner.embedders.0.model."] = "clip_g." - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - - state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") - state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) - return state_dict - - def process_clip_state_dict_for_saving(self, state_dict): - replace_prefix = {} - state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") - if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: - state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") - replace_prefix["clip_g"] = "conditioner.embedders.0.model" - state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) - return state_dict_g - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) - -class SDXL(supported_models_base.BASE): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "transformer_depth": [0, 0, 2, 2, 10, 10], - "context_dim": 2048, - "adm_in_channels": 2816, - "use_temporal_attention": False, - } - - latent_format = latent_formats.SDXL - - memory_usage_factor = 0.8 - - def model_type(self, state_dict, prefix=""): - if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5 - self.latent_format = latent_formats.SDXL_Playground_2_5() - self.sampling_settings["sigma_data"] = 0.5 - self.sampling_settings["sigma_max"] = 80.0 - self.sampling_settings["sigma_min"] = 0.002 - return model_base.ModelType.EDM - elif "edm_vpred.sigma_max" in state_dict: - self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item()) - if "edm_vpred.sigma_min" in state_dict: - self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item()) - return model_base.ModelType.V_PREDICTION_EDM - elif "v_pred" in state_dict: - if "ztsnr" in state_dict: #Some zsnr anime checkpoints - self.sampling_settings["zsnr"] = True - return model_base.ModelType.V_PREDICTION - else: - return model_base.ModelType.EPS - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) - if self.inpaint_model(): - out.set_inpaint() - return out - - def process_clip_state_dict(self, state_dict): - keys_to_replace = {} - replace_prefix = {} - - replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model" - replace_prefix["conditioner.embedders.1.model."] = "clip_g." - state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True) - - state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) - state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.") - return state_dict - - def process_clip_state_dict_for_saving(self, state_dict): - replace_prefix = {} - state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") - for k in state_dict: - if k.startswith("clip_l"): - state_dict_g[k] = state_dict[k] - - state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1)) - pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"] - for p in pop_keys: - if p in state_dict_g: - state_dict_g.pop(p) - - replace_prefix["clip_g"] = "conditioner.embedders.1.model" - replace_prefix["clip_l"] = "conditioner.embedders.0" - state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) - return state_dict_g - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) - -class SSD1B(SDXL): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "transformer_depth": [0, 0, 2, 2, 4, 4], - "context_dim": 2048, - "adm_in_channels": 2816, - "use_temporal_attention": False, - } - -class Segmind_Vega(SDXL): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "transformer_depth": [0, 0, 1, 1, 2, 2], - "context_dim": 2048, - "adm_in_channels": 2816, - "use_temporal_attention": False, - } - -class KOALA_700M(SDXL): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "transformer_depth": [0, 2, 5], - "context_dim": 2048, - "adm_in_channels": 2816, - "use_temporal_attention": False, - } - -class KOALA_1B(SDXL): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "transformer_depth": [0, 2, 6], - "context_dim": 2048, - "adm_in_channels": 2816, - "use_temporal_attention": False, - } - -class SVD_img2vid(supported_models_base.BASE): - unet_config = { - "model_channels": 320, - "in_channels": 8, - "use_linear_in_transformer": True, - "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], - "context_dim": 1024, - "adm_in_channels": 768, - "use_temporal_attention": True, - "use_temporal_resblock": True - } - - unet_extra_config = { - "num_heads": -1, - "num_head_channels": 64, - "attn_precision": torch.float32, - } - - clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." - - latent_format = latent_formats.SD15 - - sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.SVD_img2vid(self, device=device) - return out - - def clip_target(self, state_dict={}): - return None - -class SV3D_u(SVD_img2vid): - unet_config = { - "model_channels": 320, - "in_channels": 8, - "use_linear_in_transformer": True, - "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], - "context_dim": 1024, - "adm_in_channels": 256, - "use_temporal_attention": True, - "use_temporal_resblock": True - } - - vae_key_prefix = ["conditioner.embedders.1.encoder."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.SV3D_u(self, device=device) - return out - -class SV3D_p(SV3D_u): - unet_config = { - "model_channels": 320, - "in_channels": 8, - "use_linear_in_transformer": True, - "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], - "context_dim": 1024, - "adm_in_channels": 1280, - "use_temporal_attention": True, - "use_temporal_resblock": True - } - - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.SV3D_p(self, device=device) - return out - -class Stable_Zero123(supported_models_base.BASE): - unet_config = { - "context_dim": 768, - "model_channels": 320, - "use_linear_in_transformer": False, - "adm_in_channels": None, - "use_temporal_attention": False, - "in_channels": 8, - } - - unet_extra_config = { - "num_heads": 8, - "num_head_channels": -1, - } - - required_keys = { - "cc_projection.weight": None, - "cc_projection.bias": None, - } - - clip_vision_prefix = "cond_stage_model.model.visual." - - latent_format = latent_formats.SD15 - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) - return out - - def clip_target(self, state_dict={}): - return None - -class SD_X4Upscaler(SD20): - unet_config = { - "context_dim": 1024, - "model_channels": 256, - 'in_channels': 7, - "use_linear_in_transformer": True, - "adm_in_channels": None, - "use_temporal_attention": False, - } - - unet_extra_config = { - "disable_self_attentions": [True, True, True, False], - "num_classes": 1000, - "num_heads": 8, - "num_head_channels": -1, - } - - latent_format = latent_formats.SD_X4 - - sampling_settings = { - "linear_start": 0.0001, - "linear_end": 0.02, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.SD_X4Upscaler(self, device=device) - return out - -class Stable_Cascade_C(supported_models_base.BASE): - unet_config = { - "stable_cascade_stage": 'c', - } - - unet_extra_config = {} - - latent_format = latent_formats.SC_Prior - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - sampling_settings = { - "shift": 2.0, - } - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoder."] - clip_vision_prefix = "clip_l_vision." - - def process_unet_state_dict(self, state_dict): - key_list = list(state_dict.keys()) - for y in ["weight", "bias"]: - suffix = "in_proj_{}".format(y) - keys = filter(lambda a: a.endswith(suffix), key_list) - for k_from in keys: - weights = state_dict.pop(k_from) - prefix = k_from[:-(len(suffix) + 1)] - shape_from = weights.shape[0] // 3 - for x in range(3): - p = ["to_q", "to_k", "to_v"] - k_to = "{}.{}.{}".format(prefix, p[x], y) - state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)] - return state_dict - - def process_clip_state_dict(self, state_dict): - state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) - if "clip_g.text_projection" in state_dict: - state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1) - return state_dict - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.StableCascade_C(self, device=device) - return out - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel) - -class Stable_Cascade_B(Stable_Cascade_C): - unet_config = { - "stable_cascade_stage": 'b', - } - - unet_extra_config = {} - - latent_format = latent_formats.SC_B - supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] - - sampling_settings = { - "shift": 1.0, - } - - clip_vision_prefix = None - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.StableCascade_B(self, device=device) - return out - -class SD15_instructpix2pix(SD15): - unet_config = { - "context_dim": 768, - "model_channels": 320, - "use_linear_in_transformer": False, - "adm_in_channels": None, - "use_temporal_attention": False, - "in_channels": 8, - } - - def get_model(self, state_dict, prefix="", device=None): - return model_base.SD15_instructpix2pix(self, device=device) - -class SDXL_instructpix2pix(SDXL): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "transformer_depth": [0, 0, 2, 2, 10, 10], - "context_dim": 2048, - "adm_in_channels": 2816, - "use_temporal_attention": False, - "in_channels": 8, - } - - def get_model(self, state_dict, prefix="", device=None): - return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) - -class LotusD(SD20): - unet_config = { - "model_channels": 320, - "use_linear_in_transformer": True, - "use_temporal_attention": False, - "adm_in_channels": 4, - "in_channels": 4, - } - - unet_extra_config = { - "num_classes": 'sequential' - } - - def get_model(self, state_dict, prefix="", device=None): - return model_base.Lotus(self, device=device) - -class SD3(supported_models_base.BASE): - unet_config = { - "in_channels": 16, - "pos_embed_scaling_factor": None, - } - - sampling_settings = { - "shift": 3.0, - } - - unet_extra_config = {} - latent_format = latent_formats.SD3 - - memory_usage_factor = 1.2 - - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.SD3(self, device=device) - return out - - def clip_target(self, state_dict={}): - clip_l = False - clip_g = False - t5 = False - pref = self.text_encoder_key_prefix[0] - if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: - clip_l = True - if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict: - clip_g = True - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - if "dtype_t5" in t5_detect: - t5 = True - - return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect)) - -class StableAudio(supported_models_base.BASE): - unet_config = { - "audio_model": "dit1.0", - } - - sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03} - - unet_extra_config = {} - latent_format = latent_formats.StableAudio1 - - text_encoder_key_prefix = ["text_encoders."] - vae_key_prefix = ["pretransform.model."] - - def get_model(self, state_dict, prefix="", device=None): - seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True) - seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True) - return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device) - - def process_unet_state_dict(self, state_dict): - for k in list(state_dict.keys()): - if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero - state_dict.pop(k) - return state_dict - - def process_unet_state_dict_for_saving(self, state_dict): - replace_prefix = {"": "model.model."} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model) - -class AuraFlow(supported_models_base.BASE): - unet_config = { - "cond_seq_dim": 2048, - } - - sampling_settings = { - "multiplier": 1.0, - "shift": 1.73, - } - - unet_extra_config = {} - latent_format = latent_formats.SDXL - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.AuraFlow(self, device=device) - return out - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model) - -class PixArtAlpha(supported_models_base.BASE): - unet_config = { - "image_model": "pixart_alpha", - } - - sampling_settings = { - "beta_schedule" : "sqrt_linear", - "linear_start" : 0.0001, - "linear_end" : 0.02, - "timesteps" : 1000, - } - - unet_extra_config = {} - latent_format = latent_formats.SD15 - - memory_usage_factor = 0.5 - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.PixArt(self, device=device) - return out.eval() - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.PixArtT5XXL) - -class PixArtSigma(PixArtAlpha): - unet_config = { - "image_model": "pixart_sigma", - } - latent_format = latent_formats.SDXL - -class HunyuanDiT(supported_models_base.BASE): - unet_config = { - "image_model": "hydit", - } - - unet_extra_config = { - "attn_precision": torch.float32, - } - - sampling_settings = { - "linear_start": 0.00085, - "linear_end": 0.018, - } - - latent_format = latent_formats.SDXL - - memory_usage_factor = 1.3 - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.HunyuanDiT(self, device=device) - return out - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel) - -class HunyuanDiT1(HunyuanDiT): - unet_config = { - "image_model": "hydit1", - } - - unet_extra_config = {} - - sampling_settings = { - "linear_start" : 0.00085, - "linear_end" : 0.03, - } - -class Flux(supported_models_base.BASE): - unet_config = { - "image_model": "flux", - "guidance_embed": True, - } - - sampling_settings = { - } - - unet_extra_config = {} - latent_format = latent_formats.Flux - - memory_usage_factor = 3.1 # TODO: debug why flux mem usage is so weird on windows. - - supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Flux(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect)) - -class FluxInpaint(Flux): - unet_config = { - "image_model": "flux", - "guidance_embed": True, - "in_channels": 96, - } - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - -class FluxSchnell(Flux): - unet_config = { - "image_model": "flux", - "guidance_embed": False, - } - - sampling_settings = { - "multiplier": 1.0, - "shift": 1.0, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device) - return out - -class GenmoMochi(supported_models_base.BASE): - unet_config = { - "image_model": "mochi_preview", - } - - sampling_settings = { - "multiplier": 1.0, - "shift": 6.0, - } - - unet_extra_config = {} - latent_format = latent_formats.Mochi - - memory_usage_factor = 2.0 #TODO - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.GenmoMochi(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect)) - -class LTXV(supported_models_base.BASE): - unet_config = { - "image_model": "ltxv", - } - - sampling_settings = { - "shift": 2.37, - } - - unet_extra_config = {} - latent_format = latent_formats.LTXV - - memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def __init__(self, unet_config): - super().__init__(unet_config) - self.memory_usage_factor = (unet_config.get("cross_attention_dim", 2048) / 2048) * 5.5 - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.LTXV(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect)) - -class HunyuanVideo(supported_models_base.BASE): - unet_config = { - "image_model": "hunyuan_video", - } - - sampling_settings = { - "shift": 7.0, - } - - unet_extra_config = {} - latent_format = latent_formats.HunyuanVideo - - memory_usage_factor = 1.8 #TODO - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.HunyuanVideo(self, device=device) - return out - - def process_unet_state_dict(self, state_dict): - out_sd = {} - for k in list(state_dict.keys()): - key_out = k - key_out = key_out.replace("txt_in.t_embedder.mlp.0.", "txt_in.t_embedder.in_layer.").replace("txt_in.t_embedder.mlp.2.", "txt_in.t_embedder.out_layer.") - key_out = key_out.replace("txt_in.c_embedder.linear_1.", "txt_in.c_embedder.in_layer.").replace("txt_in.c_embedder.linear_2.", "txt_in.c_embedder.out_layer.") - key_out = key_out.replace("_mod.linear.", "_mod.lin.").replace("_attn_qkv.", "_attn.qkv.") - key_out = key_out.replace("mlp.fc1.", "mlp.0.").replace("mlp.fc2.", "mlp.2.") - key_out = key_out.replace("_attn_q_norm.weight", "_attn.norm.query_norm.scale").replace("_attn_k_norm.weight", "_attn.norm.key_norm.scale") - key_out = key_out.replace(".q_norm.weight", ".norm.query_norm.scale").replace(".k_norm.weight", ".norm.key_norm.scale") - key_out = key_out.replace("_attn_proj.", "_attn.proj.") - key_out = key_out.replace(".modulation.linear.", ".modulation.lin.") - key_out = key_out.replace("_in.mlp.2.", "_in.out_layer.").replace("_in.mlp.0.", "_in.in_layer.") - out_sd[key_out] = state_dict[k] - return out_sd - - def process_unet_state_dict_for_saving(self, state_dict): - replace_prefix = {"": "model.model."} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect)) - -class HunyuanVideoI2V(HunyuanVideo): - unet_config = { - "image_model": "hunyuan_video", - "in_channels": 33, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.HunyuanVideoI2V(self, device=device) - return out - -class HunyuanVideoSkyreelsI2V(HunyuanVideo): - unet_config = { - "image_model": "hunyuan_video", - "in_channels": 32, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.HunyuanVideoSkyreelsI2V(self, device=device) - return out - -class CosmosT2V(supported_models_base.BASE): - unet_config = { - "image_model": "cosmos", - "in_channels": 16, - } - - sampling_settings = { - "sigma_data": 0.5, - "sigma_max": 80.0, - "sigma_min": 0.002, - } - - unet_extra_config = {} - latent_format = latent_formats.Cosmos1CV8x8x8 - - memory_usage_factor = 1.6 #TODO - - supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.CosmosVideo(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) - -class CosmosI2V(CosmosT2V): - unet_config = { - "image_model": "cosmos", - "in_channels": 17, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.CosmosVideo(self, image_to_video=True, device=device) - return out - -class CosmosT2IPredict2(supported_models_base.BASE): - unet_config = { - "image_model": "cosmos_predict2", - "in_channels": 16, - } - - sampling_settings = { - "sigma_data": 1.0, - "sigma_max": 80.0, - "sigma_min": 0.002, - } - - unet_extra_config = {} - latent_format = latent_formats.Wan21 - - memory_usage_factor = 1.0 - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - def __init__(self, unet_config): - super().__init__(unet_config) - self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9 - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.CosmosPredict2(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) - -class CosmosI2VPredict2(CosmosT2IPredict2): - unet_config = { - "image_model": "cosmos_predict2", - "in_channels": 17, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.CosmosPredict2(self, image_to_video=True, device=device) - return out - -class Lumina2(supported_models_base.BASE): - unet_config = { - "image_model": "lumina2", - } - - sampling_settings = { - "multiplier": 1.0, - "shift": 6.0, - } - - memory_usage_factor = 1.2 - - unet_extra_config = {} - latent_format = latent_formats.Flux - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Lumina2(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect)) - -class WAN21_T2V(supported_models_base.BASE): - unet_config = { - "image_model": "wan2.1", - "model_type": "t2v", - } - - sampling_settings = { - "shift": 8.0, - } - - unet_extra_config = {} - latent_format = latent_formats.Wan21 - - memory_usage_factor = 1.0 - - supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def __init__(self, unet_config): - super().__init__(unet_config) - self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2000 - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN21(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}umt5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect)) - -class WAN21_I2V(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "i2v", - "in_dim": 36, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN21(self, image_to_video=True, device=device) - return out - -class WAN21_FunControl2V(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "i2v", - "in_dim": 48, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN21(self, image_to_video=False, device=device) - return out - -class WAN21_Camera(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "camera", - "in_dim": 32, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN21_Camera(self, image_to_video=False, device=device) - return out - -class WAN22_Camera(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "camera_2.2", - "in_dim": 36, - } - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN21_Camera(self, image_to_video=False, device=device) - return out - -class WAN21_Vace(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "vace", - } - - def __init__(self, unet_config): - super().__init__(unet_config) - self.memory_usage_factor = 1.2 * self.memory_usage_factor - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN21_Vace(self, image_to_video=False, device=device) - return out - -class WAN22_S2V(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "s2v", - } - - def __init__(self, unet_config): - super().__init__(unet_config) - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN22_S2V(self, device=device) - return out - -class WAN22_T2V(WAN21_T2V): - unet_config = { - "image_model": "wan2.1", - "model_type": "t2v", - "out_dim": 48, - } - - latent_format = latent_formats.Wan22 - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.WAN22(self, image_to_video=True, device=device) - return out - -class Hunyuan3Dv2(supported_models_base.BASE): - unet_config = { - "image_model": "hunyuan3d2", - } - - unet_extra_config = {} - - sampling_settings = { - "multiplier": 1.0, - "shift": 1.0, - } - - memory_usage_factor = 3.5 - - clip_vision_prefix = "conditioner.main_image_encoder.model." - vae_key_prefix = ["vae."] - - latent_format = latent_formats.Hunyuan3Dv2 - - def process_unet_state_dict_for_saving(self, state_dict): - replace_prefix = {"": "model."} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Hunyuan3Dv2(self, device=device) - return out - - def clip_target(self, state_dict={}): - return None - -class Hunyuan3Dv2mini(Hunyuan3Dv2): - unet_config = { - "image_model": "hunyuan3d2", - "depth": 8, - } - - latent_format = latent_formats.Hunyuan3Dv2mini - -class HiDream(supported_models_base.BASE): - unet_config = { - "image_model": "hidream", - } - - sampling_settings = { - "shift": 3.0, - } - - sampling_settings = { - } - - # memory_usage_factor = 1.2 # TODO - - unet_extra_config = {} - latent_format = latent_formats.Flux - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.HiDream(self, device=device) - return out - - def clip_target(self, state_dict={}): - return None # TODO - -class Chroma(supported_models_base.BASE): - unet_config = { - "image_model": "chroma", - } - - unet_extra_config = { - } - - sampling_settings = { - "multiplier": 1.0, - } - - latent_format = comfy.latent_formats.Flux - - memory_usage_factor = 3.2 - - supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] - - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Chroma(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) - -class ACEStep(supported_models_base.BASE): - unet_config = { - "audio_model": "ace", - } - - unet_extra_config = { - } - - sampling_settings = { - "shift": 3.0, - } - - latent_format = comfy.latent_formats.ACEAudio - - memory_usage_factor = 0.5 - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.ACEStep(self, device=device) - return out - - def clip_target(self, state_dict={}): - return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model) - -class Omnigen2(supported_models_base.BASE): - unet_config = { - "image_model": "omnigen2", - } - - sampling_settings = { - "multiplier": 1.0, - "shift": 2.6, - } - - memory_usage_factor = 1.65 #TODO - - unet_extra_config = {} - latent_format = latent_formats.Flux - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def __init__(self, unet_config): - super().__init__(unet_config) - if comfy.model_management.extended_fp16_support(): - self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.Omnigen2(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect)) - -class QwenImage(supported_models_base.BASE): - unet_config = { - "image_model": "qwen_image", - } - - sampling_settings = { - "multiplier": 1.0, - "shift": 1.15, - } - - memory_usage_factor = 1.8 #TODO - - unet_extra_config = {} - latent_format = latent_formats.Wan21 - - supported_inference_dtypes = [torch.bfloat16, torch.float32] - - vae_key_prefix = ["vae."] - text_encoder_key_prefix = ["text_encoders."] - - def get_model(self, state_dict, prefix="", device=None): - out = model_base.QwenImage(self, device=device) - return out - - def clip_target(self, state_dict={}): - pref = self.text_encoder_key_prefix[0] - hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) - return supported_models_base.ClipTarget(comfy.text_encoders.qwen_image.QwenImageTokenizer, comfy.text_encoders.qwen_image.te(**hunyuan_detect)) - - -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2, QwenImage] - -models += [SVD_img2vid] diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py deleted file mode 100644 index 54573abb110d8cc5e190ecefa0f9aecf95da0b99..0000000000000000000000000000000000000000 --- a/comfy/supported_models_base.py +++ /dev/null @@ -1,119 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -from . import model_base -from . import utils -from . import latent_formats - -class ClipTarget: - def __init__(self, tokenizer, clip): - self.clip = clip - self.tokenizer = tokenizer - self.params = {} - -class BASE: - unet_config = {} - unet_extra_config = { - "num_heads": -1, - "num_head_channels": 64, - } - - required_keys = {} - - clip_prefix = [] - clip_vision_prefix = None - noise_aug_config = None - sampling_settings = {} - latent_format = latent_formats.LatentFormat - vae_key_prefix = ["first_stage_model."] - text_encoder_key_prefix = ["cond_stage_model."] - supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] - - memory_usage_factor = 2.0 - - manual_cast_dtype = None - custom_operations = None - scaled_fp8 = None - optimizations = {"fp8": False} - - @classmethod - def matches(s, unet_config, state_dict=None): - for k in s.unet_config: - if k not in unet_config or s.unet_config[k] != unet_config[k]: - return False - if state_dict is not None: - for k in s.required_keys: - if k not in state_dict: - return False - return True - - def model_type(self, state_dict, prefix=""): - return model_base.ModelType.EPS - - def inpaint_model(self): - return self.unet_config["in_channels"] > 4 - - def __init__(self, unet_config): - self.unet_config = unet_config.copy() - self.sampling_settings = self.sampling_settings.copy() - self.latent_format = self.latent_format() - self.optimizations = self.optimizations.copy() - for x in self.unet_extra_config: - self.unet_config[x] = self.unet_extra_config[x] - - def get_model(self, state_dict, prefix="", device=None): - if self.noise_aug_config is not None: - out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) - else: - out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) - if self.inpaint_model(): - out.set_inpaint() - return out - - def process_clip_state_dict(self, state_dict): - state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True) - return state_dict - - def process_unet_state_dict(self, state_dict): - return state_dict - - def process_vae_state_dict(self, state_dict): - return state_dict - - def process_clip_state_dict_for_saving(self, state_dict): - replace_prefix = {"": self.text_encoder_key_prefix[0]} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def process_clip_vision_state_dict_for_saving(self, state_dict): - replace_prefix = {} - if self.clip_vision_prefix is not None: - replace_prefix[""] = self.clip_vision_prefix - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def process_unet_state_dict_for_saving(self, state_dict): - replace_prefix = {"": "model.diffusion_model."} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def process_vae_state_dict_for_saving(self, state_dict): - replace_prefix = {"": self.vae_key_prefix[0]} - return utils.state_dict_prefix_replace(state_dict, replace_prefix) - - def set_inference_dtype(self, dtype, manual_cast_dtype): - self.unet_config['dtype'] = dtype - self.manual_cast_dtype = manual_cast_dtype diff --git a/comfy/t2i_adapter/.DS_Store b/comfy/t2i_adapter/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/t2i_adapter/.DS_Store and /dev/null differ diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py deleted file mode 100644 index 10ea18e326693f237b3b219970c86e3808f6d334..0000000000000000000000000000000000000000 --- a/comfy/t2i_adapter/adapter.py +++ /dev/null @@ -1,299 +0,0 @@ -#taken from https://github.com/TencentARC/T2I-Adapter -import torch -import torch.nn as nn -from collections import OrderedDict - - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return nn.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -class Downsample(nn.Module): - """ - A downsampling layer with an optional convolution. - :param channels: channels in the inputs and outputs. - :param use_conv: a bool determining if a convolution is applied. - :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then - downsampling occurs in the inner-two dimensions. - """ - - def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): - super().__init__() - self.channels = channels - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.dims = dims - stride = 2 if dims != 3 else (1, 2, 2) - if use_conv: - self.op = conv_nd( - dims, self.channels, self.out_channels, 3, stride=stride, padding=padding - ) - else: - assert self.channels == self.out_channels - self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) - - def forward(self, x): - assert x.shape[1] == self.channels - if not self.use_conv: - padding = [x.shape[2] % 2, x.shape[3] % 2] - self.op.padding = padding - - x = self.op(x) - return x - - -class ResnetBlock(nn.Module): - def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): - super().__init__() - ps = ksize // 2 - if in_c != out_c or sk == False: - self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) - else: - # print('n_in') - self.in_conv = None - self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) - self.act = nn.ReLU() - self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) - if sk == False: - self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) - else: - self.skep = None - - self.down = down - if self.down == True: - self.down_opt = Downsample(in_c, use_conv=use_conv) - - def forward(self, x): - if self.down == True: - x = self.down_opt(x) - if self.in_conv is not None: # edit - x = self.in_conv(x) - - h = self.block1(x) - h = self.act(h) - h = self.block2(h) - if self.skep is not None: - return h + self.skep(x) - else: - return h + x - - -class Adapter(nn.Module): - def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): - super(Adapter, self).__init__() - self.unshuffle_amount = 8 - resblock_no_downsample = [] - resblock_downsample = [3, 2, 1] - self.xl = xl - if self.xl: - self.unshuffle_amount = 16 - resblock_no_downsample = [1] - resblock_downsample = [2] - - self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) - self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) - self.channels = channels - self.nums_rb = nums_rb - self.body = [] - for i in range(len(channels)): - for j in range(nums_rb): - if (i in resblock_downsample) and (j == 0): - self.body.append( - ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) - elif (i in resblock_no_downsample) and (j == 0): - self.body.append( - ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) - else: - self.body.append( - ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) - self.body = nn.ModuleList(self.body) - self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) - - def forward(self, x): - # unshuffle - x = self.unshuffle(x) - # extract features - features = [] - x = self.conv_in(x) - for i in range(len(self.channels)): - for j in range(self.nums_rb): - idx = i * self.nums_rb + j - x = self.body[idx](x) - if self.xl: - features.append(None) - if i == 0: - features.append(None) - features.append(None) - if i == 2: - features.append(None) - else: - features.append(None) - features.append(None) - features.append(x) - - features = features[::-1] - - if self.xl: - return {"input": features[1:], "middle": features[:1]} - else: - return {"input": features} - - - -class LayerNorm(nn.LayerNorm): - """Subclass torch's LayerNorm to handle fp16.""" - - def forward(self, x: torch.Tensor): - orig_type = x.dtype - ret = super().forward(x.type(torch.float32)) - return ret.type(orig_type) - - -class QuickGELU(nn.Module): - - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - - -class ResidualAttentionBlock(nn.Module): - - def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): - super().__init__() - - self.attn = nn.MultiheadAttention(d_model, n_head) - self.ln_1 = LayerNorm(d_model) - self.mlp = nn.Sequential( - OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), - ("c_proj", nn.Linear(d_model * 4, d_model))])) - self.ln_2 = LayerNorm(d_model) - self.attn_mask = attn_mask - - def attention(self, x: torch.Tensor): - self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None - return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] - - def forward(self, x: torch.Tensor): - x = x + self.attention(self.ln_1(x)) - x = x + self.mlp(self.ln_2(x)) - return x - - -class StyleAdapter(nn.Module): - - def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): - super().__init__() - - scale = width ** -0.5 - self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) - self.num_token = num_token - self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) - self.ln_post = LayerNorm(width) - self.ln_pre = LayerNorm(width) - self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) - - def forward(self, x): - # x shape [N, HW+1, C] - style_embedding = self.style_embedding + torch.zeros( - (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) - x = torch.cat([x, style_embedding], dim=1) - x = self.ln_pre(x) - x = x.permute(1, 0, 2) # NLD -> LND - x = self.transformer_layes(x) - x = x.permute(1, 0, 2) # LND -> NLD - - x = self.ln_post(x[:, -self.num_token:, :]) - x = x @ self.proj - - return x - - -class ResnetBlock_light(nn.Module): - def __init__(self, in_c): - super().__init__() - self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) - self.act = nn.ReLU() - self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) - - def forward(self, x): - h = self.block1(x) - h = self.act(h) - h = self.block2(h) - - return h + x - - -class extractor(nn.Module): - def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): - super().__init__() - self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) - self.body = [] - for _ in range(nums_rb): - self.body.append(ResnetBlock_light(inter_c)) - self.body = nn.Sequential(*self.body) - self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) - self.down = down - if self.down == True: - self.down_opt = Downsample(in_c, use_conv=False) - - def forward(self, x): - if self.down == True: - x = self.down_opt(x) - x = self.in_conv(x) - x = self.body(x) - x = self.out_conv(x) - - return x - - -class Adapter_light(nn.Module): - def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): - super(Adapter_light, self).__init__() - self.unshuffle_amount = 8 - self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) - self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) - self.channels = channels - self.nums_rb = nums_rb - self.body = [] - self.xl = False - - for i in range(len(channels)): - if i == 0: - self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) - else: - self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) - self.body = nn.ModuleList(self.body) - - def forward(self, x): - # unshuffle - x = self.unshuffle(x) - # extract features - features = [] - for i in range(len(self.channels)): - x = self.body[i](x) - features.append(None) - features.append(None) - features.append(x) - - return {"input": features[::-1]} diff --git a/comfy/taesd/.DS_Store b/comfy/taesd/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/taesd/.DS_Store and /dev/null differ diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py deleted file mode 100644 index ce36f1a84dae599a35e84a8da3462408c0f0ccc6..0000000000000000000000000000000000000000 --- a/comfy/taesd/taesd.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -""" -Tiny AutoEncoder for Stable Diffusion -(DNN for encoding / decoding SD's latent space) -""" -import torch -import torch.nn as nn - -import comfy.utils -import comfy.ops - -def conv(n_in, n_out, **kwargs): - return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) - -class Clamp(nn.Module): - def forward(self, x): - return torch.tanh(x / 3) * 3 - -class Block(nn.Module): - def __init__(self, n_in, n_out): - super().__init__() - self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) - self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() - self.fuse = nn.ReLU() - def forward(self, x): - return self.fuse(self.conv(x) + self.skip(x)) - -def Encoder(latent_channels=4): - return nn.Sequential( - conv(3, 64), Block(64, 64), - conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, latent_channels), - ) - - -def Decoder(latent_channels=4): - return nn.Sequential( - Clamp(), conv(latent_channels, 64), nn.ReLU(), - Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), - Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), - Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), - Block(64, 64), conv(64, 3), - ) - -class TAESD(nn.Module): - latent_magnitude = 3 - latent_shift = 0.5 - - def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): - """Initialize pretrained TAESD on the given device from the given checkpoints.""" - super().__init__() - self.taesd_encoder = Encoder(latent_channels=latent_channels) - self.taesd_decoder = Decoder(latent_channels=latent_channels) - self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) - self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) - if encoder_path is not None: - self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True)) - if decoder_path is not None: - self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) - - @staticmethod - def scale_latents(x): - """raw latents -> [0, 1]""" - return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) - - @staticmethod - def unscale_latents(x): - """[0, 1] -> raw latents""" - return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) - - def decode(self, x): - x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) - x_sample = x_sample.sub(0.5).mul(2) - return x_sample - - def encode(self, x): - return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift diff --git a/comfy/text_encoders/.DS_Store b/comfy/text_encoders/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/text_encoders/.DS_Store and /dev/null differ diff --git a/comfy/text_encoders/ace.py b/comfy/text_encoders/ace.py deleted file mode 100644 index d650bb10d5d5bfdd485f18095e4a99ed7b06b6b7..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/ace.py +++ /dev/null @@ -1,153 +0,0 @@ -from comfy import sd1_clip -from .spiece_tokenizer import SPieceTokenizer -import comfy.text_encoders.t5 -import os -import re -import torch -import logging - -from tokenizers import Tokenizer -from .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji - -SUPPORT_LANGUAGES = { - "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285, - "pt": 286, "pl": 294, "tr": 295, "ru": 267, "cs": 293, - "nl": 297, "ar": 5022, "zh": 5023, "ja": 5412, "hu": 5753, - "ko": 6152, "hi": 6680 -} - -structure_pattern = re.compile(r"\[.*?\]") - -DEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") - - -class VoiceBpeTokenizer: - def __init__(self, vocab_file=DEFAULT_VOCAB_FILE): - self.tokenizer = None - if vocab_file is not None: - self.tokenizer = Tokenizer.from_file(vocab_file) - - def preprocess_text(self, txt, lang): - txt = multilingual_cleaners(txt, lang) - return txt - - def encode(self, txt, lang='en'): - # lang = lang.split("-")[0] # remove the region - # self.check_input_length(txt, lang) - txt = self.preprocess_text(txt, lang) - lang = "zh-cn" if lang == "zh" else lang - txt = f"[{lang}]{txt}" - txt = txt.replace(" ", "[SPACE]") - return self.tokenizer.encode(txt).ids - - def get_lang(self, line): - if line.startswith("[") and line[3:4] == ']': - lang = line[1:3].lower() - if lang in SUPPORT_LANGUAGES: - return lang, line[4:] - return "en", line - - def __call__(self, string): - lines = string.split("\n") - lyric_token_idx = [261] - for line in lines: - line = line.strip() - if not line: - lyric_token_idx += [2] - continue - - lang, line = self.get_lang(line) - - if lang not in SUPPORT_LANGUAGES: - lang = "en" - if "zh" in lang: - lang = "zh" - if "spa" in lang: - lang = "es" - - try: - line_out = japanese_to_romaji(line) - if line_out != line: - lang = "ja" - line = line_out - except: - pass - - try: - if structure_pattern.match(line): - token_idx = self.encode(line, "en") - else: - token_idx = self.encode(line, lang) - lyric_token_idx = lyric_token_idx + token_idx + [2] - except Exception as e: - logging.warning("tokenize error {} for line {} major_language {}".format(e, line, lang)) - return {"input_ids": lyric_token_idx} - - @staticmethod - def from_pretrained(path, **kwargs): - return VoiceBpeTokenizer(path, **kwargs) - - def get_vocab(self): - return {} - - -class UMT5BaseModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_base.json") - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options) - -class UMT5BaseTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer = tokenizer_data.get("spiece_model", None) - super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data) - - def state_dict(self): - return {"spiece_model": self.tokenizer.serialize_model()} - -class LyricsTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") - super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data) - -class AceT5Tokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out["lyrics"] = self.voicebpe.tokenize_with_weights(kwargs.get("lyrics", ""), return_word_ids, **kwargs) - out["umt5base"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return self.umt5base.untokenize(token_weight_pair) - - def state_dict(self): - return self.umt5base.state_dict() - -class AceT5Model(torch.nn.Module): - def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): - super().__init__() - self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options) - self.dtypes = set() - if dtype is not None: - self.dtypes.add(dtype) - - def set_clip_options(self, options): - self.umt5base.set_clip_options(options) - - def reset_clip_options(self): - self.umt5base.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs_umt5base = token_weight_pairs["umt5base"] - token_weight_pairs_lyrics = token_weight_pairs["lyrics"] - - t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base) - - lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0) - return t5_out, None, {"conditioning_lyrics": lyrics_embeds} - - def load_sd(self, sd): - return self.umt5base.load_sd(sd) diff --git a/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json deleted file mode 100644 index 519ed340c36e19dfe15ccffd7aa5a31a261d70a5..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json +++ /dev/null @@ -1,15535 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 0, - "special": true, - "content": "[STOP]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 1, - "special": true, - "content": "[UNK]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 2, - "special": true, - "content": "[SPACE]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 259, - "special": true, - 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}, - { - "id": 5412, - "special": true, - "content": "[ja]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 5753, - "special": true, - "content": "[hu]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6152, - "special": true, - "content": "[ko]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6680, - "special": true, - "content": "[hi]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6681, - "special": true, - "content": "[start]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6682, - "special": true, - "content": "[intro]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6683, - "special": true, - "content": "[verse]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6684, - "special": true, - "content": "[chorus]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6685, - "special": true, - "content": "[bridge]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6686, - "special": true, - "content": "[outro]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6687, - "special": true, - "content": "[end]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6688, - "special": true, - "content": "[inst]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6689, - "special": true, - "content": "[solo]", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false - }, - { - "id": 6690, - "special": true, - "content": "[hook]", - "single_word": false, - 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"stę p", - "b ą", - "po ko", - "w em", - "g ę", - "a by", - "g e", - "al bo", - "s pra", - "z no", - "de n", - "s mo", - "je sz", - "k się", - "jest eś", - "po z", - "ni gdy", - "k sią", - "c óż", - "w s", - "po w", - "t ka", - "ś wie", - "sz ka", - "sa mo", - "s ł", - "rz ę", - "na le", - "chce sz", - "ni k", - "p ę", - "chy ba", - "cią g", - "ją cy", - "wo j", - "na sze", - "mnie j", - "wię cej", - "z wy", - "o sta", - "f e", - "wa ż", - "h o", - "se r", - "śmie r", - "wie r", - "dz ą", - "za ś", - "gdy by", - "ja ki", - "wo l", - "wi n", - "d ą", - "ści a", - "roz ma", - "wa l", - "pa nie", - "sta r", - "ka z", - "je żeli", - "d em", - "w ra", - "ko ń", - "sie bie", - "zno wu", - "p ró", - "cz em", - "st wa", - "i sto", - "pó ł", - "d ał", - "ko bie", - "ała m", - "wy ch", - "ce sa", - "ni ch", - "za wsze", - "dzi ć", - "te ż", - "le pie", - "pro szę", - "k re", - "t wa", - "o t", - "ł ą", - "ch u", - "c ą", - "p rz", - "ł e", - "sze dł", - "od powie", - "my śli", - "ś wią", - "e n", - "e r", - "d e", - "a n", - "e t", - "i j", - "i n", - "e l", - "a a", - "s t", - "o r", - "g e", - "i s", - "a t", - "i e", - "c h", - "o n", - "e en", - "h et", - "i t", - "v er", - "aa r", - "a l", - "o or", - "g en", - "v an", - "o p", - "d en", - "h e", - "o m", - "t e", - "w e", - "i k", - "r e", - "z e", - "ij n", - "d at", - "b e", - "d er", - "in g", - "o e", - "ij k", - "a an", - "ch t", - "v oor", - "l e", - "i et", - "r o", - "m o", - "k en", - "z ijn", - "m en", - "i g", - "j e", - "n iet", - "a r", - "o o", - "i d", - "u n", - "i l", - "s ch", - "mo et", - "st e", - "u r", - "o l", - "he b", - "u it", - "g el", - "w ij", - "a s", - "m e", - "t en", - "w or", - "o u", - "v en", - "l en", - "aa t", - "d it", - "m et", - "r a", - "b en", - "s p", - "o ver", - "d ie", - "n o", - "w er", - "l ijk", - "f t", - "s l", - "an d", - "v e", - "t er", - "i er", - "i en", - "t o", - "d aar", - "g r", - "b el", - "de ze", - "d u", - "a g", - "k an", - "wor den", - "in gen", - "moet en", - "n en", - "on der", - "heb ben", - "r u", - "oo k", - "s en", - "c t", - "k t", - "no g", - "aa l", - "w as", - "u l", - "e er", - "b ij", - "m ijn", - "p ro", - "v ol", - "d o", - "k om", - "at ie", - "e ft", - "k el", - "al s", - "r ij", - "he id", - "a f", - "st el", - "m aar", - "a p", - "we e", - "a d", - "he eft", - "w aar", - "i cht", - "d an", - "er en", - "n e", - "w el", - "w at", - "w il", - "a cht", - "aa g", - "ge b", - "c on", - "z o", - "k e", - "b et", - "h ij", - "d ig", - "k un", - "u w", - "d t", - "d oor", - "t ij", - "a m", - "an g", - "on d", - "er s", - "is ch", - "ge en", - "i ge", - "ge v", - "ve el", - "n u", - "m a", - "on s", - "o f", - "b l", - "n aar", - "g ro", - "p l", - "an der", - "at en", - "kun nen", - "e cht", - "h ier", - "g oe", - "an t", - "u s", - "t wee", - "on t", - "de lijk", - "el e", - "u ur", - "al le", - "t oe", - "me er", - "i st", - "n a", - "n ie", - "on ze", - "l o", - "i m", - "p en", - "h ad", - "tij d", - "h oe", - "to t", - "z ou", - "a k", - "aa k", - "a men", - "d r", - "w oor", - "s e", - "wor dt", - "o t", - "gel ijk", - "g aan", - "i c", - "g er", - "k er", - "el d", - "e m", - "h ou", - "de l", - "z en", - "z el", - "te gen", - "b o", - "kom en", - "c om", - "i gen", - "e it", - "wer k", - "goe d", - "z al", - "z ij", - "sl ag", - "e s", - "z ien", - "a st", - "echt er", - "it ie", - "t ie", - "el ijk", - "m is", - "isch e", - "bel an", - "h aar", - "i ch", - "b er", - "h an", - "v r", - "al e", - "c i", - "gr ijk", - "in d", - "do en", - "l and", - "belan grijk", - "p un", - "op en", - "ct ie", - "zel f", - "m ij", - "it eit", - "ste m", - "me e", - "ar en", - "al l", - "b r", - "re cht", - "d ien", - "h u", - "g aat", - "pro b", - "m oe", - "p er", - "a u", - "ul len", - "z ich", - "daar om", - "or m", - "k l", - "v o", - "en t", - "st aat", - "z it", - "du i", - "n at", - "du s", - "d s", - "ver slag", - "kel ijk", - "prob le", - "w et", - "ge m", - "c r", - "i on", - "p r", - "sch ap", - "g d", - "h un", - "z a", - "er d", - "z et", - "st aan", - "st r", - "m aal", - "in der", - "e id", - "st en", - "p ar", - "k ken", - "ge d", - "z ullen", - "re s", - "men sen", - "j aar", - "re gel", - "ie der", - "vol gen", - "ge ven", - "e ven", - "l u", - "bl ij", - "i ë", - "k o", - "u we", - "m an", - "ma ken", - "l ie", - "g a", - "oe k", - "nie uwe", - "b aar", - "h o", - "h er", - "in ter", - "ander e", - "ru ik", - "s u", - "a gen", - "or t", - "m er", - "ou w", - "st er", - "wil len", - "aa kt", - "h oo", - "an den", - "f f", - "l ig", - "t re", - "s amen", - "ze er", - "dui delijk", - "ant woor", - "he el", - "men t", - "pun t", - "hou den", - "we g", - "vr aag", - "gel e", - "een s", - "be sch", - "om en", - "er g", - "do el", - "d ag", - "sp e", - "ur en", - "ing s", - "or en", - "l ang", - "de len", - "m ar", - "ste un", - "in nen", - "p ol", - "o on", - "i de", - "s n", - "s ie", - "r icht", - "z onder", - "no dig", - "all een", - "m id", - "ra gen", - "iet s", - "ver sch", - "geb ruik", - "st u", - "ro uw", - "stel len", - "be g", - "men ten", - "v in", - "eer ste", - "l aat", - "gro ot", - "oo d", - "to ch", - "l aten", - "aar d", - "s le", - "de el", - "st and", - "pl aat", - "re e", - "bet re", - "d i", - "l id", - "uit en", - "ra cht", - "bel eid", - "g et", - "ar t", - "st ie", - "st aten", - "g gen", - "re ken", - "e in", - "al en", - "m ing", - "mo gelijk", - "gro te", - "al tijd", - "z or", - "en kel", - "w ik", - "pol itie", - "e igen", - "el k", - "han del", - "g t", - "k we", - "m aat", - "el en", - "i p", - "v rij", - "s om", - "je s", - "aa m", - "hu is", - "v al", - "we er", - "lid staten", - "k ing", - "k le", - "be d", - "gev al", - "stel l", - "a i", - "wik kel", - "kwe stie", - "t al", - "ste e", - "a b", - "h el", - "kom st", - "p as", - "s s", - "it u", - "i den", - "eer d", - "m in", - "c e", - "p o", - "twee de", - "proble em", - "w aren", - "us sen", - "sn el", - "t ig", - "ge w", - "j u", - "ul t", - "ne men", - "com mis", - "versch il", - "k on", - "z oek", - "k rij", - "gr aag", - "den k", - "l anden", - "re den", - "be sl", - "oe g", - "bet er", - "he den", - "m ag", - "p e", - "bo ven", - "a c", - "con t", - "f d", - "h ele", - "k r", - "v ier", - "w in", - "ge z", - "k w", - "m il", - "v or", - "he m", - "ra m", - "aa s", - "ont wikkel", - "dr ie", - "v aak", - "plaat s", - "l a", - "g ang", - "ij f", - "f in", - "nat uur", - "t ussen", - "u g", - "in e", - "d a", - "b at", - "kom t", - "w acht", - "aa d", - "u t", - "é n", - "acht er", - "geb ie", - "ver k", - "lig t", - "c es", - "nie uw", - "van d", - "s t", - "n í", - "j e", - "p o", - "c h", - "r o", - "n a", - "s e", - "t o", - "n e", - "l e", - "k o", - "l a", - "d o", - "r a", - "n o", - "t e", - "h o", - "n ě", - "v a", - "l i", - "l o", - "ř e", - "c e", - "d e", - "v e", - "b y", - "n i", - "s k", - "t a", - "n á", - "z a", - "p ro", - "v o", - "v ě", - "m e", - "v á", - "s o", - "k a", - "r á", - "v y", - "z e", - "m i", - "p a", - "t i", - "st a", - "m ě", - "n é", - "ř i", - "ř í", - "m o", - "ž e", - "m a", - "j í", - "v ý", - "j i", - "d ě", - "r e", - "d a", - "k u", - "j a", - "c i", - "r u", - "č e", - "o b", - "t ě", - "m u", - "k y", - "d i", - "š e", - "k é", - "š í", - "t u", - "v i", - "p ře", - "v í", - "s i", - "n ý", - "o d", - "so u", - "v é", - "n y", - "r i", - "d y", - "b u", - "b o", - "t y", - "l á", - "l u", - "n u", - "ž i", - "m á", - "st i", - "c í", - "z á", - "p ra", - "sk é", - "m í", - "c o", - "d u", - "d á", - "by l", - "st o", - "s a", - "t í", - "je d", - "p ří", - "p ři", - "t é", - "s í", - "č i", - "v ní", - "č a", - "d í", - "z i", - "st u", - "p e", - "b a", - "d ní", - "ro z", - "va l", - "l í", - "s po", - "k á", - "b e", - "p i", - "no u", - "ta k", - "st e", - "r y", - "l é", - "vě t", - "se m", - "p ě", - "ko n", - "ne j", - "l y", - "ko u", - "ý ch", - "b ě", - "p r", - "f i", - "p rá", - "a le", - "ja ko", - "po d", - "ž í", - "z í", - "j sou", - "j sem", - "ch o", - "l ní", - "c ké", - "t á", - "m y", - "a k", - "h u", - "va t", - "pře d", - "h la", - "k e", - "st á", - "č í", - "š i", - "s le", - "k la", - "š tě", - "lo u", - "m ů", - "z na", - "ch á", - "o r", - "p ů", - "h a", - "b i", - "ta ké", - "d ů", - "no st", - "t ře", - "te r", - "p u", - "i n", - "v r", - "ve l", - "sk u", - "v še", - "t ní", - "do b", - "by la", - "č ní", - "ja k", - "v u", - "je ho", - "b ý", - "vá ní", - "ný ch", - "po u", - "te n", - "t ři", - "v z", - "st ře", - "d va", - "h le", - "č á", - "no sti", - "c k", - "v š", - "vo u", - "s u", - "h e", - "h ra", - "je n", - "s y", - "da l", - "po z", - "s lo", - "te l", - "d ru", - "de n", - "vš ak", - "g i", - "k dy", - "by lo", - "bu de", - "st ra", - "j ší", - "m é", - "me n", - "vý ch", - "ní m", - "s m", - "ko li", - "r ů", - "t ra", - "mů že", - "ne ní", - "ho d", - "b í", - "do u", - "sk a", - "t ý", - "st ě", - "u je", - "s á", - "pě t", - "ne s", - "k rá", - "to m", - "st ví", - "v ně", - "se d", - "s vé", - "p í", - "z o", - "mu sí", - "u ž", - "tí m", - "jí cí", - "jed no", - "t r", - "ča s", - "e v", - "č ty", - "sk ý", - "ni c", - "ev ro", - "to ho", - "h y", - "k ter", - "r ní", - "st í", - "s vě", - "pa k", - "vše ch", - "k ů", - "n g", - "á d", - "chá zí", - "a ni", - "a r", - "jed na", - "bý t", - "t ro", - "k ra", - "pr vní", - "m no", - "ské ho", - "p á", - "p la", - "le m", - "ne bo", - "ke m", - "st ro", - "s la", - "né ho", - "z de", - "dal ší", - "ř a", - "čty ři", - "h rá", - "dru h", - "l ně", - "v la", - "sk ých", - "š ko", - "pů so", - "pro to", - "v ů", - "sk á", - "ve n", - "še st", - "d ně", - "je ště", - "me zi", - "te k", - "s ko", - "ch a", - "ně koli", - "be z", - "g ra", - "ji ž", - "č ně", - "j á", - "s lu", - "z ná", - "ve r", - "sed m", - "k ro", - "ta m", - "a no", - "v lá", - "o sm", - "byl y", - "vá m", - "ck ý", - "te ch", - "dě ji", - "vel mi", - "le ži", - "va la", - "l ý", - "t vo", - "spo le", - "ch u", - "stu p", - "mo ž", - "evro p", - "g e", - "sta l", - "j de", - "ch y", - "ro di", - "je jí", - "po li", - "de vět", - "s me", - "a ž", - "té to", - "re m", - "d é", - "f or", - "u ni", - "f o", - "ten to", - "a u", - "ka ž", - "nu la", - "na d", - "by ch", - "mo c", - "sto u", - "e x", - "le n", - "k do", - "z d", - "pra co", - "to mu", - "ný m", - "ži vo", - "ze m", - "f e", - "f u", - "ná sle", - "j o", - "sk y", - "ji ch", - "h á", - "mě l", - "dě la", - "j sme", - "p re", - "ni ce", - "ste j", - "ne m", - "st ní", - "he m", - "ná ro", - "z u", - "b li", - "ni t", - "pa r", - "a l", - "poz ději", - "ta ko", - "n ce", - "če r", - "ší m", - "ně co", - "vá l", - "ře j", - "krá t", - "á lní", - "u r", - ". .", - "a si", - "kter é", - "sta v", - "ma jí", - "my s", - "do bě", - "s ně", - "ce n", - "z y", - "z ku", - "t ů", - "ch od", - "s pě", - "je jich", - "sou čas", - "d r", - "va li", - "ri e", - "k te", - "pr ů", - "ze ní", - "pa t", - "a n", - "po tře", - "de m", - "d nes", - "ze mí", - "sa mo", - "zna m", - "b ra", - "má m", - "te dy", - "g o", - "hla vní", - "pou ží", - "b ní", - "ve de", - "le p", - "je k", - "pra v", - "poli ti", - "d ne", - "je m", - "le t", - "če ní", - "pro b", - "ne ž", - "dě l", - "fi l", - "č o", - "cí ch", - "st é", - "d lou", - "h i", - "a by", - "to u", - "několi k", - "d la", - "vy u", - "vi t", - "ho u", - "ck ých", - "no vé", - "či n", - "st y", - "dě lá", - "k ý", - "ob la", - "pod le", - "ra n", - "dů leži", - "ta to", - "po ku", - "ko ne", - "d ý", - "d vě", - "ž ád", - "nou t", - "t ku", - "t vr", - "cké ho", - "ro v", - "r é", - "te le", - "p sa", - "s vět", - "ti vní", - "do sta", - "te m", - "še l", - "druh é", - "s kou", - "ž o", - "jed ná", - "vý znam", - "prob lé", - "pu bli", - "vá n", - "od po", - "pod po", - "d le", - "ja ké", - "še ní", - "ví m", - "bě hem", - "na chází", - "s lou", - "pou ze", - "o tá", - "p lo", - "to vé", - "vět ši", - "ko mi", - "va jí", - "ty to", - "zá pa", - "z mě", - "mo h", - "ví ce", - "spole č", - "au to", - "pro ti", - "st ru", - "dě t", - "chá ze", - "že l", - "с т", - "е н", - "н о", - "н а", - "п р", - "т о", - "п о", - "р а", - "г о", - "к о", - "н е", - "в о", - "в а", - "е т", - "е р", - "н и", - "е л", - "и т", - "н ы", - "з а", - "р о", - "ен и", - "к а", - "л и", - "е м", - "д а", - "о б", - "л а", - "д о", - "с я", - "т ь", - "о т", - "л о", - "л ь", - "е д", - "с о", - "м и", - "р е", - "м о", - "ц и", - "пр о", - "т а", - "э то", - "к и", - "р у", - "пр и", - "т и", - "с е", - "ст а", - "в ы", - "м ы", - "в и", - "б ы", - "м а", - "е с", - "л я", - "ст и", - "л е", - "ч то", - "м е", - "р и", - "ч а", - "о д", - "е й", - "ел ь", - "ени я", - "г а", - "н у", - "с и", - "п а", - "ра з", - "б о", - "ст о", - "с у", - "с а", - "д у", - "е го", - "е ст", - "и н", - "ит ь", - "и з", - "ж е", - "м у", - "п ер", - "по д", - "ени е", - "с ь", - "к у", - "пр ед", - "но го", - "ны х", - "в ер", - "т е", - "но й", - "ци и", - "д е", - "р ы", - "д ел", - "л ю", - "в е", - "о н", - "м ен", - "г и", - "н я", - "б у", - "пр а", - "в се", - "ет ся", - "ст ь", - "ж а", - "до л", - "ж и", - "б е", - "ко н", - "с л", - "ш и", - "д и", - "ст в", - "с ко", - "ны е", - "ч и", - "ю т", - "д ер", - "ст ра", - "т ы", - "х од", - "щ и", - "з о", - "з на", - "но сти", - "ч ес", - "в ля", - "ва ть", - "о р", - "по л", - "в ет", - "та к", - "ш а", - "т у", - "с во", - "пр е", - "о на", - "ит ель", - "ны й", - "с ло", - "ка к", - "в л", - "но сть", - "х о", - "мо ж", - "п е", - "д ля", - "ни я", - "но е", - "ра с", - "дол ж", - "да р", - "т ель", - "с ка", - "п у", - "ст во", - "ко то", - "ра б", - "е е", - "ро д", - "э ти", - "с об", - "о ру", - "ж ен", - "ны м", - "ит и", - "ни е", - "ко м", - "д ет", - "ст у", - "г у", - "п и", - "ме ж", - "ени ю", - "т ер", - "раб от", - "во з", - "ци я", - "ко й", - "щ ест", - "г ра", - "з и", - "р я", - "меж ду", - "ст ва", - "в с", - "ел о", - "ш е", - "м ер", - "б а", - "з ы", - "л у", - "а ль", - "д ей", - "г ла", - "на род", - "к ти", - "пред ста", - "л ся", - "я вля", - "с ки", - "но в", - "ед ин", - "ро в", - "и с", - "ни ма", - "р ем", - "ход и", - "так же", - "д ру", - "а ть", - "сл ед", - "го во", - "на я", - "ю щи", - "ен ь", - "кото ры", - "х от", - "в у", - "и х", - "ем у", - "ч ит", - "ва ж", - "ор га", - "чес ки", - "щ е", - "к е", - "х а", - "по с", - "то м", - "бо ль", - "м не", - "па с", - "об ъ", - "пра в", - "кон ф", - "сл у", - "под дер", - "ст ви", - "на ш", - "ль ко", - "сто я", - "ну ю", - "л ем", - "ен ных", - "к ра", - "д ы", - "между народ", - "г да", - "не об", - "го су", - "ств у", - "ени и", - "госу дар", - "к то", - "и м", - "ч ест", - "р ет", - "во про", - "л ен", - "ел и", - "ро ва", - "ци й", - "на м", - "это й", - "ж ения", - "необ ходи", - "мен я", - "бы ло", - "си ли", - "ф и", - "в я", - "ш ь", - "это го", - "о ни", - "орга ни", - "бе зо", - "пр об", - "и ме", - "ре ш", - "б и", - "безо пас", - "ют ся", - "о ста", - "ен но", - "го д", - "ел а", - "предста в", - "ть ся", - "сло во", - "органи за", - "долж ны", - "это м", - "б ла", - "ч е", - "ч у", - "бла го", - "это му", - "в рем", - "с пе", - "но м", - "ени й", - "с по", - "на с", - "не т", - "з у", - "в ед", - "е ще", - "ска за", - "се й", - "ер ен", - "да н", - "са м", - "ел я", - "ра н", - "зы ва", - "явля ется", - "бу дет", - "кти в", - "т ре", - "дел е", - "м от", - "конф ерен", - "ла сь", - "ча с", - "сто ро", - "ко го", - "е з", - "не й", - "о с", - "ли сь", - "раз ору", - "пер е", - "с си", - "ны ми", - "про ц", - "го ло", - "ч ело", - "бо ле", - "чело ве", - "с ер", - "п л", - "ч ет", - "стра н", - "п я", - "бы л", - "к ла", - "то в", - "ж д", - "дел а", - "е ра", - "у же", - "со вет", - "г ен", - "безопас ности", - "ц а", - "се да", - "по з", - "от вет", - "проб лем", - "на ко", - "т ем", - "до ста", - "п ы", - "щ а", - "во й", - "су щест", - "необходи мо", - "бы ть", - "мож ет", - "д ем", - "что бы", - "е к", - "ч ер", - "у сили", - "ре с", - "ру д", - "един енных", - "д об", - "до сти", - "ств ен", - "я дер", - "год ня", - "ка за", - "се годня", - "сей час", - "то лько", - "во д", - "ес ь", - "м ного", - "бу ду", - "е в", - "ест ь", - "т ри", - "об щест", - ". .", - "я вл", - "вы сту", - "р ед", - "с чит", - "с ит", - "деле га", - "ло ж", - "это т", - "ф ор", - "к лю", - "воз мож", - "ва ния", - "б ли", - "и ли", - "в з", - "на ций", - "ско го", - "при ня", - "п ла", - "о ч", - "ить ся", - "ст е", - "на ши", - "которы е", - "а р", - "име ет", - "с от", - "зна ч", - "пер ь", - "след у", - "ен ы", - "та ки", - "объ единенных", - "ст ро", - "те перь", - "б ле", - "благо дар", - "раз в", - "а н", - "жи ва", - "оч ень", - "я т", - "бе з", - "об ес", - "г ро", - "ло сь", - "с ы", - "организа ции", - "ч лен", - "то го", - "она ль", - "ж да", - "все х", - "с вя", - "боле е", - "со в", - "ко гда", - "во т", - "к ре", - "к ры", - "по этому", - "во ль", - "о й", - "ген ера", - "ч ем", - "л ы", - "пол ити", - "в ен", - "конферен ции", - "проц ес", - "б я", - "ит е", - "от но", - "разв ити", - "а ф", - "ю щ", - "в но", - "ми р", - "ни и", - "ка я", - "а с", - "итель но", - "в то", - "ени ем", - "генера ль", - "пр от", - "вс ем", - "сам бле", - "ас самбле", - "о м", - "з д", - "с мот", - "ре ги", - "ч его", - "од нако", - "усили я", - "дей стви", - "ч но", - "у ча", - "об раз", - "во с", - "э та", - "пер его", - "гово р", - "ва м", - "мо ло", - "врем я", - "д ь", - "хот ел", - "г ру", - "за явл", - "пре доста", - "по ль", - "не е", - "ре зо", - "перего во", - "резо лю", - "к рет", - "поддер ж", - "обес пе", - "не го", - "представ ит", - "на де", - "к ри", - "ч ь", - "про ек", - "л ет", - "дру ги", - "ا ل", - "َ ا", - "و َ", - "ّ َ", - "ِ ي", - "أ َ", - "ل َ", - "ن َ", - "ال ْ", - "ه ُ", - "ُ و", - "م ا", - "ن ْ", - "م ن", - "ع َ", - "ن ا", - "ل ا", - "م َ", - "ت َ", - "ف َ", - "أ ن", - "ل ي", - "م ِ", - "ا ن", - "ف ي", - "ر َ", - "ي َ", - "ه ِ", - "م ْ", - "ق َ", - "ب ِ", - "ل ى", - "ي ن", - "إ ِ", - "ل ِ", - "و ا", - "ك َ", - "ه ا", - "ً ا", - "م ُ", - "و ن", - "ال م", - "ب َ", - "ي ا", - "ذ ا", - "س ا", - "ال ل", - "م ي", - "ي ْ", - "ر ا", - "ر ي", - "ل ك", - "م َا", - "ن َّ", - "ل م", - "إ ن", - "س ت", - "و م", - "ّ َا", - "ل َا", - "ه م", - "ّ ِ", - "ك ُ", - "ك ان", - "س َ", - "ب ا", - "د ي", - "ح َ", - "ع ْ", - "ب ي", - "ال أ", - "و ل", - "ف ِي", - "ر ِ", - "د ا", - "مِ نْ", - "ُو نَ", - "و ْ", - "ه َا", - "ّ ُ", - "ال س", - "ال َ", - "ن ي", - "ل ْ", - "ت ُ", - "ه ل", - "ر ة", - "د َ", - "س ْ", - "ت ِ", - "ن َا", - "ر ْ", - "الل َّ", - "سا مي", - "ك ن", - "ك ل", - "ه َ", - "عَ لَ", - "ع لى", - "م ع", - "إ لى", - "ق د", - "ال ر", - "ُو ا", - "ي ر", - "ع ن", - "ي ُ", - "ن ِ", - "ب ْ", - "ال ح", - "هُ مْ", - "ق ا", - "ذ ه", - "ال ت", - "ِي نَ", - "ج َ", - "ه ذا", - "ع د", - "ال ع", - "د ْ", - "قَ الَ", - "ر ُ", - "ي م", - "ي ة", - "ن ُ", - "خ َ", - "ر ب", - "ال ك", - "و َا", - "أ نا", - "ة ِ", - "ال ن", - "ح د", - "ع ِ", - "ت ا", - "ه و", - "ف ا", - "ع ا", - "ال ش", - "ل ُ", - "ي ت", - "ذ َا", - "ي ع", - "ال ذ", - "ح ْ", - "ال ص", - "إِ نَّ", - "ج ا", - "ع لي", - "ك َا", - "ب ُ", - "ت ع", - "و ق", - "م ل", - "ل َّ", - "ي د", - "أ خ", - "ر ف", - "ت ي", - "ال ِ", - "ّ ا", - "ذ لك", - "أَ نْ", - "س ِ", - "ت وم", - "م ر", - "مَ نْ", - "ب ل", - "ال ق", - "الل ه", - "ِي َ", - "ك م", - "ذ َ", - "ع ل", - "ح ب", - "س ي", - "ع ُ", - "ال ج", - "ال د", - "ش َ", - "ت ك", - "ف ْ", - "ص َ", - "ل ل", - "د ِ", - "ب ر", - "ف ِ", - "ت ه", - "أ ع", - "ت ْ", - "ق ْ", - "الْ أَ", - "ئ ِ", - "عَ نْ", - "و ر", - "ح ا", - "ال َّ", - "م ت", - "ف ر", - "د ُ", - "ه نا", - "وَ أَ", - "ت ب", - "ة ُ", - "أ ي", - "س ب", - "ري د", - "و ج", - "كُ مْ", - "ح ِ", - "ك ْ", - "د ر", - "َا ء", - "ه ذه", - "ال ط", - "الْ مُ", - "د ة", - "ق ل", - "غ َ", - "ي وم", - "الَّ ذ", - "ك ر", - "ت ر", - "ك ِ", - "ك ي", - "عَلَ ى", - "رَ ب", - "ع ة", - "ق ُ", - "ج ْ", - "ف ض", - "ل ة", - "ه ْ", - "ر َا", - "وَ لَ", - "الْ مَ", - "أَ نَّ", - "ي َا", - "أ ُ", - "ش ي", - "اللَّ هُ", - "لَ ى", - "ق ِ", - "أ ت", - "عَلَ يْ", - "اللَّ هِ", - "ال ب", - "ض َ", - "ة ً", - "ق ي", - "ا ر", - "ب د", - "خ ْ", - "سْ تَ", - "ط َ", - "قَ دْ", - "ذه ب", - "أ م", - "ما ذا", - "وَ إِ", - "ة ٌ", - "و نَ", - "لي لى", - "و لا", - "ح ُ", - "ه ي", - "ص ل", - "ال خ", - "و د", - "لي س", - "ل دي", - "ق ال", - "كَا نَ", - "م َّ", - "ح ي", - "ت م", - "ل ن", - "وَ لَا", - "ب ع", - "يم كن", - "س ُ", - "ة َ", - "ح ت", - "ر ًا", - "ك ا", - "ش ا", - "هِ مْ", - "لَ هُ", - "ز َ", - "دا ً", - "م س", - "ك ث", - "الْ عَ", - "ج ِ", - "ص ْ", - "ف َا", - "ل ه", - "و ي", - "ع َا", - "هُ وَ", - "ب ِي", - "ب َا", - "أ س", - "ث َ", - "ل ِي", - "ر ض", - "الر َّ", - "لِ كَ", - "ت َّ", - "ف ُ", - "ق ة", - "ف عل", - "مِ ن", - "ال آ", - "ث ُ", - "س م", - "م َّا", - "بِ هِ", - "ت ق", - "خ ر", - "ل قد", - "خ ل", - "ش ر", - "أن ت", - "ل َّا", - "س ن", - "الس َّ", - "الذ ي", - "س َا", - "و ما", - "ز ل", - "و ب", - "أ ْ", - "إ ذا", - "ر ِي", - "ح ة", - "ن ِي", - "الْ حَ", - "وَ قَالَ", - "ب ه", - "ة ٍ", - "س أ", - "ر ٌ", - "ب ال", - "م ة", - "ش ْ", - "و ت", - "عن د", - "ف س", - "بَ عْ", - "ه ر", - "ق ط", - "أ ح", - "إن ه", - "و ع", - "ف ت", - "غ ا", - "هنا ك", - "ب ت", - "مِ نَ", - "س ر", - "ذَ لِكَ", - "ر س", - "حد ث", - "غ ْ", - "ّ ِي", - "ال إ", - "وَ يَ", - "ج ل", - "ا ست", - "ق ِي", - "ع ب", - "و س", - "ي ش", - "الَّذ ِينَ", - "تا ب", - "د ِي", - "ج ب", - "ك ون", - "ب ن", - "ال ث", - "لَ يْ", - "ب عد", - "وَ الْ", - "فَ أَ", - "ع م", - "هُ م", - "ت ن", - "ذ ْ", - "أ ص", - "أ ين", - "رَب ِّ", - "الذ ين", - "إِ ن", - "ب ين", - "ج ُ", - "عَلَيْ هِ", - "ح َا", - "ل و", - "ست ط", - "ظ ر", - "لَ مْ", - "ء ِ", - "كُ ل", - "ط ل", - "ت َا", - "ض ُ", - "كن ت", - "ل ًا", - "م ٌ", - "ق بل", - "ـ ـ", - "ذ ِ", - "قَ وْ", - "ص ِ", - "م ًا", - "كان ت", - "ص ا", - "ي ق", - "ال ف", - "ال نا", - "م ٍ", - "إِ نْ", - "ال نَّ", - "ج د", - "وَ مَا", - "ت ت", - "ب ح", - "م كان", - "كي ف", - "ّ ة", - "ال ا", - "ج َا", - "أ و", - "سا عد", - "ض ِ", - "إ لا", - "را ً", - "ق َا", - "ر أ", - "ع ت", - "أ حد", - "ه د", - "ض ا", - "ط ر", - "أ ق", - "ما ء", - "د َّ", - "ال با", - "م ُو", - "أَ وْ", - "ط ا", - "ق ُو", - "خ ِ", - "ت ل", - "ستط يع", - "د َا", - "الن َّا", - "إ لَى", - "وَ تَ", - "هَ ذَا", - "ب ة", - "علي ك", - "ج ر", - "ال من", - "ز ا", - "ر ٍ", - "د ع", - "ّ ًا", - "س ة", - "ثُ مَّ", - "شي ء", - "ال غ", - "ت ح", - "ر ُونَ", - "ال يوم", - "م ِي", - "ن ُوا", - "أ ر", - "تُ مْ", - "ع ر", - "ي ف", - "أ ب", - "د ًا", - "ص َا", - "الت َّ", - "أ ريد", - "ال ز", - "يَ وْ", - "إ لي", - "ج ي", - "يَ عْ", - "فض ل", - "ال إن", - "أن ه", - "n g", - "i 4", - "a n", - "s h", - "z h", - "i 2", - "ng 1", - "u 4", - "i 1", - "ng 2", - "d e", - "j i", - "a o", - "x i", - "u 3", - "de 5", - "e 4", - "i 3", - "ng 4", - "an 4", - "e n", - "u o", - "sh i4", - "an 2", - "u 2", - "c h", - "u 1", - "ng 3", - "a 1", - "an 1", - "e 2", - "a 4", - "e i4", - "o ng1", - "a i4", - "ao 4", - "h u", - "a ng1", - "l i", - "y o", - "an 3", - "w ei4", - "uo 2", - "n 1", - "en 2", - "ao 3", - "e 1", - "y u", - "q i", - "e ng2", - "zh o", - "a ng3", - "a ng4", - "a ng2", - "uo 4", - "m i", - "g e4", - "y i1", - "g uo2", - "e r", - "b i", - "a 3", - "h e2", - "e 3", - "y i2", - "d i4", - "zh ong1", - "b u4", - "g u", - "a i2", - "n 2", - "z ai4", - "sh i2", - "e ng1", - "r en2", - "o ng2", - "xi an4", - "y i", - "x u", - "n 4", - "l i4", - "en 4", - "y u2", - "e i2", - "yi2 ge4", - "o u4", - "e i3", - "d i", - "u i4", - "a 2", - "yo u3", - "ao 1", - "d a4", - "ch eng2", - "en 1", - "e ng4", - "y i4", - "s i1", - "zh i4", - "ji a1", - "yu an2", - "n i", - "t a1", - "de5 yi2ge4", - "k e1", - "sh u3", - "x i1", - "j i2", - "ao 2", - "t i", - "o u3", - "o ng4", - "xi a4", - "a i1", - "g ong1", - "zh i1", - "en 3", - "w ei2", - "j u", - "xu e2", - "q u1", - "zho u1", - "er 3", - "mi ng2", - "zho ng3", - "l i3", - "w u4", - "y i3", - "uo 1", - "e 5", - "j i4", - "xi ng2", - "ji an4", - "hu a4", - "y u3", - "uo 3", - "j i1", - "a i3", - "z uo4", - "h ou4", - "hu i4", - "e i1", - "ni an2", - "q i2", - "p i", - "d ao4", - "sh eng1", - "de 2", - "d ai4", - "u an2", - "zh e4", - "zh eng4", - "b en3", - "sh ang4", - "zh u3", - "b ei4", - "y e4", - "ch u1", - "zh an4", - "l e5", - "l ai2", - "sh i3", - "n an2", - "r en4", - "yo u2", - "k e4", - "b a1", - "f u4", - "d ui4", - "y a4", - "m ei3", - "z i4", - "xi n1", - "ji ng1", - "zh u", - "n 3", - "yo ng4", - "m u4", - "ji ao4", - "y e3", - "ji n4", - "bi an4", - "l u4", - "q i1", - "sh e4", - "xi ang1", - "o ng3", - "sh u4", - "d ong4", - "s uo3", - "gu an1", - "s an1", - "b o", - "t e4", - "d uo1", - "f u2", - "mi n2", - "l a1", - "zh i2", - "zh en4", - "o u1", - "w u3", - "m a3", - "i 5", - "z i5", - "j u4", - "er 4", - "y ao4", - "xia4 de5yi2ge4", - "s i4", - "t u2", - "sh an1", - "z ui4", - "ch u", - "yi n1", - "er 2", - "t ong2", - "d ong1", - "y u4", - "y an2", - "qi an2", - "shu3 xia4de5yi2ge4", - "ju n1", - "k e3", - "w en2", - "f a3", - "l uo2", - "zh u4", - "x i4", - "k ou3", - "b ei3", - "ji an1", - "f a1", - "di an4", - "ji ang1", - "wei4 yu2", - "xi ang4", - "zh i3", - "e ng3", - "f ang1", - "l an2", - "sh u", - "r i4", - "li an2", - "sh ou3", - "m o", - "qi u2", - "ji n1", - "h uo4", - "shu3xia4de5yi2ge4 zhong3", - "f en1", - "n ei4", - "g ai1", - "mei3 guo2", - "u n2", - "g e2", - "b ao3", - "qi ng1", - "g ao1", - "t ai2", - "d u", - "xi ao3", - "ji e2", - "ti an1", - "ch ang2", - "q uan2", - "li e4", - "h ai3", - "f ei1", - "t i3", - "ju e2", - "o u2", - "c i3", - "z u2", - "n i2", - "bi ao3", - "zhong1 guo2", - "d u4", - "yu e4", - "xi ng4", - "sh eng4", - "ch e1", - "d an1", - "ji e1", - "li n2", - "pi ng2", - "f u3", - "g u3", - "ji e4", - "w o", - "v 3", - "sh eng3", - "n a4", - "yu an4", - "zh ang3", - "gu an3", - "d ao3", - "z u3", - "di ng4", - "di an3", - "c eng2", - "ren2 kou3", - "t ai4", - "t ong1", - "g uo4", - "n eng2", - "ch ang3", - "hu a2", - "li u2", - "yi ng1", - "xi ao4", - "c i4", - "bian4 hua4", - "li ang3", - "g ong4", - "zho ng4", - "de5 yi1", - "s e4", - "k ai1", - "w ang2", - "ji u4", - "sh i1", - "sh ou4", - "m ei2", - "k u", - "s u", - "f eng1", - "z e2", - "tu2 shi4", - "t i2", - "q i4", - "ji u3", - "sh en1", - "zh e3", - "ren2kou3 bian4hua4", - "ren2kou3bian4hua4 tu2shi4", - "di4 qu1", - "y ang2", - "m en", - "men 5", - "l ong2", - "bi ng4", - "ch an3", - "zh u1", - "w ei3", - "w ai4", - "xi ng1", - "bo 1", - "b i3", - "t ang2", - "hu a1", - "bo 2", - "shu i3", - "sh u1", - "d ou1", - "s ai4", - "ch ao2", - "b i4", - "li ng2", - "l ei4", - "da4 xue2", - "f en4", - "shu3 de5", - "m u3", - "ji ao1", - "d ang1", - "ch eng1", - "t ong3", - "n v3", - "q i3", - "y an3", - "mi an4", - "l uo4", - "ji ng4", - "g e1", - "r u4", - "d an4", - "ri4 ben3", - "p u3", - "yu n4", - "hu ang2", - "wo 3", - "l v", - "h ai2", - "shi4 yi1", - "xi e1", - "yi ng3", - "w u2", - "sh en2", - "w ang3", - "gu ang3", - "li u4", - "s u4", - "shi4 zhen4", - "c an1", - "c ao3", - "xi a2", - "k a3", - "d a2", - "h u4", - "b an4", - "d ang3", - "h u2", - "z ong3", - "de ng3", - "de5yi2ge4 shi4zhen4", - "ch uan2", - "mo 4", - "zh ang1", - "b an1", - "mo 2", - "ch a2", - "c e4", - "zhu3 yao4", - "t ou2", - "j u2", - "shi4 wei4yu2", - "s a4", - "u n1", - "ke3 yi3", - "d u1", - "h an4", - "li ang4", - "sh a1", - "ji a3", - "z i1", - "lv 4", - "f u1", - "xi an1", - "x u4", - "gu ang1", - "m eng2", - "b ao4", - "yo u4", - "r ong2", - "zhi1 yi1", - "w ei1", - "m ao2", - "guo2 jia1", - "c ong2", - "g ou4", - "ti e3", - "zh en1", - "d u2", - "bi an1", - "c i2", - "q u3", - "f an4", - "xi ang3", - "m en2", - "j u1", - "h ong2", - "z i3", - "ta1 men5", - "ji 3", - "z ong1", - "zhou1 de5yi2ge4shi4zhen4", - "t uan2", - "ji ng3", - "gong1 si1", - "xi e4", - "l i2", - "li4 shi3", - "b ao1", - "g ang3", - "gu i1", - "zh eng1", - "zhi2 wu4", - "ta1 de5", - "pi n3", - "zhu an1", - "ch ong2", - "shi3 yong4", - "w a3", - "sh uo1", - "chu an1", - "l ei2", - "w an1", - "h uo2", - "q u", - "s u1", - "z ao3", - "g ai3", - "q u4", - "g u4", - "l u", - "x i2", - "h ang2", - "yi ng4", - "c un1", - "g en1", - "yi ng2", - "ti ng2", - "cheng2 shi4", - "ji ang3", - "li ng3", - "l un2", - "bu4 fen4", - "de ng1", - "xu an3", - "dong4 wu4", - "de2 guo2", - "xi an3", - "f an3", - "zh e5", - "h an2", - "h ao4", - "m i4", - "r an2", - "qi n1", - "ti ao2", - "zh an3", - "h i", - "k a", - "n o", - "t e", - "s u", - "s hi", - "t a", - "t o", - "n a", - "w a", - "o u", - "r u", - "n i", - "k u", - "k i", - "g a", - "d e", - "k o", - "m a", - "r e", - "r a", - "m o", - "t su", - "w o", - "e n", - "r i", - "s a", - "d a", - "s e", - "j i", - "h a", - "c hi", - "k e", - "te ki", - "m i", - "y ou", - "s h", - "s o", - "y o", - "y a", - "na i", - "t te", - "a ru", - "b a", - "u u", - "t ta", - "ka i", - "ka n", - "shi te", - "m e", - "d o", - "mo no", - "se i", - "r o", - "ko to", - "ka ra", - "shi ta", - "b u", - "m u", - "c h", - "su ru", - "k ou", - "g o", - "ma su", - "ta i", - "f u", - "k en", - "i u", - "g en", - "wa re", - "shi n", - "z u", - "a i", - "o n", - "o ku", - "g i", - "d ou", - "n e", - "y uu", - "i ru", - "i te", - "ji ko", - "de su", - "j u", - "ra re", - "sh u", - "b e", - "sh ou", - "s ha", - "se kai", - "s ou", - "k you", - "ma shita", - "s en", - "na ra", - "sa n", - "ke i", - "i ta", - "a ri", - "i tsu", - "ko no", - "j ou", - "na ka", - "ch ou", - "so re", - "g u", - "na ru", - "ga ku", - "re ba", - "g e", - "h o", - "i n", - "hi to", - "sa i", - "na n", - "da i", - "tsu ku", - "shi ki", - "sa re", - "na ku", - "p p", - "bu n", - "ju n", - "so no", - "ka ku", - "z ai", - "b i", - "to u", - "wa ta", - "sh uu", - "i i", - "te i", - "ka re", - "y u", - "shi i", - "ma de", - "sh o", - "a n", - "ke reba", - "shi ka", - "i chi", - "ha n", - "de ki", - "ni n", - "ware ware", - "na kereba", - "o ite", - "h ou", - "ya ku", - "ra i", - "mu jun", - "l e", - "yo ku", - "bu tsu", - "o o", - "ko n", - "o mo", - "ga e", - "nara nai", - "ta chi", - "z en", - "ch uu", - "kan gae", - "ta ra", - "to ki", - "ko ro", - "mujun teki", - "z e", - "na ga", - "ji n", - "shi ma", - "te n", - "i ki", - "i ku", - "no u", - "i masu", - "r ou", - "h on", - "ka e", - "t to", - "ko re", - "ta n", - "ki ta", - "i s", - "da tta", - "ji tsu", - "ma e", - "i e", - "me i", - "da n", - "h e", - "to ku", - "dou itsu", - "ri tsu", - "k yuu", - "h you", - "rare ta", - "kei sei", - "k kan", - "rare ru", - "m ou", - "do ko", - "r you", - "da ke", - "naka tta", - "so ko", - "ta be", - "e r", - "ha na", - "c o", - "fu ku", - "p a", - "so n", - "ya su", - "ch o", - "wata ku", - "ya ma", - "z a", - "k yo", - "gen zai", - "b oku", - "a ta", - "j a", - "ka wa", - "ma sen", - "j uu", - "ro n", - "b o", - "na tte", - "wataku shi", - "yo tte", - "ma i", - "g ou", - "ha i", - "mo n", - "ba n", - "ji shin", - "c a", - "re te", - "n en", - "o ka", - "ka gaku", - "na tta", - "p o", - "ka ru", - "na ri", - "m en", - "ma ta", - "e i", - "ku ru", - "ga i", - "ka ri", - "sha kai", - "kou i", - "yo ri", - "se tsu", - "j o", - "re ru", - "to koro", - "ju tsu", - "i on", - "sa ku", - "tta i", - "c ha", - "nin gen", - "n u", - "c e", - "ta me", - "kan kyou", - "de n", - "o oku", - "i ma", - "wata shi", - "tsuku ru", - "su gi", - "b en", - "ji bun", - "shi tsu", - "ke ru", - "ki n", - "ki shi", - "shika shi", - "mo to", - "ma ri", - "i tte", - "de shita", - "n de", - "ari masu", - "te r", - "z ou", - "ko e", - "ze ttai", - "kkan teki", - "h en", - "re kishi", - "deki ru", - "tsu ka", - "l a", - "i tta", - "o i", - "ko butsu", - "mi ru", - "sh oku", - "shi masu", - "gi jutsu", - "g you", - "jou shiki", - "a tta", - "ho do", - "ko ko", - "tsuku rareta", - "z oku", - "hi tei", - "ko ku", - "rekishi teki", - "ke te", - "o ri", - "i mi", - "ka ko", - "naga ra", - "ka karu", - "shu tai", - "ha ji", - "ma n", - "ta ku", - "ra n", - "douitsu teki", - "z o", - "me te", - "re i", - "tsu u", - "sare te", - "gen jitsu", - "p e", - "s t", - "ba i", - "na wa", - "ji kan", - "wa ru", - "r t", - "a tsu", - "so ku", - "koui teki", - "a ra", - "u ma", - "a no", - "i de", - "ka ta", - "te tsu", - "ga wa", - "ke do", - "re ta", - "mi n", - "sa you", - "tte ru", - "to ri", - "p u", - "ki mi", - "b ou", - "mu ra", - "sare ru", - "ma chi", - "k ya", - "o sa", - "kon na", - "a ku", - "a l", - "sare ta", - "i pp", - "shi ku", - "u chi", - "hito tsu", - "ha tara", - "tachi ba", - "shi ro", - "ka tachi", - "to mo", - "e te", - "me ru", - "ni chi", - "da re", - "ka tta", - "e ru", - "su ki", - "a ge", - "oo ki", - "ma ru", - "mo ku", - "o ko", - "kangae rareru", - "o to", - "tan ni", - "ta da", - "tai teki", - "mo tte", - "ki nou", - "shi nai", - "k ki", - "u e", - "ta ri", - "l i", - "ra nai", - "k kou", - "mi rai", - "pp on", - "go to", - "hi n", - "hi tsu", - "te ru", - "mo chi", - "ka tsu", - "re n", - "n yuu", - "su i", - "zu ka", - "tsu ite", - "no mi", - "su gu", - "ku da", - "tetsu gaku", - "i ka", - "ron ri", - "o ki", - "ni ppon", - "p er", - "shi mashita", - "chi shiki", - "cho kkanteki", - "su ko", - "t ion", - "ku u", - "a na", - "a rou", - "ka tte", - "ku ri", - "i nai", - "hyou gen", - "i shiki", - "do ku", - "a tte", - "a tara", - "to n", - "wa ri", - "ka o", - "sei san", - "hana shi", - "s i", - "ka ke", - "na ji", - "su nawa", - "sunawa chi", - "u go", - "su u", - "ba ra", - "le v", - "hi ro", - "i wa", - "be tsu", - "yo i", - "se ru", - "shite ru", - "rare te", - "to shi", - "se ki", - "tai ritsu", - "wa kara", - "to kyo", - "k ka", - "k yoku", - "u n", - "i ro", - "mi te", - "sa ki", - "kan ji", - "mi ta", - "su be", - "r yoku", - "ma tta", - "kuda sai", - "omo i", - "ta no", - "ware ru", - "co m", - "hitsu you", - "ka shi", - "re nai", - "kan kei", - "a to", - "ga tte", - "o chi", - "mo tsu", - "in g", - "son zai", - "l l", - "o re", - "tai shite", - "a me", - "sei mei", - "ka no", - "gi ri", - "kangae ru", - "yu e", - "a sa", - "o naji", - "yo ru", - "ni ku", - "osa ka", - "suko shi", - "c k", - "ta ma", - "kano jo", - "ki te", - "mon dai", - "a mari", - "e ki", - "ko jin", - "ha ya", - "i t", - "de te", - "atara shii", - "a wa", - "ga kkou", - "tsu zu", - "shu kan", - "i mashita", - "mi na", - "ata e", - "da rou", - "hatara ku", - "ga ta", - "da chi", - "ma tsu", - "ari masen", - "sei butsu", - "mi tsu", - "he ya", - "yasu i", - "d i", - "de ni", - "no ko", - "ha ha", - "do mo", - "ka mi", - "su deni", - "na o", - "ra ku", - "i ke", - "a ki", - "me ta", - "l o", - "ko domo", - "so shite", - "ga me", - "ba kari", - "to te", - "ha tsu", - "mi se", - "moku teki", - "da kara", - "s z", - "e l", - "g y", - "e n", - "t t", - "e m", - "a n", - "a k", - "e r", - "a z", - "a l", - "e t", - "o l", - "e g", - "e k", - "m i", - "o n", - "é s", - "c s", - "a t", - "á r", - "h o", - "e z", - "á l", - "i s", - "á n", - "o r", - "a r", - "e gy", - "e s", - "é r", - "á t", - "o tt", - "e tt", - "m eg", - "t a", - "o k", - "o s", - "ho gy", - "n em", - "é g", - "n y", - "k i", - "é l", - "h a", - "á s", - "ü l", - "i n", - "mi n", - "n a", - "e d", - "o m", - "i k", - "k ö", - "m a", - "n i", - "v a", - "v ol", - "é t", - "b b", - "f el", - "i g", - "l e", - "r a", - "é n", - "t e", - "d e", - "a d", - "ó l", - "b e", - "on d", - "j a", - "r e", - "u l", - "b en", - "n ek", - "u t", - "vol t", - "b an", - "ö r", - "o g", - "a p", - "o d", - "á g", - "n k", - "é k", - "v al", - "k or", - "a m", - "i l", - "í t", - "á k", - "b a", - "u d", - "sz er", - "min d", - "o z", - "é p", - "el l", - "ér t", - "m ond", - "i t", - "sz t", - "n ak", - "a mi", - "n e", - "ő l", - "cs ak", - "n é", - "ma g", - "ol y", - "m er", - "ál l", - "án y", - "ö n", - "ö l", - "min t", - "m ár", - "ö tt", - "na gy", - "é sz", - "az t", - "el ő", - "t ud", - "o t", - "é ny", - "á z", - "m ég", - "kö z", - "el y", - "s ég", - "en t", - "s em", - "ta m", - "h et", - "h al", - "f i", - "a s", - "v an", - "ho z", - "v e", - "u k", - "k ez", - "á m", - "v el", - "b er", - "a j", - "u nk", - "i z", - "va gy", - "m os", - "sz em", - "em ber", - "f og", - "mer t", - "ü k", - "l en", - "ö s", - "e j", - "t al", - "h at", - "t ak", - "h i", - "m ás", - "s ág", - "ett e", - "l eg", - "ü nk", - "h át", - "sz a", - "on y", - "ez t", - "mind en", - "en d", - "ül t", - "h an", - "j ó", - "k is", - "á j", - "in t", - "ú gy", - "i d", - "mos t", - "ar t", - "í r", - "k er", - "i tt", - "a tt", - "el t", - "mond ta", - "k ell", - "l á", - "ak i", - "ál t", - "ér d", - "t ö", - "l an", - "v ár", - "h ol", - "t el", - "l át", - "ő k", - "v et", - "s e", - "ut án", - "k ét", - "na p", - "í v", - "ál y", - "v ég", - "ö k", - "i r", - "d ul", - "v is", - "né z", - "t er", - "á ban", - "k ül", - "ak kor", - "k ap", - "sz él", - "y en", - "ú j", - "i m", - "oly an", - "es en", - "k ed", - "h ely", - "t ör", - "b ól", - "el m", - "r á", - "ár a", - "r ó", - "l ó", - "vol na", - "t an", - "le het", - "e bb", - "t en", - "t ek", - "s ok", - "k al", - "f or", - "u g", - "ol t", - "k a", - "ek et", - "b or", - "f ej", - "g ond", - "a g", - "ak ar", - "f él", - "ú l", - "b el", - "ott a", - "mi t", - "val ami", - "j el", - "é d", - "ar c", - "u r", - "hal l", - "t i", - "f öl", - "á ba", - "ol g", - "ki r", - "ol d", - "m ar", - "k érd", - "j ár", - "ú r", - "sz e", - "z s", - "él et", - "j át", - "o v", - "u s", - "é z", - "v il", - "v er", - "ő r", - "á d", - "ö g", - "le sz", - "on t", - "b iz", - "k oz", - "á bb", - "kir ály", - "es t", - "a b", - "en g", - "ig az", - "b ar", - "ha j", - "d i", - "o b", - "k od", - "r ól", - "v ez", - "tö bb", - "sz ó", - "é ben", - "ö t", - "ny i", - "t á", - "sz ól", - "gond ol", - "eg ész", - "í gy", - "ő s", - "o bb", - "os an", - "b ől", - "a bb", - "c i", - "ő t", - "n ál", - "k ép", - "azt án", - "v i", - "t art", - "be szél", - "m en", - "elő tt", - "a szt", - "ma j", - "kö r", - "han g", - "í z", - "in cs", - "a i", - "é v", - "ó d", - "ó k", - "hoz z", - "t em", - "ok at", - "an y", - "nagy on", - "h áz", - "p er", - "p ed", - "ez te", - "et len", - "nek i", - "maj d", - "sz ony", - "án ak", - "fel é", - "egy szer", - "j e", - "ad t", - "gy er", - "ami kor", - "f oly", - "sz ak", - "ő d", - "h ú", - "á sz", - "am ely", - "h ar", - "ér e", - "il yen", - "od a", - "j ák", - "t ár", - "á val", - "l ak", - "t ó", - "m ent", - "gy an", - "él y", - "ú t", - "v ar", - "kez d", - "m ell", - "mi kor", - "h ez", - "val ó", - "k o", - "m es", - "szer et", - "r end", - "l et", - "vis sza", - "ig en", - "f ő", - "va s", - "as szony", - "r ől", - "ped ig", - "p i", - "sz ép", - "t ák", - "ö v", - "an i", - "vil ág", - "p en", - "mag a", - "t et", - "sz ik", - "é j", - "én t", - "j ött", - "s an", - "sz í", - "i de", - "g at", - "ett em", - "ul t", - "h ány", - "ás t", - "a hol", - "ők et", - "h ár", - "k el", - "n ő", - "cs i", - "tal ál", - "el te", - "lá tt", - "tör t", - "ha gy", - "e sz", - "s en", - "n él", - "p ar", - "v ál", - "k ut", - "l ány", - "ami t", - "s ő", - "ell en", - "mag át", - "in k", - "u gyan", - "kül ön", - "a sz", - "mind ig", - "l ép", - "tal án", - "u n", - "sz or", - "k e", - "il lan", - "n incs", - "z et", - "vagy ok", - "tel en", - "is mer", - "s or", - "is ten", - "ít ott", - "j obb", - "v es", - "dul t", - "j uk", - "sz en", - "r o", - "ö m", - "l ett", - "k ar", - "egy ik", - "b ár", - "sz i", - "sz ív", - "az on", - "e szt", - "föl d", - "kut y", - "p illan", - "f ér", - "k om", - "t ől", - "t ű", - "é be", - "t ött", - "bar át", - "í g", - "a hogy", - "e h", - "e p", - "s o", - "v en", - "jel ent", - "t at", - "sz eg", - "mint ha", - "f al", - "egy en", - "mi l", - "sza b", - "r i", - "é m", - "biz ony", - "j on", - "ör eg", - "d olg", - "cs ap", - "ti szt", - "áll t", - "an cs", - "id ő", - "k at", - "ü gy", - "mi ért", - "ó t", - "ü r", - "cs in", - "h az", - "b et", - "én ek", - "v ér", - "j ól", - "al att", - "m ely", - "l o", - "sem mi", - "ny ug", - "v ág", - "kö vet", - "ös sze", - "ma d", - "l i", - "a cs", - "fi ú", - "kö n", - "más ik", - "j ön", - "sz ám", - "g er", - "s ó", - "r ész", - "k ér", - "z el", - "é vel", - "e o", - "e u", - "a n", - "eu l", - "eu n", - "eo n", - "a e", - "d a", - "a l", - "s s", - "i n", - "i l", - "a g", - "an g", - "y eon", - "y eo", - "d o", - "c h", - "n g", - "j i", - "h an", - "g a", - "g o", - "u i", - "h ae", - "a m", - "u l", - "u n", - "g eo", - "s i", - "n eun", - "ss da", - "s eo", - "eon g", - "y o", - "i da", - "t t", - "k k", - "j eo", - "d eul", - "w a", - "eu m", - "g e", - "o n", - "o g", - "s al", - "m an", - "yeon g", - "geo s", - "h ag", - "an eun", - "j a", - "g i", - "s u", - "i ss", - "o l", - "d ae", - "eo b", - "h a", - "j u", - "eo l", - "g eu", - "j eong", - "s ae", - "do e", - "g eul", - "s eu", - "s in", - "eul o", - "b n", - "s ang", - "bn ida", - "h al", - "b o", - "han eun", - "m al", - "i m", - "m o", - "b u", - "jeo g", - "sae ng", - "in eun", - "an h", - "m a", - "sal am", - "j o", - "s a", - "eo m", - "n ae", - "w i", - "l o", - "g wa", - "yeo l", - "n a", - "e seo", - "y e", - "m yeon", - "tt ae", - "h w", - "j e", - "eob s", - "j ang", - "g u", - "g w", - "il eul", - "yeo g", - "j eon", - "si g", - "j ag", - "j in", - "y u", - "o e", - "s e", - "hag o", - "d eun", - "y a", - "m un", - "s eong", - "g ag", - "h am", - "d ang", - "b a", - "l eul", - "s il", - "do ng", - "kk a", - "b al", - "da l", - "han da", - "eo ssda", - "ae g", - "l i", - "ha ji", - "s eon", - "o ng", - "hae ssda", - "d e", - "i ssda", - "e ge", - "b un", - "m ul", - "ju ng", - "ji g", - "m u", - "iss neun", - "b i", - "g eun", - "seu bnida", - "w on", - "p p", - "d aneun", - "eo h", - "d eo", - "ga m", - "j al", - "hae ng", - "ag o", - "y ang", - "b ul", - "b ang", - "u m", - "s o", - "h i", - "j ae", - "si m", - "saeng gag", - "hag e", - "s og", - "eo ss", - "d an", - "ja sin", - "j il", - "eo g", - "g yeong", - "doe n", - "go ng", - "m i", - "ch i", - "d eu", - "d eon", - "hae ss", - "d u", - "n am", - "eun g", - "jo h", - "n al", - "m yeong", - "w o", - "eon a", - "i go", - "g yeol", - "y ag", - "gw an", - "ul i", - "yo ng", - "n o", - "l yeo", - "j og", - "eoh ge", - "ga t", - "b og", - "mo s", - "t ong", - "ch a", - "man h", - "jeo l", - "geo l", - "h oe", - "ag a", - "n aneun", - "g an", - "un eun", - "ch eol", - "ch e", - "do l", - "b on", - "b an", - "ba d", - "ch u", - "ham yeon", - "yeo ssda", - "i bnida", - "g ye", - "eo s", - "hw al", - "salam deul", - "ji man", - "dang sin", - "ji b", - "ttae mun", - "m ae", - "i b", - "e neun", - "eu g", - "jeo m", - "geul eon", - "h wa", - "a ssda", - "b eob", - "bu t", - "b ae", - "yeo ss", - "ch in", - "ch aeg", - "g eon", - "g ae", - "nae ga", - "i ga", - "m og", - "sig an", - "g il", - "h yeon", - "l yeog", - "gu g", - "p yeon", - "s an", - "w ae", - "j ul", - "s eul", - "deun g", - "haji man", - "eum yeon", - "p il", - "m ol", - "n eu", - "a ss", - "n yeon", - "t ae", - "h u", - "p yo", - "s ul", - "g ang", - "j ineun", - "b eon", - "ha da", - "seo l", - "si p", - "dal eun", - "a p", - "sal m", - "g yo", - "ch eon", - "hag i", - "in a", - "cheol eom", - "g al", - "il a", - "kka ji", - "anh neun", - "ha bnida", - "tt eon", - "n u", - "hae seo", - "doen da", - "s ol", - "tt al", - "l a", - "il o", - "seu b", - "b yeon", - "m yeo", - "b eol", - "s on", - "n un", - "j un", - "j am", - "j eung", - "tt o", - "e n", - "mo m", - "h o", - "ch im", - "hw ang", - "eun eun", - "jo ng", - "bo da", - "n ol", - "n eom", - "but eo", - "jig eum", - "eobs da", - "dae lo", - "i g", - "y ul", - "p yeong", - "seon eun", - "sal ang", - "seu t", - "h im", - "n an", - "h eom", - "h yang", - "p i", - "gw ang", - "eobs neun", - "hw ag", - "ge ss", - "jag i", - "il eon", - "wi hae", - "dae han", - "ga ji", - "m eog", - "j yeo", - "cha j", - "b yeong", - "eo d", - "g yeo", - "do n", - "eo ji", - "g ul", - "mo deun", - "j on", - "in saeng", - "geul ae", - "h ang", - "sa sil", - "si b", - "ch al", - "il ago", - "doe l", - "g eum", - "doe neun", - "b ol", - "ga jang", - "geul igo", - "e l", - "h yeong", - "haeng bog", - "ch ul", - "h on", - "ch ae", - "s am", - "m ang", - "in da", - "da m", - "w ol", - "ch oe", - "d ul", - "si jag", - "ch eong", - "il aneun", - "ul ineun", - "ae n", - "kk e", - "mun je", - "a do", - "t eu", - "g un", - "geun eun", - "b ge", - "ch eo", - "b aeg", - "ju g", - "t a", - "sang dae", - "geu geos", - "do g", - "eu s", - "deu s", - "ja b", - "h yeo", - "tt eohge", - "u g", - "ma j", - "ch il", - "s wi", - "j ileul", - "ch ang", - "g aneun", - "m ag", - "i ji", - "da go", - "m in", - "yo han", - "t eug", - "pp un", - "al eul", - "haeng dong", - "p o", - "m il", - "ch am", - "se sang", - "e do", - "p an", - "man deul", - "am yeon", - "a b", - "kk ae", - "b ag", - "i deul", - "p um", - "m eol", - "s un", - "n eul", - "ham kke", - "chu ng", - "da b", - "yu g", - "s ag", - "gwang ye", - "il eohge", - "bal o", - "neun de", - "ham yeo", - "go s", - "geul eoh", - "an ila", - "bang beob", - "da si", - "b yeol", - "g yeon", - "gam jeong", - "on eul", - "j aneun", - "yeo m", - "l ago", - "i gi", - "hw an", - "t eul", - "eo seo", - "si k", - "ch o", - "jag a", - "geul eom", - "geul eona", - "jeong do", - "g yeog", - "geul eohge", - "geu deul", - "eu t", - "im yeon", - "j jae", - "k eun", - "i sang", - "mal haessda", - "eu ge", - "no p", - "in gan", - "bo myeon", - "t aeg", - "seu s", - "d wi", - "s aneun", - "w an", - "anh go", - "t an", - "nu gu", - "su ng", - "da myeon", - "a deul", - "p eul", - "ttal a", - "d i", - "geos do", - "a ji", - "m eon", - "eum yeo", - "dol og", - "neun g", - "mo du", - "क े", - "ह ै", - "े ं", - "् र", - "ा र", - "न े", - "य ा", - "म ें", - "स े", - "क ी", - "क ा", - "ो ं", - "त ा", - "क र", - "स ्", - "क ि", - "क ो", - "र ्", - "न ा", - "क ्", - "ह ी", - "औ र", - "प र", - "त े", - "ह ो", - "प ्र", - "ा न", - "् य", - "ल ा", - "व ा", - "ल े", - "स ा", - "है ं", - "ल ि", - "ज ा", - "ह ा", - "भ ी", - "व ि", - "इ स", - "त ी", - "न ्", - "र ा", - "म ा", - "द े", - "द ि", - "ब ा", - "त ि", - "थ ा", - "न ि", - "क ार", - "ए क", - "ही ं", - "ह ु", - "ं ग", - "ै ं", - "न ी", - "स ी", - "अ प", - "त ्", - "न हीं", - "र ी", - "म े", - "म ु", - "ि त", - "त ो", - "प ा", - "ल ी", - "लि ए", - "ग ा", - "ल ्", - "र ह", - "र े", - "क् ष", - "म ैं", - "स म", - "उ स", - "ज ि", - "त ्र", - "म ि", - "च ा", - "ो ग", - "स ं", - "द ्", - "स ि", - "आ प", - "त ु", - "द ा", - "क ु", - "य ों", - "व े", - "ज ी", - "् या", - "उ न", - "ि क", - "य े", - "भ ा", - "् ट", - "ह म", - "स् ट", - "श ा", - "ड ़", - "ं द", - "ख ा", - "म ्", - "श ्", - "य ह", - "स क", - "प ू", - "कि या", - "अप ने", - "र ू", - "स ु", - "म ी", - "ह ि", - "ज ो", - "थ े", - "र ि", - "द ी", - "थ ी", - "ग ी", - "ल ोग", - "ग या", - "त र", - "न् ह", - "च ्", - "व ार", - "ब ी", - "प ्", - "द ो", - "ट ी", - "श ि", - "कर ने", - "ग े", - "ै से", - "इ न", - "ं ड", - "सा थ", - "प ु", - "ब े", - "ब ार", - "व ी", - "अ न", - "ह र", - "उ न्ह", - "हो ता", - "ज ब", - "कु छ", - "म ान", - "क ्र", - "ब ि", - "प ह", - "फ ि", - "स र", - "ार ी", - "र ो", - "द ू", - "क हा", - "त क", - "श न", - "ब ्", - "स् थ", - "व ह", - "बा द", - "ओ ं", - "ग ु", - "ज ्", - "्र े", - "ग र", - "रह े", - "व र्", - "ह ू", - "ार ्", - "प ी", - "ब हु", - "मु झ", - "्र ा", - "दि या", - "स ब", - "कर ते", - "अप नी", - "बहु त", - "क ह", - "ट े", - "हु ए", - "कि सी", - "र हा", - "ष ्ट", - "ज ़", - "ब ना", - "स ो", - "ड ि", - "को ई", - "व ्य", - "बा त", - "र ु", - "व ो", - "मुझ े", - "द् ध", - "च ार", - "मे रे", - "व र", - "्र ी", - "जा ता", - "न ों", - "प्र ा", - "दे ख", - "ट ा", - "क् या", - "अ ध", - "ल ग", - "ल ो", - "प ि", - "य ु", - "च े", - "जि स", - "ं त", - "ान ी", - "प ै", - "ज न", - "ार े", - "च ी", - "मि ल", - "द ु", - "दे श", - "च् छ", - "ष ्", - "स ू", - "ख े", - "च ु", - "ि या", - "ल गा", - "ब ु", - "उन के", - "ज् ञ", - "क्ष ा", - "त रह", - "्या दा", - "वा ले", - "पू र्", - "मैं ने", - "का म", - "रू प", - "हो ती", - "उ प", - "ज ान", - "प्र कार", - "भ ार", - "म न", - "हु आ", - "ट र", - "हू ँ", - "पर ि", - "पा स", - "अन ु", - "रा ज", - "लोग ों", - "अ ब", - "सम झ", - "ड ी", - "म ौ", - "श ु", - "च ि", - "प े", - "क ृ", - "सक ते", - "म ह", - "य ोग", - "द र्", - "उ से", - "ं ध", - "ड ा", - "जा ए", - "ब ो", - "ू ल", - "म ो", - "ों ने", - "ं स", - "तु म", - "पह ले", - "ब ता", - "त था", - "य ो", - "ग ई", - "उ त्", - "सक ता", - "क म", - "ज ्यादा", - "र ख", - "सम य", - "ार ा", - "अ गर", - "स् त", - "च ल", - "फि र", - "वार ा", - "कर ना", - "श ी", - "ग ए", - "ब न", - "ौ र", - "हो ने", - "चा ह", - "ख ु", - "हा ँ", - "उन्ह ें", - "उन्ह ोंने", - "छ ो", - "म् ह", - "प्र ति", - "नि क", - "व न", - "्य ू", - "र ही", - "तु म्ह", - "ज ैसे", - "ि यों", - "क् यों", - "ल ों", - "फ ़", - "ं त्र", - "हो ते", - "क् ति", - "त ्य", - "कर ्", - "क ई", - "व ं", - "कि न", - "प ो", - "कार ण", - "ड़ ी", - "भ ि", - "इस के", - "ब र", - "उस के", - "द् वारा", - "श े", - "क ॉ", - "दि न", - "न् न", - "ड़ ा", - "स् व", - "नि र्", - "मु ख", - "लि या", - "ट ि", - "ज्ञ ान", - "क् त", - "द ्र", - "ग ्", - "क् स", - "म ै", - "ग ो", - "ज े", - "ट ्र", - "म ार", - "त् व", - "ध ार", - "भा व", - "कर ता", - "ख ि", - "क ं", - "चा हि", - "य र", - "प् त", - "क ों", - "ं च", - "ज ु", - "म त", - "अ च्छ", - "हु ई", - "क भी", - "ले किन", - "भ ू", - "अप ना", - "दू स", - "चाहि ए", - "य ू", - "घ र", - "सब से", - "मे री", - "ना म", - "ढ ़", - "ं ट", - "ें गे", - "ब ै", - "फ ा", - "ए वं", - "य ी", - "ग ्र", - "क्ष े", - "आ ज", - "आप को", - "भा ग", - "ठ ा", - "क ै", - "भार त", - "उन की", - "प हु", - "स भी", - "ध ा", - "ण ा", - "स ान", - "हो गा", - "त ब", - "स ंग", - "प र्", - "अ व", - "त ना", - "ग ि", - "य न", - "स् था", - "च ित", - "ट ्", - "छ ा", - "जा ने", - "क्षे त्र", - "वा ली", - "पूर् ण", - "स मा", - "कार ी" - ] - } -} \ No newline at end of file diff --git a/comfy/text_encoders/ace_text_cleaners.py b/comfy/text_encoders/ace_text_cleaners.py deleted file mode 100644 index cd31d8d8cdffcda51cc26405c3f6f2bcee410d48..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/ace_text_cleaners.py +++ /dev/null @@ -1,395 +0,0 @@ -# basic text cleaners for the ACE step model -# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies -# TODO: more languages than english? - -import re - -def japanese_to_romaji(japanese_text): - """ - Convert Japanese hiragana and katakana to romaji (Latin alphabet representation). - - Args: - japanese_text (str): Text containing hiragana and/or katakana characters - - Returns: - str: The romaji (Latin alphabet) equivalent - """ - # Dictionary mapping kana characters to their romaji equivalents - kana_map = { - # Katakana characters - 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o', - 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko', - 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so', - 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to', - 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no', - 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho', - 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo', - 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo', - 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro', - 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n', - - # Katakana voiced consonants - 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go', - 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo', - 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do', - 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo', - 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po', - - # Katakana combinations - 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo', - 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho', - 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho', - 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo', - 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo', - 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo', - 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo', - 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo', - 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo', - 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo', - 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo', - - # Katakana small characters and special cases - 'ッ': '', # Small tsu (doubles the following consonant) - 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo', - - # Katakana extras - 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo', - 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo', - - # Hiragana characters - 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o', - 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko', - 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so', - 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to', - 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no', - 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho', - 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo', - 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo', - 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro', - 'わ': 'wa', 'を': 'wo', 'ん': 'n', - - # Hiragana voiced consonants - 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go', - 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo', - 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do', - 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo', - 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po', - - # Hiragana combinations - 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo', - 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho', - 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho', - 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo', - 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo', - 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo', - 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo', - 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo', - 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo', - 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo', - 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo', - - # Hiragana small characters and special cases - 'っ': '', # Small tsu (doubles the following consonant) - 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo', - - # Common punctuation and spaces - '　': ' ', # Japanese space - '、': ', ', '。': '. ', - } - - result = [] - i = 0 - - while i < len(japanese_text): - # Check for small tsu (doubling the following consonant) - if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'): - if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map: - next_romaji = kana_map[japanese_text[i+1]] - if next_romaji and next_romaji[0] not in 'aiueon': - result.append(next_romaji[0]) # Double the consonant - i += 1 - continue - - # Check for combinations with small ya, yu, yo - if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'): - combo = japanese_text[i:i+2] - if combo in kana_map: - result.append(kana_map[combo]) - i += 2 - continue - - # Regular character - if japanese_text[i] in kana_map: - result.append(kana_map[japanese_text[i]]) - else: - # If it's not in our map, keep it as is (might be kanji, romaji, etc.) - result.append(japanese_text[i]) - - i += 1 - - return ''.join(result) - -def number_to_text(num, ordinal=False): - """ - Convert a number (int or float) to its text representation. - - Args: - num: The number to convert - - Returns: - str: Text representation of the number - """ - - if not isinstance(num, (int, float)): - return "Input must be a number" - - # Handle special case of zero - if num == 0: - return "zero" - - # Handle negative numbers - negative = num < 0 - num = abs(num) - - # Handle floats - if isinstance(num, float): - # Split into integer and decimal parts - int_part = int(num) - - # Convert both parts - int_text = _int_to_text(int_part) - - # Handle decimal part (convert to string and remove '0.') - decimal_str = str(num).split('.')[1] - decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str) - - result = int_text + decimal_text - else: - # Handle integers - result = _int_to_text(num) - - # Add 'negative' prefix for negative numbers - if negative: - result = "negative " + result - - return result - - -def _int_to_text(num): - """Helper function to convert an integer to text""" - - ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", - "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", - "seventeen", "eighteen", "nineteen"] - - tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] - - if num < 20: - return ones[num] - - if num < 100: - return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "") - - if num < 1000: - return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "") - - if num < 1000000: - return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "") - - if num < 1000000000: - return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "") - - return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "") - - -def _digit_to_text(digit): - """Convert a single digit to text""" - digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] - return digits[digit] - - -_whitespace_re = re.compile(r"\s+") - - -# List of (regular expression, replacement) pairs for abbreviations: -_abbreviations = { - "en": [ - (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) - for x in [ - ("mrs", "misess"), - ("mr", "mister"), - ("dr", "doctor"), - ("st", "saint"), - ("co", "company"), - ("jr", "junior"), - ("maj", "major"), - ("gen", "general"), - ("drs", "doctors"), - ("rev", "reverend"), - ("lt", "lieutenant"), - ("hon", "honorable"), - ("sgt", "sergeant"), - ("capt", "captain"), - ("esq", "esquire"), - ("ltd", "limited"), - ("col", "colonel"), - ("ft", "fort"), - ] - ], -} - - -def expand_abbreviations_multilingual(text, lang="en"): - for regex, replacement in _abbreviations[lang]: - text = re.sub(regex, replacement, text) - return text - - -_symbols_multilingual = { - "en": [ - (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) - for x in [ - ("&", " and "), - ("@", " at "), - ("%", " percent "), - ("#", " hash "), - ("$", " dollar "), - ("£", " pound "), - ("°", " degree "), - ] - ], -} - - -def expand_symbols_multilingual(text, lang="en"): - for regex, replacement in _symbols_multilingual[lang]: - text = re.sub(regex, replacement, text) - text = text.replace(" ", " ") # Ensure there are no double spaces - return text.strip() - - -_ordinal_re = { - "en": re.compile(r"([0-9]+)(st|nd|rd|th)"), -} -_number_re = re.compile(r"[0-9]+") -_currency_re = { - "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"), - "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"), - "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"), -} - -_comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b") -_dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b") -_decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)") - - -def _remove_commas(m): - text = m.group(0) - if "," in text: - text = text.replace(",", "") - return text - - -def _remove_dots(m): - text = m.group(0) - if "." in text: - text = text.replace(".", "") - return text - - -def _expand_decimal_point(m, lang="en"): - amount = m.group(1).replace(",", ".") - return number_to_text(float(amount)) - - -def _expand_currency(m, lang="en", currency="USD"): - amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", ".")))) - full_amount = number_to_text(amount) - - and_equivalents = { - "en": ", ", - "es": " con ", - "fr": " et ", - "de": " und ", - "pt": " e ", - "it": " e ", - "pl": ", ", - "cs": ", ", - "ru": ", ", - "nl": ", ", - "ar": ", ", - "tr": ", ", - "hu": ", ", - "ko": ", ", - } - - if amount.is_integer(): - last_and = full_amount.rfind(and_equivalents[lang]) - if last_and != -1: - full_amount = full_amount[:last_and] - - return full_amount - - -def _expand_ordinal(m, lang="en"): - return number_to_text(int(m.group(1)), ordinal=True) - - -def _expand_number(m, lang="en"): - return number_to_text(int(m.group(0))) - - -def expand_numbers_multilingual(text, lang="en"): - if lang in ["en", "ru"]: - text = re.sub(_comma_number_re, _remove_commas, text) - else: - text = re.sub(_dot_number_re, _remove_dots, text) - try: - text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text) - text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text) - text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text) - except: - pass - - text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text) - text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text) - text = re.sub(_number_re, lambda m: _expand_number(m, lang), text) - return text - - -def lowercase(text): - return text.lower() - - -def collapse_whitespace(text): - return re.sub(_whitespace_re, " ", text) - - -def multilingual_cleaners(text, lang): - text = text.replace('"', "") - if lang == "tr": - text = text.replace("İ", "i") - text = text.replace("Ö", "ö") - text = text.replace("Ü", "ü") - text = lowercase(text) - try: - text = expand_numbers_multilingual(text, lang) - except: - pass - try: - text = expand_abbreviations_multilingual(text, lang) - except: - pass - try: - text = expand_symbols_multilingual(text, lang=lang) - except: - pass - text = collapse_whitespace(text) - return text - - -def basic_cleaners(text): - """Basic pipeline that lowercases and collapses whitespace without transliteration.""" - text = lowercase(text) - text = collapse_whitespace(text) - return text diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py deleted file mode 100644 index cf4252eea3ad6ed02e0a8ee4c13385c78909cbd8..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/aura_t5.py +++ /dev/null @@ -1,22 +0,0 @@ -from comfy import sd1_clip -from .spiece_tokenizer import SPieceTokenizer -import comfy.text_encoders.t5 -import os - -class PT5XlModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json") - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) - -class PT5XlTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1, tokenizer_data=tokenizer_data) - -class AuraT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer) - -class AuraT5Model(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): - super().__init__(device=device, dtype=dtype, model_options=model_options, name="pile_t5xl", clip_model=PT5XlModel, **kwargs) diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py deleted file mode 100644 index ed4638a9ab4882ac9f72033e3a6f07537682d807..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/bert.py +++ /dev/null @@ -1,143 +0,0 @@ -import torch -from comfy.ldm.modules.attention import optimized_attention_for_device -import comfy.ops - -class BertAttention(torch.nn.Module): - def __init__(self, embed_dim, heads, dtype, device, operations): - super().__init__() - - self.heads = heads - self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) - - - def forward(self, x, mask=None, optimized_attention=None): - q = self.query(x) - k = self.key(x) - v = self.value(x) - - out = optimized_attention(q, k, v, self.heads, mask) - return out - -class BertOutput(torch.nn.Module): - def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): - super().__init__() - self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) - self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device) - # self.dropout = nn.Dropout(0.0) - - def forward(self, x, y): - x = self.dense(x) - # hidden_states = self.dropout(hidden_states) - x = self.LayerNorm(x + y) - return x - -class BertAttentionBlock(torch.nn.Module): - def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): - super().__init__() - self.self = BertAttention(embed_dim, heads, dtype, device, operations) - self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) - - def forward(self, x, mask, optimized_attention): - y = self.self(x, mask, optimized_attention) - return self.output(y, x) - -class BertIntermediate(torch.nn.Module): - def __init__(self, embed_dim, intermediate_dim, dtype, device, operations): - super().__init__() - self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device) - - def forward(self, x): - x = self.dense(x) - return torch.nn.functional.gelu(x) - - -class BertBlock(torch.nn.Module): - def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): - super().__init__() - self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations) - self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations) - self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations) - - def forward(self, x, mask, optimized_attention): - x = self.attention(x, mask, optimized_attention) - y = self.intermediate(x) - return self.output(y, x) - -class BertEncoder(torch.nn.Module): - def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations): - super().__init__() - self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)]) - - def forward(self, x, mask=None, intermediate_output=None): - optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) - - if intermediate_output is not None: - if intermediate_output < 0: - intermediate_output = len(self.layer) + intermediate_output - - intermediate = None - for i, l in enumerate(self.layer): - x = l(x, mask, optimized_attention) - if i == intermediate_output: - intermediate = x.clone() - return x, intermediate - -class BertEmbeddings(torch.nn.Module): - def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations): - super().__init__() - self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device) - self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device) - self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device) - - self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device) - - def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None): - if embeds is not None: - x = embeds - else: - x = self.word_embeddings(input_tokens, out_dtype=dtype) - x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x) - if token_type_ids is not None: - x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype) - else: - x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x) - x = self.LayerNorm(x) - return x - - -class BertModel_(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - embed_dim = config_dict["hidden_size"] - layer_norm_eps = config_dict["layer_norm_eps"] - - self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations) - self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations) - - def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): - x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype) - mask = None - if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) - - x, i = self.encoder(x, mask, intermediate_output) - return x, i - - -class BertModel(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - self.bert = BertModel_(config_dict, dtype, device, operations) - self.num_layers = config_dict["num_hidden_layers"] - - def get_input_embeddings(self): - return self.bert.embeddings.word_embeddings - - def set_input_embeddings(self, embeddings): - self.bert.embeddings.word_embeddings = embeddings - - def forward(self, *args, **kwargs): - return self.bert(*args, **kwargs) diff --git a/comfy/text_encoders/cosmos.py b/comfy/text_encoders/cosmos.py deleted file mode 100644 index a1adb5242bc9d625bbb32b9d55ca426a0f61e419..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/cosmos.py +++ /dev/null @@ -1,42 +0,0 @@ -from comfy import sd1_clip -import comfy.text_encoders.t5 -import os -from transformers import T5TokenizerFast - - -class T5XXLModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json") - t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) - if t5xxl_scaled_fp8 is not None: - model_options = model_options.copy() - model_options["scaled_fp8"] = t5xxl_scaled_fp8 - - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options) - -class CosmosT5XXL(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) - - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data) - - -class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) - - -def te(dtype_t5=None, t5xxl_scaled_fp8=None): - class CosmosTEModel_(CosmosT5XXL): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - if dtype is None: - dtype = dtype_t5 - super().__init__(device=device, dtype=dtype, model_options=model_options) - return CosmosTEModel_ diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py deleted file mode 100644 index d61ef66689b32e52d15e962d41793a489aa44eab..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/flux.py +++ /dev/null @@ -1,70 +0,0 @@ -from comfy import sd1_clip -import comfy.text_encoders.t5 -import comfy.text_encoders.sd3_clip -import comfy.model_management -from transformers import T5TokenizerFast -import torch -import os - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) - - -class FluxTokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return self.clip_l.untokenize(token_weight_pair) - - def state_dict(self): - return {} - - -class FluxClipModel(torch.nn.Module): - def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): - super().__init__() - dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) - self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) - self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options) - self.dtypes = set([dtype, dtype_t5]) - - def set_clip_options(self, options): - self.clip_l.set_clip_options(options) - self.t5xxl.set_clip_options(options) - - def reset_clip_options(self): - self.clip_l.reset_clip_options() - self.t5xxl.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs_l = token_weight_pairs["l"] - token_weight_pairs_t5 = token_weight_pairs["t5xxl"] - - t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) - l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - return t5_out, l_pooled - - def load_sd(self, sd): - if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: - return self.clip_l.load_sd(sd) - else: - return self.t5xxl.load_sd(sd) - -def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None): - class FluxClipModel_(FluxClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options) - return FluxClipModel_ diff --git a/comfy/text_encoders/genmo.py b/comfy/text_encoders/genmo.py deleted file mode 100644 index 9dcf190a232550c9e678e819c88092a5bc518058..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/genmo.py +++ /dev/null @@ -1,38 +0,0 @@ -from comfy import sd1_clip -import comfy.text_encoders.sd3_clip -import os -from transformers import T5TokenizerFast - - -class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): - def __init__(self, **kwargs): - kwargs["attention_mask"] = True - super().__init__(**kwargs) - - -class MochiT5XXL(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) - - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) - - -class MochiT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) - - -def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None): - class MochiTEModel_(MochiT5XXL): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - if dtype is None: - dtype = dtype_t5 - super().__init__(device=device, dtype=dtype, model_options=model_options) - return MochiTEModel_ diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py deleted file mode 100644 index dbcf52784d63b6fc07c289d1e4e27a9fd7376b4c..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hidream.py +++ /dev/null @@ -1,155 +0,0 @@ -from . import hunyuan_video -from . import sd3_clip -from comfy import sd1_clip -from comfy import sdxl_clip -import comfy.model_management -import torch -import logging - - -class HiDreamTokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data) - self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) - t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) - out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens - out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return self.clip_g.untokenize(token_weight_pair) - - def state_dict(self): - return {} - - -class HiDreamTEModel(torch.nn.Module): - def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, device="cpu", dtype=None, model_options={}): - super().__init__() - self.dtypes = set() - if clip_l: - self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=True, model_options=model_options) - self.dtypes.add(dtype) - else: - self.clip_l = None - - if clip_g: - self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) - self.dtypes.add(dtype) - else: - self.clip_g = None - - if t5: - dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) - self.t5xxl = sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=True) - self.dtypes.add(dtype_t5) - else: - self.t5xxl = None - - if llama: - dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) - if "vocab_size" not in model_options: - model_options["vocab_size"] = 128256 - self.llama = hunyuan_video.LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None, special_tokens={"start": 128000, "pad": 128009}) - self.dtypes.add(dtype_llama) - else: - self.llama = None - - logging.debug("Created HiDream text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}, llama {}:{}".format(clip_l, clip_g, t5, dtype_t5, llama, dtype_llama)) - - def set_clip_options(self, options): - if self.clip_l is not None: - self.clip_l.set_clip_options(options) - if self.clip_g is not None: - self.clip_g.set_clip_options(options) - if self.t5xxl is not None: - self.t5xxl.set_clip_options(options) - if self.llama is not None: - self.llama.set_clip_options(options) - - def reset_clip_options(self): - if self.clip_l is not None: - self.clip_l.reset_clip_options() - if self.clip_g is not None: - self.clip_g.reset_clip_options() - if self.t5xxl is not None: - self.t5xxl.reset_clip_options() - if self.llama is not None: - self.llama.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs_l = token_weight_pairs["l"] - token_weight_pairs_g = token_weight_pairs["g"] - token_weight_pairs_t5 = token_weight_pairs["t5xxl"] - token_weight_pairs_llama = token_weight_pairs["llama"] - lg_out = None - pooled = None - extra = {} - - if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: - if self.clip_l is not None: - lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - else: - l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) - - if self.clip_g is not None: - g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) - else: - g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) - - pooled = torch.cat((l_pooled, g_pooled), dim=-1) - - if self.t5xxl is not None: - t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) - t5_out, t5_pooled = t5_output[:2] - else: - t5_out = None - - if self.llama is not None: - ll_output = self.llama.encode_token_weights(token_weight_pairs_llama) - ll_out, ll_pooled = ll_output[:2] - ll_out = ll_out[:, 1:] - else: - ll_out = None - - if t5_out is None: - t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device()) - - if ll_out is None: - ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device()) - - if pooled is None: - pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) - - extra["conditioning_llama3"] = ll_out - return t5_out, pooled, extra - - def load_sd(self, sd): - if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: - return self.clip_g.load_sd(sd) - elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: - return self.clip_l.load_sd(sd) - elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: - return self.t5xxl.load_sd(sd) - else: - return self.llama.load_sd(sd) - - -def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None): - class HiDreamTEModel_(HiDreamTEModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["llama_scaled_fp8"] = llama_scaled_fp8 - super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) - return HiDreamTEModel_ diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py deleted file mode 100644 index b02148b3346db8463b471d83542a59071f7d15e4..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hunyuan_video.py +++ /dev/null @@ -1,159 +0,0 @@ -from comfy import sd1_clip -import comfy.model_management -import comfy.text_encoders.llama -from transformers import LlamaTokenizerFast -import torch -import os -import numbers - - -def llama_detect(state_dict, prefix=""): - out = {} - t5_key = "{}model.norm.weight".format(prefix) - if t5_key in state_dict: - out["dtype_llama"] = state_dict[t5_key].dtype - - scaled_fp8_key = "{}scaled_fp8".format(prefix) - if scaled_fp8_key in state_dict: - out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype - - return out - - -class LLAMA3Tokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256, pad_token=128258): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "llama_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=pad_token, min_length=min_length, tokenizer_data=tokenizer_data) - -class LLAMAModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}): - llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None) - if llama_scaled_fp8 is not None: - model_options = model_options.copy() - model_options["scaled_fp8"] = llama_scaled_fp8 - - textmodel_json_config = {} - vocab_size = model_options.get("vocab_size", None) - if vocab_size is not None: - textmodel_json_config["vocab_size"] = vocab_size - - model_options = {**model_options, "model_name": "llama"} - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) - - -class HunyuanVideoTokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens - self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1, tokenizer_data=tokenizer_data) - - def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs): - out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) - - if llama_template is None: - llama_text = self.llama_template.format(text) - else: - llama_text = llama_template.format(text) - llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs) - embed_count = 0 - for r in llama_text_tokens: - for i in range(len(r)): - if r[i][0] == 128257: - if image_embeds is not None and embed_count < image_embeds.shape[0]: - r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:] - embed_count += 1 - out["llama"] = llama_text_tokens - return out - - def untokenize(self, token_weight_pair): - return self.clip_l.untokenize(token_weight_pair) - - def state_dict(self): - return {} - - -class HunyuanVideoClipModel(torch.nn.Module): - def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}): - super().__init__() - dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) - self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) - self.llama = LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options) - self.dtypes = set([dtype, dtype_llama]) - - def set_clip_options(self, options): - self.clip_l.set_clip_options(options) - self.llama.set_clip_options(options) - - def reset_clip_options(self): - self.clip_l.reset_clip_options() - self.llama.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs_l = token_weight_pairs["l"] - token_weight_pairs_llama = token_weight_pairs["llama"] - - llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama) - - template_end = 0 - extra_template_end = 0 - extra_sizes = 0 - user_end = 9999999999999 - images = [] - - tok_pairs = token_weight_pairs_llama[0] - for i, v in enumerate(tok_pairs): - elem = v[0] - if not torch.is_tensor(elem): - if isinstance(elem, numbers.Integral): - if elem == 128006: - if tok_pairs[i + 1][0] == 882: - if tok_pairs[i + 2][0] == 128007: - template_end = i + 2 - user_end = -1 - if elem == 128009 and user_end == -1: - user_end = i + 1 - else: - if elem.get("original_type") == "image": - elem_size = elem.get("data").shape[0] - if template_end > 0: - if user_end == -1: - extra_template_end += elem_size - 1 - else: - image_start = i + extra_sizes - image_end = i + elem_size + extra_sizes - images.append((image_start, image_end, elem.get("image_interleave", 1))) - extra_sizes += elem_size - 1 - - if llama_out.shape[1] > (template_end + 2): - if tok_pairs[template_end + 1][0] == 271: - template_end += 2 - llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] - llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] - if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]): - llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements - - if len(images) > 0: - out = [] - for i in images: - out.append(llama_out[:, i[0]: i[1]: i[2]]) - llama_output = torch.cat(out + [llama_output], dim=1) - - l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - return llama_output, l_pooled, llama_extra_out - - def load_sd(self, sd): - if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: - return self.clip_l.load_sd(sd) - else: - return self.llama.load_sd(sd) - - -def hunyuan_video_clip(dtype_llama=None, llama_scaled_fp8=None): - class HunyuanVideoClipModel_(HunyuanVideoClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["llama_scaled_fp8"] = llama_scaled_fp8 - super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) - return HunyuanVideoClipModel_ diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py deleted file mode 100644 index ac6994529acd8493e6ec65a975f8984fd06749e8..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hydit.py +++ /dev/null @@ -1,81 +0,0 @@ -from comfy import sd1_clip -from transformers import BertTokenizer -from .spiece_tokenizer import SPieceTokenizer -from .bert import BertModel -import comfy.text_encoders.t5 -import os -import torch - -class HyditBertModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json") - model_options = {**model_options, "model_name": "hydit_clip"} - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) - -class HyditBertTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77, tokenizer_data=tokenizer_data) - - -class MT5XLModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json") - model_options = {**model_options, "model_name": "mt5xl"} - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) - -class MT5XLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model") - tokenizer = tokenizer_data.get("spiece_model", None) - super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) - - def state_dict(self): - return {"spiece_model": self.tokenizer.serialize_model()} - -class HyditTokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None) - self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory) - self.mt5xl = MT5XLTokenizer(tokenizer_data={**tokenizer_data, "spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs) - out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return self.hydit_clip.untokenize(token_weight_pair) - - def state_dict(self): - return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]} - -class HyditModel(torch.nn.Module): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__() - self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options) - self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options) - - self.dtypes = set() - if dtype is not None: - self.dtypes.add(dtype) - - def encode_token_weights(self, token_weight_pairs): - hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"]) - mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"]) - return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]} - - def load_sd(self, sd): - if "bert.encoder.layer.0.attention.self.query.weight" in sd: - return self.hydit_clip.load_sd(sd) - else: - return self.mt5xl.load_sd(sd) - - def set_clip_options(self, options): - self.hydit_clip.set_clip_options(options) - self.mt5xl.set_clip_options(options) - - def reset_clip_options(self): - self.hydit_clip.reset_clip_options() - self.mt5xl.reset_clip_options() diff --git a/comfy/text_encoders/hydit_clip.json b/comfy/text_encoders/hydit_clip.json deleted file mode 100644 index c41c7c1ff376407f42e3ff20ab26faaa98bb5e65..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hydit_clip.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "bos_token_id": 0, - "classifier_dropout": null, - "directionality": "bidi", - "eos_token_id": 2, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 1024, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 16, - "num_hidden_layers": 24, - "output_past": true, - "pad_token_id": 0, - "pooler_fc_size": 768, - "pooler_num_attention_heads": 12, - "pooler_num_fc_layers": 3, - "pooler_size_per_head": 128, - "pooler_type": "first_token_transform", - "position_embedding_type": "absolute", - "torch_dtype": "float32", - "transformers_version": "4.22.1", - "type_vocab_size": 2, - "use_cache": true, - "vocab_size": 47020 -} - diff --git a/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json deleted file mode 100644 index a8b3208c2884c4efb86e49300fdd3dc877220cdf..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "cls_token": "[CLS]", - "mask_token": "[MASK]", - "pad_token": "[PAD]", - "sep_token": "[SEP]", - "unk_token": "[UNK]" -} diff --git a/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json deleted file mode 100644 index a14356073e11a885074a7cdbddc749463cefd911..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "cls_token": "[CLS]", - "do_basic_tokenize": true, - "do_lower_case": true, - "mask_token": "[MASK]", - "name_or_path": "hfl/chinese-roberta-wwm-ext", - "never_split": null, - "pad_token": "[PAD]", - "sep_token": "[SEP]", - "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json", - "strip_accents": null, - "tokenize_chinese_chars": true, - "tokenizer_class": "BertTokenizer", - "unk_token": "[UNK]", - "model_max_length": 77 -} diff --git a/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt deleted file mode 100644 index 6246906805d02aca01714c71e4c8d77b69a7a131..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt +++ /dev/null @@ -1,47020 +0,0 @@ -[PAD] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] -[unused25] -[unused26] -[unused27] -[unused28] -[unused29] -[unused30] -[unused31] -[unused32] -[unused33] -[unused34] -[unused35] -[unused36] -[unused37] -[unused38] -[unused39] -[unused40] -[unused41] -[unused42] -[unused43] -[unused44] -[unused45] -[unused46] -[unused47] -[unused48] -[unused49] -[unused50] -[unused51] -[unused52] -[unused53] -[unused54] -[unused55] -[unused56] -[unused57] -[unused58] -[unused59] -[unused60] -[unused61] -[unused62] -[unused63] -[unused64] -[unused65] -[unused66] -[unused67] -[unused68] -[unused69] -[unused70] -[unused71] -[unused72] -[unused73] -[unused74] -[unused75] -[unused76] -[unused77] -[unused78] -[unused79] -[unused80] -[unused81] -[unused82] -[unused83] -[unused84] -[unused85] -[unused86] -[unused87] -[unused88] -[unused89] -[unused90] -[unused91] -[unused92] -[unused93] -[unused94] -[unused95] -[unused96] -[unused97] -[unused98] -[unused99] -[UNK] -[CLS] -[SEP] -[MASK] - 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-import comfy.model_management -from . import qwen_vl - -@dataclass -class Llama2Config: - vocab_size: int = 128320 - hidden_size: int = 4096 - intermediate_size: int = 14336 - num_hidden_layers: int = 32 - num_attention_heads: int = 32 - num_key_value_heads: int = 8 - max_position_embeddings: int = 8192 - rms_norm_eps: float = 1e-5 - rope_theta: float = 500000.0 - transformer_type: str = "llama" - head_dim = 128 - rms_norm_add = False - mlp_activation = "silu" - qkv_bias = False - rope_dims = None - -@dataclass -class Qwen25_3BConfig: - vocab_size: int = 151936 - hidden_size: int = 2048 - intermediate_size: int = 11008 - num_hidden_layers: int = 36 - num_attention_heads: int = 16 - num_key_value_heads: int = 2 - max_position_embeddings: int = 128000 - rms_norm_eps: float = 1e-6 - rope_theta: float = 1000000.0 - transformer_type: str = "llama" - head_dim = 128 - rms_norm_add = False - mlp_activation = "silu" - qkv_bias = True - rope_dims = None - -@dataclass -class Qwen25_7BVLI_Config: - vocab_size: int = 152064 - hidden_size: int = 3584 - intermediate_size: int = 18944 - num_hidden_layers: int = 28 - num_attention_heads: int = 28 - num_key_value_heads: int = 4 - max_position_embeddings: int = 128000 - rms_norm_eps: float = 1e-6 - rope_theta: float = 1000000.0 - transformer_type: str = "llama" - head_dim = 128 - rms_norm_add = False - mlp_activation = "silu" - qkv_bias = True - rope_dims = [16, 24, 24] - -@dataclass -class Gemma2_2B_Config: - vocab_size: int = 256000 - hidden_size: int = 2304 - intermediate_size: int = 9216 - num_hidden_layers: int = 26 - num_attention_heads: int = 8 - num_key_value_heads: int = 4 - max_position_embeddings: int = 8192 - rms_norm_eps: float = 1e-6 - rope_theta: float = 10000.0 - transformer_type: str = "gemma2" - head_dim = 256 - rms_norm_add = True - mlp_activation = "gelu_pytorch_tanh" - qkv_bias = False - rope_dims = None - -class RMSNorm(nn.Module): - def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None): - super().__init__() - self.eps = eps - self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) - self.add = add - - def forward(self, x: torch.Tensor): - w = self.weight - if self.add: - w = w + 1.0 - - return comfy.ldm.common_dit.rms_norm(x, w, self.eps) - - - -def rotate_half(x): - """Rotates half the hidden dims of the input.""" - x1 = x[..., : x.shape[-1] // 2] - x2 = x[..., x.shape[-1] // 2 :] - return torch.cat((-x2, x1), dim=-1) - - -def precompute_freqs_cis(head_dim, position_ids, theta, rope_dims=None, device=None): - theta_numerator = torch.arange(0, head_dim, 2, device=device).float() - inv_freq = 1.0 / (theta ** (theta_numerator / head_dim)) - - inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) - position_ids_expanded = position_ids[:, None, :].float() - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() - sin = emb.sin() - if rope_dims is not None and position_ids.shape[0] > 1: - mrope_section = rope_dims * 2 - cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) - sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) - else: - cos = cos.unsqueeze(1) - sin = sin.unsqueeze(1) - - return (cos, sin) - - -def apply_rope(xq, xk, freqs_cis): - cos = freqs_cis[0] - sin = freqs_cis[1] - q_embed = (xq * cos) + (rotate_half(xq) * sin) - k_embed = (xk * cos) + (rotate_half(xk) * sin) - return q_embed, k_embed - - -class Attention(nn.Module): - def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): - super().__init__() - self.num_heads = config.num_attention_heads - self.num_kv_heads = config.num_key_value_heads - self.hidden_size = config.hidden_size - - self.head_dim = config.head_dim - self.inner_size = self.num_heads * self.head_dim - - ops = ops or nn - self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype) - self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) - self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) - self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - freqs_cis: Optional[torch.Tensor] = None, - optimized_attention=None, - ): - batch_size, seq_length, _ = hidden_states.shape - xq = self.q_proj(hidden_states) - xk = self.k_proj(hidden_states) - xv = self.v_proj(hidden_states) - - xq = xq.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) - xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) - xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) - - xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis) - - xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) - xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) - - output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True) - return self.o_proj(output) - -class MLP(nn.Module): - def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): - super().__init__() - ops = ops or nn - self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) - self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) - self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype) - if config.mlp_activation == "silu": - self.activation = torch.nn.functional.silu - elif config.mlp_activation == "gelu_pytorch_tanh": - self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh") - - def forward(self, x): - return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x)) - -class TransformerBlock(nn.Module): - def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): - super().__init__() - self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) - self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) - self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) - self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) - - def forward( - self, - x: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - freqs_cis: Optional[torch.Tensor] = None, - optimized_attention=None, - ): - # Self Attention - residual = x - x = self.input_layernorm(x) - x = self.self_attn( - hidden_states=x, - attention_mask=attention_mask, - freqs_cis=freqs_cis, - optimized_attention=optimized_attention, - ) - x = residual + x - - # MLP - residual = x - x = self.post_attention_layernorm(x) - x = self.mlp(x) - x = residual + x - - return x - -class TransformerBlockGemma2(nn.Module): - def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): - super().__init__() - self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) - self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) - self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) - self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) - self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) - self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) - - def forward( - self, - x: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - freqs_cis: Optional[torch.Tensor] = None, - optimized_attention=None, - ): - # Self Attention - residual = x - x = self.input_layernorm(x) - x = self.self_attn( - hidden_states=x, - attention_mask=attention_mask, - freqs_cis=freqs_cis, - optimized_attention=optimized_attention, - ) - - x = self.post_attention_layernorm(x) - x = residual + x - - # MLP - residual = x - x = self.pre_feedforward_layernorm(x) - x = self.mlp(x) - x = self.post_feedforward_layernorm(x) - x = residual + x - - return x - -class Llama2_(nn.Module): - def __init__(self, config, device=None, dtype=None, ops=None): - super().__init__() - self.config = config - self.vocab_size = config.vocab_size - - self.embed_tokens = ops.Embedding( - config.vocab_size, - config.hidden_size, - device=device, - dtype=dtype - ) - if self.config.transformer_type == "gemma2": - transformer = TransformerBlockGemma2 - self.normalize_in = True - else: - transformer = TransformerBlock - self.normalize_in = False - - self.layers = nn.ModuleList([ - transformer(config, device=device, dtype=dtype, ops=ops) - for _ in range(config.num_hidden_layers) - ]) - self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) - # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) - - def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]): - if embeds is not None: - x = embeds - else: - x = self.embed_tokens(x, out_dtype=dtype) - - if self.normalize_in: - x *= self.config.hidden_size ** 0.5 - - if position_ids is None: - position_ids = torch.arange(0, x.shape[1], device=x.device).unsqueeze(0) - - freqs_cis = precompute_freqs_cis(self.config.head_dim, - position_ids, - self.config.rope_theta, - self.config.rope_dims, - device=x.device) - - mask = None - if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) - - causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) - if mask is not None: - mask += causal_mask - else: - mask = causal_mask - optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) - - intermediate = None - all_intermediate = None - if intermediate_output is not None: - if intermediate_output == "all": - all_intermediate = [] - intermediate_output = None - elif intermediate_output < 0: - intermediate_output = len(self.layers) + intermediate_output - - for i, layer in enumerate(self.layers): - if all_intermediate is not None: - all_intermediate.append(x.unsqueeze(1).clone()) - x = layer( - x=x, - attention_mask=mask, - freqs_cis=freqs_cis, - optimized_attention=optimized_attention, - ) - if i == intermediate_output: - intermediate = x.clone() - - x = self.norm(x) - if all_intermediate is not None: - all_intermediate.append(x.unsqueeze(1).clone()) - - if all_intermediate is not None: - intermediate = torch.cat(all_intermediate, dim=1) - - if intermediate is not None and final_layer_norm_intermediate: - intermediate = self.norm(intermediate) - - return x, intermediate - -class BaseLlama: - def get_input_embeddings(self): - return self.model.embed_tokens - - def set_input_embeddings(self, embeddings): - self.model.embed_tokens = embeddings - - def forward(self, input_ids, *args, **kwargs): - return self.model(input_ids, *args, **kwargs) - - -class Llama2(BaseLlama, torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - config = Llama2Config(**config_dict) - self.num_layers = config.num_hidden_layers - - self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) - self.dtype = dtype - -class Qwen25_3B(BaseLlama, torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - config = Qwen25_3BConfig(**config_dict) - self.num_layers = config.num_hidden_layers - - self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) - self.dtype = dtype - -class Qwen25_7BVLI(BaseLlama, torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - config = Qwen25_7BVLI_Config(**config_dict) - self.num_layers = config.num_hidden_layers - - self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) - self.visual = qwen_vl.Qwen2VLVisionTransformer(hidden_size=1280, output_hidden_size=config.hidden_size, device=device, dtype=dtype, ops=operations) - self.dtype = dtype - - def preprocess_embed(self, embed, device): - if embed["type"] == "image": - image, grid = qwen_vl.process_qwen2vl_images(embed["data"]) - return self.visual(image.to(device, dtype=torch.float32), grid), grid - return None, None - - def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): - grid = None - for e in embeds_info: - if e.get("type") == "image": - grid = e.get("extra", None) - position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device) - start = e.get("index") - position_ids[:, :start] = torch.arange(0, start, device=embeds.device) - end = e.get("size") + start - len_max = int(grid.max()) // 2 - start_next = len_max + start - position_ids[:, end:] = torch.arange(start_next, start_next + (embeds.shape[1] - end), device=embeds.device) - position_ids[0, start:end] = start - max_d = int(grid[0][1]) // 2 - position_ids[1, start:end] = torch.arange(start, start + max_d, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] - max_d = int(grid[0][2]) // 2 - position_ids[2, start:end] = torch.arange(start, start + max_d, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] - - if grid is None: - position_ids = None - - return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids) - -class Gemma2_2B(BaseLlama, torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - config = Gemma2_2B_Config(**config_dict) - self.num_layers = config.num_hidden_layers - - self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) - self.dtype = dtype diff --git a/comfy/text_encoders/llama_tokenizer/tokenizer.json b/comfy/text_encoders/llama_tokenizer/tokenizer.json deleted file mode 100644 index 99f23954b4bade407d4f3a18892f21f80b412d68..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/llama_tokenizer/tokenizer.json +++ /dev/null @@ -1,410579 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 128000, - "content": "<|begin_of_text|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 128001, - "content": "<|end_of_text|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 128002, - "content": "<|reserved_special_token_0|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 128003, - "content": "<|reserved_special_token_1|>", - "single_word": false, - "lstrip": false, - 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x", - "Ġ S", - "a d", - "Ġ \"", - "c h", - "u t", - "i f", - "* *", - "Ġ }", - "e m", - "o l", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "t h", - ") Ċ", - "Ġ {Ċ", - "Ġ{ Ċ", - "Ġ g", - "i g", - "i v", - ", Ċ", - "c e", - "o d", - "Ġ v", - "a te", - "at e", - "Ġ T", - "a g", - "a y", - "Ġ *", - "o t", - "u s", - "Ġ C", - "Ġ st", - "Ġs t", - "Ġ I", - "u n", - "u l", - "u e", - "Ġ A", - "o w", - "Ġ '", - "e w", - "Ġ <", - "a tion", - "at ion", - "atio n", - "ati on", - "( )", - "Ġ for", - "Ġf or", - "Ġfo r", - "a b", - "o rt", - "or t", - "u m", - "a me", - "am e", - "Ġ is", - "Ġi s", - "p e", - "t r", - "c k", - "â Ģ", - "Ġ y", - "i st", - "is t", - "- ---", - "-- --", - "--- -", - ". ĊĊ", - ".Ċ Ċ", - "h e", - "Ġ e", - "l o", - "Ġ M", - "Ġ be", - "Ġb e", - "e rs", - "er s", - "Ġ on", - "Ġo n", - "Ġ con", - "Ġc on", - "Ġco n", - "a p", - "u b", - "Ġ P", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "a ss", - "as s", - "i nt", - "in t", - "> Ċ", - "l y", - "u rn", - "ur n", - "Ġ $", - "; ĊĊ", - ";Ċ Ċ", - "a v", - "p ort", - "por t", - "po rt", - "i r", - "- >", - "n t", - "c tion", - "ct ion", - "e nd", - "en d", - "Ġ de", - "Ġd e", - "0 0", - "i th", - "it h", - "o ut", - "ou t", - "t urn", - "tu rn", - "tur n", - "o ur", - "ou r", - "Ġ ĠĠĠĠ", - "ĠĠ ĠĠĠ", - "ĠĠĠĠ Ġ", - "ĠĠĠ ĠĠ", - "l ic", - "li c", - "r es", - "re s", - "p t", - "= =", - "Ġ this", - "Ġt his", - "Ġth is", - "Ġthi s", - "Ġ wh", - "Ġw h", - "Ġ if", - "Ġi f", - "Ġ D", - "v er", - "ve r", - "a ge", - "ag e", - "Ġ B", - "h t", - "e xt", - "ex t", - "= \"", - "Ġ that", - "Ġt hat", - "Ġth at", - "Ġtha t", - "* ***", - "** **", - "*** *", - "Ġ R", - "Ġ it", - "Ġi t", - "e ss", - "es s", - "Ġ F", - "Ġ r", - "o s", - "a nd", - "an d", - "Ġ as", - "Ġa s", - "e ct", - "ec t", - "k e", - "r om", - "ro m", - "Ġ //", - "Ġ/ /", - "c on", - "co n", - "Ġ L", - "( \"", - "q u", - "l ass", - "la ss", - "las s", - "Ġ with", - "Ġw ith", - "Ġwi th", - "Ġwit h", - "i z", - "d e", - "Ġ N", - "Ġ al", - "Ġa l", - "o p", - "u p", - "g et", - "ge t", - "Ġ }Ċ", - "Ġ} Ċ", - "i le", - "il e", - "Ġ an", - "Ġa n", - "a ta", - "at a", - "o re", - "or e", - "r i", - "Ġ pro", - "Ġp ro", - "Ġpr o", - "; čĊ", - "ĉ ĉĉĉ", - "ĉĉ ĉĉ", - "ĉĉĉ ĉ", - "t er", - "te r", - "a in", - "ai n", - "Ġ W", - "Ġ E", - "Ġ com", - "Ġc om", - "Ġco m", - "Ġ return", - "Ġre turn", - "Ġr eturn", - "Ġret urn", - "a rt", - "ar t", - "Ġ H", - "a ck", - "ac k", - "im port", - "imp ort", - "ub lic", - "ubl ic", - "Ġ or", - "Ġo r", - "e st", - "es t", - "m ent", - "me nt", - "men t", - "Ġ G", - "a ble", - "ab le", - "abl e", - "Ġ -", - "i ne", - "in e", - "i ll", - "il l", - "i nd", - "in d", - "e re", - "er e", - ": :", - "i ty", - "it y", - "Ġ +", - "Ġ tr", - "Ġt r", - "e lf", - "el f", - "i ght", - "ig ht", - "igh t", - "( '", - "o rm", - "or m", - "u lt", - "ul t", - "s tr", - "st r", - ". .", - "\" ,", - "Ġ you", - "Ġy ou", - "Ġyo u", - "y pe", - "yp e", - "p l", - "Ġ new", - "Ġn ew", - "Ġne w", - "Ġ j", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ from", - "Ġf rom", - "Ġfr om", - "Ġfro m", - "Ġ ex", - "Ġe x", - "Ġ O", - "2 0", - "l d", - "Ġ [", - "o c", - ": Ċ", - "Ġ se", - "Ġs e", - "Ġ le", - "Ġl e", - "- -------", - "-- ------", - "---- ----", - "--- -----", - "----- ---", - "------ --", - "------- -", - ". s", - "{ Ċ", - "' ,", - "a nt", - "an t", - "Ġ at", - "Ġa t", - "a se", - "as e", - ". c", - "Ġ ch", - "Ġc h", - "< /", - "a ve", - "av e", - "a ng", - "an g", - "Ġ are", - "Ġa re", - "Ġar e", - "Ġ int", - "Ġin t", - "Ġi nt", - "âĢ Ļ", - "_ t", - "e rt", - "er t", - "i al", - "ia l", - "a ct", - "ac t", - "} Ċ", - "i ve", - "iv e", - "o de", - "od e", - "o st", - "os t", - "Ġ class", - "Ġc lass", - "Ġcl ass", - "Ġclas s", - "Ġcla ss", - "Ġ not", - "Ġn ot", - "Ġno t", - "o g", - "o rd", - "or d", - "a lue", - "al ue", - "alu e", - "a ll", - "al l", - "f f", - "( );Ċ", - "() ;Ċ", - "(); Ċ", - "o nt", - "on t", - "i me", - "im e", - "a re", - "ar e", - "Ġ U", - "Ġ pr", - "Ġp r", - "Ġ :", - "i es", - "ie s", - "i ze", - "iz e", - "u re", - "ur e", - "Ġ by", - "Ġb y", - "i re", - "ir e", - "Ġ }ĊĊ", - "Ġ} ĊĊ", - "Ġ}Ċ Ċ", - ". p", - "Ġ sh", - "Ġs h", - "i ce", - "ic e", - "a st", - "as t", - "p tion", - "pt ion", - "t ring", - "tr ing", - "tri ng", - "o k", - "_ _", - "c l", - "# #", - "Ġ he", - "Ġh e", - "a rd", - "ar d", - ") .", - "Ġ @", - "i ew", - "ie w", - "ĉ ĉĉ", - "ĉĉ ĉ", - "Ġ was", - "Ġw as", - "Ġwa s", - "i p", - "t his", - "th is", - "Ġ u", - "Ġ The", - "ĠT he", - "ĠTh e", - "i de", - "id e", - "a ce", - "ac e", - "i b", - "a c", - "r ou", - "ro u", - "Ġ we", - "Ġw e", - "j ect", - "je ct", - "jec t", - "Ġ public", - "Ġp ublic", - "Ġpub lic", - "Ġpubli c", - "a k", - "v e", - "a th", - "at h", - "o id", - "oi d", - "Ġ =>", - "Ġ= >", - "u st", - "us t", - "q ue", - "qu e", - "Ġ res", - "Ġre s", - "Ġr es", - ") )", - "' s", - "Ġ k", - "a ns", - "an s", - "y st", - "ys t", - "un ction", - "unc tion", - "unct ion", - "* *******", - "** ******", - "**** ****", - "****** **", - "*** *****", - "***** ***", - "******* *", - "Ġ i", - "Ġ us", - "Ġu s", - "p p", - "1 0", - "o ne", - "on e", - "a il", - "ai l", - "= ===", - "== ==", - "=== =", - "n ame", - "na me", - "nam e", - "Ġ str", - "Ġs tr", - "Ġst r", - "Ġ /", - "Ġ &", - "a ch", - "ac h", - "d iv", - "di v", - "y stem", - "yst em", - "ys tem", - "e ll", - "el l", - "Ġ have", - "Ġh ave", - "Ġha ve", - "Ġhav e", - "e rr", - "er r", - "o uld", - "ou ld", - "oul d", - "u ll", - "ul l", - "p on", - "po n", - "Ġ J", - "_ p", - "Ġ ==", - "Ġ= =", - "i gn", - "ig n", - "S t", - ". Ċ", - "Ġ pl", - "Ġp l", - ") ;ĊĊ", - ");Ċ Ċ", - "); ĊĊ", - "f orm", - "fo rm", - "for m", - "p ut", - "pu t", - "o unt", - "ou nt", - "oun t", - "} ĊĊ", - "}Ċ Ċ", - "d d", - "i te", - "it e", - "Ġ get", - "Ġg et", - "Ġge t", - "r r", - "o me", - "om e", - "Ġ âĢ", - "Ġâ Ģ", - "a ram", - "ar am", - "ara m", - "c c", - "Ġ */", - "Ġ* /", - "E R", - "I n", - "l es", - "le s", - "_ s", - "o ng", - "on g", - "i e", - "Ġ can", - "Ġc an", - "Ġca n", - "Ġ V", - "e rv", - "er v", - "p r", - "Ġ un", - "Ġu n", - "r ow", - "ro w", - "b er", - "be r", - "Ġ do", - "Ġd o", - "l l", - "Ġ el", - "Ġe l", - "Ġ self", - "Ġs elf", - "Ġse lf", - "Ġsel f", - "a ted", - "at ed", - "ate d", - "a ry", - "ar y", - "Ġ .", - "' ]", - "u d", - "Ġ en", - "Ġe n", - "Ġ Th", - "ĠT h", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "t e", - "_ c", - "u ct", - "uc t", - "Ġ ab", - "Ġa b", - "o rk", - "or k", - ". get", - ".g et", - ".ge t", - "Ġ #", - "a w", - "r ess", - "re ss", - "res s", - "o b", - "N ame", - "Na me", - "Nam e", - "2 01", - "20 1", - "a pp", - "ap p", - "[ '", - "Ġ all", - "Ġa ll", - "Ġal l", - "o ry", - "or y", - "i tion", - "it ion", - "iti on", - "a nce", - "an ce", - "anc e", - "e ar", - "ea r", - "Ġ cont", - "Ġc ont", - "Ġcon t", - "Ġco nt", - "v ent", - "ve nt", - "ven t", - "i a", - "Ġ will", - "Ġw ill", - "Ġwi ll", - "Ġwil l", - "I N", - "Ġ ĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠ", - "r eturn", - "re turn", - "ret urn", - "Ġ ", - "\" ,Ċ", - "\", Ċ", - "e c", - "Ġ In", - "ĠI n", - "p h", - "Ġ |", - "_ f", - "Ġ var", - "Ġv ar", - "Ġva r", - "e nce", - "en ce", - "enc e", - "I d", - "r ee", - "re e", - "i nk", - "in k", - "l ect", - "le ct", - "lec t", - "u g", - "e th", - "et h", - "Ġ else", - "Ġe lse", - "Ġel se", - "Ġels e", - "- ---------------", - "-- --------------", - "---- ------------", - "-------- --------", - "--- -------------", - "------------ ----", - "----- -----------", - "---------- ------", - "------ ----------", - "----------- -----", - "------------- ---", - "------- ---------", - "--------- -------", - "--------------- -", - "-------------- --", - "1 9", - "c ont", - "con t", - "co nt", - "Ġ so", - "Ġs o", - "a tic", - "at ic", - "ati c", - "Ġ lo", - "Ġl o", - "p ro", - "pr o", - "t on", - "to n", - "s s", - "o wn", - "ow n", - "a bel", - "ab el", - "abe l", - "o int", - "oin t", - "oi nt", - "o us", - "ou s", - "e ld", - "el d", - "S T", - "T he", - "Th e", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "R E", - "\" :", - "o lor", - "ol or", - "olo r", - "t p", - "e g", - "k ey", - "ke y", - "u de", - "ud e", - "Ġ St", - "ĠS t", - "o und", - "ou nd", - "oun d", - "Ġ ar", - "Ġa r", - "\" );Ċ", - "\") ;Ċ", - "\"); Ċ", - "e ner", - "en er", - "ene r", - "s er", - "se r", - "1 1", - "b ject", - "bj ect", - "es sage", - "ess age", - "essa ge", - "f er", - "fe r", - "Ġ more", - "Ġm ore", - "Ġmor e", - "Ġmo re", - "at ions", - "ation s", - "atio ns", - "ati ons", - "e nts", - "en ts", - "ent s", - "Ġ his", - "Ġh is", - "Ġhi s", - "Ġ they", - "Ġt hey", - "Ġth ey", - "Ġthe y", - ". S", - "Ġ Y", - "u se", - "us e", - "n e", - "i sh", - "is h", - "o ld", - "ol d", - "_ d", - "i o", - "i eld", - "ie ld", - "iel d", - "Ġ per", - "Ġp er", - "Ġpe r", - "C ont", - "Con t", - "Co nt", - "in gs", - "ing s", - "# ###", - "## ##", - "### #", - "Ġ data", - "Ġd ata", - "Ġda ta", - "Ġdat a", - "Ġ sa", - "Ġs a", - "e f", - "f o", - "Ġ one", - "Ġo ne", - "Ġon e", - "e ng", - "en g", - "Ġ dis", - "Ġd is", - "Ġdi s", - "A T", - "Ġ name", - "Ġn ame", - "Ġna me", - "Ġnam e", - "Ġ true", - "Ġtr ue", - "v al", - "va l", - "l ed", - "le d", - ". f", - "Ġ ne", - "Ġn e", - "Ġ end", - "Ġe nd", - "Ġen d", - "3 2", - ". T", - "1 6", - "c re", - "cr e", - "a rk", - "ar k", - "l og", - "lo g", - "E x", - "e rror", - "er ror", - "err or", - "erro r", - "_ id", - "_i d", - "ur re", - "urr e", - "a nge", - "an ge", - "ang e", - "Ġ null", - "Ġn ull", - "Ġnu ll", - "r ray", - "rr ay", - "Ġ my", - "Ġm y", - "p an", - "pa n", - "i ct", - "ic t", - "a tor", - "at or", - "ato r", - "V iew", - "Vi ew", - "L ist", - "Li st", - "ĉ return", - "ĉr eturn", - "ĉret urn", - "ĉre turn", - "âĢ Ŀ", - "Ġ pre", - "Ġp re", - "Ġpr e", - "Ġ x", - "c lude", - "cl ude", - "clud e", - "a rg", - "ar g", - "1 5", - "o v", - ". h", - "Ġ >", - "Ġ their", - "Ġthe ir", - "' )", - "i rst", - "ir st", - "irs t", - "i ck", - "ic k", - "g h", - "L E", - "O R", - "Ġ private", - "Ġpr ivate", - "Ġpriv ate", - "Ġprivat e", - "t em", - "te m", - "čĊ čĊ", - "u ser", - "us er", - "use r", - "Ġ )", - "c om", - "co m", - ". A", - "\" ;Ċ", - "\"; Ċ", - "Ġ id", - "Ġi d", - "r ead", - "re ad", - "rea d", - "Ġ who", - "Ġw ho", - "Ġwh o", - "_ b", - "\" >Ċ", - "\"> Ċ", - "Ġ time", - "Ġt ime", - "Ġtim e", - "Ġti me", - "Ġ man", - "Ġm an", - "Ġma n", - "r y", - "= =======", - "== ======", - "==== ====", - "=== =====", - "====== ==", - "===== ===", - "======= =", - "r oup", - "ro up", - "rou p", - "r op", - "ro p", - "p ublic", - "pub lic", - "v el", - "ve l", - "um ber", - "umb er", - "b le", - "bl e", - "Ġ which", - "Ġwh ich", - "** **************", - "**** ************", - "******** ********", - "************ ****", - "************** **", - "Ġ any", - "Ġa ny", - "Ġan y", - "Ġ false", - "Ġf alse", - "Ġfa lse", - "Ġfal se", - "Ġfals e", - "w e", - "Ġ value", - "Ġv alue", - "Ġval ue", - "Ġva lue", - "Ġvalu e", - "Ġ li", - "Ġl i", - "\" )", - "n der", - "nd er", - "nde r", - "g r", - "Ġ no", - "Ġn o", - "p aram", - "par am", - "pa ram", - "para m", - "2 5", - "f ig", - "fi g", - ". com", - ".c om", - ".co m", - "Ġ app", - "Ġa pp", - "Ġap p", - "_ l", - "i ons", - "ion s", - "io ns", - ". D", - "Ġ Ch", - "ĠC h", - "Ġ about", - "Ġa bout", - "Ġab out", - "Ġ add", - "Ġa dd", - "Ġad d", - "Ġ su", - "Ġs u", - "Ġ string", - "Ġs tring", - "Ġst ring", - "Ġstr ing", - "Ġstri ng", - "I D", - "Ġ over", - "Ġo ver", - "Ġov er", - "s tring", - "st ring", - "str ing", - "stri ng", - ". l", - "our ce", - "0 00", - "00 0", - "_ C", - "] Ċ", - "Ġ qu", - "Ġq u", - "Ġ String", - "ĠS tring", - "ĠSt ring", - "ĠStr ing", - "c a", - "S E", - "Ġ ro", - "Ġr o", - "s h", - "u al", - "ua l", - "T ype", - "Typ e", - "Ty pe", - "s on", - "so n", - "n ew", - "ne w", - "e rn", - "er n", - "Ġ ag", - "Ġa g", - "A R", - "] ;Ċ", - "]; Ċ", - "] .", - "Ġ ?", - "i cal", - "ic al", - "ica l", - "Ġ des", - "Ġd es", - "Ġde s", - "u th", - "ut h", - "i x", - "a ys", - "ay s", - "Ġ type", - "Ġt ype", - "Ġtyp e", - "Ġty pe", - "' t", - "a ult", - "au lt", - "aul t", - "Ġ inter", - "Ġin ter", - "Ġint er", - "Ġinte r", - "v ar", - "va r", - ". b", - "Ġ part", - "Ġp art", - "Ġpar t", - "Ġpa rt", - ". d", - "ur rent", - "urre nt", - "urr ent", - "I T", - "E N", - "3 0", - "e nc", - "en c", - "( f", - "r a", - "v alue", - "val ue", - "va lue", - "valu e", - "c ho", - "ch o", - "1 8", - "ut ton", - "utt on", - "utto n", - "o se", - "os e", - "1 4", - "Ġ !=", - "Ġ! =", - "a ter", - "at er", - "ate r", - "à ©", - "re ate", - "reat e", - "rea te", - "o ll", - "ol l", - "p os", - "po s", - "y le", - "yl e", - "n g", - "A L", - "u sing", - "us ing", - "usi ng", - "a mes", - "am es", - "ame s", - "Ġ {čĊ", - "Ġ{ čĊ", - "a tes", - "at es", - "ate s", - "e ly", - "el y", - "Ġ work", - "Ġw ork", - "Ġwor k", - "Ġwo rk", - "Ġ em", - "Ġe m", - "i nal", - "in al", - "ina l", - "Ġ sp", - "Ġs p", - "Ġ when", - "Ġw hen", - "Ġwh en", - "Ġwhe n", - ". set", - ".s et", - ".se t", - "Ġ ĠĠĠĠĠ", - "ĠĠ ĠĠĠĠ", - "ĠĠĠĠ ĠĠ", - "ĠĠĠ ĠĠĠ", - "ĠĠĠĠĠ Ġ", - ") :Ċ", - "): Ċ", - "t o", - "q uire", - "qu ire", - "quir e", - "qui re", - "ind ow", - "indo w", - "l ement", - "le ment", - "lem ent", - "lemen t", - "leme nt", - "p ect", - "pe ct", - "pec t", - "a sh", - "as h", - "[ i", - "Ġ use", - "Ġu se", - "Ġus e", - ". F", - "p ec", - "pe c", - "Ġ ad", - "Ġa d", - "o ve", - "ov e", - "ce ption", - "cept ion", - "cep tion", - "e ngth", - "en gth", - "eng th", - "in clude", - "inc lude", - "incl ude", - "inclu de", - "a der", - "ad er", - "ade r", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "at us", - "atu s", - "T h", - "i tle", - "it le", - "r it", - "ri t", - "v oid", - "vo id", - "( ).", - "() .", - "( Ċ", - "Ġ off", - "Ġo ff", - "Ġof f", - "Ġ other", - "Ġo ther", - "Ġot her", - "Ġ &&", - "Ġ& &", - "' ;Ċ", - "'; Ċ", - "m s", - "Ġ been", - "Ġb een", - "Ġbe en", - "Ġbee n", - "Ġ te", - "Ġt e", - "m l", - "c o", - "n c", - "1 3", - "er vice", - "erv ice", - "Ġ %", - "* *Ċ", - "** Ċ", - "a nn", - "an n", - "a de", - "ad e", - "Ċ ĊĊĊ", - "ĊĊ ĊĊ", - "ĊĊĊ Ċ", - "l ock", - "lo ck", - "loc k", - "con st", - "co nst", - "cons t", - "1 00", - "10 0", - "p onse", - "pon se", - "pons e", - "Ġ sup", - "Ġs up", - "Ġsu p", - "+ +", - "d ate", - "da te", - "dat e", - "Ġ acc", - "Ġa cc", - "Ġac c", - "Ġ had", - "Ġh ad", - "Ġha d", - "Ġ bu", - "Ġb u", - "2 00", - "20 0", - "Ġ Re", - "ĠR e", - "Ġ were", - "Ġw ere", - "Ġwe re", - "Ġwer e", - "Ġ file", - "Ġf ile", - "Ġfil e", - "Ġfi le", - "Ġ would", - "Ġw ould", - "Ġwo uld", - "Ġ âĢľ", - "ĠâĢ ľ", - "v en", - "ve n", - "i ss", - "is s", - "Ġ our", - "Ġo ur", - "Ġou r", - "c lass", - "cl ass", - "cla ss", - "clas s", - "r aw", - "ra w", - "Ġ year", - "Ġy ear", - "Ġye ar", - "D ata", - "Da ta", - "Dat a", - "Ġ val", - "Ġv al", - "Ġva l", - "Ġ some", - "Ġs ome", - "Ġso me", - "Ġsom e", - "f ter", - "ft er", - "fte r", - "y s", - "Ġ ///", - "Ġ// /", - "Ġ/ //", - "r ound", - "ro und", - "rou nd", - "v iew", - "vi ew", - "vie w", - "Ġ pe", - "Ġp e", - "Ġ there", - "Ġt here", - "Ġth ere", - "Ġthe re", - "Ġther e", - "Ġ said", - "Ġs aid", - "Ġsa id", - "Ġsai d", - "d u", - "o f", - "l ine", - "li ne", - "lin e", - "/ *", - "d uct", - "du ct", - "duc t", - "Ġ her", - "Ġh er", - "Ġhe r", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "R es", - "Re s", - "Ġ co", - "Ġc o", - "Ġ comm", - "Ġc omm", - "Ġcom m", - "Ġco mm", - "i se", - "is e", - "m in", - "mi n", - "Ġ ĠĠĠĊ", - "ĠĠ ĠĠĊ", - "ĠĠĠĠ Ċ", - "ĠĠĠ ĠĊ", - "# include", - "et hod", - "eth od", - ". P", - "u te", - "ut e", - "Ġ ass", - "Ġa ss", - "Ġas s", - "I nt", - "In t", - "a sk", - "as k", - "l oc", - "lo c", - "Ġ like", - "Ġl ike", - "Ġli ke", - "Ġlik e", - "o dy", - "od y", - "Ġ let", - "Ġl et", - "Ġle t", - "l oad", - "lo ad", - "Ġ am", - "Ġa m", - "r ol", - "ro l", - "Ġ gr", - "Ġg r", - "y p", - "Ġ also", - "Ġal so", - "Ġals o", - "Ġ It", - "ĠI t", - "u rl", - "ur l", - "i fic", - "if ic", - "ifi c", - "o rs", - "or s", - "_ P", - "_ n", - "i gh", - "ig h", - "Ġ than", - "Ġt han", - "Ġth an", - "Ġtha n", - "C om", - "Co m", - "A N", - "U L", - "a ting", - "at ing", - "ati ng", - "atin g", - "1 7", - "Ġ This", - "ĠT his", - "ĠTh is", - "ĠThi s", - "r ef", - "re f", - "_ S", - "Ġ static", - "Ġst atic", - "Ġstat ic", - "Ġsta tic", - "Ġstati c", - "r oll", - "ro ll", - "rol l", - "Ġ just", - "Ġj ust", - "Ġju st", - "Ġjus t", - "Ġ result", - "Ġres ult", - "i an", - "ia n", - "id th", - "Ġ them", - "Ġt hem", - "Ġth em", - "Ġthe m", - ") );Ċ", - ")) ;Ċ", - ")); Ċ", - "d er", - "de r", - "re ak", - "rea k", - "C on", - "Co n", - ": //", - ":/ /", - "u le", - "ul e", - ". ..", - ".. .", - "a rch", - "ar ch", - "arc h", - "e ment", - "em ent", - "eme nt", - "emen t", - "Ġ <<", - "Ġ< <", - "5 0", - "u sh", - "us h", - "en se", - "ens e", - "a rr", - "ar r", - "Ġ into", - "Ġin to", - "Ġint o", - "c ess", - "ce ss", - "ces s", - "a mp", - "am p", - "i ed", - "ie d", - "u ment", - "um ent", - "ume nt", - "umen t", - "Ġ \\", - "] ,", - "w o", - "a ls", - "al s", - "Ġ what", - "Ġw hat", - "Ġwh at", - "a nc", - "an c", - "V alue", - "Val ue", - "Va lue", - "= '", - "o lum", - "ol um", - "olu m", - "Ġ pos", - "Ġp os", - "Ġpo s", - "a ges", - "ag es", - "age s", - "a yer", - "ay er", - "aye r", - "Ġ sc", - "Ġs c", - "u es", - "ue s", - "\" )Ċ", - "\") Ċ", - "_ T", - "Ġ list", - "Ġl ist", - "Ġli st", - "Ġlis t", - "( s", - "Ġ case", - "Ġc ase", - "Ġca se", - "Ġcas e", - "C h", - "ĉ ĉĉĉĉ", - "ĉĉ ĉĉĉ", - "ĉĉĉĉ ĉ", - "ĉĉĉ ĉĉ", - "//// ////", - "/// /////", - "///// ///", - "p onent", - "pon ent", - "pone nt", - "Ġ z", - "Ġ kn", - "Ġk n", - "l et", - "le t", - "D E", - "r ed", - "re d", - "Ġ fe", - "Ġf e", - "Ġ },Ċ", - "Ġ} ,Ċ", - "Ġ}, Ċ", - "Ġ ,", - "( t", - "Ġ first", - "Ġf irst", - "Ġfi rst", - "Ġfir st", - "' );Ċ", - "') ;Ċ", - "'); Ċ", - "w ord", - "wo rd", - "wor d", - "Ġ import", - "Ġim port", - "Ġimp ort", - "Ġ act", - "Ġa ct", - "Ġac t", - "Ġ char", - "Ġc har", - "Ġch ar", - "Ġcha r", - "C T", - "Ġ Tr", - "ĠT r", - "o ple", - "op le", - "opl e", - "= {", - "ĉ f", - "2 4", - "i ent", - "ie nt", - "ien t", - "c ent", - "ce nt", - "cen t", - ". j", - "l ection", - "le ction", - "lect ion", - "lec tion", - ") )Ċ", - ")) Ċ", - "Ġ only", - "Ġon ly", - "Ġ print", - "Ġp rint", - "Ġpr int", - "Ġpri nt", - "Ġprin t", - "m er", - "me r", - ". W", - "o ck", - "oc k", - "Ġ --", - "Ġ- -", - "T ext", - "Te xt", - "Tex t", - "Ġ op", - "Ġo p", - "a nk", - "an k", - "Ġ its", - "Ġit s", - "Ġi ts", - "Ġ back", - "Ġb ack", - "Ġba ck", - "Ġbac k", - "[ \"", - "Ġ need", - "Ġn eed", - "Ġne ed", - "Ġ cl", - "Ġc l", - "Ġ sub", - "Ġs ub", - "Ġsu b", - "Ġ la", - "Ġl a", - "( (", - ". \"", - "O bject", - "Ob ject", - "Obj ect", - "Ġ start", - "Ġst art", - "Ġstar t", - "Ġsta rt", - "f ile", - "fi le", - "fil e", - "( self", - "(s elf", - "(se lf", - "(sel f", - "n er", - "ne r", - "e y", - "Ġ user", - "Ġu ser", - "Ġus er", - "Ġuse r", - "Ġ ent", - "Ġe nt", - "Ġen t", - "Ġ Com", - "ĠC om", - "ĠCo m", - "i ts", - "it s", - "Ġ Con", - "ĠC on", - "ĠCo n", - "o uble", - "ou ble", - "oub le", - "o wer", - "ow er", - "owe r", - "i tem", - "it em", - "ite m", - "v ery", - "ver y", - "ve ry", - "Ġ We", - "ĠW e", - "6 4", - "l ick", - "lic k", - "li ck", - "Ġ Q", - "p hp", - "ph p", - "t tp", - "tt p", - "' :", - "i cs", - "ic s", - "Ġ under", - "Ġu nder", - "Ġun der", - "Ġund er", - "Ġunde r", - "Ġ *Ċ", - "Ġ* Ċ", - ". L", - ") ;", - "i ces", - "ic es", - "ice s", - "Ġ reg", - "Ġre g", - "Ġr eg", - ") čĊ", - "ĉ public", - "ĉp ublic", - "ĉpub lic", - "S S", - "Ġ then", - "Ġt hen", - "Ġth en", - "Ġthe n", - "r eat", - "re at", - "rea t", - "i ous", - "io us", - "iou s", - ". G", - "e k", - "i rect", - "ir ect", - "ire ct", - "h eck", - "he ck", - "hec k", - "cri pt", - "cr ipt", - "n ing", - "ni ng", - "nin g", - "Ġ Un", - "ĠU n", - "Ġ may", - "Ġm ay", - "Ġma y", - "Ġ Wh", - "ĠW h", - "B o", - "I tem", - "It em", - "str uct", - "stru ct", - ". st", - ".s t", - "r eam", - "re am", - "rea m", - "i ble", - "ib le", - "lo at", - "Ġ org", - "Ġo rg", - "Ġor g", - "u nd", - "un d", - "s um", - "su m", - "_ in", - "_i n", - ". ./", - ".. /", - "_ M", - "Ġ how", - "Ġh ow", - "Ġho w", - "r ite", - "ri te", - "rit e", - "' Ċ", - "T o", - "4 0", - "w w", - "Ġ people", - "Ġpe ople", - "in dex", - "ind ex", - "inde x", - ". n", - "h ttp", - "ht tp", - "htt p", - "( m", - "e ctor", - "ect or", - "ec tor", - "Ġ ind", - "Ġin d", - "Ġi nd", - "Ġ jav", - "Ġj av", - "Ġja v", - "] ,Ċ", - "], Ċ", - "Ġ He", - "ĠH e", - "_ st", - "_s t", - "f ul", - "fu l", - "o le", - "ol e", - ") {Ċ", - "){ Ċ", - "Ġ should", - "Ġsh ould", - "Ġsho uld", - "o py", - "op y", - "e lp", - "el p", - "i er", - "ie r", - "_ name", - "_n ame", - "_na me", - "er son", - "ers on", - "I ON", - "IO N", - "o te", - "ot e", - "Ġ test", - "Ġt est", - "Ġte st", - "Ġtes t", - "Ġ bet", - "Ġb et", - "Ġbe t", - "r ror", - "rr or", - "u lar", - "ul ar", - "ula r", - "ã Ģ", - "Ġ Ð", - "b s", - "t ing", - "ti ng", - "tin g", - "Ġ make", - "Ġm ake", - "Ġma ke", - "Ġmak e", - "T r", - "Ġ after", - "Ġa fter", - "Ġaf ter", - "Ġaft er", - "ar get", - "arg et", - "arge t", - "R O", - "ol umn", - "olum n", - "olu mn", - "r c", - "_ re", - "_r e", - "de fine", - "def ine", - "2 2", - "Ġ right", - "Ġr ight", - "Ġrig ht", - "Ġri ght", - "r ight", - "ri ght", - "rig ht", - "d ay", - "da y", - "Ġ long", - "Ġl ong", - "Ġlo ng", - "Ġlon g", - "[ ]", - "( p", - "t d", - "c ond", - "con d", - "co nd", - "Ġ Pro", - "ĠP ro", - "ĠPr o", - "Ġ rem", - "Ġre m", - "Ġr em", - "pt ions", - "ption s", - "v id", - "vi d", - ". g", - "Ġ ext", - "Ġe xt", - "Ġex t", - "Ġ __", - "Ġ_ _", - "' )Ċ", - "') Ċ", - "p ace", - "pa ce", - "pac e", - "m p", - "Ġ min", - "Ġm in", - "Ġmi n", - "st ance", - "sta nce", - "stan ce", - "a ir", - "ai r", - "a ction", - "act ion", - "ac tion", - "w h", - "t ype", - "ty pe", - "typ e", - "u til", - "ut il", - "uti l", - "a it", - "ai t", - "< ?", - "I C", - "t ext", - "te xt", - "tex t", - "Ġ ph", - "Ġp h", - "Ġ fl", - "Ġf l", - ". M", - "c cess", - "cc ess", - "cce ss", - "b r", - "f ore", - "fo re", - "for e", - "ers ion", - ") ,Ċ", - "), Ċ", - ". re", - ".r e", - "a teg", - "at eg", - "ate g", - "Ġ loc", - "Ġl oc", - "Ġlo c", - "i ns", - "in s", - "- s", - "t rib", - "tr ib", - "tri b", - "Ġ Int", - "ĠI nt", - "ĠIn t", - "Ġ array", - "Ġa rray", - "Ġar ray", - "Ġarr ay", - ", \"", - "P ro", - "Pr o", - "( c", - "ess ion", - "> ĊĊ", - ">Ċ Ċ", - "Ġ she", - "Ġs he", - "Ġsh e", - "\" ]", - "a ph", - "ap h", - "Ġ exp", - "Ġe xp", - "Ġex p", - "er ty", - "ert y", - "Ġ Se", - "ĠS e", - "Ġ par", - "Ġp ar", - "Ġpa r", - "u nc", - "un c", - "E T", - "Ġ read", - "Ġre ad", - "Ġr ead", - "p rint", - "pr int", - "pri nt", - "Ġ rel", - "Ġre l", - "Ġr el", - "Ġ form", - "Ġf orm", - "Ġfor m", - "Ġfo rm", - "Ġ dr", - "Ġd r", - "Ex ception", - "Except ion", - "in put", - "inp ut", - "Ġ trans", - "Ġt rans", - "Ġtr ans", - "Ġtra ns", - "Ġtran s", - "# #######", - "## ######", - "#### ####", - "### #####", - "##### ###", - "###### ##", - "####### #", - "or der", - "ord er", - "orde r", - "B y", - "Ġ aw", - "Ġa w", - "i ties", - "it ies", - "iti es", - "u ff", - "uf f", - "p lay", - "pl ay", - "pla y", - ". add", - ".a dd", - ".ad d", - "Ġ âĢĵ", - "ĠâĢ ĵ", - "Ġ want", - "Ġw ant", - "Ġwa nt", - "Ġwan t", - "Ġ comp", - "Ġc omp", - "Ġcom p", - "Ġco mp", - "m ents", - "ment s", - "me nts", - "men ts", - "Ġ ||", - "Ġ| |", - "a z", - "b e", - "Ġ number", - "Ġn umber", - "Ġnum ber", - "Ġnumb er", - "Ġ require", - "Ġre quire", - "Ġreq uire", - "Ġrequ ire", - "Ġ Ex", - "ĠE x", - "6 0", - "Ġ col", - "Ġc ol", - "Ġco l", - "Ġ key", - "Ġk ey", - "Ġke y", - "em ber", - "emb er", - "Ġ two", - "Ġt wo", - "Ġtw o", - "Ġ size", - "Ġs ize", - "Ġsi ze", - "Ġsiz e", - "Ġ where", - "Ġw here", - "Ġwh ere", - "Ġwhe re", - "U T", - "res ult", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "o ugh", - "ou gh", - "oug h", - "or ld", - "o od", - "oo d", - "u ch", - "uc h", - "at ive", - "ati ve", - "ativ e", - "g er", - "ge r", - "a rent", - "ar ent", - "are nt", - "aren t", - "Ġ /*", - "Ġ/ *", - "Ġ arg", - "Ġa rg", - "Ġar g", - "Ġ while", - "Ġwh ile", - "2 3", - "( this", - "(t his", - "(th is", - "Ġ rec", - "Ġre c", - "Ġr ec", - "Ġ dif", - "Ġd if", - "Ġdi f", - "St ate", - "Stat e", - "Ġ spec", - "Ġs pec", - "Ġsp ec", - "Ġspe c", - "r ide", - "ri de", - "rid e", - "_ F", - "Ġ look", - "Ġl ook", - "Ġlo ok", - "A M", - "il ity", - "ilit y", - "ili ty", - "e ter", - "et er", - "ete r", - "âĢĻ t", - "Ċ ĊĊ", - "ĊĊ Ċ", - "ay out", - "ayo ut", - "-- ------------------------------", - "---- ----------------------------", - "---------------- ----------------", - "------------ --------------------", - "---------------------------- ----", - "------------------------------ --", - "-------------------- ------------", - "a ger", - "ag er", - "age r", - "Ġ could", - "Ġc ould", - "Ġco uld", - "Ġcou ld", - "Ġ br", - "Ġb r", - "e nds", - "en ds", - "end s", - "u res", - "ur es", - "ure s", - "Ġ know", - "Ġk now", - "Ġkn ow", - "e ts", - "et s", - "Ġ If", - "ĠI f", - "Ġ Sh", - "ĠS h", - ". w", - "b ack", - "ba ck", - "bac k", - "Ġ ser", - "Ġs er", - "Ġse r", - "Ġ +=", - "Ġ+ =", - "Ġ fr", - "Ġf r", - "( ));Ċ", - "() );Ċ", - "()) ;Ċ", - "()); Ċ", - "Ġ hand", - "Ġh and", - "Ġha nd", - "Ġhan d", - "I nd", - "In d", - "U LL", - "UL L", - "I m", - "( );ĊĊ", - "() ;ĊĊ", - "();Ċ Ċ", - "(); ĊĊ", - "Ġ most", - "Ġm ost", - "Ġmo st", - "Ġmos t", - "Ġ try", - "Ġt ry", - "Ġtr y", - "Ġ now", - "Ġn ow", - "Ġno w", - "r ough", - "ro ugh", - "rou gh", - "> čĊ", - "ack age", - "Ġ him", - "Ġh im", - "Ġhi m", - ". _", - "i fy", - "if y", - "Ġ break", - "Ġb reak", - "Ġbre ak", - "Ġ );Ċ", - "Ġ) ;Ċ", - "Ġ); Ċ", - "r en", - "re n", - "# define", - "i tt", - "it t", - "Ġ ap", - "Ġa p", - "ĉ c", - "( n", - "Ġ You", - "ĠY ou", - "ĠYo u", - ": ĊĊ", - ":Ċ Ċ", - "- m", - "Ġ every", - "Ġe very", - "Ġever y", - "Ġev ery", - "Ġeve ry", - "us tom", - "ust om", - "usto m", - "l ient", - "li ent", - "lie nt", - "lien t", - "oc ument", - "ocu ment", - "cri ption", - "cript ion", - "E rror", - "Err or", - "Er ror", - "Erro r", - "- b", - "Ð ¾", - "] [", - "9 9", - "t rans", - "tr ans", - "tra ns", - "tran s", - "Ġ point", - "Ġp oint", - "Ġpo int", - "Ġpoi nt", - "Ġ std", - "Ġs td", - "Ġst d", - "Ġ fil", - "Ġf il", - "Ġfi l", - "T ime", - "Tim e", - "Ti me", - "8 0", - "Ġ mod", - "Ġm od", - "Ġmo d", - "Ġ ->", - "Ġ- >", - "Ġ error", - "Ġe rror", - "Ġerr or", - "Ġer ror", - "Ġerro r", - "a h", - "Ġ text", - "Ġt ext", - "Ġte xt", - "Ġtex t", - "r oller", - "ro ller", - "rol ler", - "roll er", - "l ose", - "lo se", - "los e", - "q l", - "Ġ pol", - "Ġp ol", - "Ġpo l", - "> < /", - "Ġ show", - "Ġs how", - "Ġsh ow", - "Ġsho w", - "U ser", - "Us er", - "Use r", - "a sed", - "as ed", - "ase d", - "Ġ {ĊĊ", - "Ġ{ ĊĊ", - "Ġ{Ċ Ċ", - "Ġ find", - "Ġf ind", - "Ġfin d", - "Ġfi nd", - "Ð °", - "E D", - "s pan", - "sp an", - "spa n", - "e nu", - "en u", - "Ġ current", - "Ġc urrent", - "Ġcur rent", - "Ġcurr ent", - "Ġ used", - "Ġu sed", - "Ġus ed", - "Ġuse d", - "c ept", - "ce pt", - "cep t", - "cl ud", - "Ġ play", - "Ġp lay", - "Ġpl ay", - "Ġpla y", - "Ġ log", - "Ġl og", - "Ġlo g", - "u tion", - "ut ion", - "uti on", - "f l", - "Ġ see", - "Ġs ee", - "Ġse e", - "ind ows", - "indow s", - "indo ws", - "Ġ help", - "Ġh elp", - "Ġhe lp", - "Ġhel p", - "Ġ these", - "Ġth ese", - "Ġthe se", - "Ġ pass", - "Ġp ass", - "Ġpas s", - "Ġpa ss", - "Ġ down", - "Ġd own", - "Ġdo wn", - "Ġdow n", - "Ġ even", - "Ġe ven", - "Ġev en", - "Ġeve n", - "a son", - "as on", - "aso n", - "u ild", - "ui ld", - "uil d", - "f rom", - "fr om", - "( d", - "Ġ bl", - "Ġb l", - "l abel", - "la bel", - "lab el", - "e lse", - "el se", - "els e", - "Ð µ", - "Ġ (!", - "Ġ( !", - "i zed", - "iz ed", - "ize d", - "( ),", - "() ,", - "Ġ ob", - "Ġo b", - "Ġ item", - "Ġit em", - "Ġi tem", - "u mp", - "um p", - "U R", - "o rn", - "or n", - "Ġ don", - "Ġd on", - "Ġdo n", - "S e", - "m an", - "ma n", - "2 7", - "am ple", - "amp le", - "t n", - "= ===============", - "== ==============", - "==== ============", - "======== ========", - "=== =============", - "============ ====", - "============= ===", - "=========== =====", - "============== ==", - "========= =======", - "========== ======", - "=============== =", - "====== ==========", - "===== ===========", - "======= =========", - "H e", - "g ram", - "gr am", - "gra m", - "Ġ did", - "Ġd id", - "Ġdi d", - "w n", - "_ h", - "i ver", - "iv er", - "ive r", - "Ġ sm", - "Ġs m", - "Ġ through", - "Ġth rough", - "Ġthr ough", - "Ġthro ugh", - "Ġ An", - "ĠA n", - "c he", - "ch e", - "Ġ inv", - "Ġin v", - "Ġi nv", - "o use", - "ou se", - "ous e", - "Ġ es", - "Ġe s", - "Ġ New", - "ĠN ew", - "ĠNe w", - "ex port", - "exp ort", - "expo rt", - "m ary", - "ma ry", - "mar y", - "u to", - "ut o", - "l er", - "le r", - "Ġ last", - "Ġl ast", - "Ġla st", - "Ġlas t", - "Ġ event", - "Ġe vent", - "Ġeven t", - "Ġev ent", - "Ġeve nt", - "t ry", - "tr y", - "ï ¼", - "i ly", - "il y", - "ig ned", - "ign ed", - "igne d", - "i nes", - "in es", - "ine s", - "ol low", - "oll ow", - "ollo w", - "ic ense", - "icens e", - "s ole", - "so le", - "sol e", - "l ear", - "le ar", - "lea r", - "( int", - "(i nt", - "(in t", - "Ġ again", - "Ġa gain", - "Ġag ain", - "Ġ high", - "Ġh igh", - "Ġhi gh", - "h tml", - "ht ml", - "htm l", - "In dex", - "Ind ex", - "ut hor", - "uth or", - "Ġ /**Ċ", - "Ġ/ **Ċ", - "Ġ/* *Ċ", - "Ġ/** Ċ", - "Ġ line", - "Ġl ine", - "Ġli ne", - "Ġlin e", - "E vent", - "Even t", - "Ev ent", - "_ D", - "Ġ does", - "Ġd oes", - "Ġdo es", - "Ġdoe s", - "it ial", - "iti al", - "itia l", - "Ġ cr", - "Ġc r", - "a rs", - "ar s", - "2 8", - "Ġ tem", - "Ġt em", - "Ġte m", - "c ause", - "ca use", - "f ace", - "fa ce", - "fac e", - "Ġ `", - "_ A", - "B utton", - "But ton", - "a ture", - "at ure", - "atur e", - "atu re", - "ect ed", - "ec ted", - "E S", - "i ster", - "is ter", - "ist er", - "iste r", - "ĉ Ċ", - "Ġ before", - "Ġb efore", - "Ġbe fore", - "Ġbef ore", - "a le", - "al e", - "o ther", - "ot her", - "oth er", - "Ġ because", - "Ġb ecause", - "Ġbe cause", - "Ġbec ause", - "r oid", - "ro id", - "roi d", - "Ġ ed", - "Ġe d", - "i k", - "r eg", - "re g", - "Ġ De", - "ĠD e", - "Ġ dist", - "Ġd ist", - "Ġdis t", - "Ġdi st", - "} ,Ċ", - "}, Ċ", - "Ġ state", - "Ġst ate", - "Ġstat e", - "Ġsta te", - "Ġ cons", - "Ġc ons", - "Ġcon s", - "Ġco ns", - "r int", - "ri nt", - "rin t", - "a tt", - "at t", - "Ġ here", - "Ġh ere", - "Ġhe re", - "Ġher e", - "i ned", - "in ed", - "ine d", - "Ġ final", - "Ġf inal", - "Ġfin al", - "Ġfi nal", - "Ġ \"\"", - "Ġ\" \"", - "K ey", - "Ke y", - "L O", - "Ġ del", - "Ġd el", - "Ġde l", - "p ty", - "pt y", - "th ing", - "thin g", - "2 6", - "Ġ And", - "ĠA nd", - "ĠAn d", - "Ġ run", - "Ġr un", - "Ġru n", - "Ġ X", - "y m", - ". app", - ".ap p", - ".a pp", - "Ġ very", - "Ġv ery", - "Ġver y", - "Ġve ry", - "c es", - "ce s", - "_ N", - "a red", - "ar ed", - "are d", - "w ard", - "wa rd", - "war d", - "l ist", - "li st", - "lis t", - "i ted", - "it ed", - "ite d", - "o log", - "ol og", - "olo g", - "it ch", - "B ox", - "Bo x", - "i fe", - "if e", - "3 3", - "Ġ ac", - "Ġa c", - "Ġ model", - "Ġm odel", - "Ġmod el", - "Ġmode l", - "Ġmo del", - "Ġ mon", - "Ġm on", - "Ġmo n", - "Ġ way", - "Ġw ay", - "Ġwa y", - "l ete", - "le te", - "let e", - "Ġ call", - "Ġc all", - "Ġcal l", - "Ġca ll", - "Ġ att", - "Ġa tt", - "Ġat t", - "Ġ cal", - "Ġc al", - "Ġca l", - "v ert", - "ver t", - "ve rt", - "Ġ dec", - "Ġd ec", - "Ġde c", - "l ease", - "le ase", - "lea se", - "o un", - "ou n", - "Ġ });Ċ", - "Ġ} );Ċ", - "Ġ}) ;Ċ", - "Ġ}); Ċ", - "f r", - "form ation", - "format ion", - "forma tion", - "e tail", - "et ail", - "eta il", - "Ġ num", - "Ġn um", - "Ġnu m", - "a j", - "qu ery", - "que ry", - "quer y", - "Ġ well", - "Ġw ell", - "Ġwe ll", - "Ġwel l", - "Ġ object", - "Ġo bject", - "Ġob ject", - "Ġobj ect", - "Ġ As", - "ĠA s", - "Ġ years", - "Ġy ears", - "Ġyear s", - "Ġye ars", - "C olor", - "Col or", - "Co lor", - "I S", - "Ġ default", - "Ġd efault", - "Ġde fault", - "Ġdef ault", - "Ġdefa ult", - "W h", - "Ġ ins", - "Ġin s", - "Ġi ns", - "a int", - "ain t", - "ai nt", - "Ġ java", - "Ġj ava", - "Ġjav a", - "Ġja va", - "Ġ sim", - "Ġs im", - "Ġsi m", - "Ġ Ar", - "ĠA r", - "m on", - "mo n", - "t il", - "ti l", - "( );čĊ", - "() ;čĊ", - "(); čĊ", - ") :", - "S et", - "Se t", - "2 9", - "at ter", - "att er", - "atte r", - "Ġ view", - "Ġv iew", - "Ġvi ew", - "Ġvie w", - "Ġ pres", - "Ġp res", - "Ġpr es", - "Ġpre s", - "a rray", - "ar ray", - "arr ay", - "arra y", - "W e", - "A t", - "Ġ bel", - "Ġb el", - "Ġbe l", - "Ġ many", - "Ġm any", - "Ġman y", - "Ġma ny", - "2 1", - "M an", - "Ma n", - "e nder", - "en der", - "end er", - "ende r", - "Ġ being", - "Ġb eing", - "Ġbe ing", - "Ġbei ng", - "Ġ good", - "Ġg ood", - "Ġgo od", - "Ġgoo d", - "ĉ ĉĉĉĉĉ", - "ĉĉ ĉĉĉĉ", - "ĉĉĉĉ ĉĉ", - "ĉĉĉ ĉĉĉ", - "ĉĉĉĉĉ ĉ", - "at ional", - "ation al", - "atio nal", - "ati onal", - "w are", - "wa re", - "war e", - ". log", - ".l og", - ".lo g", - "{ čĊ", - "Ġ using", - "Ġu sing", - "Ġus ing", - "_ B", - "Ġ :=", - "Ġ: =", - "_ w", - "i sts", - "is ts", - "ist s", - "l ish", - "li sh", - "lis h", - "Ġ stud", - "Ġst ud", - "Ġstu d", - "Ġ Al", - "ĠA l", - "Ġ gu", - "Ġg u", - "con fig", - "conf ig", - "u ring", - "ur ing", - "uri ng", - "t ime", - "ti me", - "tim e", - "o ken", - "ok en", - "oke n", - "ame space", - "ames pace", - "Ġ request", - "Ġre quest", - "Ġreq uest", - "Ġrequ est", - "Ġ child", - "Ġch ild", - "Ġchi ld", - "Ġ Ã", - "l ob", - "lo b", - "Ġ param", - "Ġp aram", - "Ġpar am", - "Ġpara m", - "Ġpa ram", - "Ġ }čĊ", - "Ġ} čĊ", - "0 1", - "Ġ echo", - "Ġe cho", - "Ġec ho", - "Ġech o", - "f unction", - "func tion", - "fun ction", - "**** ****************************", - "******** ************************", - "**************** ****************", - "************************ ********", - "******************** ************", - "**************************** ****", - "************ ********************", - "p s", - "E lement", - "El ement", - "Elem ent", - "Ele ment", - "a lk", - "al k", - "l ication", - "lic ation", - "li cation", - "lica tion", - "b y", - "S ize", - "Si ze", - "ra wing", - "raw ing", - "Ġ person", - "Ġp erson", - "Ġper son", - "Ġpers on", - "Ġperso n", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "\\ n", - "o bject", - "ob ject", - "obj ect", - "i nce", - "in ce", - "inc e", - "E n", - "F ile", - "Fi le", - "Fil e", - "u f", - "f fect", - "ff ect", - "ffe ct", - "A C", - "Ġ style", - "Ġst yle", - "Ġsty le", - "Ġstyl e", - "sum mary", - "summ ary", - "Ġ que", - "Ġqu e", - "Ġq ue", - "_ r", - "Ġ ($", - "Ġ( $", - "M odel", - "Mode l", - "Mod el", - "Mo del", - "i dent", - "id ent", - "ide nt", - "iden t", - "Ġ method", - "Ġm ethod", - "Ġmet hod", - "Ġmeth od", - "I L", - "o tt", - "ot t", - "l ess", - "le ss", - "les s", - "I NG", - "IN G", - "Ġ ()", - "Ġ( )", - "Ġ expect", - "Ġex pect", - "Ġexp ect", - "y nc", - "yn c", - "p ackage", - "pack age", - "3 5", - "u rs", - "ur s", - "Ġ prot", - "Ġp rot", - "Ġpro t", - "Ġpr ot", - ". /", - "p re", - "pr e", - "Ġ )Ċ", - "Ġ) Ċ", - "m a", - "Ġ sur", - "Ġs ur", - "Ġsu r", - "Ġ found", - "Ġf ound", - "Ġfo und", - "Ġfou nd", - "In fo", - "Inf o", - "p ar", - "pa r", - "i mes", - "im es", - "ime s", - ". e", - "a ins", - "ain s", - "ai ns", - "Ġ post", - "Ġp ost", - "Ġpos t", - "Ġpo st", - "- d", - "4 5", - "o lean", - "ol ean", - "ole an", - "Ġ sl", - "Ġs l", - "P E", - "Ġ such", - "Ġs uch", - "Ġsu ch", - "Ġsuc h", - "s elect", - "se lect", - "sel ect", - "a iner", - "ain er", - "ai ner", - "aine r", - "Ġ think", - "Ġth ink", - "Ġthin k", - "Ġthi nk", - "Ġd iffer", - "Ġdif fer", - "Ġdi ffer", - "Ġdiff er", - ". r", - "/ **Ċ", - "/* *Ċ", - "/** Ċ", - "F F", - "o ol", - "oo l", - "p late", - "pl ate", - "plat e", - "pla te", - "q ual", - "qu al", - "qua l", - "Ġ For", - "ĠF or", - "ĠFo r", - "Ġ much", - "Ġm uch", - "Ġmu ch", - "Ġmuc h", - "u c", - "( new", - "(n ew", - "(ne w", - "od ule", - "odu le", - "Ġ som", - "Ġs om", - "Ġso m", - "Ġ http", - "Ġh ttp", - "Ġht tp", - "Ġhtt p", - "Ġ List", - "ĠL ist", - "ĠLi st", - "ĠLis t", - "Ġ count", - "Ġc ount", - "Ġco unt", - "Ġcoun t", - "Ġcou nt", - "Ġ inst", - "Ġin st", - "Ġi nst", - "Ġins t", - "c har", - "ch ar", - "cha r", - "m it", - "mi t", - ". id", - ".i d", - "a king", - "ak ing", - "aki ng", - "akin g", - "Ġ gener", - "Ġg ener", - "Ġge ner", - "Ġgen er", - "Ġgene r", - "p x", - "v ice", - "vi ce", - "vic e", - "3 7", - "_ data", - "_d ata", - "_dat a", - "_da ta", - "Ġ NULL", - "ĠN ULL", - "ĠNU LL", - "} čĊ", - "i dd", - "id d", - "ãĢ Ĥ", - "Ġ med", - "Ġm ed", - "Ġme d", - "o rg", - "or g", - "i der", - "id er", - "ide r", - "a che", - "ac he", - "ach e", - "w ork", - "wo rk", - "wor k", - "Ġ check", - "Ġc heck", - "Ġch eck", - "Ġche ck", - "w een", - "we en", - "Ġ ((", - "Ġ( (", - "t he", - "th e", - "a nts", - "an ts", - "ant s", - "> <", - ". B", - "- c", - "Ġ open", - "Ġo pen", - "Ġop en", - "Ġ est", - "Ġe st", - "Ġes t", - "Ġ ĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĊ", - "Ġ next", - "Ġn ext", - "Ġne xt", - "Ġnex t", - "I M", - "Ñ Ĥ", - "O T", - "à ³", - "Ġ follow", - "Ġf ollow", - "Ġfol low", - "Ġfoll ow", - "c ontent", - "con tent", - "cont ent", - "conte nt", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "Ġin clud", - "Ġincl ud", - "Ġinclu d", - "H E", - "Ġ Res", - "ĠR es", - "ĠRe s", - "Ġ href", - "Ġh ref", - "Ġhr ef", - "Ð ¸", - "Ġ car", - "Ġc ar", - "Ġca r", - "y pes", - "ype s", - "yp es", - "i mage", - "im age", - "ima ge", - "imag e", - "U n", - "Ġ bool", - "Ġb ool", - "Ġbo ol", - "Ġboo l", - "A D", - "Ġ game", - "Ġg ame", - "Ġgam e", - "Ġga me", - ". Form", - ".F orm", - ".For m", - "r ows", - "ro ws", - "row s", - "* /", - "v elop", - "ve lop", - "vel op", - ". Drawing", - ".D rawing", - ".Draw ing", - "Ġ path", - "Ġp ath", - "Ġpat h", - "Ġpa th", - "is ion", - "isi on", - "Ġ each", - "Ġe ach", - "Ġea ch", - "Ġ Pl", - "ĠP l", - "_ type", - "_t ype", - "_typ e", - "_ty pe", - "P ath", - "Pa th", - "Pat h", - "n ection", - "ne ction", - "nect ion", - "Ġ av", - "Ġa v", - "' ).", - "') .", - "Ġ support", - "Ġs upport", - "Ġsup port", - "Ġsupp ort", - "E NT", - "EN T", - "r em", - "re m", - "\" ).", - "\") .", - "Ġ own", - "Ġo wn", - "Ġow n", - "Ġ cor", - "Ġc or", - "Ġco r", - "c ount", - "co unt", - "cou nt", - "m iss", - "mi ss", - "mis s", - "u ally", - "ual ly", - "Ġ mem", - "Ġm em", - "Ġme m", - "s td", - "st d", - "i ence", - "ie nce", - "ien ce", - "s earch", - "se arch", - "sea rch", - "\" ĊĊ", - "\"Ċ Ċ", - "F orm", - "For m", - "Fo rm", - "Ġ sex", - "Ġs ex", - "Ġse x", - "e name", - "en ame", - "ena me", - "Ġ sign", - "Ġs ign", - "Ġsi gn", - "Ġsig n", - "Ġ et", - "Ġe t", - "Ġ ĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠ ĠĠĠĠ", - "' ,'", - "', '", - "Ġ App", - "ĠA pp", - "ĠAp p", - "Ġ those", - "Ġth ose", - "Ġtho se", - "o ff", - "of f", - "Ġ err", - "Ġe rr", - "Ġer r", - "Ġ system", - "Ġs ystem", - "Ġsys tem", - "Ġsy stem", - "Ġsyst em", - "Ġ best", - "Ġb est", - "Ġbe st", - "Ġbes t", - "c ode", - "co de", - "cod e", - "Ġ same", - "Ġs ame", - "Ġsa me", - "Ġsam e", - "Ġ di", - "Ġd i", - "u ss", - "us s", - "Ġ create", - "Ġc reate", - "Ġcre ate", - "Ġcreat e", - "Ġcrea te", - "a ther", - "at her", - "ath er", - "athe r", - "A rray", - "Ar ray", - "Arr ay", - ". in", - ".i n", - "f e", - "S ervice", - "Ser vice", - "Serv ice", - "U N", - "a ts", - "at s", - "Ġ Z", - "al th", - "alt h", - "Ġ made", - "Ġm ade", - "Ġma de", - "Ġmad e", - "tr ue", - "tru e", - "A B", - "Ġ mark", - "Ġm ark", - "Ġmar k", - "Ġma rk", - "r id", - "ri d", - "if ied", - "ifi ed", - "ifie d", - ", čĊ", - "y n", - "p ress", - "pr ess", - "pre ss", - "pres s", - "Ġ group", - "Ġg roup", - "Ġgr oup", - "Ġgro up", - "Ġgrou p", - "Ġ fin", - "Ġf in", - "Ġfi n", - "Ġ License", - "ĠL icense", - "ĠLic ense", - "F ield", - "Fi eld", - "e ger", - "eg er", - "Ġ world", - "Ġw orld", - "Ġwor ld", - "i ness", - "in ess", - "ine ss", - "ines s", - "t y", - "Ġ process", - "Ġp rocess", - "Ġpro cess", - "Ġproc ess", - "Ġproces s", - "( b", - "Ġ cre", - "Ġc re", - "Ġcr e", - "a rn", - "ar n", - "i ves", - "iv es", - "ive s", - "Ġ main", - "Ġm ain", - "Ġma in", - "Ġmai n", - "i deo", - "id eo", - "ide o", - "3 6", - "_ g", - "A G", - "val id", - "va lid", - "i mg", - "im g", - "P I", - "Ġ color", - "Ġc olor", - "Ġco lor", - "Ġcol or", - "Ġ report", - "Ġre port", - "Ġrep ort", - "Ġrepo rt", - "Ġ take", - "Ġt ake", - "Ġta ke", - "Ġtak e", - "r ib", - "ri b", - "O M", - "Ġ day", - "Ġd ay", - "Ġda y", - "Re quest", - "Req uest", - "Ġ sk", - "Ġs k", - "b ers", - "ber s", - "be rs", - "ĉ s", - ". Add", - ".A dd", - ".Ad d", - "o ot", - "oo t", - "I mage", - "Im age", - "Ġcom ple", - "Ġcomp le", - "Ġcompl e", - "ol lection", - "oll ection", - "ollect ion", - "olle ction", - "Ġ top", - "Ġt op", - "Ġto p", - "Ġ free", - "Ġf ree", - "Ġfr ee", - "Ġfre e", - "A S", - "D e", - "Ġ On", - "ĠO n", - "I G", - "9 0", - "e ta", - "et a", - "D ate", - "Da te", - "Dat e", - "Ġ action", - "Ġa ction", - "Ġact ion", - "Ġac tion", - "3 4", - "O ver", - "i tor", - "it or", - "ito r", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "n ot", - "no t", - "Ġ index", - "Ġin dex", - "Ġind ex", - "Ġinde x", - "h er", - "he r", - "i con", - "ic on", - "ico n", - "O n", - "; čĊčĊ", - ";čĊ čĊ", - "iv ity", - "ivi ty", - "m and", - "man d", - "ma nd", - ". Windows", - ".W indows", - ".Window s", - "O L", - "Ġ real", - "Ġre al", - "Ġ max", - "Ġm ax", - "Ġma x", - "l and", - "la nd", - "lan d", - ". ...", - ".. ..", - "... .", - "r aph", - "ra ph", - "rap h", - "Ġ build", - "Ġb uild", - "Ġbu ild", - "l eg", - "le g", - "as sword", - "ass word", - "? ĊĊ", - "?Ċ Ċ", - "âĢ ¦", - "o ok", - "oo k", - "u ck", - "uc k", - "Ġ message", - "Ġm essage", - "Ġmess age", - "Ġmes sage", - "t est", - "te st", - "tes t", - "i vers", - "iv ers", - "ive rs", - "iver s", - "3 8", - "Ġ input", - "Ġin put", - "Ġinp ut", - "Ġ art", - "Ġa rt", - "Ġar t", - "Ġ between", - "Ġb etween", - "Ġbet ween", - "G et", - "Ge t", - "en ter", - "ent er", - "ente r", - "g round", - "gr ound", - "gro und", - "e ne", - "en e", - "à ¡", - ". length", - ".l ength", - ".len gth", - ".le ngth", - "N ode", - "No de", - "( i", - "C lass", - "Cl ass", - "Cla ss", - "f or", - "fo r", - "Ġ âĢĶ", - "ĠâĢ Ķ", - "t en", - "te n", - "o in", - "oi n", - "Ġ ke", - "Ġk e", - "u i", - "Ġ IN", - "ĠI N", - "Ġ table", - "Ġt able", - "Ġtab le", - "Ġta ble", - "s ub", - "su b", - "Ġ Le", - "ĠL e", - "Ġ head", - "Ġh ead", - "Ġhe ad", - "Ġ must", - "Ġm ust", - "Ġmus t", - "Ġmu st", - "//// ////////////", - "//////// ////////", - "//////////// ////", - ". util", - ".u til", - ".ut il", - "Cont ext", - "Con text", - "Ġ order", - "Ġor der", - "Ġord er", - "Ġ mov", - "Ġm ov", - "Ġmo v", - "o ver", - "ov er", - "ove r", - "Ġ contin", - "Ġcon tin", - "Ġcont in", - "Ġ say", - "Ġs ay", - "Ġsa y", - "st atic", - "stat ic", - "sta tic", - ". Text", - ".T ext", - ".Te xt", - "Ġ className", - "Ġclass Name", - "p any", - "pan y", - "pa ny", - "Ġ ter", - "Ġt er", - "Ġte r", - "h ead", - "he ad", - "hea d", - "r g", - "Ġ product", - "Ġpro duct", - "Ġprodu ct", - "Ġprod uct", - "T his", - "Th is", - ". âĢĿ", - "Ġ But", - "ĠB ut", - "ĠBu t", - "7 0", - "l oy", - "lo y", - "Ġ double", - "Ġd ouble", - "Ġdo uble", - "Ġdoub le", - "Ġdou ble", - "s g", - "Ġ place", - "Ġp lace", - "Ġpl ace", - "Ġplac e", - "Ġpla ce", - ". x", - "m essage", - "mes sage", - "mess age", - "Ġ information", - "Ġin formation", - "Ġinform ation", - "Ġinformat ion", - "pr ivate", - "priv ate", - "Ġ oper", - "Ġo per", - "Ġop er", - "c ed", - "ce d", - "d b", - "\" > < /", - "P aram", - "Par am", - "Pa ram", - "Para m", - "i cle", - "ic le", - "icl e", - "Ġ week", - "Ġwe ek", - "Ġwee k", - "Ġ prop", - "Ġp rop", - "Ġpro p", - "Ġpr op", - "t able", - "ta ble", - "tab le", - "tabl e", - "id get", - "idge t", - "p lace", - "pl ace", - "pla ce", - "P rop", - "Pro p", - "Pr op", - "Ġ All", - "ĠA ll", - "ĠAl l", - "e ls", - "el s", - "b ox", - "bo x", - ". ĊĊĊĊ", - ".ĊĊ ĊĊ", - ".Ċ ĊĊĊ", - ".ĊĊĊ Ċ", - ". R", - "Ġ To", - "ĠT o", - "i ter", - "it er", - "ite r", - "S h", - "u ration", - "ur ation", - "ura tion", - "urat ion", - "o lder", - "ol der", - "old er", - "_ list", - "_l ist", - "_li st", - "c ome", - "com e", - "co me", - "Ġ sw", - "Ġs w", - "iz ation", - "iza tion", - "ĉ for", - "ĉf or", - "b l", - "Ġ program", - "Ġp rogram", - "Ġpro gram", - "Ġpr ogram", - "Ġprog ram", - "( e", - "a pe", - "ap e", - "c heck", - "ch eck", - "che ck", - ". Forms", - ".Form s", - ".For ms", - "Ġ und", - "Ġu nd", - "Ġun d", - "ateg ory", - "ategor y", - "atego ry", - "7 5", - "a gs", - "ag s", - "Ġ response", - "Ġres ponse", - "Ġrespons e", - "Ġresp onse", - "U S", - "re quest", - "req uest", - "requ est", - "Ġ struct", - "Ġstr uct", - "Ġstru ct", - "e scription", - "es cription", - "Ġ code", - "Ġc ode", - "Ġco de", - "Ġcod e", - "_ H", - "u ffer", - "uff er", - "uf fer", - "Ġ without", - "Ġwith out", - "lo bal", - "lob al", - "M anager", - "Man ager", - "Manage r", - "Mana ger", - "il ter", - "ilt er", - "P O", - "ĉ this", - "ĉt his", - "ĉth is", - "o ption", - "op tion", - "opt ion", - "Ġ sol", - "Ġs ol", - "Ġso l", - "Ġ ===", - "Ġ= ==", - "Ġ== =", - "a kes", - "ak es", - "ake s", - "Cont roller", - "Control ler", - "Contr oller", - "4 4", - "M essage", - "Mes sage", - "Mess age", - "Ġ ref", - "Ġre f", - "Ġr ef", - "e ver", - "ev er", - "eve r", - "Ġ So", - "ĠS o", - "a ining", - "ain ing", - "ai ning", - ". append", - ".app end", - ".ap pend", - "Ġ still", - "Ġst ill", - "Ġstil l", - "Ġpro vid", - "Ġpr ovid", - "Ġprov id", - "Ġ assert", - "Ġas sert", - "Ġass ert", - "m ed", - "me d", - "Ġ cap", - "Ġc ap", - "Ġca p", - "us iness", - "usi ness", - "Ġ rep", - "Ġre p", - "Ġr ep", - "t ings", - "ting s", - "tin gs", - "v ed", - "ve d", - ". N", - "a pi", - "ap i", - "O D", - "Ġ field", - "Ġf ield", - "Ġfi eld", - "Ġfie ld", - "i ven", - "iv en", - "ive n", - "o to", - "ot o", - "âĢ ľ", - "c ol", - "co l", - "( x", - "g ht", - "gh t", - "Res ult", - "C ode", - "Co de", - "Cod e", - ". is", - ".i s", - "l ink", - "li nk", - "lin k", - "Ġ cour", - "Ġc our", - "Ġco ur", - "Ġcou r", - "A n", - "Ġ team", - "Ġt eam", - "Ġte am", - "Ġtea m", - "ĉ int", - "ĉi nt", - "ĉin t", - "i ft", - "if t", - "5 5", - "Ġ second", - "Ġse cond", - "Ġsec ond", - "Ġ going", - "Ġgo ing", - "Ġ range", - "Ġr ange", - "Ġran ge", - "Ġra nge", - "Ġrang e", - "_ E", - "n ess", - "ne ss", - "nes s", - "3 9", - "Ġf am", - "Ġfa m", - "Ġ nil", - "Ġn il", - "Ġni l", - "Ġ Cont", - "ĠC ont", - "ĠCon t", - "ĠCo nt", - "ail able", - "u tes", - "ut es", - "ute s", - "a tab", - "at ab", - "ata b", - "Ġ fact", - "Ġf act", - "Ġfa ct", - "Ġfac t", - "Ġ vis", - "Ġv is", - "Ġvi s", - "( &", - "Ġ AN", - "ĠA N", - "3 1", - "A l", - "t itle", - "ti tle", - "tit le", - "Ġ android", - "Ġand roid", - "C E", - "\\ \"", - "i rt", - "ir t", - "Ġw rit", - "Ġwr it", - "Ð ½", - "ĉ m", - "ft ware", - "o nd", - "on d", - "Ġ ret", - "Ġre t", - "Ġr et", - "os ition", - "osi tion", - "osit ion", - "Ġ home", - "Ġh ome", - "Ġhom e", - "Ġho me", - "Ġ left", - "Ġl eft", - "Ġle ft", - "ar gs", - "arg s", - "m eric", - "mer ic", - "me ric", - "4 8", - "Ġ direct", - "Ġd irect", - "Ġdi rect", - "Ġdir ect", - "Ġdire ct", - "o ci", - "oc i", - "P l", - "A s", - "r et", - "re t", - "a do", - "ad o", - "O f", - "c hn", - "ch n", - "Ġ Get", - "ĠG et", - "ĠGe t", - "e e", - "r oss", - "ro ss", - "ros s", - "( );", - "() ;", - "_ ___", - "__ __", - "___ _", - ". ph", - ".p h", - "I t", - "o ute", - "ou te", - "out e", - "Ġex per", - "Ġexp er", - "ch ool", - "cho ol", - "w ww", - "ww w", - "} ,", - "Ġ allow", - "Ġal low", - "Ġall ow", - "Ġallo w", - "Ġ Â", - "( ))", - "() )", - "s ize", - "si ze", - "siz e", - "i sm", - "is m", - "a i", - "t ract", - "tr act", - "tra ct", - "a ne", - "an e", - ". ..ĊĊ", - ".. .ĊĊ", - "... ĊĊ", - "...Ċ Ċ", - "con text", - "cont ext", - "conte xt", - "Ġ beg", - "Ġb eg", - "Ġbe g", - "C H", - "Ġ page", - "Ġp age", - "Ġpa ge", - "Ġpag e", - "h ip", - "hi p", - "n o", - "c ore", - "co re", - "cor e", - "s p", - "Ġ different", - "Ġd ifferent", - "Ġdiffer ent", - "i able", - "ia ble", - "Ġ Me", - "ĠM e", - "_ IN", - "_I N", - "b utton", - "but ton", - "butt on", - "Ġ Is", - "ĠI s", - "erv ices", - "ervice s", - "Ġ ca", - "Ġc a", - "Ġ around", - "Ġa round", - "Ġar ound", - "Ġarou nd", - "A pp", - "Ap p", - "r ation", - "ra tion", - "rat ion", - "ratio n", - "Ġ rece", - "Ġre ce", - "Ġr ece", - "Ġrec e", - "Ġ really", - "Ġre ally", - "Ġreal ly", - "Ġ image", - "Ġi mage", - "Ġim age", - "Ġimag e", - "Ġima ge", - "Ġ target", - "Ġt arget", - "Ġtar get", - "Ġtarg et", - "Ġ dep", - "Ġd ep", - "Ġde p", - "opy right", - "t ra", - "tr a", - "i ngle", - "in gle", - "ing le", - "i tal", - "it al", - "ita l", - "L ayout", - "Ġ both", - "Ġb oth", - "Ġbo th", - "Ġbot h", - "Over ride", - "a rm", - "ar m", - "= >", - "at erial", - "ate rial", - "ater ial", - "ateria l", - "i led", - "il ed", - "ile d", - "Ġ put", - "Ġp ut", - "Ġpu t", - "Q u", - "Ñ Ģ", - "u ng", - "un g", - "m ap", - "ma p", - "ĉ ĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉ ĉ", - "Ġ level", - "Ġle vel", - "Ġlev el", - "Ġleve l", - "Com ponent", - "Comp onent", - "b ook", - "bo ok", - "boo k", - "c reen", - "cre en", - "cr een", - "_ RE", - "_R E", - "Ġ config", - "Ġcon fig", - "Ġconf ig", - "ã ģ", - "O r", - ". data", - ".d ata", - ".dat a", - ".da ta", - "Ġ document", - "Ġd ocument", - "Ġdoc ument", - "\" ,\"", - "\", \"", - "trib ute", - "u x", - "L og", - "Lo g", - "f erence", - "fer ence", - "fe rence", - "p ost", - "pos t", - "po st", - "_ e", - "Ġ local", - "Ġl ocal", - "Ġlo cal", - "Ġloc al", - "an dom", - "and om", - "ando m", - "as sert", - "ass ert", - "asse rt", - "asser t", - "V al", - "Va l", - "l ected", - "lect ed", - "lec ted", - "i na", - "in a", - "at abase", - "ata base", - "atab ase", - "A dd", - "Ad d", - "Ġ content", - "Ġc ontent", - "Ġcon tent", - "Ġcont ent", - "Ġconten t", - "Ġconte nt", - ". print", - ".p rint", - ".pr int", - "s igned", - "sign ed", - "sig ned", - "r ic", - "ri c", - ". \"ĊĊ", - ".\" ĊĊ", - ".\"Ċ Ċ", - "Ġ fa", - "Ġf a", - "! ĊĊ", - "!Ċ Ċ", - "- f", - "i ved", - "iv ed", - "ive d", - "Ġ quest", - "Ġqu est", - "Ġque st", - "Ġq uest", - "Ġques t", - ". ex", - ".e x", - "Ġ float", - "Ġf loat", - "Ġflo at", - "Ġ develop", - "Ġde velop", - "Ġdev elop", - "Ġdeve lop", - "Ġdevel op", - "о Ð", - "M ap", - "Ma p", - "a ding", - "ad ing", - "adi ng", - "adin g", - "Ġ poss", - "Ġp oss", - "Ġpos s", - "Ġpo ss", - "U E", - "n amespace", - "name space", - "names pace", - "_ O", - "ĉ b", - ". Get", - ".G et", - ".Ge t", - "> (", - "j son", - "js on", - "e tails", - "et ails", - "etail s", - "eta ils", - "6 6", - "Ġ too", - "Ġt oo", - "Ġto o", - "Ġ extends", - "Ġext ends", - "Ġextend s", - "Ġ None", - "ĠN one", - "ĠNo ne", - "ĠNon e", - "Ġ fore", - "Ġf ore", - "Ġfor e", - "Ġfo re", - "( String", - "(S tring", - "(Str ing", - "form at", - "for mat", - "forma t", - "Ġ great", - "Ġg reat", - "Ġgr eat", - "Ġgre at", - "in ter", - "int er", - "inte r", - "c ale", - "ca le", - "cal e", - "Ñ ģ", - "r on", - "ro n", - "i ving", - "iv ing", - "ivi ng", - "E nt", - "En t", - "e ncy", - "en cy", - "enc y", - "x t", - "o y", - "0 5", - "Ġ month", - "Ġm onth", - "Ġmon th", - "Ġmo nth", - "Ġmont h", - "Ġh app", - "Ġha pp", - "Ġhap p", - "Ġ super", - "Ġs uper", - "Ġsu per", - "Ġsup er", - "b ar", - "ba r", - "d efault", - "de fault", - "def ault", - "_ de", - "_d e", - "or ds", - "ord s", - "l n", - "( {Ċ", - "({ Ċ", - "Ġ Ind", - "ĠI nd", - "ĠIn d", - "a ses", - "as es", - "ase s", - "Ġ title", - "Ġt itle", - "Ġtit le", - "Ġti tle", - "Ġ context", - "Ġcon text", - "Ġcont ext", - "Ġconte xt", - "0 8", - "o h", - "- p", - "E m", - "Ġ met", - "Ġm et", - "Ġme t", - "T est", - "Te st", - "Tes t", - "Ġ life", - "Ġl ife", - "Ġli fe", - "Ġlif e", - "_ v", - "Ġ US", - "ĠU S", - "U I", - "o cation", - "oc ation", - "oca tion", - "m d", - "Ġ [Ċ", - "Ġ[ Ċ", - "Ġ ]", - "s w", - "Ġ incre", - "Ġin cre", - "Ġinc re", - "Ġincr e", - "s cript", - "scri pt", - "scr ipt", - "ent ial", - "enti al", - "w ays", - "way s", - "wa ys", - ". de", - ".d e", - "Ġ src", - "Ġs rc", - "Ġsr c", - "Ġ catch", - "Ġc atch", - "Ġcat ch", - "Ġ Americ", - "ĠA meric", - "ĠAm eric", - "ĠAmer ic", - "/ /Ċ", - "// Ċ", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "Ġ pay", - "Ġp ay", - "Ġpa y", - "p lit", - "pl it", - "âĢ Ķ", - "Ġc oun", - "Ġco un", - "Ġcou n", - "o bj", - "ob j", - ". php", - ".p hp", - ".ph p", - "Ġ change", - "Ġch ange", - "Ġchang e", - "Ġcha nge", - "Ġchan ge", - "e thing", - "eth ing", - "' re", - "'r e", - "a ster", - "as ter", - "ast er", - "aste r", - "l os", - "lo s", - "l ation", - "la tion", - "lat ion", - "Ġ ĠĊ", - "ĠĠ Ċ", - "L e", - "à ¤", - "( {", - "re ady", - "read y", - "rea dy", - "Ġ No", - "ĠN o", - "Ġ position", - "Ġp osition", - "Ġpos ition", - "Ġposit ion", - "Ġposi tion", - "Ġ old", - "Ġo ld", - "Ġol d", - "Ġ book", - "Ġb ook", - "Ġbo ok", - "Ġboo k", - "a bled", - "ab led", - "able d", - "abl ed", - "b ug", - "bu g", - "2 02", - "20 2", - "H and", - "Ha nd", - "Han d", - "} ;ĊĊ", - "};Ċ Ċ", - "}; ĊĊ", - "is play", - "isp lay", - "a ving", - "av ing", - "avi ng", - "avin g", - "0 4", - "Ġg over", - "Ġgo ver", - "Ġgov er", - "Ġ version", - "Ġv ersion", - "Ġvers ion", - "S ystem", - "Sys tem", - "Sy stem", - "n ect", - "ne ct", - "res ponse", - "resp onse", - "respons e", - "St yle", - "U p", - "an gu", - "ang u", - "Ġ three", - "Ġth ree", - "Ġthr ee", - "i nit", - "in it", - "ini t", - "e ro", - "er o", - "Ġ law", - "Ġl aw", - "Ġla w", - "en dif", - "end if", - "endi f", - "Ġ base", - "Ġb ase", - "Ġbas e", - "Ġba se", - "e mail", - "em ail", - "ema il", - "( l", - "_ V", - "Ġ conf", - "Ġcon f", - "Ġco nf", - "A TE", - "AT E", - "Ġ during", - "Ġd uring", - "Ġdu ring", - "Ġdur ing", - "t es", - "te s", - "Ġ console", - "Ġcon sole", - "Ġcons ole", - "Ġ Pr", - "ĠP r", - "Ġ spe", - "Ġs pe", - "Ġsp e", - "v es", - "ve s", - "6 5", - "p ath", - "pat h", - "pa th", - "i alog", - "ial og", - "ia log", - "d ition", - "di tion", - "dit ion", - "_ to", - "_t o", - "ar ds", - "ard s", - "Ġ against", - "Ġagain st", - "et work", - "Ġ Ph", - "ĠP h", - "_ L", - "c ur", - "cu r", - "i mit", - "im it", - "imi t", - "W ith", - "Wi th", - "Ġ power", - "Ġp ower", - "Ġpo wer", - "Ġpow er", - "i um", - "iu m", - "' ;ĊĊ", - "';Ċ Ċ", - "'; ĊĊ", - "Ġw om", - "Ġwo m", - "l eft", - "le ft", - "lef t", - "our ces", - "ource s", - "a tri", - "at ri", - "atr i", - "Ġ Im", - "ĠI m", - "Ġ Man", - "ĠM an", - "ĠMa n", - "or th", - "ort h", - "$ {", - "8 8", - "qu als", - "qual s", - "qua ls", - "e se", - "es e", - "_ size", - "_s ize", - "_si ze", - "Ġ iss", - "Ġis s", - "Ġi ss", - "o tal", - "ot al", - "ota l", - "- g", - "i que", - "iqu e", - "iq ue", - "r ame", - "ra me", - "ram e", - "Ġ width", - "Ġw idth", - "Ġwid th", - "e rg", - "er g", - ") (", - "it tle", - "itt le", - "T R", - "Ġ They", - "ĠT hey", - "ĠThe y", - "ĠTh ey", - "e nces", - "en ces", - "ence s", - "enc es", - "0 2", - "r l", - "o ns", - "on s", - "Ġ label", - "Ġl abel", - "Ġla bel", - "Ġlab el", - ". y", - "- t", - "up date", - "upd ate", - "a nel", - "an el", - "ane l", - "s c", - ". to", - ".t o", - "Ġ project", - "Ġpro ject", - "Ġproj ect", - "Ġproje ct", - "à ¼", - "Ġ element", - "Ġe lement", - "Ġel ement", - "Ġele ment", - "Ġelem ent", - "Ġ success", - "Ġs uccess", - "Ġsu ccess", - "Ġsuc cess", - "Ġsucc ess", - "Ġsucces s", - "ĉ ĉĊ", - "ĉĉ Ċ", - ". sh", - ".s h", - "r am", - "ra m", - "c hed", - "ch ed", - "che d", - "( ))Ċ", - "() )Ċ", - "()) Ċ", - "Ġ (Ċ", - "Ġ( Ċ", - "Ġ date", - "Ġd ate", - "Ġda te", - "Ġdat e", - "Ġ tot", - "Ġt ot", - "Ġto t", - "_ ST", - "_S T", - "A ll", - "Al l", - "if ication", - "ific ation", - "ifi cation", - "ifica tion", - "ĉ var", - "ĉv ar", - "ĉva r", - "Ġ tri", - "Ġt ri", - "Ġtr i", - "c hem", - "ch em", - "che m", - "m y", - "Ġ big", - "Ġb ig", - "Ġbi g", - "Ġ Ad", - "ĠA d", - "Ġ At", - "ĠA t", - "o ts", - "ot s", - "n um", - "nu m", - "A ct", - "Ac t", - "Ġ map", - "Ġm ap", - "Ġma p", - "e ra", - "er a", - "c ope", - "co pe", - "cop e", - ". $", - ", âĢĿ", - "Ġ pop", - "Ġp op", - "Ġpo p", - "Ġ few", - "Ġf ew", - "Ġfe w", - "Ġ len", - "Ġl en", - "Ġle n", - "u id", - "ui d", - "e ters", - "et ers", - "eter s", - "ete rs", - "u les", - "ul es", - "ule s", - "à Ń", - "s ource", - "ht tps", - "http s", - "htt ps", - "Ġ dem", - "Ġd em", - "Ġde m", - "Ġ ear", - "Ġe ar", - "Ġea r", - "#### ############", - "######## ########", - "############ ####", - "Ġ match", - "Ġm atch", - "Ġmat ch", - "o ries", - "or ies", - "ori es", - "orie s", - "4 9", - "a ces", - "ace s", - "ac es", - "Ġ Cl", - "ĠC l", - "Ġ node", - "Ġn ode", - "Ġno de", - "Ġnod e", - "7 8", - "i rc", - "ir c", - "l ocal", - "lo cal", - "loc al", - "un ity", - "unit y", - "uni ty", - "} ;Ċ", - "}; Ċ", - "Ġ another", - "Ġan other", - "Ġano ther", - "< <", - "o gle", - "og le", - "ogl e", - "Ġ sit", - "Ġs it", - "Ġsi t", - "e work", - "ew ork", - "T E", - ". I", - "N S", - "o logy", - "ol ogy", - "olog y", - "olo gy", - "o ught", - "ou ght", - "ough t", - "oug ht", - ". Cont", - ".C ont", - ".Con t", - ".Co nt", - "> >", - "Ġ care", - "Ġc are", - "Ġcar e", - "Ġca re", - "st ate", - "stat e", - "sta te", - "ĉ private", - "ĉpr ivate", - "ĉpriv ate", - "Ġ effect", - "Ġe ffect", - "Ġeff ect", - "Ġef fect", - "+ +)", - "++ )", - "_ file", - "_f ile", - "_fil e", - "en ding", - "end ing", - "endi ng", - "L ine", - "Li ne", - "Lin e", - "F or", - "Fo r", - "i or", - "io r", - "Ġ Sc", - "ĠS c", - "Ġ fun", - "Ġf un", - "Ġfu n", - ". Size", - ".S ize", - "ĉ else", - "ĉe lse", - "ĉel se", - "] )", - "st art", - "star t", - "sta rt", - "v ious", - "vi ous", - "vio us", - "Ġ },", - "Ġ} ,", - "o urs", - "ou rs", - "our s", - "Ġ leg", - "Ġl eg", - "Ġle g", - "Ġ service", - "Ġs ervice", - "Ġser vice", - "Ġserv ice", - "Ġservi ce", - "Ġservic e", - "Ġ since", - "Ġs ince", - "Ġsi nce", - "Ġsin ce", - "Ġsinc e", - "i ron", - "ir on", - "iro n", - "L abel", - "La bel", - "Lab el", - "Ġ non", - "Ġn on", - "Ġno n", - "Ġ los", - "Ġl os", - "Ġlo s", - "i ction", - "ic tion", - "ict ion", - "Ġ full", - "Ġf ull", - "Ġful l", - "Ġfu ll", - "a cter", - "act er", - "ac ter", - "b oard", - "bo ard", - "boa rd", - "g ress", - "gr ess", - "gre ss", - "gres s", - "Ġ turn", - "Ġt urn", - "Ġtu rn", - "Ġtur n", - "i ther", - "it her", - "ith er", - "ithe r", - "0 9", - ". size", - ".s ize", - ".si ze", - "Ġ body", - "Ġb ody", - "Ġbo dy", - "Ġbod y", - "r esh", - "re sh", - "res h", - "e turn", - "et urn", - "etur n", - "etu rn", - "1 99", - "19 9", - "( _", - "y les", - "yle s", - "yl es", - "or mal", - "orm al", - "p i", - "Ġ something", - "Ġs omething", - "Ġsome thing", - "Ġsom ething", - "! --", - "u int", - "ui nt", - "uin t", - "Ġ produ", - "Ġp rodu", - "Ġpro du", - "Ġpr odu", - "Ġprod u", - "Ġ stand", - "Ġst and", - "Ġsta nd", - "Ġstan d", - "Ġpro ble", - "Ġpr oble", - "Ġprob le", - "Ġprobl e", - "Ġ available", - "Ġa vailable", - "Ġav ailable", - "Ġavail able", - "m t", - "Ġ Bl", - "ĠB l", - "Ġ ...", - "Ġ. ..", - "Ġ.. .", - "Ġ block", - "Ġb lock", - "Ġbl ock", - "Ġblo ck", - "Ġbloc k", - "In put", - "Ġ keep", - "Ġke ep", - "C ount", - "Co unt", - "Cou nt", - "o pen", - "op en", - "ope n", - "Ġ ['", - "Ġ[ '", - "Ġ throw", - "Ġth row", - "Ġthr ow", - "Ġthro w", - "u ilder", - "uild er", - "ui lder", - "uil der", - "A ction", - "Act ion", - "Ac tion", - "Ġ things", - "Ġth ings", - "Ġthing s", - "Ġthin gs", - "Tr ue", - "Ġ url", - "Ġu rl", - "Ġur l", - "Ġ Bo", - "ĠB o", - "print f", - "Ġ red", - "Ġre d", - "Ġr ed", - "j s", - ". create", - ".c reate", - "Ġ Or", - "ĠO r", - "S tatus", - "St atus", - "Stat us", - "In stance", - "Inst ance", - "Ġ control", - "Ġc ontrol", - "Ġcont rol", - "Ġcontr ol", - "Ġcontro l", - "Ġ come", - "Ġc ome", - "Ġcom e", - "Ġco me", - "Ġ custom", - "Ġc ustom", - "Ġcust om", - "Ġcus tom", - "l ocation", - "lo cation", - "loc ation", - "0 7", - "m odel", - "mod el", - "mo del", - "mode l", - "Ġ čĊ", - "Ġč Ċ", - "Ġ source", - "Ġs ource", - "Ġsour ce", - "Ġe as", - "Ġea s", - ". out", - ".o ut", - "] ĊĊ", - "]Ċ Ċ", - "o ney", - "on ey", - "one y", - "Ġ await", - "Ġa wait", - "Ġaw ait", - "Ġp artic", - "Ġpart ic", - "Ġpar tic", - "Ġparti c", - "A P", - "ub lish", - "ubl ish", - "o des", - "od es", - "ode s", - "_ pro", - "_p ro", - "_pr o", - "p ly", - "pl y", - "r iter", - "ri ter", - "rit er", - "rite r", - "Ġ prov", - "Ġp rov", - "Ġpro v", - "Ġpr ov", - "Ġ mill", - "Ġm ill", - "Ġmil l", - "Ġmi ll", - "H T", - "] )Ċ", - "]) Ċ", - "Ġ chang", - "Ġc hang", - "Ġch ang", - "Ġcha ng", - "Ġchan g", - "Ġ ask", - "Ġa sk", - "Ġas k", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "Ġ output", - "Ġout put", - "Ġ email", - "Ġe mail", - "Ġem ail", - "6 8", - ". push", - ".p ush", - "Ġ }čĊčĊ", - "Ġ} čĊčĊ", - "Ġ}čĊ čĊ", - "i nation", - "in ation", - "ina tion", - "inati on", - "4 7", - "at rix", - "atri x", - "atr ix", - "T able", - "Tab le", - "Ta ble", - "u ccess", - "uc cess", - "ucc ess", - "] );Ċ", - "]) ;Ċ", - "]); Ċ", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ disc", - "Ġd isc", - "Ġdis c", - "Ġdi sc", - "( [", - "Ġ business", - "Ġb usiness", - "Ġbus iness", - "h eight", - "he ight", - "hei ght", - ". html", - ".h tml", - ".ht ml", - ".htm l", - "t a", - "f ield", - "fi eld", - "Ġ required", - "Ġre quired", - "Ġrequire d", - "Ġrequ ired", - "_ R", - "Ġg overn", - "Ġgo vern", - "Ġgover n", - "Ġgov ern", - "} čĊčĊ", - "}čĊ čĊ", - "l ex", - "le x", - "5 00", - "50 0", - ". ,", - "Ġ Set", - "ĠS et", - "ĠSe t", - "u rch", - "ur ch", - "/ //", - "// /", - "t s", - "a f", - "Ġ might", - "Ġm ight", - "Ġmi ght", - "Ġmig ht", - "i story", - "ist ory", - "istor y", - "isto ry", - "S tr", - "St r", - "Ġ never", - "Ġn ever", - "Ġne ver", - "Ġnev er", - "Res ponse", - "Resp onse", - "Respons e", - "ar se", - "ars e", - "a da", - "ad a", - "Ġ How", - "ĠH ow", - "ĠHo w", - "Ġ *)", - "Ġ* )", - "Ġ ;", - "Ġ hard", - "Ġh ard", - "Ġhar d", - "Ġha rd", - "A d", - "Ġ intern", - "Ġin tern", - "Ġint ern", - "Ġinter n", - "Ġinte rn", - "u sed", - "us ed", - "use d", - "( data", - "(d ata", - "(dat a", - "(da ta", - "m od", - "mo d", - "an nel", - "ann el", - "anne l", - "Ġ np", - "Ġn p", - "u gg", - "ug g", - "Ġ />Ċ", - "Ġ/ >Ċ", - "Ġ/> Ċ", - "Ġ called", - "Ġc alled", - "Ġcall ed", - "Ġcal led", - "Ġcalle d", - "b ody", - "bo dy", - "Ġ cho", - "Ġc ho", - "Ġch o", - "( r", - "_ set", - "_s et", - "_se t", - "i rd", - "ir d", - "Ġ >=", - "Ġ> =", - "Ġ };Ċ", - "Ġ} ;Ċ", - "Ġ}; Ċ", - "Ġ options", - "Ġo ptions", - "Ġoption s", - "Ġopt ions", - "Ġ Gener", - "ĠG ener", - "ĠGe ner", - "ĠGen er", - "ĠGene r", - "Ġ height", - "Ġh eight", - "Ġhe ight", - "Ġhei ght", - "P oint", - "Po int", - "Y ou", - "Yo u", - "e ty", - "et y", - "C lick", - "Cl ick", - "Cli ck", - "Ġ small", - "Ġs mall", - "Ġsm all", - "Ġ ide", - "Ġi de", - "Ġid e", - "Ġ access", - "Ġa ccess", - "Ġacc ess", - "Ġac cess", - "Ġacces s", - "angu age", - "Ġ protected", - "Ġprot ected", - "Ġprotect ed", - "Ġ job", - "Ġj ob", - "Ġjo b", - "Ġ There", - "ĠT here", - "ĠThe re", - "ĠTh ere", - "ĠTher e", - "D ef", - "De f", - "Ġ address", - "Ġadd ress", - "Ġad dress", - "Ġaddr ess", - "Ġ uint", - "Ġu int", - "Ġui nt", - "N ot", - "No t", - "o o", - "a ps", - "ap s", - "< div", - "", - "() ->", - "()- >", - "ĉ ĠĠĠ", - "ĉĠ ĠĠ", - "ĉĠĠ Ġ", - "\" ))", - "\") )", - "C ontent", - "Cont ent", - "Con tent", - "_ W", - "p lement", - "pl ement", - "ple ment", - "Ġ won", - "Ġw on", - "Ġwo n", - "Ġ video", - "Ġv ideo", - "Ġvi deo", - "Ġvid eo", - "Ġvide o", - "a di", - "ad i", - "p oint", - "po int", - "poi nt", - "% %", - "0 3", - "Ġ gl", - "Ġg l", - "er ved", - "erv ed", - "erve d", - "v iron", - "vi ron", - "vir on", - "I F", - "u ted", - "ut ed", - "ute d", - "ã ĥ", - "' m", - "Ġ cert", - "Ġc ert", - "Ġce rt", - "Ġcer t", - "Ġ prof", - "Ġp rof", - "Ġpro f", - "Ġpr of", - "Ġ cell", - "Ġc ell", - "Ġce ll", - "Ġcel l", - "a ri", - "ar i", - "Ġ player", - "Ġp layer", - "Ġpl ayer", - "Ġplay er", - "Ġpla yer", - "a is", - "ai s", - "Ġ cost", - "Ġc ost", - "Ġco st", - "Ġcos t", - "Ġ hum", - "Ġh um", - "Ġhu m", - "( R", - "Ġo ffic", - "Ġof fic", - "Ġoff ic", - "k s", - ". text", - ".t ext", - ".te xt", - ".tex t", - "a tures", - "at ures", - "ature s", - "atur es", - "atu res", - "Ġ total", - "Ġt otal", - "Ġto tal", - "Ġtot al", - "Ġ */ĊĊ", - "Ġ* /ĊĊ", - "Ġ*/ ĊĊ", - "Ġ*/Ċ Ċ", - "o pe", - "op e", - "Ġ stat", - "Ġs tat", - "Ġst at", - "Ġsta t", - "U M", - "Ġ load", - "Ġl oad", - "Ġlo ad", - "Ġloa d", - "ight s", - "igh ts", - "Ġ clear", - "Ġc lear", - "Ġcl ear", - "Ġcle ar", - "u ro", - "ur o", - "Ġ techn", - "Ġt echn", - "Ġte chn", - "Ġtech n", - "Ġtec hn", - "up port", - "upp ort", - "I R", - "Ġ row", - "Ġr ow", - "Ġro w", - "Ġse em", - "Ġsee m", - "Ġ q", - "Ġ short", - "Ġs hort", - "Ġsh ort", - "Ġsho rt", - "Ġ Not", - "ĠN ot", - "ĠNo t", - "i pp", - "ip p", - "G roup", - "Gr oup", - "Gro up", - "s ection", - "se ction", - "sec tion", - "sect ion", - "m ax", - "ma x", - "i rl", - "ir l", - "Ġ override", - "Ġover ride", - "Ġ company", - "Ġcom pany", - "Ġcomp any", - "Ġcompan y", - "Ġ done", - "Ġd one", - "Ġdo ne", - "Ġdon e", - "\" );čĊ", - "\") ;čĊ", - "\"); čĊ", - "Ġ gre", - "Ġg re", - "Ġgr e", - ". Re", - ".R e", - "Ġ belie", - "Ġbe lie", - "Ġbel ie", - "r ist", - "ri st", - "ris t", - "Ġ health", - "Ġhe alth", - "Ġheal th", - "A NT", - "AN T", - "( )ĊĊ", - "() ĊĊ", - "()Ċ Ċ", - "Ġ Be", - "ĠB e", - ". value", - ".v alue", - ".val ue", - ".va lue", - "Ġ Gr", - "ĠG r", - "ot tom", - "ott om", - "otto m", - "Ġ args", - "Ġar gs", - "Ġarg s", - "P T", - "s tatus", - "st atus", - "stat us", - "f unc", - "fun c", - "fu nc", - "u ments", - "um ents", - "ument s", - "ume nts", - "umen ts", - "- h", - "N umber", - "Num ber", - ": čĊ", - "Ġ Log", - "ĠL og", - "ĠLo g", - "er ver", - "erv er", - "erve r", - "Ġ ),Ċ", - "Ġ) ,Ċ", - "Ġ), Ċ", - "a ment", - "am ent", - "ame nt", - "amen t", - "Ġ obj", - "Ġo bj", - "Ġob j", - "i nc", - "in c", - "Ġ children", - "Ġch ildren", - "Ġchild ren", - "i cy", - "ic y", - "I Z", - "a nds", - "an ds", - "and s", - "ab ly", - "abl y", - "Ġd istrib", - "Ġdis trib", - "Ġdist rib", - "Ġdistr ib", - "Ġ cur", - "Ġc ur", - "Ġcu r", - "e rial", - "er ial", - "eri al", - "eria l", - "Ġ days", - "Ġd ays", - "Ġday s", - "Ġda ys", - "r eated", - "re ated", - "reate d", - "reat ed", - "rea ted", - "r ect", - "re ct", - "rec t", - "- l", - "i rm", - "ir m", - "i dden", - "id den", - "idd en", - "o mb", - "om b", - "Ġ initial", - "Ġin itial", - "Ġinit ial", - "Ġiniti al", - ". js", - ".j s", - "Ġ â", - "Qu ery", - "Que ry", - "Ġ online", - "Ġon line", - "i mal", - "im al", - "ima l", - ". con", - ".c on", - ".co n", - "a u", - "U rl", - "Ur l", - "c ontrol", - "cont rol", - "contr ol", - "contro l", - "i rection", - "ir ection", - "ire ction", - "irect ion", - "Ġ instance", - "Ġin stance", - "Ġinst ance", - "O RT", - "OR T", - "Ġ Fr", - "ĠF r", - "w here", - "wh ere", - "Ġ javax", - "Ġj avax", - "Ġjav ax", - "Ġjava x", - "Ġ organ", - "Ġo rgan", - "Ġor gan", - "Ġorg an", - "a pter", - "ap ter", - "apt er", - "Ġ reason", - "Ġre ason", - "o ptions", - "option s", - "opt ions", - "5 9", - "Ġ Mar", - "ĠM ar", - "ĠMa r", - "( a", - "Ġ within", - "Ġwith in", - "Ġwi thin", - "Ġwit hin", - ". âĢĿĊĊ", - ".âĢĿ ĊĊ", - ".âĢĿĊ Ċ", - "O DE", - "OD E", - "_ DE", - "_D E", - "ad min", - "adm in", - "en ded", - "end ed", - "ende d", - "Ġ design", - "Ġd esign", - "Ġde sign", - "Ġdes ign", - "Ġdesi gn", - "Ġ Data", - "ĠD ata", - "ĠDa ta", - "ĠDat a", - "u ne", - "un e", - "Ġ File", - "ĠF ile", - "ĠFil e", - "ĠFi le", - "r oot", - "ro ot", - "Ġ cent", - "Ġc ent", - "Ġce nt", - "Ġcen t", - "Ġ arr", - "Ġa rr", - "Ġar r", - "_ add", - "_a dd", - "_ad d", - "l en", - "le n", - "p age", - "pa ge", - "pag e", - ", '", - "_ str", - "_s tr", - "_st r", - "Ġ bro", - "Ġb ro", - "Ġbr o", - "ab ility", - "abil ity", - "abilit y", - "o uth", - "ou th", - "out h", - "5 8", - "/ c", - "p ose", - "pos e", - "po se", - "ir tual", - "irt ual", - "e arch", - "ear ch", - "ea rch", - "_ url", - "_u rl", - "_ur l", - "ar gin", - "arg in", - "H ttp", - "Ġ school", - "Ġs chool", - "Ġsch ool", - "Ġscho ol", - "a va", - "av a", - "Ġ consider", - "Ġcons ider", - "Ġconsid er", - ". label", - ".l abel", - ".lab el", - "Ġ Array", - "ĠA rray", - "ĠAr ray", - "ĠArr ay", - "4 2", - "w eb", - "we b", - "o pt", - "op t", - ". println", - ".print ln", - "u lation", - "ul ation", - "ula tion", - "Ġ func", - "Ġf unc", - "Ġfun c", - "Ġfu nc", - "P L", - "Ġ \"\\", - "Ġ\" \\", - "Ġ Text", - "ĠT ext", - "ĠTe xt", - "ĠTex t", - "act ory", - "actor y", - "( function", - "(f unction", - "(func tion", - "(fun ction", - "n ull", - "nu ll", - "nul l", - "Ġ eng", - "Ġe ng", - "Ġen g", - "d own", - "do wn", - "Ġ include", - "Ġin clude", - "Ġinclud e", - "Ġinc lude", - "Ġincl ude", - "Ġinclu de", - "Ġ En", - "ĠE n", - "Ġ Dr", - "ĠD r", - "Ġ db", - "Ġd b", - "! !", - "s ide", - "si de", - "sid e", - "Ġ init", - "Ġin it", - "Ġi nit", - "Ġini t", - "qu ired", - "quire d", - "quir ed", - "qui red", - "Ġ She", - "ĠS he", - "ĠSh e", - "C olumn", - "Col umn", - "re act", - "rea ct", - "Ġ ann", - "Ġa nn", - "Ġan n", - "Ġ stop", - "Ġs top", - "Ġst op", - "Ġsto p", - "Ġ later", - "Ġl ater", - "Ġla ter", - "Ġlate r", - "Ġlat er", - "Ġ That", - "ĠT hat", - "ĠTh at", - "en tion", - "ent ion", - "enti on", - "d f", - "U G", - "I LE", - "IL E", - "Ġ client", - "Ġc lient", - "Ġcl ient", - "Ġcli ent", - "r aft", - "ra ft", - "raf t", - "f fer", - "ff er", - "ffe r", - "P OST", - "PO ST", - "POS T", - "el per", - "elp er", - "Ġ love", - "Ġl ove", - "Ġlo ve", - "Ġlov e", - "qu ote", - "quot e", - "quo te", - "o ud", - "ou d", - "Ġ json", - "Ġj son", - "Ġjs on", - "Ġ able", - "Ġa ble", - "Ġab le", - "Ġabl e", - "Ġ men", - "Ġm en", - "Ġme n", - "A X", - "Ġ Copyright", - "ĠC opyright", - "ĠCopy right", - "à ¶", - "a vig", - "av ig", - "avi g", - "r eq", - "re q", - "C lient", - "Cl ient", - "Cli ent", - "} );Ċ", - "}) ;Ċ", - "}); Ċ", - ". Com", - ".C om", - ".Co m", - "e rc", - "er c", - "i lt", - "il t", - "p ecial", - "pe cial", - "pec ial", - "pecia l", - "_ com", - "_c om", - "_co m", - "r oom", - "ro om", - ". Name", - ".N ame", - "Ġ give", - "Ġg ive", - "Ġgi ve", - "a mb", - "am b", - "i ke", - "ik e", - "Ġ condition", - "Ġcon dition", - "Ġcond ition", - "Ġcondi tion", - "c lient", - "cl ient", - "cli ent", - "a tors", - "at ors", - "ator s", - "ato rs", - ": \"", - "Ġ copy", - "Ġc opy", - "Ġco py", - "Ġcop y", - "u ture", - "ut ure", - "ivers ity", - "iversit y", - "er nal", - "ern al", - "erna l", - "{ {", - "Ġ Can", - "ĠC an", - "ĠCa n", - "o unc", - "ou nc", - "oun c", - "d o", - "Ġ occ", - "Ġo cc", - "Ġoc c", - "Ġ appro", - "Ġapp ro", - "Ġap pro", - "th ers", - "ther s", - "the rs", - "z e", - "Ġ either", - "Ġe ither", - "Ġei ther", - "Ġ Fl", - "ĠF l", - "Ġ important", - "Ġimport ant", - "Ġ lead", - "Ġl ead", - "Ġle ad", - "at tr", - "att r", - "A RT", - "AR T", - "E qual", - "Equ al", - "Eq ual", - "Ġ da", - "Ġd a", - "et ch", - "etc h", - "e ntity", - "ent ity", - "enti ty", - "Ġ family", - "Ġf amily", - "Ġfam ily", - "Ġfamil y", - "ad ding", - "add ing", - "addin g", - "Ġ option", - "Ġo ption", - "Ġop tion", - "Ġopt ion", - "Ġ exist", - "Ġex ist", - "i ca", - "ic a", - "Ġ Object", - "ĠO bject", - "ĠOb ject", - "ĠObj ect", - "6 9", - "' ve", - "v ers", - "ver s", - "ve rs", - "it ional", - "ition al", - "iti onal", - "6 7", - "out put", - "Ġ True", - "ĠTr ue", - "ĠTru e", - "Ġ OF", - "ĠO F", - "_ time", - "_t ime", - "_tim e", - "_ti me", - "Ġ offer", - "Ġo ffer", - "Ġof fer", - "Ġoff er", - "Ġ });ĊĊ", - "Ġ} );ĊĊ", - "Ġ});Ċ Ċ", - "Ġ}) ;ĊĊ", - "Ġ}); ĊĊ", - "H ER", - "HE R", - "e gin", - "eg in", - "\" \"", - "Ġ water", - "Ġw ater", - "Ġwa ter", - "Ġwat er", - "Ġ che", - "Ġc he", - "Ġch e", - "Ġ My", - "ĠM y", - "o red", - "or ed", - "ore d", - "Ġ step", - "Ġs tep", - "Ġst ep", - "Ġste p", - "a nces", - "an ces", - "ance s", - "anc es", - "C K", - "A Y", - "à ¸", - "str uction", - "struct ion", - "stru ction", - "( C", - "3 00", - "30 0", - "o uch", - "ou ch", - "St ream", - "Str eam", - "act ive", - "activ e", - "a ma", - "am a", - "E ntity", - "Ent ity", - "pro duct", - "produ ct", - "prod uct", - "( ){Ċ", - "() {Ċ", - "(){ Ċ", - "Ġ government", - "Ġg overnment", - "Ġgovern ment", - "Ġ ID", - "ĠI D", - "aj or", - "ajo r", - "A nd", - "An d", - "Ġ display", - "Ġd isplay", - "Ġdis play", - "Ġdisp lay", - "Ġdispl ay", - "Ð »", - "Ġ times", - "Ġt imes", - "Ġtime s", - "Ġtim es", - "Ġti mes", - "Ġ four", - "Ġf our", - "Ġfo ur", - "Ġfou r", - "Ġ far", - "Ġf ar", - "Ġfa r", - "Ġ present", - "Ġp resent", - "Ġpre sent", - "Ġpres ent", - "Ġ NS", - "ĠN S", - "Ġ \\Ċ", - "Ġ\\ Ċ", - "u est", - "ue st", - "ues t", - "Ġ bas", - "Ġb as", - "Ġba s", - "e cho", - "ec ho", - "ech o", - "ch ild", - "chi ld", - "if ier", - "ifi er", - "ifie r", - "H andler", - "Hand ler", - "Handle r", - "Ġ lib", - "Ġl ib", - "Ġli b", - "P roperty", - "Pro perty", - "Prop erty", - "trans lation", - "Ġ room", - "Ġr oom", - "Ġro om", - "Ġ once", - "Ġo nce", - "Ġon ce", - "Ġonc e", - "Ġ []", - "Ġ[ ]", - "c enter", - "cent er", - "cen ter", - "cente r", - "================ ================", - "Ġ results", - "Ġres ults", - "Ġresult s", - "Ġ continue", - "Ġcont inue", - "Ġcontin ue", - "Ġcontinu e", - "Ġ talk", - "Ġt alk", - "Ġtal k", - "Ġta lk", - "_ get", - "_g et", - "_ge t", - "Ġ grow", - "Ġg row", - "Ġgr ow", - "Ġgro w", - ". sw", - ".s w", - "e b", - "Ġ Public", - "ĠP ublic", - "ĠPub lic", - "O P", - "ec ute", - "ecut e", - "o ls", - "ol s", - "Ġ **", - "Ġ* *", - "\" );ĊĊ", - "\");Ċ Ċ", - "\") ;ĊĊ", - "\"); ĊĊ", - "Ġ mass", - "Ġm ass", - "Ġma ss", - "Ġmas s", - "u red", - "ur ed", - "ure d", - ". class", - ".c lass", - ".cl ass", - "o mic", - "om ic", - "omi c", - "Ġ mean", - "Ġm ean", - "Ġme an", - "i ps", - "ip s", - "Ġ aut", - "Ġa ut", - "Ġau t", - ") ;čĊčĊ", - ");čĊ čĊ", - "); čĊčĊ", - "Ġ until", - "Ġun til", - "Ġunt il", - "Ġ market", - "Ġm arket", - "Ġmark et", - "Ġmar ket", - "Ġ area", - "Ġa rea", - "Ġare a", - "Ġar ea", - "u it", - "ui t", - "Ġ length", - "Ġl ength", - "Ġle ngth", - "Ġlen gth", - "Ġleng th", - "Ġ With", - "ĠW ith", - "ĠWi th", - "ĠWit h", - "str uctor", - "struct or", - "stru ctor", - "e vent", - "ev ent", - "even t", - "eve nt", - "\" ><", - "\"> <", - "Ġ Sp", - "ĠS p", - "I V", - "Ġ mus", - "Ġm us", - "Ġmu s", - "i ff", - "if f", - "Ġ kind", - "Ġk ind", - "Ġki nd", - "Ġkin d", - "a uthor", - "aut hor", - "auth or", - "o unds", - "ou nds", - "ound s", - "oun ds", - "m b", - "_ key", - "_k ey", - "_ke y", - "4 1", - "w idth", - "wid th", - "pos itory", - "posit ory", - "positor y", - "Ġ light", - "Ġl ight", - "Ġli ght", - "Ġlig ht", - "u k", - "R ow", - "Ro w", - "o hn", - "oh n", - "a lf", - "al f", - "viron ment", - "a pper", - "ap per", - "app er", - "appe r", - "ol lections", - "oll ections", - "ollection s", - "ollect ions", - "olle ctions", - "Ġ side", - "Ġs ide", - "Ġsi de", - "Ġsid e", - "_ info", - "_in fo", - "_inf o", - "Ġ example", - "Ġex ample", - "Ġexam ple", - "i mary", - "im ary", - "ima ry", - "imar y", - "Ġ wr", - "Ġw r", - "Ġ camp", - "Ġc amp", - "Ġca mp", - "Ġcam p", - "cri be", - "cr ibe", - "2 55", - "25 5", - "\" /", - "Ġ miss", - "Ġm iss", - "Ġmis s", - "Ġmi ss", - "w ay", - "wa y", - "Ġ based", - "Ġb ased", - "Ġbase d", - "Ġbas ed", - "Ġba sed", - "Ġ plan", - "Ġp lan", - "Ġpl an", - "Ġpla n", - "V is", - "Vi s", - "o main", - "om ain", - "oma in", - "u nk", - "un k", - "Ġ away", - "Ġa way", - "Ġaw ay", - "U P", - "< T", - "O S", - "i od", - "io d", - "Ġ Mon", - "ĠM on", - "ĠMo n", - "âĢĻ re", - "Ġ lik", - "Ġl ik", - "Ġli k", - "à §", - "i vely", - "iv ely", - "ive ly", - "ivel y", - ". v", - "i mer", - "im er", - "ime r", - "i zer", - "iz er", - "ize r", - "S ub", - "Su b", - "Ġ button", - "Ġb utton", - "Ġbut ton", - "Ġbutt on", - "Ġbutto n", - "Ġ Up", - "ĠU p", - "Ġ experience", - "Ġex perience", - "Ġexper ience", - "Ġexperi ence", - "C L", - "Ġ render", - "Ġre nder", - "Ġr ender", - "Ġren der", - "Ġrend er", - "_ value", - "_v alue", - "_val ue", - "_va lue", - "Ġ near", - "Ġn ear", - "Ġne ar", - "U RL", - "UR L", - "a lt", - "al t", - "Ġ country", - "Ġc ountry", - "Ġcount ry", - "Ġcoun try", - "ib ility", - "ibil ity", - "ibilit y", - "ibili ty", - "5 7", - "( ),Ċ", - "() ,Ċ", - "(), Ċ", - "e ad", - "ea d", - "Ġ author", - "Ġa uthor", - "Ġaut hor", - "Ġauth or", - "Ġ specific", - "Ġs pecific", - "Ġspec ific", - "Ġspeci fic", - "b ase", - "ba se", - "bas e", - "( name", - "(n ame", - "o nes", - "on es", - "one s", - "Ġ Do", - "ĠD o", - "Ġ along", - "Ġa long", - "Ġal ong", - "Ġalo ng", - "y ear", - "ye ar", - "Ġ express", - "Ġex press", - "Ġexp ress", - "Ġexpr ess", - "Ġexpres s", - ". '", - "e nv", - "en v", - "Ġ begin", - "Ġb egin", - "Ġbe gin", - "Ġbeg in", - "Ġ software", - "Ġs oftware", - "Ġso ftware", - "Ġsoft ware", - "Ġ imp", - "Ġi mp", - "Ġim p", - "Ġ win", - "Ġw in", - "Ġwi n", - "ó n", - "Ġ thing", - "Ġth ing", - "Ġthin g", - "Ġthi ng", - "T rans", - "Tr ans", - "Tra ns", - "Ġ THE", - "ĠT HE", - "ĠTH E", - "Ġ ", - "Ġ? >", - "Ġ den", - "Ġd en", - "Ġde n", - "ob ile", - "obi le", - "obil e", - "ch ange", - "chan ge", - "cha nge", - "chang e", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "i ci", - "ic i", - "n a", - "Ġ Form", - "ĠF orm", - "ĠFor m", - "ĠFo rm", - "Ġ sort", - "Ġs ort", - "Ġso rt", - "Ġsor t", - "S elect", - "Se lect", - "Sel ect", - "Sele ct", - "p are", - "par e", - "pa re", - "Ġ thought", - "Ġth ought", - "Ġthough t", - "Ġthou ght", - "Ġtho ught", - "_ con", - "_c on", - "_co n", - "Ġ task", - "Ġt ask", - "Ġta sk", - "Ġtas k", - "o cus", - "oc us", - "ocu s", - "Ġ DE", - "ĠD E", - "Ġ Min", - "ĠM in", - "ĠMi n", - "Ġ opt", - "Ġo pt", - "Ġop t", - "ĉ break", - "ĉb reak", - "u mer", - "um er", - "ume r", - "K E", - "t hen", - "th en", - "the n", - "Ġ det", - "Ġd et", - "Ġde t", - "Ġ Test", - "ĠT est", - "ĠTe st", - "ĠTes t", - "p orts", - "port s", - "por ts", - "po rts", - "Ġ review", - "Ġre view", - "Ġr eview", - "Ġrev iew", - "( '/", - "(' /", - "m ove", - "mo ve", - "mov e", - "Ġ switch", - "Ġs witch", - "Ġsw itch", - "E RT", - "ER T", - "p atch", - "pat ch", - "an not", - "ann ot", - "anno t", - "ã Ĥ", - "Ġ above", - "Ġa bove", - "Ġab ove", - "it ive", - "iti ve", - "5 6", - "Ġ question", - "Ġqu estion", - "Ġquest ion", - "Ġq uestion", - "Ġquesti on", - "Ġques tion", - "Ġ Qu", - "ĠQ u", - "ãĢĤ ĊĊ", - "ãĢĤĊ Ċ", - "g le", - "gl e", - "Ġ word", - "Ġw ord", - "Ġwor d", - "Ġwo rd", - "Ġ provide", - "Ġpro vide", - "Ġprovid e", - "Ġprov ide", - "Ġ Return", - "ĠR eturn", - "ĠRe turn", - "ĠRet urn", - "Ġ research", - "Ġre search", - "Ġres earch", - "ã o", - "u str", - "us tr", - "ust r", - "Ġ publish", - "Ġp ublish", - "Ġpub lish", - "Ġpubli sh", - "ch ema", - "che ma", - "chem a", - "} }", - "Ġ CON", - "ĠC ON", - "ĠCO N", - "- in", - "-i n", - "all back", - "Ġ cover", - "Ġc over", - "Ġco ver", - "Ġcov er", - "\\ \\", - "c olor", - "co lor", - "col or", - "colo r", - "Ġ IS", - "ĠI S", - "Ġ whether", - "Ġwh ether", - "Ġwhe ther", - "i mate", - "im ate", - "ima te", - "imat e", - "i sc", - "is c", - "B ar", - "Ba r", - "Ġ div", - "Ġd iv", - "Ġdi v", - "B e", - "o urn", - "ou rn", - "our n", - "Ġ having", - "Ġh aving", - "Ġha ving", - "Ġhav ing", - "l em", - "le m", - "p layer", - "pl ayer", - "play er", - "pla yer", - "a bs", - "ab s", - "am era", - "ame ra", - "amer a", - "n ey", - "ne y", - "Ġ exc", - "Ġe xc", - "Ġex c", - "g ether", - "get her", - "ge ther", - "p lied", - "pl ied", - "a o", - "[ $", - "Ġ ++", - "Ġ+ +", - "i pe", - "ip e", - "s how", - "sh ow", - "/ d", - "[ :", - "a gement", - "ag ement", - "age ment", - "agem ent", - "l ev", - "le v", - "_ ID", - "_I D", - "9 7", - "r ary", - "ra ry", - "rar y", - "a des", - "ad es", - "ade s", - "_ se", - "_s e", - "a use", - "au se", - "aus e", - "Ġ employ", - "Ġe mploy", - "Ġem ploy", - "Ġemp loy", - "Ġ */čĊ", - "Ġ* /čĊ", - "Ġ*/ čĊ", - "Ġ fre", - "Ġf re", - "Ġfr e", - "Ġ' @", - "Ġcom plet", - "Ġcomp let", - "Ġcomple t", - "Ġcompl et", - "Ġ large", - "Ġl arge", - "Ġlarg e", - "Ġlar ge", - "r al", - "ra l", - "\\ x", - "Ġ fac", - "Ġf ac", - "Ġfa c", - "< String", - ">", - "Ġ> >", - "Ġ face", - "Ġf ace", - "Ġfa ce", - "Ġfac e", - "C TION", - "CT ION", - "Ġ save", - "Ġs ave", - "Ġsa ve", - "Ġsav e", - "Ġ typ", - "Ġt yp", - "Ġty p", - "d ev", - "de v", - "( \"#", - "(\" #", - "A GE", - "AG E", - "cont ainer", - "contain er", - "e dit", - "ed it", - "edi t", - "Q L", - "Ġ items", - "Ġit ems", - "Ġi tems", - "Ġitem s", - "Ġ social", - "Ġs ocial", - "Ġso cial", - "Ġsoc ial", - "Ġsoci al", - "i en", - "ie n", - "Ġ React", - "ĠRe act", - ") .ĊĊ", - "). ĊĊ", - ").Ċ Ċ", - "Ġ mar", - "Ġm ar", - "Ġma r", - "Ġre du", - "Ġr edu", - "Ġred u", - "Ġ RE", - "ĠR E", - ". put", - ".p ut", - "Ġ major", - "Ġm ajor", - "Ġmaj or", - "C ell", - "Ce ll", - "Cel l", - "n ext", - "ne xt", - "nex t", - "Ġ expected", - "Ġex pected", - "Ġexp ected", - "Ġexpect ed", - "Ġ yet", - "Ġy et", - "Ġye t", - "Ġin div", - "Ġind iv", - "trib utes", - "tribute s", - "at is", - "ati s", - "a med", - "am ed", - "ame d", - "Ġ food", - "Ġf ood", - "Ġfo od", - "Ġfoo d", - "S ource", - "( string", - "(s tring", - "(str ing", - "(st ring", - "Ġ +Ċ", - "Ġ+ Ċ", - "i tes", - "it es", - "ite s", - "d r", - "Ġ members", - "Ġm embers", - "Ġmem bers", - "Ġmember s", - "Ġmemb ers", - "Ġ comb", - "Ġc omb", - "Ġcom b", - "Ġco mb", - "i tems", - "it ems", - "ite ms", - "item s", - "Ġ Per", - "ĠP er", - "ĠPe r", - "T H", - "= True", - "Ġ bar", - "Ġb ar", - "Ġba r", - "_ SE", - "_S E", - "c omm", - "com m", - "co mm", - "( w", - ") ĊĊĊ", - ")Ċ ĊĊ", - ")ĊĊ Ċ", - "Ġ send", - "Ġs end", - "Ġse nd", - "Ġsen d", - "Ġ inc", - "Ġin c", - "Ġi nc", - "un signed", - "uns igned", - "F A", - "Ġ params", - "Ġpar ams", - "Ġparam s", - "Ġpara ms", - "Ġpa rams", - "a pping", - "ap ping", - "app ing", - "r os", - "ro s", - "u gin", - "ug in", - "ugi n", - "f a", - "Ġ connection", - "Ġcon nection", - "Ġconn ection", - "Ġconnect ion", - "Ġ };ĊĊ", - "Ġ} ;ĊĊ", - "Ġ};Ċ Ċ", - "Ġ}; ĊĊ", - "Ġb ecome", - "Ġbe come", - "Ġbec ome", - "M ode", - "Mod e", - "Mo de", - "Ġ ev", - "Ġe v", - "Ġ diff", - "Ġd iff", - "Ġdif f", - "Ġdi ff", - "Ġ United", - "ĠUn ited", - "ĠUnit ed", - "ĠUni ted", - "H eight", - "He ight", - "f ully", - "ful ly", - "full y", - "im ages", - "image s", - "ima ges", - "imag es", - "Ġ makes", - "Ġm akes", - "Ġmake s", - "Ġma kes", - "Ġmak es", - "Ġ global", - "Ġg lobal", - "Ġglob al", - "Ġglo bal", - "Ġ contact", - "Ġcont act", - "Ġconta ct", - "' :Ċ", - "': Ċ", - "Ġ abs", - "Ġa bs", - "Ġab s", - "а Ð", - "f loat", - "flo at", - "Ġ except", - "Ġex cept", - "Ġexc ept", - "Ġexce pt", - "Ġ Pol", - "ĠP ol", - "ĠPo l", - "Ch ild", - "Chi ld", - "t yp", - "ty p", - "Ġc ertain", - "Ġcert ain", - "Ġcer tain", - "i ón", - "ió n", - "O UT", - "OU T", - "Ġim pro", - "Ġimp ro", - "Ġimpr o", - "i les", - "il es", - "ile s", - "Ġ -->Ċ", - "Ġ- ->Ċ", - "Ġ-- >Ċ", - "Ġ--> Ċ", - "Ġ Part", - "ĠP art", - "ĠPar t", - "ĠPa rt", - "val ues", - "value s", - "valu es", - "o ss", - "os s", - "/ **", - "/* *", - "i lit", - "il it", - "ili t", - "Ġ Event", - "ĠE vent", - "ĠEven t", - "ĠEv ent", - "ĠEve nt", - "c urity", - "cur ity", - "s ter", - "st er", - "ste r", - "Ġ character", - "Ġchar acter", - "1 98", - "19 8", - "Ġ news", - "Ġn ews", - "Ġnew s", - "Ġne ws", - "Ġ \",", - "Ġ\" ,", - "Ġ device", - "Ġd evice", - "Ġde vice", - "Ġdev ice", - "c el", - "ce l", - "lo gin", - "log in", - "he et", - "hee t", - "D efault", - "De fault", - "Def ault", - "@ \"", - "ĉ Ġ", - "c lick", - "cl ick", - "cli ck", - "( value", - "(v alue", - "(val ue", - "(va lue", - "Ġ Ab", - "ĠA b", - "Ġ previous", - "Ġpre vious", - "Ġprev ious", - "ERR OR", - "o cal", - "oc al", - "oca l", - "Ġ material", - "Ġm aterial", - "Ġmat erial", - "Ġmate rial", - "Ġmateria l", - "Ġmater ial", - "Ġmateri al", - "Ġ below", - "Ġb elow", - "Ġbe low", - "Ġbel ow", - "Ġ Christ", - "ĠCh rist", - "ĠChris t", - "ĠChr ist", - "Ġ media", - "Ġm edia", - "Ġme dia", - "Ġmed ia", - "Ġmedi a", - "c over", - "co ver", - "cov er", - "Ġ UI", - "ĠU I", - "Ġ fail", - "Ġf ail", - "Ġfa il", - "Ġ black", - "Ġb lack", - "Ġbl ack", - "Ġbla ck", - "Ġ component", - "Ġcom ponent", - "Ġcomp onent", - "Ġ American", - "ĠA merican", - "ĠAmeric an", - "ĠAmerica n", - "ĠAmer ican", - "Ġ added", - "Ġadd ed", - "Ġad ded", - "Ġ buy", - "Ġb uy", - "Ġbu y", - "s tit", - "st it", - "sti t", - "Ġ came", - "Ġc ame", - "Ġca me", - "Ġcam e", - "Ġ delete", - "Ġde lete", - "Ġdel ete", - "Ġdelet e", - "Ġdele te", - "p roperty", - "pro perty", - "prop erty", - "proper ty", - "o ding", - "od ing", - "odi ng", - "Ġ card", - "Ġc ard", - "Ġcar d", - "Ġca rd", - "r ops", - "ro ps", - "rop s", - "Ġ https", - "Ġhttp s", - "Ġht tps", - "Ġhtt ps", - "Ġ root", - "Ġr oot", - "Ġro ot", - "Ġ handle", - "Ġh andle", - "Ġhand le", - "Ġhan dle", - "C C", - "B ack", - "Ba ck", - "em plate", - "emp late", - "empl ate", - "Ġ getting", - "Ġg etting", - "Ġget ting", - "_ by", - "_b y", - "m ail", - "ma il", - "mai l", - "_ sh", - "_s h", - ". assert", - ".as sert", - "Ġ Dec", - "ĠD ec", - "ĠDe c", - "( true", - "(tr ue", - "Ġ comput", - "Ġcom put", - "Ġcomp ut", - "Ġ claim", - "Ġcl aim", - "Ġcla im", - "' =>", - "'= >", - "Ġ Sub", - "ĠS ub", - "ĠSu b", - "Ġ air", - "Ġa ir", - "Ġai r", - "o ps", - "op s", - "n av", - "na v", - "e ments", - "em ents", - "ement s", - "eme nts", - "emen ts", - "( id", - "(i d", - "Ġ enter", - "Ġen ter", - "Ġent er", - "an ged", - "ang ed", - "ange d", - "E nd", - "En d", - "Ġ location", - "Ġl ocation", - "Ġlo cation", - "Ġloc ation", - "Ġ night", - "Ġn ight", - "Ġni ght", - "Ġnig ht", - "Ġ doing", - "Ġdo ing", - "Ġdoi ng", - "Ġ Red", - "ĠR ed", - "ĠRe d", - "l in", - "li n", - "} ĊĊĊ", - "}Ċ ĊĊ", - "}ĊĊ Ċ", - "v ider", - "vid er", - "vi der", - "vide r", - "Ġ pick", - "Ġp ick", - "Ġpi ck", - "Ġpic k", - "Ġ watch", - "Ġw atch", - "Ġwat ch", - "ess ages", - "essage s", - "essa ges", - "Ġ human", - "Ġh uman", - "Ġhum an", - "Ġhu man", - "Ġ dam", - "Ġd am", - "Ġda m", - "p end", - "pe nd", - "pen d", - "d ir", - "di r", - "Ġ tax", - "Ġt ax", - "Ġta x", - "Ġ girl", - "Ġg irl", - "Ġgi rl", - "Ġgir l", - "re et", - "ree t", - "Ġ box", - "Ġb ox", - "Ġbo x", - "Ġ strong", - "Ġst rong", - "Ġstr ong", - "Ġstro ng", - "Ġstron g", - "( v", - "r el", - "re l", - "Ġ interface", - "Ġinter face", - "Ġinterf ace", - "Ġ msg", - "Ġm sg", - "Ġms g", - "f ect", - "fe ct", - "fec t", - "_ at", - "_a t", - "Ġ house", - "Ġh ouse", - "Ġhous e", - "Ġho use", - "Ġ track", - "Ġt rack", - "Ġtr ack", - "Ġtra ck", - "' );ĊĊ", - "') ;ĊĊ", - "');Ċ Ċ", - "'); ĊĊ", - "j e", - "Ġ John", - "ĠJ ohn", - "ĠJo hn", - "ĠJoh n", - "i str", - "is tr", - "ist r", - "( S", - "u be", - "ub e", - "Ġ ce", - "Ġc e", - "it ted", - "itt ed", - "itte d", - "V ER", - "VE R", - "* )", - "p arent", - "par ent", - "pare nt", - "pa rent", - "paren t", - "Ġ application", - "Ġapp lication", - "Ġap plication", - "Ġapplic ation", - "Ġappl ication", - "a ny", - "an y", - ". swing", - ".s wing", - ".sw ing", - "Ġ pack", - "Ġp ack", - "Ġpa ck", - "Ġpac k", - "\\ u", - "Ġp ract", - "Ġpr act", - "Ġpra ct", - "Ġprac t", - "Ġ section", - "Ġs ection", - "Ġse ction", - "Ġsec tion", - "Ġsect ion", - "c tx", - "ct x", - "Ġ unsigned", - "Ġun signed", - "Ġuns igned", - ". Point", - ".P oint", - "Ġ One", - "ĠO ne", - "ĠOn e", - "Ä ±", - "i ple", - "ip le", - "ipl e", - "a id", - "ai d", - "Ñ ĥ", - "V ector", - "Vec tor", - "Ve ctor", - "Vect or", - "b yte", - "by te", - "byt e", - "Ġ wait", - "Ġw ait", - "Ġwa it", - "Ġ Ãł", - "Ġà ł", - "à ¥", - "Ġto gether", - "Ġtog ether", - "Ġ throws", - "Ġth rows", - "Ġthrow s", - "Ġthr ows", - "Ġthro ws", - "F O", - "' ))", - "') )", - "h ost", - "ho st", - "hos t", - "i sing", - "is ing", - "isi ng", - "isin g", - ". view", - ".v iew", - "Ġ terms", - "Ġte rms", - "Ġter ms", - "Ġterm s", - "f ramework", - "fr amework", - "frame work", - "fram ework", - "- r", - "Ġ apply", - "Ġapp ly", - "Ġap ply", - "Ġappl y", - "Ġ session", - "Ġs ession", - "Ġsess ion", - "O ptions", - "Option s", - "Opt ions", - "ug gest", - "ugg est", - "Ġ others", - "Ġo thers", - "Ġother s", - "w itter", - "wit ter", - "Ġ fund", - "Ġf und", - "Ġfun d", - "Ġfu nd", - "I nit", - "In it", - "Ini t", - "_ _(", - "__ (", - "ens or", - "enso r", - "G ET", - "GE T", - "Ġse veral", - "Ġsever al", - "Ġsev eral", - "i i", - "[ j", - "I O", - "Ġ template", - "Ġt emplate", - "Ġtem plate", - "Ġtemp late", - "Ġtempl ate", - "P osition", - "Pos ition", - "Ġe con", - "Ġec on", - "Ġeco n", - "a chine", - "ach ine", - "achi ne", - "Ġ il", - "Ġi l", - ". spring", - ".s pring", - ".sp ring", - "m ain", - "ma in", - "mai n", - "e lt", - "el t", - "i ment", - "im ent", - "ime nt", - "imen t", - "R ec", - "Re c", - "m m", - "Ġ University", - "ĠUn iversity", - "ĠUnivers ity", - "urs or", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "G L", - "ic ture", - "ict ure", - "it hub", - "ith ub", - "c er", - "ce r", - "c ast", - "ca st", - "cas t", - "F rom", - "Fr om", - "a les", - "al es", - "ale s", - "Ġ subject", - "Ġsu bject", - "Ġsub ject", - "Ġsubj ect", - "p assword", - "pass word", - "pas sword", - "n y", - "Ġ esc", - "Ġe sc", - "Ġes c", - ". write", - ".w rite", - ".writ e", - ".wr ite", - "ï¼ Į", - "W hat", - "Wh at", - ". H", - "Ġ history", - "Ġh istory", - "Ġhistor y", - "Ġhist ory", - "Ġhi story", - "Ġhisto ry", - "Ġ Fe", - "ĠF e", - "Ġ individual", - "Ġind ividual", - "Ġindiv idual", - "Ġindivid ual", - "u nit", - "un it", - "uni t", - "Ġ -->", - "Ġ- ->", - "Ġ-- >", - "Ġ du", - "Ġd u", - "I ST", - "IS T", - "Ġ users", - "Ġus ers", - "Ġuse rs", - "Ġuser s", - "f s", - "f alse", - "fa lse", - "fal se", - "u nt", - "un t", - "T itle", - "Tit le", - "Ti tle", - "Ġ mot", - "Ġm ot", - "Ġmo t", - "Ġ future", - "Ġf uture", - "Ġfut ure", - "Ġfu ture", - "a ched", - "ac hed", - "ach ed", - "ache d", - "Ġ started", - "Ġstart ed", - "Ġstar ted", - "Ġ mode", - "Ġm ode", - "Ġmod e", - "Ġmo de", - "Ġ '<", - "Ġ' <", - "_ array", - "_a rray", - "_arr ay", - "_ar ray", - "Ġ ax", - "Ġa x", - "' ];Ċ", - "'] ;Ċ", - "']; Ċ", - "i res", - "ir es", - "ire s", - "T here", - "The re", - "Th ere", - "u ght", - "ug ht", - "ugh t", - "t ml", - "tm l", - "p osed", - "pos ed", - "pose d", - "po sed", - "i cult", - "ic ult", - "Ġ took", - "Ġt ook", - "Ġto ok", - "Ġtoo k", - "Ġ games", - "Ġg ames", - "Ġgame s", - "Ġgam es", - "Ġga mes", - "Ġ }}", - "Ġ} }", - "Ġ ?>Ċ", - "Ġ? >Ċ", - "Ġ?> Ċ", - "Ġ products", - "Ġproduct s", - "Ġprodu cts", - "I s", - "Ġ bad", - "Ġb ad", - "Ġba d", - "Ġ Des", - "ĠD es", - "ĠDe s", - ". path", - ".p ath", - ".pa th", - ".pat h", - "' ĊĊ", - "'Ċ Ċ", - "Ġ Post", - "ĠP ost", - "ĠPo st", - "ĠPos t", - "a vel", - "av el", - "ave l", - "( :", - "1 50", - "15 0", - "Ġ needs", - "Ġne eds", - "Ġneed s", - "Ġ known", - "Ġk nown", - "Ġkn own", - "Ġknow n", - "F l", - "Ġ exec", - "Ġe xec", - "Ġex ec", - "Ġexe c", - "Ġ seen", - "Ġs een", - "Ġse en", - "Ġsee n", - "5 1", - "u me", - "um e", - "Ġ border", - "Ġb order", - "Ġbor der", - "Ġbord er", - "Ġ live", - "Ġl ive", - "Ġli ve", - "Ġliv e", - "t emp", - "te mp", - "tem p", - "P er", - "Pe r", - "Ġ variable", - "Ġvar iable", - "Ġvari able", - "i et", - "ie t", - "Ġ Def", - "ĠD ef", - "ĠDe f", - "Ġ ge", - "Ġg e", - "e me", - "em e", - "_ back", - "_b ack", - "f irst", - "fi rst", - "fir st", - "Ġ provided", - "Ġpro vided", - "Ġprovid ed", - "Ġprov ided", - "Ġprovide d", - "//////////////// ////////////////", - "Ġ filename", - "Ġf ilename", - "Ġfile name", - "Ġfil ename", - "Ġfi lename", - "Ġ hope", - "Ġh ope", - "Ġhop e", - "Ġho pe", - "u ly", - "ul y", - "a uto", - "aut o", - "au to", - "f ind", - "fin d", - "fi nd", - "_ string", - "_s tring", - "_st ring", - "_str ing", - "b tn", - "bt n", - "it ude", - "itud e", - "itu de", - "At tribute", - "Attrib ute", - "Ġ young", - "Ġyou ng", - "Ġyo ung", - ". txt", - ".t xt", - ".tx t", - "Ġ website", - "Ġwe bsite", - "Ġweb site", - "Ġwebs ite", - "Ġ Prop", - "ĠP rop", - "ĠPro p", - "ĠPr op", - "Ġ ey", - "Ġe y", - "> ();Ċ", - ">( );Ċ", - ">() ;Ċ", - ">(); Ċ", - "i onal", - "ion al", - "io nal", - "iona l", - "A RR", - "AR R", - "iction ary", - "ur ther", - "urt her", - ". ", - ")- >", - "t x", - "Ġ pur", - "Ġp ur", - "Ġpu r", - "u el", - "ue l", - "ym bol", - "ymb ol", - "u ation", - "ua tion", - "uat ion", - "a nger", - "an ger", - "ang er", - "ange r", - "Ġ background", - "Ġback ground", - "e cess", - "ec ess", - "ece ss", - "ef ined", - ". .......", - ".. ......", - "... .....", - ".... ....", - "..... ...", - "...... ..", - "....... .", - "Ġ description", - "Ġd escription", - "Ġde scription", - "Ġdes cription", - "Ġdescri ption", - "Ġ represent", - "Ġre present", - "Ġrep resent", - "\" ));Ċ", - "\") );Ċ", - "\")) ;Ċ", - "\")); Ċ", - "p ression", - "pr ession", - "press ion", - "row ser", - "rows er", - "rowse r", - "Ġ series", - "Ġs eries", - "Ġse ries", - "Ġser ies", - "Ġserie s", - "Ġseri es", - "w ards", - "ward s", - "war ds", - "5 2", - "( $_", - "($ _", - "a ise", - "ai se", - "ais e", - "Ġ hot", - "Ġh ot", - "Ġho t", - "a city", - "ac ity", - "aci ty", - "r ies", - "ri es", - "rie s", - "a ctions", - "act ions", - "action s", - "C reate", - "Cre ate", - "Creat e", - "a dio", - "ad io", - "adi o", - "am ples", - "amp les", - "ample s", - "Ġ original", - "Ġor iginal", - "Ġorig inal", - "Ġorigin al", - "ens ive", - "ensi ve", - "f ont", - "fo nt", - "fon t", - "st ream", - "str eam", - "stre am", - " using", - ".spring framework", - "0 01", - "00 1", - "s erver", - "ser ver", - "serve r", - "serv er", - "Ġ bill", - "Ġb ill", - "Ġbi ll", - "Ġbil l", - "A CK", - "AC K", - "i lename", - "il ename", - "ile name", - "ilen ame", - "Ġ frame", - "Ġf rame", - "Ġfr ame", - "Ġfra me", - "Ġfram e", - "Ġ =Ċ", - "Ġ= Ċ", - "E dit", - "Ed it", - "ad ius", - "adi us", - "Ġ draw", - "Ġd raw", - "Ġdr aw", - "Ġdra w", - "an ks", - "ank s", - "Ġd eter", - "Ġde ter", - "Ġdet er", - "Ġ comes", - "Ġc omes", - "Ġcom es", - "Ġco mes", - "Ġcome s", - "_ int", - "_in t", - "_i nt", - "Ġ foreach", - "Ġf oreach", - "Ġfor each", - "Ġfore ach", - "Ġfo reach", - "a ngle", - "an gle", - "ang le", - "angl e", - "Ġ elect", - "Ġe lect", - "Ġel ect", - "Ġele ct", - "p ected", - "pect ed", - "pec ted", - "He ader", - "Head er", - "i stration", - "ist ration", - "istr ation", - "istra tion", - "istrat ion", - "F alse", - "Fa lse", - "Fal se", - "Ġ Game", - "ĠG ame", - "ĠGa me", - "ĠGam e", - "Ġ filter", - "Ġf ilter", - "Ġfil ter", - "Ġfilt er", - "Act ivity", - "Activ ity", - "Ġ larg", - "Ġl arg", - "Ġla rg", - "Ġlar g", - "in ition", - "init ion", - "ini tion", - "Ġ \"<", - "Ġ\" <", - "2 56", - "25 6", - "i sed", - "is ed", - "ise d", - "Ġ remove", - "Ġre move", - "Ġrem ove", - "Ġ Trans", - "ĠT rans", - "ĠTr ans", - "ĠTra ns", - "ĠTran s", - "m et", - "me t", - "s ee", - "se e", - "Form at", - "For mat", - "Com mand", - "Comm and", - "Ġ EX", - "ĠE X", - "N one", - "No ne", - "Non e", - "Ġ front", - "Ġf ront", - "Ġfr ont", - "Ġfro nt", - "Ġfron t", - "A SE", - "AS E", - "Ġ Rec", - "ĠR ec", - "ĠRe c", - "ound ation", - "Ġ vo", - "Ġv o", - "9 6", - "= \\\"", - "=\\ \"", - "( *", - "Ch ange", - "Chan ge", - "Cha nge", - ". Write", - ".W rite", - "g roup", - "gr oup", - "gro up", - "i ents", - "ie nts", - "ient s", - "ien ts", - "u y", - "******** ********************************************************", - "**************** ************************************************", - "******************************** ********************************", - "************************ ****************************************", - "**************************************** ************************", - "************************************************ ****************", - "******************************************************** ********", - "Ġ dig", - "Ġd ig", - "Ġdi g", - "h r", - "( -", - "Ġ gen", - "Ġg en", - "Ġge n", - "n umber", - "num ber", - "v ec", - "ve c", - "ur ope", - "uro pe", - "en try", - "ent ry", - "entr y", - "L L", - "Ġ ste", - "Ġs te", - "Ġst e", - "Val id", - "Va lid", - "' ],", - "'] ,", - "_ param", - "_p aram", - "_par am", - "_para m", - "_pa ram", - "Ġ selected", - "Ġse lected", - "Ġselect ed", - "Ġsel ected", - "Ġ according", - "Ġa ccording", - "Ġacc ording", - "Ġaccord ing", - "Ġ Dis", - "ĠD is", - "ĠDi s", - "Ġ util", - "Ġu til", - "Ġut il", - "B uffer", - "Buf fer", - "Buff er", - "Bu ffer", - "_ error", - "_e rror", - "_err or", - "_er ror", - "Ġ associ", - "Ġass oci", - "Ġassoc i", - "_ SIZE", - "_S IZE", - "_SI ZE", - "Ġ wor", - "Ġw or", - "Ġwo r", - "Ġ printf", - "Ġprint f", - "Ġprin tf", - "r ag", - "ra g", - " ł", - "D D", - "Ġ Val", - "ĠV al", - "ĠVa l", - "Ġ activ", - "Ġact iv", - "Ġac tiv", - "E ng", - "En g", - "e time", - "et ime", - "eti me", - "etim e", - "Ġ virtual", - "Ġv irtual", - "Ġvir tual", - "Ġvirt ual", - "a ign", - "ai gn", - "a ur", - "au r", - "Ġ Pres", - "ĠP res", - "ĠPr es", - "ĠPre s", - "Ġ Exception", - "ĠEx ception", - "ĠExcept ion", - "Ġ anything", - "Ġany thing", - "Ġ Off", - "ĠO ff", - "ĠOf f", - "Ġ hours", - "Ġh ours", - "Ġhour s", - "Ġho urs", - "Ġ war", - "Ġw ar", - "Ġwa r", - "Arg s", - "Ar gs", - "a ging", - "ag ing", - "agi ng", - "Ġ models", - "Ġmod els", - "Ġmodel s", - "Ġmode ls", - "Ġ Time", - "ĠT ime", - "ĠTim e", - "ĠTi me", - "O b", - "a ms", - "am s", - "j oy", - "jo y", - "Ġ early", - "Ġear ly", - ". read", - ".re ad", - ".r ead", - "8 6", - "Ġ center", - "Ġc enter", - "Ġcent er", - "Ġcen ter", - "Ġ Initial", - "ĠIn itial", - "ĠInit ial", - "ĠIniti al", - "Ġ language", - "Ġl anguage", - "Ġlangu age", - "l ength", - "le ngth", - "len gth", - "x y", - "Ġ sn", - "Ġs n", - "Ġ inf", - "Ġin f", - "Ġi nf", - "P ost", - "Pos t", - "Po st", - "Ġ ago", - "Ġa go", - "Ġag o", - "Ġ easy", - "Ġe asy", - "Ġeas y", - "Ġea sy", - "_ code", - "_c ode", - "_co de", - "_cod e", - "Ġ ANY", - "ĠA NY", - "ĠAN Y", - "_ ch", - "_c h", - "Ġ download", - "Ġd ownload", - "Ġdown load", - "( T", - "a ved", - "av ed", - "ave d", - "âĢ ĵ", - "Ġ students", - "Ġst udents", - "Ġstud ents", - "Ġstudent s", - "Ġ fig", - "Ġf ig", - "Ġfi g", - "l ight", - "li ght", - "lig ht", - "x x", - "Ġ buffer", - "Ġb uffer", - "Ġbu ffer", - "Ġbuf fer", - "Ġbuff er", - "Ġ Dep", - "ĠD ep", - "ĠDe p", - "Ġ Math", - "ĠM ath", - "ĠMat h", - "ĠMa th", - "I TH", - "IT H", - "Ġ vari", - "Ġv ari", - "Ġvar i", - "Ġva ri", - "Ġ due", - "Ġd ue", - "Ġdu e", - "F actory", - "Fact ory", - "Factor y", - "Ġ por", - "Ġp or", - "Ġpo r", - "Ġ ep", - "Ġe p", - "o type", - "ot ype", - "otyp e", - "oty pe", - "Ġ cannot", - "Ġc annot", - "Ġcan not", - "Ġcann ot", - "Ġ white", - "Ġwh ite", - "Ġwhit e", - "< int", - "čĊ", - "\"> čĊ", - ". annot", - ".an not", - "Ġ collection", - "Ġc ollection", - "Ġcol lection", - "Ġcoll ection", - "Ġcollect ion", - "Ġcolle ction", - "' .", - "Ġ similar", - "Ġs imilar", - "Ġsim ilar", - "Ġsimil ar", - "Ġ taken", - "Ġt aken", - "Ġtake n", - "Ġta ken", - "Ġtak en", - "( \"%", - "(\" %", - "Or der", - "Ord er", - "' ]Ċ", - "'] Ċ", - "- md", - "-m d", - "Ġ TH", - "ĠT H", - "a ced", - "ace d", - "ac ed", - "Ġis n", - "Ġi sn", - "/ j", - "Ġ son", - "Ġs on", - "Ġso n", - "g raph", - "gr aph", - "gra ph", - "Ġ Integer", - "ĠInt eger", - "Ġn ecess", - "Ġne cess", - "Ġnec ess", - "Ġneces s", - "r een", - "re en", - "ree n", - "Ġ um", - "Ġu m", - "Ġ \\<", - "Ġ\\ <", - "Ġ moment", - "Ġm oment", - "Ġmom ent", - "Ġmo ment", - "Ġ bring", - "Ġb ring", - "Ġbr ing", - "Ġbri ng", - "Ġ indic", - "Ġin dic", - "Ġind ic", - "y sis", - "ys is", - "ysi s", - "Le vel", - "v erse", - "ver se", - "vers e", - "ur renc", - "urre nc", - "urr enc", - "_ test", - "_t est", - "_te st", - "Ġent ire", - "D own", - "Do wn", - "Ġ }ĊĊĊ", - "Ġ} ĊĊĊ", - "Ġ}Ċ ĊĊ", - "Ġ}ĊĊ Ċ", - "( result", - "(res ult", - "Ġ Read", - "ĠR ead", - "ĠRe ad", - "à ¨", - "M od", - "Mo d", - "Ġ trying", - "Ġt rying", - "Ġtr ying", - "Ġtry ing", - "\" ),Ċ", - "\") ,Ċ", - "\"), Ċ", - "Ġ member", - "Ġm ember", - "Ġmem ber", - "Ġmemb er", - "Ġ Cor", - "ĠC or", - "ĠCo r", - "O DO", - "OD O", - "- control", - "-c ontrol", - "-cont rol", - "un time", - "unt ime", - "Ġ Sim", - "ĠS im", - "ĠSi m", - "D ialog", - "Di alog", - "Dia log", - "p lot", - "pl ot", - "_ on", - "_o n", - "Ġ phys", - "Ġph ys", - "Ġphy s", - "} /", - "Ġ namespace", - "Ġn amespace", - "Ġname space", - "Ġnames pace", - "ĉ čĊ", - "a cc", - "ac c", - "P layer", - "Pl ayer", - "Play er", - "A RE", - "AR E", - "8 9", - "Ġ foot", - "Ġf oot", - "Ġfo ot", - "Ġfoo t", - "Ġ board", - "Ġb oard", - "Ġbo ard", - "Ġboa rd", - "p art", - "par t", - "pa rt", - "Ġ sus", - "Ġs us", - "Ġsu s", - "w ise", - "wi se", - "wis e", - "Ġ Mc", - "ĠM c", - "Ġ push", - "Ġp ush", - "Ġpu sh", - "Ġpus h", - "A TA", - "AT A", - "Ġ please", - "Ġp lease", - "Ġpl ease", - "Ġple ase", - "Ġplea se", - "Ġpleas e", - "r ied", - "ri ed", - "rie d", - "we et", - "b it", - "bi t", - "i ded", - "id ed", - "ide d", - "V E", - "Ġ Sw", - "ĠS w", - "U B", - "Ġ types", - "Ġt ypes", - "Ġtype s", - "Ġtyp es", - "Ġty pes", - "e dia", - "ed ia", - "edi a", - "Ġc los", - "Ġcl os", - "Ġclo s", - "ace book", - "W hen", - "Wh en", - "Ġ edit", - "Ġe dit", - "Ġed it", - "Ġedi t", - "i gger", - "ig ger", - "igg er", - "Ġe nerg", - "Ġen erg", - "Ġener g", - "Cont ainer", - "Contain er", - "Conta iner", - "Ġ phot", - "Ġp hot", - "Ġph ot", - "Ġ Count", - "ĠC ount", - "ĠCo unt", - "ĠCou nt", - "ĠCoun t", - "Ġ Europe", - "ĠE urope", - "ĠEuro pe", - "ĠEurop e", - "ĠEur ope", - ". Is", - ".I s", - "Ġ Russ", - "ĠR uss", - "ĠRu ss", - "ĠRus s", - "p eed", - "pe ed", - "pee d", - "Ġ Str", - "ĠS tr", - "ĠSt r", - "Ġ py", - "Ġp y", - "Ġ cult", - "Ġc ult", - "Ġcu lt", - "Ġcul t", - "Ġ defined", - "Ġd efined", - "Ġdef ined", - "Ġdefine d", - "Ġdefin ed", - "c count", - "cc ount", - "cco unt", - "Ġo bt", - "Ġob t", - ". Location", - ".L ocation", - ".Lo cation", - "Ġ thread", - "Ġt hread", - "Ġth read", - "Ġthr ead", - "i lle", - "il le", - "ill e", - "Ġ instead", - "Ġin stead", - "Ġinst ead", - "st rong", - "str ong", - "stro ng", - "Ġ Sec", - "ĠS ec", - "ĠSe c", - "U RE", - "UR E", - "Ġ idea", - "Ġi dea", - "Ġid ea", - "Ġide a", - ". se", - ".s e", - "e my", - "em y", - "se lected", - "select ed", - "sel ected", - "Con nection", - "Conn ection", - "Connect ion", - "a cing", - "ac ing", - "aci ng", - "acin g", - "t hread", - "th read", - "thr ead", - ". next", - ".n ext", - ".ne xt", - "Ġ coll", - "Ġc oll", - "Ġco ll", - "Ġcol l", - "Ġ film", - "Ġf ilm", - "Ġfil m", - "Ġfi lm", - "is tic", - "ist ic", - "isti c", - "Ġ compet", - "Ġcom pet", - "Ġcomp et", - "Ġ conn", - "Ġc onn", - "Ġcon n", - "Ġco nn", - "th ough", - "Ġ compan", - "Ġcom pan", - "Ġcomp an", - "o cket", - "oc ket", - "ock et", - "Ġt each", - "Ġte ach", - "Ġtea ch", - "= (", - "Ġ phone", - "Ġp hone", - "Ġph one", - "Ġphon e", - "Ġ active", - "Ġact ive", - "Ġactiv e", - "7 9", - "de lete", - "del ete", - "1 01", - "10 1", - "t ries", - "tr ies", - "trie s", - "tri es", - "Ġ mo", - "Ġm o", - "Ġ death", - "Ġde ath", - "} );ĊĊ", - "});Ċ Ċ", - "}) ;ĊĊ", - "}); ĊĊ", - "o col", - "oc ol", - "oco l", - "W idget", - "Ġ article", - "Ġart icle", - "Ġartic le", - "r odu", - "ro du", - "rod u", - "an did", - "and id", - "andi d", - "Ñ ĭ", - "Ġ Cr", - "ĠC r", - "k a", - "( ):", - "() :", - "l ood", - "lo od", - "loo d", - "ĉ ĉĉĊ", - "ĉĉ ĉĊ", - "ĉĉĉ Ċ", - "Ġ almost", - "Ġal most", - "Ġalm ost", - "Ġ sell", - "Ġs ell", - "Ġse ll", - "Ġsel l", - "erv let", - "r ip", - "ri p", - "U nit", - "Un it", - "Uni t", - "Ġapp lic", - "Ġappl ic", - "Ġ connect", - "Ġcon nect", - "Ġconn ect", - "Ġ feature", - "Ġf eature", - "Ġfe ature", - "Ġfeat ure", - "Ġ via", - "Ġv ia", - "Ġvi a", - "' ),", - "') ,", - "Ġ lim", - "Ġl im", - "Ġli m", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ Gu", - "ĠG u", - "E ngine", - "Eng ine", - "Ġ ens", - "Ġe ns", - "Ġen s", - "Ġ environment", - "Ġen vironment", - "Ġenviron ment", - "b lock", - "bl ock", - "blo ck", - "H ERE", - "HE RE", - "HER E", - "N ULL", - "NU LL", - "g y", - "t ag", - "ta g", - ") ).", - ")) .", - "e xp", - "ex p", - "Ġcom pl", - "Ġco mpl", - "Ġcomp l", - "Ġ install", - "Ġinst all", - "Ġinstal l", - "Ġ complete", - "Ġcom plete", - "Ġcomp lete", - "Ġcomple te", - "Ġcomplet e", - "Ġcompl ete", - "q ueue", - "que ue", - "at ural", - "atur al", - "atura l", - "atu ral", - "Ġ general", - "Ġg eneral", - "Ġgener al", - "Ġgen eral", - "Ġgene ral", - "Ġgenera l", - "t hon", - "th on", - "Ġas ked", - "Ġask ed", - "o res", - "or es", - "ore s", - "( res", - "(r es", - "(re s", - "Ġ reserved", - "Ġres erved", - "Ġreserve d", - "Ġreserv ed", - "S P", - "Ġ âĢ¦", - "ĠâĢ ¦", - "Å Ĥ", - "Ġsign ific", - "O ff", - "Of f", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ Ag", - "ĠA g", - "Ġ Just", - "ĠJ ust", - "ĠJu st", - "Ġ Error", - "ĠE rror", - "ĠEr ror", - "ĠErr or", - "Ġin fl", - "Ġinf l", - "a data", - "ad ata", - "ada ta", - "Ġ icon", - "Ġi con", - "Ġic on", - "as ks", - "ask s", - "' '", - "_ LO", - "_L O", - "? .", - "a ccount", - "ac count", - "acc ount", - "acco unt", - "Ġ (*", - "Ġ( *", - "' )ĊĊ", - "') ĊĊ", - "')Ċ Ċ", - "r ap", - "ra p", - "_ var", - "_v ar", - "_va r", - "Ġ FOR", - "ĠF OR", - "ĠFO R", - "Ġ party", - "Ġp arty", - "Ġpart y", - "Ġpar ty", - "Ġ Your", - "ĠY our", - "ĠYou r", - "ĠYo ur", - "c at", - "ca t", - "s try", - "st ry", - "str y", - ". new", - ".n ew", - ".ne w", - "b oot", - "bo ot", - "boo t", - "Ġ Nov", - "ĠN ov", - "ĠNo v", - "Ġ vector", - "Ġv ector", - "Ġve ctor", - "Ġvec tor", - "Ġvect or", - "Ġ normal", - "Ġn ormal", - "Ġnor mal", - "Ġnorm al", - "Ġf urther", - "Ġfur ther", - "Re pository", - "8 00", - "80 0", - "Ġ database", - "Ġd atabase", - "Ġdata base", - "Ġdat abase", - "Ġdatab ase", - "at tle", - "att le", - "Ġ music", - "Ġm usic", - "Ġmus ic", - "Ġmu sic", - "Ġ speed", - "Ġs peed", - "Ġsp eed", - "Ġspe ed", - "Ġ doc", - "Ġd oc", - "Ġdo c", - "p rocess", - "pro cess", - "proc ess", - "IG HT", - "IGH T", - ". parse", - ".p arse", - ".par se", - "Ġ taking", - "Ġt aking", - "Ġta king", - "Ġtak ing", - "Ġ viol", - "Ġv iol", - "Ġvi ol", - "c eed", - "ce ed", - "cee d", - "Ġ After", - "ĠA fter", - "ĠAf ter", - "Ġ forward", - "Ġfor ward", - "Ġ crit", - "Ġc rit", - "Ġcr it", - "Ġcri t", - "\" />Ċ", - "\"/ >Ċ", - "\"/> Ċ", - "r ot", - "ro t", - "Ġ failed", - "Ġf ailed", - "Ġfa iled", - "Ġfail ed", - "e fore", - "ef ore", - "Ġcon cern", - "Ġconc ern", - "Ġconce rn", - "o e", - "b a", - "Ġ sender", - "Ġs ender", - "Ġse nder", - "Ġsend er", - "Ġsen der", - "Ġ term", - "Ġt erm", - "Ġte rm", - "Ġter m", - "h as", - "ha s", - "= \"#", - "=\" #", - "Ġ potential", - "Ġpot ential", - "Ġpotent ial", - "N um", - "Nu m", - "Ġ published", - "Ġp ublished", - "Ġpublish ed", - "Ġpubli shed", - ". close", - ".c lose", - ".cl ose", - "Ġ Image", - "ĠI mage", - "ĠIm age", - "ĠImag e", - "str aint", - "stra int", - "strain t", - "U D", - "Ġ Ob", - "ĠO b", - "Ġ probably", - "Ġprob ably", - "l im", - "li m", - "\" :Ċ", - "\": Ċ", - "ol ume", - "olum e", - "olu me", - "Ġ consum", - "Ġcon sum", - "Ġcons um", - "7 6", - "a gue", - "ag ue", - "agu e", - "ens ions", - "ension s", - "ensi ons", - "Ġinvest ig", - "- year", - "-y ear", - "' );", - "') ;", - "- sm", - "-s m", - "Ġen joy", - "Ġenjo y", - "o rig", - "or ig", - "ori g", - "e ring", - "er ing", - "eri ng", - "erin g", - "c p", - "le ased", - "lease d", - "lea sed", - "p lements", - "pl ements", - "ple ments", - "plement s", - "Ġ returns", - "Ġreturn s", - "p at", - "pa t", - "B O", - "Ġ House", - "ĠH ouse", - "ĠHo use", - "ĠHou se", - ". Label", - ".L abel", - "Ġ weight", - "Ġw eight", - "Ġwe ight", - "Ġweigh t", - "Ġwei ght", - "ig hb", - "igh b", - "Ġ conditions", - "Ġcondition s", - "Ġcond itions", - "Ġ exception", - "Ġex ception", - "Ġexcept ion", - "Ġexce ption", - "d escription", - "de scription", - "des cription", - "Ġ trad", - "Ġt rad", - "Ġtr ad", - "Ġtra d", - "- to", - "-t o", - "Ġ {}", - "Ġ{ }", - "Ġ module", - "Ġm odule", - "Ġmod ule", - "E ND", - "EN D", - ". ap", - ".a p", - ". props", - ".p rops", - ".pro ps", - ".pr ops", - ".prop s", - "Ġ constructor", - "Ġcon structor", - "Ġconstruct or", - "Ġconstr uctor", - "a ves", - "av es", - "ave s", - "Ġ favor", - "Ġf avor", - "Ġfa vor", - "Ġfav or", - "Ġ Now", - "ĠN ow", - "ĠNo w", - "; i", - "Ġ Main", - "ĠM ain", - "ĠMa in", - "ĠMai n", - "_ k", - "e ries", - "er ies", - "erie s", - "eri es", - "âĢĻ ll", - "âĢĻl l", - "trans form", - "ime stamp", - "imest amp", - "P re", - "Pr e", - "Ġ mer", - "Ġm er", - "Ġme r", - ". res", - ".re s", - ".r es", - "s tant", - "st ant", - "sta nt", - "stan t", - "L ocation", - "Lo cation", - "Loc ation", - "_ NAME", - "_N AME", - "Ġ loss", - "Ġl oss", - "Ġlo ss", - "Ġlos s", - "Ġ ĊĊ", - "ĠĊ Ċ", - "n et", - "ne t", - "Ġ engine", - "Ġe ngine", - "Ġeng ine", - "B lock", - "Bl ock", - "Bloc k", - "Blo ck", - "Ġ issues", - "Ġiss ues", - "Ġissue s", - "Ġissu es", - "Ġ parse", - "Ġp arse", - "Ġpar se", - "Ġpars e", - "Ġ Bar", - "ĠB ar", - "ĠBa r", - "Ġ stay", - "Ġst ay", - "Ġsta y", - "Ġ JSON", - "ĠJ SON", - "ĠJS ON", - "Ġ dom", - "Ġd om", - "Ġdo m", - "a irs", - "air s", - "ai rs", - "w ner", - "wn er", - "Ġ lower", - "Ġl ower", - "Ġlo wer", - "Ġlow er", - "\" ,čĊ", - "\", čĊ", - "Ġ Dem", - "ĠD em", - "ĠDe m", - "u fact", - "uf act", - "ufac t", - "Ġ ps", - "Ġp s", - "Ġ perfect", - "Ġper fect", - "Ġperf ect", - "R L", - "Ġ educ", - "Ġe duc", - "Ġed uc", - "Ġedu c", - "l s", - "em ory", - "emo ry", - "ARR ANT", - "u ge", - "ug e", - "Ġ exact", - "Ġex act", - ". key", - ".k ey", - ".ke y", - "al led", - "all ed", - "alle d", - "e ch", - "ec h", - "i ef", - "ie f", - "\\ /", - "o ke", - "ok e", - "Ġ former", - "Ġfor mer", - "Ġform er", - "Ġforme r", - "al loc", - "all oc", - "allo c", - "Ġ six", - "Ġs ix", - "Ġsi x", - "i da", - "id a", - "Ġ margin", - "Ġm argin", - "Ġmar gin", - "Ġmarg in", - "Ġ heart", - "Ġhe art", - "Ġhear t", - "a ld", - "al d", - "p ack", - "pa ck", - "pac k", - ".getElement ById", - "ĠW ARRANT", - "Ġ rather", - "Ġr ather", - "Ġrat her", - "Ġra ther", - "Ġ building", - "Ġbuild ing", - "er man", - "erm an", - "l ice", - "lic e", - "li ce", - "Ġ questions", - "Ġquest ions", - "Ġquestion s", - "Ġquesti ons", - "i zes", - "iz es", - "ize s", - "le ge", - "leg e", - "irect ory", - "irector y", - "Ġ je", - "Ġj e", - "Ġ cas", - "Ġc as", - "Ġca s", - "p rops", - "pr ops", - "pro ps", - "prop s", - "u tf", - "ut f", - "Ġ security", - "Ġs ecurity", - "Ġse curity", - "Ġsec urity", - "Ġ however", - "Ġhow ever", - "w eight", - "we ight", - "wei ght", - "weigh t", - "Ġ inside", - "Ġin side", - "Ġins ide", - "Ġp resident", - "Ġpres ident", - "C har", - "Ch ar", - "Cha r", - "Ġ WITH", - "ĠW ITH", - "ĠWI TH", - ". map", - ".m ap", - ".ma p", - "Ġ graph", - "Ġg raph", - "Ġgr aph", - "Ġgra ph", - "Ġgrap h", - "Ġ tag", - "Ġt ag", - "Ġta g", - "_ status", - "_s tatus", - "_st atus", - "_stat us", - "Ġ attempt", - "Ġat tempt", - "Ġatt empt", - "o pp", - "op p", - "u ses", - "us es", - "use s", - "ĉ const", - "ĉcon st", - "Ġ round", - "Ġr ound", - "Ġro und", - "Ġrou nd", - ", $", - "Ġ friends", - "Ġf riends", - "Ġfri ends", - "Ġfriend s", - "E mail", - "Em ail", - "? >", - "Re source", - "Res ource", - "K EY", - "KE Y", - "o sp", - "os p", - ". query", - ".qu ery", - "Ġ North", - "ĠN orth", - "ĠNor th", - "a bles", - "ab les", - "able s", - "abl es", - "is trib", - "ist rib", - "istr ib", - "_ class", - "_c lass", - "_cl ass", - "el lo", - "ell o", - "T hat", - "Th at", - "Ð º", - "pec ially", - "pecial ly", - "Ġ President", - "ĠP resident", - "ĠPres ident", - "Ġ campaign", - "Ġc ampaign", - "Ġcamp aign", - "Ġ alt", - "Ġa lt", - "Ġal t", - "a rea", - "ar ea", - "are a", - "Ġc hall", - "Ġch all", - "Ġcha ll", - "Ġop port", - "Ġopp ort", - ". Con", - ".C on", - ".Co n", - "Ġ energy", - "Ġe nergy", - "Ġen ergy", - "Ġenerg y", - "Ġener gy", - "l ike", - "li ke", - "lik e", - ". string", - ".s tring", - ".st ring", - ".str ing", - "ing ton", - "ingt on", - ") *", - "y y", - "Ġ profession", - "Ġprof ession", - "Ġprofess ion", - "ir th", - "irt h", - "Ġ seg", - "Ġs eg", - "Ġse g", - "æ ľ", - "Ġ hor", - "Ġh or", - "Ġho r", - "i ers", - "ie rs", - "ier s", - "c an", - "ca n", - "Ġbe hind", - "Ġbeh ind", - "Pro duct", - "Produ ct", - "Prod uct", - "f g", - "Ġ Sk", - "ĠS k", - ". jpg", - ".j pg", - ".jp g", - "? :", - "] ;ĊĊ", - "];Ċ Ċ", - "]; ĊĊ", - "Ġ callback", - "Ġc allback", - "Ġcall back", - "Ġ Http", - "ĠH ttp", - "Ñ Į", - "l ong", - "lo ng", - "lon g", - "M S", - "A TH", - "AT H", - "Ġ raise", - "Ġr aise", - "Ġrais e", - "Ġra ise", - "Ġ wanted", - "Ġw anted", - "Ġwant ed", - "Ġwan ted", - "r own", - "ro wn", - "row n", - "u tor", - "ut or", - "uto r", - "l t", - "] =", - "e line", - "el ine", - "eli ne", - "elin e", - "M A", - "Ġs epar", - "Ġse par", - "Ġsep ar", - "c s", - "s emb", - "se mb", - "sem b", - "D is", - "Di s", - "b serv", - "bs erv", - "Ġ Will", - "ĠW ill", - "ĠWil l", - "ĠWi ll", - "Ġ policy", - "Ġp olicy", - "Ġpol icy", - "Ġpolic y", - "Ġ third", - "Ġth ird", - "Ġthi rd", - "p hone", - "ph one", - "phon e", - "Ġ bed", - "Ġb ed", - "Ġbe d", - "/ g", - ". __", - "._ _", - "Ġ Inc", - "ĠI nc", - "ĠIn c", - "i zing", - "iz ing", - "izi ng", - "izin g", - ". remove", - ".re move", - ".rem ove", - "in stance", - "inst ance", - "instanc e", - ". type", - ".t ype", - ".typ e", - "Ġ serv", - "Ġs erv", - "Ġse rv", - "Ġser v", - "E ach", - "Ġ har", - "Ġh ar", - "Ġha r", - "Ġ Message", - "ĠM essage", - "ĠMess age", - "ĠMes sage", - "( key", - "(k ey", - "SE LECT", - "SEL ECT", - "P os", - "Po s", - ") );čĊ", - ")) ;čĊ", - ")); čĊ", - "Ġre comm", - "Ġrec omm", - "Ġrecom m", - "Ġreco mm", - "Ġ training", - "Ġtr aining", - "Ġtrain ing", - "Ġtra ining", - "Ġtrai ning", - "Ġ Ent", - "ĠE nt", - "ĠEn t", - "Ġ Char", - "ĠC har", - "ĠCh ar", - "ĠCha r", - "i cht", - "ic ht", - "ich t", - "( file", - "(f ile", - "(fi le", - "(fil e", - "Ġ prior", - "Ġp rior", - "Ġpr ior", - "Ġpri or", - "Ġprio r", - "G ame", - "Gam e", - "Ga me", - "Ġ exit", - "Ġe xit", - "Ġex it", - "Param s", - "Par ams", - "Pa rams", - "Para ms", - ". core", - ".c ore", - ".co re", - ".cor e", - "P C", - "n es", - "ne s", - "an ced", - "ance d", - "anc ed", - "( request", - "(re quest", - "(req uest", - "P assword", - "Pass word", - "Pas sword", - "} >Ċ", - "}> Ċ", - "Ġ mag", - "Ġm ag", - "Ġma g", - "Ġ release", - "Ġre lease", - "Ġr elease", - "Ġrel ease", - "Ġrele ase", - "Ġ shall", - "Ġs hall", - "Ġsh all", - "Ġsha ll", - "u dent", - "ud ent", - "ude nt", - "uden t", - "Ġ South", - "ĠS outh", - "ĠSo uth", - "ĠSou th", - "a ndo", - "an do", - "and o", - ": '", - ". TabIndex", - ".Tab Index", - "s k", - "an ner", - "ann er", - "anne r", - "is set", - "iss et", - "isse t", - "Ġ outside", - "Ġout side", - "Ġouts ide", - "l edge", - "le dge", - "led ge", - "Ġ å", - "Ġ Rob", - "ĠR ob", - "ĠRo b", - "Ġ imm", - "Ġi mm", - "Ġim m", - "! Ċ", - "Ġ Web", - "ĠW eb", - "ĠWe b", - "D es", - "De s", - "B C", - "an cial", - "anc ial", - "ancia l", - "R oute", - "Ro ute", - "D ec", - "De c", - "fer ences", - "ference s", - "Ġp urch", - "Ġpur ch", - "Ġpu rch", - "Ġ Model", - "ĠM odel", - "ĠMod el", - "ĠMo del", - "ĠMode l", - "c tor", - "ct or", - "g n", - "_ start", - "_st art", - "_star t", - "_sta rt", - "_ un", - "_u n", - ". *", - "i ses", - "is es", - "ise s", - "Ġ ground", - "Ġg round", - "Ġgr ound", - "Ġgro und", - "Ġgrou nd", - "Ġ unique", - "Ġun ique", - "Ġuniqu e", - "Ġuni que", - "Ġuniq ue", - "Ġbe aut", - "Ġbeau t", - "{ \"", - "Ġ pour", - "Ġp our", - "Ġpo ur", - "Ġpou r", - "Ġ Oct", - "ĠO ct", - "ĠOc t", - "Ġ tree", - "Ġt ree", - "Ġtr ee", - "Ġtre e", - "s ets", - "se ts", - "set s", - "_ res", - "_re s", - "_r es", - "' )->", - "') ->", - "_ reg", - "_re g", - "_r eg", - "( \"\\", - "(\" \\", - "Ġ byte", - "Ġb yte", - "Ġby te", - "Ġbyt e", - "B l", - "Ġ dating", - "Ġd ating", - "Ġda ting", - "Ġdat ing", - "Ġdati ng", - "Ġ matter", - "Ġm atter", - "Ġmat ter", - "Ġmatt er", - "Ġmatte r", - "Ġ Rem", - "ĠR em", - "ĠRe m", - "Ġ' ../", - "Ġ'. ./", - "Ġ'.. /", - "Ġ Aug", - "ĠA ug", - "ĠAu g", - "Ġ La", - "ĠL a", - "Ġ $(", - "Ġ$ (", - "o urnal", - "our nal", - "ourn al", - "1 11", - "11 1", - "i am", - "ia m", - "Ġ shows", - "Ġsh ows", - "Ġshow s", - "Ġsho ws", - "w rite", - "wr ite", - "Ġ ball", - "Ġb all", - "Ġbal l", - "Ġba ll", - "Ġsim ply", - "Ġsimp ly", - "Ġsimpl y", - "Ġ fast", - "Ġf ast", - "Ġfa st", - "Ġfas t", - "Ġ memory", - "Ġm emory", - "Ġmem ory", - "Ġmemor y", - "Ġmemo ry", - "A SS", - "AS S", - "Ġ Of", - "ĠO f", - "o ved", - "ov ed", - "ove d", - "a nte", - "an te", - "ant e", - "a ul", - "au l", - "i stry", - "is try", - "ist ry", - "istr y", - ") ));Ċ", - ")) );Ċ", - "))) ;Ċ", - "))); Ċ", - "Ġ fit", - "Ġf it", - "Ġfi t", - "< string", - "_", - "-> _", - "\" )ĊĊ", - "\") ĊĊ", - "\")Ċ Ċ", - "o x", - "ap plication", - "app lication", - "appl ication", - "Ġ ]Ċ", - "Ġ] Ċ", - "Ċ ĊĊĊĊĊ", - "ĊĊ ĊĊĊĊ", - "ĊĊĊĊ ĊĊ", - "ĊĊĊ ĊĊĊ", - "ĊĊĊĊĊ Ċ", - "1 80", - "18 0", - "Ġ soon", - "Ġs oon", - "Ġso on", - "Ġsoo n", - "ct ions", - "ction s", - "i nger", - "in ger", - "ing er", - "inge r", - "Ġ join", - "Ġj oin", - "Ġjo in", - "Ġ Pe", - "ĠP e", - "Ġ ë", - "Ġ las", - "Ġl as", - "Ġla s", - ". E", - "c ss", - "cs s", - "/ or", - "/o r", - "Ġ Start", - "ĠSt art", - "ĠStar t", - "ĠSta rt", - "Ġ TO", - "ĠT O", - "Ġ subs", - "Ġs ubs", - "Ġsu bs", - "Ġsub s", - "c onn", - "con n", - "co nn", - "com ponents", - "comp onents", - "component s", - "DE BUG", - "qu are", - "qua re", - "F unction", - "Func tion", - "Fun ction", - "en dar", - "end ar", - "enda r", - ". index", - ".in dex", - ".ind ex", - "Ġ fill", - "Ġf ill", - "Ġfil l", - "Ġfi ll", - "Ä Ļ", - "Ġ choose", - "Ġch oose", - "Ġcho ose", - "h ow", - "ho w", - "Ġ America", - "ĠAmeric a", - "ĠAmer ica", - "as sets", - "ass ets", - "asset s", - "asse ts", - "- -----------", - "-- ----------", - "---- --------", - "-------- ----", - "--- ---------", - "----- -------", - "---------- --", - "------ ------", - "----------- -", - "------- -----", - "--------- ---", - "Ġ Value", - "ĠV alue", - "ĠVal ue", - "ĠVa lue", - "Ġ office", - "Ġoff ice", - "Ġoffic e", - "Ġ veh", - "Ġv eh", - "Ġve h", - "Ġ transform", - "Ġtrans form", - "Ġtransf orm", - "Ġ Art", - "ĠA rt", - "ĠAr t", - "Ġ inde", - "Ġin de", - "Ġi nde", - "Ġind e", - "Ġ fn", - "Ġf n", - "Ġ implements", - "Ġim plements", - "Ġimp lements", - "Ġimplement s", - "Ġimpl ements", - "a ngo", - "an go", - "ang o", - "p lete", - "pl ete", - "ple te", - "plet e", - "+ \"", - "t mp", - "tm p", - "am ily", - "ami ly", - "amil y", - "Ġ hash", - "Ġh ash", - "Ġhas h", - "Ġha sh", - "m issions", - "miss ions", - "mission s", - "E ST", - "ES T", - "g t", - "Pro vider", - "Provid er", - "Provide r", - "Prov ider", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "Ġ flag", - "Ġf lag", - "Ġfl ag", - "Ġfla g", - "Ġpart icip", - "Ġpartic ip", - "Ġparti cip", - "d en", - "de n", - "Ġ Returns", - "ĠReturn s", - "Ġ note", - "Ġn ote", - "Ġnot e", - "Ġno te", - "ü r", - "p m", - "id eos", - "ide os", - "ideo s", - "Ġ specified", - "Ġs pecified", - "Ġspec ified", - "Ġ EN", - "ĠE N", - "e ster", - "es ter", - "est er", - "este r", - "o lid", - "ol id", - "oli d", - "Ġ upon", - "Ġu pon", - "Ġup on", - "( std", - "(s td", - "(st d", - "ĉ v", - "Ġ '\\", - "Ġ' \\", - "u z", - "Ġ vert", - "Ġv ert", - "Ġver t", - "Ġve rt", - "Ġv ict", - "Ġvi ct", - "Ġvic t", - "ĉ self", - "ĉs elf", - "ĉse lf", - "Ġ \"$", - "Ġ\" $", - "8 5", - ". k", - "Ġ groups", - "Ġg roups", - "Ġgroup s", - "Ġgro ups", - "Ġgrou ps", - "g ithub", - "git hub", - "l ang", - "la ng", - "lan g", - "Ġ mut", - "Ġm ut", - "Ġmu t", - "T O", - "Ġ ve", - "Ġv e", - "Ġ Please", - "ĠP lease", - "ĠPl ease", - "ĠPle ase", - "; ĊĊĊ", - ";Ċ ĊĊ", - ";ĊĊ Ċ", - "a ccess", - "ac cess", - "acc ess", - "Ġ {\"", - "Ġ{ \"", - "r ea", - "re a", - "Ġ risk", - "Ġr isk", - "Ġris k", - "Ġri sk", - "i cker", - "ic ker", - "ick er", - "og gle", - "ogg le", - "ĉ while", - "A NG", - "AN G", - ". send", - ".s end", - ".se nd", - "7 2", - "Ġ woman", - "Ġw oman", - "Ġwom an", - "Ġwo man", - "Ġ gets", - "Ġg ets", - "Ġget s", - "Ġge ts", - "Ġ ign", - "Ġi gn", - "Ġig n", - "Ġ Id", - "ĠI d", - "_ log", - "_l og", - "_lo g", - "O NE", - "ON E", - "Ġe vid", - "Ġev id", - "Ġ Har", - "ĠH ar", - "ĠHa r", - "_ sub", - "_s ub", - "_su b", - "Ġ endl", - "Ġe ndl", - "Ġen dl", - "Ġend l", - "Ġ included", - "Ġin cluded", - "Ġinclud ed", - "Ġinclude d", - "Ġincl uded", - "Ġinclu ded", - "( ));ĊĊ", - "() );ĊĊ", - "());Ċ Ċ", - "()) ;ĊĊ", - "()); ĊĊ", - "Ġ Ap", - "ĠA p", - "i gr", - "ig r", - "Ġ sem", - "Ġs em", - "Ġse m", - "Ġ Black", - "ĠB lack", - "ĠBl ack", - "d oc", - "do c", - "_ table", - "_t able", - "_tab le", - "_ta ble", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "- up", - "-u p", - "Ġ cause", - "Ġc ause", - "Ġca use", - "Ġcaus e", - "Ġ ..", - "Ġ. .", - "Ġ van", - "Ġv an", - "Ġva n", - "_ dict", - "_d ict", - "_di ct", - "_dic t", - "Ġ focus", - "Ġf ocus", - "Ġfoc us", - "Ġfo cus", - "I ND", - "IN D", - "C ESS", - "CE SS", - "CES S", - ". Log", - ".L og", - ".Lo g", - "Ġ multiple", - "Ġm ultiple", - "Ġmult iple", - "Ġmulti ple", - "Ġmultip le", - "i do", - "id o", - "Ġre gard", - "Ġreg ard", - "- M", - "and ler", - "andle r", - "o urse", - "our se", - "ours e", - "Ġ deg", - "Ġd eg", - "Ġde g", - ". U", - "Ġadd ition", - "Ġad dition", - "Ġv arious", - "Ġvar ious", - "Ġvari ous", - "Ġva rious", - "Ġ receive", - "Ġre ceive", - "Ġrece ive", - "е н", - "еР½", - "Ġ HT", - "ĠH T", - "O bj", - "Ob j", - "D F", - "Ġ increase", - "Ġin crease", - "Ġincre ase", - "Ġincr ease", - "Ġ Open", - "ĠO pen", - "ĠOp en", - "] ;", - "Ġ commit", - "Ġcom mit", - "Ġcomm it", - "? Ċ", - "ateg ories", - "ategor ies", - "ategori es", - "ategorie s", - "atego ries", - "at ory", - "ator y", - "ato ry", - "s hip", - "sh ip", - "shi p", - "Ġ Mich", - "ĠM ich", - "ĠMi ch", - "ĠMic h", - "Ġ html", - "Ġh tml", - "Ġht ml", - "ro mise", - "rom ise", - "Ġ leave", - "Ġle ave", - "Ġstr ateg", - "Ġstrat eg", - "Ġstra teg", - "Ġstrate g", - "a ven", - "av en", - "ave n", - "Ġ Console", - "ĠCon sole", - "ĠCons ole", - "k nown", - "kn own", - "know n", - "- n", - "_ LE", - "_L E", - ". component", - ".com ponent", - ".comp onent", - "Ġ bre", - "Ġb re", - "Ġbr e", - "S ession", - "i ance", - "ia nce", - "ian ce", - "Ġ align", - "Ġal ign", - "Ġali gn", - "type def", - "typ edef", - "typed ef", - "_ result", - "_res ult", - "Ġ WHERE", - "ĠW HERE", - "ĠWH ERE", - ". split", - ".s plit", - ".sp lit", - "Ġ reading", - "Ġre ading", - "Ġread ing", - "FA ULT", - "Ġ clo", - "Ġc lo", - "Ġcl o", - "Ġ notice", - "Ġnot ice", - "_ pr", - "_p r", - "ar ter", - "art er", - "arte r", - "Ġ lock", - "Ġl ock", - "Ġlo ck", - "Ġloc k", - "Ġ standard", - "Ġst andard", - "Ġstand ard", - "e tic", - "et ic", - "eti c", - "el low", - "ell ow", - "ello w", - "Ġ padding", - "Ġp adding", - "Ġpad ding", - "Ġpadd ing", - "Ġ His", - "ĠH is", - "ĠHi s", - "Ġ states", - "Ġst ates", - "Ġstate s", - "Ġstat es", - "Ġsta tes", - "_ cast", - "_c ast", - "_ca st", - "( P", - "a a", - "Ġ internal", - "Ġin ternal", - "Ġint ernal", - "Ġinter nal", - "Ġintern al", - "e an", - "ea n", - "Ġ PRO", - "ĠP RO", - "ĠPR O", - "Ġ Key", - "ĠK ey", - "ĠKe y", - "Ġ especially", - "Ġes pecially", - "Ġespecial ly", - "Ġespec ially", - "m ing", - "min g", - "mi ng", - "Ġ cross", - "Ġc ross", - "Ġcr oss", - "Ġcro ss", - "Ġ national", - "Ġn ational", - "Ġnation al", - "Ġnat ional", - "_ object", - "_obj ect", - "_o bject", - "_ob ject", - "f ilter", - "fil ter", - "filt er", - "Ġ script", - "Ġs cript", - "Ġscr ipt", - "Ġscri pt", - ". update", - ".up date", - "_ i", - "Ġ Assert", - "ĠAs sert", - "ĠAss ert", - "/ core", - "/c ore", - "/co re", - "% %%%", - "%% %%", - "%%% %", - "Ġ problems", - "Ġpro blems", - "Ġproble ms", - "Ġproblem s", - "Ġprob lems", - "Ġprobl ems", - "i stor", - "is tor", - "ist or", - "isto r", - "Ġ .=", - "Ġ. =", - "Ġ arch", - "Ġa rch", - "Ġar ch", - "Ġarc h", - "Ġ written", - "Ġw ritten", - "Ġwrit ten", - "Ġwr itten", - "Ġm ilit", - "Ġmil it", - "Ġmi lit", - "Ġmili t", - "M ENT", - "ME NT", - ". ch", - ".c h", - "c ape", - "ca pe", - "cap e", - "Ġ Mus", - "ĠM us", - "ĠMu s", - "_ config", - "_con fig", - "_conf ig", - "Ġ API", - "ĠA PI", - "ĠAP I", - "f oot", - "fo ot", - "foo t", - "Ġ images", - "Ġim ages", - "Ġimage s", - "Ġimag es", - "Ġima ges", - "e ndl", - "en dl", - "end l", - ". In", - ".I n", - "F irst", - "Fi rst", - "Ġ platform", - "Ġpl atform", - "Ġplat form", - ". prot", - ".p rot", - ".pro t", - ".pr ot", - "O ption", - "Op tion", - "Opt ion", - "s te", - "st e", - "Ġ TODO", - "ĠT ODO", - "ĠTO DO", - "ĠTOD O", - "Ġ force", - "Ġf orce", - "Ġfor ce", - "Ġforc e", - ". cont", - ".c ont", - ".con t", - ".co nt", - "ĉ echo", - "ĉe cho", - "ĠD av", - "ĠDa v", - "P tr", - "Pt r", - "( B", - "R T", - "Ġ Base", - "ĠB ase", - "ĠBa se", - "ĠBas e", - "] ['", - "][ '", - "Ġann ounc", - "Ġanno unc", - "con sole", - "cons ole", - "Ġ Py", - "ĠP y", - "d s", - ". as", - ".a s", - "Ġ prevent", - "Ġpr event", - "Ġpre vent", - "Ġprev ent", - "a pan", - "ap an", - "apa n", - "Ġ {'", - "Ġ{ '", - "} '", - "Ġ dead", - "Ġd ead", - "Ġde ad", - "V AL", - "VA L", - "Q UE", - "QU E", - "******** ****************************************************************", - "**************** ********************************************************", - "******************************** ****************************************", - "**************************************************************** ********", - "************************ ************************************************", - "**************************************** ********************************", - "************************************************ ************************", - "******************************************************** ****************", - "Ġ charg", - "Ġch arg", - "Ġchar g", - "Ġcha rg", - "R eturn", - "Re turn", - "Ret urn", - "Ġ ful", - "Ġf ul", - "Ġfu l", - "d om", - "do m", - "Ġ rules", - "Ġr ules", - "Ġrule s", - "Ġru les", - "Ġ modify", - "Ġmod ify", - "Ġ eval", - "Ġe val", - "Ġev al", - "h am", - "ha m", - "a tement", - "at ement", - "ate ment", - "atem ent", - "\\ <", - "u la", - "ul a", - "= False", - "=F alse", - "R A", - "Ġ contains", - "Ġcon tains", - "Ġcont ains", - "Ġcontain s", - "Ġconta ins", - "7 4", - "Ġ stack", - "Ġst ack", - "Ġsta ck", - "m ar", - "ma r", - "Ġ {}Ċ", - "Ġ{ }Ċ", - "Ġ{} Ċ", - "Ġ undefined", - "Ġun defined", - "Ġund efined", - "Ġundef ined", - "A ss", - "As s", - "Ġ China", - "ĠCh ina", - "ĠChi na", - "ĠChin a", - "v ey", - "ve y", - "* Ċ", - "Ġ playing", - "Ġpl aying", - "Ġplay ing", - "Ġpla ying", - ") /", - "a ctor", - "act or", - "ac tor", - "Ġ bottom", - "Ġb ottom", - "Ġbot tom", - "Ġbott om", - "l ier", - "li er", - "lie r", - "Ġ Number", - "ĠN umber", - "ĠNum ber", - "Ġc ouple", - "Ġco uple", - "Ġcou ple", - "Ġcoup le", - "D C", - "Ġ SO", - "ĠS O", - "g or", - "go r", - ". setText", - ".set Text", - "s uccess", - "su ccess", - "succ ess", - "com mand", - "comm and", - "comma nd", - "F ilter", - "Fil ter", - "Ġ Our", - "ĠO ur", - "ĠOu r", - "_ item", - "_i tem", - "_it em", - "Ġ ctx", - "Ġc tx", - "Ġct x", - "Ġ road", - "Ġr oad", - "Ġro ad", - "V ersion", - "Vers ion", - "c ase", - "ca se", - "cas e", - "u rt", - "ur t", - "av ior", - "avi or", - "y ch", - "yc h", - "semb ly", - "sembl y", - "Ġ Product", - "ĠPro duct", - "ĠProdu ct", - "ĠProd uct", - "Ġ held", - "Ġh eld", - "Ġhe ld", - "Ġhel d", - "a fe", - "af e", - "Ġ includes", - "Ġin cludes", - "Ġinclud es", - "Ġinclude s", - "Ġincl udes", - "Ġinclu des", - "< quote", - "Ġ avoid", - "Ġa void", - "Ġav oid", - "Ġ Fin", - "ĠF in", - "ĠFi n", - "Ġ Mod", - "ĠM od", - "ĠMo d", - "Ġ tab", - "Ġt ab", - "Ġta b", - "a no", - "an o", - "à ±", - "i pping", - "ip ping", - "ipp ing", - "ippi ng", - "- e", - "Ġ insert", - "Ġin sert", - "Ġins ert", - "Ġinser t", - "Ġinse rt", - "t arget", - "tar get", - "c han", - "ch an", - "cha n", - ". Model", - ".M odel", - ".Mod el", - ".Mode l", - "I ME", - "IM E", - "\\ Ċ", - "Ġ machine", - "Ġm achine", - "Ġma chine", - "Ġmach ine", - "a vy", - "av y", - "Ġ NO", - "ĠN O", - "Ġ Inter", - "ĠIn ter", - "ĠInt er", - "Ġ operation", - "Ġo peration", - "Ġop eration", - "Ġoper ation", - "Ġopera tion", - "m odal", - "mod al", - "mo dal", - "T ag", - "Ta g", - "] :", - "Ġ production", - "Ġp roduction", - "Ġpro duction", - "Ġproduct ion", - "Ġprodu ction", - "Ġprod uction", - "Ġ areas", - "Ġa reas", - "Ġare as", - "Ġarea s", - "Ġ ren", - "Ġre n", - "Ġr en", - "_ from", - "_f rom", - "_fr om", - "n bsp", - "nb sp", - "Ġ operator", - "Ġo perator", - "Ġop erator", - "Ġoper ator", - "Ġopera tor", - "m en", - "me n", - "a pped", - "ap ped", - "app ed", - "appe d", - "_ per", - "_p er", - "_pe r", - "z en", - "ze n", - "( \".", - "(\" .", - ". save", - ".s ave", - ".sa ve", - ".sav e", - "=\" {{", - "=\"{ {", - "Ġ tor", - "Ġt or", - "Ġto r", - "( response", - "(res ponse", - "(resp onse", - "Ġc andid", - "Ġcan did", - "Ġcand id", - "Ġ conv", - "Ġcon v", - "Ġco nv", - "a iled", - "ail ed", - "ai led", - "Ġ Lib", - "ĠL ib", - "ĠLi b", - "c omp", - "com p", - "co mp", - "u ra", - "ur a", - "ï¿ ½", - "Ġ Here", - "ĠH ere", - "ĠHe re", - "ĠHer e", - "Ġ argument", - "Ġarg ument", - "h ood", - "ho od", - "hoo d", - "Ġ establish", - "Ġest ablish", - "ograph y", - "ogra phy", - "Ġ onClick", - "Ġon Click", - "amb da", - "Ġ sch", - "Ġs ch", - "Ġsc h", - "Ġ movie", - "Ġm ovie", - "Ġmov ie", - "Ġmo vie", - "Ġ sec", - "Ġs ec", - "Ġse c", - "Ġ activity", - "Ġact ivity", - "Ġactiv ity", - "Ø §", - "Ġ sql", - "Ġs ql", - "Ġsq l", - "_ all", - "_a ll", - "_al l", - "in cip", - "inc ip", - "inci p", - "Ġpro vides", - "Ġprovid es", - "Ġprov ides", - "Ġprovide s", - "Ġ sys", - "Ġs ys", - "Ġsy s", - "a cket", - "ack et", - "ac ket", - "Ġw asn", - "Ġwas n", - "Ġwa sn", - "Ġ uses", - "Ġu ses", - "Ġus es", - "Ġuse s", - "Ġ Function", - "ĠF unction", - "ĠFun ction", - "ĠFunc tion", - ". google", - ".g oogle", - ".go ogle", - "Ġ Result", - "ĠRes ult", - "8 4", - "V isible", - "Vis ible", - "ag ma", - "el come", - "Ġ Sy", - "ĠS y", - "Ġ Cent", - "ĠC ent", - "ĠCe nt", - "AL SE", - "ALS E", - "a ción", - "ac ión", - "aci ón", - "E XT", - "EX T", - "Ġ license", - "Ġl icense", - "Ġlic ense", - "Ġlicens e", - "Ġ Long", - "ĠL ong", - "ĠLo ng", - "ĠLon g", - "Ġ accom", - "Ġacc om", - "Ġac com", - "Ġ ability", - "Ġab ility", - ". height", - ".h eight", - ".he ight", - "Act ive", - "Activ e", - "o logical", - "olog ical", - "ologic al", - "ologi cal", - "o ly", - "ol y", - ") ),", - ")) ,", - ". Se", - ".S e", - "Ġ parameter", - "Ġparam eter", - "Ġpara meter", - "Ġparamet er", - "p rite", - "pr ite", - "prit e", - "pri te", - "AB ILITY", - ". service", - ".s ervice", - ".serv ice", - ".ser vice", - "Ġ Group", - "ĠG roup", - "ĠGr oup", - "ĠGro up", - "_ query", - "_qu ery", - "_que ry", - "Ġ Item", - "ĠI tem", - "ĠIt em", - "i ning", - "in ing", - "ini ng", - "inin g", - "Ġ jud", - "Ġj ud", - "Ġju d", - "i ms", - "im s", - "f ix", - "fi x", - "i nder", - "in der", - "ind er", - "inde r", - "a gram", - "ag ram", - "agra m", - "agr am", - "Ġ functions", - "Ġfunction s", - "Ġfun ctions", - "Ġfunct ions", - "Ġex peri", - "Ġexp eri", - "Ġexper i", - "Ġ Em", - "ĠE m", - "Ġ rot", - "Ġr ot", - "Ġro t", - "Ġ pen", - "Ġp en", - "Ġpe n", - ". btn", - ".b tn", - ".bt n", - "Ġ AS", - "ĠA S", - "# ifdef", - "#if def", - "Ġ choice", - "Ġch oice", - "Ġcho ice", - "Ġ Page", - "ĠP age", - "ĠPa ge", - "ĠPag e", - "_ PRO", - "_P RO", - "_PR O", - "Q U", - "å ı", - "a ntity", - "ant ity", - "anti ty", - " Ń", - "w ords", - "word s", - "wor ds", - "Ġ readonly", - "Ġread only", - "Ġ flex", - "Ġf lex", - "Ġfl ex", - "Ġfle x", - "prot ected", - "protect ed", - "Ġ Any", - "ĠA ny", - "ĠAn y", - "Ġ characters", - "Ġchar acters", - "Ġcharacter s", - "en ced", - "ence d", - "enc ed", - "Ġ July", - "ĠJ uly", - "ĠJul y", - "ĠJu ly", - "i ler", - "il er", - "ile r", - "C ard", - "Car d", - "Ca rd", - "u rance", - "ur ance", - "ura nce", - "uran ce", - "Ġ rev", - "Ġre v", - "Ġr ev", - ". event", - ".e vent", - ".ev ent", - "a ly", - "al y", - "1 30", - "13 0", - "Ġw onder", - "Ġwon der", - "Ġwo nder", - "Ġ Port", - "ĠP ort", - "ĠPo rt", - "ĠPor t", - "Ġ legal", - "Ġl egal", - "Ġle gal", - "Ġleg al", - "r ole", - "ro le", - "rol e", - "Ġ ten", - "Ġt en", - "Ġte n", - "Ġg oes", - "Ġgo es", - "M P", - "wh ite", - ") :čĊ", - "): čĊ", - ") )čĊ", - ")) čĊ", - "Ġ reference", - "Ġre ference", - "Ġref erence", - "Ġrefer ence", - "Ġrefere nce", - "Ġ mis", - "Ġm is", - "Ġmi s", - "Ġ Project", - "ĠPro ject", - "ĠProj ect", - "i cks", - "ic ks", - "ick s", - "> &", - "C ON", - "CO N", - "Ġre pl", - "Ġrep l", - "Ġ regular", - "Ġreg ular", - "Ġregul ar", - "St orage", - "r amework", - "rame work", - "ram ework", - "Ġ goal", - "Ġgo al", - "Ġ touch", - "Ġt ouch", - "Ġto uch", - "Ġtou ch", - ". widget", - ".w idget", - "Ġ built", - "Ġb uilt", - "Ġbu ilt", - "d es", - "de s", - "P art", - "Par t", - "Pa rt", - "( re", - "(r e", - "Ġ worth", - "Ġw orth", - "Ġwor th", - "h ib", - "hi b", - "g ame", - "ga me", - "gam e", - "9 1", - "1 92", - "19 2", - "Ġ в", - "ĠÐ ²", - "a cion", - "ac ion", - "aci on", - "acio n", - "Ġ White", - "ĠWh ite", - "ĠWhit e", - "( type", - "(t ype", - "(typ e", - "( `", - "8 1", - "Ġ natural", - "Ġn atural", - "Ġnatur al", - "Ġnat ural", - "Ġin j", - "Ġi nj", - "Ġ calcul", - "Ġcal cul", - "Ġcalc ul", - "Ġ April", - "ĠApr il", - "ĠAp ril", - ". List", - ".L ist", - "Ġ associated", - "Ġassoci ated", - "Ġassociate d", - "Ġassoc iated", - "ĉ System", - "ĉS ystem", - "~ ~", - "= [", - "Ġ storage", - "Ġst orage", - "Ġstor age", - "Ġsto rage", - "Ġ bytes", - "Ġby tes", - "Ġbyte s", - "Ġbyt es", - "Ġ travel", - "Ġt ravel", - "Ġtr avel", - "Ġtra vel", - "Ġtrav el", - "Ġ sou", - "Ġs ou", - "Ġso u", - "Ġ passed", - "Ġp assed", - "Ġpass ed", - "Ġpas sed", - "Ġpasse d", - "! =", - "a script", - "as cript", - ". open", - ".op en", - ".o pen", - "Ġ grid", - "Ġg rid", - "Ġgr id", - "Ġgri d", - "Ġ bus", - "Ġb us", - "Ġbu s", - "Ġ recogn", - "Ġrec ogn", - "Ġreco gn", - "A b", - "Ġ hon", - "Ġh on", - "Ġho n", - "Ġ Center", - "ĠC enter", - "ĠCent er", - "Ġ prec", - "Ġp rec", - "Ġpr ec", - "Ġpre c", - "b uild", - "bu ild", - "7 3", - "HT ML", - "Ġ San", - "ĠS an", - "ĠSa n", - "Ġ countries", - "Ġc ountries", - "Ġcount ries", - "Ġcoun tries", - "a led", - "al ed", - "ale d", - "t oken", - "to ken", - "tok en", - "k t", - "Ġ qual", - "Ġqu al", - "Ġq ual", - "Ġqua l", - "L ast", - "La st", - "Las t", - "ad ow", - "ado w", - "Ġ manufact", - "Ġman ufact", - "i dad", - "id ad", - "ida d", - "j ango", - "ja ngo", - "jan go", - "jang o", - "N ext", - "Ne xt", - "x f", - ". a", - "Ġ porno", - "Ġp orno", - "Ġporn o", - "Ġpor no", - "Ġ PM", - "ĠP M", - "e rve", - "er ve", - "erv e", - "i ting", - "it ing", - "iti ng", - "itin g", - "_ th", - "_t h", - "c i", - "= None", - "=N one", - "g s", - "Ġ login", - "Ġlo gin", - "Ġlog in", - "at ives", - "ative s", - "ati ves", - "ativ es", - "' ]);Ċ", - "'] );Ċ", - "']) ;Ċ", - "']); Ċ", - "Ä ħ", - "Ġ ill", - "Ġi ll", - "Ġil l", - "I A", - "ch ildren", - "child ren", - "D O", - "Ġ levels", - "Ġlevel s", - "Ġlev els", - "Ġleve ls", - "Ġ {{", - "Ġ{ {", - "Ġ looks", - "Ġl ooks", - "Ġlo oks", - "Ġlook s", - "Ġ \"#", - "Ġ\" #", - "To String", - "ToStr ing", - "Ġ necessary", - "Ġn ecessary", - "Ġnecess ary", - "Ġ ĠĠĊ", - "ĠĠ ĠĊ", - "ĠĠĠ Ċ", - "c ell", - "ce ll", - "cel l", - "En try", - "Ent ry", - "Entr y", - "Ġ '#", - "Ġ' #", - "Ġext rem", - "Ġextr em", - "Se lector", - "Select or", - "Sel ector", - "Sele ctor", - "Ġ placeholder", - "Ġplace holder", - "L oad", - "Lo ad", - "Ġ released", - "Ġre leased", - "Ġrelease d", - "Ġrele ased", - "O RE", - "OR E", - "E numer", - "En umer", - "Enum er", - "Ġ TV", - "ĠT V", - "S ET", - "SE T", - "in q", - "P ress", - "Pr ess", - "Pre ss", - "Pres s", - "Ġ Department", - "ĠDe partment", - "ĠDep artment", - "ĠDepart ment", - "Ġ properties", - "Ġp roperties", - "Ġprop erties", - "Ġproper ties", - "Ġ respond", - "Ġres pond", - "Ġresp ond", - "S earch", - "Se arch", - "Sea rch", - "a el", - "ae l", - "Ġ requ", - "Ġre qu", - "Ġr equ", - "Ġreq u", - "Ġ Book", - "ĠB ook", - "ĠBo ok", - "ĠBoo k", - "/ Ċ", - "( st", - "(s t", - "Ġ financial", - "Ġfin ancial", - "Ġfinanc ial", - "Ġfinan cial", - "i cket", - "ic ket", - "ick et", - "_ input", - "_in put", - "_inp ut", - "Ġ threat", - "Ġth reat", - "Ġthr eat", - "( in", - "(i n", - "S trip", - "St rip", - "Str ip", - "ì Ŀ", - "ç ão", - "7 1", - "Ġe vidence", - "Ġev idence", - "Ġevid ence", - ") );", - ")) ;", - "Ġ Bro", - "ĠB ro", - "ĠBr o", - "Ġ [];Ċ", - "Ġ[ ];Ċ", - "Ġ[] ;Ċ", - "Ġ[]; Ċ", - "Ġ ou", - "Ġo u", - "b uf", - "bu f", - "S cript", - "Scr ipt", - "d at", - "da t", - "Ġ rule", - "Ġr ule", - "Ġru le", - "# import", - "= \"/", - "=\" /", - "S erial", - "Se rial", - "Ser ial", - "Ġ starting", - "Ġstart ing", - "Ġstar ting", - "[ index", - "[in dex", - "[ind ex", - "a e", - "Ġ contrib", - "Ġcon trib", - "Ġcont rib", - "Ġcontr ib", - "s ession", - "sess ion", - "_ new", - "_n ew", - "_ne w", - "u table", - "ut able", - "uta ble", - "o ber", - "ob er", - "obe r", - "Ġ \"./", - "Ġ\" ./", - "Ġ\". /", - "Ġ logger", - "Ġlo gger", - "Ġlog ger", - "Ġrec ently", - "Ġrecent ly", - "Ġ returned", - "Ġre turned", - "Ġreturn ed", - "č čĊ", - ") ))Ċ", - ")) )Ċ", - "))) Ċ", - "it ions", - "ition s", - "iti ons", - "Ġ seek", - "Ġse ek", - "Ġsee k", - "Ġ communic", - "Ġcomm unic", - "Ġcommun ic", - "Ġ \".", - "Ġ\" .", - "Ġ username", - "Ġuser name", - "Ġusern ame", - "E CT", - "EC T", - "D S", - "Ġ otherwise", - "Ġother wise", - "Ġ German", - "ĠG erman", - "ĠGer man", - "ĠGerm an", - ". aw", - ".a w", - "Ad apter", - "Ada pter", - "ix el", - "ixe l", - "Ġ systems", - "Ġs ystems", - "Ġsystem s", - "Ġsys tems", - "Ġsyst ems", - "Ġ drop", - "Ġd rop", - "Ġdr op", - "Ġdro p", - "8 3", - "Ġ structure", - "Ġstruct ure", - "Ġ $(\"#", - "Ġ$ (\"#", - "Ġ$( \"#", - "Ġ$(\" #", - "en cies", - "enc ies", - "enci es", - "an ning", - "ann ing", - "anni ng", - "Ġ Link", - "ĠL ink", - "ĠLin k", - "ĠLi nk", - "Ġ Response", - "ĠRes ponse", - "ĠRespons e", - "ĠResp onse", - "Ġ stri", - "Ġs tri", - "Ġst ri", - "Ġstr i", - "Å ¼", - "Ġ DB", - "ĠD B", - "æ Ĺ", - "and roid", - "andro id", - "andr oid", - "sub mit", - "o tion", - "ot ion", - "oti on", - "9 2", - "( @", - ". test", - ".t est", - ".te st", - "8 2", - "Ċ ĊĊĊĊĊĊĊ", - "ĊĊ ĊĊĊĊĊĊ", - "ĊĊĊĊ ĊĊĊĊ", - "ĊĊĊ ĊĊĊĊĊ", - "ĊĊĊĊĊĊ ĊĊ", - "ĊĊĊĊĊ ĊĊĊ", - "ĊĊĊĊĊĊĊ Ċ", - "] ;čĊ", - "]; čĊ", - "Ġdirect ly", - "Ġ \"%", - "Ġ\" %", - "r is", - "ri s", - "el ta", - "elt a", - "A IL", - "AI L", - ") {čĊ", - "){ čĊ", - "m ine", - "min e", - "mi ne", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "( k", - "b on", - "bo n", - "a sic", - "as ic", - "asi c", - "p ite", - "pi te", - "pit e", - "_ __", - "__ _", - "M ax", - "Ma x", - "Ġ errors", - "Ġerror s", - "Ġerr ors", - "Ġer rors", - "Ġerro rs", - "Ġ While", - "ĠWh ile", - "Ġ arguments", - "Ġarg uments", - "Ġargument s", - "Ġ ensure", - "Ġen sure", - "Ġens ure", - "R ight", - "- based", - "-b ased", - "-base d", - "W eb", - "We b", - "Ġ -=", - "Ġ- =", - "Ġint rodu", - "Ġintr odu", - "Ġintro du", - "Ġ Inst", - "ĠI nst", - "ĠIn st", - "ĠIns t", - "ĠW ash", - "ĠWas h", - "ĠWa sh", - "or din", - "ord in", - "j oin", - "jo in", - "D atabase", - "Data base", - "Dat abase", - "Ġ grad", - "Ġg rad", - "Ġgr ad", - "Ġgra d", - "Ġ usually", - "Ġus ually", - "Ġusual ly", - "Ġusu ally", - "I TE", - "IT E", - "P rops", - "Pro ps", - "Prop s", - "Pr ops", - "? >Ċ", - "?> Ċ", - "Ġ Go", - "ĠG o", - "@ Override", - "R EF", - "RE F", - "Ġ ip", - "Ġi p", - "ĠA ustral", - "ĠAust ral", - "ĠAu stral", - "ĠAustr al", - "Ġ ist", - "Ġis t", - "Ġi st", - "View ById", - "Ġ serious", - "Ġse rious", - "Ġser ious", - "Ġseri ous", - "Ġ customer", - "Ġc ustomer", - "Ġcustom er", - "Ġcust omer", - ". prototype", - ".prot otype", - ".proto type", - "o do", - "od o", - "c or", - "co r", - "Ġ door", - "Ġd oor", - "Ġdo or", - "Ġ WITHOUT", - "ĠWITH OUT", - "Ġ plant", - "Ġp lant", - "Ġpl ant", - "Ġplan t", - "Ġpla nt", - "Ġb egan", - "Ġbe gan", - "Ġbeg an", - "Ġ distance", - "Ġd istance", - "Ġdist ance", - "Ġdi stance", - "( )).", - "() ).", - "()) .", - "Ġch ance", - "Ġcha nce", - "Ġchan ce", - "Ġ ord", - "Ġo rd", - "Ġor d", - "c ame", - "ca me", - "cam e", - "pr agma", - "Ġ protect", - "Ġprot ect", - "Ġprote ct", - "r agment", - "ra gment", - "rag ment", - "Ġ Node", - "ĠN ode", - "ĠNo de", - "e ning", - "en ing", - "eni ng", - "enin g", - "Ñ ĩ", - "Ġ route", - "Ġr oute", - "Ġro ute", - "Ġrout e", - "Ġrou te", - "Ġ School", - "ĠS chool", - "ĠSch ool", - "ĠScho ol", - "h i", - "Ġne ighb", - "Ġneigh b", - "A fter", - "Af ter", - "l icit", - "lic it", - "li cit", - "Ġ contr", - "Ġcon tr", - "Ġcont r", - "Ġ primary", - "Ġpr imary", - "Ġprim ary", - "Ġpri mary", - "Ġprima ry", - "A A", - ".Write Line", - "ut ils", - "util s", - "uti ls", - "Ġ bi", - "Ġb i", - "R ed", - "Re d", - ".L inq", - ". object", - ".o bject", - ".obj ect", - ".ob ject", - "Ġ leaders", - "Ġle aders", - "Ġlead ers", - "Ġleader s", - "un ities", - "unit ies", - "uni ties", - "Ġ gun", - "Ġg un", - "Ġgu n", - "o nth", - "on th", - "ont h", - "Ġ Dev", - "ĠD ev", - "ĠDe v", - "F ILE", - "FI LE", - "Ġ comments", - "Ġcom ments", - "Ġcomm ents", - "Ġcomment s", - "Ġcomme nts", - "_ len", - "_l en", - "_le n", - "ar row", - "arr ow", - "arro w", - "a mount", - "am ount", - "amo unt", - "R ange", - "Ra nge", - "s ert", - "se rt", - "ser t", - "Grid View", - "Ġ updated", - "Ġup dated", - "Ġupdate d", - "Ġupd ated", - "Ġ Mo", - "ĠM o", - "Ġ inform", - "Ġin form", - "Ġinfo rm", - "Ġinf orm", - "oci ety", - "a la", - "al a", - "A ccess", - "Ac cess", - "Acc ess", - "Ġ hab", - "Ġh ab", - "Ġha b", - "Ġ creat", - "Ġc reat", - "Ġcr eat", - "Ġcre at", - "Ġcrea t", - "_ arg", - "_a rg", - "_ar g", - "Ġ January", - "ĠJan uary", - "Ġ Day", - "ĠD ay", - "ĠDa y", - "\" )čĊ", - "\") čĊ", - "u ple", - "up le", - "d ocument", - "doc ument", - "gor ith", - "gorit h", - "m enu", - "me nu", - "men u", - "Ġ Over", - "ĠO ver", - "ĠOv er", - "b b", - ". title", - ".t itle", - "_ out", - "_o ut", - "Ġ led", - "Ġl ed", - "Ġle d", - "u ri", - "ur i", - "Ġ ?> < /", - "g l", - "Ġ bank", - "Ġb ank", - "Ġban k", - "Ġba nk", - "ay ment", - "ĉ printf", - "ĉprint f", - "M D", - "Ġ sample", - "Ġs ample", - "Ġsam ple", - "Ġsamp le", - "Ġ hands", - "Ġh ands", - "Ġhand s", - "Ġha nds", - "Ġhan ds", - "Ġ Version", - "ĠV ersion", - "ĠVers ion", - "u ario", - "ua rio", - "uar io", - "Ġ offers", - "Ġof fers", - "Ġoff ers", - "Ġoffer s", - "ity Engine", - "Ġ shape", - "Ġs hape", - "Ġsh ape", - "Ġsha pe", - "Ġ sleep", - "Ġs leep", - "Ġsle ep", - "Ġslee p", - "_ point", - "_p oint", - "_po int", - "S ettings", - "Set tings", - "Setting s", - "Ġ achie", - "Ġa chie", - "Ġach ie", - "Ġ sold", - "Ġs old", - "Ġso ld", - "Ġsol d", - "o ta", - "ot a", - ". bind", - ".b ind", - ".bin d", - ".bi nd", - "A m", - "Ġ safe", - "Ġs afe", - "Ġsa fe", - "Ġsaf e", - "St ore", - "Ġ shared", - "Ġsh ared", - "Ġshare d", - "Ġsha red", - "Ġshar ed", - "Ġ priv", - "Ġp riv", - "Ġpr iv", - "Ġpri v", - "_ VAL", - "_V AL", - "Ġs ens", - "Ġse ns", - "Ġsen s", - ") {", - "Ġ remember", - "Ġre member", - "Ġrem ember", - "sh ared", - "sha red", - "share d", - "e lement", - "el ement", - "ele ment", - "elem ent", - "Ġ shoot", - "Ġs hoot", - "Ġsh oot", - "Ġsho ot", - "V ert", - "Ver t", - "Ve rt", - "c out", - "co ut", - "cou t", - "Ġ env", - "Ġe nv", - "Ġen v", - "_ label", - "_l abel", - "_lab el", - "_la bel", - "Ġ >Ċ", - "Ġ> Ċ", - "r un", - "ru n", - "Ġ scene", - "Ġs cene", - "Ġsc ene", - "Ġscen e", - "Ġsce ne", - "( array", - "(a rray", - "(arr ay", - "(ar ray", - "d evice", - "de vice", - "dev ice", - "_ title", - "_t itle", - "_ti tle", - "a gon", - "ag on", - "ago n", - "] čĊ", - "a by", - "ab y", - "Ġbe came", - "Ġbec ame", - "bo olean", - "bool ean", - "boo lean", - "Ġ park", - "Ġp ark", - "Ġpar k", - "Ġpa rk", - "Ġ Code", - "ĠC ode", - "ĠCo de", - "ĠCod e", - "up load", - "r iday", - "ri day", - "rid ay", - "Ġ September", - "ĠSept ember", - "ĠSep tember", - "F e", - "Ġ sen", - "Ġs en", - "Ġse n", - "c ing", - "ci ng", - "cin g", - "F L", - "C ol", - "Co l", - "u ts", - "ut s", - "_ page", - "_p age", - "_pag e", - "_pa ge", - "i nn", - "in n", - "Ġim plied", - "Ġimp lied", - "Ġimpl ied", - "a ling", - "al ing", - "ali ng", - "alin g", - "Ġyour self", - "Ġyours elf", - ". Count", - ".C ount", - ".Co unt", - "con f", - "co nf", - "Ġ aud", - "Ġa ud", - "Ġau d", - "_ init", - "_in it", - "_i nit", - "_ini t", - ". )", - "Ġw rote", - "Ġwr ote", - "0 03", - "00 3", - "N G", - ". Error", - ".E rror", - ".Err or", - "ä »", - ". for", - ".f or", - "Ġ equal", - "Ġe qual", - "Ġequ al", - "Ġeq ual", - "Ġ Request", - "ĠRe quest", - "ĠReq uest", - "Ġ serial", - "Ġs erial", - "Ġse rial", - "Ġser ial", - "Ġseria l", - "Ġseri al", - "Ġ allows", - "Ġall ows", - "Ġallow s", - "Ġallo ws", - "X X", - "Ġ middle", - "Ġm iddle", - "Ġmid dle", - "Ġmidd le", - "c hor", - "ch or", - "cho r", - "1 95", - "19 5", - "9 4", - "à ¸", - "er val", - "erv al", - "erva l", - ". Column", - ".C olumn", - ".Col umn", - "re ading", - "read ing", - "rea ding", - "Ġ escort", - "Ġesc ort", - "Ġ August", - "ĠAug ust", - "Ġquick ly", - "Ġwe ap", - "Ġ CG", - "ĠC G", - "ro pri", - "rop ri", - "h o", - "Ġ cop", - "Ġc op", - "Ġco p", - "( struct", - "(str uct", - "Ġ Big", - "ĠB ig", - "ĠBi g", - "Ġ vs", - "Ġv s", - "Ġf requ", - "Ġfr equ", - "Ġfre qu", - "Ġfreq u", - ". Value", - ".V alue", - ".Val ue", - "Ġ actions", - "Ġa ctions", - "Ġact ions", - "Ġaction s", - "Ġ proper", - "Ġpro per", - "Ġpr oper", - "Ġprop er", - "Ġ inn", - "Ġin n", - "Ġi nn", - "Ġ objects", - "Ġobject s", - "Ġobj ects", - "Ġ matrix", - "Ġm atrix", - "Ġmat rix", - "av ascript", - "ava script", - "Ġ ones", - "Ġo nes", - "Ġon es", - "Ġone s", - ". group", - ".g roup", - ".gr oup", - "Ġ green", - "Ġg reen", - "Ġgr een", - "Ġgre en", - "Ġ paint", - "Ġp aint", - "Ġpain t", - "Ġpa int", - "Ġpai nt", - "o ols", - "ool s", - "oo ls", - "y cl", - "yc l", - "en code", - "enc ode", - "enco de", - "o lt", - "ol t", - "com ment", - "comm ent", - ". api", - ".ap i", - ".a pi", - "D ir", - "Di r", - "Ġ une", - "Ġu ne", - "Ġun e", - "iz ont", - "izon t", - "izo nt", - ". position", - ".p osition", - ".pos ition", - "Ġde signed", - "Ġdes igned", - "Ġdesign ed", - "_ val", - "_v al", - "_va l", - "a vi", - "av i", - "i ring", - "ir ing", - "iri ng", - "t ab", - "ta b", - "Ġ layer", - "Ġl ayer", - "Ġla yer", - "Ġlay er", - "Ġ views", - "Ġview s", - "Ġvi ews", - "Ġvie ws", - "Ġ reve", - "Ġre ve", - "Ġr eve", - "Ġrev e", - "r ael", - "ra el", - "Ġ ON", - "ĠO N", - "r ics", - "ri cs", - "ric s", - "1 60", - "16 0", - "n p", - "Ġ core", - "Ġc ore", - "Ġco re", - "Ġcor e", - "( ));čĊ", - "() );čĊ", - "()) ;čĊ", - "()); čĊ", - "M ain", - "Ma in", - "Ġ expert", - "Ġex pert", - "Ġexp ert", - "Ġexper t", - "ĉ ĉčĊ", - "ĉĉ čĊ", - "_ en", - "_e n", - "Ġ />", - "Ġ/ >", - "ut ter", - "utt er", - "I AL", - "IA L", - "a ils", - "ail s", - "ai ls", - "Ġ King", - "ĠK ing", - "ĠKi ng", - "ĠKin g", - "* /ĊĊ", - "*/ ĊĊ", - "*/Ċ Ċ", - "Ġ Met", - "ĠM et", - "ĠMe t", - "_ end", - "_e nd", - "_en d", - "ad dr", - "add r", - "o ra", - "or a", - "Ġ ir", - "Ġi r", - "M in", - "Mi n", - "Ġsur pr", - "Ġre pe", - "Ġrep e", - "Ġ directory", - "Ġd irectory", - "Ġdirect ory", - "Ġdirector y", - "P UT", - "PU T", - "- S", - "Ġ election", - "Ġe lection", - "Ġel ection", - "Ġelect ion", - "Ġele ction", - "h aps", - "ha ps", - "hap s", - ". pre", - ".p re", - ".pr e", - "c m", - "Value s", - "Val ues", - "Ġ \"Ċ", - "Ġ\" Ċ", - "c olumn", - "col umn", - "i vil", - "iv il", - "ivi l", - "Log in", - "Lo gin", - "in ue", - "inu e", - "9 3", - "Ġ beautiful", - "Ġbe autiful", - "Ġbeaut iful", - "Ġ secret", - "Ġs ecret", - "Ġse cret", - "Ġsec ret", - "Ġsecre t", - "( event", - "(e vent", - "(ev ent", - "Ġ chat", - "Ġc hat", - "Ġch at", - "Ġcha t", - "u ms", - "um s", - "Ġ origin", - "Ġor igin", - "Ġorig in", - "Ġori gin", - "Ġ effects", - "Ġe ffects", - "Ġeffect s", - "Ġeff ects", - "Ġ management", - "Ġman agement", - "Ġmanage ment", - "Ġmana gement", - "i lla", - "il la", - "ill a", - "t k", - "Ġ setting", - "Ġs etting", - "Ġset ting", - "Ġsett ing", - "Ġ Cour", - "ĠC our", - "ĠCo ur", - "ĠCou r", - "Ġ massage", - "Ġm assage", - "Ġmass age", - "Ġmas sage", - "Ġmassa ge", - "ĉ end", - "ĉe nd", - "ĉen d", - "Ġ happy", - "Ġh appy", - "Ġhapp y", - "Ġha ppy", - "Ġhap py", - "Ġ finish", - "Ġf inish", - "Ġfin ish", - "Ġfi nish", - "Ġ camera", - "Ġc amera", - "Ġcame ra", - "Ġcam era", - "Ġcamer a", - "Ġ Ver", - "ĠV er", - "ĠVe r", - "ĠDem ocr", - "ĠDemo cr", - "Ġ Her", - "ĠH er", - "ĠHe r", - "( Q", - "c ons", - "con s", - "co ns", - "i ta", - "it a", - "Ġ '.", - "Ġ' .", - "{ }", - "ĉ C", - "Ġ stuff", - "Ġst uff", - "Ġstu ff", - "1 94", - "19 4", - "Ġ :Ċ", - "Ġ: Ċ", - "Ġ AR", - "ĠA R", - "T ask", - "Ta sk", - "h idden", - "hi dden", - "hid den", - "e ros", - "er os", - "ero s", - "I GN", - "IG N", - "at io", - "ati o", - "Ġ Health", - "ĠHe alth", - "ĠHeal th", - "ol ute", - "olut e", - "olu te", - "En ter", - "Ent er", - "' >", - "Ġ Twitter", - "ĠT witter", - "ĠTw itter", - "Ġ County", - "ĠC ounty", - "ĠCount y", - "ĠCou nty", - "ĠCoun ty", - "s cribe", - "scri be", - "scr ibe", - "Ġ= >Ċ", - "Ġ=> Ċ", - "Ġ hy", - "Ġh y", - "f it", - "fi t", - "Ġm ilitary", - "Ġmilit ary", - "Ġmil itary", - "Ġmilitar y", - "Ġ sale", - "Ġs ale", - "Ġsa le", - "Ġsal e", - "re quired", - "require d", - "requ ired", - "n on", - "no n", - "boot strap", - "h old", - "ho ld", - "hol d", - "r im", - "ri m", - "- old", - "-o ld", - "Ġ Down", - "ĠD own", - "ĠDo wn", - "ĠDow n", - "Ġ mention", - "Ġm ention", - "Ġmen tion", - "Ġment ion", - "cont act", - "_ group", - "_g roup", - "_gr oup", - "o day", - "od ay", - "oda y", - "Ġ town", - "Ġt own", - "Ġto wn", - "Ġtow n", - "Ġ solution", - "Ġs olution", - "Ġsol ution", - "u ate", - "ua te", - "uat e", - "el ling", - "ell ing", - "elli ng", - "] ->", - "]- >", - "o tes", - "ot es", - "ote s", - "en tal", - "ent al", - "enta l", - "o men", - "om en", - "ome n", - "osp ital", - "Ġ Sup", - "ĠS up", - "ĠSu p", - "_ EN", - "_E N", - "Ġ slow", - "Ġs low", - "Ġsl ow", - "Ġslo w", - "SE SSION", - "SES SION", - "Ġ blue", - "Ġb lue", - "Ġbl ue", - "a go", - "ag o", - "Ġl ives", - "Ġli ves", - "Ġlive s", - "Ġliv es", - "Ġ ^", - ". un", - ".u n", - "i nst", - "in st", - "ins t", - "e nge", - "en ge", - "eng e", - "Ġ customers", - "Ġcustom ers", - "Ġcustomer s", - "Ġcust omers", - "Ġ cast", - "Ġc ast", - "Ġca st", - "Ġcas t", - "ud get", - "udge t", - "ï¼ ģ", - "ic ens", - "ice ns", - "Ġd etermin", - "Ġde termin", - "Ġdeter min", - "Ġdeterm in", - "Se lected", - "Select ed", - "Sel ected", - "_ pl", - "_p l", - "ue ue", - "Ġ dark", - "Ġd ark", - "Ġda rk", - "Ġdar k", - "/ /ĊĊ", - "// ĊĊ", - "//Ċ Ċ", - "s i", - "th ern", - "ther n", - "the rn", - "Ġ Japan", - "ĠJ apan", - "ĠJa pan", - "ĠJap an", - "/ w", - "P U", - "Ġ East", - "ĠE ast", - "ĠEa st", - "o vie", - "ov ie", - "ovi e", - "Ġ package", - "Ġp ackage", - "Ġpack age", - "Ġ nor", - "Ġn or", - "Ġno r", - "Ġ api", - "Ġa pi", - "Ġap i", - "b ot", - "bo t", - "\" ];Ċ", - "\"] ;Ċ", - "\"]; Ċ", - "_ post", - "_p ost", - "_pos t", - "_po st", - "u late", - "ul ate", - "ula te", - "Ġ club", - "Ġc lub", - "Ġcl ub", - "' ));Ċ", - "') );Ċ", - "')) ;Ċ", - "')); Ċ", - "Ġ loop", - "Ġl oop", - "Ġlo op", - "P IO", - "PI O", - "i one", - "ion e", - "io ne", - "s hot", - "sh ot", - "In itial", - "Init ial", - "Ġ played", - "Ġpl ayed", - "Ġplay ed", - "reg ister", - "regist er", - "r ought", - "ro ught", - "rou ght", - "rough t", - "_ max", - "_m ax", - "_ma x", - "ace ment", - "ac ement", - "m atch", - "mat ch", - "raph ics", - "raphic s", - "A ST", - "AS T", - "Ġ existing", - "Ġex isting", - "Ġexist ing", - "Ġ complex", - "Ġcom plex", - "Ġcomp lex", - "Ġcomple x", - "Ġcompl ex", - "D A", - ". Ch", - ".C h", - ". common", - ".com mon", - ".comm on", - "m o", - "Ġ' ../../", - "Ġ'../ ../", - "Ġ'.. /../", - "i to", - "it o", - "Ġ analysis", - "Ġan alysis", - "Ġanal ysis", - "Ġanaly sis", - "Ġanalys is", - "Ġ deliver", - "Ġdel iver", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "i dx", - "id x", - "à ł", - "o ngo", - "on go", - "ong o", - "Ġ English", - "ĠEng lish", - "ĠEngl ish", - "< !--", - " \";Ċ", - ">\" ;Ċ", - ">\"; Ċ", - "_ UN", - "_U N", - "ĉ std", - "ĉs td", - "ĉst d", - "o ded", - "od ed", - "ode d", - "Ġ calls", - "Ġc alls", - "Ġcall s", - "Ġcal ls", - "h ere", - "he re", - "her e", - "R el", - "Re l", - "Ġ brand", - "Ġb rand", - "Ġbr and", - "Ġbra nd", - "Ġbran d", - "back ground", - "g a", - "_ address", - "_add ress", - "_addr ess", - "_ad dress", - "_ params", - "_param s", - "_par ams", - "_para ms", - "_pa rams", - "C ategory", - "1 03", - "10 3", - "Ġ India", - "ĠIn dia", - "ĠInd ia", - "ĠIndi a", - "_ event", - "_e vent", - "_ev ent", - "_even t", - "Ġ ing", - "Ġin g", - "Ġi ng", - "R ender", - "Re nder", - "Ren der", - ". cl", - ".c l", - "um py", - "ump y", - "Ġ pet", - "Ġp et", - "Ġpe t", - "F C", - "Ġ Ant", - "ĠA nt", - "ĠAn t", - "E xt", - "Ex t", - "Ġ charge", - "Ġch arge", - "Ġchar ge", - "Ġcharg e", - "e ned", - "en ed", - "ene d", - "g rad", - "gr ad", - "gra d", - "E O", - "Ġ depend", - "Ġd epend", - "Ġde pend", - "Ġdep end", - "Ġ .ĊĊ", - "Ġ. ĊĊ", - "Ġ.Ċ Ċ", - "f rame", - "fr ame", - "fra me", - "fram e", - "Ġ df", - "Ġd f", - "Ġ huge", - "Ġh uge", - "Ġhug e", - "Ġhu ge", - "Ġ PART", - "ĠP ART", - "ĠPA RT", - "ĠPAR T", - "e ds", - "ed s", - "; ;", - "Ġ AM", - "ĠA M", - "Ġ basic", - "Ġb asic", - "Ġbas ic", - "Ġba sic", - "Ġ Let", - "ĠL et", - "ĠLe t", - "l ich", - "lic h", - "li ch", - "Ġ arm", - "Ġa rm", - "Ġar m", - "Ġ star", - "Ġs tar", - "Ġst ar", - "Ġsta r", - "Ġf ederal", - "Ġfed eral", - "Ġfeder al", - "W ork", - "Wo rk", - "Ġ carry", - "Ġc arry", - "Ġcar ry", - "Ġcarr y", - "Ġ Israel", - "ĠIs rael", - "( obj", - "(o bj", - "(ob j", - "= {{", - "={ {", - "Ġ saved", - "Ġs aved", - "Ġsa ved", - "Ġsave d", - "Ġsav ed", - "Ġ syn", - "Ġs yn", - "Ġsy n", - "Ġ constant", - "Ġcon stant", - "Ġconst ant", - "Ġcons tant", - "V ENT", - "VE NT", - "VEN T", - "Ġ positive", - "Ġpos itive", - "Ġposit ive", - "Ġ conduct", - "Ġcon duct", - "Ġcond uct", - "Ġconduc t", - "Ġcondu ct", - "Ġ skin", - "Ġs kin", - "Ġsk in", - "Ġski n", - "Ġear lier", - "Ġ layout", - "Ġl ayout", - "Ġlay out", - "Ġ IP", - "ĠI P", - "O UR", - "OU R", - "Ġ tim", - "Ġt im", - "Ġti m", - "style sheet", - "styles heet", - "_ cl", - "_c l", - "Ġ Card", - "ĠC ard", - "ĠCar d", - "ĠCa rd", - "++ ){Ċ", - "++) {Ċ", - "++){ Ċ", - "Ġtem per", - "Ġtemp er", - "Ġ David", - "ĠD avid", - "ĠDav id", - "ĠDa vid", - "ĉ try", - "ĉt ry", - "ĉtr y", - ". dart", - ".d art", - ".da rt", - "Ġw ants", - "Ġwant s", - "Ġwa nts", - "Ġwan ts", - "Ġ picture", - "Ġp icture", - "Ġpic ture", - "Ġpict ure", - "Ġ videos", - "Ġv ideos", - "Ġvideo s", - "Ġvid eos", - "Ġvide os", - "Ġ Comm", - "ĠC omm", - "ĠCom m", - "ĠCo mm", - "is ions", - "ision s", - "isi ons", - "_ MAX", - "_M AX", - "_MA X", - "M apping", - "Map ping", - "Ma pping", - "- content", - "-c ontent", - "-con tent", - "-cont ent", - "Ġ Ear", - "ĠE ar", - "ĠEa r", - "- de", - "-d e", - "Ġp rem", - "Ġpr em", - "Ġpre m", - "b ruary", - "br uary", - "bru ary", - "bruar y", - "Ġ components", - "Ġcom ponents", - "Ġcomp onents", - "Ġcomponent s", - "Ġthrough out", - "Ġ pull", - "Ġp ull", - "Ġpul l", - "Ġpu ll", - "Ġ pages", - "Ġp ages", - "Ġpage s", - "Ġpa ges", - "Ġpag es", - "e nte", - "en te", - "ent e", - "res pond", - "resp ond", - "Ġ gas", - "Ġg as", - "Ġga s", - "cript or", - "Ġ edge", - "Ġe dge", - "Ġed ge", - "Ġ bound", - "Ġb ound", - "Ġbo und", - "Ġbou nd", - "A CT", - "AC T", - "* *****", - "** ****", - "**** **", - "*** ***", - "***** *", - "Ġ creating", - "Ġc reating", - "Ġcr eating", - "Ġcre ating", - "Ġcreat ing", - "Ġcrea ting", - "Ġ CH", - "ĠC H", - "Ġ nullptr", - "Ġnull ptr", - "B r", - "+ '", - ". co", - ".c o", - "> ::", - ">: :", - "Ġ learning", - "Ġl earning", - "Ġle arning", - "Ġlearn ing", - "Ġlear ning", - ". Length", - ".L ength", - ".Le ngth", - ".Len gth", - "_ SH", - "_S H", - "Ġ patients", - "Ġpat ients", - "Ġpatient s", - "A IN", - "AI N", - "Ġ kids", - "Ġk ids", - "Ġkid s", - "Ġki ds", - "Ġ comfort", - "Ġcom fort", - "Ġ shown", - "Ġsh own", - "Ġshow n", - "Ġsho wn", - "ug ins", - "ugin s", - "ugi ns", - "Ġ Back", - "ĠB ack", - "ĠBa ck", - "ĠBac k", - "e lla", - "el la", - "ell a", - "_ CL", - "_C L", - "Ġ lat", - "Ġl at", - "Ġla t", - "Ġ dispatch", - "Ġdis patch", - "Ġdisp atch", - "Ġ classes", - "Ġc lasses", - "Ġclass es", - "Ġcl asses", - "Ġclasse s", - "Ġclas ses", - ". at", - ".a t", - ". begin", - ".b egin", - ".be gin", - "Ġ successful", - "Ġsuccess ful", - "b an", - "ba n", - "Ġob tain", - "Ġobt ain", - "Ġ Sl", - "ĠS l", - "Ġ lack", - "Ġl ack", - "Ġla ck", - "Ġlac k", - "it erator", - "iter ator", - "T hread", - "Th read", - "Thr ead", - "( size", - "(s ize", - "(si ze", - "Ġ none", - "Ġn one", - "Ġno ne", - "Ġnon e", - ". has", - ".h as", - "_ X", - "s ort", - "so rt", - "n ap", - "na p", - "p et", - "pe t", - "b in", - "bi n", - "7 00", - "70 0", - "Ġ Canada", - "ĠCan ada", - "T hey", - "The y", - "Th ey", - "Ġd ans", - "Ġda ns", - "Ġdan s", - "Ġ Mat", - "ĠM at", - "ĠMa t", - "< td", - "'", - "'=> '", - "'= >'", - "Ġ Paul", - "ĠP aul", - "ĠPa ul", - "m as", - "ma s", - "ĉ print", - "ĉp rint", - "ĉpr int", - "( len", - "(l en", - "(le n", - "f d", - "Ġ );", - "Ġ) ;", - ". Event", - ".E vent", - "q li", - "ql i", - "i rit", - "ir it", - "iri t", - "ie lds", - "ield s", - "iel ds", - "o man", - "om an", - "oma n", - "Ġ Top", - "ĠT op", - "ĠTo p", - "Ġ vote", - "Ġv ote", - "Ġvo te", - "Ġvot e", - "Ġ mask", - "Ġm ask", - "Ġma sk", - "Ġmas k", - "Ġ theme", - "Ġth eme", - "Ġthe me", - "Ġthem e", - "- Ċ", - "Ġ props", - "Ġp rops", - "Ġpro ps", - "Ġpr ops", - "Ġprop s", - "Ġ fine", - "Ġf ine", - "Ġfin e", - "Ġfi ne", - "Ġ writer", - "Ġw riter", - "Ġwrit er", - "Ġwr iter", - "Ġwrite r", - "_ offset", - "_off set", - "_o ffset", - "c ar", - "ca r", - "Ġ altern", - "Ġal tern", - "Ġalt ern", - "Ġalter n", - "Ġalte rn", - "Ġ copyright", - "Ġc opyright", - "Ġcopy right", - "Ġ destroy", - "Ġd estroy", - "Ġde stroy", - "Ġdest roy", - "p per", - "pp er", - "ppe r", - "Ġ generate", - "Ġg enerate", - "Ġgener ate", - "Ġgen erate", - "Ġgene rate", - "Ġgenera te", - "p ped", - "pp ed", - "ppe d", - "âĢĻ d", - "Ġ ĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĊ", - "ĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠ Ċ", - "m ake", - "ma ke", - "mak e", - "Ġ Show", - "ĠS how", - "ĠSh ow", - "ĠSho w", - "Ġ browser", - "Ġb rowser", - "Ġbrows er", - "Ġbrowse r", - "Ġbrow ser", - "Ġ favorite", - "Ġf avorite", - "Ġfavor ite", - "Ġ career", - "Ġcar eer", - "Ġcare er", - "Ġhapp ened", - "Ġhappen ed", - "( char", - "(c har", - "(ch ar", - "Ġ recommend", - "Ġre commend", - "Ġrecomm end", - "Ġ liter", - "Ġl iter", - "Ġli ter", - "Ġlit er", - "Ġlite r", - ". filter", - ".f ilter", - ".fil ter", - "g rade", - "gr ade", - "grad e", - "gra de", - "Ġ £", - "Ġ £", - "P hone", - "Ph one", - "o ms", - "om s", - "Ġ named", - "Ġn amed", - "Ġname d", - "Ġna med", - "Ġnam ed", - "- label", - "-l abel", - "-la bel", - "i po", - "ip o", - "Ġ Other", - "ĠO ther", - "ĠOt her", - "Ġ panel", - "Ġp anel", - "Ġpa nel", - "Ġpan el", - "Ġpane l", - "Ġ rock", - "Ġr ock", - "Ġro ck", - "Ġroc k", - "S cale", - "Sc ale", - "ĉ assert", - "ĉas sert", - "Ð ´", - "Ġ trust", - "Ġt rust", - "Ġtr ust", - "f ront", - "fr ont", - "Ġd emon", - "Ġde mon", - "Ġdem on", - "Ġdemo n", - "A r", - "N et", - "Ne t", - "Ġ economic", - "Ġe conomic", - "Ġecon omic", - "Ġeconom ic", - "Ġec onomic", - "f ooter", - "fo oter", - "foot er", - "foo ter", - "Ġ race", - "Ġr ace", - "Ġrac e", - "Ġra ce", - "( node", - "(n ode", - "(no de", - "Ġ Option", - "ĠO ption", - "ĠOp tion", - "ĠOpt ion", - "s plit", - "sp lit", - "spl it", - "Ġ physical", - "Ġph ysical", - "Ġphys ical", - "Ġphysic al", - "i fest", - "if est", - "ife st", - "ifes t", - "Ġ removed", - "Ġre moved", - "Ġrem oved", - "Ġremove d", - ". http", - ".h ttp", - ".ht tp", - ") ),Ċ", - ")) ,Ċ", - ")), Ċ", - "Ġlo oked", - "Ġlook ed", - "' ;", - "d ing", - "di ng", - "din g", - "g est", - "ge st", - "ges t", - "atur day", - "/ licenses", - "/lic enses", - "/license s", - "P rice", - "Pr ice", - "Pri ce", - "Ġ dro", - "Ġd ro", - "Ġdr o", - "Ġt owards", - "Ġto wards", - "Ġtoward s", - "Ġtow ards", - "Ġ uns", - "Ġu ns", - "Ġun s", - "Ġ CL", - "ĠC L", - "ĉ static", - "ĉst atic", - "ĉstat ic", - "Ġ rows", - "Ġr ows", - "Ġro ws", - "Ġrow s", - "Ġ define", - "Ġde fine", - "Ġdef ine", - "Ġdefin e", - ". replace", - ".re place", - ".rep lace", - "Ġ father", - "Ġf ather", - "Ġfa ther", - "Ġfat her", - "Ġ Design", - "ĠD esign", - "ĠDe sign", - "ĠDes ign", - "as sign", - "ass ign", - "assi gn", - "m ut", - "mu t", - "D evice", - "De vice", - "Dev ice", - "D id", - "Di d", - "' ))Ċ", - "') )Ċ", - "')) Ċ", - "o metry", - "om etry", - "ome try", - "omet ry", - "ometr y", - "ay load", - "Ġ histor", - "Ġh istor", - "Ġhis tor", - "Ġhist or", - "Ġhi stor", - "Ġhisto r", - "Ġ Param", - "ĠP aram", - "ĠPar am", - "ĠPa ram", - "ĠPara m", - "Ġ Boolean", - "ĠBo olean", - "ĠBool ean", - "ĠBoo lean", - "Ġ nature", - "Ġn ature", - "Ġna ture", - "Ġnatur e", - "Ġnat ure", - "Ġ js", - "Ġj s", - "Ġ nation", - "Ġn ation", - "Ġna tion", - "Ġnat ion", - "i h", - "Ġ discover", - "Ġdis cover", - "Ġdisc over", - "Ġdisco ver", - "s em", - "se m", - "H andle", - "Hand le", - "Han dle", - "ĉ r", - "Ġ Techn", - "ĠT echn", - "ĠTe chn", - "ĠTech n", - "ĠTec hn", - "Ġ wall", - "Ġw all", - "Ġwa ll", - "Ġwal l", - "{ $", - "@ property", - "Ġ\" ../", - "Ġ\". ./", - "Ġ\".. /", - "Ġ exam", - "Ġex am", - ". draw", - ".d raw", - ".dr aw", - "o pping", - "op ping", - "opp ing", - "Ġn early", - "Ġnear ly", - "Ġ cool", - "Ġc ool", - "Ġco ol", - "Ġin depend", - "Ġind epend", - "Ġinde pend", - "R ES", - "RE S", - "Ġ handler", - "Ġh andler", - "Ġhand ler", - "Ġhandle r", - "Ġ Monday", - "ĠMon day", - "ĠMond ay", - "Ġ sun", - "Ġs un", - "Ġsu n", - "St yles", - "Style s", - "ous ly", - "Ġ ĉ", - "v est", - "ve st", - "ves t", - "D isplay", - "Dis play", - "Disp lay", - "( y", - "at ically", - "atic ally", - "atical ly", - "Ġ predict", - "Ġp redict", - "Ġpre dict", - "Ġpred ict", - "Ġpredic t", - "y ing", - "yi ng", - "Ġ sometimes", - "Ġs ometimes", - "Ġsome times", - "Ġsom etimes", - "Ġsometime s", - "\" ]Ċ", - "\"] Ċ", - "Ġ drink", - "Ġd rink", - "Ġdr ink", - "Ġdri nk", - "Ġ bul", - "Ġb ul", - "Ġbu l", - "if ications", - "ific ations", - "ification s", - ". insert", - ".in sert", - ".ins ert", - ". reg", - ".re g", - ".r eg", - "Ġ tests", - "Ġt ests", - "Ġte sts", - "Ġtest s", - "Ġtes ts", - "Al ignment", - "Align ment", - "Ġal leg", - "Ġall eg", - "Ġalle g", - "Ġ attribute", - "Ġat tribute", - "Ġattrib ute", - "Ġ Note", - "ĠN ote", - "ĠNo te", - "ĠNot e", - "Ġmy self", - "Ġmys elf", - "a rts", - "ar ts", - "art s", - "N ow", - "No w", - "Ġ interesting", - "Ġinter esting", - "Ġinterest ing", - "Ġinteres ting", - "l ients", - "li ents", - "lient s", - "lie nts", - "lien ts", - "Ġ population", - "Ġpop ulation", - "Ġpopul ation", - "Ġ California", - "ĠCal ifornia", - "ĠCaliforn ia", - "\" I", - "å ¹", - "Ġ greater", - "Ġg reater", - "Ġgreat er", - "Ġgre ater", - "ues day", - "Ġth ous", - "Ġthou s", - "Ġtho us", - "Ġco sts", - "Ġcost s", - "Ġcos ts", - "Ġ launch", - "Ġl aunch", - "Ġla unch", - "\\ Http", - "k er", - "ke r", - "b and", - "ba nd", - "ban d", - "Ġ Play", - "ĠP lay", - "ĠPl ay", - "ĠPla y", - "Ġ band", - "Ġb and", - "Ġban d", - "Ġba nd", - ". shape", - ".s hape", - ".sh ape", - ".sha pe", - "e some", - "es ome", - "eso me", - "art icle", - "arti cle", - "artic le", - ". rf", - ".r f", - "Ġ wer", - "Ġw er", - "Ġwe r", - "á s", - "em bers", - "ember s", - "emb ers", - "u sr", - "us r", - "B A", - "i can", - "ic an", - "ica n", - "e tt", - "et t", - "valid ate", - "ul ti", - "ult i", - "Ġim mediately", - "Ġimmedi ately", - "Ġimmediate ly", - "z er", - "ze r", - "Ġ figure", - "Ġf igure", - "Ġfig ure", - "Ġfigur e", - "o es", - "oe s", - "e ller", - "el ler", - "ell er", - "elle r", - "ir cle", - "irc le", - "Ġ Sign", - "ĠS ign", - "ĠSi gn", - "ĠSig n", - ". db", - ".d b", - "Ġ rank", - "Ġr ank", - "Ġran k", - "Ġra nk", - "By tes", - "Byte s", - "Ġ projects", - "Ġproject s", - "Ġproj ects", - "Ġproje cts", - "_ rec", - "_re c", - "_r ec", - "U LAR", - "UL AR", - "ULA R", - "A PI", - "AP I", - "Ġ Line", - "ĠL ine", - "ĠLin e", - "ĠLi ne", - "P ort", - "Por t", - "Po rt", - "Ġ poll", - "Ġp oll", - "Ġpol l", - "Ġpo ll", - "Ġg iving", - "Ġgi ving", - "id ence", - "ide nce", - "iden ce", - "- -Ċ", - "-- Ċ", - "Ġ plot", - "Ġp lot", - "Ġpl ot", - "Ġplo t", - "i cial", - "ic ial", - "ici al", - "icia l", - "Ġw arrant", - "Ġwar rant", - "I TION", - "IT ION", - "Ġ Double", - "ĠD ouble", - "ĠDo uble", - "ĠDou ble", - "ĠDoub le", - "Ġb illion", - "Ġbill ion", - "Ġbil lion", - "gorith m", - "gorit hm", - "Ġ equipment", - "Ġe quipment", - "Ġequ ipment", - "Ġequip ment", - "D ATE", - "DA TE", - "DAT E", - "Ġ @\"", - "Ġ@ \"", - "E E", - "Ġ ple", - "Ġp le", - "Ġpl e", - "i ation", - "ia tion", - "iat ion", - "Ġ headers", - "Ġhe aders", - "Ġhead ers", - "Ġheader s", - "Ġpro ced", - "Ġproc ed", - ".Component Model", - "Ġ Obama", - "ĠOb ama", - "Ġ pa", - "Ġp a", - "Ġ Best", - "ĠB est", - "ĠBe st", - "ĠBes t", - "im ately", - "imate ly", - "imat ely", - ". getString", - ".get String", - ".getS tring", - ". \\", - "m ploy", - "mp loy", - "mpl oy", - "Ġ raw", - "Ġr aw", - "Ġra w", - "_ block", - "_b lock", - "_bl ock", - "_bloc k", - "und red", - "\" },Ċ", - "\"} ,Ċ", - "\"}, Ċ", - "1 12", - "11 2", - ".Group Layout", - "Ġb rought", - "Ġbr ought", - "Ġbro ught", - "NS String", - "NSS tring", - "th row", - "thr ow", - "thro w", - "c reated", - "cre ated", - "create d", - "cr eated", - "creat ed", - ". New", - ".N ew", - "_ view", - "_v iew", - "_vi ew", - "C P", - "e ps", - "ep s", - "O p", - "Ġ gratis", - "Ġgr atis", - "Ġgrat is", - "Ġ '\"", - "Ġ' \"", - "Ġint erview", - "Ġinter view", - "Ġinterv iew", - "\" \"\"Ċ", - "\"\" \"Ċ", - "\"\"\" Ċ", - "Ġ partial", - "Ġp artial", - "Ġpart ial", - "Ġparti al", - "Ġ aria", - "Ġa ria", - "Ġar ia", - "b ing", - "bin g", - "bi ng", - "A uthor", - "Auth or", - "Aut hor", - "B ook", - "Bo ok", - "Ġ Pat", - "ĠP at", - "ĠPa t", - "u man", - "um an", - "uma n", - "User s", - "Us ers", - "Use rs", - "p lus", - "pl us", - "1 93", - "19 3", - "Ġ Direct", - "ĠD irect", - "ĠDi rect", - "ĠDir ect", - "ĠDire ct", - "ven ue", - "al pha", - "alph a", - "UC CESS", - "Ġ Call", - "ĠC all", - "ĠCal l", - "ĠCa ll", - "Ġ );čĊ", - "Ġ) ;čĊ", - "Ġ); čĊ", - "im ated", - "imate d", - "ima ted", - "imat ed", - "Ġ remain", - "Ġre main", - "Ġr emain", - "Ġrem ain", - "Ġ anti", - "Ġan ti", - "Ġant i", - "Ġ London", - "ĠL ondon", - "ĠLo ndon", - "ĠLon don", - "ĠLond on", - "Ġs afety", - "Ġsaf ety", - "Ġsafe ty", - "P OSE", - "PO SE", - "POS E", - "o les", - "ol es", - "ole s", - "cont roller", - "control ler", - "contr oller", - "contro ller", - "B yte", - "By te", - "Ġ Court", - "ĠC ourt", - "ĠCo urt", - "ĠCour t", - "ĠCou rt", - "Ġ Phil", - "ĠP hil", - "ĠPh il", - "ĠPhi l", - "Ġ Associ", - "ĠAss oci", - "ĠAssoc i", - "e na", - "en a", - "å IJ", - "_ STR", - "_S TR", - "_ST R", - "c oin", - "co in", - "res hold", - "resh old", - "Ġ batch", - "Ġb atch", - "Ġbat ch", - "_ Click", - "_C lick", - "_Cl ick", - "ent ication", - "entic ation", - "enti cation", - "> ';Ċ", - ">' ;Ċ", - ">'; Ċ", - "e nty", - "en ty", - "ent y", - "Ġbeg inning", - "Ġbegin ning", - "Ġ zero", - "Ġz ero", - "Ġze ro", - "Ġzer o", - "Ġ Convert", - "ĠCon vert", - "ĠConv ert", - "Ġ terr", - "Ġt err", - "Ġte rr", - "Ġter r", - "Ġ paid", - "Ġp aid", - "Ġpa id", - "Ġpai d", - "Ġincre ased", - "Ġincrease d", - "c atch", - "cat ch", - "- size", - "-s ize", - "1 15", - "11 5", - "act ivity", - "activ ity", - "e quals", - "equ als", - "equal s", - "Ġ queue", - "Ġque ue", - "Ġq ueue", - "Ġ \"'", - "Ġ\" '", - "Ġ International", - "ĠInt ernational", - "ĠIntern ational", - "ĠInter national", - "Ġf ür", - "urs day", - "Ġ scient", - "Ġs cient", - "Ġsc ient", - "Ġsci ent", - "al low", - "all ow", - "allo w", - "a xis", - "ax is", - "Ġ appropri", - "Ġapp ropri", - "Ġap propri", - "Ġappro pri", - "e dge", - "ed ge", - "Ġ idx", - "Ġi dx", - "Ġid x", - "S uccess", - "Su ccess", - "Suc cess", - "ent ifier", - ": \\", - "x is", - "xi s", - "Ġ maximum", - "Ġmax imum", - "Ġmaxim um", - "ar ks", - "ark s", - "Ġ birth", - "Ġb irth", - "Ġbir th", - "( index", - "(in dex", - "(ind ex", - "Ġ maybe", - "Ġm aybe", - "Ġmay be", - ". py", - ".p y", - "f iles", - "file s", - "fi les", - "fil es", - "Ġ limited", - "Ġl imited", - "Ġlimit ed", - "Ġlim ited", - "Ġlimite d", - "_ check", - "_c heck", - "_ch eck", - "_che ck", - "l ook", - "lo ok", - "loo k", - "p lies", - "pl ies", - "Ġ movement", - "Ġm ovement", - "Ġmov ement", - "Ġmove ment", - "Ġmo vement", - "' ].", - "'] .", - "Ġb road", - "Ġbr oad", - "Ġbro ad", - "Ġ BE", - "ĠB E", - "Ġ UnityEngine", - "ĠUn ityEngine", - "ĠUnity Engine", - ". cpp", - ".c pp", - ".cp p", - "Ġ Every", - "ĠE very", - "ĠEv ery", - "ĠEver y", - "ĠEve ry", - "Ad min", - "Ġ fans", - "Ġf ans", - "Ġfa ns", - "Ġfan s", - "p ared", - "par ed", - "pare d", - "pa red", - "Ċ ĠĠĠĠĊ", - "Ġ foreign", - "Ġfore ign", - "Ġ pan", - "Ġp an", - "Ġpa n", - "Ġ tour", - "Ġt our", - "Ġto ur", - "Ġtou r", - "Ġ Order", - "ĠOr der", - "ĠOrd er", - "Ġ moving", - "Ġm oving", - "Ġmov ing", - "Ġmo ving", - "Ġ auf", - "Ġa uf", - "Ġau f", - "C all", - "Cal l", - "Ca ll", - "c b", - "Å Ł", - "vent ory", - "Ġ Sql", - "ĠS ql", - "ĠSq l", - "Ġ fully", - "Ġf ully", - "Ġfull y", - "Ġful ly", - "Click Listener", - "W ORD", - "WO RD", - "Ġann ounced", - "Ġannounc ed", - "Ġannounce d", - ") čĊčĊ", - ")čĊ čĊ", - "Ġag reed", - "Ġagre ed", - "Ġagree d", - "Ġagr eed", - "r ie", - "ri e", - "Ġ earn", - "Ġe arn", - "Ġear n", - "Ġea rn", - "_ link", - "_l ink", - "_lin k", - "_li nk", - ". array", - ".a rray", - ".ar ray", - ".arr ay", - "( text", - "(t ext", - "(te xt", - "(tex t", - "Ġ materials", - "Ġmaterial s", - "Ġmateria ls", - "Ġmater ials", - "Ġmateri als", - ", p", - "f fff", - "ff ff", - "fff f", - "v g", - "Ġ ©", - "Ġ ©", - "Ġ unless", - "Ġun less", - "Ġunle ss", - "Ġunl ess", - "a jax", - "aj ax", - "aja x", - "L OG", - "LO G", - "Ġ sexual", - "Ġs exual", - "Ġsex ual", - "Ġ \\\"", - "Ġ\\ \"", - "- time", - "-t ime", - "Ġ coach", - "Ġco ach", - "Ġ supported", - "Ġs upported", - "Ġsup ported", - "Ġsupport ed", - "Ġsupp orted", - "Ġ photos", - "Ġph otos", - "Ġphot os", - "Ġphoto s", - "i form", - "if orm", - "ifo rm", - ". Create", - ".C reate", - ") ]", - "r ier", - "ri er", - "rie r", - "Ġ dialog", - "Ġd ialog", - "Ġdi alog", - "Ġdia log", - "Ġdial og", - "a ver", - "av er", - "ave r", - "i ge", - "ig e", - ") +", - "_ idx", - "_id x", - "_i dx", - ": [", - "_ min", - "_m in", - "_mi n", - "Ġ Cong", - "ĠC ong", - "ĠCon g", - "ĠCo ng", - "Ġ pressure", - "Ġpres sure", - "Ġpress ure", - "Ġ teams", - "Ġte ams", - "Ġteam s", - "Ġtea ms", - "S ign", - "Si gn", - "Sig n", - "b egin", - "be gin", - "beg in", - "r ian", - "ri an", - "ria n", - "N ESS", - "NE SS", - "NES S", - "L S", - "Ġim prove", - "Ġimp rove", - "Ġimpro ve", - "Ġimpr ove", - "Ġimprov e", - "Ġ Sunday", - "ĠS unday", - "ĠSun day", - "ĠSund ay", - "Ġ definition", - "Ġdef inition", - "Ġdefinit ion", - "Ġdefin ition", - "i ger", - "ig er", - "ige r", - "rol lers", - "roll ers", - "roller s", - "Ġ thinking", - "Ġth inking", - "Ġthink ing", - "Ġthin king", - "T emplate", - "Temp late", - "Tem plate", - "- F", - "Ġem erg", - "p lates", - "pl ates", - "plate s", - "plat es", - "pla tes", - "Ġ USA", - "ĠU SA", - "ĠUS A", - ". setState", - ".set State", - "Ġ Also", - "ĠAl so", - "ĠAls o", - "r ev", - "re v", - "Ġ enable", - "Ġe nable", - "Ġen able", - "Ġ CO", - "ĠC O", - "P ECT", - "PE CT", - "PEC T", - "Ġ concept", - "Ġcon cept", - "Ġconc ept", - "Ġconce pt", - ") -", - "Ġ âĢ¢", - "ĠâĢ ¢", - "Ġ sets", - "Ġs ets", - "Ġse ts", - "Ġset s", - "Ġ meaning", - "Ġme aning", - "Ġmean ing", - "e mon", - "em on", - "emo n", - "Ġ Cons", - "ĠC ons", - "ĠCon s", - "ĠCo ns", - "c mp", - "cm p", - "e der", - "ed er", - "ede r", - "an ned", - "ann ed", - "anne d", - "ic ensed", - "icense d", - "icens ed", - "Ġ Super", - "ĠS uper", - "ĠSup er", - "ĠSu per", - "Ġ daily", - "Ġd aily", - "Ġda ily", - "Ġdai ly", - "Ġ multi", - "Ġm ulti", - "Ġmult i", - "Ġmul ti", - "_ u", - "Ġch alleng", - "Ġchall eng", - "_ mode", - "_m ode", - "_mod e", - "_mo de", - "Ġ Promise", - "ĠP romise", - "ĠPro mise", - "ĠProm ise", - "Ġ strict", - "Ġstr ict", - "Ġstri ct", - "j o", - "i nton", - "in ton", - "int on", - "into n", - "( list", - "(l ist", - "(li st", - "On ly", - "> {", - "Ġ vehicle", - "Ġv ehicle", - "Ġveh icle", - "í ķ", - "Ġ Player", - "ĠP layer", - "ĠPl ayer", - "ĠPlay er", - "ĠPla yer", - "1 06", - "10 6", - "Ġ Del", - "ĠD el", - "ĠDe l", - "Ġ pool", - "Ġp ool", - "Ġpo ol", - "Ġpoo l", - ". url", - ".u rl", - ".ur l", - "nes day", - "( );čĊčĊ", - "() ;čĊčĊ", - "();čĊ čĊ", - "(); čĊčĊ", - "9 00", - "90 0", - "Ġ \");Ċ", - "Ġ\" );Ċ", - "Ġ\") ;Ċ", - "Ġ\"); Ċ", - "L ocal", - "Lo cal", - "Loc al", - ". \");Ċ", - ".\" );Ċ", - ".\") ;Ċ", - ".\"); Ċ", - "Ġ organization", - "Ġo rganization", - "Ġorgan ization", - "Ġorganiz ation", - "r ender", - "re nder", - "ren der", - "rend er", - "rende r", - "Ġ Application", - "ĠApp lication", - "ĠAp plication", - "ĠAppl ication", - "Ġ summer", - "Ġs ummer", - "Ġsum mer", - "Ġsumm er", - "ex pected", - "exp ected", - "expect ed", - "N A", - "Ġ rap", - "Ġr ap", - "Ġra p", - "_ obj", - "_o bj", - "_ob j", - "Ġ surface", - "Ġs urface", - "Ġsur face", - "Ġsurf ace", - "Ġ PUR", - "ĠP UR", - "ĠPU R", - "Ġ },ĊĊ", - "Ġ} ,ĊĊ", - "Ġ},Ċ Ċ", - "Ġ}, ĊĊ", - "Ġ variables", - "Ġvariable s", - "Ġvari ables", - "( message", - "(m essage", - "Ġo pin", - "Ġop in", - "Ġopi n", - ". back", - ".b ack", - ".ba ck", - "а н", - "аР½", - "Ġ workers", - "Ġwork ers", - "Ġwor kers", - "Ġworker s", - "v m", - "C o", - "ught er", - "ugh ter", - "Ġ master", - "Ġm aster", - "Ġma ster", - "Ġmas ter", - "Ġmast er", - "Ġ \"\",", - "Ġ\" \",", - "Ġ\"\" ,", - "Ġ stories", - "Ġst ories", - "Ġstor ies", - "Ġsto ries", - ". User", - ".U ser", - ".Use r", - "Ġcele br", - "in ese", - "ine se", - "ines e", - "B S", - "Ġ Command", - "ĠCom mand", - "ĠComm and", - "ash board", - "Ġ og", - "Ġo g", - "k g", - ". image", - ".i mage", - ".im age", - ".imag e", - ". style", - ".st yle", - "Ġ steps", - "Ġst eps", - "Ġstep s", - "Ġste ps", - "Ġ Ben", - "ĠB en", - "ĠBe n", - "( args", - "(arg s", - "(ar gs", - "4 04", - "40 4", - "Ġ Person", - "ĠP erson", - "ĠPer son", - "ĠPers on", - ", y", - "Ġoffic ials", - "Ġofficial s", - "| Ċ", - "Ġ skills", - "Ġs kills", - "Ġsk ills", - "Ġskill s", - "v c", - "Ġ builder", - "Ġb uilder", - "Ġbu ilder", - "Ġbuild er", - "Ġ gar", - "Ġg ar", - "Ġga r", - "A ccount", - "Ac count", - "Acc ount", - "Ġ Auth", - "ĠA uth", - "ĠAut h", - "ĠAu th", - "ç Ķ", - "' ])Ċ", - "'] )Ċ", - "']) Ċ", - "Ġ AT", - "ĠA T", - "n n", - ". Int", - ".I nt", - ".In t", - "SS ERT", - "Ġ effective", - "Ġe ffective", - "Ġeffect ive", - "Ġeff ective", - "LE TE", - "LET E", - "Ġ tools", - "Ġt ools", - "Ġto ols", - "Ġtoo ls", - "Ġtool s", - "A RD", - "AR D", - "Ġ digital", - "Ġd igital", - "Ġdig ital", - "Ġdigit al", - "1 91", - "19 1", - "D ouble", - "Do uble", - "Dou ble", - "Ġ Find", - "ĠF ind", - "ĠFin d", - "ĠFi nd", - "R C", - "Ġ inline", - "Ġin line", - "/ r", - "A RAM", - "AR AM", - "ARA M", - "A SK", - "AS K", - "Ġ intent", - "Ġin tent", - "Ġint ent", - "Ġinte nt", - "a ight", - "ai ght", - "_ addr", - "_add r", - "_ad dr", - "Ġ requests", - "Ġre quests", - "Ġrequest s", - "Ġrequ ests", - ". first", - ".f irst", - ".fi rst", - "Ġ debug", - "Ġde bug", - "Ġdeb ug", - "Ġ spent", - "Ġs pent", - "Ġsp ent", - "Ġspe nt", - "( )));Ċ", - "() ));Ċ", - "()) );Ċ", - "())) ;Ċ", - "())); Ċ", - "Å Ľ", - "Ġpr incip", - "Ġprin cip", - "Ġprinc ip", - "Log ger", - "Lo gger", - "cl udes", - "clude s", - "clud es", - ". use", - ".u se", - ".us e", - "Ġs urv", - "Ġsu rv", - "Ġsur v", - "m edia", - "med ia", - "me dia", - "medi a", - "Ġ February", - "ĠFe bruary", - "ĠFeb ruary", - "Ġ Mac", - "ĠM ac", - "ĠMa c", - "Ġ missing", - "Ġm issing", - "Ġmiss ing", - "Ġmis sing", - "Ġ wife", - "Ġw ife", - "Ġwi fe", - "Ġt alking", - "Ġtalk ing", - "Ġtal king", - "Ġ Make", - "ĠM ake", - "ĠMa ke", - "ĠMak e", - "Ġ cart", - "Ġc art", - "Ġcar t", - "Ġca rt", - "Ġ located", - "Ġloc ated", - "Ġlocate d", - "E nc", - "En c", - "- a", - "ch ron", - "chr on", - "Ġ cards", - "Ġc ards", - "Ġcar ds", - "Ġcard s", - "Ġg uy", - "Ġgu y", - "Ġ pers", - "Ġp ers", - "Ġper s", - "Ġpe rs", - "Ġ Yes", - "ĠY es", - "ĠYe s", - "at ever", - "ate ver", - "Ġ Ang", - "ĠA ng", - "ĠAn g", - "o lar", - "ol ar", - "ola r", - "Ġ Even", - "ĠE ven", - "ĠEv en", - "ĠEve n", - "Ġ accur", - "Ġacc ur", - "Ġac cur", - "Ġ Power", - "ĠP ower", - "ĠPo wer", - "ĠPow er", - "Ġ Gold", - "ĠG old", - "ĠGo ld", - "ĠGol d", - "c lear", - "cl ear", - "cle ar", - "P rocess", - "Pro cess", - "Proc ess", - "Ġ records", - "Ġrec ords", - "Ġrecord s", - "Ġk illed", - "Ġkill ed", - "Ġkil led", - ". clear", - ".c lear", - ".cl ear", - "ĠWARRANT IES", - "Ġ purpose", - "Ġp urpose", - "Ġpur pose", - "Ġpurpos e", - "p anel", - "pan el", - "pa nel", - "pane l", - "J ECT", - "JE CT", - "ÃŃ a", - "Ġex erc", - "Ġexe rc", - "W S", - "/ L", - ". exports", - ".ex ports", - ".exp orts", - ".export s", - "Ġ ___", - "Ġ_ __", - "Ġ__ _", - "Ġ sin", - "Ġs in", - "Ġsi n", - "S ervlet", - "Serv let", - "Ġd é", - ". delete", - ".de lete", - ".del ete", - "r oke", - "ro ke", - "rok e", - "S l", - "u gh", - "ug h", - "e ars", - "ear s", - "ea rs", - "Ġ pointer", - "Ġpoint er", - "Ġpo inter", - "Ġ hop", - "Ġh op", - "Ġho p", - "all ery", - "alle ry", - "aller y", - "Ġ obs", - "Ġo bs", - "Ġob s", - "c overy", - "co very", - "cover y", - "cov ery", - "ĉ char", - "ĉc har", - "ĉch ar", - "ĉ ĉĉĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉĉ ĉĉĉĉ", - "ĉĉĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉĉĉĉ ĉ", - "ĉ def", - "ĉd ef", - "ĉde f", - "o city", - "oc ity", - "oci ty", - "it chen", - "itch en", - "u lations", - "ul ations", - "ulation s", - "Ġ FIT", - "ĠF IT", - "ĠFI T", - "Ġ ).", - "Ġ) .", - "straint s", - "stra ints", - "strain ts", - "v ention", - "vent ion", - "ven tion", - "Ġ requires", - "Ġre quires", - "Ġrequire s", - "Ġrequ ires", - "Ġ Oper", - "ĠO per", - "ĠOp er", - "M E", - "O UNT", - "OUN T", - "OU NT", - "al let", - "all et", - "alle t", - "Ġ norm", - "Ġn orm", - "Ġno rm", - "Ġnor m", - "I RE", - "IR E", - "ex as", - "Ġ programs", - "Ġpro grams", - "Ġpr ograms", - "Ġprogram s", - "Ġprog rams", - "Ġ weak", - "Ġwe ak", - "' .$", - "'. $", - "u ing", - "ui ng", - "uin g", - "ĉ ĠĠĠĠĠĠĠ", - "ĉĠĠĠ ĠĠĠĠ", - "ĉĠ ĠĠĠĠĠĠ", - "ĉĠĠ ĠĠĠĠĠ", - "ĉĠĠĠĠĠ ĠĠ", - "ĉĠĠĠĠ ĠĠĠ", - "ĉĠĠĠĠĠĠ Ġ", - "Ġ mil", - "Ġm il", - "Ġmi l", - "Ġ firm", - "Ġf irm", - "Ġfi rm", - "Ġfir m", - "init ely", - "inite ly", - "_ VALUE", - "_VAL UE", - "ap se", - "aps e", - "atis f", - "ati sf", - "Ġ demand", - "Ġd emand", - "Ġde mand", - "Ġdem and", - "_ mod", - "_m od", - "_mo d", - "Ġde scribed", - "Ġdes cribed", - "Ġdescri bed", - "Ġdescribe d", - "Ġ places", - "Ġp laces", - "Ġpl aces", - "Ġplace s", - "Ġplac es", - "Ġpla ces", - "V ID", - "VI D", - "Ġ alone", - "Ġal one", - "Ġalo ne", - "Ġ export", - "Ġex port", - "Ġexp ort", - "Ġexpo rt", - "Ġ vec", - "Ġv ec", - "Ġve c", - "Ġ Max", - "ĠM ax", - "ĠMa x", - "Ġ activities", - "Ġact ivities", - "Ġactiv ities", - "ic tures", - "ict ures", - "icture s", - "g ener", - "ge ner", - "gen er", - "gene r", - "Ġ ma", - "Ġm a", - "Ĥ ¬", - "Ġ expression", - "Ġex pression", - "Ġexp ression", - "Ġexpress ion", - "Ġexpr ession", - "C allback", - "Call back", - "_ content", - "_c ontent", - "_con tent", - "_cont ent", - "Ġ Most", - "ĠM ost", - "ĠMo st", - "ĠMos t", - "Ġ testing", - "Ġt esting", - "Ġtest ing", - "Ġtes ting", - "E C", - "CH ANT", - "CHA NT", - "CHAN T", - "Ġ adjust", - "Ġad just", - "Ġadj ust", - ".Th reading", - ".Thread ing", - "( ctx", - "(c tx", - "(ct x", - "Ġ agree", - "Ġa gree", - "Ġag ree", - "Ġagre e", - "Ġagr ee", - "i ghest", - "ig hest", - "igh est", - "Ġ ui", - "Ġu i", - "Ġ Law", - "ĠL aw", - "ĠLa w", - ". Y", - "> < ?", - "Ġ pod", - "Ġp od", - "Ġpo d", - "- lg", - "-l g", - "âĢĿ ĊĊ", - "âĢĿĊ Ċ", - "Ġ describe", - "Ġde scribe", - "Ġdes cribe", - "Ġdescri be", - "Ġdescr ibe", - "Ġ European", - "ĠE uropean", - "ĠEurope an", - "ĠEurop ean", - "- sh", - "-s h", - "ĠPUR POSE", - "O RY", - "OR Y", - "Ġcon vers", - "Ġconv ers", - "Ġconver s", - "Ġ Illuminate", - "ĠI lluminate", - "ĠIllum inate", - "Ġ Av", - "ĠA v", - "( ch", - "(c h", - "? \"", - "c hen", - "ch en", - "che n", - "i ma", - "im a", - "D ocument", - "Doc ument", - "Ġ operations", - "Ġoper ations", - "Ġoperation s", - "w in", - "wi n", - "ĉ function", - "ĉf unction", - "ĉfunc tion", - "ĉfun ction", - ". Image", - ".I mage", - ".Im age", - "Ġs cen", - "Ġsc en", - "Ġsce n", - "/ h", - "Ġ SC", - "ĠS C", - "Ġ explo", - "Ġexp lo", - "Ġexpl o", - ": %", - "/ **čĊ", - "/* *čĊ", - "/** čĊ", - "N AME", - "NA ME", - "æ Ī", - "( var", - "(v ar", - "(va r", - "Ġ director", - "Ġd irector", - "Ġdirect or", - "Ġdir ector", - "Ġdire ctor", - "O NG", - "ON G", - "Ġ yield", - "Ġy ield", - "Ġyi eld", - "Ġfe et", - "Ġfee t", - "Ġ Search", - "ĠS earch", - "ĠSe arch", - "ĠSea rch", - "Ġ Il", - "ĠI l", - "Ġrest aur", - "Ġresta ur", - "Ġrestau r", - "d uc", - "du c", - "Ġ integer", - "Ġint eger", - "Ġinteg er", - "Ġinte ger", - "1 07", - "10 7", - "Ġ' ';Ċ", - "Ġ'' ;Ċ", - "Ġ''; Ċ", - "Ġhigh ly", - "check ed", - "ĠPART IC", - "ER CHANT", - "ï¼ ī", - "Ġ optim", - "Ġop tim", - "Ġopt im", - "Q ueue", - "Que ue", - "Ġ LI", - "ĠL I", - "it ation", - "ita tion", - "itat ion", - "Ġ transport", - "Ġtrans port", - "Ġtran sport", - "iss ion", - "f ill", - "fi ll", - "fil l", - "us ion", - "usi on", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĉ bool", - "ĉb ool", - "- th", - "-t h", - "u pt", - "up t", - "Ġ essential", - "Ġess ential", - "an ted", - "ant ed", - "ante d", - "Ġbenef its", - "Ġbenefit s", - "Ġbene fits", - "ĉ S", - "' ;čĊ", - "'; čĊ", - "i ki", - "ik i", - "Ġ girls", - "Ġgirl s", - "Ġgir ls", - "i ced", - "ic ed", - "ice d", - "b uffer", - "buf fer", - "bu ffer", - "buff er", - "] +", - "Ġ socket", - "Ġs ocket", - "Ġso cket", - "Ġsock et", - "Ġsoc ket", - "Ġ prices", - "Ġp rices", - "Ġpr ices", - "Ġprice s", - "Ġpri ces", - "Ġ Fre", - "ĠF re", - "ĠFr e", - "Ġ sat", - "Ġs at", - "Ġsa t", - "Ġ wood", - "Ġw ood", - "Ġwo od", - "Ġwoo d", - "Menu Item", - "A RG", - "AR G", - "Ġ Admin", - "ĠAd min", - "O WN", - "OW N", - "d k", - "Ġ reset", - "Ġre set", - "Ġres et", - "Ġ forms", - "Ġfor ms", - "Ġform s", - "Ġfo rms", - "Ġ и", - "ĠÐ ¸", - "æ ĸ", - "Ġ Tuesday", - "ĠT uesday", - "ĠTues day", - "1 09", - "10 9", - "Ġ Initialized", - "ĠInitial ized", - "ĠInitialize d", - "ĠInit ialized", - "_ train", - "_t rain", - "_tr ain", - "_tra in", - "o rary", - "or ary", - "ora ry", - "ate gor", - "ateg or", - "atego r", - "Ġ dt", - "Ġd t", - "T otal", - "To tal", - "Tot al", - "con struct", - "i lies", - "il ies", - "ili es", - "Ġgu ys", - "Ġguy s", - "е ÑĢ", - "Ġ instruction", - "Ġin struction", - "Ġinstr uction", - "Ġinstruct ion", - "0 10", - "01 0", - "y led", - "yle d", - "yl ed", - "Ġ internet", - "Ġin ternet", - "Ġint ernet", - "Ġinter net", - "Ġintern et", - "et adata", - "eta data", - "a dy", - "ad y", - "f aces", - "face s", - "fa ces", - "fac es", - "j ection", - "ject ion", - "je ction", - "jec tion", - "Ġ Jack", - "ĠJ ack", - "ĠJac k", - "ĠJa ck", - "Ġ rect", - "Ġre ct", - "Ġr ect", - "Ġrec t", - "[ -", - "Ġ Leg", - "ĠL eg", - "ĠLe g", - "Ġ devices", - "Ġdev ices", - "Ġdevice s", - "O C", - "Ġ *čĊ", - "Ġ* čĊ", - "o ration", - "or ation", - "ora tion", - "er tain", - "ert ain", - "erta in", - "Ġ guard", - "Ġg uard", - "Ġgu ard", - "Ġguar d", - "o stream", - "ost ream", - "Ġ enum", - "Ġe num", - "Ġen um", - ". layout", - ".l ayout", - "Ġ \";Ċ", - "Ġ\" ;Ċ", - "Ġ\"; Ċ", - "v oke", - "vo ke", - "Ġ Ok", - "ĠO k", - "H ome", - "Ho me", - "Hom e", - "( tr", - "(t r", - "E TH", - "ET H", - "Ġ delay", - "Ġd elay", - "Ġde lay", - "Ġdel ay", - "Ġdela y", - "Ġ purchase", - "Ġp urchase", - "Ġpurch ase", - "d c", - "Ġ aren", - "Ġa ren", - "Ġare n", - "Ġar en", - "_ once", - "_on ce", - "_o nce", - "ĉ ĉĉĉĊ", - "ĉĉ ĉĉĊ", - "ĉĉĉĉ Ċ", - "ĉĉĉ ĉĊ", - "r or", - "ro r", - "d raw", - "dr aw", - "dra w", - ". run", - ".r un", - ".ru n", - "( model", - "(m odel", - "(mod el", - "(mode l", - "Time out", - "l ik", - "li k", - "Ġ Arg", - "ĠA rg", - "ĠAr g", - ". en", - ".e n", - "Ġ fish", - "Ġf ish", - "Ġfi sh", - "Ġfis h", - "c py", - "cp y", - "_ fe", - "_f e", - "ERCHANT ABILITY", - "( X", - "_ output", - "_out put", - "? ?", - "Ġ jo", - "Ġj o", - "and ard", - "anda rd", - "Ġ doll", - "Ġd oll", - "Ġdo ll", - "Ġdol l", - "er rors", - "err ors", - "error s", - "erro rs", - "_ base", - "_b ase", - "ĠPARTIC ULAR", - "Ġ leader", - "Ġle ader", - "Ġlead er", - "Ġcom par", - "Ġco mpar", - "Ġcomp ar", - "Ġd oub", - "Ġdo ub", - "Ġdou b", - "Ġ Vis", - "ĠV is", - "ĠVi s", - "Stack Trace", - "- C", - "ĠSt ud", - "stit ute", - "M ore", - "Mo re", - "Mor e", - "Ġ Description", - "ĠD escription", - "ĠDe scription", - "ĠDes cription", - "W ARE", - "WA RE", - "WAR E", - "a ds", - "ad s", - "Ġ к", - "ĠÐ º", - "b ind", - "bin d", - "bi nd", - "= self", - "=s elf", - "e mploy", - "em ploy", - "emp loy", - "empl oy", - "emplo y", - "[ n", - ". all", - ".a ll", - ".al l", - "- B", - "& &", - "a lm", - "al m", - "Ġ culture", - "Ġc ulture", - "Ġcult ure", - "Ġcul ture", - "h ouse", - "ho use", - "hou se", - "Ġs uffer", - "Ġsu ffer", - "Ġsuff er", - "Ġsuf fer", - "Ġ '%", - "Ġ' %", - "Ġ straight", - "Ġstr aight", - "Ġstra ight", - "Ġ Star", - "ĠS tar", - "ĠSt ar", - "ĠSta r", - "u do", - "ud o", - "Ġ ded", - "Ġd ed", - "Ġde d", - "Ġ COM", - "ĠC OM", - "ĠCO M", - "Ġ confirm", - "Ġcon firm", - "Ġconf irm", - "Ġ Good", - "ĠG ood", - "ĠGo od", - ". sc", - ".s c", - "____ ____________", - "________ ________", - "____________ ____", - "D R", - "Config uration", - "Date Time", - "Ġ advert", - "Ġad vert", - "Ġadv ert", - "Ġ couldn", - "Ġcould n", - "a sync", - "as ync", - "asy nc", - "st ack", - "sta ck", - "' )čĊ", - "') čĊ", - "K it", - "Ki t", - "Ġh ous", - "Ġho us", - "Ġm echan", - "Ġme chan", - "Ġmec han", - "Ġmech an", - "r ate", - "ra te", - "rat e", - "2 04", - "20 4", - "Ġ audio", - "Ġa udio", - "Ġaud io", - "Ġau dio", - "Ġaudi o", - "ĉ cout", - "ĉc out", - "c ores", - "co res", - "core s", - "cor es", - "Ġ spot", - "Ġs pot", - "Ġsp ot", - "Ġspo t", - "Ġin creasing", - "Ġincre asing", - "Ġ ##", - "Ġ# #", - ") ))", - ")) )", - "p oints", - "point s", - "po ints", - "poi nts", - "Ġcom pared", - "Ġcomp ared", - "Ġcompar ed", - "Ġcompare d", - "l ig", - "li g", - "Ġ behavior", - "Ġbeh avior", - "Ġ BY", - "ĠB Y", - "Ġ Att", - "ĠA tt", - "ĠAt t", - "c raft", - "cr aft", - "he aders", - "head ers", - "header s", - "hea ders", - "e te", - "et e", - "end region", - "Ġ detail", - "Ġd etail", - "Ġde tail", - "Ġdet ail", - "U LE", - "UL E", - "Ġ Common", - "ĠCom mon", - "ĠComm on", - "ĉ protected", - "s ton", - "st on", - "sto n", - "ĠFIT NESS", - "Ġ fresh", - "Ġf resh", - "Ġfr esh", - "Ġfre sh", - "Ġfres h", - "\" >ĊĊ", - "\"> ĊĊ", - "\">Ċ Ċ", - ". example", - ".ex ample", - ".exam ple", - "b erg", - "ber g", - "be rg", - "Ġ moved", - "Ġm oved", - "Ġmov ed", - "Ġmove d", - "Ġmo ved", - "ĉ e", - "Ġ Saturday", - "ĠS aturday", - "Ġ payload", - "Ġp ayload", - "Ġpay load", - "Ä ĩ", - ") :ĊĊ", - "):Ċ Ċ", - "): ĊĊ", - "Ġb ey", - "Ġbe y", - "u rer", - "ur er", - "ure r", - "< script", - " ,", - "\" > < ?", - "( num", - "(n um", - "ĉ inline", - "ĉin line", - "Trans action", - ". On", - ".O n", - "Ġ mail", - "Ġm ail", - "Ġma il", - "Ġmai l", - "r ey", - "re y", - "res ults", - "result s", - "Ġ nav", - "Ġn av", - "Ġna v", - "I MIT", - "IM IT", - "_ ids", - "_id s", - "_i ds", - "M ake", - "Ma ke", - "å Ĭ", - "M odal", - "Mod al", - "Mo dal", - "Ġ LOG", - "ĠL OG", - "ĠLO G", - "Ġ Sur", - "ĠS ur", - "ĠSu r", - "Ġinstance of", - "Ġ overall", - "Ġover all", - "Ġ Information", - "ĠIn formation", - "ĠInform ation", - "Ġ construction", - "Ġcon struction", - "Ġconstruct ion", - "Ġconstr uction", - "_ FILE", - "_F ILE", - "b ut", - "bu t", - "Ġm edic", - "Ġme dic", - "Ġmed ic", - "Ġmedi c", - "Ġ duration", - "Ġd uration", - "Ġdu ration", - "Ġdur ation", - "it ness", - "a gent", - "ag ent", - "age nt", - "agen t", - "A V", - "Ġ seven", - "Ġs even", - "Ġse ven", - "Ġsev en", - "o lf", - "ol f", - "Ġ }}Ċ", - "Ġ} }Ċ", - "Ġ}} Ċ", - "\" ],Ċ", - "\"] ,Ċ", - "\"], Ċ", - "1 70", - "17 0", - "1 22", - "12 2", - "Ġ calling", - "Ġc alling", - "Ġcall ing", - "Ġcal ling", - "Ġ ans", - "Ġa ns", - "Ġan s", - "th rows", - "throw s", - "thr ows", - "thro ws", - "or izontal", - "Ġ useState", - "Ġuse State", - ". fl", - ".f l", - "Ġ Status", - "ĠS tatus", - "ĠSt atus", - "ĠStat us", - "Ġ Online", - "ĠOn line", - "R R", - "Ġ Rich", - "ĠR ich", - "ĠRic h", - "ĠRi ch", - "ĠH ill", - "ĠHi ll", - "ĠHil l", - "Ġ brain", - "Ġb rain", - "Ġbr ain", - "Ġbra in", - "Ġfollow ed", - "Ġfoll owed", - "2 40", - "24 0", - "e mic", - "em ic", - "emi c", - "Ġs light", - "Ġsl ight", - "Ġ insurance", - "Ġins urance", - ". Array", - ".A rray", - ".Ar ray", - "Ġ abstract", - "Ġa bstract", - "Ġab stract", - "Ġabs tract", - "Ġabst ract", - "Ġ Sum", - "ĠS um", - "ĠSu m", - "re direct", - "red irect", - "redi rect", - "o wner", - "ow ner", - "own er", - "( msg", - "(m sg", - "(ms g", - "Ġ Clinton", - "ĠCl inton", - "ĠClin ton", - "ĠClint on", - "ĠCli nton", - "N on", - "No n", - "ĉ ex", - "ĉe x", - "Ġ volume", - "Ġv olume", - "Ġvol ume", - "Ġvolum e", - "Ġ EventArgs", - "ĠEvent Args", - "- L", - "Ġ Dim", - "ĠD im", - "ĠDi m", - "Ġ Mart", - "ĠM art", - "ĠMar t", - "ĠMa rt", - "Ġ cursor", - "Ġc ursor", - "Ġcurs or", - "Ġcurso r", - "Ġ implementation", - "Ġim plementation", - "Ġimplement ation", - "ur red", - "urre d", - "urr ed", - "Ġl arger", - "Ġlarge r", - "Ġlarg er", - "Ġlar ger", - ") ;ĊĊĊ", - ");Ċ ĊĊ", - ");ĊĊ Ċ", - "); ĊĊĊ", - "' +", - ". transform", - ".trans form", - "Ġ upload", - "Ġup load", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "D raw", - "Dr aw", - "n el", - "ne l", - "ĉ float", - "ĉf loat", - "q rt", - "qr t", - "Ġ Network", - "ĠN etwork", - "ĠNet work", - "Ġ tit", - "Ġt it", - "Ġti t", - "A xis", - "Ax is", - ". android", - ".and roid", - "Ġ completed", - "Ġcom pleted", - "Ġcomp leted", - "Ġcomple ted", - "Ġcomplet ed", - "Ġcompl eted", - "Ġcomplete d", - "Ġ mur", - "Ġm ur", - "Ġmu r", - "Ġ columns", - "Ġcolumn s", - "Ġcolum ns", - "x c", - "Ġ supply", - "Ġs upply", - "Ġsup ply", - "Ġsuppl y", - "Ġsupp ly", - "im inal", - "imi nal", - "imin al", - "Ġ spr", - "Ġs pr", - "Ġsp r", - "================ ================================================", - "================================ ================================", - "================================================ ================", - "Ġ units", - "Ġun its", - "Ġunit s", - "Ġuni ts", - "( u", - "m i", - "re place", - "rep lace", - "[ key", - "[k ey", - "à ¹", - "an tic", - "ant ic", - "anti c", - "Ġ payment", - "Ġp ayment", - "Ġpay ment", - ", B", - "Ġ Apple", - "ĠApp le", - "ĠAp ple", - "ĠAppl e", - "g in", - "gi n", - "Re quired", - "Require d", - "# +", - "l ands", - "land s", - "la nds", - "lan ds", - "Ġ squ", - "Ġs qu", - "Ġsq u", - "Ġ factor", - "Ġf actor", - "Ġfact or", - "Ġfa ctor", - "Ġfac tor", - "Ġfacto r", - "d ec", - "de c", - "Ġ strength", - "Ġst rength", - "Ġstr ength", - "Ġstre ngth", - "Ġstren gth", - "Ġ boy", - "Ġb oy", - "Ġbo y", - "Ġ balance", - "Ġb alance", - "Ġbal ance", - "Ġ sources", - "Ġs ources", - "Ġsource s", - "Ġsour ces", - "s creen", - "sc reen", - "scr een", - "- top", - "-t op", - "-to p", - "Ġ Amazon", - "ĠA mazon", - "ĠAm azon", - "ĠAma zon", - "Ġ hidden", - "Ġh idden", - "Ġhi dden", - "Ġhid den", - "е ÑĤ", - "_ client", - "_c lient", - "_cl ient", - "_cli ent", - "Ġ eat", - "Ġe at", - "Ġea t", - ". display", - ".d isplay", - ".dis play", - "Ġ »", - "Ġ »", - "Ġ trigger", - "Ġtr igger", - "Ġtri gger", - "Ġtrig ger", - "an ager", - "ana ger", - "Ġ tro", - "Ġt ro", - "Ġtr o", - "Ġ claims", - "Ġcl aims", - "Ġclaim s", - "Ġcla ims", - "f ord", - "fo rd", - "for d", - "Ġ Company", - "ĠCom pany", - "ĠComp any", - "Ġ gift", - "Ġg ift", - "Ġgi ft", - "Ġgif t", - ", :", - "_ app", - "_a pp", - "_ap p", - "h andle", - "han dle", - "hand le", - "Ġ produce", - "Ġp roduce", - "Ġpro duce", - "Ġprodu ce", - "Ġprod uce", - "/ lib", - "/l ib", - "/li b", - "5 12", - "51 2", - "Ġ -*", - "Ġ- *", - "ĉ set", - "ĉs et", - "ĉse t", - "' ];", - "'] ;", - "a rc", - "ar c", - "a nder", - "an der", - "and er", - "ande r", - "Ġ Engine", - "ĠE ngine", - "ĠEng ine", - "Ġ attributes", - "Ġat tributes", - "Ġattribute s", - "Ġattrib utes", - "t ask", - "ta sk", - "tas k", - "< =", - "( N", - "Ġ warm", - "Ġw arm", - "Ġwar m", - "Ġwa rm", - "wh ich", - "Ġ Fore", - "ĠF ore", - "ĠFor e", - "ĠFo re", - "ag nost", - "agn ost", - "m ys", - "my s", - "Ġ tal", - "Ġt al", - "Ġta l", - "Ġ Sal", - "ĠS al", - "ĠSa l", - "g i", - "Ġ Print", - "ĠP rint", - "ĠPr int", - "ĠPri nt", - "Ġ TRUE", - "ĠTR UE", - "Ġ о", - "ĠÐ ¾", - ". UI", - ".U I", - "Ġ flash", - "Ġf lash", - "Ġfl ash", - "Ġfla sh", - "ro perty", - "rop erty", - ". location", - ".l ocation", - ".loc ation", - ".lo cation", - "Ġ Mill", - "ĠM ill", - "ĠMil l", - "ĠMi ll", - "b i", - "con tr", - "cont r", - ". request", - ".re quest", - ".req uest", - "Ġ Sam", - "ĠS am", - "ĠSa m", - "Ġ negative", - "Ġn egative", - "Ġneg ative", - "k it", - "ki t", - "Ġ sett", - "Ġs ett", - "Ġse tt", - "Ġset t", - ".print StackTrace", - "a be", - "ab e", - "ĉ i", - "Ġ burn", - "Ġb urn", - "Ġbu rn", - "Ġbur n", - "Ġs ociety", - "Ġsoci ety", - "C ache", - "Ca che", - "Ġ Security", - "ĠS ecurity", - "ĠSe curity", - "ĠSec urity", - ". models", - ".model s", - ".mod els", - ".mode ls", - "ĠWARRANT Y", - "_ up", - "_u p", - "ce ive", - "Ġ clients", - "Ġc lients", - "Ġcl ients", - "Ġclient s", - "Ġcli ents", - ". Tr", - ".T r", - "Ġprovid ing", - "Ġprov iding", - "Ġr out", - "Ġro ut", - "Ġrou t", - "m aterial", - "mat erial", - "mate rial", - "Ġ ||Ċ", - "Ġ| |Ċ", - "Ġ|| Ċ", - "Ġ Ser", - "ĠS er", - "ĠSe r", - "Ġ Office", - "ĠOff ice", - "FT WARE", - "Ġ '$", - "Ġ' $", - "Ġf oc", - "Ġfo c", - "Ġex cell", - "Ġexc ell", - "Ġexcel l", - "Ġexce ll", - "Ġ cat", - "Ġc at", - "Ġca t", - "n ormal", - "norm al", - "nor mal", - "Ġd etermine", - "Ġdeter mine", - "Ġdetermin e", - "Ġdeterm ine", - "ĉ uint", - "ĉu int", - "ĉui nt", - "P ane", - "Pa ne", - "Pan e", - "Ġ employees", - "Ġemploy ees", - "Ġemployee s", - "Ġ Texas", - "ĠT exas", - "ĠTex as", - "Ġtr aff", - "Ġtra ff", - "Ġtraf f", - "Ġ Report", - "ĠRe port", - "ĠRep ort", - "ĠRepo rt", - "an ta", - "ant a", - "Ġ Box", - "ĠB ox", - "ĠBo x", - "Ġ django", - "Ġd jango", - "Ġdj ango", - "Ġ partner", - "Ġp artner", - "Ġpart ner", - "E B", - "L INE", - "LI NE", - "LIN E", - "Ġfe eling", - "Ġfeel ing", - "Ġfee ling", - "Ġ civil", - "Ġc ivil", - "Ġci vil", - "Ġciv il", - "( float", - "(f loat", - "S ql", - "Sq l", - "Ġwould n", - ". init", - ".in it", - ".i nit", - ".ini t", - ". left", - ".l eft", - ".le ft", - "- v", - "_ level", - "_le vel", - "' }", - "A F", - "Ġ loading", - "Ġlo ading", - "Ġload ing", - "Ġloa ding", - "Ġ Only", - "ĠOn ly", - "Ġ cookies", - "Ġc ookies", - "Ġco okies", - "Ġcook ies", - "Ġcookie s", - "Ġ Gl", - "ĠG l", - "C O", - "Ġ strategy", - "Ġstr ategy", - "Ġstrateg y", - "Ġstrate gy", - "(' ./", - "('. /", - "Ġ ship", - "Ġs hip", - "Ġsh ip", - "p oses", - "pos es", - "pose s", - "po ses", - "Ġ signal", - "Ġs ignal", - "Ġsign al", - "Ġsig nal", - "Ġ alpha", - "Ġal pha", - "Ġalph a", - ". pop", - ".p op", - ".po p", - "R adius", - "Rad ius", - "Radi us", - "Ġ replace", - "Ġre place", - "Ġrep lace", - "Ġrepl ace", - "_ DIR", - "_D IR", - "_DI R", - "c ounter", - "co unter", - "count er", - "bserv able", - "e la", - "el a", - "W eight", - "We ight", - "Wei ght", - "h ash", - "ha sh", - "has h", - "b ose", - "bo se", - "bos e", - "f x", - "Ġ Email", - "ĠE mail", - "ĠEm ail", - "Ġ refer", - "Ġre fer", - "Ġref er", - "local host", - "_ RO", - "_R O", - "i ques", - "ique s", - "iqu es", - "iq ues", - "S tep", - "St ep", - "Ste p", - "Ġ ahead", - "Ġa head", - "Ġah ead", - "( View", - "(V iew", - "Ġ Services", - "ĠS ervices", - "ĠService s", - "ĠServ ices", - "Ġ Json", - "ĠJ son", - "ĠJs on", - "ess or", - "esso r", - "Ġ pun", - "Ġp un", - "Ġpu n", - "Ġ appropriate", - "Ġapp ropriate", - "Ġappropri ate", - "a kers", - "ak ers", - "ake rs", - "aker s", - "o sen", - "os en", - "ose n", - "p osing", - "pos ing", - "po sing", - "Ġ agent", - "Ġa gent", - "Ġag ent", - "Ġage nt", - "f c", - "Ġ transfer", - "Ġtrans fer", - "Ġtransf er", - "Ġ invalid", - "Ġin valid", - "Ġinval id", - "Ġ Research", - "ĠRe search", - "ĠRes earch", - "Vert ex", - "Ver tex", - "Ġ gay", - "Ġg ay", - "Ġga y", - "Ġ journal", - "Ġj ournal", - "Ġjo urnal", - "Ġjour nal", - "[ x", - "Ġ \"\",Ċ", - "Ġ\" \",Ċ", - "Ġ\"\" ,Ċ", - "Ġ\"\", Ċ", - "Ġ Well", - "ĠW ell", - "ĠWe ll", - "ĠWel l", - ". Tasks", - ".T asks", - ".Task s", - "S pec", - "Sp ec", - "Spe c", - "Ġ ol", - "Ġo l", - "Ġs pend", - "Ġsp end", - "Ġspe nd", - "Ġ Australia", - "ĠA ustralia", - "ĠAustral ia", - "ĠAustr alia", - "M atch", - "Mat ch", - ".j unit", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ MAX", - "ĠM AX", - "ĠMA X", - "iz able", - "iza ble", - "cl usive", - "clus ive", - "_ valid", - "_val id", - "_va lid", - "Ġ quarter", - "Ġqu arter", - "Ġquar ter", - "Ġquart er", - "y an", - "ya n", - "0 05", - "00 5", - "Ġ Edit", - "ĠE dit", - "ĠEd it", - "ar den", - "ard en", - "arde n", - "= new", - "=n ew", - "Ġ frag", - "Ġf rag", - "Ġfr ag", - "Ġfra g", - "B it", - "Bi t", - "z i", - "a ine", - "ain e", - "ai ne", - "u dd", - "ud d", - ". Object", - ".O bject", - ".Obj ect", - "de bug", - "deb ug", - "Ġ cash", - "Ġc ash", - "Ġca sh", - "Ġcas h", - "_ IM", - "_I M", - "Ġ een", - "Ġe en", - "Ġee n", - "Ġ commercial", - "Ġcom mercial", - "Ġcomm ercial", - "Ġcommerc ial", - "Ġ Video", - "ĠV ideo", - "ĠVi deo", - "ĠVid eo", - "ĠVide o", - "l oader", - "lo ader", - "load er", - "Ġ fixed", - "Ġf ixed", - "Ġfix ed", - "Ġfi xed", - "Ġ applications", - "Ġapp lications", - "Ġapplication s", - "Ġapplic ations", - "Ġappl ications", - "Ġ _,", - "Ġ_ ,", - "Ġ Russia", - "ĠR ussia", - "ĠRuss ia", - "it ect", - "ite ct", - "_ (", - "Ġ Block", - "ĠB lock", - "ĠBl ock", - "ĠBlo ck", - "ĠBloc k", - "Ġ san", - "Ġs an", - "Ġsa n", - "Ġ Tom", - "ĠT om", - "ĠTo m", - "Ġ perhaps", - "Ġper haps", - "Ġ sig", - "Ġs ig", - "Ġsi g", - "le vant", - "lev ant", - "Ġc orpor", - "Ġcor por", - "Ġcorp or", - "Ġcorpo r", - "at aset", - "ata set", - "atas et", - "r onic", - "ro nic", - "ron ic", - "x e", - "Ġ eth", - "Ġe th", - "Ġet h", - "S ome", - "So me", - "Som e", - "p op", - "po p", - "_ OK", - "_O K", - "Ġt end", - "Ġte nd", - "Ġten d", - ". Res", - ".R es", - ".Re s", - "_ and", - "_a nd", - "_an d", - "Ġ reviews", - "Ġre views", - "Ġreview s", - "Ġ wild", - "Ġw ild", - "Ġwi ld", - "Ġwil d", - "1 17", - "11 7", - "Ġ degree", - "Ġd egree", - "Ġde gree", - "Ġdeg ree", - ". O", - ". objects", - ".object s", - ".obj ects", - "_ args", - "_arg s", - "_ar gs", - "n il", - "ni l", - "Ġ disabled", - "Ġdis abled", - "Ġdisable d", - "P arent", - "Par ent", - "Pa rent", - "Paren t", - "Ġ notes", - "Ġn otes", - "Ġnot es", - "Ġno tes", - "Ġnote s", - "Ġ \"\"Ċ", - "Ġ\" \"Ċ", - "Ġ\"\" Ċ", - "( state", - "(st ate", - "(stat e", - "i strict", - "istr ict", - "Ġ logging", - "Ġlog ging", - ". IO", - ".I O", - "Ġ Mal", - "ĠM al", - "ĠMa l", - "D M", - "Ġ xml", - "Ġx ml", - "Ġxm l", - "Ġ Robert", - "ĠRob ert", - "ĠRo bert", - "e len", - "el en", - "ele n", - "l ayout", - "lay out", - "f ol", - "fo l", - "' ]))", - "'] ))", - "']) )", - ", b", - "Ġ Jer", - "ĠJ er", - "ĠJe r", - "f ilename", - "file name", - "fi lename", - "fil ename", - "Ġ fan", - "Ġf an", - "Ġfa n", - "Ġ Custom", - "ĠC ustom", - "ĠCust om", - "= \"\"", - "=\" \"", - "Ġ Die", - "ĠD ie", - "ĠDi e", - "B undle", - ". utils", - ".util s", - ".ut ils", - "Ġ trip", - "Ġt rip", - "Ġtr ip", - "Ġtri p", - "M B", - "Ġ soft", - "Ġs oft", - "Ġso ft", - "Ġsof t", - "_ MODE", - "_M ODE", - "_MO DE", - "_MOD E", - "Ġapp licable", - "Ġapplic able", - "Ġappl icable", - "Ġ upper", - "Ġu pper", - "Ġup per", - "Ġupp er", - "ER VER", - "ERV ER", - "ERVE R", - "_ al", - "_a l", - "_ LOG", - "_L OG", - "_LO G", - "H ere", - "He re", - "Her e", - "w p", - "Ġ Server", - "ĠS erver", - "ĠSer ver", - "ĠServ er", - "ĠServe r", - "Ġ Client", - "ĠC lient", - "ĠCl ient", - "ĠCli ent", - "Ġ chem", - "Ġc hem", - "Ġch em", - "Ġche m", - "S croll", - "Sc roll", - "Scr oll", - "Ġ highest", - "Ġh ighest", - "Ġhigh est", - "Ġhi ghest", - "Ġ Select", - "ĠS elect", - "ĠSe lect", - "ĠSel ect", - "ĠSele ct", - "Ġ \"@", - "Ġ\" @", - "Ġ Why", - "ĠW hy", - "ĠWh y", - "S ec", - "Se c", - "h eel", - "he el", - "hee l", - "O peration", - "Oper ation", - "Op eration", - "Opera tion", - "Ġ connected", - "Ġconn ected", - "Ġconnect ed", - "ir med", - "irm ed", - "Ġcit iz", - "Ġ Che", - "ĠC he", - "ĠCh e", - "Ġ forces", - "Ġfor ces", - "Ġforce s", - "Ġforc es", - "Ġ www", - "Ġw ww", - "Ġww w", - "R oot", - "Ro ot", - "AN CE", - "ANC E", - "M any", - "Man y", - "Ma ny", - "i cip", - "ic ip", - "ici p", - "r gan", - "rg an", - "2 20", - "22 0", - "Ġ Tor", - "ĠT or", - "ĠTo r", - "Ġ Press", - "ĠP ress", - "ĠPr ess", - "ĠPres s", - "ĠPre ss", - "Ġ Mor", - "ĠM or", - "ĠMo r", - "- line", - "-l ine", - "-li ne", - "u led", - "ul ed", - "ule d", - "> \\", - "Ġ thus", - "Ġt hus", - "Ġth us", - "Ġthu s", - "Ġ Register", - "ĠReg ister", - "h ol", - "ho l", - "Ġ Chinese", - "ĠCh inese", - "ĠChin ese", - "Ġ posted", - "Ġpos ted", - "Ġpost ed", - "Ġpo sted", - "Ġposte d", - "Ġm agn", - "Ġmag n", - "Ġma gn", - "ab ilities", - "abil ities", - "abilit ies", - "Ġd isease", - "Ġdis ease", - "Ġdise ase", - "Ġrem ains", - "Ġremain s", - "Ġ Prof", - "ĠP rof", - "ĠPro f", - "ĠPr of", - "- form", - "-f orm", - "-for m", - "Ġ cin", - "Ġc in", - "Ġci n", - "o rgan", - "or gan", - "org an", - "i cate", - "ic ate", - "ica te", - "Ġ stress", - "Ġst ress", - "Ġstr ess", - "Ġstre ss", - "] *", - "Ġ ----------------------------------------------------------------", - "Ġ---------------- ------------------------------------------------", - "Ġ------------------------------------------------ ----------------", - "Ġ-------------------------------- --------------------------------", - "Ġ------------------------------------------------------------ ----", - "_ context", - "_con text", - "_cont ext", - "or ry", - "orr y", - "Ġd ied", - "Ġdi ed", - "Ġdie d", - "m at", - "ma t", - "Ġ starts", - "Ġst arts", - "Ġstart s", - "Ġstar ts", - "Ġsta rts", - ". Message", - ".M essage", - "Ġ runs", - "Ġr uns", - "Ġrun s", - "Ġru ns", - "Ġ guide", - "Ġg uide", - "Ġgu ide", - "Ġguid e", - "Ġgui de", - "Ġw arranty", - "Ġwarrant y", - "ent ials", - "ential s", - "enti als", - "d ict", - "di ct", - "dic t", - "Ġ Size", - "ĠS ize", - "ĠSi ze", - "ĠSiz e", - "u ler", - "ul er", - "ule r", - "Ġres ponsible", - "Ġrespons ible", - "_ SET", - "_S ET", - "_SE T", - "Ġcont aining", - "Ġcontain ing", - "Ġconta ining", - "Ġ Price", - "ĠP rice", - "ĠPr ice", - "ĠPri ce", - "| |", - "3 50", - "35 0", - "F S", - "Ġ emp", - "Ġe mp", - "Ġem p", - "_ button", - "_b utton", - "_but ton", - "( uint", - "(u int", - "(ui nt", - "Ġs uff", - "Ġsu ff", - "Ġsuf f", - "p th", - "pt h", - "Ġdef initely", - "Ġdefinit ely", - "Ġdefinite ly", - "p ute", - "put e", - "pu te", - "Ġ marketing", - "Ġmark eting", - "Ġmarket ing", - "Ġ WH", - "ĠW H", - "Ġ Sie", - "ĠS ie", - "ĠSi e", - "+ =", - "OL OR", - "Ġ consult", - "Ġcons ult", - "Ġconsul t", - "Ġ signed", - "Ġs igned", - "Ġsign ed", - "Ġsig ned", - "Ġ sequence", - "Ġse quence", - "Ġsequ ence", - "l ee", - "le e", - "Ġ requirements", - "Ġrequire ments", - "Ġrequirement s", - "h y", - "Ex press", - "Exp ress", - "Expr ess", - "M T", - "s ey", - "se y", - "Ġ ult", - "Ġu lt", - "Ġul t", - "å ®", - "ellig ence", - "elli gence", - "Ġ analy", - "Ġan aly", - "Ġanal y", - "Ġana ly", - "Ġ dress", - "Ġd ress", - "Ġdr ess", - "Ġdre ss", - "e ngine", - "eng ine", - "engin e", - "Ġ Great", - "ĠG reat", - "ĠGr eat", - "ĠGre at", - "Ġ Android", - "ĠAnd roid", - "ĠAndr oid", - "Ġ Alex", - "ĠA lex", - "ĠAl ex", - "ĠAle x", - "m ode", - "mod e", - "mo de", - "D ictionary", - ". Date", - ".D ate", - "ä ½", - "V ICE", - "VI CE", - "VIC E", - "Ġf amilies", - "Ġfam ilies", - "Ġfamil ies", - "Ġ Russian", - "ĠR ussian", - "ĠRuss ian", - "ĠRussia n", - "ĠRus sian", - "Ġ Times", - "ĠT imes", - "ĠTime s", - "ĠTim es", - "ĠTi mes", - ". call", - ".c all", - ".ca ll", - ".cal l", - "$ (", - "Pro file", - "Pr ofile", - "Prof ile", - "Ġ folder", - "Ġf older", - "Ġfol der", - "Ġfold er", - "Ġfo lder", - "c hes", - "ch es", - "che s", - "Ġl egis", - "Ġle gis", - "Ġleg is", - "_ row", - "_r ow", - "_ro w", - "u nes", - "un es", - "une s", - "Ù Ħ", - "Ġ }).", - "Ġ} ).", - "Ġ}) .", - "As sert", - "Ass ert", - "a gen", - "ag en", - "age n", - "Ġ Hand", - "ĠH and", - "ĠHa nd", - "ĠHan d", - "I ter", - "It er", - "Ġbig gest", - "o reach", - "or each", - "ore ach", - "orea ch", - "Ġp olic", - "Ġpol ic", - "Ġpo lic", - "Ġ permissions", - "Ġper missions", - "Ġpermission s", - "Ġperm issions", - "Ġsh owed", - "Ġshow ed", - "Ġsho wed", - "Ġ Element", - "ĠE lement", - "ĠEl ement", - "ĠEle ment", - "ĠElem ent", - "Ġ topic", - "Ġt opic", - "Ġto pic", - "Ġtop ic", - "âĢĶ âĢĶ", - "r oad", - "ro ad", - "Ġ Bank", - "ĠB ank", - "ĠBa nk", - "ĠBan k", - "re cord", - "rec ord", - "Ġ partners", - "Ġpart ners", - "Ġpartner s", - "Ġ Ref", - "ĠR ef", - "ĠRe f", - "ess ions", - "ession s", - "Ġas sess", - "Ġass ess", - "Ġasses s", - "U ST", - "US T", - "Ġ Party", - "ĠP arty", - "ĠPart y", - "ĠPar ty", - "p rodu", - "pr odu", - "pro du", - "prod u", - "L C", - "Ġ ul", - "Ġu l", - ". form", - ".f orm", - ".for m", - "h ide", - "hi de", - "hid e", - "c opy", - "co py", - "cop y", - "U TF", - "UT F", - "Ġ SOFTWARE", - "ĠSO FTWARE", - "čĊ čĊčĊ", - "čĊčĊ čĊ", - "Ġ Lin", - "ĠL in", - "ĠLi n", - "u na", - "un a", - "u gar", - "ug ar", - "uga r", - "Ġ administration", - "Ġadmin istration", - "Ġadministr ation", - "Ġ opening", - "Ġop ening", - "Ġopen ing", - "Ġ scan", - "Ġs can", - "Ġsc an", - "Ġsca n", - "Ġ continued", - "Ġcontin ued", - "Ġcontinue d", - "Ġcontinu ed", - "com ponent", - "comp onent", - ". sp", - ".s p", - "Ġhapp ens", - "Ġhappen s", - "um my", - "umm y", - "Ġ PR", - "ĠP R", - ". File", - ".F ile", - "Ġ Download", - "ĠD ownload", - "ĠDown load", - "Lo ading", - "Load ing", - "d i", - "Ġ waiting", - "Ġwait ing", - "Ġwa iting", - "_ ADD", - "_A DD", - "_AD D", - "T ab", - "Ta b", - ". querySelector", - ".query Selector", - "Ġe conomy", - "Ġecon omy", - "Ġeconom y", - "Ġec onomy", - "Ġ French", - "ĠF rench", - "ĠFr ench", - "t xt", - "tx t", - "Ġ fant", - "Ġf ant", - "Ġfa nt", - "Ġfan t", - "_ ;Ċ", - "_; Ċ", - "H older", - "Hold er", - "Ho lder", - "Hol der", - "S H", - "0 04", - "00 4", - "Ġ numpy", - "Ġn umpy", - "Ġnum py", - "Ġ street", - "Ġst reet", - "Ġstre et", - "Ġ male", - "Ġm ale", - "Ġma le", - "Ġmal e", - "\\ Model", - "\\M odel", - "an ging", - "ang ing", - "angi ng", - "3 33", - "33 3", - "Ġ Bill", - "ĠB ill", - "ĠBi ll", - "ĠBil l", - "Ġpre viously", - "Ġprevious ly", - "Ġprev iously", - "B I", - "Ġ Secret", - "ĠS ecret", - "ĠSe cret", - "ĠSec ret", - "Ġ mist", - "Ġm ist", - "Ġmis t", - "Ġmi st", - "Ġ Field", - "ĠF ield", - "ĠFi eld", - "u ps", - "up s", - "Ġ Process", - "ĠP rocess", - "ĠPro cess", - "ĠProc ess", - "Ġ kept", - "Ġk ept", - "Ġke pt", - "Ġkep t", - "Ġ OT", - "ĠO T", - "Ġ traditional", - "Ġtrad itional", - "Ġtradition al", - ". i", - "a min", - "am in", - "ami n", - "Ġh elps", - "Ġhelp s", - "Ġhel ps", - "A ny", - "An y", - "or igin", - "orig in", - "ori gin", - "il ters", - "ilter s", - "ilt ers", - "j u", - "d esc", - "de sc", - "des c", - "Ġ Account", - "ĠA ccount", - "ĠAc count", - "ĠAcc ount", - "Ġ )čĊ", - "Ġ) čĊ", - "k top", - "kt op", - "ol ly", - "oll y", - "Ġ fs", - "Ġf s", - "Ġ ê", - "Ġ ut", - "Ġu t", - "Ġ central", - "Ġc entral", - "Ġcent ral", - "Ġcentr al", - "( test", - "(t est", - "(te st", - ". An", - ".A n", - "Ġs atisf", - "G R", - "Ġ Full", - "ĠF ull", - "ĠFu ll", - "ĠFul l", - "Ġ heat", - "Ġh eat", - "Ġhe at", - "i ber", - "ib er", - "ibe r", - "Ġ onto", - "Ġon to", - "Ġont o", - "m os", - "mo s", - "S chema", - "Sch ema", - "Ġ factory", - "Ġf actory", - "Ġfact ory", - "Ġfactor y", - "Ġfacto ry", - "\" .$", - "\". $", - "a ws", - "aw s", - "St atement", - "State ment", - "Stat ement", - "( target", - "(t arget", - "ĉ new", - "ĉn ew", - ". be", - ".b e", - "Ġ guest", - "Ġg uest", - "Ġgu est", - "Ġ mal", - "Ġm al", - "Ġma l", - "A RY", - "AR Y", - "Ġre ached", - "Ġreach ed", - "Ġ mouse", - "Ġm ouse", - "Ġmo use", - "Ġmou se", - "Ġ challenge", - "Ġch allenge", - "Ġchall enge", - "Ġchalleng e", - "ĉ double", - "ĉd ouble", - "ĉdo uble", - "Ġ Tem", - "ĠT em", - "ĠTe m", - "Ġ terror", - "Ġt error", - "Ġte rror", - "Ġter ror", - "Ġterr or", - "Ġ extract", - "Ġex tract", - "Ġext ract", - "Ġextra ct", - "Ġextr act", - "_ TO", - "_T O", - "Ġse parate", - "Ġsepar ate", - "Ġseparat e", - "Ġ mir", - "Ġm ir", - "Ġmi r", - "h elp", - "he lp", - "hel p", - "Ġ capacity", - "Ġcap acity", - "Ġcapac ity", - "Ġcapacit y", - "Ġ Property", - "ĠP roperty", - "ĠPro perty", - "ĠProp erty", - "ĠProper ty", - "k an", - "ka n", - "_ create", - "_c reate", - "_cre ate", - "Ġ Light", - "ĠL ight", - "ĠLi ght", - "ĠLig ht", - ". parent", - ".p arent", - ".par ent", - ".pa rent", - "Ġunder standing", - "Ġunderstand ing", - "Ġunderst anding", - "Ġe asier", - "Ġeas ier", - "Ġ |=", - "Ġ| =", - "Ġ enh", - "Ġe nh", - "Ġen h", - "Ġ fat", - "Ġf at", - "Ġfa t", - "Ġpro test", - "Ġprot est", - "Ġprote st", - "a mm", - "am m", - "_ AT", - "_A T", - "- of", - "-o f", - "i ls", - "il s", - "Ġ Oh", - "ĠO h", - "Ġ psych", - "Ġps ych", - "Ġpsy ch", - "Ġ $.", - "Ġ$ .", - "i nds", - "in ds", - "ind s", - "Ġ relative", - "Ġrel ative", - "Ġrelativ e", - "Ġrelat ive", - "s hop", - "sh op", - "s hort", - "sh ort", - "Ġ Sand", - "ĠS and", - "ĠSan d", - "ĠSa nd", - "2 10", - "21 0", - "u estion", - "ues tion", - "uest ion", - "Ġf ear", - "Ġfe ar", - "/ ĊĊ", - "/Ċ Ċ", - ". context", - ".con text", - ".cont ext", - "Ġ schools", - "Ġschool s", - "Ġsch ools", - "Ġscho ols", - "Ġ serve", - "Ġs erve", - "Ġse rve", - "Ġser ve", - "Ġserv e", - "z one", - "zo ne", - "zon e", - "_ db", - "_d b", - "Ġmajor ity", - "ex ample", - "exam ple", - "Ġ lang", - "Ġl ang", - "Ġla ng", - "Ġlan g", - "ĉ ĠĠ", - "ĉĠ Ġ", - "Reg ister", - "e ndo", - "en do", - "end o", - "Ġ processing", - "Ġp rocessing", - "Ġprocess ing", - "Ġproces sing", - "_ template", - "_t emplate", - "_temp late", - "_tem plate", - "- user", - "-use r", - "-us er", - "-u ser", - "Ġ eg", - "Ġe g", - "C OM", - "CO M", - "Ġ Blue", - "ĠB lue", - "ĠBl ue", - "ĠBlu e", - "i ro", - "ir o", - "Ġ remote", - "Ġrem ote", - "Ġremot e", - "Ġ IT", - "ĠI T", - "# !/", - "#! /", - "Ġred istrib", - "Ġredis trib", - "1 24", - "12 4", - "r az", - "ra z", - "Ġ Since", - "ĠS ince", - "ĠSi nce", - "ĠSin ce", - "Ġ Tur", - "ĠT ur", - "ĠTu r", - "1 35", - "13 5", - "Back ground", - "= ==", - "== =", - "Ġ reflect", - "Ġref lect", - "Ġrefl ect", - "Ġp ros", - "Ġpro s", - "Ġpr os", - "c md", - "cm d", - "Ġw hom", - "Ġwh om", - "Ġwho m", - "Com pat", - "Comp at", - "Ġ Are", - "ĠA re", - "ĠAr e", - "Id entifier", - "Ident ifier", - "ĠT hom", - "ĠTh om", - "_ port", - "_p ort", - "_po rt", - "_por t", - "g u", - "Ġ monitor", - "Ġm onitor", - "Ġmon itor", - "r m", - "Ġ patient", - "Ġp atient", - "Ġpat ient", - "ver ter", - "vert er", - "verte r", - "Ġ gain", - "Ġg ain", - "Ġga in", - "- ui", - "-u i", - "I nst", - "In st", - "Ins t", - "Ġd ies", - "Ġdi es", - "Ġdie s", - "1 18", - "11 8", - "A rea", - "Ar ea", - "Are a", - "_ filter", - "_f ilter", - "_fil ter", - "_filt er", - "Ġg rat", - "Ġgr at", - "Ġgra t", - "Ġre ality", - "Ġreal ity", - "ord inate", - "ordin ate", - "ol ved", - "olve d", - "olv ed", - "Cont act", - "Conta ct", - "Ġcom pliance", - "Ġcompl iance", - "_ or", - "_o r", - "Ġ Var", - "ĠV ar", - "ĠVa r", - "d l", - "Ġ append", - "Ġapp end", - "Ġap pend", - "Ġappe nd", - "G ER", - "GE R", - "( max", - "(m ax", - ". render", - ".re nder", - ".r ender", - "Ġ dynamic", - "Ġd ynamic", - "Ġdynam ic", - "Ġdyn amic", - "ordin ates", - "ordinate s", - "_ options", - "_option s", - "_o ptions", - "_opt ions", - "_ column", - "_c olumn", - "_col umn", - "Ġb atter", - "Ġbatt er", - "Ġbat ter", - "s pace", - "sp ace", - "spa ce", - "L a", - "Ġ Source", - "ĠS ource", - "ĠSour ce", - "/ bin", - "/b in", - "Ġ dos", - "Ġd os", - "Ġdo s", - "Ġ Board", - "ĠB oard", - "ĠBo ard", - "Ġ Thread", - "ĠT hread", - "ĠTh read", - "ĠThr ead", - "Ġ AL", - "ĠA L", - "( config", - "(con fig", - "(conf ig", - "1 44", - "14 4", - "Ġ Mer", - "ĠM er", - "ĠMe r", - "Ġm iles", - "Ġmil es", - "Ġmi les", - "Ġmile s", - "_ header", - "_head er", - "_he ader", - "ETH OD", - "i zz", - "iz z", - "Ġb enefit", - "Ġbenef it", - "Ġbene fit", - "Ġ integr", - "Ġint egr", - "Ġinteg r", - "Ġinte gr", - "( current", - "(c urrent", - "(cur rent", - "(curr ent", - "u lo", - "ul o", - ". default", - ".d efault", - ".de fault", - ".def ault", - "Ġ Div", - "ĠD iv", - "ĠDi v", - "Ġ ton", - "Ġt on", - "Ġto n", - "o th", - "ot h", - "er vation", - "erv ation", - "erva tion", - "e dom", - "ed om", - "edo m", - "Ġ baby", - "Ġb aby", - "Ġba by", - "Ġbab y", - "ce ived", - "ceive d", - ". top", - ".t op", - ".to p", - "rior ity", - "Ġ Local", - "ĠL ocal", - "ĠLo cal", - "ĠLoc al", - "r iage", - "ri age", - "ria ge", - "Ġ attacks", - "Ġatt acks", - "Ġattack s", - "Ġ hospital", - "Ġh ospital", - "Ġhosp ital", - "1 68", - "16 8", - "Ġ female", - "Ġf emale", - "Ġfe male", - "Ġfem ale", - "Ġ Login", - "ĠLog in", - "ĠLo gin", - "ĠF lor", - "ĠFl or", - "ĠFlo r", - "Ġ chain", - "Ġch ain", - "Ġcha in", - "Ġchai n", - "ash ion", - "ashi on", - "Text ure", - "Tex ture", - "S ave", - "Sa ve", - "Ġ farm", - "Ġf arm", - "Ġfa rm", - "Ġfar m", - ". contains", - ".con tains", - ".cont ains", - ". Test", - ".T est", - ".Te st", - "Ġkn ows", - "Ġknow s", - "Ġgener ally", - "Ġgeneral ly", - "ip eline", - "ipe line", - "ipel ine", - "Ġme ant", - "Ġmean t", - "e ncia", - "en cia", - "enc ia", - "enci a", - "Ġn icht", - "Ġni cht", - "Ġnic ht", - "Ġnich t", - "Ġ contents", - "Ġcont ents", - "Ġcontent s", - "Ġconten ts", - "Ġconte nts", - "P M", - "ched ule", - "( line", - "(l ine", - "(li ne", - "C G", - "j ob", - "jo b", - "Ġ Real", - "ĠRe al", - "u er", - "ue r", - "f irm", - "fi rm", - "fir m", - "Ġ Ø", - "e tro", - "et ro", - "etr o", - "\" `Ċ", - "\"` Ċ", - "Ġ speech", - "Ġs peech", - "Ġspe ech", - "Ġ thr", - "Ġt hr", - "Ġth r", - "f oreach", - "fo reach", - "fore ach", - "for each", - "Ġ warn", - "Ġw arn", - "Ġwar n", - "Ġwa rn", - "ĉ l", - "Ġ heavy", - "Ġhe avy", - "Ġheav y", - "< li", - " )", - "_ char", - "_c har", - "_ch ar", - "re source", - "res ource", - "Ġ episode", - "Ġep isode", - "Ġepis ode", - "Ġ '_", - "Ġ' _", - "Ġ Es", - "ĠE s", - "Ġ Earth", - "ĠE arth", - "ĠEar th", - "Âł Âł", - "UP DATE", - "1 33", - "13 3", - "Ġ Sou", - "ĠS ou", - "ĠSo u", - "u is", - "ui s", - "t ypes", - "type s", - "ty pes", - "typ es", - "Ġ mas", - "Ġm as", - "Ġma s", - "Ġ fav", - "Ġf av", - "Ġfa v", - "Ġ construct", - "Ġcon struct", - "Ġconstr uct", - "_ rate", - "_r ate", - "_ra te", - "_rat e", - "e ras", - "er as", - "era s", - "Ġ |Ċ", - "Ġ| Ċ", - "rop erties", - "Ġ external", - "Ġex ternal", - "Ġext ernal", - "Ġextern al", - "Ġexter nal", - "Ġapp lied", - "Ġap plied", - "Ġappl ied", - "Ġ prefix", - "Ġp refix", - "Ġpre fix", - "Ġpref ix", - "o ted", - "ot ed", - "ote d", - "l ers", - "le rs", - "ler s", - "Ġ cold", - "Ġc old", - "Ġco ld", - "Ġcol d", - "Ġ SP", - "ĠS P", - "Ġ Church", - "ĠCh urch", - "ĠChu rch", - "Ġ Output", - "ĠOut put", - "l osed", - "lo sed", - "lose d", - "los ed", - "ç ļ", - "if icate", - "ific ate", - "ifi cate", - "ifica te", - "o peration", - "op eration", - "ope ration", - "oper ation", - "he rit", - "her it", - "x FF", - "xF F", - ". env", - ".e nv", - ".en v", - "_ err", - "_e rr", - "_er r", - "o sh", - "os h", - "D irection", - "Dir ection", - "Direct ion", - "Di rection", - "Dire ction", - "C ancel", - "Can cel", - "Ġ Frank", - "ĠF rank", - "ĠFr ank", - "ĠFra nk", - "ĠFran k", - "Ġ finding", - "Ġf inding", - "Ġfind ing", - "Ġfin ding", - ". )ĊĊ", - ".) ĊĊ", - ".)Ċ Ċ", - "Ġ router", - "Ġr outer", - "Ġro uter", - "Ġroute r", - "Ġrout er", - "Ġrou ter", - "ãĥ »", - "s es", - "se s", - "Ġ crow", - "Ġc row", - "Ġcr ow", - "Ġcro w", - "= ='", - "== '", - "Ġ sand", - "Ġs and", - "Ġsa nd", - "Ġsan d", - "Ġ rid", - "Ġr id", - "Ġri d", - "i ture", - "it ure", - "itu re", - "itur e", - "Ġ entre", - "Ġen tre", - "Ġent re", - "Ġentr e", - "Ġ observ", - "Ġo bserv", - "Ġob serv", - "Ġobs erv", - "Ġ vac", - "Ġv ac", - "Ġva c", - "ð Ł", - "- T", - "A rt", - "Ar t", - "n ight", - "ni ght", - ". search", - ".s earch", - ".se arch", - "Ġ exchange", - "Ġex change", - "Ġ district", - "Ġd istrict", - "Ġdi strict", - "Ġdistr ict", - ". os", - ".o s", - "Ġ department", - "Ġde partment", - "Ġdep artment", - "Ġdepart ment", - "Ġ documents", - "Ġdocument s", - "Ġdoc uments", - "Ġcent ury", - "Ġ Next", - "ĠN ext", - "ĠNe xt", - "ĠNex t", - "H ost", - "Ho st", - "ĠK IND", - "Ġs usp", - "Ġsu sp", - "Ġsus p", - "- P", - "r end", - "re nd", - "ren d", - ". em", - ".e m", - "u ite", - "ui te", - "uit e", - "i sters", - "is ters", - "ist ers", - "ister s", - "iste rs", - "( json", - "(j son", - "(js on", - "Ġ Ann", - "ĠA nn", - "ĠAn n", - "w t", - "a ti", - "at i", - "Ġ HTML", - "ĠHT ML", - "w hen", - "wh en", - "D irectory", - "Direct ory", - "Director y", - "Ġsh ut", - "< a", - "e dy", - "ed y", - "Ġ healthy", - "Ġhealth y", - "Ġheal thy", - "Ġ temperature", - "Ġt emperature", - "Ġtem perature", - "Ġtemper ature", - "Ġ Gen", - "ĠG en", - "ĠGe n", - "Ġ metal", - "Ġm etal", - "Ġme tal", - "Ġmet al", - "Ġmeta l", - "Ġ submit", - "Ġsub mit", - "Ġ DO", - "ĠD O", - "Ġat tract", - "Ġatt ract", - "Ġattr act", - "Ġ {};Ċ", - "Ġ{ };Ċ", - "Ġ{} ;Ċ", - "Ġ{}; Ċ", - "Ġ Word", - "ĠW ord", - "ĠWo rd", - "ĠWor d", - "Ġ ll", - "Ġl l", - "Ġse emed", - "Ġsee med", - "Ġseem ed", - "k o", - "I ED", - "IE D", - "Ġl abor", - "Ġla bor", - "Ġlab or", - ". Context", - ".Cont ext", - ".Con text", - "Ġ asset", - "Ġas set", - "Ġass et", - "y ou", - "yo u", - "Ġ cars", - "Ġc ars", - "Ġcar s", - "Ġca rs", - "Ġ Column", - "ĠC olumn", - "ĠCol umn", - "ĠColum n", - "Ġ ré", - "Ġr é", - "Ġ square", - "Ġs quare", - "Ġsqu are", - "Ġ NSString", - "ĠNS String", - "ĠNSS tring", - "âĢĿ ,", - "a pes", - "ap es", - "ape s", - ". ..Ċ", - ".. .Ċ", - "... Ċ", - "Ġ thanks", - "Ġth anks", - "Ġthan ks", - "Ġthank s", - "( props", - "(p rops", - "(pro ps", - "(pr ops", - "(prop s", - "Ġ tick", - "Ġt ick", - "Ġti ck", - "Ġtic k", - "Ġ experiment", - "Ġex periment", - "Ġexper iment", - "Ġexperi ment", - "Ġpr ison", - "Ġpri son", - "Ġpris on", - "t ree", - "tr ee", - "tre e", - "- text", - "-t ext", - "-te xt", - "Ġ IOException", - "ĠIO Exception", - "- width", - "-w idth", - "_ STATUS", - "_ST ATUS", - "_STAT US", - "f ast", - "fa st", - "fas t", - "- body", - "-b ody", - "-bo dy", - "- header", - "-head er", - "-he ader", - "Ġg uar", - "Ġgu ar", - "c rete", - "cre te", - "cret e", - "cr ete", - "Ġ Tim", - "ĠT im", - "ĠTi m", - "Ġcl early", - "Ġclear ly", - "Ġ Republican", - "ĠRepublic an", - "Ġ justify", - "Ġjust ify", - "и ÑĤ", - "ĉ ĠĠĠĠ", - "ĉĠĠĠ Ġ", - "ĉĠ ĠĠĠ", - "ĉĠĠ ĠĠ", - "c ache", - "ca che", - "cac he", - "; //", - ";/ /", - "Ġ presence", - "Ġpres ence", - "Ġf actors", - "Ġfact ors", - "Ġfa ctors", - "Ġfac tors", - "Ġfactor s", - "Ġfacto rs", - "Ġ employee", - "Ġe mployee", - "Ġemploy ee", - "] ))", - "]) )", - "M ember", - "Mem ber", - "Ġ selector", - "Ġse lector", - "Ġselect or", - "Ġsel ector", - "Ġsele ctor", - "b or", - "bo r", - "Ġ Mex", - "ĠM ex", - "ĠMe x", - "çļ Ħ", - "u tex", - "ut ex", - "ute x", - "_ tag", - "_t ag", - "_ta g", - "ail ure", - "Ġ Net", - "ĠN et", - "ĠNe t", - "Ġre li", - "Ġr eli", - "Ġrel i", - "E G", - "Ġ fprintf", - "Ġf printf", - "Ġ teen", - "Ġt een", - "Ġte en", - "Ġtee n", - "l oss", - "lo ss", - "los s", - "Ġle aving", - "1 34", - "13 4", - "De legate", - "Ġ beat", - "Ġb eat", - "Ġbe at", - "Ġ minute", - "Ġmin ute", - "Ġminut e", - "sub scribe", - "subs cribe", - "Ġred istribute", - "Ġredistrib ute", - "Ġredis tribute", - "Con stants", - "Constant s", - "Const ants", - "Ġc ancer", - "Ġcan cer", - "Ġcanc er", - "/ {", - "B L", - "Ġ span", - "Ġs pan", - "Ġsp an", - "Ġspa n", - "Ġ Child", - "ĠCh ild", - "ĠChi ld", - "C enter", - "Cent er", - "Ġ earth", - "Ġe arth", - "Ġear th", - "Y S", - "Ġ Level", - "ĠLe vel", - "ĠLev el", - "Ġ sea", - "Ġs ea", - "Ġse a", - ". support", - ".s upport", - ".sup port", - ". inner", - ".in ner", - ". Item", - ".I tem", - ".It em", - "il ling", - "ill ing", - "illi ng", - "illin g", - "Ġ ĠĠĠĊĠĠĠĠĊ", - "ĠĠ ĠĠĊĠĠĠĠĊ", - "ĠĠĠĠ ĊĠĠĠĠĊ", - "ĠĠĠ ĠĊĠĠĠĠĊ", - "ĠĠĠĠĊ ĠĠĠĠĊ", - "Ġ Label", - "ĠL abel", - "ĠLa bel", - "ĠLab el", - "3 20", - "32 0", - "Ġ Est", - "ĠE st", - "ĠEs t", - "( arg", - "(a rg", - "(ar g", - "1 45", - "14 5", - "bo Box", - "ĉ foreach", - "ĉf oreach", - "ĉfor each", - "c os", - "co s", - "F ailed", - "Fail ed", - "Fa iled", - "s wers", - "sw ers", - "swer s", - "E ditor", - "Ed itor", - "Edit or", - "r ont", - "ro nt", - "ron t", - "Ġ MP", - "ĠM P", - "ex pr", - "exp r", - "Ġ Life", - "ĠL ife", - "ĠLi fe", - "ĠLif e", - "Ġ ??", - "Ġ? ?", - "ö r", - "Ġ attend", - "Ġatt end", - "Ġatte nd", - "Ġ Que", - "ĠQ ue", - "ĠQu e", - "Ġ species", - "Ġs pecies", - "Ġsp ecies", - "Ġspec ies", - "Ġspe cies", - "Ġspeci es", - "- D", - "Ġ aus", - "Ġa us", - "Ġau s", - "Str uct", - "Ġadv antage", - "Ġadvant age", - "o ston", - "os ton", - "ost on", - "osto n", - "- block", - "-b lock", - "-bl ock", - "in itial", - "init ial", - "C RE", - "CR E", - "Ġtr uly", - "Ġ compare", - "Ġcom pare", - "Ġcomp are", - "Ġcompar e", - "or ney", - "orn ey", - "orne y", - "Ġ spect", - "Ġs pect", - "Ġsp ect", - "Ġspec t", - "Ġspe ct", - "F ull", - "Fu ll", - "b es", - "be s", - "Ġ visible", - "Ġv isible", - "Ġvis ible", - "Ġ mess", - "Ġm ess", - "Ġme ss", - "Ġmes s", - "st ances", - "stance s", - "sta nces", - "stan ces", - "Ġ cloud", - "Ġc loud", - "Ġcl oud", - "Ġclo ud", - "_ version", - "_v ersion", - "Ġf urn", - "Ġfur n", - "Ġfu rn", - "ic ago", - "ica go", - "L OW", - "LO W", - "Ġ traffic", - "Ġtraff ic", - "Ġtra ffic", - "Ġtraf fic", - "Ġ fol", - "Ġf ol", - "Ġfo l", - "ry pto", - "rypt o", - "Ġ declar", - "Ġde clar", - "Ġdec lar", - "Ġdecl ar", - "Ġ slot", - "Ġs lot", - "Ġsl ot", - "Ġslo t", - "Ġ Ext", - "ĠE xt", - "ĠEx t", - "Ġ England", - "ĠEng land", - "ĠEngl and", - "Ġ Under", - "ĠU nder", - "ĠUn der", - "ĠUnd er", - "Ġ ta", - "Ġt a", - "l etter", - "let ter", - "lette r", - "lett er", - "2 03", - "20 3", - "Ġoff icer", - "Ġoffic er", - "Ġoffice r", - "Ġ Donald", - "ĠD onald", - "ĠDon ald", - "Y es", - "Ye s", - "_ json", - "_j son", - "_js on", - "I TableView", - "IT ableView", - "Ġ USE", - "ĠU SE", - "ĠUS E", - "mploy ee", - "Ġop inion", - "Ġopin ion", - "Ġ Aut", - "ĠA ut", - "ĠAu t", - "b order", - "bor der", - "Ġad vice", - "Ġadv ice", - "Ġautom atically", - "Ġautomatic ally", - "Ġautomat ically", - "is co", - "isc o", - "Ġ mm", - "Ġm m", - ". vis", - ".v is", - "a ml", - "am l", - "Ġ initialize", - "Ġinitial ize", - "Ġ ({", - "Ġ( {", - "Ġ ;ĊĊ", - "Ġ; ĊĊ", - "Ġ;Ċ Ċ", - "Ġ generation", - "Ġg eneration", - "Ġgener ation", - "Ġgen eration", - "Ġgene ration", - "Ġgenera tion", - "Ġ bits", - "Ġb its", - "Ġbit s", - "Ġbi ts", - "cl ipse", - "clip se", - "Ġu nf", - "Ġun f", - "u tors", - "ut ors", - "uto rs", - "utor s", - "p lt", - "pl t", - "Ġ delta", - "Ġd elta", - "Ġdel ta", - "Ġdelt a", - "e stroy", - "est roy", - "estr oy", - "estro y", - "i sis", - "is is", - "isi s", - "< br", - "Ċ", - "'> Ċ", - "a pers", - "ap ers", - "ape rs", - "aper s", - "] (", - "cont inue", - "contin ue", - "s pec", - "sp ec", - "spe c", - "Ġ Road", - "ĠR oad", - "ĠRo ad", - "A SH", - "AS H", - "il iar", - "ili ar", - "ilia r", - "Ġcontin ues", - "Ġcontinue s", - "Ġcontinu es", - "Ġ appoint", - "Ġapp oint", - "Ġap point", - "Ġ #Ċ", - "Ġ# Ċ", - "Ġ Vir", - "ĠV ir", - "ĠVi r", - "Ġ ?>\"", - "Ġ? >\"", - "Ġ?> \"", - "Ġ bin", - "Ġb in", - "Ġbi n", - "} \",", - "}\" ,", - "go ing", - "e ach", - "ea ch", - "B D", - "1 85", - "18 5", - "Ġ Access", - "ĠA ccess", - "ĠAc cess", - "ĠAcc ess", - "D oc", - "Do c", - "Ġ Management", - "ĠMan agement", - "ĠManage ment", - "ĠMana gement", - "B ER", - "BE R", - "as ket", - "ask et", - ". getInstance", - ".get Instance", - ".getIn stance", - "1 29", - "12 9", - "Ġestablish ed", - "s ocket", - "so cket", - "sock et", - "soc ket", - "I NS", - "IN S", - "ĉ virtual", - "ĉv irtual", - "ĉ result", - "ĉres ult", - "RE AD", - "REA D", - "_ height", - "_h eight", - "_he ight", - "1 52", - "15 2", - "Ġ Font", - "ĠF ont", - "ĠFo nt", - "ĠFon t", - "Ġ ();Ċ", - "Ġ( );Ċ", - "Ġ() ;Ċ", - "Ġ(); Ċ", - "_ html", - "_h tml", - "_ht ml", - "Ġ neighbor", - "Ġne ighbor", - "Ġneighb or", - "Ġneigh bor", - "l or", - "lo r", - "Ġ gather", - "Ġg ather", - "Ġga ther", - "Ġ })ĊĊ", - "Ġ} )ĊĊ", - "Ġ})Ċ Ċ", - "Ġ}) ĊĊ", - "Ġ identity", - "Ġid entity", - "Ġide ntity", - "Ġident ity", - "Ġ fab", - "Ġf ab", - "Ġfa b", - "p adding", - "pad ding", - "Ġ Route", - "ĠR oute", - "ĠRo ute", - "ĠRou te", - "ĠRout e", - "Enumer able", - "Enum erable", - "à ´", - "Ġ forced", - "Ġfor ced", - "Ġforce d", - "Ġforc ed", - "/ jquery", - "/j query", - ". ĊĊĊĊĊĊ", - ".ĊĊ ĊĊĊĊ", - ".Ċ ĊĊĊĊĊ", - ".ĊĊĊĊ ĊĊ", - ".ĊĊĊ ĊĊĊ", - ".ĊĊĊĊĊ Ċ", - "res ents", - "resent s", - "rese nts", - "_ left", - "_l eft", - "_le ft", - ". Param", - ".P aram", - ".Par am", - "ĉ throw", - "ĉth row", - "Ġ Ham", - "ĠH am", - "ĠHa m", - "Ġevent ually", - "Ġeventual ly", - "a cer", - "ace r", - "ac er", - "p ub", - "pu b", - "Ġ tra", - "Ġt ra", - "Ġtr a", - "un ique", - "uni que", - "uniq ue", - "d el", - "de l", - "Ġ Florida", - "ĠFl orida", - "ĠFlor ida", - "Ġ Clean", - "ĠC lean", - "ĠCl ean", - "ĠCle an", - "x a", - "Ġ ·", - "Ġ ·", - "Ġ validate", - "Ġvalid ate", - "Ġvalida te", - "Vis ual", - "Ex pression", - "Exp ression", - "Express ion", - "Expr ession", - "_ func", - "_f unc", - "_fun c", - "_fu nc", - "m ember", - "mem ber", - "ĉ h", - "t rl", - "tr l", - "1 36", - "13 6", - "ĉ G", - "nap shot", - "Ġ PropTypes", - "ĠProp Types", - "v in", - "vi n", - "1 53", - "15 3", - "] )ĊĊ", - "]) ĊĊ", - "])Ċ Ċ", - "o wl", - "ow l", - "if ies", - "ifi es", - "ifie s", - "Ġ $('.", - "Ġ$ ('.", - "Ġ$( '.", - "Ġ$(' .", - "Ġ Context", - "ĠCon text", - "ĠCont ext", - "ĠConte xt", - "Ġ Toast", - "ĠTo ast", - ". Key", - ".K ey", - "Ġoff icers", - "Ġoffic ers", - "Ġoffice rs", - "Ġofficer s", - "/ n", - "s n", - "un defined", - "und efined", - "undef ined", - ". items", - ".i tems", - ".item s", - ".it ems", - "ut ow", - "uto w", - "a mage", - "am age", - "ama ge", - "Ġ accounts", - "Ġac counts", - "Ġaccount s", - "o okie", - "ook ie", - "oo kie", - "S ection", - "Se ction", - "Sec tion", - "ic ians", - "ici ans", - "ician s", - "icia ns", - "Ġad vis", - "Ġadv is", - "( is", - "(i s", - "[ :,", - "[: ,", - "Ġ France", - "ĠF rance", - "ĠFr ance", - "ĠFranc e", - "ĠFra nce", - "ĠFran ce", - "F unc", - "Fun c", - "Fu nc", - "ic ious", - "ici ous", - "icio us", - "Ġ tok", - "Ġt ok", - "Ġto k", - "Ch annel", - "Chan nel", - "Ġ AD", - "ĠA D", - "_ NUM", - "_N UM", - "Ġ timeout", - "Ġtime out", - "l emma", - "le mma", - "lem ma", - "r eme", - "re me", - "rem e", - "u j", - ". Al", - ".A l", - "u clear", - "uc lear", - "ucle ar", - "( os", - "(o s", - "( \"<", - "(\" <", - "[ Ċ", - "f etch", - "fet ch", - "Ġ bal", - "Ġb al", - "Ġba l", - "Ġ guid", - "Ġg uid", - "Ġgu id", - "Ġgui d", - "- align", - "-al ign", - "Ġ Write", - "ĠW rite", - "ĠWr ite", - "Ġ Once", - "ĠO nce", - "ĠOn ce", - "ĠOnc e", - "utow ired", - "OD ULE", - "Ġ pitch", - "Ġp itch", - "Ġpit ch", - "C F", - "by tes", - "byte s", - "byt es", - "Ġ Commission", - "ĠCom mission", - "ĠComm ission", - "Ġin cred", - "Ġincre d", - "Ġinc red", - "Ġincr ed", - "P ER", - "PE R", - "_ response", - "_res ponse", - "_resp onse", - "Ġ Los", - "ĠL os", - "ĠLo s", - "p arser", - "par ser", - "parse r", - "pars er", - "Ġ assume", - "Ġas sume", - "Ġass ume", - "Ġassum e", - ". Request", - ".Re quest", - "Ġ Token", - "ĠT oken", - "ĠTo ken", - "ĠTok en", - "_ position", - "_p osition", - "_pos ition", - "Ġ nom", - "Ġn om", - "Ġno m", - "- term", - "-t erm", - "-te rm", - "Ġ remaining", - "Ġrem aining", - "Ġremain ing", - "i ostream", - "io stream", - "Ġ pieces", - "Ġp ieces", - "Ġpie ces", - "Ġpiece s", - "a py", - "ap y", - "Ġ Less", - "ĠL ess", - "ĠLe ss", - "ĠLes s", - "r ange", - "ra nge", - "ran ge", - "rang e", - "um bn", - "umb n", - "p rise", - "pr ise", - "pri se", - "_ option", - "_op tion", - "_o ption", - "_opt ion", - "2 30", - "23 0", - "I mpl", - "Im pl", - "Imp l", - "k wargs", - "kw args", - "Ġbusiness es", - "Al ert", - "Ale rt", - "Ġpart ies", - "Ġpar ties", - "Ġpartie s", - "Ġparti es", - "Ġ Container", - "ĠCont ainer", - "Ġ Private", - "ĠPr ivate", - "ĠPriv ate", - "Ġ Plan", - "ĠP lan", - "ĠPl an", - "ĠPla n", - "Ġ registered", - "Ġregister ed", - "Ġregist ered", - "Ġ jour", - "Ġj our", - "Ġjo ur", - "Ġjou r", - "a cker", - "ack er", - "ac ker", - "е ни", - "ен и", - "/ >", - "c hat", - "ch at", - "cha t", - "s ect", - "se ct", - "sec t", - "Ġ creation", - "Ġc reation", - "Ġcre ation", - "Ġcreat ion", - "Ġcrea tion", - "ol utely", - "olute ly", - "olut ely", - "Ġ instant", - "Ġin stant", - "Ġins tant", - "Ġinst ant", - "Ġ delivery", - "Ġd elivery", - "Ġdel ivery", - "Ġdeliver y", - "i cken", - "ic ken", - "ick en", - "y es", - "ye s", - "1 63", - "16 3", - "Ġ Franc", - "ĠFr anc", - "ĠFra nc", - "ĠFran c", - "b ling", - "bl ing", - "e nda", - "en da", - "end a", - "[ (", - "_ range", - "_r ange", - "_ra nge", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "Ġ schedule", - "Ġs chedule", - "Ġsched ule", - "C onn", - "Con n", - "Co nn", - "Ġ thank", - "Ġth ank", - "Ġthan k", - "Ġtha nk", - "x d", - "Ġ hook", - "Ġh ook", - "Ġho ok", - "Ġ documentation", - "Ġdocument ation", - "Param eters", - "Parameter s", - "H ello", - "Hel lo", - "Hell o", - "v t", - "Ġ articles", - "Ġart icles", - "Ġarticle s", - "Ġartic les", - "Ġ west", - "Ġw est", - "Ġwe st", - "Ġwes t", - "d efined", - "def ined", - "define d", - ". select", - ".s elect", - ".se lect", - ".sel ect", - "ok ens", - "oken s", - "oke ns", - "Ġ VAL", - "ĠV AL", - "ĠVA L", - ". file", - ".f ile", - ".fi le", - ".fil e", - "re set", - "res et", - "rese t", - "Ġ mys", - "Ġm ys", - "Ġmy s", - "Ġ MA", - "ĠM A", - "] ),", - "]) ,", - "Ġ cities", - "Ġc ities", - "Ġcit ies", - "Ġci ties", - "re lated", - "rel ated", - "å Ľ", - "Ġ appeared", - "Ġappe ared", - "Ġappear ed", - "Ġ wid", - "Ġw id", - "Ġwi d", - ". panel", - ".p anel", - ".pa nel", - ".pan el", - "Ġ Ins", - "ĠI ns", - "ĠIn s", - ". entity", - ".e ntity", - ".ent ity", - "Ġde cre", - "Ġdec re", - "Ġ Lou", - "ĠL ou", - "ĠLo u", - "( time", - "(t ime", - "(ti me", - "Ġ Thank", - "ĠTh ank", - "ĠThan k", - ". createElement", - ".create Element", - "Ġ mentioned", - "Ġmention ed", - "Ġment ioned", - "o unce", - "ou nce", - "oun ce", - "ounc e", - "Ġ Try", - "ĠT ry", - "ĠTr y", - "Ġ Wall", - "ĠW all", - "ĠWal l", - "ĠWa ll", - "/ images", - "/image s", - "/im ages", - "Ġ Menu", - "ĠM enu", - "ĠMe nu", - "ĠMen u", - "' čĊ", - "Ġ Er", - "ĠE r", - "Ġc ritic", - "Ġcr itic", - "Ġcrit ic", - "Ġcri tic", - "Ġ Year", - "ĠY ear", - "ĠYe ar", - "( param", - "(p aram", - "(par am", - "(pa ram", - "(para m", - "Ġ flo", - "Ġf lo", - "Ġfl o", - "N N", - "o oter", - "oot er", - "oo ter", - "Ġ ];Ċ", - "Ġ] ;Ċ", - "Ġ]; Ċ", - "Ġ Aff", - "ĠA ff", - "ĠAf f", - "\" github", - "\"g ithub", - "ro oms", - "room s", - "Ġ hyp", - "Ġh yp", - "Ġhy p", - "g lobal", - "glob al", - "Ġ avec", - "Ġa vec", - "Ġav ec", - "Ġave c", - "æľ Ī", - "Ġ completion", - "Ġcom pletion", - "Ġcomp letion", - "Ġcomple tion", - "Ġcomplet ion", - "Ġ cond", - "Ġc ond", - "Ġcon d", - "Ġco nd", - "on ymous", - "onym ous", - "( temp", - "(t emp", - "(te mp", - "Ġ stars", - "Ġst ars", - "Ġstar s", - "Ġsta rs", - "Ġ relevant", - "Ġre levant", - "Ġrele vant", - "Ġrelev ant", - "Ġ covered", - "Ġcover ed", - "Ġcov ered", - "Ġ elim", - "Ġe lim", - "Ġel im", - "_ types", - "_t ypes", - "_type s", - "_typ es", - "_ty pes", - "( bool", - "(b ool", - "Ġ tu", - "Ġt u", - "_ exists", - "_ex ists", - "_exist s", - "Ġ secure", - "Ġs ecure", - "Ġsec ure", - "Ġ stored", - "Ġst ored", - "Ġstore d", - "Ġstor ed", - "Ġsto red", - "] /", - "x F", - "Ġ Controller", - "ĠCont roller", - "ĠControl ler", - "ĠContr oller", - "Ġm igr", - "Ġmi gr", - "Ġmig r", - "M I", - "Ġ Den", - "ĠD en", - "ĠDe n", - "Ġ annual", - "Ġann ual", - "U IL", - "UI L", - "- and", - "-a nd", - "-an d", - "Ġ crime", - "Ġcr ime", - "Ġcri me", - "Ġcrim e", - "b el", - "be l", - "Ġk itchen", - "Ġkit chen", - "@ g", - "_ ph", - "_p h", - "ourn ament", - "Ġ Social", - "ĠS ocial", - "ĠSo cial", - "ĠSoc ial", - "ĠSoci al", - "Ġ Special", - "ĠS pecial", - "ĠSp ecial", - "ĠSpec ial", - "ĠSpe cial", - "lo gger", - "log ger", - "logg er", - "Ġ tail", - "Ġt ail", - "Ġta il", - "Ġtai l", - "Ġ unknown", - "Ġun known", - "Ġunk nown", - "Ġunknow n", - "d ed", - "de d", - "Ġapp rec", - "Ġap prec", - "( db", - "(d b", - "c f", - "1 55", - "15 5", - "Ġ assign", - "Ġas sign", - "Ġass ign", - "- out", - "-o ut", - "Ġ Mont", - "ĠM ont", - "ĠMon t", - "ĠMo nt", - "d p", - "w idget", - "wid get", - "Ġ stone", - "Ġs tone", - "Ġst one", - "Ġsto ne", - "- primary", - "-pr imary", - ". grid", - ".g rid", - ".gr id", - "Res ults", - "Result s", - "a zz", - "az z", - "Ġ daughter", - "Ġda ughter", - "Ġ curr", - "Ġc urr", - "Ġcur r", - "Ġcu rr", - "1 75", - "17 5", - "Ġ lin", - "Ġl in", - "Ġli n", - "Ġ south", - "Ġs outh", - "Ġso uth", - "Ġsou th", - "Ġsout h", - "form s", - "fo rms", - "for ms", - "Ġ OUT", - "ĠO UT", - "ĠOU T", - "l ette", - "le tte", - "let te", - "lett e", - "a ks", - "ak s", - "ig ure", - "igu re", - "Ġ EU", - "ĠE U", - "var iable", - "vari able", - "Ġ brief", - "Ġb rief", - "Ġbr ief", - "Ġbri ef", - "Ġ Scott", - "ĠS cott", - "ĠSc ott", - "ĠScot t", - "ĠSco tt", - "Ġ conference", - "Ġcon ference", - "Ġconf erence", - "Ġconfer ence", - "a nda", - "an da", - "and a", - "_ lock", - "_l ock", - "_lo ck", - "_loc k", - "o ral", - "or al", - "ora l", - "Ġe ine", - "Ġein e", - "Ġei ne", - "O RS", - "OR S", - "//// ////////////////////////////////////////////////////////////", - "//////// ////////////////////////////////////////////////////////", - "//////////////// ////////////////////////////////////////////////", - "//////////////////////////////// ////////////////////////////////", - "//////////// ////////////////////////////////////////////////////", - "//////////////////////////////////////////////// ////////////////", - "//////////////////////////////////////////////////////// ////////", - "//////////////////////////////////////////////////////////// ////", - "//////////////////////////////////////////////////// ////////////", - "es so", - "ess o", - "Ġ ris", - "Ġr is", - "Ġri s", - "Ġ gender", - "Ġg ender", - "Ġge nder", - "Ġgen der", - "es tic", - "est ic", - "esti c", - "L icense", - "Lic ense", - "( out", - "(o ut", - "Ġ ms", - "Ġm s", - "S ee", - "Se e", - "Ġw illing", - "Ġwill ing", - "Ġwil ling", - "a ze", - "az e", - "Ġ sports", - "Ġs ports", - "Ġsp orts", - "Ġsport s", - "Ġspo rts", - "Ġspor ts", - "Ġ yes", - "Ġy es", - "Ġye s", - "l u", - "Ġp urs", - "Ġpur s", - "Ġpu rs", - "/ javascript", - "/j avascript", - "/java script", - "/jav ascript", - "- pro", - "-p ro", - "-pr o", - "nav bar", - "_ product", - "_pro duct", - "_prod uct", - "/ bootstrap", - "/boot strap", - "Ġdr iving", - "Ġdriv ing", - "Ġdri ving", - "Ġ Ä", - "Ġpro pos", - "Ġprop os", - "ul tip", - "ult ip", - "ulti p", - "up lic", - ". email", - ".e mail", - ".em ail", - "Ġ approx", - "Ġapp rox", - "Ġap prox", - "Ġappro x", - "( cl", - "(c l", - "Ġ wear", - "Ġw ear", - "Ġwe ar", - "Ġ reply", - "Ġre ply", - "Ġrep ly", - "Ġrepl y", - "as set", - "ass et", - "asse t", - "Ġ ice", - "Ġi ce", - "Ġic e", - "Ġ tx", - "Ġt x", - "k r", - "Ġ Germany", - "ĠGerman y", - "ĠGer many", - "ĠGerm any", - "Ġ George", - "ĠGe orge", - "ĠGeorg e", - "Ġ cb", - "Ġc b", - "ĉ err", - "ĉe rr", - "M ove", - "Mo ve", - "Mov e", - "Ġ poly", - "Ġp oly", - "Ġpol y", - "Ġpo ly", - "v oice", - "vo ice", - "} \"", - "Ġ animal", - "Ġan imal", - "Ġanim al", - "Ġani mal", - "A v", - "Ġ Location", - "ĠL ocation", - "ĠLo cation", - "ĠLoc ation", - "Ġ native", - "Ġn ative", - "Ġnat ive", - "] [\"", - "][ \"", - "< double", - " \"", - "s tat", - "st at", - "sta t", - "Ġ },čĊ", - "Ġ} ,čĊ", - "Ġ}, čĊ", - "< span", - " =", - "Ð ±", - "1 39", - "13 9", - "i va", - "iv a", - ". AutoSize", - ".Auto Size", - "Ġ Lat", - "ĠL at", - "ĠLa t", - "_ ext", - "_e xt", - "_ex t", - "Initial ize", - ". register", - ".reg ister", - "1 56", - "15 6", - "O PY", - "OP Y", - "Ġ reverse", - "Ġre verse", - "Ġrev erse", - "Ġrevers e", - "Ġrever se", - "_ dis", - "_d is", - "_di s", - "' ][", - "'] [", - "Ġ prompt", - "Ġp rompt", - "Ġprom pt", - "on to", - "ont o", - "Ġ Journal", - "ĠJ ournal", - "ĠJo urnal", - "r outer", - "ro uter", - "rou ter", - "route r", - "Ġ mysqli", - "Ġm ysqli", - "Ġmys qli", - "Ġmysql i", - "# else", - ") \"", - "- xs", - "-x s", - "l ets", - "le ts", - "let s", - "p han", - "ph an", - "pha n", - ". LE", - ".L E", - "1 37", - "13 7", - "W ill", - "Wil l", - "Wi ll", - "Ġaff ord", - "Ġaf ford", - "Ġ skill", - "Ġs kill", - "Ġsk ill", - "Ġski ll", - "- toggle", - "-t oggle", - "N C", - "B ind", - "Bin d", - "Bi nd", - "T S", - "J ust", - "Ju st", - "it eral", - "ite ral", - "iter al", - "Y P", - "ĉ unsigned", - "ĉun signed", - "Ġ wind", - "Ġw ind", - "Ġwin d", - "Ġwi nd", - "1 49", - "14 9", - ") ):Ċ", - ")) :Ċ", - ")): Ċ", - "Ġ warning", - "Ġw arning", - "Ġwar ning", - "Ġwarn ing", - "Ġ Water", - "ĠW ater", - "ĠWat er", - "ĠWa ter", - "Ġ draft", - "Ġd raft", - "Ġdr aft", - "Ġdra ft", - "Ġ cm", - "Ġc m", - "Ġ sam", - "Ġs am", - "Ġsa m", - "Ġ holding", - "Ġh olding", - "Ġhold ing", - "Ġhol ding", - "z ip", - "zi p", - "Ġ Science", - "ĠSc ience", - "ĠSci ence", - "Ġsup posed", - "Ġsuppose d", - "Ġsupp osed", - "G en", - "Ge n", - "Ġ diet", - "Ġd iet", - "Ġdi et", - "Ġdie t", - "< h", - "Ġ Pass", - "ĠP ass", - "ĠPa ss", - "ĠPas s", - "v i", - "Ġh usband", - "Ġhus band", - "� �", - "n ote", - "not e", - "no te", - "Ġ About", - "ĠA bout", - "ĠAb out", - "ĠIn stitute", - "ĠInstit ute", - "ĠInstitut e", - "Ġ climate", - "Ġcl imate", - "Ġclim ate", - "Ġcli mate", - ". Format", - ".Form at", - ".For mat", - "Ġ nut", - "Ġn ut", - "Ġnu t", - "e sted", - "es ted", - "est ed", - "este d", - "Ġapp arent", - "Ġap parent", - "Ġappar ent", - "Ġappare nt", - "Ġ holds", - "Ġh olds", - "Ġhold s", - "Ġho lds", - "Ġhol ds", - "f i", - "n ews", - "ne ws", - "new s", - "C M", - "v ideo", - "vid eo", - "vi deo", - "vide o", - "' :'", - "': '", - "D ITION", - "DI TION", - "p ing", - "pi ng", - "pin g", - "Ġsen ior", - "Ġseni or", - "w a", - "- ->Ċ", - "-- >Ċ", - "--> Ċ", - "_ default", - "_d efault", - "_de fault", - "_def ault", - "Ġ Database", - "ĠD atabase", - "ĠData base", - "ĠDat abase", - "r ep", - "re p", - "E SS", - "ES S", - "n ergy", - "ner gy", - "nerg y", - ". Find", - ".F ind", - "_ mask", - "_m ask", - "_ma sk", - "_mas k", - "Ġ rise", - "Ġr ise", - "Ġris e", - "Ġri se", - "Ġ kernel", - "Ġk ernel", - "Ġker nel", - "Ġkern el", - ": :$", - ":: $", - ". Q", - "Ġoff ering", - "Ġoffer ing", - "de cl", - "dec l", - "Ġ CS", - "ĠC S", - "Ġ listed", - "Ġl isted", - "Ġli sted", - "Ġlist ed", - "Ġliste d", - "Ġlis ted", - "Ġ mostly", - "Ġmost ly", - "e nger", - "en ger", - "eng er", - "enge r", - "Ġ blocks", - "Ġb locks", - "Ġbl ocks", - "Ġblock s", - "Ġblo cks", - "Ġbloc ks", - "o lo", - "ol o", - "Ġgover ning", - "Ġgovern ing", - "\\ F", - "Ġcon cent", - "Ġconc ent", - "Ġconce nt", - ". getText", - ".get Text", - "Ġ mb", - "Ġm b", - "Ġocc urred", - "Ġoccur red", - "Ġ changing", - "Ġch anging", - "Ġchang ing", - "Ġchan ging", - "S cene", - "Sc ene", - "_ CODE", - "_C ODE", - "_CO DE", - "_COD E", - "B eh", - "Be h", - "\" The", - "\"T he", - "Ġ tile", - "Ġt ile", - "Ġti le", - "Ġtil e", - "Ġ Association", - "ĠAssoci ation", - "ĠAssoc iation", - "ĉ P", - "al ty", - "alt y", - "_ ad", - "_a d", - "od ies", - "odi es", - "odie s", - "i ated", - "ia ted", - "iate d", - "iat ed", - "Ġ prepared", - "Ġpre pared", - "Ġprepar ed", - "Ġprepare d", - "Ġprep ared", - "p ossible", - "poss ible", - "Ġ mort", - "Ġm ort", - "Ġmor t", - "Ġmo rt", - "T EST", - "TE ST", - "TES T", - "1 42", - "14 2", - "Ġ ignore", - "Ġi gnore", - "Ġign ore", - "Ġig nore", - "Ġignor e", - "Ġ calc", - "Ġc alc", - "Ġcal c", - "Ġca lc", - "Ġ rs", - "Ġr s", - "Ġ assertEquals", - "Ġassert Equals", - "Ġ sz", - "Ġs z", - "Ġ THIS", - "ĠTH IS", - ". \"Ċ", - ".\" Ċ", - "Ġ canvas", - "Ġc anvas", - "Ġcan vas", - "Ġcanv as", - "j ava", - "ja va", - "jav a", - "Ġd ut", - "Ġdu t", - "VAL ID", - ". sql", - ".s ql", - ". input", - ".in put", - "Ġ aux", - "Ġa ux", - "Ġau x", - "S up", - "Su p", - "Ġ artist", - "Ġart ist", - "V ec", - "Ve c", - "_ TIME", - "_T IME", - "_TIM E", - "_TI ME", - ".string ify", - "et ween", - "Ġ Category", - "ĠC ategory", - "Ġ [-", - "Ġ[ -", - "Ġ DevExpress", - "ĠDev Express", - "Ġ Jul", - "ĠJ ul", - "ĠJu l", - "Ġ ring", - "Ġr ing", - "Ġri ng", - "Ġrin g", - ". ed", - ".e d", - "Y Y", - "L et", - "Le t", - "T extField", - "Text Field", - "Ġ flat", - "Ġf lat", - "Ġfl at", - "Ġfla t", - "_ print", - "_p rint", - "_pr int", - "_pri nt", - "Ġ OTHER", - "ĠOT HER", - "ad ian", - "adi an", - "adia n", - "Ġ checked", - "Ġcheck ed", - "e le", - "el e", - "Al ign", - "Ali gn", - "st anding", - "stand ing", - "stan ding", - "Ġ [],", - "Ġ[ ],", - "Ġ[] ,", - "Ġ lab", - "Ġl ab", - "Ġla b", - "u cky", - "uc ky", - "uck y", - "Ġ Christmas", - "ĠChrist mas", - "( image", - "(i mage", - "(im age", - ". module", - ".m odule", - ".mod ule", - "Ġ lots", - "Ġl ots", - "Ġlo ts", - "Ġlot s", - "Ġs lightly", - "Ġsl ightly", - "Ġslight ly", - "( final", - "(f inal", - "(fin al", - "(fi nal", - "er ge", - "erg e", - "è ¿", - "1 47", - "14 7", - "Ġ Police", - "ĠPol ice", - "ĠPo lice", - "ĠPolic e", - "1 43", - "14 3", - "Ġ Right", - "ĠR ight", - "ĠRig ht", - "ĠRi ght", - "Ġ award", - "Ġa ward", - "Ġaw ard", - "Ġ OS", - "ĠO S", - "Ġ {}ĊĊ", - "Ġ{ }ĊĊ", - "Ġ{} ĊĊ", - "Ġ{}Ċ Ċ", - "Ġ ptr", - "Ġp tr", - "Ġpt r", - "o ves", - "ov es", - "ove s", - "ic ated", - "ica ted", - "icate d", - "е м", - "еР¼", - "Ġ manage", - "Ġman age", - "Ġma nage", - "Ġmana ge", - "ol iday", - "olid ay", - "oli day", - "A mount", - "Am ount", - "ool Strip", - "t body", - "tb ody", - "N av", - "Na v", - "w rap", - "wr ap", - "B B", - "Ġw atching", - "Ġwatch ing", - "Ġwat ching", - "a rios", - "ar ios", - "ari os", - "ario s", - "Ġ optional", - "Ġoption al", - "Ġopt ional", - "_ K", - "Ġ Licensed", - "ĠL icensed", - "ĠLicense d", - "ĠLic ensed", - ". Map", - ".M ap", - ".Ma p", - "T imer", - "Time r", - "Tim er", - "Ti mer", - "Ġ AP", - "ĠA P", - "Ġ Rev", - "ĠR ev", - "ĠRe v", - "( o", - ", c", - "u min", - "um in", - "umi n", - "et ailed", - "etail ed", - "eta iled", - "Ġ Hy", - "ĠH y", - "Ġ blank", - "Ġbl ank", - "Ġbla nk", - "a gger", - "ag ger", - "agg er", - "Ġ Self", - "ĠS elf", - "ĠSe lf", - "ĠSel f", - "( )[", - "() [", - ". make", - ".m ake", - ".ma ke", - "e arn", - "ear n", - "ea rn", - "ch annel", - "chan nel", - "< pre", - "

;Ċ", - ">; Ċ", - "W orld", - "Ġ python", - "Ġp ython", - "Ġpy thon", - "Ġpyt hon", - "Ġ lif", - "Ġl if", - "Ġli f", - "Ġt rav", - "Ġtr av", - "Ġtra v", - "Ġcon ven", - "Ġconv en", - "com pany", - "comp any", - "compan y", - "Ġ Club", - "ĠC lub", - "ĠCl ub", - "1 38", - "13 8", - "V er", - "Ve r", - "B tn", - "Ġ zone", - "Ġz one", - "Ġzo ne", - "product s", - "produ cts", - "Ġ Educ", - "ĠE duc", - "ĠEd uc", - "ĠEdu c", - "Ġ verify", - "Ġver ify", - "Ġveri fy", - "Ġ Mil", - "ĠM il", - "ĠMi l", - "o no", - "on o", - "] );ĊĊ", - "]) ;ĊĊ", - "]);Ċ Ċ", - "]); ĊĊ", - "EN CE", - "ENC E", - "Ġ packet", - "Ġp acket", - "Ġpack et", - "Ġpa cket", - "Ġpac ket", - "Ġ cer", - "Ġc er", - "Ġce r", - "Ġ enumer", - "Ġe numer", - "Ġen umer", - "Ġenum er", - "Ġ pars", - "Ġp ars", - "Ġpar s", - "Ġpa rs", - "form ed", - "for med", - "forme d", - "Ġ occup", - "Ġocc up", - "Ġoc cup", - "t re", - "tr e", - "Ġ exercise", - "Ġex ercise", - "Ġexerc ise", - "D ay", - "Da y", - "_ sum", - "_s um", - "_su m", - "Ġ asking", - "Ġas king", - "Ġask ing", - "a ption", - "ap tion", - "apt ion", - "Ġ orders", - "Ġor ders", - "Ġorder s", - "Ġord ers", - "Ġs pending", - "Ġsp ending", - "Ġspend ing", - "Ġ ERR", - "ĠE RR", - "ĠER R", - ". Dis", - ".D is", - ".Di s", - "Ġ Util", - "ĠU til", - "ĠUt il", - "âĢľ I", - "\\ '", - "? )", - "/ >Ċ", - "/> Ċ", - "Ġe mot", - "Ġem ot", - "Ġemo t", - "Ġin fluence", - "Ġinflu ence", - "Ġ Africa", - "ĠA frica", - "ĠAfr ica", - "ĠAf rica", - "at ters", - "att ers", - "atter s", - "atte rs", - "Ù ħ", - ". session", - ".s ession", - ".sess ion", - "Ġ chief", - "Ġch ief", - "Ġchi ef", - "ĉ ĉĉĉĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉĉĉĉ", - "ĉĉĉĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉĉ ĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉĉĉĉ ĉ", - "Ġ tom", - "Ġt om", - "Ġto m", - "cl uded", - "clude d", - "clud ed", - "s erial", - "se rial", - "ser ial", - "_ handler", - "_h andler", - "_handle r", - "_hand ler", - ". Type", - ".T ype", - "a ped", - "ap ed", - "ape d", - "Ġp olicies", - "Ġpol icies", - "Ġpolic ies", - "- ex", - "-e x", - "- tr", - "-t r", - "bl ank", - "bla nk", - "m erce", - "mer ce", - "merc e", - "Ġ coverage", - "Ġco verage", - "Ġcover age", - "Ġ rc", - "Ġr c", - "_ matrix", - "_m atrix", - "_mat rix", - "_ box", - "_b ox", - "_bo x", - "Ġ charges", - "Ġch arges", - "Ġchar ges", - "Ġcharg es", - "Ġcharge s", - "Ġ Boston", - "ĠB oston", - "ĠBo ston", - "ĠBos ton", - "P e", - "Ġcirc um", - "Ġcir cum", - "Ġ filled", - "Ġf illed", - "Ġfil led", - "Ġfill ed", - "Ġfille d", - "1 48", - "14 8", - "Ġ north", - "Ġn orth", - "Ġnor th", - "icture Box", - "ĉ res", - "ĉr es", - "ĉre s", - "è ®", - "Ġ termin", - "Ġter min", - "Ġterm in", - "Ġ [âĢ¦", - "Ġ[ âĢ¦", - "I RECT", - "IR ECT", - "IRE CT", - "Ġ ber", - "Ġb er", - "Ġbe r", - "Ġ\" ../../", - "Ġ\"../ ../", - "Ġ\".. /../", - "r etch", - "ret ch", - ". code", - ".c ode", - ".co de", - ".cod e", - "_ col", - "_c ol", - "_co l", - "Ġ Government", - "ĠG overnment", - "ĠGovern ment", - "Ġ argv", - "Ġar gv", - "Ġarg v", - "Ġ Lord", - "ĠL ord", - "ĠLo rd", - "ĠLor d", - "a si", - "as i", - "E xec", - "Ex ec", - "ĉ let", - "ĉl et", - "vert is", - "Ġ discussion", - "Ġdisc ussion", - "Ġdiscuss ion", - "en ance", - "ena nce", - "enan ce", - "ou tube", - "out ube", - "outu be", - "type of", - "typ eof", - "Ġs erved", - "Ġser ved", - "Ġserv ed", - "Ġserve d", - "Ġ Put", - "ĠP ut", - "ĠPu t", - "ĉ x", - "Ġ sweet", - "Ġs weet", - "Ġswe et", - "B efore", - "Be fore", - "ate gy", - "ateg y", - ". of", - ".o f", - "Ġ Material", - "ĠM aterial", - "ĠMat erial", - "ĠMate rial", - "ĠMater ial", - "S ort", - "So rt", - "O NT", - "ON T", - "ig ital", - "igit al", - "igi tal", - "W hy", - "Wh y", - "Ġs ust", - "Ġsu st", - "Ġsus t", - "Ġ ç", - "a bet", - "ab et", - "abe t", - "Ġ segment", - "Ġs egment", - "Ġse gment", - "Ġseg ment", - "Ġ [],Ċ", - "Ġ[ ],Ċ", - "Ġ[] ,Ċ", - "Ġ[], Ċ", - "Ġ Muslim", - "ĠM uslim", - "ĠMus lim", - "Ġ findViewById", - "Ġfind ViewById", - "c ut", - "cu t", - "_ TEXT", - "_T EXT", - "_TE XT", - "_TEX T", - "Ġ Mary", - "ĠM ary", - "ĠMar y", - "ĠMa ry", - "Ġl oved", - "Ġlo ved", - "Ġlove d", - "Ġlov ed", - "Ġ lie", - "Ġl ie", - "Ġli e", - "Ġ JO", - "ĠJ O", - "Ġ isset", - "Ġis set", - "Ġiss et", - "m onth", - "mon th", - "mo nth", - "mont h", - "Ġ prime", - "Ġpr ime", - "Ġprim e", - "Ġpri me", - "t i", - "Ġ Carol", - "ĠCar ol", - "ĠCa rol", - "U se", - "Us e", - "1 46", - "14 6", - "Ġ Pop", - "ĠP op", - "ĠPo p", - "Ġ Save", - "ĠS ave", - "ĠSa ve", - "ĠSav e", - "Int erval", - "Inter val", - "ex ecute", - "exec ute", - "d y", - "Ġ Iran", - "ĠI ran", - "ĠIr an", - "_ cont", - "_c ont", - "_con t", - "_co nt", - "ĉ T", - "Ġ phase", - "Ġph ase", - "Ġpha se", - "check box", - "we ek", - "Ġ hide", - "Ġh ide", - "Ġhi de", - "Ġhid e", - "Ġ til", - "Ġt il", - "Ġti l", - "Ġ ju", - "Ġj u", - "C ustom", - "b urg", - "bur g", - "bu rg", - "/ M", - "T ON", - "TO N", - "Ġ quant", - "Ġqu ant", - "Ġq uant", - "Ġqua nt", - "Ġquan t", - "Ġ rub", - "Ġr ub", - "Ġru b", - "ix els", - "ixel s", - "ixe ls", - "Ġ installed", - "Ġinst alled", - "Ġinstall ed", - "Ġinstal led", - "Ġ dump", - "Ġd ump", - "Ġdu mp", - "Ġdum p", - "Ġproper ly", - "( List", - "(L ist", - "Ġdec ide", - "Ġdecid e", - "ap ply", - "app ly", - "appl y", - "H as", - "Ha s", - "Ġ keeping", - "Ġke eping", - "Ġkeep ing", - "Ġcit izens", - "Ġcitiz ens", - "Ġcitizen s", - "Ġ joint", - "Ġj oint", - "Ġjoin t", - "Ġjo int", - "p ool", - "po ol", - "S ocket", - "So cket", - "Sock et", - "_ op", - "_o p", - "Ġ weapon", - "Ġwe apon", - "Ġweap on", - "g nore", - "gn ore", - "Ġ Exec", - "ĠE xec", - "ĠEx ec", - "ot ten", - "ott en", - "otte n", - "Ġ MS", - "ĠM S", - "Ġ (-", - "Ġ( -", - "Ġ Review", - "ĠR eview", - "ĠRe view", - "ĠRev iew", - "Ġ examples", - "Ġex amples", - "Ġexample s", - "Ġexam ples", - "Ġ tight", - "Ġt ight", - "Ġti ght", - "! (", - "D P", - "Ġ MessageBox", - "ĠMessage Box", - "Ġphot ograph", - "Ġphoto graph", - "1 64", - "16 4", - "U RI", - "UR I", - "é t", - "l ow", - "lo w", - "Ġ Grand", - "ĠG rand", - "ĠGr and", - "ĠGran d", - "ĠGra nd", - ".p ersistence", - ".persist ence", - "Ġmain tain", - "Ġmaint ain", - "Ġ nums", - "Ġn ums", - "Ġnum s", - "Ġnu ms", - "Ġ zip", - "Ġz ip", - "Ġzi p", - "i als", - "ial s", - "ia ls", - "Ġ Gets", - "ĠG ets", - "ĠGet s", - "ĠGe ts", - "p eg", - "pe g", - "Ġ Buffer", - "ĠB uffer", - "ĠBuff er", - "ĠBu ffer", - "ĠBuf fer", - "~~ ~~", - "ra structure", - "Ġ PL", - "ĠP L", - "u en", - "ue n", - "ob by", - "obb y", - "size of", - "siz eof", - "Ġ pic", - "Ġp ic", - "Ġpi c", - "Ġ seed", - "Ġs eed", - "Ġse ed", - "Ġsee d", - "Ġex perienced", - "Ġexperience d", - "Ġexperi enced", - "Ġ odd", - "Ġo dd", - "Ġod d", - "Ġ kick", - "Ġk ick", - "Ġki ck", - "Ġ procedure", - "Ġpro cedure", - "Ġproced ure", - "av igator", - "avig ator", - "- on", - "-o n", - ", j", - "Ġ Although", - "ĠAl though", - "Ġ userId", - "Ġuser Id", - "ac cept", - "acc ept", - "B lue", - "Bl ue", - "I Color", - "IC olor", - "l ayer", - "la yer", - "lay er", - "a vailable", - "av ailable", - "avail able", - "Ġ ends", - "Ġe nds", - "Ġen ds", - "Ġend s", - ". table", - ".t able", - ".tab le", - ".ta ble", - "Ġ dataset", - "Ġd ataset", - "Ġdata set", - "Ġdat aset", - "Ġdatas et", - "b us", - "bu s", - "Ġ explain", - "Ġex plain", - "Ġexp lain", - "Ġexpl ain", - "( pro", - "(p ro", - "(pr o", - "ĠCommit tee", - "Ġn oted", - "Ġnot ed", - "Ġno ted", - "Ġnote d", - "] :Ċ", - "]: Ċ", - "D im", - "Di m", - "st dio", - "std io", - "1 54", - "15 4", - ". \",Ċ", - ".\" ,Ċ", - ".\", Ċ", - "_ source", - "_s ource", - "1 81", - "18 1", - "Ġ Week", - "ĠWe ek", - "Ġ Edge", - "ĠE dge", - "ĠEd ge", - "Ġoper ating", - "Ġopera ting", - "Ġ este", - "Ġe ste", - "Ġes te", - "Ġest e", - "i pl", - "ip l", - "3 30", - "33 0", - "ag ination", - "agi nation", - "agina tion", - "Ġpro ceed", - "Ġproc eed", - "Ġ animation", - "Ġan imation", - "Ġanim ation", - ". Models", - ".Model s", - ".Mod els", - ".Mode ls", - "Ġ Watch", - "ĠW atch", - "ĠWat ch", - "i at", - "ia t", - "Ġop pon", - "Ġopp on", - "/ A", - "Re port", - "Rep ort", - "Repo rt", - "Ġ sounds", - "Ġs ounds", - "Ġso unds", - "Ġsound s", - "Ġsou nds", - "_ buf", - "_b uf", - "IE LD", - "IEL D", - "Ġb und", - "Ġbu nd", - "Ġbun d", - "ĉ get", - "ĉg et", - ". pr", - ".p r", - "( tmp", - "(t mp", - "(tm p", - "Ġ kid", - "Ġk id", - "Ġki d", - "> ĊĊĊ", - ">Ċ ĊĊ", - ">ĊĊ Ċ", - "Ġ yang", - "Ġy ang", - "Ġya ng", - "Ġyan g", - "Not Found", - "Ñ Ĩ", - "m ath", - "ma th", - "mat h", - "@ gmail", - "@g mail", - "Ġ LIMIT", - "ĠL IMIT", - "ĠLI MIT", - "red ients", - "redient s", - "redi ents", - "Ġ vent", - "Ġv ent", - "Ġve nt", - "Ġven t", - "av igate", - "avig ate", - "avi gate", - "L ook", - "Lo ok", - "Ġ religious", - "Ġrel igious", - "Ġrelig ious", - "Ġ rand", - "Ġr and", - "Ġran d", - "Ġra nd", - "r io", - "ri o", - "( GL", - "(G L", - "_ ip", - "_i p", - "u an", - "ua n", - "ici ency", - "icie ncy", - "Ġ Change", - "ĠCh ange", - "ĠCha nge", - "ĠChan ge", - "ĠChang e", - "> čĊčĊ", - ">čĊ čĊ", - "Ġ Entity", - "ĠE ntity", - "ĠEnt ity", - "Ġren contre", - "Ġrencont re", - "Ġrencontr e", - "Ġ Ret", - "ĠR et", - "ĠRe t", - "p lan", - "pl an", - "pla n", - "é n", - "BO OL", - "u ries", - "ur ies", - "uri es", - "t rain", - "tr ain", - "tra in", - "Def inition", - "= ===========", - "== ==========", - "==== ========", - "======== ====", - "=== =========", - "=========== =", - "========= ===", - "========== ==", - "====== ======", - "===== =======", - "======= =====", - "z z", - "4 50", - "45 0", - "An imation", - "Anim ation", - "Ġ OK", - "ĠO K", - "_ menu", - "_m enu", - "_me nu", - ". bl", - ".b l", - "_ score", - "_s core", - "_sc ore", - "Ġ acad", - "Ġa cad", - "Ġac ad", - "( System", - "(S ystem", - "Ġ refresh", - "Ġre fresh", - "Ġref resh", - "Ġrefr esh", - "' =>$", - "'=> $", - "'= >$", - ". Graphics", - ".G raphics", - ".Graph ics", - "am ento", - "ament o", - "amen to", - "p id", - "pi d", - "t c", - "Ġ tips", - "Ġt ips", - "Ġti ps", - "Ġtip s", - "Ġ homes", - "Ġh omes", - "Ġhome s", - "Ġhom es", - "Ġho mes", - "Ġ fuel", - "Ġf uel", - "Ġfu el", - "Ġfue l", - "â ĸ", - "_ helper", - "_h elper", - "_help er", - "Ġ ĠčĊ", - "ĠĠ čĊ", - "Ġ Room", - "ĠR oom", - "ĠRo om", - "ĠRoo m", - ". Close", - ".C lose", - ".Cl ose", - "_ attr", - "_at tr", - "_att r", - "Ġ Mount", - "ĠM ount", - "ĠMo unt", - "ĠMou nt", - "Ġ Ev", - "ĠE v", - "ar ser", - "ars er", - "arse r", - "_ top", - "_t op", - "_to p", - "e ah", - "ea h", - "Ġ Delete", - "ĠDe lete", - "ĠDel ete", - "ãĢ į", - "u ke", - "uk e", - "Ġ usage", - "Ġu sage", - "Ġus age", - "Ġusa ge", - "a ria", - "ar ia", - "ari a", - "_ dev", - "_d ev", - "_de v", - "Ġ texture", - "Ġtext ure", - "Ġtex ture", - "Ġtextu re", - "Ġ conversation", - "Ġcon versation", - "Ġconvers ation", - "e per", - "ep er", - "B ean", - "Be an", - "d one", - "do ne", - "don e", - "non atomic", - "Ġ Second", - "ĠSe cond", - "ĠSec ond", - "Ġsh ooting", - "Ġshoot ing", - "Ġsho oting", - "_ pre", - "_p re", - "_pr e", - "Com ponents", - "Component s", - "Comp onents", - "Ġ ]ĊĊ", - "Ġ] ĊĊ", - "Ġ]Ċ Ċ", - "_ _,", - "__ ,", - "st itution", - "stit ution", - ". Char", - ".C har", - ".Ch ar", - "> ();ĊĊ", - ">( );ĊĊ", - ">();Ċ Ċ", - ">() ;ĊĊ", - ">(); ĊĊ", - "Ġpres ented", - "Ġpresent ed", - "Ġpresente d", - "Ġ wa", - "Ġw a", - "o ker", - "ok er", - "oke r", - "- ĊĊ", - "-Ċ Ċ", - "i ner", - "in er", - "ine r", - "Ġbe coming", - "Ġbec oming", - "Ġ incident", - "Ġinc ident", - "Ġincid ent", - "A tt", - "At t", - "1 62", - "16 2", - "Ġreve aled", - "Ġreveal ed", - "f orc", - "fo rc", - "for c", - "Ġ boot", - "Ġb oot", - "Ġbo ot", - "Ġboo t", - ". page", - ".p age", - ".pa ge", - ".pag e", - "En umerator", - "Enumer ator", - "Enum erator", - "1 65", - "16 5", - "_ ->", - "_- >", - "Ph oto", - "Phot o", - "Ġ spring", - "Ġs pring", - "Ġsp ring", - "Ġspr ing", - ". \",", - ".\" ,", - "Ġ Dictionary", - "ĠD ictionary", - "B JECT", - "BJ ECT", - "Ġ locations", - "Ġl ocations", - "Ġloc ations", - "Ġlocation s", - "Ġ samples", - "Ġs amples", - "Ġsample s", - "Ġsam ples", - "Ġsamp les", - "Input Stream", - "Ġ Brown", - "ĠB rown", - "ĠBr own", - "ĠBro wn", - "ĠBrow n", - "Ġ stats", - "Ġst ats", - "Ġstat s", - "Ġsta ts", - "q uality", - "qu ality", - "qual ity", - "Ñ ħ", - "- dis", - "-d is", - "-di s", - "Ġhelp ing", - "Ġhel ping", - "Ġ ped", - "Ġp ed", - "Ġpe d", - "2 24", - "22 4", - "( se", - "(s e", - "Ġ Who", - "ĠW ho", - "ĠWh o", - "a lian", - "al ian", - "ali an", - "alia n", - "in ternal", - "int ernal", - "inter nal", - "intern al", - "Ġ ft", - "Ġf t", - "> ().", - ">( ).", - ">() .", - "- >{", - "-> {", - "Ġ mine", - "Ġm ine", - "Ġmin e", - "Ġmi ne", - "Ġ sector", - "Ġs ector", - "Ġse ctor", - "Ġsec tor", - "Ġsect or", - "Ġ gro", - "Ġg ro", - "Ġgr o", - "Ġopport unities", - "Ġopportun ities", - "Ġ ü", - "Ġà ¼", - "Ġ mp", - "Ġm p", - "Ġalleg ed", - "Ġalle ged", - "Ġallege d", - "Ġdoub t", - "Ġdou bt", - "M ouse", - "Mo use", - "A bout", - "Ab out", - "_ part", - "_p art", - "_par t", - "_pa rt", - "Ġ chair", - "Ġc hair", - "Ġch air", - "Ġcha ir", - "Ġchai r", - "Ġ stopped", - "Ġst opped", - "Ġstop ped", - "Ġsto pped", - "1 61", - "16 1", - "l oop", - "lo op", - "loo p", - "ent ities", - "enti ties", - "Ġ apps", - "Ġa pps", - "Ġapp s", - "Ġap ps", - "ans ion", - "ansi on", - "Ġ mental", - "Ġm ental", - "Ġmen tal", - "Ġment al", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "F R", - "Ġdef end", - "Ġdefe nd", - "c are", - "ca re", - "car e", - "Ġ ideal", - "Ġi deal", - "Ġide al", - "Ġidea l", - "/ api", - "/a pi", - "/ap i", - "ur face", - "urf ace", - "0 11", - "01 1", - "Ġ ele", - "Ġe le", - "Ġel e", - "u lator", - "ul ator", - "ula tor", - "Ġ Rights", - "ĠR ights", - "ĠRight s", - "angu ages", - "anguage s", - "Ġf unds", - "Ġfun ds", - "Ġfund s", - "Ġfu nds", - "Ġ adapt", - "Ġad apt", - "Ġada pt", - "Ġadap t", - "At tributes", - "Attribute s", - "Attrib utes", - "Ġ deploy", - "Ġde ploy", - "Ġdep loy", - "o pts", - "op ts", - "opt s", - "Ġ validation", - "Ġvalid ation", - "Ġvalida tion", - "Ġconcern s", - "u ce", - "uc e", - ". num", - ".n um", - "ul ture", - "ult ure", - "ultur e", - "i la", - "il a", - "Ġ cup", - "Ġc up", - "Ġcu p", - "Ġ pure", - "Ġp ure", - "Ġpur e", - "Ġpu re", - ". Fore", - ".F ore", - ".For e", - "1 83", - "18 3", - "Ġ HashMap", - "ĠHash Map", - ". valueOf", - ".value Of", - "a sm", - "as m", - "M O", - "Ġ cs", - "Ġc s", - "Ġ stores", - "Ġst ores", - "Ġstore s", - "Ġstor es", - "Ġsto res", - "Ġ ************************************************************************", - "Ġ**************************************************************** ********", - "Ġ******************************** ****************************************", - "Ġ******** ****************************************************************", - "Ġ**************** ********************************************************", - "Ġ************************ ************************************************", - "Ġ**************************************** ********************************", - "Ġ******************************************************** ****************", - "Ġ************************************************ ************************", - "Ġ communication", - "Ġcomm unication", - "Ġcommunic ation", - "Ġcommun ication", - "m em", - "me m", - ". EventHandler", - ".Event Handler", - ". Status", - ".S tatus", - ".St atus", - ".Stat us", - "_ right", - "_r ight", - ".set On", - "S heet", - "She et", - "Ġ identify", - "Ġident ify", - "ener ated", - "ene rated", - "enerate d", - "ord ered", - "order ed", - "orde red", - "Ġ \"[", - "Ġ\" [", - "Ġs we", - "Ġsw e", - "Con dition", - "Cond ition", - "Ġ According", - "ĠA ccording", - "ĠAcc ording", - "ĠAccord ing", - "Ġ prepare", - "Ġpre pare", - "Ġprepar e", - "Ġprep are", - "Ġ rob", - "Ġr ob", - "Ġro b", - "P ool", - "Po ol", - "Ġ sport", - "Ġs port", - "Ġsp ort", - "Ġspo rt", - "Ġspor t", - "r v", - "Ġ Router", - "ĠR outer", - "ĠRoute r", - "ĠRo uter", - "ĠRou ter", - "ĠRout er", - "Ġ alternative", - "Ġaltern ative", - "Ġalter native", - "( []", - "([ ]", - "Ġ Chicago", - "ĠCh icago", - "ĠChic ago", - "i pher", - "ip her", - "iph er", - "is che", - "isc he", - "isch e", - "Ġ Director", - "ĠD irector", - "ĠDirect or", - "ĠDir ector", - "ĠDire ctor", - "k l", - "Ġ Wil", - "ĠW il", - "ĠWi l", - "ke ys", - "key s", - "Ġ mysql", - "Ġm ysql", - "Ġmy sql", - "Ġmys ql", - "Ġ welcome", - "Ġw elcome", - "Ġwel come", - "k ing", - "ki ng", - "kin g", - "Ġ Manager", - "ĠM anager", - "ĠMan ager", - "ĠManage r", - "ĠMana ger", - "Ġ caught", - "Ġca ught", - ") }Ċ", - ")} Ċ", - "S core", - "Sc ore", - "_ PR", - "_P R", - "Ġ survey", - "Ġs urvey", - "Ġsur vey", - "Ġsurv ey", - "Ġsurve y", - "h ab", - "ha b", - "He aders", - "Header s", - "Head ers", - "A DER", - "AD ER", - "ADE R", - "Ġ decor", - "Ġde cor", - "Ġdec or", - "Ġdeco r", - "Ġturn s", - "Ġtur ns", - "Ġ radius", - "Ġr adius", - "Ġrad ius", - "Ġradi us", - "er rupt", - "err upt", - "C or", - "Co r", - "Ġ mel", - "Ġm el", - "Ġme l", - "Ġ intr", - "Ġin tr", - "Ġint r", - "( q", - "Ġ AC", - "ĠA C", - "a mos", - "am os", - "amo s", - "M AX", - "MA X", - "Ġ Grid", - "ĠG rid", - "ĠGr id", - "ĠGri d", - "Ġ Jesus", - "ĠJ esus", - "ĠJes us", - "ĠJe sus", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - ". DE", - ".D E", - "Ġ ts", - "Ġt s", - "Ġ linked", - "Ġlink ed", - "Ġlin ked", - "f ree", - "fr ee", - "fre e", - "Ġ Qt", - "ĠQ t", - "Ġ /**čĊ", - "Ġ/ **čĊ", - "Ġ/* *čĊ", - "Ġ/** čĊ", - "Ġf aster", - "Ġfa ster", - "Ġfast er", - "Ġfas ter", - "c tr", - "ct r", - "_ J", - "D T", - ". Check", - ".C heck", - ".Ch eck", - "Ġ combination", - "Ġcomb ination", - "Ġcombin ation", - "Ġint ended", - "Ġintend ed", - "- the", - "-t he", - "-th e", - "- type", - "-t ype", - "1 82", - "18 2", - "e ctors", - "ect ors", - "ec tors", - "ector s", - "a mi", - "am i", - "u ting", - "ut ing", - "uti ng", - "utin g", - "Ġ uma", - "Ġu ma", - "Ġum a", - "X ML", - "XM L", - "U CT", - "UC T", - "A p", - "Ġ Random", - "ĠR andom", - "ĠRand om", - "ĠRan dom", - "Ġ ran", - "Ġr an", - "Ġra n", - ". sort", - ".s ort", - ".so rt", - "Ġ sorted", - "Ġs orted", - "Ġsort ed", - "Ġsor ted", - "Ġsorte d", - ". Un", - ".U n", - "4 01", - "40 1", - "_ PER", - "_P ER", - "_PE R", - "it ory", - "itor y", - "ito ry", - "Ġ priority", - "Ġp riority", - "Ġprior ity", - "Ġpriorit y", - "Ġ Gal", - "ĠG al", - "ĠGa l", - "Ġ Old", - "ĠO ld", - "ĠOl d", - "h ot", - "ho t", - "Ġ Display", - "ĠD isplay", - "ĠDis play", - "ĠDisp lay", - "( sub", - "(s ub", - "_ TH", - "_T H", - "_ Y", - "Ġ Care", - "ĠC are", - "ĠCar e", - "ĠCa re", - "lo ading", - "load ing", - "K ind", - "Kin d", - "Ki nd", - "_ handle", - "_h andle", - "_hand le", - ", ,", - "r ase", - "ra se", - "ras e", - "_ replace", - "_re place", - "_rep lace", - ". addEventListener", - ".add EventListener", - "Ġ RT", - "ĠR T", - "1 72", - "17 2", - "Ġ entered", - "Ġen tered", - "Ġent ered", - "Ġenter ed", - "g ers", - "ge rs", - "ger s", - "Ġ ich", - "Ġi ch", - "Ġic h", - "( start", - "(st art", - "2 05", - "20 5", - "/ app", - "/a pp", - "/ap p", - "Ġbr other", - "Ġbro ther", - "Ġbroth er", - "M emory", - "Mem ory", - "Memo ry", - "Out let", - "Ġ utf", - "Ġu tf", - "Ġut f", - "p rec", - "pr ec", - "pre c", - "Ġ navigation", - "Ġn avigation", - "Ġnav igation", - "Ġnavig ation", - "O RK", - "OR K", - "Ġ dst", - "Ġd st", - "Ġds t", - "D etail", - "De tail", - "Det ail", - "Ġaud ience", - "Ġaudi ence", - "Ġ dur", - "Ġd ur", - "Ġdu r", - "Ġ cluster", - "Ġcl uster", - "un ched", - "unc hed", - "unch ed", - "Ġ ],", - "Ġ] ,", - "Ġcomfort able", - ". values", - ".value s", - ".val ues", - "Ġ Total", - "ĠT otal", - "ĠTo tal", - "ĠTot al", - "Ġ snap", - "Ġs nap", - "Ġsn ap", - "Ġsna p", - "Ġstand ards", - "Ġstandard s", - "Ġper formed", - "Ġperform ed", - "Ġperfor med", - "h and", - "ha nd", - "han d", - "( \"@", - "(\" @", - "å Ń", - "Ġ phil", - "Ġp hil", - "Ġph il", - "Ġphi l", - "i br", - "ib r", - "t rim", - "tr im", - "tri m", - "Ġ forget", - "Ġf orget", - "Ġfor get", - "Ġforg et", - "Ġforge t", - "1 57", - "15 7", - "Ġ doctor", - "Ġdo ctor", - "Ġdoc tor", - ". TextBox", - ".Text Box", - "3 77", - "37 7", - "i cons", - "ic ons", - "icon s", - "ico ns", - ", s", - "Ġ Op", - "ĠO p", - "S m", - "S top", - "St op", - "ĉ List", - "ĉL ist", - "ĉ u", - "Com ment", - "Comm ent", - "_ VERSION", - "_V ERSION", - "_VER SION", - ".X tra", - "P erson", - "Per son", - "Pers on", - "r b", - "L OB", - "LO B", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "Ġ Central", - "ĠC entral", - "ĠCent ral", - "2 70", - "27 0", - "I CK", - "IC K", - "r aq", - "ra q", - "Ġp utting", - "Ġput ting", - "Ġputt ing", - "Ġ md", - "Ġm d", - "Ġ Love", - "ĠL ove", - "ĠLo ve", - "ĠLov e", - "P rogram", - "Pro gram", - "Pr ogram", - "Prog ram", - "B order", - "o or", - "oo r", - "Ġall owing", - "Ġallow ing", - "Ġallo wing", - "a fter", - "af ter", - "aft er", - "Ġ entries", - "Ġen tries", - "Ġent ries", - "Ġentr ies", - "Ġ Maybe", - "ĠM aybe", - "ĠMay be", - "] ).", - "]) .", - "Ġ Short", - "ĠS hort", - "ĠSh ort", - "ĠSho rt", - ") \\", - ". now", - ".n ow", - ".no w", - "f riend", - "Ġ prefer", - "Ġp refer", - "Ġpre fer", - "Ġpref er", - "Ġ GPIO", - "ĠG PIO", - "ĠGP IO", - "ĠGPI O", - "o sis", - "os is", - "osi s", - "Ġ GameObject", - "ĠGame Object", - "Ġ skip", - "Ġs kip", - "Ġsk ip", - "Ġski p", - "Ġ competition", - "Ġcom petition", - "Ġcompet ition", - "Ġcompetit ion", - "_ match", - "_m atch", - "_mat ch", - "l ications", - "lic ations", - "lication s", - "_ CONT", - "_C ONT", - "_CON T", - "_CO NT", - ". groupBox", - ".group Box", - "Ġ als", - "Ġa ls", - "Ġal s", - "6 66", - "66 6", - "\" We", - "\"W e", - "_ eq", - "_e q", - "l an", - "la n", - "_ search", - "_s earch", - "_se arch", - "Ġ Music", - "ĠM usic", - "ĠMus ic", - "ĠMu sic", - "a sis", - "as is", - "asi s", - "Ġ bind", - "Ġb ind", - "Ġbi nd", - "Ġbin d", - "ĠIs land", - "ĠIsl and", - "r um", - "ru m", - "( E", - "Ġ seat", - "Ġs eat", - "Ġse at", - "Ġsea t", - "V ideo", - "Vi deo", - "Ġ ack", - "Ġa ck", - "Ġac k", - "re ek", - "ree k", - "={ ()", - "={( )", - "Ġ rating", - "Ġr ating", - "Ġrat ing", - "Ġra ting", - "Ġ restaurant", - "Ġrest aurant", - "Ġrestaur ant", - "Ġrestau rant", - "4 56", - "45 6", - "D EX", - "DE X", - "( buf", - "(b uf", - "p ping", - "pp ing", - "u ality", - "ual ity", - "uali ty", - "Ġ league", - "Ġle ague", - "1 76", - "17 6", - "Ġ focused", - "Ġf ocused", - "Ġfocus ed", - "Ġfoc used", - "a pon", - "ap on", - "apo n", - "$ data", - "$d ata", - "CL UD", - "CLU D", - "CLUD ING", - "Ġ absolute", - "Ġa bsolute", - "Ġabs olute", - "Ġabsolut e", - "Ġabsol ute", - "( query", - "(qu ery", - "Ġt ells", - "Ġtell s", - "Ġtel ls", - "A ng", - "An g", - "Ġcomm unities", - "Ġcommun ities", - "Ġh onest", - "Ġhon est", - "Ġho nest", - "Ġhone st", - "o king", - "ok ing", - "oki ng", - "okin g", - "Ġa part", - "Ġap art", - "Ġapar t", - "Ġapa rt", - "ar ity", - "ari ty", - "/ $", - "_ module", - "_m odule", - "_mod ule", - "Ġ Enc", - "ĠE nc", - "ĠEn c", - ". an", - ".a n", - ". Config", - ".Con fig", - "C re", - "Cr e", - "Ġsh ock", - "Ġsho ck", - "ĠA rab", - "ĠAr ab", - "ĠAra b", - "I ENT", - "IE NT", - "/ re", - "/r e", - "Ġre trie", - "Ġret rie", - "Ġretr ie", - "yc ler", - "ycle r", - "ycl er", - "i sa", - "is a", - "Ġ Organ", - "ĠO rgan", - "ĠOr gan", - "ĠOrg an", - ". graph", - ".g raph", - ".gr aph", - "Ġ í", - "ĠB AS", - "ĠBA S", - "E num", - "En um", - "Ġ possibly", - "Ġposs ibly", - "ÑĢ аÐ", - "ÑĢа Ð", - "Ġ Japanese", - "ĠJ apanese", - "ĠJapan ese", - "Ġ craft", - "Ġc raft", - "Ġcr aft", - "Ġcra ft", - "Ġ Place", - "ĠP lace", - "ĠPl ace", - "ĠPla ce", - "Ġt alent", - "Ġtal ent", - "Ġtale nt", - "Ġf unding", - "Ġfun ding", - "Ġfund ing", - "Ġ confirmed", - "Ġconf irmed", - "Ġconfirm ed", - "Ġ cycle", - "Ġc ycle", - "Ġcy cle", - "Ġcycl e", - "Ġcyc le", - "/ x", - "G E", - "Ġh earing", - "Ġhe aring", - "Ġhear ing", - "Ġ plants", - "Ġpl ants", - "Ġplan ts", - "Ġplant s", - "Ġpla nts", - "Ġ mouth", - "Ġm outh", - "Ġmo uth", - "Ġmou th", - "p ages", - "page s", - "pa ges", - "pag es", - "o ria", - "or ia", - "ori a", - "Ġ Remove", - "ĠRe move", - "ĠRem ove", - "_ total", - "_t otal", - "_to tal", - "_tot al", - "Ġ od", - "Ġo d", - "oll apse", - "d oor", - "do or", - "Ġb ought", - "Ġbo ught", - "Ġbou ght", - "Ġ addr", - "Ġadd r", - "Ġad dr", - "AR CH", - "ARC H", - "_ dim", - "_d im", - "_di m", - "d den", - "dd en", - "dde n", - "Ġdec ades", - "Ġdecade s", - "Ġdecad es", - "RE QUEST", - "REQ UEST", - "Ġ versions", - "Ġv ersions", - "Ġversion s", - "Ġvers ions", - "f ire", - "fi re", - "fir e", - "0 06", - "00 6", - "Ġ moves", - "Ġm oves", - "Ġmov es", - "Ġmove s", - "Ġmo ves", - "f b", - "Ġ coffee", - "Ġc offee", - "Ġco ffee", - "Ġcoff ee", - "Ġcof fee", - ". connect", - ".con nect", - ".conn ect", - "Ġ Row", - "ĠR ow", - "ĠRo w", - "Ġ schema", - "Ġs chema", - "Ġsch ema", - "Ġschem a", - "Ġsche ma", - "S cope", - "Sc ope", - "- Type", - "-T ype", - "Ġf ighting", - "Ġfight ing", - "Ġre tail", - "Ġr etail", - "Ġret ail", - "Ġ modified", - "Ġmod ified", - "T F", - "F iles", - "File s", - "Fi les", - "Fil es", - "n ie", - "ni e", - "_ command", - "_com mand", - "_comm and", - "s tone", - "st one", - "ston e", - "sto ne", - "Ġ ÑĤ", - "ĠÑ Ĥ", - "_ thread", - "_t hread", - "_th read", - "_thr ead", - "Ġ bond", - "Ġb ond", - "Ġbo nd", - "Ġbon d", - "Ġ Development", - "ĠDe velopment", - "ĠDev elopment", - "ĠDevelop ment", - "Ġ pt", - "Ġp t", - "F ORM", - "FO RM", - "FOR M", - "p let", - "pl et", - "ple t", - "Ġ identified", - "Ġident ified", - "c pp", - "cp p", - "2 06", - "20 6", - "2 25", - "22 5", - "Ġ coding", - "Ġc oding", - "Ġco ding", - "Ġcod ing", - "o ked", - "ok ed", - "oke d", - "Ġ Master", - "ĠM aster", - "ĠMa ster", - "ĠMas ter", - "ĠMast er", - "ID TH", - "Ġres idents", - "Ġresident s", - "Ġresid ents", - "Ġreside nts", - "r edit", - "re dit", - "red it", - "redi t", - "Ġ Photo", - "ĠPh oto", - "ĠPhot o", - "= -", - "u nte", - "un te", - "unt e", - "at eur", - "ate ur", - "1 59", - "15 9", - "_ STATE", - "_ST ATE", - "_STAT E", - "_STA TE", - "Ġ Sing", - "ĠS ing", - "ĠSi ng", - "ĠSin g", - "Ġ sheet", - "Ġs heet", - "Ġshe et", - ". val", - ".v al", - ".va l", - "or se", - "ors e", - "Ġh ers", - "Ġhe rs", - "Ġher s", - "Ġd etermined", - "Ġdetermin ed", - "Ġdetermine d", - "Ġdeterm ined", - "Com mon", - "Comm on", - "Ġ wed", - "Ġw ed", - "Ġwe d", - "_ queue", - "_q ueue", - "_que ue", - "P H", - "Ġ Atl", - "ĠA tl", - "ĠAt l", - "c red", - "cre d", - "cr ed", - "/ LICENSE", - "/L ICENSE", - "Ġ mes", - "Ġm es", - "Ġme s", - "Ġ advanced", - "Ġad vanced", - "Ġadv anced", - "Ġadvance d", - ". java", - ".j ava", - ".jav a", - ". Sh", - ".S h", - "G o", - "k ill", - "ki ll", - "kil l", - "f p", - "_ settings", - "_s ettings", - "_set tings", - "_setting s", - "Ġ pal", - "Ġp al", - "Ġpa l", - "Ġ truck", - "Ġtr uck", - "Ġ combined", - "Ġcomb ined", - "Ġcombine d", - "Ġcombin ed", - "Ġ \"${", - "Ġ\" ${", - "Ġ\"$ {", - "ĠC orpor", - "ĠCor por", - "ĠCorp or", - "Ġ joined", - "Ġj oined", - "Ġjoin ed", - "Ġjo ined", - "Ġ Jose", - "ĠJ ose", - "ĠJo se", - "ĠJos e", - "ĠC up", - "ĠCu p", - "u ns", - "un s", - "est ival", - "esti val", - "le vision", - "lev ision", - "Ġ broken", - "Ġb roken", - "Ġbr oken", - "Ġbro ken", - "Ġbroke n", - "Ġmar riage", - "Ġ Western", - "ĠWest ern", - "ĠWes tern", - "Ġrep resents", - "Ġrepresent s", - "Ġ Title", - "ĠT itle", - "ĠTi tle", - "ĠTit le", - "Ġ ss", - "Ġs s", - ". Ass", - ".A ss", - ".As s", - "ong oose", - "ongo ose", - "i ento", - "ient o", - "ien to", - "< >();Ċ", - "<>( );Ċ", - "<> ();Ċ", - "Ġabs olutely", - "Ġabsolute ly", - "Ġabsolut ely", - "Ġabsol utely", - "Ġ smooth", - "Ġsm ooth", - "Ġsmo oth", - "T ERN", - "TE RN", - "TER N", - "Ġ Unless", - "ĠUn less", - "W ord", - "Wo rd", - "Ġ merge", - "Ġm erge", - "Ġmer ge", - "Ġmerg e", - "i gan", - "ig an", - "iga n", - "Ġ Vol", - "ĠV ol", - "ĠVo l", - "Ġ nn", - "Ġn n", - ". getId", - ".get Id", - "Ġ з", - "ĠÐ ·", - "1 71", - "17 1", - "Ġ sexy", - "Ġse xy", - "Ġsex y", - "Ġse eking", - "Ġsee king", - "Ġseek ing", - "S ingle", - "Si ngle", - "Sin gle", - "Sing le", - ". this", - ".t his", - ".th is", - "1 79", - "17 9", - "Ġ kom", - "Ġk om", - "Ġko m", - "b ound", - "bo und", - "bou nd", - "; \"", - "Ġ fontSize", - "Ġfont Size", - "_ df", - "_d f", - "Ġin jury", - "Ġinj ury", - "( H", - "Ġ issued", - "Ġiss ued", - "Ġissue d", - "Ġissu ed", - "_ END", - "_E ND", - "_EN D", - ": self", - ":s elf", - "0 20", - "02 0", - "Ġ patch", - "Ġp atch", - "Ġpat ch", - "Ġle aves", - "Ġleave s", - "Ġ adopt", - "Ġad opt", - "Ġado pt", - "File Name", - "ãĢ IJ", - "Ġexec utive", - "Ġexecut ive", - "Ġ Byte", - "ĠB yte", - "ĠBy te", - "] ))Ċ", - "]) )Ċ", - "])) Ċ", - "Ġ nu", - "Ġn u", - "o uting", - "ou ting", - "out ing", - "cl uding", - "clud ing", - "- R", - ". options", - ".o ptions", - ".opt ions", - ".option s", - "Ġsub stant", - "Ġsubs tant", - "Ġsubst ant", - "av ax", - "ava x", - "ĠB UT", - "ĠBU T", - "Ġ technical", - "Ġtechn ical", - "Ġtw ice", - "Ġm ás", - "Ġmá s", - "Ġun ivers", - "Ġuni vers", - "y r", - "Ġ drag", - "Ġd rag", - "Ġdr ag", - "Ġdra g", - "Ġ DC", - "ĠD C", - "Ġ sed", - "Ġs ed", - "Ġse d", - "Ġ bot", - "Ġb ot", - "Ġbo t", - "Ġ Pal", - "ĠP al", - "ĠPa l", - "Ġ Hall", - "ĠH all", - "ĠHa ll", - "ĠHal l", - "force ment", - "forc ement", - "Ġa uch", - "Ġau ch", - "Ġauc h", - ". mod", - ".m od", - ".mo d", - "n otation", - "not ation", - "nota tion", - "_ files", - "_f iles", - "_file s", - "_fil es", - ". line", - ".l ine", - ".li ne", - ".lin e", - "_ flag", - "_f lag", - "_fl ag", - "[ name", - "[n ame", - "Ġ resolution", - "Ġre solution", - "Ġres olution", - "Ġb ott", - "Ġbo tt", - "Ġbot t", - "( \"[", - "(\" [", - "e nde", - "en de", - "end e", - "( arr", - "(a rr", - "(ar r", - "F ree", - "Fr ee", - "Fre e", - "( @\"", - "(@ \"", - "Ġ District", - "ĠD istrict", - "ĠDi strict", - "P EC", - "PE C", - ": -", - "P icker", - "Pic ker", - "Pi cker", - "Pick er", - "Ġ Jo", - "ĠJ o", - "Ġ ĠĠĠĠĊ", - "ĠĠ ĠĠĠĊ", - "ĠĠĠĠ ĠĊ", - "ĠĠĠ ĠĠĊ", - "ĠĠĠĠĠ Ċ", - "Ġ River", - "ĠR iver", - "ĠRiv er", - "ĠRi ver", - "_ rows", - "_r ows", - "_row s", - "_ro ws", - "Ġhelp ful", - "Ġmass ive", - "- --Ċ", - "-- -Ċ", - "--- Ċ", - "Ġme asures", - "Ġmeasure s", - "Ġmeas ures", - "0 07", - "00 7", - "Ġ Runtime", - "ĠR untime", - "ĠRun time", - "Ġw orry", - "Ġwor ry", - "Ġ Spec", - "ĠS pec", - "ĠSp ec", - "ĠSpe c", - "ĉ D", - "ãĢ ij", - "Ġ ){Ċ", - "Ġ) {Ċ", - "Ġ){ Ċ", - "Ġw orse", - "Ġwor se", - "Ġwors e", - "( filename", - "(f ilename", - "(file name", - "(fi lename", - "(fil ename", - "Ġ lay", - "Ġl ay", - "Ġla y", - "Ġ magic", - "Ġm agic", - "Ġmag ic", - "Ġ Their", - "ĠThe ir", - "o ul", - "ou l", - "st roy", - "str oy", - "stro y", - "Ġ Where", - "ĠW here", - "ĠWh ere", - "ĠWhe re", - "2 80", - "28 0", - "Ġs udden", - "Ġsu dden", - "Ġsud den", - "Ġd efe", - "Ġde fe", - "Ġdef e", - "Ġ binding", - "Ġb inding", - "Ġbin ding", - "Ġbind ing", - "Ġ flight", - "Ġf light", - "Ġfl ight", - "Ġ OnInit", - "ĠOn Init", - "Ġ Women", - "ĠW omen", - "ĠWo men", - "Ġ Policy", - "ĠP olicy", - "ĠPol icy", - "ĠPolic y", - "Ġdr ugs", - "Ġdrug s", - "Ġdru gs", - "ish ing", - "ishi ng", - "(' ../", - "('. ./", - "Ġ Mel", - "ĠM el", - "ĠMe l", - "p eat", - "pe at", - "t or", - "to r", - "Ġpro posed", - "Ġprop osed", - "Ġpropos ed", - "Ġpropose d", - "Ġst ated", - "Ġstate d", - "Ġstat ed", - "Ġsta ted", - "_ RES", - "_RE S", - "_R ES", - "Ġ east", - "Ġe ast", - "Ġeas t", - "Ġea st", - "2 12", - "21 2", - "Ġ CONDITION", - "ĠCON DITION", - "_ desc", - "_d esc", - "_de sc", - "_des c", - "Ġw inning", - "Ġwin ning", - "f olio", - "fo lio", - "fol io", - "M apper", - "Map per", - "Ma pper", - "Ġ Pan", - "ĠP an", - "ĠPa n", - "ĠA nge", - "ĠAn ge", - "ĠAng e", - ".s ervlet", - ".serv let", - "Ġ copies", - "Ġc opies", - "Ġco pies", - "Ġcop ies", - "L M", - "Ġ vm", - "Ġv m", - "å į", - "Ġ dictionary", - "Ġd ictionary", - "S eg", - "Se g", - "1 77", - "17 7", - "e lines", - "el ines", - "eline s", - "eli nes", - "elin es", - "Ġ Send", - "ĠS end", - "ĠSe nd", - "ĠSen d", - "Ġ iron", - "Ġi ron", - "Ġir on", - "Ġ Fort", - "ĠF ort", - "ĠFor t", - "ĠFo rt", - "1 66", - "16 6", - ". domain", - ".d omain", - ".do main", - ".dom ain", - "Ġde bate", - "Ġdeb ate", - "Not Null", - "e q", - "a cher", - "ac her", - "ach er", - "ache r", - "l f", - "ĉ fmt", - "ĉf mt", - "Ġl awy", - "Ġla wy", - "Ġlaw y", - "1 78", - "17 8", - "Ä Ł", - "Ġ Men", - "ĠM en", - "ĠMe n", - "Ġ trim", - "Ġt rim", - "Ġtr im", - "Ġtri m", - "( NULL", - "(N ULL", - "Ġ !!", - "Ġ! !", - "Ġ pad", - "Ġp ad", - "Ġpa d", - "Ġfollow s", - "Ġfoll ows", - "\" ][\"", - "\"] [\"", - "\"][ \"", - "r equ", - "re qu", - "req u", - "Ġ Ep", - "ĠE p", - ". github", - ".g ithub", - ".git hub", - "( img", - "(i mg", - "(im g", - "e to", - "et o", - "( '\\", - "(' \\", - "S ervices", - "Service s", - "Serv ices", - "umbn ail", - "_ main", - "_m ain", - "_ma in", - "p leted", - "pl eted", - "ple ted", - "plete d", - "plet ed", - "fort unately", - "fortunate ly", - "Ġ windows", - "Ġw indows", - "Ġwindow s", - "Ġwind ows", - "Ġ plane", - "Ġp lane", - "Ġpl ane", - "Ġplan e", - "Ġpla ne", - "Ġ Connection", - "ĠCon nection", - "ĠConnect ion", - "ĠConn ection", - ". local", - ".l ocal", - ".loc al", - ".lo cal", - "u ard", - "ua rd", - "uar d", - "} \\", - "= =\"", - "== \"", - "a ndon", - "an don", - "and on", - "ando n", - "Ġ Roy", - "ĠR oy", - "ĠRo y", - "w est", - "we st", - "1 58", - "15 8", - "ig inal", - "igin al", - "igi nal", - "em ies", - "emi es", - "emie s", - "i tz", - "it z", - "' ):Ċ", - "') :Ċ", - "'): Ċ", - "Ġ Peter", - "ĠP eter", - "ĠPe ter", - "ĠPet er", - "ĠPete r", - "Ġt ough", - "Ġto ugh", - "Ġtou gh", - "Ġre duced", - "Ġred uced", - "Ġredu ced", - "Ġreduce d", - "Ġ calculate", - "Ġc alculate", - "Ġcal culate", - "Ġcalcul ate", - "Ġcalc ulate", - "Ġ rapid", - "Ġr apid", - "Ġrap id", - "Ġra pid", - "c ustomer", - "custom er", - "cust omer", - "Ġ efficient", - "Ġeff icient", - "Ġeffic ient", - "Ġ medium", - "Ġm edium", - "Ġmed ium", - "Ġmedi um", - "Ġ fell", - "Ġf ell", - "Ġfe ll", - "Ġfel l", - ". ref", - ".re f", - ".r ef", - "Ġ Cas", - "ĠC as", - "ĠCa s", - "Ġ feedback", - "Ġfe edback", - "Ġfeed back", - "S peed", - "Sp eed", - "Spe ed", - "( output", - "(out put", - "a je", - "aj e", - "Ġ categories", - "Ġc ategories", - "Ġcategor ies", - "Ġcategorie s", - "Ġ fee", - "Ġf ee", - "Ġfe e", - "} ;", - "Ġ deleted", - "Ġde leted", - "Ġdel eted", - "Ġdelete d", - "Ġdelet ed", - "Ġdele ted", - "r eh", - "re h", - "Ġ proof", - "Ġp roof", - "Ġpro of", - "D esc", - "De sc", - "Des c", - "B uild", - "Bu ild", - "Ġs ides", - "Ġside s", - "Ġsi des", - "Ġsid es", - ". ArrayList", - ".Array List", - "- %", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "Ø ±", - ". match", - ".m atch", - ".mat ch", - "л и", - "Ġfe els", - "Ġfeel s", - "Ġfee ls", - "Ġachie ve", - "Ġach ieve", - "Ġc lim", - "Ġcl im", - "Ġcli m", - "_ ON", - "_O N", - "Ġ CD", - "ĠC D", - "Ġ teacher", - "Ġt eacher", - "Ġte acher", - "Ġteach er", - "Ġtea cher", - "_ current", - "_c urrent", - "_cur rent", - "_curr ent", - "b n", - "_ PL", - "_P L", - "is ting", - "ist ing", - "isti ng", - "E nable", - "En able", - "G EN", - "GE N", - "Ġ tv", - "Ġt v", - "Ġ sock", - "Ġs ock", - "Ġso ck", - "Ġsoc k", - "Ġ plays", - "Ġp lays", - "Ġpl ays", - "Ġplay s", - "Ġpla ys", - "Ġ discount", - "Ġdis count", - "Ġdisc ount", - "Ġdisco unt", - "Ġ KE", - "ĠK E", - "Ġ Debug", - "ĠDe bug", - "ĠDeb ug", - "F ore", - "For e", - "Fo re", - "Ġ Iraq", - "ĠI raq", - "ĠIr aq", - "Ġ appearance", - "Ġap pearance", - "Ġappear ance", - "M on", - "Mo n", - "Ġ styled", - "Ġst yled", - "Ġstyle d", - "Ġsty led", - "Ġstyl ed", - "Ġ Human", - "ĠH uman", - "ĠHum an", - "ĠHu man", - "i ot", - "io t", - "Ġ History", - "ĠH istory", - "ĠHi story", - "ĠHistor y", - "ĠHist ory", - "Ġs ac", - "Ġsa c", - "Ġ Collection", - "ĠC ollection", - "ĠCol lection", - "ĠColl ection", - "ĠCollect ion", - "Ġ recommended", - "Ġre commended", - "Ġrecomm ended", - "Ġrecommend ed", - ". Selected", - ".Se lected", - ".Select ed", - "Ġ organizations", - "Ġorgan izations", - "Ġorganization s", - "Ġorganiz ations", - "Ġdis covered", - "Ġdiscover ed", - "co hol", - "coh ol", - "a das", - "ad as", - "ada s", - "Ġ Thomas", - "ĠTh omas", - "ĠThom as", - "M ay", - "Ma y", - "Ġcon serv", - "Ġcons erv", - "Ġconse rv", - "Ġd omin", - "Ġdo min", - "Ġdom in", - "Ġ Follow", - "ĠF ollow", - "ĠFol low", - "Ġ Section", - "ĠS ection", - "ĠSe ction", - "ĠSec tion", - "ĠSect ion", - "Ġ Thanks", - "ĠTh anks", - "ĠThank s", - "ĠThan ks", - "User name", - "Ġ recipe", - "Ġrec ipe", - "Ġrecip e", - "Ġwonder ful", - ". sleep", - ".s leep", - "_ if", - "_i f", - "ĉ ĊĉĊ", - "ĉĊ ĉĊ", - "or no", - "orn o", - "Ġ ru", - "Ġr u", - "_ target", - "_t arget", - "_tar get", - ". \"\"", - ".\" \"", - "à ¦", - "Event Args", - "Ġ inputs", - "Ġin puts", - "Ġinput s", - "Ġinp uts", - "Ġf if", - "Ġfi f", - "Ġ vision", - "Ġv ision", - "Ġvis ion", - "c y", - "Ġ Series", - "ĠS eries", - "ĠSe ries", - "ĠSer ies", - "ĠSerie s", - ") (((", - ")( ((", - ")(( (", - "Ġtr ading", - "Ġtrad ing", - "Ġtra ding", - "Ġtradi ng", - "Ġ marker", - "Ġm arker", - "Ġmark er", - "Ġmar ker", - "B egin", - "Be gin", - "Ġ typically", - "Ġtyp ically", - "Ġtypical ly", - "Ġc auses", - "Ġca uses", - "Ġcause s", - "Ġcaus es", - "d ropdown", - "drop down", - "_ DEBUG", - "_DE BUG", - "2 60", - "26 0", - "Ġ detect", - "Ġd etect", - "Ġdet ect", - "c ountry", - "count ry", - "! \");Ċ", - "!\" );Ċ", - "!\"); Ċ", - "!\") ;Ċ", - "ĉ R", - "a ppy", - "ap py", - "app y", - "Ġc ref", - "Ġcr ef", - "Ġcre f", - "( '<", - "(' <", - "\" =>", - "Ġ LE", - "ĠL E", - "re ader", - "read er", - "rea der", - "Ġ administr", - "Ġadmin istr", - "à µ", - "u cket", - "uc ket", - "uck et", - "Ġf ashion", - ". char", - ".c har", - ".ch ar", - "i zar", - "iz ar", - "iza r", - "Ġ disable", - "Ġd isable", - "Ġdis able", - "Ġs uc", - "Ġsu c", - "Ġ Live", - "ĠL ive", - "ĠLi ve", - "ĠLiv e", - "i ssue", - "iss ue", - "Ġ metadata", - "Ġm etadata", - "Ġmet adata", - "Ġmeta data", - "f lags", - "fl ags", - "flag s", - "Ġ ðŁ", - "Ġð Ł", - "Ġ committed", - "Ġcom mitted", - "Ġcomm itted", - "Ġcommit ted", - "Ġ va", - "Ġv a", - "Ġ rough", - "Ġr ough", - "Ġro ugh", - "Ġrou gh", - "Ġ '''Ċ", - "Ġ' ''Ċ", - "Ġ'' 'Ċ", - "Ġ''' Ċ", - "Ġ highlight", - "Ġhigh light", - "_ vars", - "_v ars", - "_var s", - "_va rs", - "V O", - "Ġ encoding", - "Ġen coding", - "Ġenc oding", - "- Z", - "_ sign", - "_s ign", - "_sig n", - "_si gn", - "$ (\"#", - "$( \"#", - "$(\" #", - "Ġ rain", - "Ġr ain", - "Ġra in", - "re atest", - "reate st", - "reat est", - "rea test", - "Ġ END", - "ĠE ND", - "ĠEN D", - "S election", - "Se lection", - "Select ion", - "Sel ection", - "Sele ction", - "Ġ candidates", - "Ġc andidates", - "Ġcandid ates", - "Ġcandidate s", - "Ġ sav", - "Ġs av", - "Ġsa v", - ". Empty", - "Ġdec isions", - "Ġdecision s", - "Ġdecis ions", - "Ġcoll abor", - "r idge", - "ri dge", - "rid ge", - "f eed", - "fe ed", - "fee d", - "r ession", - "ress ion", - "Ġ persons", - "Ġper sons", - "Ġperson s", - "Ġpers ons", - "Ġperso ns", - "V M", - "0 08", - "00 8", - "e ga", - "eg a", - "_ BIT", - "_B IT", - "A ccording", - "Acc ording", - "ack ed", - "ac ked", - "Ġdoll ars", - "Ġdollar s", - "_ loss", - "_l oss", - "_lo ss", - "Ġ Cost", - "ĠC ost", - "ĠCo st", - "ĠCos t", - "} \"Ċ", - "}\" Ċ", - "Not ification", - "Ġpro stit", - "Ġpros tit", - "Ġprost it", - "Ġ authority", - "Ġauthor ity", - ". rec", - ".re c", - ".r ec", - "Ġsp okes", - "Ġspoke s", - "Ġspo kes", - "Ġ Today", - "ĠT oday", - "ĠTo day", - "ĠTod ay", - "i stant", - "is tant", - "ist ant", - "istan t", - "ista nt", - "Ġ Head", - "ĠH ead", - "ĠHe ad", - "âĢĿ .", - "er tainment", - "ert ainment", - "ertain ment", - "c ean", - "ce an", - "cea n", - "c ulate", - "cul ate", - "cu late", - "Ġ ven", - "Ġv en", - "Ġve n", - "How ever", - "_ arr", - "_a rr", - "_ar r", - "Ġ tokens", - "Ġt okens", - "Ġtoken s", - "Ġtok ens", - "G raph", - "Gr aph", - "Ġ Jud", - "ĠJ ud", - "ĠJu d", - "Ġ Virgin", - "ĠVir gin", - "Ġ Serial", - "ĠS erial", - "ĠSe rial", - "ĠSer ial", - "un ning", - "unn ing", - "M utable", - "Mu table", - "Mut able", - "a gers", - "ag ers", - "age rs", - "ager s", - ". csv", - ".c sv", - ".cs v", - "Ġdevelop ing", - "Ġdevel oping", - "Ġ instructions", - "Ġin structions", - "Ġinstruction s", - "Ġinstr uctions", - "Ġinstruct ions", - "Ġ promise", - "Ġp romise", - "Ġpro mise", - "Ġprom ise", - "Ġ requested", - "Ġrequest ed", - "Ġrequ ested", - "_ encode", - "_en code", - "_enc ode", - "/ \"", - "Ġ Icon", - "ĠI con", - "u ilt", - "ui lt", - "uil t", - "- day", - "-d ay", - "-da y", - "Ġ intelligence", - "Ġint elligence", - "Ġintellig ence", - ". IS", - ".I S", - "Ġ Observable", - "ĠO bservable", - "ĠObserv able", - "Ġ Hard", - "ĠH ard", - "ĠHar d", - "ĠHa rd", - "B ool", - "Bo ol", - "2 11", - "21 1", - "id ential", - "ident ial", - ". Anchor", - ".An chor", - "Ġ selling", - "Ġs elling", - "Ġsell ing", - "Ġsel ling", - "C I", - "A GES", - "AG ES", - "AGE S", - "t le", - "tl e", - "b ur", - "bu r", - "UFF ER", - "UF FER", - "R Y", - "Ġb igger", - "Ġbig ger", - "Ġbi gger", - "Ġ rat", - "Ġr at", - "Ġra t", - "Ġf amous", - "Ġfam ous", - "Ġ typename", - "Ġtype name", - "Ġtyp ename", - "Ġ explained", - "Ġexpl ained", - "Ġexplain ed", - "} }Ċ", - "}} Ċ", - "Ġn uclear", - "Ġnu clear", - "Ġnucle ar", - "- N", - "Ġcr isis", - "Ġcri sis", - "Ġcris is", - "Ġ Enter", - "ĠEn ter", - "ĠEnt er", - "Ġ answers", - "Ġan swers", - "Ġanswer s", - "Ġans wers", - "/ ${", - "/$ {", - "/ pl", - "/p l", - "Ġ sequ", - "Ġs equ", - "Ġse qu", - "Ġseq u", - "_ next", - "_n ext", - "_ne xt", - "m ask", - "ma sk", - "mas k", - "Ġ standing", - "Ġst anding", - "Ġstand ing", - "Ġstan ding", - "Ġpl enty", - "Ġple nty", - "Ġ Cross", - "ĠC ross", - "ĠCr oss", - "ĠCro ss", - "ĠCros s", - "ĉ ret", - "ĉr et", - "ĉre t", - "d ro", - "dr o", - "Ġ Cast", - "ĠC ast", - "ĠCas t", - "ĠCa st", - "1 67", - "16 7", - "= true", - "Ġ Chris", - "ĠCh ris", - "ĠChr is", - "i cio", - "ic io", - "ici o", - "Ġ Mike", - "ĠM ike", - "ĠMi ke", - "ĠMik e", - "D ecimal", - "De cimal", - "Dec imal", - "add Component", - "L en", - "Le n", - "Ġ cock", - "Ġc ock", - "Ġco ck", - "Ġcoc k", - "Ġ #{", - "Ġ# {", - "U RN", - "UR N", - "< tr", - "", - "\\\" >", - "Ġ *=", - "Ġ* =", - "Ġ PS", - "ĠP S", - "Ġdanger ous", - "[ p", - "O ME", - "OM E", - "O ther", - "Ot her", - "Ġ StringBuilder", - "ĠString Builder", - "P oints", - "Point s", - "Po ints", - "he ading", - "head ing", - "hea ding", - "Ġ currency", - "Ġc urrency", - "Ġcurr ency", - "Ġ percentage", - "Ġper centage", - "Ġpercent age", - "_ API", - "_A PI", - "_AP I", - "Ġ classic", - "Ġclass ic", - "Ġcl assic", - "Ġclas sic", - "t head", - "th ead", - "the ad", - "Ġ MO", - "ĠM O", - "F E", - "I dx", - "Id x", - "a wait", - "aw ait", - "awa it", - "awai t", - "Ġ è", - "Ġà ¨", - "Ġacc ident", - "Ġ variant", - "Ġv ariant", - "Ġvar iant", - "Ġvari ant", - "Ġm yst", - "Ġmy st", - "Ġmys t", - "Ġ Land", - "ĠL and", - "ĠLa nd", - "ĠLan d", - "Ġ Bre", - "ĠB re", - "ĠBr e", - "Ġh arm", - "Ġhar m", - "Ġha rm", - "Ġ Acc", - "ĠA cc", - "ĠAc c", - "Ġ charged", - "Ġch arged", - "Ġchar ged", - "Ġcharg ed", - "Ġcharge d", - "i ones", - "ion es", - "io nes", - "ione s", - "Vis ibility", - "ar ry", - "arr y", - "Ġ Language", - "ĠL anguage", - "Ġ walking", - "Ġw alking", - "Ġwalk ing", - "Ġwal king", - "\" .ĊĊ", - "\". ĊĊ", - "\".Ċ Ċ", - "i fer", - "if er", - "ife r", - "Ġleaders hip", - "Ġleader ship", - ". From", - ".F rom", - "y nam", - "yn am", - "yna m", - "Ġ timestamp", - "Ġt imestamp", - "Ġtime stamp", - "i pt", - "ip t", - "Ġ Has", - "ĠH as", - "ĠHa s", - "RE FER", - "REF ER", - "Ġ Its", - "ĠI ts", - "ĠIt s", - "Ġ listener", - "Ġlist ener", - "Ġlisten er", - "Ġliste ner", - "Ġlis tener", - "U TE", - "UT E", - "2 13", - "21 3", - "_ description", - "_d escription", - "_de scription", - "_des cription", - "Ġex periences", - "Ġexper iences", - "Ġexperience s", - "Ġexperi ences", - "Ġ creates", - "Ġcreate s", - "Ġcre ates", - "Ġcreat es", - "Ġcrea tes", - "R S", - "c art", - "ca rt", - "car t", - "b lack", - "bl ack", - "bla ck", - "Ġ choices", - "Ġcho ices", - "Ġchoice s", - "w ar", - "wa r", - "7 50", - "75 0", - "Ġ '''", - "Ġ' ''", - "Ġ'' '", - "Ġ ordered", - "Ġorder ed", - "Ġord ered", - "Ġeven ing", - "Ġev ening", - "Ġeve ning", - "Ġp il", - "Ġpi l", - "Ġt un", - "Ġtu n", - "Ġ Bad", - "ĠB ad", - "ĠBa d", - "( app", - "(a pp", - "(ap p", - "r andom", - "ran dom", - "rand om", - "Ġ explicit", - "Ġexp licit", - "Ġexpl icit", - "Ġexplic it", - "Ġarr ived", - "Ġarrive d", - "Ġarriv ed", - "Ġ fly", - "Ġf ly", - "Ġfl y", - "Ġe conom", - "Ġecon om", - "Ġec onom", - "Ġeco nom", - "- mail", - "-m ail", - "Ġ lists", - "Ġl ists", - "Ġli sts", - "Ġlist s", - "Ġlis ts", - "Ġarch itect", - "Ġarchit ect", - "2 34", - "23 4", - "Ġ Pay", - "ĠP ay", - "ĠPa y", - "Ġ ds", - "Ġd s", - "Ġ Sol", - "ĠS ol", - "ĠSo l", - "Ġ vehicles", - "Ġv ehicles", - "Ġveh icles", - "Ġvehicle s", - "H z", - "- com", - "-c om", - "-co m", - "Ġ king", - "Ġk ing", - "Ġki ng", - "Ġkin g", - "_ equal", - "_e qual", - "_eq ual", - "_equ al", - "Ġ Help", - "ĠH elp", - "ĠHe lp", - "ĠHel p", - "Ġab use", - "4 80", - "48 0", - "1 69", - "16 9", - "-- ;Ċ", - "--; Ċ", - "Ġ extr", - "Ġex tr", - "Ġext r", - "Ġ chemical", - "Ġchem ical", - "ä ¿", - "Ġ orient", - "Ġo rient", - "Ġor ient", - "Ġori ent", - "Ġbre ath", - "Ġbreat h", - "Ġ Space", - "ĠS pace", - "ĠSp ace", - "ĠSpa ce", - "( element", - "(e lement", - "(el ement", - "(elem ent", - "(ele ment", - "w ait", - "wa it", - "D ED", - "DE D", - "ig ma", - "igm a", - "Ġ entr", - "Ġen tr", - "Ġent r", - "Ġ sob", - "Ġs ob", - "Ġso b", - "- name", - "-n ame", - "-na me", - "Ġ affected", - "Ġaff ected", - "Ġaffect ed", - "i ka", - "ik a", - "Ġ coal", - "Ġco al", - "_ work", - "_w ork", - "_wo rk", - "Ġh undreds", - "Ġhundred s", - "Ġpol itics", - "Ġpolit ics", - "Ġpolitic s", - "sub ject", - "su bject", - "subj ect", - "Ġ consumer", - "Ġcon sumer", - "Ġcons umer", - "Ġconsum er", - "Ġconsume r", - "AN GE", - "ANG E", - "Ġre peated", - "Ġrep eated", - "Ġrepe ated", - "Ġrepeat ed", - "S end", - "Se nd", - "Sen d", - "Ġ #[", - "Ġ# [", - "Ġ protocol", - "Ġprot ocol", - "Ġproto col", - "Ġ leads", - "Ġle ads", - "Ġlead s", - "us eum", - "use um", - "E very", - "Ev ery", - "Ever y", - "8 08", - "80 8", - "1 74", - "17 4", - "Im port", - "Imp ort", - "( count", - "(c ount", - "(co unt", - "Ġch allenges", - "Ġchalleng es", - "Ġchallenge s", - "Ġn ovel", - "Ġno vel", - "Ġnov el", - "Ġ depart", - "Ġde part", - "Ġdep art", - "b its", - "bit s", - "bi ts", - ". Current", - ".C urrent", - "Ġ `${", - "Ġ` ${", - "Ġ`$ {", - "o ting", - "ot ing", - "oti ng", - "( \\", - "Ġ creative", - "Ġc reative", - "Ġcre ative", - "Ġcreat ive", - "Ġ buff", - "Ġb uff", - "Ġbu ff", - "Ġbuf f", - "Ġint roduced", - "Ġintrodu ced", - "Ġintroduce d", - "Ġintro duced", - "u sic", - "us ic", - "usi c", - "mod ules", - "module s", - "A re", - "Ar e", - "- doc", - "-d oc", - "-do c", - "l anguage", - "_ cache", - "_c ache", - "_ca che", - "Ġ tod", - "Ġt od", - "Ġto d", - "? > < /", - "om ething", - "ome thing", - "Ġh un", - "Ġhu n", - "å º", - "a ters", - "at ers", - "ate rs", - "ater s", - "In tent", - "Int ent", - "Ġ implemented", - "Ġim plemented", - "Ġimplement ed", - "Ġ Case", - "ĠC ase", - "ĠCas e", - "ĠCa se", - "Ch ildren", - "Child ren", - "Ġ notification", - "Ġnot ification", - "Render er", - "W rapper", - "Wrap per", - "Wr apper", - "Object s", - "Obj ects", - "t l", - ". Contains", - ".Cont ains", - ".Con tains", - "Pl ugin", - "Plug in", - ". row", - ".r ow", - ".ro w", - "Ġ forg", - "Ġf org", - "Ġfor g", - "Ġfo rg", - "Ġ permit", - "Ġper mit", - "Ġperm it", - "Ġ targets", - "Ġtarget s", - "Ġtar gets", - "Ġtarg ets", - "Ġ IF", - "ĠI F", - "Ġ tip", - "Ġt ip", - "Ġti p", - "s ex", - "se x", - "Ġ supports", - "Ġsup ports", - "Ġsupport s", - "Ġsupp orts", - "Ġ fold", - "Ġf old", - "Ġfol d", - "Ġfo ld", - "ph oto", - "phot o", - "} ,čĊ", - "}, čĊ", - "Ġ google", - "Ġg oogle", - "Ġgo ogle", - "Ġgoog le", - "Ġgoo gle", - "$ ('#", - "$( '#", - "$(' #", - "Ġ sharing", - "Ġsh aring", - "Ġsha ring", - "Ġshar ing", - "Ġ goods", - "Ġg oods", - "Ġgo ods", - "Ġgood s", - "Ġgoo ds", - "v s", - "Ġ Dan", - "ĠD an", - "ĠDa n", - "R ate", - "Ra te", - "Ġ Martin", - "ĠM artin", - "ĠMar tin", - "ĠMart in", - "Ġm anner", - "Ġman ner", - "Ġmann er", - "l ie", - "li e", - ". The", - ".T he", - ".Th e", - "In ternal", - "Int ernal", - "Inter nal", - "Intern al", - "ĠCON TR", - "ĠCONT R", - "M ock", - "Mo ck", - "R IGHT", - "Ġ '{", - "Ġ' {", - "Ġ controls", - "Ġcontrol s", - "Ġcontr ols", - "Ġcontro ls", - "M at", - "Ma t", - "Ġ mand", - "Ġm and", - "Ġman d", - "Ġma nd", - "Ġ extended", - "Ġext ended", - "Ġextend ed", - "O k", - "Ġ embed", - "Ġem bed", - "Ġemb ed", - "Ġ planet", - "Ġplan et", - "Ġplane t", - "Ġpla net", - "Ġ Non", - "ĠN on", - "ĠNo n", - "- ch", - "-c h", - ") \",", - ")\" ,", - "e par", - "ep ar", - "Ġbel ieved", - "Ġbelie ved", - "Ġbelieve d", - "Ġ Environment", - "ĠEn vironment", - "Ġ Friend", - "ĠF riend", - "ĠFri end", - "- res", - "-r es", - "-re s", - "Ġ handling", - "Ġhand ling", - "Ġhan dling", - "n ic", - "ni c", - "- level", - "-le vel", - "s cri", - "sc ri", - "scr i", - "X ml", - "B E", - "u ngen", - "un gen", - "ung en", - "unge n", - "Ġ alter", - "Ġal ter", - "Ġalt er", - "Ġalte r", - "[ idx", - "[i dx", - "[id x", - "P op", - "Po p", - "c am", - "ca m", - "Ġ (((", - "Ġ( ((", - "Ġ(( (", - "Ġ shipping", - "Ġsh ipping", - "Ġship ping", - "Ġ battery", - "Ġb attery", - "Ġbatter y", - "Ġbatt ery", - "Ġbat tery", - "iddle ware", - "M C", - "Ġ impl", - "Ġi mpl", - "Ġim pl", - "Ġimp l", - "ot ation", - "ota tion", - "Ġ Lab", - "ĠL ab", - "ĠLa b", - "< form", - " {{", - ">{ {", - "Ġ Resource", - "ĠRe source", - "ĠRes ource", - "Ġ Standard", - "ĠSt andard", - "ĠStand ard", - "Ġ Prem", - "ĠP rem", - "ĠPr em", - "ĠPre m", - "up dated", - "update d", - "upd ated", - "iv alent", - "ival ent", - "Ġ assets", - "Ġas sets", - "Ġass ets", - "Ġasset s", - "_ temp", - "_t emp", - "_tem p", - "_te mp", - "Ġinter ests", - "Ġinterest s", - "Ġinteres ts", - "Ġ hardware", - "Ġh ardware", - "Ġhard ware", - "Ġ Rom", - "ĠR om", - "ĠRo m", - "Ġ Share", - "ĠS hare", - "ĠSh are", - "ĠSha re", - "ĠShar e", - "Ġ ''Ċ", - "Ġ' 'Ċ", - "Ġ'' Ċ", - "Ġ *,", - "Ġ* ,", - "Ġ Take", - "ĠT ake", - "ĠTa ke", - "ĠTak e", - "Ġ Images", - "ĠIm ages", - "ĠImage s", - "ĠImag es", - "_ CHECK", - "_C HECK", - "( typeof", - "(type of", - "(typ eof", - "Ġ Jun", - "ĠJ un", - "ĠJu n", - "\\< ^", - "Ġ liqu", - "Ġl iqu", - "Ġli qu", - "Ġw orst", - "Ġwor st", - "Ġwo rst", - "Ġwors t", - "ymb ols", - "ymbol s", - "ĉ ĉĉĠĠĠ", - "ĉĉ ĉĠĠĠ", - "ĉĉĉ ĠĠĠ", - "ĉĉĉĠ ĠĠ", - "ĉĉĉĠĠ Ġ", - "Ġ drivers", - "Ġdr ivers", - "Ġdriver s", - "Ġdrive rs", - "Ġdriv ers", - "Ġdri vers", - "Ġ Document", - "ĠD ocument", - "ĠDoc ument", - "e no", - "en o", - "Ġ Technology", - "ĠTechn ology", - "Ġ approved", - "Ġap proved", - "Ġappro ved", - "Ġapprove d", - "u mps", - "um ps", - "ump s", - "Ġ snow", - "Ġs now", - "Ġsn ow", - "Ġsno w", - "form ance", - "forma nce", - "forman ce", - "_ ASSERT", - "_A SSERT", - "_ASS ERT", - "u its", - "ui ts", - "uit s", - "2 07", - "20 7", - "Ù Ĩ", - "Ġd ifferences", - "Ġdif ferences", - "Ġdiffer ences", - "Ġdifference s", - ". Visible", - ".V isible", - "ĉ ĉĉčĊ", - "ĉĉ ĉčĊ", - "ĉĉĉ čĊ", - "Ġ Ps", - "ĠP s", - "_ fetch", - "_f etch", - "Ġ todo", - "Ġt odo", - "Ġto do", - "Ġtod o", - ". ',Ċ", - ".' ,Ċ", - ".', Ċ", - "Ġ sel", - "Ġs el", - "Ġse l", - "ur ers", - "ure rs", - "urer s", - "in valid", - "Ġ tweet", - "Ġt weet", - "Ġtwe et", - "Ġtwee t", - "V EL", - "VE L", - "Ġresearch ers", - "Ġresearcher s", - "Ġ sprintf", - "Ġs printf", - "Ġsprint f", - "Ġ RO", - "ĠR O", - "Ġ pel", - "Ġp el", - "Ġpe l", - ". Trans", - ".T rans", - ".Tr ans", - "Ġ illegal", - "Ġil legal", - "Ġill egal", - "Ġilleg al", - "d ialog", - "di alog", - "dia log", - "sm arty", - "smart y", - "l g", - "_ MIN", - "_M IN", - "_MI N", - "Ġ hero", - "Ġh ero", - "Ġhe ro", - "Ġher o", - "f inal", - "fin al", - "fi nal", - "Ġ pp", - "Ġp p", - ". Le", - ".L e", - "Ġ ci", - "Ġc i", - "ĉ RT", - "ĉR T", - "Ġs uggested", - "Ġsuggest ed", - "p df", - "pd f", - "a ching", - "ach ing", - "achi ng", - "Ġ Ro", - "ĠR o", - "Ġ Properties", - "ĠP roperties", - "ĠProp erties", - "ĠProper ties", - "Ġ Si", - "ĠS i", - "Ġbu ying", - "Ġbuy ing", - "Ġ mu", - "Ġm u", - "Ġ lands", - "Ġl ands", - "Ġla nds", - "Ġland s", - "Ġlan ds", - "if iers", - "ifier s", - "ifi ers", - "ifie rs", - "Ġ FILE", - "ĠF ILE", - "ĠFI LE", - "ĠFIL E", - "RO UP", - "Ġ holder", - "Ġh older", - "Ġhold er", - "Ġho lder", - "Ġhol der", - "Ġ Son", - "ĠS on", - "ĠSo n", - "Ġsym pt", - "Ġsymp t", - ". route", - ".r oute", - ".ro ute", - ") ?", - "Ġ argc", - "Ġar gc", - "Ġarg c", - "Ġ fort", - "Ġf ort", - "Ġfor t", - "Ġfo rt", - "Ġcas ino", - "Ġcasi no", - "_ category", - "_c ategory", - "Ġ forum", - "Ġf orum", - "Ġfor um", - "Ġfo rum", - "2 15", - "21 5", - "p refix", - "pre fix", - "pref ix", - "ap ture", - "apt ure", - "T ube", - "Tu be", - "e ms", - "em s", - "im ize", - "imi ze", - "imiz e", - "Ġn ue", - "Ġnu e", - "a us", - "au s", - "c ourse", - "co urse", - "cour se", - "A TOR", - "AT OR", - "ATO R", - "( )),", - "() ),", - "()) ,", - "Ad vertis", - "IN GS", - "ING S", - "Ġac know", - "Ġack now", - "ĠK orea", - "ĠKore a", - "ĠKo rea", - "ĠKor ea", - "p ling", - "pl ing", - "Ġ worker", - "Ġwork er", - "Ġwor ker", - "PL IED", - "h al", - "ha l", - "Ġ Richard", - "ĠRich ard", - "ĠRic hard", - "E lements", - "Element s", - "El ements", - "Elem ents", - "Ele ments", - "ĉ ĉĉĠ", - "ĉĉ ĉĠ", - "ĉĉĉ Ġ", - "s tar", - "st ar", - "sta r", - "Ġ relationships", - "Ġrelations hips", - "Ġrelationship s", - "Ġrelation ships", - "Ġ cheap", - "Ġc heap", - "Ġche ap", - "A CH", - "AC H", - "Ġ XML", - "ĠX ML", - "ĠXM L", - ", &", - "Ġ Louis", - "ĠL ouis", - "ĠLo uis", - "ĠLou is", - "Ġ ride", - "Ġr ide", - "Ġrid e", - "Ġri de", - "_ FAIL", - "_F AIL", - "_FA IL", - "Ġ chunk", - "Ġch unk", - "Ġchu nk", - "[ s", - "_ OUT", - "_O UT", - "Ġ chosen", - "Ġch osen", - "Ġcho sen", - "Ġchose n", - "_ [", - "/ (", - "Ġ Jeff", - "ĠJ eff", - "ĠJe ff", - "_ sl", - "_s l", - "p riv", - "pr iv", - "pri v", - "Ġ Canadian", - "ĠCan adian", - "Ġ unable", - "Ġu nable", - "Ġun able", - "Ġuna ble", - "Ġunab le", - "_ FLAG", - "_F LAG", - "_FL AG", - "Ġ nos", - "Ġn os", - "Ġno s", - "h igh", - "hi gh", - "Ġ lift", - "Ġl ift", - "Ġli ft", - "Ġlif t", - "f un", - "fu n", - "( ){", - "() {", - "el ly", - "ell y", - "ycler View", - "_ as", - "_a s", - "_ LIST", - "_L IST", - "Ġ radi", - "Ġr adi", - "Ġrad i", - "Ġra di", - ". getValue", - ".get Value", - "3 04", - "30 4", - "ĠAnge les", - "ĠAngel es", - "Ġ Span", - "ĠS pan", - "ĠSp an", - "ĠSpa n", - "_ instance", - "_in stance", - "_inst ance", - "i tors", - "it ors", - "itor s", - "ito rs", - "2 08", - "20 8", - "Ġ migration", - "Ġm igration", - "Ġmigr ation", - "Ġmig ration", - "A K", - "O h", - " ®", - ". selected", - ".se lected", - ".select ed", - ".sel ected", - "Ġ GT", - "ĠG T", - "Ġ advance", - "Ġadv ance", - "Ġ Style", - "ĠSt yle", - "ĠSty le", - ". DataGridView", - ".Data GridView", - "e ction", - "ect ion", - "ec tion", - "Ñ İ", - "p io", - "pi o", - "r og", - "ro g", - "Ġ shopping", - "Ġsh opping", - "Ġshop ping", - "Ġsho pping", - "Ġ Rect", - "ĠR ect", - "ĠRe ct", - "ĠRec t", - "I lluminate", - "O U", - "ĉ array", - "ĉa rray", - "ĉarr ay", - "ĉar ray", - "Ġsub stantial", - "Ġsubstant ial", - "Ġp regn", - "Ġpre gn", - "Ġpreg n", - "Ġprom ote", - "Ġpromot e", - "Ġpromo te", - "I EW", - "IE W", - ". Layout", - ".L ayout", - "Ġsign s", - "Ġsig ns", - "/ .", - "Ġ letters", - "Ġlet ters", - "Ġletter s", - "Ġlett ers", - "B oard", - "Bo ard", - "c trl", - "ct rl", - "ctr l", - "\" \\", - "Ġ Jones", - "ĠJ ones", - "ĠJo nes", - "ĠJon es", - "Ġ vertex", - "Ġver tex", - "Ġvert ex", - "Ġverte x", - "Ġ ja", - "Ġj a", - "Ġaff ili", - "Ġ wealth", - "Ġwe alth", - "ĉ default", - "ĉd efault", - "ĉdef ault", - "ĉde fault", - "Ġsign ificantly", - "Ġsignific antly", - "Ġsignificant ly", - "Ġ ec", - "Ġe c", - "Ġ xs", - "Ġx s", - "act ual", - "ac tual", - ". per", - ".p er", - ".pe r", - "_ step", - "_s tep", - "_st ep", - "_ste p", - "an vas", - "m ac", - "ma c", - "Ġtrans l", - "Ġtran sl", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "It erator", - "Iter ator", - "Ġ och", - "Ġo ch", - "Ġoc h", - "ag nostic", - "agnost ic", - "Ġ During", - "ĠD uring", - "ĠDu ring", - "ĠDur ing", - "Ġ DEFAULT", - "ĠD EFAULT", - "ĠDE FAULT", - "Ġt ill", - "Ġti ll", - "Ġtil l", - "Ġ signature", - "Ġsign ature", - "Ġsig nature", - "Ġ bird", - "Ġb ird", - "Ġbi rd", - "Ġbir d", - "Ġ Ol", - "ĠO l", - "3 10", - "31 0", - "Ġ Ir", - "ĠI r", - "H S", - "av atar", - "ava tar", - "ESS AGE", - "Ġe lev", - "Ġel ev", - "Ġele v", - "Ġ mt", - "Ġm t", - "Ġ Nav", - "ĠN av", - "ĠNa v", - "Ġrel ax", - "Ġ plate", - "Ġp late", - "Ġpl ate", - "Ġpla te", - "Ġplat e", - "I TEM", - "IT EM", - "ITE M", - "( date", - "(d ate", - "(dat e", - "(da te", - ". not", - ".n ot", - ".no t", - "Ġ grade", - "Ġg rade", - "Ġgr ade", - "Ġgrad e", - "Ġgra de", - "Ġ }),Ċ", - "Ġ} ),Ċ", - "Ġ}) ,Ċ", - "Ġ}), Ċ", - "? \"ĊĊ", - "?\" ĊĊ", - "?\"Ċ Ċ", - "i ences", - "ie nces", - "ience s", - "ien ces", - "H igh", - "Hi gh", - "Ġ DIS", - "ĠD IS", - "ĠDI S", - "2 31", - "23 1", - "dis abled", - "disable d", - "Q UI", - "QU I", - "Ġ noise", - "Ġn oise", - "Ġno ise", - "Ġnoi se", - "a ux", - "au x", - "Ġ UP", - "ĠU P", - "8 88", - "88 8", - "o sa", - "os a", - "Ġv oc", - "Ġvo c", - "Ġ ))", - "Ġ) )", - "o com", - "oc om", - "oco m", - "_ OFF", - "_O FF", - "_OF F", - "Ġ Db", - "ĠD b", - "L ock", - "Lo ck", - "Loc k", - ".e clipse", - ", d", - "Ġ Draw", - "ĠD raw", - "ĠDr aw", - "ĠDra w", - "Ġ \"(", - "Ġ\" (", - "Ġ visited", - "Ġvis ited", - "Ġvisit ed", - "Ġvisite d", - "Ġ âĪ", - "Ġâ Ī", - "Ġs ucceed", - "Ġsuc ceed", - "Ġsucc eed", - "Ġim possible", - "Ġimp ossible", - "Ġimposs ible", - "a ire", - "air e", - "ai re", - "Ġ Turn", - "ĠT urn", - "ĠTur n", - "ĠTu rn", - "Ġ dish", - "Ġd ish", - "Ġdis h", - "Ġdi sh", - "F G", - "Ġ sensor", - "Ġs ensor", - "Ġsens or", - "A NN", - "AN N", - "a ba", - "ab a", - "Ġs urg", - "Ġsu rg", - "Ġsur g", - "] );čĊ", - "]) ;čĊ", - "]); čĊ", - "Ġ fp", - "Ġf p", - "_ an", - "_a n", - "- J", - "- G", - "Ġ Job", - "ĠJ ob", - "ĠJo b", - "Con vert", - "Conv ert", - "Ġ KEY", - "ĠK EY", - "ĠKE Y", - "Ġ authors", - "Ġauthor s", - "Ġauth ors", - "_ server", - "_s erver", - "_serv er", - "_ser ver", - "\\ r", - "Ġ -*-", - "Ġ- *-", - "Ġ-* -", - "f lex", - "fl ex", - "Ġ soc", - "Ġs oc", - "Ġso c", - "R et", - "Re t", - "Ġ salt", - "Ġs alt", - "Ġsa lt", - "Ġsal t", - "Ġ âĢ¦ĊĊ", - "ĠâĢ¦ ĊĊ", - "ĠâĢ¦Ċ Ċ", - "Ġ Clear", - "ĠC lear", - "ĠCl ear", - "ĠCle ar", - "( page", - "(p age", - "(pa ge", - "- danger", - "-d anger", - "-da nger", - "Ġ rooms", - "Ġro oms", - "Ġroom s", - "con v", - "co nv", - "# {", - ". op", - ".o p", - "Ġ Area", - "ĠA rea", - "ĠAr ea", - "ĠAre a", - "_ SC", - "_S C", - "h en", - "he n", - "Ġbeg ins", - "Ġbegin s", - "- y", - "Ġexc ited", - "Ġexcit ed", - "Ġ ignored", - "Ġign ored", - "Ġignore d", - "Ġignor ed", - "Ġ bonus", - "Ġb onus", - "Ġbon us", - "st udent", - "stu dent", - "stud ent", - "Ġ Member", - "ĠM ember", - "ĠMem ber", - "Ġrel atively", - "Ġrelative ly", - "Ġrelativ ely", - "Ġrelat ively", - "Ġ Low", - "ĠL ow", - "ĠLo w", - "Ġ Produ", - "ĠP rodu", - "ĠPro du", - "ĠPr odu", - "ĠProd u", - "at eway", - "ate way", - "pos ure", - "po sure", - "Ġth ick", - "Ġthi ck", - "an iel", - "ani el", - "anie l", - "( view", - "(v iew", - "ĠC rush", - "ĠCr ush", - "ĠCru sh", - "ĠCrus h", - "Ext ension", - "I l", - "e ed", - "ee d", - "L OC", - "LO C", - ". im", - ".i m", - ". Items", - ".I tems", - ".Item s", - ".It ems", - "Ġconf lict", - "Ġconflic t", - "Ġconfl ict", - ". prevent", - ".pre vent", - ".pr event", - ".prev ent", - "2 52", - "25 2", - "Ġon Create", - "u v", - "i ser", - "is er", - "ise r", - "Ġ wave", - "Ġw ave", - "Ġwa ve", - "Ġwav e", - "M ar", - "Ma r", - "Ġ Community", - "ĠComm unity", - "ĠCommun ity", - "i che", - "ic he", - "ich e", - "Ġ Nothing", - "ĠNo thing", - "[ m", - "Ġ Lee", - "ĠL ee", - "ĠLe e", - "ri ends", - "riend s", - "rie nds", - "rien ds", - "2 32", - "23 2", - "è re", - "! !!", - "!! !", - "a nz", - "an z", - ". result", - ".res ult", - "Ġ SK", - "ĠS K", - "_ PARAM", - "_P ARAM", - "_PA RAM", - "_PAR AM", - "Ġdem ocr", - "Ġdemo cr", - "Back Color", - ". exists", - ".ex ists", - ".exist s", - "\" It", - "\"I t", - "( options", - "(o ptions", - "(opt ions", - "(option s", - "r azy", - "ra zy", - "raz y", - "a ser", - "as er", - "ase r", - "\\ Database", - "\\Data base", - "\\D atabase", - "al endar", - "alen dar", - "_ ass", - "_a ss", - "_as s", - "; }Ċ", - ";} Ċ", - "ver tex", - "vert ex", - "verte x", - "ine craft", - "W arning", - "War ning", - "Warn ing", - "ar go", - "arg o", - "Ġ actor", - "Ġa ctor", - "Ġact or", - "Ġac tor", - "Ġ Instead", - "ĠIn stead", - "ĠInst ead", - "Ġ Using", - "ĠU sing", - "ĠUs ing", - "S elf", - "Se lf", - "Sel f", - "@ interface", - "Ġspe aking", - "Ġspeak ing", - "Ġ Paris", - "ĠP aris", - "ĠPar is", - "ĠPa ris", - "Ġ LICENSE", - "ĠL ICENSE", - "ĠLIC ENSE", - ". node", - ".n ode", - ".no de", - "Ġ Food", - "ĠF ood", - "ĠFo od", - "ĠFoo d", - "E IF", - "EI F", - "Ġ Bi", - "ĠB i", - ". Start", - ".St art", - "Ġ IB", - "ĠI B", - "Ġun iversity", - "Ġunivers ity", - "2 54", - "25 4", - "Ġ Header", - "ĠHe ader", - "ĠHead er", - ". product", - ".pro duct", - ".prod uct", - "4 09", - "40 9", - "C opy", - "Co py", - "Cop y", - "e tc", - "et c", - "r ical", - "ri cal", - "ric al", - "rica l", - "Ġ >>>", - "Ġ> >>", - "Ġ>> >", - "b ooks", - "bo oks", - "book s", - "boo ks", - "Ġ algorithm", - "Ġal gorithm", - "Ġ' __", - "Ġ'_ _", - "( javax", - "(j avax", - "(java x", - "Ġnumer ous", - "Ġnumero us", - "S hare", - "Sh are", - "Shar e", - "Sha re", - "H ave", - "Ha ve", - "Ġrec ru", - "Ġ prove", - "Ġp rove", - "Ġpro ve", - "Ġpr ove", - "Ġprov e", - ". substring", - ".sub string", - ".substr ing", - "he alth", - "е л", - "еР»", - "Ġ decimal", - "Ġd ecimal", - "Ġde cimal", - "Ġdec imal", - "Ġ commission", - "Ġcom mission", - "Ġcomm ission", - "s cription", - "script ion", - "scri ption", - "x C", - "Ġ summary", - "Ġsum mary", - "Ġsummar y", - "Ġsumm ary", - "at ted", - "att ed", - "atte d", - "Ġc loser", - "Ġcl oser", - "Ġclose r", - "Ġclos er", - "Ġclo ser", - "f inished", - "fin ished", - "finish ed", - "( )){Ċ", - "() ){Ċ", - "()) {Ċ", - "()){ Ċ", - "Ġ Wood", - "ĠW ood", - "ĠWo od", - "ĠWoo d", - "3 01", - "30 1", - "_ fields", - "_f ields", - "_field s", - "k u", - "_ items", - "_i tems", - "_item s", - "_it ems", - "F lag", - "Fl ag", - "Ġ confidence", - "Ġconf idence", - "Ġ Federal", - "ĠF ederal", - "ĠFeder al", - "ĠFed eral", - "d ux", - "du x", - "Ġ compat", - "Ġcom pat", - "Ġcomp at", - "Ġ vertical", - "Ġver tical", - "Ġvert ical", - "Ð ¹", - "è s", - "; \">Ċ", - ";\" >Ċ", - ";\"> Ċ", - "_ manager", - "_m anager", - "_man ager", - "_manage r", - "( )))Ċ", - "() ))Ċ", - "()) )Ċ", - "())) Ċ", - "I DE", - "ID E", - ": \",", - ":\" ,", - "2 35", - "23 5", - "_ _Ċ", - "__ Ċ", - "Ġ Way", - "ĠW ay", - "ĠWa y", - "2 21", - "22 1", - "Ñ Ī", - "T emp", - "Te mp", - "Tem p", - "Ġ STR", - "ĠS TR", - "ĠST R", - "r itten", - "rit ten", - "ritt en", - "ritte n", - "S ync", - "Sy nc", - "Syn c", - "Ġ AV", - "ĠA V", - "Ġ CEO", - "ĠC EO", - "ĠCE O", - "Ġ Guid", - "ĠG uid", - "ĠGu id", - "ĠGui d", - "Ġenvironment al", - "Ġenviron mental", - "Ġcorrespond ing", - "ĉ console", - "ĉcon sole", - "Ġ justice", - "Ġjust ice", - "Ġju stice", - "Ġ JS", - "ĠJ S", - "Ġl ived", - "Ġli ved", - "Ġlive d", - "Ġliv ed", - "g ar", - "ga r", - "Ġ Graph", - "ĠG raph", - "ĠGr aph", - "ĠGra ph", - "Ġ Stat", - "ĠS tat", - "ĠSt at", - "ĠSta t", - "Ġ iPhone", - "Ġi Phone", - "ĠiP hone", - ". al", - ".a l", - "Ġ HD", - "ĠH D", - "Ġocc ur", - "Ġoc cur", - "Ġ threshold", - "Ġth reshold", - "Ġthresh old", - "5 09", - "50 9", - "Ġ onclick", - "Ġon click", - "Ġonc lick", - "R EG", - "RE G", - ".Graphics Unit", - "M eta", - "Me ta", - "Met a", - "Å ¾", - "Ġ cum", - "Ġc um", - "Ġcu m", - ". gnu", - ".g nu", - "à «", - "Ġobt ained", - "Ġobtain ed", - "Ġcom plaint", - "Ġcompl aint", - "Ġcomplain t", - "Ġ eating", - "Ġe ating", - "Ġeat ing", - "Ġea ting", - "Ġ tar", - "Ġt ar", - "Ġta r", - "_ task", - "_t ask", - "_ta sk", - "Ġ opts", - "Ġo pts", - "Ġop ts", - "Ġopt s", - "2 16", - "21 6", - "( to", - "(t o", - "P ass", - "Pa ss", - "Pas s", - "Ġp lastic", - "Ġpl astic", - "Ġplast ic", - "t ility", - "til ity", - "Ġ Win", - "ĠW in", - "ĠWi n", - ".prevent Default", - "p ile", - "pi le", - "Ġ Gar", - "ĠG ar", - "ĠGa r", - "Ġ quantity", - "Ġqu antity", - "Ġquant ity", - "Ġqua ntity", - "_ last", - "_l ast", - "_la st", - "Ġg reatest", - "Ġgreat est", - "Ġgre atest", - "D ao", - "Da o", - "_ DIS", - "_D IS", - "_DI S", - "Ġ Used", - "ĠU sed", - "ĠUs ed", - "ĠUse d", - "Ġ HP", - "ĠH P", - "r iting", - "ri ting", - "rit ing", - "S ION", - "SI ON", - "b lue", - "bl ue", - "d omain", - "do main", - "dom ain", - "Ġ scores", - "Ġs cores", - "Ġsc ores", - "Ġscore s", - "Ġsco res", - "Ġscor es", - "N ormal", - "Norm al", - "Nor mal", - "_ admin", - "_ad min", - "Ġ ASSERT", - "ĠA SSERT", - "ĠASS ERT", - "T hen", - "The n", - "Th en", - "* **", - "** *", - "d ist", - "dis t", - "di st", - "l on", - "lo n", - "Ġh ate", - "Ġha te", - "Ġhat e", - "s hal", - "sh al", - "sha l", - "Image View", - "d atabase", - "data base", - "dat abase", - "Ġp and", - "Ġpa nd", - "Ġpan d", - "Ġ logic", - "Ġlog ic", - "= false", - "=f alse", - "b g", - "Ġ Configuration", - "ĠConfig uration", - "Ġn ur", - "Ġnu r", - "O G", - "Ġ married", - "Ġmar ried", - ": +", - "Ġd ropped", - "Ġdr opped", - "Ġdrop ped", - "Ġdro pped", - "0 40", - "04 0", - "Ġ registration", - "Ġreg istration", - "Ġregistr ation", - "Ġregist ration", - "о м", - "оР¼", - "ult iple", - "ulti ple", - "ultip le", - "i zers", - "iz ers", - "ize rs", - "izer s", - "s hape", - "sh ape", - "sha pe", - ". copy", - ".c opy", - ".co py", - "Ġw earing", - "Ġwe aring", - "Ġwear ing", - "ĠC ath", - "ĠCa th", - "ĠCat h", - "Ġded icated", - "Ġdedic ated", - "Ġdedicate d", - "Ġ ...Ċ", - "Ġ. ..Ċ", - "Ġ... Ċ", - "Ġ.. .Ċ", - "Ġadv oc", - "Ġ Family", - "ĠF amily", - "ĠFam ily", - "ĠFamil y", - "Ġ statements", - "Ġstate ments", - "Ġstat ements", - "Ġstatement s", - "e matic", - "em atic", - "ema tic", - "emat ic", - "ampions hip", - "ampion ship", - "Ġmot iv", - "Ġmo tiv", - "Ġ Have", - "ĠH ave", - "ĠHa ve", - "ĠHav e", - "Ġb low", - "Ġbl ow", - "Ġblo w", - "J ob", - "Jo b", - "c ert", - "ce rt", - "cer t", - "_ vector", - "_v ector", - "_vec tor", - "_vect or", - "_ve ctor", - "inst all", - "Ġ COPY", - "ĠC OPY", - "ĠCO PY", - "ĠCOP Y", - "em bed", - "emb ed", - "D IR", - "DI R", - "Ġ Spring", - "ĠS pring", - "ĠSp ring", - "ĠSpr ing", - "Ġex hib", - "Ġexh ib", - "2 23", - "22 3", - "c dn", - "cd n", - "Ġ Comment", - "ĠCom ment", - "ĠComm ent", - "Ġ Optional", - "ĠOption al", - "ĠOpt ional", - ". player", - ".p layer", - ".pl ayer", - ".play er", - "Ġ Dark", - "ĠD ark", - "ĠDa rk", - "ĠDar k", - "( pos", - "(p os", - "(po s", - "Ġ Should", - "ĠSh ould", - "ĠSho uld", - "Ġ centre", - "Ġc entre", - "Ġcent re", - "Ġcentr e", - "Ġcen tre", - "Ġ Guard", - "ĠG uard", - "ĠGu ard", - "ĠGuar d", - "ó w", - "Ġtr ouble", - "Ġtro uble", - "Ġtroub le", - "Ġtrou ble", - "E NER", - "EN ER", - "ENE R", - "( unsigned", - "(un signed", - "_ service", - "_s ervice", - "_serv ice", - "_ser vice", - "Ġ ns", - "Ġn s", - "u ling", - "ul ing", - "ulin g", - "uli ng", - "Ġ Mexico", - "ĠMe xico", - "ĠMex ico", - "Ġ NY", - "ĠN Y", - "m ysql", - "my sql", - "mys ql", - "Ġ lic", - "Ġl ic", - "Ġli c", - "å ľ", - "M r", - "- fl", - "-f l", - "Ġ Customer", - "ĠC ustomer", - "ĠCustom er", - "ĠCust omer", - "i di", - "id i", - "Ġ ?>ĊĊ", - "Ġ? >ĊĊ", - "Ġ?> ĊĊ", - "Ġ?>Ċ Ċ", - "r ible", - "ri ble", - "rib le", - "Ġ пÑĢ", - "Ġп ÑĢ", - "Ġ sizes", - "Ġs izes", - "Ġsize s", - "Ġsi zes", - "Ġsiz es", - "_ STRING", - "_ST RING", - "_STR ING", - "valid ation", - "Ġ Jon", - "ĠJ on", - "ĠJo n", - "( Http", - "(H ttp", - "add Class", - "N odes", - "Node s", - "No des", - "Ġ fragment", - "Ġf ragment", - "Ġfr agment", - "Ġfra gment", - "Ġfrag ment", - "Ġs poke", - "Ġsp oke", - "Ġspo ke", - "Ġw aste", - "Ġwas te", - "Ġwa ste", - "Ġwast e", - "J oin", - "Jo in", - "Ġ illustr", - "Ġill ustr", - "Ġillust r", - "e li", - "el i", - "c ient", - "ci ent", - "cie nt", - "Ġ aid", - "Ġa id", - "Ġai d", - "Ġpro sec", - "Ġpros ec", - "Ġprose c", - "' ){Ċ", - "') {Ċ", - "'){ Ċ", - "Ġp assing", - "Ġpass ing", - "Ġpas sing", - "Ġ faces", - "Ġf aces", - "Ġfa ces", - "Ġfac es", - "Ġface s", - "S hape", - "Sh ape", - "Sha pe", - "_ Z", - "i ti", - "it i", - "Ġ alle", - "Ġa lle", - "Ġal le", - "Ġall e", - "Ġ robot", - "Ġr obot", - "Ġro bot", - "Ġrob ot", - "Ġ ĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠ ĠĊ", - "Ġ Spe", - "ĠS pe", - "ĠSp e", - "Ġre ceiving", - "Ġrece iving", - "Ġ Details", - "ĠD etails", - "ĠDe tails", - "ĠDet ails", - "ĠDetail s", - "Ġ \")", - "Ġ\" )", - "m g", - "_ REF", - "_RE F", - "_R EF", - "Ġ comparison", - "Ġcom parison", - "Ġcompar ison", - "* ,", - "Ġ Found", - "ĠF ound", - "ĠFo und", - "ĠFou nd", - "_ session", - "_s ession", - "_sess ion", - "( U", - "/ F", - "Ġ xxx", - "Ġx xx", - "Ġxx x", - "N etwork", - "Net work", - "d ers", - "de rs", - "der s", - "Ġ capture", - "Ġc apture", - "Ġcap ture", - "Ġcapt ure", - "Ġc orre", - "Ġcor re", - "Ġcorr e", - "Ġ Ltd", - "ĠL td", - "ĠLt d", - "Ġ Adv", - "ĠA dv", - "ĠAd v", - "[ @", - "Ġ clip", - "Ġc lip", - "Ġcl ip", - "Ġcli p", - "M ill", - "Mi ll", - "Mil l", - "Ġ Profile", - "ĠPro file", - "ĠPr ofile", - "ĠProf ile", - "Ġ endif", - "Ġen dif", - "Ġend if", - "Ġob lig", - "Ġobl ig", - "de scribe", - "des cribe", - "descr ibe", - ". element", - ".e lement", - ".el ement", - ".elem ent", - "riter ion", - "L D", - "e red", - "er ed", - "ere d", - "Ġf avour", - "Ġfav our", - "s core", - "sc ore", - "Ġ Filter", - "ĠF ilter", - "ĠFil ter", - "at tributes", - "attribute s", - "attrib utes", - "Ġ checks", - "Ġcheck s", - "Ġche cks", - "In flater", - "Inf later", - "Ġ Plus", - "ĠP lus", - "ĠPl us", - "Ġ scientific", - "Ġscient ific", - "Ġ privacy", - "Ġpriv acy", - "H ead", - "He ad", - "Ġ feat", - "Ġf eat", - "Ġfe at", - "Ġ degrees", - "Ġdeg rees", - "Ġdegree s", - "Ġ Pale", - "ĠP ale", - "ĠPal e", - "ĠPa le", - "; \">", - ";\" >", - "Ġ films", - "Ġfil ms", - "Ġfilm s", - "Ġ Audio", - "ĠA udio", - "ĠAud io", - "ĠAu dio", - "ĠAudi o", - "Ġ Tag", - "ĠT ag", - "ĠTa g", - "Ġ Energy", - "ĠE nergy", - "ĠEn ergy", - "ĠEnerg y", - "i tar", - "it ar", - "ita r", - "par ator", - "para tor", - "Ġf ellow", - "Ġfell ow", - "Ġfel low", - "Ġ evt", - "Ġe vt", - "Ġev t", - "Ġ Tri", - "ĠT ri", - "ĠTr i", - "ĠD AM", - "ĠDA M", - "c loud", - "cl oud", - "clo ud", - "Ġ Password", - "ĠP assword", - "ĠPass word", - "ĠPas sword", - "Ġ Democrats", - "ĠDem ocrats", - "ĠDemocr ats", - "ĠDemocrat s", - "ĠA cad", - "ĠAc ad", - "$ lang", - "$l ang", - "Ġ reb", - "Ġre b", - "Ġr eb", - "( ))ĊĊ", - "() )ĊĊ", - "()) ĊĊ", - "())Ċ Ċ", - "н Ñĭ", - "Ġ Bur", - "ĠB ur", - "ĠBu r", - "read cr", - "Ġ hex", - "Ġh ex", - "Ġhe x", - "2 09", - "20 9", - "Con sole", - "Cons ole", - "c tl", - "ct l", - "ou sel", - "ous el", - "ouse l", - "Ġ William", - "ĠWill iam", - "ĠWilli am", - "Ġ az", - "Ġa z", - "_ PORT", - "_P ORT", - "_PO RT", - "Ġpr actices", - "Ġpract ices", - "Ġpractice s", - "Ġany where", - "Ġ Position", - "ĠP osition", - "ĠPos ition", - "Ġ ->Ċ", - "Ġ- >Ċ", - "Ġ-> Ċ", - "i ams", - "ia ms", - "iam s", - ". username", - ".user name", - "place holder", - "Ġ oder", - "Ġo der", - "Ġod er", - "Ġode r", - "Ġ Secretary", - "ĠSecret ary", - "Ġ iT", - "Ġi T", - "m ond", - "mon d", - "mo nd", - "e vents", - "event s", - "ev ents", - "even ts", - "eve nts", - "? âĢĿ", - ". Sub", - ".S ub", - "Ġ attached", - "Ġatt ached", - "Ġattach ed", - "Ġn ão", - "Ġ estate", - "Ġe state", - "Ġest ate", - "Ġesta te", - "3 65", - "36 5", - ". action", - ".a ction", - ".ac tion", - ".act ion", - "Ġ figures", - "Ġfig ures", - "Ġfigure s", - "Ġfigur es", - "Ġ });čĊ", - "Ġ} );čĊ", - "Ġ}) ;čĊ", - "Ġ}); čĊ", - "Ġsub scri", - "Ġsubs cri", - ". tag", - ".t ag", - ".ta g", - "n am", - "na m", - ". plot", - ".p lot", - ".pl ot", - "n oon", - "no on", - "li ament", - "lia ment", - "Char acter", - ". tab", - ".t ab", - ".ta b", - "Ġ winter", - "Ġw inter", - "Ġwin ter", - "Ġ Variable", - "ĠVar iable", - "ĠVari able", - "Ġ trees", - "Ġt rees", - "Ġtr ees", - "Ġtre es", - "Ġtree s", - "Ġp roud", - "Ġpro ud", - "Ġpr oud", - "( V", - "_ load", - "_l oad", - "_lo ad", - "Ġh ier", - "Ġhi er", - "ĠE con", - "ĠEc on", - "ĠEco n", - "Ġ fd", - "Ġf d", - "Ġvict ims", - "Ġvictim s", - "R est", - "Re st", - "Res t", - "i ana", - "ia na", - "ian a", - "Ġ fake", - "Ġf ake", - "Ġfa ke", - "Ġfak e", - ".Print ln", - "Ġ strlen", - "Ġst rlen", - "Ġstr len", - "Ġ sad", - "Ġs ad", - "Ġsa d", - "Ġ ble", - "Ġb le", - "Ġbl e", - "P rot", - "Pro t", - "Pr ot", - "Ġ buttons", - "Ġbut tons", - "Ġbutton s", - "Ġbutt ons", - "Ġbutto ns", - "Ġte levision", - "Ġtele vision", - "Ġtelevis ion", - "Ġtelev ision", - "Ġ logo", - "Ġl ogo", - "Ġlo go", - "Ġlog o", - "ext ension", - "ĉ j", - "s tein", - "st ein", - "ste in", - "ac iones", - "acion es", - "aci ones", - "acio nes", - "Ġ \"\"\"ĊĊ", - "Ġ\"\" \"ĊĊ", - "Ġ\"\"\"Ċ Ċ", - "Ġ\"\"\" ĊĊ", - "Ġ simp", - "Ġs imp", - "Ġsim p", - "Ġsi mp", - "Ġrecord ed", - "Ġbr ings", - "Ġbring s", - "Ġ principal", - "Ġpr incipal", - "Ġprincip al", - "Ġf ees", - "Ġfe es", - "Ġfee s", - "( source", - "(s ource", - "k dir", - "kd ir", - "Ġ utils", - "Ġutil s", - "Ġut ils", - "Ġcorrect ly", - "f il", - "fi l", - "Ġ wel", - "Ġw el", - "Ġwe l", - "P air", - "Pa ir", - "- button", - "-b utton", - "-but ton", - "s cale", - "sc ale", - "scal e", - "ver ify", - "[ c", - "Ġ ---", - "Ġ- --", - "Ġ-- -", - "Ġ escape", - "Ġe scape", - "Ġes cape", - "Ġesc ape", - "Ġescap e", - "i kes", - "ik es", - "ike s", - "Lower Case", - "ic ian", - "ici an", - "icia n", - "Ġ chapter", - "Ġch apter", - "Ġcha pter", - "Ġchap ter", - "Ġ TYPE", - "ĠT YPE", - "ĠTY PE", - "Ġ shadow", - "Ġsh adow", - "Ġ awesome", - "Ġaw esome", - "Ġawe some", - "W E", - "e lif", - "el if", - "eli f", - "Ġ lambda", - "Ġl ambda", - "Ġlamb da", - "Ġ distinct", - "Ġdist inct", - "Ġ bare", - "Ġb are", - "Ġbar e", - "Ġba re", - "- off", - "-of f", - "-o ff", - "Ġ colour", - "Ġcol our", - ". appendChild", - ".append Child", - "o lec", - "ol ec", - "ole c", - "a ga", - "ag a", - ". fill", - ".f ill", - ".fi ll", - ".fil l", - "ĉ super", - "ĉs uper", - "Ġ adj", - "Ġa dj", - "Ġad j", - "( position", - "(p osition", - "(pos ition", - ". getItem", - ".get Item", - "2 42", - "24 2", - "S hort", - "Sh ort", - "Ġtot ally", - "Ġtotal ly", - "V D", - "Ġ Tre", - "ĠT re", - "ĠTr e", - "_ ep", - "_e p", - "v ements", - "ve ments", - "vement s", - "vem ents", - "Ġ Solution", - "ĠS olution", - "ĠSol ution", - "Ġfund ament", - "F ollow", - "Ġ facility", - "Ġfac ility", - "Ġfacilit y", - "Ġfacil ity", - "Ġhapp ening", - "Ġhappen ing", - "O F", - ". textBox", - ".text Box", - "S pan", - "Sp an", - "Ġ «", - "Ġ «", - "i den", - "id en", - "ide n", - "Ġex ceed", - "Ġexc eed", - "Ġexce ed", - "( parent", - "(p arent", - "(par ent", - "(paren t", - "(pa rent", - "Ġ cp", - "Ġc p", - "ç »", - "Ġh asn", - "Ġhas n", - "Ġha sn", - "Ġ pri", - "Ġp ri", - "Ġpr i", - "Ġcon sequ", - "Ġcons equ", - "Ġconse qu", - "n en", - "ne n", - "ĠIN TO", - "ĠINT O", - "I gnore", - "Ign ore", - "Ġ Future", - "ĠF uture", - "ĠFu ture", - "ĠFut ure", - "Ġ carbon", - "Ġc arbon", - "Ġcar bon", - "Ġcarb on", - "Ġ Steel", - "ĠSt eel", - "ĠSte el", - "f mt", - "fm t", - "o kie", - "ok ie", - "oki e", - "Ġ spl", - "Ġs pl", - "Ġsp l", - "( title", - "(t itle", - "(ti tle", - "- info", - "-in fo", - "-inf o", - "Ġde als", - "Ġdeal s", - "Ġ fixture", - "Ġf ixture", - "Ġfix ture", - "e a", - "D iv", - "Di v", - "Ġ tested", - "Ġt ested", - "Ġte sted", - "Ġtest ed", - "Ġtes ted", - "Ġteste d", - "_ return", - "_re turn", - "_r eturn", - "_ret urn", - ") ĊĊĊĊ", - ")Ċ ĊĊĊ", - ")ĊĊ ĊĊ", - ")ĊĊĊ Ċ", - "up ported", - "upport ed", - "upp orted", - "Ġ Cook", - "ĠC ook", - "ĠCo ok", - "Ġp aying", - "Ġpay ing", - "Ġpa ying", - "Ġ Ill", - "ĠI ll", - "ĠIl l", - "Ġarr ested", - "Ġarrest ed", - "Ġ Prime", - "ĠPr ime", - "ĠPri me", - "ĠPrim e", - "_ callback", - "_c allback", - "_call back", - "> ,Ċ", - ">, Ċ", - "d river", - "dr iver", - "drive r", - "O nce", - "On ce", - "a bb", - "ab b", - "_ bytes", - "_by tes", - "_byte s", - "Ġ Sets", - "ĠS ets", - "ĠSe ts", - "ĠSet s", - "( Object", - "(O bject", - "Ġ cc", - "Ġc c", - "Ġ shell", - "Ġs hell", - "Ġsh ell", - "Ġshe ll", - "Ġshel l", - "a lo", - "al o", - ") ;//", - "); //", - "( log", - "(l og", - "(lo g", - "2 64", - "26 4", - "c tors", - "ct ors", - "ctor s", - ") ", - "2 18", - "21 8", - "Ġ $(\".", - "Ġ$ (\".", - "Ġ$( \".", - "Ġ$(\" .", - ". pos", - ".p os", - ".po s", - "Ġ boys", - "Ġbo ys", - "Ġboy s", - "Ġwed ding", - "Ġ agents", - "Ġag ents", - "Ġage nts", - "Ġagent s", - "= \"_", - "=\" _", - "Ġ Army", - "ĠAr my", - "ĠArm y", - "Ġ hint", - "Ġh int", - "Ġhi nt", - "Ġhin t", - "v ision", - "vis ion", - "Ġ tech", - "Ġt ech", - "Ġte ch", - "Ġtec h", - "Ġ Connect", - "ĠCon nect", - "ĠConn ect", - "Ġ legend", - "Ġl egend", - "Ġle gend", - "Ġleg end", - "Ġ Bet", - "ĠB et", - "ĠBe t", - ". Base", - ".B ase", - "Sub ject", - "Su bject", - "Ġ lit", - "Ġl it", - "Ġli t", - "Re move", - "Rem ove", - "Ġ \":", - "Ġ\" :", - "Ġ Final", - "ĠF inal", - "ĠFin al", - "ĠFi nal", - "pear ance", - "ĠiT unes", - "Ġ participants", - "Ġpart icipants", - "Ġparticip ants", - "Ġparticipant s", - "Ġ Python", - "ĠP ython", - "ĠPy thon", - "Ġ busy", - "Ġbu sy", - "Ġbus y", - "i el", - "ie l", - "vert ices", - "Ġtemplate Url", - "Ġ Close", - "ĠC lose", - "ĠCl ose", - "ĠClo se", - "I mg", - "Im g", - "ĠCorpor ation", - "ĠCorp oration", - "t imestamp", - "time stamp", - "Ġ extend", - "Ġext end", - "Ġwe bsites", - "Ġweb sites", - "Ġwebsite s", - "Ġwebs ites", - "Ġposs ibility", - "Ġpossibilit y", - "о ÑĤ", - "Ġ kö", - "Ġk ö", - "Ġm eat", - "Ġme at", - "Ġ representation", - "Ġre presentation", - "Ġrep resentation", - "Ġrepresent ation", - "Ġrepresenta tion", - "2 41", - "24 1", - "Ġ ĉĉ", - "Ġĉ ĉ", - "_ START", - "_ST ART", - "_STAR T", - "_STA RT", - ". apply", - ".app ly", - ".ap ply", - "ĠV alley", - "ĠVal ley", - "ĠValle y", - "ĠVall ey", - "Ġ Success", - "ĠS uccess", - "ĠSu ccess", - "ĠSuc cess", - "ĠSucc ess", - "H i", - "Ġ nob", - "Ġn ob", - "Ġno b", - "Ġ IEnumerable", - "ĠI Enumerable", - "_ select", - "_s elect", - "_se lect", - "_sel ect", - "g eo", - "ge o", - ". \")Ċ", - ".\" )Ċ", - ".\") Ċ", - "Ġt urning", - "Ġturn ing", - "Ġtur ning", - "Ġ fabric", - "Ġf abric", - "Ġfab ric", - "(\" \");Ċ", - "(\"\" );Ċ", - "(\"\") ;Ċ", - "(\"\"); Ċ", - "Ġp erspective", - "Ġpers pective", - "é Ĺ", - "Ġ Sn", - "ĠS n", - "Th ank", - "Than k", - "; j", - ". Parameters", - ".Param eters", - ".Parameter s", - "ĉ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĉĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĉĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĉĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ facts", - "Ġf acts", - "Ġfact s", - "Ġfa cts", - "Ġfac ts", - "3 05", - "30 5", - "Ġ unt", - "Ġu nt", - "Ġun t", - ". instance", - ".in stance", - ".inst ance", - "#### ############################################################", - "######## ########################################################", - "################ ################################################", - "################################ ################################", - "################################################ ################", - "######################################## ########################", - "######################## ########################################", - "######################################################## ########", - "############################################################ ####", - "- end", - "-e nd", - "-en d", - "Ġ JOIN", - "ĠJO IN", - "Ġ Hen", - "ĠH en", - "ĠHe n", - "Ġ uri", - "Ġu ri", - "Ġur i", - "åIJ į", - "Ġ на", - "Ġн а", - "Ġ Info", - "ĠIn fo", - "ĠInf o", - "Ġcon ducted", - "Ġconduct ed", - "Ġconduc ted", - "Ġ Ã¥", - "Ġà ¥", - "OUR CE", - "Ġ wine", - "Ġw ine", - "Ġwin e", - "Ġwi ne", - "J ohn", - "Jo hn", - ".Error f", - ".Err orf", - "Ġ Age", - "ĠA ge", - "ĠAg e", - "o unded", - "ound ed", - "oun ded", - "Ġreal ize", - "Ġrealiz e", - "3 12", - "31 2", - "Ġ ];", - "Ġ] ;", - "Ġsub sequ", - "Ġsubs equ", - ", m", - "( User", - "(U ser", - "i ano", - "ia no", - "ian o", - "Ġaccom pl", - "Ġaccomp l", - "i sp", - "is p", - ". std", - ".s td", - ".st d", - "é ĩ", - "Ġ Bed", - "ĠB ed", - "ĠBe d", - ". setAttribute", - ".set Attribute", - "B R", - "ke ep", - "kee p", - "Ġ ALL", - "ĠA LL", - "ĠAL L", - "Ġ isol", - "Ġis ol", - "Ġi sol", - "Ġiso l", - "a mma", - "am ma", - "amm a", - "P ackage", - "Pack age", - "Ġ occasion", - "Ġocc asion", - "Ġoccas ion", - "- success", - "-s uccess", - "-su ccess", - "е д", - "еР´", - "ĠLIMIT ED", - "s trip", - "st rip", - "str ip", - "stri p", - "( )ĊĊĊ", - "() ĊĊĊ", - "()Ċ ĊĊ", - "()ĊĊ Ċ", - "is tribution", - "istrib ution", - "Color s", - "Col ors", - "Ġ+ :+", - "Did Load", - "a ler", - "al er", - "ale r", - "Ġ tid", - "Ġt id", - "Ġti d", - "Ġ LED", - "ĠL ED", - "ĠLE D", - "Ġ Linked", - "ĠLink ed", - "ĠLin ked", - "Ġ Cart", - "ĠC art", - "ĠCar t", - "ĠCa rt", - "( ))čĊ", - "() )čĊ", - "()) čĊ", - "_ READ", - "_RE AD", - "Ġk illing", - "Ġkill ing", - "Ġkil ling", - "Ġ PHP", - "ĠP HP", - "ĠPH P", - "f ection", - "fe ction", - "fect ion", - "fec tion", - "Ġ instances", - "Ġin stances", - "Ġinst ances", - "Ġinstance s", - "c v", - "\" />", - "\"/ >", - "Ġ sf", - "Ġs f", - "Ġt axes", - "Ġtax es", - "Ġta xes", - "_ location", - "_l ocation", - "_lo cation", - "_loc ation", - "Ġ Bitcoin", - "ĠBit coin", - "u able", - "ua ble", - "r ank", - "ra nk", - "ran k", - "i gnore", - "ig nore", - "ign ore", - "t rack", - "tr ack", - "tra ck", - "к а", - "Ġshould n", - "Ġ OP", - "ĠO P", - "= >{Ċ", - "=> {Ċ", - "Ġ km", - "Ġk m", - "Ġ helper", - "Ġh elper", - "Ġhelp er", - "Ġhel per", - "_ head", - "_h ead", - "_he ad", - "Ġ Whether", - "ĠWh ether", - "ĠWhe ther", - "o co", - "oc o", - "_ bl", - "_b l", - "Ġ statistics", - "Ġstat istics", - "Ġstatist ics", - "Ġstatistic s", - "Ġbe auty", - "Ġbeaut y", - "Ġbeau ty", - "Ġ tog", - "Ġt og", - "Ġto g", - "t ip", - "ti p", - "ëĭ ¤", - "Ġ csv", - "Ġc sv", - "Ġcs v", - "( sql", - "(s ql", - "(sq l", - "std lib", - "we ak", - "Ġ likes", - "Ġl ikes", - "Ġli kes", - "Ġlike s", - "Ġlik es", - "Ä į", - "Ġ repeat", - "Ġre peat", - "Ġrep eat", - "Ġrepe at", - "Ġa partment", - "Ġap artment", - "Ġapart ment", - "Ġe mph", - "Ġem ph", - "Ġemp h", - "_ edit", - "_e dit", - "_ed it", - "Ġ vit", - "Ġv it", - "Ġvi t", - "ĉ type", - "ĉt ype", - "ĉtyp e", - "2 17", - "21 7", - "E ven", - "Ev en", - "u ten", - "ut en", - "ute n", - "Ġcircum stances", - "Ġcircumstance s", - "b ian", - "bi an", - "bia n", - "Ġs ugar", - "Ġsu gar", - "Ġsug ar", - "W indows", - "Window s", - "Wind ows", - "ì ŀ", - "Ġobs erved", - "Ġobserv ed", - "Ġobserve d", - "/ data", - "/d ata", - "/dat a", - "Ġ calendar", - "Ġc alendar", - "Ġcal endar", - "Ġcalend ar", - "Ġ strike", - "Ġst rike", - "Ġstr ike", - "Ġstri ke", - "Ġ RES", - "ĠR ES", - "ĠRE S", - "_ sc", - "_s c", - "f ony", - "fo ny", - "fon y", - "o rem", - "or em", - "ore m", - "( z", - "p ower", - "po wer", - "pow er", - "et ect", - "ete ct", - "Ġ Sat", - "ĠS at", - "ĠSa t", - ". description", - ".d escription", - ".de scription", - ".des cription", - "Ġ gang", - "Ġg ang", - "Ġga ng", - "Ġgan g", - "Ġ Sports", - "ĠS ports", - "ĠSp orts", - "ĠSport s", - "ĠSpo rts", - "ĠSpor ts", - "on gs", - "ong s", - "Ġ Bundle", - "ĠB undle", - "ĠBund le", - "ĠBun dle", - ". sum", - ".s um", - "o nce", - "on ce", - "Ġacc used", - "Ġaccus ed", - "Ġaccuse d", - "Ġexp lore", - "Ġexpl ore", - "Ġexplo re", - "Ġexplor e", - "Ġ approximately", - "Ġapprox imately", - "Ġapproximate ly", - "Ġ losing", - "Ġl osing", - "Ġlo sing", - "Ġlos ing", - "th esis", - "the sis", - "thes is", - "Ġ Fund", - "ĠF und", - "ĠFun d", - "ĠFu nd", - "Ġdi agn", - "Ġdia gn", - "Ġdiag n", - "A utowired", - "p roperties", - "prop erties", - "proper ties", - "Ġ _.", - "Ġ_ .", - "Ġ cnt", - "Ġc nt", - "Ġcn t", - "ced ure", - "Ġ yy", - "Ġy y", - "Ġ grant", - "Ġg rant", - "Ġgr ant", - "Ġgran t", - "Ġgra nt", - "s ock", - "so ck", - "soc k", - ". innerHTML", - ".inner HTML", - "Ġ ]);Ċ", - "Ġ] );Ċ", - "Ġ]) ;Ċ", - "Ġ]); Ċ", - "Ġ CONFIG", - "ĠCON FIG", - "ĠCONF IG", - "= '$", - "=' $", - "5 50", - "55 0", - "] ];Ċ", - "]] ;Ċ", - "]]; Ċ", - "U ND", - "UN D", - "Ġ glob", - "Ġg lob", - "Ġgl ob", - "Ġglo b", - "Ġ dire", - "Ġd ire", - "Ġdi re", - "Ġdir e", - "uff le", - "_ MEM", - "_M EM", - "_ME M", - "Ġauth entic", - "> (\"", - ">( \"", - "Ġde cade", - "Ġdec ade", - "Ġdecad e", - "Ġ Import", - "ĠIm port", - "ĠImp ort", - "Ġorig inally", - "Ġoriginal ly", - "Ġorigin ally", - "Ġ jQuery", - "Ġj Query", - "Ġind icate", - "Ġindic ate", - "Ġindica te", - "Ġours elves", - "S w", - ". lbl", - ".l bl", - ".lb l", - "en erate", - "ener ate", - "ene rate", - "Ġbas ically", - "Ġbasic ally", - "Ġ Hom", - "ĠH om", - "ĠHo m", - "Ġ+ #+", - "Ġ+# +", - "Ġ Britain", - "ĠBrit ain", - "ĠBri tain", - "Ġ Kar", - "ĠK ar", - "ĠKa r", - "to Equal", - ". stop", - ".s top", - ".st op", - "Ġ modal", - "Ġm odal", - "Ġmod al", - "Ġmo dal", - "Ġmoda l", - "i si", - "is i", - "Ġsuggest s", - "Ġ dtype", - "Ġd type", - "Ġdt ype", - "Ġ tur", - "Ġt ur", - "Ġtu r", - "b f", - "Ġ connections", - "Ġconnection s", - "Ġconn ections", - "Ġconnect ions", - "Ġ Before", - "ĠB efore", - "ĠBe fore", - "i sted", - "is ted", - "ist ed", - "iste d", - "m ouse", - "mo use", - "Ġp ulled", - "Ġpull ed", - "Ġpul led", - ". build", - ".b uild", - "Ġleg islation", - "Ġlegis lation", - "Ġlegisl ation", - "Ġ forth", - "Ġf orth", - "Ġfor th", - "Ġfort h", - "p ad", - "pa d", - "e go", - "eg o", - ". Now", - ".N ow", - ".No w", - "Ġexc iting", - "Ġexcit ing", - "} ĊĊĊĊ", - "}Ċ ĊĊĊ", - "}ĊĊ ĊĊ", - "}ĊĊĊ Ċ", - "Ġcom pr", - "Ġco mpr", - "Ġcomp r", - "Ġ shares", - "Ġsh ares", - "Ġshare s", - "Ġsha res", - "Ġshar es", - "Ġ rig", - "Ġr ig", - "Ġri g", - "g reen", - "gr een", - "gre en", - "gree n", - "_ vec", - "_v ec", - "_ve c", - "Ġ enumerate", - "Ġenum erate", - "Ġenumer ate", - "A uto", - "Aut o", - "Au to", - "ic ator", - "ica tor", - "Ġ Ray", - "ĠR ay", - "ĠRa y", - "a sse", - "as se", - "ass e", - "Ġ holiday", - "Ġh oliday", - "Ġhol iday", - "Ġ nullable", - "Ġnull able", - "Ġnulla ble", - "g un", - "gu n", - "_ details", - "_d etails", - "_de tails", - "_detail s", - "_det ails", - "Ġ wrapper", - "Ġw rapper", - "Ġwr apper", - "Ġwrap per", - "s eq", - "se q", - "Ġ Young", - "ĠYou ng", - "ĠYo ung", - "ju ana", - "juan a", - "Ġ\" __", - "Ġ\"_ _", - "l icense", - "lic ense", - "s erve", - "se rve", - "ser ve", - "serv e", - "^ (", - "i ders", - "id ers", - "ide rs", - "ider s", - ". Remove", - ".Re move", - ".Rem ove", - "rop down", - "' S", - "p in", - "pi n", - "( token", - "(t oken", - "(to ken", - "(tok en", - ". Default", - ".D efault", - ".De fault", - ".Def ault", - "Ġ reasonable", - "Ġreason able", - "amp ion", - "ĠS ociety", - "ĠSoci ety", - "Ġ bei", - "Ġb ei", - "Ġbe i", - "er ves", - "erv es", - "erve s", - "r ad", - "ra d", - "Ġ Fox", - "ĠF ox", - "ĠFo x", - "_ images", - "_image s", - "_im ages", - "_imag es", - "Ġ wheel", - "Ġw heel", - "Ġwh eel", - "Ġwhe el", - "' )[", - "') [", - "Ġ cfg", - "Ġc fg", - "Ġcf g", - "( By", - "(B y", - "Con structor", - "Construct or", - "Ġ vary", - "Ġv ary", - "Ġvar y", - "Ġva ry", - ". swift", - ".sw ift", - "Ġ proxy", - "Ġpro xy", - "Ġpr oxy", - "Ġprox y", - "ĉ H", - "Ġ Another", - "ĠAn other", - "Ġ Pen", - "ĠP en", - "ĠPe n", - "Ġ checking", - "Ġcheck ing", - "Ġ jest", - "Ġj est", - "Ġje st", - "Ġjes t", - "m anager", - "man ager", - "manage r", - "mana ger", - "Or igin", - "Orig in", - "u gs", - "ug s", - "o ir", - "oi r", - "> čĊ", - "Ġ-- >čĊ", - "Ġ--> čĊ", - "Ġrel ief", - "Ġreli ef", - "Ġrelie f", - "l ap", - "la p", - "q uer", - "qu er", - "que r", - "_ parent", - "_p arent", - "_par ent", - "_pa rent", - "he ap", - "hea p", - "L OSE", - "LO SE", - "LOS E", - "Ġ combine", - "Ġc ombine", - "Ġcom bine", - "Ġcomb ine", - "Ġcombin e", - "Ġ Rose", - "ĠR ose", - "ĠRo se", - "ĠRos e", - "o wers", - "ow ers", - "ower s", - "owe rs", - "Ġpro cedures", - "Ġproced ures", - "Ġprocedure s", - "Ġ Sort", - "ĠS ort", - "ĠSo rt", - "ĠSor t", - "a nim", - "an im", - "ani m", - "v ariant", - "var iant", - "vari ant", - "eh icle", - "Ġsign ing", - "Ġsig ning", - "Ġsignin g", - "Pr imary", - "Prim ary", - "Pri mary", - "c urrency", - "curr ency", - "Ġs exe", - "Ġse xe", - "Ġsex e", - "o en", - "oe n", - "th eta", - "the ta", - "e man", - "em an", - "ema n", - "Ġim pressive", - "Ġimp ressive", - "Ġimpress ive", - "( '_", - "(' _", - "ĉ U", - "Ġ TextStyle", - "ĠText Style", - "_ cnt", - "_c nt", - "_cn t", - "Ġ slice", - "Ġs lice", - "Ġsl ice", - "Ġslic e", - "( ':", - "(' :", - "Ġunder stood", - "Ġunderst ood", - "H is", - "Hi s", - "2 77", - "27 7", - "0 13", - "01 3", - "Ġin formed", - "Ġinform ed", - "Ġ nick", - "Ġn ick", - "Ġni ck", - "Ġnic k", - "4 29", - "42 9", - "( TAG", - "(T AG", - "h d", - "Ġe lections", - "Ġel ections", - "Ġelect ions", - "Ġelection s", - "Ġele ctions", - "es ture", - "est ure", - "Ġ Santa", - "ĠS anta", - "ĠSan ta", - "ĠSant a", - "ĠCo ast", - ". pdf", - ".p df", - "inc iple", - "incip le", - "inci ple", - ". clone", - ".cl one", - "b orn", - "bo rn", - "bor n", - "u ta", - "ut a", - "Ġ licensed", - "Ġl icensed", - "Ġlicense d", - "Ġlic ensed", - "Ġlicens ed", - "C r", - "Ġ bread", - "Ġb read", - "Ġbr ead", - "Ġbre ad", - "Ġ Houston", - "ĠH ouston", - "ĠHou ston", - "Ġ nod", - "Ġn od", - "Ġno d", - "Ġh opes", - "Ġhope s", - "Ġhop es", - "Ġho pes", - "Ġ CGRect", - "ĠCG Rect", - "Ġgu ilty", - "Ġguilt y", - ". gif", - ".g if", - "Ġ rose", - "Ġr ose", - "Ġro se", - "Ġros e", - ". Common", - ".Com mon", - ".Comm on", - "T ip", - "Ti p", - "A NK", - "AN K", - "Ġ FC", - "ĠF C", - "D uring", - "Du ring", - "Dur ing", - "Ġ Symfony", - "ĠSym fony", - "Ġdef ensive", - "k m", - ") >", - "a rchive", - "arch ive", - "arc hive", - "Ġ URI", - "ĠU RI", - "ĠUR I", - "y cling", - "yc ling", - "ycl ing", - "- o", - "Ġ Website", - "ĠWe bsite", - "ĠWeb site", - "A MP", - "AM P", - "4 05", - "40 5", - "ish ment", - "Ġdo ctors", - "Ġdoc tors", - "Ġdoctor s", - "D irect", - "Dir ect", - "Di rect", - "Dire ct", - "A RI", - "AR I", - "Ġ Redirect", - "ĠRe direct", - "ĠRed irect", - "i eren", - "ie ren", - "ier en", - "iere n", - "9 60", - "96 0", - "_ dist", - "_d ist", - "_dis t", - "_di st", - "y o", - "Ġ Progress", - "ĠPro gress", - "Ġz um", - "Ġzu m", - "Ġme mor", - "Ġmem or", - "Ġmemo r", - "Ġ ED", - "ĠE D", - "Ġ jur", - "Ġj ur", - "Ġju r", - "æį ®", - "_ TABLE", - "_T ABLE", - "_TAB LE", - "_TA BLE", - "Ġ uuid", - "Ġu uid", - "Ġuu id", - "Ex pr", - "Exp r", - ". head", - ".h ead", - ".he ad", - "( '%", - "(' %", - "point er", - "po inter", - "Ġ estimate", - "Ġest imate", - "Ġestim ate", - "Ġ Greg", - "ĠG reg", - "ĠGr eg", - "ĠGre g", - "Ġ loader", - "Ġl oader", - "Ġlo ader", - "Ġload er", - "Ġloa der", - "Ġ iOS", - "Ġi OS", - "Ġ mens", - "Ġm ens", - "Ġme ns", - "Ġmen s", - "[ y", - "Ġref used", - "Ġrefuse d", - "Ġ precision", - "Ġp recision", - "Ġpre cision", - "Ġprec ision", - "Ġprecis ion", - "i sch", - "is ch", - "isc h", - "Ġ ACTION", - "ĠA CTION", - "ĠAC TION", - "ĠACT ION", - "C loud", - "Cl oud", - "Clo ud", - "s With", - "( ret", - "(r et", - "(re t", - "2 92", - "29 2", - "_ ADDR", - "_A DDR", - "_ADD R", - "_AD DR", - "_ conf", - "_con f", - "_co nf", - "( df", - "(d f", - "Ġ locked", - "Ġl ocked", - "Ġloc ked", - "Ġlock ed", - "Ġ rising", - "Ġr ising", - "Ġris ing", - "Ġri sing", - "ãĥ» ãĥ»", - "Ġ Ms", - "ĠM s", - "Ġ scenes", - "Ġsc enes", - "Ġscene s", - "Ġscen es", - "Ġsce nes", - "_ EXT", - "_E XT", - "_EX T", - "_ raw", - "_r aw", - "_ra w", - "_ the", - "_t he", - "_th e", - "pe ople", - "Ġre con", - "Ġrec on", - "Ġreco n", - "Ġ Fun", - "ĠF un", - "ĠFu n", - "Ġb less", - "Ġbl ess", - "Ġble ss", - "Ġ Updated", - "ĠUp dated", - "ĠUpdate d", - "4 22", - "42 2", - "ü n", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠčĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠčĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠčĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠčĊ", - "p ection", - "pe ction", - "pect ion", - "pec tion", - "R elease", - "Re lease", - "Rel ease", - ". logger", - ".log ger", - ".lo gger", - "Ġ SY", - "ĠS Y", - "Ġc ounsel", - "Ġcoun sel", - "u rd", - "ur d", - "_ true", - "_tr ue", - "Ġevery body", - "iv ot", - "ivo t", - "Ġh ence", - "Ġhe nce", - "Ġhen ce", - "Ġ NAS", - "ĠN AS", - "ĠNA S", - "7 89", - "78 9", - "Ġop posed", - "Ġopp osed", - "Ġoppos ed", - "Ġoppose d", - "un known", - "unk nown", - "Ġ DESC", - "ĠD ESC", - "ĠDE SC", - "ĠDES C", - "Ġ Chair", - "ĠC hair", - "ĠCh air", - "ĠCha ir", - "f ailed", - "fa iled", - "fail ed", - "Ġ INCLUDING", - "ĠIN CLUDING", - "3 86", - "38 6", - "3 52", - "35 2", - "Ġ writers", - "Ġw riters", - "Ġwrit ers", - "Ġwrite rs", - "Ġwriter s", - "{ }Ċ", - "{} Ċ", - "ÃŃ t", - "_ copy", - "_c opy", - "_co py", - "} :", - "Ġ Bat", - "ĠB at", - "ĠBa t", - "Ġ converted", - "Ġcon verted", - "Ġconvert ed", - "Ġconver ted", - "e ding", - "ed ing", - "edi ng", - "edin g", - "pl acement", - "place ment", - "Ġ Host", - "ĠH ost", - "ĠHo st", - "ĠHos t", - "S ound", - "So und", - "Sou nd", - "и м", - "Ġs ought", - "Ġso ught", - "Ġsou ght", - "4 02", - "40 2", - "m id", - "mi d", - "Ġ salary", - "Ġs alary", - "Ġsal ary", - "Ġsala ry", - "o gg", - "og g", - "âĦ ¢", - "b ul", - "bu l", - "Ġw ir", - "Ġwi r", - "valid ator", - "_ STAT", - "_ST AT", - "_STA T", - ". store", - ".st ore", - "Ġ Battle", - "ĠB attle", - "ĠBat tle", - "ĠBatt le", - "ı n", - "Ġ -->ĊĊ", - "Ġ-- >ĊĊ", - "Ġ-->Ċ Ċ", - "Ġ--> ĊĊ", - "Tr ump", - "d ot", - "do t", - "Ġ CONT", - "ĠC ONT", - "ĠCON T", - "ĠCO NT", - ". fetch", - ".f etch", - "Ġcont inu", - "Ġcontin u", - "w as", - "wa s", - "Ġf raud", - "Ġfr aud", - "Ġfra ud", - "Ġfrau d", - "_ tmp", - "_t mp", - "_tm p", - "m itter", - "mit ter", - "mitt er", - ". pictureBox", - ".p ictureBox", - ".picture Box", - "G A", - "Ġ tournament", - "Ġt ournament", - ". Input", - ".In put", - "3 43", - "34 3", - "[ r", - "ex ion", - "cent age", - "ĠK orean", - "ĠKore an", - "ĠKorea n", - "ĠKor ean", - "u ndef", - "un def", - "und ef", - "unde f", - "Ġ Available", - "ĠA vailable", - "ĠAv ailable", - "re shape", - "res hape", - "resh ape", - "Ġ kit", - "Ġk it", - "Ġki t", - "Ġ Struct", - "ĠStr uct", - "Ġ SUB", - "ĠS UB", - "ĠSU B", - "An swer", - "Ans wer", - "_ lib", - "_l ib", - "_li b", - ". twitter", - ".t witter", - ".tw itter", - "Ġ ore", - "Ġo re", - "Ġor e", - "Ġ Dragon", - "ĠD ragon", - "ĠDr agon", - "ĠDrag on", - "ĠDra gon", - ". Ext", - ".Ex t", - ".E xt", - ", k", - "Ġex planation", - "Ġexplan ation", - "r efs", - "re fs", - "ref s", - "Ġ Drive", - "ĠD rive", - "ĠDr ive", - "Ġ Training", - "ĠTr aining", - "ĠTra ining", - "ĠTrain ing", - "2 82", - "28 2", - ". Has", - ".H as", - "3 41", - "34 1", - "int age", - "inta ge", - "b ig", - "bi g", - "olog ist", - "olo gist", - "ologi st", - "en nis", - "enn is", - "4 60", - "46 0", - "Ù ĩ", - "Ġch icken", - "Ġchi cken", - "Ġchick en", - "Ġchic ken", - "Ġ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ Ċ", - "ç Ľ", - "ãģ §", - "Ġ peak", - "Ġpe ak", - "Ġpea k", - "Ġdr inking", - "Ġdrink ing", - "Ġ encode", - "Ġen code", - "Ġenc ode", - "Ġ NEW", - "ĠN EW", - "ĠNE W", - "m alloc", - "mal loc", - "mall oc", - "ĉ fprintf", - "ĉf printf", - "Ġ= ================================================================", - "Ġ================= ================================================", - "Ġ================================================= ================", - "Ġ================================= ================================", - "Ġ============================================================== ===", - "in cluding", - "incl uding", - "inclu ding", - "Ġprincip les", - "Ġprinciple s", - "Ġprinc iples", - "Ġ Mah", - "ĠM ah", - "ĠMa h", - "2 67", - "26 7", - "st orage", - "sto rage", - "stor age", - "- key", - "-k ey", - "Ġ keyword", - "Ġkey word", - "% ;", - "Ġ trained", - "Ġtr ained", - "Ġtrain ed", - "Ġtra ined", - "Ġtrai ned", - ". contrib", - ".con trib", - ".cont rib", - "Ġ kv", - "Ġk v", - "__ ':Ċ", - "Ġ Boy", - "ĠB oy", - "ĠBo y", - "param eter", - "para meter", - "Ġ suite", - "Ġs uite", - "Ġsu ite", - "Ġsuit e", - "Ġsui te", - "Ġth ousand", - "Ġthous and", - "Ġthou sand", - "Ġ coordinate", - "Ġco ordinate", - "Ġcoord inate", - "Ġcoordin ate", - "- generated", - "-g enerated", - "íķ ĺ", - "g enerated", - "gener ated", - "generate d", - "gene rated", - "Ġad mitted", - "Ġadm itted", - "Ġadmit ted", - "Ġp ussy", - "Ġpus sy", - "Ġpuss y", - "# w", - "Ġsw im", - "un ion", - "uni on", - "N a", - "2 74", - "27 4", - "Ġ Royal", - "ĠR oyal", - "ĠRoy al", - "ĠRo yal", - ". channel", - ".ch annel", - "Up dated", - "Update d", - "_ ROOT", - "_R OOT", - "_RO OT", - "Ġv ital", - "Ġvi tal", - "Ġvit al", - "Ġvita l", - "3 35", - "33 5", - "r action", - "ra ction", - "rac tion", - "ract ion", - "ĠCr usher", - "ĠCrush er", - "ĠCru sher", - "ĠCrus her", - "Ġ preced", - "Ġpr eced", - "Ġpre ced", - "Ġprec ed", - "Ġ horizontal", - "Ġh orizontal", - "Ġhor izontal", - "Ġhorizon tal", - "Blue print", - "Ġ attrs", - "Ġat trs", - "Ġatt rs", - "Ġattr s", - "Ġsm oke", - "Ġsmo ke", - "Ð Ĵ", - ". Equals", - ".E quals", - ".Equal s", - "F B", - "Ġ Resources", - "ĠRe sources", - "ĠRes ources", - "ĠResource s", - "rol ling", - "roll ing", - "Ġ passes", - "Ġp asses", - "Ġpass es", - "Ġpas ses", - "Ġpasse s", - "Ġ Num", - "ĠN um", - "ĠNu m", - "r otate", - "rot ate", - "e type", - "et ype", - "ety pe", - "\\ \",", - "\\\" ,", - "Ġs ensitive", - "Ġsens itive", - "Ġsensit ive", - "Ġt all", - "Ġtal l", - "Ġta ll", - "? âĢĿĊĊ", - "?âĢĿ ĊĊ", - "Pro xy", - "Pr oxy", - "i y", - "_ section", - "_s ection", - "_se ction", - "_sec tion", - "âĢĶâĢĶ âĢĶâĢĶ", - "b rid", - "br id", - "Ġc ircuit", - "Ġcirc uit", - "a tan", - "at an", - "ata n", - "E NC", - "EN C", - "Ġdr iven", - "Ġdrive n", - "Ġdriv en", - "Ġdri ven", - "Ġv oted", - "Ġvo ted", - "Ġvot ed", - "Ġvote d", - "Ġeduc ational", - "Ġeducation al", - "Ġ interaction", - "Ġinter action", - "Ġinteract ion", - "Ġinte raction", - "ab etes", - "abe tes", - "abet es", - "Ġ tone", - "Ġt one", - "Ġto ne", - "Ġton e", - "ĠInitialize Component", - "Ġmer ely", - "Ġmere ly", - "Ġ ìŀ", - "Ġì ŀ", - "c ookie", - "co okie", - "cook ie", - "_ div", - "_d iv", - "_di v", - "Ġ UILabel", - "ĠUI Label", - "ĠUIL abel", - "v ely", - "ve ly", - "vel y", - "} );čĊ", - "}) ;čĊ", - "}); čĊ", - "_ ENT", - "_E NT", - "_EN T", - "#+ #+", - "art icles", - "article s", - "arti cles", - "artic les", - "Ġ Southern", - "ĠSouth ern", - "ĠSou thern", - "Ġstrong er", - "Ġstro nger", - "Ġstron ger", - "Ġ Given", - "ĠG iven", - "ĠGi ven", - "ĠGive n", - "Ġ Eric", - "ĠE ric", - "ĠEr ic", - "Ġ IR", - "ĠI R", - "a bstract", - "ab stract", - "abs tract", - "U nder", - "Un der", - "Und er", - "n able", - "na ble", - "Ġ increment", - "Ġin crement", - "Ġincre ment", - "Ġinc rement", - "Ġincr ement", - "o ven", - "ov en", - "ove n", - "Ġ coin", - "Ġc oin", - "Ġco in", - "Ġcoi n", - "_ timer", - "_t imer", - "_time r", - "_tim er", - "_ti mer", - "Ġsuffer ed", - "Ġsuff ered", - "Ġ FREE", - "ĠF REE", - "ĠFR EE", - "ĠFRE E", - "' ].\"", - "'] .\"", - "']. \"", - "Ġ Queen", - "ĠQu een", - "ĠQue en", - "st ats", - "stat s", - "sta ts", - "Ġmeet ings", - "Ġmeeting s", - "Ġmee tings", - "2 76", - "27 6", - "Ġen tering", - "Ġent ering", - "Ġenter ing", - "Ġalong side", - "( session", - "(s ession", - "(sess ion", - "it als", - "ital s", - "ita ls", - "Ġ foundation", - "Ġf oundation", - "Ġfound ation", - "Ġ Credit", - "ĠC redit", - "ĠCr edit", - "ĠCre dit", - ". div", - ".d iv", - ".di v", - "_ ALL", - "_A LL", - "_AL L", - "p cion", - "pc ion", - "pci on", - "_ stat", - "_s tat", - "_st at", - "_sta t", - "ic king", - "ick ing", - "Default s", - "_ src", - "_s rc", - "_sr c", - "Ġ outputs", - "Ġout puts", - "Ġoutput s", - "/ B", - "Ġen thus", - "Ġent hus", - "Ġenth us", - "- bl", - "-b l", - ". ForeColor", - ".Fore Color", - "ĉ temp", - "ĉt emp", - "F ace", - "Fac e", - "Fa ce", - "Ġinter act", - "Ġinte ract", - "Ġwe ird", - "Ġwei rd", - "Ġweir d", - "M ount", - "Mo unt", - "r ell", - "re ll", - "rel l", - "ud ents", - "ude nts", - "udent s", - "uden ts", - "Ġrequire ment", - "Ġrequ irement", - "Ġ Sus", - "ĠS us", - "ĠSu s", - "I ER", - "IE R", - "Ġe lected", - "Ġel ected", - "Ġelect ed", - "re ference", - "ref erence", - "refer ence", - "Ġ ME", - "ĠM E", - "Ġ servers", - "Ġs ervers", - "Ġser vers", - "Ġserver s", - "Ġserv ers", - "Ġserve rs", - ". wait", - ".w ait", - "Ġ snapshot", - "Ġs napshot", - "Ġsnap shot", - "Ġsnaps hot", - "il ton", - "ilt on", - "Ġ tries", - "Ġt ries", - "Ġtr ies", - "Ġtri es", - "Ġtrie s", - "Ġ tipo", - "Ġt ipo", - "Ġti po", - "Ġtip o", - ". Time", - ".T ime", - "> w", - "Ġm ountain", - "Ġmount ain", - "Ġp ounds", - "Ġpo unds", - "Ġpou nds", - "Ġpound s", - "Ġ[ ...", - "Ġ[. ..", - "ex ists", - "exist s", - "Ġng On", - "_ MAP", - "_M AP", - "_MA P", - "Ġf lying", - "Ġfl ying", - "Ġfly ing", - "3 31", - "33 1", - "xi ety", - "ĉ value", - "ĉv alue", - "ĉval ue", - "ĉva lue", - "_ DB", - "_D B", - "u no", - "un o", - "Ġse ats", - "Ġsea ts", - "Ġseat s", - "T URN", - "TU RN", - ". author", - ".a uthor", - ".auth or", - ".aut hor", - "! )", - "or ce", - "orc e", - "Ġind icated", - "Ġindic ated", - "Ġindicate d", - "Ġindica ted", - "3 17", - "31 7", - ". sin", - ".s in", - ".si n", - "Ġ assignment", - "Ġass ignment", - "Ġassign ment", - "im iento", - "imi ento", - "Ġ Frame", - "ĠF rame", - "ĠFr ame", - "ĠFra me", - "ĠFram e", - "3 24", - "32 4", - "_ gen", - "_g en", - "_ge n", - "in ery", - "ine ry", - "iner y", - "_ )", - "m essages", - "message s", - "mess ages", - ". settings", - ".s ettings", - ".set tings", - ".setting s", - "Ġ Mean", - "ĠM ean", - "ĠMe an", - "ĠM useum", - "ĠMus eum", - "ĠMuse um", - "i rq", - "ir q", - "at tach", - "att ach", - "atta ch", - "ĠPale stin", - "ĠPalest in", - "_ QU", - "_Q U", - "_ tags", - "_t ags", - "_tag s", - "_ta gs", - "Ġcas ual", - "e men", - "em en", - "eme n", - "ASS WORD", - "4 32", - "43 2", - "$ s", - "ĠC irc", - "ĠCi rc", - "ĠCir c", - "о й", - "оР¹", - "et ric", - "etr ic", - "etri c", - "/ P", - "0 18", - "01 8", - "Ġ epoch", - "Ġep och", - "< head", - " The", - ">T he", - "Ġ Ak", - "ĠA k", - "Ġ grass", - "Ġgr ass", - "Ġgra ss", - "Ġgras s", - "/ *čĊ", - "/* čĊ", - "( dis", - "(d is", - "(di s", - "Ġ guns", - "Ġg uns", - "Ġgu ns", - "Ġgun s", - "Ġ tb", - "Ġt b", - "Ġ Kevin", - "ĠK evin", - "ĠKe vin", - ". args", - ".ar gs", - ".arg s", - "Ġ Ah", - "ĠA h", - "o ped", - "op ed", - "ope d", - "( J", - "column s", - "arg uments", - "argument s", - "ĠWith Events", - "_ full", - "_f ull", - "_fu ll", - "Ġ Defense", - "ĠDef ense", - "S imple", - "Sim ple", - "Ġde aths", - "Ġdeath s", - "2 95", - "29 5", - "Ġext ensive", - "Ġ Still", - "ĠSt ill", - "Ġ Expression", - "ĠEx pression", - "ĠExp ression", - "ĠExpress ion", - "ĠExpr ession", - "Ġ Agency", - "ĠA gency", - "ĠAg ency", - "ĠAge ncy", - "Ġper forming", - "Ġperform ing", - "Ġperfor ming", - "F X", - "Ġ usuario", - "Ġus uario", - "Ġusu ario", - "U AL", - "UA L", - "S ide", - "Si de", - "Sid e", - "o dos", - "od os", - "odo s", - "ap top", - "apt op", - "Ġ credentials", - "Ġc redentials", - "Ġcred entials", - "Ġcredential s", - "_ cap", - "_c ap", - "_ca p", - "at ient", - "ati ent", - "atie nt", - "Ġ Disney", - "ĠDis ney", - "Ġ ai", - "Ġa i", - "Ġ chip", - "Ġc hip", - "Ġch ip", - "Ġchi p", - "Ġ volt", - "Ġv olt", - "Ġvo lt", - "Ġvol t", - ".make Text", - "%%%%%%%% %%%%%%%%", - "Ġ belief", - "Ġbel ief", - "Ġbelie f", - "_ LOC", - "_L OC", - "_LO C", - "Ġ Civil", - "ĠC ivil", - "ĠCi vil", - "ĠCiv il", - "N avigation", - "Nav igation", - "Navig ation", - "Ġ reveal", - "Ġre veal", - "Ġreve al", - "Ġ violent", - "Ġviol ent", - "Ġ Fil", - "ĠF il", - "ĠFi l", - "Ġ catalog", - "Ġc atalog", - "Ġcat alog", - "Ġcata log", - "Ġcatal og", - "e med", - "em ed", - "eme d", - "s can", - "sc an", - ". control", - ".c ontrol", - ".cont rol", - "Ġ constitution", - "Ġcon stitution", - "Ġconst itution", - "Ġconstit ution", - "Ġconstitu tion", - "C ountry", - "Count ry", - "S eparator", - "Se parator", - "Separ ator", - "_ APP", - "_A PP", - "_AP P", - "t opic", - "to pic", - "top ic", - "u etooth", - "uet ooth", - "M IN", - "MI N", - "Ġ descriptor", - "Ġdes criptor", - "y t", - "ET HER", - "ETH ER", - "Ġd istribute", - "Ġdis tribute", - "Ġdistrib ute", - "' }Ċ", - "'} Ċ", - ". trim", - ".t rim", - ".tr im", - ". Line", - ".L ine", - "Ġ lbl", - "Ġl bl", - "Ġlb l", - "assert Equals", - "Ġ Det", - "ĠD et", - "ĠDe t", - "omb ok", - "ombo k", - "( width", - "(w idth", - "Ġt ort", - "Ġto rt", - "Ġtor t", - "ĠEX PRESS", - "ĠEXP RESS", - "a co", - "ac o", - "U sing", - "Us ing", - "Ġ Brand", - "ĠB rand", - "ĠBr and", - "ĠBra nd", - "ĠBran d", - "w all", - "wa ll", - "wal l", - "E MENT", - "EM ENT", - "Ġ Communic", - "ĠComm unic", - "ĠCommun ic", - "< uint", - " (Ċ", - ">( Ċ", - "? >\"", - "?> \"", - "Ġ ///Ċ", - "Ġ// /Ċ", - "Ġ/ //Ċ", - "Ġ/// Ċ", - "Ġe iner", - "Ġein er", - "Ġeine r", - "Ġei ner", - "Ġ weekly", - "Ġweek ly", - "ĉ logger", - "ĉlog ger", - "_ pop", - "_p op", - "_po p", - "_ man", - "_m an", - "_ma n", - "Ġm igrations", - "Ġmigr ations", - "Ġmigration s", - "Ġ asks", - "Ġas ks", - "Ġask s", - "Ġ bs", - "Ġb s", - "Ġ falls", - "Ġf alls", - "Ġfall s", - "Ġfal ls", - ". Where", - ".W here", - ".Wh ere", - "- height", - "-h eight", - "-he ight", - "_ feature", - "_f eature", - "_fe ature", - "_feat ure", - ". Min", - ".M in", - "Ġ hyper", - "Ġh yper", - "Ġhy per", - "Ġhyp er", - "Ġhype r", - "Ġ volatile", - "Ġv olatile", - "Ġvol atile", - "Ġ twenty", - "Ġtw enty", - "Ġtwe nty", - "Typ ography", - "U nable", - "Un able", - "Una ble", - "D et", - "De t", - ", f", - "- mod", - "-m od", - "Ġset tlement", - "Ġsett lement", - "Ġsettle ment", - "Ġ contracts", - "Ġcon tracts", - "Ġcontract s", - "Ġcontr acts", - "Ġcontra cts", - "n ome", - "no me", - "nom e", - "B ad", - "Ba d", - "Ġ Brian", - "ĠB rian", - "ĠBr ian", - "ĠBri an", - "7 68", - "76 8", - "( username", - "(user name", - "! !!!", - "!! !!", - "!!! !", - "Ġ hack", - "Ġh ack", - "Ġha ck", - "Ġhac k", - ". Field", - ".F ield", - "H R", - "Ġ Jordan", - "ĠJ ordan", - "ĠJord an", - "i za", - "iz a", - "Ġ Âł", - "Ġ ł", - "Ġ Sher", - "ĠS her", - "ĠSh er", - "ĠShe r", - ". header", - ".head er", - ".he ader", - "( other", - "(o ther", - "Ġ Dub", - "ĠD ub", - "ĠDu b", - "( op", - "(o p", - "Ġ Round", - "ĠR ound", - "ĠRo und", - "ĠRou nd", - "Ġ vie", - "Ġv ie", - "Ġvi e", - "Ġ appl", - "Ġapp l", - "Ġap pl", - "ĉ J", - "Ġ Insert", - "ĠIn sert", - "ĠIns ert", - "Ġ LP", - "ĠL P", - "re gon", - "reg on", - "rego n", - "Ġ MPI", - "ĠM PI", - "ĠMP I", - "Ġ anchor", - "Ġan chor", - "Ġanch or", - "Ġanc hor", - "a ca", - "ac a", - "ø r", - "Ġ ade", - "Ġa de", - "Ġad e", - "an chor", - "anc hor", - "anch or", - "qu ee", - "que e", - "Ġ TreeNode", - "ĠT reeNode", - "ĠTree Node", - "Ġtarget ed", - "Ġtarg eted", - "Ġl aid", - "Ġla id", - "Ġlai d", - "A BEL", - "AB EL", - "v et", - "ve t", - "Ġ Origin", - "ĠOr igin", - "ĠOri gin", - "ĠOrig in", - "A nt", - "An t", - ". ');Ċ", - ".' );Ċ", - ".') ;Ċ", - ".'); Ċ", - "ex pect", - "exp ect", - "ed Reader", - "Ġ Major", - "ĠM ajor", - "ĠMaj or", - "Ġ inch", - "Ġin ch", - "Ġinc h", - "Com par", - "Co mpar", - "Comp ar", - "Ġ preview", - "Ġp review", - "Ġpr eview", - "Ġpre view", - "Ġprev iew", - "Ġill ness", - "ĠCON TRACT", - "ĠCONTR ACT", - "ĠCONT RACT", - "Ġ Independ", - "ĠIn depend", - "ĠInd epend", - "u uid", - "uu id", - "Ġ nome", - "Ġn ome", - "Ġno me", - "Ġnom e", - "Ġ tc", - "Ġt c", - "ĠA venue", - "i san", - "is an", - "isa n", - "Ġ phrase", - "Ġph rase", - "_ move", - "_m ove", - "_mov e", - "_mo ve", - "\" )[", - "\") [", - "4 12", - "41 2", - "Ġpro vision", - "Ġprov ision", - "Ġconc entr", - "Ġconcent r", - "_ IR", - "_I R", - "Ġ Ut", - "ĠU t", - "( )+", - "() +", - "Ġ nas", - "Ġn as", - "Ġna s", - "! ,", - "Ġ Robin", - "ĠRob in", - "ĠRo bin", - "i ations", - "iation s", - "iat ions", - "at itude", - "Ġ px", - "Ġp x", - "Ġ Without", - "ĠWith out", - "/ bash", - "/b ash", - "e kt", - "ek t", - "re ement", - "ree ment", - "3 42", - "34 2", - "Ob server", - "Observ er", - "Obs erver", - "3 18", - "31 8", - "Ġ Region", - "ĠReg ion", - "UB LIC", - "UBL IC", - "Ġ {//", - "Ġ{ //", - "K N", - "å ·", - "Game Object", - "å ¾", - "en coding", - "enc oding", - "enco ding", - "Ġ ***", - "Ġ* **", - "Ġ** *", - "project s", - "proj ects", - "Ġ tk", - "Ġt k", - "Ġche ese", - "Ġchees e", - "EM PL", - "EMP L", - "a ro", - "ar o", - "Ġ اÙĦ", - "Ġا ÙĦ", - "6 10", - "61 0", - "3 37", - "33 7", - "Ġcons ists", - "Ġconsist s", - "re fresh", - "ref resh", - "u reau", - "ure au", - "Ġ Scanner", - "ĠSc anner", - "ĠScan ner", - "Ġs oil", - "Ġso il", - "Ġfl avor", - "Ġflav or", - "Ġfla vor", - "Data Source", - "Ex ecute", - "Exec ute", - "е ние", - "ен ие", - "ени е", - "Ġ shit", - "Ġs hit", - "Ġsh it", - "åĪ Ĩ", - "< any", - " < /", - "Qu antity", - "Quant ity", - "qu iry", - "quir y", - "qui ry", - "_ tab", - "_t ab", - "_ta b", - "Ġ alg", - "Ġa lg", - "Ġal g", - "To ast", - "re size", - "res ize", - "resi ze", - "quest ions", - "question s", - "s chema", - "sch ema", - "L iteral", - "Lite ral", - "Lit eral", - "Liter al", - "( entity", - "(e ntity", - "(ent ity", - "NE CTION", - "NECT ION", - "ch anged", - "change d", - "chan ged", - "chang ed", - "_ FIELD", - "_F IELD", - "_ HEIGHT", - "_HE IGHT", - "Ġ organic", - "Ġorg anic", - "Ġorgan ic", - "P RE", - "PR E", - "Ġ Cat", - "ĠC at", - "ĠCa t", - ". Draw", - ".D raw", - "E s", - "Ġ loud", - "Ġl oud", - "Ġlo ud", - "Ġlou d", - "6 80", - "68 0", - "Ġ ĠĠĠĠĠĠĠĉ", - "ĠĠ ĠĠĠĠĠĠĉ", - "ĠĠĠĠ ĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠ ĉ", - "ĠĠĠ ĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠ Ġĉ", - "ĠĠĠĠĠ ĠĠĠĉ", - "ĠĠĠĠĠĠ ĠĠĉ", - "Ġ Kat", - "ĠK at", - "ĠKa t", - "Ġ heap", - "Ġhe ap", - "âĢľ It", - "âĢľI t", - "0 70", - "07 0", - "e tr", - "et r", - "Ġ unlikely", - "Ġun likely", - "Ġunlike ly", - "er als", - "era ls", - "eral s", - "/ auth", - "/a uth", - "5 02", - "50 2", - "t odo", - "to do", - "tod o", - "P lace", - "Pl ace", - "Post ed", - "Pos ted", - "Po sted", - "Com ments", - "Comment s", - "Comm ents", - "Ġ Tech", - "ĠT ech", - "ĠTe ch", - "ĠTec h", - "Ġ Finally", - "ĠF inally", - "ĠFin ally", - "ĠFinal ly", - "eg ration", - "egr ation", - "egra tion", - "Ġ minimal", - "Ġmin imal", - "Ġmini mal", - "Ġminim al", - "Ġ Files", - "ĠF iles", - "ĠFile s", - "ĠFil es", - "ĠFi les", - "Ġt amb", - "Ġta mb", - "Ġtam b", - "ë¡ ľ", - "Ġ Release", - "ĠR elease", - "ĠRe lease", - "ĠRel ease", - "4 25", - "42 5", - ". resize", - ".re size", - ".res ize", - "Ġ Ï", - "c ollect", - "col lect", - "coll ect", - "= p", - "ĠLI ABLE", - "Ġp roducing", - "Ġprodu cing", - "Ġprod ucing", - "- wrapper", - "-w rapper", - "-wrap per", - "Ġs ingles", - "Ġsingle s", - "Ġsin gles", - "Ġsing les", - "ĠN BA", - "ĠNB A", - "o rr", - "or r", - "e ren", - "er en", - "ere n", - ". addAction", - ".add Action", - "Ġ thesis", - "Ġth esis", - "Ġthe sis", - "d n", - "P TY", - "PT Y", - ". des", - ".d es", - ".de s", - "Ġb acter", - "Ġba cter", - "Ġbac ter", - "Ġ Express", - "ĠEx press", - "ĠExp ress", - "ĠExpr ess", - "Ġ *)Ċ", - "Ġ* )Ċ", - "Ġ*) Ċ", - "å ij", - "/ admin", - "/ad min", - "se conds", - "sec onds", - "second s", - "åĬ Ł", - "uss ion", - "a beth", - "ab eth", - "abe th", - "abet h", - "Ġ Computer", - "ĠCom puter", - "ĠComp uter", - "ĠCompute r", - "ĠComput er", - "Ġr uling", - "Ġru ling", - "(\" ../", - "(\". ./", - "(\".. /", - ". GET", - ".G ET", - "ĠMe dal", - "ĠMed al", - "ition ally", - "itional ly", - "com mit", - "comm it", - "f ocus", - "fo cus", - "_ LEVEL", - "_LE VEL", - "i nda", - "in da", - "ind a", - "F act", - "Fac t", - "Fa ct", - "= np", - "=n p", - "=\" \">Ċ", - "=\"\" >Ċ", - "=\"\"> Ċ", - "Ġsub sequent", - "Ġsubsequ ent", - "pos able", - "- fluid", - "-fl uid", - "Ġth orough", - "Ġtho rough", - "Ġthor ough", - "Ġpublic ly", - "Ġpubli cly", - "ap ters", - "apt ers", - "apter s", - "Ġ Wilson", - "ĠWil son", - "_ PRE", - "_P RE", - "_PR E", - "y ard", - "ya rd", - "yar d", - "ä ¼", - "ĉ in", - "ĉi n", - "3 39", - "33 9", - "Ġre vers", - "Ġrev ers", - "Ġreve rs", - "Ġrever s", - "Ġ bullet", - "Ġb ullet", - "Ġbul let", - "Ġbull et", - "cri bed", - "cribe d", - "nes ota", - "Ġ ($_", - "Ġ( $_", - "Ġ($ _", - "an non", - "ann on", - "anno n", - "c ursor", - "curso r", - "Ġclo thing", - "Ġcloth ing", - "Ġ Multi", - "ĠM ulti", - "ĠMult i", - "ĠMul ti", - "2 87", - "28 7", - ": ',", - ":' ,", - "Ġv ess", - "Ġve ss", - "Ġves s", - "ord inator", - "ordin ator", - "Ġe inem", - "Ġein em", - "Ġeine m", - "Ġei nem", - "C annot", - "Can not", - "Ġ armed", - "Ġar med", - "Ġarm ed", - "ĉ V", - "ä¸ Ĭ", - ". Flat", - ".F lat", - ".Fl at", - "Ġ Sep", - "ĠS ep", - "ĠSe p", - "Ġ Subject", - "ĠSub ject", - "ĠSu bject", - "_ font", - "_f ont", - "Ġcharacter istics", - "Ġcharacteristic s", - "D one", - "Do ne", - "Don e", - "e ln", - "el n", - "#### ########", - "######## ####", - "##### #######", - "###### ######", - "####### #####", - "P OS", - "PO S", - "Ġ density", - "Ġd ensity", - "Ġdens ity", - "Ġ Platform", - "ĠPl atform", - "ĠPlat form", - "- items", - "-item s", - "-i tems", - "-it ems", - "Ġ overs", - "Ġo vers", - "Ġover s", - "Ġov ers", - "Ġp ushing", - "Ġpush ing", - "ç ¤", - ". Connection", - ".Con nection", - ".Connect ion", - ".Conn ection", - "_ term", - "_t erm", - "_te rm", - "_ter m", - "Ġinitial ization", - "________________ ________________", - "ç ¬", - ". document", - ".d ocument", - ".doc ument", - "l esh", - "le sh", - "les h", - "ĉ document", - "ĉd ocument", - "ĉdoc ument", - "Ġ Pin", - "ĠP in", - "ĠPi n", - "ç a", - "Ġ definitions", - "Ġdefinition s", - "Ġdefinit ions", - "Ġdefin itions", - ". Path", - ".P ath", - "_ WRITE", - "_W RITE", - "_WR ITE", - "Ġ ĉĊ", - "Ġĉ Ċ", - "? >ĊĊ", - "?> ĊĊ", - "?>Ċ Ċ", - "Ġter rible", - "Ġterr ible", - "b ean", - "be an", - "ick ets", - "icket s", - "Ġ SV", - "ĠS V", - "B uy", - "Bu y", - "( task", - "(t ask", - "Ġreg ime", - "g oogle", - "go ogle", - "goog le", - "goo gle", - "Ġc rack", - "Ġcr ack", - "Ġcra ck", - ". visit", - ".vis it", - "N UM", - "NU M", - "e nergy", - "en ergy", - "ener gy", - "Ġs truck", - "Ġstr uck", - "Ġstru ck", - "_ sample", - "_s ample", - ". payload", - ".p ayload", - ".pay load", - "Ġre vis", - "Ġrev is", - "Ġ Scene", - "ĠS cene", - "ĠSc ene", - "Ġ pg", - "Ġp g", - "Ġbreak fast", - "URRE NT", - ". charAt", - ".char At", - "_ exception", - "_ex ception", - "_except ion", - "ĠA nton", - "ĠAn ton", - "ĠAnt on", - "Ġguide lines", - "Ġguid elines", - "Ġguideline s", - "Ġex haust", - "Ġ Financial", - "ĠFin ancial", - "Ġ indent", - "Ġin dent", - "Ġind ent", - "Ġinde nt", - "Ġ desktop", - "Ġd esktop", - "Ġdes ktop", - "Ġdesk top", - "H idden", - "Hi dden", - "F ailure", - "Fail ure", - "Ġpr inciple", - "Ġprincip le", - "Ġprinc iple", - "Ġ iv", - "Ġi v", - "Ġs eks", - "Ġse ks", - "Ġsek s", - "n etwork", - "net work", - "Ġ numberOf", - "Ġnumber Of", - "Ġ Albert", - "ĠAl bert", - "ĠAlb ert", - "ĉ long", - "ĉl ong", - "8 01", - "80 1", - ", .", - "Ġ zeros", - "Ġz eros", - "Ġzero s", - "Ġze ros", - "Ġzer os", - "f ade", - "fa de", - "fad e", - "Ġ Typ", - "ĠT yp", - "ĠTy p", - "Ġ Term", - "ĠT erm", - "ĠTe rm", - "ĠTer m", - "ĠA rts", - "ĠAr ts", - "ĠArt s", - ". Application", - ".App lication", - ".Ap plication", - "Ġbe half", - "Ġbeh alf", - "æĪ ·", - "Ġ mere", - "Ġm ere", - "Ġme re", - "Ġmer e", - "( `${", - "(` ${", - "Ġaware ness", - "el pers", - "elp ers", - "elper s", - "f lix", - "fl ix", - "Ġ weigh", - "Ġwe igh", - "Ġwei gh", - "Ġest imates", - "Ġestim ates", - "Ġestimate s", - ". child", - ".ch ild", - "/ O", - "Ġ Bitmap", - "ĠB itmap", - "ĠBit map", - ". bottom", - ".b ottom", - ".bot tom", - "Ġ** ************************************************************************", - "Ġ************************************************************************ **", - "Ex pect", - "Exp ect", - "en to", - "ent o", - "Ġ Forum", - "ĠF orum", - "ĠFor um", - "ĠFo rum", - "v eral", - "ver al", - "ve ral", - "Ġj ail", - "Ġja il", - "Ġ abilities", - "Ġab ilities", - "ĠH OLD", - "ĠHO LD", - "ĠHOL D", - "Ġ Cit", - "ĠC it", - "ĠCi t", - "Ġd ynam", - "Ġdy nam", - "Ġdyn am", - "Ġ gray", - "Ġg ray", - "Ġgr ay", - "Ġgra y", - "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", - ". nextInt", - ".next Int", - "ant ly", - "ĠAR ISING", - "( private", - "(pr ivate", - "(priv ate", - "Ġ rejected", - "Ġre jected", - "Ġreject ed", - "Ġrej ected", - "Ġ Nic", - "ĠN ic", - "ĠNi c", - "Ġle ather", - "= {Ċ", - "={ Ċ", - "aly tics", - "t hetic", - "th etic", - "the tic", - ". Top", - ".T op", - ".To p", - "3 73", - "37 3", - ". Page", - ".P age", - "={ `", - "Ġ ;čĊ", - "Ġ; čĊ", - "de pth", - "dep th", - "dept h", - "m ann", - "man n", - "ma nn", - "W D", - "Ġ Som", - "ĠS om", - "ĠSo m", - ". Right", - ".R ight", - "Ġ )}Ċ", - "Ġ) }Ċ", - "Ġ trait", - "Ġt rait", - "Ġtr ait", - "Ġtra it", - "Ġtrai t", - "à Ĺ", - "i ac", - "ia c", - "Ġ rv", - "Ġr v", - "S ample", - "Sam ple", - ". Xml", - ".X ml", - "o pped", - "op ped", - "opp ed", - "Ġ ÑĦ", - "ĠÑ Ħ", - "l ists", - "li sts", - "list s", - "lis ts", - "Ġt ear", - "Ġte ar", - "Ġtea r", - "ivers ary", - ". collection", - ".c ollection", - ".col lection", - ".collect ion", - ".coll ection", - "ĠCon stitution", - "ĠConst itution", - "ĠConstit ution", - "Ġ HttpResponse", - "ĠHttp Response", - "Ġbr ill", - "Ġbri ll", - "Ġ Prom", - "ĠP rom", - "ĠPro m", - "ĠPr om", - "h over", - "ho ver", - "3 66", - "36 6", - "Ġ Miami", - "ĠM iami", - "ĠMi ami", - "ĠMia mi", - "Ġar gue", - "Ġarg ue", - "_ float", - "_f loat", - "5 04", - "50 4", - "Ġ ãĤ", - "Ġ nat", - "Ġn at", - "Ġna t", - "ĠT al", - "ĠTa l", - "Ġ integration", - "Ġint egration", - "Ġinteg ration", - "Ġintegr ation", - "( cur", - "(c ur", - "Ġre moving", - "Ġrem oving", - "Ġ coeff", - "Ġc oeff", - "Ġco eff", - "Ġcoef f", - "Ġ Though", - "ĠTh ough", - "ĠThou gh", - "Ġ forecast", - "Ġfor ecast", - "Ġfore cast", - "4 08", - "40 8", - "ĠV egas", - "ĠVe gas", - "ĠVega s", - "ĠVeg as", - "S ite", - "Si te", - "Sit e", - "3 46", - "34 6", - "Ġt rab", - "Ġtr ab", - "Ġtra b", - "Ġ Henry", - "ĠHen ry", - "- i", - "Ġinv olves", - "Ġinvol ves", - "Ġinvolve s", - "B T", - "Ġs lo", - "Ġsl o", - "In voke", - "Inv oke", - "Ġl ucky", - "Ġluck y", - "Ġlu cky", - "Ġluc ky", - "0 25", - "02 5", - "r at", - "ra t", - "Ġ ?Ċ", - "Ġ? Ċ", - "Ġ handled", - "Ġhand led", - "Ġhandle d", - "( fd", - "(f d", - "cont ents", - "content s", - "conte nts", - "Ġ OFF", - "ĠO FF", - "ĠOF F", - "R F", - "Ġ sty", - "Ġs ty", - "Ġst y", - "Ġ Motor", - "ĠM otor", - "ĠMo tor", - "ĠMot or", - "ĠMoto r", - "t ery", - "ter y", - "te ry", - "t ax", - "ta x", - "M AP", - "MA P", - "Ġ Mrs", - "ĠM rs", - "ĠMr s", - "Ġ phones", - "Ġph ones", - "Ġphone s", - "Ġphon es", - "Ġ UIView", - "ĠUI View", - "\" )));Ċ", - "\") ));Ċ", - "\")) );Ċ", - "\"))) ;Ċ", - "\"))); Ċ", - "( dev", - "(d ev", - "(de v", - "ĠI rish", - "ĠIr ish", - "ĠIris h", - "0 19", - "01 9", - "Ġ ws", - "Ġw s", - "D I", - "_ OFFSET", - "_OFF SET", - "Ġ Events", - "ĠE vents", - "ĠEvent s", - "ĠEven ts", - "ĠEv ents", - "ĠEve nts", - "Ġst ages", - "Ġstage s", - "Ġsta ges", - "Ġstag es", - "Ġ }//", - "Ġ} //", - "Ġh aben", - "Ġhab en", - "Ġha ben", - "Ġhabe n", - "ST ANCE", - "Ġ Sin", - "ĠS in", - "ĠSi n", - "Ġ Money", - "ĠM oney", - "ĠMon ey", - "ĠMo ney", - "( top", - "(t op", - "(to p", - "Ġ appointment", - "Ġapp ointment", - "Ġappoint ment", - "V ERSION", - "VER SION", - "VERS ION", - "m etadata", - "met adata", - "meta data", - "_ comment", - "_com ment", - "_comm ent", - "Ġcolle agues", - "Ġcolleague s", - "m aps", - "ma ps", - "map s", - "â ĺ", - "Ċ ĉĊ", - "( al", - "(a l", - "_ req", - "_re q", - "_r eq", - "Ġf ut", - "Ġfu t", - "Ġ architecture", - "Ġarch itecture", - "Ġarchitect ure", - "Ġarchit ecture", - "3 51", - "35 1", - "ĠWH ETHER", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "_ screen", - "_s creen", - "_sc reen", - "_scr een", - "Ġstyle Urls", - "Ġ monster", - "Ġmon ster", - ". up", - ".u p", - "ph ia", - "phi a", - "Ġ processor", - "Ġprocess or", - "Ġproc essor", - "Ġprocesso r", - "Ġ Terr", - "ĠT err", - "ĠTe rr", - "ĠTer r", - "= ',", - "=' ,", - "Ġ Manufact", - "ĠMan ufact", - "Ġ NT", - "ĠN T", - "k el", - "ke l", - "i bern", - "ib ern", - "iber n", - "ibe rn", - "ĉ file", - "ĉf ile", - "ĉfi le", - "A li", - "Al i", - "rient ation", - "Ġ //!", - "Ġ// !", - "ap ore", - "apor e", - "apo re", - "an eous", - "ane ous", - "Ġ Creat", - "ĠC reat", - "ĠCr eat", - "ĠCre at", - "f older", - "fo lder", - "fol der", - "fold er", - "4 15", - "41 5", - "Ġ hay", - "Ġh ay", - "Ġha y", - "Sup press", - "( left", - "(l eft", - "(le ft", - "Ġe uro", - "Ġeu ro", - "Ġdis claimer", - "u stry", - "us try", - "ust ry", - "ustr y", - "s hips", - "sh ips", - "ship s", - "shi ps", - "_ fd", - "_f d", - "Ġ Fa", - "ĠF a", - "_ insert", - "_in sert", - "_ins ert", - "Ġ rol", - "Ġr ol", - "Ġro l", - "if ting", - "ift ing", - "Ġ Comments", - "ĠCom ments", - "ĠComm ents", - "ĠComment s", - "_ br", - "_b r", - "Ġlos ses", - "Ġloss es", - "Ġ Added", - "ĠAd ded", - "ĠAdd ed", - "ch arg", - "char g", - "cha rg", - "Ġ по", - "Ġп о", - "_ system", - "_s ystem", - "_sys tem", - "_sy stem", - "Ġ Sometimes", - "ĠS ometimes", - "ĠSome times", - "ĠSom etimes", - "Ġ Spain", - "ĠS pain", - "ĠSp ain", - "ĠSpa in", - "( group", - "(g roup", - "(gr oup", - "i alis", - "ial is", - "ia lis", - "iali s", - "Ġd ollar", - "Ġdoll ar", - "Ġdol lar", - "Ġ Args", - "ĠAr gs", - "ĠArg s", - "4 99", - "49 9", - "2 97", - "29 7", - "qu ires", - "quire s", - "quir es", - "qui res", - "Ġ Ten", - "ĠT en", - "ĠTe n", - ". scss", - ".s css", - ".sc ss", - "Ġsurv ive", - "Ġsurviv e", - "u sage", - "us age", - "usa ge", - "Ġ jun", - "Ġj un", - "Ġju n", - "im iter", - "imit er", - "imi ter", - "ï¼ģ ĊĊ", - "ï¼ģĊ Ċ", - "Ġf ifth", - "Ġfif th", - "t oggle", - "tog gle", - "Ġde cline", - "Ġdec line", - "Ġdecl ine", - "( $\"", - "($ \"", - "( Long", - "(L ong", - "i nge", - "in ge", - "ing e", - "Ġp ilot", - "Ġpi lot", - "Ġpil ot", - "- light", - "-l ight", - "-li ght", - "- radius", - "-r adius", - "-rad ius", - "Ġp odcast", - "Ġpod cast", - "Ġn aturally", - "Ġnatural ly", - "Ġnatur ally", - "Ġnat urally", - "P ages", - "Page s", - "Pa ges", - "Pag es", - "ä¸ º", - "Ġ Despite", - "ĠDes pite", - "Ġl ighting", - "Ġlight ing", - "Ġ crate", - "Ġc rate", - "Ġcr ate", - "Ġcra te", - "Ġ Binary", - "ĠB inary", - "ĠBin ary", - "Ġred ucing", - "Ġredu cing", - "Ġe leg", - "Ġel eg", - "Ġele g", - "Ġ Mouse", - "ĠM ouse", - "ĠMo use", - "ĠMou se", - "ĠTest Bed", - "Ġbefore Each", - "_ ARRAY", - "_AR RAY", - "_ARR AY", - "Re direct", - "Red irect", - "3 29", - "32 9", - "Ġf lood", - "Ġfl ood", - "Ġflo od", - "Ġ ships", - "Ġs hips", - "Ġsh ips", - "Ġship s", - "3 63", - "36 3", - "Ġelectric ity", - "Ġelectr icity", - ") *(", - ")* (", - "ê ¸", - "Ġ Viet", - "ĠV iet", - "ĠVi et", - "ĠVie t", - "h ero", - "he ro", - "her o", - "Ġ dia", - "Ġd ia", - "Ġdi a", - "Ġ Kent", - "ĠK ent", - "ĠKe nt", - "ĠKen t", - "he art", - "hea rt", - "hear t", - "Ġthreat s", - "_ acc", - "_a cc", - "_ac c", - "Ġ symbols", - "Ġs ymbols", - "Ġsymbol s", - "Ġsymb ols", - "is chen", - "isc hen", - "ische n", - "isch en", - "_ inst", - "_in st", - "_i nst", - "_ins t", - "C riterion", - "Ġ TIM", - "ĠT IM", - "ĠTI M", - ". Height", - ".H eight", - ".He ight", - "5 80", - "58 0", - "Ġ âĢĻ", - "ĠâĢ Ļ", - "( );ĊĊĊ", - "() ;ĊĊĊ", - "();Ċ ĊĊ", - "();ĊĊ Ċ", - "(); ĊĊĊ", - "Product s", - "Produ cts", - "_ SP", - "_S P", - "Ġ Cy", - "ĠC y", - "Ġ dependent", - "Ġdep endent", - "Ġdepend ent", - "e ste", - "es te", - "est e", - "Ġ datos", - "Ġd atos", - "Ġda tos", - "Ġdat os", - "Ġdato s", - "d it", - "di t", - "а в", - "аР²", - "IG NAL", - "IGN AL", - "Ġ lesson", - "Ġl esson", - "Ġle sson", - "Ġless on", - "Ġles son", - "\" >'", - "\"> '", - "Ġ Cover", - "ĠC over", - "ĠCo ver", - "ĠCov er", - "ĠCove r", - "Ġ Hope", - "ĠH ope", - "ĠHo pe", - "ĠHop e", - "Ġ Timer", - "ĠT imer", - "ĠTime r", - "ĠTim er", - "ĠTi mer", - "Ġ dad", - "Ġd ad", - "Ġda d", - "v iders", - "vid ers", - "vider s", - "vi ders", - "vide rs", - "Ġ Phot", - "ĠP hot", - "ĠPh ot", - "/ ?", - "r opy", - "ro py", - "rop y", - "o ming", - "om ing", - "omin g", - "omi ng", - "as ion", - "asi on", - "asio n", - "Ġ \\(", - "Ġ\\ (", - "Ġ ET", - "ĠE T", - "Ġ Reading", - "ĠRe ading", - "ĠRead ing", - "Ġep isodes", - "Ġepisode s", - "Ġepis odes", - "l m", - "4 21", - "42 1", - "e cha", - "ec ha", - "ech a", - "Ġne uro", - "Ġneu ro", - "Ġneur o", - "8 20", - "82 0", - "Ġhar mon", - "Ġharm on", - "Ġlib eral", - "Ġliber al", - "- ind", - "-in d", - "-i nd", - "3 93", - "39 3", - "D ATA", - "DA TA", - "DAT A", - "Ġevery day", - "Ġdi vided", - "Ġdiv ided", - "Ġdivide d", - "Ġdivid ed", - "ĠActive Record", - "f igure", - "fig ure", - "figur e", - "U A", - "ä ¹", - "riend ly", - "t ech", - "te ch", - "tec h", - "6 01", - "60 1", - ". gameObject", - ".game Object", - "и ÑĤÑĮ", - "иÑĤ ÑĮ", - "3 74", - "37 4", - "Ġ moon", - "Ġm oon", - "Ġmo on", - "Ġmoo n", - "f time", - "ft ime", - "Ġ noch", - "Ġn och", - "Ġno ch", - "Ġnoc h", - "ĠT ORT", - "ĠTO RT", - "ĠTOR T", - "Ġ VM", - "ĠV M", - ". initial", - ".in itial", - ".init ial", - "( child", - "(ch ild", - "Ġmus ical", - "Ġmusic al", - "Ġmusica l", - "Ġ oc", - "Ġo c", - "b as", - "ba s", - "Ġ Hay", - "ĠH ay", - "ĠHa y", - "3 61", - "36 1", - "_ long", - "_l ong", - "_lo ng", - "_lon g", - "Ġ memset", - "Ġmem set", - "i ley", - "il ey", - "ile y", - "adel phia", - "S V", - "ro at", - "_ tx", - "_t x", - "Ġ lon", - "Ġl on", - "Ġlo n", - "Ġng OnInit", - "ĠngOn Init", - "b p", - "Ġ Golden", - "ĠGold en", - "ĠGol den", - "A CHE", - "AC HE", - "ACH E", - "Ġwor ried", - "a zi", - "az i", - "E ar", - "T ake", - "Ta ke", - "Tak e", - "( fp", - "(f p", - "burg h", - "bur gh", - "_ Data", - "_D ata", - "g res", - "gr es", - "gre s", - "Ġ Ont", - "ĠO nt", - "ĠOn t", - "p us", - "pu s", - "Ġ transparent", - "Ġtrans parent", - "Ġp ocket", - "Ġpo cket", - "Ġpoc ket", - "Ġ ram", - "Ġr am", - "Ġra m", - "igration s", - "igr ations", - ". čĊčĊ", - ".čĊ čĊ", - "Ġ [(", - "Ġ[ (", - "Ġadopt ed", - "Ġreport edly", - "Ġreported ly", - "Ġ Dream", - "ĠD ream", - "ĠDr eam", - "ĠDre am", - "Ġ }));Ċ", - "Ġ} ));Ċ", - "Ġ}) );Ċ", - "Ġ})) ;Ċ", - "l osing", - "lo sing", - "los ing", - "Ġte eth", - "Ġtee th", - "Ġ Books", - "ĠB ooks", - "ĠBo oks", - "ĠBook s", - "ĠBoo ks", - "\" ,&", - "\", &", - "en ny", - "enn y", - "L EMENT", - "LE MENT", - "LEM ENT", - "Ġ gel", - "Ġg el", - "Ġge l", - "Ġ Plant", - "ĠP lant", - "ĠPl ant", - "ĠPlan t", - "ĠPla nt", - "4 37", - "43 7", - "! âĢĿ", - ". host", - ".h ost", - "Ġ Reply", - "ĠRe ply", - "ĠRep ly", - "3 76", - "37 6", - "r ength", - "re ngth", - "ren gth", - "Ġrec ognition", - "Ġrecogn ition", - "Ġ }}>Ċ", - "Ġ} }>Ċ", - "Ġ}} >Ċ", - "Ġ}}> Ċ", - "L A", - "Ġ mirror", - "Ġm irror", - "Ġmir ror", - "Ġmi rror", - "Ġ assistant", - "Ġass istant", - "Ġassist ant", - "( device", - "(d evice", - "(de vice", - "(dev ice", - "Ġspirit ual", - "b uilder", - "build er", - "bu ilder", - " §", - "Ġo utr", - "Ġout r", - "Ġou tr", - "Ġ tt", - "Ġt t", - "Ġ PER", - "ĠP ER", - "ĠPE R", - "Ġrad ical", - "Ġradi cal", - "Method s", - "Ġ pace", - "Ġp ace", - "Ġpa ce", - "Ġpac e", - "u dy", - "ud y", - "Ġg ut", - "Ġgu t", - "Ġ Greek", - "ĠG reek", - "ĠGre ek", - "ĠGree k", - "Ġ nonatomic", - "Ġnon atomic", - "Ġ Paper", - "ĠP aper", - "ĠPa per", - "ĠPap er", - "_ GPIO", - "_G PIO", - "_GP IO", - "Ġo bst", - "Ġob st", - "Ġobs t", - ". Ad", - ".A d", - "viron ments", - "vironment s", - "ĠS ov", - "ĠSo v", - "3 56", - "35 6", - "( con", - "(c on", - "(co n", - "Ġ Transaction", - "ĠTrans action", - ". assign", - ".as sign", - "ĉ catch", - "ĉc atch", - "ĉcat ch", - "el ter", - "elt er", - "Ġ bitcoin", - "Ġbit coin", - "_ GR", - "_G R", - "Ġ čĊ", - "Ġ/ >čĊ", - "Ġ/> čĊ", - "m etic", - "me tic", - "met ic", - "Ġtrans formation", - "Ġtransform ation", - "åı ·", - "Ġ rgb", - "Ġr gb", - "Ġrg b", - "istrib utions", - "istribution s", - "Ġ implicit", - "Ġimp licit", - "Ġimpl icit", - "Ġimplic it", - "/ in", - "/i n", - "d estination", - "dest ination", - "а ÑĤÑĮ", - "аÑĤ ÑĮ", - "Z ero", - "Ze ro", - "Ġ unset", - "Ġun set", - "Ġuns et", - "9 20", - "92 0", - ". where", - ".w here", - ".wh ere", - ". go", - ".g o", - "Ġ formation", - "Ġform ation", - "Ġformat ion", - "Ġforma tion", - "Ġ declaration", - "Ġde claration", - "Ġdeclar ation", - "( )čĊčĊ", - "() čĊčĊ", - "()čĊ čĊ", - "Ġ Expl", - "ĠEx pl", - "ĠExp l", - "ĉ ĉĉĠĠ", - "ĉĉ ĉĠĠ", - "ĉĉĉ ĠĠ", - "ĉĉĉĠ Ġ", - "/ pro", - "/p ro", - "/pr o", - ". JSON", - ".J SON", - "4 41", - "44 1", - "Ġ desk", - "Ġd esk", - "Ġde sk", - "Ġdes k", - ". substr", - ".sub str", - "// ----------------------------------------------------------------------------", - "//---------------------------------------------------------------- ------------", - "//------------------------------------------------ ----------------------------", - "l yn", - "ly n", - "p son", - "ps on", - "4 07", - "40 7", - "d isable", - "dis able", - "Ġ Func", - "ĠF unc", - "ĠFun c", - "ĠFu nc", - "ĉ Assert", - "Ġ MARK", - "ĠM ARK", - "ĠMA RK", - "ĠMAR K", - "Ġde feat", - "Ġdef eat", - "Ġdefe at", - "Ġ blind", - "Ġbl ind", - "Ġbli nd", - "Ġ constants", - "Ġcon stants", - "Ġconst ants", - "Ġconstant s", - "3 62", - "36 2", - ". headers", - ".head ers", - ".header s", - ".he aders", - "U ILD", - "UI LD", - "UIL D", - "Ġ expenses", - "Ġexp enses", - "Ġexpense s", - "P ixel", - "Pix el", - "Ġ hr", - "Ġh r", - "Ġ fel", - "Ġf el", - "Ġfe l", - "Ġ Eastern", - "ĠEast ern", - "ĠEaster n", - "4 24", - "42 4", - "4 90", - "49 0", - "_ del", - "_d el", - "_de l", - "3 57", - "35 7", - "ĠC ub", - "ĠCu b", - "Ġ sq", - "Ġs q", - "ĉ count", - "ĉc ount", - "Ġ Directory", - "ĠD irectory", - "ĠDirect ory", - "ĠDirector y", - "Ġex clus", - "Ġexc lus", - "Ġexcl us", - "Ġ historic", - "Ġhistor ic", - "Ġhist oric", - "Ġhisto ric", - "Ġ ------------------------------------------------", - "Ġ---------------- --------------------------------", - "Ġ-------------------------------- ----------------", - "Ġ-------------------- ----------------------------", - "Ġ composition", - "Ġcom position", - "Ġcomp osition", - "Ġcompos ition", - "Ġ dataGridView", - "Ġdata GridView", - "Ġ Burn", - "ĠB urn", - "ĠBur n", - "ĠBu rn", - "Ġ BC", - "ĠB C", - "M aster", - "Ma ster", - "Mas ter", - "Ġ spawn", - "Ġs pawn", - "Ġsp awn", - "Ġspa wn", - "Ġ bearing", - "Ġb earing", - "Ġbe aring", - "Ġbear ing", - ". SetActive", - ".Set Active", - "i lo", - "il o", - "Ġ gallery", - "Ġg allery", - "Ġgall ery", - "Ġf ounded", - "Ġfound ed", - "Ġfo unded", - "Ġ availability", - "Ġa vailability", - "Ġav ailability", - "Ġavail ability", - ". sqrt", - ".s qrt", - "Ġ pes", - "Ġp es", - "Ġpe s", - "Ġ DOM", - "ĠD OM", - "ĠDO M", - "m ate", - "ma te", - "mat e", - "O ct", - "Ġ matched", - "Ġm atched", - "Ġmatch ed", - "Ġmat ched", - "it ivity", - "Ġan xiety", - ". price", - ".p rice", - ".pr ice", - "Ġ Instant", - "ĠIn stant", - "ĠInst ant", - "ĠIns tant", - "ì Ĭ", - "Ġt ut", - "Ġtu t", - "I Collection", - "IC ollection", - ". shared", - ".sh ared", - ".share d", - ".sha red", - "_ sql", - "_s ql", - "_sq l", - "t bl", - "tb l", - "l ibrary", - "lib rary", - "_ destroy", - "_d estroy", - "_de stroy", - "_dest roy", - "er mal", - "erm al", - "Ġ Notes", - "ĠN otes", - "ĠNo tes", - "ĠNot es", - "ĠNote s", - "Ġ Ein", - "ĠE in", - "Ġsou thern", - "Ġsouth ern", - "ĠOTHER WISE", - "Ġ macro", - "Ġm acro", - "Ġma cro", - "Ġmac ro", - ". lower", - ".l ower", - ".lo wer", - ".low er", - "c ls", - "cl s", - "Content View", - ". link", - ".l ink", - ".li nk", - ".lin k", - "con stant", - "const ant", - "cons tant", - "Ġ Bes", - "ĠB es", - "ĠBe s", - "Ġsome body", - "n b", - "3 99", - "39 9", - "\" >{", - "\"> {", - "( local", - "(l ocal", - "(loc al", - "(lo cal", - ". ....", - ".. ...", - "... ..", - ".... .", - "Ġ Null", - "ĠN ull", - "ĠNu ll", - "m x", - "Ġ ç", - "Ġà §", - "Ġ pause", - "Ġp ause", - "Ġpa use", - "Ġpau se", - "- ----------", - "-- ---------", - "---- -------", - "-------- ---", - "--- --------", - "----- ------", - "---------- -", - "------ -----", - "------- ----", - "--------- --", - "_ MO", - "_M O", - "Ġ CM", - "ĠC M", - "Ġfor Key", - "Ġ DVD", - "ĠD VD", - "ĠDV D", - "Ġ closest", - "Ġclose st", - "Ġclos est", - "Ġcloses t", - "_ DEVICE", - "_DE VICE", - "_DEV ICE", - "Ġ Stephen", - "ĠSte phen", - "ĠStep hen", - "ĠSteph en", - "Ġ BBC", - "ĠB BC", - "ĠBB C", - "Ġ Travel", - "ĠT ravel", - "ĠTr avel", - "ĠTra vel", - "ĠTrav el", - "P aint", - "Pa int", - "Ġ Results", - "ĠRes ults", - "ĠResult s", - "Ġ Rule", - "ĠR ule", - "ĠRu le", - "Ġ tp", - "Ġt p", - "Ġ ratings", - "Ġr atings", - "Ġrating s", - "Ġrat ings", - "Ġra tings", - "c in", - "ci n", - "c sv", - "cs v", - "> /", - "Ġ GOP", - "ĠG OP", - "ĠGO P", - "l ad", - "la d", - "Ġ ÑĢ", - "ĠÑ Ģ", - "Ġ indexPath", - "Ġindex Path", - "m atrix", - "mat rix", - "= f", - "ar sed", - "ars ed", - "arse d", - "Ġ });", - "Ġ} );", - "Ġ}) ;", - "Ġ Cos", - "ĠC os", - "ĠCo s", - "Ġ Score", - "ĠS core", - "ĠSc ore", - "ĠSco re", - "ĠScor e", - "Ġ tak", - "Ġt ak", - "Ġta k", - "Ġ ESP", - "ĠE SP", - "ĠES P", - "Ġ INC", - "ĠI NC", - "ĠIN C", - "_ NULL", - "_N ULL", - "- flex", - "-f lex", - "-fl ex", - "\" ][", - "\"] [", - "in to", - "int o", - "e land", - "el and", - "ela nd", - "elan d", - "Author ization", - "_ FALSE", - "_F ALSE", - "Ġ gate", - "Ġg ate", - "Ġga te", - "Ġ vid", - "Ġv id", - "Ġvi d", - "is tent", - "ist ent", - "iste nt", - "isten t", - "T IME", - "TI ME", - "TIM E", - "Ġ rewrite", - "Ġre write", - "Ġrew rite", - "Ġ tie", - "Ġt ie", - "Ġti e", - "Ġ archive", - "Ġa rchive", - "Ġarch ive", - "Ġarc hive", - "5 11", - "51 1", - ". events", - ".e vents", - ".event s", - ".ev ents", - ". getParameter", - ".get Parameter", - ".getParam eter", - "Ġ Permission", - "ĠPer mission", - "ĠPerm ission", - "Ġprogram me", - "Ġprogramm e", - "Ġ é", - "j ud", - "ju d", - "Ġcame ras", - "Ġcamera s", - "Ġcam eras", - "Ġcamer as", - "3 38", - "33 8", - "3 49", - "34 9", - "( sys", - "(s ys", - "ĠSy rian", - "ĠSyria n", - "Ġimpro vements", - "Ġimprove ments", - "Ġimprovement s", - "Ġimprov ements", - "Ġ hip", - "Ġh ip", - "Ġhi p", - "Ġsu icide", - "Ġsuic ide", - "Ġsch olar", - "Ġscho lar", - "Ġ compatible", - "Ġcom patible", - "Ġcompat ible", - "0 22", - "02 2", - "rem ote", - ". down", - ".d own", - ".do wn", - "F UNCTION", - "FUNC TION", - "FUN CTION", - "Ġman aging", - "Ġmana ging", - "Ġ UIKit", - "ĠUI Kit", - ". raw", - ".r aw", - ".ra w", - "> >>>", - ">> >>", - ">>> >", - "3 71", - "37 1", - "Ġdem ands", - "Ġdemand s", - "el lite", - "ell ite", - "elli te", - "Ġ dent", - "Ġd ent", - "Ġde nt", - "Ġden t", - "Ġ Micro", - "ĠM icro", - "ĠMi cro", - "ĠMic ro", - "åı ĸ", - "' ][$", - "'] [$", - "'][ $", - "Ġ IE", - "ĠI E", - "im ension", - "imens ion", - "Ġt rem", - "Ġtr em", - "Ġtre m", - "6 30", - "63 0", - "Ġg ained", - "Ġgain ed", - "Ġga ined", - ". with", - ".w ith", - ". ok", - ".o k", - "h ou", - "ho u", - "Ġb om", - "Ġbo m", - "amp aign", - "ampa ign", - "Ġ joining", - "Ġjoin ing", - "Ġjo ining", - "f ish", - "fi sh", - "Ġadd Subview", - "8 60", - "86 0", - "Ġnor thern", - "Ġnorth ern", - ". cor", - ".c or", - ".co r", - "o ret", - "or et", - "ore t", - "D ie", - "Di e", - "i nish", - "in ish", - "ini sh", - "inis h", - "_ comp", - "_c omp", - "_com p", - "_co mp", - "Ġ attended", - "Ġatt ended", - "Ġattend ed", - "Ġ collapse", - "Ġc ollapse", - "Ġcoll apse", - "Ġcollaps e", - "Ġ SS", - "ĠS S", - "a cent", - "ace nt", - "ac ent", - "acen t", - "_ EQUAL", - "_E QUAL", - "_EQ UAL", - "Ġ Deep", - "ĠDe ep", - "ĠDee p", - "R GB", - "RG B", - "ĉ test", - "ĉt est", - "ol ves", - "olve s", - "olv es", - "u set", - "us et", - "use t", - "Un ityEngine", - "Unity Engine", - "w riter", - "write r", - "wr iter", - "Re solver", - "Res olver", - "Resolve r", - ", %", - "if ference", - "iff erence", - "iffer ence", - "iffe rence", - "_ remove", - "_re move", - "_rem ove", - "o nda", - "on da", - "ond a", - "Ġf emme", - "Ġfem me", - "3 85", - "38 5", - "de code", - "dec ode", - "Br anch", - "Ġ flush", - "Ġf lush", - "Ġfl ush", - "Ġflu sh", - "Ġinnov ative", - "T ests", - "Test s", - "Te sts", - "Tes ts", - "Ġ[' ./", - "Ġ['. /", - "Ġ covering", - "Ġcover ing", - "Ġcov ering", - ". admin", - ".ad min", - "ulti part", - "ultip art", - "( lambda", - "(l ambda", - " namespace", - "Ġ Sport", - "ĠS port", - "ĠSp ort", - "ĠSpo rt", - "ĠSpor t", - "Ġ !(", - "Ġ! (", - "a cles", - "ac les", - "acle s", - "acl es", - "Ġde pression", - "Ġdep ression", - "Ġdepr ession", - "Ġdepress ion", - "ĠK ong", - "ĠKon g", - "ĠKo ng", - "5 70", - "57 0", - "Ġ pert", - "Ġp ert", - "Ġper t", - "Ġpe rt", - "Ġ Conn", - "ĠC onn", - "ĠCon n", - "ĠCo nn", - "Ġ Otherwise", - "ĠOther wise", - "/ home", - "/h ome", - "s upported", - "sup ported", - "support ed", - "Ġ pink", - "Ġp ink", - "Ġpi nk", - "Ġpin k", - "Ġinv ited", - "Ġinvite d", - "Ġinvit ed", - "ñ os", - "ño s", - "_ enabled", - "_en abled", - "_enable d", - "Ġ -Ċ", - "Ġ- Ċ", - "F W", - "e ners", - "en ers", - "ener s", - "ene rs", - "Ġ MY", - "ĠM Y", - "Ġs uggestions", - "Ġsuggest ions", - "Ġsuggestion s", - "C anvas", - "Can vas", - "Ġ fer", - "Ġf er", - "Ġfe r", - "Ġ Marketing", - "ĠMark eting", - "ĠMarket ing", - "@ Test", - "un tu", - "unt u", - "Ġ Ven", - "ĠV en", - "ĠVe n", - "Ġ Cou", - "ĠC ou", - "ĠCo u", - "i vals", - "iv als", - "ival s", - "iva ls", - "D onald", - "Don ald", - "l imited", - "lim ited", - "limit ed", - "ĉ ĉĉĉĉĉĊ", - "ĉĉ ĉĉĉĉĊ", - "ĉĉĉĉ ĉĉĊ", - "ĉĉĉ ĉĉĉĊ", - "ĉĉĉĉĉ ĉĊ", - "ĉĉĉĉĉĉ Ċ", - "Ġanal yst", - "Ġanaly st", - "Ġanalys t", - "( entry", - "(en try", - "(ent ry", - "Ġrepresent ative", - "_ attributes", - "_at tributes", - "_attribute s", - "_attrib utes", - "Ġ fur", - "Ġf ur", - "Ġfu r", - ". hide", - ".h ide", - "r esp", - "re sp", - "res p", - "ad ores", - "ado res", - "ador es", - "r ides", - "ri des", - "ride s", - "rid es", - "Ġ Josh", - "ĠJ osh", - "ĠJo sh", - "ĠJos h", - "r obot", - "ro bot", - "rob ot", - "ĠN AT", - "ĠNA T", - "Ġs esso", - "Ġses so", - "Ġsess o", - "Ġint egrated", - "Ġinteg rated", - "Ġintegr ated", - "Ġintegrate d", - ": true", - "p arts", - "par ts", - "part s", - "pa rts", - "Ġst upid", - "Ġstu pid", - "Ġstup id", - ": event", - ":e vent", - "@end section", - "Ġ pu", - "Ġp u", - ". Table", - ".T able", - ".Tab le", - "Ġ Yii", - "ĠY ii", - "ĠYi i", - "` ;ĊĊ", - "`;Ċ Ċ", - "`; ĊĊ", - "Ġ clang", - "Ġc lang", - "Ġcl ang", - "Ġclan g", - "Ġcla ng", - "=\" \">", - "=\"\" >", - "en gan", - "eng an", - "enga n", - "_ parameters", - "_param eters", - "_parameter s", - ". internal", - ".in ternal", - ".int ernal", - ".inter nal", - "Ġ Modern", - "ĠMod ern", - "ĠMode rn", - "ĠModer n", - "Ġ metric", - "Ġm etric", - "Ġmet ric", - "Ġ semi", - "Ġs emi", - "Ġse mi", - "Ġsem i", - "={ {Ċ", - "={{ Ċ", - "7 07", - "70 7", - ". amazon", - ".a mazon", - ".am azon", - "Ġ BB", - "ĠB B", - "ain ty", - "aint y", - "ai nty", - "view port", - "3 67", - "36 7", - "Ġstart Activity", - "dis patch", - "disp atch", - "* ****", - "** ***", - "**** *", - "*** **", - "Ġf lav", - "Ġfl av", - "Ġfla v", - "iffer ent", - "iffe rent", - "3 82", - "38 2", - "[ this", - "[t his", - "Ġs take", - "Ġst ake", - "Ġsta ke", - "Ġarg ued", - "Ġargue d", - "v iously", - "vious ly", - "vi ously", - ". work", - ".w ork", - "Ġ Oak", - "ĠO ak", - "O ld", - "Ol d", - "( async", - "(a sync", - "(as ync", - "n otes", - "not es", - "no tes", - "note s", - "Ġ flip", - "Ġf lip", - "Ġfl ip", - "Ġdis ag", - "Ġ TE", - "ĠT E", - "ĉ error", - "ĉe rror", - "ĉerr or", - "< '", - "Ġ »ĊĊ", - "Ġ» ĊĊ", - "Ġ»Ċ Ċ", - "Ġ filtered", - "Ġfil tered", - "Ġfilter ed", - "Ġfilt ered", - "ĠM ach", - "ĠMac h", - "ĠMa ch", - "Ġ hung", - "Ġh ung", - "Ġhun g", - "Ġhu ng", - "_ dump", - "_d ump", - "_ samples", - "_s amples", - "_sample s", - "- dismiss", - "-dis miss", - "Ġ ray", - "Ġr ay", - "Ġra y", - "Im plemented", - "Implement ed", - "D K", - "Ġ jed", - "Ġj ed", - "Ġje d", - "0 90", - "09 0", - "Ġbreak s", - "Ġbre aks", - "Ġ fits", - "Ġf its", - "Ġfit s", - "Ġfi ts", - ". gr", - ".g r", - "Ġ Zero", - "ĠZ ero", - "ĠZe ro", - "o ro", - "or o", - "Ġequ ally", - "Ġequal ly", - "Ġeq ually", - "Ġ '[", - "Ġ' [", - "Ġconcern ing", - "< meta", - "<", - "'> <", - "Ġpro mot", - "Ġprom ot", - "Ġpromo t", - "Ġ incl", - "Ġin cl", - "Ġinc l", - "_ only", - "_on ly", - "ë¥ ¼", - "ĠAtt orney", - "- date", - "-d ate", - "-da te", - "-dat e", - "Ġ landscape", - "Ġl andscape", - "Ġland scape", - "Ġlands cape", - "Ġlandsc ape", - "Ġ fu", - "Ġf u", - "S Y", - ". prop", - ".p rop", - ".pro p", - ".pr op", - "Ġ Arr", - "ĠA rr", - "ĠAr r", - "p ag", - "pa g", - "Parallel Group", - "' :čĊ", - "': čĊ", - "Ġ logs", - "Ġl ogs", - "Ġlo gs", - "Ġlog s", - "a unch", - "un ci", - "unc i", - "n ama", - "na ma", - "nam a", - "Table Cell", - "iss ues", - "issue s", - ". {", - "e curity", - "ec urity", - "_ exec", - "_e xec", - "_ex ec", - "_exe c", - "o lds", - "ol ds", - "old s", - "Ġ hosts", - "Ġhost s", - "Ġho sts", - "Ġhos ts", - "Ġ proto", - "Ġpro to", - "Ġpr oto", - "Ġprot o", - "_ import", - "_im port", - "_imp ort", - "_ sort", - "_s ort", - "_so rt", - "Ġ Bow", - "ĠB ow", - "ĠBo w", - "Ġ Normal", - "ĠN ormal", - "ĠNor mal", - "ĠNorm al", - "Ġ Farm", - "ĠF arm", - "ĠFar m", - "ĠFa rm", - ".create ParallelGroup", - "R otation", - "Rot ation", - ". err", - ".e rr", - ".er r", - "Ġp leased", - "Ġplease d", - "Ġple ased", - "Ġplea sed", - "Ġpleas ed", - "it age", - "ita ge", - "itag e", - ". Wh", - ".W h", - "ĉ ĉĠĠĠĠ", - "ĉĉ ĠĠĠĠ", - "ĉĉĠĠĠ Ġ", - "ĉĉĠ ĠĠĠ", - "ĉĉĠĠ ĠĠ", - "M R", - "Ġ MORE", - "ĠM ORE", - "ĠMO RE", - "ĠMOR E", - "Ġ Natural", - "ĠN atural", - "ĠNat ural", - "ĠNatur al", - "_ transform", - "_trans form", - "B ASE", - "BA SE", - "en eral", - "ener al", - "ene ral", - "u tdown", - "ut down", - ". commons", - ".com mons", - ".common s", - ".comm ons", - "W T", - "Ġ aan", - "Ġa an", - "Ġaa n", - ". Result", - ".Res ult", - "d og", - "do g", - "Ġcl icking", - "Ġclick ing", - "Ġclic king", - ") ,ĊĊ", - "), ĊĊ", - "),Ċ Ċ", - "# line", - "O perator", - "Oper ator", - "Op erator", - "Opera tor", - "Ġc iv", - "Ġci v", - "Ġm erg", - "Ġme rg", - "Ġmer g", - "o buf", - "ob uf", - "ng then", - "ngth en", - "Ġ [{", - "Ġ[ {", - "Ġc ancell", - "Ġcan cell", - "Ġcancel l", - "Ġcanc ell", - "tr igger", - "tri gger", - ". :", - "W ORK", - "WO RK", - "de clare", - "decl are", - "declar e", - "Ġde crease", - "Ġdecre ase", - "ÅĽ ci", - "l oom", - "lo om", - "loo m", - ". None", - ".N one", - ".No ne", - ".Non e", - "Ġ MI", - "ĠM I", - "Ġ Jason", - "ĠJ ason", - "ĠJa son", - "ĠJas on", - "Ġhealth care", - "ia mond", - "iam ond", - "iamo nd", - "s ylvania", - "* x", - "Ġ Ra", - "ĠR a", - "[ b", - "Ġ printing", - "Ġprint ing", - "Ġprin ting", - "ph abet", - "pha bet", - "Ġ Labour", - "ĠLa bour", - "ĠLab our", - "o pper", - "op per", - "opp er", - "Ġz ijn", - "Ġzi jn", - "Ġzij n", - "- target", - "-t arget", - "_ FUNCTION", - "_F UNCTION", - "_FUNC TION", - "_FUN CTION", - "Ġ oct", - "Ġo ct", - "Ġoc t", - "е ниÑı", - "ен иÑı", - "ени Ñı", - "åľ ¨", - "Ġ western", - "Ġwest ern", - "Ġwes tern", - "Ġcomp uters", - "Ġcomput ers", - "Ġcomputer s", - "Ġcompute rs", - "Ġ RET", - "ĠR ET", - "ĠRE T", - "Hash Map", - "[ String", - "[S tring", - "get Value", - "_ DATE", - "_D ATE", - "_DAT E", - "_DA TE", - ". Next", - ".N ext", - "ĠF if", - "ĠFi f", - "é l", - "ic ked", - "ick ed", - "æ İ", - "- MM", - "-M M", - "Ġ {ĊĊĊ", - "Ġ{ ĊĊĊ", - "Ġ{Ċ ĊĊ", - "Ġ{ĊĊ Ċ", - "Ġ contacts", - "Ġcont acts", - "Ġcontact s", - "Ġconta cts", - "Ġ digits", - "Ġd igits", - "Ġdig its", - "Ġdigit s", - "P rodu", - "Pro du", - "Pr odu", - "Prod u", - "Ġun usual", - "Ġunus ual", - "Ġrapid ly", - "t ures", - "ture s", - "tu res", - "tur es", - "Ġang ry", - "c ancel", - "can cel", - "x xxx", - "xx xx", - "xxx x", - "_ parser", - "_p arser", - "_parse r", - "_par ser", - "_pars er", - "id ity", - "idi ty", - "_ PREFIX", - "_P REFIX", - "_PRE FIX", - "_PREF IX", - "7 10", - "71 0", - "Ġm ehr", - "Ġme hr", - "Ġrare ly", - "Ġrar ely", - "e the", - "et he", - "eth e", - "o pes", - "op es", - "ope s", - "Ġ %.", - "Ġ% .", - "w orks", - "work s", - "wor ks", - "Ġ theta", - "Ġth eta", - "Ġthe ta", - "Ġcon tribution", - "Ġcontrib ution", - "Ġ Tony", - "ĠT ony", - "ĠTo ny", - "ĠTon y", - "Ġs quad", - "Ġsqu ad", - "5 37", - "53 7", - "а й", - "аР¹", - "Ġî n", - "t here", - "th ere", - "ther e", - "the re", - "o uted", - "ou ted", - "out ed", - "oute d", - "ĉ q", - "Ļ Ĥ", - "g ood", - "go od", - "goo d", - "L I", - "é¡ µ", - "Ġ Living", - "ĠL iving", - "ĠLi ving", - "ĠLiv ing", - "iz abeth", - "iza beth", - "Ġ kt", - "Ġk t", - "Ġ Dallas", - "ĠD allas", - "ĠDal las", - "] ],Ċ", - "]] ,Ċ", - "]], Ċ", - "Ġ />ĊĊ", - "Ġ/ >ĊĊ", - "Ġ/>Ċ Ċ", - "Ġ/> ĊĊ", - "Ġ raising", - "Ġr aising", - "Ġrais ing", - "Ġra ising", - "/ router", - "/r outer", - "_ game", - "_g ame", - "3 68", - "36 8", - "Ġ CUR", - "ĠC UR", - "ĠCU R", - "z ens", - "ze ns", - "zen s", - ". es", - ".e s", - "Ġ fontWeight", - "Ġfont Weight", - "( func", - "(f unc", - "(fun c", - "not ification", - "notif ication", - "Ġ' ../../../", - "Ġ'../ ../../", - "Ġ'../../ ../", - "Ġbl ame", - "Ġbla me", - "ãĢĤ ĊĊĊĊ", - "ãĢĤĊĊ ĊĊ", - "ãĢĤĊ ĊĊĊ", - "an co", - "anc o", - "9 80", - "98 0", - "Id entity", - "Ident ity", - "Ide ntity", - "f ollow", - "fol low", - "Ġ arts", - "Ġa rts", - "Ġar ts", - "Ġart s", - "x s", - "Ġoffic ially", - "Ġofficial ly", - "Ġ Studio", - "ĠSt udio", - "ĠStud io", - "ĠStudi o", - "Ġrecommend ations", - "Ġrecommendation s", - "Ġ locale", - "Ġl ocale", - "Ġlo cale", - "Ġloc ale", - "Ġlocal e", - "Ġam ateur", - "Ġamat eur", - "Ġ Enable", - "ĠE nable", - "ĠEn able", - "Ġ caps", - "Ġc aps", - "Ġcap s", - "Ġca ps", - ". End", - ".E nd", - ".En d", - "3 88", - "38 8", - "- add", - "-a dd", - "-ad d", - "_g shared", - "Ġ CT", - "ĠC T", - "F orce", - "For ce", - "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "Ġ orange", - "Ġo range", - "Ġor ange", - "Ġorang e", - "Ġora nge", - "Ġoran ge", - "Ġ lp", - "Ġl p", - "Ġ answered", - "Ġanswer ed", - ". Grid", - ".G rid", - ".Gr id", - "Ġd ual", - "Ġdu al", - "Ġdua l", - "Ġstr ategic", - "Ġstrateg ic", - "Ġn obody", - "Ġno body", - "Ġnob ody", - "Ġ fatal", - "Ġf atal", - "Ġfa tal", - "Ġfat al", - "_ est", - "_e st", - "_es t", - "( el", - "(e l", - "Ġ ìł", - "Ġì ł", - "ĠB udd", - "ĠBu dd", - "ĠBud d", - "A IT", - "AI T", - "_ factor", - "_f actor", - "_fac tor", - "_fact or", - "_fa ctor", - "- one", - "-on e", - "-o ne", - "Ġ HAVE", - "ĠH AVE", - "ĠHA VE", - "\" čĊčĊ", - "\"čĊ čĊ", - "7 60", - "76 0", - "P rof", - "Pro f", - "Pr of", - "Ġ är", - "Ġä r", - "str ings", - "string s", - "Ġ dirty", - "Ġd irty", - "Ġdir ty", - "Ġdirt y", - "Ġ Face", - "ĠF ace", - "ĠFac e", - "ĠFa ce", - "Ġ Begin", - "ĠB egin", - "ĠBe gin", - "ĠBeg in", - "Ġ Bus", - "ĠB us", - "ĠBu s", - "Ġ wis", - "Ġw is", - "Ġwi s", - "åŃ Ĺ", - "Ġ speaker", - "Ġs peaker", - "Ġspe aker", - "Ġspeak er", - "Ġ carrier", - "Ġcar rier", - "Ġcarr ier", - "Ġ Om", - "ĠO m", - "Ġhad n", - "Ġha dn", - "Al low", - "All ow", - ":: __", - "::_ _", - "Ġ verb", - "Ġv erb", - "Ġver b", - "Ġve rb", - "Ġ Complete", - "ĠCom plete", - "ĠComp lete", - "ĠComple te", - "Ġ Easy", - "ĠE asy", - "ĠEa sy", - "Ġb ills", - "Ġbill s", - "Ġbil ls", - "Ġ ĠĊĊ", - "ĠĠ ĊĊ", - "ĠĠĊ Ċ", - "Vert ical", - "Ver tical", - "Ġ pron", - "Ġp ron", - "Ġpro n", - "Ġpr on", - "Ġ Define", - "ĠDe fine", - "ĠDef ine", - "Ġ lookup", - "Ġlook up", - "variable s", - "vari ables", - "Ġp andas", - "Ġpan das", - "Ġpand as", - "Ġpanda s", - "u mes", - "um es", - "ume s", - "Ġin noc", - "Ġinn oc", - "Ġ setUp", - "Ġset Up", - "ĠCh ampionship", - "ĠChampions hip", - "ĠChampion ship", - "art ist", - "arti st", - "ĠC Type", - "ĠCT ype", - "F oundation", - "Found ation", - "๠Ī", - "Ġ Setup", - "ĠSet up", - "4 28", - "42 8", - "Ġ recipes", - "Ġrec ipes", - "Ġrecipe s", - "Ġrecip es", - "Ġ UIColor", - "ĠU IColor", - "ĠUI Color", - "Ġ Fight", - "ĠF ight", - "ĠFig ht", - "ĠFi ght", - "Ġ authorized", - "Ġauthor ized", - "Ġauthorize d", - "_ click", - "_c lick", - "_cl ick", - "_cli ck", - "9 90", - "99 0", - "_ success", - "_s uccess", - "_succ ess", - "_su ccess", - "an gan", - "ang an", - "anga n", - "Ġ Mountain", - "ĠM ountain", - "ĠMount ain", - "Ġ Doctor", - "ĠDo ctor", - "ĠDoc tor", - "Ġ egg", - "Ġe gg", - "Ġeg g", - "ĠM edicine", - "ĠMed icine", - "ĠMedic ine", - "c les", - "cl es", - "cle s", - "` .Ċ", - "`. Ċ", - "[ int", - "[i nt", - "[in t", - "d ashboard", - "dash board", - "Ġ Appro", - "ĠApp ro", - "ĠAp pro", - "- dr", - "-d r", - "Ġprodu ces", - "Ġproduce s", - "Ġprod uces", - "Ġr ental", - "Ġren tal", - "Ġrent al", - "Ġ reload", - "Ġre load", - "Ġr eload", - "Ġrel oad", - "3 81", - "38 1", - "Ġ arrival", - "Ġarr ival", - "Ġarriv al", - "s pot", - "sp ot", - "spo t", - "Ġunder t", - "Ġund ert", - "Ġunde rt", - "3 78", - "37 8", - "Ġequ ipped", - "Ġequip ped", - "Ġ proved", - "Ġpro ved", - "Ġpr oved", - "Ġprov ed", - "Ġprove d", - "Ġ centers", - "Ġcent ers", - "Ġcenter s", - "Ġcen ters", - "Ġ defines", - "Ġdef ines", - "Ġdefine s", - "Ġdefin es", - "al so", - "als o", - "Ġ opacity", - "Ġop acity", - "Ġ Unfortunately", - "ĠUn fortunately", - "ĠIll inois", - "Ġ не", - "Ġн е", - "ĠT emple", - "ĠTem ple", - "ĠTemp le", - "ĠTempl e", - "Ġ Trail", - "ĠT rail", - "ĠTr ail", - "ĠTra il", - "Ġ Kelly", - "ĠK elly", - "ĠKel ly", - "Ġ measurement", - "Ġme asurement", - "Ġmeasure ment", - "Ġmeas urement", - "Ġse parated", - "Ġsepar ated", - "Ġseparate d", - "Ġseparat ed", - "- circle", - "-c ircle", - "H ey", - "He y", - "Ġ READ", - "ĠRE AD", - "ig its", - "igit s", - "igi ts", - "Ġ ib", - "Ġi b", - "Ġ MOD", - "ĠM OD", - "ĠMO D", - "at tery", - "att ery", - "atter y", - "atte ry", - "а з", - "аР·", - "Ġv end", - "Ġve nd", - "Ġven d", - "е нÑĤ", - "ен ÑĤ", - "Ġ HttpClient", - "ĠHttp Client", - "3 59", - "35 9", - "s afe", - "sa fe", - "_ ASS", - "_A SS", - "_AS S", - "i cit", - "ic it", - "ici t", - "Ġ Construct", - "ĠCon struct", - "ĠConstr uct", - "Ġ Clo", - "ĠC lo", - "ĠCl o", - "Ġ Six", - "ĠS ix", - "ĠSi x", - "_ TOKEN", - "_T OKEN", - "_TO KEN", - "( block", - "(b lock", - "(bl ock", - "Ġwar ned", - "Ġwarn ed", - "/* !", - "! Ċ", - "}/ >Ċ", - "}/> Ċ", - "Ġin novation", - "Ġinn ovation", - "Ġinnov ation", - "_ \"", - "Ġ );čĊčĊ", - "Ġ) ;čĊčĊ", - "Ġ); čĊčĊ", - "Ġ);čĊ čĊ", - "Ġ spots", - "Ġsp ots", - "Ġspot s", - "Ġspo ts", - "Ġcho osing", - ". cs", - ".c s", - "Ġf lexible", - "Ġflex ible", - "U Int", - "UI nt", - "4 35", - "43 5", - "9 30", - "93 0", - "Ġ scratch", - "Ġs cratch", - "Ġscr atch", - "- al", - "-a l", - "Ġf estival", - "Ġfest ival", - "Ġout standing", - "================ ================================", - "================================ ================", - "M ean", - "Me an", - "Ġ Oregon", - "ĠO regon", - "ĠOre gon", - "s ymbol", - "sym bol", - ". account", - ".a ccount", - ".ac count", - ".acc ount", - "d ney", - "dn ey", - "' ''", - "'' '", - "! \",", - "!\" ,", - "9 01", - "90 1", - "Ġ particle", - "Ġp article", - "Ġpart icle", - "Ġpartic le", - "Ġparti cle", - "à ĥ", - "[ MAX", - "[M AX", - "I VER", - "IV ER", - "IVE R", - "ER ENCE", - "NS Mutable", - "ĠC olumbia", - "ĠColum bia", - "_ ĊĊ", - "_Ċ Ċ", - ". fr", - ".f r", - "Ġc ogn", - "Ġco gn", - "Ġcog n", - "V R", - "Ġ Methods", - "ĠMethod s", - "ĠMeth ods", - "Ġ Made", - "ĠM ade", - "ĠMad e", - "ĠMa de", - "Ġ BR", - "ĠB R", - "Ġ Else", - "ĠE lse", - "ĠEl se", - "Ġeg gs", - "Ġegg s", - "Ġ swing", - "Ġs wing", - "Ġsw ing", - "Ġ Inv", - "ĠI nv", - "ĠIn v", - "Ġdise ases", - "Ġdisease s", - "Ġf irms", - "Ġfirm s", - "Ġfi rms", - "Ġfir ms", - "Ġ lemma", - "Ġl emma", - "Ġle mma", - "Ġlem ma", - "} `);Ċ", - "}` );Ċ", - "l ings", - "ling s", - "lin gs", - "Ġg ym", - "Ġgy m", - "umin um", - "umi num", - ". Trim", - ".T rim", - ".Tr im", - "M em", - "Me m", - "Ġcrit icism", - "Ġcritic ism", - "ibern ate", - "_ TX", - "_T X", - "i oni", - "ion i", - "io ni", - "Ġguid ance", - "Ġgui dance", - "Ġrepeated ly", - "Ġrepeat edly", - "Ġ supplier", - "Ġs upplier", - "Ġsup plier", - "Ġsuppl ier", - "Ġsupp lier", - "Ġp ainting", - "Ġpaint ing", - "Ġpain ting", - "8 64", - "86 4", - ". Fragment", - ".F ragment", - "ed Exception", - "Ġw iring", - "Ġwir ing", - "Ġwi ring", - "Ġcour ts", - "Ġcou rts", - "Ġcourt s", - "W EB", - "WE B", - "æľ ī", - "\\ .", - "ill ance", - "illa nce", - "Ġb rows", - "Ġbr ows", - "Ġbro ws", - "Ġbrow s", - "Ġ Pattern", - "ĠP attern", - "ĠPat tern", - "ĠPatt ern", - "PL ICATION", - "PLIC ATION", - "Ġ Summer", - "ĠS ummer", - "ĠSum mer", - "Ch ain", - "Cha in", - "Ġc ute", - "Ġcut e", - "Ġcu te", - "m ercial", - "mer cial", - "merc ial", - "Ġd il", - "Ġdi l", - "ĠFrank lin", - "ĉ global", - "ĉg lobal", - "IN CLUDING", - "h istory", - "hi story", - "hist ory", - "histor y", - "Ġ lst", - "Ġl st", - "Ġls t", - "Q t", - "S DL", - "SD L", - "a lia", - "al ia", - "ali a", - "i ere", - "ie re", - "ier e", - "( ...", - "(. ..", - "(.. .", - "ĉ cin", - "ĉc in", - "if fs", - "iff s", - "v elope", - "ve lope", - "vel ope", - "velop e", - "Ġ Root", - "ĠR oot", - "ĠRo ot", - "ĠRoo t", - "cl uster", - "clus ter", - "User Name", - "i gne", - "ig ne", - "ign e", - "< S", - "Ġ fest", - "Ġf est", - "Ġfe st", - "4 19", - "41 9", - "Ġindic ating", - "Ġindica ting", - "k eeper", - "ke eper", - "keep er", - "kee per", - "Ġc ada", - "Ġca da", - "Ġcad a", - "é g", - "con sin", - "cons in", - "Ġ GB", - "ĠG B", - "Ġ lb", - "Ġl b", - "e mony", - "em ony", - "emo ny", - "emon y", - "- icons", - "-icon s", - "-i cons", - "_ doc", - "_d oc", - "_do c", - "A ctor", - "Act or", - "Ac tor", - "e lem", - "el em", - "ele m", - ". Delete", - ".De lete", - "Ġin fection", - "Ġinf ection", - "Ġinfect ion", - "Ġ Privacy", - "ĠPriv acy", - "Ġgreat ly", - "Ġ Pos", - "ĠP os", - "ĠPo s", - "ĠT reat", - "ĠTr eat", - "ĠTre at", - "F low", - "Fl ow", - "Flo w", - "Ġat tractive", - "Ġattr active", - "Ġattract ive", - "Ġ Marc", - "ĠM arc", - "ĠMar c", - "ĠMa rc", - "s udo", - "su do", - "t esy", - "te sy", - "tes y", - "- an", - "-a n", - "9 98", - "99 8", - "ab ama", - "aba ma", - "Ġ Would", - "ĠW ould", - "ĠWo uld", - "Ġs uck", - "Ġsu ck", - "Ġsuc k", - "index Path", - "Ġ Et", - "ĠE t", - "T imes", - "Time s", - "Tim es", - "Ti mes", - "7 80", - "78 0", - "Ġ clubs", - "Ġcl ubs", - "Ġclub s", - "_ assoc", - "_as soc", - "_ass oc", - "Ġac quired", - "Ġacqu ired", - "Ġacquire d", - "( \":", - "(\" :", - "Ġint ense", - "Ġintens e", - ". maps", - ".m aps", - ".map s", - ".ma ps", - "Ex pected", - "Exp ected", - "Expect ed", - "T oggle", - "Ġ ay", - "Ġa y", - "Ġl ifestyle", - "Ġlife style", - "Ġlif estyle", - "- called", - "-c alled", - "-cal led", - "-call ed", - "Ġ Snow", - "ĠS now", - "ĠSn ow", - "ĠSno w", - "V olume", - "Vol ume", - "Ġcann abis", - "Ġ Direction", - "ĠD irection", - "ĠDirect ion", - "ĠDi rection", - "ĠDir ection", - "ĠDire ction", - "Ġ Limited", - "ĠL imited", - "ĠLim ited", - "ĠLimit ed", - "- specific", - "-s pecific", - "-spec ific", - "Ġd owntown", - "Ġdown town", - "Ġdownt own", - "/ icons", - "/i cons", - "/icon s", - "/ic ons", - "Ġre ven", - "Ġr even", - "Ġrev en", - "Ġreve n", - "L eg", - "Le g", - "8 85", - "88 5", - "= null", - "=n ull", - "4 96", - "49 6", - "Key board", - "' )).", - "') ).", - "')) .", - "Ġ\" \";čĊ", - "Ġ\"\" ;čĊ", - "Ġ\"\"; čĊ", - "Ġatt itude", - ". navigate", - ".n avigate", - ".nav igate", - "- error", - "-e rror", - "AM PLE", - "AMP LE", - "AMPL E", - "Ġ Jay", - "ĠJ ay", - "ĠJa y", - "v r", - "c ow", - "co w", - ". compile", - ".com pile", - ".comp ile", - "Ġmem ories", - "Ġmemor ies", - "Ġmemo ries", - "_ mark", - "_m ark", - "_mar k", - "_ma rk", - "Ġ Minnesota", - "ĠMin nesota", - "Ġk osten", - "Ġko sten", - "Ġkos ten", - "Ġkost en", - "Ġ probability", - "Ġprob ability", - "Ġprobabil ity", - "w arning", - "war ning", - "warn ing", - "Ġgen etic", - "Ġgene tic", - "F ixture", - "Fix ture", - "Ġ HashSet", - "ĠHash Set", - "N ombre", - "Nom bre", - "_ month", - "_m onth", - "_mon th", - "_mo nth", - "Æ °", - "- start", - "-st art", - "-star t", - "xy gen", - "ĉ ft", - "ĉf t", - "i agnostics", - "Ġ Matthew", - "ĠMat thew", - "ĠMatth ew", - "Ġcon cepts", - "Ġconcept s", - "Ġconce pts", - "Ġcon str", - "Ġconst r", - "Ġcons tr", - ". State", - ".St ate", - ".Stat e", - "и н", - "N ov", - "No v", - "Î ±", - "Ġ Panel", - "ĠP anel", - "ĠPan el", - "ĠPa nel", - "ĠPane l", - "ä¸ ª", - "com pare", - "comp are", - "> ()Ċ", - ">( )Ċ", - ">() Ċ", - "Ġapp lying", - "Ġapply ing", - "Ġappl ying", - "Ġprom ised", - "Ġpromise d", - "Ġ ox", - "Ġo x", - "n cia", - "nc ia", - "Ġ Validation", - "ĠValid ation", - "o rts", - "or ts", - "ort s", - "_ cur", - "_c ur", - "_cu r", - "e lect", - "el ect", - "ele ct", - "e ye", - "ey e", - "( Data", - "(D ata", - "Ġre porter", - "Ġreport er", - "Ġ Buff", - "ĠB uff", - "ĠBu ff", - "ĠBuf f", - "3 95", - "39 5", - "Ġ sr", - "Ġs r", - "Ġ \";", - "Ġ\" ;", - "i cky", - "ic ky", - "ick y", - "Ġt empor", - "Ġtem por", - "Ġtemp or", - "Ġtempo r", - "S N", - "Ġ resident", - "Ġres ident", - "Ġresid ent", - "Ġreside nt", - "p ires", - "pi res", - "pir es", - "pire s", - "ys ical", - "ysi cal", - "Ġend orse", - "Ġendors e", - "Ġ Song", - "ĠS ong", - "ĠSo ng", - "ĠSon g", - "is Empty", - "le et", - "lee t", - "_ util", - "_u til", - "_ut il", - "Ġd istingu", - "Ġdist ingu", - "Ġ Talk", - "ĠT alk", - "ĠTal k", - "ĠTa lk", - "Ġ Mot", - "ĠM ot", - "ĠMo t", - "( default", - "(d efault", - "(de fault", - "(def ault", - ". Arg", - ".A rg", - ".Ar g", - "gorith ms", - "gorithm s", - "_ words", - "_w ords", - "_word s", - "im mer", - "imm er", - "_ reset", - "_re set", - "_res et", - "f amily", - "W W", - "Ġs avings", - "Ġsav ings", - "Ġsaving s", - "Ġ âĢĿ", - "ĠâĢ Ŀ", - "_ enable", - "_e nable", - "_en able", - "s idebar", - "side bar", - "R unning", - "Run ning", - "Ġ ali", - "Ġa li", - "Ġal i", - "Ġte stim", - "Ġtest im", - "Ġtes tim", - "Ġ warnings", - "Ġw arnings", - "Ġwar nings", - "Ġwarn ings", - "Ġwarning s", - "Ġ Chem", - "ĠC hem", - "ĠCh em", - "ĠChe m", - "Ġ Exit", - "ĠE xit", - "ĠEx it", - "Ġf ounder", - "Ġfound er", - "Ġfo under", - "Ġfou nder", - "p ector", - "pe ctor", - "pect or", - "pec tor", - "Ġ rm", - "Ġr m", - "_ dataset", - "_d ataset", - "_data set", - "_dat aset", - "_datas et", - "Ġ Das", - "ĠD as", - "ĠDa s", - "Ġ han", - "Ġh an", - "Ġha n", - "G etty", - "Get ty", - "Ge tty", - "á l", - "Ġ ny", - "Ġn y", - "Ġpo verty", - "Ġpov erty", - "Ġresult ed", - ". by", - ".b y", - "Ġ Visit", - "ĠVis it", - "ĠVi sit", - "Ġobt aining", - "Ġobtain ing", - "/ '.$", - "/' .$", - "/'. $", - "Ġ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "s hall", - "sh all", - "shal l", - "sha ll", - "_ LEFT", - "_LE FT", - "UI Image", - "_ Name", - "_N ame", - "h ave", - "ha ve", - "ĠN ob", - "ĠNo b", - "l r", - "- footer", - "-f ooter", - "-foot er", - "Ġn aked", - "Ġna ked", - "Ġnak ed", - "ĠG arden", - "ĠGar den", - "ĠGard en", - "\\F acades", - "Ġ graduate", - "Ġgrad uate", - "Ġgradu ate", - "4 17", - "41 7", - "Ġfr anchise", - "Ġfranch ise", - "p lane", - "pl ane", - "plan e", - "pla ne", - "Ġcontrib utions", - "Ġcontribution s", - "Ġstring With", - "Ġ crypto", - "Ġc rypto", - "Ġcrypt o", - "Ġcry pto", - "Ġmov ements", - "Ġmove ments", - "Ġmo vements", - "Ġmovement s", - "a thers", - "ath ers", - "ather s", - "athe rs", - "Ġ lifetime", - "Ġl ifetime", - "Ġlife time", - "Ġlif etime", - "Ġcommunic ate", - "Ġcommun icate", - "j ar", - "ja r", - "Ġ Fragment", - "ĠF ragment", - "ĠFr agment", - "ĠFra gment", - "ĠFrag ment", - "_ IF", - "_I F", - "ĠN avy", - "ĠNav y", - "ĠNa vy", - "Ġ Figure", - "ĠF igure", - "ĠFig ure", - "Ġ simulation", - "Ġs imulation", - "Ġsim ulation", - "Ġsimul ation", - "_ stop", - "_s top", - "_st op", - "Ġreport ers", - "Ġreporter s", - "Ġver sus", - "Ġvers us", - "a ja", - "aj a", - "Ġ α", - "ĠÎ ±", - "Ġgover nor", - "Ġgovern or", - "Ġgoverno r", - "List Item", - "Ġ sealed", - "Ġse aled", - "Ġsea led", - "Ġseal ed", - ". Background", - ".Back ground", - "e di", - "ed i", - "ash ing", - "ashi ng", - "Ġ lip", - "Ġl ip", - "Ġli p", - "ĠI h", - "m erge", - "mer ge", - "Ġn ec", - "Ġne c", - "0 24", - "02 4", - "el ocity", - "elo city", - "AT EG", - "ATE G", - "Ġse eds", - "Ġsee ds", - "Ġseed s", - "Ġ floating", - "Ġf loating", - "Ġfloat ing", - "Ġflo ating", - "7 01", - "70 1", - "_ FA", - "_F A", - "w alk", - "wa lk", - "wal k", - "ĉ user", - "ĉu ser", - "ĉuse r", - "ĉus er", - "_ depth", - "_de pth", - "_dep th", - "_dept h", - "Ġw age", - "Ġwa ge", - "Ġwag e", - "@ app", - "@a pp", - "N il", - "Ni l", - "( [\"", - "([ \"", - "( vector", - "(v ector", - "(vec tor", - "Ġsecret ary", - "4 61", - "46 1", - "Ġj Panel", - "v ez", - "ve z", - "Âł ³³³", - "³³ ³³", - "³³³ Âł", - "d irection", - "dir ection", - "di rection", - "direct ion", - "dire ction", - "Ġ EP", - "ĠE P", - "Ġ hunt", - "Ġh unt", - "Ġhun t", - "Ġhu nt", - "3 96", - "39 6", - "Json Property", - "Ġ PORT", - "ĠP ORT", - "ĠPO RT", - "ĠPOR T", - "] \",", - "]\" ,", - "а п", - "аР¿", - "Ġ Foreign", - "ĠFore ign", - "p anic", - "pan ic", - "pa nic", - "Ġtr ials", - "Ġtri als", - "Ġtrial s", - "Ġ Ale", - "ĠA le", - "ĠAl e", - "Ġr ural", - "Ġru ral", - "- value", - "-v alue", - "-val ue", - "-valu e", - "author ized", - "authorize d", - "Ġ Scotland", - "ĠSc otland", - "ĠScot land", - ". drop", - ".d rop", - ".dr op", - "Ġ MT", - "ĠM T", - "ç ±", - "3 91", - "39 1", - "row th", - "5 15", - "51 5", - "File Path", - "Ġ recall", - "Ġre call", - "Ġrec all", - "Ġrecal l", - "if le", - "Ġ cel", - "Ġc el", - "Ġce l", - "Ġ SELECT", - "ĠSE LECT", - "ĠSEL ECT", - "k n", - "_ case", - "_c ase", - "_ca se", - "Ġ crop", - "Ġc rop", - "Ġcr op", - "Ġcro p", - "5 43", - "54 3", - "s ure", - "sur e", - "su re", - "p ot", - "po t", - "I CS", - "IC S", - "Ġ stem", - "Ġs tem", - "Ġst em", - "Ġste m", - "Ġindust ries", - "Ġindustri es", - "P ut", - "Pu t", - "Ġ aber", - "Ġa ber", - "Ġab er", - "road cast", - "I cons", - "Icon s", - ") \")Ċ", - ")\" )Ċ", - ")\") Ċ", - "æĪIJ åĬŁ", - "g ui", - "gu i", - "Ġass umed", - "Ġassum ed", - "Ġassume d", - "Ġ rx", - "Ġr x", - "E A", - "è §", - "E LL", - "EL L", - "Ġd ose", - "Ġdo se", - "Ġdos e", - "Ġ ine", - "Ġin e", - "Ġi ne", - "Ġd eeper", - "Ġde eper", - "Ġdeep er", - "Ġdee per", - "l ider", - "li der", - "lide r", - "lid er", - "Ġ ordinary", - "Ġord inary", - "Ġordin ary", - "Ġg olf", - "Ġgo lf", - "Ġgol f", - "6 05", - "60 5", - "_ IMAGE", - "_IM AGE", - "Ġ NAME", - "ĠN AME", - "ĠNA ME", - "( module", - "(m odule", - "(mod ule", - "Ġ atom", - "Ġa tom", - "Ġat om", - "Ġ belt", - "Ġb elt", - "Ġbe lt", - "Ġbel t", - "Ġoff ices", - "Ġoffic es", - "Ġoffice s", - "5 06", - "50 6", - "b eta", - "be ta", - "bet a", - "Ġphilosoph y", - "( JSON", - "(J SON", - "(JS ON", - "- field", - "-f ield", - "-fi eld", - "Ġint roduce", - "Ġintrodu ce", - "Ġintro duce", - "Ġcon venience", - "Ġconven ience", - "op tim", - "opt im", - "> \"Ċ", - ">\" Ċ", - "a thy", - "at hy", - "ath y", - "Ġ employer", - "Ġemploy er", - "q uate", - "qu ate", - "qua te", - "quat e", - "Ġ edited", - "Ġed ited", - "Ġedit ed", - "Ġedi ted", - "Arg uments", - "Argument s", - "ĠN ations", - "ĠNation s", - "ĠNat ions", - "_ _)", - "__ )", - "Ġn ose", - "Ġno se", - "Ġnos e", - "Ġ Sample", - "ĠS ample", - "ĠSam ple", - "ĠSamp le", - "' )ĊĊĊ", - "') ĊĊĊ", - "')Ċ ĊĊ", - "')ĊĊ Ċ", - "Ġ cake", - "Ġc ake", - "Ġca ke", - ". getAttribute", - ".get Attribute", - "H D", - "3 92", - "39 2", - "Mod ified", - "4 45", - "44 5", - "Ġ predicted", - "Ġpred icted", - "Ġpredict ed", - "Ġpredic ted", - "Å Ħ", - "a nie", - "an ie", - "ani e", - "S orry", - "( doc", - "(d oc", - "(do c", - "w ind", - "win d", - "wi nd", - "i eve", - "ie ve", - "iev e", - "Ġpro visions", - "Ġprov isions", - "Ġprovision s", - "A TER", - "AT ER", - "ATE R", - "O TE", - "OT E", - "M Y", - ". Autowired", - ".A utowired", - "ĠB ath", - "ĠBa th", - "ĠBat h", - "4 23", - "42 3", - ". Boolean", - ".Bool ean", - "Ġ backend", - "Ġback end", - ". Mouse", - ".M ouse", - "at eral", - "ate ral", - "ater al", - "p aper", - "pa per", - "Con st", - "Co nst", - "Cons t", - "Ġ VR", - "ĠV R", - "_ entity", - "_e ntity", - "_ent ity", - "_ CTRL", - "_C TRL", - "_CT RL", - "Ġ Protection", - "ĠPro tection", - "ĠProt ection", - "ĠProte ction", - "ĠProtect ion", - "Ġ GM", - "ĠG M", - "Ġ Study", - "ĠSt udy", - "ĠStud y", - "Ġ soup", - "Ġs oup", - "Ġso up", - "Ġsou p", - "o time", - "ot ime", - "oti me", - "' use", - "'u se", - "] \"", - "/ users", - "/user s", - "/use rs", - "/us ers", - "a ug", - "au g", - "Ġ Hong", - "ĠH ong", - "ĠHon g", - "ĠHo ng", - "_ norm", - "_n orm", - "_no rm", - "ãģ ¨", - "Ġse cre", - "Ġsec re", - "( Build", - "(B uild", - "Ġ Contract", - "ĠCon tract", - "ĠCont ract", - "ĠContr act", - "o las", - "ol as", - "ola s", - "Ġs auce", - "Ġsa uce", - "Ġsau ce", - "Ġag gressive", - "Ġaggress ive", - "Ġagg ressive", - "Ġ racial", - "Ġr acial", - "Ġrac ial", - "Ġra cial", - "char acter", - "@ @", - "Ġ compile", - "Ġcom pile", - "Ġcomp ile", - "Ġcompil e", - "Ġ Void", - "ĠV oid", - "ĠVo id", - "_ rem", - "_re m", - "_r em", - "_ memory", - "_m emory", - "_mem ory", - "3 48", - "34 8", - "k k", - "Ġ mic", - "Ġm ic", - "Ġmi c", - "S ame", - "Sam e", - "Sa me", - "U tility", - "Util ity", - "Ut ility", - "Ġ Html", - "ĠH tml", - "Ġ Xml", - "ĠX ml", - "ĠXm l", - "Re ady", - "Read y", - "Ġg all", - "Ġga ll", - "Ġgal l", - "Ġalleg edly", - "Ġalleged ly", - "ĉ ĉĉĉĠĠĠ", - "ĉĉ ĉĉĠĠĠ", - "ĉĉĉĉ ĠĠĠ", - "ĉĉĉ ĉĠĠĠ", - "ĉĉĉĉĠ ĠĠ", - "ĉĉĉĉĠĠ Ġ", - "Ġ Metal", - "ĠM etal", - "ĠMe tal", - "ĠMet al", - "ĠMeta l", - "Ġ Personal", - "ĠPerson al", - "ĠPers onal", - "ĠPersona l", - "Ġborder Radius", - "rx js", - "object s", - "obj ects", - "Ġwant ing", - "Ġwan ting", - "Ġb owl", - "Ġbo wl", - "Ġbow l", - "v endor", - "offset of", - "Ġ Rs", - "ĠR s", - "Ġ Rating", - "ĠR ating", - "ĠRa ting", - "ĠRat ing", - "Ġr ally", - "Ġrall y", - "_ NODE", - "_N ODE", - "_NO DE", - "4 18", - "41 8", - "Ġ Mix", - "ĠM ix", - "ĠMi x", - "Ġad vertis", - "Ġadvert is", - "4 85", - "48 5", - "6 67", - "66 7", - "Ġnarr ative", - "s al", - "sa l", - "Ġ mc", - "Ġm c", - "S Error", - "SE rror", - "Ġf ingers", - "Ġfin gers", - "Ġfinger s", - "Ġfing ers", - "Ġac company", - "Ġaccom pany", - "Ġaccomp any", - "Ġt ired", - "Ġti red", - "Ġtire d", - "Ġtir ed", - "Ġ stride", - "Ġst ride", - "Ġstr ide", - "Ġstri de", - "Ġ gui", - "Ġg ui", - "Ġgu i", - "e list", - "el ist", - "eli st", - "L ocale", - "Lo cale", - "Local e", - "Loc ale", - "Ġre leases", - "Ġrelease s", - "Ġrele ases", - "i king", - "ik ing", - "iki ng", - "Ġ anger", - "Ġa nger", - "Ġan ger", - "Ġang er", - "Ġange r", - ") ))ĊĊ", - ")) )ĊĊ", - ")))Ċ Ċ", - "))) ĊĊ", - "al lest", - "all est", - "alle st", - "alles t", - "Sum mary", - "( O", - "( for", - "(f or", - "Ġbasket ball", - "Ġ roads", - "Ġro ads", - "Ġroad s", - "Ġ Install", - "ĠInst all", - "Ġ Fab", - "ĠF ab", - "ĠFa b", - "it map", - "itm ap", - "4 75", - "47 5", - "Ġ ))Ċ", - "Ġ) )Ċ", - "Ġ)) Ċ", - "Ġ intersection", - "Ġinter section", - "Ġintersect ion", - "Ġinters ection", - "igh bor", - "ighb or", - "ĠB ry", - "ĠBr y", - "Ġ HERE", - "ĠH ERE", - "ĠHE RE", - "ĠHER E", - "S oftware", - "So ftware", - "Soft ware", - "el fare", - "elf are", - "a cs", - "ac s", - "6 22", - "62 2", - "Ġtr ailer", - "Ġtrail er", - "Ġtra iler", - "Ġtrai ler", - ". getClass", - ".get Class", - ".getC lass", - "ch ars", - "char s", - "cha rs", - "Ġreg ulation", - "Ġregul ation", - "Ġre fers", - "Ġref ers", - "Ġrefer s", - "Ġd estruction", - "Ġde struction", - "Ġdestruct ion", - "Ġ continuous", - "Ġcontin uous", - "Ġcontinu ous", - "Ġ Austin", - "ĠA ustin", - "ĠAust in", - "ĠAus tin", - "ĠAu stin", - "é ¢", - "a kan", - "ak an", - "aka n", - ". window", - ".w indow", - ".wind ow", - "Ġ Templates", - "ĠT emplates", - "ĠTem plates", - "ĠTemplate s", - "ĠTemp lates", - "ĠTempl ates", - "Ġabs ence", - ": n", - "Ġdis order", - "f lash", - "fl ash", - "Ġde let", - "Ġdel et", - "Ġdele t", - "bo ards", - "board s", - "Ġ Ġĉ", - "ĠĠ ĉ", - "R OP", - "RO P", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġac qu", - "Ġlaw suit", - "Ġlaws uit", - "Ġ Reviews", - "ĠRe views", - "ĠReview s", - "Ġgar age", - "Ġga rage", - "t imer", - "time r", - "ti mer", - "tim er", - "Ġ ej", - "Ġe j", - "Ġ Rectangle", - "ĠRect angle", - "Ġ flowers", - "Ġfl owers", - "Ġflow ers", - "Ġflo wers", - "Ġflower s", - "3 98", - "39 8", - "i lst", - "il st", - "ils t", - "Ġ Instance", - "ĠIn stance", - "ĠInst ance", - "S uper", - "Sup er", - "Su per", - "d et", - "de t", - "dis posing", - "disp osing", - "Ġ ES", - "ĠE S", - "Ġ IC", - "ĠI C", - "v ere", - "ver e", - "ve re", - "S k", - "_ channels", - "_ch annels", - "_channel s", - "_chan nels", - "p uted", - "put ed", - "pu ted", - "pute d", - "/ null", - "/n ull", - "n nen", - "nn en", - "4 31", - "43 1", - "Ġ Gallery", - "ĠG allery", - "ĠGall ery", - "_ global", - "_g lobal", - "_glob al", - "Auth entication", - "Ġ Rank", - "ĠR ank", - "ĠRa nk", - "ĠRan k", - "Ġ blocked", - "Ġb locked", - "Ġbl ocked", - "Ġblock ed", - "Ġbloc ked", - "Ġc alm", - "Ġcal m", - "Ġca lm", - "m arket", - "mark et", - "mar ket", - "ĉ val", - "ĉv al", - "ĉva l", - "Ġ aug", - "Ġa ug", - "Ġau g", - "p eriod", - "per iod", - "peri od", - "Ġ Constant", - "ĠCon stant", - "ĠCons tant", - "ĠConst ant", - "Ġ ?>\">Ċ", - "Ġ?> \">Ċ", - "Ġ?>\" >Ċ", - "Ġ?>\"> Ċ", - "Ġ lobby", - "Ġl obby", - "Ġlob by", - "p al", - "pa l", - "3 79", - "37 9", - "Ġ sink", - "Ġs ink", - "Ġsi nk", - "Ġsin k", - "5 08", - "50 8", - "i ah", - "ia h", - "Ð ¡", - "ur name", - "urn ame", - "Ġcon ver", - "Ġconv er", - "Ġinvest igate", - "Ġinvestig ate", - "Ch rist", - "Chris t", - "Chr ist", - "H ub", - "Hu b", - "Ġ IND", - "ĠI ND", - "ĠIN D", - "Ġ Ped", - "ĠP ed", - "ĠPe d", - "u ras", - "ur as", - "ura s", - "ĉ url", - "ĉu rl", - "Ġ Tro", - "ĠT ro", - "ĠTr o", - "Ġ preferences", - "Ġp references", - "Ġpre ferences", - "Ġprefer ences", - "Ġpreference s", - "Ġguar anteed", - "Ġguarante ed", - "Ġguarantee d", - "` ĊĊ", - "`Ċ Ċ", - "Ġport ions", - "Ġportion s", - "Ġe valu", - "Ġev alu", - "Ġeval u", - "' > < /", - "( ){ĊĊ", - "() {ĊĊ", - "(){Ċ Ċ", - "(){ ĊĊ", - "en coded", - "enc oded", - "encode d", - "enco ded", - "z illa", - "zi lla", - ". Class", - ".C lass", - ".Cl ass", - "Ġ *_", - "Ġ* _", - "_ '", - "Ġview ed", - "Ġvi ewed", - "Ġvie wed", - "Ġ Philadelphia", - "ĠPhil adelphia", - ". rows", - ".r ows", - ".row s", - ".ro ws", - "Add ed", - "Ad ded", - "Ġ Touch", - "ĠT ouch", - "ĠTo uch", - "ĠTou ch", - "8 40", - "84 0", - ". delegate", - ".de legate", - "quee ze", - "s lide", - "sl ide", - "Ġ Senior", - "ĠSen ior", - "( tag", - "(t ag", - "Ġinter views", - "Ġinterview s", - "Ġs ua", - "Ġsu a", - "a tas", - "at as", - "ata s", - "@ ĊĊ", - "@Ċ Ċ", - "d istance", - "di stance", - "dist ance", - "Ġ sein", - "Ġs ein", - "Ġse in", - "Ġsei n", - "l atest", - "la test", - "late st", - "lat est", - "lates t", - "Ġ Prince", - "ĠPr ince", - "ĠPri nce", - "Ġlux ury", - "Ġre fr", - "Ġref r", - "Ġ Kitchen", - "ĠK itchen", - "ĠKit chen", - "Ñ Ħ", - "( at", - "(a t", - "F inal", - "Fin al", - "Fi nal", - "ü ck", - "üc k", - "_ zero", - "_z ero", - "Ġ ABC", - "ĠA BC", - "ĠAB C", - "Ġ Manchester", - "ĠMan chester", - "Ġ cow", - "Ġc ow", - "Ġco w", - "C OL", - "CO L", - "_ NUMBER", - "_NUM BER", - "ch anges", - "change s", - "chan ges", - "chang es", - "g enerate", - "gen erate", - "gener ate", - "gene rate", - ". Printf", - ".Print f", - "3 69", - "36 9", - "s hare", - "sh are", - "sha re", - "St ock", - "Ġ PT", - "ĠP T", - "A nim", - "An im", - "a nga", - "an ga", - "ang a", - "Ġ ig", - "Ġi g", - "up loads", - "upload s", - "Ġ packed", - "Ġp acked", - "Ġpack ed", - "Ġpac ked", - "Ġ }];Ċ", - "Ġ} ];Ċ", - "Ġ}] ;Ċ", - "( sender", - "(s ender", - "(se nder", - "(send er", - "Ġ Wire", - "ĠW ire", - "ĠWi re", - "ĠWir e", - "i sons", - "is ons", - "ison s", - "iso ns", - "Ġplay off", - "\\ E", - "6 08", - "60 8", - "/ R", - "Ġ headed", - "Ġhe aded", - "Ġhead ed", - "Al pha", - "( order", - "(ord er", - "(or der", - "Ġop ponents", - "Ġopp onents", - "Ġoppon ents", - "Ġopponent s", - "ack son", - "acks on", - "_ member", - "_m ember", - "_mem ber", - "T urn", - "Tur n", - "Tu rn", - "ĠSov iet", - "ìĹ IJ", - "a uge", - "au ge", - "aug e", - "4 48", - "44 8", - "Ġ incoming", - "Ġin coming", - "Ġinc oming", - "Ġincom ing", - "Ġ jak", - "Ġj ak", - "Ġja k", - "- game", - "-g ame", - "Ġ Male", - "ĠM ale", - "ĠMal e", - "ĠMa le", - "Ġ Month", - "ĠM onth", - "ĠMon th", - "ĠMo nth", - "ĠMont h", - "St age", - ". exe", - ".e xe", - ".ex e", - "Own Property", - ". setItem", - ".set Item", - "Ġ dc", - "Ġd c", - "ä½ ľ", - "Ġb rut", - "Ġbr ut", - "Ġbru t", - "Ġattempt ing", - ". len", - ".l en", - ".le n", - "Ġjud gment", - "Ġs ab", - "Ġsa b", - "Ġ cad", - "Ġc ad", - "Ġca d", - "Ġ Items", - "ĠI tems", - "ĠIt ems", - "ĠItem s", - "com fort", - "el ize", - "eli ze", - "/ log", - "/l og", - "/lo g", - "Ġentre prene", - "Ġ compiler", - "Ġc ompiler", - "Ġcom piler", - "Ġcomp iler", - "Ġcompile r", - "Ġcompil er", - "_ validation", - "_valid ation", - "r eview", - "re view", - "rev iew", - "Ġ textBox", - "Ġtext Box", - "Ġ fraction", - "Ġf raction", - "Ġfr action", - "Ġfra ction", - "Ġfract ion", - "Ġfrac tion", - "Ġ Bal", - "ĠB al", - "ĠBa l", - "> ;ĊĊ", - ">;Ċ Ċ", - ">; ĊĊ", - ".AutoScale Mode", - "Ġ cats", - "Ġc ats", - "Ġca ts", - "Ġcat s", - "4 65", - "46 5", - "Ġ registry", - "Ġreg istry", - "Ġregistr y", - "Ġregist ry", - "u lus", - "ul us", - "ulu s", - "F I", - "p ayload", - "pay load", - "- search", - "-s earch", - "-se arch", - "Ġst aying", - "Ġstay ing", - "Ġsta ying", - "ac ious", - "aci ous", - "acio us", - "De coration", - "Dec oration", - "Decor ation", - "R eview", - "Re view", - "Rev iew", - "I nf", - "In f", - "Ke ep", - "it is", - "iti s", - ", String", - ",S tring", - "C oord", - "Co ord", - "Ġp ero", - "Ġper o", - "Ġpe ro", - "S ex", - "Se x", - "Ġ Atlanta", - "ĠAtl anta", - "u esta", - "ue sta", - "ues ta", - "uest a", - "A rgb", - "Arg b", - "Ar gb", - "> *", - "} _", - "F ooter", - "Foo ter", - "Foot er", - "Fo oter", - "Ġ employed", - "Ġemploy ed", - "_ bound", - "_b ound", - "_bo und", - "v ide", - "vid e", - "vi de", - ". func", - ".f unc", - ".fun c", - "$ scope", - "$s cope", - "Ġ spo", - "Ġs po", - "Ġsp o", - "Ġ Anal", - "ĠA nal", - "ĠAn al", - "ĠAna l", - "oun ced", - "ounc ed", - "ounce d", - "a round", - "ar ound", - "aro und", - "Ġ restriction", - "Ġre striction", - "Ġrestrict ion", - "Ġrestr iction", - "Ġ shops", - "Ġsh ops", - "Ġshop s", - "Ġsho ps", - "å Ģ", - "Ġ Latin", - "ĠL atin", - "ĠLa tin", - "ĠLat in", - "- col", - "-c ol", - "-co l", - "Ġbar ely", - "Ġbare ly", - "Ġ Euro", - "ĠE uro", - "ĠEu ro", - "ĠEur o", - "E r", - "Ġf aire", - "Ġfa ire", - "Ġfair e", - "_ distance", - "_d istance", - "_dist ance", - "_di stance", - "_ unlock", - "_un lock", - "Qu ote", - "IV ATE", - "IVA TE", - "Ġ åĪ", - "Ġå Ī", - "Ġa imed", - "Ġaim ed", - "Ġai med", - "Ġaime d", - "ĠRe trie", - "ĠRet rie", - ". iter", - ".i ter", - ".it er", - "Ġ wrapped", - "Ġw rapped", - "Ġwr apped", - "Ġwrap ped", - "Ġag reements", - "Ġagre ements", - "Ġagree ments", - "Ġagreement s", - "str ument", - "stru ment", - "( product", - "(pro duct", - "(prod uct", - "Ġstud ied", - "Ġstudi ed", - ". setValue", - ".set Value", - "Ġ ye", - "Ġy e", - "Ġ Cache", - "ĠC ache", - "ĠCa che", - "MB OL", - "Ġquarter back", - "Ġ syntax", - "Ġs yntax", - "Ġsy ntax", - "Ġsyn tax", - "Ġsynt ax", - ".get ElementsBy", - ".getElements By", - ". version", - ".v ersion", - "we bsite", - "web site", - "webs ite", - "R unner", - "Run ner", - "_ single", - "_s ingle", - "_si ngle", - "_sin gle", - "a tiv", - "at iv", - "ati v", - "Ġ Altern", - "ĠAl tern", - "ĠAlt ern", - "ĠAlter n", - "Ġ Beautiful", - "ĠBe autiful", - "ĠBeaut iful", - "right arrow", - "Ġd iversity", - "Ġdivers ity", - "p lash", - "pl ash", - "pla sh", - "( co", - "(c o", - ". Fill", - ".F ill", - "Ġ typing", - "Ġtyp ing", - "Ġty ping", - "3 87", - "38 7", - "0 23", - "02 3", - "Ġ clar", - "Ġc lar", - "Ġcl ar", - "Ġcla r", - "H it", - "Hi t", - "O O", - "a cco", - "ac co", - "acc o", - "5 07", - "50 7", - "w orth", - "wort h", - "wor th", - "Ġ scripts", - "Ġs cripts", - "Ġscript s", - "Ġscri pts", - "ĠMuslim s", - "Ġ LL", - "ĠL L", - "er ving", - "erv ing", - "( boolean", - "(bool ean", - "Ġbase ball", - "Ġ CAN", - "ĠC AN", - "ĠCA N", - "3 94", - "39 4", - "0 44", - "04 4", - "M AIL", - "MA IL", - "d epend", - "de pend", - "dep end", - "Ġres pective", - "Ġrespect ive", - "Ġresp ective", - "Ġ constexpr", - "Ġconst expr", - ".* ;ĊĊ", - ".*;Ċ Ċ", - "' ]))Ċ", - "'] ))Ċ", - "']) )Ċ", - "'])) Ċ", - "Ġ yard", - "Ġy ard", - "Ġya rd", - "Ġyar d", - "Ġident ical", - "if ecycle", - "ife cycle", - "U SH", - "US H", - "up iter", - ". validate", - ".valid ate", - "c li", - "cl i", - "I STER", - "IS TER", - "IST ER", - "Ind icator", - "F ail", - "Fa il", - "Ġdem ocracy", - "Ġdemocr acy", - ". var", - ".v ar", - ".va r", - "Ġs atisfied", - "Ġsatisf ied", - "- ------------", - "-- -----------", - "---- ---------", - "-------- -----", - "--- ----------", - "------------ -", - "----- --------", - "---------- ---", - "------ -------", - "----------- --", - "------- ------", - "--------- ----", - "en cer", - "ence r", - "enc er", - "h or", - "ho r", - "Ġr ounds", - "Ġro unds", - "Ġround s", - "Ġrou nds", - "D AO", - "DA O", - "o a", - "Ġfl ask", - "Ġfla sk", - "= c", - "[ ]Ċ", - "[] Ċ", - "/ dist", - "/d ist", - "/dis t", - "Ġp arte", - "Ġpart e", - "Ġpar te", - "Ġ confirmation", - "Ġconfirm ation", - "e ron", - "er on", - "ero n", - "a ware", - "aw are", - "awa re", - "< ?>", - "", - "Ġ dependencies", - "Ġdep endencies", - "Ġdepend encies", - "Ġ Videos", - "ĠV ideos", - "ĠVideo s", - "ĠVid eos", - "ĠVide os", - "- row", - "-r ow", - "-ro w", - "Ġ **/Ċ", - "Ġ* */Ċ", - "Ġ** /Ċ", - "Ġ nou", - "Ġn ou", - "Ġno u", - "Ġ hover", - "Ġh over", - "Ġho ver", - "æ ŀ", - "Ġ nin", - "Ġn in", - "Ġni n", - "Ġ USD", - "ĠU SD", - "ĠUS D", - "M ac", - "Ma c", - "_ Load", - "_L oad", - "Ġout comes", - "Ġoutcome s", - "_ socket", - "_s ocket", - "_sock et", - "_so cket", - "_soc ket", - "Ġ queries", - "Ġqu eries", - "Ġque ries", - "Ġquer ies", - "w m", - "5 92", - "59 2", - "Ġh itting", - "Ġhit ting", - "in ux", - "inu x", - "M ich", - "Mi ch", - "Mic h", - "u dge", - "ud ge", - "A TAB", - "AT AB", - "ATA B", - "Ġv ulnerable", - "Ġvulner able", - "ä ¾", - "Ġ portfolio", - "Ġport folio", - ": YES", - "ĉ map", - "ĉm ap", - "B ound", - "Bo und", - "Ġ iteration", - "Ġit eration", - "Ġiter ation", - "in cess", - "ince ss", - "inc ess", - "inces s", - "Ġ actors", - "Ġa ctors", - "Ġact ors", - "Ġac tors", - "Ġactor s", - "Ġ Qual", - "ĠQ ual", - "ĠQu al", - "_ clean", - "_c lean", - "_cl ean", - "ãĢij ãĢIJ", - "M SG", - "MS G", - "G reen", - "Gr een", - "Gre en", - "ĠOff icer", - "ĠOffice r", - "Ġsm oking", - "Ġsmo king", - "> ',", - ">' ,", - "Ġ Flo", - "ĠF lo", - "ĠFl o", - "++ ;", - "4 33", - "43 3", - "oly gon", - "Ġ bulk", - "Ġb ulk", - "Ġbu lk", - "Ġbul k", - "Ġd rama", - "Ġdr ama", - "Ġdram a", - "Ġdra ma", - "Ġ exceptions", - "Ġex ceptions", - "Ġexcept ions", - "Ġexception s", - "Ġexce ptions", - "o sed", - "os ed", - "ose d", - "Ġ+ čĊ", - "Ġ legacy", - "Ġleg acy", - "C V", - "Ġcontrib uted", - "Ġcontribute d", - "Ġ Terms", - "ĠTe rms", - "ĠTer ms", - "ĠTerm s", - "Ġ bt", - "Ġb t", - "4 34", - "43 4", - "Ġun tuk", - "Ġunt uk", - "Ġ alien", - "Ġa lien", - "Ġal ien", - "Ġali en", - "= ==Ċ", - "== =Ċ", - "=== Ċ", - "ĉ Vector", - "ĉV ector", - "ĉVec tor", - "Ġ ls", - "Ġl s", - "On line", - ". facebook", - ".f acebook", - ".face book", - "n umeric", - "num eric", - "nu meric", - "numer ic", - "ock ets", - "ocket s", - "A ut", - "Au t", - "b ury", - "bur y", - "bu ry", - "- redux", - "-re dux", - "-red ux", - "ĠRed istributions", - "ĠRedistribution s", - "GLOBAL S", - "urrenc ies", - "urr encies", - "Ġ tons", - "Ġt ons", - "Ġto ns", - "Ġton s", - "âĢĻ ,", - "Ġ ê", - "Ġà ª", - "( col", - "(c ol", - "(co l", - "Ġ Symbol", - "ĠS ymbol", - "ĠSym bol", - "Ġst ayed", - "Ġstay ed", - "Ġ ML", - "ĠM L", - "Ġm unicip", - "Ġmun icip", - "Ġ sexo", - "Ġs exo", - "Ġse xo", - "Ġsex o", - "S en", - "Se n", - "n r", - "Ġg ains", - "Ġgain s", - "Ġga ins", - "Ġshort ly", - ". Menu", - ".M enu", - ".Me nu", - "à ½", - "KN OWN", - "Ġ operators", - "Ġoper ators", - "Ġoperator s", - "Ġopera tors", - "- V", - "Ġ Patrick", - "ĠPat rick", - "ĠPatri ck", - "/ add", - "/a dd", - "/ad d", - "_ CO", - "_C O", - "i ration", - "ir ation", - "ira tion", - "( post", - "(p ost", - "(pos t", - "(po st", - "Post s", - "Pos ts", - "Po sts", - "/ _", - "Ġ plug", - "Ġp lug", - "Ġpl ug", - "Ġplu g", - "Ġintel lectual", - "Ġintellect ual", - "Ġme tab", - "Ġmet ab", - "Ġmeta b", - "Ġpregn ancy", - "ĠPrem ier", - "ĠPremi er", - "n m", - "Ġ prediction", - "Ġpred iction", - "Ġpredict ion", - "Ġpredic tion", - "6 06", - "60 6", - "ĠMin istry", - "ĠMini stry", - "ĠMinist ry", - "Th ree", - "Thr ee", - "val uate", - "valu ate", - "Ġ Mini", - "ĠM ini", - "ĠMin i", - "ĠMi ni", - "b u", - "о з", - "оР·", - "< ul", - " \";čĊ", - ">\" ;čĊ", - ">\"; čĊ", - "ĠS av", - "ĠSa v", - ". Bold", - ".B old", - "Ġen ables", - "Ġenable s", - "ĉ tmp", - "ĉt mp", - "Ġman ually", - "Ġmanual ly", - "ĠS qu", - "ĠSq u", - "use rid", - "user id", - ". function", - ".f unction", - ".func tion", - ".fun ction", - ". cache", - ".c ache", - ".ca che", - "L OPT", - "LO PT", - ". Services", - ".S ervices", - ".Service s", - "5 88", - "58 8", - "d dit", - "dd it", - "t im", - "ti m", - "< img", - " >>", - ">> >", - "st ation", - "stat ion", - "sta tion", - "l ore", - "lo re", - "lor e", - "a type", - "at ype", - "aty pe", - "i shop", - "is hop", - "ish op", - "/ ****************************************************************", - "/******************************** ********************************", - "/************************ ****************************************", - "/******************************************************** ********", - "/******** ********************************************************", - "/**************** ************************************************", - "/**************************************** ************************", - "/************************************************ ****************", - "5 21", - "52 1", - "Com boBox", - "Combo Box", - "Ġvac ation", - "Ġva cation", - "Ġinit iative", - "Ġiniti ative", - "Ġ defaultValue", - "Ġdefault Value", - "7 70", - "77 0", - "con cat", - "conc at", - "Ġ Kh", - "ĠK h", - "6 32", - "63 2", - "Ġ Welcome", - "ĠW elcome", - "ĠWel come", - "ized Name", - "M igration", - "Ġ gradient", - "Ġg radient", - "Ġgrad ient", - "H ot", - "Ho t", - "Ġhard ly", - "e lo", - "el o", - "Ġ Students", - "ĠSt udents", - "ĠStud ents", - "ĠStudent s", - "Ġl oose", - "Ġlo ose", - "Ġloos e", - "7 30", - "73 0", - "a tz", - "at z", - ". Send", - ".S end", - ".Se nd", - "' /", - "Ġ universal", - "Ġun iversal", - "Ġunivers al", - "Ġ enterprise", - "Ġenter prise", - "Ġ regex", - "Ġreg ex", - "Ġ visitor", - "Ġvis itor", - "Ġvisit or", - "Ġ Fly", - "ĠF ly", - "ĠFl y", - "S eq", - "Se q", - "ภĻ", - "Ġ Visual", - "ĠVis ual", - "Ġ libraries", - "Ġl ibraries", - "Ġlib raries", - "Ġlibr aries", - "at oes", - "ato es", - "P ayment", - "Pay ment", - "4 47", - "44 7", - "Ġ pent", - "Ġp ent", - "Ġpe nt", - "Ġpen t", - "Ġgather ed", - "VR TX", - "VRT X", - "Ġ DM", - "ĠD M", - "S plit", - "Sp lit", - "Spl it", - "Ġl etting", - "Ġlet ting", - "Ġlett ing", - "Ð Ŀ", - "_ errors", - "_error s", - "_err ors", - "_er rors", - "ep och", - "P ARAM", - "PA RAM", - "PAR AM", - "c u", - "ÑģÑĤ в", - "ol utions", - "olution s", - "olut ions", - "Ed iting", - "Edit ing", - "fo nts", - "font s", - "fon ts", - "Ġ allocated", - "Ġal located", - "Ġalloc ated", - "Ġallocate d", - "Ġ Based", - "ĠB ased", - "ĠBase d", - "ĠBa sed", - "ĠBas ed", - "( Y", - "Ġ Judge", - "ĠJ udge", - "ĠJud ge", - "ĠJu dge", - "Ġbr others", - "Ġbro thers", - "Ġbrother s", - "Ġbroth ers", - "F ILES", - "FILE S", - "FI LES", - "ç o", - "5 31", - "53 1", - "w b", - "_ PI", - "_P I", - "' ^", - "Ġ sword", - "Ġs word", - "Ġsw ord", - "Ġswo rd", - ". services", - ".s ervices", - ".service s", - ".serv ices", - "Ġ nl", - "Ġn l", - "T im", - "Ti m", - "i gg", - "ig g", - "ĠMo ore", - "ĠMoo re", - "ĠMoor e", - "Ġcrypt oc", - "Ġcrypto c", - "åĩ º", - "_ posts", - "_post s", - "_pos ts", - "_po sts", - "ot ate", - "ota te", - "? '", - ". ...ĊĊ", - ".. ..ĊĊ", - "... .ĊĊ", - ".... ĊĊ", - "....Ċ Ċ", - "Ġ kl", - "Ġk l", - "= \"$", - "=\" $", - "Ġde coration", - "Ġdec oration", - "Ġdecor ation", - "Ġdeco ration", - "Ạ¡", - "Ġ DIRECT", - "ĠD IRECT", - "ĠDI RECT", - "ĠDIR ECT", - "G UI", - "GU I", - ") =>{Ċ", - ")= >{Ċ", - ")=> {Ċ", - "Ġ newsletter", - "Ġnews letter", - "Ġpre cis", - "Ġprec is", - "( point", - "(p oint", - "(po int", - "Ġ Equipment", - "ĠE quipment", - "ĠEqu ipment", - "ĠEquip ment", - "u ty", - "ut y", - "Ġ Dave", - "ĠD ave", - "ĠDav e", - "ĠDa ve", - "Ġpart icipation", - "Ġpartic ipation", - "Ġparticip ation", - "u arios", - "ua rios", - "uario s", - "uar ios", - "x it", - "xi t", - ". As", - ".A s", - "E TER", - "ET ER", - "o rous", - "or ous", - "oro us", - "Ġ shield", - "Ġsh ield", - "[ ]>", - "[] >", - "il itary", - "ilit ary", - ". origin", - ".or igin", - ".orig in", - "Ġ promotion", - "Ġpro motion", - "Ġprom otion", - "Ġpromot ion", - "Ġpromo tion", - "U nt", - "Un t", - "Ġ ct", - "Ġc t", - "T RA", - "TR A", - "5 56", - "55 6", - "View Holder", - "Ġ sigma", - "Ġs igma", - "Ġsig ma", - "d elta", - "del ta", - "are house", - "con tract", - "cont ract", - "contr act", - "contra ct", - "( Vector", - "(V ector", - "(Vec tor", - "7 21", - "72 1", - "Ġcomp ete", - "Ġcompet e", - "/ form", - "/f orm", - "/ components", - "/com ponents", - "/component s", - "Ġ nr", - "Ġn r", - "ĠInd ones", - "ĠIndo nes", - "Ġ оÑĤ", - "Ġо ÑĤ", - "Ġ Volume", - "ĠV olume", - "ĠVol ume", - ". files", - ".f iles", - ".file s", - ".fi les", - ".fil es", - "( resp", - "(r esp", - "(res p", - "(re sp", - "/ models", - "/model s", - "/mod els", - "Ġ surf", - "Ġs urf", - "Ġsu rf", - "Ġsur f", - "st andard", - "stand ard", - "/ o", - "ĠXCT Assert", - "V ICES", - "VICE S", - "VI CES", - "VIC ES", - ". Code", - ".C ode", - ".Co de", - "S ED", - "SE D", - "Ġ activate", - "Ġact ivate", - "Ġactiv ate", - "D elta", - "Del ta", - "Ġlimit ation", - "Ġlim itation", - "r ij", - "ri j", - "Ġpregn ant", - "Ġpreg nant", - ": ^(", - ":^ (", - "Ġs our", - "Ġso ur", - "Ġsou r", - "p ie", - "pi e", - "8 03", - "80 3", - "Ġ expense", - "Ġexp ense", - "i cation", - "ic ation", - "ica tion", - "Ġ Large", - "ĠL arge", - "ĠLar ge", - "Ġ ±", - "Ġ ±", - "ĠB owl", - "ĠBo wl", - "ĠBow l", - "( models", - "(model s", - "(mod els", - "(mode ls", - "/ N", - "8 57", - "85 7", - "P a", - ". reload", - ".re load", - ".r eload", - ".rel oad", - "Ġwonder ing", - "4 62", - "46 2", - "Exec ution", - "ĉ ĠĠĠĠĠĠ", - "ĉĠĠĠ ĠĠĠ", - "ĉĠ ĠĠĠĠĠ", - "ĉĠĠ ĠĠĠĠ", - "ĉĠĠĠĠĠ Ġ", - "ĉĠĠĠĠ ĠĠ", - "Ġ Graphics", - "ĠG raphics", - "ĠGraph ics", - "ĠGraphic s", - "Ġ Contin", - "ĠCon tin", - "ĠCont in", - "_ job", - "_j ob", - "Ġ getName", - "Ġget Name", - "Ġ Magn", - "ĠM agn", - "ĠMag n", - "ĠMa gn", - "Ġ DWORD", - "ĠD WORD", - "ĠDW ORD", - "m ad", - "ma d", - "Ġ nh", - "Ġn h", - "f eatures", - "fe atures", - "feature s", - "feat ures", - "fea tures", - "} \");Ċ", - "}\" );Ċ", - "}\") ;Ċ", - "he ets", - "heet s", - "hee ts", - "( train", - "(t rain", - "(tr ain", - "z n", - "Ġrec ruit", - "Ġrecru it", - ". connection", - ".con nection", - ".connect ion", - ".conn ection", - "Ġbar rel", - "Ġbarr el", - "Ġ steam", - "Ġs team", - "Ġst eam", - "Ġste am", - "_ setting", - "_s etting", - "_set ting", - "Ġ angular", - "Ġang ular", - "ane ously", - "aneous ly", - "Ġ bil", - "Ġb il", - "Ġbi l", - "Ġ Norm", - "ĠN orm", - "ĠNo rm", - "ĠNor m", - "5 22", - "52 2", - "(! $", - "i bt", - "ib t", - "% (", - "Ġ posit", - "Ġp osit", - "Ġpos it", - "Ġpo sit", - "Ġposi t", - "Ġ Father", - "ĠF ather", - "ĠFa ther", - "ĠFat her", - "int endo", - "inte ndo", - "5 65", - "56 5", - "L ive", - "Li ve", - "Liv e", - "0 41", - "04 1", - "Ġ ports", - "Ġp orts", - "Ġport s", - "Ġpo rts", - "Ġpor ts", - "Ġm ej", - "Ġme j", - "Ġ landing", - "Ġl anding", - "Ġland ing", - "Ġlan ding", - "p onder", - "pon der", - "pond er", - "po nder", - "ponde r", - "Ġ cod", - "Ġc od", - "Ġco d", - "_ HEADER", - "_HE ADER", - "_HEAD ER", - ". Margin", - ".M argin", - ".Mar gin", - "Ġ balls", - "Ġb alls", - "Ġball s", - "Ġbal ls", - "Ġdisc ussions", - "Ġdiscuss ions", - "Ġdiscussion s", - "Ġ blend", - "Ġbl end", - "Ġble nd", - "H ex", - "He x", - "Ġfar mers", - "Ġfarm ers", - "Ġfarmer s", - "Ġmaint aining", - "Ġmaintain ing", - "Ġ ĠĠčĊ", - "ĠĠ ĠčĊ", - "ĠĠĠ čĊ", - "s yn", - "sy n", - "[ T", - "r us", - "ru s", - "4 39", - "43 9", - "uff ers", - "uf fers", - "uffer s", - "Ġ contributors", - "Ġcontrib utors", - "Ġcontributor s", - "_ sys", - "_s ys", - "_sy s", - ". Debug", - ".De bug", - "Ġ constructed", - "Ġconstruct ed", - "o mes", - "om es", - "ome s", - "? id", - "s lider", - "sl ider", - "slide r", - "Ġsup pliers", - "Ġsupplier s", - "Ġsuppl iers", - "Ġsupp liers", - "6 11", - "61 1", - "scribe r", - "scri ber", - "scr iber", - "p es", - "pe s", - "Ð ŀ", - "\" :čĊ", - "\": čĊ", - "\\ Controller", - ") )ĊĊĊ", - ")) ĊĊĊ", - "))Ċ ĊĊ", - "))ĊĊ Ċ", - "Ġ lua", - "Ġl ua", - "Ġlu a", - "M ulti", - "Mult i", - "Mul ti", - "E NS", - "EN S", - "S rc", - "Sr c", - "Ġ petition", - "Ġpet ition", - "Ġpetit ion", - "Ġ slave", - "Ġsl ave", - "Ġsla ve", - "Ġslav e", - "lo oking", - "look ing", - "loo king", - "V ERT", - "VER T", - "VE RT", - "ĉ vector", - "ĉv ector", - "ĉvec tor", - "S pecial", - "Sp ecial", - "Spec ial", - "Spe cial", - "h h", - "an ne", - "ann e", - "ĠN iger", - "ĠNi ger", - "/ views", - "/view s", - "z ing", - "zi ng", - "zin g", - "end ant", - "enda nt", - "< C", - "s peed", - "sp eed", - "spe ed", - "5 14", - "51 4", - "Ġ{ };ĊĊ", - "Ġ{} ;ĊĊ", - "Ġ{};Ċ Ċ", - "Ġ{}; ĊĊ", - "Begin Init", - "Ġf open", - "Ġfo pen", - "@ RequestMapping", - "End Init", - "Ġp unch", - "Ġpun ch", - "S ender", - "Se nder", - "Send er", - "Sen der", - "6 03", - "60 3", - "é Ķ", - "get Message", - "/ types", - "/t ypes", - "/type s", - ". PI", - ".P I", - "(' ');Ċ", - "oc used", - "ocus ed", - "ocu sed", - "( all", - "(a ll", - "(al l", - "Ġ dropdown", - "Ġd ropdown", - "Ġdrop down", - ") .__", - "). __", - ")._ _", - "Ġ Vin", - "ĠV in", - "ĠVi n", - ". ForeignKey", - ".Fore ignKey", - "6 12", - "61 2", - "ca nf", - "can f", - "o ured", - "ou red", - "our ed", - "oure d", - "Ġ Organization", - "ĠO rganization", - "ĠOrgan ization", - "Ġ а", - "ĠÐ °", - "Ġ Culture", - "ĠC ulture", - "ĠCult ure", - "ĠCul ture", - "( cls", - "(c ls", - "(cl s", - ", _", - "9 02", - "90 2", - "r gba", - "rg ba", - "rgb a", - "ìĿ ĺ", - ". dataGridView", - ".data GridView", - "Ġdo zen", - "Ġdoz en", - "ĠG es", - "ĠGe s", - "8 05", - "80 5", - "4 64", - "46 4", - "_ shared", - "_sh ared", - "_share d", - "_sha red", - "n ick", - "ni ck", - "nic k", - "Ġh osp", - "Ġho sp", - "Ġhos p", - "o meter", - "om eter", - "ome ter", - "omet er", - "4 95", - "49 5", - "Ġclaim ing", - "Ġcla iming", - "0 32", - "03 2", - "i bles", - "ib les", - "ible s", - "r ik", - "ri k", - "æĺ ¯", - "en ario", - "ena rio", - "Ġd engan", - "Ġden gan", - "o bb", - "ob b", - "m ont", - "mon t", - "mo nt", - "_ rank", - "_r ank", - "_ra nk", - "(' /',", - "('/ ',", - "Ġap olog", - "Ġapo log", - "P s", - "_ power", - "_p ower", - "_pow er", - "_po wer", - "ĠG ree", - "ĠGr ee", - "ĠGre e", - "Ġf ulfill", - "Ġful fill", - "Ġfulfil l", - "Ġ firebase", - "Ġf irebase", - "Ġfire base", - "9 10", - "91 0", - "Ġ fare", - "Ġf are", - "Ġfa re", - "Ġfar e", - "ĠH im", - "ĠHi m", - "Ġ bean", - "Ġb ean", - "Ġbe an", - "âĢ¦ .", - "Ġ SPI", - "ĠS PI", - "ĠSP I", - "_ RX", - "_R X", - "Ġper ception", - "Ġperce ption", - "Ġpercept ion", - "rel ative", - "com pile", - "comp ile", - "u um", - "uu m", - "u tos", - "ut os", - "uto s", - "a uc", - "au c", - "Ġ Ask", - "ĠA sk", - "ĠAs k", - "Ġ indicator", - "Ġind icator", - "Ġindic ator", - "Ġindica tor", - "/ th", - "/t h", - ".set String", - "ĠWis consin", - ". Domain", - ".D omain", - ".Do main", - ".Dom ain", - "Ġart ificial", - "De velop", - "Dev elop", - "Ġ Sarah", - "ĠS arah", - "ĠSar ah", - "ĠSa rah", - "ĠSara h", - "Ġ lying", - "Ġl ying", - "Ġly ing", - "( search", - "(s earch", - "(se arch", - "ĠEm pire", - "ĠEmp ire", - "ur ring", - "urr ing", - "æŶ éĹ´", - "= \"${", - "=\" ${", - "=\"$ {", - "Ġ getId", - "Ġget Id", - "Ġ Payment", - "ĠP ayment", - "ĠPay ment", - "t ransition", - "trans ition", - "Ġ ].", - "Ġ] .", - "i xin", - "ix in", - "V T", - "- select", - "-s elect", - "-se lect", - "Ġdemonstr ated", - "Ġdemonstrate d", - "Ġ lastName", - "Ġlast Name", - "em ployment", - "emp loyment", - "employ ment", - ". getProperty", - ".get Property", - ".getP roperty", - "Ġf ought", - "Ġfo ught", - "Ġfou ght", - "file Name", - "Ġ Pers", - "ĠP ers", - "ĠPer s", - "ĠPe rs", - "4 52", - "45 2", - "- card", - "-c ard", - "-car d", - "-ca rd", - "a str", - "as tr", - "ast r", - "at trs", - "att rs", - "attr s", - "Ġpro minent", - "Ġprom inent", - "Ġpromin ent", - "D esign", - "De sign", - "Des ign", - "anc ouver", - "ãģĹ ãģ", - "ar do", - "ard o", - "s ecret", - "se cret", - "sec ret", - "Ġ rag", - "Ġr ag", - "Ġra g", - "Ġpo ison", - "Ġpoi son", - "Ġpois on", - "- man", - "-m an", - ", omitempty", - "7 40", - "74 0", - "ĉ un", - "ĉu n", - "it zer", - "itz er", - "ĠCas ino", - "Ġ Ross", - "ĠR oss", - "ĠRo ss", - "ĠRos s", - "- foot", - "-f oot", - "( results", - "(result s", - "(res ults", - "P lan", - "Pl an", - "Ġl aser", - "Ġla ser", - "Ġlas er", - "ê¸ °", - "_ DR", - "_D R", - "5 23", - "52 3", - "F acebook", - "Face book", - "4 49", - "44 9", - "Ġ boards", - "Ġbo ards", - "Ġboard s", - "s ta", - "st a", - "] ],", - "]] ,", - "6 75", - "67 5", - "Ġ tiles", - "Ġt iles", - "Ġti les", - "Ġtile s", - "Ġtil es", - "S IZE", - "SI ZE", - "Ġ =~", - "Ġ= ~", - "9 70", - "97 0", - "Ġprem ier", - "Ġpremi er", - "o cab", - "oc ab", - "oca b", - "Ġ encoded", - "Ġen coded", - "Ġenc oded", - "Ġencode d", - "Ġ reserve", - "Ġre serve", - "Ġres erve", - "Ġreserv e", - "6 09", - "60 9", - "ĠAfghan istan", - "Ġ ListNode", - "ĠList Node", - "ur ls", - "url s", - "Ġ submission", - "Ġsub mission", - "Ġn eu", - "Ġne u", - "4 77", - "47 7", - "Ġ# +#", - "_ POST", - "_P OST", - "_PO ST", - "_POS T", - "Ġmo ist", - "Ġmoi st", - "Ġmois t", - "e lli", - "el li", - "ell i", - "ellig ent", - "elli gent", - ". alert", - ".al ert", - "ó d", - "b re", - "br e", - "Ġ Collect", - "ĠC ollect", - "ĠCol lect", - "ĠColl ect", - "Ġ graphic", - "Ġg raphic", - "Ġgraph ic", - "Ġgrap hic", - "Ġ longitude", - "Ġlong itude", - "Ġlongitud e", - "Ġ Provid", - "ĠPro vid", - "ĠPr ovid", - "ĠProv id", - "Ġ Calculate", - "ĠC alculate", - "ĠCal culate", - "ĠCalcul ate", - "ĠCalc ulate", - "x ffff", - "xf fff", - "xff ff", - "xfff f", - "c riteria", - "crit eria", - "Ġ waters", - "Ġw aters", - "Ġwater s", - "Ġwa ters", - "Ġwat ers", - "r ock", - "ro ck", - "roc k", - "lo quent", - "ĠT rib", - "ĠTr ib", - "ĠTri b", - "5 13", - "51 3", - "Ġ burst", - "Ġb urst", - "Ġbu rst", - "Ġbur st", - "Ġ suffix", - "Ġs uffix", - "Ġsuff ix", - "Ġsuf fix", - ". Extensions", - ".Ext ensions", - ".Extension s", - "is hes", - "ish es", - "i vel", - "iv el", - "ive l", - "Ġ LIKE", - "ĠL IKE", - "ĠLI KE", - "Ġ Getty", - "ĠG etty", - "ĠGet ty", - "ĠGe tty", - ".Action Event", - ".s lf", - ".sl f", - "Ġ HAL", - "ĠH AL", - "ĠHA L", - "u pal", - "up al", - "upa l", - "E AR", - "EA R", - "5 24", - "52 4", - "u di", - "ud i", - "_ timeout", - "_time out", - "U F", - "Ġ Singapore", - "ĠSing apore", - "ĠSingap ore", - "ĠAd vent", - "ĠAdv ent", - "_ interval", - "_int erval", - "_inter val", - "c haft", - "ch aft", - "cha ft", - "Ġ Emer", - "ĠE mer", - "ĠEm er", - "Ġ telephone", - "Ġtele phone", - "ĠTur k", - "ĠTu rk", - "_ interface", - "_inter face", - "Ġ Own", - "ĠO wn", - "ĠOw n", - "Ġencour aged", - "Ġencourage d", - "< Object", - "(", - "<> (", - "5 44", - "54 4", - ". Product", - ".Pro duct", - ".Produ ct", - "Form s", - "For ms", - "Fo rms", - "N EW", - "NE W", - "P ay", - "Pa y", - "ĉ boolean", - "ĉbool ean", - "_ contact", - "_cont act", - "Ġ Electric", - "ĠE lectric", - "ĠElect ric", - "s kip", - "sk ip", - "ski p", - "Ġw ur", - "Ġch ronic", - "Ġchron ic", - "Ġchr onic", - "_ driver", - "_d river", - "_dr iver", - "_drive r", - "9 40", - "94 0", - "Ġ Sab", - "ĠS ab", - "ĠSa b", - "Ġ Ult", - "ĠU lt", - "ĠUl t", - "Ġ Rad", - "ĠR ad", - "ĠRa d", - "ST ATUS", - "STAT US", - "Ġ Lewis", - "ĠL ewis", - "ĠLe wis", - "ĠLew is", - "O B", - "Ġgift s", - "Ġgi fts", - "Ġgif ts", - ". Rec", - ".R ec", - ".Re c", - "TR UE", - "Ġint ensity", - "Ġintens ity", - "M arker", - "Mark er", - "Mar ker", - ". compare", - ".com pare", - ".comp are", - "f fic", - "ff ic", - "ffi c", - "C ookie", - "Co okie", - "Cook ie", - "Ġ Baby", - "ĠB aby", - "ĠBa by", - "ĠBab y", - "Ġ BigDecimal", - "ĠB igDecimal", - "ĠBig Decimal", - "i let", - "il et", - "ile t", - "ĠHOLD ERS", - "ĠHOLDER S", - "Ġ Lady", - "ĠL ady", - "ĠLa dy", - "ĠLad y", - "Ġ lung", - "Ġl ung", - "Ġlu ng", - "Ġlun g", - "Ġ Alabama", - "ĠAl abama", - "Ġ dess", - "Ġd ess", - "Ġde ss", - "Ġdes s", - "` );Ċ", - "`) ;Ċ", - "Ġ Builder", - "ĠB uilder", - "ĠBuild er", - "ĠBu ilder", - "_ region", - "_reg ion", - "Ġ neutral", - "Ġne utral", - "Ġneut ral", - "Ġneutr al", - "9 09", - "90 9", - "B oth", - "Bo th", - "Bot h", - "Ġ hp", - "Ġh p", - "Ġ horn", - "Ġh orn", - "Ġhor n", - "Ġho rn", - "Ġ segments", - "Ġse gments", - "Ġseg ments", - "Ġsegment s", - "Ġ EC", - "ĠE C", - "\" =>\"", - "\"=> \"", - "( rec", - "(r ec", - "(re c", - "Ġ Pi", - "ĠP i", - "G M", - "Ġl aptop", - "Ġlap top", - "S calar", - "Sc alar", - "Scala r", - "4 63", - "46 3", - "i sd", - "is d", - "- dialog", - "-d ialog", - "-di alog", - "Ġ Anderson", - "ĠAnd erson", - "ĠAnders on", - "Ġmis takes", - "Ġmist akes", - "Ġmi stakes", - "Ġmistake s", - "7 08", - "70 8", - "Ġ Han", - "ĠH an", - "ĠHa n", - "j es", - "je s", - "est ination", - "esti nation", - "4 36", - "43 6", - "Ġprom ises", - "Ġpromise s", - "b id", - "bi d", - "Ġ Scient", - "ĠS cient", - "ĠSc ient", - "ĠSci ent", - "G IN", - "GI N", - "Ġ Performance", - "ĠPer formance", - "ĠPerform ance", - "b age", - "ba ge", - "bag e", - ". users", - ".user s", - ".use rs", - ".us ers", - "le ading", - "lead ing", - "lea ding", - "Ġ oral", - "Ġo ral", - "Ġor al", - "Ġora l", - "G raphics", - "Graph ics", - "Graphic s", - "4 88", - "48 8", - "_ PTR", - "_P TR", - "_PT R", - "5 18", - "51 8", - "h ang", - "ha ng", - "han g", - "Ġin ev", - "Ġi nev", - "Ġine v", - "p rocessing", - "process ing", - "F actor", - "Fact or", - "Fac tor", - "Fa ctor", - "Ġ NA", - "ĠN A", - "$ string", - "$s tring", - "$str ing", - "Ġ grounds", - "Ġgr ounds", - "Ġground s", - "Ġgro unds", - "Ġgrou nds", - ".Save Changes", - "c lock", - "cl ock", - "clo ck", - "9 41", - "94 1", - "cri pcion", - "Ġ Newton", - "ĠNew ton", - "g c", - ". includes", - ".in cludes", - ".include s", - "Ġ blast", - "Ġb last", - "Ġbl ast", - "Ġblas t", - "Ġbla st", - "Ġ' -'", - "Ġ'- '", - "Ġp uede", - "Ġpued e", - "Ġpu ede", - "4 69", - "46 9", - ". Session", - ".S ession", - "Ġ grep", - "Ġg rep", - "Ġgr ep", - "Ġgre p", - "_ final", - "_f inal", - "_fin al", - "Ġ Gay", - "ĠG ay", - "ĠGa y", - "Ġ Give", - "ĠG ive", - "ĠGi ve", - "i ri", - "ir i", - "- star", - "-s tar", - "-st ar", - "Ġ UIImage", - "ĠUI Image", - "_ epoch", - "_ep och", - "u bb", - "ub b", - "e nth", - "en th", - "ent h", - "Ġ elite", - "Ġe lite", - "Ġel ite", - "Ġelit e", - "Ġcampaign s", - "Ġ Porno", - "ĠP orno", - "ĠPorn o", - "ĠPor no", - "_ assign", - "_as sign", - "_ass ign", - "Prot ocol", - "Proto col", - "Ġ Being", - "ĠB eing", - "ĠBe ing", - "ĠBei ng", - "Ġ Airport", - "ĠAir port", - "Ġcon ventional", - "Ġconvent ional", - "Ġconvention al", - "Ġ Wat", - "ĠW at", - "ĠWa t", - "Ġ CI", - "ĠC I", - "E TA", - "ET A", - "Ġ Anthony", - "ĠAnth ony", - "Ġ tablet", - "Ġtable t", - "Ġtab let", - "( format", - "(form at", - "(for mat", - "Ġconsist ently", - "Ġconsistent ly", - "ĠI owa", - "ĠIo wa", - "4 74", - "47 4", - "Ġ avatar", - "Ġav atar", - "Ġava tar", - "0 27", - "02 7", - ". cursor", - ".c ursor", - "! [", - "Ġh anging", - "Ġhang ing", - "Ġhan ging", - "Ġhangi ng", - "H er", - "He r", - "S uch", - "Su ch", - "Suc h", - "' ;ĊĊĊ", - "';Ċ ĊĊ", - "';ĊĊ Ċ", - "'; ĊĊĊ", - "org eous", - "orge ous", - "( )==", - "() ==", - "Ġ viewModel", - "Ġview Model", - "Ġ ãĥ", - "Ġ els", - "Ġe ls", - "Ġel s", - "Ġ Agent", - "ĠA gent", - "ĠAg ent", - "ĠAge nt", - "F etch", - "a por", - "ap or", - "apo r", - "Ġ cx", - "Ġc x", - "p read", - "pr ead", - "pre ad", - "ĠP ier", - "ĠPi er", - "ĠPie r", - "o eff", - "oe ff", - "6 16", - "61 6", - "S n", - "8 90", - "89 0", - "Ġ Virtual", - "ĠV irtual", - "ĠVir tual", - "ĠVirt ual", - "A pr", - "Ap r", - ". White", - ".Wh ite", - "6 15", - "61 5", - "_ MOD", - "_M OD", - "_MO D", - "Ġ Points", - "ĠP oints", - "ĠPoint s", - "ĠPo ints", - "å¤ ±", - "Ġ genes", - "Ġg enes", - "Ġge nes", - "Ġgen es", - "Ġgene s", - "Ġ vendor", - "Ġv endor", - "Ġvend or", - "Ġmain stream", - "< src", - "Ċ", - "Ġ< >Ċ", - "Ġ<> Ċ", - "F ilename", - "File name", - "Fi lename", - "Fil ename", - "Ġs ne", - "Ġsn e", - "Ġ Football", - "ĠF ootball", - "ĠFoot ball", - "Ġr ival", - "Ġri val", - "Ġriv al", - "Ġdis aster", - "i onic", - "ion ic", - "io nic", - "ioni c", - "Ġ Damage", - "ĠD amage", - "ĠDa mage", - "ĠDam age", - ". Resource", - ".Re source", - ".Res ource", - "- en", - "-e n", - "Ġ Types", - "ĠT ypes", - "ĠType s", - "ĠTy pes", - "ĠTyp es", - "get String", - "( board", - "(b oard", - "Ġ bol", - "Ġb ol", - "Ġbo l", - "p lain", - "pl ain", - "pla in", - "z ym", - "zy m", - "ภ²", - "Ġ scanner", - "Ġsc anner", - "Ġscan ner", - "i lder", - "il der", - "ild er", - "ilde r", - "_ msgs", - "_msg s", - "_ms gs", - "æ ı", - "( intent", - "(int ent", - "(in tent", - "Ġ destruct", - "Ġd estruct", - "Ġde struct", - "Ġb ust", - "Ġbu st", - "Ġbus t", - "Ġ Employ", - "ĠE mploy", - "ĠEm ploy", - "ĠEmp loy", - "o ni", - "on i", - "Ġ UIViewController", - "ĠUI ViewController", - "ĠUIView Controller", - "Ġo dds", - "Ġodd s", - "Ġod ds", - "e arer", - "ear er", - "ea rer", - "Ge ometry", - "Geo metry", - "Geom etry", - "Ġ yii", - "Ġy ii", - "Ġyi i", - "_ EXPORT", - "_EX PORT", - "_EXP ORT", - "Ġ Attack", - "ĠAtt ack", - "Ġn iet", - "Ġnie t", - "Ġni et", - "Ġim pression", - "Ġimp ression", - "Ġimpress ion", - "Ġimpr ession", - "Ġ Gil", - "ĠG il", - "ĠGi l", - "_ prob", - "_p rob", - "_pro b", - "_pr ob", - "5 28", - "52 8", - "Ġ CF", - "ĠC F", - "Ġ Experience", - "ĠEx perience", - "/ plugins", - "/pl ugins", - "/plugin s", - ". Method", - ".M ethod", - "Ġbel iefs", - "Ġbelie fs", - "Ġbelief s", - "N ative", - "Nat ive", - "_ build", - "_b uild", - "Ġ vig", - "Ġv ig", - "Ġvi g", - "Ġr anks", - "Ġrank s", - "Ġran ks", - "cover ed", - "cov ered", - "7 05", - "70 5", - "s uch", - "su ch", - "G uard", - "Gu ard", - ". pack", - ".p ack", - ".pa ck", - "ad der", - "add er", - "8 09", - "80 9", - "i via", - "iv ia", - "ivi a", - "l ng", - "ln g", - "Ġ вÑĭ", - "Ġв Ñĭ", - "5 52", - "55 2", - "T imestamp", - "Time stamp", - "_ now", - "_n ow", - "_no w", - "Ġp oker", - "Ġpo ker", - "Ġpok er", - "Ġpoke r", - "Ġ unc", - "Ġu nc", - "Ġun c", - "Ġ shapes", - "Ġsh apes", - "Ġshape s", - "Ġsha pes", - "- types", - "-t ypes", - "-type s", - "_ period", - "_p eriod", - "_per iod", - "p k", - "Ġveter an", - "Ġ sono", - "Ġs ono", - "Ġso no", - "Ġson o", - "Ġ appointed", - "Ġapp ointed", - "Ġappoint ed", - "over flow", - ". driver", - ".d river", - ".dr iver", - ".drive r", - "_ cat", - "_c at", - "_ca t", - "u tt", - "ut t", - "p lant", - "pl ant", - "plan t", - "pla nt", - "i mb", - "im b", - "Ġ Accept", - "ĠAc cept", - "ĠAcc ept", - "Ġ concert", - "Ġcon cert", - "Ġconc ert", - "Ġconce rt", - "ĉ node", - "ĉn ode", - "ĉno de", - "ĉ z", - "? >čĊ", - "?> čĊ", - "Ġb anned", - "Ġban ned", - "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĉĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĉĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġt oxic", - "Ġto xic", - "Ġtox ic", - "Ġdis appe", - "Ġdisap pe", - "4 73", - "47 3", - "È Ľ", - "Ġg race", - "Ġgr ace", - "Ġgra ce", - "Ġgrac e", - "at eful", - "ate ful", - "Re ply", - "Rep ly", - "ĠC ruz", - "ĠCr uz", - "ĠCru z", - "4 86", - "48 6", - "Ġs crap", - "Ġsc rap", - "Ġscr ap", - "Ġ keywords", - "Ġkey words", - "Ġkeyword s", - "s imp", - "si mp", - "sim p", - "Ġmort gage", - "Ġcy ber", - "Ġ Execute", - "ĠEx ecute", - "ĠExec ute", - "Ġ latitude", - "Ġl atitude", - "Ġlat itude", - "i fu", - "if u", - ". COM", - ".C OM", - ".CO M", - "d bo", - "db o", - "Ġs orts", - "Ġso rts", - "Ġsort s", - "Ġsor ts", - "Ġ Gas", - "ĠG as", - "ĠGa s", - "om ial", - "omi al", - ". Local", - ".L ocal", - ".Lo cal", - "C ells", - "Cell s", - "Cel ls", - ". Replace", - ".Re place", - "String s", - "Str ings", - ". fit", - ".f it", - ".fi t", - "Ġ Third", - "ĠTh ird", - "ĠThi rd", - "% \",Ċ", - "%\" ,Ċ", - "%\", Ċ", - "Ġ {}\".", - "Ġ{ }\".", - "Ġ{} \".", - "Ġ Sony", - "ĠS ony", - "ĠSo ny", - "ĠSon y", - "Ġ [:", - "Ġ[ :", - "5 85", - "58 5", - "Ġf allen", - "Ġfa llen", - "Ġfall en", - "Ġfal len", - ". ')Ċ", - ".' )Ċ", - ".') Ċ", - "i nh", - "in h", - "Ġ MC", - "ĠM C", - "Ġ redis", - "Ġre dis", - "Ġr edis", - "Ġred is", - "C odes", - "Code s", - "Co des", - "Cod es", - "Ġ profiles", - "Ġpro files", - "Ġprof iles", - "Ġprofile s", - "Ġprofil es", - "h ook", - "ho ok", - "hoo k", - "Re ducer", - "Red ucer", - "Reduc er", - "Reduce r", - "_ FUNC", - "_F UNC", - "_FUN C", - "Ġ navigate", - "Ġn avigate", - "Ġnav igate", - "Ġnavig ate", - "st rlen", - "str len", - "Ġh orm", - "Ġhor m", - "Ġho rm", - "á ŀ", - "Ġ SR", - "ĠS R", - ". boot", - ".b oot", - ".bo ot", - "Ġ digest", - "Ġd igest", - "Ġdi gest", - "Ġdig est", - "ĉ header", - "ĉhead er", - ". findOne", - ".find One", - "æ ģ", - "Db Type", - "n ia", - "ni a", - "_ merge", - "_m erge", - "Ġd onne", - "Ġdon ne", - "Ġdonn e", - "/ Getty", - "/G etty", - "_ CHAR", - "_CH AR", - "Ġ bands", - "Ġb ands", - "Ġband s", - "Ġban ds", - "Ġba nds", - ". URL", - ".U RL", - ".UR L", - "art ial", - "arti al", - "Ġ freq", - "Ġf req", - "Ġfr eq", - "Ġfre q", - "Ġs ist", - "Ġsi st", - "Ġsis t", - "N g", - "Ġrender ing", - "Ġrend ering", - "\\ Core", - "\\C ore", - "Widget s", - "Ġ VA", - "ĠV A", - "Ġactiv ists", - "Ġactivist s", - "S te", - "St e", - "= _", - "a lla", - "al la", - "all a", - "St amp", - "Ġ loads", - "Ġlo ads", - "Ġload s", - "Ġloa ds", - "Ġ xx", - "Ġx x", - "Ġ Learning", - "ĠL earning", - "ĠLe arning", - "ĠLearn ing", - "ĠLear ning", - ". Mvc", - ".M vc", - "u ir", - "ui r", - "( \"$", - "(\" $", - "Ġ connecting", - "Ġconnect ing", - "Read Only", - "u ru", - "ur u", - "ĠE ag", - "ĠEa g", - "B IT", - "BI T", - "_ DEL", - "_D EL", - "_DE L", - "å §", - "arr ass", - "arra ss", - "ex ternal", - "ext ernal", - "extern al", - "exter nal", - "Ġ YOUR", - "ĠY OUR", - "ĠYOU R", - "ĠB rew", - "ĠBr ew", - "ĠBre w", - "Ġ Five", - "ĠF ive", - "ĠFi ve", - "Ġ resize", - "Ġre size", - "Ġres ize", - "i gid", - "ig id", - "igi d", - "e ration", - "er ation", - "era tion", - "6 53", - "65 3", - "Ġ Ñį", - "ĠÑ į", - "5 36", - "53 6", - "åĬ ł", - "0 39", - "03 9", - "Ġ Catch", - "ĠC atch", - "ĠCat ch", - "Ù ģ", - "Ġ Leon", - "ĠL eon", - "ĠLe on", - "ĠLeo n", - "a mil", - "am il", - "ami l", - ". Body", - ".B ody", - "C lip", - "Cl ip", - "Cli p", - "/ list", - "/l ist", - "/li st", - ". br", - ".b r", - "Edit Text", - "ĉ db", - "ĉd b", - ". Game", - ".G ame", - "( BuildContext", - "(Build Context", - "back end", - ". Red", - ".R ed", - ".Re d", - "f acebook", - "face book", - "5 29", - "52 9", - ". urls", - ".url s", - ".ur ls", - "m r", - "rol led", - "roll ed", - "- ------", - "-- -----", - "---- ---", - "--- ----", - "----- --", - "------ -", - "Ġint ervention", - "Ġinter vention", - "Ġinterven tion", - "Ġinterv ention", - "Ġret irement", - "Ġretire ment", - "Ġretir ement", - "Ġ Kit", - "ĠK it", - "ĠKi t", - "Ġ PRE", - "ĠP RE", - "ĠPR E", - "Upper Case", - "Ġ Socket", - "ĠS ocket", - "ĠSo cket", - "ĠSoc ket", - "Ġ :-", - "Ġ: -", - "Ġstud ying", - "Ġstudy ing", - "Ġ Metro", - "ĠM etro", - "ĠMe tro", - "ĠMet ro", - "ar ded", - "ard ed", - "arde d", - "Ġcon versations", - "Ġconvers ations", - "Ġconversation s", - "C alled", - "Call ed", - "Cal led", - "Ġex amine", - "Ġexam ine", - "ert ificate", - ". gz", - ".g z", - "- responsive", - "-res ponsive", - "Ġ refund", - "Ġre fund", - "Ġref und", - "_ network", - "_n etwork", - "_net work", - "0 26", - "02 6", - "all owed", - "allow ed", - "allo wed", - "em pt", - "emp t", - "Ġme als", - "Ġmeal s", - "C ategories", - "Ġtravel ing", - "Ġtrav eling", - "Ġ kg", - "Ġk g", - "Ġsh ame", - "Ġsha me", - "Ġsham e", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġexplicit ly", - "Ġmath ematic", - "Ġ Suite", - "ĠS uite", - "ĠSu ite", - "ĠSuit e", - "Ġ RGB", - "ĠR GB", - "ĠRG B", - "****** /", - "***** */", - "Ġm ixture", - "Ġmix ture", - "l earning", - "le arning", - "lear ning", - "learn ing", - ". template", - ".t emplate", - ".temp late", - ".tem plate", - "at ts", - "att s", - "w x", - "ĉ ctx", - "ĉc tx", - "ĉct x", - ". properties", - ".p roperties", - ".prop erties", - "Ġdr inks", - "Ġdrink s", - "Ġ Either", - "ĠE ither", - "set Text", - ". getData", - ".get Data", - ".getD ata", - ". zip", - ".z ip", - "Ġreve als", - "Ġreveal s", - "< table", - ".Ċ", - "/> .Ċ", - "/>. Ċ", - "Ġr anked", - "Ġrank ed", - "Ġran ked", - "_ impl", - "_i mpl", - "_im pl", - "_imp l", - "Ġ Handles", - "ĠH andles", - "ĠHand les", - "ĠHandle s", - "Ġhost ed", - "Ġho sted", - "Ġhos ted", - "Ġ updating", - "Ġup dating", - "Ġupd ating", - "al bum", - "é Ŀ", - "Ġ shader", - "Ġsh ader", - "Ġsha der", - "Ġshade r", - "Ed itors", - "Edit ors", - "Editor s", - "- round", - "-r ound", - "-ro und", - "[ ]{", - "[] {", - "Ġ sep", - "Ġs ep", - "Ġse p", - "Ġ Hi", - "ĠH i", - "T EM", - "TE M", - "look up", - "loo kup", - ". man", - ".m an", - ".ma n", - "_ INPUT", - "_IN PUT", - "Ġthreat ened", - "Ġthreaten ed", - "_ IMPORT", - "_IM PORT", - "_IMP ORT", - "Ġ drops", - "Ġd rops", - "Ġdr ops", - "Ġdrop s", - "Ġdro ps", - "r uit", - "ru it", - "s id", - "si d", - "b oth", - "bo th", - "bot h", - "Ġ Excel", - "ĠEx cel", - "ĠExc el", - "Ġ jer", - "Ġj er", - "Ġje r", - "ord inary", - "ordin ary", - "е й", - "еР¹", - "V IEW", - "VI EW", - "re ply", - "rep ly", - "Ġ ):Ċ", - "Ġ) :Ċ", - "Ġ): Ċ", - "col ors", - "color s", - "colo rs", - "ver ified", - "_ Tr", - "_T r", - "_ parse", - "_p arse", - "_par se", - "_pars e", - "Ġcon gress", - "Ġcongr ess", - "Ġcong ress", - "6 17", - "61 7", - "P romise", - "Pro mise", - "Prom ise", - "i nts", - "in ts", - "int s", - "Ġ Mother", - "ĠM other", - "ĠMo ther", - "ĠMot her", - ". Api", - ".A pi", - ".Ap i", - "Ġ Duration", - "ĠD uration", - "ĠDu ration", - "ĠDur ation", - "Ġ firstName", - "Ġfirst Name", - "inherit doc", - "ĠM ars", - "ĠMar s", - "ĠMa rs", - "Ġ apr", - "Ġa pr", - "Ġap r", - "O DY", - "OD Y", - "Ġvis its", - "Ġvisit s", - "6 31", - "63 1", - "Ġhe aling", - "Ġheal ing", - "let ters", - "letter s", - "lette rs", - "lett ers", - ") ));čĊ", - ")) );čĊ", - "))) ;čĊ", - "))); čĊ", - "f uture", - "fu ture", - ". Framework", - ".F ramework", - ".Frame work", - "Ġk iss", - "Ġki ss", - "Ġinv olve", - "Ġinvol ve", - "Ġ silent", - "Ġs ilent", - "Ġsil ent", - "ad ows", - "ado ws", - "adow s", - "Ġany body", - "s ch", - "sc h", - "6 90", - "69 0", - "Ġsol ely", - "Ġsole ly", - "- img", - "-i mg", - "-im g", - "Ġ propri", - "Ġp ropri", - "Ġpro pri", - "Ġprop ri", - "Ġin struct", - "Ġinstr uct", - "Ġ licenses", - "Ġlicense s", - "Ġlic enses", - "Ġlicens es", - "Ġ meth", - "Ġm eth", - "Ġme th", - "Ġmet h", - "Ġcon dem", - "Ġcond em", - "Ġ Domain", - "ĠD omain", - "ĠDo main", - "ĠDom ain", - "ĠH arris", - "ĠHar ris", - "ĠHarr is", - "Ġs Ã¥", - "CE PT", - "B atch", - "Bat ch", - "@ extends", - "ĠCONTR IBUT", - ". DataFrame", - ".Data Frame", - "4 72", - "47 2", - "_ packet", - "_p acket", - "_pack et", - "_pa cket", - "re cision", - "rec ision", - "Ġf ocusing", - "Ġfocus ing", - "Ġfoc using", - ". ht", - ".h t", - "__ \":Ċ", - ": Get", - "Ġ KC", - "ĠK C", - "Ġp assage", - "Ġpass age", - "Ġpas sage", - "S egment", - "Se gment", - "Seg ment", - "_ center", - "_c enter", - "_cent er", - "-z A", - "_ BL", - "_B L", - "Ġcon vin", - "Ġconv in", - "Ġ classified", - "Ġclass ified", - "Ġ NSMutable", - "ĠNS Mutable", - "_ ap", - "_a p", - "t ile", - "til e", - "ti le", - "Rect angle", - "4 92", - "49 2", - "( nums", - "(n ums", - "(num s", - "v ens", - "ve ns", - "ven s", - "Ġ UIButton", - "ĠUI Button", - "ĠUIB utton", - "ĠF eder", - "ĠFe der", - "ĠFed er", - "a mo", - "am o", - "Ġ outline", - "Ġout line", - "Ġ Parser", - "ĠP arser", - "ĠPar ser", - "ĠParse r", - "ĠPars er", - "Ġ âī", - "Ġâ ī", - "Ġ Works", - "ĠW orks", - "ĠWork s", - "ĠWor ks", - ". Schema", - ".S chema", - "Ġeng ines", - "Ġengine s", - "6 37", - "63 7", - "5 63", - "56 3", - "_ common", - "_com mon", - "_comm on", - "5 42", - "54 2", - "_ old", - "_o ld", - "Ġset ContentView", - "ĠsetContent View", - "Ġ ///<", - "Ġ// /<", - "Ġ/// <", - "Ġ BT", - "ĠB T", - "f m", - "Ġd ivers", - "Ġdi vers", - "Ġdiv ers", - "Ġdive rs", - "Ġdiver s", - "_ weights", - "_weight s", - "_we ights", - "e mark", - "em ark", - "ema rk", - "Ġ ACT", - "ĠA CT", - "ĠAC T", - "Ġpro portion", - "Ġprop ortion", - "Ġproport ion", - "Ġpropor tion", - "over lay", - ". dirname", - ".dir name", - "Ġ Git", - "ĠG it", - "ĠGi t", - "_ REFERENCE", - "_REF ERENCE", - "_REFER ENCE", - "< >", - "l b", - "_ rule", - "_r ule", - "_ru le", - "è´ ¥", - "Ġ Putin", - "ĠP utin", - "ĠPut in", - "ĠPu tin", - "Ġsleep ing", - "Ġsle eping", - "Ġslee ping", - "( ):čĊ", - "() :čĊ", - "(): čĊ", - "Ġ preserve", - "Ġp reserve", - "Ġpre serve", - "Ġpres erve", - "Ġpar liament", - "Ġ Looking", - "ĠLo oking", - "ĠLook ing", - "Ġp icking", - "Ġpick ing", - "Ġpic king", - "Ġ Dispatch", - "ĠDis patch", - "ĠDisp atch", - "Ġs lip", - "Ġsl ip", - "ë ĵ", - "ĠL yn", - "ĠLy n", - "_ signal", - "_s ignal", - "_sign al", - "_sig nal", - "config uration", - "ĠP itt", - "ĠPi tt", - "ĠPit t", - "4 91", - "49 1", - "a den", - "ad en", - "ade n", - "pro cedure", - "Ġenthus i", - "Ġenth usi", - "f ight", - "fig ht", - "fi ght", - "Ġ Consider", - "ĠCons ider", - "Ġt orn", - "Ġto rn", - "Ġtor n", - "Conn ected", - "Connect ed", - ". cos", - ".c os", - ".co s", - "_ groups", - "_g roups", - "_group s", - "Ġ Think", - "ĠTh ink", - "ĠThi nk", - "ĠThin k", - "Ġdel iber", - "Ġre sid", - "Ġres id", - "work ing", - "wor king", - ". columns", - ".column s", - "Ġ Called", - "ĠC alled", - "ĠCal led", - "ĠCall ed", - "Ġ eslint", - "Ġes lint", - "Ġesl int", - "> \",", - ">\" ,", - "_ DOWN", - "_D OWN", - "_DO WN", - "h ist", - "hi st", - "his t", - "Ġ Advanced", - "ĠAd vanced", - "ĠAdv anced", - "ĠAdvance d", - "Ġre wards", - "Ġreward s", - "Ġrew ards", - "a ctors", - "act ors", - "ac tors", - "actor s", - "Ġsil ence", - "4 79", - "47 9", - "Ġm yth", - "Ġmy th", - "Ġn eur", - "Ġne ur", - "Ġneu r", - "5 19", - "51 9", - "Ġ auction", - "Ġa uction", - "Ġau ction", - "Ġauc tion", - ". GetString", - ".Get String", - "e ks", - "ek s", - "( project", - "(pro ject", - "(proj ect", - "5 98", - "59 8", - "ĉ msg", - "ĉm sg", - "ĉms g", - "ĉ output", - "ĉout put", - "Ġcomplaint s", - "Ġcomplain ts", - "5 51", - "55 1", - ", S", - "Ġ tbl", - "Ġt bl", - "Ġtb l", - "Ġ ,ĊĊ", - "Ġ, ĊĊ", - "Ġ,Ċ Ċ", - "r iors", - "ri ors", - "rior s", - "rio rs", - "ah ren", - "ahr en", - "Ġlaw yers", - "Ġlawy ers", - "Ġlawyer s", - "re dux", - "red ux", - "_ symbol", - "_s ymbol", - "_sym bol", - "o ffee", - "of fee", - "off ee", - "_ RESULT", - "_RES ULT", - "( Name", - "(N ame", - "U TC", - "UT C", - ". currentTime", - ".current Time", - "Ġorgan is", - ". arg", - ".a rg", - ".ar g", - "5 33", - "53 3", - "Ġmin im", - "Ġmi nim", - "Ġmini m", - "w ick", - "wi ck", - "Ġrece ives", - "Ġreceive s", - "B alance", - "Bal ance", - "Ġspe aks", - "Ġspeak s", - "Ġ Days", - "ĠD ays", - "ĠDay s", - "ĠDa ys", - "Ġ Below", - "ĠB elow", - "ĠBe low", - "ĠBel ow", - "4 83", - "48 3", - "t ipo", - "ti po", - "tip o", - "P resent", - "Pre sent", - "Pres ent", - "Ġre serv", - "Ġres erv", - "h p", - "Ġ rit", - "Ġr it", - "Ġri t", - "_ RIGHT", - "_R IGHT", - "- -)", - "-- )", - "Ġchair man", - "7 81", - "78 1", - "D IS", - "DI S", - "Ġ BOOST", - "ĠBO OST", - "Ġex periments", - "Ġexper iments", - "Ġexperi ments", - "Ġexperiment s", - "6 87", - "68 7", - "_ _);Ċ", - "__ );Ċ", - "__) ;Ċ", - "__); Ċ", - "Ġ stamp", - "Ġst amp", - "Ġsta mp", - "Ġf ert", - "Ġfe rt", - "Ġfer t", - "Ġf ond", - "Ġfo nd", - "Ġfon d", - "T er", - "Te r", - "el ve", - "u ren", - "ur en", - "ure n", - "+ i", - "end ency", - "ende ncy", - "enden cy", - "Ġvirtual ly", - "Ġvirt ually", - ". ..\"", - ".. .\"", - "... \"", - "ï½ ŀ", - "9 25", - "92 5", - "- cent", - "-c ent", - "-ce nt", - "_ unique", - "_un ique", - "Ġ pricing", - "Ġp ricing", - "Ġpr icing", - "Ġpri cing", - "m ic", - "mi c", - "R ESH", - "RE SH", - "RES H", - "Ġ :::", - "Ġ: ::", - "Ġ:: :", - "Ġ annotation", - "Ġan notation", - "Ġann otation", - "Ġannot ation", - "Ġ Circle", - "ĠC ircle", - "ĠCirc le", - "ĠCir cle", - "ong odb", - "ongo db", - "i tas", - "it as", - "ita s", - "Ġ %(", - "Ġ% (", - "( component", - "(com ponent", - "(comp onent", - "Ġ об", - "Ġо б", - "( port", - "(p ort", - "(po rt", - "- hour", - "-h our", - ". obj", - ".o bj", - ".ob j", - "L BL", - "LB L", - "Ġ jury", - "Ġj ury", - "Ġju ry", - "Ġjur y", - "G BT", - "GB T", - "Ġ spy", - "Ġs py", - "Ġsp y", - "Ġ Professional", - "ĠProf essional", - "ĠProfession al", - "Ġ\" \";ĊĊ", - "Ġ\"\" ;ĊĊ", - "Ġ\"\";Ċ Ċ", - "Ġ\"\"; ĊĊ", - "Ġstr iking", - "Ġstri king", - "Ġd iscrimination", - "Ġdiscrim ination", - "Ġdiscrimin ation", - "Ġp ays", - "Ġpay s", - "Ġpa ys", - "9 37", - "93 7", - "l ict", - "lic t", - "li ct", - "en tes", - "ent es", - "ente s", - "Ġth rowing", - "Ġthrow ing", - "Ġthr owing", - "Ġthro wing", - "Ġ Plugin", - "ĠPl ugin", - "ĠPlug in", - "( def", - "(d ef", - "(de f", - "Ġ RuntimeException", - "ĠRuntime Exception", - "Ġ Migration", - "ĠM igration", - "ĠMig ration", - "5 99", - "59 9", - "Ġ dic", - "Ġd ic", - "Ġdi c", - "b ag", - "ba g", - "o nia", - "on ia", - "oni a", - "Ġcor ruption", - "Ġcorrupt ion", - "7 04", - "70 4", - "( Map", - "(M ap", - "Ġp rz", - "Ġpr z", - ". dto", - ".d to", - ".dt o", - "Ġac quire", - "Ġacqu ire", - "State ToProps", - "Ġl oving", - "Ġlo ving", - "Ġlov ing", - "о ж", - "оР¶", - "_ pattern", - "_p attern", - "_pat tern", - "Ġem otions", - "Ġemot ions", - "Ġemotion s", - "Ġ publisher", - "Ġp ublisher", - "Ġpublish er", - "Ġpubli sher", - "_ be", - "_b e", - "Ġc ouples", - "Ġco uples", - "Ġcou ples", - "Ġcouple s", - "Ġcoup les", - "4 98", - "49 8", - "o j", - "Ġ Chart", - "ĠC hart", - "ĠCh art", - "ĠChar t", - "ĠCha rt", - "Ġt rop", - "Ġtr op", - "Ġtro p", - ". tool", - ".t ool", - ".to ol", - "Ġestablish ment", - "Ġ dol", - "Ġd ol", - "Ġdo l", - "6 54", - "65 4", - "Ġ tower", - "Ġt ower", - "Ġto wer", - "Ġtow er", - "Ġ lane", - "Ġl ane", - "Ġla ne", - "Ġlan e", - "ĠSy dney", - "Ġf illing", - "Ġfil ling", - "Ġfill ing", - "claim ed", - "cla imed", - "6 44", - "64 4", - "Ġdialog ue", - "Ġdia logue", - "Ġdial ogue", - "Ġcon vention", - "Ġconv ention", - "Ġconven tion", - "Ġconvent ion", - "bo oking", - "book ing", - "boo king", - "par ency", - "pare ncy", - "paren cy", - "æ ±", - "Ġ Generic", - "ĠG eneric", - "ĠGener ic", - "ĠGen eric", - "ĠGene ric", - "7 18", - "71 8", - "\\ Schema", - "\\S chema", - "4 82", - "48 2", - "6 18", - "61 8", - "Ġ ranges", - "Ġr anges", - "Ġrange s", - "Ġran ges", - "Ġrang es", - "/ ch", - "/c h", - "Ġ panels", - "Ġpanel s", - "Ġpa nels", - "Ġpan els", - "Ġpane ls", - "Ġr uled", - "Ġrule d", - "Ġru led", - "çĶ Ł", - ". ts", - ".t s", - "_ sets", - "_s ets", - "_set s", - "_se ts", - "Ġ cleanup", - "Ġc leanup", - "Ġclean up", - "Pre vious", - "Prev ious", - "Ġ Animal", - "ĠAn imal", - "ĠAnim al", - "6 07", - "60 7", - "( $(", - "($ (", - "ĠA ve", - "ĠAv e", - "ol lar", - "oll ar", - "olla r", - "0 28", - "02 8", - "_ eval", - "_e val", - "_ev al", - "ĉ Name", - "ĉN ame", - "( tree", - "(t ree", - "(tr ee", - "Ġ \"]", - "Ġ\" ]", - "5 71", - "57 1", - "Ġdu ties", - "Ġdut ies", - "= '/", - "=' /", - "Click ed", - "Cl icked", - "Ġdiffer ently", - "Ġdifferent ly", - "Ġ Clark", - "ĠCl ark", - "ĠClar k", - "ĠCla rk", - "Ġ dit", - "Ġd it", - "Ġdi t", - "olog ists", - "ologist s", - "ologi sts", - "Ġsy nd", - "Ġsyn d", - "Ġs ends", - "Ġse nds", - "Ġsend s", - "Ġsen ds", - "- known", - "-k nown", - "-know n", - "k b", - "Ġ Modal", - "ĠM odal", - "ĠMod al", - "ĠMo dal", - "it ative", - "itat ive", - "Ġr acing", - "Ġrac ing", - "Ġra cing", - "Ġhigh lights", - "Ġhighlight s", - "Ġ Simon", - "ĠS imon", - "ĠSim on", - "ĠSi mon", - "Ġ Captain", - "ĠCap tain", - "ĠCapt ain", - "ä¿ ¡", - "Ġ CB", - "ĠC B", - "con tin", - "cont in", - "conti n", - "a ran", - "ar an", - "ara n", - "Ġ physics", - "Ġph ysics", - "Ġphys ics", - "Ġphysic s", - "r etty", - "re tty", - "ret ty", - "rett y", - "e tal", - "et al", - "eta l", - ". md", - ".m d", - "ax ios", - "Ġspe akers", - "Ġspeak ers", - "Ġspeaker s", - "Ġ prep", - "Ġp rep", - "Ġpr ep", - "Ġpre p", - "Ġaw arded", - "Ġaward ed", - "ì§ Ģ", - "Ġ Corn", - "ĠC orn", - "ĠCo rn", - "ĠCor n", - "Ġ Nature", - "ĠN ature", - "ĠNa ture", - "ĠNat ure", - "ĠNatur e", - "UD IO", - "7 37", - "73 7", - "Ġ proj", - "Ġp roj", - "Ġpro j", - "Ġpr oj", - "- pre", - "-p re", - "-pr e", - "[ u", - "F eatures", - "Fe atures", - "Feature s", - "Feat ures", - "Ġ isEqual", - "Ġis Equal", - "B inary", - "Bin ary", - "s ig", - "si g", - "Ġcon fusion", - "Ġconf usion", - "5 46", - "54 6", - "5 68", - "56 8", - "Ġ Hat", - "ĠH at", - "ĠHa t", - "Ġkt ó", - ". configure", - ".con figure", - ".config ure", - ".conf igure", - "M ON", - "MO N", - "4 94", - "49 4", - "/ edit", - "/e dit", - "_ Add", - "_A dd", - "_Ad d", - ", true", - ",tr ue", - "5 41", - "54 1", - "Ġ cli", - "Ġc li", - "Ġcl i", - "Error Message", - "- loader", - "-l oader", - "-lo ader", - "-load er", - "Dim ensions", - "Dimension s", - "ulti ply", - "ultip ly", - "Ġ {!!", - "Ġ{ !!", - "Ġ{! !", - "Ġ SqlCommand", - "ĠSql Command", - "Ġ spoken", - "Ġsp oken", - "Ġspoke n", - "Ġspo ken", - "Ġ pics", - "Ġp ics", - "Ġpi cs", - "Ġpic s", - "Ġ toy", - "Ġt oy", - "Ġto y", - "( Key", - "(K ey", - "Ġ Loop", - "ĠL oop", - "ĠLo op", - "Ø ¨", - "E ATURE", - "EA TURE", - "in ction", - "inc tion", - "inct ion", - "_ setup", - "_set up", - "w rapper", - "wrap per", - "wr apper", - "Ġt ong", - "Ġto ng", - "Ġton g", - "c ular", - "cul ar", - "cu lar", - "O pt", - "Op t", - ". Pl", - ".P l", - "= \",", - "=\" ,", - "( length", - "(l ength", - "(len gth", - "(le ngth", - "u mn", - "um n", - "Ġ chrom", - "Ġch rom", - "Ġchr om", - "Ġs event", - "Ġse vent", - "Ġseven t", - "Ġsev ent", - "Ġ IllegalArgumentException", - "ĠIl legalArgumentException", - "ĠIllegal ArgumentException", - "4 78", - "47 8", - "ĉ start", - "ĉst art", - "Ġbe gun", - "Ġbeg un", - "CE PTION", - "CEPT ION", - "d ataset", - "data set", - "dat aset", - "datas et", - "8 25", - "82 5", - "Ġ Failed", - "ĠF ailed", - "ĠFa iled", - "ĠFail ed", - "c ols", - "co ls", - "col s", - "4 59", - "45 9", - "Ġk nee", - "Ġkn ee", - "Ġkne e", - "i more", - "im ore", - "imo re", - ". splice", - ".sp lice", - "s hell", - "sh ell", - "she ll", - "ig gers", - "igger s", - "igg ers", - "Ġ themes", - "Ġth emes", - "Ġthe mes", - "Ġthem es", - "Ġtheme s", - "9 95", - "99 5", - "Ġ DJ", - "ĠD J", - "Ġ Assistant", - "ĠAss istant", - "ĠAssist ant", - "- $", - "M aybe", - "May be", - "Ġ ordering", - "Ġorder ing", - "Ġord ering", - "ĠInt elligence", - "ĠIntelli gence", - "ĠMass achusetts", - "Ġf ailing", - "Ġfa iling", - "Ġfail ing", - "el son", - "els on", - "G reat", - "Gr eat", - "Gre at", - "= i", - ". rest", - ".re st", - ".r est", - ".res t", - "Ġ invite", - "Ġinv ite", - "Ġinvit e", - "- disable", - "-d isable", - "-dis able", - ". GroupBox", - ".Group Box", - "âĢĻ est", - "âĢĻe st", - "âĢĻes t", - "Ġt ackle", - "Ġtack le", - "Ġtac kle", - "g v", - "et ter", - "ette r", - "ett er", - "Ġ ),čĊ", - "Ġ) ,čĊ", - "Ġ), čĊ", - "_ rules", - "_r ules", - "_rule s", - "_ru les", - ". warn", - ".w arn", - "function s", - "fun ctions", - "ĠChrist ians", - "ĠChristian s", - "Ġb acked", - "Ġback ed", - "Ġbac ked", - "Ġ slider", - "Ġs lider", - "Ġsl ider", - "Ġslide r", - "Ġslid er", - "Ġenjoy ing", - "Ġenjo ying", - "n est", - "ne st", - "nes t", - "Ġh ij", - "Ġhi j", - "_ ms", - "_m s", - "/ /*", - "// *", - "An notations", - "Annotation s", - "Ġ Variables", - "ĠVariable s", - "ĠVari ables", - "< V", - "( server", - "(s erver", - "(serv er", - "Ġ Oracle", - "ĠOr acle", - "e lements", - "el ements", - "element s", - "ele ments", - "elem ents", - "Ġ organisation", - "Ġorgan isation", - "Ġorganis ation", - "_ pointer", - "_point er", - "_po inter", - "Ġ Headers", - "ĠHe aders", - "ĠHead ers", - "ĠHeader s", - "[ d", - "Ġ deadline", - "Ġdead line", - "i ssa", - "is sa", - "iss a", - "Ġ knife", - "Ġkn ife", - "Ġ NASA", - "ĠN ASA", - "ĠNAS A", - "ĠNA SA", - "Ġ Height", - "ĠH eight", - "ĠHe ight", - "7 84", - "78 4", - "Ġ Async", - "ĠA sync", - "ĠAs ync", - "Ġ venue", - "Ġven ue", - ". dom", - ".d om", - ".do m", - "bour ne", - "bou rne", - "ĠH awai", - "ĠHaw ai", - "Ġ memo", - "Ġm emo", - "Ġme mo", - "Ġmem o", - "i ctions", - "ict ions", - "iction s", - "Ġsur veillance", - "Ġsurve illance", - "o mi", - "om i", - "/ assets", - "/as sets", - "5 87", - "58 7", - "Ġ edu", - "Ġe du", - "Ġed u", - "Ä Ľ", - "Ġr oster", - "Ġro ster", - "Ġros ter", - "Ġrost er", - "Ġh ired", - "Ġhi red", - "Ġhire d", - "Ġ Tok", - "ĠT ok", - "ĠTo k", - "Ġ placement", - "Ġpl acement", - "Ġplace ment", - "Ġplac ement", - "ur ations", - "uration s", - "urat ions", - "Ġ setState", - "Ġset State", - "ĠMag azine", - "Ġhor ror", - "Ġho rror", - "Ġhorr or", - "T ry", - "Tr y", - "Ġ lag", - "Ġl ag", - "Ġla g", - "Ġ Everyone", - "ĠEvery one", - "t hur", - "th ur", - ") );čĊčĊ", - ")) ;čĊčĊ", - "));čĊ čĊ", - ")); čĊčĊ", - ". return", - ".re turn", - ".r eturn", - ".ret urn", - "Ġsy mp", - "Ġsym p", - "âĸĪ âĸĪ", - "Ġn ights", - "Ġnight s", - "work er", - "wor ker", - "Ġ ale", - "Ġa le", - "Ġal e", - "ennes see", - ". step", - ".s tep", - ".st ep", - "Ġs ynchronized", - "Ġsynchron ized", - "Ġsynchronize d", - "4 87", - "48 7", - "o uri", - "ou ri", - "our i", - "D oes", - "Do es", - ". change", - ".ch ange", - "f on", - "fo n", - ". setBackground", - ".set Background", - "ir cular", - "irc ular", - "4 76", - "47 6", - "+ -", - "ĠC IA", - "ĠCI A", - "7 29", - "72 9", - "Ġ Jane", - "ĠJ ane", - "ĠJan e", - "ĠJa ne", - "Ġ Similar", - "ĠS imilar", - "ĠSim ilar", - "- I", - "level and", - "lev eland", - "Ġpro spect", - "Ġpros pect", - "_ found", - "_f ound", - "ĉ color", - "ĉc olor", - "ĉcol or", - ".D iagnostics", - ".Di agnostics", - "Ġ announce", - "Ġann ounce", - "Ġannounc e", - "Ġanno unce", - "Ġass umes", - "Ġassum es", - "Ġassume s", - "/ tr", - "/t r", - "Ġ bd", - "Ġb d", - "9 87", - "98 7", - "Ġ Carbon", - "ĠC arbon", - "ĠCar bon", - "ĠCarb on", - "Ġanal ys", - "Ġanaly s", - "Ġana lys", - "5 64", - "56 4", - ". dest", - ".d est", - ".de st", - ".des t", - "n ik", - "ni k", - "Ġ Lie", - "ĠL ie", - "ĠLi e", - "- index", - "-in dex", - "-ind ex", - "Draw able", - "Ġ TAG", - "ĠT AG", - "ĠTA G", - "Ġ triangle", - "Ġt riangle", - "Ġtr iangle", - "Ġtri angle", - "Ġtriang le", - "_ FLOAT", - "_F LOAT", - "ĉ ĉĠĠĠĠĠ", - "ĉĉ ĠĠĠĠĠ", - "ĉĉĠĠĠ ĠĠ", - "ĉĉĠ ĠĠĠĠ", - "ĉĉĠĠ ĠĠĠ", - "ĉĉĠĠĠĠ Ġ", - ". black", - ".b lack", - ".bl ack", - "v ue", - "vu e", - "c uracy", - "cur acy", - "cura cy", - "Ġa ffects", - "Ġaff ects", - "Ġaffect s", - "9 06", - "90 6", - "Ġsur ely", - "Ġsure ly", - "S lider", - "Sl ider", - "Slide r", - "u ki", - "uk i", - "c ery", - "ce ry", - "cer y", - "Ġ unter", - "Ġun ter", - "Ġunt er", - ". profile", - ".pro file", - ".pr ofile", - ".prof ile", - "or don", - "ord on", - "ordo n", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "le ave", - "lea ve", - "Ġsmart phone", - "g ie", - "gi e", - "Ġcon spir", - "Ġcons pir", - "Ġ tutorial", - "Ġt utorial", - "Ġtut orial", - "Ġtutor ial", - "Ġtuto rial", - "ç± »", - "Ġ cab", - "Ġc ab", - "Ġca b", - "7 65", - "76 5", - "Ġ Summary", - "ĠSum mary", - "* ĊĊ", - "*Ċ Ċ", - "ä h", - "\" This", - "\"T his", - "Ġ slides", - "Ġsl ides", - "Ġslide s", - "Ġslid es", - "\" ", - "() >", - "c ycle", - "cy cle", - "cycl e", - "ĠB ull", - "ĠBul l", - "ĠBu ll", - "p aths", - "path s", - "pat hs", - "pa ths", - "Ġu np", - "Ġun p", - "Ġview DidLoad", - "_ Model", - "_M odel", - "_Mode l", - "_Mod el", - "Ġ assertTrue", - "Ġassert True", - "Ġ rated", - "Ġr ated", - "Ġrate d", - "Ġrat ed", - "Ġra ted", - "De cl", - "Dec l", - "ver ted", - "vert ed", - "verte d", - "Ġ Dat", - "ĠD at", - "ĠDa t", - "b rew", - "br ew", - "bre w", - "Ġpoint ing", - "M s", - "Ġ Pointer", - "ĠPoint er", - "ĠPo inter", - ") '", - "_ non", - "_n on", - "_no n", - "5 27", - "52 7", - "Ġ SEC", - "ĠS EC", - "ĠSE C", - "Ġ yeah", - "Ġy eah", - "Ġye ah", - "g ency", - "ge ncy", - "gen cy", - "initial ize", - "f ly", - "fl y", - "7 11", - "71 1", - "[ pos", - "[p os", - ", g", - "T ele", - "Te le", - "Tel e", - "0 34", - "03 4", - "Ġj oke", - "Ġjo ke", - "Ġ clause", - "Ġcl ause", - "Ġcla use", - ". findById", - ".find ById", - ".findBy Id", - "e nes", - "en es", - "ene s", - "( instance", - "(in stance", - "(inst ance", - "6 26", - "62 6", - " £", - "9 15", - "91 5", - "Ġs lic", - "Ġsl ic", - "_ home", - "_h ome", - "_hom e", - "Ġ */}Ċ", - "Ġ*/ }Ċ", - "_ pages", - "_p ages", - "_page s", - "_pag es", - "_pa ges", - "( service", - "(s ervice", - "(serv ice", - "9 05", - "90 5", - "R P", - "Ġ Among", - "ĠA mong", - "ĠAm ong", - ". getCurrent", - ".get Current", - ".getC urrent", - "8 06", - "80 6", - "ã Ĥ¹", - "ãĤ ¹", - "Ġs lee", - "Ġsl ee", - "Ġsle e", - "= [Ċ", - ">[ Ċ", - "o ler", - "ol er", - "ole r", - "Ġli bert", - "Ġlib ert", - "Ġliber t", - "Ġ `Ċ", - "Ġ` Ċ", - "Ġw enn", - "Ġwe nn", - "Ġwen n", - "l ated", - "la ted", - "late d", - "lat ed", - "Ġ immune", - "Ġimm une", - "Ġimmun e", - "( Node", - "(N ode", - "Ġ Problem", - "ĠPro blem", - "ĠProb lem", - "ĠProble m", - "Ġ Abs", - "ĠA bs", - "ĠAb s", - "l ogs", - "lo gs", - "log s", - "Ġ ../", - "Ġ. ./", - "Ġ.. /", - "Ġ ADC", - "ĠA DC", - "ĠAD C", - "Ġ }}\">Ċ", - "Ġ} }\">Ċ", - "Ġ}} \">Ċ", - "Ġ}}\" >Ċ", - "Ġ}}\"> Ċ", - "> ');Ċ", - ">' );Ċ", - ">') ;Ċ", - ">'); Ċ", - "= b", - "Ġ Wind", - "ĠW ind", - "ĠWin d", - "ĠWi nd", - "l ahoma", - "lah oma", - "Ġ allocate", - "Ġal locate", - "Ġall ocate", - "Ġalloc ate", - "Ġallo cate", - "o rian", - "or ian", - "oria n", - "ori an", - "Ġpr escription", - "Ġpre scription", - "Ġpres cription", - "- quality", - "-q uality", - "-qu ality", - "Ġ Mayor", - "ĠMay or", - "ĠMa yor", - "ĠMayo r", - "8 55", - "85 5", - "in ely", - "ine ly", - "inel y", - "end foreach", - "Ġ Complex", - "ĠCom plex", - "ĠComp lex", - "ĠComple x", - "k om", - "ko m", - "7 09", - "70 9", - "T Y", - "7 90", - "79 0", - "] ].", - "]] .", - ". Style", - ".St yle", - "_ many", - "_m any", - "_man y", - "_ma ny", - "', '$", - "',' $", - "Ġbar rier", - "Ġbarr ier", - "Ġ Fetch", - "ĠF etch", - "ĠFet ch", - "Ġ Marvel", - "ĠMar vel", - "Ġres ist", - "о го", - "ог о", - "b idden", - "bi dden", - "bid den", - "Ġ Runnable", - "ĠR unnable", - "ĠRun nable", - ": false", - ":f alse", - "8 99", - "89 9", - "Ġbuild s", - "Ġ Stage", - "ĠSt age", - "ĠSta ge", - "Ġ dub", - "Ġd ub", - "Ġdu b", - "em po", - "emp o", - ". site", - ".s ite", - ".si te", - "5 58", - "55 8", - "; ĊĊĊĊ", - ";Ċ ĊĊĊ", - ";ĊĊ ĊĊ", - ";ĊĊĊ Ċ", - "9 94", - "99 4", - "Ġ Denver", - "ĠDen ver", - "Ġre vel", - "Ġrev el", - "Ġreve l", - "Ġtrigger ed", - "Ġ dice", - "Ġd ice", - "Ġdi ce", - "Ġdic e", - "_ fail", - "_f ail", - "_fa il", - "Ġ gc", - "Ġg c", - "8 33", - "83 3", - "5 89", - "58 9", - "ĉ X", - "Ġ Throwable", - "ĠTh rowable", - "ĠThrow able", - "7 75", - "77 5", - ". router", - ".r outer", - ".route r", - ".ro uter", - "ĠRe volution", - "ĠRev olution", - "ÑĢ а", - "_ NON", - "_N ON", - "_NO N", - "0 55", - "05 5", - "Ł ¥", - "5 78", - "57 8", - "Ġ elder", - "Ġe lder", - "Ġel der", - "Ġelde r", - "Ġab road", - "Ġ е", - "ĠÐ µ", - "Ġ Adult", - "ĠAd ult", - "b lr", - "bl r", - "g lyphicon", - "glyph icon", - "6 13", - "61 3", - "Ġprom oting", - "Ġpromot ing", - "Ġpromo ting", - "Ġ iz", - "Ġi z", - "Ġ Solid", - "ĠS olid", - "ĠSo lid", - "ĠSol id", - "6 45", - "64 5", - "_ loader", - "_l oader", - "_lo ader", - "_load er", - "ear ly", - ". enabled", - ".en abled", - ".enable d", - "- edit", - "-e dit", - "-ed it", - "Ġ UL", - "ĠU L", - "_ play", - "_p lay", - "_pl ay", - "Ġ Interrupt", - "ĠInt errupt", - "ĠInter rupt", - "ĠInterr upt", - "Ġadv antages", - "Ġadvant ages", - "Ġadvantage s", - "u cle", - "uc le", - "Ġmechan ical", - "Ġmechanic al", - "Ġmech anical", - ".table LayoutPanel", - "Ġ Working", - "ĠWork ing", - "ĠWor king", - "Ġ anonymous", - "Ġan onymous", - "Ġanonym ous", - "Ġanon ymous", - "R ating", - "Ra ting", - "ig ious", - "igi ous", - "_ phone", - "_p hone", - "_ph one", - ".addAction Listener", - "Ġf ran", - "Ġfr an", - "Ġfra n", - "un den", - "und en", - "unde n", - "Ġ *)&", - "Ġ* )&", - "Ġ*) &", - "_ bool", - "_b ool", - "_bo ol", - "ul ative", - "Ġ cone", - "Ġc one", - "Ġcon e", - "Ġco ne", - "Ġ Mult", - "ĠM ult", - "ĠMu lt", - "ĠMul t", - "Ġm ö", - "Ġ Forward", - "ĠFor ward", - "] ):Ċ", - "]) :Ċ", - "]): Ċ", - "Ġconvin ced", - "Ġconvince d", - "Ġconvinc ed", - "act ed", - "ac ted", - "6 43", - "64 3", - "ãģ ĵ", - "Ġ Configure", - "ĠCon figure", - "ĠConfig ure", - "ĠConf igure", - "Ġce iling", - "Ġceil ing", - "D er", - "De r", - "Ġpass engers", - "Ġpassenger s", - "G roups", - "Group s", - "Gro ups", - "Ġs occer", - "Ġsoc cer", - "/ W", - "av iors", - "avior s", - "avi ors", - "s with", - "sw ith", - "Ġ Zone", - "ĠZ one", - "ĠZo ne", - ". Options", - ".O ptions", - ".Option s", - "Ġ Mom", - "ĠM om", - "ĠMo m", - "i eder", - "ie der", - "ied er", - "Array s", - "Ar rays", - "Arr ays", - "Ġtreat ments", - "Ġtreatment s", - "Ġprotect ing", - "f ac", - "fa c", - "Ġ pickle", - "Ġp ickle", - "Ġpick le", - "Ġpic kle", - "Button Item", - "7 13", - "71 3", - "Ġ blocking", - "Ġb locking", - "Ġbl ocking", - "Ġblock ing", - "Ġbloc king", - "st rar", - "str ar", - "stra r", - "à ²", - "Ġ Export", - "ĠEx port", - "ĠExp ort", - "ĠExpo rt", - "Ġth rew", - "Ġthr ew", - "ot ta", - "ott a", - "Ġ BASE", - "ĠB ASE", - "ĠBAS E", - "ĠBA SE", - ". ws", - ".w s", - ".LE ADING", - "order By", - "_ delay", - "_d elay", - "_de lay", - "_del ay", - "Ġ Pu", - "ĠP u", - ". dll", - ".d ll", - "Ġ Choose", - "ĠCh oose", - "ĠCho ose", - "9 92", - "99 2", - "Pol ice", - "Po lice", - "Ġ BEGIN", - "ĠB EGIN", - "ĠBE GIN", - "bo xes", - "box es", - "Ġ diamond", - "Ġd iamond", - "Ġdiam ond", - "Ġdia mond", - ", l", - "Ġ ĉĉĉ", - "Ġĉ ĉĉ", - "Ġĉĉ ĉ", - "Ġc urious", - "Ġcur ious", - "Ġcu rious", - "6 24", - "62 4", - "t v", - "Ġerot ische", - "Ġerotisch e", - "ack ages", - "ackage s", - "ĉ Set", - "ĉS et", - "T ick", - "Ti ck", - ". border", - ".b order", - "static method", - "Ġ cher", - "Ġc her", - "Ġch er", - "Ġche r", - "in voice", - "inv oice", - "Ġc ru", - "Ġcr u", - "Ġde fect", - "Ġdef ect", - "Ġdefe ct", - "_ metadata", - "_m etadata", - "_meta data", - "_met adata", - "re lation", - "rel ation", - "i kan", - "ik an", - "ika n", - "[ N", - "( Qt", - "(Q t", - "( Base", - "(B ase", - "æģ ¯", - "b eat", - "be at", - "Ġ Empty", - "ĠEm pty", - "ĠEmp ty", - "ĉ o", - "_ shift", - "_s hift", - "_sh ift", - "Ġreg ret", - "7 22", - "72 2", - "Th ose", - "Tho se", - "C ent", - "Ce nt", - "ĠPort ug", - "ĠIs lands", - "ĠIsl ands", - "ĠIsland s", - "Ġ TIME", - "ĠT IME", - "ĠTIM E", - "ĠTI ME", - "Man agement", - "Manage ment", - "Mana gement", - "9 96", - "99 6", - "- sp", - "-s p", - "5 39", - "53 9", - "ê me", - "êm e", - "Ġn otion", - "Ġnot ion", - "Ġno tion", - "un ifu", - "uni fu", - "P K", - "8 26", - "82 6", - "è¡ Į", - "ĠC URLOPT", - "ĠCUR LOPT", - "ĠCURL OPT", - "\\ \"\\", - "\\\" \\", - "U V", - "ç º", - "d ra", - "dr a", - "c ou", - "co u", - "= `", - "Ġ Destroy", - "ĠD estroy", - "ĠDe stroy", - "ĠDest roy", - "r p", - ". cancel", - ".c ancel", - ".can cel", - "G G", - "r untime", - "run time", - "Ġ Vue", - "ĠV ue", - "ĠVu e", - "Ġpro gressive", - "Ġprogress ive", - "Ġprog ressive", - "/ services", - "/s ervices", - "/service s", - "Ġ runner", - "Ġr unner", - "Ġrun ner", - "_ FRAME", - "_FR AME", - ". ToolStripMenuItem", - ".ToolStrip MenuItem", - "Ġ ','", - "Ġ' ,'", - "Ġ', '", - "d elay", - "de lay", - "del ay", - "= utf", - "=u tf", - "Ġscreen ing", - "Ġscre ening", - "Ġp ulling", - "Ġpull ing", - "Ġpul ling", - "o mas", - "om as", - "oma s", - "Ġ anth", - "Ġa nth", - "Ġan th", - "Ġant h", - "- new", - "-n ew", - "-ne w", - "/ local", - "/l ocal", - "/lo cal", - "Ġ iPad", - "Ġi Pad", - "ĠiP ad", - "Ġ twitter", - "Ġt witter", - "Ġtw itter", - "Ġd ying", - "Ġdy ing", - "Ġhe aven", - "Ġheav en", - "Ġ UInt", - "ĠU Int", - "ĠUI nt", - "Ġ Senator", - "ĠSen ator", - "Ġpre sum", - "Ġpres um", - "Ġ Walker", - "ĠW alker", - "ĠWalk er", - "ĠWal ker", - "Ġover come", - "e tection", - "et ection", - "ete ction", - "etect ion", - "Ġemb arrass", - "Ch ina", - "Chi na", - "6 39", - "63 9", - "In clude", - "Inc lude", - "R OLL", - "RO LL", - "ROL L", - "Ġ dataType", - "Ġdata Type", - "D avid", - "Da vid", - "ภ£", - "l op", - "lo p", - "- month", - "-m onth", - "-mon th", - "Ġ scar", - "Ġs car", - "Ġsc ar", - "Ġsca r", - "Ġ Safe", - "ĠS afe", - "ĠSaf e", - "ĠSa fe", - "Ġ ****************************************************************", - "Ġ******************************** ********************************", - "Ġ******** ********************************************************", - "Ġ**************** ************************************************", - "Ġ************************ ****************************************", - "Ġ**************************************** ************************", - "Ġ******************************************************** ********", - "Ġ************************************************ ****************", - "Ġaccess ories", - "Ġaccessor ies", - "Ġr amp", - "Ġra mp", - "Ġram p", - "_ USE", - "_U SE", - "_US E", - "Ġcon trad", - "Ġcont rad", - "Ġcontr ad", - "Ġcontra d", - ") )]Ċ", - ")) ]Ċ", - "))] Ċ", - "Ġp rest", - "Ġpr est", - "Ġpre st", - "Ġpres t", - "Ġ HR", - "ĠH R", - "ĠR ap", - "ĠRa p", - "Ġ usize", - "Ġu size", - "Ġus ize", - "Ġ capability", - "Ġcap ability", - "Ġc ort", - "Ġco rt", - "Ġcor t", - "- next", - "-n ext", - "-ne xt", - "0 77", - "07 7", - "6 27", - "62 7", - "Ġbur den", - "8 22", - "82 2", - "_ reader", - "_re ader", - "_read er", - "Ġ @@", - "Ġ@ @", - "reg ular", - "Ġ Ka", - "ĠK a", - "0 36", - "03 6", - "M AN", - "MA N", - "Ġ astr", - "Ġa str", - "Ġas tr", - "Ġast r", - "Ġ' ')Ċ", - "Ġ'' )Ċ", - "Ġ'') Ċ", - "Ġ fed", - "Ġf ed", - "Ġfe d", - "Ġp arsing", - "Ġpar sing", - "Ġpars ing", - "Ġ Years", - "ĠY ears", - "ĠYear s", - "ĠYe ars", - "Ġ broker", - "Ġb roker", - "Ġbr oker", - "Ġbro ker", - "Ġbroke r", - "\": {\"", - "Ġ akt", - "Ġa kt", - "Ġak t", - "In ventory", - "ab eled", - "abel ed", - "abe led", - "Ġarg parse", - "* ******Ċ", - "** *****Ċ", - "**** ***Ċ", - "****** *Ċ", - "*** ****Ċ", - "***** **Ċ", - "******* Ċ", - "vers ation", - "Ġ cord", - "Ġc ord", - "Ġco rd", - "Ġcor d", - "Ġ Ti", - "ĠT i", - "Ġ hopefully", - "Ġhope fully", - "Ġhop efully", - "Ġhopeful ly", - "Ġ ah", - "Ġa h", - "v erb", - "ver b", - "ve rb", - "Ġst olen", - "Ġstole n", - "Ġsto len", - "Ġstol en", - ". Entry", - ".En try", - ".Ent ry", - "Ġex pecting", - "Ġexpect ing", - "O rientation", - "Ġ powered", - "Ġp owered", - "Ġpower ed", - "Ġpow ered", - "Ġ persist", - "Ġp ersist", - "Ġpers ist", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "' ]);", - "'] );", - "']) ;", - "' )),Ċ", - "') ),Ċ", - "')) ,Ċ", - "')), Ċ", - "Ġ Cash", - "ĠC ash", - "ĠCas h", - "ĠCa sh", - "ĉ item", - "ĉi tem", - "ĉit em", - "8 18", - "81 8", - "g rades", - "gr ades", - "grad es", - "grade s", - "gra des", - "r opol", - "ro pol", - "rop ol", - "b asic", - "ba sic", - "bas ic", - "Ġ \");čĊ", - "Ġ\" );čĊ", - "Ġ\") ;čĊ", - "Ġ\"); čĊ", - "Ġa wards", - "Ġaw ards", - "Ġaward s", - "( range", - "(r ange", - "(ra nge", - "- all", - "-a ll", - "-al l", - "Ġ IBOutlet", - "ĠIB Outlet", - "Ġ Indeed", - "ĠInd eed", - "---------------------------------------------------------------- ------------", - "------------ ----------------------------------------------------------------", - "------------------------------------------------ ----------------------------", - "------ ----------------------------------------------------------------------", - "---------------------------- ------------------------------------------------", - "---------------------------------------------------------------------- ------", - "Ġstom ach", - "Ġsto mach", - "Ġ flower", - "Ġf lower", - "Ġfl ower", - "Ġflow er", - "Ġflo wer", - "Ġs ew", - "Ġse w", - "_ times", - "_t imes", - "_time s", - "_tim es", - "_ti mes", - "a vis", - "av is", - "avi s", - "Q String", - "QS tring", - "Ġ Routes", - "ĠR outes", - "ĠRoute s", - "ĠRo utes", - "ĠRou tes", - "ĠRout es", - "_ prot", - "_p rot", - "_pro t", - "_pr ot", - "Ġcom edy", - "Ġcome dy", - "Ġcomed y", - "Ġ logout", - "Ġlog out", - "Ġlogo ut", - "Ġwood en", - "Ġwo oden", - "Ġwoo den", - "Ġ poster", - "Ġp oster", - "Ġpos ter", - "Ġpost er", - "Ġpo ster", - "Ġposte r", - "p iece", - "pi ece", - "pie ce", - ". Join", - ".J oin", - "Ġ Pok", - "ĠP ok", - "ĠPo k", - "cel ona", - "m utex", - "mut ex", - "mu tex", - "mute x", - "; čĊčĊčĊ", - ";čĊ čĊčĊ", - ";čĊčĊ čĊ", - "Ġst rikes", - "Ġstr ikes", - "Ġstri kes", - "Ġstrike s", - "7 87", - "78 7", - "Lo aded", - "Load ed", - ") arg", - ")a rg", - "e sa", - "es a", - "Un ited", - "Unit ed", - "Uni ted", - "E p", - "P ELL", - "PE LL", - "8 07", - "80 7", - "Ġ Atlantic", - "ĠAtl antic", - "ul let", - "ull et", - "ulle t", - "6 52", - "65 2", - "ap ple", - "app le", - "appl e", - "Ġsett led", - "Ġsettle d", - "a con", - "ac on", - "aco n", - "Ġ printer", - "Ġpr inter", - "Ġprint er", - "Ġprin ter", - "Ġ GC", - "ĠG C", - "å® ļ", - "Ġrender ed", - "Ġrend ered", - ", âĢĻ", - "he it", - "hei t", - "s ocial", - "so cial", - "soc ial", - ". ge", - ".g e", - "7 14", - "71 4", - "Ġ Rick", - "ĠR ick", - "ĠRic k", - "ĠRi ck", - "ĠU tah", - "ĠUt ah", - "g ot", - "go t", - "on ical", - "onic al", - "oni cal", - "onica l", - "Ġ Scroll", - "ĠS croll", - "ĠSc roll", - "ĠScr oll", - "ĠSc iences", - "ĠScience s", - "ĠSci ences", - "Ġ jug", - "Ġj ug", - "Ġju g", - "Ġa mpl", - "Ġam pl", - "Ġamp l", - "en ti", - "ent i", - "LE FT", - "Ġ tabs", - "Ġt abs", - "Ġtab s", - "Ġta bs", - "Ġenorm ous", - ". getKey", - ".get Key", - "l ocate", - "lo cate", - "loc ate", - ". EX", - ".E X", - ". storage", - ".st orage", - ". We", - ".W e", - "Ġ toast", - "Ġto ast", - "Ġ Additionally", - "ĠAdd itionally", - "ĠAdditional ly", - "ĠAddition ally", - "8 82", - "88 2", - "Ġ NOW", - "ĠN OW", - "ĠNO W", - "5 47", - "54 7", - "_ UPDATE", - "_UP DATE", - "Ġtrans ferred", - "Ġtransfer red", - "t ha", - "th a", - ". Display", - ".D isplay", - ".Dis play", - "_ ui", - "_u i", - "ID EO", - "IDE O", - "Ġmeaning ful", - "ĠMos cow", - ", this", - ",t his", - "Ġ Victoria", - "ĠVict oria", - "ĠVictor ia", - "æĶ ¹", - "Ġ ÐŁ", - "ĠÐ Ł", - ". stack", - ".st ack", - "ĠB arn", - "ĠBar n", - "ĠBa rn", - "pared Statement", - ": string", - ":s tring", - ":str ing", - "Ġ bij", - "Ġb ij", - "Ġbi j", - "Ġ STATE", - "ĠST ATE", - "ĠSTAT E", - "ĠSTA TE", - "Ġemploy ers", - "Ġemployer s", - "ĉ input", - "ĉin put", - "( |", - "Ġ lex", - "Ġl ex", - "Ġle x", - "in voke", - "inv oke", - "ĉ num", - "ĉn um", - "+ +,", - "++ ,", - "at ial", - "ati al", - "or ses", - "ors es", - "orse s", - "Ġ fork", - "Ġf ork", - "Ġfor k", - "Ġfo rk", - "_ txt", - "_t xt", - "_tx t", - "ĠAnt onio", - "ĠAnton io", - "Ġ (<", - "Ġ( <", - "a verse", - "av erse", - "aver se", - "avers e", - "Ġdev ast", - "ãĢ Ģ", - ". Dec", - ".D ec", - ".De c", - "ĠG ard", - "ĠGar d", - "ĠGa rd", - "/ ui", - "/u i", - ". %", - "t ri", - "tr i", - "Ġ rolled", - "Ġroll ed", - "Ġrol led", - "Value Pair", - "it ten", - "itt en", - "itte n", - "ĠT her", - "ĠThe r", - "ĠTh er", - "Ġv rou", - "Ġvr ou", - "Ġ Flow", - "ĠF low", - "ĠFl ow", - "ĠFlo w", - "Ġ Finance", - "ĠF inance", - "ĠFin ance", - "Ġ Comb", - "ĠC omb", - "ĠCom b", - "ĠCo mb", - "H C", - ". setVisible", - ".set Visible", - "i sl", - "is l", - "Ġ pk", - "Ġp k", - "7 73", - "77 3", - "Ġup set", - "Ġups et", - "( raw", - "(r aw", - "(ra w", - "ĠV ice", - "ĠVi ce", - "ĠVic e", - "e atures", - "ea tures", - "eature s", - "eat ures", - "Ġ Lang", - "ĠL ang", - "ĠLa ng", - "ĠLan g", - "0 29", - "02 9", - "Lo oking", - "Look ing", - "7 67", - "76 7", - "Ġ AST", - "ĠA ST", - "ĠAS T", - "Ġt rips", - "Ġtr ips", - "Ġtri ps", - "Ġtrip s", - "Ġ Justin", - "ĠJ ustin", - "ĠJust in", - "ĠJu stin", - "b rowser", - "browse r", - "=\" '.$", - "=\"' .$", - "=\"'. $", - ". vertices", - ".vert ices", - "8 21", - "82 1", - "- co", - "-c o", - "} /{", - "}/ {", - "Ġ ?,", - "Ġ? ,", - "Ġ Domin", - "ĠD omin", - "ĠDo min", - "ĠDom in", - "ĠBe lg", - "ĠBel g", - "\" <", - "Ġsup pose", - "Ġsupp ose", - "a ddy", - "ad dy", - "add y", - "Ġwalk s", - "Ġwal ks", - "6 88", - "68 8", - "ER RU", - "ERR U", - "_ filters", - "_f ilters", - "_filter s", - "_fil ters", - "_filt ers", - "Pre ferred", - "s cene", - "sc ene", - "е Ñģ", - "ĠAff airs", - "Ġ\" #{", - "Ġ\"# {", - "Ġon Submit", - "Ġ stocks", - "Ġst ocks", - "Ġstock s", - "Ġsto cks", - "/ view", - "/v iew", - "g ree", - "gr ee", - "gre e", - "- get", - "-g et", - "9 03", - "90 3", - "h it", - "hi t", - "J o", - ". getC", - ".get C", - "7 25", - "72 5", - "Init ialized", - "Initial ized", - "Initialize d", - "ÑĤ и", - "c uts", - "cut s", - "cu ts", - "( Type", - "(T ype", - "ĠAg reement", - "ĠAgree ment", - "ĠViet nam", - "Ġ /*!", - "Ġ/* !", - "Ġ pizza", - "Ġp izza", - "Ġpi zza", - "- view", - "-v iew", - "_ em", - "_e m", - "Ġ lhs", - "Ġl hs", - "Ġlh s", - "Ġm uy", - "Ġmu y", - "Ġ Ident", - "ĠI dent", - "ĠId ent", - "ĠIde nt", - "Ġ Friends", - "ĠF riends", - "ĠFriend s", - "ĠFri ends", - "0 61", - "06 1", - "Ġab und", - "_ AD", - "_A D", - ". timestamp", - ".t imestamp", - ".time stamp", - "- '", - "Ġ duplicate", - "Ġd uplicate", - "Ġdup licate", - "Ġduplic ate", - "Ġh unting", - "Ġhun ting", - "Ġhunt ing", - "Ġreg ulatory", - "Ġregul atory", - "Ġregulator y", - "i ao", - "ia o", - "am ous", - "amo us", - "ĠEnt ertainment", - "ĠEnter tainment", - "[ A", - "iat ric", - "_ CLIENT", - "_CL IENT", - "_CLI ENT", - "Ġ Kids", - "ĠK ids", - "ĠKi ds", - "ĠKid s", - "/ pkg", - "/p kg", - "B reak", - "Bre ak", - ") ));ĊĊ", - ")) );ĊĊ", - ")));Ċ Ċ", - "))) ;ĊĊ", - "))); ĊĊ", - "Ġ Shape", - "ĠS hape", - "ĠSh ape", - "ĠSha pe", - "Ġrel ating", - "Ġrelat ing", - "Int errupt", - "Inter rupt", - "able Opacity", - "em bre", - "emb re", - "embr e", - "Ġm ystery", - "Ġmy stery", - "Ġmys tery", - "Ġmyst ery", - "Ġmyster y", - "Ġjournal ists", - "Ġjournalist s", - "r itable", - "ri table", - "rit able", - "rita ble", - ". Link", - ".L ink", - "Ġst opping", - "Ġstop ping", - "Ġsto pping", - "C RET", - "CR ET", - "CRE T", - ". DB", - ".D B", - "Ġpop ularity", - "Ġpopular ity", - "Ġpopul arity", - "Ġ gew", - "Ġg ew", - "Ġge w", - "Ġi mpr", - "Ġim pr", - "Ġimp r", - "set Value", - "F LAG", - "FL AG", - "ĉ max", - "ĉm ax", - "Ġb ake", - "Ġba ke", - "Ġbak e", - "w y", - "ĠE conomic", - "ĠEcon omic", - "ĠEc onomic", - "ĠEconom ic", - "Ġen contr", - "Ġ fname", - "Ġf name", - "Ġfn ame", - "/ de", - "/d e", - "R ank", - "Ra nk", - "Ġ bugs", - "Ġb ugs", - "Ġbu gs", - "Ġbug s", - ". sm", - ".s m", - "Ġ median", - "Ġm edian", - "Ġmed ian", - "Ġmedia n", - "Ġmedi an", - "D OWN", - "DO WN", - "Ġ Sure", - "ĠS ure", - "ĠSur e", - "ĠSu re", - "At Index", - "Ġ Dick", - "ĠD ick", - "ĠDi ck", - "Ġ (__", - "Ġ( __", - "Ġ(_ _", - ". delta", - ".d elta", - ".del ta", - "F r", - "Ġsuggest ing", - "Ġ RecyclerView", - "ĠRec yclerView", - ", e", - "ST ART", - "STAR T", - "STA RT", - "/ ****************************************************************************", - "/************************************************************************ ****", - "/**************************************************************** ************", - "/******************************************************** ********************", - "/************************************************ ****************************", - "x ford", - "xf ord", - "Ġ receipt", - "Ġre ceipt", - "Ġrece ipt", - "CL AIM", - "CLA IM", - "read only", - "9 68", - "96 8", - "Ġeng aging", - "6 19", - "61 9", - "C a", - "as ma", - "asm a", - "Ġens uring", - "Eng lish", - "ĠV ancouver", - "h yth", - "hy th", - "Ġpurch asing", - "Ġ PI", - "ĠP I", - ". word", - ".w ord", - "( sp", - "(s p", - ". home", - ".h ome", - ".hom e", - ": def", - ":d ef", - "Ġg ig", - "Ġgi g", - "5 74", - "57 4", - "6 71", - "67 1", - "Ġ Ve", - "ĠV e", - "f orum", - "fo rum", - "for um", - "Ġ Mitch", - "ĠM itch", - "ĠMit ch", - "B ay", - "Ba y", - "_ FL", - "_F L", - "6 51", - "65 1", - "Ġs oll", - "Ġso ll", - "Ġsol l", - "5 77", - "57 7", - "_ columns", - "_column s", - "Ġminor ity", - "b ird", - "bi rd", - "bir d", - "Ġh anded", - "Ġhand ed", - "Ġhan ded", - "S SL", - "SS L", - "ST AT", - "STA T", - "Ġnerv ous", - "Ġner vous", - "ĥ ½", - "Ġ filePath", - "Ġfile Path", - "C REATE", - "CRE ATE", - "A w", - "Ġp ens", - "Ġpe ns", - "Ġpen s", - "8 35", - "83 5", - "s eed", - "se ed", - "see d", - "Ġ Compute", - "ĠCom pute", - "ĠComp ute", - "ĠComput e", - "o lk", - "ol k", - "5 94", - "59 4", - "Ġ Asset", - "ĠAs set", - "ĠAss et", - "r each", - "re ach", - "rea ch", - "' ),čĊ", - "') ,čĊ", - "'), čĊ", - "n avigation", - "nav igation", - "L F", - "/ util", - "/u til", - "Ġ Pub", - "ĠP ub", - "ĠPu b", - "Ġ âĶ", - "Ġâ Ķ", - "c ion", - "ci on", - "cio n", - "# #Ċ", - "## Ċ", - "0 72", - "07 2", - "I II", - "II I", - "Tag Name", - "Ġa mid", - "Ġam id", - "Ġami d", - "per mission", - "perm ission", - "if iable", - "ifi able", - "x FFFFFFFF", - "xFF FFFFFF", - "xFFFF FFFF", - "xFFFFFF FF", - "н и", - ". Buffer", - ".B uffer", - "_ irq", - "_i rq", - "_ir q", - "d ark", - "da rk", - "dar k", - "Ġ retval", - "Ġret val", - ". fire", - ".f ire", - ".fi re", - "p roduction", - "pro duction", - "product ion", - "produ ction", - "prod uction", - ". listen", - ".l isten", - ".list en", - ".li sten", - "Ġ Weather", - "ĠWe ather", - "Ġbu yers", - "Ġbuy ers", - "Ġbuyer s", - ". ne", - ".n e", - "e rp", - "er p", - "ĠP ent", - "ĠPe nt", - "ĠPen t", - "6 99", - "69 9", - "Ġw elfare", - "Ġwel fare", - "Ġ pageSize", - "Ġpage Size", - "ĠSt adium", - "ĠStad ium", - "er ta", - "ert a", - "Ġ lev", - "Ġl ev", - "Ġle v", - "am pa", - "amp a", - "P ager", - "Page r", - "Pa ger", - "Pag er", - "6 65", - "66 5", - "Ġ charging", - "Ġch arging", - "Ġchar ging", - "Ġcharg ing", - "Ġ Netflix", - "ĠNet flix", - "| null", - "_ random", - "_r andom", - "_rand om", - ". xpath", - ".x path", - "Ġs tere", - "Ġst ere", - "Ġste re", - "Ġster e", - "Ġ ISIS", - "ĠIS IS", - "ĠISI S", - "pon ses", - "ponse s", - "pons es", - "( loc", - "(l oc", - "(lo c", - "5 66", - "56 6", - "ey ond", - "Ġ Official", - "ĠOff icial", - "6 57", - "65 7", - "ĠMary land", - "Data Type", - "_ par", - "_p ar", - "_pa r", - "{ },", - "{} ,", - "Ġ Enjoy", - "ĠEn joy", - "7 27", - "72 7", - "_ SHIFT", - "_SH IFT", - "ĠA wards", - "ĠAward s", - "ĠAw ards", - "_ ENTRY", - "_EN TRY", - "_ENT RY", - "Ġseem ingly", - "Ġseeming ly", - "ent icate", - "entic ate", - "enti cate", - "Ġhe arts", - "Ġheart s", - "Ġhear ts", - "5 83", - "58 3", - "_ ;ĊĊ", - "_;Ċ Ċ", - "_; ĊĊ", - "ĠH IV", - "ĠHI V", - "Ġin divid", - "Ġind ivid", - "Ġindiv id", - "Ġ Flag", - "ĠF lag", - "ĠFl ag", - "ĠFla g", - "_ ctrl", - "_c trl", - "_ct rl", - "_ctr l", - "Ġ Callback", - "ĠC allback", - "ĠCall back", - ", z", - "Ġ GPU", - "ĠG PU", - "ĠGP U", - "ĉ obj", - "ĉo bj", - "ĉob j", - "Ġ Phoenix", - "ĠPh oenix", - "Ġ BUS", - "ĠB US", - "ĠBU S", - "9 07", - "90 7", - "Ġr ubber", - "Ġrub ber", - "_ AUTH", - "_A UTH", - "_AUT H", - "ĠS olutions", - "ĠSol utions", - "ĠSolution s", - "( location", - "(l ocation", - "(loc ation", - "(lo cation", - "Variable s", - "Vari ables", - ". setEnabled", - ".set Enabled", - "_ high", - "_h igh", - "_hi gh", - "W O", - "G esture", - "Ġ retry", - "Ġre try", - "Ġr etry", - "Ġret ry", - "Ġretr y", - "Ġobject ForKey", - "allow een", - "allo ween", - "Ġ mos", - "Ġm os", - "Ġmo s", - "Ġ Cele", - "ĠC ele", - "ĠCe le", - "ĠCel e", - "Ġi kke", - "Ġik ke", - "( cell", - "(c ell", - "Ġ MODE", - "ĠM ODE", - "ĠMO DE", - "ĠMOD E", - "r ena", - "re na", - "ren a", - "Ġdes cribing", - "Ġdescri bing", - "6 41", - "64 1", - "Ġ phi", - "Ġp hi", - "Ġph i", - "Ġ rd", - "Ġr d", - "Ġde serve", - "Ġdes erve", - "Ġdese rve", - "Ġdeser ve", - "Ġw heels", - "Ġwheel s", - "Ġwhe els", - "å¸ Ĥ", - "Ġcr itics", - "Ġcrit ics", - "Ġcritic s", - "Ġcri tics", - "7 55", - "75 5", - "N amespace", - "Name space", - "Names pace", - "Ġ Fra", - "ĠF ra", - "ĠFr a", - "Ġ ĊĊĊĊ", - "ĠĊ ĊĊĊ", - "ĠĊĊ ĊĊ", - "ĠĊĊĊ Ċ", - "Ġ alla", - "Ġa lla", - "Ġal la", - "Ġall a", - "Ġre quiring", - "Ġrequ iring", - "æľ Ł", - "ut ation", - "uta tion", - "Ġdel ayed", - "Ġdelay ed", - "Ġadministr ative", - "Ġ bay", - "Ġb ay", - "Ġba y", - ". hidden", - ".h idden", - "T ex", - "Te x", - "0 51", - "05 1", - "Ġbound aries", - "Ġ ]);ĊĊ", - "Ġ] );ĊĊ", - "Ġ]);Ċ Ċ", - "Ġ]) ;ĊĊ", - "Ġ]); ĊĊ", - "Ġ Following", - "ĠFollow ing", - "~ /", - "F i", - "_ conv", - "_con v", - "_co nv", - "_ TITLE", - "_T ITLE", - "Ġdes de", - "I CollectionView", - "ICollection View", - "A lias", - "Al ias", - "Ali as", - "Ġ bite", - "Ġb ite", - "Ġbit e", - "Ġbi te", - "p atient", - "pat ient", - "_ COMMAND", - "_COM MAND", - "_COMM AND", - "Com pleted", - "Complete d", - "Comp leted", - "Comple ted", - "ĉ elif", - "ĉe lif", - "ĉel if", - "( <", - "B usiness", - "Bus iness", - "Ġ Pool", - "ĠP ool", - "ĠPo ol", - "Ġpurs ue", - "Ġ Ban", - "ĠB an", - "ĠBa n", - "_ steps", - "_st eps", - "_step s", - "_ste ps", - "_ DECL", - "_DE CL", - "_DEC L", - "um ble", - "umb le", - "Ġ combo", - "Ġc ombo", - "Ġcom bo", - "Ġcomb o", - "Ġ Layer", - "ĠL ayer", - "ĠLa yer", - "ĠLay er", - ". xr", - ".x r", - "Ġ dup", - "Ġd up", - "Ġdu p", - "- --------", - "-- -------", - "---- -----", - "-------- -", - "--- ------", - "----- ----", - "------ ---", - "------- --", - "6 28", - "62 8", - "Ġ modifier", - "Ġmod ifier", - "r ob", - "ro b", - "r ez", - "re z", - "6 96", - "69 6", - "Ġath letes", - "Ġathlete s", - "U sed", - "Us ed", - "Use d", - "w ear", - "we ar", - "8 15", - "81 5", - "Ġleg itimate", - "Ġlegit imate", - "Ġlegitim ate", - "Ġ \"ĊĊ", - "Ġ\" ĊĊ", - "Ġ\"Ċ Ċ", - "Ġ hv", - "Ġh v", - "S td", - "St d", - "0 37", - "03 7", - "Ġ Hold", - "ĠH old", - "ĠHol d", - "ĠHo ld", - "Ġsurv iv", - "ĠAll iance", - "Ġ Early", - "ĠEar ly", - "ĠEarl y", - "7 78", - "77 8", - "Beh avior", - "( font", - "(f ont", - "/ libs", - "/lib s", - "/l ibs", - "/li bs", - "Ġ rectangle", - "Ġrect angle", - "Ġs inger", - "Ġsi nger", - "Ġsin ger", - "Ġsing er", - "Ġ amp", - "Ġa mp", - "Ġam p", - "Equal To", - "Ġ \".\"", - "Ġ\" .\"", - "Ġ\". \"", - "Ġgirl friend", - "å ±", - "l inear", - "li near", - "line ar", - "lin ear", - "o bserv", - "ob serv", - "obs erv", - "Ġpi ù", - "Ġcom plement", - "Ġcomp lement", - "Ġcomple ment", - "Ġcompl ement", - "With Value", - "( password", - "(p assword", - "(pass word", - "t ake", - "ta ke", - "tak e", - "Bl ank", - "Ġ Compar", - "ĠCom par", - "ĠCo mpar", - "ĠComp ar", - "' \",", - "'\" ,", - "_ policy", - "_p olicy", - "_pol icy", - "m ongoose", - "mongo ose", - "mong oose", - "_ FAILED", - "_FAIL ED", - "_FA ILED", - ". report", - ".re port", - ".repo rt", - ".rep ort", - "R atio", - ".Perform Layout", - "7 47", - "74 7", - "us able", - "usa ble", - "m ers", - "mer s", - "me rs", - "_ render", - "_re nder", - "_r ender", - "PE ED", - "7 72", - "77 2", - "Ġle sb", - "Ġles b", - "ĉ E", - "_ tool", - "_t ool", - "_to ol", - "Ġl adies", - "Ġlad ies", - "9 08", - "90 8", - "о Ñģ", - ") )))Ċ", - ")) ))Ċ", - "))) )Ċ", - ")))) Ċ", - "; ;;;", - ";; ;;", - ";;; ;", - ". dot", - ".d ot", - ".do t", - "Ġ nest", - "Ġn est", - "Ġne st", - "Ġnes t", - "pe ak", - "uk kit", - "e ca", - "ec a", - "_ SW", - "_S W", - "Ġ &(", - "Ġ& (", - "ĠOk lahoma", - "Ġb anking", - "Ġbank ing", - "Ġban king", - "5 69", - "56 9", - "Ġ Nintendo", - "ĠN intendo", - "7 52", - "75 2", - "Ġre produce", - "Ġrep roduce", - "Ġreprodu ce", - "Ġrepro duce", - "_ elements", - "_e lements", - "_element s", - "_el ements", - "_elem ents", - "_ele ments", - "_ mac", - "_m ac", - "_ma c", - "pr oxy", - "pro xy", - "prox y", - "Ġremark able", - "} /${", - "}/ ${", - "Ġ outs", - "Ġo uts", - "Ġout s", - "Ġou ts", - ".has Next", - "M ODE", - "MO DE", - "MOD E", - "6 58", - "65 8", - "Ġ anime", - "Ġan ime", - "Ġanim e", - "Ġani me", - ". conn", - ".c onn", - ".con n", - ".co nn", - "Un ique", - "Uni que", - "D om", - "Do m", - "Ġimport antly", - "Ġimportant ly", - "i tty", - "it ty", - "itt y", - "Ġju ice", - "T w", - "ĠPart ners", - "ĠPartner s", - "Ġatt acking", - "Ġattack ing", - "Ġport able", - "Ġpor table", - "Ġporta ble", - "am iento", - "ami ento", - "amient o", - ". PictureBox", - ".P ictureBox", - ". gen", - ".g en", - ".ge n", - "Ġopt imal", - "Ġoptim al", - "5 82", - "58 2", - "Ġre cre", - "Ġrec re", - "Ġjournal ist", - "Ġ Extract", - "ĠEx tract", - "ĠExt ract", - "ĠExtra ct", - "ĠExtr act", - "Ġ Moreover", - "ĠMore over", - "Ġ marginTop", - "Ġmargin Top", - ". Ap", - ".A p", - "Ġf iring", - "Ġfi ring", - "Ġfir ing", - "N aN", - "Na N", - "ĉ template", - "ĉt emplate", - "ĉtemp late", - "а д", - "аР´", - ". En", - ".E n", - "Ġdef ence", - "Ġdefe nce", - "Ġ Tel", - "ĠT el", - "ĠTe l", - "i len", - "il en", - "ile n", - "j an", - "ja n", - "= data", - "=d ata", - "Ġ Url", - "ĠU rl", - "ĠUr l", - "Ġ Reuters", - "ĠRe uters", - "( total", - "(t otal", - "(to tal", - "ĠF ifth", - "ĠFif th", - "Ġes says", - "Ġess ays", - "Ġessay s", - "Ġessa ys", - "Ġinterpret ation", - "Ġch arity", - "Ġchar ity", - "Ġ Rules", - "ĠR ules", - "ĠRule s", - "ĠRu les", - "Ġ subsection", - "Ġsub section", - "Ġsubs ection", - "st yled", - "style d", - "sty led", - "styl ed", - "a zer", - "az er", - "aze r", - "l ags", - "la gs", - "lag s", - "L IST", - "LI ST", - "Ġ uploaded", - "Ġup loaded", - "Ġupload ed", - "Ġ trash", - "Ġtr ash", - "Ġtra sh", - "Ġtras h", - "Ġ registr", - "Ġreg istr", - "Ġregist r", - "Ġ seller", - "Ġs eller", - "Ġse ller", - "Ġsell er", - "Ġsel ler", - "> ';čĊ", - ">' ;čĊ", - ">'; čĊ", - "Ġ startTime", - "Ġstart Time", - "ç Ļ", - "s y", - "( HttpServletRequest", - "(Http ServletRequest", - "Ġ trap", - "Ġt rap", - "Ġtr ap", - "Ġtra p", - "G C", - "Ġ embedded", - "Ġembed ded", - "Ġsur rounded", - "Ġsurround ed", - "8 16", - "81 6", - "i mits", - "im its", - "imit s", - "imi ts", - "T X", - "yl inder", - "6 85", - "68 5", - "Ġ Fal", - "ĠF al", - "ĠFa l", - "Ġsent ences", - "Ġsentence s", - "Ġ Ja", - "ĠJ a", - "IF ICATION", - "IFIC ATION", - "we apon", - "o vation", - "ov ation", - "ova tion", - "ovat ion", - "Ġ coat", - "Ġco at", - "Ġinter pol", - "Ġinterp ol", - "Ġl ips", - "Ġli ps", - "Ġlip s", - "Ġ Ky", - "ĠK y", - "Ġv ectors", - "Ġvector s", - "Ġve ctors", - "Ġvec tors", - "Ġvect ors", - "_ am", - "_a m", - "Ġin take", - "Ġint ake", - ". world", - ".w orld", - "Ġ inbox", - "Ġin box", - "Ġ MAC", - "ĠM AC", - "ĠMA C", - "_ ab", - "_a b", - "( nameof", - "(name of", - "6 33", - "63 3", - "Ġent ert", - "Ġenter t", - "Ġg athering", - "Ġgather ing", - "Ġ SIM", - "ĠS IM", - "ĠSI M", - "+ +.", - "++ .", - "n ya", - "ny a", - "' }}", - "'} }", - "Ġ UPDATE", - "ĠUP DATE", - "Ġ pac", - "Ġp ac", - "Ġpa c", - "( html", - "(h tml", - "(ht ml", - "ĠS ant", - "ĠSan t", - "ĠSa nt", - "i ating", - "ia ting", - "iat ing", - "ĠIde as", - "ĠIdea s", - "Ġs pray", - "Ġsp ray", - "Ġspr ay", - "ĠH art", - "ĠHar t", - "ĠHa rt", - "Ġ verification", - "Ġver ification", - "Ġverifica tion", - "ad esh", - "ade sh", - "ades h", - "/ modules", - "/mod ules", - "/module s", - "Ġ Mind", - "ĠM ind", - "ĠMin d", - "ĠMi nd", - "ĠSized Box", - "Ġsh elter", - "Ġshel ter", - "Ġ heroes", - "Ġher oes", - "Ġhero es", - "a tty", - "at ty", - "att y", - "Ġcert ified", - "Ġcertif ied", - "s j", - "Ġ être", - "Ġê tre", - "ÅĤ o", - "Ġpublish ing", - "ĠMal ays", - "ĠMa lays", - "ĠMalay s", - ". getUser", - ".get User", - "Ġ Provider", - "ĠPro vider", - "ĠProvid er", - "ĠProvide r", - "ĠProv ider", - "Ġ LinkedList", - "ĠLink edList", - "ĠLinked List", - "ĠB or", - "ĠBo r", - "R OUND", - "RO UND", - "d id", - "di d", - "t ain", - "ta in", - "p ire", - "pi re", - "pir e", - "ĠJ enn", - "ĠJe nn", - "ĠJen n", - "t el", - "te l", - "a nde", - "an de", - "and e", - "7 57", - "75 7", - "_ front", - "_f ront", - "_fr ont", - "ĠMc G", - "Test Method", - "ภŃ", - "Ġocc asionally", - "Ġoccasion ally", - "Ġoccasional ly", - "ĠW ales", - "ĠWal es", - "ĠWa les", - "Ġex ercises", - "Ġexerc ises", - "Ġexercise s", - "Ġ ÐĴ", - "ĠÐ Ĵ", - "0 45", - "04 5", - "- plus", - "-p lus", - "-pl us", - "Ġ validator", - "Ġvalid ator", - "Ġvalida tor", - "Ġpr ayer", - "Ġpray er", - "Ġpra yer", - "L ATED", - "LA TED", - "LAT ED", - "_ author", - "_a uthor", - "_auth or", - "_aut hor", - "Ġla bour", - "Ġlab our", - "+ +Ċ", - "++ Ċ", - "- equiv", - "-e quiv", - "-equ iv", - "Ġ GPL", - "ĠG PL", - "ĠGP L", - "Ġ facebook", - "Ġf acebook", - "Ġface book", - "s imple", - "sim ple", - "simp le", - "g ly", - "gl y", - "Process or", - "Proc essor", - "i py", - "ip y", - "7 44", - "74 4", - "Ġ *>", - "Ġ* >", - "6 48", - "64 8", - "Ġc leared", - "Ġclear ed", - "Ġcle ared", - "Ġ Push", - "ĠP ush", - "ĠPu sh", - "8 58", - "85 8", - "Ġp enis", - "Ġpe nis", - "Ġpen is", - "Struct ure", - "l ij", - "li j", - "ĠM organ", - "ĠMo rgan", - "ĠMor gan", - "ĠMorg an", - "Ġhand ful", - "\" .Ċ", - "\". Ċ", - "9 84", - "98 4", - "| \\", - "Ġ ********************************", - "Ġ**** ****************************", - "Ġ******** ************************", - "Ġ**************** ****************", - "Ġ************************ ********", - "Ġ Aqu", - "ĠA qu", - "5 84", - "58 4", - "_ IC", - "_I C", - ". loads", - ".load s", - ".lo ads", - "Ġ meter", - "Ġm eter", - "Ġme ter", - "Ġmet er", - "ĠM arine", - "ĠMar ine", - "ĠMa rine", - "ĠMari ne", - "ĠMarin e", - ": :{", - ":: {", - "Ġ TS", - "ĠT S", - "7 76", - "77 6", - "Ġ Arrays", - "ĠAr rays", - "ĠArray s", - "ĠArr ays", - ". Title", - ".T itle", - "G RAM", - "GR AM", - "GRA M", - "ter min", - "term in", - "Ġco inc", - "Ġcoin c", - "Ġcoi nc", - "E lse", - "El se", - "_ states", - "_st ates", - "_state s", - "_stat es", - "_sta tes", - "- run", - "-r un", - "m embers", - "member s", - "mem bers", - "7 82", - "78 2", - "a stro", - "as tro", - "ast ro", - "astr o", - "0 66", - "06 6", - "Ġon Press", - "Ġbe ings", - "Ġbeing s", - "Ġab andoned", - "Ġabandon ed", - "Ġtax p", - "Ġta xp", - "ow ners", - "own ers", - "owner s", - ". mode", - ".m ode", - ".mod e", - ".mo de", - "Ġdi agnosis", - "Ġdiagn osis", - "Ġdiag nosis", - "Ġ _Ċ", - "Ġ_ Ċ", - "Ġ Knight", - "ĠK night", - "ĠKn ight", - "ĉ A", - "Ġ observe", - "Ġob serve", - "Ġobs erve", - "Ġobserv e", - ") ,'", - "), '", - "8 23", - "82 3", - "! \")Ċ", - "!\" )Ċ", - "!\") Ċ", - "Ġ Para", - "ĠP ara", - "ĠPar a", - "ĠPa ra", - "Ġ variation", - "Ġvar iation", - "Ġvari ation", - "( False", - "(F alse", - "Ġ Anti", - "ĠAn ti", - "ĠAnt i", - "Ġg ri", - "Ġgr i", - "Ġhome less", - "Ġhom eless", - "? v", - "Ġb ez", - "Ġbe z", - ". Server", - ".S erver", - ".Serve r", - "r elease", - "re lease", - "rel ease", - "ĠP atri", - "ĠPat ri", - "ĠPa tri", - "Ġ chars", - "Ġch ars", - "Ġchar s", - "Ġcha rs", - "Ġ ranking", - "Ġr anking", - "Ġrank ing", - "Ġran king", - "act ivation", - "activ ation", - "5 81", - "58 1", - "Ġw ides", - "Ġwide s", - "Ġwid es", - "Ġwi des", - "q r", - ". Sql", - ".S ql", - "a cular", - "ac ular", - "acula r", - "Ġ Bot", - "ĠB ot", - "ĠBo t", - "_ sync", - "_s ync", - "_syn c", - "_sy nc", - "Ġh appiness", - "Ġhapp iness", - "Ġvol unteers", - "Ġvolunte ers", - "Ġvolunteer s", - "8 77", - "87 7", - "Ġs its", - "Ġsit s", - "Ġsi ts", - "/ <", - "[ e", - "( fileName", - "(file Name", - "Ġcap ac", - "Ġca pac", - "8 32", - "83 2", - "Ġ Maria", - "ĠM aria", - "ĠMar ia", - "ĠMa ria", - "ĠMari a", - "f ather", - "fa ther", - "fat her", - "Ġ gram", - "Ġg ram", - "Ġgr am", - "Ġgra m", - "* i", - "Ġc aso", - "Ġca so", - "Ġcas o", - "_ draw", - "_d raw", - "_dr aw", - "Ġ Raw", - "ĠR aw", - "ĠRa w", - "Ġ Iterator", - "ĠIt erator", - "ĠIter ator", - "6 64", - "66 4", - "Ġ Padding", - "ĠP adding", - "ĠPad ding", - "9 24", - "92 4", - "P D", - "B OX", - "BO X", - "ĠS PECIAL", - "ĠSPEC IAL", - "Ġ fecha", - "Ġf echa", - "Ġfe cha", - "Ġfec ha", - "Ġ vide", - "Ġv ide", - "Ġvi de", - "Ġvid e", - "Ġ Leader", - "ĠLe ader", - "ĠLead er", - "ä» ¥", - "$ (\".", - "$( \".", - "$(\" .", - "Ġd iameter", - "Ġdiam eter", - "Ġdia meter", - "Ġm ild", - "Ġmil d", - "Ġmi ld", - "7 45", - "74 5", - "Ġr ocks", - "Ġro cks", - "Ġrock s", - "Ġroc ks", - "app ings", - "apping s", - "0 48", - "04 8", - "d irectory", - "direct ory", - "director y", - "5 57", - "55 7", - ". flush", - ".f lush", - ".fl ush", - "Ġ Jess", - "ĠJ ess", - "ĠJes s", - "ĠJe ss", - "UN IT", - "Ġ Pear", - "ĠP ear", - "ĠPe ar", - "Ġ mandatory", - "Ġm andatory", - "Ġmand atory", - "S ur", - "Su r", - "q t", - "Ġ streams", - "Ġstream s", - "Ġstre ams", - "Ġco operation", - "Ġcooper ation", - "Ġcoop eration", - "Ġ Sac", - "ĠS ac", - "ĠSa c", - "Ġche aper", - "Ġcheap er", - "ĉ ch", - "ĉc h", - "an imation", - "anim ation", - "f are", - "fa re", - "far e", - "( height", - "(h eight", - "( True", - "N Y", - "Ġw rest", - "Ġwr est", - "Ġwre st", - "Ġp olls", - "Ġpol ls", - "Ġpoll s", - "Ġencounter ed", - "Ġencount ered", - "ĠMark etable", - "ĠMarket able", - "_ PASSWORD", - "_P ASSWORD", - "_PASS WORD", - "7 16", - "71 6", - "_ SELECT", - "_SE LECT", - "_SEL ECT", - "ĠArab ia", - "ĠAra bia", - "_ clock", - "_c lock", - "_cl ock", - "Ġ voy", - "Ġv oy", - "Ġvo y", - "Ġ из", - "Ġи з", - "Ġs tir", - "Ġst ir", - "is ible", - "isi ble", - "- effect", - "-e ffect", - "-eff ect", - ". created", - ".c reated", - ".create d", - ".cr eated", - "Ġto ys", - "Ġtoy s", - "ĠTrad able", - "Ġ rust", - "Ġr ust", - "Ġru st", - "Ġrus t", - "Ġ strcpy", - "Ġstr cpy", - "_ timestamp", - "_t imestamp", - "_time stamp", - "Ġtal ented", - "Ġtalent ed", - ", null", - ",n ull", - "Ġ Jobs", - "ĠJ obs", - "ĠJo bs", - "ĠJob s", - "Ġ Portland", - "ĠPort land", - "Ġweak ness", - "Th row", - "Thr ow", - "Ġ Angel", - "ĠAn gel", - "ĠAng el", - "ĠAnge l", - "ä¿ ®", - "7 54", - "75 4", - "Ġun cert", - "Ġunc ert", - "ï¼ī Ċ", - "Ġ ìĿ´", - "ĠìĿ ´", - "Wh ich", - "Ġ[- ]:", - "S omething", - "Some thing", - "Som ething", - "Ġconv icted", - "Ġconvict ed", - "k le", - "kl e", - "ed ium", - "edi um", - "Ġ branches", - "Ġbr anches", - "Ġbranch es", - "Ġbran ches", - "Ġ bases", - "Ġb ases", - "Ġbase s", - "Ġbas es", - "Ġba ses", - "ç ®", - "Ġcomplex ity", - "Ġ Fig", - "ĠF ig", - "ĠFi g", - ". reshape", - ".re shape", - ".res hape", - "$ db", - "$d b", - "7 36", - "73 6", - "_ CONST", - "_CON ST", - "_CO NST", - "Ġ Tes", - "ĠT es", - "ĠTe s", - ". runtime", - ".r untime", - ".run time", - "Ġ deny", - "Ġd eny", - "Ġde ny", - "Ġden y", - "Ġ BSD", - "ĠB SD", - "ĠBS D", - "Ġ kr", - "Ġk r", - "h att", - "ha tt", - "hat t", - "Ġ Static", - "ĠSt atic", - "ĠStat ic", - "ĠSta tic", - "Ġunivers ities", - "Re place", - "Rep lace", - "Ġd rove", - "Ġdr ove", - "Ġdro ve", - "Ġad oles", - "Ġado les", - "_ plugin", - "_pl ugin", - "ĠL GBT", - "ĠLG BT", - "Ġ tex", - "Ġt ex", - "Ġte x", - "d uction", - "du ction", - "duct ion", - "duc tion", - "7 51", - "75 1", - "7 99", - "79 9", - "E DI", - "ED I", - "Ġ Ted", - "ĠT ed", - "ĠTe d", - "_ URI", - "_U RI", - "Ġre ception", - "Ġrece ption", - "Ġrecept ion", - "Ġrecep tion", - "ar ten", - "art en", - "arte n", - ". Single", - ".S ingle", - ".Sin gle", - "r ice", - "ri ce", - "ric e", - "sc ious", - "sci ous", - "8 43", - "84 3", - "_ bg", - "_b g", - "Ġw ages", - "Ġwa ges", - "Ġwage s", - "Ġwag es", - "Ġ Servlet", - "ĠS ervlet", - "ĠServ let", - "UI Layout", - "UIL ayout", - "Ġ formatted", - "Ġform atted", - "Ġformat ted", - ". Mod", - ".M od", - "< class", - " ',Ċ", - ">' ,Ċ", - ">', Ċ", - "Ġexp anding", - "Ġexpand ing", - "Ġ Hamilton", - "ĠHam ilton", - "Ġ Contrib", - "ĠCon trib", - "ĠCont rib", - "ĠContr ib", - ". Tables", - ".T ables", - ".Tab les", - ".Table s", - "7 28", - "72 8", - "Act iv", - "Ac tiv", - "H H", - "o commerce", - "ocom merce", - "_ ;", - "Ġamong st", - "o wing", - "ow ing", - "owi ng", - "8 59", - "85 9", - "Ġ Cold", - "ĠC old", - "ĠCo ld", - "ĠCol d", - "A PH", - "AP H", - "Ġpsych ological", - "Ġpsycho logical", - "_ tensor", - "_t ensor", - "Ġpack aging", - "Ġ Sweden", - "ĠSw eden", - "ĠSwe den", - "Ġ pare", - "Ġp are", - "Ġpar e", - "Ġpa re", - "Ġ aggregate", - "Ġag gregate", - "Ġaggreg ate", - "Ġmod erate", - "Ġmode rate", - "Ġmoder ate", - "8 62", - "86 2", - "_ hand", - "_h and", - "Ġdesign ated", - "Ġdesignate d", - "Ġd rum", - "Ġdr um", - "Ġdru m", - "Ġ getUser", - "Ġget User", - "ĠC reek", - "ĠCre ek", - "ĠCree k", - "_ scope", - "_s cope", - "_sc ope", - "Ġ Transfer", - "ĠTrans fer", - "Ġ Marg", - "ĠM arg", - "ĠMar g", - "ĠMa rg", - "Ġ fighters", - "Ġfight ers", - "Ġfighter s", - "W nd", - "Ġ Sel", - "ĠS el", - "ĠSe l", - "Ġ Launch", - "ĠL aunch", - "ĠLa unch", - "Ġem erging", - "Ġemerg ing", - "i frame", - "if rame", - "ifr ame", - "Ġ Additional", - "ĠAdd itional", - "ĠAddition al", - "Ġf ears", - "Ġfe ars", - "Ġfear s", - "Ġsat ellite", - "_ :", - "Ġ disposing", - "Ġdis posing", - "Ġdisp osing", - "Ġdispos ing", - "Get Value", - "Http Post", - "AT IVE", - "ul ary", - "ular y", - "ula ry", - "View s", - "Vi ews", - "Ġatt ending", - "Ġattend ing", - "ĠT ennessee", - "Ġ Mission", - "ĠM ission", - "ĠMiss ion", - "Ġmed ication", - "Ġmedic ation", - "Ġmedi cation", - "Ġ Wy", - "ĠW y", - "Ġ Anna", - "ĠAn na", - "ĠAnn a", - "Ø ¹", - "Ġ Vertex", - "ĠVer tex", - "ĠVert ex", - ". types", - ".t ypes", - ".type s", - ".typ es", - "O rgan", - "Or gan", - "Org an", - ". DataGridViewTextBoxColumn", - ".DataGridView TextBoxColumn", - "Ġ RS", - "ĠR S", - "Ġt empo", - "Ġtem po", - "Ġtemp o", - "( App", - "(A pp", - "8 92", - "89 2", - "Version UID", - ". point", - ".p oint", - ".po int", - ".poi nt", - "ĠD utch", - "ĠDut ch", - "H ours", - "Hour s", - "Ho urs", - "L U", - "Ġ quoted", - "Ġqu oted", - "Ġquote d", - "Ġquot ed", - "Ġquo ted", - ". builder", - ".b uilder", - ".build er", - "Ġ Perfect", - "ĠPer fect", - "ĠPerf ect", - "Ġ Always", - "ĠAl ways", - "_ two", - "_t wo", - "_tw o", - "Ġexclusive ly", - "Ġexclus ively", - "ĠC ra", - "ĠCr a", - "ific ar", - "ifi car", - "ifica r", - "Ġ AWS", - "ĠA WS", - "ĠAW S", - "ing ham", - "com plex", - "comp lex", - "k ernel", - "ker nel", - "Ġ gravity", - "Ġgr avity", - "Ġgrav ity", - "Ġ wi", - "Ġw i", - "0 52", - "05 2", - "Ġ overview", - "Ġover view", - "Ġov erview", - "6 61", - "66 1", - "Ġ Want", - "ĠW ant", - "ĠWa nt", - "ĠWan t", - "Ġ WP", - "ĠW P", - "( sh", - "(s h", - ". rotation", - ".r otation", - ".rot ation", - "St ates", - "State s", - "Stat es", - "Ġ Teen", - "ĠT een", - "ĠTe en", - "ĠTee n", - "_ components", - "_com ponents", - "_comp onents", - "_component s", - "ì Īĺ", - "ìĪ ĺ", - "Re ceived", - "Receive d", - "Ġly rics", - "Ġlyric s", - "Ġlyr ics", - "r ites", - "ri tes", - "rit es", - "rite s", - "ĉ ĉĉĉĉĠ", - "ĉĉ ĉĉĉĠ", - "ĉĉĉĉ ĉĠ", - "ĉĉĉ ĉĉĠ", - "ĉĉĉĉĉ Ġ", - "- American", - "-A merican", - "-Americ an", - "[ num", - "[n um", - "/ python", - "/p ython", - "/py thon", - "Ġ UART", - "ĠU ART", - "ĠUA RT", - "Ġ apple", - "Ġapp le", - "Ġap ple", - "Ġappl e", - "Ġ Jonathan", - "ĠJon athan", - "Ġm omentum", - "Ġmoment um", - "ภ±", - "Ĥ ¹", - "Ġm ich", - "Ġmi ch", - "Ġmic h", - "an dra", - "and ra", - "andr a", - "Ġb iological", - "Ġbi ological", - "Ġbio logical", - "ĠM ens", - "ĠMe ns", - "ĠMen s", - "Ġ %%", - "Ġ% %", - "el sea", - "else a", - "els ea", - "ĠMex ican", - ".rand int", - "Ġt ale", - "Ġtal e", - "Ġta le", - "Ġ Validate", - "ĠValid ate", - "Ġdef eated", - "Ġdefe ated", - "Ġdefeat ed", - ". htm", - ".h tm", - ".ht m", - "Ġc opper", - "Ġco pper", - "Ġcop per", - "Ġcopp er", - "= /", - "co system", - "cos ystem", - "Ġ rip", - "Ġr ip", - "Ġri p", - "d ecimal", - "de cimal", - "dec imal", - ". VISIBLE", - ".V ISIBLE", - "Ġ Ta", - "ĠT a", - "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", - "Ġdown loaded", - "Ġdownload ed", - "en vironment", - "Ġno mine", - "Ġnom ine", - "Ġnomin e", - "build ing", - "Ġ Spot", - "ĠS pot", - "ĠSp ot", - "ĠSpo t", - "ipher al", - "iph eral", - "Ġ alto", - "Ġal to", - "Ġalt o", - "q uet", - "qu et", - "que t", - "Ġ FT", - "ĠF T", - "/ get", - "/g et", - "/ge t", - "/ master", - "/m aster", - "W IN", - "WI N", - "åħ ĥ", - "6 76", - "67 6", - "W est", - "We st", - "ar gc", - "arg c", - "Ġpro ducers", - "Ġprodu cers", - "Ġproduce rs", - "Ġproducer s", - "Ġ Much", - "ĠM uch", - "ĠMu ch", - "_ storage", - "_st orage", - "c redit", - "cre dit", - "cr edit", - "cred it", - "C ONT", - "CON T", - "CO NT", - "Ġ vet", - "Ġv et", - "Ġve t", - "Ġ voices", - "Ġvo ices", - "Ġvoice s", - "Ġvoi ces", - "( '',", - "(' ',", - "Ġin struments", - "Ġinstr uments", - "Ġinstrument s", - "6 62", - "66 2", - "Ġ MSG", - "ĠM SG", - "ĠMS G", - "e sse", - "es se", - "ess e", - "re pository", - "repos itory", - "om ics", - "omic s", - "omi cs", - "Ġ dealer", - "Ġde aler", - "Ġdeal er", - "St ill", - "Ġ banner", - "Ġb anner", - "Ġban ner", - "asc ii", - "Ġ remarks", - "Ġre marks", - "Ġr emarks", - "Ġrem arks", - "Ġremark s", - "Ġremar ks", - "[ js", - "[j s", - "Ġshort er", - "g ulp", - "gu lp", - "Ġm yster", - "Ġmy ster", - "Ġmys ter", - "Ġmyst er", - "Ġk un", - "Ġku n", - "Ġ Bird", - "ĠB ird", - "ĠBi rd", - "ĠBir d", - "Ġt iene", - "Ġti ene", - "Ġtie ne", - "7 88", - "78 8", - "n ut", - "nu t", - "Ġ Um", - "ĠU m", - "Ġ wise", - "Ġw ise", - "Ġwis e", - "Ġwi se", - "Y eah", - "Ye ah", - "I NESS", - "IN ESS", - "INE SS", - "INES S", - "0 46", - "04 6", - "_ begin", - "_b egin", - "_be gin", - "_beg in", - "- heading", - "-head ing", - "-he ading", - "C ourse", - "Co urse", - "Cour se", - "Ġ čĊčĊ", - "ĠčĊ čĊ", - "om bie", - "omb ie", - "gr aded", - "grad ed", - "grade d", - "gra ded", - "Ġ GPS", - "ĠG PS", - "ĠGP S", - "Ġ że", - "Ġż e", - "F it", - "Fi t", - "c aption", - "ca ption", - "cap tion", - "capt ion", - "ö n", - "/ image", - "/i mage", - "/im age", - "l ia", - "li a", - "( mod", - "(m od", - "Ġle ak", - "en za", - "enz a", - "6 29", - "62 9", - "/ H", - "Ġ Happy", - "ĠH appy", - "ĠHa ppy", - "ĠHapp y", - "9 93", - "99 3", - "D ist", - "Dis t", - "Di st", - "n x", - "ĠGovern or", - "ĠGover nor", - "( last", - "(l ast", - "t eacher", - "te acher", - "tea cher", - "Ġ Sent", - "ĠS ent", - "ĠSe nt", - "ĠSen t", - "s upport", - "sup port", - "8 38", - "83 8", - "ject ory", - "Ġ Ùħ", - "ĠÙ ħ", - "Reg istration", - "Registr ation", - "0 63", - "06 3", - "Ġ Gray", - "ĠG ray", - "ĠGr ay", - "ĠGra y", - ", false", - ",f alse", - "Ġ adjusted", - "Ġadjust ed", - "Ġadj usted", - "( settings", - "(s ettings", - "(set tings", - "(setting s", - "< R", - "Ġ Mage", - "ĠM age", - "ĠMag e", - "ĠMa ge", - "Ġ plaint", - "Ġpl aint", - "Ġplain t", - "Ġpla int", - "_ )Ċ", - "_) Ċ", - "ĉ it", - "ĉi t", - "o metric", - "om etric", - "omet ric", - "ometr ic", - ". bootstrap", - ".boot strap", - "Ġcar ries", - "Ġcarr ies", - "I p", - "Ġ! $", - "Ġsw imming", - "Ġswim ming", - "Ġ Mario", - "ĠM ario", - "ĠMar io", - "ĠMa rio", - "ĠMari o", - "Ġ Questions", - "ĠQuest ions", - "ĠQuestion s", - "P ACE", - "PA CE", - "æĸ ¹", - "e or", - "eo r", - "} }\"", - "}} \"", - "Ġ oven", - "Ġo ven", - "Ġov en", - "Ġ Kon", - "ĠK on", - "ĠKo n", - "Ġwis dom", - "Ġac quisition", - "ess ment", - "ag ine", - "agi ne", - "Ġex pressions", - "Ġexpress ions", - "Ġexpression s", - "Ġexpr essions", - "Sequential Group", - "F ront", - "Fr ont", - "ul pt", - "ulp t", - "a wk", - "aw k", - "' ])ĊĊ", - "'] )ĊĊ", - "']) ĊĊ", - "'])Ċ Ċ", - "8 13", - "81 3", - "7 32", - "73 2", - "_ AR", - "_A R", - "Ġan alog", - "Ġanal og", - "Ġana log", - "u lin", - "ul in", - "uli n", - "_ PRINT", - "_PR INT", - "_PRI NT", - "Ġ LG", - "ĠL G", - "Ġ blob", - "Ġb lob", - "Ġbl ob", - "Ġblo b", - "Ġ Furthermore", - "ĠFurther more", - "_ component", - "_com ponent", - "_comp onent", - "Ġ Cole", - "ĠC ole", - "ĠCo le", - "ĠCol e", - "L AN", - "LA N", - "SC RIPTION", - "SCRI PTION", - "SCRIPT ION", - "Ġ lap", - "Ġl ap", - "Ġla p", - "ic ensing", - "icens ing", - "_TIME OUT", - "ĠF ro", - "ĠFr o", - "Ġl iability", - "Ġli ability", - "Ġ composed", - "Ġcom posed", - "Ġcomp osed", - "Ġcompose d", - "Ġcompos ed", - "6 34", - "63 4", - ".create SequentialGroup", - "_ person", - "_p erson", - "_per son", - "Ġ beam", - "Ġb eam", - "Ġbe am", - "ĉ ĠĠĠĠĠĠĠĠ", - "ĉĠĠĠ ĠĠĠĠĠ", - "ĉĠ ĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠ Ġ", - "ĉĠĠ ĠĠĠĠĠĠ", - "ĉĠĠĠĠĠ ĠĠĠ", - "ĉĠĠĠĠ ĠĠĠĠ", - "ĉĠĠĠĠĠĠ ĠĠ", - "Ġ NotFound", - "ĠNot Found", - "6 84", - "68 4", - ". 'Ċ", - ".' Ċ", - "ÃŃ s", - ". TextView", - ".T extView", - ".Text View", - "P DF", - "PD F", - "Ġ kar", - "Ġk ar", - "Ġka r", - "_ _('", - "__ ('", - "__( '", - "Ġ \":\"", - "Ġ\" :\"", - "Ġ\": \"", - "_ messages", - "_m essages", - "_message s", - "Ġhar vest", - ". history", - ".h istory", - ".hist ory", - "> 'Ċ", - ">' Ċ", - "- fold", - "-f old", - "æ Ĭ", - "Ġ Better", - "ĠB etter", - "ĠBet ter", - "ĠBett er", - "Ġ\" \\<", - "Ġ\"\\ <", - "sp acing", - "spa cing", - "Ġf urnished", - "Ġfurn ished", - "Ġfurnish ed", - "9 13", - "91 3", - "o ser", - "os er", - "ose r", - "] }Ċ", - "]} Ċ", - "Ġ $\"", - "Ġ$ \"", - "p ull", - "pu ll", - ". Post", - ".P ost", - ".Pos t", - "9 19", - "91 9", - "( ip", - "(i p", - "Ĺ ı", - ". front", - ".f ront", - ".fr ont", - "n te", - "nt e", - "Ġ FM", - "ĠF M", - "g uid", - "gu id", - "gui d", - "8 44", - "84 4", - "Ġnegot iations", - "Ġnegotiation s", - "ag onal", - "agon al", - "ago nal", - "9 34", - "93 4", - "Ġtrem end", - "ung eon", - "unge on", - "A dv", - "Ad v", - "car ousel", - "ÃŁ e", - "_ DESC", - "_D ESC", - "_DE SC", - "Ġ hammer", - "Ġh ammer", - "Ġham mer", - "Ġhamm er", - "ẠŃ", - "Ġ ĠĠĠĠĠĠĠĊĊ", - "ĠĠ ĠĠĠĠĠĠĊĊ", - "ĠĠĠĠ ĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠĠ ĊĊ", - "ĠĠĠ ĠĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠ ĠĊĊ", - "ĠĠĠĠĠ ĠĠĠĊĊ", - "ĠĠĠĠĠĠ ĠĠĊĊ", - "ĠĠĠĠĠĠĠĠĊ Ċ", - "- core", - "-c ore", - "-co re", - "-cor e", - "- service", - "-s ervice", - "-ser vice", - "Ġc orners", - "Ġcor ners", - "Ġcorner s", - "Ġcorn ers", - "Ġ SF", - "ĠS F", - "p red", - "pr ed", - "pre d", - "> A", - "ĠJ Label", - "ĠJL abel", - "Ġrom antic", - "Ġroman tic", - "Ġromant ic", - "Ġtest imony", - "Ġtestim ony", - "Ġtestimon y", - "o sc", - "os c", - "Ġ Generation", - "ĠG eneration", - "ĠGener ation", - "ĠGen eration", - "ĠGene ration", - "as ures", - "asure s", - "asu res", - "_ internal", - "_in ternal", - "_int ernal", - "_inter nal", - "Ġ prints", - "Ġpr ints", - "Ġprint s", - "Ġpri nts", - "Ġprin ts", - "Ġ ])Ċ", - "Ġ] )Ċ", - "Ġ]) Ċ", - "ĠC leveland", - "re po", - "rep o", - "D isc", - "Dis c", - "Di sc", - "6 77", - "67 7", - "7 62", - "76 2", - "Ġ \">Ċ", - "Ġ\" >Ċ", - "Ġ\"> Ċ", - "� ���", - "�� ��", - "��� �", - "Ġ nearest", - "Ġne arest", - "Ġnear est", - "5 91", - "59 1", - "_ tb", - "_t b", - "( require", - "(re quire", - "(req uire", - "E OF", - "EO F", - "- child", - "-ch ild", - "Ġb udd", - "Ġbu dd", - "Ġbud d", - ".Xtra Editors", - "al ties", - "alt ies", - "7 23", - "72 3", - "\\\" :\\\"", - "\\\": \\\"", - "W ords", - "Word s", - "9 17", - "91 7", - "Ġloc ally", - "Ġlocal ly", - "Ġp urchases", - "Ġpurch ases", - "Ġpurchase s", - "6 95", - "69 5", - "D rawer", - "Draw er", - "ex tract", - "ext ract", - "extra ct", - "extr act", - "Ġex ecut", - "Ġexec ut", - "Ġexe cut", - "} '.", - "}' .", - "user data", - "Ġfocus es", - "Ġfoc uses", - "- minute", - "-min ute", - "7 64", - "76 4", - "Ġ Publish", - "ĠP ublish", - "ĠPub lish", - "o go", - "og o", - "Ġm ountains", - "Ġmount ains", - "Ġmountain s", - "B ot", - "Bo t", - "} >{", - "}> {", - "Ġt ension", - "Ġtens ion", - "r od", - "ro d", - "m esh", - "me sh", - "mes h", - "Ġtrans formed", - "Ġtransform ed", - ", R", - "( )}Ċ", - "() }Ċ", - "()} Ċ", - ". long", - ".l ong", - ".lo ng", - ".lon g", - "Ġg orgeous", - "Ġgorge ous", - "Ġ Schedule", - "ĠS chedule", - "Ġold est", - "Ġol dest", - "Ġsub process", - "( IN", - "(I N", - "y ect", - "ye ct", - "ĠCo oper", - "ar ness", - "arn ess", - "Ġ Monitor", - "ĠM onitor", - "ĠMon itor", - ". part", - ".p art", - ".par t", - ".pa rt", - "9 72", - "97 2", - "Ġ NBC", - "ĠN BC", - "ĠNB C", - "6 68", - "66 8", - "Ġc otton", - "Ġcot ton", - "Ġcott on", - "Ġ hol", - "Ġh ol", - "Ġho l", - "7 26", - "72 6", - "Ġ rgba", - "Ġr gba", - "Ġrgb a", - "Ġrg ba", - "Ġ Bio", - "ĠB io", - "ĠBi o", - "Cont inue", - "Contin ue", - "P od", - "Po d", - "Ġparticip ating", - "cl usions", - "clus ions", - "clusion s", - "(By Val", - "7 34", - "73 4", - "à ¬", - "Ġ HOW", - "ĠH OW", - "ĠHO W", - "_set opt", - "Ġaccompany ing", - "0 91", - "09 1", - "a ton", - "at on", - "ato n", - "Ġ /\\", - "Ġ/ \\", - "Ġ Authentication", - "ĠAuth entication", - "ĠAuthentic ation", - "i én", - "ĠBar ack", - "ĠBa rack", - "/ *.", - "/* .", - "Ġe ager", - "Ġea ger", - "Ġeag er", - "Ġ Cancel", - "ĠC ancel", - "ĠCan cel", - "ĠCanc el", - "< lemma", - " $", - "O LEAN", - "OLE AN", - "OK IE", - "IB ILITY", - "U AGE", - "UA GE", - "Ġ Survey", - "ĠS urvey", - "ĠSur vey", - "ĠSurv ey", - "0 71", - "07 1", - "Ġre sign", - "Ġr esign", - "Ġres ign", - "w ing", - "win g", - "wi ng", - "Ġse crets", - "Ġsec rets", - "Ġsecret s", - "Ġsecre ts", - "Ġc hips", - "Ġch ips", - "Ġchip s", - "Ġchi ps", - "JSON Object", - "D esktop", - "Des ktop", - "Desk top", - "5 96", - "59 6", - "_SY MBOL", - "( resource", - "(res ource", - "(re source", - "Ġ< />Ċ", - "ĠĊ", - "Ġnew est", - "Ġne west", - "u li", - "ul i", - "Ġde sert", - "Ġdes ert", - "Ġdese rt", - "Ġdeser t", - "Ġd ip", - "Ġdi p", - "Ġ Pow", - "ĠP ow", - "ĠPo w", - "Ġequ ation", - "Ġeq uation", - "Ġposs ibilities", - "Ġpossibilit ies", - "Ġ Fed", - "ĠF ed", - "ĠFe d", - "os ph", - "osp h", - "Ġ [%", - "Ġ[ %", - "Ġ bubble", - "Ġb ubble", - "Ġbu bble", - "Ġbub ble", - "Ġbubb le", - "ether lands", - "7 93", - "79 3", - "Ġc ement", - "Ġce ment", - ". auto", - ".a uto", - ".au to", - ".aut o", - "_ AN", - "_A N", - "âĢĻ .", - "s election", - "se lection", - "select ion", - "sel ection", - "Ġ Bond", - "ĠB ond", - "ĠBo nd", - "ĠBon d", - "9 88", - "98 8", - "D en", - "De n", - "- O", - ". getType", - ".get Type", - "8 96", - "89 6", - ". Window", - ".W indow", - "p res", - "pr es", - "pre s", - "Ġsw inger", - "Ġswing er", - "\" })Ċ", - "\"} )Ċ", - "\"}) Ċ", - "Ġ pip", - "Ġp ip", - "Ġpi p", - "Ġm ice", - "Ġmi ce", - "Ġmic e", - "Ġ compound", - "Ġcomp ound", - "- plugin", - "-pl ugin", - "i ko", - "ik o", - "Ġcent uries", - "i cular", - "ic ular", - "- inline", - "-in line", - "ĉ key", - "ĉk ey", - "> \\<", - ">\\ <", - "EN SION", - "ENS ION", - "Ġ[ čĊ", - "Ġprecis ely", - "Ġprecise ly", - "Ġ été", - "Ġé té", - "Ġét é", - "Ġ Past", - "ĠP ast", - "ĠPa st", - "ĠPas t", - "ĠCam bridge", - "ĠCamb ridge", - "- full", - "-f ull", - "Ġ analyze", - "Ġan alyze", - "Ġanaly ze", - "Ġ Steven", - "ĠSt even", - "ĠSte ven", - "ĠSteve n", - "Ġ nem", - "Ġn em", - "Ġne m", - "d ue", - "du e", - "o ren", - "or en", - "ore n", - "Ġmus cles", - "Ġmuscle s", - "i jing", - "ij ing", - "iji ng", - "8 52", - "85 2", - "/ -", - "ĠKenn edy", - "5 97", - "59 7", - "R M", - "oss ible", - "Ġact ress", - "Ġd olor", - "Ġdo lor", - "Ġdol or", - "9 14", - "91 4", - "å½ ķ", - "N eed", - "Ne ed", - ". toggle", - ".t oggle", - "Ġ Race", - "ĠR ace", - "ĠRa ce", - "ĠRac e", - "w ers", - "we rs", - "wer s", - ". material", - ".m aterial", - ".mat erial", - "Ġ Due", - "ĠD ue", - "ĠDu e", - "Ġ Pel", - "ĠP el", - "ĠPe l", - "# print", - "Ġin dependence", - "Ġindepend ence", - "ex us", - "Sh adow", - "Ġ encoder", - "Ġe ncoder", - "Ġen coder", - "Ġenc oder", - "Ġencode r", - "( level", - "(le vel", - "Ġ Swift", - "ĠSw ift", - ". doc", - ".d oc", - ".do c", - "_ selection", - "_s election", - "_se lection", - "_select ion", - "_sel ection", - "9 52", - "95 2", - "Ġserial VersionUID", - "9 45", - "94 5", - "Label s", - "Lab els", - "Ġperform ances", - "Ġperformance s", - "Ġperforman ces", - ". Tag", - ".T ag", - "ĠN HL", - "ĠNH L", - "i zen", - "iz en", - "ize n", - "/ UIKit", - "/UI Kit", - "9 91", - "99 1", - "_ CONTROL", - "_CONT ROL", - "Ġe arnings", - "Ġear nings", - "Ġearn ings", - "Ġearning s", - "9 75", - "97 5", - "Ġ Alt", - "ĠA lt", - "ĠAl t", - "_ HANDLE", - "_H ANDLE", - "_HAND LE", - "C tx", - "Ġper su", - "Ġpers u", - "Ġ tran", - "Ġt ran", - "Ġtr an", - "Ġtra n", - "ç ¨", - "_ CHANNEL", - "_CH ANNEL", - "_CHAN NEL", - "Ġs atisfaction", - "Ġsatisf action", - "Ġ GP", - "ĠG P", - "7 69", - "76 9", - "i ox", - "io x", - "m itt", - "mit t", - "mi tt", - "l ando", - "land o", - "la ndo", - "lan do", - "Ġ pig", - "Ġp ig", - "Ġpi g", - "in als", - "inal s", - "ina ls", - "ê ncia", - "ên cia", - "7 31", - "73 1", - "S urface", - "Sur face", - "Ġ UUID", - "ĠU UID", - "Ġbenef icial", - "Ġbenefici al", - "Ġ sequences", - "Ġse quences", - "Ġsequence s", - "Ġsequ ences", - "ĉ memset", - "ĉmem set", - "Ġmag ical", - "Ġmagic al", - " «", - "Ġw orn", - "Ġwor n", - "Ġwo rn", - "A SC", - "AS C", - "p opup", - "pop up", - "C OMP", - "CO MP", - "COM P", - "_ before", - "_b efore", - "_be fore", - "e ness", - "en ess", - "ene ss", - "enes s", - "U i", - "L es", - "Le s", - ". require", - ".re quire", - ".req uire", - ". Serializable", - ".Serial izable", - "add Gap", - "Ġ authorization", - "Ġauthor ization", - "0 85", - "08 5", - ".py plot", - "u rray", - "ur ray", - "urr ay", - "l atitude", - "lat itude", - "8 45", - "84 5", - "f rames", - "fr ames", - "frame s", - "fra mes", - "fram es", - "a js", - "aj s", - "Ġ compass", - "Ġcom pass", - "Ġcomp ass", - "Ġ observations", - "Ġobs ervations", - "Ġobserv ations", - "Ġobservation s", - "_ sup", - "_s up", - "_su p", - ".en viron", - ".env iron", - "Ġtr iple", - "Ġtri ple", - "Ġtrip le", - "Ġ Ruby", - "ĠR uby", - "ĠRub y", - "ĠRu by", - "Ġd rain", - "Ġdr ain", - "Ġdra in", - "_ FILTER", - "_F ILTER", - "S an", - "Sa n", - "U MP", - "UM P", - "Null Exception", - "Ġ Gab", - "ĠG ab", - "ĠGa b", - "o we", - "ow e", - "ĠTur kish", - "ĠTurk ish", - "_ sequence", - "_se quence", - "Ġ Grant", - "ĠG rant", - "ĠGr ant", - "ĠGran t", - "ĠGra nt", - "u ela", - "ue la", - "uel a", - "Ġ wo", - "Ġw o", - "Ġ cube", - "Ġc ube", - "Ġcu be", - "Ġcub e", - "i q", - "Ġdis orders", - "Ġdisorder s", - "Ġextra ordinary", - "Ġextraordin ary", - "Ġ ctrl", - "Ġc trl", - "Ġct rl", - "Ġctr l", - "Ġ Seq", - "ĠS eq", - "ĠSe q", - "en tr", - "ent r", - "8 65", - "86 5", - "Ġsan ctions", - "Ġsanct ions", - "Ġsanction s", - "9 49", - "94 9", - "ut sch", - "uts ch", - "Re ports", - "Report s", - "Rep orts", - "Repo rts", - "Ġ inherit", - "Ġin herit", - "Ġinher it", - "P eriod", - "Per iod", - "Ġphot ography", - "Ġphotograph y", - "Ġ Framework", - "ĠF ramework", - "ĠFr amework", - "ĠFrame work", - "ĠFram ework", - "Ġspecial ist", - "Ġspeci alist", - "Ġ ?ĊĊ", - "Ġ? ĊĊ", - "Ġ?Ċ Ċ", - "_ selected", - "_se lected", - "_select ed", - "_sel ected", - ". Player", - ".P layer", - ".Pl ayer", - ".Play er", - "Ġ allocation", - "Ġal location", - "Ġall ocation", - "Ġalloc ation", - "Ġallo cation", - "( account", - "(a ccount", - "(ac count", - "(acc ount", - "Ġ structural", - "Ġstruct ural", - "v able", - "va ble", - "- offset", - "-off set", - "-o ffset", - "-offs et", - ".App CompatActivity", - "а м", - "аР¼", - ".Add WithValue", - "Ġ icons", - "Ġi cons", - "Ġicon s", - "Ġic ons", - "Ġ shutdown", - "Ġsh utdown", - "Ġshut down", - "_ low", - "_l ow", - "_lo w", - "Ġ Compare", - "ĠCom pare", - "ĠComp are", - "ĠCompar e", - "Ġ Ce", - "ĠC e", - "= head", - "=h ead", - "l am", - "la m", - ". predict", - ".p redict", - ".pre dict", - ".pred ict", - "_ DEC", - "_D EC", - "_DE C", - "Ġ Sleep", - "ĠS leep", - "ĠSlee p", - "ĠSle ep", - "Ġ Gratis", - "ĠGr atis", - "ĠGrat is", - "Ġs uggestion", - "Ġsuggest ion", - "Ġ DEL", - "ĠD EL", - "ĠDE L", - "c aff", - "ca ff", - "caf f", - "av irus", - "avi rus", - "avir us", - "No thing", - "ŀ ĭ", - "Ġwide spread", - "Ġwides pread", - "Ġmechan isms", - "Ġmechanism s", - "Ġ textAlign", - "Ġtext Align", - "oc cup", - "occ up", - "Ġ Rail", - "ĠR ail", - "ĠRa il", - "ĠRai l", - ": NS", - ":N S", - "Ġ fiber", - "Ġf iber", - "Ġfi ber", - "Ġfib er", - "Ġ mk", - "Ġm k", - "Ġv intage", - "- long", - "-l ong", - "-lo ng", - ". reduce", - ".re duce", - ".red uce", - ". Entities", - ".Ent ities", - "( record", - "(re cord", - "(rec ord", - "Ġ pleasant", - "Ġple asant", - "Ġpleas ant", - "F RING", - "FR ING", - ". Cells", - ".C ells", - ".Cell s", - "O TT", - "OT T", - "ĉ elseif", - "ĉelse if", - "6 49", - "64 9", - "7 24", - "72 4", - "_ confirm", - "_con firm", - "_conf irm", - "ĠView Group", - "s ym", - "sy m", - "Ġ pray", - "Ġp ray", - "Ġpr ay", - "Ġpra y", - "Ġsus pected", - "Ġsusp ected", - "Ġsuspect ed", - "Cont ains", - "Con tains", - "Contain s", - "Conta ins", - "9 83", - "98 3", - "Ġb orders", - "Ġborder s", - "Ġbor ders", - "Ġbord ers", - "Ġcomponent Did", - "A SSERT", - "ASS ERT", - "Ġin finite", - "Ġinf inite", - "Ġinfinit e", - "- order", - "-or der", - "Ġ hello", - "Ġh ello", - "Ġhel lo", - "Ġhell o", - "Ġ Grade", - "ĠG rade", - "ĠGr ade", - "ĠGrad e", - "ĠGra de", - ".currentTime Millis", - "ap olis", - "apol is", - "apo lis", - "z h", - "ĉ Object", - "ĉO bject", - ": \\\\", - ":\\ \\", - "H O", - "val uation", - "valu ation", - "Ġ vocab", - "Ġv ocab", - "Ġvo cab", - "Ġvoc ab", - "7 19", - "71 9", - "Ġ coupon", - "Ġc oupon", - "Ġco upon", - "Ġcou pon", - "Ġcoup on", - "ata bases", - "atab ases", - "atabase s", - ". GetType", - ".Get Type", - "L earn", - "Le arn", - "7 92", - "79 2", - "] =\"", - "]= \"", - "Ġ Gary", - "ĠG ary", - "ĠGar y", - "ĠGa ry", - "ot ive", - "oti ve", - "Ġ ash", - "Ġa sh", - "Ġas h", - "Ġ bib", - "Ġb ib", - "Ġbi b", - "X XXX", - "XX XX", - "XXX X", - "Ġ balanced", - "Ġbalance d", - "Ġbal anced", - "VAL UE", - "Ġ Nat", - "ĠN at", - "ĠNa t", - "_ Ad", - "_A d", - "< E", - "åĮ º", - "Ġ MethodInfo", - "ĠMethod Info", - "8 97", - "89 7", - "L IB", - "LI B", - "Ġconsider able", - "Ġconsid erable", - "Ġ Industry", - "ĠInd ustry", - "ĠIndust ry", - "t ests", - "te sts", - "test s", - "tes ts", - ". setTitle", - ".set Title", - "Ġ Bluetooth", - "ĠB luetooth", - "ĠBl uetooth", - "ĠBlu etooth", - "Ġ mapped", - "Ġm apped", - "Ġmap ped", - "Ġma pped", - "Ġ Bruce", - "ĠBr uce", - "ĠBru ce", - "Ġ MainWindow", - "ĠMain Window", - "ĉ status", - "ĉs tatus", - "ĉst atus", - "ĉstat us", - "Ġ raz", - "Ġr az", - "Ġra z", - "ĠM and", - "ĠMan d", - "ĠMa nd", - "Ġ classification", - "Ġclass ification", - "Per missions", - "Permission s", - "Perm issions", - "9 69", - "96 9", - "Ġ ----------------------------------------------------------------------------", - "Ġ---------------------------------------------------------------- ------------", - "Ġ------------------------------------------------ ----------------------------", - "Ġ------------ ----------------------------------------------------------------", - "Ġ------ ----------------------------------------------------------------------", - "Ġ------------------------------------------------------------ ----------------", - "Ġ------------------------------------------------------------------------- ---", - "Ġ containers", - "Ġcont ainers", - "Ġcontainer s", - "Ġcontain ers", - "Ġconta iners", - ": set", - ":s et", - "_ xml", - "_x ml", - "Ġwh ilst", - "Th rough", - "Thr ough", - "Ġv align", - "Ġval ign", - "Ġworld s", - "Ġwor lds", - "C ORD", - "CO RD", - "COR D", - "ED IA", - "EDI A", - "ÑĢ ов", - "ÑĢо в", - "Ġs pare", - "Ġsp are", - "Ġspa re", - "Ġspar e", - "Ġ Had", - "ĠH ad", - "ĠHa d", - "Ġ DEF", - "ĠD EF", - "ĠDE F", - "( ptr", - "(p tr", - "(pt r", - "Ġw arming", - "Ġwar ming", - "Ġwarm ing", - "8 98", - "89 8", - "ठ¾", - "Ġcons ensus", - "a gne", - "ag ne", - "agn e", - "C TL", - "CT L", - "Ġ ìķ", - "Ġì ķ", - ". Main", - ".M ain", - ".Ma in", - "web Element", - "Ġp ist", - "Ġpi st", - "Ġpis t", - "F lash", - "Fl ash", - "App end", - "Ap pend", - "Appe nd", - ".tw img", - "T ap", - "Ta p", - "Ġveget ables", - "Ġvegetable s", - "a lg", - "al g", - "0 58", - "05 8", - ". sample", - ".s ample", - ".sam ple", - "Ġco aching", - "Ġcoach ing", - "( ind", - "(i nd", - "(in d", - "Cell Value", - "Check Box", - "Ġ Hell", - "ĠH ell", - "ĠHe ll", - "ĠHel l", - "R OOT", - "RO OT", - "7 96", - "79 6", - "Ġst adium", - "Ġstad ium", - "Ġinvestig ating", - ") %", - "s ted", - "st ed", - "ste d", - "9 65", - "96 5", - "Ġ Writing", - "ĠW riting", - "ĠWr iting", - "Ġ ê²", - "Ġê ²", - "Ġ uno", - "Ġu no", - "Ġun o", - "Ġ {{--", - "Ġ{{ --", - "Ġ coords", - "Ġco ords", - "Ġcoord s", - "Ġun ser", - "Ġuns er", - "o rganization", - "organ ization", - "Ġ Crime", - "ĠCr ime", - "ĠCri me", - "ĠDem ocrat", - "ĠDemocr at", - "5 79", - "57 9", - "Ġ vin", - "Ġv in", - "Ġvi n", - "/ file", - "/f ile", - "0 78", - "07 8", - "- api", - "-a pi", - "-ap i", - "Ġ Ay", - "ĠA y", - "Ġf unded", - "Ġfun ded", - "Ġfund ed", - "ĠBr exit", - "ĠBre xit", - "ĠG h", - "ent ina", - "enti na", - "entin a", - "c ases", - "ca ses", - "case s", - "cas es", - "Ġ dash", - "Ġd ash", - "Ġda sh", - "Ġdas h", - "Ġ!! }Ċ", - "Ġ!!} Ċ", - "H I", - "Off ice", - "Ġcap tain", - "Ġcapt ain", - "Ġwor ship", - "Ġwors hip", - "Ġworsh ip", - "\\ C", - "7 33", - "73 3", - "8 51", - "85 1", - "Ġg lobe", - "Ġgl obe", - "Ġglob e", - "Ġglo be", - "_ board", - "_b oard", - "_bo ard", - "Ġb abies", - "Ġba bies", - "Ġbab ies", - "8 76", - "87 6", - "Ġcon secutive", - "Ġconsec utive", - "Ġenh anced", - "Ġenhance d", - "er eum", - "ere um", - "ĠAd vis", - "ĠAdv is", - "Ġg rain", - "Ġgr ain", - "Ġgra in", - "7 71", - "77 1", - "Ġc raw", - "Ġcr aw", - "Ġcra w", - "ancell ationToken", - "ancellation Token", - ". alpha", - ".al pha", - "_ WITH", - "_W ITH", - "ĠO tt", - "ĠOt t", - "Ġ Cool", - "ĠC ool", - "ĠCo ol", - ". batch", - ".b atch", - ".bat ch", - "Ġ verified", - "Ġver ified", - "( callback", - "(c allback", - "(call back", - "Ġreg ards", - "Ġregard s", - "6 83", - "68 3", - "Ġ IntPtr", - "ĠInt Ptr", - "o ucher", - "ou cher", - "ouch er", - "Ġ kin", - "Ġk in", - "Ġki n", - "Ġt ouched", - "Ġtouch ed", - "Ġtou ched", - "it Ãł", - "a thon", - "at hon", - "ath on", - "Ġadj acent", - "Ġaccom panied", - "L EAR", - "LE AR", - "Ġim plies", - "Ġimp lies", - "Ġimpl ies", - "Ġ hill", - "Ġh ill", - "Ġhi ll", - "Ġhil l", - "ĠB altimore", - "ĠBalt imore", - "= \"-", - "=\" -", - "F inally", - "Fin ally", - "Final ly", - "8 83", - "88 3", - "S am", - "Sa m", - "ic opt", - "ico pt", - "Ġs od", - "Ġso d", - "Ġ maj", - "Ġm aj", - "Ġma j", - "Ġ Shipping", - "ĠSh ipping", - "ĠShip ping", - "ĠShi pping", - "Ġ getAll", - "Ġget All", - "Ġco aches", - "Ġcoach es", - "Ġdon ations", - "Ġdonation s", - "i lot", - "il ot", - "ilo t", - "Ġ Tar", - "ĠT ar", - "ĠTa r", - "c err", - "ce rr", - "cer r", - "Ġ badge", - "Ġb adge", - "Ġbad ge", - "Ġba dge", - "Ġ markers", - "Ġm arkers", - "Ġmark ers", - "Ġmar kers", - "Ġmarker s", - "Ġ Rand", - "ĠR and", - "ĠRa nd", - "ĠRan d", - "a ised", - "ai sed", - "ais ed", - "aise d", - "iss ance", - "issa nce", - "issan ce", - "Ġexpl oring", - "Ġexplo ring", - "Ġexplor ing", - "8 27", - "82 7", - "u ced", - "uc ed", - "uce d", - "ĠInd onesia", - "ĠIndones ia", - "ĠIndo nesia", - "Ġbe neath", - "Ġbene ath", - "Ġm agnetic", - "Ġmagn etic", - "Ġmagnet ic", - "Ġm useum", - "Ġmus eum", - "Ġmuse um", - "match Condition", - "Ġdis rupt", - "Ġre mind", - "Ġrem ind", - "Ġremin d", - "Ġ TM", - "ĠT M", - "Ġ /><", - "Ġ/ ><", - "Ġ/> <", - "Ġf ool", - "Ġfo ol", - "Ġfoo l", - "Ġ esk", - "Ġe sk", - "Ġes k", - ". Null", - ".N ull", - "Ġ Dies", - "ĠD ies", - "ĠDi es", - "ĠDie s", - "_ OUTPUT", - "_OUT PUT", - "_TYPE D", - "_TYP ED", - "Ġp ainted", - "Ġpaint ed", - "Ġpain ted", - "6 73", - "67 3", - "7 35", - "73 5", - "Ġsoph istic", - "Ġ Bear", - "ĠB ear", - "ĠBe ar", - "ĠBea r", - "* n", - "_ PACK", - "_P ACK", - "_PA CK", - "Ġdel ivering", - "Ġdeliver ing", - "Ġ COUNT", - "ĠC OUNT", - "ĠCO UNT", - "åį ķ", - "Ġj eg", - "Ġje g", - "- car", - "-c ar", - "-ca r", - "f name", - "fn ame", - "Ġr anging", - "Ġran ging", - "Ġrang ing", - "8 48", - "84 8", - "Ġ Neg", - "ĠN eg", - "ĠNe g", - "/ ******/", - "Ġ CHAR", - "ĠCH AR", - "Ġu ltra", - "Ġult ra", - "Ġul tra", - "Ġultr a", - "G rad", - "Gr ad", - "= t", - "Ġjud ges", - "Ġjudge s", - "ĠD ise", - "ĠDis e", - "ĠDi se", - "an ners", - "ann ers", - "anner s", - "anne rs", - "9 85", - "98 5", - "8 91", - "89 1", - "8 61", - "86 1", - "Ġ scal", - "Ġs cal", - "Ġsc al", - "Ġsca l", - "_ cal", - "_c al", - "_ca l", - "ĠCON NECTION", - "ĠCONNECT ION", - "_ embed", - "_em bed", - "_emb ed", - "( fn", - "(f n", - "Ġ Craft", - "ĠC raft", - "ĠCr aft", - "ĠCra ft", - "0 47", - "04 7", - "Ġ Pas", - "ĠP as", - "ĠPa s", - "\" )->", - "\") ->", - ". convert", - ".con vert", - ".conv ert", - ". resource", - ".re source", - ".res ource", - "Ġ STATUS", - "ĠST ATUS", - "ĠSTAT US", - "ô ng", - "ôn g", - "Ġ Tit", - "ĠT it", - "ĠTi t", - "Ġclass room", - "ĠArch itect", - "ĠK ings", - "ĠKing s", - "ĠKin gs", - "Ġ steady", - "Ġste ady", - "Ġstead y", - "/* !Ċ", - "/*! Ċ", - "Ġ Gene", - "ĠG ene", - "ĠGe ne", - "ĠGen e", - ") \";Ċ", - ")\" ;Ċ", - "i cia", - "ic ia", - "ici a", - "s tan", - "st an", - "sta n", - "Ġ Construction", - "ĠCon struction", - "ĠConstruct ion", - "ĠConstr uction", - "um per", - "ump er", - "9 51", - "95 1", - "w c", - "Ġ CBS", - "ĠC BS", - "ĠCB S", - "in ging", - "ing ing", - "- party", - "-p arty", - "-part y", - "-par ty", - "( driver", - "(d river", - "(dr iver", - "M ARK", - "MA RK", - "MAR K", - "0 82", - "08 2", - "Ġ nested", - "Ġn ested", - "Ġne sted", - "Ġnest ed", - "Ġneste d", - "Ġnes ted", - "e ward", - "ew ard", - "Ġ dependency", - "Ġd ependency", - "Ġdep endency", - "Ġdepend ency", - "Ġm ales", - "Ġma les", - "Ġmale s", - "Ġmal es", - "9 28", - "92 8", - "Ġ ONE", - "ĠO NE", - "ĠON E", - "Ġ Production", - "ĠP roduction", - "ĠPro duction", - "ĠProduct ion", - "ĠProdu ction", - "ĠProd uction", - "] [$", - "][ $", - "ãĥ¼ ãĥ", - "_ LOAD", - "_L OAD", - "_LO AD", - "ĠB ol", - "ĠBo l", - "el ry", - "8 31", - "83 1", - "ł éĻ¤", - "Ġ Require", - "ĠRe quire", - "ĠReq uire", - "Ġ placing", - "Ġpl acing", - "Ġplac ing", - "Ġpla cing", - "x xx", - "xx x", - "C ALE", - "CA LE", - "CAL E", - "Ġ thumb", - "Ġth umb", - "Ġthu mb", - "8 24", - "82 4", - "Ch oose", - "Cho ose", - "Ġ prototype", - "Ġprot otype", - "Ġproto type", - "V OID", - "VO ID", - "Ġles bian", - "Ġlesb ian", - "Ġlesbi an", - "7 41", - "74 1", - "Ġ traits", - "Ġt raits", - "Ġtr aits", - "Ġtra its", - "Ġtrait s", - "Ġtrai ts", - "Sh arp", - "Shar p", - "Sha rp", - "Ġ consume", - "Ġcon sume", - "Ġcons ume", - "Ġconsum e", - "Tr uth", - "Ġaction Performed", - "Ġ Environmental", - "ĠEnvironment al", - "Ġ Dean", - "ĠD ean", - "ĠDe an", - "Ġ estado", - "Ġest ado", - "Ġesta do", - "Ġestad o", - "s ame", - "sa me", - "sam e", - "Ġ numeric", - "Ġn umeric", - "Ġnum eric", - "Ġnumer ic", - "Ġnu meric", - "Ġtrans it", - "Ġtran sit", - ". Email", - ".E mail", - "- side", - "-s ide", - "_ RUN", - "_R UN", - "ĠV illage", - "ĠVill age", - "ĠVilla ge", - "ĠVil lage", - "_ OPEN", - "_OP EN", - "è ¦", - ". rem", - ".re m", - ".r em", - "- warning", - "-w arning", - "-war ning", - "a nya", - "an ya", - "any a", - "Property Changed", - "Ġ (!_", - "Ġ(! _", - "( check", - "(c heck", - "(ch eck", - "i lia", - "il ia", - "ili a", - "Ġ Soft", - "ĠS oft", - "ĠSo ft", - "ĠSof t", - "st eps", - "ste ps", - "step s", - "ĠMad rid", - "Memory Warning", - "Ġ handlers", - "Ġhand lers", - "Ġhandle rs", - "Ġhandler s", - "Ġexperi encing", - "Ġ inspect", - "Ġin spect", - "Ġins pect", - "Ġinsp ect", - "button s", - "but tons", - "butt ons", - "Receive MemoryWarning", - "ch emy", - "che my", - "chem y", - "L inks", - "Link s", - "Lin ks", - "Ġurl lib", - "Ġur llib", - ".System Colors", - "Ġ Eigen", - "ĠE igen", - "ĠEig en", - "Ġpun ishment", - "Ġpunish ment", - ":UI Control", - "b ara", - "bar a", - "ba ra", - "- set", - "-s et", - "-se t", - "Ġ }čĊčĊčĊ", - "Ġ} čĊčĊčĊ", - "Ġ}čĊ čĊčĊ", - "Ġ}čĊčĊ čĊ", - "Ġt olerance", - "Ġtoler ance", - "Ġ interfaces", - "Ġinter faces", - "Ġinterface s", - "Ġinterf aces", - ". redirect", - ".re direct", - ".red irect", - "ighb ors", - "ighbor s", - "cs rf", - "csr f", - "_ background", - "_back ground", - ". Utils", - ".Util s", - "_ HT", - "_H T", - "6 92", - "69 2", - "Ġ Interest", - "ĠInter est", - "i mos", - "im os", - "imo s", - "Ġgr ants", - "Ġgrant s", - "Ġgran ts", - "Ġgra nts", - "0 83", - "08 3", - "Ġexam ined", - "Ġexamine d", - "Ð Ķ", - "Ġ cf", - "Ġc f", - "f orge", - "for ge", - "forg e", - "b acks", - "back s", - "ba cks", - "bac ks", - "Ġ Objects", - "ĠObject s", - "ĠObj ects", - "_ sent", - "_s ent", - "_se nt", - ". entry", - ".en try", - ".ent ry", - "Ġ THEN", - "ĠT HEN", - "ĠTHE N", - "ĠTH EN", - "ell ido", - "elli do", - "c ia", - "ci a", - ", res", - ",r es", - ",re s", - "6 59", - "65 9", - "6 81", - "68 1", - "/st dc", - "/std c", - ". nd", - ".n d", - "( Int", - "(I nt", - "(In t", - "Ġ Authors", - "ĠAuthor s", - "ĠAuth ors", - "ĠApp CompatActivity", - "' {", - "Ġ medi", - "Ġm edi", - "Ġme di", - "Ġmed i", - "M usic", - "Mu sic", - "Mus ic", - "i gm", - "ig m", - "ce ipt", - "Ġ auss", - "Ġa uss", - "Ġau ss", - "Ġaus s", - "Ġtarget ing", - "Ġtarg eting", - "Ġ Keys", - "ĠKey s", - "ĠKe ys", - "h n", - ": ]Ċ", - ":] Ċ", - "Ġmin eral", - "Ġmine ral", - "Ġminer al", - "à ®", - ". ca", - ".c a", - "7 61", - "76 1", - "o med", - "om ed", - "ome d", - "Ġ sheets", - "Ġs heets", - "Ġshe ets", - "Ġsheet s", - "Ġc amb", - "Ġca mb", - "Ġcam b", - "Ġdead ly", - ". inject", - ".in ject", - "( unit", - "(u nit", - "(un it", - "Ġ Selection", - "ĠS election", - "ĠSe lection", - "ĠSelect ion", - "ĠSel ection", - "ĠSele ction", - ".g ms", - "( connection", - "(con nection", - "(conn ection", - "(connect ion", - "Ġ $(\"", - "Ġ$ (\"", - "Ġ$( \"", - "é mon", - "ém on", - "Ġ Currently", - "ĠCurrent ly", - "p te", - "pt e", - "_ paths", - "_p aths", - "_path s", - "_pa ths", - "_pat hs", - "8 47", - "84 7", - "le af", - "lea f", - "Ġimp lications", - "Ġimpl ications", - "Ġimplication s", - "Ġimplic ations", - "p osal", - "pos al", - "po sal", - "ä½ į", - "[ /", - "a ncia", - "an cia", - "anc ia", - "é Ľ", - "m ul", - "mu l", - "c ie", - "ci e", - "Ġge ile", - "Ġgeil e", - "6 79", - "67 9", - "im als", - "imal s", - "ima ls", - "UI View", - "Ġs urre", - "Ġsur re", - "s erialize", - "serial ize", - "I SO", - "IS O", - "Ġar bitrary", - "Ġarbit rary", - "Ġarbitr ary", - "Ġsock addr", - ". fn", - ".f n", - "Ġ Merc", - "ĠM erc", - "ĠMe rc", - "ĠMer c", - "Ġ casting", - "Ġc asting", - "Ġcas ting", - "Ġcast ing", - "Key Down", - "Ġ newValue", - "Ġnew Value", - "ĠnewVal ue", - "op ens", - "open s", - "ope ns", - "7 17", - "71 7", - "T odo", - "To do", - "Ġflex ibility", - "ĉ ĉĉĉĠĠ", - "ĉĉ ĉĉĠĠ", - "ĉĉĉĉ ĠĠ", - "ĉĉĉ ĉĠĠ", - "ĉĉĉĉĠ Ġ", - "V elocity", - "Vel ocity", - "ú n", - "r owing", - "ro wing", - "row ing", - "Ġ computed", - "Ġcom puted", - "Ġcomp uted", - "Ġcomput ed", - "Ġcompute d", - "` )Ċ", - "`) Ċ", - "st atement", - "state ment", - "stat ement", - "sta tement", - "Ġ ri", - "Ġr i", - "_ cart", - "_c art", - "_car t", - "_ca rt", - "L ow", - "Lo w", - "trans fer", - ". nav", - ".n av", - "Ġ grave", - "Ġgr ave", - "Ġgra ve", - "Ġgrav e", - "Ġ Door", - "ĠD oor", - "ĠDo or", - "ĉ alert", - "ĉal ert", - "6 91", - "69 1", - "6 98", - "69 8", - ". subscribe", - ".sub scribe", - "- profile", - "-pro file", - "-pr ofile", - "-prof ile", - "ĉ base", - "ĉb ase", - "Ġ âĪĴ", - "ĠâĪ Ĵ", - "_ _ĊĊ", - "__ ĊĊ", - "__Ċ Ċ", - "Ġengine ers", - "Ġengineer s", - "Ġexp losion", - "Ġexplos ion", - "Ġd ari", - "Ġda ri", - "Ġdar i", - "6 82", - "68 2", - "ĉ Log", - "ĉL og", - "o nal", - "on al", - "ona l", - "Ġis olated", - "Ġisol ated", - "Ġiso lated", - "Ġisolate d", - "{ i", - "Ġ Msg", - "ĠM sg", - "ĠMs g", - "F uture", - "Fu ture", - "Ġr acist", - "Ġrac ist", - "- wrap", - "-w rap", - "Ġ Vers", - "ĠV ers", - "ĠVer s", - "ĠVe rs", - "b org", - "bo rg", - "bor g", - "I SION", - "IS ION", - "Ġ ÑĢаÐ", - "ĠÑĢ аÐ", - "ĠÑĢа Ð", - "ĠY an", - "ĠYa n", - "8 36", - "83 6", - "init With", - "Ġn omin", - "Ġno min", - "Ġnom in", - "( empty", - "(em pty", - "(emp ty", - "ÃŃ n", - "ã Ĥ¤", - "ãĤ ¤", - "ĉ width", - "ĉw idth", - "Ġch amber", - "Ġcham ber", - "/ ajax", - "/a jax", - "E MP", - "EM P", - "0 93", - "09 3", - "Ġne ces", - "Ġnec es", - "i vos", - "iv os", - "ivo s", - "log ic", - "* )&", - "*) &", - "cri pts", - "cript s", - "9 76", - "97 6", - "Row At", - "0 53", - "05 3", - "i blings", - "ib lings", - "ibling s", - "Ġ ears", - "Ġe ars", - "Ġear s", - "Ġea rs", - "Ġcomp uting", - "Ġcomput ing", - "Ġ maker", - "Ġm aker", - "Ġmake r", - "Ġma ker", - "Ġmak er", - "Ġ Neither", - "ĠN either", - "ĠNe ither", - "b readcrumb", - "Ġ serialize", - "Ġs erialize", - "Ġserial ize", - "Ġ Within", - "ĠWith in", - "ĠWi thin", - "ĠWit hin", - "Ġd ell", - "Ġde ll", - "Ġdel l", - "_ TRACE", - "_TR ACE", - "_TRA CE", - "0 92", - "09 2", - "= a", - "Ġw ishes", - "Ġwish es", - "Ġwis hes", - "- inch", - "-in ch", - "-inc h", - "ĠD or", - "ĠDo r", - "Ġinnoc ent", - "ĠD ol", - "ĠDo l", - "Ġint ens", - "Ġinte ns", - "for ced", - "force d", - "forc ed", - "0 54", - "05 4", - "Ġ BIT", - "ĠB IT", - "ĠBI T", - "Ġphot ographs", - "Ġphoto graphs", - "Ġphotograph s", - "Ġc asa", - "Ġca sa", - "Ġcas a", - "Ġ Len", - "ĠL en", - "ĠLe n", - "\\ Framework", - "\\F ramework", - ". Simple", - ".S imple", - "Ġd ear", - "Ġde ar", - "8 95", - "89 5", - ") /(", - ")/ (", - "ip pi", - "ipp i", - "Ġ owns", - "Ġown s", - "Ġow ns", - "P layers", - "Pl ayers", - "Player s", - "Play ers", - "Ġprop osals", - "Ġpropos als", - "Ġproposal s", - ". pi", - ".p i", - "us alem", - "usa lem", - "usal em", - "D amage", - "Da mage", - "Dam age", - "Ġcal ories", - "Ġcalorie s", - "Ġcalor ies", - "Ġ Creative", - "ĠC reative", - "ĠCre ative", - "ĠCreat ive", - "Ġ [$", - "Ġ[ $", - "Ġ //čĊ", - "Ġ// čĊ", - "Ġ/ /čĊ", - "7 86", - "78 6", - "And View", - "è me", - "èm e", - ". custom", - ".c ustom", - "_ factory", - "_f actory", - "_factor y", - "_fact ory", - "comm ands", - "command s", - "comma nds", - "_ look", - "_l ook", - "_lo ok", - "Ġ strcmp", - "Ġstr cmp", - "Y N", - "a ired", - "air ed", - "ai red", - "aire d", - "Ġ audit", - "Ġa udit", - "Ġaud it", - "Ġau dit", - "Ġaudi t", - "о ÑģÑĤ", - "оÑģ ÑĤ", - "Ġ Reverse", - "ĠRe verse", - "ĠRev erse", - "ĠRever se", - "ropri ate", - "e tics", - "et ics", - "etic s", - "eti cs", - "< vector", - "';Ċ", - "\"> ';Ċ", - "\">' ;Ċ", - "Ġpe pper", - "Ġpepp er", - "Ġpep per", - "9 89", - "98 9", - "Ġ shed", - "Ġs hed", - "Ġsh ed", - "Ġshe d", - "Ġ Medium", - "ĠM edium", - "ĠMed ium", - "ĠMedi um", - "Ġ Cookie", - "ĠC ookie", - "ĠCo okie", - "ĠCook ie", - "8 89", - "88 9", - "Ġoverse as", - "ed or", - "edo r", - "as urement", - "asure ment", - "asu rement", - "7 66", - "76 6", - "åŃ ĺ", - "Ġ' .'", - "Ġ'. '", - "Ġ php", - "Ġp hp", - "Ġph p", - "Ġ PROC", - "ĠP ROC", - "ĠPRO C", - "ĠPR OC", - "Ġexcept ional", - "Ġexception al", - "( th", - "(t h", - "Ġ Jet", - "ĠJ et", - "ĠJe t", - "Ġ occupied", - "Ġoccup ied", - ". setImage", - ".set Image", - "Ġ Related", - "ĠRe lated", - "ĠRel ated", - "u cker", - "uc ker", - "uck er", - "M embers", - "Member s", - "Mem bers", - "PR INT", - "PRI NT", - "ĠG lo", - "ĠGl o", - "_ VIEW", - "_V IEW", - "} \",Ċ", - "}\", Ċ", - "}\" ,Ċ", - "Ġad option", - "Ġadopt ion", - "Ġado ption", - "[ ])Ċ", - "[] )Ċ", - "[]) Ċ", - "8 42", - "84 2", - "ĠMiss ouri", - "ĠLin coln", - "er ald", - "era ld", - "eral d", - "P opup", - "Pop up", - "Ġf ate", - "Ġfa te", - "Ġfat e", - "- bootstrap", - "-boot strap", - "f ections", - "fe ctions", - "fect ions", - "fection s", - "Ġ Poll", - "ĠP oll", - "ĠPol l", - "ĠPo ll", - "_ ARGS", - "_ARG S", - "_AR GS", - "in ance", - "ina nce", - "inan ce", - "6 97", - "69 7", - "- home", - "-h ome", - ". ),", - ".) ,", - "_ done", - "_d one", - "_do ne", - "_don e", - "6 94", - "69 4", - ": ĊĊĊ", - ":Ċ ĊĊ", - ":ĊĊ Ċ", - "Ġdiscuss ing", - "Ġ SQLException", - "ĠSQL Exception", - "Ġelect ro", - "Ġelectr o", - "ĉ req", - "ĉr eq", - "ĉre q", - "Ġ zw", - "Ġz w", - "8 86", - "88 6", - "Ġl ui", - "Ġlu i", - "9 32", - "93 2", - "Ġover night", - "$ user", - "Ġ WAY", - "ĠW AY", - "ĠWA Y", - "Ġall erg", - "Ġalle rg", - "Ġaller g", - "Ġdis appointed", - "Ġdisappoint ed", - "Ġrad iation", - "Ġradi ation", - "Ġim pressed", - "Ġimp ressed", - "Ġimpress ed", - "Ġimpr essed", - "Ġimpres sed", - "if icates", - "ific ates", - "ificate s", - "ifica tes", - "Ġt ob", - "Ġto b", - "CL ASS", - "CLA SS", - "Ġ cuda", - "Ġc uda", - "Ġcu da", - "Ġcud a", - "_ det", - "_d et", - "_de t", - "- post", - "-p ost", - "-pos t", - "-po st", - "u lu", - "ul u", - "Trans lation", - "- hand", - "-h and", - ". year", - ".y ear", - "Ġ Mongo", - "ĠM ongo", - "ĠMon go", - "ĠMo ngo", - "ĠMong o", - "Ġun clear", - "Ġunc lear", - "Ġuncle ar", - ". engine", - ".e ngine", - ".eng ine", - "WEB PACK", - "r ices", - "ri ces", - "ric es", - "rice s", - "_ ACCESS", - "_AC CESS", - "_ACC ESS", - "Ġh olidays", - "Ġholiday s", - "per cent", - "perc ent", - ". Identity", - ".Id entity", - "Ġ Gov", - "ĠG ov", - "ĠGo v", - "Ġpass ionate", - "Ġpassion ate", - "! !.", - "!! .", - "ĠG reece", - "ĠGre ece", - "ĠGree ce", - "plus plus", - "' ));", - "') );", - "')) ;", - "G P", - "Ġex cit", - "Ġexc it", - ".tab Page", - "_ cond", - "_c ond", - "_con d", - "_co nd", - "Ġ sponsor", - "Ġs ponsor", - "Ġspons or", - "M ODULE", - "MOD ULE", - "_ proc", - "_p roc", - "_pro c", - "_pr oc", - "Ġ $Ċ", - "Ġ$ Ċ", - "Ġr ational", - "Ġrat ional", - "Ġratio nal", - "Ġration al", - ". Tool", - ".T ool", - ".To ol", - "Ġi hr", - "Ġih r", - "c ca", - "cc a", - "åĵ ģ", - "ĠE state", - "ĠEst ate", - "ĠEsta te", - "IB UTE", - "IBUT E", - "Action Performed", - "Ġ Solar", - "ĠS olar", - "ĠSo lar", - "ĠSol ar", - "¦ Ĥ", - "Ġequ ity", - "Ġeq uity", - "t id", - "ti d", - "9 38", - "93 8", - "Ġre cip", - "Ġrec ip", - ". simple", - ".s imple", - ".sim ple", - "m k", - "6 89", - "68 9", - "Ġ Luke", - "ĠL uke", - "ĠLu ke", - "ĠLuk e", - "ĠGuard ian", - "Ġ encrypted", - "Ġenc rypted", - "Ġencrypt ed", - "Ġdom inant", - "Ġdomin ant", - "Ġdomina nt", - ". place", - ".p lace", - ".pl ace", - "Ġ NV", - "ĠN V", - "8 39", - "83 9", - "Ġton gue", - "Ġtong ue", - "( Get", - "(G et", - "Ġst ainless", - "Ġstain less", - ". Play", - ".P lay", - ".Pl ay", - "Ġ eb", - "Ġe b", - "a ci", - "ac i", - ". buffer", - ".b uffer", - ".buf fer", - "readcr umbs", - "readcrumb s", - "Ġv accine", - "Ġvacc ine", - "p rom", - "pr om", - "pro m", - "9 79", - "97 9", - "Ġ userInfo", - "Ġuser Info", - "Ġ slug", - "Ġs lug", - "Ġsl ug", - "Ġslu g", - "Serial izedName", - "Serialized Name", - "- wide", - "-w ide", - "Ġre actions", - "Ġreaction s", - "Ġreact ions", - "Ġ Yang", - "ĠY ang", - "ĠYan g", - "ĠYa ng", - "Ġ Adds", - "ĠA dds", - "ĠAd ds", - "ĠAdd s", - "( userId", - "(user Id", - "Ġ plates", - "Ġp lates", - "Ġpl ates", - "Ġplate s", - "Ġpla tes", - "Ġplat es", - "Ġ MEM", - "ĠM EM", - "ĠME M", - "Ġb ail", - "Ġba il", - "In side", - "Ins ide", - "e ted", - "et ed", - "ete d", - "Ġ elsif", - "Ġels if", - "Ġs ake", - "Ġsa ke", - "Ġsak e", - "Ġ cycles", - "Ġc ycles", - "Ġcy cles", - "Ġcycle s", - "Ġcycl es", - "Ġcyc les", - "Ġ ìĹ", - "Ġì Ĺ", - "ĉ I", - "- collapse", - "-c ollapse", - "8 41", - "84 1", - "Ġ GMT", - "ĠG MT", - "ĠGM T", - "8 14", - "81 4", - "De claration", - "Ġg ros", - "Ġgr os", - "Ġgro s", - "Ġre aches", - "Ġreach es", - "Ġcust ody", - "Un til", - "Unt il", - "7 53", - "75 3", - "8 56", - "85 6", - "t u", - "ĠC hen", - "ĠCh en", - "ĠChe n", - "Ġ nx", - "Ġn x", - "( addr", - "(add r", - "(ad dr", - "Ġ Offer", - "ĠO ffer", - "ĠOff er", - "ĠOf fer", - "Ġcol leg", - "Ġcoll eg", - "Ġcolle g", - "ass ador", - "6 74", - "67 4", - "Ġ mapper", - "Ġm apper", - "Ġmap per", - "Ġma pper", - "8 54", - "85 4", - "ĠS IGNAL", - "ĠSIG NAL", - "ĠSIGN AL", - "ĠB loom", - "ĠBl oom", - "ĠBlo om", - "ĠH oll", - "ĠHol l", - "ĠHo ll", - "ĠIm per", - "ĠImp er", - "- des", - "-d es", - "-de s", - "_ site", - "_s ite", - "_si te", - "P roc", - "Pro c", - "Pr oc", - "E qu", - "Eq u", - "Ġ atomic", - "Ġat omic", - "Ġatom ic", - "Ġ Woman", - "ĠW oman", - "ĠWo man", - "s ent", - "se nt", - "sen t", - "7 38", - "73 8", - "8 17", - "81 7", - "s car", - "sc ar", - "Ġint elligent", - "Ġintellig ent", - "Ġ Getting", - "ĠG etting", - "ĠGet ting", - "Ġ Registration", - "ĠReg istration", - "ĠRegistr ation", - "Ġ Phill", - "ĠP hill", - "ĠPh ill", - "ĠPhil l", - "ĠPhi ll", - "Ġ killer", - "Ġk iller", - "Ġkill er", - "Ġkil ler", - "Ġki ller", - "un icode", - "unic ode", - "uni code", - "Ċ ĉĉĊ", - "Ġ Jacob", - "ĠJ acob", - "ĠJac ob", - "ĠJa cob", - "Ġ Const", - "ĠCon st", - "ĠCo nst", - "ĠCons t", - "Ġ locate", - "Ġl ocate", - "Ġlo cate", - "Ġloc ate", - "Ġc aus", - "Ġca us", - "7 49", - "74 9", - "ĠSch olar", - "ĠScho lar", - "Ġ constitutional", - "Ġconstitution al", - "Ġin flation", - "Ġinf lation", - "Ġinfl ation", - "Ġ Got", - "ĠG ot", - "ĠGo t", - "= array", - "=a rray", - "en dum", - "end um", - "Ġ translated", - "Ġtrans lated", - "Ġtransl ated", - "Ġtranslate d", - "Ġdiv orce", - "Ġdivor ce", - "En tries", - "Ent ries", - "Entr ies", - "Ġs or", - "Ġso r", - "Ġ Quote", - "ĠQu ote", - "ir lines", - "irl ines", - "U K", - "Ġ excel", - "Ġex cel", - "Ġexc el", - "Ġexce l", - "( opt", - "(o pt", - "(op t", - "Ġ ADV", - "ĠA DV", - "ĠAD V", - ", :,", - ",: ,", - "Ġcont acted", - "Ġcontact ed", - "7 42", - "74 2", - "Ġ DA", - "ĠD A", - "Ġr ings", - "Ġring s", - "Ġrin gs", - "Ġ Industrial", - "ĠInd ustrial", - "ĠIndust rial", - ". getContext", - ".get Context", - "Ġforg otten", - "Ġforgot ten", - "Ġ Tan", - "ĠT an", - "ĠTa n", - "Ġ pants", - "Ġp ants", - "Ġpa nts", - "Ġpan ts", - "Ġpant s", - "Ġ ov", - "Ġo v", - "Ġ decoder", - "Ġde coder", - "Ġdec oder", - "Ġdecode r", - "Ġdeco der", - "Ġ Partial", - "ĠP artial", - "ĠPart ial", - "ĠParti al", - "Ġ vc", - "Ġv c", - "Ġb attles", - "Ġbattle s", - "Ġbatt les", - "A rial", - "Ar ial", - "FRING EMENT", - "i rates", - "ir ates", - "ira tes", - "irate s", - ", w", - "aint enance", - "Ġ Od", - "ĠO d", - "ĠTechn ologies", - "åī į", - "ĠC arter", - "ĠCar ter", - "ĠCart er", - ". findAll", - ".find All", - "N ome", - "No me", - "Nom e", - "B en", - "Be n", - "Ġ Usage", - "ĠU sage", - "ĠUs age", - "ĠUsa ge", - "Ġ Picture", - "ĠP icture", - "ĠPic ture", - "Ġbad ly", - "_ panel", - "_p anel", - "_pa nel", - "_pan el", - "Ġpat ent", - "Ġpa tent", - "Ġ Protocol", - "ĠProt ocol", - "ĠProto col", - "l otte", - "lo tte", - "lot te", - "ĉ player", - "ĉp layer", - "ĉpl ayer", - "ĉplay er", - "j ections", - "ject ions", - "je ctions", - "jection s", - "7 46", - "74 6", - "Ġ dou", - "Ġd ou", - "Ġdo u", - "_ release", - "_re lease", - "_r elease", - "_rel ease", - "urn iture", - "_ tax", - "_t ax", - "_ta x", - "Ġ Fields", - "ĠF ields", - "ĠField s", - ". dataset", - ".d ataset", - ".data set", - ".dat aset", - ".datas et", - "_ master", - "_m aster", - "_ma ster", - "_mas ter", - "CLUD E", - "CLU DE", - "ĠPh arm", - "ĠPhar m", - "b st", - "bs t", - "Ġoper ational", - "Ġoperation al", - ". cell", - ".c ell", - ".ce ll", - "Ġident ifying", - "Ġidentify ing", - "Ġ jwt", - "Ġj wt", - "t uple", - "tu ple", - "Ġ TC", - "ĠT C", - "Ġ Cro", - "ĠC ro", - "ĠCr o", - "9 36", - "93 6", - "ix map", - "- components", - "-com ponents", - "-component s", - "-comp onents", - "g eneral", - "gen eral", - "gener al", - "gene ral", - "Ġ oz", - "Ġo z", - "_ De", - "_D e", - "_ double", - "_d ouble", - "_do uble", - "Ġ Too", - "ĠT oo", - "ĠTo o", - "0 88", - "08 8", - ".View Group", - "8 79", - "87 9", - "g ate", - "ga te", - "d ings", - "ding s", - "din gs", - "ph otos", - "photo s", - "phot os", - "Ġgr ande", - "Ġgrand e", - "Ġgran de", - "Ġgra nde", - "ol lect", - "oll ect", - "olle ct", - "_ lin", - "_l in", - "_li n", - "Ġaw ful", - "f ilters", - "filter s", - "fil ters", - "filt ers", - "Ġ alternate", - "Ġaltern ate", - "e sp", - "es p", - "Ġ compress", - "Ġcom press", - "Ġcomp ress", - "Ġcompr ess", - "e o", - "Ġ Scale", - "ĠS cale", - "ĠSc ale", - "ĠScal e", - "Ġin direct", - "Ġind irect", - "Ġindir ect", - "Ġ invoice", - "Ġin voice", - "Ġinv oice", - "Ġinvo ice", - "ĊĊ ĊĊĊĊĊĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊ", - "ĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊ", - "ĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊĊĊĊĊĊĊ ĊĊ", - "ĊĊĊĊĊĊĊĊĊĊĊ ĊĊĊĊĊ", - "Start ing", - "Star ting", - "Ġ Players", - "ĠP layers", - "ĠPl ayers", - "ĠPlay ers", - "ĠPlayer s", - "ĠPla yers", - "i ele", - "ie le", - "iel e", - ". then", - ".t hen", - ".th en", - ".the n", - "9 81", - "98 1", - "O rd", - "Or d", - "Ġ Tuple", - "ĠT uple", - "ĠTu ple", - "ĠTup le", - "Ġ bout", - "Ġb out", - "Ġbo ut", - "Ġbou t", - "Ġ Statistics", - "ĠStat istics", - "P review", - "Pr eview", - "Pre view", - "Prev iew", - "Ġp uzzle", - "Ġpu zzle", - "Ġpuzz le", - "Ġ Width", - "ĠW idth", - "ĠWid th", - "ST ATE", - "STAT E", - "STA TE", - "Ġ overlay", - "Ġover lay", - "Ġoverl ay", - "ĉ on", - "ĉo n", - "Ġin fr", - "Ġinf r", - "Ġsm allest", - "Ġsmall est", - "l ocked", - "lock ed", - "loc ked", - "ÑĤ о", - "s sl", - "ss l", - "7 79", - "77 9", - "Ġde emed", - "Ġdee med", - "Ġdeem ed", - "Ġs co", - "Ġsc o", - "r eck", - "re ck", - "rec k", - "Ġj Button", - "Ġ missions", - "Ġm issions", - "Ġmiss ions", - "Ġmission s", - "8 71", - "87 1", - "ç§ °", - ".Selected Index", - "T ABLE", - "TA BLE", - "TAB LE", - "S ept", - "Se pt", - "Sep t", - "Ġac knowledge", - "Ġack nowledge", - "Ġacknow ledge", - "Ġacknowled ge", - "Ġ strtotime", - "Ġstrt otime", - "Ġ Tell", - "ĠT ell", - "ĠTe ll", - "ĠTel l", - "ĠD ak", - "ĠDa k", - "Ġal uminum", - "Ġf ence", - "Ġfe nce", - "Ġfen ce", - "Ġ Stars", - "ĠSt ars", - "ĠStar s", - "ĠSta rs", - "CON FIG", - "CONF IG", - "Ġr etrofit", - "Ġretro fit", - "Ġ emphasis", - "Ġem phasis", - "Ġemph asis", - "Ġemphas is", - "/ header", - "/head er", - "/he ader", - "Ġ Something", - "ĠS omething", - "ĠSome thing", - "ĠSom ething", - "in ished", - "ini shed", - "inish ed", - "inis hed", - "=' \".$", - "='\" .$", - "='\". $", - "Ġ Validators", - "ĠValid ators", - "ĠValidator s", - "Ġp olar", - "Ġpol ar", - "Ġpo lar", - "s ections", - "se ctions", - "section s", - "sect ions", - "9 44", - "94 4", - ".as px", - ".asp x", - "Ġa spir", - "Ġas pir", - "Ġasp ir", - ". Mock", - ".M ock", - "Code Gen", - "Ġp eut", - "Ġpe ut", - "Ġpeu t", - "9 71", - "97 1", - "Ġaccept ing", - "Ġb acking", - "Ġback ing", - "Ġbac king", - "P icture", - "Pic ture", - "/ ap", - "/a p", - "е г", - "еР³", - "_ SEC", - "_S EC", - "_SE C", - "- use", - "-us e", - "-u se", - "an notation", - "ann otation", - "annot ation", - "Ġc ognitive", - "Ġcogn itive", - "Ġg rip", - "Ġgr ip", - "Ġgri p", - "h our", - "ho ur", - "hou r", - "Ġ Legal", - "ĠL egal", - "ĠLe gal", - "ĠLeg al", - "Ġe pic", - "Ġep ic", - ". toolStrip", - ".t oolStrip", - ".tool Strip", - ". notify", - ".n otify", - ".not ify", - ". Last", - ".L ast", - "OR IZ", - "M iddleware", - "Middle ware", - "cri ptions", - "cript ions", - "cription s", - "l ash", - "la sh", - "las h", - "_ FOUND", - "_F OUND", - "Ġ Liverpool", - "ĠLiver pool", - "Ġ {}\",", - "Ġ{ }\",", - "Ġ{} \",", - "9 31", - "93 1", - "Inst all", - "Ġ nit", - "Ġn it", - "Ġni t", - "Ġfig ured", - "Ġfigure d", - "Ġfigur ed", - "[ len", - "[l en", - ". Win", - ".W in", - ". platform", - ".pl atform", - "8 53", - "85 3", - "Ġgam bling", - "Ġgamb ling", - "( dt", - "(d t", - "a very", - "av ery", - "ave ry", - "aver y", - "ĉ include", - "ĉin clude", - "Wh ether", - "R outing", - "Ro uting", - "Ġth erap", - "Ġthe rap", - "Ġther ap", - "Rem ote", - "Ġ Loss", - "ĠL oss", - "ĠLo ss", - "ĠLos s", - "y ll", - "yl l", - "Ġappro ached", - "Ġapproach ed", - "Ġ Vehicle", - "ĠV ehicle", - "Ġ Alpha", - "ĠAl pha", - "Ġv ocê", - "Ġvoc ê", - "an swers", - "ans wers", - "answer s", - "NS Dictionary", - "9 54", - "95 4", - "cons ider", - "un used", - "unu sed", - "Ġ Fan", - "ĠF an", - "ĠFa n", - "or able", - "ora ble", - "f re", - "fr e", - "8 73", - "87 3", - "ĠDIS CLAIM", - "Ġ Actor", - "ĠA ctor", - "ĠAct or", - "ĠAc tor", - ". ]", - "to Have", - ". userId", - ".user Id", - "Ġspe eds", - "Ġspeed s", - "e way", - "ew ay", - "Ġrec urs", - "Ġrecur s", - "Ġ г", - "ĠÐ ³", - "_ priv", - "_p riv", - "_pr iv", - "_pri v", - "! âĢĿĊĊ", - "!âĢĿ ĊĊ", - "Ch oice", - "Cho ice", - "Ġs ettle", - "Ġset tle", - "Ġsett le", - "Ġ planes", - "Ġpl anes", - "Ġplan es", - "Ġplane s", - "Ġpla nes", - "' },", - "'} ,", - "T om", - "To m", - "I TER", - "IT ER", - "ITE R", - "! \"Ċ", - "!\" Ċ", - "å »", - "ach elor", - "ache lor", - "achel or", - "Ġse paration", - "Ġsepar ation", - "Ġseparat ion", - "Ġ dal", - "Ġd al", - "Ġda l", - "a dj", - "ad j", - "Ġ registers", - "Ġreg isters", - "Ġregister s", - "Ġregist ers", - "r iz", - "ri z", - "Ġ Notice", - "ĠNot ice", - "Ġ lu", - "Ġl u", - "Ġc ourage", - "Ġcour age", - "Ġcou rage", - "Ġ axes", - "Ġa xes", - "Ġax es", - "Ġaxe s", - "cell ent", - ". async", - ".as ync", - ".a sync", - "0 73", - "07 3", - "Ġcom patibility", - "Ġcompat ibility", - "ç «", - "Ġ !ĊĊ", - "Ġ! ĊĊ", - "Ġ!Ċ Ċ", - "ĉ title", - "ĉt itle", - "ĉti tle", - "Y LE", - "YL E", - "ĉ message", - "ĉm essage", - "U UID", - "UU ID", - "OL DER", - "OLD ER", - "Ġ HH", - "ĠH H", - "Ġ StyleSheet", - "ĠStyle Sheet", - "Ġacc essed", - "Ġaccess ed", - "Ġacces sed", - ". validation", - ".valid ation", - "t asks", - "task s", - "tas ks", - "Ġpoll ution", - "Ġpollut ion", - ". canvas", - ".c anvas", - ".can vas", - "Ġ ingredient", - "Ġing redient", - "ĠC abin", - "ĠCa bin", - "ĠCab in", - "A h", - "ol down", - "old own", - "ĠN OI", - "ĠNO I", - "Ġ ÃĹ", - "Ġà Ĺ", - "[ f", - "e duc", - "ed uc", - "edu c", - "y alty", - "yal ty", - "( not", - "(n ot", - "(no t", - "_ State", - "_St ate", - "9 33", - "93 3", - "a men", - "am en", - "ame n", - "7 95", - "79 5", - "7 39", - "73 9", - "Ġ dao", - "Ġd ao", - "Ġda o", - "u dad", - "ud ad", - "uda d", - "el lers", - "ell ers", - "elle rs", - "eller s", - "} &", - "l icity", - "lic ity", - "li city", - "licit y", - "_ WINDOW", - "_W INDOW", - "Ġt atto", - "Ġtat to", - "val or", - "va lor", - ". Range", - ".R ange", - "Ġreference d", - "Ġrefer enced", - "ĠRe serve", - "ĠRes erve", - "M oney", - "Mon ey", - "Mo ney", - "8 74", - "87 4", - "SC RIPT", - "SCRI PT", - "/ product", - "/pro duct", - "cho ices", - "choice s", - "Ġ tin", - "Ġt in", - "Ġti n", - "ãĤ ĵ", - "9 18", - "91 8", - "Ġ separator", - "Ġs eparator", - "Ġse parator", - "Ġsepar ator", - "Ġseparat or", - "Ġ pkg", - "Ġp kg", - "Ġpk g", - "am med", - "amm ed", - "Ġ MAT", - "ĠM AT", - "ĠMA T", - "! !ĊĊ", - "!! ĊĊ", - "!!Ċ Ċ", - "Ġ raid", - "Ġr aid", - "Ġra id", - "Ġmot ivation", - "Ġmotiv ation", - "Ġ XP", - "ĠX P", - "Ġ Background", - "ĠBack ground", - "Ġ Quaternion", - "ĠQu aternion", - ".define Property", - "i ker", - "ik er", - "ike r", - "ĉ parent", - "ĉp arent", - "Ġ Originally", - "ĠOrigin ally", - "ĠOriginal ly", - "ĠOrig inally", - "ant age", - "anta ge", - "ĠH ans", - "ĠHa ns", - "ĠHan s", - "Ġ timeline", - "Ġt imeline", - "Ġtime line", - "Ġtim eline", - ". cur", - ".c ur", - "o pic", - "op ic", - "opi c", - "ĠS equ", - "ĠSe qu", - "ĠSeq u", - "m ust", - "mu st", - "mus t", - "Ġ Coal", - "ĠCo al", - "Ġ formatter", - "Ġfor matter", - "Ġform atter", - "Ġformat ter", - "_ RGB", - "_R GB", - "_RG B", - "Ġ _(\"", - "Ġ_ (\"", - "Ġ_( \"", - "' }),Ċ", - "'} ),Ċ", - "'}) ,Ċ", - "Ġ= ================", - "Ġ== ===============", - "Ġ=== ==============", - "Ġ===== ============", - "Ġ==== =============", - "Ġ========== =======", - "Ġ======= ==========", - "Ġ FUNCTION", - "ĠF UNCTION", - "ĠFUN CTION", - "ĠFUNC TION", - "ĠFUNCT ION", - "Ġ lng", - "Ġl ng", - "Ġln g", - "ic ates", - "ica tes", - "icate s", - "l ive", - "li ve", - "liv e", - "_ engine", - "_e ngine", - "_eng ine", - "Ġt owns", - "Ġtown s", - "Ġtow ns", - "8 68", - "86 8", - "' ))ĊĊ", - "') )ĊĊ", - "')) ĊĊ", - "'))Ċ Ċ", - "Ġ PK", - "ĠP K", - "( api", - "(a pi", - "(ap i", - "ĉ scanf", - "ĉs canf", - "0 89", - "08 9", - "p acket", - "pack et", - "pa cket", - "pac ket", - ". phone", - ".p hone", - ".ph one", - "á Ģ", - "Ġ Andy", - "ĠAn dy", - "ĠAnd y", - "_N AMES", - "_NAME S", - "9 82", - "98 2", - "P LY", - "PL Y", - "9 55", - "95 5", - "Ġ mins", - "Ġm ins", - "Ġmin s", - "Ġmi ns", - "i mi", - "im i", - "Ġ brick", - "Ġb rick", - "Ġbr ick", - "Ġbri ck", - "Ġ blade", - "Ġbl ade", - "Ġbla de", - ". stdout", - ".std out", - "} `;Ċ", - "}` ;Ċ", - "S hift", - "Sh ift", - "ĉ sb", - "ĉs b", - "Ġ Checks", - "ĠCheck s", - "ĠChe cks", - "Ġphenomen on", - "Av atar", - "Ġmin istry", - "Ġmini stry", - "Ġminist ry", - "r ose", - "ro se", - "ros e", - "ĉ File", - "ĉF ile", - "8 78", - "87 8", - "Ġt itled", - "Ġtitle d", - "Ġtit led", - "( LOG", - "(L OG", - "Ġ gan", - "Ġg an", - "Ġga n", - "d esign", - "de sign", - "des ign", - "( ),čĊ", - "() ,čĊ", - "(), čĊ", - "Ġ bones", - "Ġb ones", - "Ġbo nes", - "Ġbon es", - "Ġbone s", - "s tm", - "st m", - "ÅĽ Äĩ", - "Ġ InputStream", - "ĠInput Stream", - "Ġvol unt", - "Ġ Serializable", - "ĠSerial izable", - "Ġ fighter", - "Ġf ighter", - "Ġfight er", - "Ġ Drag", - "ĠD rag", - "ĠDr ag", - "ĠDra g", - "T witter", - "Tw itter", - "Ġsub sid", - "Ġsubs id", - "ç ¼", - "Ġ forums", - "Ġfor ums", - "Ġforum s", - ". loading", - ".load ing", - ".lo ading", - "log ged", - "logg ed", - "_ this", - "_t his", - "_th is", - "Ġ terrain", - "Ġter rain", - "Ġterr ain", - "Ġterra in", - "Ġir re", - "Ġirr e", - "Ġ Ing", - "ĠI ng", - "ĠIn g", - "Ġ CN", - "ĠC N", - "_ objects", - "_object s", - "_obj ects", - ". uid", - ".ui d", - ".u id", - "Ġconscious ness", - "T INGS", - "TING S", - "ĠG all", - "ĠGal l", - "ĠGa ll", - "Ġport ray", - "0 56", - "05 6", - "Ġ Developer", - "ĠDe veloper", - "ĠDevelop er", - "Ġ participant", - "Ġpart icipant", - "Ġparticip ant", - "Ġ \";čĊ", - "Ġ\" ;čĊ", - "Ġ\"; čĊ", - "/ model", - "/m odel", - "/mod el", - "7 94", - "79 4", - "Ġ Operations", - "ĠOper ations", - "ĠOperation s", - "^ \\", - "Ġ Later", - "ĠL ater", - "ĠLa ter", - "ĠLat er", - "ĠLate r", - "Ġ raises", - "Ġr aises", - "Ġraise s", - "Ġrais es", - "Ġra ises", - "- none", - "-n one", - "-no ne", - "-non e", - ". meta", - ".m eta", - ".me ta", - ".met a", - "= '.$", - "=' .$", - "='. $", - "F inished", - "Fin ished", - "Finish ed", - "Ġre placing", - "Ġrepl acing", - "Ġ sampling", - "Ġs ampling", - "Ġsam pling", - "Ġsamp ling", - "ĠJ en", - "ĠJe n", - "\" There", - "\"The re", - "\"T here", - "RE AL", - "REA L", - "A LE", - "AL E", - "ìĬ ¤", - "Or ders", - "Order s", - "Ord ers", - "_ parameter", - "_param eter", - "_para meter", - "ĠOlymp ic", - "Ġtr ès", - "Ġ arena", - "Ġa rena", - "Ġare na", - "Ġar ena", - "Ġaren a", - "i ol", - "io l", - "; ?>", - "Ġimp acts", - "Ġimpact s", - "Ġ WS", - "ĠW S", - ": get", - ":g et", - "Ġf lights", - "Ġfl ights", - "Ġflight s", - "ĠRuss ell", - "ĠRus sell", - "c amera", - "came ra", - "cam era", - "F n", - "s igma", - "sig ma", - "Ġ forcing", - "Ġfor cing", - "Ġforc ing", - "Ġ locals", - "Ġloc als", - "Ġlocal s", - "Ġ departure", - "Ġdepart ure", - "Ġcelebr ation", - "Ġ Say", - "ĠS ay", - "ĠSa y", - "8 84", - "88 4", - "ï¼ Ĵ", - "ĠH ills", - "ĠHill s", - "ĠHil ls", - ".has OwnProperty", - "Ġ typings", - "Ġtyp ings", - "Ġtyping s", - ". API", - ".A PI", - ".AP I", - "Ġd onation", - "Ġdo nation", - "Ġdon ation", - "Operation Exception", - ". Activity", - ".Act ivity", - "c plusplus", - "Ġ Charlie", - "ĠChar lie", - "ĠCharl ie", - "Ġim ported", - "Ġimport ed", - "Ġimp orted", - "Ġd ann", - "Ġda nn", - "Ġdan n", - "Ġocc asions", - "Ġoccas ions", - "Ġoccasion s", - "Ġimplement ing", - "Ġ purple", - "Ġp urple", - "Ġpur ple", - ". dialog", - ".d ialog", - ".di alog", - "SQL Exception", - "er no", - "ern o", - "Ġw ars", - "Ġwar s", - "Ġwa rs", - "Ġ paste", - "Ġp aste", - "Ġpast e", - "Ġpas te", - "Ġpa ste", - "Ġdecre ased", - "Ġdecrease d", - "Ġhar sh", - "Ġel abor", - "Ġela bor", - "in puts", - "input s", - "inp uts", - "Ġ Views", - "ĠView s", - "ĠVi ews", - "ĠVie ws", - "Ġ errorMessage", - "Ġerror Message", - "_ mul", - "_m ul", - "_mu l", - "ĉ write", - "ĉw rite", - "Ġ Cop", - "ĠC op", - "ĠCo p", - "Ġ Annual", - "ĠAnn ual", - "( button", - "(b utton", - "Ġ vida", - "Ġv ida", - "Ġvi da", - "Ġvid a", - "b ars", - "bar s", - "ba rs", - "ĠHar vard", - "ĉ expect", - "ĉex pect", - "ĉexp ect", - "Ġ indexes", - "Ġindex es", - "Ġinde xes", - "Ġdocument ary", - "Ġf lesh", - "Ġfl esh", - "Ġfle sh", - "OR LD", - "Ġ Delta", - "ĠD elta", - "ĠDel ta", - "M AND", - "MA ND", - "MAN D", - "B rush", - "Br ush", - "Bru sh", - "- column", - "-c olumn", - "-col umn", - "Ġdevelop ments", - "Ġdevelopment s", - "9 74", - "97 4", - "7 83", - "78 3", - "method Visitor", - "s lice", - "sl ice", - "Ġ PDO", - "ĠP DO", - "ĠPD O", - "Ġinv esting", - "Ġinvest ing", - "8 67", - "86 7", - "ir able", - "ira ble", - "Ġ xmlns", - "Ġxml ns", - "ï¼ Ľ", - "ar ta", - "art a", - "Ġthe ories", - "Ġtheor ies", - "Ġtheo ries", - "_ city", - "_c ity", - "_ci ty", - "Ġ $__", - "Ġ$ __", - "Ġ$_ _", - "C reating", - "Cre ating", - "Cr eating", - "Creat ing", - "( pr", - "(p r", - "D ropdown", - "Drop down", - "is match", - "ism atch", - "Ġ NET", - "ĠN ET", - "ĠNE T", - "9 26", - "92 6", - "' ])){Ċ", - "'] )){Ċ", - "']) ){Ċ", - "'])) {Ċ", - "'])){ Ċ", - "Ġ Values", - "ĠVal ues", - "ĠValue s", - "Ġ SEO", - "ĠS EO", - "ĠSE O", - "Ġ STAT", - "ĠST AT", - "ĠSTA T", - "Ġe cosystem", - "Ġeco system", - "Ġ tempt", - "Ġt empt", - "Ġtem pt", - "Ġtemp t", - "Ġ \\\\", - "Ġ\\ \\", - "Ġ //{Ċ", - "Ġ// {Ċ", - "Ġ//{ Ċ", - "Ġ Christopher", - "ĠChrist opher", - "ĠChristoph er", - "ĠKent ucky", - "ĠHttp ServletResponse", - "ĠHttpServlet Response", - "Ġh ybrid", - "Ġhy brid", - "y on", - "yo n", - "Ġ feeding", - "Ġfe eding", - "Ġfeed ing", - "Ġfee ding", - "Ġ Extra", - "ĠEx tra", - "ĠExt ra", - "ĠExtr a", - "N orm", - "No rm", - "Nor m", - "IT CH", - "Ġ Sean", - "ĠS ean", - "ĠSe an", - "ĠSea n", - "Ġ Upload", - "ĠUp load", - "m un", - "mu n", - "p ur", - "pu r", - "Ġ persistent", - "Ġp ersistent", - "Ġpers istent", - "Ġpersist ent", - "ĠI DC", - "ĠID C", - "Ġ Perform", - "ĠPer form", - "ĠPerf orm", - "8 63", - "86 3", - ". merge", - ".m erge", - "_ room", - "_r oom", - "_ro om", - "Mean while", - "! ='", - "!= '", - "Ġ Wel", - "ĠW el", - "ĠWe l", - "Args Constructor", - "8 87", - "88 7", - ". Database", - ".D atabase", - ".Data base", - "Ġco unting", - "Ġcount ing", - "Ġcoun ting", - "( )*", - "() *", - "Ķ åĽŀ", - "Ġ TOP", - "ĠT OP", - "ĠTO P", - "m ill", - "mi ll", - "mil l", - "Ġ DT", - "ĠD T", - "IGN ED", - "9 56", - "95 6", - "Ġ KB", - "ĠK B", - "Ġcom ply", - "Ġcomp ly", - "Ġcompl y", - "S outh", - "So uth", - "Sou th", - "_ collection", - "_c ollection", - "_col lection", - "_coll ection", - "_collect ion", - "Ch apter", - "Cha pter", - "Ġexpl aining", - "Ġexplain ing", - "_ AM", - "_A M", - "_ ts", - "_t s", - "c ards", - "card s", - "car ds", - "Ġ quel", - "Ġqu el", - "Ġque l", - "Ġq uel", - "Ġ pole", - "Ġp ole", - "Ġpol e", - "Ġpo le", - "Ġtouch down", - "Ġ Others", - "ĠO thers", - "ĠOther s", - "Ġpe ers", - "Ġpeer s", - "Ġpee rs", - "Ġ TypeError", - "ĠType Error", - "7 63", - "76 3", - "Ġsix th", - "Ġch eer", - "Ġche er", - "Ġdis pute", - "Ġdisp ute", - "Ġdisput e", - "9 63", - "96 3", - "8 93", - "89 3", - "u sc", - "us c", - ") ],", - ")] ,", - "th umb", - "Ġh iding", - "Ġhi ding", - "Ġhid ing", - "Ġ SIG", - "ĠS IG", - "ĠSI G", - "l ikes", - "li kes", - "like s", - "lik es", - "Ġ PAGE", - "ĠP AGE", - "ĠPA GE", - ". Reflection", - ".Ref lection", - "Ġhead quarters", - "T ING", - "TI NG", - "Ġ Ghost", - "ĠG host", - "ĠGh ost", - "M LE", - "ML E", - "$ Ċ", - "Ġcont rary", - "Ġcontr ary", - "Ġcontra ry", - "ext end", - "' ]).", - "'] ).", - "']) .", - "FF ECT", - "FFE CT", - "Ġ Pinterest", - "ĠP interest", - "úmer o", - "ric ane", - "rica ne", - "ĉ session", - "ĉs ession", - "Ġcr ystal", - "Ġcry stal", - "Ġcryst al", - "- Control", - "-C ontrol", - "overn ment", - "o graf", - "og raf", - "ogr af", - "ogra f", - "9 61", - "96 1", - "- action", - "-a ction", - "-ac tion", - "v olume", - "vol ume", - "f ten", - "ft en", - "fte n", - "Ġun con", - "Ġunc on", - "Ġ animate", - "Ġan imate", - "Ġanim ate", - "Ġani mate", - "Ġ lease", - "Ġl ease", - "Ġle ase", - "s cr", - "sc r", - "Ġre fuse", - "Ġref use", - "ãĢ ĭ", - "f tp", - "ft p", - "in formation", - "inform ation", - "Ġeval uated", - "Ġevaluate d", - "Ġevalu ated", - "Ġin jection", - "Ġinj ection", - "Ġinject ion", - "Ġ jack", - "Ġj ack", - "Ġja ck", - "Ġjac k", - "Ġwork shop", - "Ġworks hop", - "æ³ ¨", - "P TH", - "PT H", - "Ġ Ts", - "ĠT s", - "o ffer", - "of fer", - "off er", - "ĉ os", - "ĉo s", - "Ġking dom", - "M issing", - "Miss ing", - "Mis sing", - "Ġlaw makers", - "Ġlawmaker s", - "ext Field", - "Ġs inging", - "Ġsin ging", - "Ġsing ing", - "a bi", - "ab i", - "/ client", - "/c lient", - "/cl ient", - "/cli ent", - ". media", - ".m edia", - ".me dia", - ".med ia", - "ATEG ORY", - "Sign ature", - "Sig nature", - "% ',Ċ", - "%' ,Ċ", - "%', Ċ", - "Ġ Fuck", - "ĠF uck", - "ĠFu ck", - "] [:", - "][ :", - "Ġs ensors", - "Ġsens ors", - "Ġsensor s", - "/ com", - "/c om", - "/co m", - "Ġ Primary", - "ĠPr imary", - "ĠPri mary", - "ĠPrim ary", - ". SQL", - ".S QL", - "_ program", - "_p rogram", - "_pro gram", - "_pr ogram", - "_prog ram", - "Ġp ills", - "Ġpil ls", - "Ġpill s", - "Ġ integral", - "Ġint egral", - "Ġinteg ral", - "Ġintegr al", - "Ġ fleet", - "Ġf leet", - "Ġfle et", - "Ġflee t", - "Ġd ropping", - "Ġdr opping", - "Ġdrop ping", - "Ġdro pping", - ". sl", - ".s l", - "B een", - "Be en", - "Ġ pets", - "Ġp ets", - "Ġpe ts", - "Ġpet s", - "Ġad vised", - "Ġadv ised", - "Ġadvis ed", - "Ġadvise d", - "Ġ dragon", - "Ġd ragon", - "Ġdr agon", - "Ġdrag on", - "Ġdra gon", - "_ EDIT", - "_ED IT", - "( im", - "(i m", - "9 39", - "93 9", - "F ER", - "FE R", - "Ġ Drug", - "ĠD rug", - "ĠDr ug", - "( random", - "(r andom", - "(rand om", - "Ġ compression", - "Ġcom pression", - "Ġcomp ression", - "Ġcompr ession", - "Ġcompress ion", - "o ust", - "ou st", - "ous t", - "[ %", - "Ġ buyer", - "Ġbu yer", - "Ġbuy er", - "h op", - "ho p", - "R oles", - "Role s", - "Ro les", - "Rol es", - "man age", - "ma nage", - "mana ge", - "Ġpain ful", - "Ġ Branch", - "ĠBr anch", - "ĠBran ch", - "- modal", - "-m odal", - "-mod al", - "e nant", - "en ant", - "ena nt", - "enan t", - "Ġ Mesh", - "ĠM esh", - "ĠMe sh", - "ĠMes h", - "/ font", - "/f ont", - "ĠG raham", - "ĠGra ham", - "Ġ âĺ", - "Ġâ ĺ", - "Ġ nc", - "Ġn c", - "ĠFranc is", - "ĠFran cis", - "Ġspec ification", - "Ġspecific ation", - "Ġdam ages", - "Ġdamage s", - "- config", - "-con fig", - "-conf ig", - "Ġthe oret", - "Ġtheor et", - "Ġtheo ret", - "s ecure", - "sec ure", - "_ multi", - "_m ulti", - "_mul ti", - "_mult i", - "aceut ical", - "Ġdem anding", - "Ġdemand ing", - "en ne", - "enn e", - "I STS", - "IS TS", - "IST S", - "0 94", - "09 4", - "( )));ĊĊ", - "() ));ĊĊ", - "()) );ĊĊ", - "()));Ċ Ċ", - "())) ;ĊĊ", - "())); ĊĊ", - "Re ason", - "Re cent", - "Rec ent", - "ph ase", - "pha se", - "phas e", - "Ġ psy", - "Ġp sy", - "Ġps y", - "_ MAN", - "_M AN", - "_MA N", - "Ġvol unteer", - "Ġvolunte er", - "Ġvolunt eer", - "å ¿", - "istrib uted", - "istribute d", - "l io", - "li o", - "Ġproduct ivity", - "_ comm", - "_c omm", - "_com m", - "_co mm", - "S pring", - "Sp ring", - "Spr ing", - "n is", - "ni s", - ". weight", - ".w eight", - ".we ight", - "ĠC ancer", - "ĠCan cer", - "ĠCanc er", - "Al loc", - "All oc", - "Ġ Tweet", - "ĠT weet", - "ĠTwe et", - "Ġsepar ately", - "Ġseparate ly", - "Ġseparat ely", - "ĉ check", - "ĉc heck", - "ĉch eck", - "_ properties", - "_p roperties", - "_prop erties", - ". Unit", - ".U nit", - ".Un it", - "8 29", - "82 9", - "_ CLK", - "_C LK", - "_CL K", - "Ġ gt", - "Ġg t", - "Ġ ();ĊĊ", - "Ġ( );ĊĊ", - "Ġ() ;ĊĊ", - "Ġ();Ċ Ċ", - "Ġ(); ĊĊ", - "Ġh andy", - "Ġhand y", - "Ġhan dy", - "8 34", - "83 4", - "ĠTh ompson", - "ĠThom pson", - "Ġun necessary", - "Ġunn ecessary", - "Ġ Reader", - "ĠRe ader", - "ĠRead er", - "8 94", - "89 4", - "G N", - "= request", - "=re quest", - "=req uest", - "Ġ Utility", - "ĠU tility", - "ĠUtil ity", - "ĠUt ility", - ". Repository", - ".Re pository", - "Ġ Ax", - "ĠA x", - "hy dr", - "7 91", - "79 1", - "i eu", - "ie u", - "Ġ thy", - "Ġt hy", - "Ġth y", - "Ġ lt", - "Ġl t", - "_ mail", - "_m ail", - "_ma il", - "ä¿® æĶ¹", - "a iland", - "ail and", - "ai land", - "Ġ Philip", - "ĠPh ilip", - "ĠPhil ip", - "ĠPhi lip", - "Ġb itter", - "Ġbit ter", - "Ġbitte r", - "Ġb etting", - "Ġbet ting", - "8 37", - "83 7", - "Ġt imed", - "Ġtime d", - "Ġtim ed", - "Ġti med", - "o cks", - "oc ks", - "ock s", - "0 76", - "07 6", - "' a", - "Ġal gorithms", - "Ġalgorithm s", - "Ġ reinterpret", - "Ġre interpret", - "Ġt oss", - "Ġto ss", - "r ogen", - "ro gen", - "rog en", - "Ġh oped", - "Ġhope d", - "Ġhop ed", - "Ġho ped", - "( selected", - "(se lected", - "(select ed", - "(sel ected", - "Ġ venture", - "Ġvent ure", - "Ġven ture", - "T EX", - "TE X", - "Ġ Leave", - "ĠLe ave", - ". Substring", - ".Sub string", - "Ġgr ateful", - "Ġgrat eful", - "Ġgrate ful", - "7 43", - "74 3", - "u ka", - "uk a", - "Ġ Consumer", - "ĠCon sumer", - "ĠCons umer", - "ĠConsum er", - "Ġag greg", - "Ġagg reg", - "C ircle", - "ภģ", - "_ blocks", - "_b locks", - "_block s", - "_bl ocks", - "_bloc ks", - "Ġleg ally", - "Ġlegal ly", - "Ġ \"|", - "Ġ\" |", - "ãĥ ĥ", - ". board", - ".b oard", - ".bo ard", - ". Ab", - ".A b", - "Function s", - "Fun ctions", - "rec ipe", - "è ĩ", - "ĠO xford", - "ĠOx ford", - "Ġw holes", - "Ġwh oles", - "Ġwho les", - "Ġwhole s", - ". Build", - ".B uild", - "_ changed", - "_ch anged", - "_change d", - "_chan ged", - "h ai", - "ha i", - "Ġ departments", - "Ġdepartment s", - "Ġdepart ments", - "9 64", - "96 4", - "I mp", - "Im p", - "Ġcoal ition", - "IN FRINGEMENT", - "Ġem power", - "Ġemp ower", - "it ches", - "itch es", - "N orth", - "Nor th", - "Ġin flamm", - "Ġinfl amm", - "O NSE", - "ON SE", - "ONS E", - "Ġmiss ile", - "ĠR aj", - "ĠRa j", - "Ġ Issue", - "ĠI ssue", - "ĠIss ue", - "Ġ atoi", - "Ġa toi", - "Ġat oi", - "c aled", - "ca led", - "cale d", - "cal ed", - ". Controllers", - ".Cont rollers", - ".Control lers", - ".Controller s", - "Ġ Wolf", - "ĠW olf", - "ĠWo lf", - "ĠWol f", - "Ġcrush ers", - "Ġcrusher s", - "á» ĩ", - ". Auth", - ".A uth", - ".add Attribute", - "h is", - "hi s", - "Ġbo ots", - "Ġboot s", - "Ġboo ts", - ". clean", - ".c lean", - ".cl ean", - "c amp", - "ca mp", - "cam p", - "Ġ tenant", - "Ġt enant", - "Ġte nant", - "Ġten ant", - "Ġt une", - "Ġtu ne", - "Ġtun e", - "Ġ {}'.", - "Ġ{ }'.", - "Ġ{} '.", - "Ġwork out", - "Re po", - "Rep o", - "Ġpart ially", - "Ġpartial ly", - "Ġparti ally", - "MI SSION", - "MISS ION", - "j amin", - "ja min", - "jam in", - "Ġ SB", - "ĠS B", - "Ġd etermination", - "Ġde termination", - "Ġdeter mination", - "Ġdetermin ation", - "Ġdeterm ination", - "Ġ' ');Ċ", - "Ġ'' );Ċ", - "Ġ'') ;Ċ", - "Ġ''); Ċ", - "ĠB eng", - "ĠBe ng", - "ĠBen g", - "Ġ vos", - "Ġv os", - "Ġvo s", - "Ġin hab", - "Ġinh ab", - "/ lang", - "/l ang", - "s burgh", - "sburg h", - "Exec utor", - "h one", - "ho ne", - "hon e", - "Ġ Challenge", - "ĠCh allenge", - "ĠChall enge", - "_ links", - "_l inks", - "_link s", - "_lin ks", - ". Level", - ".Le vel", - "Ġunder ground", - "- code", - "-c ode", - "-co de", - "9 59", - "95 9", - "Ġopt imization", - "Ġoptim ization", - "log ging", - "logg ing", - "_ dest", - "_d est", - "_de st", - "_des t", - "Ġ snake", - "Ġsn ake", - "Ġsna ke", - "Ġchem icals", - "Ġchemical s", - "_IMPORT ED", - "ad oop", - "ado op", - "adoo p", - "ĠTH AT", - "man aged", - "manage d", - "mana ged", - "Ġred uces", - "Ġredu ces", - "Ġreduce s", - "Ġ REAL", - "ĠRE AL", - "Ġ Guy", - "ĠG uy", - "ĠGu y", - "_GENER IC", - "_GEN ERIC", - "/ ********************************", - "/************************ ********", - "/******** ************************", - "/**************** ****************", - ". amount", - ".a mount", - ".am ount", - "Ġ dere", - "Ġd ere", - "Ġde re", - "Ġder e", - "get Time", - "Ġp ant", - "Ġpa nt", - "Ġpan t", - "an onymous", - "anon ymous", - "Ġhar mony", - "Ġharm ony", - "Ġharmon y", - "Ġ Alan", - "ĠA lan", - "ĠAl an", - "ĠAla n", - "Ġsc enarios", - "Ġscen arios", - "Ġscenario s", - "Ġd irt", - "Ġdi rt", - "Ġdir t", - "h tags", - "ht ags", - "htag s", - "hta gs", - "M c", - "S hell", - "Sh ell", - "She ll", - "r in", - "ri n", - "{ čĊčĊ", - "{čĊ čĊ", - ". pow", - ".p ow", - ".po w", - "ĉ client", - "ĉc lient", - "ĉcl ient", - "ĉcli ent", - "Ġcon spiracy", - "Ġconspir acy", - "Ġad mission", - "Ġadm ission", - "Ġ Regional", - "ĠReg ional", - "ĠRegion al", - "Ġ ViewController", - "ĠView Controller", - "ĠPhil ippines", - "ĠPhilipp ines", - "ĠPhilippine s", - "Ġde pos", - "Ġdep os", - "Ġp ap", - "Ġpa p", - "9 62", - "96 2", - "Ġ Pad", - "ĠP ad", - "ĠPa d", - "P aul", - "Pa ul", - ". ComboBox", - ".Com boBox", - "Ġt utor", - "Ġtu tor", - "Ġtut or", - "Ġtuto r", - "Ġ Recipe", - "ĠRec ipe", - "w riting", - "wr iting", - "Ġcontrib utor", - "O TH", - "OT H", - "S mall", - "Sm all", - "V I", - "Ġh acer", - "Ġha cer", - "Ġhace r", - "Ġhac er", - "e qu", - "eq u", - "Ġ Examples", - "ĠEx amples", - "ĠExample s", - "ĠExam ples", - "h uman", - "hu man", - "hum an", - ". messages", - ".m essages", - ".message s", - "ĉ typ", - "ĉt yp", - "Ġ (čĊ", - "Ġ( čĊ", - "Ġ SSL", - "ĠS SL", - "ĠSS L", - "L EN", - "LE N", - "ĠRom ney", - "( grid", - "(g rid", - "(gr id", - "ĉ min", - "ĉm in", - "Ġ >ĊĊ", - "Ġ> ĊĊ", - "Ġ>Ċ Ċ", - "Ġf ruits", - "Ġfr uits", - "Ġfruit s", - "Ġv oter", - "Ġvo ter", - "Ġvot er", - "Ġvote r", - "In line", - "p ane", - "pan e", - "pa ne", - "Ġ Collections", - "ĠC ollections", - "ĠCol lections", - "ĠCollection s", - "ĠColl ections", - "ĠCollect ions", - "char set", - "chars et", - "Ġ spam", - "Ġsp am", - "Ġspa m", - "z b", - "it emap", - "ite map", - "item ap", - "Ġs ucceeded", - "Ġsuc ceeded", - "Ġsucceed ed", - "_ COL", - "_C OL", - "_CO L", - "Ġ elapsed", - "Ġel apsed", - "i meter", - "im eter", - "ime ter", - "imet er", - "Ġre covered", - "Ġrecover ed", - "T ensor", - "hat tan", - "hatt an", - ". setup", - ".set up", - "i sto", - "is to", - "ist o", - "( head", - "(h ead", - "9 77", - "97 7", - "Ġ SIZE", - "ĠS IZE", - "ĠSI ZE", - "Ġt actics", - "Ġtact ics", - "Ġtactic s", - "Ġtac tics", - "Ġdis tur", - "Ġdist ur", - "Ġpr eval", - "Ġpre val", - "Ġprev al", - "ic ios", - "ici os", - "icio s", - "( Value", - "(V alue", - "_ cols", - "_c ols", - "_col s", - "_co ls", - "Ġ Fat", - "ĠF at", - "ĠFa t", - "Ġse al", - "Ġsea l", - "Ġ sons", - "Ġs ons", - "Ġso ns", - "Ġson s", - "Ġens ures", - "Ġensure s", - "0 95", - "09 5", - "Ġp ressing", - "Ġpres sing", - "Ġpress ing", - "= &", - "igen ous", - "Ġharass ment", - "_ JSON", - "_J SON", - "_JS ON", - "Ġign or", - "Ġig nor", - "yn omial", - "ynom ial", - "o mer", - "om er", - "ome r", - "_ static", - "_st atic", - "_stat ic", - "_sta tic", - "Ġsign ificance", - "Ġsignific ance", - "Ġsignifica nce", - "Ġc ircles", - "Ġcirc les", - "Ġcircle s", - "Ġcir cles", - "_ System", - "_S ystem", - "Ġdisc ipline", - "Ġdiscipl ine", - "Ġd ressed", - "Ġdr essed", - "Ġdress ed", - "Ġ sphere", - "Ġs phere", - "Ġsp here", - "Ġsph ere", - "9 27", - "92 7", - "Ġcl imb", - "Ġclim b", - "Ġcli mb", - "7 59", - "75 9", - "_ actions", - "_a ctions", - "_action s", - "_act ions", - "ĠB ab", - "ĠBa b", - "Ġ' =',", - "Ġ'=' ,", - "Ġ'= ',", - "_ schema", - "_s chema", - "\" use", - "Ġ unders", - "Ġun ders", - "Ġunder s", - "Ġund ers", - "Ġunde rs", - "Ġc ups", - "Ġcu ps", - "Ġcup s", - ". screen", - ".s creen", - ".sc reen", - "/ new", - "/n ew", - "/ne w", - "Ġapp earing", - "Ġappe aring", - "Ġappear ing", - "T OP", - "TO P", - "v ised", - "vis ed", - "vi sed", - "vise d", - "c lang", - "cl ang", - "cla ng", - "Ġinvest igators", - "Ġinvestig ators", - "Ġinvestigator s", - "Ġm ysterious", - "Ġmyster ious", - "Ġprom ising", - "Ġqual ify", - "Ġqua lify", - "Ġquali fy", - "Ġc ave", - "Ġca ve", - "Ġcav e", - "Ġ equip", - "Ġe quip", - "Ġequ ip", - "= x", - "G T", - "( link", - "(l ink", - "(li nk", - ". velocity", - ".v elocity", - ".vel ocity", - ". erase", - ".e rase", - ".er ase", - "o ter", - "ot er", - "ote r", - "++++ ++++", - "pro fit", - "prof it", - "Ġ zones", - "Ġz ones", - "Ġzone s", - "Ġzo nes", - "_ uid", - "_u id", - "_ui d", - "- ser", - "-s er", - "-se r", - "Ġob jectives", - "Ġobject ives", - "Ġobjective s", - "Ġmil f", - "Ġmi lf", - "web kit", - "( match", - "(m atch", - "(mat ch", - "n eh", - "ne h", - "Ġ Associated", - "ĠAssoci ated", - "ĠAssociate d", - "ĠAssoc iated", - "Ġ Todo", - "ĠT odo", - "ĠTo do", - "ĠTod o", - "= d", - "0 65", - "06 5", - "C am", - "Ca m", - "Ġv ocal", - "Ġvo cal", - "Ġvoc al", - "Ġ sudo", - "Ġs udo", - "Ġsu do", - "Ġsud o", - "( EX", - "(E X", - "Ġt rou", - "Ġtr ou", - "Ġtro u", - "A BC", - "AB C", - ". bean", - ".b ean", - ".be an", - "Ġ Ground", - "ĠG round", - "ĠGr ound", - "ĠGro und", - "Ġ REST", - "ĠR EST", - "ĠRE ST", - "ĠRES T", - "we ets", - "weet s", - "I ng", - "In g", - "i mon", - "im on", - "imo n", - "9 46", - "94 6", - "_ bus", - "_b us", - "Ġ COLOR", - "ĠC OLOR", - "ĠCOL OR", - "un to", - "unt o", - "Ġf oss", - "Ġfo ss", - "Ġfos s", - "Ġ Links", - "ĠL inks", - "ĠLink s", - "ĠLin ks", - "8 69", - "86 9", - "ä ng", - "än g", - "/ forms", - "/form s", - "pr ises", - "prise s", - "pri ses", - "Ġ achievement", - "Ġachie vement", - "Ġachieve ment", - "C ALL", - "CA LL", - "CAL L", - "е лÑĮ", - "ел ÑĮ", - "Ġ Verify", - "ĠVer ify", - "_ SOURCE", - "_S OURCE", - "apt cha", - "I DD", - "ID D", - "_ reference", - "_re ference", - "_ref erence", - "_refer ence", - "G old", - "Go ld", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "9 47", - "94 7", - "Re ceiver", - "Receive r", - "0 99", - "09 9", - "Ġ aj", - "Ġa j", - "_ direction", - "_d irection", - "_dir ection", - "_di rection", - "_direct ion", - "} ]", - "Ġ Compet", - "ĠCom pet", - "ĠComp et", - "Ġ bang", - "Ġb ang", - "Ġban g", - "Ġba ng", - "7 98", - "79 8", - "ĠC ass", - "ĠCas s", - "ĠCa ss", - "- url", - "-u rl", - "t echn", - "te chn", - "tech n", - "tec hn", - "ĠJer usalem", - "long itude", - "' );čĊčĊ", - "') ;čĊčĊ", - "'); čĊčĊ", - "');čĊ čĊ", - "Ġwin ners", - "Ġwinner s", - "T asks", - "Task s", - "Ġ DMA", - "ĠD MA", - "ĠDM A", - "Ġ tooltip", - "Ġto oltip", - "Ġtool tip", - "İ ·", - "ĠB ra", - "ĠBr a", - "_ duration", - "_d uration", - "_dur ation", - "c ury", - "cur y", - "cu ry", - "par ents", - "pare nts", - "parent s", - "paren ts", - "- --- >(", - ">> (", - "Ġ Kir", - "ĠK ir", - "ĠKi r", - "Ġ intros", - "Ġint ros", - "Ġintr os", - "Ġintro s", - "Ġsk etch", - "Ġsk illed", - "Ġskill ed", - "Ġ immer", - "Ġim mer", - "Ġimm er", - "Ġade quate", - "Ġadequ ate", - "_ rep", - "_re p", - "_r ep", - "( header", - "(head er", - "_ like", - "_l ike", - "_li ke", - "Ġper ceived", - "Ġperceive d", - "Ġperce ived", - "s sh", - "ss h", - "Ġ assuming", - "Ġas suming", - "Ġass uming", - "Ġassum ing", - "Ġ ff", - "Ġf f", - "_ uuid", - "_u uid", - "u las", - "ul as", - "ula s", - "Ġdem ocratic", - "Ġdemocr atic", - "Ġdemocrat ic", - ". entities", - ".ent ities", - "S eries", - "Se ries", - "Ser ies", - "aph ore", - "Ġnew er", - "Ġne wer", - "} (", - "S EC", - "SE C", - "a iro", - "air o", - "ai ro", - "Ġcom mod", - "Ġcomm od", - "Ġpriv ilege", - "Ġprivile ge", - "Ġde ux", - "Ġ Hop", - "ĠH op", - "ĠHo p", - ". '/", - ".' /", - "c tic", - "ct ic", - ". ';Ċ", - ".' ;Ċ", - "< ?=", - " C", - "ĠWar ren", - "Ġ optimizer", - "Ġopt imizer", - "Ġoptim izer", - "Ġoptimize r", - "ĠSER VICES", - "ĠSERVICE S", - "_ oper", - "_op er", - "_o per", - "get Attribute", - "ĠMc K", - "_ self", - "_s elf", - "_se lf", - "_sel f", - "0 84", - "08 4", - ". rs", - ".r s", - "\" )ĊĊĊ", - "\") ĊĊĊ", - "\")Ċ ĊĊ", - "\")ĊĊ Ċ", - "Get Component", - "er ce", - "erc e", - "Ġt ous", - "Ġto us", - "Ġtou s", - "un its", - "unit s", - "uni ts", - "' ]);čĊ", - "'] );čĊ", - "']) ;čĊ", - "']); čĊ", - "Z oom", - "/ E", - "Ġob sc", - "Ġobs c", - "Ġfast est", - "Ġfas test", - "on line", - "Ġpeace ful", - "f fen", - "ff en", - "ffe n", - "Ġ cargo", - "Ġc argo", - "Ġcar go", - "Ġcarg o", - "ĉ pr", - "ĉp r", - "Ġse eks", - "Ġsee ks", - "Ġseek s", - "z u", - "0 74", - "07 4", - "T rim", - "Tr im", - "Tri m", - "Ġ ward", - "Ġw ard", - "Ġwar d", - "Ġwa rd", - "Ġv erd", - "Ġver d", - "Ġve rd", - "Ġ blogs", - "Ġb logs", - "Ġbl ogs", - "Ġblog s", - "Ġblo gs", - ". exceptions", - ".ex ceptions", - ".exception s", - "Ġ Premium", - "ĠP remium", - "ĠPre mium", - "ĠPrem ium", - "ĠPremi um", - "ĠN etherlands", - "S afe", - "Sa fe", - "F inish", - "Fin ish", - "Fi nish", - "Ġ Album", - "ĠAl bum", - "ĠAlb um", - "_ ACC", - "_A CC", - "_AC C", - "= this", - "=t his", - "v irtual", - "vir tual", - "virt ual", - "] >", - "_ LABEL", - "_L ABEL", - "_LA BEL", - "Ġ Nich", - "ĠN ich", - "ĠNic h", - "ĠNi ch", - "_ win", - "_w in", - "Ġ Aaron", - "ĠA aron", - "W P", - "; $", - "a ims", - "ai ms", - "aim s", - "Ġ ImageView", - "ĠImage View", - "Ġend less", - "Ġendl ess", - "E RA", - "ER A", - "_ DISABLE", - "_DIS ABLE", - "Ġ cancelled", - "Ġcancel led", - "Ġcancell ed", - "Ġcanc elled", - "- us", - "-u s", - "Ġ inspection", - "Ġins pection", - "Ġinspect ion", - "Ġinsp ection", - "e min", - "em in", - "emi n", - "Ġ Grey", - "ĠG rey", - "ĠGr ey", - "ĠGre y", - "- open", - "-o pen", - "-op en", - "Ġ iterations", - "Ġiter ations", - "Ġiteration s", - ". owner", - ".o wner", - "Ġk eras", - "Ġke ras", - "Ġker as", - ". Password", - ".P assword", - ".Pass word", - "ĠR y", - "Ġ INS", - "ĠI NS", - "ĠIN S", - "A ir", - "Ai r", - "Ġ Several", - "ĠSe veral", - "ĠSever al", - "ĠSev eral", - ".Tab Stop", - "IN GLE", - "ING LE", - "Ġ Hair", - "ĠH air", - "ĠHa ir", - "ĠHai r", - "Ġ Canvas", - "ĠC anvas", - "ĠCan vas", - "A AAA", - "AA AA", - "AAA A", - "Ġf law", - "Ġfl aw", - "Ġfla w", - "c edes", - "ce des", - "ced es", - "cede s", - ". Report", - ".Re port", - "í Ĭ", - "Ġ Tips", - "ĠT ips", - "ĠTi ps", - "ĠTip s", - "cript ors", - "criptor s", - ". transaction", - ".trans action", - ". Spring", - ".S pring", - ".Sp ring", - "Ġ viewer", - "Ġview er", - "Ġvie wer", - "Ġins ights", - "Ġinsight s", - "è¾ ĵ", - "ord ion", - "U INT", - "UI NT", - "se ek", - "see k", - "Ġ Auf", - "ĠA uf", - "ĠAu f", - "ìŀ IJ", - "Ġ strain", - "Ġs train", - "Ġst rain", - "Ġstr ain", - "Ġstra in", - "To oltip", - "Tool tip", - "Ġ dz", - "Ġd z", - "ig nal", - "ign al", - "a dt", - "ad t", - "Ġ uc", - "Ġu c", - "f inite", - "fin ite", - "fi nite", - "Ġ nm", - "Ġn m", - ". cmd", - ".c md", - ".cm d", - "ĠMy Sql", - "[ data", - "[d ata", - ".j ackson", - ". tree", - ".t ree", - ".tr ee", - "Request Param", - "_ agent", - "_a gent", - "_ag ent", - "_age nt", - "\" )]čĊ", - "\") ]čĊ", - "\")] čĊ", - "Ġas sass", - "Ġass ass", - "( Constants", - "(Constant s", - "(Const ants", - ": ss", - ":s s", - "Ġ MAN", - "ĠM AN", - "ĠMA N", - "+- +-", - "Ġ Bottom", - "ĠB ottom", - "ĠBot tom", - "ĠBott om", - "pr ints", - "print s", - "pri nts", - "Ġ Same", - "ĠS ame", - "ĠSam e", - "ĠSa me", - "@ Autowired", - "s wap", - "sw ap", - "i ción", - "ic ión", - "ici ón", - "Ġprot esters", - "Ġprote sters", - "Ġprotest ers", - "Ġprotester s", - "Ġh oney", - "Ġhon ey", - "Ġho ney", - "Ġhone y", - "Ġ Veter", - "ĠV eter", - "ĠVe ter", - "ĠVet er", - "( Calendar", - "(C alendar", - "- ad", - "-a d", - "ĠBro oklyn", - "ĠBrook lyn", - "L ife", - "Li fe", - "_ VAR", - "_V AR", - "z ech", - "ze ch", - "Ġ CALL", - "ĠC ALL", - "ĠCA LL", - "ĠCAL L", - "_ CAST", - "_C AST", - "_CA ST", - "ĠE lection", - "ĠEl ection", - "ĠElect ion", - "ĠEle ction", - "Ġ thickness", - "Ġth ickness", - "Ġthick ness", - "V ery", - "Ver y", - "Ve ry", - "_ INTEGER", - "_IN TEGER", - "- dev", - "-d ev", - "-de v", - ") )))", - ")) ))", - "))) )", - "a pat", - "ap at", - "apa t", - "o ooo", - "oo oo", - "ooo o", - "d emo", - "de mo", - "dem o", - "Ġ parseFloat", - "Ġparse Float", - "Ġ Rather", - "ĠR ather", - "ĠRa ther", - "ĠRat her", - "ĠRath er", - "ST IT", - "m aker", - "ma ker", - "make r", - "mak er", - "[ current", - "[c urrent", - "[cur rent", - "[curr ent", - "chron o", - "chr ono", - "Ġ christ", - "Ġch rist", - "Ġchr ist", - "ãģ ª", - "Ġ Detail", - "ĠD etail", - "ĠDe tail", - "ĠDet ail", - "Æ° á»", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "Ġs ul", - "Ġsu l", - "id ency", - "ide ncy", - "iden cy", - "Q ue", - "Qu e", - "Ġe legant", - "Ġeleg ant", - "a pons", - "ap ons", - "apon s", - "apo ns", - "Ġd ishes", - "Ġdis hes", - "Ġdish es", - "Ġint egers", - "Ġinteg ers", - "Ġinteger s", - "Ġinte gers", - "( read", - "(r ead", - "(re ad", - "0 57", - "05 7", - "find ViewById", - "Ġ Amount", - "ĠA mount", - "ĠAm ount", - "Ġ Skip", - "ĠS kip", - "ĠSk ip", - "ĠSki p", - "Ġhab its", - "Ġha bits", - "Ġhabit s", - "* )(", - "*) (", - "Ġmon sters", - "Ġmonster s", - "M AC", - "MA C", - ": end", - ":e nd", - "Ġf rank", - "Ġfr ank", - "Ġfra nk", - "Ġfran k", - "As sembly", - "Ġ dfs", - "Ġd fs", - "Ġdf s", - "Ġn eut", - "Ġne ut", - "Ġneu t", - "_ TYPES", - "_T YPES", - "_TYPE S", - "_TYP ES", - "e qual", - "eq ual", - "equ al", - "lo yd", - "loy d", - "( uri", - "(u ri", - "(ur i", - "Ġ chi", - "Ġc hi", - "Ġch i", - "Ġdef endant", - "Ġdefend ant", - "Ġconf licts", - "Ġconflic ts", - "Ġconflict s", - "Ġconfl icts", - "Ġ vil", - "Ġv il", - "Ġvi l", - "- js", - "-j s", - "Ġ Peace", - "ĠPe ace", - "Ġ mutable", - "Ġm utable", - "Ġmut able", - "Ġmu table", - ") sender", - ")s ender", - "Ġ Focus", - "ĠF ocus", - "ĠFo cus", - "å» º", - "Ġapprec iated", - "Ġappreciate d", - "s leep", - "Ġ RED", - "ĠR ED", - "ĠRE D", - "C ulture", - "Ġdesign ers", - "Ġdesigner s", - "_ generator", - "_g enerator", - "_gen erator", - "_gener ator", - "c odes", - "co des", - "code s", - "cod es", - "/ ex", - "/e x", - ". GetValue", - ".Get Value", - "um bled", - "umb led", - "umble d", - ".scal ajs", - ".scala js", - "pe ror", - "per or", - "Ġveter ans", - "Ġveteran s", - "Ġ })čĊ", - "Ġ} )čĊ", - "Ġ}) čĊ", - "Ġun fortunately", - "Ġunfortunate ly", - "_ CREATE", - "_C REATE", - "_CRE ATE", - "_CREAT E", - "M ass", - "Ma ss", - "Mas s", - "Ġ CLAIM", - "ĠCL AIM", - "Ġ Meet", - "ĠMe et", - "_ support", - "_s upport", - "_sup port", - "_supp ort", - "B ank", - "Ban k", - "Ba nk", - "( ).Ċ", - "() .Ċ", - "(). Ċ", - "D ark", - "Da rk", - "Dar k", - "_ LOW", - "_L OW", - "_LO W", - "Ġ Mining", - "ĠM ining", - "ĠMin ing", - "ĠMini ng", - "ĠMi ning", - "Ġ Owner", - "ĠO wner", - "ĠOwn er", - "ĠOw ner", - "i era", - "ie ra", - "ier a", - "Client e", - "Cl iente", - "Cli ente", - "Ġencour aging", - "> S", - "Ġboy friend", - "Ġ Half", - "ĠH alf", - "ĠHa lf", - "ĠHal f", - "Ġ ACC", - "ĠA CC", - "ĠAC C", - "A ff", - "Af f", - "_ ar", - "_a r", - "- life", - "-l ife", - "-li fe", - "c x", - ".J Button", - "iz ado", - "iza do", - ". zero", - ".z ero", - ".open qa", - "o ton", - "ot on", - "oto n", - ". textContent", - ".text Content", - "Ġt oll", - "Ġto ll", - "Ġtol l", - "a tie", - "at ie", - "ati e", - "Ġball ot", - "Ġbal lot", - "Ġballo t", - "- number", - "-n umber", - "-num ber", - ". Exception", - ".Ex ception", - "ĉ params", - "ĉparam s", - "c ircle", - "circ le", - "cir cle", - "- map", - "-m ap", - "Ġ nap", - "Ġn ap", - "Ġna p", - "Ġ Robot", - "ĠR obot", - "ĠRob ot", - "ĠRo bot", - "Ġ Ich", - "ĠI ch", - "reg istration", - "registr ation", - "regist ration", - "A mazon", - "Am azon", - "roll ment", - "( exp", - "(e xp", - "(ex p", - "Ġt anks", - "Ġtank s", - "Ġtan ks", - "ĠG ordon", - "ĠGor don", - "ĠGord on", - "Ġm achinery", - "Ġmachine ry", - "Ġmach inery", - "Ġ baseline", - "Ġb aseline", - "Ġbase line", - "Ġbas eline", - "æ ĭ", - "0 86", - "08 6", - "Ø ©", - "Ġ Convention", - "ĠCon vention", - "ĠConv ention", - "ĉ config", - "ĉcon fig", - "ĉconf ig", - "o okies", - "ook ies", - "ookie s", - "m ult", - "mu lt", - "mul t", - "Rec ords", - "Record s", - "Ġ EST", - "ĠE ST", - "ĠES T", - "Ġgar bage", - "Ġcon form", - "Ġconf orm", - "i dal", - "id al", - "ida l", - "Ġb arg", - "Ġbar g", - "Ġba rg", - "Ġsurv ived", - "Ġsurvive d", - "Ġsurviv ed", - "Ġinvestig ations", - "Ġinvestigation s", - "9 35", - "93 5", - ".contains Key", - "---- ----------------------------------------------------------------------Ċ", - "---------------------------------------------------------------- ----------Ċ", - "---------------------------------------------------------------------- ----Ċ", - "or tion", - "ort ion", - "Ġh orr", - "Ġhor r", - "Ġho rr", - "_ http", - "_h ttp", - "_ht tp", - "Ġ mant", - "Ġm ant", - "Ġman t", - "Ġma nt", - "] ;čĊčĊ", - "]; čĊčĊ", - "];čĊ čĊ", - "b inary", - "bin ary", - "9 48", - "94 8", - "e mpl", - "em pl", - "emp l", - "Ġin quiry", - "Ġ Meanwhile", - "ĠMean while", - "0 98", - "09 8", - "Ġcollect ing", - ".Entity Framework", - "\" ,ĊĊ", - "\", ĊĊ", - "\",Ċ Ċ", - "Ġ Pic", - "ĠP ic", - "ĠPi c", - "@ Inject", - "ick ness", - "Ġ Binding", - "ĠB inding", - "ĠBind ing", - "ĠBin ding", - "Ġcont rolling", - "Ġcontrol ling", - "re verse", - "rev erse", - "Ġch airs", - "Ġchair s", - "Ġcha irs", - "Ġchai rs", - "semb led", - "sem bled", - "semble d", - "sembl ed", - "( add", - "(a dd", - "(ad d", - "Dis abled", - "Disable d", - "a nas", - "an as", - "ana s", - ". translate", - ".trans late", - "- ----------Ċ", - "-- ---------Ċ", - "---- -------Ċ", - "-------- ---Ċ", - "--- --------Ċ", - "----- ------Ċ", - "---------- -Ċ", - "------ -----Ċ", - "----------- Ċ", - "------- ----Ċ", - "--------- --Ċ", - "Ġref lected", - "Ġreflect ed", - "Ġrefl ected", - "\" ]ĊĊ", - "\"] ĊĊ", - "\"]Ċ Ċ", - "Ex ternal", - "Ext ernal", - "Extern al", - "Ar row", - "Arr ow", - "S ingleton", - "Single ton", - "Sing leton", - "% x", - "Ġ Å", - "Ġan cest", - "Ġance st", - "Ġanc est", - "ĠOr leans", - "ĉ cmd", - "ĉc md", - "ĉcm d", - "Ġpro hibited", - "Ġprohib ited", - "Ġprohibit ed", - "ith metic", - "( channel", - "(ch annel", - "(chan nel", - "_ css", - "_c ss", - "_cs s", - "For ward", - ". socket", - ".s ocket", - ".so cket", - ".sock et", - "Ġl uc", - "Ġlu c", - "â Ĩ", - "Ġ Firefox", - "ĠFire fox", - "Ġ Movies", - "ĠM ovies", - "ĠMovie s", - "ĠMov ies", - ") _", - ". ends", - ".e nds", - ".end s", - ".en ds", - "( shape", - "(s hape", - "(sh ape", - "Ġde alt", - "Ġdeal t", - "Ġs aves", - "Ġsa ves", - "Ġsave s", - "Ġsav es", - "Ġgl ory", - "Ġglo ry", - "Ġglor y", - "Ġmej or", - "Ġbre athing", - "Ġbreath ing", - "Ġ eller", - "Ġe ller", - "Ġel ler", - "Ġell er", - "Ġelle r", - "get Data", - "Ġ angles", - "Ġan gles", - "Ġang les", - "Ġangle s", - "Ġangl es", - "Ġ toolbar", - "Ġtool bar", - "Ġ spacing", - "Ġsp acing", - "Ġspa cing", - "Ġspac ing", - "0 59", - "05 9", - "I PS", - "IP S", - "Ġfloor s", - "Ġflo ors", - "_ ACTIVE", - "_ACT IVE", - "_ACTIV E", - "Ġ shuffle", - "Ġsh uffle", - "/ shared", - "/sh ared", - "/share d", - "Ġ Ele", - "ĠE le", - "ĠEl e", - "e dish", - "ed ish", - "edi sh", - "edis h", - "Ġweb cam", - ". expect", - ".ex pect", - ".exp ect", - "i loc", - "il oc", - "ilo c", - "Ġ Includes", - "ĠIn cludes", - "ĠInclude s", - "Ġt weeted", - "Ġtweet ed", - "Ġtwe eted", - "Ġtwee ted", - "Ġ :)", - "Ġ: )", - "Ġ Essay", - "ĠEs say", - "ĠEss ay", - "F ix", - "Fi x", - "- between", - "-b etween", - "_ web", - "_w eb", - "_we b", - ". conv", - ".con v", - ".co nv", - "Ġrac ism", - "Ġreflect s", - "Ġrefl ects", - "u mm", - "um m", - "и ÑĤе", - "иÑĤ е", - "_ footer", - "_f ooter", - "_foot er", - "/ docs", - "/d ocs", - "/doc s", - "/do cs", - "Ġ Pour", - "ĠP our", - "ĠPo ur", - "ĠPou r", - "Ng Module", - ". initialize", - ".initial ize", - "pattern s", - "_ In", - "_I n", - "Ġ Abb", - "ĠA bb", - "ĠAb b", - "* čĊ", - "Ġsent iment", - "b uff", - "buf f", - "bu ff", - "_ counts", - "_count s", - "_co unts", - "_cou nts", - "Ġ reuse", - "Ġre use", - "ch unk", - "chu nk", - "Ġim posed", - "Ġimp osed", - "Ġimpose d", - "Primary Key", - "Fore ground", - "Ġcons umed", - "Ġconsum ed", - "Ġconsume d", - "? !", - "Ġd ick", - "Ġdi ck", - "Ġdic k", - "Ġ chron", - "Ġch ron", - "Ġchr on", - "ĠF ern", - "ĠFe rn", - "ĠFer n", - "Ġ responsive", - "Ġres ponsive", - "Ġrespons ive", - "9 58", - "95 8", - "Ġin sect", - "Ġins ect", - "Ġinse ct", - "ic ulty", - "icult y", - "Ġ rw", - "Ġr w", - "Ġa like", - "Ġal ike", - "Ġali ke", - "Ġ subset", - "Ġsub set", - "Ġsubs et", - "Ġ Cookies", - "ĠC ookies", - "ĠCo okies", - "ĠCook ies", - "ĠCookie s", - "Ġ Pair", - "ĠP air", - "ĠPa ir", - "ĠPai r", - "Ġ tier", - "Ġt ier", - "Ġti er", - "Ġtie r", - "I FO", - "IF O", - "av our", - "avo ur", - "avou r", - "Ġ QU", - "ĠQ U", - ", sizeof", - ",size of", - "Ġ merged", - "Ġm erged", - "Ġmer ged", - "Ġmerge d", - "Ġmerg ed", - "m v", - "i tol", - "it ol", - "ito l", - "y lon", - "yl on", - "Ġj umped", - "Ġjump ed", - ". role", - ".r ole", - ".ro le", - "ens aje", - "ensa je", - "R ules", - "Rule s", - "Ru les", - "Ġ browse", - "Ġb rowse", - "Ġbrows e", - "Ġbrow se", - "An imator", - "Anim ator", - "Ġy oga", - "Ġyo ga", - "Ġyog a", - "Ġ variants", - "Ġvar iants", - "Ġvari ants", - "Ġvariant s", - "Ġcour tesy", - "Ġcourt esy", - "Ġcourte sy", - "u ran", - "ur an", - "ura n", - "p bs", - "pb s", - "else if", - "A lt", - "Al t", - "Ġ Lane", - "ĠL ane", - "ĠLa ne", - "ĠLan e", - "C LK", - "CL K", - "I MARY", - "IM ARY", - "IMA RY", - "_ PROPERTY", - "_P ROPERTY", - "_PRO PERTY", - "ï¼ IJ", - "Ġ chan", - "Ġc han", - "Ġch an", - "Ġcha n", - "Ġgrad ually", - "Ġgradu ally", - "Ġgradual ly", - "Ġ shake", - "Ġsh ake", - "Ġsha ke", - "Ġbl onde", - "Ġblo nde", - "Ġblond e", - ".. .\");Ċ", - "... \");Ċ", - "...\" );Ċ", - "- sex", - "-s ex", - "-se x", - "Ġgame play", - "a cies", - "ac ies", - "aci es", - "acie s", - ". refresh", - ".re fresh", - ".ref resh", - "U SB", - "US B", - "Ġ Plot", - "ĠP lot", - "ĠPl ot", - "W as", - "Wa s", - "iss ippi", - "Ġ Tensor", - "ĠT ensor", - "Ġcrypt ocurrency", - "Ġcrypto currency", - "Ġcryptoc urrency", - "Ġdifficult ies", - "De leted", - "Delete d", - "Del eted", - "With out", - "_ append", - "_app end", - "_ap pend", - "_ ver", - "_v er", - "_ve r", - "9 67", - "96 7", - "\" ))čĊ", - "\") )čĊ", - "\")) čĊ", - "Ġh onestly", - "Ġhonest ly", - "Ġ pivot", - "Ġp ivot", - "Ġpiv ot", - "Ġ temps", - "Ġte mps", - "Ġtem ps", - "Ġtemp s", - "_ ps", - "_p s", - "Ġ Unlike", - "ĠUn like", - "[ :-", - "[: -", - "V S", - "_ inf", - "_in f", - "_i nf", - "Ġjun ior", - "Ġjuni or", - "Ġ animations", - "Ġan imations", - "Ġanim ations", - "Ġanimation s", - "Ġ filepath", - "Ġfile path", - "? {{$", - ">{ {$", - ">{{ $", - "Ġ unicode", - "Ġun icode", - "Ġuni code", - "Ġunic ode", - "p laces", - "pl aces", - "place s", - "pla ces", - "Ġ Coffee", - "ĠC offee", - "ĠCo ffee", - "ĠCoff ee", - ". SE", - ".S E", - "Ġ PAR", - "ĠP AR", - "ĠPA R", - "( txt", - "(t xt", - "(tx t", - "g ebra", - "ge bra", - "geb ra", - "Ġ fires", - "Ġf ires", - "Ġfire s", - "Ġfi res", - "Ġfir es", - "Main Window", - "m edium", - "med ium", - "medi um", - "Ġ( âĢľ", - "Ġ lg", - "Ġl g", - "Ġ cmp", - "Ġc mp", - "Ġcm p", - "/ base", - "/b ase", - "_ layers", - "_l ayers", - "_layer s", - "_la yers", - "_ entries", - "_en tries", - "_ent ries", - "Ġadmin ister", - "ĠS UCH", - "ĠSU CH", - "B P", - "ĠScott ish", - "ĉ čĊĉčĊ", - "ĉčĊ ĉčĊ", - "g uard", - "gu ard", - "gua rd", - "Ġ Strong", - "ĠSt rong", - "ĠStr ong", - "In sn", - "Ins n", - "Ġ CAP", - "ĠC AP", - "ĠCA P", - "as ury", - "asu ry", - "Ġ SEE", - "ĠS EE", - "ĠSE E", - "C lock", - "Cl ock", - "Clo ck", - "e rie", - "er ie", - "eri e", - "\\ models", - "\\model s", - "Ġ $$", - "Ġ$ $", - "Ġ Cab", - "ĠC ab", - "ĠCa b", - "Ġwur de", - "Ġsold ier", - "Ġcl ips", - "Ġclip s", - "Ġcli ps", - "Ġarr angement", - "Ġarrang ement", - "Ġarrange ment", - "Ġ Wonder", - "ĠW onder", - "ĠWo nder", - "ĠWon der", - "ĠH orn", - "ĠHor n", - "ĠHo rn", - "Ġsc ared", - "Ġsca red", - "Ġscar ed", - "Ġscare d", - "Ġc ure", - "Ġcur e", - "Ġcu re", - "m kdir", - "mk dir", - "Ġ aligned", - "Ġal igned", - "Ġalign ed", - "Ġ Pink", - "ĠP ink", - "ĠPin k", - "ĠPi nk", - "Ġl anded", - "Ġland ed", - "Ġlan ded", - "D imension", - "Dim ension", - "Scroll Pane", - ". chat", - ".c hat", - ".ch at", - ". With", - ".W ith", - "Ġ Train", - "ĠT rain", - "ĠTr ain", - "ĠTra in", - "] .Ċ", - "]. Ċ", - "Ġth irty", - "Ġd urable", - "Ġdur able", - "Ġ ld", - "Ġl d", - "Ġlate init", - "Ġ charts", - "Ġch arts", - "Ġchar ts", - "Ġchart s", - "Ġcha rts", - "Ġins ult", - ". Fatal", - ".F atal", - ".Fat al", - "_ ct", - "_c t", - "Ġm asks", - "Ġmask s", - "Ġmas ks", - "CLUD ED", - "CLU DED", - "CLUDE D", - "P resident", - "Pres ident", - "Ġcol ours", - "Ġcolour s", - "g ments", - "gment s", - "gm ents", - ". attributes", - ".at tributes", - ".attribute s", - ".attrib utes", - "Ġ Flex", - "ĠF lex", - "ĠFl ex", - "ĠFle x", - "Ġ Clock", - "ĠC lock", - "ĠCl ock", - "ĠClo ck", - "ÃŃ cul", - "ÃŃc ul", - "i men", - "im en", - "ime n", - "J O", - "Ġ Regex", - "ĠReg ex", - "_ LINK", - "_L INK", - "Ġc ouch", - "Ġco uch", - "Ġcou ch", - "Ġ INPUT", - "ĠIN PUT", - "Ġb eating", - "Ġbe ating", - "Ġbeat ing", - "b usiness", - "bus iness", - "pr eced", - "pre ced", - "prec ed", - ". unit", - ".un it", - ".u nit", - ".uni t", - "Ġ Fel", - "ĠF el", - "ĠFe l", - "N ever", - "Ne ver", - "os pel", - "osp el", - ". startswith", - ".start swith", - "ĠE PA", - "ĠEP A", - ". only", - ".on ly", - "Ġpre venting", - "Ġprevent ing", - "Ġprev enting", - "y er", - "ye r", - "Column Name", - "Ġe levation", - "Ġele vation", - "Ġelev ation", - "f lu", - "fl u", - "i cycle", - "ic ycle", - "icy cle", - "Ġ offline", - "Ġoff line", - "Tool bar", - "Ġcomp eting", - "Ġcompet ing", - ") ].", - ")] .", - "Ġm og", - "Ġmo g", - "Ġ isValid", - "Ġis Valid", - "A sk", - "As k", - "_ av", - "_a v", - "_ lat", - "_l at", - "_la t", - "A NC", - "AN C", - "ĠJ oh", - "ĠJo h", - "k ers", - "ke rs", - "ker s", - "Ġ guards", - "Ġg uards", - "Ġgu ards", - "Ġguard s", - "Ġguar ds", - "Ġ chains", - "Ġch ains", - "Ġchain s", - "Ġcha ins", - "Ġchai ns", - "ĠSimple DateFormat", - ". static", - ".st atic", - ".stat ic", - "Ġv essel", - "Ġve ssel", - "Ġvess el", - "Ġves sel", - "Ġm ud", - "Ġmu d", - "Ġst abil", - "Ġstab il", - "Ġsta bil", - "Ġst ret", - "Ġstr et", - "Ġstre t", - "g m", - "am ation", - "ama tion", - "amat ion", - "ç ľ", - "- with", - "-w ith", - "Ġ ros", - "Ġr os", - "Ġro s", - "_ PA", - "_P A", - "Ġ resultado", - "Ġresult ado", - "Ġconf idential", - "Ġconfident ial", - "ĠTok yo", - "ĉ using", - "ĉu sing", - "ĉus ing", - "Ġ Mathf", - "ĠMath f", - "ĠMat hf", - "om bine", - "omb ine", - "Ġ ESPN", - "ĠESP N", - "ĠES PN", - "Ġde alers", - "Ġdeal ers", - "Ġdealer s", - "Ġdismiss ed", - "T RY", - "TR Y", - "Ġte ens", - "Ġteen s", - "Ġtee ns", - "rec ords", - "record s", - "Ġw ings", - "Ġwin gs", - "Ġwing s", - "g allery", - "ac counts", - "account s", - "acco unts", - "_ LIB", - "_L IB", - "Ġj acket", - "Ġja cket", - "Ġjack et", - "Ġjac ket", - "Ġ NSObject", - "ĠNS Object", - "Ġ stones", - "Ġs tones", - "Ġst ones", - "Ġstone s", - "Ġsto nes", - "Ġ Delivery", - "ĠD elivery", - "ĠDel ivery", - "ĠDeliver y", - "ĠD iet", - "ĠDi et", - "ĠDie t", - "/ watch", - "/w atch", - "Ġto ilet", - "Ġtoile t", - "Ġtoi let", - "Ġ Guest", - "ĠG uest", - "ĠGu est", - ". day", - ".d ay", - ".da y", - "0 67", - "06 7", - "Ġ intval", - "Ġint val", - "0 87", - "08 7", - "Vis it", - "Vi sit", - "Ġinvest igated", - "Ġinvestig ated", - "Ġinvestigate d", - "Ġpen tru", - "Ġpent ru", - "ĠThe atre", - "andid ates", - "andidate s", - "andi dates", - "L ang", - "La ng", - "Ġ Serv", - "ĠS erv", - "ĠSe rv", - "ĠSer v", - "Ġ controllers", - "Ġcont rollers", - "Ġcontrol lers", - "Ġcontroller s", - "Ġ setTitle", - "Ġset Title", - "N P", - "a my", - "am y", - "f lat", - "fl at", - "( ui", - "(u i", - "0 69", - "06 9", - "_ document", - "_d ocument", - "_doc ument", - "è ĥ½", - "èĥ ½", - "Ġ Coin", - "ĠC oin", - "ĠCo in", - "ĠAd ams", - "ĠAdam s", - "ĠAda ms", - "p tic", - "pt ic", - "Ġ productive", - "Ġpro ductive", - "Ġproduct ive", - "Ġprod uctive", - "Ġaccompl ished", - "Ġaccomplish ed", - "čĊ čĊčĊčĊ", - "čĊčĊ čĊčĊ", - "čĊčĊčĊ čĊ", - "Ġde ferred", - "Ġdefer red", - "i entes", - "ient es", - "ien tes", - "iente s", - "Ġs inc", - "Ġsi nc", - "Ġsin c", - "ol ars", - "olar s", - "ola rs", - "Right arrow", - "Ġvar iations", - "Ġvari ations", - "Ġvariation s", - "( offset", - "(o ffset", - "(off set", - "9 57", - "95 7", - ". LayoutInflater", - ".Layout Inflater", - "Ġ suspend", - "Ġs uspend", - "Ġsus pend", - "Ġsusp end", - "Ġpre vention", - "Ġprevent ion", - "Ġprev ention", - "_ private", - "_pr ivate", - "_priv ate", - "_ js", - "_j s", - "â ĺħ", - "âĺ ħ", - "Ġw ieder", - "Ġwie der", - "Ġwi eder", - "at um", - "atu m", - "Ĵ Į", - "Ġappear ances", - "Ġappearance s", - ". Document", - ".D ocument", - ".Doc ument", - "Ġvalid ates", - "Ġvalidate s", - "Ġvalida tes", - "c alendar", - "cal endar", - "} \";Ċ", - "}\" ;Ċ", - ". demo", - ".d emo", - ".de mo", - "con ut", - "co nut", - "Ġcor rection", - "Ġcorrect ion", - "Ġcorre ction", - "Ġcorr ection", - "Ġ Deal", - "ĠDe al", - "Ġbatter ies", - "Ġbatt eries", - ". duration", - ".d uration", - ", \\", - "_ marker", - "_m arker", - "_mark er", - "_mar ker", - "m ulti", - "mul ti", - "mult i", - "Ġ halt", - "Ġh alt", - "Ġha lt", - "Ġhal t", - "Ġ cms", - "Ġc ms", - "Ġcm s", - "Ġsh aped", - "Ġshape d", - "Ġsha ped", - "B ro", - "Br o", - "re duce", - "red uce", - "Ġ ####", - "Ġ# ###", - "Ġ## ##", - "Ġ### #", - "C TOR", - "CT OR", - "Ġ Benef", - "ĠB enef", - "ĠBen ef", - "ĠBene f", - "Ġicon ic", - "Ġic onic", - "Ġp iano", - "Ġpi ano", - "Ġpian o", - "Ġeffect iveness", - "Ġeffective ness", - "| .Ċ", - "|. Ċ", - "Ġ ajax", - "Ġa jax", - "Ġaj ax", - "Ġv olumes", - "Ġvol umes", - "Ġvolume s", - "Ġvolum es", - "ภ¡", - "Ġ cljs", - "Ġcl js", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "a ths", - "at hs", - "ath s", - "r aits", - "ra its", - "rait s", - "rai ts", - "å¤ §", - "Ñ ĸ", - "_ mult", - "_m ult", - "_mul t", - "_mu lt", - "Ġfasc inating", - "A verage", - "Ġp ré", - "Ġpr é", - "ĠChair man", - ".find Element", - "_ pin", - "_p in", - "_pi n", - "Ġcom paring", - "Ġcomp aring", - "Ġcompar ing", - "Ġdark ness", - "- Fi", - "-F i", - "- server", - "-s erver", - "-ser ver", - "Ġselect ing", - "s terdam", - "ster dam", - "Ġ Parts", - "ĠP arts", - "ĠPart s", - "ĠPar ts", - "ĠPa rts", - "FORM ATION", - "FORMAT ION", - "Ġn oting", - "Ġnot ing", - "Ġno ting", - "Ġ pile", - "Ġp ile", - "Ġpi le", - "Ġpil e", - "o gs", - "og s", - "Ġ palette", - "Ġp alette", - "Ġpa lette", - "Ġpal ette", - "Ġpale tte", - "_ do", - "_d o", - "it ize", - "iti ze", - "0 79", - "07 9", - "( )(", - "() (", - "Ġdef ining", - "Ġdefin ing", - "Ġ remainder", - "Ġremain der", - "Un its", - "Unit s", - "Uni ts", - "_ TASK", - "_T ASK", - "_TA SK", - "Http Client", - "S ocial", - "So cial", - "Ġf undra", - "Ġfun dra", - "Ġfund ra", - "N R", - "c hest", - "ch est", - "che st", - "ches t", - "C urrency", - "Curr ency", - ". adapter", - ".ad apter", - "Ġd op", - "Ġdo p", - "un ting", - "unt ing", - "ANG UAGE", - "\" He", - "\"H e", - "ĉ index", - "ĉin dex", - "_ package", - "_p ackage", - "_pack age", - ". Icon", - ".I con", - "Ġre pet", - "Ġrep et", - "Ġrepe t", - "m ass", - "ma ss", - "mas s", - "= \".$", - "=\" .$", - "=\". $", - "ĠS ud", - "ĠSu d", - "Ġ lid", - "Ġl id", - "Ġli d", - "pro vince", - "prov ince", - "ì ľ", - "G PIO", - "GP IO", - "Ð ļ", - "Ġ MySQL", - "ĠMy SQL", - "Ġ docs", - "Ġd ocs", - "Ġdo cs", - "Ġdoc s", - "Ġ GA", - "ĠG A", - "Ġip sum", - "Ġips um", - "K ernel", - "Ġac cepts", - "Ġaccept s", - "Ġf itting", - "Ġfit ting", - "Ġcu ando", - "Ġd uplic", - "Ġdup lic", - "ĠBr other", - "ĠBro ther", - "ĠK le", - "ĠKl e", - "n ums", - "num s", - "nu ms", - "Ġ morph", - "Ġm orph", - "Ġmor ph", - "Ġ ########", - "Ġ# #######", - "Ġ## ######", - "Ġ### #####", - "Ġ#### ####", - "Ġ##### ###", - "Ġ CGPoint", - "ĠCG Point", - "< unsigned", - "ä¾ ĭ", - "ĠD uke", - "ĠDu ke", - ".set Bounds", - "q s", - "o ric", - "or ic", - "ori c", - "j er", - "je r", - "Ġreg arded", - "Ġregard ed", - "Http Request", - "Ġb onds", - "Ġbo nds", - "Ġbon ds", - "Ġbond s", - "Ġthorough ly", - "en cent", - "ence nt", - "enc ent", - "Ġhighlight ed", - "Ġac res", - "Ġacre s", - "Ġwork place", - "Ġ Lux", - "ĠL ux", - "ĠLu x", - "Ġ quot", - "Ġqu ot", - "Ġquo t", - "9 86", - "98 6", - ". inflate", - ".in flate", - ".inf late", - "Ġd ocumented", - "Ġdocument ed", - "Ġadd iction", - "Ġaddict ion", - "Ġ mutation", - "Ġm utation", - "Ġmut ation", - ". city", - ".c ity", - ".ci ty", - "Ġbott les", - "Ġbottle s", - "Ġ Repository", - "ĠRe pository", - "ĠRepos itory", - "o nn", - "on n", - "err no", - "ARI ABLE", - "åº ¦", - "_ BEGIN", - "_B EGIN", - "_BE GIN", - "g las", - "gl as", - "' })Ċ", - "'} )Ċ", - "'}) Ċ", - "Ġ Massage", - "ĠM assage", - "ĠMass age", - "ĠMas sage", - "ĠW hit", - "ĠWh it", - "reg ex", - "W A", - "Ġout let", - "- head", - "-h ead", - "-he ad", - "Ġ expired", - "Ġex pired", - "Ġexp ired", - "Ġexpire d", - "Ġ Thai", - "ĠT hai", - "ĠTh ai", - "/ include", - "/in clude", - "/inc lude", - "g radient", - "grad ient", - "s canf", - "scan f", - "Ġs eam", - "Ġse am", - "Ġsea m", - "w al", - "wa l", - "ĉ buf", - "ĉb uf", - "B earer", - "Be arer", - "Bear er", - "Ġprec ious", - "Ġprecio us", - "i facts", - "if acts", - "ifact s", - "ifa cts", - "c oord", - "co ord", - "Ġexpl oration", - "Ġexplo ration", - "Ġexplor ation", - ". getY", - ".get Y", - "( handle", - "(h andle", - "(hand le", - "T opic", - "To pic", - "Top ic", - "Ġ Vent", - "ĠV ent", - "ĠVen t", - "ĠVe nt", - "r hs", - "rh s", - "- -----Ċ", - "-- ----Ċ", - "---- --Ċ", - "--- ---Ċ", - "----- -Ċ", - "------ Ċ", - "Ġ Bright", - "ĠB right", - "ĠBr ight", - "ĠBrig ht", - "ĠBri ght", - "Ġ guild", - "Ġg uild", - "Ġgu ild", - "Ġgui ld", - "m other", - "mo ther", - "mot her", - "moth er", - "st orm", - "sto rm", - "stor m", - "Ġmunicip al", - "Ġ ink", - "Ġin k", - "Ġi nk", - ". TYPE", - ".T YPE", - "w l", - ".. . < /", - "_ ro", - "_r o", - "( (*", - "(( *", - "? ???", - "?? ??", - "??? ?", - "_ vertex", - "_ver tex", - "_vert ex", - "ke it", - "ĠH alloween", - "T I", - "Ġ Va", - "ĠV a", - "_ car", - "_c ar", - "_ca r", - "=\" {{$", - "=\"{{ $", - "=\"{ {$", - "Ġrandom ly", - "а ние", - "ан ие", - "ани е", - "Ġsh ocked", - "Ġshock ed", - "ĠPok émon", - "s ignal", - "sign al", - "sig nal", - "Ġ SDK", - "ĠS DK", - "ĠSD K", - "m iddleware", - "middle ware", - "Ġt reating", - "Ġtr eating", - "Ġtreat ing", - "Ġtre ating", - "Ġbur ned", - "Ġburn ed", - "De partment", - "Dep artment", - "Depart ment", - "ĠS pect", - "ĠSp ect", - "ĠSpec t", - "ĠSpe ct", - "Ġ cliente", - "Ġcl iente", - "Ġclient e", - "Ġcli ente", - "Ġ Reddit", - "ĠRe ddit", - "ĠRed dit", - "_ avg", - "_a vg", - "_av g", - "Ġinst alling", - "Ġinstall ing", - "Ġinstal ling", - "_ alpha", - "_al pha", - ", data", - ",d ata", - "Ġ setId", - "Ġset Id", - "Ġ ListView", - "ĠList View", - "( property", - "(p roperty", - "(pro perty", - "(prop erty", - "Ġcross ing", - "Ġ Obj", - "ĠO bj", - "ĠOb j", - "ĠW ard", - "ĠWar d", - "ĠWa rd", - "ĠRedirect To", - "Ġ Present", - "ĠP resent", - "ĠPres ent", - "ĠPre sent", - "Ġdr aws", - "Ġdraw s", - "Ġdra ws", - "ched uled", - "chedule d", - "Ġlegisl ative", - "Ġtw ist", - "Ġ Stra", - "ĠS tra", - "ĠSt ra", - "ĠStr a", - "Ġ AFP", - "ĠA FP", - "ĠAF P", - "ĠC hap", - "ĠCh ap", - "ĠCha p", - "- pr", - "-p r", - ": CGRect", - "Ġ ces", - "Ġc es", - "Ġce s", - "R outes", - "Route s", - "Ro utes", - "n of", - "no f", - "Ġ visa", - "Ġv isa", - "Ġvis a", - "Ġvi sa", - "Ġ TCP", - "ĠT CP", - "ĠTC P", - "ĠE VEN", - "ĠEV EN", - "ĠEVE N", - "iv ial", - "ivia l", - "ivi al", - "Ġ Letter", - "ĠL etter", - "ĠLet ter", - "R AY", - "RA Y", - "Ġ implode", - "Ġim plode", - "Ġimpl ode", - ". eq", - ".e q", - "= '+", - "=' +", - "Ġmot ivated", - "Ġmotiv ated", - "Ġmotivate d", - ". visible", - ".v isible", - ".vis ible", - ". short", - ".s hort", - ".sh ort", - "> manual", - "Ġ Technical", - "ĠTechn ical", - "Ġcorpor ation", - "Ġcorp oration", - "Ġcorpo ration", - "Ġ HW", - "ĠH W", - "an ka", - "ank a", - "T AIL", - "TA IL", - "is tas", - "ist as", - "ista s", - "Ġper forms", - "Ġperform s", - "Ġperfor ms", - "Ġ Behavior", - "ĠBeh avior", - ". For", - ".F or", - "_ ORDER", - "_OR DER", - "_ORD ER", - "Ġ Kick", - "ĠK ick", - "ĠKi ck", - "Ġ callbacks", - "Ġcall backs", - "Ġcallback s", - "_ dr", - "_d r", - "u ego", - "ue go", - "h ub", - "hu b", - "uff icient", - "s ky", - "sk y", - "Ġ bp", - "Ġb p", - "h table", - "ht able", - "hta ble", - "Ġ ONLY", - "ĠON LY", - "ĠAUTH ORS", - "ĠAUTHOR S", - ". Argument", - ".Arg ument", - "\" };Ċ", - "\"} ;Ċ", - "Ġ Thunder", - "ĠTh under", - "ĠThu nder", - "Ġ Kom", - "ĠK om", - "ĠKo m", - ". Should", - ".Sh ould", - "A UTH", - "AU TH", - "AUT H", - "a hu", - "ah u", - "_ payment", - "_p ayment", - "_pay ment", - "Ġ starter", - "Ġst arter", - "Ġstart er", - "Ġstar ter", - "ìĦ ľ", - "ìļ ©", - "B log", - "Bl og", - "Blo g", - ". patch", - ".p atch", - ".pat ch", - "Ġgover ned", - "Ġgovern ed", - "as sy", - "ass y", - "- found", - "-f ound", - "Ġthe ater", - "Ġtheat er", - "ĠFont Weight", - "Ġ Batman", - "ĠBat man", - "\" If", - "\"I f", - ". Random", - ".R andom", - "_ delta", - "_d elta", - "_del ta", - "Ġ CE", - "ĠC E", - "Auth enticated", - "Authenticate d", - "Ġd rone", - "Ġdr one", - "Ġdro ne", - "Ġc ous", - "Ġco us", - "Ġcou s", - "r adius", - "rad ius", - "radi us", - "M er", - "Me r", - "( None", - "(N one", - "Ġ NJ", - "ĠN J", - "_ headers", - "_header s", - "_head ers", - "_he aders", - "Ġ amer", - "Ġa mer", - "Ġam er", - "py test", - "Ġ Actions", - "ĠA ctions", - "ĠAct ions", - "ĠAction s", - "ĉ ĉĉĠĠĠĠ", - "ĉĉ ĉĠĠĠĠ", - "ĉĉĉ ĠĠĠĠ", - "ĉĉĉĠĠĠ Ġ", - "ĉĉĉĠ ĠĠĠ", - "ĉĉĉĠĠ ĠĠ", - "Ġ ett", - "Ġe tt", - "Ġet t", - "Ġh oly", - "Ġho ly", - "Ġhol y", - "Ġun comfort", - "ĠN in", - "ĠNi n", - "Ġ Decimal", - "ĠD ecimal", - "ĠDe cimal", - "ĠDec imal", - "Ġ Messages", - "ĠM essages", - "ĠMessage s", - "ĠMess ages", - ". sender", - ".s ender", - ".se nder", - ".send er", - "] ])Ċ", - "]] )Ċ", - "]]) Ċ", - "Ġem brace", - "Ġemb race", - "Ġembr ace", - "Th ough", - "Tho ugh", - "/ sp", - "/s p", - "Ġcult ures", - "Ġculture s", - "Ġcul tures", - "Ġhigh way", - "t ar", - "ta r", - ". fail", - ".f ail", - ".fa il", - "_ hidden", - "_h idden", - "_hi dden", - "_hid den", - "ĠcomponentDid Mount", - "ĠW right", - "ĠWr ight", - "Ġj ag", - "Ġja g", - "_ il", - "_i l", - "../ ../../", - "../../ ../", - "i gu", - "ig u", - "F ood", - "Foo d", - "Fo od", - "Ġ ace", - "Ġa ce", - "Ġac e", - "Ġa ños", - "Ġaño s", - "Ġañ os", - "U SD", - "US D", - "Ġmut ual", - "Ġmu tual", - "Log ic", - "Ġt emple", - "Ġtem ple", - "Ġtemp le", - "Ġtempl e", - "Ġbrief ly", - "Ġ Trip", - "ĠT rip", - "ĠTr ip", - "ĠTri p", - "class method", - "default s", - "Ġ chunks", - "Ġch unks", - "Ġchunk s", - ", ,,,", - ",, ,,", - ",,, ,", - "Ġ Reason", - "ĠRe ason", - "$ id", - "$i d", - "- ups", - "-up s", - "-u ps", - "Ġ damn", - "Ġda mn", - "Ġdam n", - "Ġtr ucks", - "Ġtruck s", - "Ġun limited", - "Ġunl imited", - "Ġsc ulpt", - "Ġ Cards", - "ĠC ards", - "ĠCar ds", - "ĠCard s", - "Ġ autor", - "Ġa utor", - "Ġaut or", - "Ġauto r", - "Ġau tor", - "Ġ Testing", - "ĠT esting", - "ĠTest ing", - "ĠTes ting", - "Ġd iese", - "Ġdi ese", - "Ġdie se", - "Ġdies e", - "sh ops", - "shop s", - "ç ´", - "( payload", - "(p ayload", - "Ġ PATH", - "ĠP ATH", - "ĠPA TH", - "ĠPAT H", - "ĠMem orial", - "ĠMemo rial", - "ĠMemor ial", - "Ġridic ulous", - "e gree", - "eg ree", - "egr ee", - "-w inning", - "-win ning", - "Ġre hab", - "Ġreh ab", - "Ġsophistic ated", - "w pdb", - "wp db", - "ĉ path", - "ĉp ath", - "! \";Ċ", - "!\" ;Ċ", - "_ SYS", - "_S YS", - "_SY S", - ". speed", - ".s peed", - ".sp eed", - "Ġ soap", - "Ġso ap", - "s uffix", - "W rap", - "Wr ap", - "Ġenh ancement", - "Ġenhance ment", - "à ī", - "ú b", - "Ġ playlist", - "Ġplay list", - "Ġmix ing", - "Ġmi xing", - "Ġmixin g", - "ant idad", - "anti dad", - "=\" \";Ċ", - "=\"\" ;Ċ", - "Ġ Revision", - "ĠRe vision", - "ĠRev ision", - "Ġ Beat", - "ĠB eat", - "ĠBe at", - "ĠBea t", - ". inc", - ".in c", - ".i nc", - "- way", - "-w ay", - "e ncias", - "enc ias", - "encia s", - "enci as", - "u lers", - "ul ers", - "ule rs", - "uler s", - "C at", - "Ca t", - "i del", - "id el", - "ide l", - "Ġ Ship", - "ĠS hip", - "ĠSh ip", - "ĠShi p", - ". setColor", - ".set Color", - "Ġthreat ening", - "Ġthreaten ing", - ". modules", - ".module s", - ".mod ules", - "Ġafter wards", - "Ġafterward s", - "Ġ Dashboard", - "ĠD ashboard", - "ĠDash board", - "Ċ ĠĊ", - "S ignal", - "Sign al", - "Sig nal", - "Ġ primer", - "Ġpr imer", - "Ġprim er", - "Ġprime r", - "Ġpri mer", - "or neys", - "orney s", - "orne ys", - "ici ary", - "icia ry", - "iciar y", - "Ġ ligne", - "Ġl igne", - "Ġli gne", - "Ġlig ne", - "Ġlign e", - "_ predict", - "_p redict", - "_pre dict", - "_pred ict", - "Ġa est", - "Ġae st", - "Ġaes t", - "_ https", - "_http s", - "_ht tps", - "> :", - "Ġ Lex", - "ĠL ex", - "ĠLe x", - "Ġren contres", - "Ġrencont res", - "Ġrencontre s", - "Ġrencontr es", - "eg ral", - "egr al", - "egra l", - "s cala", - "sc ala", - "scal a", - "_ family", - "_f amily", - "ÃŁ en", - "ÃŁe n", - "_ sym", - "_s ym", - "_sy m", - "Ġunc ertainty", - "Ġuncert ainty", - "Ġuncertain ty", - "Ġ VALUE", - "ĠVAL UE", - "Ġ };čĊčĊ", - "Ġ} ;čĊčĊ", - "Ġ};čĊ čĊ", - "Ġ}; čĊčĊ", - "Ġbr oader", - "Ġbro ader", - "Ġbroad er", - "Ġh orses", - "Ġhor ses", - "Ġhorse s", - "Ġhors es", - "ãģ Ŀ", - "Ġ Kal", - "ĠK al", - "ĠKa l", - "o ba", - "ob a", - "_ INET", - "_IN ET", - "_I NET", - "Ġ Kill", - "ĠK ill", - "ĠKi ll", - "ĠKil l", - "j query", - "a mination", - "am ination", - "amin ation", - "ami nation", - "amina tion", - "[ @\"", - "[@ \"", - "Ġm uj", - "Ġmu j", - "# ##Ċ", - "## #Ċ", - "### Ċ", - "First OrDefault", - "then Return", - "C he", - "Ch e", - "/ footer", - "/f ooter", - "/foo ter", - "Ġp arks", - "Ġpar ks", - "Ġpark s", - "as je", - "ĠG ulf", - "ĠGu lf", - "ĠGul f", - "Ġmod est", - "Ġmode st", - "Ġmo dest", - "Ġmodes t", - ". Init", - ".I nit", - ".In it", - "ï¼Ł ĊĊ", - "ï¼ŁĊ Ċ", - "Ġpros pects", - "Ġprospect s", - "Ġ svg", - "Ġs vg", - "Ġsv g", - "Ġ åı", - "Ġå ı", - ". Dialog", - ".D ialog", - ".Di alog", - ".Dial og", - "_ NET", - "_N ET", - "_NE T", - "Ġ (($", - "Ġ( ($", - "Ġ(( $", - "Ġ ek", - "Ġe k", - "Ġ Warning", - "ĠW arning", - "ĠWar ning", - "ĠWarn ing", - "Ġ MK", - "ĠM K", - "< LM", - "", - ")= >", - "Ġ Repair", - "ĠRe pair", - "ĠRep air", - "_ BE", - "_B E", - "B rand", - "Br and", - "u art", - "ua rt", - "uar t", - "p review", - "pr eview", - "pre view", - "prev iew", - "Ġiniti atives", - "Ġinitiative s", - "r unning", - "run ning", - "b ang", - "ba ng", - "ban g", - "ĉ update", - "ĉup date", - "Ġ Coach", - "ĠCo ach", - "R ich", - "Ġ youtube", - "Ġy outube", - "Ġyou tube", - "Ġrit ual", - "Ġri tual", - "ap pa", - "app a", - "ĠRob inson", - "ĠRobin son", - "p recision", - "pre cision", - "prec ision", - "//// ////////////////////////////////////////////////////////////////////////", - "//////// ////////////////////////////////////////////////////////////////////", - "//////////////// ////////////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////// ////////////", - "//////////// ////////////////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////////////// ////", - "//////////////////////////////////////////////////////////////////// ////////", - "//////////////////////////////////////////////////////////// ////////////////", - "= []Ċ", - "=[ ]Ċ", - "=[] Ċ", - "Ġcelebr ated", - "Ġcelebrate d", - "O TO", - "OT O", - "Ġin clusion", - "Ġincl usion", - "Ġinclus ion", - "J P", - "' ;čĊčĊ", - "'; čĊčĊ", - "';čĊ čĊ", - "Ġnot able", - "Ġno table", - "Ġnota ble", - "( _.", - "(_ .", - "Man aged", - "Manage d", - "Mana ged", - "Ġgu ides", - "Ġguide s", - "Ġguid es", - "Ġgui des", - "& nbsp", - "ated Route", - "Ġ Adjust", - "ĠAd just", - "ĠAdj ust", - "Ġcol ored", - "Ġcolor ed", - "Ġcolore d", - "_ scores", - "_s cores", - "_score s", - "_sc ores", - "Ġ Tesla", - "ĠTe sla", - "ĠTes la", - "_ progress", - "_pro gress", - "_prog ress", - ". inst", - ".in st", - ".i nst", - ".ins t", - "[ '_", - "[' _", - ". flags", - ".f lags", - ".fl ags", - ".flag s", - "Ġ fclose", - "Ġf close", - "Ġfc lose", - "_ OPER", - "_O PER", - "_OP ER", - "ż y", - "_ note", - "_n ote", - "_no te", - "_not e", - "Ġtrans gender", - "å ķ", - "RI PT", - "Ġab sent", - "Ġabs ent", - "Ġ amet", - "Ġa met", - "Ġam et", - "Ġ operand", - "Ġoper and", - "Ġopera nd", - "ë ©", - "Ġ hood", - "Ġh ood", - "Ġho od", - "to LowerCase", - "a vo", - "av o", - "ĠC ircuit", - "ĠCirc uit", - "ĠL ind", - "ĠLin d", - "ĠLi nd", - "-- }}Ċ", - "= m", - "Ġ suppress", - "Ġsup press", - "Ġsupp ress", - "Ġ MAP", - "ĠM AP", - "ĠMA P", - "i ang", - "ia ng", - "ian g", - "- admin", - "-ad min", - "Ġ sidebar", - "Ġs idebar", - "Ġside bar", - "Ġ Bu", - "ĠB u", - "Ġ Hex", - "ĠH ex", - "ĠHe x", - ", F", - "Ġ Signal", - "ĠS ignal", - "ĠSign al", - "ĠSig nal", - "Ġtrans parency", - "ĠF ederation", - "ĠFeder ation", - "ĠFed eration", - "/ V", - "R eq", - "Re q", - "Ġ pulse", - "Ġp ulse", - "Ġpul se", - "Ġpu lse", - "Ġpuls e", - "Ġt ends", - "Ġte nds", - "Ġten ds", - "Ġtend s", - "Number s", - "Num bers", - "% '", - "Ġde port", - "Ġdep ort", - "d atas", - "data s", - "da tas", - "dat as", - "_ UINT", - "_U INT", - "_UI NT", - "_ tra", - "_t ra", - "_tr a", - "o ko", - "ok o", - "Ġ \"?", - "Ġ\" ?", - "com pet", - "comp et", - "so lete", - "sole te", - "sol ete", - "un dry", - "und ry", - "Ġ overlap", - "Ġover lap", - "Ġoverl ap", - "} `,Ċ", - "}` ,Ċ", - "}`, Ċ", - ". ly", - ".l y", - "_ summary", - "_sum mary", - "Ġ Lost", - "ĠL ost", - "ĠLo st", - "ĠLos t", - ". Center", - ".C enter", - "Ġdis ability", - ". Serialization", - ".S erialization", - ".Serial ization", - "Ġ geom", - "Ġge om", - "Ġgeo m", - "Ġ ?:", - "Ġ? :", - "Ġ Wo", - "ĠW o", - "Ġsh ipped", - "Ġship ped", - "Ĥ æķ°", - "Ġu gly", - "Ġug ly", - "Ġugl y", - "Ġexcit ement", - "Ġex terior", - "Ġext erior", - "Ġexter ior", - "Ġ checkout", - "Ġcheck out", - "Ġ kur", - "Ġk ur", - "Ġku r", - ", D", - "ĠAl aska", - "ĠAla ska", - "Ġsyn thetic", - "Ġsynth etic", - "Ġsynt hetic", - "Ġ Budget", - "ĠB udget", - "ĠBud get", - "Ġ Subscribe", - "ĠSub scribe", - "Ġ &Ċ", - "Ġ& Ċ", - "ÈĻ i", - "Ġ Yu", - "ĠY u", - "ĉ query", - "} .Ċ", - "}. Ċ", - "Ġtr aged", - "Ġtra ged", - "Ġtrag ed", - "as sen", - "ass en", - "asse n", - "Ġaccom modation", - "Ġaccommod ation", - "Ġphys ician", - "Ġphysic ian", - "Ġre named", - "Ġren amed", - "Ġrename d", - "Ġt idak", - "Ġtid ak", - "z Äħ", - "Ġ minus", - "Ġm inus", - "Ġmin us", - "n ych", - "ny ch", - "0 97", - "09 7", - "_EX CEPTION", - "th reads", - "thread s", - "Ġt ire", - "Ġti re", - "Ġtir e", - "_ created", - "_c reated", - "_create d", - "_cr eated", - "_cre ated", - "en sure", - "ens ure", - "Ġ worthy", - "Ġw orthy", - "Ġwor thy", - "Ġworth y", - "Ġexc use", - "Ġ cloth", - "Ġc loth", - "Ġcl oth", - "Ġclo th", - "Ġclot h", - ". parentNode", - ".parent Node", - "/ platform", - "/pl atform", - "ĠU FC", - "ĠUF C", - "Ġ Gtk", - "ĠG tk", - "un ny", - "unn y", - "Ġg ibt", - "Ġgi bt", - "Ġgib t", - "ke ley", - "kel ey", - "h um", - "hu m", - "( tx", - "(t x", - "ĉ dev", - "ĉd ev", - "ĉde v", - "Ġout fit", - "Ġoutf it", - "do ors", - "door s", - "Ġ fon", - "Ġf on", - "Ġfo n", - "i cut", - "ic ut", - "v olatile", - "vol atile", - "Ġhom osex", - "Ġhomo sex", - "Max imum", - "Ġex pend", - "Ġexp end", - "Ġ });ĊĊĊ", - "Ġ} );ĊĊĊ", - "Ġ});Ċ ĊĊ", - "Ġ});ĊĊ Ċ", - "Ġ}) ;ĊĊĊ", - "Ġ}); ĊĊĊ", - "E q", - "on ders", - "ond ers", - "onder s", - "onde rs", - "de partment", - "dep artment", - "depart ment", - "Ġ Physics", - "ĠPh ysics", - "ĠPhys ics", - "\" });Ċ", - "\"} );Ċ", - "\"}) ;Ċ", - "Ġpar ad", - "Ġpara d", - "Ġpa rad", - ". Str", - ".S tr", - ".St r", - "Ġs ele", - "Ġse le", - "Ġsel e", - "IF IED", - "IFI ED", - "Ġdel ivers", - "Ġdeliver s", - "i van", - "iv an", - "iva n", - "Ġrespons ibilities", - "Ġadvoc ates", - "Ġadvocate s", - "è µ", - "Ġ RID", - "ĠR ID", - "ĠRI D", - ". parameters", - ".param eters", - ".parameter s", - "M etrics", - "Met rics", - "Metric s", - "ron ics", - "ronic s", - "Ġ UITableViewCell", - "ĠUI TableViewCell", - "ĠUITableView Cell", - "A bsolute", - "Abs olute", - "ip se", - "ips e", - "y lum", - "yl um", - "ML Element", - "MLE lement", - "_ VALID", - "_VAL ID", - "< title", - " \\<^", - ">\\< ^", - "Ġ ios", - "Ġi os", - "Ġio s", - "s ound", - "so und", - "sou nd", - "\" ];", - "\"] ;", - "Ġf reed", - "Ġfr eed", - "Ġfree d", - "Ġfre ed", - "r ottle", - "rot tle", - "rott le", - "Ġ Lower", - "ĠL ower", - "ĠLo wer", - "ĠLow er", - "ĠLowe r", - "[ count", - "[c ount", - "å Ŀ", - "Ġp ale", - "Ġpa le", - "Ġpal e", - "ĠW ayne", - "ĠWay ne", - "ĠWa yne", - "e arth", - "ear th", - "_ categories", - "_c ategories", - "U CK", - "UC K", - ". metadata", - ".m etadata", - ".meta data", - ".met adata", - "Ġsum mon", - "Ġsumm on", - "H OME", - "HO ME", - "олÑĮ з", - "Ġmanufact ured", - "Ġmanufacture d", - "Ġ dock", - "Ġd ock", - "Ġdo ck", - "Ġdoc k", - "Ġcompet itors", - "Ġcompetitor s", - "Ġcompetit ors", - "_ MODEL", - "_MODE L", - "_MO DEL", - "_MOD EL", - "ok ia", - "oki a", - "Ġ Hey", - "ĠH ey", - "ĠHe y", - "Î ¿", - "Ġ backward", - "Ġback ward", - "ĠPO SS", - "ĠPOS S", - "r opa", - "ro pa", - "rop a", - "Ġ cri", - "Ġc ri", - "Ġcr i", - "_ OBJ", - "_O BJ", - "Trans port", - "- high", - "-h igh", - "Ġerot ik", - "Ġero tik", - "_ slot", - "_s lot", - "_sl ot", - "Ġ artic", - "Ġar tic", - "Ġart ic", - "_ framework", - "_f ramework", - "_frame work", - "_fr amework", - "-s erif", - "-se rif", - "-ser if", - "ĠSql DbType", - "' )(", - "') (", - "+ \"/", - "+\" /", - "Ġw ore", - "Ġwor e", - "Ġwo re", - "S il", - "Si l", - "Ġst oring", - "Ġstor ing", - "Ġsto ring", - "Ġ Phase", - "ĠPh ase", - "u ant", - "ua nt", - "uan t", - "Ġb ump", - "Ġbu mp", - "Ġbum p", - "in ho", - "inh o", - "Ġd ign", - "Ġdi gn", - "Ġdig n", - "Ġ backs", - "Ġb acks", - "Ġback s", - "Ġba cks", - "Ġbac ks", - "q q", - "( hash", - "(h ash", - "(has h", - "Ġ geo", - "Ġg eo", - "Ġge o", - "Ġt ender", - "Ġte nder", - "Ġten der", - "Ġtend er", - "L ogo", - "Log o", - "Lo go", - "! )Ċ", - "!) Ċ", - "Ġ MX", - "ĠM X", - "Ġ Arthur", - "ĠAr thur", - "ĠArt hur", - "ĠArth ur", - "ess oa", - "esso a", - "_ Ch", - "_C h", - "Ġbed rooms", - "Ġbedroom s", - "=\"# \"><", - "=\"#\" ><", - "=\"#\"> <", - "Ġ throat", - "Ġth roat", - "Ġthro at", - "i nsic", - "in sic", - "ins ic", - "insi c", - ". integer", - ".int eger", - "Ġ primitive", - "Ġpr imitive", - "Ġprim itive", - "Truth y", - "Ġfacilit ate", - "Ġfacil itate", - "Ġcre ativity", - "Ġcreat ivity", - "Ġ DNS", - "ĠD NS", - "ĠDN S", - "Ġ gra", - "Ġg ra", - "Ġgr a", - "u ez", - "ue z", - "Ġcount less", - "ĠP oland", - "ĠPol and", - "ĠPo land", - "' M", - "Ġ Dist", - "ĠD ist", - "ĠDis t", - "ĠDi st", - "Ġ vest", - "Ġv est", - "Ġve st", - "Ġves t", - "Ġcert ification", - "Ġcertif ication", - "á» ij", - "h eld", - "he ld", - "hel d", - "ext ensions", - "extension s", - "( static", - "(st atic", - "(stat ic", - "Ġ grades", - "Ġg rades", - "Ġgr ades", - "Ġgrad es", - "Ġgrade s", - "Ġgra des", - "Ġ Uber", - "ĠU ber", - "ĠUb er", - "ãģ Ł", - "Ġ [])Ċ", - "Ġ[ ])Ċ", - "Ġ[] )Ċ", - "Ġ[]) Ċ", - "d atos", - "da tos", - "dat os", - "dato s", - "Ġ getData", - "Ġget Data", - "ĠCh arg", - "ĠChar g", - "ĠCha rg", - "Ġ BS", - "ĠB S", - ".m icrosoft", - ".micro soft", - ". video", - ".v ideo", - ". direction", - ".d irection", - ".dir ection", - ".di rection", - ".direct ion", - "-> {'", - "->{ '", - "l ua", - "lu a", - "a pest", - "ap est", - "ape st", - "apes t", - "Ġbo iler", - "Ġboil er", - "e rek", - "er ek", - "ere k", - "Ġdec ides", - "Ġdecide s", - "Ġdecid es", - ". jar", - ".j ar", - "I SC", - "IS C", - "Ġ Words", - "ĠW ords", - "ĠWord s", - "ĠWor ds", - "( CON", - "(C ON", - "EMPL ATE", - "ree ze", - "s hots", - "sh ots", - "shot s", - "a pps", - "ap ps", - "app s", - "un ted", - "unt ed", - "unte d", - ". setName", - ".set Name", - ": :<", - ":: <", - "- bold", - "-b old", - "-bo ld", - "ê ²", - "å¯ Ĩ", - "Long rightarrow", - "Ġun fair", - "Ġunf air", - "Ġ earning", - "Ġe arning", - "Ġear ning", - "Ġearn ing", - "Ġ shelf", - "Ġsh elf", - "Ġshe lf", - "Ġshel f", - "UR EMENT", - "URE MENT", - "Ġ idle", - "Ġi dle", - "Ġid le", - "_ MENU", - "_M ENU", - "_ME NU", - ". Custom", - ".C ustom", - "A GER", - "AG ER", - "AGE R", - "- \"", - "_ switch", - "_s witch", - "_sw itch", - "b ecause", - "be cause", - "bec ause", - ") view", - ")v iew", - "m are", - "ma re", - "mar e", - "_ condition", - "_con dition", - "_cond ition", - "Ġ Starting", - "ĠStart ing", - "ĠStar ting", - "M vc", - "( pre", - "(p re", - "(pr e", - "d ump", - "du mp", - "dum p", - "_ LOCK", - "_L OCK", - "_LO CK", - "_LOC K", - "at etime", - "ate time", - ". callback", - ".c allback", - ".call back", - "ĠC er", - "ĠCe r", - "o pol", - "op ol", - "opo l", - "ib rary", - "ibr ary", - "Ġ reservation", - "Ġres ervation", - "Ġreserv ation", - "Ġreserva tion", - "ĉ ĉĉĉĉĉĉĊ", - "ĉĉ ĉĉĉĉĉĊ", - "ĉĉĉĉ ĉĉĉĊ", - "ĉĉĉ ĉĉĉĉĊ", - "ĉĉĉĉĉ ĉĉĊ", - "ĉĉĉĉĉĉ ĉĊ", - "ĉĉĉĉĉĉĉ Ċ", - "l ector", - "le ctor", - "lect or", - "lec tor", - "grad uate", - "Ġgener ous", - "Ġgene rous", - "Ġ ion", - "Ġi on", - "Ġio n", - "r icao", - "ri cao", - "ric ao", - "rica o", - "m q", - "_ complete", - "_com plete", - "_comp lete", - "( cursor", - "(c ursor", - "Ġ FormControl", - "ĠForm Control", - ": center", - ":c enter", - "Ġsub stitute", - "Ġsubstit ute", - "Ġ Planning", - "ĠPl anning", - "ĠPlan ning", - "Ġp ension", - "Ġpens ion", - "Ġrecommend ation", - "Ġ Tags", - "ĠT ags", - "ĠTag s", - "ĠTa gs", - "Ġg ef", - "Ġge f", - "Ġ albums", - "Ġalbum s", - "Ġalb ums", - "Ġ washing", - "Ġw ashing", - "Ġwash ing", - "r oc", - "ro c", - "Ġtr ains", - "Ġtrain s", - "Ġtra ins", - "Ġtrai ns", - "a tings", - "at ings", - "ating s", - "atin gs", - "Ġex ponent", - "Ġexp onent", - "ack bar", - "- ln", - "-l n", - "á g", - ".Data Annotations", - "Ġ EIF", - "ĠE IF", - "ĠEI F", - "ĠMal aysia", - "ĠMalays ia", - "ĉ PORT", - "ĉP ORT", - "on us", - "onu s", - "Ġc lever", - "Ġcl ever", - "Ġcle ver", - "Ġp eu", - "Ġpe u", - "> ĊĊĊĊ", - ">Ċ ĊĊĊ", - ">ĊĊ ĊĊ", - ">ĊĊĊ Ċ", - "Ġ Arguments", - "ĠArg uments", - "ĠArgument s", - "Ġdeb ugging", - "Ġdebug ging", - "( right", - "(r ight", - "' D", - "com pute", - "comp ute", - "comput e", - "Ġfin est", - "Ġfine st", - "Ġfi nest", - "Ġfines t", - "OR AGE", - "ORA GE", - "Ġspect acular", - "ph rase", - "Ġin dia", - "Ġind ia", - "Ġlegend ary", - "b irth", - "bir th", - "Ġ composite", - "Ġcom posite", - "Ġcomp osite", - "Ġcompos ite", - "Ġg rows", - "Ġgr ows", - "Ġgrow s", - "Ġgro ws", - "Ġ TD", - "ĠT D", - "Ġe pid", - "Ġep id", - "Ġlaunch ing", - "] ][", - "]] [", - "Min utes", - "Minute s", - "Ġ Cha", - "ĠC ha", - "ĠCh a", - "Ġclean ed", - "Ġcle aned", - "Ġwitness es", - "u kan", - "uk an", - "uka n", - "ĉ Type", - "ĉT ype", - "Ġh abe", - "Ġhab e", - "Ġha be", - "par agraph", - "para graph", - "ĠJ Panel", - "ĠJP anel", - "ĠH ann", - "ĠHa nn", - "ĠHan n", - "Ġvar ied", - "Ġvari ed", - "Ġva ried", - "Ġ Pokemon", - "ĠP okemon", - "ĠPok emon", - "ĠPoke mon", - "ĠM UST", - "ĠMU ST", - "åĬ ¨", - ". visibility", - ".vis ibility", - "op up", - "^ [", - ". expand", - ".exp and", - "Ġ \"',", - "Ġ\" ',", - "Ġ\"' ,", - ".f asterxml", - "_ auto", - "_a uto", - "_aut o", - "Ġ Sheet", - "ĠS heet", - "ĠShe et", - "m arker", - "mark er", - "mar ker", - "Par cel", - "e ws", - "ew s", - "Ġ Strategy", - "ĠStr ategy", - "ĠStrateg y", - "- making", - "-m aking", - "Ġun ve", - "Ġtr ailing", - "Ġtrail ing", - "Ġtra iling", - "Ġtrai ling", - "Ġcl icks", - "Ġclick s", - "Ġcli cks", - "Ġclic ks", - "Ġ GetComponent", - "ĠGet Component", - "ĉ content", - "ĉc ontent", - "ĉcon tent", - "ĉcont ent", - "IG ENCE", - "ER NEL", - "ERN EL", - "NSMutable Array", - "Ġb reat", - "Ġbr eat", - "Ġbre at", - "Ġharm ful", - "¶ Ī", - "Ġbes ides", - "Ġbeside s", - "Ġb oring", - "Ġbo ring", - "Ġbor ing", - "Ġbrut al", - "Ġbru tal", - "v ang", - "va ng", - "van g", - "( parse", - "(p arse", - "(par se", - "qu ick", - "qui ck", - "Ġ pytest", - "Ġpy test", - "Ġpyt est", - "Ġswitch ing", - "( )]Ċ", - "() ]Ċ", - "()] Ċ", - "Ġ ìĦ", - "Ġì Ħ", - "L ER", - "LE R", - "ĉ font", - "ĉf ont", - "Ġ nett", - "Ġn ett", - "Ġne tt", - "Ġnet t", - ") ]ĊĊ", - ")]Ċ Ċ", - ")] ĊĊ", - "( /\\", - "(/ \\", - "æŀ ľ", - "to Array", - "Ġb reed", - "Ġbr eed", - "Ġbre ed", - "Ġbree d", - "Ġ CAR", - "ĠC AR", - "ĠCA R", - "Ġ Weapon", - "ĠWe apon", - "A bs", - "Ab s", - "t ot", - "to t", - "Ġ setName", - "Ġset Name", - "a ptive", - "apt ive", - "Ġ :,", - "Ġ: ,", - "Ġ escaped", - "Ġesc aped", - "Ġescape d", - "Ġescap ed", - "or den", - "ord en", - "orde n", - "Ġ Pri", - "ĠP ri", - "ĠPr i", - "th umbnail", - "Ġde scriptions", - "Ġdes criptions", - "Ġdescription s", - "Ġdescri ptions", - "/ styles", - "/st yles", - "/style s", - "Ġ PCI", - "ĠP CI", - "ĠPC I", - "Ġ alphabet", - "Ġal phabet", - "Ġalpha bet", - "Ġalph abet", - "astic search", - "astics earch", - "N OTE", - "NO TE", - "NOT E", - "Ġc ialis", - "Ġci alis", - "ĠGr iff", - "ĠGri ff", - "Ġp orque", - "Ġpor que", - "Ġprote ins", - "Ġprotein s", - "p lays", - "pl ays", - "play s", - "pla ys", - "Ġst ating", - "Ġstat ing", - "Ġsta ting", - "Ġstati ng", - "Ġim agination", - "Ġimag ination", - "Ġimagin ation", - "Ġf acial", - "Ġfa cial", - "Ġfac ial", - "ĠM echan", - "ĠMe chan", - "ĠMech an", - "ĠMec han", - "Ġarr anged", - "Ġarrang ed", - "Ġarrange d", - "_ used", - "_u sed", - "_us ed", - "_use d", - "Ġarrang ements", - "Ġarrangement s", - "Ġarrange ments", - "Ġ Pipe", - "ĠP ipe", - "ĠPi pe", - "ĠPip e", - "host name", - "Ġpro vinc", - "Ġprov inc", - "T it", - "Ti t", - ".Flat Style", - "Ġ Split", - "ĠS plit", - "ĠSp lit", - "ĠSpl it", - "Ġ Loader", - "ĠL oader", - "ĠLo ader", - "ĠLoad er", - ". cc", - ".c c", - "Ġ clinic", - "Ġcl inic", - "Ġclin ic", - "Ġcli nic", - "-------- --------------------", - "---------------- ------------", - "------------ ----------------", - "------------- ---------------", - "--------------- -------------", - "-------------- --------------", - "-------------------- --------", - "Ġb aking", - "Ġba king", - "Ġbak ing", - "Ġ ENT", - "ĠE NT", - "ĠEN T", - "ne ath", - "nea th", - "ãĢģ ĊĊ", - "ãĢģĊ Ċ", - "A NE", - "AN E", - ".EntityFramework Core", - "a ppers", - "ap pers", - "app ers", - "apper s", - "appe rs", - ". ic", - ".i c", - "Ġ NgModule", - "ĠNg Module", - "Ġ FORM", - "ĠF ORM", - "ĠFOR M", - "ĠFO RM", - "Ġ ';", - "Ġ' ;", - "- profit", - "-pro fit", - "-prof it", - "h w", - "en emy", - "ene my", - "Ġ Eye", - "ĠE ye", - "ĠEy e", - "Ġca ution", - "Ġcaut ion", - "t own", - "to wn", - "Ġur ged", - "Ġurge d", - "Ġurg ed", - "Ġ Jimmy", - "ĠJim my", - "ynchron ous", - "-s ized", - "-size d", - "m aking", - "ma king", - "mak ing", - ", {", - "] ',", - "]' ,", - "_ Object", - "_O bject", - "_Obj ect", - "ah oma", - "aho ma", - "Ġact ivist", - "Ġactiv ist", - "IN VAL", - "INV AL", - "Ġ Commercial", - "ĠCom mercial", - "ĠComm ercial", - "ĠOr lando", - "( tab", - "(t ab", - "Ġ ب", - "ĠØ ¨", - "Al gorithm", - "Ġher itage", - "Get Mapping", - "Ġfail ures", - "Ġfailure s", - "r ios", - "ri os", - "rio s", - "at iva", - "ati va", - "ativ a", - "Ġ tet", - "Ġt et", - "Ġte t", - "Ġcar pet", - "Ġcarp et", - "( Z", - "th ree", - "thr ee", - "Ġdis closure", - "Ġdisc losure", - ". ERROR", - ".ERR OR", - "_ called", - "_c alled", - "_call ed", - "_cal led", - "Ġd ial", - "Ġdi al", - "Ġdia l", - "Ġoccas ional", - "Ġoccasion al", - ". Err", - ".E rr", - "Ġfun cion", - "Ġfunc ion", - "caff old", - "caf fold", - "Ġre leasing", - "Ġrele asing", - "ï¼ī ĊĊ", - "ï¼īĊ Ċ", - "_ Value", - "_V alue", - "_Val ue", - "Ġ Vari", - "ĠV ari", - "ĠVar i", - "ĠVa ri", - "y ellow", - "Ġstrugg les", - "Ġstruggle s", - ". cal", - ".c al", - ".ca l", - "ĠDak ota", - "ĉ close", - "ĉc lose", - "ĉcl ose", - "Ġsand wich", - "Ġ analytics", - "Ġan alytics", - "Ġanaly tics", - "Ġanalytic s", - "Ġ **)", - "Ġ* *)", - "Ġ** )", - "& #", - "Ġ Jos", - "ĠJ os", - "ĠJo s", - "Ġpass ive", - "AT TR", - "ATT R", - "Th rowable", - "Throw able", - "ĠM un", - "ĠMu n", - "Ġ Uint", - "ĠU int", - "ĠUi nt", - "( disposing", - "(dis posing", - "a rak", - "ar ak", - "ara k", - "Ġ Leaders", - "ĠLe aders", - "ĠLeader s", - "ĠLead ers", - "Ġaffect ing", - "Ġitem View", - "Ġe conomics", - "Ġecon omics", - "Ġeconomic s", - "Ġeconom ics", - "f v", - "๠Ģ", - ". rb", - ".r b", - "Ġ Overall", - "ĠOver all", - "Ġwealth y", - "Ġev olved", - "Ġevolve d", - "n da", - "nd a", - "ĠH us", - "ĠHu s", - "re strict", - "u men", - "um en", - "ume n", - "ĠA gricult", - "ĠAgr icult", - "! ĊĊĊ", - "!ĊĊ Ċ", - "!Ċ ĊĊ", - "Ġ expires", - "Ġex pires", - "Ġexp ires", - "Ġexpire s", - "Ġspokes person", - "int erval", - "inter val", - "Ġ â", - "Ġà ¢", - "Ġ queen", - "Ġqu een", - "Ġque en", - "( nil", - "(n il", - "i ngo", - "in go", - "ing o", - "He ap", - "Ù İ", - "Ġcom plain", - "Ġcomp lain", - "Ġcompl ain", - "S ym", - "Sy m", - "Ġ Clone", - "ĠCl one", - "ĠClo ne", - "Ġ Ru", - "ĠR u", - "ĠW ILL", - "ĠWI LL", - "Ġ Crystal", - "ĠCr ystal", - "ĠCry stal", - "/ content", - "/c ontent", - "/con tent", - "i ngen", - "in gen", - "ing en", - "inge n", - "oint ment", - "Last Name", - "av icon", - "avi con", - "avic on", - "Ġ IBM", - "ĠI BM", - "ĠIB M", - "Ġ Dimension", - "ĠD imension", - "ĠDim ension", - "a nh", - "an h", - "ici pants", - "icip ants", - "icipant s", - "Ġ Anne", - "ĠAn ne", - "ĠAnn e", - ". progress", - ".pro gress", - "Ġ algo", - "Ġal go", - "Ġalg o", - "o bil", - "ob il", - "obi l", - "Ġ Voice", - "ĠV oice", - "ĠVo ice", - "Ġ FE", - "ĠF E", - "Ġ gli", - "Ġg li", - "Ġgl i", - "Ġ ved", - "Ġv ed", - "Ġve d", - "Ġpr events", - "Ġpre vents", - "Ġprevent s", - "Ġprev ents", - "\\ Column", - "\\C olumn", - "Ġ folk", - "Ġf olk", - "Ġfol k", - "Ġfo lk", - "e tti", - "et ti", - "ett i", - "Ġ mn", - "Ġm n", - "Ġ CLASS", - "ĠCL ASS", - "Ġdis playing", - "Ġdisplay ing", - "Ġdispl aying", - "ĠK l", - "ĠF err", - "ĠFe rr", - "ĠFer r", - "d uto", - "du to", - ". ib", - ".i b", - "Ġ dados", - "Ġd ados", - "Ġda dos", - "Ġdad os", - "Ġdado s", - "' name", - "'n ame", - "'na me", - "- space", - "-s pace", - "-sp ace", - "Ġit alian", - "Ġitalia n", - "Ġ inverse", - "Ġin verse", - "Ġinv erse", - "Ġinvers e", - "Ġ dense", - "Ġd ense", - "Ġden se", - "Ġdens e", - "u ter", - "ut er", - "ute r", - "Ġ IEnumerator", - "ĠI Enumerator", - "- sign", - "-s ign", - "Ġnation wide", - "Ġ persona", - "Ġperson a", - "Ġpers ona", - "Ġperso na", - "Ġs olved", - "Ġsol ved", - "Ġsolve d", - "Ġdram atically", - "Ġdramatic ally", - "Log out", - "Logo ut", - "Ġ grav", - "Ġg rav", - "Ġgr av", - "Ġgra v", - "Ġanal yses", - "Ġanaly ses", - "Ġanalys es", - "Ġanalyse s", - "ol lo", - "oll o", - "Ġ lamp", - "Ġl amp", - "Ġla mp", - "Ġlam p", - ". team", - ".t eam", - ".te am", - "Ġ Erot", - "ĠE rot", - "ĠEr ot", - "= [\"", - "=[ \"", - "Ġd ancing", - "Ġdan cing", - "Ġ ?>/", - "Ġ? >/", - "Ġ?> /", - "Ġc ater", - "Ġca ter", - "Ġcat er", - "Ġcate r", - "f fe", - "ff e", - "Ġ Sha", - "ĠS ha", - "ĠSh a", - "ĠB os", - "ĠBo s", - "ĠRE QUIRE", - "Ġ Monster", - "ĠMon ster", - "ĠMons ter", - "Ġ RB", - "ĠR B", - "Ġ IDE", - "ĠI DE", - "ĠID E", - "Ġs uits", - "Ġsu its", - "Ġsuit s", - "Ġsui ts", - "Ġ formData", - "Ġform Data", - "( theta", - "(th eta", - "(the ta", - "Ġs patial", - "Ġsp atial", - "Ġspat ial", - "= NULL", - "=N ULL", - "Ġ SqlConnection", - "ĠSql Connection", - "Ġ à", - "ĠV enez", - "ĠVen ez", - "ĠVe nez", - "Ġ Morning", - "ĠM orning", - "ĠMor ning", - "Ġpublic ations", - "Ġpub lications", - "Ġpublication s", - "ĠNON INFRINGEMENT", - "first Name", - "u ds", - "ud s", - "W ould", - "Wo uld", - "_ HEAD", - "_HE AD", - "Ġinv ested", - "Ġinvest ed", - "Ġinve sted", - "s table", - "st able", - "sta ble", - "stab le", - "f red", - "fr ed", - "fre d", - "Ġcomm ander", - "Ġcommand er", - "Ġcomma nder", - "Ġcommande r", - "S ES", - "SE S", - "âĢĶ a", - "an che", - "anc he", - "anch e", - "Ġ Movement", - "ĠM ovement", - "ĠMo vement", - "ĠMove ment", - "ĠMov ement", - "ë ³", - "S uite", - "Su ite", - "Suit e", - "Ġjur isdiction", - "ë ¦¬", - "ë¦ ¬", - "Ġ Beth", - "ĠB eth", - "ĠBe th", - "ĠBet h", - "j Query", - "ĠI sa", - "ĠIs a", - "Ġd ental", - "Ġden tal", - "Ġdent al", - ", *", - "Ġ Limit", - "ĠL imit", - "ĠLim it", - "ĠLi mit", - "il iation", - "ili ation", - "ilia tion", - "= \"{", - "=\" {", - "b ast", - "ba st", - "bas t", - "Ġt urb", - "Ġtu rb", - "Ġtur b", - "i sy", - "is y", - "O OK", - "OO K", - "Ġadv ocate", - "Ġadvoc ate", - "i mag", - "im ag", - "ima g", - "LE CTION", - "LECT ION", - "LEC TION", - "л ÑĮ", - "( category", - "(c ategory", - ". dec", - ".d ec", - ".de c", - "Ġun iqu", - "Ġuni qu", - "Ġuniq u", - "_ sn", - "_s n", - "Ġat tracted", - "Ġattr acted", - "Ġattract ed", - "Ġ Ãī", - "Ġà ī", - "Ġ Running", - "ĠR unning", - "ĠRun ning", - "_ edges", - "_edge s", - "_ed ges", - "Ġ Disable", - "ĠD isable", - "ĠDis able", - "_ AS", - "_A S", - "åĽ ¾", - "Ġnetwork ing", - "Ġnet working", - "_ branch", - "_br anch", - "H aving", - "Ha ving", - "toBe Truthy", - "G I", - "Ġc amps", - "Ġca mps", - "Ġcamp s", - "Ġcam ps", - "s ep", - "se p", - "- part", - "-p art", - "-par t", - "Ġ )ĊĊĊĊĊĊĊĊ", - "Ġ) ĊĊĊĊĊĊĊĊ", - "Ġ)Ċ ĊĊĊĊĊĊĊ", - "Ġ)ĊĊ ĊĊĊĊĊĊ", - "Ġ)ĊĊĊ ĊĊĊĊĊ", - "ustr alia", - "ustral ia", - "Ġ Reports", - "ĠRe ports", - "ĠRep orts", - "ĠReport s", - "ĠRepo rts", - "r ito", - "ri to", - "rit o", - "Ġwa ist", - "_ plus", - "_p lus", - "_pl us", - "Ġ WW", - "ĠW W", - "- person", - "-p erson", - "-per son", - "Ap ril", - "Apr il", - "Ġ sar", - "Ġs ar", - "Ġsa r", - ". tar", - ".t ar", - ".ta r", - "Ġagricult ural", - "Ġagr icultural", - "t ic", - "ti c", - "Ġ tcp", - "Ġt cp", - "Ġtc p", - "Ġ setValue", - "Ġset Value", - "ag ento", - "agent o", - "agen to", - "Ġ Appe", - "ĠA ppe", - "ĠApp e", - "ĠAp pe", - "p iler", - "pi ler", - "pile r", - "C ADE", - "CA DE", - "CAD E", - "Ġ anche", - "Ġan che", - "Ġanch e", - "Ġanc he", - "at cher", - "atch er", - "Ġc omics", - "Ġcom ics", - "Ġcomic s", - "Ġ lbs", - "Ġl bs", - "Ġlb s", - "_ segment", - "_s egment", - "_se gment", - "_seg ment", - "' ]=$", - "'] =$", - "']= $", - "it ters", - "itt ers", - "itter s", - "itte rs", - "i cher", - "ic her", - "ich er", - "iche r", - "G INE", - "GIN E", - "GI NE", - "Ġutil ize", - "Ġutiliz e", - "Ġ Cursor", - "ĠC ursor", - "ĠCurso r", - "_ expression", - "_ex pression", - "_exp ression", - "_expr ession", - "Ġ dag", - "Ġd ag", - "Ġda g", - "< long", - " < ?=", - "> x", - ". Task", - ".T ask", - "m oney", - "mon ey", - "mo ney", - "ib aba", - "iba ba", - "' });Ċ", - "'} );Ċ", - "'}) ;Ċ", - "Ġ Specific", - "ĠS pecific", - "ĠSpec ific", - "Ġ Linear", - "ĠL inear", - "ĠLine ar", - "ĠLin ear", - "ĠLi near", - "_ OPT", - "_O PT", - "_OP T", - "Hash Code", - "( Player", - "(P layer", - ".Contains Key", - "Ġ collapsed", - "Ġc ollapsed", - "Ġcoll apsed", - "Ġcollapse d", - "Ġcollaps ed", - "trans parent", - "_R ANGE", - "View er", - "( cfg", - "(c fg", - "(cf g", - "Ġ sorting", - "Ġs orting", - "Ġsort ing", - "Ġsor ting", - "Ġinf ected", - "Ġinfect ed", - "Ġ Nach", - "ĠN ach", - "ĠNa ch", - "Ġaccommod ate", - ". elements", - ".e lements", - ".element s", - ".el ements", - ".elem ents", - "_ PART", - "_P ART", - "_PA RT", - "_PAR T", - "Ġ Sexy", - "ĠSe xy", - "ĠSex y", - "= get", - "=g et", - "( year", - "(y ear", - "Ġ xhr", - "Ġx hr", - ": ]", - "ow ski", - "ows ki", - "Ġsum mar", - "Ġsumm ar", - "Ġ ¿", - "Ġ ¿", - "Ġ inte", - "Ġin te", - "Ġint e", - "Ġi nte", - "Ġ workflow", - "Ġwork flow", - "ĠTai wan", - "v ersions", - "vers ions", - "version s", - "åı ij", - "Ġsur prisingly", - "Ġsurprising ly", - "Ġop tical", - "Ġopt ical", - "Ġoptic al", - "Ġpro ces", - "Ġproc es", - "Ġdis agree", - "Ġdisag ree", - "Ġn uevo", - "Ġnue vo", - "Ġ CAM", - "ĠC AM", - "ĠCA M", - "s orted", - "sort ed", - "le ases", - "lease s", - "lea ses", - "is tle", - "ist le", - "I dent", - "Id ent", - "Ide nt", - "ĉ event", - "ĉe vent", - "ĉev ent", - "j ected", - "ject ed", - "jec ted", - "Ch unk", - "V ars", - "Var s", - "Va rs", - ". provider", - ".pro vider", - "Ġproceed ings", - "Ġproceeding s", - "Ġ inclusive", - "Ġin clusive", - "Ġincl usive", - "Ġinclus ive", - "Ġart work", - "end ants", - "enda nts", - "endant s", - "ï¼ļ Ċ", - "s een", - "se en", - "see n", - "Ġ lig", - "Ġl ig", - "Ġli g", - "Ġ makers", - "Ġm akers", - "Ġmake rs", - "Ġma kers", - "Ġmaker s", - "Ġmak ers", - "_ fun", - "_f un", - "_fu n", - "Ġlength s", - "Ġleng ths", - "Path Variable", - "[ item", - "[i tem", - "[it em", - "ภµ", - "D ead", - "De ad", - "FF FFFF", - "FFFF FF", - "FFF FFF", - "Ġ Urban", - "ĠUr ban", - "ĠUrb an", - "u ples", - "up les", - "uple s", - "i chen", - "ic hen", - "ich en", - "iche n", - "( nullptr", - "(null ptr", - ". spec", - ".s pec", - ".sp ec", - ", System", - ",S ystem", - "U RATION", - "UR ATION", - "URA TION", - "( job", - "(j ob", - "å¼ ı", - "Ġ tracker", - "Ġtr acker", - "Ġtrack er", - "Ġtra cker", - "Å Ļ", - "Ġ MR", - "ĠM R", - "Ġ SQLite", - "ĠSQL ite", - "ĠSQ Lite", - "Ġ dto", - "Ġd to", - "Ġdt o", - "Ġ ;;Ċ", - "Ġ; ;Ċ", - "Ġ;; Ċ", - "Ġ mint", - "Ġm int", - "Ġmin t", - "Ġmi nt", - "Ġ Introduction", - "ĠInt roduction", - "ĠIntro duction", - "c ao", - "ca o", - "Ġquest ioned", - "Ġquestion ed", - "Ġquesti oned", - "Ġf itted", - "Ġfit ted", - "Ġfitte d", - "re vision", - "rev ision", - "s q", - "Ġm ig", - "Ġmi g", - "_ units", - "_un its", - "_unit s", - "_ async", - "_a sync", - "_as ync", - "Ġf lick", - "Ġfl ick", - "} );ĊĊĊ", - "});Ċ ĊĊ", - "});ĊĊ Ċ", - "}) ;ĊĊĊ", - "}); ĊĊĊ", - "Ġn otre", - "Ġnot re", - "Ġno tre", - "} `,", - "}` ,", - "F ilters", - "Filter s", - "Fil ters", - "Ġm undo", - "Ġmu ndo", - "Ġmund o", - "Ġmun do", - "_ days", - "_d ays", - "_day s", - "_da ys", - "Ġ frm", - "Ġf rm", - "Ġfr m", - "u tc", - "ut c", - "Ġ vals", - "Ġv als", - "Ġval s", - "Ġva ls", - "e width", - "ew idth", - "Ġ Generator", - "ĠG enerator", - "ĠGener ator", - "ĠGen erator", - "Ġ Artist", - "ĠArt ist", - "Ġ IDs", - "ĠI Ds", - "ĠID s", - "Ġ Articles", - "ĠArt icles", - "ĠArticle s", - "re ater", - "reate r", - "reat er", - "rea ter", - "ĠComponent Fixture", - ". =", - "Ġ rou", - "Ġr ou", - "Ġro u", - "- no", - "-n o", - ".b ukkit", - "e gg", - "eg g", - "Ġ Diff", - "ĠD iff", - "ĠDi ff", - "a tics", - "at ics", - "atic s", - "ati cs", - "Ñĥ Ñĩ", - "âĢĶ ĊĊ", - "Ġ Charlotte", - "ĠChar lotte", - "ĠCharl otte", - "b ye", - "by e", - "Ġ });čĊčĊ", - "Ġ} );čĊčĊ", - "Ġ}) ;čĊčĊ", - "Ġ});čĊ čĊ", - "Ġ}); čĊčĊ", - "ĠV ik", - "ĠVi k", - "ĠB row", - "ĠBr ow", - "ĠBro w", - "Ġ lv", - "Ġl v", - "ĠG ib", - "ĠGi b", - "- wing", - "-w ing", - "-win g", - "GL IGENCE", - "( Il", - "(I l", - "ĠEngine er", - ". Wait", - ".W ait", - "Ġ Pictures", - "ĠP ictures", - "ĠPicture s", - "ĠPic tures", - "Ġr het", - "Ġrh et", - "Ġrhe t", - "Ġ thermal", - "Ġth ermal", - "Ġther mal", - "Ġtherm al", - "Ġp raise", - "Ġpr aise", - "Ġpra ise", - "< >();ĊĊ", - "<>();Ċ Ċ", - "<>( );ĊĊ", - "<> ();ĊĊ", - "Ġ Spider", - "ĠSp ider", - "ĠSpi der", - "P ause", - "Pa use", - "ĠB aker", - "ĠBa ker", - "ĠBak er", - "ĠBake r", - "Ġs lower", - "Ġsl ower", - "Ġslow er", - "Ġslo wer", - "Ġ }]Ċ", - "Ġ} ]Ċ", - "Ġ}] Ċ", - "_ enqueue", - "_en queue", - "Ġdis appeared", - "Ġdisappe ared", - "Ġdisappear ed", - "Ġ Ticket", - "ĠT icket", - "ĠTi cket", - "ĠTick et", - "ĠTic ket", - "IN UX", - "INU X", - "_ LOCAL", - "_LO CAL", - "_LOC AL", - "аÑģ Ñģ", - "@Inject able", - "comm unity", - "G estureRecognizer", - "Gesture Recognizer", - "åĽ ½", - "Ġs cales", - "Ġsc ales", - "Ġscale s", - "Ġsca les", - "Ġscal es", - "Ġ -(", - "Ġ- (", - "/ '+", - "/' +", - "Ġ Sit", - "ĠS it", - "ĠSi t", - "Ġexecutive s", - "Ġexecut ives", - "ar ding", - "ard ing", - "ardi ng", - "ardin g", - "Ġad vers", - "Ġadv ers", - "Ġback wards", - "Ġbackward s", - "ĉ context", - "ĉcon text", - "ĉcont ext", - "ĠH amp", - "ĠHam p", - "ĠHa mp", - "Ġ PF", - "ĠP F", - "Ġ Deck", - "ĠD eck", - "ĠDe ck", - "ĠDec k", - "Ġ Craig", - "ĠC raig", - "ĠCra ig", - "A merican", - "Americ an", - "America n", - "Ġ bell", - "Ġb ell", - "Ġbe ll", - "Ġbel l", - "Ġp rol", - "Ġpro l", - "Ġpr ol", - "u fen", - "uf en", - "ufe n", - "Ġ rng", - "Ġr ng", - "Ġrn g", - "ar shal", - "ars hal", - "Ġ Simply", - "ĠSim ply", - "ĠSimpl y", - "first name", - "sh ore", - "J uly", - "Jul y", - "Ju ly", - "Ġm ortality", - "Ġmort ality", - "Ġmortal ity", - "ĠâĨĴ ĊĊ", - "H elpers", - "Helper s", - "Help ers", - "Hel pers", - "Ġ benchmark", - "Ġb enchmark", - "Ġbench mark", - "e made", - "em ade", - "ema de", - "Ġorgan isations", - "Ġorganis ations", - "Ġorganisation s", - ".g son", - ".gs on", - "Ġ TextField", - "ĠT extField", - "ĠText Field", - "Ġcivil ians", - "Ġciv ilians", - "Ġcivilian s", - ". Arrays", - ".Array s", - ".Ar rays", - "ĠMiss issippi", - "Ġinter mediate", - "Ġintermedi ate", - "get User", - "_ cluster", - "_cl uster", - "Rel ative", - "fore ign", - ".querySelector All", - "Fore ignKey", - "Foreign Key", - "Ġreason ably", - "- --------Ċ", - "-- -------Ċ", - "---- -----Ċ", - "-------- -Ċ", - "--- ------Ċ", - "----- ----Ċ", - "------ ---Ċ", - "------- --Ċ", - "--------- Ċ", - "C ards", - "Card s", - "Car ds", - "ĠK am", - "ĠKa m", - "Ġ Thor", - "ĠT hor", - "ĠTh or", - "Ġ roller", - "Ġr oller", - "Ġro ller", - "Ġroll er", - "Ġrol ler", - "- element", - "-e lement", - "-el ement", - "Ġ Currency", - "ĠC urrency", - "d die", - "dd ie", - "AL LY", - "ALL Y", - "Ġ RA", - "ĠR A", - "Ġper met", - "Ġperm et", - "Ġperme t", - "a aaa", - "aa aa", - "aaa a", - "Ġhome work", - "Ġhom ework", - "ĠV it", - "ĠVi t", - "Ġm old", - "Ġmo ld", - "Ġmol d", - "ĠF er", - "ĠFe r", - "[ start", - "Ġstat istical", - "Ġstatist ical", - "Ġstatistic al", - "Ġsc ary", - "Ġsca ry", - "Ġscar y", - "_ HOME", - "_H OME", - ". Begin", - ".B egin", - ".Be gin", - "Con struct", - "o genic", - "og enic", - "ogen ic", - "oge nic", - "ĠDEAL INGS", - "Ġtamb ién", - "i xon", - "ix on", - "ixo n", - ". ind", - ".in d", - ".i nd", - "a cre", - "ac re", - "acr e", - "Ġ transforms", - "Ġtrans forms", - "Ġtransform s", - "ĠN ap", - "ĠNa p", - ". Block", - ".B lock", - ".Bl ock", - "uss ia", - "p iration", - "pi ration", - "pir ation", - "ul ent", - "ule nt", - "ulen t", - "Ġ ceil", - "Ġc eil", - "Ġce il", - "Cl ause", - "Cla use", - "n aire", - "na ire", - "T ES", - "TE S", - "Ġn eat", - "Ġne at", - "S TD", - "ST D", - "Ġ RegExp", - "ĠReg Exp", - "per form", - "perf orm", - ": )", - "Ġun ions", - "Ġunion s", - "Ġuni ons", - "Ġs ublic", - "Ġsub lic", - "Ġw inds", - "Ġwin ds", - "Ġwind s", - "Ġwi nds", - "lo ating", - "loat ing", - "g lich", - "gl ich", - "gli ch", - "Ġ pagination", - "Ġp agination", - "Ġpag ination", - "Ġpagina tion", - "S kill", - "Sk ill", - "App ly", - "Ap ply", - "Ġ Operator", - "ĠO perator", - "ĠOper ator", - "ĠOp erator", - "ĠOpera tor", - "ist ogram", - "isto gram", - "Ġ qualities", - "Ġqual ities", - "Ġquali ties", - "C ross", - "Cr oss", - "Cro ss", - "Ġd ecom", - "Ġde com", - "Ġdec om", - "Ġdeco m", - "] ,\"", - "], \"", - "Ġ Juan", - "ĠJ uan", - "ĠJu an", - ". modal", - ".m odal", - ".mod al", - ".mo dal", - ". Child", - ".Ch ild", - "Ġ Roger", - "ĠR oger", - "ĠRo ger", - "ĠRog er", - "STIT UTE", - ":CGRect Make", - "a lette", - "al ette", - "ale tte", - "alet te", - "Ġ sta", - "Ġs ta", - "Ġst a", - "a side", - "as ide", - "asi de", - "Ġ blur", - "Ġbl ur", - "Ġ Wa", - "ĠW a", - "if etime", - "ife time", - "r eed", - "re ed", - "ree d", - "control s", - "contr ols", - "contro ls", - "Ġ bins", - "Ġb ins", - "Ġbi ns", - "Ġbin s", - "Ġ пол", - "Ġп ол", - "Ġпо л", - "* /,Ċ", - "*/ ,Ċ", - "*/, Ċ", - "U IS", - "UI S", - "ĠR ou", - "ĠRo u", - "Ġ Demo", - "ĠD emo", - "ĠDe mo", - "ĠDem o", - "- awesome", - "Ġ Chain", - "ĠCh ain", - "ĠCha in", - "Ġh asta", - "Ġhas ta", - "Ġha sta", - "Ġhast a", - "ĠB art", - "ĠBar t", - "ĠBa rt", - ". KEY", - ".K EY", - "Ġ vendors", - "Ġv endors", - "Ġvend ors", - "Ġvendor s", - "no follow", - "nof ollow", - "Ġ Dest", - "ĠD est", - "ĠDe st", - "ĠDes t", - "_ builder", - "_b uilder", - "_build er", - "Ġarg ues", - "Ġargue s", - "_ answer", - "_an swer", - "_ans wer", - "g oto", - "go to", - "got o", - "Ġ RESULT", - "ĠRES ULT", - "Ġ MON", - "ĠM ON", - "ĠMO N", - "Ġp oder", - "Ġpo der", - "Ġpod er", - "Ġpode r", - "o ons", - "oo ns", - "oon s", - "_ CASE", - "_C ASE", - "_CA SE", - "Ġrep lic", - "Ġrepl ic", - "Ġfin ancing", - "Ġfinanc ing", - "Ġfinan cing", - "Ġ DATE", - "ĠD ATE", - "ĠDA TE", - "ĠDAT E", - "c ern", - "ce rn", - "cer n", - "_ track", - "_t rack", - "_tr ack", - "_tra ck", - "t ies", - "ti es", - "tie s", - "/ logo", - "/l ogo", - "/log o", - "/lo go", - "ĠNE GLIGENCE", - "get Type", - "> T", - "b et", - "be t", - "g irl", - "gi rl", - "ĠINCIDENT AL", - "- site", - "-s ite", - ". trigger", - ".tr igger", - "Ġ Lisa", - "ĠL isa", - "ĠLi sa", - "ĠLis a", - "_ inputs", - "_in puts", - "_input s", - "_inp uts", - "Ġrel atives", - "Ġrelative s", - "Ġrelativ es", - "Ġrelat ives", - "Logged In", - "Con figure", - "Config ure", - "Conf igure", - "I K", - ". accept", - ".ac cept", - ".acc ept", - "Re sume", - "Res ume", - "Ġ Draft", - "ĠD raft", - "ĠDr aft", - "ĠDra ft", - "Ġ *>(", - "Ġ* >(", - "Ġ*> (", - "Ġ WA", - "ĠW A", - "ed ian", - "edia n", - "edi an", - "er ness", - "ern ess", - "erne ss", - "ernes s", - "Ġ LayoutInflater", - "ĠLayout Inflater", - "* /čĊčĊ", - "*/ čĊčĊ", - "*/čĊ čĊ", - "o thy", - "ot hy", - "oth y", - "Ġoblig ation", - "Ġobl igation", - "Sub scribe", - "Ġ thumbnail", - "Ġth umbnail", - "ex ist", - "Ġins isted", - "Ġinsist ed", - "Ġ UICollectionView", - "ĠU ICollectionView", - "ĠUI CollectionView", - "Ġ Angular", - "ĠAng ular", - "Ġtable ts", - "Ġtab lets", - "Ġtablet s", - "Ġ Impact", - "ĠImp act", - "ãĢį ĊĊ", - "ãĢįĊ Ċ", - "a ho", - "ah o", - "Ġcharacter istic", - "g d", - "Ġ= ================================================", - "Ġ================= ================================", - "Ġ================================= ================", - "o urt", - "ou rt", - "our t", - "` .", - "App ro", - "Ap pro", - "Co ordinate", - "Coord inate", - "Re member", - "Rem ember", - "Ġ marine", - "Ġm arine", - "Ġmar ine", - "Ġma rine", - "Ġmari ne", - "Ġmarin e", - "] =='", - "]= ='", - "]== '", - "Ġ Administrator", - "ĠAdmin istrator", - "ĠAdministr ator", - ". getDefault", - ".get Default", - ".getD efault", - "Ġ forgot", - "Ġf orgot", - "Ġfor got", - "Ġforg ot", - "Ġ Structure", - "ĠStruct ure", - "V ue", - "Vu e", - "ar sing", - "ars ing", - "arsi ng", - "m oment", - "mo ment", - "mom ent", - "k w", - "_ cursor", - "_c ursor", - "Att ack", - "Ġath letic", - "Ġdiagn osed", - "Ġdiagnose d", - "Ġ ende", - "Ġe nde", - "Ġen de", - "Ġend e", - "åĪ łéĻ¤", - "H ouse", - "Ho use", - "Ġ PARAM", - "ĠP ARAM", - "ĠPA RAM", - "ĠPAR AM", - "ĠPARA M", - "Ġ wiki", - "Ġw iki", - "Ġwi ki", - "Ġwik i", - "Ġ Opp", - "ĠO pp", - "ĠOp p", - "Ġcons ervation", - "Ġconserv ation", - "Ġ snd", - "Ġs nd", - "Ġsn d", - "_ tem", - "_t em", - "_te m", - "sub str", - "subst r", - "subs tr", - "ĠC ape", - "ĠCap e", - "ĠCa pe", - ". sim", - ".s im", - ".si m", - "U TION", - "UT ION", - "a nan", - "an an", - "ana n", - "âĢĻ un", - "Ġ gy", - "Ġg y", - "- work", - "-w ork", - "Ġcomp elling", - "Ġcompel ling", - "= '#", - "=' #", - "ĉ sub", - "ĉs ub", - "Ġ directories", - "Ġdirect ories", - "Ġdirector ies", - "íĬ ¸", - "Ġ touches", - "Ġtouch es", - "Ġtou ches", - "out ines", - "outine s", - ". Collection", - ".C ollection", - ".Col lection", - "s chedule", - "sched ule", - ". lat", - ".l at", - "Ġ Doctrine", - "ĠDo ctrine", - "C AA", - "CA A", - "Ġ Refer", - "ĠRe fer", - "ĠRef er", - "Ġshift s", - "Ġ likelihood", - "Ġlik elihood", - "pr eter", - "pre ter", - "pret er", - "Ġ Female", - "ĠF emale", - "ĠFe male", - "ĠFem ale", - "Ġinter cept", - "Ġ lou", - "Ġl ou", - "Ġlo u", - "çĻ »", - "Ġ rug", - "Ġr ug", - "Ġru g", - "ĠC rown", - "ĠCr own", - "ĠCro wn", - "ĠCrow n", - "Ġ ****************************************************************************", - "Ġ************************************************************************ ****", - "Ġ************************************************************************** **", - "Ġ**************************************************************** ************", - "Ġ**** ************************************************************************", - "Ġ******************************************************** ********************", - "Ġ************************************************ ****************************", - "- product", - "-pro duct", - "-produ ct", - "Ġprompt ed", - "u ngle", - "un gle", - "ung le", - "d ocker", - "do cker", - "doc ker", - "dock er", - "Ġ Tu", - "ĠT u", - "Ġ Unique", - "ĠUn ique", - "ĠUni que", - "_ Error", - "_E rror", - "_Err or", - "u los", - "ul os", - "ulo s", - "Ġ âĦ", - "Ġâ Ħ", - "Ġ (`", - "Ġ( `", - "G etting", - "Get ting", - "_ scal", - "_s cal", - "_sc al", - "Ġ Enh", - "ĠE nh", - "ĠEn h", - "ü t", - "Ġsust ained", - "Ġsustain ed", - "Ġ patches", - "Ġp atches", - "Ġpat ches", - "Ġpatch es", - "Ġpros per", - "ĠG aza", - "ĠGa za", - "ĠGaz a", - "_ light", - "_l ight", - "_li ght", - "Ġin cons", - "Ġinc ons", - "Ġincon s", - "- -------Ċ", - "-- ------Ċ", - "---- ----Ċ", - "-------- Ċ", - "--- -----Ċ", - "----- ---Ċ", - "------ --Ċ", - "------- -Ċ", - "ĉ ĉĠĠĠĠĠĠ", - "ĉĉ ĠĠĠĠĠĠ", - "ĉĉĠĠĠ ĠĠĠ", - "ĉĉĠ ĠĠĠĠĠ", - "ĉĉĠĠ ĠĠĠĠ", - "ĉĉĠĠĠĠ ĠĠ", - "ĉĉĠĠĠĠĠ Ġ", - "S F", - "C N", - ": \";Ċ", - ":\" ;Ċ", - "ĠColl ins", - "( *)", - "(* )", - "Ġcomp ilation", - "Ġcompil ation", - "' ]čĊ", - "'] čĊ", - "Ġcon sequence", - "Ġconsequ ence", - "Ġconse quence", - ", ...", - ",. ..", - "Ġ dm", - "Ġd m", - "Ġ BLOCK", - "ĠB LOCK", - "ĠBL OCK", - "Cl uster", - "Ġ ski", - "Ġs ki", - "Ġsk i", - "( argc", - "(arg c", - "(ar gc", - "T uple", - "Tu ple", - "Ġj oins", - "Ġjoin s", - "Ġjo ins", - "ĠSher iff", - "W ar", - "Wa r", - "in di", - "ind i", - "Ġcom mented", - "Ġcomm ented", - "Ġcomment ed", - "H OST", - "HO ST", - "Ġ invitation", - "Ġinv itation", - "Ġinvit ation", - "apan ese", - "Ġper mits", - "Ġpermit s", - "Ġperm its", - "preced ented", - "_ zone", - "_z one", - "Ġ Amy", - "ĠA my", - "ĠAm y", - "_ RD", - "_R D", - "Min imum", - "Ġinv ocation", - "Ġinvo cation", - ". enable", - ".e nable", - ".en able", - "i chten", - "ich ten", - "icht en", - "ichte n", - "- owned", - "\" id", - "_PO INTER", - "_POINT ER", - "F ac", - "Fa c", - "Ġspec ifications", - "Ġspecific ations", - "Ġspecification s", - "Ġn omination", - "Ġno mination", - "Ġnom ination", - "Ġnomin ation", - "Ġ gp", - "Ġg p", - "< (", - "Ġ robots", - "Ġro bots", - "Ġrob ots", - "Ġrobot s", - "Ġ Jerry", - "ĠJ erry", - "ĠJer ry", - "Ġ holders", - "Ġh olders", - "Ġhold ers", - "Ġholder s", - "Ġhol ders", - "Ġ wand", - "Ġw and", - "Ġwa nd", - "Ġwan d", - "c ms", - "cm s", - "Ġ }))Ċ", - "Ġ} ))Ċ", - "Ġ}) )Ċ", - "Ġ})) Ċ", - ". Toast", - ".To ast", - "ĠI List", - "ĠIL ist", - "B ased", - "Base d", - "Bas ed", - "Ba sed", - "z oom", - "zo om", - "/ style", - "/st yle", - "ĠB eck", - "ĠBe ck", - "ĠBec k", - "M en", - "Me n", - "Ġcontrib uting", - "Ġ undo", - "Ġu ndo", - "Ġun do", - "Ġund o", - "Ġ OH", - "ĠO H", - "Ġadd Object", - "Ġe igen", - "Ġei gen", - "Ġeig en", - "sign up", - "éĶ Ļ", - "Ġd istant", - "Ġdis tant", - "Ġdist ant", - "Ġdi stant", - "PAR ATOR", - "Ġ Mari", - "ĠM ari", - "ĠMar i", - "ĠMa ri", - "Ġ má", - "Ġm á", - "E mp", - "Em p", - "ó s", - "Ġ ìĪĺ", - "Ġì Īĺ", - "ĠìĪ ĺ", - "e vt", - "ev t", - "+ j", - "p ark", - "par k", - "pa rk", - "Ġ Stay", - "ĠSt ay", - "ĠSta y", - "ĠD un", - "ĠDu n", - "Ġs oy", - "Ġso y", - "> %", - "az ines", - "azine s", - "azi nes", - "Ġti empo", - "( me", - "(m e", - "p resent", - "pre sent", - "pres ent", - ". This", - ".T his", - ".Th is", - "Ġed itors", - "Ġedit ors", - "Ġeditor s", - "Ġedi tors", - "F IELD", - ". Work", - ".W ork", - "ĠUn iverse", - "ĠUnivers e", - "ĠUni verse", - "ĠUniv erse", - "Ġdr unk", - "Ġdru nk", - ". timer", - ".t imer", - ".time r", - ".tim er", - "Ġal tered", - "Ġalt ered", - "Ġalter ed", - "Ġalte red", - "ĠN ar", - "ĠNa r", - "ëł ¥", - ". Active", - ".Act ive", - "id or", - "ido r", - "ç Ń", - ".delta Time", - "Ġawk ward", - "& quot", - "ĠS afari", - "ĠSaf ari", - "Ġt ricks", - "Ġtr icks", - "Ġtri cks", - "Ġtrick s", - "M ENTS", - "MENT S", - "div ision", - "di vision", - "Ġvar ying", - "Ġva rying", - "Ġvary ing", - "ĠHigh way", - "Ġphot ographer", - "Ġphotograph er", - "ĠSt ewart", - "ĠSte wart", - "Ġ lasting", - "Ġl asting", - "Ġlast ing", - "Ġlas ting", - ". Pre", - ".P re", - ".Pr e", - ".amazon aws", - "Ġ Luck", - "ĠL uck", - "ĠLuc k", - "ĠLu ck", - ". Description", - ".D escription", - ".De scription", - ".Des cription", - "Ġ Naz", - "ĠN az", - "ĠNa z", - "n eg", - "ne g", - "Ġc ó", - "<< \"\\", - "<<\" \\", - "Ġ Surv", - "ĠS urv", - "ĠSur v", - "ĠSu rv", - "Ġ Unc", - "ĠU nc", - "ĠUn c", - "Rec ipe", - ". BorderStyle", - ".Border Style", - "Ġmod ifications", - "Ġmodification s", - "Ġmodific ations", - "- at", - "-a t", - "AT FORM", - "h dr", - "hd r", - "a ko", - "ak o", - "Ġsub license", - "Ġsublic ense", - "Ġ Jump", - "ĠJ ump", - "ĠJu mp", - "Ġbe im", - "Ġbei m", - "ĠMan hattan", - ". bool", - ".b ool", - ".bo ol", - "_ hw", - "_h w", - "ÑĤ ÑĮ", - "B in", - "Bi n", - "Ġ gateway", - "Ġg ateway", - "Ġgate way", - "\" \":", - "\"\" :", - "Ġ UIS", - "ĠU IS", - "ĠUI S", - ": \"+", - ":\" +", - "- def", - "-d ef", - "-de f", - "Ġ Regular", - "ĠReg ular", - "/ testing", - "/t esting", - "/test ing", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "string stream", - "Ġdis par", - "Ġdi spar", - "Ġdisp ar", - "Ġm obil", - "Ġmo bil", - "Ġmob il", - "- read", - "-r ead", - "-re ad", - "Ġ Adapter", - "ĠAd apter", - "ĠAda pter", - "ĠAdapt er", - "ĠCh ampions", - "ĠChampion s", - "ĠChamp ions", - "Ġ scheduler", - "Ġs cheduler", - "Ġsched uler", - "Ġschedule r", - "Ġ kills", - "Ġk ills", - "Ġkill s", - "Ġkil ls", - "Ġ Multiple", - "ĠM ultiple", - "ĠMulti ple", - "ĠMult iple", - "ĠMultip le", - "i rror", - "ir ror", - "Ġg ods", - "Ġgo ds", - "Ġgod s", - "A DO", - "AD O", - "a kte", - "ak te", - "akt e", - "Ġ Usuario", - "ĠUs uario", - ".c ircular", - "Ġre cept", - "Ġrec ept", - "Ġrece pt", - "Ġrecep t", - "Ġ Expr", - "ĠEx pr", - "ĠExp r", - "Ġelder ly", - "Ġnice ly", - "Ġnic ely", - "Ġb este", - "Ġbe ste", - "Ġbest e", - "Ġbes te", - "W ant", - "Wa nt", - "Ġclass ical", - "Ġclassic al", - ". sprite", - ".s prite", - ".sp rite", - "ob jc", - "obj c", - "ĠM ason", - "ĠMa son", - "ĠMas on", - "Ġs istema", - "Ġsist ema", - "Ġsistem a", - ". Black", - ".B lack", - ".Bl ack", - "e so", - "es o", - "ĠZe it", - "Ġ divid", - "Ġd ivid", - "Ġdi vid", - "Ġdiv id", - "Ġen ters", - "Ġent ers", - "Ġenter s", - "_ subject", - "_sub ject", - "_su bject", - "Ġ Planet", - "ĠPlan et", - "ĠPlane t", - "ĠPla net", - ". warning", - ".w arning", - ".warn ing", - "Ġ Gram", - "ĠG ram", - "ĠGr am", - "ĠGra m", - "_ tokens", - "_t okens", - "_token s", - "_tok ens", - "Ġhouse holds", - "Ġhousehold s", - "_ customer", - "_c ustomer", - "_custom er", - "_cust omer", - "user Name", - "c ross", - "cr oss", - "cro ss", - "Ġp ione", - "Ġpi one", - "Ġass ists", - "Ġassist s", - "_ SM", - "_S M", - "i bo", - "ib o", - "Ġl oyal", - "Ġlo yal", - "Ġus eless", - "Ġuse less", - "# elif", - "ĠUlt imate", - "C ome", - "Com e", - "Co me", - "g el", - "ge l", - "Ġd ich", - "Ġdi ch", - "Ġdic h", - "x yz", - "xy z", - "i kel", - "ik el", - "ike l", - "o bra", - "ob ra", - "_ scan", - "_s can", - "_sc an", - "Ġ Interior", - "ĠIn terior", - "ĠInt erior", - "ĠInter ior", - "Ġ Nice", - "ĠN ice", - "ĠNic e", - "ĠNi ce", - "Ġp lac", - "Ġpl ac", - "Ġpla c", - "ĉ target", - "ĉt arget", - "Ġv iral", - "Ġvi ral", - "Ġvir al", - "as so", - "ass o", - "( )/", - "() /", - "u nde", - "un de", - "und e", - "Ġ Adobe", - "ĠAd obe", - "O s", - "vis ited", - "visit ed", - "Ġ OW", - "ĠO W", - "Ġ Feed", - "ĠF eed", - "ĠFe ed", - "ĠFee d", - "Ġ Sequence", - "ĠSe quence", - "ĠSequ ence", - "Ġman ages", - "Ġmanage s", - "Ġmana ges", - "in son", - "ins on", - "ĠLouis iana", - "{ })", - "{} )", - "ĠH ab", - "ĠHa b", - "Ġ LD", - "ĠL D", - "Ġb ip", - "Ġbi p", - "p rites", - "pr ites", - "prite s", - "prit es", - "pri tes", - "( elem", - "(e lem", - "(el em", - "(ele m", - ".h ibernate", - "é lé", - "él é", - "Ġoh ne", - "_ transaction", - "_trans action", - "Ġann unci", - "P ublished", - "Publish ed", - "Ġ Honda", - "ĠH onda", - "ĠHon da", - "ĠHo nda", - "ĠHond a", - "Ġ Tam", - "ĠT am", - "ĠTa m", - "Ġ Packet", - "ĠP acket", - "ĠPac ket", - "ĠPack et", - "ĠPa cket", - "_ selector", - "_se lector", - "_select or", - "_sel ector", - "Ġchalleng ed", - "Ġchallenge d", - "P rocessing", - "Process ing", - "- hover", - "-h over", - "Ġ trainer", - "Ġtr ainer", - "Ġtrain er", - "Ġtra iner", - "Ġtrai ner", - "_ cancel", - "_c ancel", - "_can cel", - "Ġ NSDictionary", - "ĠNS Dictionary", - "ab ric", - "abr ic", - "Ġ MLS", - "ĠM LS", - "ĠML S", - "_ sensor", - "_s ensor", - "Ġsh rink", - "Ġshr ink", - "Ġshri nk", - "Ġ FX", - "ĠF X", - "th reshold", - "thresh old", - "ĉ HX", - "ĉH X", - "- mark", - "-m ark", - "-mar k", - "` .`", - "`. `", - "S cheme", - "Sch eme", - "( full", - "(f ull", - "_ writer", - "_w riter", - "_write r", - "_wr iter", - "Ġ Sys", - "ĠS ys", - "ĠSy s", - "Ġf led", - "Ġfl ed", - "Ġfle d", - "ĠC in", - "ĠCi n", - "- widget", - "-w idget", - "Ġ Previous", - "ĠPre vious", - "ĠPrev ious", - "G ender", - "Ge nder", - "Gen der", - "_ question", - "_q uestion", - "_qu estion", - "_quest ion", - "F eed", - "Fe ed", - "Fee d", - "Ġsc rut", - "Ġscr ut", - "( prefix", - "(p refix", - "(pre fix", - "(pref ix", - "ãĢĤ ãĢĤ", - "Ġin fections", - "Ġinf ections", - "Ġinfection s", - "Ġinfect ions", - "P arts", - "Par ts", - "Part s", - "Pa rts", - "Ġh ierarchy", - "Ġhier archy", - "_ DELETE", - "_DE LETE", - "Ġ Patient", - "ĠP atient", - "ĠPat ient", - "_ pay", - "_p ay", - "_pa y", - "Ġprom oted", - "Ġpromote d", - "Ġpromot ed", - "Ġpromo ted", - "Ġ ìĭ", - "Ġì ĭ", - "Ġcivil ian", - "Ġciv ilian", - "Ġagricult ure", - "Ġagr iculture", - "Ġ Piece", - "ĠP iece", - "ĠPi ece", - "ĠPie ce", - "Ġ stance", - "Ġst ance", - "Ġsta nce", - "Ġstan ce", - "uts che", - "utsch e", - "As sign", - "Ass ign", - ". ACTION", - ".A CTION", - ".AC TION", - ".ACT ION", - "F ig", - "Fi g", - "_ radius", - "_r adius", - "_rad ius", - "_radi us", - "Ġ Sync", - "ĠS ync", - "ĠSy nc", - "ĠSyn c", - "d ucer", - "du cer", - "duc er", - "duce r", - "f ailure", - "fail ure", - "en sed", - "ens ed", - "ense d", - "p time", - "pt ime", - "B M", - "_ datetime", - "_d atetime", - "_date time", - "_dat etime", - "qu ivo", - "quiv o", - "qui vo", - "QUE UE", - "èĢ ħ", - "App ear", - "Ap pear", - "Appe ar", - "Ġsum mit", - "Ġsumm it", - ": void", - ":v oid", - "Ġ vine", - "Ġv ine", - "Ġvi ne", - "Ġvin e", - "è ®¤", - "è® ¤", - "on ne", - "onn e", - "_ TRANS", - "_TR ANS", - "_TRAN S", - "_TRA NS", - ". green", - ".g reen", - ".gr een", - "_ cc", - "_c c", - "Ġhung ry", - "Ġ \">", - "Ġ\" >", - "( ));čĊčĊ", - "() );čĊčĊ", - "()) ;čĊčĊ", - "());čĊ čĊ", - "()); čĊčĊ", - "Ex tract", - "Ext ract", - "Extra ct", - "i zens", - "iz ens", - "ize ns", - "izen s", - "Ġ solver", - "Ġs olver", - "Ġsol ver", - "Ġsolve r", - "N otify", - "Not ify", - "Ġ english", - "Ġeng lish", - "Ġ Shopping", - "ĠSh opping", - "ĠShop ping", - "ĠSho pping", - "inter faces", - "interface s", - "R EQ", - "RE Q", - "Ġil leg", - "Ġill eg", - "Ġ UIImageView", - "ĠUI ImageView", - "ĠUIImage View", - "Ġ disconnect", - "Ġdis connect", - "Ġ Until", - "ĠUn til", - "ĠUnt il", - "ĠCons ervative", - "ĠConserv ative", - "@ Column", - "Ġshift ed", - "Ġ :čĊ", - "Ġ: čĊ", - "Ġf ich", - "Ġfi ch", - "Ġfic h", - "Ġd la", - "Ġdl a", - "Ġs hoe", - "Ġsh oe", - "Ġsho e", - "\" ),čĊ", - "\") ,čĊ", - "\"), čĊ", - "ul arity", - "ular ity", - "_ RESP", - "_RE SP", - "_R ESP", - "_RES P", - "We ather", - "UI Application", - ". iterator", - ".it erator", - ".iter ator", - "Ġ aging", - "Ġa ging", - "Ġag ing", - ". Parent", - ".P arent", - ".Par ent", - "o wie", - "ow ie", - "owi e", - "( equal", - "(e qual", - "(eq ual", - "Ġ Conv", - "ĠCon v", - "ĠCo nv", - "/ default", - "/d efault", - "/de fault", - "Ġme asuring", - "Ġmeas uring", - ". prev", - ".p rev", - ".pre v", - ".pr ev", - ". IsValid", - ".Is Valid", - ". Fat", - ".F at", - "Ġs Äĥ", - "key words", - "keyword s", - "with out", - "Ġs overe", - "Ġso vere", - "Ġex changes", - "Ġexchange s", - "Ġm elt", - "Ġme lt", - "Ġmel t", - "Ġis lands", - "Ġisland s", - "Ġisl ands", - "ĠInt egr", - "Ġj umping", - "Ġjump ing", - "Ġ gle", - "Ġg le", - "Ġgl e", - "Ġjournal ism", - "Ġ dated", - "Ġd ated", - "Ġdate d", - "Ġda ted", - "Ġdat ed", - "Local ized", - "Ġ Refresh", - "ĠRe fresh", - "ĠRef resh", - "P article", - "Part icle", - "Ġ aa", - "Ġa a", - "ĠST RICT", - "ĠSTR ICT", - "Ġb od", - "Ġbo d", - ". Process", - ".P rocess", - ".Pro cess", - "_ AUTO", - "_A UTO", - "_AUT O", - "Ġ Published", - "ĠP ublished", - "ĠPublish ed", - "e very", - "ever y", - "ev ery", - "eve ry", - "Ġtechn ological", - "Ġtechno logical", - "Ġtechnolog ical", - "l sx", - "ls x", - "Ġir rit", - "Ġirr it", - "Add itional", - "Ġ delimiter", - "Ġdel imiter", - "Ġdelim iter", - "_ language", - "_l anguage", - "- area", - "-a rea", - "-ar ea", - "bo ys", - "boy s", - "Ġ Tube", - "ĠT ube", - "ĠTu be", - "ĠTub e", - "Ġ wat", - "Ġw at", - "Ġwa t", - "Ġmechan ics", - "Ġmechanic s", - "_ owner", - "_o wner", - "_own er", - "S pell", - "Sp ell", - "Spe ll", - "Ġ Stories", - "ĠSt ories", - "ĠSto ries", - ".Append Line", - "T ableView", - "Table View", - "h em", - "he m", - "s tick", - "st ick", - "sti ck", - "ol lower", - "oll ower", - "ollow er", - "ollo wer", - "I FF", - "IF F", - "Ġ UV", - "ĠU V", - "oll ision", - "S UB", - "SU B", - "Ġcom parable", - "Ġcompar able", - "Ġd onde", - "Ġdo nde", - "Ġdon de", - "s ales", - "sa les", - "sal es", - "sale s", - "ll vm", - "Ġ }],Ċ", - "Ġ} ],Ċ", - "Ġ}] ,Ċ", - "OTT OM", - "Ġ Purpose", - "ĠP urpose", - "ĠPur pose", - "L ab", - "La b", - "Ġinterview ed", - "o is", - "oi s", - "a sil", - "as il", - "asi l", - ". setId", - ".set Id", - "Ġ Instruction", - "ĠIn struction", - "- ->", - "-- >", - "Ġ Modified", - "ĠMod ified", - "ation ally", - "ational ly", - "Ġ Meeting", - "ĠMe eting", - "ĠMeet ing", - "è¯ ¯", - "# region", - "Ġ routing", - "Ġr outing", - "Ġro uting", - "Ġrout ing", - "Ġrou ting", - ". focus", - ".f ocus", - "ĠY outh", - "ĠYou th", - "ĠYo uth", - "< D", - "ĠN ag", - "ĠNa g", - "cont acts", - "contact s", - "Ġ forming", - "Ġfor ming", - "Ġform ing", - "Ġ mie", - "Ġm ie", - "Ġmi e", - "',[' ../", - "Ġ BP", - "ĠB P", - "Ġapp et", - "Ġap pet", - "Ġappe t", - "Ġ Teacher", - "ĠT eacher", - "ĠTe acher", - "ĠTea cher", - "ĠTeach er", - "Ġ TP", - "ĠT P", - "Ġann ually", - "Ġannual ly", - "outed EventArgs", - "Ġ Speaker", - "ĠS peaker", - "ĠSpe aker", - "ĠSpeak er", - "Ġ rename", - "Ġre name", - "Ġr ename", - "Ġren ame", - "C FG", - "CF G", - "(\" //", - "(\"/ /", - "æİ ¥", - "/ pages", - "/p ages", - "/page s", - "Ġpr és", - "Ġpré s", - "Ġ Spell", - "ĠS pell", - "ĠSp ell", - "ĠSpe ll", - ". Allow", - ".Al low", - ".All ow", - "ĠINT ERRU", - "ĠINTER RU", - "Ġ (#", - "Ġ( #", - "âĢĻ ĊĊ", - "âĢĻĊ Ċ", - "_ Generic", - "_G eneric", - ". imshow", - ".im show", - "_ tim", - "_t im", - "_ti m", - "- face", - "-f ace", - "( &(", - "(& (", - "ati num", - "atin um", - "Ġrevolution ary", - "Ġ Hours", - "ĠH ours", - "ĠHo urs", - "ĠHour s", - "ĠHou rs", - "r ain", - "ra in", - "rai n", - "Ġany time", - "Ġ abb", - "Ġa bb", - "Ġab b", - ". jsp", - ".j sp", - ".js p", - "S crollView", - "Scroll View", - "Ġ Truth", - "ĠTr uth", - "ĠTru th", - "Ġ anticipated", - "Ġanticip ated", - "Ġanticipate d", - "Ġantic ipated", - "Ġ accent", - "Ġacc ent", - "Ġac cent", - ". checked", - ".check ed", - "Ġspec ifies", - "Ġ caf", - "Ġc af", - "Ġca f", - "Ġcell padding", - "Ġ cooked", - "Ġco oked", - "Ġcook ed", - "Ġ Hugh", - "ĠH ugh", - "ĠHu gh", - "pe ek", - "pee k", - "_ RATE", - "_R ATE", - "_RA TE", - "Ġd orm", - "Ġdo rm", - "Ġdor m", - "/ čĊ", - "IV ITY", - ". Controller", - ".Cont roller", - ".Control ler", - "( part", - "(p art", - "(par t", - "(pa rt", - ". constraint", - ".con straint", - "Ġin vasion", - "Ġinv asion", - "M OVE", - "MO VE", - "MOV E", - "Ġgl uc", - "Ġglu c", - "l ename", - "le name", - "len ame", - "lena me", - "Ġ amen", - "Ġa men", - "Ġam en", - "eng lish", - "engl ish", - "ĠSw itzerland", - "\" ;ĊĊĊ", - "\";Ċ ĊĊ", - "\";ĊĊ Ċ", - "\"; ĊĊĊ", - "p est", - "pe st", - "pes t", - ". collect", - ".c ollect", - ".col lect", - ".coll ect", - "N ib", - "Ni b", - "Ġ Dict", - "ĠD ict", - "ĠDi ct", - "Ġ Emb", - "ĠE mb", - "ĠEm b", - "( subject", - "(sub ject", - "Ġout rage", - "Ġoutr age", - "Ġoutra ge", - "Ġdec iding", - "Ġdecid ing", - "Ġsent enced", - "Ġsentence d", - "F echa", - "Fe cha", - "\" A", - "Ġ quer", - "Ġqu er", - "Ġque r", - "Ġq uer", - "Ġfont Family", - "Ġqu adr", - "Ġquad r", - "Ġqua dr", - "- Y", - "_ CACHE", - "_C ACHE", - "_CA CHE", - "Ġan alyzed", - "Ġanaly zed", - "Ġanalyze d", - "Ġg aining", - "Ġgain ing", - "Ġga ining", - "Ġ Against", - "ĠAgain st", - "ĠS oul", - "ĠSo ul", - "ĠSou l", - "t au", - "ta u", - "Ġlight weight", - "Ġ TF", - "ĠT F", - "Ġ Effects", - "ĠE ffects", - "ĠEffect s", - "ĠEff ects", - ". Types", - ".T ypes", - ".Type s", - ". addClass", - ".add Class", - "Ġv egan", - "Ġve gan", - "Ġveg an", - "é ģ", - ". '\"", - ".' \"", - "Ġ Explorer", - "ĠEx plorer", - "ĠExpl orer", - "ĠExplore r", - ". detect", - ".d etect", - ".det ect", - ". shift", - ".s hift", - ".sh ift", - "Ġoblig ations", - "Ġobligation s", - "last Name", - "Ġassoci ations", - "Ġassociation s", - "Ġassoc iations", - "ĠTime Span", - "un ter", - "unt er", - "unte r", - "Ġ Fresh", - "ĠF resh", - "ĠFr esh", - "ĠFre sh", - "ĠFres h", - "Com patible", - "Compat ible", - "P ub", - "Pu b", - "id ges", - "idge s", - ". option", - ".op tion", - ".o ption", - ".opt ion", - "v ari", - "var i", - "va ri", - ". hashCode", - ".hash Code", - "Ġ geb", - "Ġg eb", - "Ġge b", - ". section", - ".s ection", - ".se ction", - ".sec tion", - "- not", - "-n ot", - "-no t", - "Ġ Submit", - "ĠSub mit", - "T N", - "reg istry", - "registr y", - "regist ry", - "_ media", - "_m edia", - "_me dia", - "_med ia", - "Ġn aj", - "Ġna j", - "f ft", - "ff t", - "Ġ mate", - "Ġm ate", - "Ġmat e", - "Ġma te", - "- third", - "-th ird", - "Ġp ockets", - "Ġpocket s", - "e sta", - "es ta", - "est a", - "Ġb ent", - "Ġbe nt", - "Ġben t", - "ĠN ord", - "ĠNo rd", - "ĠNor d", - "Ġretail ers", - "Ġretailer s", - "ĠM orris", - "ĠMor ris", - "ĠMorr is", - ". \"\"\"ĊĊ", - ".\"\" \"ĊĊ", - ".\"\"\"Ċ Ċ", - ".\"\"\" ĊĊ", - "W rong", - "Wr ong", - "Ġ ÅĽ", - "ĠÅ Ľ", - "R ay", - "Ra y", - ". ec", - ".e c", - "Ġ Bind", - "ĠB ind", - "ĠBi nd", - "ĠBin d", - "_ HAND", - "_H AND", - "( non", - "(n on", - "(no n", - "is Valid", - "Ġsimilar ly", - "_ LIMIT", - "_L IMIT", - "Ġd ynamics", - "Ġdynamic s", - "Ġdynam ics", - "Ġdyn amics", - "Ġdist inction", - "Ġdistinct ion", - "ãģ Ĩ", - "< N", - "Ġ orth", - "Ġor th", - "Ġort h", - "Ġ Toyota", - "ĠToy ota", - "Ġ Kate", - "ĠK ate", - "ĠKat e", - "ĠKa te", - "Ġ LS", - "ĠL S", - "o rie", - "or ie", - "ori e", - "ĠSpring s", - "ĠSpr ings", - "Ġf reak", - "Ġfre ak", - "last name", - "_ MULT", - "_M ULT", - "_MUL T", - "- step", - "-s tep", - "-st ep", - "-ste p", - "\" (", - "A DDR", - "AD DR", - "ADD R", - "Ġentert aining", - "Ġentertain ing", - "_ CONF", - "_CON F", - "_CO NF", - "Ġ decoded", - "Ġde coded", - "Ġdec oded", - "Ġdecode d", - "Ġdeco ded", - "Ġst reak", - "Ġstre ak", - "Ġwait ed", - "Ġwa ited", - "Ġnot ified", - "ro duced", - "rodu ced", - "rod uced", - "roduce d", - "vis ual", - ". LayoutParams", - ".Layout Params", - "æ °", - "e sian", - "es ian", - "esi an", - "f its", - "fit s", - "fi ts", - "s pring", - "sp ring", - "spr ing", - "ĠBer nie", - "ĠBern ie", - "User Defaults", - "Ġpe dest", - "Ġped est", - "Ap pearance", - "Appear ance", - "Ġ Wiki", - "ĠW iki", - "ĠWi ki", - "ĠWik i", - "ĠNOT ICE", - "Ġ ssh", - "Ġs sh", - "Ġss h", - "Ġdur ante", - "Ġ Zip", - "ĠZ ip", - "ĠZi p", - "ı r", - "ĠN ATO", - "ĠNAT O", - "ĠNA TO", - "Ġtw elve", - "Ġr oyal", - "Ġro yal", - "Ġroy al", - "ï ¸", - "Ġ merchant", - "Ġm erchant", - "Ġmer chant", - "Ġmerch ant", - "ĠF urniture", - "ĠFurn iture", - "' ]),Ċ", - "'] ),Ċ", - "']) ,Ċ", - "']), Ċ", - ", X", - "Ġ folders", - "Ġf olders", - "Ġfolder s", - "Ġfol ders", - "Ġfold ers", - "Ġ Gate", - "ĠG ate", - "ĠGa te", - "ĠGat e", - "ĉ func", - "ĉf unc", - "ĉfun c", - "p ick", - "pi ck", - "pic k", - "_ usuario", - "_us uario", - "ĠV erm", - "ĠVer m", - "ĠVe rm", - "m ention", - "ment ion", - "men tion", - "ur pose", - "Ġ alerts", - "Ġal erts", - "Ġalert s", - "Ġale rts", - "x ious", - "xi ous", - "_ sig", - "_s ig", - "_si g", - "Ġ Fu", - "ĠF u", - "Ġ (:", - "Ġ( :", - "Ġd umb", - "Ġdu mb", - "Ġdum b", - "åħ ³", - "Ġaccur ately", - "Ġaccurate ly", - "éĩ į", - "R B", - "- screen", - "-s creen", - "-sc reen", - "Ġ VER", - "ĠV ER", - "ĠVE R", - "j our", - "jo ur", - "Ġrom ance", - "Ġroman ce", - "Ġroma nce", - "uc ceed", - "ucc eed", - ". choice", - ".ch oice", - "Ġad ip", - "_ dims", - "_d ims", - "_dim s", - "_di ms", - "Serial izable", - "ãĤ ĭ", - ". job", - ".j ob", - "Ġ prog", - "Ġp rog", - "Ġpro g", - "Ġpr og", - "u char", - "uch ar", - "uc har", - "ucha r", - "Ġg ently", - "Ġgent ly", - "Ġ RSS", - "ĠR SS", - "ĠRS S", - "ict ured", - "icture d", - "_ENABLE D", - "ĉ label", - "ĉl abel", - "aw ks", - "awk s", - "Ġ Ensure", - "ĠEn sure", - "ĠEns ure", - "re member", - "rem ember", - "ìł ķ", - "Ġtrans mit", - "{ {$", - "{{ $", - ". Transaction", - ".Trans action", - "ur se", - "urs e", - "_ relative", - "_rel ative", - "Ġs ized", - "Ġsize d", - "Ġsi zed", - "Ġsiz ed", - "Ġ XX", - "ĠX X", - "ĠPr incess", - "ĠPrince ss", - "Ġ Larry", - "ĠL arry", - "ĠLar ry", - "Ġp ró", - "Ġpr ó", - "Ġ ÑģÑĤÑĢ", - "ĠÑģ ÑĤÑĢ", - "ĠÑģÑĤ ÑĢ", - "Ġs isters", - "Ġsi sters", - "Ġsister s", - "Ġsist ers", - "Ġsis ters", - "e struct", - "estr uct", - "Ġ checkpoint", - "Ġcheck point", - ": length", - ":len gth", - ":l ength", - "Ġ Carlos", - "ĠCar los", - "ĠCarl os", - "ĠCarlo s", - "/ icon", - "/i con", - "/ic on", - "_ TARGET", - "_T ARGET", - "T okens", - "Token s", - "Tok ens", - "Ġpat ience", - "Ġ Selected", - "ĠSe lected", - "ĠSelect ed", - "ĠSel ected", - "q ty", - "qt y", - ".show Message", - "Ġwild life", - "Ġ Props", - "ĠP rops", - "ĠPro ps", - "ĠPr ops", - "ĠProp s", - "b m", - "- arrow", - "-ar row", - "Ġ parcel", - "Ġpar cel", - "Ġparc el", - "Ġparce l", - "f irebase", - "fire base", - "ĠBen jamin", - "c esso", - "cess o", - "ces so", - ". tim", - ".t im", - "ĠG arc", - "ĠGar c", - "ĠGa rc", - ". any", - ".a ny", - ".an y", - "ĠHOW EVER", - "ĠK o", - "Ġgrab bed", - "_ frames", - "_f rames", - "_frame s", - "_fr ames", - "Ġobject AtIndex", - "ĠADV ISED", - "Ġsu bur", - "Ġsub ur", - "ĉ GL", - "ĉG L", - "Ġ })}Ċ", - "Ġ} )}Ċ", - "Ġ}) }Ċ", - "- length", - "-l ength", - "-le ngth", - "-len gth", - "ìĭ ľ", - "ĠPot ter", - "_ buff", - "_b uff", - "_buf f", - ". gui", - ".g ui", - "Ġ Encoding", - "ĠEn coding", - "ĠEnc oding", - "E lect", - "El ect", - "Ele ct", - "- message", - "-m essage", - "Ġ �", - "Ġ ÈĻi", - "Ġ ArgumentNullException", - "ĠArgument NullException", - "а ÑĨи", - "Ġmin imize", - "Ġminim ize", - "Ġrespond ing", - "$_ ['", - "Ġ Individual", - "ĠInd ividual", - "á c", - "Ġ INTER", - "ĠIN TER", - "ĠINT ER", - "Ġmast urb", - "Ġmastur b", - "Ġ Bin", - "ĠB in", - "ĠBi n", - "( '$", - "(' $", - "ëĵ ľ", - "Ġopen ly", - "Ġ ><", - "Ġ> <", - "Ġ unto", - "Ġun to", - "Ġunt o", - "olog ically", - "ological ly", - "ologic ally", - "Ġ Mul", - "ĠM ul", - "ĠMu l", - "VID IA", - "Ġs lim", - "Ġsl im", - "ĠCommission er", - "( on", - "(o n", - "Ġunder neath", - "/ db", - "/d b", - "v ote", - "vo te", - "( Message", - "(M essage", - "ĠP ope", - "ĠPop e", - "ĠPo pe", - "D efined", - "Def ined", - "Define d", - "Ġ swift", - "Ġsw ift", - "u rf", - "ur f", - "Ġadapt ed", - "Ġadap ted", - "S EL", - "SE L", - "Ġre venues", - "Ġrevenue s", - "Ġreven ues", - "Ġdi vine", - "Ġdiv ine", - "= y", - "G radient", - "Grad ient", - "_ act", - "_a ct", - "_ac t", - "Ġ/* !<", - "Ġ/*! <", - "Ġ polygon", - "Ġp olygon", - "Ġpoly gon", - "Ġ FDA", - "ĠF DA", - "ĠFD A", - "ĠC arr", - "ĠCar r", - "ĠCa rr", - "a tables", - "at ables", - "ata bles", - "atab les", - "atable s", - "( stdout", - "(std out", - "Ġref riger", - "Ġrefr iger", - "Ġco ordin", - "Ġcoord in", - "avor ites", - "avorite s", - "avo rites", - "ÑĪ и", - "Ġcompass ion", - "ĠPOSS IBILITY", - "- secondary", - "-second ary", - "ur acy", - "ura cy", - "Ġcom promise", - "Ġcomp romise", - "Ġcomprom ise", - "_ AV", - "_A V", - "_ os", - "_o s", - "Ġbe side", - "Ġbes ide", - "ĥ Ŀ", - "Ġ ln", - "Ġl n", - ". plugins", - ".pl ugins", - ".plugin s", - "Cap acity", - "a lah", - "al ah", - "ala h", - ". bin", - ".b in", - ".bi n", - "Ġ CRC", - "ĠC RC", - "ĠCR C", - "_ balance", - "_b alance", - "_bal ance", - "Ġflex Direction", - "Ġam bit", - "Ġamb it", - "Ġ nickname", - "Ġn ickname", - "Ġnick name", - "ĠFor ces", - "ĠForce s", - "C LE", - "CL E", - "Ġ Shell", - "ĠS hell", - "ĠSh ell", - "ĠShe ll", - "ĠShel l", - "Ġs ail", - "Ġsa il", - "Ġsai l", - "Ġ Writer", - "ĠW riter", - "ĠWrite r", - "ĠWr iter", - "Ġ Alice", - "ĠA lice", - "ĠAl ice", - "ĠAli ce", - "ĠAlic e", - "d w", - "ĠInd ians", - "ĠIndia ns", - "ĠIndian s", - "ĠIndi ans", - "ĠMar shall", - "ĠMars hall", - "ĠMarshal l", - "ĠMarsh all", - "_ SRC", - "_S RC", - "_SR C", - "Ġ normalized", - "Ġnormal ized", - "Ġnormalize d", - "ĠJ ag", - "ĠJa g", - "ãĤ Ĵ", - "ze it", - "r pc", - "rp c", - "ÃŃ c", - ". inline", - ".in line", - "Ġtr avers", - "Ġtra vers", - "Ġtrav ers", - "_ numeric", - "_n umeric", - "_num eric", - "_numer ic", - "Ġ utilities", - "Ġutil ities", - "Ġut ilities", - "Ġe vac", - "Ġev ac", - "IN PUT", - "ĉ register", - "ĉreg ister", - "M X", - "ĠCamp bell", - "Ġ datasets", - "Ġd atasets", - "Ġdata sets", - "Ġdataset s", - "Ġdatas ets", - "Ġdem anded", - "Ġdemand ed", - "Ġdemande d", - "Ġinitial State", - "g an", - "ga n", - "Ġ ei", - "Ġe i", - "Un expected", - "- web", - "-w eb", - "-we b", - "t rait", - "tr ait", - "tra it", - ", Y", - "Ġ Todd", - "ĠT odd", - "ĠTo dd", - "ĠTod d", - "Ġs keleton", - "Ġske leton", - "Ġ optimize", - "Ġopt imize", - "Ġoptim ize", - "ç ¬¬", - "ç¬ ¬", - "Ġ Upon", - "ĠU pon", - "ĠUp on", - "ĠSt Object", - "Ġap lic", - "Ġapl ic", - ". ' P", - "v ron", - "vr on", - "vro n", - ". UN", - ".U N", - "Ġp ainter", - "Ġpaint er", - "Ġpain ter", - "Ġpa inter", - "izar re", - "Ġ lav", - "Ġl av", - "Ġla v", - "Ġ pom", - "Ġp om", - "Ġpo m", - "p reg", - "pr eg", - "pre g", - "= function", - "=f unction", - "( serial", - "(s erial", - "(se rial", - "if ica", - "ific a", - "ifi ca", - "u ming", - "um ing", - "umin g", - "umi ng", - "åľ °", - "ãģ Ĥ", - "- op", - "-o p", - "U CH", - "UC H", - "ĠH end", - "ĠHe nd", - "ĠHen d", - ". propTypes", - ".prop Types", - "Ġ yo", - "Ġy o", - "Ġr outines", - "Ġrout ines", - "Ġroutine s", - "Ġc aring", - "Ġcar ing", - "Ġca ring", - "S em", - "Se m", - "Ġres erves", - "Ġreserve s", - "Ġreserv es", - "Ġprior ities", - "Ġpriorit ies", - "red its", - "redit s", - "redi ts", - "I STR", - "IS TR", - "IST R", - "Content Type", - "ĠS chw", - "ĠSc hw", - "ĠSch w", - "/ media", - "/m edia", - "/me dia", - "Ġ estr", - "Ġe str", - "Ġes tr", - "Ġest r", - "Ġclim bing", - "Ġclimb ing", - "- week", - "-we ek", - "cher che", - "s ensor", - "To Array", - "ĠMont real", - "Ġclo uds", - "Ġcloud s", - "ĠInject able", - "ĠR ice", - "ĠRic e", - "ĠRi ce", - "Ġpropag anda", - "_ provider", - "_pro vider", - "_prov ider", - "Ġin door", - "Ġind oor", - "Ġindo or", - "Ġin aug", - "Ġdipl om", - "Ġdip lom", - "Ġm essaging", - "Ġmess aging", - "_ mut", - "_m ut", - "_mu t", - "å ¦Ĥ", - "å¦ Ĥ", - "Ġ kw", - "Ġk w", - "O NS", - "ON S", - "a rians", - "ar ians", - "ari ans", - "arian s", - "aria ns", - "R PC", - "RP C", - ") ]čĊ", - ")] čĊ", - "- ray", - "-r ay", - "-ra y", - "ĠS or", - "ĠSo r", - "m all", - "ma ll", - "mal l", - "Ġmarket place", - "Ġ vtk", - "Ġv tk", - "Ġvt k", - "M a", - "o gan", - "og an", - "oga n", - "i gi", - "ig i", - "Ġs ponsored", - "Ġspons ored", - "Ġsponsor ed", - "Ġ Dani", - "ĠD ani", - "ĠDan i", - "ĠDa ni", - ".S EVER", - ".SE VER", - "> '.$", - ">' .$", - ">'. $", - "m ultipart", - "multi part", - "multip art", - "ĠW ol", - "ĠWo l", - "Ġ tableName", - "Ġtable Name", - "Ġ Username", - "ĠUser name", - "Back groundColor", - "Background Color", - "Ġf right", - "Ġfr ight", - "Ġfri ght", - "_ EMAIL", - "_E MAIL", - "_EM AIL", - "Sept ember", - "Sep tember", - "_ vals", - "_v als", - "_val s", - "_va ls", - "op ia", - "opi a", - "Ġsp otted", - "Ġspot ted", - "- Ch", - "-C h", - "Ġ dataSource", - "Ġdata Source", - "/ \"Ċ", - "/\" Ċ", - "е кÑĤ", - "ек ÑĤ", - "Ġ RequestMethod", - "ĠRequest Method", - "Ġ Replace", - "ĠRe place", - "ĠRep lace", - "- do", - "-d o", - "a hn", - "ah n", - "ĠPh D", - "] .ĊĊ", - "]. ĊĊ", - "].Ċ Ċ", - "N ON", - "NO N", - "g ement", - "ge ment", - "gem ent", - "geme nt", - "Ġ Thr", - "ĠT hr", - "ĠTh r", - "Ġquiet ly", - "Ġtor ture", - "Ġtort ure", - "Ġte as", - "Ġtea s", - "Ġ CY", - "ĠC Y", - "Ġ atr", - "Ġa tr", - "Ġat r", - "de velopment", - "dev elopment", - "develop ment", - "- detail", - "-d etail", - "-de tail", - "-det ail", - "Ġl ighter", - "Ġlight er", - "Ġarg uing", - "Ġdes erves", - "Ġdeserve s", - "Ġdeser ves", - "Ġcur riculum", - "_ CONTEXT", - "_CON TEXT", - "_CONT EXT", - "ÅĤ y", - "H ITE", - "HI TE", - "ĉ ID", - "ĉI D", - "/ uploads", - "/upload s", - "/up loads", - "Ġt its", - "Ġtit s", - "Ġti ts", - "r eo", - "re o", - "_ drop", - "_d rop", - "_dr op", - ". UTF", - ".U TF", - "Ġ pickup", - "Ġpick up", - "Ġpic kup", - "Ġgro cery", - "Ġ Pure", - "ĠP ure", - "ĠPur e", - "ĠPu re", - "Ġeas iest", - "P hil", - "Ph il", - "Phi l", - ". feature", - ".f eature", - ".fe ature", - "( \"*", - "(\" *", - "Ġinvest or", - "Ġinve stor", - "t ok", - "to k", - "Ġ jar", - "Ġj ar", - "Ġja r", - "L os", - "Lo s", - "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", - ". queue", - ".q ueue", - "- speed", - "-s peed", - "-sp eed", - "-spe ed", - "M al", - "Ma l", - "um blr", - "umb lr", - "Ġ CONST", - "ĠCON ST", - "ĠCO NST", - "ĠCONS T", - "Ġ HRESULT", - "ĠH RESULT", - "ĠD ance", - "ĠDan ce", - "ĠDa nce", - "( filePath", - "(file Path", - "Ġattribute d", - "Ġattrib uted", - "ॠį", - "ĠB und", - "ĠBu nd", - "ĠBun d", - "c oins", - "co ins", - "coin s", - "Ġs ão", - "Ġ pir", - "Ġp ir", - "Ġpi r", - "person al", - "pers onal", - "persona l", - "Ġpr elim", - "Ġpre lim", - "Ġpro pose", - "Ġprop ose", - "Ġpropos e", - "Ġ TL", - "ĠT L", - "] ])", - "]] )", - "Ġ Subscription", - "ĠSub scription", - "ĠK re", - "ĠKr e", - ", len", - ",l en", - ". FirstOrDefault", - ".First OrDefault", - ") --", - ")- -", - "_ products", - "_product s", - ".Get Bytes", - "S hip", - "Sh ip", - "Ġ encrypt", - "Ġen crypt", - "Ġenc rypt", - "Ġ SG", - "ĠS G", - "ĠM yst", - "ĠMy st", - "h ir", - "hi r", - "Ġ iterate", - "Ġit erate", - "Ġiter ate", - "Ġint end", - "Ġinte nd", - ".mock ito", - "Ġch apters", - "Ġchapter s", - "Ġchap ters", - "( angle", - "(a ngle", - "(an gle", - "(ang le", - "ĠV lad", - "è® ¾", - "' .ĊĊ", - "'. ĊĊ", - "'.Ċ Ċ", - "Response Body", - "ĠA bd", - "ĠAb d", - "de al", - "dea l", - "Ġbar riers", - "Ġbarrier s", - "Ġbarr iers", - "- outline", - "-out line", - "b ill", - "bi ll", - "bil l", - "ĠF alls", - "ĠFall s", - "ĠFal ls", - "_ second", - "_se cond", - "_sec ond", - ". include", - ".in clude", - ".inc lude", - ". ceil", - ".c eil", - ".ce il", - "Ġ occupation", - "Ġoccup ation", - "ph ony", - "phon y", - ".move To", - "Ġ Jennifer", - "ĠJ ennifer", - "ĠJenn ifer", - "A STER", - "AS TER", - "AST ER", - "ASTE R", - "; \"><", - ";\" ><", - ";\"> <", - "Ġ Enabled", - "ĠEn abled", - "ĠEnable d", - "Ġ terminate", - "Ġter minate", - "Ġterm inate", - "Ġtermin ate", - "Ġ Io", - "ĠI o", - "l ations", - "lation s", - "lat ions", - "ĠTHE ORY", - "Ġear liest", - "Ġ rack", - "Ġr ack", - "Ġrac k", - "Ġra ck", - "Ġ Scar", - "ĠS car", - "ĠSc ar", - "sh ake", - "sha ke", - "c hip", - "ch ip", - "chi p", - "Ġ uv", - "Ġu v", - "Ġall iance", - "п иÑģ", - "пи Ñģ", - "ĠGOOD S", - "z ione", - "zi one", - "zion e", - "Ġ VI", - "ĠV I", - "Ġ {-", - "Ġ{ -", - "Ġfil tering", - "Ġfilter ing", - "Ġfilt ering", - "Ġmis con", - "Ġmisc on", - ".Dock Style", - "Ġb ush", - "Ġbu sh", - "Ġbus h", - "Ġj unk", - "Ġju nk", - "Ġjun k", - "æ Į", - "Ġ QUE", - "ĠQ UE", - "ĠQU E", - "Ġ hooks", - "Ġh ooks", - "Ġhook s", - "Ġho oks", - "Ġf irmware", - "Ġfirm ware", - "Ġ middleware", - "Ġm iddleware", - "Ġmiddle ware", - "d ic", - "di c", - "ĠOak land", - "Ġarr ives", - "Ġarrive s", - "Ġarriv es", - "P ayload", - "Pay load", - "p ixel", - "pix el", - "] |", - "Ġ startDate", - "Ġstart Date", - ". PRO", - ".P RO", - ".PR O", - "_ audio", - "_a udio", - "Ġmid field", - "igid body", - "ĠSw iss", - "Ġ Clip", - "ĠC lip", - "ĠCl ip", - "ĠCli p", - "Ġ Dump", - "ĠD ump", - "ĠDu mp", - "ĠDum p", - "Ġ TextBox", - "ĠText Box", - "Ġ geh", - "Ġg eh", - "Ġge h", - "y ield", - "yi eld", - "o ds", - "od s", - "Ġrefer endum", - "Back end", - "Ġ Cream", - "ĠC ream", - "ĠCr eam", - "ĠCre am", - "Ġd ominated", - "Ġdo minated", - "Ġdom inated", - "Ġdomin ated", - "Ġdominate d", - "Ġdomina ted", - "Ġ Archive", - "ĠA rchive", - "ĠArch ive", - "ĠArc hive", - "Ġr iders", - "Ġrid ers", - "Ġride rs", - "Ġri ders", - "Ġrider s", - ".prepare Statement", - "Ġqu ando", - "Ġquand o", - "Ġqua ndo", - "Ġquan do", - "Ġ chef", - "Ġch ef", - "Ġche f", - "w iki", - "wi ki", - "wik i", - "i nel", - "in el", - "ine l", - "am pling", - "amp ling", - "(\" \\\\", - "(\"\\ \\", - "Ġs ag", - "Ġsa g", - "_ proxy", - "_pro xy", - "_pr oxy", - "ãģ ķ", - "p do", - "pd o", - ". getElementsByTagName", - ".get ElementsByTagName", - ".getElementsBy TagName", - "Ġdemon stration", - "Ġdemonstr ation", - "Ġ NPC", - "ĠN PC", - "ĠNP C", - "Ġ archivo", - "Ġarch ivo", - "en dance", - "end ance", - "enda nce", - "Ġefficient ly", - "( actual", - "(ac tual", - "(act ual", - ". tableView", - ".t ableView", - ".table View", - "Ġm ush", - "Ġmus h", - "Ġmu sh", - "Ġb ears", - "Ġbe ars", - "Ġbear s", - "_ threads", - "_th reads", - "_thread s", - "j as", - "ja s", - "ah un", - "ahu n", - "Ġne ural", - "Ġneu ral", - "Ġneur al", - "Ġdesign ing", - "ĠG DP", - "ĠGD P", - "Ġlif ted", - "Ġlift ed", - "çĽ ®", - "Ġ Joint", - "ĠJ oint", - "ĠJo int", - "ĠJoin t", - "ĠJoi nt", - "Ġ Include", - "ĠIn clude", - "ĠInc lude", - "ĠG iants", - "ĠGi ants", - "ĠGiant s", - "ĠGian ts", - "ĠGia nts", - "Ġwithdraw al", - "Ġ Rent", - "ĠR ent", - "ĠRe nt", - "ĠRen t", - "n ative", - "nat ive", - "Ġ Seek", - "ĠSe ek", - "ĠSee k", - "g ression", - "gr ession", - "gress ion", - "_ CPU", - "_C PU", - "_CP U", - "\\ S", - "Ġ Shield", - "ĠSh ield", - "ĠShi eld", - "Ġs olic", - "Ġso lic", - "Ġsol ic", - "Ġ boom", - "Ġb oom", - "Ġbo om", - "Ġboo m", - "yect o", - "Ġmanufact ure", - "Ġ âĢĭ", - "ĠâĢ ĭ", - "Ġ bbox", - "Ġb box", - "Ġbb ox", - "Ġearth qu", - "oll ectors", - "ollect ors", - "olle ctors", - ":@\" %", - ":@ \"%", - "Ġ loops", - "Ġl oops", - "Ġlo ops", - "Ġloop s", - "J e", - "al king", - "alk ing", - "Ġ Whats", - "ĠWh ats", - "ĠWhat s", - "ĠBo ys", - "ĠBoy s", - ". book", - ".b ook", - ".bo ok", - "AR GE", - "ARG E", - "_ pixel", - "_p ixel", - "_pix el", - "Ġsus pects", - "Ġsusp ects", - "Ġsuspect s", - "Î ¹", - "u sp", - "us p", - "Ġ BMW", - "ĠB MW", - "ĠBM W", - "ie ces", - "iece s", - "iec es", - "( person", - "(p erson", - "(per son", - "å¼ Ģ", - "é »", - "ĠP odcast", - "ĠPod cast", - "Ġ bou", - "Ġb ou", - "Ġbo u", - "( Item", - "(I tem", - "(It em", - "à »", - "( Input", - "(In put", - "Http Get", - "Ġ burg", - "Ġb urg", - "Ġbu rg", - "Ġbur g", - ") ^", - "BO ARD", - "* /,", - "*/ ,", - "Ġ gulp", - "Ġg ulp", - "Ġgu lp", - "Ġgul p", - "ĠB enn", - "ĠBe nn", - "ĠBen n", - "Ġde cks", - "Ġdec ks", - "Ġdeck s", - ". statusCode", - ".status Code", - "Ġ acute", - "Ġac ute", - "Ġh ug", - "Ġhu g", - "u gu", - "ug u", - "Ġ pled", - "Ġp led", - "Ġpl ed", - "Ġple d", - ", \"%", - ",\" %", - "h ape", - "ha pe", - "hap e", - "Ġз ап", - "Ġза п", - "ĠM aine", - "ĠMain e", - "ĠMa ine", - "ĠMai ne", - ". real", - ".re al", - "Ġd alam", - "Ġda lam", - "Ġdal am", - "Ġ Minor", - "ĠMin or", - "ĠMi nor", - ". Float", - ".F loat", - "d isp", - "dis p", - "di sp", - "Ġ tl", - "Ġt l", - "Ġen count", - "Ġenc ount", - "= >$", - "=> $", - "Ġ fg", - "Ġf g", - "t ees", - "te es", - "tee s", - "ĠRe comm", - "ĠRec omm", - "ĠReco mm", - "ä l", - "Ġ chemistry", - "Ġchem istry", - "B locks", - "Block s", - "Bl ocks", - "Bloc ks", - "Blo cks", - "O ID", - "OI D", - "Ġf orex", - "Ġfor ex", - "Ġfore x", - "Ġfo rex", - "Ġ Append", - "ĠApp end", - "ĠAp pend", - "ĠAppe nd", - "Ġ {*", - "Ġ{ *", - "Ġ Supply", - "ĠS upply", - "ĠSup ply", - "CG Float", - "( bl", - "(b l", - "Ġ ate", - "Ġa te", - "Ġat e", - "ad ora", - "ado ra", - "ador a", - "Ġg ust", - "Ġgu st", - "Ass oci", - "Assoc i", - "> .Ċ", - ">. Ċ", - "F ETCH", - ". serial", - ".s erial", - ".se rial", - ".ser ial", - "widget s", - "wid gets", - "ard less", - "i efs", - "ie fs", - "ief s", - "_ FULL", - "_F ULL", - "ern etes", - "ernet es", - "erne tes", - "Ġ Pred", - "ĠP red", - "ĠPr ed", - "ĠPre d", - "Ø Ń", - "äº ĭ", - "ub ernetes", - "ubern etes", - "Ġ Laura", - "ĠL aura", - "ĠLa ura", - "ĠLaur a", - "ĠLau ra", - "Ġl abeled", - "Ġlabel ed", - "Ġlab eled", - "High light", - "Ġanno ying", - "Ġannoy ing", - "/ update", - "/up date", - "( description", - "(d escription", - "(de scription", - "(des cription", - "Ġint imid", - "Ġintim id", - "$ c", - "\" )))Ċ", - "\") ))Ċ", - "\")) )Ċ", - "\"))) Ċ", - ". AP", - ".A P", - "Ġ[ ]*", - "Ġ[] *", - "Ġ EXIT", - "ĠEX IT", - ". Host", - ".H ost", - "Ġ OPEN", - "ĠOP EN", - ". sendMessage", - ".send Message", - "_ camera", - "_c amera", - "_cam era", - "_ tile", - "_t ile", - "_ti le", - "Ġth erm", - "Ġthe rm", - "Ġther m", - "onom ous", - "Ġdis adv", - "Ġn aar", - "Ġna ar", - "index Of", - "Ġ PP", - "ĠP P", - ". protocol", - ".prot ocol", - ".proto col", - "A FE", - "AF E", - "Ġ textures", - "Ġtext ures", - "Ġtexture s", - "Ġtex tures", - "Ġtextu res", - "######## ########################################", - "################ ################################", - "################################ ################", - "######################################## ########", - "######################## ########################", - "um bai", - "umb ai", - "umba i", - ". stats", - ".st ats", - ".stat s", - "Ġ GE", - "ĠG E", - "Ġ ie", - "Ġi e", - "Ġ STD", - "ĠS TD", - "ĠST D", - "ĠM ann", - "ĠMan n", - "ĠMa nn", - ". reflect", - ".ref lect", - "K B", - "Ġd ive", - "Ġdi ve", - "Ġdiv e", - ". wav", - ".w av", - "/* ----------------------------------------------------------------", - "/*------------------------------------------------ ----------------", - "/ settings", - "/s ettings", - "/set tings", - ".l ifecycle", - ".life cycle", - "Ġda ughters", - "Ġdaughter s", - "o rus", - "or us", - "oru s", - "u ber", - "ub er", - "ube r", - "N ING", - "NI NG", - "s tri", - "st ri", - "str i", - "Ġ Tip", - "ĠT ip", - "ĠTi p", - "Ġ zn", - "Ġz n", - "Ġsw itched", - "Ġswitch ed", - "i net", - "in et", - "ine t", - "uff y", - "uf fy", - "ĠTransport ation", - "( conf", - "(con f", - "(co nf", - "f rica", - "fr ica", - "Ġ XL", - "ĠX L", - "Ġ Lead", - "ĠL ead", - "ĠLe ad", - "_ percent", - "_per cent", - "_perc ent", - "< Map", - " __", - "->_ _", - "per missions", - "perm issions", - "permission s", - "ĠD etermine", - "ĠDetermin e", - ". Man", - ".M an", - ".Ma n", - "Ġadv ances", - "Ġadvance s", - ". InputStream", - ".Input Stream", - "Ġstrong est", - "Ġstron gest", - "Ġe Bay", - "Ġ# -", - "Ġ dirname", - "Ġdir name", - "Ġ SMS", - "ĠS MS", - "ĠSM S", - "Ġmed ications", - "Ġmedic ations", - "Ġmedication s", - "Ġam ended", - "Ġamen ded", - "Ġamend ed", - "Ġchurch es", - "ĠIm perial", - "ĠImp erial", - "ĠImper ial", - "$ row", - "$r ow", - "ĠMad ison", - "Ġ Insp", - "ĠIn sp", - "ĠIns p", - "Ġaff air", - "Ġaf fair", - "Ġpsych ology", - "Ġpsycho logy", - "v h", - "Ġ severity", - "Ġse verity", - "Ġsever ity", - "âĢ IJ", - "Ġst rips", - "Ġstr ips", - "Ġstri ps", - "Ġstrip s", - "A H", - "vert ising", - "vertis ing", - "Ġc onse", - "Ġcon se", - "Ġcons e", - "IM AGE", - "IMA GE", - "Ġ Stats", - "ĠSt ats", - "ĠStat s", - "ĠSta ts", - "ĉ sc", - "ĉs c", - ". Cursor", - ".C ursor", - "Ġ freeze", - "Ġf reeze", - "Ġfree ze", - "s son", - "ss on", - "( xml", - "(x ml", - "Ġ Susan", - "ĠS usan", - "ĠSus an", - "ĠSu san", - ". tile", - ".t ile", - "e ded", - "ed ed", - "ede d", - "ĠĠ ĠĠĉĉĉ", - "ĠĠĠĠ ĉĉĉ", - "ĠĠĠ Ġĉĉĉ", - "ĠĠĠĠĉ ĉĉ", - "ĠĠĠĠĉĉ ĉ", - "u elle", - "ue lle", - "uel le", - "uell e", - "ĠMitch ell", - "b ased", - "base d", - "ba sed", - "bas ed", - "Oper and", - "Opera nd", - "½ æķ°", - "Ġ FF", - "ĠF F", - "ĉ strcpy", - "ĉstr cpy", - "ou nces", - "oun ces", - "ounc es", - "ounce s", - "il do", - "ild o", - ".execute Query", - "Ġappro aching", - "Ġapproach ing", - "Ġ Seven", - "ĠS even", - "ĠSe ven", - "ĠSev en", - "Ġ nuts", - "Ġn uts", - "Ġnut s", - "Ġnu ts", - "Ġ ric", - "Ġr ic", - "Ġri c", - "ass ignment", - "assign ment", - "Ġ calculator", - "Ġcal culator", - "Ġcalcul ator", - "Ġcalc ulator", - "ĠMur phy", - "ĠB ou", - "ĠBo u", - "í Ħ", - "Ġ butt", - "Ġb utt", - "Ġbut t", - "Ġbu tt", - "Ġ ticks", - "Ġt icks", - "Ġti cks", - "Ġtick s", - "Ġtic ks", - "Project s", - "Proj ects", - "i lib", - "il ib", - "ili b", - ".text Color", - "m ov", - "mo v", - "_ logo", - "_l ogo", - "_log o", - "_lo go", - "( template", - "(t emplate", - "(temp late", - "Ġ INIT", - "ĠIN IT", - "Ġ imageView", - "Ġimage View", - "s criptions", - "script ions", - "scri ptions", - "scription s", - "OR ITY", - "Con sumer", - "Cons umer", - "Consum er", - "Ġun precedented", - "Ġtour ist", - "Ġtou rist", - "Ġ bron", - "Ġb ron", - "Ġbr on", - "Ġbro n", - "Ġcon tractor", - "Ġcontract or", - "Ġcontr actor", - "Ġcontra ctor", - "Ġ licence", - "Ġli cence", - "Ġlic ence", - "Ġ Nam", - "ĠN am", - "ĠNa m", - "æ ¯", - "( transform", - "(trans form", - "_ ATT", - "_A TT", - "_AT T", - "P ref", - "Pr ef", - "Pre f", - "Ġ Gam", - "ĠG am", - "ĠGa m", - "Ġvess els", - "Ġvessel s", - "Ġh av", - "Ġha v", - "L ater", - "La ter", - "Lat er", - "Late r", - ". ToLower", - ".To Lower", - "Ġ urls", - "Ġurl s", - "Ġur ls", - "Ġbreak down", - "Ġpen alties", - "Ġpenal ties", - "Ġf oster", - "Ġfo ster", - "Ġfost er", - "Ġfos ter", - "Ġ UE", - "ĠU E", - "Ġc lue", - "Ġcl ue", - "c omed", - "com ed", - "co med", - "come d", - "åIJį 称", - "- main", - "-m ain", - "Ġ pts", - "Ġp ts", - "Ġpt s", - "Ġco unted", - "Ġcount ed", - "Ġcoun ted", - "i cts", - "ic ts", - "ict s", - "/ post", - "/p ost", - "Ġ getattr", - "Ġget attr", - "Ġ ping", - "Ġp ing", - "Ġpi ng", - "Ġpin g", - "AN CEL", - "ANCE L", - "ANC EL", - "Ġ pec", - "Ġp ec", - "Ġpe c", - "Ñħ од", - "Ñħо д", - "an tom", - "ant om", - "anto m", - "Ġ Blueprint", - "ĠBlue print", - "ĠEvent Emitter", - "Ġ lä", - "Ġl ä", - "æ ²", - "Ġst raw", - "Ġstr aw", - "Ġstra w", - "( comp", - "(c omp", - "(com p", - "(co mp", - "' une", - "'un e", - "'u ne", - "> N", - "- client", - "-c lient", - "-cl ient", - "-cli ent", - "es Module", - "- base", - "-b ase", - "Ġret reat", - "Ġretr eat", - "_ simple", - "_s imple", - "_sim ple", - "ĉ ĉĉĉĉĉĠ", - "ĉĉ ĉĉĉĉĠ", - "ĉĉĉĉ ĉĉĠ", - "ĉĉĉ ĉĉĉĠ", - "ĉĉĉĉĉ ĉĠ", - "ĉĉĉĉĉĉ Ġ", - "f ee", - "fe e", - "' )čĊčĊ", - "') čĊčĊ", - "')čĊ čĊ", - "Control Item", - "Ġsub scribers", - "Ġsubscri bers", - "Ġsubscribe rs", - "Ġsubscriber s", - "p lease", - "pl ease", - "ple ase", - "Ġ Eff", - "ĠE ff", - "ĠEf f", - "Ġp ound", - "Ġpo und", - "Ġpou nd", - "Ġ Bytes", - "ĠBy tes", - "ĠByte s", - "ĠT ea", - "ĠTe a", - "_ activity", - "_act ivity", - "_activ ity", - "Ġmax im", - "Ġmaxi m", - "Ġ opcode", - "Ġop code", - "Ġopc ode", - "B SD", - "BS D", - ". constant", - ".con stant", - ".const ant", - ".cons tant", - "; }", - "omb res", - "ombre s", - "Ġcare ers", - "Ġcareer s", - ") .ĊĊĊĊ", - "). ĊĊĊĊ", - ").ĊĊ ĊĊ", - ").Ċ ĊĊĊ", - ").ĊĊĊ Ċ", - "Ġsp reading", - "Ġspread ing", - "Ġspre ading", - "- expanded", - "-exp anded", - "-expand ed", - "Ġ Ord", - "ĠO rd", - "ĠOr d", - "am arin", - "ama rin", - "amar in", - "Ġmob ility", - "Ġmobil ity", - "Un fortunately", - "a kk", - "ak k", - "N L", - "_ redirect", - "_re direct", - "_red irect", - "Ġ PG", - "ĠP G", - "Ġ Sensor", - "ĠS ensor", - "ĠSens or", - "b ol", - "bo l", - "t ap", - "ta p", - "_ MEMORY", - "_MEM ORY", - "Ġ UIAlert", - "ĠUI Alert", - "pl itude", - "plit ude", - "We bsite", - "Web site", - "Ġ Logo", - "ĠL ogo", - "ĠLog o", - "ĠLo go", - "l ove", - "lo ve", - "lov e", - "[ ind", - "[i nd", - "[in d", - "Ġalto gether", - "Ġwonder ed", - "Ġ esper", - "Ġes per", - "Ġesp er", - "ĠLib eral", - "ĠLiber al", - "Ġ oss", - "Ġo ss", - "Ġos s", - "Ġe lit", - "Ġel it", - "Ġst iff", - "Ġstif f", - "od ox", - "odo x", - "_ mentions", - "_m entions", - "_ment ions", - "ĠDou glas", - "ĠDoug las", - "_ pid", - "_p id", - "_pi d", - "Ġ CK", - "ĠC K", - "ĠinitWith Frame", - ". blog", - ".b log", - ".bl og", - "p kg", - "pk g", - "ang hai", - "QUI RED", - "QUIRE D", - "u u", - "Ġ mkdir", - "Ġm kdir", - "Ġmk dir", - "AT AL", - "ATA L", - "Ġu nh", - "Ġun h", - "i nces", - "in ces", - "ince s", - "inc es", - "s th", - "st h", - "Ġhypo thesis", - "Ġhypoth esis", - "Ġc ata", - "Ġca ta", - "Ġcat a", - "Ġ TB", - "ĠT B", - "Ġ Clar", - "ĠC lar", - "ĠCl ar", - "ĠCla r", - "Ġpre decess", - "Ġpred ecess", - "Ġsit uated", - "Ġsitu ated", - "- world", - "-w orld", - ") )/", - ")) /", - "Ġhead lines", - "Ġheadline s", - ". stat", - ".s tat", - ".st at", - "Ġout break", - "s path", - "sp ath", - "spa th", - "_ FLAGS", - "_FLAG S", - "ĠServlet Exception", - "S un", - "Su n", - "F ROM", - "FR OM", - "Ġ Dir", - "ĠD ir", - "ĠDi r", - "ãĥ» ãĥ»ãĥ»", - "ãĥ»ãĥ» ãĥ»", - "_ coord", - "_c oord", - "_co ord", - "ĠOp tim", - "ĠOpt im", - "M onitor", - "Mon itor", - ". bit", - ".b it", - ".bi t", - "X XX", - "XX X", - "Ġto das", - "Ġtod as", - "Ġtoda s", - "f eld", - "fe ld", - "fel d", - "ÑĢ и", - "i mir", - "im ir", - "imi r", - "Ġpolit ically", - "Ġpolitical ly", - "Ġpolitic ally", - "Ġm olecular", - "Ġmolec ular", - "Ġmole cular", - "Ġtr aded", - "Ġtrad ed", - "Ġtrade d", - "Ġtra ded", - "Ġ {{$", - "Ġ{ {$", - "Ġ{{ $", - "ĠSw edish", - "ĠSwe dish", - "Ġ'@ /", - "_ REAL", - "_RE AL", - "Ġ warehouse", - "Ġw arehouse", - "Ġware house", - "t oday", - "to day", - "tod ay", - ", L", - "o rp", - "or p", - "< section", - " false", - ">f alse", - "Ġ spa", - "Ġs pa", - "Ġsp a", - "Ġ Near", - "ĠN ear", - "ĠNe ar", - "ì ķ", - "Ġint rig", - "Ġintr ig", - "_ members", - "_m embers", - "_mem bers", - "_member s", - "w ave", - "wa ve", - "wav e", - "Ġanal ysts", - "Ġanaly sts", - "Ġanalyst s", - "Ġanalys ts", - "_ OS", - "_O S", - "e din", - "ed in", - "edi n", - "Ġ Fri", - "ĠF ri", - "ĠFr i", - "Ġret rieved", - "Ġretrie ved", - "Ġretrieve d", - "Ġretr ieved", - "Reg ular", - "_ obs", - "_o bs", - "_ob s", - "EX PORT", - "EXP ORT", - "' )}}\"", - "') }}\"", - "')}} \"", - "')} }\"", - "\" class", - "\"c lass", - "__ ((", - "__( (", - "b ucket", - "bu cket", - "Ġ stro", - "Ġs tro", - "Ġst ro", - "Ġstr o", - "Ġ Patch", - "ĠP atch", - "ĠPat ch", - "y stick", - "yst ick", - "ys tick", - "ful ness", - "a pos", - "ap os", - "apo s", - "D a", - "ĉ ĉĉĉĉĠĠĠ", - "ĉĉ ĉĉĉĠĠĠ", - "ĉĉĉĉ ĉĠĠĠ", - "ĉĉĉ ĉĉĠĠĠ", - "ĉĉĉĉĉ ĠĠĠ", - "ĉĉĉĉĉĠ ĠĠ", - "ĉĉĉĉĉĠĠ Ġ", - "Ġen rich", - "Ġenr ich", - "un ordered", - "h ole", - "ho le", - "hol e", - "C ong", - "Con g", - "Co ng", - "< Product", - "ĠC urt", - "ĠCur t", - "ĠCu rt", - "( the", - "(t he", - "(th e", - "_ lower", - "_l ower", - "_lo wer", - "_low er", - "Ġavoid ing", - "Ġ buzz", - "Ġb uzz", - "Ġbu zz", - "Ġbuz z", - "Ġv iable", - "Ġvi able", - "Ġvia ble", - "u ba", - "ub a", - "- is", - "-i s", - "a rel", - "ar el", - "are l", - "Ġ acted", - "Ġact ed", - "Ġac ted", - "- details", - "-d etails", - "-de tails", - "-detail s", - "-det ails", - "ภĩ", - "Ġ Theory", - "ĠThe ory", - "ĠTheo ry", - "ĠP un", - "ĠPu n", - "Ġ Anonymous", - "ĠAn onymous", - ".. .\"Ċ", - "... \"Ċ", - "...\" Ċ", - "è res", - "ère s", - "åı ¯", - "Ġ Vision", - "ĠV ision", - "ĠVis ion", - "_ sem", - "_s em", - "_se m", - "a sha", - "as ha", - "ash a", - "Ġcelebr ity", - "Ġ endDate", - "Ġend Date", - "Ġ populate", - "Ġpop ulate", - "Ġpopul ate", - "Ġc uis", - "Ġcu is", - "Ġcui s", - "q uant", - "qu ant", - "qua nt", - "quan t", - "f loor", - "fl oor", - "flo or", - "Ġglobal ly", - "Ġglob ally", - "Ġc ruise", - "Ġcru ise", - "Ġcruis e", - "ĠStan ley", - "Ġb ikes", - "Ġbi kes", - "Ġbike s", - "Ġbik es", - ". getConnection", - ".get Connection", - "Ġpoor ly", - "_ other", - "_o ther", - "_ot her", - "am ping", - "amp ing", - ". \");ĊĊ", - ".\" );ĊĊ", - ".\");Ċ Ċ", - ".\") ;ĊĊ", - ".\"); ĊĊ", - "o di", - "od i", - "_ ADMIN", - "_A DMIN", - "_AD MIN", - ". colors", - ".color s", - ".col ors", - "ĠG aming", - "ĠGa ming", - "ĠGam ing", - "> ';ĊĊ", - ">' ;ĊĊ", - ">';Ċ Ċ", - ">'; ĊĊ", - "STR UCT", - "STRU CT", - "Q R", - "I Ds", - "ID s", - "( arguments", - "(arg uments", - "(argument s", - "_ aux", - "_a ux", - "( Event", - "(E vent", - "_ PRIVATE", - "_PR IVATE", - "_PRIV ATE", - "ĠT rek", - "ĠTr ek", - "ĠTre k", - "Ġ downloads", - "Ġdown loads", - "Ġdownload s", - "m utable", - "mut able", - "mu table", - "_ STRUCT", - "_STR UCT", - "( wx", - "(w x", - "Ġ domains", - "Ġdom ains", - "Ġdomain s", - "Ġdoma ins", - "js px", - "jsp x", - "ĠVi agra", - "ĠVia gra", - "Command s", - "Comm ands", - "J s", - ". cfg", - ".c fg", - ".cf g", - "Content Pane", - "Ġ EditText", - "ĠEdit Text", - "à¥į à¤", - "At tach", - "Att ach", - "Ġ ARM", - "ĠA RM", - "ĠAR M", - "pos itive", - "posit ive", - "Ġ Generated", - "ĠG enerated", - "ĠGener ated", - "ĠGenerate d", - "ĠGene rated", - "Ġse ized", - "Ġseiz ed", - "Ġsei zed", - "Ġseize d", - "= :", - "Ġ electronics", - "Ġelect ronics", - "Ġelectronic s", - "Ġelectron ics", - "Ġ AppComponent", - "ĠApp Component", - "/ ',Ċ", - "/' ,Ċ", - "/', Ċ", - ". equalsIgnoreCase", - ".equals IgnoreCase", - "Do ctrine", - "d isk", - "dis k", - "di sk", - "Ġ Political", - "ĠPol itical", - "ĠPolit ical", - "C HO", - "CH O", - "< F", - "ĉ height", - "ĉh eight", - "Ġ Bug", - "ĠB ug", - "ĠBu g", - ". le", - ".l e", - "i kh", - "ik h", - "Ġ milliseconds", - "Ġm illiseconds", - "Ġmill iseconds", - "Ġmilli seconds", - "Ġconst itu", - "Ġconstit u", - "m ag", - "ma g", - ". nl", - ".n l", - "- range", - "-r ange", - "-ra nge", - "ang gal", - "' ,[", - "', [", - "r opolitan", - "ropol itan", - "Ġ Ãľ", - "Ġà ľ", - "Ġ UC", - "ĠU C", - ". desc", - ".d esc", - ".de sc", - ".des c", - "- LAST", - "-L AST", - "f stream", - "fst ream", - "i bil", - "ib il", - "ibi l", - "Ġf ier", - "Ġfi er", - "Ġfie r", - "V ERY", - "VER Y", - "VE RY", - "Ġ ë³", - "Ġë ³", - "I RT", - "IR T", - "_ UI", - "_U I", - "( abs", - "(a bs", - "(ab s", - "Ġk nees", - "Ġkn ees", - "Ġkne es", - "Ġknee s", - "Ġr ookie", - "Ġro okie", - "Ġ Vac", - "ĠV ac", - "ĠVa c", - "a rena", - "ar ena", - "are na", - "aren a", - "comm end", - "- \\", - "ĠSUB STITUTE", - "S oft", - "So ft", - "Ġpart ir", - "Ġpar tir", - "Ġparti r", - "we alth", - "è¦ ģ", - "( dataset", - "(d ataset", - "(data set", - "(dat aset", - "(datas et", - "Ġ Climate", - "ĠCl imate", - "ĠClim ate", - "ĠCli mate", - "- show", - "-s how", - "-sh ow", - "Ġrel iability", - "Ġreli ability", - "_ chunk", - "_ch unk", - "ä» £", - "_ stock", - "_st ock", - "ĠEX EMPLARY", - "ï ¸ı", - "ï¸ ı", - "Ġ vÃŃ", - "Ġv ÃŃ", - "Ġsm iled", - "Ġsmile d", - "Ġdr ill", - "Ġdri ll", - ". Function", - ".F unction", - ".Func tion", - "Ġ SI", - "ĠS I", - "Ġre gression", - "Ġreg ression", - "Ġregress ion", - "- X", - "Ġ Jar", - "ĠJ ar", - "ĠJa r", - "p ref", - "pr ef", - "pre f", - "ĉ success", - "ĉs uccess", - "ĠH itler", - "ĠHit ler", - "Ġinst inct", - "Ġfem mes", - "Ġfemme s", - "Ġ lover", - "Ġl over", - "Ġlo ver", - "Ġlove r", - "Ġlov er", - "< Ċ", - "Ġmulti plier", - "Ġmultip lier", - "r il", - "ri l", - "Re size", - "Res ize", - "Ġ Authorization", - "ĠAuthor ization", - "ĠK an", - "ĠKa n", - "Dispatch ToProps", - "Ġc rops", - "Ġcr ops", - "Ġcro ps", - "Ġcrop s", - "t okens", - "token s", - "tok ens", - "e cn", - "ec n", - "ent ially", - "ential ly", - "enti ally", - "ĠINTERRU PTION", - "f ake", - "fa ke", - "fak e", - "Un defined", - "Und efined", - "Ġ AK", - "ĠA K", - "Ġ TestCase", - "ĠTest Case", - "Ġ rab", - "Ġr ab", - "Ġra b", - "Ġ torrent", - "Ġt orrent", - "Ġtor rent", - "Ġ Ot", - "ĠO t", - "B ars", - "Bar s", - "Ba rs", - "Ġ lecture", - "Ġl ecture", - "Ġlect ure", - "Ġen jo", - "Ġrespond s", - "Ġresp onds", - "Ġ indexed", - "Ġindex ed", - "Ġinde xed", - "Of Work", - "_ chain", - "_ch ain", - ") )->", - ")) ->", - "))- >", - "Ġ Beauty", - "ĠBe auty", - "ĠBeaut y", - "ĠBea uty", - "ĠBeau ty", - "Ġ` <", - "Ġtouch ing", - "Ġtou ching", - "Ġ |--", - "Ġ| --", - "Ġ|- -", - "ĉ flag", - "ĉf lag", - "normal ize", - "Ġt rapped", - "Ġtr apped", - "Ġtra pped", - "Ġtrap ped", - "Ġestablish ing", - "/ build", - "/b uild", - "A J", - "f y", - "- react", - "-re act", - "a vn", - "av n", - "RI PTION", - "RIPT ION", - "Ġk ut", - "Ġku t", - "Ġ Fashion", - "ĠF ashion", - "Ġ Inform", - "ĠIn form", - "ĠInfo rm", - "ĠInf orm", - "c urities", - "cur ities", - "< byte", - "{Ċ", - "Ġ= >{Ċ", - "Ġ=> {Ċ", - "Ġgar lic", - "Ġ repr", - "Ġre pr", - "Ġrep r", - "Ġre plies", - "Ġrep lies", - "Ġrepl ies", - "( prop", - "(p rop", - "(pro p", - "(pr op", - "Ġspirit s", - "Ġspir its", - "Ġins pire", - "Ġinspir e", - "Ġinsp ire", - "Ġbase ment", - "Ġbas ement", - ". reject", - ".re ject", - "Ġ hints", - "Ġh ints", - "Ġhint s", - "Ġhi nts", - "Ġhin ts", - "Ġpol ling", - "Ġpoll ing", - "ĉ ĠĊ", - "ĉĠ Ċ", - "_ rating", - "_r ating", - "_ra ting", - "_rat ing", - "Ġc ath", - "Ġca th", - "Ġcat h", - "a vier", - "av ier", - "avi er", - "Ġ compressed", - "Ġcom pressed", - "Ġcomp ressed", - "Ġcompr essed", - "Ġcompress ed", - "Ġ VS", - "ĠV S", - "] '", - "Ġjud icial", - "ĠT rend", - "ĠTr end", - "ĠTre nd", - "tr aining", - "tra ining", - "train ing", - "EST AMP", - "ogn ition", - "Ä ģ", - "S ENT", - "SE NT", - "SEN T", - "v entions", - "vent ions", - "vention s", - "Ġconsult ant", - "Ġconsulta nt", - "Ġconsul tant", - "u mph", - "um ph", - "ump h", - "Ġ userService", - "Ġuser Service", - ", NULL", - ",N ULL", - "k h", - "D ear", - "De ar", - "_ BAD", - "_B AD", - "it ations", - "itation s", - "itat ions", - "Ġmet aph", - "Ġmeta ph", - "' é", - "and ise", - "andi se", - "- font", - "-f ont", - ". chart", - ".c hart", - ".ch art", - ".char t", - "Ġ sg", - "Ġs g", - "_ Controller", - "_Control ler", - ". jpeg", - ".j peg", - ".jp eg", - "Ġ ULONG", - "ĠU LONG", - "ĠUL ONG", - "ĉ game", - "ĉg ame", - "( ss", - "(s s", - "ĠM aj", - "ĠMa j", - "ĉ go", - "ĉg o", - "Ġ Sad", - "ĠS ad", - "ĠSa d", - "ĠB erg", - "ĠBe rg", - "ĠBer g", - "Ġ Mine", - "ĠM ine", - "ĠMin e", - "ĠMi ne", - "P ack", - "Pa ck", - "Ġres istant", - "Ġresist ant", - "Ġ ROM", - "ĠR OM", - "ĠRO M", - "Ġ peg", - "Ġp eg", - "Ġpe g", - "ĠSt anford", - "ĠStan ford", - "Ġ Yahoo", - "ĠY ahoo", - "ĠYa hoo", - "ĠYah oo", - "Ġ scaled", - "Ġs caled", - "Ġsc aled", - "Ġscale d", - "Ġsca led", - "Ġscal ed", - "Ġ lan", - "Ġl an", - "Ġla n", - "= []", - "=[ ]", - "\" /> < /", - "Ġ plots", - "Ġp lots", - "Ġpl ots", - "Ġplot s", - "Ġplo ts", - ". *Ċ", - ".* Ċ", - "Ġtr aveled", - "Ġtravel ed", - "Ġtra veled", - "Ġtrav eled", - "ĠO scar", - "ĠOs car", - "ĠOsc ar", - "V L", - "Ġl inking", - "Ġlink ing", - "Ġlin king", - "Ġt ires", - "Ġti res", - "Ġtire s", - "Ġtir es", - "Ġ' *'", - "Ġ'* '", - "ĠBuffer ed", - "ĠBuff ered", - "e ri", - "er i", - "Ġ ****", - "Ġ* ***", - "Ġ** **", - "Ġ*** *", - "Ġover look", - "Ġoverl ook", - ". Non", - ".N on", - ".No n", - "Ġr és", - "Ġré s", - "Ġe gy", - "Ġeg y", - "å° ı", - "Ġatt acker", - "Ġattack er", - "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", - ". sync", - ".s ync", - ".syn c", - ".sy nc", - "AS CADE", - "ASC ADE", - "G round", - "Gr ound", - "Gro und", - "Ġ decay", - "Ġdec ay", - "Ġ Ton", - "ĠT on", - "ĠTo n", - "Ġjew elry", - "Ġjewel ry", - "Ġb ypass", - "Ġby pass", - "Ġm embr", - "Ġmem br", - "Ġmemb r", - "R NA", - "RN A", - "< System", - " ččĊ", - "Ġs ud", - "Ġsu d", - "ĉ background", - "ĉback ground", - "Ġsch olars", - "Ġscholar s", - "-m uted", - "a rá", - "ar á", - "Ġ =====", - "Ġ= ====", - "Ġ== ===", - "Ġ=== ==", - "Ġ==== =", - "Ġ ____", - "Ġ_ ___", - "Ġ__ __", - "Ġ___ _", - "C reat", - "Cre at", - "Cr eat", - "e never", - "en ever", - "ene ver", - "/ wp", - "/w p", - "Ġ VPN", - "ĠV PN", - "ĠVP N", - "Error Code", - ") ],Ċ", - ")] ,Ċ", - ")], Ċ", - "( builder", - "(b uilder", - "(build er", - "Ġ Enemy", - "ĠEn emy", - "S ensor", - "u sa", - "us a", - "Ġtr iggers", - "Ġtrigger s", - "Ġtrig gers", - "Ġplay offs", - "Ġplayoff s", - "_ REQ", - "_RE Q", - "_R EQ", - "Ġ (~", - "Ġ( ~", - "ĠB arry", - "ĠBar ry", - "ĠBarr y", - "Ġperman ently", - "Ġpermanent ly", - "Ġ RUN", - "ĠR UN", - "ĠRU N", - "Ġb ure", - "Ġbu re", - "Ġbur e", - ".Fatal f", - ".Fat alf", - "Ġch ick", - "Ġchi ck", - "Ġchic k", - "ĉ panic", - "ĉp anic", - "p si", - "ps i", - "o ka", - "ok a", - "éĢ ī", - "> [", - "Ġunder stands", - "Ġunderstand s", - "Ġunderst ands", - "Ġ Junior", - "ĠJun ior", - "ĠJuni or", - "Ġ INFO", - "ĠIN FO", - "ĠINF O", - "= mysqli", - "=m ysqli", - "=mysql i", - "us tain", - "ust ain", - "usta in", - "- source", - "-s ource", - "s erv", - "se rv", - "ser v", - "Ġ CREATE", - "ĠC REATE", - "ĠCRE ATE", - ". au", - ".a u", - "Ġs ells", - "Ġsell s", - "Ġsel ls", - "Ġ ĠĊĠĠĊ", - "ĠĠ ĊĠĠĊ", - "ĠĠĊ ĠĠĊ", - "E urope", - "Euro pe", - "z w", - "p reh", - "pr eh", - "pre h", - "ĠN SA", - "ĠNS A", - "Ġ xy", - "Ġx y", - "ภ´", - "Ġ Beyond", - "ĠB eyond", - "ĠBey ond", - "In stead", - "Inst ead", - "Non Query", - "Ġa rise", - "Ġar ise", - "Ġavoid ed", - ". emplace", - ".em place", - ".emp lace", - "_ models", - "_model s", - "_mode ls", - "_mod els", - "} ),Ċ", - "}) ,Ċ", - "}), Ċ", - "Ġ hid", - "Ġh id", - "Ġhi d", - "Ġ &_", - "Ġ& _", - ". points", - ".p oints", - ".point s", - ".po ints", - ".poi nts", - ". getWidth", - ".get Width", - ". Exec", - ".Ex ec", - ".E xec", - "Ġ ////", - "Ġ// //", - "Ġ/ ///", - "Ġ/// /", - "Ġ Sessions", - "ĠS essions", - "ĠSession s", - ". ..\\", - ".. .\\", - "... \\", - "ĠCol omb", - "ĠColo mb", - "Ġacceler ation", - "Ġaccel eration", - "re store", - "rest ore", - "Ġ ile", - "Ġi le", - "Ġil e", - "o bic", - "ob ic", - "obi c", - "< Node", - " }Ċ", - ">} Ċ", - "pl aint", - "plain t", - "pla int", - "get Text", - "Ġindividual ly", - "Ġindivid ually", - "Ġ checkbox", - "Ġcheck box", - "U Y", - "ĠL amb", - "ĠLa mb", - "ĠLam b", - "Ġdys function", - "ĠL ar", - "ĠLa r", - "à °", - "Ġ Creating", - "ĠC reating", - "ĠCr eating", - "ĠCre ating", - "ĠCreat ing", - "' );ĊĊĊ", - "') ;ĊĊĊ", - "');Ċ ĊĊ", - "');ĊĊ Ċ", - "'); ĊĊĊ", - "\" They", - "\"The y", - "\"T hey", - "l ocations", - "loc ations", - "location s", - "_ CORE", - "_C ORE", - "_CO RE", - "_COR E", - "Inter action", - "umbn ails", - "umbnail s", - "Ġ Partner", - "ĠP artner", - "ĠPart ner", - "b rit", - "br it", - "Ġl esser", - "Ġless er", - "Ġles ser", - "Ġ Slot", - "ĠS lot", - "ĠSl ot", - "ĠSlo t", - "set Attribute", - "Ġ Wave", - "ĠW ave", - "ĠWa ve", - ". po", - ".p o", - "/ store", - "/st ore", - "Ġb rowsing", - "Ġbrows ing", - "Ġbrow sing", - "_ pd", - "_p d", - "s ume", - "sum e", - "su me", - "s ed", - "se d", - "C urve", - "Cur ve", - "Cu rve", - "Ġpl asma", - "Ġsusp icious", - "ìĿ ¸", - "Ġ Bah", - "ĠB ah", - "ĠBa h", - "Ġ Explicit", - "ĠExp licit", - "ĠExpl icit", - "_ CC", - "_C C", - ".Client Size", - "\\ View", - "\\V iew", - "Ġsub stit", - "Ġsubs tit", - "Ġsubst it", - "l oon", - "lo on", - "loo n", - "Ġ GAME", - "ĠG AME", - "ĠGA ME", - "ĠGAM E", - "ĠB rid", - "ĠBr id", - "ĠBri d", - "Ľ 建", - "_ User", - "_U ser", - "Ġs quares", - "Ġsqu ares", - "Ġsquare s", - "f one", - "fo ne", - "fon e", - "Ġsa cred", - "Ġsac red", - "Ġsacr ed", - "ug hs", - "ugh s", - "] interface", - "Ġ Throw", - "ĠTh row", - "ĠThr ow", - "ĠK irk", - "ĠKir k", - "ĠKi rk", - "Ġem pire", - "Ġemp ire", - "Ġempir e", - "Ġass essed", - "Ġassess ed", - "Ġasses sed", - "T ax", - "Ta x", - "ĠHe aven", - "- buffer", - "-b uffer", - "_ STATIC", - "_ST ATIC", - "_STAT IC", - "é né", - "én é", - "-b ordered", - "-border ed", - "Ġp unct", - "Ġpun ct", - "( mode", - "(m ode", - "(mod e", - "Ġke ine", - "Ġkein e", - "S ent", - "Se nt", - "Sen t", - "Ġ Calcul", - "ĠCal cul", - "ĠCalc ul", - "ĠE ve", - "ĠEv e", - "Ġsty lish", - "Ġstyl ish", - "Ġo ils", - "Ġoil s", - "Ġoi ls", - ". TestCase", - ".Test Case", - "Ġtrad emark", - "Ġtrade mark", - "Ġliter ary", - "Ġlite rary", - "Ġconcentr ations", - "Ġconcentration s", - "Ġ Relations", - "ĠRe lations", - "ĠRel ations", - "ĠRelation s", - "( Class", - "(C lass", - "(Cl ass", - "Ġ stdin", - "Ġst din", - "Ġstd in", - "Ġv æ", - "back up", - "bac kup", - ". VERSION", - ".V ERSION", - ".AutoScale Dimensions", - "st arter", - "start er", - "star ter", - "Transaction al", - "- panel", - "-p anel", - "-pane l", - "St udio", - "k c", - "ĠCh amber", - "ĠCham ber", - "ĠS piel", - "ĠSp iel", - "ĠSpi el", - "Ġ rho", - "Ġr ho", - "Ġrh o", - "ا ÙĦ", - "ا٠Ħ", - "! '", - ". Attributes", - ".At tributes", - ".Attribute s", - "Ġmurder ed", - "apeut ic", - "Ġint imate", - "Ġintim ate", - "Ġ textField", - "Ġt extField", - "Ġtext Field", - "ĠBuff alo", - "d ummy", - "dum my", - "\" %", - "ĠLib erty", - "ĠLibert y", - "ĠLiber ty", - "o bar", - "ob ar", - "oba r", - "Ġ Tank", - "ĠT ank", - "ĠTa nk", - "ĠTan k", - "Ġ Popular", - "ĠPop ular", - "er visor", - "erv isor", - "ĠIn iti", - "ĠInit i", - "ĠIni ti", - "ĠM all", - "ĠMal l", - "ĠMa ll", - "Ġ Prior", - "ĠP rior", - "ĠPr ior", - "ĠPri or", - "C AP", - "CA P", - "ĠC lay", - "ĠCl ay", - "ĠCla y", - "Ġ Certificate", - "ĠC ertificate", - "ĠCert ificate", - "ĠCertif icate", - ". Lock", - ".L ock", - ".Lo ck", - "- strip", - "-s trip", - "-st rip", - "-str ip", - "-dr iven", - "-drive n", - "/ all", - "/a ll", - "/al l", - "ĠMessageBox Buttons", - "ĠMessageBoxButton s", - "_ SECRET", - "_SE CRET", - "_SEC RET", - "_ pb", - "_p b", - "Ġ rats", - "Ġr ats", - "Ġrat s", - "Ġra ts", - "ा à¤", - "Ġ nt", - "Ġn t", - ". Router", - ".R outer", - ".Route r", - "_ topic", - "_t opic", - "_to pic", - "_top ic", - "Ġt ennis", - "Ġten nis", - "Ġ PUBLIC", - "ĠP UBLIC", - "ĠPUB LIC", - "ĠActiv atedRoute", - "Ġ ',Ċ", - "Ġ' ,Ċ", - "Ġ', Ċ", - "Ġcost ume", - "Ġj okes", - "Ġjo kes", - "Ġjoke s", - ". Handle", - ".H andle", - ".Hand le", - "ĉ byte", - "ĉb yte", - "Ġfl avors", - "Ġflavor s", - "Ġflav ors", - "( cc", - "(c c", - "Ġperson as", - "Ġpers onas", - "Ġpersona s", - "Ġperso nas", - "ĉ image", - "ĉi mage", - "ĉim age", - "ĠN azi", - "ĠNa zi", - "ĠNaz i", - "Ġ grammar", - "Ġgram mar", - "Ġgramm ar", - "Ġú lt", - "Ġval ve", - "Ġ vic", - "Ġv ic", - "Ġvi c", - "Ġ Rachel", - "ĠR achel", - "ĠRac hel", - "ĠRach el", - "_ invalid", - "_in valid", - "P refs", - "Pr efs", - "Pre fs", - "Pref s", - "std int", - "stdin t", - "( route", - "(r oute", - "(ro ute", - "Ġ htmlspecialchars", - "Ġhtml specialchars", - "Ġpe oples", - "Ġpeople s", - "p line", - "pl ine", - "Ġ nv", - "Ġn v", - "Ġ Quant", - "ĠQ uant", - "ĠQu ant", - "o ppers", - "op pers", - "opp ers", - "opper s", - "Ġ currentUser", - "Ġcurrent User", - "ĠC atal", - "ĠCa tal", - "ĠCat al", - "ĠCata l", - "Ġre conc", - "Ġrecon c", - "Ġreco nc", - "Ġcon junction", - "Ġconj unction", - "l x", - "am burg", - "amb urg", - "Ġinflu ential", - "d anger", - "da nger", - "dan ger", - "in ders", - "ind ers", - "inder s", - "inde rs", - "Ġ %@\",", - "Ġ% @\",", - "Ġ%@ \",", - ". configuration", - ".config uration", - "o some", - "os ome", - "oso me", - ". identity", - ".id entity", - ".ident ity", - ".ide ntity", - "Ġ picker", - "Ġp icker", - "Ġpick er", - "Ġpi cker", - "Ġpic ker", - "n ost", - "no st", - "nos t", - "ĠDI Y", - "Aug ust", - "a blo", - "ab lo", - "abl o", - "Le af", - "ĠR eco", - "ĠRe co", - "ĠRec o", - "c ko", - "ck o", - "D OC", - "DO C", - "ĠH erm", - "ĠHe rm", - "ĠHer m", - ": any", - ":a ny", - "Ġ Interview", - "ĠInt erview", - "ĠInter view", - "Ġ Tex", - "ĠT ex", - "ĠTe x", - "x fe", - "xf e", - "( work", - "(w ork", - "Ġle ap", - "He ading", - "Head ing", - "Ġ quarters", - "Ġqu arters", - "Ġquarter s", - "Ġquar ters", - "Ġquart ers", - "\\ Bundle", - "r eb", - "re b", - "Per haps", - "ĠG mbH", - "B irth", - "Bir th", - "ĉ sum", - "ĉs um", - "ĠWat son", - ". nil", - ".n il", - "ç ¡", - "{ }ĊĊ", - "{} ĊĊ", - "{}Ċ Ċ", - "ic aid", - "ica id", - "G etter", - "Get ter", - "\" name", - "Ġ \"čĊ", - "Ġ\" čĊ", - "_ none", - "_n one", - "_no ne", - "_non e", - "z m", - "ac ute", - "u esto", - "ue sto", - "ues to", - "uest o", - "Ġs ous", - "Ġso us", - "Ġsou s", - "Ġre build", - "Ġreb uild", - "Ġnews papers", - "Ġnewsp apers", - "Ġnewspaper s", - "Ġ Haz", - "ĠH az", - "ĠHa z", - "Ġ kits", - "Ġk its", - "Ġkit s", - "Ġki ts", - "i fo", - "if o", - "Bl ur", - "Ġsu ited", - "Ġsuit ed", - "Ġsuite d", - "Ġsui ted", - "- In", - "-I n", - "à ¯", - "Ġ Keith", - "ĠKe ith", - "ĠNor way", - "IN IT", - "INI T", - "ire ccion", - "i eties", - "ie ties", - "iet ies", - "_ usage", - "_u sage", - "_us age", - "Ġ Doug", - "ĠD oug", - "ĠDo ug", - "ĠDou g", - "r ise", - "ri se", - "ris e", - "Ġtr illion", - "im ited", - "imit ed", - "imi ted", - "Ġ REL", - "ĠR EL", - "ĠRE L", - "a lic", - "al ic", - "ali c", - "Ġcritic ized", - "Ġcriticize d", - "the orem", - "Ġc ease", - "Ġce ase", - "Ġside w", - "Ġsid ew", - "ĠT erry", - "ĠTer ry", - "ĠTerr y", - "Ġsubs idi", - "Ġsubsid i", - "Ġfirm ly", - "Ġ aws", - "Ġa ws", - "Ġaw s", - "Ġh ott", - "Ġhot t", - "Ġho tt", - "Ġd ressing", - "Ġdress ing", - "b adge", - "ba dge", - "bad ge", - "Ġ Applications", - "ĠApp lications", - "ĠApplication s", - "ĠAppl ications", - "è¿ ĶåĽŀ", - "è¿Ķ åĽŀ", - "Ġlaugh ed", - "Ġh obby", - "Ġhob by", - "Ġmus icians", - "Ġmusic ians", - "Ġmusician s", - "Ġ *.", - "Ġ* .", - ". placeholder", - ".place holder", - "Ġc ounters", - "Ġcount ers", - "Ġcoun ters", - "Ġcounter s", - "ĠCap itol", - "S DK", - "SD K", - "Ġh elmet", - "Ġhel met", - "Ġhelm et", - "and box", - "q uit", - "qu it", - "qui t", - "Ġcriminal s", - "Ġcrim inals", - "Ġteen ager", - "Ġteenage r", - "( update", - "(up date", - "G l", - ". selection", - ".s election", - ".se lection", - ".select ion", - ".sel ection", - "Ġdis charge", - "Ġpres enting", - "Ġpresent ing", - "ufact urer", - "_ UNKNOWN", - "_UN KNOWN", - "Ġst ressed", - "Ġstr essed", - "Ġstress ed", - "å Ļ¨", - "åĻ ¨", - "Pro to", - "Pr oto", - "Prot o", - "_ correct", - "_c orrect", - "_cor rect", - "_corr ect", - "h aus", - "ha us", - "Ġre nov", - "Ġren ov", - "Ġfire arms", - "Ġfirearm s", - "Ġtechn ically", - "Ġtechnical ly", - "- browser", - "-b rowser", - "Ġc andy", - "Ġcan dy", - "Ġcand y", - "St roke", - "Str oke", - "Ġ executor", - "Ġexec utor", - "Ġexecut or", - "Ġocc urrence", - "Ġoccur rence", - "Ġ IPv", - "ĠIP v", - "_ INTERFACE", - "_INTER FACE", - "Ġ Retrieve", - "ĠRe trieve", - "ĠRet rieve", - "ĠRetrie ve", - ". bad", - ".b ad", - ".ba d", - "Ex change", - "Nav bar", - "Ġ Kid", - "ĠK id", - "ĠKi d", - "(get ApplicationContext", - "_ STOP", - "_S TOP", - "_ST OP", - "Ġ Boss", - "ĠB oss", - "ĠBo ss", - "ĠBos s", - "List eners", - "Listener s", - "Listen ers", - "Ġsh ooter", - "Ġshoot er", - "Ġsho oter", - "ĠA lb", - "ĠAl b", - "ä ch", - "Ġ pix", - "Ġp ix", - "Ġpi x", - ". keyCode", - ".key Code", - "al one", - "alo ne", - "alon e", - "Ġabs urd", - "Ġ Cum", - "ĠC um", - "ĠCu m", - "ĠNewton soft", - "i kt", - "ik t", - "Ġlaugh ing", - "Ġcapital ism", - "ree Node", - "T x", - "_ QUERY", - "_QU ERY", - ". Sleep", - ".S leep", - "( login", - "(log in", - "(lo gin", - "Web Element", - "Ġcelebr ating", - "Ġ deprecated", - "Ġde precated", - "Ġdep recated", - "Ġm aar", - "Ġma ar", - "Ġart istic", - "Ġartist ic", - "_ASS OC", - "_AS SOC", - "ĠBorder Radius", - "ĉ wp", - "ĉw p", - "Ġsurv ivors", - "Ġsurviv ors", - "Ġsurvivor s", - "In ner", - "- red", - "-r ed", - "-re d", - "Ġprosec ution", - "_ pp", - "_p p", - "( \"$", - "\"=> $", - "Ġ comma", - "Ġcom ma", - "Ġco mma", - "Ġcomm a", - "un checked", - "g raphics", - "graph ics", - "graphic s", - "r ors", - "ro rs", - "ror s", - "G ROUND", - "GR OUND", - "( public", - "(p ublic", - "(pub lic", - "Ġcustom ized", - "Ġcustomize d", - "ĠArk ansas", - "Ġ Rew", - "ĠR ew", - "ĠRe w", - "Ġ expiration", - "Ġex piration", - "Ġexp iration", - "× ķ", - "ĠC ul", - "ĠCu l", - "Ġn ons", - "Ġno ns", - "Ġnon s", - ". Filter", - ".F ilter", - "Ġsen ator", - "_ definition", - "_def inition", - "ash ington", - "ashing ton", - "y mph", - "ym ph", - "/ J", - "Ġ fuse", - "Ġf use", - "Ġfu se", - "Ġfus e", - "ra mid", - "ram id", - "Ġ Supplier", - "ĠS upplier", - "ĠSup plier", - "Ġ autocomplete", - "Ġaut ocomplete", - "Ġauto complete", - "Ġ }),", - "Ġ} ),", - "Ġ}) ,", - ". \"ĊĊĊ", - ".\" ĊĊĊ", - ".\"ĊĊ Ċ", - ".\"Ċ ĊĊ", - "_ functions", - "_function s", - "_fun ctions", - "ĉ to", - "ĉt o", - ". eval", - ".e val", - ".ev al", - "ĠT Object", - "ĠTO bject", - "Re ferences", - "Reference s", - "Refer ences", - "Ġh eated", - "Ġhe ated", - "Ġheat ed", - "H AL", - "HA L", - "Ġ ))}Ċ", - "Ġ) )}Ċ", - "Ġ)) }Ċ", - "} $", - "ĠB arr", - "ĠBar r", - "ĠBa rr", - "_ UNIT", - "_UN IT", - "+ $", - "Ġ getValue", - "Ġget Value", - "i ped", - "ip ed", - "ipe d", - "ch ied", - "chie d", - "chi ed", - "( vm", - "(v m", - "c ue", - "cu e", - "_ integer", - "_int eger", - "_ course", - "_c ourse", - "_co urse", - "th ird", - "Ġre vised", - "Ġrev ised", - "Ġrevis ed", - "Ġrevise d", - "* */Ċ", - "** /Ċ", - "_ DIRECT", - "_D IRECT", - "_DIR ECT", - "_DI RECT", - "Out Of", - "( \"(", - "(\" (", - "Ġ Feel", - "ĠF eel", - "ĠFe el", - "ĠFee l", - "Ġre ass", - "Ġ subtitle", - "Ġsub title", - "Ġsubt itle", - "p eri", - "pe ri", - "per i", - "n f", - "Ġenjoy s", - "Ġenjo ys", - "Ġtreat s", - "Ġtre ats", - ") this", - ")t his", - "- tabs", - "-t abs", - "-tab s", - "an cers", - "ance rs", - "anc ers", - "ancer s", - "Ġ continent", - "Ġcont inent", - "Ġcontin ent", - "Ġcar dio", - "Ġcard io", - "Ġcardi o", - "S er", - "Se r", - ". question", - ".q uestion", - ".qu estion", - ".quest ion", - "Ġph rases", - "Ġphrase s", - "Valid ators", - "Validator s", - "Ġpop ul", - "Ġ lÃŃ", - "Ġl ÃŃ", - "s ong", - "so ng", - "son g", - "_ INTERNAL", - "_IN TERNAL", - "_INTER NAL", - "Ġadv iser", - "Ġadvis er", - "Ġadvise r", - "Ġp uzz", - "Ġpu zz", - "Ġamb itious", - "Ġambit ious", - "ĠT ob", - "ĠTo b", - "Ġ DP", - "ĠD P", - "Ġpres idency", - "Ġsur render", - "Ġsurre nder", - "Ġw atches", - "Ġwatch es", - "Ġwat ches", - "_ binary", - "_b inary", - "_bin ary", - "Ġ Soon", - "ĠS oon", - "ĠSo on", - "Ġcan ada", - "(\" \")Ċ", - "(\"\" )Ċ", - "(\"\") Ċ", - "] ='", - "]= '", - "Ġ Brandon", - "ĠBr andon", - "ĠBrand on", - "ĠBra ndon", - "ĠBran don", - "e psilon", - "eps ilon", - "r w", - ". addChild", - ".add Child", - ". Copy", - ".C opy", - ".Co py", - "Pr incipal", - "Ph otos", - "Photo s", - "Phot os", - "Ġmargin al", - "Ġmarg inal", - "Ġb asics", - "Ġbas ics", - "Ġbasic s", - "e ing", - "ei ng", - "ein g", - "M ust", - "Mu st", - "Mus t", - "_ String", - "_S tring", - "_Str ing", - "_St ring", - "Ġ ole", - "Ġo le", - "Ġol e", - "M agento", - "Mag ento", - ". customer", - ".c ustomer", - ".custom er", - "( prev", - "(p rev", - "(pr ev", - "(pre v", - "ภ¥", - "Ġlo yalty", - "Ġloyal ty", - "C og", - "Co g", - "Ġ protocols", - "Ġprot ocols", - "Ġprotocol s", - "Ġproto cols", - "Ġ Companies", - "ĠCom panies", - "Ġthe oretical", - "Ġtheoret ical", - "Ġtheor etical", - "Ġaccess ing", - "Ġacces sing", - "Ġ Zen", - "ĠZ en", - "ĠZe n", - ". ones", - ".on es", - ".o nes", - ".one s", - "att ice", - "atti ce", - "_ world", - "_w orld", - "z es", - "ze s", - "Ġtatto o", - "Ġtat too", - "Ġm enos", - "Ġme nos", - "Ġmen os", - "Ġmeno s", - "Ġ intersect", - "Ġinter sect", - "Ġinters ect", - "\" ];ĊĊ", - "\"] ;ĊĊ", - "\"];Ċ Ċ", - "\"]; ĊĊ", - "be lie", - "bel ie", - "Ġ inactive", - "Ġin active", - ".read line", - "-label led", - ". done", - ".d one", - ".do ne", - "lic kr", - "lick r", - "Ġ WORK", - "ĠW ORK", - "Ġder ivative", - "Ġderiv ative", - "Ġd atabases", - "Ġdata bases", - "Ġdatabase s", - "Ġdatab ases", - "âĤ Ĥ", - "Ġ sx", - "Ġs x", - ". isArray", - ".is Array", - "Ġ ys", - "Ġy s", - "Ġp ada", - "Ġpa da", - "Ġpad a", - "Ġ Bullet", - "ĠB ullet", - "ĠBul let", - "ĠBull et", - "(` /", - "is Active", - "Ġ CGSize", - "ĠCG Size", - "( equalTo", - "(equal To", - "ĠColum bus", - "Ġm arry", - "Ġmar ry", - "D EV", - "DE V", - "_ limits", - "_l imits", - "_limit s", - "_li mits", - "_lim its", - "r ones", - "ro nes", - "ron es", - "rone s", - "I AS", - "IA S", - "Ġ tau", - "Ġt au", - "Ġta u", - "m ino", - "min o", - "mi no", - "_ Write", - "_W rite", - "ĠW ine", - "ĠWin e", - "ĠWi ne", - "Ġ [['", - "Ġ[ ['", - "Ġ[[ '", - "Ġ Pull", - "ĠP ull", - "ĠPu ll", - "ĠPul l", - "ri ters", - "rit ers", - "rite rs", - "riter s", - "r ients", - "ri ents", - "rie nts", - "rient s", - "rien ts", - "Ġsh ifting", - "Ġshift ing", - "u pp", - "up p", - "_ TIMER", - "_T IMER", - "_TIME R", - "_TIM ER", - "_TI MER", - "Ġ Conditions", - "ĠCondition s", - "ĠCond itions", - "Ạ¥", - "Ġ Orders", - "ĠOr ders", - "ĠOrder s", - "ĠOrd ers", - "Ġ Strength", - "ĠSt rength", - "ĠStr ength", - "ĠStre ngth", - "æī Ģ", - "Ġval idity", - "Ġvalid ity", - "Ġf ot", - "Ġfo t", - "e tur", - "et ur", - "etu r", - "Ġ bolt", - "Ġb olt", - "Ġbo lt", - "Ġbol t", - "åĨ ħ", - "Ġ Along", - "ĠA long", - "ĠAl ong", - "ĠAlo ng", - "o shi", - "os hi", - "osh i", - "Ġassum ptions", - "Ġassumption s", - "Ġmag azines", - "Ġmagazine s", - "_ SPI", - "_S PI", - "_SP I", - "Ġp unt", - "Ġpun t", - "Ġpu nt", - "_ PRODUCT", - "_PRO DUCT", - "_PROD UCT", - "Ġ relay", - "Ġre lay", - "Ġr elay", - "Ġrel ay", - "Ġ Javascript", - "ĠJ avascript", - "ĠJava script", - ". te", - ".t e", - "- es", - "-e s", - "Ġ widgets", - "Ġwidget s", - "Ġwid gets", - "( fs", - "(f s", - "< Item", - " \";", - ">\" ;", - "at ching", - "atch ing", - "Ġ Knowledge", - "ĠK nowledge", - "ĠKnow ledge", - "ĉ The", - "ĉT he", - "; margin", - ";m argin", - "less ness", - "o pard", - "op ard", - "opa rd", - "u matic", - "um atic", - "uma tic", - "umat ic", - "( )));čĊ", - "() ));čĊ", - "()) );čĊ", - "())) ;čĊ", - "())); čĊ", - "Ġf als", - "Ġfa ls", - "Ġfal s", - "( cache", - "(c ache", - "(ca che", - "Type Id", - "éĢ ļ", - "_ choice", - "_ch oice", - "ĠG oth", - "ĠGo th", - "ĠGot h", - "Ġ Sites", - "ĠS ites", - "ĠSi tes", - "ĠSite s", - "ĠSit es", - "M G", - "_ border", - "_b order", - "Ind ices", - "Com parer", - "Comp arer", - "Compar er", - "Compare r", - "ĠRe distribution", - "ĠRed istribution", - "ĠRedis tribution", - "Ġclose t", - "Ġclos et", - "Ġclo set", - "Ġvers atile", - "Ġversa tile", - "In puts", - "Input s", - "**** ****************", - "******** ************", - "**************** ****", - "****** **************", - "************ ********", - "************** ******", - "Ġob esity", - "qu iz", - "qui z", - "g ra", - "gr a", - "( global", - "(g lobal", - "åĬ ¡", - "Ġ collector", - "Ġcol lector", - "Ġcoll ector", - "Ġcollect or", - "Ġcolle ctor", - "Ġ kor", - "Ġk or", - "Ġko r", - "o vable", - "ov able", - "ova ble", - "A DC", - "AD C", - "Ġ EventHandler", - "ĠEvent Handler", - ". nc", - ".n c", - "Ġplay back", - "ient os", - "ien tos", - "iento s", - "_ perm", - "_p erm", - "_per m", - "_pe rm", - "_ WARNING", - "_W ARNING", - "_WARN ING", - "ĠOlymp ics", - "ĠOlympic s", - ". norm", - ".n orm", - ".no rm", - "Ġ Broadcast", - "ĠB roadcast", - "ĠBroad cast", - "_ small", - "_s mall", - "_sm all", - "d rive", - "dr ive", - ". iloc", - ".i loc", - ".il oc", - "Ġ typed", - "Ġt yped", - "Ġtype d", - "Ġtyp ed", - "Ġty ped", - "M EM", - "ME M", - "_ cons", - "_c ons", - "_con s", - "_co ns", - "D METHOD", - "DM ETHOD", - "Ġ lun", - "Ġl un", - "Ġlu n", - ". distance", - ".d istance", - ".di stance", - ".dist ance", - "( par", - "(p ar", - "(pa r", - "p oon", - "po on", - "Ġ bast", - "Ġb ast", - "Ġbas t", - "Ġba st", - "act ivities", - "activ ities", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - ": čĊčĊ", - ":čĊ čĊ", - "S ER", - "SE R", - ") &&", - ")& &", - "_ lst", - "_l st", - "_ls t", - "ĠPol ish", - "ĠPo lish", - "Ġkn ocked", - "Ġknock ed", - "Ġfrustr ation", - "au kee", - "Ġph osph", - "iqu id", - "iq uid", - "_ coeff", - "_c oeff", - "_co eff", - "_coef f", - "æŃ ¤", - "L atest", - "La test", - "Lat est", - "Late st", - "ĠD ust", - "ĠDu st", - "T ipo", - "Tip o", - "Ti po", - "Ġmain tains", - "Ġmaint ains", - "Ġmaintain s", - "Ġ marsh", - "Ġmar sh", - "Ġmars h", - "inc inn", - "inci nn", - "l bl", - "lb l", - "C are", - "Car e", - "Ca re", - "Ġneighborhood s", - "_ gpio", - "_g pio", - "_gp io", - "ĠAr senal", - "ĠArs enal", - "D em", - "De m", - "ĠW he", - "ĠWh e", - "_ hook", - "_h ook", - "Ġl dc", - "Ġld c", - "ĠHar per", - "ĠBer keley", - "Ġgrad uated", - "Ġgraduate d", - "Ġgradu ated", - "Per cent", - "Ġarr iving", - "Ġarriv ing", - "Ġ Adventure", - "ĠAd venture", - "ĠAdvent ure", - "( scope", - "(s cope", - "(sc ope", - "( '*", - "(' *", - "qu arter", - "ĠM arie", - "ĠMar ie", - "ĠMa rie", - "ĠMari e", - "Spe aking", - "Speak ing", - "_ codegen", - "_code gen", - "_cod egen", - "Ġim mun", - "Ġimm un", - "c aster", - "ca ster", - "cast er", - "cas ter", - "ãĤ Į", - "åķ Ĩ", - "Ġ Dimensions", - "ĠDim ensions", - "ĠDimension s", - ". record", - ".re cord", - ".rec ord", - "Ġ texto", - "Ġtext o", - "Ġtex to", - "Ġ Michelle", - "ĠMich elle", - "ĠMichel le", - "ĠMiche lle", - "P ending", - "Pen ding", - "( by", - "(b y", - "_ PAR", - "_P AR", - "_PA R", - "u cht", - "uch t", - "uc ht", - "b ee", - "be e", - ". Thread", - ".T hread", - ".Th read", - "am pire", - "amp ire", - "k now", - "kn ow", - "Ġ Clinical", - "ĠClin ical", - "ĠClinic al", - "Ġmargin Bottom", - "Ġd istinguish", - "Ġdistingu ish", - ". Full", - ".F ull", - ". undefined", - ".un defined", - "ĠSequ elize", - "#### ########################################################################", - "################ ############################################################", - "################################################################ ############", - "############ ################################################################", - "################################################ ############################", - "######################################################################## ####", - "############################ ################################################", - "############################################################ ################", - "Ġ educated", - "Ġeduc ated", - "Ġeducate d", - "_ OVER", - "_O VER", - "åº ı", - "Ġ ÂłĠÂł", - "ĠÂł ĠÂł", - "ĠÂłĠ Âł", - "_ each", - "_e ach", - "Ġ urge", - "Ġur ge", - "Ġurg e", - "de part", - "dep art", - "Ġdon ors", - "Ġdonor s", - "Ġ Au", - "ĠA u", - "Ġb illions", - "Ġbill ions", - "Ġbillion s", - "Ġbelong ing", - "_ age", - "_a ge", - "_ag e", - "_ Int", - "_I nt", - "_In t", - "Ġsub stances", - "Ġsubstance s", - "Ġsubst ances", - "m achine", - "ma chine", - "mach ine", - "! !!ĊĊ", - "!! !ĊĊ", - "!!! ĊĊ", - "!!!Ċ Ċ", - "Ġjson ify", - "ib bean", - "Ġ Cad", - "ĠC ad", - "ĠCa d", - "Ġ endTime", - "Ġend Time", - "Ġc ycling", - "Ġcy cling", - "Ġcycl ing", - "Ġcyc ling", - "Ġ UITextField", - "ĠUI TextField", - "ĠUIT extField", - "Ġle verage", - "Ġlever age", - "Ġleve rage", - "Ġvan illa", - "e at", - "ea t", - "L aunch", - "La unch", - "( pt", - "(p t", - "st ates", - "state s", - "stat es", - "sta tes", - "Ġ Controls", - "ĠControl s", - "ĠContr ols", - "Ġ Respons", - "ĠRes pons", - "ĠResp ons", - "Ġ Jake", - "ĠJ ake", - "ĠJa ke", - "ĠJak e", - "Ġa sleep", - "Ġas leep", - "fort unate", - ".next Line", - "Size Mode", - "ì Ŀ¼", - "ìĿ ¼", - "Testing Module", - "G erman", - "Ger man", - "ĠInvest ig", - ". reverse", - ".re verse", - ".rev erse", - "Ġ BACK", - "ĠB ACK", - "ĠBA CK", - "( DateTime", - "(Date Time", - "Ġnon profit", - "Ġ Expect", - "ĠEx pect", - "ĠExp ect", - "Ġt anto", - "Ġtan to", - "Ġtant o", - "' ]),", - "'] ),", - "']) ,", - "ĉ the", - "ĉt he", - "ĉth e", - "M ultiple", - "Multi ple", - "Mult iple", - "Multip le", - "(get Activity", - "_ WAIT", - "_W AIT", - "Ġj á", - "de cor", - "dec or", - "lev ance", - "Ġ GitHub", - "ĠGit Hub", - "m ination", - "min ation", - "mi nation", - "mina tion", - "_ quantity", - "_qu antity", - "_quant ity", - ". Scanner", - ".Sc anner", - ".Scan ner", - "ĠL ion", - "ĠLi on", - "éĶĻ 误", - "Ġ dre", - "Ġd re", - "Ġdr e", - "Ġtan tra", - "Ġtant ra", - "Ġtantr a", - "Ġ contentType", - "Ġcontent Type", - "Ġ fid", - "Ġf id", - "Ġfi d", - "_ alt", - "_a lt", - "_al t", - "NS IndexPath", - "- pl", - "-p l", - "åĮ ĸ", - "Ġantib iot", - "t ables", - "table s", - "ta bles", - "tab les", - "tabl es", - "a cial", - "ac ial", - "aci al", - "acia l", - "Ġ Registry", - "ĠReg istry", - "ĠRegistr y", - "Ġo live", - "Ġol ive", - "Ġoli ve", - "i gers", - "ig ers", - "ige rs", - "iger s", - "Ġ subscriber", - "Ġsub scriber", - "Ġsubscri ber", - "Ġsubscribe r", - "_ pres", - "_p res", - "_pr es", - "_pre s", - "Ġ Syntax", - "ĠS yntax", - "ĠSy ntax", - "ĠSyn tax", - "Ġl overs", - "Ġlo vers", - "Ġlove rs", - "Ġlover s", - "Ġlov ers", - ". Byte", - ".B yte", - ".By te", - "ol ders", - "old ers", - "older s", - "_ forward", - "_for ward", - "al ways", - "C aption", - "Cap tion", - "Ca ption", - "Capt ion", - "P riv", - "Pr iv", - "Pri v", - "ĠT ampa", - "ĠTam pa", - "is ateur", - "-labelled by", - "Ġ ToString", - "ĠTo String", - "Ġ ìĤ¬", - "Ġì Ĥ¬", - "ĠìĤ ¬", - "Ġinit iated", - "Ġiniti ated", - "Ġinitiate d", - "W F", - "Ġinstitution al", - "in ject", - "Ġ Scr", - "ĠS cr", - "ĠSc r", - "Ġ doctrine", - "Ġdo ctrine", - "Ġdoctr ine", - "Ġsp acious", - "Ġspac ious", - "i sure", - "is ure", - "isu re", - "Ġ Ana", - "ĠA na", - "ĠAn a", - "\" time", - "ess aging", - "essa ging", - "Ġ cid", - "Ġc id", - "Ġci d", - "ĠN an", - "ĠNa n", - "Ġin complete", - "Ġincom plete", - "T AG", - "TA G", - "- build", - "-b uild", - "Dec ember", - "Ġres idual", - "Ġresid ual", - "( PDO", - "(P DO", - "Ġ Listen", - "ĠL isten", - "ĠList en", - "ĠLi sten", - "ĠLis ten", - "ĠListe n", - "Ġ glyph", - "Ġg lyph", - "Ġgly ph", - "Ġg aps", - "Ġgap s", - "Ġga ps", - "n ea", - "ne a", - ". Rect", - ".R ect", - ".Re ct", - ".Rec t", - "Ġs au", - "Ġsa u", - "ĠPhoto graph", - "ĠPhot ograph", - "Ġ executable", - "Ġexec utable", - "Ġexecut able", - "Ġ Expert", - "ĠEx pert", - "ĠExp ert", - "Co routine", - "Cor outine", - "_ sizes", - "_s izes", - "_size s", - "_si zes", - "Ġ NL", - "ĠN L", - ". isValid", - ".is Valid", - ") ;}Ċ", - "); }Ċ", - ");} Ċ", - "- reg", - "-r eg", - "-re g", - "Ġc iting", - "Ġcit ing", - "Ġci ting", - "c wd", - "cw d", - "ĠOtt awa", - "ĠB att", - "ĠBa tt", - "ĠBat t", - "Ġrenew able", - "Ġprelim inary", - "Ġas ylum", - "Ġw rist", - "Ġwr ist", - "Ġutil iz", - "Ġut iliz", - "Ġdet ention", - "F ast", - "Fa st", - "Ġ ange", - "Ġa nge", - "Ġan ge", - "Ġang e", - "incinn ati", - "Ġste ering", - "Ġsteer ing", - "Ġ NaN", - "ĠN aN", - "ĠNa N", - "i osity", - "ios ity", - "/ page", - "/p age", - "Ġ è¿", - "Ġè ¿", - "ster ol", - "ste rol", - "Ġdis g", - "Ġdi sg", - "( DB", - "(D B", - "Ġ DESCRIPTION", - "ĠDE SCRIPTION", - "ĠDESC RIPTION", - "Ġ _$", - "Ġ_ $", - "Ġob stacle", - "Ġobst acle", - "Ġb izarre", - "Ġex traction", - "Ġext raction", - "Ġextra ction", - "Ġextract ion", - "Ġextr action", - "_ expected", - "_ex pected", - "_exp ected", - "_expect ed", - "Ġ loses", - "Ġl oses", - "Ġlo ses", - "Ġlos es", - "Ġlose s", - "Ġ Celebr", - "ĠCele br", - "Ġhtml For", - "Ġexp loit", - "Ġexpl oit", - "Ġexplo it", - "олÑĮз ов", - "X YZ", - "XY Z", - "Ġm agnet", - "Ġmag net", - "Ġmagn et", - "am ped", - "amp ed", - "Ġ atoms", - "Ġat oms", - "Ġatom s", - "S ources", - "Source s", - "pect ives", - "pective s", - "Ñģ ли", - "Ñģл и", - "Ġ= čĊ", - "Ġd are", - "Ġda re", - "Ġdar e", - "ĠW alter", - "ĠWal ter", - "ĠWalt er", - "Ġ brightness", - "Ġb rightness", - "Ġbright ness", - "Ġ annotations", - "Ġan notations", - "Ġannotation s", - "Ġannot ations", - "ë ı", - "is ke", - "isk e", - "S chedule", - ". images", - ".image s", - ".im ages", - ".imag es", - "ross o", - "ros so", - "Ġ \"..", - "Ġ\" ..", - "Ġ\". .", - "g amma", - "ga mma", - "gam ma", - "Ġin structor", - "Ġinstr uctor", - "Ġinstruct or", - "Ġ overwrite", - "Ġover write", - "- am", - "-a m", - "Ġdevast ating", - "ĠSaint s", - "ĠSa ints", - "ĠSai nts", - "Ġ hs", - "Ġh s", - "Ġbon uses", - "Ġbonus es", - "$ output", - "$out put", - "i jd", - "ij d", - "(Action Event", - "m onitor", - "mon itor", - "Ġmatt ress", - "Jan uary", - ". jp", - ".j p", - "Ġcar acter", - "Ġcara cter", - "Ġcaract er", - "Ġim pose", - "Ġimp ose", - "_ rest", - "_re st", - "_r est", - "_res t", - "Ġ Signature", - "ĠSign ature", - "ĠSig nature", - "Ġcoron avirus", - "ãģ Ĭ", - "_ compare", - "_com pare", - "_comp are", - "Me asure", - "it ated", - "ita ted", - "itate d", - "itat ed", - "e lijk", - "el ijk", - "eli jk", - "i gos", - "ig os", - "igo s", - "e sar", - "es ar", - "esa r", - "Ġr ushed", - "Ġru shed", - "Ġrush ed", - "Ġrus hed", - "m etry", - "me try", - "met ry", - "_SE PARATOR", - "_ WE", - "_W E", - "_ ATTRIBUTE", - "_ATTR IBUTE", - "_ATTRIB UTE", - "Ġ yaml", - "Ġy aml", - "Ġya ml", - "Ġ specs", - "Ġsp ecs", - "Ġspec s", - "Ġspe cs", - "ĠR ah", - "ĠRa h", - "ph eric", - "pher ic", - "phe ric", - "ĠIn vestment", - "ĠInvest ment", - "ä ll", - "äl l", - "Ġappe aling", - "Ġappeal ing", - "Ġ viewport", - "Ġview port", - "ç ©", - "Ġ marginLeft", - "Ġmargin Left", - "Ġ subtract", - "Ġsub tract", - "Ġsubt ract", - "Ġ EDIT", - "ĠED IT", - "ĉ ArrayList", - "ĉArray List", - "gr ading", - "grad ing", - "gra ding", - "Ġ Failure", - "ĠF ailure", - "ĠFail ure", - "as per", - "asp er", - "E EK", - "EE K", - "( now", - "(n ow", - "(no w", - "< object", - "Ġ Alignment", - "ĠAl ignment", - "ĠAlign ment", - "ple ado", - "q tt", - "qt t", - "( ERROR", - "(ERR OR", - "Ġ INVALID", - "ĠIN VALID", - "Ġ userid", - "Ġuse rid", - "Ġuser id", - "r aises", - "ra ises", - "raise s", - "rais es", - "rai ses", - "I DI", - "ID I", - "Ġv ariance", - "Ġvar iance", - "Ġvari ance", - "Ġ Nil", - "ĠN il", - "ĠNi l", - "/ delete", - "/de lete", - "_ MAIN", - "_M AIN", - "_MA IN", - ". Token", - ".T oken", - ".To ken", - ". Category", - ".C ategory", - "> )Ċ", - ">) Ċ", - "C ollision", - "Coll ision", - "Ġ Greater", - "ĠG reater", - "ĠGreat er", - "ĠGre ater", - "ĠR acing", - "ĠRa cing", - "ĠRac ing", - "a lan", - "al an", - "ala n", - "Ġmon etary", - "Ġmonet ary", - ", new", - ",n ew", - "Ġ Sorry", - "ĠS orry", - "ĠSor ry", - ". Enable", - ".E nable", - ".En able", - "Ġ Instantiate", - "ĠIn stantiate", - "ĠInstant iate", - "o llen", - "ol len", - "oll en", - "olle n", - "ë© ´", - "Ġ Calling", - "ĠC alling", - "ĠCal ling", - "ĠCall ing", - "_ hour", - "_h our", - "A DA", - "AD A", - "Ġs hy", - "Ġsh y", - ") **", - ")* *", - "Ġ ==>", - "Ġ= =>", - "Ġ== >", - "Ġe special", - "Ġes pecial", - "Ġesp ecial", - "Ġespec ial", - "Ġ interpreted", - "Ġinterpret ed", - "Ġinterpre ted", - "! =\"", - "!= \"", - "Ġph armacy", - "Ġpharm acy", - "Ġpharmac y", - ". single", - ".s ingle", - ".sin gle", - ".si ngle", - "ĠC ialis", - "ĠCi alis", - "Ġp aras", - "Ġpar as", - "Ġpara s", - "Ġpa ras", - ". toUpperCase", - ".to UpperCase", - "Ġ Demon", - "ĠD emon", - "ĠDe mon", - "ĠDem on", - "ĠDemo n", - "Pr ime", - "Prim e", - "Pri me", - "Ġrank ings", - "Ġranking s", - "Add ing", - "Ad ding", - "_ HASH", - "_H ASH", - "_HAS H", - "Ġ Exam", - "ĠEx am", - "Ú ©", - "ĠV ictor", - "ĠVi ctor", - "ĠVict or", - "ĠVic tor", - "Ok ay", - "\" ];čĊ", - "\"] ;čĊ", - "\"]; čĊ", - "Ġ fortune", - "Ġfort une", - "Ġ FETCH", - "ĠF ETCH", - "exp and", - ". Interop", - ".Inter op", - "Ġb arn", - "Ġbar n", - "Ġba rn", - "æ ¶Ī", - "æ¶ Ī", - "ue vo", - "Ġspec ulation", - "âĶĢâĶĢ âĶĢâĶĢ", - "Ġ Nu", - "ĠN u", - "ĠBl ues", - "ĠBlue s", - "ĠBlu es", - "( fname", - "(f name", - "(fn ame", - "Ġin habit", - "Ġinhab it", - "Ġinh abit", - "Ġ\\ \"%", - "Ġ\\\" %", - "C ES", - "CE S", - "ul ario", - "ular io", - "ula rio", - "_ cr", - "_c r", - "Ġ validated", - "Ġvalid ated", - "Ġvalidate d", - "Ġvalida ted", - "Ġmid night", - "an king", - "ank ing", - "anki ng", - "Ġincor porate", - "Ġincorpor ate", - "Ġpur suit", - "Ġpurs uit", - "E XP", - "EX P", - "pr ime", - "prim e", - "pri me", - "P id", - "Pi d", - "- US", - "-U S", - "ĠN urs", - "ĠNu rs", - "ĠNur s", - "Ġ Wheel", - "ĠW heel", - "ĠWh eel", - "ĠWhe el", - "é ĺ", - "Ġ inp", - "Ġin p", - "Ġi np", - "Ġsupport ive", - ". member", - ".m ember", - ".mem ber", - "Ġ Shot", - "ĠS hot", - "ĠSh ot", - "ĠSho t", - ". CheckBox", - ".Check Box", - "Ġaff irm", - "Ġaf firm", - "T or", - "To r", - "Full Year", - "Ġconsider ably", - "c redentials", - "cred entials", - "credential s", - "_ opts", - "_op ts", - "_o pts", - "_opt s", - "R oll", - "Ro ll", - "Rol l", - "( round", - "(r ound", - "(ro und", - "Ġc oment", - "Ġcom ent", - "Ġco ment", - "Ġcome nt", - "_ UART", - "_U ART", - "Ġext ending", - "Ġextend ing", - "R G", - "result ado", - "i tu", - "it u", - ". getSession", - ".get Session", - ".getS ession", - "Ġat traction", - "Ġatt raction", - "Ġattr action", - "Ġattract ion", - "& D", - "$ html", - "$h tml", - "Ġ Jessica", - "ĠJess ica", - "Ġ Associate", - "ĠAssoci ate", - "ĠAssoc iate", - "a ñ", - "_ ed", - "_e d", - "ĠL ag", - "ĠLa g", - "Ġorig ins", - "Ġorigin s", - "( ))->", - "() )->", - "()) ->", - "add EventListener", - "IA LOG", - "IAL OG", - "åIJ ¦", - ". Compare", - ".Com pare", - ".Comp are", - "Al bum", - "ĠK u", - "< Q", - "ar gest", - "arg est", - "arge st", - "arges t", - "Ġpro long", - "Ġprol ong", - "Ġconfig urations", - "Ġconfiguration s", - "Ġconfigur ations", - "Ġacc identally", - "Ġaccident ally", - "Ġaccidental ly", - "_ photo", - "_ph oto", - "Ġ' ';čĊ", - "Ġ'' ;čĊ", - "Ġ''; čĊ", - "Ġ verse", - "Ġv erse", - "Ġver se", - "Ġvers e", - "B ob", - "Bo b", - "Ġf arming", - "Ġfar ming", - "Ġfarm ing", - "d elivery", - "del ivery", - "deliver y", - "ĠM ack", - "ĠMac k", - "ĠMa ck", - "Ġuse Selector", - ".bootstrap cdn", - "ke eping", - "keep ing", - "kee ping", - "e ny", - "en y", - ". upload", - ".up load", - "Ġ METHOD", - "ĠM ETHOD", - "ĠMETH OD", - "c reator", - "cre ator", - "creat or", - "< _", - "ĠE aster", - "ĠEast er", - "ĠEa ster", - ". --", - ".- -", - "UI Button", - "ãĤ ī", - "om eters", - "ome ters", - "omet ers", - "ometer s", - "Ġ shine", - "Ġsh ine", - "Ġshin e", - "Ġh ogy", - "Ġho gy", - "Ġhog y", - "\\ s", - "Ġh arness", - "Ġhar ness", - ". Cell", - ".C ell", - "Ġ lifting", - "Ġl ifting", - "Ġlif ting", - "Ġlift ing", - "Ġcomb ines", - "Ġcombine s", - "Ġcombin es", - "Ġ Occup", - "ĠOcc up", - "ĠOc cup", - "ex clude", - "exc lude", - "p atial", - "pat ial", - "Ġre spir", - "Ġres pir", - "Ġresp ir", - "_ fit", - "_f it", - "Ġf ifty", - "Ġfi fty", - "Ġfif ty", - "ĠM ol", - "ĠMo l", - "Ġt uned", - "Ġtu ned", - "Ġtun ed", - "Ġtune d", - "-d imensional", - "Ġ qs", - "Ġq s", - "Ġt ops", - "Ġto ps", - "Ġtop s", - "> \";ĊĊ", - ">\";Ċ Ċ", - ">\" ;ĊĊ", - ">\"; ĊĊ", - "quis ite", - "qui site", - "ch annels", - "chan nels", - "channel s", - "/ res", - "/r es", - "/re s", - "Ġ Analytics", - "ĠAn alytics", - "ĠAnaly tics", - ".app compat", - "/ to", - "/t o", - "Ġon Error", - "( attr", - "(at tr", - "(att r", - "I RM", - "IR M", - "Ġrag az", - "- as", - "-a s", - ". Second", - ".Se cond", - "ori ented", - "orient ed", - "Ġd onn", - "Ġdo nn", - "Ġdon n", - "Ġlight ning", - "f id", - "fi d", - "ĠP le", - "ĠPl e", - "ãģ¾ ãģĻ", - "t ro", - "tr o", - ". True", - ".Tr ue", - "O bservable", - "Observ able", - "× Ļ", - "um bing", - "umb ing", - "Ġpros pective", - "Ġprospect ive", - "- filter", - "-f ilter", - "Ġpurs uant", - "( points", - "(p oints", - "(point s", - "(po ints", - ". Bind", - ".B ind", - "Ġp alm", - "Ġpa lm", - "Ġpal m", - "clear fix", - "ö s", - "ĠG onz", - "ĠGo nz", - "ĠGon z", - "Ġwe aken", - "Ġweak en", - "D rive", - "Dr ive", - "en ido", - "eni do", - "l ld", - "ll d", - "o box", - "ob ox", - "obo x", - "an ean", - "ane an", - "G ot", - "Go t", - "ä¿ Ŀ", - "Reg ex", - "æ ĥ", - "Ġsa lad", - "Ġsal ad", - "Ġsala d", - "as sis", - "ass is", - "assi s", - "\" net", - "inherit Doc", - "Ġ RV", - "ĠR V", - "qu ier", - "qui er", - "Ġ clazz", - "Ġcl azz", - "Ġcla zz", - "ı ÅŁ", - "oster one", - "oste rone", - "Ġair line", - "Ġairl ine", - ".list dir", - "Ġdown loading", - "Ġdownload ing", - "ĠP alm", - "ĠPal m", - "ĠPa lm", - "w aukee", - "& lt", - ". BL", - ".B L", - "_ INLINE", - "_IN LINE", - "of fs", - "off s", - "< <(", - "<< (", - "_ news", - "_n ews", - "_new s", - "_ne ws", - "Ġch ase", - "Ġcha se", - "/ ><", - "/> <", - "Ġe uros", - "Ġeu ros", - "Ġeuro s", - "ĠEgypt ian", - "ĠSt ainless", - "_ BOOL", - "_BO OL", - "Ġ Guild", - "ĠG uild", - "ĠGu ild", - "ĠGui ld", - "ĠGuil d", - "ĠD ynam", - "ĠDy nam", - "ĠDyn am", - "[ indexPath", - "[index Path", - "Ġ ï", - "Ġmem orable", - "Ġmemor able", - "ĠCh ampion", - "ĠChamp ion", - "Resource Manager", - ". Login", - ".Log in", - ".Lo gin", - "Ġ Former", - "ĠFor mer", - "ĠForm er", - "y ped", - "ype d", - "yp ed", - "Ġl leg", - "Ġll eg", - "Ġlle g", - "; \",", - ";\" ,", - "D WORD", - "DW ORD", - "Ġtax i", - "Ġta xi", - "Ġb ombs", - "Ġbomb s", - "Ġbom bs", - "r ah", - "ra h", - ". tags", - ".t ags", - ".tag s", - ".ta gs", - "_ tests", - "_t ests", - "_test s", - "_te sts", - "s tones", - "st ones", - "ston es", - "stone s", - "sto nes", - "âĢĿ )", - "[ g", - "r type", - "rt ype", - "Ġ vu", - "Ġv u", - "Ġhost ile", - "Ġho stile", - "Ġhos tile", - "Ch ars", - "Char s", - "Cha rs", - "ĠPatri ots", - "ĠPatriot s", - "/ status", - "/s tatus", - "/st atus", - "/stat us", - "< B", - "Ġ Income", - "ĠIn come", - "ĠInc ome", - "ĠD ad", - "ĠDa d", - "Ġpat rol", - "_ CHANGE", - "_CH ANGE", - "_CHAN GE", - "Ġup graded", - "Ġupgrade d", - "Ġ china", - "Ġch ina", - "Ġchi na", - "Ġchin a", - "set q", - "Start ed", - "Star ted", - ".U ndef", - ".Un def", - "Ġ checksum", - "Ġcheck sum", - "Ġchecks um", - "Ġfrustr ated", - "{ o", - "Ġe nf", - "Ġen f", - "Ġ woods", - "Ġw oods", - "Ġwood s", - "Ġwo ods", - "Ġwoo ds", - "Ġ Anyone", - "ĠAny one", - "En code", - "Enc ode", - "ĠQt Widgets", - "a reas", - "are as", - "area s", - "Ġsh eer", - "Ġshe er", - "s ki", - "sk i", - "end point", - "_ Test", - "_T est", - "S oup", - "So up", - "Sou p", - "~~~~~~~~ ~~~~~~~~", - "( files", - "(f iles", - "(file s", - "(fi les", - "(fil es", - "ĉ ĉĉĉĉčĊ", - "ĉĉ ĉĉĉčĊ", - "ĉĉĉĉ ĉčĊ", - "ĉĉĉ ĉĉčĊ", - "ĉĉĉĉĉ čĊ", - ". spark", - ".s park", - ".sp ark", - "Ġvalue d", - "Ġval ued", - "Ġvalu ed", - "Ġ %Ċ", - "Ġ% Ċ", - ". controls", - ".control s", - "ĠXCTAssert Equal", - "Ġf ame", - "Ġfam e", - "Ġfa me", - "ĠR ic", - "ĠRi c", - "D OT", - "DO T", - "ĠAlbert a", - "ĠAlb erta", - "ä½ ¿", - "o sal", - "os al", - "osa l", - ".Web Controls", - "Ġ ------------", - "Ġ- -----------", - "Ġ-- ----------", - "Ġ---- --------", - "Ġ--- ---------", - "Ġ----- -------", - "Ġ---------- --", - "Ġ------ ------", - "Ġ-------- ----", - "Ġ------- -----", - "Ġ--------- ---", - "Ġ----------- -", - "Ġ Mis", - "ĠM is", - "ĠMi s", - "Ġ SYS", - "ĠS YS", - "ĠSY S", - "Non null", - "= item", - "=i tem", - "Ġ expire", - "Ġex pire", - "Ġexp ire", - "De code", - "Dec ode", - "_ operation", - "_op eration", - "_o peration", - "_oper ation", - "Ġ Validator", - "ĠValid ator", - ". CENTER", - ".C ENTER", - "uff s", - "uf fs", - "* m", - "Ġa vant", - "Ġav ant", - "Ġava nt", - "Ġavan t", - "æ¬ ¡", - "âĢľ You", - ". permission", - ".per mission", - ".perm ission", - ".. .)", - "... )", - "Ġ Lic", - "ĠL ic", - "ĠLi c", - "_ coords", - "_co ords", - "_coord s", - ". nombre", - ".n ombre", - ".nom bre", - "c lo", - "cl o", - ". Internal", - ".In ternal", - ".Int ernal", - ".Inter nal", - "Ġ Cho", - "ĠC ho", - "ĠCh o", - "_ sw", - "_s w", - "ĉ Il", - "ĉI l", - "c lk", - "cl k", - "Ġ castle", - "Ġc astle", - "Ġcas tle", - "Ġcast le", - "( layer", - "(l ayer", - "p it", - "pi t", - "Ġ guided", - "Ġgu ided", - "Ġguide d", - "Ġguid ed", - "Ġgui ded", - "Ġ âĸĪ", - "Ġâĸ Ī", - "Ġsup erb", - "Ġsuper b", - "Ġsup plements", - "Ġsupplement s", - "Ġsuppl ements", - "Ġsupp lements", - "_ cent", - "_c ent", - "_ce nt", - "Ġ peek", - "Ġpe ek", - "Ġpee k", - "IN ARY", - "INA RY", - ". ContentAlignment", - ".Content Alignment", - "f alls", - "fall s", - "fal ls", - "\" ));", - "\") );", - "\")) ;", - "W all", - "Wal l", - "Wa ll", - ") .čĊ", - "). čĊ", - "Ġ Danny", - "ĠD anny", - "ĠDan ny", - "ĠDann y", - "irm ingham", - "IAL IZ", - "( create", - "(c reate", - "\" In", - "\"I n", - "Service Provider", - "Ġpr iced", - "Ġprice d", - "Ġpri ced", - "m acro", - "ma cro", - "mac ro", - "a mac", - "am ac", - "ama c", - ". box", - ".b ox", - ".bo x", - "- ---Ċ", - "-- --Ċ", - "---- Ċ", - "--- -Ċ", - "ãĥ «", - "Ġ Suit", - "ĠS uit", - "ĠSu it", - "u rst", - "ur st", - "urs t", - "b ru", - "br u", - "ourn als", - "ournal s", - "num ero", - "numer o", - "_ _()Ċ", - "__ ()Ċ", - "__( )Ċ", - "__() Ċ", - "D as", - "Da s", - "ĠM itt", - "ĠMi tt", - "ĠMit t", - "u der", - "ud er", - "ude r", - "? \\", - "f u", - "[ B", - "Ġ: )ĊĊ", - "Ġ:) ĊĊ", - "Ġ:)Ċ Ċ", - "( inter", - "(int er", - "(in ter", - "br ains", - "bra ins", - "brain s", - "Ġatt itudes", - "Ġattitude s", - "Ver ify", - "Ġsign atures", - "Ġsignature s", - "ack Bar", - "Ġ gd", - "Ġg d", - "J ack", - "Ja ck", - "Jac k", - ". cat", - ".c at", - ".ca t", - "Ġ zz", - "Ġz z", - "w arf", - "wa rf", - "war f", - "F TER", - "FT ER", - "\" );ĊĊĊ", - "\");Ċ ĊĊ", - "\") ;ĊĊĊ", - "\");ĊĊ Ċ", - "\"); ĊĊĊ", - "A live", - "Al ive", - "Ali ve", - "I CLE", - "IC LE", - "Ġ Whatever", - "ĠWh atever", - "ĠWhat ever", - "Ġ outlined", - "Ġout lined", - "Ġoutline d", - "s prite", - "sp rite", - "spr ite", - "е в", - "еР²", - "_ AB", - "_A B", - "_ DEPTH", - "_DE PTH", - "Ġcr ushed", - "Ġcrush ed", - "Ġcru shed", - "Ġcrus hed", - "a aa", - "aa a", - "( ev", - "(e v", - "æľ º", - "An ti", - "Ant i", - "I CO", - "IC O", - "is EqualTo", - "isEqual To", - ". sun", - ".s un", - "i culo", - "ic ulo", - "s ale", - "sa le", - "sal e", - "_ hex", - "_h ex", - "_he x", - "Ġ Vk", - "ĠV k", - "ap tor", - "apt or", - "Un ion", - "Uni on", - "Ġ Discount", - "ĠDis count", - "ĠDisc ount", - "ĠDisco unt", - "l ista", - "li sta", - "list a", - "lis ta", - ".Undef Or", - "Ġ automation", - "Ġa utomation", - "Ġautom ation", - "Ġautomat ion", - "N or", - "No r", - "å¯ ¹", - "åı Ĥæķ°", - "åıĤ æķ°", - "Ġre flex", - "Ġref lex", - "Ġrefl ex", - "ĠLa ure", - "ĠLaur e", - "ĠLau re", - ".showMessage Dialog", - ". temp", - ".t emp", - ".te mp", - ".tem p", - "Ġ akan", - "Ġa kan", - "Ġak an", - "Ġaka n", - "Ġ_ _____", - "Ġ__ ____", - "Ġ___ ___", - "Ġ____ __", - "Ġ_____ _", - ".Is True", - "A RED", - "AR ED", - "ARE D", - "a gle", - "ag le", - "E nergy", - "En ergy", - "Ġquant ities", - "âĢĻ é", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "Ġcitizens hip", - "Ġcitizen ship", - "m outh", - "mo uth", - "Ġin appropriate", - "Ġ Outdoor", - "ĠOut door", - "White Space", - "An onymous", - "lo ads", - "load s", - "webElement Properties", - "T en", - "Te n", - "Ġacc idents", - "Ġaccident s", - "Ġ advertisement", - "Ġad vertisement", - "Ġadvertis ement", - "Ġadvertise ment", - "ĠY emen", - "ĠYe men", - "( call", - "(c all", - "(cal l", - "(ca ll", - "Ġsl avery", - "Ġslave ry", - "Ġsla very", - "Ġslav ery", - "Ñģ п", - "ĠL am", - "ĠLa m", - "_ BITS", - "_B ITS", - "_BIT S", - "o mega", - "om ega", - "ome ga", - "ĠO le", - "ĠOl e", - "Ġkid n", - "Ġki dn", - "_ An", - "_A n", - "ĠR aid", - "ĠRa id", - "ĠRai d", - "C reation", - "Cre ation", - "Creat ion", - "s aved", - "save d", - "sa ved", - "sav ed", - "Ġpro port", - "Ġprop ort", - "Ġpropor t", - "W ARNING", - "WAR NING", - "WARN ING", - "\\ P", - "Ġ pwd", - "Ġp wd", - "Ġpw d", - "Data Reader", - "is cher", - "isc her", - "ische r", - "isch er", - "ad eon", - "ade on", - "Ġ Predict", - "ĠP redict", - "ĠPre dict", - "ĠPred ict", - "Ġreason ing", - "Ġdestroy ing", - "H el", - "He l", - "* d", - "ĠLeg isl", - "_ Pr", - "_P r", - "ĉ ĉĉĠĠĠĠĠĠĠ", - "ĉĉ ĉĠĠĠĠĠĠĠ", - "ĉĉĉ ĠĠĠĠĠĠĠ", - "ĉĉĉĠĠĠ ĠĠĠĠ", - "ĉĉĉĠ ĠĠĠĠĠĠ", - "ĉĉĉĠĠ ĠĠĠĠĠ", - "ĉĉĉĠĠĠĠ ĠĠĠ", - "ĉĉĉĠĠĠĠĠ ĠĠ", - "ĉĉĉĠĠĠĠĠĠ Ġ", - "Ġsym path", - "Ġsymp ath", - "Ġch ess", - "Ġche ss", - "Ġ mam", - "Ġm am", - "Ġma m", - ": hover", - ":h over", - "Ġcon verts", - "Ġconvert s", - "Ġconv erts", - "Ġconver ts", - "Ġp ela", - "Ġpe la", - "Ġpel a", - "Ġpro gression", - "Ġprogress ion", - "Ġprog ression", - "Ġ\" _\"", - "Ġ\"_ \"", - "ĠG ill", - "ĠGi ll", - "ĠGil l", - "ĉ show", - "ĉs how", - "ĉsh ow", - "Ġsupposed ly", - "ac curacy", - "acc uracy", - "accur acy", - "e lin", - "el in", - "eli n", - "Ġunf olding", - "Ġunfold ing", - "Ġ Hyper", - "ĠH yper", - "ĠHy per", - "ĠHyp er", - "Ġw anna", - "Ġwann a", - "Ġwan na", - "Ġ ups", - "Ġu ps", - "Ġup s", - "( #", - "ĠC riminal", - "ĠCr iminal", - "( Point", - "(P oint", - "at Lng", - "act ly", - "Ġcontract ors", - "Ġcontr actors", - "Ġcontractor s", - "Ġcontra ctors", - "' ]}", - "'] }", - "draul ic", - "ód igo", - "Ġ TT", - "ĠT T", - "Ġ Wide", - "ĠW ide", - "ĠWi de", - "ĠWid e", - "Ġ ARG", - "ĠA RG", - "ĠAR G", - "_ ic", - "_i c", - "FLAG S", - "S chool", - "Sch ool", - "Ġcl earing", - "Ġclear ing", - "Ġcle aring", - "- being", - "-b eing", - "-be ing", - "={ [", - ", const", - "man ent", - "Over lay", - "( '\"", - "(' \"", - "éĩ ı", - "Ġ Timestamp", - "ĠT imestamp", - "ĠTime stamp", - "Ġm ailing", - "Ġma iling", - "Ġmail ing", - "Ġmai ling", - "Ġ Cake", - "ĠC ake", - "ĠCa ke", - ". That", - ".T hat", - ".Th at", - "Ġmed itation", - "q p", - "Ġ empresa", - "Ġemp resa", - "Ġempres a", - "ĠL ions", - "ĠLi ons", - "ĠLion s", - "Ġw eld", - "Ġwe ld", - "Ġwel d", - "Ġ LinkedIn", - "ĠLinked In", - "Ġc ush", - "Ġcu sh", - "Ġcus h", - "Ġ genome", - "Ġge nome", - "Ġgen ome", - "Ġgenom e", - ". IndexOf", - ".Index Of", - "a gain", - "ag ain", - "aga in", - "Ġ fallback", - "Ġf allback", - "Ġfall back", - "Ġc amping", - "Ġcamp ing", - "Ġcam ping", - "r edd", - "re dd", - "red d", - "-strip ed", - "-str iped", - "Ġ dv", - "Ġd v", - "Fe bruary", - "Feb ruary", - "Ġ Proxy", - "ĠPro xy", - "ĠPr oxy", - "u sk", - "us k", - "Ġd iesel", - "Ġdi esel", - "Ġdie sel", - "Ġdies el", - "Ġdiese l", - "W RITE", - "WR ITE", - "RE AK", - "REA K", - "L orem", - "Lo rem", - ". Invoke", - ".In voke", - ".Inv oke", - "- div", - "-d iv", - "-di v", - "Inter ceptor", - "Ġ DH", - "ĠD H", - "i ales", - "ial es", - "ia les", - "iale s", - "Ġvill ages", - "Ġvillage s", - "Ġvilla ges", - "Ø ´", - "Ġ ENV", - "ĠE NV", - "ĠEN V", - "S ys", - "Sy s", - ". XR", - ".X R", - "Ġpo em", - "à Ĥ", - "c ade", - "ca de", - "cad e", - "p lots", - "pl ots", - "plot s", - "Ġ {(", - "Ġ{ (", - ". git", - ".g it", - "/ svg", - "/s vg", - "n cmp", - "nc mp", - "Ġ Äį", - "ĠÄ į", - "a ines", - "ain es", - "ai nes", - "aine s", - "åĩ ½æķ°", - "åĩ½ æķ°", - "Ġ ()ĊĊ", - "Ġ( )ĊĊ", - "Ġ() ĊĊ", - "Ġ()Ċ Ċ", - "op sis", - "ops is", - "Ġ Relationship", - "ĠRel ationship", - "ĠRelations hip", - "ĠRelation ship", - "_ aut", - "_a ut", - "Ġ Bomb", - "ĠB omb", - "ĠBo mb", - "ĠBom b", - "ĉ com", - "ĉc om", - "* sizeof", - "*size of", - "off icial", - "_ payload", - "_p ayload", - "_pay load", - "ĉ ĉĉĉĉĠĠ", - "ĉĉ ĉĉĉĠĠ", - "ĉĉĉĉ ĉĠĠ", - "ĉĉĉ ĉĉĠĠ", - "ĉĉĉĉĉ ĠĠ", - "ĉĉĉĉĉĠ Ġ", - ". manager", - ".m anager", - ".man ager", - ".manage r", - "Ġ Around", - "ĠA round", - "ĠAr ound", - "ĉ send", - "ĉs end", - "ĉse nd", - "Ġ Exercise", - "ĠEx ercise", - "Ġ Billy", - "ĠB illy", - "ĠBill y", - "ĠBil ly", - "i vi", - "iv i", - "Ġne eding", - "Ġneed ing", - "_ urls", - "_url s", - "_ur ls", - "_ tasks", - "_t asks", - "_task s", - "ĠH em", - "ĠHe m", - "Ġ tearDown", - "Ġte arDown", - "Ġtear Down", - "en crypt", - "enc rypt", - ". tie", - ".t ie", - "Ġ asm", - "Ġa sm", - "Ġas m", - "I CH", - "IC H", - "ĠCGRect Make", - "ìĦ ±", - "u long", - "ul ong", - "ulo ng", - "Ġ itr", - "Ġit r", - "Ġi tr", - "Ġ GST", - "ĠG ST", - "ĠGS T", - "Ġoffer ings", - "Ġoffering s", - "r obe", - "ro be", - "rob e", - "E EE", - "EE E", - "oper ators", - "operator s", - "_ PROP", - "_P ROP", - "_PRO P", - "_PR OP", - "in dent", - "ind ent", - "inde nt", - "inden t", - "A DE", - "AD E", - "o rf", - "or f", - "ë IJ", - "Ġbl essed", - "Ġbless ed", - "v ascular", - "vas cular", - "Ġcon oc", - "Ġco noc", - "H appy", - "Ha ppy", - "B ridge", - "Br idge", - "il itation", - "ilit ation", - "j oint", - "join t", - "jo int", - "Ġ Administr", - "ĠAdmin istr", - "- transform", - "-trans form", - "Ġmean time", - "Ġmeant ime", - "/ K", - "ĠBed room", - "Ġr igid", - "Ġrig id", - "Ġri gid", - "Ġb rowsers", - "Ġbrowser s", - "Ġbrows ers", - "Ġbrowse rs", - "EM PTY", - "EMP TY", - ". Serialize", - ".S erialize", - ".Serial ize", - "_ ED", - "_E D", - "Ġst itch", - "Ġ jan", - "Ġj an", - "Ġja n", - "el lt", - "ell t", - "Ġ brace", - "Ġb race", - "Ġbr ace", - "Ġbra ce", - "Ġt rails", - "Ġtr ails", - "Ġtrail s", - "Ġtra ils", - "Ġtrai ls", - "p ublished", - "publish ed", - "å¯Ĩ çłģ", - "} ')Ċ", - "}' )Ċ", - "}') Ċ", - "Ġac ids", - "Ġacid s", - "Ġ !!!", - "Ġ! !!", - "Ġ!! !", - "_ direct", - "_d irect", - "_dir ect", - "_di rect", - "> ());Ċ", - ">( ));Ċ", - ">() );Ċ", - ">()) ;Ċ", - "a jÄħ", - "aj Äħ", - "_O CC", - "_OC C", - "Ġplan ets", - "Ġplane ts", - "Ġplanet s", - "Ġpla nets", - "æ Ł¥", - "æŁ ¥", - "ĠDub lin", - "Ġ serie", - "Ġs erie", - "Ġse rie", - "Ġser ie", - "Ġseri e", - ". printf", - ".print f", - "de ep", - "dee p", - "` )", - "Ġ \\$", - "Ġ\\ $", - "Ġ μ", - "ĠÎ ¼", - "_ VIDEO", - "_V IDEO", - "end ors", - "endor s", - "endo rs", - "Ġ Crypto", - "ĠC rypto", - "ĠCrypt o", - "ĠCry pto", - "F ar", - "Fa r", - ". Transparent", - ".Trans parent", - ". TR", - ".T R", - "i asm", - "ia sm", - "ias m", - "_ training", - "_tr aining", - "_train ing", - "_tra ining", - "Ġte aches", - "Ġteach es", - "Ġtea ches", - "ĠB elt", - "ĠBe lt", - "ĠBel t", - "Ġlimit ing", - "Ġlim iting", - "ĠK ath", - "ĠKat h", - "ĠKa th", - "Ġ IndexPath", - "ĠIndex Path", - "Ġachie vements", - "Ġachieve ments", - "Ġachievement s", - "Ġse rá", - "Ġser á", - "interop Require", - "Ġd isse", - "Ġdis se", - "Ġdi sse", - "Ġdiss e", - ". If", - ".I f", - "ar ming", - "arm ing", - "uls ion", - "P o", - "_ DETAIL", - "_DE TAIL", - "_DET AIL", - "Prot otype", - "Proto type", - "Ġ CAL", - "ĠC AL", - "ĠCA L", - "Ġag rees", - "Ġagre es", - "Ġagree s", - "Ġagr ees", - ". vo", - ".v o", - ".Execute NonQuery", - "Ġ Topic", - "ĠT opic", - "ĠTo pic", - "ĠTop ic", - "Ġ' {}", - "Ġ'{ }", - "A rm", - "Ar m", - "Ġ ecc", - "Ġe cc", - "Ġec c", - "M ag", - "Ma g", - "Ġ serialized", - "Ġs erialized", - "Ġser ialized", - "Ġserial ized", - "Ġserialize d", - "ĉ conn", - "ĉc onn", - "ĉcon n", - "c ached", - "ca ched", - "cache d", - "cac hed", - "= tf", - "=t f", - "Ġ ByteArray", - "ĠByte Array", - "prot obuf", - "proto buf", - "var char", - "ĉ ASSERT", - "ĉA SSERT", - "Ġ liste", - "Ġl iste", - "Ġli ste", - "Ġlist e", - "Ġlis te", - "_ trigger", - "_tr igger", - "_tri gger", - "· ¸", - "F eel", - "Fe el", - "Fee l", - "T ahoma", - "Ġ Lik", - "ĠL ik", - "ĠLi k", - "Ġ structured", - "Ġstruct ured", - "Ġstructure d", - "erg us", - ". Initial", - ".In itial", - ".Init ial", - "_ ge", - "_g e", - "cl js", - ". contact", - ".cont act", - "Ġand ere", - "Ġan dere", - "Ġander e", - "$ stmt", - "_ CURRENT", - "_C URRENT", - "Ġ Discover", - "ĠDis cover", - "ĠDisc over", - "ĠDisco ver", - "$ res", - "$r es", - "form atter", - "for matter", - "format ter", - "H a", - "van gst", - "vang st", - "Ġe merge", - "Ġem erge", - "Ġemerg e", - "ãĢĤ âĢĿ", - "ĠC abinet", - "ĠCabin et", - "ĠCab inet", - "- square", - "-s quare", - "éĥ ¨", - "Ġ rage", - "Ġr age", - "Ġra ge", - "Ġrag e", - "Ġ AJ", - "ĠA J", - "Ġ VT", - "ĠV T", - "sh adow", - "ĠFa ith", - "e names", - "en ames", - "ename s", - "ena mes", - "p retty", - "pr etty", - "pre tty", - "pret ty", - "h asil", - "ha sil", - "has il", - "p arty", - "par ty", - "part y", - "Ġ varchar", - "Ġvar char", - "Ġf otos", - "Ġfo tos", - "Ġfoto s", - "Ġfot os", - "Ġa lum", - "Ġal um", - "ĠBel gium", - "ĠBelg ium", - ". ylabel", - ".y label", - "Ġ dej", - "Ġd ej", - "Ġde j", - "_ numbers", - "_num bers", - "_number s", - "Ġ hu", - "Ġh u", - ".set Adapter", - "Ġ Usually", - "ĠUs ually", - "( sample", - "(s ample", - ". Shared", - ".Sh ared", - "Ġbo oked", - "Ġbook ed", - "Ġboo ked", - "Ġ> >=", - "Ġ>> =", - "Ġmin erals", - "Ġmineral s", - "Ġminer als", - "\" > < ?=", - "\">", - "'] )->", - "']) ->", - "p rog", - "pr og", - "pro g", - "b oo", - "bo o", - "_ md", - "_m d", - "_ pack", - "_p ack", - "_pa ck", - "( express", - "(ex press", - "(exp ress", - "(expr ess", - "u tz", - "ut z", - "\\ Auth", - ", id", - ",i d", - "ĠCh ile", - "ĠChi le", - "act ice", - "actic e", - "Ġrec ruitment", - "Ġrecruit ment", - "Ġ poses", - "Ġp oses", - "Ġpos es", - "Ġpo ses", - "Ġpose s", - "Ġvulner ability", - "inst anc", - "o rum", - "or um", - "oru m", - "d ess", - "de ss", - "des s", - "Ġ xl", - "Ġx l", - "%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%", - "( fig", - "(f ig", - "(fi g", - "Ġde leting", - "Ġdel eting", - "Ġdelet ing", - "Ġdele ting", - ". del", - ".d el", - ".de l", - ") ')Ċ", - ")' )Ċ", - "Ġ Weekly", - "ĠWeek ly", - "? ??", - "?? ?", - "( strcmp", - "(str cmp", - "s mith", - "sm ith", - "Ġpurs uing", - "- so", - "-s o", - "Ġ Apps", - "ĠA pps", - "ĠApp s", - "ĠAp ps", - "/ 'Ċ", - "/' Ċ", - "Ġde cis", - "Ġdec is", - "F ORE", - "FO RE", - "FOR E", - "Every one", - "Ġl anes", - "Ġla nes", - "Ġlane s", - "Ġlan es", - "V irtual", - "Vir tual", - ". attach", - ".at tach", - ".att ach", - "( Log", - "(L og", - "ĠMed icaid", - "ĠMedic aid", - "( Path", - "(P ath", - "ĠTur ner", - "ĠTurn er", - "/ application", - "/app lication", - "/ap plication", - "Ġ portrait", - "Ġport rait", - "Ġpor trait", - "Ġop pose", - "Ġopp ose", - "Ġoppos e", - "check out", - "Ġfin ishes", - "Ġfinish es", - "_ ME", - "_M E", - "Bar rier", - "S ong", - "So ng", - "Son g", - "V AR", - "VA R", - "Ear lier", - "r ella", - "re lla", - "rel la", - "rell a", - "Ġh ast", - "Ġhas t", - "Ġha st", - "a zar", - "az ar", - "aza r", - "Ġp ulls", - "Ġpull s", - "Ġpul ls", - "n gx", - "ng x", - "Ġins piring", - "Ġinspir ing", - "Ġinsp iring", - "Ñĥ Ñİ", - "- direction", - "-d irection", - "-direct ion", - "-dir ection", - "-di rection", - "Ġexplos ive", - "Ġ createdAt", - "Ġcreated At", - "s to", - "st o", - "Ġw heat", - "Ġwh eat", - "Ġwhe at", - "Ġ Built", - "ĠB uilt", - "ĠBu ilt", - "' ai", - "'a i", - "Ġ tracked", - "Ġtr acked", - "Ġtrack ed", - "h ammad", - "ham mad", - "RowAt IndexPath", - "_ heap", - "_he ap", - "D ue", - "Du e", - "Ġconn ects", - "Ġconnect s", - ". publish", - ".p ublish", - ".pub lish", - "e mu", - "em u", - "Ġb ullets", - "Ġbul lets", - "Ġbull ets", - "Ġbullet s", - "B AR", - "BA R", - "o late", - "ol ate", - "ola te", - "Ġintern ally", - "Ġinternal ly", - "Ġc atching", - "Ġcatch ing", - "Ġcat ching", - "- password", - "-p assword", - "-pass word", - "ou ched", - "ouch ed", - "æĢ §", - "e ous", - "eo us", - "Ġx range", - "Ġxr ange", - "Q uality", - "Qu ality", - "Qual ity", - "v v", - "Man age", - "Ma nage", - "Mana ge", - "( ($", - "(( $", - "ace ments", - "ac ements", - "acement s", - "ĠBr others", - "ĠBro thers", - "ĠBrother s", - "Ġ HEAD", - "ĠHE AD", - "Ġ Unsupported", - "ĠUn supported", - "ĠUns upported", - "s an", - "sa n", - "e si", - "es i", - "* **Ċ", - "** *Ċ", - "*** Ċ", - "Ġadapt ation", - "Ġ Worker", - "ĠWork er", - "ĠWor ker", - "' ]/", - "'] /", - ".save fig", - "( trans", - "(t rans", - "(tr ans", - "Ø ¬", - "n ee", - "ne e", - "C orrect", - "Cor rect", - ".. .\")Ċ", - "... \")Ċ", - "...\" )Ċ", - "Ġsubmit ting", - "- path", - "-p ath", - "ĉ last", - "ĉl ast", - "is san", - "iss an", - "issa n", - ". xlabel", - ".x label", - "Ġ Separ", - "ĠS epar", - "ĠSe par", - "ĠSep ar", - "/ no", - "/n o", - "_ best", - "_b est", - "_be st", - "ĠM ills", - "ĠMill s", - "ĠMil ls", - "_ sock", - "_s ock", - "_so ck", - "_soc k", - "( flag", - "(f lag", - "(fl ag", - "Ġdest inations", - "Ġdestination s", - "Ġdestin ations", - "em ption", - "emp tion", - "empt ion", - "Ġ FAIL", - "ĠF AIL", - "ĠFA IL", - "å ĴĮ", - "åĴ Į", - "Ġ rp", - "Ġr p", - "f act", - "fa ct", - "fac t", - "ĉ len", - "ĉl en", - "D AY", - "DA Y", - "Ġse iz", - "Ġsei z", - "_ dst", - "_d st", - "_ds t", - "l ip", - "li p", - ". Linear", - ".L inear", - ".Line ar", - "Ġ Basket", - "ĠB asket", - "ĠBas ket", - "$ t", - "$ i", - "- brand", - "-b rand", - "-br and", - "Ġ Neil", - "ĠN eil", - "ĠNe il", - "Ġ Eq", - "ĠE q", - "Ġt hou", - "Ġth ou", - "Ġtho u", - "o gene", - "og ene", - "ogen e", - "oge ne", - "Ġscholar ship", - "Ġscholars hip", - "æĽ ´", - "Ġs wo", - "Ġsw o", - "ag inator", - "agina tor", - "e ni", - "en i", - "( book", - "(b ook", - "Ġ blink", - "Ġb link", - "Ġbl ink", - "Ġbli nk", - "t hus", - "th us", - "Ġ cancellationToken", - "Ġc ancellationToken", - "Ġcancell ationToken", - "Ġcancellation Token", - "ĠPalestin ians", - "ĠPalestinian s", - "Ġprof itable", - "Ġprofit able", - "Ġback pack", - "en son", - "ens on", - "enso n", - "< Long", - " < /", - "_ WORD", - "_W ORD", - "\\M igrations", - "\\Migration s", - "Ġ ENABLE", - "ĠEN ABLE", - "_PARAM ETER", - "ĠB ishop", - "ĠBi shop", - "ĠBis hop", - ". subject", - ".sub ject", - "il las", - "ill as", - "illa s", - ". matrix", - ".m atrix", - ".mat rix", - "urre nces", - "urrenc es", - "urr ences", - "urrence s", - "* y", - "Ġcost ly", - "Ġ Chuck", - "ĠCh uck", - "ĠChu ck", - "Ġc loses", - "Ġcl oses", - "Ġclose s", - "Ġclos es", - "Ġclo ses", - "ĠM ight", - "ĠMi ght", - "ĠMig ht", - "- store", - "-st ore", - "Ġ mall", - "Ġm all", - "Ġma ll", - "Ġmal l", - "i eten", - "ie ten", - "iet en", - "iete n", - ". Abs", - ".A bs", - ".Ab s", - "Ġcou pled", - "Ġcouple d", - "Ġcoup led", - ". basic", - ".b asic", - ".ba sic", - "Ġ ::::::::", - "Ġ: :::::::", - "Ġ:: ::::::", - "Ġ::: :::::", - "M aker", - "Make r", - "Ma ker", - "c annot", - "can not", - "Ġ ach", - "Ġa ch", - "Ġac h", - "ĠE li", - "ĠEl i", - "âĪ Ĵ", - "o rna", - "or na", - "orn a", - "Ġ cps", - "Ġc ps", - "Ġcp s", - "Ġthere of", - "Ġther eof", - "Ġ@ {", - "Ġ NSMutableArray", - "ĠNSMutable Array", - "Î ½", - "pro ductive", - "product ive", - "prod uctive", - "S quare", - "tem pts", - "temp ts", - "tempt s", - "Ġelim inated", - "Ġeliminate d", - "Ġelimin ated", - "< M", - "Ġcons ervatives", - "Ġconserv atives", - "Ġconservative s", - "ĠS urg", - "ĠSur g", - "ĠSu rg", - ". par", - ".p ar", - ".pa r", - "ĠB uch", - "ĠBu ch", - "* b", - "F ort", - "For t", - "Fo rt", - "Col our", - "Ġ Chi", - "ĠC hi", - "ĠCh i", - "e dic", - "ed ic", - "edi c", - "> true", - "ĠN YC", - "ĠNY C", - "Ġb ored", - "Ġbo red", - "Ġbor ed", - "Ġbore d", - "Ġ Detect", - "ĠD etect", - "ĠDet ect", - "Ġapp ar", - "Ġap par", - "Ġje ans", - "Ġjean s", - "Ġ Tak", - "ĠT ak", - "ĠTa k", - "I OD", - "IO D", - "ĠH orse", - "ĠHor se", - "( FILE", - "(F ILE", - "( ?", - "r ique", - "ri que", - "opt imizer", - "optim izer", - "optimize r", - "n at", - "na t", - "lo ys", - "loy s", - "ĉ Token", - "ĉT oken", - "oub ted", - "u ess", - "ue ss", - "ues s", - "oc oa", - "oco a", - "Data Member", - "_ POWER", - "_P OWER", - "_PO WER", - "class List", - "Push Button", - "Ġ WiFi", - "ĠWi Fi", - ". Stream", - ".St ream", - ".Str eam", - ". guild", - ".g uild", - ".gui ld", - "Ġn og", - "Ġno g", - "ĠPort ugal", - "ĠPortug al", - "ĠUn ter", - "ĠUnt er", - "Pr imitive", - "Prim itive", - "b oss", - "bo ss", - "bos s", - "ĠDe utsch", - "Ġer otic", - "Ġerot ic", - "Ġero tic", - "Ġ strconv", - "Ġstr conv", - ".Try Parse", - "Ġ grams", - "Ġg rams", - "Ġgr ams", - "Ġgram s", - "Ġgra ms", - ". Success", - ".S uccess", - "_ pk", - "_p k", - "ĠHar vey", - "-m inded", - "-min ded", - ". country", - ".c ountry", - ".count ry", - "[ ]\"", - "[] \"", - "Ġ angel", - "Ġan gel", - "Ġang el", - "Ġange l", - "Ġbe ats", - "Ġbeat s", - "ĠV or", - "ĠVo r", - "i lio", - "il io", - "ili o", - ". master", - ".m aster", - ".mas ter", - ".ma ster", - "s omething", - "some thing", - "som ething", - "Ġ PACK", - "ĠP ACK", - "ĠPA CK", - "ĠPAC K", - "( if", - "(i f", - "Request Body", - "Ġ antes", - "Ġan tes", - "Ġant es", - "Ġante s", - "/ widget", - "/w idget", - "Ġ modo", - "Ġm odo", - "Ġmod o", - "Ġmo do", - "Ġ AW", - "ĠA W", - "f inder", - "find er", - "fin der", - "fi nder", - "Ġ optimized", - "Ġopt imized", - "Ġoptim ized", - "Ġoptimize d", - "Ġmiss iles", - "Ġmissile s", - "N B", - "ĉ internal", - "ĉint ernal", - "ĉin ternal", - "ĉinter nal", - "t ex", - "te x", - "ĠS ri", - "ĠSr i", - "Ġdam aging", - "Ġ Mais", - "ĠM ais", - "ĠMa is", - "ĠMai s", - "- Allow", - "-Al low", - "Ġ Zh", - "ĠZ h", - "- alt", - "-a lt", - "-al t", - "Ġ ));ĊĊ", - "Ġ) );ĊĊ", - "Ġ)) ;ĊĊ", - "Ġ));Ċ Ċ", - "Ġ)); ĊĊ", - "è ī", - "Ġinflu ences", - "Ġinfluence s", - "Ġc atal", - "Ġca tal", - "Ġcat al", - "Ġcata l", - "_ REGISTER", - "_REG ISTER", - "ĠAPI s", - "ĠAP Is", - "-cent ury", - "Ġ biology", - "Ġb iology", - "Ġbi ology", - "Ġbio logy", - "Ġ Actual", - "ĠAct ual", - "ĠAc tual", - "Ġ heels", - "Ġhe els", - "Ġheel s", - "TR ACE", - "TRA CE", - "_ DIG", - "_D IG", - "_DI G", - "D ataset", - "Data set", - "Dat aset", - "Datas et", - "ĠM atter", - "ĠMat ter", - "ĠMatt er", - "ĠMatte r", - "Ġ classifier", - "Ġclass ifier", - ".w ikipedia", - "ĠRo gers", - "ĠRog ers", - "ĠRoger s", - "Ġdon ated", - "Ġdonate d", - "raw ler", - "rawl er", - "e nen", - "en en", - "ene n", - "Ġcas inos", - "Ġcasino s", - "Ġcasi nos", - "or tal", - "ort al", - "orta l", - "Ġp rive", - "Ġpr ive", - "Ġpriv e", - "Ġpri ve", - "s pe", - "sp e", - "du cers", - "duc ers", - "duce rs", - "ducer s", - ". ep", - ".e p", - "Ġgr asp", - "Ġgra sp", - "Ġgras p", - "ac ji", - "Ġd airy", - "Ġda iry", - "Ġdai ry", - "Ġdair y", - "Ġb uses", - "Ġbu ses", - "Ġbus es", - ". comm", - ".c omm", - ".com m", - ".co mm", - ". ins", - ".in s", - ".i ns", - "Ġ IRS", - "ĠI RS", - "ĠIR S", - "Ġ Beer", - "ĠB eer", - "ĠBe er", - "ĠBee r", - "a dc", - "ad c", - "o ard", - "oa rd", - "_ MET", - "_M ET", - "_ME T", - "Ġ' +'", - "Ġ'+ '", - "r ans", - "ra ns", - "ran s", - "Ġk inda", - "Ġkind a", - "Ġki nda", - "Ġkin da", - "Ġ âĶĤ", - "ĠâĶ Ĥ", - "ĠM aur", - "ĠMa ur", - "а г", - "аР³", - "Ġband width", - "i bus", - "ib us", - "ibu s", - "Ġ Different", - "ĠD ifferent", - "( mat", - "(m at", - "Ġ Resume", - "ĠRe sume", - "ĠRes ume", - "_ UNS", - "_U NS", - "_UN S", - "est ablish", - "Ġfon ction", - "Sub scription", - "_ company", - "_com pany", - "_comp any", - "Ġ lightly", - "Ġl ightly", - "Ġlight ly", - ". confirm", - ".con firm", - ".conf irm", - ". yaml", - ".y aml", - "Ġ Boost", - "ĠBo ost", - "ĠBoo st", - "Com merce", - "Comm erce", - "- template", - "-t emplate", - "-temp late", - "_ DELAY", - "_DE LAY", - "_DEL AY", - "Ġ HI", - "ĠH I", - "Ġn avig", - "Ġna vig", - "Ġnav ig", - "( Sender", - "(S ender", - "Ġ HS", - "ĠH S", - "_ \"+", - "_\" +", - "Ġ REQUEST", - "ĠRE QUEST", - "ĠREQ UEST", - "Ġ wifi", - "Ġw ifi", - "Ġwi fi", - "= \"\"Ċ", - "=\" \"Ċ", - "=\"\" Ċ", - "] )->", - "]) ->", - "])- >", - "Ġr ope", - "Ġro pe", - "Ġrop e", - "Ġvi olated", - "Ġviol ated", - "Ġviolate d", - "Ġgl ance", - "ĠK urd", - "ĠKur d", - "ĠKu rd", - "Ġ è®", - "Ġè ®", - "d eck", - "de ck", - "dec k", - "Ġ ISBN", - "ĠIS BN", - "Ġin fect", - "Ġinf ect", - "Ġ Foo", - "ĠF oo", - "ĠFo o", - "Ġ getter", - "Ġg etter", - "Ġget ter", - "Ġ tener", - "Ġt ener", - "Ġte ner", - "Ġten er", - "a ppe", - "ap pe", - "app e", - ". hh", - ".h h", - "_ hot", - "_h ot", - "< AM", - " \".$", - ">\" .$", - ">\". $", - "Ġre lies", - "Ġrel ies", - "Ġreli es", - "Ġrelie s", - "( Console", - "Int ernational", - "Inter national", - "Intern ational", - "- >{$", - "-> {$", - "->{ $", - "M id", - "Mi d", - "Ġdis sert", - "Ġdiss ert", - "Ġdisse rt", - "d ds", - "dd s", - "Ġdeposit s", - "Ġdepos its", - "ĉ driver", - "ĉd river", - "# ga", - "#g a", - "p rising", - "pr ising", - "pri sing", - "print ln", - "Ġp resenter", - "Ġpres enter", - "Ġpresent er", - "Ġpresente r", - "Ġm ines", - "Ġmin es", - "Ġmi nes", - "Ġmine s", - "C SS", - "CS S", - "Ġ Dual", - "ĠD ual", - "ĠDu al", - "( !(", - "(! (", - "Ġk am", - "Ġka m", - "Ġ isLoading", - "Ġis Loading", - "Ġ Protect", - "ĠProt ect", - "ĠProte ct", - ". upper", - ".u pper", - ".up per", - "a rium", - "ar ium", - "ari um", - "] :ĊĊĊ", - "]: ĊĊĊ", - "]:Ċ ĊĊ", - "]:ĊĊ Ċ", - "Y ii", - "- shirt", - "-sh irt", - "Ġ IMAGE", - "ĠIM AGE", - "_ colors", - "_color s", - "_col ors", - "Ġ urgent", - "Ġur gent", - "Ġurge nt", - "Ġurg ent", - ". Container", - ".Cont ainer", - "! (Ċ", - "!( Ċ", - "S aturday", - "Ġsoc ieties", - "Ġsoci eties", - "Ġ Than", - "ĠT han", - "ĠTh an", - "Ġ Cod", - "ĠC od", - "ĠCo d", - "= @", - "Ġ attachments", - "Ġattach ments", - "Ġattachment s", - ". mobile", - ".m obile", - ".mob ile", - "Ġs pite", - "Ġsp ite", - "Ġspi te", - "Ġspit e", - "Ġ bounce", - "Ġb ounce", - "Ġbo unce", - "Ġbou nce", - "r awl", - "ra wl", - "raw l", - "instance type", - "instanc etype", - "ĠTr uck", - "ĠTru ck", - "Ġmanip ulation", - "( Config", - "- inst", - "-in st", - "-i nst", - "-ins t", - "Ġ stor", - "Ġs tor", - "Ġst or", - "Ġsto r", - "it ution", - "itu tion", - "Preferred Gap", - "Ġmain AxisAlignment", - "Ġlist ened", - "Ġlisten ed", - "Ġliste ned", - "'' 'ĊĊ", - "'''Ċ Ċ", - "''' ĊĊ", - "ott age", - "otta ge", - "- project", - "-pro ject", - ". APPLICATION", - ".AP PLICATION", - "ĉ root", - "ĉr oot", - "Ġw hit", - "Ġwh it", - "Ġ bilder", - "Ġb ilder", - "Ġbi lder", - "Ġbil der", - "Ġbild er", - "Ġ ker", - "Ġk er", - "Ġke r", - "Ġappl iances", - "Ġappliance s", - "ro wave", - "row ave", - "ìĿ Ģ", - "em atics", - "ema tics", - "ematic s", - "emat ics", - "Ġ Org", - "ĠO rg", - "ĠOr g", - "o ping", - "op ing", - "opi ng", - "_ SEARCH", - "_SE ARCH", - "Ġc ham", - "Ġch am", - "Ġcha m", - "add ContainerGap", - "Ġ ().", - "Ġ( ).", - "Ġ() .", - "Ġ Arrow", - "ĠAr row", - "ĠArr ow", - "Il legal", - "Ill egal", - "Current ly", - "Curr ently", - "Ġ usa", - "Ġu sa", - "Ġus a", - "Ġpass words", - "Ġpassword s", - "Ġre nown", - "Ġren own", - "a vern", - "av ern", - "ave rn", - "aver n", - "ĠE vil", - "ĠEv il", - "Ġ concat", - "Ġcon cat", - "Ġconc at", - "Ġd uo", - "Ġdu o", - "Ġ vale", - "Ġv ale", - "Ġval e", - "Ġva le", - "Ġ Bean", - "ĠB ean", - "ĠBe an", - "ĠBea n", - "Ġind icators", - "Ġindic ators", - "Ġindicator s", - "Ġindica tors", - "c math", - "cm ath", - "ĠP ump", - "ĠPu mp", - "Nov ember", - "ific ant", - "ifi cant", - "ifica nt", - "_ DOMAIN", - "_DO MAIN", - "_DOM AIN", - "re gar", - "reg ar", - "rega r", - "Ġ Portal", - "ĠP ortal", - "ĠPort al", - "ĠPor tal", - "\" $", - "Ġ formerly", - "Ġformer ly", - "\" ]:Ċ", - "\"] :Ċ", - "\"]: Ċ", - "Ġ Visibility", - "ĠVis ibility", - ".getElementsBy ClassName", - "_ RED", - "_RE D", - "_R ED", - "Ġch ampions", - "Ġchampion s", - "Ġchamp ions", - "à ´", - "Val or", - "Va lor", - "_ es", - "_e s", - "* a", - "- repeat", - "-re peat", - "B and", - "Ban d", - "Ba nd", - ". stage", - ".st age", - "Ġbure auc", - "Ġbureau c", - "C nt", - "e ten", - "et en", - "ete n", - "- function", - "-f unction", - "Ġm uito", - "Ġmu ito", - "Ġmuit o", - "P ID", - "PI D", - "_ editor", - "_e ditor", - "_edit or", - "_ed itor", - "Ġcr ashed", - "Ġcrash ed", - "Ġcra shed", - "d ead", - "de ad", - "dea d", - "k at", - "ka t", - "a gh", - "ag h", - "Ġ EXT", - "ĠE XT", - "ĠEX T", - "as ser", - "ass er", - "asse r", - "- small", - "-s mall", - "-sm all", - "Ġre aliz", - "Ġreal iz", - "( Entity", - "(E ntity", - "ú s", - "Ġ Actually", - "ĠAct ually", - "ĠActual ly", - "Ġ Elite", - "ĠE lite", - "ĠEl ite", - "ĠEli te", - "Ġ helm", - "Ġh elm", - "Ġhe lm", - "Ġhel m", - "( nonatomic", - "(non atomic", - "a sher", - "as her", - "ash er", - "Comm unity", - "all eng", - "alle ng", - "allen g", - "i ry", - "ir y", - "ĠG rowth", - "ĠGrow th", - "Ġs ue", - "Ġsu e", - "Ġf requencies", - "Ġfrequ encies", - "_ descriptor", - "_des criptor", - ". Attribute", - ".At tribute", - "Ġrec ipients", - "Ġrecipient s", - "Ġrecip ients", - "_ NS", - "_N S", - "/ \"+", - "/\" +", - "i ban", - "ib an", - "iba n", - "Ġ athlete", - "Ġath lete", - "Ġ Ign", - "ĠI gn", - "ĠIg n", - "_ DMA", - "_D MA", - "_DM A", - "( ds", - "(d s", - "Ġ Requirements", - "ĠRequire ments", - "ĠRequirement s", - "A DI", - "AD I", - "e rez", - "er ez", - "ere z", - "\\ Admin", - "br aska", - "bra ska", - "bras ka", - "ĠR ust", - "ĠRu st", - "ĠRus t", - "Re lation", - "Rel ation", - "C OD", - "CO D", - "Ġ VERSION", - "ĠV ERSION", - "ĠVER SION", - "e mma", - "em ma", - "emm a", - ") ){", - ")) {", - ". Duration", - ".D uration", - "Ġ Camb", - "ĠC amb", - "ĠCam b", - "ĠCa mb", - "- logo", - "-l ogo", - "-lo go", - "-log o", - "Ġread able", - "Ġcre ators", - "Ġcreat ors", - "Ġcreator s", - "Ġcrea tors", - "( )];Ċ", - "() ];Ċ", - "()] ;Ċ", - "Up Down", - "- half", - "-h alf", - ".get Month", - ".getM onth", - "( sf", - "(s f", - "P ic", - "Pi c", - "Ġh unger", - "Ġhun ger", - "Ġhung er", - "Ġhu nger", - ". tx", - ".t x", - "Ġex ceeded", - "Ġexceed ed", - "Ġexce eded", - "_ seed", - "_s eed", - "_se ed", - "( ^", - "_ sk", - "_s k", - ". perform", - ".per form", - "Ġ >::", - "Ġ> ::", - "Ġ mongo", - "Ġm ongo", - "Ġmon go", - "Ġmo ngo", - "Ġmong o", - "= float", - "=f loat", - "bind Param", - "S mart", - "Sm art", - "i fa", - "if a", - "Ġse curities", - "Ġsec urities", - "Ġpre jud", - "Ġ ,\"", - "Ġ, \"", - "Ġcor ps", - "Ġcorp s", - "Ġv ra", - "Ġvr a", - "ama care", - "amac are", - "i terr", - "it err", - "ite rr", - "iter r", - "( Media", - "(M edia", - "(Me dia", - "u che", - "uch e", - "uc he", - "Ġ cob", - "Ġc ob", - "Ġco b", - "Ġl iber", - "Ġli ber", - "Ġlib er", - ". geometry", - ".ge ometry", - ".geom etry", - ".geo metry", - "L ocator", - "Loc ator", - "Ġsl iding", - "Ġslid ing", - "Ġs urgical", - "Ġsurg ical", - "_ CUR", - "_C UR", - "Ġcon sect", - "Ġcons ect", - "Ġconsec t", - "Ġconse ct", - "[ *", - "ĠRe sort", - "ĠRes ort", - "St ub", - "_ DOUBLE", - "_DO UBLE", - "Ġ Soph", - "ĠS oph", - "ĠSo ph", - "Ġelect oral", - "_ disable", - "_d isable", - "_dis able", - "Ġ Ñģо", - "ĠÑģ о", - "ĠLight ning", - "Ġ mentions", - "Ġm entions", - "Ġmention s", - "Ġment ions", - "o cy", - "oc y", - "Ġle aked", - "Ġleak ed", - "Ġrelax ing", - "P resenter", - "Pres enter", - "Present er", - "v sp", - "vs p", - "Ġg uilt", - "Ġgu ilt", - "Ġgui lt", - "=- =-", - ". reply", - ".re ply", - ".rep ly", - "Ġ Mirror", - "ĠM irror", - "ĠMir ror", - "ĠMi rror", - "C amp", - "Ca mp", - "Cam p", - "Ġ+#+ #+#+", - "Ġ+#+#+#+ #+#+", - ". Author", - ".A uthor", - ".Auth or", - "Ġ directive", - "Ġdirect ive", - "Ġdir ective", - "- hook", - "-h ook", - "íĦ °", - "} ĊĊĊĊĊ", - "}Ċ ĊĊĊĊ", - "}ĊĊ ĊĊĊ", - "}ĊĊĊ ĊĊ", - "}ĊĊĊĊ Ċ", - "@ pytest", - "_ rand", - "_r and", - "_ra nd", - "m is", - "mi s", - "Ġcolor ful", - "u je", - "uj e", - "l asses", - "lass es", - "las ses", - "Ġ Classes", - "ĠC lasses", - "ĠCl asses", - "ĠClass es", - "ĠClasse s", - ". have", - ".h ave", - "% ),", - "%) ,", - "é¢ ĺ", - "Ġdistur bing", - "Ġdisturb ing", - "sub string", - "substr ing", - "subst ring", - "subs tring", - "ĠK oh", - "ĠKo h", - "In vest", - "Inv est", - "p urchase", - "Ġrec ycling", - "Ġrecycl ing", - "Ġ ART", - "ĠA RT", - "ĠAR T", - "ier archy", - "Ġ fps", - "Ġf ps", - "Ġfp s", - ". checkBox", - ".check Box", - "íķ ´", - "_ material", - "_m aterial", - "_mat erial", - "du cation", - "duc ation", - "Ġ fw", - "Ġf w", - "u dit", - "ud it", - "udi t", - "Ġreview ing", - "Ġ Sid", - "ĠS id", - "ĠSi d", - "S yntax", - "Sy ntax", - "Syn tax", - "Ġ Written", - "ĠW ritten", - "ĠWr itten", - "ar gar", - "arg ar", - "arga r", - "U ME", - "UM E", - "/ q", - "Class ifier", - "Off icial", - "Ġj azz", - "Ġja zz", - "Ġjaz z", - "Ġ omega", - "Ġo mega", - "Ġom ega", - "Ph ysics", - "Phys ics", - "Ġl ugar", - "Ġlu gar", - "Ġlug ar", - "_access or", - "_acc essor", - ". commands", - ".command s", - ".comm ands", - "Ab ility", - "Ġ Batch", - "ĠB atch", - "ĠBat ch", - "R AM", - "RA M", - "Ġenc ounters", - "Ġencounter s", - "Ġencount ers", - ". Qu", - ".Q u", - "B YTE", - "BY TE", - "Ġ Distribution", - "ĠD istribution", - "ĠDis tribution", - "ĠDistrib ution", - "Ġ uso", - "Ġu so", - "Ġus o", - "ĠRe covery", - "ĠRec overy", - "ĠReco very", - "ĠRecover y", - "ap proved", - "appro ved", - "approve d", - "Ġden ial", - "/ share", - "/s hare", - "/sh are", - "Link edList", - "Linked List", - ") čĊčĊčĊ", - ")čĊ čĊčĊ", - ")čĊčĊ čĊ", - "u ddy", - "ud dy", - "udd y", - "Ġf ines", - "Ġfin es", - "Ġfine s", - "Ġfi nes", - "Ġ ry", - "Ġr y", - "Un icode", - "Uni code", - "ĉ render", - "ĉr ender", - "ĉre nder", - "Ġprem ises", - "Ġpremise s", - "Ġpremi ses", - "Ġ pon", - "Ġp on", - "Ġpo n", - "ali ases", - "alias es", - "alia ses", - "/ Foundation", - "/F oundation", - "c uda", - "cu da", - "ĠC ock", - "ĠCo ck", - "ĠCoc k", - ", :)", - ",: )", - "( folder", - "(f older", - "Ġm éd", - "Ġmé d", - "d rag", - "dr ag", - "dra g", - "Ġtal ents", - "Ġtalent s", - "Ġtale nts", - "Ġ ĠĠĊĊ", - "ĠĠ ĠĊĊ", - "ĠĠĠ ĊĊ", - "ĠĠĠĊ Ċ", - "е ÑģÑĤв", - "еÑģÑĤ в", - "m ob", - "mo b", - ".y ml", - "Ġ aster", - "Ġa ster", - "Ġas ter", - "Ġast er", - "Ġdis cre", - "Ġdisc re", - "go al", - "ĠG TX", - "ĠGT X", - "Ġ SUCCESS", - "ĠS UCCESS", - "Ġ LONG", - "ĠL ONG", - "ĠLO NG", - "( find", - "(f ind", - "(fin d", - "(fi nd", - "Ġ singular", - "Ġs ingular", - "Ġsing ular", - "_ sz", - "_s z", - "ĠEth ereum", - "ĠEther eum", - ". .Ċ", - ".. Ċ", - "Ġir res", - "Ġirre s", - "Ġirr es", - "' )){Ċ", - "') ){Ċ", - "')) {Ċ", - "Ġmin isters", - "Ġminister s", - "Ġmini sters", - "Ġminist ers", - "St eps", - "Step s", - "Ste ps", - "iver sal", - "ivers al", - "Ġ Nevertheless", - "ĠNever theless", - "- led", - "-l ed", - "-le d", - "Ġ( %)", - "Ġ(% )", - "ç¡ ®", - "Ġ timezone", - "Ġtime zone", - "Ġstr anger", - "Ġstrange r", - "Ġstrang er", - "Ġstran ger", - "Ġstra nger", - "( render", - "(r ender", - "(re nder", - "Ġsh util", - "Ġshut il", - "Ġ mph", - "Ġm ph", - "Ġmp h", - "Ġt rio", - "Ġtr io", - "Ġtri o", - "p py", - "pp y", - "Ġpred omin", - "Ġ endors", - "Ġend ors", - "ĠRuss ians", - "ĠRussia ns", - "ĠRussian s", - "ĉ row", - "ĉr ow", - "Ġ wizard", - "Ġw izard", - ". serialize", - ".s erialize", - ".serial ize", - "Ġcompl ained", - "Ġcomplain ed", - "Ġs ido", - "Ġsi do", - "Ġsid o", - "Ġdel ighted", - "Ġdelight ed", - "- me", - "-m e", - "ĠR av", - "ĠRa v", - "H uman", - "Hum an", - "Hu man", - "a days", - "ad ays", - "ada ys", - "aday s", - "re cv", - "rec v", - "Work ing", - "J ump", - "Ju mp", - "Ġ Ã¥r", - "ĠÃ¥ r", - "Ġ Automatic", - "ĠAuto matic", - "ĠAut omatic", - "ĠAutom atic", - "_ Base", - "_B ase", - "æł ¼", - "aur ants", - "aurant s", - "aura nts", - " ¯", - "æ ¸", - "(C Type", - "I FI", - "IF I", - "( amount", - "(a mount", - "(am ount", - "Ġbel ieving", - "Ġbelie ving", - "= mysql", - "=m ysql", - "=my sql", - "Ġ fir", - "Ġf ir", - "Ġfi r", - "Ġrest oration", - "Ġresto ration", - "er eco", - "ere co", - "Ð ¢", - "_ '+", - "_' +", - "Ġe book", - "Ġeb ook", - "Ġde bris", - "Ġdeb ris", - "( inputs", - "(input s", - "(in puts", - "(inp uts", - "AY OUT", - "Ġscre aming", - "Ġscream ing", - "a via", - "av ia", - "avi a", - "l ander", - "land er", - "la nder", - "lan der", - "Ġdist ress", - "Ġdi stress", - "Ġdistr ess", - "Ġas sembled", - "Ġassemble d", - "Ġ Avoid", - "ĠA void", - "ĠAv oid", - "( thread", - "(t hread", - "(th read", - "Ġ RPC", - "ĠR PC", - "ĠRP C", - "_ EXIT", - "_EX IT", - "( queue", - "(q ueue", - "и ÑģÑĤ", - "иÑģ ÑĤ", - "D ll", - "Ġsk ull", - "Ġsku ll", - "_ pub", - "_p ub", - "ch ez", - "che z", - "m inate", - "min ate", - "mina te", - "en sen", - "ens en", - "ense n", - "Ġins ane", - "Ġinsan e", - "b ounds", - "bo unds", - "bound s", - "bou nds", - "ĠR osen", - "ĠRo sen", - "ĠRose n", - "ĠRos en", - "Ġcondition ing", - "process ed", - "proc essed", - "v ideos", - "vid eos", - "video s", - "vide os", - "f our", - "fo ur", - ". Conv", - ".Con v", - ".Co nv", - "| ;Ċ", - "Person al", - "Pers onal", - "Persona l", - "cer pt", - ":UIControlState Normal", - "Ġd oses", - "Ġdo ses", - "Ġdos es", - "Ġdose s", - "ĠK arl", - "ĠKar l", - "ĠKa rl", - "ĠF requ", - "ĠFr equ", - "ĠFre qu", - ". BASE", - ".B ASE", - "Ġ Vote", - "ĠV ote", - "ĠVo te", - "Ġcon current", - "Ġconc urrent", - "ĠMessageBox Icon", - "Ġ Ãĸ", - "Ġà ĸ", - "ĠDu bai", - "ĠDub ai", - "Ġ Retail", - "ĠR etail", - "ĠRe tail", - "ĠRet ail", - ": number", - ":n umber", - ":num ber", - "Ġ Observer", - "ĠOb server", - "ĠObserv er", - "ĠObs erver", - "Ġ BigInteger", - "ĠB igInteger", - "ĠBig Integer", - "ĠBigInt eger", - "_ origin", - "_or igin", - "_orig in", - "_ori gin", - "_ WORK", - "_W ORK", - "F rames", - "Frame s", - "Fr ames", - "Fra mes", - "Ġnot ably", - ". âĢľ", - "Ġt ropical", - "Ġtrop ical", - "Ġn iche", - "Ġni che", - "Ġnic he", - "Ġnich e", - "a mina", - "am ina", - "amin a", - "ami na", - ". sys", - ".s ys", - ".sy s", - "( tokens", - "(t okens", - "(token s", - "(tok ens", - "mod ify", - "o sit", - "os it", - "osi t", - "st rom", - "str om", - "stro m", - "ĠC omics", - "ĠCom ics", - "ĠComic s", - "O PTION", - "OP TION", - "OPT ION", - "T icket", - "Tick et", - "Ti cket", - "Ġf actories", - "Ġfact ories", - "Ġfactor ies", - "Ġfacto ries", - "Ġdis put", - "Ġdisp ut", - "_ File", - "_F ile", - "ĠF inn", - "ĠFin n", - "ĠFi nn", - "e ee", - "ee e", - "ĠDis cord", - "ĠDisc ord", - "ĠDisco rd", - "_ money", - "_m oney", - "_mon ey", - "_mo ney", - ". tpl", - ".t pl", - ".tp l", - "_ safe", - "_s afe", - "_sa fe", - "L B", - "Ġg lut", - "Ġgl ut", - "Ġglu t", - "J K", - ". flow", - ".f low", - ".fl ow", - "- cont", - "-c ont", - "-con t", - "-co nt", - "g os", - "go s", - "Ġhor izon", - "ĠR ush", - "ĠRu sh", - "ĠRus h", - ": :*", - ":: *", - "P ipe", - "Pi pe", - "u lla", - "ul la", - "ull a", - "b orough", - "bo rough", - "bor ough", - "boro ugh", - "he imer", - "heim er", - "hei mer", - "( move", - "(m ove", - "( Text", - "(T ext", - "} );čĊčĊ", - "}) ;čĊčĊ", - "}); čĊčĊ", - "});čĊ čĊ", - "w elcome", - "wel come", - "Ġ Components", - "ĠCom ponents", - "ĠComponent s", - "ĠComp onents", - "Ġgovern ance", - "c losed", - "cl osed", - "close d", - "clo sed", - "ĉ margin", - "ĉm argin", - "Ġla undry", - "Ġ Terminal", - "ĠTerm inal", - "ĠTermin al", - "iz ards", - "izar ds", - "izard s", - ". âĢĶ", - ". remote", - ".rem ote", - ". radius", - ".r adius", - ".rad ius", - "ĠQue bec", - "Ġ dh", - "Ġd h", - "T ech", - "Te ch", - "ĠM ist", - "ĠMi st", - "ĠMis t", - "s eller", - "se ller", - "sel ler", - "sell er", - "_ literal", - "_l iteral", - "_lite ral", - "_lit eral", - "Ġgen ius", - "Ġ brains", - "Ġbr ains", - "Ġbrain s", - "Ġbra ins", - "g em", - "ge m", - "Ġ Measure", - "ĠMe asure", - "Ġcat ast", - "Ġcata st", - "r ance", - "ra nce", - "ran ce", - ". TextField", - ".T extField", - ".Text Field", - "Ġcon suming", - "Ġcons uming", - "Ġconsum ing", - "Ġ'\\ ''", - "Ġ'\\' '", - "oubted ly", - "Ġ Certain", - "ĠC ertain", - "ĠCert ain", - "ĠCer tain", - "E v", - "er ti", - "ert i", - "b eing", - "be ing", - "bei ng", - "Ex perience", - "Ġ //[", - "Ġ// [", - "Ġ/ /[", - "ĠAr abic", - "ĠArab ic", - "ĠAra bic", - "ĠC rist", - "ĠCr ist", - "ĠCri st", - "Ġ Azure", - "ĠA zure", - "ĠAz ure", - "Ġ hora", - "Ġh ora", - "Ġhor a", - "Ġho ra", - "l adesh", - "lad esh", - "\\ Blueprint", - "d ar", - "da r", - ". rel", - ".re l", - ".r el", - "Ġsup rem", - "ĠRe agan", - "Ġ Attributes", - "ĠAt tributes", - "ĠAttribute s", - "- sidebar", - "-s idebar", - "-side bar", - "Ġuse Styles", - "ĠA irlines", - "ĠAir lines", - "Ġh ills", - "Ġhill s", - "Ġhil ls", - "/x html", - "v inc", - "vin c", - "vi nc", - "_ mock", - "_m ock", - "_mo ck", - "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠP ill", - "ĠPi ll", - "ĠPil l", - ".Layout Style", - "ĠComm ander", - "ĠCommand er", - "] <", - "sign ature", - "sig nature", - "Ġ{ }čĊ", - "Ġ{} čĊ", - "Ġhat red", - "Ġ ëĭ", - "Ġë ĭ", - "ole sterol", - "Ġ ********", - "Ġ* *******", - "Ġ** ******", - "Ġ*** *****", - "Ġ**** ****", - "Ġ***** ***", - "ancel lor", - "ancell or", - "c rop", - "cr op", - "cro p", - "T IM", - "TI M", - "ĉ ĉĊĊ", - "ĉĉ ĊĊ", - "ĉĉĊ Ċ", - "ys qli", - "ysql i", - "u itive", - "uit ive", - "ĉ unset", - "ĉun set", - "_ sel", - "_s el", - "_se l", - "Ġ menus", - "Ġm enus", - "Ġmen us", - "Ġmenu s", - "t ick", - "ti ck", - "tic k", - "Ġcon stitute", - "Ġconstit ute", - "Ġconstitu te", - "Ġ Elements", - "ĠE lements", - "ĠEl ements", - "ĠElement s", - "ĠEle ments", - "ĠElem ents", - "Ġ Redis", - "ĠR edis", - "ĠRe dis", - "ĠRed is", - "ag gio", - "agg io", - "aggi o", - "_ fp", - "_f p", - "_ depend", - "_d epend", - "_de pend", - "_dep end", - "e mas", - "em as", - "ema s", - "C AST", - "CA ST", - "CAS T", - "o range", - "or ange", - "ora nge", - "oran ge", - "orang e", - "j on", - "jo n", - "Ġ Emily", - "ĠEm ily", - "ĠEmil y", - "Ġpot atoes", - "Ġpotato es", - "Ġre ceptor", - "Ġrecept or", - "Ġrecep tor", - "Ġ Electronic", - "ĠElect ronic", - "ĠElectro nic", - "ĠElectron ic", - "Ġ Lights", - "ĠL ights", - "ĠLight s", - "Ġcomb ining", - "Ġcombin ing", - "Ġ Someone", - "ĠSome one", - "Ġ######## .", - "ĠT OD", - "ĠTO D", - "/ show", - "/s how", - "/sh ow", - "X d", - ". \"'", - ".\" '", - "a fx", - "af x", - "Ġtr agic", - "Ġtrag ic", - "St yled", - "Style d", - "Ġ Marco", - "ĠMar co", - "ĠMarc o", - "G allery", - "d ale", - "da le", - "dal e", - ".âĢĿ ĊĊĊĊ", - ".âĢĿĊĊ ĊĊ", - ".âĢĿĊ ĊĊĊ", - "é rie", - "ér ie", - "éri e", - "/ service", - "/s ervice", - "äº Ĩ", - "Ġ ambient", - "Ġamb ient", - "_ SETTINGS", - "_SET TINGS", - "_SETTING S", - ". Adapter", - ".Ad apter", - "l ene", - "le ne", - "len e", - "Ġtravel s", - "Ġtrav els", - "Not ice", - "Ġc leans", - "Ġclean s", - "Ġcle ans", - "ĠF em", - "ĠFe m", - "c hair", - "ch air", - "cha ir", - "chai r", - "Ñĥ н", - "/ my", - "/m y", - "_ bad", - "_b ad", - "ĠE conomics", - "ĠEcon omics", - "ĠEconomic s", - "ĠEconom ics", - "I SA", - "IS A", - "_ CNT", - "_C NT", - "_CN T", - "( Menu", - "(M enu", - "(Me nu", - "äº İ", - "ĠR idge", - "ĠRid ge", - "ĠRi dge", - "Ġlength y", - "Ġleng thy", - "D ot", - "Do t", - "Ġj umps", - "Ġjump s", - "Ġju mps", - "Ġ hey", - "Ġh ey", - "Ġhe y", - "$ pdf", - "$p df", - "Ġ worm", - "Ġw orm", - "Ġwor m", - "Ġwo rm", - "Ġ sut", - "Ġs ut", - "Ġsu t", - "Ġ sher", - "Ġs her", - "Ġsh er", - "Ġshe r", - "i amo", - "ia mo", - "iam o", - "Ġ Calc", - "ĠC alc", - "ĠCal c", - "ĠCa lc", - "t rieve", - "tr ieve", - "trie ve", - "tri eve", - "Ġc ops", - "Ġco ps", - "Ġcop s", - "ĠCh rom", - "ĠChr om", - "Ġ regulated", - "Ġreg ulated", - "Ġregul ated", - "Ġregulate d", - "reat ment", - "Ġ Higher", - "ĠHigh er", - "o ks", - "ok s", - "Ġde ze", - "Ġdez e", - "LOC ATION", - "ongs To", - "Ġ finite", - "Ġf inite", - "Ġfin ite", - "Ġfi nite", - "Ġv aries", - "Ġvar ies", - "Ġvari es", - "Ġva ries", - "Ġposition ed", - "Ġposit ioned", - "' il", - "'i l", - "éĩ ij", - "Ġh ike", - "Ġhi ke", - "Ġhik e", - "( done", - "(d one", - "(do ne", - "play list", - "Ġ ada", - "Ġa da", - "Ġad a", - "Ġcoast al", - "ĠN ancy", - "ĠNa ncy", - "ĠNan cy", - ".DateTime Field", - "Cpp CodeGen", - "Ġ Similarly", - "ĠSimilar ly", - "r eur", - "re ur", - "reu r", - "Ġ Contr", - "ĠCon tr", - "ĠCont r", - "Ġ Hidden", - "ĠH idden", - "ĠHi dden", - "Ġ Beta", - "ĠB eta", - "ĠBe ta", - "ĠBet a", - "at ched", - "atch ed", - "_ install", - "_inst all", - ". Output", - ".Out put", - "Look up", - "ĠRich mond", - "qu ared", - "quare d", - "qua red", - "Ġm anga", - "Ġman ga", - "Ġma nga", - "Ġmang a", - "- controls", - "-control s", - "ĠBer nard", - "ĠBern ard", - "L arge", - "Ġs lices", - "Ġsl ices", - "Ġslice s", - "Ġslic es", - "Ġoff ence", - "Ġoffen ce", - "ĠM ega", - "ĠMe ga", - "ĠMeg a", - "Ġ estar", - "Ġe star", - "Ġes tar", - "Ġest ar", - "Ġesta r", - "Ġj oints", - "Ġjoin ts", - "Ġjo ints", - "Ġjoint s", - "Ġ summ", - "Ġs umm", - "Ġsu mm", - "Ġsum m", - "_ platform", - "_pl atform", - "B uff", - "Buf f", - "Bu ff", - ".add Subview", - "Ġret ained", - "Ġretain ed", - "L etter", - "Let ter", - ". dim", - ".d im", - ".di m", - "Ġess ere", - "Ġesse re", - "ĠS caffold", - "EX PECT", - "EXP ECT", - "ĉ RE", - "ĉR E", - ". longitude", - ".long itude", - "ü nd", - "ün d", - "Ġstat ue", - ". addWidget", - ".add Widget", - "ĠCar ibbean", - "add PreferredGap", - "il de", - "ild e", - "UI Label", - "UIL abel", - "ĠOp port", - "ĠOpp ort", - "Ġim perial", - "Ġimp erial", - "Ġimper ial", - "Ġimpe rial", - "urs ion", - "Ġman date", - "Ġmand ate", - "Ġprom otional", - "Ġpromot ional", - "Ġpromotion al", - "Ġ vk", - "Ġv k", - "ia ÅĤ", - "Ġp yl", - "Ġpy l", - "Ġ Creation", - "ĠC reation", - "ĠCre ation", - "ĠCreat ion", - "о зд", - "оз д", - "Ġsim pler", - "Ġsimple r", - "Ġsimp ler", - "Ġsimpl er", - ". what", - ".w hat", - ".wh at", - "Ġ Recent", - "ĠRe cent", - "ĠRec ent", - "ĠRece nt", - "St orm", - ". quantity", - ".qu antity", - ".quant ity", - "Ġ Lov", - "ĠL ov", - "ĠLo v", - "\" -", - "ub bles", - "ubble s", - "ubb les", - "_ notification", - "_not ification", - "( world", - "(w orld", - "ur ger", - "urg er", - "urge r", - "* (-", - "*( -", - ": \"Ċ", - ":\" Ċ", - "h m", - "an ship", - "ans hip", - "Ġ Almost", - "ĠAl most", - "Ġmotor cycle", - "_ fee", - "_f ee", - "_fe e", - "Ġabs orb", - "Ġabsor b", - "ĠVin cent", - "ĠVince nt", - "Ġs ounded", - "Ġso unded", - "Ġsound ed", - "ÃŃ st", - "ÃŃs t", - "Ġpharm aceutical", - "h tag", - "ht ag", - "hta g", - "ĠK indle", - "ĠKind le", - "ĠKin dle", - "ital ize", - "ĠEm peror", - "ous tic", - "oust ic", - "Ġspecial ists", - "Ġspecialist s", - "åħ ¬", - "Border Style", - "/ \\", - "RE LATED", - "REL ATED", - "(' ,',", - "(', ',", - "(',' ,", - "( expr", - "(ex pr", - "(exp r", - "Ġ ht", - "Ġh t", - "åį Ī", - "_ Create", - "_C reate", - "Ġs pecially", - "Ġspec ially", - "Ġspecial ly", - "Ġspeci ally", - "Ġ [];čĊ", - "Ġ[ ];čĊ", - "Ġ[] ;čĊ", - "Ġ[]; čĊ", - "Ġ heel", - "Ġh eel", - "Ġhe el", - "Ġs ept", - "Ġse pt", - "Ġsep t", - "_ arch", - "_a rch", - "_ar ch", - "_arc h", - "( initial", - "(in itial", - "(init ial", - "% .ĊĊ", - "%. ĊĊ", - "%.Ċ Ċ", - "\\\" ,\\\"", - "\\\", \\\"", - "\\\",\\ \"", - "Ġdisc usses", - "Ġdiscuss es", - "Ġ upt", - "Ġu pt", - "Ġup t", - "Ġ[ &", - "Ġm anus", - "Ġman us", - ". hand", - ".h and", - "Ġ MAIN", - "ĠM AIN", - "ĠMA IN", - "ĠDen mark", - "Ġ ],čĊ", - "Ġ] ,čĊ", - "Ġ], čĊ", - "Ġcr yst", - "Ġcry st", - "Ġn ack", - "Ġna ck", - "Co ords", - "Coord s", - "_ inner", - "_in ner", - "Ġmid st", - "Ġmi dst", - "Ġa wake", - "Ġaw ake", - "Ġ Ðŀ", - "ĠÐ ŀ", - "- break", - "-b reak", - "-bre ak", - "ÃŃ vel", - "ÃŃv el", - "_ PASS", - "_P ASS", - "_PA SS", - "Ġ Params", - "ĠPar ams", - "ĠParam s", - "ĠPa rams", - "ĠPara ms", - "Ġd etr", - "Ġde tr", - "Ġdet r", - "Ġsp ider", - "Ġspi der", - "Ġ Concept", - "ĠCon cept", - "ĠConc ept", - "ĠConce pt", - "Ġ prend", - "Ġp rend", - "Ġpr end", - "Ġpre nd", - "CH ED", - "CHE D", - ". Exit", - ".Ex it", - ".E xit", - "Ġpop ulated", - "Ġpopulate d", - "Ġpopul ated", - "Ġvirt ue", - "_ SESSION", - "_SE SSION", - "Ġnou vel", - "Ġnouve l", - "o auth", - "oa uth", - "Ġд аннÑĭ", - "Ġдан нÑĭ", - "r ink", - "ri nk", - "rin k", - ". HeaderText", - ".Header Text", - "atur ated", - "atura ted", - "atu rated", - "Ġe rst", - "Ġer st", - "Ġers t", - "Ġ åħ", - "Ġå ħ", - "ॠĩ", - "_ visible", - "_v isible", - "_vis ible", - "e yer", - "ey er", - "eye r", - "Ġ liable", - "Ġl iable", - "Ġli able", - "Ġlia ble", - "Ġd ebe", - "Ġde be", - "Ġdeb e", - "Ġ bw", - "Ġb w", - "{- #", - "_ WIN", - "_W IN", - "d fs", - "df s", - "H over", - "Ho ver", - "Ġ PUT", - "ĠP UT", - "ĠPU T", - "- angle", - "-a ngle", - "-an gle", - "Ġn oble", - "Ġno ble", - "Ġnob le", - "Ġtr aces", - "Ġtra ces", - "Ġtrace s", - "en cv", - "enc v", - "Ġ userData", - "Ġuser Data", - "_ ins", - "_in s", - "_i ns", - "ĠS uz", - "ĠSu z", - "Ġnews letters", - "Ġnewsletter s", - "ĠM odi", - "ĠMod i", - "ĠMo di", - "Ġentreprene urs", - "Ġentrepreneur s", - "Ġ tribute", - "Ġtrib ute", - "Ġrum ors", - "Ġrumor s", - "Ġ rr", - "Ġr r", - "Ġ Quarter", - "ĠQu arter", - "ĠQuart er", - "ĠQuar ter", - "ê³ ł", - "Ġ feeds", - "Ġfe eds", - "Ġfeed s", - "Ġfee ds", - "ó g", - "Ġen velope", - "Ġenv elope", - "Ġenvelop e", - "Ġ lear", - "Ġl ear", - "Ġle ar", - "Ġk ø", - "de veloper", - "develop er", - "S imilar", - "Sim ilar", - ": \")Ċ", - ":\" )Ċ", - ":\") Ċ", - "sub scription", - "subs cription", - "Mod ifier", - "it alic", - "ital ic", - "ita lic", - "Ġn asty", - "Ġna sty", - "Ġnas ty", - "Ġnast y", - "Ġ termination", - "Ġter mination", - "Ġterm ination", - "Ġtermin ation", - "Ġch arming", - "Ġchar ming", - "Ġcharm ing", - "Ġ âŁ", - "Ġâ Ł", - "t ons", - "ton s", - "to ns", - ". trace", - ".t race", - ".tr ace", - "h ots", - "ho ts", - "hot s", - "Ġ UR", - "ĠU R", - "M ont", - "Mon t", - "Mo nt", - "Ġjust ified", - "ĠG ang", - "ĠGa ng", - "ĠGan g", - "i nea", - "in ea", - "ine a", - "Ġb og", - "Ġbo g", - "( ap", - "(a p", - "_ $", - "Ġcont amin", - "Ġconta min", - ". Dot", - ".D ot", - ".Do t", - "ĉ Debug", - "( exports", - "(ex ports", - "(exp orts", - "Ġ paired", - "Ġp aired", - "Ġpair ed", - "Ġpa ired", - "Ġpai red", - "Ġ Assignment", - "ĠAss ignment", - "ĠAssign ment", - "Ġauto mobile", - "Ġautom obile", - "ĵ į", - "Ġph ases", - "Ġphase s", - "Ġpha ses", - "v w", - "@ SuppressWarnings", - "= \\", - "r ant", - "ra nt", - "ran t", - "- ed", - "-e d", - "ĉ await", - "ĉa wait", - "Ġcert ificates", - "Ġcertificate s", - "Ġcertif icates", - "' >\"", - "'> \"", - "Ġint act", - "C TRL", - "CT RL", - "CTR L", - "M ike", - "Mi ke", - "g regation", - "greg ation", - "AT TERN", - "ATT ERN", - "ATTER N", - "Ġre public", - "Ġrep ublic", - "_ upper", - "_u pper", - "_up per", - "ili ary", - "iliar y", - "ilia ry", - "Ġcom putation", - "Ġcomp utation", - "Ġcomput ation", - "h ire", - "hi re", - "hir e", - "ĠS hin", - "ĠSh in", - "ĠShi n", - "_ ANY", - "_A NY", - "_AN Y", - "Ġ Manufacturer", - "ĠMan ufacturer", - "ĠManufact urer", - "ĠC arm", - "ĠCar m", - "ĠCa rm", - "Ġbear ings", - "Ġbearing s", - "_ comb", - "_c omb", - "_com b", - "_co mb", - "c ad", - "ca d", - "ur istic", - "Ġwh olesale", - "Ġwhole sale", - "Ġwholes ale", - "Ġd onor", - "Ġdo nor", - "Ġdon or", - ". interfaces", - ".inter faces", - ".interface s", - "pr esso", - "press o", - "pres so", - "Ġ Brun", - "ĠB run", - "ĠBr un", - "ĠBru n", - "- close", - "-c lose", - "-cl ose", - "p rove", - "pr ove", - "pro ve", - "prov e", - "_ SK", - "_S K", - "ĉ frame", - "ĉf rame", - "ĉfr ame", - "et ros", - "etro s", - "etr os", - "ĠP ain", - "ĠPa in", - "ĠPai n", - "_ EXP", - "_E XP", - "_EX P", - "Ġ LT", - "ĠL T", - "_ fs", - "_f s", - ". datas", - ".d atas", - ".data s", - ".dat as", - ".da tas", - "ĉ ss", - "ĉs s", - "v oir", - "vo ir", - "Ġ Axis", - "ĠA xis", - "ĠAx is", - "M ajor", - "= \"<", - "=\" <", - "[ h", - "Ġprof ess", - "Ġprofes s", - "ig rate", - "igr ate", - "( score", - "(s core", - "(sc ore", - "Key word", - "\" os", - "ĠĠ ĠĠĉĊ", - "ĠĠĠĠ ĉĊ", - "ĠĠĠ ĠĉĊ", - "ĠĠĠĠĉ Ċ", - "an alysis", - "analy sis", - "anal ysis", - "Ġre play", - "Ġrep lay", - "Ġrepl ay", - ". pass", - ".p ass", - ".pa ss", - "\\ d", - "t ls", - "tl s", - "Ġsan ct", - ". light", - ".l ight", - ".li ght", - "_ mobile", - "_m obile", - "_mob ile", - "Ñģ ÑĤÑĮ", - "ÑģÑĤ ÑĮ", - "ĉ total", - "ĉt otal", - "ĉto tal", - "u ity", - "ui ty", - "uit y", - "Ġ paused", - "Ġpa used", - "Ġpause d", - "Ġpau sed", - "N AS", - "NA S", - "Ġen core", - "Ġenc ore", - "l oe", - "lo e", - "Ġ-* -ĊĊ", - "Ġ-*- ĊĊ", - "Ġ-*-Ċ Ċ", - ". high", - ".h igh", - "am pler", - "amp ler", - "ample r", - "Ġ Secure", - "ĠS ecure", - "ĠSec ure", - "Ġf ragments", - "Ġfra gments", - "Ġfrag ments", - "Ġfragment s", - "_ vel", - "_v el", - "_ve l", - "ill ary", - "illa ry", - "ĠS tein", - "ĠSt ein", - "ĠSte in", - "ĠD awn", - "ĠDa wn", - "ĠDaw n", - "Ġmax imize", - "Ġmaxim ize", - "ภ¢", - "Ġ /^", - "Ġ/ ^", - "Ġcontin ually", - "Ġcontinu ally", - "Ġcontinual ly", - "Ġsh adows", - "Ġshadow s", - "ĉ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĉĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠI ActionResult", - "Ġinform ación", - "C HECK", - "CHE CK", - ". SelectedItem", - ".Selected Item", - "b undle", - "ol ley", - "oll ey", - "olle y", - "< Int", - "<", - "\\\" ><", - "\\\"> <", - "Ġ trajectory", - "Ġtra jectory", - "_ ring", - "_r ing", - "Ġhydro gen", - "Ġhydr ogen", - "t ron", - "tr on", - "tro n", - "Ġstat ute", - "Ġ conditional", - "Ġcondition al", - "Ġcond itional", - "Ġt ray", - "Ġtr ay", - "Ġtra y", - "- school", - "-s chool", - "( widget", - "(w idget", - "$ config", - "$con fig", - "Ġrequest ing", - "Ġrequ esting", - ". uint", - ".ui nt", - ".u int", - "e ton", - "et on", - "eto n", - "br ities", - "brit ies", - "Of Type", - "A DMIN", - "AD MIN", - "ADM IN", - "p redict", - "pre dict", - "pred ict", - "Ġg egen", - "Ġge gen", - "Ġgeg en", - "ĠH app", - "ĠHa pp", - "OC UMENT", - "Ġ Apart", - "ĠA part", - "ĠAp art", - "Ġ -----", - "Ġ- ----", - "Ġ-- ---", - "Ġ---- -", - "Ġ--- --", - "r oe", - "ro e", - "u ide", - "ui de", - "uid e", - "just ify", - "ĠS quad", - "ĠSqu ad", - "Ġprof es", - ". bot", - ".b ot", - ".bo t", - "_ currency", - "_c urrency", - "_curr ency", - "i nnen", - "in nen", - "inn en", - "inne n", - "ĠM umbai", - "ĠMum bai", - "Ġ Numbers", - "ĠNumber s", - "ĠNum bers", - "avana ugh", - "agn itude", - "âĢľ There", - "âĢľThe re", - "= http", - "=h ttp", - "çī ĩ", - "Ġ vb", - "Ġv b", - "+ '{{$", - "\"> {{$", - "\">{{ $", - "\">{ {$", - "Ġ inode", - "Ġin ode", - "Ġi node", - "s il", - "si l", - "Ġh ace", - "Ġha ce", - "Ġhac e", - "Ġsever ely", - "Ġsevere ly", - "Ġ Overview", - "ĠOver view", - "ĠOv erview", - "Ġsp raw", - "Ġspr aw", - "Ġbe aches", - "Ġbeach es", - ": left", - ":l eft", - "· »", - "( ${", - "($ {", - "Ġ FIRST", - "ĠF IRST", - "ĠFIR ST", - "ĠS pa", - "ĠSp a", - "- ass", - "-a ss", - "-as s", - "Ġb aise", - "Ġba ise", - "Ġbais e", - "Ġ NODE", - "ĠN ODE", - "ĠNO DE", - "Ġ Pizza", - "ĠP izza", - "ĠPi zza", - "P et", - "Pe t", - "( seq", - "(s eq", - "(se q", - "\\ \">Ċ", - "\\\" >Ċ", - "\\\"> Ċ", - "CppMethod Pointer", - "Ġ vp", - "Ġv p", - "Ġ ia", - "Ġi a", - "_ seconds", - "_se conds", - "_sec onds", - "_second s", - "e met", - "em et", - "eme t", - "/ blob", - "/b lob", - "/bl ob", - "_TH RESH", - ".. .čĊ", - "... čĊ", - "D est", - "De st", - "Des t", - "Ġ NH", - "ĠN H", - ". dataSource", - ".data Source", - "it és", - "ité s", - "Ġ Jak", - "ĠJ ak", - "ĠJa k", - "s ell", - "se ll", - "sel l", - "Ġwork shops", - "Ġworkshop s", - "< u", - "Ġr ivals", - "Ġrival s", - "Ġri vals", - "Ġriv als", - "ĠEX ISTS", - "h om", - "ho m", - "- token", - "-t oken", - "-to ken", - "com patible", - "compat ible", - ".J Panel", - "Ġphys icians", - "Ġphysician s", - "Ġphysic ians", - "ar tin", - "art in", - "arti n", - "Ġdes irable", - "Ġdistinct ive", - ". Dep", - ".D ep", - ".De p", - "g id", - "gi d", - "il iate", - "ili ate", - "ilia te", - ", max", - ",m ax", - "Ġprem iere", - "Ġpremier e", - "Ġpremi ere", - "Ġq Debug", - "Ġadvoc acy", - "Ġwh isper", - "P t", - "Ġun changed", - "_ qty", - "_q ty", - "请 æ±Ĥ", - "Se ason", - "Sea son", - "ave length", - "avel ength", - "ĠP ul", - "ĠPu l", - "Ġd ÃŃa", - "ĠdÃŃ a", - "'] ]],Ċ", - "']] ],Ċ", - "a lis", - "al is", - "ali s", - "( \"&", - "(\" &", - "b oro", - "bo ro", - "bor o", - "Ġ bm", - "Ġb m", - "Ġ Radi", - "ĠR adi", - "ĠRa di", - "ĠRad i", - "w rong", - "wr ong", - "Ġ Going", - "ĠGo ing", - "ime Type", - "i ji", - "ij i", - "- feedback", - "-fe edback", - "-feed back", - "Ġ Names", - "ĠN ames", - "ĠName s", - "ĠNa mes", - "ĠNam es", - "ĠB apt", - "ĠBa pt", - "Ġpro bable", - "Ġprob able", - "Ġ Ether", - "ĠE ther", - "ĠEth er", - "ĠEt her", - "Ġ Politics", - "ĠPol itics", - "ĠPolit ics", - "_ protocol", - "_prot ocol", - "_proto col", - "l ining", - "li ning", - "lin ing", - "S at", - "Sa t", - "Ġcor rel", - "Ġcorre l", - "Ġcorr el", - ". Primary", - ".Pr imary", - "( nullable", - "(null able", - "RI ORITY", - "Ġcol oring", - "Ġcolor ing", - "Ġutil izing", - "Ġutiliz ing", - "d as", - "da s", - "Ġex ported", - "Ġexp orted", - "Ġexport ed", - "Ġcar riers", - "Ġcarrier s", - "Ġcarr iers", - "Con v", - "Co nv", - ". editor", - ".e ditor", - ".ed itor", - ".edit or", - "i ó", - "( handles", - "(h andles", - "(handle s", - "(hand les", - "Ġapprec iation", - ". import", - ".im port", - ".imp ort", - "ĠA ustria", - "ĠAust ria", - "ĠAustr ia", - "Ġ Strip", - "ĠS trip", - "ĠSt rip", - "ĠStr ip", - "i light", - "il ight", - "ili ght", - "ilig ht", - "Ġappropri ately", - "Ġappropriate ly", - "ĠP rest", - "ĠPr est", - "ĠPres t", - "ĠPre st", - "Ġ Wir", - "ĠW ir", - "ĠWi r", - "Ġ UIApplication", - "ĠUI Application", - "al chemy", - "Ġ Mob", - "ĠM ob", - "ĠMo b", - "Ġ Determin", - "ĠD etermin", - "ĠDe termin", - "ergus on", - "register ed", - "regist ered", - "_ convert", - "_con vert", - "_conv ert", - "ĠVlad imir", - "ĠVladim ir", - ".Show Dialog", - "ref lect", - "Ġs hook", - "Ġsh ook", - "Ġsho ok", - "Ġas sure", - "Ġass ure", - "Ġ Often", - "ĠO ften", - "ĠOf ten", - "Ġcivil ization", - "Ġv ocabulary", - "Ġvocab ulary", - "fore ground", - "Ġ Scope", - "ĠS cope", - "ĠSc ope", - "ĠSco pe", - "Ġun wanted", - "Ġunw anted", - "act ing", - "ac ting", - "Ġ ([]", - "Ġ( []", - "Ġ([ ]", - "Ġm arking", - "Ġmark ing", - "Ġmar king", - ". original", - ".origin al", - ".or iginal", - ".orig inal", - "Ġ MOVE", - "ĠM OVE", - "ĠMO VE", - "ĠMOV E", - "Ġsp orting", - "Ġsport ing", - "Ġspor ting", - "ce ptions", - "ception s", - "cept ions", - "NS Number", - "S izes", - "Size s", - "Si zes", - "Ġpro vincial", - "Ġprovinc ial", - "Ġprovincia l", - "_ Trans", - "_T rans", - "_Tr ans", - "Ġproble matic", - "Ġproblem atic", - "Ġproblema tic", - "Ġprobl ematic", - "d igit", - "di git", - "dig it", - "Ġ Emma", - "ĠE mma", - "ĠEm ma", - "ĠEmm a", - "l ocks", - "lo cks", - "lock s", - "loc ks", - "ĠC rew", - "ĠCr ew", - "ĠCre w", - "i ba", - "ib a", - "' ):", - "') :", - "i sha", - "is ha", - "ish a", - "Ġm amm", - "Ġma mm", - "Ġmam m", - "Ġocc ured", - "Ġoccur ed", - "w cs", - "wc s", - "( rule", - "(r ule", - "Ġmerch andise", - "es pecially", - "ĠT win", - "ĠTw in", - "Ġn aming", - "Ġna ming", - "Ġnam ing", - "Ġs log", - "Ġsl og", - "Ġslo g", - "Ġimpro ves", - "Ġimprove s", - "Ġimpr oves", - "Ġimprov es", - "Ġad her", - ": text", - ":t ext", - ".h adoop", - "_ HTTP", - "_HT TP", - ". toList", - ".to List", - ". disabled", - ".dis abled", - ".disable d", - "Ġl enses", - "Ġlen ses", - "Ġlens es", - ". ini", - ".in i", - ".i ni", - "Ġ Rare", - "ĠR are", - "ĠRa re", - "Ġ Ubuntu", - "ĠUb untu", - "Ġsc ram", - "Ġscr am", - "o lation", - "ol ation", - "ola tion", - "t itulo", - "tit ulo", - "Every thing", - "Ġnod ded", - "icht ig", - "_ constant", - "_con stant", - "_const ant", - "_cons tant", - "z c", - "l ift", - "li ft", - "lif t", - "Ġ Notify", - "ĠN otify", - "ĠNot ify", - "o ndo", - "on do", - "ond o", - "Ġ INF", - "ĠI NF", - "ĠIN F", - "( \"+", - "(\" +", - "ĠK az", - "ĠKa z", - "Ġd read", - "Ġdr ead", - "Ġdre ad", - ". mapper", - ".m apper", - ".map per", - ".ma pper", - "l eur", - "le ur", - "ĠCom ey", - "ĠCo mey", - "ĠCome y", - "Ġ NB", - "ĠN B", - "i cers", - "ic ers", - "ice rs", - "icer s", - ". Push", - ".P ush", - "Ġ Hack", - "ĠH ack", - "ĠHa ck", - "ĠBrazil ian", - "ĠBraz ilian", - "_ prod", - "_p rod", - "_pro d", - "_pr od", - "Ġ //ĊĊ", - "Ġ// ĊĊ", - "Ġ/ /ĊĊ", - "Ġ//Ċ Ċ", - "Ġb icycle", - "Ġbi cycle", - "Ġbicy cle", - "Ġbic ycle", - "Ġun available", - "Ġuna vailable", - "Ġadoles cent", - "b lk", - "bl k", - "Ġmit ig", - "_ blue", - "_b lue", - "_bl ue", - "ì ĺ", - "fade In", - "Ġ Utilities", - "ĠUtil ities", - "ĠUt ilities", - "Ġ MN", - "ĠM N", - "; k", - "< style", - "- status", - "-s tatus", - "-st atus", - "-stat us", - "i ndo", - "in do", - "ind o", - "Ġin nings", - "Ġinn ings", - "Ġinning s", - "Ġg j", - "Ġ| |=", - "Ġ|| =", - ". eu", - ".e u", - ": Number", - ":N umber", - "Ġc uisine", - "Ġcu isine", - "Ġcuis ine", - "ĠURL s", - "i ek", - "ie k", - "Ġw ires", - "Ġwire s", - "Ġwir es", - "Ġwi res", - "ĉ ps", - "ĉp s", - "i eg", - "ie g", - ". mk", - ".m k", - "so ap", - "Ġsome time", - "Ġsom etime", - "Ġs tap", - "Ġst ap", - "Ġsta p", - "_ series", - "_s eries", - "_se ries", - "_ser ies", - ". Target", - ".T arget", - "æ º", - ". destination", - ".d estination", - ".dest ination", - "OUN TER", - "OUNT ER", - "R aises", - "Ra ises", - "Raise s", - "& A", - "Ġsmart phones", - "Ġsmartphone s", - "NI Env", - ". sdk", - ".s dk", - ".sd k", - "Ġhel icopter", - "Ġhelicopt er", - "Ġim pe", - "Ġimp e", - "Ġ Birth", - "ĠB irth", - "ĠBir th", - "A U", - "b readcrumbs", - "breadcrumb s", - "co ords", - "coord s", - "Ġexpl ored", - "Ġexplo red", - "Ġexplore d", - "Ġexplor ed", - "Ġ lod", - "Ġl od", - "Ġlo d", - "Ġ Ip", - "ĠI p", - "g able", - "ga ble", - "i ane", - "ia ne", - "ian e", - "Ġart ifacts", - "Ġartifact s", - "Box Layout", - "ا ر", - "Ø§Ø ±", - "list ener", - "listen er", - "lis tener", - "liste ner", - ". cart", - ".c art", - ".ca rt", - ".car t", - "ĠH uff", - "ĠHu ff", - "ĠHind u", - "ĠHin du", - "ĠData Types", - "ĠDataType s", - "Ġ Drupal", - "ĠDr upal", - "IGN ORE", - "Ġoff sets", - "Ġoffset s", - "Ġoffs ets", - "Ġ RTC", - "ĠR TC", - "ĠRT C", - "- login", - "-lo gin", - "-log in", - "æ ®", - "Ġ QObject", - "ĠQ Object", - "Ġprosec utor", - "R ock", - "Ro ck", - "_ chat", - "_c hat", - "_ch at", - "W ay", - "Wa y", - "ì ²", - "Ġneg lig", - "Ġd ude", - "Ġdu de", - "; <", - "Ġde legates", - "Ġdelegate s", - "Ġdeleg ates", - "_ failed", - "_f ailed", - "_fail ed", - "_fa iled", - "/ dev", - "/d ev", - "/de v", - "/ work", - "/w ork", - "( New", - "(N ew", - "e table", - "et able", - "eta ble", - "( )\"", - "() \"", - "( Icons", - "(I cons", - "Ġp ork", - "Ġpo rk", - "Ġpor k", - "ĠModel AndView", - "Ġ VIP", - "ĠV IP", - "ĠVI P", - "ĠK or", - "ĠKo r", - "m ix", - "mi x", - "Ġ oxid", - "Ġox id", - "Ġ SCREEN", - "ĠS CREEN", - "ĠSC REEN", - "Ġ Fourth", - "ĠFour th", - "/ \",Ċ", - "/\" ,Ċ", - "/\", Ċ", - "Ġ tee", - "Ġt ee", - "Ġte e", - "ĠSte vens", - "ĠSteve ns", - "ĠSteven s", - "t icks", - "ti cks", - "tic ks", - "tick s", - "Ġp ledge", - "Ġpl edge", - "Ġple dge", - "Ġpled ge", - "ib bon", - "Ġ Loan", - "ĠLo an", - "Ġ neo", - "Ġn eo", - "Ġne o", - "n umpy", - "num py", - "Ġ SharedPreferences", - "ĠShared Preferences", - "- oriented", - "ĠLogger Factory", - "Ġ GraphQL", - "ĠGraph QL", - "z enia", - "ze nia", - "zen ia", - "\" _", - "W omen", - "Wo men", - ". cast", - ".c ast", - ".ca st", - "Ġdeliber ately", - "Ġdeliberate ly", - "+ b", - "Ġ Arn", - "ĠA rn", - "ĠAr n", - "font Size", - "Ġ maze", - "Ġm aze", - "Ġma ze", - "Ġmaz e", - "Ġbl amed", - "Ġblame d", - "Ġbla med", - ". mas", - ".m as", - ".ma s", - "} )čĊ", - "}) čĊ", - "eler ik", - "ele rik", - "eleri k", - "Ġsc anning", - "Ġscan ning", - "ĠWork shop", - "ĠWorks hop", - "Ġf inden", - "Ġfind en", - "Ġfin den", - "Ġfinde n", - "Ġc aut", - "Ġca ut", - "UI Font", - "( return", - "(r eturn", - "(re turn", - "(ret urn", - "a lin", - "al in", - "ali n", - "c astle", - "cast le", - "cas tle", - "//// ////////////////////////////////////////////////////////////////////", - "//////// ////////////////////////////////////////////////////////////////", - "//////////////// ////////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////// ////////", - "//////////// ////////////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////////// ////", - "//////////////////////////////////////////////////////// ////////////////", - "//////////////////////////////////////////////////////////// ////////////", - "Ġincent ive", - "Ġincentiv e", - "o path", - "op ath", - "opa th", - "b lob", - "bl ob", - "blo b", - "Ġcigaret te", - "Ġcigar ette", - "Ġf ertil", - "Ġfer til", - "Ġfert il", - "* /ĊĊĊ", - "*/ ĊĊĊ", - "*/Ċ ĊĊ", - "*/ĊĊ Ċ", - "Ġ Shar", - "ĠS har", - "ĠSh ar", - "ĠSha r", - "Ċ ĠĠĠĠĠĠĊ", - "Ġunc ertain", - "Ġuncert ain", - "ĠS ton", - "ĠSt on", - "ĠSto n", - "Oper ations", - "Operation s", - "ĠSp encer", - "Ġde fin", - "Ġdef in", - "Ġ Solo", - "ĠS olo", - "ĠSo lo", - "ĠSol o", - "o nest", - "on est", - "one st", - "ones t", - "·» åĬł", - "Ġu omo", - "Ġuom o", - "G ive", - "Gi ve", - "Ġden tro", - "Ġdent ro", - "; padding", - ";p adding", - "ent ai", - "enta i", - "Ġ Cars", - "ĠC ars", - "ĠCar s", - "ĠCa rs", - "Ġenthus iasm", - "Ġenthusi asm", - "Ġ Operating", - "ĠOper ating", - "ĠOpera ting", - "S kip", - "Sk ip", - "par ation", - "pa ration", - "para tion", - "Ġprot ects", - "Ġprote cts", - "Ġprotect s", - "Ġre ver", - "Ġr ever", - "Ġrev er", - "Ġreve r", - "d g", - "ĠC incinnati", - "Ġconsect etur", - "Ġm uss", - "Ġmus s", - "Ġmu ss", - "employ ed", - "a uses", - "au ses", - "ause s", - "aus es", - "in kle", - "ink le", - ". Values", - ".Value s", - ".Val ues", - "£ ¼", - "l ov", - "lo v", - "_ WARN", - "_W ARN", - "Ġ bookmark", - "Ġbook mark", - "Ġ Apollo", - "ĠA pollo", - "ĠAp ollo", - ". axis", - ".a xis", - ".ax is", - "Ġm ét", - "Ġmé t", - "Ġop ener", - "Ġopen er", - "Ġt umor", - "Ġtu mor", - "Ġtum or", - "d an", - "da n", - "Ġelement ary", - "Ġsk ipped", - "Ġskip ped", - "Ġski pped", - "ĠK er", - "ĠKe r", - "as ia", - "asi a", - "_ resp", - "_re sp", - "_r esp", - "_res p", - "Ġde mol", - "Ġdem ol", - "Ġdemo l", - "ĠCan adians", - "ĠCanadian s", - "Ġt astes", - "Ġtaste s", - "Ġtas tes", - "Ġtast es", - "U Integer", - "UInt eger", - "Ġ' ${", - "Ġ'$ {", - ". aws", - ".a ws", - ".aw s", - "R OID", - "RO ID", - "ROI D", - "r ians", - "ri ans", - "ria ns", - "rian s", - "M Q", - "ord able", - "orda ble", - "Ġcou sin", - "Ġcous in", - "Prop agation", - "( Session", - "(S ession", - "p halt", - "ph alt", - "pha lt", - "U LD", - "UL D", - "Ġ Scalar", - "ĠS calar", - "ĠSc alar", - "ĠScala r", - "ĠScal ar", - "Ġblood y", - "Ġblo ody", - "Ġ à¦", - "Ġà ¦", - ". mask", - ".m ask", - ".mas k", - ".ma sk", - ", q", - "Ġ Units", - "ĠUn its", - "ĠUnit s", - "ĠUni ts", - "Ġcent res", - "Ġcentre s", - "Ġcentr es", - "Ġcen tres", - "Ġ Prim", - "ĠP rim", - "ĠPr im", - "ĠPri m", - ". ]ĊĊ", - ".] ĊĊ", - "ĠSh aw", - "ĠSha w", - "P rom", - "Pro m", - "Pr om", - "Ġ Thought", - "ĠTh ought", - "ĠThough t", - "ĠThou ght", - "Check er", - "Che cker", - "_ outputs", - "_out puts", - "_output s", - "( chan", - "(c han", - "(ch an", - "E INVAL", - "Ġ bob", - "Ġb ob", - "Ġbo b", - "_ cmp", - "_c mp", - "_cm p", - "P ed", - "Pe d", - "Ġmat rices", - "Ġvrou wen", - "Ġvrouw en", - "Ġgenu inely", - "Ġgenuine ly", - "high light", - "( display", - "(d isplay", - "(dis play", - ") !=", - ")! =", - "Ġde licate", - "Ġdel icate", - "Ġdelic ate", - "ĠL uther", - "ĠLu ther", - "ĠM iles", - "ĠMil es", - "ĠMi les", - "ĠMile s", - "Ġ userID", - "Ġuser ID", - "% =", - "at eurs", - "ate urs", - "ateur s", - "_ BUF", - "_B UF", - "_BU F", - "- ------Ċ", - "-- -----Ċ", - "---- ---Ċ", - "--- ----Ċ", - "----- --Ċ", - "------ -Ċ", - "------- Ċ", - "im itives", - "imit ives", - "imitive s", - "Ġsh elves", - "Ġshel ves", - "s low", - "sl ow", - "_ information", - "_in formation", - "L EG", - "LE G", - "W r", - ". forms", - ".for ms", - ".form s", - "c eland", - "ce land", - "cel and", - "cela nd", - "/ un", - "/u n", - ": &", - ". âĢĻĊĊ", - ".âĢĻ ĊĊ", - "= \"%", - "=\" %", - "Ġp rost", - "Ġpro st", - "Ġpr ost", - "Ġpros t", - "Ġ fontsize", - "Ġfont size", - "Ġfonts ize", - "u ción", - "uc ión", - "uci ón", - "g etic", - "get ic", - "ge tic", - "a mt", - "am t", - "= \".", - "=\" .", - "De cor", - "Dec or", - "B rit", - "Br it", - "Ġ\" \").", - "Ġ\"\" ).", - "Ġ\"\") .", - "Ġf ounding", - "Ġfound ing", - "Ġfo unding", - ". FileName", - ".File Name", - "Ġ Tier", - "ĠT ier", - "ĠTi er", - "ĠTie r", - "Ġdis close", - "Ġdisc lose", - "á m", - ". syn", - ".s yn", - ".sy n", - ". ViewHolder", - ".View Holder", - "lic ant", - "li cant", - "lica nt", - "_ stage", - "_st age", - "_sta ge", - "Mon day", - "Ġ deserialize", - "Ġde serialize", - "Ġdes erialize", - "t alk", - "ta lk", - "tal k", - "Ġtrad itionally", - "Ġtraditional ly", - "Ġtradition ally", - "æĢ ģ", - "Ø ®", - "L EX", - "LE X", - "Ġ eh", - "Ġe h", - "ĉ ROM", - "ĉR OM", - "Ġ {})Ċ", - "Ġ{ })Ċ", - "Ġ{} )Ċ", - "Ġ{}) Ċ", - "Question s", - "Quest ions", - "n cpy", - "nc py", - "Ġfix ing", - "Ġfi xing", - "к Ñĥ", - "_ Key", - "_K ey", - ": x", - "Ġ STRING", - "ĠST RING", - "ĠSTR ING", - "ĠÑĦ ай", - "ĉ left", - "ĉl eft", - "ĠB ench", - "ĠBen ch", - "el lij", - "ell ij", - "elli j", - "UR RED", - "URRE D", - "Ġ Diagram", - "ĠDi agram", - "ĠDia gram", - "} catch", - "/ time", - "/t ime", - "Ġ Missing", - "ĠM issing", - "ĠMiss ing", - "ĠMis sing", - "db name", - "Ġs ore", - "Ġso re", - "Ġsor e", - "ĠW alt", - "ĠWal t", - "ĠWa lt", - "ug ging", - "ugg ing", - "re present", - "rep resent", - "Ġ GS", - "ĠG S", - "ne ys", - "ney s", - "ĉ page", - "ĉp age", - "Ġvol can", - "( btn", - "(b tn", - "(bt n", - "Ġexceed s", - "Ġexce eds", - "Ġ erg", - "Ġe rg", - "Ġer g", - "Ġpi lots", - "Ġpil ots", - "Ġpilot s", - "ĠS ed", - "ĠSe d", - "ers ions", - "ersion s", - "Ġp atron", - "Ġpat ron", - "Ġpa tron", - "R V", - "/ top", - "/t op", - "/to p", - ". asset", - ".as set", - "_ cross", - "_c ross", - "_cr oss", - ". Editor", - ".E ditor", - ".Edit or", - ".Ed itor", - ". tb", - ".t b", - "Ġwel coming", - "S CREEN", - "SC REEN", - ") findViewById", - "C oder", - "Code r", - "Co der", - "Cod er", - " \",Ċ", - ">\" ,Ċ", - ">\", Ċ", - "_ Pin", - "_P in", - "u ese", - "ue se", - "ues e", - "Ġ overrides", - "Ġover rides", - "Ġoverride s", - "_ ready", - "_re ady", - "_read y", - "Ad vanced", - "Adv anced", - "Advance d", - "Ġ opi", - "Ġo pi", - "Ġop i", - "- cart", - "-c art", - "-car t", - "-ca rt", - "(\" /\",", - "(\"/ \",", - "ĠD eb", - "ĠDe b", - "C RY", - "CR Y", - "Ġ Vertical", - "ĠVer tical", - "ĠVert ical", - "Ġ OVER", - "ĠO VER", - "ĠOV ER", - "Ġ Corporate", - "ĠCor porate", - "ĠCorpor ate", - "ĠCorp orate", - "Ġ\" \";", - "Ġ\"\" ;", - "Ġstep ping", - "Ġste pping", - "e j", - "Ġaccus ations", - "Ġaccusation s", - "Ġo raz", - "Ġor az", - "Ġora z", - "_ tail", - "_t ail", - "_ta il", - "Ġin duced", - "Ġind uced", - "Ġindu ced", - "Ġinduce d", - "Ġ elastic", - "Ġe lastic", - "Ġel astic", - "Ġelast ic", - "Ġbl own", - "Ġblow n", - "Ġblo wn", - ", //", - ",/ /", - "Ġback grounds", - "Ġbackground s", - "âĢĻ une", - "âĢĻun e", - "- sdk", - "-s dk", - "Ġset Interval", - "Ġincent ives", - "Ġincentive s", - "Ġincentiv es", - "Ġveget able", - "Ġveg etable", - "_ On", - "_O n", - "exp anded", - "expand ed", - "p ix", - "pi x", - "_ shader", - "_sh ader", - "_sha der", - "ĠSP DX", - "ĠSPD X", - "@ example", - "Ġ Wrapper", - "ĠW rapper", - "ĠWr apper", - "ĠWrap per", - ". Zero", - ".Z ero", - "Pos itive", - "Ġ spinner", - "Ġsp inner", - "Ġspin ner", - "Ġin vented", - "Ġinv ented", - "Ġinvent ed", - "ĠG ates", - "ĠGa tes", - "ĠGate s", - "ĠGat es", - "о ÑĤоÑĢ", - "оÑĤ оÑĢ", - "оÑĤо ÑĢ", - "Ġcompar isons", - "Ġcomparison s", - "è ·", - ". primary", - ".pr imary", - "data Provider", - "add itional", - "ĉ options", - "ĉo ptions", - "ĉopt ions", - "ĉoption s", - "s napshot", - "snap shot", - ".set Horizontal", - "Ġ\" {}", - "Ġ\"{ }", - "ĠF isher", - "ĠFish er", - "ĠFi sher", - "h alten", - "hal ten", - "halt en", - "< Type", - "", - "Ġ) ->", - "Ġ Registered", - "ĠRegister ed", - "IN ED", - "INE D", - "k al", - "ka l", - "par ison", - "Ġobj eto", - "Ġobjet o", - "V i", - "m anda", - "man da", - "ma nda", - "mand a", - "Ġren ewed", - "Ġrenew ed", - "ĠS of", - "ĠSo f", - "e ssel", - "es sel", - "ess el", - "esse l", - ".nd array", - "Ġ crap", - "Ġc rap", - "Ġcr ap", - "Ġcra p", - "ç® ¡", - ".abs path", - ".ab spath", - "( up", - "(u p", - "Ġclear ance", - "Ġ TW", - "ĠT W", - "_ COPY", - "_C OPY", - "_CO PY", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĉ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠ Ġĉ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĉ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĉ", - "Ġfor ests", - "Ġfore sts", - "Ġforest s", - "Ġfores ts", - "Ġarg uably", - "Ġ ASS", - "ĠA SS", - "ĠAS S", - "h ey", - "he y", - "a mel", - "am el", - "ame l", - "_ fore", - "_f ore", - "_for e", - "ĠSouth east", - "ĠSou theast", - "Ġab used", - "Ġabuse d", - "Ġpract icing", - "ake dirs", - "aked irs", - "ä¸ »", - "_ resources", - "_re sources", - "_res ources", - "_resource s", - "Ġ pond", - "Ġp ond", - "Ġpo nd", - "Ġpon d", - ". Fixed", - ".F ixed", - "Last Error", - "ĠPsych ology", - "ĠPsycho logy", - "Ġ\" //", - "Ġ\"/ /", - "! :", - "Re usable", - "Ġ mensaje", - "Ġm ensaje", - "Ġmens aje", - "Ġro spy", - "Ġros py", - "Ġ bour", - "Ġb our", - "Ġbo ur", - "Ġbou r", - "Ġvar ieties", - "Ġvari eties", - "Ġem path", - "Ġemp ath", - "( ({", - "(( {", - "_ org", - "_or g", - "_o rg", - "Ġ Mes", - "ĠM es", - "ĠMe s", - "Ġ Magento", - "ĠM agento", - "ĠMag ento", - "IST ORY", - "Un less", - "Ġh j", - "ĠD uty", - "ĠDu ty", - "ĠDut y", - "J un", - "Ju n", - ", size", - ",s ize", - "Ġpaint ings", - "Ġpain tings", - "Ġpainting s", - "Ġd ispens", - "Ġdisp ens", - "d art", - "da rt", - "dar t", - "Ġbehavior al", - "Ġ rpc", - "Ġr pc", - "Ġrp c", - "c alculate", - "cal culate", - "calc ulate", - "calcul ate", - "f ruit", - "fr uit", - "_ mm", - "_m m", - "ĉ pthread", - "ĉp thread", - "ĉpt hread", - "Max Length", - "Ġc urrencies", - "Ġcurr encies", - "_ capacity", - "_cap acity", - "ĠO z", - "Ġfire arm", - "Ġco efficient", - "Ġcoeff icient", - "Ġbank ruptcy", - "Ġbankrupt cy", - "w art", - "wa rt", - "war t", - "Ġfat igue", - "A VA", - "AV A", - "Ġe spa", - "Ġes pa", - "Ġesp a", - "_ pc", - "_p c", - "Ġ Quotes", - "ĠQu otes", - "ĠQuote s", - "_ LIGHT", - "_L IGHT", - "Ġ Tickets", - "ĠT ickets", - "ĠTicket s", - "ĠTick ets", - "Ġre lates", - "Ġrel ates", - "Ġrelate s", - "Ġrelat es", - "Ġpublish ers", - "Ġpublisher s", - "Ġun locked", - "Ġunlock ed", - "Ġunl ocked", - "Ġ //----------------------------------------------------------------", - "Ġ// ----------------------------------------------------------------", - "Ġ//------------------------------------------------ ----------------", - "Ġ//-------------------------------- --------------------------------", - "Ġ//---------------- ------------------------------------------------", - "Ġ InterruptedException", - "ĠInterrupt edException", - "Ġout look", - "r n", - "Ġreb els", - "Ġrebel s", - "W ritten", - "Wr itten", - "Ġa sian", - "Ġas ian", - "Ġasi an", - "Ġasia n", - "ot to", - "ott o", - "Ġ ĉĉĉĉ", - "Ġĉ ĉĉĉ", - "Ġĉĉ ĉĉ", - "Ġĉĉĉ ĉ", - "_ gpu", - "_g pu", - "_gp u", - "T xt", - "Tx t", - ". ImageView", - ".Image View", - "Ġs uis", - "Ġsu is", - "Ġsui s", - "_ tables", - "_t ables", - "_table s", - "_tab les", - "_ta bles", - ". RecyclerView", - ".Rec yclerView", - "Ġwhat soever", - "è ģ", - "] ++;Ċ", - "assert True", - "_ verify", - "_ver ify", - "ĠR ivers", - "ĠRiver s", - "ĠRiv ers", - "ĠRi vers", - "Ġ ][", - "Ġ] [", - "J et", - "Je t", - "id ian", - "idi an", - "idia n", - "S ibling", - "Si bling", - "Ġ genres", - "Ġgen res", - "Ġgenre s", - ". Access", - ".A ccess", - ".Ac cess", - ".Acc ess", - "O PS", - "OP S", - "Ġtr ivial", - "Ġtrivia l", - "ภª", - "a len", - "al en", - "ale n", - "в ед", - "ве д", - "ĠS word", - "ĠSw ord", - "Ġscrut iny", - "Ġscrutin y", - "( cb", - "(c b", - "Ġ commerce", - "Ġcom merce", - "Ġcomm erce", - "Ġcommerc e", - "Ġguarante es", - "Ġguarantee s", - "_ adv", - "_a dv", - "_ad v", - "Ġ LET", - "ĠL ET", - "ĠLE T", - "re cio", - "rec io", - "Ġh ilar", - "Ġhi lar", - "Ġhil ar", - "Ġback yard", - "ãĢ ı", - "Ġillustr ated", - "Ġillustrate d", - "Ġillust rated", - "/ vendor", - "/v endor", - ". Util", - ".U til", - "Ġ wow", - "Ġw ow", - "Ġwo w", - "LO Y", - "Ġ Marshal", - "ĠM arshal", - "ĠMar shal", - "ĠMars hal", - "ĠMarsh al", - "\" >'.$", - "\"> '.$", - "\">' .$", - "\">'. $", - "ĠB ak", - "ĠBa k", - "Ġ modifiers", - "Ġmod ifiers", - "Ġmodifier s", - "d ictionary", - "ĠS tre", - "ĠSt re", - "ĠStr e", - "m ultiple", - "mult iple", - "multi ple", - "multip le", - "\" )),", - "\") ),", - "\")) ,", - "ĠC ort", - "ĠCo rt", - "ĠCor t", - "' ]\").", - "'] \").", - "( admin", - "(ad min", - "Ġ Creator", - "ĠC reator", - "ĠCre ator", - "ĠCreat or", - "In ternet", - "Int ernet", - "Inter net", - "Intern et", - "( ms", - "(m s", - "l ogy", - "lo gy", - "log y", - "DECL ARE", - "Ġ Marcus", - "ĠMar cus", - "ĠMarc us", - "< <<<", - "<< <<", - "<<< <", - "ãģ ł", - "_ my", - "_m y", - "( inst", - "(i nst", - "(in st", - "(ins t", - "Ġsc iences", - "Ġscience s", - "Ġsci ences", - "N DER", - "ND ER", - ". enter", - ".en ter", - ".ent er", - "Ġ itu", - "Ġit u", - "Ġi tu", - "Ġbe have", - "Ġbeh ave", - "P an", - "Pa n", - "om bies", - "omb ies", - "ombie s", - "= '<", - "=' <", - "' ));čĊ", - "') );čĊ", - "')) ;čĊ", - "')); čĊ", - "Ġ MENU", - "ĠM ENU", - "ĠME NU", - "ĠMEN U", - "Ġ Workers", - "ĠWork ers", - "ĠWorker s", - "ĠWor kers", - ".No Error", - "Ġ bindings", - "Ġbin dings", - "Ġbind ings", - "Ġbinding s", - "Ġdis abilities", - "{ \\", - "ĠM unicip", - "ĠMun icip", - "Ġ cores", - "Ġc ores", - "Ġco res", - "Ġcor es", - "Ġcore s", - "ur ple", - "ĠN okia", - "us ions", - "usion s", - "usi ons", - "Ġ Fitness", - "ĠF itness", - "ĠFit ness", - ". handleChange", - ".handle Change", - "Ġ javascript", - "Ġj avascript", - "Ġjav ascript", - "Ġjava script", - "ìļ Ķ", - "( dec", - "(d ec", - "(de c", - "Ġ packing", - "Ġp acking", - "Ġpack ing", - "Ġpac king", - "- depend", - "-d epend", - "-de pend", - "Ġtrans cript", - "Ġtran script", - "z eros", - "ze ros", - "zer os", - "zero s", - "_ alert", - "_al ert", - "? \",Ċ", - "?\" ,Ċ", - "?\", Ċ", - "l ibs", - "li bs", - "lib s", - "± оÑĤ", - "Ġ |ĊĊ", - "Ġ| ĊĊ", - "Ġ|Ċ Ċ", - "tr ained", - "tra ined", - "train ed", - "ĠG ent", - "ĠGe nt", - "ĠGen t", - "ĠR ab", - "ĠRa b", - "x p", - "_ configuration", - "_config uration", - "å¤ ©", - "_ accept", - "_ac cept", - "_acc ept", - ".rec yclerview", - ": url", - "ĠMu hammad", - "ĠMuham mad", - "Ġpriv ileges", - "Ġprivile ges", - "Ġprivilege s", - "_ bank", - "_b ank", - "u ku", - "uk u", - "w allet", - "wall et", - "wal let", - "Ġ ROOT", - "ĠR OOT", - "ĠRO OT", - "Ġenc uent", - "? family", - "?f amily", - "ĉ position", - "ĉp osition", - "ĉpos ition", - "Ġ cg", - "Ġc g", - "Ġpre cip", - "Ġprec ip", - "method s", - "meth ods", - "_ fast", - "_f ast", - "_fa st", - "in crement", - "inc rement", - "incre ment", - "incr ement", - "ĠT iger", - "ĠTi ger", - "ĠTig er", - "_OCC URRED", - "qu ip", - "qui p", - "Ġ HAS", - "ĠH AS", - "ĠHA S", - "_ dom", - "_d om", - "_do m", - "Ġw reck", - "Ġwr eck", - "Ġwre ck", - "b j", - "Ġd ern", - "Ġde rn", - "Ġder n", - "Ġorg ans", - "Ġorgan s", - ". entries", - ".en tries", - ".ent ries", - "Ġ _('", - "Ġ_ ('", - "Ġ_( '", - "r amento", - "ram ento", - "Ġ Jamie", - "ĠJam ie", - "ĠJa mie", - "Ġ punk", - "Ġp unk", - "Ġpun k", - "Ġpu nk", - "I PP", - "IP P", - "Ġprogram a", - "Ġprog rama", - "Ġat tain", - "Ġatt ain", - "Ġpro ves", - "Ġpr oves", - "Ġprov es", - "Ġprove s", - "/ sign", - "/s ign", - "Ġanswer ing", - "Ġl adder", - "Ġlad der", - "**** ************************", - "******** ********************", - "**************** ************", - "************************ ****", - "******************** ********", - "************ ****************", - "************** **************", - "ĠW almart", - "ĠWal mart", - "Ġ CONTENT", - "ĠCONT ENT", - "d uctor", - "du ctor", - "duct or", - "duc tor", - "Ġver bal", - "Ġverb al", - "Ġ PID", - "ĠP ID", - "ĠPI D", - "c rypto", - "crypt o", - "cry pto", - "_CALL BACK", - "Ġ= ================================", - "Ġ================= ================", - "Ġp otent", - "Ġpo tent", - "Ġpot ent", - "Ġsh orts", - "Ġshort s", - "Ġsho rts", - ". Uri", - ".U ri", - ". uniform", - ".un iform", - ".uni form", - "; border", - ";b order", - "Ġ Wer", - "ĠW er", - "ĠWe r", - "Ġher ein", - "Ġhere in", - "l la", - "ll a", - "ĠI hr", - "ĠIh r", - "P ixmap", - "Pix map", - "l iteral", - "lit eral", - "lite ral", - "liter al", - "! )ĊĊ", - "!) ĊĊ", - "!)Ċ Ċ", - "g eneric", - "gen eric", - "gener ic", - "gene ric", - "r ust", - "ru st", - "rus t", - "_ scripts", - "_s cripts", - "_script s", - "o sto", - "os to", - "ost o", - "it us", - "itu s", - "ĠCoal ition", - "Ġre mot", - "Ġrem ot", - "de ploy", - "dep loy", - "ĠE agle", - "ĠEag le", - "ĠEa gle", - "ãĢģ ãĢĮ", - "Ġimport ante", - "Ġimportant e", - "ĉ object", - "ĉo bject", - "ĉobj ect", - "ĉob ject", - "Ġseason al", - "Ġseas onal", - "n ej", - "ne j", - "ai du", - "aid u", - "Bind View", - "ĠSi erra", - "- bg", - "-b g", - "Ġmake Styles", - "[ offset", - "[o ffset", - "G ames", - "Game s", - "Gam es", - "Ga mes", - "Ġhorm one", - "AR IO", - "ARI O", - "he ads", - "head s", - "hea ds", - "( select", - "(s elect", - "(se lect", - "(sel ect", - "Ġ Started", - "ĠStart ed", - "ĠStar ted", - "@ param", - "_ decl", - "_de cl", - "_dec l", - "_ blog", - "_b log", - "_bl og", - "Ġa ño", - "Ġañ o", - "\\ Api", - "ĠMil waukee", - "Pro vid", - "Pr ovid", - "Prov id", - "An imated", - "Anim ated", - "Animate d", - "Ġco oler", - "Ġcool er", - "Ġ Seed", - "ĠS eed", - "ĠSe ed", - "ĠSee d", - ". Edit", - ".E dit", - ".Ed it", - "Ï Ħ", - "Ġ Taking", - "ĠT aking", - "ĠTa king", - "ĠTak ing", - "Ġborder Color", - "-f ounder", - "-found er", - ".Logger Factory", - "Ġ\" \"ĊĊ", - "Ġ\"\" ĊĊ", - "Ġ\"\"Ċ Ċ", - "A LT", - "AL T", - "Ġ Late", - "ĠL ate", - "ĠLa te", - "ĠLat e", - "EDI ATE", - "EDIA TE", - "Ġ );ĊĊĊ", - "Ġ) ;ĊĊĊ", - "Ġ);Ċ ĊĊ", - "Ġ);ĊĊ Ċ", - "Ġ); ĊĊĊ", - "a fa", - "af a", - "Ġc ancellation", - "Ġcancel lation", - "Ġcancell ation", - "Ġcanc ellation", - "A tom", - "At om", - "ĠB irmingham", - "emp resa", - "empre sa", - "H EMA", - "HE MA", - "a scal", - "as cal", - "asc al", - "asca l", - "Ġup side", - "Ġups ide", - ". Version", - ".V ersion", - "Ġ Folder", - "ĠF older", - "ĠFo lder", - "ĠFol der", - "ĠFold er", - "Ġ Eight", - "ĠE ight", - "ĠEig ht", - "Ġ Vintage", - "ĠV intage", - "Ġ AppDelegate", - "ĠApp Delegate", - "ĠPre vention", - "ĠPrevent ion", - "ĠPrev ention", - ". separator", - ".s eparator", - ".se parator", - "S TM", - "ST M", - "( room", - "(r oom", - "(ro om", - "g enerator", - "gen erator", - "gener ator", - "Ġc attle", - "Ġcat tle", - "ĉ Z", - "Ġ Particle", - "ĠP article", - "ĠPart icle", - "ĠParti cle", - "' };Ċ", - "'} ;Ċ", - "Ġne ighbours", - "Ġneighb ours", - "Ġneighbour s", - "ĠState less", - "ĠStat eless", - "Ġ altitude", - "Ġalt itude", - "Ġs aint", - "Ġsa int", - "Ġsai nt", - "о бав", - "об ав", - "оба в", - "Ġcon vinc", - "Ġconv inc", - "Ġconvin c", - "Ġ Contents", - "ĠCont ents", - "ĠContent s", - "ĠConte nts", - "Ġje une", - "Ġjeu ne", - "( ts", - "(t s", - "S erialization", - "Serial ization", - "( collection", - "(c ollection", - "(col lection", - "(coll ection", - "ĠJ azz", - "ĠJa zz", - "Ġ Dod", - "ĠD od", - "ĠDo d", - "ĠR och", - "ĠRo ch", - "ĠRoc h", - "a cio", - "ac io", - "aci o", - "comm ended", - "commend ed", - "DE FINE", - "DEF INE", - ".on load", - "Ġspecial ty", - "Ġspeci alty", - "PL ACE", - "_ MOVE", - "_M OVE", - "_MO VE", - "_MOV E", - "Ġaccount able", - "Re uters", - "Ġf icken", - "Ġfi cken", - "Ġfic ken", - "Ġfick en", - "Ġde pr", - "Ġdep r", - "W ow", - "Wo w", - "V oid", - "Vo id", - ". space", - ".s pace", - ".sp ace", - "ภĹ", - "Ġt q", - "Ġ Pets", - "ĠP ets", - "ĠPe ts", - "ĠPet s", - "< $", - "( Current", - "(C urrent", - "ber ries", - "plan ation", - "pla nation", - "Ġlist Of", - "Ġ Thu", - "ĠT hu", - "ĠTh u", - "Ġ PRINT", - "ĠPR INT", - "ĠPRI NT", - "Ġm ismo", - "Ġmis mo", - "Ġmism o", - "Ġ doi", - "Ġd oi", - "Ġdo i", - "c hk", - "ch k", - "Ġ Unicode", - "ĠUn icode", - "ĠUni code", - "( role", - "(r ole", - "(ro le", - "Ġvir gin", - "< Point", - "

-->Ċ", - ">- ->Ċ", - ">-- >Ċ", - "V ol", - "Vo l", - "ĠS SD", - "ĠSS D", - ") )),", - ")) ),", - "))) ,", - ". Optional", - ".Option al", - "Ġn urses", - "Ġnur ses", - "Ġnurs es", - "Ġnurse s", - "Ġ orb", - "Ġo rb", - "Ġor b", - "_ pe", - "_p e", - ") ;čĊčĊčĊ", - ");čĊ čĊčĊ", - "); čĊčĊčĊ", - ");čĊčĊ čĊ", - "pl aced", - "place d", - "pla ced", - "es ser", - "ess er", - "esse r", - "Ġther apeutic", - "Ġwh itespace", - "Ġwhite space", - "Ġwhites pace", - "Ġ aston", - "Ġa ston", - "Ġas ton", - "Ġast on", - "Success ful", - "Ġp raised", - "Ġpr aised", - "Ġpraise d", - "Ġpra ised", - "ĠW es", - "ĠWe s", - "Ġe ighth", - "Ġeight h", - "i ral", - "ir al", - "ira l", - "Ġv rouw", - "Ġvrou w", - "Ġ faction", - "Ġf action", - "Ġfact ion", - "Ġfa ction", - "Ġfac tion", - "_ bias", - "_b ias", - "_bi as", - "Ġ witch", - "Ġw itch", - "Ġwit ch", - "Ġ npc", - "Ġn pc", - "Ġnp c", - "( sb", - "(s b", - "ĠRod rig", - "ĠRodr ig", - "_ big", - "_b ig", - "_bi g", - "D ependency", - "Dep endency", - "ĠAb raham", - "ar di", - "ard i", - "C AR", - "CA R", - "n os", - "no s", - "Ġab undance", - "Ġabund ance", - "Ġnut rients", - "Ġnutrient s", - "in stein", - "ins tein", - "inst ein", - ". Vert", - ".V ert", - ".Ver t", - "Ġ ISS", - "ĠI SS", - "ĠIS S", - "< U", - "Ġs ums", - "Ġsu ms", - "Ġsum s", - "_ hist", - "_h ist", - "_hi st", - "Ġfar mer", - "Ġfarm er", - "Ġ Abr", - "ĠA br", - "ĠAb r", - "S hot", - "Sh ot", - "Ġ BadRequest", - "ĠBad Request", - "Ġh ass", - "Ġhas s", - "Ġha ss", - "Ġ Rails", - "ĠR ails", - "ĠRa ils", - "ĠRail s", - "ĠRai ls", - "Ġaff iliated", - "Ġaffili ated", - "Ġaffiliate d", - "æĿ ¥", - "Ġe rf", - "Ġer f", - "I NF", - "IN F", - "Ġ ViewHolder", - "ĠView Holder", - "m ini", - "min i", - "mi ni", - "ĠR oth", - "ĠRo th", - "ĠRot h", - "Ġfaith ful", - "ĠPhill ips", - "ĠPhillip s", - "AN DOM", - "AND OM", - "] .[", - "]. [", - "_ PAY", - "_P AY", - "_PA Y", - "ĠAr ctic", - "ĠArc tic", - "f aker", - "fa ker", - "fake r", - "fak er", - "D igit", - "Di git", - "Dig it", - "M ale", - "Ma le", - "Mal e", - "std err", - "se ys", - "sey s", - "Ġ Å¡", - "ĠÅ ¡", - "_ remote", - "_rem ote", - "l ique", - "li que", - "liqu e", - "Ġin def", - "Ġi ndef", - "Ġind ef", - "Ġinde f", - "ĠIndust ries", - "i tra", - "it ra", - "itr a", - "_ pairs", - "_p airs", - "_pair s", - "_pa irs", - "< iostream", - " D", - "Ġs ervlet", - "Ġserv let", - "bast ian", - "Ġ >&", - "Ġ> &", - "S ID", - "SI D", - "_ clk", - "_c lk", - "_cl k", - "Ġdi visions", - "Ġdiv isions", - "Ġdivision s", - "Ġdivis ions", - "} ',Ċ", - "}' ,Ċ", - "}', Ċ", - "Ġd ildo", - "Ġdil do", - "Ġpar ade", - "Ġpara de", - "Ġpa rade", - "Ġparad e", - "m ajor", - "maj or", - "Ġa board", - "Ġab oard", - "; ++", - "Ġ fusion", - "Ġf usion", - "Ġfus ion", - "\" },{\"", - "\"} ,{\"", - "\"}, {\"", - "ĠDialog Result", - "ĉ arr", - "ĉa rr", - "ĉar r", - "- em", - "-e m", - "_ nr", - "_n r", - "( handler", - "(h andler", - "(handle r", - "(hand ler", - ". NET", - ".N ET", - ".Xtra Reports", - "ĠSh ah", - "ĠSha h", - "Ġ Brief", - "ĠB rief", - "ĠBr ief", - "ĠBri ef", - "- ,", - "Ġ precio", - "Ġp recio", - "Ġpre cio", - "Ġprec io", - "ĉ ĉĉĠĠĠĠĠĠ", - "ĉĉ ĉĠĠĠĠĠĠ", - "ĉĉĉ ĠĠĠĠĠĠ", - "ĉĉĉĠĠĠ ĠĠĠ", - "ĉĉĉĠ ĠĠĠĠĠ", - "ĉĉĉĠĠ ĠĠĠĠ", - "ĉĉĉĠĠĠĠ ĠĠ", - "ĉĉĉĠĠĠĠĠ Ġ", - "Ġ tant", - "Ġt ant", - "Ġta nt", - "Ġtan t", - "ĠGr ande", - "ĠGrand e", - "ĠGran de", - "ĠGra nde", - "/ xml", - "/x ml", - "_ ICON", - "_I CON", - "_IC ON", - "ĠR etro", - "ĠRe tro", - "ĠRet ro", - "un que", - "Ġn ag", - "Ġna g", - "to Fixed", - "X L", - "Ġ declaring", - "Ġdecl aring", - "Ġdeclar ing", - "Ġ Concrete", - "ĠCon crete", - "ĠConc rete", - "Ġ Amazing", - "ĠAm azing", - "ĠAma zing", - "ĉprint k", - "Ġdeb ates", - "Ġdebate s", - "D ATED", - "DA TED", - "DATE D", - "DAT ED", - "Ġa esthetic", - "Ġaest hetic", - "Ġaes thetic", - "eme tery", - "emet ery", - "Routing Module", - "ĠNash ville", - "W AYS", - "WA YS", - "WAY S", - "Ġ wolf", - "Ġw olf", - "Ġwo lf", - "Ġwol f", - "Ġob servers", - "Ġobs ervers", - "Ġobserv ers", - "Ġobserver s", - "Ġobserve rs", - "O TA", - "OT A", - "an son", - "ans on", - "Ġ ea", - "Ġe a", - "Ġgreen house", - "ĵį ä½ľ", - "Ġst air", - "Ġsta ir", - "Ġimm igrant", - "Ġimmigr ant", - "_ apply", - "_app ly", - "_ap ply", - "pe are", - "pear e", - "ĠB loomberg", - "ĠBloom berg", - "_ PLAYER", - "_PL AYER", - "_PLAY ER", - "R esp", - "Re sp", - "Res p", - "æŃ £", - "Choose r", - "Cho oser", - "Ġ ICollection", - "ĠI Collection", - "ĠIC ollection", - "P eter", - "Pe ter", - "Pet er", - "Err o", - "Er ro", - ".detect Changes", - "M aps", - "Map s", - "Ma ps", - "Ġ squeeze", - "Ġs queeze", - "Ġsqueez e", - "ĠH omes", - "ĠHome s", - "ĠHom es", - "ĠHo mes", - "weg ian", - "Ġformat ting", - "Ġnegot iate", - "u ld", - "ul d", - "ĠN ep", - "ĠNe p", - "Ġ QB", - "ĠQ B", - "Ġeconom ies", - "Ġec onomies", - "Ġ */,", - "Ġ* /,", - "Ġ*/ ,", - "Ġred und", - "Ġredu nd", - "ĠA ber", - "ĠAb er", - "ĠAbe r", - ".IsNullOr WhiteSpace", - "yc led", - "ycle d", - "ycl ed", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "_ Sh", - "_S h", - "Ġs kept", - "Ġsk ept", - "Ġske pt", - "Ġre created", - "Ġrec reated", - "Ġrecre ated", - "Ġrecreate d", - "Ġ getType", - "Ġget Type", - "Ġm argins", - "Ġmargin s", - "Ġmarg ins", - "Ġcolon ial", - "ch arts", - "char ts", - "cha rts", - "chart s", - "/ /@", - "// @", - "Ġ processors", - "Ġprocess ors", - "Ġprocessor s", - "Ġprocesso rs", - "è¯ ´", - "b atis", - "bat is", - "æĦ ı", - "at orio", - "ator io", - "ato rio", - "atori o", - "ment ioned", - "mention ed", - "P atient", - "Pat ient", - "Ġp rey", - "Ġpr ey", - "Ġpre y", - "Check box", - "_ xpath", - "_x path", - ". skip", - ".s kip", - ".sk ip", - "ĠMor mon", - "ĠMorm on", - "ĠMemory Stream", - "CR EMENT", - "CRE MENT", - "Ġ ku", - "Ġk u", - "m eld", - "me ld", - "mel d", - "\\ Data", - "\\D ata", - "Ġ Kernel", - "ĠK ernel", - "ĠKer nel", - "ĠKern el", - "i ltr", - "il tr", - "ilt r", - "éĢ ģ", - "( profile", - "(pro file", - "(pr ofile", - "C arbon", - "Car bon", - "R OLE", - "RO LE", - "ROL E", - "( pl", - "(p l", - "] *(", - "]* (", - ". memory", - ".m emory", - ".mem ory", - ".memo ry", - "Ġme dal", - "Ġmed al", - "Ġ advisor", - "Ġad visor", - "Ġadv isor", - "Ġadvis or", - "it ät", - "itä t", - "Ġ hdr", - "Ġh dr", - "Ġhd r", - "ie rung", - "ier ung", - "Ġ Provides", - "ĠPro vides", - "ĠProvid es", - "ĠProvide s", - "ĠProv ides", - "( alpha", - "(al pha", - "Ġteen agers", - "Ġteenager s", - "Ġteenage rs", - "- parser", - "-p arser", - "-par ser", - "-parse r", - ". LatLng", - ".L atLng", - "] ()Ċ", - "]( )Ċ", - "]() Ċ", - "Ġfel ony", - "Ġfelon y", - "ĉĉĉ ĊĉĉĉĊ", - "ĉĉĉĊ ĉĉĉĊ", - "B OOK", - "BO OK", - "Ġ slash", - "Ġs lash", - "Ġsl ash", - "Ġsla sh", - "Ġ clearfix", - "Ġclear fix", - "ĠPro phet", - "ĠProp het", - "å® ¹", - "right ness", - "- fi", - "-f i", - ". kind", - ".k ind", - "er ton", - "ert on", - "erto n", - "J im", - "Ġmanip ulate", - "Ġ worksheet", - "Ġwork sheet", - "Ġworks heet", - "o lin", - "ol in", - "oli n", - "st ars", - "star s", - "sta rs", - "Ġ artifact", - "Ġart ifact", - "_ EMPTY", - "_EM PTY", - "_EMP TY", - "ĉ main", - "ĉm ain", - "--- ---------- ';", - ">' ;", - "Ġexp ressing", - "Ġexpress ing", - "Ġexpres sing", - "Ġ IQ", - "ĠI Q", - "Ġ Fact", - "ĠF act", - "ĠFac t", - "ĠFa ct", - "/************************************************************************ *******Ċ", - "/**************************************************************************** ***Ċ", - "_ mass", - "_m ass", - "_ma ss", - "_mas s", - ") ):", - ")) :", - "Ġcon dom", - "Ġcond om", - "Ġcondo m", - "Ġcreate State", - "ome town", - "omet own", - "Ġi rr", - "Ġir r", - "Ġ >(", - "Ġ> (", - "> B", - "it eration", - "ite ration", - "iter ation", - "ãĥ ª", - "Ġsh irts", - "Ġshirt s", - "ou nty", - "ount y", - "oun ty", - "- >$", - "-> $", - "_ SIGN", - "_S IGN", - "_SIG N", - "_SI GN", - "ĠD ale", - "ĠDa le", - "ĠDal e", - "Ġ jj", - "Ġj j", - "E asy", - "F re", - "Fr e", - "Ġ Ny", - "ĠN y", - "Ġ chlor", - "Ġch lor", - "m atched", - "match ed", - "mat ched", - "ĠG erm", - "ĠGe rm", - "ĠGer m", - "- UA", - "-U A", - "ĠN athan", - "ĠNa than", - "ĠNat han", - "ĠNath an", - "e ducation", - "educ ation", - "edu cation", - "- yard", - "-y ard", - "- che", - "-c he", - "-ch e", - "h ouses", - "ho uses", - "house s", - "hou ses", - "r itional", - "rit ional", - "rition al", - "Ġpro ximity", - "Ġprox imity", - "Ġdie sem", - "Ġdies em", - "Ġdiese m", - "áºŃ p", - "Ġd rought", - "Ġdr ought", - "Ġdro ught", - ". audio", - ".a udio", - ".au dio", - "Ġ Leo", - "ĠL eo", - "ĠLe o", - "Ġfavor able", - "Ġfav orable", - "in ch", - "inc h", - "ĠD aw", - "ĠDa w", - "r ibly", - "rib ly", - "_ student", - "_st udent", - "id able", - "ida ble", - "O VE", - "OV E", - "Ġl acks", - "Ġla cks", - "Ġlack s", - "Ġlac ks", - "oun cing", - "ounc ing", - ". business", - ".b usiness", - ".bus iness", - "Ġ reopen", - "Ġre open", - "m aybe", - "may be", - "_ GLOBAL", - "_G LOBAL", - "Ġd resses", - "Ġdr esses", - "Ġdress es", - "ĠEd wards", - "ĠEdward s", - "ens ible", - "ensi ble", - "Ġ Hardware", - "ĠH ardware", - "ĠHard ware", - "Ġ Excellent", - "ĠEx cellent", - "ĠExcell ent", - "ĠTime Unit", - "CT IONS", - "CTION S", - "Ġs chedules", - "Ġsched ules", - "Ġschedule s", - "Ġ segue", - "Ġse gue", - "Ġseg ue", - "Ġsegu e", - "Open s", - "Op ens", - "am men", - "amm en", - "- Identifier", - "Ġst aring", - "Ġstar ing", - "Ġsta ring", - "Ġhapp ily", - "ĠH ob", - "ĠHo b", - "' _", - "Ġ \");", - "Ġ\" );", - "Ġ\") ;", - "ament os", - "amento s", - "amen tos", - "et ched", - "etch ed", - "etc hed", - "Ġ/ >}Ċ", - "Ġ/> }Ċ", - "Ġ/>} Ċ", - ". Users", - ".User s", - ".Use rs", - "Ġint errupted", - "Ġinter rupted", - "Ġinterrupt ed", - "Cont acts", - "Contact s", - "Conta cts", - "Ġ registro", - "Ġreg istro", - "Ġregistr o", - "Ġregist ro", - "in burgh", - "C HA", - "CH A", - "_ imp", - "_i mp", - "_im p", - "p his", - "ph is", - "phi s", - "s ay", - "sa y", - "Ġret ailer", - "Ġretail er", - ". NODE", - ".N ODE", - ".NO DE", - "/ maps", - "/m aps", - "/map s", - "_ LAST", - "_L AST", - "_LA ST", - "Ġ Charge", - "ĠCh arge", - "ĠChar ge", - "ĠCharg e", - "_ guard", - "_g uard", - "C ollider", - "Col lider", - "Coll ider", - "ĠStateless Widget", - "\" :[\"", - "\": [\"", - "\":[ \"", - "(\" ../../", - "(\"../ ../", - "(\".. /../", - "i oxide", - "iox ide", - "ioxid e", - "ĠS und", - "ĠSun d", - "ĠSu nd", - "Ġ' ';", - "Ġ'' ;", - "un set", - "uns et", - "add Widget", - "л Ñİ", - "el les", - "ell es", - "elle s", - "al ker", - "alk er", - "A rc", - "Ar c", - "Ġd educt", - "Ġde duct", - "Ġded uct", - "G UILayout", - "GUI Layout", - "ĠV illa", - "ĠVi lla", - "ĠVill a", - "ĠVil la", - "Ġfor bidden", - "Ġforb idden", - "Ġforbid den", - "_ where", - "_w here", - "_wh ere", - "Ġ \\/", - "Ġ\\ /", - "ĠT ib", - "ĠTi b", - "_ AX", - "_A X", - "] čĊčĊ", - "]čĊ čĊ", - "Ġ Bir", - "ĠB ir", - "ĠBi r", - "Ġb end", - "Ġbe nd", - "Ġben d", - "Ġ MAKE", - "ĠM AKE", - "ĠMA KE", - "ĠMAK E", - "Ġ MET", - "ĠM ET", - "ĠME T", - "Ġf utures", - "Ġfuture s", - "Ġfut ures", - "Ġfu tures", - "Ġ weighted", - "Ġwe ighted", - "Ġweight ed", - "Ġweigh ted", - "\"\" \"čĊ", - "\"\"\" čĊ", - "Ġ authorize", - "Ġauthor ize", - "( program", - "(p rogram", - "(pro gram", - "(pr ogram", - "(prog ram", - "} ,{\"", - "}, {\"", - "},{ \"", - "Ġcoeff icients", - "Ġcoefficient s", - "ê s", - "Per Page", - "ĠB athroom", - "ĠBath room", - "ĠPublish ing", - "G PL", - "GP L", - "Ġsub missions", - "Ġsubmission s", - "Ġ NUMBER", - "ĠNUM BER", - "ĠNU MBER", - "j Äħ", - "Ġadd itionally", - "Ġadditional ly", - "Ġaddition ally", - "em pre", - "emp re", - "ĠS hel", - "ĠSh el", - "ĠShe l", - "o typ", - "ot yp", - "oty p", - "S olution", - "Sol ution", - "Ġth under", - "Ġthu nder", - "_ ec", - "_e c", - "Ġ ĊĠĠĠĠĊ", - "ĠĊ ĠĠĠĠĊ", - "ĠF ellow", - "ĠFel low", - "ĠFell ow", - "Ġk ay", - "Ġka y", - "Ġnew State", - "ONT AL", - "Im plementation", - "Implement ation", - ". Look", - ".L ook", - ".Lo ok", - "Ġ ents", - "Ġe nts", - "Ġen ts", - "Ġent s", - "Ġl ors", - "Ġlo rs", - "Ġlor s", - "Ġ BIG", - "ĠB IG", - "ĠBI G", - "f ab", - "fa b", - "Ġaverage d", - "Ġaver aged", - "Ġ Feedback", - "ĠFe edback", - "ĠFeed back", - "ĠW ells", - "ĠWell s", - "ĠWel ls", - "Ġm artial", - "Ġmart ial", - "Ġind ul", - "Ġindu l", - "ĠComm unist", - "ĠCommun ist", - "ĠF orex", - "ĠFor ex", - "ĠFore x", - "ĠFo rex", - "ĠAgricult ure", - "ĠAgr iculture", - "\" [", - "Ġqu ar", - "Ġq uar", - "Ġqua r", - "ĠK ont", - "ĠKon t", - "ĠKo nt", - "ĉ view", - "ĉv iew", - ". Bytes", - ".Byte s", - ".By tes", - "d esktop", - "des ktop", - "desk top", - "Ġ Makes", - "ĠM akes", - "ĠMake s", - "ĠMa kes", - "ĠMak es", - "akes peare", - ". Nullable", - ".Null able", - "Ġspot light", - "V B", - "o wy", - "ow y", - "( torch", - "(t orch", - "(to rch", - "t ridge", - "tr idge", - "tri dge", - "_ bounds", - "_b ounds", - "_bound s", - "_bo unds", - "Ġapolog ize", - ". addItem", - ".add Item", - "an td", - "ant d", - "* );Ċ", - "*) ;Ċ", - ", u", - "( gen", - "(g en", - "ç» ĵ", - "re ator", - "reat or", - "rea tor", - "ĠC ord", - "ĠCo rd", - "ĠCor d", - "o upper", - "ou pper", - "oup per", - ". metro", - ".m etro", - ".me tro", - ".met ro", - "Ġ ew", - "Ġe w", - "Ġ WORD", - "ĠW ORD", - ". After", - ".A fter", - "Ġdet ained", - "Ġdetain ed", - "ĠH ammer", - "ĠHam mer", - "ĠHamm er", - "ex isting", - "exist ing", - "Ġ ost", - "Ġo st", - "Ġos t", - "Ġmon ument", - "- custom", - "-c ustom", - "User ID", - "Ġ Nom", - "ĠN om", - "ĠNo m", - "Ġre jection", - "Ġreject ion", - "Ġrej ection", - "( dim", - "(d im", - "(di m", - "Ġ singleton", - "Ġs ingleton", - "Ġsingle ton", - "Ġsing leton", - "ĉ die", - "ĉd ie", - "ar iance", - "ari ance", - "arian ce", - "aria nce", - "re ports", - "rep orts", - "report s", - "repo rts", - "] !=", - "e lda", - "el da", - "eld a", - "Ġpreval ence", - "_ regs", - "_re gs", - "_reg s", - ". \".", - ".\" .", - "Ġfemin ist", - "Code c", - "Co dec", - "Cod ec", - "Ġ **Ċ", - "Ġ* *Ċ", - "Ġ** Ċ", - "( labels", - "(label s", - "_ MARK", - "_M ARK", - "_MA RK", - "FA ILED", - "FAIL ED", - "Ġadminister ed", - "W N", - "ĠĠ ĠĠĠĠĠĠĉĉ", - "ĠĠĠĠ ĠĠĠĠĉĉ", - "ĠĠĠĠĠĠĠĠ ĉĉ", - "ĠĠĠĠĠĠĠ Ġĉĉ", - "ĠĠĠĠĠ ĠĠĠĉĉ", - "ĠĠĠĠĠĠ ĠĠĉĉ", - "ĠĠĠĠĠĠĠĠĉ ĉ", - "Ġ noun", - "Ġn oun", - "Ġno un", - "Ġnou n", - "w ig", - "wi g", - "Ġg otta", - "Ġgot ta", - "Ġ rif", - "Ġr if", - "Ġri f", - "- im", - "-i m", - "ĠPaul o", - "ĠPa ulo", - "ĠCommand Type", - "] ))ĊĊ", - "]) )ĊĊ", - "])) ĊĊ", - "]))Ċ Ċ", - "- zero", - "-z ero", - "Tr aining", - "Train ing", - "Tra ining", - "Ġ lord", - "Ġl ord", - "Ġlo rd", - "Ġlor d", - "_ art", - "_a rt", - "_ar t", - "re ddit", - "red dit", - "redd it", - "C ert", - "Ce rt", - "Ġp eso", - "Ġpe so", - "Ġpes o", - "R ot", - "Ro t", - "Ġen danger", - "Ġend anger", - ". dr", - ".d r", - "user Info", - "u nts", - "un ts", - "unt s", - "n v", - "ĠTr ailer", - "ĠTra iler", - "ĠTrail er", - "- first", - "-f irst", - "-fi rst", - "( make", - "(m ake", - "Ġbenef ici", - "- black", - "-b lack", - "-bl ack", - "i ÃŁ", - "Ġund oubtedly", - "Ġm ex", - "Ġme x", - "ĠAn cient", - "ĠAnc ient", - "( as", - "(a s", - "Ġdes cent", - "Ġdesc ent", - "P ick", - "Pic k", - "Pi ck", - "Ġrep lica", - "Ġrepl ica", - "Ġreplic a", - "$ obj", - "$o bj", - "ä hr", - "äh r", - "Ġar rows", - "Ġarr ows", - "Ġarrow s", - "f ty", - "ft y", - "ĠLib ya", - "u ga", - "ug a", - "ch arged", - "char ged", - "charge d", - "charg ed", - "T ur", - "Tu r", - "Ġh omic", - "Ġhom ic", - "Ġho mic", - "is sen", - "iss en", - "isse n", - "Ġ Fake", - "ĠF ake", - "ĠFa ke", - "ĠFak e", - "Ġbe ers", - "Ġbeer s", - "Ġbee rs", - "Ġsc attered", - "Ġscatter ed", - "( Time", - "(T ime", - "UT IL", - "Ġbureauc r", - "Ġbureau cr", - "/ plain", - "/p lain", - "/pl ain", - "Ġst icking", - "Ġstick ing", - "F AIL", - "FA IL", - "ĠC ovid", - "ĠCo vid", - "ĠCov id", - "Th ird", - "_ present", - "_p resent", - "_pre sent", - "_pres ent", - "ĠP ierre", - "ĠPi erre", - "ĠPier re", - "Ġ ëª", - "Ġë ª", - "Ġ[ ...]ĊĊ", - "Ġ[... ]ĊĊ", - "Ġ[...] ĊĊ", - "P rob", - "Pro b", - "Pr ob", - "Ġ Traffic", - "ĠTra ffic", - "ĠTraff ic", - "i cao", - "ic ao", - "ica o", - "do ctor", - "doc tor", - "Ġ ),ĊĊ", - "Ġ) ,ĊĊ", - "Ġ),Ċ Ċ", - "Ġ), ĊĊ", - "T abs", - "Tab s", - "Ta bs", - "a lu", - "al u", - "ï¼ļ âĢľ", - "Ġin herent", - "Ġinher ent", - "_ No", - "_N o", - "r itis", - "rit is", - "Ġ Proof", - "ĠP roof", - "ĠPro of", - ". basename", - ".b asename", - ".base name", - "ä¼ ļ", - "Ġc him", - "Ġch im", - "Ġchi m", - "Ġ Protected", - "ĠProt ected", - "ĠProtect ed", - "c rit", - "cri t", - "cr it", - "Ġp rone", - "Ġpro ne", - "Ġpr one", - "Ġpron e", - "Ġ кон", - "Ġк он", - "Ġко н", - "Ġ Heroes", - "ĠHer oes", - "ĠHero es", - "Ġan xious", - "Ġanx ious", - "Ġ anos", - "Ġa nos", - "Ġan os", - "Ġano s", - "Ġweek ends", - "Ġweekend s", - "Ġs ext", - "Ġse xt", - "Ġsex t", - "Ġre ducer", - "Ġred ucer", - "Ġredu cer", - "Ġreduce r", - "= UTF", - "h alf", - "ha lf", - "hal f", - "ĠS aw", - "ĠSa w", - ". mm", - ".m m", - "Ġn ueva", - "Ġnu eva", - "Ġnue va", - ".current Target", - ". lua", - ".l ua", - ".lu a", - "_EXT ENSION", - "ĉ reg", - "ĉr eg", - "ĉre g", - "Ġ Ctrl", - "ĠC trl", - "ĠCt rl", - "_ align", - "_al ign", - "accept able", - "Ġr ushing", - "Ġrush ing", - "f rac", - "fr ac", - "fra c", - "Ġbo asts", - "Ġboast s", - "Ġboa sts", - "F ive", - "Fi ve", - " ±", - "Ġ Temperature", - "ĠT emperature", - "ĠTem perature", - "ĠTemper ature", - "> ):", - ">) :", - "Ġch arter", - "Ġchar ter", - "Ġchart er", - "RE ATED", - "REATE D", - "REAT ED", - "REA TED", - "Ġsub jected", - "Ġsubject ed", - "Ġsubj ected", - "Ġ opc", - "Ġo pc", - "Ġop c", - "health y", - "使 çĶ¨", - "ĠScient ific", - "Ġ frau", - "Ġfr au", - "Ġfra u", - "ri ages", - "ria ges", - "riage s", - "ภĶ", - ". inventory", - ".in ventory", - "at ionale", - "ation ale", - "ational e", - "M ad", - "Ma d", - "min utes", - "minute s", - "> >();Ċ", - ">> ();Ċ", - ">>( );Ċ", - ">>() ;Ċ", - "Ġ Env", - "ĠE nv", - "ĠEn v", - "Ġrecord ings", - "Ġrecording s", - "Ġsusp icion", - "sql ite", - "sq lite", - "ĉ read", - "ĉr ead", - "ĉre ad", - "ãģ ¦", - "Ġwor ries", - ".put String", - "ĠSh anghai", - "( uid", - "(u id", - "(ui d", - "r er", - "re r", - "ĠvÃŃ de", - "\" ):", - "\") :", - "Ġmethod ology", - "Ġк оÑĤоÑĢ", - "Ġко ÑĤоÑĢ", - "ĠкоÑĤ оÑĢ", - "c cc", - "cc c", - "av ad", - "ava d", - "Ġin duction", - "Ġind uction", - "Ġindu ction", - "ĉ Thread", - "ĉT hread", - ", string", - ",s tring", - ",str ing", - ",st ring", - "ạ i", - "neh men", - "u ition", - "ui tion", - "uit ion", - "Ġ* __", - "Ġ*_ _", - ".e mf", - ".em f", - "Ġ ìľ", - "Ġì ľ", - "/ themes", - "/th emes", - "/theme s", - "/the mes", - "Ġ Nine", - "ĠN ine", - "ĠNi ne", - "ĠNin e", - ". One", - ".On e", - ".O ne", - "Ġ Embed", - "ĠEm bed", - "ĠEmb ed", - "Ġf az", - "Ġfa z", - "u ations", - "uation s", - "uat ions", - "Ġprivate ly", - "Ġpriv ately", - "Ġprivat ely", - "Ġ ling", - "Ġl ing", - "Ġli ng", - "Ġlin g", - "[ F", - "u shi", - "us hi", - "ush i", - "Ġlaunch es", - "( KEY", - "(K EY", - "G MT", - "GM T", - "Ġa iming", - "Ġaim ing", - "Ġai ming", - "pat ible", - "ĠB iden", - "ĠBi den", - "ĠBid en", - "i w", - "Ġ Degree", - "ĠD egree", - "ĠDe gree", - "ĠDeg ree", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "Ġ$ ('<", - "Ġ$( '<", - "Ġ$(' <", - "á rios", - "ário s", - "ár ios", - "to UpperCase", - "ìł ľ", - "Ġ EUR", - "ĠE UR", - "ĠEU R", - "Ġovers ight", - "Ġtable sp", - "Ġtables p", - "Up dates", - "Update s", - ".m akedirs", - ".make dirs", - "Ġ humidity", - "Ġh umidity", - "Ġhum idity", - "Ġhumid ity", - "/ template", - "/t emplate", - "/temp late", - "Al ways", - "( IS", - "(I S", - "_ cert", - "_c ert", - "_ce rt", - "D ig", - "Di g", - "Ġunder way", - "or ton", - "ort on", - "ĠHur ricane", - "Ġsp ends", - "Ġspe nds", - "Ġspend s", - "Ġ Segment", - "ĠS egment", - "ĠSe gment", - "ĠSeg ment", - "Ġ flies", - "Ġf lies", - "Ġfl ies", - "Ġ Toggle", - "ĠT oggle", - "ĠL ynch", - "ĠLyn ch", - "Ġs enses", - "Ġsense s", - "Ġsens es", - "Ġsen ses", - "ĠK os", - "ĠKo s", - "set Enabled", - "ist ically", - "istic ally", - "istical ly", - "Ġ tester", - "Ġt ester", - "Ġte ster", - "Ġtest er", - "Ġtes ter", - "Ġteste r", - "Ġadministr ators", - "Ġadministrator s", - "Ġt agged", - "Ġtag ged", - "Ð ĵ", - "Ġ shortcut", - "Ġshort cut", - "Ġ Resolution", - "ĠRe solution", - "ĠRes olution", - "Ġsuper vision", - "Ġsuperv ision", - "ĠAsh ley", - "Tr acking", - "Track ing", - "ul atory", - "ulator y", - "an del", - "and el", - "ande l", - "i sten", - "is ten", - "ist en", - "iste n", - "Ġun re", - "Ġunr e", - "( diff", - "(d iff", - "(di ff", - "AN TS", - "ANT S", - "Ġr ider", - "Ġrid er", - "Ġride r", - "Ġri der", - "Ġs Äħ", - ". Series", - ".S eries", - ".Se ries", - "_ orders", - "_order s", - "_or ders", - "_ord ers", - "ORIZ ONTAL", - "Ġret ention", - "ãĢĤ čĊčĊ", - "\"> čĊčĊ", - "\">čĊ čĊ", - "Ġdi agonal", - "Ġdiag onal", - "Ġdiagon al", - "ĠC ancellationToken", - "_ Internal", - "_In ternal", - "_Int ernal", - "_Inter nal", - "Ġr uin", - "Ġru in", - ". Qt", - ".Q t", - "ocr atic", - "ocrat ic", - "T el", - "Te l", - "Ġ Answers", - "ĠAn swers", - "ĠAnswer s", - "ĠAns wers", - "m atic", - "ma tic", - "mat ic", - "Ġ xp", - "Ġx p", - "a tem", - "at em", - "ate m", - "_ jobs", - "_j obs", - "_job s", - "_ any", - "_a ny", - "_an y", - "Ġsen iors", - "Ġsenior s", - "Ġseni ors", - "Ġland mark", - "ĠQ List", - "Ġman eu", - "Ġmane u", - "ot ify", - "oti fy", - "/ \";Ċ", - "/\" ;Ċ", - "/ server", - "/s erver", - "ĠPhil osoph", - "u tenant", - "ut enant", - "ute nant", - "uten ant", - "( io", - "(i o", - "h z", - "Ġ authenticated", - "Ġauth enticated", - "Ġauthentic ated", - "Ġauthenticate d", - "d v", - "- Compatible", - "Origin ally", - "Original ly", - "Orig inally", - ", function", - ",f unction", - "ãĢĤ čĊ", - "ĠRepresent ative", - "as ily", - "asi ly", - "asil y", - "irc uit", - ". dt", - ".d t", - "( math", - "(m ath", - "(mat h", - ". Marshal", - ".M arshal", - ".Mar shal", - "[ ,", - "Ġ Cities", - "ĠC ities", - "ĠCit ies", - "ĠCi ties", - "_ turn", - "_t urn", - "| )Ċ", - "Ġ cantidad", - "Ġc antidad", - "Ġcant idad", - "al ter", - "alt er", - "alte r", - "ĉ ui", - "ĉu i", - "ĠNe braska", - "Ġsk irt", - "Ġski rt", - ". bg", - ".b g", - "Shared Preferences", - "( style", - "(st yle", - "Ġg rief", - "Ġgr ief", - "Ġgri ef", - "g ew", - "ge w", - "Ġsaf eg", - "Ġsafe g", - "o lang", - "ol ang", - "ola ng", - "olan g", - "_ lists", - "_l ists", - "_list s", - "_li sts", - "ì Ľ", - "Ġgran ite", - "Ġgra nite", - "Ġhot test", - "Ġhott est", - ". jdbc", - ".j dbc", - ".jd bc", - ". Customer", - ".C ustomer", - ".Custom er", - "Ġ âī¤", - "Ġâī ¤", - "Ġw aar", - "Ġwa ar", - "_ scene", - "_s cene", - "_sc ene", - "+ '/", - "+' /", - "ĠJ TextField", - "ĠJText Field", - "ĠJT extField", - "Ġs eating", - "Ġse ating", - "Ġsea ting", - "Ġseat ing", - "Ġw ears", - "Ġwe ars", - "Ġwear s", - "Ġ` /", - "C ases", - "Case s", - "Ca ses", - "Cas es", - "Ġ Youtube", - "ĠY outube", - "ĠYou tube", - "ı m", - "Ġb alcon", - "Ġbal con", - ", G", - "Meta Data", - "Met aData", - "- price", - "-p rice", - "-pr ice", - "S CR", - "SC R", - "Un ity", - "Unit y", - "Uni ty", - "Ġtr unk", - "={ `${", - "={` ${", - "Ġearth quake", - "Ġearthqu ake", - "P artial", - "Part ial", - "Ġ subst", - "Ġsu bst", - "Ġsub st", - "Ġsubs t", - "Ġel imin", - "Ġelim in", - "=\" '.", - "=\"' .", - "//* [@", - "//*[ @", - "Ġsup ervisor", - "Ġsuper visor", - "Ġsuperv isor", - "vr olet", - "vro let", - "_ article", - "_art icle", - "Ġ pane", - "Ġp ane", - "Ġpa ne", - "Ġpan e", - "b io", - "bi o", - "Ġmot ors", - "Ġmo tors", - "Ġmotor s", - "Ġmoto rs", - "N M", - "F rank", - "Fr ank", - "Fran k", - "Fra nk", - "Ġon ion", - "- word", - "-w ord", - "Item ClickListener", - "ItemClick Listener", - "Ġ brit", - "Ġb rit", - "Ġbr it", - "Ġbri t", - "end encies", - "enden cies", - "Com puter", - "Comp uter", - "Compute r", - "Comput er", - "_ running", - "_r unning", - "_run ning", - "( day", - "(d ay", - "(da y", - "- he", - "-h e", - "( named", - "(n amed", - "(name d", - "ĠS ach", - "ĠSa ch", - "ĠSac h", - "о Ñĩ", - "c ampaign", - "camp aign", - ". Abstract", - ".A bstract", - ".Ab stract", - ".Abs tract", - "( wrapper", - "(w rapper", - ". pay", - ".p ay", - ".pa y", - "Ġ uw", - "Ġu w", - "G eo", - "Ge o", - "r ails", - "ra ils", - "rai ls", - "rail s", - "/ select", - "/s elect", - "/se lect", - "i chte", - "ic hte", - "ich te", - "icht e", - "s ons", - "so ns", - "son s", - "E VENT", - "EV ENT", - "Ġal iment", - "Ġali ment", - "Pro viders", - "Provider s", - "Provid ers", - "Provide rs", - "Prov iders", - "A wait", - "Aw ait", - "_INTER VAL", - ". off", - ".of f", - ".o ff", - "Ġgl uten", - "Ġglut en", - "Ġglu ten", - "_ cloud", - "_c loud", - "_cl oud", - "Ġ wen", - "Ġw en", - "Ġwe n", - ". extract", - ".ex tract", - ".ext ract", - ".extra ct", - "ĉ button", - "ĉb utton", - "/ MM", - "/M M", - "P arty", - "Par ty", - "Part y", - "Ġdem ographic", - "Ġdemo graphic", - "_ errno", - "_err no", - "Ġh iking", - "Ġhi king", - "Ġhik ing", - "(' ')Ċ", - "\", @\"", - "Ġ wit", - "Ġw it", - "Ġwi t", - "r á", - "ol ogie", - "olog ie", - "olo gie", - "ologi e", - "Ġ Styles", - "ĠSt yles", - "ĠStyle s", - "ĠSty les", - "ĠBrowser Module", - ". RequestMapping", - ".Request Mapping", - "ic ans", - "ica ns", - "ican s", - "P AGE", - "PA GE", - "c reation", - "cre ation", - "creat ion", - "ĠF erguson", - "u ded", - "ud ed", - "ude d", - "num bers", - "number s", - "Ġ GTK", - "ĠG TK", - "ĠGT K", - "Ġpresent ations", - "Ġpresentation s", - "ĠB obby", - "ĠBob by", - "_ span", - "_s pan", - "_sp an", - "e style", - "est yle", - "esty le", - "Ġillegal ly", - "Ġilleg ally", - "ab ela", - "abel a", - "abe la", - "Ġbattle field", - "cap acity", - "t error", - "ter ror", - "te rror", - "terr or", - "] \");Ċ", - "]\" );Ċ", - "]\") ;Ċ", - "Ġwar rior", - "le ader", - "lead er", - "lea der", - "Ġ DBG", - "ĠD BG", - "ĠDB G", - "Ġ Revenue", - "ĠRe venue", - "Ġvi gil", - "Ġvig il", - "Ġcounter parts", - "Ġcounterpart s", - "( Error", - "(E rror", - "AC TER", - "ACT ER", - "Ġhe eft", - "Ġse lections", - "Ġselect ions", - "Ġselection s", - "Ġsel ections", - "Ġsele ctions", - "ze ug", - "t om", - "to m", - "- two", - "-t wo", - "-tw o", - ". ;Ċ", - ".; Ċ", - "_ statement", - "_st atement", - "_state ment", - "_stat ement", - "_sta tement", - "ĠA id", - "ĠAi d", - "ĠV ul", - "ĠVu l", - "_ rgb", - "_r gb", - "_rg b", - "Ġpr izes", - "Ġpri zes", - "Ġprize s", - "Ġ editable", - "Ġed itable", - "Ġedit able", - "Ġedi table", - "ĉ form", - "ĉf orm", - "ĉfor m", - "ın ı", - ". decor", - ".de cor", - ".dec or", - "D emo", - "De mo", - "Dem o", - "l ices", - "lic es", - "li ces", - "lice s", - "Ġen ctype", - "Ġenc type", - "rat ulations", - "Ġ ROS", - "ĠR OS", - "ĠRO S", - "_ chars", - "_ch ars", - "_char s", - "ĠJ ahr", - "ĠJa hr", - "ĠJah r", - "p artial", - "part ial", - "Ñĥ ÑĤ", - "Ġ Receive", - "ĠRe ceive", - "ĠRece ive", - "ĠL ands", - "ĠLa nds", - "ĠLand s", - "ĠLan ds", - "AP TER", - "APT ER", - "Ġch opped", - "Ġcho pped", - "Ġchop ped", - ". .\"", - ".. \"", - "Ġ Analy", - "ĠAn aly", - "ĠAnal y", - "ĠAna ly", - "Ġ UID", - "ĠU ID", - "ĠUI D", - "ĠR adeon", - "ĠRad eon", - "ĠB ee", - "ĠBe e", - "Ġu nm", - "Ġun m", - "> M", - ".find all", - "Token izer", - "Ġ WHAT", - "ĠWH AT", - "Ġ sj", - "Ġs j", - "D rawing", - "Draw ing", - "E ss", - "Es s", - "O ND", - "ON D", - "Ĭ ¶", - "( packet", - "(p acket", - "(pa cket", - "(pack et", - "âĢĶ but", - "Inv ocation", - "ĠN uclear", - "ĠNu clear", - "? ;Ċ", - "Ġgr andes", - "Ġgrand es", - "Ġgran des", - "Ġgrande s", - "Ġ Crypt", - "ĠC rypt", - "ĠCry pt", - "r emark", - "re mark", - "rem ark", - "rema rk", - "Ġ' ../../../../", - "Ġ'../ ../../../", - "Ġ'../../ ../../", - "Ġ'../../../ ../", - "Ġin ability", - "m agic", - "mag ic", - "c ats", - "ca ts", - "cat s", - "Ġ simulate", - "Ġsim ulate", - "Ġsimul ate", - ": ${", - ":$ {", - "in flate", - "inf late", - "Ġ ener", - "Ġe ner", - "Ġen er", - ": NO", - ":N O", - "i ples", - "ip les", - "iple s", - "ipl es", - "Ġme rit", - "Ġmer it", - "Ġ Rated", - "ĠR ated", - "ĠRa ted", - "ĠRate d", - "ĠRat ed", - "Ġg lue", - "Ġgl ue", - "Ġglu e", - "/ blog", - "/b log", - "/bl og", - "Ġ gren", - "Ġg ren", - "Ġgr en", - "Ġgre n", - "Ġthr illed", - "Ġthrill ed", - ". CH", - ".C H", - "un can", - "unc an", - "unca n", - "Ġ PRIMARY", - "ĠPR IMARY", - "ĠPRI MARY", - "Ġper sec", - "Ġpers ec", - "Ġperse c", - "Ġfe ared", - "Ġfear ed", - ". MIN", - ".M IN", - "ĠThe ater", - "é Ĵ", - "ateg orie", - "ategor ie", - "ategori e", - "atego rie", - "æ® µ", - "Ġappet ite", - "s quare", - "squ are", - "ĠAlex and", - "ĠAlexa nd", - ". UserId", - ".User Id", - "_ gt", - "_g t", - "_ enter", - "_en ter", - "_ent er", - "Ġgrad uates", - "Ġgraduate s", - "Ġgradu ates", - "Fragment Manager", - "Author ize", - "-N LS", - "( My", - "(M y", - "Ġtri umph", - "Ġtrium ph", - "us ting", - "ust ing", - "ustin g", - "_PARAM S", - "_PAR AMS", - "Char acters", - "Character s", - "(: ,:,", - "(:, :,", - "_ BUILD", - "_B UILD", - "_BU ILD", - "M Hz", - "MH z", - "Ġw ashed", - "Ġwas hed", - "Ġwa shed", - "Ġwash ed", - "Ġun cle", - "Ġunc le", - "St eve", - "Ste ve", - "ar down", - "ard own", - "ardo wn", - "< stdio", - " ${", - ">$ {", - "_ confirmation", - "_confirm ation", - "Ġt rophy", - "Ġtr ophy", - "Ġtro phy", - "Ġtrop hy", - "W orks", - "Work s", - "ĠElect ronics", - "ĠElectronic s", - "ĠElectron ics", - "ĠMediterr anean", - "_ metrics", - "_m etrics", - "_metric s", - "_met rics", - "Ġann ouncing", - "Ġannounc ing", - "Ġ DAY", - "ĠD AY", - "ĠDA Y", - "_ proto", - "_pro to", - "_pr oto", - "_prot o", - "Ġ pear", - "Ġp ear", - "Ġpe ar", - "Ġpea r", - "base Url", - "ĉ ĉĉĉĉĉĉĉĊ", - "ĉĉ ĉĉĉĉĉĉĊ", - "ĉĉĉĉ ĉĉĉĉĊ", - "ĉĉĉ ĉĉĉĉĉĊ", - "ĉĉĉĉĉ ĉĉĉĊ", - "ĉĉĉĉĉĉ ĉĉĊ", - "ĉĉĉĉĉĉĉĉ Ċ", - "ĉĉĉĉĉĉĉ ĉĊ", - "Ġco ordination", - "Ġcoord ination", - "Ġcoordin ation", - ": N", - ". animate", - ".an imate", - ".anim ate", - "ĠC otton", - "ĠCot ton", - "_ hit", - "_h it", - "_hi t", - "â ľ", - "Ġj etzt", - "Ġjet zt", - "i fter", - "if ter", - "ift er", - "( fields", - "(f ields", - "(field s", - "own load", - "ific acion", - "ifica cion", - ". cuda", - ".c uda", - "ĠL iu", - "ĠLi u", - "> equals", - ">e quals", - "Ġ Ace", - "ĠA ce", - "ĠAc e", - "ÑĢ ам", - "ÑĢаР¼", - "ÑĢа м", - "ĠSup erman", - "ĠSuper man", - "ĠGar cia", - "ĠGarc ia", - "Ġarr ests", - "Ġarrest s", - "a gar", - "ag ar", - "aga r", - "Ġ {})", - "Ġ{ })", - "Ġ{} )", - "Ġ macros", - "Ġmac ros", - "Ġmacro s", - "ro upe", - "rou pe", - "roup e", - "ê tre", - "êt re", - "Ġtw isted", - "Ġtwist ed", - "str uments", - "strument s", - "stru ments", - "_ (\"", - "_( \"", - "_ vertices", - "_vert ices", - "Ġ Transition", - "ĠT ransition", - "ĠTrans ition", - "ĠTransit ion", - "и к", - "[ max", - "[m ax", - "m ind", - "min d", - "mi nd", - "Ġ accessToken", - "Ġaccess Token", - "Ġun le", - "Ġunl e", - "m us", - "mu s", - "c op", - "co p", - "Ġ Factor", - "ĠF actor", - "ĠFac tor", - "ĠFa ctor", - "ĠFact or", - "Ġcon ced", - "Ġconc ed", - "Ġconce d", - "Ġre tr", - "Ġr etr", - "Ġret r", - ".l inalg", - ".lin alg", - "- slider", - "-s lider", - "-slide r", - "-sl ider", - "o bl", - "ob l", - "_Static Fields", - "Ġz ombie", - "s elling", - "sel ling", - "sell ing", - "Ġ chap", - "Ġc hap", - "Ġch ap", - "Ġcha p", - "Ġsh aking", - "Ġsha king", - "Ġ Translate", - "ĠTrans late", - "ĠAm sterdam", - "Ġ ETH", - "ĠE TH", - "ĠET H", - "_ EXTERN", - "_EX TERN", - "_EXT ERN", - "k d", - "_ disc", - "_d isc", - "_dis c", - "_di sc", - "Ġprec eding", - "Ġpreced ing", - "Ġ prix", - "Ġp rix", - "Ġpr ix", - "Ġpri x", - "Object Name", - "_ modified", - "_mod ified", - "ard ware", - "Ġ?> \">", - "Ġ?>\" >", - "Ġ DW", - "ĠD W", - "` ${", - "Ġ?> \">\" >\"> \">< ?", - "u yen", - "uy en", - "uye n", - "Ġd onna", - "Ġdon na", - "Ġdonn a", - "Ġx si", - "Ġxs i", - "Ġ$ \"{", - "Ġ$\" {", - "Ġ Drawing", - "ĠD rawing", - "ĠDraw ing", - "ĠDra wing", - ", nil", - ",n il", - "Ġ onder", - "Ġo nder", - "Ġon der", - "Ġonde r", - "B G", - "O bserv", - "Ob serv", - "Obs erv", - "Ġconsider ations", - "Ġconsideration s", - "bo at", - "boa t", - "ĠB anks", - "ĠBank s", - "ĠBan ks", - "Ġin dict", - "Ġind ict", - "Ġindic t", - ", I", - "ĠB lu", - "ĠBl u", - "( version", - "(v ersion", - "cl iente", - "client e", - "cli ente", - "o lan", - "ol an", - "ola n", - "L ESS", - "LE SS", - "LES S", - "assert Same", - "_ void", - "_v oid", - "ĠW AS", - "ĠWA S", - "ĉ enum", - "ĉe num", - "ĉen um", - "Ġm ixer", - "Ġmix er", - "E W", - "a ffe", - "af fe", - "aff e", - "Ġblow job", - "t extField", - "text Field", - "Ġimm ense", - "_ repo", - "_re po", - "_rep o", - "Ġ globals", - "Ġg lobals", - "Ġglobal s", - "Ġglob als", - "ant ages", - "anta ges", - "antage s", - ". today", - ".t oday", - ".to day", - "Th ursday", - "ĠB rig", - "ĠBr ig", - "ĠBri g", - "{ })Ċ", - "{} )Ċ", - "{}) Ċ", - "Ġ Imagine", - "ĠIm agine", - "ĠImag ine", - "( GPIO", - "(G PIO", - "Ġ esto", - "Ġe sto", - "Ġes to", - "Ġest o", - "Ġ Province", - "ĠPro vince", - "ĠProv ince", - "ĠM ental", - "ĠMen tal", - "ĠMent al", - "_ cells", - "_c ells", - "_cell s", - "ĠJul ian", - "ĠJu lian", - "ĠJulia n", - "ĠJuli an", - ". Screen", - ".S creen", - ".Sc reen", - "Ġc andle", - "Ġcan dle", - "Ġcand le", - "Ġm onde", - "Ġmon de", - "Ġmo nde", - "Ġmond e", - "Ġv erg", - "Ġver g", - "Ġve rg", - "it erals", - "iter als", - "iteral s", - "- layout", - "-l ayout", - "G uest", - "Gu est", - "Ġv ind", - "Ġvi nd", - "Ġvin d", - "Ġ Echo", - "ĠE cho", - "ĠEc ho", - "' )}", - "') }", - "Ġ mann", - "Ġm ann", - "Ġman n", - "Ġma nn", - "_ BOOLEAN", - "_BO OLEAN", - "h ap", - "ha p", - "Ġnight mare", - "U GH", - "UG H", - "Ġnon etheless", - "Ġnone theless", - "Ġ athe", - "Ġa the", - "Ġat he", - "Ġath e", - "ĠH olland", - "ĠHol land", - "ĠHo lland", - "ĠHoll and", - "Ġ Born", - "ĠB orn", - "ĠBo rn", - "ĠBor n", - "\\ ORM", - "a nut", - "an ut", - "_ levels", - "_level s", - "Ġpet ite", - "Ġpetit e", - "- art", - "-a rt", - "-ar t", - "_ SHOW", - "_S HOW", - "_SH OW", - "number Of", - "_ thumbnail", - "_th umbnail", - "a mins", - "am ins", - "amin s", - "ami ns", - "Ġ Defines", - "ĠDef ines", - "ĠDefine s", - "Ġ\" =", - ". StatusCode", - ".Status Code", - "Ġdign ity", - "ĠB ike", - "ĠBi ke", - "ĠBik e", - ".New Line", - "ĠG las", - "ĠGl as", - "( logger", - "(log ger", - "(lo gger", - "Ġc atches", - "Ġcatch es", - "Ġcat ches", - "v otes", - "vo tes", - "vote s", - "Ġexam ining", - "/ register", - "/reg ister", - "Ġspec ifying", - "Ġspecify ing", - "_ fixed", - "_f ixed", - "_fix ed", - "Ġdraw ings", - "Ġdrawing s", - "Th reshold", - "A x", - "Ġ Architecture", - "ĠArch itecture", - "ĠArchitect ure", - "( pid", - "(p id", - "(pi d", - "W ire", - "Wir e", - "Wi re", - "( cont", - "(c ont", - "(con t", - "(co nt", - "l ane", - "la ne", - "lan e", - "L ists", - "List s", - "Li sts", - "Ġs print", - "Ġsp rint", - "Ġspr int", - "Ġgrand father", - "_ AG", - "_A G", - "Ġs cheduling", - "Ġsched uling", - "CL US", - "CLU S", - "at urity", - "atur ity", - "Ġ locking", - "Ġl ocking", - "Ġloc king", - "Ġlock ing", - "[ size", - "[s ize", - "_ styles", - "_st yles", - "_style s", - "Ġ wb", - "Ġw b", - "-- >ĊĊ", - "-->Ċ Ċ", - "--> ĊĊ", - "Ġsp inning", - "Ġspin ning", - "_ pending", - "_p ending", - "_pen ding", - "Match ers", - "Mat chers", - "Matcher s", - ". Keys", - ".Key s", - "Ġ PV", - "ĠP V", - "en us", - "enu s", - "ant is", - "anti s", - "Ġ discard", - "Ġd iscard", - "Ġdis card", - "Ġdisc ard", - "Ġ haul", - "Ġh aul", - "Ġha ul", - "Ġem pir", - "Ġemp ir", - "Ġpath way", - "Ġo ak", - "Ġoa k", - "м ен", - "ме н", - "-in duced", - "-ind uced", - "Ġim pair", - "Ġimp air", - "ĠCal gary", - ".is Hidden", - "d z", - "_ include", - "_in clude", - "_inc lude", - "Ġ gm", - "Ġg m", - "Ġ' ('", - "Ġ'( '", - "P Y", - "uggest ions", - "uggestion s", - "Ġcom modity", - "Ġcommod ity", - "c ro", - "cr o", - "/ sub", - "/s ub", - "Ġ getInstance", - "Ġget Instance", - "Ġ Legacy", - "ĠLeg acy", - "ĠK il", - "ĠKi l", - "B al", - "Ba l", - "( short", - "(s hort", - "(sh ort", - "In form", - "Info rm", - "Inf orm", - "+ x", - "* r", - "Ġ Hopefully", - "ĠHope fully", - "ĠHop efully", - "o rate", - "or ate", - "ora te", - "Ġm achen", - "Ġma chen", - "Ġmach en", - "Ġmac hen", - "Ġtreat y", - "Ġtre aty", - "ĠO ri", - "ĠOr i", - ". public", - ".p ublic", - ".pub lic", - "- horizontal", - "-h orizontal", - "Ġt actic", - "Ġta ctic", - "Ġtact ic", - "Ġtac tic", - "Ġb ord", - "Ġbo rd", - "Ġbor d", - "w ares", - "ware s", - "wa res", - "war es", - "Ġ ammo", - "Ġa mmo", - "Ġam mo", - "Ġ Lists", - "ĠL ists", - "ĠList s", - "ĠLi sts", - "ĠLis ts", - "Ġequ ations", - "Ġeq uations", - "Ġequation s", - "/ her", - "/h er", - "/he r", - "ĠN SW", - "ĠNS W", - "B ounding", - "Bo unding", - "Bound ing", - "_ Collections", - "_C ollections", - "Ġ avail", - "Ġa vail", - "Ġav ail", - "Ġava il", - ". DropDown", - ".Drop Down", - "è °", - "Ġ hh", - "Ġh h", - "Ġl Ãł", - ". pb", - ".p b", - "Ġmem orial", - "Ġmemor ial", - "Ġmemo rial", - "Ġmemoria l", - "Ġ ATTR", - "ĠAT TR", - "ĠATT R", - "Ġexhaust ed", - "Ġt sp", - "Ġts p", - "ĉ redirect", - "ĉre direct", - "Ġlike wise", - "Ġlik ewise", - "S TER", - "ST ER", - "STE R", - "L java", - "Ġcondem ned", - "Ġcondemn ed", - "oca ust", - "( strict", - "(str ict", - "Ġex empt", - "Ġexem pt", - "Ġexemp t", - "Ġ sms", - "Ġs ms", - "Ġsm s", - "Ġex agger", - "S YS", - "SY S", - "Ġl ounge", - "Ġlo unge", - "Ġlou nge", - "Ġloung e", - ": ^", - "Ġt odd", - "Ġto dd", - "Ġtod d", - "d eb", - "de b", - "at orial", - "ator ial", - "ato rial", - "atori al", - "atoria l", - "ĠPort er", - "ĠPor ter", - "Ġt uition", - "Ġtu ition", - "Ġex empl", - "Ġexem pl", - "Ġexe mpl", - "Ġexemp l", - "Ġ paren", - "Ġp aren", - "Ġpar en", - "Ġpa ren", - "Ġpare n", - ".line To", - "Ġkid ney", - "Ġki dney", - "Ġkidn ey", - "Ġ ça", - "Ġç a", - "Ġc ui", - "Ġcu i", - "ï¼Į 请", - "X C", - "Ġmo ż", - "Ġn ominated", - "Ġno minated", - "Ġnom inated", - "Ġnomin ated", - "Ġnominate d", - "l ung", - "lu ng", - "lun g", - "Im Gui", - "Ġ Buzz", - "ĠB uzz", - "ĠBu zz", - "Ġst ereo", - "Ġste reo", - "Ġster eo", - "Ġstere o", - "p ortal", - "port al", - "por tal", - "res as", - "resa s", - "Ġ klass", - "Ġk lass", - "Ġkl ass", - "Ġkla ss", - "Ġklas s", - "Ġd rafted", - "Ġdraft ed", - "Ġproject ile", - "/g pl", - "( parameters", - "(param eters", - "(parameter s", - "* )Ċ", - "*) Ċ", - "Ġass isted", - "Ġassist ed", - "Ġ NSInteger", - "ĠNS Integer", - "s itemap", - "site map", - "sit emap", - ": nth", - ":n th", - ". Views", - ".View s", - ".Argument Parser", - "Ġ meer", - "Ġm eer", - "Ġme er", - "Ġmee r", - "z ier", - "zi er", - "zie r", - "Ġ Dig", - "ĠD ig", - "ĠDi g", - "Ċ", - ")} >Ċ", - ")}> Ċ", - "Ġp lag", - "Ġpl ag", - "Ġpla g", - "p ine", - "pi ne", - "pin e", - "Ġblank et", - "Ġ : -", - "Ġ lcd", - "Ġl cd", - "Ġlc d", - "- --------------", - "-- -------------", - "---- -----------", - "-------- -------", - "--- ------------", - "------------ ---", - "----- ----------", - "---------- -----", - "------ ---------", - "----------- ----", - "------------- --", - "------- --------", - "--------- ------", - "-------------- -", - "( \"\"", - "(\" \"", - "Ġt actical", - "Ġtact ical", - "Ġtactic al", - "Ġtac tical", - "ĠR onald", - "ĠRon ald", - "ex tr", - "ext r", - "ĠF est", - "ĠFe st", - "Ġf uer", - "Ġfu er", - "Ġfue r", - "- navigation", - "-n avigation", - "-nav igation", - "Ġ kb", - "Ġk b", - "g host", - "gh ost", - "Ġ handleChange", - "Ġhandle Change", - "_ cls", - "_c ls", - "_cl s", - "( )!=", - "() !=", - "Com parator", - "Compar ator", - ". vm", - ".v m", - "ĠC ox", - "ĠCo x", - "_ review", - "_re view", - "_r eview", - "_rev iew", - "/ @", - "_ cookie", - "_c ookie", - "_co okie", - "Ġrecogn ised", - "Ġrecognise d", - "l dap", - "ld ap", - "lda p", - "Th reads", - "Thread s", - "ĠS exual", - "ĠSex ual", - "ĠB earing", - "ĠBe aring", - "ĠBear ing", - "ĠBea ring", - "( SQL", - "(S QL", - "Ġ xr", - "Ġx r", - "Ġt high", - "Ġth igh", - "Ġthi gh", - "URL Connection", - "ĠS UV", - "ĠSU V", - "Ġm Context", - "Ġinc idence", - "Ġincid ence", - "Ġ Este", - "ĠE ste", - "ĠEs te", - "ĠEst e", - ". sup", - ".s up", - "_ te", - "_t e", - "( EXIT", - "(EX IT", - "C MD", - "CM D", - "/ \">", - "/\" >", - "Al most", - "Ġ Une", - "ĠU ne", - "ĠUn e", - "Ġand eren", - "Ġandere n", - "Ġander en", - "Ġ Singleton", - "ĠS ingleton", - "ĠSing leton", - "ĠSingle ton", - "Ġb ore", - "Ġbo re", - "Ġbor e", - "Th ink", - "Thin k", - "Ġn arc", - "Ġna rc", - "Ġnar c", - "] initWith", - "]init With", - "_ shop", - "_s hop", - "_sh op", - "( strategy", - "(str ategy", - "! ',", - "!' ,", - "her its", - "herit s", - "Ġ Desk", - "ĠD esk", - "ĠDe sk", - "ĠDes k", - "_ machine", - "_m achine", - "_ma chine", - ".n etty", - ".net ty", - ".ne tty", - "ı nda", - "ın da", - "ınd a", - "= <", - "Ġ QR", - "ĠQ R", - "Ġ Sidebar", - "ĠS idebar", - "ĠSide bar", - ".split Container", - "Ġon Success", - "Ġ monkey", - "Ġmon key", - "Ġmonk ey", - "En joy", - "( nodes", - "(n odes", - "(node s", - "(no des", - "pect rum", - "Ġ (*(", - "Ġ( *(", - "Ġ(* (", - "ĉ UINT", - "ĉU INT", - "ĉUI NT", - ", height", - ",h eight", - "ĠNetwork s", - "ĠNet works", - ". tail", - ".t ail", - ".ta il", - ".l inspace", - ".lin space", - "Ġ \"...", - "Ġ\" ...", - "Ġ\". ..", - "Ġ\".. .", - "L isten", - "List en", - "Li sten", - "Æ ¡", - ". Channel", - ".Ch annel", - "- defined", - "-d efined", - "-def ined", - "Re peat", - "Rep eat", - "ad just", - "adj ust", - "E RM", - "ER M", - "_ application", - "_app lication", - "_ap plication", - ".assert NotNull", - ".assertNot Null", - "- stream", - "-st ream", - "-str eam", - "Ġ rabbit", - "Ġr abbit", - "Ġrab bit", - "Ġposition ing", - "Ġ woke", - "Ġw oke", - "Ġwo ke", - "Ġf ing", - "Ġfin g", - "Ġfi ng", - "Ġmulti player", - "Ġmultip layer", - "Ġregister ing", - "Ġregist ering", - "un til", - "unt il", - "Ã¥ n", - "( ::", - "(: :", - "uss ions", - "ussion s", - "Ġpot ato", - "Ġ Equals", - "ĠE quals", - "ĠEqu als", - "ĠEqual s", - ". Sup", - ".S up", - "/ apache", - "/ap ache", - "Ġ (=", - "Ġ( =", - ". \")", - ".\" )", - ". ptr", - ".p tr", - ".pt r", - "Ġ Speech", - "ĠS peech", - "ĠSpe ech", - ". clip", - ".c lip", - ".cl ip", - ".cli p", - "ĠGab riel", - "ĠGabri el", - "Ġmus ician", - "Ġmusic ian", - "/ issues", - ". shop", - ".s hop", - ".sh op", - "Ġ Hier", - "ĠH ier", - "ĠHi er", - "_ RET", - "_RE T", - "_R ET", - "_ bucket", - "_b ucket", - "ãĥ ¡", - "a vs", - "av s", - "Ġ roz", - "Ġr oz", - "Ġro z", - "f lower", - "fl ower", - "flow er", - "flo wer", - "Write Barrier", - "ĠM ilan", - "ĠMil an", - "ĠMi lan", - "Ġlegisl ature", - "ĠD oll", - "ĠDo ll", - "ĠDol l", - "Ġpro ving", - "Ġpr oving", - "Ġprov ing", - ".concat enate", - "âķ IJ", - "Ġg char", - "Ġgc har", - "cdn js", - "b les", - "ble s", - "bl es", - "Ġ Listing", - "ĠL isting", - "ĠList ing", - "ĠLis ting", - "л о", - ".xr Label", - "ĠS ak", - "ĠSa k", - "just ice", - "ju stice", - "ĠVal entine", - "ĠValent ine", - "un less", - "Ġp iger", - "Ġpi ger", - "Ġpig er", - "Ġpige r", - "( run", - "(r un", - "Ġtest ified", - "A NA", - "AN A", - "ĠRe moves", - "ĠRem oves", - "ĠRemove s", - ") )));Ċ", - ")) ));Ċ", - "))) );Ċ", - ")))) ;Ċ", - "rec ated", - "ĠRuntime Method", - "Ġcon qu", - "ãĤ ¢", - "Ġt issues", - "Ġtissue s", - "a iler", - "ail er", - "ai ler", - "é té", - "ét é", - "- Star", - "-S tar", - "-St ar", - "Ġfl ames", - "Ġflame s", - "Ġflam es", - "Ġfla mes", - ". setIcon", - ".set Icon", - "Ġsup ern", - "Ġsuper n", - "Ġv agina", - "Ġvag ina", - "- variable", - "-var iable", - "Ġwell ness", - "C UR", - "CU R", - "Ġb elle", - "Ġbe lle", - "Ġbel le", - "Ġbell e", - ". getRequest", - ".get Request", - "Ġp oco", - "Ġpo co", - "Ġpoc o", - "b enh", - "be nh", - "ben h", - "a gens", - "ag ens", - "age ns", - "agen s", - "Ġs pill", - "Ġsp ill", - "Ġspi ll", - "Ġ Jur", - "ĠJ ur", - "ĠJu r", - "Ġ dispatcher", - "Ġdispatch er", - "Ġdisp atcher", - "н ого", - "но го", - "ног о", - "e monic", - "em onic", - "emo nic", - "emon ic", - "( dirname", - "(dir name", - "Ġ ÐĶ", - "ĠÐ Ķ", - "Ġp asse", - "Ġpass e", - "Ġpas se", - "Ġpa sse", - "Ġg anz", - "Ġga nz", - "Ġgan z", - "r icing", - "ri cing", - "ric ing", - "E U", - "Ġmuj eres", - "Ġmujer es", - "es sen", - "ess en", - "esse n", - ". attribute", - ".at tribute", - ".attrib ute", - "j j", - "ĉ ĉĠĊ", - "ĉĉ ĠĊ", - "ĉĉĠ Ċ", - "[ ^", - "Ġ strtolower", - "Ġstr tolower", - "Ġstrtol ower", - "lex er", - "ect ar", - "ec tar", - "ecta r", - "h otel", - "ho tel", - "hot el", - ". square", - ".s quare", - "Ġr all", - "Ġra ll", - "Ġl owered", - "Ġlow ered", - "Ġlower ed", - "handle d", - "hand led", - "M arket", - "Mark et", - "Mar ket", - "Ġ Uses", - "ĠU ses", - "ĠUs es", - "ĠUse s", - "i vas", - "iv as", - "iva s", - ". Business", - ".B usiness", - ".Bus iness", - "ãģĹ ãģ¦", - "ãģĹãģ ¦", - "D IV", - "DI V", - "Ġw asted", - "Ġwas ted", - "Ġwa sted", - "Ġwaste d", - "Ġwast ed", - "Ġa voir", - "Ġav oir", - "ê m", - "_ ACCOUNT", - "_AC COUNT", - "_ACC OUNT", - ". et", - ".e t", - "ĉ SDL", - "ĉS DL", - "k ap", - "ka p", - "Ġ fox", - "Ġf ox", - "Ġfo x", - "up pet", - "upp et", - "uppe t", - "{ },Ċ", - "{} ,Ċ", - "{}, Ċ", - "\" ,'", - "\", '", - "F avorite", - "P END", - "PE ND", - "Ġ AES", - "ĠA ES", - "ĠAE S", - "} ),", - "}) ,", - "Ġde duction", - "Ġded uction", - "Ġdeduct ion", - "Ġpol ÃŃt", - "Ġcomponent Will", - "ĠT elerik", - "ĠTele rik", - "_ SELF", - "_SE LF", - "_SEL F", - "Ġm use", - "Ġmus e", - "Ġmu se", - "C raft", - "Cr aft", - "Ġ dens", - "Ġd ens", - "Ġde ns", - "Ġden s", - "ठ¿", - "( tp", - "(t p", - "Ġt asty", - "Ġta sty", - "Ġtas ty", - "Ġtast y", - "Ġ balances", - "Ġbalance s", - "Ġbal ances", - "Ġded ication", - "Ġdedic ation", - "Ġdedi cation", - "ĠWall ace", - "ĠWal lace", - "Ġun law", - "Ġunl aw", - "\\ \">\\", - "\\\" >\\", - "\\\"> \\", - "Ġm um", - "Ġmu m", - "- update", - "-up date", - "e mente", - "em ente", - "ement e", - "eme nte", - "emen te", - "Ġs oda", - "Ġso da", - "Ġsod a", - "Re public", - "Rep ublic", - "as mine", - "asm ine", - "é ric", - "ér ic", - "éri c", - "( Status", - "(S tatus", - "ĠJson Convert", - "Ġ Disk", - "ĠD isk", - "ĠDis k", - "ĠDi sk", - ". Redirect", - ".Re direct", - ".Red irect", - "Ġfil ming", - "Ġfilm ing", - "/ mol", - "/m ol", - "R o", - "Ġ ville", - "Ġv ille", - "Ġvi lle", - "Ġvill e", - "Ġvil le", - "Ġtr abaj", - "Ġtrab aj", - "Ġs ynthesis", - "Ġsyn thesis", - "Ġsynth esis", - "Ġsynthes is", - "r ega", - "re ga", - "reg a", - "Ġ rl", - "Ġr l", - "S cheduler", - "Schedule r", - "ISH ED", - "current User", - "( errors", - "(err ors", - "(error s", - "(er rors", - "' h", - "_ bot", - "_b ot", - "_bo t", - "x imo", - "xi mo", - "Ġ USART", - "ĠUS ART", - "ĠUSA RT", - "_ super", - "_s uper", - "_sup er", - "_su per", - "_ DECREF", - "_DEC REF", - "н ой", - "но й", - "_ ROW", - "_R OW", - "_RO W", - "Ġprom otes", - "Ġpromote s", - "Ġpromot es", - "Ġpromo tes", - "Ġ TA", - "ĠT A", - "Ġh oras", - "Ġhor as", - "Ġho ras", - "Ġhora s", - "ĠRep resents", - "ĠRepresent s", - "Ġ nameof", - "Ġname of", - "Ġnam eof", - "Ġ Exc", - "ĠE xc", - "ĠEx c", - "ĠGar age", - "ĠGa rage", - "Ġse ine", - "Ġsein e", - "Ġsei ne", - ", #", - "Ġh erb", - "Ġhe rb", - "Ġher b", - "/ resources", - "/re sources", - "/res ources", - "/resource s", - "Ġple aded", - "Ġplea ded", - "Ġplead ed", - ".r adioButton", - ".radio Button", - "Ġ æĺ", - "Ġæ ĺ", - "O ps", - "Op s", - "ĠN est", - "ĠNe st", - "ĠNes t", - "c string", - "cs tring", - "ĠDef ence", - "Ġref ere", - "Ġrefer e", - "_ leaf", - "_le af", - "Ġreve lation", - "Ġrevel ation", - "ë §", - ".execute Update", - "_W ORLD", - "Ġexp ans", - "(\" \\\"", - "(\"\\ \"", - "j ab", - "ja b", - "Ġdoub ts", - "Ġdoubt s", - "Ġ Geometry", - "ĠGe ometry", - "ĠGeo metry", - "Ġintrodu ces", - "Ġintroduce s", - "Ġsen ators", - "Ġsenator s", - "Ġc anal", - "Ġcan al", - "Ġca nal", - ". helper", - ".h elper", - ".help er", - "ĠB iology", - "ĠBi ology", - "ĠBio logy", - "ĠBiol ogy", - "_ SENS", - "_S ENS", - "_SE NS", - ". previous", - ".pre vious", - ".prev ious", - "- touch", - "-t ouch", - "-to uch", - "a bit", - "ab it", - "abi t", - "Ġimp acted", - "Ġimpact ed", - "Ġbr ackets", - "Ġbracket s", - ". direct", - ".d irect", - ".dir ect", - ".di rect", - "ac cum", - "acc um", - "Ġtest osterone", - "ĉ action", - "ĉa ction", - "ĉac tion", - "ĉact ion", - "Ġ Chance", - "ĠCh ance", - "ĠCha nce", - "ĠChan ce", - "Ġpe aks", - "Ġpeak s", - "Ġpea ks", - "CppCodeGen WriteBarrier", - "Ġun belie", - "Ġunbe lie", - "_ press", - "_p ress", - "_pr ess", - "_pre ss", - "_pres s", - ". Rel", - ".R el", - ".Re l", - "ang led", - "angle d", - "angl ed", - "/ templates", - "/t emplates", - "/template s", - "/temp lates", - "-- >čĊ", - "--> čĊ", - "l ime", - "li me", - "lim e", - "Ġsufficient ly", - "_ nt", - "_n t", - "Exp and", - ".is file", - "Ġ isEmpty", - "Ġis Empty", - "Ġ qt", - "Ġq t", - "Ġmul her", - "a cob", - "ac ob", - "aco b", - "Ge orge", - "å¸ ¸", - "Ġas sim", - "Ġass im", - "a so", - "as o", - "Ġcompr ised", - "Ġcomprise d", - "O V", - "( CONFIG", - "(CON FIG", - "ĉ writer", - "ĉw riter", - "ĉwrite r", - "Ġd esp", - "Ġde sp", - "Ġdes p", - "Ġten ure", - "( cr", - "(c r", - ". pool", - ".p ool", - ".po ol", - "ĠB rend", - "ĠBr end", - "ĠBre nd", - "ĠBren d", - "Ġc ensor", - "( timeout", - "(time out", - "Ġp lea", - "Ġpl ea", - "Ġple a", - ". Wrap", - ".W rap", - "Ġt ightly", - "Ġtight ly", - "Ġ Were", - "ĠW ere", - "ĠWe re", - "ĠWer e", - "Ġ Ignore", - "ĠI gnore", - "ĠIgn ore", - "ĠIg nore", - "a bei", - "ab ei", - "abe i", - "Ġbr idges", - "Ġbridge s", - "Ġbrid ges", - "Ġcondem n", - "Ġsimp licity", - "Ġsimpl icity", - "Ġrout inely", - "Ġroutine ly", - "Ġbl acks", - "Ġblack s", - "Ġbla cks", - "j b", - "ĠP it", - "ĠPi t", - "U tf", - "Ut f", - "Ġ /Ċ", - "Ġ/ Ċ", - "r eload", - "re load", - "rel oad", - "Ġset Object", - "/ global", - "/g lobal", - "Ġf atty", - "Ġfa tty", - "Ġfat ty", - "Ġfatt y", - "Ġs ocks", - "Ġso cks", - "Ġsock s", - "Ġsoc ks", - "Could n", - "Ġerot isk", - "æĿ ¡", - "Ġ Pressure", - "ĠPres sure", - "ĠPress ure", - "ĠM az", - "ĠMa z", - "n pos", - "np os", - "to lower", - "tol ower", - "Ġ EQ", - "ĠE Q", - "ut eur", - "ute ur", - "Ġ Moment", - "ĠM oment", - "ĠMo ment", - "ĠMom ent", - "Ġ eta", - "Ġe ta", - "Ġet a", - "{{ --", - "Ġ graphs", - "Ġgraph s", - "Ġgrap hs", - "ĠG uar", - "ĠGu ar", - "r ine", - "ri ne", - "rin e", - "( --", - "(- -", - "Ġ HttpStatus", - "ĠHttp Status", - "( student", - "(st udent", - "* np", - "*n p", - "Ġrail way", - "Ġas ynchronous", - "_ vm", - "_v m", - "' ],'", - "'] ,'", - "'], '", - ", text", - ",t ext", - "m erchant", - "mer chant", - "( Guid", - "(G uid", - "ĠG ra", - "ĠGr a", - "ix er", - "ixe r", - "fetch All", - ". addListener", - ".add Listener", - "f lip", - "fl ip", - "* $", - "> (),", - ">( ),", - ">() ,", - "Ġsun light", - "as signed", - "ass igned", - "assign ed", - "Ġ abc", - "Ġa bc", - "Ġab c", - "Ġ COLUMN", - "ĠC OLUMN", - "ĠðŁĻĤ ĊĊ", - ") ...", - "). ..", - ").. .", - "Ġ ensemble", - "Ġen semble", - "Ġens emble", - "Ġ newline", - "Ġnew line", - "_S INGLE", - "i edad", - "ie dad", - "ied ad", - "Ġd arker", - "Ġdark er", - "Ġdar ker", - "or map", - "orm ap", - "Ġ lion", - "Ġl ion", - "Ġli on", - "pl its", - "plit s", - "Ġillustr ation", - "Ġillust ration", - "Ġ IEEE", - "ĠI EEE", - "ĠIE EE", - "Ġv ista", - "Ġvis ta", - "Ġvi sta", - "ous ands", - "ousand s", - "* ******", - "** *****", - "**** ***", - "****** *", - "*** ****", - "***** **", - "ĠTom my", - "Ġ hue", - "Ġh ue", - "Ġhu e", - "S el", - "Se l", - "Ġ aura", - "Ġa ura", - "Ġau ra", - "Ġaur a", - "ĠThe rapy", - "ĠTher apy", - "Ġan imator", - "Ġanim ator", - ". constraints", - ".con straints", - ".constraint s", - "Ġv ague", - "Ġva gue", - "Ġvag ue", - "(\" \")", - "(\"\" )", - "Ġvill ain", - "Ġvil lain", - "Ġvilla in", - "Ġbless ing", - "Ġstring Builder", - "Ġ Misc", - "ĠM isc", - "ĠMi sc", - "ĠMis c", - "Ġ DIR", - "ĠD IR", - "ĠDI R", - "f ax", - "fa x", - "- node", - "-n ode", - "-no de", - "Ġ Walking", - "ĠW alking", - "ĠWalk ing", - "ĠWal king", - "Ġ AU", - "ĠA U", - "s ess", - "se ss", - "ses s", - "Ġgr ill", - "Ġgri ll", - "VERT ISE", - "ĠF oods", - "ĠFood s", - "ĠFo ods", - "ĠFoo ds", - "Ġt ournaments", - "Ġtour naments", - "Ġtournament s", - "à ĵ", - "Ġ Marsh", - "ĠMar sh", - "ĠMars h", - "Ġw onders", - "Ġwon ders", - "Ġwonder s", - "Long itude", - ".Command Text", - "= input", - "=in put", - "_ encoder", - "_e ncoder", - "_en coder", - "_encode r", - "_enc oder", - "page Size", - "Ġ getState", - "Ġget State", - "> >Ċ", - ">> Ċ", - ". grey", - ".g rey", - ".gr ey", - "p od", - "po d", - "Ġread ings", - "Ġreading s", - "Ġre consider", - "Start up", - "Ġex cer", - "Ġexc er", - "Ġexce r", - ". balance", - ".b alance", - "_ cycle", - "_c ycle", - "_ Time", - "_T ime", - "LO CAL", - "LOC AL", - "Ġ EFI", - "ĠE FI", - "ĠEF I", - "ĠR eyn", - "ĠRe yn", - "ĠRey n", - ".set Foreground", - "b yn", - "by n", - "Ġdis connected", - "Ġdisconnect ed", - "ACT IVE", - "Ġ embedding", - "Ġembed ding", - "ic kers", - "ick ers", - "icker s", - "Ġsurround ings", - "Ġsurrounding s", - "* c", - "Ġgar ant", - "Ġga rant", - "Ġ bf", - "Ġb f", - "Ġ wipe", - "Ġw ipe", - "Ġwi pe", - "Ġ ä¸ĭ", - "Ġä¸ ĭ", - "_ TRA", - "_T RA", - "_TR A", - "ad ox", - "ado x", - "ç ķ", - "Ġs ucks", - "Ġsu cks", - "Ġsuc ks", - "Ġsuck s", - "Ġ Songs", - "ĠS ongs", - "ĠSon gs", - "ĠSong s", - "ĠAssoci ates", - "ĠAssociate s", - "ĠAssoc iates", - "ĠB ald", - "ĠBa ld", - "ĠBal d", - "ĠB rett", - "ĠBr ett", - "ĠBre tt", - "ĠBret t", - "ven ile", - "Ġ vt", - "Ġv t", - "Ġin ade", - "Ġre signed", - "Ġres igned", - "Ġresign ed", - "ĠGl enn", - "ĠGlen n", - "ĠGle nn", - ". pattern", - ".p attern", - ".pat tern", - ".Data Bind", - "Ñĥ м", - "Layout Inflater", - "c het", - "ch et", - "che t", - "ĠTest ament", - ". ms", - ".m s", - "Ġp av", - "Ġpa v", - "Ġ ReactDOM", - "ĠReact DOM", - "ur dy", - "urd y", - "A DATA", - "AD ATA", - "ADA TA", - "M u", - "/ actions", - "/a ctions", - "/action s", - "Ġ Js", - "ĠJ s", - "_ extract", - "_ex tract", - "_ext ract", - "_extra ct", - "Ġ Bring", - "ĠB ring", - "ĠBr ing", - "ĠBri ng", - ": id", - ":i d", - "st rt", - "str t", - "i vation", - "iv ation", - "iva tion", - "Ġout right", - "Ġou tright", - "Ġoutr ight", - "a zu", - "az u", - "loy ment", - "и Ñı", - "al do", - "ald o", - "Ġ Publisher", - "ĠP ublisher", - "ĠPublish er", - "E ducation", - "Educ ation", - "P alette", - "Pal ette", - "Pa lette", - "Pale tte", - "_ drv", - "_d rv", - "_dr v", - "Ġ ($(", - "Ġ( $(", - "Ġ($ (", - "ĠA nda", - "ĠAn da", - "ĠAnd a", - "Ġrem edy", - "Ġremed y", - "Ġin consistent", - "Ġincons istent", - "Ġinconsist ent", - "t ection", - "te ction", - "tec tion", - "Ġreg ulators", - "Ġregul ators", - "Ġregulator s", - "Ġshort est", - "( pair", - "(p air", - "(pa ir", - "Ġ Installation", - "ĠInstall ation", - "Ġdef endants", - "Ġdefend ants", - "Ġdefendant s", - "Ġ ();", - "Ġ( );", - "Ġ() ;", - "- large", - "-l arge", - "M el", - "Me l", - "Ġthreat en", - "н Ñı", - "Ġfet ish", - "ot ine", - "oti ne", - "_ dic", - "_d ic", - "_di c", - "Ġ <$", - "Ġ< $", - "Ġst agger", - "Ġsta gger", - "Ġstag ger", - "s pi", - "sp i", - "$ response", - "$res ponse", - "S erv", - "Se rv", - "Ser v", - "- born", - "-b orn", - "-bo rn", - "j os", - "jo s", - "ĉ img", - "ĉi mg", - "ĉim g", - "ĉ WHERE", - "ĉW HERE", - "_ lt", - "_l t", - "å½ ĵ", - ". cost", - ".c ost", - ".co st", - ".cos t", - "Ġ Tue", - "ĠT ue", - "ĠTu e", - ". labels", - ".label s", - ".lab els", - "Ġ LV", - "ĠL V", - "wcs store", - "ĠJ esse", - "ĠJes se", - "ĠJe sse", - "ĠJess e", - "ภ«", - "T rade", - "Tr ade", - "Trad e", - "Tra de", - "Ġpredecess or", - "ë Ĥ", - "f inally", - "fin ally", - "final ly", - "_ general", - "_g eneral", - "_gen eral", - "_gene ral", - "_gener al", - "oggle r", - "ogg ler", - "_ REGION", - "_REG ION", - "n ement", - "ne ment", - "nem ent", - "Ġb logger", - "Ġblog ger", - "Ġblo gger", - "ĠHar bor", - "Ġ Dataset", - "ĠD ataset", - "ĠData set", - "ĠDat aset", - "[ w", - "Ġattend ees", - "Ġattendee s", - ". ico", - ".i co", - ".ic o", - "max imum", - ". Unlock", - ".Un lock", - "_ SYNC", - "_S YNC", - "_SY NC", - "_SYN C", - "ág ina", - "Ġd owns", - "Ġdown s", - "Ġdow ns", - "ĠW ii", - "ĠWi i", - "] )/", - "]) /", - "Ġk icking", - "Ġkick ing", - "un ication", - "unic ation", - "uni cation", - "Ġ DAC", - "ĠD AC", - "ĠDA C", - "Ġ IDS", - "ĠI DS", - "ĠID S", - "ĠR ental", - "ĠRen tal", - "ĠRent al", - "Ġ currentTime", - "Ġcurrent Time", - "Ġvacc ines", - "Ġvaccine s", - "ĠD evil", - "ĠDe vil", - "ĠDev il", - "Ġn ors", - "Ġno rs", - "Ġnor s", - "_ mouse", - "_m ouse", - "_mo use", - "ur rection", - "urre ction", - "urr ection", - "urrect ion", - "( no", - "(n o", - "Ġ >čĊ", - "Ġ> čĊ", - "Ġag gression", - "Ġaggress ion", - "Ġagg ression", - "Ġbre eding", - "Ġbreed ing", - "Ġbree ding", - ". symbol", - ".s ymbol", - ".sym bol", - "i man", - "im an", - "ima n", - "Absolute Path", - "Ġ WHO", - "ĠW HO", - "ĠWH O", - "_ flush", - "_f lush", - "_fl ush", - "- root", - "-r oot", - "-ro ot", - "a rna", - "ar na", - "arn a", - "& M", - "Ġf athers", - "Ġfa thers", - "Ġfather s", - "Ġ Rocket", - "ĠR ocket", - "ĠRock et", - "ĠRo cket", - "ĠRoc ket", - "i veau", - "ive au", - "Ġw ander", - "Ġwa nder", - "Ġwand er", - "Ġwan der", - "Ġcom pos", - "Ġcomp os", - "ĠWar rior", - "Ġ Seat", - "ĠS eat", - "ĠSe at", - "ĠSea t", - "ĠCl inic", - "ĠClin ic", - "ĠCli nic", - "_ invoice", - "_in voice", - "_inv oice", - "( dispatch", - "(dis patch", - "Product o", - "at uring", - "atur ing", - "atu ring", - "oss ier", - "ĠM AY", - "ĠMA Y", - "Ġd agger", - "Ġda gger", - "Ġdag ger", - "Ġsan itized", - "Ġsanit ized", - "Ġsanitize d", - "Ġ RFC", - "ĠR FC", - "ĠRF C", - "Ġp roph", - "Ġpro ph", - "Ġpr oph", - "Ġprop h", - "Ġu rine", - "Ġur ine", - "Ġuri ne", - "Ġgr ind", - "Ġgri nd", - "Ġgrin d", - "Ġ Expanded", - "ĠExp anded", - "ĠExpand ed", - "des cripcion", - "- fw", - "-f w", - "ĠK erry", - "ĠKer ry", - "ĠKerr y", - "= name", - "=n ame", - "Ġ chk", - "Ġc hk", - "Ġch k", - "Ġn ationally", - "Ġnational ly", - "Ġnation ally", - "Ġt hee", - "Ġth ee", - "Ġthe e", - "I nc", - "In c", - "Ġ ?>>", - "Ġ? >>", - "Ġ?> >", - ". RadioButton", - ".R adioButton", - ".Http ServletResponse", - ".HttpServlet Response", - "/ Y", - "ĉ field", - "ĉf ield", - "ĉfi eld", - "Ġ homme", - "Ġhom me", - "y per", - "ype r", - "yp er", - "Ph ysical", - "Phys ical", - "= v", - "Ġd riv", - "Ġdr iv", - "Ġdri v", - "Ġ Errors", - "ĠError s", - "ĠEr rors", - "ĠErr ors", - "Ġc Äĥ", - "De ath", - "Ġ WINDOW", - "ĠW INDOW", - "Ġpo et", - "Ġ Sharp", - "ĠSh arp", - "ĠSha rp", - "ĠShar p", - "Ġ Immutable", - "ĠIm mutable", - "ĠImm utable", - "ĉ create", - "ĉc reate", - "Ġge ht", - "Ġgeh t", - "ĠRe form", - "ĠRef orm", - "a iser", - "ai ser", - "ais er", - "aise r", - "Ġ Initialization", - "ĠInitial ization", - "Ġimm unity", - "Ġimmun ity", - ". compose", - ".com pose", - ".comp ose", - "Ġlate ncy", - "Ġlat ency", - "Ġlaten cy", - "ĠLeban on", - "ĠPar ad", - "ĠPa rad", - "ĠPara d", - "Ġf uels", - "Ġfuel s", - "Ġfu els", - "Ġfue ls", - "ĠEx hib", - "c oh", - "co h", - "% \">Ċ", - "%\" >Ċ", - "%\"> Ċ", - "Ġ CLI", - "ĠC LI", - "ĠCL I", - ") initWith", - ")init With", - "- Za", - "-Z a", - "_ CLEAR", - "_C LEAR", - "_CL EAR", - "re gn", - "reg n", - "Ġfin ances", - "Ġfinance s", - "Ġfinanc es", - "Ġfinan ces", - ". standard", - ".st andard", - "_ CATEGORY", - "_C ATEGORY", - ". library", - ".l ibrary", - ".lib rary", - "Ġtravel ers", - "Ġtraveler s", - "_ wp", - "_w p", - "Ġ Evaluation", - "ĠE valuation", - "ĠEval uation", - "ĠEvalu ation", - "start ing", - "star ting", - "Ġ )),Ċ", - "Ġ) ),Ċ", - "Ġ)) ,Ċ", - "ep isode", - "Ġ Variant", - "ĠV ariant", - "ĠVar iant", - "ĠVari ant", - "Ġ daemon", - "Ġda emon", - "ĠJ ulia", - "ĠJul ia", - "ĠJu lia", - "ĠJuli a", - "Ġ NR", - "ĠN R", - "Ġd oubles", - "Ġdouble s", - "Ġdoub les", - "Ġdou bles", - "< v", - "/ runtime", - "/r untime", - "/run time", - "Ġ interpreter", - "Ġinter preter", - "Ġinterpret er", - "Ġinterpre ter", - "Ġ INDEX", - "ĠIN DEX", - "ĠIND EX", - "ĠHol mes", - "_ DIM", - "_D IM", - "_DI M", - "Ġp addle", - "Ġpad dle", - "Ġpadd le", - "_ example", - "_ex ample", - "_exam ple", - "Ġ foreground", - "Ġfore ground", - ". routes", - ".r outes", - ".route s", - ".ro utes", - "Ġs owie", - "Ġso wie", - "Ġsow ie", - "S UCCESS", - "Ġ CDC", - "ĠC DC", - "ĠCD C", - "Ġ BD", - "ĠB D", - "_ -", - "as ured", - "asure d", - "asu red", - "W riting", - "Wr iting", - "Ġ currentPage", - "Ġcurrent Page", - "( answer", - "(ans wer", - "(an swer", - "Ġ ASCII", - "ĠA SCII", - "ĠASC II", - "à ¨", - "Ġsocial ly", - "Ġsoc ially", - "Ġsoci ally", - "y yy", - "yy y", - "ĠSpecial ist", - "( customer", - "(c ustomer", - "(custom er", - "ist ani", - "istan i", - "ista ni", - "k est", - "ke st", - "kes t", - "ĠM ak", - "ĠMa k", - "Ġt ho", - "Ġth o", - ". pt", - ".p t", - "( comment", - "(com ment", - "(comm ent", - "Ġ Converter", - "ĠCon verter", - "ĠConvert er", - "g am", - "ga m", - "b ins", - "bin s", - "bi ns", - ". tele", - ".t ele", - ".te le", - ".tel e", - "ĠVeter ans", - "ĠVeteran s", - "_ ALLOC", - "_AL LOC", - "_ALL OC", - "олÑĮзов аÑĤ", - "inn amon", - "; width", - "o hl", - "oh l", - "Ġf antas", - "Ġfan tas", - "Ġfant as", - "Ġs ung", - "Ġsu ng", - "Ġsun g", - "ĉ K", - "( Json", - "(J son", - "Ġneighbour hood", - "Ġv ow", - "Ġvo w", - "Ġs ins", - "Ġsi ns", - "Ġsin s", - "on acci", - "ona cci", - "Ġ epochs", - "Ġepoch s", - "im agen", - "image n", - "ima gen", - "imag en", - ". Change", - ".Ch ange", - ".my batis", - "Se ek", - "See k", - "W ER", - "WE R", - "管 çIJĨ", - "Ġinter ess", - "Ġinte ress", - "Ġinteres s", - "_ Event", - "_E vent", - "ed erland", - "eder land", - "Ġterr itor", - "Ġci udad", - "uc ked", - "uck ed", - "Ġsn ack", - "Ġsna ck", - "Ġtrans ported", - "Ġtransport ed", - "Ġtransporte d", - "Ġ Manifest", - "ĠMan ifest", - "Ġ DAT", - "ĠD AT", - "ĠDA T", - "_ theta", - "_th eta", - "_the ta", - "Ġw ont", - "Ġwon t", - "Ġwo nt", - ". ĊĊĊĊĊĊĊĊĊĊ", - ".ĊĊ ĊĊĊĊĊĊĊĊ", - ".Ċ ĊĊĊĊĊĊĊĊĊ", - ".ĊĊĊĊ ĊĊĊĊĊĊ", - ".ĊĊĊ ĊĊĊĊĊĊĊ", - ".ĊĊĊĊĊĊ ĊĊĊĊ", - ".ĊĊĊĊĊĊĊĊ ĊĊ", - ".ĊĊĊĊĊ ĊĊĊĊĊ", - "Ĭ¶ æĢģ", - "ĠE pic", - "ĠEp ic", - "D eck", - "De ck", - "Dec k", - "l tra", - "lt ra", - "ltr a", - "_ ZERO", - "_Z ERO", - "Ġ[ ];", - "Ġ[] ;", - "/ scripts", - "/s cripts", - "/script s", - "Ġ --------------------------------------------------------------------------------", - "Ġ---------------------------------------------------------------- ----------------", - "Ġ---- ----------------------------------------------------------------------------", - "Ġ---------------- ----------------------------------------------------------------", - "Ġ------------------------------------------------ --------------------------------", - "Ġ-------------------------------- ------------------------------------------------", - "Ġ---------------------------------------------------------------------------- ----", - "Ġ---------- ----------------------------------------------------------------------", - "Ġ------------------------------------------------------------ --------------------", - "Ġ------------------------------------------------------------------------- -------", - "æĥ ħ", - "Ġ weed", - "Ġw eed", - "Ġwe ed", - "Ġwee d", - "N BC", - "NB C", - "Ġr aped", - "Ġrap ed", - "Ġra ped", - "Ġrape d", - "Ġ Gateway", - "ĠG ateway", - "ĠGate way", - "ĠGat eway", - "[ M", - "Ġ Timeout", - "ĠTime out", - "ench mark", - ". ViewModel", - ".View Model", - "Ġporn os", - "Ġpor nos", - "Ġporno s", - "Ġ Ya", - "ĠY a", - "th ritis", - "thr itis", - "ĠFl ynn", - "ĠFly nn", - "Ġ mega", - "Ġm ega", - "Ġme ga", - "Ġmeg a", - "a cin", - "ac in", - "aci n", - "Ġtr ibal", - "Ġtri bal", - "Ġtrib al", - ". apple", - ".app le", - ".ap ple", - "Ġ Blo", - "ĠB lo", - "ĠBl o", - "â n", - "i bi", - "ib i", - "r ov", - "ro v", - "ĠL ives", - "ĠLive s", - "ĠLi ves", - "ĠLiv es", - "^ .", - "get Request", - "Ġ Establish", - "ĠEst ablish", - "cont ainers", - "container s", - "contain ers", - "Ġst arring", - "Ġstar ring", - "Ġcele brities", - "Ġcelebr ities", - "Ġ Relative", - "ĠRel ative", - "ĠHe ights", - "ĠHeight s", - "Ġtq dm", - "ĠNorth west", - "i vic", - "iv ic", - "ivi c", - "ĉ cl", - "ĉc l", - "Ġautom otive", - "ent ric", - "entr ic", - "Ġ fortunate", - "Ġfort unate", - "Ġfire place", - "Ġfi replace", - "se ud", - "n ickname", - "nick name", - "; s", - "_ CAL", - "_C AL", - "_CA L", - "h alt", - "ha lt", - "hal t", - "( ns", - "(n s", - "_ deleted", - "_de leted", - "_delete d", - "_del eted", - "De velopment", - "Dev elopment", - "Develop ment", - "m ovies", - "movie s", - "mov ies", - "Ġid entities", - "Ġident ities", - "Ġprompt ly", - "ا ÙĨ", - "ا٠Ĩ", - "Ġ ante", - "Ġa nte", - "Ġan te", - "Ġant e", - "Ġ\" ','", - "Ġ\"' ,'", - "Ġ\"', '", - "åı £", - "imp se", - "imps e", - "Ġy ap", - "Ġya p", - "Type Name", - "Ġb itch", - "Ġbit ch", - "Ġassoci ates", - "Ġassociate s", - "Ġassoc iates", - "HE ME", - "- empty", - "-em pty", - "Ġ ت", - "ĠØ ª", - "ol vers", - "olve rs", - "olver s", - "olv ers", - "Ġp istol", - "Ġpist ol", - "Ġpis tol", - "Sc oped", - "Scope d", - "ag ner", - "agn er", - "agne r", - "' ]=='", - "'] =='", - "']= ='", - "']== '", - "Ġ IMP", - "ĠI MP", - "ĠIM P", - "e xc", - "ex c", - "Ġo mitted", - "Ġom itted", - "Ġomit ted", - "Ġmind set", - "Ġminds et", - "Ġ [](", - "Ġ[ ](", - "Ġ[] (", - "Ġ orn", - "Ġo rn", - "Ġor n", - "_ CAM", - "_C AM", - "_CA M", - "A vg", - "Av g", - "Localized String", - "ĠN atur", - "ĠNa tur", - "ĠNat ur", - "Ġ composer", - "Ġcom poser", - "Ġcomp oser", - "Ġcompose r", - "Ġcompos er", - "Ġ Playing", - "ĠPl aying", - "ĠPlay ing", - "ĠPla ying", - "Ġover d", - "Ġov erd", - "_ utf", - "_u tf", - "_ut f", - ". sk", - ".s k", - "ĠF ol", - "ĠFo l", - "$ page", - "$p age", - ", Object", - ",O bject", - "Ġb ees", - "Ġbe es", - "Ġbee s", - "al ary", - "ala ry", - "alar y", - "b ullet", - "bul let", - "bull et", - "_ library", - "_l ibrary", - "_lib rary", - "O ffer", - "Of fer", - "Off er", - "loc ated", - "locate d", - "Ġ (_,", - "Ġ( _,", - "Ġ(_ ,", - "âĢľ He", - "Ġ Owners", - "ĠOwn ers", - "ĠOwner s", - "ĠOw ners", - ") ).Ċ", - ")) .Ċ", - ")). Ċ", - "Ġb ri", - "Ġbr i", - ". Admin", - ".Ad min", - "k tion", - "kt ion", - "лÑİ Ñĩ", - "Ġerot ici", - "Ġerotic i", - "Cancel led", - "Ġ agr", - "Ġa gr", - "Ġag r", - "re views", - "review s", - "_ dma", - "_d ma", - "_dm a", - "R ICT", - "RI CT", - "RIC T", - "Ġ gfx", - "Ġg fx", - "Ġgf x", - "m pi", - "mp i", - "p po", - "pp o", - "Ġ //@", - "Ġ// @", - "Ġ/ /@", - "Ġ uppercase", - "Ġupper case", - "Ġcomm itting", - "Ġcommit ting", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "User Data", - "Ġv ai", - "Ġva i", - "ĉ sort", - "ĉs ort", - "Ġcongr at", - "Ġcong rat", - "Ġd ioxide", - "Ġdi oxide", - "д а", - ". area", - ".a rea", - ".ar ea", - ".are a", - "ĠJosh ua", - "ĠJos hua", - "ĠK och", - "ĠKo ch", - "_ break", - "_b reak", - "a zure", - "az ure", - "azu re", - "is tical", - "ist ical", - "istic al", - "isti cal", - "istica l", - "_AL PHA", - "_ views", - "_view s", - "_vi ews", - "Ġelim inating", - "Ġelimin ating", - "O MB", - "OM B", - "e numer", - "en umer", - "enu mer", - "enum er", - "ĠH ydro", - "ĠHy dro", - "( *(", - "(* (", - "ERT ICAL", - "Ġinev itably", - "Ġst ole", - "Ġsto le", - "Ġstol e", - "- east", - "-e ast", - "i eron", - "ie ron", - "ier on", - "iero n", - "Ġ linger", - "Ġl inger", - "Ġli nger", - "Ġlin ger", - "Ġling er", - "/ doc", - "/d oc", - "/do c", - "Å º", - "Ġ Already", - "ĠAl ready", - "as io", - "asi o", - "Ġ --Ċ", - "Ġ- -Ċ", - "Ġ-- Ċ", - "Ġ abbrev", - "Ġabb rev", - "Ġ Atom", - "ĠA tom", - "ĠAt om", - "h im", - "hi m", - "Ġ INSERT", - "ĠINS ERT", - "s un", - "su n", - "âĻ ª", - "CON NECT", - "CONN ECT", - "er ator", - "era tor", - "ĠM anning", - "ĠMan ning", - "ĠMann ing", - "Ġ :(", - "Ġ: (", - "g as", - "ga s", - "= >'", - "=> '", - "Ġquery set", - "; }čĊ", - ";} čĊ", - "Ġ Population", - "ĠPop ulation", - "uted String", - "res ident", - "resi dent", - "_ FONT", - "_F ONT", - "Ġ Respond", - "ĠRes pond", - "ĠResp ond", - "Ġobsc ure", - "Ġ observable", - "Ġo bservable", - "Ġobserv able", - "ĠContrib utors", - "ĠContributor s", - "k on", - "ko n", - "ĠM usk", - "ĠMus k", - "ĠMu sk", - "ex ao", - "ĠT ub", - "ĠTu b", - "Boot Application", - "S OR", - "SO R", - ". Horizontal", - ".H orizontal", - ". findBy", - ".find By", - ". power", - ".p ower", - ".pow er", - ".po wer", - "Ġpositive ly", - "Ġposit ively", - "ven ience", - "ĠJ ong", - "ĠJo ng", - "ĠJon g", - "Ġwh istle", - "Ġ знаÑĩ", - "Ġз наÑĩ", - "Ġзна Ñĩ", - "Ġзн аÑĩ", - "Ġl ending", - "Ġlen ding", - "Ġlend ing", - "Ġdestruct ive", - "Ġ onDelete", - "Ġon Delete", - "author ization", - "() ;?>", - "(); ?>", - "_ original", - "_or iginal", - "_origin al", - "_orig inal", - "sc ience", - "sci ence", - "a tra", - "at ra", - "atr a", - "?, ?,", - "Ġ Asc", - "ĠA sc", - "ĠAs c", - "Ġconvin cing", - "Ġconvinc ing", - "$ a", - "or gen", - "org en", - "orge n", - "_ Date", - "_D ate", - "Ġ Provide", - "ĠPro vide", - "ĠProvid e", - "ĠProv ide", - "Ġlon ely", - "Ġlone ly", - ") 'Ċ", - ")' Ċ", - "ex change", - "; ?>Ċ", - ";?> Ċ", - ". fast", - ".f ast", - ".fa st", - "S amples", - "Sample s", - "Sam ples", - "L ondon", - "Lo ndon", - "Lon don", - "' ])čĊ", - "'] )čĊ", - "']) čĊ", - "Ġ Ionic", - "ĠI onic", - "ĠIo nic", - "ĠIon ic", - "Ġp esso", - "Ġpes so", - "ĠKn ights", - "ĠKnight s", - "ĠR af", - "ĠRa f", - "_ attrs", - "_at trs", - "_attr s", - "_att rs", - "Ġrepe al", - "> Main", - ">M ain", - "Ġ Ordered", - "ĠOrder ed", - "ĠOrd ered", - "_ New", - "_N ew", - "=\" \"> < /", - "url patterns", - "ATION AL", - "ATIO NAL", - "pe ech", - "pee ch", - "ĠId aho", - "Ġpr incess", - "Ġprince ss", - "Ġprin cess", - "Ġprinc ess", - "Ġprinces s", - "Ġ Customers", - "ĠCustom ers", - "ĠCustomer s", - "ĠCust omers", - "a ways", - "aw ays", - "away s", - "awa ys", - "a db", - "ad b", - "ĠBry ant", - "ĠBryan t", - "n once", - "no nce", - "non ce", - "Ġad ul", - "Ġ` `(", - "Ġ`` (", - "Ġafter math", - "= dict", - "=d ict", - "text Box", - "Ġs perm", - "Ġsp erm", - "Ġspe rm", - "Ġsper m", - "Ġc ough", - "Ġco ugh", - "Ġcou gh", - "H or", - "Ho r", - "âĢĻ S", - ".Component ResourceManager", - "Ġreg ulator", - "Ġregul ator", - "Ġpartner ships", - "Ġpartners hips", - "Ġpartnership s", - "/ projects", - "/project s", - "t rys", - "tr ys", - "try s", - "ĠL aser", - "ĠLa ser", - "ĠLas er", - "⣠©", - "ĠF unk", - "ĠFun k", - "ĠFu nk", - "Ġun conscious", - "Ġuncon scious", - "Ġc rust", - "Ġcr ust", - "Ġcru st", - "Ġcrus t", - "Ġ Teams", - "ĠTe ams", - "ĠTeam s", - "ĠTea ms", - "Ġ Banner", - "ĠB anner", - "ĠBan ner", - "ĠH oney", - "ĠHon ey", - "ĠHo ney", - "l ems", - "le ms", - "lem s", - "Ġmax Width", - "Pointer Exception", - "fade Out", - "- St", - "-S t", - "Ġstr angers", - "Ġstrange rs", - "Ġstranger s", - "Ġstrang ers", - "Ġstran gers", - "_ GO", - "_G O", - "W ritable", - "Wr itable", - "_ Info", - "_In fo", - ". NonNull", - ".Non Null", - "an notations", - "annot ations", - "annotation s", - "Ġ GD", - "ĠG D", - "Ġendorse d", - "Ġendors ed", - "ĉToken Name", - "Ġ Depending", - "ĠDe pending", - "ĠDep ending", - "ĠDepend ing", - "YN AM", - "Ġ Meteor", - "ĠM eteor", - "ĠMet eor", - "Ġ Increase", - "ĠIn crease", - "ĠIncre ase", - ". Many", - ".M any", - ".Man y", - ".Ma ny", - "= =(", - "== (", - ". UUID", - ".U UID", - "_ KERNEL", - "_K ERNEL", - "Ġvid é", - "Ġ pq", - "Ġp q", - "ĠQt Gui", - "Ġ Various", - "ĠV arious", - "ĠVar ious", - "ĠVa rious", - "ĠVari ous", - "Ġ john", - "Ġj ohn", - "Ġjo hn", - "_ patch", - "_p atch", - "_pat ch", - "Ġt outes", - "Ġto utes", - "Ġtou tes", - "Ġtout es", - "Ġtoute s", - "Ġ Fail", - "ĠF ail", - "ĠFa il", - "Ġsurv iving", - "Ġsurviv ing", - "( \"${", - "(\" ${", - "(\"$ {", - "Ġ ĠĠĠĠĠĠčĊ", - "ĠĠ ĠĠĠĠĠčĊ", - "ĠĠĠĠ ĠĠĠčĊ", - "ĠĠĠ ĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠ čĊ", - "ĠĠĠĠĠ ĠĠčĊ", - "ĠĠĠĠĠĠ ĠčĊ", - "Ġ imageUrl", - "Ġimage Url", - ". wordpress", - ".word press", - "s ources", - "source s", - "ĉgl Vertex", - "âĢĻ a", - "Ġes col", - "Ġesc ol", - "R ARY", - "RA RY", - "RAR Y", - "Ġ Snake", - "ĠSn ake", - "Ġqu int", - "Ġq uint", - "Ġqui nt", - "Ġl asts", - "Ġla sts", - "Ġlast s", - "Ġlas ts", - "ĠHar mon", - "ĠHarm on", - "Ġ coil", - "Ġc oil", - "Ġco il", - "Ġcoi l", - "Ġexplo itation", - "Ġexploit ation", - "l een", - "le en", - "lee n", - "' >\";Ċ", - "'> \";Ċ", - "'>\" ;Ċ", - "Ġ SERVER", - "ĠS ERVER", - "ĠSER VER", - "Ġ HEADER", - "ĠHE ADER", - "ĠHEAD ER", - "_ velocity", - "_v elocity", - "_vel ocity", - "Ġ Invoke", - "ĠIn voke", - "ĠInv oke", - ". timestamps", - ".timestamp s", - "Ġs ulf", - "Ġsu lf", - "Ġsul f", - "I QUE", - "IQ UE", - "Ġinhabit ants", - "ph ins", - "phi ns", - "phin s", - "a zzo", - "az zo", - "azz o", - "Ġ mono", - "Ġm ono", - "Ġmon o", - "Ġmo no", - "L egend", - "Le gend", - "Leg end", - "Ġ nonce", - "Ġn once", - "Ġno nce", - "Ġnon ce", - "I FE", - "IF E", - "; \";Ċ", - ";\" ;Ċ", - "- create", - "-c reate", - "\" \",Ċ", - "\"\" ,Ċ", - "\"\", Ċ", - "per mit", - "perm it", - "ĠIm migration", - "ĠImm igration", - "Ġ pathname", - "Ġpath name", - "ff ective", - "ffect ive", - "âĻĢ âĻĢ", - "Ġex ams", - "Ġexam s", - "- event", - "-e vent", - "-ev ent", - "-even t", - "ĠT ill", - "ĠTi ll", - "ĠTil l", - "[ mid", - "[m id", - "F IX", - "FI X", - "; color", - ";c olor", - "( Order", - "_ traits", - "_t raits", - "_tr aits", - "_tra its", - "_trait s", - "Ġ orderBy", - "Ġorder By", - "Ġs unt", - "Ġsu nt", - "Ġsun t", - "ĠNich olas", - "Ø ²", - "Ġs unny", - "Ġsun ny", - "i ners", - "in ers", - "ine rs", - "iner s", - "Ġaccess ibility", - "Ġ HB", - "ĠH B", - ". comp", - ".c omp", - ".com p", - ".co mp", - "ĉ op", - "ĉo p", - "Ġminor ities", - "eth eus", - "ethe us", - "Ġcollabor ative", - "p rit", - "pr it", - "pri t", - "H IR", - "HI R", - "Ġwr aps", - "Ġwrap s", - "ĉ draw", - "ĉd raw", - "g od", - "go d", - "Ġ IX", - "ĠI X", - ". apps", - ".app s", - ".ap ps", - ".a pps", - "Ġ NM", - "ĠN M", - "Ġir relevant", - "Ġirre levant", - "ĠT igers", - "ĠTi gers", - "ĠTiger s", - "ĠTig ers", - "Ġ diag", - "Ġd iag", - "Ġdi ag", - "Ġdia g", - "G V", - "ĠAccess ories", - "k ont", - "ko nt", - "kon t", - "Ġs implify", - "Ġsimp lify", - "Ġsimpl ify", - "Ġ Favorite", - "ĠF avorite", - "ĠFavor ite", - "_ tools", - "_t ools", - "_to ols", - "_tool s", - "( []);Ċ", - "([ ]);Ċ", - "([] );Ċ", - "Ġt owers", - "Ġto wers", - "Ġtow ers", - "Ġtower s", - "B es", - "Be s", - "Ġ hunter", - "Ġh unter", - "Ġhun ter", - "Ġhunt er", - "Ġs alon", - "Ġsa lon", - "Ġsal on", - "( buff", - "(b uff", - "(buf f", - "ĉ debug", - "ĉde bug", - "Ġmal ware", - "M oving", - "Mo ving", - "Mov ing", - "- options", - "-o ptions", - "-option s", - "-opt ions", - ") +'", - ")+ '", - "ĠL OVE", - "ĠLO VE", - "_S OCKET", - "_SO CKET", - "_ fin", - "_f in", - "ĠDel aware", - "Ġsher iff", - "- invalid", - "-in valid", - "Ġ FULL", - "ĠF ULL", - "ĠFU LL", - "Ġ под", - "Ġп од", - "Ġпо д", - "e las", - "el as", - "ela s", - "\" strings", - "ĠRepresent atives", - "ĠRepresentative s", - "s urface", - "sur face", - "surf ace", - "res olved", - "resolve d", - "ht docs", - ") ):čĊ", - ")) :čĊ", - ")): čĊ", - "Ġpress ures", - "Ġpressure s", - "Ġno rms", - "Ġnor ms", - "Ġnorm s", - "Ġ pla", - "Ġp la", - "Ġpl a", - "Ġ surname", - "Ġs urname", - "Ġsur name", - "Ġ postal", - "Ġpos tal", - "Ġpost al", - "Ġpo stal", - "Ġ Depart", - "ĠDe part", - "ĠDep art", - "Ġs laughter", - "Ġsla ughter", - "or ida", - "ori da", - "Ġhe bben", - "Ġheb ben", - "Ġd esar", - "Ġde sar", - "Ġdes ar", - "comp act", - "_ LANG", - "_L ANG", - "_LA NG", - "åIJ Ī", - "o poly", - "op oly", - "opol y", - "opo ly", - "_ rad", - "_r ad", - "_ra d", - "ĠST DMETHOD", - "ĠSTD METHOD", - "L azy", - "La zy", - "Ġ ĠĠĉ", - "ĠĠ Ġĉ", - "ĠĠĠ ĉ", - ".. .,", - "... ,", - "( web", - "(w eb", - "Ġ Pont", - "ĠP ont", - "ĠPo nt", - "ĠPon t", - "Ġet was", - "Ġetwa s", - "Ġup ward", - "_ hat", - "_h at", - "Ġ ],ĊĊ", - "Ġ] ,ĊĊ", - "Ġ],Ċ Ċ", - "Ġ], ĊĊ", - "Ġ baseUrl", - "Ġbase Url", - "Ġwor rying", - "Ġworry ing", - "- addon", - "-add on", - "-ad don", - "( getClass", - "(get Class", - "S PI", - "SP I", - "Ġcapt uring", - ") },Ċ", - ")} ,Ċ", - ")}, Ċ", - "E ffects", - "Effect s", - "Eff ects", - "Ġcompet ent", - "Ġcompete nt", - "Ġf oul", - "Ġfo ul", - "Ġfou l", - "Ġsubs cribing", - "Ġsubscri bing", - "Ġ OBJECT", - "ĠO BJECT", - "ĠOBJ ECT", - "ĠOB JECT", - "IX EL", - "b ucks", - "bu cks", - "( edge", - "(e dge", - "(ed ge", - "( pass", - "(p ass", - "(pa ss", - "ĠPeter son", - "ĠPet erson", - "ĠPeters on", - "Ġbo obs", - "Ġboo bs", - "Ġboob s", - "Ġ Delay", - "ĠD elay", - "ĠDe lay", - "ĠDel ay", - "_ square", - "_s quare", - "e lim", - "el im", - "eli m", - "o ters", - "ot ers", - "ote rs", - "oter s", - "_ PC", - "_P C", - "% E", - "on click", - "Ġ SVG", - "ĠS VG", - "ĠSV G", - "Ġt opped", - "Ġto pped", - "Ġtop ped", - "Ġtopp ed", - "Ġf ist", - "Ġfi st", - "Ġfis t", - "s mart", - "sm art", - "ĠR alph", - "( owner", - "(o wner", - "j ours", - "jo urs", - "jour s", - "Ġbro nze", - "Ġbron ze", - "Ġ ArgumentException", - "ĠArgument Exception", - "( original", - "(origin al", - "(orig inal", - "(or iginal", - "_ SCALE", - "_S CALE", - "_SC ALE", - "_ cp", - "_c p", - "Ġrecomm ends", - "Ġrecommend s", - ".set Style", - "S ure", - "Sur e", - "Su re", - "L AND", - "LA ND", - "LAN D", - "Ġre peating", - "Ġrep eating", - "Ġrepe ating", - "Ġrepeat ing", - "M att", - "Mat t", - "Ma tt", - ". Visibility", - "Ġenter prises", - "Ġenterprise s", - ". Setup", - ".Set up", - "( scene", - "(s cene", - "(sc ene", - "ĠRe active", - "ĠReact ive", - "ur ge", - "urg e", - "b w", - ". Put", - ".P ut", - "p ersist", - "pers ist", - ". cookie", - ".c ookie", - ".co okie", - "ĠA udi", - "ĠAud i", - "ĠAu di", - "` s", - "s upplier", - "sup plier", - "( Form", - "(F orm", - " ¡", - "_ so", - "_s o", - "Į Ģ", - "ĠLeg ion", - "t te", - "tt e", - "N d", - "L oss", - "Lo ss", - "Los s", - "( attrs", - "(at trs", - "(attr s", - "(att rs", - ". scatter", - ".sc atter", - "Ġg room", - "Ġgr oom", - "Ġgro om", - "Ġgl impse", - "Ġglimps e", - "Ġn ails", - "Ġna ils", - "Ġnail s", - "Ġcum ulative", - "Ġf azer", - "Ġfa zer", - "Ġfaz er", - "_ services", - "_s ervices", - "_service s", - "_serv ices", - ". Num", - ".N um", - "ib ilit", - "ibil it", - "ibi lit", - "ibili t", - "_ resolution", - "_re solution", - "_res olution", - "Ġ Tx", - "ĠT x", - "um inium", - "umin ium", - "o pa", - "op a", - ". schedule", - ".s chedule", - "sm tp", - "ภķ", - "ur ry", - "urr y", - "ü k", - "g oog", - "go og", - "goo g", - "_ signature", - "_sign ature", - "_sig nature", - ". into", - ".in to", - ".int o", - "Ġ Steps", - "ĠSt eps", - "ĠSte ps", - "ĠStep s", - "Ġhome owners", - "Ġhomeowner s", - "Ġ NSURL", - "ĠNS URL", - "ĠP AC", - "ĠPA C", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĊĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĊĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĊĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĊĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĊĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĊ Ċ", - "> ')Ċ", - ">' )Ċ", - ">') Ċ", - "e nh", - "en h", - "Ġin cap", - "Ġinc ap", - "$ MESS", - "Ġm oins", - "Ġmo ins", - "Ġmoi ns", - "Ġ Fi", - "ĠF i", - "Ġoff season", - "pr essions", - "press ions", - "pression s", - "> .. < /", - "Ġpro vinces", - "Ġprov inces", - "Ġprovince s", - "Ġprovinc es", - "_ RAW", - "_R AW", - "_RA W", - "\\ App", - "Ġprostit uer", - "Ġprostitu er", - "_ gain", - "_g ain", - ".t encent", - "ff ects", - "ffect s", - "ffe cts", - "( pk", - "(p k", - "s ku", - "sk u", - "Ġ usable", - "Ġus able", - "Ġusa ble", - "ER VED", - "ERV ED", - "ERVE D", - "Ġant enna", - "Ġantenn a", - "h ea", - "he a", - "p list", - "pl ist", - "_ PLUGIN", - "_PL UGIN", - "Ñģ л", - ". lookup", - ".look up", - "á» ģ", - "Ġen larg", - "Ġp iss", - "Ġpi ss", - "Ġpis s", - "H am", - "Ha m", - "i map", - "im ap", - "ima p", - "Ġ invalidate", - "Ġin validate", - "Ġinvalid ate", - "Ġs ilk", - "Ġsi lk", - "Ġsil k", - "=\"# \">Ċ", - "=\"#\" >Ċ", - "=\"#\"> Ċ", - "ĠGr ass", - "ĠGra ss", - "Ġ Goal", - "ĠGo al", - "ĠGoa l", - "_ pdf", - "_p df", - "_pd f", - "Hand lers", - "Handler s", - "Handle rs", - "Ġst acks", - "Ġstack s", - "Ġsta cks", - ". getFullYear", - ".get FullYear", - "= [];Ċ", - "=[ ];Ċ", - "=[] ;Ċ", - "è½ ¦", - ", V", - "( split", - "(s plit", - "(sp lit", - "Ñĥн к", - "Ġbake ca", - "Ġbak eca", - "Ġ~ /.", - "Ġ~/ .", - "p ez", - "pe z", - "t ails", - "ta ils", - "tail s", - "ĠG len", - "ĠGl en", - "ĠGle n", - "Ġ setImage", - "Ġset Image", - "Ġ Comic", - "ĠC omic", - "ĠCom ic", - "ĠCo mic", - "B LOCK", - "BL OCK", - "ĉ This", - "ĉT his", - "o ader", - "oad er", - "oa der", - "Ġcapital ist", - "Ġcapita list", - "Ġcapit alist", - "_ STEP", - "_ST EP", - "( Boolean", - "Ġ Correct", - "ĠC orrect", - "ĠCor rect", - "r ina", - "ri na", - "rin a", - "Ġconc aten", - "Ġconcat en", - "å® ŀ", - "( ):ĊĊ", - "() :ĊĊ", - "():Ċ Ċ", - "(): ĊĊ", - "Ġun anim", - "Ġuna nim", - "l li", - "ll i", - "al ars", - "ala rs", - "alar s", - "- ne", - "-n e", - "Ġd ivor", - "Ġdi vor", - "Ġdiv or", - "ĠKick starter", - "] ._", - "]. _", - "< number", - " * * < /", - ": d", - "m di", - "md i", - "bind Value", - "Ġ Decision", - "ĠDe cision", - "ĠDec ision", - "Return Value", - ", index", - ",in dex", - "x fc", - "xf c", - "Ġse rum", - "Ġser um", - "get Field", - "Connection String", - "- object", - "-o bject", - "-ob ject", - ". recv", - ".re cv", - ".rec v", - "Ġunder graduate", - "Ġundergrad uate", - ". Infrastructure", - ".Inf rastructure", - "ĠK ab", - "ĠKa b", - "Ġadv isory", - "Ġadvis ory", - "Ġadvisor y", - "- tree", - "-t ree", - "-tr ee", - "Ġ mue", - "Ġm ue", - "Ġmu e", - "in form", - "info rm", - "inf orm", - ". embed", - ".em bed", - "Ġ errorCode", - "Ġerror Code", - "m icro", - "mi cro", - "mic ro", - "Ġsp arked", - "Ġspark ed", - "Ġspar ked", - "Ġimage ry", - "Ġimag ery", - "con c", - "co nc", - "_ missing", - "_m issing", - "_miss ing", - "Ġsur plus", - "K S", - "ĉR THOOK", - "ĉRT HOOK", - "T ell", - "Te ll", - "Tel l", - "r ium", - "ri um", - "Ġ Radius", - "ĠR adius", - "ĠRad ius", - "ĠRadi us", - "r ika", - "ri ka", - "rik a", - "los ion", - "ĠH ern", - "ĠHe rn", - "ĠHer n", - "G amma", - "Gam ma", - "Ga mma", - "Ġ Fee", - "ĠF ee", - "ĠFe e", - "Ġ Named", - "ĠN amed", - "ĠName d", - "ĠNa med", - "ĠNam ed", - "ĠCan yon", - "Ġ JSONArray", - "ĠJSON Array", - "Ġz wei", - "Ġzw ei", - "Ġzwe i", - "Ġ SSH", - "ĠS SH", - "ĠSS H", - "Ġser vant", - "Ġserv ant", - "co al", - "Ġden ying", - "Ġdeny ing", - "Ġs plits", - "Ġsplit s", - "Ġspl its", - "In correct", - "Inc orrect", - "Ġt ox", - "Ġto x", - "ĠAnal yst", - "ĠAnaly st", - "Ġacc red", - "Ġac cred", - "Ġaccr ed", - "u ble", - "ub le", - "ubl e", - "Ġ wt", - "Ġw t", - "Ġ Trial", - "ĠT rial", - "ĠTr ial", - "ĠTri al", - ". extension", - ".ext ension", - "Ġ Career", - "ĠCar eer", - "ĠCare er", - "Ġsec uring", - "ĠL il", - "ĠLi l", - "Ġpro jections", - "Ġproject ions", - "Ġproj ections", - "Ġprojection s", - "Ġproje ctions", - "Ġy east", - "Ġye ast", - "M ade", - "Ma de", - "Mad e", - "Ġfound ations", - "Ġfoundation s", - "ac ific", - "aci fic", - ". volume", - ".v olume", - ".vol ume", - "Ġmir rors", - "Ġmirror s", - "#### ############################################################################", - "######## ########################################################################", - "################ ################################################################", - "################################ ################################################", - "################################################################ ################", - "################################################ ################################", - "############################################################################ ####", - "######################################################################## ########", - "######################################## ########################################", - "######################## ########################################################", - "######################################################## ########################", - "Ġvi olate", - "Ġviol ate", - "ars ers", - "arse rs", - "arser s", - "Ġs ocio", - "Ġso cio", - "Ġsoc io", - "Ġsoci o", - "Ġtk inter", - "Ġ LINK", - "ĠL INK", - "ĠLI NK", - "ĠLIN K", - ". getSize", - ".get Size", - ".getS ize", - "Ġ Whole", - "ĠW hole", - "ĠWh ole", - "ĠWho le", - ")view DidLoad", - "ĉ done", - "ĉd one", - "ĉdo ne", - "ude au", - "\\ \"> < /", - "And rew", - "Andre w", - "e rb", - "er b", - "Ġf ö", - ". cluster", - ".cl uster", - "Ġdis course", - "Ġdisc ourse", - "Ġdiscour se", - "Ġdisco urse", - "_DE FIN", - "_DEF IN", - "Ġpued en", - "Ġpu eden", - "Ġpuede n", - "Ġ LOW", - "ĠL OW", - "ĠLO W", - ". av", - ".a v", - "Ġpr eca", - "Ġpre ca", - "Ġprec a", - "Ġ quo", - "Ġqu o", - "Ġq uo", - "Ġve loc", - "Ġvel oc", - ", ''", - ",' '", - "Ġ xyz", - "Ġx yz", - "Ġxy z", - "ĉ padding", - "ĉp adding", - "Ġtom atoes", - "Ġtomato es", - "ĠB ent", - "ĠBe nt", - "ĠBen t", - "_ curr", - "_c urr", - "_cur r", - "_cu rr", - "NS Date", - "Ġ getCurrent", - "Ġget Current", - "ĠgetC urrent", - "Ġ [`", - "Ġ[ `", - "Wed nesday", - ". Bar", - ".B ar", - "Ġ Vous", - "ĠV ous", - "ĠVo us", - "i nz", - "in z", - "ĠQu inn", - "ĠQui nn", - "ex cel", - "exc el", - "d os", - "do s", - "Ġout dated", - "O UTH", - "OUT H", - "OU TH", - "Ġ Maker", - "ĠM aker", - "ĠMake r", - "ĠMa ker", - "ĠMak er", - "ep endency", - "epend ency", - "Ġd ull", - "Ġdu ll", - "Ġdul l", - "ĠW inn", - "ĠWin n", - "ĠWi nn", - "o ge", - "og e", - "cl ave", - "cla ve", - "Ġ nova", - "Ġn ova", - "Ġno va", - "Ġnov a", - "Ġ aval", - "Ġa val", - "Ġav al", - "Ġava l", - "C apt", - "Cap t", - "Ca pt", - "ĠSp otify", - "ĠSpot ify", - "Ġj ul", - "Ġju l", - ") tableView", - ")t ableView", - "Ġf ilenames", - "Ġfile names", - "Ġfil enames", - "Ġfilename s", - "Ġesk ort", - "åij ¨", - "Ġsk ew", - "Ġske w", - "t erior", - "ter ior", - "te rior", - "teri or", - "Ġfin anc", - "Ġfinan c", - "Ġ tabla", - "Ġtab la", - "Ġta bla", - "ĠU IB", - "ĠUI B", - "Ġ ():", - "Ġ( ):", - "Ġ() :", - "ĠD ocker", - "ĠDo cker", - "ĠDoc ker", - "ĠDock er", - "per centage", - "percent age", - "Me et", - "i chi", - "ic hi", - "ich i", - "Ġinter im", - "Ġinte rim", - "Ġ' ='", - "Ġ'= '", - ". JSONObject", - ".JSON Object", - "( fid", - "(f id", - "(fi d", - "Ġd ownt", - "Ġdown t", - "Ġdow nt", - "Ġtrans ient", - "ĠSt eph", - "ĠSte ph", - "ĠStep h", - "Ġignor ance", - "Ġ Codes", - "ĠC odes", - "ĠCo des", - "ĠCode s", - "ĠCod es", - "= '',", - "=' ',", - "='' ,", - "Ġ ICE", - "ĠI CE", - "ĠIC E", - "Ġtran qu", - "Ġ Extended", - "ĠExt ended", - "ĠExtend ed", - "Ġ mund", - "Ġm und", - "Ġmu nd", - "Ġmun d", - "Ġ HOME", - "ĠH OME", - "ĠHO ME", - "Ġkil ometers", - "Ġkilomet ers", - "Ġ imagen", - "Ġim agen", - "Ġimage n", - "Ġimag en", - "Ġima gen", - "o ux", - "ou x", - "( sz", - "(s z", - "You ng", - "Yo ung", - "uff ed", - "uf fed", - "Ġ Wake", - "ĠW ake", - "ĠWa ke", - "ĠWak e", - "Ġa ide", - "Ġaid e", - "Ġai de", - "P ROC", - "PR OC", - "PRO C", - "ĠR at", - "ĠRa t", - "ĠL ith", - "ĠLi th", - "ĠLit h", - "b art", - "bar t", - "ba rt", - "Ġ Arrange", - "ĠAr range", - "ĠArr ange", - "p rompt", - "prom pt", - "Ð £", - "( ct", - "(c t", - "Ġ Interval", - "ĠInt erval", - "ĠInter val", - "d ept", - "de pt", - "dep t", - "D aniel", - "Dan iel", - "Dani el", - "Ġ fills", - "Ġf ills", - "Ġfil ls", - "Ġfill s", - ". tensor", - ".t ensor", - "( trim", - "(t rim", - "(tr im", - "Ġje alous", - "F eb", - "Fe b", - "\\ Common", - "Ġamendment s", - "Ġamend ments", - "_ operator", - "_op erator", - "_o perator", - "_oper ator", - "_ customize", - "_custom ize", - "Ġ ]]", - "Ġ] ]", - "Ġ bn", - "Ġb n", - "Ġdis appointment", - "Ġdisappoint ment", - "Ġmill enn", - ". when", - ".w hen", - ".wh en", - "Ġob ey", - "Ġobe y", - "Ġoff enders", - "Ġoffender s", - "Ġoffend ers", - "Ġoffen ders", - "W ild", - "Wil d", - "Wi ld", - "Ġcell For", - "Ġappar atus", - ". after", - ".a fter", - ".af ter", - "Ġ EPS", - "ĠE PS", - "ĠEP S", - "Ġad orable", - "ope rand", - "oper and", - "( listener", - "(list ener", - "ve al", - "Ġ )(", - "Ġ) (", - "Ġcardio vascular", - "uplic ates", - "uplicate s", - "r istol", - "rist ol", - "ris tol", - "Ġref uses", - "Ġrefuse s", - "( QWidget", - "(Q Widget", - "Ġel emento", - "Ġelement o", - "Ġelem ento", - "Number Of", - ". delay", - ".d elay", - ".de lay", - ".del ay", - ". groups", - ".g roups", - ".group s", - "\" >'+", - "\"> '+", - "\">' +", - "åĿ Ģ", - "ace ncy", - "ac ency", - "acen cy", - "( URL", - "(U RL", - "_ half", - "_h alf", - "_hal f", - "= l", - "Ġlist View", - "( section", - "(s ection", - "(se ction", - "(sec tion", - ". toArray", - ".to Array", - "+ /", - "ĠRodrig uez", - "i stream", - "ist ream", - "istr eam", - "Ġelig ibility", - ": :-", - ":: -", - ". newInstance", - ".new Instance", - "P B", - "Ġ Assets", - "ĠAs sets", - "ĠAss ets", - "ĠAsset s", - "Ġ Composite", - "ĠCom posite", - "ĠComp osite", - "ĠL abs", - "ĠLa bs", - "ĠLab s", - "ĠH amas", - "ĠHam as", - "ĠHa mas", - "++ );Ċ", - "++) ;Ċ", - "Ġ blk", - "Ġb lk", - "Ġbl k", - "Ġ Neo", - "ĠN eo", - "ĠNe o", - "L uc", - "Lu c", - "@ login", - "Ġun aware", - "Ġuna ware", - ". met", - ".m et", - ".me t", - "_ RELEASE", - "_RE LEASE", - "( ST", - "(S T", - "AM IL", - "AMI L", - "r ike", - "ri ke", - "rik e", - "Ġ (){Ċ", - "Ġ( ){Ċ", - "Ġ() {Ċ", - "Ġ(){ Ċ", - "( sprintf", - "(s printf", - "Ġ Accounts", - "ĠAc counts", - "ĠAccount s", - "Ġ VIEW", - "ĠV IEW", - "ĠVI EW", - "Ġ Aj", - "ĠA j", - "ãĤ °", - "Ġwh isk", - "Ġ idi", - "Ġi di", - "Ġid i", - "Ġr ode", - "Ġro de", - "Ġrod e", - "Ġ ihn", - "Ġi hn", - "Ġih n", - "ĠElement ary", - "Q ty", - "Qt y", - "Ġintrig uing", - "Ġ å¤", - "Ġå ¤", - "J obs", - "Job s", - "Jo bs", - "ĉ offset", - "ĉo ffset", - "ĠAh med", - "ĠTal iban", - "Ġ èİ·åıĸ", - "Ġè İ·åıĸ", - "Ġin jected", - "Ġinj ected", - "Ġinject ed", - ". Authentication", - ".Auth entication", - "_ linear", - "_l inear", - "_line ar", - "_lin ear", - "_li near", - ". Decimal", - ".D ecimal", - ".De cimal", - ".Dec imal", - "Ġapp les", - "Ġap ples", - "Ġappl es", - "Ġapple s", - "Ġshare holders", - "Ġshareholder s", - "Ġb aked", - "Ġba ked", - "Ġbake d", - "Ġbak ed", - ". diff", - ".d iff", - ".di ff", - "ĠE ddie", - "ĠEd die", - "o kers", - "ok ers", - "oke rs", - "oker s", - "Ġconfront ed", - "vo ices", - "voice s", - "Ġt us", - "Ġtu s", - "Ġ Spin", - "ĠS pin", - "ĠSp in", - "ĠSpi n", - "N ODE", - "NO DE", - "_ Un", - "_U n", - "C TX", - "CT X", - "/ google", - "/g oogle", - "/go ogle", - "T emperature", - "Tem perature", - "Ġ' ').", - "Ġ'' ).", - "Ġ'') .", - "Ġmagn ificent", - "Ġ startIndex", - "Ġstart Index", - "semb les", - "sem bles", - "semble s", - "sembl es", - "Any one", - "z k", - "e hen", - "eh en", - "ĠD ame", - "ĠDa me", - "ĠDam e", - ". strict", - ".str ict", - "Ġre places", - "Ġrep laces", - "Ġrepl aces", - "Ġreplace s", - "Ġline back", - "Ġpush es", - "Ġpus hes", - "Ġche ek", - "ĠS hi", - "ĠSh i", - "_ BYTES", - "_BY TES", - "_BYTE S", - "R EA", - "RE A", - "ả n", - "_CON NECTION", - "_CONNECT ION", - "G ateway", - "Gate way", - "ĠTr avis", - "ĠTra vis", - "ĠTrav is", - "Ġ AX", - "ĠA X", - "Ġ Basically", - "ĠBasic ally", - "ĠBas ically", - "Ġ Upgrade", - "ĠUp grade", - "à ª", - "th emes", - "the mes", - "theme s", - "them es", - "er mo", - "erm o", - "k or", - "ko r", - "F emale", - "Fe male", - "_ attach", - "_at tach", - "_att ach", - "ĠìĤ¬ ìļ©", - "Ġ poz", - "Ġp oz", - "Ġpo z", - "= =============Ċ", - "== ============Ċ", - "==== ==========Ċ", - "======== ======Ċ", - "=== ===========Ċ", - "============ ==Ċ", - "============= =Ċ", - "=========== ===Ċ", - "============== Ċ", - "========= =====Ċ", - "========== ====Ċ", - "====== ========Ċ", - "===== =========Ċ", - "======= =======Ċ", - "( symbol", - "(s ymbol", - "(sym bol", - "Ġ Sector", - "ĠS ector", - "ĠSe ctor", - "ĠSec tor", - "ĠSect or", - "__ )ĊĊ", - "__) ĊĊ", - "__)Ċ Ċ", - "_ padding", - "_p adding", - "_pad ding", - "ï¼ļ \"", - "Ġ fabs", - "Ġf abs", - "Ġfa bs", - "Ġfab s", - "Ġr anged", - "Ġrange d", - "Ġran ged", - "Ġrang ed", - "set Name", - "Ġp error", - "Ġper ror", - "Ġpe rror", - "â Ĺ", - "ĠFile Reader", - "Ġf ulfilled", - "Ġful filled", - "Ġfulfill ed", - "Ġfulfil led", - "_ Current", - "_C urrent", - "Ġdo minate", - "Ġdom inate", - "Ġdomin ate", - "Ġdomina te", - "Ġsm ugg", - "Post Mapping", - "_ force", - "_f orce", - "_for ce", - "Ġb loc", - "Ġbl oc", - "Ġblo c", - "ĠG iant", - "ĠGi ant", - "ĠGian t", - "ĠGia nt", - "( video", - "(v ideo", - "Ġ CU", - "ĠC U", - "System Service", - "Ġ elf", - "Ġe lf", - "Ġel f", - "Ġkont akt", - "ë ª", - "k ees", - "ke es", - "kee s", - "g tk", - "gt k", - "Ġparam Int", - "Ġ markup", - "Ġmark up", - "Ġmar kup", - "u ales", - "ual es", - "ua les", - "uale s", - "Ġaccount ed", - "Ġgang bang", - "RY PT", - "Ġ Wrong", - "ĠW rong", - "ĠWr ong", - "Ġ credited", - "Ġcr edited", - "Ġcred ited", - "Ġcredit ed", - "Ġ MESSAGE", - "ĠM ESSAGE", - "Ġf laws", - "Ġfl aws", - "Ġflaw s", - "Ġfla ws", - "Ġb bw", - "Ġbb w", - "Ġmet abolic", - "Ġmetab olic", - "Ġmetabol ic", - "ĠO EM", - "ĠOE M", - "/ event", - "/e vent", - "(C ollectors", - "mon ton", - "mo nton", - "mont on", - "monto n", - "ap pear", - "app ear", - "appe ar", - "Ġop ted", - "Ġopt ed", - "Ġc heat", - "Ġch eat", - "Ġche at", - "Ġd av", - "Ġda v", - "Ġ Proceed", - "ĠPro ceed", - "ĠProc eed", - "Ġ ê¸", - "Ġê ¸", - "an ked", - "ank ed", - "anke d", - "и з", - "an sk", - "ans k", - "Ġ Hang", - "ĠH ang", - "ĠHa ng", - "ĠHan g", - "ĠC ler", - "ĠCl er", - "ĠCle r", - "Ġdis gu", - "Ġdisg u", - "Ġc map", - "Ġcm ap", - ". cljs", - ".cl js", - "Ġa ument", - "Ġau ment", - "l ez", - "le z", - "Ġ Joined", - "ĠJ oined", - "ĠJo ined", - "ĠJoin ed", - "ĠJoi ned", - "_ received", - "_re ceived", - "_receive d", - "Ġa erial", - "Ġaer ial", - "Ġae rial", - "o tel", - "ot el", - "ote l", - "Ġg reet", - "Ġgre et", - "\" s", - "Ġ Genesis", - "ĠGen esis", - "ĠGene sis", - "ĠCal if", - "ĠCa lif", - "pan ion", - "Ġtail ored", - "Ġtailor ed", - "m apping", - "ma pping", - "map ping", - "and Expect", - ". track", - ".t rack", - ".tr ack", - "at omy", - "ato my", - "atom y", - "ĠO w", - "ul lah", - "ull ah", - "ulla h", - ". Yes", - ".Y es", - "Ġ SimpleName", - "ĠSimple Name", - "d bh", - "db h", - "' en", - "'e n", - "Ġn onsense", - "Ġnon sense", - "Ġnons ense", - "Ġphilosoph ical", - "( getContext", - "(get Context", - "Ġis so", - "Ġiss o", - "Ġ ACE", - "ĠA CE", - "ĠAC E", - "start Date", - "Ġb ÄĻd", - "ĠAUTH OR", - "ĠG lobe", - "ĠGl obe", - "ĠGlo be", - "ĠGlob e", - "Ġin sects", - "Ġins ects", - "Ġinsect s", - "Ġinse cts", - "_ Al", - "_A l", - "ush ing", - "ushi ng", - "è® °", - "/ Home", - "/H ome", - "ĠLocal Date", - "ne eded", - "need ed", - "nee ded", - "hes ive", - "Ġ illusion", - "Ġill usion", - "äº Į", - "Ġt rat", - "Ġtr at", - "Ġtra t", - "x o", - "/ detail", - "/d etail", - "/de tail", - "_ MATCH", - "_M ATCH", - "_MAT CH", - "Ġbroad band", - "Ġ wal", - "Ġw al", - "Ġwa l", - "ĠIllegal StateException", - "IRE CTION", - "IRECT ION", - "Ġnor theast", - "Ġnorth east", - "es ium", - "esi um", - "Ġ Cliente", - "ĠCl iente", - "ĠClient e", - "ĠCli ente", - "ul ance", - "ula nce", - "ulan ce", - "n ty", - "nt y", - "Ġt ecn", - "Ġte cn", - "Ġtec n", - "Device s", - "Dev ices", - "Ġgr ains", - "Ġgrain s", - "Ġgra ins", - "ĠO g", - "Ġ SEL", - "ĠS EL", - "ĠSE L", - "ud iant", - "udi ant", - "Ġ ++;Ċ", - "Ġ++ ;Ċ", - "Ġexplan ations", - "Ġexplanation s", - "o cco", - "oc co", - "occ o", - "Ġd iets", - "Ġdi ets", - "Ġdie ts", - "Ġdiet s", - "Ġco hort", - "Ġcoh ort", - "( controller", - "(cont roller", - "(control ler", - ". Iterator", - ".It erator", - ".Iter ator", - "- rich", - "-r ich", - "ro cess", - "roc ess", - "G D", - "Ġcar bohydr", - "Ġ fried", - "Ġf ried", - "Ġfr ied", - "Ġfri ed", - "ĠEm ployment", - "ĠEmp loyment", - "ĠEmploy ment", - "ìŀ ¥", - "ĠLeon ard", - "ĠLeo nard", - "_ ${", - "_$ {", - "qu ares", - "quare s", - "qua res", - "Ġcompan ions", - "Ġcompanion s", - "Ġp aris", - "Ġpar is", - "Ġpa ris", - "Ġpari s", - "Ġst imulation", - "Ġstim ulation", - "ĠZ oo", - "ĠZo o", - "Ġre levance", - "Ġrelev ance", - "Ġ Colour", - "ĠCol our", - "ĠColo ur", - "Ġs pear", - "Ġsp ear", - "Ġspe ar", - "ot ional", - "otion al", - "oti onal", - "Ġ Lite", - "ĠL ite", - "ĠLi te", - "ĠLit e", - "ĠK osten", - "ĠKo sten", - "ĠKos ten", - "Ġ ó", - "Ġà ³", - "_ attachment", - "_att achment", - "_attach ment", - "orph ic", - "orp hic", - "Ġda mit", - "Ġdam it", - "Ġ dlg", - "Ġd lg", - "Ġdl g", - "Ġth rive", - "Ġthr ive", - "CH ANGE", - "CHAN GE", - "Ġ Apparently", - "ĠApp arently", - "Ġa tual", - "Ġat ual", - "Ġro oted", - "Ġroot ed", - "( images", - "(image s", - "(im ages", - "a wi", - "aw i", - "ar iat", - "ari at", - "aria t", - "Ġch erry", - "Ġcher ry", - "ST ATIC", - "STAT IC", - "m nt", - "mn t", - "Ġ UserId", - "ĠUser Id", - "il let", - "ill et", - "ille t", - "ĠHis panic", - "ĠHispan ic", - "Ġ nak", - "Ġn ak", - "Ġna k", - "Ġcent ro", - "Ġcentr o", - "Ġcen tro", - "Ġ dims", - "Ġd ims", - "Ġdi ms", - "Ġdim s", - "_ initialize", - "_initial ize", - "ı k", - "ĠCent ers", - "ĠCenter s", - "R EN", - "RE N", - "Ġevolution ary", - "Ġ Topics", - "ĠTo pics", - "ĠTop ics", - "ĠTopic s", - "_ damage", - "_d amage", - "_da mage", - "e mer", - "em er", - "eme r", - "Ġr und", - "Ġrun d", - "Ġru nd", - "Ġpun ished", - "Ġpunish ed", - "Ġc ubic", - "Ġcu bic", - "Ġcub ic", - "f air", - "fa ir", - "[ ];ĊĊ", - "[] ;ĊĊ", - "[];Ċ Ċ", - "Ġin stantiate", - "Ġinstant iate", - "Ġover see", - "Ġovers ee", - "Ġoverse e", - "- delete", - "-de lete", - "-del ete", - "unt eer", - "unte er", - "start Time", - "Ġ Pipeline", - "ĠP ipeline", - "ĠPipe line", - "ĠPip eline", - "_ GAME", - "_G AME", - "ĠC ir", - "ĠCi r", - "ĉ Null", - "ĉN ull", - ". Formatting", - ".Format ting", - "uc umber", - "ĠR ide", - "ĠRid e", - "ĠRi de", - "Ġz oo", - "Ġzo o", - "Ġ checker", - "Ġcheck er", - "Ġche cker", - "åIJ Į", - "= C", - "Ġg rit", - "Ġgr it", - "Ġgri t", - "\" );//", - "\") ;//", - "\"); //", - "_ xy", - "_x y", - "Ġ Declaration", - "ĠDe claration", - "Ġ callable", - "Ġcall able", - "F oo", - "Fo o", - "Ġ ListItem", - "ĠList Item", - "Ġin accur", - "m lin", - "ml in", - "ĉ Data", - "ĉD ata", - "Ġev olving", - "a wan", - "aw an", - "awa n", - "Ġc afe", - "Ġca fe", - "Ġcaf e", - "f olk", - "fo lk", - "fol k", - "_ IDX", - "_ID X", - "_I DX", - "Ġ Anything", - "ĠAny thing", - "ĠPalest ine", - "ĠPalestin e", - "Ġ GridView", - "ĠGrid View", - "Ġcol ony", - "Ġcolon y", - "ĠGerman s", - "ĠGer mans", - "ĠGerm ans", - "( +", - ". pid", - ".p id", - ".pi d", - ". jsx", - ".j sx", - ".js x", - "ĠSup erior", - "ĠSuper ior", - "Christ ian", - "ĠL ect", - "ĠLe ct", - "ĉ Game", - "ĉG ame", - "Ġinstrument al", - "An imations", - "Animation s", - "Anim ations", - "д ал", - "да л", - "ĠM oses", - "ĠMo ses", - "ĠMos es", - "ĉĉ čĊĉĉčĊ", - "ĉĉčĊ ĉĉčĊ", - "z s", - "k te", - "kt e", - "ä¸ ļ", - "_ DIST", - "_D IST", - "_DIS T", - "_DI ST", - "b itmap", - "bit map", - "d B", - "Ġp ersistence", - "Ġpers istence", - "Ġpersist ence", - "ÑĢ оÑģ", - "ÑĢо Ñģ", - "$ l", - "B ron", - "Br on", - "Bro n", - "Ġ {|", - "Ġ{ |", - "_ chart", - "_c hart", - "_ch art", - "_char t", - "Ġ Consum", - "ĠCon sum", - "ĠCons um", - "Ġh emp", - "Ġhe mp", - "Ġhem p", - "Ġ \"))Ċ", - "Ġ\" ))Ċ", - "Ġ\") )Ċ", - "Ġ\")) Ċ", - "Ġatt ackers", - "Ġattack ers", - "Ġattacker s", - "Ġknowledge able", - "Ġc et", - "Ġce t", - "Ġvir uses", - "Ġvirus es", - "' I", - "Ġpitch er", - "Ġpit cher", - "Ġswe eping", - "Ġsweep ing", - "= list", - "=l ist", - "apt ops", - "aptop s", - ". depth", - ".de pth", - ".dep th", - "Ġinstruct ed", - "Ġ Rus", - "ĠR us", - "ĠRu s", - "benh avn", - "Ġ ин", - "Ġи н", - "S ports", - "Sp orts", - "Sport s", - "Spo rts", - "Ġon set", - "Ġons et", - "æĿ ĥ", - ". RED", - ".R ED", - ".RE D", - "_ si", - "_s i", - "ĠP ST", - "ĠPS T", - ". onChange", - ".on Change", - "> tag", - ">t ag", - "ĠR oh", - "ĠRo h", - "_ character", - "_char acter", - "ĠL aws", - "ĠLa ws", - "ĠLaw s", - "Ġ Bachelor", - "ĠB achelor", - "ĠBach elor", - "_ swap", - "_s wap", - "_sw ap", - ".re activex", - "Ġreward ing", - "Ġrew arding", - "M edium", - "Med ium", - "- [", - "Ġ Recently", - "ĠRec ently", - "ĠRecent ly", - "J oint", - "Join t", - "Jo int", - "part ition", - "Ġ Minutes", - "ĠMin utes", - "ĠMinute s", - "Ġ indo", - "Ġin do", - "Ġi ndo", - "Ġind o", - "Ġabsor bed", - "Ġabsorb ed", - "Ġ GN", - "ĠG N", - "_ IND", - "_IN D", - "_I ND", - "Ġs aber", - "Ġsa ber", - "Ġsab er", - "Ġsabe r", - "S pawn", - "Sp awn", - "out puts", - "output s", - "ĠJeff rey", - "Ġmed ieval", - "Ġmedi eval", - "h ed", - "he d", - "G uide", - "Gui de", - "Guid e", - "Gu ide", - "Ġpsych o", - "Ġpsy cho", - "Ġg lam", - "Ġgl am", - "E lim", - "El im", - "äd chen", - "_ plain", - "_p lain", - "_pl ain", - "Ġ Sau", - "ĠS au", - "ĠSa u", - "- four", - "-f our", - "Ġanaly zing", - "QUE RY", - "QU ERY", - "Ġtom ato", - "_ buttons", - "_button s", - "_but tons", - "V EN", - "VE N", - ". setStatus", - ".set Status", - ". Url", - ".U rl", - "+ ĊĊ", - "+Ċ Ċ", - "Ġcompl aining", - "Ġcomplain ing", - "d egree", - "de gree", - "deg ree", - "conf irmed", - "confirm ed", - "Ġsu bt", - "Ġsub t", - "p arsed", - "par sed", - "parse d", - "pars ed", - "Ġt orque", - "Ġtor que", - "Ġtrouble d", - "Ġtroub led", - "Ġtrou bled", - "Ġ TARGET", - "ĠT ARGET", - "ĠTAR GET", - "Ġtrad emarks", - "Ġtrade marks", - "Ġtrademark s", - "Ġ Coordinate", - "ĠCo ordinate", - "ĠCoord inate", - "ĠV iv", - "ĠVi v", - "Ġ //}ĊĊ", - "Ġ// }ĊĊ", - "Ġ//}Ċ Ċ", - "Ġapr ès", - ". getPosition", - ".get Position", - ".getP osition", - "( KeyCode", - "(Key Code", - "ĠSil va", - "Ġ meteor", - "Ġm eteor", - "Ġmet eor", - "Ġendorse ment", - "Ġendors ement", - "Over view", - "Ġ Poss", - "ĠP oss", - "ĠPo ss", - "ĠPos s", - ". Inject", - ".In ject", - "Ġeven ly", - "Ġ visualization", - "Ġvisual ization", - "Ġ wchar", - "Ġw char", - "Ġwc har", - "ĠH DMI", - "ĠHD MI", - "Ġf unct", - "Ġfun ct", - "Ġfunc t", - "ick name", - "',' ','", - "','', '", - "Ġfor wards", - "Ġforward s", - "Managed Object", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĉ server", - "ĉs erver", - "ĠOut look", - "ĠChron icle", - "ĠChronic le", - "Ġdub bed", - "Ġd ok", - "Ġdo k", - "ĠW ear", - "ĠWe ar", - ". AL", - ".A L", - "p aren", - "par en", - "pare n", - "pa ren", - ". Interface", - ".Inter face", - "Inter faces", - "Interface s", - ". cod", - ".c od", - ".co d", - "Ġd ib", - "Ġdi b", - ".Global ization", - "ĠAc ademic", - "ĠAcad emic", - "ĠAcadem ic", - "Ġas sms", - "Ġass ms", - "A utom", - "Auto m", - "Aut om", - "Au tom", - "Ġ lw", - "Ġl w", - "Ġ NW", - "ĠN W", - "Ġ&& čĊ", - "Ġproble ma", - "Ġproblem a", - "Ġprobl ema", - "ĠManufact uring", - "l imits", - "li mits", - "lim its", - "limit s", - "- mobile", - "-m obile", - "Ġ filme", - "Ġfil me", - "Ġfilm e", - "/ map", - "/m ap", - "Ġd oit", - "Ġdo it", - "Ġdoi t", - "ĠI nk", - "ĠIn k", - "Ġs ued", - "Ġsu ed", - "Ġsue d", - ". arr", - ".a rr", - ".ar r", - "Ġunder min", - "Ġ Proc", - "ĠP roc", - "ĠPro c", - "ĠPr oc", - "croll View", - "_ _$", - "__ $", - "Ġside walk", - "Ġsidew alk", - "( that", - "(t hat", - "(th at", - "ภ·", - "[ q", - "gram mar", - "Ġt ë", - "q uito", - "qu ito", - "quit o", - "qui to", - "Ġsp iral", - "Ġspir al", - "Ġspi ral", - "ext ended", - "extend ed", - "Ġf ocal", - "Ġfoc al", - "Ġfo cal", - "Ġdig ging", - "p as", - "pa s", - "ĠT all", - "ĠTal l", - "ĠTa ll", - ". proxy", - ".pro xy", - ".pr oxy", - "i tures", - "it ures", - "iture s", - "itu res", - "itur es", - "T RACT", - "TR ACT", - "TRA CT", - "Ġ Realm", - "ĠRe alm", - "ĠReal m", - "Ġf eder", - "Ġfe der", - "Ġfed er", - "Ġ oriented", - "Ġorient ed", - "Ġori ented", - "Ġ Alternative", - "ĠAltern ative", - "ĠAlter native", - "Ġ owe", - "Ġo we", - "Ġow e", - "Ġs ourced", - "Ġsource d", - "Ġsour ced", - "in ker", - "ink er", - "inke r", - ". det", - ".d et", - ".de t", - "S ep", - "Se p", - "Ġ Qui", - "ĠQ ui", - "ĠQu i", - "ĠPal mer", - "ĠPalm er", - "( _,", - "(_ ,", - "s amples", - "sample s", - "sam ples", - "samp les", - "o yer", - "oy er", - "ul lan", - "ull an", - "ulla n", - "q uez", - "qu ez", - "que z", - "Ed ges", - "Edge s", - "Ġsh out", - "Ġsho ut", - "Ġ Achie", - "ĠA chie", - "ĠAch ie", - "Ġ haar", - "Ġh aar", - "Ġha ar", - "_ Construct", - "_Con struct", - "Ġprem ature", - "Ġre vert", - "Ġrev ert", - "Ġreve rt", - "Ġrever t", - "' ).Ċ", - "') .Ċ", - "'). Ċ", - "Ġs chn", - "Ġsc hn", - "Ġsch n", - "filter ed", - "fil tered", - "filt ered", - "null ptr", - "S aved", - "Save d", - "Sa ved", - "it ecture", - "itect ure", - "C LA", - "CL A", - "Ġ vl", - "Ġv l", - "s tell", - "st ell", - "ste ll", - "ĉ Me", - "ĉM e", - "ĠL ip", - "ĠLi p", - "n ational", - "nat ional", - "nation al", - "Ġwh olly", - "Ġspr ings", - "Ġspring s", - ". Timer", - ".T imer", - ".Time r", - "ĉ src", - "ĉs rc", - "e lsen", - "el sen", - "else n", - "els en", - "åħ ¶", - "Ġcommunic ating", - "Ġ Quiz", - "ĠQu iz", - "ĠQui z", - "Ġt eng", - "Ġte ng", - "Ġten g", - "Ġg ez", - "Ġge z", - "Ġ Outside", - "ĠOut side", - "ĠOuts ide", - ". Sign", - ".S ign", - "( cs", - "(c s", - "Ġdisp utes", - "Ġdispute s", - "Ġdisput es", - "ĠWe iss", - "ĠWei ss", - "an nes", - "ann es", - "anne s", - "> No", - ">N o", - "ĠB ach", - "ĠBa ch", - "ĠBac h", - ".remove All", - "re fer", - "ref er", - "/ dashboard", - "/d ashboard", - "Ġ Ajax", - "ĠA jax", - "ĠAj ax", - "Index Changed", - "Ġ Weak", - "ĠWe ak", - "' \"Ċ", - "'\" Ċ", - "Ġs ights", - "Ġsight s", - "Ġsigh ts", - "access Token", - "ĠJ oi", - "ĠJo i", - "( domain", - "(d omain", - "(dom ain", - "(do main", - "ĉ cv", - "ĉc v", - "Ġcontin uation", - "Ġcontinu ation", - "Ġcontinua tion", - "Ġp lum", - "Ġpl um", - "Ġplu m", - "a dir", - "ad ir", - "adi r", - ". setMessage", - ".set Message", - "Ġ ï¼Į", - "Ġï¼ Į", - "Ġsw allow", - "Ġswal low", - "ĠL amp", - "ĠLa mp", - "ĠLam p", - "Ġ qw", - "Ġq w", - "Ġ uu", - "Ġu u", - "C oin", - "Co in", - "u bic", - "ub ic", - "ubi c", - "ĠDe als", - "ĠDeal s", - "r ace", - "ra ce", - "rac e", - "Ġdict ator", - "Ġm eme", - "Ġme me", - "Ġmem e", - "turn ed", - "tur ned", - "ĠJul ie", - "ĠJu lie", - "ĠJuli e", - ".grid Column", - "Ġp uppy", - "Ġpup py", - "Ġpu ppy", - "Ġp am", - "Ġpa m", - "Ġ ){čĊ", - "Ġ) {čĊ", - "Ġ){ čĊ", - "Ġinv iting", - "Ġinvit ing", - "Ġf rench", - "Ġfr ench", - "Ġfren ch", - "v im", - "vi m", - "Ġwr apping", - "Ġwrap ping", - "Ġ#- }Ċ", - "( [-", - "([ -", - "Ear ly", - "Ġsh iny", - "Ġshin y", - ". faces", - ".f aces", - ".face s", - ".fac es", - ".fa ces", - "Ġre bell", - "Ġreb ell", - "Ġrebel l", - "abc def", - "abcd ef", - "ä lt", - "äl t", - "Ġest imation", - "Ġestim ation", - "ph ys", - "phy s", - "los ures", - "losure s", - "_ REL", - "_RE L", - "_R EL", - "Ġex clusion", - "Ġexclus ion", - "Ġexcl usion", - "ĠSk ype", - "ĠSky pe", - "we ise", - "wei se", - "weis e", - "- stop", - "-s top", - "-st op", - "no thing", - "ĠE gg", - "ĠEg g", - "is ors", - "iso rs", - "isor s", - "Rich ard", - "Ġcounsel ing", - "Ġcom mem", - "Ġcomm em", - "Ġcomme m", - "ĠQ MessageBox", - "ĠSy nd", - "ĠSyn d", - "ĠF rost", - "ĠFr ost", - "ĠFro st", - "ĠCom petition", - "ĠCompet ition", - "ĠA wake", - "ĠAw ake", - "Ġ ted", - "Ġt ed", - "Ġte d", - "ic iones", - "ici ones", - "icio nes", - "icion es", - "ĠDev Components", - "VERTISE MENT", - "o tti", - "ot ti", - "ott i", - ". runner", - ".r unner", - ".run ner", - "Ġunique ly", - "Ġuniqu ely", - "Ġuniq uely", - ". flag", - ".f lag", - ".fl ag", - "ĉ rs", - "ĉr s", - "_ generic", - "_g eneric", - "_gen eric", - "_gene ric", - "_gener ic", - "Ġ` ``Ċ", - "Ġ`` `Ċ", - "Ġ``` Ċ", - "ACH INE", - "ACHI NE", - "Ġm ein", - "Ġme in", - "( Application", - "(App lication", - "( br", - "(b r", - "Ġrat ios", - "Ġratio s", - ": ,", - "ĠX CTest", - "ĠXCT est", - "ĠXC Test", - "ustain able", - "- www", - "-w ww", - "it les", - "itle s", - "_ TEMP", - "_T EMP", - "_TE MP", - "_TEM P", - "Ġs yst", - "Ġsys t", - "Ġsy st", - "umeric UpDown", - "ĉ assertTrue", - "ĉassert True", - "Ġ wf", - "Ġw f", - ". peek", - ".pe ek", - "ĠB ulg", - "ĠBul g", - "ĠBu lg", - "Ġterr ifying", - ". MODE", - ".M ODE", - ".MOD E", - "Ġ GW", - "ĠG W", - "á r", - "Ġ fic", - "Ġf ic", - "Ġfi c", - "Ġcommit ments", - "Ġcommitment s", - "- tech", - "-t ech", - "-te ch", - "Ġ Liquid", - "ĠL iquid", - "ĠLiqu id", - "o pez", - "op ez", - "ope z", - "z heimer", - "a ña", - "añ a", - "- media", - "-m edia", - "-me dia", - "-med ia", - "( animated", - "(an imated", - "_ goal", - "_go al", - "Ġg um", - "Ġgu m", - "y stone", - "yst one", - "ys tone", - ". SET", - ".S ET", - ".SE T", - "ĠW end", - "ĠWe nd", - "ĠWen d", - "set CellValue", - "Ġ msgs", - "Ġmsg s", - "Ġms gs", - "c ash", - "ca sh", - "cas h", - "AL LOC", - "ALL OC", - "/ aws", - "/a ws", - "Ġmicro wave", - "Ġmic rowave", - ". Pointer", - ".Point er", - "ĉ Console", - "ĉCon sole", - "_ sorted", - "_s orted", - "_sort ed", - "ĠF ilip", - "ĠFil ip", - "ĠFi lip", - "P rod", - "Pro d", - "Pr od", - "Ġ// !<", - "Ġ//! <", - "in group", - "ing roup", - "Ġ ks", - "Ġk s", - "_T RI", - "_TR I", - "Ġteas poon", - "Ġ ATT", - "ĠA TT", - "ĠAT T", - "Ġre covering", - "Ġrecover ing", - "Ġ GLOBAL", - "ĠG LOBAL", - ". Par", - ".P ar", - "Ġ/ >;Ċ", - "Ġ/> ;Ċ", - "Ġmar ble", - "ul ators", - "ula tors", - "ulator s", - "Ġ Cycle", - "ĠC ycle", - "ĠCy cle", - "ĠCycl e", - "ĠCyc le", - "Ġher bs", - "Ġherb s", - "_ metric", - "_m etric", - "_met ric", - ") !", - "_C LOCK", - "_CL OCK", - "_ Button", - "_B utton", - "H arry", - "Har ry", - "è¿ Ľ", - "Ġstr ains", - "Ġstrain s", - "Ġstra ins", - "Ġ AppBar", - "ĠApp Bar", - "Ġ Chan", - "ĠC han", - "ĠCh an", - "ĠCha n", - "/ video", - "/v ideo", - "Ġ bam", - "Ġb am", - "Ġba m", - ". Progress", - ".Pro gress", - "$ f", - "l emen", - "le men", - "lem en", - "leme n", - "Ġir regular", - "ĠD uncan", - "ĠDun can", - "ĠM int", - "ĠMin t", - "ĠMi nt", - "- video", - "-v ideo", - "ঠ¾", - "ó wn", - "ów n", - "Ġ EMPTY", - "ĠEM PTY", - "ĠEMP TY", - "Ġst acked", - "Ġstack ed", - "Ġ HA", - "ĠH A", - "_ cut", - "_c ut", - "_cu t", - "Ġwhere in", - "ĠW ays", - "ĠWay s", - "ĠWa ys", - "( counter", - "(c ounter", - "(count er", - "(co unter", - "è¯ ķ", - "Form Group", - "Ġb lew", - "Ġbl ew", - "Ġble w", - "c ourses", - "co urses", - "course s", - "cour ses", - "Ġ productos", - "Ġproduct os", - "Ġproducto s", - "r ys", - "ry s", - "Ġ Restr", - "ĠR estr", - "ĠRe str", - "ĠRes tr", - "ĠRest r", - "Ġst yling", - "Ġsty ling", - "Ġstyl ing", - "> s", - "Ġp iv", - "Ġpi v", - "Ġit ertools", - "Ġiter tools", - "get Repository", - "Ġ Ik", - "ĠI k", - "_ devices", - "_device s", - "_dev ices", - "lay ui", - "Ġhalf way", - "Ġfran ç", - "Ġt uning", - "Ġtu ning", - "Ġtun ing", - "O A", - "_ Node", - "_N ode", - "_No de", - "ar de", - "ard e", - "Ġf ierce", - "Ġfi erce", - "Ġfier ce", - "Ġfierc e", - "l icted", - "lic ted", - "lict ed", - "# čĊ", - "Ġbreak through", - "ĠE rik", - "ĠEr ik", - "Ġb ride", - "Ġbr ide", - "Ġbri de", - "Ġbrid e", - "Ġ .\"", - "Ġ. \"", - "c ulus", - "cul us", - "cu lus", - "in side", - "ins ide", - "insi de", - "ĠIndian apolis", - "Ġ EE", - "ĠE E", - "Ġy og", - "Ġyo g", - "ur ret", - "urre t", - "urr et", - ". fs", - ".f s", - ". grad", - ".g rad", - ".gr ad", - "_ cards", - "_c ards", - "_card s", - "_car ds", - "_ accuracy", - "_ac curacy", - "_acc uracy", - "_e pi", - "_ep i", - "qu eda", - "que da", - "/ org", - "/or g", - "/o rg", - "é ªĮ", - "éª Į", - "Ġcom pte", - "Ġcomp te", - "Ġcompt e", - ") )[", - ")) [", - "Out side", - "G reater", - "Great er", - "Gre ater", - "Ġ Renderer", - "ĠRender er", - ". actor", - ".a ctor", - ".ac tor", - ".act or", - "Account s", - "Ac counts", - "I dle", - "Id le", - "_ hours", - "_h ours", - "_hour s", - "er ner", - "ern er", - "erne r", - "J oined", - "Join ed", - "Jo ined", - "Ġme nj", - "Ġmen j", - "re quires", - "require s", - "requ ires", - "Ġ OPER", - "ĠO PER", - "ĠOP ER", - ".remove Child", - "ĉ sp", - "ĉs p", - "Ġ esse", - "Ġe sse", - "Ġes se", - "Ġess e", - "r ift", - "ri ft", - "rif t", - "x FE", - "xF E", - "ĠSh akespeare", - "____ ________", - "________ ____", - "Ġbudget s", - "Ġbud gets", - "Model State", - "fill able", - "- component", - "-com ponent", - "-comp onent", - "o cos", - "oc os", - "oco s", - "Ġ BUTTON", - "ĠB UTTON", - "ĠBUT TON", - "/ io", - "/i o", - ", out", - ",o ut", - "s ms", - "sm s", - "Th omas", - "Tho mas", - "ĠAr med", - "ĠArm ed", - "re sume", - "res ume", - "Ġrot ating", - "Ġ Vault", - "ĠV ault", - "ĠVa ult", - "Ġs eus", - "Ġse us", - "Ġseu s", - ". (*", - ".( *", - "Ġa mino", - "Ġam ino", - "Ġami no", - "Ġ[ ]);ĊĊ", - "Ġ[] );ĊĊ", - "Ġ[]) ;ĊĊ", - "Ġ[]);Ċ Ċ", - "Ġprov oc", - "n ox", - "no x", - ". GetEnumerator", - ".Get Enumerator", - "= ======Ċ", - "== =====Ċ", - "==== ===Ċ", - "=== ====Ċ", - "====== =Ċ", - "===== ==Ċ", - "======= Ċ", - "æĸ Ļ", - "_ scroll", - "_s croll", - "_sc roll", - "_scr oll", - "Ġfil med", - "Ġfilm ed", - "Ġfilme d", - "ĠS oci", - "ĠSo ci", - "ĠSoc i", - "g ap", - "ga p", - "g ro", - "gr o", - "V ote", - "Vo te", - "\" But", - "\"B ut", - "_ RC", - "_R C", - "An imal", - "Anim al", - " Ģ", - "ib ile", - "ibil e", - "ibi le", - "Ġaw aken", - "Ġawake n", - "o rest", - "or est", - "ore st", - "ores t", - "in ja", - "ĠI van", - "ĠIv an", - "( Command", - "Ġ *****", - "Ġ* ****", - "Ġ** ***", - "Ġ*** **", - "Ġ**** *", - "Î ·", - "Ġkv inder", - "Ġkvin der", - "Ġkvinde r", - "/ helpers", - "/h elpers", - "/help ers", - "/helper s", - "_ cases", - "_c ases", - "_case s", - "_ca ses", - "t g", - "ìĦ ¸", - "Register ed", - "ĉ pass", - "ĉp ass", - "_ digits", - "_d igits", - "_digit s", - "Ġcon tour", - "Ġcont our", - "Ġinf ants", - "Ġinfant s", - "Ġjust ification", - "Ġ Fortunately", - "ĠFort unately", - "Cont r", - "Con tr", - "ĠonCreate View", - "_ SAMPLE", - "_S AMPLE", - "_SAMPL E", - "Ġallow Null", - "Ġn ud", - "Ġnu d", - "Ġf etched", - "Ġfetch ed", - "Ġfet ched", - "_ equ", - "_e qu", - "_eq u", - "Ġ Unable", - "ĠU nable", - "ĠUn able", - "ĠUna ble", - "= \\\"\"", - "=\\\" \"", - "=\\ \"\"", - "> {Ċ", - ">{ Ċ", - "Ġcommit tees", - "Ġcommittee s", - "ist ema", - "iste ma", - "istem a", - "+ \".", - "+\" .", - "ÃŃ an", - "ÃŃa n", - "m ant", - "man t", - "ma nt", - "Ġsou theast", - "Ġsouth east", - "ï¼Į Ċ", - "dialog s", - "dia logs", - "PRO JECT", - "ch arger", - "char ger", - "charge r", - "charg er", - "- port", - "-p ort", - "-po rt", - "( uuid", - "(u uid", - ". export", - ".ex port", - ".exp ort", - "S ix", - "Si x", - "Ġ RP", - "ĠR P", - "P rem", - "Pr em", - "Pre m", - "Ġcon science", - "Ġconsc ience", - "Ġmargin Right", - "_ distribution", - "_d istribution", - "_dis tribution", - "y aml", - "ya ml", - "res izing", - "resi zing", - "D ock", - "Do ck", - "Doc k", - "Ġ Locations", - "ĠL ocations", - "ĠLocation s", - "ĠLoc ations", - "G Y", - "S eed", - "Se ed", - "See d", - "B UFFER", - "BUF FER", - "BUFF ER", - "os sip", - "oss ip", - "u llen", - "ul len", - "ull en", - "ulle n", - "Th ings", - "Thing s", - "Thin gs", - "- self", - "-s elf", - "-se lf", - ". poll", - ".p oll", - ".po ll", - ".pol l", - "PL AYER", - "PLAY ER", - "Ġ å®", - "Ġå ®", - "G ROUP", - "Ġ Away", - "ĠA way", - "ĠAw ay", - "Ġg ospel", - "x fd", - "xf d", - "M ary", - "Mar y", - "Ma ry", - "Ġ Portable", - "ĠPort able", - "ĠPor table", - "T URE", - "TU RE", - "Ġutil is", - "Ġut ilis", - "Ġse it", - "Ġsei t", - "Ġ strand", - "Ġs trand", - "Ġst rand", - "Ġstr and", - "Ġstran d", - "Ġstra nd", - "Ġtrans c", - "Ġtran sc", - "Ġ (^", - "Ġ( ^", - "ĠAl fred", - "ĠAlf red", - ". mem", - ".m em", - ".me m", - ". circle", - ".c ircle", - "Ġ ~/", - "Ġ~ /", - "for cing", - "forc ing", - "Ġ riot", - "Ġr iot", - "Ġri ot", - "Ġrio t", - "p rox", - "pr ox", - "pro x", - "TH ON", - "iz ación", - "iza ción", - "Ġ NI", - "ĠN I", - "r ost", - "ro st", - "ros t", - "Ġdis pro", - "Ġdisp ro", - "_ instances", - "_in stances", - "_instance s", - "_inst ances", - "ï¼Į âĢľ", - "ograph er", - "ogra pher", - "en das", - "end as", - "enda s", - "ĠIs aac", - "ĠIsa ac", - "ĠP ine", - "ĠPin e", - "ĠPi ne", - "/ dis", - "/d is", - "Ġcolor With", - "it erate", - "ite rate", - "iter ate", - "_ stride", - "_st ride", - "_str ide", - "Ġp unto", - "Ġpun to", - "Ġpunt o", - ". EventArgs", - ".Event Args", - "( center", - "(c enter", - "Ġneighb oring", - "Ġneighbor ing", - "ĠPr ison", - "ĠPri son", - "Ġ Messenger", - "ĠM essenger", - "ĠMess enger", - "Ġepid emic", - "Ġepidemi c", - "d ao", - "da o", - "_ complex", - "_com plex", - "_comp lex", - "Ġg ravel", - "Ġgr avel", - "Ġgrave l", - "Ġgra vel", - "Ġgrav el", - "_D IP", - "_DI P", - "é ment", - "ém ent", - "ĠA ri", - "ĠAr i", - "_ bitmap", - "_b itmap", - "_bit map", - ". quit", - ".q uit", - ".qu it", - "( valid", - "(val id", - "(va lid", - "Ġ pend", - "Ġp end", - "Ġpe nd", - "Ġpen d", - "Ġrespir atory", - "Ġre bound", - "Ġreb ound", - "Default Value", - "ãĥ Ń", - "Ġcom mits", - "Ġcomm its", - "Ġcommit s", - ". tests", - ".t ests", - ".test s", - ".te sts", - "_ fr", - "_f r", - "i tet", - "it et", - "ite t", - ". sf", - ".s f", - "Ġspace craft", - "c ritical", - "cri tical", - "cr itical", - "crit ical", - "Ġde pressed", - "Ġdep ressed", - "Ġdepr essed", - "Ġdepress ed", - "ĠAny Object", - "Ġu nb", - "Ġun b", - "Ġdis cern", - "Ġdisc ern", - "( mysql", - "(m ysql", - "(my sql", - "L atin", - "La tin", - "Lat in", - "ĠB og", - "ĠBo g", - "ĠWild life", - "To File", - "ToF ile", - "i oxid", - "iox id", - "@ RestController", - "Ġ\" $(", - "Ġ\"$ (", - "Ġ <<\"", - "Ġ< <\"", - "Ġ<< \"", - "Ġdef ects", - "Ġdefe cts", - "Ġdefect s", - "Ġ datum", - "Ġd atum", - "Ġdat um", - "h in", - "hi n", - "Ġreal izar", - "Ġrealiz ar", - "Ġrealiza r", - "any ahu", - "anya hu", - "Ġ Sig", - "ĠS ig", - "ĠSi g", - "@ Data", - "ad aptive", - "ada ptive", - "adapt ive", - "ĠC atherine", - ". cr", - ".c r", - "Ġ COOKIE", - "ĠCO OKIE", - "Ġ pictured", - "Ġp ictured", - "Ġpicture d", - "Ġpict ured", - "ĠF ighter", - "ĠFight er", - "Query able", - "Ġ Anyway", - "ĠAny way", - "ĠGL FW", - "_ namespace", - "_n amespace", - "_name space", - "_names pace", - "_ ft", - "_f t", - "Ġ ])", - "Ġ] )", - "O rganization", - "Organ ization", - "Ġconstit utes", - "Ġconstitu tes", - "Ġconstitute s", - "Ġqu and", - "Ġqua nd", - "Ġquan d", - "( chunk", - "(ch unk", - "\" />čĊ", - "\"/ >čĊ", - "\"/> čĊ", - "ĠL akes", - "ĠLa kes", - "ĠLake s", - "ĠLak es", - "main window", - "Car thy", - "Cart hy", - "s pin", - "sp in", - "spi n", - "( csv", - "(c sv", - "(cs v", - ": red", - ":r ed", - "- commerce", - "-com merce", - "-comm erce", - "ภ¹", - "Ġdis covering", - "Ġdiscover ing", - "Ġ eco", - "Ġe co", - "Ġec o", - "_ fac", - "_f ac", - "_fa c", - "ince ton", - "inc eton", - "ĠGreen s", - "ĠGre ens", - "ĠGree ns", - "j wt", - "Ø µ", - "ĠBron cos", - "Ġ Goods", - "ĠG oods", - "ĠGo ods", - "ĠGood s", - "( GTK", - "(G TK", - "Ġ returnValue", - "Ġreturn Value", - "Ġsi empre", - "Ġne utr", - "Ġneu tr", - "Ġneut r", - "w ent", - "we nt", - "wen t", - "ĠN atal", - "ĠNa tal", - "ĠNat al", - "Ġenthusi astic", - "Ġenthusiast ic", - "á» į", - "F N", - "/ database", - "/d atabase", - "/data base", - "/dat abase", - "C atalog", - "Cat alog", - "Ġb run", - "Ġbr un", - "Ġbru n", - "ĠK ash", - "ĠKa sh", - "ĠKas h", - "_ Pl", - "_P l", - "isc rim", - ", width", - ",w idth", - "Ġin mates", - "Ġinmate s", - "Ass ignment", - "Assign ment", - "ĠH aven", - "ĠHave n", - "ĠHa ven", - "ĠHav en", - "Ġplay ground", - "ex am", - "@ Controller", - "ul iar", - "uli ar", - "ulia r", - ". getParent", - ".get Parent", - ".getP arent", - "Ġ \";ĊĊ", - "Ġ\" ;ĊĊ", - "Ġ\";Ċ Ċ", - "Ġ\"; ĊĊ", - ": size", - ":s ize", - "iss ors", - "issor s", - "Ġf is", - "Ġfi s", - "Ġ alc", - "Ġa lc", - "Ġal c", - "ens ation", - "ensa tion", - "ĠN ixon", - "ĠNi xon", - "Ġ mighty", - "Ġmight y", - "- str", - "-s tr", - "-st r", - "_ special", - "_s pecial", - "_sp ecial", - "_spec ial", - "_ ADC", - "_A DC", - "_AD C", - "Ġ Twig", - "ĠT wig", - "ĠTw ig", - "um bling", - "umb ling", - "- address", - "-add ress", - "-ad dress", - "Ġher oin", - "Ġhero in", - "Y TE", - "YT E", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "F riend", - "Fri end", - "Ġ ave", - "Ġa ve", - "Ġav e", - "Ġ PNG", - "ĠP NG", - "ĠPN G", - "ĠKur dish", - "ĠKurd ish", - "DataSet Changed", - "Ġbl ades", - "Ġblade s", - "Ġbla des", - "b ral", - "br al", - "bra l", - "S team", - "St eam", - "Ste am", - "Ġs igu", - "Ġsi gu", - "Ġsig u", - "IRT UAL", - "a cos", - "ac os", - "aco s", - "U DP", - "UD P", - "( database", - "(d atabase", - "(data base", - "(dat abase", - "h ec", - "he c", - "Ġ Strings", - "ĠString s", - "ĠStr ings", - "_ scalar", - "_s calar", - "_sc alar", - "_scal ar", - "ĉ desc", - "ĉd esc", - "ĉdes c", - "ĉde sc", - "Ġ TLS", - "ĠT LS", - "ĠTL S", - "; \"Ċ", - ";\" Ċ", - "ĠCor byn", - "Simple Name", - "u ell", - "ue ll", - "uel l", - "Ġ Entre", - "ĠEn tre", - "ĠEnt re", - "ĠEntr e", - "ell ites", - "ellite s", - "elli tes", - "- place", - "-p lace", - "-pl ace", - "Ġfrank ly", - "ĠE rf", - "ĠEr f", - "C EL", - "CE L", - "Ġpa ÃŃs", - "Ġh edge", - "Ġhe dge", - "Ġhed ge", - "Ġ latent", - "Ġla tent", - "Ġlate nt", - "Ġlat ent", - "Ġlaten t", - "Ġ IRQ", - "ĠIR Q", - "ĠH erald", - "ĠHer ald", - "ĠHera ld", - "Ġ Prec", - "ĠP rec", - "ĠPr ec", - "ĠPre c", - "ë³ ´", - ". TEXT", - ".T EXT", - "S alary", - "Sal ary", - "Ġaut umn", - "Ġtr avail", - "Ġtra vail", - "Ġtrav ail", - "Ġtrava il", - ". Sum", - ".S um", - "Ġc ared", - "Ġcar ed", - "Ġca red", - "Ġcare d", - "M or", - "Mo r", - "Ġint uitive", - "Ġintuit ive", - "Ġj ournals", - "Ġjournal s", - "_ IT", - "_I T", - "Ġ Trou", - "ĠT rou", - "ĠTr ou", - "ĠTro u", - "ä¼ ł", - "Has ColumnName", - "Com posite", - "Comp osite", - "Ġsp ice", - "Ġspi ce", - "_ disk", - "_d isk", - "_dis k", - "_di sk", - "_CODE S", - "_CO DES", - "_COD ES", - "Ġ Introduced", - "ĠInt roduced", - "ĠIntro duced", - "i ona", - "ion a", - "io na", - "Ġn uestra", - "Ġnue stra", - "Ġnuest ra", - "Ġnues tra", - "o ct", - "oc t", - "ĠĠĠĠ ĊĠĠĠĠĊĠĠĠĠĊ", - "ĠĠĠĠĊ ĠĠĠĠĊĠĠĠĠĊ", - "ĠĠĠĠĊĠĠĠĠĊ ĠĠĠĠĊ", - "( parameter", - "(param eter", - "(para meter", - "Ġst udios", - "Ġstud ios", - "Ġstudio s", - "Ġstudi os", - "Ġ projectId", - "Ġproject Id", - "Ġbd sm", - ".Sql Client", - "im izer", - "imize r", - "imi zer", - "imiz er", - "Ġ CARD", - "ĠC ARD", - "ĠCA RD", - "ĠCAR D", - "+ t", - "a an", - "aa n", - ". sol", - ".s ol", - ".so l", - "_ Adjust", - "_Ad just", - "Ġright eous", - "Ġ Logging", - "ĠLog ging", - ". filters", - ".f ilters", - ".filter s", - ".fil ters", - "_ TAB", - "_T AB", - "_TA B", - "ĉ sys", - "ĉs ys", - "rop hic", - "roph ic", - "o therapy", - "other apy", - "Ġ Browse", - "ĠB rowse", - "ĠBrow se", - "key board", - "R ON", - "RO N", - "+ \\", - "r opped", - "ro pped", - "rop ped", - "ropp ed", - "Ġext ensively", - "Ġextensive ly", - "f k", - "Ġ lime", - "Ġl ime", - "Ġli me", - "Ġlim e", - "y ears", - "year s", - "ye ars", - "E xc", - "Ex c", - "Ġs ph", - "Ġsp h", - "Ġch eating", - "Ġche ating", - "Ġcheat ing", - "an dro", - "and ro", - "andr o", - "ÃŃ o", - "Ġpr ince", - "Ġpri nce", - "Ġprin ce", - "Ġprinc e", - "o ire", - "oi re", - "oir e", - "Ġ Destination", - "ĠD estination", - "ĠDest ination", - "ĠCon verts", - "ĠConvert s", - "ĠConv erts", - "Ġup stream", - "o led", - "ol ed", - "ole d", - "Ġserv ants", - "Ġservant s", - "Ġ semantic", - "Ġsem antic", - "Ġcr unch", - "Ġeven tual", - "Ġevent ual", - "r unner", - "run ner", - "/ error", - "/e rror", - "S pin", - "Sp in", - "Spi n", - "Ġsecret ly", - "Ġ assemble", - "Ġas semble", - "Ġass emble", - ". Person", - ".P erson", - ".Per son", - "end error", - "ender ror", - "ende rror", - "_ <", - "Ġp endant", - "Ġpend ant", - "S leep", - "ĠChem istry", - "Ġboss es", - "Ġbos ses", - "l k", - ") )),Ċ", - ")) ),Ċ", - "))) ,Ċ", - "))), Ċ", - "Block ly", - "DE VICE", - "DEV ICE", - "Ġreflect ing", - "Ġ ample", - "Ġam ple", - "Ġampl e", - "Ġamp le", - "M illiseconds", - "Mill iseconds", - "ĠPres idential", - "ĠPresident ial", - "Ġ usuarios", - "Ġus uarios", - "Ġusuario s", - "Ġusu arios", - "Ġ NZ", - "ĠN Z", - "Ġ Salary", - "ĠS alary", - "ĠSal ary", - "ĠSala ry", - "ĠA manda", - "ĠAm anda", - "ĠAma nda", - "_ np", - "_n p", - "j ury", - "ju ry", - "jur y", - "Ġk ön", - "Ġkö n", - "Ġther apist", - "Ġtherap ist", - "Ġhom osexual", - "Ġhomosex ual", - "Ġhomo sexual", - "ĠDr ake", - "ĠDra ke", - "- window", - "-w indow", - "Ġ Located", - "ĠLoc ated", - "ĠLocate d", - ". Driver", - ".D river", - "Ġ VIDEO", - "ĠV IDEO", - "ĠVID EO", - "Ġmer chants", - "Ġmerch ants", - "Ġmerchant s", - "ĠC hest", - "ĠCh est", - "ĠChe st", - "ĠChes t", - "- lock", - "-l ock", - "-lo ck", - "/ php", - "/p hp", - "/ph p", - "Ġmil ano", - "Ġmilan o", - "_ STYLE", - "_ST YLE", - "ar ger", - "arg er", - "arge r", - "i dea", - "id ea", - "ide a", - "G UID", - "GUI D", - "GU ID", - "ad vanced", - "adv anced", - "advance d", - "me al", - "Options ItemSelected", - "= '%", - "=' %", - "ĠC ham", - "ĠCh am", - "ĠCha m", - ": data", - ":d ata", - "( stat", - "(s tat", - "(st at", - "Will Appear", - "Ġinf ormal", - "Ġinform al", - "a ji", - "aj i", - "Ġre productive", - "Ġrepro ductive", - "Ġ CAS", - "ĠC AS", - "ĠCA S", - "ãģ £", - "F UNC", - "FUN C", - "FU NC", - "ĠR uth", - "ĠRu th", - "ĠRut h", - ") +(", - ")+ (", - "CON ST", - "CO NST", - "CONS T", - "Ġ Fans", - "ĠF ans", - "ĠFa ns", - "ĠFan s", - "Ġ groupId", - "Ġgroup Id", - "x ffffffff", - "xf fffffff", - "xff ffffff", - "xffff ffff", - "xffffff ff", - "Ġs ampler", - "Ġsample r", - "Ġsam pler", - "Ġsamp ler", - "Ġ}} \">", - "Ġ}}\" >", - ". the", - ".t he", - ".th e", - "Ġh ollow", - "Ġhol low", - "W AY", - "WA Y", - "Ġ Faculty", - "ĠFac ulty", - "Attrib utedString", - "Ġ Looks", - "ĠL ooks", - "ĠLo oks", - "ĠLook s", - "ĠR ex", - "ĠRe x", - "j k", - "ĠM IL", - "ĠMI L", - "Ġ bard", - "Ġb ard", - "Ġbar d", - "Ġba rd", - ". Long", - ".L ong", - ".Lo ng", - "Ġli vest", - "Ġlive st", - "Ġlives t", - "Ġliv est", - "Ġs kal", - "Ġsk al", - "Ġska l", - "ic ism", - "ici sm", - "M AIN", - "MA IN", - "Ġmuch o", - "Ġmu cho", - "Ġmuc ho", - "B ODY", - "BO DY", - "Ġ ese", - "Ġe se", - "Ġes e", - "ĉ use", - "ĉu se", - "ĉus e", - "F oot", - "Foo t", - "Fo ot", - ". SQLException", - ".SQL Exception", - "Ġin heritance", - "Ġinherit ance", - "re ceived", - "receive d", - "rece ived", - "Ġp utas", - "Ġput as", - "Ġpu tas", - "Ġputa s", - "e dis", - "ed is", - "edi s", - "a lsa", - "al sa", - "als a", - "Ġ ErrorMessage", - "ĠError Message", - "Bo oking", - "Book ing", - "Ġ tract", - "Ġt ract", - "Ġtr act", - "Ġtra ct", - "a cz", - "ac z", - "ĠC ant", - "ĠCan t", - "ĠCa nt", - "_ regex", - "_reg ex", - "Ġide ological", - "Ġj ihad", - "Ġji had", - "Ġjih ad", - "h os", - "ho s", - "/ sys", - "/s ys", - "co lm", - "col m", - "( pool", - "(p ool", - "(po ol", - "Ġest án", - "Ġestá n", - "Ġ Pending", - "ĠP ending", - "ĠPen ding", - "ĠPend ing", - "em ás", - "Ġktó ry", - ") );ĊĊĊ", - ")) ;ĊĊĊ", - "));Ċ ĊĊ", - "));ĊĊ Ċ", - ")); ĊĊĊ", - "trans actions", - "transaction s", - "Ġw ield", - "Ġwie ld", - "Ġwi eld", - "Ġwiel d", - "i tere", - "it ere", - "ite re", - "iter e", - "er ture", - "ert ure", - "_ ss", - "_s s", - "Ġstretch ing", - "Ġstret ching", - "Ġpr isoner", - "Ġprison er", - "Ġpris oner", - ".Read All", - "Ġb esch", - "Ġbe sch", - "Ġbes ch", - "-- ;čĊ", - "--; čĊ", - "Ġcr isp", - "Ġcri sp", - "Ġcris p", - "_ SCAN", - "_S CAN", - "_SC AN", - "Ġ ae", - "Ġa e", - "Str ict", - "ĠMin neapolis", - "ĠBo eing", - "a ris", - "ar is", - "ari s", - "r ek", - "re k", - "_ pipe", - "_p ipe", - "_pi pe", - "Ġpri ests", - "Ġpriest s", - "( EIF", - "(E IF", - "eh icles", - "ehicle s", - "Ġ Interactive", - "ĠInter active", - "b etween", - "bet ween", - "ĉNull Check", - "ĠBl air", - "Ġ Lt", - "ĠL t", - "_ inline", - "_in line", - "eth yl", - " ¼", - "_ packages", - "_p ackages", - "_package s", - "_pack ages", - "Ġbar rels", - "Ġbarrel s", - "Ġbarr els", - "_ he", - "_h e", - "Ġ regexp", - "Ġreg exp", - "Ġregex p", - "_ pts", - "_p ts", - "_pt s", - "_ Handler", - "_H andler", - "_Handle r", - "ing ular", - "ingu lar", - "ĠN issan", - "ĠR anch", - "ĠRan ch", - "Ġper ch", - "Ġpe rch", - "Ġperc h", - "Un supported", - "S mith", - "Sm ith", - "ĠLeg ends", - "ĠLegend s", - "M i", - "Ġ gf", - "Ġg f", - "st eder", - "ste der", - "sted er", - "Ġac quiring", - "Ġacqu iring", - "Ġs imulator", - "Ġsim ulator", - "Ġsimul ator", - "( ),\"", - "() ,\"", - "(), \"", - "re ceive", - "rece ive", - "Ġin place", - "Ġinp lace", - "A CTION", - "AC TION", - "ACT ION", - "Ġ WebDriver", - "ĠWeb Driver", - "file system", - "files ystem", - "< Order", - "l open", - "lo pen", - "lop en", - "lope n", - "Ġ HEIGHT", - "ĠHE IGHT", - ".set Border", - "į °", - "__ [\"", - "__[ \"", - "Ġ clamp", - "Ġc lamp", - "Ġcl amp", - "Ġclam p", - "Ġcla mp", - "Seg oe", - "b ands", - "ba nds", - "ban ds", - "band s", - "to List", - "am ba", - "amb a", - "> '+Ċ", - ">' +Ċ", - ">'+ Ċ", - "Ġ credible", - "Ġcred ible", - "a mat", - "am at", - "ama t", - "pl aying", - "play ing", - "pla ying", - ".setImage Resource", - "q uel", - "qu el", - "que l", - "Ġpo dr", - "Ġpod r", - "ge om", - "geo m", - "E k", - "ĠQ atar", - "Ġg eld", - "Ġge ld", - "Ġgel d", - "? ',Ċ", - "?' ,Ċ", - "?', Ċ", - "Ġc yl", - "Ġcy l", - "( ax", - "(a x", - "Ġ WI", - "ĠW I", - "ur ally", - "ural ly", - "ĠBr asil", - "ĠBra sil", - "ĠBras il", - "Ġs enza", - "Ġsen za", - "a ley", - "al ey", - "ale y", - "o nen", - "on en", - "one n", - "Ġ bah", - "Ġb ah", - "Ġba h", - "Ġm olecule", - "Ġmolec ule", - "R ad", - "Ra d", - "è¿ °", - "AN CH", - "ANC H", - "- background", - "-back ground", - "- agent", - "-a gent", - "-ag ent", - "-age nt", - "Ġprol ifer", - ": boolean", - "Ġt ide", - "Ġti de", - "Ġtid e", - "erial izer", - "erialize r", - "_ ;čĊ", - "_; čĊ", - "F ee", - "Fe e", - "* *)", - "** )", - "er gy", - "erg y", - "ĠH onor", - "ĠHon or", - "ĠHo nor", - ". Logging", - ".Log ging", - "i ris", - "ir is", - "iri s", - "Ġunder mine", - "Ġundermin e", - "ĠD y", - "Ġt yr", - "Ġty r", - "Ġ deque", - "Ġde que", - "Ġd amer", - "Ġda mer", - "Ġdam er", - "Ġdame r", - "( [])Ċ", - "([ ])Ċ", - "([] )Ċ", - ".layout ControlItem", - ".layoutControl Item", - "p eated", - "pe ated", - "peat ed", - "C AN", - "CA N", - "ra gments", - "rag ments", - "ragment s", - "L and", - "La nd", - ") ]);Ċ", - ")] );Ċ", - ")]) ;Ċ", - "ĠS ah", - "ĠSa h", - "Ġ DECL", - "ĠDE CL", - "ĠDEC L", - "With in", - "Wi thin", - "Ġ Namespace", - "ĠN amespace", - "ĠName space", - "ĠNames pace", - "an other", - "ano ther", - "semb ling", - "sem bling", - "sembl ing", - ". describe", - ".de scribe", - ".des cribe", - "Con sum", - "Cons um", - "Ġ Fear", - "ĠF ear", - "ĠFe ar", - "g iven", - "gi ven", - "give n", - "O range", - "Or ange", - "< boolean", - " This", - ">T his", - "Ġdata Index", - "Ġprint able", - "Ġprin table", - "ĠE yes", - "ĠEye s", - "ĠEy es", - "_ targets", - "_target s", - "_tar gets", - "( Py", - "(P y", - ". over", - ".o ver", - ".ov er", - "Ġ bru", - "Ġb ru", - "Ġbr u", - "am pton", - "amp ton", - "Ġplaint iff", - "< Key", - " );Ċ", - ">) ;Ċ", - "in vest", - "inv est", - ". *ĊĊ", - ".* ĊĊ", - ".*Ċ Ċ", - "Ġt élé", - "Ġté lé", - "Ġsu perf", - "Ġsuper f", - "Ġ cascade", - "Ġc ascade", - "Ġcas cade", - "Ġcasc ade", - "D TD", - "DT D", - "Ġv ivid", - "Ġvi vid", - "Ġviv id", - "Ġsubsid ies", - "Ġsubsidi es", - "ĠH ass", - "ĠHas s", - "ĠHa ss", - "Ġcol laps", - "Ġcoll aps", - "Ġcer amic", - "{ }\".", - "{} \".", - "ĠLeak age", - "- trash", - "-tr ash", - "-tra sh", - "c ollapsed", - "coll apsed", - "collapse d", - "- social", - "-s ocial", - "-so cial", - "ĠC had", - "ĠCh ad", - "ĠCha d", - "Ġinc lined", - "Ġincl ined", - "Ġ sto", - "Ġs to", - "Ġst o", - "Ġstory board", - ". payment", - ".p ayment", - ".pay ment", - "stack overflow", - "ĠRa iders", - "ĠRaid ers", - "ĠRaider s", - "ĠRai ders", - "Ġ #'", - "Ġ# '", - "ol icies", - "olic ies", - "oli cies", - "ìľ¼ ë¡ľ", - "e map", - "em ap", - "ema p", - "Ġ kj", - "Ġk j", - "Ġ quota", - "Ġqu ota", - "Ġquot a", - "Ġquo ta", - "ĠGar dens", - "ĠGarden s", - "ĠGard ens", - "ë² Ī", - "ĠAng els", - "ĠAnge ls", - "ĠAngel s", - "Ġ oft", - "Ġo ft", - "Ġof t", - "Ġlower case", - "Ġ iParam", - "Ġi Param", - "ĠiP aram", - "Ġche apest", - "Ġcheap est", - "un ta", - "unt a", - "_ pkt", - "_p kt", - "_pk t", - "ic ators", - "ica tors", - "icator s", - "Ġ leurs", - "Ġl eurs", - "Ġle urs", - "Ġleur s", - "Ġdecre ases", - "Ġdecrease s", - "ĉ define", - "ĉdef ine", - "ĉde fine", - "P REC", - "PR EC", - "PRE C", - "am mers", - "amm ers", - "ammer s", - "Ġ PreparedStatement", - "ĠPre paredStatement", - "ĠPrepared Statement", - "( direction", - "(d irection", - "(dir ection", - "(di rection", - "Ġcr ews", - "Ġcre ws", - "Ġcrew s", - "ar ked", - "ark ed", - "ĠMem phis", - "Ġ Sell", - "ĠS ell", - "ĠSe ll", - "ĠSel l", - "G TK", - "GT K", - "Ġ maid", - "Ġm aid", - "Ġma id", - "Ġmai d", - ": disable", - ":d isable", - "éĽ Ĩ", - "ĠP f", - "Ġal beit", - "op enh", - "open h", - "ope nh", - "?> \">Ċ", - "?>\" >Ċ", - ". getSource", - ".get Source", - ".getS ource", - "( scale", - "(s cale", - "(sc ale", - "D u", - "ĠP IL", - "ĠPI L", - "_ refresh", - "_re fresh", - "_ref resh", - "Ġb ets", - "Ġbe ts", - "Ġbet s", - "( car", - "(c ar", - "(ca r", - "ĠV on", - "ĠVo n", - "| --------------------------------------------------------------------------Ċ", - "ĠG rat", - "ĠGr at", - "ĠGra t", - "M uch", - "Mu ch", - "( Dialog", - "(D ialog", - ".stop Propagation", - "Ġ tek", - "Ġt ek", - "Ġte k", - "Ġex its", - "Ġexit s", - "' ],$", - "'] ,$", - "'], $", - "Ġ phoneNumber", - "Ġphone Number", - "u cs", - "uc s", - "e cimal", - "ec imal", - "eci mal", - "- -------------", - "-- ------------", - "---- ----------", - "-------- ------", - "--- -----------", - "------------ --", - "----- ---------", - "---------- ----", - "------ --------", - "----------- ---", - "------------- -", - "------- -------", - "--------- -----", - "i np", - "in p", - ".po jo", - "Ġcor pus", - "Ġcorp us", - "Ġpractition ers", - "Ġpractitioner s", - ". pic", - ".p ic", - ".pi c", - "\" testing", - "Ġstring By", - ". NotNull", - ".Not Null", - "Ġ rang", - "Ġr ang", - "Ġran g", - "Ġra ng", - ". Dynamic", - ".D ynamic", - "_ Render", - "_R ender", - "_Re nder", - "а ÑĤа", - "аÑĤ а", - "Wait ing", - "Wa iting", - "Ġ Wik", - "ĠW ik", - "ĠWi k", - "Ġoverwhel med", - "Ġoverwhelm ed", - "% \">", - "%\" >", - "Ġ AE", - "ĠA E", - "} }>Ċ", - "}} >Ċ", - "}}> Ċ", - "u w", - "_ typ", - "_t yp", - "_ty p", - "Ġ buckets", - "Ġb uckets", - "Ġbucket s", - "Ġbuck ets", - "Ġg reeting", - "Ġgre eting", - "Ġgreet ing", - "Ġ laughter", - "Ġla ughter", - "Ġlaugh ter", - "Ġant agon", - "ugg estion", - "uggest ion", - "- email", - "-e mail", - "-em ail", - "ĉ top", - "ĉt op", - "ĉto p", - "Ġ eros", - "Ġe ros", - "Ġer os", - "Ġero s", - "_ tri", - "_t ri", - "_tr i", - "Ġiss uing", - "Ġissu ing", - "Ġ há", - "Ġh á", - "Ġis olate", - "Ġisol ate", - "Ġiso late", - "Over flow", - ", E", - "Ġnut ritional", - "Ġnutrition al", - "Ġnutrit ional", - "ĠAbb ott", - "Ġ nf", - "Ġn f", - ". touch", - ".t ouch", - ".to uch", - ".fetch all", - "_ zip", - "_z ip", - "\" )}Ċ", - "\") }Ċ", - "\")} Ċ", - "Ġ amat", - "Ġa mat", - "Ġam at", - "Ġama t", - "Ġ Cisco", - "ĠC isco", - "Ġn Ã¥", - "P LEX", - "PL EX", - "PLE X", - "Ġ sei", - "Ġs ei", - "Ġse i", - "f oto", - "fo to", - ". toJson", - ".to Json", - "å¤ ļ", - "ĠK lein", - "ĠKle in", - "ĠKl ein", - "Ġ libc", - "Ġli bc", - "Ġlib c", - "Ġm iners", - "Ġmin ers", - "Ġmi ners", - "Ġmine rs", - "Ġminer s", - "å ¢", - "- print", - "-p rint", - "-pr int", - "ĠP ride", - "ĠPr ide", - "ĠPri de", - "T odos", - "To dos", - "Todo s", - "Ġ masked", - "Ġmask ed", - "Ġmas ked", - "Ġ setData", - "Ġset Data", - "Ġtele fon", - "Ġtel efon", - "Ġun happy", - "Ġunh appy", - "Ġ Tables", - "ĠT ables", - "ĠTable s", - "ĠTab les", - "ĠTa bles", - "g eb", - "ge b", - "( debug", - "(de bug", - "_ allowed", - "_all owed", - "_allow ed", - "- access", - "-a ccess", - "-ac cess", - "Ġlog istics", - "Ġlogistic s", - "Ġ gems", - "Ġg ems", - "Ġge ms", - "Ġgem s", - "ĠM ature", - "ĠMat ure", - "ĠMa ture", - "Ġ rsp", - "Ġr sp", - "Ġrs p", - "Ġ Alle", - "ĠA lle", - "ĠAl le", - "ĠAll e", - ". getBytes", - ".get Bytes", - ".getBy tes", - "\\ web", - "ynchron ized", - "ynchronize d", - "Par agraph", - "Para graph", - "Ġth rottle", - "Ġthr ottle", - "Ġthrott le", - ". sqlite", - ".sql ite", - "cons ulta", - "consult a", - "ĠS eah", - "ĠSe ah", - "ĠSea h", - "C e", - "Ġsub mar", - "E RE", - "ER E", - "V ous", - "Vo us", - "Ġ reddit", - "Ġre ddit", - "Ġred dit", - "Ġredd it", - "Ġsql alchemy", - "- mile", - "-m ile", - "oc ide", - "oci de", - "P our", - "Po ur", - "} }\">Ċ", - "}} \">Ċ", - "}}\" >Ċ", - "st ead", - "ste ad", - "Ġ @(", - "Ġ@ (", - "Ġ [])", - "Ġ[ ])", - "Ġ[] )", - "Ġ Ads", - "ĠA ds", - "ĠAd s", - "Ġover load", - "Ġoverl oad", - "r idden", - "ri dden", - "rid den", - "ĠDe sert", - "ĠDes ert", - "Ġ Wrap", - "ĠW rap", - "ĠWr ap", - "ĠPortug uese", - "e tz", - "et z", - "ĉ first", - "ĉf irst", - "ĉfi rst", - "Ġm ilestone", - "Ġmil estone", - "Ġmiles tone", - "Ġmile stone", - "æĹ ł", - "Ñĥ Ñī", - "( success", - "(s uccess", - "< Vector", - " \")Ċ", - ">\" )Ċ", - ">\") Ċ", - "ĠD ollar", - "ĠDol lar", - "ĠDoll ar", - "Ġ emoji", - "Ġem oji", - "Ġemo ji", - "Car ousel", - "- player", - "-p layer", - "-play er", - "-pl ayer", - "Ġadjust ing", - "Ġadj usting", - "Ġj uga", - "Ġju ga", - "Ġjug a", - "allenge s", - "alleng es", - "allen ges", - "g ene", - "ge ne", - "gen e", - "(body Parser", - "lo pedia", - "lop edia", - "lope dia", - "Ġ Behind", - "ĠBe hind", - "ĠBeh ind", - "Ġslee ves", - "Ġsleeve s", - "Ġdrag ging", - "ĠChe vrolet", - "Ġ biz", - "Ġb iz", - "Ġbi z", - "iv ities", - "ivi ties", - "Ġ Frequency", - "ĠF requency", - "ĠFrequ ency", - ", char", - ",c har", - ",ch ar", - ". WHITE", - ".W HITE", - "_ preview", - "_p review", - "_pr eview", - "_pre view", - "_prev iew", - ") ';Ċ", - ")' ;Ċ", - "_ ax", - "_a x", - "I ONS", - "ION S", - "IO NS", - ". cpu", - ".c pu", - ".cp u", - ". inputs", - ".in puts", - ".input s", - "U BE", - "UB E", - "_ feed", - "_f eed", - "_fe ed", - "_fee d", - "ĠSup plement", - "! ).", - "!) .", - "e sus", - "es us", - "Ġ UDP", - "ĠU DP", - "ĠUD P", - "Ġmicro phone", - "Ġconf irms", - "Ġconfirm s", - ".is NotEmpty", - "\" :\"\",Ċ", - "\": \"\",Ċ", - "\":\" \",Ċ", - "\":\"\" ,Ċ", - "_ SCREEN", - "_S CREEN", - "_SC REEN", - "ĉ expected", - "ĉex pected", - "ĉexpect ed", - "ĉexp ected", - "+-+- +-+-", - "ĠH ait", - "ĠHa it", - "ĠHai t", - "fast call", - "Ġdep ict", - "v b", - "_ picture", - "_p icture", - "_pic ture", - "ĉ description", - "ĉd escription", - "ĉdes cription", - "ĉde scription", - "ĠW ife", - "ĠWi fe", - "u ci", - "uc i", - "Ġv icious", - "Ġvic ious", - "ä» ĸ", - "u eba", - "ue ba", - "Ġset User", - "ãģ ¡", - "Ġd iving", - "Ġdi ving", - "Ġdiv ing", - "Ġop era", - "Ġoper a", - "user content", - "a rah", - "ar ah", - "ara h", - ") },", - ")} ,", - "y un", - "yu n", - "v elt", - "ve lt", - "vel t", - "Ġun covered", - "Ġuncover ed", - "Ġ hips", - "Ġh ips", - "Ġhi ps", - "Ġhip s", - "Ġosc ill", - "Ġassert ing", - "Ġ Xi", - "ĠX i", - ". restore", - ".re store", - ".rest ore", - "k ea", - "ke a", - "Ġsp elling", - "Ġspell ing", - "Ġspel ling", - "Ġ derive", - "Ġde rive", - "Ġder ive", - "Ġderiv e", - "ab we", - "ĠD ow", - "ĠDo w", - ". setType", - ".set Type", - "_ vs", - "_v s", - "Ġc ozy", - "Ġco zy", - "Ġcoz y", - ". categories", - ".c ategories", - "O rg", - "Or g", - "_ mgr", - "_m gr", - "Ġd ungeon", - "Ġdung eon", - "collection View", - "Ġ Blank", - "ĠBl ank", - "ac ias", - "aci as", - "acia s", - "ä ä", - "_ cleanup", - "_c leanup", - "_clean up", - "_ACT IVITY", - "_ACTIV ITY", - "Ġtri angles", - "Ġtriangle s", - "Ġtriang les", - ". MenuItem", - ".Menu Item", - "Ġ iphone", - "Ġi phone", - "Ġip hone", - "Ġ Won", - "ĠW on", - "ĠWo n", - "] ]ĊĊ", - "]] ĊĊ", - "]]Ċ Ċ", - "Ġ Comparison", - "ĠCom parison", - "ĠCompar ison", - ". Doc", - ".D oc", - ".Do c", - "Ġ canonical", - "Ġcan onical", - "Ġcanon ical", - "ĠSu dan", - "ĠSud an", - "' ){", - "') {", - "Up Inside", - "b uiltin", - "built in", - "E NCY", - "EN CY", - "ENC Y", - "x be", - "xb e", - "Ġch uck", - "Ġchu ck", - "Ġcontrad ict", - "Ġcontra dict", - "Ġnu estro", - "Ġnue stro", - "Ġnuest ro", - "Ġnues tro", - "Ġarchitect ural", - "ĠF ib", - "ĠFi b", - "Ġcomp ares", - "Ġcompar es", - "Ġcompare s", - "* k", - "C fg", - "çĦ ¡", - "n ten", - "nt en", - "nte n", - "M atches", - "Match es", - "Mat ches", - "Ġ DOWNLOAD", - "ĠDOWN LOAD", - "_HANDLE R", - "_HAND LER", - "man agement", - "manage ment", - "mana gement", - "[ S", - "E NG", - "EN G", - "ÂĢ Â", - "f ang", - "fa ng", - "fan g", - "Ġsl ipped", - "Ġslip ped", - "ĠL anka", - "ĠLan ka", - "esc aping", - "Ġtack les", - "Ġtackle s", - "ĠPe dro", - "ĠPed ro", - ". Prop", - ".P rop", - ".Pro p", - ".Pr op", - ". ''", - ".' '", - ". Generated", - ".G enerated", - ".Generate d", - ".New Guid", - "at rigesimal", - "il lon", - "ill on", - "illo n", - "Ġstat istic", - "Ġstatist ic", - "s pecies", - "sp ecies", - "spec ies", - "spe cies", - "h olding", - "hold ing", - "hol ding", - "Dr upal", - "Ġfundament ally", - "Ġfundamental ly", - "Ġbond age", - "Ġres olutions", - "Ġresolution s", - "Inline Data", - "\\ Type", - "es tion", - "est ion", - "esti on", - ". wrap", - ".w rap", - ".wr ap", - "Ġwar riors", - "Ġwarrior s", - "Ġ LOCAL", - "ĠLO CAL", - "ĠLOC AL", - "A rchive", - "Arch ive", - "Arc hive", - "Ġembr aced", - "Ġembrace d", - "á» §", - ". Ver", - ".V er", - "ĠAff ordable", - "ole sale", - "oles ale", - "Ġ Applied", - "ĠApp lied", - "ĠAp plied", - "ĠAppl ied", - "Ġ Conversion", - "ĠCon version", - "ĠConv ersion", - "ĠConvers ion", - "m ega", - "me ga", - "meg a", - "_ cam", - "_c am", - "_ca m", - "Ġcer emon", - "Ġcere mon", - "a urus", - "au rus", - "aur us", - "ĠV olk", - "ĠVol k", - "ĠVo lk", - ". opens", - ".open s", - ".op ens", - "/ about", - "/a bout", - "Ġ Std", - "ĠS td", - "ĠSt d", - "j ournal", - "jo urnal", - "jour nal", - "( )){čĊ", - "() ){čĊ", - "()) {čĊ", - "()){ čĊ", - ", \"\\", - ",\" \\", - "( Arrays", - "(Array s", - "ĠD ense", - "ĠDen se", - "ase ña", - "än ner", - "änn er", - "/ stat", - "/s tat", - "/st at", - "user Data", - "Ġg erman", - "Ġger man", - "Ġgerm an", - "Ġ tz", - "Ġt z", - "w orthy", - "worth y", - "wort hy", - "wor thy", - "Format Exception", - "ph erd", - "pher d", - "phe rd", - "Ġsm iles", - "Ġsmile s", - "Ġ Whenever", - "ĠWh enever", - "ĠWhen ever", - "ĠWhe never", - "( adapter", - "(ad apter", - ".bad logic", - "Ġbrief ing", - ". GridColumn", - ".Grid Column", - "- char", - "-c har", - "-ch ar", - "d imension", - "dim ension", - "ĠC opper", - "ĠCo pper", - "ĠCop per", - "ĠCopp er", - "Ġn inth", - "Ġni nth", - "Ġnin th", - "Ġ' {{", - "Ġ'{ {", - "Ġ rav", - "Ġr av", - "Ġra v", - "_ Table", - "_T able", - "_Tab le", - "Ġderiv atives", - "Ġderivative s", - "Ġ Raise", - "ĠR aise", - "ĠRa ise", - "ĠRai se", - "ĠF ut", - "ĠFu t", - "ar mor", - "arm or", - "- padding", - "-p adding", - "-pad ding", - "Ġre min", - "Ġr emin", - "Ġrem in", - "ĉ style", - "ĉst yle", - "Ġ Membership", - "ĠMember ship", - "ĠMembers hip", - "Ġsp reads", - "Ġspread s", - "Ġspre ads", - "Ġg alleries", - "Ġgall eries", - "ĠClark e", - "ĠClar ke", - "Ġcon ception", - "Ġconcept ion", - "Ġconce ption", - "min ute", - "Ġab usive", - "_ adj", - "_a dj", - "_ad j", - "Ġterr ific", - "Ġo vert", - "Ġover t", - "Ġov ert", - "our cing", - "Ġ entrada", - "Ġent rada", - "Ġentr ada", - "Ġentra da", - "level s", - "lev els", - "Ġcrit ique", - "Ġres pects", - "Ġrespect s", - "Ġresp ects", - "ĠM MA", - "ĠMM A", - "i ene", - "ie ne", - "ien e", - "Ġen caps", - "Ġenc aps", - "ĠRay mond", - "Div ider", - "Di vider", - "i vable", - "iv able", - "iva ble", - "b az", - "ba z", - "Ġ@ _;Ċ", - "Ġ@_ ;Ċ", - "ĠCl aire", - "ĠCla ire", - "ĠClair e", - "Ġur ging", - "Ġurg ing", - "C EE", - "CE E", - "Ġtrans former", - "Ġtransform er", - "dis cord", - "disc ord", - "ĠJ ourney", - "t os", - "to s", - "Ġcompet itions", - "Ġcompetition s", - "Ġcompetit ions", - "Ġ OBJ", - "ĠO BJ", - "ĠOB J", - "ĠB is", - "ĠBi s", - "Ġrelax ation", - "i dy", - "id y", - "_ INSTANCE", - "_IN STANCE", - "_INST ANCE", - "Ġ Pref", - "ĠP ref", - "ĠPr ef", - "ĠPre f", - "d ados", - "da dos", - "dad os", - "ici encies", - "ĠMedia Query", - "Ġ Cube", - "ĠC ube", - "ĠCub e", - "ĠCu be", - "Ġ Strange", - "ĠSt range", - "ĠStr ange", - "ĠStra nge", - "g pu", - "gp u", - "( days", - "(d ays", - "(day s", - "(da ys", - "_ InitStruct", - "_Init Struct", - "Ġf ingerprint", - "Ġfinger print", - "e mat", - "em at", - "ema t", - "ĠG ecko", - "ĠGe cko", - "Ġ rails", - "Ġr ails", - "Ġrail s", - "Ġra ils", - "ĠL um", - "ĠLu m", - "s traction", - "st raction", - "str action", - "stract ion", - "stra ction", - "ig ung", - "igu ng", - "( movie", - "(m ovie", - "_ dictionary", - "_d ictionary", - "_ interrupt", - "_int errupt", - "_inter rupt", - "Ġ QC", - "ĠQ C", - "i ked", - "ik ed", - "ike d", - "append Child", - "rec ipient", - "r é", - "V e", - "Ġt owel", - "Ġto wel", - "Ġtow el", - ".last IndexOf", - "Ġplace bo", - "Ġplac ebo", - "Ġ Wie", - "ĠW ie", - "ĠWi e", - ". esp", - ".e sp", - ".es p", - "( Debug", - "oper ative", - "Ġde ceased", - "Ġdece ased", - "& id", - "ĉ mutex", - "ĉm utex", - "e lic", - "el ic", - "eli c", - "Ġb apt", - "Ġba pt", - "ĉ čĊčĊ", - "ĉčĊ čĊ", - "Ġfar ther", - "Ġfart her", - "H alf", - "Ha lf", - "Hal f", - ". disable", - ".d isable", - ".dis able", - ".menu Strip", - "le ccion", - "lec cion", - "Ġ resultCode", - "Ġresult Code", - "Ġc ans", - "Ġcan s", - "Ġca ns", - "- election", - "-e lection", - "-elect ion", - "-el ection", - "f emale", - "fe male", - "_ FIX", - "_F IX", - "aus ible", - "Ġ POWER", - "ĠP OWER", - "ĠPO WER", - "ĠPOW ER", - "Ġre construction", - "Ġrecon struction", - "Ġreconstruct ion", - "Ġsc ans", - "Ġscan s", - "Ġsca ns", - ".Xtra Bars", - "âĢĺ s", - "Re moved", - "Rem oved", - "Remove d", - "Ġpara graphs", - "Ġparagraph s", - "_ margin", - "_m argin", - "_mar gin", - "Ġl ymph", - "Ġly mph", - "Ġ bos", - "Ġb os", - "Ġbo s", - "l ington", - "ling ton", - "ĠBapt ist", - "Ġadvertis ements", - "Ġadvertisement s", - "Ġadvertise ments", - "Ġ Manage", - "ĠMan age", - "ĠMa nage", - "ĠMana ge", - "/ yyyy", - "/y yyy", - "I OUS", - "IO US", - "EN CES", - "ENCE S", - "ENC ES", - "ĠF iction", - "ĠFi ction", - "ĉ menu", - "ĉm enu", - "ĉme nu", - "ĠFile OutputStream", - "o van", - "ov an", - "ova n", - "ĠF eng", - "ĠFe ng", - "ĠFen g", - "Ġsk ipping", - "Ġskip ping", - "Ġski pping", - "get Class", - "getC lass", - "an ni", - "ann i", - "Ġre bounds", - "Ġreb ounds", - "Ġrebound s", - "Ġpublic ity", - "Ġpub licity", - "Ġpubli city", - "Ġin gres", - "Ġing res", - "Ġingr es", - "us ement", - "use ment", - "Ġthought ful", - ". Chart", - ".C hart", - ".Ch art", - ".Char t", - "Ġh atte", - "Ġha tte", - "Ġhat te", - "pass port", - "pas sport", - "Ġhook ed", - "Ġho oked", - "Ġ Lens", - "ĠL ens", - "ĠLe ns", - "ĠLen s", - "Ġflag ship", - "Ġflags hip", - "Ġs tip", - "Ġst ip", - "Ġ GEN", - "ĠG EN", - "ĠGE N", - "Ġcl ues", - "Ġclue s", - "i pv", - "ip v", - "ĠR ise", - "ĠRi se", - "ĠRis e", - "ĠG ew", - "ĠGe w", - "table name", - "tab lename", - "tabl ename", - "Ġfore most", - "_ validate", - "_valid ate", - "_ analysis", - "_an alysis", - "o lla", - "ol la", - "oll a", - "Ġqual ifications", - "Ġqualification s", - "Ġd istributions", - "Ġdistrib utions", - "Ġdistribution s", - "ĠF lower", - "ĠFl ower", - "ĠFlo wer", - "ĠFlow er", - "Ġt ense", - "Ġten se", - "Ġtens e", - "Ġthank ful", - "Ġcl utch", - "Ġun ified", - "ro ads", - "road s", - "Ġs iti", - "Ġsit i", - "Ġsi ti", - "Ġst all", - "Ġsta ll", - "Ġstal l", - "_P RIORITY", - "_PRI ORITY", - "c stdlib", - "_ USERNAME", - "_USER NAME", - ". bytes", - ".by tes", - ".byte s", - "? page", - "?p age", - "er malink", - "erm alink", - "ermal ink", - "ĠVe get", - "ĠVeg et", - "/v nd", - "- author", - "-a uthor", - "-auth or", - "-aut hor", - ". NONE", - ".N ONE", - ".NO NE", - "ĠCon current", - "ĠConc urrent", - "ĠC ry", - "ĠCr y", - "Ġst arters", - "Ġstart ers", - "Ġstar ters", - "Ġstarter s", - "Ġ Interaction", - "ĠInter action", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "Ġ LEVEL", - "ĠLE VEL", - "E ll", - "El l", - "Ġ comboBox", - "Ġcom boBox", - "Ġcombo Box", - "ĠThe resa", - "ĠTh eresa", - "ĠThere sa", - "ĠTher esa", - "t ek", - "te k", - "_ Handle", - "_H andle", - "Ġ aby", - "Ġa by", - "Ġab y", - ".g dx", - ", end", - ",e nd", - ",en d", - "( Local", - "(L ocal", - "O l", - "kn ife", - "a rial", - "ar ial", - "ari al", - "aria l", - "ĠH off", - "ĠHo ff", - "ĠHof f", - "Ġprostituer ade", - "Do ctor", - "Doc tor", - "In stances", - "Instance s", - "Inst ances", - ". SetValue", - ".Set Value", - "ĉ from", - "ĉf rom", - "ĉfr om", - "Ġlux urious", - "In dent", - "Ind ent", - "Al locator", - "All ocator", - "Alloc ator", - "_ DRAW", - "_D RAW", - "_DR AW", - "(\" ,\",", - "(\", \",", - "(\",\" ,", - "ĠF rances", - "ĠFr ances", - "ĠFrance s", - "ĠFranc es", - "ĠFra nces", - "ĠFran ces", - "Ġ groupBox", - "Ġgroup Box", - "( schema", - "(s chema", - "Print f", - "O RIES", - "OR IES", - "- gradient", - "-g radient", - "Ġre put", - "Ġrep ut", - "a rin", - "ar in", - "ari n", - "_ DONE", - "_D ONE", - "_DO NE", - "in cre", - "inc re", - "incr e", - "ig nty", - "ign ty", - "Ġex ert", - "Ġexe rt", - "Ġ -.", - "Ġ- .", - "/ App", - "/A pp", - "- through", - "-th rough", - "Ġdec lining", - "Ġdecl ining", - "Ġdes sert", - "Ġdess ert", - "Ġinc umb", - "Ġ designation", - "Ġdesign ation", - ". PORT", - ".P ORT", - ".PO RT", - ", strong", - ",str ong", - ",st rong", - "Ġ sandbox", - "Ġs andbox", - "Ġsand box", - "Ġw ines", - "Ġwin es", - "Ġwine s", - "Ġwi nes", - "ĠP av", - "ĠPa v", - "$ str", - "$s tr", - "ask ell", - "Ġh ö", - "Ġ PY", - "ĠP Y", - "Get Instance", - "Text Input", - "game Object", - "/ events", - "/e vents", - "/event s", - "created At", - "Ġlocal Var", - "Ġ WHITE", - "ĠW HITE", - "ĠWH ITE", - "p ered", - "pe red", - "per ed", - "i lege", - "ile ge", - "eff icient", - ", color", - ",c olor", - ",col or", - "c ate", - "ca te", - "cat e", - "ĠC afe", - "ĠCa fe", - "ĠCaf e", - "Ġsimilar ities", - "Ġp umps", - "Ġpump s", - "Ġpu mps", - "ĠHun gary", - "ĠHung ary", - ". Username", - ".User name", - "Ġs kate", - "Ġsk ate", - "Ġska te", - "Ġtouchdown s", - "Ġacceler ate", - "Ġaccel erate", - "ĠH elen", - "ĠHe len", - "ĠHel en", - "O MEM", - "OM EM", - "OME M", - "ĠK un", - "ĠKu n", - "_ vol", - "_v ol", - "Ġ findAll", - "Ġfind All", - "ĠMens chen", - "a head", - "ah ead", - ") ;\"", - "); \"", - "k ommen", - "kom men", - "Ġposs essed", - "Ġpossess ed", - ".arg max", - ". transition", - ".t ransition", - ".trans ition", - "A RP", - "AR P", - "OL UME", - "OLUM E", - "( script", - "(s cript", - "Ġ Ðĺ", - "ĠÐ ĺ", - "Ġ Finding", - "ĠF inding", - "ĠFin ding", - "ĠFind ing", - "o nces", - "on ces", - "once s", - "I o", - "B old", - "Bo ld", - "Ġrenew al", - "_D IALOG", - "Ġdis reg", - "IN TERN", - "INT ERN", - "INTER N", - "Ġt oute", - "Ġto ute", - "Ġtou te", - "Ġtout e", - "Ġelect r", - "Ġele ctr", - "ĠG ross", - "ĠGr oss", - "ĠGro ss", - "ĠGros s", - "ĉ true", - "ĉtr ue", - ". Fields", - ".F ields", - ".Field s", - "Ġ WIDTH", - "ĠW IDTH", - "ĠD ent", - "ĠDe nt", - "ĠDen t", - "Ġ Ãģ", - "Ġà ģ", - "NS Notification", - "Ġ aos", - "Ġa os", - "Ġao s", - "Ġme lee", - "Ġmel ee", - ". Validation", - ".Valid ation", - "Ġ DEC", - "ĠD EC", - "ĠDE C", - "- dependent", - "-depend ent", - "Ġsu ic", - "Ġsui c", - "T raits", - "Tr aits", - "Tra its", - "Trait s", - "$ message", - "$m essage", - "Ġ Dear", - "ĠD ear", - "ĠDe ar", - "ĉ FILE", - "ĉF ILE", - "l anguages", - "language s", - ". Prot", - ".P rot", - ".Pro t", - ".Pr ot", - ". addr", - ".add r", - ".ad dr", - "- generation", - "-g eneration", - "-gen eration", - "I CON", - "IC ON", - "ICO N", - "Ġtrans plant", - "- description", - "-d escription", - "-de scription", - "-des cription", - "Ġch asing", - "Ġcha sing", - "Ġch ees", - "Ġche es", - "Ġ }*/Ċ", - "Ġ} */Ċ", - "T rad", - "Tr ad", - "Tra d", - "qu eries", - "que ries", - "quer ies", - "/ widgets", - "/widget s", - "sub package", - "Ġe spec", - "Ġes pec", - "Ġesp ec", - "Ġcr acked", - "Ġcrack ed", - "Ġcompet itor", - "Ġcompetit or", - "P urchase", - "- team", - "-t eam", - "-te am", - "ole cular", - "olec ular", - "or Thunk", - "& P", - "Ġrel ent", - "Ġrele nt", - "/ #{", - "/# {", - "Ġ productId", - "Ġproduct Id", - "Ġ è¾", - "Ġè ¾", - "ĠL av", - "ĠLa v", - "Ġ Alter", - "ĠAl ter", - "ĠAlt er", - ". Mode", - ".M ode", - ".Mod e", - "AD IO", - "ADI O", - "g rp", - "gr p", - "æ ·»åĬł", - "æ·» åĬł", - "Q uit", - "Qu it", - "Qui t", - "Ġdep ths", - "Ġdepth s", - "Ġdept hs", - "- category", - "-c ategory", - "Ġ DATABASE", - "ĠD ATABASE", - "ĠDATA BASE", - "S PELL", - "SP ELL", - "ĠF alcon", - "ĠFal con", - "ĠQString List", - "Ġ ''.", - "Ġ' '.", - "Ġ'' .", - "ĠIn stitution", - "ĠInst itution", - "ĠInstit ution", - "ĠInstitut ion", - "d amage", - "da mage", - "dam age", - "az or", - "azo r", - "bel ongsTo", - "belongs To", - "ver ages", - "verage s", - "Ġ NONE", - "ĠN ONE", - "ĠNO NE", - "ĠNON E", - "ip pets", - "ipp ets", - "ippet s", - ", \\Ċ", - ",\\ Ċ", - "Ġfoot print", - "_ archive", - "_a rchive", - "_arch ive", - "_arc hive", - "n ak", - "na k", - ". getField", - ".get Field", - "Ġ Reflection", - "ĠRef lection", - "ĠReflect ion", - "Ġ ']", - "Ġ' ]", - "ĠH BO", - "ĠHB O", - "_ discount", - "_dis count", - "_disc ount", - "Ġin cest", - "Ġinc est", - "Ġince st", - "ĠD odge", - "ĠDo dge", - "ĠDod ge", - "ĠW ade", - "ĠWa de", - ". NO", - ".N O", - "\" encoding", - "Ġ Blockchain", - "ĠBlock chain", - "Ġlaws uits", - "Ġlawsuit s", - "Ġ Maint", - "ĠM aint", - "ĠMain t", - "ĠMa int", - "ĠMai nt", - "ch ten", - "cht en", - "chte n", - "Ġét ait", - "Ġktó re", - "_ ctl", - "_c tl", - "_ct l", - "( timer", - "(t imer", - "(time r", - "(ti mer", - "B attle", - "Bat tle", - "i zo", - "iz o", - "ay ed", - "aye d", - "I OR", - "IO R", - "ĠGlas gow", - "Ġs ynth", - "Ġsy nth", - "Ġsyn th", - "Ġsynt h", - "_ logs", - "_l ogs", - "_log s", - "_lo gs", - ". pose", - ".p ose", - ".pos e", - ".po se", - "_Adjust orThunk", - "( (&", - "(( &", - "Ġun sure", - "Ġuns ure", - "Ġunsur e", - "y state", - "yst ate", - "íķĺ ëĬĶ", - "O ULD", - "OU LD", - ". ng", - ".n g", - "Ġdefault dict", - "work space", - "works pace", - "Ġselect ive", - "Ġsel ective", - "Picker Controller", - "YNAM IC", - ". methods", - ".method s", - "Ġpath ways", - "Ġpathway s", - "Ġ Few", - "ĠF ew", - "ĠFe w", - "K G", - "C RYPT", - "CRY PT", - "follow ing", - "ĠD LC", - "ĠDL C", - "ĠS ara", - "ĠSar a", - "ĠSa ra", - "Ġ preset", - "Ġp reset", - "Ġpre set", - "Ġpres et", - "e structor", - "estr uctor", - "estruct or", - "ĠK urt", - "ĠKur t", - "ĠKu rt", - "Ġair plane", - "Ġ omp", - "Ġo mp", - "Ġom p", - "Ġ Parents", - "ĠPar ents", - "ĠParent s", - "ĠParen ts", - "ĠPare nts", - "ĠMart inez", - "ĠMartin ez", - ". complete", - ".com plete", - ".comp lete", - "Ġbroad ly", - "Ġs care", - "Ġsc are", - "Ġsca re", - "Ġscar e", - "ĠM é", - "Ġelim ination", - "Ġelimin ation", - "Ġp oured", - "Ġpo ured", - "Ġpour ed", - "Ġpou red", - "/ sw", - "/s w", - "Ġcom un", - "Ġco mun", - "Ġm asc", - "Ġma sc", - "Ġmas c", - "ĠOrgan ic", - "ĠOrg anic", - "Ġ StringUtils", - "ĠString Utils", - "ĠStringUtil s", - "il ateral", - "ilate ral", - "ilater al", - "Ġreluct ant", - "- age", - "-a ge", - "-ag e", - "Ġ nz", - "Ġn z", - ". \"\\", - ".\" \\", - "Ġpast or", - "Ġpas tor", - "Ġpa stor", - "a lez", - "al ez", - "ale z", - "Ġe fect", - "Ġef ect", - "p rov", - "pr ov", - "pro v", - "/ init", - "/i nit", - "/in it", - "Ġp enn", - "Ġpe nn", - "Ġpen n", - "u nds", - "un ds", - "und s", - "Ġ ssize", - "Ġs size", - "Ġss ize", - "Ġ Proj", - "ĠP roj", - "ĠPro j", - "ĠPr oj", - "b asename", - "base name", - "bas ename", - "Ġsh ells", - "Ġshell s", - "Ġshel ls", - "ĠN eck", - "ĠNe ck", - "ĠNec k", - "ĠEn forcement", - "v ided", - "vid ed", - "vi ded", - "vide d", - "s town", - "st own", - "sto wn", - "S phere", - "Sp here", - "$ r", - "us sen", - "uss en", - "a fil", - "af il", - "afi l", - "Ġ Telegram", - "ĠTele gram", - "Ġanaly tical", - "Ġanalytic al", - "н Ñĭе", - "нÑĭ е", - "us ually", - "usu ally", - "usual ly", - "x n", - "Ġhistor ian", - "Ġhist orian", - "Ġhistoria n", - "Ġhisto rian", - "ĠGreg ory", - "ol ph", - "Ġ Una", - "ĠU na", - "ĠUn a", - "Ġcon tributes", - "Ġcontrib utes", - "Ġcontribute s", - "% -", - "anti ago", - "ÑĢ ед", - "ÑĢе д", - ". region", - ".reg ion", - "Ġab rupt", - "ĠUnsupported OperationException", - "Ġ TASK", - "ĠT ASK", - "ĠTA SK", - "ĠTAS K", - "_ finish", - "_f inish", - "_fin ish", - "Ġnot orious", - "Ġ Vs", - "ĠV s", - "Ġ MQ", - "ĠM Q", - "Ġs unset", - "Ġsun set", - "Ġun acceptable", - "ar cer", - "arc er", - "Ġill umin", - "Ġillum in", - "ĠO rb", - "ĠOr b", - "Ġ bh", - "Ġb h", - "E ste", - "Est e", - "Es te", - "_ dispatch", - "_dis patch", - "_disp atch", - "Ġr ipped", - "Ġrip ped", - "Ġri pped", - "Ġtou jours", - "Ġ Parcel", - "ĠPar cel", - "_ ll", - "_l l", - ". userName", - ".user Name", - ". classes", - ".c lasses", - ".class es", - ".cl asses", - "S OURCE", - "( Number", - "(N umber", - "е лÑı", - "ел Ñı", - "Ġhead phones", - "Ġheadphone s", - "( side", - "(s ide", - "(si de", - "(sid e", - "con stitution", - "const itution", - "an nah", - "ann ah", - "anna h", - "čĊ ĠĠĠĠĠĠĠĠčĊ", - "Ġcl iff", - "Ġcli ff", - "- ref", - "-r ef", - "-re f", - "Ġmost rar", - "Ġmo strar", - "Ġmostr ar", - "Ġmostra r", - "ĠP owell", - "ĠPo well", - "ĠPow ell", - "+ y", - "Ġ BG", - "ĠB G", - "_ fragment", - "_f ragment", - "_fr agment", - "_frag ment", - ". Port", - ".P ort", - "Ġreal izing", - "Ġrealiz ing", - "param ref", - "Ġh ometown", - "Ġhome town", - "@ Table", - "+ \" --}}Ċ", - ">-- }}Ċ", - "F rench", - "Fr ench", - "Entity Manager", - "Ġ Plain", - "ĠP lain", - "ĠPl ain", - "ĠPla in", - "//// ////////////////////////////////////////////////////////////////", - "//////// ////////////////////////////////////////////////////////////", - "//////////////// ////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////// ////", - "//////////// ////////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////// ////////////", - "//////////////////////////////////////////////////////////// ////////", - "//////////////////////////////////////////////////// ////////////////", - " ³", - "( RE", - "(R E", - "c apt", - "ca pt", - "cap t", - "Ġ organisms", - "Ġorgan isms", - "Ġorganis ms", - "Ġorganism s", - "Ġ jets", - "Ġj ets", - "Ġje ts", - "Ġjet s", - "o location", - "ol ocation", - "olo cation", - "ĠApp RoutingModule", - "Ġgl orious", - "Ġglo rious", - "Ġglor ious", - "æľ į", - "Ġdisc arded", - "Ġdiscard ed", - "ĉ ĉĉĉĠĠĠĠĠ", - "ĉĉ ĉĉĠĠĠĠĠ", - "ĉĉĉĉ ĠĠĠĠĠ", - "ĉĉĉ ĉĠĠĠĠĠ", - "ĉĉĉĉĠ ĠĠĠĠ", - "ĉĉĉĉĠĠĠ ĠĠ", - "ĉĉĉĉĠĠ ĠĠĠ", - "ĉĉĉĉĠĠĠĠ Ġ", - "ĠArn old", - "l ug", - "lu g", - "Ġp arl", - "Ġpar l", - "Ġpa rl", - "Ġhorm ones", - "Ġhormone s", - "Ġ mah", - "Ġm ah", - "Ġma h", - "ĠS onic", - "ĠSo nic", - "ĠSon ic", - "Ġorgan izers", - "Ġorganiz ers", - "Ġorganize rs", - "Ġorganizer s", - "_ PLATFORM", - "_PL ATFORM", - ". inv", - ".in v", - ".i nv", - "Ġch ord", - "Ġcho rd", - "Ġchor d", - "vent ional", - "vention al", - "ĉ of", - "ĉo f", - "Ep isode", - ". Enum", - ".E num", - ".En um", - "un kt", - "unk t", - "ĠD h", - "ĠJ ared", - "ĠJa red", - "ĠJar ed", - "ĠN ak", - "ĠNa k", - "Ġint ends", - "Ġinte nds", - "Ġintend s", - "End ian", - "Ġa ustralia", - "_ cv", - "_c v", - "( resolve", - "(res olve", - "(re solve", - "Ġclin ics", - "Ġclinic s", - "l iked", - "li ked", - "like d", - "lik ed", - "ASH INGTON", - "in ha", - "inh a", - "' *", - "Ġ NP", - "ĠN P", - "_ beh", - "_b eh", - "_be h", - "Ġ hf", - "Ġh f", - "Ġw ür", - "c ategoria", - "$ form", - "$f orm", - "Ġsub way", - "Ġ isActive", - "Ġis Active", - "pop ular", - "C our", - "Co ur", - "Cou r", - "Ġ cooldown", - "Ġco oldown", - "Ġcool down", - "Ġa insi", - "Ġain si", - "Ġ GLuint", - "ĠGL uint", - "e real", - "ere al", - "erea l", - "Ġarray Of", - "Ġh atch", - "Ġhat ch", - "= =========", - "== ========", - "==== ======", - "======== ==", - "=== =======", - "========= =", - "====== ====", - "===== =====", - "======= ===", - "r esses", - "res ses", - "ress es", - "resse s", - "_ PP", - "_P P", - ". ^", - "_ decay", - "_dec ay", - "ĠB less", - "ĠBl ess", - "ĠBle ss", - "m etrics", - "met rics", - "metric s", - "ĠCOPY ING", - "ĠDump ster", - "ĠJos é", - "ĠDesign s", - "< Void", - "<", - "Ġ? ><", - "Ġ?> <", - "Ġ \"}Ċ", - "Ġ\" }Ċ", - "Ġ\"} Ċ", - "time zone", - "Ġ eer", - "Ġe er", - "Ġee r", - "max cdn", - "Ġ ESC", - "ĠE SC", - "ĠES C", - "ig aret", - "iga ret", - "igar et", - "_ connected", - "_connect ed", - "_conn ected", - "_ reverse", - "_re verse", - "_rev erse", - "Ġquestion able", - "ĠU SC", - "ĠUS C", - "Ġtu tti", - "Ġtut ti", - "Ġ dropout", - "Ġdrop out", - "Ġ Activities", - "ĠAct ivities", - "ĠActiv ities", - "ĠW inds", - "ĠWin ds", - "ĠWi nds", - "ĠWind s", - "' )));Ċ", - "') ));Ċ", - "')) );Ċ", - "'))) ;Ċ", - "Ġcon gest", - "Ġcong est", - "ÄŁ ı", - "Ġprolong ed", - "è¿ Ļ", - "ĠCross AxisAlignment", - "L EEP", - "LE EP", - "LEE P", - "Ġ VALID", - "ĠVAL ID", - "ĠG az", - "ĠGa z", - "Ġ dependence", - "Ġdepend ence", - "ĠP rix", - "ĠPr ix", - "ĠPri x", - ".Compiler Services", - "j ump", - "ju mp", - "Ġst rat", - "Ġstr at", - "Ġstra t", - "c irc", - "ci rc", - "cir c", - "Ġ CUSTOM", - "ĠC USTOM", - "x aa", - "xa a", - "Ġ bmp", - "Ġb mp", - "Ġbm p", - "Ġb ureau", - "Ġbu reau", - "Ġbure au", - "Ġw aren", - "Ġwar en", - "Ġwa ren", - "Ġware n", - "N X", - "( Window", - "(W indow", - "ĠChrist ie", - "ĠChris tie", - "_ FE", - "_F E", - "Ġ tn", - "Ġt n", - "Ġ Omega", - "ĠO mega", - "ĠOm ega", - "communic ations", - "communication s", - "Home Page", - "com pletion", - "comp letion", - "Ġsupply ing", - "Ġsuppl ying", - "Ġsupp lying", - "YPE S", - "YP ES", - "á vel", - "áv el", - "åĪ ¶", - "( click", - "(c lick", - "(cl ick", - "(cli ck", - "\\ Contracts", - "/ questions", - "/question s", - "Ġ ez", - "Ġe z", - "A MS", - "AM S", - ". mesh", - ".m esh", - ".me sh", - "Ġ' \\Ċ", - ">\\ Ċ", - "R obot", - "Rob ot", - "Ro bot", - "Json Object", - "Ġ DF", - "ĠD F", - "Ġ Processor", - "ĠProcess or", - "ĠProc essor", - "_ should", - "_sh ould", - ". protobuf", - ".prot obuf", - ".proto buf", - "- users", - "-user s", - "-use rs", - "-us ers", - "Ġemb ry", - "Ġembr y", - "F ONT", - "FO NT", - "Ġstart ups", - "Ġstartup s", - "Ġ DataSource", - "ĠData Source", - ") #", - "u ros", - "ur os", - "uro s", - "_ Color", - "_C olor", - "Ġst andalone", - "Ġstand alone", - "} [", - "j d", - "Ġfor give", - "Ġforg ive", - "Ġ ngx", - "Ġn gx", - "Ġng x", - "Ġ Generally", - "ĠGener ally", - "ĠGeneral ly", - "Ġconfig urable", - "Ġconfigur able", - "/ order", - "/or der", - "Ġ vas", - "Ġv as", - "Ġva s", - "' )\";Ċ", - "') \";Ċ", - "')\" ;Ċ", - "Ġ RR", - "ĠR R", - "ĠT roy", - "ĠTr oy", - "ĠTro y", - "Ġcomprom ised", - "Ġcompromise d", - "ĠS wan", - "ĠSw an", - "int endent", - "C entral", - "Cent ral", - "_ keeper", - "_k eeper", - "_ke eper", - "_keep er", - "Ġar quivo", - "Ġ ReadOnly", - "ĠRead Only", - "_ curve", - "_c urve", - "_cur ve", - "_cu rve", - "k v", - "en tin", - "ent in", - "enti n", - "è ±", - "Ġ Ey", - "ĠE y", - ".im read", - "ĠP am", - "ĠPa m", - "i ffe", - "if fe", - "iff e", - "at ivity", - "ativ ity", - "x bc", - "xb c", - "Ġ grim", - "Ġg rim", - "Ġgr im", - "Ġgri m", - "- filled", - "-f illed", - "-fill ed", - "name se", - "names e", - "nam ese", - "' ]:", - "'] :", - "Ġ aur", - "Ġa ur", - "Ġau r", - "ĠGi bson", - "ĠGib son", - ". MouseEvent", - ".Mouse Event", - "Ġl ado", - "Ġla do", - "Ġlad o", - "ava doc", - "avad oc", - "Ġf amil", - "Ġfam il", - "Ġfa mil", - "Ġ Moder", - "ĠM oder", - "ĠMod er", - "ĠMo der", - "ĠMode r", - "f ps", - "fp s", - "ãĢĢ ãĢĢ", - "- example", - "-ex ample", - "ĠAl zheimer", - "Ġ Utf", - "ĠU tf", - "ĠUt f", - "_ arguments", - "_arg uments", - "_argument s", - "Con clusion", - "text Content", - "rem aining", - "remain ing", - "rema ining", - "Ġinterrupt s", - "Ġ Backup", - "ĠBack up", - "ĠBac kup", - "ĠM ong", - "ĠMon g", - "ĠMo ng", - "Ġre ceptors", - "Ġrecept ors", - "Ġreceptor s", - "Ġrecep tors", - "h istor", - "hi stor", - "hist or", - "his tor", - ".cor outines", - "Ġsh outed", - "Ġshout ed", - "Ġsho uted", - "Al arm", - "Ġcomb ust", - "Ġg rote", - "Ġgr ote", - "Ġgro te", - "ult ural", - "ultur al", - "( ids", - "(i ds", - "(id s", - "---- ----------------------------------------------------------------------------", - "---------------- ----------------------------------------------------------------", - "-------------------------------- ------------------------------------------------", - "---------------------------------------------------------------- ----------------", - "------------------------------------------------ --------------------------------", - "---------- ----------------------------------------------------------------------", - "---------------------------------------------------------------------------- ----", - "---------------------------------------------------------------------- ----------", - "ipl inary", - "iplina ry", - "O pts", - "Op ts", - "Opt s", - "ĠY ale", - "ĠYa le", - "local Storage", - "Ġequ ival", - "Ġequiv al", - "ĠF leet", - "ĠFle et", - "\\ b", - "* pi", - "*p i", - "ĠQ Label", - "æ ¡", - "Ġ vx", - "Ġv x", - "Ġ ACL", - "ĠA CL", - "ĠAC L", - "Ġsu cesso", - "Ġsuc esso", - "Ġsucess o", - "Ġ perc", - "Ġp erc", - "Ġper c", - "Ġpe rc", - "ĠN otre", - "ĠNo tre", - "ĠNot re", - "Ġan arch", - "Ġana rch", - "R ing", - "s pb", - "sp b", - "Ġ strpos", - "Ġstr pos", - "st ores", - "store s", - "sto res", - "stor es", - "ĠMap le", - "ĠMa ple", - "( MainActivity", - "(Main Activity", - "(\" \"))", - "(\"\" ))", - "(\"\") )", - "Ġview Holder", - "Qu ad", - "Ġig ual", - "ors che", - "orsch e", - ". margin", - ".m argin", - ".mar gin", - "Ġin die", - "Ġind ie", - "Ġfr anc", - "Ġfra nc", - "Ġfran c", - "ĠForm Builder", - "ĠPart icip", - "ĠParti cip", - ". flash", - ".f lash", - ".fl ash", - "Ġ storms", - "Ġstorm s", - "Ġstor ms", - "Ġsto rms", - "U lt", - "Ul t", - "Ġ fen", - "Ġf en", - "Ġfe n", - "[ new", - "[n ew", - "E ver", - "Ev er", - "= \"Ċ", - "=\" Ċ", - "Ġ localized", - "Ġlocal ized", - "Ġlocalize d", - "_ follow", - "_f ollow", - "Ġ nave", - "Ġn ave", - "Ġna ve", - "Ġnav e", - "Ġdom inance", - "Ġdomin ance", - "Ġdomina nce", - "( tile", - "(t ile", - "(ti le", - "J ournal", - "Jo urnal", - "Ġ VC", - "ĠV C", - "Ġpen etration", - "Ġpenet ration", - "Ġpenetr ation", - "ï¼ ķ", - "Ġcom partment", - "Ġcomp artment", - "Ġcompart ment", - "Ġb ids", - "Ġbi ds", - "Ġbid s", - "Form atted", - "Format ted", - "**** **/ĊĊ", - "****** /ĊĊ", - "*** ***/ĊĊ", - "***** */ĊĊ", - "******/ ĊĊ", - "******/Ċ Ċ", - "( city", - "(c ity", - "(ci ty", - "âĢĶ it", - "[ C", - "Ġuse Callback", - "a ub", - "au b", - ") ?.", - ")? .", - "Ġ VAR", - "ĠV AR", - "ĠVA R", - "ĠSe bastian", - "ĠSebast ian", - "ĠM oss", - "ĠMo ss", - "ĠMos s", - "Ġabund ant", - "G reg", - "Gr eg", - "Gre g", - "ÑĤ а", - "_ ci", - "_c i", - "Ġb ibli", - "Ġbib li", - "C RM", - "CR M", - "Ġ Attempt", - "ĠAt tempt", - "ĠAtt empt", - "is me", - "ism e", - "d ash", - "da sh", - "das h", - "ãĢ İ", - "_ mu", - "_m u", - ".Formatting Enabled", - "Ind eed", - "- direct", - "-d irect", - "-dir ect", - "-di rect", - "Ġs ucking", - "Ġsuc king", - "Ġsuck ing", - "Ġp ne", - "Ġpn e", - "ocab ulary", - "ĠP ackers", - "ĠPac kers", - "ĠPack ers", - ". Navigation", - ".N avigation", - ".Nav igation", - "Ġp ied", - "Ġpie d", - "Ġpi ed", - "cri bing", - "ĠSt uart", - ".To Double", - "Ġ Secondary", - "ĠSecond ary", - "S aving", - "Sa ving", - "ĠD ut", - "ĠDu t", - "ĠM add", - "ĠMad d", - "ĠMa dd", - "M agic", - "Mag ic", - ", H", - ".document Element", - "Ġ BST", - "ĠB ST", - "ĠBS T", - "Ġdif fers", - "Ġdiffer s", - "Ġdiff ers", - "Ġmore over", - "_ nd", - "_n d", - "SE ARCH", - "п ÑĢав", - "пÑĢа в", - "пÑĢ ав", - "æ ´", - "to Match", - "Ġde creasing", - "Ġdecre asing", - "- member", - "-m ember", - "am pus", - "amp us", - "( boost", - "D aily", - "Da ily", - "Data GridView", - "Ġ HttpContext", - "ĠHttp Context", - "Ġh ipp", - "Ġhi pp", - "Ġhip p", - "_ workers", - "_work ers", - "_worker s", - "- language", - "-l anguage", - "é ĵ", - "Ġcons isted", - "Ġconsist ed", - "a thing", - "ath ing", - "athi ng", - "ĠMer cury", - "ĠMerc ury", - "$ content", - "$c ontent", - "$con tent", - "Ġpract iced", - "Ġpractice d", - "Ġ Modules", - "ĠMod ules", - "ĠModule s", - "_ DAY", - "_D AY", - "_DA Y", - "Ġweakness es", - "ĠL odge", - "ĠLo dge", - "ĠLod ge", - "Ġ nar", - "Ġn ar", - "Ġna r", - "Ġ Mate", - "ĠM ate", - "ĠMat e", - "ĠMa te", - "Ġ jp", - "Ġj p", - "ĠHttp Headers", - "Ġs mo", - "Ġsm o", - "Ġ TOKEN", - "ĠT OKEN", - "ĠTO KEN", - "ĠTOK EN", - "] )(", - "]) (", - "Ġa qui", - "Ġaqu i", - "sw agen", - "Ġ srv", - "Ġs rv", - "Ġsr v", - "ĉ ans", - "ĉa ns", - "ĉan s", - "A round", - "Ar ound", - "ĠMan uel", - "Ġfiction al", - "Ġfict ional", - "Ġ IMG", - "ĠI MG", - "ĠIM G", - "Ġ .'", - "Ġ. '", - "Ġ Berry", - "ĠB erry", - "ĠBer ry", - "Ġwall paper", - "s exual", - "sex ual", - "i ero", - "ie ro", - "ier o", - "Ġ çļĦ", - "ìĨ Į", - "Backing Field", - "ĠAd rian", - "ĠAdri an", - "BASE PATH", - "Ġre peats", - "Ġrepe ats", - "Ġrepeat s", - "Ġbl ues", - "Ġblue s", - "Ġun predict", - "Ġunp redict", - "_ coll", - "_c oll", - "_col l", - "_co ll", - "st acle", - "sta cle", - "Ġ Tumblr", - "ĠT umblr", - "Ġ Elf", - "ĠE lf", - "ĠEl f", - "Ġass urance", - "Ġc ensus", - "Ġcen sus", - "Ġ IMPORT", - "ĠIM PORT", - "ĠIMP ORT", - "E NDER", - "EN DER", - "END ER", - "a nos", - "an os", - "ano s", - "Ġ =(", - "Ġ= (", - "ĠEl lis", - "ĠEll is", - "ĠElli s", - "\" ĊĊĊĊ", - "\"Ċ ĊĊĊ", - "\"ĊĊ ĊĊ", - "\"ĊĊĊ Ċ", - ". win", - ".w in", - "Ġ Above", - "ĠA bove", - "ĠAb ove", - "a lon", - "al on", - "alo n", - "_ tick", - "_t ick", - "_ti ck", - "Ġrepresent ations", - "Ġrepresentation s", - "Ġ æķ", - "Ġæ ķ", - "w id", - "wi d", - "ĠA rms", - "ĠAr ms", - "ĠArm s", - "L ista", - "List a", - "Li sta", - "_ failure", - "_f ailure", - "_fail ure", - "_ cm", - "_c m", - ".Flat Appearance", - "Ġth rone", - "Ġthr one", - "Ġthro ne", - "P atch", - "Pat ch", - "ĠV oy", - "ĠVo y", - "en gl", - "eng l", - "Ġnegot iating", - "> `", - "Ġshoot s", - "Ġsho ots", - "Ġ FPS", - "ĠF PS", - "ĠFP S", - ". Year", - ".Y ear", - "ĠK iss", - "ĠKi ss", - "ĠKis s", - "en ción", - "enc ión", - "enci ón", - "re eting", - "ree ting", - "reet ing", - "From File", - "Ġresign ation", - "Ø ·", - "Ġt wins", - "Ġtw ins", - "Ġtwin s", - "Æ° ợ", - "Æ°á» £", - "Ġge bru", - "Ġgeb ru", - ". getContent", - ".get Content", - ".getC ontent", - ". Tree", - ".T ree", - ".Tr ee", - "Ġ Employees", - "ĠEmployee s", - "ĠEmploy ees", - "ĠF IFA", - "ĠFI FA", - "Ġc ertainty", - "Ġcert ainty", - "Ġcertain ty", - "( Cl", - "(C l", - "Ġ totals", - "Ġtot als", - "Ġtotal s", - "ed itable", - "edit able", - "edi table", - "ॠĢ", - ". Reporting", - ".Report ing", - "M as", - "Ma s", - "qu iet", - "qui et", - ". rules", - ".r ules", - ".ru les", - ".rule s", - "Ġ VO", - "ĠV O", - "con exion", - ", K", - "Ġ allocator", - "Ġal locator", - "Ġall ocator", - "Ġalloc ator", - "ĠPow der", - "\\ Repository", - "B eat", - "Be at", - "_ tipo", - "_t ipo", - "_tip o", - "_ti po", - "Ġ[ '',", - "Ġ[' ',", - "_ INTR", - "_IN TR", - "_INT R", - "Ġ <<<", - "Ġ< <<", - "Ġ<< <", - "< hr", - " \");čĊ", - ">\" );čĊ", - ">\") ;čĊ", - "drop IfExists", - "ĠB eg", - "ĠBe g", - "_ HAL", - "_H AL", - "Ġcross AxisAlignment", - "Ġ Evidence", - "ĠE vidence", - "ĠEv idence", - "Ġpec uliar", - "Ġin stitute", - "Ġinstit ute", - "ve is", - "Ġ fft", - "Ġf ft", - "Ġff t", - "à ģ", - "Ġzo ekt", - "Ġzoek t", - "an aly", - "ana ly", - "anal y", - "ĠHome land", - "ĠHom eland", - "Ġpen etr", - "Ġpenet r", - "udden ly", - "ĉ element", - "ĉe lement", - "ĉel ement", - "ĉelem ent", - "ĠB ren", - "ĠBr en", - "ĠBre n", - "ĠTr udeau", - "ĠCub an", - "ĠCu ban", - "ĠCuba n", - "j am", - "ja m", - "us lim", - "_ ev", - "_e v", - "Ġs tems", - "Ġst ems", - "Ġste ms", - "Ġstem s", - "} %", - "Ŀ å§ĭ", - "Ġbr anding", - "Ġbrand ing", - "Ġbran ding", - "Ġcorrespond ence", - ". jquery", - ".j query", - "¢ åįķ", - "ĠRe ads", - "ĠRead s", - "(Http StatusCode", - "(HttpStatus Code", - "as sin", - "ass in", - "assi n", - "( slot", - "(s lot", - "(sl ot", - "ĠGrad uate", - "// /<", - "/// <", - "Ġinformation s", - "Ġinform ations", - "Ġinformat ions", - "EN ABLE", - "ENA BLE", - "Ġp uis", - "Ġpu is", - "Ġ finder", - "Ġf inder", - "Ġfind er", - "Ġfin der", - "Ġfi nder", - "Ġfinde r", - "ĠB ris", - "ĠBr is", - "ĠBri s", - "Ġnett steder", - "_ mid", - "_m id", - "_mi d", - "Ġ ogs", - "Ġo gs", - "Ġog s", - "ĠSter ling", - "Ġar rog", - "Ġarr og", - "str ftime", - "| ĊĊ", - "|Ċ Ċ", - "Ġ vox", - "Ġv ox", - "Ġvo x", - "Ġ Regardless", - "ĠReg ardless", - "Ġ eso", - "Ġe so", - "Ġes o", - "Ġ Comfort", - "ĠCom fort", - ".Boolean Field", - "Ġ uh", - "Ġu h", - "A CY", - "AC Y", - "Ġsque ez", - "ĠV ic", - "ĠVi c", - "con tro", - "cont ro", - "contr o", - ". lo", - ".l o", - "Ġ ire", - "Ġi re", - "Ġir e", - "ĠCom edy", - "ĠCome dy", - "ë ¶", - "Ġorig inated", - "Ġorigin ated", - "Ġoriginate d", - "Ġ shipment", - "Ġsh ipment", - "Ġship ment", - "| max", - "|m ax", - "_ guid", - "_g uid", - "_gui d", - "le vation", - "lev ation", - "н аÑı", - "на Ñı", - "( undefined", - "(un defined", - "Ġ DDR", - "ĠD DR", - "ĠDD R", - "Ġshoot ings", - "Ġshooting s", - "ĠLat ino", - "ĠLatin o", - "END OR", - "Ġaver aging", - "Ġgre eted", - "Ġgreet ed", - "Ġthe aters", - "Ġtheater s", - "Ġtheat ers", - "о е", - "оРµ", - "Ġ dB", - "Ġd B", - "Ġ gst", - "Ġg st", - "Ġgs t", - "Ġde finite", - "Ġdef inite", - "Ġdefinit e", - "Ġdefin ite", - ". Storage", - ".St orage", - ". her", - ".h er", - ".he r", - "Ġa fore", - "Ġaf ore", - "Ġ Reality", - "ĠRe ality", - "ĠReal ity", - "ĠG ods", - "ĠGod s", - "ĠGo ds", - "v ersed", - "ver sed", - "vers ed", - "verse d", - "Ġhand some", - "Ġhands ome", - "Ġ excluding", - "Ġex cluding", - "Ġexcl uding", - "( ad", - "(a d", - "Qu otes", - "Quote s", - "Ġ Scheme", - "ĠS cheme", - "ĠSch eme", - "ĠSche me", - "? q", - "ĠT amil", - "ĠTa mil", - "ĠTam il", - "T icks", - "Tick s", - "Ti cks", - "Ġ pest", - "Ġp est", - "Ġpe st", - "Ġpes t", - "' n", - "Ġporn ography", - "_ modal", - "_m odal", - "_mod al", - "_mo dal", - "Ġ ----------", - "Ġ- ---------", - "Ġ-- --------", - "Ġ---- ------", - "Ġ--- -------", - "Ġ----- -----", - "Ġ------ ----", - "Ġ-------- --", - "Ġ------- ---", - "Ġ--------- -", - "Ġd isposable", - "Ġdis posable", - "Ġdispos able", - "F REE", - "FR EE", - "Ġsh ark", - "Ġsha rk", - "Ġshar k", - "C HE", - "CH E", - "Ġdep icted", - "Ġdepict ed", - "Ġdemonstr ations", - "Ġdemonstration s", - "ĠK illed", - "ĠKill ed", - "ĠKil led", - "Ġ RULE", - "ĠR ULE", - "ĠRU LE", - "Ġobs essed", - "Ġobsess ed", - "Ġs implified", - "Ġsimpl ified", - "Post al", - "Pos tal", - "Po stal", - "Ġconcept ual", - "Ġ pst", - "Ġp st", - "Ġps t", - "L as", - "La s", - "_ PROJECT", - "_PRO JECT", - "uc ceeded", - "ucceed ed", - "o lu", - "ol u", - "ÄŁ i", - "Ġpersonal ities", - "Ġ reshape", - "Ġre shape", - "Ġres hape", - "Ġresh ape", - "Ġen closed", - "Ġenc losed", - "ĉ ptr", - "ĉp tr", - "ĉpt r", - "Ġt utorials", - "Ġtutorial s", - "Ġtutor ials", - "Ġexpl oded", - "Ġexplo ded", - "Ġexplode d", - "_ DIRECTORY", - "_DIRECT ORY", - "åĨħ 容", - "Ġc anon", - "Ġcan on", - "Ġca non", - "Ġrecogn ise", - "P AD", - "PA D", - "Ġ Approx", - "ĠApp rox", - "ĠAp prox", - "ĠAppro x", - "Ġ Restore", - "ĠRe store", - "ĠRest ore", - "Ġ Important", - "ĠImport ant", - "Ġhe avier", - "Ġheav ier", - ". Sequential", - ".Se quential", - "E arth", - "Ear th", - "ĠM ilk", - "ĠMil k", - "ĠMi lk", - ".set Request", - ". tem", - ".t em", - ".te m", - "Ġre construct", - "Ġrecon struct", - "Ġskept ical", - "Ġskeptic al", - "_ Private", - "_Pr ivate", - "B UF", - "BU F", - "q ua", - "qu a", - ": a", - "Ġ sek", - "Ġs ek", - "Ġse k", - "Ġd well", - "Ġdw ell", - "o ssa", - "os sa", - "oss a", - "Ġreward ed", - "Ġrew arded", - "и й", - "( topic", - "(t opic", - "(to pic", - "(top ic", - "_ partition", - "_part ition", - "Ġ__ ________________", - "Ġ______ ____________", - "Key words", - "Keyword s", - "ĠFr anco", - "ĠFranc o", - "ĠFran co", - "L ite", - "Li te", - "Lit e", - "Ġn aken", - "Ġna ken", - "Ġnak en", - "Ġ за", - "Ġз а", - "O BJECT", - "OB JECT", - "OBJ ECT", - "Ġcraft s", - "Ġcra fts", - "Ġ Swap", - "ĠS wap", - "ĠSw ap", - ".X na", - ". Connect", - ".Con nect", - ".Conn ect", - "Ġbalcon y", - "( real", - "(re al", - "ĠBar nes", - "ĠBarn es", - "b ir", - "bi r", - "Ġ Twenty", - "ĠTw enty", - "ĠTwe nty", - "a yan", - "ay an", - "aya n", - "at ars", - "ata rs", - "atar s", - "ĠPro pel", - "ĠProp el", - "ĠIh nen", - "Up grade", - "Ġc urb", - "Ġcur b", - "Ġcu rb", - "- second", - "-se cond", - "Ġn eph", - "Ġne ph", - "Ġnep h", - ". pres", - ".p res", - ".pre s", - ".pr es", - "ìŀ ħ", - ". seq", - ".s eq", - ".se q", - "Ġp added", - "Ġpad ded", - "Ġpadd ed", - "\" ?", - "j l", - "ãĥ ¬", - "' ) a", - "Co ordinates", - "Coordinate s", - "Ġen acted", - "Ġenact ed", - "EN TS", - "ENT S", - "Ġ lac", - "Ġl ac", - "Ġla c", - ". final", - ".f inal", - ".fi nal", - ".fin al", - "ĠPhp Storm", - "c alled", - "cal led", - "call ed", - "Ġin quiries", - ". middleware", - ".m iddleware", - ".middle ware", - "ĠD owntown", - "ĠDown town", - "/ ';Ċ", - "/' ;Ċ", - "Ġkil omet", - "ac cel", - "acc el", - "Ġqu ien", - "Ġq uien", - "Ġqui en", - "w string", - "ws tring", - "set Data", - "Ġman era", - "Ġmane ra", - "Ġmod ular", - "r imp", - "ri mp", - "rim p", - "Ġtar iffs", - "Ġtariff s", - "Ġtarif fs", - "âĢĻ il", - "âĢĻi l", - "_TH ROW", - "/ color", - "/c olor", - "/co lor", - "Ġ HTMLElement", - "ĠHT MLElement", - "ĠHTML Element", - "Ġc arro", - "Ġcar ro", - "Ġcarr o", - "Ġpr ere", - "Ġpre re", - "Ġprer e", - "Ġplot ting", - "Ġ Positive", - "ĠPos itive", - "ĠM achines", - "ĠMachine s", - "ĠMach ines", - "O TES", - "OT ES", - "OTE S", - "á» Ľ", - "ple asant", - "Ġ alte", - "Ġa lte", - "Ġal te", - "Ġalt e", - "Ġa inda", - "Ġai nda", - "Ġain da", - "th ese", - "the se", - "thes e", - "Ġ cors", - "Ġc ors", - "Ġco rs", - "Ġcor s", - "i pay", - "ip ay", - "ipa y", - "ĠAdv isory", - "ĠAdvis ory", - "ĠAdvisor y", - "ĠRub io", - "ĠRu bio", - "j q", - "Ġl imestone", - "Ġlim estone", - "Ġlime stone", - "Ġdet ached", - "Ġdetach ed", - "设 ç½®", - "t enant", - "te nant", - "ten ant", - "Ġ Depth", - "ĠDe pth", - "ĠDep th", - "ĠDept h", - "a lore", - "al ore", - "alo re", - "ĠÑģÑĤ ÑĢок", - "ĠÑģÑĤÑĢ ок", - "ĠÑģÑĤÑĢо к", - "Ġ FORE", - "ĠF ORE", - "ĠFOR E", - "ĠFO RE", - "ĠL ay", - "ĠLa y", - "p resentation", - "present ation", - ") ');Ċ", - ")' );Ċ", - ".sub plots", - ".subplot s", - "Ï ĥ", - "N OW", - "NO W", - "G ar", - "Ga r", - "h andles", - "handle s", - "hand les", - "a bra", - "ab ra", - "abr a", - "put ies", - "pu ties", - "ĠElect rical", - "ĠElectric al", - "M iddle", - "Mid dle", - "r opic", - "ro pic", - "rop ic", - "Ġ JD", - "ĠJ D", - "Ġ Dyn", - "ĠD yn", - "ĠDy n", - "ĠB ristol", - "ĠBr istol", - "ĠBris tol", - "ĠMc Carthy", - "ĠMcCart hy", - "Ġstr iker", - "Ġstri ker", - "Ġstrike r", - "Ġenum erable", - "Ġenumer able", - "ĠE van", - "ĠEv an", - "ĠEva n", - ". defaults", - ".default s", - "qu ences", - "que nces", - "quence s", - ") ||", - ")| |", - "ĉ token", - "ĉt oken", - "ĉto ken", - "â Ĺı", - "âĹ ı", - "- dropdown", - "-d ropdown", - "-drop down", - "ST ORE", - "Ġ Graphic", - "ĠG raphic", - "ĠGraph ic", - "( pp", - "(p p", - "Ex pl", - "Exp l", - "Ġup wards", - "Ġupward s", - "ĠD istributed", - "ĠDistrib uted", - "Ġ WEB", - "ĠW EB", - "ĠWE B", - "J er", - "Je r", - "is NaN", - "çĶŁ æĪIJ", - "> R", - "üss en", - "üs sen", - "e fs", - "ef s", - "Ġun cover", - "Ġunc over", - "Ġl ud", - "Ġlu d", - ". calculate", - ".c alculate", - ".cal culate", - ".calc ulate", - "Ġ intptr", - "Ġint ptr", - "Ġmidfield er", - ". Headers", - ".Header s", - ".He aders", - ".Head ers", - "Ġ mf", - "Ġm f", - "e ref", - "er ef", - "ere f", - ". Metro", - ".M etro", - ".Me tro", - "Ġ Speaking", - "ĠSpe aking", - "ĠSpeak ing", - ": b", - "Ġcryptoc urrencies", - "Ġd emons", - "Ġde mons", - "Ġdem ons", - "Ġdemon s", - "Ġdemo ns", - "ĉ EXPECT", - "Ġw icked", - "y outube", - "you tube", - "youtu be", - ": Int", - ":I nt", - "ĠH indi", - "ĠHind i", - "ĠHin di", - "Ġ CAT", - "ĠC AT", - "ĠCA T", - "Ġ ع", - "ĠØ ¹", - "r ar", - "ra r", - "o more", - "om ore", - "omo re", - "omor e", - "/ per", - "/p er", - "/ license", - "/lic ense", - "/l icense", - "Ġre im", - "Ġa waiting", - "Ġawait ing", - "Ġle thal", - "Ġlet hal", - "Ġleth al", - "Ġ EF", - "ĠE F", - "r ounded", - "ro unded", - "round ed", - "ĠPl atinum", - "Ġв Ñģе", - "ĠвÑģ е", - ". coords", - ".co ords", - ".coord s", - ". Device", - ".D evice", - ".De vice", - ".Dev ice", - "/ item", - "/i tem", - "Ġ Wenn", - "ĠW enn", - "ĠWe nn", - "ĠWen n", - "compile Components", - "ĠK inder", - "ĠKind er", - "ĠKi nder", - "ĠKin der", - ".remove Item", - "Ġ anda", - "Ġa nda", - "Ġand a", - "Ġan da", - "b nb", - "bn b", - "Ġ pra", - "Ġp ra", - "Ġpr a", - "( transaction", - "(trans action", - "Ġembarrass ing", - "ĉ BOOL", - ".content View", - "Ġevent data", - "at ore", - "ator e", - "ato re", - "Ġprovided In", - "ir ma", - "irm a", - "Ġz ona", - "Ġzo na", - "_ HW", - "_H W", - "æ Ļ", - "Ġst ove", - "Ġsto ve", - "Ġcounter part", - "_ Product", - "_Pro duct", - "_MAN AGER", - "Ġinf ring", - "Ġinfr ing", - "Ġ ERA", - "ĠE RA", - "ĠER A", - "_ party", - "_p arty", - "_part y", - "_par ty", - "Ñ ij", - "Ġin ici", - "Ġi nici", - "Ġini ci", - "_ Request", - "_Re quest", - "Ġmir acle", - "Ġmirac le", - "Ġcancel Button", - "S py", - "Sp y", - "at ó", - "Ġpol ish", - "Ġpo lish", - "Ġpolis h", - "ĠNic ole", - "ĠNi cole", - "ĠNico le", - "ĠNicol e", - ". displayName", - ".display Name", - "\\ Requests", - "\\Request s", - "Ġuse History", - "Router Module", - "Ġst ared", - "Ġstar ed", - "Ġsta red", - "Ġstare d", - "I DER", - "ID ER", - "IDE R", - "Ñĥнк ÑĨи", - "Ġ nota", - "Ġn ota", - "Ġnot a", - "Ġno ta", - "$ arr", - "$a rr", - "$ar r", - "pec ified", - "Ġt opp", - "Ġto pp", - "Ġtop p", - "_DR IVER", - "_DRIVE R", - "/ ng", - "/n g", - "å ł", - "_ tm", - "_t m", - "% timeout", - "< s", - "Ġ (*)", - "Ġ( *)", - "Ġ(* )", - "Ġ HttpRequest", - "ĠHttp Request", - "_ TRACK", - "_TR ACK", - "_TRA CK", - "( note", - "(n ote", - "(not e", - "(no te", - "Ġ Explore", - "ĠExp lore", - "ĠExpl ore", - "_ serv", - "_s erv", - "_se rv", - "_ser v", - "Ġ ç»", - "Ġç »", - "B inder", - "Bind er", - "Bin der", - "Bi nder", - "+ \",", - "+\" ,", - ". att", - ".a tt", - ".at t", - "ĠEth i", - "ĠEt hi", - "Ġc ódigo", - "= '\\", - "=' \\", - ". lines", - ".l ines", - ".line s", - ".li nes", - ".lin es", - "( Of", - "(O f", - "å° Ĩ", - "miss ible", - "Ġ vé", - "Ġv é", - "Ġac oustic", - "Ġcraft ing", - "n it", - "ni t", - ". ba", - ".b a", - "ĠLuc y", - "ĠLu cy", - "Ġi Pod", - "ĠiP od", - "Ġpup ils", - "Ġpupil s", - "- max", - "-m ax", - "_ wr", - "_w r", - "( cp", - "(c p", - "Ġ REPORT", - "ĠRE PORT", - "ĠREP ORT", - "Ġ dns", - "Ġd ns", - "Ġdn s", - "Ġ References", - "ĠRe ferences", - "ĠReference s", - "ĠRefer ences", - "Ġunder taken", - "Ġundert aken", - "Ġundertake n", - "Ġkø benhavn", - "Ġ chai", - "Ġc hai", - "Ġch ai", - "Ġcha i", - "ĠC roat", - "ĠCro at", - "_ Log", - "_L og", - "r owned", - "row ned", - "rown ed", - "_ med", - "_m ed", - "_me d", - "ĉ date", - "ĉd ate", - "# __", - "Ġcost umes", - "Ġcostume s", - "Ġ Requires", - "ĠRe quires", - "ĠRequire s", - "aff le", - "ç Ĭ¶æĢģ", - "çĬ¶ æĢģ", - "-S emit", - "-Se mit", - "ela ide", - "еÑĤ од", - "Ġp estic", - "Ġpes tic", - "Ġpest ic", - "Ġ dra", - "Ġd ra", - "Ġdr a", - "D OCUMENT", - "DOC UMENT", - "Ġ ...čĊ", - "Ġ... čĊ", - "Ġ.. .čĊ", - "} `}Ċ", - "}` }Ċ", - "}`} Ċ", - "ĠA uction", - "ĠAu ction", - "Ġ Dock", - "ĠD ock", - "ĠDo ck", - "ĠDoc k", - "xxxx xxxx", - "( getString", - "(get String", - "ħ į", - "Ġborder Width", - "ĠM achinery", - "ĠMachine ry", - "ĠMach inery", - "Ġpredict able", - "Ġpredic table", - ". SH", - ".S H", - "Ġam plitude", - "Ġampl itude", - ".for Root", - "I Navigation", - "IN avigation", - "Table Model", - "at trib", - "att rib", - "attr ib", - "Ġmaneu ver", - "Ġexc av", - "B ERS", - "BER S", - "BE RS", - "Ġd apat", - "Ġda pat", - "Ġdap at", - "Ġinstall ations", - "Ġinstallation s", - "Ġinstal lations", - ". Async", - ".A sync", - ".As ync", - "Ġ rays", - "Ġr ays", - "Ġra ys", - "Ġray s", - "= âĢĿ", - "; ččĊ", - ". crypto", - ".c rypto", - "_ dbg", - "_d bg", - "_db g", - "Ġ Enumerable", - "ĠEnum erable", - "Of Size", - "_ epochs", - "_epoch s", - "m w", - "M ENU", - "ME NU", - "out line", - "ĠP apers", - "ĠPa pers", - "ĠPaper s", - "ĠPap ers", - "= ===========Ċ", - "== ==========Ċ", - "==== ========Ċ", - "======== ====Ċ", - "=== =========Ċ", - "============ Ċ", - "=========== =Ċ", - "========= ===Ċ", - "========== ==Ċ", - "====== ======Ċ", - "===== =======Ċ", - "======= =====Ċ", - "Ġuniform s", - "Ġuni forms", - "ĠG ig", - "ĠGi g", - "- package", - "-p ackage", - "-pack age", - "ĠJ enkins", - "ĠJen kins", - "Ġ HomePage", - "ĠHome Page", - ". isSelected", - ".is Selected", - "Ġmechan ic", - "Ġmech anic", - "M K", - "Ġ Sounds", - "ĠS ounds", - "ĠSo unds", - "ĠSou nds", - "ĠSound s", - "//---------------------------------------------------------------- -------------Ċ", - "//---------------------------------------------------------------------------- -Ċ", - "Ġresearch ing", - "Ġ infos", - "Ġin fos", - "Ġinfo s", - "Ġinf os", - "o graphics", - "og raphics", - "ograph ics", - "ographic s", - "er set", - "ers et", - "erse t", - "([ '/", - "([' /", - "ĠTim ber", - ". agent", - ".a gent", - ".ag ent", - ".age nt", - ".to JSON", - "_ commands", - "_command s", - "_comm ands", - "p aring", - "par ing", - "pa ring", - "_ adjust", - "_ad just", - "_adj ust", - ". nome", - ".n ome", - ".no me", - ".nom e", - "( glm", - "(g lm", - "(gl m", - "Status Bar", - "file path", - "? âĢĻ", - "Ġdet ective", - "Ġdetect ive", - "Ġuns erer", - "Ġunser er", - "Ġunsere r", - "ĠTi bet", - "ĠTib et", - "EN DED", - "END ED", - "( seed", - "(s eed", - "(se ed", - "Ġsne ak", - "Ġa mor", - "Ġam or", - "Ġamo r", - "=\" //", - "=\"/ /", - "ĠPan thers", - "ĠPanther s", - "all ax", - "alla x", - "ĠL IVE", - "ĠLI VE", - "ĉ DWORD", - "ĉD WORD", - "] =-", - "]= -", - "Ġt ornado", - "Ġtorn ado", - "/ min", - "/m in", - "Ġl ungs", - "Ġlung s", - "Ġlun gs", - "- current", - "-c urrent", - "-cur rent", - "Ġ Booking", - "ĠBo oking", - "ĠBook ing", - "ĠBoo king", - "åĪĹ è¡¨", - "Ġenjoy ment", - "ठ°", - "J A", - "t yped", - "type d", - "ty ped", - "typ ed", - ". Btn", - ".B tn", - "f at", - "fa t", - "u gal", - "ug al", - "uga l", - "Ġ Shares", - "ĠSh ares", - "ĠShare s", - "ĠSha res", - "ĠShar es", - "Ġdis gr", - "Ġdisg r", - "Ġ BAR", - "ĠB AR", - "ĠBA R", - "Ġ FOX", - "ĠF OX", - "ĠFO X", - "Op code", - "Ġ Sz", - "ĠS z", - "key down", - "iction aries", - "Ġdet ailing", - "Ġdetail ing", - "} ))Ċ", - "}) )Ċ", - "})) Ċ", - "Ġ pok", - "Ġp ok", - "Ġpo k", - "Ġdemonstr ating", - "Ġ notation", - "Ġn otation", - "Ġnot ation", - "Ġnota tion", - "l ayers", - "la yers", - "lay ers", - "layer s", - "@ if", - "ĠN PR", - "ĠNP R", - ".strict Equal", - "Ġ Recipes", - "ĠRec ipes", - "ĠRecipe s", - ". Tensor", - ".T ensor", - "Ġliqu or", - "Ġdeb ts", - "Ġdebt s", - ". endsWith", - ".end sWith", - ".ends With", - "W heel", - "Wh eel", - ". Pos", - ".P os", - "C SV", - "CS V", - "$ arity", - "$ar ity", - "Ġun stable", - "Ġuns table", - "Ġunst able", - "( loss", - "(l oss", - "(lo ss", - "EN SOR", - "ENS OR", - "Ġel even", - "Ġele ven", - "Ġelev en", - "ĠL opez", - "ĠLo pez", - "ĠHop kins", - "c onom", - "con om", - "co nom", - "cono m", - "ĠS eth", - "ĠSe th", - "ĠSet h", - "Ġpo ems", - "Ġpoem s", - "Q uant", - "Qu ant", - "Ġg sl", - "Ġgs l", - "Ġsy rup", - "Ġs ibling", - "Ġsi bling", - "Ġc ass", - "Ġca ss", - "Ġcas s", - "- vous", - "-v ous", - "ö t", - "_P ATTERN", - "_ SECTION", - "_SE CTION", - "_SEC TION", - "est imated", - "estimate d", - "up grade", - ". mongodb", - ".m ongodb", - ".mongo db", - "ĠBo at", - "_ CTX", - "_C TX", - "_CT X", - "Ġfetch ing", - "Ġfet ching", - "u stin", - "us tin", - "ust in", - "p iel", - "pi el", - "pie l", - "M arg", - "Mar g", - "Ma rg", - "Ref lection", - "Reflect ion", - "Ġ duct", - "Ġd uct", - "Ġdu ct", - "ĠMunicip al", - "Ġ bx", - "Ġb x", - ". GetCurrent", - ".Get Current", - "m link", - "ml ink", - "mlin k", - "ĠAccount ing", - "ĠGen eva", - "ĠGene va", - "_ Pos", - "_P os", - "Ġp asser", - "Ġpass er", - "Ġpas ser", - "Ġpasse r", - "Ġhear ings", - "Ġhearing s", - "com pan", - "comp an", - "Ġfrag ile", - "Initial izer", - "Initialize r", - "w alker", - "walk er", - "wal ker", - ". Material", - ".M aterial", - "ĠH unting", - "ĠHun ting", - "ĠHunt ing", - "try side", - "trys ide", - "Ġ kat", - "Ġk at", - "Ġka t", - "Ġcl erk", - "Ġcle rk", - "Ġcler k", - "á Ł", - "do ing", - "doi ng", - "ĉ group", - "ĉg roup", - "ĉgr oup", - "Ġsan ction", - "Ġsanct ion", - ". lb", - ".l b", - "Ġ Lazy", - "ĠL azy", - "ĠLa zy", - "ĠLaz y", - "Ġ Constraint", - "ĠCon straint", - "ĠConstr aint", - "P agination", - "Pag ination", - "Ġpou vez", - "ĠInd icates", - "M ER", - "ME R", - "Ġc ours", - "Ġco urs", - "Ġcour s", - "Ġcou rs", - "Ġy early", - "Ġyear ly", - "Ġg rosse", - "Ġgro sse", - "Ġgross e", - "Ġgros se", - "abb rev", - "abbr ev", - "Ġ DON", - "ĠD ON", - "ĠDO N", - "Ġpro ceeded", - "Ġproceed ed", - "ent lich", - "Ġ propertyName", - "Ġproperty Name", - "ĠTe aching", - "ĠTea ching", - "ĠTeach ing", - "st adt", - "sta dt", - "stad t", - "Ġc utoff", - "Ġcut off", - "or ners", - "orn ers", - "orne rs", - "Ġa frica", - "Ġaf rica", - "Ġafr ica", - "Ġ renders", - "Ġr enders", - "Ġrender s", - "Ġren ders", - "Ġrend ers", - "ĠYan kees", - "ĠYankee s", - "Ġ Toolbar", - "ĠTool bar", - "s paces", - "sp aces", - "space s", - "spa ces", - ".fill Style", - "Ġseg undo", - "Ġsegu ndo", - "_ strlen", - "_st rlen", - "_str len", - ". Firebase", - ".F irebase", - ".Fire base", - "å¤ Ħ", - "Ġmention ing", - "\\ (", - "ĠVal ve", - "S etter", - "Set ter", - "Ġsp ans", - "Ġspan s", - "Ġspa ns", - "ĠAl cohol", - "Ġ Letters", - "ĠLet ters", - "ĠLetter s", - "\\ xe", - "\\x e", - "Ġ TK", - "ĠT K", - "_ BLE", - "_B LE", - "_BL E", - ". getResult", - ".get Result", - "< Player", - "

\"", - "=> \"", - "t lement", - "tle ment", - "tl ement", - "$ (\"", - "$( \"", - "From String", - "ĠB ild", - "ĠBi ld", - "ĠBil d", - "Ġcon ventions", - "Ġconv entions", - "Ġconvent ions", - "Ġconvention s", - "_ native", - "_n ative", - "_nat ive", - "Ġ Inspector", - "ĠIns pector", - "ĠInsp ector", - "ĠP ist", - "ĠPi st", - "ĠPis t", - "u bar", - "ub ar", - "uba r", - "Ġ regs", - "Ġre gs", - "Ġreg s", - "ĠP ilot", - "ĠPi lot", - "ĠPil ot", - "T hus", - "Th us", - "Thu s", - "> '+", - ">' +", - "Ġ cela", - "Ġc ela", - "Ġce la", - "Ġcel a", - ". news", - ".n ews", - ".new s", - ".ne ws", - "( Product", - "L iving", - "Li ving", - "Liv ing", - "R ussia", - "Russ ia", - "Ġ facet", - "Ġf acet", - "Ġfac et", - "Ġface t", - "e tical", - "et ical", - "etic al", - "eti cal", - "Ġ[ '$", - "Ġ[' $", - "/ [", - "Ġ Dire", - "ĠD ire", - "ĠDi re", - "ĠDir e", - "Ġg ases", - "Ġgas es", - "Ġga ses", - "ĠIN FORMATION", - "Ġ Eat", - "ĠE at", - "ĠEa t", - "ĠFor ums", - "ĠForum s", - "Ġ Characters", - "ĠChar acters", - "ĠCharacter s", - "_ met", - "_m et", - "_me t", - "Ġ ìĭľ", - "Ġìĭ ľ", - "Ġk ings", - "Ġking s", - "Ġkin gs", - "a chie", - "ach ie", - "achi e", - "Ġ Lambda", - "ĠL ambda", - "ĠLamb da", - "Ġt imers", - "Ġtime rs", - "Ġtim ers", - "Ġti mers", - "Ġtimer s", - "ĠL ighting", - "ĠLight ing", - "ĠCas ey", - "ĠCase y", - "ĠCa sey", - "ad dir", - "add ir", - "an dex", - "and ex", - "ande x", - ". answer", - ".an swer", - "Ġ Hip", - "ĠH ip", - "ĠHi p", - "ĠPr incip", - "Start Date", - "Ġ ãĢĮ", - "ĠãĢ Į", - "t res", - "tr es", - "tre s", - "Ġ &#", - "Ġ& #", - ".Max Value", - "ĠPro blems", - "ĠProblem s", - "ĠProb lems", - "ĠProble ms", - "Ġ latex", - "Ġla tex", - "Ġlate x", - "Ġlat ex", - "Of Class", - "ĠL ynn", - "ĠLy nn", - "ĠLyn n", - "/ /'", - "// '", - "Ġvoy age", - "Ġsh uttle", - "Ġshut tle", - "ĠR oller", - "ĠRo ller", - "ĠRoll er", - "ĠRol ler", - "ĠRuntime Error", - "u ya", - "uy a", - "D ic", - "Di c", - "ĉ builder", - "ĉb uilder", - "ĉbuild er", - "Ġbul lying", - "Ġbull ying", - "Ġbully ing", - "Ġsimple st", - "Ġsimp lest", - "Ġsimpl est", - "Ġsimples t", - ". called", - ".c alled", - ".call ed", - ".cal led", - "Ġ LR", - "ĠL R", - "Ġmor ality", - "Ġmoral ity", - "Ġst urdy", - "tr acking", - "track ing", - ". swagger", - ".sw agger", - "_ BIND", - "_B IND", - "_BIN D", - "I TOR", - "IT OR", - "ITO R", - "-url encoded", - "Ġ Ñħ", - "ĠÑ ħ", - "ĠTr inity", - "Ġtr aps", - "Ġtra ps", - "Ġtrap s", - "Ġ |-", - "Ġ| -", - "Ġ setText", - "Ġset Text", - "Ġbar gain", - "Ġbarg ain", - "Ġbr akes", - "Ġbra kes", - "Ġbrake s", - ". getCode", - ".get Code", - ".g etCode", - ".getC ode", - "Ġm igrate", - "Ġmigr ate", - "Ġmig rate", - "Ġ ribbon", - "Ġr ibbon", - "Ġrib bon", - ") return", - ")r eturn", - "Ġ charger", - "Ġch arger", - "Ġchar ger", - "Ġcharg er", - "Ġcharge r", - "a com", - "ac om", - "aco m", - "ADI US", - "ĠAmb assador", - "- after", - "-a fter", - "Ġ anni", - "Ġan ni", - "Ġann i", - "ĉ spin", - "ĉs pin", - "ĉsp in", - "Con cept", - "ĠHend erson", - "Ġ HOST", - "ĠH OST", - "ĠHO ST", - ". rank", - ".r ank", - ".ra nk", - "ĠNorth east", - "ĠNor theast", - "Ġber lin", - "Ġre quis", - "Ġreq uis", - "Ġrequ is", - ". feed", - ".f eed", - ".fe ed", - "Ġsource Mapping", - "ĠRen contre", - ". ajax", - ".a jax", - "nest js", - "Ġt rek", - "Ġtr ek", - "Ġtre k", - "ĠN acional", - "Ġ& [", - "Ġpay able", - "or tex", - "ort ex", - "orte x", - "Ġ dept", - "Ġd ept", - "Ġde pt", - "Ġdep t", - "field Name", - "Ġcomp letes", - "Ġcomple tes", - "Ġcomplet es", - "Ġcompl etes", - "Ġcomplete s", - "ĠR VA", - "ĠRV A", - "Ġon ions", - "Ġonion s", - "al ignment", - "align ment", - "Form ats", - "Format s", - "Ġ' {$", - "Ġ'{ $", - "Hash Set", - "ĠB od", - "ĠBo d", - ".Invariant Culture", - "Ġsett lements", - "Ġsettlement s", - "Ġsettle ments", - "Ġ hydr", - "Ġhy dr", - ". updated", - ".update d", - ".up dated", - "v enth", - "ve nth", - "vent h", - "ven th", - "( seconds", - "(se conds", - "(second s", - "(sec onds", - "=\" /\"", - "=\"/ \"", - "Ġweb page", - "( ĊĊ", - "(Ċ Ċ", - "Ġ tir", - "Ġt ir", - "Ġti r", - "Ġt oes", - "Ġto es", - "Ġtoe s", - "ĠB rick", - "ĠBr ick", - "ĠBri ck", - "Ġamb ition", - "Ġambit ion", - "P ot", - "Po t", - "= max", - "=m ax", - "E TIME", - "ET IME", - "Ġde pot", - "Ġdep ot", - "c alls", - "cal ls", - "call s", - "ĠNor wegian", - "` :", - "Ġ burger", - "Ġb urger", - "Ġbur ger", - "Ġburg er", - "Ġburge r", - "Ġprofessor s", - "Ġprofess ors", - "Ġ Allocate", - "ĠAl locate", - "ĠAll ocate", - "ĠAlloc ate", - "-third s", - "- chart", - "-c hart", - "-ch art", - "-char t", - "Ġ ford", - "Ġf ord", - "Ġfor d", - "Ġfo rd", - "* N", - ".k otlin", - "Ġpaper work", - "Ġ DEVICE", - "ĠDE VICE", - "ĠDEV ICE", - "% @\",", - "%@ \",", - "re spect", - "res pect", - "resp ect", - "( mp", - "(m p", - "é «ĺ", - "é« ĺ", - "- if", - "-i f", - "Ġcush ion", - "o bot", - "ob ot", - "obo t", - "Ġp arc", - "Ġpar c", - "Ġpa rc", - "S PACE", - "SP ACE", - "SPA CE", - "ĠNet anyahu", - "Ġself ish", - "Ġsel fish", - "f eat", - "fe at", - "fea t", - "Ġ clientes", - "Ġcl ientes", - "Ġclient es", - "Ġcli entes", - "Ġcliente s", - "- tools", - "-t ools", - "-to ols", - "-tool s", - "-too ls", - "Ġp orch", - "Ġpo rch", - "Ġpor ch", - "Ġ jq", - "Ġj q", - ". verbose", - ".ver bose", - "Ġlib erals", - "Ġliberal s", - "Ġliber als", - "] )ĊĊĊ", - "]) ĊĊĊ", - "])Ċ ĊĊ", - "])ĊĊ Ċ", - "p ies", - "pi es", - "pie s", - "Not Blank", - "( term", - "(t erm", - "(te rm", - "È Ľi", - "ÈĽ i", - "_ Params", - "_Param s", - ". normalize", - ".normal ize", - "B ullet", - "AS IC", - "ASI C", - "( hex", - "(h ex", - "_ cliente", - "_cl iente", - "_client e", - "_cli ente", - "+ ,", - "_ DI", - "_D I", - "Ġforth coming", - "} \")]Ċ", - "}\" )]Ċ", - "}\") ]Ċ", - "s eo", - "se o", - "U m", - "> Name", - ">N ame", - "Ġcomfort ably", - "irect ional", - "irection al", - "W ITH", - "WI TH", - "/ pr", - "/p r", - "Ġ Poor", - "ĠP oor", - "ĠPo or", - "ĠV itamin", - "ĠVit amin", - "ĠVita min", - "v ic", - "vi c", - "G H", - "Ġprior it", - "Ġprio rit", - "Ġ NN", - "ĠN N", - "Ġ Closed", - "ĠC losed", - "ĠCl osed", - "ĠClose d", - "ĠClo sed", - "¤ í", - "Ġ isOpen", - "Ġis Open", - "\\ Console", - "And Feel", - ". SUCCESS", - ".S UCCESS", - "_OPER ATION", - "p olation", - "po lation", - "pol ation", - "ĠT as", - "ĠTa s", - "p sz", - "ps z", - "> '.", - ">' .", - "C URRENT", - "V endor", - "host s", - "ho sts", - "hos ts", - "ĠE rd", - "ĠEr d", - ">tag ger", - ">t agger", - "ĠsourceMapping URL", - "Ġmar athon", - "_ closed", - "_c losed", - "_cl osed", - "_close d", - "Ġex emption", - "Ġexem ption", - "Ġexempt ion", - "Ġexemp tion", - "Ġrecogn izes", - "Ġrecognize s", - "ide show", - "ides how", - "' $", - "(' /');Ċ", - "('/ ');Ċ", - "('/') ;Ċ", - "m its", - "mit s", - "mi ts", - "wa rz", - "war z", - "ĠCh erry", - "ĠCher ry", - "µ ¬", - "n or", - "no r", - "p orte", - "port e", - "por te", - "Ġ wl", - "Ġw l", - "_ backup", - "_back up", - ".get Boolean", - ". getResource", - ".get Resource", - "Ġdefinit ive", - "Ġdefin itive", - ". EditText", - ".Edit Text", - "Ġ sÃŃ", - "Ġs ÃŃ", - ". CONT", - ".C ONT", - ".CON T", - ".CO NT", - "Ġ PLAYER", - "ĠPL AYER", - "ĠPLAY ER", - ". cards", - ".c ards", - ".card s", - ".car ds", - "ĠSh ore", - "ĠSho re", - "(' /')Ċ", - "('/ ')Ċ", - "('/') Ċ", - "cl uir", - "Web Driver", - "( month", - "(m onth", - "(mon th", - "- release", - "-r elease", - "-re lease", - "-rel ease", - "Ġins pector", - "Ġinspect or", - "Ġinsp ector", - "å £", - "Ġ NF", - "ĠN F", - "_ clip", - "_c lip", - "_cl ip", - "_cli p", - "å ŃIJ", - "åŃ IJ", - "Ġinter acting", - "Ġinteract ing", - ". tmp", - ".t mp", - ".tm p", - "Ġ '''ĊĊ", - "Ġ'' 'ĊĊ", - "Ġ'''Ċ Ċ", - "Ġ''' ĊĊ", - "Ġ dee", - "Ġd ee", - "Ġde e", - "Ġf rost", - "Ġfr ost", - "Ġfro st", - "\" ]))Ċ", - "\"] ))Ċ", - "\"]) )Ċ", - "\"])) Ċ", - "Ġ Places", - "ĠP laces", - "ĠPl aces", - "ĠPlace s", - "ĠPla ces", - "Th rows", - "Throw s", - "Thr ows", - "f ork", - "fo rk", - "for k", - "/ day", - "/d ay", - "i Phone", - "Ġ MIC", - "ĠM IC", - "ĠMI C", - "Ġf olding", - "Ġfol ding", - "Ġfold ing", - "Ġcr ore", - "Ġcro re", - "ĠCh iefs", - "ĠChief s", - "ĠChi efs", - "pher ical", - "pheric al", - "phe rical", - "( price", - "(p rice", - "(pr ice", - ".Write String", - "Ġex iting", - "Ġexit ing", - "] ',Ċ", - "]', Ċ", - "]' ,Ċ", - "ight ing", - "igh ting", - "Ing redient", - "( vertex", - "(ver tex", - "Ġ scrollView", - "Ġs crollView", - "Ġscroll View", - "h f", - ": new", - ":n ew", - "S EN", - "SE N", - "s ector", - "se ctor", - "sec tor", - "sect or", - "Ġs pins", - "Ġsp ins", - "Ġspin s", - "Ġspi ns", - "Ġ Scheduler", - "ĠS cheduler", - "ĠSchedule r", - "o techn", - "ot echn", - "ote chn", - "otech n", - "otec hn", - "sem icolon", - "semi colon", - "Font OfSize", - "ĠSpecific ally", - "fl amm", - ". ObjectId", - ".Object Id", - "Ġc onta", - "Ġcon ta", - "Ġcont a", - "_ permissions", - "_per missions", - "_perm issions", - "_permission s", - "ĉ FROM", - "ĉF ROM", - "I CODE", - "IC ODE", - "ICO DE", - "/ kg", - "/k g", - "ĠHot els", - "ĠHotel s", - "- med", - "-m ed", - "-me d", - "ĠD in", - "ĠDi n", - "Ġn avy", - "Ġna vy", - "Ġnav y", - "get Param", - "Ġm end", - "Ġme nd", - "Ġmen d", - "Ġportray ed", - "ĠMet ropolitan", - "P ainter", - "Paint er", - "Pa inter", - "Ġref erral", - "Ġrefer ral", - "_ good", - "_g ood", - "_go od", - "Ġmar vel", - "os aic", - "osa ic", - "> (&", - ">( &", - ". ur", - ".u r", - "Ġes tos", - "Ġest os", - "Ġesto s", - "Will iam", - "Ġtim ber", - "Ġquel ques", - "Ġquelque s", - "Ġ Documents", - "ĠDocument s", - "ĠDoc uments", - ".X aml", - "Ġb atches", - "Ġbatch es", - "Ġbat ches", - "éģ ĵ", - "Ġ Released", - "ĠRe leased", - "ĠRelease d", - "T ail", - "Ta il", - "Tai l", - "CO OKIE", - "h eid", - "he id", - "hei d", - "_ station", - "_st ation", - "_stat ion", - "_sta tion", - "Ġ Via", - "ĠV ia", - "ĠVi a", - "S ale", - "Sal e", - "Sa le", - "Ġ Repeat", - "ĠRe peat", - "ĠRep eat", - "Ġpro min", - "Ġpr omin", - "Ġprom in", - "ĠZ o", - "- forward", - "-for ward", - "Ġ Ion", - "ĠI on", - "ĠIo n", - "it ary", - "ita ry", - "itar y", - "Ġj us", - "Ġju s", - "- request", - "-re quest", - "Ġproud ly", - "Ġ Streaming", - "ĠStream ing", - "ĠStre aming", - "( MouseEvent", - "(Mouse Event", - "ĠS print", - "ĠSp rint", - "ĠSpr int", - "_ rotation", - "_r otation", - "_rot ation", - "Re positories", - "Ġt art", - "Ġta rt", - "Ġtar t", - "ĠÑģ в", - "Ġm appings", - "Ġmapping s", - "è ª", - "C u", - "C ycle", - "Cy cle", - "Ġb un", - "Ġbu n", - "ĉ lua", - "ĉl ua", - "ãĥ ī", - "Ġ( (!", - "Ġ(( !", - "Ġcollect ively", - "Ġcollective ly", - "Ġ Cond", - "ĠC ond", - "ĠCon d", - "ĠCo nd", - "Ġws zyst", - "Ġwsz yst", - "( lib", - "(l ib", - "(li b", - "openh agen", - "_ skip", - "_s kip", - "_sk ip", - ".Column Header", - "é Ĥ", - "perience d", - "peri enced", - "ı è¿°", - "_ props", - "_p rops", - "_pro ps", - "_pr ops", - "_prop s", - "Ġcon trace", - "Ġcont race", - "Ġcontr ace", - "Ġcontra ce", - "Ġmatch up", - "ab etic", - "abe tic", - "abet ic", - ". members", - ".m embers", - ".member s", - ".mem bers", - "R ECT", - "RE CT", - "REC T", - "( dat", - "(d at", - "(da t", - "Ġs og", - "Ġso g", - "re nom", - "ren om", - "reno m", - "_ Method", - "_M ethod", - "Custom ers", - "Customer s", - "ful lname", - "full name", - "Z N", - "r etry", - "re try", - "ret ry", - "Ġ kap", - "Ġk ap", - "Ġka p", - "ĠN eu", - "ĠNe u", - "è Ĭ", - "add Child", - "will Return", - "_ permalink", - "_p ermalink", - "_per malink", - "_perm alink", - "Ġenerg etic", - "Ġener getic", - "ĠW et", - "ĠWe t", - "ĠM orr", - "ĠMo rr", - "ĠMor r", - "Ġ gcd", - "Ġg cd", - "Ġgc d", - "co unts", - "count s", - "cou nts", - ", type", - ",t ype", - "d ig", - "di g", - "( Login", - "(Log in", - "Ġcr acks", - "Ġcrack s", - "Ġcra cks", - "Ġb acterial", - "Ġbacter ial", - "Ġbacteria l", - "ĠM eat", - "ĠMe at", - "ĠArm strong", - "ĠBro nze", - "ĠBron ze", - "Ġapprox imate", - "_ dirs", - "_d irs", - "_dir s", - "_di rs", - "l iga", - "li ga", - "lig a", - "ÅĤ ad", - "ÅĤa d", - "Ġkind ness", - "Ġ contre", - "Ġcon tre", - "Ġcont re", - "Ġcontr e", - "ĠE VERY", - "ĠEV ERY", - "ĠEVER Y", - "ĠEVE RY", - "M ET", - "ME T", - "Ġann ouncements", - "Ġannounc ements", - "Ġannouncement s", - "Ġannounce ments", - "g pio", - "gp io", - "ĠWaitFor Seconds", - "ĠPhoto shop", - "ĠPhotos hop", - "Ġdis contin", - "/ dd", - "/d d", - "Ġtop ology", - "Ġtopo logy", - "an ical", - "ani cal", - "anic al", - ". interface", - ".inter face", - "auc oup", - ". HashSet", - ".Hash Set", - "ARI ANT", - "( routes", - "(r outes", - "(route s", - "(ro utes", - "ĠT eh", - "ĠTe h", - "Ġh ype", - "Ġhy pe", - "Ġhyp e", - "] \").", - "]\" ).", - "]\") .", - "Ġs lam", - "Ġsl am", - "Ġsla m", - "Ġbr oth", - "Ġbro th", - "- inter", - "-in ter", - "-int er", - "ĠR id", - "ĠRi d", - "- manager", - "-m anager", - "-man ager", - "Cancel ar", - "Ġ Pagination", - "ĠP agination", - "ĠPag ination", - "Ġsound track", - "Ġpos terior", - "Ġpost erior", - "Ġposter ior", - "Ġposte rior", - "Ġsc rub", - "Ġscr ub", - "c reating", - "cre ating", - "cr eating", - "creat ing", - "- *", - "ir teen", - "irt een", - ". dy", - ".d y", - ".s ymmetric", - ".sym metric", - "Ġ \"\".", - "Ġ\" \".", - "Ġ\"\" .", - "= ==============", - "== =============", - "==== ===========", - "======== =======", - "=== ============", - "============ ===", - "============= ==", - "=========== ====", - "============== =", - "========= ======", - "========== =====", - "====== =========", - "===== ==========", - "======= ========", - "Ġch assis", - "ĠnumberOf Rows", - "De veloper", - "Develop er", - "_ bins", - "_b ins", - "_bin s", - "_bi ns", - "Ġ OUR", - "ĠO UR", - "ĠOU R", - "ri eb", - "rie b", - "P ros", - "Pro s", - "Pr os", - "Ġ wiÄĻ", - "Ġw iÄĻ", - "Ġwi ÄĻ", - "\" d", - "Ġasync io", - "ze igen", - "_ spi", - "_s pi", - "_sp i", - ". ALL", - ".A LL", - ".AL L", - "Ġscre ws", - "Ġscr ews", - "Ġscrew s", - "Ch inese", - "Ġ apiKey", - "Ġapi Key", - "Ġun successful", - "ĠSea hawks", - "ĠSeah awks", - "O RG", - "OR G", - "ç« ł", - "Ġprofession ally", - "Ġprofessional ly", - "Ġ Coupon", - "ĠC oupon", - "ĠCo upon", - "ĠCou pon", - "åŃĹ æ®µ", - "Con vention", - "Conv ention", - "Ġpol ym", - "Ġpoly m", - "æī ĭ", - "Ġsal vation", - "Ġsalv ation", - "Ġengine ered", - "Ġengineer ed", - "ĠW rest", - "ĠWr est", - "Ġ GCC", - "ĠG CC", - "ĠGC C", - "Ġwar mer", - "Ġwarm er", - "Layout Constraint", - "Ġag grav", - "Ġagg rav", - "S cripts", - "Script s", - "vent ure", - "ven ture", - "Ġrefriger ator", - "Ġinnov ations", - "Ġinnovation s", - "Ġ Runner", - "ĠR unner", - "ĠRun ner", - "N IC", - "NI C", - "ĠRoll ing", - "ĠRol ling", - "Control Events", - "Ġlo os", - "p ac", - "pa c", - "ĉ panel", - "ĉp anel", - "e fe", - "ef e", - "ĠBudd ha", - "ĠBuddh a", - "- -------------Ċ", - "-- ------------Ċ", - "---- ----------Ċ", - "-------- ------Ċ", - "--- -----------Ċ", - "------------ --Ċ", - "----- ---------Ċ", - "---------- ----Ċ", - "------ --------Ċ", - "----------- ---Ċ", - "------------- -Ċ", - "------- -------Ċ", - "--------- -----Ċ", - "-------------- Ċ", - "åº ĵ", - "(for Key", - "Ġl umin", - "Ġlu min", - "Ġlum in", - "Ġ (?", - "Ġ( ?", - "ĠA IDS", - "ĠAI DS", - ", user", - ",u ser", - "im ientos", - "imiento s", - "content Type", - "ant lr", - "é ¦", - "ĠW elt", - "ĠWe lt", - "ĠWel t", - "P roduction", - "Pro duction", - "Product ion", - "Produ ction", - "Prod uction", - "m ight", - "mi ght", - "ĠV II", - "ĠVI I", - "\" ,(", - "\", (", - "Ġobs erving", - "Ġobserv ing", - "Ġdeliber ate", - "( control", - "(c ontrol", - "(cont rol", - "Ġwith d", - "Ġwit hd", - "Ġse mana", - "Ġsem ana", - "ST ACK", - "STA CK", - "u chen", - "uch en", - "uc hen", - "uche n", - "N ice", - "Ni ce", - "Nic e", - "ĠDeutsch land", - "Ġ Specifies", - "ĠSpec ifies", - "d ma", - "dm a", - "iz io", - "izi o", - "ĠF acts", - "ĠFac ts", - "ĠFa cts", - "ĠFact s", - "_ popup", - "_p opup", - "_pop up", - "ĠDirect ors", - "ĠDirector s", - "ĠDir ectors", - "ĠDire ctors", - "{ :", - "[ R", - "ĠÑį леменÑĤ", - "ĠÑįлем енÑĤ", - "Ġ plat", - "Ġp lat", - "Ġpl at", - "Ġpla t", - "Ġdirect ing", - "ä¸ ī", - "ĠGil bert", - "âĢ¦ .ĊĊ", - "âĢ¦. ĊĊ", - ". qml", - ".q ml", - "Ġthere after", - "Ġdis position", - "Ġdisp osition", - "Ġdispos ition", - "Ġdisposit ion", - "d raft", - "dr aft", - "dra ft", - "Ġs urgeon", - "Ġsurg eon", - "Ġsurge on", - "ĠIns ider", - "ĠInside r", - "Bl end", - "ĠT rev", - "ĠTr ev", - "ĠTre v", - "tr insic", - "tri nsic", - "To pics", - "Top ics", - "Topic s", - "r ieve", - "ri eve", - "rie ve", - "_ FILENAME", - "_FILE NAME", - "Ġaut res", - "Ġau tres", - "Ġautre s", - "J ose", - "Jo se", - "Jos e", - "Pro ducer", - "Produ cer", - "Prod ucer", - "e rus", - "er us", - "eru s", - "Ġpe tit", - "Ġpet it", - "Ġ NEXT", - "ĠN EXT", - "ĠNE XT", - "Ġ Filters", - "ĠF ilters", - "ĠFilter s", - "ĠFil ters", - "Ġrep licate", - "Ġrepl icate", - "Ġreplic ate", - "Ġreplica te", - "\" ]).", - "\"] ).", - "\"]) .", - "Ġl enders", - "Ġlen ders", - "Ġlend ers", - "Ġlender s", - "] \",Ċ", - "]\", Ċ", - "]\" ,Ċ", - "; charset", - "Cpp Object", - "Ġfl oral", - "Ġflo ral", - "Ġflor al", - "Ġflora l", - "Ġ Tipo", - "ĠT ipo", - "ĠTi po", - "ĠTip o", - "Ġcirc uits", - "Ġcircuit s", - "e asy", - "ea sy", - "(& $", - "it ta", - "itt a", - "er yl", - "ery l", - "_ COMMON", - "_COM MON", - "_COMM ON", - "' }}>Ċ", - "'} }>Ċ", - "'}} >Ċ", - "'}}> Ċ", - "-b acked", - "-back ed", - "( variable", - "(var iable", - "( Index", - "(In dex", - "Ġ voir", - "Ġv oir", - "Ġvo ir", - "Ġvoi r", - "_ locations", - "_l ocations", - "_location s", - "_loc ations", - "++ ){", - "++) {", - "ĠLouis ville", - "Ġgr atitude", - "Ġgrat itude", - ".Mock ito", - "ĠP owers", - "ĠPower s", - "ĠPo wers", - "ĠPow ers", - "i eurs", - "ie urs", - "ieu rs", - "ieur s", - "Ġge ographic", - "Ġgeo graphic", - "r ale", - "ra le", - "ral e", - "Ġc ra", - "Ġcr a", - "ĠSp urs", - "ipher text", - "iph ertext", - "AC ION", - "- common", - "-com mon", - "-comm on", - "Ġvict ories", - "ĠF inals", - "ĠFin als", - "ĠFinal s", - ". shuffle", - ".sh uffle", - "- million", - "-m illion", - "_ PROC", - "_P ROC", - "_PRO C", - "_PR OC", - "as sume", - "ass ume", - "Ġ ils", - "Ġi ls", - "Ġil s", - "D BC", - "DB C", - "Boot Test", - "Ġl avor", - "Ġla vor", - "Ġlav or", - ". testing", - ".t esting", - ".test ing", - ". ast", - ".as t", - ".a st", - "\" ]/", - "\"] /", - "m oid", - "mo id", - "Ġ qualification", - "Ġqual ification", - "g esch", - "ge sch", - "ges ch", - "ĉ put", - "ĉp ut", - "Ġair ports", - "Ġairport s", - "J I", - "T eacher", - "Te acher", - "_ uniform", - "_un iform", - "Ġ nama", - "Ġn ama", - "Ġna ma", - "Ġnam a", - "ĠB ast", - "ĠBa st", - "ĠBas t", - "e rtype", - "er type", - "ert ype", - "erty pe", - "c apture", - "cap ture", - "capt ure", - "get All", - "ĠReyn olds", - "o oled", - "ool ed", - "oo led", - ". comments", - ".com ments", - ".comment s", - ".comm ents", - "Ġ chin", - "Ġc hin", - "Ġch in", - "Ġchi n", - ") .*", - "). *", - "Ġ или", - "Ġи ли", - "t gl", - "tg l", - "u dos", - "ud os", - "udo s", - "Ġd ÃŃas", - "ĠdÃŃa s", - "ĠdÃŃ as", - "c hai", - "ch ai", - "cha i", - ". program", - ".p rogram", - ".pro gram", - ".pr ogram", - "Ġ psz", - "Ġp sz", - "Ġps z", - "ĉ icon", - "ĉi con", - "p hil", - "ph il", - "phi l", - "ent ral", - "entr al", - "_W RAP", - "_WR AP", - "o vi", - "ov i", - "Ġnost alg", - "In finity", - "Inf inity", - "ĉ yield", - "ĉy ield", - "Ġvit amins", - "Ġvitamin s", - "Ġvita mins", - "Ġvitam ins", - "Qu aternion", - "S ink", - "Si nk", - "Sin k", - "_ goods", - "_g oods", - "_go ods", - "_good s", - "Ġ ........", - "Ġ. .......", - "Ġ... .....", - "Ġ.. ......", - "Ġ.... ....", - "Ġ..... ...", - "Ġ...... ..", - "ĠW ings", - "ĠWin gs", - "ĠWing s", - "ur idad", - "uri dad", - "- story", - "-st ory", - "\" ])ĊĊ", - "\"] )ĊĊ", - "\"]) ĊĊ", - "\"])Ċ Ċ", - "idel ity", - "Type Def", - "G tk", - "Ġ íĮ", - "Ġí Į", - "_ Main", - "_M ain", - "Ġ chez", - "Ġch ez", - "Ġche z", - "ĠR aven", - "ĠRa ven", - "ĠRav en", - "Ġpay roll", - "Ġfreel ance", - "L LU", - "LL U", - "ĠM end", - "ĠMe nd", - "ĠMen d", - "e day", - "ed ay", - "eda y", - "Api ModelProperty", - ".Form BorderStyle", - "Ġeconom ist", - "stan bul", - "Ġfr eight", - "Ġfre ight", - "Ġfrei ght", - "- Agent", - "-A gent", - "( meta", - "(m eta", - "(me ta", - "Ġsym metry", - "Ġ' ..", - "Ġ'. .", - ". Calendar", - ".C alendar", - "- aut", - "-a ut", - "-au t", - "g f", - "p ent", - "pe nt", - "pen t", - "yc lopedia", - "Ġw ishing", - "Ġwish ing", - "Ċ ĊĊĊĊĊĊĊĊĊĊĊ", - "ĊĊ ĊĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊ ĊĊĊĊĊĊĊĊ", - "ĊĊĊ ĊĊĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊ ĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊ ĊĊĊĊ", - "ĊĊĊĊĊ ĊĊĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊĊĊ ĊĊ", - "ĊĊĊĊĊĊĊ ĊĊĊĊĊ", - "ĊĊĊĊĊĊĊĊĊ ĊĊĊ", - "ĊĊĊĊĊĊĊĊĊĊĊ Ċ", - "Ġgentle man", - "Ġ ê³", - "Ġê ³", - "= #", - "Ġlect ures", - "Ġlecture s", - "âĢľ In", - "âĢľI n", - "Ġ! _", - "Ġ hb", - "Ġh b", - "Ġ Vendor", - "ĠV endor", - "ĠVend or", - "Rec ently", - "Recent ly", - "_ notes", - "_n otes", - "_no tes", - "_not es", - "_note s", - "æıIJ 示", - "\" My", - "\"M y", - "Headers Height", - "_ SO", - "_S O", - "Ġunw illing", - "Ġsuper hero", - "g io", - "gi o", - "p sy", - "ps y", - "Ġ Peer", - "ĠP eer", - "ĠPe er", - "ĠPee r", - "j avax", - "java x", - "jav ax", - "& apos", - "&a pos", - "ĠCr isis", - "ĠCri sis", - "ord inal", - "ordin al", - "Mem cpy", - "++++++++ ++++++++", - "- val", - "-v al", - "Ġwork book", - "- ap", - "-a p", - "= k", - "Ġmetal lic", - "Ġmetall ic", - "_ peer", - "_p eer", - "_pe er", - "By PrimaryKey", - "_ SD", - "_S D", - "u ator", - "ua tor", - "uat or", - "_SH ADER", - "_SHA DER", - ") Math", - ". Transform", - ".Trans form", - "Ġc ows", - "Ġco ws", - "Ġcow s", - "P hi", - "Ph i", - "ĠC lem", - "ĠCl em", - "ĠCle m", - "( _(\"", - "(_ (\"", - "ĠL ud", - "ĠLu d", - "- delay", - "-d elay", - "-de lay", - "-del ay", - "ĠSe curities", - "ĠSec urities", - "ĠOrth odox", - "Sym fony", - "( report", - "(re port", - "(repo rt", - "(rep ort", - "Ġent ertain", - "Ġenter tain", - "Ġentert ain", - "E PS", - "EP S", - "iz oph", - "izo ph", - "ex ual", - "I RD", - "IR D", - "ä» İ", - "Ġl ith", - "Ġli th", - "Ġlit h", - "Ġ sanitize", - "Ġs anitize", - "Ġsan itize", - "Ġsanit ize", - "Ġfem inine", - "Ġfemin ine", - "IS BN", - ". authentication", - ".auth entication", - "_ pipeline", - "_p ipeline", - "_pipe line", - "/ constants", - "/con stants", - "Ġ CONF", - "ĠCON F", - "ĠCO NF", - "Ġlu cr", - "Ġluc r", - "r icia", - "ri cia", - "ric ia", - ".t tf", - ".tt f", - ". setContent", - ".set Content", - "Ġ stan", - "Ġs tan", - "Ġst an", - "Ġsta n", - "or ean", - "ore an", - "orea n", - "ĠL loyd", - ".raw Value", - "Ġ gor", - "Ġg or", - "Ġgo r", - "ĠBr owns", - "ĠBrown s", - "ĠBrow ns", - "Re gression", - "Reg ression", - "Ġl owering", - "Ġlow ering", - "Ġlower ing", - "na issance", - "Ġbl ows", - "Ġblow s", - "Ġblo ws", - "Ġam azed", - "Ġama zed", - "Ġun related", - "Ġunre lated", - "Re views", - "Review s", - "Ġ ruby", - "Ġr uby", - "Ġrub y", - "Ġru by", - "Ġ Modifier", - "ĠMod ifier", - "Ġg iants", - "Ġgi ants", - "Ġgiant s", - "Ġgia nts", - "Ġgian ts", - ". thread", - ".t hread", - ".th read", - "Ġcon tainment", - "Ġcont ainment", - "Ġcontain ment", - "ĠStart Coroutine", - "u mat", - "um at", - "uma t", - "o release", - "or elease", - "ore lease", - "ĠR andy", - "ĠRand y", - "ĠRan dy", - "@ endif", - "@end if", - "D igest", - "Di gest", - "Dig est", - "Ġsub urban", - "Ġsubur ban", - "Ġsuburb an", - "= \");Ċ", - "=\" );Ċ", - "Ġ annonce", - "Ġan nonce", - "Ġann once", - "Ġanno nce", - "Ġannon ce", - ". variable", - ".var iable", - "\\ Foundation", - "\\F oundation", - "Ġ acre", - "Ġa cre", - "Ġac re", - "V an", - "Va n", - "Ġt uples", - "Ġtu ples", - "Ġtuple s", - "Ġtup les", - "d ns", - "dn s", - "Ġ Standing", - "ĠSt anding", - "ĠStan ding", - "ĠStand ing", - "_ large", - "_l arge", - "Ġ boxing", - "Ġbo xing", - "Ġbox ing", - "Support ActionBar", - "ĠFort une", - "ĠR um", - "ĠRu m", - "_ multiple", - "_m ultiple", - "_multi ple", - "_mult iple", - "_multip le", - "arch ical", - "Ġ fwrite", - "Ġf write", - "Ġfw rite", - "_ quote", - "_qu ote", - "Ġfoo lish", - "Ġfool ish", - "Ġcom prising", - "Ġcomp rising", - "Ġcompr ising", - "Ġ оп", - "Ġо п", - "- selected", - "-se lected", - "-select ed", - "v f", - "m aid", - "ma id", - "mai d", - "N ama", - "Na ma", - "Nam a", - "( datetime", - "(d atetime", - "(date time", - "(dat etime", - "Ġindirect ly", - "g art", - "ga rt", - "gar t", - "fix tures", - "fixture s", - "c hos", - "ch os", - "cho s", - "ĠH alo", - "ĠHa lo", - "ĠHal o", - "Ġre curring", - "Ġrec urring", - "Ġrecur ring", - "- news", - "-n ews", - "-new s", - "-ne ws", - "v il", - "vi l", - "ĠNurs ing", - "ĠNur sing", - "- produ", - "-p rodu", - "-pro du", - "-pr odu", - "Ġ HQ", - "ĠH Q", - "\\Http Foundation", - "en ci", - "enc i", - "a uen", - "au en", - "Ġ vy", - "Ġv y", - "ocr acy", - "Ġde legation", - "Ġdeleg ation", - "Ġas phalt", - "Ġasp halt", - "Ġset Selected", - "k ok", - "ko k", - "/ rest", - "/r est", - "/re st", - "/res t", - "m etics", - "me tics", - "met ics", - "metic s", - "Ġ NSDate", - "ĠNS Date", - "Ġtravel led", - "Ġtrav elled", - "Ġrec ib", - "Ġ mime", - "Ġm ime", - "Ġmi me", - "Ġmim e", - "CL IENT", - "CLI ENT", - "Ġ GU", - "ĠG U", - "Ġ HANDLE", - "ĠH ANDLE", - "ĠHAND LE", - "/ Q", - "[ z", - "Ġboth ered", - "Ġbother ed", - "ĠBB Q", - "ç as", - "ça s", - "_ examples", - "_ex amples", - "_example s", - "_exam ples", - "_ FIN", - "_F IN", - "Ġwhite Color", - "Ġastr onom", - "Ġastro nom", - "- dir", - "-d ir", - "-di r", - "Ġsovere ign", - "Ġb reeze", - "Ġbree ze", - "Ġ inning", - "Ġin ning", - "Ġinn ing", - "ĠEd monton", - "g li", - "gl i", - ".blog spot", - "j sx", - "js x", - "Ġver sa", - "Ġve rsa", - "Ġvers a", - "ĠMoh ammed", - ". Job", - ".J ob", - "-t oggler", - "-toggle r", - "Ġп олÑĮзоваÑĤ", - "ar don", - "ard on", - "ardo n", - "Ġnew born", - "Ġn aval", - "Ġna val", - "Ġnav al", - "not eq", - "note q", - "Ġt umblr", - "Ġtum blr", - "Ġh entai", - "ĠTyp ically", - "ĠTypical ly", - "Ġl oot", - "Ġlo ot", - ". Sprite", - ".S prite", - ".Sp rite", - "F light", - "Fl ight", - "Ġw avelength", - "Ġwave length", - "- sk", - "-s k", - "ĠE lle", - "ĠEl le", - "ĠEll e", - "_ exports", - "_ex ports", - "_exp orts", - "_export s", - "Ġ Ñı", - "ĠÑ ı", - "Ġ IH", - "ĠI H", - "izoph ren", - "Ġ íģ", - "Ġí ģ", - "_ primary", - "_pr imary", - "_pri mary", - "_prim ary", - "Ġm ois", - "Ġmo is", - "Ġmoi s", - "Ġ BN", - "ĠB N", - "Ġsystem ic", - "Ġsyst emic", - "Ġdifer entes", - "Ġdiferente s", - "IN CT", - "INC T", - "Ġ' 'ĊĊ", - "Ġ'' ĊĊ", - "Ġ''Ċ Ċ", - "$ q", - "Widget Item", - "c lide", - "cl ide", - "cli de", - "clid e", - "$ file", - "$f ile", - "L emma", - "Le mma", - "/ table", - "/t able", - "/tab le", - "a grid", - "ag rid", - "agr id", - "ĠMongo DB", - "i nte", - "in te", - "int e", - "Ġapp rent", - "ÂŃ ing", - "ÂŃi ng", - ". Db", - ".D b", - "Ġ ÃĤ", - "Ġà Ĥ", - "h ammer", - "ham mer", - "=' ';Ċ", - "='' ;Ċ", - "Ġbr okers", - "Ġbro kers", - "Ġbroke rs", - "Ġbroker s", - "i tlement", - "it lement", - "itle ment", - "semb lies", - "sembl ies", - "E le", - "El e", - "{ x", - "Ġ lastname", - "Ġlast name", - "< -", - "Ġ flatten", - "Ġfl atten", - "Ġflat ten", - "Ġflatt en", - "_ band", - "_b and", - ". Root", - ".R oot", - ".read FileSync", - ".readFile Sync", - "= =====", - "== ====", - "==== ==", - "=== ===", - "===== =", - ". rx", - ".r x", - "? čĊ", - "Ġmetaph or", - "T i", - "c onte", - "con te", - "cont e", - "co nte", - "Ġ debit", - "Ġde bit", - "Ġdeb it", - "Ġcon tempt", - "Ġcont empt", - "Cpp Type", - "æĶ ¯", - "Form Field", - "r atio", - "rat io", - "os opher", - "osoph er", - "oso pher", - "Ġim plant", - "Ġimp lant", - "Ġimpl ant", - "P URE", - "PU RE", - "PUR E", - "Ġ alta", - "Ġal ta", - "Ġalt a", - "_ management", - "_man agement", - "_manage ment", - "Ġre fine", - "Ġref ine", - "Ġrefin e", - "Ġ CheckBox", - "ĠCheck Box", - "ĠCh arl", - "ĠChar l", - "ĠCha rl", - "- version", - "-v ersion", - "-vers ion", - "cond itional", - "condition al", - "ven ues", - "venue s", - "Ġrifle s", - "Ġrif les", - "Ġoff spring", - "Ġoffs pring", - "Ġm illing", - "Ġmill ing", - "Ġmil ling", - "Ġmilli ng", - "Ġsharp ly", - "Ġshar ply", - "Ġunder water", - "( origin", - "(orig in", - "(or igin", - "_ Control", - "_C ontrol", - "Ġ .$", - "Ġ. $", - "Pl ugins", - "Plugin s", - "Plug ins", - "Ġd rying", - "Ġdr ying", - "Ġdry ing", - "Ġillustr ates", - "Ġillustrate s", - "Ġillust rates", - "- u", - "Ġveget arian", - "n pc", - "np c", - "He art", - "; ',Ċ", - ";' ,Ċ", - ";', Ċ", - "com ma", - "co mma", - "comm a", - "t eenth", - "te enth", - "tee nth", - "teen th", - "a san", - "as an", - "asa n", - "/ spec", - "/s pec", - "/sp ec", - "_ moves", - "_m oves", - "_move s", - "_mov es", - "_mo ves", - "- margin", - "-m argin", - "-mar gin", - "Ġ ingen", - "Ġin gen", - "Ġi ngen", - "Ġing en", - "Âł ³³", - "³³ Âł", - "Ġpro jet", - "Ġproj et", - "Ġproje t", - "Ġo tra", - "Ġot ra", - "Ġ bras", - "Ġb ras", - "Ġbr as", - "Ġbra s", - ". utc", - ".u tc", - ".ut c", - "Ġsl ept", - "Ġsle pt", - "= sub", - "=s ub", - "ab ilit", - "abil it", - "abi lit", - "p oster", - "pos ter", - "post er", - "po ster", - "Ġ sdk", - "Ġs dk", - "Ġsd k", - "ounc ill", - "ouncil l", - "Ġ wd", - "Ġw d", - "Pre paredStatement", - "ĠD rum", - "ĠDr um", - "( attribute", - "(at tribute", - "Ġ Ethernet", - "ĠEth ernet", - "ĠEther net", - "ĉ DB", - "ĉD B", - "Cal ifornia", - "c ube", - "cu be", - "[ I", - ". Created", - ".C reated", - ".Create d", - "Ġ HM", - "ĠH M", - "Ġtr acing", - "Ġtra cing", - "Forms Module", - "- you", - "-y ou", - ". currency", - ".c urrency", - ".curr ency", - "fe eding", - "feed ing", - "fee ding", - "Ġ tbody", - "Ġt body", - "Ġtb ody", - "L i", - "a ccion", - "ac cion", - "acc ion", - "acci on", - "n as", - "na s", - "Ġtr ouver", - "Ġtrou ver", - "Ġtrouve r", - "N ONE", - "NO NE", - "NON E", - "\" },čĊ", - "\"} ,čĊ", - "\"}, čĊ", - "Ġ ftp", - "Ġf tp", - "Ġft p", - "With Identifier", - "p olate", - "po late", - "pol ate", - "File Info", - "Ġpurs ued", - "Ġpursue d", - "ĠĠĠĠ čĊĠĠĠĠčĊ", - "ĠĠĠĠčĊ ĠĠĠĠčĊ", - "DE SCRIPTION", - "DESC RIPTION", - "} */Ċ", - "}* /Ċ", - "From Nib", - "Ġdec orative", - "Ġdecor ative", - "_ SSL", - "_S SL", - "_SS L", - "( chat", - "(c hat", - "(ch at", - "T LS", - "TL S", - "Ġsur prises", - "Ġsurpr ises", - "Ġsurprise s", - "al culate", - "alc ulate", - "Ġ Splash", - "ĠS plash", - "ĠSp lash", - "ĠSpl ash", - "( Configuration", - "(Config uration", - "Ġ SEM", - "ĠS EM", - "ĠSE M", - "im son", - "ims on", - "/ library", - "/lib rary", - "/l ibrary", - "< Double", - "", - "Ġ} }>", - "Ġ}} >", - "G ED", - "GE D", - "f aq", - "fa q", - "Ġoption ally", - "Ġoptional ly", - "_ Dis", - "_D is", - "Ġ Successful", - "ĠSuccess ful", - "ĠC ensus", - "Ġinc arcer", - "_ CARD", - "_C ARD", - "_CA RD", - "_CAR D", - "Ġav iation", - "Ġavi ation", - "ĠG ym", - "ĠGy m", - "Author ity", - ". Bean", - ".B ean", - ".Be an", - "sh ader", - "sha der", - "shade r", - "Not Exist", - "_ TextChanged", - "_Text Changed", - "Ġ STOP", - "ĠS TOP", - "ĠST OP", - "( team", - "(t eam", - "(te am", - "\" H", - "w g", - "Ġgr inder", - "Ġgri nder", - "Ġgrind er", - "Ġgrin der", - "Ġ stripe", - "Ġst ripe", - "Ġstr ipe", - "Ġstri pe", - "Ġstrip e", - "Ġp reservation", - "Ġpres ervation", - "Cl aim", - "Cla im", - "aver sal", - "avers al", - "w arehouse", - "ware house", - "target s", - "tar gets", - "T rust", - "Tr ust", - "Ġal lev", - "Ġall ev", - "Ġalle v", - ", www", - ",w ww", - "ou sse", - "ous se", - "_ chan", - "_c han", - "_ch an", - "_ Size", - "_S ize", - "s ystems", - "sys tems", - "system s", - "Ġob jection", - "Ġobject ion", - "Ġobj ection", - "ĠK ane", - "ĠKa ne", - "ĠKan e", - "Ġcor ros", - "Ġcorr os", - "Ġcorro s", - "Ġ DSL", - "ĠD SL", - "ĠDS L", - "Ġ ua", - "Ġu a", - "Ġ MH", - "ĠM H", - "ĠStr ategic", - "ĠStrateg ic", - "_ tcp", - "_t cp", - "_tc p", - "Ġ ê°Ĵ", - "Ġê° Ĵ", - "Ġborrow ed", - "Ġborr owed", - "ĠA ch", - "ĠAc h", - "ĉ command", - "ĉcom mand", - "Ġ gps", - "Ġg ps", - "Ġgp s", - "le ston", - "les ton", - "lest on", - "ich ever", - "iche ver", - "Ġ UA", - "ĠU A", - "Ġassault ed", - "Ġspecial izes", - "Ġspecialize s", - "ĉ search", - "ĉs earch", - "ĉse arch", - "H otel", - "Hot el", - "Ho tel", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠčĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠčĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠčĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ čĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠčĊ", - "Ġ Pitch", - "ĠP itch", - "ĠPit ch", - "Ġ Ùģ", - "ĠÙ ģ", - "READ Y", - "REA DY", - "Ġpar ental", - "Ġparent al", - "Ġparen tal", - "Ġg éné", - "Ġgé né", - "Ġgén é", - "Ġdonn ées", - "Ġde tain", - "Ġdet ain", - "T ARGET", - "Ġprotagon ist", - "Ġclear Interval", - "Ġ IconButton", - "ĠIcon Button", - "ĠGet All", - "Type Info", - "E H", - "âĢľ They", - "âĢľThe y", - "Ġ{ [", - "Ġg ag", - "Ġga g", - "Ġ Ú©", - "Ġ Dropdown", - "ĠD ropdown", - "ĠDrop down", - ". free", - ".f ree", - ".fr ee", - ".fre e", - "g one", - "go ne", - "gon e", - "i mens", - "im ens", - "ime ns", - "imen s", - "Ġin stal", - "Ġins tal", - "Ġinst al", - "ĉ curl", - "ĉc url", - "ĉcur l", - "_ CAN", - "_C AN", - "_CA N", - "Ġ Bone", - "ĠB one", - "ĠBo ne", - "ĠBon e", - "ï¼ Ķ", - "on yms", - "ony ms", - "onym s", - "- government", - "-g overnment", - ".binding Navigator", - "Ġ Dans", - "ĠD ans", - "ĠDan s", - "ĠDa ns", - "ĠMc L", - "( en", - "(e n", - "> (_", - ">( _", - "ÐĴ Ñĭ", - ".* ;čĊ", - "= j", - "- cor", - "-c or", - "-co r", - "S on", - "So n", - ".ToolStrip Item", - "- around", - "-a round", - "-ar ound", - "_ XML", - "_X ML", - "end Date", - "Ġ slack", - "Ġs lack", - "Ġsl ack", - "Ġsla ck", - "Ġrot ated", - "Ġrotate d", - "Ġno qa", - "Ġc ottage", - "Ġcott age", - "Ġencontr ar", - "_ skill", - "_s kill", - "_sk ill", - "hou ette", - "! čĊ", - ". weather", - ".we ather", - "Ġemphas ized", - "Ġemphasize d", - "å® ¶", - "ĠÑģ пиÑģ", - "ĠÑģп иÑģ", - "Ġ Compiler", - "ĠC ompiler", - "ĠCom piler", - "ĠComp iler", - "ĠCompile r", - "( android", - "(and roid", - "Ġ âĢº", - "ĠâĢ º", - ". turn", - ".t urn", - "Ġsup pression", - "Ġsuppress ion", - "Ġsupp ression", - "_ calls", - "_c alls", - "_call s", - "_cal ls", - "Ġ *@", - "Ġ* @", - "( strlen", - "(str len", - "(st rlen", - ". hex", - ".h ex", - ".he x", - "ĠB ills", - "ĠBill s", - "ĠBil ls", - "Ġ RSA", - "ĠR SA", - "ĠRS A", - "Ï Ĥ", - "Ġ Escape", - "ĠE scape", - "ĠEs cape", - "ĠEsc ape", - "ement ia", - "Ġ frontend", - "Ġfront end", - "Ġp int", - "Ġpi nt", - "Ġpin t", - "_ exc", - "_e xc", - "_ex c", - "z zo", - "zz o", - "[ ],Ċ", - "[] ,Ċ", - "[], Ċ", - "Ġ\"' ,'\"", - "Ġ\"', '\"", - "Ġ\"',' \"", - ". Environment", - ".En vironment", - "Ġafore mentioned", - "Ġend ure", - "prot otype", - "proto type", - "ther apy", - "the rapy", - "s si", - "ss i", - "D eg", - "De g", - "_ plugins", - "_pl ugins", - "_plugin s", - ". userInfo", - ".user Info", - "Pr inter", - "Print er", - "Ġ PROGRAM", - "ĠPRO GRAM", - "Ġru ins", - "Ġruin s", - "Ġemp irical", - "Ġempir ical", - "Ġ crawl", - "Ġc rawl", - "Ġcr awl", - "Ġcraw l", - "Ġcra wl", - "ĠBo iler", - "- comment", - "-com ment", - "-comm ent", - ". subplot", - ".sub plot", - "_ et", - "_e t", - "Ġ' .',", - "Ġ'. ',", - "Ġ'.' ,", - "min or", - "mi nor", - "mino r", - "ĠCustom s", - "ĠCust oms", - "Ġ yaw", - "Ġy aw", - "Ġya w", - "under line", - "Ġ Como", - "ĠC omo", - "ĠCom o", - "ĠCo mo", - "( ('", - "(( '", - "( mean", - "(m ean", - "(me an", - "Ġch aque", - "Ġcha que", - "Ġ Blocks", - "ĠB locks", - "ĠBl ocks", - "ĠBlock s", - "ĠBlo cks", - "ĠBloc ks", - ". rad", - ".r ad", - ".ra d", - "ilib rium", - "Ġ webdriver", - "Ġweb driver", - "Ġmel hor", - "d ana", - "da na", - "dan a", - "ĠAb use", - "ĠAbu se", - "ĠSouth west", - "Ġ Paren", - "ĠP aren", - "ĠPar en", - "ĠPa ren", - "ĠPare n", - "PERT IES", - "ĉ IL", - "ĉI L", - "Ġs cream", - "Ġsc ream", - "Ġscre am", - "Ġscr eam", - "v u", - "Ġin comes", - "Ġinc omes", - "Ġincome s", - "Ġincom es", - "Ġ nim", - "Ġn im", - "Ġni m", - "Ġ lace", - "Ġl ace", - "Ġla ce", - "Ġlac e", - "Ġcompens ate", - "Re verse", - "Rev erse", - "D at", - "Da t", - "_ attack", - "_att ack", - "Ġn our", - "Ġno ur", - "Ġnou r", - "a chen", - "ac hen", - "ach en", - "ache n", - "c ek", - "ce k", - "< Func", - " \"+", - ">\" +", - "Ġ tokenizer", - "Ġtoken izer", - "Ġtokenize r", - "Ġsovere ignty", - "Ġsovereign ty", - "ĠP ence", - "ĠPe nce", - "ĠPen ce", - "( )\");Ċ", - "() \");Ċ", - "()\" );Ċ", - "Ġpesso as", - "Ġpessoa s", - ". Ge", - ".G e", - "Ġ Included", - "ĠIn cluded", - "ĠInclude d", - "Ġ pagina", - "Ġp agina", - "Ġpag ina", - "Ġex posing", - "Ġexp osing", - "Ġexpos ing", - "Ġexpo sing", - "е ÑĪ", - "_ SCRIPT", - "_SC RIPT", - "/ $',", - "/$ ',", - "Th umbnail", - "× Ķ", - "webElement X", - "webElementX paths", - "press ure", - "pres sure", - "ĠC urry", - "ĠCur ry", - "_ CP", - "_C P", - "OL UTION", - "I LES", - "IL ES", - "ILE S", - "prot ect", - "o ola", - "ool a", - "oo la", - "Work space", - "Works pace", - "{ };Ċ", - "{} ;Ċ", - "Ġ UNS", - "ĠU NS", - "ĠUN S", - "Ġsymp athy", - "Ġsympath y", - "r oker", - "ro ker", - "roke r", - "rok er", - "Ġre model", - "Ġr emodel", - "Ġrem odel", - "ĉ cell", - "ĉc ell", - "Ġa top", - "Ġat op", - ". FullName", - ".Full Name", - "Ġf aut", - "Ġfa ut", - "ĠE asily", - "_ dynamic", - "_d ynamic", - "_dyn amic", - "Ġfr amed", - "Ġframe d", - "Ġfra med", - "Ġfram ed", - "Ġm otive", - "Ġmot ive", - "Ġmotiv e", - "è· ¯", - "s am", - "sa m", - "Ġ marca", - "Ġmar ca", - "Ġmarc a", - "ĠText EditingController", - "Ġd estructor", - "Ġde structor", - "Ġdestruct or", - "c ream", - "cre am", - "cr eam", - "Ġr ude", - "Ġru de", - "Ġrud e", - "Ġ Bold", - "ĠB old", - "ĠBo ld", - "ĠBol d", - "ĠInd igenous", - "Ġ gens", - "Ġg ens", - "Ġge ns", - "Ġgen s", - "Ġrel acion", - "( system", - "(s ystem", - "(sys tem", - "Ġ UIFont", - "ĠUI Font", - "ĠUIF ont", - "_ charge", - "_ch arge", - "_char ge", - "U STER", - "US TER", - "UST ER", - "E V", - ". Namespace", - ".N amespace", - ".Name space", - ".Names pace", - "Ġmer ger", - "Ġmerge r", - "Ġmerg er", - "Ġ calloc", - "Ġc alloc", - "Ġcall oc", - "Ġcal loc", - "g ang", - "ga ng", - "gan g", - "Bad Request", - "Ġs per", - "Ġsp er", - "Ġspe r", - "- design", - "-d esign", - "-de sign", - "-des ign", - "Ġ âĩ", - "Ġâ ĩ", - "C han", - "Ch an", - "Cha n", - "Ġorg anism", - "Ġorgan ism", - "Ġorganis m", - ", )", - "= id", - "=i d", - "_ plane", - "_p lane", - "_pl ane", - "_plan e", - "Ġ Cases", - "ĠC ases", - "ĠCas es", - "ĠCase s", - "ĠCa ses", - "el fast", - "elf ast", - "ĠLegisl ature", - "ĠF aker", - "ĠFa ker", - "ĠFake r", - "ĠFak er", - "Ġinv oking", - "Ġinvo king", - "- utils", - "-util s", - "( ).'", - "() .'", - "(). '", - ". face", - ".f ace", - ".fac e", - ".fa ce", - "Ġguard ian", - "my Modal", - "Ġ clipboard", - "Ġclip board", - "ĠA TM", - "ĠAT M", - "Ġpe as", - "Ġpea s", - "ĠS ylv", - "ĠSy lv", - ". calc", - ".c alc", - ".ca lc", - ".cal c", - "Ġ Contacts", - "ĠCont acts", - "ĠContact s", - "int Value", - "Ġmod ifying", - "Ġmodify ing", - "ĠB arb", - "ĠBar b", - "ĠBa rb", - ". loss", - ".l oss", - ".lo ss", - "_ percentage", - "_per centage", - "_percent age", - "As ked", - "Ask ed", - "( lst", - "(l st", - "(ls t", - "ateg orical", - "ategor ical", - "ategori cal", - "atego rical", - "- files", - "-f iles", - "-file s", - "-fi les", - "ĠRom ania", - "ĠRo mania", - "ĠRoman ia", - "ĠRoma nia", - ". Ac", - ".A c", - "Ġ hai", - "Ġh ai", - "Ġha i", - "Ġ Flying", - "ĠF lying", - "ĠFl ying", - "ĠFly ing", - "Ġ ż", - "ĠÅ ¼", - "j p", - "ĠTr ainer", - "ĠTra iner", - "ĠTrain er", - ". arc", - ".a rc", - ".ar c", - "_ deg", - "_d eg", - "_de g", - "Ġtrace back", - "Or Fail", - "F LOW", - "FL OW", - ". old", - ".o ld", - ".ol d", - "o ya", - "oy a", - "g mt", - "gm t", - "is empty", - "Ġvacc ination", - "Ġ obsolete", - "Ġob solete", - "recogn ized", - "Ġru ined", - "Ġruin ed", - "ĠR ein", - "ĠRe in", - "ĠRei n", - "Ġ Tracking", - "ĠTr acking", - "ĠTrack ing", - "x fb", - "xf b", - "ا ÛĮ", - "Ġv ære", - "Ġvæ re", - "Ġbr yster", - "Ġ ITS", - "ĠI TS", - "ĠIT S", - "Ġdes tiny", - "Ġdest iny", - "Ġdestin y", - "Ġs wear", - "Ġsw ear", - "Ġswe ar", - "Ġre des", - "Ġr edes", - "Ġred es", - "Ġrede s", - "Ġ clf", - "Ġc lf", - "Ġcl f", - "Ġfl ipped", - "Ġflip ped", - "ĉ head", - "ĉh ead", - "B luetooth", - "Bl uetooth", - "Ġ Overrides", - "ĠOver rides", - "ĠOverride s", - ": Boolean", - "_ =", - "_ lr", - "_l r", - "s pawn", - "sp awn", - "spa wn", - ": index", - "VAL UES", - "VALUE S", - "is key", - "isk ey", - "iske y", - "? \");Ċ", - "?\" );Ċ", - ".syn thetic", - "Ġ Checking", - "ĠCheck ing", - "struct ures", - "structure s", - "i ping", - "ip ing", - "ipi ng", - "Ġvoc als", - "Ġvocal s", - "- Up", - "-U p", - "ĠManufact urers", - "ĠManufacturer s", - "ĠMar riage", - "ĠMarr iage", - "代 çłģ", - "Ġgar ner", - "Ġgarn er", - "_ Client", - "_C lient", - "_Cl ient", - "par allel", - "paralle l", - "RI END", - "Ġvine gar", - "se gue", - "seg ue", - "J B", - "Ġcont acting", - "Ġcontact ing", - "ĠCar roll", - "ĠCarr oll", - "Ġout reach", - "Ġoutr each", - "t ensor", - "_ variant", - "_v ariant", - "_var iant", - "Ġt heat", - "Ġth eat", - "Ġthe at", - "l icable", - "lic able", - "lica ble", - "{ |", - "t iny", - "ti ny", - "tin y", - "_ letter", - "_l etter", - "Ġp encil", - "Ġpen cil", - "Ġpenc il", - "HeadersHeight SizeMode", - "il tro", - "ilt ro", - "iltr o", - ".auto configure", - ". drag", - ".d rag", - ".dr ag", - ". useState", - ".use State", - "Ġ BMI", - "ĠB MI", - "ĠBM I", - "h int", - "hi nt", - "hin t", - "Com pile", - "Comp ile", - "* \\", - "en ary", - "ena ry", - "Ġ lvl", - "Ġl vl", - "Ġlv l", - ". Cache", - ".C ache", - "+ =\"", - "+= \"", - "_ tv", - "_t v", - "ruit ment", - "Ġf read", - "Ġfr ead", - "Ġfre ad", - "Art icles", - "Article s", - "f ila", - "fi la", - "fil a", - "Ġpack aged", - "Ġpackage d", - "âĺ Ĩ", - "AT HER", - "ATH ER", - "ĠPl anned", - "ĠPlan ned", - "s cheme", - "sch eme", - "Ġdi ary", - "Ġdia ry", - "Ġoff enses", - "Ġoffense s", - "Ġoffen ses", - "/ F", - "Ġ Stick", - "ĠS tick", - "ĠSt ick", - "Ġc erc", - "Ġce rc", - "Ġcer c", - "ĠS lee", - "ĠSl ee", - "ĠSle e", - "ĉ ĉĠĠĠĠĠĠĠĠ", - "ĉĉ ĠĠĠĠĠĠĠĠ", - "ĉĉĠĠĠ ĠĠĠĠĠ", - "ĉĉĠ ĠĠĠĠĠĠĠ", - "ĉĉĠĠ ĠĠĠĠĠĠ", - "ĉĉĠĠĠĠĠĠĠ Ġ", - "ĉĉĠĠĠĠ ĠĠĠĠ", - "ĉĉĠĠĠĠĠ ĠĠĠ", - "ĉĉĠĠĠĠĠĠ ĠĠ", - "< Image", - "", - ";' >", - "ĉ col", - "ĉc ol", - "V G", - "_ boolean", - "_bool ean", - "_bo olean", - "re cent", - "rec ent", - "rece nt", - "Ġ *)ĊĊ", - "Ġ* )ĊĊ", - "Ġ*) ĊĊ", - "Ġ*)Ċ Ċ", - "ĠRain bow", - "om men", - "omm en", - "Ġl ur", - "Ġlu r", - "Ġop pression", - "Ġopp ression", - "Ġoppress ion", - "(\" ,\");Ċ", - "(\", \");Ċ", - "(\",\" );Ċ", - "ĠFac ility", - "DEF INED", - "DEFINE D", - "Ġn eon", - "Ġne on", - "Ġneo n", - "Ġoff ender", - "Ġoffend er", - "Ġoffen der", - "A FP", - "AF P", - "Ġ Cleaning", - "ĠC leaning", - "ĠClean ing", - "ĠCle aning", - "[ ]):", - "[] ):", - "[]) :", - "Ġund ocumented", - ". Repositories", - ".Re positories", - "ĠG uitar", - "ĠGu itar", - "ĠGui tar", - "аÑģÑģ ив", - "S kills", - "Sk ills", - "Skill s", - "Ġtest imon", - "Ġtestim on", - "rypt ography", - "ĠAm ber", - "ĠAmb er", - "ĠSt alin", - "ĠSta lin", - "Ġl one", - "Ġlo ne", - "Ġlon e", - "Ġap enas", - "Ġape nas", - "Ġdi eses", - "Ġdie ses", - "Ġdies es", - "Ġdiese s", - "Ġ Arduino", - "ĠAr duino", - "è½ ¬", - "= =-", - "== -", - "_ Act", - "_A ct", - "Ġ coded", - "Ġc oded", - "Ġco ded", - "Ġcode d", - "Ġcod ed", - "âĸ ł", - "am burger", - "amb urger", - "amburg er", - "- links", - "-l inks", - "-link s", - "Ġar mour", - "Ġarm our", - ". High", - ".H igh", - "get Content", - "getC ontent", - "s tag", - "st ag", - "sta g", - "Ġ heck", - "Ġh eck", - "Ġhe ck", - "Ġ ìĹĨ", - "ĠìĹ Ĩ", - "ĠMc Connell", - "ĠCon cert", - "ĠConc ert", - "ĠConce rt", - "Ġ Alloc", - "ĠAl loc", - "ĠAll oc", - "ä re", - "är e", - ". replaceAll", - ".replace All", - "Ġpart itions", - "Ġpartition s", - "r ott", - "ro tt", - "rot t", - "ĠF le", - "ĠFl e", - "_ TREE", - "_T REE", - "_TR EE", - "reason able", - "Ġ Reporting", - "ĠRep orting", - "ĠReport ing", - "Ġbillion aire", - "s cores", - "sc ores", - "score s", - "m ins", - "min s", - "mi ns", - "- eye", - "-e ye", - "M ORE", - "MO RE", - "ab ort", - "abor t", - "abo rt", - "ĠS WT", - "ĠSW T", - "Ġin verted", - "Ġinvert ed", - "Ġ Teachers", - "ĠTe achers", - "ĠTeacher s", - "ĠTea chers", - "ĠTeach ers", - "; n", - "Ġ astro", - "Ġa stro", - "Ġas tro", - "Ġast ro", - "Ġastr o", - "н ов", - "но в", - "а ниÑĨ", - "ан иÑĨ", - "ани ÑĨ", - "product o", - "c ountries", - "count ries", - "ĠO wen", - "ĠOw en", - "Ġcont amination", - "Ġcontamin ation", - "Ġconta mination", - "Ġv ibe", - "Ġvi be", - "Ġvib e", - "ĠE lli", - "ĠEl li", - "ĠEll i", - ". script", - ".s cript", - "ĠO live", - "ĠOl ive", - "ĠOliv e", - "D MA", - "DM A", - "v ier", - "vi er", - "vie r", - ": semicolon", - "- module", - "-m odule", - "-mod ule", - "g ressive", - "gress ive", - "a gu", - "ag u", - "_ players", - "_p layers", - "_pl ayers", - "_player s", - "_play ers", - "Ġresult ados", - "Ġresultado s", - "start ed", - "star ted", - "scroll Top", - "= ====", - "== ===", - "==== =", - "=== ==", - "Ġweigh ing", - "Ġ[ [[", - "Ġ[[ [", - "z ahl", - "za hl", - "( NS", - "(N S", - "Ġ Assertion", - "ĠAssert ion", - "le ague", - "lea gue", - ".set TextColor", - ".setText Color", - "ĉ Message", - "ĉM essage", - "Ġm oms", - "Ġmom s", - "Ġmo ms", - "_ AF", - "_A F", - ". wh", - ".w h", - "A LS", - "AL S", - "Ġaut re", - "Ġau tre", - "] ĊĊĊĊ", - "]Ċ ĊĊĊ", - "]ĊĊ ĊĊ", - "]ĊĊĊ Ċ", - ". opacity", - ".op acity", - "ĠBudd hist", - "ĠBuddh ist", - "Ġde af", - "ĠOrgan isation", - "( Global", - "(G lobal", - "en sch", - "ens ch", - "Ġhead ache", - "ĠA lien", - "ĠAl ien", - "ĠAli en", - "_ inode", - "_in ode", - "_i node", - "ĠSt ark", - "ĠStar k", - "ĠSta rk", - "Ġ æī", - "Ġæ ī", - "-l nd", - "-ln d", - "o ref", - "or ef", - "ore f", - "_ feat", - "_f eat", - "_fe at", - "Ġpedest rian", - "Ġnom inal", - "Ġnomin al", - "Ġball oon", - "Ġbal loon", - "Ġballo on", - "Ġ sprites", - "Ġs prites", - "Ġsp rites", - "Ġspr ites", - "Ġsprite s", - "Prototype Of", - "ĠA post", - "ĠAp ost", - "Ġ FEATURE", - "ĠF EATURE", - "ĠFE ATURE", - "O H", - "Ġre cess", - "Ġr ecess", - "Ġrec ess", - "Ġrece ss", - "ĠD onna", - "ĠDon na", - "con sumer", - "cons umer", - "consum er", - "consume r", - "$ GLOBALS", - "ĠG IF", - "ĠGI F", - "- frame", - "-f rame", - "-fr ame", - "In icio", - "Ini cio", - "Ġpass ages", - "Ġpassage s", - "Date String", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - ". byte", - ".b yte", - ".by te", - "B ug", - "Bu g", - "initial izer", - "initialize r", - "p kt", - "pk t", - "od ium", - "odi um", - "Ġ DER", - "ĠD ER", - "ĠDE R", - ". ops", - ".op s", - ".o ps", - "l eri", - "le ri", - "ler i", - "Ġgift ed", - "Ġgif ted", - "Ġ detach", - "Ġde tach", - "Ġdet ach", - "ter rain", - "terra in", - "terr ain", - "el ters", - "elt ers", - "elter s", - "ãģ ı", - ". loader", - ".l oader", - ".load er", - ".lo ader", - "ĠN GO", - "ĠNG O", - "str ncmp", - "K h", - "( fontSize", - "(font Size", - "r ocket", - "ro cket", - "rock et", - "roc ket", - "Ġpreced ent", - "ĠAur ora", - "Ġ Experiment", - "ĠEx periment", - "i sphere", - "is phere", - "isp here", - "En coded", - "Enc oded", - "Encode d", - "Ġ âĢĵĊĊ", - "ĠâĢĵ ĊĊ", - "ĠâĢĵĊ Ċ", - "Ġpy ramid", - "ĠAnn iversary", - "o fil", - "of il", - "ofi l", - "ë Ł", - "( plugin", - "(pl ugin", - "C oeff", - "Co eff", - "Ġco operate", - "Ġcooper ate", - "Ġcoop erate", - "Ġpredomin antly", - "Ġpredominant ly", - "I SM", - "IS M", - "Ph rase", - "_ DEFINE", - "_DE FINE", - "_DEF INE", - "_DEFIN E", - "F lip", - "Fl ip", - "AMIL Y", - "AMI LY", - "ĠMark ets", - "ĠMarket s", - "Ġ StreamReader", - "ĠStream Reader", - "Ġ Combine", - "ĠC ombine", - "ĠCom bine", - "ĠComb ine", - "Ġmanus cript", - "z za", - "zz a", - ", tp", - ",t p", - "Wh atever", - "What ever", - "IT ICAL", - "igh bour", - "ighb our", - "Data Provider", - ". Texture", - ".Text ure", - "priv acy", - ". SDK", - ".S DK", - "Ġre charge", - "Ġ cpp", - "Ġc pp", - "Ġcp p", - "Ġ CFG", - "ĠC FG", - "ĠCF G", - "( holder", - "(h older", - "( py", - "(p y", - "m ot", - "mo t", - "Ġsa voir", - "Ġsav oir", - "ĠR osa", - "ĠRo sa", - "ĠRos a", - "ĠP Cs", - "ĠPC s", - "Ġ íĻ", - "Ġí Ļ", - ".her oku", - ".hero ku", - "Ġf ren", - "Ġfr en", - "Ġfre n", - "ĠR iley", - "ĠRi ley", - "a gate", - "ag ate", - "aga te", - "Ġs ond", - "Ġso nd", - "Ġson d", - ". xlsx", - ".x lsx", - ".xls x", - "Ġh acked", - "Ġhack ed", - "Ġhac ked", - "st ad", - "sta d", - "G i", - "Ġs anity", - "Ġsan ity", - "Ġsanit y", - "ĠSql DataAdapter", - ".. .\",", - "... \",", - "...\" ,", - "ĠP ussy", - "Ġ ****************", - "Ġ** **************", - "Ġ**** ************", - "Ġ******** ********", - "Ġhass le", - "_P ARENT", - "_PAR ENT", - "ĠU AE", - "ĠUA E", - "Ġbegin ners", - "Ġbeginner s", - "( Client", - "(C lient", - "(Cl ient", - "Ġstat istically", - "Ġstatist ically", - "Ġstatistical ly", - "Ġstatistic ally", - ". hour", - ".h our", - "e delta", - "ed elta", - "Ġ traction", - "Ġt raction", - "Ġtr action", - "Ġtra ction", - "Ġtract ion", - "u elve", - "uel ve", - "a rat", - "ar at", - "ara t", - "Ġsa una", - "Ġsau na", - "IN VALID", - "INVAL ID", - "Ġindict ment", - "AL LE", - "ALL E", - "Ġd issent", - "Ġdis sent", - "Ġdiss ent", - "Ġdisse nt", - "Ġ Typography", - "ĠTyp ography", - "Ġintent ional", - "Ġintention al", - "s it", - "si t", - "ĠAn imals", - "ĠAnimal s", - "ĠAnim als", - "Ġcoun tryside", - "Ġcountry side", - "Ġ uart", - "Ġu art", - "Ġua rt", - "} \\\"", - "}\\ \"", - "Ġseam less", - "¾ 示", - "Ġ autos", - "Ġa utos", - "Ġaut os", - "Ġauto s", - "Ġau tos", - "Ġ\" '\";Ċ", - "Ġ\"' \";Ċ", - "Ġ\"'\" ;Ċ", - "F lush", - "Fl ush", - "AN NOT", - "ANN OT", - "Ġal gebra", - "Ġalg ebra", - "as soc", - "ass oc", - "asso c", - "ĠW aters", - "ĠWater s", - "ĠWat ers", - "ĠWa ters", - "Ġprepar ations", - "Ġpreparation s", - "r onym", - "ro nym", - "ron ym", - "[, ]", - "S ans", - "San s", - "Sa ns", - "Ġarm ies", - "i peg", - "ip eg", - "ipe g", - "Ġcre amy", - "Ġcream y", - "Ġcrea my", - ". art", - ".a rt", - ".ar t", - "e tre", - "et re", - "etr e", - "Ġ Animated", - "ĠAn imated", - "ĠAnim ated", - "Ġun pleasant", - "e mean", - "em ean", - "eme an", - "g reat", - "gr eat", - "gre at", - "i Äħ", - "Ġ Earlier", - "ĠEar lier", - "ĠEarl ier", - "Ġc hic", - "Ġch ic", - "Ġchi c", - "Ġpres erving", - "( exec", - "(e xec", - "(ex ec", - "ĠInvest igation", - "ĠInvestig ation", - "ĉ GPIO", - "ĉG PIO", - "Ġrig orous", - "Ġrigor ous", - "i jo", - "ij o", - "= num", - "=n um", - "Ġ toolStrip", - "Ġt oolStrip", - "Ġtool Strip", - ") set", - ")s et", - "+ \"&", - "+\" &", - "Ġ Acceler", - "ĠAcc eler", - "Ġdevelop mental", - "Ġdevelopment al", - "is posable", - "Ġflaw ed", - "Ġfla wed", - "r ene", - "re ne", - "ren e", - "Up dating", - "Ġwatch dog", - "Ġden ominator", - "Ġdenom inator", - "Ġdenomin ator", - "Ġsubur bs", - "Ġsuburb s", - "Ġ ...)", - "Ġ... )", - "Ġ.. .)", - "Ġconv ictions", - "Ġconviction s", - "Ġconvict ions", - "c losure", - "clo sure", - ". IP", - ".I P", - "Ġtrans lates", - "Ġtransl ates", - "Ġtranslate s", - ".s wt", - ".sw t", - ". Trace", - ".T race", - ".Tr ace", - "Ġmet tre", - ". isEnabled", - ".is Enabled", - "Ġ Effective", - "ĠE ffective", - "ĠEffect ive", - "ĠEff ective", - ". toInt", - ".to Int", - "Ġen chant", - "Ġench ant", - "Ġst unned", - "Ġstun ned", - "Ġ poi", - "Ġp oi", - "Ġpo i", - "/ code", - "/c ode", - "/co de", - "a dm", - "ad m", - ".data binding", - ".datab inding", - ".databind ing", - "Ġ Lorem", - "ĠL orem", - "ĠLo rem", - "ĠLore m", - "ĠLor em", - "________________________________ ________________________________", - "Ġ ledger", - "Ġled ger", - "Ġledge r", - "Ġc ara", - "Ġcar a", - "Ġca ra", - "ĠG ir", - "ĠGi r", - "Ġw aits", - "Ġwait s", - "Ġwa its", - "U no", - "Un o", - "Ġ cwd", - "Ġc wd", - "Ġcw d", - "è¾ ij", - "ĠT Result", - "Ġre jo", - "Ġrej o", - "Ġe mitted", - "Ġem itted", - "Ġemit ted", - "ĠWest minster", - "ä¸Ģ 个", - "n ek", - "ne k", - "_T is", - "Ġen act", - "ĉ with", - "ĉw ith", - "or gia", - "org ia", - "Ġj ue", - "Ġju e", - "Per form", - "S PATH", - "SP ATH", - "SPA TH", - ". topic", - ".t opic", - ".to pic", - ".top ic", - "ĠD aten", - "ĠDate n", - "ĠDa ten", - "ĠDat en", - "Ạ§", - "Ġsit io", - "Ġsiti o", - "_ MM", - "_M M", - "\" So", - "\"S o", - "b ial", - "bi al", - "bia l", - "Ġ scoped", - "Ġsc oped", - "Ġscope d", - "Ġsco ped", - "Ġscop ed", - "Re quires", - "Require s", - "Ġ TOTAL", - "ĠT OTAL", - "ĠCh ancellor", - "( contents", - "(content s", - "(cont ents", - "Ġste alth", - "Ġsteal th", - "dev ices", - "device s", - "- pass", - "-p ass", - "il ih", - "ili h", - "ĠMal colm", - "ĠDe pot", - "ĠDep ot", - "Ġcon figur", - "Ġconfig ur", - "a ussian", - "aus sian", - "auss ian", - "_ constraint", - "_con straint", - "в еÑĤ", - "ве ÑĤ", - "G RA", - "GR A", - "Ġ Rates", - "ĠR ates", - "ĠRa tes", - "ĠRate s", - "ĠRat es", - ".dataGridView TextBoxColumn", - "ĠN obel", - "ĠNo bel", - "ĠNob el", - "i tics", - "it ics", - "iti cs", - "itic s", - "Ġignor ant", - "Ġ Reporter", - "ĠRe porter", - "ĠReport er", - "ĠEb ola", - "Ġ Shock", - "ĠSh ock", - "ĠSho ck", - "_ relation", - "_re lation", - "_rel ation", - "ĠN inja", - "ĠNin ja", - ") c", - "Ġ ticker", - "Ġt icker", - "Ġti cker", - "Ġtick er", - "Ġtic ker", - ". isChecked", - ".is Checked", - "ĠSup pliers", - "ĠSupplier s", - "ĠR apid", - "ĠRa pid", - "ĠRap id", - "Level s", - "âĤ¬ âĦ¢", - "ĉ queue", - "ĉq ueue", - "Ġ chop", - "Ġc hop", - "Ġch op", - "Ġcho p", - "Ġ Unix", - "ĠUn ix", - "ĠUni x", - "re ject", - "rej ect", - "- calendar", - "-c alendar", - "-cal endar", - "( sort", - "(s ort", - "(so rt", - "è ne", - "èn e", - "erc icio", - "Ġh ect", - "Ġhe ct", - "CALL TYPE", - "r oupon", - "ro upon", - "rou pon", - "roup on", - "Ġrent als", - "Ġrental s", - "author s", - "auth ors", - "{ name", - "{n ame", - "ĠF IFO", - "ĠFI FO", - "Ġ lassen", - "Ġl assen", - "Ġlas sen", - "Ġ Nous", - "ĠN ous", - "ĠNo us", - "ĠNou s", - "Ġsn apped", - "Ġsnap ped", - "Ġsna pped", - "Ġf ertility", - "Ġfer tility", - "Ġfert ility", - "Ġfertil ity", - "\" log", - "cl icked", - "click ed", - "Ġplan ting", - "Ġplant ing", - "Ġ gb", - "Ġg b", - "/ output", - "/out put", - "PE AT", - "Ġ categoria", - "Ġc ategoria", - "Ġcategor ia", - "Ġ bach", - "Ġb ach", - "Ġba ch", - "Ġbac h", - "Prof essor", - "i nth", - "in th", - "int h", - "\" ]čĊ", - "\"] čĊ", - "Rec order", - "Record er", - "s erde", - "ser de", - "Ġ Transmission", - "ĠTrans mission", - "t rad", - "tr ad", - "tra d", - "Ġtur bo", - "Ġturb o", - "_ VERTEX", - "_VER TEX", - "\\ Event", - "\\E vent", - "il ver", - "Ġbod ily", - "Ġ Sources", - "ĠS ources", - "ĠSource s", - "ĠSour ces", - "Ġkill ings", - "Ġkilling s", - "Ġkil lings", - ".xr TableCell", - "Ġfol ded", - "Ġfold ed", - "/ legal", - "/l egal", - "u ner", - "un er", - "une r", - "ĠR ifle", - "ĠRif le", - "ĠM IDI", - "ĠMI DI", - "ĠMID I", - "_Selected IndexChanged", - ".Size Type", - "Ġ WebSocket", - "ĠWeb Socket", - "Ġse leccion", - "Ġsele ccion", - "S and", - "San d", - "Sa nd", - "ot ros", - "otr os", - "Ġen vision", - "Ġenv ision", - "Ġenvis ion", - "/ etc", - "/e tc", - "ĠMel issa", - "S pot", - "Sp ot", - "Spo t", - "н ое", - "но е", - "_ ARM", - "_A RM", - "_AR M", - "At tempt", - "Att empt", - "Ġ BI", - "ĠB I", - "ãģ Ķ", - "Ġ DU", - "ĠD U", - "Ġback lash", - "st ride", - "str ide", - "stri de", - "/ classes", - "/c lasses", - "/class es", - "/cl asses", - "Ġtext Color", - "_ staff", - "_st aff", - "_sta ff", - "ob lin", - "obl in", - "ag enta", - "agent a", - "agen ta", - ". collections", - ".c ollections", - ".col lections", - ".collection s", - ".collect ions", - ".coll ections", - "il lage", - "ill age", - "illa ge", - "' čĊčĊ", - "'čĊ čĊ", - "fl atten", - "flat ten", - "_ sales", - "_s ales", - "_sale s", - "_sal es", - "_sa les", - "_ MASTER", - "_M ASTER", - "_MA STER", - "T W", - "_ da", - "_d a", - "P itch", - "ph ies", - "phi es", - "Ġz ombies", - "Ġzombie s", - "Ġ VERY", - "ĠV ERY", - "ĠVER Y", - "ĠVE RY", - "ĠPh armacy", - "ĠPharm acy", - "ĠPharmac y", - "ĠPharma cy", - "Ġprogress Bar", - "Ġhas htag", - "Ġhash tag", - "S idebar", - "Side bar", - "@ stop", - "@s top", - "( pc", - "(p c", - "ол ж", - "M AKE", - "MA KE", - "ĠC oron", - "ĠCo ron", - "ĠCor on", - "Ġkv inner", - "Ġkvin ner", - "Ġkvinn er", - "Ġkvinne r", - "ĠM aid", - "ĠMa id", - "ĠMai d", - "b ob", - "bo b", - ". titleLabel", - ".title Label", - "Ġsuccess es", - "Ġsucc esses", - "Ġsucces ses", - "ĠDem ocracy", - "ĠDemocr acy", - "ĠS urgery", - "ĠSurg ery", - "ĠSurge ry", - "Ġco ugar", - "Ġcou gar", - "Ġ curso", - "Ġcur so", - "Ġcurs o", - "Ġl oro", - "Ġlo ro", - "Ġlor o", - "ist ency", - "iste ncy", - "isten cy", - "Sen ior", - "æ k", - "Ġ AAA", - "ĠA AA", - "ĠAA A", - "Ġ BOOK", - "ĠB OOK", - "ĠBO OK", - "к о", - "W STR", - "WS TR", - "Ġ */,Ċ", - "Ġ* /,Ċ", - "Ġ*/ ,Ċ", - "Ġ*/, Ċ", - "o yal", - "oy al", - "oya l", - ". vector", - ".v ector", - ".vec tor", - "Ġ SPEC", - "ĠS PEC", - "ĠSP EC", - "ĠSPE C", - "S SF", - "SS F", - "Ġcomp uls", - "ĠAppe als", - "ĠAppeal s", - "ĠW inston", - "ĠWin ston", - "ĠWins ton", - "ĠMock ito", - "con trib", - "cont rib", - "contr ib", - "contri b", - ". available", - ".a vailable", - ".av ailable", - "entity Manager", - "a rias", - "ar ias", - "ari as", - "aria s", - "_ sale", - "_s ale", - "_sal e", - "_sa le", - "_ rs", - "_r s", - "Ġde coding", - "Ġdec oding", - "Ġdeco ding", - "Ġ locator", - "Ġl ocator", - "Ġloc ator", - "ol ith", - "oli th", - "olit h", - "Ġ kol", - "Ġk ol", - "Ġko l", - "Ġ ascii", - "Ġasc ii", - "ĠR ut", - "ĠRu t", - "/ interface", - "ĉ ĉĉĉĉĉĠĠĠ", - "ĉĉ ĉĉĉĉĠĠĠ", - "ĉĉĉĉ ĉĉĠĠĠ", - "ĉĉĉ ĉĉĉĠĠĠ", - "ĉĉĉĉĉ ĉĠĠĠ", - "ĉĉĉĉĉĉ ĠĠĠ", - "ĉĉĉĉĉĉĠ ĠĠ", - "ĉĉĉĉĉĉĠĠ Ġ", - "Ġ Numer", - "ĠN umer", - "ĠNum er", - "ĠNu mer", - ". flip", - ".f lip", - ".fl ip", - "- del", - "-d el", - "-de l", - "Ġbol ster", - "Ġbols ter", - "on omic", - "ono mic", - "onom ic", - "Ġ zm", - "Ġz m", - "L G", - "Find By", - "Ġ adaptive", - "Ġad aptive", - "Ġadapt ive", - "Ġada ptive", - "l oo", - "lo o", - "Ġ vue", - "Ġv ue", - "Ġvu e", - "( reverse", - "(re verse", - "_ canvas", - "_c anvas", - "_can vas", - ". roles", - ".r oles", - ".role s", - ".ro les", - "ific ado", - "ifica do", - "ven ient", - "\" As", - "\"A s", - "Ġ Entr", - "ĠEn tr", - "ĠEnt r", - "al igned", - "align ed", - "Ġbere its", - "/ //ĊĊ", - "// /ĊĊ", - "/// ĊĊ", - "///Ċ Ċ", - ".g wt", - ". employee", - ".e mployee", - "_ cli", - "_c li", - "_cl i", - "Ġanticip ate", - "éĻ IJ", - "Ġp ik", - "Ġpi k", - "Ġmush rooms", - "Ġmushroom s", - "( tt", - "(t t", - "Ġ oma", - "Ġo ma", - "Ġom a", - "ĠSan chez", - "_ google", - "_g oogle", - "_go ogle", - ". Valid", - ".Val id", - "Ġ FileName", - "ĠFile Name", - "iv ative", - "k ed", - "ke d", - "- war", - "-w ar", - "Ġm aturity", - "Ġmat urity", - "и д", - "Ġ miner", - "Ġm iner", - "Ġmin er", - "Ġmi ner", - "Ġmine r", - "Re ducers", - "Reduc ers", - "Reducer s", - "Reduce rs", - "Ġ LatLng", - "ĠL atLng", - "ĠLat Lng", - "_ STD", - "_S TD", - "_ST D", - "D igits", - "Digit s", - "Dig its", - "C alc", - "Cal c", - "Ca lc", - "- upload", - "-up load", - "Ġhand ic", - "Ġhan dic", - "ี à¹Ī", - "eg rated", - "egr ated", - "egrate d", - "egra ted", - "Ġ STM", - "ĠS TM", - "ĠST M", - "C lients", - "Client s", - "Cl ients", - "Cli ents", - "ĠTur bo", - "S YNC", - "SY NC", - "Ġphot ographers", - "Ġphotograph ers", - "Ġphotographer s", - ". Out", - ".O ut", - ". character", - ".char acter", - "B UILD", - "BU ILD", - ". unlock", - ".un lock", - "Ġar ises", - "Ġarise s", - "Ġ Commands", - "ĠComm ands", - "ĠCommand s", - "(\" \");čĊ", - "(\"\" );čĊ", - "(\"\") ;čĊ", - "(\"\"); čĊ", - "_ FORE", - "_F ORE", - "_FOR E", - "; ',", - ";' ,", - "+ \"'", - "+\" '", - ". Images", - ".Image s", - ".Im ages", - "\" ){", - "\") {", - "ĠM eyer", - "ĠMe yer", - "ĠMey er", - "Ġneg atively", - "Ġnegative ly", - "Ġ DLL", - "ĠD LL", - "ĠDL L", - "Ġ exe", - "Ġe xe", - "Ġex e", - "Ġdef iciency", - "Ġwild ly", - "- switch", - "-s witch", - "-sw itch", - "con struction", - "construct ion", - "Ġexception ally", - "Ġexceptional ly", - "ĠL iz", - "ĠLi z", - "/ java", - "/j ava", - "/jav a", - "Ġthe irs", - "Ġtheir s", - "ĠCon temporary", - "ĠCont emporary", - "l is", - "li s", - ".fill Rect", - "ĠN FC", - "ĠNF C", - "Ġre he", - "Ġreh e", - "( numbers", - "(num bers", - "(number s", - "Ġr aster", - "Ġra ster", - "Ġras ter", - "Ġrast er", - "Ġfig uring", - "Ġfigur ing", - "Ġshow c", - "Ġsho wc", - "ĠJ ill", - "ĠJi ll", - "Ġar cade", - "Ġarc ade", - "ĠConstruct s", - "m dl", - "md l", - "( '|", - "(' |", - "Ġident ifiers", - "Ġidentifier s", - "Ġ stellar", - "Ġst ellar", - "( Connection", - "Ġ\" {{", - "Ġ\"{ {", - "y or", - "yo r", - "( mysqli", - "(m ysqli", - "(mysql i", - "Ġd ove", - "Ġdo ve", - "Ġdov e", - "Of Birth", - ". disconnect", - ".dis connect", - "_ hi", - "_h i", - "Ġzw ischen", - "ĠGr und", - "i ros", - "ir os", - "iro s", - "_ Array", - "_A rray", - ". onclick", - ".on click", - "an som", - "ans om", - "An swers", - "Answer s", - "Ans wers", - "ĉ remove", - "ĉre move", - "F a", - "Ġh urry", - "Ġhur ry", - "- inf", - "-in f", - "-i nf", - "Ġ getClass", - "Ġget Class", - "ĠgetC lass", - "ĠReg ulation", - "Ġ FLAGS", - "ĠFLAG S", - "m isc", - "mi sc", - "mis c", - "K en", - "Ke n", - "_ heading", - "_head ing", - "_he ading", - "G Hz", - "GH z", - "- entry", - "-en try", - "Ġbi ography", - "S ig", - "Si g", - "- mf", - "-m f", - "W atcher", - "Watch er", - "Wat cher", - "âĢľ A", - "} px", - "Ġsp icy", - "Ġspi cy", - "_ sq", - "_s q", - "L ost", - "Lo st", - "Los t", - "( track", - "(t rack", - "(tr ack", - "а ли", - "ал и", - "Desc ending", - "< bits", - " ((", - ">( (", - "s urvey", - "sur vey", - "Ġ íĺ", - "Ġí ĺ", - ".. .')Ċ", - "... ')Ċ", - "...' )Ċ", - "Ġ Divider", - "ĠDi vider", - "ĠDiv ider", - "ĠDivide r", - "o sl", - "os l", - "_ CANCEL", - "_C ANCEL", - "_CAN CEL", - "_ prepare", - "_pre pare", - "_prep are", - "s tin", - "st in", - "sti n", - "ĠHe ath", - "ĠHeat h", - ". PrimaryKey", - ".Primary Key", - "Ġ âĨIJ", - "ĠâĨ IJ", - "ĠLocal DateTime", - "ĠLocalDate Time", - "Ġco operative", - "Ġcooper ative", - "L earning", - "Le arning", - "Learn ing", - ". enqueue", - ".en queue", - "Ġ goog", - "Ġg oog", - "Ġgo og", - "Ġgoo g", - "Ġ Regression", - "ĠRe gression", - "ĠReg ression", - "i mates", - "im ates", - "imate s", - "ima tes", - "imat es", - "Ġvoy eur", - "Ġ Drink", - "ĠD rink", - "ĠDr ink", - "p lug", - "pl ug", - "Ġl ender", - "Ġle nder", - "Ġlen der", - "Ġlend er", - "m ana", - "man a", - "ma na", - "Ġperson nes", - "Ġpersonne s", - "Ġpersonn es", - "yp se", - "yps e", - "Ġ unlink", - "Ġun link", - "Ġunl ink", - "ĠRa vens", - "ĠRav ens", - "ĠRaven s", - "Ġh urd", - "Ġhur d", - "Ġhu rd", - "Ġperiod ically", - "Ġperiodic ally", - "AR GS", - "ARG S", - "Ġ GH", - "ĠG H", - "char acters", - "character s", - ".. .\"ĊĊ", - "... \"ĊĊ", - "...\" ĊĊ", - "...\"Ċ Ċ", - "- establish", - "Ġ dn", - "Ġd n", - "( condition", - "(con dition", - "(cond ition", - "Ġ Gravity", - "ĠGr avity", - "Ġes tas", - "Ġest as", - "Ġesta s", - "_ focus", - "_f ocus", - "C reature", - "Cre ature", - "Cr eature", - "Creat ure", - "( site", - "(s ite", - "(si te", - "Ġc arr", - "Ġcar r", - "Ġca rr", - "Ġ RL", - "ĠR L", - "Ġ RI", - "ĠR I", - "ĠM oto", - "ĠMo to", - "ĠMot o", - "A SF", - "AS F", - "Ġ Luckily", - "ĠLuck ily", - "ĉ Route", - "ĉR oute", - "Ġ entropy", - "Ġent ropy", - "Ġentr opy", - "( \",\"", - "(\" ,\"", - "(\", \"", - "C ollect", - "Col lect", - "Coll ect", - "( contact", - "(cont act", - "ĠFlor ence", - "ĠFlo rence", - "Ġpremium s", - "Ġpremi ums", - "Ġl ifecycle", - "Ġlife cycle", - "Ġlif ecycle", - "Ġb ans", - "Ġban s", - "Ġba ns", - "x ef", - "xe f", - "Web Kit", - "Ġ Floating", - "ĠF loating", - "ĠFloat ing", - "ĠFlo ating", - "Ġ cosa", - "Ġc osa", - "Ġco sa", - "Ġcos a", - "S pecific", - "Spec ific", - "ĠLo ans", - "ĠLoan s", - "b read", - "br ead", - "bre ad", - "Ġdes criptors", - "Ġdescriptor s", - "Ġ{ :.", - "Ġ{: .", - "TH READ", - "ĠT rent", - "ĠTr ent", - "ĠTre nt", - "Ġs cop", - "Ġsc op", - "Ġsco p", - "Q A", - "ĠAn tar", - "ĠAnt ar", - "p el", - "pe l", - "_ difference", - "_d ifference", - "_diff erence", - "_ changes", - "_ch anges", - "_change s", - "_chan ges", - "( ...)", - "(... )", - "(.. .)", - "Ġ Rotation", - "ĠR otation", - "ĠRot ation", - "ĠL GPL", - "ĠLG PL", - "Ġ JUST", - "ĠJ UST", - "( Task", - "(T ask", - "_ subset", - "_sub set", - "_subs et", - "Ġ TRANS", - "ĠTR ANS", - "ĠTRAN S", - "åĬ Ľ", - "ĠS cout", - "ĠSc out", - "ĠSco ut", - "- popup", - "-p opup", - "-pop up", - "Ġsm oked", - "Ġsmoke d", - "Ġsmo ked", - "_ Class", - "_C lass", - "_Cl ass", - "Ġturn over", - "Ġturno ver", - "br akk", - "bra kk", - "ĠRock y", - "ĠRo cky", - "ĠRoc ky", - "t as", - "ta s", - ".Regular Expressions", - "ĠElli ott", - "ĠElliot t", - "Ġ Spinner", - "ĠSp inner", - "ĠSpin ner", - "DUCT ION", - "DU CTION", - "Ġl ibre", - "Ġli bre", - "Ġlib re", - "Ġlibr e", - "Ġmol to", - "Ġmolt o", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠ", - "Ġ FTP", - "ĠF TP", - "ĠFT P", - "m peg", - "mp eg", - "( features", - "(f eatures", - "(feature s", - "(fe atures", - "Ġb ald", - "Ġbal d", - "Ġba ld", - "ĠV id", - "ĠVi d", - "Ġsh outing", - "Ġshout ing", - "Ġsho uting", - "L int", - "Li nt", - "Lin t", - "Ġ sockets", - "Ġs ockets", - "Ġsocket s", - "Ġsock ets", - "Ġp row", - "Ġpro w", - "Ġpr ow", - "Ġnou velle", - "Ġnouvel le", - "Ġnouve lle", - "is card", - "isc ard", - "ĠS ponsor", - "Ġ consulta", - "Ġcons ulta", - "Ġconsult a", - "Ġconsul ta", - ") ));", - ")) );", - "))) ;", - "Ind ian", - "India n", - "ĠR aspberry", - "Ġteam mate", - "Ġ JWT", - "ĠJ WT", - "ĠJW T", - "ĠG hana", - "ĠGh ana", - "Ġ cakes", - "Ġc akes", - "Ġca kes", - "Ġcake s", - "pr imer", - "prim er", - "prime r", - "pri mer", - "form a", - "for ma", - "erg arten", - "_ Manager", - "_M anager", - "_Man ager", - "Ġpre season", - "G AME", - "GA ME", - "| \"", - "ĠB rock", - "ĠBr ock", - "ĠBro ck", - "Ġocc upy", - "Ġoccup y", - "Ġdecor ations", - "Ġdecoration s", - "á nd", - "án d", - "Ġ cot", - "Ġc ot", - "Ġco t", - "Ġp aran", - "Ġpar an", - "Ġpara n", - "Ġpa ran", - "D isk", - "Dis k", - "Di sk", - "r emain", - "re main", - "rem ain", - "rema in", - "> ?", - "St rong", - "Str ong", - "Ġf rance", - "Ġfr ance", - "Ġfra nce", - "Ġfran ce", - "Ġfranc e", - "ĠE ra", - "ĠEr a", - "- cr", - "-c r", - ".Buffer edReader", - ".Buffered Reader", - "ĠParad ise", - "ĠV AT", - "ĠVA T", - "ĠAn ders", - "ĠAnd ers", - "Ġl imb", - "Ġli mb", - "Ġlim b", - "amp oo", - "ampo o", - "Ġimper ative", - "UT ILITY", - "UTIL ITY", - "Ġ Recognition", - "ĠRec ognition", - "ĠRecogn ition", - "Ġragaz ze", - "Ġp ops", - "Ġpop s", - "Ġpo ps", - "y press", - "yp ress", - "Ġemb argo", - "Ġembar go", - "// {Ċ", - "Ġs yll", - "Ġsy ll", - "P TR", - "PT R", - "åŃĺ åľ¨", - "Ġdid nt", - "Ġdidn t", - "M ailer", - "Mail er", - "Ma iler", - "Ġacad emics", - "Ġacademic s", - "ĠFr auen", - "ĠFra uen", - "ĠFrau en", - "ne ider", - "- rel", - "-r el", - "-re l", - "Ġrain bow", - "( In", - "(I n", - "Ġs liced", - "Ġsl iced", - "Ġslice d", - "Ġslic ed", - "= ============Ċ", - "== ===========Ċ", - "==== =========Ċ", - "======== =====Ċ", - "=== ==========Ċ", - "============ =Ċ", - "============= Ċ", - "=========== ==Ċ", - "========= ====Ċ", - "========== ===Ċ", - "====== =======Ċ", - "===== ========Ċ", - "======= ======Ċ", - "( send", - "(s end", - "(se nd", - "NSMutable Dictionary", - "v os", - "vo s", - "( package", - "(p ackage", - "(pack age", - "Ġord inance", - "Ġordin ance", - "view er", - "vie wer", - "ĠSan tos", - "ĠSant os", - "ĠSanto s", - "- selling", - "-s elling", - "Ġ gov", - "Ġg ov", - "Ġgo v", - "et tle", - "ett le", - "Ġfound ers", - "Ġfo unders", - "Ġfounder s", - "Ġw aking", - "Ġwa king", - "s lashes", - "sl ashes", - "slash es", - "-p ound", - "-po und", - "re cht", - "rec ht", - "rech t", - "ا ت", - "Ø§Ø ª", - ". onClick", - ".on Click", - "Ġn ord", - "Ġno rd", - "Ġnor d", - "st änd", - "_ when", - "_w hen", - "_wh en", - "U TERS", - "UT ERS", - "UTE RS", - "i cc", - "ic c", - "Ġcaps ule", - "ĠW id", - "ĠWi d", - "M arc", - "Mar c", - "Ma rc", - "ภ¸", - "r ored", - "ro red", - "ror ed", - "U GE", - "UG E", - "LO UD", - "Ġ Audit", - "ĠA udit", - "ĠAud it", - "ĠAu dit", - "ĠAudi t", - "ip ients", - "ipient s", - "ipi ents", - "op ian", - "opia n", - "opi an", - "ĠS ue", - "ĠSu e", - "Ġwur den", - "Ġwurde n", - ". Helpers", - ".H elpers", - ".Helper s", - ".Help ers", - "Ġf actions", - "Ġfact ions", - "Ġfa ctions", - "Ġfaction s", - "[ np", - "[n p", - "- than", - "-t han", - "-th an", - "Ġre co", - "Ġr eco", - "Ġrec o", - "Ġ kas", - "Ġk as", - "Ġka s", - "Ġ cmds", - "Ġcmd s", - "Ġcm ds", - "/ network", - "/n etwork", - "/net work", - "x bf", - "xb f", - "get Color", - "getC olor", - "Ġ biased", - "Ġbi ased", - "Ġbias ed", - "ĠL ak", - "ĠLa k", - "D atas", - "Data s", - "Da tas", - "Dat as", - "v ents", - "ve nts", - "vent s", - "ven ts", - "Ġ ë²", - "Ġë ²", - "_ PS", - "_P S", - ". Validate", - ".Valid ate", - "Inv oker", - "Invoke r", - "Ġne uen", - "Ġneu en", - "Ġneue n", - "Ġju venile", - "Ġjuven ile", - "V ISION", - "VI SION", - "VIS ION", - "Ġde vote", - "Ġdev ote", - "Ġ linha", - "Ġl inha", - "Ġlin ha", - "Ġlinh a", - "Ġdiscount ed", - "Ġdisco unted", - "\\ Config", - "Ġworth while", - "Ġskin ny", - "Ġ Courses", - "ĠC ourses", - "ĠCo urses", - "ĠCour ses", - "ĠCourse s", - "le ys", - "ley s", - "ĠMort gage", - "K evin", - "Ke vin", - "Ġann ounces", - "Ġannounc es", - "Ġannounce s", - "] )*", - "]) *", - "res ervation", - "Ġ æķ°", - "Ġæķ °", - "Ġprejud ice", - "ĠString Comparison", - "Ġbe ard", - "Ġbear d", - "- win", - "-w in", - "ĠS ão", - "ĉ ms", - "ĉm s", - "j al", - "ja l", - "Ġ Earn", - "ĠE arn", - "ĠEar n", - "ĠEa rn", - "_ ports", - "_p orts", - "_port s", - "_po rts", - "_por ts", - "Ġ Nombre", - "ĠN ombre", - "ĠNom bre", - "_ COR", - "_C OR", - "_CO R", - "Ġ BUILD", - "ĠB UILD", - "ĠBU ILD", - ". sound", - ".s ound", - ".so und", - "Y ellow", - "Ġlineback er", - "Ġchar itable", - "Ġcha ritable", - "j ug", - "ju g", - "_NON NULL", - "ĠD ental", - "ĠDen tal", - "ĠDent al", - "\" >${", - "\"> ${", - "\">$ {", - "ĉ match", - "ĉm atch", - "ĉmat ch", - "R ussian", - "Russia n", - "Russ ian", - "Rus sian", - "Ġver sch", - "Ġvers ch", - "Ġp inned", - "Ġpin ned", - "Ġadopt ing", - "Options Menu", - "P ag", - "Pa g", - "Ġpair ing", - "Ġpa iring", - "Ġpai ring", - "Ġt read", - "Ġtr ead", - "Ġtre ad", - "erc ises", - "ercise s", - "Ġ Spread", - "ĠS pread", - "ĠSp read", - "ĠSpr ead", - ") i", - "Ġ BAD", - "ĠB AD", - "ĠBA D", - "_ tf", - "_t f", - "UI ImageView", - "UIImage View", - "pop ulate", - "b ab", - "ba b", - "Ġ Ïĥ", - "ĠÏ ĥ", - "[ ++", - "Ġopi oid", - "Ġ ##Ċ", - "Ġ# #Ċ", - "Ġ## Ċ", - "d type", - "dt ype", - "ĠSt arts", - "ĠStart s", - "ĠStar ts", - "ĠSta rts", - "(' /')", - "('/ ')", - "Ġperson als", - "Ġpersonal s", - "Ġpersona ls", - "- market", - "-m arket", - "-mark et", - "-mar ket", - "Ġredund ant", - "ĠEss ential", - "Ġsc rapy", - "Ġscr apy", - "Ġscrap y", - "Ġ им", - "Ġи м", - "a cl", - "ac l", - "Ġ crear", - "Ġc rear", - "Ġcr ear", - "Ġcre ar", - "Ġcrea r", - "ĠB end", - "ĠBe nd", - "ĠBen d", - "Ġrel ieve", - "Ġreli eve", - "Ġrelie ve", - "- room", - "-r oom", - "-ro om", - "w ife", - "wi fe", - "Ġv Ãł", - "ĠQ Point", - "Ġqu asi", - "Ġqua si", - "Ġ methodName", - "Ġmethod Name", - "\\ xc", - "\\x c", - "ĠP eru", - "ĠPer u", - "ĠPe ru", - "/ The", - "/T he", - ". orm", - ".o rm", - ".or m", - "Ġ viz", - "Ġv iz", - "Ġvi z", - "/ pdf", - "/p df", - "Loc ated", - "Ġconfront ation", - "ĠChampionship s", - "ĠChampions hips", - "ĠChampion ships", - "Ġhy pert", - "Ġhyp ert", - "Ġhyper t", - "Ġhype rt", - "Ġ dj", - "Ġd j", - "Ġ UserInfo", - "ĠUser Info", - "Ġ åĪĽå»º", - "ĠåĪ Ľå»º", - "\\ xb", - "\\x b", - "( sim", - "(s im", - "(si m", - "Ġ ==Ċ", - "Ġ= =Ċ", - "Ġ== Ċ", - "Ġst aging", - "Ġsta ging", - "Ġstag ing", - "Ġdr astically", - "Ġdrastic ally", - "åŃ ¦", - "l ords", - "lor ds", - "lord s", - ". less", - ".l ess", - ".le ss", - "вед иÑĤе", - "Ġ Bucket", - "ĠB ucket", - "ĠBuck et", - "ĠBu cket", - "ĠM am", - "ĠMa m", - ". term", - ".t erm", - ".te rm", - "_ pi", - "_p i", - "c zy", - "cz y", - ". pub", - ".p ub", - "p recio", - "pre cio", - "prec io", - "preci o", - "ĠV irt", - "ĠVir t", - "ĠVi rt", - "Ġ roman", - "Ġr oman", - "Ġro man", - "Ġrom an", - "Ġroma n", - "i tat", - "it at", - "ita t", - "L ex", - "Le x", - "_ infos", - "_in fos", - "_info s", - "_inf os", - "Ä °", - ". other", - ".o ther", - ".ot her", - "VE LO", - "VEL O", - "Ġ ponder", - "Ġp onder", - "Ġpo nder", - "Ġpon der", - "Ġpond er", - "Ġh anno", - "Ġhan no", - "Ġhann o", - "( Page", - "(P age", - "d oi", - "do i", - "Ġpol ite", - "Ġpo lite", - "Ġpolit e", - "Ġprogram mer", - "Ġprogramme r", - "Ġprogramm er", - "D ies", - "Die s", - "Di es", - "$ d", - "Ġre plication", - "Ġrep lication", - "Ġrepl ication", - "Ġreplic ation", - "Ġreplica tion", - "add Column", - "fr ican", - "frica n", - "Ġl eng", - "Ġle ng", - "Ġlen g", - "b eer", - "be er", - "bee r", - "o it", - "oi t", - "Ġw asting", - "Ġwas ting", - "Ġwast ing", - "y lim", - "yl im", - "me asure", - "N eg", - "Ne g", - "Ġpart ie", - "Ġpar tie", - "Ġparti e", - ". console", - ".con sole", - ".cons ole", - "ĠGu inea", - "ĠGui nea", - "T EL", - "TE L", - "_ fact", - "_f act", - "_fac t", - "_fa ct", - ". chunk", - ".ch unk", - "Ġl ent", - "Ġle nt", - "Ġlen t", - "Ġ aller", - "Ġa ller", - "Ġal ler", - "Ġall er", - "Ġalle r", - "Ġ à¤ķ", - "Ġठķ", - "_ idle", - "_id le", - "_i dle", - "Ġad missions", - "Ġadm issions", - "Ġadmission s", - "JSON Array", - "Ġv ibration", - "Ġvibr ation", - "Ġvib ration", - ". helpers", - ".h elpers", - ".helper s", - ".help ers", - "å¤ ĸ", - "Ġ hen", - "Ġh en", - "Ġhe n", - "j ohn", - "jo hn", - "Ġ ìĥĿ", - "Ġì ĥĿ", - "Ġìĥ Ŀ", - "Ġjud gement", - "Ġjudge ment", - "Ġg een", - "Ġge en", - "Ġgee n", - "t erra", - "ter ra", - "terr a", - "^ {", - "ĠI z", - "Ġc â", - "in stances", - "instance s", - "inst ances", - "instanc es", - "Ġthreat ens", - "Ġthreaten s", - "Ġm üssen", - "Kind OfClass", - "Ġstoryt elling", - "_ demo", - "_d emo", - "_de mo", - "_dem o", - "r ias", - "ri as", - "ria s", - "Priv acy", - "h ift", - "hi ft", - "ĠY i", - "es or", - "eso r", - "íķ ł", - "ens itivity", - ". Writer", - ".W riter", - ".Write r", - "ภĤ", - "D istrict", - "Di strict", - ".get JSONObject", - "Im pro", - "Imp ro", - "(get Resources", - "Ġ SPELL", - "ĠS PELL", - "ĠSP ELL", - "ĠSPE LL", - "ro duce", - "rodu ce", - "rod uce", - "Ġsl owed", - "Ġslow ed", - "Ġslo wed", - "Ġ linewidth", - "Ġline width", - "Ġlin ewidth", - "Ġhon esty", - "Ġhonest y", - "Ġho nesty", - "Ġhone sty", - "Ġ Coord", - "ĠC oord", - "ĠCo ord", - "ĠF ork", - "ĠFor k", - "ĠFo rk", - "ĠDispatch Queue", - "ĠCl iff", - "ĠCli ff", - "ĠW iring", - "ĠWi ring", - "ĠWir ing", - "_TIM ESTAMP", - "ol lah", - "oll ah", - "olla h", - "a void", - "av oid", - "avo id", - "++ ];Ċ", - "++] ;Ċ", - "sem antic", - "- css", - "-c ss", - "Ġv eto", - "Ġve to", - "Ġvet o", - "ĠM err", - "ĠMe rr", - "ĠMer r", - "Ġlegisl ators", - "C EEDED", - "CEE DED", - "CEED ED", - "Ġquestion naire", - "ĠP ills", - "ĠPill s", - "ĠPil ls", - "C alculate", - "Cal culate", - "Calc ulate", - "Calcul ate", - "( core", - "(c ore", - "(co re", - "(cor e", - "' e", - "Ġdis like", - "Ġ Preferences", - "ĠP references", - "ĠPre ferences", - "ĠPreference s", - "ĠPrefer ences", - "_ EXTERNAL", - "_EX TERNAL", - "_EXTERN AL", - "è° ĥ", - "Ġd odge", - "Ġdo dge", - "Ġdod ge", - "æľį åĬ¡", - ". names", - ".n ames", - ".name s", - ".draw Image", - "_ prom", - "_p rom", - "_pro m", - "_pr om", - "uck land", - "Ġ<$ >", - "ı z", - "/ site", - "/s ite", - "é¡ ¹", - "r ophe", - "ro phe", - "rop he", - "roph e", - "Ġcomp elled", - "Ġcompel led", - "Ġl aptops", - "Ġlaptop s", - "Ġ uni", - "Ġu ni", - "Ġun i", - "C LOSE", - "CL OSE", - "Ġcasual ties", - "Ġ Uniform", - "ĠUn iform", - "ĠUni form", - "Term inal", - ". \",\"", - ".\" ,\"", - ".\", \"", - "D AT", - "DA T", - "( TreeNode", - "(T reeNode", - "(Tree Node", - "ĠGand hi", - "( stmt", - "(st mt", - "A XB", - "AX B", - "* M", - "Ġumb rella", - "an imal", - "ani mal", - "anim al", - "Ġ grpc", - "Ġg rpc", - "Ġgr pc", - "Ġgrp c", - "Ġwhere by", - "Ġfloat s", - "Ġflo ats", - "ĉ arg", - "ĉa rg", - "ĉar g", - "Ġ dbg", - "Ġd bg", - "Ġdb g", - "Ġexceed ing", - "Ġexce eding", - "Event Type", - ".SaveChanges Async", - "Ġ {{{", - "Ġ{ {{", - "Ġ{{ {", - "Ġ owed", - "Ġo wed", - "Ġow ed", - "Ġowe d", - "ahren heit", - "Ġ ì§", - "Ġì §", - "Ġequ ipo", - "Ġequip o", - "u rai", - "ur ai", - "ura i", - "Ġi dol", - "Ġid ol", - "] \")Ċ", - "]\" )Ċ", - "]\") Ċ", - "_ major", - "_m ajor", - "Ġentire ty", - "inger print", - "ç os", - "ço s", - "/ account", - "/a ccount", - "/ac count", - "ĉ right", - "ĉr ight", - "urs os", - "ĠE DT", - "ĠED T", - "_ INSERT", - "_INS ERT", - "Ġsh ining", - "Ġshin ing", - "Ġ< :", - "Edge Insets", - "Ġcolon ies", - ". IM", - ".I M", - "ĉ Ġĉ", - "ĉĠ ĉ", - "R OAD", - "RO AD", - "C CCC", - "CC CC", - "CCC C", - "pl acing", - "pla cing", - "Ġget Activity", - "em acs", - "ema cs", - "' %(", - "'% (", - ". clicked", - ".cl icked", - ".click ed", - "Ġ Them", - "ĠT hem", - "ĠThe m", - "ĠTh em", - "is ia", - "isi a", - "Bus car", - "Bu scar", - ". rename", - ".re name", - ".r ename", - "Ġo ath", - "Ġoat h", - "Ġoa th", - "Ġafter ward", - "ĠU FO", - "ĠUF O", - "A PS", - "AP S", - "ĠJackson ville", - ". some", - ".s ome", - ".so me", - "Conf irmed", - "Confirm ed", - ". scan", - ".s can", - ".sc an", - "ig Integer", - "Decor ator", - "sh ield", - "shi eld", - "ress ive", - ". did", - ".d id", - ".di d", - "请 è¾ĵåħ¥", - "Ġsh utter", - "Ġshut ter", - "D am", - "Da m", - "Ġpar enting", - "Ġparent ing", - "Ġparen ting", - "ey ed", - "eye d", - "$ item", - "$i tem", - "- develop", - "-de velop", - "-dev elop", - "-devel op", - "Ġex tracts", - "Ġextra cts", - "Ġextract s", - "Ġextr acts", - "Ġdecentral ized", - "ĠE lsa", - "ĠEl sa", - "_ spin", - "_s pin", - "_sp in", - "_spi n", - "] )+", - "]) +", - "- initial", - "-in itial", - "-init ial", - "Ġmult itude", - "Ġmultit ude", - "Ġsens ory", - "Ġsensor y", - "Ġ MODEL", - "ĠMO DEL", - "ĠMOD EL", - "ĠMODE L", - "Ġsaf eguard", - "Ġsafe guard", - "Ġsafeg uard", - "ì ¹", - "Ġhun ters", - "Ġhunt ers", - "Ġhunter s", - "Ġ Tiny", - "ĠT iny", - "ĠTi ny", - "ĠTin y", - "I NO", - "IN O", - "dec orate", - "decor ate", - "Ġ NoSuch", - "ĠNo Such", - "H o", - "( Response", - "Ġr uler", - "Ġrule r", - "Ġru ler", - "ĉ short", - "ĉs hort", - "ĉsh ort", - "Ġ caster", - "Ġc aster", - "Ġca ster", - "Ġcas ter", - "Ġcast er", - "Ġcaste r", - "Ġ clientId", - "Ġclient Id", - "Ġ pdb", - "Ġp db", - "Ġpd b", - "ëı Ħ", - "i tic", - "it ic", - "iti c", - "Ġ GameState", - "ĠGame State", - "Ġnew Item", - ") ĊĊĊĊĊĊ", - ")Ċ ĊĊĊĊĊ", - ")ĊĊ ĊĊĊĊ", - ")ĊĊĊ ĊĊĊ", - ")ĊĊĊĊ ĊĊ", - ")ĊĊĊĊĊ Ċ", - "o uis", - "ou is", - "oui s", - "n oc", - "no c", - ". BLACK", - ".BL ACK", - "_ VECTOR", - "_V ECTOR", - "_VEC TOR", - "_VE CTOR", - "---------- ();", - ">( );", - ">() ;", - ".get P", - "an ye", - "any e", - "Ġne uron", - "Ġneuro n", - "Ġneu ron", - "Ġneur on", - "i fold", - "if old", - "ifo ld", - "Ġ Known", - "ĠK nown", - "ĠKn own", - "ĠKnow n", - "Bit coin", - "Any way", - "ay ette", - "aye tte", - "ayet te", - "Ġ' ['", - "Ġ'[ '", - "Ãł nh", - "Ãłn h", - "m gr", - "mg r", - "Ġcor related", - "Ġcorre lated", - "Ġcorrel ated", - "Ġcorrelate d", - "Ġn ause", - "Ġna use", - "Ġnau se", - "Ġmental ity", - "Ġment ality", - "has Many", - "Ġ FG", - "ĠF G", - "am pie", - "amp ie", - "I TU", - "IT U", - "F s", - ". Sp", - ".S p", - "_ between", - "_b etween", - "_bet ween", - "Dep endencies", - "o ug", - "ou g", - "Place holder", - "= text", - "=t ext", - "Ġ Managing", - "ĠMan aging", - "ĠMana ging", - "ocal ypse", - "åĮ Ĺ", - "_ mag", - "_m ag", - "_ma g", - "f ld", - "fl d", - "â ij", - "C AM", - "CA M", - "Ġ Helpers", - "ĠH elpers", - "ĠHelp ers", - "ĠHelper s", - "ĠHel pers", - "Ġd ost", - "Ġdo st", - "Ġdos t", - "/ out", - "/o ut", - "Ġassass ination", - "Ġassassin ation", - ". getImage", - ".get Image", - "ĠK enny", - "ĠKen ny", - "ĠKenn y", - ". ')ĊĊ", - ".' )ĊĊ", - ".')Ċ Ċ", - ".') ĊĊ", - ") {//", - "){ //", - "ĠR anger", - "ĠRange r", - "ĠRa nger", - "ĠRan ger", - "Ġg ek", - "Ġge k", - "Ġsince re", - "Ġsinc ere", - "Ġsincer e", - "< Value", - "čĊ", - "/> čĊ", - ".get Resources", - ".getResource s", - "Ġl ump", - "Ġlu mp", - "Ġlum p", - "_ consts", - "_con sts", - "_const s", - "_cons ts", - "( ext", - "(e xt", - "(ex t", - "ĉ dir", - "ĉd ir", - "â Ŀ", - "Ġpadding Top", - "Ġob session", - "Ġobs ession", - "Ġobsess ion", - "Ġb anning", - "Ġban ning", - "ĠApp Module", - "Ġp artisan", - "Ġpart isan", - "Ġparti san", - "Ġcatalog ue", - "Ġcata logue", - "Ġcatal ogue", - "Ġmin ors", - "Ġminor s", - "Ġp itches", - "Ġpitch es", - "Ġpit ches", - "we ep", - "Ġunder take", - "Ġundert ake", - "Ġth emed", - "Ġthe med", - "Ġthem ed", - "Ġtheme d", - "a udit", - "au dit", - "aud it", - "audi t", - ". scrollTop", - ".scroll Top", - ".scrollTo p", - "Ġ rer", - "Ġre r", - "Ġr er", - "Ġsym ptom", - "Ġsympt om", - "Ġsymp tom", - "Ġopen ings", - "Ġopening s", - ". blocks", - ".b locks", - ".bl ocks", - ".block s", - "open id", - "ope nid", - "Ġa ssh", - "Ġas sh", - "Ġass h", - "- save", - "-s ave", - "ĠP ig", - "ĠPi g", - "Ġre gain", - "Ġreg ain", - "Ġin icial", - "Ġini cial", - "Ġinici al", - "/ favicon", - "/f avicon", - "ĉ exp", - "ĉe xp", - "ĉex p", - "Ġsp ices", - "Ġspi ces", - "Ġspice s", - "i ska", - "is ka", - "isk a", - "cl aims", - "claim s", - "cla ims", - "m ak", - "ma k", - "definition s", - "Ġcorrespond ent", - "ĠCann abis", - "_ _,Ċ", - "__ ,Ċ", - "__, Ċ", - "ĠL ucky", - "ĠLuc ky", - "ĠLu cky", - "ĠLuck y", - "ĠG aussian", - "ĠGa ussian", - "ĠGauss ian", - "Ġ Nearly", - "ĠN early", - "ĠNear ly", - "C AD", - "CA D", - "' ]]Ċ", - "'] ]Ċ", - "']] Ċ", - "Ġadequate ly", - "Ġadequ ately", - "Ġ TITLE", - "ĠT ITLE", - "constitution al", - "- mm", - "-m m", - "_ override", - "_over ride", - "Ġ blas", - "Ġb las", - "Ġbl as", - "Ġbla s", - ".ready State", - "Ġrem inis", - "Ġremin is", - "Ġrein forced", - "Ġreinforce d", - "ĠColl abor", - "Ġdecor ating", - "Ġdeco rating", - "Ġb achelor", - "Ġbach elor", - "ERRU PT", - "Ġup right", - "ip ation", - "ipa tion", - "ĠN oble", - "ĠNo ble", - "ĠNob le", - "Ġvalue ForKey", - "Ġset Loading", - ". Ignore", - ".I gnore", - "å ģ", - "G lobals", - "Global s", - "ĠM ent", - "ĠMe nt", - "ĠMen t", - "AS SES", - "ASS ES", - "Ġlim bs", - "Ġlimb s", - "Ġ HUD", - "ĠH UD", - "in ci", - "inc i", - ". iv", - ".i v", - "ĠQ ModelIndex", - "F use", - "Fu se", - "Ġpe dal", - "Ġped al", - "_F REQ", - "_FR EQ", - "_FRE Q", - "( verbose", - "(ver bose", - "Ġlong itud", - "ĠCh arter", - "ĠChar ter", - "ĠChart er", - "ê ·¸", - "ê· ¸", - "Ġ bundles", - "Ġb undles", - "Ġbund les", - "Ġbundle s", - ". ignore", - ".i gnore", - "um bo", - "umb o", - "E MA", - "EM A", - ". ......", - ".. .....", - "... ....", - ".... ...", - "..... ..", - "...... .", - "s x", - ". Card", - ".C ard", - ".Car d", - "Ġhe ute", - "Ġst eer", - "Ġste er", - "j umlah", - "Ġ {_", - "Ġ{ _", - "_ Checked", - "_Check ed", - "Ġ fax", - "Ġf ax", - "Ġfa x", - "ĠG ust", - "ĠGu st", - "ĠGus t", - "itch ens", - "itchen s", - "Ġ ))ĊĊ", - "Ġ) )ĊĊ", - "Ġ)) ĊĊ", - "Ġ))Ċ Ċ", - "Ġremark ably", - "/ XML", - "/X ML", - "- remove", - "-re move", - "_ bt", - "_b t", - "Ġinc ub", - ". package", - ".p ackage", - ".pack age", - ".current Thread", - "ĠHigh lander", - "ĠHighland er", - ". side", - ".s ide", - ".sid e", - ".si de", - "s plash", - "sp lash", - "spl ash", - "Ġ ici", - "Ġi ci", - "Ġic i", - "= D", - "Ġp uck", - "Ġpu ck", - "Ġball ots", - "Ġbal lots", - "Ġballot s", - "Ġballo ts", - "Ġhuge ly", - "Ġhug ely", - "c oeff", - "co eff", - "coef f", - "coe ff", - "Ġ pData", - "Ġp Data", - ". COLUMN", - ".C OLUMN", - "ĠHe aling", - "ĠHeal ing", - "Ġ ordin", - "Ġor din", - "Ġord in", - "! ),", - "!) ,", - "Ġ' ',čĊ", - "Ġ'' ,čĊ", - "Ġ'', čĊ", - "( md", - "(m d", - "ĠS ask", - "ĠSa sk", - "ĠSas k", - "< strong", - "Ġsurv ivor", - "Ġsurviv or", - ". series", - ".s eries", - ".se ries", - ".ser ies", - "Ġcaffe ine", - "Ġ `(", - "Ġ` (", - ".TRA ILING", - "_ Input", - "_In put", - "( \"^", - "(\" ^", - "z d", - "& );Ċ", - "&) ;Ċ", - "Ġ Ping", - "ĠP ing", - "ĠPin g", - "ĠPi ng", - "Ġ voucher", - "Ġv oucher", - "Ġvo ucher", - "Ġvou cher", - ". rating", - ".r ating", - ".ra ting", - "-sh irts", - "-shirt s", - "ĠRetrie ves", - "ĠRetrieve s", - ".al ibaba", - "Or acle", - "_ MOV", - "_M OV", - "_MO V", - "Old Data", - "Ġ /*čĊ", - "Ġ/ *čĊ", - "Ġ/* čĊ", - "Ġ gboolean", - "Ġg boolean", - "Ġ= >čĊ", - "Ġ=> čĊ", - "Ġ rá", - "Ġr á", - "Ġbl unt", - "ĠImage Icon", - "i fik", - "if ik", - "ifi k", - "R TC", - "RT C", - "Ġfi bers", - "Ġfib ers", - "Ġfiber s", - "Ġto ile", - "Ġtoi le", - ". sent", - ".s ent", - ".se nt", - "ĠPy Qt", - "$ app", - "$a pp", - "Ġm edio", - "Ġme dio", - "Ġmed io", - "Ġmedi o", - "Ġgrant ing", - "Ġgran ting", - "Ġts lint", - "Ġtsl int", - "ĠM ö", - "(fig size", - "Ġhur ricane", - "Ġl ifes", - "Ġlife s", - "Ġlif es", - "Ġ ÃĦ", - "Ġà Ħ", - "rocess ing", - "_ standard", - "_st andard", - "_stand ard", - "- option", - "-o ption", - "-op tion", - "-opt ion", - "' )))", - "') ))", - "')) )", - "Ġvac ant", - "Ġva cant", - "å· ¥", - "ĠH ollow", - "ĠHol low", - "ĠHoll ow", - "handle Change", - "Ġ divider", - "Ġdi vider", - "Ġdiv ider", - "Ġdivide r", - "Ġdivid er", - "ĠEngine ers", - "ĠEngineer s", - "Ġs vens", - "Ġsv ens", - "Ġsve ns", - "Ġcom pliant", - "Ġcompl iant", - "t anggal", - "Ġ Credits", - "ĠC redits", - "ĠCredit s", - "ĠEm irates", - "Rule Context", - "Ġreal ization", - "Ġrealiz ation", - "Ġrealiza tion", - "Ġdis tracted", - "Ġdistr acted", - "Ġdistract ed", - "] +=", - "]+ =", - "Ġau gment", - "Ġaug ment", - "ĠD w", - "o tp", - "ot p", - "or rent", - "orr ent", - "orre nt", - "Ed itar", - "Edit ar", - ". stock", - ".st ock", - "St udy", - "p ections", - "pe ctions", - "pect ions", - "pection s", - "Ġ GameManager", - "ĠGame Manager", - "= cut", - "=c ut", - "Ġf lock", - "Ġfl ock", - "Ġflo ck", - "ĠRom ans", - "ĠRo mans", - "ĠRoman s", - "ĠRoma ns", - "t hem", - "th em", - "the m", - "- hop", - "-h op", - "Ġscreen shots", - "Ġscreens hots", - "Ġscreenshot s", - "Ġ /*!Ċ", - "Ġ/* !Ċ", - "Ġ/*! Ċ", - "Ġcon versions", - "Ġconv ersions", - "Ġconvers ions", - "Ġconversion s", - "Ġnormal ization", - "( configuration", - "(config uration", - "Ġa eros", - "Ġaer os", - "Ġae ros", - "_ security", - "_s ecurity", - "_se curity", - "_sec urity", - "! 'Ċ", - "!' Ċ", - "B onus", - "Bon us", - "ĠDR IVER", - "ĠDRIVE R", - "ĉ Date", - "ĉD ate", - "t ie", - "ti e", - "ĠWy oming", - "St and", - "Stan d", - "i tre", - "it re", - "itr e", - "Ġsh oppers", - "Ġshop pers", - "Ġsho ppers", - "Ġshopper s", - "Ġdisadv antage", - "Ġl iking", - "Ġli king", - "Ġlik ing", - "ç¬ ij", - "Ġunderstand able", - "S EE", - "SE E", - "Ġh oy", - "Ġho y", - "Ġnine te", - "Ġni nete", - "Ġnin ete", - "Ġcon fer", - "Ġconf er", - "Ġ nowrap", - "Ġno wrap", - "Ġnow rap", - "ĠV ern", - "ĠVer n", - "ĠVe rn", - ", čĊčĊ", - ",čĊ čĊ", - "ime step", - "imes tep", - "imest ep", - "Layout Manager", - "à ·", - "ĉ wait", - "ĉw ait", - "P LETED", - "PLE TED", - "J apan", - "Ja pan", - "Ġin duce", - "Ġind uce", - "Ġindu ce", - "Ġ å¯", - "Ġå ¯", - "о зв", - "оз в", - "_END POINT", - ". horizontal", - ".h orizontal", - "Ġacceler ated", - "Ġaccelerate d", - "r imon", - "ri mon", - "rim on", - "I VES", - "IV ES", - "IVE S", - "Trans actions", - "Transaction s", - "L ean", - "Le an", - "ĠS OUR", - "ĠSO UR", - "wh ether", - "y g", - "Ġ oid", - "Ġo id", - "Ġoi d", - "Ġ EntityManager", - "ĠEntity Manager", - "OUN TRY", - "OUNT RY", - "Ġ fila", - "Ġf ila", - "Ġfil a", - "Ġfi la", - "OLUM NS", - "OLUMN S", - "IN UE", - "INU E", - "Ġ Anchor", - "ĠAn chor", - "ĠAnc hor", - "ĠAnch or", - "TR AN", - "TRA N", - "w oo", - "wo o", - "block quote", - "ĠN urse", - "ĠNurs e", - "ĠNur se", - "ĠC arp", - "ĠCar p", - "ĠCa rp", - "Ġrede em", - ". try", - ".t ry", - ".tr y", - "Ġ JP", - "ĠJ P", - "Ġ timestamps", - "Ġtimestamp s", - "Ġ?> \"><", - "Ġ?>\" ><", - "Ġ?>\"> <", - "Ġ REMOVE", - "ĠRE MOVE", - "ĠREM OVE", - "ĠStar bucks", - "Re ally", - "Real ly", - "Ġflo oded", - "Ġflood ed", - ". Callback", - ".C allback", - ".Call back", - "Drop Down", - "i pro", - "ip ro", - "Ġt ended", - "Ġten ded", - "Ġtend ed", - "l te", - "lt e", - "Ġproportion s", - "Ġproport ions", - "- te", - "-t e", - "ĠR ena", - "ĠRe na", - "ĠRen a", - "l icate", - "lic ate", - "li cate", - "lica te", - "for ces", - "force s", - "forc es", - ". extra", - ".ex tra", - ".ext ra", - ". authenticate", - ".auth enticate", - "в од", - "во д", - "¡ °", - "Ġfor ControlEvents", - "Ġ senha", - "Ġs enha", - "Ġsen ha", - "Ġk ein", - "Ġke in", - "Ġmin ist", - "Ġmi nist", - "Ġmini st", - "Ġ Preference", - "ĠP reference", - "ĠPre ference", - "ĠPref erence", - "ĠPrefer ence", - "ĠTele graph", - "Ñĥ п", - "str pos", - "Ġillness es", - "Ġp igs", - "Ġpi gs", - "Ġpig s", - "Ġget Intent", - "ĠgetInt ent", - "S ol", - "So l", - "Ġ ¡", - "Ġ ¡", - "( cpu", - "(c pu", - "(cp u", - "[ prop", - "[p rop", - "s creens", - "screen s", - "') ;?>", - "'); ?>", - "Ġ Acts", - "ĠA cts", - "ĠAct s", - "ĠAc ts", - "Ġstr dup", - "Ġa verages", - "Ġaverage s", - "Ġaver ages", - "a nal", - "an al", - "ana l", - "ĠCas ual", - "Group Box", - "ĠHand book", - "/ comments", - "/com ments", - "/comment s", - "Ġnumber ed", - "Ġnumb ered", - "Ġbroad casting", - "Ġbroadcast ing", - "çĽ ij", - ".native Element", - ". mu", - ".m u", - "Ġ updatedAt", - "Ġupdated At", - "ĠDoes n", - "ĠDoe sn", - ". AC", - ".A C", - ". coll", - ".c oll", - ".co ll", - ".col l", - "Ġrec order", - "Ġrecord er", - "_ sha", - "_s ha", - "_sh a", - "B g", - "b il", - "bi l", - "Ġbol ts", - "Ġbolt s", - "Ġ ç¬", - "Ġç ¬", - "Ġim posing", - "Ġimp osing", - "ĠInformation en", - "_ flashdata", - "_flash data", - "e conomic", - "ec onomic", - "R emark", - "Re mark", - "Rem ark", - "u cas", - "uc as", - "Ġ Officers", - "ĠOff icers", - "ĠOffice rs", - "ĠOfficer s", - "Ġ TER", - "ĠT ER", - "ĠTE R", - "W alk", - "Wal k", - "Wa lk", - "Ġmerc ado", - "_ generate", - "_g enerate", - "_gen erate", - "_gene rate", - "_gener ate", - "H Y", - "C alling", - "Call ing", - "Cal ling", - "s nap", - "sn ap", - "script Id", - ". operation", - ".op eration", - ".o peration", - ".oper ation", - "ĠFl ame", - "ĠFla me", - "ĠFlam e", - "l iness", - "li ness", - "line ss", - "lin ess", - "lines s", - "Ġr ented", - "Ġren ted", - "Ġrent ed", - "_ toggle", - "_t oggle", - "- changing", - "-ch anging", - "-chan ging", - "Ġ TY", - "ĠT Y", - "' util", - "'u til", - "E EP", - "EE P", - "Ġ graphql", - "Ġgraph ql", - "Ġ Uni", - "ĠU ni", - "ĠUn i", - "Ġim pulse", - "Ġimp ulse", - "Ġimpuls e", - ". Basic", - ".B asic", - "Ġenerg ies", - "Ġener gies", - "Ġenergie s", - "M ARY", - "MA RY", - "MAR Y", - "ĠMar cel", - "ĠMarc el", - "Ġm ortal", - "Ġmor tal", - "Ġmort al", - "Ġf res", - "Ġfr es", - "Ġfre s", - "m ens", - "me ns", - "men s", - "m otion", - "mo tion", - "mot ion", - "Ġs ampled", - "Ġsample d", - "Ġsam pled", - "Ġsamp led", - "âĢľ That", - "i day", - "id ay", - "ida y", - "qu ipment", - "quip ment", - "get Int", - "Ġ Absolute", - "ĠA bsolute", - "ĠAbs olute", - ", '\"", - ",' \"", - "u ned", - "un ed", - "une d", - ". share", - ".s hare", - ".sh are", - ".sha re", - "Ġ })(", - "Ġ} )(", - "Ġ}) (", - "m mm", - "mm m", - "ĠR ising", - "ĠRi sing", - "ĠRis ing", - "ä» »", - "Ġun employed", - "x fa", - "xf a", - ". follow", - ".f ollow", - "ĉ ĉĉĉĠĠĠĠĠĠ", - "ĉĉ ĉĉĠĠĠĠĠĠ", - "ĉĉĉĉ ĠĠĠĠĠĠ", - "ĉĉĉ ĉĠĠĠĠĠĠ", - "ĉĉĉĉĠ ĠĠĠĠĠ", - "ĉĉĉĉĠĠĠ ĠĠĠ", - "ĉĉĉĉĠĠ ĠĠĠĠ", - "ĉĉĉĉĠĠĠĠ ĠĠ", - "ĉĉĉĉĠĠĠĠĠ Ġ", - "s lt", - "sl t", - ". Phone", - ".P hone", - ".Ph one", - "Ġkn ives", - "Ġ eve", - "Ġe ve", - "Ġev e", - "on Click", - "] ))čĊ", - "]) )čĊ", - "])) čĊ", - "Ġ Witness", - "ĠW itness", - "ĠWit ness", - "ĉ NS", - "ĉN S", - "Ġ EOS", - "ĠE OS", - "ĠEO S", - "ĠSte fan", - "ĠStef an", - "ĠPr iest", - "ĠPri est", - "âĢĶ which", - "Get String", - ". By", - ".B y", - "Ġup stairs", - "Ġdetr iment", - "b roken", - "br oken", - "bro ken", - "em bro", - "emb ro", - "embr o", - "Ġnic otine", - "i lion", - "il ion", - "ili on", - "ilio n", - "Ġaston ishing", - "_ aff", - "_a ff", - "_af f", - "Ġ Lesson", - "ĠL esson", - "ĠLe sson", - "ĠLess on", - "ĠLes son", - "Ġacc idental", - "Ġaccident al", - "od or", - "odo r", - "Ġde cir", - "Ġdec ir", - "Ġnew Name", - "+ .", - "çĽ ¸", - "igs list", - "Ġ Github", - "ĠG ithub", - "ĠGit hub", - "Ġsuccess ive", - "Ġsuc cessive", - "r acial", - "ra cial", - "rac ial", - "raci al", - "Ġen viron", - "Ġenv iron", - "Ġenvi ron", - "éªĮ è¯ģ", - "Ġred irected", - "Ġredirect ed", - "T OTAL", - "TOT AL", - "Ġgrab bing", - "Ġgra bbing", - "ĠL ance", - "ĠLa nce", - "ĠLan ce", - "ĠLanc e", - "Ġfor fe", - "_ CB", - "_C B", - "å¾ ®", - "El apsed", - "_ way", - "_w ay", - "(Dialog Interface", - "_ measure", - "_me asure", - "_meas ure", - "x bb", - "xb b", - "D og", - "Do g", - "De part", - "Dep art", - "- src", - "-s rc", - "re solver", - "res olver", - "resolve r", - "with standing", - "_ shell", - "_s hell", - "_sh ell", - "Ġ LastName", - "ĠLast Name", - "ĠAv iation", - "Ġbeg inner", - "Ġbegin ner", - "(\" %.", - "(\"% .", - "( tool", - "(t ool", - "(to ol", - "Ġ нов", - "Ġн ов", - "Ġно в", - ": init", - ":i nit", - "( API", - "(A PI", - "(AP I", - "ĠMorris on", - "ĠMorr ison", - "vt Color", - "Ġsta ple", - "Ġstap le", - "/ INFO", - "Ġsuper natural", - "Ġsupern atural", - "Ġste ak", - "t imeline", - "time line", - "tim eline", - "zz le", - "\" `ĊĊ", - "\"`Ċ Ċ", - "\"` ĊĊ", - "Second ary", - "ĠNe pal", - "ĠNep al", - ". StringUtils", - ".String Utils", - "Ġ adam", - "Ġa dam", - "Ġad am", - "Ġada m", - "Ġ (...", - "Ġ( ...", - "Ġ(. ..", - "Ġsub stitution", - "Ġsubstit ution", - "Ġsubst itution", - "Ġ boarding", - "Ġbo arding", - "Ġboard ing", - "Ġ Keyword", - "ĠKey word", - "ĠAss ault", - "dbc Template", - "Ġ orderId", - "Ġorder Id", - "( engine", - "(e ngine", - ".assert That", - "ĠV enus", - "ĠVen us", - "Ġhom icide", - "Ġhomic ide", - "ĠA val", - "ĠAv al", - "ĠAva l", - "Ġg utter", - "Ġgut ter", - "Ġ Supported", - "ĠS upported", - "ĠSup ported", - "ĠSupport ed", - "/ part", - "/p art", - "Ġac claimed", - "Ġacclaim ed", - "H istor", - "Hi stor", - "His tor", - "Hist or", - "Ġm eses", - "Ġme ses", - "Ġmes es", - "ü ber", - "üb er", - "ĠRe new", - "ĠRen ew", - "ĠRene w", - "Ġg ras", - "Ġgr as", - "Ġgra s", - "Ġ Ek", - "ĠE k", - "Ġ infile", - "Ġin file", - "Ġinf ile", - "in dy", - "ind y", - ". music", - ".m usic", - ".mu sic", - ". Scroll", - ".S croll", - ".Sc roll", - "ĠA ges", - "ĠAg es", - "ĠAge s", - "ĠNar uto", - "ĠG ather", - "ĠGa ther", - "ĠGat her", - "Ġconfirm ing", - "= (\"", - "=( \"", - "Ġp itched", - "Ġpitch ed", - "Ġpit ched", - "o ley", - "ol ey", - "ole y", - "F rance", - "Fr ance", - "Fran ce", - "Fra nce", - "Franc e", - "+ '\"", - "+' \"", - "$ total", - "$t otal", - "Ġ onde", - "Ġo nde", - "Ġon de", - "Ġd itch", - "Ġdit ch", - "_ sigma", - "_s igma", - "_sig ma", - "Ġcontin uity", - "Ġcontinu ity", - "r eward", - "re ward", - "rew ard", - "- load", - "-l oad", - "-lo ad", - "Ġpro ceso", - "Ġproc eso", - "Ġproces o", - "L ocked", - "Loc ked", - "Lock ed", - "st aw", - "sta w", - "Ġsp inal", - "Ġspin al", - "Ġspi nal", - "l azy", - "la zy", - "laz y", - "! ==", - "!= =", - "j est", - "je st", - "jes t", - "Ġd un", - "Ġdu n", - "ĠRod gers", - "ĉ grid", - "ĉg rid", - "ĉgr id", - "Ġlo gos", - "Ġlog os", - "Ġlogo s", - "ĠBen gal", - "ĠBeng al", - ". super", - ".s uper", - ".sup er", - "Pro vides", - "Provid es", - "Provide s", - "Prov ides", - "Ġnut rient", - ". Timestamp", - ".T imestamp", - ".Time stamp", - "IZ ATION", - "åĨ Į", - "Ġf ats", - "Ġfa ts", - "Ġfat s", - "ĠX xx", - "c tica", - "ct ica", - "ctic a", - "Target s", - "Tar gets", - "Ġcont ours", - "Ġcontour s", - "Ġre ordered", - "Ġreorder ed", - ": Array", - ":A rray", - "Ġtoler ate", - "Ġtol erate", - "V ir", - "Vi r", - "Ġter ribly", - "Ġterr ibly", - "Ġb ricks", - "Ġbr icks", - "Ġbrick s", - "Ġbri cks", - "( &_", - "(& _", - "h b", - "P ortal", - "Port al", - "Por tal", - "ĠB read", - "ĠBr ead", - "ĠBre ad", - ". which", - ".wh ich", - "ÂŃ t", - "as InstanceOf", - "Ġj object", - "Ġjob ject", - "Ġjo bject", - "ĉ length", - "ĉl ength", - "ĉlen gth", - "_ MT", - "_M T", - "; \">čĊ", - ";\" >čĊ", - ";\"> čĊ", - "_ EXIST", - "_EX IST", - "Ġmat ernal", - "Ġma ternal", - "Ġmater nal", - "R EL", - "RE L", - "Ġê²½ ìļ°", - "h ee", - "he e", - "Ġ layouts", - "Ġlayout s", - "Ġlay outs", - "ĠL ap", - "ĠLa p", - "a isy", - "ai sy", - "ais y", - "Ġst umbled", - "Ġstumble d", - "ĠU IG", - "ĠUI G", - "ĠS co", - "ĠSc o", - "Ġim paired", - "Ġimp aired", - "Ġimpair ed", - "RES SED", - "RESS ED", - "Ġab uses", - "Ġabuse s", - "V F", - "A RB", - "AR B", - ". NAME", - ".N AME", - "r ch", - "rc h", - "pr imir", - "prim ir", - "pri mir", - "_ completed", - "_com pleted", - "_comp leted", - "_complete d", - "Ġp enny", - "Ġpen ny", - "Ġpenn y", - "Ch rome", - "Chr ome", - "( begin", - "(b egin", - "(be gin", - "er nen", - "ern en", - "erne n", - "- checkbox", - "-check box", - "Plain OldData", - "ĠL PC", - "ĠLP C", - "r ade", - "ra de", - "rad e", - "s pir", - "sp ir", - "spi r", - "Ġcon ceived", - "Ġconce ived", - "Ġconceive d", - "T ips", - "Tip s", - "Ti ps", - "ĠIo T", - "ĠG an", - "ĠGa n", - "èģ Ķ", - "Ġbi ases", - "Ġbias es", - "Ġconsult ants", - "Ġconsultant s", - "Ġconsulta nts", - "p led", - "pl ed", - "ple d", - "_ ht", - "_h t", - "associ ated", - "assoc iated", - "associate d", - "] ,ĊĊ", - "], ĊĊ", - "],Ċ Ċ", - "Ġdelight ful", - "ĠÑĤ ек", - "ĠÑĤе к", - "Hel vetica", - "( load", - "(l oad", - "(lo ad", - "- expand", - "-exp and", - "_W IDGET", - "t oa", - "to a", - "Ġ Akt", - "ĠA kt", - "ĠAk t", - "Ġo mn", - "Ġom n", - "Ġcl auses", - "Ġclause s", - "Ġcla uses", - "In tel", - "Int el", - "*/ }Ċ", - "_ registration", - "_reg istration", - "Ġold Value", - "Ġrest oring", - "Ġresto ring", - "Ġun real", - "Ġunre al", - "O VER", - "OVE R", - "OV ER", - "ĉĊ ĉĊĉĊ", - "ĉĊĉĊ ĉĊ", - "A TS", - "AT S", - "_ probe", - "_p robe", - "_pro be", - "_pr obe", - "_prob e", - "Ġdi visor", - "Ġdiv isor", - "Ġdivis or", - ".update Dynamic", - "å¹ ³", - "Produ ces", - "Prod uces", - "st amp", - "sta mp", - ".j boss", - "ĉ task", - "ĉt ask", - "! (:", - "!( :", - "Ġpsych ic", - "@ class", - "@c lass", - "M artin", - "Mar tin", - "Mart in", - "Ġ Passed", - "ĠP assed", - "ĠPass ed", - "ĠPas sed", - "clar ations", - "claration s", - "h el", - "he l", - "а Ñĩ", - "ĉ copy", - "ĉc opy", - "- bin", - "-b in", - "z an", - "za n", - "i gram", - "ig ram", - "igr am", - "া à¦", - "( sig", - "(s ig", - "(si g", - "ĠC aval", - "ĠCa val", - "ĠCav al", - "_ ##", - "Ġ %=", - "Ġ% =", - "out lined", - "outline d", - "ĠA cid", - "ĠAc id", - "Ġunpredict able", - "- dashboard", - "-d ashboard", - "Hex String", - "+ c", - ". Public", - ".P ublic", - "Ạ©", - "Ġcon veyor", - "Ġconvey or", - "Ġ EB", - "ĠE B", - "Ġselect s", - "Ġsel ects", - "Ġsele cts", - "Ġkn ocking", - "Ġknock ing", - "ĠC ec", - "ĠCe c", - "IB UTES", - "IBUT ES", - "IBUTE S", - "ow aÄĩ", - "owa Äĩ", - "g atsby", - "* v", - "ent ropy", - "entr opy", - "Ġdispatch ed", - "Ġdisp atched", - "Ġ camel", - "Ġc amel", - "Ġca mel", - "Ġcame l", - "Ġcam el", - "ĠSat urn", - "ĠSa turn", - "Ġover weight", - "( phone", - "(p hone", - "(ph one", - "par able", - "para ble", - "% B", - "_v ectors", - "_vector s", - "_vec tors", - "_vect ors", - "_ve ctors", - "Ġbr ewing", - "Ġbre wing", - "Ġbrew ing", - "Ġ Tk", - "ĠT k", - "Ġ Downloads", - "ĠDown loads", - "ĠDownload s", - "Ġ Saved", - "ĠS aved", - "ĠSave d", - "ĠSa ved", - "ĠSav ed", - ". Price", - ".P rice", - ".Pr ice", - "Ġc urved", - "Ġcur ved", - "Ġcurve d", - "ĠParent hood", - "ĠParen thood", - "è ¶", - ".p nl", - "plete ly", - "plet ely", - ". Day", - ".D ay", - "Ġadvert isers", - "Ġadvertis ers", - "Ġadvertise rs", - "Ġadvertiser s", - "Ġe jec", - "Ġej ec", - "Ġpr zed", - "Ġprz ed", - "Ġprze d", - "ë ¯", - "! ';Ċ", - "!' ;Ċ", - "ĠK ush", - "ĠKu sh", - "Ġ TAB", - "ĠT AB", - "ĠTA B", - "Ġ quests", - "Ġqu ests", - "Ġque sts", - "Ġquest s", - "Ġques ts", - "Ġcoinc idence", - "Ġcoincide nce", - "umm ies", - "ĠKash mir", - "ĠEth ics", - "ĠEthi cs", - "_ growth", - "_g rowth", - "Ġ aktiv", - "Ġak tiv", - "Ġakt iv", - "Ġgroup ing", - "Ġgrou ping", - "å¢ ŀ", - "_ truth", - "_tr uth", - "åIJ ¬", - "t odos", - "to dos", - "todo s", - "tod os", - "i set", - "is et", - "ise t", - "Tex Coord", - "ä tt", - "ät t", - "ĠZ ur", - "ĠZu r", - "ro ys", - "roy s", - "_M AGIC", - "_MAG IC", - "Ġbrew ery", - "( State", - "ĠSM ALL", - "ĠSMA LL", - "ĠPl ants", - "ĠPlan ts", - "ĠPlant s", - "ĠPla nts", - "it bart", - "e acher", - "each er", - "ea cher", - "ĠAd elaide", - "L u", - "Ġf ick", - "Ġfi ck", - "Ġfic k", - "und les", - "undle s", - "_ loaded", - "_lo aded", - "_load ed", - "и е", - "P oll", - "Pol l", - "Po ll", - "r itic", - "ri tic", - "rit ic", - "E LY", - "EL Y", - "Ġ +'", - "Ġ+ '", - "ĠProf ession", - "Ġst amps", - "Ġstamp s", - "Ġsta mps", - "ĠS ew", - "ĠSe w", - "s crollView", - "scroll View", - "Ġcomm unist", - "Ġcommun ist", - "/ problems", - "/pro blems", - "/problem s", - "} čĊčĊčĊčĊ", - "}čĊ čĊčĊčĊ", - "}čĊčĊ čĊčĊ", - "}čĊčĊčĊ čĊ", - ", o", - "Ġ udp", - "Ġu dp", - "Ġud p", - "Ġob ese", - "Ġobe se", - "ap prove", - "app rove", - "appro ve", - "anc ellation", - "ancel lation", - "ancell ation", - "_ Game", - "_G ame", - "Ġ Hashtable", - "ĠHash table", - "ĠHas htable", - "adaptive Styles", - "Ġposs esses", - "Ġpossess es", - ". matcher", - ".m atcher", - ".match er", - ".mat cher", - "function al", - "M rs", - "Mr s", - "ĉ save", - "ĉs ave", - "Ġ DbType", - "ĠDb Type", - "Ġ ken", - "Ġk en", - "Ġke n", - "get Context", - "Ġ mans", - "Ġm ans", - "Ġman s", - "Ġma ns", - "( rel", - "(r el", - "(re l", - "ĠBrother hood", - ") `Ċ", - ")` Ċ", - "è§ £", - ". Information", - ".In formation", - "OutOfRange Exception", - "ĠS ek", - "ĠSe k", - "C as", - "Ca s", - "Ġblog gers", - "Ġblogger s", - "E ither", - "( \"\"\"", - "(\" \"\"", - "(\"\" \"", - "Ġp inch", - "Ġpin ch", - "Ġco arse", - ") p", - "ĠP ulse", - "ĠPu lse", - "ĠPul se", - "Ġlearn t", - "Ġlear nt", - "Ġdent ist", - "Ġon change", - "Ġdirect ives", - "Ġdirective s", - "( actions", - "(a ctions", - "(action s", - "(act ions", - "ny der", - "ĠS hir", - "ĠSh ir", - "ĠShi r", - "T rait", - "Tr ait", - "Tra it", - "_ dep", - "_d ep", - "_de p", - "Ġ PET", - "ĠP ET", - "ĠPE T", - "Ġ REP", - "ĠR EP", - "ĠRE P", - ".App Settings", - "cu ador", - "ide nav", - "iden av", - "Ġen vi", - "Ġenv i", - "Ġsl ammed", - "Ġslam med", - "Ġ Shoot", - "ĠS hoot", - "ĠSh oot", - "ĠSho ot", - "Ġ dateFormat", - "Ġdate Format", - ".j oda", - "ve ys", - "vey s", - "Ġ ).ĊĊ", - "Ġ) .ĊĊ", - "Ġ). ĊĊ", - "Ġ).Ċ Ċ", - "Ġcar eg", - "Ġca reg", - "Ġcare g", - "Ġ Parallel", - "ĠPar allel", - "_ translation", - "_trans lation", - ". functions", - ".function s", - ".fun ctions", - ". obs", - ".o bs", - ".ob s", - "Runtime Exception", - "[ ]=", - "[] =", - "ov erview", - "over view", - "ĠS chl", - "ĠSc hl", - "ĠSch l", - "Ġno isy", - "Ġnoi sy", - "ĠOn PropertyChanged", - "S ending", - "Send ing", - "Sen ding", - "Ġunf amiliar", - "U pon", - "Up on", - "ĠPr ints", - "ĠPrint s", - "ĠPri nts", - ". typ", - ".t yp", - "Ġfle eing", - "Ġflee ing", - "ĉ move", - "ĉm ove", - "ĉmov e", - "( Un", - "(U n", - "Ġ qr", - "Ġq r", - "× ľ", - "_ beta", - "_b eta", - "_be ta", - "_bet a", - "Ġsk ies", - "Ġski es", - "ĉ me", - "ĉm e", - "W ND", - "WN D", - "Ġst ickers", - "Ġstick ers", - "Ġsticker s", - "b las", - "bl as", - "bla s", - "Ġins erts", - "Ġinsert s", - "Ġinser ts", - "Ġinse rts", - "Ġver ses", - "Ġvers es", - "Ġverse s", - "ĠD ew", - "ĠDe w", - "Ġt angible", - "Ġtang ible", - "Ġh echo", - "Ġhe cho", - "P OL", - "PO L", - "Ġte ardown", - "Ġtear down", - "om nia", - "I BE", - "IB E", - ". cover", - ".c over", - ".co ver", - "_ strategy", - "_str ategy", - "^ -", - "set Position", - "u ale", - "ual e", - "ua le", - "S igned", - "Sign ed", - "Sig ned", - "Ġ iface", - "Ġif ace", - "Ġi face", - "as eline", - "ase line", - "asel ine", - ".set Time", - "ĠMin eral", - "ĠMine ral", - "ĠMiner al", - "ĠF ighting", - "ĠFight ing", - "s kins", - "sk ins", - "ski ns", - "skin s", - "Ġdiscrim in", - "Ġd ansk", - "Ġdans k", - "Ġdan sk", - "ĠPr inceton", - "ĠPrince ton", - "ac ist", - "aci st", - "Ġ ());Ċ", - "Ġ( ));Ċ", - "Ġ() );Ċ", - "Ġ()) ;Ċ", - "tr acks", - "tra cks", - "track s", - "imon ial", - "a decimal", - "ad ecimal", - "ade cimal", - "EP ROM", - "ug gle", - "ugg le", - ". Notification", - ".Not ification", - "$ mail", - "$m ail", - "c antidad", - "cant idad", - "ĠJ ung", - "ĠJun g", - "ĠJu ng", - "Ġsee kers", - "Ġseek ers", - "Ġseeker s", - "Ġpl ausible", - "t ier", - "ti er", - "tie r", - "е ж", - "еР¶", - "Ġ rapper", - "Ġr apper", - "Ġrap per", - "Ġra pper", - "Ġrapp er", - "Ġ Mana", - "ĠM ana", - "ĠMan a", - "ĠMa na", - "ĠHttp StatusCode", - "ĠHttpStatus Code", - "Ġbur nt", - "Ġburn t", - "l oses", - "lo ses", - "lose s", - "los es", - "Ġ Foto", - "ĠF oto", - "ĠFo to", - "ĠFot o", - "Ġ JsonObject", - "ĠJson Object", - "In stagram", - "Inst agram", - "Ġ syscall", - "Ġsys call", - "Ġreal ities", - "ĠMAT LAB", - ":^ {Ċ", - "T ERM", - "TE RM", - "TER M", - "ĠC bd", - "Ġ Paragraph", - "ĠPar agraph", - "ĠPara graph", - "Ġtrav és", - "Ġconstruct ing", - "Ġs wal", - "Ġsw al", - "Ġp ige", - "Ġpi ge", - "Ġpig e", - "L LLL", - "LL LL", - "LLL L", - "- existing", - "-ex isting", - "G ets", - "Get s", - "Ge ts", - "Ġmel ted", - "Ġmelt ed", - "Ġmit igate", - "Ġmitig ate", - "H en", - "He n", - "Ġ hm", - "Ġh m", - "i mas", - "im as", - "ima s", - "Ġ Ao", - "ĠA o", - "ĠP erez", - "ĠPer ez", - "ĠPe rez", - "ĠPere z", - "Ġ DAL", - "ĠD AL", - "ĠDA L", - "Ġ ëĭ¤", - "Ġëĭ ¤", - "Ġdi vis", - "Ġdiv is", - "Storyboard Segue", - "Ġ Modify", - "ĠMod ify", - "ĠModi fy", - "ĠÃľ ber", - "_O VERRIDE", - ".p em", - ".pe m", - "un tos", - "unt os", - "unto s", - "Ġesp añ", - "Ġespa ñ", - "Ġ{ ?", - "Ġ PAY", - "ĠP AY", - "ĠPA Y", - "_ ipv", - "_i pv", - "_ip v", - "ĠF ury", - "ĠFu ry", - "ĠFur y", - "__ .__", - "__. __", - "e low", - "el ow", - "elo w", - "-c entered", - "-center ed", - "-cent ered", - "che cks", - "check s", - "_ Reg", - "_R eg", - "_Re g", - "-J avadoc", - "ĉ load", - "ĉl oad", - "ĠLike wise", - "ĠLik ewise", - "ا Ùħ", - "ا٠ħ", - "U NE", - "UN E", - ". sem", - ".s em", - ".se m", - "x cb", - "xc b", - "ĠC ave", - "ĠCa ve", - "ĠCav e", - "_ sleep", - "_s leep", - "Ġsil ently", - "Ġsilent ly", - "Ġ Extreme", - "ĠExt reme", - "ĠExtr eme", - ". ToUpper", - ".To Upper", - "ĉ CHECK", - "ĉC HECK", - "Ġ cue", - "Ġc ue", - "Ġcu e", - "ĠQ ByteArray", - "Ġcor rupted", - "Ġcorrupt ed", - "Ġ Dé", - "ĠD é", - "Ġim ped", - "Ġimp ed", - "Ġimpe d", - "Get Name", - "Ġinaccur ate", - "Ġs ober", - "Ġso ber", - "Ġsob er", - "е е", - "еРµ", - "Ġ barcode", - "Ġbar code", - "Ġba rcode", - "-- ){Ċ", - "--) {Ċ", - "in ki", - "ink i", - "Ġ ép", - "Ġé p", - "Ġd ri", - "Ġdr i", - "Ġ ALT", - "ĠA LT", - "ĠAL T", - "> >>>>>>>", - ">>>> >>>>", - ">>>>>>> >", - "on ta", - "ont a", - "[ L", - "Ġint eres", - "Ġinter es", - "Ġinte res", - "ver ting", - "vert ing", - "Ġd iagnostics", - "Ġdi agnostics", - "Ġdiagnostic s", - "p dev", - "pd ev", - "è ©", - "Ġ Integrated", - "ĠInt egrated", - "ĠIntegr ated", - ") .'", - "). '", - "_ gc", - "_g c", - "$ text", - "$t ext", - ". games", - ".g ames", - ".game s", - ".ga mes", - "ĠT erra", - "ĠTer ra", - "ĠTerr a", - "' Re", - "'R e", - ". transfer", - ".trans fer", - "_F IFO", - "get Model", - "Ġb land", - "Ġbl and", - "Ġbla nd", - "ĠCol eman", - "ĠCole man", - "Ġpr imes", - "Ġprim es", - "Ġprime s", - "Ġpri mes", - "Ġ æĪ", - "Ġæ Ī", - "Ġcross es", - "n k", - "G ING", - "GIN G", - "GI NG", - "Ġ '^", - "Ġ' ^", - "Ġ Blob", - "ĠB lob", - "ĠBl ob", - "ĠBlo b", - "Ġinter course", - "ĠBl vd", - "Ġweigh s", - "_ regular", - "_reg ular", - "ĠPer th", - "ĠPert h", - "Ġsepar ating", - "Ġseparat ing", - "Ġb illed", - "Ġbill ed", - "Ġbil led", - ".tab Control", - "Ġp uppet", - "Ġpup pet", - "Ġutil ization", - "Ġutiliz ation", - "Ġutiliza tion", - "Ġ âĸł", - "Ġâĸ ł", - "Ġsuc ces", - "Ġsucc es", - "Ġl amps", - "Ġla mps", - "Ġlamp s", - "Ġlam ps", - "_ proj", - "_p roj", - "_pro j", - "_pr oj", - "E ric", - "Er ic", - "Ġre novation", - "Ġren ovation", - "Ġrenov ation", - "ĠF amilies", - "ĠFam ilies", - "ĠFamil ies", - "ĠFamilie s", - "Ġ Bits", - "ĠB its", - "ĠBit s", - "ĠBi ts", - "part ials", - "partial s", - "- Men", - "-M en", - "s olution", - "sol ution", - "Ġd warf", - "Ġdw arf", - "Ġdwar f", - ". INTEGER", - ".IN TEGER", - "Ġ LOCK", - "ĠL OCK", - "ĠLO CK", - "ĠLOC K", - ". ct", - ".c t", - "Ġ excerpt", - "Ġex cerpt", - "Ġexcer pt", - "Ġ Pix", - "ĠP ix", - "ĠPi x", - "Ġ FirstName", - "ĠFirst Name", - "AN TED", - "ANT ED", - "ANTE D", - "ĠAd mir", - "- help", - "-h elp", - "-he lp", - "P rior", - "Pr ior", - "Pri or", - "Ġ Align", - "ĠAl ign", - "ĠAli gn", - ". INSTANCE", - ".IN STANCE", - "Line Edit", - "(' /:", - "('/ :", - "Ġ inet", - "Ġin et", - "Ġi net", - "Ġine t", - "od us", - "odu s", - ".p kl", - ".pk l", - "Ġ KY", - "ĠK Y", - "u pert", - "up ert", - "uper t", - "upe rt", - "Ġn erves", - "Ġnerv es", - "Ġnerve s", - "Ġner ves", - "_ gradient", - "_g radient", - "_grad ient", - "} ','", - "}' ,'", - "}', '", - "_un ref", - "Ġs aturated", - "Ġsatu rated", - "Ġsatur ated", - "Ġ Connected", - "ĠConnect ed", - "ĠConn ected", - "Ġ FN", - "ĠF N", - "EX IT", - "Ġtele port", - "Ġav ait", - "Ġava it", - "Page Route", - "Ġdivorce d", - "Ġdivor ced", - "( lang", - "(l ang", - "f st", - "fs t", - "ĠT yr", - "ĠTy r", - "Ġm essenger", - "Ġmess enger", - "i fstream", - "if stream", - "X S", - "ĠB anking", - "ĠBank ing", - "ĠBan king", - "Ġinfect ious", - "ĠM ons", - "ĠMon s", - "ĠMo ns", - "_ LOOP", - "_LO OP", - "Ġzur ück", - "Ġob tener", - "Ġobt ener", - "Ġobten er", - "/ repos", - "/re pos", - "V el", - "Ve l", - "a cro", - "ac ro", - "acr o", - "Ġ userRepository", - "Ġuser Repository", - "style Type", - "Ġ SRC", - "ĠS RC", - "ĠSR C", - "VML INUX", - "rec ursive", - "/ bar", - "/b ar", - "_ chip", - "_c hip", - "_ch ip", - "_chi p", - "o minated", - "om inated", - "omin ated", - "Ġ Nit", - "ĠN it", - "ĠNi t", - "âĢĶ to", - "ĠBudd h", - "ĠBud dh", - "о меÑĢ", - "ом еÑĢ", - "ĠM AG", - "ĠMA G", - "Ġ CHE", - "ĠC HE", - "ĠCH E", - "_ den", - "_d en", - "_de n", - ". raises", - ".r aises", - ".raise s", - ".ra ises", - "_ degree", - "_d egree", - "_de gree", - "_deg ree", - "Ġpump kin", - "_ templates", - "_t emplates", - "_template s", - "_temp lates", - "_tem plates", - "_ MEDIA", - "_M EDIA", - "_MED IA", - "Ġ Timeline", - "ĠT imeline", - "ĠTime line", - "ĠTim eline", - "Ġ bots", - "Ġb ots", - "Ġbo ts", - "Ġbot s", - "Object Type", - "Ġbu ys", - "Ġbuy s", - ". posts", - ".post s", - ".pos ts", - ".po sts", - "C AL", - "CA L", - "wa iting", - "wait ing", - "ĠDaniel s", - "ĠDani els", - "Ġd abei", - "Ġda bei", - "Ġdab ei", - "Ġ Sigma", - "ĠS igma", - "ĠSig ma", - "i lor", - "il or", - "ilo r", - "i gel", - "ig el", - "ige l", - ", W", - "A DS", - "AD S", - "( panel", - "(p anel", - "(pa nel", - "ì² ´", - "it ating", - "ita ting", - "itat ing", - ". palette", - ".p alette", - ".pa lette", - "Ġmos quito", - "Ġt ego", - "Ġte go", - "Ġteg o", - "( parseInt", - "(parse Int", - "Ġdes pués", - "p romise", - "pro mise", - "prom ise", - "Ġ wij", - "Ġw ij", - "Ġwi j", - "type script", - "types cript", - "Ġ Tv", - "ĠT v", - "_IDENT IFIER", - ") .ĊĊĊ", - "). ĊĊĊ", - ").ĊĊ Ċ", - ").Ċ ĊĊ", - "_ flat", - "_f lat", - "_fl at", - "it su", - "its u", - "U SR", - "US R", - "ex perience", - "- fit", - "-f it", - "-fi t", - "ph inx", - "phi nx", - "phin x", - "_ thresh", - "_th resh", - "_thr esh", - "Ġide ally", - "Ġideal ly", - "ĠFree man", - "ĠFre eman", - "ĠFreem an", - ", DB", - ",D B", - "_ rw", - "_r w", - "çŃ ī", - "U b", - "_ statistics", - "_stat istics", - "=\" \"><", - "=\"\" ><", - "=\"\"> <", - "Ġch ore", - "Ġcho re", - "Ġchor e", - "Ġy ork", - "Ġyo rk", - "inst alled", - "install ed", - "Add itionally", - "Additional ly", - "Ġp stmt", - "Ġpst mt", - "yl ko", - ": :Ċ", - ":: Ċ", - "F orest", - "For est", - "Fore st", - "Fo rest", - "Ġhead set", - "Ġheads et", - "Ġg allon", - "Ġgal lon", - "Ġgall on", - "ÑĢ ем", - "ÑĢе м", - "Ġwithdraw n", - "Ġwithd rawn", - "Ġ Candidate", - "ĠC andidate", - "ĠCandid ate", - "Ġmel ting", - "Ġmelt ing", - "Ġfree zer", - "Ġfreeze r", - "Ġ hl", - "Ġh l", - "_ HELP", - "_HE LP", - "_HEL P", - "m ime", - "mi me", - "( /*", - "(/ *", - "Ġth irst", - "Ġthi rst", - "$ return", - "$r eturn", - "$ret urn", - "member of", - "е б", - "еР±", - "Ġ HttpServletRequest", - "ĠHttp ServletRequest", - "ĠHttpServlet Request", - "( ob", - "(o b", - "_ Result", - "_Res ult", - "Ġassert ed", - "Ġfulfill ing", - "Ġfulfil ling", - "Ġstretch es", - "Ġstret ches", - "par ated", - "pa rated", - "para ted", - "parate d", - "-f unded", - "Ġ åĽ", - "Ġå Ľ", - "in gles", - "ing les", - "ingle s", - "_ ca", - "_c a", - ". condition", - ".con dition", - ".cond ition", - "Ġ Displays", - "ĠDis plays", - "ĠDisplay s", - "ĠDisp lays", - "Ġ orang", - "Ġo rang", - "Ġor ang", - "Ġora ng", - "Ġoran g", - "Ġ CRE", - "ĠC RE", - "ĠCR E", - "Ġgl Bind", - "Ġ Selector", - "ĠSe lector", - "ĠSelect or", - "ĠSel ector", - "ĠSele ctor", - "/ type", - "/t ype", - "ĠAlex a", - "ĠAle xa", - "ched ules", - "chedule s", - "ĠPen insula", - "Ġ parity", - "Ġp arity", - "Ġpar ity", - "Ġpari ty", - "ĉ dest", - "ĉd est", - "ĉdes t", - "ĉde st", - "ĠDo ors", - "ĠDoor s", - "čĊ ĉčĊ", - "_ dimension", - "_d imension", - "_dim ension", - "Ġ aload", - "Ġa load", - "Ġal oad", - "Ġalo ad", - ".St oredProcedure", - "( paren", - "(p aren", - "(par en", - "(pa ren", - "ĠBur ke", - "ĠBurk e", - "' )]Ċ", - "') ]Ċ", - "')] Ċ", - "- engine", - "-e ngine", - "-eng ine", - "Ġ quir", - "Ġqu ir", - "Ġq uir", - "Ġqui r", - "ĠH ybrid", - "ĠHy brid", - "ĠD oe", - "ĠDo e", - "Ġout lines", - "Ġoutline s", - "ĠT rends", - "ĠTr ends", - "ĠTre nds", - "ĠTrend s", - "_ NV", - "_N V", - "per iments", - "periment s", - "peri ments", - "ĠH in", - "ĠHi n", - "? ',", - "?' ,", - "ĉ Text", - "ĉT ext", - "F UL", - "FU L", - "Ġsm ells", - "Ġsmell s", - "Ġ slick", - "Ġs lick", - "Ġsl ick", - "Ġslic k", - "Ġmis erable", - "Ġmiser able", - "ĠArray Adapter", - "Ġparam String", - "H om", - "Ho m", - "_l iterals", - "_literal s", - "_lit erals", - "us uarios", - "usuario s", - "usu arios", - "Ġprompt ing", - "_ lazy", - "_l azy", - "_la zy", - "Ġ Activation", - "ĠAct ivation", - "ĠActiv ation", - "_ oc", - "_o c", - "We ak", - "Ġan ecd", - "ĠU CLA", - "ĠUC LA", - "= re", - "=r e", - "iss ement", - "isse ment", - "ĠEsc orts", - "ĠEscort s", - "Ex cellent", - "Ġ Pause", - "ĠP ause", - "ĠPa use", - "Ġ repositories", - "Ġre positories", - "Ġrepos itories", - "T OR", - "TO R", - "ar iate", - "ari ate", - "aria te", - "ariat e", - "_ iso", - "_i so", - "_is o", - "up dates", - "update s", - "upd ates", - "ha lb", - "hal b", - "udi ante", - "udiant e", - "ë¡ Ŀ", - "Ġna ive", - "ĠP eg", - "ĠPe g", - "ĠL ounge", - "ĠLo unge", - "ĠLou nge", - "AR GIN", - "ARG IN", - "( bin", - "(b in", - "On ClickListener", - "OnClick Listener", - "Ġ FAILED", - "ĠFA ILED", - "ĠFAIL ED", - "Ġ lite", - "Ġl ite", - "Ġli te", - "Ġlit e", - "Ġd zie", - "Ġdz ie", - "Ġdzi e", - "Ġ Literal", - "ĠL iteral", - "ĠLiter al", - "ĠLit eral", - "ĠLite ral", - "i vor", - "iv or", - "ivo r", - "f cntl", - "fc ntl", - "fcn tl", - "Ġe ats", - "Ġeat s", - "Ġea ts", - "Ġ qed", - "Ġq ed", - "Un lock", - "r iding", - "ri ding", - "rid ing", - "und ai", - "unda i", - "= M", - "AT TER", - "ATT ER", - "Configure Await", - "ic ias", - "ici as", - "icia s", - "ust omed", - "ustom ed", - "usto med", - "Ġsuccess ion", - "Ġsuc cession", - "Ġsucc ession", - "end Time", - "ĠJ upiter", - "Ġj udging", - "Ġjud ging", - "d ration", - "dr ation", - "dra tion", - "_ docs", - "_d ocs", - "_doc s", - "_do cs", - ". mo", - ".m o", - "Ġeduc ators", - "Ġeducator s", - "ĠV ine", - "ĠVi ne", - "ĠVin e", - "C ond", - "Con d", - "Co nd", - "[ out", - "[o ut", - "q b", - "\\ Validator", - "Ġmean ings", - "Ġmeaning s", - "Ġpres ently", - "Ġpresent ly", - "Ġdiv iding", - "Ġdivid ing", - "otten ham", - "as cular", - "asc ular", - "Ġtrail ers", - "Ġtra ilers", - "Ġtrailer s", - "Ġtrai lers", - "Ġ CLOSE", - "ĠC LOSE", - "ĠCL OSE", - "а ми", - "ам и", - "âĢĻ ai", - "âĢĻa i", - "Ġ Gain", - "ĠG ain", - "ĠGa in", - "w or", - "wo r", - "Ġpl anner", - "Ġplan ner", - "Ġdistrib uting", - "v at", - "va t", - "mon ths", - "month s", - "mont hs", - "x label", - "xl abel", - "H F", - "V iol", - "Vi ol", - ".BASE LINE", - "еÑĤ ÑģÑı", - "Ġ Rotate", - "ĠR otate", - "ĠRot ate", - "Ġ txn", - "Ġt xn", - "Ġtx n", - ": bold", - ":b old", - "Ġb loss", - "Ġbl oss", - "Ġblo ss", - "Forg ery", - "Forge ry", - "( embed", - "(em bed", - "Ġj ako", - "Ġja ko", - "Ġjak o", - "s printf", - "the ir", - "Ġexhib its", - "Ġexhibit s", - "- static", - "-st atic", - "-stat ic", - "he cy", - "hec y", - "get ActiveSheet", - ". clients", - ".c lients", - ".client s", - ".cl ients", - ".cli ents", - "ãģ į", - "_ hide", - "_h ide", - "_hi de", - "_hid e", - "[ word", - "[w ord", - "C b", - "add Item", - "a xe", - "ax e", - "_ radio", - "_r adio", - "_rad io", - "_ra dio", - "_radi o", - "a lion", - "al ion", - "ali on", - "mod ifier", - "Ġsat uration", - "Ġsatu ration", - "Ġsatur ation", - "Ġde nom", - "Ġden om", - "_ pixels", - "_p ixels", - "_pixel s", - "_pix els", - "m ess", - "me ss", - "mes s", - "( fl", - "(f l", - "a tif", - "at if", - "ati f", - "Ġ secs", - "Ġs ecs", - "Ġse cs", - "Ġsec s", - "Ġpro stitution", - "Ġprostit ution", - "Ġprost itution", - "Ġprostitu tion", - "Ġprostitut ion", - "Ġgrand children", - "Ġparad ise", - "ĠF eld", - "ĠFe ld", - "ĠFel d", - "_B INARY", - "_BIN ARY", - "it ous", - "ito us", - "itou s", - "à ¹Ħ", - "๠Ħ", - "Ġfl ashing", - "Ġflash ing", - "-s ided", - "-side d", - "Ġcontrad iction", - "Ġcontradict ion", - "/ *ĊĊ", - "/* ĊĊ", - "/*Ċ Ċ", - "y label", - "yl abel", - "yla bel", - "ĠT et", - "ĠTe t", - "Ġadm ire", - "Ġadmir e", - "r eso", - "re so", - "res o", - "Ġl etz", - "Ġle tz", - "Ġlet z", - "Ġ SEARCH", - "ĠSE ARCH", - "s lots", - "sl ots", - "slot s", - "ĠRe wards", - "ĠRew ards", - "ĠReward s", - "ĠH og", - "ĠHo g", - "Ġ NSData", - "ĠNS Data", - "st ash", - "sta sh", - "F all", - "Fa ll", - "Fal l", - "ĠA mer", - "ĠAm er", - "Line arLayout", - "Linear Layout", - "/ photos", - "/photo s", - "/ph otos", - "Ġfe ather", - "Ġfeat her", - "Ġ |čĊ", - "Ġ| čĊ", - "Down loads", - "Download s", - ".Start sWith", - "Ġ //#", - "Ġ// #", - "Ġ/ /#", - "ine Transform", - "Ġaff id", - "Ġaf fid", - "V tbl", - "ĠR ogue", - "ĠRo gue", - "ĠRog ue", - "s cribed", - "scribe d", - "scri bed", - "Ġf auc", - "Ġfa uc", - "ĠMon roe", - "Ġdecl ares", - "Ġdeclar es", - "Ġdeclare s", - "mod ern", - "mode rn", - "r eon", - "re on", - "reo n", - "ay be", - "P ASS", - "PA SS", - "f ers", - "fer s", - "fe rs", - "_MULT I", - "_MUL TI", - "ĠMath ematics", - "ĠMathematic s", - "Ġsud ah", - "_ATT ACH", - "Ġnumber With", - "ĠSol omon", - "ĠSolo mon", - "j in", - "ji n", - "ograf ia", - "ogr afia", - "ö l", - "_ design", - "_d esign", - "_de sign", - "_des ign", - "c ulated", - "cul ated", - "culate d", - "cu lated", - "ĠL una", - "ĠLu na", - "ĠLun a", - "i esz", - "ies z", - "ie sz", - "Ġ =>'", - "Ġ= >'", - "Ġ=> '", - "Ġreve lations", - "Ġrevel ations", - "Ġrevelation s", - "A long", - "Al ong", - "( ed", - "(e d", - "Ġ Filename", - "ĠF ilename", - "ĠFile name", - "ĠFil ename", - "ĠFi lename", - "Ġ ylabel", - "Ġy label", - "S ecure", - "Sec ure", - "Ġbus ca", - "Ġbusc a", - "ag nosis", - "agn osis", - "_RE CE", - "_REC E", - "Ġover lapping", - "Ġoverlap ping", - "Ġoverl apping", - "Ex tent", - "Ext ent", - "Ġant icipation", - "Ġanticip ation", - "Ġantic ipation", - "Check s", - "Che cks", - "ĠAL SO", - "ĠALS O", - "o rc", - "or c", - "iling ual", - "it ational", - "itation al", - "itat ional", - "Ġadv ancement", - "Ġadvance ment", - "o uro", - "ou ro", - "our o", - "Ġ Predicate", - "ĠP redicate", - "ĠPred icate", - "å¾ Ĺ", - "e ria", - "er ia", - "eri a", - "ĠP ierce", - "ĠPi erce", - "ĠPier ce", - "o rio", - "or io", - "ori o", - "Ġmer its", - "Ġmerit s", - "Ġpe anut", - "Ġpea nut", - ". Package", - ".P ackage", - "ĠCon duct", - "ĠCond uct", - "_ SENSOR", - "_S ENSOR", - "_SENS OR", - "Ġbo iling", - "Ġboil ing", - "Ġin tra", - "Ġint ra", - "Ġintr a", - "Ġ IGN", - "ĠI GN", - "ĠIG N", - "ĠF ur", - "ĠFu r", - ". Refresh", - ".Re fresh", - ".Ref resh", - "Ġ Reach", - "ĠR each", - "ĠRe ach", - "_ decoder", - "_de coder", - "_dec oder", - "_decode r", - ". Exp", - ".Ex p", - ".E xp", - "Ġ ÑĤак", - "ĠÑĤ ак", - "ĠÑĤа к", - "p ill", - "pi ll", - ", Q", - "ĠGr ill", - "ĠGri ll", - "Ġp opping", - "Ġpop ping", - "Ġpo pping", - ". Ag", - ".A g", - "Ġpro yecto", - "Ġmile age", - "Ġec ological", - "Ġeco logical", - "] ]);Ċ", - "]] );Ċ", - "]]) ;Ċ", - "Ġ ÂŃ", - "Ġ Ń", - "sub plot", - "a cad", - "ac ad", - "aca d", - "Ġ Trying", - "ĠT rying", - "ĠTr ying", - "ĠTry ing", - "rec ipes", - "recipe s", - "$ criteria", - "$c riteria", - "ĠPer sian", - "ĠPers ian", - "- bound", - "-b ound", - "-bo und", - "M ASK", - "MA SK", - "MAS K", - "Ġ Gesture", - "ĠG esture", - "ĠGes ture", - "ĠGest ure", - "Ġ kk", - "Ġk k", - "ĠP VC", - "ĠPV C", - "Ġpro hibition", - "Ġprohib ition", - "Ġprohibit ion", - "Ġcom ando", - "Ġco mando", - "Ġcoma ndo", - "Ġ LOOK", - "ĠL OOK", - "ĠLO OK", - "Sh opping", - "Shop ping", - "Ġdist ortion", - "Ġdistort ion", - "< Boolean", - ".Get Length", - "um pt", - "ump t", - "\\ Product", - "ell ery", - "elle ry", - "eller y", - "Ġfire wall", - "form atted", - "format ted", - ". redis", - ".re dis", - ".r edis", - ".red is", - "Ġ esa", - "Ġe sa", - "Ġes a", - "ĠRh ode", - "S om", - "So m", - ". non", - ".n on", - ".no n", - "Ġ ').", - "Ġ' ).", - "Ġ') .", - "Ġ getView", - "Ġget View", - "ạ n", - "p rus", - "pr us", - "Mat thew", - "Ġs ia", - "Ġsi a", - "ĠF ors", - "ĠFor s", - "ĠFo rs", - "G PU", - "GP U", - "ient ras", - "ien tras", - "_ INST", - "_IN ST", - "_I NST", - "_INS T", - "Ġo larak", - "Ġol arak", - "Ġola rak", - "Ġimport ing", - "Ġimp orting", - "T CP", - "TC P", - "/ \");Ċ", - "/\" );Ċ", - "/\") ;Ċ", - "e ither", - "ei ther", - "Ġfresh ly", - "c ascade", - "cas cade", - "( character", - "(char acter", - "ĠJe ep", - "o tics", - "ot ics", - "otic s", - "oti cs", - "_ UTIL", - "_UT IL", - ".Xtra Printing", - ".first Child", - "ĠEx cell", - "ĠExcel l", - "ĠExc ell", - "Ġd vd", - "Ġdv d", - "Ġt aller", - "Ġtal ler", - "Ġta ller", - "Ġtall er", - "Ġ ras", - "Ġr as", - "Ġra s", - "y pass", - "yp ass", - "Ġassign s", - "Ġgr iev", - "Ġgri ev", - "- more", - "-m ore", - "J D", - "ĠBur ns", - "ĠBurn s", - "' >čĊ", - "'> čĊ", - ". Dependency", - ".D ependency", - ".Dep endency", - ". QueryString", - ".Query String", - ". Owner", - ".O wner", - "Ġ expiry", - "Ġex piry", - "Ġexp iry", - "T hu", - "Th u", - "( Vec", - "(V ec", - "Ġhazard ous", - "Ġ rpm", - "Ġr pm", - "Ġrp m", - "AP ON", - "APO N", - "Ġadd Target", - "s ville", - "sv ille", - "p Net", - "Ġ Img", - "ĠI mg", - "ĠIm g", - "Ġ TIMER", - "ĠT IMER", - "ĠTIM ER", - "ĠTIME R", - "ĠTI MER", - ". Animation", - ".An imation", - "Ġ bek", - "Ġb ek", - "Ġbe k", - "Ġas sort", - "Ġass ort", - "Ġle bih", - "Ġbody Parser", - "Ġvibr ating", - "Ġvib rating", - "I DL", - "ID L", - "Ġbutter knife", - "in ters", - "int ers", - "inter s", - "inte rs", - "Ġpersu ade", - "ĠLGBT Q", - "è ĭ", - ". soft", - ".s oft", - ".so ft", - "Ġbe ams", - "Ġbeam s", - "_ sur", - "_s ur", - "_su r", - ". Def", - ".D ef", - ".De f", - "Ġ labs", - "Ġl abs", - "Ġla bs", - "Ġlab s", - "ĉ plt", - "ĉp lt", - "ĉpl t", - "Ġ skins", - "Ġs kins", - "Ġsk ins", - "Ġskin s", - "Ġski ns", - "Ġtransfer ring", - "Ġtransf erring", - "Ġimag inary", - "Ġimagin ary", - "_ End", - "_E nd", - "; background", - "Ġ laps", - "Ġl aps", - "Ġla ps", - "Ġlap s", - "_ COMMENT", - "_COM MENT", - "_COMM ENT", - "( SDL", - "(S DL", - "o nds", - "on ds", - "ond s", - ". Record", - ".Re cord", - ".Rec ord", - "ĠIm plements", - "ĠImp lements", - "ĠImplement s", - "ĠImpl ements", - "_ ticks", - "_t icks", - "_tick s", - "_ti cks", - "( )))ĊĊ", - "() ))ĊĊ", - "()) )ĊĊ", - "())) ĊĊ", - "()))Ċ Ċ", - "Ġa rose", - "Ġar ose", - "] ?", - "Ġ Mp", - "ĠM p", - "ĠI Command", - "Ġsculpt ure", - "Ġcon tracted", - "Ġcontract ed", - "Ġcontr acted", - "< HTML", - "Ġcal end", - "a ty", - "at y", - "/ Sub", - "/S ub", - "Ġkv inn", - "Ġkvin n", - "_ IGNORE", - "ĠSh ane", - "ĠSha ne", - "ĠShan e", - "M LS", - "ML S", - "Ġstim ulate", - "Part ition", - "Ġ mun", - "Ġm un", - "Ġmu n", - "ó m", - "er ala", - "era la", - "eral a", - "- account", - "-a ccount", - "-ac count", - ". Binary", - ".B inary", - "c é", - "Ġse ize", - "Ġseiz e", - "Ġsei ze", - "conn ections", - "connect ions", - "connection s", - "Ġ ĊĠĠĠĠĠĠĠĠĊ", - "ĠĊ ĠĠĠĠĠĠĠĠĊ", - "Ġ Diagnostic", - "ĠDi agnostic", - "V ISIBLE", - "VIS IBLE", - "Ġ Runs", - "ĠR uns", - "ĠRun s", - "ĠRu ns", - "Ġim pressions", - "Ġimpress ions", - "Ġimpression s", - "Ġimpr essions", - "s uite", - "su ite", - "suit e", - "o ble", - "ob le", - "obl e", - "~ -", - "ak ukan", - "aku kan", - "< Person", - "

\">\" >\"> \">< /", - "_ indexes", - "_index es", - "Ġ valuation", - "Ġval uation", - "Ġvalu ation", - "Ġlife long", - "Ġlif elong", - "Ġexp edition", - "Ġexped ition", - "( Yii", - "(Y ii", - "Ġp ains", - "Ġpain s", - "Ġpa ins", - "Ġpai ns", - "Ġ PRI", - "ĠP RI", - "ĠPR I", - "Ġ Mixed", - "ĠM ixed", - "ĠMix ed", - "ĠMi xed", - "Ġ Changing", - "ĠCh anging", - "ĠChan ging", - "ĠChang ing", - "German y", - "Ger many", - "comm unication", - "communic ation", - ". organ", - ".org an", - ".o rgan", - ".or gan", - "ĠMar athon", - "ĠMara thon", - "get Path", - "Ġ Accuracy", - "ĠAc curacy", - "ĠAcc uracy", - "Ġ Globals", - "ĠG lobals", - "ĠGlobal s", - "ĠGlob als", - "') }}'", - "Ġ'\" >'", - "Ġ'\"> '", - "k inson", - "kin son", - "kins on", - "Ġ кол", - "Ġк ол", - "Ġко л", - "ogn itive", - "_ li", - "_l i", - "Ġim minent", - "Ġimm inent", - "Ġaff inity", - "Ġaf finity", - ". signal", - ".s ignal", - ".sign al", - ".sig nal", - "Ġn otch", - "Ġnot ch", - "ĠSteel ers", - "ĠSteele rs", - "max length", - "K K", - "ĠEu gene", - "ĠEug ene", - "_ PWM", - "_P WM", - "_PW M", - "r oi", - "ro i", - "Ġ âĹı", - "Ġâ Ĺı", - "ĠâĹ ı", - "ĠH amburg", - "ĠHam burg", - ". Must", - ".M ust", - "Ġ axe", - "Ġa xe", - "Ġax e", - "en ef", - "ene f", - "Ġamb itions", - "Ġambit ions", - "Ġambition s", - "Ġ Species", - "ĠS pecies", - "ĠSp ecies", - "ĠSpec ies", - "ĠSpe cies", - "ĠSt ress", - "ĠStr ess", - "ĠStre ss", - "Ġa while", - "Ġ бÑĥд", - "Ġб Ñĥд", - "ĠбÑĥ д", - "Ġwith stand", - "Ġ Decoder", - "ĠDe coder", - "ĠDec oder", - "ĠDecode r", - "_ inventory", - "_in ventory", - "Ġ{ ččĊ", - "Ġ tgt", - "Ġt gt", - "Ġtg t", - "Ġrail road", - "W ASHINGTON", - "Ġnegot iated", - "Ġnegotiate d", - "N ST", - "NS T", - "- phone", - "-p hone", - "-ph one", - ", U", - "Ġexerc ising", - "á» ¥", - "_P IXEL", - "_PIX EL", - "av ors", - "avor s", - "avo rs", - "ite rated", - "iter ated", - "iterate d", - "Ġv ampire", - "Ġvamp ire", - "a dal", - "ad al", - "ada l", - "In grese", - "Ing rese", - "Ġ ung", - "Ġu ng", - "Ġun g", - "j ective", - "ject ive", - ". cells", - ".c ells", - ".cell s", - "Ġ nano", - "Ġn ano", - "Ġna no", - "Ġnan o", - "Ġ markdown", - "Ġmark down", - "_ RULE", - "_R ULE", - "( events", - "(e vents", - "(event s", - "(ev ents", - "Ġl uggage", - "Ġlug gage", - "M ESSAGE", - "MESS AGE", - "ig keit", - "$ count", - "$c ount", - "Attribute Name", - "IG INAL", - "IGIN AL", - "_ Ent", - "_E nt", - "Ġ BF", - "ĠB F", - "Ġ COMMENT", - "ĠCOM MENT", - "ĠCOMM ENT", - "_ ini", - "_in i", - "_i ni", - "ĠEurope ans", - "ĠEuropean s", - "ĠB elle", - "ĠBe lle", - "ĠBel le", - "ĠBell e", - "åij ½", - ") ['", - ")[ '", - "åº Ķ", - "ĠUs eful", - "ĠUse ful", - ". reference", - ".re ference", - ".ref erence", - "( )\",", - "() \",", - "()\" ,", - "_ grade", - "_g rade", - "_gr ade", - "_grad e", - "ĠK aw", - "ĠKa w", - "Ġsent encing", - "Ġsocial ism", - "mon ster", - "mons ter", - "_L AYER", - "Ġdeep est", - "Ġdee pest", - "w k", - "Ġ Noise", - "ĠN oise", - "ĠNo ise", - "# ##ĊĊ", - "## #ĊĊ", - "### ĊĊ", - "###Ċ Ċ", - "Ġpr éc", - "Ġpré c", - "o tle", - "ot le", - "ÑĤ е", - "a uf", - "au f", - "i bal", - "ib al", - "iba l", - "Ġcon quer", - "Ġconqu er", - "> Email", - ">E mail", - "Ġamb ulance", - "O AD", - "OA D", - "Ġ (\"%", - "Ġ( \"%", - "Ġ(\" %", - "Ġ FI", - "ĠF I", - ". fixture", - ".f ixture", - ".fix ture", - "Ġt erse", - "Ġter se", - "Ġters e", - "ĠĠ ĠĠĉĉĉĉ", - "ĠĠĠĠ ĉĉĉĉ", - "ĠĠĠ Ġĉĉĉĉ", - "ĠĠĠĠĉ ĉĉĉ", - "ĠĠĠĠĉĉ ĉĉ", - "ĠĠĠĠĉĉĉ ĉ", - "Ġsanct uary", - "u gi", - "ug i", - "Ġ Comparator", - "ĠCom parator", - "ĠCompar ator", - "Definition s", - "Ġast hma", - "Ġl act", - "Ġla ct", - "Ġlac t", - "Ġhard wood", - ". clock", - ".c lock", - ".cl ock", - "Ġattr acting", - "Ġattract ing", - "ĠM our", - "ĠMo ur", - "ĠMou r", - "( distance", - "(d istance", - "(dist ance", - "(di stance", - "ic its", - "ici ts", - "icit s", - "Ġb onne", - "Ġbon ne", - "Ġ ACCESS", - "ĠAC CESS", - "ĠACC ESS", - ".Deserialize Object", - "Ġ Typed", - "ĠT yped", - "ĠType d", - "ĠTy ped", - "ĠTyp ed", - "Ġj eu", - "Ġje u", - "Ġ appId", - "Ġapp Id", - "ĠC lara", - "ĠCl ara", - "ĠClar a", - "ĠCla ra", - "Ġ HF", - "ĠH F", - "ĠRe ich", - "ĠRei ch", - "ip ples", - "ipp les", - "ipple s", - "// --------------------------------------------------------------------------------", - "//---------------------------------------------------------------- ----------------", - "//---------------------------------------------------------------------------- ----", - "//------------------------------------------------ --------------------------------", - "//-------------------------------- ------------------------------------------------", - "//---------------- ----------------------------------------------------------------", - "_ delivery", - "_d elivery", - "_del ivery", - "erial ization", - "Ġplaint iffs", - "Ġplaintiff s", - "S cient", - "Sc ient", - "Sci ent", - "sh opping", - "shop ping", - "Ġ Dummy", - "ĠD ummy", - "ĠDum my", - "ĠW ald", - "ĠWal d", - "ĠWa ld", - "Group Name", - "Ġ inscription", - "Ġin scription", - "Ġins cription", - "e log", - "el og", - "elo g", - ": :::::::", - ":: ::::::", - ":::: ::::", - ":::::: ::", - "::: :::::", - "::::: :::", - "::::::: :", - "_ ld", - "_l d", - "Back Pressed", - ". Raw", - ".R aw", - "ĠOn Trigger", - "Ġmuseum s", - "Ġmuse ums", - "Ġ Been", - "ĠB een", - "ĠBe en", - "ĠBee n", - "ĠAdvent ures", - "ĠAdventure s", - "Ġs late", - "Ġsl ate", - "Ġsla te", - "Ġ lett", - "Ġl ett", - "Ġle tt", - "Ġlet t", - "Ġs und", - "Ġsu nd", - "Ġsun d", - "ĠG in", - "ĠGi n", - "ĠMechan ical", - "ĠMech anical", - ". ship", - ".s hip", - ".sh ip", - "App Component", - "Ġdest ined", - "Ġdestin ed", - "Ġdw elling", - "Ġdwell ing", - "Pro filer", - "Profile r", - "Prof iler", - "Pre pare", - "ze ich", - "Ġsil icon", - "( has", - "(h as", - "Ġ# %", - "V IDEO", - "VID EO", - "Ġcollabor ate", - "L in", - "Li n", - "Ġ scopes", - "Ġsc opes", - "Ġscope s", - "Ġsco pes", - "Ġscop es", - "( className", - "(class Name", - "( sd", - "(s d", - "an din", - "and in", - "andi n", - ". ham", - ".h am", - "Service Impl", - "-de scribed", - "-des cribed", - "Ġir ony", - "Ġiron y", - "st ial", - "sti al", - "ĠHu awei", - "( repo", - "(re po", - "(rep o", - "Ġunexpected ly", - "ĠK ai", - "ĠKa i", - ". install", - ".inst all", - "\\ xf", - "\\x f", - "Ġex hibited", - "Ġexhib ited", - "Ġexhibit ed", - "_ TCP", - "_T CP", - "_TC P", - "ĠO x", - "_ CHO", - "_C HO", - "_CH O", - "Ġprostitu erte", - "Ġprostituer te", - "Ġ vä", - "Ġv ä", - "Ġs ito", - "Ġsit o", - "Ġsi to", - "Ġconstitu ents", - "Ġconstituent s", - "ĠContinue d", - "ĠContin ued", - "Ġ SAVE", - "ĠS AVE", - "ĠSA VE", - "r ss", - "rs s", - "/ message", - "/m essage", - "u bes", - "ub es", - "ube s", - "Ġmisd emean", - "Ġtax ation", - "Ġtaxa tion", - "Ġstory line", - "h air", - "ha ir", - "hai r", - "ĠF inds", - "ĠFin ds", - "ĠFind s", - "ĠFi nds", - "S IG", - "SI G", - "ver ification", - "~ =", - ". hp", - ".h p", - "It erable", - "Iter able", - "Ñĭ е", - "at ori", - "ator i", - "ato ri", - "Ġ ctr", - "Ġc tr", - "Ġct r", - "R x", - "_ );ĊĊ", - "_);Ċ Ċ", - "_) ;ĊĊ", - "d ag", - "da g", - ". pin", - ".p in", - ".pi n", - "Ġp seud", - "Ġin vo", - "Ġinv o", - "Ñģ ÑĤÑĢ", - "ÑģÑĤ ÑĢ", - "_ pix", - "_p ix", - "_pi x", - "为 空", - "Ġsw orn", - "Ġswo rn", - "âĢĶ or", - "_ registry", - "_reg istry", - "Ġdis asters", - "Ġdisaster s", - "Ġ ROI", - "ĠR OI", - "ĠRO I", - "Ġ âĢķ", - "ĠâĢ ķ", - "ak tu", - "akt u", - "f orest", - "fo rest", - "fore st", - "for est", - "be iten", - "beit en", - "bei ten", - "âĢĶ I", - "u eva", - "ue va", - "e gt", - "eg t", - "Ġsp ikes", - "Ġspi kes", - "Ġspike s", - "U RES", - "UR ES", - "URE S", - "Ġ Recommended", - "ĠRe commended", - "ĠRecomm ended", - "ĠRecommend ed", - "Ġexplo ited", - "Ġexploit ed", - "ĠFreder ick", - "_ COMPLETE", - "_COMP LETE", - "ĠDr ugs", - "ĠDrug s", - "!!! !!!!!", - "!!!! !!!!", - "!!!!! !!!", - "ĠR iv", - "ĠRi v", - "S TOP", - "ST OP", - "R OOM", - "RO OM", - "Ġ PASSWORD", - "ĠP ASSWORD", - "ĠPASS WORD", - "C ookies", - "Co okies", - "Cookie s", - "Cook ies", - ". El", - ".E l", - "á» Ń", - "ĠB ert", - "ĠBe rt", - "ĠBer t", - "Ġ hashed", - "Ġh ashed", - "Ġhas hed", - "Ġhash ed", - "Ġha shed", - "ic ester", - "ice ster", - "ices ter", - "Ġdecor ator", - "Ġ queryString", - "Ġquery String", - ": ;Ċ", - "Ġ\" [\"", - "Ġ\"[ \"", - "ot ope", - "oto pe", - "- Americ", - "-A meric", - "-Am eric", - "ĠMatthew s", - "ĠMatth ews", - "U RAL", - "UR AL", - "URA L", - "âĢľ ,", - "S ummer", - "Sum mer", - "f os", - "fo s", - "_CONT AINER", - "_ ACK", - "_A CK", - "_AC K", - "Ġ filtr", - "Ġf iltr", - "Ġfil tr", - "Ġfi ltr", - "Ġfilt r", - "_ disp", - "_d isp", - "_dis p", - "_di sp", - "_ Re", - "_R e", - "Ġfac ile", - "Ġfacil e", - "а ÑĪ", - "Ġìķ Ĭ", - "Ġe ben", - "Ġeb en", - "Ġsp rink", - "Ġspr ink", - "ĠQ uint", - "ĠQu int", - "ĠQui nt", - "> V", - "Ġhistor ians", - "Ġhistorian s", - "Ġhistoria ns", - "Ġhisto rians", - "our met", - "Ġ Monitoring", - "ĠMonitor ing", - "led ger", - "ledge r", - "c ott", - "co tt", - "cot t", - "Ġ ware", - "Ġw are", - "Ġwar e", - "Ġwa re", - "G GLE", - "GG LE", - "c ars", - "ca rs", - "car s", - "ĠM EDIATEK", - "Ġvol upt", - "_ View", - "_V iew", - "H EL", - "HE L", - "( copy", - "(c opy", - "(co py", - "( stats", - "(st ats", - "(stat s", - "Ġch romosome", - "Ġchrom osome", - "ĠCurt is", - "- conf", - "-con f", - "-co nf", - "( asset", - "(as set", - "Ġh vor", - "Ġhv or", - "File System", - "< >();čĊ", - "<>( );čĊ", - "<> ();čĊ", - "o coder", - "oc oder", - "oco der", - "ocode r", - "ĠC annon", - "ĠCan non", - "ĠCann on", - ") x", - "Ġ Smooth", - "ĠSm ooth", - "ĠS AS", - "ĠSA S", - "_ ce", - "_c e", - "ĉ prev", - "ĉp rev", - "ĉpr ev", - "ĉpre v", - "_ movie", - "_m ovie", - "_mov ie", - "_mo vie", - "E c", - "_ wall", - "_w all", - "< Button", - ".ĊĊ", - "/> .ĊĊ", - "/>.Ċ Ċ", - "/>. ĊĊ", - "o genesis", - "ogen esis", - "ogene sis", - "Ġ OPTIONS", - "ĠOPTION S", - "ĠOPT IONS", - "up tools", - "upt ools", - "Ġmilit ant", - "Ġmil itant", - "Ġmili tant", - "Ġex ited", - "Ġexit ed", - "i gar", - "ig ar", - "iga r", - "Ġ COMM", - "ĠCO MM", - "ĠCOM M", - "Ġ Disposable", - "ĠD isposable", - "ĠDis posable", - "ay cast", - "Ġrow span", - "Ġrows pan", - "Ġsyn thes", - "Ġsynth es", - "Ġsynt hes", - "Ġso ndern", - "Ġsond ern", - "Ġ Ċ", - "]- ->Ċ", - "ĠJ acket", - "ĠJack et", - "ĠJac ket", - "ĠJa cket", - "R ATION", - "RA TION", - ".get SelectedItem", - ".getSelected Item", - "- init", - "-in it", - "-i nit", - "Ġ Registers", - "ĠReg isters", - "ĠRegister s", - "_ sep", - "_s ep", - "_se p", - "Ġ Toolkit", - "ĠTool kit", - ". dict", - ".d ict", - ".di ct", - "Ġ xlabel", - "Ġx label", - "Ġxl abel", - "\\ Table", - "t oc", - "to c", - "_ combo", - "_c ombo", - "_com bo", - "_comb o", - "Ġ Compact", - "ĠComp act", - "Ġr ugged", - "Ġrug ged", - "à¥ĩ à¤", - "- management", - "-man agement", - "') }}\">Ċ", - "')}} \">Ċ", - "')}}\" >Ċ", - "')} }\">Ċ", - "')}}\"> Ċ", - "Ġ Stamp", - "ĠSt amp", - "ĠSta mp", - "ĠStam p", - "ı l", - "r ox", - "ro x", - "Ġlandscape s", - "Ġlandsc apes", - "_ NOTE", - "_N OTE", - "_NO TE", - "_NOT E", - "mon ary", - "c ab", - "ca b", - "Ġmo et", - "x af", - "xa f", - "r code", - "rc ode", - "- cli", - "-c li", - "-cl i", - "_ gate", - "_g ate", - "[ event", - "[e vent", - "S PORT", - "SP ORT", - "g ia", - "gi a", - "Ġ SUPER", - "ĠS UPER", - "ĠSU PER", - "ĠSUP ER", - "/ Login", - "_ shutdown", - "_sh utdown", - "int errupt", - "inter rupt", - "Ġpret ending", - "Ġpretend ing", - "Ġf ringe", - "Ġfr inge", - "Ġfri nge", - "ĠR eds", - "ĠRe ds", - "ĠRed s", - "Ġ CUDA", - "ĠC UDA", - "ĠCU DA", - "Ġ UNIX", - "ĠUN IX", - "v it", - "vi t", - "Ġ brig", - "Ġb rig", - "Ġbr ig", - "Ġbri g", - "d rv", - "dr v", - "Ġ Connector", - "ĠConnect or", - "ĠConn ector", - "There fore", - "Ġ lia", - "Ġl ia", - "Ġli a", - "D etection", - "De tection", - "Det ection", - "Detect ion", - "_ actor", - "_a ctor", - "_ac tor", - "_act or", - "Ġtemp file", - "Ġecc entric", - "- role", - "-r ole", - "-ro le", - "Ġp adx", - "Ġpa dx", - "Ġpad x", - "d ent", - "de nt", - "den t", - "West ern", - "Ġ ê·¸", - "Ġê ·¸", - "Ġê· ¸", - "ĠApplication Record", - "Ġcampaign ing", - "_ runner", - "_r unner", - "_run ner", - "ĠC ivic", - "ĠCi vic", - "ĠCiv ic", - "a leigh", - "ale igh", - "Ġdir ekt", - "Ġdire kt", - ".s ul", - "Ġ Ġĉĉĉ", - "ĠĠ ĉĉĉ", - "ĠĠĉ ĉĉ", - "ĠĠĉĉ ĉ", - "a nten", - "an ten", - "ant en", - "ante n", - "Ġ issuer", - "Ġiss uer", - "Ġissue r", - "Ġissu er", - "Ġassert ions", - "Ġassertion s", - "( orig", - "(o rig", - "(or ig", - "AT IO", - "Ġle aned", - "Ġlean ed", - "ä s", - ". DTO", - ".D TO", - "ex plode", - "expl ode", - "explo de", - ". Observable", - ".O bservable", - "Ġstagger ing", - "Ġkidn apped", - "Ġprogram mers", - "Ġprogramme rs", - "Ġprogrammer s", - "Ġprogramm ers", - "ĠIn nov", - "ĠInn ov", - ". parameter", - ".param eter", - "Ġd omination", - "Ġdo mination", - "Ġdom ination", - "Ġdomin ation", - "Ġdomina tion", - "Ġske ptic", - "Ġskept ic", - "Ġ æĺ¯", - "Ġæĺ ¯", - "Ġav oids", - "Ġavoid s", - ". Verify", - ".Ver ify", - "ub by", - "ubb y", - "Ġ ASN", - "ĠA SN", - "ĠAS N", - "Ġform ato", - "Ġformat o", - "Ġforma to", - "ĠBeat les", - "_ brand", - "_b rand", - "_br and", - "Ġin set", - "Ġins et", - "Ġinse t", - "y outu", - "you tu", - "Ġ toc", - "Ġt oc", - "Ġto c", - "- final", - "-f inal", - "-fi nal", - "-fin al", - "Sh owing", - "Show ing", - "ĠD oub", - "ĠDo ub", - "ĠDou b", - "ĠM esa", - "ĠMe sa", - "ĠMes a", - "A dj", - "Ad j", - "_ medium", - "_m edium", - "_med ium", - "Create s", - "Cre ates", - "Creat es", - "( endpoint", - "(end point", - "ĉ UP", - "ĉU P", - "b bie", - "bb ie", - "Ġ stalk", - "Ġs talk", - "Ġst alk", - "Ġsta lk", - "Ġstal k", - ".data bind", - ".datab ind", - ". Scan", - ".S can", - ".Sc an", - "ag ents", - "age nts", - "agent s", - "agen ts", - "$ ,", - "ind ividual", - "+ )/", - "+) /", - "ĉ vm", - "ĉv m", - "( notification", - "(not ification", - "Ġin ex", - "Ġi nex", - "Ġine x", - "Ġ Classification", - "ĠClass ification", - "r eno", - "re no", - "ren o", - "Ġo lig", - "Ġol ig", - "Ġoli g", - "- rated", - "-r ated", - "-rate d", - "-ra ted", - "Ġform ulation", - "Ġformula tion", - "Ġformul ation", - "' ,{", - "', {", - "Ġa cept", - "Ġac ept", - "Ġace pt", - "_ unpack", - "_un pack", - "_ CA", - "_C A", - ". Pow", - ".P ow", - "ĉ im", - "ĉi m", - "Ġal uminium", - "Ġalum inium", - "A NO", - "AN O", - "Ġ xn", - "Ġx n", - "Ġc ómo", - "Ġcó mo", - "Ġ Ingredient", - "ĠIng redient", - "Ġseiz ures", - "Ġseizure s", - "åħ ±", - "ific ador", - "ificado r", - "Ġs iguiente", - "Ġsigu iente", - "ĠIn fragistics", - "Ġd uplicated", - "Ġduplicate d", - "Ġdup licated", - "Ġduplic ated", - "ĠD ee", - "ĠDe e", - "Ġn ø", - "Ġ ACCEPT", - "ĠAC CEPT", - "( crate", - "(c rate", - "(cr ate", - "иÑĤ елÑĮ", - "иÑĤе лÑĮ", - "- less", - "-l ess", - "-le ss", - "Ġ infinity", - "Ġin finity", - "Ġinf inity", - "Ġinfinit y", - "An alyzer", - "Analy zer", - "- Day", - "-D ay", - "r itt", - "ri tt", - "rit t", - "( cin", - "(c in", - "(ci n", - "ĠG y", - "Ġmulti plied", - "Ġmultip lied", - "u chi", - "uch i", - "uc hi", - "ĠBald win", - "/ ip", - "/i p", - "Ġshort cuts", - "Ġshortcut s", - ". ADD", - ".A DD", - ".AD D", - "Ġv igor", - "Ġvi gor", - "Ġvig or", - "_ instruction", - "_in struction", - "_instr uction", - "( ;", - "_ eta", - "_e ta", - "_et a", - "è¿ ŀ", - "utor ials", - "utorial s", - "Ġboost ing", - "b v", - "Ġacknowled ges", - "Ġacknowledge s", - "List ening", - "Listen ing", - "F AQ", - "FA Q", - "; b", - "( (-", - "(( -", - "Ġarchitect s", - "Ġarchit ects", - "Ġz we", - "Ġzw e", - "Ġp uls", - "Ġpul s", - "Ġpu ls", - "Ġget Count", - "ĠgetC ount", - "ver bs", - "verb s", - "ãĢ ľ", - "( Collection", - "(C ollection", - "k re", - "kr e", - "Ġjurisdiction s", - "Ġjuris dictions", - "_ bridge", - "_b ridge", - "_br idge", - "ĠC rack", - "ĠCr ack", - "ĠCra ck", - "Ġ Difficulty", - "ĠDiff iculty", - "K O", - "Res ervation", - "_ requires", - "_re quires", - "_require s", - "T our", - "To ur", - "ãģĹ ãģŁ", - "ãģĹãģ Ł", - ". setCurrent", - ".set Current", - "Ġ ky", - "Ġk y", - "ĠAlb any", - "ĠAlban y", - "Ġ è§", - "Ġè §", - "l ler", - "ll er", - "lle r", - "ag na", - "agn a", - "work ers", - "worker s", - "wor kers", - ". blank", - ".bl ank", - "ĠPr ayer", - "ĠPra yer", - "M IC", - "MI C", - "Ġresil ience", - "Te X", - "Ġ Languages", - "ĠL anguages", - "ĠLanguage s", - "st udy", - "stu dy", - "stud y", - "ĉ curr", - "ĉc urr", - "ĉcur r", - "Ġenzym es", - "Ġenzyme s", - "S lug", - "Sl ug", - "Ġ íĮĮ", - "ĠíĮ Į", - "st ral", - "str al", - "stra l", - "Ġtum ors", - "Ġtumor s", - "Ġseg unda", - "Ġsegu nda", - "= '{", - "=' {", - "in struction", - "instr uction", - "ĠL isp", - "ĠLi sp", - "ĠLis p", - "/ info", - "/in fo", - "Ġ\" {$", - "Ġ\"{ $", - ",: ),", - ",:) ,", - "Ġ gv", - "Ġg v", - "( ErrorMessage", - "(Error Message", - "Ġ '=", - "Ġ' =", - "} -${", - "}- ${", - ". Documents", - ".Document s", - ".Doc uments", - "\" Well", - "\"We ll", - "\"W ell", - "Ġreminis cent", - "Ġg az", - "Ġga z", - "ir opr", - "iro pr", - "e hr", - "eh r", - "Ġsup pressed", - "Ġsuppress ed", - "Ġsupp ressed", - "er sh", - "ers h", - ".scroll To", - "Ġ cadena", - "Ġc adena", - "Ġcad ena", - "Ġcade na", - "Ġgame State", - "ÃŃ m", - "( conv", - "(con v", - "(co nv", - "Ġ Tomorrow", - "ĠTom orrow", - "ĠC CT", - "ĠCC T", - "M ongo", - "Mon go", - "Mo ngo", - "u lg", - "ul g", - ". Camera", - ".C amera", - ". handlers", - ".handle rs", - ".handler s", - ".hand lers", - "m ph", - "mp h", - "Ġ stk", - "Ġs tk", - "Ġst k", - "Ġgen etics", - "Ġgene tics", - "Ġgenetic s", - "AC ING", - "Tr ivia", - "Tri via", - "ĠB am", - "ĠBa m", - "( marker", - "(m arker", - "(mark er", - ". Stretch", - ".St retch", - ".Str etch", - "ĠSun ni", - "ĠB etty", - "ĠBe tty", - "ĠBet ty", - "ĠBett y", - ". tolist", - ".t olist", - ".to list", - "un likely", - ". Rectangle", - ".Rect angle", - "ob solete", - "IL ON", - "inner Text", - "em bourg", - "emb ourg", - "a N", - "ĠV ehicles", - "ĠVehicle s", - "un lock", - ": utf", - "n ob", - "no b", - "Ġ Seeing", - "ĠSe eing", - "ĠSee ing", - "ĠN EVER", - "ĠNE VER", - "Ġ tls", - "Ġt ls", - "Ġtl s", - "Ġf illes", - "Ġfil les", - "Ġfill es", - "Ġfille s", - "Ġbenef ited", - "Ġbenefit ed", - "ĠC lint", - "ĠCl int", - "ĠClin t", - "ĠCli nt", - "*/ ),", - "*/) ,", - ". fold", - ".f old", - "Ġpos ible", - "Ġposi ble", - "A DED", - "AD ED", - "ADE D", - "t house", - "th ouse", - ". DAL", - ".D AL", - "Ġ Odd", - "ĠO dd", - "ĠOd d", - "r okes", - "ro kes", - "roke s", - "rok es", - "ĠS unny", - "ĠSun ny", - "ĠPartial Eq", - "_ Buffer", - "_B uffer", - "ĠL evi", - "ĠLe vi", - "ĠLev i", - "long rightarrow", - "el don", - "eld on", - "eldo n", - "g ages", - "ga ges", - "gage s", - "_ warn", - "_w arn", - "_war n", - ".Create Table", - "ĠD ip", - "ĠDi p", - "_ questions", - "_question s", - "_quest ions", - ". logic", - ".log ic", - "Ġ #\"", - "Ġ# \"", - "={ ()=>", - "={() =>", - "={( )=>", - "Ġ tep", - "Ġt ep", - "Ġte p", - "Ġju icy", - "ì Ĥ¬", - "ìĤ ¬", - "en ko", - "enk o", - "ial ect", - "ia lect", - "iale ct", - "Ù ī", - "Ġon board", - "Ġ æı", - "Ġæ ı", - "ĉ rt", - "ĉr t", - "_ UTF", - "_U TF", - "_UT F", - "ĠQ Action", - "ĠQA ction", - "âĢ ŀ", - "( Component", - "( audio", - "(a udio", - ". hit", - ".h it", - "g te", - "gt e", - "Ġprogram med", - "Ġprogramme d", - "Ġprogramm ed", - "state Params", - "Ġpoly ester", - "f ires", - "fi res", - "fire s", - "fir es", - "by ss", - "] =(", - "]= (", - "_ quality", - "_q uality", - "_qu ality", - "_qual ity", - "Of Day", - "ĠF airy", - "ĠFair y", - "ĠFa iry", - "Ġy elled", - "Ġyell ed", - "o pl", - "op l", - "( userName", - "(user Name", - "Ġ Difference", - "ĠD ifference", - "ĠDiff erence", - "Ġeval uations", - "Ġevaluation s", - "Ġevalu ations", - "iff any", - "Ġcycl ists", - "Ġcyc lists", - "Ġcyclist s", - "Ġ cidade", - "Ġc idade", - "Ġcid ade", - "Ġtext book", - "Ġprof iling", - "Ġprofil ing", - "_ _),", - "__ ),", - "__) ,", - "d ea", - "de a", - ". activate", - ".act ivate", - ".activ ate", - "Ġind ications", - "Ġindic ations", - "Ġindication s", - "Ð ķ", - "Touch UpInside", - "Ġinval uable", - "Ġ MASK", - "ĠM ASK", - "ĠMA SK", - "ĠMAS K", - "Ġcont end", - "Ġconten d", - "Ġconte nd", - "F req", - "Fr eq", - "Fre q", - "Ġrec ruits", - "Ġrecru its", - "Ġrecruit s", - "( interval", - "(int erval", - "(inter val", - "Ġ UserProfile", - "ĠUser Profile", - "Ġ'./ ../", - "Ġ'. /../", - "e du", - "ed u", - "_ Callback", - "_C allback", - "_Call back", - "Ġan alogy", - "Ġanal ogy", - "Ġanalog y", - "Ġana logy", - "ĠT rophy", - "ĠTr ophy", - "ĠTro phy", - "app hire", - "V ideos", - "Video s", - "ĠC her", - "ĠCh er", - "ĠChe r", - "ĠH av", - "ĠHa v", - "âĢ¦ \"", - ". validator", - ".valid ator", - "g fx", - "gf x", - "ĠU Object", - "class names", - "classname s", - "t riangle", - "tr iangle", - "tri angle", - "Ġ Encoder", - "ĠE ncoder", - "ĠEn coder", - "ĠEnc oder", - "ĠEncode r", - ". spy", - ".s py", - ".sp y", - "Ġpred ators", - "Ġpredator s", - "= status", - "=s tatus", - "- safe", - "-s afe", - ": \",Ċ", - ":\" ,Ċ", - ":\", Ċ", - "Ġ Including", - "ĠIn cluding", - "Ġ{ };čĊ", - "Ġ{} ;čĊ", - "Ġ{}; čĊ", - "* cos", - "*c os", - "Ġend ured", - "Ġendure d", - ".sul ake", - "Ġnurs ery", - "Ġnurse ry", - "Ġfrag rance", - "Ġre building", - "Ġrebuild ing", - "Ġ nth", - "Ġn th", - "Ġnt h", - "ĠFr aser", - "ĠFra ser", - ".set Date", - "ĠV ince", - "ĠVi nce", - "ĠVin ce", - "_ REST", - "_RE ST", - "_R EST", - "_RES T", - "Ġvent ilation", - "Ġventil ation", - "æµ ·", - "cri bes", - "cribe s", - ". asm", - ".as m", - ".a sm", - "lp Vtbl", - "ĠA be", - "ĠAb e", - "u isine", - "uis ine", - ", array", - ",a rray", - ",arr ay", - "ĉ className", - "ĉclass Name", - "err als", - "erra ls", - "erral s", - "Ġ 'ĊĊ", - "Ġ' ĊĊ", - "Ġ'Ċ Ċ", - "Check out", - "Ġs olicit", - "Ġso licit", - "Ġsol icit", - "Ġsolic it", - "A ux", - "Au x", - "_ capture", - "_c apture", - "_cap ture", - "Ġr ibs", - "Ġrib s", - "Ġri bs", - "r agon", - "ra gon", - "rag on", - "v iol", - "vi ol", - "vio l", - "to pics", - "top ics", - "topic s", - "Function Flags", - "ĠM arty", - "ĠMar ty", - "ĠMart y", - "b ike", - "bi ke", - "ĠT ucker", - "ĠTu cker", - "( kernel", - "(k ernel", - "Ġ Ops", - "ĠO ps", - "ĠOp s", - "Close Operation", - "/ demo", - "/d emo", - "/de mo", - "i lda", - "il da", - "ild a", - "ĠlÃŃ nea", - "AP PING", - "APP ING", - "Ġsu ites", - "Ġsuit es", - "Ġsuite s", - "Ġsui tes", - ".visit VarInsn", - "u rus", - "ur us", - "uru s", - "Ġ Minute", - "ĠMin ute", - "( manager", - "(m anager", - "(man ager", - "Ġbutter fly", - "Ġa pare", - "Ġap are", - "Ġapar e", - "Ġapa re", - "Ġw olves", - "Ġwol ves", - "J WT", - "ĠS alon", - "ĠSal on", - "ĠSa lon", - "ĉ delay", - "ĉd elay", - "ĉde lay", - "ĉdel ay", - "- eslint", - "-es lint", - "is ations", - "isation s", - ". rpc", - ".r pc", - ") |(", - ")| (", - "ĠSnap chat", - "/ mm", - "/m m", - "M N", - "c eries", - "ce ries", - "cer ies", - ".t extAlignment", - ".text Alignment", - "ĠFrank furt", - "Ġ ado", - "Ġa do", - "Ġad o", - "( newValue", - "(new Value", - "( access", - "(a ccess", - "(ac cess", - "(acc ess", - "( Expression", - "Ġ SignIn", - "ĠSign In", - "ĠHa iti", - "ĠHait i", - "ĠHai ti", - "_ tp", - "_t p", - ". setParameter", - ".set Parameter", - "Min ute", - "Ġmanual s", - "ric anes", - "ricane s", - "rica nes", - "Ġ PTR", - "ĠP TR", - "ĠPT R", - "Ġ Outer", - "ĠO uter", - "ĠOut er", - "ĠOu ter", - "Ġ getline", - "Ġget line", - "oc ations", - "ocation s", - "_ CD", - "_C D", - "ĠL yon", - "ĠLy on", - "/ gui", - "/g ui", - "_ live", - "_l ive", - "_li ve", - "i dan", - "id an", - "ida n", - ". geom", - ".ge om", - ".geo m", - "Ġborder Bottom", - "im uth", - "imu th", - "_ checkpoint", - "_check point", - "Ġm eu", - "Ġme u", - "ĠIr ving", - "Ġpeu vent", - "( MAX", - "(M AX", - "Ġ ARCH", - "ĠAR CH", - "ĠARC H", - "Ġp ov", - "Ġpo v", - ".source forge", - "Ġjam ais", - "Ġ ark", - "Ġa rk", - "Ġar k", - "ĠBaghd ad", - "Ġ CLEAR", - "ĠC LEAR", - "ĠCL EAR", - "Menu Bar", - "Ġtr ois", - "Ġtro is", - "CHED ULE", - "Ġ #čĊ", - "Ġ# čĊ", - "( Call", - "(C all", - "$ order", - "( Material", - "(M aterial", - "(Mat erial", - "Ġencontr ado", - "$ list", - "$l ist", - "ĠMETHOD S", - ". beginTransaction", - ".begin Transaction", - "_M AG", - "_MA G", - "Style Sheet", - "Ġmajor s", - "Ġmaj ors", - "Ġindef initely", - "Ġindefinite ly", - "c leanup", - "clean up", - "Ġhome land", - "Ġhom eland", - "( dto", - "(d to", - "(dt o", - "D ates", - "Date s", - "Da tes", - "Dat es", - "P resentation", - "Present ation", - "Ġ DK", - "ĠD K", - "={` /", - "ĉ Key", - "ĉK ey", - "( Block", - "(B lock", - "_ checkbox", - "_check box", - "ne eds", - "need s", - "nee ds", - "Ġon Complete", - "r ico", - "ri co", - "ric o", - "Ġg leich", - "Ġgle ich", - "Ġ xm", - "Ġx m", - "O OD", - "OO D", - "B etter", - "Bet ter", - "ĠSQL ITE", - ". Book", - ".B ook", - "x ad", - "xa d", - "ĠG one", - "ĠGo ne", - "ĠGon e", - "ĉ dp", - "ĉd p", - "Ġdev otion", - "Ġ stm", - "Ġs tm", - "Ġst m", - "Ġob sess", - "Ġobs ess", - "Ġ Backend", - "ĠBack end", - "Qu eries", - "Que ries", - "I k", - "/ /****************************************************************", - "// ****************************************************************", - "Ġdivide nds", - "Ġdivid ends", - "Ġdividend s", - ".parent Element", - "} \")ĊĊ", - "}\" )ĊĊ", - "}\")Ċ Ċ", - "}\") ĊĊ", - "ĠMaterial PageRoute", - ": num", - ":n um", - "Ġexp lic", - "Ġexpl ic", - "Ġ OL", - "ĠO L", - "l east", - "le ast", - "lea st", - "O ops", - "iment os", - "imento s", - "imen tos", - "Ġins urers", - "Ġinsure rs", - "Ġinsurer s", - "Ġhero ic", - "ĉ fields", - "ĉf ields", - "ĉfield s", - ".img ur", - ".btn Cancel", - "ĠDet ective", - "ĠDetect ive", - "( sm", - "(s m", - "ĠMutable LiveData", - ". lab", - ".l ab", - "( ([", - "(( [", - "Ġha irst", - "Ġhair st", - "Ġhai rst", - "Ġhairs t", - "Ġ Transactions", - "ĠTrans actions", - "ĠTransaction s", - "å¼Ģ å§ĭ", - "Ġ stdClass", - "Ġstd Class", - "u ento", - "uen to", - "uent o", - "G IS", - "GI S", - "_ cod", - "_c od", - "_co d", - "In structions", - "Instruction s", - "Instr uctions", - "C alls", - "Call s", - "Cal ls", - "Pointer Type", - "ĠR w", - "Ġassort ment", - "Ġ DIG", - "ĠD IG", - "ĠDI G", - "+ r", - "_ CERT", - "_C ERT", - "_CE RT", - "Ġinst ability", - "Ġv ib", - "Ġvi b", - "o nas", - "on as", - "ona s", - "Ġr oku", - "Ġro ku", - "Ġrok u", - "ap ellido", - "Ġ angl", - "Ġan gl", - "Ġang l", - "prene ur", - "Ġfl uids", - "Ġfluid s", - "Ġflu ids", - "is ease", - "ise ase", - "Ġd eed", - "Ġde ed", - "Ġdee d", - "qu ist", - "quis t", - "qui st", - "_CONST ANT", - "Ġequ ilibrium", - "_ delegate", - "_de legate", - "ĠQuant um", - "r ei", - "re i", - "Cap abilities", - "rect angle", - "? ><", - "?> <", - "a lien", - "al ien", - "ali en", - "alie n", - "ĠJ ug", - "ĠJu g", - "D NA", - "DN A", - "T ickets", - "Tick ets", - "Ticket s", - "Occ urs", - "ĠH awk", - "ĠHaw k", - "ĠHa wk", - ".setHorizontal Group", - "\\ Collection", - "\\C ollection", - "ff iti", - "ffi ti", - "Ġre arr", - "Ġrear r", - ".setVertical Group", - "Ġc avity", - "Ġcav ity", - "Ġadult e", - "Ġadul te", - "Fac ade", - "Fa cade", - "- wh", - "-w h", - "ĠL OL", - "ĠLO L", - "Ø °", - "Ġgrand parents", - "Sw ift", - "ĉ wx", - "ĉw x", - "æīĢ æľī", - "i fen", - "if en", - "ife n", - "ff set", - "B eyond", - "// }ĊĊ", - "//}Ċ Ċ", - "Ġw ager", - "Ġwa ger", - "Ġwage r", - "Ġwag er", - "Ġ bury", - "Ġb ury", - "Ġbu ry", - "Ġbur y", - "Ġcomm ence", - "Ġcomme nce", - "Ġcommenc e", - "reg istro", - "registr o", - "regist ro", - "s cient", - "sc ient", - "sci ent", - "Ġ Percent", - "ĠPer cent", - "ĠPerc ent", - "Ġд олж", - "Ġдол ж", - "( identifier", - "(id entifier", - "(ident ifier", - ".set Model", - "Ġs eldom", - "Ġsel dom", - "n ton", - "nt on", - "Ġap pliance", - "Ġappl iance", - "a mus", - "am us", - "amu s", - "rys ler", - "Ġpan ties", - "Ġpant ies", - "engu ins", - "enguin s", - "Ġmi mic", - "Ġmim ic", - "Ġon Changed", - "ĠonChange d", - "Ġal coholic", - "Ġalcohol ic", - ".reload Data", - "Ch arge", - "Char ge", - "Ġ Fax", - "ĠF ax", - "ĠFa x", - "Ġj ScrollPane", - "Emp resa", - "Ġsh attered", - "x ba", - "xb a", - "Font s", - "Fo nts", - "? s", - "Ġpost season", - "re tain", - "ret ain", - "reta in", - "_ rates", - "_r ates", - "_rate s", - "_ra tes", - "_rat es", - "Ġ requestCode", - "Ġrequest Code", - ". todo", - ".t odo", - ".to do", - "´ s", - "C HK", - "CH K", - "Ġ Keeping", - "ĠKe eping", - "ĠKeep ing", - "ĠKee ping", - "enge ance", - "Ġvs code", - "IP PING", - "IPP ING", - "Default CloseOperation", - "_ raise", - "_r aise", - "_ra ise", - "ĠO culus", - "ĠOc ulus", - "o grams", - "og rams", - "ogram s", - "ogr ams", - "ogra ms", - "r aj", - "ra j", - "p ci", - "pc i", - "Ġcorros ion", - ". handleSubmit", - ".handle Submit", - "Access ible", - "ĠP iano", - "ĠPi ano", - "l ittle", - "lit tle", - "A CL", - "AC L", - "Äĩ e", - ". unwrap", - ".un wrap", - "ĠCon vers", - "ĠConv ers", - "ĠLe ben", - "ion eer", - "ione er", - "Ġ Merchant", - "ĠM erchant", - "ĠMer chant", - "ĠMerch ant", - "ĠJ orge", - "Ġembr acing", - "Ġ venta", - "Ġv enta", - "Ġvent a", - "Ġven ta", - "á st", - "ás t", - "Ġv iene", - "Ġvi ene", - "Ġvie ne", - "< QString", - "Ċ", - "-g rowing", - "-gr owing", - "-grow ing", - "Ġdeep copy", - "A ck", - "Ac k", - "eg gies", - "egg ies", - "Ġ __(\"", - "Ġ_ _(\"", - "Ġ__ (\"", - "Ġ__( \"", - "Ġn oir", - "Ġno ir", - "Ġnoi r", - "terror ism", - "Ġan them", - "Ġant hem", - "Ġanth em", - "a gency", - "ag ency", - "age ncy", - "agen cy", - "_ PACKAGE", - "_PACK AGE", - "Ġ Closure", - "ĠC losure", - "ĠClo sure", - ". registry", - ".reg istry", - "Ġmamm als", - "Ġmamma ls", - "< L", - "U ICollectionView", - "UI CollectionView", - "ĠLE Ds", - "ĠLED s", - "Ġv olley", - "Ġvol ley", - "Ġvoll ey", - "( Buffer", - "(B uffer", - "_N ATIVE", - "li bc", - "lib c", - "im plode", - "impl ode", - "Scroll Bar", - "ĠMar ion", - "ĠMario n", - "ĠMari on", - ". Contracts", - ".Con tracts", - ".Contract s", - "_ At", - "_A t", - "ĠWe instein", - "ĠWein stein", - "compare To", - "ĠH ose", - "ĠHo se", - "ĠHos e", - "en ity", - "eni ty", - ". createQuery", - ".create Query", - "_ router", - "_r outer", - "_ro uter", - "_route r", - "Ġstim uli", - "Ġ ++)", - "Ġ+ +)", - "Ġ++ )", - "ĠCh amp", - "ĠCha mp", - "ĠCham p", - "ĠBay ern", - "ĠBayer n", - "a ssa", - "as sa", - "ass a", - ". va", - ".v a", - "Ġdistrib utors", - "Ġdistributor s", - "Ġfile private", - "Ġdepart ed", - "c ccc", - "cc cc", - "ccc c", - "@ click", - "@c lick", - "ĠL unch", - "ĠLun ch", - "> L", - "Ġb luetooth", - "Ġbl uetooth", - ". Deep", - ".De ep", - "- standing", - "-st anding", - "á cil", - "ác il", - "áci l", - "Ġro oft", - "Ġroof t", - "Ġ Paths", - "ĠP aths", - "ĠPat hs", - "ĠPath s", - "ĠPa ths", - "_ iterations", - "_iter ations", - "_iteration s", - "Invalid ArgumentException", - ". spi", - ".s pi", - ".sp i", - "Ġ UIAlertAction", - "ĠUIAlert Action", - "u ye", - "uy e", - "sign in", - "sig nin", - ". priority", - ".p riority", - "ĠEs says", - "ĠEss ays", - "ĠEssay s", - "=' {$", - "='{ $", - "Ġ è¿ĶåĽŀ", - "Ġè¿ ĶåĽŀ", - "_ signed", - "_s igned", - "_sign ed", - "_sig ned", - ". persist", - ".p ersist", - "Ġre design", - "Ġred esign", - "Ġrede sign", - "Ġredes ign", - "To Lower", - "ĠNew man", - "= start", - "ĠIsrael is", - "ĠIsraeli s", - "as iswa", - "asis wa", - "S peech", - "Spe ech", - "Ġnum eros", - "Ġnumer os", - "Ġnumero s", - "handle rs", - "hand lers", - "handler s", - "ĠW ong", - "ĠWo ng", - "ĠWon g", - "Ġм еÑĤод", - "ĠмеÑĤ од", - "We ights", - "Weight s", - "ĠGu jar", - "t eil", - "te il", - "ĠNone theless", - "ĠNon etheless", - "_E FFECT", - "Ġ vect", - "Ġv ect", - "Ġve ct", - "Ġvec t", - "ĠO sc", - "ĠOs c", - "Ġco ats", - "Ġcoat s", - "ĠW heat", - "ĠWh eat", - "ĠWhe at", - "Ġge ek", - "Ġgee k", - "Ġ PROPERTY", - "ĠP ROPERTY", - "ĠPRO PERTY", - "w orm", - "wo rm", - "wor m", - "_ constants", - "_con stants", - "_const ants", - "_constant s", - "ĠB oulder", - "ĠBou lder", - "Ġ Parm", - "ĠP arm", - "ĠPar m", - "ĠPa rm", - "c ole", - "co le", - "col e", - "Ġdefault Center", - "ĠRo uge", - "ĠRou ge", - ": A", - "x cf", - "xc f", - "ĠVen ice", - "ĠVe nice", - "m edian", - "med ian", - "medi an", - "media n", - "Ġred emption", - "F resh", - "Fr esh", - "Fre sh", - "Ġco sm", - "Ġcos m", - "Ġ figur", - "Ġfig ur", - "Ġref urb", - "CO PE", - ". cd", - ".c d", - "Ġch ords", - "Ġchord s", - "Ġchor ds", - "ĠS gt", - "Å į", - "V PN", - "VP N", - "Ġ SEND", - "ĠS END", - "ĠSE ND", - "ĠSEN D", - "a inen", - "ain en", - "ai nen", - "aine n", - "_ accounts", - "_account s", - "_ac counts", - "Ġt enth", - "Ġte nth", - "Ġten th", - "Ġtent h", - "Ġdiss olved", - "Ġdissolve d", - "< App", - "", - "Ġ' >", - "Ġlegitim acy", - "Ġ oo", - "Ġo o", - "S linky", - "Sl inky", - "Ġnational s", - "Ġnation als", - ". words", - ".w ords", - ".word s", - "; p", - "t rap", - "tr ap", - "tra p", - "oman ip", - "oma nip", - "Ġc ues", - "Ġcu es", - "Ġcue s", - "Ġgrad uating", - "Ġgradu ating", - "Ġsem aphore", - "\" ]);ĊĊ", - "\"] );ĊĊ", - "\"]) ;ĊĊ", - "\"]);Ċ Ċ", - "\"]); ĊĊ", - "ace y", - "ac ey", - "RE ET", - "REE T", - "G rab", - "Gr ab", - "ĠF elix", - "ĠFe lix", - "ĠFel ix", - "( Id", - "(I d", - "_ neighbors", - "_ne ighbors", - "_neighbor s", - "Ġmeaning less", - "( del", - "(d el", - "(de l", - "Ġj eder", - "Ġje der", - "Ġjed er", - "Ġjede r", - "ĠContent Values", - ". absolute", - ".a bsolute", - ".abs olute", - "/ cl", - "/c l", - "Ġ xb", - "Ġx b", - "d atum", - "dat um", - "Ġtort ured", - "Ġtorture d", - "Ġrub bing", - "Ġru bbing", - "S cores", - "Sc ores", - "Score s", - "ĠðŁĺ ī", - "Ġav ons", - "Ġam sterdam", - "E OS", - "EO S", - "H al", - "Ha l", - "Ġtrust worthy", - "# =", - ".EX TRA", - "Ġm ano", - "Ġman o", - "Ġma no", - "is icing", - "isi cing", - "- support", - "-s upport", - "-sup port", - "ĉ cursor", - "ĉc ursor", - "Ġ Spo", - "ĠS po", - "ĠSp o", - "ai massage", - "aim assage", - "M ission", - "Miss ion", - "[] {\"", - "[]{ \"", - "Ġpr inters", - "Ġprint ers", - "Ġprinter s", - "Ġprin ters", - "G REEN", - "GRE EN", - "GREE N", - "Ġ teg", - "Ġt eg", - "Ġte g", - "Ġabdom inal", - "! ĊĊĊĊĊĊ", - "!ĊĊ ĊĊĊĊ", - "!Ċ ĊĊĊĊĊ", - "!ĊĊĊĊ ĊĊ", - "!ĊĊĊ ĊĊĊ", - ". Short", - ".S hort", - ".Sh ort", - "а зв", - "аз в", - "ĠGi fts", - "ĠGift s", - "} \")", - "}\" )", - "( binding", - "(b inding", - "(bin ding", - "(bind ing", - "x ce", - "xc e", - "âĢ ij", - "in fos", - "info s", - "inf os", - "Form Data", - "Ġ dart", - "Ġd art", - "Ġda rt", - "Ġdar t", - "Ġ elems", - "Ġe lems", - "Ġel ems", - "Ġele ms", - "Ġelem s", - "( inv", - "(i nv", - "(in v", - "Y L", - "t in", - "ti n", - "G ENER", - "GE NER", - "GEN ER", - "á» ¯", - "Ġ Taken", - "ĠT aken", - "ĠTake n", - "ĠTa ken", - "ĠTak en", - "uc kle", - "uck le", - ": e", - "Ġs pectral", - "Ġspect ral", - "Ġspectra l", - ".b aidu", - "/ ');Ċ", - "/' );Ċ", - "/') ;Ċ", - "Ġgre edy", - "Ġgreed y", - "es ion", - "esi on", - ",,,, ,,,,", - "Ġ/ >,Ċ", - "Ġ/> ,Ċ", - "Ġ/>, Ċ", - "Internal ServerError", - "NS NotificationCenter", - "NSNotification Center", - "Ġ Ai", - "ĠA i", - "Ġs pit", - "Ġsp it", - "Ġspi t", - "Ġaug mented", - "Ġaugment ed", - "Ġstandard UserDefaults", - "FIN ITY", - "R ace", - "Ra ce", - ": C", - "ĠRE CORD", - "ĠREC ORD", - "Ġ Highlight", - "ĠHigh light", - "Ġ' `", - "Ġdef icits", - "Ġdeficit s", - "Ġn ei", - "Ġne i", - "Ġresearch ed", - "T a", - "Ġc opp", - "Ġco pp", - "Ġcop p", - ".Get HashCode", - ") :čĊčĊ", - "): čĊčĊ", - "):čĊ čĊ", - "On Click", - "ĠWell ington", - "ĠWel lington", - "Ġrev ival", - "æ¯ Ķ", - "éĹ ®", - "Ġ NSS", - "ĠN SS", - "ĠNS S", - "Ġf orn", - "Ġfor n", - "Ġfo rn", - "Ġin té", - "Ġint é", - "ĠKu wait", - "_ flip", - "_f lip", - "_fl ip", - "_ bo", - "_b o", - "_ \\", - "Ġocc urrences", - "Ġoccurrence s", - "Ġ Scientists", - "ĠScient ists", - "ĠScientist s", - "S RC", - "SR C", - "o gens", - "og ens", - "ogen s", - "oge ns", - "i grant", - "ig rant", - "igr ant", - "RE MOTE", - "REM OTE", - "Ġ SID", - "ĠS ID", - "ĠSI D", - ". opts", - ".op ts", - ".o pts", - ".opt s", - "u ve", - "uv e", - "( )])Ċ", - "() ])Ċ", - "()] )Ċ", - "Ġlibert arian", - "ĠG lide", - "ĠGl ide", - "l esen", - "le sen", - "les en", - "Ġ forme", - "Ġfor me", - "Ġform e", - "ow ania", - "owa nia", - "owan ia", - "Ġannoy ed", - "D efs", - "De fs", - "Def s", - "Ġ Executor", - "ĠExec utor", - "Ġ casts", - "Ġc asts", - "Ġca sts", - "Ġcas ts", - "Ġcast s", - ". setChecked", - ".set Checked", - "Ġ Sharing", - "ĠSh aring", - "ĠSha ring", - "ĠShar ing", - ".Serialize Object", - "Ġ selectors", - "Ġselect ors", - "Ġselector s", - "Ġsel ectors", - "Ġsele ctors", - "_ OTHER", - "_OT HER", - "ë ¯¸", - "ë¯ ¸", - "( super", - "(s uper", - "( OS", - "(O S", - "_ VERIFY", - "_VER IFY", - "id unt", - "< header", - "';Ċ", - "Ġ/> ';Ċ", - "Ġ/>' ;Ċ", - "Ġvid éo", - "Ġvidé o", - "ĠNe gro", - "ĠNeg ro", - "ĠL ords", - "ĠLord s", - "ĠLor ds", - "ĠT ours", - "ĠTo urs", - "ĠTour s", - "ĠTou rs", - "Ġsoft ly", - ". receive", - ".re ceive", - "Ġ ERC", - "ĠE RC", - "ĠER C", - "Ġdata Set", - "B adge", - "Bad ge", - "Ba dge", - "ĉ Event", - "ĉE vent", - "Ġ perl", - "Ġper l", - "Ġpe rl", - "Ġ {}\\", - "Ġ{ }\\", - "Ġ{} \\", - "( sentence", - "(s entence", - "(sent ence", - "Or Update", - "Ġdim inish", - "Ġdimin ish", - "P IN", - "PI N", - "( draw", - "(d raw", - "(dr aw", - ".To DateTime", - ". EqualTo", - ".Equal To", - "( pin", - "(p in", - "(pi n", - "-p encil", - "l uent", - "lu ent", - "lue nt", - "Ġ Caller", - "ĠC aller", - "ĠCal ler", - "ĠCall er", - "ĠCa ller", - "Ġplay ful", - "- '+", - "-' +", - "x ca", - "xc a", - "s wick", - "sw ick", - ") {}Ċ", - "){ }Ċ", - "} :${", - "}: ${", - "ĠM eth", - "ĠMe th", - "ĠMet h", - ". getCell", - ".get Cell", - ".getC ell", - ". break", - ".b reak", - "Ġ ymax", - "Ġy max", - "=' Ċ", - "}`} >Ċ", - "ĠH iro", - "ĠHi ro", - "ĠHir o", - "( TRUE", - "(TR UE", - "as urer", - "asure r", - "asu rer", - "Ġc uer", - "Ġcu er", - "Ġcue r", - "U ber", - "Ub er", - ". Operation", - ".O peration", - ".Op eration", - "Ġ olan", - "Ġo lan", - "Ġol an", - "Ġola n", - "Ġthr illing", - "Ġthrill ing", - "< Response", - "ĠF emin", - "ĠFe min", - "ĠFem in", - "Ġtr aversal", - "Ġtravers al", - "Ġp oc", - "Ġpo c", - "Ġ setStatus", - "Ġset Status", - "de clar", - "dec lar", - "decl ar", - "std afx", - "Ġaddict ive", - "Ġ Btn", - "ĠB tn", - "Ġexplos ives", - "Ġexplosive s", - "ĠCo oking", - "ĠCook ing", - "ĠPl aint", - "ĠPlain t", - "ĠPla int", - "Ġ accumulator", - "Ġaccum ulator", - "Ġ Appointment", - "ĠApp ointment", - ", password", - ",p assword", - "ĠF AR", - "ĠFA R", - "l uet", - "lu et", - "lue t", - "Further more", - "decl spec", - "_ Statics", - "_Static s", - "_St atics", - ". Dictionary", - ".D ictionary", - "\" >'.", - "\"> '.", - "\">' .", - "ĉ valid", - "ĉval id", - "ĉva lid", - "\" \",", - "\"\" ,", - "In strument", - "Instr ument", - "> J", - "Ġno str", - "Ġnos tr", - "Ġnost r", - "ĠR ift", - "ĠRi ft", - "ĠRif t", - "_ Port", - "_P ort", - "Ġve ces", - "Ġvec es", - "[ ['", - "[[ '", - "Ġrall ies", - "- series", - "-s eries", - "-se ries", - "-ser ies", - "Ġ vv", - "Ġv v", - ". uc", - ".u c", - "Ġr tn", - "Ġrt n", - "State Changed", - "( ins", - "(i ns", - "(in s", - "Ġ Cla", - "ĠC la", - "ĠCl a", - "- -----------Ċ", - "-- ----------Ċ", - "---- --------Ċ", - "-------- ----Ċ", - "--- ---------Ċ", - "------------ Ċ", - "----- -------Ċ", - "---------- --Ċ", - "------ ------Ċ", - "----------- -Ċ", - "------- -----Ċ", - "--------- ---Ċ", - "c us", - "cu s", - "Ġ Reload", - "ĠR eload", - "ĠRe load", - "ĠRel oad", - "// ------------------------------------------------------------------------------------------------", - "//---------------------------------------------------------------- --------------------------------", - "//---------------------------------------------------------------------------- --------------------", - "//------------------------------------------------ ------------------------------------------------", - "//-------------------------------- ----------------------------------------------------------------", - "//-------------------------------------------------------------------------------- ----------------", - "//---------------- --------------------------------------------------------------------------------", - ". seconds", - ".se conds", - ".second s", - ".sec onds", - "_ destination", - "_d estination", - "_dest ination", - "Ġscre wed", - "Ġscr ewed", - "Ġscrew ed", - "> c", - "Th ickness", - "Des igner", - "Design er", - "Ġgr ids", - "Ġgrid s", - "Ġgri ds", - "n Äħ", - "( cookie", - "(c ookie", - "(co okie", - "T rip", - "Tr ip", - "Tri p", - "- Mobile", - "-M obile", - "Ġv oll", - "Ġvo ll", - "Ġvol l", - "Ġgen ital", - "Ġconf isc", - "ĠConfeder ate", - "Ġ webView", - "Ġweb View", - "Ġ mise", - "Ġm ise", - "Ġmis e", - "Ġmi se", - "Ġc ler", - "Ġcl er", - "Ġcle r", - "( selection", - "(s election", - "(se lection", - "(select ion", - "(sel ection", - "$ date", - "$d ate", - "Ġsharp en", - "Ġshar pen", - "r agen", - "ra gen", - "rag en", - "rage n", - "And Update", - "Ġre mix", - "Ġrem ix", - "Ġh tons", - "Ġht ons", - "Ġhton s", - "R W", - "M PI", - "MP I", - "Ġretrie val", - "Ġretr ieval", - "Ġrich est", - "Ġri chest", - "Ġric hest", - "Ġriches t", - ". Decode", - ".De code", - ".Dec ode", - ":init Components", - "ĠT Value", - "ĠTV alue", - "S aint", - "Sa int", - "@ include", - "Ġ PERSON", - "ĠPER SON", - ". sep", - ".s ep", - ".se p", - "Ġ LDAP", - "ĠLD AP", - "g ba", - "gb a", - "Ġgro ÃŁe", - "ĠgroÃŁ e", - "Ġreli ably", - "Ġ DFS", - "ĠD FS", - "ĠDF S", - ".get ItemId", - ".getItem Id", - "Ġpré sent", - "Ġprés ent", - ". getToken", - ".get Token", - "Ġch inese", - "Ġchin ese", - "Ġ Meal", - "ĠMe al", - "Y OU", - "YO U", - "\" > >ĊĊ", - "Ġ> >ĊĊ", - "Ġ>> ĊĊ", - "b ower", - "bo wer", - "bow er", - "Ġsw apped", - "Ġswap ped", - "/ install", - "Ġs inks", - "Ġsin ks", - "Ġsink s", - "etr ize", - "etri ze", - "Ġdec lines", - "Ġdecl ines", - "Ġdecline s", - "ĉ mysql", - "ĉm ysql", - "ĉmy sql", - "Ġ CString", - "ĠC String", - "ĠCS tring", - "ĠM otionEvent", - "ĠMotion Event", - ". Language", - ".L anguage", - "R oad", - "Ro ad", - "ÑĤ еÑĢ", - "ÑĤе ÑĢ", - "asc imento", - "' ))->", - "') )->", - "')) ->", - ". about", - ".a bout", - ".ab out", - "( editor", - "(e ditor", - "(ed itor", - "(edit or", - "ĠR atings", - "ĠRa tings", - "ĠRating s", - "ĠRat ings", - "in come", - "inc ome", - "Å¡ e", - ".de queueReusableCell", - "ĠAust rian", - "ĠAustria n", - "ĠAustr ian", - "Ġs ulla", - "Ġsu lla", - "Ġsul la", - "ĠTrib unal", - "Ġ Didn", - "ĠDi dn", - "ĠDid n", - "о ваÑĢ", - "ов аÑĢ", - "ова ÑĢ", - "Ġins pections", - "Ġinspect ions", - "Ġinspection s", - "Ġinsp ections", - "B oss", - "Bo ss", - "Ġcock tails", - "Ġcocktail s", - "Ġapolog ized", - "Ġapologize d", - "_ subplot", - "_sub plot", - "o pal", - "op al", - "opa l", - "+ =(", - "+= (", - "Ġreson ance", - "i bu", - "ib u", - "Ġ 리", - "Ġë ¦¬", - "Ġë¦ ¬", - "r oma", - "ro ma", - "rom a", - "re serve", - "res erve", - "rese rve", - "p ls", - "pl s", - "ĠT ah", - "ĠTa h", - "a xies", - "ax ies", - "O PLE", - "OP LE", - "ĠDar ren", - "ĠZ ombie", - "_ Map", - "_M ap", - "Ġ ])ĊĊ", - "Ġ] )ĊĊ", - "Ġ])Ċ Ċ", - "Ġ]) ĊĊ", - "Ġ Qi", - "ĠQ i", - "ĠS ail", - "ĠSa il", - "ĠSai l", - "Ġrestrict ive", - "Ġeros ion", - "- par", - "-p ar", - "W HITE", - "WH ITE", - "Ġold u", - "Ġol du", - "Ġap erture", - "Ġbit coins", - "Ġbitcoin s", - "text o", - "tex to", - "ĠCom cast", - "Ġtime less", - "Ġtim eless", - "en kins", - "enk ins", - "Ġfe eder", - "Ġfeed er", - "Ġfee der", - "/ tmp", - "/t mp", - "res den", - "+ '_", - "+' _", - ". Destroy", - ".D estroy", - ".De stroy", - "Ġ çok", - "Ġç ok", - "Ġ DOCUMENT", - "ĠD OCUMENT", - "ĠDOC UMENT", - ". lng", - ".l ng", - ". tagName", - ".tag Name", - "Ġk ullan", - "Ġkul lan", - "eg rate", - "egr ate", - "egra te", - "Ġ( *.", - "Ġ(* .", - "ç¼ĸ è¾ij", - "Ġhand shake", - "s oc", - "so c", - "_ geometry", - "_ge ometry", - "_geo metry", - "_geom etry", - "ĠDam ascus", - "Min or", - "Mi nor", - "ĠK afka", - "ĠKaf ka", - "ìĹ ¬", - "Fl orida", - "_ compute", - "_com pute", - "_comp ute", - ". expr", - ".ex pr", - ".exp r", - "Ġ paralle", - "Ġpar alle", - "Ġpara lle", - "ĠD iaz", - "ĠDi az", - "ĠDia z", - "c ir", - "ci r", - "[ target", - "[t arget", - "Ġj oking", - "Ġjo king", - "Ġg lor", - "Ġgl or", - "Ġglo r", - "( setq", - "(set q", - "_ handlers", - "_handler s", - "_handle rs", - "_hand lers", - "H ang", - "Ha ng", - "Han g", - "Ġf err", - "Ġfe rr", - "Ġfer r", - "r iminal", - "rim inal", - "ĉ ĠĠĠĠĉĉ", - "ĉĠĠĠ Ġĉĉ", - "ĉĠ ĠĠĠĉĉ", - "ĉĠĠ ĠĠĉĉ", - "ĉĠĠĠĠ ĉĉ", - "ĉĠĠĠĠĉ ĉ", - "en ties", - "ent ies", - "enti es", - "def ines", - "define s", - "- tax", - "-t ax", - "json p", - "Ġ UPS", - "ĠU PS", - "ĠUP S", - "m etro", - "me tro", - "met ro", - "_ _;Ċ", - "__ ;Ċ", - "__; Ċ", - "ĠUg anda", - "] )):Ċ", - "]) ):Ċ", - "])) :Ċ", - "_ td", - "_t d", - "x ae", - "xa e", - "l w", - ". OS", - ".O S", - "Ġ Logged", - "ĠLog ged", - "a cid", - "ac id", - "aci d", - "ĠM ayo", - "ĠMay o", - "ĠMa yo", - "a spect", - "as pect", - "asp ect", - "Ġvag inal", - "Ġvagina l", - "Ġinitial izing", - "Ġste roids", - "Ġster oids", - "Ġsteroid s", - "f iction", - "fi ction", - "fic tion", - "G RE", - "GR E", - "g end", - "ge nd", - "gen d", - "Ġli abilities", - "Ġ Lets", - "ĠL ets", - "ĠLe ts", - "ĠLet s", - "M ech", - "Me ch", - "( nc", - "(n c", - "( change", - "(ch ange", - "(chan ge", - "Ġconn ectors", - "Ġconnect ors", - "Ġconnector s", - ": k", - "Ġt ast", - "Ġta st", - "Ġtas t", - "! \");ĊĊ", - "!\" );ĊĊ", - "!\");Ċ Ċ", - "!\"); ĊĊ", - "!\") ;ĊĊ", - "th ings", - "thing s", - "thin gs", - "r ophy", - "ro phy", - "rop hy", - "roph y", - "l uetooth", - "lu etooth", - "luet ooth", - "Ġ SignUp", - "ĠSign Up", - ". ctrl", - ".c trl", - ".ct rl", - "Ġthere in", - "Ġther ein", - "or da", - "ord a", - ". escape", - ".e scape", - ".es cape", - "ig ator", - "iga tor", - "Ġpet rol", - "Ġspec imen", - "Ġspeci men", - "Ġdeb uted", - "Ġdebut ed", - "- Pro", - "-P ro", - "Ġcr ises", - "Ġcri ses", - "Ġcris es", - ".add View", - "ëı Ļ", - "- door", - "-d oor", - "-do or", - "Ġm onet", - "Ġmon et", - "Ġmo net", - "Ġm illis", - "Ġmill is", - "Ġmil lis", - "Ġmilli s", - "Ġ vier", - "Ġv ier", - "Ġvi er", - "Ġvie r", - "Internal Enumerator", - "Ġ admins", - "Ġad mins", - "Ġadmin s", - "Ġadm ins", - "ĠL air", - "ĠLa ir", - "z in", - "zi n", - "get Query", - "um bles", - "umb les", - "umble s", - "L IMIT", - "LI MIT", - "ĠV ig", - "ĠVi g", - "_ song", - "_s ong", - "_so ng", - "< Character", - ": :.", - ":: .", - "_ hom", - "_h om", - "_ bp", - "_b p", - "ĠSup ervisor", - "ĠSuper visor", - "ĠSuperv isor", - "sub mission", - "ab ile", - "abil e", - "abi le", - "Ġn oi", - "Ġno i", - "Or Create", - "Ġp eel", - "Ġpe el", - "Ġpee l", - "Ġon Start", - "Ġsent iments", - "Ġsentiment s", - "v ehicles", - "veh icles", - "vehicle s", - "Ġclass rooms", - "Ġclassroom s", - "Ġs zer", - "Ġsz er", - "Ġb ending", - "Ġben ding", - "Ġbend ing", - "Ġlong evity", - "Ġ acl", - "Ġa cl", - "Ġac l", - "ĠAle ppo", - "Ġ UM", - "ĠU M", - "ĠR icht", - "ĠRich t", - "ĠRic ht", - "ĠRi cht", - "Ġmulti processing", - "Ġmultip rocessing", - "DO MAIN", - "DOM AIN", - "\", \"+", - "\",\" +", - "_ YEAR", - "_Y EAR", - "Ġsc rape", - "Ġscr ape", - "Ġscrap e", - "Ġsol itary", - "Ġ\" ]\";Ċ", - "Ġ\"] \";Ċ", - "Ġ\"]\" ;Ċ", - "/ errors", - "/error s", - "ìŀ ¬", - "ľ ëł¥", - "b etter", - "bet ter", - "bett er", - "bette r", - "ĉ number", - "ĉn umber", - "ĉnum ber", - "Ġ LF", - "ĠL F", - "Ġ Across", - "ĠA cross", - "ĠAc ross", - "Pub Med", - "\\ \"\"", - "\\\" \"", - "ĠExcell ence", - "Ġus ando", - "Ġusa ndo", - "ĠU IP", - "ĠUI P", - "Activity Indicator", - "_ VOID", - "_V OID", - "_VO ID", - "Ġbre eds", - "Ġbreed s", - "Ġbree ds", - "ï½ ¥", - "ues tas", - "uest as", - "uesta s", - "ĠTre asure", - "ustr alian", - "ustral ian", - "ustralia n", - "( face", - "(f ace", - "ĠT ennis", - "ĠTen nis", - "ĠTenn is", - "ĉ Int", - "ĉI nt", - "ĉIn t", - "ĠH ansen", - "ĠHan sen", - "ĠHans en", - "ç µ", - ": I", - "Ġ âľĶ", - "Ġâľ Ķ", - "G RAY", - "GR AY", - "GRA Y", - "O USE", - "OU SE", - "OUS E", - "Ġhe pat", - "Ġhep at", - "ł í", - "A IR", - "AI R", - "ó ż", - "Ġ queued", - "Ġque ued", - "Ġqueue d", - "vin cia", - "vi ncia", - "vinc ia", - "ĠCh romium", - "ĠChrom ium", - "Ġcompet ence", - "Ġcompete nce", - "un gal", - "ung al", - "unga l", - "i lli", - "il li", - "ill i", - "Ġget By", - "Ġ Finder", - "ĠF inder", - "ĠFin der", - "ĠFind er", - "ĠFi nder", - "Ġincap able", - "Ġs add", - "Ġsa dd", - "Ġsad d", - "Ġc ites", - "Ġcit es", - "Ġci tes", - "Ġcite s", - "ĠChurch ill", - "S dk", - "More over", - "A spNet", - "As pNet", - "( Float", - "(F loat", - "$ password", - "$p assword", - "Ġ Connor", - "ĠCon nor", - "ĠConn or", - "- session", - "-s ession", - "_ dm", - "_d m", - "* ))", - "*) )", - "Ġde utsch", - "Ġdeut sch", - "Ġ NX", - "ĠN X", - "Ġper ks", - "Ġperk s", - "_ SORT", - "_S ORT", - "_SO RT", - "_TO OL", - "_TOO L", - "_ VISIBLE", - "_V ISIBLE", - "_VIS IBLE", - ". asp", - ".as p", - ".a sp", - "æĪ ĸ", - "ĠBre ath", - "D etect", - "Det ect", - "ĠD uel", - "ĠDu el", - "ĠDue l", - ". cmb", - ".c mb", - ".cm b", - "[ it", - "[i t", - ".Set Bool", - "Ġnarc iss", - "Ġab ide", - "Ġabi de", - "Ġej emplo", - "ĠâĦ ķ", - "Ġm ornings", - "Ġmor nings", - "Ġmorning s", - "Ġcomp utes", - "Ġcomput es", - "Ġcompute s", - ". ssl", - ".s sl", - ".ss l", - "j t", - "Ġm uchos", - "Ġmuch os", - "Ġmu chos", - "Ġmucho s", - "Ġmuc hos", - "_ SS", - "_S S", - "[ end", - "[e nd", - "Ġb asin", - "Ġbas in", - "Ġba sin", - "Ġalg unos", - "Ġalgun os", - "ĠCroat ia", - "line width", - "lin ewidth", - "( tags", - "(t ags", - "(tag s", - "( hidden", - "(h idden", - "ÃŃ cio", - "ÃŃc io", - "Ġa par", - "Ġap ar", - "Ġapa r", - "Ġ ж", - "ĠÐ ¶", - "ä¸ İ", - ". food", - ".f ood", - ".foo d", - "ĠR ural", - "ĠRu ral", - "Ġbread th", - "å½ ±", - "( sess", - "(s ess", - "(se ss", - "+ \")", - "+\" )", - "Ġ Paste", - "ĠP aste", - "ĠPa ste", - "ĠPast e", - "ĠPas te", - "Ġserv idor", - "ĠBit Set", - "ĠT ran", - "ĠTr an", - "ĠTra n", - "l aus", - "la us", - "v ette", - "ve tte", - "vet te", - "e yes", - "ey es", - "eye s", - "Ġ CLICK", - "ĠCL ICK", - "ĠCLI CK", - "ĠV III", - "ĠVI II", - "ĠVII I", - "ĠTur ns", - "ĠTurn s", - "ĠLe Bron", - "ĠM uj", - "ĠMu j", - "Ġ Deg", - "ĠD eg", - "ĠDe g", - "ĠAd ults", - "ĠAdult s", - "_ suite", - "_s uite", - "_su ite", - "process able", - "Ġ PHY", - "ĠP HY", - "ĠPH Y", - "g hest", - "gh est", - ". Fail", - ".F ail", - "ĠS lack", - "ĠSl ack", - "c ej", - "ce j", - "\\ Carbon", - "\\C arbon", - "Ġsuper star", - "Ġsupers tar", - "Ġsuperst ar", - "Ġhold ings", - "Ġholding s", - "Ġhol dings", - "( forms", - "(form s", - "(for ms", - "Ġ' #'", - "Ġ'# '", - "M ultip", - "Multi p", - "Mult ip", - "Mul tip", - "(\" [%", - "(\"[ %", - "- solid", - "-s olid", - "-so lid", - "/ url", - "/u rl", - "- tier", - "-t ier", - "[ length", - "[l ength", - "[len gth", - "Ġ StreamWriter", - "ĠStream Writer", - "ĠMarket place", - "get text", - "gett ext", - "_T ICK", - "_TI CK", - "Ġ Forge", - "ĠF orge", - "ĠFor ge", - "ĠForg e", - "Ġblack jack", - "ĠDO ES", - "ĠDOE S", - "ĠM atters", - "ĠMat ters", - "ĠMatt ers", - "ĠMatter s", - "ĠMatte rs", - "w aves", - "wa ves", - "wave s", - "wav es", - "Ġwhisper ed", - "Ġ lush", - "Ġl ush", - "Ġlu sh", - "ìĺ ¤", - "d igital", - "digit al", - "dig ital", - "Ġw rink", - "Ġwr ink", - "ĠH ogan", - "ĠHo gan", - "ĠHog an", - "Ġrust ic", - "Ġrus tic", - ".Apply Resources", - "ĠH ardy", - "ĠHar dy", - "ĠHard y", - "os omes", - "oso mes", - "osome s", - "A UT", - "AU T", - ". STATE", - ".ST ATE", - "Ġnarr atives", - "Ġnarrative s", - "ĉ store", - "ĉst ore", - "b ib", - "bi b", - "ĉ Scanner", - "ĠC ody", - "ĠCo dy", - "ĠCod y", - "\\ Repositories", - "Ġre union", - "Ġreun ion", - "an dum", - "and um", - "âĢĻ h", - "Ġsn iff", - "NS Bundle", - "Ġcompreh end", - "_ USAGE", - "_US AGE", - "_ occ", - "_o cc", - "_oc c", - "URRE NCY", - "J NI", - "Ġspecial izing", - "Ġ visions", - "Ġv isions", - "Ġvis ions", - "Ġvision s", - "Ġdo lore", - "Ġdol ore", - "Ġdolor e", - "Ġ vá", - "Ġv á", - "ĠChe vy", - "Ġ Styled", - "ĠSt yled", - "ĠStyle d", - "ĠSty led", - "imp act", - "a llen", - "al len", - "all en", - "alle n", - "Ġ kart", - "Ġk art", - "Ġka rt", - "Ġkar t", - "ĠTable t", - "ĠTab let", - "st uff", - "stu ff", - "re esome", - "ree some", - "rees ome", - "а ÑĤоÑĢ", - "аÑĤ оÑĢ", - "аÑĤо ÑĢ", - "//---------------------------------------------------------------- -----------Ċ", - "//- --------------------------------------------------------------------------Ċ", - "_ Admin", - "_Ad min", - "Ġcell phone", - "Ġ autoplay", - "Ġaut oplay", - "Ġauto play", - "Ġautop lay", - "Ġc ambio", - "Ġcam bio", - "Ġcamb io", - "Ġcambi o", - "Ġmar itime", - "Ġmari time", - "_ BOOT", - "_B OOT", - "_BO OT", - "- quarter", - "-qu arter", - "Ġlat ina", - "Ġlatin a", - "ĠAJ AX", - "e quiv", - "equ iv", - "ĠFront ier", - "Ġ XY", - "ĠX Y", - "} ]Ċ", - "}] Ċ", - "ĠR ough", - "ĠRo ugh", - "ĠRou gh", - ". proto", - ".pro to", - ".prot o", - ".pr oto", - "Ġcorrect ness", - "Ġfa cil", - "Ġfac il", - "Ġ Reached", - "ĠRe ached", - "ĠReach ed", - "ãģĿ ãģ®", - "V IS", - "VI S", - ". ps", - ".p s", - "Ġstr ncpy", - "Ġdif fusion", - "Ġdiff usion", - ".start Activity", - "� ��", - "�� �", - "Ġacc omp", - "Ġac comp", - "Ġaccom p", - "AME SPACE", - "AMES PACE", - "imon ials", - "imonial s", - "ĠB last", - "ĠBl ast", - "aby rin", - "Ġd ome", - "Ġdo me", - "Ġdom e", - "Ġext rav", - "Ġextra v", - "Ġextr av", - "Ġ yen", - "Ġy en", - "Ġye n", - "Ġcul inary", - "P RI", - "PR I", - "ĠComm unities", - "ĠCommun ities", - "n id", - "ni d", - "_ operations", - "_oper ations", - "_operation s", - ". hs", - ".h s", - "ĠM ilton", - "ĠMil ton", - "Ġno ises", - "Ġnoise s", - "Ġnoi ses", - "Autoresizing Mask", - "( cid", - "(c id", - "(ci d", - "} ĊĊĊĊĊĊ", - "}Ċ ĊĊĊĊĊ", - "}ĊĊ ĊĊĊĊ", - "}ĊĊĊ ĊĊĊ", - "}ĊĊĊĊ ĊĊ", - "}ĊĊĊĊĊ Ċ", - "] },Ċ", - "]} ,Ċ", - "]}, Ċ", - "Ġ Detection", - "ĠD etection", - "ĠDe tection", - "ĠDet ection", - "ĠDetect ion", - "ta bla", - "tab la", - "tabl a", - "Ġlib erties", - "Ġlibert ies", - "Ġliber ties", - "_D YNAMIC", - "w get", - "wg et", - "ĠT ür", - "ĠP ascal", - "ĠPa scal", - "ĠPas cal", - "Trans parent", - "Del ayed", - "Delay ed", - "] ()", - "]( )", - "ĠHer bert", - "ĠHerb ert", - "< ActionResult", - "", - "}- >", - "Ġpas ado", - "Ġpasa do", - "th ank", - "tha nk", - "than k", - "_ Delete", - "_De lete", - "ĠBr ighton", - "ĠBright on", - "ĠBrig hton", - ", unsigned", - "ä½ľ èĢħ", - "Ġaspir ations", - "Ġaspiration s", - "- how", - "-h ow", - "R ose", - "Ro se", - "Ros e", - "= ((", - "=( (", - "_ needed", - "_ne eded", - "_need ed", - "_ plural", - "_pl ural", - "< Application", - " >ĊĊ", - ">> ĊĊ", - ">>Ċ Ċ", - "Ġsurface d", - "Ġsurf aced", - "Ġìł Ģìŀ¥", - "ĠìłĢ ìŀ¥", - "pl atz", - "plat z", - "pla tz", - "ĉ email", - "ĉe mail", - "ĉem ail", - "cept ors", - "ceptor s", - "cep tors", - "\" >(", - "\"> (", - "Ġe pile", - "Ġep ile", - "è¯ »", - "ĠDe bt", - "ĠDeb t", - "åij Ĭ", - "N OP", - "NO P", - "\" https", - "\"http s", - ": j", - "Form Item", - "_ LICENSE", - "_L ICENSE", - ".get Double", - ".getD ouble", - "ĠAg enda", - "ĠAge nda", - "ĉ finally", - "ĉf inally", - "ĉfinal ly", - "( filters", - "(f ilters", - "(filter s", - "(fil ters", - "( av", - "(a v", - "ç¾ İ", - "A PER", - "AP ER", - "APE R", - "Ġ lava", - "Ġl ava", - "Ġla va", - "Ġlav a", - "еÑĢ ж", - ") )))ĊĊ", - ")) ))ĊĊ", - "))) )ĊĊ", - "))))Ċ Ċ", - ")))) ĊĊ", - "Ġfa ulty", - "Ġfault y", - "_ nm", - "_n m", - "Ġt rava", - "Ġtr ava", - "Ġtra va", - "Ġtrav a", - "( Bitmap", - "(B itmap", - "(Bit map", - "Ġspe eding", - "Ġspeed ing", - "> ').", - ">' ).", - ">') .", - "Ġscreen ed", - "Ġscre ened", - "_ roll", - "_r oll", - "_ro ll", - "ĠMac Book", - "Ġ AUD", - "ĠA UD", - "ĠAU D", - "Ġdiagn ose", - ". Generate", - ".G enerate", - ".Gen erate", - "Ġ ^^", - "Ġ^ ^", - "Ġs trs", - "Ġst rs", - "Ġstr s", - "[ Test", - "[T est", - "Ġr ansom", - "Ġran som", - "ĠDH CP", - "el den", - "eld en", - "Ġinterpret ations", - "Ġinterpretation s", - "( )].", - "() ].", - "()] .", - "flat Map", - "Ġline Height", - "_ mount", - "_m ount", - "_mo unt", - "ĠW izards", - "ĠWizard s", - "Ġsl uts", - "Ġslut s", - "Ġslu ts", - "eh ler", - "o dal", - "od al", - "oda l", - "Ġmilit ia", - "Ġmil itia", - "å ²", - "ear ned", - "earn ed", - "Ġmis ery", - "Ġmise ry", - "Ġmiser y", - "int val", - "f und", - "fun d", - "fu nd", - "Ġh ides", - "Ġhide s", - "Ġhi des", - "Ġhid es", - "Ġdi arr", - "Ġdia rr", - "ĠWes ley", - "Ġ xmm", - "Ġx mm", - "Ġxm m", - "Ġqu em", - "Ġque m", - "Ġq uem", - "ĠAr abs", - "ĠArab s", - "ĠAra bs", - "if th", - "ift h", - "ategor ized", - "ategori zed", - "D isposable", - "Dis posable", - "P ure", - "Pu re", - "_NOT IFY", - "sn ippet", - "ĠGar rett", - "ĠGarr ett", - ". running", - ".r unning", - ".run ning", - ". weights", - ".weight s", - ".we ights", - "Ġ (--", - "Ġ( --", - "Ġ(- -", - "Ġin variant", - "Ġinv ariant", - "äºĭ 件", - "Ġ Allowed", - "ĠAll owed", - "ĠAllow ed", - "d irs", - "dir s", - "di rs", - "Ġpass ions", - "Ġpassion s", - "Ġ lad", - "Ġl ad", - "Ġla d", - "Ġ Flush", - "ĠF lush", - "ĠFl ush", - "ĠFlu sh", - "m enus", - "men us", - "menu s", - ": block", - ":b lock", - "Ġcom pra", - "Ġcomp ra", - "Ġcompr a", - ".ch omp", - "al locator", - "all ocator", - "alloc ator", - "alloca tor", - "Ġcur ated", - "Ġcu rated", - "Ġ Knowing", - "ĠKn owing", - "ĠKnow ing", - "ĠPatt erson", - "Ġt elah", - "Ġte lah", - "Ġtel ah", - "Ġtela h", - "' ex", - "'e x", - "Ġdo omed", - "Ġdoom ed", - "Ġphil anth", - "o tty", - "ot ty", - "ott y", - ". styles", - ".st yles", - ".style s", - "Own ed", - "Ġallerg ies", - "Ġaller gies", - "= params", - "oc ese", - "oce se", - "it elist", - "ite list", - "itel ist", - "iteli st", - "Ġ Sending", - "ĠS ending", - "ĠSen ding", - "ĠSend ing", - "b ef", - "be f", - "or rar", - "orr ar", - "orra r", - "Ġ Não", - "ĠN ão", - "ĠF argo", - "ĠFar go", - "ĠL ub", - "ĠLu b", - "Ġ Combined", - "ĠComb ined", - "ĠCombine d", - "_ given", - "_g iven", - "ĉ ĉĉĉĉĠĠĠĠ", - "ĉĉ ĉĉĉĠĠĠĠ", - "ĉĉĉĉ ĉĠĠĠĠ", - "ĉĉĉ ĉĉĠĠĠĠ", - "ĉĉĉĉĉ ĠĠĠĠ", - "ĉĉĉĉĉĠ ĠĠĠ", - "ĉĉĉĉĉĠĠĠ Ġ", - "ĉĉĉĉĉĠĠ ĠĠ", - "Ġre conciliation", - "Ġreconc iliation", - "Pattern s", - "az ard", - "aza rd", - "azar d", - "Ġbio mass", - "Ġbiom ass", - "ĠH ouses", - "ĠHouse s", - "ĠHo uses", - "ĠHou ses", - "resp uesta", - "c co", - "cc o", - "/ topics", - "/to pics", - "/top ics", - "/topic s", - "ĠY uk", - "ĠYu k", - "Ġweak ened", - "Ġweaken ed", - "_ calendar", - "_c alendar", - "_cal endar", - "Ġmulher es", - "ĠM arl", - "ĠMar l", - "ĠMa rl", - "Ġs ine", - "Ġsi ne", - "Ġsin e", - "ĠT il", - "ĠTi l", - "ĠSo uls", - "ĠSou ls", - "ĠSoul s", - "ĠDe utsche", - "ĠDeutsch e", - "ĠF OLLOW", - "Ġp ipelines", - "Ġpipe lines", - "Ġpipeline s", - "Ġpip elines", - "ĠBever ly", - "_DIP SETTING", - "\" #", - "Ġ Proto", - "ĠPro to", - "ĠPr oto", - "ĠProt o", - ". big", - ".b ig", - ".bi g", - "ĠS avings", - "ĠSav ings", - "ĠSaving s", - "ĠT anz", - "ĠTa nz", - "ĠTan z", - "j un", - "ju n", - "Ġ Gamma", - "ĠG amma", - "ĠGa mma", - "ĠGam ma", - "ĠS add", - "ĠSa dd", - "ĠSad d", - "Ġadv isors", - "Ġadvis ors", - "Ġadvisor s", - "Ġro ast", - "Ġun ters", - "Ġunt ers", - "Ġunter s", - "ud ies", - "udi es", - "_ lon", - "_l on", - "_lo n", - "- pointer", - "-point er", - "-po inter", - "ĠElement Ref", - "\\ Builder", - "example Input", - ". webdriver", - ".web driver", - "data Type", - "Ġ Quite", - "ĠQ uite", - "ĠQu ite", - "ĠQui te", - "ĠQuit e", - "ĠCelt ics", - "ĠCel tics", - "ĠCeltic s", - "u il", - "ui l", - "- defense", - "-def ense", - "b ish", - "bi sh", - "bis h", - "ĠUI Window", - "Ġ Suddenly", - "ĠS uddenly", - ". hot", - ".h ot", - ". reason", - ".re ason", - "Ġg ör", - "Ġgö r", - "A MD", - "AM D", - ". Multi", - ".M ulti", - ".Mult i", - "auth enticated", - "authenticate d", - "reg ions", - "region s", - "; (", - "а ÑĢам", - "аÑĢ ам", - "аÑĢа м", - "ĠKir by", - "$ route", - "$r oute", - "PREC ATED", - "ĠDur ham", - "o wo", - "ow o", - "ĠPer forms", - "ĠPerform s", - "Ġdisreg ard", - "n st", - "ns t", - "ĠP ols", - "ĠPol s", - "ĠPo ls", - "Ġget P", - "\" ]:", - "\"] :", - "-color ed", - "-col ored", - "( Keys", - "(Key s", - "ĠAl leg", - "ĠAll eg", - "ĠAlle g", - "_ modify", - "_mod ify", - "_ loading", - "_lo ading", - "_load ing", - "s trained", - "str ained", - "stra ined", - "strain ed", - "Ġat roc", - "Ġatr oc", - "_p hr", - "_ph r", - "< Sprite", - "", - "c eph", - "ce ph", - "cep h", - ".DateTime Picker", - ". \";ĊĊ", - ".\" ;ĊĊ", - ".\";Ċ Ċ", - ".\"; ĊĊ", - "ĠT ie", - "ĠTi e", - ", item", - ",i tem", - ",it em", - "Ġm enn", - "Ġme nn", - "Ġmen n", - "G as", - "Ga s", - "o cha", - "oc ha", - "och a", - "_ virtual", - "_v irtual", - "Ġmaster piece", - "_ sequences", - "_se quences", - "_sequence s", - "L TE", - "LT E", - "Ġ Submission", - "ĠSub mission", - "C aller", - "Call er", - "Cal ler", - "Ca ller", - "$ \\", - "S port", - "Sp ort", - "Spo rt", - "ag us", - "agu s", - "Constraint Maker", - "Ġco loc", - "Ġcol oc", - "Ġ wig", - "Ġw ig", - "Ġwi g", - "Ġ У", - "ĠÐ £", - "ĉ Array", - "ĉA rray", - "L ooks", - "Lo oks", - "Look s", - "ĠG TA", - "ĠGT A", - ". steps", - ".st eps", - ".step s", - "atch ewan", - "_ ranges", - "_r anges", - "_range s", - "ext Alignment", - "ĠBren nan", - "Ġab straction", - "Ġabs traction", - "Ġabstract ion", - "Ġabst raction", - "uler Angles", - ". misc", - ".m isc", - ".mi sc", - "Ġantib odies", - "Ġex ponential", - "Ġexponent ial", - "Ġ CHANNEL", - "ĠCH ANNEL", - "exp ense", - "' y", - "Ġdetect ives", - "Ġdetective s", - "Ġpur ported", - "Y STEM", - "YS TEM", - "YST EM", - "Ġradio active", - "ĠLat ina", - "ĠLatin a", - ". Encoding", - ".En coding", - ".Enc oding", - ". TAG", - ".T AG", - "x in", - "xi n", - "D egree", - "De gree", - "Deg ree", - "ur acion", - "ura cion", - "p rices", - "pr ices", - "price s", - "pri ces", - "ĠRefer entialAction", - "Ġr arity", - "Ġrar ity", - "Ġp iles", - "Ġpi les", - "Ġpil es", - "Ġpile s", - "g ende", - "ge nde", - "gen de", - "gend e", - "_ projects", - "_project s", - "_proj ects", - "_ globals", - "_g lobals", - "_global s", - "_glob als", - ". startTime", - ".start Time", - "Ġ 구", - "Ġê µ¬", - "Ġêµ ¬", - "SE CTION", - "SEC TION", - "_ publish", - "_p ublish", - "_pub lish", - "F ault", - "Fa ult", - "D DL", - "DD L", - "_ prior", - "_p rior", - "_pr ior", - "_pri or", - "M om", - "Mo m", - "Ġth icker", - "Ġthick er", - "Ġthi cker", - "Ġ sequelize", - "Ġsequ elize", - "Ġsequel ize", - "Ġess entials", - "Ġessential s", - "s tras", - "st ras", - "str as", - "stra s", - "in tr", - "int r", - "> (()", - ">( ()", - ">(( )", - ". management", - ".man agement", - ".manage ment", - "e il", - "ei l", - "éĹ Ń", - "A ware", - "Aw are", - ". City", - ".C ity", - "ĠAr bit", - "ĠArb it", - "_ DM", - "_D M", - "_ keyboard", - "_key board", - "L Object", - "LO bject", - "- webpack", - "-web pack", - "ĠNew port", - "Ġprincipal Column", - "leg ant", - "Ġp allet", - "Ġpal let", - "Ġpall et", - "Ġfract ure", - "Ġfrac ture", - "Ġ gmail", - "Ġg mail", - "Ġgm ail", - ". Meta", - ".M eta", - ".Me ta", - "A bove", - "Ab ove", - ". KeyEvent", - ".Key Event", - "j it", - "ji t", - "_ macro", - "_m acro", - "_mac ro", - "_ma cro", - "_P USH", - "_PUS H", - "á» ©", - "/ controller", - "/control ler", - "åĬł è½½", - "Ġsuperf icial", - "exter ity", - "Ġ mensagem", - "Ġm ensagem", - "Ġmens agem", - "W ind", - "Win d", - "Wi nd", - "i ston", - "is ton", - "ist on", - "isto n", - ".open api", - "и ÑĢов", - "иÑĢ ов", - "Ġ Serializer", - "ĠS erializer", - "ĠSerial izer", - "ĠSerialize r", - "uct ive", - "Ġ zar", - "Ġz ar", - "Ġza r", - "P laces", - "Pl aces", - "Place s", - ". Static", - ".St atic", - ".Stat ic", - "B a", - "Ġin advert", - "ĠIndones ian", - "ĠIndonesia n", - "_I PV", - "_IP V", - "( horizontal", - "(h orizontal", - "Ġ getTitle", - "Ġget Title", - "ide press", - "ĠConsole Color", - "i pers", - "ip ers", - "ipe rs", - "iper s", - "$ out", - "$o ut", - "Ġfest ive", - "Ġeven ings", - "Ġevening s", - "Ġeve nings", - ". GetData", - ".Get Data", - "uit ka", - "ĠManual s", - "us sed", - "uss ed", - "_ Max", - "_M ax", - ". Chat", - ".C hat", - ".Ch at", - "ĠA ircraft", - "ĠAir craft", - "= com", - "=c om", - "F OUND", - "FO UND", - "a pro", - "ap ro", - "apr o", - "Ġtre asures", - "Ġtreasure s", - "_ alive", - "_a live", - "_al ive", - "Ġg adget", - "Ġgad get", - "e king", - "ek ing", - "eki ng", - "Button Down", - "B rowsable", - ".PER MISSION", - "P ASSWORD", - "PASS WORD", - "Ġ HASH", - "ĠH ASH", - "ĠHAS H", - "ĠHA SH", - "f é", - "\\ TestCase", - "\\Test Case", - "LO SS", - "LOS S", - "o thers", - "other s", - "oth ers", - ", J", - "Ġass hole", - "Ġassh ole", - "w erk", - "we rk", - "wer k", - "Ġm ã", - ". ie", - ".i e", - "e vil", - "ev il", - "evi l", - "kont akte", - "/ ///////////////////////////////////////////////////////////////////////////////Ċ", - "/// /////////////////////////////////////////////////////////////////////////////Ċ", - "//////////////////////////////////////////////////////////////////////////// ////Ċ", - "//////////////////////////////////////////////////////////////////////////////// Ċ", - "= sys", - "=s ys", - "ĉ lock", - "ĉl ock", - "ĉloc k", - "-- ;ĊĊ", - "--;Ċ Ċ", - "--; ĊĊ", - "_ FUN", - "_F UN", - "Fill Color", - "ó a", - "p rend", - "pr end", - "pre nd", - "Ġcom pressor", - "Ġcompr essor", - "Ġcompress or", - "M other", - "Mo ther", - "Mot her", - "ĠAr cher", - "ĠArch er", - "ĠArc her", - ". goto", - ".g oto", - ".go to", - "Ġwür de", - "Ġbam boo", - "Ġbamb oo", - "ï¼ İ", - "Ġ Trees", - "ĠT rees", - "ĠTr ees", - "ĠTree s", - "ĠTre es", - "Ġb umper", - "Ġbump er", - "Ġbum per", - "Ġsa usage", - "Ġsau sage", - "ĠEl asticsearch", - "ĠElastic search", - "Ġhor izontally", - "Ġhorizontal ly", - "ĠG ul", - "ĠGu l", - "Im mutable", - "Imm utable", - "Ġ loser", - "Ġl oser", - "Ġlo ser", - "Ġlos er", - "Ġlose r", - "Ġab orted", - "Ġabort ed", - "- demo", - "-d emo", - "-de mo", - "-dem o", - "ĠH atch", - "ĠHat ch", - "Ġ unde", - "Ġu nde", - "Ġun de", - "Ġund e", - "Ġpro cesso", - "Ġprocess o", - "Ġproc esso", - "Ġproces so", - "- call", - "-c all", - "-cal l", - "-ca ll", - "In come", - "Inc ome", - "å ĥ", - "_ returns", - "_return s", - "'] .\"'", - "']. \"'", - "'].\" '", - "( sw", - "(s w", - "C BS", - "CB S", - "am ilies", - "ami lies", - "amil ies", - "ĠYour self", - "ĠYours elf", - "ĠH olt", - "ĠHol t", - "ĠHo lt", - ". MON", - ".M ON", - "ৠĩ", - "ÑĪ е", - "a non", - "an on", - "ano n", - "Ġ FontAwesome", - "ĠFont Awesome", - "pro ducer", - "produ cer", - "prod ucer", - "produce r", - "j r", - "Ġm au", - "Ġma u", - "ĉ inter", - "ĉint er", - "ĉin ter", - "Ġdish onest", - "Ġm agna", - "Ġmag na", - "Ġmagn a", - "ĠColl ective", - "ĠCollect ive", - "Ġvra iment", - "Ġvrai ment", - "Ġcho ix", - "st ay", - "sta y", - "Ġwel ding", - "Ġweld ing", - "r ising", - "ri sing", - "ris ing", - ", min", - ",m in", - "ĠF ate", - "ĠFa te", - "ĠFat e", - "g lob", - "gl ob", - "RGB A", - "RG BA", - "Ġd ette", - "Ġde tte", - "Ġdet te", - "V en", - "Ve n", - "Ġembarrass ment", - ". DELETE", - ".DE LETE", - "g regar", - "greg ar", - "gre gar", - "- render", - "-r ender", - "-re nder", - "-ren der", - "( bucket", - "(b ucket", - "\" >ĊĊĊ", - "\"> ĊĊĊ", - "\">Ċ ĊĊ", - "\">ĊĊ Ċ", - ".wait Key", - "Bus y", - "Bu sy", - "Ġdifferent iation", - "ĠC ST", - "ĠCS T", - ". Constant", - ".Con stant", - ".Cons tant", - "Ġline Number", - "( matches", - "(m atches", - "(match es", - "(mat ches", - "Ġ websocket", - "Ġweb socket", - "Ġwebs ocket", - "Ġbar red", - "Ġbarr ed", - "Ġpued es", - "Ġpu edes", - "Ġpuede s", - "M ono", - "Mon o", - "Mo no", - "C ORE", - "CO RE", - "COR E", - "I ID", - "II D", - "ĠĠ ĠĠčĊčĊ", - "ĠĠĠĠ čĊčĊ", - "ĠĠĠ ĠčĊčĊ", - "ĠĠĠĠčĊ čĊ", - "Ġpúb lico", - "le aning", - "lean ing", - "lea ning", - "Ġclean sing", - "Ġcleans ing", - "Ġc ris", - "Ġcr is", - "Ġcri s", - "ĠDev ils", - "ĠDevil s", - "_ SETTING", - "_SET TING", - "unt ary", - "unta ry", - ". );Ċ", - ".) ;Ċ", - "Ċ ĠĠĠĊ", - "[ curr", - "[c urr", - "[cur r", - "t sy", - "ts y", - "ĠAlex is", - "ĠAle xis", - "r itel", - "ri tel", - "rit el", - "rite l", - "Ġpet roleum", - "Ġpetrol eum", - ".pre processing", - "m atter", - "mat ter", - "For Result", - "- license", - "-l icense", - "Ġtravel lers", - "Ġtrav ellers", - "Ġtraveller s", - "Ġ Dispatcher", - "ĠDispatch er", - "ĠDisp atcher", - "enn ifer", - "Ġdigest ive", - "P ED", - "PE D", - "hib ition", - "hibit ion", - "MAS ConstraintMaker", - "ĠW att", - "ĠWat t", - "ĠWa tt", - "B enef", - "Ben ef", - ".set View", - "d to", - "dt o", - "T EE", - "TE E", - "ĠPel osi", - "_EX TRA", - "_EXT RA", - "Ġmed als", - "Ġmedal s", - "x hr", - "fore cast", - "for ecast", - "Ġn argin", - "Ġnar gin", - "o uns", - "ou ns", - "oun s", - "- fill", - "-f ill", - "-fi ll", - "_CUR SOR", - "Ġsup ervised", - "Ġsuper vised", - "Ġsuperv ised", - "Ġsupervise d", - "Ġt urf", - "Ġtu rf", - "Ġtur f", - "ĠEd gar", - "POS ITION", - "POSIT ION", - "Ġ categoryId", - "Ġcategory Id", - "â ī", - "_ ER", - "_E R", - "ủ a", - "Sh own", - "Show n", - ". ll", - ".l l", - "_POL ICY", - "( ),'", - "() ,'", - "(), '", - "Ġ Prev", - "ĠP rev", - "ĠPr ev", - "ĠPre v", - "ĠString Field", - "ĉ Global", - "ĉG lobal", - "as sed", - "ass ed", - "asse d", - "Through out", - "o stringstream", - ".awt extra", - "Ġsl opes", - "Ġslo pes", - "Ġslope s", - "Ġ Sequential", - "ĠSe quential", - "ĠSequ ential", - "Ġgi orn", - "Ġgio rn", - "Ġ zelf", - "Ġz elf", - "Ġze lf", - "Ġzel f", - "Ġvers atility", - "Ġversa tility", - "le neck", - "len eck", - "lene ck", - ". cgi", - ".c gi", - ".cg i", - "Ġdoub ling", - "Ġdou bling", - "ĠBang kok", - "Ġbu urt", - "Ġusu ário", - "st udio", - "stu dio", - "stud io", - "Ġje unes", - "Ġjeune s", - "Ġjeu nes", - "Ġm uted", - "Ġmut ed", - "Ġmu ted", - "Ġmute d", - "Ġ ips", - "Ġi ps", - "Ġip s", - "_ fraction", - "_f raction", - "_fr action", - "_frac tion", - "& &(", - "&& (", - "Ġst unt", - "Ġstu nt", - "Ġstun t", - "') ;?> čĊ", - "}> čĊ", - "Ġev apor", - "b able", - "ba ble", - "bab le", - "Ġ PRICE", - "ĠPR ICE", - "ĠPRI CE", - "Ġ æ³", - "Ġæ ³", - "lu cent", - "Ġv amp", - "Ġva mp", - "ĠTechn ician", - "Ġunique ness", - "Ġuniqu eness", - "M es", - "Me s", - "ur ban", - "urb an", - ".param etrize", - "ĠRe play", - "ĠRep lay", - "S essions", - "Session s", - "em br", - "emb r", - "- Americans", - "-American s", - "-Americ ans", - "_PRO XY", - "Ġp ian", - "Ġpi an", - "Ġ trie", - "Ġt rie", - "Ġtr ie", - "Ġtri e", - "Ġ Destructor", - "ĠD estructor", - "ĠDe structor", - "Game State", - "ĠI MF", - "ĠIM F", - "c hin", - "ch in", - "chi n", - "Ġ porte", - "Ġp orte", - "Ġport e", - "Ġpor te", - "ĠS wal", - "ĠSw al", - "åŁ İ", - "Sub string", - "i ming", - "im ing", - "imi ng", - "imin g", - "/ Library", - "/L ibrary", - "Ġfright ened", - "w rites", - "write s", - "wr ites", - "Ġrec ursos", - "Ġrecurs os", - "ar Result", - "_INIT IALIZ", - "_INITIAL IZ", - "Ġ Badge", - "ĠB adge", - "ĠBad ge", - "ĠBa dge", - "_ crc", - "_c rc", - "_cr c", - "E ight", - "ĠDIST INCT", - "Ġ thro", - "Ġth ro", - "Ġthr o", - "@ Xml", - "Ġ Legendary", - "ĠLegend ary", - "- twitter", - "-t witter", - "-tw itter", - "_ easy", - "_e asy", - "Ġ +++", - "Ġ+ ++", - "Ġ++ +", - "( DATA", - "(D ATA", - ". Locale", - ".L ocale", - ".Local e", - ".Lo cale", - "Ġk ä", - "Ġn urt", - "Ġnu rt", - "Ġnur t", - "Ġcr uis", - "Ġcru is", - "_ ios", - "_i os", - "_io s", - "Ġs ensing", - "Ġsens ing", - "Ġsen sing", - "_ Line", - "_L ine", - "Ċ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "p ong", - "pon g", - "po ng", - "o leon", - "ol eon", - "ole on", - "Ġwild card", - "çĶ¨æĪ· åIJį", - "Ġbeg ging", - "R od", - "Ro d", - "Ġ Ãİ", - "Ġà İ", - "_ CELL", - "_C ELL", - "_CE LL", - "Research ers", - ". selector", - ".se lector", - ".select or", - ".sel ector", - "_ ing", - "_in g", - "_i ng", - "Ġas piring", - "Ġaspir ing", - "Ġasp iring", - "Ġimm ortal", - "Ġy min", - "_ robot", - "_r obot", - "_ro bot", - "Ġpl ur", - "Ġplu r", - "B TC", - "BT C", - "ĠD ID", - "ĠDI D", - "Ġpier cing", - "* u", - "_ DEFINED", - "_DEF INED", - "_DEFIN ED", - "_DEFINE D", - "ĠT hi", - "ĠTh i", - "i taire", - "it aire", - "ita ire", - "( media", - "(m edia", - "(me dia", - "- ons", - "-on s", - "-o ns", - "Ġch efs", - "Ġche fs", - "Ġchef s", - "Ġ\" *.", - "Ġ\"* .", - "/ AP", - "/A P", - "Ġr azor", - "Ġraz or", - "Ġsearch Data", - "Ġ =&", - "Ġ= &", - "Ġ ãĢĤ", - "ĠãĢ Ĥ", - "Ġm ourn", - "Ġmo urn", - "Ġmou rn", - "Ġmour n", - "t ingham", - "ting ham", - "Ġ oli", - "Ġo li", - "Ġol i", - "ĠVer non", - "ĠVern on", - "_ RS", - "_R S", - "ŀ æĢ§", - "Ġf ácil", - "a ngen", - "an gen", - "ang en", - "ange n", - "ce lain", - "cel ain", - "cela in", - "Ġ ail", - "Ġa il", - "Ġai l", - "l est", - "le st", - "les t", - "ĠQ COMPARE", - "g ain", - "ga in", - "Ġ ε", - "ĠÎ µ", - "ĠK ob", - "ĠKo b", - "Ġ Fault", - "ĠF ault", - "ĠFa ult", - "ĠFaul t", - "_ configs", - "_config s", - "_conf igs", - "ç»ĵ æŀľ", - ". +", - "c alar", - "ca lar", - "cal ar", - "cala r", - "( colors", - "(color s", - "(col ors", - "M ul", - "Mu l", - "_ ART", - "_A RT", - "_AR T", - "Ġexperiment ing", - "er men", - "erm en", - "ĠAng lo", - ".Fixed Single", - "S ea", - "Se a", - "Ġ ctxt", - "Ġc txt", - "Ġctx t", - "Ġct xt", - ". slider", - ".s lider", - ".sl ider", - ".slide r", - "C ollapse", - "Coll apse", - "G rey", - "Gr ey", - "Gre y", - "Ġ fld", - "Ġf ld", - "Ġfl d", - "- proof", - "-p roof", - "-pro of", - ". capacity", - ".cap acity", - "get Parent", - "ĠCom pliance", - "Ġbur gl", - "Ġburg l", - "- rec", - "-r ec", - "-re c", - "Ġover written", - "M U", - "Ġr outers", - "Ġro uters", - "Ġroute rs", - "Ġrout ers", - "Ġrouter s", - "Ġrou ters", - "ĉ Model", - "ĉM odel", - "Ġfantas ies", - "av ian", - "avi an", - "avia n", - "_ prec", - "_p rec", - "_pr ec", - "_pre c", - "ĠSc andin", - "ĠScan din", - "Ġ// <", - "Ġ/ /<", - "/ oct", - "/o ct", - "Ġceremon ies", - "Mon ths", - "Month s", - "Mont hs", - "un dy", - "und y", - "Ġqu ed", - "Ġque d", - "Ġq ued", - "ĠN ou", - "ĠNo u", - "ĠV ibr", - "ĠVi br", - "ĠVib r", - ". rgb", - ".r gb", - "Ġcit rus", - "Ġbr aces", - "Ġbra ces", - "Ġbrace s", - "- uppercase", - "-upper case", - "get Table", - "Ġd opo", - "Ġdo po", - "Ġdop o", - "ĠK err", - "ĠKe rr", - "ĠKer r", - "_ CHILD", - "_CH ILD", - "- cloud", - "-c loud", - "-cl oud", - "ĉ Matrix", - "ĉM atrix", - "ĉMat rix", - "Ġgarden ing", - "Ġgard ening", - "S ing", - "Si ng", - "Sin g", - "al most", - "alm ost", - "Require ments", - "Requirement s", - "ugu ay", - "( Property", - "(P roperty", - "sub scriber", - "subscribe r", - "F AST", - "FA ST", - "re action", - "react ion", - "rea ction", - "( lp", - "(l p", - ") })Ċ", - ")} )Ċ", - ")}) Ċ", - "` ).", - "`) .", - ". wallet", - ".w allet", - ".wall et", - "_ exchange", - "_ex change", - ". Maximum", - ".Max imum", - "Ġ Verb", - "ĠV erb", - "ĠVer b", - "ĠVe rb", - "âĶ ģ", - "( )<", - "() <", - "ï¼Ľ Ċ", - "R OT", - "RO T", - "C ARD", - "CA RD", - "CAR D", - "u bit", - "ub it", - "ubi t", - "{ @", - "_ kel", - "_k el", - "_ke l", - "Ġ Tooltip", - "ĠTo oltip", - "ĠTool tip", - "My SQL", - "Main Activity", - "a rf", - "ar f", - "Ġm align", - "Ġmal ign", - "Ġse inen", - "Ġsein en", - "Ġseine n", - "Ġsei nen", - "ap ist", - "api st", - "apis t", - "Ġ< %", - "Method Impl", - "M il", - "Mi l", - "ĠM ick", - "ĠMi ck", - "ĠMic k", - ". depend", - ".d epend", - ".de pend", - ".dep end", - "< ID", - " >&", - ">> &", - "ĉ ok", - "ĉo k", - "- low", - "-l ow", - "-lo w", - ". usuario", - ".us uario", - "n ested", - "ne sted", - "nes ted", - "nest ed", - "X B", - "OUR S", - "OU RS", - ". BorderColor", - ".Border Color", - "Ġb row", - "Ġbr ow", - "Ġbro w", - "Ġ Ðķ", - "ĠÐ ķ", - "c orr", - "co rr", - "cor r", - "ĠRed skins", - "ĠReds kins", - ".get Tag", - ".get Transaction", - "Ġst igma", - "har dt", - "hard t", - "ĠPlayer Prefs", - "al sy", - "als y", - "uc son", - "ucs on", - "L anguages", - "Language s", - "ĠOl ivia", - "ĠOliv ia", - "Ġt ac", - "Ġta c", - "Ġb li", - "Ġbl i", - "Ġc aval", - "Ġca val", - "Ġcav al", - "Ġconsolid ated", - "Ġconsolidate d", - "Ġper il", - "Ġpe ril", - "Ġperi l", - "Ġd ele", - "Ġde le", - "Ġdel e", - "Ġform ulated", - "Ġformula ted", - "Ġformul ated", - "Ġformulate d", - "Ġhigh ways", - "Ġhighway s", - ". spawn", - ".s pawn", - ".sp awn", - "= =$", - "== $", - "ĠN iet", - "ĠNi et", - "ĠNie t", - "Ġv eggies", - "Ġveg gies", - "y po", - "yp o", - "- rule", - "-r ule", - "ĠV ie", - "ĠVi e", - "/e pl", - "Ġenf ants", - "string Literal", - "Ġtough est", - "Ġtou ghest", - "bu yer", - "buy er", - "Ġcov ariance", - "Ġ ili", - "Ġi li", - "Ġil i", - "ĠSoph ie", - "Ġ BAB", - "ĠB AB", - "ĠBA B", - "Ġ \"),", - "Ġ\" ),", - "Ġ\") ,", - "ĠU k", - "current Index", - "_ userdata", - "_user data", - ". codec", - ".co dec", - ".code c", - ".cod ec", - "ĠPun jab", - "ĠS NP", - "ĠSN P", - "l ol", - "lo l", - "adv ance", - "Ġcom fy", - "Json Ignore", - "Ġfashion able", - "Ġ ICON", - "ĠI CON", - "ĠIC ON", - "ĠICO N", - "Ġ ora", - "Ġo ra", - "Ġor a", - "ĠP ricing", - "ĠPr icing", - "ĠPri cing", - "< num", - " E", - "t ering", - "ter ing", - "te ring", - "teri ng", - "/ screens", - "/s creens", - "/screen s", - "Ġheight ened", - "аÑĢ ÑĤ", - "Author ities", - "_ bbox", - "_b box", - "_bb ox", - "ü nst", - "ün st", - "üns t", - ". fontSize", - ".font Size", - "Ġ BOOLEAN", - "ĠBO OLEAN", - "div ide", - "di vide", - "divid e", - "ĠS loven", - "ĠSl oven", - "ĠSlo ven", - "ĠSlov en", - "u cer", - "uc er", - "uce r", - "Ù Ĵ", - "st ub", - "stu b", - "Ġnavig ating", - ": animated", - "_ NOW", - "_N OW", - "_NO W", - "_ vect", - "_v ect", - "_vec t", - "_ve ct", - "} {Ċ", - "}{ Ċ", - "@ (", - "Ġtele com", - "Ġtel ecom", - "Ġcontract ing", - "Ġcontr acting", - "ĠAss ange", - "Ġextract ing", - "Ġextr acting", - "Ġgr ö", - "c obra", - "co bra", - "cob ra", - ". DIS", - ".D IS", - "Ġc rab", - "Ġcr ab", - "Ġcra b", - "Ġt witch", - "Ġtw itch", - "Ġ verts", - "Ġv erts", - "Ġver ts", - "Ġvert s", - "Ġve rts", - "Ġreject s", - "Ġrej ects", - "ĉ format", - "ĉfor mat", - "ĉform at", - "Ġre generation", - "Ġreg eneration", - ". Sys", - ".S ys", - "s olve", - "sol ve", - "ĉ dialog", - "ĉd ialog", - "s hi", - "sh i", - "m eter", - "me ter", - "met er", - "( best", - "(b est", - "(be st", - "valid ators", - "validator s", - "Ġon wards", - "Ġonward s", - "Ġg uru", - "Ġgu ru", - "Ġmod erator", - "Ġmoder ator", - "ow ied", - "owie d", - "owi ed", - "ex periment", - "r ub", - "ru b", - "Ġ mqtt", - "Ġm qtt", - "Ġmq tt", - "ĠCa ucas", - "Ġnational ism", - "Ġm ange", - "Ġman ge", - "Ġma nge", - "Ġmang e", - "ĉ ImGui", - "/ Edit", - "/E dit", - "Ġ inh", - "Ġin h", - "Ġi nh", - "Ġint ellig", - "Ġintel lig", - "ero kee", - "ĉ export", - "ĉex port", - "ĉexp ort", - "Ġdiscrim inate", - "Ġdiscrimin ate", - "sub tract", - "ĠM oodle", - "ĠMoo dle", - "ĠMood le", - "en ser", - "ens er", - "ense r", - "ĠGu ides", - "ĠGuid es", - "ĠGuide s", - "ĠGui des", - "R AP", - "RA P", - "- hot", - "-h ot", - "_ grp", - "_g rp", - "_gr p", - ". picture", - ".p icture", - ".pic ture", - "X A", - "Ġinit View", - "_ Comm", - "_C omm", - "_Com m", - "Ġoverd ose", - "Ġ +ĊĊ", - "Ġ+ ĊĊ", - "Ġ+Ċ Ċ", - "ĠS ilent", - "ĠSil ent", - "sh ows", - "show s", - "Ġinter polate", - "Ġinterpol ate", - "Ġinterp olate", - "Form ation", - "Format ion", - "Ġb isc", - "Ġbi sc", - "Ġbis c", - "mark ets", - "market s", - "( SC", - "(S C", - "Z e", - "Ġ Networking", - "ĠNetwork ing", - "ĠNet working", - "Ġad renal", - "Ġadr enal", - "ĠG uns", - "ĠGu ns", - "ĠGun s", - "et eor", - "ete or", - "De clared", - "Decl ared", - "Declare d", - "orge town", - "orget own", - "Ġk arena", - "Ġka rena", - "Ġkar ena", - "/ password", - "/p assword", - "/pass word", - "_ addresses", - "_add resses", - "_address es", - "_addr esses", - "IT ERAL", - "ITE RAL", - "ITER AL", - "B uzz", - "Bu zz", - "ĠCon way", - "( case", - "(c ase", - "(ca se", - "P WD", - "PW D", - "he iro", - "hei ro", - "( act", - "(a ct", - "(ac t", - "* *čĊ", - "** čĊ", - "( ));ĊĊĊ", - "() );ĊĊĊ", - "());Ċ ĊĊ", - "()) ;ĊĊĊ", - "());ĊĊ Ċ", - "()); ĊĊĊ", - "Ġa nv", - "Ġan v", - "Ġ ..ĊĊ", - "Ġ. .ĊĊ", - "Ġ.. ĊĊ", - "Ġ..Ċ Ċ", - "( MenuItem", - "(Menu Item", - "( mail", - "(m ail", - "_ sections", - "_s ections", - "_se ctions", - "_section s", - "ĉ net", - "ĉn et", - "Ġp lut", - "Ġpl ut", - "Ġplu t", - "Ġw rench", - "Ġwr ench", - "/ object", - "/o bject", - "ĠI st", - "ĠIs t", - "Ġ VIS", - "ĠV IS", - "ĠVI S", - "/ pub", - "/p ub", - "al ten", - "alt en", - "alte n", - "Ġguitar s", - "Ġguit ars", - "Ġantib iotic", - "Ġantibiot ic", - "ï¼ ĸ", - " ¹", - "Ġ \"+\"", - "Ġ\" +\"", - "Ġ\"+ \"", - "form ula", - "Ġba bes", - "Ġbab es", - "Ġbabe s", - "Ġ Prompt", - "ĠP rompt", - "ĠProm pt", - "Ġe nim", - "Ġen im", - "/ player", - "/p layer", - "/pl ayer", - "/play er", - "ĉ ref", - "ĉr ef", - "ĉre f", - "Ġby Äĩ", - "Ġcons umes", - "Ġconsum es", - "Ġconsume s", - "ĠH ast", - "ĠHas t", - "ĠHa st", - "ĠT ao", - "ĠTa o", - "Ġ '))Ċ", - "Ġ' ))Ċ", - "Ġ') )Ċ", - "Ġc lam", - "Ġcl am", - "Ġcla m", - "Ġthigh s", - "Ġmot if", - "Ġmo tif", - "Api Operation", - "Ġ WL", - "ĠW L", - "get C", - "ĉ flags", - "ĉf lags", - "ĉflag s", - "oint ments", - "ointment s", - "Ġeconomic al", - "Ġeconom ical", - "need le", - "nee dle", - "x ls", - "xl s", - "pr actice", - "ut zer", - "utz er", - "time ofday", - "- output", - "-out put", - "Ġ findById", - "Ġfind ById", - "ĠfindBy Id", - "ĠB uddy", - "ĠBudd y", - "ĠBu ddy", - "ĠBud dy", - "Ðŀ ÑĤ", - "S even", - "Se ven", - "ĠB ark", - "ĠBar k", - "ĠBa rk", - "Ġen voy", - "Ġenv oy", - "_ algorithm", - "_al gorithm", - "åĪ ©", - "Ġball istic", - "ç§ »", - "r ades", - "ra des", - "rad es", - "rade s", - "ĉ doc", - "ĉd oc", - "ĉdo c", - "rodu cing", - "rod ucing", - "ĠE ating", - "ĠEat ing", - "ĠEa ting", - "Un mount", - "/data Tables", - "_ bonus", - "_b onus", - "Ġl itt", - "Ġli tt", - "Ġlit t", - "p ps", - "pp s", - ") localObject", - "pe rf", - "per f", - "Ġ Helvetica", - "ĠHel vetica", - "sh utdown", - "/ ml", - "/m l", - ". tokens", - ".t okens", - ".token s", - "ĠHard core", - ", row", - ",r ow", - "/ bg", - "/b g", - "S caler", - "Sc aler", - "Scale r", - "âĢĶ as", - "âĢĶa s", - "_log its", - "âĢĻ int", - "âĢĻin t", - "âĢĻi nt", - "ĉ App", - "ĉA pp", - "Impl icit", - "Imp licit", - ".F printf", - "E TO", - "ET O", - "Ġ terra", - "Ġt erra", - "Ġter ra", - "Ġterr a", - "Ġpossess ing", - ". rstrip", - ".r strip", - ".rs trip", - ", ),", - ",) ,", - "= yes", - "=y es", - "Ġ Stripe", - "ĠSt ripe", - "ĠStr ipe", - "ĠStrip e", - "? =", - "ne utral", - ". good", - ".g ood", - ".go od", - "Ġk ennen", - "Ġke nnen", - "Ġken nen", - "Ġkenn en", - "ĠS ung", - "ĠSun g", - "ĠSu ng", - "f ault", - "fa ult", - "ystate change", - "Can adian", - "',' \".$", - "ĠM its", - "ĠMi ts", - "ĠMit s", - "æ nd", - "Ġ STRUCT", - "ĠSTR UCT", - "ĠURL WithString", - "ĠCom pass", - "ĠComp ass", - "Ġ --ĊĊ", - "Ġ- -ĊĊ", - "Ġ-- ĊĊ", - "Ġ--Ċ Ċ", - "ĠNS LayoutConstraint", - "| min", - "|m in", - "- adjust", - "-ad just", - "Ġre built", - "Ġreb uilt", - "L IGHT", - "/ se", - "/s e", - "- mount", - "-m ount", - "v pn", - "vp n", - "valid ated", - "validate d", - "( QObject", - "(Q Object", - "Ġign ition", - "ĠChar gers", - "ĠCharg ers", - "ĠCharge rs", - "ĠCharger s", - "RYPT O", - "]initWith Frame", - "Ġ Fluid", - "ĠFl uid", - "ĠFlu id", - "Ġca dre", - "Ġcad re", - "Ġnom inations", - "Ġnomin ations", - "Ġnomination s", - "Ne ill", - "Neil l", - "ĠH ou", - "ĠHo u", - "Ġcurrent s", - "Ġcurr ents", - "_ gene", - "_g ene", - "_gen e", - "_ge ne", - "( inp", - "(i np", - "(in p", - "P aris", - "Par is", - "Pa ris", - "z ÄĻ", - "ag gregate", - "Ġ assoc", - "Ġas soc", - "Ġass oc", - "we eted", - "weet ed", - "er rat", - "err at", - "erra t", - "âĢĵ ĊĊ", - "Ġ' /',Ċ", - "Ġ'/ ',Ċ", - "Ġ'/' ,Ċ", - "Ġ'/', Ċ", - "f ixture", - "fix ture", - "Ġ Highest", - "ĠH ighest", - "ĠHigh est", - "ĠHi ghest", - "amb ient", - "ambi ent", - "Ġ chmod", - "Ġch mod", - "Ġ conte", - "Ġc onte", - "Ġcon te", - "Ġcont e", - "Ġco nte", - "Ġs ensual", - "Ġsens ual", - "Ġgar ment", - "z ers", - "ze rs", - "zer s", - "Ġ Powered", - "ĠP owered", - "ĠPower ed", - "ĠPow ered", - "dom ains", - "domain s", - "R eward", - "Re ward", - "Rew ard", - "i omanip", - "Ġcock pit", - "out file", - "Ġ builtin", - "Ġb uiltin", - "Ġbuilt in", - "Ġins isting", - "Ġinsist ing", - ". vars", - ".v ars", - ".var s", - ".va rs", - "zip code", - "Ġ ����", - "Ġ� ���", - "f ails", - "fa ils", - "fail s", - "Ġconsolid ation", - "_ oid", - "_o id", - "Plan et", - "Plane t", - "Ġ =\",", - "Ġ= \",", - "Ġ=\" ,", - "ĉ el", - "ĉe l", - "U ILT", - "UI LT", - "UIL T", - "ä tz", - "ät z", - "af ari", - "afa ri", - "ĠMc Cl", - "ĠMcC l", - "T imeline", - "Time line", - "Tim eline", - "E sta", - "Est a", - "Es ta", - "Ġ fram", - "Ġf ram", - "Ġfr am", - "Ġfra m", - "Y E", - "Ġcere bral", - "Of Month", - "ĠP regn", - "ĠPre gn", - "Ġкл аÑģÑģ", - "ĠклаÑģ Ñģ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠF res", - "ĠFr es", - "ĠFre s", - "Ap proved", - "Appro ved", - ". Special", - ".S pecial", - ".Spec ial", - ".Sp ecial", - "ĠProte stant", - "ĠProtest ant", - "Ġall ergy", - "Ġallerg y", - "Ġaller gy", - "_ pcm", - "_p cm", - "_pc m", - "ĉ Copyright", - "ĉC opyright", - "Ġsuper Class", - "\" strconv", - "ĠMoh amed", - "Ġ' //", - "Ġ'/ /", - "Fore Color", - "Ar thur", - "Art hur", - "ĠJ ungle", - "ĠJun gle", - "ĠJu ngle", - "ĠJung le", - "Ġve ins", - "Ġvein s", - "S ad", - "Sa d", - "Ġback ups", - "Ġbackup s", - "ĠOp inion", - "û t", - "Ġinter mitt", - "o dyn", - "od yn", - "ody n", - "ĠChrist ina", - "Ġ andre", - "Ġand re", - "Ġan dre", - "Ġevac uation", - "p alette", - "pa lette", - "pal ette", - "h orse", - "hor se", - "ĠRes ident", - "ĠHas san", - "ĠHass an", - ". Nil", - ".N il", - "Ġa isle", - "Ġais le", - "Ġ Growing", - "ĠG rowing", - "ĠGr owing", - "ĠGro wing", - "ĠGrow ing", - "Ġblog info", - "/ sql", - "/s ql", - "_ ioctl", - "_io ctl", - "S caling", - "Sc aling", - "Ġ Monad", - "ĠMon ad", - "ĠMo nad", - "ĠMona d", - "_ cpp", - "_c pp", - "_cp p", - "ĠH utch", - "ĠHut ch", - "ĠApple WebKit", - "Exp ense", - "_ JOB", - "_J OB", - "_JO B", - "Ġpoint less", - "From Body", - "an tal", - "ant al", - "anta l", - "Ġdepict ing", - "Ġ CELL", - "ĠC ELL", - "ĠCE LL", - "ĠCEL L", - "Ġre fin", - "Ġref in", - "ĠC NC", - "ĠCN C", - "ì¹ ĺ", - "_ dimensions", - "_dim ensions", - "_dimension s", - "Ġ SAN", - "ĠS AN", - "ĠSA N", - "Ġ aft", - "Ġa ft", - "Ġaf t", - "Ġfoot steps", - "c coli", - "cc oli", - "cco li", - "_ PHONE", - "_P HONE", - "_PH ONE", - "/ math", - "/m ath", - "/mat h", - "- kind", - "-k ind", - "Ġ Means", - "ĠMe ans", - "ĠMean s", - "ich ael", - "icha el", - ". guna", - ".g una", - "Ġinaug uration", - "Ġinaugur ation", - "-dr iving", - "( delete", - "(de lete", - "(del ete", - "Ġ totalCount", - "Ġtotal Count", - "_ MC", - "_M C", - ". Extension", - ".Ext ension", - "Com mercial", - "Comm ercial", - "Ġz Index", - "< Customer", - "$", - "\"> $", - "Ġe bay", - "Ġeb ay", - "Ġc aptive", - "Ġca ptive", - "Ġcapt ive", - "pl iant", - "ĠCalculate s", - "ĠCalcul ates", - "ĠCalc ulates", - "ol ta", - "olt a", - "es ting", - "est ing", - "esti ng", - "_ revision", - "_re vision", - "_rev ision", - "Ġm ús", - "Ġmú s", - "+ m", - "\",\" \",\"", - "\",\"\", \"", - "WH AT", - "Ġcompass ionate", - "Ġcompassion ate", - "h arga", - "har ga", - "[ random", - "[r andom", - "[rand om", - "Ġ modulo", - "Ġmod ulo", - "( sn", - "(s n", - "Ġoccup ations", - "Ġoccupation s", - "/ ///Ċ", - "// //Ċ", - "//// Ċ", - "/// /Ċ", - "ĉ board", - "ĉb oard", - "ĠB alk", - "ĠBa lk", - "ĠBal k", - "w iÄħ", - "wi Äħ", - "Ġ Wifi", - "ĠW ifi", - "ĠWi fi", - ". Profile", - ".Pro file", - ".Pr ofile", - ": maj", - ":m aj", - "ĉ mat", - "ĉm at", - "LOCK S", - "LOC KS", - "(j Button", - "Ġ ('$", - "Ġ( '$", - "Ġ(' $", - "M ur", - "Mu r", - "æĮ ī", - "b ble", - "bb le", - "Ġ frog", - "Ġf rog", - "Ġfr og", - "Ġfro g", - "- hide", - "-h ide", - "Ġbroad caster", - "Ġbroadcast er", - "ภŀ", - "h aled", - "ha led", - "hal ed", - "Ġam using", - "_ predictions", - "_pre dictions", - "_pred ictions", - "_predict ions", - "_prediction s", - "_ intr", - "_in tr", - "_int r", - "Ġe agle", - "Ġea gle", - "Ġeag le", - "аÑĤ елÑĮ", - "аÑĤе лÑĮ", - "Ġ getList", - "Ġget List", - "ps ilon", - "psi lon", - "Ġcharacter ization", - "AR DS", - "ARD S", - "Ġre location", - "Ġrel ocation", - "Ġreloc ation", - "Ġr ulers", - "Ġrule rs", - "Ġru lers", - "Ġruler s", - "P AY", - "PA Y", - "ĠDef initely", - "_ Action", - "_A ction", - "_Act ion", - "Ġc losures", - "Ġclos ures", - "Ġclosure s", - "Ġf actual", - "Ġfact ual", - "Ġfac tual", - "o dynamic", - "od ynamic", - "odyn amic", - "odynam ic", - "Ġpreca utions", - "Ġprecaution s", - "n iej", - "ni ej", - "nie j", - "ĠPart ies", - "ĠPar ties", - "ĠParti es", - "ĠSub aru", - "ĠSu baru", - "Ġcous ins", - "Ġcousin s", - "ar beit", - ". money", - ".m oney", - ".mo ney", - ".mon ey", - "g unta", - "gun ta", - "( and", - "(a nd", - "(an d", - "get item", - ".Style Priority", - "Ġs lid", - "Ġsl id", - "s ingleton", - "single ton", - "sing leton", - "Ġg arn", - "Ġgar n", - "Ġga rn", - "ĠP AS", - "ĠPA S", - "Ġd azz", - "Ġda zz", - "a ż", - "Ġbog us", - "ĠM og", - "ĠMo g", - "Ġrival ry", - "i sol", - "is ol", - "iso l", - "Ġland marks", - "Ġlandmark s", - "ñ as", - "ña s", - "B ern", - "Be rn", - "Ber n", - "ĠS achs", - "ĠSa chs", - "ĠSac hs", - "ĠSach s", - "Ġ \")ĊĊ", - "Ġ\" )ĊĊ", - "Ġ\") ĊĊ", - "Ġ\")Ċ Ċ", - "Ġhost ility", - "Ġhos tility", - "_m ex", - "_me x", - "m ere", - "mer e", - "me re", - "M ot", - "Mo t", - "p ictureBox", - "picture Box", - "Def ense", - "Ġaffid avit", - "other wise", - ". directory", - ".d irectory", - ".direct ory", - "_ UnityEngine", - "_Un ityEngine", - "- blog", - "-b log", - "-bl og", - ". skin", - ".s kin", - ".sk in", - "p hem", - "ph em", - "phe m", - "Ap ellido", - "er chant", - "[ class", - "[c lass", - "Ġ wart", - "Ġw art", - "Ġwar t", - "Ġwa rt", - ". \"[", - ".\" [", - "a leur", - "al eur", - "ale ur", - "/ back", - "/b ack", - "ĠĠĠĠ ĉĠĠĠ", - "ĠĠĠ ĠĉĠĠĠ", - "ĠĠĠĠĉ ĠĠĠ", - "ĠĠĠĠĉĠ ĠĠ", - "Ġprecip itation", - "Ġob struction", - "Ġobstruct ion", - "Ġp Obj", - "Ġ rupt", - "Ġr upt", - "Ġru pt", - "U CKET", - "UCK ET", - "a ye", - "ay e", - "æİ Ĵ", - "g x", - "Ġe cl", - "Ġec l", - "Ġsecre cy", - "/ Header", - "ĠLe sb", - "ĠLes b", - "Ġ lei", - "Ġl ei", - "Ġle i", - "Ġ Bulletin", - "ĠBull etin", - "ĠBullet in", - "Ġgive away", - ". Home", - ".H ome", - "_ ROOM", - "_R OOM", - "_RO OM", - "\" W", - "Ġco work", - "Ġcow ork", - "_ ra", - "_r a", - "ĠC ycling", - "ĠCy cling", - "ĠCycl ing", - "ĠCyc ling", - "ĠP aw", - "ĠPa w", - "Ġp upil", - "Ġpup il", - "/ arch", - "/a rch", - "/ar ch", - "ĠFile Utils", - "é¦ ĸ", - "r sp", - "rs p", - "Ġfreedom s", - "Ġfreed oms", - "ĠL ear", - "ĠLe ar", - "} `).", - "}` ).", - "Ġbow ls", - "Ġbowl s", - "/ block", - "/b lock", - "/bl ock", - "_ logging", - "_log ging", - "Ġme thane", - "Ġmeth ane", - "Ġh orns", - "Ġhor ns", - "Ġhorn s", - "Ġwonder fully", - "Ġwonderful ly", - "Ġalter ations", - "Ġalteration s", - "Ġex ile", - "l sen", - "ls en", - "lse n", - "_ pause", - "_p ause", - "_pa use", - "_ LANGUAGE", - "_L ANGUAGE", - "_LANG UAGE", - "ĠUS DA", - "ĠUSD A", - "_ mysql", - "_m ysql", - "_my sql", - "_AM OUNT", - "ĠL IFE", - "ĠLI FE", - "Ġyoung sters", - "Ġyoungster s", - "Ġri ots", - "Ġriot s", - "Ġrio ts", - "[ E", - "Ġun forgettable", - ", },Ċ", - "Dis posed", - "Dispose d", - "Disp osed", - "ĠAss assin", - "ĠAssass in", - "U NG", - "UN G", - "ĠNew sp", - "ĠNews p", - "User Service", - ": aload", - ":a load", - "+ ',", - "+' ,", - "Ġsett lers", - "Ġsettle rs", - "Ġscre ams", - "Ġscream s", - "Ġincon venience", - ". Rotate", - ".R otate", - "Ġj ars", - "Ġja rs", - "Ġjar s", - "ĠP uzzle", - "ĠPu zzle", - "Ġm est", - "Ġme st", - "Ġmes t", - "ar si", - "ars i", - "ĠS harma", - "ĠSh arma", - "ĠShar ma", - "| (", - ". ds", - ".d s", - "ĠSa cred", - "ĠSac red", - "ĠSacr ed", - "_ evt", - "_e vt", - "_ev t", - "Ġexp resses", - "Ġexpress es", - "Ġexpr esses", - "Ġexpres ses", - "Ġh och", - "Ġho ch", - "Ġhoc h", - "ĠD uch", - "ĠDu ch", - "ĠDuc h", - ". calls", - ".c alls", - ".call s", - ".cal ls", - "t hr", - "th r", - "ĠShe ffield", - ". AlertDialog", - ".Alert Dialog", - "Ġrad ically", - "Ġradical ly", - "Ġt rous", - "Ġtr ous", - "Ġtro us", - "Ġtrou s", - "Ġprev ailing", - "Ġprevail ing", - "ĠWW II", - "âĢĻ n", - "ens ely", - "ense ly", - "Ġ Yesterday", - "ĠY esterday", - "ĠSir ius", - "ĠSiri us", - "Ġkill ers", - "Ġkil lers", - "Ġkiller s", - "Ġ FFT", - "ĠF FT", - "ĠFF T", - "Ġ oval", - "Ġo val", - "Ġov al", - "' ):čĊ", - "') :čĊ", - "'): čĊ", - "Ġ ìłķë³´", - "Ġìłķ ë³´", - "ou rage", - "our age", - "Ġ Checkbox", - "ĠCheck box", - "Work book", - ". defer", - ".de fer", - ".def er", - "_ floor", - "_f loor", - "_fl oor", - "Ġc ouncill", - "Ġcouncil l", - "Ġnors ke", - "Ġnorsk e", - "m oil", - "mo il", - "o rea", - "or ea", - "ore a", - "Ġmark eted", - "Ġmarket ed", - "_ SUR", - "_S UR", - "_SU R", - "x AA", - "xA A", - "Ġst ained", - "Ġsta ined", - "Ġstain ed", - "e ut", - "eu t", - "ĠM eng", - "ĠMe ng", - "ĠMen g", - "Ġ ieee", - "Ġi eee", - "Ġie ee", - ". extern", - ".ex tern", - ".ext ern", - "e gie", - "eg ie", - "Ġr app", - "Ġrap p", - "Ġra pp", - "ĠPy ongyang", - "' class", - "M ob", - "Mo b", - "Ġinitial Value", - "_ wave", - "_w ave", - "Ġ jab", - "Ġj ab", - "Ġja b", - "Ġmascul ine", - "Ġampl ifier", - "Ġ tty", - "Ġt ty", - "Ġtt y", - "Path Component", - "_ xt", - "_x t", - "ĠG FP", - "ĠGF P", - "/ sec", - "/s ec", - "/se c", - "ĉ dispatch", - "ĉdis patch", - "mark down", - "ĠS chn", - "ĠSc hn", - "ĠSch n", - "b ole", - "bo le", - "bol e", - "· ·", - "mouse move", - "Ġ errMsg", - "Ġerr Msg", - "Ġa sign", - "Ġas ign", - "Ġasi gn", - "_ mono", - "_m ono", - "_mon o", - "_mo no", - "To Selector", - "ĠZ u", - "( Rect", - "(R ect", - "Ġ ErrorCode", - "ĠError Code", - "l atin", - "la tin", - "lat in", - "ang ible", - "angi ble", - "v tk", - "vt k", - "CG Size", - "P okemon", - "Pok emon", - "Ġclass mates", - "Ġat tracts", - "Ġattr acts", - "Ġattract s", - "ĠT atto", - "ĠTat to", - "ul tan", - "ult an", - "ulta n", - "ol óg", - "Ġh alted", - "Ġhal ted", - "Ġhalt ed", - "ठ¨", - "ĠK art", - "ĠKar t", - "ĠKa rt", - "Ġ ue", - "Ġu e", - "_Init Structure", - "_InitStruct ure", - "Test Class", - "ĠAir bnb", - "_ \",", - "_\" ,", - "Ġchar coal", - "Ġ ipc", - "Ġi pc", - "Ġip c", - "Ġ Stretch", - "ĠSt retch", - "ĠStr etch", - ".g lide", - ".gl ide", - "lates AutoresizingMaskIntoConstraints", - "Ġp otion", - "Ġpo tion", - "Ġpot ion", - "ITT LE", - "Ġcount ert", - "Ġcounter t", - "_ hd", - "_h d", - "pre pared", - "prepare d", - "prep ared", - "A ds", - "Ad s", - "ĠV ampire", - "ro bots", - "robot s", - "rob ots", - ".Create Index", - "Status Label", - "Ġt ucked", - "af ür", - "U t", - "Ġswe ater", - "Ġsweat er", - "_ FN", - "_F N", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĉ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ġĉ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĉ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĉ", - "at aka", - "ata ka", - "atak a", - "Ġeyeb rows", - "Ġeyebrow s", - "a coes", - "ac oes", - "aco es", - "u den", - "ud en", - "ude n", - ".Linear LayoutManager", - ".LinearLayout Manager", - "Ġs way", - "Ġsw ay", - "Ġmult in", - "Ġmulti n", - "Ġmul tin", - "( ))))Ċ", - "() )))Ċ", - "()) ))Ċ", - "())) )Ċ", - "()))) Ċ", - "Ġ NSUInteger", - "ĠNS UInteger", - "ĠMy Base", - "P artner", - "Part ner", - "uts chen", - "utsch en", - "utsche n", - "ĠC ater", - "ĠCa ter", - "ĠCat er", - ".set BackgroundColor", - ".setBackground Color", - "Ġaccompl ishment", - "Ġaccomplish ment", - "_ problem", - "_pro blem", - "_prob lem", - ".d td", - ".dt d", - "Ġ pageNumber", - "Ġpage Number", - "ĠpageNum ber", - "Ġj ackets", - "Ġjack ets", - "Ġjacket s", - "Ġc ropped", - "Ġcr opped", - "Ġcro pped", - "Ġcrop ped", - "u els", - "ue ls", - "uel s", - "ĠH ep", - "ĠHe p", - "Ġc apped", - "Ġcap ped", - "Ġca pped", - "* Math", - "*M ath", - "_ callbacks", - "_callback s", - "_call backs", - "Ġp ubb", - "Ġpub b", - "Ġpu bb", - "ĠBrun swick", - ". respond", - ".res pond", - ".resp ond", - "[ \"_", - "[\" _", - "Ġbed ding", - "hyth m", - "O X", - "( speed", - "(s peed", - "(sp eed", - "Ġpest icides", - "Ġpestic ides", - "Ġpesticide s", - "Ġ -------", - "Ġ- ------", - "Ġ-- -----", - "Ġ---- ---", - "Ġ--- ----", - "Ġ----- --", - "Ġ------ -", - ". Blue", - ".B lue", - ".Bl ue", - "Ġn oodles", - "Ġnood les", - "ĠG oes", - "ĠGo es", - "Ġs aver", - "Ġsa ver", - "Ġsave r", - "Ġsav er", - "o xy", - "ox y", - "_ completion", - "_com pletion", - "_comp letion", - "ĠSw inger", - "ĠSwing er", - "Ġ getDate", - "Ġget Date", - "Ġm inded", - "Ġmin ded", - "Ġmind ed", - "int egration", - "integr ation", - "ĠLot us", - "( stop", - "(s top", - "(st op", - "(', ');Ċ", - "(',' );Ċ", - "(',') ;Ċ", - "Ġfl oods", - "Ġflo ods", - "Ġflood s", - "Ġ Workflow", - "ĠWork flow", - "Ġe rupted", - "Ġerupt ed", - "M acro", - "Mac ro", - "Ma cro", - "ĠS auce", - "ĠSa uce", - "ĠSau ce", - "Ġ eventName", - "Ġevent Name", - "\\ Input", - "Break ing", - "Bre aking", - "ĉ when", - "ĉw hen", - "_ pw", - "_p w", - "I NDER", - "IN DER", - "IND ER", - "ĠWell ness", - "Ġv oxel", - "Ġvox el", - "ĠM ell", - "ĠMe ll", - "ĠMel l", - "Ġ MEDIA", - "ĠM EDIA", - "ĠMED IA", - "S ENS", - "SE NS", - "SEN S", - "ĠF unds", - "ĠFund s", - "ĠFun ds", - "ĠFu nds", - "ĠM ild", - "ĠMil d", - "ĠMi ld", - "< Array", - "Ċ", - "') ;?>Ċ", - "'); ?>Ċ", - "');?> Ċ", - "Ġtemp ting", - "Ġtempt ing", - "Ġtest ament", - "Ġb ible", - "Ġbi ble", - "Ġbib le", - "Ġconsult ed", - "Ġconsul ted", - "ĠIndex Error", - "è¨ ĺ", - "Ġkey pad", - "Ġke ypad", - "i zzo", - "iz zo", - "izz o", - "( ok", - "(o k", - "Ġwh atsapp", - "Ġwhats app", - "ĠRemote Exception", - "Ġte amed", - "Ġteam ed", - "Ġtea med", - "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", - "» ,", - "Ġ getTime", - "Ġget Time", - "d iag", - "di ag", - "dia g", - "is sy", - "iss y", - "Ġ hed", - "Ġh ed", - "Ġhe d", - "Ġkn ots", - "Ġknot s", - "j om", - "jo m", - "Ġf unnel", - "Ġfun nel", - "-m ails", - "-mail s", - "Ġexp orting", - "Ġexport ing", - "Ġ VL", - "ĠV L", - "ĠK arn", - "ĠKar n", - "ĠKa rn", - "ĠBuddh ism", - "ĠAl lan", - "ĠAll an", - "ĠAlla n", - "_R ADIUS", - "Ġw ording", - "Ġword ing", - "Ġwor ding", - "Ġ Forget", - "ĠF orget", - "ĠFor get", - "ĠForge t", - "ĠForg et", - "ĠCor ona", - "ĠCoron a", - "i phy", - "ip hy", - "iph y", - "Ġlim burg", - "Ġlimb urg", - "ug gy", - "ugg y", - "ĠUser Repository", - "i min", - "im in", - "imi n", - "( ele", - "(e le", - "(el e", - "Ġlabel led", - "Ġlab elled", - "ç¤ ¾", - "ĠH erman", - "ĠHer man", - "ĠHerm an", - ". qq", - ".q q", - "Ġ \"));Ċ", - "Ġ\" ));Ċ", - "Ġ\") );Ċ", - "Ġ\")) ;Ċ", - "ie ber", - ". Translate", - ".Trans late", - "r yn", - "ry n", - "Ġdes env", - "Ġdese nv", - "u md", - "um d", - "Sim ply", - "ĉ mode", - "ĉm ode", - "ĉmod e", - "R pc", - "Rp c", - "ĠVal encia", - "ĠVale ncia", - "Ġstaff ers", - "Ġstaffer s", - "Ġse lv", - "Ġsel v", - "ĠS pike", - "ĠSp ike", - "ĠSpi ke", - "Ġd elic", - "Ġde lic", - "Ġdel ic", - "Ġ eru", - "Ġe ru", - "Ġer u", - "_ DT", - "_D T", - "J udge", - "Jud ge", - "Ju dge", - "á» ķ", - "ĠB asin", - "ĠBa sin", - "ĠBas in", - ". mutable", - ".m utable", - ".mu table", - "\" url", - "Ġtar iff", - "Ġtarif f", - "ĠSlee ve", - "ĠSle eve", - "Ġ flare", - "Ġfl are", - "Ġfla re", - ". dropout", - ".drop out", - "Ġb rides", - "Ġbr ides", - "Ġbri des", - "Ġbride s", - "Ġbrid es", - ") ),čĊ", - ")) ,čĊ", - ")), čĊ", - "_ constraints", - "_con straints", - "_constraint s", - "d estruct", - "de struct", - "Out line", - "Ġdisappe ars", - "Ġdisappear s", - "_ locked", - "_l ocked", - "_lock ed", - "_loc ked", - "ĠNS LocalizedString", - "c ke", - "ck e", - "ĉ null", - "ĉn ull", - "ad resse", - "adr esse", - "adress e", - "Ġt opping", - "Ġto pping", - "Ġtop ping", - "Ġtopp ing", - "ĠJ oker", - "ĠJo ker", - "b ishop", - "bi shop", - "bis hop", - "bish op", - "н оÑģÑĤÑĮ", - "но ÑģÑĤÑĮ", - "ноÑģÑĤ ÑĮ", - "ноÑģ ÑĤÑĮ", - "and ering", - "ander ing", - "ande ring", - "_ amp", - "_a mp", - "_am p", - "= time", - "=t ime", - "_ Space", - "_S pace", - "_P ULL", - "' =", - "Ġant iqu", - "Ġanti qu", - "Ġc ach", - "Ġca ch", - "Ġcac h", - "_ __ĊĊ", - "__ _ĊĊ", - "___ ĊĊ", - "O NES", - "ON ES", - "ONE S", - "о Ñı", - "Ġ unread", - "Ġun read", - "Ġunre ad", - "Ġunr ead", - ". policy", - ".p olicy", - ".pol icy", - "oooo oooo", - "ëŁ ¬", - "Ġ usted", - "Ġu sted", - "Ġus ted", - "Ġust ed", - "ĠR ece", - "ĠRe ce", - "ĠRec e", - "Ġal lem", - "Ġall em", - "Ġalle m", - "ãĥ¼ ãĤ¹", - "ãĥ¼ãĤ ¹", - "ĠThough ts", - "ĠThought s", - "ve illance", - "i strate", - "ist rate", - "istr ate", - "istra te", - "istrat e", - "_ lane", - "_l ane", - "_la ne", - "Ġf amed", - "Ġfam ed", - "Ġfa med", - "Ġfame d", - ". GetName", - ".Get Name", - "Ġsmooth er", - "Ġsmo other", - "Ġ Qualified", - "ĠQual ified", - "a zers", - "az ers", - "aze rs", - "azer s", - "_ geo", - "_g eo", - "_ge o", - "F ax", - "Fa x", - "ĠM inds", - "ĠMin ds", - "ĠMi nds", - "ĠMind s", - "Ġ Raises", - "ĠR aises", - "ĠRa ises", - "ĠRaise s", - "ĠRai ses", - "Ġtrans cripts", - "Ġtran scripts", - "Ġtranscript s", - "Con versation", - "Ġre marked", - "Ġrem arked", - "Ġremark ed", - "Ġremar ked", - "ëĤ ĺ", - "d ling", - "dl ing", - "Ġdeploy ing", - "Ġshared Application", - "Ġ kp", - "Ġk p", - "FontAwesome Icon", - "_ dummy", - "_d ummy", - "re iben", - "reib en", - "rei ben", - "ĠJane iro", - "Dir ections", - "Direction s", - "Direct ions", - "Di rections", - "Dire ctions", - ".get Bean", - ".getB ean", - "s ass", - "sa ss", - "Ġcomm anders", - "Ġcommand ers", - "Ġcommander s", - "Ġcommande rs", - "v ation", - "va tion", - "vat ion", - "error Code", - "ĠAl loy", - "ĠAll oy", - ". localized", - ".local ized", - "Ð ij", - "Ġdish washer", - "Ġ Soup", - "ĠS oup", - "ĠSo up", - "ĠSou p", - "N u", - "_ Default", - "_D efault", - "_De fault", - "_Def ault", - "Ġun even", - "Ġune ven", - "Ġ />\";Ċ", - "Ġ/ >\";Ċ", - "Ġ/> \";Ċ", - "- Based", - "-B ased", - "Ġseam lessly", - "Ġseamless ly", - "- null", - "-n ull", - "Ġ XC", - "ĠX C", - "Ġst ew", - "Ġste w", - "( delay", - "(d elay", - "(de lay", - "(del ay", - "AT ORS", - "ATOR S", - "ATO RS", - "ĠWhe eler", - "ĠWheel er", - "\" H", - "e ast", - "ea st", - ". air", - ".a ir", - ".ai r", - "âĢľ But", - "Object Context", - "success fully", - "successful ly", - "_ land", - "_l and", - "_la nd", - "Ġf olds", - "Ġfol ds", - "Ġfold s", - "Ġfo lds", - "_ COORD", - "_CO ORD", - "Ġsub po", - ".get Address", - ".g etAddress", - "in str", - "ins tr", - "inst r", - "Material s", - "Ñĥ ÑģÑĤ", - "ÑĥÑģ ÑĤ", - "de posit", - "dep osit", - "- last", - "-l ast", - "-la st", - "_ GRAY", - "_G RAY", - "_GR AY", - "_GRA Y", - "= find", - "=f ind", - "Ġmut ant", - "Ġmu tant", - "Ġlesb ienne", - "Ġlesbi enne", - "let cher", - "R OUGH", - "RO UGH", - "ur eka", - "ure ka", - ". capture", - ".c apture", - ".cap ture", - "Ġ enn", - "Ġe nn", - "Ġen n", - "Ġ ([[", - "Ġ( [[", - "Ġ([ [", - "ĠF lu", - "ĠFl u", - "Ġ taskId", - "Ġtask Id", - "ĠHus sein", - "ĠHuss ein", - ". folder", - ".f older", - ".fold er", - "Ġa usterity", - "IST RATION", - "ISTR ATION", - "_ Impl", - "_I mpl", - "注 æĦı", - "Ġdec ree", - "Ġdecre e", - "- chat", - "-c hat", - "-ch at", - "Ġim plication", - "Ġimp lication", - "Ġimpl ication", - "Ġimplic ation", - "Ġgu esses", - "Ġguess es", - "ul kan", - "ulk an", - "An alytics", - "Analy tics", - ". plus", - ".p lus", - ".pl us", - "COM MAND", - "COMM AND", - "е ли", - "ел и", - "» ĊĊ", - "»Ċ Ċ", - "_ SITE", - "_S ITE", - "_SI TE", - "Ġ equalTo", - "Ġequal To", - "Support FragmentManager", - "Ġ Recording", - "ĠRec ording", - "ĠRecord ing", - "å®Į æĪIJ", - "Ġbag gage", - "Ġpitch ers", - "Ġpit chers", - "Ġpitcher s", - "ĠE h", - "o que", - "oq ue", - "ĉ cnt", - "ĉc nt", - "Ġ =>$", - "Ġ= >$", - "Ġ=> $", - "/ foo", - "/f oo", - "I RA", - "IR A", - "ĠSat ellite", - "bo rah", - "bor ah", - "Ġ }}\"Ċ", - "Ġ} }\"Ċ", - "Ġ}} \"Ċ", - "Ġ}}\" Ċ", - "ĠE nds", - "ĠEn ds", - "ĠEnd s", - "ĠS pray", - "ĠSp ray", - "ĠSpr ay", - ", param", - ",p aram", - ". Chrome", - ".Ch rome", - "* q", - "th ought", - "though t", - "ib rated", - "ibr ated", - "ibrate d", - "Ġth ieves", - "Ġbenefici aries", - "En tered", - "Ent ered", - "Enter ed", - "ottes ville", - "otte sville", - "Ġveter in", - "Ġvet erin", - "By ID", - "qu ipe", - "quip e", - "qui pe", - "um ption", - "ump tion", - "umpt ion", - "- unit", - "-un it", - "-u nit", - "Execution Context", - "@ s", - "ĠG iov", - "ĠGi ov", - "ĠGio v", - ". ToolTip", - ".Tool Tip", - "_ friend", - "_f riend", - "( attributes", - "(at tributes", - "(attribute s", - "Ġd umping", - "Ġdump ing", - "Ġdum ping", - "Ġ JC", - "ĠJ C", - "_ DOCUMENT", - "_D OCUMENT", - "_DOC UMENT", - "ĠAr mour", - "ĠArm our", - "( insert", - "(in sert", - "(ins ert", - ". HorizontalAlignment", - ".Horizontal Alignment", - "Ġ Qed", - "ĠQ ed", - "ãģĦ ãģ¾ãģĻ", - "/ git", - "/g it", - "Ġ YYYY", - "ĠY YYY", - "ĠYY YY", - "ĠCar diff", - "ĠCard iff", - "Ġ apa", - "Ġa pa", - "Ġap a", - "org anic", - "organ ic", - "ĠWhere as", - "ĠWhe reas", - "Ġ æĿ", - "Ġæ Ŀ", - "ĠM ia", - "ĠMi a", - "Ġdemol ition", - "Ġs cars", - "Ġsc ars", - "Ġsca rs", - "Ġscar s", - "Ġ pai", - "Ġp ai", - "Ġpa i", - "Ġre tries", - "Ġret ries", - "Ġretrie s", - "Ġretr ies", - "Ġ rq", - "Ġr q", - "ĠD enis", - "ĠDe nis", - "ĠDen is", - "( Utils", - "(Util s", - "Ġallev iate", - "Ġ PIC", - "ĠP IC", - "ĠPI C", - "i due", - "id ue", - "Ġacknowled ging", - "Ġ// ////////////////////////////////", - "Ġ////////////////// ////////////////", - "ç¡® å®ļ", - "Ä «", - "\\ Json", - ". binary", - ".b inary", - ".bin ary", - "Ġx type", - "Ġxt ype", - "sign als", - "signal s", - "Ġ Appearance", - "ĠAp pearance", - "& r", - "} s", - "C i", - "ĠI llum", - "ĠIl lum", - "ĠIll um", - "p orate", - "por ate", - "po rate", - "pora te", - "h og", - "ho g", - "Ġ indexOf", - "Ġindex Of", - "\\ Command", - "_ parallel", - "_par allel", - "ĠSher lock", - "í ĥ", - "Ġ\" \")čĊ", - "Ġ\"\" )čĊ", - "Ġ\"\") čĊ", - "//////////////// ////////////////////////////////////////////////////////////////////////////////", - "//////////////////////////////// ////////////////////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////// ////////////////////////////////", - "//////////////////////////////////////////////// ////////////////////////////////////////////////", - "//////////////////////////////////////////////////////////////////////////////// ////////////////", - "Ġcritic ize", - "Ġ Soap", - "ĠSo ap", - "Ġ Matcher", - "ĠM atcher", - "ĠMat cher", - "ĠMatch er", - "Ġgr illed", - "Ġgrill ed", - "Ġgrille d", - "* T", - "Ġad ore", - "Ġado re", - "ul ling", - "ull ing", - "Ġje doch", - "Ġjed och", - "_ refs", - "_re fs", - "_r efs", - "_ref s", - "lean up", - "ĠJ AXB", - "ĠJA XB", - "Ġr oses", - "Ġro ses", - "Ġrose s", - "Ġros es", - "ĠL iam", - "ĠLi am", - "ĠLia m", - "size i", - "siz ei", - "Ġget char", - "Ġgetch ar", - "Ġt arde", - "Ġtar de", - "Ġtard e", - "- tooltip", - "-to oltip", - "-tool tip", - "Ġqual ifier", - "Ġ Intermediate", - "ĠInter mediate", - "_ Window", - "_W indow", - "ĠM alta", - "ĠMal ta", - "Dis connect", - "e where", - "ew here", - "C ampo", - "Cam po", - "Camp o", - "Ġirr ational", - "l edo", - "le do", - "led o", - "Ġ DN", - "ĠD N", - "AR GV", - "ARG V", - "Ġout ro", - "Ġou tro", - "Ġoutr o", - "Ġth irteen", - "Jose ph", - "Jos eph", - "M AR", - "MA R", - "/ gl", - "/g l", - "J ess", - "Je ss", - "ĠPsych iat", - "Ġpadding Bottom", - "- loop", - "-l oop", - "-lo op", - "/ fonts", - "/font s", - "_ seen", - "_s een", - "_se en", - "Te ams", - "Team s", - "React DOM", - "( man", - "(m an", - "( xpath", - "(x path", - ". getSimpleName", - ".get SimpleName", - "> (*", - ">( *", - "ĠP vt", - "ĠPv t", - "Ġel ders", - "Ġelder s", - "Ġelde rs", - "Ġ pies", - "Ġp ies", - "Ġpie s", - "Ġpi es", - ".user Agent", - "- region", - "-reg ion", - "ĠGre eks", - "ĠGreek s", - "ĠGree ks", - "( fragment", - "(f ragment", - "(fr agment", - "s tu", - "st u", - "Ġcouncil s", - "Ġst amina", - "Ġsta mina", - "ĠGod dess", - "è ¥¿", - "è¥ ¿", - "Ġphilosoph ers", - "Ġphilosopher s", - "Ġperson e", - "Ġpers one", - "Ġperso ne", - "ĠL ose", - "ĠLo se", - "ĠLos e", - "Ġ CLR", - "ĠC LR", - "ĠCL R", - "Ġ Docs", - "ĠD ocs", - "ĠDo cs", - "ĠDoc s", - "Ġso ak", - "Ġ HOLDER", - "ĠH OLDER", - "ĠHOLD ER", - "ĠHOL DER", - "Ġb ells", - "Ġbel ls", - "Ġbell s", - "hash Code", - "R ATE", - "RA TE", - "_WE IGHT", - "in ous", - "ino us", - "inou s", - "en dra", - "end ra", - "oph obic", - "Ġp rose", - "Ġpro se", - "Ġpr ose", - "Ġpros e", - "Ġf inely", - "Ġfin ely", - "Ġfine ly", - "/ oauth", - "/o auth", - "( space", - "(s pace", - "(sp ace", - "a dge", - "ad ge", - "ĠM ama", - "ĠMa ma", - "ĠMam a", - "Ġstring Buffer", - "Ġst int", - "Ġm isma", - "Ġmis ma", - "Ġmism a", - "Ġvill ains", - "Ġvillain s", - "Ġvilla ins", - "ĠCrime a", - "Ġdipl oma", - "Ġdiplom a", - "Ġпо Ñģл", - "ĠпоÑģ л", - "ĠB ea", - "ĠBe a", - "( join", - "(j oin", - "Ġ íķ´", - "Ġíķ ´", - "CH AT", - "CHA T", - "p ering", - "pe ring", - "per ing", - "peri ng", - "ĠC ros", - "ĠCr os", - "ĠCro s", - "Ġmon keys", - "Ġmonkey s", - "Ġp reds", - "Ġpr eds", - "Ġpre ds", - "Ġpred s", - "y la", - "yl a", - ", ,,", - ",, ,", - "Ġv ibrator", - "Ġvibr ator", - "Ġ NU", - "ĠN U", - "åħ Ī", - "f ant", - "fa nt", - "fan t", - "z et", - "ze t", - "Ġb ietet", - "un ft", - "s worth", - "sw orth", - ". Flow", - ".F low", - ".Fl ow", - "Ġpsych ed", - "Ġpsy ched", - "Ġpsyche d", - "ĠContin ental", - "ĠContinent al", - "> t", - "Ġqu ilt", - "Ġq uilt", - "Ġqui lt", - "Ġquil t", - ". UP", - ".U P", - "Ġexpans ive", - "Dis pose", - "Disp ose", - "( language", - "(l anguage", - "C aps", - "Cap s", - "Ca ps", - "_ ZONE", - "_Z ONE", - "Ġre cycle", - "Ġr ecycle", - "Ġrec ycle", - "Ġrecycl e", - "Ġ Managed", - "ĠMan aged", - "ĠManage d", - "ĠMana ged", - "current Color", - ". broadcast", - ".b roadcast", - "sign In", - ". prom", - ".p rom", - ".pro m", - ".pr om", - "l lu", - "ll u", - "ue blo", - "Ġpun ches", - "Ġpunch es", - "Ġaut omat", - "Ġauto mat", - "Ġautom at", - "Ġassign ing", - "Ġcreate User", - "ĠAl lied", - "ĠAll ied", - "Ġcon ductor", - "Ġconduct or", - "Ġcond uctor", - "Ġconduc tor", - "Ġcondu ctor", - "Ĥ ¨", - "Ġs addle", - "Ġsad dle", - "Ġsadd le", - "Ġ dni", - "Ġd ni", - "Ġdn i", - "o medical", - "omed ical", - "- West", - "-W est", - "Positive Button", - "Ġ italic", - "Ġit alic", - "? [", - "( trigger", - "(tr igger", - "Ġele phants", - "Ġelephant s", - "\":\" \",\"", - "\":\"\" ,\"", - "Ġcal iber", - "raft ed", - "raf ted", - "d igits", - "digit s", - "dig its", - "Ġ marshal", - "Ġm arshal", - "Ġmar shal", - "Ġmarsh al", - "Ġmars hal", - "m illiseconds", - "mill iseconds", - "m arkers", - "mark ers", - "mar kers", - "marker s", - "m om", - "mo m", - "/ place", - "/p lace", - "/pl ace", - "Ġhol istic", - ": t", - "# ,", - "Ġb oto", - "Ġbo to", - "Ġbot o", - "Ġnause a", - "Ġnau sea", - "ĠSh ooting", - "ĠShoot ing", - "ĠSho oting", - "i tech", - "it ech", - "ite ch", - "Ġtext Status", - "< Class", - " ())Ċ", - ">( ))Ċ", - ">() )Ċ", - ">()) Ċ", - "ADD RESS", - "ADDR ESS", - "B ST", - "BS T", - "et zt", - "etz t", - "ĠQ gs", - "S ense", - "Sen se", - "Exception Handler", - "ĠC hu", - "ĠCh u", - ".get OwnProperty", - "Ġexerc ised", - "Ġexercise d", - "i otic", - "io tic", - "iot ic", - "ĠRe leases", - "ĠRelease s", - "Ġp interest", - "o lie", - "ol ie", - "oli e", - "i soft", - "is oft", - "iso ft", - "Ġsequ encing", - "Ġpa dre", - "Ġpad re", - "Ġpadr e", - "] ));čĊ", - "]) );čĊ", - "])) ;čĊ", - "])); čĊ", - "( radius", - "(r adius", - "(rad ius", - ". med", - ".m ed", - ".me d", - "ain ties", - "aint ies", - ".Object Model", - "Ġ emple", - "Ġem ple", - "Ġemp le", - "Ġseg uro", - "Ġsegu ro", - "St ars", - "Star s", - "Ġqual itative", - "le mn", - "lem n", - "á» ±", - "> \").", - ">\" ).", - ">\") .", - "Ġ gx", - "Ġg x", - "- cert", - "-c ert", - "-ce rt", - "ĠA STM", - "ĠAS TM", - "ĠAST M", - "Ġ fullname", - "Ġfull name", - "Ġful lname", - "Ġte lemetry", - "Ġtele metry", - "ĠCamb odia", - "_ ul", - "_u l", - "ĠCl are", - "ĠClar e", - "ĠCla re", - "C USTOM", - "Q C", - "ĠU ns", - "ĠUn s", - "Ġ HTTPS", - "ĠHTTP S", - "ĠPar kinson", - "ĠPark inson", - "ancy box", - "', '.", - "',' .", - "T ue", - "Tu e", - ". getLast", - ".get Last", - "Ġ abi", - "Ġa bi", - "Ġab i", - "Äħ d", - "A st", - "As t", - "Ġ Editing", - "ĠEd iting", - "ĠEdit ing", - ". Unity", - ".Un ity", - ".Unit y", - "j mp", - "jm p", - "Ġm ats", - "Ġmat s", - "Ġma ts", - "Ġshared Preferences", - "Cap tain", - "Capt ain", - ". pageSize", - ".page Size", - "Ġ rtl", - "Ġr tl", - "Ġrt l", - "Ġan meld", - "Runtime Object", - "Ġdem ande", - "Ġdemand e", - "( \";", - "(\" ;", - "se ite", - "sei te", - "- headed", - "-head ed", - "-he aded", - "ĠK ra", - "ĠKr a", - "Ġ FONT", - "ĠF ONT", - "ĠFO NT", - "` \\", - "Class NotFoundException", - ". avg", - ".a vg", - ".av g", - "a tical", - "at ical", - "atic al", - "ati cal", - "atica l", - "A j", - "Ġpermit ting", - "Ġperm itting", - "P roj", - "Pro j", - "Pr oj", - "ERR Q", - "Ġcre ampie", - "Ġcream pie", - "ĠBuy er", - "ĠBu yer", - "- modules", - "-mod ules", - "-module s", - "ĠSunday s", - "ĠSun days", - "ĠSund ays", - "| `Ċ", - "Ġday time", - "Ġ +(", - "Ġ+ (", - "Ġgl itch", - "Ġ Operand", - "ĠOper and", - "ĠOpera nd", - "Ġtox ins", - "Ġtoxin s", - "i nya", - "in ya", - "iny a", - "D NS", - "DN S", - "ĠS as", - "ĠSa s", - "C ake", - "Ca ke", - "ĠNational s", - "ĠNation als", - ". addTo", - ".add To", - "Ġs inking", - "Ġsin king", - "Ġsink ing", - "Ġcompreh ension", - "Ġs cor", - "Ġsc or", - "Ġsco r", - "a gements", - "ag ements", - "age ments", - "agement s", - "agem ents", - "Ġt ard", - "Ġta rd", - "Ġtar d", - "Ġm arching", - "Ġmar ching", - "Ġmarch ing", - "ĠM TV", - "ĠMT V", - "Ġs ane", - "Ġsa ne", - "Ġsan e", - "Create Info", - "Ạ¯", - "Ġend Index", - "ĉ layout", - "ĉl ayout", - "Ġ åIJį", - "ĠåIJ į", - "S ITE", - "SI TE", - "ĠT HERE", - "ĠTHE RE", - "ĠTH ERE", - "Ġ[ {'", - "Ġ[{ '", - "opath ic", - "opa thic", - "Ġtrans mitter", - "Ġtransmit ter", - "/ body", - "/b ody", - "Ġp und", - "Ġpun d", - "Ġpu nd", - "Ġ Closing", - "ĠC losing", - "ĠCl osing", - "ĠClo sing", - "Ġ setattr", - "Ġset attr", - "Ġ bounded", - "Ġb ounded", - "Ġbo unded", - "Ġbound ed", - "At las", - "Atl as", - "s uming", - "sum ing", - "su ming", - "( times", - "(t imes", - "(time s", - "(ti mes", - "p arer", - "par er", - "pare r", - "pa rer", - "y nom", - "yn om", - "fe it", - "Ġf rem", - "Ġfr em", - "Ġfre m", - "- leg", - "-l eg", - "-le g", - "ĠB ras", - "ĠBr as", - "ĠBra s", - "> #", - "Ġì¶ ľëł¥", - "Ġì¶ľ ëł¥", - "Ġ INSTANCE", - "ĠIN STANCE", - "ĠINST ANCE", - "ĠC ouch", - "ĠCo uch", - "ĠCou ch", - "_ hosts", - "_host s", - "lik elihood", - ". Marker", - ".M arker", - ".Mark er", - ".Mar ker", - "ĠM asks", - "ĠMas ks", - "ĠMask s", - "Ġc ereal", - "Ġce real", - "Ġcere al", - "ut ilities", - "util ities", - "Ġelement al", - "Ġele mental", - "Ġelem ental", - "Ġdist orted", - "Ġdistort ed", - "in active", - "c ry", - "cr y", - "W L", - "UPPORT ED", - ". Throws", - ".Th rows", - ".Throw s", - "/ schema", - "/s chema", - "s erie", - "se rie", - "ser ie", - ". \"',", - ".\" ',", - ".\"' ,", - "ĠBened ict", - "ĠBene dict", - "- picker", - "-p icker", - "-pic ker", - "ig gs", - "igg s", - "ĠP irate", - "ĠPi rate", - "ĠPir ate", - "åij¨ æľŁ", - "ĠThe ma", - "ĠTh ema", - "ĠThem a", - "ĠSouth ampton", - "Ġarray With", - "ĠPaul a", - "ĠPa ula", - "Ġpred ictor", - "Ġpredict or", - "Ġpredic tor", - "- Ass", - "-A ss", - ". userid", - ".user id", - ".use rid", - "Ġ peri", - "Ġp eri", - "Ġper i", - "Ġpe ri", - "Ġexagger ated", - "u rate", - "ur ate", - "ura te", - "urat e", - "arse ille", - "ĠCon cent", - "ĠConc ent", - "ĠConce nt", - "ĠP ik", - "ĠPi k", - "Ġ@ _;ĊĊ", - "Ġ@_;Ċ Ċ", - "Ġ@_ ;ĊĊ", - "Ġform ations", - "Ġformat ions", - "Ġformation s", - "Ġden omin", - "Ġdenom in", - "\" />.Ċ", - "\"/ >.Ċ", - "\"/> .Ċ", - "end edor", - "ended or", - "Ġpan cre", - "Ġpanc re", - "Ġ amt", - "Ġa mt", - "Ġam t", - "Ġon Resume", - "on Delete", - "ĠB CH", - "ĠBC H", - ") (\"", - ")( \"", - "m ovement", - "move ment", - "mo vement", - "mov ement", - "Ġpot assium", - "", - "Ġ-- ->", - "Ġ--- >", - "ĠP PC", - "ĠPP C", - "i sz", - "is z", - "ake FromNib", - "Ġ Disp", - "ĠD isp", - "ĠDis p", - "ĠDi sp", - "ĠAth letics", - "ĠAthletic s", - "Ġnight club", - "G OOD", - "GO OD", - ".set Geometry", - "+ [", - "/ send", - "/s end", - "/se nd", - "Ġbin aries", - "Ġr áp", - "Ġrá p", - ": req", - ":r eq", - "-con suming", - "-cons uming", - "er time", - "ert ime", - "erti me", - "UP DATED", - "UPDATE D", - "_ nullable", - "_null able", - "V IN", - "VI N", - "u lia", - "ul ia", - "uli a", - "c yan", - "cy an", - "Ġmisunder standing", - "Ġmisunderstand ing", - "o rical", - "or ical", - "ori cal", - "oric al", - "deg rees", - "degree s", - "Le ading", - "Lead ing", - ". AR", - ".A R", - "ic kest", - "ick est", - "N uevo", - "uf oria", - "Ġgo odies", - "Ġgood ies", - "Ġf ores", - "Ġfor es", - "Ġfore s", - "Ġfo res", - "() <<\"", - "()<< \"", - "()< <\"", - "ad emic", - "ade mic", - "adem ic", - "Action Creators", - "server name", - "( nt", - "(n t", - "db Context", - "Ġair borne", - "Ġexhib itions", - "Ġexhibition s", - "Ġexhibit ions", - "c ele", - "ce le", - "cel e", - "Ġt ela", - "Ġte la", - "Ġtel a", - "< Movie", - "", - "() \">", - "()\" >", - ".set PreferredSize", - "ĠM ID", - "ĠMI D", - "ĠA less", - "ĠAl ess", - "ĠAle ss", - "Ġhorse power", - "Ġa tm", - "Ġat m", - "ĠPack aging", - "Ġc iphertext", - "Ġcipher text", - "Request Method", - "Ġbe iden", - "Ġbei den", - "Ġbeide n", - "è £", - "ĠP OW", - "ĠPO W", - ".Write Header", - "d irector", - "dir ector", - "direct or", - "dire ctor", - "- but", - "-b ut", - "ãģł ãģķãģĦ", - "in cer", - "ince r", - "inc er", - "_ dn", - "_d n", - "! !!!!", - "!! !!!", - "!!! !!", - "!!!! !", - "Ġmanufact ures", - "Ġmanufacture s", - ". TextUtils", - ".Text Utils", - "Ġcon sciously", - "Ġconsc iously", - "Ġconscious ly", - "Ġb ounced", - "Ġbounce d", - "c ulture", - "cul ture", - "cult ure", - "ĠS par", - "ĠSp ar", - "ĠSpa r", - "ĠP iper", - "ĠPi per", - "ĠPipe r", - "ĠPip er", - ". press", - ".p ress", - ".pre ss", - ".pr ess", - ".pres s", - "- owner", - "-o wner", - "Ġe valuator", - "Ġeval uator", - "Ġevalu ator", - "Ġ STREAM", - "ĠST REAM", - ".PictureBox SizeMode", - "Ġsu gars", - "Ġsugar s", - "Ġsug ars", - "Screen Width", - "Ġnext State", - "Ġiv ory", - "Ġbr unch", - "Ġbrun ch", - "d ensity", - "dens ity", - "_ OW", - "_O W", - "ĠCoron avirus", - "ĠC FR", - "ĠCF R", - "b ak", - "ba k", - "\\ Category", - "\\C ategory", - "æķ° ç»Ħ", - "Ġinvoke virtual", - "} ()Ċ", - "}( )Ċ", - "Ġs ujet", - "Ġsu jet", - "- marker", - "-m arker", - "-mark er", - "-mar ker", - "is digit", - "isd igit", - "ĠM obil", - "ĠMo bil", - "ĠMob il", - "ĠJsonRequest Behavior", - "_ REMOTE", - "_RE MOTE", - ".exists Sync", - "Ġrich es", - "Ġri ches", - "Ġric hes", - ".p resenter", - ".present er", - ".pres enter", - "Ġgl Color", - "Ġh anya", - "Ġha nya", - "Ġhan ya", - "Ġfort ress", - "Ġfl ashed", - "Ġflash ed", - "Ġfla shed", - "v iz", - "vi z", - "requ ently", - "requent ly", - "b uat", - "bu at", - "$ con", - "$c on", - "> |", - ". Func", - ".F unc", - "Ġhum orous", - "Ġhumor ous", - "u em", - "ue m", - ". ZERO", - ".Z ERO", - "ĠS TL", - "ĠST L", - "ĠB uk", - "ĠBu k", - "/ sample", - "/s ample", - "ĠG ros", - "ĠGr os", - "ĠGro s", - "Rec ipes", - "Recipe s", - "Ġinf lated", - "Ġinfl ated", - "Ġinflate d", - "Ġsw ung", - ": F", - "F acing", - "Fac ing", - "Fa cing", - ". Theme", - ".Th eme", - ".The me", - "н ик", - "ни к", - "Ġspl endid", - "Ġrequest Id", - ".Center Screen", - "/ autoload", - "/auto load", - "embed ded", - "_ depart", - "_de part", - "_dep art", - "Ġ Ports", - "ĠP orts", - "ĠPort s", - "ĠPo rts", - "ĠPor ts", - "๠ĥ", - "ай д", - "disc ussion", - "_ consum", - "_con sum", - "_cons um", - "Ġsc outs", - "Ġsco uts", - "Ġscout s", - "Ġcol abor", - "Ġcola bor", - ". Stage", - ".St age", - ". nano", - ".n ano", - ".nan o", - "el dorf", - "eld orf", - "eldo rf", - "Ġgem acht", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "Ġpolicy makers", - "Ġpolicym akers", - "_P KT", - "_PK T", - ", Th", - ",T h", - "o ky", - "ok y", - "_ UID", - "_U ID", - "_UI D", - "P ing", - "Pin g", - "Pi ng", - "Ġor chest", - "Ġorch est", - "Ġorc hest", - "Ġop tics", - "Ġopt ics", - "Ġoptic s", - "u han", - "uh an", - "ĠX OR", - "ĠXO R", - "Ġespañ ol", - "ĠAd idas", - "r ng", - "rn g", - "m ans", - "man s", - "ma ns", - ".v stack", - "Ġget away", - "Ġh ierarchical", - "Ġhier archical", - "ano ia", - "anoi a", - "ĠBitmap Factory", - "re alm", - "rea lm", - "real m", - "ĉ ap", - "ĉa p", - "_ apps", - "_a pps", - "_app s", - "_ap ps", - "- divider", - "-div ider", - "-di vider", - ". drawer", - ".d rawer", - ".draw er", - "ĠH ARD", - "ĠHA RD", - "ĠHAR D", - "'] ;?>Ċ", - "']; ?>Ċ", - "'];?> Ċ", - "- packed", - "-p acked", - "-pack ed", - "æ² »", - "_STRUCT URE", - "[ Y", - "i Param", - "( eq", - "(e q", - "Ġencompass es", - "Ġ\\ ĊĊ", - "Ġ\\Ċ Ċ", - "- >[", - "-> [", - "& utm", - "g roupon", - "gr oupon", - "group on", - "gro upon", - "st rate", - "str ate", - "stra te", - "D Y", - "om orphic", - "' :[", - "': [", - "Ġgrav itational", - "ĠM icha", - "ĠMich a", - "ĠMi cha", - "ĠMic ha", - "ĠT encent", - "ĠTen cent", - "Ġco ached", - "Ġcoach ed", - "ì¶ ľ", - "Ñĥ менÑĤ", - "Ñĥм енÑĤ", - "/ mobile", - "/m obile", - "Mouse Down", - "b ud", - "bu d", - "ĠY as", - "ĠYa s", - "Ġ Providers", - "ĠPro viders", - "ĠProvid ers", - "ĠProvider s", - "ĠProvide rs", - "ĠProv iders", - "N Z", - "ĉ report", - "ĉre port", - "ĉrep ort", - "err msg", - "Ġimage Path", - "act erial", - "acter ial", - "acteria l", - "ĠM anga", - "ĠMan ga", - "ĠMa nga", - "ĠMang a", - "wick lung", - "( usuario", - "(us uario", - "\" ));čĊčĊ", - "\") );čĊčĊ", - "\")) ;čĊčĊ", - "\"));čĊ čĊ", - "\")); čĊčĊ", - "/ ***", - "/* **", - "/** *", - "Ġorgan ise", - "Ġorganis e", - "Index ed", - "_ QUAL", - "_Q UAL", - "_QU AL", - "( PyObject", - "(Py Object", - "Ġsurrender ed", - "PO CH", - "ĠN OTES", - "ĠNOT ES", - "ĠNO TES", - "ĠNOTE S", - "\\ \\\"", - "\\\\ \"", - "- job", - "-j ob", - "Ġseven ty", - "Ġsevent y", - "Ġsev enty", - "# ###Ċ", - "## ##Ċ", - "#### Ċ", - "### #Ċ", - "ĠMan or", - "ĠMa nor", - "Ġdown right", - "Ġtime frame", - "ins urance", - "che cker", - "check er", - "Ġ SECRET", - "ĠSE CRET", - "ĠSEC RET", - "Ġecho es", - "Ġech oes", - "ĠCar men", - "ĠCarm en", - ".set HorizontalAlignment", - ".setHorizontal Alignment", - "Ġ isChecked", - "Ġis Checked", - "Ġ TOR", - "ĠT OR", - "ĠTO R", - "_ nn", - "_n n", - "( '(", - "(' (", - "Fetch Request", - "ĠPrint ed", - "Fl uid", - "Ġ STACK", - "ĠST ACK", - "ĠSTA CK", - "G ES", - "GE S", - "a igned", - "aign ed", - "i gor", - "ig or", - "igo r", - ". Unknown", - ".Un known", - "C BC", - "CB C", - "ĠCarl son", - ". URI", - ".U RI", - ".UR I", - "Ġp light", - "Ġpl ight", - "/ start", - "/st art", - "/star t", - "ĠPerson nel", - "Ġ PREFIX", - "ĠP REFIX", - "ĠPRE FIX", - "ĠPREF IX", - ", **", - ",* *", - "Ġli mite", - "Ġlimit e", - "Ġlim ite", - "_ heat", - "_h eat", - "_he at", - "% ï¼Į", - "ĠD onne", - "ĠDon ne", - "get Node", - "ĠScient ology", - "Ġc omet", - "Ġcom et", - "Ġco met", - "Ġcome t", - "Ġwen ig", - "A side", - "As ide", - "ĠM PEG", - "ĠMP EG", - "' ?", - "vari ably", - ". endDate", - ".end Date", - "Ġun cont", - "Ġunc ont", - "Ġuncon t", - "Ġ Scores", - "ĠS cores", - "ĠSc ores", - "ĠScore s", - "ĠSco res", - "ĠScor es", - "Ġ LoginForm", - "ĠLogin Form", - ". generated", - ".g enerated", - ".generate d", - ".gener ated", - ", ch", - ",c h", - "- mar", - "-m ar", - "ĠN ed", - "ĠNe d", - "Ġ eventId", - "Ġevent Id", - "+ p", - "ĠS IN", - "ĠSI N", - "/ reset", - "/re set", - "/res et", - ".RE ACT", - "ĠMe ssi", - "ĠMess i", - "ĠMes si", - "_R ANK", - "_RA NK", - ".write File", - "Ġc ripp", - "Ġcr ipp", - "Ġcri pp", - "es thetic", - "est hetic", - "ERS IST", - "Ġreim bursement", - "Ġreimburse ment", - "Current Value", - "Ġu nin", - "Ġun in", - "Ġuni n", - "Down Latch", - "Ġpadding Right", - "Ġst ocked", - "Ġstock ed", - "/ '.", - "/' .", - "Ġre payment", - "Ġrep ayment", - "Ġrepay ment", - "t rak", - "tr ak", - "tra k", - "/ backend", - "/back end", - "Ġиз мен", - "C SR", - "CS R", - "Ġprevent ive", - "Ġpant alla", - "_ trim", - "_t rim", - "_tr im", - "_tri m", - "P edido", - "Ped ido", - "h ospital", - "Ġmanage able", - "route Params", - "text ures", - "texture s", - "tex tures", - ". .....ĊĊ", - ".. ....ĊĊ", - "... ...ĊĊ", - ".... ..ĊĊ", - "..... .ĊĊ", - "...... ĊĊ", - "Ġsé lection", - "Name ValuePair", - "Ġpol lut", - "Ġpoll ut", - "M odes", - "Mode s", - "Mod es", - "Mo des", - "ĠL aud", - "ĠLa ud", - "ĠLau d", - "j ay", - "ja y", - "ĠU rs", - "ĠUr s", - "Ġs igner", - "Ġsign er", - "Ġsig ner", - "Ġ JJ", - "ĠJ J", - "ĠCh erokee", - "_EX ISTS", - "_EXIST S", - "Ġd war", - "Ġdw ar", - "Ġ ($('#", - "Ġ( $('#", - "Ġ($ ('#", - "Ġ($( '#", - "Ġre ef", - "> {$", - ">{ $", - "ĠB aylor", - "ĠBay lor", - "Ġ ModelState", - "ĠModel State", - "- _", - "ĠStruct ures", - "ĠStructure s", - "Ġsou vent", - "Spec ify", - "( pipe", - "(p ipe", - "(pi pe", - "Ġfr acking", - "Ġfrac king", - "ĠG PA", - "ĠGP A", - "Ġb ele", - "Ġbe le", - "Ġbel e", - "ĉ ĉĉĉĉĉĉĠĠĠ", - "ĉĉ ĉĉĉĉĉĠĠĠ", - "ĉĉĉĉ ĉĉĉĠĠĠ", - "ĉĉĉ ĉĉĉĉĠĠĠ", - "ĉĉĉĉĉ ĉĉĠĠĠ", - "ĉĉĉĉĉĉ ĉĠĠĠ", - "ĉĉĉĉĉĉĉ ĠĠĠ", - "ĉĉĉĉĉĉĉĠ ĠĠ", - "ĉĉĉĉĉĉĉĠĠ Ġ", - "ĠMinor ity", - "Ġt ud", - "Ġtu d", - "Ġopen ness", - "ĠIllustr ated", - "Ġoxid ation", - "Ġ NK", - "ĠN K", - "ĉ Update", - "Ġ EMS", - "ĠE MS", - "ĠEM S", - "ĠTe ddy", - "ĠTed dy", - "Ġgener als", - "Ġgen erals", - "Ġgeneral s", - "Ġgenera ls", - "ĉ Mat", - "ĉM at", - "Ġrad ios", - "Ġradio s", - "Ġradi os", - "ĠAnt ique", - "ĠAnti que", - "c onomy", - "con omy", - "conom y", - "cono my", - "ĠSquad ron", - ") ','", - ")', '", - ")' ,'", - "å£ °", - "Ġy oure", - "Ġyou re", - "Ġyour e", - "Ġyo ure", - "ĠMain Page", - "Ġbeh aviours", - "Ġbehaviour s", - "en ght", - "eng ht", - "(@\" %@\",", - "Ġtest case", - "Ġ Compilation", - "ĠComp ilation", - "Ġflav ours", - "Ġflavour s", - "Ġ Extend", - "ĠExt end", - "il lator", - "ill ator", - "illa tor", - "Ġ coh", - "Ġc oh", - "Ġco h", - "Ġs pline", - "Ġsp line", - "Ġspl ine", - "Ġ KG", - "ĠK G", - "- pay", - "-p ay", - "Ġcommun ism", - "ĠBusiness es", - "oc king", - "ock ing", - ". MaxLength", - ".Max Length", - "ass andra", - "qu iring", - "quir ing", - "qui ring", - "a dden", - "ad den", - "add en", - "ĠJ eb", - "ĠJe b", - "_ fault", - "_f ault", - "_fa ult", - "[ file", - "[f ile", - "Ġpromin ence", - "disc iplinary", - "âĢĶ they", - "âĢĶthe y", - "_ extent", - "_ex tent", - "_ext ent", - "Ġ VIC", - "ĠV IC", - "ĠVI C", - "Ġen tails", - "Ġent ails", - "Ġentail s", - ". partner", - ".p artner", - ".part ner", - "Ġhipp oc", - "Le ague", - "çĶ ·", - "w ipe", - "wi pe", - "- spinner", - "-sp inner", - "-spin ner", - "Ġsal ute", - "ĠS urgical", - "ĠSurg ical", - "( outputs", - "(out puts", - "(output s", - "work ed", - "wor ked", - "[ strlen", - "[str len", - "app ointed", - "appoint ed", - "ĠH eg", - "ĠHe g", - "ĠAC PI", - "( [^", - "([ ^", - "u ala", - "ual a", - "ua la", - "_ tol", - "_t ol", - "_to l", - "ĠR it", - "ĠRi t", - ". Payment", - ".P ayment", - ".Pay ment", - "k owski", - "Ġw almart", - "Ġwal mart", - "require ments", - "ĠFIN SEQ", - "_ BACKGROUND", - "_BACK GROUND", - "ĠOs borne", - "( errorMessage", - "(error Message", - "Report ing", - "Rep orting", - "Ġa uctions", - "Ġau ctions", - "Ġauction s", - "Ġcom bos", - "Ġcomb os", - "Ġcombo s", - "ĠNot iced", - "ĠNotice d", - "_ oct", - "_o ct", - "_oc t", - "Ġprim ero", - "Ġprime ro", - "Ġprimer o", - "t aire", - "ta ire", - "_ hr", - "_h r", - "Ġм од", - "Ġмо д", - "Ġcontrad ictory", - "Ġcontradict ory", - "= \"@", - "=\" @", - "ach ines", - "achine s", - "achi nes", - "(opt arg", - "ĠP enguin", - "ĠPeng uin", - "ĠAb bas", - "ĠAbb as", - "Ġsub lime", - "Ġpage able", - "ĠDef ensive", - "Ġdistinct ly", - "ĠAutom atically", - "ĠAutomatic ally", - "Under standing", - "Equality Comparer", - "g ota", - "go ta", - "got a", - "Ġ\" ::", - "Ġ\": :", - "Ġpul ver", - "ĠB attles", - "ĠBattle s", - "ĠBatt les", - "Ġun paralleled", - "T CHA", - "TC HA", - "Ġconstr ued", - "- aff", - "-a ff", - "Ġpre cursor", - "Ġprec ursor", - "-l fs", - "Ġmad uras", - "ĠD aisy", - "ĠDa isy", - "ĠDai sy", - "ĠAr beits", - "ĠArbeit s", - ". Management", - ".Man agement", - "ĉ In", - "ĉI n", - "Ġro bes", - "Ġrob es", - "Ġrobe s", - "Ġsp éc", - "âĢľ (", - "Ġm aternity", - "Ġmat ernity", - "ex tent", - "ext ent", - "Ġ Spacer", - "ĠSp acer", - "ĠSpace r", - "ĠSpa cer", - "Did Appear", - "ĉ us", - "ĉu s", - ".getRequest Dispatcher", - "( cols", - "(c ols", - "(co ls", - "(col s", - "Ġplum met", - "ì ħ", - "Ġ{ ĊĊĊĊ", - "Ġ{Ċ ĊĊĊ", - "Ġ{ĊĊ ĊĊ", - "Ġ{ĊĊĊ Ċ", - "é rica", - "ér ica", - "éri ca", - "éric a", - "Ġ Sizes", - "ĠS izes", - "ĠSize s", - "ĠSi zes", - "ĠSiz es", - ". enum", - ".e num", - ".en um", - ". Highlight", - ".High light", - "Ġ!! }ĊĊĊ", - "Ġ? >ĊĊĊ", - "Ġ?> ĊĊĊ", - "Ġ?>Ċ ĊĊ", - "Ġ?>ĊĊ Ċ", - "W enn", - "We nn", - "Ġcl imax", - "Ġclim ax", - "Ġcli max", - "Ġc rem", - "Ġcr em", - "Ġcre m", - "_ that", - "_t hat", - "_th at", - "[ âĢ¦", - "_ domains", - "_domain s", - "_dom ains", - "_RE PLY", - "Ġcomp leta", - "Ġcomple ta", - "Ġcomplet a", - "Ġcompl eta", - "V EST", - "VE ST", - "VES T", - "_ particle", - "_p article", - "_part icle", - "Ġs op", - "Ġso p", - "Ġfatal ities", - "impl ify", - "imp lify", - "ĠS KF", - "ĠSK F", - "Ġin fusion", - "Ġinf usion", - "ĠJ avier", - "ĠJa vier", - "Ġb allet", - "Ġball et", - "Ġbal let", - "Ġam igo", - "Ġami go", - ". want", - ".w ant", - "Ġcol lagen", - "Ġcoll agen", - "Ġcollage n", - "ĠLaw yer", - ". Statement", - ".St atement", - ".State ment", - ".Stat ement", - ". rt", - ".r t", - "b aar", - "ba ar", - "End Point", - "ĠB ek", - "ĠBe k", - "S HIP", - "SH IP", - "Ġpatri arch", - "ĠA unt", - "ĠAu nt", - "_ TM", - "_T M", - "Ġ mÃŃn", - "Ġm ÃŃn", - "ĠmÃŃ n", - "Ġm astered", - "Ġmaster ed", - "Ġma stered", - "Ġmas tered", - "Ġmast ered", - "W XYZ", - "WX YZ", - "Ġes pos", - "Ġesp os", - "= logging", - "=log ging", - "Ġrighteous ness", - "t orrent", - "tor rent", - "Ġ bst", - "Ġb st", - "Ġbs t", - "_ CHAIN", - "_CH AIN", - "Ġout skirts", - "( rotation", - "(r otation", - "(rot ation", - "Ġ' .')", - "Ġ'. ')", - "Ġ'.' )", - "igr ants", - "igrant s", - "+ lsi", - "+l si", - "ĠCC TV", - "ĠCCT V", - "_PH ASE", - ". azure", - ".a zure", - "_ Process", - "_P rocess", - "_Pro cess", - "v ae", - "va e", - "ĠT ropical", - "ĠAn kara", - "ĠAnk ara", - "image View", - "_RUN NING", - "Ġ* )__", - "Ġ*) __", - "ế n", - "( cli", - "(c li", - "(cl i", - "sc atter", - "Ġs che", - "Ġsc he", - "Ġsch e", - "Reg istrar", - "Registr ar", - "Ġa iring", - "Ġair ing", - "Ġai ring", - "Ġpy plot", - "is ión", - "isi ón", - "/ customer", - "/c ustomer", - "/custom er", - "Ġs implement", - "Ġsim plement", - "Ġsimple ment", - "Ġsimp lement", - "Ġsimpl ement", - "Ġclass y", - "Ġcl assy", - "Ġclas sy", - "ĠD WC", - "ĠDW C", - "ĠBas har", - "ĠBash ar", - "ĠDE VELO", - "ĠV ick", - "ĠVi ck", - "ĠVic k", - "a vail", - "av ail", - "ava il", - "ĠH ö", - "_ extend", - "_ext end", - "dr Fc", - ".is NotBlank", - "Ġpl ais", - "Ġpla is", - "| }Ċ", - "Ġporn ofil", - "Ġporno fil", - "l abs", - "la bs", - "lab s", - "Ġ haus", - "Ġh aus", - "Ġha us", - "Ġorig inating", - "Ġorigin ating", - "Ġsurround s", - "Ġ QUAL", - "ĠQ UAL", - "ĠQU AL", - "m eg", - "me g", - "/ logger", - "/log ger", - "/lo gger", - "[ obj", - "[o bj", - "Ġirres ponsible", - "Ġ PublicKey", - "ĠPublic Key", - "H ONE", - "HO NE", - ": '/", - ":' /", - "i box", - "ib ox", - "ibo x", - "ĠF Vector", - "| {Ċ", - "ata loader", - "atal oader", - "h awks", - "hawk s", - "H DR", - "HD R", - "Ġescal ation", - "ĠPods Dummy", - "e lite", - "el ite", - "eli te", - "Ġpre sup", - "Ġpres up", - "C ached", - "Cache d", - "Ca ched", - "> G", - ". optimizer", - ".opt imizer", - ".optim izer", - ".optimize r", - "Ġ Visible", - "ĠV isible", - "ĠVis ible", - "´ Ģ", - "Ġ nen", - "Ġn en", - "Ġne n", - "Ġ pcs", - "Ġp cs", - "Ġpc s", - "Ġ Idle", - "ĠI dle", - "ĠId le", - "[ Any", - "[A ny", - "Ġkey boards", - "Ġkeyboard s", - "ĠCOMP ONENT", - "Ġtit anium", - "Ġtitan ium", - "( mut", - "(m ut", - "(mu t", - "ĠLed ger", - "Ġprosper ous", - "etro fit", - "_ LL", - "_L L", - "_ patient", - "_p atient", - "_pat ient", - "Ġ pdata", - "Ġp data", - "Ġpd ata", - "Ġ kontakte", - "Ġkont akte", - "Ġkontakt e", - "S wipe", - "Sw ipe", - "Ġcheer ful", - "ĠHond uras", - "\" ][$", - "\"] [$", - "\"][ $", - "Ġhem orrh", - "\" :\"+", - "\": \"+", - "\":\" +", - "Ġ leasing", - "Ġle asing", - "Ġinst alls", - "Ġinstall s", - "Ġinstal ls", - "ĠP ax", - "ĠPa x", - "ĠLog istics", - "ĠLogistic s", - "Ġkin etic", - "ĠP hon", - "ĠPh on", - "_ movement", - "_m ovement", - "_move ment", - "_mov ement", - "_mo vement", - "ĉ bytes", - "ĉbyte s", - "Ġcin co", - "ĠMad ness", - "\" )+", - "\") +", - "Ġ JE", - "ĠJ E", - "_ ij", - "_i j", - "Scene Manager", - "ĠB ust", - "ĠBus t", - "ĠBu st", - "p test", - "pt est", - "pte st", - "a ea", - "ae a", - "Ġb esser", - "Ġbes ser", - "ÃŃ g", - "д ин", - "ди н", - "( tasks", - "(t asks", - "(task s", - "(\" (\"", - "(\"( \"", - "set Type", - "( outfile", - "(out file", - "ĉ reset", - "ĉres et", - "ĉre set", - "Ġ ARC", - "ĠA RC", - "ĠAR C", - "Ġmús ica", - "ĠSh elf", - "ĠShe lf", - "ĠShel f", - "Ġmin Y", - "p ch", - "pc h", - "Ġwe iber", - "Ġwei ber", - "iss or", - "Ġtr ouve", - "Ġtro uve", - "Ġtrou ve", - "ĉ Button", - "ĉB utton", - "Ġre generated", - "Ġreg enerated", - "Ġregenerate d", - "Å £i", - "Å£ i", - "im achinery", - "b locking", - "bl ocking", - "block ing", - ".data Tables", - "_ frac", - "_f rac", - "_fr ac", - "ĠAdv antage", - ".visit Method", - "éĩį æĸ°", - "Ġextra pol", - "Ġextr apol", - "Ġte asing", - "Ġtea sing", - "Ġteas ing", - "ĠH itch", - "ĠHit ch", - "ĠGe ek", - "ĠGee k", - "E SCO", - "ES CO", - "ESC O", - "Ġ wich", - "Ġw ich", - "Ġwi ch", - "ĉ ax", - "ĉa x", - "_ decor", - "_de cor", - "_dec or", - "Ġscreen Width", - "ĠSo phia", - "ĠSoph ia", - "F orgot", - "For got", - "Forg ot", - ". uni", - ".un i", - ".u ni", - "ĠVen ture", - "ĠVent ure", - "_ collision", - "_c ollision", - "_coll ision", - "Ġlaw maker", - "( Edit", - "(E dit", - "b lers", - "ble rs", - "bl ers", - "bler s", - "Ġ getNext", - "Ġget Next", - "âĢĶ you", - "Media Player", - "ĠH orde", - "ĠHor de", - "ĠCongress man", - "obs ervations", - "observ ations", - "observation s", - "ĉ property", - "ĉp roperty", - "ĉprop erty", - "Ġ< --", - "Ġ<- -", - "Created At", - "u byte", - "ub yte", - "uby te", - "Ġquar antine", - "Ġdist ressed", - "Ġdistr essed", - "Ġdistress ed", - "_A PB", - "_AP B", - "ĠGood man", - "ãĤ «", - "Ġrecom end", - "_ PRINTF", - "_PRINT F", - "D ONE", - "DO NE", - "DON E", - "Bind able", - "r strip", - "rs trip", - "rst rip", - "cent aje", - "Ġ Unexpected", - "ĠUn expected", - "ĠS CHOOL", - "ĠProfessional s", - "ĠProfession als", - "ĠGPU s", - "ĠGP Us", - "L esson", - "Le sson", - "Les son", - "Less on", - "Ex clusive", - "Ġat rav", - "Ġatr av", - "ĠD ank", - "ĠDan k", - "ĠDa nk", - "ĠLaw yers", - "ĠLawyer s", - "ĠWal ton", - "ĠWalt on", - "> []", - ">[ ]", - "Ġa loud", - "Ġal oud", - "Ġalo ud", - "=\" ../../../", - "=\"../ ../../", - "=\"../../ ../", - "Ġdeb ating", - "ĠA VG", - "ĠAV G", - "_V OL", - "_VO L", - "/ cgi", - "/c gi", - ". deg", - ".d eg", - ".de g", - ": g", - ".Info f", - ".Inf of", - "Measure Spec", - ". song", - ".s ong", - ".so ng", - ".son g", - "m tree", - "mt ree", - "ul ls", - "ull s", - "J ordan", - "ĠC overs", - "ĠCo vers", - "ĠCover s", - "ĠCov ers", - "ĠCove rs", - "Ġattrib utable", - "Ġj edis", - "Ġje dis", - "Ġjed is", - "iat rics", - "iatric s", - "Ġrot terdam", - "Ġ meld", - "Ġm eld", - "Ġme ld", - "Ġmel d", - "Ġ ContentType", - "ĠContent Type", - "Ġman tle", - "Ġmant le", - "Ġ alice", - "Ġa lice", - "Ġal ice", - "Ġali ce", - "_ duplicate", - "_d uplicate", - "_dup licate", - "/ Internal", - "Ġ filesize", - "Ġfile size", - "Ġfiles ize", - "ĉ fire", - "ĉf ire", - "ĉfi re", - "r ese", - "re se", - "res e", - "on dere", - "ond ere", - "onder e", - "onde re", - "Ġfamiliar ity", - "ĠC rest", - "ĠCr est", - "ĠCre st", - "ĠCres t", - "Ġk arma", - "Ġkar ma", - "Ġtor ino", - "Ġm esa", - "Ġme sa", - "Ġmes a", - "/ temp", - "/t emp", - "Ġc hir", - "Ġch ir", - "Ġchi r", - "Ġ Overflow", - "ĠOver flow", - "Ġten emos", - "u nik", - "un ik", - "uni k", - "N EXT", - "NE XT", - "A lle", - "Al le", - "All e", - "Ġn xt", - "Ġnx t", - "M art", - "Mar t", - "Ma rt", - "Ġ atl", - "Ġa tl", - "Ġat l", - "Ġperiod o", - "Ġperi odo", - "_ you", - "_y ou", - "Ġ} )).", - "Ġ}) ).", - "Ġ})) .", - "int estinal", - ".Adapter View", - "Ġhes itant", - "Ġcompar atively", - "Ġcomparative ly", - ". UInt", - ".U Int", - ".UI nt", - "( viewModel", - "(view Model", - "Ġsang at", - "Ġ Responsive", - "ĠRes ponsive", - "ĠRespons ive", - "ĠZ ack", - "ĠZa ck", - "ĠZac k", - "â ħ", - "J AVA", - "JA VA", - "ĠFull er", - "ĠFu ller", - "ĠFul ler", - "Ġ âĿ¤", - "ĠâĿ ¤", - ". Consumer", - ".Con sumer", - ".Cons umer", - "Ġ ank", - "Ġa nk", - "Ġan k", - "Ġre actors", - "Ġreact ors", - "Ġreactor s", - "f uck", - "fu ck", - "_ rat", - "_r at", - "_ra t", - "Ġsession Factory", - "_ backward", - "_back ward", - "Ġscram bled", - "Ġscramble d", - "ĉ th", - "ĉt h", - "Ġins ensitive", - "Ġch amps", - "Ġcha mps", - "Ġcham ps", - "Ġchamp s", - "Ġ nginx", - "Ġng inx", - "Ġcon hec", - "Ġconhe c", - "ĠJ asper", - "ĠJas per", - ". fm", - ".f m", - "Strict Equal", - "ach sen", - "achs en", - "- Nov", - "-N ov", - "-No v", - "l assen", - "lass en", - "las sen", - ". integration", - ".int egration", - "( lbl", - "(l bl", - "Com pose", - "Comp ose", - "ĠF on", - "ĠFo n", - "à ļ", - "Gr atis", - "ĠL ime", - "ĠLim e", - "ĠLi me", - "ĠAdapter View", - "Ġpoison ed", - "Ġpois oned", - "anch ors", - "anchor s", - "设 计", - "'] ?>\"", - "']?> \"", - "Ġpro cur", - "Ġproc ur", - "It aly", - ". MONTH", - ".MON TH", - "ĠL UA", - "ĠLU A", - "ĠLith uania", - "ĠHe ads", - "ĠHead s", - "_CH UNK", - "ĠP USH", - "ĠPU SH", - "ĠPUS H", - "Aspect Ratio", - "Ġ weg", - "Ġw eg", - "Ġwe g", - "Ġv ids", - "Ġvi ds", - "Ġvid s", - "ĠW ein", - "ĠWe in", - "ĠWei n", - "ĉ INT", - "ĉI NT", - "ĉIN T", - "session Id", - "Ind ustry", - "Ġden ounced", - "JK LM", - "ĠVan essa", - ". Identifier", - ".Id entifier", - "p ropri", - "pro pri", - "prop ri", - "Ġ иг", - "Ġи г", - "Ġté cn", - "Ġtéc n", - "Ġm osaic", - "Ġmos aic", - "Stream Reader", - "- Th", - "-T h", - "f orth", - "for th", - "fort h", - "Ġad herence", - "Ġadher ence", - "Ġadhere nce", - "b ate", - "ba te", - "bat e", - "Ġkn ights", - "Ġknight s", - "s ounds", - "so unds", - "sound s", - "sou nds", - "Ġs alle", - "Ġsa lle", - "Ġsal le", - "O MET", - "OM ET", - "OME T", - "ãĤ¹ ãĥĪ", - "- tm", - "-t m", - "ĠR he", - "ĠRh e", - ".File OutputStream", - "åĪĨ ç±»", - "Ġ ENG", - "ĠE NG", - "ĠEN G", - "h oliday", - "hol iday", - "Ġ Congratulations", - "ĠCong ratulations", - ") (Ċ", - ")( Ċ", - "Ġaggregate s", - "Ġaggreg ates", - "H OOK", - "HO OK", - "e wire", - "ew ire", - "Sen ator", - "Ġembed dings", - "Ġembedding s", - "e py", - "ep y", - "( COM", - "(C OM", - "Ġrob ber", - "ä ter", - "ät er", - "w ang", - "wa ng", - "wan g", - "_ teacher", - "_t eacher", - "_te acher", - "Ġresent ment", - "Ġlett uce", - "er reur", - "err eur", - "erre ur", - "( ic", - "(i c", - "ĠT actical", - "ĠTac tical", - "Ġ Contracts", - "ĠCon tracts", - "ĠContract s", - "ĠContr acts", - "Ġm ænd", - "Ġsit ios", - "Ġsiti os", - "Ġsitio s", - "Ġbast ante", - "Ġnue vos", - "Ġnuevo s", - "ĉN drFc", - "Ġprivate Key", - "uc ch", - "ucc h", - "MM dd", - "Ġ è¾ĵåĩº", - "Ġè¾ĵ åĩº", - "um ba", - "umb a", - "@ foreach", - ": \");ĊĊ", - ":\" );ĊĊ", - ":\");Ċ Ċ", - ":\") ;ĊĊ", - "Ġslip pery", - "ĠKey stone", - "ĠKe ystone", - "ĠKeys tone", - "Ġpione ering", - "Ġpioneer ing", - "_ triangle", - "_t riangle", - "_tr iangle", - "_tri angle", - "( \"Ċ", - "(\" Ċ", - "ĉ ĉĉĉĉĉĉĉĠĠ", - "ĉĉ ĉĉĉĉĉĉĠĠ", - "ĉĉĉĉ ĉĉĉĉĠĠ", - "ĉĉĉ ĉĉĉĉĉĠĠ", - "ĉĉĉĉĉ ĉĉĉĠĠ", - "ĉĉĉĉĉĉ ĉĉĠĠ", - "ĉĉĉĉĉĉĉĉ ĠĠ", - "ĉĉĉĉĉĉĉ ĉĠĠ", - "ĉĉĉĉĉĉĉĉĠ Ġ", - "ĠInt ervention", - "ĠInter vention", - "S CI", - "SC I", - "Ġc JSON", - "Ġter minating", - "Ġterm inating", - "Ġtermin ating", - "ë ¹Ħ", - "ë¹ Ħ", - "Ġbaby s", - "Ġbab ys", - "Sub set", - "Ġ ë¡", - "Ġë ¡", - "Ġseu lement", - "Ġseul ement", - "Ġseule ment", - "Ġm uestra", - "Ġmue stra", - "En tre", - "Ent re", - "Entr e", - "以 ä¸Ĭ", - "n go", - "ng o", - "\" bytes", - "QR ST", - "QRS T", - "Ġy pos", - "Ġyp os", - "person a", - "pers ona", - "Ġ Deploy", - "ĠDe ploy", - "ĠDep loy", - "c ee", - "ce e", - "Ġ à®", - "Ġà ®", - ". goal", - ".go al", - "Ġhabit ats", - "Ġhabitat s", - "Ġ isAdmin", - "Ġis Admin", - "Ġexplo iting", - "Ġexploit ing", - "Ġvent il", - "Ġven til", - "ĠB alls", - "ĠBall s", - "ĠBal ls", - "ا ب", - "Ø§Ø ¨", - "Ġmind fulness", - "Ġmindful ness", - "( kwargs", - "(k wargs", - "Ġre sembling", - "Ġresembl ing", - "Ġch oir", - "Ġcho ir", - "Ġon BackPressed", - "ĠSEC URITY", - "/ gtest", - "/g test", - "Ġjust ices", - "Ġjustice s", - "Ġinteger Value", - "b lah", - "bl ah", - "bla h", - "ĠA im", - "ĠAi m", - "_ finalize", - "_final ize", - "k eh", - "ke h", - "ĠComplex ity", - "Ġaug ust", - "get ElementsByTagName", - "Ġp reach", - "Ġpr each", - "Ġpre ach", - "Ġpron unciation", - "Ġ Trash", - "ĠTr ash", - "ĠTra sh", - "- percent", - "-per cent", - "_PR IV", - "_PRI V", - "ĠH unts", - "ĠHun ts", - "ĠHu nts", - "ĠHunt s", - "ĠC urse", - "ĠCur se", - "u ellen", - "ue llen", - "uel len", - "uelle n", - "uell en", - "Ġheavy weight", - "X i", - "ĉ selected", - "ĉselect ed", - "ĉse lected", - "ĠMcC oy", - "å¼Ĥ 常", - "| =Ċ", - "|= Ċ", - "ĠBattle field", - "Item Image", - "Ġded uctions", - "Ġdeduct ions", - "Ġdeduction s", - "ĠElement al", - "ĠEle mental", - "ĠElem ental", - "( ));//", - "() );//", - "()) ;//", - "()); //", - "ĠBur k", - "ĠBu rk", - "} )čĊčĊ", - "}) čĊčĊ", - "})čĊ čĊ", - "sw ift", - "/ function", - "/f unction", - "Us ually", - "Usu ally", - "_ St", - "_S t", - "_fe ats", - "_feat s", - "Ġ IsValid", - "ĠIs Valid", - "Ġz ad", - "Ġza d", - "Image Context", - "Ġ classname", - "Ġclass name", - "Ġdon ner", - "Ġdonne r", - "Ġdonn er", - "Ġ-- >ĊĊĊ", - "Ġ-->Ċ ĊĊ", - "Ġ--> ĊĊĊ", - "Ġ-->ĊĊ Ċ", - "Ġmotor cycles", - "Ġmotorcycle s", - "+' /'+", - "+'/ '+", - "Ġ setBackground", - "Ġset Background", - "\\ CMS", - "\\C MS", - ". AllArgsConstructor", - ".All ArgsConstructor", - "ĠLex ington", - ". examples", - ".ex amples", - ".example s", - ".exam ples", - "ĠP urs", - "ĠPur s", - "ĠPu rs", - "Push Matrix", - "Ġ================================================= =============", - ".add Target", - "p ora", - "por a", - "po ra", - "Full screen", - "Ġgo of", - "Ġgoo f", - "h len", - "hl en", - "hle n", - "ä ge", - "ĠC URL", - "ĠCUR L", - "ĠCU RL", - "Ġ Interesting", - "ĠInter esting", - "ĠInterest ing", - "Ġretrie ves", - "Ġretrieve s", - "Ġretr ieves", - "_ Obj", - "_O bj", - "in ness", - "inn ess", - "inne ss", - "- ----ĊĊ", - "-- ---ĊĊ", - "---- -ĊĊ", - "--- --ĊĊ", - "----- ĊĊ", - "-----Ċ Ċ", - ".t sv", - ".ts v", - "( IM", - "(I M", - "ĠBr aves", - "ĠBra ves", - "ĠBrave s", - "_ ISR", - "_I SR", - "_IS R", - "o sti", - "os ti", - "ost i", - "á» ĵ", - "ĠEx terior", - "ĠExt erior", - "ĠCourt ney", - "Ġresid ues", - "Ġresidue s", - "T ier", - "Ti er", - ".* ;čĊčĊ", - ".*;čĊ čĊ", - ": black", - ":b lack", - "web View", - "\" path", - "Ġm asa", - "Ġma sa", - "Ġmas a", - "] !='", - "]!= '", - "Ġ Matching", - "ĠM atching", - "ĠMat ching", - "ĠMatch ing", - "d ur", - "du r", - "J vm", - "= context", - "_ RING", - "_R ING", - "Ġpro ponents", - "Ġprop onents", - "ĠQString Literal", - "Ġ inflate", - "Ġin flate", - "Ġinf late", - "Ġinfl ate", - "< Float", - " \">čĊ", - "Ġ?>\" >čĊ", - "Ġ?>\"> čĊ", - "_C OST", - "_CO ST", - "i linear", - "il inear", - "ili near", - "iline ar", - "ilin ear", - "Ġ Workspace", - "ĠWork space", - "ĠWorks pace", - "Ġs pel", - "Ġsp el", - "Ġspe l", - "ag ogue", - "ago gue", - "agog ue", - "ĠMillenn ium", - "ĠPop ulate", - "Ġ nid", - "Ġn id", - "Ġni d", - ".parse Color", - "S olar", - "So lar", - "Sol ar", - "ĠG ad", - "ĠGa d", - "Ġ ì¤ij", - "Ġì ¤ij", - "Ġì¤ ij", - "ĠK amp", - "ĠKa mp", - "ĠKam p", - "ĉ rm", - "ĉr m", - "Ġb enz", - "Ġbe nz", - "Ġben z", - "Ġ Honestly", - "ĠH onestly", - "ĠHonest ly", - "Ġelectro de", - "Ġelectr ode", - "ĠPr airie", - "ĠPra irie", - "Ġ PROFILE", - "ĠPRO FILE", - "ĠPROF ILE", - "ĠOri ental", - "ĠOrient al", - "ĠO LED", - "ĠOL ED", - "/cop yleft", - "awa ii", - "awai i", - "( products", - "(product s", - ") \\<", - ")\\ <", - "- created", - "-c reated", - "-create d", - "-cr eated", - ".Many ToMany", - "\" How", - "\"H ow", - "Ġв Ñĭп", - "ĠвÑĭ п", - "Ġmitochond rial", - "_ testing", - "_t esting", - "_test ing", - "( created", - "(c reated", - "(create d", - "(cr eated", - "Ġ getField", - "Ġget Field", - "_E VAL", - "_EV AL", - "] .\"", - "]. \"", - "ĠF SM", - "ĠFS M", - "ĠR ita", - "ĠRi ta", - "ĠRit a", - "Ġ åıĤæķ°", - "Ġåı Ĥæķ°", - "ĠåıĤ æķ°", - "Ġc ôt", - "Ġcô t", - "ĠIns ight", - "ĉ mysqli", - "ĉm ysqli", - "ĉmysql i", - "_ timing", - "_t iming", - "_tim ing", - "_ti ming", - "I DO", - "ID O", - ") ))))Ċ", - ")) )))Ċ", - "))) ))Ċ", - ")))) )Ċ", - "CO VERY", - "COVER Y", - ". imag", - ".i mag", - ".im ag", - "C DF", - "CD F", - "l ust", - "lu st", - "lus t", - "i ckt", - "ic kt", - "ick t", - "_ FP", - "_F P", - ". ','", - ".' ,'", - ".', '", - "g cc", - "gc c", - "Ġkur z", - "Ġku rz", - "_p wm", - "_pw m", - "Ġodp owied", - "Ġ Barrier", - "ĠBar rier", - "ĠBarr ier", - "/************************************************************************ ***Ċ", - "p ak", - "pa k", - "- Israel", - "ĠRut gers", - "Ġselected Item", - "ĠRam irez", - "F arm", - "Far m", - "Fa rm", - "Ġcal endars", - "Ġcalendar s", - "Ġcalend ars", - "g zip", - "gz ip", - "Ġblock buster", - "ĠPly mouth", - "çľ Į", - "res ponses", - "response s", - "respons es", - ".Dialog Interface", - "- grand", - "-g rand", - "-gr and", - "Ġ getSource", - "Ġget Source", - "ĠgetS ource", - "Ġdej tings", - "Ġdejting s", - "Ġt ieten", - "Ġti eten", - "Ġtie ten", - "Ġcondem nation", - "Ġcondemn ation", - "Ġcontin uar", - "Ġcontinu ar", - "Ġcontinua r", - ".Mock Mvc", - "/ english", - "Ġ MediaPlayer", - "ĠMedia Player", - "com puted", - "comp uted", - "compute d", - "comput ed", - "ĠCl ippers", - "ĠClip pers", - "ĠCli ppers", - "( delegate", - "(de legate", - ". Slf", - ".S lf", - "Ġ ë¡ľ", - "Ġë¡ ľ", - "ĠT ide", - "ĠTi de", - "Ġih rem", - "Ġihr em", - "Ġihre m", - "ĠW an", - "ĠWa n", - "Ñĥ ÑİÑī", - "ÑĥÑİ Ñī", - "} ><", - "}> <", - "Disc ussion", - "Discuss ion", - "Ġw atts", - "Ġwat ts", - "Ġwatt s", - "- minus", - "-m inus", - "-min us", - "ĠJul iet", - "ĠJulie t", - "ĠJuli et", - "éĽ ħ", - "Ġcon cluding", - "Ġconcl uding", - "and scape", - "ands cape", - "Ġúlt ima", - "ĠD ERP", - "ĠDE RP", - "ĠDER P", - "Ġsign Up", - "ĠSecond ly", - "W AIT", - "WA IT", - "l ds", - "ld s", - ". callbacks", - ".call backs", - ".callback s", - "( hour", - "(h our", - "im ators", - "ima tors", - "imator s", - "imat ors", - "vol ent", - "A AF", - "AA F", - "e driver", - "ed river", - "ĠMath ematic", - "< Tuple", - "'", - "Ġ/> '", - "{ j", - "_AB ORT", - "E ther", - "Et her", - "Eth er", - "Ġeduc ator", - "Ġpreca ution", - "Ġfinger tips", - "Ġfingert ips", - "get Var", - "cam atan", - "- debug", - "-de bug", - "ĠR AF", - "ĠRA F", - "[ arg", - "[a rg", - "Ġr aced", - "Ġrace d", - "Ġrac ed", - "Ġra ced", - "Ġts unami", - ".f link", - ".fl ink", - "Ġgl yc", - "Ġgly c", - "u ko", - "uk o", - "Ġ Multiply", - "ĠM ultiply", - "ĠMulti ply", - "ĠMultip ly", - "Ġre distribution", - "Ġred istribution", - "Ġredistrib ution", - "Ġredis tribution", - "A GO", - "AG O", - "Ġ Routine", - "ĠR outine", - "ĠRout ine", - "Ġ opr", - "Ġo pr", - "Ġop r", - "( lower", - "(l ower", - "(low er", - "(lo wer", - "ĠFun ktion", - "ĠFunk tion", - ". dk", - ".d k", - "Ġ egt", - "Ġe gt", - "Ġeg t", - "_B ASIC", - "sys call", - "ĠL SD", - "ĠLS D", - "Ġ Duplicate", - "ĠD uplicate", - "ĠDup licate", - "_ sell", - "_s ell", - "_se ll", - "_sel l", - "Ġerror Handler", - "_ ips", - "_i ps", - "_ip s", - "Ġ erv", - "Ġe rv", - "Ġer v", - "an nie", - "ann ie", - "anni e", - "(resource Name", - "Ġbott led", - "Ġbottle d", - "Ġcraw ling", - "Ġcrawl ing", - "e gment", - "eg ment", - ".set Tag", - "Ġ rss", - "Ġr ss", - "Ġrs s", - "ĠQu arry", - "ĠQuar ry", - "_ exact", - "_ex act", - ". jwt", - ".j wt", - "ĠBo ards", - "ĠBoard s", - "o pi", - "op i", - "Ġn asal", - "Ġna sal", - "Ġnas al", - "Ġ XYZ", - "ĠX YZ", - "ĠXY Z", - ". ud", - ".u d", - "North ern", - "Nor thern", - "Ġact ivating", - "Ġactiv ating", - "e dx", - "ed x", - "ov ah", - "ova h", - "Ġ indx", - "Ġin dx", - "Ġi ndx", - "Ġind x", - "Alert Dialog", - "Ġt ienes", - "Ġti enes", - "Ġtie nes", - "Ġtiene s", - "an nya", - "ann ya", - "anny a", - "_ pan", - "_p an", - "_pa n", - "( decimal", - "(d ecimal", - "(de cimal", - "(dec imal", - ". Dict", - ".D ict", - ".Di ct", - "Ġsubsidi aries", - "Product Name", - "F ew", - "Fe w", - "d ato", - "da to", - "dat o", - "od ied", - "odi ed", - "odie d", - "- under", - "-un der", - "-u nder", - "Ġ ê²ĥ", - "Ġê² ĥ", - "çīĪ æľ¬", - "at ism", - "atis m", - "ati sm", - "[ Math", - "[M ath", - ". '<", - ".' <", - "( infile", - "(in file", - "Ġde notes", - "Ġden otes", - "Ġdenote s", - "$ class", - "$c lass", - "_SEC URITY", - "Ġsew age", - "m elon", - "me lon", - "mel on", - "( Character", - "(Char acter", - "/ github", - "/g ithub", - "/git hub", - "Ġgl aring", - ". Guid", - ".G uid", - "_ sparse", - "_s parse", - "_sp arse", - "Ġ Margin", - "ĠM argin", - "ĠMar gin", - "ĠMarg in", - "_ dns", - "_d ns", - "_dn s", - "Ġme iner", - "Ġmein er", - "Ġmeine r", - "Ġleft ist", - "ĉ loc", - "ĉl oc", - "a bytes", - "aby tes", - "abyte s", - "Ġequipment s", - "Ġequip ments", - "ex po", - "exp o", - "ĠSom erset", - "E K", - "æį ¢", - "Ġlect urer", - "Ġlecture r", - "Ġmem iliki", - "æł ¸", - "ç´ ł", - "p ron", - "pr on", - "pro n", - ": pointer", - "b orrow", - "bor row", - "ĠProt ective", - "ĠProtect ive", - "_ cf", - "_c f", - "Ġ ÐķÑģли", - "ĠÐķ Ñģли", - "b pp", - "bp p", - "' ;ĊĊĊĊ", - "';Ċ ĊĊĊ", - "';ĊĊ ĊĊ", - "'; ĊĊĊĊ", - "';ĊĊĊ Ċ", - "at urally", - "atur ally", - "atural ly", - "_ NAV", - "_N AV", - "Ġpe ptide", - "> d", - "Ġ ifstream", - "Ġif stream", - "Ġi fstream", - "_FACT ORY", - "_FACTOR Y", - "' );//", - "') ;//", - "'); //", - "j oined", - "join ed", - "jo ined", - "m ong", - "mon g", - "mo ng", - "Ġtime spec", - "Ġtimes pec", - "Ġdest abil", - "Ġdesta bil", - "Ġ autop", - "Ġaut op", - "Ġauto p", - "Ġau top", - "- limit", - "-l imit", - "-li mit", - "public ation", - "pub lication", - "ĠD enn", - "ĠDe nn", - "ĠDen n", - ". Memory", - ".M emory", - "( skb", - "(s kb", - "(sk b", - "ĠAna heim", - "_RETURN TRANSFER", - "o ueur", - "ou eur", - "( _('", - "(_ ('", - "l egt", - "le gt", - "leg t", - "ist ingu", - "isting u", - "ĉ priv", - "ĉp riv", - "ĉpr iv", - "Ġredirect s", - "M t", - "Ġal leen", - "Ġall een", - "Ġalle en", - "Ġ PointF", - "ĠPoint F", - "Ġ omin", - "Ġo min", - "Ġom in", - "Ġc itt", - "Ġcit t", - "Ġci tt", - "ĠT age", - "ĠTag e", - "ĠTa ge", - "ĠW alls", - "ĠWall s", - "ĠWal ls", - "á» ī", - "Ġoccup ying", - "Ġoccupy ing", - "x BF", - "xB F", - "r angle", - "ra ngle", - "ran gle", - "rang le", - "Ġrel ational", - "Ġrelation al", - "Ġrelat ional", - "- org", - "-o rg", - "-or g", - "Ġ jpg", - "Ġj pg", - "Ġjp g", - "- derived", - "Ġmal function", - "ĠB enson", - "ĠBen son", - "( scroll", - "(s croll", - "(sc roll", - "Ġ XD", - "ĠX D", - "H oly", - "Ho ly", - "Hol y", - "( commands", - "(command s", - "(comm ands", - "Ġt ipping", - "Ġti pping", - "Ġtip ping", - "Ġpr imitives", - "Ġprim itives", - "Ġprimitive s", - "Ġsex le", - "Call Check", - "Ġ MASTER", - "ĠM ASTER", - "ĠMA STER", - "ĠMAS TER", - "_ TEAM", - "_TE AM", - ".setRequest Header", - "_ specs", - "_sp ecs", - "_spec s", - "Ġs erge", - "Ġser ge", - "Ġserg e", - ". Master", - ".M aster", - ".Ma ster", - "Ġ ims", - "Ġi ms", - "Ġim s", - ".Spring BootTest", - "pay pal", - "ĠW ANT", - "ĠWA NT", - "ĠWAN T", - ". Inst", - ".I nst", - ".In st", - "ĠCar pet", - "ĠCarp et", - "Ġwrong ly", - "( $('.", - "($ ('.", - "($( '.", - "($(' .", - "Ġ bild", - "Ġb ild", - "Ġbi ld", - "Ġbil d", - ". Roll", - ".R oll", - "ĠU rb", - "ĠUr b", - "- can", - "-c an", - "-ca n", - "ãģı ãģłãģķãģĦ", - "ãģıãģł ãģķãģĦ", - "olib eral", - " čĊčĊ", - "Ġ-->čĊ čĊ", - "ĠMa hm", - "ĠMah m", - "} \";ĊĊ", - "}\" ;ĊĊ", - "}\";Ċ Ċ", - "Ġ dq", - "Ġd q", - "ĠPublish ers", - "ĠPublisher s", - "ĠA mpl", - "ĠAm pl", - "ĠAmp l", - "ĠDaniel le", - "ĠDani elle", - "Ġ tern", - "Ġt ern", - "Ġte rn", - "Ġter n", - "èµ ·", - "no ÅĽÄĩ", - "e in", - "ei n", - "ĠAsync Storage", - "u nger", - "un ger", - "ung er", - "unge r", - "ro uw", - "rou w", - "Ġsc issors", - "/ assert", - "/as sert", - ". bucket", - ".b ucket", - "/ archive", - "/a rchive", - "/arch ive", - "_ Man", - "_M an", - "Ġint oler", - "Ġinto ler", - "Ġ ()=>", - "Ġ( )=>", - "Ġ() =>", - "Ġ ÐĴÑĭ", - "ĠÐĴ Ñĭ", - "Ġs ai", - "Ġsa i", - ". xy", - ".x y", - ". \"čĊ", - ".\" čĊ", - "Ġur inary", - "e sub", - "es ub", - "IST ICS", - "ISTIC S", - "Ġ κ", - "ĠÎ º", - "Ġcompl iments", - "Ġcompliment s", - "Ġtypings Japgolly", - "i har", - "ih ar", - "Exp ansion", - "ĠS erving", - "ĠSer ving", - "ĠServ ing", - "_ students", - "_st udents", - "_student s", - "ĠX BOOLE", - "( il", - "(i l", - "Ġ ì²ĺ", - "Ġì² ĺ", - "Ġj ó", - "( tol", - "(t ol", - "(to l", - "( JS", - "(J S", - "ĉ CG", - "ĉC G", - "Ġ DRAW", - "ĠD RAW", - "ĠDR AW", - "t wig", - "tw ig", - "Ġo at", - "Ġoa t", - "_ smooth", - "_sm ooth", - "ĠC SL", - "ĠCS L", - "Ġo sob", - "Ġos ob", - "Ġens uing", - "Ġb anker", - "Ġbank er", - "Ġban ker", - "ĠBack pack", - "_ ping", - "_p ing", - "_pin g", - "_pi ng", - "Ġ wishlist", - "Ġw ishlist", - "Ġwish list", - "= ax", - "=a x", - "ĉ ĠĠĠĊ", - "ĉĠĠĠ Ċ", - "ĉĠ ĠĠĊ", - "ĉĠĠ ĠĊ", - "Dis ney", - "ste ady", - "stead y", - "\" >%", - "\"> %", - "Ġproph ets", - "Ġprophet s", - "Ġ ZX", - "ĠZ X", - "Ġminimal ist", - "Ġminim alist", - ". PLAIN", - ".PL AIN", - "Se attle", - "Seat tle", - ". ordinal", - "Ġ PIPE", - "ĠPI PE", - "Ġret orna", - "Ġretorn a", - "Ġj ugador", - "Ġjug ador", - "ĠB ret", - "ĠBr et", - "ĠBre t", - "ĠâĶ ľ", - "Ġp lush", - "Ġpl ush", - "Ġplus h", - "Ġplu sh", - "UL ATOR", - "ULA TOR", - "S orting", - "Sort ing", - ".grid y", - ".gr idy", - "ect omy", - "_ activ", - "_ac tiv", - "_act iv", - "r ack", - "ra ck", - "rac k", - "Inter active", - "ĠAntar ctica", - "ĠAntarctic a", - "Ġv engeance", - "en so", - "ens o", - "_ known", - "_k nown", - "up plier", - "upp lier", - ". Modules", - ".Mod ules", - ".Module s", - "ĠConnection State", - "éļ IJèĹı", - "éļIJ èĹı", - "@ FindBy", - "Ġ placer", - "Ġpl acer", - "Ġplace r", - "Ġplac er", - "Ġpla cer", - "\\ model", - "< ()>", - "<( )>", - ".is Successful", - ".isSuccess ful", - "- good", - "-g ood", - "-go od", - "b z", - "ĠDr aco", - "ĠDra co", - "Ass istant", - "- extra", - "-ex tra", - "-ext ra", - "аб лиÑĨ", - "Ġhyp ocrisy", - "Ġt st", - "Ġts t", - "ĠA gr", - "ĠAg r", - "$ txt", - "$t xt", - "Ġlog istic", - "l icensed", - "lic ensed", - "license d", - "ĠH of", - "ĠHo f", - "Ġ tat", - "Ġt at", - "Ġta t", - "( iv", - "(i v", - "Ġint oxic", - "Ġinto xic", - "Ġintox ic", - "post Id", - "_ strike", - "_st rike", - "_str ike", - "Ġhum iliation", - "Ġhumili ation", - "p codes", - "pc odes", - "\" sync", - "\"s ync", - "( recipe", - "(rec ipe", - "+ N", - "r ente", - "re nte", - "ren te", - "rent e", - "ĉ Client", - "ĉC lient", - "ycop g", - "ĠZur ich", - "ĠZu rich", - "Ġ Profiles", - "ĠPro files", - "ĠProf iles", - "ĠProfile s", - "C ountries", - "Count ries", - "Ġp ict", - "Ġpi ct", - "Ġpic t", - "Ġroll out", - "requ encies", - "Ġp atched", - "Ġpat ched", - "Ġpatch ed", - "Ġcar tridges", - "Ġcartridge s", - "Ġsh ading", - "Ġsha ding", - "J ar", - "Ja r", - "Ġsalv age", - "ĠT axes", - "ĠTax es", - "ĠTa xes", - "Ġstand by", - "ap oran", - "apor an", - "apo ran", - "E igen", - ". angular", - "Ġ Nested", - "ĠN ested", - "ĠNe sted", - "ĠNest ed", - "ĠNes ted", - "ä º«", - "äº «", - "Ġ isVisible", - "Ġis Visible", - "ĠDw ight", - "_BR ANCH", - ". Delay", - ".D elay", - ".De lay", - "Ġk end", - "Ġke nd", - "Ġken d", - "Ġfacilit ated", - "Ġfacilitate d", - "Ġfacil itated", - ". flatMap", - ".flat Map", - "Ġs anta", - "Ġsan ta", - "Ġsant a", - "ĉ Send", - "ĉS end", - "/ messages", - "/m essages", - "/message s", - "Ġof Type", - "ĉ swap", - "ĉs wap", - "ĉsw ap", - "# plt", - "ĠTur ks", - "ĠTurk s", - "N ES", - "NE S", - "Ġprogress ively", - "Ġprogressive ly", - "ĠRes idence", - "Ġ TREE", - "ĠT REE", - "ĠTR EE", - "ĠTRE E", - "Ġn oen", - "Ġno en", - "Ġnoe n", - "d io", - "di o", - "Ġ nelle", - "Ġn elle", - "Ġne lle", - "Ġnel le", - "Ġnell e", - "Ġso gar", - "Ġsog ar", - "i tti", - "it ti", - "itt i", - "week ly", - "Ġambigu ity", - "_ Settings", - "_S ettings", - "_Set tings", - "W are", - "War e", - "Wa re", - ". neo", - ".n eo", - ".ne o", - "_ DST", - "_D ST", - "_DS T", - "Ġ æĸ¹", - "Ġæĸ ¹", - "p rep", - "pr ep", - "pre p", - "l obby", - "lob by", - "@ email", - "/ movie", - "/m ovie", - "Ġfun kc", - "Ġfunk c", - "Ġ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ Ċ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĠĠĠĊ", - "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĊ", - "ÂŃ s", - "Ġguard ians", - "Ġguardian s", - "- pos", - "-p os", - "-po s", - "Ġconfig uring", - "Ġconfigur ing", - "ĠC PS", - "ĠCP S", - "ĠD eus", - "ĠDe us", - "Ġvidé os", - "Ġvidéo s", - "_ empresa", - "_emp resa", - "Ġsl apped", - "Ġsla pped", - "Ġslap ped", - "< Model", - "',Ċ", - "\"> ',Ċ", - "\">' ,Ċ", - "\">', Ċ", - "_X DECREF", - "ĠBuzz Feed", - "_M ARGIN", - "P LOY", - ". small", - ".s mall", - ".sm all", - "Ġm imeType", - "Ġmime Type", - "Ġh olog", - "Ġho log", - "Ġhol og", - "ĉ camera", - "ĉc amera", - "l ias", - "li as", - "lia s", - "Ġsusp ense", - "od ynam", - "ody nam", - "odyn am", - "b au", - "ba u", - "Ġgrave yard", - "_ named", - "_n amed", - "_name d", - "_na med", - "\": \"'", - "\":\" '", - "Ġ ************************************************", - "Ġ******************************** ****************", - "Ġ******** ****************************************", - "Ġ**************** ********************************", - "Ġ************************ ************************", - "Ġ**************************************** ********", - "Ġgame Over", - "Ġ LENGTH", - "ĠLE NGTH", - "ĠLENG TH", - "ĉ screen", - "ĉs creen", - "ĉsc reen", - "Ġdo InBackground", - "_ dependencies", - "_depend encies", - "_dep endencies", - "Ġ rtc", - "Ġr tc", - "Ġrt c", - "/ up", - "/u p", - "_ ROM", - "_R OM", - "_RO M", - "H all", - "Ha ll", - "Hal l", - "Ġdef iciencies", - "( te", - "(t e", - "' #", - "_ equiv", - "_e quiv", - "_equ iv", - "Ġpre order", - "ĠA xe", - "ĠAx e", - "о мÑĥ", - "ом Ñĥ", - ".send File", - "Ġ filt", - "Ġf ilt", - "Ġfil t", - "Ġfi lt", - "Ġ Limits", - "ĠL imits", - "ĠLim its", - "ĠLi mits", - "ĠLimit s", - "ĠCaval iers", - ". discount", - ".dis count", - ".disc ount", - "âĨ IJ", - "ĠW it", - "ĠWi t", - "QRST UV", - "Ġ ij", - "Ġi j", - "Ġt egen", - "Ġte gen", - "Ġteg en", - "Ġ :\",", - "Ġ: \",", - "Ġ:\" ,", - "diff iculty", - "p unkt", - "pun kt", - "punk t", - "ĠEm ails", - "ĠEmail s", - "ch lor", - "chl or", - "( fun", - "(f un", - ". Uint", - ".U int", - ".Ui nt", - "ĠSt all", - "ĠSta ll", - "_ verified", - "_ver ified", - "u D", - "File Type", - "Ġple asures", - "Ġpleasure s", - "Ġpleas ures", - "Ġjud iciary", - "Ġs ham", - "Ġsh am", - "Ġsha m", - "i pur", - "ip ur", - "_ PLUS", - "_PL US", - "of fers", - "off ers", - "offer s", - "( foo", - "(f oo", - "_ GT", - "_G T", - "ĉ core", - "ĉc ore", - "EN TION", - "ENT ION", - "ĠLib eration", - "ĠLiber ation", - "Command Line", - "_ department", - "_de partment", - "_dep artment", - "_depart ment", - ". Ar", - ".A r", - "_ neighbor", - "_ne ighbor", - "Ġ Submitted", - "ĠSub mitted", - "ĠSubmit ted", - "Ġ Ċ", - "\"> -->Ċ", - "Ġdro its", - "Ġdroit s", - "Ġhomosexual s", - "Ġab duction", - "ĉ widget", - "ĉw idget", - "$ headers", - "$header s", - "ĠD AR", - "ĠDA R", - "Ġf la", - "Ġfl a", - "th reat", - "thr eat", - "Ġl ouis", - "Ġlo uis", - "Ġlou is", - ". GetProperty", - ".Get Property", - "\" Just", - "( frames", - "(f rames", - "(frame s", - "(fr ames", - "r yo", - "ry o", - "prof ession", - "| i", - "íķ´ ìĦľ", - "( sv", - "(s v", - "Ġun recognized", - "I onic", - "Io nic", - "Ion ic", - "F ashion", - "Screen State", - "Ġ Incoming", - "ĠIn coming", - "ĠInc oming", - "Not Nil", - "Ġsyn cing", - "Ġsync ing", - "e mie", - "em ie", - "emi e", - "Ġth ermo", - "Ġther mo", - "Ġtherm o", - "_ procs", - "_pro cs", - "_pr ocs", - "_proc s", - "Ġincons istency", - "Ġinconsist ency", - "rel igious", - ". mj", - ".m j", - "Ġperson n", - "Ġpers onn", - "Ġperso nn", - "Ġmoment os", - "Ġmomento s", - "or arily", - "Ġ æĬ", - "Ġæ Ĭ", - "_ne urons", - "Ill ustr", - "im oto", - "imo to", - "i lik", - "il ik", - "ili k", - "ĠW oj", - "ĠWo j", - "Tr ading", - "Trad ing", - "Tra ding", - "Ġapp are", - "Ġap pare", - "Ġappar e", - "Ġentre prises", - "Ġentreprise s", - "a chat", - "ac hat", - "ach at", - "acha t", - "Ġ ¬", - "Ġ ¬", - "Ġne igh", - "Ġnei gh", - "BUTTON DOWN", - "ĠMa her", - "ĠMah er", - "a ghan", - "ag han", - "agh an", - "- hash", - "-h ash", - "-has h", - "\" f", - "Ġclient ele", - "Ġcliente le", - ".add Button", - "ĉ SP", - "ĉS P", - "Q i", - "Ġg rated", - "Ġgr ated", - "Ġgrat ed", - "Ġgra ted", - "Ġgrate d", - "PO SITE", - "POS ITE", - "POSIT E", - ": >", - "ĠH owell", - "ĠHow ell", - "ĠHo well", - "ĠHowe ll", - "ĠCompar ative", - "Ġ ISC", - "ĠI SC", - "ĠIS C", - "ÂŃ i", - "O cean", - "D avis", - "Da vis", - "ĠFil me", - "ĠFilm e", - "W ins", - "Win s", - "Wi ns", - "ĠJ IT", - "oc cer", - "occ er", - "ĠC orm", - "ĠCo rm", - "ĠCor m", - "ENCH MARK", - "rc hive", - "rch ive", - "i cação", - "ic ação", - "ica ção", - "Ġm ata", - "Ġmat a", - "Ġma ta", - "Ġchild birth", - "ĠOption ally", - "ĠOptional ly", - "E ns", - "En s", - "Ġx http", - "Ġel ucid", - "_Osc InitStruct", - ") )):Ċ", - ")) ):Ċ", - "))) :Ċ", - "Ġint uit", - "Ġ Donate", - "ĠDon ate", - "Ġcorre lates", - "Ġcorrel ates", - "Ġcorrelate s", - "> Delete", - "Ġe quipe", - "Ġequ ipe", - "Ġequip e", - "Ġb oca", - "Ġbo ca", - "Ġinf latable", - "Ġinfl atable", - "e rah", - "er ah", - "era h", - "ĠDateTime Kind", - "Ġcal ves", - "\\ Lib", - "\\L ib", - "Ġem lrt", - "ĠTr ilogy", - "ĠTri logy", - "ĠP anc", - "ĠPan c", - "ĠPa nc", - "ĠD uis", - "ĠDu is", - "ĠpelÃŃcul a", - "W ARDS", - "WARD S", - "WAR DS", - "_DE TECT", - "_DET ECT", - "-section al", - "d hcp", - "dh cp", - "For Row", - "- destruct", - "-d estruct", - "-de struct", - "Ġ Presenter", - "ĠP resenter", - "ĠPres enter", - "ĠPresent er", - "/ slick", - "/s lick", - "/sl ick", - ", on", - ",o n", - "ĠCit adel", - "logged in", - "logg edin", - "_ subtype", - "_sub type", - "Ġs igue", - "Ġsi gue", - "Ġsig ue", - "Ġsigu e", - "Ġc uring", - "Ġcur ing", - "Ġcu ring", - "ĠFire wall", - "Ġfluores cence", - "ĠItalian s", - "ĠItalia ns", - "ĠItal ians", - "иÑĤ ÑģÑı", - ". getStyle", - ".get Style", - "In Seconds", - "j ie", - "ji e", - "- Smith", - "-S mith", - "Ġx link", - "Ġxl ink", - "Ġsub missive", - "о нÑĤ", - "он ÑĤ", - "arbon ate", - "ĠF aul", - "ĠFa ul", - "_ goals", - "_go als", - "_goal s", - "ĠCommission ers", - "ĠCommissioner s", - "chart Instance", - "_POST FIELDS", - "Ġmed ial", - "Ġmedia l", - "Ġmedi al", - "Ġm anos", - "Ġman os", - "Ġma nos", - "Ġmano s", - "Ġd elt", - "Ġde lt", - "Ġdel t", - "s vm", - "sv m", - ". Apis", - ".A pis", - ".Api s", - ".Ap is", - "e phy", - "ep hy", - "eph y", - "Ġasym pt", - "Ġapp Delegate", - "Ġimpro bable", - "c ka", - "ck a", - "s imd", - "si md", - "sim d", - "/ Error", - "/E rror", - ". âĢĵ", - "Ġ PTS", - "ĠP TS", - "ĠPT S", - "d eer", - "de er", - "dee r", - "Ġs ina", - "Ġsi na", - "Ġsin a", - "m agnitude", - "ID ADE", - "IDA DE", - "IDAD E", - "'] }'", - "']} '", - "Ġmay ores", - "Ġmayor es", - "Ġmayo res", - "ĉ comment", - "ĉcom ment", - "/ console", - "/con sole", - "\" @", - "v olt", - "vo lt", - "vol t", - ". sell", - ".s ell", - ".se ll", - ".sel l", - "ĠM acy", - "ĠMac y", - "ĠMa cy", - "Ġme lod", - "Ġmel od", - "Ġim ágenes", - "_ chg", - "_c hg", - "_ch g", - "Ġin out", - "Ġi nout", - "id ente", - "ide nte", - "ident e", - "iden te", - ") '),Ċ", - ")' ),Ċ", - ")'), Ċ", - "d ni", - "dn i", - ". blob", - ".b lob", - ".bl ob", - "Ġtyp ography", - "Ġe erie", - "Ġee rie", - "Ġeer ie", - "_ OID", - "_O ID", - "p esan", - "pe san", - "pes an", - "a jan", - "aj an", - "aja n", - "Ġch opping", - "Ġcho pping", - "Ġchop ping", - "Ġbl uff", - "a df", - "ad f", - "_ bases", - "_b ases", - "_base s", - ". Formatter", - ".Form atter", - ".Format ter", - ".For matter", - "Ġ\\ %", - "ĠPage Info", - "Car rier", - "ĠCal ibration", - "c omo", - "com o", - "co mo", - "-b odied", - "Ġfinanc ier", - "Ġ INA", - "ĠI NA", - "ĠIN A", - ". ERR", - ".E RR", - "Ġho odie", - "Ġhood ie", - "ĠS anity", - "ĠSan ity", - "gu arded", - "guard ed", - ".opend aylight", - "IS MATCH", - "ISM ATCH", - "High lights", - "Highlight s", - "ü nk", - "ün k", - "an iem", - "ani em", - "anie m", - "ang ered", - "ange red", - "anger ed", - "assign ments", - "assignment s", - "Ġregistr ado", - "Ġregist rado", - "ĠU PPER", - "ĠUP PER", - "ampil kan", - "a shire", - "as hire", - "ash ire", - "ashi re", - "ĠNik ola", - "ĠNi kola", - "ĠNikol a", - "ĠC FL", - "ĠCF L", - "ĠH DC", - "ĠHD C", - "Ġp oids", - "Ġpo ids", - "Ġpoi ds", - "ĠI Ps", - "ĠIP s", - "Ġprevent ative", - "ips oid", - "i fix", - "if ix", - "ifi x", - ". camel", - ".c amel", - ".ca mel", - ".cam el", - ". ga", - ".g a", - "V olumes", - "Volume s", - "Vol umes", - "- ste", - "-s te", - "-st e", - "Y ahoo", - "Ya hoo", - "_s ibling", - "_si bling", - "H ighest", - "High est", - "Hi ghest", - "opt group", - "Ġkvin na", - "Ġkvinn a", - "âĢĿ ãĢĤĊĊ", - "âĢĿãĢĤ ĊĊ", - "ĠAppl iances", - "Ġ \"><", - "Ġ\" ><", - "Ġ\"> <", - "' )\")Ċ", - "') \")Ċ", - "')\" )Ċ", - "h tt", - "ht t", - "ĠIdent ified", - "Ġpencil s", - "Ġpenc ils", - "Ġmember Id", - "Ġappend String", - ".load Data", - "Ġmock Mvc", - "Ġj ub", - "Ġju b", - "ĠS lut", - "ĠSl ut", - "ĠTai pei", - "st att", - "stat t", - "sta tt", - "P olit", - "Pol it", - "Po lit", - "Ġpart ager", - "Did Change", - "Incre ases", - "Increase s", - ") }.", - ")} .", - "ĠB aba", - "ĠBa ba", - "ĠBab a", - "_CL IP", - "_CLI P", - "[ unit", - "[u nit", - "Ġ клÑİÑĩ", - "Ġк лÑİÑĩ", - "Ġalc uni", - "ĠL ola", - "ĠLo la", - "ĠLol a", - "Ġcl inging", - "Ġclin ging", - "Ġcling ing", - "@ PostMapping", - "( concat", - "(con cat", - "Ġ ssid", - "Ġs sid", - "Ġss id", - "ĠF auc", - "ĠFa uc", - "o kit", - "ok it", - "oki t", - "ĠRecord ed", - "á lez", - "ál ez", - "ále z", - "($ ('<", - "($( '<", - "($(' <", - ".assertIs Not", - "Ġk ali", - "Ġka li", - "Ġkal i", - "V olt", - "Vo lt", - "Vol t", - "Ġwarm ly", - "Ġsc ares", - "Ġsca res", - "Ġscar es", - "Ġscare s", - "g etti", - "get ti", - "ge tti", - "gett i", - "füh rt", - "führ t", - "_ does", - "_d oes", - "_do es", - ". EMAIL", - ".E MAIL", - "im ations", - "imation s", - "imat ions", - "Ġspring fox", - "ĠD ecom", - "ĠDe com", - "ĠDec om", - "ar cy", - "arc y", - "Ġgl itches", - "Ġglitch es", - "ĠM off", - "ĠMo ff", - "ĠV oll", - "ĠVol l", - "ĠVo ll", - ". between", - ".b etween", - "Ġco orden", - "Ġcoord en", - "ĠPart icularly", - "G BP", - "GB P", - "Ġ semble", - "Ġs emble", - "Ġsem ble", - "Ġsembl e", - "East ern", - "_M SB", - "_MS B", - "] ){čĊ", - "]) {čĊ", - "]){ čĊ", - "m organ", - "mo rgan", - "mor gan", - "ĠE VAL", - "ĠEV AL", - "d ere", - "de re", - "der e", - "H OUSE", - "HO USE", - "m oire", - "mo ire", - "ist ique", - "isti que", - "_l stm", - "_lst m", - "_ls tm", - "- commit", - "-com mit", - "-comm it", - "yster ious", - "Ġtw ink", - "Ġtwin k", - "- thumbnails", - "-th umbnails", - "-thumbnail s", - "e nÃŃ", - "en ÃŃ", - ": '',", - ":' ',", - ":'' ,", - "Ġblack out", - "ĠFloor s", - "ĠFlo ors", - "Ġso fas", - "Ġsofa s", - "Ġsof as", - "Ġ oui", - "Ġo ui", - "Ġou i", - "le shoot", - "les hoot", - "lesh oot", - "ĠR aq", - "ĠRa q", - "- abs", - "-a bs", - "-ab s", - "Ġk ra", - "Ġkr a", - "M ining", - "Min ing", - "Mi ning", - "Mini ng", - "s haft", - "sh aft", - "sha ft", - ".set Columns", - ".setColumn s", - "Cl azz", - "Cla zz", - "PRE TTY", - ". playlist", - ".play list", - "éĸ ¢", - "-Sah aran", - "M ING", - "MI NG", - "MIN G", - "ĉ bl", - "ĉb l", - "è® ®", - "j f", - "DO CKER", - "DOC KER", - "hop efully", - "hope fully", - "( ignore", - "(i gnore", - "ĠUsers Controller", - "ĠMitar beiter", - "Ġ LES", - "ĠL ES", - "ĠLE S", - "Ham ilton", - "- metadata", - "-m etadata", - "-meta data", - "Ġ KK", - "ĠK K", - "ikt ig", - "Ġwoll te", - "Ġwol lte", - "egr ator", - "egra tor", - "] bool", - ", current", - ",c urrent", - "Ġvalue Type", - "Ġexcav ation", - "o land", - "ol and", - "ola nd", - "olan d", - "Ġv erv", - "Ġver v", - "Ġve rv", - "/ filepath", - "/file path", - "Auth Provider", - "Ġpro crast", - "ĉ ULONG", - "ĉU LONG", - "_MEM BERS", - "_MEMBER S", - "Ġup lift", - "ĠAut onomous", - "Ġart works", - "Ġartwork s", - "ĠOut reach", - "Ġp ore", - "Ġpo re", - "Ġpor e", - "Home page", - "Dialog Title", - "Ġ Generating", - "ĠG enerating", - "ĠGener ating", - "ĠGene rating", - "P ARSE", - "PAR SE", - "Ġsem anas", - "Ġsemana s", - "Ġhum ano", - "Ġhuman o", - "JSGlobal Scope", - "Ġvo lte", - "Ġvol te", - "Ġvolt e", - "Ġb ella", - "Ġbe lla", - "Ġbel la", - "Ġbell a", - "(is instance", - "Ġp lc", - "Ġpl c", - "\\ Catalog", - "\\C atalog", - "Ġeste emed", - "Ġesteem ed", - "éĽ ·", - "( suffix", - "(s uffix", - "Ġswe eps", - "Ġsweep s", - "ĉ ORDER", - "Ġdo ivent", - "Ġdoi vent", - "ĠS warm", - "ĠSw arm", - "Ġ Compiled", - "ĠComp iled", - "ĠCompile d", - "get Page", - "A DR", - "AD R", - ".R ichTextBox", - "Ġ Naming", - "ĠN aming", - "ĠNa ming", - "ĠNam ing", - "ag ged", - "agg ed", - "ĠG ANG", - "ĠGA NG", - "r asing", - "ra sing", - "ras ing", - "od eled", - "ode led", - "odel ed", - "Ġg ala", - "Ġga la", - "Ġgal a", - "ĠJS Name", - "d df", - "dd f", - "Ġil lust", - "Ġill ust", - "ĠLan sing", - "ĠLans ing", - "[ port", - "[p ort", - "- death", - "-de ath", - "Ġdin heiro", - "ĠE ighth", - "ĠEight h", - "Ġ bian", - "Ġb ian", - "Ġbi an", - "st Ã¥", - "Ġvers ión", - "ĠLinear Gradient", - "ĠH arding", - "ĠHar ding", - "ĠHard ing", - ". *)", - ".* )", - "e czy", - "ec zy", - "ecz y", - "$ header", - "Ġv Ã¥r", - "ĠvÃ¥ r", - "Un checked", - "Ġk oje", - "Ġko je", - "ĠPal adin", - "( ))),", - "() )),", - "()) ),", - "())) ,", - "G iving", - "Gi ving", - "( )})Ċ", - "() })Ċ", - "()} )Ċ", - "Ġd ips", - "Ġdi ps", - "Ġdip s", - "F riendly", - "Friend ly", - "Ġport rays", - "Ġportray s", - "Ġhel ium", - "Ġinsurg ency", - "_ expiry", - "_ex piry", - "_exp iry", - "ĠstringByAppending String", - "Ġa antal", - "Ġaan tal", - "s lope", - "sl ope", - "m ast", - "ma st", - "mas t", - ".get Integer", - ".getInt eger", - "Ġ ########################", - "Ġ######## ################", - "Ġ################ ########", - "Ġ############ ############", - "_PIPE LINE", - "Ġd ensely", - "Ġdense ly", - "Ġdens ely", - "Ġmut ating", - "m idi", - "mi di", - "mid i", - "ĠSe it", - "a yne", - "ay ne", - "NOW LED", - "ĠDes mond", - "ĠF Name", - "ĠFN ame", - "ĠN airobi", - "\\ Context", - "Ġcal cular", - "Ġcalcul ar", - "Ġcalc ular", - "- den", - "-d en", - "-de n", - "Ġ cott", - "Ġc ott", - "Ġco tt", - "Ġcot t", - "] ):čĊ", - "]) :čĊ", - "]): čĊ", - "ĠRecommend ation", - "ĠRo lex", - "ĠRole x", - "ĠRol ex", - "Ġvalidation Result", - ". pat", - ".p at", - ".pa t", - "Ġn Ãły", - "ĠRest Client", - "ĠG PI", - "ĠGP I", - "ĠAshe ville", - "Ġ OSP", - "ĠO SP", - "ĠOS P", - "ĠPER MISSION", - "ÐĶ аÑĤа", - "/ notification", - "/not ification", - "K night", - "Kn ight", - "_ Word", - "_W ord", - "ĠB ender", - "ĠBe nder", - "ĠBen der", - "ĠBend er", - "r anking", - "ran king", - "rank ing", - "Ġpart ida", - "Ġparti da", - "_ reservation", - "_res ervation", - "Ì Ģ", - "Ġm Name", - "Ġg etch", - "Ġget ch", - "Ġb orr", - "Ġbo rr", - "Ġbor r", - "Ġdilig ent", - "Disc uss", - "æŃ£ åľ¨", - "ape ake", - "i oned", - "ion ed", - "io ned", - "ione d", - "-N azi", - ". cum", - ".c um", - "ĠK ron", - "ĠKr on", - "ĠKro n", - "= $('#", - "=$ ('#", - "=$( '#", - "/ single", - "/s ingle", - "Ġerot isch", - "ĠV ib", - "ĠVi b", - "Ġrat ified", - "Ġconcert ed", - "ĠREG ARD", - "Ġdo br", - "Ġdob r", - ".Driver Manager", - "' r", - "Port able", - "Por table", - "ĉ suite", - "ĉs uite", - "Ġrel aciones", - "Ġrelacion es", - "ĠD op", - "ĠDo p", - "emp loi", - "empl oi", - "emplo i", - "D OB", - "DO B", - "Ġcr umbs", - "Ġ xls", - "Ġx ls", - "Ġxl s", - "_ Application", - "_App lication", - "(' :',", - "(': ',", - "Ġ-- ----------------------------------------------------------------------Ċ", - "Ġ---------------------------------------------------------------- --------Ċ", - "Ġ------------------------------------------------------------ ------------Ċ", - "m se", - "ms e", - "Ġb erk", - "Ġbe rk", - "Ġber k", - "Ġ ReturnValue", - "ĠReturn Value", - "ĠB elly", - "ĠBel ly", - "ĠBell y", - "Ġc amar", - "Ġca mar", - "Ġcam ar", - "ĠPe ek", - "ĠPee k", - "el sing", - "els ing", - "Ġnot ifies", - "ĠTr istan", - "ĠTri stan", - "ĠG AR", - "ĠGA R", - "em me", - "emm e", - "ĠElev ated", - "_ CSV", - "_C SV", - "_CS V", - "( chalk", - "(ch alk", - "Ġtw enties", - "ĠSearch Result", - "= search", - "=s earch", - "ĠMix ing", - "ĠMi xing", - "ý t", - "Ġrecru iter", - "Ġrecruit er", - "ĠIDE OGRAPH", - "ĠA go", - "ĠAg o", - "( Operation", - "(O peration", - "(Op eration", - "$ values", - "$value s", - "$val ues", - "Ġworld ly", - "ĠRos enberg", - "ĠRosen berg", - "ĠConfigure Services", - "> ** Ċ", - "...\" >Ċ", - "Ġsn ork", - "Ġsno rk", - "_ opacity", - "_op acity", - "ĠinitWith NibName", - "i ado", - "ia do", - "iad o", - "A AC", - "AA C", - "Ġ ]).", - "Ġ] ).", - "Ġ]) .", - "; z", - "_ paragraph", - "_par agraph", - "_para graph", - "Ġn oses", - "Ġno ses", - "Ġnos es", - "Ġnose s", - "st ands", - "stand s", - "sta nds", - "stan ds", - "i fr", - "if r", - "_m E", - "I raq", - "Ir aq", - ". Predicate", - ".P redicate", - "e naire", - "en aire", - "ena ire", - "] ]];Ċ", - "]] ];Ċ", - "Ġ unidad", - "Ġun idad", - "Ġuni dad", - "Ġretire es", - "Ġretir ees", - "_ hello", - "_h ello", - "Ġ modele", - "Ġmod ele", - "Ġmodel e", - "Ġmode le", - "ĠUITableView Controller", - "ĠUIT ableViewController", - "f write", - "fw rite", - "_ numero", - "_num ero", - "_numer o", - "_ visited", - "_vis ited", - "_visit ed", - "Ġrec ebe", - "Ġrece be", - "( Notification", - "Fant astic", - "_ submenu", - "_sub menu", - "ĠP EM", - "ĠPE M", - "ĠC upertino", - "ĠCup ertino", - "approx imately", - "cl assed", - "class ed", - "clas sed", - ".Read String", - "Ġdomic ile", - "_ PW", - "_P W", - "Ġball park", - "ĠK ale", - "ĠKa le", - "ĠKal e", - "con tra", - "cont ra", - "contr a", - "_ favorite", - "_f avorite", - "/ of", - "/o f", - "Q uite", - "Qu ite", - "Quit e", - "Qui te", - "Ġ OTA", - "ĠO TA", - "ĠOT A", - "Ġacceler ometer", - "di dn", - "did n", - "| ^", - "ĠRohing ya", - "ivi crm", - "ivic rm", - "ann abin", - "anna bin", - "обÑĭ ÑĤи", - "o rado", - "or ado", - "ora do", - "' )+", - "') +", - "Ha unted", - ", ID", - ",I D", - "( UIAlertAction", - "u rv", - "ur v", - "_ bel", - "_b el", - "_be l", - "ĠMex icans", - "ĠMexican s", - "/ terms", - "Ġ Painter", - "ĠP ainter", - "ĠPa inter", - "ĠPaint er", - "ĠPain ter", - "Input Label", - "ĠV inci", - "ĠVin ci", - "ĠRo sie", - "ĠRos ie", - "\\ uc", - "\\u c", - "< Menu", - "", - "Ġ'\" >", - "_ gs", - "_g s", - "Ġcomp il", - "n ard", - "na rd", - "nar d", - "- exc", - "-e xc", - "-ex c", - "Ġrh yme", - "Ġb utto", - "Ġbut to", - "Ġbutt o", - "s ays", - "sa ys", - "say s", - "ant asy", - "anta sy", - "antas y", - "ë ¸", - "Ġcitt Ãł", - "Ġch eg", - "Ġche g", - "Time String", - "Ġpos itivity", - "Ġposit ivity", - "ĠD abei", - "ĠDa bei", - "Ġ wang", - "Ġw ang", - "Ġwa ng", - "Ġwan g", - "Ġes cre", - "Ġesc re", - "\" c", - "ĉ video", - "ĉv ideo", - "Ġ Ranked", - "ĠR anked", - "ĠRank ed", - "ĠRan ked", - ". strings", - ".string s", - ".str ings", - "> >>(", - ">> >(", - ">>> (", - "Ġин ÑĤеÑĢ", - "ĠинÑĤ еÑĢ", - "Ġre sta", - "Ġr esta", - "Ġres ta", - "Ġrest a", - "[: ,:", - "[:, :", - "Ġren dre", - "Ġrend re", - "Ġde ser", - "Ġdes er", - "Ġdese r", - "J os", - "Jo s", - "Ġdis ruptions", - "Ġdisrupt ions", - "Ġdisruption s", - "Ġо пеÑĢ", - "Ġоп еÑĢ", - "s ampling", - "sam pling", - "samp ling", - "sup press", - "Ġcontainer View", - "ĠSeam less", - "Ġ airy", - "Ġa iry", - "Ġair y", - "Ġai ry", - "Ġon load", - ".Window Manager", - "ĠP LA", - "ĠPL A", - "br aco", - "bra co", - ".set PositiveButton", - "Ġp du", - "Ġpd u", - "Ġg si", - "Ġgs i", - "Ġ Cli", - "ĠC li", - "ĠCl i", - "_gr adients", - "_grad ients", - "_gradient s", - "Ñı д", - "ĠWh isper", - "c stdint", - "Ġl äng", - "Ġlä ng", - "Ġform ulations", - "Ġformulation s", - "Ġformul ations", - "é nom", - "én om", - "ourn emouth", - "[ $_", - "[$ _", - "Ġordin arily", - ".set Username", - ".setUser name", - "Ġfacult ies", - "MIT TED", - "/ values", - "/value s", - "Ġwe ir", - "Ġwei r", - "ĠA pt", - "ĠAp t", - "M Z", - "ĉ cf", - "ĉc f", - "u cken", - "uc ken", - "uck en", - "ĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉĉ", - "ĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉĉ ĉ", - "def ense", - "[ iVar", - "[i Var", - "ĠBusiness Exception", - "Select ors", - "Selector s", - "Sel ectors", - "Sele ctors", - "( coordinates", - "(co ordinates", - "ĠRe sets", - "ĠRes ets", - "ĠReset s", - "ĠDr inks", - "ĠDrink s", - "o leans", - "ole ans", - "olean s", - "(st ypy", - "_ IOC", - "_I OC", - "_IO C", - ". xxx", - ".x xx", - ".xx x", - "ĠS later", - "ĠSl ater", - "ĠSlate r", - "ĠB elize", - "ĠBel ize", - "Ġ /************************************************************************", - "Ġ/ ************************************************************************", - "Ġ/**************************************************************** ********", - "ad din", - "add in", - "_ep isodes", - "_episode s", - "Ġis chem", - "Ġisc hem", - "legal ArgumentException", - "D anny", - "Dan ny", - "Ġ pared", - "Ġp ared", - "Ġpar ed", - "Ġpa red", - "Ġpare d", - ".code haus", - "ĠAs sy", - "ĠAss y", - "ĉ Rect", - "ĉR ect", - "â ŀ", - ". lista", - ".l ista", - ".list a", - ".li sta", - "Ġв аÑĪ", - "Ġва ÑĪ", - "Ġv ets", - "Ġve ts", - "Ġvet s", - "H WND", - "HW ND", - "is oner", - "ison er", - "iso ner", - "Ġ xo", - "Ġx o", - "Ġor ally", - "Ġoral ly", - "Ġ Stmt", - "ĠSt mt", - ".r nn", - "ĠD PI", - "ĠDP I", - "ĠSt rikes", - "ĠStr ikes", - "ĠStrike s", - ".setViewport View", - "Ġèĩª åĬ¨çĶŁæĪIJ", - "Y ELLOW", - "GL enum", - "part ners", - "partner s", - "Ġ Implicit", - "ĠImp licit", - "ĠImpl icit", - "Ġt ako", - "Ġta ko", - "Ġtak o", - "âĢĻ elle", - "âĢĻe lle", - "Ġerm ög", - "total Count", - "G il", - "Gi l", - "ĉ work", - "ĉw ork", - "Ġpr atic", - "Ġpra tic", - "Ġprat ic", - "in ati", - "ina ti", - "a bies", - "ab ies", - "abi es", - "ĠSk inner", - "ĠSkin ner", - "Ġspirit ed", - "Ġspir ited", - "Ġpancre atic", - "Ġh df", - "Ġhd f", - "' em", - "'e m", - "Ġpsych osis", - "Ġpsycho sis", - "o licit", - "ol icit", - "olic it", - "oli cit", - "Ġ\" {\"", - "Ġ\"{ \"", - "_at ual", - "_a tual", - "Ġé lect", - "Ġél ect", - "TE AM", - "Ġd ak", - "Ġda k", - "ĠSW AT", - ". FragmentManager", - ".Fragment Manager", - "Ġprovision ing", - "l ifetime", - "life time", - "lif etime", - "_EXT ENSIONS", - "_EXTENSION S", - "Ġ CASCADE", - "ĠC ASCADE", - "ĠCAS CADE", - "Ġ ![", - "Ġ! [", - "( KP", - "(K P", - "Ġ vem", - "Ġv em", - "Ġve m", - "ĠInter racial", - "ĠInterr acial", - "' ]},Ċ", - "'] },Ċ", - "']} ,Ċ", - "sp acer", - "space r", - "spa cer", - "_ kv", - "_k v", - "W arehouse", - "Ware house", - "R DD", - "RD D", - "_f sm", - "_fs m", - ".Stretch Image", - ", Yes", - ",Y es", - "ĠRefuge e", - "ĠBr inging", - "ĠBring ing", - "Ġv álido", - "Ġvál ido", - ". intersection", - ".inter section", - "Ġsp ooky", - "Ġspo oky", - "_ portal", - "_p ortal", - "_port al", - "_por tal", - "Ġ moth", - "Ġm oth", - "Ġmot h", - "Ġmo th", - "ĠZ odiac", - "ĠSOC IAL", - "M imeType", - "'] }}", - "Ġ----- ->", - "Ġ------ >", - "_ Blue", - "_B lue", - "_Bl ue", - "Ġbot anical", - "Ġfr ags", - "Ġfra gs", - "Ġfrag s", - "Ġfamil ial", - "Ġfamilia l", - "- du", - "-d u", - "Ġse izing", - "Ġseiz ing", - "Ġsei zing", - "( blocks", - "(b locks", - "(block s", - "(bl ocks", - ". rd", - ".r d", - ".check NotNull", - "Ġm iser", - "Ġmis er", - "Ġmi ser", - "Ġmise r", - "Ġmax x", - "Ġma xx", - "ĠK nee", - "ĠKn ee", - "View Item", - "Inner HTML", - "D anger", - "Da nger", - "Dan ger", - "( (__", - "(( __", - "((_ _", - "Ġprz ypad", - "Ġprzy pad", - "create Url", - "* *,", - "** ,", - "ĠDecor ating", - "ATE GY", - "ATEG Y", - "? >/", - "?> /", - ". Designer", - ".Des igner", - ".Design er", - "hex digest", - "ĠEvery where", - "all eries", - "alle ries", - "aller ies", - ".TEXT URE", - ". Blocks", - ".B locks", - ".Bl ocks", - ".Block s", - "z ell", - "ze ll", - "zel l", - "Ġpre ço", - "S uddenly", - "input Email", - "( sync", - "(s ync", - ". bd", - ".b d", - "gold en", - "> ');", - ">' );", - ">') ;", - "ĠDick inson", - "> >(Ċ", - ">> (Ċ", - ">>( Ċ", - "Ġ QUEUE", - "ĠQUE UE", - "Ġ getColumn", - "Ġget Column", - "ĠgetC olumn", - "ĠS AND", - "ĠSA ND", - "ĠSAN D", - ". piece", - ".p iece", - ".pi ece", - "l icer", - "lic er", - "li cer", - "lice r", - "Fl utter", - "Ġget Version", - "Ġresource Id", - "o gl", - "og l", - "ÅĤ aw", - "ÅĤa w", - ". Branch", - ".Br anch", - "ĉ web", - "ĉw eb", - "Ġfr amerate", - "Ġframe rate", - "Ġfram erate", - "P PP", - "PP P", - "Ġf ray", - "Ġfr ay", - "Ġfra y", - "C NT", - "CN T", - "Ġinform atie", - "Ġinformat ie", - "' ]čĊčĊ", - "'] čĊčĊ", - "']čĊ čĊ", - "ne as", - "nea s", - "Header Code", - "Ġ æ¸", - "Ġæ ¸", - "Ġt rg", - "Ġtr g", - "raw types", - "H onda", - "Ho nda", - "Hon da", - "Ġmark eter", - "Ġmarket er", - "Ġ requestData", - "Ġrequest Data", - "Ġ Pg", - "ĠP g", - "ĉ not", - "ĉn ot", - "ĉno t", - "Ġpage Info", - "Ġakt uellen", - "Ġaktu ellen", - "ãģ ķãĤĵ", - "ãģķ ãĤĵ", - "Ġ AMS", - "ĠA MS", - "ĠAM S", - "push ViewController", - "ĉ AL", - "ĉA L", - "Ġv ests", - "Ġve sts", - "Ġvest s", - "Ġves ts", - "p roduce", - "pro duce", - "produ ce", - "prod uce", - "-m ême", - "ĠRah man", - "F unny", - "Fun ny", - "E Z", - "_ Valid", - "_Val id", - "Ġsquad ron", - "Ġ lash", - "Ġl ash", - "Ġla sh", - "Ġlas h", - "Ġ irm", - "Ġi rm", - "Ġir m", - "i asco", - "ias co", - "ĠP aran", - "ĠPar an", - "ĠPa ran", - "ĠPara n", - "Ġpet ites", - "Ġpetite s", - "Ġpetit es", - "ĠDec ay", - "Ġun initialized", - "priv ileged", - "Ġm bedtls", - "å¤ĩ 注", - "Ġ ^.", - "Ġ^ .", - "Ġec static", - "D etroit", - "Det roit", - "Ġp arten", - "Ġpart en", - "Ġpar ten", - "Ġparte n", - "Ġsou venir", - ".get Login", - ".getLog in", - "моÑĤ ÑĢ", - "мо ÑĤÑĢ", - "en ção", - "ĠmÃŃn imo", - "ĠAccess ed", - "ĠAcc essed", - "r ió", - "ri ó", - "M ic", - "Mi c", - "ĠV ocal", - "ĠVo cal", - "ĠVoc al", - ".Set String", - "Ġmens ajes", - "Ġmensaje s", - "åĢ į", - "Ġattr avers", - "ĠA ph", - "ĠAp h", - "Ġ ');čĊ", - "Ġ' );čĊ", - "Ġ') ;čĊ", - "Ġ'); čĊ", - "ü nde", - "ün de", - "ünd e", - "Ġenchant ed", - "Ġench anted", - "ĠRoot State", - "ĠCLOSE D", - "ĉ ĉĉĉĉĉĉĉčĊ", - "ĉĉ ĉĉĉĉĉĉčĊ", - "ĉĉĉĉ ĉĉĉĉčĊ", - "ĉĉĉ ĉĉĉĉĉčĊ", - "ĉĉĉĉĉ ĉĉĉčĊ", - "ĉĉĉĉĉĉ ĉĉčĊ", - "ĉĉĉĉĉĉĉĉ čĊ", - "ĉĉĉĉĉĉĉ ĉčĊ", - "Ġcal iente", - "or ris", - "orr is", - "Ġphysic ists", - "Ġphysicist s", - "h wnd", - "hw nd", - "_ vi", - "_v i", - "Ġráp ido", - "Ġcapital ized", - "Ġcapitalize d", - "ed By", - "Ġmach ining", - "Ġh ubby", - "Ġhub by", - "ĠSt acy", - "ĠSta cy", - ". Bus", - ".B us", - "d rink", - "dr ink", - "H ur", - "Hu r", - "Ġpr opia", - "Ġprop ia", - "Unit Test", - "Ġmiscon ception", - "_ _));Ċ", - "__ ));Ċ", - "__) );Ċ", - "__)) ;Ċ", - "/ dc", - "/d c", - "ĠMay weather", - "_m C", - ". createFrom", - ".create From", - "ĠQ Painter", - "ro psych", - "rops ych", - "inn itus", - "a yas", - "ay as", - "aya s", - "Ġg eg", - "Ġge g", - "( dw", - "(d w", - "Ġus ado", - "Ġusa do", - "Ġtr ickle", - "Ġtrick le", - "Ġann ihil", - "Ġanni hil", - "ĠP asta", - "ĠPa sta", - "ĠPast a", - "ĠPas ta", - "Ġ ++Ċ", - "Ġ+ +Ċ", - "Ġ++ Ċ", - "(Expected Conditions", - ".post Value", - "i cap", - "ic ap", - "ica p", - "ĠDon etsk", - "_ soup", - "_s oup", - "_so up", - "- publish", - "-p ublish", - "ĠP b", - "m entions", - "ment ions", - "mention s", - "AC CEPT", - ". Pull", - ".P ull", - ", âĢĻâĢĻ", - ",âĢĻ âĢĻ", - "Ġret arded", - "Ġretard ed", - "_ ATOM", - "_AT OM", - "ĠTerm inator", - "ĠTermin ator", - "- court", - "-c ourt", - "-co urt", - "ĠCLLocation Coordinate", - "Ġrev erence", - "Ġreve rence", - "Ġrever ence", - "ĠS SC", - "ĠSS C", - "ut ely", - "ute ly", - "ĠW ON", - "ĠG SL", - "ĠGS L", - "f rei", - "fr ei", - "fre i", - ".get Longitude", - ".getLong itude", - "Ġopen FileDialog", - ".B utter", - ".But ter", - "- important", - "-import ant", - "_M ANY", - "_MAN Y", - "_MA NY", - "ĠG ong", - "ĠGo ng", - "ĠGon g", - "âĢľ How", - "Ġg orge", - "Ġgor ge", - "= msg", - "=m sg", - "ĠE zek", - "ĠEz ek", - "create Command", - ": checked", - "Ġinfo graphic", - "Ġinf ographic", - ". WEST", - ".W EST", - "D irs", - "Dir s", - "Di rs", - "Ġgu arda", - "Ġguard a", - "Ġguar da", - "Ġbee tle", - "Ġbeet le", - "< small", - " Loading", - "_ mA", - "_m A", - ".get Random", - "b lings", - "bl ings", - "bling s", - "Ġche eses", - "Ġcheese s", - "Ġchees es", - "t ti", - "tt i", - ". âĢ¢", - "ĠBurg ess", - "ender it", - "ende rit", - ". ',čĊ", - ".' ,čĊ", - ".', čĊ", - "(\" \"+", - "(\"\" +", - "a cb", - "ac b", - "% p", - "index ed", - "inde xed", - "_ predicate", - "_p redicate", - "_pred icate", - "nes ia", - "Ġb ied", - "Ġbi ed", - "ĠC IT", - "ĠCI T", - "( Pos", - "(P os", - "_ radi", - "_r adi", - "_rad i", - "_ra di", - "ä»· æł¼", - "B iz", - "Bi z", - "ĠAdoles cent", - "Ġv iên", - "Ġvi ên", - "c ycl", - "cy cl", - "_ Cancel", - "_C ancel", - "Ġcon clusive", - "Ġconclus ive", - "Ġconcl usive", - "Ġappell ate", - "Ġappel late", - "inform atics", - "S J", - "Ġel ective", - "Ġelect ive", - "role Id", - "Fetch er", - "ĉ Command", - "(\" (%", - "(\"( %", - "Ġf art", - "Ġfa rt", - "Ġfar t", - "I LA", - "IL A", - "get Block", - "A USE", - "AU SE", - "Ġ дан", - "Ġд ан", - "Ġда н", - "ĠAr te", - "ĠArt e", - "Ġnot ifying", - "Ġnotify ing", - "Ġg ele", - "Ġge le", - "Ġgel e", - ". same", - ".s ame", - ".sa me", - ".sam e", - "ĠRe gel", - "ĠReg el", - "Ġ BaÅŁ", - "ĠB aÅŁ", - "ĠBa ÅŁ", - ". creation", - ".c reation", - "Ġ VN", - "ĠV N", - "_ community", - "_comm unity", - "Ġuns ustainable", - "S EX", - "SE X", - "Ġgrid Size", - "res cia", - "avers able", - "(', ')[", - "(',' )[", - "(',') [", - "ĠPh elps", - "á»ķ i", - "ANCE LED", - "ANCEL ED", - "- IS", - "-I S", - ".run ners", - ".runner s", - "ĠSt okes", - "ĠSto kes", - "ĠStoke s", - ". Produ", - ".P rodu", - ".Pro du", - ".Pr odu", - "Ġwh ipping", - "Ġwhip ping", - "_ac quire", - "Ġinvestig ación", - "f ried", - "fr ied", - ".copy With", - "ĠHard cover", - "- Se", - "-S e", - "áŀ¶ áŀ", - "inv itation", - "les ai", - "ĠD orm", - "ĠDo rm", - "ĠDor m", - "ĠÑģпиÑģ ка", - "Ġconcaten ated", - "Ġconcatenate d", - "o phil", - "op hil", - "oph il", - "Ġth inker", - "Ġthink er", - "Ġthin ker", - "/font awesome", - "ĠLe opard", - "ĠLeo pard", - "Ġ\" /\");Ċ", - "Ġ\"/ \");Ċ", - "Ġ\"/\" );Ċ", - "Ġresidual s", - "ĠMicro wave", - "ĠMic rowave", - "Ġcon forme", - "Ġconform e", - "th rop", - "thr op", - "thro p", - "Ġdis emb", - "Ġdi semb", - "Ġdise mb", - "ĠO MG", - "ĠOM G", - "ĠDisc ipline", - "ĠAc robat", - "/ repository", - "/re pository", - "/repos itory", - "d fa", - "df a", - "_ MED", - "_M ED", - "_ME D", - "buf io", - "Ġméth ode", - "_H OLD", - "i asi", - "ia si", - "ias i", - "_ legacy", - "_leg acy", - ") ččĊ", - "æ£ Ģ", - "Get ProcAddress", - "Ġy ay", - "Ġya y", - "ot ence", - "ote nce", - "oten ce", - "order id", - "orde rid", - "- tw", - "-t w", - "Ġd early", - "Ġdear ly", - "In coming", - "Inc oming", - "/ il", - "/i l", - "Ġneuro p", - "Ġneu rop", - "Ġneur op", - "u cz", - "uc z", - ") ;čččĊ", - "); čččĊ", - "ĠInnov ative", - "Ġpro fund", - "Ġprof und", - "ig mat", - "igma t", - "igm at", - "Selection Mode", - "re levant", - ". GO", - ".G O", - "Ġbru ises", - "Ġs ach", - "Ġsa ch", - "Ġsac h", - "o def", - "od ef", - "ode f", - "Ġre imb", - "Ġreim b", - "/ desktop", - "/d esktop", - "- spot", - "-s pot", - "-sp ot", - "un dance", - "und ance", - "unda nce", - "undan ce", - "Ent ropy", - "Entr opy", - "\\ core", - "Ġs uger", - "Ġsu ger", - "Ġsug er", - "Ġ Mvc", - "ĠM vc", - "ĠGN OME", - "_ indx", - "_in dx", - "_i ndx", - "_ind x", - "ĠYY STYPE", - "ĠYYS TYPE", - "ĠMat lab", - "ĠC IF", - "ĠCI F", - "Ġ *))", - "Ġ* ))", - "Ġ*) )", - "Ġproduct List", - "Ġ Alright", - "ĠAl right", - "ace mark", - "ac emark", - "ÑĤ ив", - "ÑĤи в", - "mod ification", - "int ernational", - "inter national", - "intern ational", - "Ġh omers", - "Ġhome rs", - "Ġhom ers", - "Ġho mers", - "Ġhomer s", - "Ġd icts", - "Ġdi cts", - "Ġdict s", - "Ġdic ts", - "ĠQ Font", - ". SQLite", - ".SQL ite", - "Ġtransplant ation", - "ĠMessageBox Button", - "ĠEl ves", - "' ]])Ċ", - "'] ])Ċ", - "']] )Ċ", - "(Q Icon", - "Ġcin emas", - "Ġcinema s", - "Ġcine mas", - "CO ORD", - "- China", - "-Ch ina", - "Ġkh ẩu", - "æĪij çļĦ", - "Ġsk ulls", - "Ġskull s", - "Ġpain staking", - "Ġpains taking", - "f ce", - "fc e", - ".XR Label", - "Ġ specifier", - "Ġspec ifier", - "Ġprefer ring", - "Ġpref erring", - "/ activity", - "( Photo", - "á lt", - "ál t", - ". lot", - ".l ot", - ".lo t", - "' '.", - "'' .", - "an nonce", - "ann once", - "annon ce", - "anno nce", - ".google code", - "- pdf", - "-p df", - "ĠP oke", - "ĠPo ke", - "ĠPok e", - "_ ACL", - "_A CL", - "_AC L", - "Ġend owed", - "dis cover", - "disc over", - ".o mg", - ".om g", - "Ġwood land", - ". Magic", - ".M agic", - "Ġvol ont", - "Not Allowed", - "Ġc have", - "Ġch ave", - "Ġcha ve", - "B MW", - "BM W", - "',' =',", - "','= ',", - "ĠS IX", - "ĠSI X", - "æĪij 们", - "Ġko sher", - "Ġkos her", - "Ġas piration", - "Ġaspir ation", - "Ġasp iration", - "i ntl", - "in tl", - "int l", - "_ref ptr", - "' +Ċ", - "'+ Ċ", - "ment or", - "men tor", - ". club", - ".c lub", - ".cl ub", - "Window State", - ". ARR", - ".A RR", - ".AR R", - "Ġ zza", - "Ġz za", - "Ġzz a", - "Ġmessage Type", - ". equ", - ".e qu", - ".eq u", - "T hor", - "Th or", - "Tho r", - "Ġin just", - "Ġinj ust", - "Ġg ums", - "Ġgu ms", - "Ġgum s", - "Ġborder Side", - "/ ////", - "// ///", - "//// /", - "/// //", - "ĠTrans mit", - "Ġbuf size", - "Ġh ak", - "Ġha k", - "Ġ ellas", - "Ġel las", - "Ġell as", - "Ġella s", - "R ANDOM", - "RAND OM", - "ĉ mc", - "ĉm c", - "Ġp ea", - "Ġpe a", - "e ko", - "ek o", - "document o", - "Ġhyster ia", - "Ġare nas", - "Ġar enas", - "Ġaren as", - "Ġarena s", - "Ġgun men", - "Ġm ike", - "Ġmi ke", - "Ġmik e", - "Ġimp unity", - "at isation", - "atis ation", - "_ Zero", - "_Z ero", - "_COMP ANY", - "ĠG ors", - "ĠGo rs", - "ĠGor s", - "Ġuse Class", - "( redis", - "(r edis", - "(re dis", - "(red is", - "ĠRUN NING", - "ĠB air", - "ĠBa ir", - "ĠBai r", - "ve lte", - "vel te", - "velt e", - "Ġ ','.", - "Ġ', '.", - "Ġ',' .", - "а ÑĤÑĮÑģÑı", - "аÑĤÑĮ ÑģÑı", - "ö st", - "ös t", - "encode URIComponent", - "_ restrict", - "_re strict", - "Ġdec als", - "Ġ Pedido", - "ĠP edido", - "ĠPed ido", - "Ġalter cation", - "Dis plays", - "Display s", - "Disp lays", - "ĠApp licants", - "ĠApplicant s", - "C US", - "CU S", - "Text area", - "ĠAng ola", - ". future", - ".f uture", - "ĠU SHORT", - "ĠUS HORT", - "Ġsuppress ing", - "Ġsupp ressing", - "Ġset zen", - "AP olynomial", - "Ġt och", - "Ġto ch", - "Ġtoc h", - "Ġhall mark", - "Ġ $$$", - "Ġ$ $$", - "Ġ$$ $", - "ĠCHAR SET", - ". rpm", - ".r pm", - "ĠD ich", - "ĠDi ch", - "---- ----------------", - "-------- ------------", - "---------------- ----", - "------------ --------", - "----- ---------------", - "---------- ----------", - "------ --------------", - "----------- ---------", - "------------- -------", - "------- -------------", - "--------- -----------", - "--------------- -----", - "-------------- ------", - "_ parm", - "_p arm", - "_par m", - "_pa rm", - "è¿ ĺ", - "a cciones", - "acc iones", - "acci ones", - "accion es", - "h ait", - "ha it", - "hai t", - "WARD ED", - "WAR DED", - "_ routing", - "_r outing", - "_ro uting", - "ĠN OM", - "ĠNO M", - "Ġen clave", - "ĠL otto", - "ĠLot to", - "ĉ fr", - "ĉf r", - "complex Content", - "ĠBall ard", - "k ube", - "ku be", - "/ win", - "/w in", - ".getColumn Model", - "_RE PLACE", - "Header Value", - "Ġest udiantes", - "Ġ apis", - "Ġa pis", - "Ġap is", - "Ġapi s", - "Ġb pm", - "Ġbp m", - "Ġ TypeName", - "ĠType Name", - "And Get", - "r ita", - "ri ta", - "rit a", - "Pl ans", - "Plan s", - "> Note", - ">N ote", - ">No te", - "Ġfet isch", - "Ġt oned", - "Ġto ned", - "Ġton ed", - "Ġtone d", - "_ goto", - "_g oto", - "_go to", - "on sense", - "ons ense", - "Ġm olds", - "Ġmo lds", - "Ġmol ds", - "Ġmold s", - "Ġinfiltr ation", - "ĠGuerr ero", - "ub bo", - "ubb o", - "c ki", - "ck i", - "( $(\".", - "($ (\".", - "($( \".", - "_ activities", - "_act ivities", - "_activ ities", - "( changes", - "(ch anges", - "(chan ges", - "(change s", - "Ġof App", - "ĠKe pler", - "ĠD emp", - "ĠDe mp", - "ĠDem p", - "ĠCont inent", - "ĠContin ent", - ". Ticks", - ".T icks", - ".Tick s", - "Ġ Unsigned", - "ĠUn signed", - "ĠUns igned", - "ĠJah res", - "ĠJahr es", - "ĠJahre s", - "Ġfresh men", - "ĠArch ived", - "ĠArchive d", - "ĠкоÑĤоÑĢ Ñĭй", - "Ġ' ::", - "Ġ': :", - "T utorial", - "C c", - "Ġtable LayoutPanel", - "from Json", - ". levels", - ".level s", - "_trans ient", - "Ġendors ing", - "Ġ DIC", - "ĠD IC", - "ĠDI C", - "l auf", - "la uf", - "Ġsh red", - "Ġshr ed", - "_E MIT", - "_EM IT", - "ific antly", - "ificant ly", - "A LA", - "AL A", - "/ proto", - "/pro to", - "/pr oto", - "Ġnarr owing", - "Ġnarrow ing", - "Ġnar rowing", - "U tc", - "Ut c", - "F actors", - "Fact ors", - "Factor s", - "Fac tors", - "Fa ctors", - "Ġsent ient", - "æŀ IJ", - "lix ir", - "ĠC ROSS", - "m eteor", - "met eor", - "Ġgr oin", - "Ġgro in", - "Ġ mdb", - "Ġm db", - "Ġmd b", - "ĠRot terdam", - "Ġcom ida", - "ĠOp Code", - "Ġ DefaultValue", - "ĠDefault Value", - "Permissions Result", - "Ġheter ogeneous", - "Ġm oot", - "Ġmo ot", - "Ġmoo t", - "Ġde ceived", - "Ġdece ived", - "Ġdeceive d", - "-in dependent", - "ĠObject OutputStream", - "Ġover power", - ". dup", - ".d up", - "Ġ ldb", - "Ġl db", - "Ġld b", - "Ġdomestic ally", - "Ġdomest ically", - "Ġbe stellen", - "Ġbest ellen", - "Ġbeste llen", - "Ġ lov", - "Ġl ov", - "Ġlo v", - "ĠContract ors", - "ĠContr actors", - "ĠContractor s", - "Tri angles", - "Triangle s", - "Ġfod der", - "Ġfil mes", - "Ġfilm es", - "Ġfilme s", - "ä¼ ģ", - "Ġrev olver", - "Startup Script", - "/ validation", - "ĠResource Type", - "i ÅŁ", - "ĠL az", - "ĠLa z", - "f ef", - "fe f", - "Ġl stm", - "Ġlst m", - "Ġls tm", - "{ *", - ". attachment", - ".attach ment", - ".att achment", - ". hits", - ".h its", - ".hit s", - "e with", - "ew ith", - "D OG", - "DO G", - "Al abama", - "Ġmedium s", - "Ġmedi ums", - ".m Context", - "- cols", - "-c ols", - "-col s", - "-co ls", - "åı ĭ", - ". notice", - ".not ice", - "Ġat tn", - "Ġatt n", - "ĠP acking", - "ĠPac king", - "ĠPack ing", - "Ġ Ln", - "ĠL n", - "_COM PLEX", - "_COMP LEX", - "/ Users", - "/User s", - ".save txt", - ".sav etxt", - "ĠR ounds", - "ĠRo unds", - "ĠRound s", - "ĠRou nds", - "?,?, ?,?,", - "Ġin gl", - "Ġing l", - "Ġ ROC", - "ĠR OC", - "ĠRO C", - "_ female", - "_f emale", - "_fe male", - "ĠSt ard", - "ĠStar d", - "ĠSta rd", - "] ];", - "]] ;", - "Ġwrest lers", - "Ġwrestler s", - "Ġtorrent s", - "Ġs inh", - "Ġsi nh", - "Ġsin h", - " ĊĊ", - "Ċ Ċ", - "ë³ µ", - "s ense", - "sen se", - "how ever", - ". Physics", - ".Ph ysics", - "Inf rastructure", - "ĠS acr", - "ĠSa cr", - "ĠSac r", - "F el", - "Fe l", - "ĠD ISTRIBUT", - "é ments", - "ém ents", - "ément s", - "ĠValid ates", - "ĠValidate s", - "#### ########################################################", - "################################ ############################", - "############ ################################################", - "################################################ ############", - "############################ ################################", - "######################################################## ####", - "Ġ |/", - "Ġ| /", - "Ġe sl", - "Ġes l", - "Ġré seau", - "ĠB ip", - "ĠBi p", - "BY TES", - "BYTE S", - "_W ATER", - "T urning", - "Turn ing", - "Tur ning", - "E LS", - "EL S", - "Ġj uxtap", - "Ġlesb ische", - "ý ch", - "( Unknown", - "(Un known", - "N eo", - "Ne o", - "@ JsonProperty", - "@Json Property", - "Ġal umnos", - "Ġalum nos", - "Ġalumno s", - "ĠRaq qa", - "im ei", - "ime i", - ".get Bounds", - ".getB ounds", - ".Mouse EventHandler", - ".MouseEvent Handler", - "# ######", - "## #####", - "#### ###", - "### ####", - "##### ##", - "###### #", - "Generic Type", - "/ cms", - "/c ms", - "/cm s", - "Ġturn o", - "Ġtur no", - "Ġ мин", - "Ġм ин", - "Ġми н", - "Ġfolk lore", - "ĠE vo", - "ĠEv o", - "Ġconduct ivity", - "Ġle ben", - "Ġgear box", - "- vs", - "-v s", - "Ġ ÏĨ", - "ĠÏ Ĩ", - "Ġdrink ers", - "Ġ conexao", - "Ġcon exao", - "Ġconex ao", - "ĠTe eth", - "ĠTee th", - "Ġget Arguments", - "ĠR AT", - "ĠRA T", - "ent ious", - "enti ous", - "E duc", - "Ed uc", - "+ W", - "ĠInstitution al", - "ĠInstitut ional", - "ĠB ord", - "ĠBo rd", - "ĠBor d", - "is Equal", - "( pwd", - "(p wd", - "Ġign ited", - "Ġignite d", - "ĠR ousse", - "ĠRou sse", - "Ġimpact ful", - "ĠM alk", - "ĠMal k", - "ĠMa lk", - "Ġg eral", - "Ġge ral", - "Ġger al", - "ĠP ivot", - "Ġa zt", - "Ġaz t", - "Ġcsv file", - "ĠR ope", - "ĠRo pe", - "ĠS OLUTION", - "ĠSOL UTION", - "ĠAr bitrary", - "ĠArbit rary", - "Ġl etto", - "Ġlet to", - "Ġlett o", - ".Mouse Adapter", - "Ġ }}}", - "Ġ} }}", - "Ġ}} }", - "ĠSa ilor", - "ĠSail or", - "ĠSai lor", - "d era", - "de ra", - "der a", - "P utting", - "Put ting", - "Ġconcent rates", - "Ġconcentr ates", - "Ġconcentrate s", - "Ġauth Domain", - "âĢĿ çļĦ", - "-f inals", - "-final s", - "-fin als", - ", strlen", - ",str len", - ",st rlen", - "Mu on", - "ĠOrd inary", - "fire fox", - "ĠLa TeX", - "ĠH und", - "ĠHun d", - "ĠHu nd", - "engine ering", - "/ blue", - "/b lue", - "/bl ue", - "ed TextBox", - "(\" \");", - "(\"\" );", - "(\"\") ;", - "ĠC DDL", - "ĠCD DL", - "k ept", - "ke pt", - "Ġ GetString", - "ĠGet String", - "K ir", - "Ki r", - "() ='", - "ĠO CD", - "ĠOC D", - "ant ium", - "anti um", - "$ menu", - "$m enu", - "ĠAppalach ian", - "Secret ary", - "ë¥ ĺ", - "ี ย", - "Sem antic", - "Ġ *[", - "Ġ* [", - "e stone", - "es tone", - "est one", - "esto ne", - "ung kin", - "Max Y", - "- tone", - "-t one", - "-to ne", - "-ton e", - "\" };čĊ", - "\"} ;čĊ", - "_ Part", - "_P art", - "< Member", - "ĊĊ", - "'> ĊĊ", - "'>Ċ Ċ", - "L ic", - "Li c", - "ĠMir age", - "ĠMi rage", - "ĠAssembly FileVersion", - "Te V", - "ĠValue EventListener", - "-s olving", - "T ho", - "Th o", - "rou lette", - "_ WP", - "_W P", - "Ġunint errupted", - "Ġfield Type", - ". Typed", - ".T yped", - ".Type d", - "Ġa mour", - "Ġam our", - "Ġamo ur", - "Ġmock ery", - "Ġmocker y", - "( vol", - "(v ol", - "(vo l", - "ĠSub committee", - "ĠR uf", - "ĠRu f", - "e rox", - "er ox", - "ero x", - ":UIButtonType Custom", - "Ġ Blur", - "ĠBl ur", - "ĠBlu r", - "Ġwy kon", - "n ces", - "nc es", - "nce s", - "ASH BOARD", - "! !\");Ċ", - "!! \");Ċ", - "Ġmurder ers", - "Ġmurderer s", - ". daily", - ".d aily", - ".da ily", - "ĠDI AG", - "j ing", - "ji ng", - "jin g", - "Ġdol phin", - "Ġl òng", - "Ġb ö", - "ĠV ocabulary", - ".St Object", - "' )\">", - "') \">", - "')\" >", - "Ġz un", - "Ġzu n", - "Ġscrim mage", - "tr éal", - "ĠL ig", - "ĠLi g", - "[ vi", - "[v i", - "C ole", - "Col e", - "Co le", - "Ġfrost ing", - ". Players", - ".P layers", - ".Pl ayers", - ".Player s", - ".Play ers", - "- translate", - "-trans late", - "Fe els", - "Feel s", - "Fee ls", - "=\\\" /", - "=\\ \"/", - ".Butter Knife", - "Ġ? >;Ċ", - "Ġ?> ;Ċ", - "Ġ avi", - "Ġa vi", - "Ġav i", - "in nie", - "inn ie", - ". Failure", - ".F ailure", - ".Fail ure", - "Ġsp indle", - "Ġspin dle", - "Configuration Exception", - "_ hop", - "_h op", - "Ġpos ição", - "Ġposi ção", - "Ġ Await", - "ĠA wait", - "ĠAw ait", - "UIImage PickerController", - "ĉ day", - "ĉd ay", - "Ġge nom", - "Ġgen om", - "C ab", - "Ca b", - "ĠÑĢ езÑĥлÑĮÑĤаÑĤ", - "ĠÑĢезÑĥлÑĮÑĤ аÑĤ", - "OR IGINAL", - "Ġejac ulation", - "( tcp", - "(t cp", - "(tc p", - "SE COND", - "SEC OND", - "Ġt onic", - "Ġto nic", - "Ġton ic", - "Ġ ListBox", - "ĠList Box", - "Ġ ĉĉĊ", - "Ġĉ ĉĊ", - "Ġĉĉ Ċ", - "( )>Ċ", - "() >Ċ", - "()> Ċ", - "Ġqu atre", - "Ġquat re", - "Ġqua tre", - "ượ ng", - "with Errors", - ". Maybe", - ".M aybe", - ", âĢ¦", - "token Id", - "_UN DEF", - "Ġfresh ness", - "ĠAmendment s", - "ĠAmend ments", - ".map box", - ". CV", - ".C V", - "( blog", - "(b log", - "(bl og", - "_get time", - ". quest", - ".q uest", - ".qu est", - "s parse", - "sp arse", - "spar se", - "Ġre sale", - "Ġres ale", - "Ġenthusi astically", - "Ġenthusiastic ally", - "Ġenthusiast ically", - "ĠProstit utas", - "W a", - "C argo", - "Car go", - ". Parcelable", - ".Parcel able", - "S ENSOR", - "SEN SOR", - "SENS OR", - "ĠR yu", - "ĠRy u", - "La ughs", - "Laugh s", - "_ Native", - "_N ative", - "/ pg", - "/p g", - "y sts", - "yst s", - "ys ts", - "Ġphot oc", - "Ġphoto c", - "ç® Ģ", - "ad opt", - "ado pt", - ". species", - ".s pecies", - ".sp ecies", - ".spec ies", - "conc iliation", - "Adjust ed", - "Adj usted", - ".Firebase Auth", - "ut tle", - "utt le", - "ord ination", - "ordin ation", - "Ġm unch", - "Ġmun ch", - "ĠS take", - "ĠSt ake", - "ĠSta ke", - ". ping", - ".p ing", - ".pi ng", - ".pin g", - "an ker", - "ank er", - "anke r", - "(QString Literal", - "Ġsub script", - "Ġsubs cript", - "Ġsubscri pt", - "Ġ ĠĉĊ", - "ĠĠ ĉĊ", - "ĠĠĉ Ċ", - "ĠM CC", - "ĠMC C", - "_ Cmd", - "_C md", - "se xy", - "sex y", - "i ou", - "io u", - "ĠM ANY", - "ĠMA NY", - "ĠMAN Y", - "Ġn anny", - "Ġnan ny", - "T RAIN", - "TR AIN", - "TRA IN", - "Ġflour ishing", - "Ġflourish ing", - "ĠW atches", - "ĠWatch es", - "ĠWat ches", - "ĠQ Map", - "ĠF erm", - "ĠFe rm", - "ĠFer m", - "Ġw asm", - "Ġwas m", - "Ġwa sm", - "ĠA bed", - "ĠAb ed", - "ĠAbe d", - "_ UD", - "_U D", - "ĠG lasses", - "ĠGl asses", - "ĠGlass es", - "ĠGlas ses", - "+ v", - "Att end", - ". Chain", - ".Ch ain", - "Ġdec ency", - "Ġdece ncy", - "ĠSup plementary", - "ĠSupplement ary", - "h unter", - "hunt er", - "- txt", - "-t xt", - "Ġ\" }\";Ċ", - "Ġ\"} \";Ċ", - ".set WindowTitle", - "( \"", - "Ġmasc ara", - "( Profile", - "åĬ Łèĥ½", - "åĬŁ èĥ½", - "im ité", - "imit é", - "imi té", - "Ġwild fires", - "Ġwildfire s", - "- ROM", - "-R OM", - ".is On", - "( groupId", - "(group Id", - "Re pair", - "Rep air", - "accum ulate", - "Ġ< \",", - "Ġhand written", - "Ġach eter", - "Ġache ter", - "ĠM GM", - "ĠMG M", - "ĠIr ma", - "-> {_", - "->{ _", - "g ee", - "ge e", - "c riminal", - "cr iminal", - "Ġèĭ¥ è¦ģ", - "Ġmoment arily", - "\" )!=", - "\") !=", - "_ lit", - "_l it", - "_li t", - "Ġexpires In", - ". \").", - ".\" ).", - ".\") .", - "éķ¿ 度", - "Ġfr ække", - "v lc", - "vl c", - "Ġor bs", - "Ġorb s", - ") ,$", - "), $", - "Ġvent ured", - "Ġventure d", - "/ >\\", - "/> \\", - "ch arm", - "char m", - "cha rm", - "N uitka", - "el dig", - "eld ig", - "ato nin", - "aton in", - "W itness", - "- lat", - "-l at", - "-la t", - "Ġset Hidden", - "Ġrel ics", - "Ġreli cs", - "Ġrelic s", - "Ġcons ulate", - "Ġconsul ate", - ". IGNORE", - "\" After", - "\"A fter", - "Ġs etAddress", - "Ġset Address", - "Ġbeste ht", - "Ġ' ')ĊĊ", - "Ġ'' )ĊĊ", - "Ġ'') ĊĊ", - "Ġ'')Ċ Ċ", - ".x axis", - "Ġser ão", - "Ġmis led", - "Ġmi sled", - "_UN IFORM", - "ĠV IA", - "ĠVI A", - "in cr", - "inc r", - "Ġzen ith", - "Ġvis cosity", - "Ġvisc osity", - "Ġthin ly", - ".get SharedPreferences", - ". ErrorCode", - ".Error Code", - "\" ),\"", - "\") ,\"", - "\"), \"", - "ĠMillion en", - "ĠMilli onen", - "Ġ/ >)Ċ", - "Ġ/> )Ċ", - "Scroll Indicator", - "-se eking", - "ĠPOLIT ICO", - "as ca", - "asc a", - "_ rl", - "_r l", - "N avig", - "Nav ig", - "Na vig", - "(full file", - "Ġsol itude", - "Ġ juven", - "Ġju ven", - "Ġha uling", - "Ġhaul ing", - "ĠMac ros", - "ĠMacro s", - "ĠG ry", - "ĠGr y", - "Ġexerc itation", - "ĠATT ACK", - "Tick Count", - "Ġ rites", - "Ġr ites", - "Ġrit es", - "Ġri tes", - "Ġd oe", - "Ġdo e", - "Particle System", - "Ġ slu", - "Ġs lu", - "Ġsl u", - "Window Text", - "Ġ ClassName", - "ĠClass Name", - "Ġs lander", - "Ġsl ander", - "Ġsla nder", - "ĉ Port", - "ĉP ort", - "j ong", - "jo ng", - "jon g", - "? a", - ".D ial", - ".Di al", - "âĢĶ at", - "âĢĶa t", - "$ objPHPExcel", - "$obj PHPExcel", - "Ġso ar", - "E NN", - "EN N", - "appe ared", - "appear ed", - "Ġquot id", - "Ġquo tid", - "e machine", - "em achine", - "ema chine", - "Ġ nip", - "Ġn ip", - "Ġni p", - "Ġmicro time", - "ĠAl ma", - "; !", - "---------------- --------------------------------------------------------------------------------", - "-------------------------------- ----------------------------------------------------------------", - "---------------------------------------------------------------- --------------------------------", - "------------------------------------------------ ------------------------------------------------", - "---------------------------------------------------------------------------- --------------------", - "-------------------------------------------------------------------------------- ----------------", - "-------------------- ----------------------------------------------------------------------------", - "ĠP assage", - "ĠPass age", - "ĠPas sage", - "Ġdump sters", - "Ġdumpster s", - "Ġdumps ters", - "Ġ Exclude", - "ĠEx clude", - "ĠExc lude", - "Ġsuggest ive", - "ĠCircularProgress Indicator", - "_ clr", - "_c lr", - "_cl r", - "Array Type", - "IL LA", - "ILL A", - "Elapsed Time", - "Dr iven", - "Drive n", - "Ġresource Name", - "ĠG arrison", - "ĠGarr ison", - "se rir", - "ser ir", - "- ahead", - "-a head", - "Ġp innacle", - "ĠEs presso", - "S parse", - "Sp arse", - "Ġas says", - "Ġass ays", - "Ġassay s", - "ĠGirl friend", - "i mid", - "im id", - "imi d", - "] ='\\", - "]= '\\", - "]=' \\", - "ONG LONG", - "ONGL ONG", - "Ġportray ing", - "L ane", - "La ne", - "Ġb úsqueda", - "Ġrein forcements", - "Ġreinforce ments", - "Ġreinforcement s", - "ĠSpread sheet", - "ĠArray Collection", - ", arr", - ",a rr", - "light box", - "ic ana", - "ica na", - "ican a", - "< \"", - "build ers", - "builder s", - "K id", - "Ki d", - "ĠMat SnackBar", - "EX PR", - "EXP R", - "od cast", - "ĠFoundation s", - "ĠFound ations", - "Ġ inds", - "Ġin ds", - "Ġi nds", - "Ġind s", - "=' ${", - "='$ {", - "F izz", - "Fi zz", - "- functional", - "-function al", - "( workspace", - "(work space", - "Ġstem med", - "_ patches", - "_p atches", - "_patch es", - "_pat ches", - "ĠJar vis", - "RE ADING", - "READ ING", - "Ġdisrespect ful", - "ĠQ Dom", - "Ġ$ {Ċ", - "Ġ${ Ċ", - "e status", - "es tatus", - "est atus", - "Re ached", - "Reach ed", - "! .ĊĊ", - "!. ĊĊ", - "I LT", - "IL T", - "ĠN DEBUG", - "ĠC ourage", - "ĠCour age", - "ĠCou rage", - "birth date", - "ĠT ing", - "ĠTi ng", - "ĠTin g", - "Ġutil izado", - "Ġutiliz ado", - "Ġutiliza do", - "án chez", - "Out door", - "Ġhand guns", - "Ġhandgun s", - "Ref Count", - "É Ļ", - "r omo", - "ro mo", - "rom o", - "Ġt ts", - "Ġtt s", - ". She", - ".S he", - ".Sh e", - "Ġ Pane", - "ĠP ane", - "ĠPan e", - "ĠPa ne", - "ãĢij, ãĢIJ", - "ĠIO CTL", - "ĠIOC TL", - "/ black", - "/b lack", - "/bl ack", - "in scription", - "ins cription", - "Ġbi opsy", - "Ġbio psy", - "Ġ TimeInterval", - "ĠT imeInterval", - "ĠTime Interval", - ".Test Check", - "ĠGUI Style", - "Ġ Capability", - "ĠCap ability", - "ĠBei trag", - "ĠBeit rag", - "don nees", - "T reatment", - ". backup", - ".back up", - "Ġsign ings", - "Ġsig nings", - "Ġsigning s", - "Ġsignin gs", - "ĠB oca", - "ĠBo ca", - "d rm", - "dr m", - ". MAIN", - ".M AIN", - "Ġgo ede", - "Ġgoed e", - "Ġ Markup", - "ĠMar kup", - "ĠMark up", - "G REE", - "GR EE", - "GRE E", - "ĠBase Service", - ". Creator", - ".C reator", - "Ġj ails", - "Ġja ils", - "Ġjail s", - "ĠK ahn", - "ĠKa hn", - "ĠKah n", - "Ip Address", - "AC HI", - "ACH I", - "Ġin hibited", - "Ġinhib ited", - "Ġinhibit ed", - "Ġ@ $_", - "Ġ@$ _", - "ĠAs sass", - "ĠAss ass", - "Ġenv iado", - "Ġenvi ado", - "Her oes", - "Hero es", - "ÐŁ еÑĢ", - "ĠM aven", - "ĠMa ven", - ". ls", - ".l s", - "Ġ ive", - "Ġi ve", - "Ġiv e", - "| RF", - "|R F", - "Ġresize Mode", - "Ġrum pe", - "_ attachments", - "_attach ments", - "_attachment s", - "T U", - "Ġtact ile", - "Ġtac tile", - "Attempt ing", - "Ġro bin", - "Ġrob in", - "y aw", - "ya w", - "Ġmerc enaries", - "ĠHab itat", - "ĠHabit at", - "end date", - "Ġ oxy", - "Ġo xy", - "Ġox y", - "ĉ Random", - "ĉR andom", - "o hon", - "oh on", - "oho n", - "Is Null", - "ĠValidation Result", - "ãĥ ļ", - "um bed", - "umb ed", - "p pv", - "pp v", - "Ġ arp", - "Ġa rp", - "Ġar p", - "ich ick", - "ichi ck", - "_r nn", - "ĠT FT", - "ĠTF T", - "Tex Image", - "\" On", - "Ġ Sampler", - "ĠS ampler", - "ĠSam pler", - "ĠSample r", - "ĠSamp ler", - "t opl", - "to pl", - "top l", - "Ġj ane", - "Ġja ne", - "Ġjan e", - "y ling", - "yl ing", - "ĠUN ICODE", - "Tab Index", - "< {Ċ", - "<{ Ċ", - "s uspend", - "sus pend", - "uv ian", - ", application", - "ол иÑĩеÑģÑĤво", - "y at", - "ya t", - "e zier", - "ez ier", - "ezi er", - "ĠCH UNK", - "ĠAd ler", - "/ Add", - "/A dd", - "Ġ KeyValue", - "ĠKey Value", - "Ġspos ób", - "S ampling", - "Sam pling", - "ch ers", - "che rs", - "cher s", - "_ AMD", - "_A MD", - "_AM D", - "R u", - ".Must Compile", - "N ation", - "Na tion", - "Nat ion", - "As soc", - "Ass oc", - "Man aging", - "Mana ging", - "ĠEn gl", - "ĠEng l", - "_ GB", - "_G B", - "Ġsucc inct", - "Ġdis liked", - "Ġdislike d", - "ĠI ke", - "ĠIk e", - "Bullet in", - "_ARCH IVE", - "Pro posal", - "Prop osal", - "Ġjog ging", - ".C REATED", - ".CREATE D", - "Ġc hol", - "Ġch ol", - "Ġcho l", - "è£ ħ", - "Į ¨", - "- push", - "-p ush", - "Ġres erva", - "Ġreserv a", - "co rev", - "core v", - "cor ev", - "è tre", - "T HR", - "TH R", - "Ġincompet ence", - "Ġchar isma", - "æĦ Ł", - "Ġ\" ==", - "Ġ\"= =", - "B TN", - "BT N", - "Ġ Locator", - "ĠL ocator", - "ĠLoc ator", - "i vet", - "iv et", - "ive t", - "(' .')Ċ", - "('. ')Ċ", - "('.') Ċ", - "('.' )Ċ", - "Ġfor IndexPath", - "ô me", - "ôm e", - "Ġcapac it", - "w aters", - "wa ters", - "water s", - "wat ers", - "ĠWR ONG", - "h oa", - "ho a", - "ĠM IPS", - "ĠMI PS", - "Ġe miss", - "Ġem iss", - "ĠJacqu eline", - "( cmp", - "(c mp", - "(cm p", - "Ġe ens", - "Ġeen s", - "Ġee ns", - "L eo", - "Le o", - ". timing", - ".t iming", - ".tim ing", - "CLU SION", - "CLUS ION", - "Ġ (\"-", - "Ġ( \"-", - "Ġ(\" -", - "åĵ Ī", - ". kode", - ".k ode", - "ĠUnder t", - "ĠUnd ert", - "Ġbe wild", - "Ġbew ild", - "ĠEs sen", - "ĠEss en", - ". hd", - ".h d", - "Ġren egot", - "Ġm ower", - "Ġmo wer", - "Ġl sp", - "Ġls p", - "Ġpen chant", - "Ġman oe", - "Ġmano e", - "Ġ agli", - "Ġa gli", - "Ġag li", - "Ġre cal", - "Ġr ecal", - "Ġrec al", - "ĠOPER ATION", - "(^ )(", - "Ġ ν", - "ĠÎ ½", - "Ġ Scoped", - "ĠSc oped", - "ĠScope d", - "ĠSco ped", - "Ġ @\"Ċ", - "Ġ@ \"Ċ", - "Ġ@\" Ċ", - "= label", - "=l abel", - "[ loc", - "[l oc", - "I ntl", - "In tl", - "Int l", - "ĠN z", - "table t", - "tab let", - "tabl et", - ". ColumnName", - ".Column Name", - "Ġscreen Size", - "D Bus", - "DB us", - "co oked", - "cook ed", - "- registration", - "-reg istration", - "âĢľ One", - "- non", - "-n on", - "-no n", - "ĠwiÄĻ c", - "Ġc osta", - "Ġco sta", - "Ġcost a", - "Ġcos ta", - ".add Tab", - ". conditions", - ".condition s", - ".cond itions", - "ĠH ess", - "ĠHe ss", - "MEM ORY", - "ĠAval anche", - "() }}Ċ", - "()} }Ċ", - "Ġtri plet", - "Ġtrip let", - "Ġtriple t", - "Ġl abyrinth", - "ĠNode List", - "ĠN YT", - "ĠNY T", - "Ġy eni", - "Ġye ni", - "Ġyen i", - "d ff", - "df f", - ".Html Controls", - "A VIS", - "AV IS", - "/ Math", - "/M ath", - "Ġ memcmp", - "Ġmem cmp", - "ا Ø¡", - "Ø§Ø ¡", - "о ÑģÑĮ", - "оÑģ ÑĮ", - "c rap", - "cr ap", - "( pages", - "(p ages", - "(page s", - "(pa ges", - "Ġl xml", - "Ġlx ml", - "ĠQ DateTime", - "_t cb", - "_tc b", - "Ġ openid", - "Ġopen id", - "Ġsyn aptic", - "ĠM DMA", - "ĠMD MA", - "( slug", - "(s lug", - "(sl ug", - "ig matic", - "igma tic", - "igm atic", - "igmat ic", - "e nor", - "en or", - "eno r", - "Ġcr amped", - "Ġcram ped", - "G OP", - "GO P", - "Ń IJ", - ".is File", - "ĠD ifferential", - "ĠDifferent ial", - "Ġ =\"\";Ċ", - "Ġ=\" \";Ċ", - "ĉ ĉĉĠĠĠĠĉ", - "ĉĉ ĉĠĠĠĠĉ", - "ĉĉĉ ĠĠĠĠĉ", - "ĉĉĉĠĠĠ Ġĉ", - "ĉĉĉĠ ĠĠĠĉ", - "ĉĉĉĠĠ ĠĠĉ", - "ĉĉĉĠĠĠĠ ĉ", - "ĠC ooke", - "ĠCo oke", - "ĠCook e", - "ĉU FUNCTION", - "Ġpersever ance", - "Relative Layout", - "IMPORT ANT", - "Ġe xon", - "Ġex on", - "Ġ он", - "Ġо н", - "i base", - "ib ase", - "iba se", - "( CONT", - "(C ONT", - "(CON T", - "n ovation", - "no vation", - "nov ation", - "nova tion", - "ä½ ķ", - "[ sub", - "[s ub", - "Admin Controller", - "HTTP Header", - "c rear", - "cre ar", - "cr ear", - "ĠN IR", - "ĠNI R", - "ĠDrop DownList", - "Ġval ide", - "Ġvalid e", - "Ġva lide", - "Ġde hydration", - ". ']", - ".' ]", - "( WIN", - "(W IN", - "Ġ ...\\", - "Ġ. ..\\", - "Ġ... \\", - "Ġ.. .\\", - "Ġphoto shop", - "Ġphotos hop", - "ĉ Init", - "ĉI nit", - "ĉIn it", - "_ cou", - "_c ou", - "_co u", - "Ġtime Zone", - "dar win", - "r omatic", - "ro matic", - "rom atic", - "roma tic", - "Navigation ItemSelectedListener", - "b rates", - "br ates", - "bra tes", - "brate s", - "] --;Ċ", - "Ġtraged ies", - "ĠPed iatrics", - "ĠPediatric s", - "SM ART", - "- API", - "-A PI", - "ĠMessage Lookup", - "ĉ vo", - "ĉv o", - "Ġprejud ices", - "Ġprejudice s", - "Ġ mA", - "Ġm A", - "U ps", - "Up s", - "ĠMISS ING", - "ĉ ad", - "ĉa d", - "C ream", - "Cre am", - "Cr eam", - "ĠT b", - "ĠM ona", - "ĠMon a", - "ĠMo na", - "_ ghost", - "_g host", - "ĉ types", - "ĉt ypes", - "ĉtype s", - "ĉtyp es", - "E mb", - "Em b", - "ĠDocument ary", - "' );ĊĊĊĊ", - "') ;ĊĊĊĊ", - "');Ċ ĊĊĊ", - "');ĊĊ ĊĊ", - "'); ĊĊĊĊ", - "');ĊĊĊ Ċ", - "Ġl up", - "Ġlu p", - "_ Reference", - "_Re ference", - "_Ref erence", - "ĠB ATCH", - "ĠBAT CH", - "Ġintertw ined", - "< Cell", - "", - "Ġf oyer", - "Ġfo yer", - "'util isation", - "ĠMü ller", - "ĠFet ish", - "Ġdefault Manager", - "Ġback track", - "B ah", - "Ba h", - "Exp licit", - "Expl icit", - "_ ASCII", - "_A SCII", - "_ASC II", - "Ġm Activity", - "( Msg", - "(M sg", - "Ġ ê²Į", - "Ġê² Į", - "ĠTER MS", - "ĠTERM S", - "ĠAn gie", - "ĠAng ie", - "H SV", - "HS V", - "ĠMos que", - ". Names", - ".N ames", - ".Name s", - "íĬ ¼", - "r este", - "re ste", - "res te", - "rest e", - "_ parms", - "_p arms", - "_par ms", - "_pa rms", - "_parm s", - "Ġg aping", - "Ġgap ing", - "Ġga ping", - "Ġc ropping", - "Ġcr opping", - "Ġcro pping", - "Ġcrop ping", - "Data Frame", - "Ġrespons iveness", - "Ġresponsive ness", - "_ undo", - "_un do", - "_u ndo", - "_ tran", - "_t ran", - "_tr an", - "_tra n", - ". terminate", - ".term inate", - "Ġitalian e", - "Ġitalia ne", - "Ġwalk through", - "Ġattract iveness", - "Ġattractive ness", - "д е", - "_ STS", - "_S TS", - "_ST S", - "_ learn", - "_l earn", - "_le arn", - "Ġchocolate s", - "Ġchocol ates", - "ier archical", - "- thinking", - "-th inking", - "Ġ )))", - "Ġ) ))", - "Ġ)) )", - "ish ments", - "ishment s", - ".Log f", - ".Lo gf", - "ĠT MZ", - "ĠTM Z", - "ĠCan ary", - "f oil", - "fo il", - "ĠV accine", - "ĠVacc ine", - ". vx", - ".v x", - "ĠSur round", - "Inter mediate", - "Ġ iov", - "Ġi ov", - "Ġio v", - "v ais", - "va is", - "' ;\";Ċ", - "'; \";Ċ", - "ï½ŀ ĊĊ", - "éĢģ æĸĻ", - "âĢ¦ it", - "Se ats", - "Sea ts", - "Seat s", - "C lar", - "Cl ar", - "Cla r", - "W ars", - "War s", - "Wa rs", - "ĠHutch inson", - "ĠH asan", - "ĠHas an", - "ĠHa san", - "! ')ĊĊ", - "!' )ĊĊ", - "!')Ċ Ċ", - "ĠRich ie", - "ĠRi chie", - "che iden", - "cheid en", - "( $('", - "($ ('", - "($( '", - "Y ork", - "Yo rk", - "Ġl ids", - "Ġli ds", - "Ġlid s", - "Ġal phanumeric", - "Ġalpha numeric", - "ĠG lock", - "ĠGl ock", - "ĠGlo ck", - ". shapes", - ".sh apes", - ".shape s", - ".sha pes", - "Ġsp arking", - "Ġspark ing", - "Ġspar king", - "_ epsilon", - "_e psilon", - "_eps ilon", - "up licated", - "uplic ated", - "uplicate d", - ". dirty", - ".d irty", - ".dir ty", - "] )==", - "]) ==", - "ĠìľĦ ì¹ĺ", - "Ġs cn", - "Ġsc n", - "Ġ /****************************************************************", - "Ġ/ ****************************************************************", - "_PRE VIEW", - "_ HC", - "_H C", - "ield ing", - "iel ding", - "f gets", - "fg ets", - "ĠAdd ison", - "Ġproduct Service", - "- figure", - "-f igure", - "( retval", - "(ret val", - "z ano", - "za no", - "zan o", - "Ġaut ob", - "Ġauto b", - "ĉ sd", - "ĉs d", - "_ numer", - "_n umer", - "_num er", - "ĠSet LastError", - "ĠF ior", - "ĠFi or", - "ific ance", - "ifica nce", - "Unt itled", - "Ġin field", - "Ġinf ield", - "Ġ{ }));Ċ", - "Ġ{} ));Ċ", - "Ġ{}) );Ċ", - "Ġs pac", - "Ġsp ac", - "Ġspa c", - "Ġr ookies", - "Ġro okies", - "Ġrookie s", - "(des cribing", - "n gen", - "ng en", - "nge n", - "ி à®", - ". rdf", - ".r df", - ".rd f", - ". Mutex", - ".M utex", - "Ġkne eling", - "Ġknee ling", - "Ġ QE", - "ĠQ E", - "set Max", - "Read Stream", - "Ġ ventas", - "Ġvent as", - "Ġven tas", - "Ġventa s", - "s ut", - "su t", - "cm peq", - "cmp eq", - ".WriteAll Text", - "ĠEx perienced", - "ĠExperience d", - "$ __", - "$_ _", - "Ġka um", - "ĠL IS", - "ĠLI S", - "Ġdocument os", - "Ġdocumento s", - "_HE ALTH", - "i contains", - "icon tains", - "icont ains", - "Ġart isans", - "Ġartisan s", - "OW NER", - "OWN ER", - "Ġb linked", - "Ġblink ed", - "get Display", - "Ġt oen", - "Ġto en", - "Ġtoe n", - "Ġrow Num", - "Ġav ril", - "Ġin vis", - "Ġinv is", - "ĠK ear", - "ĠKe ar", - "toBe InTheDocument", - "a pur", - "ap ur", - "Ġr acked", - "Ġrac ked", - "Ġrack ed", - "ĠMc Master", - "ĠMcM aster", - "_ATTR IB", - "H az", - "Ha z", - "Ġfact ura", - "Ġfac tura", - "/ ts", - "/t s", - "ĠÑĢаз меÑĢ", - "ĠÑĢазм еÑĢ", - "Ġ zf", - "Ġz f", - "Ġshort fall", - ". fasta", - ".f asta", - ".fast a", - ".fa sta", - "ĠCONST ANT", - ". managed", - ".man aged", - ".manage d", - "g ems", - "ge ms", - "gem s", - "Shared Pointer", - "Ġbl urry", - "Ġblur ry", - "b rightness", - "bright ness", - "( components", - "(com ponents", - "(component s", - "(comp onents", - "Ġ ...\"ĊĊ", - "Ġ... \"ĊĊ", - "Ġ.. .\"ĊĊ", - "Ġ...\" ĊĊ", - "Ġ...\"Ċ Ċ", - "S ELL", - "SE LL", - "SEL L", - "ĠIllustr ator", - ".get Channel", - "Ġtrou vé", - "y sters", - "yst ers", - "ys ters", - "yster s", - "Ġv ois", - "Ġvo is", - "Ġvoi s", - "ĠL inden", - "ĠLin den", - "ĠLind en", - "Ġem ojis", - "Ġemoji s", - "Ġemo jis", - "Ġb rawl", - "Ġbr awl", - "Ġbra wl", - "ĠM SR", - "ĠMS R", - "ĠE lo", - "ĠEl o", - "ĠCroat ian", - "ĠCroatia n", - "Popup Menu", - "L ewis", - "Le wis", - ". JWT", - ".J WT", - "Ġaston ished", - "B ush", - "Bus h", - "Bu sh", - "( itemId", - "(item Id", - "Ġdet achment", - "Ġdetach ment", - "ĠEn core", - "ĠEnc ore", - "å° Ķ", - "Ġre kl", - "Ġr ekl", - "Ġrek l", - "Ġc ram", - "Ġcr am", - "Ġcra m", - ") $/", - ")$ /", - ".get Host", - "_ recommend", - "_re commend", - "- HT", - "-H T", - "_cal ibration", - "Auth enticate", - ".firebase app", - "UN IX", - "ĉ Camera", - "ĉC amera", - "ĠHE AP", - "I deal", - "Ide al", - ". office", - ".off ice", - "Ġgoof y", - "Ġgoo fy", - "( Symbol", - "(S ymbol", - "Ġjo uer", - "Ġjou er", - "_part itions", - "_partition s", - "Ġrapid ement", - "Ġrapide ment", - "ĠGNU NET", - "ĠGN UNET", - "id User", - "Ġsuper vise", - "Ġsuperv ise", - "( Contact", - "A WN", - "AW N", - "ãģ ĺ", - "Ġna am", - "Ġa ust", - "Ġau st", - "Ġaus t", - "åľ¨ 线", - "_ softmax", - "_soft max", - "Allow Anonymous", - "amm able", - "amma ble", - "RO UTE", - "ROUT E", - "* D", - "Ġ aden", - "Ġa den", - "Ġad en", - "Ġade n", - "ĠCrist ina", - "ĠCrist iano", - "Ġblood stream", - "sub class", - "_ persona", - "_person a", - "CH ILD", - "- know", - "-k now", - "Ġnavigation Options", - "ĠZuk unft", - "ĠPix ar", - "Ty ler", - "Ġunder world", - "Ġsincer ity", - "Ġdisp enser", - "Ġdispens er", - "Ġk ter", - "Ġkt er", - "id ders", - "idd ers", - ".add Node", - "- checked", - "-check ed", - "Ġkey st", - "Ġke yst", - "Ġkeys t", - "ĠW TO", - "ĠWT O", - ". signals", - ".sign als", - ".signal s", - "Ġadvent urer", - "Ġadventure r", - "ĠP ang", - "ĠPan g", - "ĠPa ng", - "\\ R", - "= pos", - "=p os", - "Ġdispens aries", - "ĠClose t", - "ĠClo set", - "(\" {\\\"", - "(\"{ \\\"", - "id eon", - "ide on", - "ideo n", - "Ġnécess aire", - "( )\"Ċ", - "() \"Ċ", - "()\" Ċ", - "_RECE IVED", - "Ġrésult ats", - "Ġm oden", - "Ġmod en", - "Ġmode n", - "Ġmo den", - "ĠIceland ic", - "; d", - ". allowed", - ".all owed", - ".allow ed", - "(new User", - "Ġmerc iless", - ".Wait For", - "Ġday care", - "ĠCon veyor", - "Ġ Ù", - "ا Ù", - "า à¸", - "Ñ Ł", - "ÑŁ ÑŁ", - "Ġ à¸", - "Ġà ¸", - "à¹Ģ à¸", - "i á»", - "ãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢ", - "Ġ اØ", - "Ġا Ø", - "ॠĪ", - "Ġ ãĢĢ", - "ĠãĢ Ģ", - "Ñ Ĺ", - "i á»ĩ", - "iá» ĩ", - "ÑŁ ÑŁÑŁÑŁ", - "ÑŁÑŁ ÑŁÑŁ", - "ÑŁÑŁÑŁ ÑŁ", - "à¥ĩ à¤Ĥ", - "à¥ĩठĤ", - "Ñĸ д", - "ा र", - "ाठ°", - "ÙĨ د", - "Ñĸ в", - "Ġ ब", - "Ġठ¬", - "Ġ à¤ľ", - "Ġठľ", - "à ¥¤", - "ॠ¤", - "н Ñĸ", - "ठĹ", - "Ġ Ø¢", - "ĠØ ¢", - "Ġ न", - "Ġठ¨", - "Ñ Ķ", - "Ġ ÑĢа", - "ĠÑĢ а", - "Ġ à¤ħ", - "Ġठħ", - "Ñģ ÑĮ", - "Ġ व", - "Ġठµ", - "ÑĨ Ñĸ", - "Ġv á»", - "³ ت", - "Ġ द", - "Ġठ¦", - "n ÄĽ", - "Ġ ल", - "Ġठ²", - "Ġ ãĢĢĠãĢĢ", - "ĠãĢĢ ĠãĢĢ", - "ĠãĢĢĠ ãĢĢ", - "ॠĤ", - "ठ¦", - "à¸Ń à¸ĩ", - "ÙĪ ÙĨ", - "ठµ", - "a ÅŁ", - "๠Ĥ", - "ι κ", - "Ġ र", - "Ġठ°", - "Ġ ви", - "Ġв и", - "à¥į य", - "à¥įठ¯", - "ा न", - "ाठ¨", - "Ġ از", - "Ġا ز", - "ĠØ§Ø ²", - "ا Ùĩ", - "ا٠ĩ", - "Ľ i", - "Ġh á»", - "à¥ĭ à¤Ĥ", - "i ế", - "ĠÄij á»", - "ठ¯", - "Ï į", - "Ġc ủ", - "Ġ بر", - "Ġب ر", - "Ġ ÙħÛĮ", - "ĠÙħ ÛĮ", - "Ġ اÛĮ", - "Ġا ÛĮ", - "Ġ à¤Ĩ", - "ĠठĨ", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ि य", - "िठ¯", - "ÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁ", - "в и", - "ر د", - "н Ñĥ", - "ÙĬ ÙĨ", - "ι α", - "Ġ त", - "Ġठ¤", - "Ñĩ и", - "Ġ à¤ķर", - "Ġà¤ķ र", - "ا ز", - "Ø§Ø ²", - "a ÄŁ", - "Ġ à¤ī", - "Ġठī", - "ठ¬", - "ÏĦ α", - "ت ر", - "Ùĩ ا", - "ร ะ", - "j ÃŃ", - "Î ij", - "а ÑĤи", - "аÑĤ и", - "Ġ à¤Ĺ", - "ĠठĹ", - "Ġ ÑĤа", - "ĠÑĤ а", - "Ú Ĩ", - "ठľ", - "า à¸Ļ", - "าภĻ", - "Ġ à¤Ń", - "ĠठŃ", - "ि à¤ķ", - "िठķ", - "á v", - "Ġ Ú¯", - "Ï İ", - "า ย", - "าภ¢", - "Ġ à¤Ķ", - "ĠठĶ", - "ÅĻ ÃŃ", - "ا ÙĪ", - "ا٠Ī", - "Ġ Ñī", - "ĠÑ ī", - "Ġ à¤Ķर", - "Ġà¤Ķ र", - "ен нÑı", - "Ġ Ú©Ùĩ", - "ĠÚ© Ùĩ", - "ठ¡", - "ÏĦ ο", - "ε ι", - "Ġ à¤ĩ", - "Ġठĩ", - "à¥į त", - "à¥įठ¤", - "ठŁ", - "Û ±", - "Ġ ØĮ", - "ĠØ Į", - "Ïģ ο", - "η ÏĤ", - "ë ¬", - "Ñĸ н", - "i á»ģ", - "iá» ģ", - "i ên", - "iê n", - "Ġ вÑĸд", - "Ġв Ñĸд", - "ĠвÑĸ д", - "d ı", - "ÙĦ ÛĮ", - "Ġ ز", - "ĠØ ²", - "Ïģ α", - "Ġ ÛĮ", - "า à¸ĩ", - "าภĩ", - "Ġth á»", - "Ġ à¹Ģà¸", - "Ġà¹Ģ à¸", - "i á»ĩn", - "iá»ĩ n", - "ا ÙĬ", - "ا٠Ĭ", - "ан нÑı", - "ÑĢ е", - "Î Ł", - "å Ĵ", - "ا Ø´", - "Ø§Ø ´", - "ा ल", - "ाठ²", - "ëħ Ħ", - "Ġ य", - "Ġठ¯", - "Ġ را", - "Ġر ا", - "ठ¼", - "Ñĥ в", - "ÙĪ Ùħ", - "Ġ عÙĦ", - "Ġع ÙĦ", - "ί α", - "à¥Ī à¤Ĥ", - "à¥ģ à¤", - "า ม", - "าภ¡", - "Ġm á»Ļt", - "Ġ à¤ı", - "Ġठı", - "ãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢ", - "Ġ पर", - "Ġप र", - "Ġ اÙĨ", - "Ġا ÙĨ", - "Ġ اÛĮÙĨ", - "Ġا ÛĮÙĨ", - "ĠاÛĮ ÙĨ", - "Ġv Ỽi", - "Ġvá» Ľi", - "Î £", - "ठļ", - "Û °", - "i á»ĥ", - "iá» ĥ", - "า à¸ģ", - "าภģ", - "Î Ļ", - "ا ع", - "Ø§Ø ¹", - "Ñĸ й", - "à¹ģ ล", - "Ùĩ اÛĮ", - "Ùĩا ÛĮ", - "Ñĩ а", - ". :.:", - ".: .:", - ".:. :", - "ÏĦ η", - "Ġ Îij", - "ĠÎ ij", - "ر ÛĮ", - "Ġn gh", - "Ġng h", - "ν α", - "à¹ĥ à¸Ļ", - "ि त", - "िठ¤", - "Ġ και", - "Ġκ αι", - "Ġκα ι", - "ÏĦ ε", - "à¥į à¤Ł", - "à¥įठŁ", - "μ α", - "л Ñĥ", - "ý m", - "ÏĢ ο", - "à¥Ī ।", - "ï¼ ¼", - "ر ÙĬ", - "н иÑħ", - "ни Ñħ", - "Ïģ ι", - "Ù Ģ", - "ÑĢ о", - "Ġ à¤ļ", - "Ġठļ", - "ा त", - "ाठ¤", - "ا ÙĤ", - "ا٠Ĥ", - "Ġ श", - "Ġठ¶", - "ĠÄij á»Ļ", - "ĠÄijá» Ļ", - "é ho", - "iá»ģ u", - "ภ¨", - "Ñĸ лÑĮ", - "Ñĸл ÑĮ", - "uy á»", - "Û ²", - "Ġn Äĥ", - "Ïī ν", - "Ġ ÏĦοÏħ", - "ĠÏĦ οÏħ", - "ĠÏĦο Ïħ", - "к ий", - "ки й", - "í ĸ", - "Ġ Ñīо", - "ĠÑī о", - "à¥į व", - "à¥įठµ", - "Ġ اÙĦØ£", - "ĠاÙĦ Ø£", - "ا ئ", - "Ø§Ø ¦", - "t ı", - "Ġ ÏĦο", - "ĠÏĦ ο", - "¬ ¬", - "Ġ Ø·", - "ĠØ ·", - "Ùħ اÙĨ", - "Ùħا ÙĨ", - "Ġ Îł", - "ĠÎ ł", - "д и", - "ภ¶", - "ि à¤ı", - "िठı", - "ãģ£ ãģŁ", - "ãģ£ãģ Ł", - "ÛĮ Ùħ", - "ÃŃ nh", - "ÃŃn h", - "r av", - "ra v", - "ÄĽ t", - "Î ķ", - "Ġ Ñıк", - "ĠÑı к", - "ç Ĥ", - "à¸Ń à¸Ļ", - "ãģ¦ ãģĦ", - "ि ल", - "िठ²", - "Ñĸ ÑĤ", - "з а", - "á p", - "ठ§", - "Ġ êµ", - "Ġê µ", - "à¹ģ ละ", - "à¹ģล ะ", - "ÃŃ ch", - "ÃŃc h", - "Ġ Ø¢ÙĨ", - "ĠØ¢ ÙĨ", - "ت Ùĩ", - "Ġ Ùħع", - "ĠÙħ ع", - "н ий", - "ни й", - "Æ°á»Ľ c", - "Ġ اÙĦع", - "Ġا ÙĦع", - "ĠاÙĦ ع", - "ر ب", - "ा म", - "ाठ®", - "Ġ رÙĪ", - "Ġر ÙĪ", - "é «", - "ı y", - "Ġh á»į", - "Ġhá» į", - "ÑĤÑĮ ÑģÑı", - "Ġ Îļ", - "ĠÎ ļ", - "Ġ à¤ĩस", - "Ġà¤ĩ स", - "ï¼ ¿", - "Ġ ÚĨ", - "Ġ ÙĪاÙĦ", - "ĠÙĪ اÙĦ", - "ĠÙĪا ÙĦ", - "íķ Ļ", - "ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ", - "Ġ vý", - "Ġv ý", - "ि स", - "िठ¸", - "ữ ng", - "س ÛĮ", - "Ġ ìĥ", - "Ġì ĥ", - "ा à¤Ĥ", - "ाठĤ", - "ï½ ¤", - "à¹ĩ à¸Ļ", - "Ġ थ", - "Ġठ¥", - "l arak", - "la rak", - "lar ak", - "lara k", - "â y", - "t ÄĽ", - "ν ο", - "Ġ ÙħÙĪ", - "ĠÙħ ÙĪ", - "Ġng Æ°á»Ŀi", - "å ¦", - "ÙĬ د", - "il ir", - "ili r", - "ا ØŃ", - "Ø§Ø Ń", - "Ġ ãĢ", - "Ù ĭ", - "Ġ ÑĢоз", - "ĠÑĢ оз", - "Ġ й", - "ĠÐ ¹", - "Ġd ụ", - "à¹Ģ à¸Ľ", - "à¹ĢภĽ", - "ั à¸ĩ", - "л е", - "ा य", - "ाठ¯", - "ï¿ £", - "ÙĪ اÙĨ", - "ÙĪا ÙĨ", - "Ġth á»ĥ", - "Ġthá» ĥ", - "ã ĥ½", - "ãĥ ½", - "ü ÅŁ", - "ç Ł", - "Ġ ο", - "ĠÎ ¿", - "Ġ Σ", - "ĠÎ £", - "ÛĮ ت", - "ั à¸ģ", - "Î ¤", - "Ġ à¤ıà¤ķ", - "Ġà¤ı à¤ķ", - "Ġ ÙĩÙħ", - "ĠÙĩ Ùħ", - "ìĽ Ķ", - "Ġ Îľ", - "ĠÎ ľ", - "Ġ à¸Ħ", - "ĠภĦ", - "¯ ¸", - "ا رÛĮ", - "ار ÛĮ", - "ि न", - "िठ¨", - "Ġnh ững", - "Ġnh Æ°", - "и ÑĤи", - "иÑĤ и", - "ãĥ³ ãĥ", - "à¹Ģ ร", - "à¹Ģภ£", - "Ġ ÐĽ", - "ĠÐ Ľ", - "ÑĢ Ñĸ", - "á d", - "ü y", - "i ye", - "iy e", - "Ġ Îķ", - "ĠÎ ķ", - "Ġ ส", - "Ġภª", - "Ïĥ η", - "Ġ ë¬", - "Ġë ¬", - "ï »", - "ठ£", - "Î Ĺ", - "ठ¶", - "Ġ ÙħØŃ", - "ĠÙħ ØŃ", - "ÙĦ ÙĬ", - "Ġ με", - "Ġμ ε", - "Ġp ÅĻÃŃ", - "ĠpÅĻ ÃŃ", - "Î Ŀ", - "à¥į ष", - "à¥įठ·", - "t ir", - "ti r", - "ر اÙĨ", - "را ÙĨ", - "ĠÄij á»ĭ", - "ĠÄijá» ĭ", - "Ġ коÑĤ", - "Ġк оÑĤ", - "Ġко ÑĤ", - "к ÑĢа", - "λ ο", - "Ġ ÏĦη", - "ĠÏĦ η", - "Ñī е", - "ÏĦ ικ", - "ÏĦι κ", - "ั à¹ī", - "i ết", - "iế t", - "α ν", - "í Ķ", - "к иÑħ", - "ки Ñħ", - "Ġ поÑģ", - "Ġп оÑģ", - "Ġпо Ñģ", - "t ır", - "tı r", - "à¥į म", - "à¥įठ®", - "ر Ùģ", - "ÄĽ l", - "ठŃ", - "o vé", - "ov é", - "Ġl á»", - "à¹Ħ à¸Ķ", - "ãģª ãģĦ", - "ภ©", - "i á»ĩu", - "iá»ĩ u", - "Î ¾", - "Ġ عÙĦÙī", - "Ġع ÙĦÙī", - "ĠعÙĦ Ùī", - "д Ñĥ", - "Ġdụ ng", - "а ÑĢа", - "аÑĢ а", - "ा द", - "ाठ¦", - "o ž", - "ÙĦ Ùĩ", - "ÙĦ Ùħ", - "н оÑĹ", - "но ÑĹ", - "Û± Û", - "à¸Ĥ à¸Ńà¸ĩ", - "Î ¡", - "à¥Ģ à¤Ĥ", - "Ġ пÑĸд", - "Ġп Ñĸд", - "Ġ फ", - "Ġठ«", - "ภĺ", - "ε ÏĤ", - "ा स", - "ाठ¸", - "à¹ĥ ห", - "о ва", - "ов а", - "ت ÛĮ", - "à¸Ń ย", - "ภį", - "Ġn Äĥm", - "ĠnÄĥ m", - "ÏĦ ι", - "ÙĪ ÛĮ", - "Ġ мÑĸ", - "Ġм Ñĸ", - "Ġ اÙħ", - "Ġا Ùħ", - "ÏĢ ÏĮ", - "Ġ zá", - "Ġz á", - "ठĪ", - "Ġ à¤ĸ", - "Ġठĸ", - "Ġ nÄĽ", - "Ġn ÄĽ", - "c ÃŃ", - "ÙĨ Ú¯", - "Ñģ и", - "Î ¶", - "n á", - "Ŀ i", - "Å ©", - "Ø ¦", - "Ġ اÙĦس", - "Ġا ÙĦس", - "ĠاÙĦ س", - "á»ij c", - "Ạ½", - "ا ج", - "Ø§Ø ¬", - "Ùħ ا", - "êµ Ń", - "о Ñİ", - "د ر", - "à¹Ģ à¸ģ", - "à¹Ģภģ", - "ภł", - "à ¡ng", - "á ng", - "án g", - "íķ ©", - "Ġ ÏĦηÏĤ", - "ĠÏĦ ηÏĤ", - "ĠÏĦη ÏĤ", - "Ġ Ñĸн", - "ĠÑĸ н", - "о ÑĹ", - "à¥ĩ श", - "à¥ĩठ¶", - "ภĭ", - "à¥ĭ à¤Ĺ", - "л Ñĸ", - "Ġp ÅĻed", - "ĠpÅĻ ed", - "ĠpÅĻe d", - "Äį nÃŃ", - "Ġ ка", - "Ġк а", - "Ġ Τ", - "ĠÎ ¤", - "á»Ļ i", - "v ÃŃ", - "ÑĢ Ñı", - "ा à¤ľ", - "ाठľ", - "а Ñħ", - "ि र", - "िठ°", - "า ส", - "าภª", - "d ır", - "dı r", - "Ø ¢", - "Î ļ", - "Ġ ÎŃ", - "ĠÎ Ń", - "Ġt ại", - "iá»ĩ c", - "i ến", - "iế n", - "Ġ غ", - "ĠØ º", - "ا Ø®", - "Ø§Ø ®", - "Ġ اÙĦØŃ", - "Ġا ÙĦØŃ", - "ĠاÙĦ ØŃ", - "Ġ бÑĥ", - "Ġб Ñĥ", - "Ġv á»ģ", - "Ġvá» ģ", - "м Ñĸ", - "Ùħ ÙĦ", - "m Ä±ÅŁ", - "à¸Ľ ระ", - "à¸Ľà¸£ ะ", - "ο Ïį", - "ε ί", - "Ġर ह", - "н им", - "ни м", - "ع د", - "Ġ باÙĦ", - "Ġب اÙĦ", - "Ġبا ÙĦ", - "¤ ij", - "ç ł", - "Ġo lm", - "Ġol m", - "Ïİ Î½", - "Ġh á»įc", - "Ġhá»į c", - "ا ست", - "Ø§Ø ³Øª", - "اس ت", - "า ว", - "าภ§", - "ÙĪ ب", - "Ñĸ Ñı", - "Ġ ÙĩاÛĮ", - "ĠÙĩ اÛĮ", - "ĠÙĩا ÛĮ", - "ë§ Ī", - "ॠĮ", - "Ġ ÄĮ", - "ĠÄ Į", - "ठı", - "ا دÙĩ", - "اد Ùĩ", - "Ġ اÙĪ", - "Ġا ÙĪ", - "н Ñĭм", - "нÑĭ м", - "Ạ±", - "Ùħ ÙĨ", - "iá»ĩ t", - "l aÅŁ", - "la ÅŁ", - "Ñĸ з", - "ÙĪ س", - "Ġl Ãłm", - "ĠlÃł m", - "ĠÄij ến", - "ĠÄijế n", - "प न", - "Ġ ÛĮÚ©", - "ĠÛĮ Ú©", - "Ġ ÙĦÙĦ", - "ĠÙĦ ÙĦ", - "Ġ mÄĽ", - "Ġm ÄĽ", - "Ġ براÛĮ", - "Ġبر اÛĮ", - "ा ह", - "ाठ¹", - "Ġ Ùħر", - "ĠÙħ ر", - "e ç", - "à¸Ń ร", - "ε Ïģ", - "ั à¸Ķ", - "к он", - "ко н", - "n ou", - "no u", - "Ġ год", - "Ġг од", - "ู à¹ī", - "à¹Ģ ล", - "à¹Ģภ¥", - "Ú ĺ", - "ĠÄij á»ĭnh", - "ĠÄijá»ĭ nh", - "ĠÄij ó", - "а нов", - "ан ов", - "ано в", - "Ġ Ùģر", - "ĠÙģ ر", - "ا رد", - "ار د", - "Ñĸ ÑĹ", - "à¸Ħ ร", - "à¥į थ", - "à¥įठ¥", - "c ak", - "ca k", - "ÑĨ ÑĸÑĹ", - "ÑĨÑĸ ÑĹ", - "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", - "Ùĩ ر", - "ॠī", - "Ġg iá»", - "Ġgi á»", - "í Ĩ", - "âĢĮ ÙĩاÛĮ", - "âĢĮÙĩا ÛĮ", - "à¥ģ र", - "à¥ģठ°", - "Ġ à¸ģ", - "Ġภģ", - "Å Ī", - "æ ¨", - "ÎŁ Î", - "า à¸Ħ", - "าภĦ", - "кÑĢа ÑĹ", - "ả o", - "o ÄŁ", - "Ġ सम", - "Ġस म", - "Ġv iá»ĩc", - "Ġs ẽ", - "Ġ ná", - "Ġn á", - "ÙĬ Ùħ", - "£ p", - "ö y", - "ÙĪ ز", - "Ġ κα", - "Ġκ α", - "Ùħ د", - "n ÃŃm", - "nÃŃ m", - "o vá", - "ov á", - "ा व", - "ाठµ", - "ा ।", - "à¥į स", - "à¥įठ¸", - "ç ·", - "ặ c", - "Ġ à¸ŀ", - "Ġภŀ", - "ï½ Ģ", - "ô i", - "Ġ ợ", - "Ġá» Ł", - "ο ÏĤ", - "Ġtr ên", - "м Ñĥ", - "ÑģÑĮ к", - "ภŁ", - "o vat", - "ov at", - "ova t", - "Ġm á»", - "í ı", - "Ġ во", - "Ġв о", - "ε ν", - "à¥Ĥ र", - "Ú¯ اÙĩ", - "ĠÄij á»Ļng", - "ĠÄijá»Ļ ng", - "Ú© ÙĨ", - "Ñī и", - "Ġ пÑĢа", - "Ġп ÑĢа", - "ĠпÑĢ а", - "ü rk", - "ür k", - "ÙĪ ع", - "ấ p", - "n ý", - "Ġ quan", - "Ġqu an", - "Ġq uan", - "Ġqua n", - "Ñĸ Ñĩ", - "Ġ να", - "Ġν α", - "Ġन ह", - "Ġ Ú©ÙĨ", - "ĠÚ© ÙĨ", - "c ı", - "çĿ Ģ", - "б о", - "Ġ اس", - "Ġا س", - "ĠØ§Ø ³", - "è »", - "ا ÙĨÛĮ", - "اÙĨ ÛĮ", - "à¸ķ ร", - "ÏĦ ά", - "Ġ Ø£ÙĨ", - "ĠØ£ ÙĨ", - "éĤ £", - "Ġ ม", - "Ġภ¡", - "к ÑĤ", - "i ê", - "Ġhá» £p", - "ت Ùħ", - "Ġ بÙĨ", - "Ġب ÙĨ", - "h od", - "ho d", - "ι Ïĥ", - "ห à¸Ļ", - "Ġ ÑĹ", - "ĠÑ Ĺ", - "л ив", - "ли в", - "Ġ کرد", - "ĠÚ© رد", - "Ġکر د", - "Ġ ÙħØ´", - "ĠÙħ Ø´", - "ا Ø·", - "Ø§Ø ·", - "ب ÙĬ", - "Ġ ร", - "Ġภ£", - "د Ùħ", - "ÙĦ اÙħ", - "ÙĦا Ùħ", - "à¹Ī ว", - "Ġ ÙĨÙħ", - "ĠÙĨ Ùħ", - "Ġ æĹ", - "Ġæ Ĺ", - "é ħ", - "н оÑģÑĤ", - "но ÑģÑĤ", - "ноÑģ ÑĤ", - "i á»ĥm", - "iá»ĥ m", - "êµ IJ", - "a yı", - "ay ı", - "Ġ بÙĪد", - "Ġب ÙĪد", - "ĠبÙĪ د", - "Ú¯ ر", - "Ġh iá»ĩn", - "Ġhi á»ĩn", - "ç ³", - "ÑģÑĤ вен", - "ÑģÑĤв ен", - "ÑģÑĤве н", - "Ġà¤ķर न", - "Ġ ÏĦην", - "ĠÏĦ ην", - "ĠÏĦη ν", - "Ġ à¸Ń", - "ĠภŃ", - "Ġ Ùħت", - "ĠÙħ ت", - "ģ n", - "ج Ùħ", - "λ λ", - "Ġ ÑĢе", - "ĠÑĢ е", - "ิ à¸Ķ", - "Ġ اÙĦÙĤ", - "Ġا ÙĦÙĤ", - "ĠاÙĦ ÙĤ", - "α Ïģ", - "Ġ यह", - "Ġय ह", - "n ÃŃch", - "nÃŃ ch", - "ÑĶ ÑĤÑĮÑģÑı", - "Ġ à¸Ĺ", - "ĠภĹ", - "ÛĮ Ø´", - "ÅĻ e", - "Ġn ebo", - "Ġne bo", - "Ġneb o", - "Ġ Ñĩа", - "ĠÑĩ а", - "l ou", - "lo u", - "ÑģÑĤ во", - "ÑģÑĤв о", - "Ġ Ч", - "ĠÐ §", - "à¸Ħ ว", - "Ùĩ Ùħ", - "à¹Ģ à¸Ķ", - "à¹ĢภĶ", - "Ġ à¹ģ", - "Ġ à¹Ĥ", - "Û ³", - "Å© ng", - "Ġ nej", - "Ġn ej", - "Ġne j", - "ÛĮ Ú©", - "Ġs á»Ń", - "Ùģ ر", - "Î ł", - "Ġп ок", - "Ġпо к", - "ĠاÙĦ ÙĨ", - "Ġv Å¡", - "á º«", - "Ạ«", - "Ġnh Ãł", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ή ÏĤ", - "ο Ïģ", - "Ġ Ïĩ", - "ĠÏ ĩ", - "à¹Ģ à¸Ĺ", - "à¹ĢภĹ", - "Ñĥ лÑĮ", - "Ñĥл ÑĮ", - "ãħ ĩ", - "Ġ yıl", - "Ġy ıl", - "Ġyı l", - "ÑĢ од", - "ÑĢо д", - "ί ν", - "ìĹ Īëĭ¤", - "ìĹĪ ëĭ¤", - "ا ص", - "Ø§Ø µ", - "ĠÄij ầu", - "à¥ĩ à¤ķ", - "à¥ĩठķ", - "ÑĢ ом", - "ÑĢо м", - "ãģĵ ãģ¨", - "Ġ ار", - "Ġا ر", - "ĠØ§Ø ±", - "å¥ ¹", - "Ġ تØŃ", - "Ġت ØŃ", - "Å¡ tÄĽ", - "Å¡t ÄĽ", - "à¥į ल", - "à¥įठ²", - "à¥į à¤ķ", - "à¥įठķ", - "Ġ کار", - "ĠÚ© ار", - "u jÃŃ", - "uj ÃŃ", - "Ġ à¤īन", - "Ġà¤ī न", - "Ġ αÏĢÏĮ", - "Ġα ÏĢÏĮ", - "ĠαÏĢ ÏĮ", - "Ġm Ãł", - "ž ÃŃ", - "Ġ à¸Ī", - "ĠภĪ", - "a lı", - "al ı", - "ठ«", - "Ñĩ еÑģ", - "Ñĩе Ñģ", - "Ġ عÙĨ", - "Ġع ÙĨ", - "æķ Ļ", - "ï¾ Ĩ", - "ि à¤Ĥ", - "िठĤ", - "Ġs á»±", - "в оÑĢ", - "во ÑĢ", - "Ġth á»±c", - "ë į°", - "ëį °", - "ãģ¦ ãģĦãĤĭ", - "ãģ¦ãģĦ ãĤĭ", - "à¹Ī à¸ĩ", - "ت ب", - "Ġnh iá»ģu", - "ĥ n", - "ĠÄij á»ĵ", - "ĠÄijá» ĵ", - "Ġ ห", - "Ġภ«", - "Û µ", - "m ÄĽ", - "ạ t", - "Ġch ÃŃnh", - "ĠchÃŃ nh", - "ĠchÃŃn h", - "μ ÎŃ", - "an ı", - "Ġb á»ĭ", - "ằ ng", - "ÅĻ ed", - "ÅĻe d", - "é Ł", - "á nh", - "án h", - "ÙĢ ÙĢ", - "Ġ Ùħس", - "ĠÙħ س", - "á»ĭ ch", - "Ä ĥn", - "Äĥ n", - "o vánÃŃ", - "ov ánÃŃ", - "ová nÃŃ", - "ován ÃŃ", - "à¹Ī าà¸ĩ", - "à¹Īา à¸ĩ", - "Ġ à¸Ľ", - "ĠภĽ", - "Ġn Æ°á»Ľc", - "Ð ±Ð¾ÑĤ", - "б оÑĤ", - "бо ÑĤ", - "ı yor", - "ıy or", - "ĠØ® ÙĪد", - "ĠØ®ÙĪ د", - "Û ¹", - "Ġ Ùħد", - "ĠÙħ د", - "Ġ üz", - "Ġü z", - "ì ½", - "ÙĪ ÙĤ", - "ë¥ ´", - "л ек", - "ле к", - "Ġc ả", - "ол ог", - "оло г", - "à¹ī à¸Ńà¸ĩ", - "à¹īà¸Ń à¸ĩ", - "m iÅŁ", - "mi ÅŁ", - "à¹ī ว", - "Ä ©", - "Î ľ", - "à¸Ń à¸ģ", - "＿ ＿", - "ठĸ", - "Ġ Я", - "ĠÐ ¯", - "ë ¬´", - "ë¬ ´", - "اÛĮ ÛĮ", - "s ké", - "sk é", - "uy ên", - "e ÅŁ", - "á i", - "ú ng", - "ún g", - "Ãł o", - "Ñĸ Ñģ", - "ç ¶", - "Ġ à¤Ĩप", - "Ġà¤Ĩ प", - "ï º", - "Î Ľ", - "Ġ ê³µ", - "Ġê³ µ", - "Ġ ÐĨ", - "ĠÐ Ĩ", - "Ġà¤ħ पन", - "Ġà¤ħप न", - "ứ ng", - "ÏĮ ÏĤ", - "Ġngh iá»ĩ", - "Ġnghi á»ĩ", - "Ġ اÙĦب", - "Ġا ÙĦب", - "ĠاÙĦ ب", - "à¥ĭ न", - "Ġ à¤Ł", - "ĠठŁ", - "Ġ ìľł", - "Ġìľ ł", - "Ġc Å©ng", - "ĠcÅ© ng", - "Ġà¤ī स", - "Ġ ड", - "Ġठ¡", - "ĠØ´ دÙĩ", - "Ġشد Ùĩ", - "ี à¹ī", - "Û ´", - "ặ t", - "æĸ ¯", - "Ġ ëį", - "Ġë į", - "Ġп л", - "б и", - "ê³ Ħ", - "ο ν", - "Ġç ık", - "Ġçı k", - "Ġbu lun", - "Ġbul un", - "س Ùħ", - "a ç", - "ا ÙĨÙĩ", - "اÙĨ Ùĩ", - "ÛĮ ز", - "l eÅŁ", - "le ÅŁ", - "ắ c", - "ا Ú©", - "Ġस à¤ķ", - "Ġ оÑĢг", - "Ġо ÑĢг", - "ĠоÑĢ г", - "Ġ à¸Ļ", - "ĠภĻ", - "ा थ", - "ाठ¥", - "Ġ ÙħÙĤ", - "ĠÙħ ÙĤ", - "ĠÎĶ E", - "Ñİ ÑĤÑĮ", - "ÑİÑĤ ÑĮ", - "á»Ļ c", - "Ġ η", - "ĠÎ ·", - "s ob", - "so b", - "Ġth eo", - "Ġthe o", - "å ŀ", - "Ġ اÙĦØ´", - "ĠاÙĦ Ø´", - "à¹Ģ à¸ŀ", - "à¹Ģภŀ", - "ÎŃ ÏĤ", - "à¹Ģ à¸Ĥ", - "à¹ĢภĤ", - "å Ļ", - "ि श", - "िठ¶", - "Ġ باز", - "Ġب از", - "Ġبا ز", - "ÑĢ об", - "ÑĢо б", - "Ġγ ια", - "μ ε", - "Ġ باش", - "Ġب اش", - "Ġبا Ø´", - "ा à¤ĩ", - "ाठĩ", - "Ġqu y", - "Ġq uy", - "λ ε", - "ا Ùĥ", - "ا٠ĥ", - "Ġ ÑĢок", - "ĠÑĢ ок", - "Ġ Türk", - "ĠT ürk", - "ĠTür k", - "Ġ Ð¥", - "ĠÐ ¥", - "ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ ÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁÑŁ", - "æ ©", - "Ġph ải", - "à¸Ħ วาม", - "à¸Ħว าม", - ": ::", - ":: :", - "l ÃŃ", - "Ġj sou", - "Ġjs ou", - "ÛĮ ÙĦ", - "ál nÃŃ", - "áln ÃŃ", - "Ķ Ķ", - "í ĸī", - "íĸ ī", - "æĥ ³", - "l á", - "Ġ ÏĥÏħ", - "ĠÏĥ Ïħ", - "Ñĭ ва", - "Ñĭв а", - "Ġnh ất", - "à¸Ń ม", - "Û ¸", - "e cek", - "ec ek", - "ece k", - "Ñĸ ÑĢ", - "ÙĪ Ø´", - "λ α", - "Ġ ÎĴ", - "ĠÎ Ĵ", - "о ÑĢа", - "оÑĢ а", - "Ùģ ت", - "e dir", - "ed ir", - "edi r", - "Ñĥ Ñħ", - "ä¸ ĸ", - "ĠУ кÑĢаÑĹ", - "ĠУкÑĢа ÑĹ", - "Ġ íĶ", - "Ġí Ķ", - "ά ν", - "Ġ شر", - "ĠØ´ ر", - "ĠاÙĦ ج", - "е ÑĢед", - "еÑĢ ед", - "еÑĢе д", - "ìĺ ģ", - "Ġh Ãłnh", - "ï¿£ ï¿£", - "м е", - "ÑİÑĤ ÑģÑı", - "ĠØ¥ ÙĦÙī", - "ĠØ¥ÙĦ Ùī", - "ìĹ ħ", - "Ġ تر", - "Ġت ر", - "к ом", - "ко м", - "Ġ شد", - "ĠØ´ د", - "Ġ اÙĦÙĥ", - "Ġا ÙĦÙĥ", - "ĠاÙĦ Ùĥ", - "Ġ ÏĥÏĦο", - "ĠÏĥ ÏĦο", - "à¥į द", - "à¥įठ¦", - "ëł ¤", - "Ñĥ ваннÑı", - "Ñĥв аннÑı", - "Ġth ì", - "ê ´Ģ", - "ê´ Ģ", - "κ ε", - "س ب", - "íĥ Ģ", - "Ġ ï¼ı", - "Ġï¼ ı", - "Ġ à¹ģละ", - "Ġà¹ģ ละ", - "Ġà¹ģล ะ", - "Ġ ÏĮ", - "ĠÏ Į", - "н иÑĨ", - "ни ÑĨ", - "Ġ ÐĿа", - "ĠÐĿ а", - "Ñı в", - "l ü", - "ι ο", - "ÙĨ دÙĩ", - "ÙĨد Ùĩ", - "ÙĦ Ùĥ", - "Ġng Ãły", - "Ġnh ân", - "Ġ ^{", - "Ġ^ {", - "ॠĥ", - "Ġg erek", - "Ġge rek", - "Ġger ek", - "Ġgere k", - "ا رÙĩ", - "ار Ùĩ", - "Ġc Æ¡", - "Ġ à¸ķ", - "Ġภķ", - "æ Ĥ", - "çĶ °", - "à¥Īà¤Ĥ ।", - "ั ว", - "v ÄĽ", - "ö z", - "и ли", - "ил и", - "Ġph áp", - "Ġphá p", - "ê¸ Ī", - "Ġ ÎŁ", - "ĠÎ Ł", - "Ġp ÅĻi", - "ĠpÅĻ i", - "Ġ ìĸ´", - "Ġìĸ ´", - "Ġд ол", - "Ġдо л", - "ÙĪ رد", - "ÙĪر د", - "à¹Ģ ม", - "à¹Ģภ¡", - "Ïĥ ε", - "า à¸Ĺ", - "าภĹ", - "o Ãłi", - "ร ม", - "Û ¶", - "Ġ à¸ļ", - "Ġภļ", - "i yet", - "iy et", - "iye t", - "ÏĦ αι", - "ÏĦα ι", - "ìĦ ł", - "Ġ εÏĢ", - "Ġε ÏĢ", - "ि व", - "िठµ", - "ê¹ Į", - "г а", - "ĠÑģ лÑĥ", - "ĠÑģл Ñĥ", - "Ġh ình", - "Ġ داÙĨ", - "Ġد اÙĨ", - "Ġà¤Ĺ य", - "ÙĬ ا", - "è ij", - "à¤Ĥ त", - "Ġ ساÙĦ", - "Ġس اÙĦ", - "ëł Ī", - "l erin", - "le rin", - "ler in", - "leri n", - "à¥ĩ त", - "à¥ĩठ¤", - ".: .:.:.:", - ".:.: .:.:", - ".:. :.:.:", - ".:.:.: .:", - ".:.:. :.:", - ".:.:.:. :", - "Ġ ëħ", - "Ġë ħ", - "Ġ اÙĦØ¥", - "ĠاÙĦ Ø¥", - "ả ng", - "ản g", - "è Ħ", - "ο λ", - "п ов", - "по в", - "Ġ θ", - "ĠÎ ¸", - "Û ·", - "Ġn ó", - "Ġd Ã¼ÅŁ", - "Ġdü ÅŁ", - "Ġt iế", - "Ġti ế", - "ÙĪ ج", - "Ġj sem", - "Ġjs em", - "Ạ¡ng", - "ạ ng", - "ạn g", - "ãģĤ ãĤĭ", - "à¸Ń à¸ļ", - "ÙĪ ÙĬ", - "à¤ķ र", - "Ġ де", - "Ġд е", - "¯ ¼", - "Ġ но", - "Ġн о", - "ÑĨ Ñĸй", - "ÑĨÑĸ й", - "Ïĥ ÏĦ", - "к ие", - "ки е", - "Ïĥ ει", - "Ïĥε ι", - "ìķ Ī", - "Ġh Æ¡n", - "Ġà¤ķ ह", - "ا ض", - "Ø§Ø ¶", - "ì ¸", - "ãĥ Ł", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ãĤĪ ãģĨ", - "ा ,", - "е ÑĢи", - "еÑĢ и", - "ë© °", - "í ĶĦ", - "íĶ Ħ", - "Ġп оÑģÑĤ", - "Ġпо ÑģÑĤ", - "ĠпоÑģ ÑĤ", - "Ø® ر", - "à¥ĭ त", - "â u", - "к ой", - "ко й", - "d aki", - "da ki", - "í ħ", - ": :::::::::::::::", - ":: ::::::::::::::", - ":::: ::::::::::::", - ":::::: ::::::::::", - ":::::::: ::::::::", - "::: :::::::::::::", - "::::: :::::::::::", - "::::::: :::::::::", - "::::::::: :::::::", - ":::::::::: ::::::", - "::::::::::: :::::", - ":::::::::::: ::::", - "::::::::::::: :::", - ":::::::::::::: ::", - "::::::::::::::: :", - "Ġ öz", - "Ġö z", - "ÑĢ аж", - "ÑĢаР¶", - "ÑĢа ж", - "nÃŃ ho", - "ห ล", - "Ġ ÏĥÏĦη", - "ĠÏĥ ÏĦη", - "ĠÄij á»ģ", - "ĠÄijá» ģ", - "Ġk á»", - "i á»ĥn", - "iá» ĥn", - "iá»ĥ n", - "ÅĻ i", - "Ġkter é", - "¢ ħ", - "ü ç", - "ÙĬ Ùģ", - "Ġ lý", - "Ġl ý", - "Ġth á»Ŀi", - "Ġthá» Ŀi", - "Ġthá»Ŀ i", - "Ġ ìĨĮ", - "ĠìĨ Į", - "н ÑĮ", - "Ð Ĩ", - "ÑĤ ÑĢ", - "à¸ĩ าà¸Ļ", - "к оÑĹ", - "ко ÑĹ", - "μ ο", - "Ġs ür", - "Ġsü r", - "uy á»ģn", - "uyá» ģn", - "Ġ Ùħا", - "ĠÙħ ا", - "à¤Ĥ à¤Ĺ", - "ĠÄij á»ĵng", - "ĠÄijá»ĵ ng", - "ò n", - "à¥ģ ल", - "à¥ģठ²", - "à¥į प", - "à¥įठª", - "λ η", - "Ùħ ر", - "п ÑĢи", - "пÑĢ и", - "i yle", - "iy le", - "ा प", - "ाठª", - "Ġà¤ħ न", - "Ġ ÑĶ", - "ĠÑ Ķ", - "Ġy ön", - "Ġyö n", - "ÙĦ Ùģ", - "a dır", - "ad ır", - "adı r", - "á ½", - "Ġ ê³ł", - "Ġê³ ł", - "Ø® ص", - "im iz", - "imi z", - "åľ ĭ", - "Ġ над", - "Ġн ад", - "Ġна д", - "Ġ ÅĻ", - "ĠÅ Ļ", - "н оÑģÑĤÑĸ", - "но ÑģÑĤÑĸ", - "ноÑģÑĤ Ñĸ", - "ноÑģ ÑĤÑĸ", - "Ġ اÙģ", - "Ġا Ùģ", - "а нÑĸ", - "ан Ñĸ", - "à¥ĩ à¤Ł", - "à¥ĩठŁ", - "Ġ ë§IJ", - "Ġë§ IJ", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Ġ ìĬ¤", - "ĠìĬ ¤", - "ิ à¸ķ", - "å Ħ", - "ÛĮ Ùĩ", - "о ÑĪ", - "ž it", - "ži t", - "ìĭ ¤", - "à¥Ģ न", - "Ġ î", - "æ¥ Ń", - "à¥ĩ न", - "à¥ĩठ¨", - "Ġ Ø°", - "ĠØ °", - "Ġl oại", - "Ġlo ại", - "à¹Ħ à¸Ľ", - "Ñĸ к", - "Ġ кÑĢа", - "Ġк ÑĢа", - "à¥ĭ र", - "ุ à¸Ķ", - "ĠاÙĦ ر", - "ĠÑģ об", - "ĠÑģо б", - "า à¸Ĭ", - "าภĬ", - "Ġसà¤ķ त", - "Ġ ÎĿ", - "ĠÎ Ŀ", - "ا ÙħÙĩ", - "اÙħ Ùĩ", - "à¹ī าà¸Ļ", - "à¹īา à¸Ļ", - "Ġtr ình", - "Ġtrì nh", - "Ġ اÙĦÙģ", - "Ġا ÙĦÙģ", - "ĠاÙĦ Ùģ", - "Ġ اÙĦد", - "ĠاÙĦ د", - "u nun", - "un un", - "unu n", - "о ÑĤов", - "оÑĤ ов", - "оÑĤо в", - "Æ° ợ", - "Æ°á» Ł", - "ĠÑģ во", - "ĠÑģв о", - "ί αÏĤ", - "ία ÏĤ", - "ấ n", - "ог да", - "à¸Ĺ ย", - "Ġb yl", - "Ġby l", - "ØŃ Ø¯", - "à¸ģ ล", - "ب Ùĩ", - "Ġ vÄĽ", - "Ġv ÄĽ", - "è¢ «", - "Ġ Ø¢Ùħ", - "ĠØ¢ Ùħ", - "ĠÄij iá»ģu", - "å ¨", - "Ġk dy", - "Ġkd y", - "Ġ بÙĪ", - "Ġب ÙĪ", - "ẫ n", - "ìľ ł", - "ा à¤ķ", - "ाठķ", - "k ů", - "Ġtr Æ°á»Ŀng", - "ic ké", - "ick é", - "н иÑı", - "ни Ñı", - "Ġ ÏĢοÏħ", - "ĠÏĢ οÏħ", - "ĠÏĢο Ïħ", - "Æ°á»Ł ng", - "н омÑĥ", - "но мÑĥ", - "ном Ñĥ", - "à¹Ī à¸Ļ", - "ู à¹Ī", - "Ġk ết", - "Ġkế t", - "Ġ ï¼¼", - "Ġï¼ ¼", - "Ġ ìĭł", - "Ġìĭ ł", - "i ç", - "Ġn Äĥng", - "ĠnÄĥ ng", - "Äį ÃŃ", - "ÑĤ Ñı", - "ÑĢ еб", - "ÑĢе б", - "Ùĭ ا", - "Ú¯ ÛĮ", - "ãĥ İ", - "Ġkar ÅŁ", - "в Ñĸ", - "Ġph ần", - "à¸Ī ะ", - "ắ t", - "ر Ø©", - "ิ à¸ĩ", - "ิ à¹Ī", - "ा à¤Ī", - "ाठĪ", - "า à¸ŀ", - "าภŀ", - "ÙĨ ÛĮ", - "ìĹ °", - "b ÄĽ", - "Ġ اÙĦص", - "ĠاÙĦ ص", - "í Ĺ", - "Ġ سر", - "Ġس ر", - "l ara", - "la ra", - "lar a", - "ëĭ ¨", - "Ġ ÙĤر", - "ĠÙĤ ر", - "è İ", - "ب د", - "Ġй ого", - "à¥į ह", - "à¥įठ¹", - "Ġc ách", - "Ġcá ch", - "Ġcác h", - "íķĺ ê³ł", - "Ġ ÏĢÏģο", - "ĠÏĢ Ïģο", - "Ġ تع", - "Ġت ع", - "Ĵ Ī", - "Ġ вод", - "Ġв од", - "Ġво д", - "ç¥ ŀ", - "к им", - "ки м", - "Ġd á»±", - "à¹Ģ ห", - "à¹Ģภ«", - "а на", - "ан а", - "Ġ ï½", - "Ġï ½", - "Ġb aÄŁ", - "Ġba ÄŁ", - "Ġप ह", - "Ġ cao", - "Ġc ao", - "Ġca o", - "Ïģ ÏĮ", - "ÙĨ ج", - "ा à¤ı", - "ाठı", - "Ġ å¹´", - "Ġå¹ ´", - "Ġngh iá»ĩp", - "Ġnghiá»ĩ p", - "Û² Û°", - "к аÑı", - "ка Ñı", - "Ïģ ί", - "Ġ бол", - "Ġб ол", - "Ġбо л", - "Ġgi á", - "Ġ зд", - "Ġз д", - "à¥ĩ ल", - "à¥ĩठ²", - "Ġc ấp", - "à¹Ģ ส", - "à¹Ģภª", - "Ïģ γ", - "Ġ ìĤ", - "Ġì Ĥ", - "d ÄĽ", - "à¥ģ न", - "à¥ģठ¨", - "ì Ī", - "ı lan", - "ıl an", - "л аÑģ", - "ла Ñģ", - "Ġ ว", - "Ġภ§", - "Ġ Ïĥε", - "ĠÏĥ ε", - "Ġ Ø«", - "ĠØ «", - "Ġ Ц", - "ĠÐ ¦", - "çĤ º", - "Ġb üy", - "Ġbü y", - "е ÑĨ", - "å¤ ª", - "Ġब न", - "о гÑĢа", - "ог ÑĢа", - "Ġп ÑĢоÑĤ", - "ĠпÑĢ оÑĤ", - "ĠпÑĢо ÑĤ", - "Ġl ượng", - "Ġd ön", - "Ġdö n", - "ร à¸ĩ", - "а ло", - "ал о", - "Ġ جÙħ", - "Ġج Ùħ", - "à¥Ī ,", - "Ġ 미", - "Ġë ¯¸", - "Ġ ê¹", - "Ġê ¹", - "ÙĪ ت", - "à¥Ģ य", - "à¸Ī าà¸ģ", - "Ġch ất", - "Î ©", - "Ġkh ác", - "Ġkhá c", - "Ġth áng", - "j Å¡ÃŃ", - "ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", - "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", - "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł", - "á»ij t", - "ห ร", - "Ñĸ л", - "åħ ī", - "å Ĥ", - "ÙĦ Ø©", - "Ġ ê±°", - "Ġê± °", - "о воÑĢ", - "ов оÑĢ", - "ово ÑĢ", - "iá»ĥ u", - "Ġ меÑĤ", - "Ġм еÑĤ", - "а ÑĶ", - "Ġ ÑĩаÑģ", - "ĠÑĩ аÑģ", - "ĠÑĩа Ñģ", - "Ïģ ε", - "ì¹ ´", - "âĢĮ Ø´", - "ë¬ ¼", - "ú c", - "âĢĮ Ùĩا", - "i á»ģn", - "iá» ģn", - "iá»ģ n", - "st av", - "sta v", - "í ŀ", - "ĠÙĨ ظ", - "Ĩ Ĵ", - "Ġ ÏĦα", - "ĠÏĦ α", - "Ġ заб", - "Ġз аб", - "Ġза б", - "Ùĥ Ø©", - "Ġг ÑĢÑĥ", - "ĠгÑĢ Ñĥ", - "в о", - "Ġ Ùħج", - "ĠÙħ ج", - "Ġ sah", - "Ġs ah", - "Ġsa h", - "ب ÙĦ", - "ع Ø©", - "Ñĥ ÑĪ", - "ĠÑĤ ем", - "ĠÑĤе м", - "í ĭ", - "e ck", - "ec k", - "Ïī ÏĤ", - "ÙĬ ت", - "ìĹ Ī", - "ç ĭ", - "Ø° ا", - "ì łĢ", - "ìł Ģ", - "Ġн аÑģ", - "Ġна Ñģ", - "Ġ поÑĩ", - "Ġп оÑĩ", - "Ġпо Ñĩ", - "æł ¡", - "Ï Ī", - "Ñģ кой", - "Ñģк ой", - "Ñģко й", - "ü c", - "ÙĤ ÙĦ", - "Ġп оз", - "Ġпо з", - "Ġ оÑģоб", - "ĠоÑģ об", - "า ล", - "าภ¥", - "н Ñĭми", - "нÑĭ ми", - "нÑĭм и", - "о лод", - "ол од", - "оло д", - "è ¼", - "Ġ دÛĮ", - "Ġد ÛĮ", - "Ġ ÑĥÑģÑĤ", - "ĠÑĥ ÑģÑĤ", - "ĠÑĥÑģ ÑĤ", - "Ġ 무", - "Ġë ¬´", - "Ġë¬ ´", - "ÙĬ س", - "ë° ©", - "à¥į à¤ļ", - "à¥įठļ", - "и ла", - "ил а", - "Ġn ên", - "н ие", - "ни е", - "ι ν", - "lar ını", - "ların ı", - "à¹Ģ à¸Ļ", - "à¹ĢภĻ", - "ÙĨ ت", - "a ģı", - "aÄŁ ı", - "ım ız", - "ımı z", - "ĠاÙĦ Ø®", - "à¹Ģ ว", - "à¹Ģภ§", - "à¥į न", - "à¥įठ¨", - "Ġ Ïħ", - "ĠÏ ħ", - "Ġ íĨ", - "Ġí Ĩ", - "Ạ»", - "ิ à¹Ĥ", - "α ÏĤ", - "м еÑĤ", - "ме ÑĤ", - "Ġ zp", - "Ġz p", - "Ġje ho", - "ี ยà¸Ļ", - "ีย à¸Ļ", - "ÑĦ оÑĢ", - "ın ız", - "ını z", - "k lad", - "kl ad", - "kla d", - "íĮ Į", - "uy á»ĩ", - "uyá» ĩ", - "ι ά", - "Ġ ãĢģ", - "ĠãĢ ģ", - "Ø´ ر", - "æ© Ł", - "Ġ تا", - "Ġت ا", - "Ġ зна", - "Ġз на", - "Ġзн а", - "س تاÙĨ", - "ست اÙĨ", - "à¥ĩ र", - "à¥ĩठ°", - "ë§ ¤", - "ç ĥ", - "Ġ же", - "Ġж е", - "า à¸Ķ", - "าภĶ", - "Ġ ض", - "ĠØ ¶", - "é Ń", - "Ġн аз", - "Ġна з", - "Ġ ÛĮا", - "ĠÛĮ ا", - "e né", - "en é", - "ั ย", - "íĸ Īëĭ¤", - "íĸĪ ëĭ¤", - "Ġ بد", - "Ġب د", - "à¥ģ à¤ķ", - "à¥ģठķ", - "ÑĤ ов", - "ÑĤо в", - "ì° ¨", - "Ùĩ د", - "à¸Ķ ย", - "Ġho ặc", - "Ġ ÐŁÑĢи", - "ĠÐŁ ÑĢи", - "ĠÐŁÑĢ и", - "ÙĨ ا", - "çİ ĭ", - "Ñĥ ваÑĤи", - "Ñĥв аÑĤи", - "Ñĥва ÑĤи", - "à¸ļ ร", - "Ġà¤ķ रत", - "Ġà¤ķर त", - "Ïĥ ηÏĤ", - "Ïĥη ÏĤ", - "Ø ¤", - "éķ ·", - "åħ ĭ", - "Ġ دار", - "Ġد ار", - "ั à¹Ī", - "Æ¡ i", - "า à¸Ī", - "าภĪ", - "ý mi", - "ým i", - "ấ u", - "Ġد ست", - "Ġدس ت", - "k em", - "ke m", - "Ġ оÑģнов", - "ĠоÑģ нов", - "ëª ¨", - "Ïģ ά", - "æ ħ", - "Ġ اب", - "Ġا ب", - "ĠØ§Ø ¨", - "å£ «", - "Ħ ĸ", - "Î Ķ", - "ÙĬ Ùĥ", - "í İ", - "Ġy üz", - "a dı", - "ad ı", - "า à¸ķ", - "าภķ", - "ä» Ģ", - "ìĿ´ ëĭ¤", - "Ġ zv", - "Ġz v", - "Ġ tÄĽ", - "Ġt ÄĽ", - "Ġ íĸ", - "Ġí ĸ", - "ठ¥", - "Ġ लà¤Ĺ", - "Ġल à¤Ĺ", - "ìĺ Ģ", - "Ġ ан", - "Ġа н", - "ç Ĺ", - "ìĹ Ń", - "н ÑĸÑģÑĤÑĮ", - "нÑĸ ÑģÑĤÑĮ", - "нÑĸÑģÑĤ ÑĮ", - "Å ŀ", - "Ġph át", - "Ġphá t", - "ÙĤ Ø©", - "Ġth ế", - "Ġ ï¾", - "Ġï ¾", - "ì² ľ", - "Ġ ìĦł", - "ĠìĦ ł", - "à¹ĥ à¸Ĭ", - "i êu", - "iê u", - "ÄŁ ini", - "ÄŁi ni", - "ÄŁin i", - "ÙĤ د", - "Ġkter ý", - "Ñģ кий", - "Ñģк ий", - "Ñģки й", - "à¥į ड", - "à¥įठ¡", - "t adır", - "ta dır", - "Ġ Ñģм", - "ĠÑģ м", - "ÙĪ Ùģ", - "ا رÙĬ", - "ار ÙĬ", - "å¾ ·", - "ิ ม", - "Ø® ت", - "å¾ Ī", - "Ġ гоÑĢ", - "Ġг оÑĢ", - "ï¼Į æĪij", - "Ġ ìĺģ", - "Ġìĺ ģ", - "Ġ ëıĻ", - "Ġëı Ļ", - "Ñģ а", - "à¹Ģ à¸Ħ", - "à¹ĢภĦ", - "ë ¯¼", - "ë¯ ¼", - "ึ à¹Ī", - "Ġl iên", - "Ġli ên", - "Ġ Ùĩا", - "ĠÙĩ ا", - "ler ini", - "leri ni", - "lerin i", - "Ġ ÑĨе", - "ĠÑĨ е", - "ا ÙĦÛĮ", - "اÙĦ ÛĮ", - "Ġ मह", - "Ġम ह", - "Ġv ụ", - "Ġvá» ¥", - "Ġxu ất", - "ิ à¸ģ", - "ĠпÑĢо ÑĨ", - "Ġ αν", - "Ġα ν", - "ÑĢ им", - "ÑĢи м", - "Ġc ần", - "Ġ иÑħ", - "Ġи Ñħ", - "н оÑİ", - "но Ñİ", - "Ġt ÃŃnh", - "ĠtÃŃ nh", - "ĠtÃŃn h", - "Ġb á»Ļ", - "Ñĸ м", - "Ġnh áºŃn", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ÙĬ Ùĩ", - "äº ļ", - "Ġоб ла", - "Ġобл а", - "Ġ à¤ĺ", - "Ġठĺ", - "n ých", - "ný ch", - "æĿ ij", - "ÙĦ س", - "Ġне об", - "ا بة", - "اب Ø©", - "v á", - "ο Ïħν", - "οÏħ ν", - "ÑĢ еÑĤ", - "ÑĢе ÑĤ", - "a sında", - "as ında", - "ası nda", - "Ġ yar", - "Ġy ar", - "Ġya r", - "ĠÄij iá»ĥm", - "ĠÄiji á»ĥm", - "н Ñİ", - "ा à¤Ĺ", - "ाठĹ", - "Ġ Ú©Ø´", - "ĠÚ© Ø´", - "Ñĥ з", - "Ġ à¸Ķ", - "ĠภĶ", - "ả m", - "к ами", - "ка ми", - "кам и", - "Ġ ÎĻ", - "ĠÎ Ļ", - "à¹Ģ à¸ķ", - "à¹Ģภķ", - "Ġl Ỽ", - "Ġlá» Ľ", - "ÙĤ ÛĮ", - "k ou", - "ko u", - "ÙĦ ب", - "и ва", - "ив а", - "æ ĵ", - "Ạ¹", - "κ α", - "ë² ķ", - "èĤ ²", - "á»ij n", - "Ġbel ir", - "íĨ ł", - "ÏĦ ή", - "Ñĭ ÑĪ", - "ãĤ ĥ", - "Ġ або", - "Ġа бо", - "Ġаб о", - "s ký", - "sk ý", - "à¥Ī स", - "Ġп ÑĢоÑģÑĤ", - "ĠпÑĢ оÑģÑĤ", - "ĠпÑĢо ÑģÑĤ", - "ĠпÑĢоÑģ ÑĤ", - "ekt edir", - "ekte dir", - "a ž", - "à¹Ī à¸Ń", - "Ġ оÑģÑĤ", - "Ġо ÑģÑĤ", - "ĠоÑģ ÑĤ", - "Ġb ảo", - "Ġ 大", - "Ġå¤ §", - "Ñĭ м", - "Ġm ů", - "Æ°á»Ľ ng", - "åı Ĺ", - "ÙĪ Ùĩ", - "Ġ Ñĥп", - "ĠÑĥ п", - "Ùĥ ÙĨ", - "Ġ ÏĦÏīν", - "ĠÏĦ Ïīν", - "ëħ ¸", - "Ġ à¸Ĭ", - "ĠภĬ", - "Ġ ÑĤого", - "ĠÑĤ ого", - "ĠÑĤо го", - "Ġ Ш", - "ĠÐ ¨", - "ìĿ´ íĬ¸", - "à¹Ģ à¸Ń", - "à¹ĢภŃ", - "и нÑĥ", - "ин Ñĥ", - "ĺ ħ", - "uy á»ĥn", - "uyá» ĥn", - "í ĴĪ", - "íĴ Ī", - "ạ nh", - "ạn h", - "Ġ ãĥ½", - "Ġãĥ ½", - "ÑĤ обÑĭ", - "ÑĤо бÑĭ", - "Ġt ạo", - "å· Ŀ", - "ĠÄij á»iji", - "Ġ ëıĦ", - "Ġëı Ħ", - "ä¹ ħ", - "Ġ تÙħ", - "Ġت Ùħ", - "а ÑĢи", - "аÑĢ и", - "st vÃŃ", - "Ġc ùng", - "íŀ Ī", - "Ġt arih", - "Ġtar ih", - "ì ¤ij", - "ì¤ ij", - "í Ĥ", - "Ġ دÙĪ", - "Ġد ÙĪ", - "ì ¡", - "а лÑĸ", - "ал Ñĸ", - "ภIJ", - "Ġc òn", - "и ÑĤÑĮÑģÑı", - "иÑĤÑĮ ÑģÑı", - "Ġव ह", - "ÅĻ eb", - "ÅĻe b", - "éĽ »", - "Ġ ми", - "Ġм и", - "o vÄĽ", - "ov ÄĽ", - "Ġd ân", - "ÑĨ ÑĸÑı", - "ÑĨÑĸ Ñı", - "ÛĮ ست", - "ÛĮس ت", - "åŃ ¸", - "Ġ ür", - "Ġü r", - "ص ÙĦ", - "ÑĢ иÑĤ", - "ÑĢи ÑĤ", - "า ห", - "าภ«", - "ãģ¦ ãģĦãģŁ", - "ãģ¦ãģĦ ãģŁ", - "θ η", - "ç ĸ", - "Ø Ł", - "i ÅŁtir", - "iÅŁ tir", - "iÅŁti r", - "ĠУкÑĢаÑĹ Ð½Ð¸", - "ĠУкÑĢаÑĹн и", - "ë° ĺ", - "à¥ĩ à¤ĸ", - "à¥ĩठĸ", - "Ġv á»ĭ", - "Ġvá» ĭ", - "Î ¥", - "Ġ ãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢ ĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢ ĠãĢĢ", - "ĠãĢĢĠ ãĢĢĠãĢĢ", - "Ġb ằng", - "Ġt á»ķ", - "Ġtá» ķ", - "о ли", - "ол и", - "๠Ĩ", - "e zi", - "ez i", - "Ġ ни", - "Ġн и", - "Ġ ÎĽ", - "ĠÎ Ľ", - "Ġr ất", - "μ ÏĢ", - "ж д", - "ा रत", - "ार त", - "Ġu ž", - "à¥ĩ स", - "à¥ĩठ¸", - "ا ÙĨد", - "اÙĨ د", - "Ġb ý", - "à¥ĭ ल", - "d ÄĽl", - "dÄĽ l", - "ìķ ĺ", - "Ġ جد", - "Ġج د", - "å ³", - "ื à¹ī", - "Ġb ản", - "ạ ch", - "ạc h", - "ĠÅŁ ey", - "Ġ Ùĩر", - "ĠÙĩ ر", - "Ġ jen", - "Ġj en", - "Ġje n", - "Ġв Ñĸн", - "ĠвÑĸ н", - "es inde", - "esi nde", - "esin de", - "Ġ हम", - "Ġह म", - "çł Ķ", - "à¸ļ à¸ļ", - "Ġch ức", - "Ġchứ c", - "ึ à¸ĩ", - "m alar", - "ma lar", - "mal ar", - "ĠdeÄŁ il", - "æĿ ±", - "Ġt ác", - "Ġtá c", - "Ġk iÅŁ", - "Ġki ÅŁ", - "Ġt á»±", - "Ġtá» ±", - "à¥į ध", - "à¥įठ§", - "à¸Ļ à¸Ĺ", - "ÎŁ Î¥", - "ÎŁÎ ¥", - "ÑģÑĮ кого", - "ÑģÑĮк ого", - "ÑģÑĮко го", - "Ġ ध", - "Ġठ§", - "Ġ ìĿĺ", - "ĠìĿ ĺ", - "ÙĨ Ø©", - "ü s", - "è «", - "Ġtaraf ından", - "ħ n", - "Ġk inh", - "Ġki nh", - "Ġkin h", - "Ïĥ ι", - "à¥Ģ à¤ķ", - "íı ¬", - "ا ÙħÙĦ", - "اÙħ ÙĦ", - "ĠV iá»ĩt", - "Ġ ÏĦον", - "ĠÏĦ ον", - "ĠÏĦο ν", - "Ġ تÙĨ", - "Ġت ÙĨ", - "Ġà¤ħ ध", - "à¹Ī าà¸Ļ", - "à¹Īา à¸Ļ", - "r ı", - "à¤Ĥ द", - "é ª", - "Ġch úng", - "Ġchú ng", - "г и", - "ÏĦ αν", - "ÏĦα ν", - "Ġд оп", - "Ġдо п", - "н Ñĸй", - "нÑĸ й", - "он алÑĮ", - "она лÑĮ", - "Î ĵ", - "Ġb üyük", - "Ġbü yük", - "Ġbüy ük", - "á ¼", - "à¥Ģ र", - "Ø° Ùĩ", - "Ġ ìķĦìĿ´", - "ĠìķĦ ìĿ´", - "Ġdo anh", - "Ġ ÅĻÃŃ", - "ĠÅĻ ÃŃ", - "ÑĨ Ñı", - "Ġt Æ°", - "Ġ सर", - "Ġस र", - "Ġm ÃŃst", - "ĠmÃŃ st", - "Ġë° ı", - "Ø´ ÙĨ", - "Ñĸ б", - "Ġ ãĢĢãĢĢ", - "ĠãĢĢ ãĢĢ", - "çĻ ½", - "о Ñģп", - "оÑģ п", - "к Ñĸв", - "кÑĸ в", - "Ġt ế", - "ãģ Ń", - "Ġt Ỽi", - "Ġtá» Ľi", - "Ġ ìļ°", - "Ġìļ °", - "æľ ĥ", - "ا ÛĮد", - "اÛĮ د", - "æ §", - "ìł IJ", - "Ġd urum", - "Ġdu rum", - "Ġdur um", - "à¹Ģ à¸Ĭ", - "à¹ĢภĬ", - "à¥Ģ त", - "ĠÙĩ ÙĪ", - "à¥Ĥ प", - "Ġgö re", - "Ġgör e", - "Ġ ÑĢоб", - "ĠÑĢ об", - "Ġth iết", - "Ġthi ết", - "a jÃŃ", - "aj ÃŃ", - "ĠاÛĮ راÙĨ", - "âĢ ı", - "ÑģÑĮ коÑĹ", - "ÑģÑĮк оÑĹ", - "ÑģÑĮко ÑĹ", - "ç ħ", - "Ġ ìĦ¸", - "ĠìĦ ¸", - "á» «", - "Ġ à¸Ĥ", - "ĠภĤ", - "ů m", - "ëŀ Į", - "ι κή", - "ικ ή", - "Ġм ог", - "Ġмо г", - "ÙĨ ÙĬ", - "ãģ ļ", - "ा ब", - "ाठ¬", - "æ ¢", - "ع Ùĩ", - "ÑĶ м", - "Ġ ά", - "ĠÎ ¬", - "οÏħ ÏĤ", - "ز ار", - "زا ر", - "ê± ´", - "s ká", - "sk á", - "Ġ اÙĬ", - "Ġا ÙĬ", - "Ġi lg", - "Ġil g", - "Ġ sı", - "Ġs ı", - "e leri", - "el eri", - "eler i", - "ele ri", - "Ġ ÎĹ", - "ĠÎ Ĺ", - "u yor", - "uy or", - "uyo r", - "ठ·", - "ि म", - "िठ®", - "е ва", - "ев а", - "ä»Ģ ä¹Ī", - "ุ à¹Ī", - "à¹ī าà¸ĩ", - "à¹īา à¸ĩ", - "Ġh iá»ĩu", - "Ġhi á»ĩu", - "Ġ اع", - "Ġا ع", - "ĠØ§Ø ¹", - "Ġö zel", - "Ġöz el", - "ν η", - "ëĦ ¤", - "Ġto Ãłn", - "Ġm oh", - "Ġmo h", - "ĠÑı кÑĸ", - "ĠÑıк Ñĸ", - "ç Ĭ", - "mak tadır", - "makta dır", - "makt adır", - "ت اب", - "تا ب", - "Ġ ÑģÑĥ", - "ĠÑģ Ñĥ", - "Ġ yük", - "Ġy ük", - "Ġ Χ", - "ĠÎ §", - "з на", - "зн а", - "о Ñħ", - "Æ° u", - "à¸Ĺ ร", - "ãħ ĭ", - "Ġkar ÅŁÄ±", - "ĠkarÅŁ ı", - "Ùħ ÛĮ", - "Ġ ÑĨÑĸ", - "ĠÑĨ Ñĸ", - "ا دÛĮ", - "اد ÛĮ", - "à¥Ģ ।", - "Ïģ η", - "л ов", - "ло в", - "å¤ «", - "Ġph ân", - "Ġп оп", - "Ġпо п", - "ç· ļ", - "Ñı н", - "ุ à¸ĵ", - "ÑģÑĤ Ñĥп", - "ίν αι", - "ίνα ι", - "ĠÑĢ окÑĥ", - "ĠÑĢок Ñĥ", - "l arda", - "lar da", - "è» Ĭ", - "Ïģ Ïī", - "ÙĪ اÙĩ", - "ÙĪا Ùĩ", - "è ħ", - "à¥į रत", - "à¥įर त", - "å· ±", - "Ġ ÑĢÑĥ", - "ĠÑĢ Ñĥ", - "Ġth á»ĭ", - "Ġthá» ĭ", - "ĠÄij iá»ĩn", - "ĠÄiji á»ĩn", - "ìĸ ij", - "n ého", - "né ho", - "ส ม", - "ê° ģ", - "a cÃŃ", - "ac ÃŃ", - "Ġг ода", - "Ġгод а", - "k az", - "ka z", - "Ġb öl", - "Ġbö l", - "Ġg ian", - "Ġgi an", - "Ġgia n", - "à¸Ľ ร", - "ï¾ ŀ", - "ั à¸ķ", - "Ġg erç", - "Ġger ç", - "Ġ اج", - "Ġا ج", - "ĠØ§Ø ¬", - "Ġ ή", - "ĠÎ ®", - "Ùij Ùİ", - "Ñģ кого", - "Ñģк ого", - "Ñģко го", - "ÑĢ аÑħ", - "ÑĢа Ñħ", - "Ġ Åł", - "ĠÅ ł", - "Ġ à¤Ľ", - "ĠठĽ", - "о ÑģÑĤÑĸ", - "оÑģ ÑĤÑĸ", - "оÑģÑĤ Ñĸ", - "ë³ ¸", - "ÑģÑĮ кий", - "ÑģÑĮк ий", - "Û± Û¹", - "Û±Û ¹", - "Ñĥ ва", - "Ñĥв а", - "ا ÙĦÙħ", - "اÙĦ Ùħ", - "ĠÙħ ص", - "ëį ĺ", - "b ÃŃ", - "Ġ ÙĪج", - "ĠÙĪ ج", - "ÏĦ ÏĮ", - "e bilir", - "eb ilir", - "Ġt iếp", - "Ġti ếp", - "Ġtiế p", - "é ¤", - "Ġ ä¸Ģ", - "Ġä¸ Ģ", - "ĠÑģ ÑĢед", - "ë Ĥ¨", - "ëĤ ¨", - "ε Ïģι", - "εÏģ ι", - "ا Ø«", - "Ø§Ø «", - "Ñģ ов", - "Ñģо в", - "Ïĩ ε", - "Ġ ë¶Ħ", - "Ġë¶ Ħ", - "Ġta ké", - "Ġtak é", - "Ġd üz", - "Ġdü z", - "Ġ íıī", - "Ġíı ī", - "Ġ اص", - "Ġا ص", - "ĠØ§Ø µ", - "ĠÏĥ ÏĦην", - "ĠÏĥÏĦη ν", - "ë° Ķ", - "Ġh á»Ļi", - "Ġhá»Ļ i", - "ر Ùĩ", - "ب ÛĮ", - "в е", - "Ġا ÙĦØ·", - "ĠاÙĦ Ø·", - "Ġ ÑĢез", - "ĠÑĢ ез", - "ĠÑĢе з", - "ب ار", - "با ر", - "Ġgi ải", - "Ġgiả i", - "ãģ« ãģª", - "ol eÄį", - "ole Äį", - "ठł", - "; :", - "ä½ ı", - "Ú© Ùĩ", - "Ġ Φ", - "ĠÎ ¦", - "Ġ ÑĥÑĩ", - "ĠÑĥ Ñĩ", - "âĹı âĹı", - "ู à¸ģ", - "à¥ĩ व", - "à¥ĩठµ", - "Ïĥ α", - "Ġ اÙĨت", - "Ġا ÙĨت", - "ĠاÙĨ ت", - "Ġв п", - "Ġqu ả", - "e nin", - "en in", - "eni n", - "Ġ êµIJ", - "Ġêµ IJ", - "μ ά", - "Ú© ت", - "ÙĤ Ùĩ", - "Ġ Türkiye", - "ĠTür kiye", - "ĠTürk iye", - "Ġth ức", - "Ġthứ c", - "íĹ ĺ", - "iá»ĩ m", - "Ġत à¤ķ", - "Ġ éĩ", - "Ġé ĩ", - "़ ा", - "ĠØ£ ÙĪ", - "á le", - "ál e", - "ç© ¶", - "ĠÅŁ ekil", - "ĠÅŁek il", - "к ого", - "ко го", - "ког о", - "ÑĪ иÑħ", - "ÑĪи Ñħ", - "ا ÛĮØ´", - "اÛĮ Ø´", - "ت ÙĨ", - "н ей", - "не й", - "à¸Ĺ ำ", - "Ġ Ñıв", - "ĠÑı в", - "ر Ùħ", - "Ġm áy", - "Ġmá y", - "ห ม", - "ı yla", - "ıy la", - "Ġc ầu", - "Ġд об", - "Ġдо б", - "Ġ ìŀ¥", - "Ġìŀ ¥", - "o vý", - "ov ý", - "ι κÏĮ", - "ικ ÏĮ", - "Ġ ãħĩ", - "Ġãħ ĩ", - "Ġ ÑĤеÑĢ", - "ĠÑĤ еÑĢ", - "ĠÑĤе ÑĢ", - "Į Ĵ", - "س ÙĬ", - "Ġol uÅŁ", - "Ġb yla", - "Ġby la", - "Ġbyl a", - "ع ÙĦ", - "Ġ ÙĥاÙĨ", - "ĠÙĥ اÙĨ", - "б оÑĢ", - "бо ÑĢ", - "ì² Ń", - "ãĥ ı", - "u bl", - "ub l", - "Ġ اخ", - "Ġا Ø®", - "ĠØ§Ø ®", - "ÙĦ ÙĪد", - "ÙĦÙĪ د", - "ت ÙĬ", - "l adı", - "la dı", - "lad ı", - "Ġ Ã¶ÄŁ", - "Ġö ÄŁ", - "r uh", - "ru h", - "ç ¿", - "Ġ بعد", - "Ġب عد", - "Ġبع د", - "ÎĻ Îij", - "i dir", - "id ir", - "idi r", - "ãģ« ãģ¯", - "Ġs öy", - "Ġsö y", - "Ġkh ách", - "Ġkhác h", - "Ġkhá ch", - "ÑĨ е", - "Ġ Ø´ÙĪد", - "ĠØ´ ÙĪد", - "ĠØ´ÙĪ د", - "ç ¸", - "Ġ ëħ¸", - "Ġëħ ¸", - "ú p", - "Ġn eden", - "Ġne den", - "Ġned en", - "Ġh óa", - "Ġà¤ī प", - "Ïĥ ειÏĤ", - "Ïĥει ÏĤ", - "æĪ ¿", - "Ġ ³³", - "ĠÂł Âł", - "Ġ ìķĮ", - "Ġì ķĮ", - "Ġìķ Į", - "à¥Ģ ,", - "´ ij", - "ê u", - "ÑĢ ок", - "ÑĢо к", - "à¹Ģ à¸Ī", - "à¹ĢภĪ", - "Ġε ίναι", - "Ġ بÙĦ", - "Ġب ÙĦ", - "Ġ Ñģов", - "ĠÑģ ов", - "ĠÑģо в", - "Ġö nem", - "Ġön em", - "Ġöne m", - "Ġ à¸ĭ", - "Ġภĭ", - "ì§Ģ ë§Į", - "å® ĺ", - "ê² ©", - "ìĦ Ŀ", - "Ġ až", - "Ġa ž", - "Ġd uy", - "Ġdu y", - "ãģ¨ ãģĦ", - "Ø Ľ", - "δ ο", - "θ ε", - "Ùĥ اÙĨ", - "ठ¢", - "ा à¤ĵ", - "ाठĵ", - "Ġd á»ĭch", - "Ġdá»ĭ ch", - "á»Ļ ng", - "á»Ļn g", - "ส ำ", - "Ä ı", - "Ġ ÑĹÑħ", - "ĠÑĹ Ñħ", - "α λ", - "e Äį", - "ç² ¾", - "Ġ зв", - "Ġз в", - "èĩª å·±", - "Ġ اÙĦÙĦÙĩ", - "ĠاÙĦ ÙĦÙĩ", - "ĠاÙĦÙĦ Ùĩ", - "Ġ СÑĤ", - "ĠС ÑĤ", - "Ġ سÙĨÚ¯", - "Ġس ÙĨÚ¯", - "ĠسÙĨ Ú¯", - "Ġ дом", - "Ġд ом", - "Ġдо м", - "г оÑĤов", - "го ÑĤов", - "гоÑĤ ов", - "п овÑĸд", - "пов Ñĸд", - "по вÑĸд", - "Ġ Bá»Ļ", - "ĠB á»Ļ", - "à¥įय à¤ķ", - "Ø· Ø©", - "м ов", - "мо в", - "à¸Ĺ าà¸ĩ", - "ึ à¸ģ", - "Ġ Ñĸз", - "ĠÑĸ з", - "à¥ĭ à¤ľ", - "Ġgö ster", - "Ġ باشد", - "Ġبا شد", - "Ġباش د", - "i leri", - "il eri", - "ile ri", - "iler i", - "ĠÑģ еб", - "Ñī о", - "Ġãħĩ ãħĩ", - "ب ت", - "Ñģ е", - "à¥ĩ à¤ľ", - "à¥ĩठľ", - "Ġl ên", - "Ġ تÙĪ", - "Ġت ÙĪ", - "Ñĸ ÑģÑĤÑĮ", - "ÑĸÑģ ÑĤÑĮ", - "ÑĸÑģÑĤ ÑĮ", - "ï¾Ĩ ï¾Ĩ", - "Ġth Æ°á»Ŀng", - "Ġol duÄŁu", - "Ġoldu ÄŁu", - "ĠolduÄŁ u", - "v ÄĽt", - "vÄĽ t", - "ìĨ į", - "ãģĿ ãģĨ", - "Ġ ìĦ±", - "ĠìĦ ±", - "ë° ľ", - "Ġ à¸ģาร", - "Ġà¸ģ าร", - "Ġ Ø´Ùĩر", - "ĠØ´ Ùĩر", - "ĠØ´Ùĩ ر", - "s led", - "sl ed", - "ả nh", - "ản h", - "æŀ Ĺ", - "l acak", - "la cak", - "lac ak", - "Ġm ình", - "Ú© ÛĮ", - "Ġ à¹ĥà¸Ļ", - "Ġd ùng", - "Ġdù ng", - "Ġм аÑģ", - "Ġма Ñģ", - "ÑĦ ек", - "æ° Ķ", - "é §", - "Ġ اØŃ", - "Ġا ØŃ", - "ĠØ§Ø Ń", - "èµ °", - "ÎĻ Îļ", - "à¥ĩ ।", - "ÑģÑĮ ка", - "ÑģÑĮк а", - "Ġ ÑĩаÑģÑĤ", - "ĠÑĩ аÑģÑĤ", - "ĠÑĩа ÑģÑĤ", - "ĠÑĩаÑģ ÑĤ", - "lar ının", - "ların ın", - "larını n", - "Ġ ê¹Ģ", - "Ġê¹ Ģ", - "ì¸ µ", - "н ими", - "ни ми", - "ним и", - "èª ŀ", - "åĢ ĭ", - "Ġ êµŃ", - "Ġêµ Ń", - "к оÑĢ", - "ко ÑĢ", - "m aya", - "ma ya", - "may a", - "ิ à¹Ĥà¸Ļ", - "ิà¹Ĥ à¸Ļ", - ". ศ", - "Ġh á»ĩ", - "Ġhá» ĩ", - "Ġ تÙĤ", - "Ġت ÙĤ", - "γ κ", - "Ġà¤Ĩप à¤ķ", - "Ñģ ÑĤоÑĢ", - "ÑģÑĤ оÑĢ", - "ĠÄij o", - "Ġch ủ", - "ا ÛĮت", - "اÛĮ ت", - "ĠQu á»ijc", - "г лÑı", - "гл Ñı", - "ãĢĤ ãĢįĊĊ", - "ãĢĤãĢį ĊĊ", - "Ġn Ãło", - "à¸Ń ล", - "æĬ Ĭ", - "ÙĪ رت", - "ÙĪر ت", - "Ġb ude", - "Ġbu de", - "Ġbud e", - "æĽ ¸", - "e lik", - "el ik", - "eli k", - "Ġ جÙĩ", - "Ġج Ùĩ", - "ĠبÙĪ ابة", - "èĬ ±", - "د ار", - "دا ر", - "Ġb ýt", - "Ġbý t", - "Ñĩ е", - "ãĤĵ ãģł", - "ĠÙħ Ø·", - "l ere", - "le re", - "ler e", - "ÎĹ Î£", - "íĺ ķ", - "âĸ į", - "ÄŁ u", - "Ġв з", - "ÙĬ ز", - "ĠÐł оÑģ", - "íĭ °", - "Ġد اش", - "ì§ ij", - "a tı", - "at ı", - "m esi", - "me si", - "mes i", - "ãĤī ãĤĮ", - "ů v", - "r át", - "rá t", - "оÑģ об", - "åIJ Ħ", - "uy á»ĩn", - "uyá»ĩ n", - "åģ ļ", - "ü st", - "üs t", - "éĩ İ", - "α Ïĥ", - "Ġm ặt", - "е лов", - "ел ов", - "ело в", - "åį ļ", - "д ж", - "Ġد ارد", - "Ġدار د", - "Ġf ark", - "Ġfa rk", - "Ġfar k", - "à¹ī วย", - "à¹īว ย", - "о ни", - "он и", - "Ġب Ø®", - "à¥ģ त", - "à¥ģठ¤", - "ĠÄij ây", - "α Ïģα", - "αÏģ α", - "Ġ δια", - "Ġδ ια", - "Ġδι α", - "Ġ è¯", - "Ġè ¯", - "к аÑħ", - "ка Ñħ", - "ch áz", - "z enÃŃ", - "ze nÃŃ", - "zen ÃŃ", - "ÑĢ оп", - "ÑĢо п", - "à¥Ģ म", - "í Ĩµ", - "íĨ µ", - "d ü", - "à¸ł าà¸ŀ", - "Ġ íĬ", - "Ġí Ĭ", - "ÙĪ ا", - "Ġt á»ijt", - "Ġtá»ij t", - "ï¼Ł ãĢįĊĊ", - "ï¼ŁãĢį ĊĊ", - "Ġ æľĪ", - "Ġnh Æ°ng", - "ĠnhÆ° ng", - "Ġne ž", - "à¥ĭ ड", - "ìĹIJ ê²Į", - "à¤Ĥ ड", - "¶ Į", - "Ġ меÑģÑĤ", - "Ġм еÑģÑĤ", - "ा à¤ģ", - "ाठģ", - "ì¦ Ŀ", - "ĠÄij ang", - "ĠÄija ng", - "à¸Ń à¸Ķ", - "í ĽĦ", - "á»į i", - "sk ého", - "ské ho", - "Ġд ок", - "Ġдо к", - "Ġ تص", - "Ġت ص", - "Ġph òng", - "Ġ ê°ķ", - "Ġê° ķ", - "Ġtr Æ°á»Ľc", - "í ijľ", - "Ù Ķ", - "Ġph ÃŃ", - "Ġch á»įn", - "ä¹ IJ", - "ĠÅŁek ilde", - "ĠÅŁekil de", - "Ġ íİ", - "Ġí İ", - "é º", - "ë £¨", - "ë£ ¨", - "à¥Ī ।Ċ", - "à¥Ī। Ċ", - "ÙĪ رÛĮ", - "ÙĪر ÛĮ", - "Ñģ ÑĤÑĢа", - "ÑģÑĤ ÑĢа", - "ÑģÑĤÑĢ а", - "il di", - "ild i", - "Ġα Ïħ", - "в аннÑı", - "ван нÑı", - "ìļ ¸", - ". âĢľĊĊ", - ".âĢľ ĊĊ", - "ĠÑĤак же", - "ëĵ ±", - "е ка", - "ек а", - "æī į", - "Ùħ Ø©", - "Ġph Æ°Æ¡ng", - "é© ¬", - "ãĢĢ ĠãĢĢ", - "ãĢĢĠ ãĢĢ", - "ov ých", - "ový ch", - "ี ยà¸ĩ", - "ีย à¸ĩ", - "ĠT ru", - "ĠTr u", - "е Ñģп", - "еÑģ п", - "st up", - "stu p", - "Ä Į", - "Ġdal Å¡ÃŃ", - "ز ÛĮ", - "Ġ 매", - "Ġë§ ¤", - "Ġ обÑĢаз", - "Ġоб ÑĢаз", - "ĠобÑĢа з", - "Ġaç ık", - "Ġaçı k", - "ê° ķ", - "Ùģ ادÙĩ", - "Ú¯ اÙĨ", - "à¹ī à¸Ļ", - "ẩ n", - "å·¥ ä½ľ", - "Ġ तर", - "Ġत र", - "ÙĬ ع", - "Ġ ãĢĬ", - "ĠãĢ Ĭ", - ", âĢľ", - "Ġ nev", - "Ġn ev", - "Ġne v", - "ั à¸į", - "ÄŁ ını", - "ģın ı", - "Ġ jin", - "Ġj in", - "Ġji n", - "ا خت", - "اخ ت", - "س ر", - "Ġt Ãłi", - "Ġkter á", - "Ġا ÙĦÙĦ", - "ĠاÙĦ ÙĦ", - "ठħ", - "iz met", - "izm et", - "à¥ģ म", - "à¥ģठ®", - "า ะ", - "าภ°", - "Ġ ê·", - "Ġê ·", - "l ıģı", - "lı ģı", - "lıģ ı", - "çı ¾", - "li ÄŁi", - "liÄŁ i", - "êµ °", - "a lık", - "al ık", - "alı k", - "Ġد ÙĪر", - "ĠدÙĪ ر", - "Ġ ìĭ¤", - "Ġìĭ ¤", - "Ġз аÑģ", - "Ġза Ñģ", - "ÙĤ ÙĬ", - "Ġ ứng", - "Ġ ÙĥÙĩ", - "ĠÙĥ Ùĩ", - "ÎŁ Σ", - "ÎŁÎ £", - "è¨ Ń", - "ç Į", - "ãģĦ ãģŁ", - "íĺ Ħ", - "Ġ ÑĤе", - "ĠÑĤ е", - "е ÑĢÑĸ", - "еÑĢ Ñĸ", - "s ız", - "sı z", - "Ġ ý", - "Ġà ½", - "д ов", - "до в", - "Ġ à¤ĩसà¤ķ", - "Ġà¤ĩस à¤ķ", - "г од", - "го д", - "Ġby lo", - "Ġbyl o", - "าà¸Ħ ม", - "е нием", - "ен ием", - "ени ем", - "ение м", - "Ð ¨", - "æľ ¯", - "Ġप हल", - "Ġपह ल", - "Ġ aÅŁ", - "Ġa ÅŁ", - "ि à¤ľ", - "िठľ", - "åĵ ¡", - "в аÑĢ", - "ва ÑĢ", - "à¹ī ำ", - "â ĮĴ", - "ov án", - "ová n", - "Ġgi úp", - "Ð ¥", - "ĠÑģ Ñĥд", - "ĠÑģÑĥ д", - "Ġà¤ķ म", - "ạ m", - "ر س", - "Ġ 人", - "Ġ بÛĮ", - "Ġب ÛĮ", - "Ġà¤īन à¤ķ", - "ë¦ ½", - "áºŃ y", - "Ġv áºŃt", - "л ÑıеÑĤÑģÑı", - "лÑı еÑĤÑģÑı", - "лÑıеÑĤ ÑģÑı", - "Ġs eç", - "Ġse ç", - "Ġ ì½", - "Ġì ½", - "ÑĢ Ñĥж", - "ÑĢÑĥ ж", - "ت ص", - "| :", - "Ġ ëł", - "Ġë ł", - "и ми", - "им и", - "Ġ лÑİб", - "ĠлÑİ Ð±", - "Ġ à¸ľ", - "Ġภľ", - "ï¼Į ä½Ĩ", - "Ġ нав", - "Ġн ав", - "Ġна в", - "âĢ ¬", - "à¹Ī าย", - "à¹Īา ย", - "Ġ رس", - "Ġر س", - "s iniz", - "sin iz", - "ë ¨", - "е ниÑİ", - "ен иÑİ", - "ени Ñİ", - "Ġ ล", - "Ġภ¥", - "ا سÛĮ", - "اس ÛĮ", - "ॠľ", - "ĠÙ¾ ÛĮØ´", - "ĠÙ¾ÛĮ Ø´", - "ί δ", - "Ġ Ù¾ÛĮ", - "ĠÙ¾ ÛĮ", - "еÑĢж ав", - "ठĨ", - "ĠdÃ¼ÅŁ ün", - "å¿ «", - "ÑĢ еÑģ", - "ÑĢе Ñģ", - "åħ «", - "ÑĤ Ñĸ", - "ि à¤Ł", - "िठŁ", - "Ġ ÑĤеÑħ", - "ĠÑĤ еÑħ", - "ĠÑĤе Ñħ", - "ú t", - "ÙĨ Ùĩ", - "Ġ ÙĨØ´", - "ĠÙĨ Ø´", - "çĻ º", - "Ġ ê°¤", - "Ġê° ¤", - "л ед", - "ле д", - "Ġ ëĵ¤", - "Ġëĵ ¤", - "Ġbi lg", - "Ġbil g", - "Ġsp oleÄį", - "Ġspol eÄį", - "Ġspole Äį", - "ĠÄij Æ¡n", - "Ġ à¤īत", - "Ġà¤ī त", - "Ġtr á»ĭ", - "Ġ عÙħ", - "Ġع Ùħ", - "Ġ ।", - "Ġà ¥¤", - "Ġॠ¤", - "Ġú Äį", - "ãģ ¸", - "ว à¸ģ", - "ĠÑģл ÑĥÑĩа", - "ĠÑģлÑĥÑĩ а", - "ĠÑģлÑĥ Ñĩа", - "á» įng", - "á»į ng", - "á»įn g", - "åı Ī", - "и ÑĤÑĥ", - "иÑĤ Ñĥ", - "æľī éĻIJ", - "ë¦ °", - "ëĭ ĺ", - "Ġho ạt", - "ĠìĿ´ ëıĻ", - "з наÑĩ", - "зна Ñĩ", - "зн аÑĩ", - "Ġاست ÙģادÙĩ", - "ĠпÑĢо ÑĨеÑģ", - "ĠпÑĢоÑĨ еÑģ", - "an ın", - "anı n", - "г Ñĥ", - "Ġ اÙĦØ«", - "ĠاÙĦ Ø«", - "æĹ¥ æľ¬", - "ι κά", - "ικ ά", - "ĠÑĹ ÑĹ", - "ì§ ģ", - "i nu", - "in u", - "Ġس از", - "ãĤ ¡", - "ï¾ ī", - "Ġ اÙĤ", - "Ġا ÙĤ", - "Ġk ế", - "ů sob", - "à¹ĩ à¸ģ", - "åIJ §", - "æ¼ Ķ", - "Ñī ие", - "Ñīи е", - "ç Ĩ", - "ÑĮ ого", - "à¥ĭ à¤Ł", - "ا Ù¾", - "ا٠¾", - "å ®¤", - "å® ¤", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Ġtr iá»ĥn", - "Ġtri á»ĥn", - "Ġt áºŃp", - "é£ Ł", - "ë ¹", - "ĠÑĩеÑĢ ез", - "Ġ ÑĨи", - "ĠÑĨ и", - "Ñģ Ñĥ", - "Ġ нем", - "Ġн ем", - "Ġне м", - "Ġ аÑĢ", - "Ġа ÑĢ", - "Ġ ÙĦا", - "ĠÙĦ ا", - "Ġ ì§Ħ", - "Ġì§ Ħ", - "ç Ł³", - "çŁ ³", - "Ġп ÑĢоб", - "ĠпÑĢ об", - "ĠпÑĢо б", - "Ġ ìĽIJ", - "ĠìĽ IJ", - "ÛĮ ÙĨÛĮ", - "ÛĮÙĨ ÛĮ", - "Ñİ Ñĩи", - "âĢ į", - "Û± Û³", - "Û±Û ³", - "ã Ĥ¬", - "ãĤ ¬", - "çłĶ 究", - "í Ĥ¤", - "íĤ ¤", - "Ġger çek", - "Ġgerç ek", - "ĠØŃ Ø³", - "íĶ ¼", - "èĤ ¡", - "Ġ ÏĦι", - "ĠÏĦ ι", - "ĠvÅ¡ ech", - "ĠvÅ¡e ch", - "Ġv ì", - "ا ÙĨÙĬ", - "اÙĨ ÙĬ", - "ĠÙĩ ست", - "Ġ ëĤ¨", - "Ġë Ĥ¨", - "ĠëĤ ¨", - "ÅĻ ej", - "ÅĻe j", - "е ÑĢг", - "еÑĢ г", - "Ġs öz", - "Ġsö z", - "ĠاÙĦ Ùħت", - "ĠاÙĦÙħ ت", - "Ġch ế", - "á»ĵ i", - "åı ¤", - "âĸį âĸį", - "á»ĵ ng", - "á»ĵn g", - "ãĥ ¢", - "Ġ ди", - "Ġд и", - "ε λ", - "Ġ она", - "Ġо на", - "Ġон а", - "Ġ най", - "Ġн ай", - "Ġна й", - "Ġ _{", - "Ġ_ {", - "п ол", - "по л", - "al iz", - "ali z", - "Ġt Äĥng", - "Ġ dÃŃ", - "Ġd ÃŃ", - "é p", - "Ġ ÙĦÙħ", - "ĠÙĦ Ùħ", - "Ġm ož", - "Ġmo ž", - "Ġng oÃłi", - "è Ĺ", - "Ġ Ñĩем", - "ĠÑĩ ем", - "ĠÄij á»ķ", - "ĠÄijá» ķ", - "е ÑĤа", - "еÑĤ а", - "åı ²", - "ĠÑģ каз", - "ĠÑģк аз", - "ĠÑģка з", - "ãĤ¿ ãĥ¼", - "а нÑĮ", - "ан ÑĮ", - "Ġg öz", - "Ġgö z", - "ë³ Ħ", - "ãģĭ ãģ£ãģŁ", - "Ġ ëįĶ", - "Ġëį Ķ", - "ĠÙĨ ÙĤ", - "Ġ ÑĥÑĩа", - "ĠÑĥ Ñĩа", - "ĠÑĥÑĩ а", - "Ġsa hip", - "Ġsah ip", - "ĠÑģ пе", - "ĠÑģп е", - "ί οÏħ", - "ίο Ïħ", - "ì ½Ķ", - "ì½ Ķ", - "Ġ ëĪ", - "Ġë Ī", - "m am", - "ma m", - "Ġr oce", - "Ġro ce", - "Ġroc e", - "Ġ ÙĨاÙħ", - "ĠÙĨ اÙħ", - "еÑĢ аÑĤÑĥ", - "еÑĢа ÑĤÑĥ", - "ı p", - "ãģĦ ãģ¦", - "Ġ íķĻ", - "Ġíķ Ļ", - "Ġ à¤ĩन", - "Ġà¤ĩ न", - "å ©", - "Ġnh iên", - "a tır", - "at ır", - "atı r", - "ÅĻ enÃŃ", - "ÅĻe nÃŃ", - "ÅĻen ÃŃ", - "د Ø©", - "ãĥª ãĥ¼", - "ล à¸ĩ", - "Ġ éĢ", - "Ġé Ģ", - "Ġ à¹Ģà¸Ľ", - "Ġà¹Ģ à¸Ľ", - "Ġà¹ĢภĽ", - "д Ñĸ", - "ÎŃ Ïģ", - "ìĦ ¤", - "г ÑĢа", - "es ine", - "esi ne", - "esin e", - "Ġ ее", - "Ġе е", - "Ġ iki", - "Ġi ki", - "Ġik i", - "Ġت ج", - "lar ına", - "ları na", - "ların a", - "d ür", - "dü r", - "ĠاÙĦ Ø°", - "Ùħ ت", - "ĠठIJ", - "ि द", - "िठ¦", - "Ġ ë¹", - "Ġë ¹", - "ÑĦоÑĢм а", - "ÑĦоÑĢ ма", - "Ġ они", - "Ġо ни", - "Ġон и", - "г оÑĢ", - "го ÑĢ", - "н еÑģ", - "не Ñģ", - "ìĺĢ ëĭ¤", - "ıl dı", - "Ġ çek", - "Ġç ek", - "Ġ дов", - "Ġд ов", - "Ġдо в", - "د ÛĮ", - "Ġ ÄĮesk", - "ĠÄĮ esk", - "ĠÄĮes k", - "ÑĪ а", - "Ġ ات", - "Ġا ت", - "ĠØ§Ø ª", - "å± ĭ", - "æĸ ¼", - "Ġ práv", - "Ġp ráv", - "Ġpr áv", - "Ġprá v", - "é mu", - "ém u", - "å¸ Ī", - "ãħĭ ãħĭ", - "Ġil gili", - "Ġilg ili", - "Ġilgi li", - "ห ว", - "ठĩ", - "ा ष", - "ाठ·", - "ëŀ ij", - "as yon", - "asy on", - "ÑĨ ÑĮ", - "à¹ģ à¸ķ", - "ợ i", - "Ġв ÑĭÑģ", - "ĠвÑĭ Ñģ", - "ÑĸлÑĮ ки", - "ĠкоÑĤоÑĢ Ñĭе", - "н ики", - "ни ки", - "ник и", - "Ġ اد", - "Ġا د", - "ĠØ§Ø ¯", - "Ġ živ", - "Ġž iv", - "Ġži v", - "Ġα ÏĢο", - "ĠαÏĢ ο", - "ر ض", - "ا Ø©", - "Ø§Ø ©", - "Ġk dyž", - "Ġkdy ž", - "ữ a", - "Ġ ëĮĢíķľ", - "ĠëĮĢ íķľ", - "Ġt ôi", - "Ġtô i", - "Ñĥ ÑĶ", - "ز ر", - "Ġ å¥", - "Ġå ¥", - "ãĥĭ ãĥĭ", - "ب Ø©", - "ÏĦ οÏĤ", - "ÏĦο ÏĤ", - "ÑĨи он", - "Ġ ÙħÙĬ", - "ĠÙħ ÙĬ", - "Ġ Äĥn", - "ĠÄ ĥn", - "à¥ĩ à¤Ĺ", - "à¥ĩठĹ", - "Ġ ÑĢег", - "ĠÑĢ ег", - "ĠÑĢе г", - "ĠlỼ n", - "ì¤ Ģ", - "ìĭ ¬", - "Ġb iết", - "Ġbi ết", - "a ları", - "al arı", - "ala rı", - "alar ı", - "Ùģ ÙĬ", - "ä¸ĸ çķĮ", - "Ġне обÑħодим", - "Ġнеоб Ñħодим", - "à¸Ļ ว", - "ν ÏĦ", - "Ġ ảnh", - "íĸ Ī", - "Ġ वर", - "Ġव र", - "h led", - "hl ed", - "hle d", - "ิ à¸Ī", - "æŃ »", - "ĠاÙĦ تÙĬ", - "ĠاÙĦت ÙĬ", - "н оÑģ", - "но Ñģ", - "p rav", - "pr av", - "pra v", - "Ñı ÑĤи", - "ÑıÑĤ и", - "Ñī а", - "ÙĪ ÙĨÙĩ", - "ÙĪÙĨ Ùĩ", - "Ġ aÄŁ", - "Ġa ÄŁ", - "à¸ŀ ระ", - "à¸ŀร ะ", - "Ġth á»ijng", - "Ġthá» ijng", - "ÑĦ и", - "Ġг олов", - "Ġгол ов", - "Ġk hoa", - "Ġkh oa", - "Ġkho a", - "Ġ ëłĪ", - "Ġëł Ī", - "ãģ Ĵ", - "Ġget ir", - "Ġge tir", - "Ø´ ت", - "ж еннÑı", - "жен нÑı", - "е нÑĸ", - "ен Ñĸ", - "Ġgi ữ", - "Ġgiá» ¯", - "ler inin", - "leri nin", - "lerin in", - "lerini n", - "à¥Ģ व", - "éģ ¸", - "स र", - "ĠÑĩ елов", - "à¥į à¤ľ", - "à¥įठľ", - "ĠбÑĥ ло", - "Ġ اÙĨد", - "Ġا ÙĨد", - "ĠاÙĨ د", - "ั à¸Ļà¸Ĺ", - "ัà¸Ļ à¸Ĺ", - "è® ©", - "Ġq uyá»ģn", - "Ġquy á»ģn", - "ĠØŃ Ø§ÙĦ", - "ì² ĺ", - "Ġ лÑİд", - "ĠлÑİ Ð´", - "Ïģ Ïĩ", - "алÑĮ но", - "ãĢĢ ãĥ½", - "ê¸ ī", - "ãĤ ±", - "ĠÙħ رد", - "ĠÙħر د", - "Ġ ông", - "Ġô ng", - "Ġ اش", - "Ġا Ø´", - "ĠØ§Ø ´", - "大 åѦ", - "ì¦ Ī", - "æĪ ¦", - "e yi", - "ey i", - "Ġ ÐIJн", - "ĠÐIJ н", - "ि प", - "िठª", - "Ġt iêu", - "Ġti êu", - "Ø´ ÛĮ", - "ắ n", - "é ŃĶ", - "éŃ Ķ", - "ãģ¨ ãģĦãģĨ", - "ãģ¨ãģĦ ãģĨ", - "Ġ ìłĢ", - "Ġì łĢ", - "Ġìł Ģ", - "к ÑĤи", - "кÑĤ и", - "Ġ ÙħØŃÙħد", - "ĠÙħØŃ Ùħد", - "Ġ íĨµ", - "Ġí Ĩµ", - "ĠíĨ µ", - "ุ ม", - "åį ¡", - "о ÑĢов", - "оÑĢ ов", - "оÑĢо в", - "к оÑİ", - "ко Ñİ", - "Ġl á»±c", - "å³ ¶", - "Ġ رÙĪز", - "Ġر ÙĪز", - "ĠرÙĪ ز", - "Ñħ Ñĸд", - "Ġh á»ĵ", - "Ġhá» ĵ", - "Ġ ül", - "Ġü l", - "Ġ Ø´Ùħ", - "ĠØ´ Ùħ", - "ÙĨ س", - "ب ÙĪ", - "Ġth êm", - "ạ c", - "åº ľ", - "e chn", - "ec hn", - "ech n", - "Ġ Îļα", - "ĠÎļ α", - "èij Ĺ", - "çľ ¼", - "á h", - "Ġ ι", - "ĠÎ ¹", - "ê¹Į ì§Ģ", - "m az", - "ma z", - "λο γ", - "Ġjs me", - "Ġ á¼", - "Ġá ¼", - "Ġп ÑĢави", - "ĠпÑĢ ави", - "ĠпÑĢав и", - "ĠпÑĢа ви", - "к лад", - "Ġth ủ", - "Ġthá» §", - "s ah", - "sa h", - "ÄŁ it", - "ÄŁi t", - "Ġ ÙģÛĮ", - "ĠÙģ ÛĮ", - "ен но", - "à¥ģ à¤Ľ", - "à¥ģठĽ", - "ãģ »", - "çĻ ¾", - "и ÑĤа", - "иÑĤ а", - "ĠбÑĭ ло", - "ĠбÑĭл о", - "Ġv ys", - "Ġvy s", - "Ġ ì¶ľ", - "Ġì¶ ľ", - "ắ ng", - "ắn g", - "ĠÄij ại", - "ĠÙħ ÙĪرد", - "ĠÙħÙĪ رد", - "ĠÙħÙĪر د", - "е ла", - "ел а", - "Ñĸ ÑĪ", - "л еннÑı", - "лен нÑı", - "æ IJ", - "Ġ нед", - "Ġн ед", - "Ġне д", - "i yat", - "iy at", - "iya t", - "ì ¼", - "Ġol duÄŁunu", - "ĠolduÄŁ unu", - "ĠolduÄŁu nu", - "د اÙĨ", - "دا ÙĨ", - "í Ŀ", - "Ġ سÛĮ", - "Ġس ÛĮ", - "ี à¸ģ", - "ÄĽ st", - "ım ı", - "ä¸ī ä¸ī", - "ãĤ ½", - "ĠÑĤ еп", - "ĠÑĤе п", - "Ġ ÑĢай", - "ĠÑĢ ай", - "ĠÑĢаР¹", - "ĠÑĢа й", - "ा ध", - "ाठ§", - "Ġ ìĤ¬ëŀĮ", - "ĠìĤ¬ ëŀĮ", - "ĠT rung", - "ĠTr ung", - "ĠTru ng", - "ï¼ı ï¼ı", - "Ġt âm", - "Å¡ enÃŃ", - "Å¡e nÃŃ", - "Å¡en ÃŃ", - "ãĥ į", - "Ġ ÏĦοÏħÏĤ", - "ĠÏĦ οÏħÏĤ", - "ĠÏĦοÏħ ÏĤ", - "Ġ нÑĸ", - "Ġн Ñĸ", - "в ид", - "ви д", - "æ ¿", - "Ġ ظ", - "ĠØ ¸", - "ãĥ ¯", - "ì ¢ħ", - "ì¢ ħ", - "в аÑĤи", - "ва ÑĤи", - "ваÑĤ и", - "Ġqu á", - "ภ¤", - "ĠÄij Æ°á»Ŀng", - "à¥ģ द", - "à¥ģठ¦", - "r oj", - "ro j", - "Ġ ÑĥÑģ", - "ĠÑĥ Ñģ", - "é¦ Ļ", - "ì½ ĺ", - "Ġ ÙĪت", - "ĠÙĪ ت", - "ม าà¸ģ", - "มา à¸ģ", - "åĪ ĩ", - "Ġ án", - "Ġá n", - "Ġм ед", - "ìĹIJ ëĬĶ", - "Ġh lav", - "Ġhl av", - "ر ت", - "à¹ĥ à¸Ī", - "æ´ ²", - "Ġ лÑĸ", - "Ġл Ñĸ", - "æ Īĺ", - "æĪ ĺ", - "ÙĪ ÙĨد", - "ÙĪÙĨ د", - "è¶ ³", - "åĭ Ļ", - "çĶ ³", - "Ġ ì±", - "Ġì ±", - "ĠìĿ´ëıĻ íķ©ëĭĪëĭ¤", - "Ñī еÑģÑĤв", - "Ñīе ÑģÑĤв", - "Ġ ë¶Ī", - "Ġë ¶Ī", - "Ġë¶ Ī", - "ÙĦ ÙĪ", - "ü ven", - "èĪ ĩ", - "Ġgi Ỽi", - "Ġgiá» Ľi", - "Ġ ÙĪÙĤ", - "ĠÙĪ ÙĤ", - "Ġê°¤ ë¡ľê·¸", - "Ġ عاÙħ", - "Ġع اÙħ", - "ĺ IJ", - ": ::::", - ":: :::", - ":::: :", - "::: ::", - "Ġ Ñĥд", - "ĠÑĥ д", - "- ÑĤо", - "-ÑĤ о", - "Ġ ÑĦоÑĢ", - "ĠÑĦ оÑĢ", - "и ни", - "ин и", - "ãģĹ ãģĦ", - "ãģĹãģ Ħ", - "Ġê°¤ë¡ľê·¸ ë¡ľ", - "ãģ ³", - "ãĥ© ãĤ¤", - "e ná", - "en á", - "Ġ nez", - "Ġn ez", - "Ġne z", - "Ġönem li", - "Ġ ниÑħ", - "Ġн иÑħ", - "Ġни Ñħ", - "à¤Ĥ स", - "Ġà¤īस à¤ķ", - "à¥įर द", - "Ġn ói", - "Ġnó i", - "Ùĥ ÙĦ", - "ิ ว", - "κ ο", - "à¥ģ à¤ĸ", - "à¥ģठĸ", - "ö yle", - "öy le", - "ά λ", - "ó ng", - "ón g", - "ĠداÙĨ Ø´", - "Ġз б", - "ì »", - "à¸ľ ล", - "ëĵ¤ ìĿ´", - "Ġe tk", - "Ġet k", - "ر ات", - "را ت", - "Ġ εκ", - "Ġε κ", - "ÑĤ ÑĢа", - "ÑĤÑĢ а", - "à¥į तर", - "à¥įत र", - "à¤Ĥ ब", - "Ġм ÑĸÑģ", - "ĠмÑĸ Ñģ", - "æł ¹", - "ãĥ Ļ", - "Ġt á»ī", - "Ġtá» ī", - "à¹Ģ à¸ĭ", - "à¹Ģภĭ", - "ìĪ ł", - "ï¼Į ä¸į", - "ìĺ ¨", - "Ġm ÄĽst", - "ĠmÄĽ st", - "ģ µ", - "a zı", - "az ı", - "r ada", - "ra da", - "rad a", - "ÏĢ α", - "m é", - "ÙĨ اÙħÙĩ", - "ÙĨا ÙħÙĩ", - "ÙĨاÙħ Ùĩ", - "ا ÛĮÙĦ", - "اÛĮ ÙĦ", - "μ η", - "l uk", - "lu k", - "Ùĥ ÙĬ", - "Ġ ï¼ī", - "Ġï¼ ī", - "Ġ деÑĤ", - "Ġд еÑĤ", - "Ġде ÑĤ", - "Ġiç inde", - "Ġiçin de", - "Ġiçi nde", - "Ñı м", - "Ġd Æ°á»", - "ĠdÆ° á»", - "ĠпÑĢед ÑģÑĤав", - "ü re", - "ür e", - "åķ Ĭ", - "ĠÑĤ ÑĢÑĥ", - "ĠÑĤÑĢ Ñĥ", - "es ini", - "esi ni", - "esin i", - "Ġ але", - "Ġа ле", - "Ġал е", - "ãĥ³ ãĥī", - "ãĥ³ãĥ ī", - "à¥ĥ त", - "ε Ïħ", - "à¥ģ à¤Ĩ", - "à¥ģठĨ", - "Ġh iç", - "Ġhi ç", - "çĶ º", - "Ġ Ðĸ", - "ĠÐ ĸ", - "ç ħ§", - "çħ §", - "k á", - "Ġtr á»įng", - "Ġ تش", - "Ġت Ø´", - "ा श", - "ाठ¶", - "ĠÙħ Ø«", - "e tim", - "et im", - "eti m", - "Ġth ấy", - "Ġब ह", - "ع ت", - "ึ à¹ī", - "Ġs ev", - "Ġse v", - "Ñģ ÑĤа", - "ÑģÑĤ а", - "Ġc ứ", - "Ġt iá»ģn", - "Ġti á»ģn", - "à¥Ģ à¤ľ", - "Ñı г", - "ĠоÑĢг ани", - "ĠоÑĢган и", - "Ġб Ñĭл", - "ĠбÑĭ л", - "t ür", - "tü r", - "Ġب ازÛĮ", - "Ġبا زÛĮ", - "Ġباز ÛĮ", - "Ġ ìŀ¬", - "Ġìŀ ¬", - "व र", - "æľīéĻIJ åħ¬åı¸", - "k up", - "ku p", - "Ġ iyi", - "Ġi yi", - "Ġiy i", - "íķĺ ê²Į", - "ãĢĢ l", - "ãĤ· ãĥ§", - "ا رة", - "ار Ø©", - "ส ร", - "Ġt ÃŃch", - "ĠtÃŃ ch", - "Ġ каÑĢ", - "Ġк аÑĢ", - "Ġка ÑĢ", - "и б", - "ĠвÑĸд повÑĸд", - "ĠвÑĸдпов Ñĸд", - "Ġpo dle", - "Ġpod le", - "à¥įर à¤ķ", - "i yon", - "iy on", - "к оном", - "ко ном", - "кон ом", - "Ġ μÎŃ", - "Ġμ ÎŃ", - "Ġп ÑĢоиз", - "ĠпÑĢо из", - "Ġ âĢı", - "ĠâĢ ı", - "m ektedir", - "mekte dir", - "Ω ÎĿ", - "Ġb áo", - "à¸Ī ำ", - "ëį Ķ", - "ë¸ Į", - "Ġs ợ", - "ÛĮ رÛĮ", - "ÛĮر ÛĮ", - "о нÑĥ", - "он Ñĥ", - "ın daki", - "ında ki", - "ınd aki", - "алÑĮ ного", - "алÑĮно го", - "μ β", - "л из", - "ли з", - "Ġjej ich", - "æĸ ½", - "ä¾ ¿", - "l eÅŁtir", - "le ÅŁtir", - "leÅŁ tir", - "ĠÙĪ Ø£", - "Ġस ब", - "l erde", - "ler de", - "Ġ ÚĨÙĩ", - "ĠÚĨ Ùĩ", - "ÏĦ ÎŃ", - "Ġg ì", - "Ġ Ãļ", - "Ġà ļ", - "ĠÑĢаÑģ п", - "ĠÑĢа Ñģп", - "Ġt üm", - "à¹Ģ à¸ĩ", - "à¹Ģภĩ", - "èIJ ½", - "ìĨ ¡", - "à¹Ħ à¸Ĺย", - "m Ä±ÅŁtır", - "mÄ±ÅŁ tır", - "mÄ±ÅŁtı r", - "Ġ ÙĤرار", - "ĠÙĤر ار", - "Ġ à¸Ħาส", - "Ġà¸Ħ าส", - "Ġk ıs", - "Ġkı s", - "о ваниÑı", - "ов аниÑı", - "ова ниÑı", - "овани Ñı", - "ован иÑı", - "ãĤĤ ãģ®", - "د اÙħ", - "دا Ùħ", - "ìľ ¡", - "ol oj", - "olo j", - "ĠпоÑģл е", - "ĠпоÑģ ле", - "Ġ Так", - "ĠТ ак", - "ĠТа к", - "Ġб олее", - "Ġбол ее", - "ĠÄij á»ķi", - "ĠÄijá»ķ i", - "l ak", - "la k", - "í ħĮ", - "íħ Į", - "Ġa yn", - "Ġay n", - "Ñı Ñģ", - "Ġп ог", - "Ġпо г", - "Ġar asında", - "Ġaras ında", - "Ġara sında", - "Ġarası nda", - "Ī ¬", - "à¥Ĥ ल", - "Ġ ανα", - "Ġα να", - "Ġαν α", - "Ġq uyết", - "Ġquy ết", - "Ġthu á»Ļc", - "Ġd ün", - "Ġdü n", - "Ġp ÅĻes", - "ĠpÅĻ es", - "ĠpÅĻe s", - "ÑĦ Ñĸ", - "Ġ å¸", - "Ġå ¸", - "ا ÙĦÙĬ", - "اÙĦ ÙĬ", - "Ġп овеÑĢ", - "Ġпо веÑĢ", - "Ġпов еÑĢ", - "Ñĩ ина", - "Ñĩи на", - "Ñĩин а", - "s ko", - "sk o", - "çµ IJ", - "Ø ¡", - "Ġ гÑĢа", - "Ġг ÑĢа", - "ĠгÑĢ а", - "о ÑĤи", - "оÑĤ и", - "Ġqu á»ijc", - "ÑĨ Ñĸв", - "ÑĨÑĸ в", - "l endir", - "len dir", - "lendi r", - "в Ñĸд", - "вÑĸ д", - "Ġж иÑĤ", - "ü yor", - "üy or", - "ï¼Į ä»ĸ", - "lar ında", - "ları nda", - "ların da", - "Ġu yg", - "Ġuy g", - "Ġtr ÃŃ", - "Ġ Ø´ÙĨ", - "ĠØ´ ÙĨ", - "ا بÙĦ", - "اب ÙĦ", - "æ· ±", - "Âł p", - "Ñģ каÑı", - "Ñģк аÑı", - "Ñģка Ñı", - "о ÑĤа", - "оÑĤ а", - "ÙĪ Ø·", - "Ġ اط", - "Ġا Ø·", - "ĠØ§Ø ·", - "ä¾ Ĩ", - "Ġз аÑĤ", - "Ġза ÑĤ", - "Ġ име", - "Ġи ме", - "Ġим е", - "à¹Ģà¸Ĺ ศ", - "ëĭ ´", - "n ÄĽnÃŃ", - "nÄĽ nÃŃ", - "nÄĽn ÃŃ", - "Ñĥ лÑı", - "Ñĥл Ñı", - "- п", - "å ĺ", - "Ġв ип", - "Ġви п", - "аÑĢа кÑĤ", - "à¹Ģ à¸ļ", - "à¹Ģภļ", - "ç¦ ı", - "Ïģ Ïİ", - "س Ùĩ", - "à¥Į र", - "Ġdi ÄŁer", - "à¹Ĥ à¸Ķย", - "à¹Ĥà¸Ķ ย", - "ĠÑģп оÑģоб", - "ĠÑģпоÑģ об", - "åį ·", - "è ĸ", - "а нÑĤ", - "ан ÑĤ", - "Ñİ ÑĤÑĮÑģÑı", - "ÑİÑĤÑĮ ÑģÑı", - "ĠÑį ÑĤом", - "ĠÑįÑĤ ом", - "ĠÑįÑĤо м", - "Ġ ï½Ģ", - "Ġï½ Ģ", - "ส าม", - "ì m", - "ĠÑĪ к", - "Ġ à¸Ľà¸£à¸°", - "Ġà¸Ľ ระ", - "Ġà¸Ľà¸£ ะ", - "़ à¥Ģ", - "e kl", - "ek l", - "m uÅŁ", - "mu ÅŁ", - "ĠÑĤак ож", - "ÙĪ سط", - "ÙĪس Ø·", - "Ġ Äįi", - "ĠÄį i", - "ี à¸Ļ", - "ÛĮ ÙĨÙĩ", - "ÛĮÙĨ Ùĩ", - "ÄĽ k", - "å½ ¼", - "le rine", - "ler ine", - "leri ne", - "lerin e", - "ĠÄij ất", - "à¥ģ à¤ı", - "à¥ģठı", - "ол оÑģ", - "оло Ñģ", - "Ġ å°ı", - "Ġå° ı", - "ز ÙĬØ©", - "زÙĬ Ø©", - "Ġв ла", - "à¥Ģ ल", - "Ġ etti", - "Ġe tti", - "Ġet ti", - "Ġett i", - "ĠÑģ оÑģÑĤав", - "ĠÑģо ÑģÑĤав", - "ĠÑģоÑģÑĤ ав", - "ÙĦ اÙĦ", - "ÙĦا ÙĦ", - "Ġ çİ", - "Ġç İ", - "ĠpÅĻÃŃ pad", - "ëŁ °", - "ุ à¸ģ", - "Ġ Ñĩи", - "ĠÑĩ и", - "å ħį", - "åħ į", - "n ÄĽjÅ¡ÃŃ", - "nÄĽ jÅ¡ÃŃ", - "ิ ล", - "åį Ģ", - "s kých", - "sk ých", - "ský ch", - "า ศ", - "าภ¨", - "åIJ Ĺ", - "Ġ íĺĦ", - "Ġíĺ Ħ", - "Ġal ın", - "å§ Ķ", - "à¸ŀ ร", - "až d", - "Ġб ÑĸлÑĮ", - "ĠбÑĸ лÑĮ", - "ĠбÑĸл ÑĮ", - "à¹Ī วà¸Ļ", - "à¹Īว à¸Ļ", - "o og", - "oo g", - "a cı", - "ac ı", - "l ıģ", - "lı ÄŁ", - "Ġk hu", - "Ġkh u", - "Ġh izmet", - "Ġ éĽ", - "Ġé Ľ", - "Ġ Îĺ", - "ĠÎ ĺ", - "Ġde ÄŁer", - "ĠdeÄŁ er", - "åħ Ń", - "Ġ دÙĩ", - "Ġد Ùĩ", - "Ġn ÄĽk", - "ĠnÄĽ k", - "à¸Ħ à¸Ļ", - "е ÑĤÑĮ", - "еÑĤ ÑĮ", - "ب اÙĨ", - "با ÙĨ", - "ÏĦ ική", - "ÏĦικ ή", - "ÏĦι κή", - "ĠÄij á»ĭa", - "ĠÄijá»ĭ a", - "Ġ Công", - "ĠC ông", - "íĮ IJ", - "Ġк огда", - "ĠÚ© ÙĨد", - "ĠÚ©ÙĨ د", - "ãģ§ ãģį", - "ĠÏĢ εÏģι", - "ĠÏĢεÏģ ι", - "ĠÏĢε Ïģι", - "lar dan", - "larda n", - "Ġ зем", - "Ġз ем", - "ت ÙĪاÙĨ", - "تÙĪ اÙĨ", - "è³ ĩ", - "li kle", - "lik le", - "Ġt ụ", - "Ġtá» ¥", - "Ġd ẫn", - "Ġn ay", - "Ġna y", - "Ġ ÑģÑĤоÑĢ", - "ĠÑģ ÑĤоÑĢ", - "ĠÑģÑĤ оÑĢ", - "ĠÑģÑĤо ÑĢ", - "ĠØ´ Ùħا", - "ĠØ´Ùħ ا", - "Ø« ر", - "Ġd edi", - "Ġde di", - "Ġded i", - "к ое", - "ко е", - "ë ijIJ", - "ëij IJ", - "ÑĨ ев", - "ÑĨе в", - "ج Ùĩ", - "Ġm ůže", - "Ġmů že", - "Ġmůž e", - "à¥ģ प", - "à¥ģठª", - "à¥įर म", - "Ġ taÅŁ", - "Ġt aÅŁ", - "Ġta ÅŁ", - "оÑĢ ÑĤ", - "γ Ïģα", - "çĻ ¼", - "า à¸ļ", - "าภļ", - "iá» ħn", - "iá»ħ n", - "ĠÙħ ست", - "ĠÙħس ت", - "л екÑģ", - "ле кÑģ", - "лек Ñģ", - "Ġ prav", - "Ġp rav", - "Ġpr av", - "Ġpra v", - "Ġд оÑģ", - "Ġдо Ñģ", - "Ġd Ä±ÅŁ", - "Ġ zem", - "Ġz em", - "Ġze m", - "Ġg iao", - "Ġgi ao", - "Ġgia o", - "Ġv last", - "Ġvl ast", - "Ġvlas t", - "ĠÑį ÑĤого", - "ĠÑįÑĤ ого", - "ĠÑįÑĤо го", - "ï½ °", - "ว à¸ĩ", - "ÑĢ ой", - "ÑĢо й", - "Ġbir lik", - "e ný", - "en ý", - "Ġ ëĭ¨", - "Ġëĭ ¨", - "ов ани", - "ова ни", - "ован и", - "é£ İ", - "íı ī", - "Ġz ah", - "Ġza h", - "б а", - "åĬ ©", - "éĢ ²", - "ê ¶Į", - "Ġd iye", - "Ġdi ye", - "Ġdiy e", - "à¤Ĥ à¤ķ", - "Ġch uyá»ĥn", - "Ġ ìĹŃ", - "ĠìĹ Ń", - "Ġ ÑĤÑĢи", - "ĠÑĤ ÑĢи", - "ĠÑĤÑĢ и", - "Ġö nce", - "Ġön ce", - "Ġönc e", - "ï¼Į è¿Ļ", - "o ại", - "л еÑĤ", - "ле ÑĤ", - "ĠÏĥ Ïħν", - "ĠÏĥÏħ ν", - "l ád", - "lá d", - "ç e", - "t ü", - "Ġ Äįást", - "ĠÄį ást", - "ĠÄįá st", - "Ġ εν", - "Ġε ν", - "Ġb iá»ĩt", - "Ġ é«", - "Ġé «", - "à¥ĭ à¤ķ", - "ÙĦ ات", - "ÙĦا ت", - "ب اÙĦ", - "با ÙĦ", - "e cies", - "ec ies", - "eci es", - "Ġ ëĭ¹", - "Ġëĭ ¹", - "à¸Ĭ à¸Ļ", - "ÏĦ αÏĤ", - "ÏĦα ÏĤ", - "à¥į ण", - "à¥įठ£", - "u jÃŃcÃŃ", - "uj ÃŃcÃŃ", - "ujÃŃ cÃŃ", - "Äį et", - "Äįe t", - "Ġп об", - "Ġпо б", - "ÙĪ ار", - "ÙĪا ر", - "i yas", - "iy as", - "iya s", - "Ġd ruh", - "Ġdr uh", - "Ġdru h", - "د د", - "ÏĮ ν", - "ÑĢ ен", - "ÑĢе н", - "า รà¸ĸ", - "าร à¸ĸ", - "ä½ İ", - "ìķ ½", - "ÑĢ оз", - "ÑĢо з", - "ëĬĶ ëį°", - "ãĤĵ ãģª", - "Äį enÃŃ", - "Äįe nÃŃ", - "Äįen ÃŃ", - "**** ********", - "******** ****", - "****** ******", - "***** *******", - "******* *****", - "Ġ Ρ", - "ĠÎ ¡", - "ĠÑĤ омÑĥ", - "ĠÑĤо мÑĥ", - "ĠÑĤом Ñĥ", - "ร à¸ģ", - "à¥ģ स", - "à¥ģठ¸", - "ä¹ Ŀ", - "å°± æĺ¯", - "£ i", - "éĺ ²", - "Ùĥ ر", - "ĠÑį ÑĤи", - "ĠÑįÑĤ и", - "ĠÚ© Ø´ÙĪر", - "ĠÚ©Ø´ ÙĪر", - "Ġ ê°IJ", - "Ġê° IJ", - "Ġ ад", - "Ġа д", - "Ġ داد", - "Ġد اد", - "éģ İ", - "Ù «", - "Ġl áºŃp", - "Ġ اÙĦÙĩ", - "Ġا ÙĦÙĩ", - "ĠاÙĦ Ùĩ", - "æľ Ľ", - "Ġ تÙĩ", - "Ġت Ùĩ", - "ì§ Ī", - "ãģ§ ãģĤãĤĭ", - "ãģ§ãģĤ ãĤĭ", - "Ġ меж", - "Ġм еж", - "ĠÑĢ езÑĥлÑĮÑĤ", - "ĠÑĢез ÑĥлÑĮÑĤ", - "ç į", - "е мÑĥ", - "ем Ñĥ", - "Ġ تÙĪاÙĨ", - "Ġت ÙĪاÙĨ", - "ĠتÙĪ اÙĨ", - "Ġ راÙĩ", - "Ġر اÙĩ", - "Ġرا Ùĩ", - "ãĥ¼ ãĥł", - "ãĥ¼ãĥ ł", - "åĦ ¿", - "å± ŀ", - "б Ñĭ", - "á ¿", - "à¸Ħ ล", - "à¥ĭ à¤Ī", - "üt ün", - "à¤Ĺ र", - "ìķĺ ëĭ¤", - "âĪ §", - "Ġ ì°¨", - "Ġì° ¨", - "çµ Ħ", - "μα ÏĦα", - "ุ à¸Ļ", - "Ġ ÑĤом", - "ĠÑĤ ом", - "ĠÑĤо м", - "еÑĢ в", - "Îij Σ", - "ĠiÅŁ lem", - "ع Ùħ", - "ë ĥ", - "ãĥ Ħ", - "ا Ùģت", - "اÙģ ت", - "åĬ ŀ", - "Ġ nes", - "Ġn es", - "Ġne s", - "av aÅŁ", - "ava ÅŁ", - "ĠÙĨ ÛĮز", - "ĠÙĨÛĮ ز", - "å¼ º", - "Ġ éĻ", - "Ġé Ļ", - "Ñĸн нÑı", - "æ² ³", - "á ÅĻ", - "æĿ IJ", - "ĠØ£ ÙĬ", - "Ġ ì¹´", - "Ġì¹ ´", - "Ġn enÃŃ", - "Ġne nÃŃ", - "Ġnen ÃŃ", - "Ġ ÙĪÙħ", - "ĠÙĪ Ùħ", - "Ġ Ú©Ùħ", - "ĠÚ© Ùħ", - "i ếu", - "iế u", - "Ġ æ°", - "Ġæ °", - "åĮ »", - "Ġz or", - "Ġzo r", - "ί Ïĥ", - "ि ध", - "िठ§", - "Ġп оказ", - "Ġпо каз", - "Ġпок аз", - "Ġпока з", - "ह र", - "Ġiç er", - "ØŃ Ø©", - "ि à¤ĸ", - "िठĸ", - "а да", - "ад а", - "تر ÛĮÙĨ", - "ترÛĮ ÙĨ", - "Ġ bao", - "Ġb ao", - "Ġba o", - "Ġx ã", - "à¹Ģ à¸Ħร", - "à¹Ģà¸Ħ ร", - "Ġngh Ä©", - "à¹ģ à¸ļà¸ļ", - "à¹ģà¸ļ à¸ļ", - "ĠdoÄŁ ru", - "ĠdoÄŁr u", - "Ñĸ ÑĤи", - "ÑĸÑĤ и", - "Ġ بÙĬÙĨ", - "Ġب ÙĬÙĨ", - "ĠبÙĬ ÙĨ", - "Ġ леÑĤ", - "Ġл еÑĤ", - "ا غ", - "Ø§Ø º", - "ÛĮ Ú©ÛĮ", - "ÛĮÚ© ÛĮ", - "r áv", - "rá v", - "à¥į âĢį", - "âĢĻ nin", - "âĢĻn in", - "Ġ ย", - "Ġภ¢", - "åį Ĭ", - "Ġк оли", - "Ġкол и", - "Ġко ли", - "Ġtr ợ", - "éĿ Ĵ", - "ëŀ Ģ", - "Ġ ë¨", - "Ġë ¨", - "Ġ ÙĪر", - "ĠÙĪ ر", - "ï¾ Ĭ", - "è§ Ĥ", - "Ġ пи", - "Ġп и", - "н Ñĥв", - "нÑĥ в", - "il mesi", - "ilm esi", - "س تÙĩ", - "ست Ùĩ", - "Ġд еÑĢжав", - "ĠдеÑĢж ав", - "å® ĥ", - "åĪ ¥", - "ëħ Ģ", - "л ÑģÑı", - "à¤Ĥ ध", - "Ġ ÑĤи", - "ĠÑĤ и", - "ĠpÅĻ ip", - "ĠpÅĻi p", - "п и", - "á» ĵn", - "á»ĵ n", - "о ваÑĤÑĮ", - "ов аÑĤÑĮ", - "ова ÑĤÑĮ", - "ìĿ´ ëĿ¼", - "æľ Ŀ", - "Ġ ëĺIJ", - "Ġë ĺIJ", - "Ġëĺ IJ", - "ĠÎŃ Î½Î±", - "ĠÎŃν α", - "ãģ¾ ãģ§", - "ج اÙħ", - "جا Ùħ", - "Ġ ëĬ", - "Ġë Ĭ", - "н Ñĸв", - "нÑĸ в", - "ÏĢ οÏħ", - "ÏĢο Ïħ", - "Ġ زÙħاÙĨ", - "Ġز ÙħاÙĨ", - "ĠزÙħ اÙĨ", - "æĽ ²", - "Ġ ÙħÙĩ", - "ĠÙħ Ùĩ", - "ëł ¨", - "ä¸ ĥ", - "ãģ¨ ãģĹãģ¦", - "l abilir", - "la bilir", - "lab ilir", - "о же", - "ож е", - "å¤ ľ", - "ĠнÑĥж но", - "å½ ©", - "çĪ ±", - "Ġho Ãłn", - "ün ü", - "Ġ ëĦ¤", - "ĠëĦ ¤", - "Ġ جÙĨ", - "Ġج ÙĨ", - "Ġn ÄĽj", - "ĠnÄĽ j", - "к ими", - "ки ми", - "ким и", - "Ġa ynı", - "Ġayn ı", - "Ġ ÙĥÙĦ", - "ĠÙĥ ÙĦ", - "Ġnh au", - "Ạ³", - "ÙĬ ات", - "ÙĬا ت", - "Ġm ezi", - "Ġme zi", - "Ġmez i", - "Ġ ÑĢек", - "ĠÑĢ ек", - "ĠÑĢе к", - "Ġ tür", - "Ġt ür", - "Ġ говоÑĢ", - "Ġг овоÑĢ", - "Ġfaz la", - "åĩ Ĩ", - "ÑĪ ий", - "ÑĪи й", - "ÐŁ ÑĢи", - "ÐŁÑĢ и", - "ÑĢ оÑģÑĤ", - "ÑĢоÑģ ÑĤ", - "ÑĢо ÑģÑĤ", - "ĠоÑĢг ан", - "ĠоÑĢ ган", - "n ým", - "ný m", - "Ġ ÑĢод", - "ĠÑĢ од", - "Ġ ÙĪÛĮ", - "ĠÙĪ ÛĮ", - "ic ký", - "ick ý", - "ë¦ ¼", - "ï½ ²", - "æĢ İ", - "ĠÙĩ ذا", - "ĠÑĩ аÑģÑĤи", - "ĠÑĩа ÑģÑĤи", - "ĠÑĩаÑģ ÑĤи", - "ĠÑĩаÑģÑĤ и", - "ÃŃ r", - "á»ĩ nh", - "á»ĩn h", - "Ġ íĹ", - "Ġí Ĺ", - "ê »", - "lu ž", - "ÃŃ l", - "c ÃŃch", - "cÃŃ ch", - "å® Ł", - "ãģł ãģ£ãģŁ", - "ÙĬ رة", - "ÙĬر Ø©", - "Ġv Äĥn", - "æ¸ ¯", - "Ġ ÏĦιÏĤ", - "ĠÏĦι ÏĤ", - "ا رت", - "ار ت", - "Ġv ấn", - "âĶģâĶģ âĶģâĶģ", - "å¯ ¾", - "Ïģ ÎŃ", - "Ġг одÑĥ", - "Ġгод Ñĥ", - "Ġ سب", - "Ġس ب", - "ا رات", - "ار ات", - "ارا ت", - "е лей", - "ел ей", - "еле й", - "Ġз аÑħ", - "Ġза Ñħ", - "Ġ важ", - "Ġв аж", - "Ġва ж", - "Ġt á»īnh", - "Ġtá» īnh", - "Ġtá»ī nh", - "ا بع", - "اب ع", - "Ġ à¤ľà¤¬", - "Ġà¤ľ ब", - "Ġà¤IJ स", - "Ġ дÑĥ", - "Ġд Ñĥ", - "Ġ é«ĺ", - "Ġé «ĺ", - "Ġé« ĺ", - "ê² ł", - "н ее", - "не е", - "ï½ Į", - "Ġм ал", - "Ġма л", - "è¾ ¹", - "ãģł ãģij", - "à¹ī ร", - "ÙĤ Ø·", - "Ġb ên", - "Ġs eb", - "Ġse b", - "ĠØ® ÙĪاÙĩ", - "ĠØ®ÙĪ اÙĩ", - "s iz", - "si z", - "Ġol ur", - "Ġ ëĶ°", - "ĠëĶ °", - "Ġ ì¢ĭ", - "Ġì¢ ĭ", - "Ġs vÄĽt", - "Ġsv ÄĽt", - "ĠsvÄĽ t", - "ic ká", - "ick á", - "á» ¹", - "Ġqu ản", - "Ġquả n", - "Ġ иÑģ", - "Ġи Ñģ", - "Ġz aÄį", - "Ġza Äį", - "ื à¸Ńà¸Ļ", - "ืà¸Ń à¸Ļ", - "ÑĶ Ñİ", - "ि ष", - "िठ·", - "ç Ĭ¶", - "çĬ ¶", - "Ïĥ μ", - "ั ส", - "ó c", - "Ġ беÑĢ", - "Ġб еÑĢ", - "Ġ íĿ", - "Ġí Ŀ", - ";: ;:", - "Ġ پس", - "ĠÙ¾ س", - "Ġ ëijIJ", - "Ġë ijIJ", - "Ġëij IJ", - "н иÑĩ", - "ни Ñĩ", - "Ġо ÑĩенÑĮ", - "ĠоÑĩ енÑĮ", - "ĠìķĦìĿ´ ì½ĺ", - "Ġ θα", - "Ġθ α", - "Ġв ÑģÑĤ", - "ĠвÑģ ÑĤ", - "ا دة", - "اد Ø©", - "Ġdev am", - "ื à¸Ńà¸ĩ", - "ืà¸Ń à¸ĩ", - "ĠлÑİ Ð´Ð¸", - "ĠлÑİд и", - "ìĺ Ī", - "á»± a", - "Ñı Ñħ", - "âĢĮ اÛĮ", - "Ġ سÙĪ", - "Ġس ÙĪ", - "å° ¼", - "Ġth ứ", - "Ġthá» ©", - "m eye", - "me ye", - "mey e", - "Ġ èµ", - "Ġè µ", - "èī ¯", - "ĠdeÄŁ iÅŁ", - "ÑĪ Ñĸ", - "Ġtr ợ", - "ĠâĢİ #", - "çĹ ħ", - "ìĽ Į", - "Ġk de", - "Ġkd e", - "Î §", - "æ ¤", - "ĠÑħ аÑĢакÑĤ", - "æ ĩ", - "Ġb iến", - "Ġbi ến", - "ÙĤ ع", - "åŁ Ł", - "Ġн еп", - "Ġне п", - "Ġd ů", - "Ġп иÑĤ", - "Ġпи ÑĤ", - "ĠÑĤ ÑĢеб", - "ĠÑĤÑĢ еб", - "ا زÛĮ", - "از ÛĮ", - "Ġ طر", - "ĠØ· ر", - "Ġ ÙħÙĦ", - "ĠÙħ ÙĦ", - "Ġt ham", - "Ġth am", - "Ġtha m", - "Ġ ÙĪجÙĪد", - "ĠÙĪج ÙĪد", - "Ġs vé", - "Ġsv é", - "é§ ħ", - "ا ÛĮÙĨ", - "اÛĮ ÙĨ", - "Ġt iên", - "Ġti ên", - "s tru", - "st ru", - "str u", - "Ġv áºŃy", - "ü ne", - "ün e", - "Ġ à¹Ģม", - "Ġà¹Ģ ม", - "Ġà¹Ģภ¡", - "Ġr ằng", - "а ÑĤÑĥ", - "аÑĤ Ñĥ", - "äº ij", - "н иÑĤ", - "ни ÑĤ", - "ä¼ Ĭ", - "ÙĪ ص", - "Ġ éĿ", - "Ġé Ŀ", - "ĠпÑĢоб лем", - "d eki", - "de ki", - "dek i", - "** ************", - "******** ******", - "****** ********", - "******* *******", - "************ **", - "ò a", - "ĠÄijá»ģ u", - "ãĤĮ ãģŁ", - "ا رس", - "ار س", - "ãģª ãģı", - "ا ÙĤع", - "اÙĤ ع", - "è» į", - "Ùĥ Ùħ", - "Äį as", - "Ġk ỳ", - "Ġká» ³", - "Ø´ Ùħ", - "à¥ĩ ड", - "à¥ĩठ¡", - "éĺ ¿", - "Ġje jÃŃ", - "Ġjej ÃŃ", - "Ġ æĻ", - "Ġæ Ļ", - "Ġ Ä°ÅŁ", - "ĠÄ° ÅŁ", - "ar dım", - "ard ım", - "Ġसम य", - "Ġ ÐĿо", - "ĠÐĿ о", - "i lerin", - "il erin", - "ile rin", - "iler in", - "ileri n", - "Ġع بد", - "Ġعب د", - "n ÃŃk", - "nÃŃ k", - "ĠØ´ Ú©ÙĨ", - "ĠØ´Ú© ÙĨ", - "ิ à¸Ĺย", - "ิà¸Ĺ ย", - "á» ħ", - "ÑĢ ез", - "ÑĢе з", - "Ġch ứng", - "Ġchứ ng", - "Ġ :.", - "Ġ: .", - "Ġ पत", - "Ġप त", - "Ġž ivot", - "Ġživ ot", - "å¢ ĥ", - "« a", - "Ġt rung", - "Ġtr ung", - "ни кÑĸв", - "ник Ñĸв", - "ĠاÙĦ ÙħÙĨ", - "ĠاÙĦÙħ ÙĨ", - "ĠÑĢ аÑģÑģ", - "ĠÑĢаÑģ Ñģ", - "Ġ жив", - "Ġж ив", - "Ġз акон", - "Ġза кон", - "Ġзак он", - "Ġзако н", - "Ġ 목", - "Ġëª ©", - "Ġz áv", - "Ġzá v", - "Ġh akk", - "Ġha kk", - "Ġhak k", - "ä» ¤", - "ĠÑı кий", - "ĠÑıк ий", - "Ġ بÙĬ", - "Ġب ÙĬ", - "λ ÎŃ", - "oc uk", - "ocu k", - "Ġ Ñİ", - "ĠÑ İ", - "à¸ģ ว", - "Ġ اÙĨÚ¯", - "Ġا ÙĨÚ¯", - "ĠاÙĨ Ú¯", - "à¥ģ à¤Ĥ", - "à¥ģठĤ", - "Ġ nám", - "Ġn ám", - "Ġná m", - "á»ķ ng", - "Ġж ел", - "Ġже л", - "ĠÄij ặc", - "Äį it", - "Äįi t", - "Ġ ê±´", - "Ġê± ´", - "Ġب ÛĮØ´", - "ĠبÛĮ Ø´", - "кÑĢаÑĹ Ð½", - "Ġ ÙĪÙĩ", - "ĠÙĪ Ùĩ", - "н еннÑı", - "нен нÑı", - "Ġ à¹Ģà¸ŀ", - "Ġà¹Ģ à¸ŀ", - "Ġà¹Ģภŀ", - "о мен", - "ом ен", - "Ġl ần", - "Ġ عÙħÙĦ", - "Ġع ÙħÙĦ", - "ĠعÙħ ÙĦ", - "Ġî ģµ", - "Ä ŀ", - "ÑĸÑģ лÑı", - "Æ° ng", - "ा फ", - "ाठ«", - "à¸Ĺ à¸ĺ", - "д ен", - "де н", - "ĠÑī об", - "ĠÑīо б", - "Ñĩ ив", - "Ñĩи в", - "ılı r", - "ıl ır", - "ا عات", - "اع ات", - "j ÃŃcÃŃ", - "jÃŃ cÃŃ", - "ë² ¨", - "ÚĨ Ùĩ", - "ا رج", - "ار ج", - "ĠÙ¾ رÙĪ", - "Ġپر ÙĪ", - "Ġо дин", - "Ġод ин", - "Ġоди н", - "л ин", - "ли н", - "б Ñĥ", - "Ġसर à¤ķ", - "åĢ Ļ", - "ë¶Ģ íĦ°", - "à¥Īà¤Ĥ ,", - "å ´", - "à¹Ĥ ล", - "Ġv Å¡ak", - "ĠvÅ¡ ak", - "Ġоп ÑĢед", - "ì ±", - "æ ½", - "Ġdá»± ng", - "p ráv", - "pr áv", - "ิ ส", - "Ġnh iá»ĩm", - "Ġil iÅŁ", - "Ġili ÅŁ", - "Ġе Ñīе", - "Ġje Å¡tÄĽ", - "Ġ ÑĢаÑģÑĤ", - "ĠÑĢ аÑģÑĤ", - "ĠÑĢаÑģ ÑĤ", - "ĠÑĢа ÑģÑĤ", - "ภ®", - "à¤Ĥ à¤Ł", - "âĢĮ Ú©", - "Ġ بÛĮÙĨ", - "Ġب ÛĮÙĨ", - "ĠبÛĮ ÙĨ", - "o vou", - "ov ou", - "ovo u", - "æĻ ®", - "ί εÏĤ", - "о ÑĢоÑĪ", - "оÑĢ оÑĪ", - "оÑĢо ÑĪ", - "Ġol mak", - "Ġolm ak", - "Ġolma k", - "Ġst át", - "di ÄŁi", - "Ġt ình", - "Ġ dÄĽ", - "Ġd ÄĽ", - "ĠÚ¯ رÙģ", - "Ġگر Ùģ", - "Ïĥ ο", - "Ġ ÑĥÑĤ", - "ĠÑĥ ÑĤ", - "íķĻ êµIJ", - "ั à¸IJ", - "า à¸Ń", - "าภŃ", - "ĠÄij ặt", - "Ġмог ÑĥÑĤ", - "ĠмогÑĥ ÑĤ", - "ë° °", - "t ik", - "ti k", - "ª ½", - "li ÄŁ", - "ÏĢ ε", - "Ġ èĢ", - "Ġè Ģ", - "k ü", - "ad ece", - "ade ce", - "κ ÏĮ", - "Ġ дÑĸ", - "Ġд Ñĸ", - "ầ m", - "çĦ¡ ãģĹ", - "Û²Û° Û±", - "èµ Ľ", - "оÑģ Ñĥд", - "Ġ ìķĪëĤ´", - "ĠìķĪ ëĤ´", - "Ġ ÐĶж", - "ĠÐĶ ж", - "åº §", - "ic kých", - "ick ých", - "ický ch", - "Ġ ìłģ", - "Ġì łģ", - "Ġìł ģ", - "à¥ĩ ,", - "ov ého", - "ové ho", - "Ġv ẫn", - "Ġbirlik te", - "Ġर à¤ĸ", - "Ġ ÙĨÙĩ", - "ĠÙĨ Ùĩ", - "ÙĤ ر", - "प र", - "e tÃŃ", - "et ÃŃ", - "Ġ ÑĤÑĭ", - "ĠÑĤ Ñĭ", - "Ģ ìĿ´", - "Ġà¤ħ ल", - "Ġм оже", - "Ġмож е", - "Ġмо же", - "ãĤ ´", - "Ġs tran", - "Ġst ran", - "Ġstr an", - "Ġstra n", - "Ø· ر", - "è¿Ļ 个", - "Ġ بع", - "Ġب ع", - "åĨ Ľ", - "ek tir", - "ekt ir", - "Ġh Æ°á»Ľng", - "ÙĨ اÙĨ", - "ÙĨا ÙĨ", - "Ġठij", - "Ġà ¤ij", - "ÏĮ ÏĦη", - "о Ñģк", - "оÑģ к", - "åį ĥ", - "as ına", - "ası na", - "Ġ Ø´Ùĩ", - "ĠØ´ Ùĩ", - "Ġ деÑĢ", - "Ġд еÑĢ", - "Ġде ÑĢ", - "ĠÙħ خت", - "ĠÙħØ® ت", - "Ġ ØŃÙĤ", - "ĠØŃ ÙĤ", - "ãĥ ¾", - "س اÙĨ", - "Ġc ung", - "Ġcu ng", - "ко ÑĢиÑģÑĤ", - "коÑĢ иÑģÑĤ", - "ÏĦ ικά", - "ÏĦικ ά", - "ÏĦι κά", - "Ġв она", - "Ġво на", - "ب ا", - "ãģķ ãĤĮãģŁ", - "ãģķãĤĮ ãģŁ", - "n out", - "no ut", - "nou t", - "Ġ ı", - "ĠÄ ±", - "è§ ī", - "ĠÃ¶ÄŁ ren", - "Ġ ì½Ķ", - "Ġì ½Ķ", - "Ġì½ Ķ", - "å¸ ¦", - "Ñģ лов", - "Ñģл ов", - "Ġε ÏĢι", - "ĠεÏĢ ι", - "ê° IJ", - "ĠÙħ رب", - "ĠÙħر ب", - "ĠÙģÛĮ ÙĦÙħ", - "Ġк ÑĢов", - "Ġ ëį°", - "Ġë į°", - "Ġëį °", - "ा ण", - "ाठ£", - "Ġel ekt", - "Ġele kt", - "Ġelek t", - "Ġ наÑĢод", - "Ġн аÑĢод", - "Ġна ÑĢод", - "ĠнаÑĢ од", - "ÛĮ دÙĩ", - "ÛĮد Ùĩ", - "ç´ Ħ", - "Ġп ÑĢоÑĦ", - "ĠпÑĢ оÑĦ", - "ĠпÑĢо ÑĦ", - "Ïģ οÏĤ", - "Ïģο ÏĤ", - "Ġ ãħ", - "ä¸į æĺ¯", - "Ġ à¤ľà¤¨", - "Ġà¤ľ न", - "ั ล", - "Ġص ÙĪرت", - "ĠصÙĪر ت", - "ãĥ ľ", - "Ġà¤Ĺ à¤Ī", - "ÄŁi tim", - "ÄŁit im", - "ÑģÑĮ киÑħ", - "ÑģÑĮк иÑħ", - "Ġ лег", - "Ġл ег", - "Ġت ÙĪÙĦ", - "ĠتÙĪ ÙĦ", - "Ġ ìļ´", - "Ġìļ ´", - "ع ر", - "Ġm Ãłu", - "ĠmÃł u", - "г ов", - "го в", - "æ³ ¢", - "in deki", - "ind eki", - "inde ki", - "ìłģ ìĿ¸", - "ấ m", - "Ġ íĻķ", - "ĠíĻ ķ", - "Ġب اÛĮد", - "Ġبا ÛĮد", - "ĠباÛĮ د", - "à¹Į à¸Ĺ", - "Ġk endi", - "Ġken di", - "Ġkend i", - "ี ว", - "ิ à¸ģาร", - "ิà¸ģ าร", - "ิà¸ģา ร", - "ĠÚ© ردÙĩ", - "Ġکرد Ùĩ", - "Ġکر دÙĩ", - "å· ´", - "ठģ", - "ร าà¸Ĭ", - "à¥į श", - "à¥įठ¶", - "Ġ ÐĶлÑı", - "ĠÐĶ лÑı", - "å¥ ĩ", - "ĠÑĥ ÑģÑĤанов", - "ĠÑĥÑģÑĤ анов", - "ĠÑĥÑģÑĤан ов", - "й ÑĤе", - "ãĤ ĩ", - "ά Ïģ", - "Ġ Ю", - "ĠÐ ®", - "Ġlu áºŃt", - "ãĢ ī", - "è´ ¨", - "د ا", - "Ġdü zen", - "Ġdüz en", - "ส à¸Ļ", - "ÑĢ он", - "ÑĢо н", - "d ıģı", - "dı ģı", - "dıģ ı", - "âĢĻ da", - "âĢĻd a", - "Ġfark lı", - "Ñħ ов", - "Ñħо в", - "l án", - "lá n", - "Ñĩ аÑģ", - "Ñĩа Ñģ", - "Ñĩ ин", - "Ñĩи н", - "Ġ ì°¸", - "Ġì° ¸", - "ì ´Ī", - "ì´ Ī", - "ÑĨ ип", - "ÑĨи п", - "ç ¹", - "éĸ Ģ", - "ж а", - "ÑĢ ован", - "ÑĢов ан", - "ÑĢо ван", - "ÑĢова н", - "à¸ĵ ะ", - "ÙĦÙĬ زÙĬØ©", - "Ïĩ ει", - "Ïĩε ι", - "à¥Ī .", - "к Ñģп", - "кÑģ п", - "ا ÙĪر", - "اÙĪ ر", - "Ġng uyên", - "Ġnguy ên", - "ãģ« ãĤĪ", - "à¥ĩ म", - "à¥ĩठ®", - "Ïĥ ÏĦε", - "ÏĥÏĦ ε", - "ت ÙĪ", - "Äį ek", - "Äįe k", - "ÑĨ Ñĭ", - "Ġ 물", - "Ġë¬ ¼", - "Ñį ÑĤ", - "Ġka zan", - "Ġkaz an", - "Ùģ س", - "e hir", - "eh ir", - "в ÑĸÑĤ", - "вÑĸ ÑĤ", - "Ġد ÙĪÙĦ", - "ĠدÙĪ ÙĦ", - "Ġ ëĵľ", - "Ġëĵ ľ", - "Ġà¤ļ ल", - "е ÑģÑĤва", - "еÑģÑĤв а", - "еÑģÑĤ ва", - "δ α", - "Ġб Ñĥв", - "ĠбÑĥ в", - "Ġ ÐĿе", - "ĠÐĿ е", - "ØŃ Ø±", - "огÑĢа ÑĦ", - "Ġroz hod", - "Ġrozh od", - "Ġви коÑĢиÑģÑĤ", - "Ġвико ÑĢиÑģÑĤ", - "Ġy êu", - "λ οÏĤ", - "λο ÏĤ", - "Ú© س", - "Ġ شب", - "ĠØ´ ب", - "ิ ษ", - "æ¯ į", - "Ġд оÑĢ", - "Ġдо ÑĢ", - "Ġngh á»ĩ", - "Ġt rang", - "Ġtr ang", - "Ġtra ng", - "Ġtran g", - "à¥ĩ द", - "à¥ĩठ¦", - "Ġt ìm", - "Ñĩ но", - "Ġ اÙħا", - "Ġا Ùħا", - "ĠاÙħ ا", - "éģ ĭ", - "Ú© ر", - "k é", - "Ġ vÄĽt", - "Ġv ÄĽt", - "ĠvÄĽ t", - "Ġн аÑģÑĤ", - "Ġна ÑģÑĤ", - "ĠнаÑģ ÑĤ", - "Ġ æ±", - "Ġæ ±", - "Ġ åĽ½", - "ĠåĽ ½", - "Ġgi ảm", - "Ġgiả m", - "ا دÙĬ", - "اد ÙĬ", - "ëĤ ľ", - "ë¡ ł", - "Ġ ､", - "Ġï½ ¤", - "Ġд енÑĮ", - "Ġде нÑĮ", - "Ġден ÑĮ", - "ÑĨ ÑĸÑİ", - "ÑĨÑĸ Ñİ", - "Ġh ạn", - "Ġhạ n", - "ẳ ng", - "ẳn g", - "λ ή", - "e yen", - "ey en", - "eye n", - "ä¸ Ķ", - "æŃ ¦", - "ĠÑĦ ак", - "à¹Ī à¸Ńà¸Ļ", - "à¹Īà¸Ń à¸Ļ", - "Ġ οι", - "Ġο ι", - "ز Ùħ", - "ãģĹ ãģ¦ãģĦãĤĭ", - "ãģĹãģ¦ ãģĦãĤĭ", - "ãģĹãģ¦ãģĦ ãĤĭ", - "л ива", - "ли ва", - "лив а", - "âĢķ âĢķ", - "Ġ öl", - "Ġö l", - "Ġ à¤ĵ", - "Ġठĵ", - "Ñģ ÑĤÑĸ", - "ÑģÑĤ Ñĸ", - "à¸ģ รรม", - "à¸ģร รม", - "Ġt ục", - "Ġtụ c", - "Ġgö rün", - "Ġgör ün", - "ãģĹ ãģ¾", - "ãģĹãģ ¾", - "Ġ ì¦", - "Ġì ¦", - "é ¦¬", - "é¦ ¬", - "Ġмож на", - "Ġ Ú©ÙĦ", - "ĠÚ© ÙĦ", - "Ġ ÑĨенÑĤ", - "ĠÑĨ енÑĤ", - "ĠÑĨе нÑĤ", - "ĠÑĨен ÑĤ", - "Ġ ìĻ¸", - "ĠìĻ ¸", - "Î ĺ", - "ç ĩ", - "Ġg elen", - "Ġge len", - "Ġgel en", - "Ġgele n", - "Ġ اÙĬÙĨ", - "Ġا ÙĬÙĨ", - "ĠاÙĬ ÙĨ", - "ĠØ¢ ب", - "Ġà¤Ĩ य", - "ัà¸ģ ษ", - "Ñģ им", - "Ñģи м", - "Ġб олÑĮÑĪ", - "ĠболÑĮ ÑĪ", - "Ġм н", - "о ди", - "од и", - "ĠÄ° l", - "Ġ à¤Ĩर", - "Ġà¤Ĩ र", - "е ÑĤе", - "еÑĤ е", - "ÑĨ иÑİ", - "ÑĨи Ñİ", - "áºŃ u", - "Ġt iếng", - "Ġtiế ng", - "Ġtiến g", - "ë ¶ģ", - "ë¶ ģ", - "æ§ ĺ", - "Ġн аÑĪ", - "Ġна ÑĪ", - "ม า", - "âĢĻ ın", - "âĢĻı n", - "ãĥĥ ãĥĹ", - "ÙĪ جÙĩ", - "ÙĪج Ùĩ", - "Ġ ØŃد", - "ĠØŃ Ø¯", - "á vá", - "áv á", - "ر ÙĪØ´", - "رÙĪ Ø´", - "Ġ дейÑģÑĤв", - "Ġд ейÑģÑĤв", - "ãģ£ ãģ¦ãģĦãĤĭ", - "ãģ£ãģ¦ ãģĦãĤĭ", - "ãģ£ãģ¦ãģĦ ãĤĭ", - "Ïģ ή", - "Ġ üst", - "Ġü st", - "Ġt iết", - "Ġti ết", - "Ġtiế t", - "ac aÄŁ", - "aca ÄŁ", - "Ġ ÐŁÐ¾", - "ĠÐŁ о", - "é Ĭ", - "ë¨ ¸", - "c hod", - "ch od", - "cho d", - "ĠØ¢Ùħ ÙĪز", - "ãģŁ ãĤģ", - "Ġch uyên", - "Ġuy gu", - "Ġuyg u", - "н ÑĸÑģÑĤ", - "нÑĸ ÑģÑĤ", - "ë ´", - "æİ §", - "Ñĥ ÑİÑĤÑĮ", - "ÑĥÑİ ÑĤÑĮ", - "ÑĥÑİÑĤ ÑĮ", - "Äį i", - "ãģ ¹", - "à¥Ĥ न", - "æĹ ©", - "ãĥĩ ãĤ£", - "è Ĵ", - "ĠØ´ خص", - "ĠÑħ оÑĤ", - "ĠÚ©ÙĨ ÛĮد", - "г л", - "à¸Ń à¸Ńà¸ģ", - "à¸Ńà¸Ń à¸ģ", - "éĢ Ļ", - "Ġز ÛĮر", - "ĠزÛĮ ر", - "íķ Ń", - "ĠÃĸ z", - "åij ³", - "ØŃ Ø¯Ø©", - "ØŃد Ø©", - "Ġk ažd", - "Ġ ÑĨвеÑĤ", - "ĠÑĨ веÑĤ", - "Ġ ç¾", - "Ġç ¾", - "Ġк ож", - "Ġко ж", - "Ġ ÐŃÑĤо", - "ĠÐŃ ÑĤо", - "ÑıÑĤ елÑĮ", - "ла ÑģÑĮ", - "лаÑģ ÑĮ", - "âĢĮ Ø´ÙĪد", - "âĢĮØ´ ÙĪد", - "μ ι", - "Ġ æ²", - "Ġæ ²", - "Ġs üre", - "Ġsü re", - "Ġsür e", - "ล ะ", - "éħ Ĵ", - "ึà¸ģ ษ", - "λ λά", - "λλ ά", - "ç ij", - "Ġ ìĥĪ", - "Ġìĥ Ī", - "Ġस ह", - "ĠH Ãł", - "리 ê³ł", - "ص ر", - "Ġ æĬķ", - "ĠæĬ ķ", - "éł Ń", - "Ġb á»ĩnh", - "ĠìĥĿ ê°ģ", - "Ġà¤ħ à¤Ń", - "ê³µ ì§Ģ", - "ì Ķ", - "á» Ŀi", - "á»Ŀ i", - "ç ŃĶ", - "çŃ Ķ", - "Ġb Ãłi", - "ĠbÃł i", - "о дÑĸ", - "од Ñĸ", - "า à¸Ĥ", - "าภĤ", - "ни ков", - "ник ов", - "Ġdön em", - "Ġdö nem", - "ว ม", - "ãĥĨ ãĤ£", - "ा रण", - "ार ण", - "о ги", - "ог и", - "Ġk iá»ĥm", - "Ġki á»ĥm", - "о ÑĦ", - "äº Ī", - "åĨ ³", - "ا ÙĦات", - "اÙĦ ات", - "اÙĦا ت", - "Ġn ếu", - "Ġ cest", - "Ġc est", - "Ġce st", - "Ġces t", - "ز Ø´", - "Ùİ ÙĦ", - "Ġت Ø£", - "ĠÄij ạo", - "Ïį ν", - "Ġв нÑĥ", - "Ġ جاÙħ", - "Ġج اÙħ", - "Ġجا Ùħ", - "i vnÃŃ", - "iv nÃŃ", - "Ġìŀ ĪìĬµëĭĪëĭ¤", - "ĠìŀĪ ìĬµëĭĪëĭ¤", - "Ï Ĭ", - "æĦ Ľ", - "ãĥ Ľ", - "м Ñĸн", - "мÑĸ н", - "Ġt ÃŃm", - "ĠtÃŃ m", - "ằ m", - "ê· ł", - "äº ķ", - "Ġx ây", - "Ġ ìĽĶ", - "ĠìĽ Ķ", - "е лен", - "ел ен", - "еле н", - "Ġ à¹Ĥà¸Ķย", - "Ġà¹Ĥ à¸Ķย", - "ا ÙĦÙĩ", - "اÙĦ Ùĩ", - "Ġb ất", - "á»ĵ m", - "âĢĮ Ú¯", - "ÙĪ رة", - "ÙĪر Ø©", - "ب ات", - "با ت", - "Ġb án", - "ẫ u", - "اÙĨ ÙĪÙĨ", - "اÙĨÙĪ ÙĨ", - "Ġzá kon", - "á ž", - "ì¶ Ķ", - "à¹ģ à¸ģ", - "ãĤį ãģĨ", - "ÑĢ оÑĤ", - "ÑĢо ÑĤ", - "ç ĵ", - "Ġв они", - "Ġво ни", - "Ġx ác", - "Ġ دÛĮگر", - "ĠدÛĮ گر", - "ÏĢ οι", - "ÏĢο ι", - "Ġне Ñģк", - "ĠнеÑģ к", - "ر سÛĮ", - "رس ÛĮ", - "Ġ ëĿ¼", - "Ġë Ŀ¼", - "ت ÙĦ", - "λ ά", - "ĠÑıв лÑıеÑĤÑģÑı", - "ä¾ Ŀ", - "Ġ åħ¬", - "Ġåħ ¬", - "Ĺ i", - "Ġ íĬ¹", - "ĠíĬ ¹", - "Ùĥ ÙĪÙĨ", - "ÙĥÙĪ ÙĨ", - "ắ p", - "جÙħ ÙĪع", - "ÏĨ οÏģ", - "ÏĨο Ïģ", - "е ло", - "ел о", - "Ġg üven", - "Ġgü ven", - "Ġм ай", - "Ġма й", - "ĠÑģ оз", - "ĠÑģо з", - "à¸ģ ระ", - "à¸ģร ะ", - "Ġا سÙĦاÙħ", - "Ġاس ÙĦاÙħ", - "Ġ Ñīе", - "ĠÑī е", - "Ġs á»ijng", - "Ġsá»ij ng", - "à¥į ब", - "à¥įठ¬", - "Ú© ار", - "کا ر", - "Ġthu áºŃt", - "Ġ nÃŃ", - "Ġn ÃŃ", - "第 ä¸Ģ", - "è¦ ĸ", - "à¹Ģà¸ģ ม", - "ا ÙĬØ©", - "اÙĬ Ø©", - "Ġ ÎĪ", - "ĠÎ Ī", - "ãĤ ¶", - "ĠÙħ ÙĪÙĤع", - "ĠÙħÙĪ ÙĤع", - "Ġ åĴ", - "Ġå Ĵ", - "è¡ ĵ", - "Ġ Ðŀд", - "ĠÐŀ д", - "Ġ ä¸ī", - "Ġä¸ ī", - "ler inde", - "leri nde", - "lerin de", - "ĠÑģв оÑĹ", - "ĠÑģво ÑĹ", - "à¥Ģ à¤ı", - "Ġth Æ°Æ¡ng", - "Ïĥ ÏĦο", - "ÏĥÏĦ ο", - "Ġ غÙĬر", - "Ġغ ÙĬر", - "Ġ پر", - "ĠÙ¾ ر", - "ĠÑģеб е", - "Ġв к", - "Ġk hai", - "Ġkh ai", - "ãĤ Ģ", - "ĠÙĨ ظر", - "ĠÙĨظ ر", - "Ġдок Ñĥм", - "à¹ĩ à¸ļ", - "Ġ íķľêµŃ", - "Ġíķľ êµŃ", - "ï½ ī", - "å·¥ ç¨ĭ", - "Ġ ÙĪÙĦ", - "ĠÙĪ ÙĦ", - "ØŃ ÙĬ", - "Ġп ла", - "Ġпл а", - "Ġ Ä°stanbul", - "ĠÄ° stanbul", - "âĢĻ de", - "âĢĻd e", - "а лÑģÑı", - "ал ÑģÑı", - "ĠØ¢ÙĨ Ùĩا", - "Ġ اÙĩ", - "Ġا Ùĩ", - "Ġ ê´Ģ리", - "Ġê´Ģ 리", - "Ġ anh", - "Ġa nh", - "Ġan h", - "Å¡ ÃŃm", - "Å¡ÃŃ m", - "lar la", - "ï¼ Ŀ", - "n ostÃŃ", - "no stÃŃ", - "nost ÃŃ", - "nos tÃŃ", - "ÑģÑĤ ве", - "ÑģÑĤв е", - "ÛĮ Ùģ", - "Ġ گرد", - "ĠÚ¯ رد", - "Ġگر د", - "ãĤĮ ãĤĭ", - "Ġv á»±", - "Ġvá» ±", - "ÄĽ nÃŃ", - "ÄĽn ÃŃ", - "Ġgö rev", - "Ġgör ev", - "Ġgöre v", - "Ġyıl ında", - "Ġyılı nda", - "Ġyı lında", - "Ġl ợi", - "Ġlá» £i", - "Ġan lam", - "Ġп ÑĢовод", - "ĠпÑĢо вод", - "ĠпÑĢов од", - "ÑĨ Ñİ", - "Ġ åī", - "Ġå ī", - "Ġë§ İ", - "ÑĢ аÑģ", - "ÑĢа Ñģ", - "Ġ Ž", - "ĠÅ ½", - "Ú© اÙĨ", - "کا ÙĨ", - "Ð Ļ", - "ãģ£ ãģ¨", - "ãģ£ãģ ¨", - "Ú© ÙĦ", - "า ยà¸Ļ", - "าย à¸Ļ", - "ع اÙĦ", - "عا ÙĦ", - "Ġ ký", - "Ġk ý", - "ĠмаÑĤ еÑĢи", - "Ġма ÑĤеÑĢи", - "ê» ĺ", - "ıl ması", - "μ ÎŃν", - "μÎŃ Î½", - "ĠÙĨ ÙħÛĮ", - "ĠÙĨÙħ ÛĮ", - "Ġcu á»Ļc", - "Ġδ εν", - "Ġδε ν", - "å¹ ²", - "＿ ＿＿＿", - "＿＿ ＿＿", - "＿＿＿ ＿", - "à¥Ģ à¤Ł", - "Ġçık ar", - "Ġçı kar", - "Ġkon uÅŁ", - "Ġkonu ÅŁ", - "иÑĤ елÑĮно", - "иÑĤелÑĮ но", - "lan tı", - "lant ı", - "à¹Ħ ล", - "å¾ ĭ", - "Ġ íĶ¼", - "ĠíĶ ¼", - "ìĻ ¸", - "Ġs áng", - "éģ Ķ", - "о жд", - "ож д", - "Ġ آخر", - "ĠØ¢ خر", - "il ece", - "ile ce", - "à¥Ī न", - "Ġ jedn", - "Ġj edn", - "Ġje dn", - "Ġjed n", - "ĠÑģпе ÑĨи", - "´ Ŀ", - "Ġ Úĺ", - "Ġ ãĢĤĊ", - "ĠãĢĤ Ċ", - "èģ Į", - "Ġ ÙĨÛĮ", - "ĠÙĨ ÛĮ", - "ÑĤ оÑĢа", - "ÑĤо ÑĢа", - "ÑĤоÑĢ а", - "λ ι", - "Ġ ÙĪب", - "ĠÙĪ ب", - "iÅŁ im", - "iÅŁi m", - "ç» ´", - "ãĢĢ i", - "Ġm ua", - "Ġmu a", - "Ġj iž", - "Ġji ž", - "è¶ Ĭ", - "ãĤĴ è¦ĭ", - "Ġn á»Ļi", - "à¥į à¤Ĺ", - "à¥įठĹ", - "ç¨ ®", - "Ġ ãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢ", - "à¹ĥ หม", - "à¹ĥห ม", - "Ġ ÎĨ", - "ĠÎ Ĩ", - "ÙĨ دÛĮ", - "ÙĨد ÛĮ", - "ĠÑģ Ñĩ", - "Ġl á»ĩ", - "Ġlá» ĩ", - "l ub", - "lu b", - "еÑĢ ÑĤ", - "Ġ اطÙĦ", - "Ġا Ø·ÙĦ", - "Ġاط ÙĦ", - "ĠÑģ еÑĢед", - "ĠÑģеÑĢ ед", - "Ġ éģ", - "Ġé ģ", - "Ġз ал", - "Ġза л", - "ÙĨ ÛĮÙĨ", - "ÙĨÛĮ ÙĨ", - "çŁ¥ éģĵ", - "Ø¢ ÙĨ", - "Ġ кап", - "Ġк ап", - "Ġка п", - "Ġ à¹Ħม", - "Ġà¹Ħ ม", - "ů vod", - "ův od", - "ĠÙ¾ اÛĮ", - "Ġپا ÛĮ", - "ÑĤ ÑĢи", - "ÑĤÑĢ и", - "Ġi ht", - "Ġih t", - "๠Ĭ", - "Ġв ÑģÑĸ", - "ĠвÑģ Ñĸ", - "Ġt hay", - "Ġth ay", - "Ġtha y", - "å Ĩµ", - "åĨ µ", - "Ġ عÙĨÙĪاÙĨ", - "ĠعÙĨ ÙĪاÙĨ", - "Ġ Î¥", - "ĠÎ ¥", - "ภĿ", - "ε ÏĦαι", - "εÏĦ αι", - "iyor du", - "ï¼Į èĢĮ", - "çļĦ 人", - "Ġ सà¤Ń", - "Ġस à¤Ń", - "à¹ī à¸Ńย", - "à¹īà¸Ń ย", - "ι κο", - "ικ ο", - "ãĤĵ ãģ§", - "ì¡ ±", - "ÙĨج ÙĦÙĬزÙĬØ©", - "Ġž ád", - "ÑĢ ави", - "ÑĢа ви", - "ÑĢав и", - "γ γ", - "æµ ĭ", - "о ÑĨÑĸ", - "ãĢĢ ãĢĢĠãĢĢ", - "ãĢĢãĢĢ ĠãĢĢ", - "ãĢĢãĢĢĠ ãĢĢ", - "Ġतर ह", - "Ġ ëĨ", - "Ġë Ĩ", - "à¥Ģ à¤ļ", - "à¹Ī ม", - "Ġg á»ĵm", - "Ġk iá»ĩn", - "Ġki á»ĩn", - "è· Ł", - "Î ¦", - "es inin", - "esi nin", - "esini n", - "esin in", - "é ¥", - "é« Ķ", - "о Ñĩно", - "оÑĩ но", - "र ण", - "æĺ ¥", - "ç¶ ĵ", - "Ġ بار", - "Ġب ار", - "Ġبا ر", - "ê· ¼", - "éĻ ħ", - "Ġ سÙĬ", - "Ġس ÙĬ", - "Ñģ ÑĥÑĤ", - "ÑģÑĥ ÑĤ", - "ì µľ", - "å± ħ", - "ĠÄį esk", - "ĠÄįe sk", - "Îij ÎĿ", - "Ġd iá»ĩn", - "Ġdi á»ĩn", - "Ġ εί", - "Ġε ί", - "à¸ĩ à¸Ĺ", - "ãĤ ©", - "Ġv á»±c", - "Ġvá»± c", - "в ав", - "ва в", - "t ıģı", - "tı ģı", - "tıģ ı", - "Ġ ëªħ", - "Ġëª ħ", - "η ν", - "в иÑĤ", - "ви ÑĤ", - "Ġ Ø£Ùĥ", - "ĠØ£ Ùĥ", - "Ġп ÑĢоп", - "ĠпÑĢ оп", - "ĠпÑĢо п", - "r ak", - "ra k", - "ÑĢ аÑĤи", - "ÑĢа ÑĤи", - "ÑĢаÑĤ и", - "ĠÄij ánh", - "ĠÄijá nh", - "ÑĢ еп", - "ÑĢе п", - "ê ´ij", - "ê´ ij", - "е ÑĨÑĮ", - "еÑĨ ÑĮ", - "Ġब त", - "Ġ åĮĹ", - "ĠåĮ Ĺ", - "Ġs át", - "l edi", - "le di", - "led i", - "ìłģ ìľ¼ë¡ľ", - "ů j", - "Û° Û°", - "Ġnas ıl", - "Ġ ÙĪس", - "ĠÙĪ س", - "Ġ εξ", - "Ġε ξ", - "в Ñĭ", - "ç½ Ĺ", - "ارÛĮ Ø®", - "à¸Ľ ล", - "ί κ", - "Ġ ê¸Ī", - "Ġê¸ Ī", - "åĩ ł", - "å¼ ·", - "è¿ Ķ", - "Ġnh á»ı", - "å¾ Ģ", - "Ġда же", - "Ġç ev", - "к Ñĸ", - "Ġ Ø£Ùħ", - "ĠØ£ Ùħ", - "ี ส", - "ส ามารà¸ĸ", - "สาม ารà¸ĸ", - "Ġ ÐĦ", - "ĠÐ Ħ", - "Ñħод иÑĤ", - "ë ĸ", - "Ġtr uyá»ģn", - "Ġtruy á»ģn", - "Ġ ÑģÑĤан", - "ĠÑģÑĤ ан", - "ĠÑģÑĤа н", - "ëĵ¤ ìĿĢ", - "ا ÙĦت", - "اÙĦ ت", - "़ à¥ĩ", - "Ġ à¤ħब", - "Ġà¤ħ ब", - "æķ ¸", - "Ġд ÑĸÑı", - "ĠдÑĸ Ñı", - "ĠÙħ تر", - "ĠÙħت ر", - "Ġ ë¸", - "Ġë ¸", - "ï¾ į", - "Ġ ê³¼", - "Ġê³ ¼", - "Ġ زÛĮ", - "Ġز ÛĮ", - "ëŁ ¼", - "Ġ ÐŁÐµÑĢ", - "ĠÐŁ еÑĢ", - "Ġs ık", - "Ġsı k", - "н оÑģÑĤÑĮÑİ", - "ноÑģÑĤÑĮ Ñİ", - "ноÑģÑĤ ÑĮÑİ", - "Ġ eden", - "Ġe den", - "Ġed en", - "ا در", - "اد ر", - "ã Ħ", - "Ġ леÑĩ", - "Ġл еÑĩ", - "ĠÙĩ Ø°Ùĩ", - "ض ÙĪع", - "ضÙĪ ع", - "ĠìķĦ ëĭĪ", - "ĠìķĦëĭ Ī", - "ir ket", - "irk et", - "Ġ اگر", - "Ġا گر", - "ĠÑħ оÑĩ", - "Ġб ан", - "Ġба н", - "íĶ Į", - "æĢİ ä¹Ī", - "è Ľ", - "Ġब à¤ļ", - "ĠÚ© تاب", - "çī Į", - "Ġд ва", - "Ġдв а", - "ج ر", - "Ġп ÑĢоÑģÑĤо", - "ĠпÑĢоÑģÑĤ о", - "ĠпÑĢоÑģ ÑĤо", - "Ġà¤Ĩ व", - "Ġm ức", - "į ¼", - "Ġ jÃŃ", - "Ġj ÃŃ", - "íİ ĺ", - "Ġt amam", - "Ġta mam", - "Ġtam am", - "åĪ Ľ", - "ภĴ", - "п еÑĩ", - "пе Ñĩ", - "à¥ĭ स", - "Ġ Ñģем", - "ĠÑģ ем", - "Ġt Æ°Æ¡ng", - "ä¸ ģ", - "ī ´", - "Ġ ÑĢоÑģ", - "ĠÑĢ оÑģ", - "Ġ маÑĶ", - "Ġм аÑĶ", - "Ġма ÑĶ", - "æŃ Į", - "Ġ داÙĨÙĦÙĪد", - "ĠداÙĨ ÙĦÙĪد", - "ĠL oÃłi", - "ĠLo Ãłi", - "Ġed ilm", - "Ġedi lm", - "Ġedil m", - "Ġk onu", - "Ġko nu", - "Ġkon u", - "ĠاÙĦ Ùħر", - "ĠاÙĦÙħ ر", - "Ġu laÅŁ", - "Ġul aÅŁ", - "Ġyük sek", - "ο ι", - "Ùİ ÙĨ", - "Ġ bÄĽ", - "Ġb ÄĽ", - "ãĤ·ãĥ§ ãĥ³", - "ï¿£ ￣￣￣", - "￣￣ ￣￣", - "￣￣￣ ï¿£", - "Ġg üç", - "Ġgü ç", - "Ġ اÙĪÙĦ", - "Ġا ÙĪÙĦ", - "ĠاÙĪ ÙĦ", - "Ġ ма", - "Ġм а", - "Ġب خش", - "Ġبخ Ø´", - "ा à¤ĸ", - "ाठĸ", - "Ġв иÑģ", - "Ġви Ñģ", - "ž enÃŃ", - "že nÃŃ", - "žen ÃŃ", - "Ġz působ", - "Ġzp ůsob", - "z nam", - "zn am", - "Ġ رÙĪÛĮ", - "Ġر ÙĪÛĮ", - "ĠرÙĪ ÛĮ", - "åĭ Ŀ", - "। Ċ", - "ÙĦ ÙĤ", - "Ġж из", - "ÑĢ Ñĸв", - "ÑĢÑĸ в", - "ĠÑĥ пÑĢав", - "ĠÑĥп ÑĢав", - "Ġph á»ij", - "ic ros", - "icro s", - "Ġ à¹ģà¸ķ", - "Ġà¹ģ à¸ķ", - "Ġ ë°ķ", - "Ġë° ķ", - "ÙĪ ات", - "ÙĪا ت", - "ï¼Į ä¸Ģ", - "ан Ñģ", - "ç´ ļ", - "ย à¸Ļ", - "à¹ģ à¸Ĥ", - "Ġgi áo", - "Ġgiá o", - "äºĮ äºĮ", - "Ġ Ä°s", - "ĠÄ° s", - "ìĬ ¹", - "Ġo lacak", - "Ġol acak", - "Ġola cak", - "Ġ Các", - "ĠC ác", - "Ġ ÑĢÑĥб", - "ĠÑĢ Ñĥб", - "ĠÑĢÑĥ б", - "ẹ p", - "ÄŁ iniz", - "ÄŁini z", - "ÄŁin iz", - "ãģª ãģ©", - "Ġ моÑĢ", - "Ġм оÑĢ", - "Ġмо ÑĢ", - "ĠÑģ дел", - "ÙĦ ÙħاÙĨ", - "ÙĦÙħ اÙĨ", - "n ém", - "né m", - "å° į", - "Ġd ne", - "Ġdn e", - "ì¶ľ ìŀ¥", - "ع ب", - ": ::::::", - ":: :::::", - ":::: :::", - ":::::: :", - "::: ::::", - "::::: ::", - "Î Ĵ", - "e ket", - "ek et", - "Ġ ÑĢеÑĪ", - "ĠÑĢ еÑĪ", - "ĠÑĢе ÑĪ", - "è Ī¬", - "èĪ ¬", - "Ġ íĻĶ", - "ĠíĻ Ķ", - "ص د", - "Ġ маÑĢ", - "Ġм аÑĢ", - "Ġма ÑĢ", - "Ñı ж", - "Ø´ ار", - "ãģ ²", - "Ġ اÙĦÙĬ", - "Ġا ÙĦÙĬ", - "ĠاÙĦ ÙĬ", - "Ù į", - "à¤Ĥ à¤ľ", - "м Ñĭ", - "Ġka rar", - "Ġkar ar", - "Ġkara r", - "ÙĦÛĮ سÛĮ", - "ÙĦÛĮس ÛĮ", - "า à¸ĵ", - "าภĵ", - "ç¾ ¤", - "Ġol ması", - "Ġolm ası", - "Ġolma sı", - "Ġhaz ır", - "γÏģα ÏĨ", - "¯ u", - "в ол", - "во л", - "ĠÑģ ÑĤаÑĢ", - "ĠÑģÑĤ аÑĢ", - "ĠÑģÑĤа ÑĢ", - "o vala", - "ov ala", - "ova la", - "oval a", - "Ġв озмож", - "Ġвоз мож", - "Ġ дав", - "Ġд ав", - "Ġда в", - "é¢ ¨", - "ر ا", - "Ġдоп ом", - "ê² ĥ", - "Ġ ìĺ¬", - "Ġìĺ ¬", - "Ġ åİ", - "Ġå İ", - "Ġ 못", - "Ġëª »", - "u ç", - "í ļ", - "l ük", - "lü k", - "ä¸Ń å¿ĥ", - "Ġ दर", - "Ġद र", - "Ġ âĹĨ", - "ĠâĹ Ĩ", - "Ġt ay", - "Ġta y", - "Ġب سÛĮ", - "Ġبس ÛĮ", - "Ġ ÏĥÏĦα", - "ĠÏĥ ÏĦα", - "ĠÙħ Ø®", - "Ñı Ñī", - "å· ®", - "ภī", - "ëł ¹", - "à¹ĥà¸Ļ à¸ģาร", - "Ġ ÙĩÙĨ", - "ĠÙĩ ÙĨ", - "ãģ ¶", - "л Ñĸд", - "лÑĸ д", - "å į°", - "åį °", - "Ġs ao", - "Ġsa o", - "ÅĻ ad", - "리 ëĬĶ", - "Ñģ лед", - "Ñģл ед", - "åĶ ®", - "Ġ |:", - "Ġ| :", - "æķĻ èĤ²", - "Ġм ол", - "Ġмо л", - "ĠÙĩ ÙĬ", - "ë ģ", - "Ġ кÑĥлÑĮ", - "Ġк ÑĥлÑĮ", - "ĠкÑĥ лÑĮ", - "' nin", - "'n in", - "Ġ خر", - "ĠØ® ر", - "Ġge nel", - "Ġgen el", - "Ġgene l", - "Ġt á»Ń", - "Ġtá» Ń", - "Ġkur ul", - "Ġkuru l", - "ен ÑĤи", - "енÑĤ и", - "à¥ĭ à¤ľà¤¨", - "à¥ĭà¤ľ न", - "è¿Ļ æł·", - "Ġм Ñĸж", - "ĠмÑĸ ж", - "Ġngh iá»ĩm", - "Ġnghiá»ĩ m", - "ĠÏĢ ολ", - "ĠÏĢο λ", - "æĭ Ľ", - "Ġà¤Ĺ à¤ı", - "ầ y", - "Ġc ảm", - "Ġcả m", - "ç´ °", - "rı ca", - "Ġ عÙĦÛĮ", - "Ġع ÙĦÛĮ", - "ĠعÙĦ ÛĮ", - "ิ à¹ī", - "h ur", - "hu r", - "Ġch Æ°a", - "Ñĥ ÑĶÑĤÑĮÑģÑı", - "ÑĥÑĶ ÑĤÑĮÑģÑı", - "ãģ© ãģĨ", - "Ñĥ л", - "ิ ร", - "Ġ æľī", - "ä¼ ¼", - "ÑĦ еÑĢ", - "ÑįÑĤ омÑĥ", - "æĹ ħ", - "ĠÙħ ÙĪج", - "ĠÙħÙĪ ج", - "Ġ 본", - "Ġë³ ¸", - "Ġgi á»Ŀ", - "Ġgiá» Ŀ", - "Ġk iến", - "Ġki ến", - "à¹Ī วย", - "à¹Īว ย", - "Ġd üny", - "Ġdü ny", - "Ġdün y", - "Ġ زÙħ", - "Ġز Ùħ", - "о вÑĸ", - "ов Ñĸ", - "ĠÑĨ ÑĮого", - "ิ à¸ļ", - "Ġ ìĨIJ", - "ĠìĨ IJ", - "èIJ ¥", - "Ġ ÑĢÑĸз", - "ĠÑĢ Ñĸз", - "Ġh á»Ĺ", - "Ġhá» Ĺ", - "ÑĢ Ñĸб", - "ÑĢÑĸ б", - "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ìľ¼ ë©°", - "äºĨ ä¸Ģ", - "ĠÙĤ بÙĦ", - "ĠÙĤب ÙĦ", - "é¾ Ļ", - "Ġ گذ", - "ĠÚ¯ Ø°", - "Ġ ÙĤد", - "ĠÙĤ د", - "ãģª ãģĭãģ£ãģŁ", - "Ġ à¹Ģร", - "Ġà¹Ģ ร", - "Ġà¹Ģภ£", - "å¸ Į", - "ĠÑģ Ñħ", - "Ġг ÑĢом", - "ĠгÑĢ ом", - "ĠгÑĢо м", - "åĽ ¢", - "Ġ ì§ij", - "Ġì§ ij", - "Ġ лÑĥÑĩ", - "Ġл ÑĥÑĩ", - "åħ µ", - "Ġ ÐŀÑĤ", - "ĠÐŀ ÑĤ", - "Ġmu á»ijn", - "ãģĺ ãĤĥ", - "o vnÃŃ", - "ov nÃŃ", - "ë©´ ìĦľ", - "ë³ Ģ", - "Ġн еб", - "Ġне б", - "Ñģ ии", - "Ñģи и", - "ÙĨ Ùħ", - "ÄŁ in", - "ÄŁi n", - "Ġt oho", - "Ġto ho", - "Ġtoh o", - "en iz", - "eni z", - "ب اش", - "با Ø´", - "ĠÑģ лÑĥж", - "ĠÑģл Ñĥж", - "ĠÑģлÑĥ ж", - "Ġb ợi", - "Ġüzer e", - "Ġüz ere", - "Ġs adece", - "Ġsad ece", - "ĠÏĢ αÏģ", - "ĠÏĢα Ïģ", - "³³³³³³³³ ³³³³³³³³", - "³³³³³³³ ³³³³³³³³³", - "³³³³³ ³³³³³³³³³³³", - "³³³³³³³³³ ³³³³³³³", - "³³³³³³³³³³³ ³³³³³", - "èĮ ĥ", - "ÏĦ ικÏĮ", - "ÏĦικ ÏĮ", - "ÏĦι κÏĮ", - "Ġ äºĮ", - "ãĤĪãģĨ ãģ«", - "è ŀ", - "ãģ® ãģ¯", - "Ġ ÑĥкÑĢаÑĹн", - "ĠÑĥ кÑĢаÑĹн", - "Ġb ắt", - "в ан", - "ва н", - "Ġ ÑģÑĤÑĢа", - "ĠÑģ ÑĤÑĢа", - "ĠÑģÑĤ ÑĢа", - "ĠÑģÑĤÑĢ а", - "è¡ Ģ", - "nu tÃŃ", - "nut ÃŃ", - "o kt", - "ok t", - "รà¸ĩ à¹Ģร", - "Ġ صÙģ", - "Ġص Ùģ", - "åħ ļ", - "ÏĦ ί", - "ï¼ģ ãĢįĊĊ", - "ï¼ģãĢį ĊĊ", - "ĠÑĤем п", - "ĠÑĤе мп", - "é¡ Į", - "Ġs luž", - "Ġslu ž", - "Ñĥ ков", - "Ñĥк ов", - "Ġngh Ä©a", - "ĠnghÄ© a", - "çĶ ²", - "Ġd á»ħ", - "о ви", - "ов и", - "ÏĦ Ïħ", - "ر ÛĮÚ©", - "رÛĮ Ú©", - "ĠA nh", - "ĠAn h", - "ĠвÑģ его", - "ĠвÑģе го", - "âĢĮ Ú©ÙĨ", - "âĢĮÚ© ÙĨ", - "te ÅĻÃŃ", - "Ġm ục", - "Ùĩ ÙĨÚ¯", - "ÙĩÙĨ Ú¯", - "Ġ ÐŁÐ¾Ñģ", - "ĠÐŁ оÑģ", - "ĠÐŁÐ¾ Ñģ", - "Úĺ Ùĩ", - "ĠاÙĦ غ", - "æĿ ¾", - "y sl", - "ys l", - "Ġyap ılan", - "Ġyapı lan", - "Ġyapıl an", - "çĪ ¶", - "Ġm ạnh", - "ر اÙĩ", - "را Ùĩ", - "à¸Ķ à¸ĩ", - "o Äį", - "ë§ IJ", - "åł Ĥ", - "м аÑĤ", - "ма ÑĤ", - "Ġ eÅŁ", - "Ġe ÅŁ", - "ÙĪÙħ ات", - "Ġس اخت", - "åĽł 为", - "Ġп ÑĢий", - "ĠпÑĢ ий", - "ĠпÑĢи й", - "ıl mÄ±ÅŁ", - "é¤ ¨", - "ุ à¸ĩ", - "Ġ ëģ", - "Ġë ģ", - "à¸ķ าม", - "à¸ķา ม", - "åIJ ī", - "μ ή", - "Ġ æľ¬", - "Ġzá klad", - "ت ØŃ", - "è¾ ¼", - "Ġв Ñĸй", - "ĠвÑĸ й", - "ĠÙħÙĨ Ø·", - "Ġto án", - "к аÑĢ", - "ка ÑĢ", - "Ġ ÐĹа", - "ĠÐĹ Ð°", - "ĠпÑĢи мен", - "ĠпÑĢим ен", - "ãĤĭ ãģ¨", - "ั à¸Ĺ", - "ÛĮ س", - "ĠاÙĨ جاÙħ", - "ĠاÙĨج اÙħ", - "Ġع ÙĦÙĬ", - "ĠعÙĦ ÙĬ", - "़ ि", - "e ných", - "en ých", - "ený ch", - "ĠL iên", - "ĠLi ên", - "Ġ па", - "Ġп а", - "éļ Ĭ", - "Ġmo hou", - "Ġmoh ou", - "Ġк ÑĸлÑĮ", - "Ġ Το", - "ĠΤ ο", - "ا ÙĦب", - "اÙĦ ب", - "ÎŃ Î½", - "Ġna bÃŃ", - "Ġnab ÃŃ", - "ç i", - "ler den", - "lerde n", - "Ġth anh", - "Ġthan h", - "Ġtha nh", - "Ġb ütün", - "Ġ åŁ", - "Ġå Ł", - "ì¸ ł", - "Ġz at", - "Ġza t", - "ÙĬ ÙĪ", - "Ġμ ια", - "uy ết", - "Ñij н", - "åĪ Ĵ", - "ли во", - "лив о", - "à¹Ī à¸Ńà¸ĩ", - "à¹Īà¸Ń à¸ĩ", - "ä»ĸ 们", - "Ġб аг", - "Ġба г", - "ि à¤Ń", - "िठŃ", - "ĠÑĤ ам", - "ĠÑĤа м", - "Ġп ÑĢеп", - "ĠпÑĢ еп", - "ĠпÑĢе п", - "ิ à¸Ĭ", - "âĢĻ Ñıз", - "âĢĻÑı з", - "ĠPh ân", - "ж ен", - "же н", - "à¥Ī à¤ķ", - "ĠÑģлÑĥÑĩа е", - "Ġ .:", - "Ġ. :", - "åѦ æł¡", - "Ä° N", - "ç¾ ©", - "ĠÑģ ÑĤо", - "ĠÑģÑĤ о", - "Ġ हर", - "Ġह र", - "Ïħ ν", - "Ġx em", - "Ġxe m", - "Ġб ÑĥÑĤи", - "ĠбÑĥ ÑĤи", - "Ñģ иÑĤ", - "Ñģи ÑĤ", - "çª ģ", - "à¥į à¤Ľ", - "à¥įठĽ", - "åij ¢", - "ï¼Į ä¹Ł", - "e nÄĽ", - "en ÄĽ", - "Ġ κά", - "Ġκ ά", - "iy orum", - "iyor um", - "ĠÚ¯ Ùģت", - "âĹıâĹı âĹıâĹı", - "ั ม", - "Ġ Ðļон", - "ĠÐļ он", - "ĠÐļо н", - "н оÑĪ", - "но ÑĪ", - "ниÑĨ ÑĤ", - "ü zel", - "üz el", - "s ÃŃ", - "å¸ «", - "ص ÙĪÙĦ", - "çĥ Ń", - "ĠÄij ủ", - "ĠÄijá» §", - "ãĤ ®", - "æķ ħ", - "ĠÅ¡ kol", - "ĠÅ¡k ol", - "Ñĩ ен", - "Ñĩе н", - "à¹Ģ ย", - "à¹Ģภ¢", - "à¸Ļ à¸Ļ", - "ÙĢ ÙĢÙĢÙĢ", - "ÙĢÙĢ ÙĢÙĢ", - "ÙĢÙĢÙĢ ÙĢ", - "Ġ üç", - "Ġü ç", - "å¿ µ", - "ãĥª ãĤ¢", - "Ġ íĻĺ", - "ĠíĻ ĺ", - "Ġ éĩij", - "Ġéĩ ij", - "çı Ń", - "Ġ Ñģклад", - "ĠÑģ клад", - "ĠÑģк лад", - "Ñı ми", - "Ñıм и", - "ü f", - "Ġh ã", - "ĠÄIJ ại", - " Ĥ", - "åĦ ª", - "Ġbul unan", - "Ġbulun an", - "ĠاÙĦ ÙħØŃ", - "ĠاÙĦÙħ ØŃ", - "æĪ ı", - "Ġ è©", - "Ġè ©", - "Ġн оÑĢм", - "ĠноÑĢ м", - "Ġchu ẩn", - "Ġз аÑģÑĤ", - "Ġза ÑģÑĤ", - "ĠзаÑģ ÑĤ", - "Ġ vÃŃce", - "ĠvÃŃ ce", - "ĠvÃŃc e", - "Ð ĸ", - "Ġà¤Ĩ ध", - "Ġ Äįas", - "ĠÄį as", - "Ġ боÑĢ", - "Ġб оÑĢ", - "Ġбо ÑĢ", - "Ïģ ια", - "Ïģι α", - "ĠÙħ اÙĩ", - "ĠÙħا Ùĩ", - "Ġ íħ", - "Ġí ħ", - "ÅĻ el", - "ÅĻe l", - "Ñı ви", - "Ñıв и", - "ÏĦ εÏĤ", - "ÏĦε ÏĤ", - "i nÄĽ", - "in ÄĽ", - "Ġп еÑĢе", - "ĠпеÑĢ е", - "éķ ĩ", - "à¥įठŀ", - "Ġ éĺ", - "Ġé ĺ", - "à¹Ī าว", - "à¹Īา ว", - "ร ร", - "Ġ سÙĩ", - "Ġس Ùĩ", - "в али", - "ва ли", - "вал и", - "çķ Ļ", - "ĠÑĦ Ñĥнк", - "ĠÑĦÑĥн к", - "Ġ íĸī", - "Ġí ĸī", - "Ġíĸ ī", - "Ùģ Ùĩ", - "çĶŁ æ´»", - "èģ ŀ", - "o kud", - "ok ud", - "oku d", - "Ġ ìĤ´", - "ĠìĤ ´", - "ı zı", - "ız ı", - "Ġпо лÑĥ", - "Ġпол Ñĥ", - "ï¼Į ä½ł", - "Ø´ اÙĨ", - "æ± º", - "б ÑĢÑı", - "оÑģÑĥд аÑĢ", - "Ġo yun", - "Ġoy un", - "а нии", - "ан ии", - "ани и", - "Ġp rů", - "Ġpr ů", - "Ġn áv", - "Ġná v", - "Ġм енÑı", - "Ġмен Ñı", - "Ġìŀ ĺ", - "Ġ Ä°n", - "ĠÄ° n", - "Ġth ÃŃch", - "ĠthÃŃ ch", - "ĠÄij ảm", - "åľ Ĵ", - "Ġв же", - "Ġl oÃłi", - "Ġlo Ãłi", - "Ġ Ðŀн", - "ĠÐŀ н", - "м еÑģÑĤ", - "ме ÑģÑĤ", - "Ġ ξ", - "ĠÎ ¾", - "ãĢ ħ", - "Ġch iế", - "Ġchi ế", - "Ñĩ Ñĸ", - "Ġ íijľ", - "Ġí ijľ", - "ëĭ ¬", - "Ġ ëĭ¬", - "Ġëĭ ¬", - "à¥Ģ ड", - "ÑĢ алÑĮ", - "ÑĢа лÑĮ", - "ÑĢал ÑĮ", - "d ik", - "di k", - "Ġ íĨł", - "ĠíĨ ł", - "ëŁ ī", - "Ġ صÙĨ", - "Ġص ÙĨ", - "Ġs tej", - "Ġst ej", - "Ġste j", - "Ġа кÑĤив", - "Ġак ÑĤив", - "ĠакÑĤ ив", - "ĠакÑĤи в", - "Ġ é¦", - "Ġé ¦", - "Ġ à¹Ħà¸Ķ", - "Ġà¹Ħ à¸Ķ", - "æĬĢ æľ¯", - "Ġp rostÅĻed", - "Ġpro stÅĻed", - "Ġprost ÅĻed", - "å® ³", - "ãģ IJ", - "Ġol uÅŁtur", - "ĠoluÅŁ tur", - "e lop", - "el op", - "elo p", - "ãģ¡ ãĤĥ", - "éĥ İ", - "ض ا", - "Ġ خط", - "ĠØ® Ø·", - "ë° ķ", - "е ÑģÑı", - "еÑģ Ñı", - "ĠÙĩ ÛĮ", - "н ад", - "на д", - "Ġng Ãłnh", - "ÑĢ ÑĥÑĪ", - "ÑĢÑĥ ÑĪ", - "ãģĦ ãģĦ", - "Ġü rün", - "Ġür ün", - "à¸Ń à¸ķ", - "à¥ĭ प", - "Ġs ayı", - "Ġsa yı", - "Ġsay ı", - "à¥Ģ स", - "е ниÑħ", - "ен иÑħ", - "ени Ñħ", - "Ġ Ñģим", - "ĠÑģ им", - "ĠÑģи м", - "à¥Ģ द", - "å¤ ī", - "à¹Ī วม", - "à¹Īว ม", - "Ġ à¹Ģà¸Ĥ", - "Ġà¹Ģ à¸Ĥ", - "Ġà¹ĢภĤ", - "å·² ç»ı", - "а ÑĤо", - "аÑĤ о", - "ĠÑĢай он", - "í ĥĿ", - "íĥ Ŀ", - "Ġ ÑĤÑĢа", - "ĠÑĤ ÑĢа", - "ĠÑĤÑĢ а", - "l ayan", - "la yan", - "lay an", - "ế p", - "ा à¤Ł", - "ाठŁ", - "Ø® اب", - "人 æ°ij", - "å® Ŀ", - "è Ĩ", - "èª į", - "n aÄį", - "na Äį", - "Ġî ł", - "ĠÐļ и", - "ĠbaÅŁ ka", - "ĠbaÅŁk a", - "c ů", - "ض ع", - "èĪ ª", - "ี ม", - "Ñĭ ми", - "Ñĭм и", - "ÎĻ Σ", - "Ġشر کت", - "ย ว", - "Ġmus ÃŃ", - "Ġmu sÃŃ", - "Ġн ал", - "Ġна л", - "ี à¸Ĺ", - "Ġ áp", - "Ġá p", - "ร าย", - "æ² ¹", - "l eme", - "le me", - "lem e", - "Ġ मन", - "Ġम न", - "à¹Ħ à¸Ł", - "а ÑĤив", - "аÑĤ ив", - "аÑĤи в", - "¸ ı", - "èŃ °", - "Ïĥ ÏĦα", - "ÏĥÏĦ α", - "íĸ ¥", - "е ÑĤÑĥ", - "еÑĤ Ñĥ", - "ĠÑģв Ñıз", - "ĠÑģвÑı з", - "ед еÑĢа", - "ĠØ® ارج", - "า ษ", - "าภ©", - "âĢĮ Ù¾", - "Ñĸ г", - "é¡ ŀ", - "Ġkh ả", - "ĠÑģ пÑĢав", - "ĠÑģп ÑĢав", - "è¡ Ĺ", - "ãĥķ ãĤ¡", - "ãĥķãĤ ¡", - "Ġм еждÑĥ", - "Ġмеж дÑĥ", - "Ñĥ ли", - "Ñĥл и", - "Ġب زر", - "ÑĨ ен", - "ÑĨе н", - "Ġek onom", - "د ÙĨ", - "ا ÙħÛĮ", - "اÙħ ÛĮ", - "าส à¸ķร", - "ĠnÄĽ kol", - "ĠnÄĽk ol", - "g ün", - "з и", - "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", - "ç¦ »", - "Ġtr Æ°á»Łng", - "ı i", - "íİ ¸", - "Ġ ÑĢеб", - "ĠÑĢ еб", - "ĠÑĢе б", - "åº ķ", - "Ġت ارÛĮØ®", - "н има", - "ни ма", - "ним а", - "Ġth ân", - "аÑĤ елÑĮно", - "аÑĤелÑĮ но", - "ĠاÙĦ Ø°ÙĬ", - "ĠاÙĦØ° ÙĬ", - "ÙĪ ÙĨÛĮ", - "ÙĪÙĨ ÛĮ", - "Ġ éĥ", - "Ġé ĥ", - "Ġb ình", - "Ġbì nh", - "ικ ήÏĤ", - "ική ÏĤ", - "à¸ŀ ล", - "تÙħ اع", - "ĠPr aha", - "ĠPra ha", - "Ġ ÑģÑĤав", - "ĠÑģÑĤ ав", - "ĠÑģÑĤа в", - "د ÙĬد", - "دÙĬ د", - "Ġgi ữa", - "Ġgiữ a", - "ĠпÑĢо вед", - "ĠпÑĢов ед", - "Âł k", - "ÙĨد Ú¯ÛĮ", - "ÑĨ ий", - "ÑĨи й", - "ç Ĵ", - "ĠاÙĦ Ø£Ùħ", - "ĠاÙĦØ£ Ùħ", - "Ġ è´", - "Ġè ´", - "Ø¥ ÙĨجÙĦÙĬزÙĬØ©", - "ĠìŀĪ ìĹĪëĭ¤", - "ĠìŀĪìĹĪ ëĭ¤", - "ç ·¨", - "ç· ¨", - "ัà¸Ļ à¸ĺ", - "ĠÑĢок Ñĸв", - "Ġc áo", - "Ġcá o", - "Ġkh ó", - "Ġ ÙĨÙĪع", - "ĠÙĨ ÙĪع", - "ĠÙĨÙĪ ع", - "س ÙĦ", - "Ġ ÑĥÑģлов", - "ĠÑĥ Ñģлов", - "ĠÑĥÑģл ов", - "ĠÑĥÑģ лов", - "Ġcứ u", - "ов ого", - "ово го", - "ि à¤Ĺ", - "िठĹ", - "Ķ ëĭ¤", - "æĿ İ", - "Ġbö lg", - "Ġböl g", - "Ġn gu", - "Ġng u", - "Ġh ữu", - "Ġhá» ¯u", - "н ии", - "ни и", - "ìł Ī", - "Ġп ÑĢом", - "ĠпÑĢ ом", - "ĠпÑĢо м", - "åı Į", - "Ġd Æ°á»Ľi", - "ĠdÆ°á» Ľi", - "ĠdÆ° Ỽi", - "Ð ®", - "ÙĬ Ø´", - "æ¸ ©", - "ëı ħ", - "Ġз мÑĸ", - "Ġзм Ñĸ", - "θη κε", - "ĠbaÄŁ lı", - "Ġüzer inde", - "Ġ تغ", - "Ġت غ", - "Ġп ÑĢогÑĢа", - "ĠпÑĢ огÑĢа", - "ĠпÑĢо гÑĢа", - "ĠпÑĢог ÑĢа", - "i ž", - "Ġ ç¥", - "Ġç ¥", - "Ġy ardım", - "Ġyard ım", - "Ġyar dım", - "ÂĢ ÂĢ", - "ÂĢ Ģ", - "Ġ Ñĥв", - "ĠÑĥ в", - "Ġ rů", - "Ġr ů", - "Ġch iến", - "Ġchi ến", - "Ġchiế n", - "ν οÏĤ", - "νο ÏĤ", - "ãģ¨ ãģª", - "ا ÙĨت", - "اÙĨ ت", - "è° ·", - "ÃŃ sk", - "ÃŃs k", - "is inde", - "isi nde", - "isin de", - "Ġд ог", - "Ġдо г", - "è¿ ½", - "Ġп ÑĢоÑĤив", - "ĠпÑĢо ÑĤив", - "ĠпÑĢоÑĤ ив", - "ĠпÑĢоÑĤи в", - "Ïģ οÏħ", - "Ïģο Ïħ", - "ãģ® ãģĭ", - "Ġb azı", - "Ġba zı", - "Ġbaz ı", - "ı rak", - "ır ak", - "à¥ĩ ष", - "à¥ĩठ·", - "ĠÙħ شار", - "ĠÙħØ´ ار", - "Ġ ìĸij", - "Ġìĸ ij", - "Ġ нез", - "Ġн ез", - "Ġне з", - "Ġ Ø°ÙĦÙĥ", - "ĠØ° ÙĦÙĥ", - "èª ¿", - "åĤ Ļ", - "ĠÑĤ ÑĢан", - "ĠÑĤÑĢ ан", - "ĠÑĤÑĢа н", - "ĠÏĢ αÏģα", - "ĠÏĢαÏģ α", - "ĠÏĢα Ïģα", - "ÛĮ Ùħت", - "ÛĮÙħ ت", - "Ġt iến", - "Ġti ến", - "Ġtiế n", - "ĠÙĩ ÙħÙĩ", - "ĠÙĩÙħ Ùĩ", - "e fon", - "ef on", - "» .ĊĊ", - "». ĊĊ", - "».Ċ Ċ", - "Ġ ÙĨد", - "ĠÙĨ د", - "ج ÙĦ", - "Ġد ادÙĩ", - "Ġداد Ùĩ", - "Ġ вед", - "Ġв ед", - "Ġве д", - "Ġ sın", - "Ġs ın", - "Ġsı n", - "ĠÑģ вÑĸÑĤ", - "ĠÑģв ÑĸÑĤ", - "e lerin", - "el erin", - "eler in", - "ele rin", - "eleri n", - "âĪ ¨", - "Ġy ür", - "д ан", - "да н", - "Ġ ÐŀÑģ", - "ĠÐŀ Ñģ", - "Ġh ạng", - "Ġhạn g", - "Ġhạ ng", - "è® ¸", - "Ïĥ ÏĦη", - "ÏĥÏĦ η", - "uy ến", - "Ġн аб", - "Ġна б", - "Ġ оÑħ", - "Ġо Ñħ", - "Ïĥ Ïī", - "Ġby ly", - "Ġbyl y", - "Ñģ киÑħ", - "Ñģк иÑħ", - "Ñģки Ñħ", - "l amak", - "la mak", - "lam ak", - "lama k", - "и ÑĤоÑĢ", - "иÑĤ оÑĢ", - "Ġy atır", - "Ġya tır", - "Ġyat ır", - "ĠпÑĢоиз вод", - "Ġ جÙħع", - "Ġج Ùħع", - "ĠجÙħ ع", - "Å ł", - "æıIJ ä¾Ľ", - "Ġpr vnÃŃ", - "Ġprv nÃŃ", - "Ġα ÏĢ", - "íĻ ©", - "ĠпÑĢа кÑĤи", - "ler inden", - "lerin den", - "lerinde n", - "ĠнеобÑħодим о", - "åº ·", - "Ùİ Ø§", - "Ġ سÙĨ", - "Ġس ÙĨ", - "Ä° L", - "Ġ ê´ij", - "Ġê ´ij", - "Ġê´ ij", - "Ġ PÅĻ", - "ĠP ÅĻ", - "ç ŀ", - "ĠÑĤемп еÑĢаÑĤÑĥ", - "Ġka bul", - "Ġkab ul", - "Ġbu dou", - "Ġbud ou", - "ÑĨÑĸ оналÑĮ", - "ÑĨÑĸон алÑĮ", - "ï½ ľ", - "Ġç ocuk", - "Ġçocu k", - "ĠÑĤ ÑĸлÑĮки", - "b yt", - "by t", - "ãĥ ¤", - "ĠÑģÑĤ аÑĤ", - "ĠÑģÑĤа ÑĤ", - "Ġ æĿ±", - "ĠæĿ ±", - "le žit", - "اس طة", - "ุ ร", - "i êm", - "iê m", - "ĠкÑĥлÑĮ ÑĤÑĥ", - "Ġ пон", - "Ġп он", - "Ġпо н", - "Ä© nh", - "åĸ ľ", - "н ев", - "не в", - "ÑĶ н", - "ĠÑģо оÑĤ", - "ë Ŀ", - "çĪ ¾", - "Ġtu á»ķi", - "k anı", - "kan ı", - "สำ หร", - "ا عت", - "اع ت", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "д еÑĢж", - "деÑĢ ж", - "Ġоб лаÑģÑĤи", - "Ġобла ÑģÑĤи", - "ĠоблаÑģ ÑĤи", - "ĠоблаÑģÑĤ и", - "Ġобл аÑģÑĤи", - "Ġv ừa", - "Ġvá» «a", - "Ġ ÙħÙħ", - "ĠÙħ Ùħ", - "à¸ģ ำ", - "à¹ģ ม", - "iver sit", - "ivers it", - "à¹ģ ส", - "æ¬ §", - "l anan", - "la nan", - "lan an", - "ÙĬ ÙĨØ©", - "ÙĬÙĨ Ø©", - "س Ø©", - "ĠлÑİд ей", - "ร รม", - "รร ม", - "Ġ ì±Ħ", - "Ġì± Ħ", - "Ġ 天", - "Ġå¤ ©", - "ен нÑĭÑħ", - "à¹ģ ห", - "Ġs práv", - "Ġsp ráv", - "Ġspr áv", - "èŃ ¦", - "ï¼ ľ", - "ั à¸Ĵ", - "il ecek", - "ile cek", - "ilece k", - "Ġ æŁ", - "Ġæ Ł", - "Ġ èĭ±", - "Ġèĭ ±", - "ĠÑħ оÑĢоÑĪ", - "ëł ĩ", - "Û² Û°Û°", - "Û²Û° Û°", - "æĬ ¤", - "Ġl ã", - "ÅĻÃŃ zenÃŃ", - "ÅĻÃŃz enÃŃ", - "ĠتÙĪÙĦ ÛĮد", - "éļ Ľ", - "ãĤĮ ãģ°", - "á Å¡", - "ارÙĬ Ø®", - "æĶ »", - "Ġkho ảng", - "Ġkhoản g", - "éĻ į", - "о ван", - "ов ан", - "ова н", - "Ġg ây", - "âĢĻn ın", - "Ø£ ÙĨ", - "m iÅŁtir", - "mi ÅŁtir", - "miÅŁ tir", - "miÅŁti r", - "Ġs ức", - "Ġsứ c", - "к ÑĥÑģ", - "кÑĥ Ñģ", - "Ġüzer ine", - "ÄŁ ü", - "ا بر", - "اب ر", - "ï¼Į å°±", - "Ì £", - "Ġ ëıĮ", - "Ġëı Į", - "Ġtr á»±c", - "æĶ¶ å½ķ", - "æī ¿", - "ĠN á»Ļi", - "Ġ çĻ¾", - "ĠçĻ ¾", - "ÑĪ ÑĮ", - "ج Ø©", - "ë² ł", - "ठī", - "à ¸ı", - "ภı", - "Ġü lk", - "Ġül k", - "ĠÙĩست ÙĨد", - "ัà¸ļ à¸ģาร", - "ĠÑı ка", - "ĠÑıк а", - "ã İ", - "Ġ Як", - "ĠЯ к", - "Ġг де", - "t iv", - "ti v", - "ãĢ Ī", - "лÑİ Ñĩа", - "лÑİÑĩ а", - "ा ।Ċ", - "ा। Ċ", - "Ġ ÙħاÙĨ", - "ĠÙħ اÙĨ", - "ĠÙħا ÙĨ", - "Ġd lou", - "Ġdl ou", - "Ġ ãĥķ", - "Ġãĥ ķ", - "ठĽ", - "Ġph ục", - "Ġphụ c", - "a kat", - "ak at", - "aka t", - "Ð ¬", - "as ını", - "ĠæĬķ 稿", - "ÑĢ ев", - "ÑĢе в", - "Ġv yt", - "Ġvy t", - "Ġz mÄĽ", - "Ġzm ÄĽ", - "ÏĦ Ïī", - "è ¬", - "Ġ Ñĥм", - "ĠÑĥ м", - "Ġuz un", - "Ġp roti", - "Ġpro ti", - "Ġpr oti", - "Ġprot i", - "ĠÑģо ÑģÑĤоÑı", - "ĠÑģоÑģÑĤ оÑı", - "ัà¸Ĵ à¸Ļ", - "a tik", - "at ik", - "ati k", - "Ġ à¸ł", - "Ġภł", - "Ġà¤Ĩ द", - "lar ından", - "ların dan", - "larında n", - "æĢ ¥", - "ãĥ¼ ãĤ¯", - "ãĥ¼ãĤ ¯", - "ĠÙĦ ÙĦÙħ", - "ĠÙĦÙĦ Ùħ", - "Ùģ تÙĩ", - "Ùģت Ùĩ", - ". :.", - ".: .", - "üç ük", - "ол ева", - "à¹Į Ċ", - "ĠпеÑĢ ев", - "ĠпеÑĢе в", - "ĠÙĨ سب", - "ĠÙĨس ب", - "е леннÑı", - "ел еннÑı", - "елен нÑı", - "' ın", - "'ı n", - "ν Ïī", - "è¡ £", - "Ġ دÙĬ", - "Ġد ÙĬ", - "åį ĩ", - "Ġbel irt", - "Ġbelir t", - "Ġ /:", - "Ġ/ :", - "èij ī", - "Ġv yh", - "Ġvy h", - "çļĦ ä¸Ģ", - "èĥ Į", - "Ġ ìĹ´", - "ĠìĹ ´", - "о ла", - "ол а", - "Ġ تب", - "Ġت ب", - "á ci", - "ác i", - "ा à¤ī", - "ाठī", - "ภİ", - "çĶ ¢", - "à¥Ī ल", - "Ġ ÙĤØ·", - "ĠÙĤ Ø·", - "ëĦ Ī", - "ắ m", - "ÑĢ Ñıд", - "ÑĢÑı д", - "Ġph ụ", - "ĠÙĪ اÙĤع", - "ĠÙĪا ÙĤع", - "Ġm erk", - "Ġme rk", - "Ġmer k", - "Ġch á»ijng", - "å¯ Ł", - "ا بط", - "اب Ø·", - "us unda", - "usu nda", - "Ġод на", - "ž el", - "že l", - "ĠÑģ Ñĥм", - "ĠÑģÑĥ м", - "Ġph ù", - "Ġ ζ", - "ĠÎ ¶", - "Ġz av", - "Ġza v", - "e dn", - "ed n", - "Ġp otÅĻeb", - "Ġpot ÅĻeb", - "ĠÚ©ÙĨ ÙĨد", - "ĠÑĢ азв", - "ĠÑĢаз в", - "ĠÑĢа зв", - "¿ ł", - "ĠاÙĦ ز", - "Ġm ÄĽl", - "ĠmÄĽ l", - "Ġ ÑģÑĤанов", - "ĠÑģÑĤ анов", - "ĠÑģÑĤан ов", - "ĠÑģÑĤа нов", - "Ġ درÛĮ", - "Ġد رÛĮ", - "Ġدر ÛĮ", - "Ġt ượng", - "ã ģµ", - "ãģ µ", - "Ġд ви", - "Ġдв и", - "ÑĮ Ñı", - "è £½", - "è£ ½", - "Ġ تÙĦ", - "Ġت ÙĦ", - "Å¡ Å¥", - "ãģª ãĤī", - "Ġà¤ķ à¤Ī", - "Å¡ i", - "âĢĮ است", - "Ġk ỹ", - "Ġká» ¹", - "ë§ Ŀ", - "Ġà¤Ĩ à¤ľ", - "ãĥ ´", - "Ġb á»ı", - "du ÄŁu", - "duÄŁ u", - "Ġ æ¯", - "Ġæ ¯", - "п еÑĢ", - "пе ÑĢ", - "ا ÙĦÙĬØ©", - "اÙĦ ÙĬØ©", - "اÙĦÙĬ Ø©", - "æīĢ 以", - "åħ °", - "Ġ oran", - "Ġo ran", - "Ġor an", - "Ġora n", - "Ġ íŀ", - "Ġí ŀ", - "Ïĥ ία", - "Ïĥί α", - "Ġph ủ", - "ĠбÑĭ ла", - "ĠбÑĭл а", - "Ñĩ ива", - "Ñĩи ва", - "Ñĩив а", - "Ġ ê°Ħ", - "Ġê° Ħ", - "о лÑĸ", - "ол Ñĸ", - "Ùĥ ت", - "å ħ§", - "åħ §", - "à¥Ĥ à¤Ł", - "Ġ ëĸ", - "Ġë ĸ", - "Ġ ÙĦÙĩ", - "ĠÙĦ Ùĩ", - "ëłĪ ìĿ´", - "Ġh ız", - "å¤ ı", - "ĠæĬķ稿 æĹ¥", - "éļ ¾", - "ĵ °", - "г лÑıд", - "глÑı д", - "гл Ñıд", - "ì n", - "Ġ меÑĢ", - "Ġм еÑĢ", - "Ġ ãĢij", - "ĠãĢ ij", - "Ġ обÑī", - "Ġоб Ñī", - "um hur", - "çł ´", - "л иÑģÑĮ", - "ли ÑģÑĮ", - "sp ÄĽ", - "ر ÙĬÙĤ", - "رÙĬ ÙĤ", - "Ġ تÙģ", - "Ġت Ùģ", - "Ġا ÙĦÙĪ", - "ĠاÙĦ ÙĪ", - "çµ ±", - "а лоÑģÑĮ", - "ал оÑģÑĮ", - "ало ÑģÑĮ", - "Ġm ô", - "Ġv á»ĩ", - "Ġvá» ĩ", - "Ġ δι", - "Ġδ ι", - "Ġ зн", - "Ġз н", - "Ġ بØŃ", - "Ġب ØŃ", - "ت Ùī", - "Ġ ì§ģ", - "Ġì§ ģ", - "Ġvel mi", - "uyá» ħn", - "Ġph ạm", - "ÑģÑĤв ом", - "ÑģÑĤво м", - "ĠÙĪ اÙĦÙħ", - "ĠÙĪاÙĦ Ùħ", - "ĠÙĪا ÙĦÙħ", - "ĠбÑĭ ли", - "ĠбÑĭл и", - "ا Ø°", - "Ø§Ø °", - "ÄĽ ÅĻ", - "â Ħĸ", - "âĦ ĸ", - "Ġп олож", - "Ġпо лож", - "Ġпол ож", - "า à¸ģาร", - "าà¸ģ าร", - "ĠÄį lán", - "Îķ Ρ", - "Ġ ìĤ°", - "ĠìĤ °", - "β α", - "Ġ æĹ¥æľ¬", - "ĠæĹ¥ æľ¬", - "ز د", - "ĠÙĨ ÛĮست", - "ĠÙĨÛĮ ست", - "Ġha yat", - "Ġhay at", - "Ġhaya t", - "ç¢ º", - "à¹Ģ วล", - "à¹Ģว ล", - "ĠCh ÃŃnh", - "ĠChÃŃ nh", - "ï¼Į æĺ¯", - "ĠÙĪ اØŃ", - "ĠÙĪا ØŃ", - "èı ¯", - "Ġή ÏĦαν", - "Ġx á»Ń", - "ĠÄį erv", - "ĠÄįer v", - "ĠÄįe rv", - "ĠÙħد ÛĮر", - "é Ĩ", - "ĠëĪ Ī", - "ç» Ń", - "Ġt ên", - "ìĸ ¸", - "Ġort aya", - "Ġorta ya", - "Ġ жен", - "Ġж ен", - "Ġже н", - "Ġn Æ¡i", - "ен нÑĭе", - "ÑĦ екÑĤив", - "ÑĦек ÑĤив", - "íĿ ¬", - "Ġkh á»ı", - "ĠÄij a", - "os yal", - "osy al", - "à¸Ľà¸£à¸° à¹Ģà¸Ĺศ", - "Ġo dst", - "Ġod st", - "Ġ à¸ĸ", - "Ġภĸ", - "Ġο ÏĢο", - "æĶ¿ åºľ", - "Ġb Ãłn", - "ĠbÃł n", - "ĠG iá»", - "ĠGi á»", - "Ġold uk", - "Ġol duk", - "Ġoldu k", - "о вание", - "ов ание", - "ова ние", - "овани е", - "ован ие", - "à¸Ń ส", - "Ġ нев", - "Ġн ев", - "Ġне в", - "ÏĦ Ïģο", - "ÏĦÏģ ο", - "Ġ ìĨį", - "ĠìĨ į", - "k ı", - "Ġब ड", - "Ġ ÏħÏĢ", - "ĠÏħ ÏĢ", - "Ġ Vý", - "ĠV ý", - "ï¾ Ħ", - "çŃ ĸ", - "ε ÏĨ", - "Ġ åħ¨", - "Ġåħ ¨", - "ĠÙģ رÙĪØ´", - "ĠÙģر ÙĪØ´", - "ĠÙģرÙĪ Ø´", - "ÙĤÛĮ ÙĤ", - "ä¼ģ ä¸ļ", - "ε Ïį", - "èĻ Ł", - "Ġa yr", - "Ġay r", - "ض ÙĪ", - "Å¡ el", - "Å¡e l", - "Ġп ÑĸÑģлÑı", - "ĠпÑĸÑģ лÑı", - "Ñĸй Ñģ", - "é¢ Ĩ", - "Ú© تر", - "کت ر", - "л Ñĥб", - "лÑĥ б", - "è« ĸ", - "æ° ¸", - "ез пеÑĩ", - "Ġ кам", - "Ġк ам", - "Ġка м", - "ع داد", - "عد اد", - "ê±° ëŀĺ", - "ู à¸ĩ", - "ĠتÙĩ راÙĨ", - "Ġ ëĦĪ", - "ĠëĦ Ī", - "ÑĢ ив", - "ÑĢи в", - "Ġ ÑĤоÑĢ", - "ĠÑĤ оÑĢ", - "ĠÑĤо ÑĢ", - "ا Ùī", - "ا٠ī", - "' Ñıз", - "'Ñı з", - "ÙIJ ÙĬ", - "Ġkh ÃŃ", - "Ġ ÑĪÑĤ", - "ĠÑĪ ÑĤ", - "Ġ ξε", - "ĠÎľ ε", - "Ġb iri", - "Ġbi ri", - "Ġbir i", - "è ĩ´", - "èĩ ´", - "Ñĥ вав", - "Ñĥв ав", - "Ñĥва в", - "ãģĪ ãĤĭ", - "Ġд иÑģ", - "Ġди Ñģ", - "а ÑİÑĤ", - "аÑİ ÑĤ", - "ص ب", - "åĿ ĩ", - "о лÑİ", - "ол Ñİ", - "èĭ ¥", - "Ġ اث", - "Ġا Ø«", - "ĠØ§Ø «", - "s ou", - "so u", - "åIJ ĥ", - "ãģ® ãģł", - "ub lik", - "ubl ik", - "л ей", - "ле й", - "Âł m", - "Ġíıī ê·ł", - "ạ y", - "ε ÏĢ", - "t ık", - "tı k", - "Ġv yu", - "Ġvy u", - "ع ÙĪد", - "Ġд оз", - "Ġдо з", - "Ġl á»ĭch", - "è³ ª", - "à¥ģ à¤Ī", - "à¥ģठĪ", - "ั à¸ŀ", - "Ġt ém", - "Ġté m", - "Ġ kaç", - "Ġk aç", - "Ġka ç", - "Ġc ái", - "Ġcá i", - "Ġ μα", - "Ġμ α", - "âĢ¦âĢ¦ ãĢįĊĊ", - "í Ī¬", - "ر ÙĪÙĩ", - "رÙĪ Ùĩ", - "Ġ rych", - "Ġr ych", - "Ġry ch", - "Îij Τ", - "Ġ ÑĢÑĸв", - "ĠÑĢ Ñĸв", - "ë³ ij", - "åģ ¥", - "Ġzd rav", - "Ġ عدد", - "Ġع دد", - "Ġعد د", - "èį ī", - "δ ια", - "δι α", - "Ġv áºŃn", - "Ñĭ ÑĤ", - "Ġкол иÑĩ", - "Ġко лиÑĩ", - "Ġколи Ñĩ", - "ÏĮ ÏĦε", - "Ġb ırak", - "Ġ ØŃÙħ", - "ĠØŃ Ùħ", - "Ġch á»ĭ", - "é» Ħ", - "ĠاÙĦÙħت ØŃدة", - "ื à¸Ńà¸ģ", - "ืà¸Ń à¸ģ", - "Ġз али", - "Ġза ли", - "Ġзал и", - "Ġnh anh", - "âĢĮ تÙĪاÙĨ", - "ëĿ ½", - "Ġت ÙĪسط", - "ĠتÙĪ سط", - "ĠتÙĪس Ø·", - "è¦ģ æ±Ĥ", - "а лÑĥ", - "ал Ñĥ", - "ün kü", - "ünk ü", - "ãģª ãĤĵ", - "Ġ Trong", - "ĠT rong", - "ĠTr ong", - "ĠTro ng", - "à¸Ļ ะ", - "åij ¼", - "Ġ ÙĬÙħ", - "ĠÙĬ Ùħ", - "и ки", - "ик и", - "ĠÑĤ ÑĥÑĤ", - "ĠÑĤÑĥ ÑĤ", - "Ġya ÅŁam", - "ĠyaÅŁ am", - "Ġm á»įi", - "é ĽĦ", - "éĽ Ħ", - "ĠØŃ Ø¶", - "Ġав ÑĤом", - "ĠавÑĤ ом", - "Ġसब स", - "Ġy ếu", - "ãĤ¹ ãĤ¿", - "Ïĩ ή", - "Ñĸ Ñİ", - "è ĺ", - "ิ ย", - "Ġm ev", - "Ġme v", - "ick ého", - "ické ho", - "ि ह", - "िठ¹", - "åŃ £", - "θ ή", - "Ġब ढ", - "ĠاÙĦ Ùħس", - "ĠاÙĦÙħ س", - "ÏĦ οÏħ", - "ÏĦο Ïħ", - "ek li", - "ekl i", - "Ġде ÑĢев", - "ĠдеÑĢ ев", - "å¸ Ń", - "æ² Ļ", - "ãģ« ãĤĤ", - "Ġo blast", - "Ġob last", - "Ġobl ast", - "Ġh á»Ļ", - "Ġhá» Ļ", - "Ġ å¹³", - "Ġå¹ ³", - ".:.:.:.: .:.:.:.:", - ".:.:.:. :.:.:.:.:", - "Ġ éĸ", - "Ġé ĸ", - "Ġ جز", - "Ġج ز", - "ĠÙĩÙħ ÚĨ", - "ä¸ ¦", - "ÑĨ еп", - "ÑĨе п", - "ा Ċ", - "ä¸Ń çļĦ", - "'n ın", - "Ġ íķĺëĬĶ", - "Ġíķĺ ëĬĶ", - "ÑĶ ÑĹ", - "Ġ بش", - "Ġب Ø´", - "åį ´", - "ä¹ ł", - "ĠاطÙĦ اعات", - "ĠاطÙĦاع ات", - "Ġ ë²ł", - "Ġë² ł", - "Ġکرد ÙĨ", - "Ġکر دÙĨ", - "ा ड", - "ाठ¡", - "Ġà¤ħ र", - "ĠH á»į", - "ĠHá» į", - "ĠгÑĢом ад", - "Ġ ست", - "ĠØ ³Øª", - "Ġس ت", - "ÏĦι ÏĤ", - "Ġan cak", - "Ġanc ak", - "Ġ ог", - "Ġо г", - "Ġk teÅĻÃŃ", - "Ġ æ¬", - "Ġæ ¬", - "Ġ Ngh", - "ĠN gh", - "ĠNg h", - "Ġt edy", - "Ġte dy", - "Ġted y", - "Ġ ÏĢο", - "ĠÏĢ ο", - "Ġqu ân", - "Ġб Ñĥли", - "ĠбÑĥ ли", - "è¯ Ĩ", - "Ġt ừng", - "Ġtá» «ng", - "Ġtừ ng", - "人 çļĦ", - "ี à¸ģาร", - "ีà¸ģ าร", - "Ġκα ÏĦα", - "Ġpo uze", - "Ġpou ze", - "¡ ng", - "ĠØ¢ ر", - "Ġ ÑĤÑĥ", - "ĠÑĤ Ñĥ", - "Ġt á»·", - "Ġtá» ·", - "ĠD anh", - "ĠDan h", - "ĠDa nh", - "о ном", - "он ом", - "Ñģ ий", - "Ñģи й", - "Ġ à¹Ģà¸Ķ", - "Ġà¹Ģ à¸Ķ", - "Ġà¹ĢภĶ", - "£ ¨", - "Å¡ k", - "ãĥĥ ãĥī", - "ar dır", - "ard ır", - "Ġyö net", - "Ġyön et", - "Ñĥ вали", - "Ñĥв али", - "Ñĥва ли", - "åħĪ çĶŁ", - "Ġ ÐIJÑĢ", - "ĠÐIJ ÑĢ", - "Ġprot ože", - "Ġproto že", - "Ġ íģ¬", - "Ġíģ ¬", - "Ġjed not", - "Ġjedn ot", - "Ġjedno t", - "Ġt ý", - "éĩ ĩ", - "Ġ หร", - "Ġห ร", - "Ġ åľ°", - "Ġåľ °", - "çº ¢", - "Ġм олод", - "Ġмол од", - "Ġмо лод", - "iên g", - "iê ng", - "ĠÏĮ ÏĦι", - "Ġد اشتÙĩ", - "Ġداش تÙĩ", - "Ġداشت Ùĩ", - "Ġuy gun", - "Ġuyg un", - "Ġuygu n", - "Ġоп еÑĢа", - "ĠопеÑĢ а", - "åı «", - "Ġ ап", - "Ġа п", - "Ġ кÑĥÑĢ", - "Ġк ÑĥÑĢ", - "ĠкÑĥ ÑĢ", - "ا عة", - "اع Ø©", - "un uz", - "unu z", - "Ġ ìĤ¬ì§Ħ", - "ĠìĤ¬ ì§Ħ", - "Ġv ô", - "ç ok", - "ço k", - "Ġ èģ", - "Ġè ģ", - "ÑĤе ÑĢеÑģ", - "ÑĤеÑĢ еÑģ", - "Ġ استاÙĨ", - "Ġا ستاÙĨ", - "Ġاست اÙĨ", - "Ġاس تاÙĨ", - "а лаÑģÑĮ", - "ала ÑģÑĮ", - "à¥ģ व", - "à¥ģठµ", - "á» ³", - "Ġl Æ°u", - "Ġ Та", - "ĠТ а", - "Ġl á»±a", - "' ÑĶ", - "Ġ üy", - "Ġü y", - "Ġ ÛĮÚ©ÛĮ", - "ĠÛĮ Ú©ÛĮ", - "ĠÛĮÚ© ÛĮ", - "æ ¾", - "н ем", - "не м", - "Ġ خاÙĨ", - "ĠØ® اÙĨ", - "ĠÑį лек", - "ÙĤ اÙĦ", - "л ок", - "ло к", - "ĠÄij ẹp", - "à¥ī ल", - "Ġm ůž", - "Ġmů ž", - "ëĭ¤ ëĬĶ", - "Ġ íķĺëĤĺ", - "Ġíķĺ ëĤĺ", - "ÙĦ ت", - "çİ° åľ¨", - "м о", - "Ïħ Ïĥ", - "ãģŁ ãģ¡", - "ĠìłĦ ìĦ¸", - "à¥į à¤Łà¤°", - "à¥įà¤Ł र", - "ع ات", - "عا ت", - "د ÙĪ", - "ä¿ º", - "æ¥ ½", - "æ£ ®", - "Ġл иÑģÑĤ", - "Ġли ÑģÑĤ", - "δ ι", - "å¯ Į", - "ĠÄij Æ°a", - "в еÑģÑĤи", - "ве ÑģÑĤи", - "веÑģÑĤ и", - "д о", - "ан нÑĸ", - "Ġü ret", - "Ġür et", - "Ġg á»įi", - "ĠÑģ воÑİ", - "ĠÑģв оÑİ", - "ĠÑģво Ñİ", - "á» «ng", - "ừ ng", - "Ġt ất", - "äºļ æ´²", - "á ce", - "ác e", - "N Ãį", - "Ġ ÑĢÑĭ", - "ĠÑĢ Ñĭ", - "æ» ¡", - "Ïģ εÏĤ", - "Ïģε ÏĤ", - "åħį è´¹", - "л оÑĤ", - "ло ÑĤ", - "æĻ º", - "Ġα γ", - "Ġà¤ħ म", - "Ġ ç´", - "Ġç ´", - "о до", - "од о", - "Ñħ и", - "Ġngu á»ĵn", - "éĥ¨ åĪĨ", - "в аÑĤ", - "ва ÑĤ", - "ĠÑĤ еб", - "ĠÑĤе б", - "з аÑĨÑĸÑĹ", - "за ÑĨÑĸÑĹ", - "Ġ ÐŁÑĢо", - "ĠÐŁ ÑĢо", - "ĠÐŁÑĢ о", - "ع ÛĮ", - "Ġ ÙĪÙĬ", - "ĠÙĪ ÙĬ", - "ëŀ ľ", - "Ġne by", - "Ġneb y", - "Ġج دÛĮد", - "Ġجد ÛĮد", - "ÄŁ imiz", - "ÄŁim iz", - "£ ½", - "Ġà¤Ĩ त", - "Ġà¤Ń र", - "æī ĺ", - "å®ī åħ¨", - "Ġëĵ¤ ìĸ´", - "ب رد", - "بر د", - "Ġê²ĥ ìĿ´", - "äº ²", - "æ° ı", - "ал Ñĸз", - "алÑĸ з", - "l ack", - "la ck", - "lac k", - "ĠÙħخت ÙĦÙģ", - "ا ÙĨÙĬØ©", - "اÙĨ ÙĬØ©", - "اÙĨÙĬ Ø©", - "Ġ ì²Ń", - "Ġì² Ń", - "Ġ виÑĤ", - "Ġв иÑĤ", - "Ġви ÑĤ", - "Ġhar eket", - "Ġhare ket", - "Ġharek et", - "é ¨", - "à¸Ļ ำ", - "Ġب رخ", - "Ġبر Ø®", - "å£ ²", - "Ñĩ ай", - "Ñĩа й", - "Ġan lat", - "Ġà¤ħ व", - "ĠاÙģ ز", - "Ġh ết", - "ĠÚĨ ÙĨد", - "éĹ ľ", - "пÑĢи ÑĶм", - "g ı", - "Ġk omp", - "Ġkom p", - "Ġko mp", - "Ġl Ỽp", - "ĠlỼ p", - "Ġm á»Ĺi", - "Ġmá» Ĺi", - "à¸Ľà¸£à¸° à¸ģ", - "Ġ haf", - "Ġh af", - "Ġha f", - "Ġ eder", - "Ġe der", - "Ġed er", - "Ġзд оÑĢов", - "à¥Ĥ म", - "ëł ¸", - "Ġo nun", - "Ġon un", - "Ġonu n", - "ĠÙħر دÙħ", - "ĠÙħرد Ùħ", - "ĠÐľ аÑĢ", - "ĠÐľÐ° ÑĢ", - "Ġìĸ´ ëĸ", - "м ан", - "ма н", - "Ġ ÑģилÑĮ", - "ĠÑģ илÑĮ", - "ĠÑģи лÑĮ", - "ĠÑģил ÑĮ", - "ç¶ ²", - "ë¸ Ķ", - "л ÑıеÑĤ", - "лÑı еÑĤ", - "ĠнеÑģк олÑĮко", - "ĠнеÑģколÑĮ ко", - "l andır", - "land ır", - "lan dır", - "landı r", - "Ġв д", - "ĠÙĨ ÙĪ", - "ãģ İ", - "ÑĤ ин", - "ÑĤи н", - "ت Ø´", - "а ний", - "ан ий", - "ани й", - "Ġt ÅĻ", - "Ñģ иÑħ", - "Ñģи Ñħ", - "л ом", - "ло м", - "æŃ ©", - "ãİ ¡", - "Ġ ØŃر", - "ĠØŃ Ø±", - "æĭ į", - "e nou", - "en ou", - "eno u", - "Ġв ели", - "Ġвел и", - "Ġве ли", - "Ġ δη", - "Ġδ η", - "s ka", - "sk a", - "主 è¦ģ", - "ا ÙģØ©", - "اÙģ Ø©", - "ĠболÑĮ ÑĪе", - "ĠболÑĮÑĪ е", - "ิ ศ", - "çĽ Ĭ", - "ĠÙģ ÙĤØ·", - "ĠÙģÙĤ Ø·", - "å¨ ģ", - "Ġh Æ°á»Łng", - "ĠD oÄŁ", - "ĠDo ÄŁ", - "Ġd Ãłi", - "Ġ гоÑĤов", - "Ġг оÑĤов", - "ĠгоÑĤ ов", - "Ġв ам", - "Ġва м", - "âĢ ī", - "ा à¤ļ", - "ाठļ", - "åħ ¸", - "à¹ĥ หà¸į", - "à¹ĥห à¸į", - "Ġ ç«", - "Ġç «", - "ekt ör", - "Ġв ел", - "Ġве л", - "Ġ ÙĦÙĪ", - "ĠÙĦ ÙĪ", - "Ø´ تÙĩ", - "شت Ùĩ", - "æĺ ¾", - "ả y", - "à¹Ĥ ม", - "Ġt á»ķng", - "Ġtá»ķ ng", - "Ġtá»ķn g", - "ĠповеÑĢ Ñħ", - "ÑĹ Ð²", - "Ġph ép", - "çļ ĩ", - "Ġп оÑĢÑıд", - "Ġпо ÑĢÑıд", - "ĠпоÑĢ Ñıд", - "ĠÑģооÑĤ веÑĤ", - "ठĿ", - "ĠÑģеб Ñı", - "Ġ ëĤł", - "ĠëĤ ł", - "Ġб Ñĥла", - "ĠбÑĥ ла", - "à¹ī าย", - "à¹īา ย", - "Ġ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢ", - "ĠÙħ جÙħÙĪع", - "ï¼Į 以", - "Ġب ÙĪدÙĩ", - "ĠبÙĪد Ùĩ", - "ĠبÙĪ دÙĩ", - "μ ÏĮ", - "Ġ íݸ", - "Ġíİ ¸", - "eÅŁ it", - "Ñİ Ñīие", - "ÑİÑī ие", - "Ñİ ÑīиÑħ", - "ÑİÑī иÑħ", - "åŁº éĩij", - "Ġت ØŃت", - "ĠتØŃ Øª", - "Ġв лаÑģ", - "Ġвла Ñģ", - "ler le", - "ãĤ ²", - "ëĬ ĺ", - "è ĵ", - "m anın", - "man ın", - "manı n", - "ìŀ Ī", - "Ġz ast", - "Ġza st", - "Ġzas t", - "ĠÑĩелов ек", - "à¥ĩ ब", - "à¥ĩठ¬", - "p eÄį", - "pe Äį", - "Ġبر ÙĨاÙħÙĩ", - "Ġs lov", - "Ġsl ov", - "Ġslo v", - "ĠnÄĽ jak", - "ĠnÄĽj ak", - "ê· ľ", - "à¥ĩ ह", - "à¥ĩठ¹", - "èĹ ¤", - "ĠبÛĮ شتر", - "ĠبÛĮØ´ تر", - "il iz", - "ili z", - "Ġ ëĶĶ", - "Ġë ĶĶ", - "ĠëĶ Ķ", - "ا زÙĩ", - "از Ùĩ", - "ت د", - "Ġe tm", - "Ġet m", - "Ġëĭ¤ 른", - "Ġ vů", - "Ġv ů", - "å° Ħ", - "Ġк лаÑģ", - "Ġкл аÑģ", - "Ġкла Ñģ", - "в ÑĢоп", - "æ´ ¾", - "ĠÄij ình", - "Ñĥ ÑİÑĤ", - "ÑĥÑİ ÑĤ", - "Ñĥ еÑĤÑģÑı", - "ÑĥеÑĤ ÑģÑı", - "éľ ²", - "Ġ ÑģкоÑĢ", - "ĠÑģ коÑĢ", - "ĠÑģк оÑĢ", - "Ġв аÑģ", - "Ġва Ñģ", - "íķĺ ìĺĢëĭ¤", - "íķĺìĺĢ ëĭ¤", - "Ġ داشت", - "Ġد اشت", - "Ġداش ت", - "Ġ çĦ", - "Ġç Ħ", - "Ġ 西", - "Ġè ¥¿", - "Ġκα ÏĦά", - "ॠ¦", - "ìĹ Ĩ", - "Ġ خدÙħ", - "ĠØ® دÙħ", - "Ġخد Ùħ", - "ا سÙħ", - "اس Ùħ", - "Îij Ρ", - "ĠA ma", - "ĠAm a", - "å¥ ¥", - "Ġبزر Ú¯", - "Ġ ÐĴÑĸн", - "ĠÐĴ Ñĸн", - "Ġ Åĺ", - "ĠÅ ĺ", - "Ġ à¸Īาà¸ģ", - "Ġà¸Ī าà¸ģ", - "ĠÑħаÑĢакÑĤ еÑĢ", - "ĠÄij á»Ļi", - "ĠÄijá»Ļ i", - "ĠÑĢоз виÑĤ", - "ĠÑĢозви ÑĤ", - "ĠпÑĢоÑĦ еÑģ", - "Ġкон ÑĤÑĢ", - "ĠконÑĤ ÑĢ", - "ÎŁ ÎĽ", - "ÎŁÎ Ľ", - "Ġm inh", - "Ġmin h", - "Ġmi nh", - "ä¼ ij", - "ì ª½", - "Ġch Æ¡i", - "з аÑĨии", - "за ÑĨии", - "ĠдÑĸÑı лÑĮ", - "ë Ĩ", - "Ġn gay", - "Ġng ay", - "Ġnga y", - "à¥Ĥ à¤Ĥ", - "Ġiht iy", - "éĽ ª", - "Ġ ê´Ģ리ìŀIJ", - "Ġê´Ģ리 ìŀIJ", - "Ġc ụ", - "Ġ ì§Ī", - "Ġì§ Ī", - "ÙĬ Ø«", - "ặ p", - "ÙĪ اع", - "ÙĪا ع", - "ãģĤ ãģ£ãģŁ", - "Ġ çľ", - "Ġç ľ", - "Ġ ìļ°ë¦¬", - "Ġìļ° 리", - "à¹Ī à¸ĩà¸Ĥ", - "à¹Īà¸ĩ à¸Ĥ", - "Ġ çŃ", - "Ġç Ń", - ") ØĮ", - "Ãł m", - "ÙĦ ÛĮÙĦ", - "ÙĦÛĮ ÙĦ", - "Ġ 걸", - "Ġê± ¸", - "алÑĮ ниÑħ", - "æŶ åĢĻ", - "un dan", - "und an", - "unda n", - "Ġ Gün", - "ĠG ün", - "ĠGü n", - "Ġ topl", - "Ġt opl", - "Ġto pl", - "Ġtop l", - "ĠÑĢек омен", - "ĠاÙĨت خاب", - "Ãł u", - "Äį ka", - "ë° Ģ", - "Ġк ÑĢаÑģ", - "ĠкÑĢа Ñģ", - "л оп", - "ло п", - "å¼ µ", - "ĠاÙĦ Ùħع", - "ĠاÙĦÙħ ع", - "m ÃŃn", - "mÃŃ n", - "Ġv iết", - "Ġvi ết", - "Ġ ê°ĻìĿĢ", - "Ġê°Ļ ìĿĢ", - "ut eÄį", - "ute Äį", - "Ġ nech", - "Ġn ech", - "Ġne ch", - "Ġnec h", - "çµ Ĥ", - "ãģª ãģĮ", - "ay ın", - "ayı n", - "Ġ Äįin", - "ĠÄį in", - "ĠÄįi n", - "ch ázÃŃ", - "cház ÃŃ", - "اÙģ ظ", - "ÑĢ оваÑĤÑĮ", - "ÑĢов аÑĤÑĮ", - "ÑĢо ваÑĤÑĮ", - "ÑĢова ÑĤÑĮ", - "à¹Ħ ร", - "Ġ ãĤ¤", - "ĠãĤ ¤", - "Ġзаб олева", - "Ġ å±±", - "Ġå± ±", - "Ġka dın", - "Ġkad ın", - "ÏĦ ηÏĤ", - "ÏĦη ÏĤ", - "а лиÑģÑĮ", - "ал иÑģÑĮ", - "али ÑģÑĮ", - "Ġh ük", - "åĵ ¥", - "Ġп еÑĢи", - "ĠпеÑĢ и", - "ÅĻ ád", - "Ġà¤ħ स", - "Ġ ÑģÑĤвоÑĢ", - "ĠÑģÑĤ воÑĢ", - "ĠÙĪ ÛĮÚ©ÛĮ", - "ĠÙĪÛĮ Ú©ÛĮ", - "Ġ ì¡", - "Ġì ¡", - "Ġc á»Ńa", - "Ġcá»Ń a", - "Ġh iá»ĥu", - "游 æĪı", - "ÑĮ омÑĥ", - "Ġg ó", - "Ġt oh", - "Ġto h", - "Ġб ла", - "Ġбл а", - "Ġ åij", - "Ġå ij", - "Ġп ло", - "Ġпл о", - "и ÑĪ", - "ĠÄij ấu", - "s kou", - "sk ou", - "sko u", - "ãĤĪ ãĤĬ", - "ู à¸Ľ", - "Ġr á»ĵi", - "оп ÑĢоÑģ", - "н олог", - "ĠÑĤ ÑĢав", - "ĠÑĤÑĢ ав", - "ĠÑĤÑĢа в", - "ĠWay back", - "Ġ à¹Ĩ", - "ĠÑĥ ÑĩаÑģÑĤ", - "ĠÑĥÑĩ аÑģÑĤ", - "ĠÑĥÑĩа ÑģÑĤ", - "ĠÑĥÑĩаÑģ ÑĤ", - "ĠпÑĢеп аÑĢа", - "Ġd ạng", - "ĠÃľ n", - "à¹Ħล à¸Ļ", - "Ġد اخ", - "Ġs Æ¡", - "Ġk oy", - "Ġko y", - "ëĿ¼ ê³ł", - "ĠÄij úng", - "à¥ĩà¤Ĥ ,", - "Ġgeç ir", - "ĠÑıк Ñīо", - "Ñģ ÑĤÑĢо", - "ÑģÑĤ ÑĢо", - "ÑģÑĤÑĢ о", - "ен ÑĤов", - "енÑĤ ов", - "Ñĸ ж", - "к ÑĥÑİ", - "кÑĥ Ñİ", - "Ġe ÄŁitim", - "ĠeÄŁit im", - "à¥įर स", - "Ġ Сп", - "ĠС п", - "ا تÛĮ", - "ات ÛĮ", - "ãģij ãĤĭ", - "ÏĦ Ïīν", - "ÏĦÏī ν", - "Ġ км", - "Ġк м", - "âĸįâĸį âĸįâĸį", - "j ist", - "ji st", - "jis t", - "ÑĤ ак", - "ÑĤа к", - "Ġ åIJįåīį", - "ĠåIJį åīį", - "é¡ Ķ", - "л Ñĭ", - "Ġkh ảo", - "Ġkhả o", - "âĢĻ Ñı", - "ĠÙħ ÙĦÛĮ", - "ĠÙħÙĦ ÛĮ", - "l ož", - "lo ž", - "Ġ ìĸ¸", - "Ġìĸ ¸", - "Ġg ần", - "Ġ à¤ľà¤°", - "Ġà¤ľ र", - "ब र", - "Îķ Σ", - "า à¸Ľ", - "าภĽ", - "Ġn ás", - "Ġná s", - "form ace", - "forma ce", - "Ġet mek", - "Ġetm ek", - "Ġetme k", - "в еÑģÑĤ", - "ве ÑģÑĤ", - "ìĸ´ ìļĶ", - "Ġत थ", - "ĠÑģ ек", - "ξ η", - "æ¯ Ľ", - "B ir", - "Bi r", - "Ġ ìŀĦ", - "Ġìŀ Ħ", - "Ġv ardır", - "Ġvar dır", - "Ġvardı r", - "ÙĪ اÙĦ", - "ÙĪا ÙĦ", - "Ä° R", - "ov ané", - "ova né", - "ovan é", - "н аÑĢод", - "на ÑĢод", - "наÑĢ од", - "à¸Ħ ำ", - "e mek", - "em ek", - "eme k", - "ĠÎķ ÏĢ", - "Ġ ÅĻe", - "ĠÅĻ e", - "ãģ¾ ãģĽ", - "uyá»ĩ t", - "Ġ ìĸ¼", - "Ġìĸ ¼", - "r ů", - "Ġ onu", - "Ġo nu", - "Ġon u", - "à¹Ģà¸ķ à¸Ńร", - "од аÑĢ", - "ода ÑĢ", - "ز Ùĩ", - "Ġk av", - "Ġka v", - "о нÑĭ", - "он Ñĭ", - "Ġв еÑģ", - "Ġве Ñģ", - "ìĤ¬ ì§Ģ", - "Ġг ла", - "à Ŀ", - "ĠÙĤ ÛĮÙħت", - "çķ ¥", - "à¸ĸ าà¸Ļ", - "Äį il", - "Äįi l", - "Ġ ä¸ĩ", - "Ġä¸ ĩ", - "è¾ ĥ", - "åħ ħ", - "Ġ ÑĢед", - "ĠÑĢ ед", - "ĠÑĢе д", - "ม ห", - "am ilia", - "ami lia", - "amil ia", - "à¥ĩ à¤ķर", - "à¥ĩà¤ķ र", - "Ġt á»iji", - "Ġtá»ij i", - "Ùģ ÛĮ", - "ÑĢ ÑĸÑĪ", - "ÑĢÑĸ ÑĪ", - "ìķ ł", - "à¸Ļ ส", - "à¸Ī ร", - "à¥ĩ शन", - "à¥ĩश न", - "ĠÙħÙĪ ضÙĪع", - "æī ¹", - "Ġob sah", - "Ġobs ah", - "Ġнав Ñĩ", - "Ġdes tek", - "Ġdest ek", - "Ġdeste k", - "Ġ zas", - "Ġz as", - "Ġza s", - "å ĵį", - "åĵ į", - "üm üz", - "ümü z", - "Ġ çŁ", - "Ġç Ł", - "Ġ è¨", - "Ġè ¨", - "Ù ¬", - "ç» Ī", - "Ġz de", - "Ġzd e", - "Ġz áp", - "Ġzá p", - "à¥Ĥ सर", - "à¥Ĥस र", - "ìĿ´ ì§Ģ", - "çļ ®", - "l om", - "lo m", - "ॠ§", - "ÙĦ اÙĤ", - "ÙĦا ÙĤ", - "à¸Ļ à¸ķ", - "íĮ ħ", - "л ада", - "ла да", - "лад а", - "m asına", - "mas ına", - "ması na", - "ãģ® ãģ§", - "ëĵ¤ ìĿĦ", - "Ġн аг", - "Ġна г", - "m asını", - "mas ını", - "ãĤ Ŀ", - "ın ıf", - "ını f", - "åĽ ´", - "Ġböl üm", - "å¥ ĸ", - "æ¨ Ļ", - "ÙĦ اØŃ", - "ÙĦا ØŃ", - "Ġг оÑģÑĥдаÑĢ", - "داÙĨ ÙĦÙĪد", - "Ġп оÑĤÑĢеб", - "ĠпоÑĤ ÑĢеб", - "ĠÑĢ оÑĨÑĸ", - "о га", - "ог а", - "ĠÑģлед ÑĥеÑĤ", - "Ġп аÑĢа", - "ĠпаÑĢ а", - "Ġпа ÑĢа", - "é ¼", - "ãģį ãģŁ", - "ί ζ", - "Ġb á»ij", - "ÑĤ Ñĸв", - "ÑĤÑĸ в", - "ï¼Į 她", - "f amilia", - "éł ħ", - "Ġد ÙĦ", - "Ġs kup", - "Ġsk up", - "Ġsku p", - "еÑĩ ение", - "ãģĵãģ¨ ãģĮ", - "à¥Ģ ब", - "ุ ล", - "¨ ë¶Ģ", - "ĠاÙĦع رب", - "Ġ ç¾İ", - "Ġç¾ İ", - "ĠاÙĦ ÙħÙĪ", - "ĠاÙĦÙħ ÙĪ", - "Ġ Ø¥ÙĨ", - "ĠØ¥ ÙĨ", - "Ġná sled", - "Ġnás led", - "Ġt omu", - "Ġto mu", - "Ġtom u", - "Î Ħ", - "Ġз ави", - "Ġза ви", - "Ġзав и", - "Ġn hu", - "Ġnh u", - "ĠpÅĻed stav", - "ìłķ ë³´", - "o kol", - "ok ol", - "oko l", - "Ġк ÑĢи", - "a du", - "ad u", - "Ġ каÑĤ", - "Ġк аÑĤ", - "Ġка ÑĤ", - "Ġ ÑįÑĦ", - "ĠÑį ÑĦ", - "в ал", - "ва л", - "m ayı", - "ma yı", - "may ı", - "ĠÑĩаÑģ ÑĤо", - "ĠÑĩаÑģÑĤ о", - "Ġtr anh", - "Ġtra nh", - "Ġtran h", - "ائ ÙĦ", - "ãĤĪãģĨ ãģª", - "Ġp oh", - "Ġpo h", - "ìĥģ ìľĦ", - "Ġs ắc", - "Ùĥ س", - "Ġ мÑĥ", - "Ġм Ñĥ", - ". ::", - ".: :", - "ë Ī", - "» Ċ", - "Ġ ÙĨÚ¯", - "ĠÙĨ Ú¯", - "ÙIJ ÙĨ", - "н иком", - "ни ком", - "ник ом", - "Ñħ а", - "Ġ μοÏħ", - "Ġμ οÏħ", - "Ġμο Ïħ", - "ĠNg uyá»ħn", - "ĠвÑĭ Ñģок", - "ĠвÑĭÑģ ок", - "Ġ ÐŁÐ¾Ð´", - "ĠÐŁ од", - "ĠÐŁÐ¾ д", - "ĠпÑĢи ÑĢод", - "à¥ĭ ध", - "िà¤ķ ल", - "и ÑĢа", - "иÑĢ а", - "ëĭ¤ ê³ł", - "Ġm ajÃŃ", - "Ġma jÃŃ", - "Ġmaj ÃŃ", - "Ġv ùng", - "Ġtarih inde", - "Ġtarihi nde", - "Ġ ваÑĢ", - "Ġв аÑĢ", - "Ġва ÑĢ", - "н иÑĤÑĮ", - "ни ÑĤÑĮ", - "ниÑĤ ÑĮ", - "ει ÏĤ", - "Ġ åĩº", - "Ġåĩ º", - "dy ž", - "ÏĦ Ïİν", - "ÏĦÏİ Î½", - "ä½ĵ èĤ²", - "Ġ à¹Ģว", - "Ġà¹Ģ ว", - "Ġà¹Ģภ§", - "Ġà¤ħ à¤ļ", - "Ġ اÙĨÚ¯ÙĦÛĮسÛĮ", - "ĠاÙĨÚ¯ ÙĦÛĮسÛĮ", - "à¥įय म", - "Ġgel iÅŁ", - "æ¹ ĸ", - "Ġ اک", - "Ġا Ú©", - "Ġп лан", - "Ġпл ан", - "Ġпла н", - "k yt", - "ky t", - "ا بÛĮ", - "اب ÛĮ", - "κ ι", - "Ġc hung", - "Ġch ung", - "Ġchu ng", - "ान à¤ķ", - "s ı", - "Ġt inh", - "Ġti nh", - "Ġtin h", - "ĠÑģÑĤ ол", - "ĠÑģÑĤо л", - "ÑģÑĤ ÑĢÑĥ", - "ÑģÑĤÑĢ Ñĥ", - "Ġли ÑĪе", - "ĠлиÑĪ е", - "Ġви ÑĢоб", - "il miÅŁ", - "ilm iÅŁ", - "Ġ зÑĸ", - "Ġз Ñĸ", - "ç» Ĩ", - "åĢ Ĵ", - "ãĤ· ãĥ£", - "åŃ ©", - "Ġ à¹Ĥรà¸ĩà¹Ģร", - "Ġà¹Ĥ รà¸ĩà¹Ģร", - "Ġà¹Ĥรà¸ĩ à¹Ģร", - "íĻ ľ", - "ĠбÑĥд е", - "ĠбÑĥ де", - "Ġyak laÅŁ", - "èĩª åĪĨ", - "Ġ ÙģÙĪ", - "ĠÙģ ÙĪ", - "С Т", - "Ġso run", - "Ġsor un", - "Ġsoru n", - "à¹Ģ à¸ł", - "à¹Ģภł", - "Ġc ô", - "в иÑĩ", - "ви Ñĩ", - "ëĵ¤ ìĿĺ", - "Ġtr iá»ĩu", - "Ġtri á»ĩu", - "Ġr õ", - "Ġ ãģ«", - "ÄŁ im", - "ÄŁi m", - "iyor uz", - "è ľ", - "à¥įर व", - "Ġس Ù¾", - "Ġ ìĦľìļ¸", - "ĠìĦľ ìļ¸", - "δ ε", - "еÑĢ ÑĪ", - "Ġ أس", - "ĠØ£ س", - "äº ŀ", - "è¯ į", - "п ÑĤом", - "ฤ ษ", - "Ġساز ÙħاÙĨ", - "Ġlu ôn", - "Ùĩ ÙĪر", - "c ü", - "аÑĤ кÑĥ", - "Ġo labilir", - "Ġol abilir", - "Ġolab ilir", - "Ġola bilir", - "Ġ ìĹ°êµ¬", - "ĠìĹ° 구", - "ен ной", - "енно й", - "Ġ æĪij", - "ĠæĪ ij", - "Ġ него", - "Ġн его", - "Ġне го", - "Ġнег о", - "Ġ. **************", - "ิ à¸ĺ", - "Ġ ãĤ·", - "ĠãĤ ·", - "ت Ùģ", - "ÐŁ ÑĢо", - "ÐŁÑĢ о", - "Ġhakk ında", - "Ġhakkı nda", - "Äį nÄĽ", - "ĠM ỹ", - "é ½", - "ĠÏĥ ÏĦον", - "ĠÏĥÏĦο ν", - "Ġ âm", - "Ġâ m", - "§ ظ", - "ĠÅŁ irket", - "æĥħ åĨµ", - "ĠØ¢ÙħÙĪز Ø´", - "λ εÏħ", - "λε Ïħ", - "Ùħ Ùĩ", - "è¦ ı", - "ãģ¨ æĢĿ", - "Ġ ÙĪع", - "ĠÙĪ ع", - "ÏĪ η", - "Ïģ οÏį", - "Ïģο Ïį", - "Ġ ÂłĊ", - "ĠÂł Ċ", - "δ η", - "ÑĪ ов", - "åĪ ¤", - "Ġm ắt", - "æĭ ¿", - "à¸Ļ à¸Ķ", - "éĻ Ħ", - "à¹ī ม", - "ĠÄij ạt", - "Ġg üzel", - "Ġgü zel", - "m Ã¼ÅŁ", - "Ðŀ ÐĴ", - "çĭ ¬", - "리 를", - "Ġп лаÑĤ", - "Ġпл аÑĤ", - "Ġпла ÑĤ", - "Ġngh á»ĭ", - "ĠÑĤак иÑħ", - "ĠÑĤа киÑħ", - "б иÑĢа", - "би ÑĢа", - "Ġн ек", - "Ġне к", - "ÑģÑĮ кÑĸ", - "ÑģÑĮк Ñĸ", - "رÙĬ اض", - "o nu", - "on u", - "à¥ĭ म", - "ĠGi Ỽi", - "ĠGiá» Ľi", - "èŀ į", - "é ²", - "ĠGe nel", - "ĠGen el", - "ĠGene l", - "åĬ ¿", - "Ġ вÑĸ", - "Ġв Ñĸ", - "å§ IJ", - "è© ¦", - "ĠжиÑĤ ÑĤÑı", - "Ġ ìĺ¨", - "Ġìĺ ¨", - "åĩº æĿ¥", - "Ġt á»ij", - "Ġtá» ij", - "Ġl ao", - "Ġla o", - "ί ο", - "ĠÎł α", - "н иÑĤелÑĮ", - "ниÑĤ елÑĮ", - "ниÑĤе лÑĮ", - "éļ İ", - "Ġви кон", - "Ġвик он", - "Ġвико н", - "ĠÙģ عاÙĦ", - "ĠÙģع اÙĦ", - "à¹Ģ ศ", - "à¹Ģภ¨", - "ÏĮ γ", - "ĠоÑĢгани з", - "ĠоÑĢган из", - "Ġ емÑĥ", - "Ġе мÑĥ", - "Ġем Ñĥ", - "Ġ ÙĬع", - "ĠÙĬ ع", - "ĠÙħ ب", - "ाल य", - "ĠÎľ ÏĢ", - "é ¸", - "ù a", - "ê¸ ¸", - "Ġ ÄIJiá»ģu", - "ĠÄIJ iá»ģu", - "ε ίο", - "εί ο", - "äº ī", - "ượ t", - "ÑĢа зÑĥ", - "ÑĢаз Ñĥ", - "ĠоÑĤ ÑĢим", - "ĠоÑĤÑĢи м", - "Ġ طب", - "ĠØ· ب", - "Ġ 以", - "æĸ Ĺ", - "ë° ±", - "à¤ĩ स", - "ë§Į ìĽIJ", - "ãĢģ ãģĿãģ®", - "ĠëķĮ 문", - "ĠØ¢ ÛĮ", - "С Ðł", - "ض ÙĦ", - "æ ĵį", - "æĵ į", - "k azy", - "ka zy", - "kaz y", - "ส ว", - "â ng", - "ân g", - "à¤Ĥ à¤Ń", - "н ÑĸÑĩ", - "нÑĸ Ñĩ", - "ั à¸ĩà¸ģ", - "ัà¸ĩ à¸ģ", - "Ġبر رسÛĮ", - "ر دÙĩ", - "رد Ùĩ", - "Ġm ẫu", - "à¹Ī วà¸ĩ", - "à¹Īว à¸ĩ", - "ĠداÙĨØ´ گاÙĩ", - "d ıģ", - "dı ÄŁ", - "ĠT á»ķng", - "ĠTá»ķ ng", - "第 äºĮ", - "c ÃŃm", - "cÃŃ m", - "Ġb öyle", - "Ġbö yle", - "ë ¶Ī", - "ë¶ Ī", - "ĠÙħÙĨ ابع", - "à¥ĥ ष", - "е ÑĤÑĭ", - "еÑĤ Ñĭ", - "åĨ ·", - "åĽ Ń", - "Ġت ÙĪجÙĩ", - "ĠتÙĪ جÙĩ", - "åĪ »", - "æŀ ģ", - "à¤Ł न", - "л ан", - "ла н", - "Ġ íĥĢ", - "Ġíĥ Ģ", - "ä½ IJ", - "Ġ обÑĭ", - "Ġо бÑĭ", - "Ġоб Ñĭ", - "å¸ Ŀ", - "ì» ¤", - "å® Ī", - "èµ· æĿ¥", - "Ġ ãĥ¬", - "Ġãĥ ¬", - "çİ ī", - "à¹Ģ หล", - "à¹Ģห ล", - "и не", - "ин е", - "ห าร", - "หา ร", - "éļ ı", - "Ġг аз", - "ĠاÙĦ عÙħÙĦ", - "ĠاÙĦع ÙħÙĦ", - "ĠاÙĦعÙħ ÙĦ", - "à¥ģ à¤Ŀ", - "à¥ģठĿ", - "Ïģ ιο", - "Ïģι ο", - "Ġv ám", - "Ġvá m", - "Ġع ÙĨد", - "ĠعÙĨ د", - "ÙĨد گاÙĨ", - "ï¼Į éĤ£", - "Ġна Ñħод", - "á no", - "án o", - "ÛĮ اÙĨ", - "ÛĮا ÙĨ", - "ĠØ£ ع", - "Ġ ÑĢади", - "ĠÑĢ ади", - "ĠÑĢа ди", - "ĠÑĢад и", - "Ġм ене", - "Ġмен е", - "Ġú da", - "Ïĩ ν", - "ÑĥлÑı ÑĢ", - "à¥Ģ प", - "Ġpou žÃŃ", - "Ġ ä¸", - "ĠÙĤ اÙĨÙĪÙĨ", - "ι κοÏį", - "ικ οÏį", - "ικο Ïį", - "á y", - "Ġç öz", - "ÏĦ Ïģ", - "ÙĨ اÙħ", - "ÙĨا Ùħ", - "ุ à¸ķ", - "åĵ ª", - "ÙĬ ب", - "ä¹ °", - "ÐĶ лÑı", - "Ġ ëłĪ벨", - "ĠëłĪ 벨", - "ุ à¸ļ", - "н ÑĥÑĤи", - "нÑĥ ÑĤи", - "нÑĥÑĤ и", - "è½ »", - "ĠÎľ α", - "Ġ è¦", - "Ġè ¦", - "аÑĤ ков", - "Ġ ëĪĦ", - "ĠëĪ Ħ", - "Ġt uyá»ĥn", - "Ġtuy á»ĥn", - "Ùİ Ùħ", - "ĠвÑĭ пол", - "ĠвÑĭп ол", - "Ġst udi", - "Ġstud i", - "Ġstu di", - "ĠpÅĻ ek", - "ĠpÅĻe k", - "Ġз ам", - "Ġза м", - "Ġmat eri", - "Ġma teri", - "Ġmate ri", - "Ġmater i", - "åİ ĭ", - "Ġ ал", - "Ġа л", - "Ġ à¸ļร", - "Ġà¸ļ ร", - "Ø· ØŃ", - "ĠÙħر Ú©", - "Ġ ìĭ¬", - "Ġìĭ ¬", - "ĠÙĤ ابÙĦ", - "ĠÙĤاب ÙĦ", - "ĠÐIJ ле", - "ĠÐIJл е", - "ın tı", - "Ġ å»", - "Ġå »", - "Ä° K", - "ëħĦ ëıĦ", - "Ñĭ ваÑĤÑĮ", - "Ñĭв аÑĤÑĮ", - "Ñĭва ÑĤÑĮ", - "Ġdev let", - "社 ä¼ļ", - "ëĤ ł", - "Ġko lay", - "Ġkol ay", - "Ġkola y", - "ĠÑĢазв иÑĤи", - "ĠÑĢазви ÑĤи", - "а ди", - "ад и", - "ئ ÙĬس", - "a dıģı", - "ad ıģı", - "adı ģı", - "adıģ ı", - "Îij ÎĽ", - "Ġ hoa", - "Ġh oa", - "Ġho a", - "Ġ ศ", - "Ġภ¨", - "ı ÅŁtır", - "Ä±ÅŁ tır", - "ÑĢ Ñİ", - "Ġк аÑĩе", - "Ġка Ñĩе", - "¼ åIJĪ", - "åħ ´", - "Ġ ê·¸ëŁ¬", - "Ġê·¸ 룬", - "Ġм ÑĸÑģÑĤ", - "ĠмÑĸ ÑģÑĤ", - "ĠмÑĸÑģ ÑĤ", - "Ġм не", - "Ġмн е", - "ãĥ¼ ãĤº", - "ãĥ¼ãĤ º", - "ç§ Ģ", - "Ġع ÙĦÙĬÙĩ", - "ĠعÙĦ ÙĬÙĩ", - "ĠعÙĦÙĬ Ùĩ", - "Ġ ìĭľê°Ħ", - "Ġìĭľ ê°Ħ", - "Ġà¤ĺ र", - "Ġ Ñĥг", - "ĠÑĥ г", - "åıij å±ķ", - "ı ÅŁÄ±", - "Ä±ÅŁ ı", - "Ġ ìĪľ", - "ĠìĪ ľ", - "Ġ íĻľ", - "ĠíĻ ľ", - "æ¡ £", - "Ġn okt", - "Ġno kt", - "Ġnok t", - "l ém", - "lé m", - "ен нÑĭй", - "Ġب Ùħ", - "à¥ĩ य", - "à¥ĩठ¯", - "о дав", - "од ав", - "ода в", - "à¹Ĥ ร", - "ï¼Į æľī", - "ا ÙĬات", - "اÙĬ ات", - "اÙĬا ت", - "ا ÛĮÙĩ", - "اÛĮ Ùĩ", - "Ġà¤īप य", - "Ġs mÄĽ", - "Ġsm ÄĽ", - "Ø´ د", - "Ш ÐIJ", - "Ġا ÙħاÙħ", - "ĠاÙħ اÙħ", - "ĠاÙħا Ùħ", - "æ¿ Ģ", - "Ġho ạch", - "об ÑĢаз", - "обÑĢаР·", - "à¥ĭ ह", - "ĠÑĢеб ен", - "иÑĤ елÑı", - "иÑĤе лÑı", - "ãģªãģĮ ãĤī", - "س اÙĦ", - "Ġ à¸Īำ", - "Ġà¸Ī ำ", - "Ġ خاص", - "ĠØ® اص", - "Ġg eri", - "Ġge ri", - "Ġger i", - "ठĺ", - "Ġ ìº", - "Ġì º", - "à¹ģ à¸Ĺ", - "âĢĮ ÛĮ", - "Ú¯ رÛĮ", - "گر ÛĮ", - "ا Ùħبر", - "اÙħ بر", - "ÑĪ Ñĥ", - "Ġp hong", - "Ġph ong", - "Ġphon g", - "и мо", - "им о", - "п а", - "Ġ ìµľê³ł", - "Ġìµľ ê³ł", - "Ġ нам", - "Ġн ам", - "Ġна м", - "o stÃŃ", - "os tÃŃ", - "ost ÃŃ", - "is ini", - "isi ni", - "isin i", - "Ġд Ñĥже", - "ĠдÑĥ же", - "Ñģ ком", - "Ñģк ом", - "Ñģко м", - "ĠпÑĢод Ñĥк", - "ÏĮ ÏĦηÏĦα", - "ÏĮÏĦη ÏĦα", - "a ln", - "al n", - "is ine", - "isi ne", - "isin e", - "è¿ ľ", - "алÑĮ ной", - "алÑĮно й", - "त र", - "t ıģ", - "tı ÄŁ", - "Ġë Ĵ", - "è¿ĺ æĺ¯", - "ĠÙħ Ø«ÙĦ", - "ĠÙħØ« ÙĦ", - "ìľ ¨", - "ï¾ ĺ", - "åĪ ¸", - "ç ¶ļ", - "ç¶ ļ", - "ج اد", - "جا د", - "Ġ кÑĥ", - "Ġк Ñĥ", - "åĢ ij", - "o vu", - "ov u", - "Ġs Ä©", - "Ġ ìłIJ", - "Ġìł IJ", - "ĠÑĥ ÑĢов", - "ि à¤ļ", - "िठļ", - "ov ali", - "ova li", - "oval i", - "Ġ ÙĪÙĨ", - "ĠÙĪ ÙĨ", - "Ġ ìĿĮ", - "ĠìĿ Į", - "Ġк г", - "า à¸ĺ", - "าภĺ", - "ÏĦ Ïģα", - "ÏĦÏģ α", - "ž dy", - "à¹Į à¸ķ", - "Ġ nÄĽm", - "ĠnÄĽ m", - "Ġ Це", - "ĠЦ е", - "n oho", - "no ho", - "Ġëĭ¤ ìĭľ", - "Ġté to", - "Ġb iá»ĥu", - "ĠY ön", - "Ġpr áce", - "Ġprá ce", - "à¥ī र", - "Ġch ÃŃ", - "ов ой", - "ово й", - "Ġm ợ", - "Ġmá» Ł", - "èª ª", - "Ïİ ÏĤ", - "в олÑı", - "во лÑı", - "вол Ñı", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "å¯ ¦", - "é» ŀ", - "Ġà¤ı व", - "Ïħ να", - "Ïħν α", - "å² ¡", - "kh ông", - "ĠpÅĻÃŃpad ÄĽ", - "å Ĺ", - "Ġبد ÙĪÙĨ", - "Ïĥ κε", - "Ïĥκ ε", - "Ġdik kat", - "ĠAn cak", - "ĠAnc ak", - "Ġt iá»ĩn", - "Ġti á»ĩn", - "éĿ Ļ", - "Ġ ìĿ¼ë°ĺ", - "ĠìĿ¼ ë°ĺ", - "ĠÄį len", - "ìķ ħ", - "ा à¤ĩन", - "ाà¤ĩ न", - "ãģ£ ãģ¦ãģĦãģŁ", - "ãģ£ãģ¦ ãģĦãģŁ", - "ãģ£ãģ¦ãģĦ ãģŁ", - "ĠìĿ´ ìļ©", - "ÙĪ ÙħÛĮ", - "ÙĪÙħ ÛĮ", - "i ná", - "in á", - "ặ ng", - "ặn g", - "Ïİ Ïģα", - "ÙĨ ÙĬØ©", - "ÙĨÙĬ Ø©", - "в аж", - "ва ж", - "è İ·", - "èİ ·", - "å© ļ", - "ĠÅŁ u", - "Ġ ãģĬ", - "Ġد رب", - "Ġدر ب", - "Ġd iá»ħn", - "ÅĻ eba", - "ÅĻe ba", - "ÅĻeb a", - "as ının", - "asını n", - "ç½ij ç«Ļ", - "н ÑĮого", - "нÑĮ ого", - "нÑĮо го", - "ĠاÙĦØ£ ÙĪÙĦ", - "ικ ÎŃÏĤ", - "Ġz ÃŃsk", - "о ло", - "ол о", - "ĠÑį ÑĤоÑĤ", - "ĠÑįÑĤ оÑĤ", - "ĠÑįÑĤо ÑĤ", - "Ġp okud", - "Ġpo kud", - "Ġpok ud", - "è² »", - "е ÑĢÑĸв", - "еÑĢ Ñĸв", - "еÑĢÑĸ в", - "ãĥķ ãĤ£", - "ãĥķãĤ £", - "иÑĤÑĥ а", - "Ġv yd", - "Ġvy d", - "о лож", - "ол ож", - "оло ж", - "л ÑıÑĤÑĮ", - "лÑı ÑĤÑĮ", - "ÙĤ Ùħ", - "æ´ ĭ", - "æ§ ĭ", - "Ġغ ÛĮر", - "Ġ stÅĻed", - "Ġst ÅĻed", - "ĠstÅĻ ed", - "ظ ر", - "Ġhiç bir", - "θ εί", - "θε ί", - "z nik", - "zn ik", - "д Ñĭ", - "l uv", - "lu v", - "ĠÙħ ؤ", - "ĠÚ¯ رÙĪÙĩ", - "Ġگر ÙĪÙĩ", - "Ġ ï¼īĊ", - "Ġï¼ī Ċ", - "t eri", - "ter i", - "te ri", - "ĠÏħ ÏĢο", - "ĠÏħÏĢ ο", - "v oj", - "vo j", - "Ġبع ض", - "Ġb ilin", - "Ġbi lin", - "Ġbil in", - "Ġ رÙĪØ´", - "Ġر ÙĪØ´", - "ĠرÙĪ Ø´", - "Ġоб Ñıз", - "Ġ ï»", - "Ġï »", - "س ÙĨ", - "Ġ ÏĢα", - "ĠÏĢ α", - "í į¼", - "Ġt ÃŃn", - "ĠtÃŃ n", - "Ġ ´", - "Ġ ´", - "ìĤ¬ ìĿ´íĬ¸", - "ìĤ¬ìĿ´ íĬ¸", - "Ġ podob", - "Ġpo dob", - "Ġpod ob", - "Ñī ее", - "Ñīе е", - "Ġ åįĹ", - "Ġåį Ĺ", - "Ġb ych", - "Ġby ch", - "о зи", - "оз и", - "ĠV Äĥn", - "ØŃ Ùĩ", - "åѦ éĻ¢", - "ĠÅĻ ekl", - "ĠÅĻe kl", - "ĠÅĻek l", - "립 ëĭĪëĭ¤", - "Ġп ÑĢоÑģ", - "ĠпÑĢ оÑģ", - "ĠпÑĢо Ñģ", - "κ ά", - "ĠbaÅŁ ladı", - "ủ y", - "Ñİ Ð´Ð¶", - "ाà¤ı à¤Ĺ", - "à¤Ĥ à¤ļ", - "Ġ ê´Ģ볨", - "Ġê´Ģ 볨", - "Ġв опÑĢоÑģ", - "ĠÑģÑĤ аÑĤÑĮ", - "ĠÑģÑĤаÑĤ ÑĮ", - "ĠÑģÑĤа ÑĤÑĮ", - "Ġy atırım", - "Ġyatır ım", - "н Ñĥла", - "нÑĥ ла", - "нÑĥл а", - "ر اÙģ", - "را Ùģ", - "Ġç eÅŁit", - "Ġà¤ī द", - "å¤ ®", - "Ġпо Ñıв", - "åĽ½ 家", - "ĠÑģооÑĤвеÑĤ ÑģÑĤв", - "ìķ ¡", - "ĠØ® ÙĪاÙĩد", - "ĠØ®ÙĪاÙĩ د", - "ĠØ®ÙĪ اÙĩد", - "Å¡ Å¡ÃŃ", - "Âł п", - "ĠNh Ãł", - "' '''", - "'' ''", - "''' '", - "ï½ ¨", - "à ħ", - "Ġ ïº", - "Ġï º", - "ĠØ¢Ùħ رÛĮÚ©", - "lar ımız", - "larım ız", - "ج ا", - "Ùģ ÙĤ", - "Ġ á»", - "Ġá »", - "Ġ ìķł", - "Ġìķ ł", - "Ġز باÙĨ", - "ĠÑĤ воÑĢ", - "ĠÑĤв оÑĢ", - "н иÑĩеÑģ", - "ни ÑĩеÑģ", - "ниÑĩ еÑģ", - "Ġк ни", - "Ø® داÙħ", - "à¸Ł ร", - "Ġ ì¹ĺ", - "Ġì¹ ĺ", - "ว าม", - "ĠÙħ ÙĩÙħ", - "ĠÙħÙĩ Ùħ", - "Ġs tol", - "Ġst ol", - "Ġsto l", - "Ġed ilen", - "Ġedi len", - "Ġedil en", - "Ġ pek", - "Ġp ek", - "Ġpe k", - "اÙĨ ات", - "اÙĨا ت", - "алÑĮ нÑĸ", - "Ġнеоб ÑħÑĸд", - "à¹Ħ ว", - "Ġश र", - "Ġ íĮIJ", - "ĠíĮ IJ", - "Ò ij", - "Ġ ним", - "Ġн им", - "Ġни м", - "Ġ à¸ĺ", - "Ġภĺ", - "æĺ ł", - "äº Ĵ", - "ĠbaÅŁ ar", - "ž i", - "Ġм ног", - "Ġмн ог", - "l endi", - "len di", - "á vajÃŃ", - "áv ajÃŃ", - "n ict", - "ni ct", - "nic t", - "Ġд Ñĥм", - "ĠдÑĥ м", - "éĻ ©", - "Ïĥ Ïĥ", - "i ky", - "ik y", - "алÑĮ нÑĭй", - "Ġ ÙħÙĨت", - "ĠÙħ ÙĨت", - "ĠÙħÙĨ ت", - "å® ®", - "- за", - "-з а", - "еÑĢ к", - "å¡ Ķ", - "Ġμε ÏĦα", - "oÄŁ un", - "oÄŁu n", - "ÎĹ Îľ", - "à¥Īà¤Ĥ ।Ċ", - "à¥Īà¤Ĥ। Ċ", - "Äį ky", - "å¹³ åı°", - "à¥ĭ श", - "Ġ ona", - "Ġo na", - "Ġon a", - "Ġ bec", - "Ġb ec", - "Ġbe c", - "ì ¢", - "Ġc ây", - "Ġcâ y", - "k ün", - "kü n", - "Ġ à¤Ī", - "ĠठĪ", - "Ġr á»Ļng", - "еÑĢ б", - "å¹ ¸", - "ï¾ IJ", - "ĠпÑĸд пÑĢиÑĶм", - "çĶ £", - "Ġ ÏĦε", - "ĠÏĦ ε", - "ĠÙĨÙĤ Ø´", - "о виÑħ", - "ов иÑħ", - "ови Ñħ", - "ĠÙģ Ùī", - "Ðļ ак", - "Ùİ Ø±", - "Ġ Щ", - "ĠÐ ©", - "алÑĮ нÑĭÑħ", - "Ġk üçük", - "èŃ ·", - "æĭ ħ", - "i caret", - "ic aret", - "ica ret", - "icare t", - "Ġ رÙģت", - "Ġر Ùģت", - "ĠرÙģ ت", - "Ġод ного", - "Ġодно го", - "ÑĪ им", - "ÑĪи м", - "Ġ бÑĸ", - "Ġб Ñĸ", - "Ġuygu lam", - "Ġ æĭ", - "Ġæ ĭ", - "ä½ Ľ", - "u cu", - "uc u", - "d ÃŃ", - "Å ĺ", - "ئ Ø©", - "ê± ¸", - "Ù Į", - "Ġ ÎłÏģο", - "ĠÎł Ïģο", - "Ġye rine", - "Ġyer ine", - "Ġyeri ne", - "ĠÑĸн ÑĦоÑĢма", - "Ġ å¤ĸ", - "Ġå¤ ĸ", - "ä» ķ", - "н ав", - "на в", - "a rası", - "ar ası", - "ara sı", - "aras ı", - "à¸Ńà¸Ļ à¹Ħลà¸Ļ", - "ا شت", - "اش ت", - "ز ÙĬ", - "æ© ĭ", - "Ġ ãĤ«", - "ĠãĤ «", - "èĥ½ åĬĽ", - "å¥ Ĺ", - "Ġpro h", - "Ġpr oh", - "Ġп ÑĢава", - "ĠпÑĢ ава", - "ĠпÑĢав а", - "ĠпÑĢа ва", - "Ỽ p", - "Ġ à¸Ĥà¸Ńà¸ĩ", - "Ġà¸Ĥ à¸Ńà¸ĩ", - "Ġ ë´", - "Ġë ´", - "Ġl úc", - "Ġ éķ", - "Ġé ķ", - "ب ÙĪد", - "بÙĪ د", - "r upa", - "ru pa", - "rup a", - "ا زÙħ", - "از Ùħ", - "Ġ кан", - "Ġк ан", - "Ġка н", - "ılı m", - "ıl ım", - "Ġ Ùĩد", - "ĠÙĩ د", - "ãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢ ĠãĢĢ", - "ãĢĢĠ ãĢĢĠãĢĢ", - "Ñĭ ваеÑĤ", - "Ñĭв аеÑĤ", - "Ñĭва еÑĤ", - "Ø® اÙĨÙĩ", - "خاÙĨ Ùĩ", - "Ñĥ кÑĤ", - "Ñĥк ÑĤ", - "ĠçĻ¾ 度", - "ĠnÄĽ co", - "е мон", - "ем он", - "емо н", - "Ġà¤ħ प", - "Ġ ÎĮ", - "ĠÎ Į", - "ün ün", - "ünü n", - "æĸĩ åĮĸ", - "ä¹ İ", - "ä¸Ĭ çļĦ", - "ÙĦ ÙĬÙħ", - "ÙĦÙĬ Ùħ", - "ĠtÄĽ ch", - "ا سب", - "اس ب", - "âĢĻ ÑĶ", - "Ġ Ú¯ÛĮ", - "ĠÚ¯ ÛĮ", - "Ġ ê·¼", - "Ġê· ¼", - "Ġtr ẻ", - "μÎŃ Î½Î¿", - "μÎŃν ο", - "ãģĵãģ¨ ãĤĴ", - "ìĿ´ ëĤĺ", - "åĸ Ħ", - "Ġtr ả", - "åĪĨ æŀIJ", - "Ġ dÄĽl", - "Ġd ÄĽl", - "ĠdÄĽ l", - "Ñĥ Ñģка", - "ÑĥÑģ ка", - "ÑĥÑģк а", - "Ġм ного", - "Ġмн ого", - "Ġмног о", - "à¥Ī र", - "μα ÏĦοÏĤ", - "μαÏĦο ÏĤ", - "Ġm ÃŃsto", - "ĠmÃŃ sto", - "ĠmÃŃst o", - "Ġ ê°ģ", - "Ġê° ģ", - "Ġп ÑĢог", - "ĠпÑĢ ог", - "ĠпÑĢо г", - "b aÅŁ", - "ba ÅŁ", - "а йÑĤе", - "ай ÑĤе", - "айÑĤ е", - "Ġc á»ķ", - "å¿ ľ", - "ï¼ģ Ċ", - "ç ı", - "Ġbir çok", - "Ġ íĺķ", - "Ġíĺ ķ", - "çµ Į", - "ĠEv rop", - "ĠÑģ оÑĨÑĸ", - "ĠÑģо ÑĨÑĸ", - "ä»ĸ çļĦ", - "Ġ μÏĢο", - "Ġμ ÏĢο", - "ĠμÏĢ ο", - "å¥ Ī", - "Ġ Ú¯ÙĦ", - "ĠÚ¯ ÙĦ", - "ÙĪ ÙĦØ©", - "ÙĪÙĦ Ø©", - "æµ İ", - "Ġ Ú©ÙĪ", - "ĠÚ© ÙĪ", - "± ä¹IJ", - "ãģĹ ãģı", - "ãģĹãģ ı", - "çº ³", - "ÑģÑĤв енно", - "ÑģÑĤвен но", - "éĽ ¢", - "ा .", - "Ġgerçek leÅŁtir", - "ĠgerçekleÅŁ tir", - "Ġ kır", - "Ġk ır", - "Ġkı r", - "ì ³", - "Ġг оÑģп", - "å¹ ķ", - "ìĦ ¼", - "» .Ċ", - "». Ċ", - "к ÑĥÑĢ", - "кÑĥ ÑĢ", - "Ġ رÛĮ", - "Ġر ÛĮ", - "æĽ ¾", - "ÙĪ رÙĬ", - "ÙĪر ÙĬ", - "лекÑģ анд", - "ص Ùģ", - "Ġc ảnh", - "Ġcả nh", - "å± Ĥ", - "ãĤ Ĩ", - "Ġ تس", - "Ġت س", - "ì° ½", - "기 를", - "Ġ à¹Ģà¸Ħ", - "Ġà¹Ģ à¸Ħ", - "Ġà¹ĢภĦ", - "çŁ Ń", - "Ġ ÑģÑĤÑĢо", - "ĠÑģ ÑĤÑĢо", - "ĠÑģÑĤ ÑĢо", - "ĠÑģÑĤÑĢ о", - "ĠÏĥ ÏĦιÏĤ", - "ĠÏĥÏĦι ÏĤ", - "à¥įय व", - "Ġع ÙĦÙħ", - "ĠعÙĦ Ùħ", - "ĠÑģ иÑĤÑĥа", - "ĠÑī одо", - "ĠÑīо до", - "åIJ Ľ", - "Ùħ س", - "ĠоÑĤ кÑĢÑĭ", - "ĠоÑĤк ÑĢÑĭ", - "Ġsp oj", - "Ġspo j", - "ĠÄij Äĥng", - "Ġs avaÅŁ", - "Ġsav aÅŁ", - "ี ร", - "sk ém", - "ské m", - "Ġ è¡Į", - "Ġè¡ Į", - "é ¹", - "Ġ ÙĬÙħÙĥÙĨ", - "ĠÙĬÙħ ÙĥÙĨ", - "о вано", - "ов ано", - "ова но", - "ован о", - "Ġп ÑĢавилÑĮ", - "ĠпÑĢав илÑĮ", - "ĠпÑĢави лÑĮ", - "ĠпÑĢавил ÑĮ", - "Ġchiế c", - "èĪ ¹", - "éĵ ¶", - "ĠоÑĤ д", - "Ġ ìĿĢ", - "ĠìĿ Ģ", - "íħ Ķ", - "Ġ Nej", - "ĠN ej", - "ĠNe j", - "о не", - "он е", - "Ġk ız", - "Ġkı z", - "олог иÑĩеÑģ", - "Ġ кÑĢаÑĹ", - "ĠкÑĢа ÑĹ", - "à¸ļ à¸Ńล", - "æ¥ ¼", - "Ġت ÙħاÙħ", - "ĠتÙħ اÙħ", - "Ġب ÛĮÙħ", - "ĠبÛĮ Ùħ", - "ĠÑģ Ñĥб", - "ĠÑģÑĥ б", - "v ý", - "Ñģ кие", - "Ñģк ие", - "Ñģки е", - "ëĮĢ ë¡ľ", - "ëĮ Ģë¡ľ", - "???? ????", - "abilir siniz", - "ан Ñģов", - "анÑģ ов", - "代 表", - "Ġ매 매", - "олог ÑĸÑĩ", - "μ αν", - "μα ν", - "ак Ñģим", - "акÑģ им", - "ãĤ¤ ãĥ«", - "Ġt ải", - "Ġtả i", - "Ùħ ÙĪ", - "å® Ĺ", - "n em", - "ne m", - "Ġkho ản", - "Ġ паÑĤ", - "Ġп аÑĤ", - "Ġпа ÑĤ", - "ан ÑĤа", - "анÑĤ а", - "Ġпом оÑī", - "Ġ vod", - "Ġv od", - "Ġvo d", - "Ġkay nak", - "Ġkayn ak", - "Ïĥ ÏĨ", - "à¥Ĥ त", - "du ÄŁ", - "а ÑĤиÑģÑı", - "аÑĤи ÑģÑı", - "Ġ ç¥ŀ", - "Ġç¥ ŀ", - "ĠÑģ лова", - "ĠÑģл ова", - "ĠÑģлов а", - "ĠÑģло ва", - "ÑĢÑĥ кÑĤÑĥ", - "ÑĢÑĥк ÑĤÑĥ", - "ÑĢÑĥкÑĤ Ñĥ", - "ĠmÄĽ sÃŃ", - "Ùı Ùħ", - "зна Ñĩа", - "знаÑĩ а", - "Ġ èī", - "Ġè ī", - "åѦ çĶŁ", - "æ´ ¥", - "Ùİ ÙĬ", - "è§ Ī", - "Ġ å®ī", - "Ġå® ī", - "Ġgör Ã¼ÅŁ", - "ál nÄĽ", - "áln ÄĽ", - "ĠëĶ° ëĿ¼", - "ĠÙħ ÙĪجÙĪد", - "ĠÙħÙĪج ÙĪد", - "ĠÄij ứ", - "ĠÄijá» ©", - "ĠçalÄ±ÅŁ malar", - "ĠçalÄ±ÅŁma lar", - "ĠÑı киÑħ", - "ĠÑıк иÑħ", - "Ġاج تÙħاع", - "μ εν", - "με ν", - "èİ ī", - "ç§ ¯", - "ì¶ ķ", - "à¥į शन", - "à¥įश न", - "Ġx ét", - "Ġв ÑĤоÑĢ", - "ĠвÑĤ оÑĢ", - "çİ ©", - "Âł ÐĿ", - "ÑĪ ие", - "ÑĪи е", - "о ÑĢи", - "оÑĢ и", - "Ø£ س", - "Ġthu á»ijc", - "ëĭĪ ê¹Į", - "ë ķĮ", - "ÑĢ Ñĥп", - "ÑĢÑĥ п", - "Ñģ ÑıÑĤ", - "ÑģÑı ÑĤ", - "з Ñĭ", - "ĠÑģ меÑĢ", - "ĠÑģм еÑĢ", - "Ġv yb", - "Ġvy b", - "ĠìĿ´ ìĥģ", - "à¤ļ न", - "Ġgel di", - "Ġgeld i", - "Û± Û°", - "Û±Û °", - "ικ Ïİν", - "ĠÄIJ ức", - "Ġд оÑģÑĤаÑĤ", - "ĠдоÑģÑĤ аÑĤ", - "Ġö nc", - "Ġön c", - "è¦ ª", - "Ġ adı", - "Ġa dı", - "Ġad ı", - "un ca", - "unc a", - "ĠاÙĦ تر", - "ĠاÙĦت ر", - "çķ ¶", - "ĠФ едеÑĢа", - "ĠФед еÑĢа", - "лÑı ÑİÑĤÑģÑı", - "лÑıÑİÑĤ ÑģÑı", - "ĠÙĥ اÙĨت", - "ĠÙĥاÙĨ ت", - "æİ ¢", - "Ġ Ñĥб", - "ĠÑĥ б", - "Ġ κο", - "Ġκ ο", - "ाà¤ĩ à¤Ł", - "з н", - "Ġm ôi", - "Ġmô i", - "Ġ ãĤµ", - "ĠãĤ µ", - "Ġна вÑĸ", - "Ġнав Ñĸ", - "ç» ¼åIJĪ", - "Ġмин ÑĥÑĤ", - "Ġми нÑĥÑĤ", - "ĠминÑĥ ÑĤ", - "d ık", - "dı k", - "ÑĢ Ñĥд", - "ÑĢÑĥ д", - "åľ ĸ", - "ê° ¤", - "ĠÄijo Ãłn", - "è ¤", - "à¥į वर", - "à¥įव र", - "ĠÃľn iversit", - "а но", - "ан о", - "éĽ ¨", - "ĠvÅ¡ech ny", - "Ġëĭ¤ ìĿĮ", - "ĠC umhur", - "ĠCum hur", - "Ġм Ñĥз", - "ĠмÑĥ з", - "a ÅŁtır", - "aÅŁ tır", - "Ġ ê±°ëŀĺ", - "Ġê±° ëŀĺ", - "Ġ é¡", - "Ġé ¡", - "žit ÃŃ", - "ži tÃŃ", - "Ġ à¸Ł", - "ĠภŁ", - "Ġthu ế", - "Ġм Ñĥж", - "ĠмÑĥ ж", - "ĠÎij ν", - "Ġد ÙĪÙħ", - "ĠدÙĪ Ùħ", - "ĠÑģ ин", - "ĠÑģи н", - "Ġ ÏīÏĤ", - "ĠÏī ÏĤ", - "m eler", - "me ler", - "mel er", - "Ġ poÄį", - "Ġp oÄį", - "Ġpo Äį", - "Ġколи Ñĩе", - "ĠколиÑĩ е", - "ĠK Äį", - "è³ ½", - "ĠоÑģ Ñĸб", - "åı ¥", - "ĠB öl", - "à¸ĺ รรม", - "Ġc ạnh", - "å° ĩ", - "Ġ ноÑģ", - "Ġн оÑģ", - "Ġно Ñģ", - "èĦ ¸", - "Ġgel ir", - "о ÑĢон", - "оÑĢ он", - "оÑĢо н", - "à¥įर à¤Ń", - "ç» ĩ", - "ุ à¹ī", - "ाम ल", - "Ġc âu", - "Ġcâ u", - "Ñij ÑĤ", - "Ġ :|", - "Ġ: |", - "ãĤĮ ãģ¦", - "Ġpos led", - "Ġpo sled", - "ãĤ¹ ãĥĨ", - "ÑĸлÑĮ ÑĪ", - "ен ÑĤÑĭ", - "енÑĤ Ñĭ", - "Ø® دÙħ", - "Ġباش گاÙĩ", - "Ġth Æ°", - "á vánÃŃ", - "áv ánÃŃ", - "ává nÃŃ", - "ëĬ IJ", - "ĠØ£ ØŃ", - "ر اد", - "را د", - "ĠبسÛĮ ار", - "åĪ° äºĨ", - "\" ;\"", - "\"; \"", - "å° İ", - "Ġ ör", - "Ġö r", - "à¸Ĭ าà¸ķ", - "g enus", - "gen us", - "Ġya kın", - "Ġyak ın", - "Ġ ÃŃt", - "ĠÃŃ t", - "reg num", - "regn um", - "Ġf iyat", - "Ġfi yat", - "н ÑĸÑħ", - "нÑĸ Ñħ", - "åľ° æĸ¹", - "Ġbil gi", - "Ġbilg i", - "к ам", - "ка м", - "Ġs pol", - "Ġsp ol", - "Ġspo l", - "ائ ÙĬ", - "Ġ ÙĬÙĨ", - "ĠÙĬ ÙĨ", - "า หาร", - "าห าร", - "Ġب Ú¯", - "é ĺħ", - "éĺ ħ", - "ĠاÙĦ شر", - "ĠاÙĦØ´ ر", - " ģ", - "ĠÑĸн ÑĪиÑħ", - "ĠÑĸнÑĪ иÑħ", - "Ġtr ạng", - "çģ £", - "Ġc á»±c", - "к ан", - "ка н", - "èĭ ı", - "à Ķ", - "Ġl á»Ŀi", - "Ġlá» Ŀi", - "Ñı Ñĩ", - "Ġ ÙĪØŃ", - "ĠÙĪ ØŃ", - "ìĪ ľ", - "Å ¸", - "Ġв оÑģп", - "Ġво Ñģп", - "ĠвоÑģ п", - "ì¡ Į", - "Äį nÃŃch", - "ÄįnÃŃ ch", - "Ø® رÙī", - "خر Ùī", - "ائ ÙĬØ©", - "ائÙĬ Ø©", - "Ġsu ất", - "æĩ ī", - "ا ØŃÛĮ", - "اØŃ ÛĮ", - "Ġn áz", - "Ġná z", - "è¿Ļ ç§į", - "Ġзаб езпеÑĩ", - "Ġ ЧеÑĢ", - "ĠЧ еÑĢ", - "Ġзд ÑĸйÑģ", - "åı ¦", - "æĭ ¬", - "à¥ģ ष", - "à¥ģठ·", - "μ ÏĨ", - "ëĥ IJ", - "Ðķ Ñģли", - "é ¬", - "Ġ íĥľ", - "Ġíĥ ľ", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "Ġм л", - "å´ İ", - "Ùģ ع", - "Ġ ÙĤدر", - "ĠÙĤ در", - "ĠÙĤد ر", - "Ġv á»ijn", - "å¦ ¹", - "Ġ ÐĿаÑģ", - "ĠÐĿ аÑģ", - "ĠÐĿа Ñģ", - "à¥į फ", - "à¥įठ«", - "ãĤ¸ ãĥ£", - "Ġm ı", - "ен Ñģ", - "б Ñĥд", - "бÑĥ д", - "ĠØŃ ØªÙī", - "Ġ ì²´", - "Ġì² ´", - "ĠÑĸ ÑģÑĤоÑĢ", - "ĠÑĸÑģ ÑĤоÑĢ", - "Ġgi ấy", - "γ οÏģ", - "γο Ïģ", - "ëIJĺ ìĸ´", - "Ġ íĤ", - "Ġí Ĥ", - "ĠÐŀд на", - "ĠÙĨ ÙħÙĪد", - "ĠÙĨÙħ ÙĪد", - "Ġвип ад", - "ĠìŀIJ ìĭł", - "Ġj ste", - "Ġjs te", - "Ġ ëĵ±ë¡Ŀ", - "Ġëĵ± ë¡Ŀ", - "ek ten", - "ekt en", - "ekte n", - "ĠÑĢ еÑĩ", - "ĠÑĢе Ñĩ", - "r odnÃŃ", - "rod nÃŃ", - "س تر", - "ست ر", - "ı t", - "ä¹ħ ä¹ħ", - "ĠØ® ÙĦاÙĦ", - "ĠØ®ÙĦ اÙĦ", - "Ġ ç¦", - "Ġç ¦", - "u luk", - "ul uk", - "ulu k", - "l enen", - "le nen", - "len en", - "lene n", - "i lip", - "il ip", - "ili p", - "è´ ¢", - "Ġà¤ħ à¤ķ", - "ĠY ıl", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Ġ à¤Ŀ", - "ĠठĿ", - "ĠB ình", - "Ġol muÅŁ", - "Ġolm uÅŁ", - "اÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©", - "اÙĦØ¥ ÙĨجÙĦÙĬزÙĬØ©", - "м енно", - "мен но", - "aln ız", - "Ġ شرÙĥØ©", - "Ġشر ÙĥØ©", - "Ġس ÙĨØ©", - "ĠسÙĨ Ø©", - "è´ Ł", - "ä½ľ åĵģ", - "Ġ ìķ½", - "Ġìķ ½", - "ĠдÑĢÑĥг иÑħ", - "ĠbaÄŁ lantı", - "о дÑĥ", - "од Ñĥ", - "çļĦ æĺ¯", - "ั à¸Ļà¸Ķ", - "ัà¸Ļ à¸Ķ", - "ĠкоÑĤоÑĢ ÑĭÑħ", - "ĠاÙĦ ÙĪÙĦ", - "ĠاÙĦÙĪ ÙĦ", - "ê¸Ģ ìĥģìľĦ", - "ĠÏĢ εÏģ", - "ĠÏĢε Ïģ", - "리 ìķĦ", - "i bar", - "ib ar", - "iba r", - "Ġ èĥ", - "Ġè ĥ", - "ãģŁ ãģĦ", - "á j", - "ĠìľĦ íķ´", - "? âĢľĊĊ", - "?âĢľ ĊĊ", - "Ġ íİĺ", - "Ġíİ ĺ", - "Ġ ней", - "Ġн ей", - "Ġне й", - "ĠÐĹ Ð°Ðº", - "ĠÐĹа к", - "Ġ ÐĴÑĸд", - "ĠÐĴ Ñĸд", - "е лÑĸ", - "ел Ñĸ", - "è¯ ¾", - "åī ¯", - "m adan", - "ma dan", - "mad an", - "mada n", - "æľ «", - "ĠÏĢ ÏģÏĮ", - "Ġп ÑģиÑħ", - "Ġ ÑĤÑĸ", - "ĠÑĤ Ñĸ", - "Ùĥ ات", - "Ġvys ok", - "ê´Ģ 리", - "ül tür", - "ült ür", - "Ġ à¹Ģà¸Ń", - "Ġà¹Ģ à¸Ń", - "Ġà¹ĢภŃ", - "Ġ íķ©", - "Ġíķ ©", - "çĿ £", - "Ġ ÑĢиÑģ", - "ĠÑĢ иÑģ", - "е ÑĢÑĮ", - "еÑĢ ÑĮ", - "ĠÚ© ÙĦÛĮ", - "ĠÚ©ÙĦ ÛĮ", - "Ġ ãĥŀ", - "Ġãĥ ŀ", - "Ġph ÃŃa", - "ĠphÃŃ a", - "å «", - "ا Ú¯", - "Ġ é¢", - "Ġé ¢", - "ĠÙĨ Ùģر", - "ĠÙĨÙģ ر", - "Ġ جاÙĨ", - "Ġج اÙĨ", - "Ġجا ÙĨ", - "Ġ yas", - "Ġy as", - "Ġya s", - "ж ениÑı", - "же ниÑı", - "жен иÑı", - "ĠлÑĥÑĩ ÑĪе", - "Ġ çº", - "Ġç º", - "Ġ мон", - "Ġм он", - "Ġмо н", - "Ġت Ø®", - "Ġ Ø´ÛĮ", - "ĠØ´ ÛĮ", - "Ġн екоÑĤоÑĢ", - "Ġнек оÑĤоÑĢ", - "алÑĮ нÑĭе", - "Ġob chod", - "Ġíķ¨ ê»ĺ", - "Ġr iêng", - "ãģķ ãĤĮãĤĭ", - "ãģķãĤĮ ãĤĭ", - "о кÑĥ", - "ок Ñĥ", - "ĠС ШÐIJ", - "ë§ ģ", - "Ġ Nếu", - "ĠN ếu", - "ĠA ÄŁ", - "Ġд веÑĢ", - "Ġдв еÑĢ", - "Ġдве ÑĢ", - "à¥ĭ ष", - "Ġkh iến", - "Ġkhi ến", - "н его", - "не го", - "ì± ħ", - "ั à¸ķร", - "ัà¸ķ ร", - "m alı", - "ma lı", - "mal ı", - "Ġ ÙĬا", - "ĠÙĬ ا", - "ç§ij æĬĢ", - "ื à¸Ļ", - "ห มาย", - "หม าย", - "Ġ خص", - "ĠØ® ص", - "åĨ ľ", - "ÃŃ me", - "ÃŃm e", - "ĠÑįÑĤ ой", - "ĠÑįÑĤо й", - "Ġ ìĹħ", - "ĠìĹ ħ", - "Ġ ä¹", - "ä¼ ¯", - "' ´", - "Ùħ ÙĬÙĦ", - "ÙħÙĬ ÙĦ", - "à¸Ń à¸ĩà¸Ħ", - "à¸Ńà¸ĩ à¸Ħ", - "k ová", - "ko vá", - "kov á", - "è¿Ļ ä¹Ī", - "ãĢĤ æĪij", - "ìĹIJ ìĦľëĬĶ", - "ìĹIJìĦľ ëĬĶ", - "Ġ ìļ©", - "Ġìļ ©", - "ë¹Ħ ìĬ¤", - "Ġ ì¦Ŀ", - "Ġì¦ Ŀ", - "IT TE", - "ITT E", - "Ġ모 ëĵł", - "ĠspoleÄį nosti", - "ĠspoleÄįnost i", - "Ġв ик", - "Ġви к", - "Ġt ÅĻÃŃ", - "ĠtÅĻ ÃŃ", - "é ³", - "Ġ Ø®ÛĮ", - "ĠØ® ÛĮ", - "Ġp ož", - "Ġpo ž", - "Ġиме еÑĤ", - "Ġd ÄĽt", - "ĠdÄĽ t", - "ĠÙħد ÙĦ", - "Ġ мо", - "Ġм о", - "åį ı", - "e nÃŃm", - "en ÃŃm", - "enÃŃ m", - "é ī", - "ا ظ", - "Ø§Ø ¸", - "Ġ teÅŁ", - "Ġt eÅŁ", - "Ġte ÅŁ", - "Ġ veÅĻej", - "Ġve ÅĻej", - "L IC", - "LI C", - "ì§Ģ ëĬĶ", - "Ñĭ ваÑİÑĤ", - "Ñĭв аÑİÑĤ", - "Ñĭва ÑİÑĤ", - "ĠоÑĢг анÑĸ", - "ĠоÑĢган Ñĸ", - "nÃŃ mi", - "nÃŃm i", - "θ ÎŃ", - "ãĤ¯ ãĥ©", - "ãĥ¼ ãĥ³", - "ãĥ¼ãĥ ³", - "ли ÑģÑı", - "i mdi", - "im di", - "imd i", - "æ Ĩ", - "ïº İ", - "Ġìļ´ ìĺģ", - "κ αν", - "κα ν", - "Ġ ë³µ", - "Ġë³ µ", - "Ġ ÐĨн", - "ĠÐĨ н", - "p lication", - "pl ication", - "t ah", - "ta h", - "Ġ ÐIJв", - "ĠÐIJ в", - "Ġc á»Ļng", - "алÑĮ ноÑĹ", - "алÑĮно ÑĹ", - "Ġد ÙĪرÙĩ", - "ĠدÙĪ رÙĩ", - "ĠدÙĪر Ùĩ", - "à¥įर य", - "Ġ Ø®ÙĪ", - "ĠØ® ÙĪ", - "Ġв ÑĢа", - "Ø¥ ÙĨ", - "èĤ ī", - "Ġo yn", - "Ġoy n", - "ĠT Æ°", - "ĠÙĩ ÙħاÙĨ", - "ĠÙĩÙħ اÙĨ", - "ĠбÑĸлÑĮ ÑĪе", - "ĠбÑĸлÑĮÑĪ е", - "æĮ ¯", - "ا ÙħØ©", - "اÙħ Ø©", - "å º«", - "åº «", - "Ġ ÑĢеж", - "ĠÑĢ еж", - "ĠÑĢе ж", - "Ġدار ÙĨد", - "ÑĢ ий", - "ÑĢи й", - "Ġ æĮ", - "Ġæ Į", - "Ġson uç", - "Ġsonu ç", - "Ġt ả", - "ั à¸ĩà¸Ħ", - "ัà¸ĩ à¸Ħ", - "ë° Ľ", - "Ġ мом", - "Ġм ом", - "Ġмо м", - "ви Ñĩай", - "виÑĩ ай", - ". à¸Ħ", - "Ġ à¤Ĩà¤Ī", - "Ġà¤Ĩ à¤Ī", - "åģ ĩ", - "Ġpos kyt", - "Ġpo skyt", - "ĠÑģ Ñĥп", - "ĠÑģÑĥ п", - "ıyor du", - "а ле", - "ал е", - "и ÑĨ", - "Ġ θÎŃ", - "Ġθ ÎŃ", - "ãĤĩ ãģĨ", - "ĠÑģв ой", - "ĠÑģво й", - "ม à¸Ļ", - "Ġn ữa", - "Ġnữ a", - "v oÅĻ", - "vo ÅĻ", - "ا سÙĬ", - "اس ÙĬ", - "éĴ ±", - "ãģĹ ãģ¦ãģĦãģŁ", - "ãģĹãģ¦ ãģĦãģŁ", - "ãģĹãģ¦ãģĦ ãģŁ", - "ĠÄij ầy", - "ا ÙĬر", - "اÙĬ ر", - "Ġar aÅŁtır", - "Ġara ÅŁtır", - "ì £", - "ãģ¨ ãģ¯", - "ĠÑģ поÑĢ", - "ĠÑģп оÑĢ", - "Ġê° Ģìŀ¥", - "Ġê°Ģ ìŀ¥", - "è¼ ī", - "âĸ ¡", - "Ġ ìĻĦ", - "ĠìĻ Ħ", - "оÑĢ аÑı", - "оÑĢа Ñı", - "Ïģ εί", - "Ïģε ί", - "ĠÑį ÑĤа", - "ĠÑįÑĤ а", - "ë©´ ìłģ", - "ìĿ´ ìĬ¤", - "ä½ ³", - "æĻ ļ", - "Ġk val", - "Ġkv al", - "Ġn á»ķi", - "ÑĤ ами", - "ÑĤа ми", - "Ġпол ÑĸÑĤи", - "ĠполÑĸ ÑĤи", - "Ġ Ä°ng", - "ĠÄ° ng", - "ĠÄ°n g", - "нÑĸ ÑģÑĤÑİ", - "нÑĸÑģÑĤ Ñİ", - "Ġ à¹Ģà¸ģ", - "Ġà¹Ģ à¸ģ", - "Ġà¹Ģภģ", - "Ġ 민", - "Ġë ¯¼", - "è Ķ", - "Ïģ ία", - "Ïģί α", - "æİ Ī", - "Ġ çĤ", - "Ġç Ĥ", - "ĠÙĨÙħ اÛĮ", - "Ġ ìŀ¡", - "Ġìŀ ¡", - "æŀ ¶", - "اب ÙĤ", - "Ñģ он", - "Ñģо н", - "ен ного", - "енно го", - "ĠÙħ ÛĮÙĦÛĮ", - "ĠÙħÛĮ ÙĦÛĮ", - "ĠÙħÛĮÙĦ ÛĮ", - "Ġk urum", - "Ġkur um", - "Ġku rum", - "Ġkuru m", - "à¹Į ส", - "Ġ ì´Ŀ", - "Ġì ´Ŀ", - "Ġì´ Ŀ", - "ĠnÄĽk olik", - "ĠnÄĽkol ik", - "Ġ ÙĢ", - "ĠÙ Ģ", - "ĠзаÑģÑĤ оÑģ", - "à¸Ķ à¸Ļ", - "ÙĨ داÙĨ", - "ÙĨد اÙĨ", - "ÙĨدا ÙĨ", - "ĠJ ap", - "ĠJa p", - "éĥ ¡", - "à¥į à¤Ń", - "à¥įठŃ", - "Ġ à¹Ģà¸Ĭ", - "Ġà¹Ģ à¸Ĭ", - "Ġà¹ĢภĬ", - "Ġ âĢ«", - "ĠâĢ «", - "é£ ŀ", - "o vatel", - "ov atel", - "ova tel", - "ovat el", - "ĠÑĩа ÑģÑĤÑĮ", - "ĠÑĩаÑģ ÑĤÑĮ", - "ĠÑĩаÑģÑĤ ÑĮ", - "Ġb á»ķ", - "ãĤ¯ ãĥª", - "ิ à¹Į", - "Ġвид е", - "Ġви де", - "v ail", - "va il", - "Ì ī", - "ÄŁ inde", - "ÄŁi nde", - "ÄŁin de", - "ãģ¨ ãĤĤ", - "âĢĮÚ© ÙĨد", - "âĢĮÚ©ÙĨ د", - "Ġ ëħĦ", - "Ġëħ Ħ", - "Ġ اÙĤتص", - "ĠاÙĤ تص", - "ï½ Ĺ", - "Ïģ ιÏĥ", - "Ïģι Ïĥ", - "з д", - "èĻ ½", - "Ġth oại", - "Ġtho ại", - "Ġ ÙĪز", - "ĠÙĪ ز", - "Ġ mÃŃt", - "Ġm ÃŃt", - "ĠmÃŃ t", - "ĠÑħ олод", - "ĠÑħол од", - "Ġ кÑĥп", - "Ġк Ñĥп", - "ĠкÑĥ п", - "а ниÑħ", - "ан иÑħ", - "ани Ñħ", - "Ġnh ìn", - "ãģĭ ãģª", - "Ġ Ðļом", - "ĠÐļ ом", - "ĠÐļо м", - "ÏĦ εÏģ", - "ÏĦε Ïģ", - "ï¼Į åıª", - "Ġol up", - "Ġhá» ıi", - "ë ij", - "ĠnÄĽk ter", - "i sÃŃ", - "is ÃŃ", - "ĠвикоÑĢиÑģÑĤ ов", - "ìŀ ¡", - "Ġà¤ķ ल", - "Ġìľł ìłĢ", - "ĠпÑĢ иб", - "ĠпÑĢи б", - "èĭ ¦", - "Ġ мов", - "Ġм ов", - "Ġмо в", - "Ġ หà¸Ļ", - "Ġห à¸Ļ", - "ëIJĺ ëĬĶ", - "о ко", - "ок о", - "Ġоб еÑģп", - "Ġk ez", - "Ġke z", - "л ÑıÑħ", - "лÑı Ñħ", - "ĠпÑĢо иÑģ", - "Ġпо вин", - "Ġпов ин", - "ĠÐļ оÑĢ", - "ĠÐļо ÑĢ", - "ì¼ Ģ", - "Ġ Ñģи", - "ĠÑģ и", - "Ġ ä¹ĭ", - "Ġä¹ ĭ", - "ĠâĢĶ Ċ", - "ÑģÑĥÑĤ ÑģÑĤв", - "ç °", - "Ġ à¤ł", - "Ġठł", - "н аÑĤ", - "на ÑĤ", - "Ġs uy", - "Ġsu y", - "Ġ ÑģÑĭ", - "ĠÑģ Ñĭ", - "ĠÙĨ شاÙĨ", - "ĠÙĨØ´ اÙĨ", - "Ġна пÑĢав", - "Ġнап ÑĢав", - "ĠÑĨ ÑĮомÑĥ", - "æĺ¯ ä¸Ģ", - "Ġm üm", - "Ġmü m", - "ÑĶ мо", - "ÑĶм о", - "ĠاسÙĦاÙħ ÛĮ", - "Ġza manda", - "Ġzam anda", - "Ġzaman da", - "ÙĪ ÙħاÙĨ", - "ÙĪÙħ اÙĨ", - "ا ÙĦØŃ", - "اÙĦ ØŃ", - "Å¡t ÄĽnÃŃ", - "Å¡tÄĽ nÃŃ", - "Ġ Ðļак", - "ĠÐļ ак", - "ĠÐļа к", - "¤ íĶĦ", - "¤í ĶĦ", - "ĠÙ¾ رد", - "Ġپر د", - "C ác", - "ε ια", - "ει α", - "Ġ جÙĪ", - "Ġج ÙĪ", - "ĠÄijo ạn", - "Ġà¤ĩ त", - "Ġз ан", - "Ġза н", - "ĠÙħÙĨØ· ÙĤÙĩ", - "ĠÙħ عÙĦ", - "ĠÙħع ÙĦ", - "Ġdo kon", - "Ġdok on", - "åIJ ¸", - "ic kou", - "ick ou", - "å° ģ", - "Ġк иÑģ", - "Ġки Ñģ", - "ัà¸ĩ หว", - "i species", - "is pecies", - "isp ecies", - "Ġнап ÑĢÑı", - "æº ĸ", - "Ġà¤ľ ल", - "à¹Ģ à¸ī", - "à¹Ģภī", - "L AR", - "LA R", - "ĠÑĥÑģлов иÑı", - "ĠWiki species", - "ĠWik ispecies", - "ระ à¸Ķ", - "Ġ mey", - "Ġm ey", - "Ġme y", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "à¹ĩ à¸Ī", - "å¾ Ĵ", - "t ach", - "ta ch", - "u muz", - "um uz", - "umu z", - "κ η", - "à Ĭ", - "Ġ ün", - "Ġü n", - "ĠB ITTE", - "ĠBIT TE", - "ĠÙħ ربع", - "ĠÙħر بع", - "ĠÙħرب ع", - "ãĤ· ãĥ¥", - "िस à¤ķ", - "Ø· ÙĪر", - "Ġв оÑģ", - "Ġво Ñģ", - "ï¾ Ł", - "Ġy ayın", - "Ġyay ın", - "ãģĭ ãĤĬ", - "л иÑı", - "ли Ñı", - "Ġп ÑĢин", - "ĠпÑĢ ин", - "ĠпÑĢи н", - "ij ng", - "ĠÙĨ Ø®", - "Ġl ze", - "Ġlz e", - "à¥įष ण", - "Ġ бо", - "Ġб о", - "Ġ ê¸Ģ", - "Ġê¸ Ģ", - "Ġgel iÅŁtir", - "ĠgeliÅŁ tir", - "à¸Ľà¸£à¸° à¸Ĭ", - "å½ ¡", - "Ġ ãĤª", - "ĠãĤ ª", - "ãģĪ ãģ¦", - "н ÑĥÑĤÑĮ", - "нÑĥ ÑĤÑĮ", - "нÑĥÑĤ ÑĮ", - "Ġ ç½", - "Ġç ½", - "Ġ маг", - "Ġм аг", - "Ġма г", - "ãģ« ãģ¤", - "н оÑģÑĤей", - "ноÑģÑĤ ей", - "Ġ ÙĦÙĬ", - "ĠÙĦ ÙĬ", - "æĢ ª", - "ÑıÑĤ ÑģÑı", - "ภij", - "िय म", - "Ġ ãĢİ", - "ĠãĢ İ", - "ÑĢ ÑĮ", - "Ġm ạng", - "t ım", - "tı m", - "ĠпеÑĢи од", - "о гÑĥ", - "ог Ñĥ", - "ĠкоÑĤоÑĢ аÑı", - "ĠкоÑĤ оÑĢаÑı", - "리 ê°Ģ", - "Ġãħ ¡", - "Ġج اÛĮ", - "Ġجا ÛĮ", - "ĠпоÑĤ ÑĢÑĸб", - "Å¡ en", - "Å¡e n", - "à¸Ń ะ", - "ب ع", - "ØŁ Ċ", - "Ġ ë°©ë²ķ", - "Ġë°© ë²ķ", - "Ġ гоÑĢод", - "Ġг оÑĢод", - "ĠгоÑĢ од", - "Ġ Ðĺн", - "ĠÐĺ н", - "Ġ оказ", - "Ġо каз", - "Ġок аз", - "ر ÙĪز", - "رÙĪ ز", - "Ġili ÅŁk", - "ĠiliÅŁ k", - "å® £", - "f orman", - "form an", - "for man", - "forma n", - "a daÅŁ", - "ad aÅŁ", - "ada ÅŁ", - "ÙĬ ÙĦØ©", - "ÙĬÙĦ Ø©", - "ĠÐļ аÑĢ", - "ĠÐļа ÑĢ", - "Ġm ất", - "æħ ĭ", - "м п", - "à¹Ĥ à¸Ļ", - "ĠØŃÙĤ ÙĪÙĤ", - "Ġд нÑı", - "ĠëĴ ¤", - "ा à¤ķर", - "ाà¤ķ र", - "ì²ĺ ëŁ¼", - "âĢĮ Ø¢", - "h angi", - "han gi", - "hang i", - "è¡Į æĶ¿", - "al iyet", - "ali yet", - "Ġ ì²ľ", - "Ġì² ľ", - "ĠY ap", - "ĠYa p", - "à¹Ĥ รà¸ĩ", - "à¹Ĥร à¸ĩ", - "ì§Ģ ëħ¸", - "Ùİ Ùij", - "Îij ÎĻ", - "á na", - "án a", - "an dır", - "and ır", - "ระ à¸ļà¸ļ", - "ระà¸ļ à¸ļ", - "oÄŁ lu", - "า à¸Īะ", - "าà¸Ī ะ", - "ẩ y", - "ا ÙĪÙĦ", - "اÙĪ ÙĦ", - "ĠмаÑĤ еÑĢÑĸ", - "ÎŁ ÎĿ", - "ÎŁÎ Ŀ", - "Ġin formace", - "Ġinform ace", - "ت ع", - "à¸ļ à¸Ļ", - "ĠÄĮesk é", - "ĠÄĮes ké", - "Ġte mel", - "Ġtem el", - ":::::::::::::::: ::::::::::::::::", - "Ġ chia", - "Ġch ia", - "Ġchi a", - "- Ñģ", - "н еÑĢг", - "не ÑĢг", - "неÑĢ г", - "Ġì° ¾", - "ÑĢ ид", - "ÑĢи д", - "л оÑģÑĮ", - "ло ÑģÑĮ", - "ز ÙĦ", - "ê°Ģ ëĬĶ", - "a né", - "an é", - "Ġнав ÑĸÑĤÑĮ", - "ĠнавÑĸ ÑĤÑĮ", - "ä¸ĵ ä¸ļ", - "Ġ 경기", - "Ġê²½ 기", - "Ġp ÅĻev", - "ĠpÅĻ ev", - "ĠpÅĻe v", - "е ÑĤи", - "еÑĤ и", - "Ġ íĶĮ", - "ĠíĶ Į", - "н ÑıÑĤ", - "нÑı ÑĤ", - "à¥ģ श", - "à¥ģठ¶", - "лÑİ Ð´", - "в иÑī", - "ви Ñī", - "å° ¾", - "çļĦ äºĭ", - "Ġ ëIJľ", - "Ġë IJľ", - "ĠëIJ ľ", - "ر ÙĪÙģ", - "رÙĪ Ùģ", - "Ġ 女", - "Ġå¥ ³", - "κ ή", - "Ġ Tuy", - "ĠT uy", - "ĠTu y", - "Ġê²ĥ ìĿĦ", - "Ġb unu", - "Ġbu nu", - "Ġbun u", - "ĠÑĢаз лиÑĩ", - "ĠD ün", - "ãĤŃ ãĥ£", - "ÑĢ ÑĥÑģ", - "ÑĢÑĥ Ñģ", - "Ġ мм", - "Ġм м", - "l oven", - "lo ven", - "love n", - "lov en", - "Ġot ev", - "n oloj", - "ES Ä°", - "ü p", - "Ġ èĤ", - "Ġè Ĥ", - "ικ ÏĮÏĤ", - "ικÏĮ ÏĤ", - "ض اء", - "ضا Ø¡", - "Ġ пеÑĩ", - "Ġп еÑĩ", - "ÅĻÃŃ klad", - "ãģĵ ãĤį", - "Å¡ tÃŃ", - "Å¡t ÃŃ", - "Ġبر Ú¯", - "ãģĮ ãģĤãĤĭ", - "Ñĸ ÑģÑĤ", - "ÑĸÑģ ÑĤ", - "à¥ī à¤ķ", - "ÏĢ η", - "ĠاÙĦÙħ ست", - "ĠاÙĦÙħس ت", - "Ġз ай", - "Ġза й", - "Ġch Æ°Æ¡ng", - "о ÑĤÑĥ", - "оÑĤ Ñĥ", - "Ġ Сам", - "ĠС ам", - "Å¡ et", - "Å¡e t", - "ĠìŀĪ ìĹĪ", - "ĠÙģ ار", - "Ñĸ он", - "ãĥĹ ãĥŃ", - "Ġnh iá»ĩt", - "in izi", - "ini zi", - "iniz i", - "Ġc ož", - "Ġco ž", - "Ġà¤Ĩ न", - "Ġsyst ém", - "ر ÙĪع", - "رÙĪ ع", - "a yet", - "ay et", - "aye t", - "ĠÙģر ÙĩÙĨÚ¯", - "Ġ è¶", - "Ġè ¶", - "èģ ·", - "è§Ĥ çľĭ", - "н ок", - "но к", - "à¸IJ าà¸Ļ", - "êµIJ ìľ¡", - "k la", - "kl a", - "ãĤģ ãģ¦", - "Îķ ÎĻ", - "åĿ Ĺ", - "Ġsk uteÄį", - "à¥Ĥ à¤ľ", - "ãģij ãģ¦", - "N GC", - "NG C", - "Ġ åĢ", - "Ġå Ģ", - "ĠÑĢоз п", - "nÃŃ ků", - "nÃŃk ů", - "ãĥ³ ãĤ¹", - "ĠÐĴ еÑĢ", - "Ġyüz de", - "Ġ미 êµŃ", - "ĠÙħ Ùī", - "д еÑĢ", - "де ÑĢ", - "а ва", - "ав а", - "Ġmerk ez", - "į ng", - "Ġ ìĤ¼", - "ĠìĤ ¼", - "ĠÑĢоб оÑĤи", - "ĠÑĢобоÑĤ и", - "Ġ нÑĮого", - "Ġн ÑĮого", - "Ġ економ", - "Ġе коном", - "Ġек оном", - "ĠÑĩелов ека", - "ĠÑĩеловек а", - "Ġ à¸ŀระ", - "Ġà¸ŀ ระ", - "Ġà¸ŀร ะ", - "ãĥ Ĵ", - "ãģ£ ãģ¦ãģĦ", - "ãģ£ãģ¦ ãģĦ", - "ä¼ Ĺ", - "ĠпÑĢод ÑĥкÑĤ", - "ĠпÑĢодÑĥк ÑĤ", - "Ġy anı", - "Ġyan ı", - "à¥Ģव न", - "Ġc áºŃp", - "ĠAv rupa", - "ा à¤Ń", - "ाठŃ", - "ĠìłĦ ìļ©", - "æķ £", - "ĠìľĦ íķľ", - "Ñħод иÑĤÑĮ", - "ÑħодиÑĤ ÑĮ", - "Ġsın ır", - "ü cret", - "üc ret", - "s uz", - "su z", - "æ¨ Ĥ", - "Ġ ì°½", - "Ġì° ½", - "Ïģ ίοÏħ", - "Ïģί οÏħ", - "åĪ ļ", - "Ø® ÙĦ", - "ëłĩ ê²Į", - "ج د", - "Ġμ αÏĤ", - "Ġμα ÏĤ", - "áºŃ m", - "k ara", - "ka ra", - "kar a", - "ãĤ« ãĥ¼", - "Ġkter ou", - "ìĽ ¨", - "ÑĦи ÑĨи", - "oÄŁ raf", - "Ġна пÑĢи", - "Ġнап ÑĢи", - "ãģij ãģ©", - "Ġ éļ", - "Ġé ļ", - "ت باÙĦ", - "تب اÙĦ", - "ëŁ ½", - "ìĶ ¨", - "íĮĮ ìĿ¼", - "Ïĩ α", - "Ġuz ak", - "Ġd òng", - "Ġг олоÑģ", - "Ġгол оÑģ", - "Ïĥ ÏĦή", - "ÏĥÏĦ ή", - "ι λ", - "Ø· Ùģ", - "Ġê·¸ ëħĢ", - "ãĤ¿ ãĤ¤", - "ا ÙĨÚ¯", - "اÙĨ Ú¯", - "i nou", - "in ou", - "ino u", - "л он", - "ло н", - "à¹ĩ ม", - "Ġब द", - "Ġkon usunda", - "Ġkonusu nda", - "Ġkonus unda", - "Ġn âng", - "ãģ¾ãģĽ ãĤĵ", - "Ñĥ ÑİÑĤÑĮÑģÑı", - "ÑĥÑİ ÑĤÑĮÑģÑı", - "ÑĥÑİÑĤÑĮ ÑģÑı", - "åŁ ¹", - "ен ко", - "ìł ij", - "Ġ ÑĤов", - "ĠÑĤ ов", - "ĠÑĤо в", - "Ġt ÅĻeba", - "ĠtÅĻ eba", - "ز اÙĨ", - "زا ÙĨ", - "is yon", - "isy on", - "Ġ ген", - "Ġг ен", - "Ġге н", - "Ġ Pokud", - "ĠP okud", - "ĠPo kud", - "ĠPok ud", - "âĢĮ اÙĨد", - "âĢĮاÙĨ د", - "Ġг ÑĢÑĥд", - "ĠгÑĢÑĥ д", - "ĠгÑĢ Ñĥд", - "Ġ خرÛĮد", - "Ġخر ÛĮد", - "λ λα", - "λλ α", - "Ġp ÅĻÃŃm", - "ĠpÅĻ ÃŃm", - "ĠpÅĻÃŃ m", - "Ġ æ³ķ", - "Ġæ³ ķ", - "Ġز ÙĨدگÛĮ", - "ĠزÙĨد Ú¯ÛĮ", - "ạ p", - "Ġ íĬ¸", - "ĠíĬ ¸", - "ĠÄij á»Ļc", - "ĠÄijá»Ļ c", - "Ġê·¸ ë¦¬ê³ł", - "Ġ그리 ê³ł", - "н из", - "ни з", - "Ġ ÙĬÙĤ", - "ĠÙĬ ÙĤ", - "l aÅŁtır", - "la ÅŁtır", - "laÅŁ tır", - "ĠпÑĢав о", - "ĠпÑĢа во", - "Ñĥ Ñģк", - "ÑĥÑģ к", - "å° ½", - "Ġप ड", - "éĵ ģ", - "Ġ ì·¨", - "Ġì ·¨", - "ĠاÙĦ بÙĬ", - "ĠاÙĦب ÙĬ", - " ¸", - "ิม à¸ŀ", - "Ġs vÄĽ", - "Ġsv ÄĽ", - "Ġб ал", - "Ġба л", - "Ġm ôn", - "Ġmô n", - "ĠD ữ", - "ĠØ´ دÙĨ", - "Ġشد ÙĨ", - "Ġ ÙģÙĦ", - "ĠÙģ ÙĦ", - "Ġv znik", - "Ġvz nik", - "Ġch ứ", - "ĠÑģÑĤ ÑĢÑĥкÑĤÑĥ", - "ç¸ £", - "ĠH oa", - "ĠHo a", - "í ĮĢ", - "íĮ Ģ", - "Ġ ÑĢÑĸÑĪ", - "ĠÑĢ ÑĸÑĪ", - "Ġвоз дÑĥ", - "олÑĮ ÑĪ", - "οÏħ με", - "ู à¸Ļ", - "Ġп ÑĢид", - "ĠпÑĢ ид", - "ĠпÑĢи д", - "il mek", - "ilm ek", - "ĠاÙĦ ÙĤر", - "ĠاÙĦÙĤ ر", - "Į ĵ", - "Ġ uç", - "Ġu ç", - "å¨ ĺ", - "ec ektir", - "ecek tir", - "Ġ íħĮ", - "Ġí ħĮ", - "Ġíħ Į", - "Ġ εÏħ", - "Ġε Ïħ", - "Ġh òa", - "Ïģ Ïħ", - "ึà¸ģษ า", - "ĠÑĤеÑħ нолог", - "ú i", - "Ġbilg iler", - "Ġbilgi ler", - "Ġ ÙĤاÙĦ", - "ĠÙĤ اÙĦ", - "e dl", - "ed l", - "z nám", - "zn ám", - "á ly", - "ál y", - "åºĶ 该", - "алÑĮ ний", - "аÑĤ елÑı", - "аÑĤе лÑı", - "à¸Ļ วà¸Ļ", - "à¸Ļว à¸Ļ", - "Ġ ÐŁÐ¾Ð»", - "ĠÐŁ ол", - "ĠÐŁÐ¾ л", - "à¸ŀ à¸Ļ", - "ç¤ ¼", - "Ġt asar", - "Ġta sar", - "Ġtas ar", - "ĠÑĤ ой", - "ĠÑĤо й", - "Ġм еÑģÑı", - "Ġ иÑģк", - "Ġи Ñģк", - "ĠиÑģ к", - "Ġप द", - "γ ή", - "ا ختÙĩ", - "اخ تÙĩ", - "اخت Ùĩ", - "è¿Ļ éĩĮ", - "Ġch á»īnh", - "Ġchá»ī nh", - "ĠÙĤ سÙħ", - "Ùİ Ùĩ", - "er li", - "åĽ½ éĻħ", - "il iyor", - "ili yor", - "ĠØ´Ùĩر ستاÙĨ", - "Ġve lk", - "Ġvel k", - "åĽ º", - "Ġб ÑĸлÑĮÑĪ", - "ĠбÑĸлÑĮ ÑĪ", - "ãĥ¼ ãĥĹ", - "ãĥ¼ãĥ Ĺ", - "æŁ IJ", - "ì§ ľ", - "ĠÄĮ R", - "Ġд ек", - "Ġде к", - "ر بÛĮ", - "رب ÛĮ", - "о виÑĩ", - "ов иÑĩ", - "ови Ñĩ", - "Ġkap sam", - "Ġkaps am", - "ĠÙĦ Ø£", - "Ġ анÑĤи", - "Ġан ÑĤи", - "Ġ ücret", - "Ġü cret", - "ê² ¬", - "о ÑĢож", - "оÑĢ ож", - "оÑĢо ж", - "ÛĮ ÙħÛĮ", - "ÛĮÙħ ÛĮ", - "è© ķ", - "Ġ ë§ŀ", - "Ġë§ ŀ", - "Ġ ÑĢÑıд", - "ĠÑĢ Ñıд", - "ĠÑĢÑı д", - "ĠÙĩÙħ راÙĩ", - "â r", - "ا بت", - "اب ت", - "ĠиÑģполÑĮзов аÑĤÑĮ", - "ĠиÑģполÑĮз оваÑĤÑĮ", - "к Ñģ", - "âī ¡", - "Ġo lay", - "Ġol ay", - "Ġola y", - "èį ¯", - "Ġo prav", - "Ġop rav", - "Ġopr av", - "Ġدرب ارÙĩ", - "Ġ ä¸ŃåĽ½", - "Ġä¸Ń åĽ½", - "и лÑģÑı", - "ил ÑģÑı", - "åį «", - "ĠاÙĦ است", - "ĠاÙĦاس ت", - "ÙĪÛĮ ÛĮ", - "ÑĢ еÑĪ", - "ÑĢе ÑĪ", - "Ġ ÙĨس", - "ĠÙĨ س", - "ãĢĤ åľ¨", - "Ġ ÙĦØŃ", - "ĠÙĦ ØŃ", - "Ġko run", - "Ġkor un", - "ĠÙģ رد", - "ĠÙģر د", - "Ġо боÑĢ", - "Ġоб оÑĢ", - "Ġобо ÑĢ", - "е ÑĪÑĮ", - "еÑĪ ÑĮ", - "Ġpod mÃŃn", - "Ġ ë¬¸ìłľ", - "Ġ문 ìłľ", - "ĠdeÄŁer lendir", - "ä¸į åIJĮ", - "æ¶ ²", - "ा हर", - "ाह र", - "íļ į", - "à¥į à¤ł", - "à¥įठł", - "и ÑĤиÑģÑı", - "иÑĤи ÑģÑı", - "ا ÙĦع", - "اÙĦ ع", - "Ġd vÄĽ", - "Ġdv ÄĽ", - "ĠпеÑĢ ек", - "ĠпеÑĢе к", - "Ġ åħĥ", - "Ġåħ ĥ", - "Ġ aras", - "Ġa ras", - "Ġar as", - "Ġara s", - "Ġalt ında", - "Ġaltın da", - "Ġaltı nda", - "Ġв за", - "Ġвз а", - "æĴ ĥ", - "Ġmil yon", - "Ġ åѦ", - "ĠåŃ ¦", - "Ġв аÑĢи", - "ĠваÑĢ и", - "Ġва ÑĢи", - "ĠاÙĦع اÙĦÙħ", - "' Ñı", - "ÙĪ ÛĮس", - "ÙĪÛĮ س", - "Ġмож ÑĥÑĤÑĮ", - "ãģij ãģŁ", - "ìĿ´ ìĹĪëĭ¤", - "ìĿ´ìĹĪ ëĭ¤", - "ο Ïįν", - "οÏį ν", - "Ġ éŁ", - "Ġé Ł", - "Ġpost up", - "Ġpo stup", - "ü yük", - "üy ük", - "åĪ Ĭ", - "Ġ ÙĤب", - "ĠÙĤ ب", - "Ġاص ÙĦÛĮ", - "ĠاصÙĦ ÛĮ", - "ÙĪ Ùī", - "Ġrep ublik", - "Ġ ÐĻ", - "ĠÐ Ļ", - "ģ m", - "Ġб ел", - "ा -", - "Ñģ кое", - "Ñģк ое", - "Ñģко е", - "Ġcu á»iji", - "è² ·", - "ี ยว", - "ีย ว", - "éĩį è¦ģ", - "ู ม", - "ĠÑĢозвиÑĤ кÑĥ", - "Ġ ë°±", - "Ġë° ±", - "åĥ ¹", - "Ġ åīį", - "Ġåī į", - "à¹Ħ à¸ĭ", - "ãĢĮ âĢ¦âĢ¦", - "à¥Į त", - "Ú© رد", - "کر د", - "Ġza ÅĻÃŃzenÃŃ", - "ส าร", - "Ġle tech", - "Ġlet ech", - "l emek", - "le mek", - "lem ek", - "leme k", - "人 ãģ®", - "Ġd ưỡng", - "ĠdÆ°á» ¡ng", - "ت ÙĤ", - "Ġ åĵ", - "Ġå ĵ", - "åħ »", - "Ġ ëıħ", - "Ġëı ħ", - "Ġ 루", - "Ġë £¨", - "Ġë£ ¨", - "Ø° ÙĦÙĥ", - "Ġ ìĿ¼ë³¸", - "ĠìĿ¼ 본", - "ĠAy rıca", - "ĠÙ¾ Úĺ", - "is inin", - "isi nin", - "isin in", - "isini n", - "Ġìĭ ¶", - "Ú¯ ÛĮرÛĮ", - "Ú¯ÛĮ رÛĮ", - "Ú¯ÛĮر ÛĮ", - "خص ص", - "³ ç´°", - "ĠмаÑĤеÑĢи ал", - "k ové", - "ko vé", - "kov é", - "ë§ ī", - "ãģķ ãģĽ", - "ĠÑĤак ой", - "ĠÑĤа кой", - "Ġtr áºŃn", - "Ġ лиÑĨ", - "Ġл иÑĨ", - "Ġли ÑĨ", - "Ġ åĽĽ", - "ĠåĽ Ľ", - "Ñĩ Ñĥ", - "Ġ æ°´", - "Ġæ° ´", - "Ġdo lay", - "Ġdol ay", - "å½ ¹", - "ÑĢ ива", - "ÑĢи ва", - "ÑĢив а", - "Ġг ÑĢÑĥпп", - "ĠгÑĢÑĥ пп", - "ĠгÑĢÑĥп п", - "Ġmüm kün", - "л ена", - "лен а", - "ле на", - "ëĿ¼ ëĬĶ", - "åĪ© çĶ¨", - "Ġr ahat", - "Ġra hat", - "ï¼ıï¼ı ï¼ıï¼ı", - "æģ ©", - "Ġ íķŃ", - "Ġíķ Ń", - "Ġ íĴ", - "Ġí Ĵ", - "Ġ ìĬ¹", - "ĠìĬ ¹", - "Ġch ân", - "Ġ ãĤ¨", - "ĠãĤ ¨", - "Ġжиз ни", - "çĸ ij", - "ãĢĤ ä»ĸ", - "리 ìĬ¤", - "Ñĩ иÑħ", - "Ñĩи Ñħ", - "Ġ é¦ĸ", - "Ġé¦ ĸ", - "ÄĽ r", - "Ġй омÑĥ", - "Ġth áºŃt", - "Ġìķ ŀ", - "c ih", - "ci h", - "س ÙĦاÙħ", - "سÙĦ اÙħ", - "Ġs iyas", - "Ġsi yas", - "Ġ íĸĪ", - "Ġíĸ Ī", - "Ġк оÑĪ", - "Ġко ÑĪ", - "Ïĥ αν", - "Ïĥα ν", - "ÙĬ اÙĨ", - "ÙĬا ÙĨ", - "Ġd ö", - "ाह त", - "о ÑĢод", - "оÑĢ од", - "оÑĢо д", - "о ваÑı", - "ов аÑı", - "ова Ñı", - "ÑĨи оналÑĮ", - "ÑĨион алÑĮ", - "ائ Ùĩ", - "Ġà¤ĸ र", - "ĠÄij á»Ŀi", - "ĠÄijá» Ŀi", - "ä¸į ä¼ļ", - "Ùĥ ز", - "ี à¸Ħวาม", - "ีà¸Ħ วาม", - "l ıyor", - "lı yor", - "à¥ĭ द", - "Ġ 충", - "Ġì¶ ©", - "Ġc á»ij", - "à¹Ĥ à¸ķ", - "Ġε ÏĢί", - "ĠεÏĢ ί", - "ĠпÑĢ Ñıм", - "æ³ °", - "ا ÙĦØ©", - "اÙĦ Ø©", - "j ÃŃm", - "jÃŃ m", - "Ġ би", - "Ġб и", - "Å¡ em", - "Å¡e m", - "ĠH á»Ļi", - "à¸Ħ รà¸ĩ", - "à¸Ħร à¸ĩ", - "Ġh uyá»ĩn", - "Ġhuy á»ĩn", - "ç¯ Ģ", - "l iÅ¡", - "li Å¡", - "ĠجÙĩ ت", - "ç§ ĭ", - "ĠÑĨ ел", - "ĠÑĨе л", - "Ġ лÑĸÑĤ", - "Ġл ÑĸÑĤ", - "ĠлÑĸ ÑĤ", - "Ġ æ·", - "Ġæ ·", - "ж Ñĥ", - "ãģĪ ãģŁ", - "ë´ ī", - "Ġ 머", - "Ġë¨ ¸", - "åł´ åIJĪ", - "éĿ ©", - "ãĥª ãĥ³", - "ег да", - "Ġbe nim", - "Ġben im", - "Ġbeni m", - "çĽ Ł", - "ãģ® ä¸Ń", - "åĿ IJ", - "ĠÃľniversit esi", - "Ġko ÅŁ", - "Ġп ож", - "Ġпо ж", - "iá»ĩ p", - "ĠpÅĻ ij", - "ĠpÅĻi j", - "ëŀ ¨", - "ĠاÙĦ أس", - "ĠاÙĦØ£ س", - "ár nÃŃ", - "iế m", - "Ġ èĬ", - "Ġè Ĭ", - "Ġ δε", - "Ġδ ε", - "å¨ ±ä¹IJ", - "Ġ Æ°u", - "Ġ çĦ¡", - "ĠçĦ ¡", - "Ġг ÑĢи", - "ĠгÑĢ и", - "Ġпо ÑįÑĤомÑĥ", - "ĠÄij óng", - "ĠÄijó ng", - "ĠÄijón g", - "ج اÙĨ", - "جا ÙĨ", - "Ġngh iên", - "Ġnghi ên", - "Ġا ÙĦاÙĨ", - "ĠاÙĦ اÙĨ", - "ÑĪ ей", - "ÑĪе й", - "à¹ģ รà¸ģ", - "ĠÚĨ Ùĩار", - "ĠÚĨÙĩ ار", - "Ñİ Ñīий", - "ÑİÑī ий", - "ÏĮ Ïģ", - "Ġ رÙħ", - "Ġر Ùħ", - "ì² ł", - "Ġدست گاÙĩ", - "Ġ دÛĮد", - "Ġد ÛĮد", - "ĠدÛĮ د", - "ãĥĥãĤ¯ ãĤ¹", - "ा मन", - "ाम न", - "ĠTh Ãłnh", - "Ġth ẩm", - "Ġc Ãłng", - "ĠcÃł ng", - "Ġdön Ã¼ÅŁ", - "ĠпÑĢи гоÑĤов", - "ĠпÑĢиг оÑĤов", - "Ġk iÅŁi", - "Ġki ÅŁi", - "ĠkiÅŁ i", - "ØŃ Øª", - "Ġ ë²ķ", - "Ġë² ķ", - "é£ Ľ", - "Ġit ibar", - "Ġг лав", - "Ġгла в", - "Ġor tam", - "Ġort am", - "Ġorta m", - "Ġm add", - "Ġma dd", - "Ġmad d", - "Ġ оÑģÑĤав", - "Ġо ÑģÑĤав", - "ĠоÑģÑĤ ав", - "ĠÙģÙĪ تباÙĦ", - "ĠÙģÙĪت باÙĦ", - "Ġan laÅŁ", - "l eyen", - "le yen", - "ley en", - "ç ´Ģ", - "ç´ Ģ", - "Ġ é£", - "Ġé £", - "/ lo", - "/l o", - "Ùħ ÙĪÙĦ", - "ÙħÙĪ ÙĦ", - "Ġд ÑĥÑħ", - "ĠдÑĥ Ñħ", - "Ġ ÙĦب", - "ĠÙĦ ب", - "л ег", - "ле г", - "Ġgö nder", - "Ġgön der", - "ÙĬ Ø·", - "Ġ สำ", - "Ġส ำ", - "Ġv ás", - "Ġvá s", - "ĠÐŁ еÑĤ", - "а лоÑģÑı", - "ало ÑģÑı", - "ì ¿ł", - "ì¿ ł", - "éĻ ½", - "åĸ ®", - "èĪ ŀ", - "н Ñĥл", - "нÑĥ л", - "ÄŁ ine", - "ÄŁi ne", - "ÄŁin e", - "Ġ ghi", - "Ġg hi", - "Ġgh i", - "Ġ çµ", - "Ġç µ", - "ÙĬ ÙĨÙĬ", - "ÙĬÙĨ ÙĬ", - "Å ½", - "Ġhük üm", - "ĠD Ä±ÅŁ", - "ĠÎŃ Ïĩει", - "ĠÎŃÏĩ ει", - "Ġ Ñģка", - "ĠÑģ ка", - "ĠÑģк а", - "Ġ ÑĤим", - "ĠÑĤ им", - "ĠÑĤи м", - "Ġп оÑģÑĤав", - "Ġпо ÑģÑĤав", - "ĠпоÑģÑĤ ав", - "à¸Ļ าà¸Ķ", - "à¸Ļา à¸Ķ", - "d ül", - "dü l", - "Ġd va", - "Ġdv a", - "Ġ à¸Ħà¸Ļ", - "Ġà¸Ħ à¸Ļ", - "Ġchá»ĭ u", - "Ġ èı", - "Ġè ı", - "à¹ģส à¸Ķà¸ĩ", - "æ° £", - "Ġ íĪ¬", - "Ġí Ī¬", - "Ġ Ñĩин", - "ĠÑĩ ин", - "ĠÑĩи н", - "ãģ« ãģĬ", - "ен ноÑģÑĤи", - "енно ÑģÑĤи", - "ÐIJ ÐĿ", - "Ġh emen", - "Ġhe men", - "Ġhem en", - "Ġ ait", - "Ġa it", - "Ġai t", - "Ġ à¤Ĭ", - "ĠठĬ", - "æī §", - "ĠA BD", - "ĠAB D", - "Ġκα θ", - "æ´ Ľ", - "ãĤ¢ ãĥ«", - "à¹ī าà¸Ĺ", - "à¹īา à¸Ĺ", - "ÅĻ ez", - "ÅĻe z", - "d ÄĽji", - "dÄĽ ji", - "Ġt á»ĭch", - "еннÑı м", - "Ġв ÑģÑĤанов", - "ĠвÑģÑĤ анов", - "ĠاÙĦ بر", - "ĠاÙĦب ر", - "ÙĪÙħ تر", - "k ách", - "ká ch", - "åº Ĭ", - "л Ñĥж", - "лÑĥ ж", - "Ġ تد", - "Ġت د", - "ä¸ ½", - "ر Ø®", - "à¤Ĥ à¤ĸ", - "èĩªå·± çļĦ", - "å®ĺ ç½ij", - "- Ñı", - "à¹ĩ à¸Ķ", - "èĦ ļ", - "Ġ çķ", - "Ġç ķ", - "Ġiçer isinde", - "Ġb iá»ĥn", - "Ġbi á»ĥn", - "Ġ à¸ģล", - "Ġà¸ģ ล", - "Ġy aÄŁ", - "Ġya ÄŁ", - "Ġ æ´", - "Ġæ ´", - "Ġ бÑĢа", - "Ġб ÑĢа", - "ع ار", - "عا ر", - "æĪ °", - "à¥Ģ Ċ", - "Ġlé Äį", - "a ların", - "alar ın", - "aları n", - "Ġ Îĸ", - "ĠÎ ĸ", - "а ÑĢÑı", - "аÑĢ Ñı", - "ãģĿ ãĤĵãģª", - "ÅĪ uje", - "ãĢĢ Ġ", - "ĠsaÄŁ lık", - "Ġдо ÑģлÑĸд", - "ĠдоÑģ лÑĸд", - "ÃŃ Å¡", - "à¥įर श", - "à¥ī न", - "Ġgi ả", - "بÙĪ اسطة", - "å® ģ", - "Ġs oud", - "Ġso ud", - "Ġsou d", - "Ġк ÑĤо", - "e sel", - "es el", - "ese l", - "Ġп ам", - "Ġпа м", - "Ġ ÂłĠ", - "ĠÂł Ġ", - "ĠÄį lov", - "æ· ·", - "ห à¸į", - "ĠOs man", - "æ ¦Ĥ", - "æ¦ Ĥ", - "Ġ åĭ", - "Ġå ĭ", - "ï¼Į åħ¶", - "Ġ à¸Ħร", - "Ġà¸Ħ ร", - "Ġmá» ģm", - "Ġ ÑģоÑĢ", - "ĠÑģ оÑĢ", - "ĠÑģо ÑĢ", - "çĨ ±", - "Ġthu ê", - "ر ج", - "à¹Ĥล à¸ģ", - "Ġ íķĺê³ł", - "Ġíķĺ ê³ł", - "ÙĬ دة", - "ÙĬد Ø©", - "ĠaÅŁ aģı", - "Ġk á»ĥ", - "Ġká» ĥ", - "à¸ķ ำ", - "λ ει", - "λε ι", - "çļĦ è¯Ŀ", - "æ± ł", - "ĠÑģ ÑĤен", - "ĠÑģÑĤ ен", - "Ġin cel", - "Ġinc el", - "Ġince l", - "åº Ń", - "ÑĤ оÑĩ", - "ÑĤо Ñĩ", - "Ġprob lém", - "Ġprobl ém", - "ÏĦ Ïĥ", - "à¹ī à¸Ńà¸Ļ", - "à¹īà¸Ń à¸Ļ", - "ë³´ ëĭ¤", - "Ġà¤Ĩ à¤Ĺ", - "ν αÏĤ", - "να ÏĤ", - "ãģĦ ãĤĭ", - "Ġd ục", - "Ġdụ c", - "Ġtoho to", - "Ġtoh oto", - "ëIJĺ ìĹĪëĭ¤", - "ëIJĺìĹĪ ëĭ¤", - "T J", - "Ġви знаÑĩ", - "ĠB unun", - "ĠBu nun", - "ĠBun un", - "ĠBunu n", - "à¤Ĥ बर", - "à¤Ĥब र", - "ĠÙĩÙħÚĨ ÙĨÛĮÙĨ", - "Ġб Ñİдж", - "Ñĥ ÑĢг", - "ÑĥÑĢ г", - "äº ®", - "Ġμε γ", - "Ġtop lum", - "Ġtopl um", - "ãģ£ ãģ", - "о ÑĤо", - "оÑĤ о", - ": |", - "éĿŀ 常", - "ิ à¸Ĺà¸ĺ", - "ิà¸Ĺ à¸ĺ", - "éģ ķ", - "âĢĮÙ¾ دÛĮ", - "Ġз ÑĢоб", - "à¹Į à¸Ķ", - "Ġдолж ен", - "Ġдол жен", - "ĠmÄĽ sta", - "ĠmÄĽst a", - "ÛĮ Ø´Ùĩ", - "ÛĮØ´ Ùĩ", - "v atel", - "va tel", - "vat el", - "Ġprov oz", - "Ġ inan", - "Ġin an", - "Ġi nan", - "à¤Ĥ प", - "Ġpar ç", - "ÑĢ аÑģÑĤ", - "ÑĢа ÑģÑĤ", - "ÑĢаÑģ ÑĤ", - "üm ü", - "Ġgi á»ijng", - "Ġgiá» ijng", - "æ¬ ¢", - "Ø« ÙĬر", - "ĠB akan", - "ĠBa kan", - "ĠBak an", - "Ġ âĪ¨", - "ĠâĪ ¨", - "Ġ باÙĨ", - "Ġب اÙĨ", - "Ġبا ÙĨ", - "Û± Û¸", - "Û±Û ¸", - "ãĤĤ ãģĨ", - "land ı", - "lan dı", - "Ġyen iden", - "Ġyeni den", - "ÑĨ енÑĤ", - "ÑĨе нÑĤ", - "ÑĨен ÑĤ", - "Ġде ÑıÑĤелÑĮ", - "Ð ©", - "Ġ rov", - "Ġr ov", - "Ġro v", - "å®Į åħ¨", - "ĠK ỳ", - "s lu", - "sl u", - "Ġl ấy", - "é¤ IJ", - "ĠÑĩ олов", - "ä¼ Ŀ", - "ĠbaÅŁ v", - "å° Ī", - "ê³ ¡", - "ãĢģ ãģĿãĤĮ", - "Ġ PÅĻÃŃ", - "ĠP ÅĻÃŃ", - "ĠPÅĻ ÃŃ", - "д ем", - "де м", - "ĠпÑĢо ек", - "ร à¸ĸ", - "建 设", - "Ġмож лив", - "æ® º", - "ãģ¡ãĤĥ ãĤĵ", - "æķ ij", - "ĠÄį ty", - "ĠÄįt y", - "é¦ Ĩ", - "о ÑĢÑĥ", - "оÑĢ Ñĥ", - "Ġ æĦ", - "Ġæ Ħ", - "Ġk ÃŃch", - "λ οÏħ", - "λο Ïħ", - "ãģĦ ãģ¤", - "Ġc Äĥn", - "ĠcÄĥ n", - "Ạµ", - "Ġel de", - "éº »", - "ÄŁ e", - "Ġdo bÄĽ", - "Ġdob ÄĽ", - "ा यर", - "ाय र", - "Ġ ãĥı", - "Ġãĥ ı", - "н ен", - "не н", - "Ġmůže te", - "Ġmůž ete", - "Ġна ÑģÑĤÑĥп", - "ĠнаÑģÑĤ Ñĥп", - "ìĭľ ê°Ħ", - "ĠÑģим пÑĤом", - "Ġ ÏĥÏį", - "ĠÏĥ Ïį", - "Ġ سÙĦ", - "Ġس ÙĦ", - "ε κ", - "ร à¸ĵ", - "á te", - "át e", - "ek ler", - "ekl er", - "ĠвÑĢем ени", - "ĠвÑĢемен и", - "âĢĮ ÙĩاÛĮÛĮ", - "âĢĮÙĩاÛĮ ÛĮ", - "ãģĬ ãĤĬ", - "ж и", - "Ñĭ ваеÑĤÑģÑı", - "Ñĭв аеÑĤÑģÑı", - "Ñĭва еÑĤÑģÑı", - "ÑĭваеÑĤ ÑģÑı", - "Ùħ اÙĨÛĮ", - "ÙħاÙĨ ÛĮ", - "Ùħا ÙĨÛĮ", - "à¸ķ ล", - "Ġ صد", - "Ġص د", - "Ġ вол", - "Ġв ол", - "Ġво л", - "ìĬ Ī", - "ĠÙĥ Ùħا", - "ĠÙĥÙħ ا", - "Ġnh ằm", - "èģ ¯", - "ov acÃŃ", - "ova cÃŃ", - "Ġë§Į ëĵ¤", - "ÙĪ Ù¾", - "Ġ ë¸Į", - "Ġë¸ Į", - "ب ÙĬØ©", - "بÙĬ Ø©", - "u yla", - "uy la", - "л ено", - "лен о", - "ле но", - "èĮ ¶", - "ÑĢ ей", - "ÑĢе й", - "Ġk li", - "Ġkl i", - "Ġüzer inden", - "Ġüzerinde n", - "н еÑĤ", - "не ÑĤ", - "r aÄį", - "ra Äį", - "ĠпÑĢа ÑĨÑİ", - "Ġed iyor", - "Ġedi yor", - "ãģı ãģł", - "Ġ Äįast", - "ĠÄį ast", - "ĠÄįas t", - "i yi", - "iy i", - "éĬ Ģ", - "Ġd ù", - "Ùİ Ø¨", - "ÙĪ ÙĬØ©", - "ÙĪÙĬ Ø©", - "å ª", - "Ġs ınıf", - "Ġsın ıf", - "Ġس اعت", - "Ġ ราย", - "Ġร าย", - "Ġза Ñıв", - "Ġg ặp", - "à¸Ń ว", - "ĠØ« Ùħ", - "ĠZ á", - "ĠвÑĸд к", - "i zik", - "iz ik", - "izi k", - "Ġm ón", - "Ġmó n", - "Ġпов ÑĭÑĪ", - "Ġ à¸ļาà¸Ĺ", - "Ġà¸ļ าà¸Ĺ", - "ĠÑģ ил", - "ĠÑģи л", - "æĥħ åł±", - "Âł t", - "ĠÐľ оÑģк", - "Ġê²ĥ ìĿ´ëĭ¤", - "Ġê²ĥìĿ´ ëĭ¤", - "Ġ çIJ", - "Ġç IJ", - "ĠÙħدÛĮر ÛĮت", - "ов оÑĹ", - "ово ÑĹ", - "Τ ο", - "çº ª", - "нÑĸ ÑĪе", - "нÑĸÑĪ е", - "Ġ ÐĽÑİ", - "ĠÐĽ Ñİ", - "η Ïĥη", - "ĠÙĨسب ت", - "ĠÙĨس بت", - "m uz", - "mu z", - "ร ว", - "ãĢģ ãģĤ", - "Ġбол ез", - "Ġtr ách", - "ãĥ ¦", - "à¹Ģà¸Ĥ า", - "Ġê·¸ ëĬĶ", - "ب رÛĮ", - "بر ÛĮ", - "æł ª", - "ëĿ¼ ìĿ´", - "Ġ íĮ¨", - "Ġí Į¨", - "ĠíĮ ¨", - "íĬ ¹", - "ľ ´", - "ि ड", - "िठ¡", - "ÑĢо ме", - "ÑĢом е", - "è® ²", - "Ġ ÑĤон", - "ĠÑĤ он", - "ĠÑĤо н", - "Ñģ Ñĸ", - "Ġ ç®", - "Ġç ®", - "åıĸ ãĤĬ", - "ì° °", - "Ġ ÙĪÙĦÛĮ", - "ĠÙĪ ÙĦÛĮ", - "ĠÙĪÙĦ ÛĮ", - "Ġس Ø·ØŃ", - "èı ľ", - "н ами", - "на ми", - "нам и", - "T ürk", - "åİ Ĥ", - "Ġf inan", - "Ġfin an", - "Ġfi nan", - "ãģ« ãģªãĤĭ", - "ãģ«ãģª ãĤĭ", - "Ġ oby", - "Ġo by", - "Ġob y", - "T rong", - "Tr ong", - "Tro ng", - "Ġv yp", - "Ġvy p", - "à¥ģ ड", - "à¥ģठ¡", - "ìŀIJ ê°Ģ", - "Ġ æīĢ", - "Ġæī Ģ", - "ÐĹ Ð°", - "um lu", - "uml u", - "ëĵ Ŀ", - "Ġм енÑĸ", - "Ġмен Ñĸ", - "ол ниÑĤелÑĮ", - "олн иÑĤелÑĮ", - "Ġú Äįin", - "ĠúÄį in", - "Ġb unun", - "Ġbu nun", - "Ġbun un", - "Ġbunu n", - "ĠÐłÐ¾Ñģ Ñģии", - "в ÑģÑı", - "Ġн Ñĸж", - "ĠнÑĸ ж", - "ิà¸Ķ à¸ķ", - "غ Ø©", - "Ä ļ", - "Ġ سÙħ", - "Ġس Ùħ", - "Ġ Ðĺз", - "ĠÐĺ з", - "à¥ĩ प", - "à¥ĩठª", - "大 çļĦ", - "ì¹ ľ", - "Ġ иÑģÑĤ", - "Ġи ÑģÑĤ", - "ĠиÑģ ÑĤ", - "Ġкон ÑģÑĤÑĢÑĥк", - "Û± Û²", - "Û±Û ²", - "â l", - "Ġ ÑĪиÑĢ", - "ĠÑĪ иÑĢ", - "ĠÑĪи ÑĢ", - "ï¼ ł", - "Ġar tık", - "Ġart ık", - "æŁ ĵ", - "ä¹ ¡", - "ÃŃ te", - "ÃŃt e", - "ĠNh áºŃt", - "ĠÎĶ η", - "Ġöl ç", - "êµ ´", - "о Ñıн", - "оÑı н", - "ëĵ± ë¡Ŀ", - "Ġng ân", - "Ġ бÑĥдÑĮ", - "ĠбÑĥд ÑĮ", - "ÎŁ Ρ", - "ÎŁÎ ¡", - "ì ´", - "Ùħ ÙĪد", - "ÙħÙĪ د", - "ν ον", - "νο ν", - "Îķ ÎĿ", - "çij ŀ", - "ĠÅĻ ek", - "ĠÅĻe k", - "- âĢIJ", - "ĠM erk", - "ĠMe rk", - "ĠMer k", - "Ġоп ÑĢедел", - "ĠопÑĢед ел", - "Ïģ ιν", - "Ïģι ν", - "л аб", - "ла б", - "ëĦ¤ ìļĶ", - "Ġб лиз", - "Ġбл из", - "Ġбли з", - "Ġph á»iji", - "Ġphá»ij i", - "Ġдолж нÑĭ", - "ĠÑį кÑģп", - "ĠÑįк Ñģп", - "à¸ļ à¸Ĺ", - "à¸Ľà¸£à¸° ส", - "ĠÙ¾Úĺ ÙĪÙĩ", - "Ġ íķľëĭ¤", - "Ġíķľ ëĭ¤", - "ÏĦ οÏį", - "ÏĦο Ïį", - "Ùĩ ÙĨ", - "Ġд од", - "Ġдо д", - "Ġk ayı", - "Ġka yı", - "Ġkay ı", - "Ł ģ", - "Ñģ иÑı", - "Ñģи Ñı", - "à¤Ĥ तर", - "à¤Ĥत र", - "Ġpod nik", - "e vi", - "ev i", - "ÛĮ ÛĮر", - "Т ак", - "Та к", - "к оп", - "ко п", - "н аÑħ", - "на Ñħ", - "ا سÙĩ", - "اس Ùĩ", - "à¸ĵ à¸ij", - "Ġk há", - "Ġkh á", - "Ġy arat", - "Ġya rat", - "Ġyar at", - "ĠاÛĮÙĨ Ú©Ùĩ", - "Ø· بÙĬ", - "طب ÙĬ", - "Ġs ır", - "Ġsı r", - "ĠØ¢ÙħرÛĮÚ© ا", - "Ġ बल", - "Ġब ल", - "k aç", - "ka ç", - "Ġ åı¯", - "Ġåı ¯", - "Ġ åħ¶", - "Ġåħ ¶", - ". ***", - ".* **", - "л ÑĸннÑı", - "лÑĸн нÑı", - "ä¹ ±", - "o q", - "æ ¦", - "ãĤ ¼", - "Ġf ır", - "Ġk ê", - "Ġìłľ ê³µ", - "Ġ Ïĥη", - "ĠÏĥ η", - "а нÑĭ", - "ан Ñĭ", - "н ова", - "но ва", - "нов а", - "à¸Ĭ าย", - "ĠØ· ÙĪÙĦ", - "à¥Ī य", - "Ġ ì¹ľ", - "Ġì¹ ľ", - "ìĤ ´", - "Ġп Ñĸв", - "Ġlu áºŃn", - "Ġà¤ī म", - "åº ĥ", - "à¹ĩ à¸Ńà¸ķ", - "Ġس اÛĮت", - "л Ñıн", - "лÑı н", - "ĠíķĦ ìļĶ", - "Ġgör ül", - "ĠÑĤеÑĢ иÑĤоÑĢ", - "ĠÙĨ ØŃ", - "е ма", - "ем а", - "Ġmn oh", - "Ġ ãģ¯", - "غ ÙĬر", - "ĠÑģдел аÑĤÑĮ", - "ç ģµ", - "çģ µ", - "Ġ ÐłÐ°Ð·", - "ĠÐł аз", - "ĠÐłÐ° з", - "Ġг еÑĢ", - "Ġге ÑĢ", - "γ μα", - "íķĺ ë©´", - "ĠdeÄŁ iÅŁtir", - "ĠdeÄŁiÅŁ tir", - "ãĥ³ ãĥĨ", - "ãĥ³ãĥ Ĩ", - "å¸Ĥ åľº", - "个 人", - "ìĥ Ī", - "ì¹ ¨", - "èī º", - "ÙĤ ت", - "ĠÚ¯ رÙģتÙĩ", - "ĠگرÙģ تÙĩ", - "Ġگر ÙģتÙĩ", - "ĠگرÙģت Ùĩ", - "Ġ çİĭ", - "Ġçİ ĭ", - "ĠاÙĦ Ø°Ùĩ", - "ĠاÙĦØ° Ùĩ", - "λ Ïħ", - "à¤ľ र", - "Ġв ним", - "ë¦ Ń", - "ิ à¸Ĺ", - "Ġ شاÙĩ", - "ĠØ´ اÙĩ", - "æĬķ èµĦ", - "æĿIJ æĸĻ", - "ĠÙĨ Ùģ", - "èª ¬", - "æĬ Ĺ", - "Ġ аб", - "Ġа б", - "iy eti", - "iye ti", - "iyet i", - "ç¾ ħ", - "ÑĢ Ñĸз", - "ÑĢÑĸ з", - "Ġ สม", - "Ġส ม", - "i cÃŃ", - "ic ÃŃ", - "к ÑĥваннÑı", - "кÑĥ ваннÑı", - "Ġ ìķ¼", - "Ġìķ ¼", - "Ġ è½", - "Ġè ½", - "âĢ «", - "Ġ διά", - "Ġδ ιά", - "Ġδι ά", - "Ġд еп", - "Ġде п", - "ãĥ¼ ãĤ¿", - "ãĥ¼ãĤ ¿", - "Ġob jev", - "Ġobj ev", - "mé na", - "Ġbe lg", - "Ġbel g", - "Ġ æ¥", - "Ġæ ¥", - "Ġn á»ģn", - "Ġг ол", - "Ġpost av", - "Ġpo stav", - "Ġت Ú©", - "Ð «", - "ĠпÑĸд ÑĤ", - "ĠоÑĤ ноÑĪ", - "Ġп ÑĢив", - "ĠпÑĢ ив", - "ĠпÑĢи в", - "Ġ åŁº", - "ĠåŁ º", - "Ġн али", - "Ġна ли", - "Ġнал и", - "ů ž", - "Ġ yat", - "Ġy at", - "Ġya t", - "ÅŁ a", - "ÏĦ ήÏĤ", - "ÏĦή ÏĤ", - "ÑĨ ем", - "ÑĨе м", - "次 æķ°", - "Ġb Ãł", - "ÙĪ Ùĥ", - "Ġ íĶĦë¡ľ", - "ĠíĶĦ ë¡ľ", - "ĠPh áp", - "Ġ êµ°", - "Ġêµ °", - "è³ ŀ", - "Ġoch ran", - "Ġgere kir", - "Ġgerek ir", - "Ġ íļ", - "Ġí ļ", - "à¸ļ ล", - "á me", - "ám e", - "Ġ بÛĮر", - "Ġب ÛĮر", - "ĠبÛĮ ر", - "à¸Ĥ าย", - "ов аний", - "ова ний", - "овани й", - "ован ий", - "Ġmož né", - "âĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģ", - "á lu", - "ál u", - "н ÑĤ", - "¦ æĥħ", - "à¹ģ รม", - "ĠÑĦ Ñĸн", - "ĠÄ° ç", - "à¹Ī à¸Ńย", - "à¹Īà¸Ń ย", - "ê² ¨", - "Ġh edef", - "Ġhe def", - "Ġhed ef", - "ĠاÙĦ ÙħØ´", - "ĠاÙĦÙħ Ø´", - "à¹ī าม", - "à¹īา ม", - "å¯ Ħ", - "Ġ ëĭµ", - "Ġëĭ µ", - "Ġ ô", - "Ġà ´", - "ла ÑģÑı", - "лаÑģ Ñı", - "Ä° T", - "à¸Ķ ำ", - "Ġher hangi", - "Ġger eken", - "Ġgere ken", - "Ġgerek en", - "е ÑĢеж", - "еÑĢ еж", - "еÑĢе ж", - "ÙĪ Ø©", - "ĠpÅĻ est", - "ĠpÅĻes t", - "ĠpÅĻe st", - "ç§ij åѦ", - "оÑģÑĤ аÑĤ", - "ün den", - "ünd en", - "ünde n", - "åĮħ æĭ¬", - "Ġد Ùĩد", - "ĠدÙĩ د", - "ÑĪ иÑģÑĮ", - "ÑĪи ÑģÑĮ", - "н еÑĢ", - "не ÑĢ", - "Ñĸ дом", - "Ñĸд ом", - "Ġb iç", - "Ġbi ç", - "ìĭ Ń", - "Ġhod not", - "Ġze mÄĽ", - "Ġzem ÄĽ", - "ĠاÛĮ جاد", - "Ġy ine", - "Ġyi ne", - "ि ण", - "िठ£", - "ĠاÙĦ بÙĦ", - "ĠاÙĦب ÙĦ", - "ĠN ÄĽ", - "Ġpol ož", - "Ġpo lož", - "Ġpolo ž", - "éĺħ 读", - "å¸ ģ", - "å¼ Ł", - "ξ ε", - "Ġ Má»Ļt", - "ĠM á»Ļt", - "ç £", - "Û±Û³ Û¹", - "ĠØ¢ ز", - "ãģ ŀ", - "Ġм еÑħ", - "ย ม", - "Ġ æ¨", - "Ġæ ¨", - "Ġo tur", - "Ġot ur", - "Ġd ầu", - "Ġ ëĭ¤ìļ´", - "Ġëĭ¤ ìļ´", - "çĮ «", - "Ġ Có", - "ĠC ó", - "Ġli dÃŃ", - "Ġlid ÃŃ", - "Ġark adaÅŁ", - "Ġα λλά", - "é¡ »", - "ĠÙĩ ÙħÛĮÙĨ", - "ĠÙĩÙħ ÛĮÙĨ", - "è» ¢", - "Ġ âĹĭ", - "ĠâĹ ĭ", - "ëıĦ ë¡Ŀ", - " ĥ", - "âĢĮØ´ دÙĩ", - "âĢĮشد Ùĩ", - "ĠØŃ ÙĬØ«", - "ĠØŃÙĬ Ø«", - "Ġnh óm", - "Ïĥ Ïĩ", - "ĠÑĤÑĢан Ñģп", - "ĠÑĤÑĢанÑģ п", - "Ġtan ım", - "Ġtanı m", - "ç´ į", - "Ġba his", - "Ġbah is", - "ä¸ ¾", - "Ġин ÑĦоÑĢма", - "ĠинÑĦоÑĢм а", - "ĠÑģ лож", - "ĠÑģл ож", - "ĠÑģло ж", - "Ġk raj", - "Ġkr aj", - "Ġkra j", - "Ġ ØŃÙĦ", - "ĠØŃ ÙĦ", - "Ġ ãĥĸ", - "Ġãĥ ĸ", - "ĠÙĨ ÙĤÙĦ", - "ĠÙĨÙĤ ÙĦ", - "Ġ ÐłÐ¾Ð·", - "ĠÐł оз", - "ĠÎij Ïħ", - "lar dı", - "ĠÙ¾ اس", - "Ġپا س", - "Ġ ìĭĿ", - "Ġìĭ Ŀ", - "ĠìłĦìļ© ë©´ìłģ", - "ĠاÙĦ سÙĬ", - "ĠاÙĦس ÙĬ", - "با شد", - "باش د", - "ศ าสà¸ķร", - "Ġk öy", - "Ġkö y", - "Ġ rok", - "Ġr ok", - "Ġro k", - "Ġ 죽", - "Ġì £½", - "Ġì£ ½", - "ĠÑģ ог", - "ĠÑģо г", - "Ġch ú", - "éĺ ª", - "ĠÄįást i", - "ĠÄįá sti", - "Ġз веÑĢ", - "Ġзв еÑĢ", - "Ġ низ", - "Ġн из", - "Ġни з", - "ĠÃ¶ÄŁ ret", - "Ġ ãĥİ", - "Ġãĥ İ", - "п е", - "çĴ °", - "Ġ èª", - "Ġè ª", - "ÙĪ ÙĦÙĩ", - "ÙĪÙĦ Ùĩ", - "Ä° M", - "/ REC", - "/R EC", - "å¡ ŀ", - "ĠÐĴ и", - "/l oose", - "/lo ose", - "Ġп оÑħ", - "Ġпо Ñħ", - "Ġgen iÅŁ", - "Ġth iá»ĩn", - "Ġthi á»ĩn", - "ti ÄŁi", - "Ñĩ ие", - "Ñĩи е", - "о нд", - "он д", - "Ġп ÑĢиÑģ", - "ĠпÑĢ иÑģ", - "ĠпÑĢи Ñģ", - "áz ky", - "ĠDev let", - "ç¦ ģ", - "Ġ аг", - "Ġа г", - "i lere", - "il ere", - "ile re", - "iler e", - "ин кÑĥ", - "Ġvar dı", - "ãĢĢ ãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢ ĠãĢĢ", - "Ġë ĨĴ", - "ĠëĨ Ĵ", - "à¤Ĥ पन", - "à¤Ĥप न", - "Ġözel lik", - "éļ ľ", - "ìĸ´ ìĦľ", - "ر ÙĬÙĥ", - "رÙĬ Ùĥ", - "ÙĪ بÛĮ", - "ÙĪب ÛĮ", - "ãĥ³ ãĥĢ", - "ãĥ³ãĥ Ģ", - "í Į¨", - "íĮ ¨", - "Ġसम à¤Ŀ", - "ï¾Ĩï¾Ĩ ï¾Ĩï¾Ĩ", - "Ġ ÙģÙĨ", - "ĠÙģ ÙĨ", - "ॠĿ", - "Ġuv eden", - "ÑĪ ими", - "ÑĪи ми", - "ÑĪим и", - "Ġ à¹Ģล", - "Ġà¹Ģ ล", - "Ġà¹Ģภ¥", - "Ġ 문ìĿĺ", - "Ġ문 ìĿĺ", - "ĠØŃ Ø±Ùģ", - "ĠØŃر Ùģ", - "Ġ عب", - "Ġع ب", - "ãĥ¬ ãĥĵ", - "Ġ æŃ£", - "ĠæŃ £", - "ĠëĺIJ ëĬĶ", - "ĠÚ©ÙĨ ÙĨدÙĩ", - "ĠÚ©ÙĨÙĨد Ùĩ", - "Ġα ÏħÏĦÏĮ", - "ĠαÏħ ÏĦÏĮ", - "Ġ 길", - "Ġê¸ ¸", - "Ġif ade", - "Ġi fade", - "Ġifad e", - "Ġyap mak", - "ãĥķ ãĤ©", - "ãĥķãĤ ©", - "Ġm ẹ", - "Ġst rán", - "Ġstr án", - "Ġs vou", - "Ġsv ou", - "Ġsvo u", - "Ġv ždy", - "Ġtek rar", - "ิ à¸į", - "Ġ ìĵ°", - "Ġì ĵ°", - "Ġìĵ °", - "o ÄŁu", - "oÄŁ u", - "Ġ Ú©ÛĮÙĦ", - "ĠÚ© ÛĮÙĦ", - "ĠÚ©ÛĮ ÙĦ", - "и вÑģÑı", - "ив ÑģÑı", - "Ġë§IJ íĸĪëĭ¤", - "ä¸ Ŀ", - "à¤ı स", - "ĠÑģÑĤ ÑĢаÑħ", - "ĠÑģÑĤÑĢ аÑħ", - "ĠÑģÑĤÑĢа Ñħ", - "Ġsou Äįas", - "Ġê·¸ 룰", - "Ġ mÃ¼ÅŁ", - "Ġm Ã¼ÅŁ", - "Ġmü ÅŁ", - "λ οÏį", - "λο Ïį", - "γ Ïī", - "Ġt Æ°á»Łng", - "Ġ å·¥", - "Ġå· ¥", - "Ġ اسÙħ", - "Ġا سÙħ", - "Ġاس Ùħ", - "ÑĢ Ñĸм", - "ÑĢÑĸ м", - "à¹Ģ à¸Ľà¸¥", - "à¹Ģà¸Ľ ล", - "Ġ³³ Ġ³³", - "Ùĩ اÛĮÛĮ", - "ÙĩاÛĮ ÛĮ", - "å¯ º", - "Ġس رÛĮ", - "Ġسر ÛĮ", - "Ġк ваÑĢ", - "Ġкв аÑĢ", - "ĠØ´Ùħ ارÙĩ", - "ĠØ´Ùħا رÙĩ", - "Ġ صØŃ", - "Ġص ØŃ", - "о ÑģÑĤав", - "оÑģÑĤ ав", - "ॠ¨", - "Ġ à¸Ħวาม", - "Ġà¸Ħ วาม", - "í ĥģ", - "íĥ ģ", - "éĢ Ĥ", - "ب ØŃ", - "ĠdeÄŁiÅŁ ik", - "éĮ ²", - "е ди", - "ед и", - "Ġ okol", - "Ġo kol", - "Ġok ol", - "ĠÑģ оп", - "ĠÑģо п", - "Ġol mayan", - "Ġolm ayan", - "Ġolma yan", - "çŃ ij", - "Û± Û´", - "Û±Û ´", - "Ġ inclu", - "Ġinc lu", - "Ġincl u", - "Ġ ê²ĮìŀĦ", - "Ġê²Į ìŀĦ", - "ÛĮ ستÙħ", - "ÛĮست Ùħ", - "ÛĮس تÙħ", - "Ġ ç©", - "Ġç ©", - "ĠاÙĦÙĪÙĦ اÙĬات", - "il mektedir", - "ilm ektedir", - "à Į", - "Ùİ Ø¹", - "ĠaÄŁ ır", - "è¡ Ľ", - "Ġe ski", - "Ġes ki", - "Ġesk i", - "ê° Ŀ", - "본 ëĭ¤", - "人 åijĺ", - "Úĺ ÛĮ", - "Ġ ç¨", - "Ġç ¨", - "Ġм еÑģÑĤо", - "ĠмеÑģÑĤ о", - "v ů", - "à¥įर ह", - "ĠØ· رØŃ", - "Ġطر ØŃ", - "Ġا بÙĨ", - "Ġاب ÙĨ", - "Ġh iss", - "Ġhis s", - "Ġhi ss", - "о ÑĢÑıд", - "оÑĢ Ñıд", - "Ġد Ùģ", - "ÑĢ иÑģÑĤ", - "ÑĢи ÑģÑĤ", - "ÑĢиÑģ ÑĤ", - "à¸Ĭ ม", - "д еÑĤ", - "де ÑĤ", - "à¹Ģ หม", - "à¹Ģห ม", - "ë§Ī ìĤ¬ì§Ģ", - ": .:.:", - ":. :.:", - ":.: .:", - "éħ ¸", - "Ġα ÏģÏĩ", - "ĠαÏģ Ïĩ", - "Ġn ữ", - "ĠпоÑģ ад", - "l um", - "lu m", - "ì º", - "ãģ§ãģį ãĤĭ", - "ìĸ µ", - "ĠاÙĦ Ùħد", - "ĠاÙĦÙħ د", - "н Ñĸм", - "нÑĸ м", - "ر اÙĤ", - "را ÙĤ", - "Ġ ãĥĪ", - "Ġãĥ Ī", - "Ġod povÄĽ", - "Ġodp ovÄĽ", - "Ġbir bir", - "Ġh ãy", - "Ġhã y", - "о вий", - "ов ий", - "ови й", - "æ® ĭ", - "éĥ½ æĺ¯", - "è¿ ª", - "Ġa raç", - "Ġar aç", - "Ġara ç", - "ен ÑĤÑĸв", - "енÑĤ Ñĸв", - "æĬ ±", - "d ál", - "ĠÄIJ ông", - "Ġhe sap", - "Ġhes ap", - "Ġا ÙĨساÙĨ", - "ĠاÙĨ ساÙĨ", - "ĠÙĬ ÙĪÙħ", - "ĠÙĬÙĪ Ùħ", - "ĠÙĨ ÙĪر", - "ĠÙĨÙĪ ر", - "åī ĩ", - "çĹ Ľ", - "Ġ ÙĨÙĬ", - "ĠÙĨ ÙĬ", - "алÑĮ на", - "تب اط", - "ल ब", - "Ġkom un", - "Ġko mun", - "Ġs nad", - "Ġsn ad", - "Ġsna d", - "åĽ £", - "ر ÙĬد", - "رÙĬ د", - "elop ment", - "Ġ иÑİ", - "Ġи Ñİ", - "à¥Ģ .", - "Ġkıs a", - "Ġkı sa", - "ĠdeÄŁil dir", - "ĠdeÄŁildi r", - "à¹ī าร", - "à¹īา ร", - "Ġsv ého", - "Ġsvé ho", - "Ġobl asti", - "Ġoblast i", - "ÑĪ ли", - "à¹Ģà¸Ĺ à¸ŀ", - "ÑĢ еÑĤÑĮ", - "ÑĢе ÑĤÑĮ", - "ÑĢеÑĤ ÑĮ", - "о во", - "ов о", - "Ġ íĤ¤", - "Ġí Ĥ¤", - "ĠíĤ ¤", - "át ky", - "ĠاÙĦ Ùģر", - "ĠاÙĦÙģ ر", - "èĺ Ń", - "ÏĦ ον", - "ÏĦο ν", - "ĠÑģÑĤ оиÑĤ", - "ĠÑģÑĤо иÑĤ", - "Ùħ ØŃ", - "Ġ à¹Ħ", - "Ġà ¹Ħ", - "ĠÑĤе бе", - "ĠÑĤеб е", - "íģ ´", - "Ġm ÄĽla", - "ĠmÄĽ la", - "ĠmÄĽl a", - "æݧ åĪ¶", - "ĠCh ủ", - "ìĬ ¨", - "ÐIJ Т", - "ا جع", - "اج ع", - "ìĻ ķ", - "ç© ¿", - "ол ее", - "ห ลาย", - "หล าย", - "Ġd vou", - "Ġdv ou", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ุ à¸Ĥ", - "Ġb oz", - "Ġbo z", - "ิ à¸Ļà¸Ħ", - "ิà¸Ļ à¸Ħ", - "å¤ Ł", - "Ġfa aliyet", - "ĠÄį ÃŃs", - "ãģ» ãģ©", - "Ġ :/", - "Ġ: /", - "к ÑĸÑģÑĤÑĮ", - "кÑĸ ÑģÑĤÑĮ", - "Ġ ì¤Ģ", - "Ġì¤ Ģ", - "Ïģ αÏĤ", - "Ïģα ÏĤ", - "Ġод но", - "æ ¢ħ", - "æ¢ ħ", - "Ñĥб ли", - "н оз", - "но з", - "à¹Į ม", - "Ġvý rob", - "Ġvýro b", - "Ġ κÏħ", - "Ġκ Ïħ", - "ÅĻ ev", - "ÅĻe v", - "Âł B", - "ů že", - "ůž e", - "ä¼ļ 社", - "ι β", - "ÑĢ ованиÑı", - "ÑĢов аниÑı", - "ÑĢо ваниÑı", - "ÑĢован иÑı", - "ÑĢова ниÑı", - "Ġc ev", - "Ġce v", - "ìĽ Ģ", - "ál nÃŃch", - "áln ÃŃch", - "álnÃŃ ch", - "Ġ ÑĢав", - "ĠÑĢ ав", - "ĠÑĢаР²", - "ĠÑĢа в", - "ç´ §", - "åĢ Ł", - "Ġ ÑŁ", - "ĠÑ Ł", - "ÙĪ ÙĨÙĬ", - "ÙĪÙĨ ÙĬ", - "о зÑı", - "оз Ñı", - "Ġз ов", - "Ġk olem", - "Ġko lem", - "Ġkol em", - "Ġkole m", - "민 êµŃ", - "ç¿ Ĵ", - "Ġzam ÄĽst", - "Ġ ìłij", - "Ġìł ij", - "Ġ زÙĨ", - "Ġز ÙĨ", - "ĠØ£ Ùģ", - "Ġ 먹", - "Ġë¨ ¹", - "Ġtom to", - "Ġ 첨ë¶Ģ", - "Ġì² ¨ë¶Ģ", - "s age", - "sa ge", - "ä¸į è¿ĩ", - "е год", - "ег од", - "его д", - "ÑĢ ож", - "ÑĢо ж", - "ĠпÑĢоÑĨ ед", - "à¹Į à¸Ļ", - "san ız", - "âĢŀ Ø·", - "æ´» åĬ¨", - "о Ñĩки", - "оÑĩ ки", - "ë³´ 기", - "åŁº æľ¬", - "- Ñħ", - "ло ÑģÑı", - "ĠÙĩÛĮ ÚĨ", - "ìĹ Ķ", - "Ñĩ ного", - "Ñĩно го", - "Ġ à¤Ĺर", - "Ġà¤Ĺ र", - "Ġà¤ħ à¤Ĺ", - "ãħĭãħĭ ãħĭãħĭ", - "Ġ ãĤ¸", - "ĠãĤ ¸", - "ا سة", - "اس Ø©", - "åĬ ĩ", - "à¹ī à¸ĩ", - "Ġ 커", - "Ġì» ¤", - "n ými", - "ný mi", - "ným i", - "ãĥ¬ ãĤ¹", - "åĭ Ĵ", - "Ġобла ÑģÑĤÑĸ", - "ĠоблаÑģ ÑĤÑĸ", - "ĠоблаÑģÑĤ Ñĸ", - "ĠдÑĸÑıлÑĮ ноÑģÑĤÑĸ", - "ãĥ¬ ãĤ¤", - "Ïĩ αν", - "Ïĩα ν", - "à¹Ī าส", - "à¹Īา ส", - "ĠФ ÑĢан", - "Ùĩ ÙĦ", - "l ardır", - "lar dır", - "lardı r", - "ØŃ Ø§Øª", - "ů st", - "Ġв одÑĭ", - "Ġво дÑĭ", - "Ġвод Ñĭ", - "ĠدÙĪ ÙĦت", - "ĠدÙĪÙĦ ت", - "ĠÑģпе ÑĨÑĸ", - "Ġth ất", - "à¸Ń าหาร", - "éł ĺ", - "Ġter cih", - "ĠÏĢÏģο Ïĥ", - "Ġ ÅĻÃŃzenÃŃ", - "ĠÅĻÃŃ zenÃŃ", - "è§ī å¾Ĺ", - "Ġd nes", - "Ġdn es", - "Ġdne s", - "е Ñĩно", - "еÑĩ но", - "ãĥ ĺ", - "Ġدار اÛĮ", - "ĠÅŁ art", - "ĠÅŁar t", - "ë² ¤", - "Ġ ë¶ģ", - "Ġë ¶ģ", - "Ġë¶ ģ", - "е Ñı", - "н ÑıÑĤÑĮ", - "нÑı ÑĤÑĮ", - "нÑıÑĤ ÑĮ", - "Ġk vÄĽt", - "Ġkv ÄĽt", - "Ġتغ ÛĮÛĮر", - "é¾ į", - "Ġر ÙĨÚ¯", - "ï¼Į åı¯", - "Ġp iyas", - "Ġpi yas", - "Ġuyg ulan", - "Ġuygu lan", - "Ùİ Ø©", - "ب ÙĬر", - "بÙĬ ر", - "и ваÑĤÑĮ", - "ив аÑĤÑĮ", - "ива ÑĤÑĮ", - "Ġ íĹĪ", - "ĠíĹ Ī", - "ä¸ ¶", - "è¿Ļ äºĽ", - "Ġ گر", - "ĠÚ¯ ر", - "ç½ ª", - "ä¸Ģ æł·", - "Ġ ãĥª", - "Ġãĥ ª", - "Ġв ой", - "Ġво й", - "Ġs osyal", - "Ġsos yal", - "ุ à¸Ĺà¸ĺ", - "ุà¸Ĺ à¸ĺ", - "หม à¸Ķ", - "ç» Ŀ", - "ĠاÙĦ جÙħ", - "ĠاÙĦج Ùħ", - "ĠØ« بت", - "Ġج ÙĨÚ¯", - "ĠجÙĨ Ú¯", - "л ении", - "лен ии", - "ле нии", - "в аÑı", - "ва Ñı", - "Ġв оÑĤ", - "Ġво ÑĤ", - "ä¼ ¤", - "Ġ หล", - "Ġห ล", - "ĠÙħÙĤ اÙĦÙĩ", - "мÑĸ нÑĸ", - "мÑĸн Ñĸ", - "ìĺ ¬", - "Ñĩ ий", - "Ñĩи й", - "ĠÙħ Ú©", - "à¹Ĥ à¸Ľà¸£", - "à¹Ĥà¸Ľ ร", - "k rv", - "kr v", - "Ġ ÃŃch", - "ĠÃŃ ch", - "Ïī Ïĥη", - "ек ÑĤоÑĢ", - "екÑĤ оÑĢ", - "Я к", - "Ġp ÃŃs", - "ĠÃĸ zel", - "ĠÃĸz el", - "Ġt Æ°á»Ľng", - "Ġ ÐĶо", - "ĠÐĶ о", - "δ ιο", - "δι ο", - "ู à¸Ķ", - "Ġt ük", - "رÛĮ ÙĤ", - ". ÐĴ", - "Ġ åIJĪ", - "ĠåIJ Ī", - "ä¿ Ĥ", - "Ġob dob", - "Ġist edi", - "ÑĪ ла", - "æľī ä¸Ģ", - "Ġвк лÑİÑĩа", - "ĠвклÑİÑĩ а", - "ĠتØŃ ÙĤÛĮÙĤ", - "Ġ ÙĪÙĥ", - "ĠÙĪ Ùĥ", - "Ġ èĪ", - "Ġè Ī", - "Æ Ĵ", - "μ εÏģ", - "με Ïģ", - "Ġ åģ", - "Ġå ģ", - "Ġ ìĹĨëĬĶ", - "ĠìĹĨ ëĬĶ", - "Âł d", - "ĠB ắc", - "à¸ģล าà¸ĩ", - "ĠÑĩ Ñĥв", - "Ġc ấu", - "ĠH á»ĵ", - "ĠHá» ĵ", - "ĠÙģ اÛĮÙĦ", - "ÏĦη γοÏģ", - "ç± į", - "Ġ بت", - "Ġب ت", - "ĠобÑĢаз ом", - "æ± ī", - "èĦ ij", - "Ġgi ản", - "Ġgiả n", - "ε Ïģγ", - "εÏģ γ", - "ĠÐľ Ñĸ", - "èĻ½ çĦ¶", - "Ġ Khi", - "ĠK hi", - "ĠKh i", - "Ñĩ ини", - "Ñĩи ни", - "Ñĩин и", - "Ġà¤ħ à¤Ĺर", - "Ġà¤ħà¤Ĺ र", - "íķĺ ë©°", - "ë² Ķ", - "ãģ ģ", - "в иÑħ", - "ви Ñħ", - "ĠвÑģ егда", - "Ġ ç¶", - "Ġç ¶", - "ÑģÑĤв енной", - "ÑģÑĤвен ной", - "ÑģÑĤвенно й", - "Ġyük sel", - "æ¸ ¬", - "Ġsı ras", - "Ġsır as", - "Ġsıra s", - "ĠÏĢ ÏģÏİ", - "èĢ ³", - "ا ÛĮر", - "اÛĮ ر", - "د ÙĪد", - "دÙĪ د", - "ĠAl man", - "ĠAlma n", - "Ġver di", - "Ġverd i", - "ĠاÙĦ Ùħج", - "ĠاÙĦÙħ ج", - "ĠاÙĦ تع", - "ĠاÙĦت ع", - "ص Ø©", - "Ġsı ra", - "Ġsır a", - "Äį in", - "Äįi n", - "Ġп еÑĢÑĪ", - "ĠпеÑĢ ÑĪ", - "æĬ ĺ", - "ç© į", - "ĠÑĤ об", - "ĠÑĤо б", - "Ġ ï¾ī", - "Ġï¾ ī", - "ภ¬", - "æĿ Ģ", - "iy di", - "ี à¸ŀ", - "çĵ ¦", - "ĠавÑĤом об", - "ä¸Ń æĸĩ", - "à¥Ĥ द", - "ĠbÄĽ hem", - "Ġ PÅĻed", - "ĠP ÅĻed", - "ĠPÅĻ ed", - "ãģĵ ãģĨ", - "ั à¸Ī", - "Ġ ï½Į", - "Ġï½ Į", - "Ġ ÙĩاÙĬ", - "ĠÙĩ اÙĬ", - "ĠÙĩا ÙĬ", - "Ġs ạch", - "æĸ¹ éĿ¢", - "çķ °", - "ÑĥÑĢ н", - "Ġvý sled", - "Ġth ần", - "ï¼Į æīĢ以", - "Ñĥ ка", - "Ñĥк а", - "íķĺ ëĭ¤", - "Ġ बर", - "Ġब र", - "Ġж Ñĸн", - "Äį nÃŃho", - "ÄįnÃŃ ho", - "Ġ ãģĮ", - "ab ı", - "v ánÃŃ", - "vá nÃŃ", - "æ´ Ĺ", - "Ġи ÑģÑĤоÑĢ", - "ĠиÑģ ÑĤоÑĢ", - "ĠиÑģÑĤ оÑĢ", - "ìĿ´ íĦ°", - "Ġе лек", - "а лаÑģÑı", - "ала ÑģÑı", - "Ġ znám", - "Ġz nám", - "Ġzn ám", - "ĠØ· رÙģ", - "Ġطر Ùģ", - "Ġs ektör", - "ê¹ Ģ", - "ÙĪ ÙĤع", - "ÙĪÙĤ ع", - "ĠÙħ Ùĥ", - "ÑĢе жд", - "ÑĢеж д", - "Ġk nih", - "Ġkn ih", - "Ġت عداد", - "Ġتع داد", - "Ġتعد اد", - "åį ł", - "ÑģÑĮ ке", - "ÑģÑĮк е", - "Ġ çĶµ", - "京 éĥ½", - "Ġر اÛĮ", - "Ġرا ÛĮ", - "g ın", - "gı n", - "ĠÙĨ ظاÙħ", - "ĠÙĨظ اÙħ", - "ĠÎł ολ", - "ĠÎłÎ¿ λ", - "ä¸Ģ èĪ¬", - "Ġst ále", - "Ġstál e", - "ĠиÑģ Ñģлед", - "Ġz práv", - "Ġzp ráv", - "Ġ ÑĩиÑģÑĤ", - "ĠÑĩ иÑģÑĤ", - "ĠÑĩиÑģ ÑĤ", - "ĠÑĩи ÑģÑĤ", - "ãĥ¼ ãĥŀ", - "ãĥ¼ãĥ ŀ", - "Ðŀ Ñģ", - "ÑģÑĮ комÑĥ", - "ÑģÑĮк омÑĥ", - "ÑģÑĮко мÑĥ", - "ĠpÅĻi prav", - "ĠpÅĻip rav", - "ëĮĢ íĸī", - "Ġh alk", - "Ġha lk", - "Ġhal k", - "çĪ Ĩ", - "ãĢģ ãģĬ", - "ï¼Ł âĢĿĊĊ", - "ï¼ŁâĢĿ ĊĊ", - "éĢ ı", - "ç« ŀ", - "ни ÑĨÑĮ", - "ниÑĨ ÑĮ", - "çĽ ĺ", - "à¹Ģ à¸Ńà¸ĩ", - "à¹Ģà¸Ń à¸ĩ", - "ì Łģ", - "à¥ĩव ल", - "ä¹ĭ åIJİ", - "ãĥ« ãĥĪ", - "Ġ stru", - "Ġs tru", - "Ġst ru", - "Ġstr u", - "Ġ ＿", - "Ġï¼ ¿", - "Îķ ÎĽ", - "h le", - "hl e", - "ĠÙĨ ÙĪØ´", - "ĠÙĨÙĪ Ø´", - "ìĿ µ", - "ĠÙħ Ùģ", - "æĪĸ èĢħ", - "Ġö ld", - "Ġöl d", - "éĢ Ķ", - "ãĥ³ ãĥĹ", - "ãĥ³ãĥ Ĺ", - "íĺ ¼", - "Ġu ÄŁ", - "ĠÄij á", - "Ġvlast nÃŃ", - "ĠÙħج ÙĦس", - "åį Ķ", - "ÏĦ ικήÏĤ", - "ÏĦικ ήÏĤ", - "ÏĦική ÏĤ", - "Ġpo vin", - "Ġpov in", - "ů l", - "ĠاÙĦ ØŃÙĬ", - "ĠاÙĦØŃ ÙĬ", - "Ġsm lou", - "ãĥĥ ãĥģ", - "Ġ ÙĥÙĨ", - "ĠÙĥ ÙĨ", - "Ġch ấp", - "èIJ ¬", - "ج ب", - "? âĢľ", - "д ав", - "да в", - "ร วม", - "รว ม", - "Ùİ Ø¯", - "ĠاÙĦد ÙĪÙĦ", - "ĠëĦ¤ ìĿ´íĬ¸", - "Ġà¤Ĩ स", - "ظ ÙĬÙģ", - "ãĥ¼ ãĥ©", - "ãĥ¼ãĥ ©", - "ãģł ãĤįãģĨ", - "ĠÙĪاØŃ Ø¯", - "ĠÙĪا ØŃد", - "ر ÙĪس", - "رÙĪ س", - "Ġzákon a", - "ĠпеÑĢ еб", - "ĠпеÑĢе б", - "à¥Ģ -", - "à¹Ī à¹Ħà¸Ķ", - "为 äºĨ", - "ÎĻ ÎĿ", - "ĠìĽĶ ìĦ¸", - "ส à¸Ńà¸ĩ", - "Ġ æīĭ", - "Ġæī ĭ", - "Ġ ÐĴÑģе", - "ĠÐĴ Ñģе", - "ĠÐĴÑģ е", - "à¹Ĥ ย", - "Ġkal dır", - "Ġkaldı r", - "ÏĦ ÎŃÏĤ", - "ÏĦÎŃ ÏĤ", - "Ġ ï¿£", - "Ġ íĸĪëĭ¤", - "Ġíĸ Īëĭ¤", - "ĠíĸĪ ëĭ¤", - "ãĤģ ãģŁ", - "Ġ Äįer", - "ĠÄį er", - "ĠÄįe r", - "c ela", - "ce la", - "cel a", - "üs ü", - "ê³ ³", - "ìĹIJ ëıĦ", - "ز Ø©", - "ãģª ãĤĭ", - "ÙĪ ÛĮÙĨ", - "ÙĪÛĮ ÙĨ", - "çī Ľ", - "Ġ voj", - "Ġv oj", - "Ġvo j", - "Ġ ëĬIJ", - "ĠëĬ IJ", - "Ġ ÙĥÙħ", - "ĠÙĥ Ùħ", - "æ³ ī", - "з Ñı", - "è£ Ŀ", - "ĠØ¢ ÙĦ", - "Ġ ανά", - "Ġα νά", - "Ġαν ά", - "Âł ÐĴ", - "Ġyap ıl", - "Ġyapı l", - "æı Ľ", - "ĠÑģ ÑĥÑīеÑģÑĤв", - "ĠÑģÑĥ ÑīеÑģÑĤв", - "ĠÑģÑĥÑīе ÑģÑĤв", - "Ġn á»iji", - "ÙĪ ئ", - "ĠëĦ¤ìĿ´íĬ¸ ìĺ¨", - "Ġpolit ik", - "Å¡ ka", - "Å¡k a", - "ebilir siniz", - "ld kf", - "Ñĥб лÑĸ", - "Ġe oq", - "Ġeo q", - "ĠÙħØŃ ØµÙĪÙĦ", - "krv ldkf", - "Ġeoq krvldkf", - "Ïĥε Ïīν", - "بÙĦ غ", - "Įĵ ê¸Ģ", - "ĠÑģ ÑĢок", - "ĠU y", - "ĠN ÄĽk", - "ĠNÄĽ k", - "Ġ див", - "Ġд ив", - "Ġди в", - "ãĤµ ãĤ¤", - "Ġ ìĤ¬ìĿ´", - "ĠìĤ¬ ìĿ´", - "Ġ éĹ", - "Ġé Ĺ", - "Ġб аÑĤÑĮ", - "Ġба ÑĤÑĮ", - "Ġп еÑĢÑĸ", - "ĠпеÑĢ Ñĸ", - " ĸ", - "交 éĢļ", - "ен з", - "ÙĪ ست", - "ÙĪس ت", - "ีย à¸ļ", - "Ġ à¸Īะ", - "Ġà¸Ī ะ", - "ë¡ Ģ", - "üf us", - "Ùij ÙIJ", - "ç¸ ½", - "ัà¸Ķ ส", - "ê² Ģ", - "ĠÑĤ иÑħ", - "ĠÑĤи Ñħ", - "ĠØ¢ زÙħ", - "Ġآز Ùħ", - "Ġ اض", - "Ġا ض", - "ĠØ§Ø ¶", - "ì ¡´", - "ì¡ ´", - "ÙĴ ت", - "æĪ ¸", - "ĠìŀĪ ìĿĦ", - "Ġ çĶ·", - "Ñī Ñĸ", - "о ма", - "ом а", - "ĠاÙģز اÛĮØ´", - "Ġ Thông", - "ĠTh ông", - "ĠاجتÙħاع ÛĮ", - "е лÑİ", - "ел Ñİ", - "ĠÑħоÑĢоÑĪ о", - "à¸ł าษ", - "Ġ rám", - "Ġr ám", - "Ġrá m", - "å¾ ¡", - "ãĥ¼ ãĥĦ", - "ãĥ¼ãĥ Ħ", - "ĠL Ỽp", - "Ġ Ø´ÙĬ", - "ĠØ´ ÙĬ", - "Ġh iá»ĥm", - "Ġhi á»ĥm", - "θ ν", - "ο ÏħÏĥ", - "οÏħ Ïĥ", - "å¾ ©", - "Ġú zem", - "à¹ģ à¸ľ", - "å ·¨", - "å· ¨", - "à¸Ī à¸Ļ", - "Ú¯ راÙĨ", - "گر اÙĨ", - "Ġت ÛĮÙħ", - "ĠتÛĮ Ùħ", - "Ġ ilet", - "Ġi let", - "Ġil et", - "Ġile t", - "า à¸Ĥà¸Ńà¸ĩ", - "าà¸Ĥ à¸Ńà¸ĩ", - "Ġ تÙĪر", - "Ġت ÙĪر", - "ĠتÙĪ ر", - "Ġдо говоÑĢ", - "Ġдог овоÑĢ", - "Ġдогов оÑĢ", - "Ġt ento", - "Ġten to", - "Ġtent o", - "в Ñĥ", - "Ġз ада", - "Ġза да", - "Ġзад а", - "Ġstole tÃŃ", - "Ġstol etÃŃ", - "Âł Ġ", - "âĢĮ اÙĦ", - "Ë ĺ", - "ÅŁ iv", - "ÅŁi v", - "н ÑıÑĤи", - "нÑı ÑĤи", - "нÑıÑĤ и", - "ãĤī ãĤĮãģŁ", - "ãĤīãĤĮ ãģŁ", - "ĠS b", - "ĠاÙĦÙħ ص", - "ĠУкÑĢаÑĹ Ð½Ñĸ", - "ĠУкÑĢаÑĹн Ñĸ", - "ĠØ´ Ú©", - "iế ng", - "iến g", - "ÑĮ ÑĤе", - "è° ¢", - "ĠÙħ تÙĨ", - "ĠÙħت ÙĨ", - "Ġ ÑĢад", - "ĠÑĢ ад", - "ĠÑĢаР´", - "ĠÑĢа д", - "ĠÙħÙĪ اد", - "ì± Ħ", - "é¡ ¶", - "Ġbo ÅŁ", - "ت ÙĪر", - "تÙĪ ر", - "ĠÄij áng", - "ĠÄijá ng", - "Ġkit ap", - "Ġki tap", - "Ġkita p", - "Ġho din", - "Ġhod in", - "Ġtarih i", - "ãĤĦ ãĤĭ", - "Ñģ ÑĤеÑĢ", - "ÑģÑĤ еÑĢ", - "ÑģÑĤе ÑĢ", - "Ġ Ñħод", - "ĠÑħ од", - "в ание", - "ва ние", - "ван ие", - "ĠоÑģ вÑĸ", - "ĠÑģиÑģÑĤем Ñĭ", - "़ न", - "Ïĩ ο", - "Ġ åı°", - "Ġåı °", - "o ÅĻ", - "ç»ı æµİ", - "Ġ ä½ľ", - "Ġthu áºŃn", - "Ľ Ī", - "Ġy alnız", - "a let", - "al et", - "ale t", - "ì¦Ŀ ê¸Ī", - "Ġза Ñī", - "Ġе кÑģп", - "Ġек Ñģп", - "âĦĸ âĦĸ", - "Ġ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠÚ¯ ÙĪØ´", - "ĠÚ¯ÙĪ Ø´", - "ãģ« åħ¥", - "Ġu dÄĽl", - "Ġud ÄĽl", - "Ġ áº", - "Ġá º", - "à¤Ĩ à¤Ī", - "âĢĮ دÙĩ", - "âĢĮد Ùĩ", - "æĤ ª", - "Ġtr ò", - "æļ Ĺ", - "λλ ην", - "λλη ν", - "ĠпÑĢи зна", - "ĠпÑĢиз на", - "Ġس ÛĮستÙħ", - "ĠسÛĮ ستÙħ", - "Ġà¤ħ त", - "è o", - "è¿ İ", - "Ġз Ñĥб", - "ĠзаÑģ об", - "Ġس Ùģ", - "ĠÙħاÙĨ ÙĨد", - "Ø® Ø´", - "v ajÃŃ", - "va jÃŃ", - "nit ÅĻ", - "æ¯ Ĵ", - "æ¤ į", - "Ġgir iÅŁ", - "ĠÄij áp", - "ĠÄijá p", - "@ n", - "ов аÑĢи", - "оваÑĢ и", - "ова ÑĢи", - "ĠØ® دا", - "Ġخد ا", - "Ġv ÄĽtÅ¡", - "ĠvÄĽt Å¡", - "ĠΣ Ïħ", - "Ùģ Ø©", - "аннÑı м", - "ĠÑĩ лен", - "æĶ¯ æĮģ", - "å¨ ľ", - "lar arası", - "lara rası", - "Ρ Îij", - "Ġz iy", - "Ġzi y", - "Ġ êµIJìľ¡", - "ĠêµIJ ìľ¡", - "Ġh á»ĵi", - "Ġhá»ĵ i", - "าà¸Ħ าร", - "าà¸Ħา ร", - "im leri", - "imler i", - "è³ ¼", - "ĠجÙĩ اÙĨ", - "ĠÑĢоз мÑĸ", - "Ñħ Ñĸв", - "γ ε", - "æ¨ ª", - "ÎĻ ÎijΣ", - "ÎĻÎij Σ", - "ç¶ Ń", - "Ġbi raz", - "Ġbir az", - "ĠÑĤак ого", - "ĠÑĤа кого", - "íĥ Ħ", - "ĠбÑĥд ÑĥÑĤ", - "ĠбÑĥ дÑĥÑĤ", - "ĠбÑĥдÑĥ ÑĤ", - "ĠÑĪ вид", - "Ġ неÑģ", - "Ġн еÑģ", - "Ġне Ñģ", - "ĠÙħ عÙĦÙĪÙħات", - "ĠÙħعÙĦ ÙĪÙħات", - "à¥ĩ यर", - "à¥ĩय र", - "Ġдв ÑĥÑħ", - "å¿ħ è¦ģ", - "å§ Ĩ", - "Ġpo hled", - "Ġpoh led", - "ìĬ¤ íĦ°", - "Ġ åįģ", - "Ġåį ģ", - "ĠØ£ ب", - "веÑĢ дж", - "веÑĢд ж", - "Ġà¤ľ म", - "ल त", - "åľ° åĮº", - "Ġ |[", - "Ġ| [", - "Ġв меÑģÑĤ", - "ĠÚ© اÙħ", - "Ġ ãĥIJ", - "Ġãĥ IJ", - "ãĥ¼ ãĥĸ", - "ãĥ¼ãĥ ĸ", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Ġ ìĥģíĴĪ", - "Ġìĥģ íĴĪ", - "à¹Ģล ย", - "Äį né", - "ĠÑģÑĢед ÑģÑĤва", - "ĠÑģÑĢедÑģÑĤв а", - "Ġ ÑĤаб", - "ĠÑĤ аб", - "ĠÑĤа б", - "Ġ Ùħار", - "ĠÙħ ار", - "ĠÙħا ر", - "Ġ hled", - "Ġh led", - "Ġhl ed", - "д аÑĤ", - "да ÑĤ", - "ÙĪ ÛĮد", - "ÙĪÛĮ د", - "Ġ ãĥ©", - "Ġãĥ ©", - "ĠØ® د", - "è¤ ĩ", - "ç§ ĺ", - "Ġ برد", - "Ġب رد", - "Ġبر د", - "ĠÏĥ αÏĤ", - "Ïİ ÏĥειÏĤ", - "æĿ ¯", - "λ Ïį", - "å® ¿", - "Ġ ëĤľ", - "ĠëĤ ľ", - "ï» Ł", - "Ġözel likle", - "Ġözellik le", - "Ġкон Ñģ", - "ĠÙħ غ", - "ع ÙĬ", - "à¹Į à¸ģ", - "Ġ ÙĬت", - "ĠÙĬ ت", - "ĠÙħ شاÙĩ", - "ĠÙħØ´ اÙĩ", - "ĠTh anh", - "ĠThan h", - "ा à¤ľà¤¨", - "à¤¾à¤ľ न", - "¥ ¤", - "Ġv lá", - "Ġvl á", - "ĠÙģ ض", - "Τ ÎĻÎļ", - "Ġна Ñĥков", - "ĠнаÑĥк ов", - "е лем", - "ел ем", - "еле м", - "Ġd Ãłng", - "ĠгоÑģп одаÑĢ", - "Âł S", - "и ÑĩеÑģкиÑħ", - "иÑĩеÑģ киÑħ", - "иÑĩеÑģки Ñħ", - "ĠتÙĨ Ùĩا", - "à¤ľ न", - "Ġп ан", - "Ġпа н", - "åĨ ł", - "Ġ ëĤĺëĬĶ", - "ĠëĤĺ ëĬĶ", - "t ÃŃ", - "ä¸Ģ èµ·", - "Ġlã nh", - "Âł v", - "ov ým", - "ový m", - "ز ب", - "ĠجÙħع ÛĮت", - "Ġ æµ·", - "Ġæµ ·", - "ĠоÑģ ÑĥÑīеÑģÑĤв", - "à £i", - "ã i", - "ائ ر", - "Ġ ë³ij", - "Ġë³ ij", - "á»ĭ nh", - "Ġs á»Ńa", - "Ġsá»Ń a", - "à¥ĩà¤Ĥ ।", - "ÄĽ jÅ¡ÃŃ", - "ÄĽj Å¡ÃŃ", - "Ġд ÑĸÑĤ", - "ĠдÑĸ ÑĤ", - "Ġ æĥ", - "Ġæ ĥ", - "mÄ±ÅŁ tı", - "ر ØŃ", - "Ġì§Ģ ê¸Ī", - "å¦ »", - "âĹ ĭ", - "Ġ ì§ĢìĹŃ", - "Ġì§Ģ ìĹŃ", - "ÙĴ ÙĨ", - "Ġur Äįit", - "ĠurÄį it", - "ÙĴ Ùħ", - "z ÃŃ", - "è ķ", - "Ġ Ø´ÙĪر", - "ĠØ´ ÙĪر", - "ĠØ´ÙĪ ر", - "Ġ Không", - "ĠKh ông", - "ÛĮ زÛĮ", - "ÛĮز ÛĮ", - "Ġз г", - "Ġв не", - "Ġpr ávÄĽ", - "Ġprá vÄĽ", - "Ġpráv ÄĽ", - "è« ĭ", - "ا ÙĬت", - "اÙĬ ت", - "ั à¸ģร", - "ัà¸ģ ร", - "Ġolduk ça", - "ãĤģ ãĤĭ", - "ĠT ây", - "ëĿ¼ ìĿ¸", - "èĻ ķ", - "Ġs Æ°", - "Ġ ник", - "Ġн ик", - "Ġни к", - "Ù ł", - "اش ÛĮÙĨ", - "اشÛĮ ÙĨ", - "e lerde", - "el erde", - "eler de", - "ìĭľ ìķĦ", - "Ġ Ñĥмов", - "ĠÑĥ мов", - "ĠÑĥм ов", - "ĠçalÄ±ÅŁ an", - "Ġ ë¸Ķ", - "Ġë¸ Ķ", - "ĠÑĤак им", - "ĠÑĤа ким", - "ÑĢ ин", - "ÑĢи н", - "Ġ Ø®ÙĦ", - "ĠØ® ÙĦ", - "a yd", - "ay d", - "Ġ ãĥ¡", - "Ġãĥ ¡", - "ей ÑĩаÑģ", - "Ġdo prav", - "Ġdop rav", - "ãģĵ ãģ¨ãģ¯", - "ãģĵãģ¨ ãģ¯", - "Ġ ì¶Ķì²ľ", - "Ġì¶Ķ ì²ľ", - "å» ¶", - "Ġ kı", - "Ġk ı", - "åı ¶", - "ÑĢ иг", - "ÑĢи г", - "íħ ľ", - "çĶ³ åįļ", - "Ġ веÑĤ", - "Ġв еÑĤ", - "Ġве ÑĤ", - "ĠпомоÑī ÑĮÑİ", - "ĠاÙģ راد", - "ĠاÙģر اد", - "ÏĢ ει", - "ÏĢε ι", - "à¹Ģ สร", - "à¹Ģส ร", - "Ġgi ám", - "Ġgiá m", - "é İ", - "h las", - "hl as", - "man ız", - "manı z", - "ан гл", - "анг л", - "Ġmu ž", - "Âł K", - "ÑĢед иÑĤ", - "ÑĢеди ÑĤ", - "设 å¤ĩ", - "ι Ïĥμ", - "ιÏĥ μ", - "Ġc ải", - "Ġcả i", - "Ġ éĢļ", - "ĠéĢ ļ", - "Ġ Ùĥار", - "ĠÙĥ ار", - "Ġпод об", - "ĠмеÑĤ ал", - "ĠÑģам е", - "л ÑĥÑĩ", - "лÑĥ Ñĩ", - "åĤ ³", - "ĠÙĪÙĩ ÙĪ", - "Ġ éĩį", - "Ġéĩ į", - "в ий", - "ви й", - "æ³ ģ", - "Ġ æĿİ", - "ĠæĿ İ", - "ĠiliÅŁ kin", - "ĠiliÅŁk in", - "Ġεί Ïĩε", - "çĬ ¯", - "ÅĻej mÄĽ", - "èŃ ĺ", - "ç¨ ±", - "μ μα", - "μμ α", - "Ġ ÙĦÛĮ", - "ĠÙĦ ÛĮ", - "Ùĩ اÙĬ", - "Ùĩا ÙĬ", - "Ġ опиÑģ", - "Ġо пиÑģ", - "Ġоп иÑģ", - "Ú¯ رد", - "گر د", - "Ġг ÑĢ", - "ĠAnimal ia", - "ĠAnim alia", - "ÐŁ о", - "Ġb óng", - "ĠдеÑĤ ей", - "Ġl âu", - "Ġ æķĻ", - "Ġæķ Ļ", - "Ġпо ÑıÑģ", - "ĠاÙĦ Ø¢", - "ั à¸Ļà¸ķ", - "ัà¸Ļ à¸ķ", - "Ġд ев", - "Ġде в", - "Ġ ÑĨей", - "ĠÑĨ ей", - "ĠÑĨе й", - "ÑĮ в", - "æĥ ł", - "m aları", - "ma ları", - "mal arı", - "malar ı", - "im ler", - "à¥Ī ।ĊĊ", - "à¥Ī। ĊĊ", - "à¥Ī।Ċ Ċ", - "Ġ ном", - "Ġн ом", - "Ġно м", - "z v", - "Ġ à¸ģร", - "Ġà¸ģ ร", - "Ġpay laÅŁ", - "Âł s", - "ि सम", - "िस म", - "ÑģÑĤв еннÑĭÑħ", - "ÑģÑĤвен нÑĭÑħ", - "st oup", - "sto up", - "о нÑĸ", - "он Ñĸ", - "s tÃŃ", - "st ÃŃ", - "ĠØŃ Ú©", - "ĠÚ¯ رÙģت", - "ĠگرÙģ ت", - "Ġگر Ùģت", - "าà¸Ħ า", - "д Ñı", - "ÙĦ اث", - "ÙĦا Ø«", - "Ġzdrav ot", - "ä¸Ĭ ãģĴ", - "ãģ ¼", - "e lere", - "el ere", - "eler e", - "ele re", - "ظ Ùħ", - "ĠÑģ веÑĤ", - "ĠÑģв еÑĤ", - "о ÑĢг", - "оÑĢ г", - "ç« ¥", - "ĠпеÑĢ еп", - "ĠпеÑĢе п", - "Ġम द", - "а за", - "аз а", - "å¦Ĥ ä½ķ", - "ÑģÑĮ кÑĸй", - "ÑģÑĮк Ñĸй", - "ÑģÑĮкÑĸ й", - "Ġb Æ°á»Ľc", - "Ġger ekli", - "Ġgerek li", - "大 家", - "Ġtr ái", - "éģ ©", - "ä¸Ń 央", - "Ġph ản", - "Ġع رض", - "ĠÙĥ تاب", - "æĭ ©", - "ÑĪ его", - "ÑĪе го", - "å¸ ®", - "ĠÙĨÛĮ از", - "è¿ ·", - "ุ à¸Ľ", - "ิ à¸Ľ", - "Ġد Ø®", - "ÏĦ ικÎŃÏĤ", - "ÏĦικ ÎŃÏĤ", - "ĠU z", - "Ġت ÙĪÙħاÙĨ", - "ĠتÙĪ ÙħاÙĨ", - "ĠتÙĪÙħ اÙĨ", - "ĠÙĪ اÙĦØ£", - "ĠÙĪاÙĦ Ø£", - "ÅĻ es", - "ÅĻe s", - "Ñij м", - "Ġ å¸Ĥ", - "Ġå¸ Ĥ", - "ĠÑĤ оже", - "ĠÑĤо же", - "Ġy apan", - "Ġya pan", - "Ġyap an", - "å½¼ 女", - "ĠÙħ در", - "ĠÙħد ر", - "¶ ģ", - "Ġ æŶ", - "ĠæĹ ¶", - "à¹Ģ à¸ĺ", - "à¹Ģภĺ", - "Ġ ÙħاÙĦ", - "ĠÙħ اÙĦ", - "ĠÙħا ÙĦ", - "ĠB üyük", - "ĠBü yük", - "Ġ ÙĦت", - "ĠÙĦ ت", - "å° ļ", - "d eme", - "de me", - "dem e", - "ü b", - "ĠÑħ Ñĥд", - "Ġlé ka", - "çĽ Ľ", - "缴 æİ¥", - "ниÑĨÑĤ ва", - "ĠпÑĢи Ñĩин", - "ĠпÑĢиÑĩ ин", - "е ÑĢап", - "еÑĢ ап", - "еÑĢа п", - "ĠÑģозд а", - "ĠÑģоз да", - "æ ¢°", - "æ¢ °", - "Ġm üz", - "Ġmü z", - "ç³» åĪĹ", - "o uz", - "ou z", - "Ġà¤ĵ र", - "ÑĢ ÑĥÑĩ", - "ÑĢÑĥ Ñĩ", - "Ġ á½", - "Ġá ½", - "μÎŃ Î½Î±", - "μÎŃν α", - "ĠпÑĢед меÑĤ", - "Ġ å²", - "Ġå ²", - "ãĥ³ ãĥģ", - "ãĥ³ãĥ ģ", - "μÎŃ Î½Î·", - "μÎŃν η", - "л Ñĥг", - "лÑĥ г", - "Âł n", - "ĠT arih", - "ĠTar ih", - "Ġ ãĢĪ", - "ĠãĢ Ī", - "Ġb ana", - "Ġban a", - "Ġba na", - "Ġ cÃŃ", - "Ġc ÃŃ", - "Ġvý kon", - "åĽł æѤ", - "Ġt ÅĻi", - "ĠtÅĻ i", - "า à¸ĭ", - "าภĭ", - "v ailable", - "vail able", - "Ġ istem", - "Ġis tem", - "Ġi stem", - "Ġist em", - "ãĥ¥ ãĥ¼", - "Ðķ ÐĿ", - "Ġ гаÑĢ", - "Ġг аÑĢ", - "οÏħ λ", - "ॠĽ", - "ĠÙĪ ضع", - "ส ะ", - "è· Ŀ", - "ĠØŃ Ùģ", - "ิà¸Ĺย าล", - "ิà¸Ĺยา ล", - "她 çļĦ", - "н ÑĸÑĪ", - "нÑĸ ÑĪ", - "ж ение", - "же ние", - "жен ие", - "기 ìĹIJ", - "Ġ éĺ¿", - "Ġéĺ ¿", - "ĠÙħ ارس", - "ĠÙħا رس", - "ĠÙħار س", - "ĠçeÅŁit li", - "Ġ ÅŁehir", - "ĠÅŁ ehir", - "ĠÅŁeh ir", - "á tor", - "át or", - "à¹ī à¸Ĺ", - "ìĿ´ ëĬĶ", - "Ġ è²", - "Ġè ²", - "é¡ į", - "ç ĻĤ", - "çĻ Ĥ", - "Ġ ниÑĩ", - "Ġн иÑĩ", - "Ġни Ñĩ", - "Ġ ê°Ģì§Ģ", - "Ġê°Ģ ì§Ģ", - "ä¼ ¦", - "r án", - "rá n", - "o stat", - "os tat", - "ost at", - "osta t", - "Ġ ÙĦÙĥ", - "ĠÙĦ Ùĥ", - "è º", - "ĠNg Ãłnh", - "Ġस द", - "æľ Ĺ", - "çĦ¶ åIJİ", - "ãĤ¸ ãĤ§", - "л еÑĢ", - "ле ÑĢ", - "ĠÐŀ на", - "ĠÐŀн а", - "س ÙĪÙĨ", - "سÙĪ ÙĨ", - "Ïģ ον", - "Ïģο ν", - "ĠدرÛĮ اÙģت", - "ĠدرÛĮا Ùģت", - "à¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ", - "Ġ dál", - "Ġd ál", - "Ġdá l", - "ĠмÑĸÑģ ÑĨе", - "Ġд ней", - "Ġ اÙĦات", - "Ġا ÙĦات", - "ĠاÙĦ ات", - "Ġरह त", - "ï¼Į 对", - "è³ĩ æĸĻ", - "ä»» ä½ķ", - "é Ħ", - "t aj", - "ta j", - "β ά", - "Ġна до", - "Ġнад о", - "ĠÑģÑĤ Ñĥд", - "ĠÅŁ eh", - "ัà¸į à¸į", - "à¥ĭ ब", - "ãĥ© ãĥ¼", - "Û± Ûµ", - "Û±Û µ", - "e pt", - "ep t", - "Ġbil dir", - "Ġbild ir", - "ส à¸ĸาà¸Ļ", - "สà¸ĸ าà¸Ļ", - "е ÑĤÑĮÑģÑı", - "еÑĤÑĮ ÑģÑı", - "sk ým", - "ský m", - "Ġобла ÑģÑĤÑĮ", - "ĠоблаÑģ ÑĤÑĮ", - "ĠоблаÑģÑĤ ÑĮ", - "Ġìŀ ł", - "ĠG ör", - "ĠGö r", - "Ġd ayan", - "Ġday an", - "Ġda yan", - "ĠÛĮ اد", - "ĠÛĮا د", - "çĶŁ 产", - "íĺ ij", - "å¾ ģ", - "Ġ اجر", - "Ġا جر", - "Ġاج ر", - "Ġп ÑĢе", - "ĠпÑĢ е", - "ä¸īä¸ī ä¸īä¸ī", - "åŁİ å¸Ĥ", - "Ġ пÑĢимеÑĢ", - "ĠпÑĢ имеÑĢ", - "ĠпÑĢи меÑĢ", - "ĠпÑĢим еÑĢ", - "Äį ást", - "èģ ĺ", - "ĠÙħرب ÙĪØ·", - "æŀ ļ", - "åĪ Ģ", - "æŁ¥ çľĭ", - "Ġ모 ëijIJ", - "ìŀIJ ë£Į", - "- ､", - "Ġê°Ļ ìĿ´", - "Ġ ì¡´", - "Ġì ¡´", - "Ġì¡ ´", - "е гоÑĢ", - "ег оÑĢ", - "его ÑĢ", - "e dik", - "ed ik", - "edi k", - "и мÑĥ", - "им Ñĥ", - "ĠAr th", - "ĠArt h", - "åºĶ çĶ¨", - "m iÅŁti", - "miÅŁ ti", - "Ġkhá»ı e", - "Ġ Ñĸд", - "ĠÑĸ д", - "λ λη", - "λλ η", - "â h", - "м аг", - "ма г", - "éļ Ĩ", - "ĠвнÑĥ ÑĤÑĢ", - "ĠвнÑĥÑĤ ÑĢ", - "Ġ بط", - "Ġب Ø·", - "( æĹ¥", - "Ä° Y", - "л ик", - "ли к", - "ĠB ản", - "Ġت ÙĪس", - "ĠتÙĪ س", - "़ त", - "a mak", - "am ak", - "ama k", - "åķı é¡Į", - "ĠÑģам оÑģÑĤ", - "ĠÑģамо ÑģÑĤ", - "ï¼¼ Ċ", - "Ġ ç¦ı", - "Ġç¦ ı", - "Ù ¡", - "Ġ ÑĦоÑĢми", - "ĠÑĦоÑĢм и", - "ĠÑĦоÑĢ ми", - "Ġ ÑĢозÑĥм", - "ĠÑĢоз Ñĥм", - "ĠÙħ طاÙĦ", - "ĠÙħØ· اÙĦ", - "ä¹Ł æĺ¯", - "ç¾İ åĽ½", - "ëĵľ 립ëĭĪëĭ¤", - "Ġl Ä©nh", - "ĠпоÑĤ омÑĥ", - "ĠпоÑĤом Ñĥ", - "Ñı бÑĢÑı", - "Ñıб ÑĢÑı", - "æ¼ «", - "Ġng oại", - "à¸Ń ำ", - "ÙĬ ÙĨا", - "ÙĬÙĨ ا", - "Ġm lad", - "Ġml ad", - "Ïĥ ÏĦά", - "ÏĥÏĦ ά", - "ا تر", - "ات ر", - "주 ìĿĺ", - "ен нÑĸ", - "о за", - "оз а", - "ÙĤ ات", - "ĠÐĴ аÑģ", - "è® Ń", - "é IJ", - "Ñĥ ÑİÑĩи", - "ÑĥÑİ Ñĩи", - "Ġ کر", - "ĠÚ© ر", - "Ġ .|", - "Ġ. |", - "Ġgen ç", - "è© ²", - "ä» ģ", - "о дÑĭ", - "од Ñĭ", - "ĠØ£ ÙĪÙĦ", - "ĠØ£ÙĪ ÙĦ", - "Ġ ìĤ¬íļĮ", - "ĠìĤ¬ íļĮ", - "Ġ à¹Ģส", - "Ġà¹Ģ ส", - "Ġà¹Ģภª", - "ĠëķĮ문 ìĹIJ", - "âĢĮ ب", - "Ġли ÑĪÑĮ", - "ĠлиÑĪ ÑĮ", - "Ġи менно", - "Ġим енно", - "m adı", - "ma dı", - "mad ı", - "Ġ éĤ", - "Ġé Ĥ", - "ĠÙĪ ارد", - "ĠÙĪا رد", - "Ġtak ım", - "Ġ à¹Ģห", - "Ġà¹Ģ ห", - "Ġà¹Ģภ«", - "Ġ à¸Ńย", - "Ġà¸Ń ย", - "Ġkon usu", - "Ġkonu su", - "Ġkonus u", - "Ø® ÙĪ", - "ĠÑģ ид", - "ĠÑģи д", - "èµ ¤", - "о ÑıÑĤелÑĮ", - "оÑıÑĤ елÑĮ", - "ëĭ µ", - "ε Ïī", - "Ñĸ Ñħ", - "Ġय द", - "ĠÚ© ÛĮÙģ", - "ĠÚ©ÛĮ Ùģ", - "μ οÏĤ", - "μο ÏĤ", - "Ġal dı", - "Ġald ı", - "Ġ íĻį", - "ĠíĻ į", - "к Ñĥп", - "кÑĥ п", - "ĠÙĨÙħ اÛĮØ´", - "ĠÙĨÙħاÛĮ Ø´", - "ãģ ¥", - "Ġ íķ©ëĭĪëĭ¤", - "Ġíķ ©ëĭĪëĭ¤", - "Ġíķ© ëĭĪëĭ¤", - "Ġë Įĵê¸Ģ", - "б оÑĢа", - "бо ÑĢа", - "боÑĢ а", - "éī Ħ", - "Ġ à¹Ģà¸Ī", - "Ġà¹Ģ à¸Ī", - "Ġà¹ĢภĪ", - "à¹ī à¸ģ", - "§ Ø·", - "ر بÙĩ", - "رب Ùĩ", - "Ġ Ñĥз", - "ĠÑĥ з", - "Ġм аÑİÑĤÑĮ", - "Ġма ÑİÑĤÑĮ", - "Ġby li", - "Ġbyl i", - "ี à¸ķ", - "Ġ ì§ĢìĽIJ", - "Ġì§Ģ ìĽIJ", - "èĩª çĦ¶", - "ù y", - "Ġç aÄŁ", - "Ġça ÄŁ", - "е дин", - "ед ин", - "еди н", - "ë ī´", - "åį ±", - "Ġпоз волÑı", - "Ġпозвол Ñı", - "ØŃ Ø§Ø¯", - "ĠÑĩ его", - "ีย ร", - "Ġyön tem", - "Ġ ders", - "Ġd ers", - "Ġde rs", - "Ġder s", - "Ġ ÑģÑĤоÑı", - "ĠÑģÑĤ оÑı", - "ĠÑģÑĤо Ñı", - "Ġк ÑĢÑĥп", - "Ġ ð", - "Ġдом аÑĪ", - "Ġдома ÑĪ", - "е нд", - "ен д", - "ç» §", - "ĠÄij ô", - "Ġch tÄĽ", - "计 åĪĴ", - "ÎŃ Î±", - "Ġdob ÅĻe", - "ส à¸Ńà¸ļ", - "е ление", - "ел ение", - "еле ние", - "елен ие", - "ĠÄij ông", - "ĠÄijô ng", - "ãģ¾ ãĤĬ", - "Ġboy unca", - "à¥ģ à¤Ĺ", - "à¥ģठĹ", - "ĠÑĦ из", - "ãĤ³ ãĥ³", - "Ġde ney", - "Ġden ey", - "ÑĩеÑģ киÑħ", - "Ñĩе ÑģкиÑħ", - "ÑĩеÑģки Ñħ", - "λ ον", - "λο ν", - "以 åıĬ", - "ا ÙĪت", - "اÙĪ ت", - "Âł ³³³³", - "³³ ³³³", - "³³³³ Âł", - "³³³ ³³", - "Ġ ì¤Ħ", - "Ġì¤ Ħ", - "ि फ", - "िठ«", - "ĠÑĤ ол", - "ĠÑĤо л", - "ĠëĤ´ ê°Ģ", - "âĸ ı", - "Ġp há", - "Ġph á", - "ĠÑģп Ñĸв", - "Ġ جÙħÙĬع", - "ĠجÙħ ÙĬع", - "Ġb ezpeÄį", - "Ġbez peÄį", - "Ġ æĹł", - "ĠæĹ ł", - "Ġv Å¡e", - "ĠvÅ¡ e", - "ÑģÑĤ вÑĥ", - "ÑģÑĤв Ñĥ", - "d ust", - "du st", - "o Å¡", - "Ġت ارÙĬØ®", - "ا ØŃØ©", - "اØŃ Ø©", - "ĠÙħشار ÙĥØ©", - "Ġ ακ", - "Ġα κ", - "ั à¸Ļà¸Ļ", - "ัà¸Ļ à¸Ļ", - "éģ Ĭ", - "Ġ ÑģоÑĤ", - "ĠÑģ оÑĤ", - "ĠÑģо ÑĤ", - "Ġ каз", - "Ġк аз", - "Ġка з", - "ĠÑĤ еÑĩение", - "ĠÑĤеÑĩ ение", - "ê¸ ´", - "acak tır", - "ê±° ëĤĺ", - "ี ยม", - "ีย ม", - "ĠÑģ ÑĥÑħ", - "ĠÑģÑĥ Ñħ", - "ĠëĦĪ 무", - "ãģı ãĤĭ", - "ĠкоÑĤоÑĢ ой", - "ا ÙĤØ©", - "اÙĤ Ø©", - "y ıl", - "yı l", - "ãĤ» ãĥĥãĥĪ", - "ĠÑį лем", - "æģ IJ", - "ÙĨ اء", - "ÙĨا Ø¡", - "åħ ©", - "Ġte Äı", - "ä¸ ¥", - "Ġì§Ī 문", - "Ġ 为", - "Ġä¸ º", - "ìĭľ íĹĺ", - "Ġп ÑĢок", - "ĠпÑĢ ок", - "ĠпÑĢо к", - "u jeme", - "uj eme", - "uje me", - "ü cü", - "üc ü", - "ĠاÙĦÙħ غ", - "ĠØŃ Ø³Ø§Ø¨", - "ĠØŃس اب", - "ãģĹ ãģ¦ãģĦ", - "ãģĹãģ¦ ãģĦ", - "к ова", - "ко ва", - "ков а", - "ĠÄij Ãło", - "Ġп ÑĢиз", - "ĠпÑĢ из", - "ĠпÑĢи з", - "ĠÙĪ ÙħÙĨ", - "ĠÙĪÙħ ÙĨ", - "Ġ оÑĢ", - "Ġо ÑĢ", - "à¸ģ à¸ķ", - "а ÑĦ", - "Ġ à¸ŀร", - "Ġà¸ŀ ร", - "ÑĨи ей", - "æ ª", - "Ġ působ", - "Ġp ůsob", - "Ġpů sob", - "åŃ© åŃIJ", - "Ġb ánh", - "Ġbán h", - "ĠÑĦоÑĢм Ñĥ", - "ĠÑĦоÑĢ мÑĥ", - "Ġ á»ķ", - "Ġá» ķ", - "Ġмен ее", - "Ġмене е", - "à¹ī าห", - "à¹īา ห", - "ни ÑĨа", - "ниÑĨ а", - "ี Ċ", - "Ġв олоÑģ", - "Ġвол оÑģ", - "Ġار ائÙĩ", - "第 ä¸ī", - "ëIJĺ ìĹĪ", - "Ġkıs m", - "Ġkı sm", - "ãĥ¼ ãĥĬ", - "ãĥ¼ãĥ Ĭ", - "ler imiz", - "ÙĨ ÙĬÙĨ", - "ÙĨÙĬ ÙĨ", - "Ġ NgÆ°á»Ŀi", - "ĠNg Æ°á»Ŀi", - "ĠоÑĤ дел", - "ĠоÑĤд ел", - "çļĦ æŶåĢĻ", - "о нов", - "он ов", - "Äį an", - "i zm", - "iz m", - "ĠÑģоб ой", - "à¹ĩ à¸ķ", - "Ġ ÑģлÑĸд", - "ĠÑģ лÑĸд", - "ĠÑģл Ñĸд", - "Ġ à¤ľà¤¹", - "Ġà¤ľ ह", - "ï¼Į æĪij们", - "ï¼ĮæĪij 们", - "ãĢĤ ãģĿãģ®", - "ÏĢ ÏīÏĤ", - "çĨ Ł", - "ภ¯", - "ëĦ IJ", - "æľ ĭ", - "Ġë¹Ħ ë°Ģ", - "ëį ķ", - "Ġm Ãłn", - "ĠmÃł n", - "ìĿ´ ê³ł", - "ëŀľ ëĵľ", - "éĤ Ħ", - "Ä±ÅŁ ık", - "Ä±ÅŁÄ± k", - "Ġ 个", - "Ġä¸ ª", - "Ġn ád", - "Ġná d", - "б ÑĢа", - "æĮĩ å®ļ", - "lar ıyla", - "ları yla", - "ĠÐŀ ни", - "ĠÐŀн и", - "Ġ hra", - "Ġh ra", - "Ġhr a", - "ĠÑĢе ÑĨеп", - "ĠÐłÐ¾Ñģ Ñģий", - "å½± åĵį", - "Ġ Když", - "ĠK dyž", - "ĠÃ¶ÄŁ renc", - "ĠÃ¶ÄŁren c", - "åī µ", - "Ġ jist", - "Ġj ist", - "Ġji st", - "èĪ Ī", - "è§ ¦", - "åıij çİ°", - "ม าย", - "มา ย", - "er ken", - "erk en", - "Ġзд еÑģÑĮ", - "ĠÙħس ئ", - "@n ate", - "ĠëĤ´ ìļ©", - "Ġnab ÃŃd", - "ĠnabÃŃ d", - "Û Ģ", - "Ġмо менÑĤ", - "Ġмом енÑĤ", - "ãģł ãģĮ", - "ί δα", - "ίδ α", - "T ak", - "Ta k", - "Ġ ë³´ê³ł", - "Ġë³´ ê³ł", - ": ::::::::", - ":: :::::::", - ":::: :::::", - ":::::: :::", - ":::::::: :", - "::: ::::::", - "::::: ::::", - "::::::: ::", - "ÄŁ men", - "Ġпо меÑī", - "Ġпом еÑī", - "ãģ«ãģ¤ ãģĦãģ¦", - "ĠÙģ ÙĪÙĤ", - "ĠÙģÙĪ ÙĤ", - "Ġع ضÙĪ", - "ĠÙħ ÛĮاÙĨ", - "ĠÙħÛĮ اÙĨ", - "Ġm üc", - "Ġmü c", - "ĠпÑĢо Ñıв", - "ÑĩеÑģ ки", - "Ñĩе Ñģки", - "ãģł ãģĭãĤī", - "éĤ ¦", - "Ġ ë¶ĦìĦĿ", - "Ġë¶Ħ ìĦĿ", - "éŁ ©", - "į ¨", - "ĠD aha", - "ĠDa ha", - "ĠDah a", - "Ġ κÏĮ", - "Ġκ ÏĮ", - "Ġна Ñĩина", - "ĠнаÑĩ ина", - "ĠÐŁ оÑĤ", - "ĠÐŁÐ¾ ÑĤ", - "Ïĥκε Ïħ", - "Ïĥκ εÏħ", - "Ġ ÑĢан", - "ĠÑĢ ан", - "ĠÑĢаР½", - "ĠÑĢа н", - "ÙĪ ÙĬس", - "ÙĪÙĬ س", - ": :::::::::", - ":: ::::::::", - ":::: ::::::", - ":::::: ::::", - ":::::::: ::", - "::: :::::::", - "::::: :::::", - "::::::: :::", - "::::::::: :", - "Û±Û¹ Û¹", - "Ġard ından", - "à¹Ĥ à¸Ķ", - "ا راÙĨ", - "ار اÙĨ", - "ارا ÙĨ", - "د اد", - "دا د", - "Ġqu ý", - "ĠØ£Ùĥ ثر", - "âĹ Ĩ", - "ĠØ£ خرÙī", - "Ġأخ رÙī", - "Ġë§Ī ìĿĮ", - "ë¦ ´", - "Ġ عÙĦÙĪÙħ", - "ĠعÙĦ ÙĪÙħ", - "Ġe ÄŁ", - "воÑĢ Ñİ", - "во ÑĢÑİ", - "Ġ ãĥĹ", - "Ġãĥ Ĺ", - "Ñĥ ÑĩаÑģ", - "ÑĥÑĩ аÑģ", - "ÑĥÑĩа Ñģ", - "Ġب Ø£", - "ÏĨ ο", - "ни ками", - "ник ами", - "ника ми", - "никам и", - "à¹ĥ à¸ķ", - "Äįet nÄĽ", - "à¸ļ าà¸ĩ", - "çī Ļ", - "ãĥª ãĤ«", - "í Ĵ", - "åĩº çīĪ", - "γ ι", - "ãĢĤ ãģĿãĤĮ", - "Ġy ani", - "Ġya ni", - "Ġyan i", - "l ech", - "le ch", - "lec h", - "ĠLu áºŃt", - "çļĦ ãģª", - "Ġneden iyle", - "Ġnedeni yle", - "d ej", - "de j", - "ĠÑģов еÑĢÑĪ", - "Ġph á»ķ", - "ıs ından", - "ısında n", - "Ġch ắc", - "d eÅŁ", - "de ÅŁ", - "Ġком ан", - "Ġко ман", - "æĽ ¿", - "Ġp lán", - "Ġpl án", - "Ġplá n", - "Ġd ữ", - "ĠêµŃ ê°Ģ", - "Ġta kip", - "Ġtak ip", - "Ġth ủy", - "Ġthủ y", - "Ñģ лÑĸд", - "Ñģл Ñĸд", - "âī §", - "ĠI IC", - "ĠII C", - "θ Ïħ", - "á vat", - "áv at", - "Ġ Ñģок", - "ĠÑģ ок", - "ĠÑģо к", - "Ġб агаÑĤо", - "Ġбаг аÑĤо", - "ĠбагаÑĤ о", - ";:;: ;:;:", - "Ïģ ιοÏĤ", - "Ïģι οÏĤ", - "Ïģιο ÏĤ", - "il miÅŁtir", - "ilm iÅŁtir", - "ilmiÅŁ tir", - "Ġ znam", - "Ġz nam", - "Ġzn am", - "Ġ Τα", - "ĠΤ α", - "a maz", - "am az", - "ama z", - "à¹ģ à¸ŀ", - "ãĥģ ãĥ£", - "Ġkullan ı", - "æĶ¾ éĢģ", - "д н", - "ĠÙĪ اب", - "ĠÙĪا ب", - "Ġtr ắng", - "Ñģ Ñıг", - "ÑģÑı г", - "Ġار تباط", - "Ġв Ñħод", - "å·ŀ å¸Ĥ", - "Ġ सत", - "Ġस त", - "Ñĩ аеÑĤÑģÑı", - "Ñĩа еÑĤÑģÑı", - "ÑĩаеÑĤ ÑģÑı", - "íĮĮ íĬ¸", - "Ġ Những", - "ĠNh ững", - "ä¸į åı¯", - "å± Ĭ", - "Ġ ãĤŃ", - "ĠãĤ Ń", - "ار ÙĩاÛĮ", - "ارÙĩ اÛĮ", - "Ġar ÅŁiv", - "Ġ اÙĦÙī", - "Ġا ÙĦÙī", - "ĠاÙĦ Ùī", - "ाय à¤ķ", - "ãģĹ ãĤĩãģĨ", - "ãģĹãĤĩ ãģĨ", - "Ġ ulus", - "Ġu lus", - "Ġul us", - "al axy", - "ala xy", - "기 ê°Ģ", - "ãİ¡ (", - "μά ÏĦÏīν", - "è n", - "ù i", - "Ġна ÑģÑĤоÑı", - "ĠнаÑģÑĤ оÑı", - "ĠС в", - "ĠоÑģ оби", - "ĠоÑģоб и", - "к ово", - "ко во", - "ков о", - "ĠÑĢеб енка", - "ĠÑĢебен ка", - "ĠÑĤ Ñıж", - "ĠÑĤÑı ж", - "Ġxu á»ijng", - "Ġ ê¶Į", - "Ġê ¶Į", - "о год", - "ог од", - "ого д", - "Ġ ấy", - "è² ł", - "ว à¸Ļ", - "Ġ stanov", - "Ġsta nov", - "Ġstan ov", - "Ġk rál", - "Ġkr ál", - "Ġà¤ĩ सल", - "Ġà¤ĩस ल", - "e be", - "eb e", - "å® ¾", - "ĠдоÑģÑĤаÑĤ оÑĩно", - "II IK", - "III K", - "ÏĢ ά", - "Ġbir kaç", - "ĠاÙĦ ÙħÙĤ", - "ĠاÙĦÙħ ÙĤ", - "ãĥ ¶", - "ĠBaÅŁ kanı", - "ĠBaÅŁkan ı", - "Ġ첨ë¶Ģ íĮĮìĿ¼", - "Ġya rar", - "Ġyar ar", - "äº ¡", - "Ġ ÏĢÏĮ", - "ĠÏĢ ÏĮ", - "Âł Ñģ", - "δ ή", - "e lerini", - "eler ini", - "eleri ni", - "elerin i", - "Ġs uç", - "Ġsu ç", - "Ġд ома", - "Ġдо ма", - "Ġдом а", - "Ġна ÑĢÑĥÑĪ", - "ĠнаÑĢ ÑĥÑĪ", - "Ġ ί", - "ĠÎ ¯", - "Ġê·¸ ìĿĺ", - "çĶµ å½±", - "ا بÙĩ", - "اب Ùĩ", - "к омÑĥ", - "ко мÑĥ", - "ком Ñĥ", - "Ġत ब", - "à¥Ī à¤ł", - "Ġ모 ì§ij", - "Ġ æ±Ł", - "Ġæ± Ł", - "Ġê²ĥ ìĿĢ", - "ον ÏĦαι", - "ĠاÙĦ رÙĬاض", - "è¨ ±", - "Ġhal inde", - "Ġاش ارÙĩ", - "Ġ кÑĢÑĭ", - "Ġк ÑĢÑĭ", - "л ений", - "лен ий", - "ле ний", - "lu ÄŁ", - "Ġdo bu", - "Ġdob u", - "s ik", - "si k", - "à¥ģ à¤Ł", - "à¥ģठŁ", - "Ġ кÑĸн", - "Ġк Ñĸн", - "ãģ¨ ãģį", - "à¥Ĥ स", - "æħ ¢", - "ĠdÄ±ÅŁ ında", - "ĠdÄ±ÅŁÄ± nda", - "ç· ı", - "Ġ bÃŃ", - "Ġb ÃŃ", - "ĠCL IIIK", - "ĠIIC III", - "Ġh erk", - "Ġhe rk", - "Ġher k", - "ãĤı ãģĽ", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Âł ³³³³³", - "³³ ³³³³", - "³³³³ ³³", - "³³³ ³³³", - "³³³³³ Âł", - "اÙĦ د", - "Ġdav ran", - "Äį er", - "Äįe r", - "Ġ ØŁ", - "ĠØ Ł", - "ãģĺãĤĥ ãģªãģĦ", - "Ġd air", - "Ġda ir", - "Ġdai r", - "Ġî ¥¤", - "ั à¸ĩส", - "ัà¸ĩ ส", - "Ġ ëĭ´", - "Ġëĭ ´", - "å¾ ŀ", - "ĠÑįÑĤ иÑħ", - "ĠÑįÑĤи Ñħ", - "è¯ º", - "á» ·", - "е ÑĢиÑģÑĤи", - "еÑĢи ÑģÑĤи", - "ов ÑĭÑħ", - "Ġ ãĥĩ", - "Ġãĥ ĩ", - "ض ÙĬ", - "Ġà¤ī à¤ł", - "Ġnap ÅĻÃŃklad", - "è ´Ŀ", - "è´ Ŀ", - "Ġ Å¡k", - "ĠÅ¡ k", - "ĠبÙĪد ÙĨد", - "ĠبÙĪدÙĨ د", - "vů li", - "éģ ĩ", - "Ġз най", - "Ġзна й", - "Ġзн ай", - "ĠT ham", - "ĠTh am", - "r ani", - "ra ni", - "ran i", - "ا ØŃت", - "اØŃ Øª", - "Ø´ Ùĩ", - "мÑĸнÑĸ ÑģÑĤÑĢа", - "๠ĭ", - "ĠÎij να", - "ĠÎijν α", - "à¥ĭ à¤ļ", - "ç»Ħ ç»ĩ", - "ÑģÑĤ иÑĤ", - "ÑģÑĤи ÑĤ", - "im li", - "åIJį çĦ¡ãģĹãģķãĤĵ", - "åIJįçĦ¡ãģĹ ãģķãĤĵ", - "Ùij Ø©", - "θ μ", - "о лоÑĤ", - "ол оÑĤ", - "оло ÑĤ", - "ย à¸ĩ", - "ãĤī ãĤĮãĤĭ", - "ãĤīãĤĮ ãĤĭ", - "Ġ лиÑĩ", - "Ġл иÑĩ", - "Ġли Ñĩ", - "ов Ñĭе", - "éĢ ĥ", - "Ġ 广", - "Ġå¹ ¿", - "ìĬ ¬", - "Ùħ ÛĮÙĨ", - "ÙħÛĮ ÙĨ", - "ĠìłĦ ì²´", - "ĠÎŃ Ïĩ", - "Ġ ì±ħ", - "Ġì± ħ", - "Ġ hlas", - "Ġh las", - "Ġhl as", - "е кÑĤив", - "ек ÑĤив", - "екÑĤ ив", - "екÑĤи в", - "ĠÏĢ λη", - "lu ÄŁu", - "luÄŁ u", - "好 çļĦ", - "ĠÚĨ ÙĪÙĨ", - "ĠB eled", - "ĠBe led", - "ĠBel ed", - "Ġen gel", - "Ġeng el", - "нÑı Ñı", - "ĠyaÅŁ an", - "Ñĩ ниÑħ", - "ار ÙĬØ©", - "ارÙĬ Ø©", - "म त", - "ãĥĭãĥĭ ãĥĭãĥĭ", - "åĭ ¢", - "Ġ åĨħ", - "ĠåĨ ħ", - "Ġíı¬ íķ¨", - "Ġоб Ñģ", - "Ġth ấp", - "Ġd ây", - "ãĥĸ ãĥ©", - "а ÑĤÑĭ", - "аÑĤ Ñĭ", - "ĠÑģво ей", - "ĠÑģвое й", - "ãĤī ãģªãģĦ", - "åıij çĶŁ", - "e rece", - "er ece", - "ere ce", - "Ġod bor", - "Ġв неÑģ", - "Ġвне Ñģ", - "ĠÄIJ ảng", - "ĠëıĮ ìķĦ", - "ÄĽ li", - "ÄĽl i", - "ı sında", - "ıs ında", - "ısı nda", - "Ġबद ल", - "v nÃŃ", - "vn ÃŃ", - "ãģ® ãģ«", - "Ġпо ÑĤом", - "ĠпоÑĤ ом", - "im de", - "imd e", - "a lama", - "al ama", - "ala ma", - "alam a", - "âĢ ª", - "Ġstej nÄĽ", - "е ÑĢе", - "еÑĢ е", - "éĴ ¢", - "æľº æŀĦ", - "Ġ è³", - "Ġè ³", - "åĶ ±", - "Ġ ëħ¸ì¶ľ", - "Ġëħ¸ ì¶ľ", - "Ġли бо", - "âĢ Ĭ", - "Ġc ez", - "Ġce z", - "ro mÄĽ", - "rom ÄĽ", - "ί Ïīν", - "ÏĨ ή", - "Ġ íĻ©", - "ĠíĻ ©", - "Ġdlou h", - "éª ¨", - "åħ¬ éĩĮ", - "ä¼ ¸", - "Ġ ãĥij", - "Ġãĥ ij", - "ä» Ļ", - "Ġol madı", - "Ġolm adı", - "Ġolma dı", - "е лиÑĩ", - "ел иÑĩ", - "ели Ñĩ", - "ожд ениÑı", - "Ġsöy ledi", - "Ġsöyl edi", - "á tek", - "át ek", - "áte k", - "ìĥ µ", - "ย วà¸ģ", - "ยว à¸ģ", - "Ġ 鼻", - "ĠéĽ »", - "Ġп ев", - "ĠдÑĢÑĥг ие", - "át ku", - "Ġع ÙĪ", - "ov ána", - "ová na", - "ován a", - "ض ر", - "Ġëģ Ŀ", - "ĠíĨµ íķ´", - "Î ĸ", - "Ġv ur", - "Ġvu r", - "åĨ ²", - "Ġп ÑĢек", - "ĠпÑĢ ек", - "ĠпÑĢе к", - "Ġप à¤ķ", - "Ġ à¹Ģà¸Ĺ", - "Ġà¹Ģ à¸Ĺ", - "Ġà¹ĢภĹ", - "ãģ¨ ãģĭ", - "ع ÙĨ", - "å® ĩ", - "ÏĦ ζ", - "Ġn ằm", - "ĠÑģ воб", - "ĠÑģв об", - "ĠÑģво б", - "Ġδ Ïį", - "çĸ Ĺ", - "- й", - "é¦Ļ 港", - "ت ا", - "Ïĥι μο", - "íķ Ħ", - "Ġ 详æĥħ", - "Ġè¯ ¦æĥħ", - "ä¸ ¡", - "Ùİ Ø§ÙĦ", - "Ùİا ÙĦ", - "ĠTr Æ°á»Ŀng", - "e ného", - "en ého", - "ené ho", - "ĠÑĢекомен дÑĥ", - "ÛĮ رÙĩ", - "ÛĮر Ùĩ", - "า à¸ĸ", - "าภĸ", - "ĠÚ© اÙħÙĦ", - "ĠکاÙħ ÙĦ", - "ب Ø·", - "ز ÛĮÙĨÙĩ", - "زÛĮ ÙĨÙĩ", - "Ġдолж на", - "Ġë§İ ìĿĢ", - "âĹıâĹıâĹıâĹı âĹıâĹıâĹıâĹı", - "lep Å¡ÃŃ", - "ал ог", - "ало г", - "ãĤª ãĥ³", - "Ġ ë³Ħ", - "Ġë³ Ħ", - "ı rı", - "ır ı", - "ĠجاÙħ عÙĩ", - "ĠجاÙħع Ùĩ", - "æĽ ľ", - "o jÃŃ", - "oj ÃŃ", - "ĠÑĪ лÑıÑħ", - "Ġhız lı", - "Ġ خصÙĪص", - "Ġخص ÙĪص", - "ÐIJ ÑĢ", - "å ľĺ", - "åľ ĺ", - "Ġжив оÑĤ", - "é ±", - "Ġng ữ", - "Ġv òng", - "èİ «", - "Ġза Ñħод", - "ĠзаÑħ од", - "ìĻ Ħ", - "ĠÑģлед ÑĥÑİÑī", - "éĹ »", - "Ñij ÑĢ", - "Ġch vÃŃ", - "èĥ ľ", - "ãģª ãģĹ", - "Ġtek noloj", - "Ġtekn oloj", - "ej ména", - "Ġ ìłĪ", - "Ġìł Ī", - "ì³ IJ", - "æĻ® éĢļ", - "Ġvý ro", - "Ġay rı", - "Ġayr ı", - "Ġп ÑĢев", - "ĠпÑĢ ев", - "ĠпÑĢе в", - "Ġgó p", - "à¹Ĥ à¸ģ", - "à¸Ĺำ à¹ĥห", - "åı İ", - "åĺ ī", - "Ġte lev", - "Ġtele v", - "Ġtel ev", - "ãģ¨ ãģĵãĤį", - "ëı Į", - "ph yl", - "phy l", - "ร าะ", - "Ġ çĪ", - "Ġç Ī", - "ÑģÑĤ иÑĤÑĥ", - "ÑģÑĤи ÑĤÑĥ", - "ÑģÑĤиÑĤ Ñĥ", - "ï¼Į è¿ĺ", - "ĠÎij γ", - "Äį ku", - "æı ´", - "ाय त", - "æı ı", - "ãĤĤ ãģĹ", - "ĠпеÑĢ еÑģ", - "ĠпеÑĢе Ñģ", - "Ġìĺģ íĻĶ", - "id la", - "idl a", - "åİ ħ", - "ï¼ı :", - "ت رÛĮ", - "تر ÛĮ", - "à¸Ľ à¸ı", - "ĠнаÑģ еленнÑı", - "Ġam aç", - "Ġama ç", - "Ġk do", - "Ġkd o", - "Ġиз веÑģÑĤ", - "ÑĪ иÑĢ", - "ÑĪи ÑĢ", - "ì£ ł", - "Å¡ it", - "Å¡i t", - "Ġt á»ijc", - "Ġtá»ij c", - "ìŀIJ ìĿĺ", - "Ñĩ аÑĤ", - "Ñĩа ÑĤ", - "åı ĥ", - "éĽ ¶", - "å° º", - "Ġ indir", - "Ġin dir", - "Ġind ir", - "Ġна ÑĨÑĸоналÑĮ", - "Ġx anh", - "Ġxa nh", - "ÛĮ دÛĮ", - "ÛĮد ÛĮ", - "Ġин ÑĤеÑĢеÑģ", - "ĠинÑĤеÑĢ еÑģ", - "ĠØ¢ سÛĮ", - "Ġآس ÛĮ", - "éĤ£ 个", - "Ġb ilm", - "Ġbi lm", - "Ġbil m", - "а не", - "ан е", - "ĠtÄĽch to", - "Ñĩ ик", - "Ñĩи к", - "Ġдо Ñħод", - "èĤ¡ 份", - "åħ³ ç³»", - "ãģ«ãģª ãģ£ãģŁ", - "ĠпÑĢед пÑĢи", - "Ġgeç en", - "Ġب ÙĤ", - "Ġvý znam", - "Ġ à¹Ģà¸Ħร", - "Ġà¹Ģ à¸Ħร", - "Ġà¹Ģà¸Ħ ร", - "ĠÑħ ÑĤо", - "Ø´ ÙĬ", - "åıĤ åĬł", - "ÑģÑĤв енного", - "ÑģÑĤвен ного", - "ÑģÑĤвенно го", - "ÑĤ ÑĢон", - "ÑĤÑĢ он", - "ÑĤÑĢо н", - "ÂĢÂĢ ÂĢÂĢ", - "æ¢ Ŀ", - "б ав", - "ба в", - "Û± Û¶", - "Û±Û ¶", - "é¡ º", - "Ġj az", - "Ġja z", - "ĠاÙĦ ÙħÙĦ", - "ĠاÙĦÙħ ÙĦ", - "Ġا ثر", - "Ġاث ر", - "ĠпÑĢи вод", - "ĠпÑĢив од", - "а нÑĥ", - "ан Ñĥ", - "à¥ģ à¤Ń", - "à¥ģठŃ", - "æĹ §", - "ÑĮ е", - "ส ล", - "л ÑıÑİÑĤ", - "лÑı ÑİÑĤ", - "ว à¸Ķ", - "Æ° Ỽi", - "Æ°á» Ľi", - "Æ°á»Ľ i", - "ÙĬ ÙħØ©", - "ÙĬÙħ Ø©", - "ãĤ¯ ãĥŃ", - "л ий", - "ли й", - "γ Ïģά", - "Ġper forman", - "Ġperform an", - "Ġperf orman", - "Ġperfor man", - "è¯ ī", - "ä½ł çļĦ", - "ìħ Ķ", - "н ениÑı", - "не ниÑı", - "нен иÑı", - "á»Ń i", - "ÙĪ زÛĮ", - "ÙĪز ÛĮ", - "éŁ ¿", - "à¥Ī द", - "Ġëª ¸", - "Ġe ser", - "Ġes er", - "Ġese r", - "ĠÙģعاÙĦ ÛĮت", - "нÑĸ веÑĢ", - "нÑĸв еÑĢ", - "κ Ïģα", - "è¨ ¼", - "Ġn emoc", - "Ġnem oc", - "Ġyardım cı", - "Ġ çī¹", - "Ġçī ¹", - "Ġ коп", - "Ġк оп", - "Ġко п", - "ĠÐľ ож", - "़ à¤ķ", - "Ġ ëľ", - "Ġë ľ", - "ĠÑĢе ак", - "Ġp ozor", - "Ġpoz or", - "Âł ÐIJ", - "Ġ ÙĬÙĥ", - "ĠÙĬ Ùĥ", - "ĠÑģ ад", - "Ġ åħ«", - "Ġåħ «", - "Ġп олÑĮз", - "ĠполÑĮ з", - "Ġra ÄŁmen", - "ter nÃŃ", - "tern ÃŃ", - "s iyon", - "si yon", - "Ñģ ÑıÑĩ", - "ÑģÑı Ñĩ", - "ov aný", - "ova ný", - "ovan ý", - "ĠëĮĢíķľ 민êµŃ", - "ĠвÑĸд б", - "ĠÐIJ нд", - "ĠÐIJн д", - "st va", - "éĮ Ħ", - "Ġ ëij", - "Ġë ij", - "ิ à¸Ħ", - "j ÃŃt", - "jÃŃ t", - "Ġkullan ıcı", - "Ġkullanı cı", - "Ġ æŁ¥çľĭ", - "ĠæŁ¥ çľĭ", - "Ùģ ÙĦ", - "Ġ ЯкÑīо", - "ĠЯк Ñīо", - "çľĭ åĪ°", - "ÑĢ еÑħ", - "ÑĢе Ñħ", - "ĠاÙĦع ربÙĬØ©", - "ĠاÙĦعرب ÙĬØ©", - "ĠاÙĦعربÙĬ Ø©", - "ë¡ľê·¸ ëŀ¨", - "Ġब à¤ľ", - "Ġп ÑĢип", - "ĠпÑĢ ип", - "ĠпÑĢи п", - "Ġs chop", - "Ġsc hop", - "Ġsch op", - "Ġscho p", - "Ġب اÙĦا", - "Ġبا ÙĦا", - "ĠباÙĦ ا", - "å® ħ", - "Ġا ÙĦÙħÙĩ", - "ĠاÙĦ ÙħÙĩ", - "ĠاÙĦÙħ Ùĩ", - "α να", - "αν α", - "à¥ĭ व", - "åģ ´", - "å¼Ģ åıij", - "Ùħ اÙĦ", - "Ùħا ÙĦ", - "Ġ धर", - "Ġध र", - "Ġda hil", - "Ġdah il", - "Ġdahi l", - "ãĢģ ãģĵãģ®", - "ัà¸Ī à¸Ī", - "Ñģп ÑĸлÑĮ", - "Ġà¤ķ प", - "Ġв еÑĩ", - "Ġве Ñĩ", - "Ġвид а", - "Ġви да", - "ĠÙħ عÙĨ", - "ĠÙħع ÙĨ", - "ĠоÑĤ ли", - "i á»ħ", - "iá» ħ", - "л иÑĪ", - "ли ÑĪ", - "Ġ ÐŁÐ¾Ñģле", - "ĠÐŁÐ¾Ñģ ле", - "ãģĵ ãģĵ", - "Ġk ültür", - "Ġ جر", - "Ġج ر", - "Ġ æ¼", - "Ġæ ¼", - "èĩ º", - "Ġmev cut", - "Ù¾ ÛĮ", - "ĠاÙĦ سÙĦاÙħ", - "ĠاÙĦس ÙĦاÙħ", - "иÑĤ елей", - "иÑĤе лей", - "Ġ ÑĢоÑģÑĤ", - "ĠÑĢ оÑģÑĤ", - "ĠÑĢоÑģ ÑĤ", - "Ġed il", - "Ġedi l", - "Ġ å·²", - "Ġå· ²", - "ç²¾ åĵģ", - "ä» ħ", - "âĢĻ ye", - "âĢĻy e", - "à¥Īà¤Ĥ .", - "Ġ åĨĨ", - "ĠåĨ Ĩ", - "ëĪ Ħ", - "Ġ ìĻķ", - "ĠìĻ ķ", - "æĺ Ń", - "ĠÎļ ο", - "m eden", - "med en", - "me den", - "Ġo lab", - "Ġol ab", - "Ġola b", - "ĠÚ© ÙĪد", - "ĠÚ©ÙĪ د", - "à¸Ħ าส", - "ен наÑı", - "æĬ ¼", - "yl ül", - "Ġsev iy", - "Ġd ÄĽti", - "ĠdÄĽ ti", - "ĠdÄĽt i", - "âĢ¬ Ċ", - "Ġع ز", - "Ġu á»ijng", - "Ġس رÙħ", - "Ġسر Ùħ", - "е не", - "ен е", - "Ġмал енÑĮ", - "Ġ вÑĸдом", - "Ġв Ñĸдом", - "ĠвÑĸд ом", - "ĠвÑĸ дом", - "ั à¸ļà¸Ĺ", - "ัà¸ļ à¸Ĺ", - "ĠTh ái", - "Ġà¤Ĩव श", - "rove ÅĪ", - "çĽ £", - "ĠÑı зÑĭ", - "ĠO y", - "å£ ģ", - "в аÑĤÑĮ", - "ва ÑĤÑĮ", - "ваÑĤ ÑĮ", - "л адÑĥ", - "ла дÑĥ", - "лад Ñĥ", - "ا صÙĦ", - "اص ÙĦ", - "ot ÅĻeb", - "د ÙĬØ«", - "دÙĬ Ø«", - "íı °", - "νο μ", - "г оÑĢод", - "го ÑĢод", - "гоÑĢ од", - "Ġm uh", - "Ġmu h", - "âĢĻ l", - "ÑģÑĤ воÑĢ", - "ÑģÑĤв оÑĢ", - "ÑģÑĤво ÑĢ", - "åħ Ħ", - "Ðķ Ðł", - "Ø· ÙĦ", - "éľ ĩ", - "Ùİ Øª", - "Ġb lÃŃ", - "Ġbl ÃŃ", - "Ġed ildi", - "Ġedil di", - "éĿ ł", - "äºĮ åįģ", - "æĹ Ĺ", - "Ġç iz", - "ĠÄij ảo", - "Ġo pat", - "Ġop at", - "o ÄŁan", - "oÄŁ an", - "ë² Į", - "Ġ éł", - "Ġé ł", - "Ġseb ep", - "Ġsebe p", - "Ñĥ ÑĤи", - "ÑĥÑĤ и", - "åĪ º", - "Ø· ب", - "ev Å¡ÃŃm", - "c hop", - "ch op", - "cho p", - "çĶ ļ", - "Ġngh á»ģ", - "Ġп аÑĢÑĤ", - "ĠпаÑĢ ÑĤ", - "ุ à¸Ħ", - "Ú© ÛĮÙĦ", - "Ú©ÛĮ ÙĦ", - "d um", - "du m", - "Ġor tak", - "Ġort ak", - "Ġorta k", - "ãģŁ ãģĹ", - "Ġoby vatel", - "Ġv ých", - "Ġvý ch", - "Ġv eren", - "Ġver en", - "Ġve ren", - "Ġvere n", - "Ġв еÑģÑĮ", - "ĠвеÑģ ÑĮ", - "Ġве ÑģÑĮ", - "ĠÐĶ а", - "Ġ íķĺì§Ģë§Į", - "Ġíķĺ ì§Ģë§Į", - "Ġíķĺì§Ģ ë§Į", - "å¦Ĥ æѤ", - "Ġमह त", - "ัà¸ĩà¸ģ ฤษ", - "ãĢĤ è¿Ļ", - "Ġ гал", - "Ġг ал", - "Ġsa nat", - "Ġsan at", - "Ġsana t", - "éł Ĩ", - "ĠÑģам о", - "å Ľ°", - "åĽ °", - "ี à¸Ń", - "ĠBaÅŁ kan", - "ÏĦ οÏħÏĤ", - "ÏĦοÏħ ÏĤ", - "Ġyap tıģı", - "Ġyaptı ģı", - "Ġyaptıģ ı", - "ÅĻ it", - "ÅĻi t", - "ĠÑģ ÑĸлÑĮ", - "ान त", - "Ġ ÙĨت", - "ĠÙĨ ت", - "Ġkh Äĥn", - "à¸Ĭ à¸Ļะ", - "à¸Ĭà¸Ļ ะ", - "м ини", - "ми ни", - "мин и", - "ãĥ¬ ãĥ¼", - "ë Ĥ¬", - "ëĤ ¬", - "éħĴ åºĹ", - "ĠاÙĦÙĬ ÙĪÙħ", - "ä¹ Ĺ", - "à¸Ħรà¸ĩ à¸ģาร", - "Ùģ اÙĤ", - "Ġ à¤ıस", - "Ġà¤ı स", - "Ġ æ¡", - "Ġæ ¡", - "Ú¯ Ø°", - "Ġà¤ĩ ल", - "е лениÑı", - "ел ениÑı", - "еле ниÑı", - "елен иÑı", - "à¸ģ รà¸ĵ", - "à¸ģร à¸ĵ", - "举 西", - "ÎŁ Îľ", - "ÎŁÎ ľ", - "Ġm áºŃt", - "Ġs nÃŃ", - "Ġsn ÃŃ", - " IJ", - "à¹Ģร า", - "íķ´ ìķ¼", - "Ġ ìĦľë¹ĦìĬ¤", - "ĠìĦľ ë¹ĦìĬ¤", - "Ġداخ ÙĦ", - "Ġth ắng", - "íĥ Ī", - "а вÑģÑı", - "ав ÑģÑı", - "Ġ Ñĸм", - "ĠÑĸ м", - "ا Ùħت", - "اÙħ ت", - "Ġ ÙĪÙĤت", - "ĠÙĪ ÙĤت", - "ĠÙĪÙĤ ت", - "à¥Ĥ à¤ģ", - "Ġ èIJ", - "Ġè IJ", - "Ġ سÙĦاÙħ", - "Ġس ÙĦاÙħ", - "ĠسÙĦ اÙħ", - "Ġvz dÄĽl", - "å¸Į æľĽ", - "åŃĺ æ¡£", - "Ġ à¸Ĺำ", - "Ġà¸Ĺ ำ", - "ĠвÑĸй ÑģÑĮ", - "а ÑĢан", - "аÑĢ ан", - "аÑĢа н", - "ĠÑĢ Ñĸк", - "Ġп иÑģÑĮ", - "Ġпи ÑģÑĮ", - "ĠпиÑģ ÑĮ", - "Ġá¼ IJ", - "기 ëıĦ", - "ĠпоÑģÑĤ оÑıн", - "Ġ åĮĹ京", - "ĠåĮĹ äº¬", - "ĠNÄĽ m", - "Ø´ ÙĨاÙħÙĩ", - "Ø´ÙĨ اÙħÙĩ", - "Ġdal Å¡ÃŃch", - "ĠdalÅ¡ÃŃ ch", - "Ġب اع", - "Ġبا ع", - "Ġpo hy", - "Ġpoh y", - "ا ÙĦÙģ", - "اÙĦ Ùģ", - "à¸ŀ วà¸ģ", - "é ĭ", - "Ġ cih", - "Ġc ih", - "Ġci h", - "Ù ¢", - "ä¸ ´", - "ãĤ¯ ãĥĪ", - "п нÑı", - "Ġ дал", - "Ġд ал", - "Ġда л", - "ÙĴ ر", - "ãĢĢ ãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢãĢĢĠãĢĢ ĠãĢĢ", - "ãĢĢãĢĢĠ ãĢĢĠãĢĢ", - "æĬ¥ åijĬ", - "ÙĪ دÛĮ", - "ÙĪد ÛĮ", - "á» £i", - "ợ i", - "ÑĨ ÑĸÑĶÑİ", - "ÑĨÑĸ ÑĶÑİ", - "Ġ ãĥĢ", - "Ġãĥ Ģ", - "ĠÑģÑĤ еп", - "r až", - "ra ž", - "ĠS aÄŁ", - "ĠSa ÄŁ", - "Ġt uyến", - "Ġtuy ến", - "Ġal mak", - "Ġalma k", - "Ġalm ak", - "Ġзаболева ниÑı", - "Ġ ÏĥÏĩ", - "ĠÏĥ Ïĩ", - "Ġ íĭ", - "Ġí ĭ", - "Ġв им", - "Ġви м", - "ç¡ ¬", - "Ġ äºĶ", - "Ġi kinci", - "Ġik inci", - "ุ à¸į", - "ส าว", - "ĠìĦ¸ ê³Ħ", - "ĠÙħ ØŃÙĦ", - "ĠÙħØŃ ÙĦ", - "ระ หว", - "Ġelek tron", - "Ġelekt ron", - "Ġelektr on", - "Ġh ại", - "Ġhạ i", - "æĹ ¢", - "Ġ íĸ¥", - "Ġíĸ ¥", - "Ġji né", - "Ġjin é", - "Ġng he", - "Ġngh e", - "æij ©", - "ĠÑģо бÑĸ", - "ĠÑģоб Ñĸ", - "Æ ¯", - "ÑĤ ÑĥÑĢ", - "ÑĤÑĥ ÑĢ", - "æ±½ 车", - "Ø´ اÙĩ", - "Ġd Ãłnh", - "ä¸ ¹", - "ä»Ĭ æĹ¥", - "ãĥIJ ãĥ¼", - "в аниÑı", - "ва ниÑı", - "ван иÑı", - "Ġ ساÙħ", - "Ġس اÙħ", - "çݯ å¢ĥ", - "ĠاÙĦ ÙħÙĨت", - "ĠاÙĦÙħ ÙĨت", - "ĠاÙĦÙħÙĨ ت", - "ĠÑģеÑĢ д", - "éģ ł", - "ε ÏĦ", - "Ġав ÑĤ", - "าà¸ĩ ว", - "Ġvz tah", - "ru ž", - "алÑĮ наÑı", - "алÑĮна Ñı", - "Ġطر اØŃÛĮ", - "à¹Ĥรà¸ĩ à¹ģรม", - "ĠÄį asto", - "ĠÄįas to", - "ĠÄįast o", - "Ġ ê¼", - "Ġê ¼", - "Ïĥ ÏĦÏĮ", - "ÏĥÏĦ ÏĮ", - "Ġbu rada", - "Ġbur ada", - "ĠÄ° z", - "Ġ ê·¸ëŀĺ", - "Ġê·¸ ëŀĺ", - "å² Ľ", - "ĠØ´ ÙĪÙĨد", - "ĠØ´ÙĪ ÙĨد", - "Å¡ ek", - "Å¡e k", - "Ġ ìĿ´ìķ¼", - "ĠìĿ´ ìķ¼", - "ãĤĮ ãģªãģĦ", - "ê· ¹", - "la mÄ±ÅŁ", - "lam Ä±ÅŁ", - "ä» į", - "cház et", - "cháze t", - "Ġ ÑģÑĥÑĤ", - "ĠÑģ ÑĥÑĤ", - "ĠÑģÑĥ ÑĤ", - "æĹł æ³ķ", - "æµ ¦", - "ÄĽ la", - "ÄĽl a", - "à¹ĥà¸Ļ à¸Ĭ", - "Ġc ân", - "Ġcâ n", - "ÎŁ Îĵ", - "ÎŁÎ ĵ", - "Ġz vý", - "Ġzv ý", - "ĠÙ¾ ار", - "Ġپا ر", - "Ġк лÑĸ", - "Ġкл Ñĸ", - "Ġn ové", - "Ġno vé", - "Ġnov é", - "çĶ ĺ", - "ë¹ ł", - "m á", - "Ġ Ñģол", - "ĠÑģ ол", - "ĠÑģо л", - "à¤ķ रण", - "à¤ķर ण", - "н оÑĩ", - "но Ñĩ", - "Ġ fik", - "Ġf ik", - "Ġfi k", - "Ġà¤ľ à¤Ĺ", - "à¹ĩ à¸Ļà¸ķ", - "à¹ĩà¸Ļ à¸ķ", - "ĠÙħ تØŃ", - "ĠÙħت ØŃ", - "Ġph iên", - "Ġphi ên", - "Ġol sun", - "Ġк аб", - "Ġка б", - "Ġh út", - "èĦ ±", - "Ġ åĸ", - "Ġå ĸ", - "ĠH ải", - "Ġ tÄĽÅ¾", - "Ġt ÄĽÅ¾", - "ĠtÄĽ ž", - "Ġth ái", - "Ġ تاب", - "Ġت اب", - "Ġتا ب", - "- ÐŁ", - "Ø« ار", - "çĨ Ĭ", - "Ġ ними", - "Ġн ими", - "Ġни ми", - "Ġним и", - "Ġzp rac", - "Ġत ह", - "Ġм акÑģим", - "Ġмак Ñģим", - "m eyi", - "me yi", - "mey i", - "ĠÑģ оÑĨи", - "ĠÑģо ÑĨи", - "æ² Ĵ", - "ĠìķĬ ëĬĶ", - "＿ _", - "åķ ¦", - "ĠاÙĨ ÙĪاع", - "æļ ´", - "ä¸Ĭ æµ·", - "åħ· æľī", - "à¥ģ ब", - "à¥ģठ¬", - "ìķ Ļ", - "Ġíģ °", - "Ġíŀ ĺ", - "Ġtr ánh", - "ि यन", - "िय न", - "ãģ¾ ãģ¾", - "п оÑĩ", - "по Ñĩ", - "m ÄĽr", - "mÄĽ r", - "å³ °", - "ĠÙħ صر", - "ĠÙħص ر", - "ĠÑįÑĦ ÑĦекÑĤив", - "ĠÑįÑĦÑĦек ÑĤив", - "Ġ çı", - "Ġç ı", - "ler iyle", - "leri yle", - "âĪ ļ", - "Ġ ì¶ķ", - "Ġì¶ ķ", - "Ġ ê²Įìĭľ", - "Ġê²Į ìĭľ", - "ìĿ ij", - "Ġ poÅĻád", - "Ġpo ÅĻád", - "Ġشب Ú©Ùĩ", - "اÙĩ Ø´", - "ĠخدÙħ ات", - "Ġna Å¡e", - "ĠnaÅ¡ e", - "ν οÏį", - "νο Ïį", - "Ġyön elik", - "Ġk ork", - "Ġko rk", - "Ġkor k", - "ا ÙĩÙħ", - "اÙĩ Ùħ", - "è° Ī", - "Ġ μη", - "Ġμ η", - "Ġd olar", - "Ġdo lar", - "Ġdol ar", - "çµ ¦", - "ĠÎķ Ïħ", - "Ġobdob ÃŃ", - "Ġ μÏĮ", - "Ġμ ÏĮ", - "à¹Ģ à¸Ńà¸ģ", - "à¹Ģà¸Ń à¸ģ", - "Ġپاس Ø®", - "è¡ ¥", - "ا عد", - "اع د", - "ãĤī ãģĦ", - "ÎŃ Î»", - "и ÑĤÑĭ", - "иÑĤ Ñĭ", - "Ġ ëħ¼", - "Ġëħ ¼", - "Ġ^{ [", - "ί γ", - "æł ij", - "l ında", - "lı nda", - "ĠìŬ 룬", - "£ £", - "ÅĻ il", - "ÅĻi l", - "Ġав ÑĤоÑĢ", - "ĠавÑĤ оÑĢ", - "ÏĦ ικÏĮÏĤ", - "ÏĦικ ÏĮÏĤ", - "ÏĦικÏĮ ÏĤ", - "u dur", - "ud ur", - "udu r", - "Ġc Æ°", - "Ġk ıy", - "Ġkı y", - "Ñģ ем", - "Ñģе м", - "ĠØ£ بÙĪ", - "Ġأب ÙĪ", - "ÏĦ ικÏİν", - "ÏĦικ Ïİν", - "Û± Û·", - "Û±Û ·", - "è² ¸", - "Ġп ÑĢож", - "ĠпÑĢ ож", - "ĠпÑĢо ж", - "ün cü", - "Ġ нÑĸÑĩ", - "Ġн ÑĸÑĩ", - "ĠнÑĸ Ñĩ", - "Ġ मत", - "Ġम त", - "ãģķ ãĤĮãģ¦ãģĦãĤĭ", - "ãģķãĤĮ ãģ¦ãģĦãĤĭ", - "ãģķãĤĮãģ¦ ãģĦãĤĭ", - "ا صر", - "اص ر", - "Ġع ÙĤ", - "ĠкаÑĩе ÑģÑĤве", - "ĠÐĵ еÑĢ", - "ĠÐĵе ÑĢ", - "åº Ĩ", - "Ù ¹", - "a larda", - "al arda", - "alar da", - "ĠÙ¾ رس", - "Ġپر س", - "и ÑĩеÑģкой", - "иÑĩеÑģ кой", - "Ġp him", - "Ġph im", - "Ġphi m", - "ί νη", - "ίν η", - "ä¸ĩ åĨĨ", - "i lerini", - "iler ini", - "ileri ni", - "ilerin i", - "ãĢģ 大", - "Ġo lsa", - "Ġol sa", - "æł¹ æį®", - "âĢĮ س", - "ĠTh ủ", - "r oje", - "ro je", - "roj e", - "нÑĮ оÑĹ", - "нÑĮо ÑĹ", - "Ġs lou", - "Ġsl ou", - "Ġslo u", - "ี ฬ", - "ıy orum", - "ıyor um", - "ÄĽ j", - "Ġ خبر", - "ĠØ® بر", - "è® Ĭ", - "Ġ 缸", - "ĠçĽ ¸", - "e lerinin", - "eler inin", - "eleri nin", - "elerin in", - "elerini n", - "íķĻ ëħĦëıĦ", - "íķĻëħĦ ëıĦ", - "ÑĩеÑģ кие", - "Ñĩе Ñģкие", - "ÑĩеÑģки е", - "ĠÅŁ ekl", - "ĠÅŁek l", - "Ġز ÙħاÙĨÛĮ", - "ĠزÙħاÙĨ ÛĮ", - "ĠزÙħ اÙĨÛĮ", - "Ġ xin", - "Ġx in", - "Ġxi n", - "ัà¸ģ à¸ĩาà¸Ļ", - "ĠE kim", - "ĠEk im", - "æĦ ¿", - "Ġод ной", - "Ġодно й", - "ν ή", - "æľĢ æĸ°", - "ĩ ¼", - "Ġн иж", - "Ġни ж", - "Ġ ë³¼", - "Ġë³ ¼", - "è· ij", - "Ġна пиÑģ", - "Ġнап иÑģ", - "èģ ĸ", - "Ġ âĢĮ", - "ĠâĢ Į", - "æłĩ åĩĨ", - "Ġv rát", - "Ġvr át", - "ĠV ì", - "Ġ ÙģراÙĨ", - "ĠÙģ راÙĨ", - "ĠÙģر اÙĨ", - "æĿ¥ çļĦ", - "å§ ¿", - "Ñħ Ñĥ", - "ĠبÛĮر ÙĪÙĨ", - "Ġд ÑĥÑĪ", - "ĠдÑĥ ÑĪ", - "в аÑİÑĤ", - "ва ÑİÑĤ", - "Ġs ebe", - "Ġse be", - "Ġseb e", - "é» ĺ", - "Ġkay ıt", - "Ġkayı t", - "Ïģ θ", - "ãģ¨ ãģ®", - "ĠпÑĢоÑĨ еÑģÑģ", - "ĠпÑĢоÑĨеÑģ Ñģ", - "æĮģ ãģ¡", - "Ñĸ на", - "Ñĸн а", - "Ġ ÑĤоÑĤ", - "ĠÑĤ оÑĤ", - "ĠÑĤо ÑĤ", - "ĠÑĤак ие", - "ĠÑĤа кие", - "The o", - "Th eo", - "ĠÙĨ ÛĮر", - "ĠÙĨÛĮ ر", - "ÑĨ Ñĥ", - "Ġay ak", - "à¸Ļ à¸Ń", - "Ġsit esinde", - "Ġsites inde", - "Ġsitesi nde", - "ĠÚ©ÙĨ ÛĮÙħ", - "ĠÑģ оÑħ", - "ĠÑģо Ñħ", - "Ġम à¤ľ", - "Ġol uyor", - "ç½ij åĿĢ", - "ĠÙ¾ زش", - "ĠE ylül", - "d Ã¼ÄŁ", - "dü ÄŁ", - "Ġبر Ø®ÛĮ", - "Ġبرخ ÛĮ", - "ĠÙħع رÙģ", - "Ġ obec", - "Ġo bec", - "Ġob ec", - "Ġobe c", - "ĠçalÄ±ÅŁ ma", - "Ġçal Ä±ÅŁma", - "ìĦ¼ íĦ°", - "ĠÑģво ÑĶ", - "оÑģÑĤ ей", - ": ::::::::::", - ":: :::::::::", - ":::: :::::::", - ":::::: :::::", - ":::::::: :::", - "::: ::::::::", - "::::: ::::::", - "::::::: ::::", - "::::::::: ::", - ":::::::::: :", - "Ġ алÑĮ", - "Ġа лÑĮ", - "Ġал ÑĮ", - "ç« Ł", - "Ġباش ÙĨد", - "اÙĦ Ø«", - "Ġнай б", - "Ġп ока", - "Ġпо ка", - "Ġпок а", - "Î ŀ", - "ĠÙĪ Ø¥", - "Ġ Ø®ÙĪاÙĨ", - "ĠØ® ÙĪاÙĨ", - "ĠØ®ÙĪ اÙĨ", - "à¥ģप य", - "Ġ à¹ĥห", - "ĠбÑĭ ÑģÑĤÑĢо", - "Ġth á»Ń", - "Ġthá» Ń", - "ëģ ¼", - "Ġ å¤ļ", - "Ġå¤ ļ", - "两 个", - "ม à¸ķ", - "ز ارش", - "زار Ø´", - "زا رش", - "Ġ ëŁ", - "Ġë Ł", - "य ह", - "Ñī ина", - "Ñīи на", - "Ñīин а", - "ầ ng", - "ần g", - "ï½Ĺ ï½Ĺ", - "à¹Ģà¸ŀ ลà¸ĩ", - "à¹Ģà¸ŀล à¸ĩ", - "tv rt", - "ĠÑĸн ÑĪÑĸ", - "ĠÑĸнÑĪ Ñĸ", - "λ εί", - "λε ί", - "Ġv iá»ĩn", - "Ġvi á»ĩn", - "ij ¸", - "Ġ çĻ½", - "ĠçĻ ½", - "Ùİ ÙĪ", - "Ġch ứa", - "Ġchứ a", - "st vo", - "ĠdoÄŁ r", - "Ġ iler", - "Ġi ler", - "Ġil er", - "Ġile r", - "à¥ĭ ,", - "à¹ĥà¸Ļ à¸Ľ", - "Ġر ÙĪست", - "ĠرÙĪ ست", - "ÙĪ ÙĦÙĪ", - "ÙĪÙĦ ÙĪ", - "Å¡ lo", - "ал иÑģÑĤ", - "али ÑģÑĤ", - "åħ± åĴĮ", - "à¸ŀ ย", - "Ġ ìĻĢ", - "ĠìĻ Ģ", - "ÙĦ ÙĬÙĦ", - "ÙĦÙĬ ÙĦ", - "ĠÑı кого", - "ĠÑıк ого", - "е ÑģÑĤÑĮ", - "еÑģ ÑĤÑĮ", - "еÑģÑĤ ÑĮ", - "ĠÑĦ ин", - "ĠØ£ ÙĨÙĩ", - "ĠØ£ÙĨ Ùĩ", - "ĠMü dür", - "ĠÎĶ ια", - "ĠÎĶι α", - "ĠÑĤ ел", - "ĠÑĤе л", - "ि ,", - "Ñĥ ки", - "Ñĥк и", - "ĠÐł Ф", - "ĠMay ıs", - "à¹Ī à¸Ńม", - "à¹Īà¸Ń ม", - "ar ken", - "ark en", - "æĢ ķ", - "ب ÛĮÙĨ", - "بÛĮ ÙĨ", - "ÑĤ аÑħ", - "ÑĤа Ñħ", - "e bo", - "eb o", - "ë³´ ì¦Ŀê¸Ī", - "ĠÙ¾ ÙĦ", - "Ġг Ñĥб", - "Ġв клÑİÑĩ", - "Ġвк лÑİÑĩ", - "æĶ¿ æ²»", - "Ġε ÏĢιÏĥ", - "ĠεÏĢ ιÏĥ", - "ĠεÏĢι Ïĥ", - "ĠÙģار سÛĮ", - "ĠÙģارس ÛĮ", - "èŃ ī", - "ÏĨ η", - "( éĩij", - "ศ ร", - "åī §", - "âĢĻ ya", - "âĢĻy a", - "å¹´ 度", - "ĠÙĨ رÙħ", - "Ùĥ ÙĪÙħ", - "ÙĥÙĪ Ùħ", - "è¢ ĭ", - "Ġneden le", - "à¹īà¸Ńà¸ĩ à¸ģาร", - "à¹īà¸Ńà¸ĩà¸ģ าร", - "ãĢĮ ãģĤ", - "Ġп оÑģÑĤÑĥп", - "Ġпо ÑģÑĤÑĥп", - "ĠпоÑģÑĤ Ñĥп", - "ìľĦ ìĽIJ", - "åį ĺ", - "èİ ±", - "Ġum ož", - "p ok", - "po k", - "Ñĥ ÑģÑĤи", - "ÑĥÑģ ÑĤи", - "ÑĥÑģÑĤ и", - "Ġ éħ", - "Ġé ħ", - "ĠÑĦ Ñĸз", - "å» £", - "ิ หาร", - "Ġж ÑĥÑĢн", - "ĠдÑĸÑĤ ей", - "Ñĥ ÑİÑīие", - "ÑĥÑİ Ñīие", - "ÑĥÑİÑī ие", - "ä»Ĭ 天", - "ìĿ´ ëĿ¼ê³ł", - "ìĿ´ëĿ¼ ê³ł", - "ç² ī", - "èĴ Ļ", - "ĠDün ya", - "ĠDüny a", - "егод нÑı", - "Ġm imo", - "Ġmi mo", - "Ġmim o", - "Ġ вин", - "Ġв ин", - "Ġви н", - "ãģĿ ãģĵ", - "æ¯ ķ", - "ĠØ£ Ø®", - "Ġ åIJĮ", - "ĠåIJ Į", - "س اÙĨÛĮ", - "ساÙĨ ÛĮ", - "Ġ kah", - "Ġk ah", - "Ġka h", - "ि यर", - "िय र", - "ÏĢ οÏĤ", - "ÏĢο ÏĤ", - "j ez", - "je z", - "ÙĬ ج", - "ĠsaÄŁ lay", - "ا جÙĩ", - "اج Ùĩ", - "Ġ çł", - "Ġç ł", - "ï ľ", - "Ġج ست", - "Ġt ức", - "Æ° Æ¡i", - "Æ°Æ¡ i", - "Ø´ Ùģ", - "ส à¸ķ", - "Ġ ÑĢеÑģ", - "ĠÑĢ еÑģ", - "ĠÑĢе Ñģ", - "Ġ å£", - "Ġå £", - "Ġbi zim", - "Ġbiz im", - "Ġbizi m", - "Ġ ê·Ģ", - "Ġê· Ģ", - "ि ब", - "िठ¬", - "ë¡ľ ìļ´", - "ĠÑģ ÑĤал", - "ĠÑģÑĤ ал", - "ĠÑģÑĤа л", - "Ġ ÑĢÑĥÑģ", - "ĠÑĢ ÑĥÑģ", - "ĠÑĢÑĥ Ñģ", - "ĠO cak", - "ĠOc ak", - "åľ £", - "Ġ úÄįast", - "Ġú Äįast", - "ĠúÄį ast", - "ive rz", - "iver z", - "ëĤĺ ëĬĶ", - "о ÑĢоÑĤ", - "оÑĢ оÑĤ", - "оÑĢо ÑĤ", - "Ñĩ инÑĭ", - "Ñĩи нÑĭ", - "Ñĩин Ñĭ", - "Ġihtiy aç", - "ÐĿ Ðŀ", - "ĠÐĿ ов", - "ĠÐĿо в", - "ีย à¸Ķ", - "ĠпоÑĤÑĢÑĸб но", - "Ú¯ ز", - "ĠÑģказ ал", - "ĠG ia", - "ĠGi a", - "m esini", - "mes ini", - "mesi ni", - "Ġbulun ur", - "æ¸ ¡", - "г оÑĤ", - "го ÑĤ", - "Ġh uku", - "Ġhu ku", - "ëĦ ·", - "ã Ĩ", - "Ġ اÙĥ", - "Ġا Ùĥ", - "Ġد ÙĦÛĮÙĦ", - "ĠدÙĦ ÛĮÙĦ", - "Ġ اساس", - "Ġا ساس", - "Ġاس اس", - "ìĹ° 구", - "ĠÎĺ ε", - "Ġس ÙĪر", - "ĠسÙĪ ر", - "Ġ ì¢Ģ", - "Ġì¢ Ģ", - "ĠاÙĦ در", - "ĠاÙĦد ر", - "ĠÑģÑĤÑĢо иÑĤелÑĮ", - "Ġ Ñĥк", - "ĠÑĥ к", - "ĠìĻ ľ", - "е лик", - "ел ик", - "ели к", - "O VID", - "OV ID", - "Ġt emiz", - "Ġtem iz", - "äº ¦", - "Ġth iếu", - "Ġthi ếu", - "Ġп ÑĥÑĤ", - "ĠпÑĥ ÑĤ", - "Ñİ Ñīей", - "ÑİÑī ей", - "Ġur Äį", - "Ġ ÄIJây", - "ĠÄIJ ây", - "æ¥ µ", - "μ οÏħ", - "μο Ïħ", - "Ġ à¹Ģà¸Ļ", - "Ġà¹Ģ à¸Ļ", - "Ġà¹ĢภĻ", - "е веÑĢ", - "ев еÑĢ", - "Âł ÐĶ", - "ì ´Ŀ", - "ì´ Ŀ", - "è¶ £", - "Ġà¤ħ लà¤Ĺ", - "Ġà¤ħल à¤Ĺ", - "Æ°á»Ŀ n", - "Ġ ãĥŃ", - "Ġãĥ Ń", - "Ġ ê³³", - "Ġê³ ³", - "é² ģ", - "Ġرس ÛĮد", - "身 ä½ĵ", - "ั à¸ĵà¸ij", - "y nÃŃ", - "yn ÃŃ", - "ج ات", - "جا ت", - "ì§Ģ 를", - "न ल", - "ì ķĮ", - "ìķ Į", - "Ñĸ п", - "Ġv Ãłng", - "ĠvÃł ng", - "Ġпл оÑī", - "Ġпло Ñī", - "оз мож", - "åī ²", - "Ġth ảo", - "л ади", - "ла ди", - "лад и", - "Ġ åĿ", - "Ġå Ŀ", - "ĠÐľ и", - "Ġдел аÑĤÑĮ", - "Ġдела ÑĤÑĮ", - "é ij", - "Ġh uy", - "Ġhu y", - "ا ÛĮØ·", - "اÛĮ Ø·", - "Ġпов ÑĤоÑĢ", - "ü len", - "ül en", - "üle n", - "Ġ ÙĪÙģ", - "ĠÙĪ Ùģ", - "ĠÙĬ تÙħ", - "ĠÙĬت Ùħ", - "ĠÑĢеж им", - "Ġ ìºIJ", - "Ġìº IJ", - "ĠÃĩ ünkü", - "ع دد", - "عد د", - "ни веÑĢ", - "нив еÑĢ", - "ĠÐĿ ик", - "å¸ ĸ", - "Ïį ÏĢ", - "an lar", - "س تÛĮ", - "ست ÛĮ", - "Ġbulun maktadır", - "à¹ģ à¸ļ", - "v ek", - "ve k", - "Ġгла за", - "Ġглаз а", - "å¹ ħ", - "Ġúda j", - "Ġг ÑĢо", - "ĠгÑĢ о", - "Ġкон кÑĥÑĢ", - "Ġd ůležit", - "Ġdů ležit", - "Ġ Ø·ÙĪر", - "ĠØ· ÙĪر", - "à¸ĺ าà¸Ļ", - "ĠÙĦ ÙĥÙĨ", - "ĠÙĦÙĥ ÙĨ", - "ر ÙĤ", - "Ðļ ÐIJ", - "Ġ éĿĴ", - "ĠéĿ Ĵ", - "Ġ ìĤ¬ëŀij", - "ĠìĤ¬ ëŀij", - "ĠÑħ воÑĢ", - "ĠÑħв оÑĢ", - "s unuz", - "sun uz", - "ĠÙħØ´ خص", - "éĻ ¸", - "Ġ ढ", - "Ġठ¢", - "Ġv az", - "Ġva z", - "交 æĺĵ", - "ĠÑĤеÑĢ ÑĢиÑĤ", - "ÑĩеÑģ кой", - "Ñĩе Ñģкой", - "ี à¹Ĥ", - "rop oda", - "ıl dıģı", - "ıldı ģı", - "Ġ ëī´", - "Ġë ī´", - "íķĻ 기", - "ë³´ íĹĺ", - "Ġз аÑĤем", - "ĠзаÑĤ ем", - "Âł в", - "ãĥ¼ ãĥĨ", - "ãĥ¼ãĥ Ĩ", - "Ġ ÐŀÑģнов", - "ĠÐŀÑģ нов", - "ãĨ į", - "Ġد ع", - "ÐŁ оÑģ", - "ÐŁÐ¾ Ñģ", - "æ² ī", - "Ġ лож", - "Ġл ож", - "çĶµ åŃIJ", - "Ġ رد", - "Ġر د", - "ĠÑģ ÑĢазÑĥ", - "e jte", - "ej te", - "Ġà¤ij फ", - "Ġt Ãłu", - "ÃŃ k", - "lan ması", - "lanma sı", - "к аÑĤ", - "ка ÑĤ", - "าà¸ģ าศ", - "ãĤ¢ ãĤ¤", - "ÏĦ ιο", - "ÏĦι ο", - "Ġ å§", - "Ġå §", - "प त", - "E Y", - "Ġj mé", - "Ġjm é", - "Ġod kazy", - "Ġê°ľ ìĿ¸", - "éģ ¿", - "bÄĽ h", - "Ðł Ðŀ", - "çĥ Ī", - "Ġza rar", - "Ġzar ar", - "Ú¯ ÙĪÙĨÙĩ", - "Ú¯ÙĪ ÙĨÙĩ", - "Ġtr ì", - "Ġm ại", - "ен нÑĭм", - "ĠÑį коном", - "ĠÑįк оном", - "éĽ £", - "Ġ íĦ", - "Ġí Ħ", - "æİ ī", - "Ġs oru", - "Ġso ru", - "Ġsor u", - "ĠФедеÑĢа ÑĨии", - "ĠÑģиÑģÑĤем и", - "æĸĻ çĦ¡æĸĻ", - "Ġà¤ķ à¤Ń", - "ĠÙĩ ÙĨد", - "ĠÙĩÙĨ د", - "ุà¸ĩ à¹Ģà¸Ĺà¸ŀ", - "ĠOsman lı", - "ĠпÑĢод олж", - "Ġ ÙĪÙĦا", - "ĠÙĪ ÙĦا", - "ĠÙĪÙĦ ا", - "ĠÄįlán ku", - "Ġa dım", - "Ġad ım", - "Ġadı m", - "ĠÏĢ αÏģά", - "ĠÏĢαÏģ ά", - "ĠÏĢα Ïģά", - "Ġzá ÅĻÃŃ", - "Ġ à¸Īำà¸ģ", - "Ġà¸Īำ à¸ģ", - "Ġп ен", - "m enin", - "me nin", - "men in", - "meni n", - "Ġìĺ¤ ëĬĺ", - "em iz", - "emi z", - "οÏį ÏĤ", - "- स", - "íķĺ ìĭľ", - "ĠÑħ ви", - "ĠÑħв и", - "ãĤ° ãĥ©", - "Ġп оÑĪ", - "Ġпо ÑĪ", - "ĠÐŀдна ко", - "ĠÐŀднак о", - "Ñĸд но", - "íĺ ľ", - "Ñī ими", - "Ñīи ми", - "Ñīим и", - "èĥ ¸", - "ĠÄ° lk", - "ĠÄ°l k", - "m ey", - "me y", - "Ġз да", - "Ġзд а", - "κ λη", - "а лом", - "ал ом", - "ало м", - "à¹Ģศ ษ", - "ا ÙĨا", - "اÙĨ ا", - "Ġ ÎŁÎ¹", - "ĠÎŁ ι", - "Ġ åıĮ", - "Ġåı Į", - "ี à¸Ĥ", - "Ġ بس", - "Ġب س", - "è§Ħ å®ļ", - "i say", - "is ay", - "isa y", - "uk arı", - "uka rı", - "æµģ éĩı", - "v ÃŃm", - "vÃŃ m", - "λ Ïİ", - "ä¹ Ļ", - "Ġल ड", - "ĠÙĨد ارد", - "ĠÙĨدار د", - "е ÑĢом", - "еÑĢ ом", - "еÑĢо м", - "Ġsır asında", - "Ġsıras ında", - "Ġsıra sında", - "Ġr Äĥng", - "Æ¡ m", - "Ġl ạnh", - "Ġlạ nh", - "ठĥ", - "à¥ģ ण", - "à¥ģठ£", - "uz ey", - "uze y", - "Ġ Ñĥва", - "ĠÑĥ ва", - "ĠÑĥв а", - "vÄĽ d", - "Ñĭ Ñģ", - "Ġ κι", - "Ġκ ι", - "Ñ ķ", - "ÛĮ ا", - "à¸ĩ à¸Ħ", - "ph ylum", - "phy lum", - "phyl um", - "Ġber aber", - "ี à¸Ķ", - "æµ ®", - "ा सन", - "ास न", - "o vice", - "ov ice", - "ovic e", - "ovi ce", - "è¦ §", - "Ġस फ", - "å°ij 女", - "ан ÑĤи", - "анÑĤ и", - "é¨ ĵ", - "Ġso át", - "é¬ ¼", - "lan mÄ±ÅŁ", - "Ġb ếp", - "ÙIJ ÙĦ", - "Ġsay ısı", - "Ġsayı sı", - "ĠÙĤ دÙħ", - "ĠÙĤد Ùħ", - "à¥Ī म", - "ह म", - "ĠÑĢ Ñĥки", - "ĠÑĢÑĥ ки", - "ĠÑĢÑĥк и", - "ĠصÙģ ØŃÙĩ", - "Å¡ ky", - "Å¡k y", - "é» Ĵ", - "èģ ļ", - "ãģĭ ãģ«", - "Ġs âu", - "ед аг", - "ĠÑģÑĤоÑĢ онÑĭ", - "ĠÑģÑĤоÑĢон Ñĭ", - "Ġ ruk", - "Ġr uk", - "Ġru k", - "âĢĮ âĢĮ", - "ĠØ¢ ÙĪر", - "Ġع دÙħ", - "Ġعد Ùħ", - "õ i", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Ġبا زار", - "Ġباز ار", - "Ġe deb", - "Ġed eb", - "Ġv ÄįetnÄĽ", - "оп аÑģ", - "Ġн ег", - "Ġне г", - "m ayan", - "ma yan", - "may an", - "maya n", - "к оÑģÑĤÑĮ", - "ко ÑģÑĤÑĮ", - "Ġsv ůj", - "ÄŁ ında", - "ģı nda", - "ģın da", - "Ø° ÛĮر", - "M á»Ļt", - "Ð Ħ", - "Ġyap tı", - "ि थ", - "िठ¥", - "ĠÙħ Ùĩر", - "ĠÙħÙĩ ر", - "Ġд оÑģÑĤи", - "Ġдо ÑģÑĤи", - "ĠдоÑģÑĤ и", - "ĠдоÑģ ÑĤи", - "Ġ صÙĪر", - "Ġص ÙĪر", - "m esine", - "mes ine", - "mesi ne", - "ĠD ân", - "ä¸Ģ ä¸ĭ", - "çį İ", - "ĠÐľ иÑħ", - "ĠÐľÐ¸ Ñħ", - "Ġо Ñĩи", - "ĠоÑĩ и", - "ãĤ¦ ãĤ§", - "Ġ ÑĸÑģ", - "ĠÑĸ Ñģ", - "Ġgi ác", - "Ġgiá c", - "åľ¨çº¿ è§Ĥçľĭ", - "Ġاد اÙħÙĩ", - "ÑĨ ов", - "ÑĨо в", - "Ġ комÑĥ", - "Ġк омÑĥ", - "Ġком Ñĥ", - "Ġко мÑĥ", - "ĠÄ°ng iliz", - "Ġг ÑĢаж", - "ĠгÑĢа ж", - "ĠгÑĢ аж", - "ãģ¦ ãĤĤ", - "Ġch ữ", - "олÑĮ кÑĥ", - "m ÄĽt", - "mÄĽ t", - "Ñıг ом", - "Ñĩ аÑģÑĤ", - "Ñĩа ÑģÑĤ", - "ÑĩаÑģ ÑĤ", - "ìĸ ¼", - "Ġkh óa", - "Ġkhó a", - "ĠÐIJ д", - "ĠØ¢ ÙĤ", - "Ġkurul uÅŁ", - "ά ζ", - "Ġж ов", - "Ġв ÑģÑĤÑĢе", - "ĠвÑģÑĤ ÑĢе", - "ĠÙĪ ÙĦÙĥ", - "ĠÙĪÙĦ Ùĥ", - "Ġt uyá»ĩt", - "y ı", - "Ġ ÐĴо", - "ĠÐĴ о", - "Ġv á»įng", - "Ġvá» įng", - "ع ÙĬØ©", - "عÙĬ Ø©", - "Ġop ÄĽt", - "ا ÙĬد", - "اÙĬ د", - "à¥Ī .Ċ", - "à¥Ī. Ċ", - "ĠÑģ ами", - "ĠÑģам и", - "åª Ĵ", - "Ġsv ých", - "ĠëĤĺ íĥĢ", - "ìĨ IJ", - "Ġ ÙĦع", - "ĠÙĦ ع", - "Ġet kin", - "Ġetk in", - "Ġetki n", - "ĠN á", - "Ġsou tÄĽ", - "Ġsout ÄĽ", - "층 ìĿĺ", - "Ġ çŃī", - "ĠçŃ ī", - "Ġر سÙħ", - "Ġرس Ùħ", - "Ġ خاÙĨÙĩ", - "ĠØ® اÙĨÙĩ", - "ĠخاÙĨ Ùĩ", - "Ġ 家", - "Ġå® ¶", - "iá» ģm", - "iá»ģ m", - "ëħ IJ", - "ê° Ī", - "ì° ©", - "ž il", - "ži l", - "ÑģÑĤиÑĤ ÑĥÑĤ", - "ÑģÑĤиÑĤÑĥ ÑĤ", - "or uÄį", - "oru Äį", - "ĠØ¥ ذا", - "Ġإذ ا", - "à¹Ħ à¸Ĥ", - "ี à¸Ĭ", - "ÑĢ аб", - "ÑĢаР±", - "ÑĢа б", - "íķĻ ìĥĿ", - "Ġ ìī", - "Ġì ī", - "r nek", - "rn ek", - "rne k", - "Ġاست خداÙħ", - "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", - "Ġв Ñģем", - "ĠвÑģ ем", - "ĠвÑģе м", - "Ġìłķ ëıĦ", - "Ġvy j", - "éĢ ±", - "алÑĮ ное", - "алÑĮно е", - "Ġch uyá»ĩn", - "ì§Ģ ìĽIJ", - "i lerine", - "ile rine", - "iler ine", - "ileri ne", - "ilerin e", - "ĠìķĦ 무", - "Ġок оло", - "ाव न", - "à¸Ļ า", - "о пÑĢи", - "оп ÑĢи", - "d rž", - "dr ž", - "ĠÑģÑĥ ÑģпÑĸлÑĮ", - "Ġب Ùĥ", - "u ky", - "uk y", - "Ġ ÏĩÏī", - "ĠÏĩ Ïī", - "Ġtu ần", - "nict vÃŃ", - "Ġ ÙĩدÙģ", - "ĠÙĩد Ùģ", - "Ġch iá»ģu", - "ÎĹ ÎĿ", - "å°ı å§IJ", - "íķĺ ìĺĢ", - "Ġk las", - "Ġkl as", - "Ġkla s", - "á»Ļ n", - "ĠìĿ´ íĽĦ", - "ÙĨ اÙħج", - "ÙĨا Ùħج", - "ÙĨاÙħ ج", - "Äį ast", - "Äįas t", - "ĠاÙĦ خاص", - "ĠاÙĦØ® اص", - "l Ä±ÅŁ", - "lı ÅŁ", - "Ġع Ùħر", - "ĠعÙħ ر", - "ãĢį Ċ", - "иб олее", - "ãĤĬ ãģ®", - "ãħ ł", - "ä¹Ł ä¸į", - "к ÑĢеÑĤ", - "Ġ ìĶ", - "Ġì Ķ", - "ÏĦ ια", - "ÏĦι α", - "ĠÑĥпÑĢав лÑĸннÑı", - "æ² ¢", - "Ġk esin", - "Ġke sin", - "Ġkes in", - "ì¡Į ëĭ¤", - "머 ëĭĪ", - "羣 çļĦ", - "Ġbak ım", - "æĿ± 京", - "¾ ¸", - "ÙħÙĦ ÙĥØ©", - "оÑĤ ÑĢеб", - "d ın", - "dı n", - "Ġ PÅĻi", - "ĠP ÅĻi", - "ĠPÅĻ i", - "Ġm ÄĽli", - "ĠmÄĽ li", - "ĠmÄĽl i", - "Ġδη μο", - "å ¯¸", - "å¯ ¸", - "ĠÙĪ ÙĥاÙĨ", - "ĠÙĪÙĥ اÙĨ", - "Ġप ढ", - "ĠвеÑĢ Ñħ", - "Ġе Ñij", - "C ách", - "Các h", - "ä½ľ 为", - "ĠÐļ ол", - "ĠÐļо л", - "Ġ ве", - "Ġв е", - "Ġ деÑĢж", - "Ġд еÑĢж", - "ĠдеÑĢ ж", - "em oc", - "emo c", - "ãģ¸ ãģ®", - "Ġ аÑĢÑħ", - "ĠаÑĢ Ñħ", - "Ġk iếm", - "Ġ æĺİ", - "Ġæĺ İ", - "ĠлÑİд ини", - "ĠлÑİди ни", - "ë ·", - "ĠÙĪ اÙĦت", - "ĠÙĪاÙĦ ت", - "ĠÙĪا ÙĦت", - "Ġ è°", - "Ġè °", - "çģ ¯", - "íĻ ķ", - "Ġ구 매", - "Ġ ç§ij", - "Ġç§ ij", - "it nÃŃ", - "и ÑĩеÑģкие", - "иÑĩеÑģ кие", - "иÑĩеÑģки е", - "ĠÙĨ Ùģس", - "ĠÙĨÙģ س", - "Ġت ÙĦÙģ", - "ĠتÙĦ Ùģ", - "ا ÙģÛĮ", - "اÙģ ÛĮ", - "ĠØŃ Ø³ÙĨ", - "ĠØŃس ÙĨ", - "âĸ¡ âĸ¡", - "ý vá", - "ýv á", - "ÄŁ ın", - "ģı n", - "ıyor uz", - "ĠCh ÃŃ", - "ĠÙ¾ÚĺÙĪÙĩ Ø´", - "Ġ ÏĦÎŃ", - "ĠÏĦ ÎŃ", - "ĠÏĥ Ïĩε", - "ĠÏĥÏĩ ε", - "о леÑĤ", - "ол еÑĤ", - "α ιδ", - "αι δ", - "Ġh ạt", - "Ġhạ t", - "à¸ł าà¸Ħ", - "åĨ °", - "Ġrych le", - "it eli", - "ite li", - "itel i", - "Âł z", - "ย à¸ģ", - "æ¨ ¹", - "Ġج ÙĪاÙĨ", - "ĠجÙĪ اÙĨ", - "æĺ Į", - "Ġü retim", - "Ġür etim", - "Ġüret im", - "ระ à¸ļ", - "à¸Ľà¸£à¸° ม", - "ά Ïĥ", - "å² ©", - "ĠÑĥ ÑģÑĤÑĢой", - "ĠÑĥÑģÑĤ ÑĢой", - "Ġver ilen", - "Ġveri len", - "ich ni", - "ĠpÅĻÃŃ mo", - "ĠpÅĻÃŃm o", - "ĠاÙĦØ°Ùĩ اب", - "ì½ ľ", - "æľ ±", - "Ġس Ø®", - "Ñĸ ла", - "Ñĸл а", - "Ñĥ ма", - "Ñĥм а", - "ห า", - "ÛĮ دا", - "ÛĮد ا", - "å² ¸", - "ä¸Ģ å®ļ", - "Ġ ä¼ļ", - "Ġ ÐŁÑĸд", - "ĠÐŁ Ñĸд", - "Ġ ÑĩиÑĤ", - "ĠÑĩ иÑĤ", - "ĠÑĩи ÑĤ", - "и Ñİ", - "Ġ ÐĹап", - "ĠÐĹ Ð°Ð¿", - "ĠÐĹа п", - "ÑĤ иÑı", - "ÑĤи Ñı", - "Ġ ê°ľë°ľ", - "Ġê°ľ ë°ľ", - "ĠÑĤе оÑĢ", - "Ñı ÑģÑĮ", - "ÑıÑģ ÑĮ", - "ĠpÅĻÃŃ prav", - "( åľŁ", - "Ùħ ÙĬ", - "ĠpÅĻed evÅ¡ÃŃm", - "ĠTem muz", - "Ġпод деÑĢж", - "Ġнед оÑģÑĤаÑĤ", - "ĠìĿ´ ìľł", - "Ġkhá»ı i", - "ĠاÙĦ تØŃ", - "ĠاÙĦت ØŃ", - "ĠÙħÙħ Ú©ÙĨ", - "Ġv hod", - "Ġvh od", - "ев ой", - "ево й", - "о вал", - "ов ал", - "ова л", - "Ġн алеж", - "Ġна леж", - "Ġнал еж", - "ï¼¼ :", - "ย ะ", - "ĠÙħ اشÛĮÙĨ", - "Ġg á»Ńi", - "al ım", - "alı m", - "Ġìµľ ìłĢ", - "Ùij Ùĩ", - "á»Ļ p", - "à¥Ģ ।Ċ", - "à¥Ģ। Ċ", - "Ġ пиÑģ", - "Ġп иÑģ", - "Ġпи Ñģ", - "Ġ вÑģÑı", - "Ġв ÑģÑı", - "ĠвÑģ Ñı", - "Ñĩ ем", - "Ñĩе м", - "o zenÃŃ", - "oz enÃŃ", - "oze nÃŃ", - "Ġ äºļæ´²", - "Ġäºļ æ´²", - "е ÑĢалÑĮ", - "еÑĢ алÑĮ", - "еÑĢа лÑĮ", - "기 ëĬĶ", - "Ġп ÑĢез", - "ĠпÑĢ ез", - "ĠпÑĢе з", - "ĠعÙħ ÙĪÙħÛĮ", - "и ÑĩниÑħ", - "иÑĩ ниÑħ", - "Ġ æ²³", - "Ġæ² ³", - "od nÃŃ", - "åıª æĺ¯", - "Ġpo dp", - "Ġpod p", - "à¹īà¸Ńà¸ĩ à¸ŀ", - "ाय द", - "ाà¤ĩ ल", - "ล à¸Ķ", - "ĠÑĢÑĸÑĪ еннÑı", - "Ġ ÑĤÑĥÑĢ", - "ĠÑĤ ÑĥÑĢ", - "ĠÑĤÑĥ ÑĢ", - "ÑģÑĮ кÑĥ", - "ÑģÑĮк Ñĥ", - "Ġsal dır", - "ĠÐĽ ÑĮв", - "ãĢģ Ċ", - "ĠÙ¾ÛĮ ÙĪÙĨد", - "åѦ ä¹ł", - "λ Ïī", - "o vit", - "ov it", - "ovi t", - "ü le", - "ül e", - "女 æĢ§", - " Ł", - "e mez", - "em ez", - "eme z", - "Ġh ale", - "Ġha le", - "Ġhal e", - "âī ¦", - "ĠÎķ κ", - "ÏĦηγοÏģ ία", - "k ý", - "ìĦ± ìĿĦ", - "Ġt ým", - "Ġtý m", - "à¥ĩ -", - "Ġz ejména", - "æĻ ¶", - "Ġn gon", - "Ġng on", - "ãĢı ĊĊ", - "软 件", - "éĤ£ ä¹Ī", - "ĠкваÑĢ ÑĤи", - "ĠÙħÙĨ ظ", - "on ec", - "one c", - "Ġг ли", - "à¥ģर à¤ķ", - "ĠS okol", - "ĠSo kol", - "ĠSok ol", - "Ġ ä¿Ŀ", - "д ив", - "ди в", - "ál nÃŃm", - "áln ÃŃm", - "álnÃŃ m", - "ac aģı", - "aca ģı", - "acaÄŁ ı", - "a ÅŁa", - "aÅŁ a", - "ĠÙħ اÙĦÛĮ", - "ĠÙħا ÙĦÛĮ", - "ĠÙħاÙĦ ÛĮ", - "ĠÃĸ n", - "иÑĤ ели", - "иÑĤе ли", - "ĠØ® رد", - "Ġخر د", - "Ġkullan ıl", - "Ġkullanı l", - "Ġ ÙħÛĮÙĦ", - "ĠÙħ ÛĮÙĦ", - "ĠÙħÛĮ ÙĦ", - "Ġ íļ¨", - "Ġíļ ¨", - "ã n", - "Ġ rost", - "Ġr ost", - "Ġro st", - "Ġros t", - "Ġëĸ ł", - "u bat", - "ub at", - "uba t", - "Ġ åıĤ", - "Ġåı Ĥ", - "Ġبر اÙĬ", - "Ġм енÑĮ", - "Ġмен ÑĮ", - "ั à¸Ħร", - "ัà¸Ħ ร", - "Ġпом ог", - "ĠØŃض ÙĪر", - "Ġthá»ĭ t", - "ä¹ ³", - "Ġ ìĭłì²Ń", - "Ġìĭł ì²Ń", - "Ġíĺ Ħìŀ¬", - "ĠíĺĦ ìŀ¬", - "Ġ ë¹ł", - "Ġë¹ ł", - "вÑĢоп ей", - "Ġne jen", - "Ġnej en", - "Ñĸ ка", - "Ñĸк а", - "Ġ ìļ¸", - "Ġìļ ¸", - "ĠÙħ بار", - "ĠÙħب ار", - "Ġ Äįek", - "ĠÄį ek", - "ĠÄįe k", - "Ġk alk", - "Ġka lk", - "Ġkal k", - "Ġ amac", - "Ġa mac", - "Ġam ac", - "Ġama c", - "اد ت", - "ĠÙħ اسÙĩ", - "ĠÙħا سÙĩ", - "Ġarasında ki", - "Ġaras ındaki", - "Ġб еÑģ", - "ĠоÑĤд елÑĮ", - "ĠоÑĤдел ÑĮ", - "á½ ¶", - "ĠΤ ζ", - "v yk", - "vy k", - "ج ÙĨ", - "» ê²Į", - "ĠниÑĩ его", - "ĠØ´ اÙħÙĦ", - "ĠÑĥÑģловиÑı Ñħ", - "la ması", - "lam ası", - "lama sı", - "è½ ī", - "ç¾ ½", - "Ġж ид", - "ĠоÑĤ ноÑģ", - "ĠздÑĸйÑģ нÑİ", - "Ġ VỼi", - "ĠV Ỽi", - "ÙĪ ÙĦÛĮ", - "ÙĪÙĦ ÛĮ", - "Ġt isÃŃ", - "Ġti sÃŃ", - "ĠÏĩ ÏģÏĮ", - "Ġprac ovnÃŃ", - "Ġpracov nÃŃ", - "ĠÙĬ ÙĥÙĪÙĨ", - "ĠÙĬÙĥ ÙĪÙĨ", - "Ġb eÅŁ", - "Ġbe ÅŁ", - "ج ز", - "ั à¸ļร", - "ัà¸ļ ร", - "ĠYön et", - "Ġشر اÛĮØ·", - "ĠتÙĪس عÙĩ", - "çĹ ĩ", - "à¸ĩ à¹Ģà¸Ľ", - "ä¸Ģ 次", - "ĠÐłÐ¾ÑģÑģий Ñģкой", - "æľĢ é«ĺ", - "Ġsp olu", - "Ġspo lu", - "Ġspol u", - "д аеÑĤÑģÑı", - "да еÑĤÑģÑı", - "даеÑĤ ÑģÑı", - "Ñĸ ÑĤÑĥ", - "ÑĸÑĤ Ñĥ", - "Ġоб ÑĢаÑĤ", - "ĠобÑĢа ÑĤ", - "e nek", - "en ek", - "ene k", - "Ġ mek", - "Ġm ek", - "Ġme k", - "å¦ Ī", - "Ġдоп олниÑĤелÑĮ", - "Ġ ç²", - "Ġç ²", - "ĠÙĦ ÙĦت", - "ĠÙĦÙĦ ت", - "ĠHaz iran", - "æ¸ Ī", - "à¹Į à¸Ĥà¸Ńà¸ĩ", - "ĠÑĦ он", - "Ġê²ĥ ìľ¼ë¡ľ", - "Ġnh é", - "Ġbu gün", - "Ġbug ün", - "ov ém", - "ové m", - "Ġза веÑĢ", - "Ġзав еÑĢ", - "Ġд виг", - "Ġдв иг", - "Ġдви г", - "ä¼ Ļ", - "Ġnu ôi", - "меÑĢ ик", - "ме ÑĢик", - "ĠÙĨÙħ ÙĪÙĨÙĩ", - "èį ·", - "Ñĥ вала", - "Ñĥв ала", - "Ñĥва ла", - "ç¿ »", - "Ġs ân", - "ог оÑİ", - "ого Ñİ", - "ا سÙĬØ©", - "اس ÙĬØ©", - "اسÙĬ Ø©", - "Ñĥн кÑĤ", - "Ñĥнк ÑĤ", - "á nÃŃm", - "án ÃŃm", - "ánÃŃ m", - "ен ное", - "енно е", - "Ġph út", - "Ġ मर", - "Ġम र", - "ĠاÙĦ ÙĪØ·", - "ĠاÙĦÙĪ Ø·", - "Ġлег ко", - "Ġ ãĢĭ", - "ĠãĢ ĭ", - "ë¡ľ ëĵľ", - "ĠKas ım", - "ÙĬ ÙĦÙĬ", - "ÙĬÙĦ ÙĬ", - "ĠbaÄŁlantı lar", - "ĠÑĤ ÑĢÑĥд", - "ĠÑĤÑĢ Ñĥд", - "ĠÑĤÑĢÑĥ д", - "Ø· Ùĩ", - "Ġk vůli", - "ÑģÑĤ оÑı", - "Ġsat Ä±ÅŁ", - "Ġh áºŃu", - "ĠبÙĩ ترÛĮÙĨ", - "ĠبÙĩتر ÛĮÙĨ", - "ĠÑģ елÑĮ", - "ĠÑģел ÑĮ", - "ั à¸Ļว", - "ัà¸Ļ ว", - "o su", - "os u", - "य न", - "åĽ ³", - "ι δ", - "ÛĮ تÛĮ", - "ÛĮت ÛĮ", - "ĠQu áºŃn", - "Ġ ей", - "Ġе й", - "à¹Ģว ลา", - "à¹Ģวล า", - "ìĬ¤ íĥĢ", - "ìĤ¬ 를", - "Ġا ÙĩÙĦ", - "ĠاÙĩ ÙĦ", - "η γ", - "Ġk á»·", - "Ġká» ·", - "Ġ наÑĤ", - "Ġн аÑĤ", - "Ġна ÑĤ", - "âĢ ¡", - "Ñĸ ÑĩниÑħ", - "ÑĸÑĩ ниÑħ", - "ĠÑĢазвиÑĤи Ñı", - "ĠÑĢазви ÑĤиÑı", - "e cial", - "ec ial", - "eci al", - "ĠÑħ озÑı", - "в аеÑĤ", - "ва еÑĤ", - "ĠÄIJ á»Ļ", - "ĠÄIJá» Ļ", - "Ġ éĵ", - "Ġé ĵ", - "Ġok am", - "ĠвÑģ ÑĸÑħ", - "ĠвÑģÑĸ Ñħ", - "ĠPr aze", - "ĠPra ze", - "ë¥ ł", - "ι κα", - "ικ α", - "æ¬ ²", - "Ġgerçek leÅŁ", - "ç¥ ĸ", - "Ġод ним", - "Âł M", - "Ġre nk", - "Ġr enk", - "Ġren k", - "Ġल à¤ķ", - "ãĥķ ãĤ§", - "ãĥķãĤ §", - "ĠÙĨ زد", - "å¹ »", - "Ġúzem ÃŃ", - "æı ¡", - "а лиÑģÑı", - "али ÑģÑı", - "Ġ ÃĶ", - "Ġà Ķ", - "Ġy orum", - "Ġyo rum", - "ĠÏĢ ÏģÏī", - "ãĥ³ ãĥĩ", - "ãĥ³ãĥ ĩ", - "éĸĭ å§ĭ", - "ãĥ¼ ãĥª", - "ãĥ¼ãĥ ª", - "Ġìĸ¼ êµ´", - "Û± Û±", - "Û±Û ±", - "lü ÄŁÃ¼", - "lÃ¼ÄŁ ü", - "ÙĨ Ø´", - "à¹Ī ำ", - "èĽ ĭ", - "ĠØ£ د", - "ĠW illi", - "ĠWill i", - "ĠWil li", - "ĠWi lli", - "èª ²", - "Ġsür dür", - "ĠEx ternÃŃ", - "Ġp ůvod", - "Ġpů vod", - "ĠØ® اÙĨÙĪ", - "ĠخاÙĨ ÙĪ", - "ĠкоÑĤоÑĢ ое", - "Ġm ohl", - "Ġmo hl", - "Ġmoh l", - "Ġs tÄĽ", - "Ġst ÄĽ", - "åĩ ı", - "ìĤ ¼", - "aban cı", - "à¹ģ à¸Ļ", - "สำ à¸Ħ", - "æĤ £", - "ab ilece", - "abil ece", - "abile ce", - "éĺ³ åŁİ", - "Îij Îļ", - "Ġch ữa", - "Ġchữ a", - "ĠìķĦ ëĭ", - "طبÙĬ ÙĤ", - "طب ÙĬÙĤ", - "ÎĻ ÎŁÎ¥", - "ÎĻÎŁ Î¥", - "ÑĢ ование", - "ÑĢов ание", - "ÑĢо вание", - "ÑĢован ие", - "ÑĢова ние", - "åĩ ½", - "Ġ ì¼", - "Ġì ¼", - "ÑĢ оÑĦ", - "ÑĢо ÑĦ", - "à¹ĩ à¸Ļส", - "à¹ĩà¸Ļ ส", - "Ġ ãĤ¦", - "ĠãĤ ¦", - "ï¼ļ ãĢĮ", - "á»ĭ a", - "Ġ hPa", - "Ġh Pa", - "m anı", - "man ı", - "ál nÃŃho", - "álnÃŃ ho", - "ÙĪ تÛĮ", - "ÙĪت ÛĮ", - "ĠлеÑĩ ениÑı", - "j te", - "jt e", - "- д", - "åħ¨ åĽ½", - "ĠбÑĥд Ñĸв", - "Ġz atÃŃm", - "Ġzat ÃŃm", - "Ġ öyle", - "Ġö yle", - "ìĿ´ ê°Ģ", - "s tal", - "st al", - "sta l", - "i vatel", - "iv atel", - "ivate l", - "iva tel", - "Ġ æľª", - "Ġpož ad", - "ĠÑģ ни", - "Ġpos lednÃŃ", - "Ġposled nÃŃ", - "ĠÑģÑĤ анд", - "ĠÑģÑĤан д", - "ĠÑģÑĤа нд", - "à¥Ģ à¤ıम", - "à¥Ģà¤ı म", - "Ġ عکس", - "Ġع کس", - "ÑĢ иÑı", - "ÑĢи Ñı", - "ã y", - "á»ĭ p", - "Ġo kul", - "Ġok ul", - "Ġoku l", - "à¸ĩ หมà¸Ķ", - "Ġвоз ник", - "m ÃŃ", - "ç§ Ł", - "ĠÄij á»ijc", - "Ġp odÃŃ", - "Ġpo dÃŃ", - "Ġpod ÃŃ", - "ĠÅĻÃŃ j", - "ĠÑĤак Ñĸ", - "ĠÑĤа кÑĸ", - "à¸ļ าà¸Ĺ", - "Ġ 보기", - "Ġë³´ 기", - "ล า", - "еÑģ ÑĤо", - "еÑģÑĤ о", - "Ġ çĶ¨", - "и нÑĭ", - "ин Ñĭ", - "ĠÑĢ ÑĥÑħ", - "ĠÑĢÑĥ Ñħ", - "ĠÑĢаÑģп олож", - "Ñī еннÑı", - "Ġc á»Ń", - "à¹ī à¸ļร", - "à¥įयव स", - "ï¾ ļ", - "Ġд алÑĮ", - "Ġда лÑĮ", - "Ġдал ÑĮ", - "Ġض د", - "ÙĦ ÙĬØ©", - "ÙĦÙĬ Ø©", - "ĠкоÑĤоÑĢ ого", - "Ġd ve", - "Ġdv e", - "Ġnh ạc", - "ÑĦ Ñĸка", - "ÑĦÑĸ ка", - "ÑĦÑĸк а", - "à¥Ī à¤Ł", - "èĩª çĶ±", - "Ġпо ÑĢÑĥÑĪ", - "ĠпоÑĢ ÑĥÑĪ", - "æľĭ åıĭ", - "Ġd ört", - "Ġdö rt", - "ĠÑĢаÑģп ÑĢоÑģÑĤ", - "ãģ§ ãģ¯ãģªãģĦ", - "ãģ§ãģ¯ ãģªãģĦ", - "ĠпеÑĢ ег", - "ĠпеÑĢе г", - "Ġ ánh", - "Ġá nh", - "Ġán h", - "ĠV ÃŃ", - "ظ Ù¹", - "à¥į रण", - "à¥įर ण", - "Ġb ilim", - "Ġbi lim", - "Ġbil im", - "Ġlid é", - "Ġd ÃŃky", - "ĠdÃŃ ky", - "ĠÄIJ á»ĵng", - "Ġ εÏģγ", - "Ġε Ïģγ", - "Ġzn ovu", - "Ïĥ ια", - "Ïĥι α", - "Ñ ŀ", - "स à¤Ń", - "e kk", - "ek k", - "Ġμε ÏĦά", - "ÑģÑĤ иÑĩ", - "ÑģÑĤи Ñĩ", - "ÛĮ ÙĨÚ¯", - "ÛĮÙĨ Ú¯", - "ĠÑıв лÑıÑİÑĤÑģÑı", - "Ġ 建", - "Ġå» º", - "Ïĥ Ïĥα", - "ÏĥÏĥ α", - "ав лива", - "à¸ģ รม", - "à¸ģร ม", - "ç¬ Ķ", - "Ġ ге", - "Ġг е", - "Ġ رÙĩ", - "Ġر Ùĩ", - "Ġм ел", - "Ġна пÑĢимеÑĢ", - "ĠнапÑĢи меÑĢ", - "Ġм ик", - "Ġми к", - "ĠاÙĦس ÙĥاÙĨ", - "æ¤ ľ", - "ĠÐļ ÑĢа", - "Ġv Ãłi", - "ĠvÃł i", - "ائ Ùħ", - "ĠÏĩ Ïģή", - "leÅŁ me", - "Ġ jas", - "Ġj as", - "Ġja s", - "ê²Į ìŀĦ", - "Ġm aç", - "Ġma ç", - "Ġì§Ħ íĸī", - "à¥ĩद न", - "Ġvů bec", - "ĠÙĦ ÙĨ", - "è« ĩ", - "âī¡ âī¡", - "л ением", - "ление м", - "лен ием", - "ле нием", - "ع ÙĨÛĮ", - "عÙĨ ÛĮ", - "ãĥŀ ãĥ³", - "Ä° Z", - "ĠÃĸ ÄŁ", - "ĠìŬ ìŀIJ", - "y Å¡", - "Ġ ÑģÑĤа", - "ĠÑģ ÑĤа", - "ĠÑģÑĤ а", - "Ġ สำหร", - "Ġสำ หร", - "Ġन व", - "ãĢĤ ä½Ĩ", - "олÑĮ но", - "Ġyan ında", - "Ġyanı nda", - "è² ´", - "Ġjednot liv", - "Ġ åİŁ", - "Ġåİ Ł", - "éłħ 缮", - "Ġमद द", - "리 ìĹIJ", - "ĠÙħ اÙĬ", - "ĠÙħا ÙĬ", - "ĠÑĩ еÑĢв", - "ĠÑĩеÑĢ в", - "Ġd áv", - "Ġdá v", - "ÙĦ ÛĮÙĩ", - "ÙĦÛĮ Ùĩ", - "? #", - "Äį nÃŃm", - "ÄįnÃŃ m", - "ÑĢ ег", - "ÑĢе г", - "ĠпÑĢимен Ñı", - "ĠпÑĢим енÑı", - "ãĤĬ ãģ¨", - "ê° Ļ", - "Ġtop lam", - "Ġtopl am", - "i leÅŁ", - "il eÅŁ", - "ile ÅŁ", - "Ġk ategor", - "ÑĤ ал", - "ÑĤа л", - "ãģ«ãĤĪ ãĤĭ", - "Ġdom ác", - "Ġ ê·ľ", - "Ġê· ľ", - "ĠÙĩ زار", - "ĠpÅĻÃŃ stup", - "ĠpÅĻÃŃst up", - "ı lıyor", - "ılı yor", - "ıl ıyor", - "ж ди", - "жд и", - "ĠD Æ°Æ¡ng", - "ĠPh áºŃt", - "Ġç ünkü", - "구 ê¸ĢìĥģìľĦ", - "ov aných", - "ova ných", - "ovan ých", - "ovaný ch", - "Ġع Ø´", - "Ġà¤ķर à¤ķ", - "ž ÃŃt", - "žÃŃ t", - "Ġ vÄĽtÅ¡ÃŃ", - "ĠvÄĽt Å¡ÃŃ", - "ĠvÄĽtÅ¡ ÃŃ", - "ĠاÙħ کاÙĨ", - "Ġn ông", - "Ġz ám", - "Ġzá m", - "à¥Į न", - "е каÑĢ", - "ек аÑĢ", - "ека ÑĢ", - "Âł Т", - "k ami", - "ka mi", - "ĠÑĢеÑģ ÑĥÑĢ", - "п оÑģ", - "по Ñģ", - "Ùİ ÙĤ", - "ί λ", - "Ġ سازÛĮ", - "Ġس ازÛĮ", - "Ġساز ÛĮ", - "Ġçık an", - "Ġçı kan", - "ĠdÃŃ tÄĽ", - "Ġتص ÙĪ", - "ç¯ ĩ", - "н д", - "Ġrám ci", - "h ong", - "ho ng", - "hon g", - "Ġ ÑģÑĸм", - "ĠÑģ Ñĸм", - "s ak", - "sa k", - "к еÑĤ", - "ке ÑĤ", - "д Ñĸл", - "дÑĸ л", - "ç¹ Ķ", - "Ġth Æ°á»Łng", - "Ġне ÑĹ", - "з Ñĸ", - "ÅĻ ÃŃd", - "ÅĻÃŃ d", - "ित न", - "à¤ı à¤ķ", - "Ġs ữa", - "ĠÙħ رØŃ", - "ĠÙħر ØŃ", - "é ŀ", - "Ġc Æ°á»Ŀng", - ": .:", - ":. :", - "ÑĤ ен", - "ÑĤе н", - "èī ¦", - "Ġkh ợi", - "Ġ 기ì¤Ģ", - "Ġ기 ì¤Ģ", - "lan ır", - "彩 票", - "ض ÛĮ", - "Ġuz av", - "Ġb oh", - "Ġbo h", - "è m", - "Ġ æ£", - "Ġæ £", - "n ici", - "ni ci", - "nic i", - "( çģ«", - "åħ³ äºİ", - "Ñĸ ÑĩнÑĸ", - "ÑĸÑĩ нÑĸ", - "à¸ģ ารà¸ĵ", - "à¸ģาร à¸ĵ", - "Ġì² «", - "ÑĢ ÑĥеÑĤ", - "ÑĢÑĥ еÑĤ", - "ĠarÅŁiv lendi", - "ÑĤ им", - "ÑĤи м", - "า à¸ł", - "าภł", - "Ġبر ابر", - "Ġ à¹Ģà¸ĭ", - "Ġà¹Ģ à¸ĭ", - "Ġà¹Ģภĭ", - "ĠÄij êm", - "è· ³", - "Ġyön etim", - "Ġyönet im", - "Ġ éķ·", - "Ġéķ ·", - "ãĥĨ ãĥ¬ãĥĵ", - "м аÑĤи", - "ма ÑĤи", - "маÑĤ и", - "è´£ ä»»", - "ick ým", - "ický m", - "è ¸", - "à¹Ģห à¸ķ", - "ëł Į", - "Ġ رÙĬ", - "Ġر ÙĬ", - "ĠвÑĭ дел", - "åĩº çİ°", - "Ġп еÑģ", - "Ġì¢ĭ ìĿĢ", - "Ġà¤ī सन", - "Ġà¤īस न", - "ĠAr alık", - "ĠAra lık", - "ĠÑĩа ÑģÑĥ", - "ĠÑĩаÑģ Ñĥ", - "l ava", - "la va", - "lav a", - "Ġ ï½ŀ", - "Ġï½ ŀ", - "æģ ĭ", - "د ÛĮد", - "دÛĮ د", - "âĢĻ den", - "âĢĻd en", - "âĢĻde n", - "Ġ åĪĿ", - "ĠåĪ Ŀ", - "ÙĪ دة", - "ÙĪد Ø©", - "Ñĩ или", - "Ñĩи ли", - "Ñĩил и", - "ĠÑħаÑĢакÑĤ еÑĢиÑģÑĤи", - "ا ستاÙĨ", - "اس تاÙĨ", - "است اÙĨ", - "द र", - "ĠبÙĪد ÙĨ", - "ĠبÙĪ دÙĨ", - "Ġп алÑĮ", - "Ġпа лÑĮ", - "Ġпал ÑĮ", - "ĠÑĤ ÑĢади", - "ĠÑĤÑĢ ади", - "ĠÑĤÑĢа ди", - "Ġд еÑı", - "Ġде Ñı", - "Ġ خش", - "ĠØ® Ø´", - "Ġpok raÄį", - "Ġ구 ê¸Ģ", - "к овÑĸ", - "ко вÑĸ", - "ков Ñĸ", - "Ġ tık", - "Ġt ık", - "Ġh ấp", - "Ġza lož", - "Ġzal ož", - "१ à¥", - "Ġëĭµ ë³Ģ", - "м еÑĪ", - "ме ÑĪ", - "íļ ¨", - "Ġspol up", - "Ġspolu p", - "Ë Ĩ", - "è¾ ¦", - "Ġg á»Ĺ", - "Ġ å®ļ", - "Ġå® ļ", - "ĵ n", - "as ından", - "asında n", - "- ı", - "ĠбеÑĢ ез", - "大 åѸ", - "Ġз нов", - "Ġзн ов", - "ĠHo Ãłng", - "Ġد ÙĪÙĨ", - "ĠدÙĪ ÙĨ", - "Ġan lay", - "ĠÙĪ زار", - "ĠÙĪز ار", - "ĠعÙĦ ÙħÛĮ", - "ĠعÙĦÙħ ÛĮ", - "è£ ľ", - "Ġdü nya", - "Ġdün ya", - "Ġdüny a", - "Ġза лиÑĪ", - "Ġзал иÑĪ", - "Ġзали ÑĪ", - "д аеÑĤ", - "да еÑĤ", - "ν ε", - "и ÑĩеÑģкого", - "иÑĩеÑģ кого", - "ìĬ¤ íħľ", - "ĠÐij еÑĢ", - "Ġ дж", - "Ġд ж", - "Ġ опаÑģ", - "Ġоп аÑģ", - "ÏĨ α", - "Ġzv lá", - "Ġt ô", - "б еÑĢ", - "бе ÑĢ", - "ĠÎľ αÏģ", - "Ġξα Ïģ", - "ti ÄŁini", - "tiÄŁi ni", - "ãĥ¬ ãĥ³", - "ĠK ho", - "ĠKh o", - "ĠÑĸн ÑĪ", - "Ġ ï¿¥", - "ì° ¬", - "ï½ ¡", - "Ġ ноÑĩ", - "Ġн оÑĩ", - "Ġно Ñĩ", - "è¨ Ĭ", - "ÄĽ ti", - "ÄĽt i", - "å¿ Ļ", - "Ġکرد ÙĨد", - "ĠکردÙĨ د", - "ĠÄij ẩy", - "ĠÑģказ ав", - "ëĥ ¥", - "å± ¬", - "Ġश हर", - "ĠÚ©Ùħ Ú©", - "Âł ÐŁ", - "ın ca", - "нÑĸвеÑĢ ÑģиÑĤ", - "Ġ Ú¯ÙĪÙĨÙĩ", - "ĠÚ¯ ÙĪÙĨÙĩ", - "ĠÚ¯ÙĪ ÙĨÙĩ", - "ĠTop lam", - "ĠiÅŁ aret", - "ä½ł 们", - "Ġd erece", - "Ġde rece", - "Ġder ece", - "Ġdere ce", - "Ġderec e", - "ĠìĤ¬ ìĭ¤", - "Ġ ìŀIJ기", - "ĠìŀIJ 기", - "å®ŀ çİ°", - "çĶŁ çī©", - "ãģ® ä¸Ģ", - "Ġ ÑĢом", - "ĠÑĢ ом", - "ÙĪ زÙĩ", - "ÙĪز Ùĩ", - "Ġ ãģ¨", - "íĻ į", - "ÙĬ ÙĤ", - "Ġ åIJįçĦ¡ãģĹãģķãĤĵ", - "ĠåIJį çĦ¡ãģĹãģķãĤĵ", - "ĠåIJįçĦ¡ãģĹ ãģķãĤĵ", - "ĠÙ¾ ÛĮر", - "ĠÙ¾ÛĮ ر", - "Ġпол ез", - "ì¶ ©", - "ĠкоÑĢ п", - "IJ ëĭ¤", - "á» «a", - "ừ a", - "Îķ Τ", - "Ġжел ез", - "ãģ£ãģ ±", - "Ġx uyên", - "Ġ ë¥", - "Ġë ¥", - "à¥ĩ ।Ċ", - "à¥ĩ। Ċ", - "ĠÑģÑĤ али", - "ĠÑģÑĤал и", - "ĠÑģÑĤа ли", - "Ġpomoc ÃŃ", - "Ġdurum da", - "Ġп ÑĢоÑĪ", - "ĠпÑĢ оÑĪ", - "ĠпÑĢо ÑĪ", - "l enÃŃ", - "le nÃŃ", - "len ÃŃ", - "β ολ", - "βο λ", - "Ġ æĸĩç«ł", - "Ġæĸĩ ç«ł", - "tÄĽ z", - "d ÃŃl", - "dÃŃ l", - "Ġdruh é", - "ĠÑĤ огда", - "Ġh rá", - "Ġhr á", - "о ÑĤÑĮ", - "оÑĤ ÑĮ", - "า à¸ģร", - "าà¸ģ ร", - "Ġتص Ùħ", - "ĠÙħد ت", - "ка дем", - "Ġpat ÅĻÃŃ", - "ä¹ĭ åīį", - "س بة", - "سب Ø©", - "Ġпо кÑĢÑĭ", - "Ġпок ÑĢÑĭ", - "Ġn áp", - "Ġná p", - "Ġ_ {}", - "Ġ_{ }", - "ëĵ± íķĻêµIJ", - "ĠØ¥ ÙĦÙĬ", - "ĠØ¥ÙĦ ÙĬ", - "Ġöz g", - "çļ Ĩ", - "Ġhay van", - "ĠN isan", - "ĠNi san", - "غ از", - "Ġت ت", - "ĠдÑĥ Ñħов", - "ĠдÑĥÑħ ов", - "ĠÐŁÐ¾ ÑįÑĤомÑĥ", - "ÑĮ огод", - "ÑĮого д", - "Ġk uÅŁ", - "Ġku ÅŁ", - "Ġà¤ĩ सम", - "Ġà¤ĩस म", - "ج ÛĮ", - "Ġ ãĤ¿", - "ĠãĤ ¿", - "Ġв кÑĥÑģ", - "Ġвк ÑĥÑģ", - "ĠвкÑĥ Ñģ", - "ç Ģ", - "ĠвÑĭ ÑĪе", - "âĢĻ dan", - "âĢĻd an", - "âĢĻda n", - "ĠاØŃ Ùħد", - "Ġtal ep", - "Ġta lep", - "Ġtale p", - "Ġ ÏĪ", - "ĠÏ Ī", - "Ġdol ayı", - "Ġdolay ı", - "ĠÚ¯ زارش", - "б ол", - "бо л", - "ĠاÛĮÙĨ تر", - "ÑĢ оÑĩ", - "ÑĢо Ñĩ", - ") âĢı", - "Ġ ëIJł", - "ĠëIJ ł", - "Ġk oup", - "Ġko up", - "Ġkou p", - "( æľĪ", - "é± ¼", - "Ġ огÑĢа", - "Ġо гÑĢа", - "Ġог ÑĢа", - "ĠÑĢаз м", - "ĠÑĢа зм", - "Ġت ست", - "Ġتس ت", - "ĠpÅĻÃŃ slu", - "í ĽĪ", - "ĠëĮĢ íķ´", - "à¹ģ à¸Ľ", - "ан нÑĭе", - "аннÑĭ е", - "ĠìĿ¸ íĦ°", - "Ġkullan ılan", - "Ġkullanı lan", - "Ġkullanıl an", - "Ġz tr", - "æĬĢ è¡ĵ", - "ि à¤Ľ", - "िठĽ", - "ĠاÙĦÙħ ؤ", - "ov aly", - "ova ly", - "oval y", - "us tos", - "ust os", - "usto s", - "Ġö rg", - "Ġör g", - "Ġ 太", - "Ġå¤ ª", - "ε ιο", - "ει ο", - "Ġ uÄį", - "Ġu Äį", - "ĠØ´ Ú©ÙĦ", - "ĠØ´Ú© ÙĦ", - "建 çŃij", - "Ġch ạy", - "ĠÏĩ Ïģη", - "н ÑĥÑĤ", - "нÑĥ ÑĤ", - "Ġباع Ø«", - "ĠNÄĽk ter", - "ÑĥÑĤ ÑĤÑı", - "ãģ§ãģĻ ãģĭ", - "Ġsay ılı", - "Ġsayı lı", - "им оÑģÑĤÑĮ", - "имо ÑģÑĤÑĮ", - "ĠпиÑĤ аннÑı", - "Ġk ÃŃnh", - "Ġh ran", - "Ġhr an", - "Ġhra n", - "ok rat", - "Ġed ilir", - "Ġedil ir", - "Ġà¤ķह त", - "Ġp aci", - "Ġpa ci", - "Ġpac i", - "ाल न", - "Ġи де", - "Ġид е", - "ĠZ em", - "ĠZe m", - "Ġsluž by", - "ÑģÑĤв еннÑĭй", - "ÑģÑĤвен нÑĭй", - "ĠØ¢ ÙĨاÙĨ", - "ĠØ¢ÙĨ اÙĨ", - "ĠÑĤ оваÑĢи", - "ĠÑĤоваÑĢ и", - "ĠÑĤов аÑĢи", - "ĠتØŃ ÙħÙĬÙĦ", - "ĠY ük", - "Ġк аÑĤегоÑĢ", - "ĠкаÑĤ егоÑĢ", - "íĭ Ģ", - "Ġк оÑģ", - "Ġко Ñģ", - "Ġ обов", - "Ġо бов", - "Ġоб ов", - "Ġобо в", - "ĠprostÅĻed ÃŃ", - "ĠÑģ оÑģ", - "ĠÑģо Ñģ", - "ĠÐIJ лекÑģанд", - "ĠÐIJлекÑģ анд", - "Ġ à¹Ģà¸Ĥà¸ķ", - "Ġà¹Ģà¸Ĥ à¸ķ", - "å¿ħ é¡»", - "ั à¸Ĭ", - "ĠÙĦ د", - "ãĢģ ä¸Ģ", - "ĠÎľ ÎŃ", - "Ñĥ ваÑĤиÑģÑı", - "Ñĥв аÑĤиÑģÑı", - "ÑĥваÑĤи ÑģÑı", - "Ñĥва ÑĤиÑģÑı", - "æķ ı", - "ãĥ¼ ãĥIJ", - "ãĥ¼ãĥ IJ", - "اÙĦ ÙĦÙĩ", - "Ġب Ùĩا", - "ĠبÙĩ ا", - "åĸ ¶", - "è´ µ", - "æĸ¹ åIJij", - "Ġ ì¸", - "Ġì ¸", - "Ġ ÙĨاÙħÙĩ", - "ĠÙĨ اÙħÙĩ", - "ĠÙĨاÙħ Ùĩ", - "ÑĮ ко", - "Ġv ody", - "Ġvo dy", - "Ġvod y", - "v ÃŃc", - "vÃŃ c", - "à¹ģ à¸Ī", - "Ġع ÙĦÛĮÙĩ", - "ĠعÙĦ ÛĮÙĩ", - "ĠعÙĦÛĮ Ùĩ", - "à¹ģ รà¸ĩ", - "ί να", - "ίν α", - "ãģ ¬", - "Ġ Ðŀп", - "ĠÐŀ п", - "Ġsay f", - "ï¼Į çĶ±", - "ä¼ ´", - "ĠÑĥд об", - "ãģ¾ ãģł", - "Ġне пÑĢи", - "Ġнеп ÑĢи", - " İ", - "à¤¾à¤ľ प", - "pl nÄĽ", - "Ġ ìĹĦ", - "ĠìĹ Ħ", - "Ġrů zn", - "Ġrůz n", - "Ġx ếp", - "ãĥĸ ãĥ«", - "ĠзаÑħ иÑģÑĤ", - "ĠÙħص رÙģ", - "ĠÙħصر Ùģ", - "ĠvÅ¡ech no", - "ãģ® ãģĬ", - "ĠTh á»ĭ", - "Ġm ùa", - "¿ IJ", - "ĠпÑĢин ÑĨип", - "ĠاÙĨ ÙĤÙĦ", - "г аÑĢ", - "га ÑĢ", - "Ġmož nost", - "ÙĤ ÙĬÙĤ", - "ÙĤÙĬ ÙĤ", - "Ġotev ÅĻ", - "Ġ fak", - "Ġf ak", - "Ġfa k", - "Ġng uy", - "Ġngu y", - "б ов", - "бо в", - "l acaÄŁ", - "lac aÄŁ", - "ا طر", - "اط ر", - "ãģ« ãĤĪãĤĬ", - "ãģ«ãĤĪ ãĤĬ", - "æĺ¯ åľ¨", - "Ġt ầng", - "ìĿ¸ ìĿ´", - "a ÅĻ", - "ç ¢°", - "ç¢ °", - "ÏĮ με", - "Ġ ê°Ī", - "Ġê° Ī", - "ĠØ£ ØŃد", - "ĠØ£ØŃ Ø¯", - "غ راÙģ", - "غر اÙģ", - "ĠÙĬ ØŃ", - "ï½ §", - "ĠاÙĦØŃÙĬ اة", - "Ġ lep", - "Ġl ep", - "Ġle p", - "Ġ ฮ", - "Ġภ®", - "t ae", - "ta e", - "Ġl Æ°Æ¡ng", - "è½ ®", - "Ġз мÑĸн", - "ĠзмÑĸ н", - "Ġзм Ñĸн", - "ĠÐļи ÑĹв", - "ĠмÑĸ ÑģÑı", - "ĠмÑĸÑģ Ñı", - "к ав", - "ка в", - "à¸ķ ะ", - "Ġm noho", - "Ġmn oho", - "Ġmnoh o", - "ĠNgh á»ĭ", - "èĻ İ", - "Ġ ãĥŁ", - "Ġãĥ Ł", - "Ġp ráci", - "Ġpr áci", - "Ġprá ci", - "Ġg á»ijc", - "ĠY eni", - "ĠYe ni", - "ĠYen i", - "ا ضÙĬ", - "اض ÙĬ", - "Ġ èij", - "Ġè ij", - "Ġк ла", - "Ġкл а", - "ı ng", - "ÏĦ εί", - "ÏĦε ί", - "Ġb eni", - "Ġbe ni", - "Ġben i", - "Ġ عد", - "Ġع د", - "Ġ aktu", - "Ġak tu", - "Ġakt u", - "ĠÙĪ ÙĤد", - "ĠÙĪÙĤ د", - "Ġпод гоÑĤов", - "Ġgi ai", - "Ġgia i", - "( æ°´", - "Ġs aç", - "Ġsa ç", - "ĠÙħÙĨ اسب", - "ĠÙħÙĨاس ب", - "âĸ ĭ", - "ÙIJ Ùĩ", - "é į", - "à¸Ń à¸Ĺ", - "ĠسÛĮ اسÛĮ", - "o lit", - "ol it", - "oli t", - "ĠاÙĦ جز", - "ĠاÙĦج ز", - "Ø· ÙĦب", - "Ø·ÙĦ ب", - "Ġ sey", - "Ġs ey", - "Ġse y", - "e rence", - "er ence", - "ere nce", - "eren ce", - "ì´ Į", - "ĠвнÑĥÑĤÑĢ ен", - "ĠвнÑĥÑĤ ÑĢен", - "Ġ à¸Ļาย", - "Ġà¸Ļ าย", - "ĠìķĬ ìķĺëĭ¤", - "ĠìķĬìķĺ ëĭ¤", - "o lik", - "ol ik", - "oli k", - "æľĢ åIJİ", - "ä» ª", - "Ġ ÑĢÑĸд", - "ĠÑĢ Ñĸд", - "è¼ ĥ", - "Ġ باب", - "Ġب اب", - "Ġبا ب", - "Ñĥ ди", - "Ñĥд и", - "Ġ ÑģÑĤÑĥп", - "ĠÑģÑĤ Ñĥп", - "ĠÄij ứng", - "ĠÄijứ ng", - "ĠÅŁ öyle", - "Ġ íķĻìĥĿ", - "ĠíķĻ ìĥĿ", - "Ġв лаÑģÑĤи", - "Ġвла ÑģÑĤи", - "ĠвлаÑģ ÑĤи", - "Ġh ãng", - "Ġhã ng", - "à¹ī าว", - "à¹īา ว", - "ĠÚ© اÙĩØ´", - "Ġ ëĵ¯", - "Ġëĵ ¯", - "ĠجÙħ ÙĦÙĩ", - "Ġد کتر", - "ad olu", - "ado lu", - "adol u", - "Ġت بد", - "Ġتب د", - "ظ اÙħ", - "Ġz naÄį", - "Ġzn aÄį", - "Ġد ÙĨÛĮ", - "ĠدÙĨ ÛĮ", - "Ġs ạn", - "å¼ ±", - "ÏĢ ι", - "Ġ çIJĨ", - "ĠçIJ Ĩ", - "Ġ ÙģصÙĦ", - "ĠÙģ صÙĦ", - "и нг", - "ин г", - "Ðļ Ðŀ", - "ĠС ов", - "ĠСо в", - "Ġziy aret", - "Ġ دÙħ", - "Ġد Ùħ", - "ç« ¹", - "Ġsah ibi", - "is ayar", - "isa yar", - "isay ar", - "ÄŁ a", - "ĠпеÑĢÑĸ од", - "Ġs na", - "Ġsn a", - "( æľ¨", - "Ġ нее", - "Ġн ее", - "Ġне е", - "ĠÑĦак ÑĤоÑĢ", - "ĠÑĦакÑĤ оÑĢ", - "м еж", - "ме ж", - "åº Ħ", - "r áž", - "rá ž", - "ок ÑĢем", - "Ġž al", - "ิ à¹Ģศษ", - "è± ª", - "ou cÃŃ", - "ĠU lus", - "ĠUl us", - "Ġtak že", - "ا ÙĪÙĨ", - "اÙĪ ÙĨ", - "н иÑĤи", - "ни ÑĤи", - "ниÑĤ и", - "нÑĮ о", - "ëį ¸", - "Ġ Ùĥرة", - "ĠÙĥ رة", - "ĠÙĥر Ø©", - "åľ ³", - "ĠArth ropoda", - "ĠÑĤ одÑĸ", - "ĠÑĤо дÑĸ", - "Ġدر صد", - "ุ รà¸ģ", - "ุร à¸ģ", - "ĠÑģв ого", - "ĠÑģво го", - "说 éģĵ", - "Ġc ánh", - "Ġcá nh", - "Ġcán h", - "æĵ Ĭ", - "Ġ ä¸ĭè½½", - "Ġä¸ĭ è½½", - "èī ¾", - "Ġnik dy", - "Ø® Ø·", - "ĠÑģ ейÑĩаÑģ", - "ÙĪ ÙĬÙĦ", - "ÙĪÙĬ ÙĦ", - "a met", - "am et", - "ame t", - "문 ìĿĺ", - "ĠE ÄŁitim", - "大 ä¼ļ", - "Ġb ÅĻez", - "за ÑĨÑĸÑı", - "Ġty to", - "н ай", - "на й", - "غ Ùħ", - "Ġ é©", - "Ġé ©", - "计 ç®Ĺ", - "Türk iye", - "Ġmn ož", - "åIJĪ ä½ľ", - "æľį åĭĻ", - "Ġkažd ý", - "ĠÑİ ÑĢид", - "Ġ βα", - "Ġβ α", - "à¥Ĥ à¤ļ", - "åIJĮ ãģĺ", - "Ġ çĭ", - "Ġç ĭ", - "ί ÏĦ", - "ÙĪÛĮ ÙĨت", - "ÙĪÛĮÙĨ ت", - "ا ÙĨس", - "اÙĨ س", - "æľĢ 大", - "Ġ Từ", - "ĠT ừ", - "éŃĶ æ³ķ", - "Ġб ли", - "Ġбл и", - "ĠÑĤак ое", - "ĠÑĤа кое", - "ãģ ľ", - "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ìĿ´ ë©°", - "ĠÙĤسÙħ ت", - "Ġ оÑĨÑĸ", - "Ġо ÑĨÑĸ", - "н икÑĥ", - "ни кÑĥ", - "ник Ñĥ", - "Ġ Bạn", - "ĠB ạn", - "ĠоÑĢг анÑĸз", - "ĠоÑĢган Ñĸз", - "ĠоÑĢганÑĸ з", - "ü ph", - "üp h", - "Ġ izin", - "Ġi zin", - "Ġiz in", - "Ġ ï¾Ĭ", - "Ġï¾ Ĭ", - "εί ÏĤ", - "à¸ĩ à¹ģà¸ķ", - "ãģ¡ ãĤī", - "ва жа", - "важ а", - "Ġ 欧", - "Ġæ¬ §", - "ι Ïİ", - "ÏĢ ÎŃ", - "Ġк ÑĢеп", - "ĠÑĨ иÑħ", - "ĠÑĨи Ñħ", - "æĦŁ ãģĺ", - "çķ «", - "Ùĥ ÙĪ", - "е мо", - "ем о", - "ž en", - "že n", - "å¹³ æĸ¹", - "ĠÙħجÙħÙĪع Ùĩ", - "ĠÑģво и", - "Ġ ãĦ", - "à¸Ľà¸£à¸°à¸ģ à¸Ńà¸ļ", - "ĠпÑĢ оÑĤи", - "ĠпÑĢо ÑĤи", - "ĠпÑĢоÑĤ и", - "ÙĪ ÛĮÙĩ", - "ÙĪÛĮ Ùĩ", - "认 为", - "ÏĨ ÎŃ", - "и ÑĩеÑģкий", - "иÑĩеÑģ кий", - "иÑĩеÑģки й", - "æ¥ ļ", - "Ġп ап", - "Ġпа п", - "δ Ïģο", - "Ġkullan ım", - "Ġkullanı m", - "Ġz bo", - "Ġzb o", - "Ġú spÄĽ", - "Ġ Ùħز", - "ĠÙħ ز", - "ĠF ak", - "ĠFa k", - "елÑĮ зÑı", - "æ´» åĭķ", - "ĠÐŁ ÑĢав", - "ĠÐŁÑĢ ав", - "ĠÐŁÑĢа в", - "¦ y", - "åĥ ķ", - "æij ĺ", - "Ġر ئ", - "Ġ ÏĨοÏģ", - "ĠÏĨ οÏģ", - "м иÑĤ", - "ми ÑĤ", - "Ġt icaret", - "Ġti caret", - "Ġtic aret", - "æ³ķ å¾ĭ", - "å¹´ 代", - "ìĪ Ļ", - "å ¿ł", - "å¿ ł", - "à¹ĩ à¸Ļà¸Ĺ", - "à¹ĩà¸Ļ à¸Ĺ", - "Ġ Ñĥж", - "ĠÑĥ ж", - "ĠÙħتØŃ Ø¯Ùĩ", - "Ġtr á»Ŀi", - "Ġ رØŃ", - "Ġر ØŃ", - "ĠÚ©ÙĪ ÚĨ", - "ĠопÑĢед елен", - "ĠопÑĢедел ен", - "ĠزÙħ ÛĮÙĨÙĩ", - "ĠزÙħÛĮÙĨ Ùĩ", - "Ġn óng", - "Ġnó ng", - "Ġng ủ", - "Nh ững", - "Ġк иÑĪ", - "Ġки ÑĪ", - "Ġ jde", - "Ġj de", - "Ġjd e", - "Ġ ä¸Ĭæµ·", - "Ġä¸Ĭ æµ·", - "åĭ ĩ", - "Ġt anı", - "Ġtan ı", - "à¹Į à¹ģละ", - "à¹Įà¹ģ ละ", - "ĠÑĢа ÑģÑĤвоÑĢ", - "ĠÑĢаÑģÑĤ воÑĢ", - "ĠÑģÑĢед ÑģÑĤв", - "Ġc án", - "Ġcá n", - "Ġsyst ému", - "Ġsystém u", - "ÛĮ Ø·", - "ĠÑģиÑģÑĤем а", - "Ġ ëŀ", - "Ġë ŀ", - "Ġ ÑĩеÑĤ", - "ĠÑĩ еÑĤ", - "éĥ¨ éŨ", - "å¸ °", - "Ġm illet", - "Ġmill et", - "Ġmil let", - "ĠÎķ λλά", - "à¥ĩà¤ĸ न", - "Ġrepublik y", - "ÑĢ ами", - "ÑĢа ми", - "ÑĢам и", - "Ġसम स", - "Ġaç ısından", - "اد ÙĦ", - "Ġб еÑģп", - "ĠбеÑģ п", - "ãĥ» âĶģ", - "åľ Ń", - "o cu", - "oc u", - "k ánÃŃ", - "ká nÃŃ", - "ÙĪ رش", - "ÙĪر Ø´", - "ëŀ µ", - "Ġ çģ", - "Ġç ģ", - "è° ģ", - "Ġs ám", - "Ġν εÏĨ", - "Ġνε ÏĨ", - "b ilir", - "bil ir", - "ĠmÃŃst ÄĽ", - "Ġ žen", - "Ġž en", - "Ġže n", - "Ġil ç", - "Ġ ë§ģ", - "Ġë§ ģ", - "ãĢij Ċ", - "ĠÙħÙĪ ارد", - "ĠاÙĦ Ø´ÙĬ", - "ĠاÙĦØ´ ÙĬ", - "Ġ기 ë¡Ŀ", - "Ġt ady", - "Ġta dy", - "Ġtad y", - "à¸Ń าà¸Ī", - "ĠÑģ ÑĦ", - "ĠspoleÄį nost", - "Ġtém atu", - "Ùħ اÙħ", - "Ùħا Ùħ", - "Ùħ ع", - "Ġ леж", - "Ġл еж", - "ĠÚĨ Ø´Ùħ", - "ĠiÅŁ let", - "ĠÙĨس Ø®", - "ä¼ °", - "ãģį ãģª", - "ãĢ ĥ", - "å² Ĺ", - "Ġ åŃIJ", - "Ġå ŃIJ", - "ĠåŃ IJ", - "Ġb ảng", - "Ġbản g", - "çĮ ®", - "Ġc ứng", - "Ġcứ ng", - "Ġк ÑĢай", - "ĠкÑĢа й", - "Ġ èĭ±è¯Ń", - "Ġèĭ± è¯Ń", - "Ðł ÐIJ", - "ز ÙĨ", - "èĥ ŀ", - "Ġsür eç", - "Ġsüre ç", - "ãĥķ ãĥĪ", - "ĠкÑĸлÑĮ ка", - "ne ÄŁin", - "neÄŁi n", - "ov ány", - "ová ny", - "ován y", - "л Ñĸн", - "лÑĸ н", - "Ġvý raz", - "ĠÑģÑĩ иÑĤа", - "ĠÑģÑĩиÑĤ а", - "ĠпÑĢав ило", - "ĠпÑĢави ло", - "ĠпÑĢавил о", - "ĠиÑģполÑĮз Ñĥ", - "Ġk éo", - "Ġké o", - "ĠyaklaÅŁ ık", - "ĠÙĪاب ستÙĩ", - "ов аÑĤелÑĮ", - "Ġ ì²ł", - "Ġì² ł", - "ĠاÙĦ عاÙħ", - "ĠاÙĦع اÙħ", - "åĿ ı", - "Ġ à¸ī", - "Ġภī", - "ĠS Æ¡n", - "λ ιο", - "λι ο", - "ì¶Ķ ì²ľ", - "Ġsluž eb", - "ĠдеÑıÑĤелÑĮ ноÑģÑĤи", - "з м", - "Ġп ози", - "Ġпо зи", - "Ġпоз и", - ".; .;", - "ĠпÑĢоиÑģ ÑħодиÑĤ", - "าย à¹ĥà¸Ļ", - "çļĦ ãģ«", - "Ġà¤ĩस स", - "о меÑĤ", - "ом еÑĤ", - "Ġ αÏģ", - "Ġα Ïģ", - "ा à¤Ĺर", - "ाà¤Ĺ र", - "i cÃŃch", - "ic ÃŃch", - "icÃŃ ch", - "Ġpolož ky", - "ê³ ¨", - "æĥ Ĭ", - "Ġö ner", - "Ġön er", - "Ġöne r", - "Ġx ảy", - "ĠÙĨظ رÛĮ", - "ĠÙĨظر ÛĮ", - "Ġngh á»ī", - "Ġ à¸ľà¸¥", - "Ġà¸ľ ล", - "ĠÑĢ олÑĮ", - "ĠÑĢ емон", - "ĠÑĢе мон", - "ص ÙĪر", - "V ý", - "ĠS á»ij", - "ĠÑģ ÑĥÑĩаÑģ", - "ĠÑģÑĥ ÑĩаÑģ", - "ห ย", - "ĠاÙĤ داÙħ", - "Ġer kek", - "Ġerk ek", - "Ġ èį", - "Ġè į", - "ĠÄij ôi", - "ĠÄijô i", - "Ġкон кÑĢеÑĤ", - "æ¬ Ĭ", - "Ġ 缮", - "ĠçĽ ®", - "ÙĪ Ú©", - "lı kla", - "lık la", - "Ġp azar", - "Ġpa zar", - "Ġpaz ar", - "ά νÏī", - "άν Ïī", - "Ñĥ ÑģÑĤа", - "ÑĥÑģ ÑĤа", - "ÑĥÑģÑĤ а", - "ãģª ãģŁ", - "Ġ ÙĩÙĨÚ¯", - "ĠÙĩ ÙĨÚ¯", - "ĠÙĩÙĨ Ú¯", - "Ю ÐĽ", - "Ġв елик", - "Ġвели к", - "Ġвел ик", - "Ġве лик", - "Ġnh Ỽ", - "Ġ ìĭľíĹĺ", - "Ġìĭľ íĹĺ", - ") ìĿĺ", - "Ùĥ Ùĩ", - "Ġ à¹ģล", - "Ġà¹ģ ล", - "Û² Ûµ", - "Ġار ساÙĦ", - "Ġ окÑĢем", - "Ġок ÑĢем", - "ά ÏĤ", - "ĠвÑĭ Ñħод", - "vÄĽt Å¡ÃŃ", - "ĠØ· رÛĮÙĤ", - "Ġк оÑĢоÑĤ", - "ĠкоÑĢ оÑĤ", - "Ġко ÑĢоÑĤ", - "н ÑĶ", - "ãĤĬ ãģ«", - "Ġ ä¹Ł", - "Ġä¹ Ł", - "ØŃ Øµ", - "ع ÙħاÙĦ", - "عÙħ اÙĦ", - "oloj ik", - "oloji k", - "Ġر ابط", - "Ġرا بط", - "çª Ĺ", - "Ġg iz", - "Ġgi z", - "Ġch ết", - "Ġchế t", - "æ¨ £", - "ส à¸ĩ", - "ÙĪ تر", - "ÙĪت ر", - "ĠÑı кÑĥ", - "ĠÑıк Ñĥ", - "çı¾ åľ¨", - "ĠоÑĤ ÑģÑĥÑĤÑģÑĤв", - "Ġ ê´ijê³ł", - "Ġê´ij ê³ł", - "Ñĸ ки", - "Ñĸк и", - "åĢ ¤", - "è® ¢", - "Ġ dle", - "Ġd le", - "Ġdl e", - "Ġ åł", - "Ġå ł", - "æ¨ ©", - "è® ¯", - "åĶ IJ", - "Ġ âĸ²", - "Ġâĸ ²", - "Ġli stop", - "Ġlist op", - "Ġlis top", - "Ġdat ové", - "Ġdato vé", - "ÏĦ ÏĮÏĤ", - "ÏĦÏĮ ÏĤ", - "Ġ оз", - "Ġо з", - "δ ÏĮ", - "èĴ Ĥ", - "Û³ Û°", - "ãĥª ãĥ¼ãĤº", - "ãĥªãĥ¼ ãĤº", - "ĠÙħر کز", - "ĠÙħرک ز", - "ĠпÑĸдÑĤ ÑĢим", - "ĠÑģ ез", - "é¡ ĺ", - "Ġol acaktır", - "Ġolacak tır", - "æº Ģ", - "ĠÏĢεÏģι ο", - "ĠÏĢεÏģ ιο", - "ĠÏĢε Ïģιο", - "ÑĦ а", - "ÏĦ ηÏĥη", - "ÏĦη Ïĥη", - "ç» ĥ", - "Ðŀ д", - "δ Ïħ", - "âĦ ĥ", - "Ġl ắp", - "ĠëĦ ĺ", - "Ø· اÙĨ", - "ĠÙ¾ ÙĨج", - "ĠÙ¾ÙĨ ج", - "ت اÙĨ", - "تا ÙĨ", - "i lerinin", - "iler inin", - "ileri nin", - "ilerin in", - "ilerini n", - "à Ī", - "ĠØ® ÙĪØ´", - "ĠØ®ÙĪ Ø´", - "Ġ ìĬ¬", - "ĠìĬ ¬", - "ĠاÙĦر ئÙĬس", - "ẵ n", - "Ġ شار", - "ĠØ´ ار", - "e ru", - "er u", - "ж ив", - "жи в", - "à¸Ļ าย", - "à¸Ļา ย", - "Ġs ẻ", - "Ġà¤ī à¤ļ", - "ãģ« ãģĭ", - "ç¡ Ģ", - "Ġyür üt", - "ĠС еÑĢг", - "ĠСеÑĢ г", - "Ġ каÑģ", - "Ġк аÑģ", - "Ġка Ñģ", - "ĠÐij ог", - "Ġìĸ´ëĸ »ê²Į", - "Ġ çŁ³", - "Ġç Ł³", - "ĠçŁ ³", - "Ġöl dür", - "Ġöld ür", - "л Ñĸв", - "лÑĸ в", - "Ġho Ãłng", - "ĠhoÃłn g", - "Ġb á»Ļt", - "Ġbá»Ļ t", - "çŀ ¬", - "Ġ 침", - "Ġì¹ ¨", - "N ếu", - "Ġne vy", - "Ġnev y", - "Ġ ìľ¤", - "Ġìľ ¤", - "Ġsou Äįást", - "ıs ıyla", - "ısı yla", - "Ġtük et", - "b ou", - "bo u", - "Ġд во", - "Ġдв о", - "س Ø·", - "å½ĵ çĦ¶", - "ãĥ ¨", - "Ġ زادÙĩ", - "Ġز ادÙĩ", - "Ġزاد Ùĩ", - "Ġ éĥ¨", - "Ġéĥ ¨", - "Ġر ÙĪØŃ", - "ĠرÙĪ ØŃ", - "Ġ ï¼į", - "Ġï¼ į", - "ĠмÑĸÑģ ÑĨев", - "ĠмÑĸÑģÑĨе в", - "θ εν", - "θε ν", - "ภĨ", - "л енÑĸ", - "лен Ñĸ", - "ле нÑĸ", - "çį ²", - "ĠH OH", - "ĠHO H", - "s ın", - "sı n", - "ิ à¸ķร", - "ิà¸ķ ร", - "è² ¡", - "ĠpÅĻ id", - "ĠpÅĻi d", - "à¹Ģ หà¸Ļ", - "à¹Ģห à¸Ļ", - "l ý", - "è¨Ģ èijī", - "ठĵ", - "âĸįâĸįâĸįâĸį âĸįâĸįâĸįâĸį", - "ب اب", - "با ب", - "ãĥ¼ ãĥķ", - "ãĥ¼ãĥ ķ", - "м оÑĢ", - "мо ÑĢ", - "è¿ĩ ç¨ĭ", - "Ġ ãĥĽ", - "Ġãĥ Ľ", - "ĠK inh", - "ĠKi nh", - "ĠKin h", - "íķľ êµŃ", - "Ġìĸ´ëĸ ¤", - "Ġв лиÑı", - "Ġf ayd", - "Ġfa yd", - "Ġص ÙĨع", - "ĠصÙĨ ع", - "Ġal ır", - "Ġet tiÄŁi", - "Ġetti ÄŁi", - "ά κ", - "im izin", - "imi zin", - "imiz in", - "imizi n", - "ัà¸ļ à¸ľ", - "Ġзем елÑĮ", - "ÙĬÙĦ اد", - "ÙĬÙĦا د", - "æ¶ ¨", - "çı ł", - "ĠØ£ غ", - "Ġz ku", - "Ġzk u", - "âĢŀ A", - "า à¸ķร", - "าà¸ķ ร", - "a yi", - "ay i", - "ãĥ© ãĤ¹", - "и ло", - "ил о", - "ĠÄij á»į", - "ĠÄijá» į", - ". Îķ", - "ë ľ", - "ĠμÏĢο Ïģεί", - "å¸ ¶", - "Ġar tır", - "Ġart ır", - "า à¸į", - "าภį", - "å¿ ĺ", - "ta lya", - "tal ya", - "Ġpoz dÄĽji", - "ĠpozdÄĽ ji", - "Ġnep ÅĻ", - "Ġ æ¹", - "Ġæ ¹", - "اÙĩ ÛĮ", - "Ġsat ın", - "Ġ ë²Į", - "Ġë² Į", - "ج ÙĪ", - "ä¸Ģ 缴", - "ìķĦ ìļĶ", - "Âł P", - "Ġ ØĽ", - "ĠØ Ľ", - "Ġп ал", - "Ġпа л", - "表 æĥħ", - "Ġc anlı", - "Ġcan lı", - "æĪIJ 为", - "ÙĪ ÙĨا", - "ÙĪÙĨ ا", - "Ġ âĢ¯", - "ĠâĢ ¯", - "à¸ģำ ล", - "åį ĸ", - "Ġ αÏĥ", - "Ġα Ïĥ", - "и нок", - "ин ок", - "а мп", - "ам п", - "ล à¸Ńà¸ĩ", - "ÙĤ ÙĤ", - "ĠпÑĢо Ñħод", - "ĠпÑĢоÑħ од", - "ãĤĦãĤĭ 夫", - "Ïĩ η", - "è² ¨", - "ĠÙģ ÙĬÙĩ", - "ĠÙģÙĬ Ùĩ", - "ÙĬ رÙĬ", - "ÙĬر ÙĬ", - "Ġвне ÑĪ", - "Ġk arak", - "Ġka rak", - "Ġkar ak", - "Ġkara k", - "Ø« ÙĦ", - "Ùĩ ÙĪرÛĮ", - "ÙĩÙĪر ÛĮ", - "اÙĪر Ù¾", - "ĠÄij á»ı", - "ĠÄijá» ı", - "ji Å¡tÄĽnÃŃ", - "jiÅ¡tÄĽ nÃŃ", - "ت بر", - "تب ر", - "Ġê·¸ ê²ĥ", - "Ġg ül", - "Ġgü l", - "Ġпо кÑĥп", - "Ġпок Ñĥп", - "l ilik", - "li lik", - "lili k", - "lil ik", - "Ġz da", - "Ġzd a", - "åīį ãģ«", - "ĠÙħÙĩ ÙĨد", - "Ġ ÎijÎĿ", - "ĠÎij ÎĿ", - "ĠÚ©ÛĮÙĦ ÙĪÙħتر", - "Ġp ÅĻeh", - "ĠpÅĻ eh", - "ĠpÅĻe h", - "а леж", - "ал еж", - "але ж", - "Ġka yn", - "Ġkay n", - "è® ¿", - "Ġì¤ij êµŃ", - "ĠÑĪиÑĢ ок", - "ĠÑĪи ÑĢок", - "ĠÙħشار کت", - "âĢ Ĥ", - "Ġ íŤ", - "ĠíĹ ¤", - "Ġìłľ íĴĪ", - "ĠØ´ ÛĮر", - "ĠØ´ÛĮ ر", - "es inden", - "esinde n", - "esin den", - "ÑĢ ÑĸÑĩ", - "ÑĢÑĸ Ñĩ", - "èı ²", - "Ñģ коÑĢ", - "Ñģк оÑĢ", - "Ñģко ÑĢ", - "e tik", - "et ik", - "eti k", - "า à¸ľ", - "าภľ", - "ĠØ· بÛĮ", - "Ġطب ÛĮ", - "κ ÎŃ", - "ĠìŀĪ ìĸ´", - "Ġ dek", - "Ġd ek", - "Ġde k", - "ÑĢ Ñĸй", - "ÑĢÑĸ й", - "å ĨĴ", - "åĨ Ĵ", - "nÃŃ ci", - "® ¤", - "ĠÙħر تب", - "Ġy azı", - "Ġya zı", - "Ġyaz ı", - "üs lü", - "ìľ¼ ëĤĺ", - "e lerine", - "eler ine", - "ele rine", - "eleri ne", - "elerin e", - "Ġy oÄŁun", - "Ġб ак", - "Ġба к", - "ÎĻ ÎŁ", - "ά λÏħ", - "άλ Ïħ", - "ç´ Ļ", - "ĠÑĢÑĥ ками", - "ĠÑĢÑĥк ами", - "Ġçöz üm", - "ìłķ ìĿĦ", - "Ġgüç lü", - "λ ÏĮ", - "Ġb elli", - "Ġbe lli", - "Ġbel li", - "Ġbell i", - "ÃŃ Å¡e", - "ÃŃÅ¡ e", - "ĠÏĮ ÏĢÏīÏĤ", - "Ġna Å¡", - "Ġp ár", - "Ġpá r", - "ÑĪ ÑĤ", - "Ġ ìĨ¡", - "ĠìĨ ¡", - "à¥Ĥ रत", - "à¥Ĥर त", - "ĠÏĢολ Ïį", - "ĠÏĢο λÏį", - "ç° ¡", - "èĤ ¯", - "æ¹ ¾", - "Ġ äºĭ", - "Ġब स", - "Ġ무 ë£Į", - "д ина", - "дин а", - "ди на", - "èª °", - "л еж", - "ле ж", - "Ġú ÅĻad", - "ĠоÑģвÑĸ ÑĤи", - "ĠоÑģвÑĸÑĤ и", - "ĠвÑĸд Ñĩ", - "ĠпÑĢи знаÑĩ", - "ĠпÑĢизна Ñĩ", - "ĠпÑĢиз наÑĩ", - "çĶ³ 请", - "' ya", - "'y a", - "ä¿ Ĭ", - "Ġ ÙĬÙĪÙĨ", - "ĠÙĬ ÙĪÙĨ", - "ĠÙĬÙĪ ÙĨ", - "Ġس ع", - "Ġ ÐĶаÑĤа", - "ĠÐĶ аÑĤа", - "ĠÐĶа ÑĤа", - "è¨Ģ ãģĨ", - "ĠØŃ ØªÛĮ", - "ĠJi ÅĻÃŃ", - "ĠÐ¥ аÑĢ", - "éĻ Ī", - "à¹Ī าà¸Īะ", - "à¹Īา à¸Īะ", - "Ġsay esinde", - "ĠÑĤÑĢеб а", - "ê°Ģ ì§Ģ", - "Ġy emek", - "Ġye mek", - "Ġyem ek", - "Ġyeme k", - "è¦ ļ", - "ặ n", - "ãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", - "Ġ 举", - "Ġä¸ ľ", - "Ġ ÙĪا", - "ĠÙĪ ا", - "ĠÙħ ÙĪس", - "ĠÙħÙĪ س", - "Ġком анд", - "Ġкоман д", - "Ġseç im", - "Ñĩ еннÑı", - "Ñĩен нÑı", - "Ġtot iž", - "Ġr á»Ńa", - "ı a", - "Ø¢ Ùħ", - "ÑĨ Ñĸон", - "ÑĨÑĸ он", - ": :::::::::::", - ":: ::::::::::", - ":::: ::::::::", - ":::::: ::::::", - ":::::::: ::::", - "::: :::::::::", - "::::: :::::::", - "::::::: :::::", - "::::::::: :::", - ":::::::::: ::", - "::::::::::: :", - "ÐĿ ÐIJ", - "ı za", - "ız a", - "h end", - "he nd", - "hen d", - "Ġफ र", - "ัà¸Ķ à¸ģาร", - "Ġ Cách", - "ĠC ách", - "ĠCác h", - "ĠпоÑĤ Ñĸм", - "Ġá¼ Ģ", - "ا ÙĦا", - "اÙĦ ا", - "á» ¡", - "ر ÛĮÙħ", - "رÛĮ Ùħ", - "å® «", - "Ġز ÙħÛĮÙĨ", - "ĠزÙħ ÛĮÙĨ", - "ÑĢ еÑģÑĤ", - "ÑĢе ÑģÑĤ", - "ÑĢеÑģ ÑĤ", - "б аÑĩ", - "ба Ñĩ", - "Ùĩر ست", - "н ог", - "но г", - "ï¼Į 大", - "ĠëĺIJ íķľ", - "Ġz ůst", - "Ġzů st", - "ĠÐĴ она", - "ĠÐĴо на", - "å¤ĩ 份", - "Ġ اÙģت", - "Ġا Ùģت", - "ĠاÙģ ت", - "o je", - "oj e", - "Ñģк ÑĸлÑĮки", - "Ġnh ẹ", - "Ġк еÑĢÑĸв", - "ĠкеÑĢ Ñĸв", - "á¿ ¦", - "æĸ¹ æ¡Ī", - "з аÑĨиÑı", - "за ÑĨиÑı", - "ĠвÑĸдповÑĸд но", - "ĠвÑĸдпов Ñĸдно", - "ãĤ¤ ãĤ¹", - "г ал", - "га л", - "ĠобÑĭ Ñĩно", - "ĠобÑĭÑĩ но", - "اÙĪرپ ÙĪÛĮÙĨت", - "å® ľ", - "l osti", - "lo sti", - "los ti", - "lost i", - "è¿Ľ åħ¥", - "uyor du", - "벤 íĬ¸", - "æīĭ ãĤĴ", - "ÐŁ од", - "ÐŁÐ¾ д", - "ĠÙħØŃ Ø¯ÙĪد", - "Ġ Ø¢Ùħد", - "ĠØ¢ Ùħد", - "ĠØ¢Ùħ د", - "ar akter", - "arak ter", - "çļĦ 大", - "Ġsı cak", - "l ant", - "la nt", - "lan t", - "Ġd ấu", - "ĠÙĨ Ú©", - "èĢħ ãģ®", - "Ġkend ini", - "Ġkendi ni", - "Ġп аÑĨи", - "Ġпа ÑĨи", - "Ġ 기íĥĢ", - "Ġ기 íĥĢ", - "ĠвмеÑģÑĤ е", - "в аеÑĤÑģÑı", - "ва еÑĤÑģÑı", - "ваеÑĤ ÑģÑı", - "Ġ ë§ī", - "Ġë§ ī", - "ĠchvÃŃ li", - "Ø® ÛĮ", - "ÙĦ ع", - "n ÃŃky", - "nÃŃ ky", - "nÃŃk y", - "､ :", - "ëIJľ ëĭ¤", - "ì§ ķ", - "Ġк вÑĸÑĤ", - "Ġкв ÑĸÑĤ", - "¨ ìĸ´", - "l iž", - "li ž", - "Ġë¹Ħë°Ģ ê¸Ģ", - "Ġkh á»iji", - "Ġ ë°©ìĨ¡", - "Ġë°© ìĨ¡", - "e chan", - "ec han", - "ech an", - "echa n", - "Ġзакон одав", - "Ġа кÑĤ", - "Ġак ÑĤ", - "문 ìłľ", - "ĠN ó", - "Ġ çĤ¹", - "Ġç Ĥ¹", - "ĠçĤ ¹", - "hled em", - "hle dem", - "ĠÑģво ÑĹÑħ", - "ĠÑģвоÑĹ Ñħ", - "Ġر ÙĤÙħ", - "ĠرÙĤ Ùħ", - "æĽ ¼", - "ि वर", - "िव र", - "åİ ļ", - "ĠÐļ од", - "ĠÐļо д", - "à¤Ń à¤Ĺ", - "ìŀIJ ëĬĶ", - "à¸Ļ ม", - "Ñĥ Ñģа", - "ÑĥÑģ а", - "Ġg ünü", - "Ġgün ü", - "ĠÄij ÃŃch", - "Ġtr ữ", - "å ·»", - "å· »", - "éĵ¶ è¡Į", - "ØŃ ÙĨ", - "è® ¨", - "γ Ïĩ", - "á½ ¸", - "a larında", - "alar ında", - "aları nda", - "aların da", - "Ġk af", - "Ġka f", - "ÙĪ اج", - "ÙĪا ج", - "ĠиÑģ клÑİÑĩ", - "ĠиÑģк лÑİÑĩ", - "Ġnh iá»ħ", - "á»į t", - "ĠìĽ ¹", - "Ġ éĿ¢", - "ĠéĿ ¢", - "ãģ® ãģĮ", - "Ġм ало", - "Ġмал о", - "Ġма ло", - "Ñĸ лÑĸ", - "Ñĸл Ñĸ", - "Ġ biên", - "Ġb iên", - "Ġbi ên", - "n ému", - "né mu", - "ném u", - "пÑĢи меÑĢ", - "пÑĢ имеÑĢ", - "âĸł âĸł", - "Ġk amp", - "Ġka mp", - "Ġkam p", - "Ġ веÑī", - "Ġв еÑī", - "Ġве Ñī", - "Äį em", - "Äįe m", - "à¥ģ ध", - "à¥ģठ§", - "æŁ »", - "ت ÙĪÙĨ", - "تÙĪ ÙĨ", - "åıª æľī", - "ãģ¯ ãģĦ", - "Ġ รวม", - "Ġร วม", - "ãĤ ŀ", - "ãģĻ ãĤĭãģ¨", - "ãģĻãĤĭ ãģ¨", - "å¾Ī å¤ļ", - "à¹Ī à¸ķ", - "ĠsvÄĽt a", - "ĠsvÄĽ ta", - "Ġ ê°Ģ격", - "Ġê°Ģ 격", - "Ú¯ Ùĩ", - "an daÅŁ", - "and aÅŁ", - "anda ÅŁ", - "ãĥª ãĤ¹", - "Ïī μα", - "ĠØ® ÙĪب", - "ĠØ®ÙĪ ب", - "ç´ ħ", - "Ñĩ иÑģ", - "Ñĩи Ñģ", - "ì¢ Į", - "ĠØŃض رت", - "Ġви ÑĢÑĸÑĪ", - "Ù¾ ر", - "Ġtý d", - "Ġkon tro", - "Ġkont ro", - "д ейÑģÑĤв", - "ãģŁãĤģ ãģ«", - "ì ī", - "мини ÑģÑĤÑĢа", - "мин иÑģÑĤÑĢа", - "âĢ ¯", - "åī ij", - "ни ÑĨÑĸ", - "ниÑĨ Ñĸ", - "å¦ ĩ", - "Ġ лиÑĪ", - "Ġл иÑĪ", - "Ġли ÑĪ", - "ãģ£ ãģ¦ãĤĭ", - "ãģ£ãģ¦ ãĤĭ", - "на ÑĢÑĥж", - "наÑĢ Ñĥж", - "Ñī иÑħ", - "Ñīи Ñħ", - "ÏĦ οκ", - "ÏĦο κ", - "ov áno", - "ová no", - "ován o", - "تر ÙĦ", - "ÑĢ ек", - "ÑĢе к", - "غ ات", - "Ġ omez", - "Ġo mez", - "Ġom ez", - "ì ĵ°", - "ĠÃľ l", - "ï½ Ĵ", - "lı ģını", - "lıģ ını", - "lıģın ı", - "Ġv ượt", - "Ġb ÄĽÅ¾", - "ĠbÄĽ ž", - "Ãľ R", - "Ġ ãĥ¾", - "Ġãĥ ¾", - "ĠdoÄŁ al", - "Ġh atır", - "Ġha tır", - "Ġhat ır", - "Ġsv ým", - "ì§Ģ ëıĦ", - "à¹Ģà¸ł à¸Ĺ", - "Ġv ay", - "Ġva y", - "Ġ æĻĤ", - "Ġæ ĻĤ", - "ĠæĻ Ĥ", - "à¥įव प", - "Ġp lo", - "Ġpl o", - "é¢Ħ è§Ī", - "Ġçık tı", - "Ġ دÙĨ", - "Ġد ÙĨ", - "n ánÃŃ", - "ná nÃŃ", - "ê· Ģ", - "íĺ Ģ", - "à¸ŀ à¸ļ", - "m uÅŁtur", - "muÅŁ tur", - "å®ĺ æĸ¹", - "ĠíĶĦ ë¡ľê·¸ëŀ¨", - "éĢŁ 度", - "ler dir", - "lerdi r", - "ÑĩеÑģ кого", - "Ñĩе Ñģкого", - "ĠÄ°n san", - "âĶ ĥ", - "Ġà¤ĩत न", - "С Ð¡Ðł", - "Ġا Ùħر", - "ĠاÙħ ر", - "Ġkö tü", - "Ùģ Ø´", - "Ġb oj", - "Ġbo j", - "ĠÑĨÑĸ ÑĶÑĹ", - "Ġsöy lem", - "Ġsöyl em", - "ни ÑĨÑĭ", - "ниÑĨ Ñĭ", - "ãĢĤ 她", - "âĢĿ .Ċ", - "âĢĿ. Ċ", - "Ġm ilion", - "Ġmil ion", - "Ġmi lion", - "Ġmili on", - "Ġson unda", - "Ġsonu nda", - "з Ñĥ", - "à¥į मà¤ķ", - "à¥įम à¤ķ", - "人 åı£", - "n ÄĽÅ¾", - "nÄĽ ž", - "ĠÑģ моÑĤ", - "ĠÑģм оÑĤ", - "Ġкомп лекÑģ", - "Ġкомплек Ñģ", - "Ġзави Ñģим", - "Ġиме ÑİÑĤ", - "Ġl ạc", - "Ġlạ c", - "Ġ hangi", - "Ġh angi", - "Ġhang i", - "Ġhan gi", - "ëĶ ©", - "åĬ ³", - "ĠvÄĽ ci", - "ĠvÄĽc i", - "е ÑĢов", - "еÑĢ ов", - "еÑĢо в", - "κ Ïģι", - "Ġdur umu", - "Ġdurum u", - "Ġ بÙĪاسطة", - "ĠبÙĪ اسطة", - "ĠØ£ بÙĬ", - "Ġأب ÙĬ", - "ĠAÄŁ ustos", - "ε Ïĩ", - "Ġд иÑĤи", - "Ġди ÑĤи", - "ĠдиÑĤ и", - "ÑĦ ика", - "ÑĦи ка", - "ÑĦик а", - "Ġ NÄĥm", - "ĠN Äĥm", - "Ġ 기ìĪł", - "Ġ기 ìĪł", - "Ġhlav nÃŃ", - "ä¿ ĥ", - "Ġलà¤Ĺ त", - "ĠO br", - "ĠOb r", - ". ย", - "ко вод", - "ков од", - "ково д", - "o pis", - "op is", - "opi s", - "Ġ ãĥī", - "Ġãĥ ī", - "Ġبش ÙĥÙĦ", - "н ием", - "ни ем", - "ние м", - "Ġtém ÄĽÅĻ", - "ĠاÙĦ ØŃر", - "ĠاÙĦØŃ Ø±", - "ĠÙĦ ازÙħ", - "ĠÙĦا زÙħ", - "Ġm ái", - "Ġmá i", - "i liÄŁi", - "ili ÄŁi", - "ë³ ¼", - "Ġy ık", - "Ġyı k", - "ç½ ²", - "ÑĢ ава", - "ÑĢа ва", - "ÑĢав а", - "Ñī ин", - "Ñīи н", - "ãģ« 対", - "ç²¾ ç¥ŀ", - "à¹ī ส", - "Ġtem sil", - "à Ĩ", - "ìķ Ķ", - "ĠпÑĢавилÑĮ но", - "ÑĢ оÑİ", - "ÑĢо Ñİ", - "Û±Û³ Û¸", - "è© ŀ", - "اء Ø©", - "ÙĪ ارÙĩ", - "ÙĪا رÙĩ", - "ÙĪار Ùĩ", - "ï¼ ħ", - "ĠÐľ ик", - "ĠÐľÐ¸ к", - "æģ ¶", - "æı Ĵ", - "ा पन", - "ाप न", - "ĠÚ©ÛĮÙģ ÛĮت", - "ĠT Ãłi", - "Ġt iá»ĥu", - "ov alo", - "ova lo", - "oval o", - "çĿ ¡", - "Ñĩ ил", - "Ñĩи л", - "Ġ лиÑĤ", - "Ġл иÑĤ", - "Ġли ÑĤ", - "λεÏħ ÏĦα", - "Ġ окон", - "Ġо кон", - "Ġок он", - ": :|", - ":: |", - "в ала", - "ва ла", - "вал а", - "ĠÙħرک زÛĮ", - "ĠÙħرکز ÛĮ", - "Ġ alÄ±ÅŁ", - "Ġa lÄ±ÅŁ", - "Ġal Ä±ÅŁ", - "Ġдолж но", - "Ġдол жно", - "æĻĤ 代", - "Ġ sert", - "Ġs ert", - "Ġse rt", - "Ġser t", - "е ÑĤом", - "еÑĤ ом", - "ัà¸Ļ ย", - "åģ ·", - "Ġ vÃŃc", - "Ġv ÃŃc", - "ĠvÃŃ c", - "ĠÑħ оÑĤÑı", - "ĠÑħоÑĤ Ñı", - "a larını", - "alar ını", - "aların ı", - "len mesi", - "lenme si", - "ãĥ³ ãĥIJ", - "ãĥ³ãĥ IJ", - "Ġëª ĩ", - "Ġá» ¦y", - "ĠاÙĦ کتر", - "vy Å¡Å¡ÃŃ", - "è² ¬", - "주 ìĭľ", - "á ÅĻe", - "áÅĻ e", - "Ġy ere", - "Ġye re", - "Ġyer e", - "ãĤ¢ ãĥ³", - "ĠاÙĦس عÙĪد", - "ĠØ¢ Ø´", - "Ġch óng", - "Ġchó ng", - "Ġ è»", - "Ġè »", - "г аÑĶ", - "га ÑĶ", - "Ġ ãģĤ", - "ç¨ ³", - "δ εÏĤ", - "δε ÏĤ", - "缮 çļĦ", - "Ġce vap", - "Ġcev ap", - "Ñģ ÑĤе", - "ÑģÑĤ е", - "é¡ ¿", - "म न", - "é¡ ¾", - "Ġк ÑĢедиÑĤ", - "ĠÙħس تÙĤ", - "ĠÙħست ÙĤ", - "Ġ миÑĤ", - "Ġм иÑĤ", - "Ġми ÑĤ", - "Ġt á»ĵn", - "Ġtá» ĵn", - "Ġ جÙĦ", - "Ġج ÙĦ", - "Ä© a", - "ĠاÙĦع ÙĦÙħ", - "ĠاÙĦعÙĦ Ùħ", - "á ků", - "ák ů", - "Ġ íķĻêµIJ", - "ĠíķĻ êµIJ", - "à¸Ĺ à¸Ńà¸ĩ", - "ห à¸Ļà¸Ķ", - "หà¸Ļ à¸Ķ", - "ĠлÑĸÑĤ еÑĢаÑĤÑĥ", - "ëIJ ł", - "ά ÏģÏĩ", - "άÏģ Ïĩ", - "ĠÙĤد رت", - "ĠÙĤدر ت", - "à¸Ļ าà¸ĩ", - "à¸Ļา à¸ĩ", - "Ġa rac", - "Ġar ac", - "Ġara c", - "Ġj ÃŃd", - "ĠjÃŃ d", - "Ġtür lü", - "íĶ ½", - "er siz", - "ers iz", - "е ним", - "ен им", - "ени м", - "Ġyüz yıl", - "Ġ ãģĦ", - "ĠÎļ Ïħ", - "Ġ æļ", - "Ġæ ļ", - "Ġp ůj", - "Ġpů j", - "Ġt á»Ļi", - "Ġth iên", - "Ġthi ên", - "Ä° S", - "Ġth úc", - "Ġthú c", - "æĹ ģ", - "ìŀIJ ìĿ¸", - "Ġöl üm", - "ر ÛĮÙģ", - "رÛĮ Ùģ", - "ÑĢ еж", - "ÑĢе ж", - "ص اÙĦ", - "ر ÙģØ©", - "رÙģ Ø©", - "i ếp", - "iế p", - "Ñı ÑĤиÑı", - "ÑıÑĤ иÑı", - "ÑıÑĤи Ñı", - "Ġpou žit", - "á tu", - "át u", - "为 ä»Ģä¹Ī", - "ì ģ", - "Ġ krát", - "Ġk rát", - "Ġkr át", - "ĠپرÙĪ ÚĺÙĩ", - "Ġrozhod nutÃŃ", - "ĠÑĥ нивеÑĢ", - "Ñĸй но", - "Ġ åij¨", - "Ġåij ¨", - "Ġk iá»ĥu", - "缮 åīį", - "ä¿ Ħ", - "ÏĦ οι", - "ÏĦο ι", - "ÑĦеÑĢ ен", - "uÅŁ tur", - "Ġ nÃŃm", - "Ġn ÃŃm", - "ĠnÃŃ m", - "âĢĮ Ø®", - "Ġ ủy", - "Ġ ÑģÑĤаÑĤи", - "ĠÑģÑĤ аÑĤи", - "ĠÑģÑĤаÑĤ и", - "ĠÑģÑĤа ÑĤи", - "ÑĩеÑģ кий", - "Ñĩе Ñģкий", - "ÑĩеÑģки й", - "Ġj estli", - "Ġjest li", - "ĠÙ¾ ÙĨ", - "Ġob ce", - "ĠجÙĩ اÙĨÛĮ", - "ĠجÙĩاÙĨ ÛĮ", - "едаг ог", - "ãģ§ ãģ®", - "Ġbu á»Ļc", - "ì¹´ ì§Ģëħ¸", - "à¹ĩ à¸Ħ", - "ĠÄį tvrt", - "Ġ ника", - "Ġн ика", - "Ġни ка", - "Ġник а", - "Ġвп лив", - "Ġд иÑĢ", - "Ġди ÑĢ", - "ĠÑģоб ÑģÑĤвен", - "Ġë§İ ìĿ´", - "æ¾ ³", - "ÑĢ Ñĥб", - "ÑĢÑĥ б", - "æ£ ĭ", - "声 éŁ³", - "ä¹ ĥ", - "تÛĮ جÙĩ", - "å¹ ¼", - "o nya", - "on ya", - "ony a", - "ĠPlan tae", - "ĠPlant ae", - "Ч ÑĤо", - "æIJ Ń", - "ä½ľ çĶ¨", - "ìħ ¨", - "Ġк ÑĢÑĥг", - "Ġ ÙĪÙģÙĬ", - "ĠÙĪ ÙģÙĬ", - "ĠÙĪÙģ ÙĬ", - "Ġ ï¼ŀ", - "Ġï¼ ŀ", - "ÑĪ ки", - "Âł Ðľ", - "ا Ø´ÛĮ", - "اش ÛĮ", - "ĠÅŀ ubat", - "ĠÅŀu bat", - "Ġع شر", - "Ġعش ر", - "l if", - "li f", - "Ġpou žitÃŃ", - "Ġpoužit ÃŃ", - "íĨ ¡", - "Ġб лок", - "Ġбл ок", - "èĢ ¶", - "ู ร", - "Ġv üc", - "Ø´ ÙĪد", - "Ø´ÙĪ د", - "и ма", - "им а", - "ни ÑĨип", - "ниÑĨ ип", - "ìĿ´ ëĵľ", - "Ġ âĢIJ", - "ĠâĢ IJ", - "Ġ назнаÑĩ", - "Ġна знаÑĩ", - "Ġназ наÑĩ", - "Ġназна Ñĩ", - "Ġstr any", - "Ġstran y", - "Ġstra ny", - "æ® ¿", - "ĠاÙĦ رÙĪ", - "ĠاÙĦر ÙĪ", - "çº ¸", - "åĪ ij", - "ï¼Į ä»İ", - "Ġ ë©´", - "Ġë© ´", - "ĠпÑĢовед еннÑı", - "Ġh ava", - "Ġha va", - "Ġhav a", - "ĠìĹĨ ìĹĪëĭ¤", - "ĠìĹĨìĹĪ ëĭ¤", - "å¢ŀ åĬł", - "Ú ¾", - "ç¼ º", - "Ġع بار", - "Ġعب ار", - "Ġt ắc", - "Ġin ÅŁa", - "er se", - "ers e", - "ر ÙĬب", - "رÙĬ ب", - "Ġá»ķ n", - "Ø£ Ø©", - "ĠÏĢολ ι", - "ĠÏĢο λι", - "Ġm ắc", - "Ñģ ол", - "Ñģо л", - "æ´ ŀ", - "- го", - "-г о", - "ç¨ĭ 度", - "ĠвикоÑĢиÑģÑĤ аннÑı", - "âĢŀ ظ", - "e lerinde", - "eler inde", - "eleri nde", - "elerin de", - "ĠNh Æ°ng", - "ĠNhÆ° ng", - "st ÅĻed", - "Ġhasta lık", - "Ġhast alık", - "à¹ī à¹Ģà¸Ľ", - "Ġd efa", - "Ġde fa", - "Ġdef a", - "Ġ زÙĬ", - "Ġز ÙĬ", - "اط ÙĤ", - "Ġп ÑĢой", - "ĠпÑĢ ой", - "ĠпÑĢо й", - "Ġок ÑĢÑĥг", - "ν ια", - "νι α", - "l adu", - "la du", - "lad u", - "k oli", - "ko li", - "kol i", - "Ġ oÄŁ", - "Ġo ÄŁ", - "Ġви Ñģок", - "ĠвиÑģ ок", - "Ð ĩ", - "çĽ ĸ", - "ãĤı ãģij", - "ãĥ¼ ãĥģ", - "ãĥ¼ãĥ ģ", - "æ¡ ¥", - "ĠÅ¡kol y", - "ĠÅ¡k oly", - "i tom", - "it om", - "ito m", - "Ġت ØŃص", - "ĠتØŃ Øµ", - "a lara", - "al ara", - "ala ra", - "alar a", - "Ġк ал", - "Ġка л", - "ĠпÑĢи Ñħод", - "Ġ é¦ĸ页", - "Ġé¦ĸ 页", - " į", - "ĠÛĮ عÙĨÛĮ", - "Ġt ùy", - "Ģ ë¡ľ", - "볤 ê³ł", - "á ze", - "áz e", - "Ġ ек", - "Ġе к", - "èħ ¹", - "ĠF akat", - "ĠFa kat", - "ĠFak at", - "п о", - "ĠÄij á»įc", - "ĠÄijá»į c", - "å Īĺ", - "åĪ ĺ", - "áz al", - "ÑĤ он", - "ÑĤо н", - "Ú¯ ÙĪ", - "ä¸ Ī", - "ìĹ ¼", - "ĠÙĦÙĦ Ø£", - "ĠE ÄŁer", - "åħ±åĴĮ åĽ½", - "Ø° ر", - "Ġd aÄŁ", - "Ġda ÄŁ", - "è¡Į ä¸ļ", - "ê±°ëŀĺ ê°Ģ", - "è´Ł è´£", - "C ông", - "ĠÑĦ илÑĮ", - "ĠÑĦил ÑĮ", - "Ġ аÑģ", - "Ġа Ñģ", - "Ġch ẳng", - "ним аÑĤÑĮ", - "нима ÑĤÑĮ", - "Ġif ad", - "Ġi fad", - "Ġ ìħ", - "Ġì ħ", - "çĪ µ", - "ĠÅĻe Å¡enÃŃ", - "åĽ½ 产", - "Ġкак ой", - "Ġка кой", - "Ġम ध", - "ĠY ar", - "ĠYa r", - "ob raz", - "obra z", - "Ġon emoc", - "Ġ âĤ", - "Ġâ Ĥ", - "åİŁ åĽł", - "ĠÙĥ رد", - "ĠÙĥر د", - "Ġآز اد", - "Ġad lı", - "ĠH izmet", - "ãĥ¼ ãĥij", - "ãĥ¼ãĥ ij", - "ÙĨ سÙĬØ©", - "ÙĨس ÙĬØ©", - "Ġв нÑĥÑĤ", - "ĠвнÑĥ ÑĤ", - "Ġd ále", - "Ġdál e", - "Ġdá le", - "Îķ Î¥", - "Ġ ÑĥÑħ", - "ĠÑĥ Ñħ", - "Ġ ÑĢев", - "ĠÑĢ ев", - "ĠÑĢе в", - "Ġ меÑĪ", - "Ġм еÑĪ", - "ĠkoÅŁ ul", - "ĠاÛĮ راÙĨÛĮ", - "ĠاÛĮراÙĨ ÛĮ", - "éĺ µ", - "Ġ ëıĻìķĪ", - "ĠëıĻ ìķĪ", - "à¹Ģ à¸Ł", - "à¹ĢภŁ", - "ëłĪ 벨", - "è¨Ń è¨Ī", - "p rak", - "pr ak", - "pra k", - "p oÄį", - "po Äį", - "اع دة", - "اعد Ø©", - "Ġas ker", - "Ġask er", - "ĠÙĪÛĮ ÚĺÙĩ", - "ĠÙĪÛĮÚĺ Ùĩ", - "ĠТ еÑĢ", - "ĠТе ÑĢ", - "mak ta", - "makt a", - "ĠÄįty ÅĻ", - "Âł С", - "âĢĮÚ©ÙĨ ÙĨد", - "ï¼Į 並", - "ĠÑĢоÑģ Ñĸй", - "Ġu nut", - "Ġun ut", - "è¿Ļ ä¸Ģ", - "o pak", - "op ak", - "opa k", - "èĢ IJ", - "Ġз амеÑĤ", - "Ġза меÑĤ", - "Ġзам еÑĤ", - "à¹Į ล", - "ب ÙĨ", - "Ġ 몰", - "Ġëª °", - "Ġins anlar", - "Ġinsan lar", - "åı¯ æĺ¯", - "æ¢ ¦", - "к од", - "ко д", - "èĽ Ľ", - "kl adnÃŃ", - "klad nÃŃ", - "ÑĢов од", - "ÑĢо вод", - "ĠмÑĸ ÑģÑĤа", - "ĠмÑĸÑģ ÑĤа", - "ĠмÑĸÑģÑĤ а", - "åĩº äºĨ", - "Ġп аÑģ", - "Ġпа Ñģ", - "о бов", - "об ов", - "Ú¯ اÙĩÛĮ", - "گاÙĩ ÛĮ", - "в ин", - "ви н", - "à¥įर ध", - "Ġком пон", - "Ġкомп он", - "Ġ аÑĤ", - "Ġа ÑĤ", - "Ġa det", - "Ġad et", - "Ġade t", - "Ġ ãĥģ", - "Ġãĥ ģ", - "ĠØ° ات", - "ĠØŃ ÙĪ", - "Ġtro chu", - "à¹ģ หà¸Ļ", - "à¹ģห à¸Ļ", - "Ġзав жди", - "ĠPart isi", - "ĠParti si", - "ĠS avaÅŁ", - "ĠSav aÅŁ", - "Ġs ÃŃd", - "ĠsÃŃ d", - "Ġ Ñģон", - "ĠÑģ он", - "ĠÑģо н", - "ر ÙĬÙģ", - "رÙĬ Ùģ", - "Ġz cela", - "åĺ ´", - "ĠÑĦ ÑĥÑĤ", - "il erek", - "ile rek", - "iler ek", - "ilere k", - "m alıdır", - "malı dır", - "Ġd á»±a", - "Ġdá»± a", - "à¸Ĺำ à¸ĩาà¸Ļ", - "ĠÙĪÙĦ ÙĥÙĨ", - "ĠÙĪÙĦÙĥ ÙĨ", - "ãģª ãĤĵãģł", - "ãģªãĤĵ ãģł", - "ĠÚ© ÙħÛĮ", - "ĠÚ©Ùħ ÛĮ", - "Ġléka ÅĻ", - "Ïģ Ïį", - "ج Ùħع", - "جÙħ ع", - "ın ızı", - "ını zı", - "ınız ı", - "ĠAn adolu", - "ãģ«ãĤĪ ãģ£ãģ¦", - "Ġê·¸ëŁ¬ ëĤĺ", - "Ġ íĮĶ", - "ĠíĮ Ķ", - "Ñĸ ÑĤÑĮ", - "ÑĸÑĤ ÑĮ", - "Ġ ¦", - "Ġ ¦", - "ä¸į è¦ģ", - "à¸ĸ ม", - "Ġ ÙĬد", - "ĠÙĬ د", - "ĠpÅĻ ep", - "ĠpÅĻe p", - "Ġ è¦ģ", - "Ġè¦ ģ", - "ĠпÑĢо екÑĤ", - "ĠпÑĢоек ÑĤ", - "ĠÑĢе ги", - "ĠÑĢег и", - "Ġd ạy", - "к ового", - "ков ого", - "ково го", - "Ġ ıs", - "Ġı s", - "ĠK ı", - "ĠÙģÙĬ Ùĩا", - "ĠÙģÙĬÙĩ ا", - "ÛĮ ات", - "ÛĮا ت", - "ĠÑģÑĤ ала", - "ĠÑģÑĤал а", - "ĠÑģÑĤа ла", - "æĬ ľ", - "Ñĥ ÑĢа", - "ÑĥÑĢ а", - "ĠÙ¾ اÛĮاÙĨ", - "ĠپاÛĮ اÙĨ", - "Ġپا ÛĮاÙĨ", - "Ġitibar en", - "а нÑĸÑĹ", - "ан ÑĸÑĹ", - "анÑĸ ÑĹ", - "Ġо ÑĦоÑĢм", - "л еÑĩ", - "ле Ñĩ", - "ε ξ", - "æĶ¿ çŃĸ", - "Ġ ç½ij", - "Ġç½ ij", - "å Ĥ¬", - "åĤ ¬", - "ĠìĿ´ 룰", - "Ġkar deÅŁ", - "Ñİ Ñīего", - "ÑİÑī его", - "л ки", - "ĠاÛĮ اÙĦات", - "ت Ùĩا", - "تÙĩ ا", - "Ġпод Ñħод", - "ĠØŃ ÙĪÙĦ", - "ĠØŃÙĪ ÙĦ", - "ĠÑģов ÑĢем", - "íĿ ¥", - "Ġ 詳細", - "Ġè© ³ç´°", - "ı yı", - "ıy ı", - "ĠتÙĤ ÙĪ", - "æ¯Ķ è¾ĥ", - "Ġαν ÏĦι", - "Ġ ΣΤ", - "ĠΣ Τ", - "ji Å¡Å¥", - "yn ı", - "Ġpo cházet", - "- Ðļ", - "Ġзав д", - "Ùİ Ø³", - "ç»ĵ æŀĦ", - "Ùħ ار", - "Ùħا ر", - "ν οι", - "νο ι", - "ĠÎł εÏģι", - "ĠγεÏģ ι", - "èĩ £", - "Ġna cházÃŃ", - "Ġnach ázÃŃ", - "ÏĦ Ïİ", - "à¥įय त", - "u yu", - "uy u", - "æķ Ĺ", - "e bi", - "eb i", - "Ġë°Ķ ë¡ľ", - "ĠгÑĢ н", - "ĠاÙĦ اس", - "Ġorg án", - "Ġ edin", - "Ġe din", - "Ġed in", - "Ġedi n", - "åŁ ĥ", - "à¹ģ à¸Ħ", - "ĠØŃ Ø¯ÙĪد", - "ĠØŃد ÙĪد", - "ĠдÑĢÑĥг ой", - "ĠдÑĢÑĥго й", - "оÑģ ков", - "оÑģк ов", - "ĠS ợ", - "ĠpÅĻ ib", - "ĠpÅĻi b", - "ä¿Ŀ æĬ¤", - "Ùħ بر", - "Ġ ãĥĨ", - "Ġãĥ Ĩ", - "Ġd oz", - "Ġdo z", - "op tera", - "opt era", - "ิล à¸Ľ", - "د ارÛĮ", - "دار ÛĮ", - "دا رÛĮ", - "æĦŁ è§ī", - "代 çIJĨ", - "ÙĨ دا", - "ÙĨد ا", - "ا ÙĬا", - "اÙĬ ا", - "ص ÙĨ", - "Ġce lé", - "Ġcel é", - "Ġ è©ķ", - "Ġè© ķ", - "à¸ĩ à¸Ļ", - "Ġ leh", - "Ġl eh", - "Ġle h", - "èİ· å¾Ĺ", - "ãĢĢ ï¾ī", - "ĠìĦł ìĪĺ", - "르 ëĬĶ", - "à¤Ĩ र", - "å§Ķ åijĺ", - "æĹł çłģ", - "Ġ è·", - "Ġè ·", - "Ġza jÃŃm", - "Ġzaj ÃŃm", - "ec ké", - "eck é", - "æ µľ", - "æµ ľ", - "ĠÑĥ нÑĸвеÑĢÑģиÑĤ", - "ĠбÑİдж еÑĤ", - "à¥ĩ .", - "Ġv stup", - "Ġ оÑī", - "Ġо Ñī", - "Ġ åľĭ", - "Ġåľ ĭ", - "ä¸ģ 缮", - "Ġв едÑĮ", - "Ġвед ÑĮ", - "Ġë§IJ ìĿĦ", - "Ġtek nik", - "Ġtekn ik", - "ãĢĢ ï½Į", - "ãĢĢï½ Į", - "ĠпÑĸд виÑī", - "ĠÑģвÑıз и", - "ĠÑģвÑı зи", - "Ġتر جÙħ", - " ī", - "ĠÄij âu", - "Ñĸ Ñĩного", - "ÑĸÑĩ ного", - "å°ij å¹´", - "e cta", - "ect a", - "ec ta", - "ि लत", - "िल त", - "ι οÏĤ", - "ιο ÏĤ", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "t eg", - "te g", - "á» īnh", - "á»ī nh", - "¯ ¿", - "Ġne bu", - "Ġneb u", - "ÙĬ ÙĬÙĨ", - "о ÑıÑĤ", - "оÑı ÑĤ", - "é¤ Ĭ", - "ĠاÙĤتص ادÛĮ", - "ĠاÙĤتصاد ÛĮ", - "âĢĻ nun", - "âĢĻn un", - "ĠÐĴ Ñĸк", - "Ġng Äĥn", - "ëĮĢ íķĻêµIJ", - "ëĮĢíķĻ êµIJ", - "é ı", - "़ र", - "ا باÙĨ", - "اب اÙĨ", - "Ùİ Ùĥ", - "Ġet kil", - "Ġetk il", - "Ġetki l", - "Ġch ắn", - "Ġë°ľ ìĥĿ", - "Ġtam amen", - "Ġtamam en", - "ĠÙħØŃ ÛĮØ·", - "ü lü", - "ül ü", - "åģ¥ 康", - "ĠÑĢаÑģÑĤ ениÑı", - "ÏĢο ί", - "Ġ è¶ħ", - "Ġè¶ ħ", - "á Äį", - "ĠìĻ¸ ë¶Ģ", - "ĠØ® ÛĮÙĦÛĮ", - "ĠØ®ÛĮ ÙĦÛĮ", - "Ġد ÙĪست", - "ĠدÙĪ ست", - "à¹Ģà¸Ĥ à¸ķ", - "Ġk alan", - "Ġka lan", - "Ġkal an", - "ë¨ ¼", - "a vÄĽ", - "av ÄĽ", - "문 íĻĶ", - "Ġди аг", - "ĠÙĨ ÙĪÙĬس", - "ĠÙĨÙĪ ÙĬس", - "íķ ij", - "à¸ŀ าะ", - "ëĭ¤ ê°Ģ", - "Ġn iá»ĩm", - "Ġس ÙĪÙħ", - "ĠسÙĪ Ùħ", - "- м", - "à¸Ķำ à¹Ģà¸Ļ", - "à¹ĩ ว", - "ãĢĤ ãģĵãģ®", - "ç¯ ī", - "Width Space", - "Zero WidthSpace", - "ائ ÙħØ©", - "ائÙħ Ø©", - "à¹Ħà¸ĭ à¸ķ", - "ä¸ĭè½½ 次æķ°", - "ä¼¼ ä¹İ", - "ĠÑĤ в", - "Ġzá kaz", - "Ġج دا", - "Ġجد ا", - "Ġg ider", - "Ġgi der", - "Ġgid er", - "ãĥ¼ ãĥĵ", - "ãĥ¼ãĥ ĵ", - "n ů", - "Ġë§ģ íģ¬", - "ĠdÃ¼ÅŁ ük", - "Ñĥ нок", - "Ñĥн ок", - "Ġt óc", - "ĠÑĤ ÑĢÑĥб", - "ĠÑĤÑĢ Ñĥб", - "ĠÑĤÑĢÑĥ б", - "о кÑģ", - "ок Ñģ", - "Ġtr ải", - "Ġtrả i", - "Ġm iá»ħn", - "Ġth Æ°á»Ľc", - "Ġnh áºŃt", - "Âł D", - "m asının", - "mas ının", - "masını n", - "è¼ ª", - "ĠÎĿ ο", - "er ç", - "Ġdok once", - "Ġdokon ce", - "ĠG üven", - "ĠGü ven", - "ov aná", - "ova ná", - "ovan á", - "е зд", - "ез д", - "Ñĸ нÑĮ", - "Ñĸн ÑĮ", - "èģ ²", - "اÙĦ Ø£", - "ï¼Į ä½Ĩæĺ¯", - "ï¼Įä½Ĩ æĺ¯", - "Ġпол ноÑģÑĤÑĮÑİ", - "Port ály", - "ĠØŃ Ø§Ùģظ", - "à¥Ĥ à¤ķ", - "ÑĢ Ñĥн", - "ÑĢÑĥ н", - "人 çī©", - "Ġa çı", - "Ġaç ı", - "Ġp oru", - "Ġpo ru", - "Ġpor u", - "er iod", - "eri od", - "ĠAmer ika", - "ĠAmerik a", - "çĩ Ł", - "Ġ رÙĪد", - "Ġر ÙĪد", - "ĠرÙĪ د", - "ĠкÑĢов и", - "ÙĪ ÙĤت", - "ÙĪÙĤ ت", - "éĺ ¶", - "ãĥ»âĶģ ãĥ»âĶģ", - "ر ÙĬÙħ", - "رÙĬ Ùħ", - "åİĨ åı²", - "ä¸ ¸", - "Ġзн овÑĥ", - "Ġзнов Ñĥ", - "ĠÑģво его", - "ĠÑģвое го", - "бÑĥд ÑĮ", - "ĠØŃ Ø¬Ùħ", - "ĠØŃج Ùħ", - "ĠδÏį ο", - "ìķĪ ëĤ´", - "Ġ ãģ§", - "à¹Ī ะ", - "Ùij Ùı", - "çµIJ æŀľ", - "âĢĻ i", - "à¹Į ,", - "åŃĺ äºİ", - "Ġरà¤ĸ न", - "ĠسرÙħ اÛĮÙĩ", - "Ġг лÑĥб", - "ĠглÑĥ б", - "ĠÑĢаз Ñĸ", - "ĠÑĢа зÑĸ", - "Ñĩ нÑĸ", - "ï¼Į åıĪ", - "c ısı", - "cı sı", - "æľī åħ³", - "ãĤ» ãĥ³", - "èIJ ¨", - "ĠGi áo", - "ĠGiá o", - "ĠاÙĦØ« اÙĨÙĬ", - "ĠÑĢаз ом", - "Ġ ÑĤÑĢо", - "ĠÑĤ ÑĢо", - "ĠÑĤÑĢ о", - "Ġaçık lam", - "åĨ³ å®ļ", - "à¸Ń à¸Ľ", - "åĶ ¯", - "ĠÅŁ ark", - "ĠÅŁar k", - "Ġsist emi", - "Ġsistem i", - "Ġto prak", - "Ġtop rak", - "èĢĥ ãģĪ", - "Ġпоп ÑĥлÑıÑĢ", - "Ġ ëĨį", - "ĠëĨ į", - "ا ÙĬÙĨ", - "اÙĬ ÙĨ", - "स म", - "Ġ ÂĢ", - "Ġ Ģ", - "Ġed erek", - "Ġeder ek", - "Ġg ec", - "Ġge c", - "ìĤ¬ ìĹħ", - "ĠÑĢ оки", - "ĠÑĢок и", - "ĠбеÑĢ ем", - "ĠخاÙĨÙĪ ادÙĩ", - "Ġ èµ·", - "Ġèµ ·", - "Ġ ЧÑĤо", - "ĠЧ ÑĤо", - "Ġo bÄĽ", - "Ġob ÄĽ", - "и нÑĸ", - "ин Ñĸ", - "ìĿ´ ìĹĪ", - "ĠIn di", - "ĠInd i", - "Ġд иÑĤ", - "Ġди ÑĤ", - "ãĥ¶ æľĪ", - "Ġнем ного", - "Ġzáklad ÄĽ", - "à¹Ĥ à¸Ħ", - "ĠÑģам ого", - "ĠÑģамо го", - "Ġب ØŃØ«", - "ĠبØŃ Ø«", - "Ġ æ¶", - "Ġæ ¶", - "ов ж", - "Ġоб ÑĢаÑī", - "ĠобÑĢа Ñī", - "à Ĵ", - "ว รร", - "วร ร", - "à¤Ĥ श", - "ĠоÑĩ еÑĢед", - "ĠÙģ رز", - "ĠÙģر ز", - "ëĮĢ íķľ", - "Ġs izin", - "Ġsi zin", - "Ġsiz in", - "Ġsizi n", - "ر Ùģت", - "رÙģ ت", - "Ñİ Ñīим", - "ÑİÑī им", - "æ» ij", - "a vir", - "av ir", - "avi r", - "ĠÙĪ صÙĦ", - "ĠÙĪص ÙĦ", - "Ġqu ay", - "Ġqua y", - "Ġг ип", - "ÑĢ ениÑı", - "ÑĢе ниÑı", - "ÑĢен иÑı", - "à¥į वत", - "à¥įव त", - "ιν Ïīν", - "à¤ľ ह", - "Ġh Æ¡i", - "Ġpo važ", - "Ġpov až", - "Ġع رب", - "м енÑĤа", - "мен ÑĤа", - "менÑĤ а", - "Ġо ÑģÑĤан", - "ĠоÑģÑĤ ан", - "ä¹ĭ éĹ´", - "a cÃŃch", - "ac ÃŃch", - "acÃŃ ch", - "ĠÑģказ ала", - "ĠÑģказал а", - "ìĿ´ ëĿ¼ëĬĶ", - "ìĿ´ëĿ¼ ëĬĶ", - "ĠØ´ اخ", - "Ġëĭ¹ ìĭł", - "ar lar", - "arl ar", - "Ġмл н", - "åĨ ¬", - ". :.:.:", - ".: .:.:", - ".:.: .:", - ".:. :.:", - ".:.:. :", - "Ġ θε", - "Ġθ ε", - "Ġher kes", - "Ġherk es", - "л Ñıд", - "лÑı д", - "ا Ùħا", - "اÙħ ا", - "Ġë ŃIJ", - "Ïĥιμο ÏĢοι", - "Ġ obraz", - "Ġob raz", - "Ġobr az", - "Ġobra z", - "غ اÙĦ", - "B Æ°á»Ľc", - "å° Ĭ", - "ìŀIJ 를", - "æĢ Ĵ", - "οÏħ Ïģγ", - "å¼ķ ãģį", - "Ġkon uda", - "Ġkonu da", - "ĠاÙĦت ج", - "Ġ krit", - "Ġk rit", - "Ġkr it", - "å¿ į", - "ĠìłĦìĦ¸ ê°Ģ", - "г овоÑĢ", - "го воÑĢ", - "гов оÑĢ", - "Ġist iyor", - "о ки", - "ок и", - "ĠобеÑģп еÑĩ", - "ĠобеÑģпе Ñĩ", - "Ġay rıca", - "Ġayrı ca", - "à¹Ģ à¸ľ", - "à¹Ģภľ", - "а ÑĢод", - "аÑĢ од", - "Ä° Åŀ", - "ĠجÙħ ÙĩÙĪرÛĮ", - "ĠجÙħÙĩÙĪر ÛĮ", - "ĠÑģво иÑħ", - "ĠÑģвои Ñħ", - "Ġprov ád", - "Ġ ÑĢам", - "ĠÑĢ ам", - "ĠÑĢаР¼", - "ĠÑĢа м", - "ĠÙĤ ض", - "л иÑĤелÑĮ", - "лиÑĤ елÑĮ", - "ãĤ± ãĥĥãĥĪ", - "оÑģ оÑĦ", - "Ġर हन", - "Ġरह न", - "k ový", - "ko vý", - "kov ý", - "ì° ¸", - "γ κα", - "γκ α", - "λ οι", - "λο ι", - "μ ÏĢο", - "μÏĢ ο", - "ĠÄij au", - "ĠÄija u", - "н иÑİ", - "ни Ñİ", - "Ġman žel", - "Ġ íĺ¼", - "Ġíĺ ¼", - "ĠÑĤ иÑģ", - "ĠÑĤи Ñģ", - "ãĥĨ ãĥ«", - "ab ilecek", - "abil ecek", - "abile cek", - "abilece k", - "н ин", - "ни н", - "à¸ģรรม à¸ģาร", - "éł IJ", - "Ġph ê", - "j edn", - "je dn", - "jed n", - "交 æµģ", - "Ġвним ание", - "об ÑĢеÑĤ", - "Ġжиз нÑĮ", - "ÑĢи ÑģÑĤи", - "ÑĢиÑģÑĤ и", - "ÑĢиÑģ ÑĤи", - "à¥Ī à¤ļ", - "Ġyüz den", - "Ġyüzde n", - "Ġg iy", - "Ġgi y", - "éļ Ķ", - "ä» ²", - "Ġ èĻ", - "Ġè Ļ", - "ĠP arti", - "ĠPart i", - "ĠPar ti", - "Ġ éĸ¢", - "Ġéĸ ¢", - "ัà¸ļ ส", - "Ġnej lepÅ¡ÃŃ", - "Ùİ Ùī", - "ĠìĿ´ ìłľ", - "Ġc ắt", - "ÑĢоз Ñĥм", - "Ġnej sou", - "l ÃŃd", - "lÃŃ d", - "θ ο", - "à¹ĩ à¸ĩ", - "ĠÑģп ÑĢоÑģ", - "ma mÄ±ÅŁ", - "mam Ä±ÅŁ", - "Ġ 쪽", - "Ġì ª½", - "ا ÙģÙĤ", - "اÙģ ÙĤ", - "ÑĨÑĸй ниÑħ", - "Ġ é¦Ļ", - "Ġé¦ Ļ", - "ĠÙħÛĮÙĦÛĮ ÙĪÙĨ", - "å¤ ¢", - "ĠÙģ Ùĩرست", - "r ý", - "Ġпо вÑĸдом", - "Ġпов Ñĸдом", - "ec eÄŁi", - "ece ÄŁi", - "ĠзабезпеÑĩ еннÑı", - " Ķ", - "ãģĹ ãģªãģĦ", - "åŁº ç¡Ģ", - "ĠÚĨ ÙĨÛĮÙĨ", - "ĠÑĢоз ÑĢоб", - "ä¸Ģ äºĽ", - "ãĥ³ ãģ®", - "ĠпÑĢа ÑĨÑĸв", - "ĠпÑĢаÑĨÑĸ в", - "å¾Ĺ åĪ°", - "Ġt ấn", - "åŃĺæ¡£ å¤ĩ份", - "Ġ íĻĪ", - "ĠíĻ Ī", - "Ġ à¸Ķาว", - "Ġà¸Ķ าว", - "ìĭ ±", - "л ина", - "ли на", - "лин а", - "ĠвоÑģп ал", - "ÄŁ inden", - "ÄŁin den", - "ÄŁinde n", - "аÑĤ елей", - "аÑĤе лей", - "r ž", - "ĠÑĦ Ñĥн", - "ĠÐIJ л", - "ĠпоÑĩ ÑĤи", - "о вÑĸд", - "ов Ñĸд", - "овÑĸ д", - "ا عب", - "اع ب", - "าะ ห", - "Ġвоз ÑĢаÑģÑĤ", - "ิà¸ĩ ห", - "ĠÙģ ÙĦس", - "ĠÙģÙĦ س", - "ĠÅ¡ est", - "à¸Ĭ าว", - "Ġ 골", - "Ġê³ ¨", - "Ġ oÄį", - "Ġo Äį", - "ãĤ¸ ãĥ§", - "к оÑģÑĤи", - "ко ÑģÑĤи", - "éĽĨ åĽ¢", - "æ± ĩ", - "ĠpÅĻÃŃ liÅ¡", - "Ġ ìĿij", - "ĠìĿ ij", - "ди ви", - "див и", - "ĠдокÑĥм енÑĤа", - "ĠдокÑĥменÑĤ а", - "ĠCh âu", - "Ġm áu", - "Ġmá u", - "Ġkh ô", - "à ķ", - "Ñī ий", - "Ñīи й", - "Ġs ẵn", - "Ġкон ÑĦ", - "Ġз ÑĥÑģÑĤ", - "åĽŀ çŃĶ", - "Ġ коÑĢиÑģÑĤ", - "ĠкоÑĢ иÑģÑĤ", - "Ġко ÑĢиÑģÑĤ", - "ĠÏĢεÏģ ί", - "ĠÏĢε Ïģί", - "ä¸ °", - "Ġm ạch", - "ан к", - "ä¸ĭ æĿ¥", - "èµĦ æĸĻ", - "ย à¸Ńà¸Ķ", - "ĠÏĢ ιο", - "à¹ī à¸ĩาà¸Ļ", - "à¹īà¸ĩ าà¸Ļ", - "Ġum ÃŃst", - "æ½ ®", - "çªģ çĦ¶", - "Ġk ultur", - "Ġkul tur", - "ĠاÙĦ صÙģ", - "ĠاÙĦص Ùģ", - "a larının", - "alar ının", - "aların ın", - "alarını n", - "ĠÎĶη μο", - "Ġвикон аннÑı", - "Ġвико наннÑı", - "ï½ ¿", - "Ġбез опаÑģ", - "ĠÑģ аÑħ", - "Ġn oh", - "Ġno h", - "à¹ĥ à¸ļ", - "éĥ½ å¸Ĥ", - "ÅŁ am", - "ÅŁa m", - "б ÑĥÑĤ", - "бÑĥ ÑĤ", - "Ġ모 ìĬµ", - "Ġв аг", - "Ġва г", - "çIJĨ 解", - "Ġekonom ik", - "Ġkh ắc", - "Ġs vat", - "Ġsv at", - "лиÑĪ ком", - "ัà¸ĩ à¸Īาà¸ģ", - "iz yon", - "èĥ½ å¤Ł", - "ί νει", - "ίν ει", - " Ĭ", - "ì¦ Į", - "Ġ ÙĩاÛĮÛĮ", - "ĠÙĩ اÛĮÛĮ", - "ĠÙĩاÛĮ ÛĮ", - "ĠkiÅŁ iler", - "ĠkiÅŁi ler", - "Ġк леÑĤ", - "Ġкл еÑĤ", - "Ġкле ÑĤ", - "íĺ ģ", - "à¥ĥ द", - "i Å¡", - "ëĶĶ ìĺ¤", - "ÙĬ راÙĨ", - "ÙĬر اÙĨ", - "ÙĬرا ÙĨ", - "ĠÐĿ Ñĥ", - "à¸Ń à¸Ļà¸Ĺ", - "à¸Ńà¸Ļ à¸Ĺ", - "ĠÑģ оÑĩ", - "ĠÑģо Ñĩ", - "Ġist eyen", - "ĠS ez", - "ĠSe z", - "Ġ ãĤ»", - "ĠãĤ »", - "ĠA ç", - "âĢĮ ÙĨ", - "ĠÑĤ оп", - "ĠÑĤо п", - "ĠÑĤеÑĢÑĢиÑĤ оÑĢ", - "a cılık", - "acı lık", - "Ġод нÑĥ", - "Ġv eri", - "Ġver i", - "Ġve ri", - "ĠÚ© د", - "ĠÚ¯ ÙģتÙĩ", - "ĠÚ¯Ùģت Ùĩ", - "Ġcin sel", - "Ġcins el", - "олог ии", - "ĠpÅĻed mÄĽt", - "à¤Ĥ à¤ĺ", - "Ġ 空", - "Ġç© º", - "γ α", - "' ye", - "'y e", - "ت رة", - "تر Ø©", - "Ġd ÅĻÃŃ", - "ĠH Ãłn", - "ĠHÃł n", - "Ġر شتÙĩ", - "Ġv idea", - "Ġvi dea", - "Ġvid ea", - "Ġvide a", - "Ġ ног", - "Ġн ог", - "Ġно г", - "æ ·»", - "æ· »", - "è¿ĺ æľī", - "ÙĨ در", - "ÙĨد ر", - "Ġy erde", - "Ġyer de", - "Ġk ent", - "Ġke nt", - "Ġken t", - "à¸ļ าล", - "Ġд еÑģÑı", - "Ġде ÑģÑı", - "ä¸ļ åĬ¡", - "Ġоб ÑĬек", - "ĠобÑĬ ек", - "ĠвнÑĥÑĤÑĢ ÑĸÑĪ", - "ĠвнÑĥÑĤ ÑĢÑĸÑĪ", - "k ola", - "ko la", - "kol a", - "eb nÃŃ", - "ี ล", - "Ġ ,.", - "Ġ, .", - "ĠмÑĸж наÑĢод", - "ãģªãĤĵ ãģ¦", - "ĠS öz", - "Ġ chod", - "Ġc hod", - "Ġch od", - "Ġcho d", - "Ġtr úc", - "Ġtrú c", - "ìļĶ ìĿ¼", - "Ġph áºŃn", - "Ñģ ка", - "Ñģк а", - "ĠÑħ лоп", - "Ñģ ким", - "Ñģк им", - "Ñģки м", - "Ġka pit", - "Ġkap it", - "ëĵ¤ ìĹIJê²Į", - "Ġb Ãło", - "ĠbÃł o", - "lı ģın", - "lıģı n", - "lıģ ın", - "Ä° ÅŁ", - "Äį nÃŃk", - "ÄįnÃŃ k", - "Ġ NgoÃłi", - "ĠNg oÃłi", - "Ġب ÛĮاÙĨ", - "ĠبÛĮ اÙĨ", - "Ġpro Äį", - "Ġpr oÄį", - "ĠпÑĢоÑĤ Ñıгом", - "åĢ ī", - "е Ñİ", - "Ġ νο", - "Ġν ο", - "ëĿ¼ ëıĦ", - "ì ·¨", - "Ġви Ñıв", - "Ġпо над", - "Ġпон ад", - "Ġжов ÑĤ", - "Ġ æ¯Ķ", - "Ġæ¯ Ķ", - "Ġd oby", - "Ġdo by", - "Ġdob y", - "л ам", - "ла м", - "Ñij л", - "Ġ ÑĢаÑħ", - "ĠÑĢ аÑħ", - "ĠÑĢа Ñħ", - "Ġвоз ника", - "Ġвозник а", - "ниÑĨÑĤ во", - "å± ¤", - "ĠоÑĤ лиÑĩ", - "ĠоÑĤли Ñĩ", - "çĤ İ", - "é£ ¯", - "Ġživ ota", - "Ġživot a", - "at ör", - "Ġce lý", - "Ġcel ý", - "Ġ aday", - "Ġa day", - "Ġad ay", - "Ġada y", - "ر ÙĬÙĥÙĬ", - "رÙĬ ÙĥÙĬ", - "رÙĬÙĥ ÙĬ", - "Ġب ص", - "m eyen", - "me yen", - "meye n", - "mey en", - "ìļ° ìĬ¤", - "ب ÙĪÙĦ", - "بÙĪ ÙĦ", - "Ġ озна", - "Ġо зна", - "Ġоз на", - "é º¼", - "éº ¼", - "æĵ ļ", - "Ġz kou", - "Ġzk ou", - "ëĤĺ ìļĶ", - "Ġk ry", - "Ġkr y", - "Ġnem oh", - "Ġvyu žÃŃ", - "Ġ æľ¨", - "Ġад мÑĸнÑĸÑģÑĤÑĢа", - "ا Ùĩا", - "اÙĩ ا", - "à¹ĥ à¸ģล", - "＿＿＿＿ ＿＿＿＿", - "Ġ гоÑĤ", - "Ġг оÑĤ", - "ĠدÛĮ گرÛĮ", - "ĠدÛĮگر ÛĮ", - "Ġл екаÑĢ", - "è§ Ģ", - "Ġ íĺij", - "Ġíĺ ij", - "ĠB öyle", - "ist rov", - "istr ov", - "istro v", - "女 åŃIJ", - "Ġпоп еÑĢед", - "ĠÙĨÙĪÙĬس ÙĨدÙĩ", - "ÙĴ ÙĦ", - "ĠÐŁ ав", - "Ġö rnek", - "Ġör nek", - "Ġп ÑĢик", - "ĠпÑĢ ик", - "ĠпÑĢи к", - "Ġ ÑĪи", - "ĠÑĪ и", - "üslü man", - "ĠÙħÙĤ ابÙĦ", - "åįģ äºĮ", - "Ġb ekl", - "Ġbe kl", - "Ġbek l", - "Ġver ir", - "Ġve rir", - "Ġveri r", - "ÙĪ Ø°", - "ض Ø©", - "ÑĢо ÑĤив", - "ÑĢоÑĤ ив", - "æĮ ij", - ". .:", - ".. :", - "Ġخارج ÙĬØ©", - "a dık", - "ad ık", - "adı k", - "ĠÐŁ оÑĩ", - "ĠÐŁÐ¾ Ñĩ", - "ĠÑħÑĥд ож", - "客 æĪ·", - "μ ον", - "μο ν", - "ek tiv", - "ekt iv", - "Ġt vá", - "Ġtv á", - "Û² Û²", - "Ġl á»įc", - "Ġо но", - "Ġон о", - "ÑĨ иÑĤ", - "ÑĨи ÑĤ", - "ĠÐĴ Ñģ", - "Ġ å¢", - "Ġå ¢", - "æµ ª", - "а ÑĢÑĸ", - "аÑĢ Ñĸ", - "Ġsür ekli", - "Ġ stra", - "Ġs tra", - "Ġst ra", - "Ġstr a", - "Ġb ize", - "Ġbi ze", - "Ġbiz e", - "Ġtes pit", - "Ġch âu", - "ĠاÙĦ ض", - "à¹īà¸Ń à¸ĩà¸ģ", - "à¹īà¸Ńà¸ĩ à¸ģ", - "Ġ èĢħ", - "ĠèĢ ħ", - "ĠH á»", - "Ġкажд Ñĭй", - "а Ñİ", - "à¸Ļ à¸Ħร", - "à¸Ļà¸Ħ ร", - "à¸Ĺ ะ", - "ĠÙħر اجع", - "ĠÙħراج ع", - "Ġha line", - "Ġhal ine", - "δ οÏĤ", - "δο ÏĤ", - "e ÄŁi", - "ĠÙħÛĮ زاÙĨ", - "Ġ ÙĩÙĦ", - "ĠÙĩ ÙĦ", - "Ġb olest", - "Ġbo lest", - "Ġbol est", - "Ġ åľŁ", - "Ġåľ Ł", - "Ġu zman", - "Ġuz man", - "ÑĢ ог", - "ÑĢо г", - "確 èªį", - "ĠÑĢÑĸз ниÑħ", - "Ġза кÑĢÑĭ", - "Ġзак ÑĢÑĭ", - "лÑĥ ги", - "лÑĥг и", - "ĠÑģо веÑĤ", - "ĠÑģов еÑĤ", - "id di", - "idd i", - "åIJĪ ãĤıãģĽ", - "Ġ åIJī", - "ĠåIJ ī", - "Ġk iá»ĩm", - "ë² ½", - "ĠÙħع ÙħÙĪÙĦ", - "ĠопÑĢед елÑı", - "ĠопÑĢедел Ñı", - "Ġmik tar", - "Ġ ìŀIJëıĻ", - "ĠìŀIJ ëıĻ", - "Ġil aç", - "л оÑĩ", - "ло Ñĩ", - "Ġy ılı", - "Ġyıl ı", - "Ġyı lı", - "Ġ ÄIJá»ĥ", - "ĠÄIJ á»ĥ", - "ĠÄIJá» ĥ", - "Ġab ych", - "Ġaby ch", - "Ġrek lam", - "Ġrekl am", - "Ġv ypad", - "Ġvy pad", - "Ġvyp ad", - "Ġна ÑĥÑĩ", - "à¹Ģà¸Ħร าะห", - "Ġ ä»ĸ", - "p ovÄĽ", - "po vÄĽ", - "ï¼Į 让", - "ç¥ Ŀ", - "ا ÙĪÙĨد", - "اÙĪ ÙĨد", - "اÙĪÙĨ د", - "Ġ: |:", - "Ġ:| :", - "Ġre ž", - "Ġvyb av", - "ìľ ¤", - "æŃ ´", - "огÑĢаÑĦ иÑı", - "ez peÄį", - "ezpe Äį", - "± n", - "о вÑĥ", - "ов Ñĥ", - "Ġд Ñĥма", - "ĠдÑĥ ма", - "ĠдÑĥм а", - "Ġjedn odu", - "Ġjedno du", - "о Ñīи", - "оÑī и", - "ĠÙħ شتر", - "ĠÙħØ´ تر", - "è¦ ³", - "Ġyok tur", - "Ġyoktu r", - "Ġob Äįan", - "ĠTr ần", - "ım sız", - "α ιν", - "αι ν", - " Į", - "ر ÛĮاÙĨ", - "رÛĮ اÙĨ", - "ĠJe ho", - "ĠJeh o", - "ĠاÙĦ Ø¢ÙĨ", - "ĠاÙĦØ¢ ÙĨ", - "ÑģÑĮ ким", - "ÑģÑĮк им", - "Ġk dyby", - "Ġkdy by", - "ĠbaÅŁ ına", - "Ġprez ident", - "Ġ Viá»ĩc", - "ĠV iá»ĩc", - "åħ ¼", - "à¥Į à¤ľ", - "Ġ매매 ê°Ģ", - "模 å¼ı", - "nÃŃ mu", - "nÃŃm u", - "Ġ åĤ", - "Ġå Ĥ", - "Ġ deniz", - "Ġd eniz", - "Ġden iz", - "ĺ èĽĽ", - "Ġ èĢĮ", - "ĠèĢ Į", - "ÙĪ ØŃ", - "Ñĭ п", - "Ġâĸ ¼", - "n ul", - "nu l", - "ĠS ev", - "ĠSe v", - "Ġ ruh", - "Ġr uh", - "Ġru h", - "Ġh ạ", - "Ġ Ñıн", - "ĠÑı н", - "Ġ기 본", - "Ġv elik", - "Ġve lik", - "Ġvel ik", - "ĠT ân", - "и лиÑģÑĮ", - "ил иÑģÑĮ", - "или ÑģÑĮ", - "ĠÑħ ÑĢа", - "åĤ ·", - "Ġà¤Ĩ à¤ı", - "Ġn ynÃŃ", - "Ġny nÃŃ", - "» ØĮ", - "ĠØ´ ع", - "æĿ Ĥ", - "Ġм ÑĭÑĪ", - "ĠмÑĭ ÑĪ", - "ãģĻ ãģIJ", - "Ġ ê³µì§Ģ", - "Ġê³µ ì§Ģ", - "Ġt á»Ļc", - "ãĥ¼ ãĥĩ", - "ãĥ¼ãĥ ĩ", - "ĠÑģ ело", - "ĠÑģел о", - "Ġا عÙĦاÙħ", - "Ġاع ÙĦاÙħ", - "ĠÅŁ imdi", - "ĠÅŁi mdi", - "ĠاÙĦÙħ ÙĬÙĦاد", - "ĠاÙĨÙĤÙĦ اب", - "Ġشخص ÙĬØ©", - "ĠK ür", - "ĠKü r", - "Ġ вÑĸÑĤ", - "Ġв ÑĸÑĤ", - "ĠвÑĸ ÑĤ", - "ĠاÙĨد ازÙĩ", - "Ġм оÑī", - "Ġмо Ñī", - "t ernet", - "ter net", - "tern et", - "ĠαÏħ ÏĦή", - "ĠÑĢоз ÑĤа", - "Ġв ив", - "Ġви в", - "l ej", - "le j", - "Ġ 表", - "Ġè¡ ¨", - "ÏĥÏĥ ÏĮÏĦε", - "ĠÙĬ ست", - "ĠÙĬس ت", - "Ġм аÑĪ", - "Ġма ÑĪ", - "åĿ ļ", - "Ġком наÑĤ", - "า หล", - "าห ล", - "Ġ çĻ¼", - "ĠçĻ ¼", - "ĠاÙĪÙĦ ÛĮÙĨ", - "è¿IJ åĬ¨", - "Ġп ÑĥнкÑĤ", - "ĠпÑĥнк ÑĤ", - "ĠоÑģоб енно", - "Ġм ам", - "Ġма м", - "ç» ©", - "￣￣￣￣ ￣￣￣￣", - "алÑĮ нÑĭм", - "ĠЦ енÑĤ", - "ĠЦе нÑĤ", - "- Ðľ", - "ç· Ĵ", - "Ġह à¤ľ", - "о ÑĤÑĭ", - "оÑĤ Ñĭ", - "ãĤ¤ ãĥī", - "د ارة", - "دار Ø©", - "دا رة", - "ãģ¨ ãģĹãģŁ", - "ั à¸ŀย", - "ัà¸ŀ ย", - "Ġot áz", - "Ġдопом огоÑİ", - "à¹ģละ à¸ģาร", - "ĠÑĤÑĢанÑģп оÑĢÑĤ", - "ĠÑĤÑĢанÑģпоÑĢ ÑĤ", - "ĠÙĤر Ø¢ÙĨ", - "Ġ 第ä¸Ģ", - "Ġ第 ä¸Ģ", - "Ġм ил", - "Ġми л", - "Ġng ôi", - "Ġl inh", - "Ġli nh", - "Ġlin h", - "ĠNh ân", - "ÑĮогод нÑĸ", - "æĢ Ģ", - "à¹ī าส", - "à¹īา ส", - ".:: .::", - "Ġbi rey", - "Ġbir ey", - "æĢĿ ãģĦ", - "à¹ĥ à¸Ķ", - "веÑĢ д", - "ве ÑĢд", - "Ġlistop adu", - "Ġ à¹ģม", - "Ġà¹ģ ม", - "г е", - "Ġк ÑĥÑħ", - "ĠкÑĥ Ñħ", - "Ġ íĻľëıĻ", - "ĠíĻľ ëıĻ", - "Ġ èİ", - "Ġè İ", - "ĠÐIJ лÑĮ", - "ĠÐIJл ÑĮ", - "íļĮ ìĿĺ", - "ĠÏĢ Ïģα", - "Ġv ui", - "Ġvu i", - "ว ร", - "à¤Ĥ व", - "Ġg ece", - "Ġge ce", - "Ġgec e", - "ç« ¶", - "Ġk uv", - "Ġku v", - "м еÑī", - "ме Ñī", - "ĠÑĤеп еÑĢÑĮ", - "ĠÑĤепеÑĢ ÑĮ", - "à¸Ń à¹Ģม", - "åĪ¶ 度", - "ĠÑĤ ÑĢеÑĤ", - "ĠÑĤÑĢ еÑĤ", - "ĠÙĨ تÛĮجÙĩ", - "ä»ĺ ãģį", - "Ġ ï¾ŀ", - "Ġï¾ ŀ", - "Ġ Ñĩого", - "ĠÑĩ ого", - "âĢIJ -", - "ĠÅĻÃŃ ká", - "à¸ĩ à¹ĥà¸Ļ", - "ĠnÄĽkol ika", - "ĠnÄĽkolik a", - "Ġb una", - "Ġbu na", - "Ġbun a", - "ï¼Į åŃĺäºİ", - "ล ำ", - "ãĢģ ãģ¨", - "Ġn á»Ļp", - "ĠاÙĦ جÙĨ", - "ĠاÙĦج ÙĨ", - "ĠÎł αν", - "Ġγα ν", - "Ðŀ Ðł", - "Ġدخ تر", - "Ġúda je", - "Ġúdaj e", - "Ġ å¼ł", - "Ġå¼ ł", - "r etim", - "re tim", - "ret im", - "s ınız", - "sın ız", - "ĠÙĩÙĨ اÙĥ", - "ĠÙĩÙĨا Ùĥ", - "ÐĽ Ь", - "æķ ¬", - "Îij Îľ", - "页éĿ¢ åŃĺæ¡£å¤ĩ份", - "ìĤ¬ ê°Ģ", - "Ġt rest", - "Ġtr est", - "Ġtre st", - "Ġtres t", - "v iÄį", - "vi Äį", - "ĠÙ¾ ÛĮدا", - "ĠÙ¾ÛĮ دا", - "ζ ε", - "ĠÐŁ ов", - "ĠÐŁÐ¾ в", - "ÙĦÙħ ات", - "o rex", - "or ex", - "ore x", - "è¬ Ľ", - "ĠвÑĸдк ÑĢиÑĤ", - "м аÑħ", - "ма Ñħ", - "ĠÑĩиÑģ ле", - "ت بار", - "تب ار", - "ĠÎŃ Îº", - "ìķĦ íĮĮíĬ¸", - "r avel", - "ra vel", - "rav el", - "α Ïĥία", - "αÏĥ ία", - "a Äį", - "Ġ à¤ıन", - "Ġà¤ı न", - "ละ à¹Ģà¸Ń", - "Ġз алеж", - "Ġза леж", - "Ġзал еж", - "Ġ æģ", - "Ġæ ģ", - "Ġмож еÑĤе", - "ĠможеÑĤ е", - "Ġможе ÑĤе", - "Ġпо вед", - "Ġпов ед", - "ĠبسÛĮ ارÛĮ", - "ĠبسÛĮار ÛĮ", - "Ġ poÄįet", - "Ġpo Äįet", - "ĠpoÄį et", - "ر بع", - "رب ع", - "e lez", - "el ez", - "ele z", - "ا ÙĪرÛĮ", - "اÙĪ رÛĮ", - "اÙĪر ÛĮ", - "Ġba ÅŁk", - "ĠbaÅŁ k", - "å° Ĥ", - "Ġhal de", - "æĭ Ł", - "S au", - "Sa u", - "о ÑĨи", - "ี à¸Ħ", - "Ġв лади", - "Ġвла ди", - "Ġвлад и", - "ÙIJ Ùħ", - "k ud", - "ku d", - "à¥Ĥ ब", - "å§Ķ åĵ¡", - "า รà¸ĵ", - "าร à¸ĵ", - "o rů", - "or ů", - "Ġ ÙħÙĪÙĦ", - "ĠÙħ ÙĪÙĦ", - "ĠÙħÙĪ ÙĦ", - "Ġ byt", - "Ġb yt", - "Ġby t", - "ĠpÅĻÃŃslu Å¡", - "èĭ± è¯Ń", - "éĢ IJ", - "Ġvel ké", - "Ġvelk é", - "Ġà¤Ĩ श", - "Ġph iếu", - "Ġphi ếu", - "à¹ĥ ส", - "Ġاس Ù¾", - "Ġzbo žÃŃ", - "ãģĵ ãĤĵãģª", - "ãģĵãĤĵ ãģª", - "ĠÙĪÙĩ ÙĬ", - "ĠÑĥÑĩа ÑģÑĤÑĮ", - "ĠÑĥÑĩаÑģÑĤ ÑĮ", - "ĠÑĥÑĩаÑģ ÑĤÑĮ", - "à¸Īำ à¸Ļวà¸Ļ", - "Ġتر Ú©", - "åįģ åĪĨ", - "ÎŁ Îł", - "ÎŁÎ ł", - "κ ολ", - "κο λ", - "Ġf akat", - "Ġfa kat", - "Ġfak at", - "Ġch á»Ĺ", - "éĢļ çŁ¥", - "Ġв одÑĥ", - "Ġво дÑĥ", - "Ġвод Ñĥ", - "ĠÎļα ÏĦηγοÏģία", - "aca ģını", - "acaÄŁ ını", - "л ого", - "ло го", - "ĠmÃ¼ÅŁ ter", - "Ġj ednou", - "Ġjed nou", - "Ġjedn ou", - "Ġjedno u", - "Ġб аÑĢ", - "Ġба ÑĢ", - "i dae", - "id ae", - "ida e", - "d ım", - "dı m", - "è¾ ²", - "åIJ ¹", - "ëIJ ©ëĭĪëĭ¤", - "ĠÅŁekl inde", - "e ným", - "en ým", - "ený m", - "ëĵ ¯", - "i tÄĽ", - "it ÄĽ", - "Ġк олÑĮ", - "Ġкол ÑĮ", - "Ġко лÑĮ", - "ëĮĢ íķĻ", - "ĠÃĸ r", - "Ġ ê½", - "Ġê ½", - "ĠUB ND", - "Ġh ik", - "Ġhi k", - "ãĤī ãģĹãģĦ", - "ãĤīãģĹ ãģĦ", - "åĩº åĵģ", - "C ó", - "Ġ Îŀ", - "ĠÎ ŀ", - "Ġ åħ¥", - "Ġåħ ¥", - "ĠNg uyên", - "ĠÙ¾ ÙĪØ´", - "лÑı ÑĶ", - "ĠØ¢ غاز", - "Ġnhiá»ħ m", - "d ivid", - "div id", - "di vid", - "ç ĺ", - "ا ÙģتÙĩ", - "اÙģ تÙĩ", - "اÙģت Ùĩ", - "а меÑĤ", - "ам еÑĤ", - "нÑĥ лÑģÑı", - "нÑĥл ÑģÑı", - "ä¼ģ æ¥Ń", - "ÑĢоб ÑĸÑĤ", - "dü ÄŁÃ¼", - "dÃ¼ÄŁ ü", - "Ġ کاÙĨ", - "ĠÚ© اÙĨ", - "à¸Ń à¸ĩà¸Ĺ", - "à¸Ńà¸ĩ à¸Ĺ", - "й н", - "Ġpoh yb", - "Ġpohy b", - "Ġb iá»ĩn", - "Ġbi á»ĩn", - "Ġ ï¼Ľ", - "Ġï¼ Ľ", - "Ùħ ÙĨد", - "ÙħÙĨ د", - "Ġà¤Ĩ à¤ķ", - "ĠÄįlov ÄĽk", - "ĠÄįlovÄĽ k", - "ãĤĴè¦ĭ ãĤĭ", - "ë· °", - "ĠÑĥв елиÑĩ", - "ĠÑĥвели Ñĩ", - "Ġ ê´", - "Ġê ´", - "Ġyan lÄ±ÅŁ", - "éº ¦", - "Ġå¤ĸ éĥ¨", - "ÏĦ οÏħÏģγ", - "ÏĦοÏħ Ïģγ", - "Ġп ÑĢоÑĩ", - "ĠпÑĢ оÑĩ", - "ĠпÑĢо Ñĩ", - "ĠÑĢÑĥ ковод", - "çĽ ¤", - "èľ ĺèĽĽ", - "å®ī è£ħ", - "ĠУ кÑĢа", - "Ġtart Ä±ÅŁ", - "ÑĤ аж", - "ÑĤа ж", - "ĠoluÅŁ an", - "ĠRus ya", - "Ġк лÑĥб", - "Ġкл Ñĥб", - "ĠклÑĥ б", - "ĠÎł Ρ", - "alı dır", - "k ın", - "kı n", - "ĠзмÑĸ ни", - "ĠзмÑĸн и", - "leÅŁ ik", - "еÑĢ п", - "об Ñīе", - "обÑī е", - "Ġqu áºŃn", - "Ġप श", - "ãĤĴ åıĹ", - "à¹Ģล à¸Ĥ", - "ا ضر", - "اض ر", - "Ġuž ivatel", - "λ ία", - "λί α", - "ĠÐĴ они", - "ĠÐĴо ни", - "ุà¸Ķ à¸Ĺ", - "ĠV Ãł", - "ãĥ³ ãĤ¿", - ") ëĬĶ", - "æ¸ Ľ", - "Ġ μÏĢ", - "Ġμ ÏĢ", - "å· §", - "ĠÑĪ кол", - "ĠÑĪк ол", - "Ġì²ĺ ìĿĮ", - "ัà¸ģ à¸Ķ", - "æ® Ĭ", - "Ġnh á»Ŀ", - "ĠοÏĢο ία", - "à¹ģ à¸Ļว", - "à¹ģà¸Ļ ว", - "меÑĢик ан", - "nÃŃ ka", - "nÃŃk a", - "Ġíĺ¸ íħĶ", - "سب ب", - "à¸ĩ ม", - "ìŀĪ ëĬĶ", - "غ Ø·", - "Ùı ÙĦ", - "¹ æŀľ", - "Ñĩ Ñĸв", - "ÑĩÑĸ в", - "ÑĪ аÑı", - "ÑĪа Ñı", - "ĠØ¥ ÙĦا", - "ĠØ¥ÙĦ ا", - "خص ÙĪص", - "ll ll", - "ĠÑį ÑĤим", - "ĠÑįÑĤ им", - "ĠÑįÑĤи м", - "Ġz vÃŃ", - "Ġzv ÃŃ", - "Ġqu án", - "Ġquá n", - "à¸Ļ à¸ģ", - "Ġп олов", - "Ġпо лов", - "Ġпол ов", - "Ġ æ·±", - "Ġæ· ±", - "Ġm iá»ģn", - "Ġmi á»ģn", - "人 éĸĵ", - "Ġз им", - "Ġmey dana", - "е ÑĦ", - "Ġb á»ģn", - "Ġbá»ģ n", - "ز ÙĬد", - "زÙĬ د", - "ĠÐł еÑģп", - "ĠÐłÐµ Ñģп", - "ÎĻ ΣΤ", - "ÎĻΣ Τ", - "Ġ æĶ¶", - "ĠæĶ ¶", - "r aya", - "ra ya", - "ray a", - "ĠتÙĪ اÙĨد", - "ĠتÙĪاÙĨ د", - "Ġ ister", - "Ġis ter", - "Ġi ster", - "Ġist er", - "Ġ ë°Ģ", - "Ġë° Ģ", - "ĠмеÑħ ани", - "Ġ à¸ķำ", - "Ġà¸ķ ำ", - "Ġд ека", - "Ġде ка", - "Ġдек а", - "à¤Ĥ à¤Ĺल", - "à¤Ĥà¤Ĺ ल", - "ãĥ¼ ãĤ«ãĥ¼", - "Ġnep ÅĻÃŃ", - "ĠnepÅĻ ÃŃ", - "ĠÑģ ÑĩиÑĤ", - "ĠÑģÑĩ иÑĤ", - "Ġο μά", - "Ġç ift", - "ب ÛĮÙĨÛĮ", - "بÛĮ ÙĨÛĮ", - "بÛĮÙĨ ÛĮ", - "m eleri", - "me leri", - "mel eri", - "meler i", - "Ġвоз дейÑģÑĤв", - "d ou", - "do u", - "ìĥģ ìĿĦ", - "ĠÐĴ олод", - "ĠÐĴо лод", - "ĠÐĴол од", - "ε β", - "ÐĿ Ðĺ", - "Ñı к", - "Ïį ÏĦε", - "з ано", - "за но", - "len ir", - "c elik", - "ce lik", - "cel ik", - "ĠÑģоÑģÑĤав лÑıеÑĤ", - "ι αÏĤ", - "ια ÏĤ", - "ĠÐĵ оÑĢ", - "ä¹ĭ ä¸Ģ", - "Ïĥμ ÏĮÏĤ", - "ÏĥμÏĮ ÏĤ", - "ãģ« éĸ¢", - "Ġв Ñĩ", - "Ġп оÑģк", - "Ġпо Ñģк", - "ĠпоÑģ к", - "è¼ ¯", - "à¥Ģ श", - "ĠØ¢ ثار", - "à¸Ħวาม ร", - "Ġ един", - "Ġе дин", - "Ġеди н", - "íħ IJ", - "å¹³ æĪIJ", - "ĠkiÅŁ inin", - "ĠkiÅŁi nin", - "ãĤ² ãĥ¼ãĥł", - "à¥įत व", - "Ġkapsam ında", - "Ġak tar", - "Ġakt ar", - "Ġtr ừ", - "Ġر شد", - "Ġна каз", - "Ġнак аз", - "ر ÙĬÙĦ", - "رÙĬ ÙĦ", - "à¸Ń à¸Ħ", - "Ġگذ شتÙĩ", - "Ġ æ°ij", - "Ġæ° ij", - "ĠÑĤеб Ñı", - "s por", - "sp or", - "spo r", - "Ñİ ÑīаÑı", - "ÑİÑī аÑı", - "окÑĢем а", - "в ад", - "ва д", - "ĠCh úng", - "ĠزÛĮ ادÛĮ", - "ĠزÛĮاد ÛĮ", - "е ного", - "ен ого", - "ено го", - "ĠÚ© سÛĮ", - "à ŀ", - "Ġad ına", - "Ġadı na", - "Ñĥ да", - "Ñĥд а", - "Ñĸ ÑĶ", - "аÑĤ ели", - "аÑĤе ли", - "Ġnáv Å¡tÄĽ", - "çĶ¨ äºİ", - "ĠپرÙĪ ÙĨدÙĩ", - "ĠÙĨ بÙĪد", - "ĠÙĨب ÙĪد", - "س ات", - "ìĹ ĺ", - "ãģ£ ãģ¦ãĤĤ", - "ãģ£ãģ¦ ãĤĤ", - "Ġ çī©", - "Ġçī ©", - "Ðĺ з", - "åĪ ·", - "Ġ íľ´", - "Ġí ľ´", - "ĠоÑģоб лив", - "ãģĹ ãģ¾ãģ£ãģŁ", - "ãģĹãģ¾ ãģ£ãģŁ", - "a ydı", - "ay dı", - "ayd ı", - "åĩº çļĦ", - "ĠìķĦëĭĪ ëĿ¼", - "ıs ını", - "à¸Ĺาà¸ĩ à¸ģาร", - "Ġzv uky", - "Ġ 管", - "Ġç® ¡", - "âĸĭ âĸĭ", - "ĠÑĤ елеÑĦ", - "ĠÑĤел еÑĦ", - "Ġн елÑĮзÑı", - "ãĥ« ãģ®", - "Ïĥ ÏĢ", - "Ġ ç³", - "Ġç ³", - "åł ¡", - "ÑĨ Ñĥз", - "ÑĨÑĥ з", - "رÙĬ ÙĤØ©", - "رÙĬÙĤ Ø©", - "à¤¿à¤Ľ ल", - "è² ©", - "ĠУ кÑĢаÑĹн", - "ĠУкÑĢаÑĹ Ð½", - "ĠÙħسئ ÙĪÙĦ", - "Ġо ÑĩÑĸ", - "ĠоÑĩ Ñĸ", - "æľĢ å¾Į", - "Ġзна Ñİ", - "Ġзн аÑİ", - "à¹ī à¸Ļà¸Ĺ", - "à¹īà¸Ļ à¸Ĺ", - "ĠÑĤ еÑĢап", - "ĠÑĤеÑĢ ап", - "ĠÑĤе ÑĢап", - "ĠÑģп ок", - "ĠØ®ÙĪد رÙĪ", - "éĺ »", - "Ġdüz ey", - "ä¸Ģ åĢĭ", - "ا ÙģÙĩ", - "اÙģ Ùĩ", - "à¤Ĥ य", - "èµĦ 产", - "继 ç»Ń", - "ĠÑģ лаб", - "ĠÑģл аб", - "æĦı æĢĿ", - "ĠíĻĺ ìĤ°", - "ĠÑı ÑĢ", - "Ġd ůvod", - "Ġdů vod", - "çĿ Ľ", - "تÛĮ ب", - "ĠÙĪ ÛĮر", - "ĠÙĪÛĮ ر", - "ĠÙĩ زÛĮÙĨÙĩ", - "Ġben zer", - "Ġbenz er", - "ĠÙħ ادÙĩ", - "ĠÙħا دÙĩ", - "ĠÙħاد Ùĩ", - "à¥Į à¤ķ", - "Ġ à¹Ģà¸ķ", - "Ġà¹Ģ à¸ķ", - "Ġà¹Ģภķ", - "ãĤĪ ãģı", - "ид енÑĤ", - "èĭ± èªŀ", - "е ÑĢÑĭ", - "еÑĢ Ñĭ", - "Ġê¸Ī ìķ¡", - "Ġ ãĥ¼", - "Ġãĥ ¼", - "Ġ ëį¤íĶĦ", - "Ġëį ¤íĶĦ", - "ÑĢ аÑĤÑĮ", - "ÑĢа ÑĤÑĮ", - "ÑĢаÑĤ ÑĮ", - "Ġ åįķ", - "Ġåį ķ", - "à¹Ģà¸ī à¸ŀาะ", - "Ġ æĶ¿", - "ĠæĶ ¿", - "Ġà¤Ĩ म", - "Ġз ни", - "Ġзн и", - "Ġ ëĿ¼ìĿ´", - "ĠëĿ¼ ìĿ´", - "æİ Į", - "çIJĨ çĶ±", - "Ġ اغ", - "Ġا غ", - "ĠØ§Ø º", - "ĠÑģ иг", - "ĠÑģи г", - "Ġе ÑĦекÑĤив", - "ĠÐŁ ÑĢед", - "ĠÐŁÑĢ ед", - "ãĥ´ ãĤ£", - "Ġви ко", - "Ġвик о", - "Ġt vrd", - "Ġtv rd", - "ëĤ´ 기", - "ãĥĭ ãĤ¢", - "ĠÙħشاÙĩ دÙĩ", - "Ġस à¤ļ", - "l Ã¼ÄŁ", - "lü ÄŁ", - "è¯ģ åĪ¸", - "Ġs iêu", - "Ġsi êu", - "Ġ оÑĤв", - "ĠоÑĤ в", - "Ġvyt voÅĻ", - "ĠØŃ ÙħÙĦ", - "ĠØŃÙħ ÙĦ", - "ĠÑĦ ÑĢан", - "à¹ī à¸Ķ", - "åĮ» éĻ¢", - "Ġв лад", - "Ġвла д", - "غ ÙĦ", - "建 ç«ĭ", - "os loven", - "osl oven", - "и лаÑģÑĮ", - "ила ÑģÑĮ", - "عÙĦ ÙĪÙħات", - "عÙĦÙĪÙħ ات", - "Ġ ترÛĮÙĨ", - "Ġتر ÛĮÙĨ", - "ÎŃ Ïģει", - "ÎŃÏģ ει", - "Ġb áºŃt", - "ĠÙħØ´ Ú©", - "Ġر ئÙĬس", - "Ġرئ ÙĬس", - "Ġìłľ ìŀij", - "γ η", - "Ġн Ñĸк", - "ĠнÑĸ к", - "Ġ구 ìĦ±", - "ĠÄij en", - "Ġà¤ļ र", - "Ġgeç miÅŁ", - "äºĨ 解", - "Ġл еÑģ", - "Ġqu anh", - "Ġqua nh", - "Ġquan h", - "ãĢĮ æĪij", - "ĠNÄĽkter á", - "ëŀ į", - "Ãħ Ÿ", - "à¤Ĥ दर", - "à¤Ĥद र", - "ìķĦ ìĿ´", - "å°ij ãģĹ", - "ĠØ´Ùĩر ÛĮ", - "ĠØ´Ùĩ رÛĮ", - "κ ÏĦη", - "ĠâĹ Ħ", - "Ġ Ùĥس", - "ĠÙĥ س", - "è· Į", - "à ı", - "å·¥ åħ·", - "åĬ ĥ", - "p om", - "po m", - "ĠнавÑĩ аннÑı", - "Ġ رج", - "Ġر ج", - "ÑĢ ÑĥеÑĤÑģÑı", - "ÑĢÑĥ еÑĤÑģÑı", - "ÑĢÑĥеÑĤ ÑģÑı", - "Ġν ÎŃ", - "ÛĮÙĨ Ú©", - "à¹Ĥ à¸ĭ", - "åĭ ¤", - "ãģĹãģ¾ ãģĨ", - "ĠÑģ оглаÑģ", - "ĠÑģог лаÑģ", - "éĩij èŀį", - "ç »¿", - "ç» ¿", - "ĠС ан", - "æķ µ", - "Ġпо вÑĸÑĤ", - "Ġпов ÑĸÑĤ", - "Ġпом оÑīи", - "ĠпомоÑī и", - "ãĥ¡ ãĥªãĤ«", - "ãĤ· ãĤ¢", - "ĠÏĢ ÏģοÏĤ", - "ĠÏĢÏģο ÏĤ", - "èĪª 空", - "ĠваÑĢи анÑĤ", - "ĠваÑĢиан ÑĤ", - "Ġyalnız ca", - "ç³» çµ±", - "ĠÙģ ÙĪر", - "ĠÙģÙĪ ر", - "оÑĩ ной", - "оÑĩно й", - "à¹Ģว à¸Ńร", - "ĠкÑĥлÑĮ ÑĤÑĥÑĢ", - "ĠкÑĥлÑĮÑĤÑĥ ÑĢ", - "Ïĩ ι", - "ÄįÃŃ ta", - " ĵ", - "人 ãģĮ", - "κ οÏį", - "κο Ïį", - "ĠÑĢе ÑĶ", - "Ġв ÑģÑİ", - "ĠвÑģ Ñİ", - "éº Ĺ", - "Ġز ÙĨاÙĨ", - "ĠزÙĨ اÙĨ", - "çĭ Ĥ", - "Ġ หม", - "Ġห ม", - "Ġx úc", - "åħ Ĵ", - "ÄŁ inin", - "ÄŁi nin", - "ÄŁini n", - "ÄŁin in", - "åĸľ 欢", - "ĠÑģÑĤ ад", - "ĠÑģÑĤа д", - "iy esi", - "iye si", - "ìļ ±", - "è Ŀ", - "Ġ kus", - "Ġk us", - "Ġku s", - "ÏĦ ολ", - "ÏĦο λ", - "г Ñĸв", - "Ñĸ ли", - "Ñĸл и", - "ãģĦ ãĤĦ", - "é© Ĺ", - "ont rol", - "ا ÙĦÙĥ", - "اÙĦ Ùĥ", - "к овиÑħ", - "ко виÑħ", - "ков иÑħ", - "ĠÑģÑĤ ало", - "ĠÑģÑĤал о", - "ĠÑģÑĤа ло", - "ĠÎł αÏģα", - "Ġγα Ïģα", - "ĠγαÏģ α", - "Ġ chy", - "Ġc hy", - "Ġch y", - "Ġcih az", - "ĩ ´", - "ìŀ¥ ìĿ´", - "a ceae", - "ace ae", - "acea e", - "Ø´ Ùĩر", - "Ø´Ùĩ ر", - "ил аннÑı", - "çļĦ å°ı", - "Ġth ụ", - "Ġthá» ¥", - "ÙĪ ÙĨت", - "ÙĪÙĨ ت", - "л оÑĢ", - "ло ÑĢ", - "ãĤĴ æĮģ", - "ĠÎĶ ι", - "Ġ 羣", - "Ġçľ Ł", - "ÐĽ Ðŀ", - "é½ IJ", - "çİ Ħ", - "ا ÙĪÙĩ", - "اÙĪ Ùĩ", - "Ġи нÑĤ", - "Ġин ÑĤ", - "à¥Ģ à¤Łà¤°", - "à¥Ģà¤Ł र", - "Ġ обÑīе", - "Ġоб Ñīе", - "ĠобÑī е", - "Ġдеп ÑĥÑĤ", - "μÎŃν εÏĤ", - "ĠÙĥ ÙĬÙģ", - "ع ÙħÙĦ", - "عÙħ ÙĦ", - "ï¼Į å¦Ĥæŀľ", - "ï¼Įå¦Ĥ æŀľ", - "Ġин ÑĦек", - "i tele", - "it ele", - "ite le", - "itel e", - "Ġ ãĢĢãĢĢĠãĢĢ", - "ĠãĢĢ ãĢĢĠãĢĢ", - "ĠãĢĢãĢĢ ĠãĢĢ", - "ãĤ¤ ãĥ³ãĥĪ", - "ãĤ¤ãĥ³ ãĥĪ", - "л ÑĸÑĤ", - "лÑĸ ÑĤ", - "Ġ ÑģÑİ", - "ĠÑģ Ñİ", - "Ġz ase", - "Ġza se", - "Ġzas e", - "d ech", - "de ch", - "dec h", - "е ко", - "ек о", - "è® ĵ", - "åı ¬", - "з ем", - "Îł Îij", - "Ġvz du", - "า à¸Īาà¸ģ", - "าà¸Ī าà¸ģ", - "ko liv", - "kol iv", - "koli v", - "zk um", - "èģ Ĭ", - "Ġì±Ħ ìļ©", - "๠į", - "Ġ asp", - "Ġa sp", - "Ġas p", - "Û² Û´", - "ìĿ¸ ëį°", - "ĠkarÅŁÄ± laÅŁ", - "ï¼Į åı¯ä»¥", - "ï¼Įåı¯ 以", - "Ġà¤ĩन à¤ķ", - "Ġ ìĬ¤íĥĢ", - "ĠìĬ¤ íĥĢ", - "éĥ¨ å±ĭ", - "åĪ¶ ä½ľ", - "ãĥ¼ ãĤ·ãĥ§ãĥ³", - "ον ÏĦαÏĤ", - "γ ο", - "Ġìŀij ìĦ±", - "èij £", - "oz ÅĻejmÄĽ", - "ĠÑĢезÑĥлÑĮÑĤаÑĤ е", - "ĠÑĢезÑĥлÑĮÑĤ аÑĤе", - "ĠIns ecta", - "Ġs kon", - "Ġsk on", - "o tu", - "ot u", - "Ġp ÄĽt", - "ĠpÄĽ t", - "Ñģ ÑĮого", - "ÑģÑĮ ого", - "ĠÄ°s lam", - "Ġl á»ħ", - "Ġlá» ħ", - "ä¸Ń åľĭ", - "ĠÐľÑĸ нÑĸÑģÑĤ", - "åIJĪ åIJĮ", - "asy onu", - "asyon u", - "ож еÑĤ", - "оже ÑĤ", - "èĩª åĬ¨", - "ÑģÑĮ коÑİ", - "ÑģÑĮк оÑİ", - "ÑģÑĮко Ñİ", - "ĠkiÅŁ isel", - "ĠkiÅŁi sel", - "ÏĦ ικοÏį", - "ÏĦικ οÏį", - "ÏĦι κοÏį", - "ÏĦικο Ïį", - "Ġ ÑĥÑĩаÑģ", - "ĠÑĥ ÑĩаÑģ", - "ĠÑĥÑĩ аÑģ", - "ĠÑĥÑĩа Ñģ", - "ıl mÄ±ÅŁtır", - "ılmÄ±ÅŁ tır", - "ĠÑı ке", - "ĠÑıк е", - "Ñī инÑĭ", - "Ñīи нÑĭ", - "Ñīин Ñĭ", - "м аÑĢ", - "ма ÑĢ", - "Ġso udu", - "Ġsou du", - "Ġsoud u", - "Âł Я", - "Ġд ÑĢÑĥ", - "ĠдÑĢ Ñĥ", - "ãģ¡ ãĤĩ", - "à¥ĭ à¥ľ", - "ï¾ ij", - "Ġ ÏĦÏĮ", - "ĠÏĦ ÏĮ", - "Ġ ضر", - "Ġض ر", - "l áš", - "lá Å¡", - "Ġд Ñĸв", - "ĠдÑĸ в", - "Ġج دÙĬد", - "Ġجد ÙĬد", - "Ġнеб олÑĮÑĪ", - "ĠнеболÑĮ ÑĪ", - "éģ Ń", - "ç» į", - "ĠKur ulu", - "ĠKurul u", - "ÑģÑĤÑĢ ÑĥменÑĤ", - "ÑģÑĤÑĢÑĥ менÑĤ", - "è¿Ļ æĺ¯", - "ìĻ Ķëĭ¤", - "ìĻĶ ëĭ¤", - "м елÑĮ", - "ме лÑĮ", - "Ġ ä¼Ĭ", - "ủ ng", - "ĠзавиÑģим оÑģÑĤи", - "ëį ¤íĶĦ", - "çĩ ĥ", - "è¿ĩ åİ»", - "ĠзаÑģÑĤоÑģ ÑĥваннÑı", - "Ġداخ ÙĦÛĮ", - "ĠداخÙĦ ÛĮ", - "Ñī Ñij", - "ĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", - "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂł", - "ïº ®", - "ĠاÙĦÙħ ÙħÙĦÙĥØ©", - "s ında", - "sı nda", - "sın da", - "è³ Ģ", - "å± ı", - "Ġ ê¿", - "Ġê ¿", - "Ġdo ktor", - "Ġdok tor", - "ĠÙĤ اب", - "ĠS ist", - "ĠSi st", - "ĠмеÑģÑĤ е", - "ĠÑģоÑħ ÑĢа", - "ا شتÙĩ", - "اش تÙĩ", - "اشت Ùĩ", - "Ġ æľŁ", - "ĠпоÑģк олÑĮкÑĥ", - "Ġp ev", - "Ġpe v", - "ا گر", - "اگ ر", - "Ùħ ز", - "Ġض ÙħÙĨ", - "ॠ©", - "g esi", - "ge si", - "ges i", - "a ÄŁa", - "aÄŁ a", - "解 åĨ³", - "ëħ¸ ì¶ľ", - "Ġl uyá»ĩn", - "Ġкон ÑĤак", - "ĠконÑĤ ак", - "ภº", - "Ġ NgÃły", - "ĠNg Ãły", - "Ġvý stav", - "Ġth uyết", - "اÛĮ ع", - "Ġ: /:", - "Ġ:/ :", - "Ġph ạt", - "ĠÎij ÏĢÏĮ", - "ĠÎijÏĢ ÏĮ", - "Ġ muz", - "Ġm uz", - "Ġmu z", - "Ġ ìĥī", - "Ġìĥ ī", - "ĠÃĩ in", - "Ġکار برد", - "Ġکاربر د", - "ائ د", - "ب اد", - "با د", - "à¥į तम", - "à¥įत म", - "Ġ ëijĺ", - "Ġëij ĺ", - "Ġм оз", - "Ġмо з", - "Å¡ ÃŃch", - "Å¡ÃŃ ch", - "Ġ มห", - "Ġม ห", - "ĠØ¢ س", - "ĠÑģ лиÑĪком", - "èĥ ¡", - "è£ ģ", - "æĪ »", - "ĠìĦ¤ ëªħ", - "Ġo tom", - "Ġot om", - "Ġoto m", - "Ġलà¤Ĺ à¤Ńà¤Ĺ", - "à¸ĩ à¸ģ", - "ا بد", - "اب د", - "à¸Ļ าม", - "à¸Ļา ม", - "èĤ ©", - "Ġشد ÙĨد", - "ĠشدÙĨ د", - "ãģĿãģ® ä»ĸ", - "ad lo", - "ÄĽ n", - "ĠÙĦ Ùĩا", - "ĠÙĦÙĩ ا", - "Ġмин им", - "Ġми ним", - "Ġd ÅĻev", - "ĠTh iên", - "ĠThi ên", - "ëŀ Ļ", - "en gin", - "eng in", - "à¥Ģ मत", - "à¥Ģम त", - "ĠÑĥп оÑĤÑĢеб", - "âĢĮ تر", - "Ġç¥ŀ 马", - "ov ánÃŃm", - "ová nÃŃm", - "ovánÃŃ m", - "ován ÃŃm", - "Ġд ело", - "Ġдел о", - "Ġде ло", - "Ġ ç¼ĸ", - "Ġç¼ ĸ", - "ĠاÙĦ ظ", - "Ġ вий", - "Ġв ий", - "Ġви й", - "а ÑĤом", - "аÑĤ ом", - "аÑĤо м", - "åħ¬ åijĬ", - "ĠÄij em", - "ãĤ· ãĥªãĥ¼ãĤº", - "ä¸ĭ çļĦ", - "l ası", - "la sı", - "las ı", - "ĠвÑĭ боÑĢ", - "ĠвÑĭб оÑĢ", - "ÑĤ оÑĤ", - "ÑĤо ÑĤ", - "ëıĦ ë³Ħ", - "ĠÑĥ ÑģÑĤан", - "ĠÑĥÑģÑĤ ан", - "Ġ íŀĪ", - "Ġíŀ Ī", - "лÑĥ аÑĤа", - "Ġth ác", - "а нием", - "ан ием", - "ани ем", - "ание м", - "ов аÑĤÑĮÑģÑı", - "ова ÑĤÑĮÑģÑı", - "оваÑĤÑĮ ÑģÑı", - "ÑĤ ÑĶ", - "ÐŃ ÑĤо", - "ï¼Į è¦ģ", - "ĠV z", - "ĠØŃ ÙĪزÙĩ", - "ĠØŃÙĪ زÙĩ", - "- к", - "V Ỽi", - "ent ů", - "Ġbulun duÄŁu", - "Ġbulundu ÄŁu", - "ر ÙĪØ·", - "رÙĪ Ø·", - "ĠÑĹ Ð¹", - "Ġçev r", - "Ġ ÅĻed", - "ĠÅĻ ed", - "ĠÅĻe d", - "Ġس اختÙĩ", - "Ġساخت Ùĩ", - "åĬŀ æ³ķ", - "Ġ ÙĤÙĦ", - "ĠÙĤ ÙĦ", - "i ÅŁi", - "iÅŁ i", - "ï¼Ŀ ï¼Ŀ", - "س اس", - "Ġúdaj ů", - "å ¬", - "æį Ł", - "á ct", - "ác t", - "ĠÎij ÏĢ", - "çĪ ·", - "Ġ ÅĻád", - "ĠÅĻ ád", - "Ġl á»Ĺi", - "Ġlá» Ĺi", - "Ġlá»Ĺ i", - "on tent", - "ont ent", - "onte nt", - "ĠÙħ Ø°", - "ol oji", - "olo ji", - "oloj i", - "Ġپرد اخت", - "à¹ī าà¸ŀ", - "à¹īา à¸ŀ", - "ĠдейÑģÑĤв иÑı", - "Ġmnož stvÃŃ", - "ìķĪ ë§Ī", - "åģ ¶", - "Ġ ÃĶng", - "ĠÃĶ ng", - "Ġdak ika", - "hen dis", - "hend is", - "Ġb ác", - "å¯ ¶", - "à¹ĩà¸ģ หà¸į", - "noc enÃŃ", - "ĠErd oÄŁan", - ": ::::::::::::", - ":: :::::::::::", - ":::: :::::::::", - ":::::: :::::::", - ":::::::: :::::", - "::: ::::::::::", - "::::: ::::::::", - "::::::: ::::::", - "::::::::: ::::", - ":::::::::: :::", - "::::::::::: ::", - ":::::::::::: :", - "аÑĤ ем", - "аÑĤе м", - "d ız", - "dı z", - "ĠØ£ÙĬ ضا", - "ĠØ£ÙĬض ا", - "ĠÑįÑĦ ÑĦек", - "ãĤĮ ãģ¦ãģĦãĤĭ", - "ãĤĮãģ¦ ãģĦãĤĭ", - "ĠbaÅŁv uru", - "ĠbaÅŁvur u", - "ά νει", - "άν ει", - "ĠÏĦε λεÏħÏĦα", - "Ġê²Ģ ìĥī", - "ĠÚ©ÙĨ ترÙĦ", - "Ġ शà¤ķ", - "Ġश à¤ķ", - "å¼ ¹", - "Ġol muÅŁtur", - "Ġolm uÅŁtur", - "ĠolmuÅŁ tur", - "Ġв ÑģÑĤÑĥп", - "ĠвÑģÑĤ Ñĥп", - "Ñĩ ила", - "Ñĩи ла", - "Ñĩил а", - "ย า", - "ĠØ£ØŃ Ùħد", - "os lav", - "osl av", - "ĠÑĩа Ñģов", - "ĠÑĩаÑģ ов", - "Ġzá kladnÃŃ", - "Ġzáklad nÃŃ", - "Ġस व", - "д он", - "до н", - "ĠÅĻÃŃj na", - "κ οÏħ", - "κο Ïħ", - "éĢģ æĸĻçĦ¡æĸĻ", - "éĢģæĸĻ çĦ¡æĸĻ", - "Ïĥ ίαÏĤ", - "Ïĥία ÏĤ", - "Ïĥί αÏĤ", - "ãĤ´ ãĥª", - "Ġв иб", - "Ġви б", - "å½ Ĵ", - "Ġназ ад", - "ĠçĻ¾åº¦ æĶ¶å½ķ", - "á» Ĩ", - "Ġkal dı", - "ì¼ ľ", - "Ġ íıŃ", - "Ġíı Ń", - "ĠÑĩи ном", - "ĠÑĩин ом", - "è ¹", - "Ñı л", - "ĠÑĢаз дел", - "ĠÑĢазд ел", - "d G", - "ĠT ento", - "ĠTen to", - "ĠTent o", - "Ñı ÑĤÑĮÑģÑı", - "ÑıÑĤÑĮ ÑģÑı", - "éĿ¢ çļĦ", - "ĠÎķ ÏĢι", - "ĠÎķÏĢ ι", - "ê° ij", - "Ġk èm", - "ни ÑĨÑı", - "ниÑĨ Ñı", - "çĸ «", - "éĽ Ļ", - "ĠÙħر Ùĥز", - "Ġна Ñĥк", - "å¢ Ĺ", - "ĠÑĤе пеÑĢ", - "ĠÑĤеп еÑĢ", - "ा à¤ł", - "ाठł", - "à¹ĩà¸ļ à¹Ħà¸ĭà¸ķ", - "μβ ÏģίοÏħ", - "ĠÑĦÑĸн анÑģов", - "ĠÑĦÑĸнанÑģ ов", - "Ñĸ ÑĶÑİ", - "ÑĸÑĶ Ñİ", - "Ïģ ίζ", - "Ïģί ζ", - "ì¤ Ħ", - "ĠباÙĨ Ú©", - "t ul", - "tu l", - "li ÄŁini", - "liÄŁi ni", - "liÄŁ ini", - "liÄŁin i", - "ĠпозволÑı еÑĤ", - "Ġпозвол ÑıеÑĤ", - "Ïĥ ί", - "Ġ ìĽĥ", - "ĠìĽ ĥ", - "à¹Į à¸Ħ", - "Ġpol ov", - "Ġpo lov", - "Ġpolo v", - "ìŀ¥ ìĿĦ", - "is té", - "ist é", - "ĠС Ð¡Ð¡Ðł", - "á hl", - "áh l", - "è ¥", - "Ġкомп лек", - "à¸Ĥ à¸Ļาà¸Ķ", - "ั ศ", - "ν αν", - "να ν", - "Ġç¥ŀ马 æĶ¶å½ķ", - "ìĭľ ìĺ¤", - "Ġé¦ĸ页 第", - "ĠçĻ¾åº¦ æµģéĩı", - "åij¨ æĶ¶å½ķ", - "Ġh atta", - "Ġhat ta", - "ÐĴ Ñĸд", - "ĠвÑĭ ÑģÑĤÑĥп", - "Ú© ارÛĮ", - "کار ÛĮ", - "کا رÛĮ", - "K hi", - "Kh i", - "Ġì°¾ ìķĦ", - "Ġn ặng", - "éĨ «", - "ĠV Å¡", - "ĠпеÑĢ ен", - "ĠпеÑĢе н", - "л ава", - "ла ва", - "лав а", - "ÙĬ ÙħÙĬ", - "ÙĬÙħ ÙĬ", - "Ġvat andaÅŁ", - "Ġ ιÏĥÏĦο", - "Ġι ÏĥÏĦο", - "Ġ à¸ĵ", - "Ġภĵ", - "स ल", - "г ен", - "ге н", - "Ġ بÙĪر", - "Ġب ÙĪر", - "ĠبÙĪ ر", - "âĢĮدÙĩ د", - "âĢĮد Ùĩد", - "l ıklı", - "lık lı", - "Ġ strate", - "Ġst rate", - "Ġstr ate", - "Ġstrat e", - "Ġstra te", - "ب ÙĪر", - "بÙĪ ر", - "ãĢģ ãĤ¢", - "Ġson uc", - "Ġsonu c", - "Ġна иболее", - "- в", - "Ġвод ой", - "oj enÃŃ", - "oje nÃŃ", - "Ġغ رب", - "Ġغر ب", - "Ġb eri", - "Ġbe ri", - "Ġber i", - "a dÄĽ", - "ad ÄĽ", - "Ġd ovol", - "Ġdo vol", - "Ġdov ol", - "âĢĮÚ©ÙĨ ÙĨدگاÙĨ", - "âĢĮÚ©ÙĨÙĨد گاÙĨ", - "ãģķ ãĤī", - "ãĥ³ ãĤº", - "ãĤ« ãĥ«", - "om etr", - "ome tr", - "omet r", - "åĩ Ģ", - "ĠÙģ ÙĪÙĦ", - "ĠÙģÙĪ ÙĦ", - "ĠÙħ ÙĪسÛĮ", - "ĠÙħÙĪ سÛĮ", - "ĠÙħÙĪس ÛĮ", - "ĠاÙĦÙħغ رب", - "e cko", - "ec ko", - "eck o", - "ÙĢÙĢÙĢÙĢ ÙĢÙĢÙĢÙĢ", - "ê°Ģ 격", - "ÑĢ ÑĥÑĤ", - "ÑĢÑĥ ÑĤ", - "Ġ ë¶Ģë¶Ħ", - "Ġë¶Ģ ë¶Ħ", - "ĠpÅĻed pis", - "Ġoprav du", - "еÑĤ иÑĩ", - "еÑĤи Ñĩ", - "à¹Ĥ à¸Ħรà¸ĩà¸ģาร", - "æ ħ§", - "æħ §", - "æĭ ľ", - "س Ùĥ", - "ìŀ¡ ëĭ´", - "à¸Ľà¸£à¸°à¸¡ าà¸ĵ", - "è´¨ éĩı", - "Ġголов Ñĥ", - "Ġгол овÑĥ", - "л ениÑİ", - "лен иÑİ", - "ле ниÑİ", - "Ġन à¤ı", - "Ġprojekt u", - "ا Ùģر", - "اÙģ ر", - "at ivnÃŃ", - "ati vnÃŃ", - "ativ nÃŃ", - "ÎŃ Î½ÏĦ", - "ÎŃν ÏĦ", - "ãĥī ãĥ©", - "Ġted av", - "ê ¼", - "à¸Ľà¸£à¸°à¸ģ าศ", - "Ġt uto", - "Ġtu to", - "Ġtut o", - "Ġch iếu", - "Ġchi ếu", - "Ġchiế u", - "Ġv yz", - "Ġvy z", - "ÑĢ оÑĪ", - "ÑĢо ÑĪ", - "åıĸ å¾Ĺ", - "Ġм иÑģÑĤ", - "Ġми ÑģÑĤ", - "ĠмиÑģ ÑĤ", - "ĠÑģлÑĥÑĩа ÑıÑħ", - "Ġغ Ø°", - "ĠÑĥ клад", - "ĠÑĥк лад", - "ĠÑĥÑģÑĤанов лен", - "Ġtes lim", - "Ġ ãĢį", - "ĠãĢ į", - "Ġ è£", - "Ġè £", - "æ¯ «", - "éĬĢ è¡Į", - "e cts", - "ect s", - "ec ts", - "k emiz", - "kem iz", - "ν ηÏĤ", - "νη ÏĤ", - "è¾ º", - "Ġп ÑĢем", - "ĠпÑĢ ем", - "ĠпÑĢе м", - "Ġson ucu", - "Ġsonuc u", - "Ġsonu cu", - "P okud", - "Po kud", - "Pok ud", - "ĠÐŀÑģ об", - "è¾ Ľ", - "è¼ ¸", - "ë³´ ê³ł", - "à¸ļ à¸Ħ", - "ãĢĤ ãĢį", - "ा ।ĊĊ", - "ा। ĊĊ", - "ा।Ċ Ċ", - "ĠÑģамоÑģÑĤ оÑıÑĤелÑĮ", - "ÙĦ ÛĮت", - "ÙĦÛĮ ت", - "λ εκ", - "λε κ", - "ĠÑĢай она", - "ĠÑĢайон а", - "ÑĮ и", - "à¹Ī าà¸Ĺ", - "à¹Īา à¸Ĺ", - "Ġ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", - "Ġà¸Ľà¸£à¸° à¹Ģà¸Ĺศ", - "ม à¸Ń", - "ا Ùĩر", - "اÙĩ ر", - "Ġви боÑĢ", - "Ġвиб оÑĢ", - "ÑİÑĩи ÑģÑĮ", - "Ġp ovol", - "Ġpo vol", - "Ġpov ol", - "a base", - "ab ase", - "aba se", - "âĢ³ N", - "Ú© ÙĪ", - "ĠУкÑĢаÑĹ Ð½Ð°", - "ĠУкÑĢа ÑĹна", - "ĠУкÑĢаÑĹн а", - "sta nov", - "stan ov", - "ĠÑĥÑĩ аÑģÑĤи", - "ĠÑĥÑĩа ÑģÑĤи", - "ĠÑĥÑĩаÑģÑĤ и", - "ĠÑĥÑĩаÑģ ÑĤи", - "Ġh lad", - "Ġhl ad", - "ĠÑĢаÑģÑģ каз", - "ãģ¿ ãģŁãģĦ", - "á½ °", - "Ġ åĽŀ", - "ĠåĽ ŀ", - "Ġ Æ°Æ¡ng", - "α Ïģά", - "αÏģ ά", - "Ø® ب", - "æį ķ", - "ÃŃ ÅĻ", - "Ġ سÛĮÙĨ", - "Ġس ÛĮÙĨ", - "ĠسÛĮ ÙĨ", - "Âł in", - "ĠM ÄĽst", - "æķĻ åѦ", - "ĠоÑģоб иÑģÑĤ", - "ĠоÑģоби ÑģÑĤ", - "u ji", - "uj i", - "çĶ» åĥı", - "ĠداÙĨ Ø´ÙĨاÙħÙĩ", - "ĠداÙĨØ´ ÙĨاÙħÙĩ", - "ìĿ´ ìķ¼", - "Ġзап иÑĤ", - "ĠÑģво ими", - "ĠÑģвои ми", - "ĠÑģвоим и", - "Û²Û° Û²", - "ï¼Į å°Ĩ", - "ãĥ¼ ãģ®", - "Ġth ÃŃ", - "ĠÙħت ÙĪسط", - "à¥ĩ Ċ", - "å¤ļ å°ij", - "ï¼Į çĦ¶åIJİ", - "íĹ Ī", - "Ġ à¤Ńà¤Ĺ", - "Ġà¤Ń à¤Ĺ", - "Ġ åı·", - "Ġåı ·", - "Ġt eor", - "Ġte or", - "å Ĥ¨", - "åĤ ¨", - "Ġ ÑĢÑĸÑĩ", - "ĠÑĢ ÑĸÑĩ", - "ĠÑģÑĤаÑĤ ÑĤÑĸ", - "Ġرابط Ùĩ", - "Ġ ï¼ľ", - "Ġï¼ ľ", - "ب اØŃ", - "با ØŃ", - "ิà¸Ļ à¸Ĺาà¸ĩ", - "ิà¸Ļà¸Ĺ าà¸ĩ", - "à¥ĩà¤Ĥ Ċ", - "ائ ÙĤ", - "ĠاÙĦج دÙĬد", - "l iÄį", - "li Äį", - "ا ØŃÙĦ", - "اØŃ ÙĦ", - "mé nÄĽ", - "Ġb ầu", - "ĠÐĴ ал", - "Ġб лагод", - "Ġбла год", - "Ġблаг од", - "еÑĤ елÑĮ", - "еÑĤе лÑĮ", - "å¹³ åĿĩ", - "м ин", - "ми н", - "Ġsü rec", - "Ġsür ec", - "Ġsüre c", - "Ġза вод", - "Ġзав од", - "èį IJ", - "ÑĤ ий", - "ÑĤи й", - "л об", - "ло б", - "Ġ вок", - "Ġв ок", - "Ġво к", - "l adıģı", - "la dıģı", - "lad ıģı", - "ladı ģı", - "ladıģ ı", - "اÙĬ ÙĬ", - "ê²ł ìĬµëĭĪëĭ¤", - "Ġamac ıyla", - "Ġamacı yla", - "ï¼Į åĽłä¸º", - "ï¼ĮåĽł 为", - "ãģ§ ãģĤãģ£ãģŁ", - "ãģ§ãģĤ ãģ£ãģŁ", - "ĠØ´ رÙĪع", - "Ġشر ÙĪع", - "æŁ Ķ", - "' nun", - "'n un", - "о кол", - "ок ол", - "око л", - "Ġc iddi", - "Ġcid di", - "Ġb ụ", - "Ġyap ılacak", - "Ġyapı lacak", - "Ġyapıl acak", - "ĠÑĩÑĥв ÑģÑĤв", - "ìĤ¬ ìĿĺ", - "à¸Ń à¸Ļà¸Ķ", - "à¸Ńà¸Ļ à¸Ķ", - "ΊΤ", - "Ġëĭ¤ ìĸij", - "ëĭ¤ ë©´", - "im izi", - "imi zi", - "imiz i", - "ä¹ Ĥ", - "ãģ² ãģ¨", - "Ġ éĿŀ", - "ĠéĿ ŀ", - "âĢĮپدÛĮ ا", - "ä¹ ĺ", - "ãĥĬ ãĥ«", - "ĠпÑĸдпÑĢиÑĶм ÑģÑĤва", - "ĠпÑĸдпÑĢиÑĶмÑģÑĤв а", - "๠ij", - "è¿ Ŀ", - "ĠÙħ ÙĨÙĩ", - "ĠÙħÙĨ Ùĩ", - "ÑĢ ик", - "ÑĢи к", - "а ÑĢÑĸв", - "аÑĢ Ñĸв", - "аÑĢÑĸ в", - "Ġ кого", - "Ġк ого", - "Ġко го", - "ĠÙĤ ص", - "Ġ æĿ¥", - "ĠæĿ ¥", - "ĠPh òng", - "Ġ ово", - "Ġо во", - "Ġов о", - "ĠпеÑĢ ева", - "ĠпеÑĢе ва", - "ĠпеÑĢев а", - "é£ ²", - "à¤Ĥ à¤Łà¤°", - "à¤Ĥà¤Ł र", - "ÙĬ را", - "ÙĬر ا", - "il diÄŁi", - "ildi ÄŁi", - "e tin", - "et in", - "eti n", - "Ïĩε ία", - "Ïĩεί α", - "Ġzah rani", - "ÙĪ جد", - "ÙĪج د", - "Ġ ç¯", - "Ġç ¯", - "าร ย", - "Ġза ко", - "Ġзак о", - "ĠتÙĤ س", - "ãĤ¹ ãĤ¿ãĥ¼", - "ãĤ¹ãĤ¿ ãĥ¼", - "æĿ °", - "Ġ ãĤ°", - "ĠãĤ °", - "Ġ é»Ħ", - "Ġé» Ħ", - "Ġ Ðļогда", - "ĠÐļ огда", - "ॠ«", - "Ġ 次", - "Ġæ¬ ¡", - "ĠвÑĭ ÑĢаж", - "Ġch Äĥm", - "лÑı ÑĶÑĤÑĮÑģÑı", - "лÑıÑĶ ÑĤÑĮÑģÑı", - "د ÙĩÙħ", - "دÙĩ Ùħ", - "Ġv rch", - "Ġvr ch", - "çº Į", - "п оÑĢ", - "по ÑĢ", - "Ġm aÄŁ", - "Ġma ÄŁ", - "å¾Ĵ æŃ©", - "po dob", - "pod ob", - "ะ à¹ģ", - "éģ¸ æīĭ", - "å¸ ¯", - "Ġse bou", - "Ġseb ou", - "in ize", - "ini ze", - "iniz e", - "ĠÐľ ак", - "ĠÐľÐ° к", - "Ġ æĻ®", - "ĠæĻ ®", - "ĠÏħÏĢ άÏģÏĩ", - "ĠÄIJ Ãł", - "ĠBr no", - "Ġ Å¡ÃŃ", - "ĠÅ¡ ÃŃ", - "اÙĦ ص", - "Ġngh iêm", - "Ġnghi êm", - "Ġon ları", - "Ġonlar ı", - "Ġu žÃŃ", - "Ġuž ÃŃ", - "èĩªåĪĨ ãģ®", - "ĠнаÑħод иÑĤÑģÑı", - "Ġj si", - "Ġjs i", - "Ġस मर", - "Ġसम र", - "ĠÏĨ Ïī", - "Û±Û¹ Û¸", - "Ġà¤ľà¤Ĺ ह", - "éŃ ļ", - "ìĿ¸ ê°Ģ", - "ÄIJ iá»ģu", - "ĠØ£ عÙĦاÙħ", - "Ġأع ÙĦاÙħ", - "à¥ĩà¤Ĥ ।Ċ", - "à¥ĩà¤Ĥ। Ċ", - "å½¢ æĪIJ", - "Ġ ikt", - "Ġi kt", - "Ġik t", - "Ġzd roj", - "ĠAmer ik", - "Ρ Îĵ", - "à¸ĩ ส", - "ĠíĴ Ģ", - "Ñģол ÑİÑĤ", - "ÙĪ ÙĬت", - "ÙĪÙĬ ت", - "Ġgörün tü", - "ан нÑĭÑħ", - "аннÑĭ Ñħ", - "ĠØ£ ÙĤ", - "Ġ миÑĢ", - "Ġм иÑĢ", - "Ġми ÑĢ", - "å« Į", - "Ġm á»iji", - "Ġd erin", - "Ġde rin", - "Ġder in", - "é ĴĪ", - "éĴ Ī", - "Ġма ÑĪи", - "ĠмаÑĪ и", - "ì¸ ¡", - "ĠجÙĨ ÙĪب", - "ĠÑģ ло", - "ĠÑģл о", - "ãĢĤ ä¸Ģ", - "ени ÑıÑħ", - "ениÑı Ñħ", - "ĠÑĩолов Ñĸк", - "Ġy ana", - "Ġya na", - "Ġyan a", - "Ġо кÑĤ", - "Ġок ÑĤ", - "Ġ неÑĢ", - "Ġн еÑĢ", - "Ġне ÑĢ", - "æĪ ¶", - "н ÑĮомÑĥ", - "нÑĮ омÑĥ", - "нÑĮо мÑĥ", - "ĠÑĸ мен", - "ĠÑĸм ен", - "ãĤı ãģŁãģĹ", - "ĠÎĵ ια", - "ĠÎĵι α", - "ãĢģ ç§ģ", - "Ġ kou", - "Ġk ou", - "Ġko u", - "ĠÑĨ еÑĢк", - "ĠÑĨеÑĢ к", - "lay arak", - "ãĢ ĩ", - "ا ÙĦس", - "اÙĦ س", - "Âł T", - "Ġд ÑĢÑĥж", - "ĠдÑĢÑĥ ж", - "ĠдÑĢ Ñĥж", - "Ġд воÑĢ", - "Ġдв оÑĢ", - "Ġдво ÑĢ", - "λ ί", - "ĠëĨ Ģ", - "Ġte plot", - "Ġtep lot", - "Ùģ ات", - "б Ñĸ", - "Ġgüven lik", - "Ġgüvenli k", - "n ÄĽn", - "nÄĽ n", - "è© ©", - "Ġinsan ların", - "Ġinsanlar ın", - "ĠìĦ¤ ì¹ĺ", - "èĵ Ŀ", - "a vatel", - "av atel", - "ava tel", - "j ev", - "je v", - "ĠÚĨ را", - "Ġgerek iyor", - "ãĥĥ ãĤ°", - "ĠÃĩ ok", - "Ġ ÙĪجÙĩ", - "ĠÙĪ جÙĩ", - "ĠÙĪج Ùĩ", - "Ġ Ñĥли", - "ĠÑĥ ли", - "ĠÑĥл и", - " ij", - "åij Ģ", - "ĠоÑĢгани заÑĨии", - "ĠоÑĢганиз аÑĨии", - "ĠоÑĢганиза ÑĨии", - "ĠÑĸÑģ нÑĥ", - "Ġneb ude", - "Ġnebu de", - "Ġë° ¤", - "ä¸Ĭ ãģĮ", - "Ġध न", - "ĠرÙĪ ابط", - "γγ ελ", - "Ġдо ÑģÑıг", - "ĠдоÑģ Ñıг", - "ĠاÙĦÙĤ دÙħ", - "ĠاÙĦÙĤد Ùħ", - "Ġзна Ñħод", - "ĠÄįÃŃs lo", - "ÅŁ k", - "ĠاÙĦد ÙĬÙĨ", - "Ġgün lük", - "ÙĥÙĬ ÙĬÙģ", - "ÎŃ Ïģα", - "ÎŃÏģ α", - "à¸ķ รว", - "à¸ķร ว", - "Ġнали Ñĩи", - "ا ÙħÛĮÙĨ", - "اÙħ ÛĮÙĨ", - "اÙħÛĮ ÙĨ", - "Ġμ ικ", - "Ġdönem de", - "à¹Ī à¸Ĺ", - "æĥ ij", - "à¥ĭà¤Ĥ ,", - "Ñĩ Ñı", - "ãģ¾ ãĤĭ", - "ĠاÙĦ تÙĨ", - "ĠاÙĦت ÙĨ", - "ÑĢ аг", - "ÑĢаР³", - "ÑĢа г", - "ëĵ¤ ê³¼", - "Ń Ķ", - "ĠÙħÙĨ Ùĩا", - "ĠÙħÙĨÙĩ ا", - "ĠTh ế", - "éIJ µ", - "Ġ ï¾Ħ", - "Ġï¾ Ħ", - "ĠاÙĦØ¥ سÙĦاÙħ", - "ãĤ¦ ãĤ¹", - "ÙĬ دÙĬ", - "ÙĬد ÙĬ", - "Ġ å¾Ĺ", - "Ġå¾ Ĺ", - "Ġза ÑĢаз", - "ãĤ¸ ãĥ¥", - "Ġت عد", - "Ġتع د", - "i ÃŃ", - "Ġç ocu", - "oz ici", - "Ġ ë²Ķ", - "Ġë² Ķ", - "ĠØ¢Ùħ دÙĩ", - "ĠØ¢Ùħد Ùĩ", - "ÑĦ ик", - "ÑĦи к", - "Ġпо ÑģÑĤанов", - "ĠпоÑģÑĤ анов", - "Ġkrál ov", - "¨ ¨", - "Ġì¤ij ìļĶ", - "ĠG Wei", - "ĠGW ei", - "Ġvý voj", - "Ġboy ut", - "Ġ nek", - "Ġn ek", - "Ġne k", - "ا ÙĩاÛĮ", - "اÙĩ اÛĮ", - "اÙĩا ÛĮ", - "Ġst ranÄĽ", - "Ġstran ÄĽ", - "Ġstra nÄĽ", - "и ем", - "ие м", - "Ġпо ÑĢаж", - "ĠпоÑĢ аж", - "à¥įर दर", - "à¥įरद र", - "é¡Ķ ãĤĴ", - "ĠY üz", - "Ġо знаÑĩа", - "Ġозна Ñĩа", - "à¹ģล à¸Ļà¸Ķ", - "Ġب ÙĩرÙĩ", - "ĠبÙĩ رÙĩ", - "ен ÑĤÑĥ", - "енÑĤ Ñĥ", - "ĠÐĿ ад", - "ĠÐĿа д", - "ĠÐŁ олÑĮ", - "ĠÐŁÐ¾ лÑĮ", - "ĠÐŁÐ¾Ð» ÑĮ", - "ãĥĹ ãĥª", - "á¿ ¶", - "âĢĮپدÛĮ اÛĮ", - "âĢĮپدÛĮا ÛĮ", - "ĠÙ¾ اÙĪرپÙĪÛĮÙĨت", - "ิà¸ģ า", - "Ġε νÏİ", - "Ġεν Ïİ", - "Ġس اÛĮر", - "éģ º", - "ãĢģ ä»Ĭ", - "ĠL ê", - "äºĭ æĥħ", - "ĠY er", - "ĠYe r", - "èħ °", - "ĠاÙĦر سÙħ", - "ĠاÙĦÙħ ÙĪÙĤع", - "ĠاÙĦÙħÙĪ ÙĤع", - "Ġh Ãłm", - "Ġд ÑĢев", - "ĠдÑĢ ев", - "á tel", - "át el", - "áte l", - "ĠвÑģ Ñij", - "ìĺ ¥", - "ĠM ec", - "ĠMe c", - "ãĤ Ľ", - "Ġص اد", - "ĠÚ¯ ردد", - "Ġگرد د", - "Ġگر دد", - "Ġkr ás", - "èĮĥ åĽ´", - "a larına", - "alar ına", - "aları na", - "aların a", - "èĻ ļ", - "ĠØ¢ ÙĪرد", - "ĠØ¢ÙĪر د", - "ç¼ ĵ", - "ิ à¸ŀ", - "Ġ ãĥĭ", - "Ġãĥ ĭ", - "Ġ æĢ§", - "ĠæĢ §", - "ĠÙħÙĨ Ø°", - "ç· ´", - "Ġ ê¶ģ", - "Ġê ¶ģ", - "в аем", - "ва ем", - "Ġζ Ïī", - "Ġn avr", - "Ġna vr", - "Ġnav r", - "Ïĥ ÏĦαÏĥη", - "ÏĥÏĦα Ïĥη", - "Ġر Ø£", - "Ġd opl", - "Ġdo pl", - "Ġdop l", - "＿ ＿＿", - "＿＿ ＿", - "çĶļ èĩ³", - "Äį el", - "Äįe l", - "æĦı åij³", - "ç¥ Ń", - "à ĺ", - "ÑģÑĤв еннÑĭе", - "ÑģÑĤвен нÑĭе", - "è£ ¡", - "Ġ ãĢī", - "ĠãĢ ī", - "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ĠãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "Ġ вал", - "Ġв ал", - "Ġва л", - "Ġ ẩm", - "Ġd iyor", - "Ġdi yor", - "Ġdiy or", - "à¸Ńà¸ĩ à¸Īาà¸ģ", - "ĠPh ó", - "ĠÐĵ е", - "ĠвеÑĢ еÑģ", - "Ġве ÑĢеÑģ", - "Ġk onz", - "Ġko nz", - "Ġkon z", - "ر ز", - "ĠÑģоб оÑİ", - "Ġεκ εί", - "ìĺģ ìĸ´", - "i ag", - "ia g", - "ĠÑģ енÑĤ", - "Ġn ấu", - "Ġja ké", - "Ġjak é", - "Ġro zh", - "Ġroz h", - "Ġб ог", - "Ġбо г", - "ÙĨ اد", - "ÙĨا د", - "ĠاÙħ ÙĪر", - "à¹Į à¸ģาร", - "à¹Įà¸ģ าร", - "ĠY aÅŁ", - "ĠYa ÅŁ", - "é Ī", - "åķ ª", - "Ġon ay", - "Ġona y", - "ìĹ ĩ", - "o mu", - "om u", - "ÑĨ Ñĸйного", - "ÑĨÑĸй ного", - "ÑĨÑĸйно го", - "ĠÑģ ал", - "ĠΣ Ïħν", - "ĠΣÏħ ν", - "Ġsav un", - "å¦ Ļ", - "à¸Īะ ม", - "ãĤ¹ ãĤ¯", - "Ġd osy", - "Ġdo sy", - "Ġdos y", - "ľ ĺ", - "ë¨ ¹", - "Ġmin ul", - "Ġmi nul", - "ãĢĭ Ċ", - "åģ ı", - "ĠÐļ аÑĤ", - "ĠÐļа ÑĤ", - "Ġed ilmesi", - "Ġedilm esi", - "Ġedil mesi", - "ÑĨÑĸ ÑĶн", - "ìĦ± ìĿ´", - "åĸ Ķ", - "Ġв ÑĸÑĢ", - "ĠвÑĸ ÑĢ", - "è¯ ij", - "ाà¤ĩ ड", - "ĠÙĪÙĤ تÛĮ", - "ĠÙĪÙĤت ÛĮ", - "ÄIJ á»ĥ", - "Ġ vyÅ¡Å¡ÃŃ", - "Ġvy Å¡Å¡ÃŃ", - "Äį ila", - "Äįi la", - "Äįil a", - "а дÑĥ", - "ад Ñĥ", - "çī¹ åĪ¥", - "ĠìĿ¸ 기", - "u jÃŃcÃŃch", - "ujÃŃ cÃŃch", - "ujÃŃcÃŃ ch", - "ĠPo dle", - "ĠPod le", - "Ġy avaÅŁ", - "Ļ æ±Ł", - "Ġka yb", - "Ġkay b", - "åĬ ª", - "ç´ ¹", - "Ġоб ÑĢабоÑĤ", - "ĠобÑĢа боÑĤ", - "Ġм аÑı", - "Ġма Ñı", - "Ġ åıĬ", - "Ġåı Ĭ", - "æİ¥ åıĹ", - "ÙĨ تÛĮ", - "ÙĨت ÛĮ", - "Ġ ÏĩÏİ", - "ĠÏĩ Ïİ", - "ÑĤ ÑĢо", - "ÑĤÑĢ о", - "Ġu yar", - "Ġuy ar", - "ĠعÙħÙĦ کرد", - "Ġо ÑĨен", - "ĠмеÑģÑĤ а", - "à¸ķ ลาà¸Ķ", - "à¸ķล าà¸Ķ", - "Ùħ ÙĤ", - "ild ren", - "Ġзави ÑģиÑĤ", - "Âł ĠÂł", - "ÂłĠ Âł", - "Ġm ožná", - "Ġmož ná", - "æĺŃ åĴĮ", - "ır ken", - "к ин", - "ки н", - "åĿ Ĥ", - "ÏĦ Ïĥι", - "ÏĦÏĥ ι", - "ĠÑĩ Ñĥд", - "Ðļ он", - "is lav", - "isl av", - "ĠÐļ ÑĢаÑģ", - "ĠÐļÑĢа Ñģ", - "N ej", - "Ne j", - "Âł b", - "r of", - "ro f", - "Ġ ileri", - "Ġi leri", - "Ġil eri", - "Ġile ri", - "Ġiler i", - "ĠÐŀ ÑĢ", - "ĠCh á»ī", - "Ġn üfus", - "ĠÑĸ нÑĤ", - "ĠÑĸн ÑĤ", - "! âĢľ", - "Ġन र", - "主 ä¹ī", - "ĠتÙĨ ظ", - "ův odu", - "ůvod u", - "ĠгоÑĢ ода", - "ĠгоÑĢод а", - "Ġk ural", - "Ġkur al", - "Ġku ral", - "Ġj edin", - "Ġje din", - "Ġjed in", - "ÑĢ аÑĤег", - "ÑĢаÑĤ ег", - "åĢ º", - "Ġzpůsob em", - "ìĿ¸ ìĿĺ", - "Ġ ÙĨب", - "ĠÙĨ ب", - "ĠN ga", - "ĠNg a", - "ĠÐĿ ай", - "ĠÐĿа й", - "ĠاÙģ زار", - "ĠاÙģز ار", - "нÑĥ вÑģÑı", - "нÑĥв ÑģÑı", - "Ġдв оÑħ", - "Ġдво Ñħ", - "Ġro zp", - "Ġroz p", - "ε ίοÏħ", - "εί οÏħ", - "είο Ïħ", - "Ġο ικο", - "Ġοι κο", - "ĠG eç", - "ĠGe ç", - " Ĺ", - "Ġch iếm", - "Ġchiế m", - "ĠÑĢаÑģпÑĢоÑģÑĤ ÑĢан", - "Ġh Æ°Æ¡ng", - "èĩª åĭķ", - "ĠÙħÙĪ ÙģÙĤ", - "æĮ ¥", - "ï¼ģ âĢĿĊĊ", - "ï¼ģâĢĿ ĊĊ", - "Ïģο ÏĨοÏģ", - "èı Į", - "ãĥ´ ãĤ¡", - "欧 ç¾İ", - "ĠÑĤеп ло", - "ãģĤ ãģĤ", - "ãĤ¦ ãĥ³", - "ĠÅŁ eyi", - "ĠÅŁey i", - "Ġs üt", - "Ġsü t", - "ãģ¹ ãģ¦", - "ãĥ³ ãĥij", - "ãĥ³ãĥ ij", - "μÎŃν Ïīν", - "Ġgenel likle", - "Ġدر ÙħاÙĨ", - "Ù ª", - "Ġak ıl", - "ĠÐľ Ñĭ", - "Ġet miÅŁ", - "Ġetm iÅŁ", - "Å¡ la", - "Ġвозмож ноÑģÑĤÑĮ", - "Ġвозможно ÑģÑĤÑĮ", - "Ġgün cel", - "Ġná ro", - "å½¢ å¼ı", - "Ġα ÏĢοÏĦε", - "ĠαÏĢο ÏĦε", - "ĠмÑĸÑģ ÑĨÑı", - "Ġ رض", - "Ġر ض", - "ä¸į çŁ¥éģĵ", - "ä¸įçŁ¥ éģĵ", - "r ava", - "ra va", - "rav a", - "ĠÎļ ά", - "ิà¸Ļ à¸Ĺร", - "ิà¸Ļà¸Ĺ ร", - "Ġли ÑģÑĤÑĮ", - "ĠлиÑģÑĤ ÑĮ", - "èĨ ľ", - "ãģ«ãģª ãĤĬ", - "Ġ æĿ¾", - "ĠæĿ ¾", - "å® ı", - "Ġм иÑģ", - "Ġми Ñģ", - "át nÃŃ", - "Ġyıl lık", - "ĠMerk ezi", - "ĠMerkez i", - "Ġiç eri", - "Ġiçer i", - "ÅĻ ÃŃž", - "ÅĻÃŃ Å¾", - "Ġp ÅĻe", - "ĠpÅĻ e", - "Ïĩ Ïģι", - "Ġ åįĥ", - "Ġåį ĥ", - "Ġs rp", - "Ġsr p", - "à¹Ĥ à¸Ĺร", - "à¹Ĥà¸Ĺ ร", - "ĠK rál", - "ĠKr ál", - ". Σ", - "á val", - "áv al", - "l éd", - "lé d", - "Ġ λα", - "Ġλ α", - "ี ยวà¸ģ", - "ีย วà¸ģ", - "ียว à¸ģ", - "ãģı ãģª", - "ĠvÅ¡ ichni", - "ĠпÑĢед оÑģÑĤав", - "ì ¿", - "Ġ 구ê¸ĢìĥģìľĦ", - "Ġ구 ê¸ĢìĥģìľĦ", - "Ġ구ê¸Ģ ìĥģìľĦ", - "Ġà¤īप लब", - "в оз", - "во з", - "ĠëħĦ ëıĦë³Ħ", - "､ _", - "à¸ļ รร", - "à¸ļร ร", - "ĠÑģв ÑĸÑĤÑĥ", - "ĠÑģвÑĸÑĤ Ñĥ", - "ĠÑĢÑĥб лей", - "len me", - "lÃŃ Äį", - "ÏĦ ει", - "ÏĦε ι", - "Ġ åı¤", - "Ġåı ¤", - "ĠObr ázky", - "Ġìĺģ íĸ¥", - "ĠгÑĢаж дан", - "í Ĥ¹", - "íĤ ¹", - "Ġsahip tir", - "Ġп оÑĩаÑĤкÑĥ", - "ĠпоÑĩ аÑĤкÑĥ", - "ĠØ£ÙĬ ض", - "ĠÑĤоÑĢ гов", - "Ġgel ecek", - "Ġgele cek", - "Ġ 문íĻĶ", - "Ġ문 íĻĶ", - "ik leri", - "ikler i", - "ĠнеобÑħÑĸд но", - "Ġ äºij", - "o vol", - "ov ol", - "ovo l", - "Ġद ल", - "ĠìķĬ ê³ł", - "Ġм г", - "Ġz jist", - "an lı", - "ั à¸ĩà¸Ļ", - "ัà¸ĩ à¸Ļ", - "ÑĢа Ñħов", - "ÑĢаÑħ ов", - "ι νη", - "ιν η", - "Ġп лоÑĤ", - "Ġпл оÑĤ", - "Ġпло ÑĤ", - "Ġn itel", - "Ġni tel", - "Ġnit el", - "ìĬ¤ íģ¬", - "ĠSon ra", - "ĠÑģ боÑĢ", - "ĠÑģб оÑĢ", - "Ġ ÏĥοÏħ", - "ĠÏĥ οÏħ", - "Ġol mam", - "Ġolm am", - "Ġolma m", - "Ġan aliz", - "Ġanal iz", - "à¹Į ว", - "Ġm ỹ", - "Ġmá» ¹", - "ce ae", - "cea e", - "Ġ ден", - "Ġд ен", - "Ġде н", - "веÑĢ жд", - "веÑĢж д", - "Ạ¢", - "ãģĵ ãģ¨ãĤĤ", - "ãģĵãģ¨ ãĤĤ", - "ìĤ¬ íķŃ", - "è¨Ģ ãģ£ãģŁ", - "Ġ ì¹´ì§Ģëħ¸", - "Ġì¹´ ì§Ģëħ¸", - "ÑĢ иÑĤи", - "ÑĢи ÑĤи", - "ÑĢиÑĤ и", - "Ġch ce", - "Ġçev ir", - "ÛĮ ÛĮÙĨ", - "ä¼ļ è®®", - "ัม à¸ŀ", - "Ġ åĦ", - "Ġå Ħ", - "ĠÙ¾ در", - "å¼ı ä¼ļ社", - "Ġ ÑĨен", - "ĠÑĨ ен", - "ĠÑĨе н", - "ิ à¸ĸ", - "Ġji nak", - "Ġjin ak", - "Ġб лÑİ", - "Ġбл Ñİ", - "и ÑĨин", - "иÑĨ ин", - "ÙĴ Ùĩ", - "Ú© ÙĪر", - "Ú©ÙĪ ر", - "Ġ ìķħ", - "Ġìķ ħ", - "e ksiyon", - "ek siyon", - "eks iyon", - "ĠÑģ веÑĢ", - "ĠÑģв еÑĢ", - "ĠобÑĢаз ованиÑı", - "Ġ ãĥĻ", - "Ġãĥ Ļ", - "æľī 人", - "Ġbilg ileri", - "Ġbilgi leri", - "Ġbilgiler i", - "Ġh ầu", - "еÑĢ Ñĸг", - "еÑĢÑĸ г", - "Ġva Å¡e", - "Ġn edir", - "Ġne dir", - "Ġned ir", - "ä¸į å¾Ĺ", - "ĠbaÅŁar ılı", - "ĠbaÅŁarı lı", - "Ġkay bet", - "Ġkayb et", - "å© ·", - "ĠÐĿ ав", - "ĠÐĿа в", - "Ġê´Ģ íķľ", - "Ñģ ÑĤÑİ", - "ÑģÑĤ Ñİ", - "å®ŀ éĻħ", - "k lady", - "kl ady", - "klad y", - "kla dy", - "д аÑĤÑĮ", - "да ÑĤÑĮ", - "даÑĤ ÑĮ", - "r aç", - "ra ç", - "Ġkuv vet", - "à¸ģาร à¸Ĺ", - "å ļ", - "Ġ ÑĢеп", - "ĠÑĢ еп", - "ĠÑĢе п", - "Ġ à¸Ŀ", - "ĠภĿ", - "ĠDi ÄŁer", - "íĶĦ íĬ¸", - "Ġnej vÄĽtÅ¡ÃŃ", - "Ġìłģ ìļ©", - "Ġonemoc nÄĽnÃŃ", - "а ка", - "ак а", - "Ðł аз", - "ĠÙģ Ø¥ÙĨ", - "ãĤµ ãĤ¤ãĤº", - "ãĤµãĤ¤ ãĤº", - "Ġv lád", - "Ġvl ád", - "Ġvlá d", - "Ġr ady", - "Ġrad y", - "Ġra dy", - "ãĢģ ãģĵãĤĮ", - "ÑģÑĤв ие", - "lı ÄŁa", - "lıģ a", - "å ŃĶ", - "åŃ Ķ", - "Ġ áo", - "Ġá o", - "à¸Ń าà¸ģาศ", - "Ġ à¤ıम", - "Ġà¤ı म", - "δ αÏĤ", - "δα ÏĤ", - "Ġа пÑĢ", - "Ġап ÑĢ", - "æİ Ľ", - "Ġ ç«ĭ", - "Ġç« ĭ", - "âĸı âĸı", - "ĠС м", - "Ġne má", - "Ġnem á", - "Ġ è¢", - "Ġè ¢", - "νο μα", - "νομ α", - "ĠÙģ رÙĪد", - "ĠÙģر ÙĪد", - "ĠÙģرÙĪ د", - "Ġül ke", - "Ġülk e", - "Ġ æĺŁ", - "Ġæĺ Ł", - "ั à¸Ļà¸ģ", - "ัà¸Ļ à¸ģ", - "ãģķãĤĵ ãģ®", - "eÅŁ il", - "ÄŁ iz", - "ÄŁi z", - "ĠÐij оÑĢ", - "Ġt ầm", - "ει ÏĦοÏħÏģγ", - "Ġ γÏģα", - "Ġγ Ïģα", - "à¥įष à¤ķ", - "Ġv ẻ", - "Ġkend isine", - "Ġkendisi ne", - "ĠìķĮ ê³ł", - "Ġêµ Ńìłľ", - "ĠêµŃ ìłľ", - "ĠnÄĽk do", - "Ġ ÛĮÙĩ", - "ĠÛĮ Ùĩ", - "Ġکار بر", - "ãĥĻ ãĥ«", - "ï» ´", - "Ġt uyên", - "Ġtuy ên", - "Ġç at", - "Ġça t", - "âĢIJ âĢIJ", - " ı", - "Ġ ìĤ¬ìĹħ", - "ĠìĤ¬ ìĹħ", - "é ĨĴ", - "éĨ Ĵ", - "æıIJ é«ĺ", - "æ· ¡", - "Ġ ÄŁ", - "ĠÄ Ł", - "èĸ ¦", - "ãĢĭ ï¼Ī", - "æ¡ ĥ", - "ìĹ Ħ", - "Ġ æŀĹ", - "Ġæŀ Ĺ", - "Ä Ĥ", - "ĠÄĮ ech", - "α ιο", - "αι ο", - "ĠØ· رÙĬÙĤ", - "Ġطر ÙĬÙĤ", - "Ġзав еÑĢÑĪ", - "ĠзавеÑĢ ÑĪ", - "تÙĪ بر", - "ĠØŃ Ø¬", - "ĠÎŃÏĩ οÏħν", - "¿ ÃĤ", - "Ġd ÄĽtÃŃ", - "ĠdÄĽ tÃŃ", - "ĠdÄĽt ÃŃ", - "Ġiç ine", - "Ġiçin e", - "Ġiçi ne", - "ĠCh úa", - "ан нÑĭй", - "аннÑĭ й", - "ĠÙĪÛĮ Úĺ", - "Ġna stav", - "Ġnast av", - "ıs ına", - "ısı na", - "ĠÑĹ Ð¼", - "п он", - "по н", - "е нÑı", - "ен Ñı", - "ĠÙĪ ظ", - "Ú¯ ÙĦ", - "หล วà¸ĩ", - "Ġza stav", - "Ġzast av", - "а кон", - "ак он", - "³³³³³³³³³³³³³³³³ ³³³³³³³³³³³³³³³³", - "ĠK ır", - "ĠKı r", - "çµ ¶", - "ĠоÑĢганÑĸ заÑĨÑĸÑĹ", - "ĠоÑĢганÑĸз аÑĨÑĸÑĹ", - "ĠоÑĢганÑĸза ÑĨÑĸÑĹ", - "ãģŁ ãĤĬ", - "Ø° ÙĬ", - "Ġर à¤ķ", - "amp iyon", - "Ġ æ¸ħ", - "Ġæ¸ ħ", - "çľ¼ çĿĽ", - "Ġìķ ĬìĿĢ", - "ĠìķĬ ìĿĢ", - "é¹ ¿", - "Ġ å¿ĥ", - "Ġå¿ ĥ", - "ĠпÑĢек ÑĢаÑģ", - "ĠÑģ егоднÑı", - "Ġ सल", - "Ġस ल", - "ĠÏħ ÏĢÏĮ", - "ĠÏħÏĢ ÏĮ", - "ĠÐķ го", - "ĠÐĽ и", - "ãĤ¨ ãĥ«", - "Ġл ÑİÑĤ", - "ĠлÑİ ÑĤ", - "é¥ °", - "Ġvz dál", - "¯ ÃĤ", - "Ġна Ñıв", - "Ġتش Ú©ÛĮÙĦ", - "Ġس ÙĪÛĮ", - "ĠسÙĪ ÛĮ", - "Ġt ái", - "Ġtá i", - "Ġk apı", - "Ġkap ı", - "ĠsvÄĽt ÄĽ", - "ĠsvÄĽ tÄĽ", - "δ ÏĮν", - "δÏĮ ν", - "æ¼ ¢", - "ì į¨", - "ĠbaÅŁv ur", - "ÑĢ ина", - "ÑĢи на", - "ÑĢин а", - "Ġk elim", - "Ġke lim", - "Ġkel im", - "аÑĤ ок", - "аÑĤо к", - "Ġκά θε", - "ĠYük sek", - "à¹ĩà¸Ļ à¸ľ", - "éł Ĥ", - "åIJĮ æĻĤ", - "ÅŁ tır", - "ÅŁt ır", - "ว à¸ĩศ", - "วà¸ĩ ศ", - "o ty", - "ot y", - "Ġ ارد", - "Ġا رد", - "Ġار د", - "ĠìŀIJìĭł ìĿĺ", - "ĠÑıн ва", - "üyor du", - "æĿ ¨", - "ĠâĢĵ Ċ", - "ï¼Į å®ĥ", - "е йн", - "ей н", - "ĠпеÑĢ еÑĤ", - "ĠпеÑĢе ÑĤ", - "ĠdeÄŁiÅŁik lik", - "ĠогÑĢа ниÑĩ", - "ìĦľ ìļ¸", - "Ġgel iyor", - "ĠÙ¾ Ø°ÛĮر", - "åĵ ²", - "ey in", - "eyi n", - "Ġëı Ī", - "Ġun iverz", - "Ġh ned", - "Ġhn ed", - "Ġt áºŃn", - "vo ÅĻÃŃ", - "voÅĻ ÃŃ", - "Ġn iên", - "Ġni ên", - "dÄĽ podob", - "ìĤ¬ íļĮ", - "ãģĮ ãģĤãĤĬ", - "ĠÑģ ÑĸÑĩ", - "' '\"", - "'' \"", - "Ġtop lantı", - "ĠÑģ ÑĩеÑĤ", - "ĠÑģÑĩ еÑĤ", - "åĩĨ å¤ĩ", - "ан ÑĸÑı", - "анÑĸ Ñı", - "Ġ zel", - "Ġz el", - "Ġze l", - "v ala", - "val a", - "va la", - "Ġа пп", - "Ġап п", - "ĠاÙĦÙħ ÙĦÙĥ", - "ĠاÙĦÙħÙĦ Ùĥ", - "Ġho ÅŁ", - "ĠÐĵ ен", - "ĠÐĵе н", - "ÑĤ аб", - "ÑĤа б", - "ĠÄĮesk o", - "ĠÄĮes ko", - "Ġмай же", - "ĠmÄĽ sto", - "ĠmÄĽst o", - "yo nel", - "yon el", - "ê±° 리", - "Ġìĺ¨ ëĿ¼ìĿ¸", - "ç´ ¯", - "Ġde rec", - "Ġder ec", - "Ġdere c", - "Ġок ÑĢÑĥж", - "Ġy abancı", - "Ġ íĦ°", - "ĠíĦ °", - "Ġ èµĦ", - "Ġèµ Ħ", - "ÎĻÎļ ÎĹ", - "Ġп Ñĭ", - "Ġv ÄĽn", - "ĠvÄĽ n", - "и нки", - "ин ки", - "ụ p", - "æľº 械", - "ĠìķĮ 볤", - "ëħ ķ", - "Ġ λÏĮγ", - "Ġλ ÏĮγ", - "e yn", - "ey n", - "Ġ ëIJĺìĹĪëĭ¤", - "ĠëIJĺ ìĹĪëĭ¤", - "ĠëIJĺìĹĪ ëĭ¤", - "æ± ¡", - "Ġve dle", - "Ġved le", - "ĠÙĥ تب", - "ë§ ¨", - "ĠÙħÙĤ اÙĪ", - "å¹´ ãģ«", - "ाà¤ĩ à¤ķ", - "ĠÑģÑĤ оÑģ", - "ĠÑģÑĤо Ñģ", - "ĠÏĥ ÏĦοÏħÏĤ", - "м еÑĤÑĮ", - "ме ÑĤÑĮ", - "меÑĤ ÑĮ", - "Ġes as", - "Ġesa s", - "ëIJĺ ê³ł", - "ĠkvÄĽt na", - "Ġ éľ", - "Ġé ľ", - "d ük", - "dü k", - "åŁ ·", - "è ªĮ", - "èª Į", - "Ġm luv", - "Ġml uv", - "ĠпÑĢи нÑı", - "ĠпÑĢин Ñı", - "Ġpo té", - "Ġpot é", - "ĠÚ© ÙĨÙħ", - "ĠÚ©ÙĨ Ùħ", - "ĠпÑĢед лож", - "ĠÐľÐ¾Ñģк ва", - "ï¼Į å¦Ĥ", - "Ġsv ém", - "Ġsvé m", - "Ġا ÙħÙĨ", - "ĠاÙħ ÙĨ", - "ส าย", - "ĠÑĥм енÑĮ", - "Ġ ãģĵãģ®", - "åī Ĥ", - "ĠÑģ еÑĢÑĮ", - "ĠÑģеÑĢ ÑĮ", - "Ġm á»ĩ", - "Ġmá» ĩ", - "Ġ ä¹Ŀ", - "Ġä¹ Ŀ", - "Ġза кÑĸн", - "Ġзак Ñĸн", - "Ġв елиÑĩ", - "Ġвели Ñĩ", - "Ġвел иÑĩ", - "Ġве лиÑĩ", - "Ġкон ÑĤÑĢа", - "ĠконÑĤ ÑĢа", - "ĠконÑĤÑĢ а", - "ĠS osyal", - "Ġy ukarı", - "Ġد ÙĪب", - "ĠدÙĪ ب", - "ä¾ §", - "Ġза мен", - "Ġзам ен", - "ï» ®", - "Ġso bÄĽ", - "Ġsob ÄĽ", - "ĠТак же", - "Ð İ", - "ε δ", - "Ùħ ارÛĮ", - "Ùħا رÛĮ", - "Ùħار ÛĮ", - "ξ ι", - "ì¹ Ń", - "Ġп лаÑģÑĤи", - "Ġпл аÑģÑĤи", - "Ġпла ÑģÑĤи", - "Ïĥ οÏħν", - "Ïĥο Ïħν", - "ÏĥοÏħ ν", - "èľĺèĽĽ è¯į", - "ÙĪ ÛĮزÛĮ", - "ÙĪÛĮ زÛĮ", - "Ġnap ÅĻ", - "ĠÑĤип а", - "ĠÑĤи па", - "à¥Ĥ à¤Ľ", - "ĠÅŁ ah", - "л ÑıÑĤи", - "лÑı ÑĤи", - "ب ÛĮر", - "بÛĮ ر", - "ระ ยะ", - "ĠболÑĮ ÑĪин", - "ĠболÑĮÑĪ ин", - "ÏĦη ÏĦα", - "Ġíıī ê°Ģ", - "Ġpro jev", - "Ġproj ev", - "Ġproje v", - "ò i", - "Ġк нÑı", - "ÏĨ εÏģ", - "е ÑĢÑĥ", - "еÑĢ Ñĥ", - "Ñį н", - "ĠعÙħ ÙĦÛĮ", - "ĠعÙħÙĦ ÛĮ", - "à¤ł न", - "ãĥ³ ãĤ¯", - "ĠìķĦ ëŀĺ", - "Î Ī", - "Ġب است", - "Ġبا ست", - "Ġ تÙĥ", - "Ġت Ùĥ", - "a ÄįnÃŃ", - "aÄį nÃŃ", - "ĠлÑĸ кÑĥваннÑı", - "ĠлÑĸк ÑĥваннÑı", - "à¸Ħ à¹Ĥà¸Ļ", - "Ġ èĥ½", - "Ġè ĥ½", - "Ġèĥ ½", - "θ λη", - "len miÅŁ", - "Ġl á»Ļ", - "Ġlá» Ļ", - "Ġsi lah", - "Ġsil ah", - "ĠA ustr", - "ĠAust r", - "ĠAus tr", - "ĠAu str", - "ØŃ ÙĤ", - ".*** .***", - "ì ©", - "Ġg Ãł", - "Ġباز بÛĮÙĨÛĮ", - "ĠÄij Ãłn", - "ÃŃ ky", - "ÃŃk y", - "ĠÎķ ν", - "ض Ùħ", - "å§ ĵ", - "Ġ ÙĨÙĪÛĮس", - "ĠÙĨ ÙĪÛĮس", - "ĠÙĨÙĪ ÛĮس", - "Ġskup iny", - "Ġس ÛĮد", - "ĠسÛĮ د", - "Ġal dıģı", - "Ġald ıģı", - "Ġaldı ģı", - "m eli", - "me li", - "mel i", - "в иж", - "ви ж", - "ì¹ĺ ëĬĶ", - "ов аÑħ", - "ова Ñħ", - "Ġ æ©", - "Ġæ ©", - "Ø´ÙĨ اسÛĮ", - "Ø´ÙĨاس ÛĮ", - "Ġn imi", - "Ġni mi", - "Ġnim i", - "ĠÐĵ ÑĢи", - "íĹ Į", - "Ġк в", - "éŁ ĵ", - "Ġ íĽĦ기", - "ĠíĽĦ 기", - "Ġ stÅĻÃŃ", - "Ġst ÅĻÃŃ", - "ĠstÅĻ ÃŃ", - "ĠкÑĸлÑĮ кÑĸÑģÑĤÑĮ", - "ĠBakan lıģı", - "ĠменÑĮ ÑĪе", - "ا ÙĪÛĮ", - "اÙĪ ÛĮ", - "Ġار ÙĪÙ¾", - "Ġ èī²", - "Ġèī ²", - "ĠÚ©ÙĪÚĨ Ú©", - "ĠA ynı", - "Ġ äºĨ", - "Ġس Ùģر", - "ĠسÙģ ر", - "ĠÑĤе аÑĤ", - "Ġ vÄĽd", - "ĠvÄĽ d", - "а ÑĢов", - "аÑĢ ов", - "Ġоб меж", - "ĠìķĬ ìķĺ", - "追 åĬł", - "éł Ī", - "dÄĽ lenÃŃ", - "dÄĽl enÃŃ", - "dÄĽlen ÃŃ", - "Ġk ims", - "Ġki ms", - "Ġkim s", - "Ġ èı²", - "Ġèı ²", - "Ġг ÑĢÑĥн", - "ĠгÑĢÑĥ н", - "ĠгÑĢ Ñĥн", - "ĠØ¢ ÙĦÙħاÙĨ", - "ĠØ¢ÙĦ ÙħاÙĨ", - "Ġав г", - "ĠÑī оÑģÑĮ", - "ĠÑīо ÑģÑĮ", - "Ġ å¾·", - "Ġå¾ ·", - "ĠÐĿа ÑĨÑĸоналÑĮ", - "æĪIJ ç«ĭ", - "ูà¸Ļ ย", - "ãĥ¼ ãĥ«ãĥī", - "ãĥ¼ãĥ« ãĥī", - "éĽ ²", - "ĠT á»ķ", - "cı lık", - "ĠAlma nya", - "ĠAlman ya", - "Ġov Å¡em", - " ĭ", - "ĠÏĩÏģη ÏĥιμοÏĢοι", - "Ġörg üt", - "िस स", - "èĹ Ŀ", - "ĠGi ải", - "Ġsv ob", - "Ġsvo b", - "Ġrůzn ých", - "Ġrůz ných", - "Ġsmlou vy", - "ÑĢ еÑģÑģ", - "ÑĢеÑģ Ñģ", - "ี à¹Ģà¸Ķ", - "ĠاÙħ رÙĪز", - "ĠاÙħر ÙĪز", - "ãĤ ħ", - "åĿ ¦", - "à¹ī à¸Ħ", - "Ġ каж", - "Ġк аж", - "Ġка ж", - "å¼ Ĺ", - "Ñĩ ноÑĹ", - "Ñĩно ÑĹ", - "åľ Ī", - "ĠØ¢ ÙĩÙĨÚ¯", - "ëª °", - "Ġ æº", - "Ġæ º", - "Ġ èĦ", - "Ġè Ħ", - "ä¸Ģ æŃ¥", - "оÑĩ ка", - "Ġpro stor", - "Ġpros tor", - "Ġprost or", - "Ġng ắn", - "Ġ ç·", - "Ġç ·", - "н аÑĢ", - "на ÑĢ", - "Ġà¤ľ व", - "ĠнаÑĩ алÑĮ", - "Ġне дел", - "Ġнед ел", - "ĠÑģиÑģÑĤем Ñĥ", - "ج ÙĬ", - "اد ات", - "ادا ت", - "Ġ æ¢", - "Ġæ ¢", - "ĠجاÙħ عة", - "ĠجاÙħع Ø©", - "Ġ ä»İ", - "Ġà¤ħ फ", - "èĸ Ħ", - "Ġب اÙĤ", - "Ġبا ÙĤ", - "ب ÙĬع", - "بÙĬ ع", - "ãģķ ãĤĮãģ¦", - "ãģķãĤĮ ãģ¦", - "ĠÃĩ alÄ±ÅŁ", - "Ø®ÙĪ است", - "ãĥĥ ãĤ·ãĥ¥", - "ĠØŃ Ø³ÛĮÙĨ", - "ĠØŃس ÛĮÙĨ", - "Ġоб наÑĢÑĥж", - "в Ñĸдом", - "вÑĸ дом", - "вÑĸд ом", - "Ġh ôm", - "л анд", - "ла нд", - "лан д", - "Ġव à¤ľà¤¹", - "س ÙĬÙĨ", - "سÙĬ ÙĨ", - "æł ı", - "Ġna vÃŃc", - "Ġnav ÃŃc", - "ãĤµ ãĤ¤ãĥĪ", - "ãĤµãĤ¤ ãĥĪ", - "ĠÑı комÑĥ", - "ĠÑıк омÑĥ", - "Ġí Ľ", - "ĠY ani", - "ĠYan i", - "ĠYa ni", - "ãĤĵ ãģ§ãģĻ", - "ãĤĵãģ§ ãģĻ", - "Ġг ÑĢÑĥп", - "ĠгÑĢÑĥ п", - "ĠгÑĢ Ñĥп", - "Äį ný", - "ÑĨ ик", - "ÑĨи к", - "ÙĪ ÙĬر", - "ÙĪÙĬ ر", - "Ġ Xã", - "ĠX ã", - "Ġf yz", - "Ġfy z", - "Ġ ï½ī", - "Ġï½ ī", - "âĢĮ ترÛĮÙĨ", - "âĢĮتر ÛĮÙĨ", - "à¤Ł à¤ķ", - "ÑĦоÑĢм и", - "ÑĦоÑĢ ми", - "ĠO yun", - "ĠOy un", - "åł´ æīĢ", - "ØŃ Ø«", - "ĠìķĮ ìķĦ", - "ÑĢав илÑĮ", - "ÑĢави лÑĮ", - "ï¼Į âĢĿ", - "b oru", - "bo ru", - "bor u", - "ĠK ullan", - "ĠKul lan", - "ĠKay nak", - "Ġê° ĸ", - "ç´ Ķ", - "ï¼Į æ¯ı", - "ÎĹ Î¡", - "Ġp ůl", - "Ġpů l", - "Ġг оÑģÑĤ", - "ر ÙĪÙħ", - "رÙĪ Ùħ", - "ï¼Į åį³", - "Û² Û³", - "ĠÙĨØ® ست", - "ĠÚ© سب", - "Ġ à¹Ģà¸ļ", - "Ġà¹Ģ à¸ļ", - "Ġà¹Ģภļ", - "Ġy azar", - "Ġya zar", - "Ġyaz ar", - "j ekt", - "je kt", - "à¹Ĥล ย", - "Ġдоб ÑĢе", - "Ġپزش Ú©ÛĮ", - "ĠتÙĩ ÛĮÙĩ", - "ç¾İ åľĭ", - "но ÑģÑıÑĤ", - "ноÑģ ÑıÑĤ", - "ноÑģÑı ÑĤ", - "ëłĪ ìĬ¤", - "åĹ ¯", - "Ġr Ãłng", - "ĠÎķ ξ", - "а ÑĤаÑĢ", - "аÑĤ аÑĢ", - "аÑĤа ÑĢ", - "k ova", - "ko va", - "kov a", - "ĠÅŁey ler", - "Ø® اص", - "ĠìķĪ ìłĦ", - "Ñī ей", - "Ñīе й", - "Ġë° Ŀ", - "âĢĮتÙĪاÙĨ د", - "ãģĪ ãģ°", - "Ġv ữ", - "Ġvá» ¯", - "ĠÑģ ама", - "ĠÑģам а", - "ĠобоÑĢ Ñĥд", - "Ġобо ÑĢÑĥд", - "âĢĮ باشد", - "à¹Į à¸Ń", - "Ġdet ay", - "æĤ ²", - " Ī", - "ãĤ¦ ãĤ£", - "ĠпÑĢав ила", - "ĠпÑĢави ла", - "ĠпÑĢавил а", - "kr ét", - "à¹Į ร", - "åĮ ¹", - "Ġ åħį", - "Ġå ħį", - "Ġåħ į", - "ĠÑģилÑĮ но", - "ĠиÑģ ÑĤоÑĩ", - "ĠиÑģÑĤ оÑĩ", - "ĠsaÄŁ lar", - "Ġ æѦ", - "ĠæŃ ¦", - "íĸ ĪìĬµëĭĪëĭ¤", - "íĸĪ ìĬµëĭĪëĭ¤", - "Kh ông", - "à¹Īาà¸ĩ à¹Ĩ", - "Û° Û°Û°", - "Û°Û° Û°", - "Ġ رÙĤ", - "Ġر ÙĤ", - "âĢĻ ÑıÑĤ", - "âĢĻÑı ÑĤ", - "åĽ ²", - "à¹ģ à¸Ķà¸ĩ", - "Ġžád né", - "c ouz", - "co uz", - "cou z", - "à ĭ", - "ĠпÑĸд гоÑĤов", - "Ġ ëĮĢíķĻ", - "ĠëĮĢ íķĻ", - "Ġdüny anın", - "èĢģ å¸Ī", - "èģĮ ä¸ļ", - "Ġy eri", - "Ġye ri", - "Ġyer i", - "à¥ĭ à¤ķर", - "à¥ĭà¤ķ र", - "ĠبÙĩ تر", - "ëĭĪ ìķĦ", - "ìĿĮ ìĿĦ", - "Ġ æĮĩ", - "ĠæĮ ĩ", - "ãĢį ï¼Ī", - "ĠÑģооÑĤвеÑĤÑģÑĤв ии", - "æĬ ĵ", - "à¹Ĥ à¸Ĺ", - "Ġtr á»ĵng", - "ĠпÑĢа ÑĨÑĸ", - "Ġ ëĨĵ", - "ĠëĨ ĵ", - "à¤ĩ न", - "Ġìłķ ë§IJ", - "ãĢ ķ", - "Ġc áºŃn", - "åĸ Ŀ", - "Ġê³Ħ ìĨį", - "Ġ ä¸İ", - "Ġä¸ İ", - "å¥ ı", - "Ġع اÙĦÙħ", - "Ġvys vÄĽt", - "Ġдо ÑĢог", - "ĠдоÑĢ ог", - "Ġн еÑĢв", - "ĠнеÑĢ в", - "Ġб еÑĤ", - "Ġп ÑĢиÑĤ", - "ĠпÑĢ иÑĤ", - "ĠпÑĢи ÑĤ", - "ов Ñĭй", - "å· ¡", - "Ùģ اع", - "Ðļ Ðĺ", - "à¸ķ รวà¸Ī", - "à¸ķรว à¸Ī", - "ĠÐľ ай", - "ĠÐľÐ° й", - "ëıĦ ë¡ľ", - "Ġz lat", - "ĠsaÄŁ lam", - "Ïģ αν", - "Ïģα ν", - "à¸Ĭ ร", - "å¹´ ãģ®", - "à¸Ħ รà¸Ńà¸ĩ", - "à¸Ħร à¸Ńà¸ĩ", - " ħ", - "Ġho á", - "Ġдов олÑĮно", - "Ġol maz", - "Ġolm az", - "Ġolma z", - "ĠpodmÃŃn ky", - "ĠÑħозÑı й", - "æĻ ´", - "ÑĢ ова", - "ÑĢов а", - "ÑĢо ва", - "Ġl ược", - "ान न", - "Ġкап иÑĤ", - "ĠÚĺ اÙĨ", - "æľī äºĽ", - "ĠповеÑĢÑħ ноÑģÑĤи", - "ĠÑĨ Ñĸн", - "ĠÑĨÑĸ н", - "ü yle", - "üy le", - "Ġj azy", - "Ġja zy", - "Ġjaz y", - "ĠPh ú", - "Ġ सन", - "Ġस न", - "åĩº åĶ®", - "Âł д", - "Ġ ãĤ¯", - "ĠãĤ ¯", - "çĶ± äºİ", - "à¥į पत", - "à¥įप त", - "ĠاÙĦØ® اÙħ", - "Ġاص ÙĦاØŃ", - "ĠاصÙĦ اØŃ", - "Ġ تÛĮ", - "Ġت ÛĮ", - "Ġt ato", - "Ġta to", - "Ġtat o", - "å¹ ¹", - "æ³ ½", - "à¸Ńà¸ģ à¸Īาà¸ģ", - "Ñĥ лÑİ", - "Ñĥл Ñİ", - "Ġв Ñģп", - "ĠвÑģ п", - "m ekte", - "me kte", - "mek te", - "à¥Ģ फ", - "ĠÚĺ ÙĪئ", - "Ġl á»ĩnh", - "Ġlá»ĩ nh", - "âĢĮ کرد", - "âĢĮÚ© رد", - "íı¬ ì¸ł", - "an ki", - "ank i", - "Ġëĵ±ë¡Ŀ ëĮĢíĸī", - "Ġ ãĤĿ", - "ĠãĤ Ŀ", - "Ġار زش", - "Ġارز Ø´", - "Ġth ú", - "Ġ ấn", - "è¡Į 为", - "ĠÑģ нова", - "ê ¾¸", - "Ġsou hlas", - "Ġв озв", - "Ġвоз в", - "Ġво зв", - "ÏģÎŃ ÏĢει", - "ĠнÑĸ Ñĩого", - "ĠнÑĸÑĩ ого", - "н ож", - "но ж", - "ÑĤ ик", - "ÑĤи к", - "ãģ© ãģĵ", - "ĠоÑģнов е", - "ãĤ ¥", - "à¸Ľà¸£à¸° à¸Īำ", - "Ġ à¸Ĺà¸Ńà¸ĩ", - "Ġà¸Ĺ à¸Ńà¸ĩ", - "Ġek sik", - "Ġeks ik", - "ĠÙĦ Ø¥", - "ãģĭ ãģ®", - "Ġ ãģª", - "- प", - "Ïģ ει", - "Ïģε ι", - "ĠìłĦ 문", - "า à¸ģล", - "าà¸ģ ล", - "β ε", - "íĬ¹ ë³Ħ", - "íķĺ ë©´ìĦľ", - "íķĺë©´ ìĦľ", - "à¸Ħà¹Ĥà¸Ļ à¹Ĥลย", - "Ġ 好", - "Ġå¥ ½", - "Ġy ayım", - "Ġyay ım", - "ë§Į ëĤ¨", - "ĠкиÑģ лоÑĤ", - "ĠкиÑģл оÑĤ", - "ĠÑį неÑĢг", - "çĸ ¾", - "Ġد Ø´", - "Ġsor uml", - "Ġsoru ml", - "Ġза клад", - "Ġзак лад", - "à¸Ĭ à¸Ńà¸ļ", - "ĠÙģرÙĩÙĨÚ¯ ÛĮ", - "Ġà¤ı ल", - "Ġë¹Ħ êµIJ", - "l erce", - "ler ce", - "Ġ Ø·ÙĦب", - "ĠØ· ÙĦب", - "ĠØ·ÙĦ ب", - "ãģ« ãģĹãģ¦", - "ĠÑı коÑĹ", - "ĠÑıк оÑĹ", - "ĠاÙĦب تÙĩ", - "ĠÐľ аÑĤ", - "ĠÐľÐ° ÑĤ", - "åį ĵ", - "Ġ åħ¬åı¸", - "Ġåħ¬ åı¸", - "Ġsöy ley", - "Ġsöyl ey", - "ĠìĥĪ ë¡ľìļ´", - "ĠÑĦ аÑĢ", - "Ġalt ına", - "Ġaltın a", - "Ġaltı na", - "Ġsta vu", - "Ġstav u", - "âĢĻ ı", - "al izace", - "aliz ace", - "Ġви ÑģÑĤÑĥп", - "æķĻ å¸Ī", - "à¥Ģ à¤ıस", - "à¥Ģà¤ı स", - "o dÄĽ", - "od ÄĽ", - "ĠÑĨ Ñĸл", - "ĠÑĨÑĸ л", - "ĠëĮĢ ìĥģ", - "ĠкоÑĤоÑĢ ом", - "ĠкоÑĤ оÑĢом", - "Ġظ رÙģ", - "éİ ®", - "اÙģ ÙĬØ©", - "اÙģÙĬ Ø©", - "Ġ ìĹĨìĿ´", - "ĠìĹĨ ìĿ´", - "ĠμÏĮ νο", - "ĠC Æ¡", - "å¯ »", - "ÏĦ ιÏĥ", - "ÏĦι Ïĥ", - "Ġ ãĤĦ", - "ĠãĤ Ħ", - "Ġjed noho", - "Ġjedn oho", - "Ġjedno ho", - "ا ا", - "Ø§Ø §", - "et ler", - "Ġव स", - "ĠÑĢазлиÑĩ нÑĭÑħ", - "Ġج غراÙģ", - "Ġth ừa", - "Ġthá» «a", - "ĠгÑĢомад Ñıн", - "ॠ°", - "ĠاÙĦØ£ Ø®", - "Ġнаг ÑĢÑĥз", - "ç¸ ¾", - "à¥Ĥ ह", - "ĠпÑĢÑıм о", - "â Ĭ", - "ĠاÙĦØ£ÙĪÙĦ Ùī", - "æĸ° èģŀ", - "Ġìĥģ íĻ©", - "it esi", - "ite si", - "ites i", - "ëį° ìĿ´íĬ¸", - "æŃ ·", - "ï¼ĮèĢĮ ä¸Ķ", - "ãģ¯ ãģļ", - "产 çĶŁ", - "æ°Ĺ ãģĮ", - "y slu", - "ys lu", - "ysl u", - "ìĸ´ ëĤĺ", - "ا Ú©Ùħ", - "اک Ùħ", - "âĢ ĥ", - ") ìĿĢ", - "Ġجست ارÙĩاÛĮ", - "ÙĪ Ø«", - "ãħ İ", - "Ġkav ram", - "v ál", - "vá l", - "æľ Ń", - "æĤ ł", - "ìħ Ģ", - "h rad", - "hr ad", - "hra d", - "Ġت ÙĥÙĪÙĨ", - "ĠتÙĥ ÙĪÙĨ", - "ĠH òa", - "å¹´ çļĦ", - "Ġç arp", - "Ġça rp", - "Ġy olu", - "Ġyo lu", - "Ġyol u", - "Ġdub na", - "ĠÐĴ елик", - "ĠÐĴели к", - "Ġt ôn", - "Ġtô n", - "æ ķĮ", - "æķ Į", - "Ġc oi", - "Ġco i", - "Ġnak onec", - "ĠÑį ÑĤÑĥ", - "ĠÑįÑĤ Ñĥ", - "íĨµ ëł¹", - "ÑĪ ел", - "ÑĪе л", - "Ġneb yl", - "Ġneby l", - "in ç", - "ب اÙĦØ¥ÙĨجÙĦÙĬزÙĬØ©", - "باÙĦ Ø¥ÙĨجÙĦÙĬزÙĬØ©", - "ï¼ ¡", - "о нÑĮ", - "он ÑĮ", - "Ġне маÑĶ", - "Ġнем аÑĶ", - "Ġê³ł ê°Ŀ", - "ĠÙĤ طع", - "ĠÙĤØ· ع", - "ĠÑĤеÑĢиÑĤоÑĢ ÑĸÑĹ", - "人 ãģ¯", - "ĠΣ α", - "éĤ£ äºĽ", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ios per", - "í Ĥ¨", - "íĤ ¨", - "r aki", - "ra ki", - "rak i", - "اÛĮ ج", - "Âł C", - "Ġан алÑĸз", - "ãĤı ãĤĬ", - "ĠìķĦëĭ Į", - "ĠاÙĦعÙħÙĦ ÙĬØ©", - "ĠاÙĦعÙħ ÙĦÙĬØ©", - "l ament", - "la ment", - "lam ent", - "é» ¨", - "u jÃŃcÃŃm", - "ujÃŃ cÃŃm", - "ujÃŃcÃŃ m", - "Ġr ẻ", - "ä¸į åĪ°", - "Ġrez erv", - "ĠاÙĦØ° ÙĬÙĨ", - "ĠاÙĦØ°ÙĬ ÙĨ", - "æĭ ¥", - "Ðĺ н", - "Ġतह त", - "r esi", - "re si", - "res i", - "Ġ ãĥ¢", - "Ġãĥ ¢", - "л ев", - "ле в", - "ãĢĢ r", - "Ġ ä»Ĭ", - "Ġö dem", - "Ġpot rav", - "ĠêµIJ ìĪĺ", - "ÑĢ еди", - "ÑĢед и", - "ÑĢе ди", - "ĠÎļ ÎijÎĻ", - "Ġ наÑĩала", - "ĠнаÑĩ ала", - "Ġиз б", - "ĠbÅĻez na", - "Ġle dna", - "Ġled na", - "ÑĢ ÑĥÑİÑĤ", - "ÑĢÑĥ ÑİÑĤ", - "Ġ моÑĤ", - "Ġм оÑĤ", - "Ġмо ÑĤ", - "åıĹ åĪ°", - "ĠÑĢÑĥ кÑĥ", - "ĠÑĢÑĥк Ñĥ", - "Ỽ m", - "ad ele", - "ade le", - "adel e", - "ĠÑĢоз глÑı", - "åħ IJ", - "Ġر ÙĪاÙĨ", - "ĠرÙĪ اÙĨ", - "а ков", - "ак ов", - "Ñĥ ÑĢÑĭ", - "ÑĥÑĢ Ñĭ", - "Ġaz al", - "ĠÑĥ кÑĢа", - "ĠÑĥк ÑĢа", - "пи он", - "ĠÄįlov ÄĽ", - "äºĮäºĮ äºĮäºĮ", - "ا بÙĬ", - "اب ÙĬ", - "Ġas lında", - "ë¹ Ī", - "Ġв ÑĢаÑĩ", - "ĠвÑĢа Ñĩ", - "ë£ ¹", - "Ġген еÑĢа", - "à¸ģาร ส", - "ĠÑģов Ñģем", - "ÙĪ ÙĦا", - "ÙĪÙĦ ا", - "Ġश ब", - "ाà¤ĸ ण", - "ست اÙĨÛĮ", - "ستاÙĨ ÛĮ", - "æĬ ½", - "Ġrů z", - "ĠíĮIJ 매", - "à¸ģาร à¸ķ", - "ائ ÛĮ", - "a sal", - "as al", - "asa l", - "ĠÑĢабоÑĤ Ñĥ", - "ĠÑĢаб оÑĤÑĥ", - "ĠÑĢабо ÑĤÑĥ", - "à¥ĭल न", - "Ġ 马", - "Ġé© ¬", - "Ġl ai", - "Ġla i", - "ó i", - "v ap", - "va p", - "ëħĦ ìĹIJëĬĶ", - "ëħĦìĹIJ ëĬĶ", - "ĠпеÑĢед баÑĩ", - "Ġп леÑĩ", - "Ġпл еÑĩ", - "id det", - "idd et", - "ĠÑĩ оÑĢ", - "i yan", - "iy an", - "iya n", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", - "ĠØŃر ÙģÙĩ", - "ĠØŃرÙģ Ùĩ", - "大 éĺª", - "Ñĩ ого", - "Ġ ки", - "Ġк и", - "ا ÙĪÙĬ", - "اÙĪ ÙĬ", - "ĠbaÅŁ lan", - "Ġmerk ezi", - "Ġmerkez i", - "© ©", - "Ġر است", - "Ġرا ست", - "Ġ ëĬĶ", - "ĠëĬ Ķ", - "ĠÑģ ÑĢав", - "ĠвнÑĥ ÑĤÑĢи", - "ĠвнÑĥÑĤÑĢ и", - "ĠвнÑĥÑĤ ÑĢи", - "ãĢĢ ãĥİ", - "åĿ Ľ", - "Ġв ÑĤ", - ": :/", - ":: /", - "Ġsöz leÅŁ", - "Ġver diÄŁi", - "Ġverdi ÄŁi", - "ิ ยม", - "ิย ม", - "ĠÐŁ ÑĢоÑĤ", - "ĠÐŁÑĢ оÑĤ", - "ĠÐŁÑĢо ÑĤ", - "Ùĥ ار", - "Ġب ÙĨدÛĮ", - "ĠبÙĨ دÛĮ", - "ĠبÙĨد ÛĮ", - "Ùı ÙĪ", - "缴 æĴŃ", - "ĠÙħ ÙĦÙĬ", - "ĠÙħÙĦ ÙĬ", - "Ġnut né", - "ะà¹ģ à¸Ļà¸Ļ", - "ĠM ã", - "Ġ ì´", - "Ġì ´", - "à¹Ī าม", - "à¹Īา ม", - "м оÑģ", - "мо Ñģ", - "Ġпо Ñıви", - "ĠпоÑıв и", - "Ġn ghi", - "Ġng hi", - "Ġngh i", - "Ġ ëIJĺëĬĶ", - "ĠëIJĺ ëĬĶ", - "Ñģ клад", - "Ñģк лад", - "à¤Ĺ ल", - "ĠC á»Ļng", - "çŁ¥ è¯Ĩ", - "Ġ taj", - "Ġt aj", - "Ġta j", - "Ġع بر", - "Ġعب ر", - "éĻĦ è¿ij", - "ü ÄŁ", - "Ġê³µ ê³ł", - "è£ ķ", - "âĢĮ Ø´ÙĨ", - "âĢĮØ´ ÙĨ", - "Ġgerç ekten", - "Ġgerçek ten", - "n un", - "nu n", - "Ùħ Ø´", - "ê°Ģ ëĬ¥", - "ãĥ© ãĥ³ãĥī", - "ãĥ©ãĥ³ ãĥī", - "ay acak", - "aya cak", - "åįģ ä¸Ģ", - "ĠB ảo", - "Ġyet erli", - "Ġyeter li", - "ž iv", - "ži v", - "ĠÙĬÙĨ اÙĬر", - "Ġb ýval", - "Ġbý val", - "ìĽĶ ê¹Įì§Ģ", - "Ġn ợ", - "Ġ ê´Ģê³Ħ", - "Ġê´Ģ ê³Ħ", - "Ġ íĿ¬", - "ĠíĿ ¬", - "а ÑİÑĤÑĮ", - "аÑİÑĤ ÑĮ", - "аÑİ ÑĤÑĮ", - "Ġgö tür", - "Ġваж но", - "Ġва жно", - "æµ ©", - "ĠìĿ¼ ë¶Ģ", - "ÑĨÑĸй ний", - "ëł¥ ìĿĦ", - "Ġл еÑĩение", - "ĠлеÑĩ ение", - "éĸ¢ ä¿Ĥ", - "ĠT üm", - "ìĻ Ķ", - "éģ Ĺ", - "ĠD ön", - "Ġ ÑģпÑĸлÑĮ", - "ĠÑģп ÑĸлÑĮ", - "ĠÑģпÑĸл ÑĮ", - "ãĥģ ãĤ§", - "н ÑıеÑĤÑģÑı", - "нÑı еÑĤÑģÑı", - "нÑıеÑĤ ÑģÑı", - "il tere", - "ilter e", - "ilt ere", - "Ġ íĮĢ", - "Ġí ĮĢ", - "ĠíĮ Ģ", - "è¨Ń å®ļ", - "Ġro din", - "Ġrod in", - "Ġrodi n", - "ĠاÙĤتص اد", - "алÑĮ не", - "à¥į à¤ķर", - "à¥įà¤ķ र", - "Ġvý bÄĽ", - "Ġteh lik", - "âĶ IJ", - "Ġ çĶ°", - "Ïģί ÏĤ", - "iy el", - "iye l", - "Ġth iá»ĩu", - "Ġthi á»ĩu", - "ÏĪ ηÏĤ", - "ÏĪη ÏĤ", - "Ġд ве", - "Ġдв е", - "ĠEl ekt", - "ĠEle kt", - "à¸ģ à¸İ", - "о ÑĢÑĥж", - "оÑĢ Ñĥж", - "оÑĢÑĥ ж", - "a ÅŁÄ±", - "aÅŁ ı", - "è© ³ç´°", - "Ġات ÙģاÙĤ", - "Ġg ắn", - "æ²Ĵ æľī", - "ĠÙħطاÙĦ عÙĩ", - "ÏĦ ιν", - "ÏĦι ν", - "Ġok res", - "Ñ ľ", - "ê° Ķëĭ¤", - "Ðł оз", - "å¾ĭ 宾", - "ï¼ī ï¼Ī", - "Ġìļ´ìĺģ ìŀIJ", - "ãĤ« ãĥĨ", - "l aÄį", - "la Äį", - "à¥ĩब स", - "Ġo Äįi", - "ĠoÄį i", - "- б", - "e lerden", - "eler den", - "elerde n", - "k ových", - "kov ých", - "kový ch", - "ĠÄ°z mir", - "สม าà¸Ĭ", - "lad atel", - "Ġ æ»", - "Ġæ »", - "éĶĢ åĶ®", - "ĠдоÑģлÑĸд женнÑı", - "ĠлÑĸ каÑĢ", - "ĠлÑĸка ÑĢ", - "ĠлÑĸк аÑĢ", - "Ġодна ко", - "ĠV ác", - "Ġ è«", - "Ġè «", - "éĢ² è¡Į", - "以 å¤ĸ", - "é³ ¥", - "Ġ ÙĨج", - "ĠÙĨ ج", - "ĠbaÅŁ kan", - "ĠbaÅŁka n", - "ĠbaÅŁk an", - "Ġopat ÅĻenÃŃ", - "ا رش", - "ار Ø´", - "ض اÙģØ©", - "ضا ÙģØ©", - "ãĤ¹ ãĥ¬", - "ή ν", - "ÄĽ tÃŃ", - "ÄĽt ÃŃ", - "ว ย", - "Ġرس ÙĪÙĦ", - "ÅĻ ich", - "ÅĻi ch", - "ĠpÅĻ ih", - "ĠpÅĻi h", - "ÑĮ ми", - "çĦ¶ èĢĮ", - "Ġth ẳng", - "l amaz", - "la maz", - "lam az", - "lama z", - "ÙĢ ÙĢÙĢ", - "ÙĢÙĢ ÙĢ", - "Ġì°¸ ìŬ", - "ĠÙĨÙĪ شتÙĩ", - "ĠÙĨÙĪØ´ تÙĩ", - "ĠÑģÑĤ ек", - "ãģ® ãģ¿", - "ĠÙĪ اÙĦع", - "ĠÙĪاÙĦ ع", - "ĠÙĪا ÙĦع", - "æķ ¢", - "à¥Ģà¤Ĥ ,", - "ÐŀÑģ нов", - "им оÑģÑĤи", - "имо ÑģÑĤи", - "ĠÄĮesk á", - "ĠÄĮes ká", - "Ñĸ Ñĩний", - "ÑĸÑĩ ний", - "าม ารà¸ĸ", - "ekk ür", - "Âł h", - "ι κη", - "ικ η", - "Ġتع ÛĮÛĮÙĨ", - "к оÑģÑĤÑĸ", - "ко ÑģÑĤÑĸ", - "ĠMust afa", - "Ġì¦ ī", - "ãģ§ ãģĤãĤĬ", - "ãģ§ãģĤ ãĤĬ", - "å·¥ ä¸ļ", - "ov ÃŃd", - "ovÃŃ d", - "ÐĿ о", - "Ġس پس", - "Ġسپ س", - "Ú¯ÛĮ رد", - "Ú¯ÛĮر د", - "Ġп едагог", - "Ġ کارÛĮ", - "ĠÚ© ارÛĮ", - "Ġکار ÛĮ", - "ĠÑĪ ÑĤÑĥ", - "ĠÑĪÑĤ Ñĥ", - "æĮ Ĥ", - "Ø¢ Ùħد", - "Ø¢Ùħ د", - "羣 æĺ¯", - "Ġ ابت", - "Ġا بت", - "Ġاب ت", - "Ġرئ ÛĮس", - "Ġد ÛĮÙĨ", - "ĠدÛĮ ÙĨ", - "ÏĪ ε", - "Ġse zon", - "Ġsez on", - "Ġ çĨ", - "Ġç Ĩ", - "स न", - "ãĥ» ãĤ¢", - "Ġ åħŃ", - "Ġåħ Ń", - "Ġ è±", - "Ġè ±", - "Ġìłľ 목", - "ĠÙħ عد", - "ĠÙħع د", - "ĠÙģ ÙĤد", - "ĠÙģÙĤ د", - "éĤ Ĭ", - "Ω Σ", - "Ġ å¡", - "Ġå ¡", - "Ġob vyk", - "ĠìĿ´ ëłĩê²Į", - "ĠбоÑĢ оÑĤÑĮ", - "Û² Û±", - "Ġ á»ijng", - "Ġá» ijng", - "è¯ Ĺ", - "Ġ ÄIJá»iji", - "ĠÄIJ á»iji", - "ĠбеÑĢез нÑı", - "Ġs oÄŁ", - "Ġso ÄŁ", - "Ġ ï¾į", - "Ġï¾ į", - "ãĤĴ ãģ¤", - "ãģĹ ãĤĥ", - "еÑĢ еÑĩ", - "еÑĢе Ñĩ", - "ãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢ", - "æĪ ª", - "ĠاÙĦسعÙĪد ÙĬØ©", - "ĠëĤ¨ ìŀIJ", - "ĠAng iosper", - "???????? ????????", - "Ġpr ům", - "Ġprů m", - "ĠплоÑī ад", - "Ġ ÏĦÏģα", - "ĠÏĦ Ïģα", - "д аÑİÑĤ", - "да ÑİÑĤ", - "Ġsı nav", - "Ġsın av", - "Ġm ặc", - "æ°´ å¹³", - "Ġви глÑı", - "Ġвиг лÑı", - "Ġn ást", - "Ġná st", - "Ġnás t", - "ĠобÑĭ Ñĩ", - "ĠìĿ´ìķ¼ 기", - "ë¹ Ľ", - "ĠB aÄŁ", - "ĠBa ÄŁ", - "ĠاÙĦØ« اÙĦØ«", - "Ġser vis", - "Ġserv is", - "Ġservi s", - "Ġ 룬", - "ĠëŁ ¬", - "ом ина", - "ί θ", - "Ġ Ấ", - "ĠẠ¤", - "ê²½ 기", - "Ġì¡ ¸", - "ี à¸ļ", - "Ġà¤ĺ à¤Łà¤¨", - "Ġ à¸Ļาà¸ĩ", - "Ġà¸Ļ าà¸ĩ", - ". Îł", - "ìķ ķ", - "r ün", - "Ġon ların", - "Ġonları n", - "Ġonlar ın", - "Ġзб ÑĸлÑĮÑĪ", - "à¹ģ à¸Ł", - "ĠìŬ 기", - "Ġ ëĮĢíijľ", - "ĠëĮĢ íijľ", - "ĠÑģи лÑĥ", - "ĠÑģил Ñĥ", - "à¹Ĥ à¸Ľ", - "Ġت ÙĤد", - "ĠتÙĤ د", - "ĠÐŁ ом", - "ĠÐŁÐ¾ м", - "ĠмаÑģ ла", - "Ġ ìĺģìĥģ", - "Ġìĺģ ìĥģ", - "н ение", - "не ние", - "нен ие", - "λα μβ", - "ĠB yl", - "ĠBy l", - "æĬ µ", - "æİ ª", - "Ġκαθ ÏİÏĤ", - "m ızı", - "æĸ° çļĦ", - "éĩį è¤ĩ", - "ั à¸Ľ", - "çŃ Ĩ", - "ĠÑĤ ка", - "ĠзнаÑĩ еннÑı", - "Ġзна ÑĩеннÑı", - "л аÑĤи", - "ла ÑĤи", - "лаÑĤ и", - "Ġv liv", - "Ġvl iv", - "ÐIJ н", - "ĠÚĨ اپ", - "ĠпиÑĤ анÑĮ", - ": ï½ī", - "æķĻ æİĪ", - "Ġì¹ľ 구", - "Ġtr ao", - "Ġtra o", - "à¥įयà¤ķ त", - "ุà¸Ħ à¸Ħล", - "ĠرÙĪ Ø´ÙĨ", - "ĠرÙĪØ´ ÙĨ", - "ĠعÙĦÙĬ Ùĩا", - "ĠعÙĦÙĬÙĩ ا", - "ãĢģ ãģĦ", - "ëħĦ ìĹIJ", - "éĢ Ĩ", - "Ġмаг аз", - "ï¾ŀ ï¾ŀ", - "Ġs ice", - "Ġsi ce", - "Ġsic e", - "âĢĻ te", - "âĢĻt e", - "ĠاÙĦÙĦ غة", - "á u", - "èĩª 身", - "Ġng Å©", - "ĠÑģк ладÑĥ", - "ĠÑģклад Ñĥ", - "Ġz ru", - "Ġtr uy", - "Ġ ilan", - "Ġi lan", - "Ġil an", - "ĠÙ¾ اÛĮÙĩ", - "ĠپاÛĮ Ùĩ", - "Ġپا ÛĮÙĩ", - ": :::::::::::::", - ":: ::::::::::::", - ":::: ::::::::::", - ":::::: ::::::::", - ":::::::: ::::::", - "::: :::::::::::", - "::::: :::::::::", - "::::::: :::::::", - "::::::::: :::::", - ":::::::::: ::::", - "::::::::::: :::", - ":::::::::::: ::", - "::::::::::::: :", - "f ak", - "fa k", - "ÑĤ еÑħ", - "ÑĤе Ñħ", - "Ġt aky", - "Ġta ky", - "Ġtak y", - "Ġìĸ¸ ìĸ´", - "ed enÃŃ", - "eden ÃŃ", - "ede nÃŃ", - "Ġà¤ļ लत", - "Ġà¤ļल त", - "Ġë°° ìļ°", - "Ġjmé no", - "ĠÙĦ Ø£ÙĨ", - "ĠÙĦØ£ ÙĨ", - "α νά", - "αν ά", - "к ÑĥлÑĮ", - "кÑĥ лÑĮ", - "кÑĥл ÑĮ", - "ĠØŃÙģ ظ", - "ĠآزÙħ ÙĪÙĨ", - "иÑĤелÑĮ нÑĭе", - "ĠÐŀ лекÑģанд", - "èį £", - "Ġà¤ľà¤¬ à¤ķ", - "Ġr odi", - "Ġro di", - "Ġrod i", - "Ġبرخ ÙĪرد", - "Ġhaf ta", - "Ġhaft a", - "λ ικά", - "λι κά", - "λικ ά", - "à¸ķ à¸Ļ", - "ĠбеÑĢ ег", - "αν δ", - "- С", - "Ġprav idel", - "ĠбÑĸ лÑı", - "ĠбÑĸл Ñı", - "íĴ į", - "ĠпÑĢед ÑĥÑģ", - "ĠмÑĥ ниÑĨип", - "åĮĸ åѦ", - "ĠتÙħ اس", - "Ġà¤ī ल", - "Ðĵ Ðŀ", - "غ ر", - "r adan", - "ra dan", - "rad an", - "rada n", - "ĠëĤĺ ìĺ¤", - "è¨ Ĥ", - "à¹Ģà¸ĺ à¸Ń", - "âĢĮ سÛĮ", - "âĢĮس ÛĮ", - "ĠобÑıз аÑĤелÑĮно", - "ĠобÑıзаÑĤелÑĮ но", - "о ÑĤе", - "оÑĤ е", - "à¹Į à¸Ĭ", - "çĶ¨ çļĦ", - "Ġalt ın", - "Ġaltı n", - "ĠÑģоÑĤ ÑĢÑĥд", - "Ñĸ нки", - "Ñĸн ки", - "озмож но", - "Î IJ", - "ë¹ Į", - " ķ", - "ĠÑĤ оÑĩно", - "ĠÑĤо Ñĩно", - "ĠÑĤоÑĩ но", - "Ġj men", - "Ġjm en", - "اÙĦ ÛĮا", - "اÙĦÛĮ ا", - "èĪ į", - "ch odu", - "cho du", - "chod u", - "ê³ ¤", - "ick ém", - "ické m", - "ĠÙħ ÙĪر", - "ĠÙħÙĪ ر", - "ãĥª ãĥ³ãĤ¯", - "ãĥªãĥ³ ãĤ¯", - "Ġa ÅŁam", - "ĠaÅŁ am", - "Ġ иÑĤ", - "Ġи ÑĤ", - "Ġन य", - "Ġ μο", - "Ġμ ο", - "éķ ľ", - "ĠبÙĨ ابر", - "ĠبÙĨا بر", - "Ġت خصص", - "Ġส à¸ŀ", - "ĠпÑĢоÑĦеÑģ Ñģи", - "Ġp uan", - "Ġpu an", - "ĠÙģر ÙħاÙĨ", - "ĠÙģرÙħ اÙĨ", - "ëĮĢ íļĮ", - "Ġп ÑıÑĤ", - "ĠÙħ ÙĪب", - "ĠÙħÙĪ ب", - "ĠvÄĽ ku", - "Ġ ëĥ", - "Ġë ĥ", - "ec ký", - "eck ý", - "ĠìĪĺ ëıĦ", - "Ġth ao", - "Ġtha o", - "Ġk apat", - "Ġka pat", - "Ġkap at", - "ĠзаÑħ воÑĢÑİ", - "Ġ åħī", - "Ġåħ ī", - "ر اÙĨÛĮ", - "راÙĨ ÛĮ", - "را ÙĨÛĮ", - "éĢł æĪIJ", - "ĠÑģв Ñĸй", - "ĠдоÑģ иÑĤÑĮ", - "Ġmil yar", - "Ġener ji", - "Ġenerj i", - "Ġк ип", - "Ġки п", - "Ġì¢ĭ ìķĦ", - "Ġب Ø¥", - "ê²Į ìĭľ", - "ĠL Æ°u", - "ĠÙħÙĨظ ÙĪر", - "Ïī μά", - "ζ ί", - "ım da", - "Ġ ìĿ´ë¥¼", - "ĠìĿ´ 를", - "๠Ĵ", - "Ġв важ", - "Ġвв аж", - "Ġga zet", - "Ġgaze t", - "Ġgaz et", - "à¥įत न", - "à¹īำ หà¸Ļ", - "åľŁ åľ°", - "Ġसद स", - "ت بة", - "تب Ø©", - "Ġpo ÄįÃŃta", - "Ġìĭľ ìĬ¤íħľ", - "ร à¸Ħ", - "Ġed ecek", - "ĠتØŃ ÙĦÛĮÙĦ", - "æĮī çħ§", - "åĿ ª", - "Ġê·¸ ê°Ģ", - "ت ÙĩÙħ", - "تÙĩ Ùħ", - "Ġб аж", - "Ġба ж", - "ا Ùģع", - "اÙģ ع", - "éĢļ 常", - "ĠТ и", - "γ νÏī", - "ì¹ Ļ", - "Ġznam ená", - "ï¼¼ ï¼¼", - "α ÏĢÏĮ", - "åĨĻ 羣", - "Ġ ï¼¼Ċ", - "Ġï¼¼ Ċ", - "åĬł å·¥", - "èĤ¡ä»½ æľīéĻIJåħ¬åı¸", - "Ñı ÑĤий", - "ÑıÑĤ ий", - "ÑıÑĤи й", - "Ġh âl", - "Ġç ab", - "Ġça b", - "ĠØŃ Ø§Ø¶Ø±", - "P ÅĻ", - "ĠاÙĦ تÙĤ", - "ĠاÙĦت ÙĤ", - "ξ ηÏĤ", - "ξη ÏĤ", - "б е", - "Ġkh ám", - "Ġkhá m", - "Ġ âĮĴ", - "Ġâ ĮĴ", - "Ġ éķ¿", - "Ġéķ ¿", - "Ġ âĢ¦Ċ", - "ĠâĢ¦ Ċ", - "द म", - "ĠSt udi", - "ĠStud i", - "Ġk odu", - "Ġko du", - "Ġkod u", - "Ġkom unik", - "Ġkomun ik", - "Ġkat kı", - "n ete", - "ne te", - "net e", - "Ġr apor", - "Ġrap or", - "Ġra por", - "éĨ ´", - "ãĤī ãģĽ", - "ĠнеÑģк олÑĮ", - "Ġhá»į p", - "ï¿£ ￣￣", - "￣￣ ï¿£", - "º ¼", - "è£ Ĥ", - "ед ÑĮ", - "Ġا ÙĦاØŃ", - "ĠاÙĦ اØŃ", - "l adık", - "la dık", - "lad ık", - "ladı k", - "Ġfot oÄŁraf", - "æĹ¥ ãģ®", - "ĠØŃ Ø§ÙĦت", - "ĠØŃاÙĦ ت", - "ĠØ« ÙĦاث", - "а ÑĤов", - "аÑĤ ов", - "аÑĤо в", - "ey se", - "Ġê°IJ ìĤ¬", - "á že", - "áž e", - "Ġн ада", - "Ġна да", - "Ġнад а", - "Ġà¤ķ हन", - "Ġà¤ķह न", - "Ġ ãĥĿ", - "Ġãĥ Ŀ", - "ãģ« ãģĤãĤĭ", - "ãģ«ãģª ãģ£ãģ¦", - "ÙĪ دÙĩ", - "ÙĪد Ùĩ", - "Ġpo Å¡k", - "太 éĺ³åŁİ", - "ç»ı éªĮ", - "æĴŃ æĶ¾", - "Ġma jet", - "Ġmaj et", - "Ñħ о", - "ĠÑĤ еÑģÑĤ", - "ĠÑĤе ÑģÑĤ", - "ï¼ı Ċ", - "Ïĥε ÏĦε", - "ĠТ омÑĥ", - "ĠТо мÑĥ", - "ĠТом Ñĥ", - "Ùİ ØŃ", - "ĠìŀĪ ìľ¼ë©°", - "Ġза знаÑĩ", - "éļ IJ", - "Ġд ÑĸÑĹ", - "ĠдÑĸ ÑĹ", - "к ÑĤив", - "кÑĤ ив", - "кÑĤи в", - "ÙĪ ÙģÙĬ", - "ÙĪÙģ ÙĬ", - "Ġt á»Ŀ", - "Ġtá» Ŀ", - "à¸¹à¸Ľ à¹ģà¸ļà¸ļ", - "ĠÑĢ едак", - "ĠÑĢед ак", - "Ġa teÅŁ", - "Ġat eÅŁ", - "Ġate ÅŁ", - "Ġkh iá»ĥn", - "Ġkhi á»ĥn", - "ü ny", - "ün y", - "ี ยà¸ģ", - "ีย à¸ģ", - "ĠÑĩа Ñīе", - "Ġt uy", - "Ġtu y", - "γ Ïīν", - "γÏī ν", - "ร à¸Ńà¸ļ", - "Ġtr ùng", - "à¹ģà¸Ĺ à¸Ļ", - "Ġα κÏĮ", - "Ġακ ÏĮ", - "ĠÐĴеÑĢ Ñħов", - "à¹ĥ à¸Ļส", - "à¹ĥà¸Ļ ส", - "ãĢģ ä½ķ", - "åĩ ¦", - "Ġ ç»ı", - "Ġç» ı", - "æ¨ ĵ", - "اÙĨÚ¯ ÙĦÛĮسÛĮ", - "Ġ lepÅ¡ÃŃ", - "Ġlep Å¡ÃŃ", - "Ġ å¼Ģå§ĭ", - "Ġå¼Ģ å§ĭ", - "éĻ º", - "ĠÑĩ еÑĤÑĭ", - "ĠÑĩеÑĤ Ñĭ", - "ĠС еÑĢ", - "оÑİ Ð·", - "Ġx ung", - "Ġxu ng", - "åĵģ çīĮ", - "Ġìĥģ íĥľ", - "ĠÙĨ صب", - "ĠÙĨص ب", - "ĠÑĩ омÑĥ", - "Ġتر Ú©ÛĮ", - "Ġترک ÛĮ", - "- ли", - "o vÃŃ", - "ov ÃŃ", - "Ġا ÙĨج", - "ĠاÙĨ ج", - "çµ ¡", - "Ġت ÙĪص", - "ĠتÙĪ ص", - "Ġ ì¿ł", - "Ġì ¿ł", - "Ġvar sa", - "Ġva rsa", - "Ġvars a", - "ĠÑĢаз ÑĢабоÑĤ", - "à¸Ĥ à¸Ńà¸ĩà¸Ħ", - "à¸Ĥà¸Ńà¸ĩ à¸Ħ", - "éŃ Ĥ", - "Ġà¤Ĭ पर", - "æĿ¥ 说", - "ĠÑĨенÑĤ ÑĢалÑĮ", - "ĠÑĨенÑĤÑĢ алÑĮ", - "ĠÑĨенÑĤÑĢа лÑĮ", - "ĠTak ım", - "Ġon lar", - "Ġسر عت", - "好 åĥı", - "Ġbu á»ķi", - "ĠÐij ел", - "Âł c", - "Ø£ ت", - "à¸Ĥ à¸ĵะ", - "ãģ« åĩº", - "Ġ+ **************", - "ÏĦη κε", - "ا جر", - "اج ر", - "Ġ âĢ²", - "ĠâĢ ²", - "ãĥ¼ ãĥ¬", - "ãĥ¼ãĥ ¬", - "é¥ Ń", - "Ġج ÙĦس", - "ĠجÙĦ س", - "Ġب ستÙĩ", - "Ġبس تÙĩ", - "ว าà¸ĩ", - "Ġ βά", - "Ġβ ά", - "Ġа меÑĢикан", - "ĠPr emi", - "ĠPre mi", - "ĠPrem i", - "m ae", - "ma e", - "ĠÑģ ÑĢеди", - "ĠÑģÑĢед и", - "Ạł", - "Ġв ÑĢед", - "ãĢĤ èĢĮ", - "åĴ ²", - "Ġê³µ ê°ľ", - "èĤ ¥", - "з виÑĩай", - "Ġpro cent", - "Ġproc ent", - "и лоÑģÑĮ", - "ил оÑģÑĮ", - "ило ÑģÑĮ", - "श न", - "é łģ", - "éł ģ", - "е кÑĤи", - "ек ÑĤи", - "екÑĤ и", - "د اشت", - "دا شت", - "íķĻ íļĮ", - "ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ", - "ĠÙħد ÙĬÙĨØ©", - "िल न", - "Ġ èĹ", - "Ġè Ĺ", - "м иÑĢ", - "ми ÑĢ", - "Ġн оÑĢ", - "Ġно ÑĢ", - "Ġ íķĺì§Ģ", - "Ġíķĺ ì§Ģ", - "в еÑī", - "ве Ñī", - "nÄĽ m", - "е ÑĢами", - "еÑĢ ами", - "еÑĢа ми", - "Ġpra cov", - "Ġprac ov", - "ĠبÙĬ اÙĨات", - "ĠÏĥ Ïįν", - "ĠÏĥÏį ν", - "Ġج Ø°", - "ãģĦ ãģ§", - "ĠB ÃŃ", - "è± Ĩ", - "Ġh mot", - "Ġhm ot", - "il eceÄŁi", - "ilece ÄŁi", - "Ġت اث", - "Ġتا Ø«", - "è´ ´", - "Ġ ê¸ī", - "Ġê¸ ī", - "Ġm ysl", - "Ġmy sl", - "Ġmys l", - "ĠìĿ´ íķ´", - "Ġ기 ëĬ¥", - "ĠТ ам", - "ĠТа м", - "ĠнаÑģ елениÑı", - "ĠM ez", - "ĠMe z", - "Ġ모 르", - "íĻĶ 를", - "ĠÙĨسخ Ùĩ", - "ĠتÙĦ ÙĪÛĮزÛĮ", - "ĠÄįerv na", - "Æ°á» ¡ng", - "ص ØŃ", - "ĠÑĤ ÑĢен", - "ĠÑĤÑĢ ен", - "Õ ¡", - "Ġce lou", - "Ġcel ou", - "Å© i", - "ìĹĨ ìĿ´", - "nÃŃ ku", - "nÃŃk u", - "Ġprogram u", - "à¥į पन", - "à¥įप न", - "Ġп ÑĢеж", - "ĠпÑĢ еж", - "ĠпÑĢе ж", - "ا رب", - "ار ب", - "æľŁ éĸĵ", - "Ġ μά", - "Ġμ ά", - "ëįĶ ëĭĪ", - "ụ n", - "ĠпеÑĢ еÑģÑĤ", - "ĠпеÑĢе ÑģÑĤ", - "ĠпеÑĢеÑģ ÑĤ", - "对 äºİ", - "è¿IJ è¡Į", - "ĠÑĤ ан", - "ĠÑĤа н", - "Ġ ìĤ¬ìĿ´íĬ¸", - "ĠìĤ¬ ìĿ´íĬ¸", - "ĠìĤ¬ìĿ´ íĬ¸", - "ĠQu ảng", - "ĠQuản g", - "Ġst ojÃŃ", - "Ġsto jÃŃ", - "ãĥŃ ãĥ¼", - "Ú¯ ار", - "Ġе неÑĢг", - "Ġkter ým", - "Ġkterý m", - "ĠпÑĢи мÑĸ", - "ĠпÑĢим Ñĸ", - "ĠкаÑĢÑĤ и", - "ĠкаÑĢ ÑĤи", - "Ġz engin", - "Ġzen gin", - "ï¼Į åĨį", - "Ġت رب", - "Ġتر ب", - "ĠÑĨенÑĤ ÑĢ", - "ĠÑĨен ÑĤÑĢ", - "ĠsaÄŁ lamak", - "ĠsaÄŁlam ak", - "ëĭ Ŀ", - "ãģ® åŃIJ", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "Ġs Æ¡n", - "ĠsÆ¡ n", - "z ı", - "ÑĤ аки", - "ÑĤа ки", - "ÑĤак и", - "ÄĽ stÃŃ", - "ÄĽst ÃŃ", - "Ġ à¥", - "Ġà ¥", - "é ®", - "åŁ¹ è®Ń", - "Ġ ìĶ¨", - "ĠìĶ ¨", - "Ġbel ki", - "ĠìĿ´ 벤íĬ¸", - "ëĶĶ ìĸ´", - "Ġs Ãłn", - "ни кам", - "ник ам", - "ника м", - "a lim", - "al im", - "ali m", - "対 å¿ľ", - "ĠS á»±", - "éģĵ è·¯", - "é«ĺ æ¸ħ", - "Ġd õi", - "ĠÙĦ ÙĢ", - "Ġ èĤ¡", - "ĠèĤ ¡", - "ν ι", - "âĢŀ J", - "' nde", - "'n de", - "Îij Îĵ", - "ãģ¨ ãģªãĤĭ", - "ãģ¨ãģª ãĤĭ", - "çĪ ¸", - "ع ÙĦÛĮ", - "عÙĦ ÛĮ", - "Ïģι ÏĥÏĦ", - "ÏģιÏĥ ÏĦ", - "Ġe ÄŁit", - "ĠeÄŁ it", - "Ġзов нÑĸÑĪ", - "Ġп ÑĢим", - "ĠпÑĢ им", - "ĠпÑĢи м", - "س Ùħبر", - "سÙħ بر", - "ĠmÄĽst ÄĽ", - "ĠÏĢεÏģι ÏĥÏĥÏĮÏĦε", - "ÐIJ Ðł", - "æĦŁ åĪ°", - "Ġ문 ìĦľ", - "ãģĭ ãĤĭ", - "ÙĤÙĬ ÙĤØ©", - "ÙĤÙĬÙĤ Ø©", - "Ġв ÑĤÑĢа", - "ĠвÑĤ ÑĢа", - "Ġ à¸Ńำ", - "Ġà¸Ń ำ", - "Ñģ кÑĥÑİ", - "Ñģк ÑĥÑİ", - "د ÙĩاÛĮ", - "دÙĩ اÛĮ", - "ĠÄ° st", - "ĠÄ°s t", - "ĠÐĹ Ð°Ð²", - "ĠÐĹа в", - "Ġ éĥ½", - "Ġé ĥ½", - "Ġéĥ ½", - "ÑĪ ем", - "ÑĪе м", - "Ġе ÑīÑij", - "ĠÐľÐ¸Ñħ ай", - "ĠÑĥпÑĢав лениÑı", - "л еннÑĭе", - "лен нÑĭе", - "ĠzaÄį al", - "æ¡ Į", - "Ġп Ñĸз", - "л ÑıÑĤÑĮÑģÑı", - "лÑı ÑĤÑĮÑģÑı", - "лÑıÑĤÑĮ ÑģÑı", - "Ġ ìŀIJë£Į", - "ĠìŀIJ ë£Į", - "ãĢĢ ãĢĢĠ", - "ãĢĢãĢĢ Ġ", - "ĠK ral", - "ĠKr al", - "ĠKra l", - "èĪ ī", - "Ġà¤Ń व", - "Ġ Ø®Ùħ", - "ĠØ® Ùħ", - "Ġа кадем", - "Ġ isten", - "Ġis ten", - "Ġi sten", - "Ġist en", - "ĠиÑģ кÑĥÑģ", - "ĠиÑģк ÑĥÑģ", - "ĠعÙĨد Ùħا", - "Ġا ÙĦاÙħ", - "ĠاÙĦ اÙħ", - "is mus", - "ism us", - "ismu s", - "Ġayr ıntı", - "Ġ Що", - "ĠЩ о", - "ĠÙĩ ÙĪØ´", - "ĠÙĩÙĪ Ø´", - "د ÙĪاج", - "دÙĪ اج", - "л аж", - "ла ж", - "ĠÚ©ÙĨ ار", - "Âł R", - "æĢ§ çļĦ", - "Ñģ Ñĸм", - "ÑģÑĸ м", - "ĠM üz", - "ĠMü z", - "ÑĢ овиÑĩ", - "ÑĢов иÑĩ", - "ÑĢо виÑĩ", - "Ġ Ω", - "ĠÎ ©", - "Ġìĸ´ ëĶĶ", - "س ÙħØ©", - "سÙħ Ø©", - "Ġ ÑĢÑı", - "ĠÑĢ Ñı", - "Ġt Æ°Æ¡i", - "ĠtÆ° Æ¡i", - "ĠÑĢаÑģ Ñħод", - "åı° çģ£", - "ĠاÙĦ ÙĪÙĤت", - "ĠاÙĦÙĪ ÙĤت", - "بر اÛĮ", - "ĠзÑĢоб иÑĤи", - "Ġб ÑĥÑĢ", - "ĠбÑĥ ÑĢ", - "ĠÄįin nosti", - "ĠÄįinnost i", - "Ġص اØŃ", - "ĠصÙĨ عت", - "ĠصÙĨع ت", - "Ġ Ø·ÙĦ", - "ĠØ· ÙĦ", - "ξ Ïį", - "ĠtisÃŃ c", - "ĠFr ansa", - "ĠFran sa", - "ì¦ ĺ", - "è» ½", - "Ñ ĺ", - "ÏĮÏĦη ÏĦαÏĤ", - "ÏĮÏĦηÏĦα ÏĤ", - "ĠM illet", - "ĠMill et", - "ĠMil let", - "ãĢĢ ãĥ¾", - "ĠпÑĢ ием", - "ĠпÑĢи ем", - "ĠترجÙħ Ùĩ", - "Ġس ÙĪد", - "ĠسÙĪ د", - "ĠsouÄįást ÃŃ", - "ÐĶ о", - "Ġtr ụ", - "足 çIJĥ", - "à¸Ľ à¸ģ", - "Ġu stanov", - "ÎŁ ÎĻ", - "ÎŁÎ Ļ", - "Ðŀ н", - "Ġн еж", - "Ġне ж", - "к ог", - "ко г", - "ä¸Ģ çĤ¹", - "Ġد ÙĪراÙĨ", - "ĠدÙĪ راÙĨ", - "ĠدÙĪر اÙĨ", - "å½± éŁ¿", - "el idir", - "eli dir", - "âĢŀ N", - "es iyle", - "esi yle", - "ÑĢем енно", - "ÑĢе менно", - "Ġilet iÅŁim", - "ม à¹Ģà¸ķ", - "以 åīį", - "ãĥĭ ãĥ¼", - "鼻 話", - "à¹Ĥ à¸ŀ", - "ov ky", - "Ġза мÑĸ", - "Ġзам Ñĸ", - "Ġव à¤ķ", - " Ļ", - "ĠвÑĸй ни", - "Ġol madıģı", - "Ġolm adıģı", - "Ġolmadı ģı", - "Ġolma dıģı", - "æ¢ ģ", - "ĠТ еп", - "ĠТе п", - "nÄĽ te", - "nÄĽt e", - "èħ ķ", - "ìĤ¬ ëĬĶ", - "m amak", - "ma mak", - "mam ak", - "Ġc iz", - "Ġci z", - "æ£ Ĵ", - "Ġ ï¼ı:", - "Ġï¼ı :", - "éģĭ åĭķ", - "ĠÙĩ ÙĨا", - "ĠÙĩÙĨ ا", - "Ġ ê°ij", - "Ġê° ij", - "ĠÙĩÙĨÚ¯ اÙħ", - "ĠuÄŁ ra", - "å½ ¦", - "Ġob jekt", - "Ġobj ekt", - "ãģ¨ ãģĻãĤĭ", - "åĽ½ åĨħ", - "ĠдеÑĢжав и", - "ĠдеÑĢж ави", - "Ġ èĮ", - "Ġè Į", - "Ġulus lararası", - "Ù £", - "Ġmut lak", - "Ġз обов", - "Ġ γεν", - "Ġγ εν", - "Ġγε ν", - "à¹Ħà¸Ł à¸Ł", - "Ġözg ür", - "íĦ ¸", - "Ġвипад кÑĥ", - "Ġà¤ķ ब", - "ĠاÙĦ خط", - "ĠاÙĦØ® Ø·", - "θη καν", - "ï¼Į æĬĬ", - "ÑıÑĤ ÑĤÑı", - "Ġolmadı ģını", - "Ġolma dıģını", - "Âłk W", - "ĠnÄĽkter ých", - "ãĥĩ ãĥ«", - "æ¤į çī©", - "μι λοÏĤ", - "ÐIJÑĢ ÑħÑĸв", - "ĠТ о", - "èĸ ¬", - "ÑģÑĤв иÑı", - "Ġ Ø®ÙĪاست", - "ĠØ®ÙĪ است", - "олог ÑĸÑĹ", - "ÙĪ اÙĩد", - "ÙĪاÙĩ د", - "ÙĪا Ùĩد", - "Ġ нак", - "Ġн ак", - "Ġна к", - "ĠкоÑĤоÑĢ ÑĥÑİ", - "Ġद à¤ķ", - "âĢŀ M", - "λ ια", - "λι α", - "æŃ ²", - "第 åĽĽ", - "à¤¾à¤ľ स", - "Ġ( «", - "Ġth ẻ", - "､ Ċ", - "ç£ ģ", - "Ġ ÙĦÙĤ", - "ĠÙĦ ÙĤ", - "Ġ ìķĶ", - "Ġìķ Ķ", - "Ġн ового", - "Ġнов ого", - "ĠìķĦ 주", - "Ġ ëIJĺìĸ´", - "ĠëIJĺ ìĸ´", - "Ġo lun", - "Ġol un", - "à ¾", - "Ġkar iy", - "Ġkari y", - "ĠØŃ Ø³Ø¨", - "ĠØŃس ب", - "ĠìĿĺ 미", - ". Ðľ", - "Ġoz naÄį", - "ÙĦ سÙĦ", - "ÙĦس ÙĦ", - "ĠÐĴ ид", - "ĠÐĴи д", - "ë¡ľ ëĤĺ", - "à¥įà¤Ł म", - "í ľ´", - "Ġbilg isayar", - "ìĿ¸ ì§Ģ", - "Ġв ов", - "Ġво в", - "nict vÃŃm", - "nictvÃŃ m", - "า à¸Ńย", - "าà¸Ń ย", - "Ġشخص ÛĮ", - "п Ñĸон", - "æľ¬ å½ĵ", - "Ġب ÙĢ", - "ĠмаÑģ ло", - "ĠPh át", - "Ġ ба", - "Ġб а", - "алÑĮ номÑĥ", - "алÑĮно мÑĥ", - "алÑĮном Ñĥ", - "社 åĮº", - "Ġ Ò", - ": ::|", - ":: :|", - "::: |", - "ê ´", - "Ġ ä¸ĥ", - "Ġä¸ ĥ", - "ĠÙĪ اÙĦد", - "ĠÙĪاÙĦ د", - "ни ке", - "ник е", - "à¸Ń ลล", - "à¸Ńล ล", - "Ġyer leÅŁ", - "Ġkom bin", - "Ġkomb in", - "u Å¡", - "Ġо ÑĤÑĢи", - "ĠоÑĤ ÑĢи", - "ä¹ Į", - "iÅŁ ti", - "Ġs óng", - "Ġsó ng", - "λ ηÏĤ", - "λη ÏĤ", - "Ġк ÑĥÑĢÑģ", - "ĠкÑĥÑĢ Ñģ", - "à¹Ī าà¸Ħ", - "à¹Īา à¸Ħ", - "Ġ ÙĬس", - "ĠÙĬ س", - "Ġ داÙħ", - "Ġد اÙħ", - "çĴ° å¢ĥ", - "Ñĩ енко", - "Ñĩен ко", - "ãĢį ãģ®", - "ĠmÃŃ sta", - "ĠmÃŃst a", - "ĠÑĦ оÑĤ", - "ĠpÅĻÃŃ zn", - "ĠÑĢ аза", - "ĠÑĢаз а", - "ĠÑĢа за", - "ç´ «", - "lá da", - "lád a", - "ĠÑģпеÑĨи алиÑģÑĤ", - "ĠبÛĮ ÙħارÛĮ", - "ĠبÛĮÙħ ارÛĮ", - "ĠبÛĮÙħار ÛĮ", - "Ġëĵ £", - "çĭ Ĺ", - "ÙĪ ÙĪ", - "ан ÑĸÑĤ", - "анÑĸ ÑĤ", - "ĠدÙĨ باÙĦ", - "ĠÙħجÙħÙĪع Ø©", - "ÃŃ na", - "ÃŃn a", - "ĠH alk", - "ĠHa lk", - "ĠHal k", - "á jem", - "áj em", - "enÃŃ ze", - "Ġim z", - "« ng", - "Ġ ÎķÎł", - "ĠÎķ Îł", - "ĠÙħ Ùĩد", - "ĠÙħÙĩ د", - "ìľĦìĽIJ íļĮ", - "Ġìľł íĺķ", - "ाप स", - "Ġje ž", - "ан Ñĸз", - "анÑĸ з", - "иÑĤ ай", - "иÑĤа й", - "á¿ ĸ", - "ir ler", - "irl er", - "기 ê°Ħ", - "Ġ воÑĢ", - "Ġв оÑĢ", - "Ġво ÑĢ", - "Ġ Ïİ", - "ĠÏ İ", - "Ġpo zn", - "Ġpoz n", - "Ġ ساÙĨ", - "Ġس اÙĨ", - "å ¯¿", - "å¯ ¿", - "æĸ¯ çī¹", - "Ġtu rist", - "Ġtur ist", - "ĠìŀIJ ìľł", - "à¥Ģ à¤ĸ", - "μ με", - "μμ ε", - "an sı", - "ans ı", - "ìĨĮ ëħĦ", - "Ġted avi", - "Ġtedav i", - "Ñĩ еÑģÑĤва", - "Ñĩе ÑģÑĤва", - "å£ ĵ", - "о ве", - "ов е", - "ï¼Į çľĭ", - "ĠпоÑģл Ñĥг", - "ĠпоÑģ лÑĥг", - "ĠÑĤÑĢ анÑģ", - "ĠÑĤÑĢан Ñģ", - "Ġz áz", - "Ġzá z", - "æĪ ´", - "Ġм она", - "Ġмон а", - "Ġмо на", - "ิ à¹Ģà¸Ħราะห", - "ĠÙĨ ÛĮÙħ", - "ĠÙĨÛĮ Ùħ", - "ĠìĤ¬ëŀĮ ìĿ´", - "a hat", - "ah at", - "aha t", - "Ïħ κ", - "ĠоÑĤ каз", - "ĠоÑĤк аз", - "ĠÐĴолод ими", - "ĠС к", - "िà¤ķ त", - "å¦ ĸ", - "Ġëĭ¤ìļ´ ë¡ľëĵľ", - "ìĺģ ìĥģ", - "Ġन à¤Ī", - "c ete", - "ce te", - "ĠгÑĢи б", - "ĠгÑĢ иб", - "ece ÄŁini", - "eceÄŁi ni", - "Ġç oÄŁu", - "ĠçoÄŁ u", - "ĠмаÑĤеÑĢи ала", - "ĠмаÑĤеÑĢиал а", - "ứ t", - "Ġz aten", - "Ġza ten", - "Ġzat en", - "ĠF RA", - "ĠFR A", - "ĠBir liÄŁi", - "Ġs itesi", - "Ġsit esi", - "Ġsite si", - "Ġsites i", - "Ġ åĶ", - "Ġå Ķ", - "ĠÐĴ ол", - "ĠÐĴо л", - "Âł PS", - "ÂłP S", - "ा लत", - "ाल त", - "Ġ баÑĩ", - "Ġб аÑĩ", - "Ġба Ñĩ", - "алÑĸ заÑĨÑĸÑĹ", - "алÑĸз аÑĨÑĸÑĹ", - "ĠS lov", - "ĠSl ov", - "ĠSlo v", - "ç³ ĸ", - "ĠговоÑĢ иÑĤ", - "Ġв вед", - "Ġвв ед", - "ุà¸ķ à¸ļà¸Ńล", - "ãģĨ ãģ¡", - "Ġyap tık", - "Ġyaptı k", - "Ġìłķ ì¹ĺ", - "ê°ľ 를", - "à¥Ī सल", - "à¥Īस ल", - "ج ÙĬÙĦ", - "جÙĬ ÙĦ", - "ĠзаÑģÑĤоÑģ ов", - "è¿ «", - "ĠKur ul", - "ĠNas ıl", - "ĠнапÑĢÑı м", - "Ġ ä½į", - "à¹Į à¸ļ", - "Ġ éģĵ", - "Ġéģ ĵ", - "Ġни же", - "Ġниж е", - "Ġк оÑģÑĤ", - "Ġко ÑģÑĤ", - "ĠкоÑģ ÑĤ", - "ظ Ùĩر", - "ظÙĩ ر", - "Т а", - "ì§ Ŀ", - "Ġön ünde", - "ж Ñĸ", - "Ġاجر اÛĮ", - "Ġاجرا ÛĮ", - "ĠоÑĢган Ñĸв", - "ĠоÑĢганÑĸ в", - "v ise", - "vis e", - "vi se", - "Ġ ìĿĦ", - "ĠìĿ Ħ", - "à¸ķ รà¸ĩ", - "à¸ķร à¸ĩ", - "Ú©ÙĨ ÙĪÙĨ", - "Ġdlou ho", - "Ġdlouh o", - "Ðŀ ÐĿ", - "Ġ ìľ¡", - "Ġìľ ¡", - "缮 æłĩ", - "ë¯ Ģë¡ľ", - "ï¼ıï¼ıï¼ıï¼ı ï¼ıï¼ıï¼ıï¼ı", - "ĠпоÑĩ емÑĥ", - "æķħ äºĭ", - "ÑĤ еÑģÑĮ", - "ÑĤе ÑģÑĮ", - "ĠÙĤ ÙĦب", - "ĠÙĤÙĦ ب", - "Ġت جÙĩ", - "Ġتج Ùĩ", - "i lendir", - "il endir", - "ilen dir", - "Ġи гÑĢа", - "Ġиг ÑĢа", - "ĠÐĶ он", - "ĠÐĶо н", - "ĠpÅĻÃŃ jem", - "è¦ Ĩ", - "С п", - "- ни", - "on se", - "ons e", - "и ной", - "ин ой", - "о Ñĩного", - "оÑĩ ного", - "оÑĩно го", - "ا ساÙĨ", - "اس اÙĨ", - "ĠполÑĥÑĩ иÑĤÑĮ", - "ĠполÑĥ ÑĩиÑĤÑĮ", - "ÑĤ ап", - "ÑĤа п", - "ĠL ý", - "ĠÃĤ u", - "Ġh üc", - "e bek", - "eb ek", - "ebe k", - "ĠY ayın", - "æĹ ĭ", - "ัà¸Ļ à¸Ĺร", - "ัà¸Ļà¸Ĺ ร", - "Ġвикон ав", - "Ġвико нав", - "Ġs ông", - "à¥ģ à¤ľ", - "à¥ģठľ", - "ĠÐĹ Ð°Ð³", - "ĠÐĹа г", - "¤ ëĭ¤", - "Ġc Å©", - "ĠÚ¯ رÙħ", - "Ġگر Ùħ", - "ä¼ ı", - "ãģ« ãģĻãĤĭ", - "- Ф", - "Ġ ÙĤÙħ", - "ĠÙĤ Ùħ", - "Ġo lacaÄŁ", - "Ġol acaÄŁ", - "æĿ¥ äºĨ", - "æĭĽ èģĺ", - "ĠÐĿаÑģ еленнÑı", - "Ġ ìĺģìĸ´", - "Ġìĺģ ìĸ´", - "Ġ æѤ", - "ĠæŃ ¤", - "Ġب دÙĨ", - "Ġبد ÙĨ", - "Û² Û¸", - "оÑĢ аÑĤив", - "оÑĢа ÑĤив", - "ï¼ ³", - "Ġneby lo", - "Ġnebyl o", - "ĠÑĥ ÑĩиÑĤ", - "ĠÑĥÑĩ иÑĤ", - "æĿ ľ", - "Ġд анÑĸ", - "Ġда нÑĸ", - "Ġдан Ñĸ", - "Ġsp otÅĻeb", - "Ġspot ÅĻeb", - "ãĥ¼ ãĥĨãĤ£", - "ãĥ¼ãĥĨ ãĤ£", - "ен нÑĥÑİ", - "ê¹Į ìļĶ", - "v em", - "ve m", - "P ÅĻÃŃ", - "PÅĻ ÃŃ", - "Ġy andan", - "Ġyan dan", - "é¼ ĵ", - "Ġدست ÙĪر", - "Ġدس تÙĪر", - "Ġhaf if", - "h ů", - "Ġv áž", - "Ġvá ž", - "ĠìķĦ ì§ģ", - "Ùı ر", - "Ġ ла", - "Ġл а", - "ëł ī", - "า à¸Ľà¸£à¸°", - "à¸²à¸Ľ ระ", - "lık lar", - "lıkla r", - "ĠÑģÑĤанд аÑĢÑĤ", - "à¸Ń à¹ĥห", - "å¥ ´", - "ĠоÑĤ п", - "âĪ ł", - "ãĥ¼ ãĥĢ", - "ãĥ¼ãĥ Ģ", - "ch áze", - "cház e", - "Ġê·¸ ëłĩê²Į", - "Ġê·¸ëłĩ ê²Į", - "os tel", - "ost el", - "oste l", - "Ġгал Ñĥз", - "â k", - "еÑĨ ÑĤ", - "ëŀij ìĬ¤", - "ĠÄį ist", - "ĠÄįi st", - "ÑĢ ана", - "ÑĢа на", - "ÑĢан а", - "Ġv ững", - "Ġvữ ng", - "Ġs eni", - "Ġse ni", - "Ġsen i", - "Ġg óc", - "Ġgó c", - "ÏĨ ÏĮ", - "á nu", - "án u", - "Ġ öt", - "Ġö t", - "Ġs óc", - "Ġsó c", - "ãģĦ ãģ®", - "ĠÑģк лада", - "ĠÑģклад а", - "ÐIJÑĢÑħÑĸв овано", - "ĠìĿ´ ë²Ī", - "ãĤ¹ ãģ®", - "il ebilir", - "ile bilir", - "ï½Ģ ãĥ½", - "ีย à¸į", - "Ġκα ὶ", - "Ġë ¯¿", - "æĽ´ å¤ļ", - "ıs ının", - "ısını n", - "ĠGi ám", - "ĠGiá m", - "æŃ£ å¼ı", - "Ïĥ μÏĮ", - "Ïĥμ ÏĮ", - "Ġarch it", - "Ġarc hit", - "Ġ ï½²", - "Ġï½ ²", - "Ñĩ аÑİÑĤÑģÑı", - "Ñĩа ÑİÑĤÑģÑı", - "ÑĩаÑİÑĤ ÑģÑı", - "ë²Ħ ì§Ģ", - "ãĤ¤ ãĥ¤", - "é«ĺ æł¡", - "è¨ ³", - "ĠÙħ ÛĮÚ©", - "ĠÙħÛĮ Ú©", - "Ġ æĥħ", - "Ġæĥ ħ", - "Ġ pha", - "Ġp ha", - "Ġph a", - "太 éĥİ", - "à¸ŀระ ราà¸Ĭ", - "ÙĤ ÙĬØ©", - "ÙĤÙĬ Ø©", - "ĠÑĥ лÑĥÑĩ", - "ĠÑĥл ÑĥÑĩ", - "ÑģÑĤв ÑĥеÑĤ", - "ÑģÑĤвÑĥ еÑĤ", - "Ġk eÅŁ", - "Ġke ÅŁ", - "é«ĺ çŃī", - "Ġs Ỽm", - "Ïģ κε", - "Ïģκ ε", - "μ οÏģ", - "μο Ïģ", - "Ġzá stup", - "o zÃŃ", - "oz ÃŃ", - "Ġm ili", - "Ġmil i", - "Ġmi li", - "Ġмог ли", - "Ġз ÑĢозÑĥм", - "Ġباش ÛĮد", - "Ġak ci", - "Ġд ÑĢа", - "ĠдÑĢ а", - "Ġα Ïģι", - "ĠαÏģ ι", - "ãģĭ ãĤīãģ®", - "ãģĭãĤī ãģ®", - "å¯ Ĵ", - "ĠZ aman", - "ĠZa man", - "ĠZam an", - "ĠÑĸ де", - "ĠÑĸд е", - "Ġ ãĢĢĠ", - "ĠãĢĢ Ġ", - "Ġk lu", - "Ġkl u", - "ak lı", - "à¥ĩ à¤ļ", - "à¥ĩठļ", - "ĠÑģвоб од", - "س اÙħ", - "Ġ ов", - "Ġо в", - "Ġu byt", - "Ġub yt", - "éĩĩ çĶ¨", - "Ġdavran Ä±ÅŁ", - "ĠnabÃŃ zÃŃ", - "ĠÐij Ñĥд", - "Ġ Ïī", - "ĠÏ ī", - "ĠاÙĦ رØŃ", - "ĠاÙĦر ØŃ", - "ั à¸ķà¸Ļ", - "ัà¸ķ à¸Ļ", - "и ме", - "им е", - "Ġت ÙĦÙĥ", - "ĠتÙĦ Ùĥ", - "ت Ùħع", - "تÙħ ع", - "Ġад миниÑģÑĤÑĢа", - "Ġzor unda", - "Ġzorun da", - "ĠÙĨ سبة", - "ĠÙĨسب Ø©", - "ĠÙĨس بة", - "ĠصÙĨع تÛĮ", - "ĠصÙĨعت ÛĮ", - "ĠÑĦÑĥн да", - "éı ¡", - "Ġpo tom", - "Ġpot om", - "Ġп ÑĢеÑģÑĤ", - "ĠпÑĢ еÑģÑĤ", - "ĠпÑĢе ÑģÑĤ", - "ĠпÑĢеÑģ ÑĤ", - "Ġsı rada", - "Ġsır ada", - "Ġsıra da", - "Ġ ayar", - "Ġa yar", - "Ġay ar", - "ا ÙĤÙĦ", - "اÙĤ ÙĦ", - "æº ª", - "ĠØ¢ÙĤ اÛĮ", - "ĠпеÑĢе Ñħод", - "ĠпÑĢакÑĤи ÑĩеÑģки", - "é» ĥ", - "ĠÑĥ Ñħод", - "ĠÑĥÑħ од", - "ĠÙħ تÙģ", - "ĠÙħت Ùģ", - "Ġsiyas i", - "Ġпо ÑĤен", - "ĠпоÑĤ ен", - "Ùİ Ùģ", - "ĠÐĽ Ñĥ", - "ĠконÑĤÑĢ олÑĮ", - "ĠÑģказ аÑĤÑĮ", - "à¥Ģ à¤ķरण", - "à¥Ģà¤ķ रण", - "åħ¨ çIJĥ", - "Û² Û¶", - "Ġt oto", - "Ġto to", - "Ġtot o", - "Ġ ÙĪد", - "ĠÙĪ د", - "ãĤ¿ãĤ¤ ãĥĹ", - "åľ į", - "å¼ķ çĶ¨", - "ï¼ £", - "èĬ ¸", - "ä»ĭ ç»į", - "ĠÑĤеÑĢÑĢиÑĤоÑĢ ии", - "æĹ¥ ãģ«", - "m ÃŃt", - "mÃŃ t", - "am ız", - "amı z", - "ìĿ´ ìĸ´", - "Ġyar Ä±ÅŁ", - "Ġyarı ÅŁ", - "Ġg üc", - "Ġgü c", - "Ġ Ïĩα", - "ĠÏĩ α", - "ัà¸Ļย ายà¸Ļ", - "ãĤĴ è¡Į", - "Ġm illi", - "Ġmill i", - "Ġmil li", - "Ġmi lli", - "Ġ çı¾", - "Ġçı ¾", - "K dyž", - "m azon", - "ma zon", - "maz on", - "ë³´ ëĤ´ê¸°", - "ĠÑĤÑĢÑĥ дов", - "ĠÑĤÑĢÑĥд ов", - "é£ ¾", - "Ġви ник", - "Ġвин ик", - "ĠÙĪز ارت", - "ĠÙĪزار ت", - "éĩĮ çļĦ", - "м аз", - "ма з", - "ĠR US", - "ĠRU S", - "е кÑĤÑĥ", - "ек ÑĤÑĥ", - "екÑĤ Ñĥ", - "Ġع اش", - "Ġk once", - "Ġko nce", - "Ġkon ce", - "ãĤĪãģĨ ãģ§ãģĻ", - "Ġмал ÑĭÑĪ", - "m eni", - "me ni", - "men i", - "е Ñģа", - "еÑģ а", - "ا ضÛĮ", - "اض ÛĮ", - "Ġb rat", - "Ġbr at", - "Ġbra t", - "ĠвÑĸд ноÑģ", - "θ εÏģ", - "θε Ïģ", - "ĠЧ ем", - "æij ĩ", - "ĠÙħ ادر", - "ĠÙħا در", - "ĠÙħاد ر", - "çĶ¨ åĵģ", - "ĠÙħØŃ Ø§Ùģظ", - "Ġm yÅ¡", - "Ġmy Å¡", - "ج ع", - "Ġis im", - "Ġi sim", - "Ġisi m", - "æ³ Ĭ", - "ıl maz", - "ĠÎĽ α", - "å¯ ©", - "Ġay ır", - "е ними", - "ен ими", - "ени ми", - "еним и", - "à¥ĩह तर", - "åľ Ĩ", - "ãģ¾ ãģ£ãģŁ", - "çĶ¢ åĵģ", - "ĠÑĸнÑĦоÑĢма ÑĨÑĸÑĹ", - "Ġt ủ", - "Ġtá» §", - "สม à¸ļ", - "Ġst ÅĻ", - "Ġë°ľ íijľ", - "а ÑĢÑĮ", - "аÑĢ ÑĮ", - "ĠC ao", - "ĠCa o", - "Ρ ÎĻ", - "à¸ģาร à¸Ī", - "Ġпод Ñĥм", - "ä»ķ äºĭ", - "ĠÐļ ÑĢоме", - "Ġ ìĹĶ", - "ĠìĹ Ķ", - "Ġ Ñĥда", - "ĠÑĥ да", - "ĠÑĥд а", - "ĠавÑĤом аÑĤи", - "Ġ à¸Ħà¸ĵะ", - "Ġà¸Ħ à¸ĵะ", - "ĠK iÅŁ", - "ĠKi ÅŁ", - "ĠÑģоÑģÑĤоÑı ние", - "l isi", - "li si", - "lis i", - "Ġëĸ ¨ìĸ´", - "oot ball", - "Ġ íį¼", - "Ġí į¼", - "Ġ лим", - "Ġл им", - "Ġли м", - "Ġç erç", - "ÙĪÙĦ ÙĬÙĪ", - "ÙĪÙĦÙĬ ÙĪ", - "Ġs lož", - "Ġsl ož", - "Ġslo ž", - "Ġ 먼", - "Ġë¨ ¼", - "ร à¸Ńà¸ĩ", - "ÑĪ ее", - "ÑĪе е", - "âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦", - "ãģĵ ãģ¡ãĤī", - "о ÑĢÑĭ", - "оÑĢ Ñĭ", - "çĥ Ł", - "Âł F", - "а ного", - "ан ого", - "ано го", - "Ø« ÛĮر", - "çı į", - "å¸Ĥ åł´", - "vÄĽ dom", - "vÄĽd om", - "ì² ¨ë¶Ģ", - "ĠìĤ¬ ê±´", - "ï¾ Į", - "à¹ĥ à¸Ļว", - "à¹ĥà¸Ļ ว", - "Ġzvlá Å¡t", - "ÏĦ εÏħ", - "ÏĦε Ïħ", - "Ġкак ие", - "Ġка кие", - "ÏĨοÏģ ά", - "ÏĨο Ïģά", - "åĦ Ħ", - "Ġzp ÄĽt", - "íķľ íħĮ", - "Ġz vol", - "Ġzv ol", - "Ġ çĹ", - "Ġç Ĺ", - "ÑĢа нениÑı", - "ÑĢан ениÑı", - "ĠسÛĮ است", - "ĠÐļ оли", - "ĠÐļол и", - "ĠÐļо ли", - "ĠоÑĢганиз ма", - "ĠоÑĢганизм а", - "ĠÑıнва ÑĢÑı", - "Ġد ادÙĨ", - "Ġداد ÙĨ", - "п ÑĢа", - "пÑĢ а", - "ï¼Į ä»ĸ们", - "ï¼Įä»ĸ 们", - "æijĺ è¦ģ", - "Ġqu ần", - "ÙĬ ÙĪÙĨ", - "ÙĬÙĪ ÙĨ", - "Ġви Ñħов", - "Âł à¹Ģà¸Ķ", - "Ġ елем", - "Ġе лем", - "eb ilecek", - "Ġд оÑĩ", - "Ġдо Ñĩ", - "Ġб лаг", - "Ġбл аг", - "Ġбла г", - "ĠÑı й", - "ad nÃŃ", - "Ġzá roveÅĪ", - "en stvÃŃ", - "âĢĮ اÙĨ", - "ãģķãĤĵ ãģ¯", - "/ |", - "ĠاÙĦع اÙħØ©", - "ĠاÙĦعاÙħ Ø©", - "éł ¼", - "Ġخدا ÙĪÙĨد", - "Ġخد اÙĪÙĨد", - "н ам", - "на м", - "ĠÑģ лиз", - "ĠÑģл из", - "æ¶ ī", - "ร ษ", - "e ÅŁtir", - "eÅŁ tir", - "ĠÙĨ دار", - "ĠÙĨد ار", - "ร าà¸Ħ", - "è¨Ģ ãĤı", - "Ġ èŃ", - "Ġè Ń", - "Ġк ÑĢиÑĤ", - "ĠкÑĢи ÑĤ", - "ĠвоздÑĥ Ñħа", - "ĠвоздÑĥÑħ а", - "Ġà¤Ĺ त", - "Ġprá vo", - "Ġpráv o", - "à¥ĭष ण", - "Ġs ắp", - "íı Ń", - "Ġص رÙģ", - "ĠراÛĮ گاÙĨ", - "ĠоÑĤ к", - "ëĨ ĵ", - "ĠÑģек ÑĢеÑĤ", - "Ä° n", - "on avir", - "ona vir", - "ĠV ys", - "ĠVy s", - "ĠbaÅŁ lat", - "ĠMu ham", - "ĠлиÑģÑĤ оп", - "ĠT icaret", - "ĠTi caret", - "ĠTic aret", - "Ġad landır", - "ĠÐĶ миÑĤ", - "Ïĥμ οÏį", - "Ïĥμο Ïį", - "ä¾ µ", - "ìĭľ ëĬĶ", - "à¹ģà¸Ľ ลà¸ĩ", - "ın ıza", - "ını za", - "ınız a", - "- г", - "и ÑĩноÑĹ", - "иÑĩ ноÑĹ", - "иÑĩно ÑĹ", - "Ñĥ ÑĢи", - "ÑĥÑĢ и", - "U Z", - "ìĽ ł", - "Ġتبد ÛĮÙĦ", - "æ º«", - "æº «", - "ĠÑĢам каÑħ", - "Ġn ét", - "Ġné t", - "æ² ¿", - "Ġroz Å¡ÃŃ", - "Ġस प", - "ĠÑĤак е", - "ĠÑĤа ке", - "ÑĢ аÑĩ", - "ÑĢа Ñĩ", - "ĠاÙĦ ÙĤد", - "ĠاÙĦÙĤ د", - "íķĻ ê³¼", - "Ñĥв аннÑıм", - "ÑĥваннÑı м", - "Ġm ám", - "Ġmá m", - "ë¡ ¯", - "á½ IJ", - "Ġet kili", - "Ġetk ili", - "Ġetkil i", - "Ġetki li", - "Ġار تÙģ", - "Ġtechn olog", - "Ġtechno log", - "Ġ ì½ĺ", - "Ġì½ ĺ", - "Ġت ÙĥÙĬÙĬÙģ", - "ĠpÅĻ ece", - "ĠpÅĻe ce", - "家 åºŃ", - "Ġ ãģı", - "âĶ ´", - "íģ ¼", - "ĠÎľ ά", - "à¹Ģ à¸ķร", - "à¹Ģà¸ķ ร", - "ĠÑģÑĤанов иÑĤÑģÑı", - "ç«ĭ ãģ¡", - "Ġ éĸĭ", - "Ġéĸ ĭ", - "ĠÄ° yi", - "ĠnÄĽkter é", - "ĠÑĢоб оÑĤ", - "ĠÄIJ Æ°á»Ŀng", - "ĠاÙĦ اج", - "Ġsp eci", - "Ġspec i", - "Ġspe ci", - "çī¹ åĪ«", - "åŃ Ŀ", - "âĢĮ گذ", - "âĢĮÚ¯ Ø°", - "a lıģı", - "al ıģı", - "alı ģı", - "Ġм иÑĢа", - "Ġми ÑĢа", - "ĠмиÑĢ а", - "í İĺìĿ´ì§Ģ", - "íİĺ ìĿ´ì§Ģ", - "Ø® Ùģ", - "ãĤª ãĥª", - "Ġس ÛĮÙħ", - "ĠسÛĮ Ùħ", - "Ġìĸ´ ëĬIJ", - "алÑĮ нÑĥ", - "Ñĩ ний", - "ümüz de", - "æĻº èĥ½", - "ý n", - "ĠتÙĤÙĪ ÛĮت", - "Ġп ÑĢиг", - "ĠпÑĢ иг", - "ĠпÑĢи г", - "ĠгÑĢÑĥпп Ñĭ", - "am ı", - "γ οÏį", - "γο Ïį", - "оÑĢ ÑĤÑĥ", - "оÑĢÑĤ Ñĥ", - "ĠG iang", - "ĠGi ang", - "ĠGian g", - "ĠGia ng", - "ÅĻ en", - "ÅĻe n", - "Ġokol ÃŃ", - "产 ä¸ļ", - "Ġ зм", - "Ġз м", - "Ġ é¾", - "Ġé ¾", - "ÙĬ ار", - "ÙĬا ر", - "ĠاÙĦØ´ÙĬ Ø®", - "иÑĤелÑĮ нÑĭй", - "Ġ اÙĩÙħ", - "Ġا ÙĩÙħ", - "ĠاÙĩ Ùħ", - "ĠباÙĦ رÙĬاض", - "ĠÙ¾ÛĮ اÙħ", - "Ġk redi", - "Ġkr edi", - "Ġkre di", - "Ġkred i", - "ĠA rap", - "ĠAr ap", - "ĠAra p", - "Ġ ÑĢаб", - "ĠÑĢ аб", - "ĠÑĢаР±", - "ĠÑĢа б", - "ĠнекоÑĤоÑĢ ÑĭÑħ", - "ĠØŃاÙģظ Ùĩ", - "иÑĤелÑĮ ного", - "иÑĤелÑĮно го", - "Ġgerek mektedir", - "ĠD eniz", - "ĠDen iz", - "ĠتÙĦ اش", - "st agram", - "sta gram", - "stag ram", - "áv ky", - "åĬł åħ¥", - "oz or", - "ozo r", - "Ġdurum unda", - "Ġdurumu nda", - "Ġíıī ëĭ¹", - "Ġ ë´ī", - "Ġë´ ī", - "Ġp enÄĽ", - "Ġpe nÄĽ", - "Ġpen ÄĽ", - "Ú¯ اÙĨÛĮ", - "گاÙĨ ÛĮ", - "ĠK up", - "ĠKu p", - "Ġ ÑĨеÑĢ", - "ĠÑĨ еÑĢ", - "ĠÑĨе ÑĢ", - "ul ması", - "âij ł", - "ĠÑģÑĸÑĩ нÑı", - "ım ıza", - "ımız a", - "ımı za", - "å®ļ çļĦ", - "Âł ÑĤ", - "åĬŀ åħ¬", - "ìľ¼ ëĭĪ", - "ĠاÙĦ Ø¥ÙĨ", - "ĠاÙĦØ¥ ÙĨ", - "Ġ çĥ", - "Ġç ĥ", - "ãĢį ï¼Į", - "ÑĹ Ð½Ð°", - "ĠпÑĢигоÑĤов лениÑı", - "Ð ħ", - "ĠÑģ олн", - "ĠÑģол н", - "Ġë¶Ģ ìĤ°", - "æħ ¶", - "ãĤ ¾", - "v oje", - "vo je", - "voj e", - "ÛĮ دÙĨ", - "ÛĮد ÙĨ", - "ìĥĿ ëĭĺ", - "ç¹ ģ", - "á du", - "ád u", - ": ::::::::::::::", - ":: :::::::::::::", - ":::: :::::::::::", - ":::::: :::::::::", - ":::::::: :::::::", - "::: ::::::::::::", - "::::: ::::::::::", - "::::::: ::::::::", - "::::::::: ::::::", - ":::::::::: :::::", - "::::::::::: ::::", - ":::::::::::: :::", - "::::::::::::: ::", - ":::::::::::::: :", - "س ÙĨÚ¯", - "سÙĨ Ú¯", - "éĶ ĭ", - "Ġ звиÑĩай", - "Ġз виÑĩай", - "å§Ķåijĺ ä¼ļ", - "ĠμÎŃ Ïĥα", - "ĠÑĢ ождениÑı", - "æĪIJ 人", - "Ġ dÃŃl", - "Ġd ÃŃl", - "ĠdÃŃ l", - "ĠÐĶ об", - "ĠÐĶо б", - "Ġ à¹ĥà¸Ĭ", - "ÏĢ ί", - "g amber", - "gam ber", - "ĠÙĪÛĮÚĺ Ú¯ÛĮ", - "Ġ èĬ±", - "ĠèĬ ±", - "Ġb Ãły", - "ĠbÃł y", - "ĠжовÑĤ нÑı", - "åħ¬ å¼Ģ", - "ĠÑĤ оÑĩки", - "ĠÑĤо Ñĩки", - "ĠÑĤоÑĩ ки", - "ãģĤ ãģ®", - "а лÑĸв", - "ал Ñĸв", - "алÑĸ в", - "Ġch arakter", - "Ġchar akter", - "ĠÎĴ α", - "Ġzku Å¡en", - "Ġà¤Ńà¤Ĺ व", - "Ñĩ ика", - "Ñĩи ка", - "Ñĩик а", - "à¥Ģà¤Ĥ ।", - "è£ ı", - "åijĬ è¯ī", - "iy atı", - "iya tı", - "iyat ı", - "ĠÑĨ елÑĮ", - "ĠÑĨе лÑĮ", - "ĠÑĨел ÑĮ", - "Ġ ìĬĪ", - "ĠìĬ Ī", - "а ÑĢд", - "аÑĢ д", - "ĠÃľl ke", - "Ġpro since", - "Ġpros ince", - "ĠÙĨ گاÙĩ", - "ĠÙĨÚ¯ اÙĩ", - "ãĢĮ ãģĬ", - "ÎŁ Τ", - "ÎŁÎ ¤", - "ìĦľ ëĬĶ", - "ÙĪ گر", - "ض اÙĨ", - "ضا ÙĨ", - "Ġdů sled", - "çIJ ´", - "à¸ķำ à¹ģหà¸Ļ", - "к ÑĤÑĸв", - "кÑĤ Ñĸв", - "lád á", - "á¿ Ĩ", - "ĠD oÄŁu", - "ĠDo ÄŁu", - "ĠDoÄŁ u", - "ãģij ãĤĮãģ°", - "缮 ãĤĴ", - "Ġ 缴", - "ĠçĽ ´", - "æ Ľ°", - "æĽ °", - "ĠвÑĤоÑĢ ой", - "Ġг лÑĥ", - "ĠìĿ ½", - "기 ì¤Ģ", - "Ġma dde", - "Ġmad de", - "Ġmadd e", - "Ġjed né", - "Ġjedn é", - "Ġо ÑĦÑĸ", - "ìĭĿ ìĿĦ", - "Ġch út", - "Ġchú t", - "åĩº ãģĹãģŁ", - "åĩºãģĹ ãģŁ", - "и ÑĩеÑģкаÑı", - "иÑĩеÑģ каÑı", - "Ġ лок", - "Ġл ок", - "Ġal tı", - "Ġalt ı", - "ëĵľ ëĬĶ", - "ey gamber", - "ĠÑģв ое", - "ĠÑģво е", - "ĠtaÅŁ ım", - "ĠÑĤо Ñīо", - "Ġgeç ti", - "Ġpr emi", - "Ġpre mi", - "Ġprem i", - "ĠMeh met", - "ï¼Į åĽłæѤ", - "ï¼ĮåĽł æѤ", - "ί κη", - "ίκ η", - "Ġönce ki", - "Ġönc eki", - "Ġ à¤ķन", - "Ġà¤ķ न", - "ĠÑĤемп еÑĢаÑĤÑĥÑĢа", - "ĠÑĤемпеÑĢаÑĤÑĥ ÑĢа", - "éĺ ´", - "Ġìĸ¼ ë§Ī", - "Ø´ ب", - "á ky", - "ák y", - "ãĢĢ V", - "воÑĢ еннÑı", - "l asyon", - "las yon", - "Ġд оказ", - "Ġдо каз", - "Ġдок аз", - "Ġëľ »", - "Ġоб лиÑĩ", - "Ġобл иÑĩ", - "ÎĻ ÎijÎļ", - "ÎĻÎij Îļ", - "Ġ ÑĢазд", - "ĠÑĢаз д", - "ĠÑĢа зд", - "ï¼Į 为", - "å® ½", - "Ġk orum", - "Ġko rum", - "Ġkor um", - "åķĬ åķĬ", - "ĠÅĻe kla", - "ĠÅĻekl a", - "ĠÅĻek la", - "ãĥĹ ãĥ¬", - "Ġв аÑĢÑĤ", - "ĠваÑĢ ÑĤ", - "ĠпÑĢоблем Ñĭ", - "Ġ ä½ł", - "Ġth Æ¡m", - "Ġta kové", - "Ġtak ové", - "Ġtako vé", - "л енÑĭ", - "лен Ñĭ", - "ле нÑĭ", - "Ġ åĪ¶", - "ĠåĪ ¶", - "Ġji ných", - "Ġjin ých", - "Ġjiný ch", - "ĠÙĨ ص", - "ĠгÑĢÑĥд нÑı", - "Ġ ãģĹ", - "иÑĤелÑĮ ной", - "иÑĤелÑĮно й", - "ĠاØŃ ØªÙħ", - "Ñİ ÑĢ", - "ÏĨ Ïħ", - "Ġ Ø´ÙħاÙĦÛĮ", - "ĠØ´Ùħ اÙĦÛĮ", - "ĠØ´Ùħا ÙĦÛĮ", - "ĠØ´ÙħاÙĦ ÛĮ", - "Ġ ì»´", - "Ġì» ´", - "acaÄŁ ız", - "acaģı z", - "ì§Ģ ë§ī", - "ĠÑĦин анÑģов", - "Ġ ê·¹", - "Ġê· ¹", - "ĠÚĨ ÛĮزÛĮ", - "ĠÚĨÛĮز ÛĮ", - "à¥Ģ à¤Ľ", - "ص ات", - "ान म", - "Ġв озможно", - "Ġвозмож но", - "è¨ İ", - "çĦ ¦", - "ĠاÙĦبÙĦ د", - "Ġ çĶ³åįļ", - "ç¥ ¥", - "Ġë°Ķ ëĿ¼", - "Ú¯ ÛĮر", - "Ú¯ÛĮ ر", - "Ûµ Û°", - "μι οÏħÏģγ", - "ĠpÅĻed sed", - "ç»ı èIJ¥", - "å§ ij", - "e mey", - "em ey", - "eme y", - "ĠÙĨ ÙĪÙģ", - "ĠÙĨÙĪ Ùģ", - "å¾ ½", - "Ġprá va", - "Ġpráv a", - "Ġво обÑīе", - "Ġ íĭ°", - "Ġíĭ °", - "Ġب Ø£ÙĨ", - "Ġبأ ÙĨ", - "ĠFr anti", - "ĠFran ti", - "ĠP aÅŁa", - "ĠPa ÅŁa", - "ĠÙ¾ ست", - "Ġپس ت", - "k ân", - "ĠÑģиг н", - "Ġd ần", - "æ IJľ", - "æIJ ľ", - "Ġr oky", - "Ġro ky", - "Ġrok y", - "Ùĥ ÙĪر", - "ÙĥÙĪ ر", - "ĠÎĶ ή", - "али заÑĨии", - "ализ аÑĨии", - "ализа ÑĨии", - "ä¼ł å¥ĩ", - "ı da", - "lÃŃ b", - "ĠÑĢÑĸв нÑı", - "Ġн оÑı", - "Ġно Ñı", - "bÄĽ hu", - "bÄĽh u", - "ิà¸ĩห าà¸Ħม", - "ï¼Į åį´", - "Ġ ÑĩеÑģ", - "ĠÑĩ еÑģ", - "lan mÄ±ÅŁtır", - "lanmÄ±ÅŁ tır", - "Ġ Æ°á»Ľc", - "áv acÃŃ", - "ีฬ า", - "δ ÎŃ", - "âĢĮØ´ ÙĪÙĨد", - "Ġ ÑĢобÑĸÑĤ", - "ĠÑĢоб ÑĸÑĤ", - "Ġ å·´", - "Ġå· ´", - "ĠM ev", - "ĠMe v", - "ĠÙħرØŃ ÙĦÙĩ", - "Ġвз ÑĢоÑģ", - "ç½ ļ", - "Ġب اÙĦÙħ", - "Ġبا ÙĦÙħ", - "ĠباÙĦ Ùħ", - "Ġиз гоÑĤов", - "ĠS por", - "ĠSp or", - "ĠSpo r", - "åĦ Ģ", - "ĠاÙĦ Ø£ÙĨ", - "ĠاÙĦØ£ ÙĨ", - "à¹Īา à¸ĩà¸ģ", - "à¹Īาà¸ĩ à¸ģ", - "л аÑģÑĤи", - "ла ÑģÑĤи", - "лаÑģ ÑĤи", - "ÎŁ Îļ", - "ÎŁÎ ļ", - "Ġ Ú©ÛĮ", - "ĠÚ© ÛĮ", - "åij½ 令", - "ØŃ Ø¯Ø«", - "ØŃد Ø«", - "ÙĬ ÙĥÙĬ", - "ÙĬÙĥ ÙĬ", - "ĠпеÑĢв Ñĭй", - "ãĤ¹ ãĤ³", - "ĠÅ¡ pat", - "ĠÅ¡p at", - "Ġnik do", - "ั à¸ĩม", - "ัà¸ĩ ม", - "èµ «", - "æĺ ¨", - "Ġв Ñĥли", - "ĠвÑĥл и", - "ĠÐļ а", - "à¹Ī ละ", - "Ġsa mot", - "Ġsam ot", - "Ġsamo t", - "ĠобеÑģп е", - "ĠÙħعرÙģ ÛĮ", - "ĠÙħØŃصÙĪÙĦ ات", - "в анов", - "ва нов", - "ван ов", - "вано в", - "ĠÙħستÙĤ ÛĮÙħ", - "å¢ Ļ", - "Âł Ðļ", - "Ġд оÑĤ", - "Ġдо ÑĤ", - "z im", - "zi m", - "ÙIJ ر", - "Ġ Ø´ÙĪ", - "ĠØ´ ÙĪ", - "åľ¨ åľ°", - "Ġ çİ°", - "Ġçİ °", - "Ġ åĮĸ", - "ĠåĮ ĸ", - "ز ÙĪ", - "Ġyay gın", - "Ġо ÑĢиг", - "ĠоÑĢ иг", - "Ùı ÙĨ", - "Ġev rop", - "Ġ ï½ľ", - "Ġï½ ľ", - "Ġëħ¸ì¶ľ ëĵ±ë¡Ŀ", - "åĩ Ŀ", - "л еннÑĭÑħ", - "лен нÑĭÑħ", - "Ġje nom", - "Ġjen om", - "Ġ ЧÑĤобÑĭ", - "ĠЧ ÑĤобÑĭ", - "ĠЧÑĤо бÑĭ", - "ĠìĹĨ ëĭ¤", - "ĠìŬ ìĦ±", - "Ġres mi", - "im álnÃŃ", - "缮 ãģ®", - "s ian", - "si an", - "-ни бÑĥдÑĮ", - "ο κ", - "çĭ¬ ç«ĭ", - "ÅŁ ehir", - "åIJ IJ", - "åĪ¶ éĢł", - "Ġ ÎĶεν", - "ĠÎĶ εν", - "ĠÎĶε ν", - "ãĥĭ ãĥ¥", - "иÑĤелÑĮ нÑĭÑħ", - "Ġ ÙĥاÙħ", - "ĠÙĥ اÙħ", - "Ïģ κ", - "Ġr au", - "Ġra u", - "ĠÑģм еÑĢÑĤи", - "ĠÑģмеÑĢ ÑĤи", - "ĠÏĮ ÏĦαν", - "Ġ Tại", - "ĠT ại", - "Ġ رب", - "Ġر ب", - "ε νο", - "εν ο", - "ر دد", - "رد د", - "Ġ à¸ģระ", - "Ġà¸ģ ระ", - "Ġà¸ģร ะ", - "Ïĥ μο", - "Ïĥμ ο", - "Ġ æ¼Ķ", - "Ġæ¼ Ķ", - "ิà¸Ī à¸ģรรม", - "ĠÑĢаз ви", - "ĠÑĢазв и", - "ãĤ¹ ãĥļ", - "Ñĸ ÑĩноÑĹ", - "ÑĸÑĩ ноÑĹ", - "lá Å¡enÃŃ", - "láš enÃŃ", - "اب عة", - "ابع Ø©", - "ov ými", - "ový mi", - "ovým i", - "а нг", - "ан г", - "Ġкап ÑĸÑĤ", - "ãĢģ âĢĭ", - "íĸĪ ëįĺ", - "ĠÑĥ ÑģÑĸ", - "ĠÑĥÑģ Ñĸ", - "ย าว", - "ยา ว", - "Ø£ Ùħ", - "ãĥ© ãĥĥãĤ¯", - "Ġë ķ", - "ĠسÙĨ ÙĪات", - "ĠÑģÑĤаÑĤ ÑĮи", - "ĠÑģÑĤаÑĤÑĮ и", - "ÑĹ Ñħ", - "Ïģο Ïĩή", - "ĠØ£Ùĥ تÙĪبر", - "lan ma", - "Ġmal zem", - "ç £¨", - "ç£ ¨", - "Ġб окÑĥ", - "Ġбо кÑĥ", - "Ġбок Ñĥ", - "åŃĹ å¹ķ", - "ĠоÑĢганÑĸ за", - "ĠоÑĢганÑĸз а", - "ãĥ© ãĤ¤ãĥ³", - "ãĥ©ãĤ¤ ãĥ³", - "ĠÙħع دÙĨ", - "ĠÙħعد ÙĨ", - "çĶ· åŃIJ", - "Ġ æĤ", - "Ġæ Ĥ", - "Ạ¾", - "Ġmez iná", - "Ġmezi ná", - "и ваÑİÑĤ", - "ив аÑİÑĤ", - "ива ÑİÑĤ", - "ĠطبÛĮ عÛĮ", - "èĻ ij", - "à¤Ł र", - "Ġпод Ñģ", - "ĠÅŁ aÅŁ", - "à¸Ļ à¹Ĩ", - "ĠÅ¡ p", - "v ÄĽÅĻ", - "vÄĽ ÅĻ", - "з ÑĮ", - "ëĿ¼ ë§Ī", - "ุ à¸ĺ", - "âĢ¦ Ø·", - "리 ì§Ģ", - "âĦĸâĦĸ âĦĸâĦĸ", - "Ġb ức", - "ĠSp oj", - "ĠSpo j", - "ĠиÑģполÑĮзов ани", - "ĠиÑģполÑĮз овани", - "å·¦ åı³", - "en ler", - "ĠоÑī ÑĥÑī", - "Ġоб лÑĸ", - "Ġобл Ñĸ", - "ظ ËĨ", - "ÙĦ ÛĮس", - "ÙĦÛĮ س", - "æıIJ åįĩ", - "ĠговоÑĢ иÑĤÑĮ", - "ĠговоÑĢиÑĤ ÑĮ", - "Ġk ür", - "Ġkü r", - "Ġλ ειÏĦοÏħÏģγ", - "ла га", - "лаг а", - "ĠÑģÑĥ дÑĥ", - "ĠÑģÑĥд Ñĥ", - "Ġ 측", - "Ġì¸ ¡", - "θε Ïĥη", - "Ġ нен", - "Ġн ен", - "Ġне н", - "Ġbiç imde", - "Ġbiçim de", - "ÑĨÑĸй ноÑĹ", - "ÑĨÑĸйно ÑĹ", - "à¹Ģà¸Ħ ย", - "ĠDal Å¡ÃŃ", - "Ġи меÑĤÑĮ", - "Ġим еÑĤÑĮ", - "Ġиме ÑĤÑĮ", - "èĭ Ĺ", - "ĠÙħع رÙĪÙģ", - "Ġt ạp", - "Ġm eÅŁ", - "Ġme ÅŁ", - "Âł N", - "оÑĢ они", - "оÑĢон и", - "оÑĢо ни", - "ع Ùģ", - "à¹Ĥ รà¸ĩà¹Ģร", - "à¹Ĥรà¸ĩ à¹Ģร", - "âĶ ¬", - "Ġ à¹Ģà¸ŀราะ", - "Ġà¹Ģà¸ŀ ราะ", - "Ġèı² å¾ĭ宾", - "ÑģÑĤв енное", - "ÑģÑĤвен ное", - "ÑģÑĤвенно е", - "Ġاز دÙĪاج", - "ĠÑĦ ев", - "éł »", - "Ġ สล", - "Ġส ล", - "à¸ķ à¸Ńà¸Ļ", - "Ġ 기ê°Ħ", - "Ġ기 ê°Ħ", - "ä½ ©", - "ÏĦ ην", - "ÏĦη ν", - "ëĤ¬ ëĭ¤", - "ĠQ uy", - "ĠQu y", - "Ġë¶ Ļ", - "ĠС Ñĥд", - "и ж", - "Ġ à¹Ģà¸ģม", - "Ġà¹Ģà¸ģ ม", - "ĠÑģв ÑıÑĤ", - "ĠÑģвÑı ÑĤ", - "et ooth", - "eto oth", - "ε Ïģο", - "εÏģ ο", - "ÙĦ ÙħØ©", - "ÙĦÙħ Ø©", - "Ø´ ÙĪر", - "Ø´ÙĪ ر", - "Ġd omu", - "Ġdo mu", - "Ġdom u", - "èį Ĵ", - "m î", - "ëıĦ 를", - "ĠÑĢекомендÑĥ еÑĤÑģÑı", - "Ġsonra sında", - "Ġsonrası nda", - "Ġд нÑĸв", - "Ġç al", - "Ġça l", - "ãĤ«ãĥĨ ãĤ´ãĥª", - "Ġ еж", - "Ġе ж", - "Ġìķ ī", - "èī² çļĦ", - "âĢĻ nde", - "âĢĻn de", - "Ġ ÏĢÏīÏĤ", - "ĠÏĢ ÏīÏĤ", - "ĠÑĩеÑĤ веÑĢ", - "k ili", - "ki li", - "kil i", - "æĢ§ èĥ½", - "اد ÙĬØ©", - "ادÙĬ Ø©", - "çº ¯", - "ĠاÙĦ تش", - "ĠاÙĦت Ø´", - "ĠÑĤ ела", - "ĠÑĤе ла", - "ĠÑĤел а", - "Ġоб ÑĬем", - "ĠобÑĬ ем", - "å²Ĺ ä½į", - "Ġkon krét", - "Ġa rada", - "Ġar ada", - "Ġara da", - "ìĭľ ìĹIJ", - "Ġor anı", - "Ġoran ı", - "ر Ùĥ", - "ÐĽ ÐIJ", - "Ġ ménÄĽ", - "Ġmé nÄĽ", - "ج ÙĪÛĮ", - "جÙĪ ÛĮ", - "Ġv ợ", - "Ġvá» £", - "ĠAngiosper mae", - "èĥ İ", - "Ġh ôn", - "äºĭ æ¥Ń", - "ĠоÑĤ веÑĢ", - "ĠоÑĤв еÑĢ", - "Ġs rd", - "Ġsr d", - "Å¡ li", - "ส à¸ģ", - "æ¼ ı", - "ĠØ´ رØŃ", - "Ġشر ØŃ", - "ÑĨ Ñıми", - "ÑĨÑı ми", - "Ġs lav", - "Ġsl av", - "Ġsla v", - "Ġc eny", - "Ġce ny", - "Ġcen y", - "à¸Ń à¹Ģร", - "Ġ ÙĪÙĦد", - "ĠÙĪÙĦ د", - "Ġк оÑĢа", - "ĠкоÑĢ а", - "Ġко ÑĢа", - "Ġб ÑĢон", - ": .:.:.:.:", - ":.:.: .:.:", - ":.: .:.:.:", - "Ġne mus", - "Ġnem us", - "è¿Ļ æł·çļĦ", - "è¿Ļæł· çļĦ", - "Ġبر ÙĨاÙħج", - "Ġú plnÄĽ", - "ีà¸Ļ าà¸Ħม", - "Ġë°Ľ ìķĦ", - "με Ïģα", - "μεÏģ α", - "ç¼ ©", - "Ġn ắm", - "ĠобÑĬ ÑıÑģ", - "ĠконÑĤÑĢ олÑİ", - "á vajÃŃcÃŃ", - "ávajÃŃ cÃŃ", - "Ġk um", - "Ġku m", - "çĶ· 人", - "Ġv nitÅĻ", - "Ġب دÙĩ", - "Ġبد Ùĩ", - "Ġأب رÙĬÙĦ", - "人æ°ij åħ±åĴĮåĽ½", - "Ġyap ılır", - "Ġyapıl ır", - "Ġna Å¡ÃŃ", - "ĠnaÅ¡ ÃŃ", - "ãĥ¼ ãĥŃ", - "ãĥ¼ãĥ Ń", - "Ġt ạm", - "Ġhen üz", - "Ġz emi", - "Ġze mi", - "Ġzem i", - "Ġkh áng", - "Ġkhá ng", - "åħ¬ åħ±", - "Ġ èĢģ", - "ĠèĢ ģ", - "ĠعÙĪ اÙħÙĦ", - "Âł V", - "à¹ī à¹ģà¸ģ", - "άν ÏĦα", - "ĠÑĤÑĢав нÑı", - "Ġη μÎŃ", - "è´ ¸", - "ส à¸Ķ", - "Ġس Ùħت", - "ĠسÙħ ت", - "ĠØ® اک", - "ĠÑĤак ий", - "ĠÑĤа кий", - "Ġet tik", - "Ġett ik", - "Ġetti k", - "ĠÏĮ λ", - "Ġп оли", - "Ġпо ли", - "Ġпол и", - "Ġ нож", - "Ġн ож", - "Ġно ж", - "غ اÙĨ", - "ÙĨ دÙĬ", - "ÙĨد ÙĬ", - "ĠÄįty ÅĻi", - "ĠÄįtyÅĻ i", - "ĠPh Æ°Æ¡ng", - "ĠÙĪر زش", - "ĠÙĪرز Ø´", - "ãģĦ ãģĭ", - "r vé", - "rv é", - "Ġतर फ", - "Ġन à¤Ĺर", - "m asında", - "ma sında", - "mas ında", - "ması nda", - "е виÑĩ", - "ев иÑĩ", - "еви Ñĩ", - "ve ÅĻej", - "ä¿Ŀ æĮģ", - "æĬĢ èĥ½", - "æݨ èįIJ", - "l âm", - "Ġ Ïį", - "ĠÏ į", - "å¢ŀ éķ¿", - "Ġاص ÙģÙĩ", - "ĠÐĹак онÑĥ", - "ĠÐŁ ÑĢез", - "ĠÐŁÑĢ ез", - "Ġpod por", - "Ġpodp or", - "기 íĥĢ", - "Ġ íıIJ", - "Ġíı IJ", - "Ġ ëĭĪ", - "Ġëĭ Ī", - "lar ınız", - "ların ız", - "larını z", - "ãĥĸ ãĥŃ", - "ĠÑĦÑĢан ÑĨÑĥз", - "ãĥĬ ãĥ¼", - "Ġb eled", - "Ġbe led", - "Ġbel ed", - "Ġbele d", - "ัà¸Ļว าà¸Ħม", - "ĠÙģ رÙĪ", - "ĠÙģر ÙĪ", - "ÑĦ ÑĢов", - "ĠìĿ´ 룬", - "ượ u", - "Ġê³µ ìĭĿ", - "Ġbird en", - "Ġbir den", - "Ġз елен", - "Ġзел ен", - "çĴ ĥ", - "Ġh á»ĵng", - "Ġhá»ĵ ng", - "ĠÅ¡ kola", - "ĠÅ¡kol a", - "ĠÅ¡k ola", - "ĠÑģам ом", - "ĠÑģамо м", - "an lık", - "anlı k", - "空 éĹ´", - "åįĹ çľģ", - "л еÑĢг", - "ле ÑĢг", - "леÑĢ г", - "Ñĸз неÑģ", - "Âł A", - "ãĢį ãĤĴ", - "Ġkend ine", - "Ġkendi ne", - "Ġ اÙĪÙĨ", - "Ġا ÙĪÙĨ", - "ĠاÙĪ ÙĨ", - "ãĢ Ķ", - "ĠΣ Ïį", - "à¹Ģ à¸Ħล", - "à¹Ģà¸Ħ ล", - "å¥ ¶", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "Ġú Äįet", - "ĠúÄį et", - "Ñĥ ла", - "Ñĥл а", - "éĢļ ä¿¡", - "Ġì¦ IJ", - ". čĊĊ", - ".čĊ Ċ", - "ĠÐľ ед", - "ا عÙĬ", - "اع ÙĬ", - "Ġjeho ž", - "ĠGü ney", - "ĠGün ey", - "ĠÎij ÏĢο", - "ĠÎijÏĢ ο", - "Ġп олÑĸ", - "Ġпо лÑĸ", - "Ġпол Ñĸ", - "ü me", - "üm e", - "ho dob", - "hod ob", - "ĠÎĿ α", - "ĠØ¢ ÙĦات", - "ĠØ¢ÙĦ ات", - "ĠpÅĻ iz", - "ĠpÅĻi z", - "Ġt avs", - "Ġta vs", - "Ġtav s", - "Ġتب ÙĦÛĮ", - "ãĥ³ ãĥĶ", - "ãĥ³ãĥ Ķ", - "Ø® رج", - "خر ج", - "Ġак кÑĥ", - "Ġú prav", - "ĠاØŃ Ø³Ø§Ø³", - "ì¹´ ëĿ¼", - "ı mızı", - "ım ızı", - "ımız ı", - "ımı zı", - "Ġд окÑĥменÑĤ", - "Ġдок ÑĥменÑĤ", - "ĠдокÑĥм енÑĤ", - "Ġ اصÙĦ", - "Ġا صÙĦ", - "Ġاص ÙĦ", - "ظ Ùĩ", - "ĠìĿ¸ ê°Ħ", - "Ġج رÛĮاÙĨ", - "Ġجر ÛĮاÙĨ", - "Î¥ ÎĿ", - "ÑĩеÑģ каÑı", - "Ñĩе ÑģкаÑı", - "ÙĬ ÙĨÙĬØ©", - "ÙĬÙĨ ÙĬØ©", - "ÙĬÙĨÙĬ Ø©", - "åĴ ¨", - "æĹħ 游", - "Ġ à¸Īำà¸Ļวà¸Ļ", - "Ġà¸Īำ à¸Ļวà¸Ļ", - "Ġ анг", - "Ġа нг", - "Ġан г", - "Ïħ Ïĩ", - "èĻ «", - "ĠÙħ ÙĤر", - "ĠÙħÙĤ ر", - "ĠÙħÙĪسÛĮ ÙĤÛĮ", - "ut ut", - "ĠÐĽ е", - "ĠÐŁ ÑĸÑģлÑı", - "ãĤŃ ãĥ¼", - "ุล าà¸Ħม", - "åĩ ¡", - "ÏĢ οÏį", - "ÏĢο Ïį", - "ĠÃĸ dül", - "Ïĥ κ", - "Ġ ÑĨÑİ", - "ĠÑĨ Ñİ", - "Ñĭ ваÑı", - "Ñĭв аÑı", - "Ñĭва Ñı", - "ï½ŀ ï½ŀ", - "ĠØ´ ÙħاÙĦ", - "ĠØ´Ùħ اÙĦ", - "ĠØ´Ùħا ÙĦ", - "è¿ ħ", - "ĠبÙĦ Ú©Ùĩ", - "çİ Ľ", - "Ġì§Ģ ëĤĺ", - "ĠÙģ کر", - "ĠÑģÑĤеп ени", - "Ġна Ñĥки", - "ĠнаÑĥк и", - "çī© çIJĨ", - "ÄĽ le", - "ÄĽl e", - "Ġо ÑģкÑĸлÑĮки", - "ĠкÑĥлÑĮÑĤÑĥ ÑĢи", - "ĠкÑĥлÑĮÑĤÑĥÑĢ и", - "èĢĥ è¯ķ", - "Ġmateri ál", - "ĠÑģÑĤ воÑĢеннÑı", - "ĠÑģÑĤвоÑĢ еннÑı", - "Ġà¤ħ द", - "æıIJ åĩº", - "Ġè©ķ 価", - "ÙĴ د", - "Ġë§Įëĵ¤ ìĸ´", - "duÄŁu nu", - "duÄŁ unu", - "ÙĬ ÙĨÙĩ", - "ÙĬÙĨ Ùĩ", - "åĵ ¦", - "оÑĩ нÑĭÑħ", - "ĠÙħ ض", - "is mu", - "ism u", - "Ġ Ñĩай", - "ĠÑĩ ай", - "ĠÑĩа й", - "ÙĪ رÙĪب", - "ÙĪر ÙĪب", - "Ġ англ", - "Ġан гл", - "Ġанг л", - "oÄŁun luk", - "ĠпÑĢед пол", - "ĠÎŃ ÏīÏĤ", - "ส à¸ĸ", - "ĠÎķλλά δα", - "ĠBi lg", - "ĠBil g", - "Ġ بات", - "Ġب ات", - "Ġبا ت", - "ĠÐĽÑĮв Ñĸв", - "Ġyap ılması", - "Ġyapıl ması", - "æ£Ģ æŁ¥", - "æķ° åѦ", - "Ġ :.:", - "Ġ: .:", - "Ġ:. :", - "Ġ çİ©", - "Ġçİ ©", - "Îļ α", - "à¹Ģà¸Ĺ à¸Ħà¹Ĥà¸Ļà¹Ĥลย", - "Ġساخت ÙħاÙĨ", - "ĠìĨĮ 리", - "é¼ »", - "Ġs mr", - "Ġsm r", - "Ġëĭ¤ìĸij íķľ", - "Ġjed nánÃŃ", - "Ġjedn ánÃŃ", - "Ġjedná nÃŃ", - "Ġserv isi", - "Ġservi si", - "Ġservis i", - "Ġey lem", - "Ġм али", - "Ġмал и", - "Ġма ли", - "Ġvý hod", - "éĻ¤ äºĨ", - "ĠпоÑĢÑıд кÑĥ", - "Ġn ový", - "Ġno vý", - "Ġnov ý", - "å¤ ķ", - "ĠнекоÑĤоÑĢ Ñĭе", - "Ġ^ {}", - "Ġ^{ }", - "γÏī γ", - "Ñĥ ÑĪки", - "ÑĥÑĪ ки", - "Ġp sik", - "Ġps ik", - "Ġpsi k", - "ĠíĶĦ 리", - "Ø´ اء", - "Ġ ван", - "Ġв ан", - "Ġва н", - "Ġس ÙĥاÙĨ", - "ç¢ ¼", - "ĠÎľ η", - "ĠÑĥÑĢов енÑĮ", - "ãĤµ ãĥ¼", - "ĠاÙĦب ØŃر", - "Ġd nÃŃ", - "Ġdn ÃŃ", - "à¸ģาร ศ", - "e diÄŁi", - "edi ÄŁi", - "Ġbelir li", - "Ùĭ ØĮ", - "ĠzamÄĽst nan", - "ĠzamÄĽstn an", - "æŁ ±", - "ا ÙģÙĬ", - "اÙģ ÙĬ", - "Ġh ải", - "æĢĿ æĥ³", - "Ġn eler", - "Ġne ler", - "Ġnel er", - "Ġرس ÙħÛĮ", - "ĠرسÙħ ÛĮ", - "Ñģ еÑĢ", - "Ñģе ÑĢ", - "ãģĵãģ¨ ãģ§", - "ĠZá kladnÃŃ", - "л ова", - "ло ва", - "лов а", - "к ÑĤÑĥ", - "кÑĤ Ñĥ", - "ÙĪس Ùģ", - "Ñĸб лÑĸ", - "Ì Ĥ", - "ÑĢ д", - "éĻ ³", - "æį ·", - "ĠyaÅŁ ayan", - "à¥ģ à¤ļ", - "à¥ģठļ", - "ÑĸÑĤ ÑĤÑı", - "Ġb á»ģ", - "ëĤĺ ëĿ¼", - "Ġм ÑıÑģ", - "Ġ{ [%", - "Ġ{[ %", - "θ α", - "Ġдоз волÑı", - "Ġдозвол Ñı", - "Ġ åIJĦ", - "ĠåIJ Ħ", - "ĠÐŁ еÑĢв", - "ĠÐŁÐµÑĢ в", - "ĠSaÄŁ lık", - "ÑģÑĤоÑĢ иÑı", - "Ġbun lar", - "Ġs á»ķ", - "़ à¥į", - "Ġ åĪ©", - "ĠåĪ ©", - "ĠÑģ поÑģ", - "ĠÑģп оÑģ", - "Ġyap tır", - "Ġyaptı r", - "Ġt Æ°á»Ŀng", - "ÙĪ ÙĨØ©", - "ÙĪÙĨ Ø©", - "Ġ еп", - "Ġе п", - "ãģ§ãģį ãģªãģĦ", - "Ùģ تÙħ", - "Ùģت Ùħ", - "ĠÐĵ ол", - "íķĺ ì§Ģë§Į", - "íķĺì§Ģ ë§Į", - "Ġì§Ħ ì§ľ", - "Ġob jedn", - "Ġobj edn", - "Ġизмен ениÑı", - "女 人", - "Ġпл ани", - "Ġпла ни", - "Ġплан и", - "ĠFak ült", - "Ġt zv", - "Ġtz v", - "ĠобÑıз аÑĤелÑĮ", - "Ġблиз ÑĮко", - "r ası", - "ra sı", - "ras ı", - "ĠεÏĢί ÏĥηÏĤ", - "ĠÑĦак ÑĤи", - "ĠÑĦакÑĤ и", - "ĠÄIJ ặc", - "ĠAlt ın", - "л иÑĤ", - "ли ÑĤ", - "Ġл ÑĸÑģ", - "ĠлÑĸ Ñģ", - "çī §", - "Ġп ÑĥÑģÑĤ", - "ĠпÑĥ ÑģÑĤ", - "Ġком ÑĸÑģ", - "ä¿Ŀ éļľ", - "åħ· ä½ĵ", - "- ÑĤ", - "Ġtr hu", - "Ġtrh u", - "Ġâī Ī", - "Ġдека бÑĢÑı", - "ĠÑĦоÑĢм Ñĭ", - "ĠÑĦоÑĢ мÑĭ", - "Ng oÃłi", - "Ġdo hod", - "رÙĬ ÙĥÙĬØ©", - "رÙĬÙĥ ÙĬØ©", - "رÙĬÙĥÙĬ Ø©", - "ĠØ¢ÙħÙĪز Ø´ÛĮ", - "ĠØ¢ÙħÙĪزش ÛĮ", - "ĠzajÃŃm av", - "Ġkat ılım", - "Ġkatıl ım", - "ä¸ ĺ", - "Ġko num", - "Ġkon um", - "Ġkonu m", - "Ġм оÑĩ", - "Ġмо Ñĩ", - "ãĥ³ ãĥķ", - "ãĥ³ãĥ ķ", - "диви дÑĥ", - "Ġ äºļ", - "Ġ æĴ", - "Ġæ Ĵ", - "γÏģά ÏĨ", - "ãĥIJ ãĤ¹", - "Ġп Ñĥнк", - "ĠBir leÅŁik", - "Ġqu en", - "Ġque n", - "Ġq uen", - "Ġв каз", - "Ġвк аз", - "à¥ĩ शà¤ķ", - "à¥ĩश à¤ķ", - "ĠY unan", - "ĠYu nan", - "ĠYun an", - "ãģł ãģ¨", - "Û±Û¹ Û·", - "á ty", - "át y", - "Ġ ÙĪص", - "ĠÙĪ ص", - "Ġнег аÑĤив", - "ãģ¤ ãģ®", - "Ġ åĬ¨", - "ĠåĬ ¨", - "ãĥį ãĥĥãĥĪ", - "Ġд Ñĸй", - "ĠдÑĸ й", - "ĠbaÅŁ ında", - "Ġtr Æ°ng", - "Ġm akin", - "Ġma kin", - "Ġmak in", - "Ġ æĦĽ", - "ĠæĦ Ľ", - "м еÑĩ", - "ме Ñĩ", - "Ġ è¿ij", - "Ġè¿ ij", - "ÙĤ در", - "ÙĤد ر", - "Ġاست اÙĨد", - "ĠاستاÙĨ د", - "Ġinform acÃŃ", - "ार à¤ķ", - "è¬ Ŀ", - "ÑĢаб аÑĤ", - "Ġ çŃĶ", - "Ġç ŃĶ", - "ĠçŃ Ķ", - "Ġ èĩ³", - "Ġèĩ ³", - "Ġп олÑĮ", - "Ġпо лÑĮ", - "Ġпол ÑĮ", - "ĠÙĩÙĨ ر", - "ëĮĢ ë¹Ħ", - "Ġخارج ÛĮ", - "r act", - "ra ct", - "rac t", - "ãĢĤ ãģĵãĤĮ", - "ĠØ´ÙĪر اÛĮ", - "л енно", - "лен но", - "Ġh isset", - "Ġhis set", - "Ġhiss et", - "Ġc Ãłi", - "ĠcÃł i", - "ĠÑĦ оÑĤо", - "ĠÑĦоÑĤ о", - "æģ Ĵ", - "Ġмед иÑĨин", - "Ġмеди ÑĨин", - "ÑģÑĤ вÑĸ", - "ÑģÑĤв Ñĸ", - "ĠاÙĦ عÙĦ", - "ĠاÙĦع ÙĦ", - "ĠпиÑģÑĮ мен", - "ãĢĤ ãģ¾ãģŁ", - "Ġvlast nÄĽ", - "Ġп ода", - "Ġпо да", - "Ġпод а", - "Ïģ οι", - "Ïģο ι", - "Ġ ìĦĿ", - "ĠìĦ Ŀ", - "ĠìĿ¼ ìĿ´", - "Ġ ìĽĮ", - "ĠìĽ Į", - "ок Ñģи", - "окÑģ и", - "Ġos oby", - "Ġosob y", - "ÐŁÐ¾Ñģ ле", - "ĠÑĸÑģÑĤоÑĢ ÑĸÑĹ", - "ع ÙĦÙī", - "عÙĦ Ùī", - "н ка", - "ت Ùħبر", - "تÙħ بر", - "à¥ĩ हर", - "à¥ĩह र", - "ĠJ ana", - "ĠJan a", - "ĠJa na", - "ÙĦ ÙĬات", - "ÙĦÙĬ ات", - "ĠмаÑĢ ÑĤа", - "ĠÐļи ÑĶ", - "ĠÑĢоб оÑĤÑĥ", - "ĠÑĢобоÑĤ Ñĥ", - "Ġnh ấn", - "и Ñģлов", - "иÑģ лов", - "ëŁ Ń", - "Ġo dv", - "Ġod v", - "ĠT á»īnh", - "âĢľ ê·¸", - "ãģ» ãģĨ", - "é² ľ", - "м еÑĨÑĮ", - "ме ÑĨÑĮ", - "า ศาสà¸ķร", - "าศ าสà¸ķร", - "à¥ģ à¤ĵ", - "à¥ģठĵ", - "ิ à¸Ļà¸Ĺ", - "ิà¸Ļ à¸Ĺ", - "m ada", - "ma da", - "mad a", - "ز اÙħ", - "زا Ùħ", - "ĠÙĥ بÙĬر", - "å®ŀ æĸ½", - "ze ÅĪ", - "Ġl ái", - "Ġlá i", - "Ïĥ μα", - "Ïĥμ α", - "ا سات", - "اس ات", - "ÑĦ ÑĤ", - "è° ±", - "çĮ ľ", - "Ġpro bÃŃ", - "Ġprob ÃŃ", - "æľĢ è¿ij", - "ÑĢ ад", - "ÑĢаР´", - "ÑĢа д", - "ãĤ½ ãĥ³", - "Ġ клад", - "Ġк лад", - "Ġкл ад", - "Ġкла д", - "à¥ľ à¤ķ", - "é v", - "ล าย", - "ลา ย", - "èİ İ", - "ĠμÎŃ ÏĩÏģι", - "Ġ кÑĥÑģ", - "Ġк ÑĥÑģ", - "ĠкÑĥ Ñģ", - "Ġ íĻĺê²½", - "ĠíĻĺ ê²½", - "Ñĩ оÑĹ", - "åıĺ åĮĸ", - "Ġب تÙĪاÙĨ", - "Ġبت ÙĪاÙĨ", - "Ġt ắt", - "Ġgöster en", - "а лÑİ", - "ал Ñİ", - "Ġкоман ди", - "Ġкоманд и", - "Ġ 컨", - "Ġì» ¨", - "Ñĥ нд", - "Ñĥн д", - "Ġج ÙĦÙĪ", - "ĠجÙĦ ÙĪ", - "åŃIJ çļĦ", - "ĠÑģ б", - "ĠÐł аÑģ", - "ĠÐłÐ° Ñģ", - "P CP", - "PC P", - "ĠCumhur baÅŁ", - "од аÑĤелÑĮ", - "ÃŃ sto", - "ÃŃs to", - "ÃŃst o", - "Ġo znám", - "Ġoz nám", - "ãĥ¼ ãĥĭ", - "ãĥ¼ãĥ ĭ", - "Ġok uy", - "Ġoku y", - "o phy", - "op hy", - "oph y", - "า à¸Ļà¸Ħร", - "าà¸Ļ à¸Ħร", - "ĠÎķ θν", - "ay ım", - "ayı m", - "Ùİ Ø£", - "æİ ¡", - "Ġfunk ce", - "Ġfunkc e", - "æļ ĸ", - "Ø· ار", - "ĠÐĿ аг", - "ĠÐĿа г", - "Ġ ä¸ĩåĨĨ", - "Ġä¸ĩ åĨĨ", - "Ġ íĴį", - "ĠíĴ į", - "Ġ ä½ı", - "Ġ ï¼İ", - "Ġï¼ İ", - "Ñĭ ваÑİÑĤÑģÑı", - "Ñĭв аÑİÑĤÑģÑı", - "Ñĭва ÑİÑĤÑģÑı", - "ÑĭваÑİÑĤ ÑģÑı", - "ĠP la", - "ĠPl a", - "ا ÙĬÙĦ", - "اÙĬ ÙĦ", - "Ġ무 ìĹĩ", - "Ġкон еÑĩно", - "к м", - "à¤Ĥ पर", - "à¤Ĥप र", - "Ġ ìłķë¶Ģ", - "Ġìłķ ë¶Ģ", - "ĠëĤ´ 볤", - "ãĤ° ãĥ«", - "çģ °", - "Ġc yk", - "Ġcy k", - "Ġжел Ñĥд", - "ĠëĨĴ ìĿĢ", - "çĶŁ åij½", - "æµ ´", - "Ġart Ä±ÅŁ", - "Ġ Ðĩ", - "ĠÐ ĩ", - "ï¼ ²", - "e kim", - "ek im", - "eki m", - "ĠÑĦ едеÑĢа", - "ĠвеÑĢеÑģ нÑı", - "н иÑĤе", - "ни ÑĤе", - "ниÑĤ е", - "ĠÄ°ÅŁ te", - "ĠÙĪضع ÛĮت", - "ãģķ ãģ¾", - "ĠtÅĻ etÃŃ", - "ĠtÅĻet ÃŃ", - "u luÄŁ", - "ulu ÄŁ", - "ĠCumhur iyet", - "ä¼ Ł", - "Ġ ë§Ŀ", - "Ġë§ Ŀ", - "Ġver mek", - "Ġverm ek", - "Ġn alez", - "Ġna lez", - "Ġnal ez", - "Ġnale z", - "çĵ ¶", - "Ġd iÅŁ", - "Ġdi ÅŁ", - "ĠH á»ĵng", - "ĠHá»ĵ ng", - "غ ÙĬرة", - "غÙĬر Ø©", - "å© Ĩ", - "н ив", - "ни в", - "Ġr út", - "' nda", - "'n da", - "Ġh roz", - "Ġhr oz", - "à¥ī प", - "Ġза коном", - "Ġзак оном", - "Ġзакон ом", - "Ġзако ном", - "Ġjed nu", - "Ġjedn u", - "ĠKa dın", - "ĠKad ın", - "in dir", - "ind ir", - "indi r", - "س ازÛĮ", - "åĮº åŁŁ", - "ĠkonuÅŁ tu", - "Ġز ÙĨد", - "ĠزÙĨ د", - "ा ĊĊ", - "ाĊ Ċ", - "ĠÐIJ з", - "à¸ĩ à¸Ĥà¸Ńà¸ĩ", - "à¸ĩà¸Ĥ à¸Ńà¸ĩ", - "ĠÑģвой ÑģÑĤва", - "Ġìŀij íĴĪ", - "пе ки", - "Ġ å°±", - "Ġå° ±", - "ев ого", - "ево го", - "ĠtaÅŁ ıy", - "ĠÙħÙĨ Ø·ÙĤØ©", - "ĠÙħÙĨØ· ÙĤØ©", - "ĠÃĩ ocuk", - "Û² Û·", - "ĠÏĥÏħ μÏĢ", - "é£Ł åĵģ", - "h á", - "ï¼ ¯", - "ÙĦ ÙħÙĩ", - "ÙĦÙħ Ùĩ", - "ãģ¨ãģª ãģ£ãģŁ", - "о ÑĢÑĸ", - "оÑĢ Ñĸ", - "° }", - "ĠtaÅŁ ın", - "çŁ ¿", - "ĠÑĩаÑģÑĤ ини", - "ĠÑĩаÑģÑĤи ни", - "ĠدÙĬ سÙħبر", - "Ġ èī¯", - "Ġèī ¯", - "st ÅĻÃŃ", - "Ġ ÑĨик", - "ĠÑĨ ик", - "ĠÑĨи к", - "âĢķâĢķ âĢķâĢķ", - "ĠÄ°ng iltere", - "ĠÑģÑĤ ÑĢаÑĤег", - "ĠÑģÑĤÑĢ аÑĤег", - "ÃĦ Ÿ", - "и Ñĩного", - "иÑĩ ного", - "иÑĩно го", - "ÃŃ rk", - "ÃŃr k", - "ĠÎij Ïģ", - "! âĢľĊĊ", - "!âĢľ ĊĊ", - "Ġ 깨", - "Ġê¹ ¨", - "à¥ģà¤Ĩ त", - "ĠدÙĨÛĮ ا", - "ĠدÙĨ ÛĮا", - "l ÃŃn", - "lÃŃ n", - "Ġà¤ķ ड", - "ĠÙħ بت", - "ĠÙħب ت", - "ем ÑĭÑħ", - "о би", - "об и", - "ย à¸Ļà¸ķ", - "ยà¸Ļ à¸ķ", - "à¤Ĥध न", - "ÚĨ ÛĮ", - "Ġ çŁ¥", - "Ġç Ł¥", - "ĠçŁ ¥", - "ĠXu ân", - "a daki", - "ad aki", - "ada ki", - "Ġ orta", - "Ġor ta", - "Ġort a", - "æł¹ æľ¬", - "åħ± åIJĮ", - "н ений", - "не ний", - "нен ий", - "ب ÙĬرة", - "بÙĬ رة", - "بÙĬر Ø©", - "çŃ ĭ", - "ïº Ķ", - "âĢĮ ÙĩاÙĬ", - "âĢĮÙĩا ÙĬ", - "Ġö deme", - "Ġödem e", - "ĠØ¢ÙĨ ÚĨÙĩ", - "Ġза Ñıви", - "ĠзаÑıв и", - "ĠÙĨÙĤ Ø´Ùĩ", - "ĠÙĨÙĤØ´ Ùĩ", - "Ġ ç³»", - "Ġç ³»", - "Ġç³ »", - "à¥ĭ ।", - "Ġì§Ģ ìłķ", - "Ġin sp", - "Ġins p", - "Ġ ÑĤен", - "ĠÑĤ ен", - "ĠÑĤе н", - "Ġت Ø·", - "Ġqu ảng", - "Ġquả ng", - "Ġquản g", - "åī £", - "ãģı ãģ®", - "ĠÑĨ им", - "ĠÑĨи м", - "k ovi", - "ko vi", - "kov i", - "i yah", - "iy ah", - "iya h", - "Ġ ëIJľëĭ¤", - "ĠëIJľ ëĭ¤", - "ص Ùĩ", - "ĠÄij u", - "Ġsu á»ijt", - "ı ma", - "ım a", - "ì§Ģ ê³ł", - "Ì ĥ", - "à¸ļ าย", - "ĠCert if", - "ĠCer tif", - "ĠÑĥÑģ ÑĸÑħ", - "ĠÑĥÑģÑĸ Ñħ", - "à¸ķะ ว", - "εί ÏĦε", - "Ġ č", - "Ġмож ливÑĸÑģÑĤÑĮ", - "Ġможлив ÑĸÑģÑĤÑĮ", - "Ġ -âĢIJ", - "Ġ- âĢIJ", - "Ġ íĺ¹", - "Ġíĺ ¹", - "ìĤ¬ ì§Ħ", - "Ġд аниÑħ", - "Ġда ниÑħ", - "Ġдан иÑħ", - "Ġzah áj", - "주 ëĬĶ", - "Ġг ид", - "n iž", - "ni ž", - "Ġ^{ °}", - "Ġk ro", - "Ġkr o", - "Äį en", - "Äįe n", - "ÏĨ ι", - "ımız da", - "Ġ æ¹ĸ", - "Ġæ¹ ĸ", - "Ġпов ÑĢежд", - "Ġì¡´ ìŀ¬", - "à¸Ļ าà¸Ļ", - "à¸Ļา à¸Ļ", - "μÎŃ Î½Î¿ÏĤ", - "μÎŃν οÏĤ", - "μÎŃνο ÏĤ", - "æ½ ľ", - "ï¼Į 使", - "Ġd osp", - "Ġdo sp", - "Ġdos p", - "Ġl iá»ģn", - "Ġli á»ģn", - "ัà¸ļ à¸Ħวาม", - "ัà¸ļà¸Ħ วาม", - "ĠÑĢабоÑĤ е", - "ĠÑĢаб оÑĤе", - "ĠÑĢабо ÑĤе", - "Ġмай бÑĥÑĤ", - "à¹Ģà¸ģ ษ", - "B aÅŁ", - "Ba ÅŁ", - "Ġ æĿ±äº¬", - "ĠæĿ± 京", - "наÑĩ ала", - "δ ει", - "δε ι", - "à¥Ī प", - "Ñĸ мÑĸ", - "Ñĸм Ñĸ", - "Ġf izik", - "Ġfi zik", - "Ġfiz ik", - "ว ล", - "ä¼ į", - "Ġ à¸Ĭà¸Ļะ", - "Ġà¸Ĭ à¸Ļะ", - "' ÑıÑĤ", - "'Ñı ÑĤ", - "н ил", - "ни л", - "и нов", - "ин ов", - "ĠÄijo án", - "รว à¸Ī", - "f et", - "fe t", - "à¹Į à¹Ĥ", - "Ġ маÑĤи", - "Ġм аÑĤи", - "ĠмаÑĤ и", - "Ġма ÑĤи", - "é¨ İ", - "Ðļ Т", - "à¹Ģส à¸Ļà¸Ń", - "à¹Ģสà¸Ļ à¸Ń", - "Ġм ав", - "Ġма в", - "lı ģına", - "lıģı na", - "lıģ ına", - "lıģın a", - "Ġпо Ñĩина", - "ĠпоÑĩ ина", - "ู à¸ķร", - "ูà¸ķ ร", - "ÑĨ еÑĢ", - "ÑĨе ÑĢ", - "uj ete", - "uje te", - "ujet e", - "Ġtah min", - "Ġвим ог", - "า à¸Ł", - "าภŁ", - "е дж", - "ед ж", - "ÏĦ εÏį", - "ÏĦε Ïį", - "ad la", - "ĠÄij Æ°Æ¡ng", - "Ġد استاÙĨ", - "Ġbas ın", - "Ġba sın", - "ĠÑħ в", - "Ġ reak", - "Ġre ak", - "ĠоÑĤ меÑĤ", - "æ³ ¥", - "Ġm áte", - "Ġmá te", - "Ġmát e", - "Ġzo run", - "Ġzor un", - "ãģ¨æĢĿ ãģĨ", - "Ġدر جة", - "ĠвÑĸд ÑģÑĥÑĤ", - "Ġع اÙħÙĦ", - "ĠعاÙħ ÙĦ", - "èĶ µ", - "Ġson raki", - "Ġsonra ki", - "Ġmoh li", - "Ġmohl i", - "и ваеÑĤ", - "ив аеÑĤ", - "ива еÑĤ", - "ĠпÑĸд ÑģÑĤав", - "Ġost rov", - "Ġostr ov", - "ान व", - "âĢŀ P", - "Ġви знаÑĩа", - "ĠвизнаÑĩ а", - "Ġprav dÄĽpodob", - "Ġz az", - "Ġza z", - "ìĿ´ 를", - "Ġдж еÑĢ", - "ĠÐł ад", - "ĠÐłÐ° д", - "ĠÑģеÑĢÑĮ ез", - "Ġ дем", - "Ġд ем", - "Ġде м", - "ÏĢ ή", - "ĠÐĦ вÑĢоп", - "ĠÐĦв ÑĢоп", - "ĠÄįesk é", - "ĠÄįe ské", - "ï¾ ı", - "Ġ ØŃÙĬ", - "ĠØŃ ÙĬ", - "ì¼ ĢìĿ´", - "ì¼Ģ ìĿ´", - "ĠØ® ÙĪÙĨ", - "ĠØ®ÙĪ ÙĨ", - "Âł L", - "ãģĦ ãģ«", - "из неÑģ", - "ĠÙħ ÙĤاÙħ", - "ĠÙħÙĤ اÙħ", - "ĠاÙĦ ØŃÙĦ", - "ĠاÙĦØŃ ÙĦ", - "ëĨ į", - "ĠØ¢ ÛĮا", - "ĠØ¢ÛĮ ا", - "ç¿ ¼", - "ï¼ ½", - "æ¸ IJ", - "ли вÑĸ", - "лив Ñĸ", - "ãģĦ ãģ¦ãģĦãĤĭ", - "ãģĦãģ¦ ãģĦãĤĭ", - "Ġ ÎijÎł", - "ĠÎij Îł", - "ĠиÑģполÑĮз ÑĥеÑĤÑģÑı", - "ĠиÑģполÑĮзÑĥ еÑĤÑģÑı", - "Ġm át", - "Ġmá t", - "Ġμε γά", - "Ġμεγ ά", - "ëħ ¼", - "æµ· éģĵ", - "ĠÙħØ´Ú© ÙĦات", - "ĠÙħØ´Ú©ÙĦ ات", - "Ñĩ на", - "'; ';", - "Ġ μία", - "Ġμ ία", - "Ïģ Ïİν", - "ÏģÏİ Î½", - "Ġby ste", - "ĠÑįлек ÑĤÑĢи", - "ĠÑįлекÑĤÑĢ и", - "ĠY ardım", - "ĠYard ım", - "ĠYar dım", - "Ġh át", - "Ġhá t", - "ĠÐĶ еÑĢжав", - ". С", - "Ġo rada", - "Ġor ada", - "Ġora da", - "Ġal anı", - "Ġalan ı", - "åľ° åŁŁ", - "ĠدÙĩ ÙĨد", - "мен ÑĪ", - "ĠоÑĢг анов", - "ĠоÑĢган ов", - "Ġع ص", - "ู à¸ĩส", - "ูà¸ĩ ส", - "ĠØ´ عر", - "Ġشع ر", - "Ġìĸ »", - "Ġά λλ", - "Ġάλ λ", - "Ġg ói", - "Ġgó i", - "ĠÙĨ اØŃ", - "å¼ ĺ", - "à¥įथ ल", - "i lim", - "il im", - "ili m", - "ëIJĺ ì§Ģ", - "Ġкон ÑĨе", - "ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", - "Ġì¤Ģ ë¹Ħ", - "Ġostat nÃŃ", - "Ġvlá dy", - "Ġvlád y", - "ĠÑģо биÑĢа", - "ĠÑģоб иÑĢа", - "ĠìĹŃ ìĤ¬", - "à¹ģ à¸ģรม", - "à¹ģà¸ģ รม", - ". ï¼ı", - "Ùı ÙĪÙĨ", - "ÙıÙĪ ÙĨ", - "Ù¾ س", - "ĠW ikip", - "ĠWi kip", - "ĠWiki p", - "ĠWik ip", - "Ġ æ¾", - "Ġæ ¾", - "Ġж аÑĢ", - "容 æĺĵ", - "ĠprostÅĻed nictvÃŃm", - "Ġž eny", - "Ġže ny", - "Ġžen y", - "Ġèı²å¾ĭ宾 çĶ³åįļ", - "а ÑİÑĤÑģÑı", - "аÑİÑĤ ÑģÑı", - "Ġm iêu", - "Ġmi êu", - "Ġp enÃŃze", - "δ ιά", - "δι ά", - "ol dur", - "old ur", - "ĠпÑĢимеÑĢ но", - "ĠìŀĪ ê³ł", - "à¸ĩ à¸Ńย", - "к овий", - "ко вий", - "ков ий", - ". ÎŁ", - "à¹ĥ à¸Ħร", - "çĭ ł", - "ĠÐŁ Ñĸв", - "æĶ¹ éĿ©", - "ĠÐĿаÑģ еление", - "Å¡et ÅĻ", - "ÙĴ ب", - "Ġ âĶĢ", - "ĠâĶ Ģ", - "غ ÙĬÙĦ", - "ĠдÑĸÑıлÑĮ нÑĸÑģÑĤÑĮ", - "ĠÙĦ ÙĬس", - "ĠÙĦÙĬ س", - "Ġìĭľ ìŀ¥", - "ãĥŁ ãĥ¥", - "ĠÚ© ÙĪت", - "ĠÚ©ÙĪ ت", - "ĠÎĵ ι", - "ิ à¹Ģว", - "e ktor", - "ek tor", - "ekt or", - "ĠбÑĥд Ñĥ", - "ĠбÑĥ дÑĥ", - "но важ", - "нов аж", - "нова ж", - "Ñī аеÑĤÑģÑı", - "Ñīа еÑĤÑģÑı", - "Ġng ôn", - "ĠvÄĽ c", - "å¾ IJ", - "à¸Ńà¹Ģม ร", - "ัà¸į à¸Ĭ", - "ĠиÑģполÑĮз ÑĥÑİÑĤ", - "ĠиÑģполÑĮзÑĥ ÑİÑĤ", - "r ubu", - "ru bu", - "rub u", - "Ġnh á»±a", - "ãģĮ ãģĬ", - "ĠÐĵ аÑĢ", - "о ÑĢе", - "оÑĢ е", - "Ġз олоÑĤ", - "æ Ł³", - "æŁ ³", - "Ġ ÙĬØ´", - "ĠÙĬ Ø´", - "Ġповин нÑĸ", - "اÙĤ تص", - "ÙĦ ØŃ", - "ĠокÑĤ ÑıбÑĢÑı", - "ĠnÄĽk dy", - "Ġо бÑĢа", - "Ġоб ÑĢа", - "ست Ú¯ÛĮ", - "符 åIJĪ", - "Ġth iá»ĥu", - "æĺ¯ ä»Ģä¹Ī", - "Ġro zs", - "Ġroz s", - "ì½ľ 걸", - "Ġк аÑĦ", - "Ġка ÑĦ", - "åIJĮ æŃ¥", - "ì¼ ĵ", - "ÏĢ ÏĦÏħ", - "à¸ł ายà¹ĥà¸Ļ", - "ι ÏĥÏĦή", - "ιÏĥ ÏĦή", - "ĠدÙĪÙĦ ار", - "ĠÙħا ÙĬÙĪ", - "ĠÙħاÙĬ ÙĪ", - "Ġ peÄį", - "Ġp eÄį", - "Ġpe Äį", - "ัà¸ļ ม", - "ÎĻ ÎĶ", - "ı ydı", - "ıy dı", - "ัà¸ģ à¸Ĺ", - "à¸Ľà¸£à¸° à¸ĸม", - "κ αι", - "κα ι", - "Ġpro dej", - "Ġprod ej", - "ĠиÑİ Ð»Ñı", - "Ġv Å©", - "é© ±", - "Ġh vÄĽ", - "Ġhv ÄĽ", - "æĥ³ è¦ģ", - "ç¯ Ħ", - "ç ak", - "ça k", - "Ġм Ñıг", - "ım ın", - "ımı n", - "Ġdisp ozici", - "Ġu kaz", - "Ġuk az", - "r acak", - "ra cak", - "rac ak", - "Ġболез ни", - "ว à¹Ĥม", - "Ġз ел", - "ĠÐĴ ики", - "ĠÐĴи ки", - "ĠÐĴик и", - "ĠÐł од", - "ูà¸ģ à¸Ħ", - "í ij¸", - "Ġth ải", - "ĠbaÄŁ ımsız", - "ĠÑĢоÑģ Ñģий", - "ĠÐļ ам", - "ĠÐļа м", - "ĠиÑģполÑĮзов аниÑı", - "ĠиÑģполÑĮз ованиÑı", - "ĠиÑģполÑĮзовани Ñı", - "ĠØŃ Ø°", - "Âł ³³³³³³³³", - "³³ ³³³³³³³", - "³³³³ ³³³³³", - "³³³ ³³³³³³", - "³³³³³³³³ Âł", - "³³³³³³³ ³³", - "³³³³³ ³³³³", - "³³³³³³ ³³³", - "ĠاÙĨت ÙĤاÙĦ", - "Ġаб ÑģолÑİÑĤ", - "Ġ Ä±ÅŁÄ±k", - "ĠÄ±ÅŁÄ± k", - "ÏĦο γÏģαÏĨ", - "ĠболÑĮÑĪ ой", - "Ġعب ارت", - "Ġعبار ت", - "ÃŃ Å¾", - "Ġدر ست", - "Ġدرس ت", - "ĠÑģл ово", - "ĠÑģлов о", - "ĠÑģло во", - "à¥Ī Ċ", - "ب ÙĪب", - "بÙĪ ب", - "ĠÐĴ оÑĤ", - "ĠÐĴо ÑĤ", - "ว à¹Ħà¸Ľ", - "Ġbil inen", - "Ġbilin en", - "Ġ ÙĤÙĬ", - "ĠÙĤ ÙĬ", - "Ġbun ların", - "Ġbunlar ın", - "Ġbunları n", - "Ùij ت", - "Ġbas it", - "Ġba sit", - "ë¦ ¿", - "ائ رة", - "ائر Ø©", - "Ġp ů", - "Ġed ilmiÅŁ", - "Ġedilm iÅŁ", - "Ġedil miÅŁ", - "Ġ ä½IJ", - "ĠYön etim", - "ĠYönet im", - "Ùħ ÛĮر", - "ÙħÛĮ ر", - "Ġsp ou", - "Ġspo u", - "æ·± åľ³", - "Ġвза ÑĶм", - "ÎĻ ÎĽ", - "Ð ĥ", - "ĠдеÑĢжав ноÑĹ", - "Ġ mrt", - "Ġm rt", - "Ġmr t", - "ĠDe mir", - "ĠDem ir", - "é» İ", - "ĠÑĢег ÑĥлÑıÑĢ", - "Ġник огда", - "å¼ ¾", - "à¥ī ड", - "Ġг лаз", - "Ġгла з", - "ĠÙħÛĮ Ú©ÙĨ", - "ĠÙħÛĮÚ© ÙĨ", - "éĻIJ å®ļ", - "Ġнав к", - "Ġпод ÑĤ", - "ĠتصÙĪ ÛĮر", - "ĠاÙĦØŃ Ø¯ÙĬØ«", - "Ġdo Å¡lo", - "нÑİ Ñİ", - "ĠÑģ Ñħод", - "ĠÑģÑħ од", - "Ø· ÙĤØ©", - "ĠÑģенÑĤ ÑıбÑĢÑı", - "çī¹ æ®Ĭ", - "à¸ģาร à¹ģà¸Ĥ", - "á zd", - "áz d", - "ÑĶ ÑĤе", - "ĠΣ ε", - "ĠÙĦ ÙĥÙĦ", - "ĠÙĦÙĥ ÙĦ", - "åIJį åŃĹ", - "اÙĨ ÛĮا", - "اÙĨÛĮ ا", - "Ġc ins", - "Ġcin s", - "Ġci ns", - "기 ìĹħ", - "Ġ éŁ³", - "Ġé Ł³", - "ĠéŁ ³", - "éł ĥ", - "ย าย", - "ยา ย", - "ìļ ķ", - "ĠvÃŃ tÄĽz", - "à¥įर ब", - "Ġشر ÙĤÛĮ", - "ĠشرÙĤ ÛĮ", - "ĠbezpeÄį nost", - "Ġçerç ev", - "Ġ 맼", - "Ġë§ Ľ", - "c ky", - "ck y", - "ĵ ¨", - "ĠÑĥм оваÑħ", - "ĠÑĥмов аÑħ", - "л иÑħ", - "ли Ñħ", - "m eniz", - "men iz", - "meni z", - "Ġب Ú¯ÛĮر", - "Ġبگ ÛĮر", - "ÙĨ Ùī", - "Ġ à¸ģารà¹ģà¸Ĥ", - "Ġà¸ģาร à¹ģà¸Ĥ", - "ι Ïĥε", - "ιÏĥ ε", - "âĢ³ E", - "Ġdönem inde", - "리 ì¹´", - "Ġ åĪ°", - "ĠåĪ °", - "Ġhu kuk", - "Ġhuku k", - "а ÑĤоÑĢа", - "аÑĤ оÑĢа", - "аÑĤоÑĢ а", - "аÑĤо ÑĢа", - "ĠاÙĦ عÙĨ", - "ĠاÙĦع ÙĨ", - "ïº ĺ", - "ün üz", - "ünü z", - "Ñģ оÑĤ", - "Ñģо ÑĤ", - "ุ ษ", - "Ġd Æ°Æ¡ng", - "ov ny", - "Ġ ÑĦоÑĢма", - "ĠÑĦоÑĢм а", - "ĠÑĦоÑĢ ма", - "ãģĹ ãģ®", - "ãģĹãģ ®", - "ز ÙĬز", - "زÙĬ ز", - "ĠاÙĦÙĨ اس", - "Ġ Ñĩим", - "ĠÑĩ им", - "ĠÑĩи м", - "大 人", - "Ú¯ ÙĬ", - "ĠÐĵ оÑģп", - "é¢Ĩ 导", - "Ġn inh", - "Ġni nh", - "Ġnin h", - "Ġร าà¸Ħา", - "Ġราà¸Ħ า", - "ÙĤ اء", - "ìī ¬", - "ĠìĿ´ ìłĦ", - "ĠÃ¶ÄŁret men", - "ĠÑĨвеÑĤ а", - "ен ноÑģÑĤÑĮ", - "енно ÑģÑĤÑĮ", - "大 ãģį", - "ĠмиÑģÑĤ еÑĨÑĤ", - "ر ÙĪت", - "رÙĪ ت", - "p oÅĪ", - "po ÅĪ", - "ĠÅŀ irket", - "ĠкÑĢаÑģ ив", - "ĠÑĢеÑģ ÑĥÑĢÑģ", - "ĠÑĢеÑģÑĥÑĢ Ñģ", - "ä¹ ¾", - "Ġ ÙģÙĩ", - "ĠÙģ Ùĩ", - "ĠY Ãĸ", - "èĬ ³", - "μ ÏīÏĤ", - "ÄĽ ji", - "ÄĽj i", - "Ġв лаж", - "Ġвла ж", - "ĠÑĥв ели", - "ا ذا", - "اذ ا", - "ãĢĤ å¦Ĥæŀľ", - "ĠпÑĢи ÑģÑĥÑĤÑģÑĤв", - "ĠẤ n", - "æĢ ĸ", - "ĠÐľ еÑĤ", - "Ġje dna", - "Ġjed na", - "Ġjedn a", - "Ġc ục", - "Ġcụ c", - "ĠاÙĨت شار", - "Ġз окÑĢема", - "и ÑĩеÑģки", - "иÑĩеÑģ ки", - "ĠкÑĢаÑĹ Ð½Ð¸", - "ĠкÑĢаÑĹн и", - "и ÑĢÑĥ", - "иÑĢ Ñĥ", - "ĠÑĸн ÑĤеÑĢ", - "ĠÑĸнÑĤ еÑĢ", - "Ġан алог", - "Ñ Ľ", - "ี à¸ĭ", - "н Ñĥли", - "нÑĥ ли", - "нÑĥл и", - "ĠN inh", - "ĠNi nh", - "ĠNin h", - "еÑĢ аÑĤоÑĢ", - "еÑĢа ÑĤоÑĢ", - "Ġr uce", - "Ġru ce", - "ĠÑĪ кÑĸ", - "ĠÑĪк Ñĸ", - "تر ÙĨت", - "Ġson rası", - "Ġsonra sı", - "Ġ æį", - "Ġæ į", - "ÑĨен ÑĤÑĢа", - "ÑĨенÑĤ ÑĢа", - "Ġà¸Ńำ à¹Ģà¸ł", - "Ø· ÙĬ", - "ï¼Į å½ĵ", - "ĠÑĤ ÑĢеÑħ", - "ĠÑĤÑĢ еÑħ", - "Âł H", - "æ´ ª", - "ãĥ³ ãĥĦ", - "ãĥ³ãĥ Ħ", - "ĠвÑĸдповÑĸд алÑĮ", - "âĢĻ daki", - "âĢĻd aki", - "âĢĻda ki", - "á ÅĻi", - "áÅĻ i", - "ĠpÅĻ em", - "ĠpÅĻe m", - "t uk", - "tu k", - "ĠÙģر ÙħÙĪد", - "ĠÙģرÙħ ÙĪد", - "Ġ ìĿ¸ì¦Ŀ", - "ĠìĿ¸ ì¦Ŀ", - "สำ à¸Ļ", - "ìĥģ ìĿĺ", - "ÅĻ ÃŃm", - "ÅĻÃŃ m", - "æ¾ ¤", - "Ġ ÑĢей", - "ĠÑĢ ей", - "ĠÑĢе й", - "ĠлÑİб ой", - "u jte", - "uj te", - "ë³µ ì§Ģ", - "Ġ درس", - "Ġد رس", - "Ġدر س", - "ĠÐĴ лади", - "ĠÑģво им", - "ĠÑģвои м", - "ĠìĿ¸íĦ° ëĦ·", - "è± Ĭ", - "Ġн алог", - "Ġнал ог", - "ãĤĪ ãģ³", - "ĠØ® اطر", - "Ġ ìŀħëĭĪëĭ¤", - "Ġìŀħ ëĭĪëĭ¤", - "ãĢĤ ãģĹãģĭãģĹ", - "л аг", - "ла г", - "å° ĸ", - "ëĭ ¥", - "ìĬ¤ ëĬĶ", - "ìĭł ì²Ń", - "ãĥĩ ãĥ¼ãĤ¿", - "ĠÑĥÑĢов нÑı", - "Ġ무 ìĬ¨", - "ĠاÙĦØ£ رض", - "à¹ī à¸ķ", - "Ỽ t", - "ĠÙĨÛĮ رÙĪ", - "ĠÙĨÛĮر ÙĪ", - "å¢ ¨", - "ãĤ¶ ãĥ¼", - "r uba", - "ru ba", - "rub a", - "ĠÙĨØ´ دÙĩ", - "и лÑı", - "ил Ñı", - "a cÃŃm", - "ac ÃŃm", - "acÃŃ m", - "ãĥ© ãĤ¯", - "X H", - "Ġس رد", - "Ġسر د", - "Ġद स", - "t ember", - "tem ber", - "ĠDoÄŁ um", - "ĠDoÄŁu m", - "ĠпÑĢ оÑĢ", - "ĠпÑĢо ÑĢ", - "θ οÏĤ", - "θο ÏĤ", - "ĠiÅŁ e", - "à¸Ń à¸Ł", - "л аÑĪ", - "ла ÑĪ", - "اص ÙĦÙĩ", - "اصÙĦ Ùĩ", - "l ivÄĽ", - "li vÄĽ", - "liv ÄĽ", - "ë¶Ģ ë¶Ħ", - "н ак", - "на к", - "åįģ ä¸ī", - "ส าห", - "à¸Ľà¸£à¸°à¹Ģà¸Ĺศ à¹Ħà¸Ĺย", - "ãĤŃ ãĥ³ãĤ°", - "ĠмеÑĤ оÑİ", - "Ġkullan arak", - "âij ¡", - "ÛĮز ات", - "ĠÙħÙĪب اÛĮÙĦ", - "ĠзнаÑĩ иÑĤ", - "Ġзна ÑĩиÑĤ", - "Ġorgan izace", - "Ġorganiz ace", - "ÑĢ ии", - "ÑĢи и", - "ov na", - "Ġ ê²½ìłľ", - "Ġê²½ ìłľ", - "ãĢģ å½¼", - "Ġम स", - "Ġ à¹Ĥà¸Ľà¸£", - "Ġà¹Ĥ à¸Ľà¸£", - "L ARI", - "LA RI", - "LAR I", - "æĩ Ĥ", - "Ġ ва", - "Ġв а", - "ĠÙĥ ÙĨت", - "ĠÙĥÙĨ ت", - "ĠÑĢабоÑĤ а", - "ĠÑĢаб оÑĤа", - "ĠÑĢабо ÑĤа", - "Âł ĠÂłĠÂł", - "ÂłĠ ÂłĠÂł", - "ÂłĠÂł ĠÂł", - "好 äºĨ", - "ĠzamÄĽst n", - "ж енÑĮ", - "же нÑĮ", - "жен ÑĮ", - "Ġu kon", - "Ġuk on", - "nÄĽ né", - "nÄĽn é", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "ĠاÙĦخاص Ø©", - "ĠÄį asu", - "ĠÄįas u", - "å°ı 说", - "ĠØŃر کت", - "æij Ħ", - "Ïĩ Ïī", - "ĠÑģв еж", - "æĸ° éĹ»", - "Ġ ìĭ±", - "Ġìĭ ±", - "Ġe ÄŁer", - "ĠeÄŁ er", - "Ġsitu ace", - "Ġ ç·¨", - "Ġç ·¨", - "Ġç· ¨", - "f ik", - "fi k", - "ë§ Īëĭ¤", - "ë§Ī ëĭ¤", - "Îķ Îļ", - "Ġê°ľ ìµľ", - "Ġc Ãł", - "ا دث", - "اد Ø«", - "Ġsay ıda", - "Ġsayı da", - "ĠØ£ ÙģضÙĦ", - "ĠØ£Ùģ ضÙĦ", - "æ³ķ éĻ¢", - "Ġ .,", - "Ġ. ,", - "ĠTh Æ°Æ¡ng", - "Ïģ ÏĮÏĤ", - "ÏģÏĮ ÏĤ", - "ãģĹ ãĤĪãģĨ", - "Ç İ", - "æ ij¸", - "æij ¸", - "Ġ éĻ³", - "ĠéĻ ³", - "¥ IJ", - "ฤ à¸Ķ", - "Ġgi ảng", - "Ġgiả ng", - "Ġgiản g", - "ĠлÑİ Ð±Ð¾Ð²", - "ĠлÑİб ов", - "Ġek ran", - "о пиÑģ", - "оп иÑģ", - "еж дÑĥ", - "Ġназ ва", - "æĭ ĵ", - "ı f", - "à¹Ī à¸ģ", - "и ÑĩнÑĸ", - "иÑĩ нÑĸ", - "Ġ ê³Ħíļį", - "Ġê³Ħ íļį", - "à¸ł าà¸Ħม", - "à¸łà¸²à¸Ħ ม", - "Ġ اپ", - "Ġا Ù¾", - "리 ìĿĺ", - "ãģ§ãģĻ ãģĮ", - "Ġkon ci", - "Ġکار خاÙĨÙĩ", - "Ġ ä½ķ", - "ĠÑĤ ва", - "ĠÑĤв а", - "ĠÐŁ оÑģÑĤ", - "ĠÐŁÐ¾ ÑģÑĤ", - "ĠÐŁÐ¾Ñģ ÑĤ", - "ĠапÑĢ елÑı", - "ĠاÙĦع راÙĤ", - "ä¸Ń åįİ", - "à¹ĩ à¸Ńà¸ģ", - "à¥įत à¤ķ", - "Ġz ájem", - "Ġzá jem", - "Ġدر جÙĩ", - "Ġब à¥ľ", - "ĠÑģÑĤ ÑĢан", - "ĠÑģÑĤÑĢ ан", - "ĠÑģÑĤÑĢа н", - "èѦ å¯Ł", - "Ġyer leÅŁtir", - "ĠyerleÅŁ tir", - "ĠV Å©", - "ç¾İ åħĥ", - "Ġì¡° ê¸Ī", - "Ġ รà¸Ńà¸ĩ", - "Ġร à¸Ńà¸ĩ", - "Ġak adem", - "Ġaka dem", - "à¸Ħ à¸ĵะ", - "Ġpoz it", - "Ġkon eÄį", - "Ġkone Äį", - "è°ĥ æŁ¥", - "Ġ ãģĭ", - "ĠÄįerv ence", - "ĠOd kazy", - "ĠëıĦ ìĭľ", - "ั สà¸Ķ", - "ัส à¸Ķ", - "Ġg ái", - "ĠÐł об", - "Ġб оÑı", - "Ġбо Ñı", - "æī ©", - "å¼Ģ å±ķ", - "a nik", - "an ik", - "ani k", - "Ġvy ž", - "ĠbaÅŁ lay", - "Ġbak Ä±ÅŁ", - "ek ce", - "ÑģÑĤ ика", - "ÑģÑĤи ка", - "еÑĢа ÑĤÑĥÑĢа", - "еÑĢаÑĤÑĥ ÑĢа", - "Ġë¶Ħ ë¥ĺ", - "ĠPo Äįet", - "od áÅĻ", - "ëĭĺ ìĿĺ", - "Ġk lid", - "Ġkl id", - "Ġkli d", - "Û² Û¹", - "ĠÚĨ ÛĮز", - "m ür", - "Ġs ứ", - "ÙĬا ÙĨØ©", - "ÙĬاÙĨ Ø©", - "åĬ ±", - "Ġ oku", - "Ġo ku", - "Ġok u", - "Ġв оди", - "Ġво ди", - "Ġвод и", - "ĠزÛĮر ا", - "ĠزÛĮ را", - "大 åĪ©", - "ĠÙĦ ÛĮÙĨÚ©", - "ĠÙĬ جب", - "ĠÙĬج ب", - "Ùħ ÛĮÙĦ", - "ÙħÛĮ ÙĦ", - "ĠÏĥ ÏĦÏģα", - "æĻ ĵ", - "ิ สà¸ķ", - "ิส à¸ķ", - "ĠÅŁ iddet", - "ĠÑĢекомен да", - "Ġpožad av", - "Ġп ÑĸÑģ", - "åħ¬ å¼ı", - "Ġ Ú¯ÛĮرÛĮ", - "ĠÚ¯ ÛĮرÛĮ", - "ĠÚ¯ÛĮ رÛĮ", - "ĠÚ¯ÛĮر ÛĮ", - "к ÑĤа", - "кÑĤ а", - "ĠÙħÙĨ اطÙĤ", - "Ġfirm y", - "Ġfir my", - "Ġ à¹Ħà¸Ľ", - "Ġà¹Ħ à¸Ľ", - "Ġ ÎŃÏģγ", - "ĠÎŃ Ïģγ", - "å¿« éĢŁ", - "ãģĮ ãģªãģĦ", - "н еÑģÑĤи", - "не ÑģÑĤи", - "неÑģ ÑĤи", - "Ġ ç²¾", - "Ġç² ¾", - "ÑĢ ади", - "ÑĢа ди", - "ÑĢад и", - "ãĤĴ ãģĭ", - "ïº ª", - "ky nÄĽ", - "Ġह त", - "t ak", - "ta k", - "ĠÙĬÙĪÙĨ ÙĬÙĪ", - "ö ÄŁ", - "Ġ ÑĢÑĥк", - "ĠÑĢ Ñĥк", - "ĠÑĢÑĥ к", - "åľĭ éļĽ", - "Ñİ ÑģÑĮ", - "Ġдав но", - "Ġp opis", - "Ġpop is", - "Ġpo pis", - "ĠB Ä°L", - "ĠÙĨ ÙĤد", - "ĠÙĨÙĤ د", - "ĠÑģп ож", - "ÑĨион нÑĭÑħ", - "ĠÑĪ п", - "Ñĥ ÑİÑīиÑħ", - "ÑĥÑİ ÑīиÑħ", - "ÑĥÑİÑī иÑħ", - "ĠвоздÑĥ Ñħ", - "ÑĤ ие", - "ÑĤи е", - "ĠU ž", - "ÏĮ δ", - "à¸ģร าà¸Ħม", - "Ġalan ında", - "Ġalanı nda", - "Ġs ắt", - "ãĥIJ ãĤ¤", - "Ng Ãły", - "Ġ ë¹Į", - "Ġë¹ Į", - "ï¼ī ãģ¯", - "Ġ ä¿¡", - "Ðķ С", - "ĠT ato", - "ĠTa to", - "ĠTat o", - "Ġún ora", - "e rap", - "er ap", - "era p", - "Ä ł", - "ĠT áºŃp", - "Ġкомп ании", - "ãĥ© ãĤ¤ãĥĪ", - "ãĥ©ãĤ¤ ãĥĪ", - "éľĢ æ±Ĥ", - "Ġت ÙĪÙĤ", - "ĠتÙĪ ÙĤ", - "âĢĻ âĢĻ", - "ëŀį ëĭĪëĭ¤", - "ĠквÑĸÑĤ нÑı", - "Ġoyun cu", - "ÂĢÂĢÂĢÂĢ ÂĢÂĢÂĢÂĢ", - "åĨ Ĭ", - "Ġyap mÄ±ÅŁ", - "ัà¸ĩ à¹Ħม", - "Ġзап аÑħ", - "á la", - "ál a", - "ĠÑĤеÑħ ниÑĩеÑģ", - "Ġ ØŃص", - "ĠØŃ Øµ", - "ร à¸Ķ", - "å¼ Ħ", - "ĠÚ¯ÛĮ اÙĩ", - "اÙĩ رة", - "اÙĩر Ø©", - "Ġà¤ı ड", - "ним аеÑĤ", - "нима еÑĤ", - "ا دÙĨ", - "اد ÙĨ", - "Îľ Îij", - "Ġ 社", - "Ġç¤ ¾", - "аÑĢ Ñĩ", - "ت ز", - "æ¶ ¦", - "in izin", - "ini zin", - "iniz in", - "inizi n", - "Ġbey az", - "Ġ بÙĪÙĦ", - "Ġب ÙĪÙĦ", - "ĠبÙĪ ÙĦ", - "åĿ ¡", - "ãģ® ãĤĪãģĨãģ«", - "Ġyap tıģ", - "Ġyaptı ÄŁ", - "Ġd aģı", - "Ġda ģı", - "ĠdaÄŁ ı", - "ĠbaÅŁ arı", - "ĠbaÅŁar ı", - "Ġ ÏĢά", - "ĠÏĢ ά", - "ĠпÑĢод аж", - "B á»Ļ", - "Ġत त", - "Ġpod stat", - "Ġpods tat", - "Ġ æµģ", - "Ġæµ ģ", - "Ġzdrav ÃŃ", - "Ġ ç¡", - "Ġç ¡", - "Ġ opak", - "Ġo pak", - "Ġop ak", - "Ġhá»į a", - "æĭ Ķ", - "Ñĥ жд", - "Ñĥж д", - "Ġtr ứng", - "ÙĪر ÙĬØ©", - "ÙĪرÙĬ Ø©", - "Ñĭ л", - "um suz", - "ums uz", - "Ġ سبب", - "Ġسب ب", - "许 å¤ļ", - "å®ŀ éªĮ", - "Ġб оли", - "Ġбол и", - "Ġбо ли", - "Ġd uyá»ĩt", - "áºŃ c", - "ĠÐij ез", - "ĠبÙĦ ÙĨد", - "м м", - "ÑĢ ел", - "ÑĢе л", - "N Ä°", - "Ġ ãĥ¯", - "Ġãĥ ¯", - "éĭ ¼", - "ĠÑģв Ñı", - "Ġ åIJİ", - "ĠåIJ İ", - "Ġmu ht", - "Ġmuh t", - "ĠпÑĢоблем и", - "ĠÑĤÑıж ел", - "ĠС ем", - "ฤษ à¸łà¸²à¸Ħม", - "à¹Ī าà¸ķ", - "à¹Īา à¸ķ", - "ör ü", - "üy orum", - "üyor um", - "ĠاÙĦØ£ ØŃ", - "ĠÑģÑĤÑĢ аÑĪ", - "ĠÑģÑĤÑĢа ÑĪ", - "h oo", - "ho o", - "ध र", - "Ġt lak", - "Ġtl ak", - "Ġsrp na", - "ifik ace", - "Ġ reh", - "Ġre h", - "Ġr eh", - "Ġм инÑĥ", - "Ġмин Ñĥ", - "Ġми нÑĥ", - "ãĢĢ j", - "ĠгÑĢÑĥ пи", - "ĠгÑĢÑĥп и", - "Ġ άλ", - "Ġά λ", - "Ġolur sa", - "λογ ία", - "ĠÐĴ ик", - "ĠÐĴи к", - "Ġmüc adel", - "Ġz ávÄĽ", - "Ġzá vÄĽ", - "Ġzáv ÄĽ", - "ĠÑĦев ÑĢа", - "Äį ná", - "à¹Į à¹Ģà¸ĭ", - "ĠÙĦ ÙĦØŃ", - "ĠÙĦÙĦ ØŃ", - "ÑĢ ип", - "ÑĢи п", - "Ġб Ñĥк", - "ĠбÑĥ к", - "ãģĪ ãģªãģĦ", - "Ġpo rad", - "Ġpor ad", - "Ġsa mostat", - "Ġsam ostat", - "Ġsamo stat", - "Ġt esis", - "Ġte sis", - "Ġtes is", - "اب ÙĤÙĩ", - "ابÙĤ Ùĩ", - "Ġجد ÙĬدة", - "ĠجدÙĬد Ø©", - "éĢ Ĵ", - "âĶģ âĶ", - "س ÛĮÙĨ", - "سÛĮ ÙĨ", - "Ġgerek tiÄŁini", - "ียà¸Ļ à¸ļ", - "è¨Ģ ãģ£ãģ¦", - "ĠÑĸн ÑĤеÑĢеÑģ", - "ĠÑĸнÑĤеÑĢ еÑģ", - "ĠÑı ким", - "ĠÑıк им", - "Ġ æĢ»", - "ĠæĢ »", - "k ovou", - "ko vou", - "kov ou", - "Ġd emek", - "Ġde mek", - "Ġdem ek", - "اÙĨ ÙĬا", - "اÙĨÙĬ ا", - "Ġdom ů", - "Å¡ nÃŃ", - "ate ÅĻ", - "åĢ «", - "δο Ïĥη", - "Ġ 기ìĹħ", - "Ġ기 ìĹħ", - "åĶ ĩ", - "ì¹ ł", - "Ñĸ дÑĥ", - "Ñĸд Ñĥ", - "린 ìĿ´", - "æľĢ åĪĿ", - "è ¸ı", - "è¸ ı", - "æĥ³ åĪ°", - "à¥į बर", - "à¥įब र", - "Ġ ìŀĶ", - "Ġìŀ Ķ", - "ĠÑĢаз нÑĭÑħ", - "k rom", - "kr om", - "ι αν", - "ια ν", - "Ġд ÑĢÑĥз", - "ĠдÑĢÑĥ з", - "ĠдÑĢ Ñĥз", - "ä »¿", - "ä» ¿", - "Ġê·¸ ëłĩ", - "Ġд алÑĸ", - "Ġда лÑĸ", - "Ġдал Ñĸ", - "æķĪ æŀľ", - "Ġह व", - "è¼ Ŀ", - "Ġì°¸ ê³ł", - "Ġ ìĨĶ", - "ĠìĨ Ķ", - "Ġz nal", - "Ġzn al", - "ĠпеÑĢ Ñģ", - "ÙIJ Ùij", - "ĠÑĤ еж", - "ĠÑĤе ж", - "åĭ Ł", - "ι θ", - "Äį ů", - "Ġe kip", - "Ġek ip", - "Ġk hung", - "Ġkh ung", - "Ġkhu ng", - "éĹ ĺ", - "ĠتصÙħ ÛĮÙħ", - "о иÑĤ", - "ĠÑħ ол", - "æĬ ŀ", - "a mam", - "am am", - "ama m", - "Ġâĸ ³", - "ãģ ĩ", - "Ġع ÙĨÙĩ", - "ĠعÙĨ Ùĩ", - "Ġì°¸ ê°Ģ", - "ĠÎļ ÏĮ", - "åı¤ å±ĭ", - "к овоÑĹ", - "ков оÑĹ", - "ково ÑĹ", - "ศ à¸Ī", - "олог иÑı", - "ĠÙħØ« بت", - "ĠÐļÑĢа ÑĹна", - "ĠмеÑģÑı ÑĨев", - "Ġalın an", - "ĠÏĢÏģα γμα", - "Ġ ìŀ¡ëĭ´", - "Ġìŀ¡ ëĭ´", - "Ġп лод", - "Ġпл од", - "Ġпло д", - "ĠÑĤка ни", - "ÑģÑĭ лки", - "ÑģÑĭл ки", - "سط س", - "ra nÄĽ", - "ran ÄĽ", - "к аж", - "ка ж", - "е маÑĤи", - "ем аÑĤи", - "ема ÑĤи", - "Ġز ÛĮست", - "ĠزÛĮ ست", - "æ¿ Ł", - "Ġpop lat", - "Ġpo plat", - "γ ÎŃν", - "íĨł íĨł", - "Ġt ây", - "Ġìµľ ê·¼", - "ãĥ© ãĥ³ãĤ¹", - "ãĥ©ãĥ³ ãĤ¹", - "Ġgün eÅŁ", - "Ġ ÙģÙĤ", - "ĠÙģ ÙĤ", - "ĠsaÄŁ layan", - "ĠsaÄŁlay an", - "ĠØŃ Ø²Ø¨", - "à¥ģल न", - "ĠB ilim", - "ĠBi lim", - "ĠBil im", - "ĠB atı", - "ĠBa tı", - "ĠBat ı", - "æł· çļĦ", - "δ ικ", - "δι κ", - "α ÏģίοÏħ", - "αÏģ ίοÏħ", - "Ġ ìĽĢ", - "ĠìĽ Ģ", - "Ġl á»Ńa", - "ÙĨ ÙĪع", - "çİ ²", - "а ном", - "ан ом", - "ано м", - "Ġst átnÃŃ", - "Ġstát nÃŃ", - "Ġ äºİ", - "Ġm ùi", - "ĠÄij á»Ļt", - "ĠÄijá»Ļ t", - "æ² ĥ", - "åħ¬ åľĴ", - "ĠÑģ ÑĮогоднÑĸ", - "но Ñģи", - "ноÑģ и", - "Z a", - "Ġд ли", - "ĠÏĥÏħν ÎŃ", - "ĠV á»ĭ", - "m av", - "ma v", - "ĠM üslüman", - "/ ï¼ı", - "ĠзаÑī иÑĤ", - "é ĸī", - "éĸ ī", - "Ġ çģ«", - "Ġçģ «", - "Ġ å·Ŀ", - "Ġå· Ŀ", - "Ġ аж", - "Ġа ж", - "è¿ĩ æĿ¥", - "à¸Ĺ าà¸Ļ", - "ĠAr aÅŁtır", - "ĠAra ÅŁtır", - "Õ¡ Õ", - "Ġpo mÄĽr", - "Ġpom ÄĽr", - "Ġd ům", - "Ġdů m", - "å¦ ®", - "Ġhlav nÄĽ", - "Ġfin ans", - "Ġfinan s", - "Ġ γνÏī", - "Ġγ νÏī", - "ÏĥÏĦη μα", - "ï¼Į çĶ¨", - "ìĭŃ ìĭľìĺ¤", - "ĠÙħ ثاÙĦ", - "ĠÙħØ« اÙĦ", - "- Ðij", - "ÑĨÑĸй нÑĸ", - "Ġد ستÙĩ", - "Ġدست Ùĩ", - "Ġدس تÙĩ", - "à¥ī स", - "ÑĢ Ñĸп", - "ÑĢÑĸ п", - "ĠpÅĻi pom", - "ĠpÅĻip om", - "Ġ ÙĪÙĦÙĬ", - "ĠÙĪ ÙĦÙĬ", - "ĠÙĪÙĦ ÙĬ", - "ĠÙĪ زÙĨ", - "ĠÙĪز ÙĨ", - "Ġelekt rik", - "Ġelektr ik", - "ĠQu ân", - "i vé", - "iv é", - "Ġl ẽ", - "ç®Ģ åįķ", - "Ġon lara", - "Ġonlar a", - "оÑģ лав", - "ìĭľ íĤ¤", - "ëª ¬", - "ĠÙħÙĤ دار", - "ĠÙħÙĤد ار", - "ĠOr ta", - "ĠOrt a", - "ĠS eç", - "ĠSe ç", - "ĠÙĨÙĪÙģ Ùħبر", - "ุà¸Ļ ายà¸Ļ", - "ĠÑĥм ови", - "ĠÑĥмов и", - "Ġपर म", - "Ġ strom", - "Ġst rom", - "Ġstr om", - "Ġstro m", - "ĠкÑĢа Ñīе", - "ç§ ¦", - "缸 æīĭ", - "鼻 è¦ĸ", - "Ġuygu lama", - "Ġuygulam a", - "Ġ ÑĢиз", - "ĠÑĢ из", - "æĪ ²", - "य र", - "ĠH lav", - "Ġ ìĭ¸", - "Ġìĭ ¸", - "Ġли пнÑı", - "ÅĪ ujÃŃ", - "ÑĢ из", - "ÑĢи з", - "é«ĺ éĢŁ", - "缸 å½ĵ", - "k enin", - "ke nin", - "ken in", - "Ġо ÑģÑĤанов", - "ĠоÑģÑĤ анов", - "ĠоÑģÑĤан ов", - "Ġbit k", - "Ġbi tk", - "ova ného", - "ovan ého", - "ované ho", - "ĠÐľ аÑĢи", - "ĠÐľÐ°ÑĢ и", - "ĠÐľÐ° ÑĢи", - "èµ ¶", - "ì½ ©", - "Ġölç ü", - "ĠС еÑĢед", - "ĠСеÑĢ ед", - "ĠTh á»Ŀi", - "Ïī να", - "Ïīν α", - "ÙĪ بة", - "ÙĪب Ø©", - "Ġch ụp", - "âĢĮ د", - "Ġch áy", - "ĠÐĴ ели", - "Ġоб ÑģÑĤ", - "ĠобÑģ ÑĤ", - "Ġìĭľ ì¦Į", - "د ÙħØ©", - "دÙħ Ø©", - "п од", - "по д", - "l ue", - "lu e", - "ĠдÑĸ лÑıн", - "ĠÙ¾ ÙĪست", - "ĠاÙĦ ÙĨس", - "ĠاÙĦÙĨ س", - "èĤ Į", - "ìĪĺ 를", - "Ġú rov", - "ĠÙħØ´ Ú©ÙĦ", - "ĠÙħØ´Ú© ÙĦ", - "éĩįè¤ĩ éĩįè¤ĩ", - "н ез", - "не з", - "Ġdop oruÄį", - "Ġtas arım", - "Ġtasar ım", - "íģ¬ 기", - "ìĿ´ ìħĺ", - "Ġde set", - "Ġdes et", - "Ġdese t", - "ĠÙħرتب Ø·", - "ัà¸Ĵ à¸Ļา", - "ัà¸Ĵà¸Ļ า", - "' ı", - "Ñĩ ки", - "ĠìŀĪ ëįĺ", - "ÑĪ ка", - "n ám", - "ná m", - "ÑģÑĤ ÑĢов", - "ÑģÑĤÑĢ ов", - "ÑģÑĤÑĢо в", - "à¥į सर", - "à¥įस र", - "нÑĥ лаÑģÑĮ", - "нÑĥла ÑģÑĮ", - "ãģ¡ãĤĩ ãģ£ãģ¨", - "Ġ å¦", - "Ġå ¦", - "γ ÏĮ", - "Ġ é»ij", - "Ġé» ij", - "X em", - "Ġt á»ĩ", - "Ġtá» ĩ", - "ĠëĮĢ íĨµëł¹", - "기 ê´Ģ", - "æīį èĥ½", - "è¯Ń è¨Ģ", - "ed eyse", - "ĠТ Ñĭ", - "ĠÑģо един", - "ĠìĹĨ ìĬµëĭĪëĭ¤", - "Ñı ÑİÑĤ", - "à¹ģ หล", - "à¹ģห ล", - "Ġì§Ģ ë°©", - "Ġosob nÃŃ", - "ÛĮ ÙĦÛĮ", - "ÛĮÙĦ ÛĮ", - "Ġавг ÑĥÑģÑĤа", - "Ñī ик", - "Ñīи к", - "Ġvý Å¡e", - "g th", - "gt h", - "ĠÏĢ αν", - "ĠÏĢα ν", - "ج ار", - "جا ر", - "Ġвид ов", - "Ġви дов", - "ìĿ´ ìĬĪ", - "ĠÐij аÑĢ", - "ĠÏĮ ÏĢοÏħ", - "æ¤ ħ", - "Ġع اÙĦÛĮ", - "ĠQ uyết", - "ĠQuy ết", - "Ãľ M", - "ãĥĿ ãĤ¤ãĥ³ãĥĪ", - "Ġ ê¹Į", - "Ġê¹ Į", - "Ġкан ди", - "k ového", - "kov ého", - "kové ho", - "ĠMerk ez", - "Ġy iy", - "Ġyi y", - "ĠpÅĻÃŃ spÄĽ", - "ĠÑĤемпеÑĢаÑĤÑĥ ÑĢÑĭ", - "ĠÙ¾ ÙĬ", - "ฤ ศà¸Ī", - "è°ĥ çĶ¨", - "ĠÑģÑĤоÑĢ онÑĥ", - "ĠÑģÑĤоÑĢон Ñĥ", - "à¹ī à¸Ĭ", - "好 ãģį", - ". Åŀ", - "Ġп ÑĢоз", - "ĠпÑĢ оз", - "ĠпÑĢо з", - "ÙĨت اج", - "鼻 åŃIJ", - ".: .:.", - ".:.: .", - ".:. :.", - "è¨ ĵ", - "и ÑĩеÑģкое", - "иÑĩеÑģ кое", - "Ġн оги", - "Ġно ги", - "Ġног и", - "Ġ λÎŃ", - "Ġλ ÎŃ", - "Ġsık ıntı", - "Ġê°Ģ 족", - "ĠتÙĨ ظÙĬÙģ", - "ĠتÙĨظ ÙĬÙģ", - "Ġö dül", - "ĠaÅŁaģı daki", - "Ġž elez", - "Ġže lez", - "ĠاÙĦع دÙĬد", - "غ ÙĨ", - "Ġокон Ñĩ", - "ÑĢем Ñı", - "ÑĢе мÑı", - "L Ä°", - "Ġne jd", - "Ġnej d", - "Ġ ÏĢλα", - "ĠÏĢ λα", - "Ñģ ко", - "Ñģк о", - "Ġ ìĪĻ", - "ĠìĪ Ļ", - "ĠÙ¾ ÙĪÙĦ", - "θεν ήÏĤ", - "Ġ주 ìļĶ", - "Ġ æĬ¥", - "ĠæĬ ¥", - "ĠÙħ Ùħا", - "ĠÙħÙħ ا", - "Ðł Ð¡Ðł", - "ĠÑĢа дÑĸ", - "ĠÑĢад Ñĸ", - "ä¸Ģ ç§į", - "é¾ Ħ", - "Ġsö yl", - "Ġsöy l", - "Ïģκε ια", - "Ïģκ εια", - "Ġзем лÑĸ", - "Ġve Äįer", - "g eç", - "ge ç", - "س تÙħ", - "ست Ùħ", - "Ġse fer", - "ĠÑģ вÑĸд", - "ĠÑģв Ñĸд", - "ï»Ł ï»", - "а лов", - "ал ов", - "ало в", - "ìĬ¤ 를", - "âī ¥", - "ĠتÙĦ ÙģÙĨ", - "ĠتÙĦÙģ ÙĨ", - "åİ» äºĨ", - "़ à¥ĭà¤Ĥ", - "़à¥ĭ à¤Ĥ", - "ĠÑĦоÑĢм е", - "ĠÑĦоÑĢ ме", - "d üm", - "dü m", - "åħ ģ", - "ÑĢ ап", - "ÑĢаР¿", - "ÑĢа п", - "ĠV Æ°Æ¡ng", - "à¸Ńะ à¹Ħร", - "ัà¸ģษ à¸ĵ", - "Ġ åį³", - "Ġåį ³", - "ĠاÙĦ رÙħ", - "ĠاÙĦر Ùħ", - "ĠзаÑħиÑģÑĤ Ñĥ", - "° E", - "o dÃŃ", - "od ÃŃ", - "Ġव न", - "ĠÄij èn", - "Ġ åıĹ", - "Ġåı Ĺ", - "èIJ½ ãģ¡", - "Ġ zim", - "Ġz im", - "Ġzi m", - "리 ì¦Ī", - "èĪ Ĵ", - "Ġзб ÑĸÑĢ", - "Ġ ä»·æł¼", - "ĠлÑİ Ð´Ð¸Ð½Ð°", - "ĠлÑİд ина", - "ĠлÑİди на", - "ĠÐŁÐ¾Ñģ иланнÑı", - "и Ñī", - "ĠÎ ¨", - "ิà¸ģ ายà¸Ļ", - "ิà¸ģา ยà¸Ļ", - "Ġbu dete", - "Ġbud ete", - "Ġbude te", - "Ġз ÑĢоÑģÑĤ", - "Ġ vyk", - "Ġv yk", - "Ġvy k", - "ĠÐĹ ÐµÐ¼", - "ĠиÑİ Ð½Ñı", - "ĠmÄĽ lo", - "ĠmÄĽl o", - "ÙĦ اÙģ", - "ÙĦا Ùģ", - "Ġ ÙĪØ´", - "ĠÙĪ Ø´", - "ĠÑģп ÑĢави", - "ĠÑģпÑĢав и", - "ãģĻ ãģİ", - "ĠгÑĢа дÑĥ", - "ĠгÑĢ адÑĥ", - "R oz", - "Ro z", - "ι νή", - "ιν ή", - "Ġch á»ĵng", - "ä¸Ģ åį·", - "Ġ Xem", - "ĠX em", - "ĠÑģимв ол", - "ĠÑģим вол", - "Ġod mÃŃt", - "ĠÑĢÑıд ом", - "ĠÑĢÑı дом", - "ĠÑĩеÑĢв нÑı", - "à¸ģระ à¸Ĺ", - "人 人", - "æ°Ĺ æĮģãģ¡", - "un daki", - "und aki", - "unda ki", - "åľĭ 家", - "εÏģ μαν", - "Ġ лÑĮ", - "Ġл ÑĮ", - "ĠN üfus", - "Ġм еÑĢе", - "ĠмеÑĢ е", - "بر اÙĬر", - "н аннÑı", - "Ġ наÑĢ", - "Ġн аÑĢ", - "Ġна ÑĢ", - "Ġt ấm", - "æĸ½ å·¥", - "é¡ ¯", - "Ġh è", - "æĺİ çĻ½", - "Ġдо гов", - "Ġдог ов", - "ĠÙģ رÙħ", - "ĠÙģر Ùħ", - "èĢ Ĺ", - "ìĬ¤ ìĿĺ", - "ìĦ¸ ëĮĢ", - "è¯ ļ", - "Ġнеб олÑĮ", - "Ġ à¸Ľà¸£à¸°à¸ģ", - "Ġà¸Ľà¸£à¸° à¸ģ", - "Ġì¹ ¼", - "Ġov liv", - "Ġ NGC", - "ĠN GC", - "ĠNG C", - "ãĢĤ ä¸į", - "ا ÙĦÙī", - "اÙĦ Ùī", - "æī £", - ". ÐIJ", - "ÑĢа ÑģÑĤа", - "ÑĢаÑģ ÑĤа", - "ÑĢаÑģÑĤ а", - "ĠÃĩ ev", - "ãģ£ ãģ¡", - "ãģ£ãģ ¡", - "ï¼Į éĥ½", - "Ġrov nÄĽÅ¾", - "ĠÏĩÏģÏĮ νια", - "Ġì¡° ìĦł", - "ĠØ¢ باد", - "Ġآب اد", - "ĠÐľ аÑģ", - "ĠÐľÐ° Ñģ", - "çĻ¼ å±ķ", - "ä» Ķ", - "Ġkend isini", - "Ġkendisi ni", - "à¹Īà¸Ńà¸ĩ à¹Ģà¸Ĺ", - "ĠV ÄĽ", - "Ġr ượu", - "Ġm áme", - "Ġmá me", - "Ġmám e", - "ĠоÑĩеÑĢед ÑĮ", - "Ġسب تÙħبر", - "Ġб ок", - "Ġбо к", - "ì§Ģ ìĹŃ", - "Ġتا Ø«ÛĮر", - "Ġتاث ÛĮر", - "Ġl isans", - "Ġli sans", - "Ġlis ans", - "Ġger ektir", - "Ġgerek tir", - "Ġs izi", - "Ġsi zi", - "Ġsiz i", - "Ñĸ но", - "Ñĸн о", - "ĠM Ã¼ÅŁ", - "ĠMü ÅŁ", - "ãģı ãĤīãģĦ", - "ãģıãĤī ãģĦ", - "Ġза клÑİÑĩ", - "Ġзак лÑİÑĩ", - "ãģĵãģ¨ ãģ«", - "è¨Ģ ãģĦ", - "ãĢģ å°ı", - "Ġet mektedir", - "Ġetm ektedir", - "åł± åijĬ", - "Ġkar Ä±ÅŁ", - "Ġоб лад", - "Ġобла д", - "Ġобл ад", - "å¥ ij", - "ra cat", - "rac at", - "ĠارتÙģ اع", - "μ αι", - "μα ι", - "íĶ Ī", - "ĠÙĪ ÙĦÙħ", - "ĠÙĪÙĦ Ùħ", - "ëĬĶ ì§Ģ", - "lom ou", - "Ġли ÑĨа", - "ĠлиÑĨ а", - "ĠìĿĮ ìķħ", - "Ġhod nÄĽ", - "èĭ± æĸĩ", - " Ħ", - "à¹ī าà¸Ĥà¸Ńà¸ĩ", - "à¹īา à¸Ĥà¸Ńà¸ĩ", - "Ġê³Ħ ìķ½", - "åIJĦ ç§į", - "ĠÙħر Ú¯", - "éĶ ģ", - "Ġन द", - "ãĥĭ ãĥ¡", - "Ġ ем", - "Ġе м", - "Ġe leÅŁtir", - "Ġel eÅŁtir", - "Ġele ÅŁtir", - "Ġ íĬ¹ë³Ħ", - "ĠíĬ¹ ë³Ħ", - "ĠÎ¥ ÏĢο", - "Å¡ ker", - "Å¡k er", - "L ERÄ°", - "LER Ä°", - "æ² Ī", - "l ikleri", - "lik leri", - "likle ri", - "likler i", - "ĠÙħÙĩÙĨد سÛĮ", - "ĠbaÄŁ ır", - "dı ģını", - "dıģ ını", - "ĠاÙĦ تد", - "ĠاÙĦت د", - "à¸¸à¸Ľ à¸ģรà¸ĵ", - "ĠÑģлед ÑĥÑİÑīие", - "ĠÑģледÑĥÑİÑī ие", - "Ġì§ģ ìłij", - "å° ¤", - "ĠоÑģнов Ñĸ", - "Ġt ÄĽla", - "ĠtÄĽ la", - "ĠtÄĽl a", - "Ġп ак", - "Ġпа к", - "iz ace", - "iza ce", - "Ġná rod", - "Ġnáro d", - "a ný", - "an ý", - "ĠÑį п", - "Ġüç üncü", - "Î¥ Ρ", - "éĨ´ éĨ´", - "à¹Ģà¸ģ à¸Ńร", - "âĢĮاÙĨ بار", - "ç¶ Ļ", - "Îij Îł", - "ı lıģı", - "ılı ģı", - "ıl ıģı", - "ılıģ ı", - "ĠÃľ rün", - "Ġдоз вол", - "Ġ íĥĪ", - "Ġíĥ Ī", - "Ġà¤ĵ वर", - "è« ¸", - "èĺ ĩ", - "ĠпÑĢоÑģÑĤ ÑĢан", - "éĿĴ å¹´", - "ãģ® æĸ¹", - "ĠÚĨ Ú¯ÙĪÙĨÙĩ", - "ÙĦ Ø·", - "âĢľ æĪij", - "Ġëĭ¤ìļ´ ë°Ľ", - "ा .Ċ", - "ा. Ċ", - "Ġmüc adele", - "Ġmücadel e", - "Ġc ÃŃt", - "ĠcÃŃ t", - "à¹Īวม à¸ģ", - "ÄŁ ına", - "ģı na", - "ģın a", - "ê°ľ ë°ľ", - "ĠÏĢ αιδ", - "ĠÏĢα ιδ", - "ĠÏĢαι δ", - "ض اÛĮ", - "ضا ÛĮ", - "Ġbor ç", - "íĬ ľ", - "ĠخدÙħ ت", - "Ġخد Ùħت", - "Ġu dál", - "Ġud ál", - "Ġ виг", - "Ġв иг", - "Ġви г", - "Ġ ë°°ìĨ¡", - "Ġë°° ìĨ¡", - "å¹ ¾", - "Ùİ Ø¬", - "Ġ ìĹĺ", - "ĠìĹ ĺ", - "çĢ ¬", - "ï Ģ", - "ĠÎij θή", - "пÑĢи клад", - "ĠпÑĢи Ñĩина", - "ĠпÑĢиÑĩин а", - "ĠпÑĢиÑĩ ина", - "ĠÙģ شار", - "æ »¿", - "æ» ¿", - "Ġd ostat", - "Ġdo stat", - "Ġdos tat", - "Ġdost at", - "Ġ졸 ìĹħ", - "Ġا رز", - "Ġار ز", - "ÙĪÙĦ ÙĪج", - "ÙĪÙĦÙĪ ج", - "س ÙĪ", - "æĺł çĶ»", - "Ġth ôi", - "Ġ ³³³", - "ĠÂł ³³", - "Ġ³³ Âł", - "à¹ģ à¸Ļะ", - "à¹ģà¸Ļ ะ", - "è¨Ń åĤĻ", - "Ġмног ие", - "ÑĤ оÑĦ", - "ÑĤо ÑĦ", - "i Å¡tÄĽ", - "iÅ¡ tÄĽ", - "à¤Ĺ ढ", - "Ġин дивидÑĥ", - "Ġ ìĥĿíĻľ", - "ĠìĥĿ íĻľ", - "Ġзов ÑģÑĸм", - "íĥ ķ", - "çľ ł", - "ĠêµŃ ëĤ´", - "e ptal", - "ep tal", - "ept al", - "r aci", - "ra ci", - "rac i", - "è¡ ¡", - "ãĦ ·", - "ĠSt ÅĻed", - "اÙĦ ÙĬا", - "اÙĦÙĬ ا", - "Σ Τ", - "Ľ °", - "ãĥī ãĥ«", - "á zÃŃ", - "áz ÃŃ", - "Ġа Ñģп", - "ĠаÑģ п", - "ĠdÄ±ÅŁ arı", - "ĠвиÑĢоб ниÑĨÑĤва", - "e za", - "ez a", - "ï¼Į ä¸įè¿ĩ", - "ï¼Įä¸į è¿ĩ", - "çĥ ¦", - "ãĥ³ ãĤ°ãĥ«", - "ãĥ³ãĤ° ãĥ«", - "Ġroz voj", - "ĠÙħÙĨت شر", - "ĠÑĥÑĤ еп", - "Ġد ÙĬÙĨ", - "ĠدÙĬ ÙĨ", - "ĠзаÑģоб Ñĸв", - "Ng Æ°á»Ŀi", - "ãĤ· ãĥ¼", - "ĠFran sız", - "ÎĻ Τ", - "ائ Ùģ", - "ι Ïĩ", - "ี à¹Ģม", - "à¥į मन", - "à¥įम न", - "à¥įम à¤ļ", - "Ġس عر", - "Ġسع ر", - "ï¾ Ŀ", - "ë°© ë²ķ", - "ĠС о", - "Ġà¤ĸ बर", - "ìĨĮ ê°ľ", - "Ġsl ova", - "Ġslo va", - "Ġslov a", - "Q PCP", - "QP CP", - "ĠK ız", - "ĠKı z", - "Ø· ÙģاÙĦ", - "Ø·Ùģ اÙĦ", - "Ġк оÑĢм", - "ĠкоÑĢ м", - "ĠìĹħ ëį°ìĿ´íĬ¸", - "es poÅĪ", - "esp oÅĪ", - "à¸Ķ าว", - "à¸Ķา ว", - "о ÑĢом", - "оÑĢ ом", - "оÑĢо м", - "ĠгÑĢа ÑĦ", - "ĠгÑĢ аÑĦ", - "Ġп ÑĸÑĪ", - "Ġ ë¿IJ", - "Ġë ¿IJ", - "ý v", - "С ам", - "Ġk rev", - "Ġkr ev", - "Ġkre v", - "ĠB unu", - "ĠBu nu", - "ĠBun u", - "Ġz obraz", - "Ġسخ ÙĨ", - "Ġ æĶ¯", - "ĠæĶ ¯", - "лÑİ Ð±", - "Ùİ Ø§ÙĨ", - "Ùİا ÙĨ", - "маÑĤ ÑĢива", - "λ εÏį", - "λε Ïį", - "Ġпо Ñħод", - "ĠпоÑħ од", - "Ġг ÑĢе", - "ĠгÑĢ е", - "çľĭ çĿĢ", - "à¸Īำ à¸ģ", - "ัà¸ĩà¸Ħ ม", - "Ġseç enek", - "Ä° stanbul", - "ĠвÑĸд мов", - "m iyor", - "mi yor", - "Ġm ụn", - "ìĿ´ ìĹIJ", - "ĠNh Æ°", - "Âł tom", - "Âłt om", - "lık ları", - "lıkla rı", - "lıklar ı", - "Âł Äij", - "ãĥ» ãĥŀ", - "Ġ Ùģت", - "ĠÙģ ت", - "ĠFakült esi", - "ìłĦ íŀĪ", - "éª ij", - "Ġìŀij ìĿĢ", - "ç¼ ĺ", - "ìº IJ", - "Ġmü zik", - "Ġmüz ik", - "а лÑĭ", - "ал Ñĭ", - "Ġp ozem", - "Ġpo zem", - "Ġpoz em", - "çĥ §", - "Ġ 常", - "Ġå¸ ¸", - "Å¡ il", - "Å¡i l", - "à¤Ĩ प", - "à¸ģำ หà¸Ļà¸Ķ", - "Ġگرد Ø´", - "λ ιά", - "λι ά", - "Ġö den", - "åıª è¦ģ", - "ĠÄIJ o", - "Ġstrat ej", - "Ġstra tej", - "Ġstrate j", - "ĠÙĩ تÙĦ", - "ÙĤ Ùģ", - "Ġkullan ılır", - "Ġkullanıl ır", - "ĠÑģп оÑģÑĤ", - "ĠÑģпоÑģ ÑĤ", - "ĠnÄĽ ho", - "ĠÐŁ еÑĢед", - "ĠÐŁÐµÑĢ ед", - "Ġиз меÑĢ", - "] ]>", - "]] >", - "ĠнÑĸк оли", - "Ġha yal", - "Ġhay al", - "Ġhaya l", - "Ġдод аÑĤков", - "Ġन à¤ķ", - "Ġins anın", - "Ġinsan ın", - "ุม à¸łà¸²à¸ŀ", - "ograf ie", - "в об", - "во б", - "ĠاÙĨ ساÙĨÛĮ", - "ĠاÙĨساÙĨ ÛĮ", - "Ġm ük", - "Ġmü k", - "ĠÑĥ меÑĢ", - "ĠÑĥм еÑĢ", - "оÑĩ нÑĭе", - "ëıĦ ìĿĺ", - "Ġ ara", - "Ġa ra", - "Ġar a", - "Ġë¹ ¨", - "Ġκ Ïį", - "л ой", - "ло й", - "Ñģи он", - "Ġroz dÃŃl", - "ay ıf", - "ayı f", - "ĠÙĪاØŃ Ø¯Ø©", - "ĠÙĪاØŃد Ø©", - "ĠÙĪا ØŃدة", - "о ÑĢалÑĮ", - "оÑĢ алÑĮ", - "оÑĢа лÑĮ", - "Ġpo chop", - "Ġpoc hop", - "éļ ¨", - "à¹īà¸Ń à¸ĩà¸Ļ", - "à¹īà¸Ńà¸ĩ à¸Ļ", - "Ġ ÙĪاÙĨ", - "ĠÙĪ اÙĨ", - "ĠÙĪا ÙĨ", - "Îľ ε", - "Ġ μον", - "Ġμ ον", - "Ġμο ν", - "Ñĥ ÑĪка", - "ÑĥÑĪ ка", - "or dum", - "ord um", - "æ¸ħ æ¥ļ", - "ĠDe ÄŁ", - "ÏĢ Ïģο", - "ĠÙĪاÙĦ تÙĬ", - "ĠÙĪاÙĦت ÙĬ", - "Ġp okus", - "Ġpo kus", - "Ġpok us", - "íĽĦ 기", - "é¥ ®", - "æĹħ è¡Į", - "Ġжен Ñīин", - "ĠdoÄŁru dan", - "Ġ Ñıб", - "ĠÑı б", - "Ġza ÄįÃŃ", - "ĠzaÄį ÃŃ", - "Ġë³´ ìŬ", - "- CP", - "-C P", - "åIJ ¨", - "à¥ĭ à¤ĸ", - "ÑĢ огÑĢа", - "ÑĢо гÑĢа", - "ÑĢог ÑĢа", - "ler di", - "ìĬ ´", - "Ùı ÙĪا", - "ÙıÙĪ ا", - "Ġustanov enÃŃ", - "Ġд оÑģÑĤав", - "Ġдо ÑģÑĤав", - "ĠдоÑģÑĤ ав", - "Ġfır sat", - "ĠاÙĦÙħÙĩ ÙĨØ©", - "ĠвеÑī еÑģÑĤва", - "ĠвеÑīеÑģÑĤв а", - "Ġн еÑģп", - "Ġне Ñģп", - "ĠнеÑģ п", - "ĠاÙĦکتر ÙĪÙĨ", - "t aÅŁ", - "ta ÅŁ", - "æĪ Ĵ", - "Ġy urt", - "Ġyu rt", - "Ġgir di", - "ĠÐļ Ñĥб", - "Ġ 를", - "Ġë¥ ¼", - "ุ à¹Į", - "ãģĿãģĨ ãģª", - "à¹ī Ċ", - "ĠвÑĭ бÑĢа", - "ĠвÑĭб ÑĢа", - "k ovÄĽ", - "ko vÄĽ", - "kov ÄĽ", - "ĠS iz", - "ĠSi z", - "Ġ گاÙĩ", - "ĠÚ¯ اÙĩ", - "ĠЧ аÑģ", - "Ġзг Ñĸдно", - ". ÐŁ", - "å§ Ĭ", - "ĠÐļ ÑĥÑĢ", - "ĠìĿĺ íķ´", - "Ġet raf", - "Ġк аÑĪ", - "Ġка ÑĪ", - "ĠØ· ÛĮ", - "ξ ει", - "ξε ι", - "ç² Ĵ", - "ĠØ¢ Ø°", - "Ġböl ge", - "Ġbölg e", - "Ġम à¤ľà¤¬", - "Ġà¤®à¤ľ ब", - "ÙIJ Ùĥ", - "Ġvál ky", - "ãģł ãĤĪ", - "Ġmes aj", - "Ġmesa j", - "ĠpÅĻ ist", - "ĠpÅĻi st", - "Ġtyp u", - "Ġty pu", - "ĠкиÑĪ еÑĩ", - "ãĤī ãģ®", - "Ġkend isi", - "Ġkendi si", - "ĠвÑĸдб Ñĥва", - "ĠвÑĸдбÑĥ ва", - "ä¾ ¯", - "Ġди за", - "ãĢĢ Ċ", - "ĠпÑĢоÑĨеÑģ Ñĥ", - "ĠÑįлек ÑĤÑĢ", - "_P US", - "Ġмног иÑħ", - "Ġk ém", - "Ġké m", - "æŀ ª", - "çİ° 代", - "Ġ éħį", - "Ġé ħį", - "Ġéħ į", - "ë¡ Ń", - "ÑĤи ÑģÑı", - "Ġl ục", - "ĠÙĪ اÙĦØŃ", - "ĠÙĪاÙĦ ØŃ", - "ĠÙĪا ÙĦØŃ", - "p tal", - "pt al", - "pta l", - "ẵ ng", - "ẵn g", - "ÏĢ λ", - "Ġd olu", - "Ġdo lu", - "Ġdol u", - "Ġt òa", - "Ġин огда", - "ĠпоÑĢÑıд ок", - "Як Ñīо", - "âĶ ĺ", - "Ġغ ربÛĮ", - "Ġغرب ÛĮ", - "Ġغر بÛĮ", - "移 åĬ¨", - "ยà¸Ļ à¸ķร", - "ยà¸Ļà¸ķ ร", - "H DATA", - "HD ATA", - "_PUSH DATA", - "_PUS HDATA", - "ĠØ« ابت", - "åĮħ åIJ«", - "ĠÏĢ ÏģÎŃÏĢει", - "़ à¥ĭ", - "åIJį åīį", - "ÑĤ еÑĢи", - "ÑĤе ÑĢи", - "ÑĤеÑĢ и", - "ï½ ¯", - "Ġ åħĪ", - "Ġåħ Ī", - "н ед", - "не д", - "Ïģ οÏįν", - "Ïģο Ïįν", - "ÏģοÏį ν", - "в ей", - "ве й", - "èĤ ĸ", - "ĠÅĻed itel", - "Ġth ép", - "Ġthé p", - "ĠÙĩ ÙģتÙĩ", - "ĠÙĩÙģت Ùĩ", - "ĠдÑĢÑĥг а", - "ĠдÑĢÑĥ га", - "ER Ä°", - "Ġ Ả", - "ĠẠ¢", - "ĠпеÑĢ еÑĢ", - "ĠпеÑĢе ÑĢ", - "Ġж еÑģÑĤ", - "Ġже ÑģÑĤ", - "ĠÄij ẳng", - "ç¦ ®", - "алÑĮ ном", - "алÑĮно м", - "िष य", - "ид енÑĤа", - "иденÑĤ а", - "Ġآخر ÛĮÙĨ", - "Ġ æĵ", - "Ġæ ĵ", - "Ġ มหาว", - "Ġมห าว", - "ĠлÑİ ÑĤого", - "ĠлÑİÑĤ ого", - "Ġб ÑĸзнеÑģ", - "gı ç", - "Ġng á»ĵi", - "оÑĩ нÑĭй", - "Ġo Äįek", - "ĠoÄį ek", - "ĠÙħ رة", - "ĠÙħر Ø©", - "Ġt var", - "Ġtv ar", - "Ġsam ozÅĻejmÄĽ", - "ĠBeled iye", - "Ġв ода", - "Ġво да", - "Ġвод а", - "Ġ Ú¯ÛĮرد", - "ĠÚ¯ÛĮ رد", - "ĠÚ¯ÛĮر د", - "Ġг одÑĭ", - "Ġгод Ñĭ", - "ãģ« è¡Į", - "æĺ¯ æĪij", - "ÑĪ или", - "ÑĪи ли", - "Ġ åĽ½äº§", - "ĠåĽ½ 产", - "ủ i", - "ĠбÑĥд ÑĥÑĤÑĮ", - "ĠбÑĥдÑĥÑĤ ÑĮ", - "ĠбÑĥдÑĥ ÑĤÑĮ", - "ĠÑĢай онÑĥ", - "ĠÑĢайон Ñĥ", - "Ġì ĵ", - "ĠÙĪ اس", - "ĠÙĪا س", - "ĠاÛĮ شاÙĨ", - "ενο δο", - "Ġнез алеж", - "ĠÙ¾ شت", - "Ġپش ت", - "Ġgir iÅŁim", - "ĠgiriÅŁ im", - "Ġд еле", - "Ġдел е", - "Ġде ле", - "ĠاصÙģÙĩ اÙĨ", - "à¸Ķ วà¸ģ", - "ĠاÙĦ ÙĤÙĬ", - "ĠاÙĦÙĤ ÙĬ", - "à¹Į à¸Ī", - "ëª »", - "Ġd ru", - "Ġdr u", - "è¿ ¹", - "ад женнÑı", - "адж еннÑı", - "Ùģ ÙĨ", - "Ïĩ οÏĤ", - "Ïĩο ÏĤ", - "à¹Ĥ à¸Ī", - "e yle", - "ey le", - "å¡ ij", - "Ġu prav", - "Ġup rav", - "Ġз даÑĤ", - "Ġзд аÑĤ", - "Ġзда ÑĤ", - "Ġvid ÄĽt", - "Ġ à¸Ľà¸£", - "Ġà¸Ľ ร", - "Ġ ÑĦеÑĢ", - "ĠÑĦ еÑĢ", - "ÐĨ н", - "Ġ ìµľìĭł", - "Ġìµľ ìĭł", - "l oha", - "lo ha", - "loh a", - "ĠиÑģп ÑĭÑĤ", - "Ġ avan", - "Ġa van", - "Ġav an", - "Ġava n", - "γ οÏħ", - "γο Ïħ", - "ĠGi ấy", - "ãĤ»ãĥ³ ãĤ¿ãĥ¼", - "éģ į", - "е ÑĢаÑħ", - "еÑĢ аÑħ", - "еÑĢа Ñħ", - "Ġê°Ģ ì§Ģê³ł", - "Ġê°Ģì§Ģ ê³ł", - "Ġ ид", - "Ġи д", - "Ġmnoh em", - "æ£Ģ æµĭ", - "Ġet me", - "Ġetm e", - "Ġ تÙħر", - "Ġت Ùħر", - "ĠتÙħ ر", - "ĠbaÅŁ layan", - "ĠbaÅŁlay an", - "ãģı ãĤĮ", - "à¹ĩà¸Ļ à¸ģาร", - "ĠÑħаÑĢакÑĤеÑĢ из", - "Ġanlam ına", - "Ùı Ùĩ", - "ĠÑģеÑĢ пнÑı", - "çķª çµĦ", - "Ġ msgid", - "Ġmsg id", - "Ġms gid", - "Ġzv ÃŃÅĻ", - "ĠzvÃŃ ÅĻ", - "ĠíļĮ ìĽIJ", - "Ġya par", - "Ġyap ar", - "ä¼ĺ åĬ¿", - "ен нÑĭми", - "еннÑĭм и", - "ĠØ£ Ø«", - "ì² Ļ", - "Ġji ného", - "Ġjin ého", - "Ġjiné ho", - "Ġد Ùģاع", - "ĠدÙģ اع", - "ĠØŃÚ© ÙĪÙħ", - "Ġr izik", - "Ġri zik", - "ά λι", - "άλ ι", - "à¸ĩ à¸Ĥ", - "èµ ¢", - "Ġ ÎķÎĽ", - "ĠÎķ ÎĽ", - "Ġok um", - "Ġoku m", - "æĶ¶ åħ¥", - "ĠÚĨ ÛĮÙĨ", - "æľī çļĦ", - "ÑĨ ами", - "ÑĨа ми", - "d ÄĽnÃŃ", - "dÄĽ nÃŃ", - "ĠкоÑĢ аб", - "Ġко ÑĢаб", - "ĠкоÑĢа б", - "Ġa landa", - "Ġal anda", - "Ġalan da", - "ส à¸Ļาม", - "สà¸Ļ าม", - "ï¼ī ãģ®", - "ı sız", - "ıs ız", - "ısı z", - "ÙĬ ÙĬر", - "Ùĥ ÙĬØ©", - "ÙĥÙĬ Ø©", - "Ġnebo Å¥", - "Ġbit ir", - "Ġbi tir", - "Ġ ãĥľ", - "Ġãĥ ľ", - "Ùij ا", - "ï¼ Ĩ", - "ĠاÙĦت ارÙĬØ®", - "มห าà¸Ļà¸Ħร", - "at ürk", - "ãĤ¹ãĥĨ ãĥł", - "θή κη", - "Ġ καν", - "Ġκ αν", - "Ġκα ν", - "ĠS ür", - "ĠSü r", - "Ġd Ä±ÅŁÄ±", - "ĠdÄ±ÅŁ ı", - "Ġk ancel", - "Ġkan cel", - "ĠÙ¾ خش", - "h Pa", - "ĠÄį t", - "ĠпÑĢ оÑħ", - "ĠпÑĢо Ñħ", - "à¹ī à¸Ī", - "Ġê±° ìķ¼", - "ĠдеÑĢжав ного", - "èĤ¡ 举", - "ìĿ´ íģ¬", - "Ùĥ تÙĪر", - "Ùĥت ÙĪر", - "ĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "è¨ º", - "Ġب Ùħا", - "ĠبÙħ ا", - "ĠноÑĢм аÑĤив", - "ç iler", - "çi ler", - "à¸ĩ ศ", - "éĽĨ ä¸Ń", - "ÑĢ иÑģ", - "ÑĢи Ñģ", - "Ñĩ аÑĶ", - "Ñĩа ÑĶ", - "li ÄŁin", - "liÄŁi n", - "liÄŁ in", - "ãĥ¼ ãĤ¿ãĥ¼", - "ãĥ¼ãĤ¿ ãĥ¼", - "а ÑĢаÑĤ", - "аÑĢ аÑĤ", - "аÑĢа ÑĤ", - "åĬĽ éĩı", - "ĠÑģÑħ ем", - "åħ¥ åı£", - "离 å¼Ģ", - "ÏģοÏĨοÏģ ίεÏĤ", - "ĠÐĹ Ð°ÑĤем", - "ĠkarÅŁ ısında", - "ĠkarÅŁÄ± sında", - "ĠاÙĨت ظ", - "ï½ Ĭ", - "Ġ eÅŁit", - "ĠeÅŁ it", - "Ġyaz ılı", - "Ġyazı lı", - "Ðļ ом", - "ا زÙĬ", - "از ÙĬ", - "Ġki mse", - "Ġkim se", - "Ġkims e", - "ÑĢа Ñīи", - "ÑĢаÑī и", - "ัà¸ģ ส", - "Ġkan un", - "Ġka nun", - "Ġ ëIJĺìĹĪ", - "ĠëIJĺ ìĹĪ", - "Ġι ÏĥÏĩ", - "Ġм еди", - "Ġмед и", - "æ° §", - "ï¼Į åħ¶ä¸Ń", - "ï¼Įåħ¶ ä¸Ń", - "Ġyok tu", - "Ġ ãĤ½", - "ĠãĤ ½", - "ĠпÑĢи обÑĢеÑĤ", - "ÙĪ ÛĮØ´", - "ÙĪÛĮ Ø´", - "ãħł ãħł", - "Ġکرد Ùħ", - "Ġکر دÙħ", - "Ġdu var", - "Ġ ç¸", - "Ġç ¸", - "ıs ır", - "ısı r", - "Ġïº į", - "ĠÐłÐ¾Ñģ ÑģиÑı", - "à¹ī à¹ĥà¸Ļ", - "Ġ iÅŁi", - "Ġi ÅŁi", - "ĠiÅŁ i", - "d ol", - "do l", - "ĠÙħØŃ ÙħÙĪد", - "ĠÑģам ÑĭÑħ", - "ĠبÙĨابر اÛĮÙĨ", - "ãĤĮ ãģ©", - "ุà¸ķ สาห", - ". »", - "ู à¸Ĭ", - "ĠT ep", - "ĠTe p", - "ãģı ãĤĵ", - "Ġ å¸ĥ", - "Ġå¸ ĥ", - "Ġत ल", - "Ġs erm", - "Ġse rm", - "Ġser m", - "λ ÏĮγ", - "λÏĮ γ", - "ĠÅŀ imdi", - "Ġà¤ľà¤¨ त", - "- ÐĴ", - "è¨ ª", - "ĠвÑĸд пов", - "ิ à¸Ļà¸Ķ", - "ิà¸Ļ à¸Ķ", - "ι ÏĥμÏĮÏĤ", - "ιÏĥμ ÏĮÏĤ", - "Ω Τ", - "âĨĴ âĨĴ", - "ικο ί", - "ĠÑģп ÑĢава", - "ĠÑģпÑĢав а", - "æľº åħ³", - "Ġ ÃĿ", - "Ġà Ŀ", - "Ġм ова", - "Ġмо ва", - "Ġмов а", - "Ġмог ла", - "Ġд лиÑĤелÑĮ", - "ãģĹ ãģ¦ãĤĤ", - "ãģĹãģ¦ ãĤĤ", - "Ġβ Ïģί", - "Ġж од", - "éĹ ª", - "ĠмÑĸ ÑģÑĮкоÑĹ", - "η Ïģε", - "çł Ĥ", - "Ġkter ých", - "Ġkterý ch", - "ĠÐĵ олов", - "ĠÐĵол ов", - "Ġh á»Ļp", - "Ġhá»Ļ p", - "Ġpa nÃŃ", - "Ġpan ÃŃ", - "تÙħ اد", - " ľ", - "åįģ åħŃ", - "κ οÏĤ", - "κο ÏĤ", - "ев ÑĭÑħ", - "æĭ Ĵ", - "ĠÑģÑĤ оÑĢон", - "ĠÑģÑĤоÑĢ он", - "ĠÑģÑĤо ÑĢон", - "Ġph óng", - "ĠÑĥлÑĥÑĩ ÑĪ", - "m rt", - "mr t", - "m par", - "mp ar", - "ĠS lav", - "ĠSl av", - "Ġ kov", - "Ġk ov", - "Ġko v", - "ìĿ¸ ìĿĢ", - "Ġ åºĶ", - "Ġåº Ķ", - "ั à¸ļà¸Ħ", - "ัà¸ļ à¸Ħ", - "Ġk ì", - "Ġa Å¥", - "ÅĻ ÃŃt", - "ÅĻÃŃ t", - "ì° Į", - "Ùħ ÙĨت", - "ÙħÙĨ ت", - "ıyor lar", - "æŃ£ 常", - "н ÑıÑĤÑĤÑı", - "нÑıÑĤ ÑĤÑı", - "r acÃŃ", - "ra cÃŃ", - "rac ÃŃ", - "ĠпиÑĤ аниÑı", - "à¸Īะ à¹Ģà¸Ľ", - "ĠاÙĦÙĩ ÙĨد", - "ĠD ost", - "ĠDo st", - "ĠDos t", - "ĠÐĴаÑģ илÑĮ", - "Ġ íĥĦ", - "Ġíĥ Ħ", - "Ġn ạn", - "à¹Īà¸Ń à¹Ħà¸Ľ", - "رÙĪ ض", - "± ظ", - "Ġbych om", - "à¸Ļ วย", - "à¸Ļว ย", - "ãģł ãģ£ãģ¦", - "ĠÐĺ Ñģп", - "ĠÐĺÑģ п", - "à¸Ħร à¸ļ", - "Ġ สà¸ĸาà¸Ļ", - "Ġส à¸ĸาà¸Ļ", - "ĠëĤ ®", - "j iÅ¡tÄĽ", - "ji Å¡tÄĽ", - "ĠÙģ ÙĪت", - "ĠÙģÙĪ ت", - "ĠCh Æ°Æ¡ng", - "ĠìĿ´ 루", - "ĠpÅĻÃŃ tom", - "t ual", - "tu al", - "b ette", - "be tte", - "bet te", - "bett e", - "Ġsa bah", - "Ġsab ah", - "μ ί", - "Ġm á»ĩnh", - "Ġmá»ĩ nh", - "ãģ® ãģłãĤįãģĨ", - "ãģ®ãģł ãĤįãģĨ", - "Ġzam ÄĽÅĻ", - "åįģ äºĶ", - "ĠìķĬ ìĿĦ", - "اÙĨ ÙĪ", - "е нÑĥ", - "ен Ñĥ", - "ĠÑĥ год", - "ĠÑĥг од", - "ĠV Æ°á»Ŀn", - "Ġëĵ± ìĿĦ", - "Ġbelirt ilen", - "æŁ Ħ", - "Ġtek lif", - "¬ Ĥ", - "Ġпод аÑĤков", - "ĠاÙĦ ÙĨÙĩ", - "ĠاÙĦÙĨ Ùĩ", - "ï¼ ´", - "ìĽ ĥ", - "Ġ हल", - "Ġह ल", - "Ġ имÑĥ", - "Ġи мÑĥ", - "Ġим Ñĥ", - "ĠкоÑĤоÑĢ Ñĭм", - "ï¼Į 以åıĬ", - "ï¼Į以 åıĬ", - "ĠÑĤаб ли", - "ा :", - "Ġب رج", - "Ġبر ج", - "ĠÎŃ Î½Î±Î½", - "ĠÎŃνα ν", - "ĠÎŃν αν", - "ĠÙĬ ÙĪÙĦÙĬÙĪ", - "ý Å¡", - "Ġ ÙĬج", - "ĠÙĬ ج", - "ĠÑĤÑĢо Ñħи", - "æŀ Ŀ", - "Ġd Ãły", - "ĠBur ada", - "ĠBu rada", - "ĠÏĥÏħ μβ", - "ĠÎij ÏģÏĩ", - "ĠÎijÏģ Ïĩ", - "Ġsoci álnÃŃ", - "Ġ Ú¯ÙĪ", - "ĠÚ¯ ÙĪ", - "Ġyan ıt", - "Ġyanı t", - "ãģ¯ ãģªãģĦ", - "ãģ® ä¸Ĭ", - "Ġn úi", - "ĠرÙģت ار", - "ĠÙħ رات", - "ĠÙħر ات", - "ز ÙħاÙĨ", - "زÙħ اÙĨ", - "าà¸Ī ารย", - "ĠÑĩиÑģ лÑĸ", - "Ġس ÙĨت", - "ĠسÙĨ ت", - "ĠÃĸzel likle", - "ì ĩ¼", - "ìĩ ¼", - "ĠÄį ÃŃm", - "AD DE", - "ADD E", - "ãģ® ãĤĪãģĨãģª", - "ÙĪÙĦÙĪ ÚĺÛĮ", - "ĠíĻľ ìļ©", - "ãĢģ ãģ©ãģĨ", - "ĠÎł ÏģÏī", - "çĻ» åł´", - "Ġнад аннÑı", - "Ġм еÑĢеж", - "ĠмеÑĢ еж", - "ĠмеÑĢе ж", - "Ġ ìĿµ", - "ĠìĿ µ", - "jÃŃ cÃŃch", - "jÃŃcÃŃ ch", - "it ou", - "ito u", - "ÙĤ ÙĪÙĦ", - "Ùħ ج", - "Ġب ÙĨد", - "ĠبÙĨ د", - "Ġön üne", - "Ġ ï½°", - "Ġï½ °", - "з в", - "Ġе ÑģÑĤе", - "Ðł Ðĺ", - "ÑĢ ол", - "ÑĢо л", - "a yla", - "ay la", - "Ġк лÑĥ", - "Ġкл Ñĥ", - "æݨ èĸ¦", - "ĠÑĢоз ÑĢаÑħ", - "Ġ ìĥģëĭ´", - "Ġìĥģ ëĭ´", - "ĠÙĨ سÙħØ©", - "ĠÙĨس ÙħØ©", - "Ġви Ñħод", - "à¥Ģ à¤Ĩà¤Ī", - "ĠпÑĢи ÑģÑĤÑĥп", - "ÙĴ ع", - "ĠteÅŁ ekkür", - "дÑı ки", - "Ġfi kir", - "Ġfik ir", - "ัศ à¸Ļ", - "ĠآزÙħ اÛĮØ´", - "Ġb izi", - "Ġbi zi", - "Ġbiz i", - "ÏĨ αÏģ", - "ÏĨα Ïģ", - "æľª æĿ¥", - "æIJ º", - "Ġδ Ïħνα", - "ĠδÏħ να", - "Ġ رÙĪÙħ", - "Ġر ÙĪÙħ", - "ĠرÙĪ Ùħ", - "Ġb undan", - "Ġbund an", - "Ġbun dan", - "ĠÙĤ اÙĦب", - "ĠÙĤاÙĦ ب", - "Ġ haft", - "Ġh aft", - "Ġha ft", - "Ġhaf t", - "å¿ ½", - "ĠÐľ оÑĢ", - "Ġzá pas", - "Ġzáp as", - "Ġ ë¹Ľ", - "Ġë¹ Ľ", - "å» ·", - "äºĪ ç´Ħ", - "Ġkh uyến", - "Ġ ÎijÎĵ", - "ĠÎij Îĵ", - "Ġìŀij ìĹħ", - "ड र", - "Ġjednodu ch", - "à¥ī म", - "ĠdeÄŁ ildi", - "ĠdeÄŁil di", - "Ġk olo", - "Ġko lo", - "Ġkol o", - "Ġد ÙĤÛĮ", - "л ами", - "ла ми", - "лам и", - "ĠH á»įc", - "ĠHá»į c", - "Ġप स", - "ĠÎł ÏģÏĮ", - "ĠâĹ ij", - "Ġ наÑģлÑĸд", - "Ġна ÑģлÑĸд", - "ĠнаÑģ лÑĸд", - "Ġ диви", - "Ġди ви", - "Ġдив и", - "ĠpÅĻes nÄĽ", - "ĠТак им", - "ĠТа ким", - "Ġru kou", - "Ġruk ou", - "ä¸Ģ åĪĩ", - "ĠÑģ пÑĢи", - "ĠÑģп ÑĢи", - "en ské", - "ens ké", - "æĹ ¦", - "ĠÙĤ ÙĨ", - "Ġú stav", - "िश त", - "à¹Į )", - "ĠT rang", - "ĠTr ang", - "ĠTra ng", - "ĠTran g", - "Ġmoh la", - "Ġmohl a", - "ĠÎķ λλην", - "Ġп оки", - "Ġпо ки", - "Ġпок и", - "ĠØ¢ Ùħار", - "ĠØ¢Ùħ ار", - "åIJ ¾", - "ĠÑĢ еÑģп", - "ĠÑĢе Ñģп", - "ĠÑĢеÑģ п", - "Ġta kdir", - "Ġtak dir", - "Ġrahat sız", - "éŁ³ ä¹IJ", - "Ġ âĶĥ", - "ĠâĶ ĥ", - "i lis", - "il is", - "ili s", - "ĠÙĪ اÙĦØ¥", - "ĠÙĪاÙĦ Ø¥", - "å® Ļ", - "Ñĥ мов", - "Ñĥм ов", - "ĠÐĽ иÑĤ", - "ĠÐĽÐ¸ ÑĤ", - ": :::|", - ":: ::|", - ":::: |", - "::: :|", - "åħ ½", - "ĠÙĨزد ÛĮÚ©", - "е лÑĸв", - "ел Ñĸв", - "елÑĸ в", - "θ οÏįν", - "θο Ïįν", - "ìĹIJìĦľ ëıĦ", - "èµĦ æł¼", - "çIJĨ 论", - "ĠKe mal", - "ĠKem al", - "Ġк еÑĢ", - "ษ ายà¸Ļ", - "Ġ åįİ", - "Ġåį İ", - ") ìĹIJ", - "Ġ ëĬĺ", - "ĠëĬ ĺ", - "ãĥĿ ãĥ¼ãĥĪ", - "ĠÐĹ Ð´", - "اص ÙĬÙĦ", - "Ġk atı", - "Ġka tı", - "Ġkat ı", - "ãĤĤãģĹ ãĤĮãģªãģĦ", - "Ġкажд ого", - "Ġ дÑĢ", - "Ġд ÑĢ", - "Ġfut bol", - "ÙĦ ÙĬÙģ", - "ÙĦÙĬ Ùģ", - "Ġì§Ģ ëĤľ", - "ĠÙ¾ÛĮØ´ ÙĨÙĩ", - "ü lük", - "ül ük", - "ülü k", - "Ġ à¸ķำà¸ļล", - "Ġà¸ķำ à¸ļล", - "Ġb áºŃc", - "Ġ åĽł", - "ĠåĽ ł", - "ik ler", - "Ïģ ιά", - "Ïģι ά", - "Ġв важа", - "Ġвваж а", - "Ġvy pl", - "Ġvyp l", - "Ġв низ", - "í Ģ", - "çľ ¾", - "ĠÑģ ила", - "ĠÑģи ла", - "ĠÑģил а", - "ĠналиÑĩи и", - "Ġع راÙĤ", - "ĠاÙĦÙħ Ùĥ", - "å°± ä¼ļ", - "Ġм Ñĸг", - "ĠмÑĸ г", - "ĠÎĮ μιλοÏĤ", - "Ñī его", - "Ñīе го", - "Ġíĸī ìłķ", - "Âł mph", - "Âłm ph", - "Ġma lé", - "Ġmal é", - "ĠÛĮ اÙģتÙĩ", - "ĠÛĮا ÙģتÙĩ", - "ĠÛĮاÙģت Ùĩ", - "Ġmn oha", - "Ġmnoh a", - "γ ά", - "Ġпо ÑģÑĤÑĢо", - "ĠпоÑģ ÑĤÑĢо", - "ĠпоÑģÑĤ ÑĢо", - "ĠاÙĦÙħ ÙĪس", - "ĠاÙĦÙħÙĪ س", - "Ġol ma", - "Ġolm a", - "ëī´ ìĬ¤", - "Ġt utar", - "Ġtu tar", - "Ġtut ar", - "ãĥ¼ãĥĵ ãĤ¹", - "à¥įथ न", - "-ли бо", - "æ¥Ń åĭĻ", - "ĠоÑģоб ливо", - "ĠоÑģоблив о", - "è® Ģ", - "ÙģÙĩ ÙĪÙħ", - "Ġk ẻ", - "Ġ Å¡tÄĽ", - "ĠÅ¡ tÄĽ", - "ĠÅ¡t ÄĽ", - "Ġc ầm", - "ĠÄįlán ky", - "ĠÄIJ iá»ĩn", - "( =", - "OV Ãģ", - "ul du", - "uld u", - "a ft", - "af t", - "Ġl ãi", - "Ġlã i", - "Ġd oldur", - "Ġdol dur", - "³³ ³³³³³³³³³", - "³³³³ ³³³³³³³", - "³³³ ³³³³³³³³", - "³³³³³³³³ ³³³", - "³³³³³³³ ³³³³", - "³³³³³ ³³³³³³", - "³³³³³³ ³³³³³", - "³³³³³³³³³ ³³", - "β ι", - "ãģ£ãģ¦ ãģįãģŁ", - "ì¶ľìŀ¥ ìķĪë§Ī", - "å¯ Ŀ", - "Ġë¶Ģ íĥģ", - "ĠاÙĦ اخ", - "Ġγ Ïħνα", - "à¤ı म", - "à¥Į ल", - "ع ادة", - "عا دة", - "عاد Ø©", - "Ġ κοÏħ", - "Ġκ οÏħ", - "Ġκο Ïħ", - "ĠÙħØ· رØŃ", - "ĠÑĩелов еÑĩ", - "Ġn umar", - "Ġnum ar", - "Ġnu mar", - "Ġnuma r", - "Ġ дина", - "Ġд ина", - "Ġди на", - "ÏĦ ÏģÎŃ", - "ÏĦÏģ ÎŃ", - "λ ικ", - "λι κ", - "Ġдол го", - "Ġnh iêu", - "ĠвоÑģ ÑģÑĤанов", - "ap ı", - "Ġ kanı", - "Ġk anı", - "Ġkan ı", - "ĠK ế", - "ãĤī ãģļ", - "Ġhar ek", - "Ġha rek", - "Ġhare k", - "ãģłãģij ãģ§", - "æ» ħ", - "Ġo hled", - "Ġoh led", - "е ÑĢим", - "еÑĢ им", - "еÑĢи м", - "ĠØŃ ÙĬÙĨ", - "ĠØŃÙĬ ÙĨ", - "ĠÙĤ Ùĩر", - "Ġब à¥Ŀ", - "اپ ÛĮÙħ", - "è¶ħ è¿ĩ", - "Ġ æħ", - "Ġæ ħ", - "Ġت Ùģس", - "ĠتÙģ س", - "as ıyla", - "ası yla", - "б иÑĤ", - "би ÑĤ", - "ĠØŃ Ø§Ø¬", - "ĠÑĤÑĢеб ованиÑı", - "Ġ æݨ", - "Ġæİ ¨", - "Ġ ç±³", - "Ġç± ³", - "ãĤ³ ãĥ¼ãĥī", - "ĠÑĥ Ñģи", - "ĠÑĥÑģ и", - "Ġاخ ÙĦاÙĤ", - "Ġdo stup", - "Ġdost up", - "Ġع ÙĦاÙĤ", - "ĠعÙĦ اÙĤ", - "िव स", - "Ġ оди", - "Ġо ди", - "Ġод и", - "t ej", - "te j", - "Ġthá» ıa", - "ัà¸ģษ à¸ĵะ", - "ัà¸ģษà¸ĵ ะ", - "ĠÑĢаÑģ к", - "ĠÑĢа Ñģк", - "ĠÐĿ аÑĢод", - "ĠÐĿа ÑĢод", - "Ġза кÑĥп", - "Ġзак Ñĥп", - "o že", - "ož e", - "Ġاج را", - "Ġاجر ا", - "ê´ij ê³ł", - "аÑĢÑĤ ам", - "Ġп еÑĢеж", - "ĠпеÑĢ еж", - "ĠпеÑĢе ж", - "èij£ äºĭ", - "ĠÑı коÑģÑĤÑĸ", - "ĠÑıк оÑģÑĤÑĸ", - "Ġв Ñĥл", - "м он", - "мо н", - "Ġch lap", - "Ġ ÑįÑĤомÑĥ", - "ĠÑįÑĤ омÑĥ", - "ĠÑįÑĤо мÑĥ", - "ĠÑįÑĤом Ñĥ", - "а ÑĤÑĸ", - "аÑĤ Ñĸ", - "Ġ íĴĪ", - "Ġí ĴĪ", - "ĠíĴ Ī", - "è¡Ĺ éģĵ", - "س د", - "ÙĪ رÙĩ", - "ÙĪر Ùĩ", - "ĠزÛĮ اد", - "åľ¨çº¿ è§Ĩé¢ij", - "ا ÙĪÙĬØ©", - "اÙĪ ÙĬØ©", - "اÙĪÙĬ Ø©", - "ï¼Į å°±æĺ¯", - "ï¼Įå°± æĺ¯", - "e lerinden", - "eler inden", - "elerin den", - "elerinde n", - "ÑĢ ажд", - "ÑĢа жд", - "ÑĢаж д", - "Ġп озд", - "Ġпо зд", - "Ġпоз д", - "Ġзна ÑĤÑĮ", - "Ġзн аÑĤÑĮ", - "ัà¸ļ สà¸Ļ", - "ัà¸ļส à¸Ļ", - "à¥ĩà¤ĸ त", - "Ġ æĽ°", - "Ġæ Ľ°", - "ĠæĽ °", - "ê³¼ ìłķ", - "é® ®", - "ĠV iá»ĩn", - "ĠVi á»ĩn", - "Ġd voj", - "Ġdv oj", - "ίν εÏĦαι", - "Ġosob nÃŃch", - "ĠosobnÃŃ ch", - "Ġ âĢª", - "ĠâĢ ª", - "éĻ µ", - "ĠØ®ÙĪد Ø´", - "ĠاÙĨ ر", - "ĠпÑĢоÑĦеÑģÑģи оналÑĮ", - "k ám", - "ká m", - "ĠÙħ ÙĥاÙĨ", - "ĠÙħÙĥ اÙĨ", - "ĠاÙĦØ£ د", - "Ġ ê³µë¶Ģ", - "Ġê³µ ë¶Ģ", - "ĠÄij ức", - "ĠÄijứ c", - "ĠCumhur iyeti", - "ĠCumhuriyet i", - "åĩº ãģĹ", - "д ами", - "да ми", - "дам и", - "ĠìĪĺ ìĥģ", - "ĠÙģ براÙĬر", - "Ġsü resi", - "Ġsür esi", - "Ġsüre si", - "Ġب ج", - "Ġ æĶ¾", - "ĠæĶ ¾", - "ØŃ ÛĮ", - "çłĶ究 æīĢ", - "åĩºçīĪ 社", - "ĠÙħÙĪ تÙĪر", - "&& &&", - "ĠпеÑĢ ей", - "ĠпеÑĢе й", - "Ġ ìĦłê±°", - "ĠìĦł ê±°", - "ĠúspÄĽ Å¡", - "ار Ú©", - "Ġet tir", - "Ġett ir", - "Ġetti r", - "Ġ ì¶ľìŀ¥", - "Ġì¶ľ ìŀ¥", - "ĠKa nun", - "ĠKan un", - "ĠÑĥменÑĮ ÑĪ", - "ĠзаÑĤ веÑĢдж", - "ĠاÙĦد ÙĪÙĦÙĬ", - "ĠاÙĦدÙĪÙĦ ÙĬ", - "Ġ ãĥĵ", - "Ġãĥ ĵ", - "ĠB azı", - "ĠBa zı", - "ĠBaz ı", - "åŃIJ ãģ®", - "åĩ ¯", - "Ġse beb", - "Ġseb eb", - "Ġsebe b", - "Ġ åħ±", - "Ġåħ ±", - "Ġd nů", - "Ġdn ů", - "ä½į äºİ", - "ĠZ d", - "æī ±", - "Ġتج ربÙĩ", - "ÃĶ NG", - "Ġìĺ¬ ëĿ¼", - "Ïī ÏĦεÏģ", - "ĠÑģ вид", - "ĠÑģв ид", - "ĠÑģви д", - "æ¯Ķ èµĽ", - "ãģ« åIJij", - "ìľĦ 를", - "ãģĹ ãģ¾ãģĹãģŁ", - "ãģĹãģ¾ ãģĹãģŁ", - "Ġd á»ĭ", - "ĠÐł ÑĥÑģ", - "Ġv á»ı", - "Ġvá» ı", - "à¤Ĥड ल", - "Ġп иÑī", - "Ġпи Ñī", - "Ġsmr ti", - "Ġsmrt i", - "à¸Īาà¸ģ à¸ģาร", - "ĠÑģаÑħ аÑĢ", - "Ġtho át", - "ج ÙħØ©", - "جÙħ Ø©", - "Ġпоз вол", - "ĠاÙĦØ« اÙĨÙĬØ©", - "ĠاÙĦثاÙĨÙĬ Ø©", - "ز ادÙĩ", - "زا دÙĩ", - "ãĢģ ä¸Ń", - "ή μεÏģα", - "æ¦ ľ", - "l acaģı", - "lac aģı", - "lacaÄŁ ı", - "Ġна ÑĪиÑħ", - "ĠнаÑĪ иÑħ", - "ìĶ Ģ", - "ĠÐĺ ÑģÑĤоÑĢиÑı", - "ün deki", - "ünd eki", - "ünde ki", - "ĠпеÑĢ ел", - "ĠпеÑĢе л", - "Ġ목 ìĨĮ", - "ĠÑģÑĤаÑĤ ÑĥÑģ", - "о вали", - "ов али", - "ова ли", - "овал и", - "ÅĻ az", - "ĠдÑĢÑĥг ого", - "ĠдÑĢÑĥго го", - "ÙĥÙĪÙħ Ø©", - "ÙĥÙĪ ÙħØ©", - "Ñĩ иÑģÑĤ", - "Ñĩи ÑģÑĤ", - "ÑĩиÑģ ÑĤ", - "μ μ", - "åıį åºĶ", - "ic ari", - "ica ri", - "ĠÙ¾ اک", - "Ġپا Ú©", - "алÑĮ ним", - "ĠB una", - "ĠBu na", - "ĠBun a", - "и ÑĤив", - "иÑĤ ив", - "иÑĤи в", - "ÑĦ ÑĢа", - "ãĥ¼ ãĥĸãĥ«", - "ãĥ¼ãĥĸ ãĥ«", - "ĠÑĤоб ÑĤо", - "룬 ìĬ¤", - "ĠاÙĦ اع", - "åħ¬ éĸĭ", - "å¥ ī", - "ÙĪÙĦ د", - "åIJį çĦ¡ãģĹ", - "æ°ij 主", - "à¥ģ à¤ľà¤°", - "à¥ģà¤ľ र", - "ìĤ¬ 무", - "Ġön celik", - "Ġönce lik", - "Ġönc elik", - "Ġ å¨", - "Ġå ¨", - "Ñı б", - "çľ ī", - "à¥įव य", - "ĠH ình", - "çļĦ åľ°æĸ¹", - "çļĦåľ° æĸ¹", - "ĠاÙĦ تس", - "ĠاÙĦت س", - "ä¸Ī 夫", - "Ġп ÑĥблÑĸ", - "ĠnÄĽjak é", - "ÄIJ á»iji", - "ĠÑģоÑģÑĤоÑı ниÑı", - "à¥Ģ )", - "ĠÄij áºŃu", - "j ed", - "je d", - "ê ¶ģ", - "Ġs enin", - "Ġse nin", - "Ġsen in", - "Ġseni n", - "ĠH óa", - "âĻ ł", - "лÑı ÑİÑĤÑĮ", - "лÑıÑİÑĤ ÑĮ", - "éĹ ²", - "ìĿ¸ íĬ¸", - "ت بÙĩ", - "تب Ùĩ", - "Ġरà¤ĸ त", - "ĠÑģлов ами", - "ĠÑģлова ми", - "ĠÑģло вами", - "Ġطب ÙĤ", - "Ġuy du", - "ุà¸ĩà¹Ģà¸Ĺà¸ŀ มหาà¸Ļà¸Ħร", - "ĠSan at", - "ĠSa nat", - "à¹ī าà¸Ĭ", - "à¹īา à¸Ĭ", - "Ġкни ж", - "Ìģ c", - "ا Ùħج", - "اÙħ ج", - "δ Ïİ", - "Å ®", - "Ġb inh", - "Ġbi nh", - "Ġbin h", - "è¾ Ĩ", - "n eÄŁi", - "ne ÄŁi", - "Ø· ÙĨ", - "å¸ ķ", - "Ġ ìĩ¼", - "Ġì ĩ¼", - "оÑģ ÑĢед", - "ĠοÏĢο ίο", - "k ır", - "kı r", - "à¥Ī श", - "Ġ à¸ĩาà¸Ļ", - "Ġà¸ĩ าà¸Ļ", - "Ġd ruž", - "Ġdru ž", - "em atik", - "ema tik", - "emat ik", - "a dıģ", - "ad ıģ", - "adı ÄŁ", - "è¾ ŀ", - "ĠpoužÃŃ vá", - "Ġkur tar", - "ĠsaÄŁ lan", - "ãĢı ï¼Ī", - "Ġmůže me", - "Ġmůž eme", - "Ġ باد", - "Ġب اد", - "Ġبا د", - "æľŁ éĹ´", - "ا تÙģ", - "ات Ùģ", - "Ġyaz ılım", - "Ġyazılı m", - "ĠìĹ° ê²°", - "ÙĬ ÙģØ©", - "ÙĬÙģ Ø©", - "Ġ emin", - "Ġe min", - "Ġem in", - "ĠнеÑģколÑĮ киÑħ", - "Û´ Û°", - "å¯ §", - "ί ζει", - "ίζ ει", - "Ġd él", - "Ġdé l", - "ver iÅŁ", - "価 æł¼", - "Ġاست اد", - "Ġал ког", - ".H CM", - "ί οÏĤ", - "ίο ÏĤ", - "α κ", - "Ø· ع", - "ãģ£ ãģį", - "ãģ£ãģ į", - "Ñı еÑĤÑģÑı", - "ÑıеÑĤ ÑģÑı", - "л ика", - "ли ка", - "лик а", - "Ġ ÑĨÑı", - "ĠÑĨ Ñı", - "Ġë§Ī ì§Ģë§ī", - "ĠаÑĢ ми", - "Ġγ λÏİ", - "E NÃį", - "EN Ãį", - "ë ®¤", - "ŃIJ ï¸ı", - "Ġ æ¯ı", - "Ġæ¯ ı", - "Ġ æĸ¼", - "Ġæĸ ¼", - "Ġκα λÏį", - "ĠТ ом", - "ĠТо м", - "ul ur", - "ulu r", - "Ġak ce", - "ĠÙħÙĪ جب", - "ĠÙħÙĪج ب", - "e siz", - "es iz", - "esi z", - "н Ñıв", - "нÑı в", - "алÑĮ нÑĥÑİ", - "алÑĮнÑĥ Ñİ", - "ал ÑĸÑģÑĤ", - "алÑĸ ÑģÑĤ", - "Ġв аÑĢÑĸ", - "ĠваÑĢ Ñĸ", - "Ġва ÑĢÑĸ", - "ĠÙħؤ س", - "ĠÙħ اÛĮÙĦ", - "ĠÙħا ÛĮÙĦ", - "ĠμεÏĦα ξÏį", - "åĩº ãģĻ", - "Ġv á»Ŀi", - "Ġvá» Ŀi", - "ëŁ ´", - "ï¼ ĭ", - "æ¯ İ", - "Ġt abi", - "Ġtab i", - "Ġta bi", - "âĤ ĥ", - "æ£ĭ çīĮ", - "Ġ ÃIJ", - "Ġà IJ", - "ĠпÑĢоÑĦеÑģ Ñĸй", - "Ñĥв аннÑĸ", - "Îľ Îł", - "Ġж ил", - "Úĺ ÙĨ", - "л ÑĥÑĪ", - "лÑĥ ÑĪ", - "á½ ´", - "о веÑĢ", - "ов еÑĢ", - "ове ÑĢ", - "è¾¼ ãģ¿", - "ĠÐľ акÑģим", - "ĠÐľÐ°Ðº Ñģим", - "Ġвз глÑıд", - "Ġн аÑĤÑĥ", - "Ġна ÑĤÑĥ", - "ĠнаÑĤ Ñĥ", - "म à¤ķ", - "ĠÑħ ими", - "ĠÑĢозÑĤа ÑĪ", - "ÙĪ راÙĨ", - "ÙĪر اÙĨ", - "ÙĪرا ÙĨ", - "ĠØ´Ùĩر ÙĩاÛĮ", - "æ© Łèĥ½", - "æ©Ł èĥ½", - "Ø® Ø°", - "ĠÑģво ÑĶÑĹ", - "ĠÑģвоÑĶ ÑĹ", - "н ÑıеÑĤ", - "нÑı еÑĤ", - "Ġgh ế", - "ĠpÅĻed ch", - "ÑĶ ÑĪ", - "огÑĢаÑĦ ÑĸÑı", - "Ġ à¸Ĺำà¹ĥห", - "Ġà¸Ĺำ à¹ĥห", - "åĿ Ĭ", - "Ïģ Ïīν", - "ÏģÏī ν", - "า ระ", - "าร ะ", - "ĠK ết", - "ĠKế t", - "Ġch ặt", - "Ġ éĻĪ", - "ĠéĻ Ī", - "ĠdÄĽ lat", - "ĠdÄĽl at", - "ĠбÑĥд ÑĥÑī", - "ĠбÑĥдÑĥ Ñī", - "ĠAç ık", - "æłª å¼ıä¼ļ社", - "ĠÐŁ аÑĢ", - "ĠK hu", - "ĠKh u", - "ãĢģ æĸ°", - "Ġб ой", - "Ġбо й", - "ë§Ī íĬ¸", - "ĠÑģоп ÑĢов", - "س اب", - "н иÑģÑĤ", - "ни ÑģÑĤ", - "å¼ ĥ", - "Ġ Ø´ÙĨاس", - "ĠØ´ÙĨ اس", - "ен ном", - "енно м", - "Ġ 项", - "Ġé¡ ¹", - "èīº æľ¯", - "о зем", - "оз ем", - "ĠÑĢеÑĪ ениÑı", - "l ady", - "la dy", - "lad y", - "ĠвÑģ ей", - "ĠвÑģе й", - "æĶ» åĩ»", - "Ġê²° ìłķ", - "ãĢĢ ï¾ŀ", - "Ġê°IJ ëıħ", - "- ÐIJ", - "Ġm ÃŃr", - "ĠmÃŃ r", - "à¥ģप à¤ı", - "нÑĸ ÑĨип", - "б ом", - "бо м", - "Ġ Å¡t", - "ĠÅ¡ t", - "éľ į", - "ĠÑĢеÑĪ ение", - "Ġдиаг ноÑģÑĤи", - "i par", - "ip ar", - "ipa r", - "ا ÛĮز", - "اÛĮ ز", - "ã ng", - "ãn g", - "ั วร", - "ัว ร", - "ĠÑĨ аÑĢ", - "Ġs ly", - "Ġsl y", - "ν Ïİ", - "ĠK uzey", - "رÛĮ ب", - "Ġc enu", - "Ġce nu", - "Ġcen u", - "Ġcert if", - "Ġcer tif", - "ĠÑĤ ÑĢеÑĤÑĮ", - "ĠÑĤÑĢ еÑĤÑĮ", - "ĠÑĤÑĢеÑĤ ÑĮ", - "ิà¸Ķ à¸Ĥ", - "Ġпа ÑĨÑĸÑĶн", - "ÅĻ iv", - "ÅĻi v", - "èĦ Ĥ", - "¢ °", - "ĠPh ần", - "ĠмеÑĤод и", - "ĠмеÑĤ оди", - "Ạ¤", - "ìĨ Ķ", - "åIJĮ åѦ", - "Ġ åĢĭ", - "ĠåĢ ĭ", - "моÑĤ ÑĢÑı", - "моÑĤÑĢ Ñı", - "Ġuv ád", - "Û±Û¹ Û¶", - "éģ¸ æĬŀ", - "! »", - "ë ĺIJ", - "ĠÛĮ ÙĪتÛĮ", - "ĠاÙĦØŃ Ø±Ø¨", - "ĠاÙĦØŃر ب", - "олог ÑĸÑı", - "n ila", - "ni la", - "nil a", - "ĠÄij ảng", - "á zi", - "áz i", - "ÑĢ оÑī", - "ÑĢо Ñī", - "Ġort adan", - "Ġorta dan", - "Ġاخ بار", - "Ġà¤ħ à¤ľ", - "Ġ매 ìļ°", - "Ġп ой", - "Ġпо й", - "Ġ جÙĬ", - "Ġج ÙĬ", - "к ÑĥваÑĤи", - "кÑĥ ваÑĤи", - "Ġá» ŀ", - "Ġب شر", - "Ġبش ر", - "Ġ ÙĥÙĬÙĦ", - "ĠÙĥ ÙĬÙĦ", - "Ñī еÑģÑĤво", - "Ñīе ÑģÑĤво", - "ÑīеÑģÑĤв о", - "ĠìŬ íĸī", - "ا ÙħÙĬ", - "اÙħ ÙĬ", - "в ÑĸлÑĮ", - "вÑĸ лÑĮ", - "ĠPr vnÃŃ", - "Ġ ÙĪسÛĮ", - "ĠÙĪ سÛĮ", - "ĠÙĪس ÛĮ", - "ĠÄIJ á»", - "æĪ¿ éĹ´", - "åľ¨çº¿ éĺħ读", - "æķ ·", - "Ġt rai", - "Ġtr ai", - "Ġtra i", - "ä¿ Ĺ", - "ĠÑģамоÑģÑĤоÑıÑĤелÑĮ но", - "ĠÑĤÑĢеб ÑĥеÑĤÑģÑı", - "ĠÑĤÑĢебÑĥеÑĤ ÑģÑı", - "δ Ïģα", - "ĠÑĢеÑĩ ов", - "Ġв Ñĸк", - "ĠвÑĸ к", - "Ġ ÑĢÑĥÑĩ", - "ĠÑĢ ÑĥÑĩ", - "ĠÑĢÑĥ Ñĩ", - "å¥ §", - "ĠolduÄŁ una", - "ĠolduÄŁu na", - "ев Ñĭе", - "Ġ à¸Ħล", - "Ġà¸Ħ ล", - "ا ÙĦÙĤ", - "اÙĦ ÙĤ", - "ĠÑĸм енÑĸ", - "ĠÑĸмен Ñĸ", - "æĶ» æĴĥ", - "ĠÑĥнивеÑĢ ÑģиÑĤ", - "Ġth Äĥm", - "ĠлиÑģÑĤоп ада", - "२ ०", - "Ø® ÙĬ", - "Îķ Îł", - "Ġart tır", - "Ġس خت", - "Ġسخ ت", - "ï¼Ī æĺŃåĴĮ", - "ĠÎŁ Ïħ", - "и ваниÑı", - "ив аниÑı", - "ива ниÑı", - "Ġstav eb", - "âħ ¥", - "γÏī γή", - "γÏīγ ή", - "Ù ©", - "ĠиÑģÑģлед ованиÑı", - "åĢĭ 人", - "Ġëĭ¤ìļ´ë°Ľ 기", - "ĠÏĦ ελ", - "ĠÏĦε λ", - "° N", - "ĠباÙĦ ÙĨ", - "à¹Į à¸ŀ", - "Ġnem ůže", - "Ġголов а", - "Ġгол ова", - "à¹Į à¹ģ", - "æ¢ ¯", - " ĺ", - "δ ηÏĤ", - "δη ÏĤ", - "ìĿ¸ ì¦Ŀ", - "l ayın", - "lay ın", - "á½ ·", - "ĠÙĨت اÛĮج", - "ĠÑģоб лÑİд", - "Ġдви жениÑı", - "Ġдвиж ениÑı", - "ì Į", - "Ġ povÄĽ", - "Ġp ovÄĽ", - "Ġpo vÄĽ", - "Ġpov ÄĽ", - "Ġ ìłĦìĹIJ", - "ĠìłĦ ìĹIJ", - "å¦Ĥ ä¸ĭ", - "ĠاÙĦÙħ در", - "ĠاÙĦÙħد ر", - "ï¼Į æĪĸ", - "ا را", - "ار ا", - "æ°ij æĹı", - "Ġب رÙĤ", - "Ġبر ÙĤ", - "Ġзап аÑģ", - "à¸Ļ à¹ĥà¸Ī", - "é f", - "Ġ à¸Łà¸£", - "Ġà¸Ł ร", - "Ġë³´ ëĤ´", - "Ġ 欧ç¾İ", - "Ġ欧 ç¾İ", - "- ÑĤаки", - "-ÑĤ аки", - "é© ļ", - "ÑĢ ÑĸÑı", - "ÑĢÑĸ Ñı", - "æŁ ı", - "ĠповÑĸÑĤ ÑĢÑı", - "çµĦ ç¹Ķ", - "d aÅŁ", - "da ÅŁ", - "Ġहम ल", - "ĠÑĢеÑĶ ÑģÑĤÑĢа", - "ά β", - "ĠÎł ο", - "Ġê·¸ 림", - "Ñĩ аÑİÑĤ", - "Ñĩа ÑİÑĤ", - "à¸ĩ à¸ķ", - "íĥ ĢìĿ´", - "íĥĢ ìĿ´", - "æī ¬", - "Ġpo jist", - "Ġpoj ist", - "Ġ çłĶ", - "Ġçł Ķ", - "Ġ åıĸ", - "Ġåı ĸ", - "Ġüzer indeki", - "Ġüzerinde ki", - "j Å¡ÃŃch", - "jÅ¡ÃŃ ch", - "à¥Ģद व", - "æª ¢", - "ĠмаÑĤеÑĢи алов", - "ĠмаÑĤеÑĢиал ов", - "и ваннÑı", - "ив аннÑı", - "Ġ å°Ĩ", - "Ġå° Ĩ", - "л л", - "Ġнаб лÑİд", - "ĠнаблÑİ Ð´", - "ĠG öz", - "ĠGö z", - "Ġв зÑı", - "Ġвз Ñı", - "çĶµ è§Ĩ", - "Ġв ак", - "Ġва к", - "ç¿ Ķ", - "Ġвза им", - "Ġg itti", - "Ġgi tti", - "Ġgit ti", - "it eleri", - "ite leri", - "itel eri", - "itele ri", - "ä»· åĢ¼", - "ĠاÙĦ تص", - "ĠاÙĦت ص", - "िन à¤ķ", - "éĢļ ãĤĬ", - "ĠÑģ ÑĦеÑĢ", - "ĠÑģÑĦ еÑĢ", - "çĻº 売", - "âĿ ¤", - "ĠÚ¯ÙĪØ´ ÛĮ", - "ĠÚ¯ÙĪ Ø´ÛĮ", - "аг аÑĤо", - "ĠÏĥÏħ γκ", - "ав иÑģ", - "ави Ñģ", - "æĤ£ èĢħ", - "ĠØ® اÙħ", - "ÎĻÎļ ÎĹΣ", - "ÎĻÎļÎĹ Î£", - "ınız da", - "pan ÄĽl", - "pa nÄĽl", - "ĠÄIJ á»ĭa", - "à¹ģละ ส", - "Ġ ãĤĤ", - "ĠãĤ Ĥ", - "Ġsonuc unda", - "Ġsonucu nda", - "ìĿ į", - "e less", - "el ess", - "ele ss", - "ĠN ha", - "ĠNh a", - "Ġzak áz", - "Ġв оÑģÑĤ", - "Ġво ÑģÑĤ", - "ĠвоÑģ ÑĤ", - "ĠvzdÄĽl ávánÃŃ", - "- ม", - "Ġmet rů", - "ĠپاÛĮ ÛĮÙĨ", - "Ġپا ÛĮÛĮÙĨ", - "ĠÑĢаÑģÑĤ ение", - "Ġmu á»iji", - "èµĦ éĩij", - "ĠÅŁ üph", - "ÙĬ ÙĦÙħ", - "ÙĬÙĦ Ùħ", - "ĠdÃ¼ÅŁÃ¼n c", - "Ġк Ñĸм", - "ĠÏĩÏī ÏģίÏĤ", - "áz ev", - "áze v", - "ĠDe ÄŁer", - "ĠDeÄŁ er", - "å·¥ æ¥Ń", - "Ġر Ùħز", - "ĠرÙħ ز", - "Ġal espoÅĪ", - "ĠпÑĢе ÑģÑĤÑĥп", - "ĠпÑĢеÑģÑĤ Ñĥп", - "ĠعÙĦ اÙĪÙĩ", - "Ġme rak", - "Ġmer ak", - "à¹Į :", - "çİ° åľº", - "ÑĨ веÑĤ", - "Ġप à¥ľ", - "Ġëĭ¤ìĿĮ ê³¼", - "u dic", - "ud ic", - "udi c", - "ĠL ep", - "ĠLe p", - "Ġод нÑĸ", - "Ġa larak", - "Ġal arak", - "å®ī æİĴ", - "Ġ à¸Ĥà¸Ļาà¸Ķ", - "Ġà¸Ĥ à¸Ļาà¸Ķ", - "re zent", - "rez ent", - "is inden", - "isin den", - "isinde n", - "ر ÙĪÛĮ", - "رÙĪ ÛĮ", - "Ġp lu", - "Ġpl u", - "ç«ĭ ãģ¦", - "Ñĭ ваниÑı", - "Ñĭв аниÑı", - "Ñĭва ниÑı", - "Ġr ast", - "Ġra st", - "Ġras t", - "Ġdüzen lem", - "je zd", - "jez d", - "Ġве ÑīеÑģÑĤв", - "ĠвеÑī еÑģÑĤв", - "ĠдиÑĢ екÑĤоÑĢ", - "ÑĦ ÑĦ", - "t ainment", - "tain ment", - "ĠاÙĦ ÙĪز", - "ĠاÙĦÙĪ ز", - "l anda", - "land a", - "la nda", - "lan da", - "ĠÙĨÚ¯ Ùĩد", - "ĠпÑĢоÑĤив оп", - "ãģ£ ãģı", - "ãģ£ãģ ı", - "ãģ¨ãģª ãĤĬ", - "Ġë°ľ 견", - "i ctor", - "ic tor", - "ict or", - "ãĤ¸ ãĤª", - "ÎŁ Φ", - "ÎŁÎ ¦", - "ĠÑģклад Ñĸ", - "Ġob sahuje", - "Ġobsah uje", - "ĠUkr a", - "ĠUk ra", - "æķ ¦", - "ĠÏĩ αÏģα", - "ĠÏĩα Ïģα", - "ĠÑĢег Ñĥли", - "俺 ãģ¯", - "ัà¸ķ ว", - "éĦ ī", - "Ġب اÛĮ", - "Ġبا ÛĮ", - "éĬ ·", - "ĠN ẵng", - "л од", - "ло д", - "ا رÙģ", - "ار Ùģ", - "æ´ ģ", - "ĠëıĻ ìĿ¼", - "ÑĤив ного", - "âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ âĶģâĶģâĶģâĶģâĶģâĶģâĶģâĶģ", - "Ġ- :-", - "Ġ-: -", - "ì» ¬", - "ĠÑĪ аг", - "ìłĦ ìŀIJ", - "çļĦ äºĭæĥħ", - "çļĦäºĭ æĥħ", - "ĠÑĢег Ñĸ", - "िय ल", - "ĠÐĿ аз", - "ĠÐĿа з", - "ĠÐĻ ого", - "ĠÐł ом", - "ĠÃĸr neÄŁin", - "Ġп ÑĢеÑģ", - "ĠпÑĢ еÑģ", - "ĠпÑĢе Ñģ", - "u luÄŁu", - "ulu ÄŁu", - "uluÄŁ u", - "Ġза дов", - "Ġзад ов", - "ÅĻ eh", - "ÅĻe h", - "æ¯ķ ä¸ļ", - "Ġth áºŃp", - "ëĤ ¸", - "Ġdlou hodob", - "Ġdlouho dob", - "дÑĸ лÑĥ", - "дÑĸл Ñĥ", - "a lat", - "al at", - "ala t", - "ä» °", - "о ком", - "ок ом", - "око м", - "ĠÑĦ ÑĸлÑĮ", - "ĠÑĦÑĸл ÑĮ", - "ĠNg ân", - "Ġ ترÙĥ", - "Ġت رÙĥ", - "Ġتر Ùĥ", - "ĠÑĤ Ñī", - "ر ÙĪد", - "رÙĪ د", - "ç uk", - "çu k", - "ra nÃŃ", - "ran ÃŃ", - "Ġdo laÅŁ", - "Ġdol aÅŁ", - "ĠQ uang", - "ĠQu ang", - "ĠpÅĻed pok", - "Ġnám ÄĽstÃŃ", - "ой Ñĩив", - "çĭ Ģ", - "Ġб изнеÑģ", - "ãģŁ ãģı", - "ĠìĿ¸ ì²ľ", - "о ÑĢо", - "оÑĢ о", - "ĠKü rt", - "ĠKür t", - "ê·¸ 룬", - "ÑĨ аÑĤÑĮ", - "ÑĨа ÑĤÑĮ", - "ĠB ên", - "Ġ acı", - "Ġa cı", - "Ġac ı", - "Ú© Ø´", - "ï¼Ī å¹³æĪIJ", - "Ġ èģĶ", - "Ġèģ Ķ", - ") ãĢģ", - "d iler", - "di ler", - "Ñĩ иÑĤÑĮ", - "ÑĩиÑĤ ÑĮ", - "Ñĩи ÑĤÑĮ", - "Ư á»", - "éĻ ¶", - "il eceÄŁini", - "ilece ÄŁini", - "ileceÄŁi ni", - "Ġv Å¡em", - "ĠvÅ¡ em", - "ĠvÅ¡e m", - "å¼Ģ å¥ĸ", - "è§Ħ 模", - "ul muÅŁ", - "Ġ åĪĺ", - "Ġå Īĺ", - "ĠåĪ ĺ", - "е о", - "еР¾", - "ĠпеÑĢев ÑĸÑĢ", - "åĪĨ åĪ«", - "Ġjed ná", - "Ġjedn á", - "li ÄŁe", - "liÄŁ e", - "ĠرÙħ ضاÙĨ", - "ık lı", - "ıkl ı", - "Ùĩ ÙĢ", - "éĩį çĤ¹", - "Ñĩ иваеÑĤÑģÑı", - "Ñĩи ваеÑĤÑģÑı", - "Ñĩив аеÑĤÑģÑı", - "Ñĩива еÑĤÑģÑı", - "ë¡ľ ìĦľ", - "ÏĦ εÏģο", - "ÏĦε Ïģο", - "ÏĦεÏģ ο", - "åľ° ä¸ĭ", - "д наннÑı", - "дн аннÑı", - "Ġng ược", - "ॠª", - "ĠÎij λ", - "Ġa lacak", - "Ġal acak", - "Ġ à¹Ģà¸ĩ", - "Ġà¹Ģ à¸ĩ", - "Ġà¹Ģภĩ", - "اÛĮ ÙĨد", - "اÛĮÙĨ د", - "Ġh Ãłi", - "ÑĢо из", - "ĠЧ и", - "Ġ ÑıÑģ", - "ĠÑı Ñģ", - "خر ÛĮد", - "Ġhu deb", - "Ġhud eb", - "åľ §", - "Ġ ìĦ¼", - "ĠìĦ ¼", - "åĶ¯ ä¸Ģ", - "Ġ вÑĸлÑĮ", - "Ġв ÑĸлÑĮ", - "ĠвÑĸ лÑĮ", - "ĠباÙĦ اتر", - "ĠباÙĦا تر", - "à¸Ńà¸ģ าส", - "Ġ Tôi", - "ĠT ôi", - "ม à¸Ĥ", - "o mor", - "om or", - "omo r", - "ĠO lomou", - "Ġx ong", - "Ġxo ng", - "Ġdomác ÃŃ", - "Ġ اختÛĮ", - "Ġاخ تÛĮ", - "Ġاخت ÛĮ", - "ĠÑĤеÑħ нÑĸÑĩ", - "ĠÑĤеÑħнÑĸ Ñĩ", - "ĠiÅŁ te", - "à¥Į द", - "Ġнад еж", - "Ø®ÛĮ ص", - "åĬª åĬĽ", - "ĠتجÙĩ ÛĮزات", - "Ġv ole", - "Ġvo le", - "Ġvol e", - "k inci", - "kin ci", - "Ġhes ab", - "ĠÑģ еÑģÑĤ", - "Ú© ا", - "ÑĤеÑĢ н", - "ร รà¸Ħ", - "รร à¸Ħ", - "åıĤ èĢĥ", - "ĠÐļ аб", - "ĠÐļа б", - "ĠÄ° mpar", - "Ġnáv rh", - "Ġnávr h", - "åĴ¨ 询", - "à¸ĸ าม", - "Ġye rel", - "Ġyer el", - "Ġyere l", - "ĠÃĸ l", - "çĮ Ľ", - "ĠاÙĦÙĪØ· ÙĨÙĬ", - "Ġ ìĿ´ìĸ´", - "ĠìĿ´ ìĸ´", - "ิà¸Ĺย าศาสà¸ķร", - "ิà¸Ĺยา ศาสà¸ķร", - "ĠA ÅŁ", - "Ġзем лÑİ", - "ĠдомаÑĪ ниÑħ", - "ĠÑĥ веÑĢ", - "ĠÑĥв еÑĢ", - "A LI", - "AL I", - "г ан", - "га н", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "Ġdo stan", - "Ġdos tan", - "Ġdost an", - "ez pe", - "ãģĭ ãģĦ", - "ر ÙģتÙĩ", - "رÙģ تÙĩ", - "رÙģت Ùĩ", - "Ġм ÑĥÑģ", - "ĠмÑĥ Ñģ", - "à¹Į à¸Ł", - "è¦ º", - "али за", - "ализ а", - "ĠÑĥÑĩ ÑĢежд", - "ĠÚ© اÙĦ", - "Ġetk isi", - "Ġetki si", - "ä½Ĩ æĺ¯", - "Ġsou vis", - "ĠSav aÅŁÄ±", - "ĠSavaÅŁ ı", - "Ġب سبب", - "ÎŁ ι", - "ÎŁÎ ¹", - "è ļ", - "Ġ æ®", - "Ġæ ®", - "Ġìĺģ êµŃ", - "ا سÛĮÙĪÙĨ", - "اسÛĮ ÙĪÙĨ", - "ĠاÙĦات ØŃاد", - "Ġ глÑı", - "Ġг лÑı", - "à¹ĩà¸ģ à¸ĭ", - "Ġج ÙĪÙĨ", - "ĠجÙĪ ÙĨ", - "ĠاÙĦرسÙħ ÙĬ", - "Âł G", - "ĠÑĤо бÑĸ", - "ĠÑĤоб Ñĸ", - " ĩ", - "Ġ ëĮĢíĸī", - "ĠëĮĢ íĸī", - "çĬ¶ æħĭ", - "Ġê·¸ ëĥ¥", - "Ġи мп", - "Ġим п", - "ĠتÙĨظ ÛĮÙħ", - "ÙĦ اÛĮÙĨ", - "ÙĦا ÛĮÙĨ", - "ÑģÑĤв еннÑĭм", - "ÑģÑĤвен нÑĭм", - "о пол", - "оп ол", - "ر ÙĪج", - "رÙĪ ج", - "Ġ à¸ĩ", - "Ġภĩ", - "Ġ çĤº", - "ĠçĤ º", - "ĠUlus lararası", - "à¥Į à¤Ĥ", - "ãĢģ ãģĿãģĨ", - "Ġس ادÙĩ", - "ÎŃ Î±ÏĤ", - "ÎŃα ÏĤ", - "Ġà¤Ĩ ल", - "- ÑĦ", - "ĠÎłÎ¿Î» ι", - "ĠÎłÎ¿ λι", - "Ġно ÑıбÑĢÑı", - "ĠноÑı бÑĢÑı", - "ÙĪ ÙĦÙĬ", - "ÙĪÙĦ ÙĬ", - "æĽľ æĹ¥", - "æĮģ ç»Ń", - "Ġê¼ Ń", - "ece ÄŁiz", - "eceÄŁi z", - "ĠÛĮ اÙģت", - "ĠÛĮا Ùģت", - "Ġ åı¸", - "Ġåı ¸", - "ाà¤Ĺ त", - "Ġ æķħ", - "Ġæķ ħ", - "Ġал леÑĢг", - "Ġt uz", - "Ġtu z", - "еÑĢ ÑĤи", - "еÑĢÑĤ и", - "Ġth ầu", - "ãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ĠãĢĢ", - "- à¤ħ", - "Ġим мÑĥ", - "ÑĢ ай", - "ÑĢаР¹", - "ÑĢа й", - "主 義", - "ĠbaÅŁ lar", - "Ġä¸Ĭ 涨", - "ع ا", - "ĠÎĻ Ïī", - "ียà¸ĩ à¹ĥหม", - "ĠاÙĦÙħد ÙĬÙĨØ©", - "Ñģ ÑĮко", - "ÑģÑĮ ко", - "ÑģÑĮк о", - "ĠتارÛĮØ® ÛĮ", - "at ÃŃm", - "âĢļ Ø·", - "Ø¢ خر", - "ĠëĦ £", - "ĠÙĨÙħ اÛĮد", - "ĠÙĨÙħاÛĮ د", - "ãģķãĤĵ ãģĮ", - "Ġb ò", - "Ġ à¸ķาม", - "Ġà¸ķ าม", - "ë³´ ìķĺëĭ¤", - "а ÑĤÑĸв", - "аÑĤ Ñĸв", - "аÑĤÑĸ в", - "ĠÑĦ ил", - "Ġkısm ı", - "iá»ĩ ng", - "iá»ĩn g", - "Ġay dın", - "éģķ ãģĦ", - "е ви", - "ев и", - "Ġ å¾®", - "Ġå¾ ®", - "( íģ¬ê¸°", - "Ġ Ú¯ÛĮر", - "ĠÚ¯ ÛĮر", - "ĠÚ¯ÛĮ ر", - "ìķĦ ìĦľ", - "Ġδη μιοÏħÏģγ", - "ãģ«ãģĬ ãģĦãģ¦", - "ĠÃľ NÄ°", - "и ÑĤом", - "иÑĤ ом", - "ع ÙĦاÙħ", - "عÙĦ اÙħ", - "åIJİ çļĦ", - "Ġp lá", - "Ġpl á", - "à¸Ľà¸£à¸° à¹Ĥย", - "ç¢ İ", - "Ġ éĺ²", - "Ġéĺ ²", - "ëĬ Ķëĭ¤", - "ëĬĶ ëĭ¤", - "Ġ æĹ¥æľŁ", - "ĠæĹ¥ æľŁ", - "Ġgeç erli", - "л аÑĤÑĭ", - "ла ÑĤÑĭ", - "лаÑĤ Ñĭ", - "Ġmutlak a", - "ÙĪ غ", - "à¹Ģ ฮ", - "à¹Ģภ®", - "Ġï» £", - "e deki", - "ed eki", - "ede ki", - "à¹Į à¹Ģà¸Ļ", - "Ġнайб ÑĸлÑĮÑĪ", - "ĠнайбÑĸлÑĮ ÑĪ", - "ï¼ Ĭ", - "Ġ à¹Ĥรà¸ĩ", - "Ġà¹Ĥ รà¸ĩ", - "Ġfot bal", - "Ġ éĢģ", - "ĠéĢ ģ", - "âĢĮاÙĦ ÙħÙĦ", - "Ïīμά ÏĦιο", - "Ġú kol", - "åįļ 士", - "d ub", - "du b", - "ı lıģ", - "ılı ÄŁ", - "ıl ıģ", - "ëĵľ 를", - "çĭ IJ", - "α λλ", - "αλ λ", - "æŃ» 亡", - "ĠпÑĢед поÑĩ", - "çµ µ", - "Ġм ÑĥзÑĭ", - "ĠмÑĥ зÑĭ", - "ĠмÑĥз Ñĭ", - "еÑĢÑĤ в", - "ĠÙĥ ÙĨد", - "ĠÙĥÙĨ د", - "Ġu lož", - "Ġul ož", - "ÎŁÎ¥ ÎĽ", - "g ili", - "gi li", - "gil i", - "üs tü", - "üst ü", - "н ки", - "ĠÙĤ ÙĪاÙĨ", - "ι ακ", - "ια κ", - "ĠÅŁ er", - "ĠкиÑģ л", - "Ġки Ñģл", - "Ùģ ضÙĦ", - "ĠÐIJ ÑĦ", - "γ εν", - "γε ν", - "Ġdo stal", - "Ġdos tal", - "Ġdost al", - "ĠsaÄŁ lıklı", - "ĠsaÄŁlık lı", - "家 æĹı", - "ÄIJ T", - "е ÑĢин", - "еÑĢ ин", - "еÑĢи н", - "ĠìĿ´ëŁ¬ íķľ", - "Ġdüny ada", - "Ġdünya da", - "Ġnh ắc", - "Âł ÂłĊ", - "³³ Ċ", - "ν ηÏĥη", - "νη Ïĥη", - "γÏģα μμα", - "Ġtak son", - "ĠTürk çe", - "ĠÙģراÙĨ سÙĩ", - "天 åłĤ", - "æº ¶", - "Ġ oto", - "Ġo to", - "Ġot o", - "èµ µ", - "ch yb", - "chy b", - "Ġ å¾Ĵ", - "Ġå¾ Ĵ", - "ÏĦ Ïį", - "áh nout", - "à¥į पर", - "à¥įप र", - "Ġv las", - "Ġvl as", - "Ġíļ¨ ê³¼", - "Ġt hang", - "Ġth ang", - "Ġthan g", - "Ġtha ng", - "Ġol masına", - "Ġolm asına", - "Ġolması na", - "ĠпоÑĢÑĥÑĪ еннÑı", - "Ġqu ỹ", - "ĠíĿ IJ", - "ĠìĪ ¨", - "Ġ ë²Ī째", - "Ġë²Ī 째", - "ẹ n", - "Ġз год", - "Ġзг од", - "Ġ تز", - "Ġت ز", - "Ġاخ تص", - "Ġاخت ص", - "ĠзÑĥÑģÑĤ ÑĢÑĸ", - "Ġt ặng", - "ῶ ν", - "Ġ ì½ľ", - "Ġì½ ľ", - "ов аниÑħ", - "ова ниÑħ", - "овани Ñħ", - "ован иÑħ", - "âĢĮ شد", - "âĢĮØ´ د", - "Ġa raya", - "Ġar aya", - "Ġara ya", - "Ġaray a", - "r ové", - "ro vé", - "rov é", - "Ġاخ تÙĦ", - "Ġاخت ÙĦ", - "ли вий", - "лив ий", - "Ġات ØŃاد", - "Ġak ÅŁam", - "ĠÚ©ÙĦ اس", - "ãĤ¢ ãĥĥãĥĹ", - "Ġz ih", - "Ġzi h", - "å ĩĮ", - "åĩ Į", - "å±± å¸Ĥ", - "Ġçev res", - "Ġçevr es", - "Ġçevre s", - "Ġог ÑĢом", - "ĠØ¢ دÙħ", - "ĠtÄĽ lo", - "ĠtÄĽl o", - "ï¼Į æľ¬", - "ĠÚĺاÙĨ ÙĪÛĮÙĩ", - "Ġkr aje", - "Ġkra je", - "Ġkraj e", - "μ ία", - "μί α", - "èħ ¿", - "âĢŀ To", - "決 å®ļ", - "ì ĩ", - "Ġ éĴ", - "Ġé Ĵ", - "ĠΣ ÏĦα", - "ĠجÙħ ÙĩÙĪر", - "ĠGen ç", - "r ám", - "rá m", - "ĠÐł ез", - "ĠÐłÐµ з", - "Ġvyt vá", - "ĠпÑĢоизвод ÑģÑĤва", - "ĠÙħ Ø°Ùĩ", - "ĠÙħØ° Ùĩ", - "Ġihtiy ac", - "ãĤ¯ ãĤ»", - "Ġn êu", - "å¾ ³", - "Ġ ëĵĿ", - "Ġëĵ Ŀ", - "н аÑĩе", - "на Ñĩе", - "наÑĩ е", - "ĠÏĥÏħ μμε", - "ÏĨ Ïīν", - "в авÑģÑı", - "ва вÑģÑı", - "вав ÑģÑı", - "Ġви ÑĤами", - "ĠвиÑĤ ами", - "Ìģ t", - "Ġfinan ÄįnÃŃ", - "åıĬ åħ¶", - "âĢ ħ", - "çĭ ¼", - "ัà¸ļ à¸ķ", - "ãģĽ ãĤĭ", - "ÎĻÎļ ÎŁ", - "λ λι", - "λλ ι", - "ÑĤ оÑİ", - "ÑĤо Ñİ", - "ا عÙĬØ©", - "اع ÙĬØ©", - "اعÙĬ Ø©", - "vÃŃ ce", - "vÃŃc e", - "о нÑĸв", - "он Ñĸв", - "онÑĸ в", - "ì£ Ħ", - "å» ł", - "ĠØ´ÙĬ Ø¡", - "ĠТ ем", - "ĠТе м", - "Ġاب زار", - "ĠTH PT", - "γ γÏģαÏĨ", - "ĠëĮĢ íķ´ìĦľ", - "ĠëĮĢíķ´ ìĦľ", - "ĠPh ạm", - "ÑĨион ной", - "| /", - "Ġ ãĤ¸ãĥ£", - "ĠãĤ¸ ãĥ£", - "ÑĮ ÑİÑĤ", - "ÑĮÑİ ÑĤ", - "Ñĥ зÑĭ", - "Ñĥз Ñĭ", - "ĠÙħ اد", - "ĠÙħا د", - "ĠmÄĽ ly", - "ĠmÄĽl y", - "Ġ çĪ±", - "ĠçĪ ±", - "Ġr ád", - "Ġrá d", - "à¸Ħว à¸ļà¸Ħ", - "à¥Ī ?", - "Ġl idi", - "Ġli di", - "Ġlid i", - "m amız", - "mam ız", - "Ġ à¹ģà¸ģ", - "Ġà¹ģ à¸ģ", - "ãĤ¯ ãĤ·ãĥ§ãĥ³", - "à¸Ńำ à¸Ļวย", - "es át", - "Ġv iêm", - "Ġvi êm", - "è¡Į åĬ¨", - "มาà¸ģ à¸ģว", - "ĠØ®ÙĪ اب", - "Ġser best", - "ÅĻÃŃ z", - "ĠíĺĦ ëĮĢ", - "ãĢĮ ãģĿãģĨ", - "çĤ ¸", - "om ik", - "omi k", - "ĠÄ° ran", - "Ġer iÅŁ", - "ĠÑģ ела", - "ĠÑģел а", - "Ġار زÛĮ", - "Ġارز ÛĮ", - "ãĥĪ ãĥª", - "ĠB ÄĽ", - "е кÑĥ", - "ек Ñĥ", - "Ч ÑĤобÑĭ", - "ЧÑĤо бÑĭ", - "Ġanlam da", - "Îij Îĺ", - "ĠLINE AR", - "ĠLIN EAR", - "æľī çĤ¹", - "ÑĤ аÑĢ", - "ÑĤа ÑĢ", - "it ler", - "itle r", - "Ġn ÃŃž", - "ĠnÃŃ Å¾", - "ĠС ÑģÑĭлки", - "å ¶", - "Ġв пол", - "Ġвп ол", - "ĠدÙĤÛĮ ÙĤÙĩ", - "ĠدÙĤÛĮÙĤ Ùĩ", - "Ġ ä½ĵ", - "ر Ùī", - "ëĶ °", - "Ġà¤ķ व", - "Ġж иÑĢ", - "æij Ĩ", - "Ġì¤ij ìĭ¬", - "Ġк Ñĥб", - "ĠкÑĥ б", - "Ġz lep", - "ĠÑĢÑĭ б", - "é³ ´", - "à¹ģà¸ľ à¸Ļ", - "Ġ íĢ", - "Ġí Ģ", - "ĠÐĿ еÑĤ", - "ĠÐĿе ÑĤ", - "ž itÄĽ", - "žit ÄĽ", - "ži tÄĽ", - "Ġb Äĥng", - "ĠH ava", - "ĠHa va", - "ĠHav a", - "Ġ모 ëį¸", - "ĠH ãy", - "ĠìĿ´ ê²ĥ", - "Ġìĥģ ìĦ¸", - "me miÅŁ", - "mem iÅŁ", - "ĠθÎŃ Ïĥη", - "ण न", - "ĠskuteÄį nÄĽ", - "ĠTarih i", - "Ġtext u", - "Ġtex tu", - "ï¼Į éĢĻ", - "ĠاÛĮÙĨتر ÙĨتÛĮ", - "ĠÙ¾ اد", - "Ġپا د", - "ิà¸Ļ à¸ģาร", - "ĠNg á»įc", - "ĠÑĢоб иÑĤи", - "íĸĪ ê³ł", - "Ġम ण", - "ÐĽ Ðĺ", - "Ġпо ÑĤеÑĢ", - "ĠпоÑĤ еÑĢ", - "Ñģ ом", - "Ñģо м", - "ĠاÙĪ ÙĦÛĮÙĩ", - "ĠاÙĪÙĦ ÛĮÙĩ", - "éĽ ij", - "ĠGi á", - "Ġk anal", - "Ġkan al", - "Ġka nal", - "Ġavant aj", - "Ġavan taj", - "Ġr yb", - "Ġry b", - "Ø® تÙĩ", - "خت Ùĩ", - "ĠÙĪ رÙĪد", - "ĠÙĪر ÙĪد", - "ÐĴ ÑĤ", - "Ïī Ïĥε", - "기 ë¡ľ", - "ĠÐĽ Ñĸ", - "Ġt ảng", - "Ġtả ng", - "Ġص ÙĦÙī", - "ĠÑĥ лÑĭ", - "ĠÑĥл Ñĭ", - "Ġcu á»ijn", - "ĠÐIJ нг", - "ĠÐIJн г", - "Ġد اÙĪ", - "ĠÑĪлÑıÑħ ом", - "ĠÄįlovÄĽk a", - "ĠÄįlovÄĽ ka", - "d ete", - "de te", - "det e", - "ÑĬ ем", - "à¹Į à¹ĥà¸Ļ", - "à¤ķ न", - "åĪ ¤æĸŃ", - "åĪ¤ æĸŃ", - "ĸ ìĹIJ", - "ÏĦ ÏīÏĥη", - "ÏĦÏī Ïĥη", - "ĠÙģÙĨ اÙĪرÛĮ", - "ĠyaÅŁ ında", - "ĠÏĥÏĩ ÎŃ", - "Ġ yı", - "Ġy ı", - "Ġp ÅĻen", - "ĠpÅĻ en", - "ĠpÅĻe n", - "ĠÑĦоÑĢм ÑĥваннÑı", - "ĠÑĦоÑĢмÑĥ ваннÑı", - "ü mÃ¼ÅŁ", - "üm Ã¼ÅŁ", - "ümü ÅŁ", - "Ġ δο", - "Ġδ ο", - "ımız ın", - "ımızı n", - "Ġ é¢Ħ", - "Ġé¢ Ħ", - "оÑģÑĤ ÑĮÑİ", - "оÑģÑĤÑĮ Ñİ", - "ĠоÑĤкÑĢÑĭ ÑĤ", - "Ġأغ سطس", - "ĠA sp", - "ĠAs p", - "ĠÑĥ зн", - "ĠÑĥз н", - "ĠÙĪ است", - "ĠÙĪا ست", - "ĠÙĪاس ت", - "e lerle", - "eler le", - "èķ ī", - "Ġت Ú©ÙĨ", - "Ġتک ÙĨ", - "Ñĥ мÑĥ", - "Ñĥм Ñĥ", - "à¹Į à¸ĭ", - "ाद न", - "ĠâĢĭ âĢĭâĢĭ", - "ĠâĢĭâĢĭ âĢĭ", - "Ġa lıyor", - "Ġal ıyor", - "Ġî ¡", - "Ùħ دة", - "Ùħد Ø©", - "Ġ Ïĥει", - "ĠÏĥ ει", - "ĠÏĥε ι", - "Ġ è¿Ļ", - "Ġè¿ Ļ", - "ĠÅŀ ehir", - "ен ÑĤами", - "енÑĤ ами", - "енÑĤа ми", - "ãĤ¿ ãĥ«", - "ห าย", - "หา ย", - "ай ÑĤ", - "Ġh arc", - "Ġhar c", - "Ġha rc", - "ãĢĤ ãģĬ", - "Ġتأ Ø«ÛĮر", - "า à¸Ĭà¸Ļ", - "าà¸Ĭ à¸Ļ", - "Ġth áºŃm", - "Ġ æ¿", - "Ġæ ¿", - "Ġm Å©i", - "Ġprv nÃŃm", - "ĠprvnÃŃ m", - "Ġбаг аÑĤÑĮ", - "ĠбагаÑĤ ÑĮ", - "ãģķãĤī ãģ«", - "b iên", - "bi ên", - "åºĶ å½ĵ", - "ìĿ´ ë²Ħ", - "Ġpou žÃŃt", - "ĠpoužÃŃ t", - "Ġokam ž", - "e sin", - "es in", - "esi n", - "v ÄĽl", - "vÄĽ l", - "Ġ ضÙĪ", - "Ġض ÙĪ", - "è» Ł", - "- з", - "à¥Ī त", - "è¨Ī ç®Ĺ", - "r abilir", - "ra bilir", - "rab ilir", - "ĠÐłÐ¾Ñģ ÑĸÑĹ", - "Ġpla tÃŃ", - "Ġplat ÃŃ", - "Ġdosp ÄĽl", - "Ġر ضا", - "Ġرض ا", - "Ġn ového", - "Ġnov ého", - "Ġnové ho", - "Ġна ÑĨионалÑĮ", - "ĠÐIJ б", - "ãģĮ ãģĤãģ£ãģŁ", - "Ġ ë¹Ī", - "Ġë¹ Ī", - "âĢĮ Ùħ", - "å±ŀ äºİ", - "Ġt ane", - "Ġta ne", - "Ġtan e", - "ÙĬ اÙĩ", - "ÙĬا Ùĩ", - "Ġ βο", - "Ġβ ο", - "Ġ ëĬ¥", - "ĠëĬ ¥", - "ãĥĩãĤ£ ãĥ¼ãĤ¹", - "Ġ Ø°Ùĥر", - "ĠØ° Ùĥر", - "Ġobvyk le", - "Ġbir inci", - "ĠاÙĦ زر", - "ĠاÙĦز ر", - "ìĿ´ ë¹Ħ", - "ĠØ¥ د", - "ĠE kon", - "ĠEk on", - "ÐŁ ол", - "ÐŁÐ¾ л", - "ĠвеÑĢ оÑıÑĤ", - "Ġyarar lan", - "Ġа ÑĢом", - "ĠаÑĢ ом", - "Ġ éĦ", - "Ġé Ħ", - "Ġ iddi", - "Ġid di", - "i Äįka", - "iÄį ka", - "struk ce", - "mÃ¼ÅŁ tür", - "Ïħ ÏĦÏĮ", - "ë¡ ±", - "Ġal maktadır", - "Ġalmak tadır", - "ени Ñıми", - "ениÑı ми", - "ียà¸Ļ ร", - "à¹ĩ à¸Ļว", - "à¹ĩà¸Ļ ว", - "и кÑĥ", - "ик Ñĥ", - "е нка", - "ен ка", - "âĢĻ yi", - "âĢĻy i", - "Ġpo hod", - "Ġpoh od", - "Ġ زر", - "Ġز ر", - "Ġx ấu", - "Ġ à¸łà¸²à¸©", - "Ġà¸ł าษ", - "Âł Ðŀ", - "Ġ δικ", - "Ġδ ικ", - "Ġδι κ", - "Ġназ ива", - "åıª èĥ½", - "大 éĩı", - "ĠÄij ế", - "Ġ 第äºĮ", - "Ġ第 äºĮ", - "ĠkiÅŁ ilerin", - "ĠkiÅŁi lerin", - "ĠkiÅŁiler in", - "Ġdob ré", - "Ġdobr é", - "é© ¾", - "Ġdůležit é", - "ë¡ ¤", - "μÎŃ Î½Î¿Ïħ", - "μÎŃν οÏħ", - "μÎŃνο Ïħ", - "Ġtr ú", - "Ġbiç im", - "Ġ ÐĿÐIJ", - "ĠÐĿ ÐIJ", - "Ġ å¾Į", - "Ġå¾ Į", - "Ġdu yg", - "Ġduy g", - "åŀ Ĥ", - "ÐĨ ÐĨ", - "Ġet meye", - "Ġetm eye", - "Ġetme ye", - "ĠÙĦب اس", - "Ġд вÑĸ", - "Ġдв Ñĸ", - "Ġ 긴", - "Ġê¸ ´", - "ÑĨ Ñĸйно", - "ÑĨÑĸй но", - "κ ÏĦή", - "ï½ Ŀ", - "ĠÑĦевÑĢа лÑı", - "å¯ «", - "Ġ 겨", - "Ġê² ¨", - "Ġyıl larda", - "Ġyıllar da", - "Ġз Ñĥп", - "Ġobchod nÃŃ", - "Ġاض اÙģÙĩ", - "в еÑĢж", - "веÑĢ ж", - "Ġ æłĩ", - "Ġæł ĩ", - "ج اج", - "جا ج", - "Ġر ÙĪسÛĮ", - "ĠرÙĪ سÛĮ", - "Ġstand art", - "Ġstan dart", - "é ru", - "ér u", - ") ìĿĦ", - "д екÑģ", - "де кÑģ", - "Ġ âĪļ", - "ĠâĪ ļ", - "ĠÄ°ngiliz ce", - "èĬ Ŀ", - "身 ä¸Ĭ", - "ØŁ ØŁ", - "Ġm ẽ", - "Îij ÎĶ", - "енÑģ ив", - "âĢĻ ta", - "âĢĻt a", - "à¹ī าà¸ģ", - "à¹īา à¸ģ", - "ÎŁÎĽ ÎŁÎĵ", - "ä»ĺ ãģij", - "Ġs Ãłng", - "ĠsÃłn g", - "Ġह à¤Ł", - "ÑĭÑĪ лен", - "ĠØ® طر", - "Ġخط ر", - "Ġнай ÑĤи", - "缸 ä¿¡", - "Ïī δ", - "ठĶ", - "Ġdo pad", - "Ġdop ad", - "à¹Ħà¸Ł ล", - "æ ģµ", - "æģ µ", - "í Ĥ¬", - "íĤ ¬", - "Ä±ÅŁ ma", - "ãģı ãĤĮãģŁ", - "ãģıãĤĮ ãģŁ", - "Ġnap rost", - "ĠÑģоÑģÑĤав е", - "Ġ ÙĪسط", - "ĠÙĪ سط", - "ĠÙĪس Ø·", - "๠ķ", - "éĸĭ çĻº", - "ĠдеÑĢ ева", - "ĠдеÑĢев а", - "- ÐĶ", - "à¸ĩ à¸Ĭ", - "ิà¸ķ ย", - "ĠاÙĦÙĤ اÙĨÙĪÙĨ", - "ãĤ¹ ãĤ«", - "l ÃŃž", - "lÃŃ Å¾", - "Ġан ализ", - "Ġproblém y", - "æĸĩ åѦ", - "çĹħ éĻ¢", - "Ñģ ед", - "Ñģе д", - "ï¼Į å°ı", - "Ġعش ÙĤ", - "ãģ° ãģĭãĤĬ", - "Ġع ÙĤد", - "ĠعÙĤ د", - "ØŃ ÙĬØ©", - "ØŃÙĬ Ø©", - "Ġë°Ķ ëŀįëĭĪëĭ¤", - "inc lu", - "incl u", - "Ġ ëĵľë¦½ëĭĪëĭ¤", - "Ġëĵľ 립ëĭĪëĭ¤", - "åį« çĶŁ", - "Ġвид Ñĥ", - "Ġви дÑĥ", - "ุ à¸ļาล", - "ุà¸ļ าล", - "ÑĢ ÑĥкÑĤ", - "ÑĢÑĥ кÑĤ", - "ÑĢÑĥк ÑĤ", - "ĠоÑģ вÑĸÑĤ", - "ĠоÑģвÑĸ ÑĤ", - "Ġvel ký", - "Ġvelk ý", - "Ġch tÄĽl", - "ĠchtÄĽ l", - "æīĵ å¼Ģ", - "Ġзакон одаÑĤелÑĮ", - "ан Ñģи", - "анÑģ и", - "ì¶ ĺ", - "ĠÙħر اج", - "åģľ æŃ¢", - "Ġво но", - "ìłķ ìĿ´", - "Ġroz sah", - "Ġrozs ah", - "Ġ æĻ´", - "ĠæĻ ´", - "Ġza jist", - "Ġzaj ist", - "Âł м", - "tı ģını", - "tıģ ını", - "Ġhizmet i", - ". Îij", - "ĠÙħعÙħÙĪÙĦ ا", - "Ġ ži", - "Ġž i", - "Ġg á»įn", - "èĮ Ĥ", - "Ġh uz", - "Ġhu z", - "ζ ει", - "ζε ι", - "à¥ī à¤Ł", - "Ġиз дел", - "ìŀ ĸ", - "ĠëĶ° 른", - "Ġk ia", - "Ġki a", - "Ġz nÄĽnÃŃ", - "Ġzn ÄĽnÃŃ", - "ĠоÑĢгани за", - "ĠоÑĢганиз а", - "از ات", - "Ġrež im", - "Ġв енÑĤи", - "b ách", - "Ġод номÑĥ", - "Ġодно мÑĥ", - "Ġодном Ñĥ", - "Ġkit ab", - "Ġki tab", - "Ġkita b", - "Ġfran couz", - "Ġfranc ouz", - "ĠØ£ ÙĦ", - "Ġس رÙĪ", - "Ġسر ÙĪ", - "Ùij ÙĦ", - "Ġ ман", - "Ġм ан", - "Ġма н", - "ë° į", - "Ġк Ñĥда", - "ĠкÑĥ да", - "Ùı س", - "ãĢĤ æѤ", - "ا شة", - "اش Ø©", - "à¸Ĥà¸Ńà¸ĩ à¸ľ", - "主 ä»»", - "ив ÑĪи", - "Ġà¸ģ รà¸ģ", - "Ġà¸ģร à¸ģ", - "ек Ñģи", - "екÑģ и", - "иÑĤ еÑĤ", - "иÑĤе ÑĤ", - "ĠØ£ ÙĦÙģ", - "ĠØ£ÙĦ Ùģ", - "а ними", - "ан ими", - "ани ми", - "ãĥļ ãĥ¼ãĤ¸", - "ĠпÑĢав ил", - "ĠпÑĢави л", - "åªĴ ä½ĵ", - "Ñİ Ñīее", - "ÑİÑī ее", - "ä¸Ģ 人", - "β ο", - "ìĭ ¸", - "о зна", - "оз на", - "å¤ī æĽ´", - "ĠÙħØ´ Ùĩد", - "æ³ķ 人", - "ĠBa kanı", - "ĠBak anı", - "ĠBakan ı", - "ĠÑħоÑĩ а", - "Ġα ξ", - "Ġver ilm", - "Ġveri lm", - "Ġk onus", - "Ġkon us", - "Ġkonu s", - "με νη", - "μεν η", - "Ġ 馬", - "Ġé ¦¬", - "Ġé¦ ¬", - "Ġìĭ¤ ìłľ", - "Ġjed no", - "Ġjedn o", - "Ġб аб", - "Ġба б", - "åĥ į", - "æĺ¯ ä¸Ģ个", - "æĺ¯ä¸Ģ 个", - "- е", - "ĠpÅĻek vap", - "à¸Ń à¸ŀ", - "ĠY ol", - "ĠYo l", - "ĠÑĥÑģÑĤан авлива", - "ê² ¼", - "Ġ 件", - "اÙĦ Ø´", - "Ġоб ÑĥÑĩ", - "åĺ Ľ", - "ĠÑħоÑĩ Ñĥ", - "ĠÐķ в", - "ÑĦ оÑĢÑĤ", - "ÑĦоÑĢ ÑĤ", - "Ġर न", - "âĢŀ V", - "èľ ľ", - "Ġd oma", - "Ġdo ma", - "Ġdom a", - "æĶ¯ æı´", - "Ġ اخت", - "Ġا خت", - "Ġاخ ت", - "å¾ ª", - "à¥Ĥ à¤ļन", - "à¥Ĥà¤ļ न", - "ा हन", - "ाह न", - "Ġ å¤ı", - "Ġå¤ ı", - "ĠاÙĦØ£ Ùħر", - "ĠاÙĦØ£Ùħ ر", - "ĠбеÑĢем енноÑģÑĤи", - "ĠTh á»±c", - "é£İ éĻ©", - "Ġül kemiz", - "Ġülk emiz", - "çķª åı·", - "ÑģÑĤ ÑĢе", - "ÑģÑĤÑĢ е", - "ÑĪ ло", - "ĠصاØŃ Ø¨", - "ι νε", - "ιν ε", - "ĠK ıs", - "ĠKı s", - "ĠPr ahy", - "ĠPra hy", - "æ¹ ¿", - "Ġv ým", - "Ġvý m", - "çĽ Ĵ", - "ÎŁ ÎĶ", - "ÎŁÎ Ķ", - "ãģł ãģª", - "ĠpÅĻÃŃ ležit", - "Ġìĸ¸ ìłľ", - "ĠÑĪвид ко", - "Ġsitu aci", - "åħĥ ç´ł", - "Ä°T ESÄ°", - "ĠV ak", - "ĠVa k", - "Ġner edeyse", - "i iii", - "ii ii", - "iii i", - "ÑĢа зд", - "ÑĢаз д", - "Ġп олиÑĤ", - "Ġпо лиÑĤ", - "Ġпол иÑĤ", - "Ġполи ÑĤ", - "Ġп огод", - "Ġпо год", - "Ġпог од", - "ĠпÑĢоÑĨеÑģ Ñģе", - "ĠпÑĢоÑĨеÑģÑģ е", - "Ġмен ÑĪе", - "äºĮ 人", - "ĠÙħÙĪ اط", - "Ġp ÅĻik", - "ĠpÅĻ ik", - "ĠpÅĻi k", - "è· ¡", - "Ġs erg", - "Ġse rg", - "Ġser g", - "ĠÑĢаÑģ ÑģÑĤоÑı", - "и Ñĩно", - "иÑĩ но", - "ĠÎĶ ÎĹÎľ", - "¨ Ø·", - "ص بØŃ", - "صب ØŃ", - "สะ à¸Ķวà¸ģ", - "د رÛĮ", - "در ÛĮ", - "k ům", - "ků m", - "ç§ģ ãģ¯", - "Ġt vor", - "Ġtv or", - "à¥įव व", - "Ġp ÅĻiv", - "ĠpÅĻ iv", - "ĠpÅĻi v", - "Ġ íı´", - "Ġíı ´", - "Ġst átu", - "Ġstát u", - "Ġed ilmiÅŁtir", - "Ġedilm iÅŁtir", - "Ġedil miÅŁtir", - "ĠedilmiÅŁ tir", - "ØŃ Ùħ", - "Ġб ÑĥÑħ", - "ĠбÑĥ Ñħ", - "สำ à¹Ģร", - "ĠتÙĪ ضÛĮ", - "ãģĿãĤĮ ãģ¯", - "Ġà¤ħव ध", - "é ŀĭ", - "éŀ ĭ", - "âĤ¬ Ċ", - "Ġ éº", - "Ġé º", - "ĠÄĮ es", - "Ġpop rvé", - "ï¼Į åĽł", - "Ġal mÄ±ÅŁ", - "Ġalm Ä±ÅŁ", - "l al", - "la l", - "ĠØ® ÙĪبÛĮ", - "ĠØ®ÙĪ بÛĮ", - "ĠØ®ÙĪب ÛĮ", - "Ġκ οÏģ", - "Ġκο Ïģ", - "ìļ´ ëıĻ", - "m ayın", - "may ın", - "mayı n", - "Ġak tif", - "Ġakt if", - "ĠاÙĨج ÙħÙĨ", - "ĠÑģ ÑĤак", - "ĠÑģÑĤ ак", - "ĠÑģÑĤа к", - "ĠÑģÑĤ аÑĢа", - "ĠÑģÑĤаÑĢ а", - "ĠÑģÑĤа ÑĢа", - "ÙĦ ÙģØ©", - "ÙĦÙģ Ø©", - "Ġparç ası", - "ĠкоÑĢп ÑĥÑģ", - "ãĢģ é«ĺ", - "! ..", - "!. .", - "ĠÎł ÎijÎĿ", - "ĠÙĩÙĨ ÙĪز", - "ion álnÃŃ", - "Ġprá vnÃŃ", - "Ġpráv nÃŃ", - " Ŀ", - "Ġت ÛĮر", - "ĠتÛĮ ر", - "Ġ åŁİ", - "ĠåŁ İ", - "Ġзг ад", - "Ġsaldır ı", - "æŁ¥çľĭ æijĺè¦ģ", - "é« ª", - "Ùģ صÙĦ", - "ãģĻ ãģ¹ãģ¦", - "е во", - "ев о", - "ê´Ģ리 ìŀIJ", - "Ġìĺ Ĩ", - "udic ots", - "ÙĪر ÙĨ", - "Ġcel kem", - "ãĤ¤ ãĤº", - "ìĬ¤ ê°Ģ", - "販 売", - "ĠíĮĮìĿ¼ 첨ë¶Ģ", - "ë ¢°", - "Ġe nergie", - "Ġenerg ie", - "Ġener gie", - "es idir", - "esi dir", - "Ġm iá»ĩng", - "éĻ ·", - "Ġг аÑĢа", - "ĠгаÑĢ а", - "Ġb iliyor", - "Ġbil iyor", - "çį² å¾Ĺ", - "еÑĤ еÑĢб", - "à¹Īา à¹Ģà¸Ľ", - "Ġμα ζί", - "Ġzprac ovánÃŃ", - "Ñģ м", - "Ġh ala", - "Ġha la", - "Ġhal a", - "Ġز ÙĪج", - "ĠвÑĸд нов", - "à¹Ģหม าะ", - "ĠÐłÐµÑģп Ñĥбли", - "åĩºåĵģ èĢħ", - "Ñī ини", - "Ñīи ни", - "Ñīин и", - "ัà¸Ļ à¹Ģà¸Ľ", - "Ġtý den", - "Ġtýd en", - "Ġب ÙĬت", - "ĠبÙĬ ت", - "Ñģ комÑĥ", - "Ñģк омÑĥ", - "Ñģком Ñĥ", - "Ñģко мÑĥ", - "ĠÙĩÙĪ اپÛĮÙħ", - "оÑģ нов", - "é¸ Ł", - "Ġsou krom", - "Ġfa iz", - "Ġdem ok", - "Ġdemo k", - "Ġkter ém", - "Ġkteré m", - "Ġëħ ¹", - "л аÑĩ", - "ла Ñĩ", - "ĠоÑĤвеÑĤ ÑģÑĤвен", - "Ġ ï¼¼:", - "Ġï¼¼ :", - "Ġ λο", - "Ġλ ο", - "ÄĮ esk", - "ê°Ģ ìļĶ", - "Ġ ãĥĬ", - "Ġãĥ Ĭ", - "Ġnhu áºŃn", - "ĠÑģ или", - "ĠÑģи ли", - "ĠÑģил и", - "ĠÐľ он", - "Ġç ap", - "Ġça p", - "ĠRow Box", - "Ġм аÑģÑĤ", - "ĠмаÑģ ÑĤ", - "Ġма ÑģÑĤ", - "ĠÐľ а", - "ĠдÑĢÑĥг о", - "ĠдÑĢÑĥ го", - "ĠØ£ Ø´", - "ë°© ìĨ¡", - "ĠпÑĸд пиÑģ", - "èĩ ¨", - "åī ©", - "Ġh iá»ĥn", - "Ġhi á»ĥn", - "ĠÙĤر ارد", - "ĠÙĤرار د", - "ist rat", - "istr at", - "istra t", - "ÐŁ Ñĸд", - "ÏĦε Ïģα", - "ÏĦεÏģ α", - "Ġpoz dÄĽ", - "ĠbaÅŁ ta", - "夫 人", - "л ини", - "ли ни", - "лин и", - "Ġка ÑĩеÑģÑĤва", - "ĠкаÑĩе ÑģÑĤва", - "Ġkur tul", - "Ġ ì¢Į", - "Ġì¢ Į", - "ãģ«ãģĬ ãģijãĤĭ", - "åľ° åįĢ", - "ĠÑĩа Ñģом", - "ĠÑĩаÑģ ом", - "ìµľ ê³ł", - "Ġn gang", - "Ġng ang", - "Ġnga ng", - "ا Ùĩد", - "اÙĩ د", - "ĠШ ев", - "ĠpÅĻ itom", - "ĠpÅĻi tom", - "Ġch ấm", - "ĠÐľ еÑģÑĤо", - "ĠÑģовеÑĢÑĪ енно", - "ÃŃ cÃŃ", - "ÃŃc ÃŃ", - "Ń å·ŀ", - "åĪĽ æĸ°", - "äºĶ æľĪ", - "Ġا عÙħاÙĦ", - "Ġاع ÙħاÙĦ", - "Ġвозмож ноÑģÑĤи", - "Ġвозможно ÑģÑĤи", - "ĠпÑĢод овж", - "n ÄĽt", - "nÄĽ t", - "ĠÐĿа пÑĢимеÑĢ", - "ĠاÙĦ دÙħ", - "ĠاÙĦد Ùħ", - "Ġ à¹ģà¸ļà¸ļ", - "Ġà¹ģ à¸ļà¸ļ", - "çĶŁ çļĦ", - "ĠÑħ аÑĢÑĩ", - "ĠSon uç", - "Ġrůzn é", - "Ġrůz né", - "Ġ اذ", - "Ġا Ø°", - "ĠØ§Ø °", - "à¸ķ à¸Ńà¸ļ", - "P ÅĻed", - "PÅĻ ed", - "ĠдеÑĢев Ñıн", - "ë´ IJ", - "ĠëĬIJ ëĤ", - "جÙħ ÙĬع", - "ĠBöyle ce", - "èµ ı", - "Ġب سÙĬ", - "Ġبس ÙĬ", - "ĠÃĩ aÄŁ", - "Ġت اÛĮ", - "Ġتا ÛĮ", - "Ġnej vyÅ¡Å¡ÃŃ", - "èĸ ©", - "Ïĩε δÏĮν", - "Ġëĵ± ìĿĺ", - "e yh", - "ey h", - "æĸĻ çIJĨ", - "ا تÙĩ", - "ات Ùĩ", - "æī «", - "Ġ å©", - "Ġå ©", - "ĠпÑĢи вед", - "ĠпÑĢив ед", - "æī ¶", - "Ġ 견", - "Ġê² ¬", - "Ġا ÙħÛĮر", - "ĠاÙħ ÛĮر", - "ाय ल", - "æ¡ ij", - "à¸Ļ à¹Ģà¸ķ", - "ила кÑĤи", - "家 ä¼Ļ", - "Ġbulun uyor", - "y sa", - "ys a", - " Ĩ", - "ĠB Ä°R", - "íĨ ¤", - "à¤Ĥà¤Ĺ à¤łà¤¨", - "ÎĶ εν", - "à¥Į à¤ķर", - "à¥Įà¤ķ र", - "éĸĵ ãģ«", - "Ġм об", - "Ġмо б", - "ĠMo rav", - "ĠMor av", - "è§Ħ åĪĴ", - "ĠÑģвÑĸÑĤ Ñĸ", - "ul ts", - "ult s", - "Ġze mÃŃ", - "Ġzem ÃŃ", - "Âł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", - "ÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂłĠÂłĠÂł", - "ĠÐŁ оп", - "ĠÐŁÐ¾ п", - "ãģĤ ãģĴ", - "Ġpom oci", - "Ġpomoc i", - "ĠзмÑĸ ÑģÑĤ", - "Ġзм ÑĸÑģÑĤ", - "主 人", - "ĠS ı", - "ãĥĽ ãĥĨãĥ«", - "ĠÑĥва гÑĥ", - "å» ³", - "à¹Ģม à¸ķร", - "est li", - "Ġlo ạt", - "ãĤ¢ ãĥ¼", - "ĠÎĶ ε", - "Ġbun ları", - "Ġbunlar ı", - "Ġ çĤ¹åĩ»", - "ĠçĤ¹ åĩ»", - "Ġ BÃłi", - "ĠB Ãłi", - "ĠBÃł i", - "Ġ ä¸ĸ", - "Ġä¸ ĸ", - "Ġê³ł ê°ľë¥¼", - "ĠÐŃ ÑĤоÑĤ", - "ĠÐŃÑĤо ÑĤ", - "Ġmem nun", - "Ġ ।Ċ", - "Ġ। Ċ", - "ĠиÑģÑĤоÑĢ ии", - "Ġ ì°©", - "Ġì° ©", - "१ ९", - "१ॠ¯", - "ĠÐŀд нак", - "ĠÐŀдна к", - "Ġv ede", - "Ġve de", - "Ġved e", - "ÏĨ ÎŃÏģει", - "ÏĨÎŃ Ïģει", - "â b", - "çĬ¶ åĨµ", - "åįı è®®", - "Ġ ê°Ŀ", - "Ġê° Ŀ", - "е вид", - "ев ид", - "еви д", - "j mu", - "jm u", - "Ġколи ÑĩеÑģÑĤва", - "ĠколиÑĩ еÑģÑĤва", - "ĠколиÑĩе ÑģÑĤва", - "à Ľ", - "i Äįe", - "iÄį e", - "Ġfirm alar", - "Ġfir malar", - "Ġfirma lar", - "èĢ Ģ", - "к Ñĸн", - "кÑĸ н", - "ĠêµŃ 민", - "Ġ목 ë¡Ŀ", - "ĠÎļ αÏģ", - "ĠÎļα Ïģ", - "Ġhis sed", - "Ġhiss ed", - "ï¼ «", - "Ġ Tên", - "ĠT ên", - "ĠÑĤÑĭ ÑģÑıÑĩ", - "ØŃÙĬ ØŃ", - "Ġвпол не", - "ĠS ınıf", - "ĠSın ıf", - "Ġμ ην", - "Ġμη ν", - "Ġ íij¸", - "Ġí ij¸", - "ĠاÙĦ طبÙĬ", - "ĠاÙĦØ· بÙĬ", - "ĠاÙĦطب ÙĬ", - "ĠزÛĮ ب", - "Ġп Ñĥ", - "Ġp raž", - "Ġpr až", - "Ġpra ž", - "ìĹĨ ëĬĶ", - "θ ÏģÏī", - "Ġi çi", - "Ġiç i", - "Ġб Ñĸл", - "ĠбÑĸ л", - "Ðł Ñij", - "Ġì¶ķ 구", - "Ġl ạ", - "Ġ ãĥķãĤ¡", - "Ġãĥķ ãĤ¡", - "Ġ èĸ", - "Ġè ĸ", - "μα ÏĦο", - "éĩij å±ŀ", - "á li", - "ál i", - "ĠÙģ Ø£", - "ĠKar lov", - "ĠKarl ov", - "ĠZ áp", - "ĠZá p", - "ãĥª ãĥ³ãĤ°", - "ãĥªãĥ³ ãĤ°", - "ab ilmek", - "abil mek", - "ĠС и", - "Ġc ÃŃrk", - "ĠcÃŃ rk", - "Ġk á»ĭp", - "Ġà¤ij नल", - "ĠÙĪ ØŃدة", - "ĠÙĪØŃ Ø¯Ø©", - "ãĥĭ ãĥĥãĤ¯", - "Ġn Æ°á»Ľng", - "Ġа кÑĤÑĥ", - "Ġак ÑĤÑĥ", - "ĠакÑĤ Ñĥ", - "å¸Ŀ åĽ½", - "Ġn ázev", - "Ġnáz ev", - "ĠÑĢемон ÑĤ", - "ĠÑĢ инкÑĥ", - "ĠÏĢ άνÏī", - "ĠÏĢά νÏī", - "ÏĦ ικο", - "ÏĦικ ο", - "ÏĦι κο", - "ĠìĤ¼ ìĦ±", - "ĠÑģимпÑĤом Ñĭ", - "ĠÑĢа нÑĸÑĪе", - "ĠJ á", - "ĠÑģÑĩиÑĤа еÑĤÑģÑı", - "ĠÑģÑĩиÑĤ аеÑĤÑģÑı", - "Ġп оÑĢÑĸв", - "Ġпо ÑĢÑĸв", - "ĠпоÑĢ Ñĸв", - "ĠÐľ ал", - "ĠÐľÐ° л", - "éĿ¢ 积", - "ĠÙĦ غ", - "Ġج Ø´ÙĨ", - "Ġнед ели", - "Ġнедел и", - "Ġì¦Ŀ ê°Ģ", - "ãĨį ëıĻ", - "Ġl ượt", - "ĠÄIJ á»ĭnh", - "Ġ à¸Ńà¸Ńà¸Ļà¹Ħลà¸Ļ", - "Ġà¸Ń à¸Ńà¸Ļà¹Ħลà¸Ļ", - "Ġyap arak", - "Ġyapar ak", - "ĠÄij ai", - "ĠÄija i", - "Ġо ÑĦиÑĨи", - "Ġε μÏĢ", - "ξ ειÏĤ", - "ξει ÏĤ", - "Ġкон ÑĦеÑĢен", - "Ġ arası", - "Ġa rası", - "Ġar ası", - "Ġaras ı", - "Ġara sı", - "à¸ķ า", - "Ġ ë´IJ", - "Ġë´ IJ", - "о вана", - "ов ана", - "ова на", - "ован а", - "ì§Ģ ê°Ģ", - "ĠV ám", - "ि à¤ľà¤¨", - "à¤¿à¤ľ न", - "Ġ ç¼ĸè¾ij", - "Ġç¼ĸ è¾ij", - "ζ ÏĮ", - "ĠÏĦ ÏģÏĮ", - "Ġücret siz", - "ĠکاÙħ ÙĦا", - "ĠکاÙħÙĦ ا", - ": ::/", - ":: :/", - "::: /", - "à¹Į ĊĊ", - "à¹ĮĊ Ċ", - "Ġéĸ¢ éĢ£", - "Ġ kara", - "Ġk ara", - "Ġka ra", - "Ġkar a", - "Ġбез пеки", - "ĠzmÄĽ ny", - "ĠzmÄĽn y", - "Ġê¿ Ī", - "v rd", - "vr d", - "li ÄŁine", - "liÄŁi ne", - "liÄŁ ine", - "liÄŁin e", - "ĠاÙĨتخاب ات", - "ĠдоÑģ вÑĸд", - "Ġkter ého", - "Ġkteré ho", - "ен ÑĤом", - "енÑĤ ом", - "ê³µ ë¶Ģ", - "ìł Ŀ", - "Ġë§Į 족", - "Ġ æij", - "Ġæ ij", - "åĩº åı£", - "建 è®®", - "о ÑĤÑı", - "оÑĤ Ñı", - "Ġ Òij", - "ĠÒ ij", - "íĶĦ ë¡ľ", - "Ġg ió", - "Ġgi ó", - "ãĤ· ãĤ§", - "Ġλ εÏĢ", - "íķĺ 볤", - "Ġyok sa", - "Ġist ih", - "ï¼ ¶", - "ĠاÙĦ عÙħ", - "ĠاÙĦع Ùħ", - "Ġکار گرد", - "à¹Ģà¸ŀ ราะ", - "Ġn ových", - "Ġnov ých", - "Ġnový ch", - "ĠÑģ на", - "Ġs ana", - "Ġsa na", - "Ġsan a", - "व त", - "Ä±ÅŁ man", - "Ä±ÅŁma n", - "åı¦ å¤ĸ", - "ì¶ľìŀ¥ ìĥµ", - "å© ¦", - "ĠкоÑĪ ÑĤÑĸв", - "ĠÙĪاÙĦ ÙĨ", - "Ġب اÙĦØ¥", - "ĠباÙĦ Ø¥", - "Ġ æĬĢ", - "ĠæĬ Ģ", - "Ġмн оже", - "à¥Ĥ ड", - "ĠC ục", - "Ġe vet", - "Ġev et", - "Ġeve t", - "èģĶ åIJĪ", - "Ġ³³Ġ³³ Ġ³³Ġ³³", - "çļĦ å¿ĥ", - "Ġd áng", - "Ġdá ng", - "Ġdán g", - "اÛĮ سÙĩ", - "Ġ erken", - "Ġer ken", - "Ġerk en", - "æ³ ¡", - "ائ ب", - "Ġyap ıldı", - "Ġyapıl dı", - "ĠQu ản", - "æŶ 代", - "ìĽ ¨ìĸ´", - "ìĽ¨ ìĸ´", - "Ġг ÑĸÑĢ", - "ok oj", - "oko j", - "Ùĥ رة", - "Ùĥر Ø©", - "Ñİ Ðº", - "Ġvý j", - "Ġhod iny", - "Ġhodin y", - "Ġелек ÑĤÑĢон", - "m ıyor", - "ĠìŀĪ ëĭ¤ëĬĶ", - "ĠìŀĪëĭ¤ ëĬĶ", - "à¹ī à¹ī", - "иÑĤелÑĮ ное", - "иÑĤелÑĮно е", - "Ġyıl lar", - "Äı te", - "ĠÄįin nost", - "ุà¸ĵ à¸łà¸²à¸ŀ", - "í ĵ¨", - "н г", - "ู รà¸ĵ", - "ูร à¸ĵ", - "ĠпоÑĢÑıд ке", - "Ġëĭ¹ ìĭľ", - "ĠÐľ оÑģков", - "ĠÐľÐ¾Ñģк ов", - "Ġk red", - "Ġkr ed", - "Ġkre d", - "u rum", - "ur um", - "uru m", - "Ġ ÑĤÑı", - "ĠÑĤ Ñı", - "Ú© ÙĨاÙĨ", - "Ú©ÙĨ اÙĨ", - "д ии", - "ди и", - "ÑĢи мÑĸн", - "ÑĢим Ñĸн", - "ĠоÑĢгани зм", - "ĠоÑĢганиз м", - "Ġ éĽĨ", - "ĠéĽ Ĩ", - "ι ÏĥÏĦο", - "ιÏĥ ÏĦο", - "ä¿¡ çĶ¨", - "åįģ åĽĽ", - "à¹Ī à¹ĥà¸Ĭ", - "ĠÑĥ вид", - "ĠÑĥв ид", - "ัà¸ĩ à¸ģล", - "ัà¸ĩà¸ģ ล", - "åı¦ ä¸Ģ", - "ãĥ« ãĥķ", - "ัà¸ļ à¸Ľà¸£", - "ĠÃľ st", - "説 æĺİ", - "в ай", - "ва й", - "а Ñĩе", - "аÑĩ е", - "æ¬ £", - "Ġkat ıl", - "Ġkatı l", - "ĠC em", - "ĠCe m", - "ĠاÙĦ جÙĩ", - "ĠاÙĦج Ùĩ", - "Ġг ÑĢÑĥз", - "ĠгÑĢÑĥ з", - "ĠгÑĢ Ñĥз", - "Ġза ÑģÑĤав", - "ĠзаÑģÑĤ ав", - "cı lar", - "ĠÑħоÑĤ ел", - "Ġs nÃŃm", - "Ġsn ÃŃm", - "ĠsnÃŃ m", - "ï¼Į 被", - "Ġ виÑī", - "Ġв иÑī", - "Ġви Ñī", - "Ġdem okrat", - "Ġdemok rat", - "à¥ĩ à¤Łà¤°", - "à¥ĩà¤Ł र", - "åij¨ å¹´", - "Ġod pad", - "Ġodp ad", - "Ġda ÅĪ", - "Ġ 代", - "à¹ĩ à¸Ļà¸Ń", - "à¹ĩà¸Ļ à¸Ń", - "ĠÑģк олÑĮко", - "Ġα ÏĨ", - "ĠpÅĻes vÄĽd", - "Ġ åĵģ", - "Ġåĵ ģ", - "ĠинÑĦоÑĢм аÑĨии", - "ĠинÑĦоÑĢма ÑĨии", - "çĽ Ĺ", - "ãģ¾ ãģ¨", - "ĠÑģам ов", - "ĠÑģамо в", - "Ġpo cit", - "Ġpoc it", - "Ġíݸ ì§ij", - "ĠÑģм еÑģÑĮ", - "Ġpo jiÅ¡tÄĽnÃŃ", - "ãģ® ãĤĤ", - "à¹Ī าà¸ģาร", - "à¹Īา à¸ģาร", - "à¹Īาà¸ģ าร", - "ĠÛĮ ÙĪÙĨ", - "Ġ기 ìĸµ", - "ick ými", - "ický mi", - "ickým i", - "a lace", - "al ace", - "ala ce", - "鼻 å½±", - "Ñİ Ð²Ð°Ð½Ð½Ñı", - "缸 åIJĮ", - "Ġ ãĢĥ", - "ĠãĢ ĥ", - "ĠдокÑĥм енÑĤÑĸв", - "ĠдокÑĥменÑĤ Ñĸв", - "ï¼ ¹", - "åĪ° åºķ", - "ó z", - "ĠAh met", - "ĠÙħس اØŃت", - "Ġhl avou", - "Ġhlav ou", - "ül ebilir", - "üle bilir", - "ãĢĤ ä½ł", - "à¹ĩà¸ģ à¸Ĭาย", - "¤ ¤", - "Ġ æĦı", - "ĠæĦ ı", - "Ġch áºŃm", - ". д", - "Ġ cca", - "Ġc ca", - "Ġcc a", - "Ġol umsuz", - " ŀ", - "çĬ ¬", - "ĠпоÑģÑĤоÑıн но", - "Ġ.************** Ċ", - "Ġا ستر", - "Ġاست ر", - "Ġاس تر", - "ĠдалÑĮ ней", - "ů r", - "ä¿Ŀ èŃ·", - "боÑĢ аÑĤоÑĢ", - "боÑĢа ÑĤоÑĢ", - "à ·", - "Ïĥ ÏĦαν", - "ÏĥÏĦ αν", - "ÏĥÏĦα ν", - "ĠÙģ ÙĬÙĦÙħ", - "ĠÙģÙĬ ÙĦÙħ", - "ç ek", - "çe k", - "ìŀIJ 기", - "Ġ æ¥Ń", - "Ġæ¥ Ń", - "н Ñĸп", - "нÑĸ п", - "èī ĩ", - "Ġm oci", - "Ġmo ci", - "Ġmoc i", - "ìľ µ", - "리 ê·¸", - "ĠÐļ о", - "éĤ£ éĩĮ", - "ĠС ÑĤаÑĢ", - "ĠСÑĤ аÑĢ", - "ĠСÑĤа ÑĢ", - "ĠتÙĪاÙĨ ÛĮد", - "Ġng uyá»ĩn", - "Ġnguy á»ĩn", - "Ġ สามารà¸ĸ", - "Ġส ามารà¸ĸ", - "Ñĸ Ñĩна", - "ÑĸÑĩ на", - "Ġ 被", - "Ġè¢ «", - "ุà¸ķสาห à¸ģรรม", - "Ġع صر", - "Ġعص ر", - "ĠÃľNÄ° VERS", - "Ġteh dy", - "ĠÙĪصÙĦ ات", - "ĠÙĪص ÙĦات", - "ä¿Ŀ è¯ģ", - "ĠE udicots", - "ĠÎł ÎŃ", - "建 è¨Ń", - "ĠìłĦ êµŃ", - "Ġ ØŃÛĮ", - "ĠØŃ ÛĮ", - "ãĤ¤ ãĥĦ", - "ĠØŃ Ø§ØµÙĦ", - "ĠجÙĨ ÙĪبÛĮ", - "ĠجÙĨÙĪب ÛĮ", - "ãĢģ æĹ¥æľ¬", - "à Ļ", - "Ġ à¸Ĺาà¸ĩ", - "Ġà¸Ĺ าà¸ĩ", - "ĠÙĨØŃ ÙĪ", - "اÙĩ ÙĬÙħ", - "å¾Į ãģ«", - "à¸Īะ à¹Ħà¸Ķ", - "åĩł 个", - "à¥ģ à¤ģ", - "à¥ģठģ", - "ëĮĢ ìĿĺ", - "Ġl Ãłn", - "ĠlÃł n", - "ìĽĶ ë¶ĢíĦ°", - "Æ ł", - "Ġ еди", - "Ġе ди", - "Ġs pis", - "Ġsp is", - "Ġspi s", - "æľī ä»Ģä¹Ī", - "Ġneb yla", - "Ġneby la", - "Ġnebyl a", - "Ġíķ´ ìĻ¸", - "ë¡ľ ë¶ĢíĦ°", - "аÑĢ Ñħ", - "l ili", - "li li", - "lil i", - "Ġíķĺ 루", - "ma ması", - "mam ası", - "Ñĩ аеÑĤ", - "Ñĩа еÑĤ", - "ĠØŃ Ø§ÙĦØ©", - "ĠØŃاÙĦ Ø©", - "ĠBöl üm", - "缸 éĹľ", - "ĠдÑĢÑĥг ими", - "ĠдÑĢÑĥгим и", - "çĽ£ çĿ£", - "à¥Ī à¤ľ", - "Ġعبد اÙĦÙĦÙĩ", - "ĠعبداÙĦ ÙĦÙĩ", - "Ġ è¿ŀ", - "Ġè¿ ŀ", - "ĠÐľ ин", - "ĠÐľÐ¸ н", - "Ġê¸ °ëĭ¤", - "Ġ기 ëĭ¤", - "Ġê³µ 격", - "è¡Į åĭķ", - "ा मà¤ķ", - "ाम à¤ķ", - "æ±Ĥ è´Ń", - "模 åŀĭ", - "Ñģ оÑĢ", - "Ñģо ÑĢ", - "r ane", - "ra ne", - "ran e", - "à¹ĩà¸Ī à¸ŀระ", - "ĠÙħس ÛĮر", - "è£ħ ç½®", - "ìķ ¤", - "nÄĽ jÅ¡ÃŃch", - "nÄĽjÅ¡ÃŃ ch", - "αλ ÏįÏĦε", - "ĠH akk", - "ĠHa kk", - "ĠHak k", - "访 éĹ®", - "ĠÑĤ еÑĩ", - "ĠÑĤе Ñĩ", - "ĠL á»ĭch", - "Ġدش ÙħÙĨ", - "Î Į", - "Ġ ÏĢε", - "ĠÏĢ ε", - "Ġза мов", - "Ġзам ов", - "Ġb irim", - "Ġbi rim", - "Ġbir im", - "Ġbiri m", - "ãĤ· ãĤ¹ãĥĨãĥł", - "ĠÏĢÏģο ÏĬ", - "Ĭ ìĿĢ", - "в иг", - "ви г", - "Ġëıħ ìĿ¼", - "ĠÑĢев олÑİ", - "Ġ é¦Ļ港", - "Ġé¦Ļ 港", - "Ġ lez", - "Ġl ez", - "Ġle z", - "ĠبÛĮ Ùħار", - "ĠبÛĮÙħ ار", - "Ġduy gu", - "Ġduyg u", - "Ġë Ľ°", - "Ġa macı", - "Ġam acı", - "Ġama cı", - "Ġamac ı", - "à¥įय प", - "ĠìŀIJ ìĦ¸", - "اÙĪ ÛĮر", - "اÙĪÛĮ ر", - "Ġs pole", - "Ġsp ole", - "Ġspo le", - "Ġspol e", - "Ãĸ L", - "Ġ جع", - "Ġج ع", - "ÙĦ ÛĮÙħ", - "ÙĦÛĮ Ùħ", - "ãģªãģ© ãģ®", - "à¸Ľà¸£à¸°à¸ª à¸ļ", - "ĠnaÅ¡ ich", - "ĠпÑĢедÑģÑĤав лÑıеÑĤ", - "Ġзд об", - "Ġo bou", - "Ġob ou", - "Ø® ÙĪاÙĨ", - "Ø®ÙĪ اÙĨ", - "ãĥ¬ ãĥĥãĥĪ", - "о дейÑģÑĤв", - "од ейÑģÑĤв", - "Ú© رÛĮ", - "کر ÛĮ", - "Ġات اÙĤ", - "ĠÑįкÑģп лÑĥаÑĤа", - "ï½ ¢", - "ĠÙĦÙĦ Ø¥", - "ĠاÙĦÙĨ ظاÙħ", - "ĠíĶĦ ëŀijìĬ¤", - "ıs ıt", - "ısı t", - "åŃ Ļ", - "Ġžád ný", - "ÙĤ Ùī", - "ัà¸ģ à¹Ģร", - "Ġë²ł ìĬ¤íĬ¸", - "Ġ ãĥ«", - "Ġãĥ «", - "åı Ķ", - "n ické", - "nic ké", - "nick é", - "Ġε ιÏĥ", - "Ġει Ïĥ", - "ãĥ« ãĥī", - "Ġدار Ùħ", - "Ġг ем", - "Ġге м", - "Ġ åѸ", - "ĠåŃ ¸", - "ान सà¤Ń", - "ानस à¤Ń", - "али зи", - "ализ и", - "ов анÑĸ", - "ова нÑĸ", - "ован Ñĸ", - "Ġо бо", - "Ġоб о", - "ìłĦ ìĹIJ", - "ĠS inh", - "ĠSi nh", - "ĠSin h", - "Ġ ÙĨع", - "ĠÙĨ ع", - "Ġоб лаÑģ", - "Ġобла Ñģ", - "Ġобл аÑģ", - "Ïħ ÏĢ", - "èĥ ¶", - "Ġaz alt", - "Ġazal t", - "åħ¨ éĿ¢", - "ĠK romÄĽ", - "ĠKro mÄĽ", - "ĠC z", - "æĬ¥ åIJį", - "Ġnásled ujÃŃcÃŃ", - "Ġна пÑĢиклад", - "ĠнапÑĢи клад", - "ãģª ãģijãĤĮãģ°", - "à¸Ń าย", - "çľĭ çľĭ", - "Ġà¸ģร à¸ģà¸İ", - "Ġà¸ģรà¸ģ à¸İ", - "ed nou", - "edn ou", - "ا زÙĦ", - "از ÙĦ", - "ãĢģ æľ¬", - "е Ñģи", - "еÑģ и", - "Ġta rz", - "Ġtar z", - "ãĢĢ ï¾Ĭ", - "Ġroz um", - "ãĤ« ãĥ¼ãĥī", - "ãĤ«ãĥ¼ ãĥī", - "Ġà¤ĩ à¤ķ", - "Ġpros tÄĽ", - "Ġprost ÄĽ", - "ĠÎĵ κ", - "ç© ´", - "ĠH ük", - "la vÃŃ", - "lav ÃŃ", - "ê ¿", - "é¸ ¡", - "Ġвозник аеÑĤ", - "Ġвозника еÑĤ", - "ÑŁ ÑŁÑŁ", - "ÑŁÑŁ ÑŁ", - "Ġпо нима", - "Ġпон има", - "ÐŁ Ðŀ", - "ãģĶãģĸ ãģĦãģ¾ãģĻ", - "ãģ ħ", - "Ġtr val", - "Ġдал еко", - "ĠÙĨ ÙĬز", - "ĠÙĨÙĬ ز", - "ĠвÑĭ Ñıв", - "ิà¸Ĺย า", - "ิà¸Ĺ ยา", - "Ġl á»Ĺ", - "Ġlá» Ĺ", - "à¹Ģ สà¸Ļ", - "à¹Ģส à¸Ļ", - "ĠÑģÑĤ енÑĭ", - "ĠÑģÑĤен Ñĭ", - "à¥įड ल", - "Ġjednotliv ých", - "ĠпÑĢиб лиз", - "i kat", - "ik at", - "ika t", - "Ġп одав", - "Ġпо дав", - "Ġпод ав", - "Ġпода в", - "ر ÛĮز", - "رÛĮ ز", - "ĠØ¢ÙĨ جا", - "社 æľĥ", - "Ġà¤ľà¤¨ वर", - "Ġa ile", - "Ġai le", - "Ġail e", - "ี à¸Ľ", - "Ġ èħ", - "Ġè ħ", - "ãģ§ ãģĹãĤĩãģĨ", - "С Ðŀ", - "ãĢģ ãĢĬ", - "ìĿ¼ 본", - "ov anou", - "ova nou", - "ovan ou", - "ν ÏĮ", - "å± ¥", - "ع ÙĦÙĤ", - "عÙĦ ÙĤ", - "Ġìī ½", - "Ġгли б", - "Ġê²ĥ ìŀħëĭĪëĭ¤", - "ĠнеобÑħодим оÑģÑĤи", - "ĠнеобÑħодимо ÑģÑĤи", - "Ġتخصص ÛĮ", - "ا سر", - "اس ر", - "ï¼Į 说", - "ĠÐĿ Ñĸ", - "Ġvy rob", - "ÑĪ ÑĥÑİ", - "ÑĪÑĥ Ñİ", - "æĪ¿ å±ĭ", - "Âł ÐĹ", - "à¹Ģ à¸ŀล", - "à¹Ģà¸ŀ ล", - "åĨħ éĥ¨", - "ĠدÙĦ ار", - "Ġп ÑĤи", - "Å¡ ti", - "Å¡t i", - "ĠaraÅŁtır ma", - "Ġзна ком", - "Ġε λλην", - "Ġ ấm", - "ÑĢ ак", - "ÑĢаРº", - "ÑĢа к", - "ãĤŃ ãĥ¥", - "Ġth áºŃn", - "èŃ ľ", - "ëªħ ìĿĺ", - "Ġy eter", - "Ġyet er", - "Ġye ter", - "Ġна Ñģлед", - "ĠнаÑģ лед", - "ĠÐļ ан", - "ĠÐļа н", - "ĠвÑĭ биÑĢа", - "ĠвÑĭб иÑĢа", - "ĠΣ Ïĩ", - "ĠÑĤеÑĢ мÑĸн", - "Ġ æ´»", - "Ġæ´ »", - "ĠاÙĦ تÙģ", - "ĠاÙĦت Ùģ", - "ĠJ apon", - "ĠJa pon", - "ĠJap on", - "éĤ ª", - "ë¶Ħ ìĦĿ", - "Ġли ÑĨо", - "ĠлиÑĨ о", - "Ġm ê", - "à¸Ħ วร", - "à¸Ħว ร", - "Ġà¤ħ à¤Ĺल", - "Ġà¤ħà¤Ĺ ल", - "ĠÙĩ ج", - "룬 ìļ´", - "Ġвой нÑĭ", - "اÙĪر زÛĮ", - "ĠÑģп ÑĢÑı", - "çĦ ¼", - "è¢ ĸ", - "Ġiç eren", - "Ġiçer en", - "Ġëħ¸ ëŀĺ", - "ĠЧеÑĢ ез", - "ÙĪج ÙĪد", - "Ñı ÑĤие", - "ÑıÑĤ ие", - "ÑıÑĤи е", - "à¸Ńลล าร", - "è ·¨", - "è· ¨", - "ĠM illi", - "ĠMill i", - "ĠMil li", - "ĠMi lli", - "件 äºĭ", - "Ġ æľĿ", - "βολ ή", - "βο λή", - "Ġ ков", - "Ġк ов", - "Ġко в", - "ĠØ´Ùĩ ÛĮد", - "ä¸ĭ åİ»", - "Ġìłķ ìĭł", - "оÑĩ кÑĥ", - "ï¼Į 便", - "γ κε", - "γκ ε", - "ĠÙħ باش", - "ĠÙħب اش", - "Ġay ında", - "Ġ ä»»", - "ÑģÑĤоÑĢ ÑĸÑı", - "ä¸Ń åѦ", - "ç¸ ®", - "ĠÑĦ Ñĸл", - "ãĢģ ãĤĦ", - "Ġ æĺ¥", - "Ġæĺ ¥", - "Ġter ör", - "Ġповин ен", - "Ġmilion ů", - "ĠÙģ ارس", - "ĠÙģار س", - "Ġв вод", - "Ġвв од", - "Ø· اÙĦ", - "Ġê¶ģ ê¸Ī", - "Ġuk áz", - "çĶ ľ", - "æļ Ĥ", - "ص ت", - "Ðļ огда", - "Ġम ल", - "ά να", - "άν α", - "Ġдок ÑĤоÑĢ", - "Ġком мÑĥ", - "ĠпÑĸд Ñģ", - "Ġà¸ģรà¸ģà¸İ าà¸Ħม", - "Âł г", - "Ġö ne", - "Ġön e", - "ĠÄIJ á»ģ", - "ĠÄIJá» ģ", - "äºĭ åĭĻ", - "Ġs rov", - "Ġsr ov", - "Ġ άν", - "Ġά ν", - "ëıĦ ê°Ģ", - "acaÄŁ ım", - "acaģı m", - "к ол", - "ко л", - "Ġb á»ĵi", - "Ġپرد از", - "Ġ ä¸ļ", - "Ġä¸ ļ", - "ëĭ¤ ìļ´", - "Ġп ÑĢедел", - "ĠпÑĢед ел", - "ĠпÑĢе дел", - "ĠÑĦедеÑĢа лÑĮ", - "ĠاÙĦ Ø£Ùĥ", - "ĠاÙĦØ£ Ùĥ", - "ãĢĢ ãĢĢãĢĢãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢ", - "Ġtr ấn", - "Ġд лин", - "Ġдли н", - "ĠÑĸ мп", - "ĠÑĸм п", - "ĠsmÄĽ rem", - "° ëĭ¤", - "Ġr ừng", - "ici álnÃŃ", - "è¡ Ĩ", - "μ ιο", - "μι ο", - "Ġاد ارÙĩ", - "ĠÑĤ ÑĢÑĮ", - "ĠÑĤÑĢ ÑĮ", - "ĠÄ° li", - "ĠÄ°l i", - "มà¸Ļ à¸ķร", - "à¥įव à¤ļ", - "е ÑĢо", - "еÑĢ о", - "ĠK UR", - "sk ými", - "ský mi", - "ským i", - "δ ί", - "u tin", - "ut in", - "uti n", - "Ġver iler", - "Ġveri ler", - "สà¸ĸาà¸Ļ à¸Ĺ", - "ĠзаÑħод Ñĸв", - "ĠÙģرÙĪد گاÙĩ", - "Ġ çĶ±", - "ู à¹ģล", - "éĥ ij", - "ĠJ ako", - "ĠJa ko", - "ĠJak o", - "ĠÑĢазвиÑĤи е", - "ĠÑĢазви ÑĤие", - "à¤ī न", - "ÙĬ دا", - "ÙĬد ا", - "Ġà¸ŀ à¸¤à¸©à¸łà¸²à¸Ħม", - "물 ìĿĦ", - "ë łĢ", - "ëł Ģ", - "- ÐĽ", - "ãĢĤ ãģĤ", - "Ġпод в", - "ï¼ī ï¼ļ", - "论 åĿĽ", - "ائ ع", - "ãĤĴ ãģĻãĤĭ", - "ĠØ£ ص", - "Ñĩ ики", - "Ñĩи ки", - "Ñĩик и", - "ĠÑģÑĤ ил", - "ley ici", - "Ñģ илÑĮ", - "Ñģи лÑĮ", - "Ġbulun du", - "ĠÑģеÑĢед ови", - "à¤Ĥ र", - "ĠاÛĮÙĨ جا", - "åľŃ åľŃ", - "ĠmyÅ¡ len", - "ĠÑĢозвиÑĤ ок", - "Ġiy ileÅŁ", - "Ġiyi leÅŁ", - "Ġв Ñĸз", - "ĠвÑĸ з", - "ëĤĺ 무", - "æĦı è§ģ", - "ι ÏĥÏĦη", - "ιÏĥ ÏĦη", - "ãĥĥ ãĥĦ", - "äºĭ æķħ", - "m adıģı", - "ma dıģı", - "mad ıģı", - "madı ģı", - "Ġà¤ħ पर", - "Ġà¤ħप र", - "ĠÚĨ رخ", - "Ġп лав", - "Ġпл ав", - "Ġпла в", - "以 æĿ¥", - "Ġë© Ģ", - "T uy", - "Tu y", - "ãĥ¼ ãĥį", - "ãĥ¼ãĥ į", - "Ġиз ÑĥÑĩ", - "ĠstÅĻed nÃŃ", - "课 ç¨ĭ", - "Ġê·¸ëħĢ ëĬĶ", - "ĠдоговоÑĢ Ñĥ", - "Ġдогов оÑĢÑĥ", - "ĠÄij á»ĭch", - "ĠÄijá»ĭ ch", - "Ġkar arı", - "Ġkarar ı", - "Ġkara rı", - "åIJ ´", - "Ùĥ اÙħ", - "ĠпоÑĤ ол", - "в ок", - "во к", - "ĠD üz", - "Τ α", - "å µ", - "âĢĻ na", - "âĢĻn a", - "а дж", - "ад ж", - "ĠdÅĻÃŃ ve", - "æ¢ ¨", - "ĠAv ust", - "åĬĽ ãĤĴ", - "à¹Ģ à¸ģล", - "à¹Ģà¸ģ ล", - "Ġпоб ед", - "Ġп ÑĢиÑĩ", - "ĠпÑĢ иÑĩ", - "ĠпÑĢи Ñĩ", - "ĠÐij Ñĸ", - "åŃ ¤", - "ĠÐł ег", - "ĠÐłÐµ г", - "Ġyet iÅŁ", - "Ġн еÑİ", - "Ġне Ñİ", - "Ġb ÃŃl", - "ĠbÃŃ l", - "ìĹĨ ìĿĮ", - "ĠÄ° talya", - "ÐĴ Ñģе", - "å¾Į ãģ®", - "Ġje jÃŃm", - "Ġjej ÃŃm", - "ĠjejÃŃ m", - "ĠвиглÑı дÑĸ", - "о гÑĢад", - "ог ÑĢад", - "огÑĢа д", - "Ġbo hat", - "Ġboh at", - "Ġ åħĭ", - "Ġåħ ĭ", - "ĠдиÑĤи ни", - "ĠдиÑĤ ини", - "лÑı ÑĤоÑĢ", - "ма га", - "маг а", - "ëĭĪ ìĬ¤", - "ĠÐł ади", - "ĠÐłÐ°Ð´ и", - "ĠÐłÐ° ди", - "ÏĢ οÏħÏģγ", - "ÏĢοÏħ Ïģγ", - "& ZeroWidthSpace", - "Ġ struk", - "Ġst ruk", - "Ġstr uk", - "Ġstru k", - "æIJ ŀ", - "Ġ ãģĿãģ®ä»ĸ", - "ìĿ¸ ìĿĦ", - "ĠпÑĢо веÑģÑĤи", - "ĠпÑĢов еÑģÑĤи", - "漫 çĶ»", - "Ġçİ© 家", - "ĠÙĪ رز", - "ĠÙĪر ز", - "ĠÑģвоÑĹ Ð¼", - "ĠL RV", - "ĠLR V", - "ิà¸ķ à¸ł", - "स त", - "ĠíĿ Ķ", - "âĹıâĹıâĹıâĹıâĹıâĹıâĹıâĹı âĹıâĹıâĹıâĹıâĹıâĹıâĹıâĹı", - "Ġt voÅĻÃŃ", - "ĠtvoÅĻ ÃŃ", - "Ġ ÐŁÐŀ", - "ĠÐŁ Ðŀ", - "é«ĺ 度", - ".h wp", - ".hw p", - "à¸ķำ à¸ļล", - "Ġد س", - "ìĪĺ ê°Ģ", - "ìĶ ©", - "ï¼ī ãĢĤĊ", - "ï¼īãĢĤ Ċ", - "æĭ ³", - "Ġl ô", - "ĠK ültür", - "اط عة", - "Ġku chy", - "Ġst roj", - "Ġstr oj", - "Ġstro j", - "μ ενο", - "με νο", - "μεν ο", - "ĠконÑģÑĤÑĢÑĥк ÑĨии", - "å°ı åѦ", - "Ġ åįļ", - "Ġåį ļ", - "Ġ èĢĥ", - "ĠèĢ ĥ", - "Ġas ıl", - "æĪij åĢij", - "خر اج", - "ĠO nun", - "ĠOn un", - "Ġ ç¾İåĽ½", - "Ġç¾İ åĽ½", - "à¥Ĥ बर", - "à¥Ĥब र", - "Ġmu ži", - "Ġmuž i", - "å§ «", - "Ġв б", - "Ġдо ме", - "Ġдом е", - "Ġ ам", - "Ġа м", - "Ġk uru", - "Ġkur u", - "Ġku ru", - "æ± Ĺ", - "l ediÄŁi", - "le diÄŁi", - "ledi ÄŁi", - "Ġv ẽ", - "å¾ ĵ", - "ĠгÑĥб еÑĢ", - "ĠÑģÑĤанов иÑĤÑĮ", - "ĠzemÄĽ dÄĽl", - "ÙĦ ÙĦ", - "Ġr amen", - "Ġra men", - "Ġram en", - "Ġprů bÄĽhu", - "Ġb lok", - "Ġbl ok", - "Ġblo k", - "ý val", - "ýv al", - "v ou", - "vo u", - "ν ά", - "ëĶĶ ìĭľ", - "ÑĨион нÑĭе", - "Ġê²Įìĭľ íĮIJ", - "ãĥ³ ãĥĩãĤ£", - "ãĥ³ãĥĩ ãĤ£", - "ä¸Ģ 级", - "и Ñĩа", - "иÑĩ а", - "ĠسرÛĮ اÙĦ", - "i lin", - "il in", - "ili n", - "ा यन", - "ाय न", - "ÙĨ ÙĪÛĮس", - "ĠÐĶ и", - "Ġاد بÛĮ", - "ĠÑĥ дов", - "ĠÑĥд ов", - "ĠÐĹ Ð°Ð¼", - "ĠÐĹа м", - "à¥ģà¤Ń व", - "Ñģ ок", - "Ñģо к", - "ĠÑĢай оне", - "ĠÑĢайон е", - "Ġ EK", - "ĠE K", - "æĤ ī", - "Ġsor umlu", - "Ġsoruml u", - "Ġzv yÅ¡", - "à¹Ģà¸ĭ à¸Ńร", - "in áÅĻ", - "iná ÅĻ", - "Ġu drž", - "Ġud rž", - "но вид", - "нов ид", - "ĠspoleÄį nÄĽ", - "Ġspole ÄįnÄĽ", - "æĪIJ äºĨ", - "ï¼ ¤", - "ัà¸ŀ à¸Ĺ", - "а ÑĪа", - "аÑĪ а", - "ĠÙĨ ادÙĬ", - "à¹ĥ à¸Ļà¸Ĺ", - "à¹ĥà¸Ļ à¸Ĺ", - "å¡ ļ", - "Ġس Ú©", - "ãĥģ ãĥ¥", - "ĠмаÑĢ ÑĪ", - "а леннÑı", - "ал еннÑı", - "ĠØŃÙħ اÛĮت", - "ãĥ³ ãĤ¸", - "รษ à¸IJ", - "Ġк ÑĢем", - "ĠK ažd", - "ê ½", - "Ġpar lament", - "Ġparl ament", - "ĠÅŁ un", - "ĠÅŁu n", - "Ġk ys", - "Ġky s", - "ÏĦ ÏĤ", - "ê°ľ ìĿĺ", - "Ġve lice", - "Ġvel ice", - "Ġce stu", - "Ġces tu", - "Ġcest u", - "ظ Ø©", - "è¯ Ĭ", - "Ġ út", - "Ġú t", - "ĠØ® ÙĪر", - "ĠØ®ÙĪ ر", - "ĠТ е", - "Ġобла ÑģÑĤ", - "ĠоблаÑģ ÑĤ", - "Ġобл аÑģÑĤ", - "à¹Ī à¸Ńà¸ķ", - "à¹Īà¸Ń à¸ķ", - "ĠAc adem", - "ĠAcad em", - "ãĢĤ æľ¬", - "Ġ 風", - "Ġé¢ ¨", - "Ñģ ен", - "Ñģе н", - "ãĥ¢ ãĥĩãĥ«", - "Ġзавд аннÑı", - "ãģ¾ ãĤĮ", - "моÑĤ ÑĢеÑĤÑĮ", - "моÑĤÑĢ еÑĤÑĮ", - "Ġkh á»ķ", - "à¹Ī ร", - "د رس", - "در س", - "ĠÄĮesk osloven", - "Ġ 计", - "Ġè® ¡", - "ĠÑĤак ом", - "ĠÑĤа ком", - "ĠÙĦ اعب", - "ĠÙĦا عب", - "ĠMuham med", - "ĠÙħ ÙĦÙģ", - "ĠÙħÙĦ Ùģ", - "ĠÙĪس ÙĦÙħ", - "ãĤ·ãĥ£ ãĥ«", - "Ġо кÑĢа", - "Ġок ÑĢа", - "à¥ģ मत", - "à¥ģम त", - "ĠëĪĦ 구", - "Ġned eni", - "Ġneden i", - "ĠëĤł ì§ľ", - "/ km", - "/k m", - "Ġд емон", - "Ġде мон", - "Ġдем он", - "ĠصÙĨ اÛĮع", - "m asından", - "mas ından", - "masında n", - "åīį ãģ®", - "æĪIJ 绩", - "ल à¤Ĺ", - "Ġ åĮħ", - "ĠåĮ ħ", - "à¸Ńà¸ģà¸Īาà¸ģ à¸Ļ", - "ا دا", - "اد ا", - "Ġay lık", - "ĠÙħ ÙĤد", - "ĠÙħÙĤ د", - "Ġönemli dir", - "ĠìĪľ ê°Ħ", - "Ġd inh", - "Ġdi nh", - "Ġdin h", - "Ġná kup", - "ist ické", - "istic ké", - "åº Ł", - "ìĬ¤ íĨł", - "Ġd ny", - "Ġdn y", - "ĠìŀĪ ëıĦë¡Ŀ", - "ìĽIJ ìĿĺ", - "ãĥķ ãĥ¬", - "p oz", - "po z", - "Ġ ев", - "Ġе в", - "ĠdÃ¼ÅŁ ür", - "à¥įर à¤ļ", - "Ġê²° íĺ¼", - "Ġ ÑĨенÑĤÑĢа", - "ĠÑĨенÑĤ ÑĢа", - "ĠÑĨен ÑĤÑĢа", - "ĠÑĨенÑĤÑĢ а", - "åŁ ĭ", - "ï¿£ ï½Ģ", - "æѦ åĻ¨", - "à¹Īาà¸Ļ มา", - "Ġर व", - "Ùij د", - "μÎŃ Î½Î¿Î¹", - "μÎŃν οι", - "μÎŃνο ι", - "Ġë§IJ ìĶĢ", - "Ġpo ÅĻad", - "Ġب غ", - "ĠÏĮ λα", - "ĠÏĮλ α", - "à¹ī à¹Ħà¸Ĥ", - "à¹Ģà¸ģ าะ", - "Ġb ạc", - "Ġd á", - "d ÄĽla", - "dÄĽ la", - "dÄĽl a", - "Ġt eb", - "Ġte b", - "Ġk èo", - "ãĤı ãĤĮ", - "Ġist iyorum", - "Ġistiyor um", - "λ ήÏĤ", - "λή ÏĤ", - "ÐIJ в", - "Ġa sla", - "Ġas la", - "Ġperform ans", - "Ġperfor mans", - "Ġperforman s", - "ĠVác lav", - "Ïģ ίαÏĤ", - "Ïģί αÏĤ", - "Ïģία ÏĤ", - "Ġ tÄĽl", - "Ġt ÄĽl", - "ĠtÄĽ l", - "æĮ Ļ", - "о ба", - "об а", - "ãģij ãĤĮãģ©", - "ĠëĶ ¸", - "ÙĪ اء", - "ÙĪا Ø¡", - "ĠÚ©ÙĪد کاÙĨ", - "ĠÚ©ÙĪدک اÙĨ", - "Ġп лиÑĤ", - "Ġпл иÑĤ", - "Ġ bilir", - "Ġb ilir", - "Ġbil ir", - "Ñĥ же", - "Ñĥж е", - "ÏĦÎŃ Î»Îµ", - "Ġà¤Ĩ à¤ķर", - "Ġà¤Ĩà¤ķ र", - "ĠÑĤÑĢ Ñĥда", - "ĠÑĤÑĢÑĥ да", - "ĠÑĤÑĢÑĥд а", - "Ġدر ÛĮا", - "ĠدرÛĮ ا", - "Ì §", - "Ġng á»įt", - "ÙĨس ا", - "а ÑģÑĤи", - "аÑģ ÑĤи", - "аÑģÑĤ и", - "ï½ £", - "Âł на", - "ем Ñĭе", - "Ġس عÙĪد", - "Ġسع ÙĪد", - "Ġ alım", - "Ġal ım", - "è´ «", - "åĪ° çļĦ", - "Ġkesin likle", - "Ġzá sad", - "Ġ ìĬ¤íĬ¸", - "ĠìĬ¤ íĬ¸", - "Ġd ahi", - "Ġda hi", - "Ġdah i", - "t é", - "åįģ åħ«", - "Ġz ayıf", - "Ø° ار", - "ذا ر", - "Ġا ÙĬراÙĨ", - "ĠاÙĬ راÙĨ", - "Ġhod nocenÃŃ", - "D ST", - "DS T", - "Ġìĸ ĺ", - "æĺ ĩ", - "éĻ £", - "Ġк ле", - "Ġкл е", - "Ġu plat", - "Ġup lat", - "ĠاÙĦتع ÙĦÙĬÙħ", - "ÏĢοί ηÏĥη", - "ек ÑĤоÑĢа", - "екÑĤ оÑĢа", - "екÑĤоÑĢ а", - "Ġë§IJ ìĿ´", - "ĠÙģ رÙĬÙĤ", - "ĠÙģر ÙĬÙĤ", - "帮 åĬ©", - "çĶŁ ãģį", - "åĨħ ãģ®", - "èģĶ 缣", - "г ÑĢад", - "гÑĢа д", - "Ġch uyến", - "ãĤĤ ãĤĬ", - "ĠÑĩаÑģÑĤ ина", - "ĠÑĩаÑģÑĤи на", - "ãģª ãģıãģª", - "ãģªãģı ãģª", - "ÑĶ в", - "ĠÑĦ аÑħ", - "k uk", - "ku k", - "çĶ· æĢ§", - "ĠÙħÛĮÙĦ ادÛĮ", - "Ġb eden", - "Ġbe den", - "Ġbed en", - "ê°Ģ 를", - "म र", - "Ġìĸ´ 머ëĭĪ", - "èģĶ ç½ij", - "Âł mi", - "Âłm i", - "Ġzah rn", - "æ² ĸ", - "Ġkhu ẩn", - "Ġo práv", - "Ġop ráv", - "Ġopr áv", - "ाह à¤ķ", - "ĠÚ©ÙĪت اÙĩ", - "Ġо бол", - "Ġоб ол", - "Ġобо л", - "Ġph úc", - "r ánÃŃ", - "rá nÃŃ", - "rán ÃŃ", - "à¥įर थ", - "æݪ æĸ½", - "Ġв олод", - "Ġво лод", - "Ġвол од", - "Ġsp ÃŃÅ¡e", - "Ġm Æ¡", - "ÑĬ ек", - "ng ör", - "à¤ī त", - "k siyon", - "ks iyon", - "ksi yon", - "а ÑĤе", - "аÑĤ е", - "Ġجز Ø¡", - "áv ka", - "ÐĴ С", - "laÅŁ ma", - "Ġ ç¿", - "Ġç ¿", - "à¸Ń าà¸Ĭ", - "ни ÑĨÑĥ", - "ниÑĨ Ñĥ", - "Ġ หาà¸ģ", - "Ġห าà¸ģ", - "ãģĭ ãģĹ", - "íı ´", - "Ġг аÑĢан", - "ĠгаÑĢ ан", - "ĠгаÑĢа н", - "Ġ Ïĥαν", - "ĠÏĥ αν", - "Ġдобав иÑĤÑĮ", - "ĠÑĢаз ÑĢеÑĪ", - "á ¾", - "æĺ¯ ä¸ª", - "μ ÎŃÏĤ", - "μÎŃ ÏĤ", - "ĠÄ°mpar ator", - "æ¨Ļ æºĸ", - "Ñģ ÑĤÑĭ", - "ÑģÑĤ Ñĭ", - "Ġg ücü", - "Ġgü cü", - "Ġgüc ü", - "Ġ íĥĢìĿ´", - "Ġíĥ ĢìĿ´", - "ĠíĥĢ ìĿ´", - "Ġ åħ¶ä»ĸ", - "Ġåħ¶ ä»ĸ", - "Ġt ông", - "Ġtô ng", - "Ġtôn g", - "Ġv edenÃŃ", - "Ġved enÃŃ", - "Ġvede nÃŃ", - "ëĵľ ë¡ľ", - "Ġm esel", - "Ġme sel", - "Ġmes el", - "Ġ Äįe", - "ĠÄį e", - "j de", - "jd e", - "Ïģ εια", - "Ïģε ια", - "Ïģει α", - "ãĤĪ ãģŃ", - "Ðł ÐĿ", - "è·Ŀ 离", - "ĠÙĤ ائÙħØ©", - "า à¸ļาล", - "าà¸ļ าล", - "ĠÑģай ÑĤÑĸ", - "Ġर स", - "ĠÙĤر ÙĨ", - "Ġná vr", - "Ġnáv r", - "Ú© Ùħ", - "çļĦ æīĭ", - "Ġsor unu", - "Ġsorun u", - "Ġsoru nu", - "/N ÄIJ", - "nut ÃŃm", - "nutÃŃ m", - "ĠØ® ÙĪرد", - "ĠØ®ÙĪ رد", - "ĠØ®ÙĪر د", - "Ġng á»Ŀ", - "Ġ: .|", - "Ġ:. |", - "Ġbudou c", - "i Äįky", - "iÄį ky", - "Ġد رد", - "Ġدر د", - "ÑĢо ниÑĩеÑģ", - "ÑĢон иÑĩеÑģ", - "ç¾ Ĭ", - "ĠìķĦ ë²Ħì§Ģ", - "ĠKan unu", - "ĠKanun u", - "ĠпÑĢивод иÑĤ", - "άλÏħ ÏĪηÏĤ", - "ĠVlad im", - "Ġal ıp", - "Ġе ÑĤап", - "Ġà¤Ĺ लत", - "ĠراÙĩ ÙĨÙħ", - "Ġpoz isyon", - "Ġgö ç", - "èµ ŀ", - "Ġм ой", - "Ġмо й", - "ĠÎł ά", - "Ġ ìĪł", - "ĠìĪ ł", - "ĠØ¢ÛĮ ÙĨدÙĩ", - "a ná", - "an á", - "举 çľģ", - "ĠÙħت عدد", - "Ġ åįĬ", - "Ġåį Ĭ", - "ãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ãĢĢãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "Ġth á»Ŀ", - "Ġthá» Ŀ", - "Ġвд ÑĢÑĥг", - "п аÑĤ", - "па ÑĤ", - "ĠпÑĢовед ениÑı", - "ÙĨ ز", - "ĠاÙĦب ØŃØ«", - "æģ ¢", - "Ġbak tı", - "Ġ è·¯", - "Ġè· ¯", - "Ġзаболева ний", - "ĠÐķ вÑĢоп", - "ĠÐķв ÑĢоп", - "Ġtarih li", - "ê¹ ¨", - "ĠÚ© ÙĪÙĩ", - "ĠÚ©ÙĪ Ùĩ", - "Ġìĸ´ 볤", - "Ġtit ul", - "Ġti tul", - "Ġvyd ánÃŃ", - "éĺ¶ æ®µ", - "à¸Īะ à¸ķ", - "Ġм оÑı", - "Ġмо Ñı", - "ĠкоÑĢ ол", - "Ġко ÑĢол", - "Ġб анк", - "Ġбан к", - "วรร à¸ĵ", - "วร รà¸ĵ", - "ĠÙĥس ارة", - "ĠK hoa", - "ĠKh oa", - "ĠKho a", - "ĠÑĥнÑĸвеÑĢÑģиÑĤ еÑĤ", - "ãģ«éĸ¢ ãģĻãĤĭ", - "r uary", - "ru ary", - "Ġ à¸Ĥาย", - "Ġà¸Ĥ าย", - "Ġsv az", - "ĠØ´ رÙĤ", - "Ġشر ÙĤ", - "Ġд ÑĭÑħ", - "Ġиз бав", - "Ġизб ав", - "ĠÑı кÑĸй", - "ĠÑıк Ñĸй", - "ĠÑıкÑĸ й", - "ĠÎľ ον", - "Ġg ön", - "Ġgö n", - "ĠUkr aj", - "ĠUk raj", - "ĠUkra j", - "ัà¸Ļ à¸Ńà¸Ńà¸ģ", - "ัà¸Ļà¸Ń à¸Ńà¸ģ", - "Ġม à¸ģราà¸Ħม", - "и ÑĤов", - "иÑĤ ов", - "Ġanal ý", - "Ġana lý", - "ĠоÑĤ меÑĩ", - "Ġبر اÙī", - "âĪ ı", - "ัà¸ģ à¸ģ", - "æĭ¥ æľī", - "ĠÑĸнÑĪ ого", - "Ġкомп анÑĸÑĹ", - "Ġk ÅĻes", - "ĠÑĢаб оÑĩ", - "ĠÑĢабо Ñĩ", - "a dÃŃ", - "ad ÃŃ", - "ìł ł", - "à¹Ħ หà¸Ļ", - "à¥ģब ह", - "âĢĻ deki", - "âĢĻd eki", - "âĢĻde ki", - "çħ ¤", - "ĠпаÑĢ Ñĥ", - "Ġпа ÑĢÑĥ", - "ìĦ Ń", - "Ġнеп оÑģÑĢед", - "ĠÄ° b", - "Ġà¸ŀ ฤศà¸Ī", - "íĭ ´", - "Ġ ëłĪìĿ´", - "ĠëłĪ ìĿ´", - "ĠTh á»ķ", - "Ñı еÑĤ", - "ائ ج", - "» çĴĥ", - "ÐĴ Ðŀ", - "åĸ Ĭ", - "Ġ 第ä¸ī", - "Ġ第 ä¸ī", - "Ġвок ÑĢÑĥг", - "Ñĩ енÑĮ", - "Ñĩе нÑĮ", - "Ñĩен ÑĮ", - "Ġolan ak", - "Ġola nak", - "t ura", - "tu ra", - "tur a", - "Ġ ÙħÙĬÙĦ", - "ĠÙħ ÙĬÙĦ", - "ĠÙħÙĬ ÙĦ", - "ey di", - "ĠÙħد ÙĬر", - "Ġnel ze", - "ัว à¸Ńย", - "ìħ ľ", - "Ġhlav u", - "Ġkor uy", - "ÑĨ ин", - "ÑĨи н", - "ĠдиÑģ ÑĨип", - "ĠÙħ اÙĨد", - "ĠÙħا ÙĨد", - "ĠÙħاÙĨ د", - "Ġпод ÑĢоб", - "Т Ðŀ", - "ÙĤر ار", - "à¹ģà¸Ļะ à¸Ļำ", - "문 ìĿĦ", - "æĮ¯ ãĤĬ", - "P ÅĻi", - "PÅĻ i", - "Ġy ên", - "श à¤ķ", - "Âł je", - "ĠÐļон ÑģÑĤиÑĤÑĥ", - "à¥ģ ह", - "à¥ģठ¹", - "ĠÙ¾ ا", - "ìĨĮ 를", - "Ġд ела", - "Ġдел а", - "Ġде ла", - "к ид", - "ки д", - "à¹Ĥ à¸Ĭ", - "커 ìĬ¤", - "dÄĽ len", - "dÄĽl en", - "à¤Ķ र", - "äºİ æĺ¯", - "ĠÙĩÙħ ÛĮØ´Ùĩ", - "ĠbaÅŁ lam", - "Ġ ìĽ¨", - "ĠìĽ ¨", - "Ġden eyim", - "Ġdeney im", - "Ġü ye", - "Ġüy e", - "Ġ νÏĮ", - "Ġν ÏĮ", - "Ġà¤ĸ ड", - "n ÄĽl", - "nÄĽ l", - "ĠÑģÑĦ еÑĢÑĸ", - "ĠÑģÑĦеÑĢ Ñĸ", - "à¸Ńà¸Ķ à¸ł", - "ä¸Ģ å¹´", - "Ġvur gu", - "Äŀ Ä°", - "âĢĻ Ċ", - "ĠÑĸн ÑĪими", - "ĠÑĸнÑĪ ими", - "Ġз менÑĪ", - "Ġठĭ", - "Ġв ека", - "Ġве ка", - "ĠØŃÚ©ÙĪÙħ ت", - "ĠتÙħ اÙħÛĮ", - "ĠتÙħاÙħ ÛĮ", - "Ġs mrt", - "Ġsm rt", - "Ġsmr t", - "Ġh ủy", - "Ġyap ılmÄ±ÅŁ", - "Ġyapıl mÄ±ÅŁ", - "à¹ī à¸ľ", - "ĠY en", - "ĠYe n", - "Ġ Ñĥл", - "ĠÑĥ л", - "ĠS vÄĽt", - "ĠSv ÄĽt", - "ั à¸Ħ", - "ĠmÄĽsÃŃ ců", - "д енÑĤи", - "ден ÑĤи", - "Ġ ï¾ĺ", - "Ġï¾ ĺ", - "Ġпол иÑĤи", - "Ġполи ÑĤи", - "ĠполиÑĤ и", - "s kyt", - "sk yt", - "sky t", - "ä¹Ł æľī", - "Ġê°Ļ ìĬµëĭĪëĭ¤", - "Ġê·¸ëŀĺ ìĦľ", - "ÏĦε Ïģη", - "ÏĦεÏģ η", - "Ñĩ еÑĢ", - "Ñĩе ÑĢ", - "ĠÃľNÄ°VERS Ä°TESÄ°", - "ส à¸ł", - "Ġ สร", - "Ġส ร", - "ान द", - "ĠaÅŁ ırı", - "λ ίοÏħ", - "λί οÏħ", - "Ġ ÙĦÙģ", - "ĠÙĦ Ùģ", - "ÃŃ nu", - "ÃŃn u", - "à¸Ń าร", - "ÑĤ ÑĥÑĢа", - "ÑĤÑĥ ÑĢа", - "ÑĤÑĥÑĢ а", - "ĠÄįesk ých", - "ĠÄįe ských", - "ĠÄįeský ch", - "Ġph ức", - "以 为", - "ÏģÏī ÏĢα", - "ĠاÙĨر ÚĺÛĮ", - "» )", - "a lardan", - "alar dan", - "alarda n", - "ĠÑģÑĤ воÑĢÑİ", - "ĠÑģÑĤвоÑĢ Ñİ", - "Ġt ráv", - "Ġtr áv", - "ॠ¬", - "ãģĬ ãĤĪãģ³", - "ïľ ĭ", - "ad il", - "adi l", - "ĠΤ ι", - "Ġ ëIJ©ëĭĪëĭ¤", - "ĠëIJ ©ëĭĪëĭ¤", - "Ġε μÏĨ", - "Ġ구 ì¡°", - "ìĹŃ ìĭľ", - "ĠاÙĦ جاÙħ", - "ĠاÙĦج اÙħ", - "主 é¢ĺ", - "ãĤ¹ ãĥĿ", - "Ġ ìĹŃìĭľ", - "ĠìĹŃ ìĭľ", - "ĠÚ©Ùħ تر", - "ĠSp oleÄį", - "ол оÑĪ", - "оло ÑĪ", - "ĠSur iye", - "Ч еÑĢ", - "æĪĺ æĸĹ", - "Ġz ávis", - "Ġzá vis", - "Ġzáv is", - "æĽ¸ 館", - "Ġmus el", - "Ġmu sel", - "Ġmuse l", - "Ġ çĿ", - "Ġç Ŀ", - "Ùħ Ùħ", - "ĠاÙĦØ® ارج", - "Ġ ÐĵÐŀ", - "ĠÐĵ Ðŀ", - "ĠваÑĢ ÑĤо", - "ĠваÑĢÑĤ о", - "Ïģα β", - "Ġपह à¤ļ", - "ub lice", - "ublic e", - "ubl ice", - "ÑĨион ного", - "è Į¨", - "èĮ ¨", - "ĠدÙģ تر", - "Ġ Ùģس", - "ĠÙģ س", - "Ġन à¤ľà¤°", - "t arı", - "ta rı", - "tar ı", - "Ġоб ÑĢоб", - "ĠÐł а", - "ĠاÙĦ صÙĨ", - "ĠاÙĦص ÙĨ", - "Ø´ Ø©", - "ĠìĹĨ ìĹĪ", - "ož ná", - "æľĢ çµĤ", - "Ù ¥", - "r ech", - "re ch", - "rec h", - "ĠاÙĦØ£ سر", - "ĠاÙĦأس ر", - "Ġм ови", - "Ġмо ви", - "Ġмов и", - "Ġì¡° êµIJ", - "Ñĸ меÑĩ", - "Ñĸм еÑĩ", - "ãĥ¯ ãĥ¼", - "б ÑĥÑĢг", - "бÑĥ ÑĢг", - "Ġس ÙĦس", - "ĠسÙĦ س", - "åѦ ä¼ļ", - "Ġ ë¦", - "Ġë ¦", - "åħĭ æĸ¯", - "æĸĩ çĮ®", - "Ġx Æ°Æ¡ng", - "Ġyo lc", - "Ġyol c", - "Ġ ìĤ¬ë¬´", - "ĠìĤ¬ 무", - "ãĤı ãģļ", - "ĠÑĢаÑģÑĤ ений", - "ĠÙģ ضاÛĮ", - "ĠÙģض اÛĮ", - "Ġna opak", - "Ġnao pak", - "ĠпÑĢи вÑĭ", - "ĠпÑĢив Ñĭ", - "Ġد ÛĮدÙĩ", - "ĠدÛĮ دÙĩ", - "ĠدÛĮد Ùĩ", - "à¸ģาร à¹ĥà¸Ĭ", - "Ġ åŀ", - "Ġå ŀ", - "çij Ł", - "以 åIJİ", - "ĠpÅĻib liž", - "ĠdÃ¼ÅŁ man", - "Ġt emin", - "Ġte min", - "Ġtem in", - "ĠÑĥÑģл Ñĥг", - "ĠÑĥÑģ лÑĥг", - "Ġद ब", - "ĠìĥĪ ê¸Ģ", - "ĠÑĥÑģÑĤÑĢой ÑģÑĤва", - "ĠТ ÑĥÑĤ", - "ÏĦ ίοÏħ", - "ÏĦί οÏħ", - "ĠÄ°s lâm", - "Ù ¤", - "åıĤ ä¸İ", - "Ġк ÑĥÑģÑĤ", - "ĠкÑĥ ÑģÑĤ", - "ĠкÑĥÑģ ÑĤ", - "éĻIJ åĪ¶", - "ت ÙĬÙĨ", - "تÙĬ ÙĨ", - "ĠоÑģÑĤ аннÑĸ", - "ĠоÑģÑĤан нÑĸ", - "ic ations", - "ication s", - "ا Ú©ÛĮ", - "اک ÛĮ", - "но ÑģÑı", - "ноÑģ Ñı", - "ÄŁ an", - "ÄŁa n", - "ãģı ãĤĮãĤĭ", - "ãģıãĤĮ ãĤĭ", - "Ġyap ıyor", - "Ġyapı yor", - "Ġê°ķ ëĤ¨", - "Ùħ ÙĬÙħ", - "ÙħÙĬ Ùħ", - "æ ŃIJ", - "æŃ IJ", - "Ġر ع", - "Ġb oÄŁ", - "Ġbo ÄŁ", - "ĠиÑģ Ñħод", - "èª ł", - "æł· åŃIJ", - "Ġbu deme", - "Ġbud eme", - "Ġbude me", - "ĠÑģ еÑĤ", - "ι ÏĥμοÏį", - "ιÏĥμ οÏį", - "Ġ å¾ĴæŃ©", - "Ġå¾Ĵ æŃ©", - "u álnÃŃ", - "ĠاÙĦع ÙĤ", - "Ġسب Ú©", - "ĠاÙĦØ£ خرÙī", - "ĠاÙĦأخ رÙī", - "E FA", - "EF A", - "åĽº å®ļ", - "Ġ ãĤ¬", - "ĠãĤ ¬", - "ĠìŀIJ ìĹ°", - "ยว à¸Ĥ", - "ب س", - "un ma", - "Ġза ним", - "Ġзан им", - "à¹ĥà¸Ļ ร", - "èĢĥ èĻij", - "æ·· åIJĪ", - "å° ĭ", - "Ġçık Ä±ÅŁ", - "Ġm aliyet", - "Ġmal iyet", - "éľ Ĭ", - "ãģŁãĤģ ãģ®", - "ĠÙ¾ Ø´", - "Ġз лоÑĩ", - "Ġvý Å¡i", - "Ġsch vál", - "ĠÙĨÙħ ÙĪدÙĩ", - "ĠÙĨÙħÙĪد Ùĩ", - "Î Ĩ", - "Ġz ách", - "Ġzá ch", - "Ġ Ïĥκ", - "ĠÏĥ κ", - "ãĤ¹ ãĥŀ", - "ĠÙħس ائÙĦ", - "ĠاÙĦاج تÙħاع", - "åľ° çĤ¹", - "ا ÛĮاÙĨ", - "اÛĮ اÙĨ", - "ĠÐŀ к", - "ê¸ Ķ", - "e lease", - "el ease", - "ele ase", - "Ġطب ÙĤÙĩ", - "ĠطبÙĤ Ùĩ", - "éij ij", - "Ġì½Ķ ë¡ľëĤĺ", - "é¼ ł", - "大 åħ¨", - "ĠпÑĢи веÑģÑĤи", - "ĠпÑĢив еÑģÑĤи", - "Ġاب تد", - "Ġابت د", - "리 ë¡ľ", - "ĠÑģÑĤÑĢ анÑĭ", - "ĠÑģÑĤÑĢа нÑĭ", - "ĠÑģÑĤÑĢан Ñĭ", - "ĠzatÃŃm co", - "Ġh uyết", - "Ġhuy ết", - "سÛĮ ÙĪÙĨ", - "Ġsor du", - "âĢĮ رس", - "ĠÑĦ ÑĢон", - "Ġed ip", - "Ġedi p", - "ÙĨ Ú¯ÛĮ", - "ÙĨÚ¯ ÛĮ", - "Ġк иÑĢ", - "Ġки ÑĢ", - "Ġ íķ´ìķ¼", - "Ġíķ´ ìķ¼", - "ì» ´", - "ÑĨик лоп", - "ĠпÑĢимен ениÑı", - "Ġоб л", - "éļ ª", - "Ġk romÄĽ", - "Ġkro mÄĽ", - "æł¸ å¿ĥ", - "ra him", - "rah im", - "о ÑĢд", - "оÑĢ д", - "Ġl Ãłnh", - "ĠlÃł nh", - "ĠlÃłn h", - "Ġо ÑģÑĤÑĢов", - "ĠоÑģÑĤ ÑĢов", - "; |", - "b uz", - "bu z", - "Ġ ÏĦÏģο", - "ĠÏĦ Ïģο", - "ĠÐĴ аÑĢ", - "æī İ", - "ı lÄ±ÅŁ", - "ılı ÅŁ", - "ıl Ä±ÅŁ", - "éĿ¢ ç©į", - "身 份", - "é¢Ĩ åŁŁ", - "ĠاÙĦÙĤر ÙĨ", - "Ġ пÑĢиклад", - "ĠпÑĢи клад", - "ĠпÑĢик лад", - "ãĥģ ãĥ¼ãĥł", - "Ġสà¸ŀ à¸Ľ", - "Ġо ÑĩиÑģÑĤ", - "ĠоÑĩ иÑģÑĤ", - "ĠоÑĩи ÑģÑĤ", - "Ġмил ли", - "а ÑĨÑĸÑĹ", - "ี à¹Ģà¸Ń", - "Ġt anın", - "Ġtan ın", - "Ġtanı n", - "çĪ¶ 亲", - "Ġmsg str", - "Ġmsgs tr", - "ĠØ´ ÛĮÙħÛĮ", - "ĠØ´ÛĮ ÙħÛĮ", - "ĠÙģر اÙĩÙħ", - "Ġ 맥", - "Ġë§ ¥", - "ãĢĤ å½ĵ", - "Ġкон ÑĨенÑĤÑĢа", - "êµIJ íļĮ", - "ãĤī ãĤĮãģ¦", - "ãĤīãĤĮ ãģ¦", - "Ġya sak", - "Ġyas ak", - "ĠÐij ол", - "Ġ æ¾³", - "Ġæ¾ ³", - "çĩ ķ", - "Ġ جا", - "Ġج ا", - "ëij ĺ", - "Ġدر Ø®ÙĪاست", - "ĠmÃŃst nÃŃ", - "ÂĤ ÃĮ", - "Ġbas kı", - "Ġu çak", - "Ġuç ak", - "ä» ĵ", - "Ġìľł ì§Ģ", - "Ġп оба", - "Ġпо ба", - "Ġпоб а", - "Ġz eptal", - "Ġze ptal", - "ç»Ļ æĪij", - "ĠAt atürk", - "ĠÙħÙĨ اس", - "Ñ Ĵ", - "Ġar acı", - "Ġarac ı", - "Ġara cı", - "лÑİ ÑĶ", - "Ġnit elik", - "Ġnitel ik", - "ĠM ezi", - "ĠMe zi", - "ĠMez i", - "ĠÎŃ Î½Î±ÏĤ", - "ĠÎŃνα ÏĤ", - "ĠÎŃν αÏĤ", - "Ïİν ÏĦαÏĤ", - "v až", - "va ž", - "Ġk uzey", - "Ġ ÏİÏģα", - "ĠÏİ Ïģα", - "ĠÑĢоз пов", - "ĠÑĢозп ов", - "à¹Ī าà¸ģ", - "à¹Īา à¸ģ", - "ãĢģ ä¸ī", - "ĠÑģÑĤ аÑĢи", - "ĠÑģÑĤаÑĢ и", - "ĠÑģÑĤа ÑĢи", - "Ġhak kı", - "Ġhakk ı", - "ĠØ¢Ùħ ادÙĩ", - "íĮ Ķ", - "о мÑĸ", - "ом Ñĸ", - "Ġ âĢł", - "ĠâĢ ł", - "ãģĭ ãĤı", - "ãĢĮ ä½ł", - "æ³ķ åĽ½", - "ÙIJ ÙĬÙĨ", - "ÙIJÙĬ ÙĨ", - "æī ķ", - "н или", - "ни ли", - "нил и", - "ĠÑĥÑģÑĤанов ки", - "Ġl ông", - "Ġlô ng", - "त म", - "ÙĪ ÙĨÙĬØ©", - "ÙĪÙĨ ÙĬØ©", - "ÙĪÙĨÙĬ Ø©", - "ÙĬ تÙĬ", - "ÙĬت ÙĬ", - "Ġê²Įìĭľ 물", - "Ġve Å¡ker", - "ÎŃ Ïģγ", - "ÎŃÏģ γ", - "ĠÑĥ Ñģе", - "ĠÑĥÑģ е", - "Ġk ıl", - "Ġkı l", - "Ġil gi", - "Ġilg i", - "μ Ïīν", - "Ġз вÑĸлÑĮ", - "Ġзв ÑĸлÑĮ", - "Ġön lem", - "à¸ģà¸İ หมาย", - "ĠH iá»ĩp", - "Ġг оÑĢм", - "ĠгоÑĢ м", - "лÑı ÑİÑĤÑĮÑģÑı", - "лÑıÑİÑĤÑĮ ÑģÑı", - "la maya", - "lam aya", - "lama ya", - "ĠÑģпоÑģоб ом", - "ãģ¸ ãģ¨", - "ç¦ģ æŃ¢", - "ĠÑĢаÑħ Ñĥнок", - "ĠоÑĤвеÑĢ ÑģÑĤи", - ".: .:.:.", - ".:.: .:.", - ".:.:.: .", - ".:.:. :.", - "Ġmü da", - "о наÑħ", - "он аÑħ", - "она Ñħ", - "Ì£ c", - "Ġyap acak", - "Ġн азвание", - "Ġназ вание", - "Ġназва ние", - "对 æĸ¹", - "ëĮĢ íijľ", - "çĪ Ń", - "в ана", - "ва на", - "ван а", - "ह न", - "ĠпÑĢоблем а", - "Ġжен ÑīинÑĭ", - "ĠженÑīин Ñĭ", - "èŀ º", - "Ġhosp odáÅĻ", - "ĠСÑĤ еп", - "ĠodpovÄĽ d", - "ĠS á»Ń", - "e view", - "ev iew", - "evi ew", - "åĩł ä¹İ", - "çŁ ¢", - "æĿ¥ ãģŁ", - "Ġп олоÑģ", - "Ġпол оÑģ", - "ĠÑģ ел", - "å± Ĩ", - "ĠпеÑĢв ой", - "ĠпÑĢоÑĨеÑģ Ñģа", - "ĠпÑĢоÑĨеÑģÑģ а", - "ãĢĢ ãĤĿ", - "ت اÙħبر", - "تا Ùħبر", - "и лаÑģÑı", - "ила ÑģÑı", - "ï¼Į æĹł", - "ĠвлаÑģ ноÑģÑĤÑĸ", - "íķĺ ìŀIJ", - "аÑĤ ки", - "ĠB Ãł", - "ĠK arel", - "ĠKar el", - "ĠKa rel", - "ĠKare l", - "è· µ", - "ر ÛĮÙĩ", - "رÛĮ Ùĩ", - "ĠëĤĺ 를", - "ĠобеÑģпеÑĩ ива", - "ĠобеÑģпе Ñĩива", - "à¥įर पत", - "ãģĹ ãĤĩ", - "åį Ĵ", - "Ġ 奥", - "Ġå¥ ¥", - "ĠпÑĢ оÑĤе", - "ĠпÑĢо ÑĤе", - "ĠпÑĢоÑĤ е", - "Ġ æĭĽ", - "Ġæĭ Ľ", - "ĠСÑĤ ÑĢана", - "ĠÑĢабоÑĤ аÑĤÑĮ", - "ĠÑĢабоÑĤа ÑĤÑĮ", - "Ġتش Ø®ÛĮص", - "ек ÑģÑĥ", - "екÑģ Ñĥ", - "Ġ 리그", - "Ġ리 ê·¸", - "Ġص اÙĦØŃ", - "ĠbaÅŁ lamÄ±ÅŁ", - "ĠbaÅŁlam Ä±ÅŁ", - "ĠÙ¾ÛĮ اÙħبر", - "ĠÙ¾ÛĮاÙħ بر", - "ز ا", - "Ġм аÑģÑģ", - "ĠмаÑģ Ñģ", - "ĠÎł αÏģ", - "Ġγα Ïģ", - "ëĿ¼ íĶ¼", - "Ġy arı", - "Ġya rı", - "Ġyar ı", - "ĠÑĤип Ñĥ", - "Ðŀ п", - "ãģij ãģªãģĦ", - "e mem", - "em em", - "eme m", - "ĠnÄĽ mu", - "ĠnÄĽm u", - "ĠÙĨ شر", - "ĠÙĨØ´ ر", - "ĠÎijθή να", - "Ùģ راÙĨ", - "Ùģر اÙĨ", - "Ġ 網", - "Ġç¶ ²", - "ĠпÑĢом иÑģлов", - "ĠBu gün", - "ĠBug ün", - "ìŀ Ķ", - "ĠжÑĸн ок", - "Ġ à¸Ľà¸£à¸°à¹Ģà¸łà¸Ĺ", - "Ġà¸Ľà¸£à¸° à¹Ģà¸łà¸Ĺ", - "ĠвикоÑĢиÑģÑĤов ÑĥваÑĤи", - "ĠТ им", - "ĠТи м", - ") 를", - "еж аÑĤÑĮ", - "Ġs ona", - "Ġso na", - "Ġson a", - "Ø´ÙĨ بÙĩ", - "Ġnich ž", - "åī Ľ", - "ĠÙģ تØŃ", - "ĠÙģت ØŃ", - "ĠÙħÙĤ دÙħ", - "ĠÙħÙĤد Ùħ", - "ĠGüven lik", - "e um", - "eu m", - "ç»ı è¿ĩ", - "è·Ŀ éĽ¢", - "Âł не", - "Ġا صÙĪÙĦ", - "Ġاص ÙĪÙĦ", - "ĠzaÄį átku", - "ิà¹Ģว à¸ĵ", - "Ġà¤ķ à¤Ł", - "Ġk riz", - "Ġkr iz", - "Ġp án", - "Ġpá n", - "ĠбоÑĢ ÑĮ", - "Ġбо ÑĢÑĮ", - "ظ ÙħØ©", - "ظÙħ Ø©", - "Ġê²½ ë¶ģ", - "ĠاÙĦÙĬ ÙħÙĨ", - "ĠاÙĦعرب ÙĬ", - "Ġh lub", - "Ġhl ub", - "Ġch á»Ŀ", - "è¥ ²", - "ëĵľ 리", - "ãĥĸ ãĥª", - "ĠÑģÑĤол ÑĸÑĤÑĤÑı", - "ر بÙĬØ©", - "رب ÙĬØ©", - "Ġ æ°¸", - "Ġæ° ¸", - "Ġê±° ìĿĺ", - "Ġβ αÏĥ", - "Ġβα Ïĥ", - "Ġa rz", - "Ġar z", - "ãĥ¢ ãĥ³", - "ĠÑĢÑĸв енÑĮ", - "ä¸į çŁ¥", - "导 èĩ´", - "ا ÙĬØ´", - "اÙĬ Ø´", - "ĠпÑĢев ÑĭÑĪ", - "Ġп н", - "ĠÎĴ ÏģοÏĩή", - "Ġ 身", - "Ġè º«", - "ĠÄIJ ầu", - "ĠÏĮ μÏīÏĤ", - "j ÃŃž", - "jÃŃ Å¾", - "Ġλ ίγ", - "ĠÑĪк оли", - "ĠÑĪкол и", - "ãģ£ãģ± ãģĦ", - "z dy", - "zd y", - "Ġê³ §", - "t eÅŁ", - "te ÅŁ", - "ÑĢ еÑī", - "ÑĢе Ñī", - "κ ει", - "κε ι", - "sah uje", - "Ġà¤īस स", - "ĠTan rı", - "ä¸į 好", - "éĥ Ń", - "ĠвÑĭ глÑıд", - "Ġç oÄŁ", - "Ġин ÑģÑĤÑĢÑĥменÑĤ", - "r ej", - "re j", - "èĪ Į", - "ãģĭ ãĤīãģªãģĦ", - "ãģĭãĤī ãģªãģĦ", - "ĠнепÑĢи ÑıÑĤ", - "Ġк ÑĢоме", - "ζ η", - "Ġл ог", - "ा वर", - "ाव र", - "ëħķ íķĺìĦ¸ìļĶ", - "ाह रण", - "ाहर ण", - "Ġgüven ilir", - "T ại", - "ĠØ´Ùĩر د", - "ĠØ´Ùĩ رد", - "ĠΤ ε", - "о ÑĢаз", - "оÑĢ аз", - "оÑĢа з", - "Ġl Ãłng", - "ĠlÃł ng", - "ĠlÃłn g", - "ï¼ ©", - "æĬķ 注", - "Ġsiyas et", - "ÐĽ Ñİ", - "Ġt ÅĻet", - "ĠtÅĻ et", - "ĠÏĢÏģÏİ ÏĦη", - "ĠÑĥлÑĭ б", - "ĠL âm", - "ÑĥлÑĮÑĤ а", - "ÑĥлÑĮ ÑĤа", - "åŁº åľ°", - "Ġskup ina", - "æ°¸ ä¹ħ", - "лÑĥ гов", - "лÑĥг ов", - "Ġ ÑĨÑĸй", - "ĠÑĨ Ñĸй", - "ĠÑĨÑĸ й", - "ĠP oh", - "ĠPo h", - "i д", - "ĠTr uy", - "ĠTru y", - "çļĦ ä¸Ģ个", - "çļĦä¸Ģ 个", - "ë²Ħ ìłĦ", - "Ġx ứ", - "à¸ĩ à¹ģรà¸ģ", - "à¸Ħ à¸Ńม", - "Ġelektron ik", - "ĠaÄŁ aç", - "Ġà¤ľ य", - "ĠповеÑĢÑħ ноÑģÑĤÑĮ", - "ĠاÙĩÙħ ÛĮت", - "ли виÑħ", - "лив иÑħ", - "ĠolduÄŁ undan", - "ï¼ī :", - "ÑĨи ÑıÑħ", - "ÑĨиÑı Ñħ", - "製 ä½ľ", - "à¸Ĺ รà¸ĩ", - "à¸Ĺร à¸ĩ", - "ey im", - "eyi m", - "Ġná klad", - "c ilik", - "ci lik", - "cil ik", - "ĠÐĵ лав", - "ĠUy gu", - "ĠÑĢег ÑĥлÑİ", - "à¤Ĥ à¤ľà¤¨", - "à¤Ĥà¤ľ न", - "Ġkayn aģı", - "à¹ī าà¸Ń", - "à¹īา à¸Ń", - "Ġgör mek", - "ĠíĮ ¬", - "Ġ å®Į", - "Ġå® Į", - "Ø« ÙħاÙĨ", - "ĠÑĤак аÑı", - "ĠÑĤа каÑı", - "ĠÑĤака Ñı", - "Ġне из", - "Ġzpráv y", - "ĠاÙĦØ´ خص", - "Ġìĺ¤ íĽĦ", - "ĠاÙĦ طب", - "ĠاÙĦØ· ب", - "atır ım", - "ر ÙĬر", - "رÙĬ ر", - "ĠÙħع ÙħارÛĮ", - "Ãľ RK", - "ÃľR K", - "ĠÒ IJ", - "ĠìĦ ¬", - "æīĭ ãģ«", - "Ġë³Ģ íĻĶ", - "u lace", - "ul ace", - "ula ce", - "Ġs ợ", - "ÑĢ иÑĩ", - "ÑĢи Ñĩ", - "มห าว", - "Ġk â", - "ĠÑģп ÑĢоб", - "Ùĩ رÙĩ", - "Ùĩر Ùĩ", - "ाध न", - "ĠÏĢ αι", - "ĠÏĢα ι", - "ب عد", - "بع د", - "ĠاÙĦ تÙĪ", - "ĠاÙĦت ÙĪ", - "ç»ı çIJĨ", - "p ůsob", - "æ¬ ł", - "ĠзаÑħвоÑĢÑİ Ð²Ð°Ð½Ð½Ñı", - "Ø® Ø©", - "ÚĨ ار", - "Ġboz uk", - "] âĢı", - "ĠSoc orro", - "Ġ hrad", - "Ġh rad", - "Ġhr ad", - "Ġhra d", - "над леж", - "ĠÑĥÑĩаÑģÑĤ ие", - "ĠÑĥÑĩаÑģ ÑĤие", - "ĠÑĥÑĩаÑģÑĤи е", - "å¤ī ãĤı", - "Ġy ans", - "Ġya ns", - "Ġyan s", - "ĠØ¥ ÙĦ", - "Ø® بر", - "خب ر", - "ÑĨиклоп ед", - "ι Ïİν", - "ιÏİ Î½", - "Ïĥ ÏĦÏģο", - "ÏĥÏĦ Ïģο", - "Ġb anka", - "Ġbank a", - "Ġban ka", - "ĠsoÄŁ uk", - "Ġün lü", - "é¢ ľ", - "Ġر Ùģع", - "ĠرÙģ ع", - "çIJ ³", - "ĠÑģоÑģÑĤоÑı нии", - "ν ονÏĦαÏĤ", - "νον ÏĦαÏĤ", - "Ġа кÑĤи", - "Ġак ÑĤи", - "ĠакÑĤ и", - "ĠÏĢολ Ïħ", - "ĠÏĢο λÏħ", - "Ġм оÑĹ", - "Ġмо ÑĹ", - "Ġ æł¼", - "Ġæł ¼", - "ç² Ĺ", - "ĠÑģлÑĥÑĩ ай", - "ĠÑģлÑĥ Ñĩай", - "ĠÑģлÑĥÑĩа й", - "ìĿ¼ ìĹIJ", - "ĠÑĤÑĢеб ÑĥеÑĤ", - "Ġ åıĤèĢĥ", - "ĠåıĤ èĢĥ", - "an gl", - "ang l", - "am ik", - "ami k", - "Ġ Ä°Åŀ", - "ĠÄ° Åŀ", - "æ¹ ¯", - "ĠÄij áo", - "ĠÄijá o", - "ละ à¸Ħร", - "Ñģ о", - "Âł ob", - "Ġk lim", - "Ġkl im", - "Ġkli m", - "èĥ Ĩ", - "ìĥĿ íĻľ", - "ãĥij ãĥ³", - "- ब", - "Ġк ад", - "Ġка д", - "à¹Ī สามารà¸ĸ", - "ĠÙħس ÙĦÙħاÙĨ", - "ç¿ °", - "ĠB ütün", - "ĠK raj", - "ĠKr aj", - "ĠKra j", - "ĠпеÑĢ Ñģп", - "ĠпеÑĢÑģ п", - "Ġener j", - "ãģķ ãģĽãĤĭ", - "ãģķãģĽ ãĤĭ", - "è¾¾ åĪ°", - "ा à¤Ĭ", - "ाठĬ", - "ĠگرÙģ تÙĨ", - "ĠگرÙģت ÙĨ", - "ÑĪ кÑĥ", - "ĠÐŁ ло", - "ÃŃ ny", - "ÃŃn y", - "ĠH ra", - "ĠÚĨ ÙĨاÙĨ", - "Ġ à¹Ħà¸Ĺย", - "Ġà¹Ħ à¸Ĺย", - "vise jÃŃcÃŃ", - "Û³ Û³", - "ĠÐľÑĸнÑĸÑģÑĤ еÑĢ", - "à¹Ĥ à¸Ń", - "ĠدÙĩ ÛĮد", - "æ¯Ķ ä¾ĭ", - "Ïĥι εÏį", - "Ç IJ", - "ãĢģ ãģª", - "Ġत स", - "ĠÄ° t", - "ĠìłĦ ìŁģ", - "à¹Ģ à¸Īร", - "à¹Ģà¸Ī ร", - "Ġelek tr", - "Ġelekt r", - "Ġd Æ°", - "â ĶĶ", - "âĶ Ķ", - "Ġ ìĥ¤", - "Ġìĥ ¤", - "ä» ®", - "à¸ģาร à¹Ģล", - "Ġм ÑĥлÑĮ", - "ĠмÑĥ лÑĮ", - "Ġ 度", - "Ġåº ¦", - "ĠH uyá»ĩn", - "в ен", - "ве н", - "Ġl Æ°á»Ľi", - "Ġprovoz u", - "Ñĥ ÑĢÑĥ", - "ÑĥÑĢ Ñĥ", - "ÑĢ ÑĸÑĹ", - "ÑĢÑĸ ÑĹ", - "Ġçocu ÄŁ", - "ัà¸IJ à¸ļาล", - "ÙĦ ÙĬÙĩ", - "ÙĦÙĬ Ùĩ", - "Ġ[âĢ¦] ...Ċ", - "åİŁ å§ĭ", - "Ġs klad", - "Ġsk lad", - "Ġskl ad", - "Ġسپ تاÙħبر", - "ĠTom áš", - "Ġس ÙĪاÙĦ", - "ĠسÙĪ اÙĦ", - "çģ Ń", - "ãĤĵ ãģ©", - "на знаÑĩ", - "ĠÄij Ä©a", - "ĠudÄĽl at", - "Ġà¤Ĩ दम", - "Ġà¤Ĩद म", - "ï¼ ¬", - "ι νÏĮ", - "ιν ÏĮ", - "iÅŁ leri", - "ÄIJ ây", - "Ġرس اÙĨÙĩ", - "ع اÙħ", - "عا Ùħ", - "ãĥ¼ãĥij ãĥ¼", - "Ġdo prov", - "Ġdop rov", - "ĠмÑĸÑģ ÑĤо", - "ĠмÑĸÑģÑĤ о", - "ï¼ ¥", - "ел Ñĸг", - "елÑĸ г", - "ائ ز", - "ä¸į äºĨ", - "ĠÐIJлекÑģанд ÑĢ", - "ĠвÑĢем ен", - "Ġdve ÅĻe", - "Ġch ảy", - "Ġ otel", - "Ġo tel", - "Ġot el", - "èĤ¯ å®ļ", - "ĠÑĥÑĤ веÑĢжд", - "ĠÐļом п", - "ĠÐļо мп", - "Ġ ëĤĺëĿ¼", - "ĠëĤĺ ëĿ¼", - "ĠвÑĸдбÑĥва ÑĶÑĤÑĮÑģÑı", - "ãĢģ ãĢİ", - "ĠkarÅŁÄ± lık", - "Ġl ẫn", - "çħ Ļ", - "ع کس", - "å¼ ¥", - "Ġte cr", - "Ġtec r", - "Ġne od", - "Ġneo d", - "æĪIJ çĤº", - "åħ¥ ãĤĬ", - "ĠÐŁ ÑĢод", - "ĠÐŁÑĢ од", - "ĠÐŁÑĢо д", - "ĠÏĢ Ïģά", - "ื à¸Ńà¸Ķ", - "ืà¸Ń à¸Ķ", - "ÑģÑĤ аÑĤи", - "ÑģÑĤа ÑĤи", - "е ноÑĹ", - "ен оÑĹ", - "ено ÑĹ", - "Ñĩи Ñģл", - "ÑĩиÑģ л", - "羣 æŃ£", - "Ġ ราà¸Ħ", - "Ġร าà¸Ħ", - "Ñĥ ÑĢе", - "ÑĥÑĢ е", - "ĠØ´ اÙĩد", - "ĠشاÙĩ د", - "ا عر", - "اع ر", - "Ġê²½ íĹĺ", - "à¸Ļ à¸Ħ", - "ãĥį ãĥ«", - "ÏĢοÏħ λοÏĤ", - "Ġम à¤Ī", - "ìĬ¤ ì½Ķ", - "itel né", - "å¼Ģ æĶ¾", - "ç į¨", - "çį ¨", - "ĠpÅĻ ech", - "ĠpÅĻe ch", - "ú Äįast", - "å¢ ĵ", - "Ġ å½±", - "Ġå½ ±", - "ÙĨ ساÙĨ", - "ÙĨس اÙĨ", - "ÙĨسا ÙĨ", - "Ġд вад", - "Ġдв ад", - "Ġдва д", - "Ġи деÑĤ", - "Ġиде ÑĤ", - "Ġид еÑĤ", - "Ġпод клÑİÑĩ", - "Ġподк лÑİÑĩ", - "íĬ¹ë³Ħ ìĭľ", - "B Ãłi", - "Å¡ ku", - "Å¡k u", - "i lerden", - "iler den", - "åıĺ å¾Ĺ", - "ëıĻ ìķĪ", - "Ġpostup nÄĽ", - "ĠиÑĤ ог", - "Ġd ůvodu", - "Ġdůvod u", - "siz lik", - "ÙĦ اÙĨ", - "ÙĦا ÙĨ", - "éĤ£ ç§į", - "ĠÑĩа Ñģа", - "ĠÑĩаÑģ а", - "ä¸į æĸŃ", - "ĠØ®ÛĮ اباÙĨ", - "ĠاÙĦد اخ", - "ĠÑģÑĤоÑĢ Ñĸн", - "Ġì¶ľ ìĹ°", - "æ² Ł", - "Ġh ry", - "Ġhr y", - "ĠG Ãľ", - "ĠìĿ¸ 구", - "l ied", - "li ed", - "lie d", - "Ġع اÙĦÙĬØ©", - "ĠпÑĢед ваÑĢ", - "ан ной", - "åı¥ è¯Ŀ", - "éł ĵ", - "ë°Ķ ìĿ¼", - "ï¼ı /", - "ĠÙħخت صات", - "ëŀ «", - "ĠçalÄ±ÅŁ maları", - "ĠçalÄ±ÅŁmalar ı", - "ĠçalÄ±ÅŁma ları", - "Ġrepublik a", - "Ġ ì³", - "Ġì ³", - "ा )", - "Ġê±´ ê°ķ", - "Ġê³µ ëıĻ", - "èħ ¦", - "ĠìĦľ ë¡ľ", - "ĠпÑĢовод иÑĤÑĮ", - "ĠдейÑģÑĤв иÑĤелÑĮно", - "v eç", - "ve ç", - "Ø« اÙĦ", - "Ġgöster ir", - "ır lar", - "ĠÑģам Ñĭм", - "á lo", - "ál o", - "é¢ij 次", - "à¥Ī à¤Ĺ", - "ا دÙħ", - "اد Ùħ", - "çĮ ª", - "ĠS ản", - "Ġ çı", - "Ġç ı", - "Ġl ety", - "Ġle ty", - "Ġlet y", - "Ġrep ublice", - "Ġrepublic e", - "æĿ¥ èĩª", - "Ġv ết", - "Ġbi rik", - "Ġbir ik", - "Ġbiri k", - "Ġm ekt", - "Ġme kt", - "Ġmek t", - "ĠاÙĦ ÙĪÙģ", - "ĠاÙĦÙĪ Ùģ", - "Ġj ich", - "Ġji ch", - "ä¸Ģ 覧", - "éľ² åĩº", - "ĠH iá»ĩn", - "ĠHi á»ĩn", - "Ġd iá»ĩt", - "ĠÑħ ÑĢиÑģÑĤи", - "åĪļ æīį", - "k ate", - "ka te", - "kat e", - "Ġb azen", - "Ġba zen", - "Ġbaz en", - "ĠurÄįit ÄĽ", - "ĠurÄį itÄĽ", - "Ġumož ÅĪuje", - "é¡ĺ ãģĦ", - "/Q ÄIJ", - "Ġmen Å¡ÃŃ", - "ÏĥκεÏħ ή", - "ĠÑĨеÑĢк ов", - "ĠÑĨеÑĢ ков", - "Ġ è´Ń", - "Ġè´ Ń", - "ок ÑĢаÑĤи", - "ĠÑĢоз к", - "α νοÏħ", - "αν οÏħ", - "Ġyön etic", - "Ġyönet ic", - "Ġol madan", - "Ġolm adan", - "Ġolma dan", - "åĨľ ä¸ļ", - "Ġë°Ķ ëŀĮ", - "çĵ ľ", - "ÑĪ аеÑĤÑģÑı", - "ÑĪа еÑĤÑģÑı", - "ĠÐļ оÑģÑĤ", - "ĠÐļо ÑģÑĤ", - "ĠÙħ عت", - "ĠÙħع ت", - "Ġ à¸ŀล", - "Ġà¸ŀ ล", - "ĠÙħتÙģ اÙĪت", - "ãĤī ãģı", - "èĪ Ĺ", - "Ġتع رÛĮÙģ", - "éīĦ éģĵ", - "Ġpé Äįe", - "ì» µ", - "Ġпод ÑĢаз", - "Ġбан кÑĥ", - "Ġбанк Ñĥ", - "Ä°S Ä°", - "æ¡ IJ", - "à¹Ĥ รà¸Ħ", - "à¹Ĥร à¸Ħ", - "ĠØŃØ° Ùģ", - "Ġ ë£", - "Ġë £", - "л иж", - "ли ж", - "Ġ ìĤ°ìĹħ", - "ĠìĤ° ìĹħ", - "ĠпÑĢи ÑĩинÑĭ", - "ĠпÑĢиÑĩин Ñĭ", - "ĠпÑĢиÑĩ инÑĭ", - "Ġна зна", - "Ġназ на", - "ãĥª ãĤ¹ãĥĪ", - "ãĥªãĤ¹ ãĥĪ", - "ìłķ ë¶Ģ", - "Ïĥ ÏĨα", - "ÏĥÏĨ α", - "å¦ ĥ", - "Ġголов и", - "Ġгол ови", - "ëIJĺìĹĪ ìĬµëĭĪëĭ¤", - "Ġεν ÏĮÏĤ", - "ãĤ¤ ãĥ³ãĤ¿", - "ãĤ¤ãĥ³ ãĤ¿", - "Ġs lun", - "Ġsl un", - "Ġslu n", - "ëł ´", - "ĠÑģÑĥÑīеÑģÑĤв ÑĥеÑĤ", - "ĠÑģÑĥÑīе ÑģÑĤвÑĥеÑĤ", - "з аб", - "за б", - "æĽ´ åĬł", - "Ġблагод аÑĢÑı", - "ĠëĮĢ 구", - "è¾ ħ", - "ห าà¸ģ", - "หา à¸ģ", - "Ġ æİ¥", - "Ġæİ ¥", - "ëĮĢ 를", - "人 ç±»", - "j eme", - "je me", - "jem e", - "åĪĨ å¸ĥ", - "ìŀ¥ ìĿĢ", - "Ġдопом оги", - "ìĻĦ ë£Į", - "o sy", - "os y", - "èĭ± éĽĦ", - "Ġ ÙĦس", - "ĠÙĦ س", - "म ह", - "Ġ à¸ģำ", - "Ġà¸ģ ำ", - "Ġداش تÙĨ", - "Ġداشت ÙĨ", - "Ń ìłľ", - "Ä° ng", - "Ä°n g", - "ĠTh Æ°á»Ŀng", - "íĻ Ģ", - "Ñį ÑĦ", - "íķ´ ìļĶ", - "ĠÐľ Ñĸж", - "ĠÐľÑĸ ж", - "еÑĢÑĸ га", - "еÑĢÑĸг а", - "Ġε á¼", - "à¹ģ สà¸ĩ", - "à¹ģส à¸ĩ", - "ãĥĢ ãĤ¤", - "Ġc esty", - "Ġce sty", - "Ġces ty", - "Ġcest y", - "Ġpr ázd", - "Ġprá zd", - "第 ä¸Ģ次", - "第ä¸Ģ 次", - "ĠÙĩÙħ سر", - "Ġz ev", - "Ġze v", - "Âł E", - "ĠBeled iyesi", - "ĠBelediye si", - "ĠпÑĢоп ози", - "Ġanlay Ä±ÅŁ", - "Âł Ùħ", - "ĠÑĢаÑģÑģ ÑĩиÑĤ", - "ĠاÙĦØ£Ùħ رÙĬÙĥÙĬØ©", - "ĠاÙĦØ£ÙħرÙĬÙĥÙĬ Ø©", - "Ġž ena", - "Ġže na", - "Ġžen a", - "d eniz", - "den iz", - "Ġn oci", - "Ġno ci", - "Ġnoc i", - "Ġst ál", - "ุ ย", - "주 ìĨĮ", - "Ġз еÑĢ", - "Ġ ìĨĮê°ľ", - "ĠìĨĮ ê°ľ", - "Ġkh ẳng", - "at ıcı", - "atı cı", - "ÄĽ ž", - "ĠÑĩ ÑĥÑĤÑĮ", - "Ġc áºŃu", - "ĠاطÙĦ اع", - "æµ ħ", - "Ġst rav", - "Ġstr av", - "Ġstra v", - "ĠSan ayi", - "Ġ طبÙĬ", - "ĠØ· بÙĬ", - "Ġطب ÙĬ", - "Ġhız la", - "Ïİ Î½Î±", - "Ïİν α", - "à¤¿à¤ľ ल", - "ÙħØŃ Ùħد", - "à¸ļ à¸ģ", - "Ġvzdál en", - "ĠÑĤак ими", - "ĠÑĤа кими", - "ĠÑĤаким и", - "ãĢĤ ãģĿãģĹãģ¦", - "Ġka lp", - "Ġkal p", - "Ġкож ного", - "Ðł µ", - "ÙĦع اب", - "ĠÙħ ÙĪÙĨ", - "ĠÙħÙĪ ÙĨ", - "ĠìĿ¼ ìĿĦ", - "Ġ ë°ĶìĿ´", - "Ġë°Ķ ìĿ´", - "Ġme kan", - "Ġmek an", - "ĠجاÙħ ع", - "Ġجا Ùħع", - "ĠÙĨ Ùģت", - "ĠÙĨÙģ ت", - "ĠاÙĦ سÙħ", - "ĠاÙĦس Ùħ", - "л ÑĭÑħ", - "лÑĭ Ñħ", - "èĥĮ æĻ¯", - "Ġê²ĥ ëıĦ", - "ĠìĤ´ ìķĦ", - "y dı", - "yd ı", - "Ġна веÑĢ", - "Ġнав еÑĢ", - "åŃIJ ãģ¯", - "l uluk", - "lu luk", - "Ġhá»Ĺ n", - "Ġ Ø´Ùģ", - "ĠØ´ Ùģ", - "Ġع ÙĦت", - "ĠعÙĦ ت", - "à¸Ħร าม", - "ĠÎļ ÏįÏĢ", - "Ġà¹Ģม ษายà¸Ļ", - "ÙĨد ÙĤ", - "ĠÑĥ ÑģÑĤÑĢа", - "ĠÑĥÑģÑĤ ÑĢа", - "ĠÑĥÑģ ÑĤÑĢа", - "ĠÎĵ εν", - "ĠÐĨ ван", - "ĠP hong", - "ĠPh ong", - "ĠPhon g", - "家 çļĦ", - "ĠÐIJ лекÑģ", - "ĠÐIJле кÑģ", - "ĠÐIJл екÑģ", - "Ġзб еÑĢÑĸг", - "ĠÅŁark ı", - "ĠÅŁar kı", - "ĠظرÙģ ÛĮت", - "ĠÙħ عÙĨÛĮ", - "ĠÙħع ÙĨÛĮ", - "ĠÙħعÙĨ ÛĮ", - "Ġ лов", - "Ġл ов", - "ĠìĤ ¶", - "èħ IJ", - "Ġ å¯Į", - "Ġå¯ Į", - "E RG", - "ER G", - "ĠÑģÑĤо имоÑģÑĤÑĮ", - "ÅĻ et", - "ÅĻe t", - "à¥ī य", - "à¹Ī าร", - "à¹Īา ร", - "ĠارÙĪÙ¾ ا", - "Ġб ÑĢоÑģ", - "ĠоÑĤ ноÑģÑıÑĤ", - "ĠоÑĤноÑģ ÑıÑĤ", - "ĠÎŁ κ", - "ÑĨÑĮ кий", - "ÏĬ κ", - "ãģĤãĤĬ ãģ¾ãģĽãĤĵ", - "ĠÑĥ ник", - "ĠÄij iá»ĥn", - "ĠÄiji á»ĥn", - "Ġvý zkum", - "Ġh ứ", - "Ġhá» ©", - "Ġ ÙĪات", - "ĠÙĪ ات", - "ĠÙĪا ت", - "Ġ å¹³æĸ¹", - "Ġå¹³ æĸ¹", - "Ïħ μ", - "ãĤĴ 使", - "εί ÏĦαι", - "两 人", - "Ġ åĮ»", - "ĠåĮ »", - "ÑĢаÑĤ иÑĤÑĮ", - "ÑĢаÑĤи ÑĤÑĮ", - "ĠاÙĦ اÙĨت", - "ĠاÙĦاÙĨ ت", - "ãģ® 人", - "ر Ø´", - "ĠТ ÑĥÑĢ", - "r nÄĽ", - "rn ÄĽ", - "天 天", - "ม าร", - "มา ร", - "Ġort alama", - "Ġorta lama", - "ĠпеÑĢе пиÑģ", - "ĠпеÑĢеп иÑģ", - "ĠìĥĿ ìĤ°", - "å¿ Ĩ", - "í ĩ´", - "ï¼Į 该", - "éĮ ¢", - "ÏĢα ίδ", - "ĠмеÑĢ опÑĢи", - "Ġг ÑĢав", - "ĠгÑĢа в", - "ĠгÑĢ ав", - "ÃĶ ng", - "Ġ æ¤", - "Ġæ ¤", - "ĠاÙĦد ÙĪÙĦØ©", - "ĠاÙĦدÙĪÙĦ Ø©", - "Ġ оÑģÑĮ", - "Ġо ÑģÑĮ", - "ĠоÑģ ÑĮ", - "å¥ Ķ", - "Ġgüven li", - "íķĺ ìĭł", - "Ġ éĬ", - "Ġé Ĭ", - "éŁ³ æ¨Ĥ", - "Ġmed ya", - "Ġب ÙĨا", - "ĠبÙĨ ا", - "а ма", - "ам а", - "Ġ ãĤŃãĥ£", - "ĠãĤŃ ãĥ£", - "èĹ ¥", - "l arım", - "lar ım", - "ları m", - "ĠT iếng", - "iyor lar", - "ï¼ ¢", - "æĶ Ŀ", - "Ñĸй ÑģÑĮкоÑĹ", - "Ġyet iÅŁtir", - "ĠyetiÅŁ tir", - "ĠÙ¾ سر", - "Ġپس ر", - "ãĤī ãģĹ", - " ļ", - "ìĥ ¤", - "à¸Ķ าห", - "à¸Ķา ห", - "ĠتØŃص ÛĮÙĦ", - "Ġб енз", - "éģ £", - "Ġнаб лÑİ", - "ä½ĵ ç³»", - "ãĥ¯ ãĤ¤ãĥĪ", - "Âł ÂłĠ", - "³³ Ġ", - "书 è®°", - "ĠMü hendis", - "p lor", - "pl or", - "l az", - "la z", - "лÑı ли", - "Ġpom áh", - "Ġб лиж", - "Ġбл иж", - "Ġбли ж", - "ĠÑĩиÑģ ла", - "Ġubyt ovánÃŃ", - "ÑĢаÑĤ но", - "Ġtr Äĥm", - "Ġاب راÙĩ", - "át ka", - "Ġiç indeki", - "Ġiçin deki", - "Ġiçinde ki", - "ั à¸ļà¸Ļ", - "ัà¸ļ à¸Ļ", - "ĠاÙħ ÛĮد", - "n ave", - "na ve", - "nav e", - "e cut", - "ec ut", - "å°± åľ¨", - "Ġt radi", - "Ġtr adi", - "Ġtrad i", - "Ġtra di", - "Ø· ÙĦÙĤ", - "Ø·ÙĦ ÙĤ", - "ãĤ¦ ãĤ©", - "Ġkhu ôn", - "ìĬ¤ ë¡ľ", - "ÏĦ ÎŃÏģα", - "ÏĦÎŃ Ïģα", - "ĠÏĥ κο", - "ĠÏĥκ ο", - "ë§ Ľ", - "ĠÙģ ÙĨÛĮ", - "ĠÙģÙĨ ÛĮ", - "à¹Į à¹Ģà¸ŀ", - "ĠاÙĦع ظ", - "Ġth ôn", - "기 ìĿĺ", - "Ġภ¿", - "Ñĥ ÑİÑĤÑģÑı", - "ÑĥÑİÑĤ ÑģÑı", - "ĠÙħ کاÙĨ", - "ĠÙħÚ© اÙĨ", - "Ġ âĹİ", - "ĠâĹ İ", - "Ġ çľģ", - "Ġçľ ģ", - "Ġ åį¡", - "Ġåį ¡", - "ĠпеÑĢ ÑĪий", - "ĠпеÑĢÑĪ ий", - "ĠíĽĦ ë³´", - "Ġآر اÙħ", - "ãģĮ ãģĦ", - "ย าà¸Ļ", - "ยา à¸Ļ", - "μ ει", - "με ι", - "ĠM áy", - "Ġz ů", - "Ġpodp oru", - "Ġpodpor u", - "ì» ¨", - "Ñģ ÑĤÑĢи", - "ÑģÑĤ ÑĢи", - "ÑģÑĤÑĢ и", - "ÏĢ ÏĦÏīÏĥη", - "Ф ÐĽ", - "åĵª éĩĮ", - "ĠпеÑĢв ÑĥÑİ", - "Ġyer inde", - "Ġyeri nde", - "ĠزÛĮ با", - "ĠزÛĮب ا", - "Ġodst ran", - "à¥Ģ à¤Ĺ", - "ĠÑĢÑĸз нÑĸ", - "Ïģ ηÏĥη", - "Ïģη Ïĥη", - "âĢĮاÙĦÙħÙĦ ÙĦÛĮ", - "ع اد", - "عا د", - "à¥įप ष", - "ÑŁ N", - "ï½ Ľ", - "ãĥ¼ ãĥľ", - "ãĥ¼ãĥ ľ", - "è´Ń ä¹°", - "ĠìĿ¸ê¸° ê¸Ģ", - "ĠÙħÛĮ Ø´ÙĪد", - "ĠбезопаÑģ ноÑģÑĤи", - "ĠνεÏĨ οκ", - "ãģ« ãģ¨", - "ĠÑĨеÑĢк ви", - "ت Ùĥ", - "ĠH Ãłng", - "ĠHÃł ng", - "ĠHÃłn g", - "ĠÙĦ ÙĦس", - "ĠÙĦÙĦ س", - "ĠνεÏĨοκ άλÏħÏĪηÏĤ", - "r aman", - "ra man", - "ram an", - "rama n", - "Ġvy vol", - "n iÄį", - "ni Äį", - "ر اÙĨÙĩ", - "راÙĨ Ùĩ", - "را ÙĨÙĩ", - "Ġp eÅŁ", - "Ġpe ÅŁ", - "ãĥ« ãĤ¯", - "å´ ĩ", - "Ġim kân", - "åĮ» çĸĹ", - "Ġप à¥Ŀ", - "άν νηÏĤ", - "Ġ جÛĮ", - "Ġج ÛĮ", - "Ġp roje", - "Ġpro je", - "Ġpr oje", - "Ġproj e", - "Ġül kenin", - "Ġülk enin", - "Ġülke nin", - "ĠK ew", - "ĠKe w", - "ĠاÙĦÙħ Ùģ", - "Ø£ Ùĥ", - "çĻº 表", - "Ġ δÏħ", - "Ġδ Ïħ", - "Ġ åĽ½å®¶", - "ĠåĽ½ 家", - "ĠKiÅŁ isel", - "ãĥ³ ãĤ¬", - "Ġzpráv a", - "V iá»ĩc", - "e rif", - "er if", - "eri f", - "Ġstrán ky", - "éļ ł", - "è¼ ķ", - "к оз", - "ко з", - "Ġस à¤ľ", - "Ùĩد اÙģ", - "l oub", - "lo ub", - "lou b", - "à¸łà¸²à¸ŀ ยà¸Ļà¸ķร", - "Ġíķł ìĿ¸", - "ĠÄIJ Ãło", - "ĠÄIJÃł o", - "ĠÙĨاØŃ ÛĮÙĩ", - "(= )", - "ĠÅŀ ampiyon", - "Ġp iÅŁ", - "Ġpi ÅŁ", - "Ġ Ø°Ùĩ", - "ĠØ° Ùĩ", - "ॠ¯", - "ĠÑģÑĢед ÑģÑĤво", - "ĠÑģÑĢедÑģÑĤв о", - "Ġ à¹Ģวลา", - "Ġà¹Ģว ลา", - "ĠÑĩ Ñĥж", - "Ġver ileri", - "Ġveri leri", - "Ġveriler i", - "ĠÚ© ارت", - "Ġکار ت", - "а ви", - "ав и", - "Ġà¤ķर व", - "Ġres tau", - "Ġrest au", - "Ġresta u", - "ê°ľ ìĽĶ", - "Ġм иÑĢов", - "Ġми ÑĢов", - "ĠмиÑĢ ов", - "ì° ®", - "ĠnÄĽjak ý", - "Ġses siz", - "Ġsess iz", - "اء ات", - "ĠÐĹ Ð°Ñħ", - "ĠÐĹа Ñħ", - "Ñı ÑīиÑħ", - "ÑıÑī иÑħ", - "п ÑĢ", - "Ġпод алÑĮ", - "Ġпода лÑĮ", - "ĠопÑĢедел иÑĤÑĮ", - "ॠŃ", - "Ġ رÙģ", - "Ġر Ùģ", - "幸 ç¦ı", - "à »", - "Ġ vÄĽdom", - "ĠvÄĽ dom", - "ĠvÄĽd om", - "ĠÑģвид еÑĤелÑĮ", - "ĠÎĵ οÏħ", - "ılıģı yla", - "ılıģ ıyla", - "çĻ» éĮ²", - "Ġä¸ĭ è·Į", - "Ġп лÑİ", - "Ġпл Ñİ", - "н од", - "но д", - "ĠØ£ جÙĦ", - "Ġأج ÙĦ", - "Ġà¤ķ थ", - "éĥ½ ä¸į", - "Ġs ene", - "Ġse ne", - "Ġsen e", - "Ġp ÄĽ", - "è¨Ī åĬĥ", - "Ġа Ñĥд", - "Ġод ном", - "Ġодно м", - "Ġ ä¸ĩåħĥ", - "Ġä¸ĩ åħĥ", - "ĠÙĪ Ùħا", - "ĠÙĪÙħ ا", - "ĠÐĶ ÑĢÑĥг", - "èµ· ãģĵ", - "в аÑİÑĤÑģÑı", - "ва ÑİÑĤÑģÑı", - "ваÑİÑĤ ÑģÑı", - "л аÑĤÑĥ", - "ла ÑĤÑĥ", - "лаÑĤ Ñĥ", - "Ġ تÙĪÙĨ", - "Ġت ÙĪÙĨ", - "ĠتÙĪ ÙĨ", - "Ñī аÑı", - "Ñīа Ñı", - "ή λ", - "ĠÐŁ ÑĢа", - "ĠÐŁÑĢ а", - "Ġاست رات", - "Ġاستر ات", - "ิà¸Ļ à¹Ģà¸Ķ", - "à¥įà¤Ĺ त", - "Âł з", - "Ġп олоÑĤ", - "Ġпо лоÑĤ", - "Ġпол оÑĤ", - "æ® ĸ", - "æ¡ Ĩ", - "ĠS istem", - "ĠSi stem", - "ĠSist em", - "Ġr uku", - "Ġru ku", - "Ġruk u", - "ãĥĥ ãĤ«ãĥ¼", - "ĠобÑıз ан", - "Ġkö ÅŁ", - "Ġad ını", - "Ø´Ùħ اÙĦÛĮ", - "na ÄįenÃŃ", - "naÄį enÃŃ", - "Ġ .ï¼ı", - "Ġ. ï¼ı", - "Ġ å®ĺ", - "Ġå® ĺ", - "Ġtoplum sal", - "èª ¤", - "ĠبÙĩ بÙĪد", - "ÑģÑĤв еннаÑı", - "ÑģÑĤвен наÑı", - "ĠØ¢ Ù¾", - "ĠجÙĦ سÙĩ", - "ĠجÙĦس Ùĩ", - "ãĢĢ ï½", - "åĵ Ń", - "æīĢ å±ŀ", - "æĴ ®", - "ì¢ Ģ", - "Ġ ει", - "Ġε ι", - "ì¹ĺ 를", - "Ġ ê³¼ìłķ", - "Ġê³¼ ìłķ", - "u uml", - "uum l", - "uu ml", - "δ ά", - "Ġ زد", - "Ġز د", - "ìĽIJ ìĿĦ", - "ĠvÄĽ cÃŃ", - "ĠvÄĽc ÃŃ", - "د Ø«", - "Ġs anki", - "Ġsan ki", - "Ġsank i", - "åĥı æĺ¯", - "л аÑĢа", - "ла ÑĢа", - "ìĤ¬ ìĿ´", - "ãĤı ãĤĮãģŁ", - "ãĤıãĤĮ ãģŁ", - "ĠÄij ón", - "ĠÄijó n", - "åIJ¯ åĬ¨", - "Ġgi Ãłnh", - "ĠgiÃł nh", - "Ġkır mızı", - "Ø® Ùħ", - "æIJ į", - "åĪĩ ãĤĬ", - "ãĤµ ãĥ¼ãĥĵãĤ¹", - "Ùĩ ار", - "Ùĩا ر", - "Ø° Ùĥر", - "о ÑĢоз", - "оÑĢ оз", - "оÑĢо з", - "à¥Īà¤Ĥ ।ĊĊ", - "à¥Īà¤Ĥ। ĊĊ", - "à¥Īà¤Ĥ।Ċ Ċ", - "ĠíĻĪ íİĺìĿ´ì§Ģ", - "ĠÙĥ بÙĬرة", - "ĠÙĥبÙĬر Ø©", - "н ина", - "ни на", - "нин а", - "íķĺ ìļ°", - "å¼ķçĶ¨ é¢ij次", - "ॠ®", - "ĠбаÑĤÑĮ кÑĸв", - "à¸Ł à¸Ńร", - "ี .", - "ìłĿ íĬ¸", - "éĺħ读 次æķ°", - "Ġit ir", - "Ġi tir", - "ÑĪ ин", - "ÑĪи н", - "ĠV áºŃy", - "çĤ ®", - "ла год", - "лаг од", - "Ø´ÙĨ اس", - "á» IJ", - "ĠÑı год", - "Ġì¤ij ìķĻ", - "ر ÙĬØ·", - "رÙĬ Ø·", - "ĠìĪĺ íĸī", - "Ġ ä¸ĢèĪ¬", - "Ġä¸Ģ èĪ¬", - "ĠÑħви лин", - "ĠÐľÐ¾Ð¶ но", - "ĠнаÑĩ але", - "Ġод нов", - "Ġодно в", - "ĠÃľ ç", - "ÑĨион нÑĭй", - "Ġ ìļķ", - "Ġìļ ķ", - "æ¼ Ĥ", - "å² ³", - "ت دÙī", - "تد Ùī", - "κ ηÏĤ", - "κη ÏĤ", - "âĢĻ nda", - "âĢĻn da", - "ï¼IJ ï¼IJ", - "èª ī", - "é§ħ å¾ĴæŃ©", - "ĠÙģرز ÙĨد", - "åħ¬ è·¯", - "α ÏĥίαÏĤ", - "αÏĥ ίαÏĤ", - "αÏĥία ÏĤ", - "าà¸ĵ าà¸Ī", - "ëij ¥", - "Ġ ÏĢοι", - "ĠÏĢ οι", - "ĠÏĢο ι", - "Ġب داÙĨ", - "Ġبد اÙĨ", - "к ап", - "ка п", - "ĠìŀĪ ëĬĶëį°", - "ĠìŀĪëĬĶ ëį°", - "ï¼Į æѤ", - "à¸Ľà¸£à¸°à¹Ĥย à¸Ĭà¸Ļ", - "ĠÚ©Ø´ÙĪر ÙĩاÛĮ", - "ุ ส", - "ãģ¹ ãģį", - "ĠÑģам Ñĭй", - "Ġп лÑı", - "Ġпл Ñı", - "Ġб ед", - "人 æīį", - "ส หร", - "ู à¸ķ", - "Ġkullan ımı", - "Ġkullanım ı", - "íķĻ ëħĦ", - "æ²» çĸĹ", - "ãĢĤ ä¸įè¿ĩ", - "ãĢĤä¸į è¿ĩ", - "æ£ ļ", - "ëĤ¨ ëıĦ", - "ĠØ¢ تش", - "Ïĩ ÎŃÏĤ", - "Ġfunk ci", - "Ġfunkc i", - "н ообÑĢаз", - "но обÑĢаз", - "à¥ĭ फ", - "Ġk aps", - "Ġka ps", - "Ġkap s", - "าษ à¸İ", - "( ع", - "ï¼Į åĬł", - "à¹Ĭ à¸ģ", - "ĠÙĩ Ø´", - "Ġدر ÙĪÙĨ", - "Ġ меÑĩ", - "Ġм еÑĩ", - "ĠпÑĢеж де", - "à¹Ī ย", - "Ġار شد", - "า à¹Ģล", - "æ¯Ķ è¼ĥ", - "ĠØ° کر", - "Ġ æĿ¡", - "ĠæĿ ¡", - "Ð Ĭ", - "Ñĥ кÑĢаÑĹн", - "ÙĬÙĨ ات", - "ÙĬÙĨا ت", - "ì¢ ĭ", - "д иÑı", - "ди Ñı", - "ÏĦ Ïģι", - "ÏĦÏģ ι", - "ĠÐļ аз", - "ĠÐļа з", - "ÙĤ ÙĦاÙĦ", - "ÙĤÙĦ اÙĦ", - "_ ,,", - "_, ,", - "ĠÚĨ ت", - "ĠìĿ¼ ìłķ", - "ĠÐŁ ÑĢоÑĦ", - "ĠÐŁÑĢ оÑĦ", - "ĠÐŁÑĢо ÑĦ", - "æ³ Ľ", - "Ġdruh ý", - "Ñĩ Ñĥк", - "ÑĩÑĥ к", - "l edik", - "le dik", - "led ik", - "ledi k", - "Ġhey ec", - "Ñĭ вал", - "Ñĭв ал", - "Ñĭва л", - "ĠD üny", - "ĠDün y", - "Ġ çĻº", - "ĠçĻ º", - "ĠpÅĻ átel", - "β άλ", - "βά λ", - "Ġ غر", - "Ġغ ر", - "ëĭ¨ ì²´", - "ìĽ¨ ëĶĶìĭľ", - "ÑĢаÑī ениÑı", - "н ÑĨиклопед", - "Ġpodnik atel", - "Ġìĭł ìŀħ", - "ĠÙģر Ø¢", - "и лиÑģÑı", - "или ÑģÑı", - "Ġol umlu", - "à¥įष मत", - "ĠÙħت خصص", - "й ом", - "ؤ اÙĦ", - "ĠÐĿ аÑĤ", - "ĠÐĿа ÑĤ", - "ìĺ¤ ëĬĶ", - "ĠMüdür lÃ¼ÄŁÃ¼", - "ĠH Ãłnh", - "ĠHÃł nh", - "ĠHÃłn h", - "Ġس ابÙĤ", - "ï¼ī çļĦ", - "ĠQu ý", - "lád ánÃŃ", - "ládá nÃŃ", - "Ġ ìļ´ëıĻ", - "Ġìļ´ ëıĻ", - "ĠÐĺ Ñħ", - "è« ¾", - "lıģ ının", - "lıģını n", - "lıģın ın", - "l il", - "li l", - "u Äį", - "ĠÑĩем пÑĸон", - "ÑĤ ож", - "ÑĤо ж", - "Ġ ä½Ľ", - "ни ÑĨе", - "ниÑĨ е", - "ĠпеÑĢв ого", - "Ġ Ñģом", - "ĠÑģ ом", - "ĠÑģо м", - "Ïĩ Ïİ", - "ÅĻ ik", - "ÅĻi k", - "иÑĤелÑĮ ÑģÑĤва", - "ĠÄ° ki", - "Ġask eri", - "Ġasker i", - "c isi", - "ci si", - "cis i", - "Ġjed nÃŃm", - "Ġjedn ÃŃm", - "Ġsta nice", - "Ġstan ice", - "èĤ¡ 票", - "à¸ľ ม", - "T ừ", - "Å¡ ak", - "ÏĦ ία", - "ÏĦί α", - "м ами", - "ма ми", - "ãģĮ åĩº", - "μο ί", - "м аÑĶ", - "ма ÑĶ", - "ëł¥ ìĿ´", - "ãĤĦ ãģ£ãģ¦", - "Ġ å¼µ", - "Ġå¼ µ", - "ØĮ Ċ", - "Ġ »Ċ", - "Ġ» Ċ", - "ا جات", - "اج ات", - "á½ ³", - "æĻĤ ãģ®", - "Ġп окол", - "Ġпо кол", - "Ġпок ол", - "ÑĸÑĤ еÑĤ", - "Ġíķ´ ê²°", - "Ġde dim", - "Ġded im", - "Ġdedi m", - "ĠÑĤ веÑĢд", - "Ġжен Ñīина", - "ĠженÑīин а", - "ед ини", - "еди ни", - "един и", - "ĠÙ¾ ÛĮÚ©", - "ĠÙ¾ÛĮ Ú©", - "iver site", - "ivers ite", - "iversit e", - "ĠآسÛĮ اب", - "ĠÑħаÑĢакÑĤеÑĢиÑģÑĤи ки", - "ĠØ£ÙĨ Ùĩا", - "ĠØ£ÙĨÙĩ ا", - "ĠÑĥкÑĢаÑĹн ÑģÑĮкоÑĹ", - "ĠاختÙĦ اÙģ", - "Ġاخت ÙĦاÙģ", - "Ġt ez", - "Ġte z", - "Ïģ εÏħ", - "Ïģε Ïħ", - "Ġkon umu", - "Ġkonu mu", - "Ġkonum u", - "ĠÑĤеÑħ нÑĸ", - "м Ñĸв", - "мÑĸ в", - "èĬ ¯", - "ĠÏĥ ελ", - "ĠÏĥε λ", - "Ä ¢", - "μ ιÏĥ", - "μι Ïĥ", - "ี à¹īĊ", - "ีà¹ī Ċ", - "Ġm ne", - "Ġmn e", - "ĠоÑĤв еÑĩ", - "ĠÎ ī", - "Ġ éĩİ", - "Ġéĩ İ", - "Ġg ấp", - "ĠпÑĢодÑĥк ÑĤÑĭ", - "ĠпÑĢодÑĥкÑĤ Ñĭ", - "ĠС ÑĢед", - "Ñĸл лÑı", - "à¸ļ à¸Ńà¸ģ", - "ĠtÅĻÃŃ dy", - "Ġth á»ķ", - "Ġthá» ķ", - "ãĥĩãĤ£ ãĤ¢", - "ÏĢοι η", - "ν ει", - "νε ι", - "æĪij们 çļĦ", - "Ġprofes yonel", - "ĠRa kou", - "ĠRak ou", - "Ġвид но", - "Ġz by", - "Ġzb y", - "ĠØŃ Ø§ÙĦÛĮ", - "ĠØŃاÙĦ ÛĮ", - "Ġ é£Ł", - "Ġé£ Ł", - "ĠL Ãłm", - "ĠÚ¯ ست", - "ĠТ ип", - "ĠТи п", - "θ ι", - "á vis", - "áv is", - "ÙIJ ب", - "åı¯èĥ½ æĢ§", - "ĠÑģем ей", - "ãĤī ãĤĮãģ¦ãģĦãĤĭ", - "ãĤīãĤĮ ãģ¦ãģĦãĤĭ", - "ãĤīãĤĮãģ¦ ãģĦãĤĭ", - "ìĥģ íĴĪ", - "Ġ οÏħ", - "Ġο Ïħ", - "Ġà¤ħà¤Ĺ स", - "о лом", - "ол ом", - "оло м", - "γ ον", - "γο ν", - "ĠÑģв ÑıÑī", - "ĠÑģвÑı Ñī", - "æĵ ¦", - "Ïĥ ÏĦηκε", - "ÏĥÏĦη κε", - "èĢħ çļĦ", - "- à¤ķ", - "ÑĤ ии", - "ÑĤи и", - "ĠвизнаÑĩ еннÑı", - "åıij åĩº", - "д аÑħ", - "да Ñħ", - "ĠмоÑĢ Ñı", - "Ġмо ÑĢÑı", - "æī¾ åĪ°", - "ÙĦ ÙĪب", - "ÙĦÙĪ ب", - "èĬ Ļ", - "ĠÑĦак ÑĤ", - "æ¯į 亲", - "id lo", - "idl o", - "ĠSt ad", - "ĠSta d", - "Ñį й", - "ìĽIJ ìĿ´", - "à¤ı न", - "æķ´ 个", - "Ġf ık", - "ĠÙħ ات", - "ĠÙħا ت", - "ÏĢ ον", - "ÏĢο ν", - "Ġê²½ 기ëıĦ", - "Ġ경기 ëıĦ", - "Ġα δ", - "Ġvz pom", - "Ġn á»ĵi", - "ĠÙĨÙĤ اط", - "ожд ение", - "Ġз алÑĸз", - "Ġзал Ñĸз", - "Ġr ủi", - "è¾ °", - ".:.:.:.:.:.:.:.: .:.:.:.:.:.:.:.:", - "ĠM Ãľ", - "Ġk ari", - "Ġka ri", - "Ġkar i", - "ĠÑģ обÑĭ", - "ĠÑģо бÑĭ", - "ĠÑģоб Ñĭ", - "ìĸ´ ì§Ħ", - "ر ÙĬس", - "رÙĬ س", - "u bu", - "ub u", - "ĠØ® ÙĦÙģ", - "ĠØ®ÙĦ Ùģ", - "ظٹ Ø·", - "æĿ ī", - "Ġ æĻ®éĢļ", - "ĠæĻ® éĢļ", - "ĠÙħÙĪاط ÙĨØ©", - "ĠÑģÑĤ анÑĥ", - "ĠÑģÑĤан Ñĥ", - "ĠÑģÑĤа нÑĥ", - "Ġê·¸ëħĢ ìĿĺ", - "ĠÙĦ Ùĥرة", - "ĠÙĦÙĥ رة", - "Ġo sm", - "Ġos m", - "ĠÑĥ ÑĢож", - "е га", - "ег а", - "Ġf else", - "Ġfe lse", - "Ġfel se", - "æĢĿ èĢĥ", - "ãĢĮ ãģĪ", - "Ġн овиÑħ", - "Ġнов иÑħ", - "Ġно виÑħ", - "๠IJ", - "ü ml", - "üm l", - "ĠíĶ¼ íķ´", - "ìĿ¼ ë°ĺ", - "Ġtür ü", - "ĠмÑĸ ÑģÑĤÑĸ", - "ĠмÑĸÑģ ÑĤÑĸ", - "ĠмÑĸÑģÑĤ Ñĸ", - "Ġkažd é", - "ĠÙħس جد", - "ấ c", - "ĠÙģ Ú©ÛĮ", - "Ġ yasal", - "Ġy asal", - "Ġya sal", - "Ġyas al", - "å°± ç®Ĺ", - "ĠоблиÑĩ ÑĩÑı", - "ĠÙĦ دÙĬ", - "ĠÙĦد ÙĬ", - "ا بات", - "اب ات", - "ĠÑģп аÑģ", - "êµ° ìļĶ", - "Ġп ад", - "Ġпа д", - "Ġб ÑĢаÑĤ", - "ĠбÑĢа ÑĤ", - "éĩį 大", - "Ġdüzen lenen", - "G ün", - "Ġaplik ace", - "à¸Ń ห", - "Ġ çħ", - "Ġç ħ", - "ĠÑģоÑģÑĤ оиÑĤ", - "è¯Ħ ä»·", - "ĠD uy", - "ĠDu y", - "Ø· اÙĤ", - "ĠпÑĢид еÑĤÑģÑı", - "Ġt olik", - "Ġto lik", - "Ġtol ik", - "Ġob rov", - "Ġobr ov", - "ĠpÅĻip oj", - "Ġ Ä±ÅŁÄ±", - "Ġı ÅŁÄ±", - "Ú¯ ÙĪÛĮ", - "Ú¯ÙĪ ÛĮ", - "æľŁ å¾ħ", - "ип лом", - "Ġ ince", - "Ġin ce", - "Ġi nce", - "Ġinc e", - "ĠС об", - "ĠСо б", - "ен ÑĮÑİ", - "енÑĮ Ñİ", - "è§Ĵ èī²", - "Ġ à¸ķร", - "Ġà¸ķ ร", - "Ġb ại", - "Ġê°ĢëĬ¥ íķľ", - "ĠblÃŃ zk", - "Ġt ách", - "Ġtá ch", - "Ġtác h", - "Ġвид Ñĭ", - "Ġви дÑĭ", - "и Ñĩна", - "иÑĩ на", - "Ġvyž ad", - "ĠìĨIJ ìĿĦ", - "ĠÐĿ ÑĸмеÑĩ", - "ĠÐĿÑĸ меÑĩ", - "åŁº äºİ", - "ĠÐļ ÑĢи", - "Ġعز ÛĮز", - "t iler", - "til er", - "ti ler", - "tile r", - "е вÑĸ", - "ев Ñĸ", - "Ġmož nosti", - "Ġmožnost i", - "ب از", - "با ز", - "ĠìĤ¬ ë§Ŀ", - "Ġz ÅĻejmÄĽ", - "íĹ ¤", - "Ġürün leri", - "ĠÎł λη", - "а ки", - "ак и", - "ãĤĴ éĸĭ", - "a nou", - "an ou", - "ano u", - "åĽ½ ãģ®", - "ĠyaÅŁ anan", - "ĠyaÅŁan an", - "ĠÑģ евеÑĢ", - "Ġ æ©Ł", - "Ġæ© Ł", - "มาà¸ģ มาย", - "Ġíijľ íĺĦ", - "ร ส", - "Ġض ربÙĩ", - "Ġضر بÙĩ", - "ĠE vet", - "ĠEv et", - "ĠEve t", - "æĨ ¶", - "Ġد ÙĤÛĮÙĤ", - "ĠدÙĤÛĮ ÙĤ", - "Ġвозник нов", - "ìľł 머", - "Ġíijľ ìĭľ", - "ÛĮ Ø´ÙĨ", - "ÛĮØ´ ÙĨ", - "ãĥĹ ãĥ©", - "ÑĤ Ñİ", - "ÙĪ سÛĮ", - "ÙĪس ÛĮ", - ") ìĿ´", - "è¯ģ æĺİ", - "ãģ§ãģį ãģ¾ãģĻ", - "ìĪĺ ìĿĺ", - "çĸ Ĩ", - "ĠÙħ ÙģÙĩÙĪÙħ", - "оÑĩ аÑĤкÑĥ", - "ाल à¤ķ", - "æ¡ Ĥ", - "ĠоÑħ оÑĢони", - "ĠارزÛĮ ابÛĮ", - "Ġìµľ ëĮĢ", - "Ġtho ải", - "ĠЦенÑĤ ÑĢалÑĮ", - "Ġ çķĻ", - "Ġçķ Ļ", - "à¸Ľà¸£à¸° à¹Ģà¸łà¸Ĺ", - "æµ· å¤ĸ", - "ĠÅŀ u", - "íĻľ ëıĻ", - "ĠdvÄĽ ma", - "istrov stvÃŃ", - "Ġarac ılıģıyla", - "Ġtr á»Ļn", - "» :", - "íĭ ±", - "ĠÙĦÛĮ Ú¯", - ". Ðļ", - "ĠÙħÙĤ اÛĮسÙĩ", - "Ġв мÑĸ", - "ر ÙĪب", - "رÙĪ ب", - "ĠاÙĦ Ø´Ùħ", - "ĠاÙĦØ´ Ùħ", - "Ġden nÄĽ", - "Ġdenn ÄĽ", - "Ñĥ Ñĩа", - "ÑĥÑĩ а", - "åħ ¹", - "Ñī им", - "Ñīи м", - "ĠíĬ¹ íŀĪ", - "ĠاستاÙĨد ارد", - "à¥Ģ ध", - "ãĤ¸ ãĤ¢", - "à¹ĩ à¹ĩ", - "иÑģ Ñģ", - "Ġkazan ç", - "ĠzÃŃsk al", - "åĽŀ æĿ¥", - "Ġп ÑıÑĤÑĮ", - "ĠпÑıÑĤ ÑĮ", - "ĠÄij ãi", - "ĠÄijã i", - "Ġ ÙĪرد", - "ĠÙĪ رد", - "ĠÙĪر د", - "Ġ ìķķ", - "Ġìķ ķ", - "ุ à¸Ĺร", - "ุà¸Ĺ ร", - "åĬ¨ çī©", - "Ġp ublik", - "Ġpub lik", - "Ġpubli k", - "æĪIJ æľ¬", - "æĪIJ åijĺ", - "ãĤ¤ ãĤ¯", - "شر ÙĥØ©", - "á¿Ĩ ÏĤ", - "Ġy ola", - "Ġyo la", - "Ġyol a", - "üyor uz", - "Ġк ÑĥÑĢи", - "ĠкÑĥÑĢ и", - "ĠкÑĥ ÑĢи", - "ĠпоÑħ ож", - "Ġìłľ ê°Ģ", - "िय त", - "ائ ÙĦØ©", - "ائÙĦ Ø©", - "Ġ ãģ¾", - "़ à¥ĩà¤Ĥ", - "़à¥ĩ à¤Ĥ", - "ÑģÑĮ кими", - "ÑģÑĮк ими", - "ÑģÑĮким и", - "âĢľ ä½ł", - "imiz de", - "ìµľ ìĭł", - "Ạ¬", - "è Ł", - "à¸Ħ รà¸Ńà¸ļ", - "à¸Ħร à¸Ńà¸ļ", - "ãĢĢ ãĢĢãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢ ãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢãĢĢãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "ت غ", - "ĠVÅ¡ ech", - "à¸±à¸Ľ à¸Ķาห", - "Ġa td", - "Ġat d", - "в оÑİ", - "во Ñİ", - "Ġyap ım", - "Ġyapı m", - "olog ické", - "ologic ké", - "Ġп лен", - "Ġпл ен", - "Ġlaz ım", - "r ung", - "ru ng", - "run g", - "ìĦľ ê´Ģ", - "Ġji ný", - "Ġjin ý", - "Ġtr òn", - "Ġtrò n", - "ĠполÑĸÑĤи ки", - "ا ÙĥÙħ", - "اÙĥ Ùħ", - "دÛĮ گر", - "à¥Īà¤Ĥ .Ċ", - "à¥Īà¤Ĥ. Ċ", - "Ġ اÙĩد", - "Ġا Ùĩد", - "ĠاÙĩ د", - "Ġ ãĥį", - "Ġãĥ į", - "ĠпÑĢодÑĥк ÑĤов", - "ĠпÑĢодÑĥкÑĤ ов", - "æĤ Ł", - "ĠpÅĻÃŃpad ech", - "ĠzaÄį ala", - "ĠzaÄįal a", - "åħ¥ ãĤĮ", - "ĠÑĢÑĸв нÑĸ", - "æĦŁ æĥħ", - "ĠΧ α", - "ì £½", - "ì£ ½", - "ิà¸Ī ารà¸ĵ", - "Âł б", - "Ñĸ ÑĹв", - "ÑĸÑĹ Ð²", - "ب Ø´", - "çļĦ éĹ®é¢ĺ", - "Ġza stup", - "Ġzast up", - "볤 ìļĶ", - "ãģ§ãģĻ ãģŃ", - "âĢĮ داÙĨ", - "âĢĮد اÙĨ", - "ï¼Į æĤ¨", - "Ġu vÄĽdom", - "ãģ¦ ãĤĭ", - "ìĤ¬ ëŀĮ", - "l un", - "lu n", - "éĽĨ åIJĪ", - "ë§ ¹", - "Ġž id", - "Ġži d", - "ठĬ", - "Ġt rp", - "Ġtr p", - "л ениÑħ", - "лен иÑħ", - "ле ниÑħ", - "＿ __", - "＿_ _", - "Ðľ Ðŀ", - "å¼ ĭ", - "λÎŃ Î¿Î½", - "ĠÄij òi", - "Ġк ÑĢок", - "lay ıcı", - "ì¶ľìŀ¥ ë§ĪìĤ¬ì§Ģ", - "åij Ī", - "éľ ŀ", - "Ġпо глÑıд", - "Ġпог лÑıд", - "ت رÙĥ", - "تر Ùĥ", - "ĠتÙģ اÙĪت", - "Ġ å®®", - "Ġå® ®", - "ĠدÙĪر بÛĮÙĨ", - "æĶ¾ åľ¨", - "ĠÑģлÑĥÑĩа ев", - "ĠÑģлÑĥÑĩае в", - "ĠÏħÏĢ ηÏģε", - "ë§ ŀ", - "ãģĻ ãģĻ", - "ê²ł ëĭ¤", - "ราย à¸ģาร", - "ĠÏĢ Ïģιν", - "ĠÑģ меÑĪ", - "ĠÑģм еÑĪ", - "å§ ī", - "Ġvýsled ky", - "Ġpot vr", - "åıij è¡Į", - "Ġt úi", - "Ġtú i", - "ĠìĤ¬ ëĿ¼", - "ç«Ļ åľ¨", - "Ġja ký", - "Ġjak ý", - "Ġ à¸ļาà¸ĩ", - "Ġà¸ļ าà¸ĩ", - "Ġdik kate", - "Ġdikkat e", - "Ġدر Ø¢Ùħد", - "æİĴ åIJį", - "r álnÃŃ", - "rál nÃŃ", - "ê³¼ ìĿĺ", - "ä½ µ", - "о лаг", - "ол аг", - "ола г", - "is iyle", - "isi yle", - "Ġ æ½", - "Ġæ ½", - "Ġ तम", - "Ġत म", - "Ġd ij", - "Ġdi j", - "Ġnh ánh", - "ĠR ek", - "ĠRe k", - "设 æĸ½", - "ĠpodmÃŃn ek", - "并 ä¸į", - "к ÑĥÑĤ", - "кÑĥ ÑĤ", - "Ġê³ł 볤", - "çļĦ å£°éŁ³", - "æĪĺ äºī", - "д аÑı", - "да Ñı", - "Ġê´Ģ ìĭ¬", - "ĠÑĦÑĸн анÑģ", - "ĠK öy", - "ĠKö y", - "Ġж ал", - "ĠÑģлÑĥж би", - "м ена", - "мен а", - "ме на", - "ت ÙĬار", - "تÙĬ ار", - "ĠÑĩем пион", - "ÏĢ ιÏĥ", - "ÏĢι Ïĥ", - "landır ma", - "mak tan", - "makta n", - "makt an", - "Ġ 丶", - "Ġä¸ ¶", - "à¹Ī à¸Ńส", - "à¹Īà¸Ń ส", - "ĠmÃ¼ÅŁ teri", - "ĠmÃ¼ÅŁter i", - "Ġص ÙĨد", - "ĠصÙĨ د", - "Ġet mesi", - "Ġetm esi", - "Ġetme si", - "Ġп оÑĢÑĤ", - "ĠпоÑĢ ÑĤ", - "ν ονÏĦαι", - "νον ÏĦαι", - "Ġ ãħĭãħĭ", - "ĠK AR", - "ĠKA R", - "Ġ uch", - "Ġu ch", - "Ġuc h", - "ĠØ® ÙĦÙĤ", - "ĠØ®ÙĦ ÙĤ", - "าษà¸İ ร", - "æŃ ¡", - "Ġи мени", - "Ġим ени", - "Ġиме ни", - "ãģł ãģijãģ©", - "ãģłãģij ãģ©", - "Ġìĭ¤ ìĭľ", - "ÏĥÏī ÏĢ", - "Ġ ì£", - "Ġì £", - "t ÄĽÅ¾", - "tÄĽ ž", - "Ġözel likleri", - "Ġözellik leri", - "Ġözellikle ri", - "Ġب Ù¾", - "Ġиз обÑĢаж", - "ÙĬÙħ ÙĥÙĨ", - "Ġ ãĥĶ", - "Ġãĥ Ķ", - "ĠÐĶ ив", - "ĠÐĶи в", - "ĠØ¥ ÙĬ", - "Ùĥ ÙĬÙĦ", - "ÙĥÙĬ ÙĦ", - "ĠÅŁ ik", - "ĠÅŁi k", - "Ġà¤Ĩ à¤ĸ", - "lar ınızı", - "ların ızı", - "larını zı", - "larınız ı", - "ĠвÑĸд ÑĢÑĸз", - "ĠÑĢоб оÑĤа", - "ĠÑĢобоÑĤ а", - "Ġta rif", - "Ġtar if", - "Ġ اÙĪت", - "Ġا ÙĪت", - "ĠاÙĪ ت", - "ın ma", - "é£Ł ãģ¹", - "Ġuzav ÅĻ", - "ë£ ¸", - "çĽij çĿ£", - "Ġ: ï¼¼", - "θ Ïħν", - "θÏħ ν", - "à¸Ķ ร", - "a larından", - "alar ından", - "aların dan", - "alarında n", - "èĩª æĭį", - "Ġro ÄįnÃŃ", - "ाà¤ĩ व", - "Ġ ÙĥÙĪر", - "ĠÙĥ ÙĪر", - "ĠÏĦ αιν", - "ĠÏĦα ιν", - "ĠÑĸн див", - "r ve", - "rv e", - "ĠνεÏĨ ÏİÏĥειÏĤ", - "Ġb á»ijn", - "Ġbá»ij n", - "Ġ å¿«", - "Ġå¿ «", - "ĠÑģ олÑĮ", - "ĠÑģо лÑĮ", - "ĠÑģол ÑĮ", - "li ÄŁinde", - "liÄŁi nde", - "liÄŁ inde", - "liÄŁin de", - "िन à¤Ł", - "a htar", - "ah tar", - "Ġneb ezpeÄį", - "æĹ¢ çĦ¶", - "ĠëĮĢ ìłĦ", - "ĠÙĨÚ¯Ùĩد ارÛĮ", - "ĠzÃŃsk at", - "Ġнали Ñĩие", - "ĠналиÑĩи е", - "Ġ aks", - "Ġa ks", - "Ġak s", - "ï¼ī ãĢĤĊĊ", - "ï¼īãĢĤ ĊĊ", - "ï¼īãĢĤĊ Ċ", - "Ġrod iny", - "Ġrodin y", - "Ġrodi ny", - "Ġза ÑħÑĸд", - "ĠзаÑħ Ñĸд", - "å¾® ç¬ij", - "ÂłÐĶ а", - "r adu", - "ra du", - "rad u", - "ī nh", - "p les", - "pl es", - "ple s", - "ĠK ons", - "ĠKon s", - "ĠKo ns", - "ิ à¹Ĥล", - "ิà¹Ĥ ล", - "ĠاÙĦ ÙĪص", - "ĠاÙĦÙĪ ص", - "åIJ¬ åĪ°", - "ĠÑģпоÑĢ ÑĤив", - "ĠÑģ айÑĤе", - "ĠÑģай ÑĤе", - "Ġ اظ", - "Ġا ظ", - "ĠØ§Ø ¸", - "lar ındaki", - "ların daki", - "larında ki", - "Ġtá»ķ n", - "ÐĿ ÐĨ", - "Ġned ost", - "ĠÑĤоÑĢ гÑĸв", - "Ġ اÛĮت", - "Ġا ÛĮت", - "ĠاÛĮ ت", - "Ġاختص اص", - "ĠÃľ y", - "ĠS adece", - "ĠSad ece", - "ĠÙħØ® رÙĪØ·", - "Ä ģn", - "Äģ n", - "ç esi", - "çe si", - "Ġ çĬ", - "Ġç Ĭ", - "ãĤĤ ãģ£ãģ¨", - "Ġ éŁĵ", - "ĠéŁ ĵ", - "èµ ĸ", - "ĠполÑĥÑĩ ениÑı", - "Ġë ĺ", - "âĢĻ ÑĹ", - "b ÃŃr", - "bÃŃ r", - "Ġб ÑĸблÑĸ", - "ĠD á»±", - "же неÑĢ", - "жен еÑĢ", - "ç½ij åĪĬ", - "Ġल à¥ľà¤ķ", - "ĠÑĥÑĩ нÑĸв", - "èĪ °", - "ĠÃĸÄŁ ren", - "Ġ ola", - "Ġo la", - "Ġol a", - "Ġ। âĢĿĊĊ", - "ระ à¹Ģà¸ļ", - "á½ ²", - "Ġ رز", - "Ġر ز", - "е и", - "еР¸", - "Ñı Ñĩи", - "ÑıÑĩ и", - "ØŃ Ø¨", - "æĴ ¤", - "ãģ¾ãģŁ ãģ¯", - "б ина", - "би на", - "бин а", - "ĠÎł εÏģ", - "ĠоÑĤноÑģ иÑĤÑģÑı", - "åīį çļĦ", - "Ġ Å¡Å¥", - "ĠÅ¡ Å¥", - "Ġyıl da", - "Ġyı lda", - ": ::::|", - ":: :::|", - ":::: :|", - "::: ::|", - "::::: |", - "us til", - "ust il", - "اÙĦ Ø¥", - "ĠsouÄįas né", - "ĠÙĨÛĮ رÙĪÛĮ", - "ĠÙĨÛĮر ÙĪÛĮ", - "ĠÙĨÛĮرÙĪ ÛĮ", - "ÑĩеÑģ кое", - "Ñĩе Ñģкое", - "ظ Ùģ", - "ĠÙ¾ÛĮØ´ ÛĮÙĨÙĩ", - "Ġع ÙģØ´", - "Ġrost lin", - "ç½ijåĪĬ ä¸ĭ载次æķ°", - "ĠпÑĢигоÑĤов иÑĤÑĮ", - "ãĥ Į", - "ĠÙĪ Ùħع", - "ĠÙĪÙħ ع", - "Ġb ecer", - "Ġbe cer", - "Ġbec er", - "Ġ ãĤ±", - "ĠãĤ ±", - "Ïĩ ήÏĤ", - "Ïĩή ÏĤ", - "о ÑģÑĤÑĥп", - "оÑģÑĤ Ñĥп", - "Ġë°ľ 매", - "Ñĸй ного", - "Ñĸйно го", - "Ġh rd", - "Ġhr d", - "ĠпÑĢепаÑĢа ÑĤÑĭ", - "ĠÙģ رض", - "ĠÙģر ض", - "ĠTy to", - "Ġ кÑĢаÑĹн", - "ĠкÑĢаÑĹ Ð½", - "Ġز اد", - "Ġikt idar", - "ì§ ĵ", - "Ùij ر", - "ÑĢÑı дÑĥ", - "ÑĢÑıд Ñĥ", - "к Ñĸй", - "кÑĸ й", - "âĶ £", - "Ġко жи", - "Ġкож и", - "Ġت ازÙĩ", - "Ġتا زÙĩ", - "o bec", - "ob ec", - "obe c", - "in ae", - "ina e", - "Ġvyj ád", - "Ġ رÙģتÙĩ", - "Ġر ÙģتÙĩ", - "ĠرÙģت Ùĩ", - "ĠرÙģ تÙĩ", - "Щ о", - "ĠBy lo", - "ĠByl o", - "оÑĤ в", - "ĠденÑĮ ги", - "é§ Ĩ", - "Ġма ÑĪин", - "ĠмаÑĪ ин", - "ĠмаÑĪи н", - "ĠØ£ ج", - "ì´Ī ëĵ±íķĻêµIJ", - "dı ģında", - "dıģı nda", - "dıģ ında", - "б аÑģ", - "ба Ñģ", - "Ġ æł¹", - "Ġæł ¹", - "ÎijÎĿ Τ", - "ÙĴ ØŃ", - "Ġjejich ž", - "ìĹIJìĦľ ìĿĺ", - "Ġад же", - "Ġì ı", - "Ïĥ οÏħ", - "Ïĥο Ïħ", - "et leri", - "etler i", - "Ġبعد ÛĮ", - "Ġبع دÛĮ", - "ĠìŀIJëıĻ ì°¨", - "ิà¸į à¸į", - "Ġt isk", - "Ġti sk", - "ãĥ¼ ãĤ¹ãĥĪ", - "ãĥ¼ãĤ¹ ãĥĪ", - "Ġमत लब", - "ê³Ħ íļį", - "ãĤ¦ ãĥĪ", - "Ġ à¹Ģมà¸ķร", - "Ġà¹Ģม à¸ķร", - "Ġop siyon", - "Ġops iyon", - "ĠÑĢав но", - "ĠبÛĮ ÙħÙĩ", - "ĠبÛĮÙħ Ùĩ", - "Ġ먼 ìłĢ", - "иÑĤелÑĮ нÑĭм", - "ĠнÑĸ би", - "Ġде ÑģÑıÑĤ", - "ĠдеÑģÑı ÑĤ", - "ĠÑģиÑĤÑĥа ÑĨии", - "еÑĢ ÑĪе", - "еÑĢÑĪ е", - "Ä ¾", - "ุ à¸ķร", - "ุà¸ķ ร", - "Ġyönet imi", - "Ġyönetim i", - "éIJ ĺ", - "ĠÙħÛĮ تÙĪاÙĨ", - "Ġز ÙĨدÙĩ", - "ĠزÙĨ دÙĩ", - "ĠزÙĨد Ùĩ", - "ãĥŃ ãĥ³", - "ĠK BS", - "ĠKB S", - "ìĦľ ë¹ĦìĬ¤", - "ï» ł", - "eck ého", - "ecké ho", - "ĠÙĤابÙĦ ÛĮت", - "ĠÙĤاب ÙĦÛĮت", - "ãĢĤ ä»Ĭ", - "ÃŃ nÄĽ", - "ÃŃn ÄĽ", - "ĠÑģм ог", - "ĠÑģл ÑĭÑĪ", - "ÙĴ Ùģ", - "po ÅĻád", - "елÑĮ но", - "Ġεί Ïĩαν", - "-ÐŁ еÑĤеÑĢб", - "ĠCh iến", - "ĠChi ến", - "é ry", - "ér y", - "ĠÑĸн ÑģÑĤиÑĤÑĥÑĤ", - "ç»Ĩ èĥŀ", - "Ñĭ ÑŁN", - "Ġv ua", - "Ġvu a", - "Ġà¤ħ श", - "ÑĢоÑģ ÑĤо", - "ÑĢоÑģÑĤ о", - "Ġvů Äįi", - "ë ¿IJ", - "Ġl iá»ĩt", - "Ġíķ µ", - "Ġ اÙģر", - "Ġا Ùģر", - "ĠاÙģ ر", - "ĠTek nik", - "Ġr oli", - "Ġro li", - "Ġrol i", - "Ġпоп ÑĭÑĤ", - "аÑĤ кÑĸв", - "Ġün iversit", - "аÑĤ оÑĢÑĭ", - "аÑĤоÑĢ Ñĭ", - "аÑĤо ÑĢÑĭ", - "ÑİÑīиÑħ ÑģÑı", - "Ġت ض", - "лÑİ ÑĩаеÑĤÑģÑı", - "лÑİÑĩ аеÑĤÑģÑı", - "лÑİÑĩа еÑĤÑģÑı", - "Ġíĸī ë³µ", - "Ġayrıntı lı", - "ĠкиÑĢ п", - "æĭ ¼", - "ëģ Ķ", - "л аÑĤа", - "ла ÑĤа", - "лаÑĤ а", - "Ġkho án", - "Ġhâl â", - "Ïĥ Ïħ", - "ог лаÑģ", - "æİ¥ çĿĢ", - "éĿ© åij½", - "Ġp ÅĻeb", - "ĠpÅĻ eb", - "ĠpÅĻe b", - "à¹Ģà¸ī ล", - "ĠاÙĦÙħÙĦ ÙĦÛĮ", - "åł Ĩ", - "íı IJ", - "à¸ķล à¸Ńà¸Ķ", - "° С", - "ìĤ¬ ëŀij", - "Ġг иб", - "ë²Ī 째", - "æĶ¹ åıĺ", - "表 çİ°", - "и ÑĩеÑģким", - "иÑĩеÑģ ким", - "иÑĩеÑģки м", - "สม à¹Ģà¸Ķ", - "å±ħ æ°ij", - " Ľ", - "ĠìķĦìĿ´ ëĶĶ", - "ĠмеждÑĥ наÑĢод", - "Ġy em", - "Ġye m", - "Ġm ül", - "Ġmü l", - "Ġا ÛĮست", - "ĠاÛĮ ست", - "Ġ ãĥ´", - "Ġãĥ ´", - "ัà¸Ļ à¹Ħà¸Ķ", - "à¥Ģ ण", - "åħ¶ å®ŀ", - "Ġgel enek", - "Ġgele nek", - "Ġgelen ek", - "ë¶ģ ëıĦ", - "à¹ī าà¸ķ", - "à¹īา à¸ķ", - "Ġ ìī¬", - "Ġìī ¬", - "Ġ ÏĢÎŃ", - "ĠÏĢ ÎŃ", - "ĠÙĥ اÙħÙĦ", - "ĠÙĥاÙħ ÙĦ", - "Ġتع ÙħÛĮر", - "è¨ ´", - "ë¹ Ļ", - "iy im", - "iyi m", - "å° ¿", - "éĤ£ æł·", - "êµŃ ìĿĺ", - "ãģĹãģ¦ ãģĬãĤĬ", - "Ġ niž", - "Ġn iž", - "Ġni ž", - "Ġκ ον", - "Ġκο ν", - "à¹Ī าà¸Ń", - "à¹Īา à¸Ń", - "Ġ γε", - "Ġγ ε", - "ĠС евеÑĢ", - "edi álnÃŃ", - "ãģŁãģ¡ ãģ®", - "m ayacak", - "may acak", - "maya cak", - "Ñ Ļ", - "ĠÑĥ гл", - "ĠÑĥг л", - "Ġk apas", - "Ġka pas", - "Ġkap as", - "Ñĥв алиÑģÑı", - "Ñĥва лиÑģÑı", - "Ñĥвали ÑģÑı", - "ĠмеÑģÑı ÑĨа", - "á» ¯u", - "ữ u", - "ิ ลล", - "ิล ล", - "ãĤĪãĤĬ ãĤĤ", - "à¥ĩ ण", - "à¥ĩठ£", - "Ġ 客", - "Ġå® ¢", - "ĠdeÄŁ erli", - "ĠdeÄŁer li", - "ÙĪ از", - "ÙĪا ز", - "ี à¸Ńย", - "ีà¸Ń ย", - "Ġ åıĪ", - "Ġåı Ī", - "Ġ à¸Ķร", - "Ġà¸Ķ ร", - "ĠÙĨ اب", - "ĠتÙĦÙĪÛĮزÛĮ ÙĪÙĨ", - "Ġol anlar", - "Ġolan lar", - "ä¼ĺ ç§Ģ", - "Ùĥ اÙĦ", - "ĠдеÑģÑı ÑĤи", - "ĠдеÑģÑıÑĤ и", - "m án", - "má n", - "ĠÑĢ анÑĮ", - "ĠÑĢа нÑĮ", - "ĠÑĢан ÑĮ", - "Ġìłľ ì¶ľ", - "è³ ¢", - "а бо", - "аб о", - "Ġtechn ik", - "Ġtech nik", - "ĠK iá»ĥm", - "ĠKi á»ĥm", - "t eki", - "te ki", - "tek i", - "á ¹", - "Ġm nÄĽ", - "Ġmn ÄĽ", - "Ġê³µ ê°Ħ", - "ĠM ek", - "ĠMe k", - "Ġاع تÙħاد", - "à¹Į à¹Ħà¸Ķ", - "ε ÏģÏĮ", - "εÏģ ÏĮ", - "ĠÑĥд аÑĢ", - "ĠÑĥда ÑĢ", - "оÑĩ ÑĮ", - "æ¦Ĥ 念", - "ÑĢ ал", - "ÑĢаР»", - "ÑĢа л", - "алÑĮ нÑĭми", - "алÑĮнÑĭм и", - "à¥ģर स", - "r áci", - "rá ci", - "Ġ ÙĤÙĪÙĦ", - "ĠÙĤ ÙĪÙĦ", - "Ġद व", - "ĠпÑĢав да", - "Ġ å¿ħ", - "Ġå¿ ħ", - "Ġdos ud", - "нÑĥ ÑĤÑĮÑģÑı", - "нÑĥÑĤÑĮ ÑģÑı", - "N Äĥm", - "à¸ĺ à¸Ļ", - "Ġdok un", - "Ġ åľ¨çº¿", - "Ġåľ¨ 线", - "ู à¹Ħ", - "ụ y", - "Ġн овÑĭÑħ", - "Ġнов ÑĭÑħ", - "Ġmez un", - "ĠC ần", - "à¸ģาร à¸ŀ", - "ĠìĺĪ ìłķ", - "Ïĥ ή", - "à¹Īà¸Ļ à¹Ģà¸ģม", - "ĠÙĪ اÙĦس", - "ĠÙĪاÙĦ س", - "ĠÙĪا ÙĦس", - "ãĥ³ ãĥĨãĤ£", - "ãĥ³ãĥĨ ãĤ£", - "çľĭ è§ģ", - "Ġس اÙĦÙħ", - "ĠساÙĦ Ùħ", - "ĠбагаÑĤÑĮ оÑħ", - "ĠÄij Ãłi", - "Ġد ستÛĮ", - "Ġدست ÛĮ", - "Ġدس تÛĮ", - "à¸ŀ à¸Ń", - "еп ÑĤи", - "ĠìłĦ íĻĶ", - "æĻĤ ãģ«", - "ĠSe znam", - "ĠSez nam", - "мÑĸ нÑĥ", - "мÑĸн Ñĥ", - "; ?#", - "à¥Ģ सर", - "à¥Ģस र", - "ĠÚĨ ÛĮست", - "νο ια", - "νοι α", - "ั à¸Ļà¸Ń", - "ัà¸Ļ à¸Ń", - "Ġ à¸Ħำ", - "Ġà¸Ħ ำ", - "Ġë³´ íĺ¸", - "Ġid dia", - "Ġiddi a", - "Ġβ ιβ", - "é«ĺ ä¸Ń", - "Ù ¨", - "ÐĴ аж", - "ĠиÑģп олн", - "ÑĪ ÑĤов", - "ÑĪÑĤ ов", - "ĠT aÅŁ", - "ĠTa ÅŁ", - "ìĽ ħ", - "åĬ ¹", - "Ġ åıĥ", - "Ġåı ĥ", - "Ġprost oru", - "Ġprostor u", - "ĠÑģп ад", - "е ÑĢина", - "еÑĢ ина", - "еÑĢи на", - "еÑĢин а", - "ĠpÅĻek lad", - "ĠpÅĻe klad", - "Å¡ ov", - "ĠÙģ ÙĩÙħ", - "ĠÙģÙĩ Ùħ", - "æĬ ij", - "Ġابت دا", - "Ġابتد ا", - "ãĤĴ ãģĬ", - "l ikler", - "lik ler", - "likle r", - "ĠÙħ اÙĥ", - "ĠÙħا Ùĥ", - "Ġko nut", - "Ġkon ut", - "Ġkonu t", - "ĠداÙĨØ´ جÙĪÛĮ", - "Ġоп ÑĤим", - "Ġб Ñĥма", - "ĠбÑĥ ма", - "ĠлÑİд Ñıм", - "Ġл Ñĸка", - "ĠлÑĸ ка", - "ĠлÑĸк а", - "ĠÑĢоз повÑĸд", - "ĠÑĢозп овÑĸд", - "ĠÑĢозпов Ñĸд", - "nes enÃŃ", - "Ġ à¸łà¸²à¸ŀ", - "Ġà¸ł าà¸ŀ", - "и Ñĩний", - "иÑĩ ний", - "ا Ø·ÙĦ", - "اط ÙĦ", - "Ñİ Ñīими", - "ÑİÑī ими", - "ÑİÑīим и", - "ãģı ãģ¨", - "éŃ ¯", - "ĠجÙĨ سÛĮ", - "Ðĺ Т", - "र ल", - "ĠÚ©ÙĪد Ú©", - "о лиÑĤ", - "ол иÑĤ", - "оли ÑĤ", - "ĠÑģÑĤÑĢÑĥкÑĤÑĥ ÑĢ", - "ve kili", - "vek ili", - "Ġब य", - "Ġgel miÅŁ", - "िर फ", - "Ġнай кÑĢа", - "ĠÐĶж он", - "Ġ ãĥĹãĥŃ", - "ĠãĥĹ ãĥŃ", - "ĠyaÅŁ lı", - "Ġkar Ä±ÅŁtır", - "ĠkarÄ±ÅŁ tır", - "ĠvÄĽtÅ¡ inou", - "Ġvaz geç", - "à¹ī าà¸Ħ", - "à¹īา à¸Ħ", - "lendir me", - "Ġ ç¨ĭ", - "Ġç¨ ĭ", - "说 è¯Ŀ", - "ĠíķĦìļĶ íķľ", - "aÅĻ ilo", - "Ġle žÃŃ", - "ĠAmer ikan", - "ĠAmerika n", - "ĠAmerik an", - "ãĤĦ ãģĻ", - "va jÃŃcÃŃ", - "vajÃŃ cÃŃ", - "ÐĿ Я", - "ĠìĹĦ ë§Ī", - "Ġ åĥ", - "Ġå ĥ", - "r ál", - "rá l", - "Ġç ay", - "Ġça y", - "tu ÄŁ", - "ุà¸į าà¸ķ", - "ĠÑģ лив", - "ĠÑģл ив", - "ν οÏħ", - "νο Ïħ", - "ĠO v", - "ĠC HP", - "ĠCH P", - "ĠZe mÄĽ", - "ĠZem ÄĽ", - "ĠÄįesk ý", - "ĠÄįe ský", - "ĠTh ánh", - "иÑĤелÑĮ ноÑģÑĤÑĮ", - "иÑĤелÑĮно ÑģÑĤÑĮ", - "æĦı ä¹ī", - "à¥įरम ण", - "Ġди амеÑĤ", - "Ġk lin", - "Ġkl in", - "Ġkli n", - "Ġ کرÛĮ", - "ĠÚ© رÛĮ", - "Ġکر ÛĮ", - "ãģ§ãģ¯ ãģªãģı", - "飯 åºĹ", - "Ġk ênh", - "Ġkê nh", - "ĠÑĢанÑĮ ÑĪе", - "ãĤĴ ãģĹãģŁ", - "ĠпÑĢи боÑĢ", - "ĠпÑĢиб оÑĢ", - "Ġà¤ĸ तर", - "Ġ yu", - "Ġy u", - "é§ IJ", - "ĠÑĢ або", - "ĠÑĢа бо", - "ĠÑĢаб о", - "ĠС ÐłÐ¡Ðł", - "èĬ ¬", - "ž ila", - "ži la", - "žil a", - "еÑĢ ÑĤа", - "еÑĢÑĤ а", - "и ÑģÑĤÑĢа", - "иÑģ ÑĤÑĢа", - "иÑģÑĤ ÑĢа", - "Ġкни ги", - "ĠFranc ie", - "ĠFran cie", - "ĠÚĺ اپ", - "ĠÎļ οÏħ", - "ĠÎļο Ïħ", - "ัว à¹Ģà¸Ńà¸ĩ", - "Ġl ắng", - "Ġ нами", - "Ġн ами", - "Ġна ми", - "Ġнам и", - "Ġпод ой", - "д ÑĢом", - "дÑĢ ом", - "o bus", - "ob us", - "ÐĴ Ñĸн", - "Ġst alo", - "Ġsta lo", - "Ġstal o", - "Ġà¤ı à¤ľ", - "ĠL inh", - "ĠLin h", - "ĠLi nh", - "ebilir iz", - "Ġзав ÑĤÑĢа", - "μ εÏģο", - "με Ïģο", - "μεÏģ ο", - "Ġ ÎŃν", - "ĠÎŃ Î½", - "ÑıÑĤ но", - "Ġд оÑĢож", - "Ġдо ÑĢож", - "ĠдоÑĢ ож", - "åıĤ çħ§", - "Ïĥ ιο", - "Ïĥι ο", - "à¹ī à¹Ģà¸ģ", - "a ných", - "an ých", - "aný ch", - "ç· ł", - "Ġ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢãĢĢ", - "ĠãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢãĢĢ ãĢĢ", - "åĬĽ çļĦ", - "ĠS ır", - "ĠSı r", - "Ġ ì§ĢëıĦ", - "Ġì§Ģ ëıĦ", - "ç· Ĭ", - "ĠpoÄį tu", - "ï¼Į ä¸İ", - "ä¸ĸ ç´Ģ", - "ем ого", - "емо го", - "Ġhus us", - "Ġhu sus", - "Ġölçü de", - "Ġtr ục", - "Ġtrụ c", - "à¸Ľà¸¥ à¸Ńà¸Ķà¸ł", - "Âłp ÅĻÃŃ", - "ĠBöl gesi", - "м ом", - "мо м", - "ãģ« ãģ¦", - "Ġ쪽 ì§Ģ", - "ÄĽt Å¡", - "ĠìĦ± ê³µ", - "र त", - "ur du", - "urd u", - "ĠìĽĢ ì§ģ", - "ÑŁ ÐŃ", - "nÃŃ kem", - "nÃŃk em", - "ĠskuteÄį nosti", - "Ġ даÑĤ", - "Ġд аÑĤ", - "Ġда ÑĤ", - "n eum", - "ne um", - "ĠÑĤаб леÑĤ", - "j vu", - "Ġs edm", - "Ġse dm", - "Ġsed m", - "س ÙĬØ©", - "سÙĬ Ø©", - "ĠкоÑĢ об", - "Ġко ÑĢоб", - "em mel", - "emm el", - "emme l", - "ãģ¤ ãģij", - "é¦Ļ èķī", - "Ġشخص ÛĮت", - "ĠشخصÛĮ ت", - "ä¸Ĭ äºĨ", - "ÙĪ را", - "ÙĪر ا", - "ĠаÑĤ моÑģ", - "Ġ лей", - "Ġл ей", - "Ġz prav", - "Ġzp rav", - "Ġëķ ħ", - "ู à¸Ĺ", - "Ġ اسر", - "Ġا سر", - "Ġاس ر", - "ĠAy dın", - "ĠعÙħ ÙĦÙĬØ©", - "ĠعÙħÙĦ ÙĬØ©", - "Ġд ÑĸÑĶ", - "ĠдÑĸ ÑĶ", - "Ġd ök", - "Ġdö k", - "Ġफ ल", - "ĠìĤ¬ëŀĮ ëĵ¤ìĿ´", - "ĠнаÑĤÑĥ ÑĢалÑĮ", - "æŁ ľ", - "温 度", - "Ġk les", - "Ġkl es", - "Ġkle s", - "Ġин веÑģÑĤи", - "s üz", - "æĴ °", - "Ġ ãĤ¢ãĥ«", - "ĠãĤ¢ ãĥ«", - "Ġ èĴ", - "Ġè Ĵ", - "ад ки", - "Ġk lÃŃÄį", - "Ïĩ εί", - "Ïĩε ί", - "ĠTh iết", - "ĠThi ết", - "ĠسرÛĮ ع", - "ĠÏĢεÏģιο Ïĩή", - "ÙĪ ÙĤÙģ", - "ÙĪÙĤ Ùģ", - "Ïģ ÏīÏĥη", - "ÏģÏī Ïĥη", - "ĠسÙĦ اÙħت", - "ĠسÙĦاÙħ ت", - "ëĵ¤ ëıĦ", - "ĠveÅĻej né", - "Ġvi tam", - "Ġvit am", - "Ġvita m", - "ĠبازÛĮ گر", - "ĠÑĢеÑĨеп ÑĤ", - "ĠìľĦ íķ´ìĦľ", - "ĠìľĦíķ´ ìĦľ", - "ĠØ£Ùĥ بر", - "Ġk üt", - "Ġkü t", - "민 주", - "Ġté ž", - "Ġ å¼ķ", - "Ġå¼ ķ", - "ÑĩаÑģ но", - "çļĦ åľ°", - "Ġarchit ekt", - "Ġбак ÑĤеÑĢ", - "Ġ ãģį", - "Ġ одеÑĢж", - "Ġо деÑĢж", - "Ġод еÑĢж", - "Ġتج ارÛĮ", - "éĿ Ī", - "Ġre cep", - "Ġrec ep", - "Ġrece p", - "é© ¶", - "Ġد ÙĩÙĩ", - "ĠدÙĩ Ùĩ", - "è² Į", - "çµIJ å©ļ", - "ılı ç", - "ãģĭãĤī ãģ¯", - "å¿ĥ éĩĮ", - "æĬķ è³ĩ", - "è² Ŀ", - "ĠкÑĥлÑĮÑĤÑĥ ÑĢÑĭ", - "ĠкÑĥлÑĮÑĤÑĥÑĢ Ñĭ", - "Ġ å°ij", - "Ġå° ij", - "à¹ģ à¸ŀร", - "à¹ģà¸ŀ ร", - "γ κÏĮ", - "γκ ÏĮ", - "ar ım", - "arı m", - "Ġاس اسÛĮ", - "Ġاساس ÛĮ", - "Ġposled nÃŃch", - "ĠposlednÃŃ ch", - "ĠÙħ ÙħÙĨ", - "ĠÙħÙħ ÙĨ", - "Ġпоз иÑĤив", - "Ġпози ÑĤив", - "ìł ¤", - "Ñĥ вавÑģÑı", - "Ñĥв авÑģÑı", - "Ñĥва вÑģÑı", - "Ñĥвав ÑģÑı", - "Ġجز ئ", - "ìĿ´ ìŀIJ", - "Ġин ÑģÑĤÑĢÑĥк", - "Ġη λεκ", - "Ġde mir", - "Ġdem ir", - "Ġdemi r", - "ä¸Ńæĸĩ åŃĹå¹ķ", - "Ġعاش ÙĤ", - "Ġب اÙĦÙĤ", - "Ġبا ÙĦÙĤ", - "ĠباÙĦ ÙĤ", - "Ġ maz", - "Ġm az", - "Ġma z", - "ά νι", - "άν ι", - "Ġ dÃ¼ÄŁ", - "Ġd Ã¼ÄŁ", - "Ġdü ÄŁ", - "Ġ κÏģα", - "Ġκ Ïģα", - "ĠбÑĥд ÑĤо", - "ç¦ı åĪ©", - "ĠпÑĢед назнаÑĩ", - "Ùħ ÙĦØ©", - "ÙħÙĦ Ø©", - "ĠбÑĥд инкÑĥ", - "Å¥ an", - "íķ Ģ", - "ç´¹ ä»ĭ", - "Ú© ز", - "ĠкаÑĦ ед", - "ãģ« è¦ĭ", - "าà¸ķร à¸IJาà¸Ļ", - "ë¡ľ ëĬĶ", - "i vÄĽ", - "iv ÄĽ", - "èĥ½ æºIJ", - "ï¼Į åħ¨", - "ĠÑĥ див", - "ĠÑĥд ив", - "Ġë§Į ëĤĺ", - "ÐĴ ÐIJ", - "ĠG ür", - "ĠGü r", - "ĠдÑĢÑĥг им", - "Ïĥ Ïį", - "Ġ oÄŁlu", - "ĠoÄŁ lu", - "Ġê°Ģ ê¹Į", - "ĠзнаÑĩ иÑĤелÑĮно", - "ĠзнаÑĩиÑĤ елÑĮно", - "о зÑĸ", - "оз Ñĸ", - "Ġm á»±c", - "ĠB eÅŁ", - "ĠBe ÅŁ", - "Ġ jezd", - "Ġje zd", - "á vÄĽ", - "áv ÄĽ", - "ÏĦη Ïĥε", - "ãģ¦ãģĦ ãģªãģĦ", - "ĠСв ÑıÑĤ", - "Ġम श", - "ĠΤ οÏħ", - "ĠΤο Ïħ", - "声 ãĤĴ", - "ĠÑģам ое", - "ĠÑģамо е", - "Ġ åĮº", - "ĠåĮ º", - "ĠìĤ¬ëŀĮ ìĿĢ", - "ĠÙħ ÙĦت", - "ĠÙħÙĦ ت", - "Ġj oker", - "Ġjo ker", - "Ġjoke r", - "Ġne ob", - "Ġneo b", - "ĠÑĤ ака", - "ĠÑĤак а", - "ĠÑĤа ка", - "ĠÙĩ Ùģت", - "Ġδε δο", - "ĠзаÑħ оп", - "ĠاÙĦÙħ خت", - "ез да", - "езд а", - "Ġíķľ ë²Ī", - "Ġع اÙħØ©", - "ĠعاÙħ Ø©", - "Ġdo state", - "Ġdost ate", - "Ġdostat e", - "Ġp lav", - "Ġpl av", - "Ġpla v", - "楽 ãģĹ", - ".;.; .;.;", - "в аÑĶ", - "ва ÑĶ", - "Ġbụ i", - "ĠÄij ỡ", - "ĠÄijá» ¡", - "Ġmys lÃŃ", - "Ġmysl ÃŃ", - "ĠÙĨ ار", - "Ġn út", - "Ġм ала", - "Ġмал а", - "Ġма ла", - "Τ Ρ", - "ĠاÙĦرÙħ زÙĬØ©", - "la dım", - "lad ım", - "ladı m", - "ä¸Ģ ç·Ĵ", - "ĠiÅŁ ç", - "l ivé", - "li vé", - "liv é", - "르 ê²Į", - "ан наÑı", - "ظËĨ Ø·", - "Ġd ừng", - "ÙĦÙĥ تر", - "çŃĶ æ¡Ī", - "ĠÙħÙĪÙĤع ÛĮت", - "ĠÑĸн озем", - "ĠиÑģ Ñĩ", - "Ġнеп ÑĢавилÑĮ", - "b akan", - "ba kan", - "bak an", - "Ġ çīĪ", - "Ġçī Ī", - "ен нÑİ", - "à¸ĩ à¹Ģศ", - "à¸Ħวาม à¸Ħ", - "% .Ċ", - "%. Ċ", - "à¹Ī à¹Ģà¸Ľ", - "ĠØ¢ بÛĮ", - "Ġآب ÛĮ", - "Ġst áty", - "Ġstát y", - "Ġتر تÛĮب", - "Äįem ž", - "Ġ é¹", - "Ġé ¹", - "Ġ ÙģاÙĦ", - "ĠÙģ اÙĦ", - "Ġbelir len", - "ĠâĨ ĺ", - "èĩ³ å°ij", - "ĠBun lar", - "Ġ ä¸ĵ", - "Ġä¸ ĵ", - "ĠÙħØŃ Ø§Ø³", - "ĠìĦľ ë²Ħ", - "Ġc anh", - "Ġcan h", - "Ġca nh", - "ĠпÑĢоÑĤ Ñıж", - "ĠнÑĸ меÑĨÑĮ", - "à¥Īà¤ł à¤ķ", - "ëĭ ī", - "Ġна неÑģ", - "Ġвоз ÑĢаÑģÑĤа", - "ĠвозÑĢаÑģÑĤ а", - "Ġ[âĢ¦ ]Ċ", - "Ġ[âĢ¦] Ċ", - ". à¸ŀ", - "ิ ศาสà¸ķร", - "ิศ าสà¸ķร", - "çģ ½", - "ê°Ļ ìĿĢ", - "ล à¸ĩà¸Ĺ", - "ลà¸ĩ à¸Ĺ", - "ãĤ± ãĥ¼ãĤ¹", - "Ġ ãĤ¢ãĤ¤", - "ĠãĤ¢ ãĤ¤", - "Ñģ Ñİ", - "ĠÙĦ ر", - "ãģĭ ãģ£ãģ¦", - "Ġ기 ë°ĺ", - "Ġ !:", - "Ġ! :", - "ĠÑģ ÑĬ", - "Ġ Ø´ÙĨاسÛĮ", - "ĠØ´ÙĨ اسÛĮ", - "ĠØ´ÙĨاس ÛĮ", - "ĠìķĦ 침", - "Ġعب اس", - "Ġ à¸ķà¸Ńà¸Ļ", - "Ġà¸ķ à¸Ńà¸Ļ", - "ĠмеÑĤал ли", - "ÑĪ ила", - "ÑĪи ла", - "Ġpod rob", - "Ġpodr ob", - "ÑĸÑģ но", - "Ġ 赤", - "Ġèµ ¤", - "c iler", - "ci ler", - "cil er", - "o zem", - "oz em", - "oze m", - "ĠоÑģнов нÑĭÑħ", - "Âł à¤ķ", - "à¸ĸ à¸Ļà¸Ļ", - "ан ÑĤаж", - "анÑĤ аж", - "анÑĤа ж", - "ĠD ÃŃky", - "Ġگذ ارÛĮ", - "æľº ä¼ļ", - "οÏħ λίοÏħ", - "οÏħλ ίοÏħ", - "оÑĩ ек", - "Ġнап иÑĤ", - "ĠبÛĮØ´ ترÛĮ", - "ĠبÛĮشتر ÛĮ", - "ä¾ į", - "ĠاÙĦ ÙħÙħ", - "ĠاÙĦÙħ Ùħ", - "ÙĪز ÙĬع", - "Ġgöz lem", - "è°ĥ æķ´", - "Âłm iles", - "Âłmi les", - "Ġk oc", - "Ġko c", - "ัà¸į ห", - "æ³ ³", - "ĠÎij γγ", - "ĠÎijγ γ", - "ĠÙĨÙħ از", - "ุ à¸Ĺ", - "ãĥı ãĤ¤", - "Ġth ù", - "к ÑĥлÑı", - "кÑĥ лÑı", - "кÑĥл Ñı", - "ĠпÑĥÑĤ ем", - "èĩº çģ£", - "Ġver gi", - "Ġverg i", - "åł´åIJĪ ãģ¯", - "ĠÑĤÑĢÑĮ оÑħ", - "Ġë³´ ë©´", - "âĸ ²", - "Ïħ γ", - "ĠдоÑĤ ÑĢим", - "æľ µ", - "Ġum ÄĽnÃŃ", - "èī¯ ãģĦ", - "Âł à¸Ļาà¸ĩ", - "Ðİ ÑĭÑŁN", - "ä¸ī 个", - "ียร à¸ķ", - "ï¼Į åIJĮæŶ", - "ĠÑĢозÑĢаÑħ Ñĥн", - "ĠD ers", - "ĠDe rs", - "ĠDer s", - "ãģª ãģ®", - "Ġê·¸ 를", - "d ikleri", - "dik leri", - "Ġhay ata", - "Ġhaya ta", - "Ġhayat a", - "è§Ħ èĮĥ", - "ç»ĵ åIJĪ", - "Ġs cé", - "Ġsc é", - "Ġc Æ¡m", - "ĠcÆ¡ m", - "åѸ éĻ¢", - "ĠÐĦ в", - "ĠÄįlán ek", - "ĠдоÑģÑĤ иг", - "ĠдоÑģÑĤи г", - "ा à¤ĩस", - "ाà¤ĩ स", - "εÏħ Ïĥη", - "éģ© çĶ¨", - "Ïĥ ον", - "Ïĥο ν", - "ıl maktadır", - "ëªħ ìĿĦ", - "ı b", - "Ġstar Å¡ÃŃ", - "Ġch ÃŃn", - "ĠchÃŃ n", - "ä¸Ģ 个人", - "ä¸Ģ个 人", - "ĠFranti Å¡ek", - "n ÄĽji", - "nÄĽ ji", - "ï» ¨", - "ĠÙĦÙĦ د", - "Ġp okoj", - "Ġpok oj", - "Ġj ih", - "Ġji h", - "ãĢį ãĢĤ", - "Ġعبد اÙĦ", - "ãĤĵãģ§ ãģĦãĤĭ", - "Ġмод елÑĮ", - "ĠteÅŁ kil", - "ĠÄĮ er", - "à¹Ģà¸Ķ à¸Ńร", - "' na", - "'n a", - "λο γή", - "λογ ή", - "Ġ kola", - "Ġk ola", - "Ġko la", - "Ġkol a", - "ãĥĢ ãĥ¼", - "иÑĤ елем", - "иÑĤе лем", - "ĠÏĥÏħ νο", - "ĠÏĥÏħν ο", - "ĠK urum", - "ĠKur um", - "ĠKu rum", - "Ġsnad no", - "ĠاÙĦÙĤر Ø¢ÙĨ", - "ĠV á»ģ", - "é«ĺ ãģĦ", - "Ġyıl dız", - "Ġbir isi", - "Ġbiri si", - "Ġkh úc", - "ÙĪ ÛĮÙĦ", - "ÙĪÛĮ ÙĦ", - "æľĢ ä½³", - "Ġส าà¸Ĥ", - "ĠÐŁ ок", - "ĠÐŁÐ¾ к", - "âī ł", - "à¹Ĥà¸Ľà¸£ à¹ģà¸ģรม", - "à¥įय यन", - "èij ¡", - "Ġn ovÄĽ", - "Ġno vÄĽ", - "Ġnov ÄĽ", - "ay ıp", - "ayı p", - "ĠSing ap", - "ĠSin gap", - "è° ĵ", - "ãĤ¶ ãĤ¤ãĥ³", - "Ġн овÑĭе", - "Ġнов Ñĭе", - "Ġh ảo", - "Ġ èŤ", - "ĠèĹ ¤", - "ãĥ³ ãĥĸ", - "ãĥ³ãĥ ĸ", - "Âł ĊĊ", - "ÂłĊ Ċ", - "θ εια", - "θε ια", - "Ġпоп ада", - "ĠëĶĶ ìŀIJìĿ¸", - "Ġداشت ÙĨد", - "ĠداشتÙĨ د", - "ĠØ´ÙĨ اختÙĩ", - "Ïĥ μαÏĦα", - "Ïĥμα ÏĦα", - "å¹³æĸ¹ åħ¬éĩĮ", - "Ġg öl", - "Ġgö l", - "ек оÑĤоÑĢ", - "еко ÑĤоÑĢ", - "Ġm álo", - "Ġmá lo", - "Ġاج ازÙĩ", - "Ú© اراÙĨ", - "کار اÙĨ", - "کا راÙĨ", - "ĠпÑĸдпÑĢиÑĶм ÑģÑĤв", - "ä¸ī å¹´", - "ĠسÙģ ÛĮد", - "ĠμÎŃ ÏģοÏĤ", - "ÐĻ ÐĻ", - "Ġh Æ°", - "س ÙĪب", - "سÙĪ ب", - "ĠÙĦ ذا", - "Ġnem ovit", - "Ġd ÃŃv", - "ĠdÃŃ v", - "Ä° s", - "¶ ¶", - "Ġph Æ°á»Ŀng", - "ĠÙĨØŃ ÙĪÙĩ", - "ĠÙĨØŃÙĪ Ùĩ", - "Ð ĭ", - "Ġz byt", - "Ġzb yt", - "Ġzby t", - "ed ii", - "edi i", - "n ech", - "ne ch", - "ĠадмÑĸнÑĸÑģÑĤÑĢа ÑĤив", - "Ġne vÄĽ", - "Ġnev ÄĽ", - "Ġ ож", - "Ġо ж", - "ĠÄIJ ó", - "à¸Ľà¸£à¸° ว", - "Ġvhod né", - "Ġum ÄĽl", - "ĠÑĢазлиÑĩ нÑĭе", - "ĠpÅĻi roz", - "Ġبخ Ø´ÛĮ", - "Ġبخش ÛĮ", - "ãģ® 大", - "ĠاÙĦ ÙĥÙĩ", - "ĠاÙĦÙĥ Ùĩ", - "ec ká", - "eck á", - "Ġzorun lu", - "ĠÐľÐ¸Ðº ола", - "Ġ amel", - "Ġa mel", - "Ġam el", - "к овÑĭе", - "ков Ñĭе", - ": :::/", - ":: ::/", - ":::: /", - "::: :/", - "ä¸įåIJĮ çļĦ", - "ĠÙĪÙĥ اÙĨت", - "ĠÙĪÙĥاÙĨ ت", - "à¸Ń à¸Ń", - "lá sil", - "ĠпÑĢедпол аг", - "ï½ ±", - "Ġ νε", - "Ġν ε", - "Ġн овÑĭй", - "Ġнов Ñĭй", - "Ġìĺģíĸ¥ ìĿĦ", - "Ġê°Ģ ì§Ħ", - "åĥ ħ", - "Y D", - "Ġب اغ", - "Ġبا غ", - "ĠØ´Ú© ست", - "Ġgü ney", - "Ġgün ey", - "и ÑģÑĮ", - "иÑģ ÑĮ", - "ãģĭ ãģªãģĦ", - "ãģĭãģª ãģĦ", - "ĠT òa", - "Ġگرد ÛĮد", - "Ġگر دÛĮد", - "ØŃ ÙĦ", - "lu vÃŃ", - "luv ÃŃ", - "v éd", - "vé d", - "Ġìĺ ·", - "Ġε ÏĢα", - "ĠεÏĢ α", - "ĠÑĤи ÑģÑıÑĩ", - "ĠÑĤиÑģ ÑıÑĩ", - "Ġê½ ĥ", - "ĠP US", - "ĠPU S", - "ĠдÑĥм кÑĥ", - "Ġ âĢĿĊ", - "ĠâĢĿ Ċ", - "ĠìĬ¤ íı¬ì¸ł", - "Ùĩ Ùĩ", - "Ġg ắng", - "Ġgắn g", - "ิ à¸łà¸²à¸ŀ", - "éĩĮ éĿ¢", - "br ıs", - "Ġz áb", - "Ġzá b", - "κ αÏĤ", - "κα ÏĤ", - "ĠåıĮ 线", - "ล ล", - "ĠÄIJ Ãłi", - "ĠÄIJÃł i", - "åѸ æł¡", - "ĠÑĢаÑģп ÑĢед", - "ĠÑģÑĤан еÑĤ", - "ĠÑģÑĤа неÑĤ", - "Ġл ак", - "Ġла к", - "Ġпод к", - "Ġg ören", - "Ġgö ren", - "Ġgör en", - "Ġgöre n", - "르 ê³ł", - "ĠÑĦ ÑĢÑĥкÑĤ", - "íĵ¨ íĦ°", - "ãģĻ ãĤĮãģ°", - "ãĤĴ ä½ľ", - "à¸Ńà¸Ńà¸ģ à¹ģà¸ļà¸ļ", - "Ġku lak", - "Ġkul ak", - "ĠíĶĮ ëłĪìĿ´", - "ĠØŃ Ø¯ÙĬØ«", - "ĠØŃد ÙĬØ«", - "ãģĨ ãĤĵ", - "Ġм Ñĸк", - "ĠмÑĸ к", - "à¤ĩस à¤ķ", - "ĠÑĥ ÑĤоÑĩ", - "ĠÑĥÑĤ оÑĩ", - "ĠÙĥ Ø«ÙĬر", - "ĠY ine", - "ĠYi ne", - "ĠYin e", - "ัว หà¸Ļ", - "н ÑĸÑĹ", - "нÑĸ ÑĹ", - "åį ¢", - "Ñĥ Ñģлов", - "ÑĥÑģ лов", - "ìĽĮ íģ¬", - "Ġà¤ħ à¤ĸ", - "ĠÑĨ Ñĸка", - "ĠÑĨÑĸ ка", - "ìĦł ìĿĦ", - "ĠØ£ ر", - "гал ÑĤеÑĢ", - "angl icky", - "ĠÑģ оÑģÑĥд", - "ĠÑģоÑģ Ñĥд", - "ĠÑĥ Ñıв", - "ĠпÑĢодÑĥк ÑĨÑĸÑĹ", - "Ġc hua", - "Ġch ua", - "Ġchu a", - "Ġd án", - "Ġdá n", - "ाम à¤Ĺ", - "ئ ت", - "ĠФ ед", - "Ġh rom", - "Ġhr om", - "íķ´ ë³´", - "ĠØ¢ÙĨ ÙĦاÛĮÙĨ", - "- пÑĢав", - "-п ÑĢав", - "Ġì¤ijìļĶ íķľ", - "Ġв кÑĥ", - "Ġвк Ñĥ", - "Ġ 大éĺª", - "Ġ大 éĺª", - "Ġt erk", - "Ġte rk", - "Ġter k", - "Ġпод Ñĸб", - "ĠвÑĸд вÑĸд", - "à¥Į à¤Ł", - "è³ £", - "Ġب تÙĨ", - "Ġبت ÙĨ", - "Ġبع ضÛĮ", - "Ġبعض ÛĮ", - "ãģª ãģĬ", - "ä»ĸ åĢij", - "Ġtavs iye", - "ĠM ısır", - "ĠØ¥ Ø°", - "Ġ æIJ", - "Ġæ IJ", - "íķĺ ëĤĺ", - "ĠÙĪ Ø®", - "ãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠ ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢĠãĢĢ", - "ãĢĢĠãĢĢĠãĢĢĠãĢĢĠãĢĢ ĠãĢĢ", - "Ġta kový", - "Ġtak ový", - "Ġtako vý", - "Ġबन न", - "Ġз ÑĢениÑı", - "ĠÙĪ ÙģÙĤ", - "ĠÙĪÙģ ÙĤ", - "ë¹Ħ ìķĦ", - "Ġпом ожеÑĤ", - "åĮĹ å¸Ĥ", - "dık ları", - "Ġ éĵģ", - "Ġéĵ ģ", - "Ġakt uálnÃŃ", - "Ġaktu álnÃŃ", - "Ġв в", - "ãĤĤ ãģªãģĦ", - "íĨµ ìĭł", - "ÏĦα Ïĥη", - "Ġìĥģ ëĮĢ", - "Ġ æł¡", - "Ġæł ¡", - "ãĢĤ éĤ£", - "ĠرÙĪسÛĮ Ùĩ", - "Ġtelev izyon", - "å¹´ é¾Ħ", - "ĠÐijоÑĢ иÑģ", - "리 ìĸ´", - "Ġz veÅĻej", - "ж но", - "ĠÐŀ ÑģÑĤ", - "ĠÐŀÑģ ÑĤ", - "ĠмÑĥж Ñĩин", - "Ġy eÅŁil", - "ĠСов еÑĤ", - "ĠСо веÑĤ", - "ĠB ÃĸL", - "ĠТак ож", - "Ġob nov", - "ĠпÑĢи надлеж", - "ĠвиÑģ нов", - "Ø· Ùħ", - "ĠìĹĨ ìĸ´", - "ĠM ùa", - "ä½ı å®ħ", - "åĮ» åѦ", - "Ġна ÑĢез", - "ĠнаÑĢ ез", - "ãĥĭ ï¾Ĩ", - "ĠM ặt", - "Ġvu ông", - "ä¸Ģ åĮº", - "ĠẢ nh", - "ÑĢ иÑĦ", - "ÑĢи ÑĦ", - "ä¿Ŀ éĻ©", - "ĠÏĩÏģή Ïĥη", - "åIJĮ æĦı", - "Ġ æīĵ", - "Ġæī ĵ", - "e tÄĽ", - "et ÄĽ", - "ĠÙĪ Ø°ÙĦÙĥ", - "ĠÑĤ иж", - "ĠÑĤи ж", - "ĠÎŁ ικο", - "ĠÎŁÎ¹ κο", - "ĠмÑĸÑģ ÑĨÑĸ", - "ĠÑĢебен ок", - "ĠÅŀ ah", - "عÙĦ ÙĪÙħ", - "l adıģ", - "la dıģ", - "lad ıģ", - "ladı ÄŁ", - "Ġg iden", - "Ġgi den", - "Ġgid en", - "лив оÑģÑĤÑĸ", - "ливо ÑģÑĤÑĸ", - "ÙĴ س", - "ĠT HB", - "ĠTH B", - "Ġmes lek", - "Âł ÐĿе", - "ÂłÐĿ е", - "μÏĨ Ïīνα", - "Ġ ÙĪاج", - "ĠÙĪ اج", - "ĠÙĪا ج", - "на ÑģлÑĸд", - "æĺŁ æľŁ", - "ÐĶ ж", - "ĠÑĢабоÑĤ аеÑĤ", - "ĠÑĢабоÑĤа еÑĤ", - "Ġs ánh", - "ìļ° 리", - "Ġا بÙĪ", - "Ġاب ÙĪ", - "çļĦ æĥħ", - "ĠìĻ¸ êµŃ", - "Ġk abil", - "Ġka bil", - "Ġkab il", - "еÑĢв Ñĭе", - "Ġgi Ãłu", - "ĠgiÃł u", - "Ġt á»ı", - "Ġtá» ı", - "Âł Ðij", - "å®Į æķ´", - "Ġmuž ů", - "ĠpomÄĽr nÄĽ", - "ĠÙħ خصÙĪص", - "ĠÐĶ ем", - "ãĤı ãĤĮãĤĭ", - "ãĤıãĤĮ ãĤĭ", - "ĠпÑĢи бÑĭ", - "ĠпÑĢиб Ñĭ", - "ĠکاÙħ Ù¾ÛĮ", - "ï¼ Ń", - "Ġt rh", - "Ġtr h", - "ĠÐij олÑĮÑĪ", - "´ :", - "и ваеÑĤÑģÑı", - "ив аеÑĤÑģÑı", - "ива еÑĤÑģÑı", - "иваеÑĤ ÑģÑı", - "Ġ ìĤ¬íķŃ", - "ĠìĤ¬ íķŃ", - "è¿Ľ ä¸ĢæŃ¥", - "ÑĨ ей", - "ÑĨе й", - "ãģ¾ ãģļ", - "аÑĤ елем", - "аÑĤе лем", - "éĮ ¯", - "Ġžal ob", - "ÑĨ ез", - "ÑĨе з", - "и нÑĥв", - "ин Ñĥв", - "инÑĥ в", - "Ġver ze", - "Ġve rze", - "Ġverz e", - "åĽŀ åĪ°", - "Ġd ược", - "ائ ÙĬÙĦ", - "ائÙĬ ÙĦ", - "sto upil", - "stoup il", - "论 æĸĩ", - "ĠÐŁ аÑĢи", - "ĠÐŁÐ°ÑĢ и", - "Ġдек оÑĢаÑĤив", - "اخ تÛĮ", - "اخت ÛĮ", - "ĠÑģÑĤ ÑĢем", - "ĠÑģÑĤÑĢ ем", - "ãĥ»âĶģãĥ»âĶģ ãĥ»âĶģãĥ»âĶģ", - "ĠÑģам ой", - "ĠÑģамо й", - "Ñĩ ÑĤо", - "ìĥģ ëĭ´", - "âī ¤", - "ÑĤ ого", - "ÑĤо го", - "ëIJ ¨", - "ı lacak", - "ıl acak", - "ä¸Ń ãģ«", - "ĠÏħÏĢάÏģÏĩ οÏħν", - "ĠвÑĸд бÑĥ", - "ĠвÑĸдб Ñĥ", - "çİ »çĴĥ", - "Ġвп еÑĢед", - "ĠPl zeÅĪ", - "Ú¯ اب", - "à¹Ģศ รษà¸IJ", - "ï¼Į æľĢ", - "Ùħ ÙĨÛĮ", - "ÙħÙĨ ÛĮ", - "çħ§ çīĩ", - "缮 å½ķ", - "ÑĢиÑĤ ÑĤÑı", - "âĢĮ اش", - "Ġ ëĮĢíļĮ", - "ĠëĮĢ íļĮ", - "ĠÅĻ adu", - "- ÑĤеÑħ", - "-ÑĤ еÑħ", - "Ġ ÙĬÙĪ", - "ĠÙĬ ÙĪ", - "Ġ à¹ģà¸ŀ", - "Ġà¹ģ à¸ŀ", - "ا ÙĥÙĨ", - "اÙĥ ÙĨ", - "Ġ기 ìŀIJ", - "Ġг Ñĸд", - "Ġìļ° 리ëĬĶ", - "Ġìļ°ë¦¬ ëĬĶ", - "Ø´ ÙħارÛĮ", - "Ø´Ùħ ارÛĮ", - "Ġt icari", - "Ġtic ari", - "âij ¢", - "ĠاÙĦ بد", - "ĠاÙĦب د", - "ĠÑĢаÑģ Ñĩ", - "Ġ اÙĦÛĮ", - "Ġا ÙĦÛĮ", - "ĠاÙĦ ÛĮ", - "Ġsü rede", - "Ġsür ede", - "Ġsüre de", - "Ġاع تر", - "Ġпо нÑıÑĤÑĮ", - "Ġпон ÑıÑĤÑĮ", - "γ κο", - "γκ ο", - "ï¼Į æ¯Ķ", - "ĠS eb", - "ĠSe b", - "Ġìĭł ê·ľ", - "æĶ¶ çĽĬ", - "ĠÙ¾ÛĮØ´ÙĨÙĩ اد", - "Îľ ÎijΤ", - "ÎľÎij Τ", - "ë°Ķ ìĿ´", - "ä¾Ľ åºĶ", - "б ин", - "би н", - "人 æ°Ĺ", - "ãģı ãĤī", - "Ġsk vÄĽl", - "Ġëĵ± ìŀ¥", - "æĭħ å½ĵ", - "Ġim kan", - "æ Ļ¨", - "æĻ ¨", - "ï¼Į çİ°åľ¨", - "Ġsrd ce", - "ìĤ° ìĹħ", - "Ġмод ели", - "æľ¬å½ĵ ãģ«", - "а нка", - "ан ка", - "анк а", - "Ġyür üy", - "ĠоÑĩ евид", - "ĠØŃ Ø³ÙĬÙĨ", - "ĠØŃس ÙĬÙĨ", - "Ñī аÑİÑĤ", - "Ñīа ÑİÑĤ", - "lé dl", - "léd l", - "ÑĨ о", - "ĠcÃŃ sa", - "ãģĭ ãģij", - "èĹ į", - "ĠØ®ÙĪاÙĩ ÙĨد", - "Ġmu že", - "Ġmuž e", - "Ġна коп", - "Ġнак оп", - "di ÄŁini", - "diÄŁi ni", - "er seniz", - "ers eniz", - "ersen iz", - "ĠпÑĢаÑĨÑĸв никÑĸв", - "д лÑı", - "Ġα ÏĥÏĦ", - "ĠαÏĥ ÏĦ", - "æ¶Ī è´¹", - "Ġ è¨Ģ", - "Ġè¨ Ģ", - "Ġb át", - "ĠØ´ ÙĥÙĦ", - "ĠÑģп иÑĢ", - "ÏĢο ÏĦε", - "Ġس اÙĦÙĩ", - "ĠساÙĦ Ùĩ", - "e kil", - "ek il", - "eki l", - "à¹ģ à¸Ĭม", - "ĠÏĥ ÏĦι", - "ĠÙħ Ø·ÙĦب", - "ĠÙħØ· ÙĦب", - "Ġìłķ ì±ħ", - "ê´Ģ ê³Ħ", - "å¹¹ ç·ļ", - "Ġ 京", - "éĢļ éģİ", - "ĠدÛĮ گراÙĨ", - "ĠدÛĮگر اÙĨ", - "ĠØ£ Ùħا", - "ĠØ£Ùħ ا", - "æĺ¯ ä¸į", - "ĠëĮĢ ëĭµ", - "ĠE rk", - "ĠEr k", - "p erty", - "per ty", - "pert y", - "ĠнаÑĩина еÑĤ", - "Ġê·¸ 리", - "ë£ ¡", - "ĠìĽ¹ ìĤ¬ìĿ´íĬ¸", - "ार न", - "æĦı è¯Ĩ", - "ĠС ÐŁ", - "Ġب اÙĬد", - "Ġبا ÙĬد", - "Ġbakım ından", - "/ TT", - "/T T", - "ĠÙģ اصÙĦÙĩ", - "ĠÙħØ« ÙĦا", - "ĠÙħØ«ÙĦ ا", - "Ġк вад", - "Ġкв ад", - "ĠØ´ اÛĮد", - "ĠuÄį itel", - "çĪ ½", - "Ġعرض Ùĩ", - "Ġ 交", - "ĠÑĩ еÑģÑĤÑĮ", - "ĠÑĩеÑģ ÑĤÑĮ", - "à¥Ī ?Ċ", - "à¥Ī? Ċ", - "ĠخاÙĨ Ùħ", - "et iyle", - "eti yle", - "Ġε γκα", - "ĠÑģÑĥ Ñīе", - "ĠìĿ¼ ìĸ´", - "ĠÐĽ ени", - "ĠÐĽÐµ ни", - "Ġ 声", - "Ġå£ °", - "á lie", - "ál ie", - "áli e", - "ãĥ¡ ãĥ¼ãĤ¸", - "à¥Ģ तर", - "à¥Ģत र", - "г алÑĸ", - "га лÑĸ", - "гал Ñĸ", - "ĠмÑĸ нÑĸм", - "ĠE ÅŁ", - "ĠпÑĢоиз оÑĪ", - "ÐĿ аÑģ", - "ÐĿа Ñģ", - "Ġب ÙĨÛĮ", - "ĠبÙĨ ÛĮ", - "让 æĪij", - "ĠпоÑģÑĤ еп", - "ĠìļĶ 구", - "ılı p", - "ıl ıp", - "Ġج ÙĪر", - "ĠجÙĪ ر", - "ĠëĮĢ ë¶Ģë¶Ħ", - "à¹ĩ à¸ķาม", - "à¹ĩà¸ķ าม", - "ĠÑĦ аÑģ", - "Ġìłķ ê·ľ", - "ла менÑĤ", - "лам енÑĤ", - "ÄŁ en", - "ÄŁe n", - "à¥ĩà¤Ĥ ĊĊ", - "à¥ĩà¤ĤĊ Ċ", - "ĠÐĺ ванов", - "ĠØŃ Ú©Ùħ", - "ĠØŃÚ© Ùħ", - "Ġ ï¾ļ", - "Ġï¾ ļ", - "ï¼ »", - "Ġne vid", - "Ġnev id", - "Ġла боÑĢаÑĤоÑĢ", - "à¸ŀย าà¸ļาล", - "Ġed iyorum", - "Ġediyor um", - "Ġhl avy", - "Ġhlav y", - "ĠEvrop ské", - "Ġph ái", - "Ġphá i", - "ãĥĵ ãĥ¼", - "ê´ij ìĹŃìĭľ", - "äº ľ", - "ØŃد اث", - "ĠпÑĢоÑĦ илакÑĤи", - "ro stÅĻed", - "rost ÅĻed", - "Ġм алÑĮ", - "Ġмал ÑĮ", - "Ġма лÑĮ", - "Ġmü dür", - "ا ساس", - "اس اس", - "ĠгалÑĥз Ñĸ", - "ี à¸Ł", - "Ġغذ اÛĮÛĮ", - "åŃIJ ä¾Ľ", - "Ġbah sed", - "ĠKrál ové", - "åį »", - "Ġ %,", - "Ġ% ,", - "ç½Ĺ æĸ¯", - "ë ļ", - "Ġ çij", - "Ġç ij", - "Ġξε ÏĦα", - "ĠÐŃ ÑĤи", - "ĠíĨµ íķ©", - "Ġاک تبر", - "ĠmÄĽsÃŃ ce", - "ìĪĺ ë¡ľ", - "ÑĦ Ñĸк", - "ÑĦÑĸ к", - "ĠÐĴ оз", - "ĠÐĴо з", - "ÑĩеÑģ ким", - "Ñĩе Ñģким", - "ÑĩеÑģки м", - "ìļ´ ëĵľ", - "Ġná klady", - "Ġnáklad y", - "ĠпоÑĤ ÑĢап", - "ĠÑĢÑĥ каÑħ", - "ĠÑĢÑĥк аÑħ", - "ι λο", - "ιλ ο", - "ĠG ül", - "ĠGü l", - "ë© ĺ", - "à¹ī ย", - "m akt", - "ma kt", - "mak t", - "ãĥ³ ãĥIJãĥ¼", - "ãĥ³ãĥIJ ãĥ¼", - "Ġн ÑĸÑı", - "ĠнÑĸ Ñı", - "ĠоÑĤ ÑĤен", - "m esinin", - "mes inin", - "mesi nin", - "mesini n", - "ĠвÑģп ом", - "Ġ ìĿ´ëĬĶ", - "ĠìĿ´ ëĬĶ", - "dy by", - "ãĤ¿ ãĥ³", - "âĹ İ", - "à¹īา หà¸Ļ", - "à¹īาห à¸Ļ", - "اد Ú¯ÛĮ", - "Ïĩ ία", - "Ġsna žÃŃ", - "Ġà¤ļ à¤ķ", - "μή μα", - "Ġ Ùĥر", - "ĠÙĥ ر", - "Ġκ οι", - "Ġκο ι", - "éĢ ¸", - "Ġne ust", - "Ġneu st", - "ĠÙĨظ اÙħÛĮ", - "ĠÙĨظاÙħ ÛĮ", - "åįļ çī©", - "Ġ ë²½", - "Ġë² ½", - "á½ ±", - "Ġì¶ľ ìĭľ", - "Ġar má", - "Ġarm á", - "ĠÙĩÙħ کارÛĮ", - "çļĦ æĥħåĨµ", - "çļĦæĥħ åĨµ", - "ÙĤ اÙħ", - "ÙĤ ب", - "Ġ éĤ£", - "ĠéĤ £", - "Ġë§ ¡", - "Ġo lası", - "Ġol ası", - "Ġola sı", - "β ÎŃÏģ", - "ä½ķ ãģĭ", - "ĠÑĥÑĩ еб", - "Ġв Ñĥз", - "Ġبر گز", - "Ġبرگ ز", - "' yi", - "'y i", - "Ġп ÑĢазд", - "ĠпÑĢа зд", - "ĠÐŀ ÑĢг", - "ĠÐŀÑĢ г", - "Ġ 并", - "Ġå¹ ¶", - "ĠÑģ ви", - "ĠÑģв и", - "ĠÙħÛĮ داÙĨ", - "ĠnaÅ¡e ho", - "ĠBA Åŀ", - "å» Ĭ", - "Ì Ī", - "ãģĵ ãģĿ", - "à¹ĩà¸Ļ à¸ŀ", - "οÏģ ειο", - "Ġбаг аÑĤ", - "γ ει", - "γε ι", - "μ είο", - "με ίο", - "à¹Īà¸ĩ à¸Ĭาà¸ķ", - "ĠHizmet leri", - "ĠAfr ika", - "ĠAf rika", - "Ġted bir", - ", ､", - "ä¸ī 级", - "ÐİÑĭÑŁN ÐİÑĭÑŁN", - "ĠÐļ ÑĢÑĸм", - "Ġa ray", - "Ġar ay", - "Ġara y", - "Ġböyle ce", - "к оÑĤ", - "ко ÑĤ", - "éĻ °", - "åĽ½ éļĽ", - "t ÄĽl", - "tÄĽ l", - "Ġp olis", - "Ġpol is", - "Ġpo lis", - "Ġu vol", - "Ġuv ol", - "ĠìĪĺ ê°ķ", - "çĶµ èĦij", - "Ġs ami", - "Ġsa mi", - "Ġsam i", - "Ġشاخ Ùĩ", - "Ġв ÑģÑĮого", - "ĠвÑģ ÑĮого", - "ĠØŃد اÙĤÙĦ", - "Ġ iken", - "Ġi ken", - "Ġik en", - "ãĤ¯ãĥ© ãĥĸ", - "Ġzá vod", - "Ġzáv od", - "ब ल", - "ë°° ìĨ¡", - "éĩĩ è´Ń", - "ëł ¬", - "Ġ ।ĊĊ", - "Ġ। ĊĊ", - "Ġ।Ċ Ċ", - "Ġê°ģ ê°ģ", - "Ġм ак", - "Ġма к", - "Ïģα Ïĥη", - "ĠiÅŁlem i", - "ãģĹ ãģ¦ãģĦãģ¾ãģĻ", - "ãģĹãģ¦ ãģĦãģ¾ãģĻ", - "ãģĹãģ¦ãģĦ ãģ¾ãģĻ", - "ĠP ek", - "ĠPe k", - "Ñİ Ð½", - "Ġvel kou", - "Ġvelk ou", - "åĬŀ çIJĨ", - "å®ĥ 们", - "Ġ èIJ¬", - "ĠèIJ ¬", - "ĠнаÑĢод Ñĥ", - "ĠнаÑĢ одÑĥ", - "Ġch ó", - "ĠH iç", - "ĠHi ç", - "Û³ Ûµ", - "Ġ รà¸Ńà¸ļ", - "Ġร à¸Ńà¸ļ", - "Û³ Û¶", - "à¸Ĥ ว", - "ä½į æĸ¼", - "ĠС ÑĤа", - "ĠСÑĤ а", - "ั à¸Ļม", - "ัà¸Ļ ม", - "ाप à¤ķ", - "ĠÑĥ ÑĢок", - "ãĤ¢ ãĥ¡ãĥªãĤ«", - "Ġз мож", - "Ġзм ож", - "sk ému", - "ské mu", - "ském u", - "Ġ è»Ĭ", - "Ġè» Ĭ", - "ĠاختÛĮ ار", - "ĠP Åĺ", - "л Ñıв", - "лÑı в", - "Ġ маз", - "Ġм аз", - "Ġма з", - "Ġözel liÄŁi", - "åij¼ ãģ°", - "Ġbir inin", - "Ġbiri nin", - "Ġод не", - "Ì Ĩ", - "ä»ĸ ãģ®", - "建 ç¯ī", - "поÑģ еÑĢед", - "ห ลà¸Ķ", - "หล à¸Ķ", - "å¤ļ ãģĦ", - "ÏĦή ÏĥειÏĤ", - "Ġر ÙĪÙĨد", - "ĠرÙĪ ÙĨد", - "èģ ½", - "ì¤ij ìĹIJ", - "ìĬ¤ íĭ°", - "Ġз вÑĸÑĤ", - "Ġзв ÑĸÑĤ", - "ĠаÑĢ ÑĤи", - "Ġc Æ°á»Ŀi", - "ĠcÆ° á»Ŀi", - "ın dır", - "ınd ır", - "Ġг олод", - "Ġгол од", - "ا زد", - "از د", - "à¹Īาว ว", - "ãĥ¡ ãĥ©", - "عÙĨ ÙĪاÙĨ", - "% )Ċ", - "%) Ċ", - "ĠÑħолод илÑĮ", - "人 们", - "C Ä°", - "ÐĹ Ð°Ð¿", - "ÐĹа п", - "ĠpÅĻ isp", - "ĠpÅĻi sp", - "Ġdurum larda", - "ÑĢ Ñĸд", - "ÑĢÑĸ д", - "Âł У", - "Ġε ÏĨαÏģ", - "Ġs prav", - "Ġsp rav", - "Ġspr av", - "ĠоÑĤÑĢим аннÑı", - "ï¼Į 没æľī", - "о вала", - "ов ала", - "ова ла", - "овал а", - "Ġng ại", - "ãĢĤ 大", - "Ġ даеÑĤ", - "Ġд аеÑĤ", - "Ġда еÑĤ", - "ĠpÃŃs em", - "ÑĨ ÑıÑĤÑĮ", - "ÑĨÑı ÑĤÑĮ", - "ov nÄĽ", - "ë¦ ī", - "Ġê² ģ", - "Ñģ ÑĤин", - "ÑģÑĤ ин", - "ÑģÑĤи н", - "ĠS ayı", - "ĠSa yı", - "ĠSay ı", - "ãĢĭ çļĦ", - "Ġyol uyla", - "Ġyolu yla", - "елеÑĦ он", - "Ġr áno", - "Ġrá no", - "Ġíĸī ëıĻ", - "ĠاÙĦخاÙħ سة", - "Ġповин на", - "ÅĻ ila", - "ÅĻi la", - "ÅĻil a", - "Ġà¤ļ रण", - "Ġà¤ļर ण", - "Ġبرگ زار", - "Ġبرگز ار", - "ìļ´ ëį°", - "à¹Ģà¸Ľ à¸Ńร", - "Ġdal eko", - "led nÃŃ", - "åIJį 稱", - "лив ÑĸÑģÑĤÑĮ", - "ливÑĸ ÑģÑĤÑĮ", - "Ġ몸 ìĿĦ", - "о ÑĢÑĸв", - "оÑĢ Ñĸв", - "оÑĢÑĸ в", - "Ц е", - "بد Ø£", - "ë°ĺ 기", - "k rát", - "kr át", - "ä¸į 足", - "Ġolduk ları", - "len iyor", - "Ġìĭľ íĸī", - "ĠпÑĢи нимаÑĤÑĮ", - "à¸Ĥà¸Ńà¸ĩ ร", - "ÏĪ ει", - "ÏĪε ι", - "Ġ ẩn", - "ت س", - "ĠÑĤ ай", - "ĠÑĤа й", - "Ġнев озможно", - "åıĬ ãģ³", - "r oti", - "ro ti", - "rot i", - "ï½ Ń", - "д ом", - "до м", - "ой но", - "å£ Ĭ", - "说 çļĦ", - "Ġsk oro", - "ni ÄįnÃŃ", - "niÄį nÃŃ", - "ĠProf es", - "ĠÑħ ÑĢониÑĩеÑģ", - "Ġ주 문", - "ĠZ n", - "ĠÑģ лой", - "ĠÑģл ой", - "ĠÑģло й", - "Îł Ïģο", - "æĮĩ æķ°", - "ĠпеÑĢ еÑĪ", - "ĠпеÑĢе ÑĪ", - "à¥ģà¤ķ स", - "Ġê°Ģ ìłķ", - "Ġ íķĺë©´", - "Ġíķĺ ë©´", - "Û±Û¹ Û´", - "к Ñĥл", - "кÑĥ л", - "ÙĬ ÙĦا", - "ÙĬÙĦ ا", - "ĠدÙĪب ارÙĩ", - "| l", - "ĠÐľ Ñĥ", - "н ила", - "ни ла", - "нил а", - "ãģ¦ ãģĦãģ¾ãģĻ", - "ãģ¦ãģĦ ãģ¾ãģĻ", - "m acı", - "ma cı", - "mac ı", - "ãģŁ ãģ¡ãģ¯", - "ãģŁãģ¡ ãģ¯", - "ĠاÙĦÙĥ تاب", - "移 åĭķ", - "λ μ", - "_ ï¼ı", - "Ġê°Ģ ìŀħ", - "èħ ¾", - "ĠпÑĢез иденÑĤ", - "Ġë¶Ħ ìķ¼", - "a hy", - "ah y", - "Å¡et ÅĻenÃŃ", - "Å¡etÅĻ enÃŃ", - "éĵ º", - "ĠpÅĻÃŃ ro", - "Ðķ Т", - "ĠìļĶ ì²Ń", - "Ġmoh lo", - "Ġmohl o", - "å¿ĥ çIJĨ", - "Ġvysok é", - "& uuml", - "ÏĦ ικα", - "ÏĦικ α", - "ÏĦι κα", - "ìĹħ ì²´", - "ãģ§ ãģĤ", - "ราย à¸ĩาà¸Ļ", - "ĠpÅĻÃŃspÄĽ v", - "Ġet miÅŁtir", - "Ġetm iÅŁtir", - "ĠetmiÅŁ tir", - "她 们", - "ÏĢ λα", - "ÏĢλ α", - "ứ a", - "Ġ 说", - "Ġè¯ ´", - "ĠÑģо Ñģед", - "ĠÑģоÑģ ед", - "åĩ ī", - "ĠÐł е", - "åİŁ æĿ¥", - "ĠÐIJÑĢ Ñħ", - "ب ÙĬÙĨ", - "بÙĬ ÙĨ", - "åľ° 说", - "Ġ ört", - "Ġö rt", - "Ġör t", - "ĠΣ εÏĢ", - "ĠΣε ÏĢ", - "ÂŃ ÙĩاÛĮ", - "ĠاÙĦ اÙĤتص", - "å°½ 管", - "ÑĤ Ñĭй", - "ÑĤÑĭ й", - "t ains", - "ta ins", - "tain s", - "ÙĢ ÙĦ", - "ç§ijæĬĢ æľīéĻIJåħ¬åı¸", - "æı ®", - "ัà¸ķ à¸ĸ", - "á»Ĺ ng", - "ล าà¸Ķ", - "ลา à¸Ķ", - "æļ ®", - "ĠÙĨÙģ سÙĩ", - "ĠÙĨÙģس Ùĩ", - "Ġ çľĭ", - "Ġçľ ĭ", - "Ġ ãģ¿", - "Ġt arım", - "Ġtar ım", - "Û±Û¹ Ûµ", - "ĠÎ Ĭ", - "Ġkom plex", - "Ġkomple x", - "Ġkomp lex", - "ĠNh Ä©", - "è´¹ çĶ¨", - "Ġکاربر اÙĨ", - "ÅĪ ovánÃŃ", - "Ġ ků", - "Ġk ů", - "д ап", - "да п", - "Îķ Χ", - "ê·¸ ëŀĺ", - "Ġdön dü", - "人 åĵ¡", - "ĠT iá»ĥu", - "ĠÙĪÛĮر اÛĮØ´", - "Ġö ngör", - "ĠÙĪ غÙĬر", - "ĠÑģ кÑĢÑĭ", - "ĠÑģк ÑĢÑĭ", - "âĢIJ '", - "Ġ немÑĥ", - "Ġн емÑĥ", - "Ġне мÑĥ", - "Ġнем Ñĥ", - "ĠH á»ĩ", - "ĠHá» ĩ", - "Ġdüzen li", - "ĠsoutÄĽ že", - "ãĢģ ãĥŀ", - "ÏĦο μα", - "ÄĽ lÃŃ", - "ÄĽl ÃŃ", - "ĠØ£ ÙĦÙħاÙĨ", - "ĠØ£ÙĦ ÙħاÙĨ", - "çł ²", - "Ġtr Ãł", - "Ġ ä¸ĸçķĮ", - "Ġä¸ĸ çķĮ", - "ay ız", - "ayı z", - "ım lı", - "ĠاÙĦØ£ Ùģ", - "íķĺ ëĬĶëį°", - "íķĺëĬĶ ëį°", - "в ано", - "ва но", - "ван о", - "ĠpÅĻi Äįemž", - "Ùĥ ÙĬب", - "ÙĥÙĬ ب", - "ĠмаÑĤ емаÑĤи", - "м ени", - "мен и", - "ме ни", - "ĠпÑĢо екÑĤÑĥ", - "ĠпÑĢоек ÑĤÑĥ", - "ĠпÑĢоекÑĤ Ñĥ", - "ี à¹Ĥà¸Ń", - "ีà¹Ĥ à¸Ń", - "о Ñĥ", - "ĠاÙĦ شرÙĥØ©", - "ĠاÙĦشر ÙĥØ©", - "æ³ £", - "ÙĪÙĤ ÙĬت", - "ÑĪ ив", - "ÑĪи в", - "Ġperson el", - "Ġpersone l", - "Ġperso nel", - "Ø´ تر", - "شت ر", - "à¸Ķ า", - "Ġë ª½", - "Ġëª ½", - "åĿIJ åľ¨", - "о ке", - "ок е", - "Ġë§Ī ë²ķ", - "ĠØ£ ÙĨا", - "ĠØ£ÙĨ ا", - "ëł µ", - "ĠÙħب اÙĨÛĮ", - "èĭ ¹æŀľ", - "Ġ ศร", - "Ġศ ร", - "ĠÐĽ ÑĥÑĩ", - "ĠÐĽÑĥ Ñĩ", - "ÎŁ ΥΣ", - "ÎŁÎ¥ Σ", - "ĠÄį á", - "ãģĽ ãģ¦", - "Ġk Ä±ÅŁ", - "Ġkı ÅŁ", - "ÑĪ ев", - "ÑĪе в", - "æĮĩ 导", - "à¹ģละ ม", - "Ġvol eb", - "Ġvole b", - "ĠÑģи лÑĭ", - "ĠÑģил Ñĭ", - "Ġdruh ou", - "Ġdru hou", - "Ġ ì°¬", - "Ġì° ¬", - "ĠìŀĪ ìĿĮ", - "Î¥ Σ", - "ä¸į å®ī", - "Ġ ìĹĨìĿĮ", - "ĠìĹĨ ìĿĮ", - "Ġde term", - "Ġdet erm", - "Ġdeter m", - "ĠاÙĦÙħ عÙĦÙĪÙħات", - "íĺ ¹", - "âĻ ¡", - "à¥įब न", - "Ġخش Ú©", - "ĠN ová", - "ĠNo vá", - "ĠNov á", - "ĠÑĦÑĥнда менÑĤ", - "ĠпÑĢогÑĢам и", - "ĠпÑĢогÑĢа ми", - "ĠпÑĢог ÑĢами", - "ĠعÙĦ ÙĬÙĥ", - "ĠعÙĦÙĬ Ùĥ", - "। ĊĊ", - "।Ċ Ċ", - "Ġver iyor", - "Ġveri yor", - "Ġ ÑĶв", - "ĠÑĶ в", - "ĠìŀĪ ëĭ¤ê³ł", - "ĠìŀĪëĭ¤ ê³ł", - "ĠاÙĦØ£Ùħ رÙĬÙĥÙĬ", - "ĠاÙĦØ£Ùħر ÙĬÙĥÙĬ", - "Ġå¤ĸéĥ¨ ãĥªãĥ³ãĤ¯", - "Ġ ä¿®", - "Ġп ÑĥÑĤи", - "ĠпÑĥÑĤ и", - "ĠпÑĥ ÑĤи", - "Ġο Ïģγ", - "ĠоÑģнов ном", - "Ġ наÑĢÑĥж", - "Ġна ÑĢÑĥж", - "ĠнаÑĢ Ñĥж", - "Ġми ÑĢе", - "ĠмиÑĢ е", - "o vÄĽt", - "ov ÄĽt", - "ovÄĽ t", - "Ġíĥ IJ", - "Ġsok ak", - "Ġspolup ráci", - "ÐĶ Ðļ", - "Ġ åĺ", - "Ġå ĺ", - "âĸįâĸįâĸįâĸįâĸįâĸįâĸįâĸį âĸįâĸįâĸįâĸįâĸįâĸįâĸįâĸį", - "Ġ ³³³³", - "ĠÂł ³³³", - "Ġ³³ ³³", - "Ġ³³³ Âł", - "Ġhay ır", - "Ġ ìĻĶ", - "ĠìĻ Ķ", - "æĤ¨ çļĦ", - "æĮ º", - "Ġ 민주", - "Ġ민 주", - "Ġhot elu", - "Ġhotel u", - "ี à¸ľ", - "ìŀIJ ëıĻ", - "ä¼¼ çļĦ", - "ÎŃν ÏĦÏģο", - "ÎŃνÏĦ Ïģο", - "Ø´ ÙĪ", - "Ġ é¤", - "Ġé ¤", - "Ġ λι", - "Ġλ ι", - "Ġol maktadır", - "Ġolmak tadır", - "ĠоÑģ веÑī", - "Ġв ина", - "Ġви на", - "Ġвин а", - "Ġخاص Ø©", - "r ana", - "ra na", - "ran a", - "γÏģα ÏĨή", - "γÏģαÏĨ ή", - "ÑĨ еÑģ", - "ÑĨе Ñģ", - "ĠdoÄŁru lt", - "ĠdoÄŁr ult", - "ĠÙĤرار داد", - "ĠÙĤرارد اد", - "ĠÐļ ал", - "ĠÐļа л", - "ê²½ ìłľ", - "Ïĩ ÏĮ", - "Ñĥ ÑİÑīий", - "ÑĥÑİ Ñīий", - "ÑĥÑİÑī ий", - "ëĭ ĺìĿ´", - "ëĭĺ ìĿ´", - "ë Į", - "л аз", - "ла з", - "Ġng ừng", - "i sku", - "is ku", - "isk u", - "ìĦł ê±°", - "ĠÑįлек ÑĤÑĢон", - "ĠÑįлекÑĤÑĢ он", - "ĠV oj", - "ĠVo j", - "н Ñıми", - "нÑı ми", - "ĠÙĪ Ø£ÙĨ", - "ĠÙĪØ£ ÙĨ", - "äº Ń", - "绣 计", - "ĠÅŁ iÅŁ", - "ĠÅŁi ÅŁ", - "ãĢį çļĦ", - "æŃ ¯", - "Ġкол лек", - "Ġд виж", - "Ġдв иж", - "Ġдви ж", - "Ġn á»Ńa", - "Äįas ÃŃ", - "Ġs onu", - "Ġso nu", - "Ġson u", - "ĠмеÑħ анÑĸз", - "ž ený", - "že ný", - "žen ý", - "Ġза ÑģÑĤÑĥп", - "ĠзаÑģÑĤ Ñĥп", - "ê´Ģ 볨", - "ĠÑĤоваÑĢ Ñĸв", - "ĠÑĤов аÑĢÑĸв", - "Ġ ì¼ĢìĿ´", - "Ġì¼ ĢìĿ´", - "à¥ģà¤Ĺ त", - "Ġzá sob", - "мов ÑĸÑĢ", - "u fac", - "uf ac", - "ů ležit", - "Ġви гоÑĤов", - "Ġвиг оÑĤов", - "ĠاÙĦÙĨ ÙĪ", - "Ġع اÙħا", - "ĠعاÙħ ا", - "æģ ¨", - "ĠìĿ´ë¯¸ ì§Ģ", - "Ġt voÅĻ", - "Ġtv oÅĻ", - "Ġvyu žitÃŃ", - "Ġgel iÅŁim", - "ĠgeliÅŁ im", - "ì³ ¤ëĭ¤", - "หà¸Ļ à¸Ńà¸ĩ", - "ĠìĿ¸ ìłķ", - "à¥į दर", - "à¥įद र", - "ĠпеÑĢед а", - "ĠпеÑĢе да", - "ĠздÑĸйÑģ неннÑı", - "ÙĨ ع", - "è¡£ æľį", - "Ġl oa", - "Ġlo a", - "íĻ Ī", - "èĭ± åĽ½", - "ĠD ruh", - "ĠDr uh", - "Ø® اÙĨ", - "д ам", - "да м", - "аÑĤелÑĮ нÑĭÑħ", - "θ ÏģÏİ", - "ĠØ£ Ùħر", - "ĠØ£Ùħ ر", - "ĠÅĻ ada", - "Ġbul uÅŁ", - "ĠÑĤÑĢанÑģп оÑĢ", - "ĠÑĤÑĢанÑģ поÑĢ", - "ĠÙĤ تÙĦ", - "ĠTa rif", - "ĠTar if", - "R us", - "Ru s", - "ĠзаÑģ Ñĸд", - "ĠÄ° h", - "l eyin", - "ley in", - "Ġvy rá", - "ĠD ÄĽ", - "иб ли", - "a vou", - "av ou", - "avo u", - "ĠÐĵеÑĢ м", - "н емÑĥ", - "не мÑĥ", - "нем Ñĥ", - "Ġкон ÑĨеп", - "ĠконÑĨе п", - "ĠÙĤ ادر", - "Ġsou bor", - "Ġl á»iji", - "Ġ çµIJ", - "Ġçµ IJ", - "л еннÑĭй", - "лен нÑĭй", - "κ Ïħ", - "Ġдопом аг", - "à¸ŀวà¸ģ à¹Ģà¸Ĥ", - "Ġqu ang", - "Ġq uang", - "Ġqua ng", - "Ġquan g", - "ĠØ· ÙĦا", - "ĠØ·ÙĦ ا", - "Ġ éĩĮ", - "Ġé ĩĮ", - "Ġéĩ Į", - "ĠÙĨÙħÙĪد ار", - "ĠÅŁ ar", - "ĠÑģп Ñĸл", - "ÂŃ n", - "ì§Ģ ìļĶ", - "åīį å¾Ģ", - "åħ³ éĶ®", - "å®ŀ åľ¨", - "éŁ³ 楽", - "ĠÙħسئ ÙĦÙĩ", - "Ġy eme", - "Ġye me", - "Ġyem e", - "ĠÑĪ аÑħ", - "기 ìĪł", - "Ġ สำà¸Ļ", - "Ġสำ à¸Ļ", - "ĠÙĪرزش ÛĮ", - "ĠÙĪرز Ø´ÛĮ", - "ãģĹ ãģŁãĤī", - "ãģĹãģŁ ãĤī", - "ί ÏĥÏī", - "ίÏĥ Ïī", - "о кон", - "ок он", - "око н", - "ãģŁ ãĤī", - "ĠØ¥ ÙĦÙĬÙĩ", - "ĠØ¥ÙĦÙĬ Ùĩ", - "ĠØ¥ÙĦ ÙĬÙĩ", - "Ġآذ رب", - "Ġr á»Ŀi", - "Ġod ak", - "Ġм огÑĥ", - "Ġмог Ñĥ", - "Ġмо гÑĥ", - "ĠÚ¯ ÙĨ", - "è² ¼", - "ed la", - "edl a", - "Ġоп ÑĭÑĤ", - "la maktadır", - "lamak tadır", - "å°¼ äºļ", - "éĥ½ ä¼ļ", - "ĠÎĺε ÏĥÏĥα", - "Ġв ог", - "Ġво г", - "ç»Ī äºİ", - "ĠÑĥÑĢов не", - "Ġv lak", - "Ġvl ak", - "ĠØ¢ ÙĦØ©", - "ĠØ¢ÙĦ Ø©", - "Ġε ιδ", - "Ġει δ", - "â ĩ", - "д ÑĥÑĤ", - "дÑĥ ÑĤ", - "Ñĸ нг", - "Ñĸн г", - "ĠØ£Ùħ رÙĬÙĥÙĬ", - "ĠØ£Ùħر ÙĬÙĥÙĬ", - "از ÙĨد", - "Ġب اÙĦØ£", - "ĠباÙĦ Ø£", - "Ġत न", - "Ġkay det", - "룬 리", - "Ġ drž", - "Ġd rž", - "Ġdr ž", - "Ġп енÑģ", - "Ġпен Ñģ", - "ĠpÅĻÃŃ Äį", - "ĠТ олÑĮко", - "Ġб аÑĤаÑĢ", - "Ġба ÑĤаÑĢ", - "éĵģ è·¯", - "ĠÙ¾ÛĮ ÚĨ", - "ĠÎĵ εÏī", - "ĠαÏħ ÏĦά", - "Äŀ I", - "ĠакÑĤив но", - "ÎĹ ÎľÎij", - "ÎĹÎľ Îij", - "Ġvar lık", - "Ġ åıª", - "Ġåı ª", - "ĠзаÑī иÑĤÑĭ", - "ĠзаÑīиÑĤ Ñĭ", - "л им", - "ли м", - "ĠÙħشاÙĩ دة", - "и ком", - "ик ом", - "Ġì¡° ìĤ¬", - "о ген", - "ог ен", - "Ġm ấy", - "g ii", - "gi i", - "èĽ ĩ", - "ĠØ® ÙĪÛĮØ´", - "ĠØ®ÙĪ ÛĮØ´", - "Ġn ová", - "Ġno vá", - "Ġnov á", - "к овой", - "ков ой", - "ково й", - "Ġkan ıt", - "Ġkanı t", - "éĿ¢ è®®", - "ĠرÙĪست ا", - "ìĸ´ ê°Ģ", - "ĠоÑĤноÑĪ ениÑı", - "Ġhodnot y", - "ÙĪ رات", - "ÙĪر ات", - "ÙĪرا ت", - "ĠpÅĻ ÃŃst", - "ĠpÅĻÃŃ st", - "Ġth á»į", - "Ġthá» į", - "Ġçık art", - "Ġçıkar t", - "Ġçı kart", - "о обÑĢаз", - "Ġnem ÄĽl", - "Âł ro", - "ĠدÙĪÙĦ تÛĮ", - "ĠدÙĪÙĦت ÛĮ", - "ี ,", - "ä¸Ģ 度", - "ia omi", - "iao mi", - "åĹ İ", - "Ùı ع", - "ĠваÑĢи ан", - "Ġpod aÅĻilo", - "ĠëĤĺ ê°Ģ", - "èIJ¥ ä¸ļ", - "ĠабÑģолÑİÑĤ но", - "Ġë¸Į ëĿ¼", - "ĠгоÑĢ из", - "a ģın", - "aÄŁ ın", - "aģı n", - "Ġyer ini", - "Ġyeri ni", - "à¹īา à¸Ļà¸Ķ", - "à¹īาà¸Ļ à¸Ķ", - "æIJ ¬", - "Ġb alık", - "Ġbal ık", - "Ġba lık", - "ĠÅŁ ans", - "认 è¯Ĩ", - "Ġistedi ÄŁiniz", - "Ġjist ÄĽ", - "Ġ ìĪĺê°Ģ", - "ĠìĪĺ ê°Ģ", - "ï¼Į ä¸Ĭ", - "à¤ľ ब", - "Ġви Ñıви", - "ĠвиÑıв и", - "ë§ ¥", - "ãģĹ ãģ¦ãĤĭ", - "ãģĹãģ¦ ãĤĭ", - "ÙĬÙĥ ا", - "ĠH üs", - "c ının", - "Ġश त", - "ĠÑĢаÑģп олаг", - "ĠÑģпÑĢав ж", - "ืà¸Ń à¸ĸ", - "ĠвеÑĢ ÑĤик", - "Ġvy stav", - "ĠÑĢе алÑĸзаÑĨÑĸÑĹ", - "в ами", - "ва ми", - "ãĤ¹ ãĥĨãĤ£", - "ãĤ¹ãĥĨ ãĤ£", - "ëħ ģ", - "ĠÑĢе ÑĩÑĸ", - "ĠÑĢеÑĩ Ñĸ", - "Ùģ اÙĦ", - "िà¤ķ à¤Ł", - "ĠвозÑĢаÑģÑĤ е", - "к аÑģ", - "ка Ñģ", - "ĠÐĺ Ñģ", - "Ġл Ñĸк", - "ĠлÑĸ к", - "ĠÏĥη μαν", - "м енÑĤÑĥ", - "мен ÑĤÑĥ", - "менÑĤ Ñĥ", - "н ÑıÑİÑĤ", - "нÑı ÑİÑĤ", - "æŁ ´", - "Ġθ εÏī", - "Ġθε Ïī", - "çĬ¯ 罪", - "ĠÙĤ طر", - "ĠÙĤØ· ر", - "ÐĶ ÐIJ", - "- |", - "Ġ ÑģÑĤÑĸ", - "ĠÑģ ÑĤÑĸ", - "ĠÑģÑĤ Ñĸ", - "Ġu yum", - "Ġuy um", - "Ġpot ÅĻeba", - "ĠpotÅĻeb a", - "ĠعÙħÙĦ ÛĮات", - "ĠعÙħÙĦÛĮ ات", - "å¥ ª", - "ا خر", - "اخ ر", - "ĠÚ© ساÙĨÛĮ", - "ت Ùħر", - "تÙħ ر", - "ÑĮ еÑĢ", - "ÑĮе ÑĢ", - "ĠN ez", - "ĠNe z", - "íļĮ ìĤ¬", - "ĠBank ası", - "е гÑĢа", - "ег ÑĢа", - "à¸Ĥà¸ĵะ à¸Ĺ", - "åIJĪ æł¼", - "ĠìŬ룬 ë¶Ħ", - "y asal", - "ya sal", - "yas al", - "Ġ è¡ĮæĶ¿", - "Ġè¡Į æĶ¿", - "åĬ ī", - "dık tan", - "ãĤ¢ãĥ« ãĥIJ", - "ĠاÛĮÙĨ ÚĨ", - "Ġdij ital", - "å° ĺ", - "ĠÑĢаз меÑī", - "ĠÑĢазм еÑī", - "ĠкÑĸлÑĮ коÑģÑĤÑĸ", - "ĠEv ropy", - "ĠEvrop y", - "ĠÑĢоз ви", - "ÑİÑī ÑĥÑİ", - "Ġ ong", - "Ġo ng", - "Ġon g", - "Ġhe psi", - "Ġhep si", - "v ailability", - "vail ability", - "Ġتص ÙħÙĬÙħ", - "ĠتصÙħ ÙĬÙħ", - "Ñĥ йÑĤе", - "Ñĥй ÑĤе", - "ह ल", - "ĠÅ¡ iro", - "Ġp ás", - "Ġpá s", - ";; ;;;;", - ";;;; ;;", - ";;; ;;;", - "éħį åIJĪ", - "ĠاÙĦعاÙĦÙħ ÙĬØ©", - "ÐĴ о", - "h af", - "ha f", - "l áv", - "lá v", - "Ġb ì", - "Ġm ůj", - "Ġmů j", - "ê»ĺ ìĦľ", - "ÂłB f", - "ĠÑģпÑĢоÑģ ил", - "âĢĮÚ©ÙĨ ÙĨدÙĩ", - "âĢĮÚ©ÙĨÙĨد Ùĩ", - "ÙĨد ÙĬØ©", - "ÙĨدÙĬ Ø©", - "çī¹ èī²", - "Ġìķ ¨", - "ุษ ย", - "ĠФ оÑĢ", - "пиÑģ ок", - "пи Ñģок", - "u žel", - "ım lar", - "çĬ¶ æ³ģ", - "Ġãĥ¬ ãĥĩãĤ£ãĥ¼ãĤ¹", - "Ñħ ови", - "Ñħов и", - "Ñħо ви", - "ÂłK Äį", - "Ñĩ им", - "Ñĩи м", - "Ġت ÙĪÙħ", - "ĠتÙĪ Ùħ", - "à¹Ģà¸ģษ à¸ķร", - "Ġìĭ± ê¸Ģ", - "Ùħ ارات", - "Ùħا رات", - "Ùħار ات", - "ê nh", - "ên h", - "ĠÅĻ id", - "æĬ ¬", - "Ñģ иÑİ", - "Ñģи Ñİ", - "æħ İ", - "Ġçev re", - "Ġçevr e", - "ãĥĪ ãĥ«", - "Ġyıl dır", - "Ġzá znam", - "Ġzáz nam", - "æľº åľº", - "Ġпо ÑĶ", - "ĠвÑĭ ÑĢаÑīи", - "Ġ Ùģع", - "ĠÙģ ع", - "ë »", - "Ġدار ÛĮÙħ", - "ï¼Į æĽ´", - "Ġзем ли", - "اب ÙĤات", - "ابÙĤ ات", - "Ġm á»Ŀi", - "Ġmá» Ŀi", - "k ých", - "ký ch", - "ÙĦ اة", - "ÙĦا Ø©", - "å¸ ½", - "بر اÙĩÙĬÙħ", - "Ġпо баÑĩ", - "Ġпоб аÑĩ", - "Ġпоба Ñĩ", - "ाà¤ĩ म", - "à¹Īาà¸ĩ à¸Ľà¸£à¸°à¹Ģà¸Ĺศ", - "ĠìĦ¸ ìĥģ", - "Ġпомог аеÑĤ", - "ĠÏĦÏĮ Ïĥο", - "æĸ ·", - "ĠÙģر اÙĪ", - "à¹Ħà¸Ľ ย", - "erg isi", - "Ġ éĻIJ", - "ĠéĻ IJ", - ". xz", - ".x z", - "ĠÑģл ÑĥÑħ", - "ĠÑģлÑĥ Ñħ", - "е коном", - "ек оном", - "еко ном", - "ĠNh ất", - "± Ø·", - "ĠëĪĪ ìĿĦ", - "Ġ íļĮìĤ¬", - "ĠíļĮ ìĤ¬", - "Ñ ĵ", - "Ġ åIJįçĦ¡ãģĹ", - "ĠåIJį çĦ¡ãģĹ", - "Ġομά δα", - "ĩ Į", - "li ÄŁinin", - "liÄŁi nin", - "liÄŁ inin", - "liÄŁini n", - "liÄŁin in", - "ع اÙĨ", - "عا ÙĨ", - "Ġز ÙĨÛĮ", - "ĠزÙĨ ÛĮ", - "T ôi", - "Ġet ki", - "Ġetk i", - "ĠìĹ° ëĿ½", - "Ġкон ÑĨа", - "è° ĭ", - "Ġзем лÑı", - "íĻĺ ê²½", - "ĠÙħÚ© اÙĨÛĮ", - "ĠÙħکاÙĨ ÛĮ", - "çĸ ²", - "Ġ ç¢", - "Ġç ¢", - "Ġkur ulan", - "Ġkurul an", - "Ġkuru lan", - "ؤ ÙĪÙĦ", - "د Ùī", - "ĠاÙĦÙħÙĨ Ø·ÙĤØ©", - "Ġn ắng", - "ÐŁ Ðļ", - "ол ай", - "ола й", - "Y K", - "åij Ĩ", - "λ αν", - "λα ν", - "西 çľģ", - "ĠÎĴ αÏĥ", - "ĠÎĴα Ïĥ", - "ĠíĻķ ìĭ¤", - "Z D", - "п Ñĸд", - "Ġ наÑĩе", - "Ġн аÑĩе", - "Ġна Ñĩе", - "ĠнаÑĩ е", - "Ġ ÏĦά", - "ĠÏĦ ά", - "å½ »", - "âĢŀ D", - "Ġ èĩº", - "Ġèĩ º", - "Ġна ÑĪей", - "ĠнаÑĪ ей", - "ĠtÃŃm to", - "Ġت سÙħ", - "Ġتس Ùħ", - "Ïģθ Ïģο", - "令 人", - "ĠP azar", - "ĠPa zar", - "ĠPaz ar", - "ãĤĵ ãģ¨", - "ç«ĭ åĪ»", - "Âģ @", - "Ġb ắc", - "ìĬ¤ íħĮ", - "Ġkadın lar", - "fig ur", - "ãģ¤ ãģ¶", - "Ġæµ Ļæ±Ł", - "Ġдек ÑĸлÑĮ", - "è¡ Ŀ", - "ยà¸Ļ à¹ģà¸Ľà¸¥à¸ĩ", - "o let", - "ol et", - "ole t", - "Ġned ok", - "n amen", - "name n", - "na men", - "nam en", - "åħĦ å¼Ł", - "ืà¸Ń à¸Ĥ", - "èĤ ĥ", - "Ġb üny", - "Ġbü ny", - "ĠÑĢад Ñıн", - "ãĢģ äºĮ", - "ан нÑİ", - "Ġ æīĭæľº", - "Ġæīĭ æľº", - "ĠоÑģ лож", - "Ġо глÑı", - "Ġог лÑı", - "Ġسب ز", - "Ġaktiv it", - "Ġà¤ı प", - "ç« ľ", - "Ġd iren", - "Ġdi ren", - "Ġdir en", - "Ġdire n", - "i в", - "ĠY atırım", - "ÑĨÑĸй на", - "Ġдо мов", - "Ġдом ов", - "ẳ n", - "ĠC oÄŁraf", - "Ùģ ÙĪ", - "æ°Ĺ ãģ«åħ¥", - "ç§ģ ãģ®", - "ï½ į", - "à¥Į ड", - "ĠÐĵÑĢи гоÑĢ", - "ĠP eygamber", - "ĠPey gamber", - "Ġα γα", - "Ġαγ α", - "Ġef ekt", - "ĠìŀĪ ìĸ´ìĦľ", - "ĠìŀĪìĸ´ ìĦľ", - "ĠплаÑĤ еж", - "ĠT rab", - "ĠTr ab", - "ĠTra b", - "o very", - "ov ery", - "ove ry", - "over y", - "âĢ¦âĢ¦ ãĢĤ", - "Ġyap maya", - "Ġнайб ÑĸлÑĮ", - "ĠÙħÙĨ زÙĦ", - "ÙĪ ÙĬÙĥ", - "ÙĪÙĬ Ùĥ", - "ıl dıģında", - "ıldı ģında", - "ıldıģı nda", - "ĠpÅĻÃŃpad nÄĽ", - "ĠμÏĢο ÏģοÏį", - "Ġëĵľ ëĿ¼ë§Ī", - "Ġë°© 문", - "ĠС им", - "ĠСи м", - "Ú© ات", - "کا ت", - "е ком", - "ек ом", - "еко м", - "ر ÙĬع", - "رÙĬ ع", - "Ùĩد Ùģ", - "æĹı èĩªæ²»", - "Ġzm ÄĽn", - "ĠzmÄĽ n", - "Ġв клад", - "Ġвк лад", - "Ġ بÙĦغ", - "ĠبÙĦ غ", - "Ġ ç§ĭ", - "Ġç§ ĭ", - "N gh", - "Ng h", - "Ġend iÅŁ", - "ĠCumhurbaÅŁ kanı", - "ĠK af", - "ĠKa f", - "Ġ à¹ģหล", - "Ġà¹ģ หล", - "Ġmut lu", - "ĠÑģ иÑĢ", - "ĠÑģи ÑĢ", - "Ġг Ñĥм", - "æ¿ ĥ", - "çĤ ī", - "ĠB áo", - "à¥Ĥ ष", - "Ġìłķ íĻķ", - "ान स", - "ï» ¤", - "наÑģлÑĸд ок", - "po Äįet", - "poÄį et", - "ë§ĮìĽIJ ìŀħëĭĪëĭ¤", - "ĠìĦľìļ¸ íĬ¹ë³Ħìĭľ", - "Îķ ÎĻΣ", - "ÎķÎĻ Σ", - "ุม à¸Ĭà¸Ļ", - "Ġм ÑĸлÑĮ", - "ĠмÑĸ лÑĮ", - "æ ħĮ", - "æħ Į", - "Ïĥκε ÏĦαι", - "Ïĥκ εÏĦαι", - "Ġ ãĢľ", - "ĠãĢ ľ", - "Ġkal iteli", - "ĠÑģмеÑĢ ÑĤÑĮ", - "è¼ Ķ", - "Ġ биÑĤ", - "Ġб иÑĤ", - "Ġби ÑĤ", - "ĠΣ ÏĦο", - "à¸ĩà¹Ģศ ส", - "åİŁ æľ¬", - "Ġk nÃŃ", - "Ġkn ÃŃ", - "äºĴ èģĶç½ij", - "ĠÑĩеловеÑĩ еÑģ", - "çŃ Ĵ", - "à¸Īำ หà¸Ļ", - "åĩº åİ»", - "ãĤ¢ ãĥĭãĥ¡", - "å±ķ 示", - "r ych", - "ry ch", - "à¤ħ ब", - "o ÅĪ", - "jÃŃ cÃŃm", - "jÃŃcÃŃ m", - "ا ØŃØ«", - "اØŃ Ø«", - "ĠÙĪاÙĤع ÛĮ", - "ĠФедеÑĢа лÑĮ", - "ĠФед еÑĢалÑĮ", - "Ñģ ам", - "Ñģа м", - "Ġ ìĺ¥", - "Ġìĺ ¥", - "åľ° çIJĥ", - "Ġs uyu", - "Ġsu yu", - "Ġsuy u", - "s eniz", - "sen iz", - "à¥ī फ", - "Ġê°Ļ ëĭ¤", - "ĠпÑĢизна ÑĩеннÑı", - "ĠпÑĢизнаÑĩ еннÑı", - "ĠS ın", - "ĠSı n", - "ĠاÙħÙĨ ÛĮت", - "Ġl átky", - "ĠÐij и", - "Ġsür eci", - "Ġsüre ci", - "Ġsürec i", - "·· ··", - "Ġê²½ ì°°", - "Ġк алÑĮ", - "Ġка лÑĮ", - "Ġкал ÑĮ", - "Ġник ÑĤо", - "Ùij Ùħ", - "ĠدÙĬ گر", - "Ġalın ması", - "л еннÑĸ", - "лен нÑĸ", - "ิว à¹Ģà¸ķà¸Ńร", - "à¸Ľà¸ģ à¸Ħรà¸Ńà¸ĩ", - "Ġзаконодав ÑģÑĤва", - "ãĢĢ ãĤ¤", - "Ġëħ¸ íķĺìļ°", - "ĠD Ã¼ÅŁ", - "Ġг ÑĥÑģÑĤ", - "ĠÐĴ аÑĪ", - "ĠاÙħ تÛĮ", - "Ġpar amet", - "Ġparam et", - "Ġpara met", - "ĠÎłÎ±Î½ εÏĢ", - "à¹Į à¸ģร", - "à¹Įà¸ģ ร", - "ζ α", - "ĠëįĶ ìļ±", - "ÙĪ ÙĦات", - "ÙĪÙĦ ات", - "ÙĪÙĦا ت", - "в аÑĤиÑģÑı", - "ва ÑĤиÑģÑı", - "ваÑĤи ÑģÑı", - "Ġk ök", - "Ġkö k", - "ÙĨ ب", - "ĠвÑĭÑģок ой", - "ãĥ¼ ãĥ¼", - "ãĥ¼ãĥ ¼", - "éĶ ¦" - ] - } -} \ No newline at end of file diff --git a/comfy/text_encoders/llama_tokenizer/tokenizer_config.json b/comfy/text_encoders/llama_tokenizer/tokenizer_config.json deleted file mode 100644 index 0b336f5a5199c43e7305481f4e9e26e292b2f0fc..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/llama_tokenizer/tokenizer_config.json +++ /dev/null @@ -1,2095 +0,0 @@ -{ - 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"lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - } - }, - "bos_token": "<|begin_of_text|>", - "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", - "clean_up_tokenization_spaces": true, - "eos_token": "<|end_of_text|>", - "legacy": true, - "model_input_names": [ - "input_ids", - "attention_mask" - ], - "model_max_length": 1000000000000000019884624838656, - "pad_token": "", - "padding_side": "right", - "processor_class": "LlavaProcessor", - "tokenizer_class": "LlamaTokenizer", - "unk_token": "", - "use_default_system_prompt": false -} diff --git a/comfy/text_encoders/long_clipl.py b/comfy/text_encoders/long_clipl.py deleted file mode 100644 index 8d4c7619d4b2007462cf14721bcdae093a5b4a7b..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/long_clipl.py +++ /dev/null @@ -1,27 +0,0 @@ - - -def model_options_long_clip(sd, tokenizer_data, model_options): - w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) - if w is None: - w = sd.get("clip_g.text_model.embeddings.position_embedding.weight", None) - else: - model_name = "clip_g" - - if w is None: - w = sd.get("text_model.embeddings.position_embedding.weight", None) - if w is not None: - if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: - model_name = "clip_g" - elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: - model_name = "clip_l" - else: - model_name = "clip_l" - - if w is not None: - tokenizer_data = tokenizer_data.copy() - model_options = model_options.copy() - model_config = model_options.get("model_config", {}) - model_config["max_position_embeddings"] = w.shape[0] - model_options["{}_model_config".format(model_name)] = model_config - tokenizer_data["{}_max_length".format(model_name)] = w.shape[0] - return tokenizer_data, model_options diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py deleted file mode 100644 index 48ea67e6782388637dbe55b3e6366cda051ec519..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/lt.py +++ /dev/null @@ -1,18 +0,0 @@ -from comfy import sd1_clip -import os -from transformers import T5TokenizerFast -import comfy.text_encoders.genmo - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) #pad to 128? - - -class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) - - -def ltxv_te(*args, **kwargs): - return comfy.text_encoders.genmo.mochi_te(*args, **kwargs) diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py deleted file mode 100644 index 674461b75077ac21d92667d96a6787b1277367fe..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/lumina2.py +++ /dev/null @@ -1,39 +0,0 @@ -from comfy import sd1_clip -from .spiece_tokenizer import SPieceTokenizer -import comfy.text_encoders.llama - - -class Gemma2BTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer = tokenizer_data.get("spiece_model", None) - super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data) - - def state_dict(self): - return {"spiece_model": self.tokenizer.serialize_model()} - - -class LuminaTokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer) - - -class Gemma2_2BModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) - - -class LuminaModel(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options) - - -def te(dtype_llama=None, llama_scaled_fp8=None): - class LuminaTEModel_(LuminaModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["scaled_fp8"] = llama_scaled_fp8 - if dtype_llama is not None: - dtype = dtype_llama - super().__init__(device=device, dtype=dtype, model_options=model_options) - return LuminaTEModel_ diff --git a/comfy/text_encoders/mt5_config_xl.json b/comfy/text_encoders/mt5_config_xl.json deleted file mode 100644 index 092fefd6e32dac566e443fc03eae53f6a8b57400..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/mt5_config_xl.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 5120, - "d_kv": 64, - "d_model": 2048, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "mt5", - "num_decoder_layers": 24, - "num_heads": 32, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 250112 -} diff --git a/comfy/text_encoders/omnigen2.py b/comfy/text_encoders/omnigen2.py deleted file mode 100644 index 1a01b2dd430461a20af92b75e9a1930b8d153fea..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/omnigen2.py +++ /dev/null @@ -1,44 +0,0 @@ -from transformers import Qwen2Tokenizer -from comfy import sd1_clip -import comfy.text_encoders.llama -import os - - -class Qwen25_3BTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) - - -class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer) - self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n' - - def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs): - if llama_template is None: - llama_text = self.llama_template.format(text) - else: - llama_text = llama_template.format(text) - return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs) - -class Qwen25_3BModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) - - -class Omnigen2Model(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options) - - -def te(dtype_llama=None, llama_scaled_fp8=None): - class Omnigen2TEModel_(Omnigen2Model): - def __init__(self, device="cpu", dtype=None, model_options={}): - if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["scaled_fp8"] = llama_scaled_fp8 - if dtype_llama is not None: - dtype = dtype_llama - super().__init__(device=device, dtype=dtype, model_options=model_options) - return Omnigen2TEModel_ diff --git a/comfy/text_encoders/pixart_t5.py b/comfy/text_encoders/pixart_t5.py deleted file mode 100644 index 5f383de076fad52fb934c9747b3f95ff453d8e6a..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/pixart_t5.py +++ /dev/null @@ -1,42 +0,0 @@ -import os - -from comfy import sd1_clip -import comfy.text_encoders.t5 -import comfy.text_encoders.sd3_clip -from comfy.sd1_clip import gen_empty_tokens - -from transformers import T5TokenizerFast - -class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def gen_empty_tokens(self, special_tokens, *args, **kwargs): - # PixArt expects the negative to be all pad tokens - special_tokens = special_tokens.copy() - special_tokens.pop("end") - return gen_empty_tokens(special_tokens, *args, **kwargs) - -class PixArtT5XXL(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding - -class PixArtTokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) - -def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None): - class PixArtTEModel_(PixArtT5XXL): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - if dtype is None: - dtype = dtype_t5 - super().__init__(device=device, dtype=dtype, model_options=model_options) - return PixArtTEModel_ diff --git a/comfy/text_encoders/qwen25_tokenizer/merges.txt b/comfy/text_encoders/qwen25_tokenizer/merges.txt deleted file mode 100644 index 31349551d90c7606f325fe0f11bbb8bd5fa0d7c7..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/qwen25_tokenizer/merges.txt +++ /dev/null @@ -1,151388 +0,0 @@ -#version: 0.2 -Ġ Ġ -ĠĠ ĠĠ -i n -Ġ t -ĠĠĠĠ ĠĠĠĠ -e r -ĠĠ Ġ -o n -Ġ a -r e -a t -s t -e n -o r -Ġt h -Ċ Ċ -Ġ c -l e -Ġ s -i t -a n -a r -a l -Ġth e -; Ċ -Ġ p -Ġ f -o u -Ġ = -i s -ĠĠĠĠ ĠĠĠ -in g -e s -Ġ w -i on -e d -i c -Ġ b -Ġ d -e t -Ġ m -Ġ o -ĉ ĉ -r o -a s -e l -c t -n d -Ġ in -Ġ h -en t -i d -Ġ n -a m -ĠĠĠĠĠĠĠĠ ĠĠĠ -Ġt o -Ġ re -- - 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')Ċ -.B itmap -sk in -_C OLUMN -Re v -G RESS -g ow -Ġw ished -tract s -.assert False -Ġscreens hot -Ġfo is -Com b -Line Width -ĠGr ab -Ġint ensive -ĉ sh -+ ) -.first Name -_PRO CESS -Ġt ilt -it ored -.L OG -Ġb ak -Ġintention ally -.play ers -(c anvas -)) )čĊ -.Pro vider -_P UBLIC -T alk -ĠL iv -ched ulers -Ġl c -ad ic -feature d -.res ources -Full Name -Ġmean while -B uffers -Ġres olver -ĠS AP -_T E -G NU -ĠForms Module -_ wh -ĠS we -.widget s -Ġcabin ets -Ġsus cept -ĠB ott -activ ex -av ar -ant ics -Ġ" =" -_k wargs -Ġgame Object -ĠAng le -.I ter -mar sh -ĠB irthday -ĠC MS -request s -ĠPear l -_E OL -Ġlin ux -( org -_M ouse -.con structor -Ġz d -Ġk icks -art isan -Ġe ax -K n -pon ge -ĠFin land -Ġmet res -ĠAss essment -part ner -/ pre -! ',Ċ -[ Int -Ġos lo -date picker -/ String -op lay -ĠHe brew -, double -Ġtrab al -+" \ -ĉ EIF -/ text -_F IRST -ĠP ete -Ġe go -Ġextr as -P DO -Ġreg ulate -ĠQ Widget -st s -ĠSh ows -ĠN HS -.c ourse -p thread -ĠF uel -.t imes -Ġ ° -Ġstr ides -($ ('# -( words -Ġrhyth m -Ġsp ont -Ġsens ation -Ġsp ike -C losing -页 éĿ¢ -N umeric -Ġbreat he -Ġfin ale -_F ACT -in ion -Ġch ill -Ġform ally -ANG ED -Ġ' :' -ĠпÑĢ и -a q -ĠFab ric -(l at -ĠPr incipal -Ġer ro -oc ale -N om -Ġf ost -_C USTOM -.int ellij -ert ools -Ġcl asse -adi ents -Ġfundra ising -EN E -_OPTION S -_ ob -// }Ċ -Ġprote ctions -.se ed -N V -term inal -;; ; -P redicate -Ġì ¶ -Ġbomb ing -G F -Ġch ew -)) ). -qual ified -] ={ -list en -C ENT -d igest -E ast -Ġd iver -Ġend points -Ġe e -Ġcolle ague -Ġdissert ation -_com mit -_D AT -. rc -Ġbre asts -ĠR ug -ĠP il -Contract s -ĠBry an -Web View -Ġconcent rate -ĠIn ner -Ġ' | -std out -_S ub -> -->Ċ -V ol -ĠS SD -)) ), -. Optional -Ġnurs es -Ġor b -_ pe -);čĊ čĊčĊ -pl aced -ess er -Ġther apeutic -Ġwhites pace -Ġa ston -Success ful -Ġpr aised -ĠW es -Ġe ighth -ir al -Ġvrou w -Ġf action -_b ias -Ġw itch -Ġnp c -(s b -ĠRod rig -_b ig -Dep endency -ĠAb raham -ard i -C AR -n os -Ġabund ance -Ġnut rients -in stein -.V ert -ĠI SS -< U -Ġsum s -_h ist -Ġfar mer -ĠA br -Sh ot -ĠBad Request -Ġh ass -ĠR ails -Ġaffili ated -æĿ ¥ -Ġer f -IN F -ĠView Holder -min i -ĠR oth -Ġfaith ful -ĠPhill ips -AND OM -]. [ -_P AY -ĠAr ctic -f aker -D igit -M ale -std err -se ys -Ġ Å¡ -_rem ote -li que -Ġin def -ĠIndust ries -it ra -_p airs -< iostream -Ġsal aries -ik en -.F rame -PL IC -_S PEC -ĠMed iterr -Ġsystem atic -Ġinter rog -Icon Button -se a -int ro -ĠIss ues -enc rypted -Ġintern ationally -Ġsn printf -Ġpast a -ĠBrad ley -_ Status -AL K -_P AD -.l aunch -< select -Ġhar dest -Ġph y -Ġ(( * --s lide -ĠNob ody -S u -Ġas ÃŃ -close st -_initial izer -Ġsupport er --g en -Ġt ales -Ġcor p -_f u -s at -ne ighbor -.M igrations -Ġal gun -Ġsin on -.S pec -? ,Ċ -.G L -m ale -Ġmon itors -yl an --L icense -.m atches -ĠA BS -ĠM ast -ĠW allet -($ ("# -Dir ty -Ġco pe -Ġinterpol ation -ous ed -ĠJ ets -.F LAG -.C ancel -.Event s -ne ver -ĠM Hz -> D -Ġs ervlet -bast ian -Ġ> & -S ID -_cl k -Ġdiv isions -} ',Ċ -Ġd ildo -Ġpar ade -m ajor -Ġab oard -; ++ -Ġf usion -"}, {" -ĠDialog Result -ĉ arr -- em -_n r -(h andler -.N ET -.Xtra Reports -ĠSh ah -ĠB rief -- , -Ġprec io -ĉĉĉ ĠĠĠĠĠĠ -Ġt ant -ĠGrand e -/ xml -_IC ON -ĠR etro -un que -Ġn ag -to Fixed -X L -Ġdecl aring -ĠCon crete -ĠAm azing -ĉprint k -Ġdeb ates -D ATED -Ġaest hetic -emet ery -Routing Module -ĠNash ville -W AYS -Ġw olf -Ġobserv ers -OT A -ans on -Ġe a -Ġgreen house -ĵį ä½ľ -Ġst air -Ġimmigr ant -_app ly -pe are -ĠBloom berg -_PL AYER -Res p -æŃ £ -Cho oser -ĠI Collection -P eter -Er ro -.detect Changes -Map s -Ġs queeze -ĠHom es -weg ian -Ġformat ting -Ġnegot iate -ul d -ĠN ep -ĠQ B -Ġeconom ies -Ġ*/ , -Ġredu nd -ĠA ber -.IsNullOr WhiteSpace -yc led -ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ ĠĠĊ -_S h -Ġske pt -Ġre created -Ġget Type -Ġmarg ins -Ġcolon ial -ch arts -// @ -Ġprocess ors -è¯ ´ -b atis -æĦ ı -ator io -mention ed -P atient -Ġpre y -Check box -_x path -.s kip -ĠMorm on -ĠMemory Stream -CRE MENT -Ġk u -m eld -\ Data -ĠK ernel -il tr -éĢ ģ -( profile -Car bon -RO LE -( pl -] *( -.m emory -Ġmed al -Ġadvis or -it ät -Ġh dr -ier ung -ĠProvid es -( alpha -Ġteen agers -- parser -.L atLng -] ()Ċ -Ġfel ony -ĉĉĉĊ ĉĉĉĊ -BO OK -Ġsl ash -Ġclear fix -ĠPro phet -å® ¹ -right ness --f i -.k ind -ert on -J im -Ġmanip ulate -Ġworks heet -ol in -st ars -Ġart ifact -_EM PTY -ĉm ain -------------- ' ; -Ġexpress ing -ĠI Q -ĠF act -/************************************************************************ *******Ċ -_m ass -)) : -Ġcon dom -Ġcreate State -omet own -Ġir r -Ġ> ( -> B -iter ation -ãĥ ª -Ġshirt s -ount y --> $ -_S IGN -ĠD ale -Ġj j -E asy -F re -ĠN y -Ġch lor -match ed -ĠG erm -- UA -ĠN athan -educ ation --y ard -- che -h ouses -r itional -Ġprox imity -Ġdies em -áºŃ p -Ġd rought -.a udio -ĠLe o -Ġfavor able -in ch -ĠD aw -rib ly -_st udent -id able -O VE -Ġlack s -ounc ing -.b usiness -Ġre open -may be -_G LOBAL -Ġdress es -ĠEd wards -ens ible -ĠHard ware -ĠEx cellent -ĠTime Unit -CTION S -Ġsched ules -Ġseg ue -Op ens -am men -- Identifier -Ġst aring -Ġhapp ily -ĠH ob -' _ -Ġ" ); -ament os -et ched -Ġ/> }Ċ -. Users -Ġinterrupt ed -Contact s -Ġreg istro -in burgh -CH A -_ imp -ph is -s ay -Ġretail er -.N ODE -/ maps -_L AST -ĠCh arge -_g uard -Coll ider -ĠStateless Widget -": [" -(" ../../ -iox ide -ĠS und -Ġ'' ; -un set -add Widget -л Ñİ -el les -alk er -A rc -Ġded uct -G UILayout -ĠV illa -Ġfor bidden -_ where -Ġ\ / -ĠT ib -_A X -] čĊčĊ -ĠB ir -Ġb end -ĠMA KE -ĠM ET -Ġfut ures -Ġweight ed -"" "čĊ -Ġauthor ize -(pro gram -}, {" -Ġcoeff icients -ê s -Per Page -ĠBath room -ĠPublish ing -G PL -Ġsub missions -ĠNUM BER -j Äħ -Ġaddition ally -em pre -ĠSh el -ot yp -S olution -Ġth under -_ ec -ĠĊ ĠĠĠĠĊ -ĠF ellow -Ġk ay -Ġnew State -ONT AL -Im plementation -.L ook -Ġ ents -Ġl ors -ĠB IG -f ab -Ġaver aged -ĠFe edback -ĠW ells -Ġm artial -Ġind ul -ĠComm unist -ĠFore x -ĠAgricult ure -" [ -Ġqu ar -ĠK ont -ĉ view -. Bytes -des ktop -ĠM akes -akes peare -.Null able -Ġspot light -V B -ow y -(t orch -tr idge -_b ounds -Ġapolog ize -.add Item -ant d -* );Ċ -, u -(g en -ç» ĵ -re ator -ĠC ord -ou pper -.m etro -Ġ ew -ĠW ORD -.A fter -Ġdet ained -ĠHam mer -ex isting -Ġo st -Ġmon ument --c ustom -User ID -ĠN om -Ġre jection -(d im -Ġsingle ton -ĉd ie -ari ance -re ports -] != -eld a -Ġpreval ence -_reg s -." . -Ġfemin ist -Code c -Ġ **Ċ -(label s -_M ARK -FA ILED -Ġadminister ed -W N -ĠĠĠĠĠĠĠĠ ĉĉ -Ġn oun -w ig -Ġg otta -Ġr if -- im -ĠPaul o -ĠCommand Type -] ))ĊĊ --z ero -Tr aining -Ġl ord -_ art -re ddit -C ert -Ġpes o -R ot -Ġend anger -.d r -user Info -un ts -n v -ĠTrail er --f irst -(m ake -Ġbenef ici --bl ack -i ÃŁ -Ġund oubtedly -Ġm ex -ĠAnc ient -( as -Ġdes cent -P ick -Ġrep lica -$ obj -ä hr -Ġar rows -ft y -ĠLib ya -ug a -charg ed -T ur -Ġh omic -iss en -ĠF ake -Ġbe ers -Ġsc attered -( Time -UT IL -Ġbureauc r -/pl ain -Ġstick ing -FA IL -ĠC ovid -Th ird -_p resent -ĠPier re -Ġë ª -Ġ[... ]ĊĊ -Pro b -ĠTra ffic -ica o -do ctor -Ġ), ĊĊ -T abs -al u -ï¼ļ âĢľ -Ġinher ent -_N o -rit is -ĠPro of -.b asename -ä¼ ļ -Ġch im -ĠProt ected -c rit -Ġpr one -Ġк он -ĠHero es -Ġan xious -Ġan os -Ġweek ends -Ġs ext -Ġredu cer -= UTF -h alf -ĠS aw -.m m -Ġnue va -.current Target -.l ua -_EXT ENSION -ĉ reg -ĠC trl -_ align -accept able -Ġrush ing -fr ac -Ġbo asts -F ive - ± -ĠTem perature -> ): -Ġchar ter -RE ATED -Ġsubject ed -Ġop c -health y -使 çĶ¨ -ĠScient ific -Ġfra u -ri ages -ภĶ -.in ventory -ation ale -M ad -min utes ->> ();Ċ -ĠEn v -Ġrecord ings -Ġsusp icion -sql ite -ĉ read -ãģ ¦ -Ġwor ries -.put String -ĠSh anghai -( uid -r er -ĠvÃŃ de -") : -Ġmethod ology -Ġк оÑĤоÑĢ -cc c -av ad -Ġindu ction -ĉ Thread -, string -ạ i -neh men -u ition -Ġ* __ -.em f -Ġì ľ -/th emes -ĠN ine -. 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ĥ -à´ ´ -འħ -áĨ º -áĪ Ĭ -áĪ ¨ -áĪ ¾ -áī IJ -áĮ ĥ -áĮ ½ -áĶ Ń -áł Ĥ -áł ¬ -ᨠ¸ -á© ĭ -ᶠı -á¾ Ķ -á¿ IJ -á¿ ļ -âĻ Ļ -âļ Ĥ -âļ Ĺ -â¡ ¢ -⤠¦ -ëĸ ° -ë¤ Ĥ -ë§ ł -ë± ĭ -ë± IJ -ìĽ ¢ -ìľ ¾ -ì³ ħ -ì» ģ -íģ » -íĥ Ļ -íĵ ĸ -íĵ Ń -íķ ± -íĽ ľ -ï¤ ħ -ï¤ Ĩ -ï¦ ĥ -ï§ © -ï¨ Ĥ -ðIJ¤ Ķ -ðIJŃ ĵ -ðIJ° ¼ -ðĿĵ ŀ -ðĿĵ ° -ðĿĻ ľ -ðĿļ ģ -ðŁħ ¢ -ðŁı ĩ -È ² -Ê ¶ -Ô Ī -Ô ij -Ý ĵ -Ý ¥ -ठij -ॠ± -ଠī -à° ³ -à° µ -ಠŁ -áĢ ı -áģ ¼ -áī ¨ -áĬ Ĵ -áĭ © -áĮ Ħ -áĮ Ķ -áIJ § -á ĴĮ -áĶ ħ -áĶ Ĭ -áł Ħ -ᨠģ -Ḡĥ -Ḡ» -âĶ ŀ -âĺ µ -âļ £ -â² ¢ -ãĪ ª -ä¶ µ -ê² Ļ -ê² ´ -ê³ Ĥ -ë¡ ¼ -ìĨ Ĭ -ì¼ ĩ -íĭ į -íĵ ¬ -íĵ ® -íĵ ¶ -íĵ » -ï¤ ¦ -ï¥ ł -ï¥ ± -ïŃ ² -ðIJŃ Ĭ -ðIJ ±ħ -ðĸ ¥ -ðĸ¥ ¨ -ðĿij ³ -ðĿĵ ķ -ðĿĵ ¬ -ðĿĵ ¹ -ðĿĵ ¾ -ðĿĶ ĵ -ðĿķ į -ðĿķ ¡ -ðĿķ ± -ðĿĸ ĸ -ðĿĺ ı -ðĿĺ IJ -ðĿĺ ļ -ðĿĻ ® -ðĿĻ ° -ðĿĻ ¸ -ðĿĻ º -ðĿĻ ¼ -ðĿĻ ½ -ðĿĻ ¿ -ðĿļ Ħ -ðĿļ ı -ðŁħ ħ -ðŁħ ĵ -Æ Ī -àł Į -áĻ ³ -á ļĮ -ἠħ -ἠIJ -ᤠĬ -ḠĬ -âĶ ½ -âķ Ĭ -⼠ĩ -⼠ı -âĿ ª -âĿ « -⣠° -ãĦ į -ãĦ ĵ -ãĦ § -ãħ ĸ -ãī « -ê¦ Ķ -ï± Ĭ -ຠĤ -áħ £ -ᥠĶ -ᥠ¤ -âĨ ¤ -âĨ · -âĩ ŀ -âĸ ¤ -âŀ ¶ -ãĪ ¼ -ï¨ · -ðĵı § -âĶ ² -âĢ ´ -âĴ Ł -âĴ ¡ -â° Ĥ -â° į -â° İ -â° IJ -â° ij -â° Ł -â° ł -â° ¡ -â¼ Ń -ãĬ ¥ -âĴ ł -â½ º -ãĩ º -ãĩ ½ -ï¨ Ĭ -áķ · -âį ¨ -⺠Ł -â½ Ĺ diff --git a/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json deleted file mode 100644 index 67688e82ccf6bf5b0fa2d95130fb8564acce6398..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json +++ /dev/null @@ -1,241 +0,0 @@ -{ - "add_bos_token": false, - "add_prefix_space": false, - "added_tokens_decoder": { - "151643": { - "content": "<|endoftext|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151644": { - "content": "<|im_start|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151645": { - "content": "<|im_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151646": { - "content": "<|object_ref_start|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151647": { - "content": "<|object_ref_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151648": { - "content": "<|box_start|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151649": { - "content": "<|box_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151650": { - "content": "<|quad_start|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151651": { - "content": "<|quad_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151652": { - "content": "<|vision_start|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151653": { - "content": "<|vision_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151654": { - "content": "<|vision_pad|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151655": { - "content": "<|image_pad|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151656": { - "content": "<|video_pad|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151657": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151658": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151659": { - "content": "<|fim_prefix|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151660": { - "content": "<|fim_middle|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151661": { - "content": "<|fim_suffix|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151662": { - "content": "<|fim_pad|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151663": { - "content": "<|repo_name|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151664": { - "content": "<|file_sep|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": false - }, - "151665": { - "content": "<|img|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151666": { - "content": "<|endofimg|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151667": { - "content": "<|meta|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "151668": { - "content": "<|endofmeta|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - } - }, - "additional_special_tokens": [ - "<|im_start|>", - "<|im_end|>", - "<|object_ref_start|>", - "<|object_ref_end|>", - "<|box_start|>", - "<|box_end|>", - "<|quad_start|>", - "<|quad_end|>", - "<|vision_start|>", - "<|vision_end|>", - "<|vision_pad|>", - "<|image_pad|>", - "<|video_pad|>" - ], - "bos_token": null, - "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role 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\ No newline at end of file diff --git a/comfy/text_encoders/qwen_image.py b/comfy/text_encoders/qwen_image.py deleted file mode 100644 index 6646b1003b439b2f943354530f4093de2e10a655..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/qwen_image.py +++ /dev/null @@ -1,85 +0,0 @@ -from transformers import Qwen2Tokenizer -from comfy import sd1_clip -import comfy.text_encoders.llama -import os -import torch -import numbers - -class Qwen25_7BVLITokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=3584, embedding_key='qwen25_7b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) - - -class QwenImageTokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_7b", tokenizer=Qwen25_7BVLITokenizer) - self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" - self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" - - def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs): - if llama_template is None: - if len(images) > 0: - llama_text = self.llama_template_images.format(text) - else: - llama_text = self.llama_template.format(text) - else: - llama_text = llama_template.format(text) - tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) - key_name = next(iter(tokens)) - embed_count = 0 - qwen_tokens = tokens[key_name] - for r in qwen_tokens: - for i in range(len(r)): - if r[i][0] == 151655: - if len(images) > embed_count: - r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:] - embed_count += 1 - return tokens - - -class Qwen25_7BVLIModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) - - -class QwenImageTEModel(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) - - def encode_token_weights(self, token_weight_pairs): - out, pooled, extra = super().encode_token_weights(token_weight_pairs) - tok_pairs = token_weight_pairs["qwen25_7b"][0] - count_im_start = 0 - for i, v in enumerate(tok_pairs): - elem = v[0] - if not torch.is_tensor(elem): - if isinstance(elem, numbers.Integral): - if elem == 151644 and count_im_start < 2: - template_end = i - count_im_start += 1 - - if out.shape[1] > (template_end + 3): - if tok_pairs[template_end + 1][0] == 872: - if tok_pairs[template_end + 2][0] == 198: - template_end += 3 - - out = out[:, template_end:] - - extra["attention_mask"] = extra["attention_mask"][:, template_end:] - if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]): - extra.pop("attention_mask") # attention mask is useless if no masked elements - - return out, pooled, extra - - -def te(dtype_llama=None, llama_scaled_fp8=None): - class QwenImageTEModel_(QwenImageTEModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["scaled_fp8"] = llama_scaled_fp8 - if dtype_llama is not None: - dtype = dtype_llama - super().__init__(device=device, dtype=dtype, model_options=model_options) - return QwenImageTEModel_ diff --git a/comfy/text_encoders/qwen_vl.py b/comfy/text_encoders/qwen_vl.py deleted file mode 100644 index 3b18ce730f62b7975da7b49b61533e4c2b8791c1..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/qwen_vl.py +++ /dev/null @@ -1,428 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Optional, Tuple -import math -from comfy.ldm.modules.attention import optimized_attention_for_device - - -def process_qwen2vl_images( - images: torch.Tensor, - min_pixels: int = 3136, - max_pixels: int = 12845056, - patch_size: int = 14, - temporal_patch_size: int = 2, - merge_size: int = 2, - image_mean: list = None, - image_std: list = None, -): - if image_mean is None: - image_mean = [0.48145466, 0.4578275, 0.40821073] - if image_std is None: - image_std = [0.26862954, 0.26130258, 0.27577711] - - batch_size, height, width, channels = images.shape - device = images.device - # dtype = images.dtype - - images = images.permute(0, 3, 1, 2) - - grid_thw_list = [] - img = images[0] - - factor = patch_size * merge_size - - h_bar = round(height / factor) * factor - w_bar = round(width / factor) * factor - - if h_bar * w_bar > max_pixels: - beta = math.sqrt((height * width) / max_pixels) - h_bar = max(factor, math.floor(height / beta / factor) * factor) - w_bar = max(factor, math.floor(width / beta / factor) * factor) - elif h_bar * w_bar < min_pixels: - beta = math.sqrt(min_pixels / (height * width)) - h_bar = math.ceil(height * beta / factor) * factor - w_bar = math.ceil(width * beta / factor) * factor - - img_resized = F.interpolate( - img.unsqueeze(0), - size=(h_bar, w_bar), - mode='bilinear', - align_corners=False - ).squeeze(0) - - normalized = img_resized.clone() - for c in range(3): - normalized[c] = (img_resized[c] - image_mean[c]) / image_std[c] - - grid_h = h_bar // patch_size - grid_w = w_bar // patch_size - grid_thw = torch.tensor([1, grid_h, grid_w], device=device, dtype=torch.long) - - pixel_values = normalized - grid_thw_list.append(grid_thw) - image_grid_thw = torch.stack(grid_thw_list) - - grid_t = 1 - channel = pixel_values.shape[0] - pixel_values = pixel_values.unsqueeze(0).repeat(2, 1, 1, 1) - - patches = pixel_values.reshape( - grid_t, - temporal_patch_size, - channel, - grid_h // merge_size, - merge_size, - patch_size, - grid_w // merge_size, - merge_size, - patch_size, - ) - - patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8) - flatten_patches = patches.reshape( - grid_t * grid_h * grid_w, - channel * temporal_patch_size * patch_size * patch_size - ) - - return flatten_patches, image_grid_thw - - -class VisionPatchEmbed(nn.Module): - def __init__( - self, - patch_size: int = 14, - temporal_patch_size: int = 2, - in_channels: int = 3, - embed_dim: int = 3584, - device=None, - dtype=None, - ops=None, - ): - super().__init__() - self.patch_size = patch_size - self.temporal_patch_size = temporal_patch_size - self.in_channels = in_channels - self.embed_dim = embed_dim - - kernel_size = [temporal_patch_size, patch_size, patch_size] - self.proj = ops.Conv3d( - in_channels, - embed_dim, - kernel_size=kernel_size, - stride=kernel_size, - bias=False, - device=device, - dtype=dtype - ) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - hidden_states = hidden_states.view( - -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size - ) - hidden_states = self.proj(hidden_states) - return hidden_states.view(-1, self.embed_dim) - - -def rotate_half(x): - x1 = x[..., : x.shape[-1] // 2] - x2 = x[..., x.shape[-1] // 2 :] - return torch.cat((-x2, x1), dim=-1) - - -def apply_rotary_pos_emb_vision(q, k, cos, sin): - cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float() - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) - return q_embed, k_embed - - -class VisionRotaryEmbedding(nn.Module): - def __init__(self, dim: int, theta: float = 10000.0): - super().__init__() - self.dim = dim - self.theta = theta - - def forward(self, seqlen: int, device) -> torch.Tensor: - inv_freq = 1.0 / (self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float, device=device) / self.dim)) - seq = torch.arange(seqlen, device=inv_freq.device, dtype=inv_freq.dtype) - freqs = torch.outer(seq, inv_freq) - return freqs - - -class PatchMerger(nn.Module): - def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2, device=None, dtype=None, ops=None): - super().__init__() - self.hidden_size = context_dim * (spatial_merge_size ** 2) - self.ln_q = ops.RMSNorm(context_dim, eps=1e-6, device=device, dtype=dtype) - self.mlp = nn.Sequential( - ops.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype), - nn.GELU(), - ops.Linear(self.hidden_size, dim, device=device, dtype=dtype), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.ln_q(x).reshape(-1, self.hidden_size) - x = self.mlp(x) - return x - - -class VisionAttention(nn.Module): - def __init__(self, hidden_size: int, num_heads: int, device=None, dtype=None, ops=None): - super().__init__() - self.hidden_size = hidden_size - self.num_heads = num_heads - self.head_dim = hidden_size // num_heads - self.scaling = self.head_dim ** -0.5 - - self.qkv = ops.Linear(hidden_size, hidden_size * 3, bias=True, device=device, dtype=dtype) - self.proj = ops.Linear(hidden_size, hidden_size, bias=True, device=device, dtype=dtype) - - def forward( - self, - hidden_states: torch.Tensor, - position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, - cu_seqlens=None, - optimized_attention=None, - ) -> torch.Tensor: - if hidden_states.dim() == 2: - seq_length, _ = hidden_states.shape - batch_size = 1 - hidden_states = hidden_states.unsqueeze(0) - else: - batch_size, seq_length, _ = hidden_states.shape - - qkv = self.qkv(hidden_states) - qkv = qkv.reshape(batch_size, seq_length, 3, self.num_heads, self.head_dim) - query_states, key_states, value_states = qkv.reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) - - if position_embeddings is not None: - cos, sin = position_embeddings - query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) - - query_states = query_states.transpose(0, 1).unsqueeze(0) - key_states = key_states.transpose(0, 1).unsqueeze(0) - value_states = value_states.transpose(0, 1).unsqueeze(0) - - lengths = cu_seqlens[1:] - cu_seqlens[:-1] - splits = [ - torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) - ] - - attn_outputs = [ - optimized_attention(q, k, v, self.num_heads, skip_reshape=True) - for q, k, v in zip(*splits) - ] - attn_output = torch.cat(attn_outputs, dim=1) - attn_output = attn_output.reshape(seq_length, -1) - attn_output = self.proj(attn_output) - - return attn_output - - -class VisionMLP(nn.Module): - def __init__(self, hidden_size: int, intermediate_size: int, device=None, dtype=None, ops=None): - super().__init__() - self.gate_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype) - self.up_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype) - self.down_proj = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype) - self.act_fn = nn.SiLU() - - def forward(self, hidden_state): - return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) - - -class VisionBlock(nn.Module): - def __init__(self, hidden_size: int, intermediate_size: int, num_heads: int, device=None, dtype=None, ops=None): - super().__init__() - self.norm1 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype) - self.norm2 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype) - self.attn = VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops) - self.mlp = VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops) - - def forward( - self, - hidden_states: torch.Tensor, - position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, - cu_seqlens=None, - optimized_attention=None, - ) -> torch.Tensor: - residual = hidden_states - hidden_states = self.norm1(hidden_states) - hidden_states = self.attn(hidden_states, position_embeddings, cu_seqlens, optimized_attention) - hidden_states = residual + hidden_states - - residual = hidden_states - hidden_states = self.norm2(hidden_states) - hidden_states = self.mlp(hidden_states) - hidden_states = residual + hidden_states - - return hidden_states - - -class Qwen2VLVisionTransformer(nn.Module): - def __init__( - self, - hidden_size: int = 3584, - output_hidden_size: int = 3584, - intermediate_size: int = 3420, - num_heads: int = 16, - num_layers: int = 32, - patch_size: int = 14, - temporal_patch_size: int = 2, - spatial_merge_size: int = 2, - window_size: int = 112, - device=None, - dtype=None, - ops=None - ): - super().__init__() - self.hidden_size = hidden_size - self.patch_size = patch_size - self.spatial_merge_size = spatial_merge_size - self.window_size = window_size - self.fullatt_block_indexes = [7, 15, 23, 31] - - self.patch_embed = VisionPatchEmbed( - patch_size=patch_size, - temporal_patch_size=temporal_patch_size, - in_channels=3, - embed_dim=hidden_size, - device=device, - dtype=dtype, - ops=ops, - ) - - head_dim = hidden_size // num_heads - self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2) - - self.blocks = nn.ModuleList([ - VisionBlock(hidden_size, intermediate_size, num_heads, device, dtype, ops) - for _ in range(num_layers) - ]) - - self.merger = PatchMerger( - dim=output_hidden_size, - context_dim=hidden_size, - spatial_merge_size=spatial_merge_size, - device=device, - dtype=dtype, - ops=ops, - ) - - def get_window_index(self, grid_thw): - window_index = [] - cu_window_seqlens = [0] - window_index_id = 0 - vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size - - for grid_t, grid_h, grid_w in grid_thw: - llm_grid_h = grid_h // self.spatial_merge_size - llm_grid_w = grid_w // self.spatial_merge_size - - index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) - - pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size - pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size - num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size - num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size - - index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) - index_padded = index_padded.reshape( - grid_t, - num_windows_h, - vit_merger_window_size, - num_windows_w, - vit_merger_window_size, - ) - index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( - grid_t, - num_windows_h * num_windows_w, - vit_merger_window_size, - vit_merger_window_size, - ) - - seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) - index_padded = index_padded.reshape(-1) - index_new = index_padded[index_padded != -100] - window_index.append(index_new + window_index_id) - - cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_size * self.spatial_merge_size + cu_window_seqlens[-1] - cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) - window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() - - window_index = torch.cat(window_index, dim=0) - return window_index, cu_window_seqlens - - def get_position_embeddings(self, grid_thw, device): - pos_ids = [] - - for t, h, w in grid_thw: - hpos_ids = torch.arange(h, device=device).unsqueeze(1).expand(-1, w) - hpos_ids = hpos_ids.reshape( - h // self.spatial_merge_size, - self.spatial_merge_size, - w // self.spatial_merge_size, - self.spatial_merge_size, - ) - hpos_ids = hpos_ids.permute(0, 2, 1, 3).flatten() - - wpos_ids = torch.arange(w, device=device).unsqueeze(0).expand(h, -1) - wpos_ids = wpos_ids.reshape( - h // self.spatial_merge_size, - self.spatial_merge_size, - w // self.spatial_merge_size, - self.spatial_merge_size, - ) - wpos_ids = wpos_ids.permute(0, 2, 1, 3).flatten() - - pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) - - pos_ids = torch.cat(pos_ids, dim=0) - max_grid_size = grid_thw[:, 1:].max() - rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size, device) - return rotary_pos_emb_full[pos_ids].flatten(1) - - def forward( - self, - pixel_values: torch.Tensor, - image_grid_thw: Optional[torch.Tensor] = None, - ) -> torch.Tensor: - optimized_attention = optimized_attention_for_device(pixel_values.device, mask=False, small_input=True) - - hidden_states = self.patch_embed(pixel_values) - - window_index, cu_window_seqlens = self.get_window_index(image_grid_thw) - cu_window_seqlens = torch.tensor(cu_window_seqlens, device=hidden_states.device) - cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) - - position_embeddings = self.get_position_embeddings(image_grid_thw, hidden_states.device) - - seq_len, _ = hidden_states.size() - spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size - - hidden_states = hidden_states.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1) - hidden_states = hidden_states[window_index, :, :] - hidden_states = hidden_states.reshape(seq_len, -1) - - position_embeddings = position_embeddings.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1) - position_embeddings = position_embeddings[window_index, :, :] - position_embeddings = position_embeddings.reshape(seq_len, -1) - position_embeddings = torch.cat((position_embeddings, position_embeddings), dim=-1) - position_embeddings = (position_embeddings.cos(), position_embeddings.sin()) - - cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum( - dim=0, - dtype=torch.int32, - ) - cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) - - for i, block in enumerate(self.blocks): - if i in self.fullatt_block_indexes: - cu_seqlens_now = cu_seqlens - else: - cu_seqlens_now = cu_window_seqlens - hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention) - - hidden_states = self.merger(hidden_states) - return hidden_states diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py deleted file mode 100644 index 2803926ac01867edc686c4ca6625a5c5aba32e0b..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/sa_t5.py +++ /dev/null @@ -1,22 +0,0 @@ -from comfy import sd1_clip -from transformers import T5TokenizerFast -import comfy.text_encoders.t5 -import os - -class T5BaseModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json") - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True) - -class T5BaseTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) - -class SAT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer) - -class SAT5Model(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): - super().__init__(device=device, dtype=dtype, model_options=model_options, name="t5base", clip_model=T5BaseModel, **kwargs) diff --git a/comfy/text_encoders/sd2_clip.py b/comfy/text_encoders/sd2_clip.py deleted file mode 100644 index 700a23bf09f03f09290f677092bd25f29d6f065a..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/sd2_clip.py +++ /dev/null @@ -1,23 +0,0 @@ -from comfy import sd1_clip -import os - -class SD2ClipHModel(sd1_clip.SDClipModel): - def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}): - if layer == "penultimate": - layer="hidden" - layer_idx=-2 - - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") - super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options) - -class SD2ClipHTokenizer(sd1_clip.SDTokenizer): - def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): - super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data) - -class SD2Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer) - -class SD2ClipModel(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): - super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs) diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json deleted file mode 100644 index 00893cfdc9b00f8eb7cf5aaa9c343e7fcd298d82..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/sd2_clip_config.json +++ /dev/null @@ -1,23 +0,0 @@ -{ - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "gelu", - "hidden_size": 1024, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 77, - "model_type": "clip_text_model", - "num_attention_heads": 16, - "num_hidden_layers": 24, - "pad_token_id": 1, - "projection_dim": 1024, - "torch_dtype": "float32", - "vocab_size": 49408 -} diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py deleted file mode 100644 index ff5d412db1481fee09cf7742b9e32e90f6ed74d9..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/sd3_clip.py +++ /dev/null @@ -1,166 +0,0 @@ -from comfy import sd1_clip -from comfy import sdxl_clip -from transformers import T5TokenizerFast -import comfy.text_encoders.t5 -import torch -import os -import comfy.model_management -import logging - -class T5XXLModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json") - t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) - if t5xxl_scaled_fp8 is not None: - model_options = model_options.copy() - model_options["scaled_fp8"] = t5xxl_scaled_fp8 - - model_options = {**model_options, "model_name": "t5xxl"} - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) - - -def t5_xxl_detect(state_dict, prefix=""): - out = {} - t5_key = "{}encoder.final_layer_norm.weight".format(prefix) - if t5_key in state_dict: - out["dtype_t5"] = state_dict[t5_key].dtype - - scaled_fp8_key = "{}scaled_fp8".format(prefix) - if scaled_fp8_key in state_dict: - out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype - - return out - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data) - - -class SD3Tokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}): - self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - - def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): - out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) - return out - - def untokenize(self, token_weight_pair): - return self.clip_g.untokenize(token_weight_pair) - - def state_dict(self): - return {} - -class SD3ClipModel(torch.nn.Module): - def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}): - super().__init__() - self.dtypes = set() - if clip_l: - self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) - self.dtypes.add(dtype) - else: - self.clip_l = None - - if clip_g: - self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) - self.dtypes.add(dtype) - else: - self.clip_g = None - - if t5: - dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) - self.t5_attention_mask = t5_attention_mask - self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask) - self.dtypes.add(dtype_t5) - else: - self.t5xxl = None - - logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5)) - - def set_clip_options(self, options): - if self.clip_l is not None: - self.clip_l.set_clip_options(options) - if self.clip_g is not None: - self.clip_g.set_clip_options(options) - if self.t5xxl is not None: - self.t5xxl.set_clip_options(options) - - def reset_clip_options(self): - if self.clip_l is not None: - self.clip_l.reset_clip_options() - if self.clip_g is not None: - self.clip_g.reset_clip_options() - if self.t5xxl is not None: - self.t5xxl.reset_clip_options() - - def encode_token_weights(self, token_weight_pairs): - token_weight_pairs_l = token_weight_pairs["l"] - token_weight_pairs_g = token_weight_pairs["g"] - token_weight_pairs_t5 = token_weight_pairs["t5xxl"] - lg_out = None - pooled = None - out = None - extra = {} - - if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: - if self.clip_l is not None: - lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - else: - l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) - - if self.clip_g is not None: - g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) - if lg_out is not None: - cut_to = min(lg_out.shape[1], g_out.shape[1]) - lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1) - else: - lg_out = torch.nn.functional.pad(g_out, (768, 0)) - else: - g_out = None - g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) - - if lg_out is not None: - lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1])) - out = lg_out - pooled = torch.cat((l_pooled, g_pooled), dim=-1) - - if self.t5xxl is not None: - t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) - t5_out, t5_pooled = t5_output[:2] - if self.t5_attention_mask: - extra["attention_mask"] = t5_output[2]["attention_mask"] - - if lg_out is not None: - out = torch.cat([lg_out, t5_out], dim=-2) - else: - out = t5_out - - if out is None: - out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device()) - - if pooled is None: - pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) - - return out, pooled, extra - - def load_sd(self, sd): - if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: - return self.clip_g.load_sd(sd) - elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: - return self.clip_l.load_sd(sd) - else: - return self.t5xxl.load_sd(sd) - -def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False): - class SD3ClipModel_(SD3ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options) - return SD3ClipModel_ diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py deleted file mode 100644 index caccb3ca283b3f129f942ebfc9e5c274a71d9f3d..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/spiece_tokenizer.py +++ /dev/null @@ -1,34 +0,0 @@ -import torch -import os - -class SPieceTokenizer: - @staticmethod - def from_pretrained(path, **kwargs): - return SPieceTokenizer(path, **kwargs) - - def __init__(self, tokenizer_path, add_bos=False, add_eos=True): - self.add_bos = add_bos - self.add_eos = add_eos - import sentencepiece - if torch.is_tensor(tokenizer_path): - tokenizer_path = tokenizer_path.numpy().tobytes() - - if isinstance(tokenizer_path, bytes): - self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) - else: - if not os.path.isfile(tokenizer_path): - raise ValueError("invalid tokenizer") - self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) - - def get_vocab(self): - out = {} - for i in range(self.tokenizer.get_piece_size()): - out[self.tokenizer.id_to_piece(i)] = i - return out - - def __call__(self, string): - out = self.tokenizer.encode(string) - return {"input_ids": out} - - def serialize_model(self): - return torch.ByteTensor(list(self.tokenizer.serialized_model_proto())) diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py deleted file mode 100644 index e8588992a4b54713de4018975b8d58170a4d0d7e..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5.py +++ /dev/null @@ -1,249 +0,0 @@ -import torch -import math -from comfy.ldm.modules.attention import optimized_attention_for_device -import comfy.ops - -class T5LayerNorm(torch.nn.Module): - def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None): - super().__init__() - self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device)) - self.variance_epsilon = eps - - def forward(self, x): - variance = x.pow(2).mean(-1, keepdim=True) - x = x * torch.rsqrt(variance + self.variance_epsilon) - return comfy.ops.cast_to_input(self.weight, x) * x - -activations = { - "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"), - "relu": torch.nn.functional.relu, -} - -class T5DenseActDense(torch.nn.Module): - def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): - super().__init__() - self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) - self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) - # self.dropout = nn.Dropout(config.dropout_rate) - self.act = activations[ff_activation] - - def forward(self, x): - x = self.act(self.wi(x)) - # x = self.dropout(x) - x = self.wo(x) - return x - -class T5DenseGatedActDense(torch.nn.Module): - def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations): - super().__init__() - self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) - self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device) - self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device) - # self.dropout = nn.Dropout(config.dropout_rate) - self.act = activations[ff_activation] - - def forward(self, x): - hidden_gelu = self.act(self.wi_0(x)) - hidden_linear = self.wi_1(x) - x = hidden_gelu * hidden_linear - # x = self.dropout(x) - x = self.wo(x) - return x - -class T5LayerFF(torch.nn.Module): - def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations): - super().__init__() - if gated_act: - self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) - else: - self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations) - - self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) - # self.dropout = nn.Dropout(config.dropout_rate) - - def forward(self, x): - forwarded_states = self.layer_norm(x) - forwarded_states = self.DenseReluDense(forwarded_states) - # x = x + self.dropout(forwarded_states) - x += forwarded_states - return x - -class T5Attention(torch.nn.Module): - def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations): - super().__init__() - - # Mesh TensorFlow initialization to avoid scaling before softmax - self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device) - self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device) - self.num_heads = num_heads - - self.relative_attention_bias = None - if relative_attention_bias: - self.relative_attention_num_buckets = 32 - self.relative_attention_max_distance = 128 - self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype) - - @staticmethod - def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): - """ - Adapted from Mesh Tensorflow: - https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 - - Translate relative position to a bucket number for relative attention. The relative position is defined as - memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to - position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for - small absolute relative_position and larger buckets for larger absolute relative_positions. All relative - positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. - This should allow for more graceful generalization to longer sequences than the model has been trained on - - Args: - relative_position: an int32 Tensor - bidirectional: a boolean - whether the attention is bidirectional - num_buckets: an integer - max_distance: an integer - - Returns: - a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) - """ - relative_buckets = 0 - if bidirectional: - num_buckets //= 2 - relative_buckets += (relative_position > 0).to(torch.long) * num_buckets - relative_position = torch.abs(relative_position) - else: - relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) - # now relative_position is in the range [0, inf) - - # half of the buckets are for exact increments in positions - max_exact = num_buckets // 2 - is_small = relative_position < max_exact - - # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance - relative_position_if_large = max_exact + ( - torch.log(relative_position.float() / max_exact) - / math.log(max_distance / max_exact) - * (num_buckets - max_exact) - ).to(torch.long) - relative_position_if_large = torch.min( - relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) - ) - - relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) - return relative_buckets - - def compute_bias(self, query_length, key_length, device, dtype): - """Compute binned relative position bias""" - context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] - memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] - relative_position = memory_position - context_position # shape (query_length, key_length) - relative_position_bucket = self._relative_position_bucket( - relative_position, # shape (query_length, key_length) - bidirectional=True, - num_buckets=self.relative_attention_num_buckets, - max_distance=self.relative_attention_max_distance, - ) - values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads) - values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) - return values.contiguous() - - def forward(self, x, mask=None, past_bias=None, optimized_attention=None): - q = self.q(x) - k = self.k(x) - v = self.v(x) - if self.relative_attention_bias is not None: - past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype) - - if past_bias is not None: - if mask is not None: - mask = mask + past_bias - else: - mask = past_bias - - out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask) - return self.o(out), past_bias - -class T5LayerSelfAttention(torch.nn.Module): - def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations): - super().__init__() - self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations) - self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) - # self.dropout = nn.Dropout(config.dropout_rate) - - def forward(self, x, mask=None, past_bias=None, optimized_attention=None): - output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention) - # x = x + self.dropout(attention_output) - x += output - return x, past_bias - -class T5Block(torch.nn.Module): - def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations): - super().__init__() - self.layer = torch.nn.ModuleList() - self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations)) - self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations)) - - def forward(self, x, mask=None, past_bias=None, optimized_attention=None): - x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention) - x = self.layer[-1](x) - return x, past_bias - -class T5Stack(torch.nn.Module): - def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations): - super().__init__() - - self.block = torch.nn.ModuleList( - [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)] - ) - self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) - # self.dropout = nn.Dropout(config.dropout_rate) - - def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): - mask = None - if attention_mask is not None: - mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) - - intermediate = None - optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) - past_bias = None - - if intermediate_output is not None: - if intermediate_output < 0: - intermediate_output = len(self.block) + intermediate_output - - for i, l in enumerate(self.block): - x, past_bias = l(x, mask, past_bias, optimized_attention) - if i == intermediate_output: - intermediate = x.clone() - x = self.final_layer_norm(x) - if intermediate is not None and final_layer_norm_intermediate: - intermediate = self.final_layer_norm(intermediate) - return x, intermediate - -class T5(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - self.num_layers = config_dict["num_layers"] - model_dim = config_dict["d_model"] - inner_dim = config_dict["d_kv"] * config_dict["num_heads"] - - self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations) - self.dtype = dtype - self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype) - - def get_input_embeddings(self): - return self.shared - - def set_input_embeddings(self, embeddings): - self.shared = embeddings - - def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs): - if input_ids is None: - x = embeds - else: - x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) - if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: - x = torch.nan_to_num(x) #Fix for fp8 T5 base - return self.encoder(x, attention_mask=attention_mask, **kwargs) diff --git a/comfy/text_encoders/t5_config_base.json b/comfy/text_encoders/t5_config_base.json deleted file mode 100644 index 71f68327c27280ce150d0c8e92fd61eca0b52a63..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5_config_base.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 3072, - "d_kv": 64, - "d_model": 768, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "relu", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": false, - "layer_norm_epsilon": 1e-06, - "model_type": "t5", - "num_decoder_layers": 12, - "num_heads": 12, - "num_layers": 12, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 32128 -} diff --git a/comfy/text_encoders/t5_config_xxl.json b/comfy/text_encoders/t5_config_xxl.json deleted file mode 100644 index 28283b51a11bed6a874499f82d411c16cc646eb1..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5_config_xxl.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 10240, - "d_kv": 64, - "d_model": 4096, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "t5", - "num_decoder_layers": 24, - "num_heads": 64, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 32128 -} diff --git a/comfy/text_encoders/t5_old_config_xxl.json b/comfy/text_encoders/t5_old_config_xxl.json deleted file mode 100644 index c9fdd7782197ca0523ee82133e9b417fa947a5c5..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5_old_config_xxl.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 65536, - "d_kv": 128, - "d_model": 1024, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "relu", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": false, - "layer_norm_epsilon": 1e-06, - "model_type": "t5", - "num_decoder_layers": 24, - "num_heads": 128, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 32128 -} diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json deleted file mode 100644 index ee4e03f97a5b3a9927fc676816f210a364ee234b..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5_pile_config_xl.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 5120, - "d_kv": 64, - "d_model": 2048, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 2, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "umt5", - "num_decoder_layers": 24, - "num_heads": 32, - "num_layers": 24, - "output_past": true, - "pad_token_id": 1, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 32128 -} diff --git a/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model b/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model deleted file mode 100644 index 6c00c742ce03c627d6cd5b795984876fa49fa899..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 -size 499723 diff --git a/comfy/text_encoders/t5_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_tokenizer/special_tokens_map.json deleted file mode 100644 index 17ade346a1042cbe0c1436f5bedcbd85c099d582..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/t5_tokenizer/special_tokens_map.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "additional_special_tokens": [ - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "" - ], - "eos_token": { - "content": "", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "pad_token": { - "content": "", - "lstrip": false, 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"", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "", - "" - ], - "clean_up_tokenization_spaces": true, - "eos_token": "", - "extra_ids": 100, - "legacy": false, - "model_max_length": 512, - "pad_token": "", - "sp_model_kwargs": {}, - "tokenizer_class": "T5Tokenizer", - "unk_token": "" -} diff --git a/comfy/text_encoders/umt5_config_base.json b/comfy/text_encoders/umt5_config_base.json deleted file mode 100644 index 6b3618f075eb4503d0666bd50f2259cc34b653c4..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/umt5_config_base.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 2048, - "d_kv": 64, - "d_model": 768, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "umt5", - "num_decoder_layers": 12, - "num_heads": 12, - "num_layers": 12, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 256384 -} diff --git a/comfy/text_encoders/umt5_config_xxl.json b/comfy/text_encoders/umt5_config_xxl.json deleted file mode 100644 index dfcb4b54bc95469e7b519aed847bb1efb034bdc1..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/umt5_config_xxl.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "d_ff": 10240, - "d_kv": 64, - "d_model": 4096, - "decoder_start_token_id": 0, - "dropout_rate": 0.1, - "eos_token_id": 1, - "dense_act_fn": "gelu_pytorch_tanh", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "umt5", - "num_decoder_layers": 24, - "num_heads": 64, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "vocab_size": 256384 -} diff --git a/comfy/text_encoders/wan.py b/comfy/text_encoders/wan.py deleted file mode 100644 index d50fa4b28df39ab358ae19bd9745b702985ba74d..0000000000000000000000000000000000000000 --- a/comfy/text_encoders/wan.py +++ /dev/null @@ -1,37 +0,0 @@ -from comfy import sd1_clip -from .spiece_tokenizer import SPieceTokenizer -import comfy.text_encoders.t5 -import os - -class UMT5XXlModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_xxl.json") - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) - -class UMT5XXlTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer = tokenizer_data.get("spiece_model", None) - super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data) - - def state_dict(self): - return {"spiece_model": self.tokenizer.serialize_model()} - - -class WanT5Tokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer) - -class WanT5Model(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): - super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs) - -def te(dtype_t5=None, t5xxl_scaled_fp8=None): - class WanTEModel(WanT5Model): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["scaled_fp8"] = t5xxl_scaled_fp8 - if dtype_t5 is not None: - dtype = dtype_t5 - super().__init__(device=device, dtype=dtype, model_options=model_options) - return WanTEModel diff --git a/comfy/utils.py b/comfy/utils.py deleted file mode 100644 index fab28cf088c0049de71527268d0bb4dadc9cfe7e..0000000000000000000000000000000000000000 --- a/comfy/utils.py +++ /dev/null @@ -1,1104 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Comfy - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - - -import torch -import math -import struct -import comfy.checkpoint_pickle -import safetensors.torch -import numpy as np -from PIL import Image -import logging -import itertools -from torch.nn.functional import interpolate -from einops import rearrange -from comfy.cli_args import args - -MMAP_TORCH_FILES = args.mmap_torch_files -DISABLE_MMAP = args.disable_mmap - -ALWAYS_SAFE_LOAD = False -if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated - class ModelCheckpoint: - pass - ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint" - - from numpy.core.multiarray import scalar - from numpy import dtype - from numpy.dtypes import Float64DType - from _codecs import encode - - torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode]) - ALWAYS_SAFE_LOAD = True - logging.info("Checkpoint files will always be loaded safely.") -else: - logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.") - -def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False): - if device is None: - device = torch.device("cpu") - metadata = None - if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): - try: - with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f: - sd = {} - for k in f.keys(): - tensor = f.get_tensor(k) - if DISABLE_MMAP: # TODO: Not sure if this is the best way to bypass the mmap issues - tensor = tensor.to(device=device, copy=True) - sd[k] = tensor - if return_metadata: - metadata = f.metadata() - except Exception as e: - if len(e.args) > 0: - message = e.args[0] - if "HeaderTooLarge" in message: - raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt)) - if "MetadataIncompleteBuffer" in message: - raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt)) - raise e - else: - torch_args = {} - if MMAP_TORCH_FILES: - torch_args["mmap"] = True - - if safe_load or ALWAYS_SAFE_LOAD: - pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args) - else: - logging.warning("WARNING: loading {} unsafely, upgrade your pytorch to 2.4 or newer to load this file safely.".format(ckpt)) - pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) - if "state_dict" in pl_sd: - sd = pl_sd["state_dict"] - else: - if len(pl_sd) == 1: - key = list(pl_sd.keys())[0] - sd = pl_sd[key] - if not isinstance(sd, dict): - sd = pl_sd - else: - sd = pl_sd - return (sd, metadata) if return_metadata else sd - -def save_torch_file(sd, ckpt, metadata=None): - if metadata is not None: - safetensors.torch.save_file(sd, ckpt, metadata=metadata) - else: - safetensors.torch.save_file(sd, ckpt) - -def calculate_parameters(sd, prefix=""): - params = 0 - for k in sd.keys(): - if k.startswith(prefix): - w = sd[k] - params += w.nelement() - return params - -def weight_dtype(sd, prefix=""): - dtypes = {} - for k in sd.keys(): - if k.startswith(prefix): - w = sd[k] - dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel() - - if len(dtypes) == 0: - return None - - return max(dtypes, key=dtypes.get) - -def state_dict_key_replace(state_dict, keys_to_replace): - for x in keys_to_replace: - if x in state_dict: - state_dict[keys_to_replace[x]] = state_dict.pop(x) - return state_dict - -def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): - if filter_keys: - out = {} - else: - out = state_dict - for rp in replace_prefix: - replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) - for x in replace: - w = state_dict.pop(x[0]) - out[x[1]] = w - return out - - -def transformers_convert(sd, prefix_from, prefix_to, number): - keys_to_replace = { - "{}positional_embedding": "{}embeddings.position_embedding.weight", - "{}token_embedding.weight": "{}embeddings.token_embedding.weight", - "{}ln_final.weight": "{}final_layer_norm.weight", - "{}ln_final.bias": "{}final_layer_norm.bias", - } - - for k in keys_to_replace: - x = k.format(prefix_from) - if x in sd: - sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) - - resblock_to_replace = { - "ln_1": "layer_norm1", - "ln_2": "layer_norm2", - "mlp.c_fc": "mlp.fc1", - "mlp.c_proj": "mlp.fc2", - "attn.out_proj": "self_attn.out_proj", - } - - for resblock in range(number): - for x in resblock_to_replace: - for y in ["weight", "bias"]: - k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) - k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) - if k in sd: - sd[k_to] = sd.pop(k) - - for y in ["weight", "bias"]: - k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) - if k_from in sd: - weights = sd.pop(k_from) - shape_from = weights.shape[0] // 3 - for x in range(3): - p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] - k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) - sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] - - return sd - -def clip_text_transformers_convert(sd, prefix_from, prefix_to): - sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32) - - tp = "{}text_projection.weight".format(prefix_from) - if tp in sd: - sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp) - - tp = "{}text_projection".format(prefix_from) - if tp in sd: - sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous() - return sd - - -UNET_MAP_ATTENTIONS = { - "proj_in.weight", - "proj_in.bias", - "proj_out.weight", - "proj_out.bias", - "norm.weight", - "norm.bias", -} - -TRANSFORMER_BLOCKS = { - "norm1.weight", - "norm1.bias", - "norm2.weight", - "norm2.bias", - "norm3.weight", - "norm3.bias", - "attn1.to_q.weight", - "attn1.to_k.weight", - "attn1.to_v.weight", - "attn1.to_out.0.weight", - "attn1.to_out.0.bias", - "attn2.to_q.weight", - "attn2.to_k.weight", - "attn2.to_v.weight", - "attn2.to_out.0.weight", - "attn2.to_out.0.bias", - "ff.net.0.proj.weight", - "ff.net.0.proj.bias", - "ff.net.2.weight", - "ff.net.2.bias", -} - -UNET_MAP_RESNET = { - "in_layers.2.weight": "conv1.weight", - "in_layers.2.bias": "conv1.bias", - "emb_layers.1.weight": "time_emb_proj.weight", - "emb_layers.1.bias": "time_emb_proj.bias", - "out_layers.3.weight": "conv2.weight", - "out_layers.3.bias": "conv2.bias", - "skip_connection.weight": "conv_shortcut.weight", - "skip_connection.bias": "conv_shortcut.bias", - "in_layers.0.weight": "norm1.weight", - "in_layers.0.bias": "norm1.bias", - "out_layers.0.weight": "norm2.weight", - "out_layers.0.bias": "norm2.bias", -} - -UNET_MAP_BASIC = { - ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), - ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), - ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), - ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), - ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), - ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), - ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), - ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias") -} - -def unet_to_diffusers(unet_config): - if "num_res_blocks" not in unet_config: - return {} - num_res_blocks = unet_config["num_res_blocks"] - channel_mult = unet_config["channel_mult"] - transformer_depth = unet_config["transformer_depth"][:] - transformer_depth_output = unet_config["transformer_depth_output"][:] - num_blocks = len(channel_mult) - - transformers_mid = unet_config.get("transformer_depth_middle", None) - - diffusers_unet_map = {} - for x in range(num_blocks): - n = 1 + (num_res_blocks[x] + 1) * x - for i in range(num_res_blocks[x]): - for b in UNET_MAP_RESNET: - diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) - num_transformers = transformer_depth.pop(0) - if num_transformers > 0: - for b in UNET_MAP_ATTENTIONS: - diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) - for t in range(num_transformers): - for b in TRANSFORMER_BLOCKS: - diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) - n += 1 - for k in ["weight", "bias"]: - diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) - - i = 0 - for b in UNET_MAP_ATTENTIONS: - diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) - for t in range(transformers_mid): - for b in TRANSFORMER_BLOCKS: - diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) - - for i, n in enumerate([0, 2]): - for b in UNET_MAP_RESNET: - diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) - - num_res_blocks = list(reversed(num_res_blocks)) - for x in range(num_blocks): - n = (num_res_blocks[x] + 1) * x - l = num_res_blocks[x] + 1 - for i in range(l): - c = 0 - for b in UNET_MAP_RESNET: - diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) - c += 1 - num_transformers = transformer_depth_output.pop() - if num_transformers > 0: - c += 1 - for b in UNET_MAP_ATTENTIONS: - diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) - for t in range(num_transformers): - for b in TRANSFORMER_BLOCKS: - diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) - if i == l - 1: - for k in ["weight", "bias"]: - diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) - n += 1 - - for k in UNET_MAP_BASIC: - diffusers_unet_map[k[1]] = k[0] - - return diffusers_unet_map - -def swap_scale_shift(weight): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - -MMDIT_MAP_BASIC = { - ("context_embedder.bias", "context_embedder.bias"), - ("context_embedder.weight", "context_embedder.weight"), - ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"), - ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"), - ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"), - ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"), - ("x_embedder.proj.bias", "pos_embed.proj.bias"), - ("x_embedder.proj.weight", "pos_embed.proj.weight"), - ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"), - ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"), - ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"), - ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"), - ("pos_embed", "pos_embed.pos_embed"), - ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), - ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), - ("final_layer.linear.bias", "proj_out.bias"), - ("final_layer.linear.weight", "proj_out.weight"), -} - -MMDIT_MAP_BLOCK = { - ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"), - ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"), - ("context_block.attn.proj.bias", "attn.to_add_out.bias"), - ("context_block.attn.proj.weight", "attn.to_add_out.weight"), - ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"), - ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"), - ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"), - ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"), - ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"), - ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"), - ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"), - ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"), - ("x_block.attn.proj.bias", "attn.to_out.0.bias"), - ("x_block.attn.proj.weight", "attn.to_out.0.weight"), - ("x_block.attn.ln_q.weight", "attn.norm_q.weight"), - ("x_block.attn.ln_k.weight", "attn.norm_k.weight"), - ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"), - ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"), - ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"), - ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"), - ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"), - ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"), - ("x_block.mlp.fc2.bias", "ff.net.2.bias"), - ("x_block.mlp.fc2.weight", "ff.net.2.weight"), -} - -def mmdit_to_diffusers(mmdit_config, output_prefix=""): - key_map = {} - - depth = mmdit_config.get("depth", 0) - num_blocks = mmdit_config.get("num_blocks", depth) - for i in range(num_blocks): - block_from = "transformer_blocks.{}".format(i) - block_to = "{}joint_blocks.{}".format(output_prefix, i) - - offset = depth * 64 - - for end in ("weight", "bias"): - k = "{}.attn.".format(block_from) - qkv = "{}.x_block.attn.qkv.{}".format(block_to, end) - key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) - key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) - key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) - - qkv = "{}.context_block.attn.qkv.{}".format(block_to, end) - key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset)) - key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset)) - key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) - - k = "{}.attn2.".format(block_from) - qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end) - key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset)) - key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset)) - key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset)) - - for k in MMDIT_MAP_BLOCK: - key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) - - map_basic = MMDIT_MAP_BASIC.copy() - map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift)) - map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift)) - - for k in map_basic: - if len(k) > 2: - key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) - else: - key_map[k[1]] = "{}{}".format(output_prefix, k[0]) - - return key_map - -PIXART_MAP_BASIC = { - ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"), - ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"), - ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"), - ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"), - ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"), - ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"), - ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"), - ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"), - ("x_embedder.proj.weight", "pos_embed.proj.weight"), - ("x_embedder.proj.bias", "pos_embed.proj.bias"), - ("y_embedder.y_embedding", "caption_projection.y_embedding"), - ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"), - ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"), - ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"), - ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"), - ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"), - ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"), - ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"), - ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"), - ("t_block.1.weight", "adaln_single.linear.weight"), - ("t_block.1.bias", "adaln_single.linear.bias"), - ("final_layer.linear.weight", "proj_out.weight"), - ("final_layer.linear.bias", "proj_out.bias"), - ("final_layer.scale_shift_table", "scale_shift_table"), -} - -PIXART_MAP_BLOCK = { - ("scale_shift_table", "scale_shift_table"), - ("attn.proj.weight", "attn1.to_out.0.weight"), - ("attn.proj.bias", "attn1.to_out.0.bias"), - ("mlp.fc1.weight", "ff.net.0.proj.weight"), - ("mlp.fc1.bias", "ff.net.0.proj.bias"), - ("mlp.fc2.weight", "ff.net.2.weight"), - ("mlp.fc2.bias", "ff.net.2.bias"), - ("cross_attn.proj.weight" ,"attn2.to_out.0.weight"), - ("cross_attn.proj.bias" ,"attn2.to_out.0.bias"), -} - -def pixart_to_diffusers(mmdit_config, output_prefix=""): - key_map = {} - - depth = mmdit_config.get("depth", 0) - offset = mmdit_config.get("hidden_size", 1152) - - for i in range(depth): - block_from = "transformer_blocks.{}".format(i) - block_to = "{}blocks.{}".format(output_prefix, i) - - for end in ("weight", "bias"): - s = "{}.attn1.".format(block_from) - qkv = "{}.attn.qkv.{}".format(block_to, end) - key_map["{}to_q.{}".format(s, end)] = (qkv, (0, 0, offset)) - key_map["{}to_k.{}".format(s, end)] = (qkv, (0, offset, offset)) - key_map["{}to_v.{}".format(s, end)] = (qkv, (0, offset * 2, offset)) - - s = "{}.attn2.".format(block_from) - q = "{}.cross_attn.q_linear.{}".format(block_to, end) - kv = "{}.cross_attn.kv_linear.{}".format(block_to, end) - - key_map["{}to_q.{}".format(s, end)] = q - key_map["{}to_k.{}".format(s, end)] = (kv, (0, 0, offset)) - key_map["{}to_v.{}".format(s, end)] = (kv, (0, offset, offset)) - - for k in PIXART_MAP_BLOCK: - key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0]) - - for k in PIXART_MAP_BASIC: - key_map[k[1]] = "{}{}".format(output_prefix, k[0]) - - return key_map - -def auraflow_to_diffusers(mmdit_config, output_prefix=""): - n_double_layers = mmdit_config.get("n_double_layers", 0) - n_layers = mmdit_config.get("n_layers", 0) - - key_map = {} - for i in range(n_layers): - if i < n_double_layers: - index = i - prefix_from = "joint_transformer_blocks" - prefix_to = "{}double_layers".format(output_prefix) - block_map = { - "attn.to_q.weight": "attn.w2q.weight", - "attn.to_k.weight": "attn.w2k.weight", - "attn.to_v.weight": "attn.w2v.weight", - "attn.to_out.0.weight": "attn.w2o.weight", - "attn.add_q_proj.weight": "attn.w1q.weight", - "attn.add_k_proj.weight": "attn.w1k.weight", - "attn.add_v_proj.weight": "attn.w1v.weight", - "attn.to_add_out.weight": "attn.w1o.weight", - "ff.linear_1.weight": "mlpX.c_fc1.weight", - "ff.linear_2.weight": "mlpX.c_fc2.weight", - "ff.out_projection.weight": "mlpX.c_proj.weight", - "ff_context.linear_1.weight": "mlpC.c_fc1.weight", - "ff_context.linear_2.weight": "mlpC.c_fc2.weight", - "ff_context.out_projection.weight": "mlpC.c_proj.weight", - "norm1.linear.weight": "modX.1.weight", - "norm1_context.linear.weight": "modC.1.weight", - } - else: - index = i - n_double_layers - prefix_from = "single_transformer_blocks" - prefix_to = "{}single_layers".format(output_prefix) - - block_map = { - "attn.to_q.weight": "attn.w1q.weight", - "attn.to_k.weight": "attn.w1k.weight", - "attn.to_v.weight": "attn.w1v.weight", - "attn.to_out.0.weight": "attn.w1o.weight", - "norm1.linear.weight": "modCX.1.weight", - "ff.linear_1.weight": "mlp.c_fc1.weight", - "ff.linear_2.weight": "mlp.c_fc2.weight", - "ff.out_projection.weight": "mlp.c_proj.weight" - } - - for k in block_map: - key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k]) - - MAP_BASIC = { - ("positional_encoding", "pos_embed.pos_embed"), - ("register_tokens", "register_tokens"), - ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"), - ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"), - ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"), - ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"), - ("cond_seq_linear.weight", "context_embedder.weight"), - ("init_x_linear.weight", "pos_embed.proj.weight"), - ("init_x_linear.bias", "pos_embed.proj.bias"), - ("final_linear.weight", "proj_out.weight"), - ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift), - } - - for k in MAP_BASIC: - if len(k) > 2: - key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) - else: - key_map[k[1]] = "{}{}".format(output_prefix, k[0]) - - return key_map - -def flux_to_diffusers(mmdit_config, output_prefix=""): - n_double_layers = mmdit_config.get("depth", 0) - n_single_layers = mmdit_config.get("depth_single_blocks", 0) - hidden_size = mmdit_config.get("hidden_size", 0) - - key_map = {} - for index in range(n_double_layers): - prefix_from = "transformer_blocks.{}".format(index) - prefix_to = "{}double_blocks.{}".format(output_prefix, index) - - for end in ("weight", "bias"): - k = "{}.attn.".format(prefix_from) - qkv = "{}.img_attn.qkv.{}".format(prefix_to, end) - key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) - key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) - key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) - - k = "{}.attn.".format(prefix_from) - qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end) - key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) - key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) - key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) - - block_map = { - "attn.to_out.0.weight": "img_attn.proj.weight", - "attn.to_out.0.bias": "img_attn.proj.bias", - "norm1.linear.weight": "img_mod.lin.weight", - "norm1.linear.bias": "img_mod.lin.bias", - "norm1_context.linear.weight": "txt_mod.lin.weight", - "norm1_context.linear.bias": "txt_mod.lin.bias", - "attn.to_add_out.weight": "txt_attn.proj.weight", - "attn.to_add_out.bias": "txt_attn.proj.bias", - "ff.net.0.proj.weight": "img_mlp.0.weight", - "ff.net.0.proj.bias": "img_mlp.0.bias", - "ff.net.2.weight": "img_mlp.2.weight", - "ff.net.2.bias": "img_mlp.2.bias", - "ff_context.net.0.proj.weight": "txt_mlp.0.weight", - "ff_context.net.0.proj.bias": "txt_mlp.0.bias", - "ff_context.net.2.weight": "txt_mlp.2.weight", - "ff_context.net.2.bias": "txt_mlp.2.bias", - "attn.norm_q.weight": "img_attn.norm.query_norm.scale", - "attn.norm_k.weight": "img_attn.norm.key_norm.scale", - "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale", - "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale", - } - - for k in block_map: - key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) - - for index in range(n_single_layers): - prefix_from = "single_transformer_blocks.{}".format(index) - prefix_to = "{}single_blocks.{}".format(output_prefix, index) - - for end in ("weight", "bias"): - k = "{}.attn.".format(prefix_from) - qkv = "{}.linear1.{}".format(prefix_to, end) - key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) - key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) - key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) - key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4)) - - block_map = { - "norm.linear.weight": "modulation.lin.weight", - "norm.linear.bias": "modulation.lin.bias", - "proj_out.weight": "linear2.weight", - "proj_out.bias": "linear2.bias", - "attn.norm_q.weight": "norm.query_norm.scale", - "attn.norm_k.weight": "norm.key_norm.scale", - } - - for k in block_map: - key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) - - MAP_BASIC = { - ("final_layer.linear.bias", "proj_out.bias"), - ("final_layer.linear.weight", "proj_out.weight"), - ("img_in.bias", "x_embedder.bias"), - ("img_in.weight", "x_embedder.weight"), - ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"), - ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"), - ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"), - ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"), - ("txt_in.bias", "context_embedder.bias"), - ("txt_in.weight", "context_embedder.weight"), - ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"), - ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"), - ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"), - ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"), - ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"), - ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"), - ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"), - ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"), - ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), - ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), - ("pos_embed_input.bias", "controlnet_x_embedder.bias"), - ("pos_embed_input.weight", "controlnet_x_embedder.weight"), - } - - for k in MAP_BASIC: - if len(k) > 2: - key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) - else: - key_map[k[1]] = "{}{}".format(output_prefix, k[0]) - - return key_map - -def repeat_to_batch_size(tensor, batch_size, dim=0): - if tensor.shape[dim] > batch_size: - return tensor.narrow(dim, 0, batch_size) - elif tensor.shape[dim] < batch_size: - return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size) - return tensor - -def resize_to_batch_size(tensor, batch_size): - in_batch_size = tensor.shape[0] - if in_batch_size == batch_size: - return tensor - - if batch_size <= 1: - return tensor[:batch_size] - - output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) - if batch_size < in_batch_size: - scale = (in_batch_size - 1) / (batch_size - 1) - for i in range(batch_size): - output[i] = tensor[min(round(i * scale), in_batch_size - 1)] - else: - scale = in_batch_size / batch_size - for i in range(batch_size): - output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] - - return output - -def resize_list_to_batch_size(l, batch_size): - in_batch_size = len(l) - if in_batch_size == batch_size or in_batch_size == 0: - return l - - if batch_size <= 1: - return l[:batch_size] - - output = [] - if batch_size < in_batch_size: - scale = (in_batch_size - 1) / (batch_size - 1) - for i in range(batch_size): - output.append(l[min(round(i * scale), in_batch_size - 1)]) - else: - scale = in_batch_size / batch_size - for i in range(batch_size): - output.append(l[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]) - - return output - -def convert_sd_to(state_dict, dtype): - keys = list(state_dict.keys()) - for k in keys: - state_dict[k] = state_dict[k].to(dtype) - return state_dict - -def safetensors_header(safetensors_path, max_size=100*1024*1024): - with open(safetensors_path, "rb") as f: - header = f.read(8) - length_of_header = struct.unpack(' max_size: - return None - return f.read(length_of_header) - -def set_attr(obj, attr, value): - attrs = attr.split(".") - for name in attrs[:-1]: - obj = getattr(obj, name) - prev = getattr(obj, attrs[-1]) - setattr(obj, attrs[-1], value) - return prev - -def set_attr_param(obj, attr, value): - return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False)) - -def copy_to_param(obj, attr, value): - # inplace update tensor instead of replacing it - attrs = attr.split(".") - for name in attrs[:-1]: - obj = getattr(obj, name) - prev = getattr(obj, attrs[-1]) - prev.data.copy_(value) - -def get_attr(obj, attr: str): - """Retrieves a nested attribute from an object using dot notation. - - Args: - obj: The object to get the attribute from - attr (str): The attribute path using dot notation (e.g. "model.layer.weight") - - Returns: - The value of the requested attribute - - Example: - model = MyModel() - weight = get_attr(model, "layer1.conv.weight") - # Equivalent to: model.layer1.conv.weight - - Important: - Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when - accessing nested model objects under `ModelPatcher.model`. - """ - attrs = attr.split(".") - for name in attrs: - obj = getattr(obj, name) - return obj - -def bislerp(samples, width, height): - def slerp(b1, b2, r): - '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' - - c = b1.shape[-1] - - #norms - b1_norms = torch.norm(b1, dim=-1, keepdim=True) - b2_norms = torch.norm(b2, dim=-1, keepdim=True) - - #normalize - b1_normalized = b1 / b1_norms - b2_normalized = b2 / b2_norms - - #zero when norms are zero - b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 - b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 - - #slerp - dot = (b1_normalized*b2_normalized).sum(1) - omega = torch.acos(dot) - so = torch.sin(omega) - - #technically not mathematically correct, but more pleasing? - res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized - res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) - - #edge cases for same or polar opposites - res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] - res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] - return res - - def generate_bilinear_data(length_old, length_new, device): - coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) - coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") - ratios = coords_1 - coords_1.floor() - coords_1 = coords_1.to(torch.int64) - - coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 - coords_2[:,:,:,-1] -= 1 - coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") - coords_2 = coords_2.to(torch.int64) - return ratios, coords_1, coords_2 - - orig_dtype = samples.dtype - samples = samples.float() - n,c,h,w = samples.shape - h_new, w_new = (height, width) - - #linear w - ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) - coords_1 = coords_1.expand((n, c, h, -1)) - coords_2 = coords_2.expand((n, c, h, -1)) - ratios = ratios.expand((n, 1, h, -1)) - - pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) - pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) - ratios = ratios.movedim(1, -1).reshape((-1,1)) - - result = slerp(pass_1, pass_2, ratios) - result = result.reshape(n, h, w_new, c).movedim(-1, 1) - - #linear h - ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) - coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) - coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) - ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) - - pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) - pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) - ratios = ratios.movedim(1, -1).reshape((-1,1)) - - result = slerp(pass_1, pass_2, ratios) - result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) - return result.to(orig_dtype) - -def lanczos(samples, width, height): - images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] - images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] - images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] - result = torch.stack(images) - return result.to(samples.device, samples.dtype) - -def common_upscale(samples, width, height, upscale_method, crop): - orig_shape = tuple(samples.shape) - if len(orig_shape) > 4: - samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1]) - samples = samples.movedim(2, 1) - samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1]) - if crop == "center": - old_width = samples.shape[-1] - old_height = samples.shape[-2] - old_aspect = old_width / old_height - new_aspect = width / height - x = 0 - y = 0 - if old_aspect > new_aspect: - x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) - elif old_aspect < new_aspect: - y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) - s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2) - else: - s = samples - - if upscale_method == "bislerp": - out = bislerp(s, width, height) - elif upscale_method == "lanczos": - out = lanczos(s, width, height) - else: - out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) - - if len(orig_shape) == 4: - return out - - out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width)) - return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width)) - -def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): - rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap)) - cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap)) - return rows * cols - -@torch.inference_mode() -def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None): - dims = len(tile) - - if not (isinstance(upscale_amount, (tuple, list))): - upscale_amount = [upscale_amount] * dims - - if not (isinstance(overlap, (tuple, list))): - overlap = [overlap] * dims - - if index_formulas is None: - index_formulas = upscale_amount - - if not (isinstance(index_formulas, (tuple, list))): - index_formulas = [index_formulas] * dims - - def get_upscale(dim, val): - up = upscale_amount[dim] - if callable(up): - return up(val) - else: - return up * val - - def get_downscale(dim, val): - up = upscale_amount[dim] - if callable(up): - return up(val) - else: - return val / up - - def get_upscale_pos(dim, val): - up = index_formulas[dim] - if callable(up): - return up(val) - else: - return up * val - - def get_downscale_pos(dim, val): - up = index_formulas[dim] - if callable(up): - return up(val) - else: - return val / up - - if downscale: - get_scale = get_downscale - get_pos = get_downscale_pos - else: - get_scale = get_upscale - get_pos = get_upscale_pos - - def mult_list_upscale(a): - out = [] - for i in range(len(a)): - out.append(round(get_scale(i, a[i]))) - return out - - output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device) - - for b in range(samples.shape[0]): - s = samples[b:b+1] - - # handle entire input fitting in a single tile - if all(s.shape[d+2] <= tile[d] for d in range(dims)): - output[b:b+1] = function(s).to(output_device) - if pbar is not None: - pbar.update(1) - continue - - out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) - out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device) - - positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)] - - for it in itertools.product(*positions): - s_in = s - upscaled = [] - - for d in range(dims): - pos = max(0, min(s.shape[d + 2] - overlap[d], it[d])) - l = min(tile[d], s.shape[d + 2] - pos) - s_in = s_in.narrow(d + 2, pos, l) - upscaled.append(round(get_pos(d, pos))) - - ps = function(s_in).to(output_device) - mask = torch.ones_like(ps) - - for d in range(2, dims + 2): - feather = round(get_scale(d - 2, overlap[d - 2])) - if feather >= mask.shape[d]: - continue - for t in range(feather): - a = (t + 1) / feather - mask.narrow(d, t, 1).mul_(a) - mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a) - - o = out - o_d = out_div - for d in range(dims): - o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2]) - o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2]) - - o.add_(ps * mask) - o_d.add_(mask) - - if pbar is not None: - pbar.update(1) - - output[b:b+1] = out/out_div - return output - -def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): - return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar) - -PROGRESS_BAR_ENABLED = True -def set_progress_bar_enabled(enabled): - global PROGRESS_BAR_ENABLED - PROGRESS_BAR_ENABLED = enabled - -PROGRESS_BAR_HOOK = None -def set_progress_bar_global_hook(function): - global PROGRESS_BAR_HOOK - PROGRESS_BAR_HOOK = function - -class ProgressBar: - def __init__(self, total, node_id=None): - global PROGRESS_BAR_HOOK - self.total = total - self.current = 0 - self.hook = PROGRESS_BAR_HOOK - self.node_id = node_id - - def update_absolute(self, value, total=None, preview=None): - if total is not None: - self.total = total - if value > self.total: - value = self.total - self.current = value - if self.hook is not None: - self.hook(self.current, self.total, preview, node_id=self.node_id) - - def update(self, value): - self.update_absolute(self.current + value) - -def reshape_mask(input_mask, output_shape): - dims = len(output_shape) - 2 - - if dims == 1: - scale_mode = "linear" - - if dims == 2: - input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1])) - scale_mode = "bilinear" - - if dims == 3: - if len(input_mask.shape) < 5: - input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1])) - scale_mode = "trilinear" - - mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode) - if mask.shape[1] < output_shape[1]: - mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]] - mask = repeat_to_batch_size(mask, output_shape[0]) - return mask - -def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out): - hi, wi = img_size_in - ho, wo = img_size_out - # if it's already the correct size, no need to do anything - if (hi, wi) == (ho, wo): - return mask - if mask.ndim == 2: - mask = mask.unsqueeze(0) - if mask.ndim != 3: - raise ValueError(f"Got a mask of shape {list(mask.shape)}, expected [b, q, k] or [q, k]") - txt_tokens = mask.shape[1] - (hi * wi) - # quadrants of the mask - txt_to_txt = mask[:, :txt_tokens, :txt_tokens] - txt_to_img = mask[:, :txt_tokens, txt_tokens:] - img_to_img = mask[:, txt_tokens:, txt_tokens:] - img_to_txt = mask[:, txt_tokens:, :txt_tokens] - - # convert to 1d x 2d, interpolate, then back to 1d x 1d - txt_to_img = rearrange (txt_to_img, "b t (h w) -> b t h w", h=hi, w=wi) - txt_to_img = interpolate(txt_to_img, size=img_size_out, mode="bilinear") - txt_to_img = rearrange (txt_to_img, "b t h w -> b t (h w)") - # this one is hard because we have to do it twice - # convert to 1d x 2d, interpolate, then to 2d x 1d, interpolate, then 1d x 1d - img_to_img = rearrange (img_to_img, "b hw (h w) -> b hw h w", h=hi, w=wi) - img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear") - img_to_img = rearrange (img_to_img, "b (hk wk) hq wq -> b (hq wq) hk wk", hk=hi, wk=wi) - img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear") - img_to_img = rearrange (img_to_img, "b (hq wq) hk wk -> b (hk wk) (hq wq)", hq=ho, wq=wo) - # convert to 2d x 1d, interpolate, then back to 1d x 1d - img_to_txt = rearrange (img_to_txt, "b (h w) t -> b t h w", h=hi, w=wi) - img_to_txt = interpolate(img_to_txt, size=img_size_out, mode="bilinear") - img_to_txt = rearrange (img_to_txt, "b t h w -> b (h w) t") - - # reassemble the mask from blocks - out = torch.cat([ - torch.cat([txt_to_txt, txt_to_img], dim=2), - torch.cat([img_to_txt, img_to_img], dim=2)], - dim=1 - ) - return out diff --git a/comfy/weight_adapter/.DS_Store b/comfy/weight_adapter/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy/weight_adapter/.DS_Store and /dev/null differ diff --git a/comfy/weight_adapter/__init__.py b/comfy/weight_adapter/__init__.py deleted file mode 100644 index b40f920e400fc895443d4016076d1ded406ae258..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/__init__.py +++ /dev/null @@ -1,34 +0,0 @@ -from .base import WeightAdapterBase, WeightAdapterTrainBase -from .lora import LoRAAdapter -from .loha import LoHaAdapter -from .lokr import LoKrAdapter -from .glora import GLoRAAdapter -from .oft import OFTAdapter -from .boft import BOFTAdapter - - -adapters: list[type[WeightAdapterBase]] = [ - LoRAAdapter, - LoHaAdapter, - LoKrAdapter, - GLoRAAdapter, - OFTAdapter, - BOFTAdapter, -] -adapter_maps: dict[str, type[WeightAdapterBase]] = { - "LoRA": LoRAAdapter, - "LoHa": LoHaAdapter, - "LoKr": LoKrAdapter, - "OFT": OFTAdapter, - ## We disable not implemented algo for now - # "GLoRA": GLoRAAdapter, - # "BOFT": BOFTAdapter, -} - - -__all__ = [ - "WeightAdapterBase", - "WeightAdapterTrainBase", - "adapters", - "adapter_maps", -] + [a.__name__ for a in adapters] diff --git a/comfy/weight_adapter/base.py b/comfy/weight_adapter/base.py deleted file mode 100644 index 43644b106b0f647b4534af45f0273c38c7ed319b..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/base.py +++ /dev/null @@ -1,175 +0,0 @@ -from typing import Optional - -import torch -import torch.nn as nn - -import comfy.model_management - - -class WeightAdapterBase: - name: str - loaded_keys: set[str] - weights: list[torch.Tensor] - - @classmethod - def load(cls, x: str, lora: dict[str, torch.Tensor], alpha: float, dora_scale: torch.Tensor) -> Optional["WeightAdapterBase"]: - raise NotImplementedError - - def to_train(self) -> "WeightAdapterTrainBase": - raise NotImplementedError - - @classmethod - def create_train(cls, weight, *args) -> "WeightAdapterTrainBase": - """ - weight: The original weight tensor to be modified. - *args: Additional arguments for configuration, such as rank, alpha etc. - """ - raise NotImplementedError - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - raise NotImplementedError - - -class WeightAdapterTrainBase(nn.Module): - # We follow the scheme of PR #7032 - def __init__(self): - super().__init__() - - def __call__(self, w): - """ - w: The original weight tensor to be modified. - """ - raise NotImplementedError - - def passive_memory_usage(self): - raise NotImplementedError("passive_memory_usage is not implemented") - - def move_to(self, device): - self.to(device) - return self.passive_memory_usage() - - -def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): - dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) - lora_diff *= alpha - weight_calc = weight + function(lora_diff).type(weight.dtype) - - wd_on_output_axis = dora_scale.shape[0] == weight_calc.shape[0] - if wd_on_output_axis: - weight_norm = ( - weight.reshape(weight.shape[0], -1) - .norm(dim=1, keepdim=True) - .reshape(weight.shape[0], *[1] * (weight.dim() - 1)) - ) - else: - weight_norm = ( - weight_calc.transpose(0, 1) - .reshape(weight_calc.shape[1], -1) - .norm(dim=1, keepdim=True) - .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) - .transpose(0, 1) - ) - weight_norm = weight_norm + torch.finfo(weight.dtype).eps - - weight_calc *= (dora_scale / weight_norm).type(weight.dtype) - if strength != 1.0: - weight_calc -= weight - weight += strength * (weight_calc) - else: - weight[:] = weight_calc - return weight - - -def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: - """ - Pad a tensor to a new shape with zeros. - - Args: - tensor (torch.Tensor): The original tensor to be padded. - new_shape (List[int]): The desired shape of the padded tensor. - - Returns: - torch.Tensor: A new tensor padded with zeros to the specified shape. - - Note: - If the new shape is smaller than the original tensor in any dimension, - the original tensor will be truncated in that dimension. - """ - if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): - raise ValueError("The new shape must be larger than the original tensor in all dimensions") - - if len(new_shape) != len(tensor.shape): - raise ValueError("The new shape must have the same number of dimensions as the original tensor") - - # Create a new tensor filled with zeros - padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) - - # Create slicing tuples for both tensors - orig_slices = tuple(slice(0, dim) for dim in tensor.shape) - new_slices = tuple(slice(0, dim) for dim in tensor.shape) - - # Copy the original tensor into the new tensor - padded_tensor[new_slices] = tensor[orig_slices] - - return padded_tensor - - -def tucker_weight_from_conv(up, down, mid): - up = up.reshape(up.size(0), up.size(1)) - down = down.reshape(down.size(0), down.size(1)) - return torch.einsum("m n ..., i m, n j -> i j ...", mid, up, down) - - -def tucker_weight(wa, wb, t): - temp = torch.einsum("i j ..., j r -> i r ...", t, wb) - return torch.einsum("i j ..., i r -> r j ...", temp, wa) - - -def factorization(dimension: int, factor: int = -1) -> tuple[int, int]: - """ - return a tuple of two value of input dimension decomposed by the number closest to factor - second value is higher or equal than first value. - - examples) - factor - -1 2 4 8 16 ... - 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 - 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16 - 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25 - 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30 - 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32 - 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64 - """ - - if factor > 0 and (dimension % factor) == 0 and dimension >= factor**2: - m = factor - n = dimension // factor - if m > n: - n, m = m, n - return m, n - if factor < 0: - factor = dimension - m, n = 1, dimension - length = m + n - while m < n: - new_m = m + 1 - while dimension % new_m != 0: - new_m += 1 - new_n = dimension // new_m - if new_m + new_n > length or new_m > factor: - break - else: - m, n = new_m, new_n - if m > n: - n, m = m, n - return m, n diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py deleted file mode 100644 index b2a2f1bd46bed5b177f9c583e8d329dc167af1c3..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/boft.py +++ /dev/null @@ -1,115 +0,0 @@ -import logging -from typing import Optional - -import torch -import comfy.model_management -from .base import WeightAdapterBase, weight_decompose - - -class BOFTAdapter(WeightAdapterBase): - name = "boft" - - def __init__(self, loaded_keys, weights): - self.loaded_keys = loaded_keys - self.weights = weights - - @classmethod - def load( - cls, - x: str, - lora: dict[str, torch.Tensor], - alpha: float, - dora_scale: torch.Tensor, - loaded_keys: set[str] = None, - ) -> Optional["BOFTAdapter"]: - if loaded_keys is None: - loaded_keys = set() - blocks_name = "{}.oft_blocks".format(x) - rescale_name = "{}.rescale".format(x) - - blocks = None - if blocks_name in lora.keys(): - blocks = lora[blocks_name] - if blocks.ndim == 4: - loaded_keys.add(blocks_name) - else: - blocks = None - if blocks is None: - return None - - rescale = None - if rescale_name in lora.keys(): - rescale = lora[rescale_name] - loaded_keys.add(rescale_name) - - weights = (blocks, rescale, alpha, dora_scale) - return cls(loaded_keys, weights) - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - v = self.weights - blocks = v[0] - rescale = v[1] - alpha = v[2] - dora_scale = v[3] - - blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) - if rescale is not None: - rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) - - boft_m, block_num, boft_b, *_ = blocks.shape - - try: - # Get r - I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype) - # for Q = -Q^T - q = blocks - blocks.transpose(-1, -2) - normed_q = q - if alpha > 0: # alpha in boft/bboft is for constraint - q_norm = torch.norm(q) + 1e-8 - if q_norm > alpha: - normed_q = q * alpha / q_norm - # use float() to prevent unsupported type in .inverse() - r = (I + normed_q) @ (I - normed_q).float().inverse() - r = r.to(weight) - inp = org = weight - - r_b = boft_b//2 - for i in range(boft_m): - bi = r[i] - g = 2 - k = 2**i * r_b - if strength != 1: - bi = bi * strength + (1-strength) * I - inp = ( - inp.unflatten(0, (-1, g, k)) - .transpose(1, 2) - .flatten(0, 2) - .unflatten(0, (-1, boft_b)) - ) - inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp) - inp = ( - inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2) - ) - - if rescale is not None: - inp = inp * rescale - - lora_diff = inp - org - lora_diff = comfy.model_management.cast_to_device(lora_diff, weight.device, intermediate_dtype) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function((strength * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(self.name, key, e)) - return weight diff --git a/comfy/weight_adapter/glora.py b/comfy/weight_adapter/glora.py deleted file mode 100644 index 939abbba584549b049da0073b4336efab05e1b65..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/glora.py +++ /dev/null @@ -1,93 +0,0 @@ -import logging -from typing import Optional - -import torch -import comfy.model_management -from .base import WeightAdapterBase, weight_decompose - - -class GLoRAAdapter(WeightAdapterBase): - name = "glora" - - def __init__(self, loaded_keys, weights): - self.loaded_keys = loaded_keys - self.weights = weights - - @classmethod - def load( - cls, - x: str, - lora: dict[str, torch.Tensor], - alpha: float, - dora_scale: torch.Tensor, - loaded_keys: set[str] = None, - ) -> Optional["GLoRAAdapter"]: - if loaded_keys is None: - loaded_keys = set() - a1_name = "{}.a1.weight".format(x) - a2_name = "{}.a2.weight".format(x) - b1_name = "{}.b1.weight".format(x) - b2_name = "{}.b2.weight".format(x) - if a1_name in lora: - weights = (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale) - loaded_keys.add(a1_name) - loaded_keys.add(a2_name) - loaded_keys.add(b1_name) - loaded_keys.add(b2_name) - return cls(loaded_keys, weights) - else: - return None - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - v = self.weights - dora_scale = v[5] - - old_glora = False - if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: - rank = v[0].shape[0] - old_glora = True - - if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: - if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: - pass - else: - old_glora = False - rank = v[1].shape[0] - - a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) - a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) - b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) - b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) - - if v[4] is not None: - alpha = v[4] / rank - else: - alpha = 1.0 - - try: - if old_glora: - lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora - else: - if weight.dim() > 2: - lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) - else: - lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) - lora_diff += torch.mm(b1, b2).reshape(weight.shape) - - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(self.name, key, e)) - return weight diff --git a/comfy/weight_adapter/loha.py b/comfy/weight_adapter/loha.py deleted file mode 100644 index 55c97a3af0185029e03a68bb9a722d571051b407..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/loha.py +++ /dev/null @@ -1,232 +0,0 @@ -import logging -from typing import Optional - -import torch -import comfy.model_management -from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose - - -class HadaWeight(torch.autograd.Function): - @staticmethod - def forward(ctx, w1u, w1d, w2u, w2d, scale=torch.tensor(1)): - ctx.save_for_backward(w1d, w1u, w2d, w2u, scale) - diff_weight = ((w1u @ w1d) * (w2u @ w2d)) * scale - return diff_weight - - @staticmethod - def backward(ctx, grad_out): - (w1d, w1u, w2d, w2u, scale) = ctx.saved_tensors - grad_out = grad_out * scale - temp = grad_out * (w2u @ w2d) - grad_w1u = temp @ w1d.T - grad_w1d = w1u.T @ temp - - temp = grad_out * (w1u @ w1d) - grad_w2u = temp @ w2d.T - grad_w2d = w2u.T @ temp - - del temp - return grad_w1u, grad_w1d, grad_w2u, grad_w2d, None - - -class HadaWeightTucker(torch.autograd.Function): - @staticmethod - def forward(ctx, t1, w1u, w1d, t2, w2u, w2d, scale=torch.tensor(1)): - ctx.save_for_backward(t1, w1d, w1u, t2, w2d, w2u, scale) - - rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1d, w1u) - rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2d, w2u) - - return rebuild1 * rebuild2 * scale - - @staticmethod - def backward(ctx, grad_out): - (t1, w1d, w1u, t2, w2d, w2u, scale) = ctx.saved_tensors - grad_out = grad_out * scale - - temp = torch.einsum("i j ..., j r -> i r ...", t2, w2d) - rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w2u) - - grad_w = rebuild * grad_out - del rebuild - - grad_w1u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w) - grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w1u.T) - del grad_w, temp - - grad_w1d = torch.einsum("i r ..., i j ... -> r j", t1, grad_temp) - grad_t1 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w1d.T) - del grad_temp - - temp = torch.einsum("i j ..., j r -> i r ...", t1, w1d) - rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w1u) - - grad_w = rebuild * grad_out - del rebuild - - grad_w2u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w) - grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w2u.T) - del grad_w, temp - - grad_w2d = torch.einsum("i r ..., i j ... -> r j", t2, grad_temp) - grad_t2 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w2d.T) - del grad_temp - return grad_t1, grad_w1u, grad_w1d, grad_t2, grad_w2u, grad_w2d, None - - -class LohaDiff(WeightAdapterTrainBase): - def __init__(self, weights): - super().__init__() - # Unpack weights tuple from LoHaAdapter - w1a, w1b, alpha, w2a, w2b, t1, t2, _ = weights - - # Create trainable parameters - self.hada_w1_a = torch.nn.Parameter(w1a) - self.hada_w1_b = torch.nn.Parameter(w1b) - self.hada_w2_a = torch.nn.Parameter(w2a) - self.hada_w2_b = torch.nn.Parameter(w2b) - - self.use_tucker = False - if t1 is not None and t2 is not None: - self.use_tucker = True - self.hada_t1 = torch.nn.Parameter(t1) - self.hada_t2 = torch.nn.Parameter(t2) - else: - # Keep the attributes for consistent access - self.hada_t1 = None - self.hada_t2 = None - - # Store rank and non-trainable alpha - self.rank = w1b.shape[0] - self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) - - def __call__(self, w): - org_dtype = w.dtype - - scale = self.alpha / self.rank - if self.use_tucker: - diff_weight = HadaWeightTucker.apply(self.hada_t1, self.hada_w1_a, self.hada_w1_b, self.hada_t2, self.hada_w2_a, self.hada_w2_b, scale) - else: - diff_weight = HadaWeight.apply(self.hada_w1_a, self.hada_w1_b, self.hada_w2_a, self.hada_w2_b, scale) - - # Add the scaled difference to the original weight - weight = w.to(diff_weight) + diff_weight.reshape(w.shape) - - return weight.to(org_dtype) - - def passive_memory_usage(self): - """Calculates memory usage of the trainable parameters.""" - return sum(param.numel() * param.element_size() for param in self.parameters()) - - -class LoHaAdapter(WeightAdapterBase): - name = "loha" - - def __init__(self, loaded_keys, weights): - self.loaded_keys = loaded_keys - self.weights = weights - - @classmethod - def create_train(cls, weight, rank=1, alpha=1.0): - out_dim = weight.shape[0] - in_dim = weight.shape[1:].numel() - mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype) - mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype) - torch.nn.init.normal_(mat1, 0.1) - torch.nn.init.constant_(mat2, 0.0) - mat3 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype) - mat4 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype) - torch.nn.init.normal_(mat3, 0.1) - torch.nn.init.normal_(mat4, 0.01) - return LohaDiff( - (mat1, mat2, alpha, mat3, mat4, None, None, None) - ) - - def to_train(self): - return LohaDiff(self.weights) - - @classmethod - def load( - cls, - x: str, - lora: dict[str, torch.Tensor], - alpha: float, - dora_scale: torch.Tensor, - loaded_keys: set[str] = None, - ) -> Optional["LoHaAdapter"]: - if loaded_keys is None: - loaded_keys = set() - - hada_w1_a_name = "{}.hada_w1_a".format(x) - hada_w1_b_name = "{}.hada_w1_b".format(x) - hada_w2_a_name = "{}.hada_w2_a".format(x) - hada_w2_b_name = "{}.hada_w2_b".format(x) - hada_t1_name = "{}.hada_t1".format(x) - hada_t2_name = "{}.hada_t2".format(x) - if hada_w1_a_name in lora.keys(): - hada_t1 = None - hada_t2 = None - if hada_t1_name in lora.keys(): - hada_t1 = lora[hada_t1_name] - hada_t2 = lora[hada_t2_name] - loaded_keys.add(hada_t1_name) - loaded_keys.add(hada_t2_name) - - weights = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale) - loaded_keys.add(hada_w1_a_name) - loaded_keys.add(hada_w1_b_name) - loaded_keys.add(hada_w2_a_name) - loaded_keys.add(hada_w2_b_name) - return cls(loaded_keys, weights) - else: - return None - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - v = self.weights - w1a = v[0] - w1b = v[1] - if v[2] is not None: - alpha = v[2] / w1b.shape[0] - else: - alpha = 1.0 - - w2a = v[3] - w2b = v[4] - dora_scale = v[7] - if v[5] is not None: #cp decomposition - t1 = v[5] - t2 = v[6] - m1 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) - - m2 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) - else: - m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) - m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) - - try: - lora_diff = (m1 * m2).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(self.name, key, e)) - return weight diff --git a/comfy/weight_adapter/lokr.py b/comfy/weight_adapter/lokr.py deleted file mode 100644 index 563c835f55bab56c38a22666c9dd2674570d7fee..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/lokr.py +++ /dev/null @@ -1,220 +0,0 @@ -import logging -from typing import Optional - -import torch -import comfy.model_management -from .base import ( - WeightAdapterBase, - WeightAdapterTrainBase, - weight_decompose, - factorization, -) - - -class LokrDiff(WeightAdapterTrainBase): - def __init__(self, weights): - super().__init__() - (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) = weights - self.use_tucker = False - if lokr_w1_a is not None: - _, rank_a = lokr_w1_a.shape[0], lokr_w1_a.shape[1] - rank_a, _ = lokr_w1_b.shape[0], lokr_w1_b.shape[1] - self.lokr_w1_a = torch.nn.Parameter(lokr_w1_a) - self.lokr_w1_b = torch.nn.Parameter(lokr_w1_b) - self.w1_rebuild = True - self.ranka = rank_a - - if lokr_w2_a is not None: - _, rank_b = lokr_w2_a.shape[0], lokr_w2_a.shape[1] - rank_b, _ = lokr_w2_b.shape[0], lokr_w2_b.shape[1] - self.lokr_w2_a = torch.nn.Parameter(lokr_w2_a) - self.lokr_w2_b = torch.nn.Parameter(lokr_w2_b) - if lokr_t2 is not None: - self.use_tucker = True - self.lokr_t2 = torch.nn.Parameter(lokr_t2) - self.w2_rebuild = True - self.rankb = rank_b - - if lokr_w1 is not None: - self.lokr_w1 = torch.nn.Parameter(lokr_w1) - self.w1_rebuild = False - - if lokr_w2 is not None: - self.lokr_w2 = torch.nn.Parameter(lokr_w2) - self.w2_rebuild = False - - self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) - - @property - def w1(self): - if self.w1_rebuild: - return (self.lokr_w1_a @ self.lokr_w1_b) * (self.alpha / self.ranka) - else: - return self.lokr_w1 - - @property - def w2(self): - if self.w2_rebuild: - if self.use_tucker: - w2 = torch.einsum( - 'i j k l, j r, i p -> p r k l', - self.lokr_t2, - self.lokr_w2_b, - self.lokr_w2_a - ) - else: - w2 = self.lokr_w2_a @ self.lokr_w2_b - return w2 * (self.alpha / self.rankb) - else: - return self.lokr_w2 - - def __call__(self, w): - diff = torch.kron(self.w1, self.w2) - return w + diff.reshape(w.shape).to(w) - - def passive_memory_usage(self): - return sum(param.numel() * param.element_size() for param in self.parameters()) - - -class LoKrAdapter(WeightAdapterBase): - name = "lokr" - - def __init__(self, loaded_keys, weights): - self.loaded_keys = loaded_keys - self.weights = weights - - @classmethod - def create_train(cls, weight, rank=1, alpha=1.0): - out_dim = weight.shape[0] - in_dim = weight.shape[1:].numel() - out1, out2 = factorization(out_dim, rank) - in1, in2 = factorization(in_dim, rank) - mat1 = torch.empty(out1, in1, device=weight.device, dtype=weight.dtype) - mat2 = torch.empty(out2, in2, device=weight.device, dtype=weight.dtype) - torch.nn.init.kaiming_uniform_(mat2, a=5**0.5) - torch.nn.init.constant_(mat1, 0.0) - return LokrDiff( - (mat1, mat2, alpha, None, None, None, None, None, None) - ) - - def to_train(self): - return LokrDiff(self.weights) - - @classmethod - def load( - cls, - x: str, - lora: dict[str, torch.Tensor], - alpha: float, - dora_scale: torch.Tensor, - loaded_keys: set[str] = None, - ) -> Optional["LoKrAdapter"]: - if loaded_keys is None: - loaded_keys = set() - lokr_w1_name = "{}.lokr_w1".format(x) - lokr_w2_name = "{}.lokr_w2".format(x) - lokr_w1_a_name = "{}.lokr_w1_a".format(x) - lokr_w1_b_name = "{}.lokr_w1_b".format(x) - lokr_t2_name = "{}.lokr_t2".format(x) - lokr_w2_a_name = "{}.lokr_w2_a".format(x) - lokr_w2_b_name = "{}.lokr_w2_b".format(x) - - lokr_w1 = None - if lokr_w1_name in lora.keys(): - lokr_w1 = lora[lokr_w1_name] - loaded_keys.add(lokr_w1_name) - - lokr_w2 = None - if lokr_w2_name in lora.keys(): - lokr_w2 = lora[lokr_w2_name] - loaded_keys.add(lokr_w2_name) - - lokr_w1_a = None - if lokr_w1_a_name in lora.keys(): - lokr_w1_a = lora[lokr_w1_a_name] - loaded_keys.add(lokr_w1_a_name) - - lokr_w1_b = None - if lokr_w1_b_name in lora.keys(): - lokr_w1_b = lora[lokr_w1_b_name] - loaded_keys.add(lokr_w1_b_name) - - lokr_w2_a = None - if lokr_w2_a_name in lora.keys(): - lokr_w2_a = lora[lokr_w2_a_name] - loaded_keys.add(lokr_w2_a_name) - - lokr_w2_b = None - if lokr_w2_b_name in lora.keys(): - lokr_w2_b = lora[lokr_w2_b_name] - loaded_keys.add(lokr_w2_b_name) - - lokr_t2 = None - if lokr_t2_name in lora.keys(): - lokr_t2 = lora[lokr_t2_name] - loaded_keys.add(lokr_t2_name) - - if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - weights = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) - return cls(loaded_keys, weights) - else: - return None - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - v = self.weights - w1 = v[0] - w2 = v[1] - w1_a = v[3] - w1_b = v[4] - w2_a = v[5] - w2_b = v[6] - t2 = v[7] - dora_scale = v[8] - dim = None - - if w1 is None: - dim = w1_b.shape[0] - w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) - else: - w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) - - if w2 is None: - dim = w2_b.shape[0] - if t2 is None: - w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) - else: - w2 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) - else: - w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) - - if len(w2.shape) == 4: - w1 = w1.unsqueeze(2).unsqueeze(2) - if v[2] is not None and dim is not None: - alpha = v[2] / dim - else: - alpha = 1.0 - - try: - lora_diff = torch.kron(w1, w2).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(self.name, key, e)) - return weight diff --git a/comfy/weight_adapter/lora.py b/comfy/weight_adapter/lora.py deleted file mode 100644 index 47aa17d13403af76d436cccc2d0531695b402c30..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/lora.py +++ /dev/null @@ -1,211 +0,0 @@ -import logging -from typing import Optional - -import torch -import comfy.model_management -from .base import ( - WeightAdapterBase, - WeightAdapterTrainBase, - weight_decompose, - pad_tensor_to_shape, - tucker_weight_from_conv, -) - - -class LoraDiff(WeightAdapterTrainBase): - def __init__(self, weights): - super().__init__() - mat1, mat2, alpha, mid, dora_scale, reshape = weights - out_dim, rank = mat1.shape[0], mat1.shape[1] - rank, in_dim = mat2.shape[0], mat2.shape[1] - if mid is not None: - convdim = mid.ndim - 2 - layer = ( - torch.nn.Conv1d, - torch.nn.Conv2d, - torch.nn.Conv3d - )[convdim] - else: - layer = torch.nn.Linear - self.lora_up = layer(rank, out_dim, bias=False) - self.lora_down = layer(in_dim, rank, bias=False) - self.lora_up.weight.data.copy_(mat1) - self.lora_down.weight.data.copy_(mat2) - if mid is not None: - self.lora_mid = layer(mid, rank, bias=False) - self.lora_mid.weight.data.copy_(mid) - else: - self.lora_mid = None - self.rank = rank - self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) - - def __call__(self, w): - org_dtype = w.dtype - if self.lora_mid is None: - diff = self.lora_up.weight @ self.lora_down.weight - else: - diff = tucker_weight_from_conv( - self.lora_up.weight, self.lora_down.weight, self.lora_mid.weight - ) - scale = self.alpha / self.rank - weight = w + scale * diff.reshape(w.shape) - return weight.to(org_dtype) - - def passive_memory_usage(self): - return sum(param.numel() * param.element_size() for param in self.parameters()) - - -class LoRAAdapter(WeightAdapterBase): - name = "lora" - - def __init__(self, loaded_keys, weights): - self.loaded_keys = loaded_keys - self.weights = weights - - @classmethod - def create_train(cls, weight, rank=1, alpha=1.0): - out_dim = weight.shape[0] - in_dim = weight.shape[1:].numel() - mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype) - mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype) - torch.nn.init.kaiming_uniform_(mat1, a=5**0.5) - torch.nn.init.constant_(mat2, 0.0) - return LoraDiff( - (mat1, mat2, alpha, None, None, None) - ) - - def to_train(self): - return LoraDiff(self.weights) - - @classmethod - def load( - cls, - x: str, - lora: dict[str, torch.Tensor], - alpha: float, - dora_scale: torch.Tensor, - loaded_keys: set[str] = None, - ) -> Optional["LoRAAdapter"]: - if loaded_keys is None: - loaded_keys = set() - - reshape_name = "{}.reshape_weight".format(x) - regular_lora = "{}.lora_up.weight".format(x) - diffusers_lora = "{}_lora.up.weight".format(x) - diffusers2_lora = "{}.lora_B.weight".format(x) - diffusers3_lora = "{}.lora.up.weight".format(x) - mochi_lora = "{}.lora_B".format(x) - transformers_lora = "{}.lora_linear_layer.up.weight".format(x) - qwen_default_lora = "{}.lora_B.default.weight".format(x) - A_name = None - - if regular_lora in lora.keys(): - A_name = regular_lora - B_name = "{}.lora_down.weight".format(x) - mid_name = "{}.lora_mid.weight".format(x) - elif diffusers_lora in lora.keys(): - A_name = diffusers_lora - B_name = "{}_lora.down.weight".format(x) - mid_name = None - elif diffusers2_lora in lora.keys(): - A_name = diffusers2_lora - B_name = "{}.lora_A.weight".format(x) - mid_name = None - elif diffusers3_lora in lora.keys(): - A_name = diffusers3_lora - B_name = "{}.lora.down.weight".format(x) - mid_name = None - elif mochi_lora in lora.keys(): - A_name = mochi_lora - B_name = "{}.lora_A".format(x) - mid_name = None - elif transformers_lora in lora.keys(): - A_name = transformers_lora - B_name = "{}.lora_linear_layer.down.weight".format(x) - mid_name = None - elif qwen_default_lora in lora.keys(): - A_name = qwen_default_lora - B_name = "{}.lora_A.default.weight".format(x) - mid_name = None - - if A_name is not None: - mid = None - if mid_name is not None and mid_name in lora.keys(): - mid = lora[mid_name] - loaded_keys.add(mid_name) - reshape = None - if reshape_name in lora.keys(): - try: - reshape = lora[reshape_name].tolist() - loaded_keys.add(reshape_name) - except: - pass - weights = (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape) - loaded_keys.add(A_name) - loaded_keys.add(B_name) - return cls(loaded_keys, weights) - else: - return None - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - v = self.weights - mat1 = comfy.model_management.cast_to_device( - v[0], weight.device, intermediate_dtype - ) - mat2 = comfy.model_management.cast_to_device( - v[1], weight.device, intermediate_dtype - ) - dora_scale = v[4] - reshape = v[5] - - if reshape is not None: - weight = pad_tensor_to_shape(weight, reshape) - - if v[2] is not None: - alpha = v[2] / mat2.shape[0] - else: - alpha = 1.0 - - if v[3] is not None: - # locon mid weights, hopefully the math is fine because I didn't properly test it - mat3 = comfy.model_management.cast_to_device( - v[3], weight.device, intermediate_dtype - ) - final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] - mat2 = ( - torch.mm( - mat2.transpose(0, 1).flatten(start_dim=1), - mat3.transpose(0, 1).flatten(start_dim=1), - ) - .reshape(final_shape) - .transpose(0, 1) - ) - try: - lora_diff = torch.mm( - mat1.flatten(start_dim=1), mat2.flatten(start_dim=1) - ).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose( - dora_scale, - weight, - lora_diff, - alpha, - strength, - intermediate_dtype, - function, - ) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(self.name, key, e)) - return weight diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py deleted file mode 100644 index 9d498208327f2d25b6cac3e6cb7c272c1bea91a1..0000000000000000000000000000000000000000 --- a/comfy/weight_adapter/oft.py +++ /dev/null @@ -1,161 +0,0 @@ -import logging -from typing import Optional - -import torch -import comfy.model_management -from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose, factorization - - -class OFTDiff(WeightAdapterTrainBase): - def __init__(self, weights): - super().__init__() - # Unpack weights tuple from LoHaAdapter - blocks, rescale, alpha, _ = weights - - # Create trainable parameters - self.oft_blocks = torch.nn.Parameter(blocks) - if rescale is not None: - self.rescale = torch.nn.Parameter(rescale) - self.rescaled = True - else: - self.rescaled = False - self.block_num, self.block_size, _ = blocks.shape - self.constraint = float(alpha) - self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) - - def __call__(self, w): - org_dtype = w.dtype - I = torch.eye(self.block_size, device=self.oft_blocks.device) - - ## generate r - # for Q = -Q^T - q = self.oft_blocks - self.oft_blocks.transpose(1, 2) - normed_q = q - if self.constraint: - q_norm = torch.norm(q) + 1e-8 - if q_norm > self.constraint: - normed_q = q * self.constraint / q_norm - # use float() to prevent unsupported type - r = (I + normed_q) @ (I - normed_q).float().inverse() - - ## Apply chunked matmul on weight - _, *shape = w.shape - org_weight = w.to(dtype=r.dtype) - org_weight = org_weight.unflatten(0, (self.block_num, self.block_size)) - # Init R=0, so add I on it to ensure the output of step0 is original model output - weight = torch.einsum( - "k n m, k n ... -> k m ...", - r, - org_weight, - ).flatten(0, 1) - if self.rescaled: - weight = self.rescale * weight - return weight.to(org_dtype) - - def passive_memory_usage(self): - """Calculates memory usage of the trainable parameters.""" - return sum(param.numel() * param.element_size() for param in self.parameters()) - - -class OFTAdapter(WeightAdapterBase): - name = "oft" - - def __init__(self, loaded_keys, weights): - self.loaded_keys = loaded_keys - self.weights = weights - - @classmethod - def create_train(cls, weight, rank=1, alpha=1.0): - out_dim = weight.shape[0] - block_size, block_num = factorization(out_dim, rank) - block = torch.zeros(block_num, block_size, block_size, device=weight.device, dtype=weight.dtype) - return OFTDiff( - (block, None, alpha, None) - ) - - def to_train(self): - return OFTDiff(self.weights) - - @classmethod - def load( - cls, - x: str, - lora: dict[str, torch.Tensor], - alpha: float, - dora_scale: torch.Tensor, - loaded_keys: set[str] = None, - ) -> Optional["OFTAdapter"]: - if loaded_keys is None: - loaded_keys = set() - blocks_name = "{}.oft_blocks".format(x) - rescale_name = "{}.rescale".format(x) - - blocks = None - if blocks_name in lora.keys(): - blocks = lora[blocks_name] - if blocks.ndim == 3: - loaded_keys.add(blocks_name) - else: - blocks = None - if blocks is None: - return None - - rescale = None - if rescale_name in lora.keys(): - rescale = lora[rescale_name] - loaded_keys.add(rescale_name) - - weights = (blocks, rescale, alpha, dora_scale) - return cls(loaded_keys, weights) - - def calculate_weight( - self, - weight, - key, - strength, - strength_model, - offset, - function, - intermediate_dtype=torch.float32, - original_weight=None, - ): - v = self.weights - blocks = v[0] - rescale = v[1] - alpha = v[2] - if alpha is None: - alpha = 0 - dora_scale = v[3] - - blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) - if rescale is not None: - rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) - - block_num, block_size, *_ = blocks.shape - - try: - # Get r - I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype) - # for Q = -Q^T - q = blocks - blocks.transpose(1, 2) - normed_q = q - if alpha > 0: # alpha in oft/boft is for constraint - q_norm = torch.norm(q) + 1e-8 - if q_norm > alpha: - normed_q = q * alpha / q_norm - # use float() to prevent unsupported type in .inverse() - r = (I + normed_q) @ (I - normed_q).float().inverse() - r = r.to(weight) - _, *shape = weight.shape - lora_diff = torch.einsum( - "k n m, k n ... -> k m ...", - (r * strength) - strength * I, - weight.view(block_num, block_size, *shape), - ).view(-1, *shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function((strength * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(self.name, key, e)) - return weight diff --git a/comfy_api/.DS_Store b/comfy_api/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/.DS_Store and /dev/null differ diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py deleted file mode 100644 index 0d4389a6e9e731a8b9e0f994584ab494981eacbc..0000000000000000000000000000000000000000 --- a/comfy_api/feature_flags.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Feature flags module for ComfyUI WebSocket protocol negotiation. - -This module handles capability negotiation between frontend and backend, -allowing graceful protocol evolution while maintaining backward compatibility. -""" - -from typing import Any, Dict - -from comfy.cli_args import args - -# Default server capabilities -SERVER_FEATURE_FLAGS: Dict[str, Any] = { - "supports_preview_metadata": True, - "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes -} - - -def get_connection_feature( - sockets_metadata: Dict[str, Dict[str, Any]], - sid: str, - feature_name: str, - default: Any = False -) -> Any: - """ - Get a feature flag value for a specific connection. - - Args: - sockets_metadata: Dictionary of socket metadata - sid: Session ID of the connection - feature_name: Name of the feature to check - default: Default value if feature not found - - Returns: - Feature value or default if not found - """ - if sid not in sockets_metadata: - return default - - return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default) - - -def supports_feature( - sockets_metadata: Dict[str, Dict[str, Any]], - sid: str, - feature_name: str -) -> bool: - """ - Check if a connection supports a specific feature. - - Args: - sockets_metadata: Dictionary of socket metadata - sid: Session ID of the connection - feature_name: Name of the feature to check - - Returns: - Boolean indicating if feature is supported - """ - return get_connection_feature(sockets_metadata, sid, feature_name, False) is True - - -def get_server_features() -> Dict[str, Any]: - """ - Get the server's feature flags. - - Returns: - Dictionary of server feature flags - """ - return SERVER_FEATURE_FLAGS.copy() diff --git a/comfy_api/generate_api_stubs.py b/comfy_api/generate_api_stubs.py deleted file mode 100644 index 604a7eced8a5a408e77c75a7889f9c25fe3771d4..0000000000000000000000000000000000000000 --- a/comfy_api/generate_api_stubs.py +++ /dev/null @@ -1,86 +0,0 @@ -#!/usr/bin/env python3 -""" -Script to generate .pyi stub files for the synchronous API wrappers. -This allows generating stubs without running the full ComfyUI application. -""" - -import os -import sys -import logging -import importlib - -# Add ComfyUI to path so we can import modules -sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from comfy_api.internal.async_to_sync import AsyncToSyncConverter -from comfy_api.version_list import supported_versions - - -def generate_stubs_for_module(module_name: str) -> None: - """Generate stub files for a specific module that exports ComfyAPI and ComfyAPISync.""" - try: - # Import the module - module = importlib.import_module(module_name) - - # Check if module has ComfyAPISync (the sync wrapper) - if hasattr(module, "ComfyAPISync"): - # Module already has a sync class - api_class = getattr(module, "ComfyAPI", None) - sync_class = getattr(module, "ComfyAPISync") - - if api_class: - # Generate the stub file - AsyncToSyncConverter.generate_stub_file(api_class, sync_class) - logging.info(f"Generated stub file for {module_name}") - else: - logging.warning( - f"Module {module_name} has ComfyAPISync but no ComfyAPI" - ) - - elif hasattr(module, "ComfyAPI"): - # Module only has async API, need to create sync wrapper first - from comfy_api.internal.async_to_sync import create_sync_class - - api_class = getattr(module, "ComfyAPI") - sync_class = create_sync_class(api_class) - - # Generate the stub file - AsyncToSyncConverter.generate_stub_file(api_class, sync_class) - logging.info(f"Generated stub file for {module_name}") - else: - logging.warning( - f"Module {module_name} does not export ComfyAPI or ComfyAPISync" - ) - - except Exception as e: - logging.error(f"Failed to generate stub for {module_name}: {e}") - import traceback - - traceback.print_exc() - - -def main(): - """Main function to generate all API stub files.""" - logging.basicConfig(level=logging.INFO) - - logging.info("Starting stub generation...") - - # Dynamically get module names from supported_versions - api_modules = [] - for api_class in supported_versions: - # Extract module name from the class - module_name = api_class.__module__ - if module_name not in api_modules: - api_modules.append(module_name) - - logging.info(f"Found {len(api_modules)} API modules: {api_modules}") - - # Generate stubs for each module - for module_name in api_modules: - generate_stubs_for_module(module_name) - - logging.info("Stub generation complete!") - - -if __name__ == "__main__": - main() diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py deleted file mode 100644 index 68ff782706e8ab4f8d40917f8ff5b8aea54c7f51..0000000000000000000000000000000000000000 --- a/comfy_api/input/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._input import ( - ImageInput, - AudioInput, - MaskInput, - LatentInput, - VideoInput, -) - -__all__ = [ - "ImageInput", - "AudioInput", - "MaskInput", - "LatentInput", - "VideoInput", -] diff --git a/comfy_api/input/basic_types.py b/comfy_api/input/basic_types.py deleted file mode 100644 index 5eadce86ac4462c7fd825e1171afa6b315a6be38..0000000000000000000000000000000000000000 --- a/comfy_api/input/basic_types.py +++ /dev/null @@ -1,14 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._input.basic_types import ( - ImageInput, - AudioInput, - MaskInput, - LatentInput, -) - -__all__ = [ - "ImageInput", - "AudioInput", - "MaskInput", - "LatentInput", -] diff --git a/comfy_api/input/video_types.py b/comfy_api/input/video_types.py deleted file mode 100644 index 9ace78cbc039eb0de063ea1ec080134a2b44cac6..0000000000000000000000000000000000000000 --- a/comfy_api/input/video_types.py +++ /dev/null @@ -1,6 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._input.video_types import VideoInput - -__all__ = [ - "VideoInput", -] diff --git a/comfy_api/input_impl/.DS_Store b/comfy_api/input_impl/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/input_impl/.DS_Store and /dev/null differ diff --git a/comfy_api/input_impl/__init__.py b/comfy_api/input_impl/__init__.py deleted file mode 100644 index b78ff0c08fbe31f073a38c580f4648eebcc47db3..0000000000000000000000000000000000000000 --- a/comfy_api/input_impl/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents - -__all__ = [ - "VideoFromFile", - "VideoFromComponents", -] diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py deleted file mode 100644 index bd2e56ad51a2fa2340e63409b9038de463af3b2d..0000000000000000000000000000000000000000 --- a/comfy_api/input_impl/video_types.py +++ /dev/null @@ -1,2 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._input_impl.video_types import * # noqa: F403 diff --git a/comfy_api/internal/.DS_Store b/comfy_api/internal/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/internal/.DS_Store and /dev/null differ diff --git a/comfy_api/internal/__init__.py b/comfy_api/internal/__init__.py deleted file mode 100644 index 4ca02e320bcb06ad7f6a8a7c8bd63f6efc18ea98..0000000000000000000000000000000000000000 --- a/comfy_api/internal/__init__.py +++ /dev/null @@ -1,150 +0,0 @@ -# Internal infrastructure for ComfyAPI -from .api_registry import ( - ComfyAPIBase as ComfyAPIBase, - ComfyAPIWithVersion as ComfyAPIWithVersion, - register_versions as register_versions, - get_all_versions as get_all_versions, -) - -import asyncio -from dataclasses import asdict -from typing import Callable, Optional - - -def first_real_override(cls: type, name: str, *, base: type=None) -> Optional[Callable]: - """Return the *callable* override of `name` visible on `cls`, or None if every - implementation up to (and including) `base` is the placeholder defined on `base`. - - If base is not provided, it will assume cls has a GET_BASE_CLASS - """ - if base is None: - if not hasattr(cls, "GET_BASE_CLASS"): - raise ValueError("base is required if cls does not have a GET_BASE_CLASS; is this a valid ComfyNode subclass?") - base = cls.GET_BASE_CLASS() - base_attr = getattr(base, name, None) - if base_attr is None: - return None - base_func = base_attr.__func__ - for c in cls.mro(): # NodeB, NodeA, ComfyNode, object … - if c is base: # reached the placeholder – we're done - break - if name in c.__dict__: # first class that *defines* the attr - func = getattr(c, name).__func__ - if func is not base_func: # real override - return getattr(cls, name) # bound to *cls* - return None - - -class _ComfyNodeInternal: - """Class that all V3-based APIs inherit from for ComfyNode. - - This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward.""" - @classmethod - def GET_NODE_INFO_V1(cls): - ... - - -class _NodeOutputInternal: - """Class that all V3-based APIs inherit from for NodeOutput. - - This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward.""" - ... - - -def as_pruned_dict(dataclass_obj): - '''Return dict of dataclass object with pruned None values.''' - return prune_dict(asdict(dataclass_obj)) - -def prune_dict(d: dict): - return {k: v for k,v in d.items() if v is not None} - - -def is_class(obj): - ''' - Returns True if is a class type. - Returns False if is a class instance. - ''' - return isinstance(obj, type) - - -def copy_class(cls: type) -> type: - ''' - Copy a class and its attributes. - ''' - if cls is None: - return None - cls_dict = { - k: v for k, v in cls.__dict__.items() - if k not in ('__dict__', '__weakref__', '__module__', '__doc__') - } - # new class - new_cls = type( - cls.__name__, - (cls,), - cls_dict - ) - # metadata preservation - new_cls.__module__ = cls.__module__ - new_cls.__doc__ = cls.__doc__ - return new_cls - - -class classproperty(object): - def __init__(self, f): - self.f = f - def __get__(self, obj, owner): - return self.f(owner) - - -# NOTE: this was ai generated and validated by hand -def shallow_clone_class(cls, new_name=None): - ''' - Shallow clone a class while preserving super() functionality. - ''' - new_name = new_name or f"{cls.__name__}Clone" - # Include the original class in the bases to maintain proper inheritance - new_bases = (cls,) + cls.__bases__ - return type(new_name, new_bases, dict(cls.__dict__)) - -# NOTE: this was ai generated and validated by hand -def lock_class(cls): - ''' - Lock a class so that its top-levelattributes cannot be modified. - ''' - # Locked instance __setattr__ - def locked_instance_setattr(self, name, value): - raise AttributeError( - f"Cannot set attribute '{name}' on immutable instance of {type(self).__name__}" - ) - # Locked metaclass - class LockedMeta(type(cls)): - def __setattr__(cls_, name, value): - raise AttributeError( - f"Cannot modify class attribute '{name}' on locked class '{cls_.__name__}'" - ) - # Rebuild class with locked behavior - locked_dict = dict(cls.__dict__) - locked_dict['__setattr__'] = locked_instance_setattr - - return LockedMeta(cls.__name__, cls.__bases__, locked_dict) - - -def make_locked_method_func(type_obj, func, class_clone): - """ - Returns a function that, when called with **inputs, will execute: - getattr(type_obj, func).__func__(lock_class(class_clone), **inputs) - - Supports both synchronous and asynchronous methods. - """ - locked_class = lock_class(class_clone) - method = getattr(type_obj, func).__func__ - - # Check if the original method is async - if asyncio.iscoroutinefunction(method): - async def wrapped_async_func(**inputs): - return await method(locked_class, **inputs) - return wrapped_async_func - else: - def wrapped_func(**inputs): - return method(locked_class, **inputs) - return wrapped_func diff --git a/comfy_api/internal/api_registry.py b/comfy_api/internal/api_registry.py deleted file mode 100644 index 7e3375cf60f8c27aed2774fcd58da4597516f1b7..0000000000000000000000000000000000000000 --- a/comfy_api/internal/api_registry.py +++ /dev/null @@ -1,39 +0,0 @@ -from typing import Type, List, NamedTuple -from comfy_api.internal.singleton import ProxiedSingleton -from packaging import version as packaging_version - - -class ComfyAPIBase(ProxiedSingleton): - def __init__(self): - pass - - -class ComfyAPIWithVersion(NamedTuple): - version: str - api_class: Type[ComfyAPIBase] - - -def parse_version(version_str: str) -> packaging_version.Version: - """ - Parses a version string into a packaging_version.Version object. - Raises ValueError if the version string is invalid. - """ - if version_str == "latest": - return packaging_version.parse("9999999.9999999.9999999") - return packaging_version.parse(version_str) - - -registered_versions: List[ComfyAPIWithVersion] = [] - - -def register_versions(versions: List[ComfyAPIWithVersion]): - versions.sort(key=lambda x: parse_version(x.version)) - global registered_versions - registered_versions = versions - - -def get_all_versions() -> List[ComfyAPIWithVersion]: - """ - Returns a list of all registered ComfyAPI versions. - """ - return registered_versions diff --git a/comfy_api/internal/async_to_sync.py b/comfy_api/internal/async_to_sync.py deleted file mode 100644 index f5f805a6215697a499f2bf48656e8cc10b4a2560..0000000000000000000000000000000000000000 --- a/comfy_api/internal/async_to_sync.py +++ /dev/null @@ -1,987 +0,0 @@ -import asyncio -import concurrent.futures -import contextvars -import functools -import inspect -import logging -import os -import textwrap -import threading -from enum import Enum -from typing import Optional, Type, get_origin, get_args - - -class TypeTracker: - """Tracks types discovered during stub generation for automatic import generation.""" - - def __init__(self): - self.discovered_types = {} # type_name -> (module, qualname) - self.builtin_types = { - "Any", - "Dict", - "List", - "Optional", - "Tuple", - "Union", - "Set", - "Sequence", - "cast", - "NamedTuple", - "str", - "int", - "float", - "bool", - "None", - "bytes", - "object", - "type", - "dict", - "list", - "tuple", - "set", - } - self.already_imported = ( - set() - ) # Track types already imported to avoid duplicates - - def track_type(self, annotation): - """Track a type annotation and record its module/import info.""" - if annotation is None or annotation is type(None): - return - - # Skip builtins and typing module types we already import - type_name = getattr(annotation, "__name__", None) - if type_name and ( - type_name in self.builtin_types or type_name in self.already_imported - ): - return - - # Get module and qualname - module = getattr(annotation, "__module__", None) - qualname = getattr(annotation, "__qualname__", type_name or "") - - # Skip types from typing module (they're already imported) - if module == "typing": - return - - # Skip UnionType and GenericAlias from types module as they're handled specially - if module == "types" and type_name in ("UnionType", "GenericAlias"): - return - - if module and module not in ["builtins", "__main__"]: - # Store the type info - if type_name: - self.discovered_types[type_name] = (module, qualname) - - def get_imports(self, main_module_name: str) -> list[str]: - """Generate import statements for all discovered types.""" - imports = [] - imports_by_module = {} - - for type_name, (module, qualname) in sorted(self.discovered_types.items()): - # Skip types from the main module (they're already imported) - if main_module_name and module == main_module_name: - continue - - if module not in imports_by_module: - imports_by_module[module] = [] - if type_name not in imports_by_module[module]: # Avoid duplicates - imports_by_module[module].append(type_name) - - # Generate import statements - for module, types in sorted(imports_by_module.items()): - if len(types) == 1: - imports.append(f"from {module} import {types[0]}") - else: - imports.append(f"from {module} import {', '.join(sorted(set(types)))}") - - return imports - - -class AsyncToSyncConverter: - """ - Provides utilities to convert async classes to sync classes with proper type hints. - """ - - _thread_pool: Optional[concurrent.futures.ThreadPoolExecutor] = None - _thread_pool_lock = threading.Lock() - _thread_pool_initialized = False - - @classmethod - def get_thread_pool(cls, max_workers=None) -> concurrent.futures.ThreadPoolExecutor: - """Get or create the shared thread pool with proper thread-safe initialization.""" - # Fast path - check if already initialized without acquiring lock - if cls._thread_pool_initialized: - assert cls._thread_pool is not None, "Thread pool should be initialized" - return cls._thread_pool - - # Slow path - acquire lock and create pool if needed - with cls._thread_pool_lock: - if not cls._thread_pool_initialized: - cls._thread_pool = concurrent.futures.ThreadPoolExecutor( - max_workers=max_workers, thread_name_prefix="async_to_sync_" - ) - cls._thread_pool_initialized = True - - # This should never be None at this point, but add assertion for type checker - assert cls._thread_pool is not None - return cls._thread_pool - - @classmethod - def run_async_in_thread(cls, coro_func, *args, **kwargs): - """ - Run an async function in a separate thread from the thread pool. - Blocks until the async function completes. - Properly propagates contextvars between threads and manages event loops. - """ - # Capture current context - this includes all context variables - context = contextvars.copy_context() - - # Store the result and any exception that occurs - result_container: dict = {"result": None, "exception": None} - - # Function that runs in the thread pool - def run_in_thread(): - # Create new event loop for this thread - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - - try: - # Create the coroutine within the context - async def run_with_context(): - # The coroutine function might access context variables - return await coro_func(*args, **kwargs) - - # Run the coroutine with the captured context - # This ensures all context variables are available in the async function - result = context.run(loop.run_until_complete, run_with_context()) - result_container["result"] = result - except Exception as e: - # Store the exception to re-raise in the calling thread - result_container["exception"] = e - finally: - # Ensure event loop is properly closed to prevent warnings - try: - # Cancel any remaining tasks - pending = asyncio.all_tasks(loop) - for task in pending: - task.cancel() - - # Run the loop briefly to handle cancellations - if pending: - loop.run_until_complete( - asyncio.gather(*pending, return_exceptions=True) - ) - except Exception: - pass # Ignore errors during cleanup - - # Close the event loop - loop.close() - - # Clear the event loop from the thread - asyncio.set_event_loop(None) - - # Submit to thread pool and wait for result - thread_pool = cls.get_thread_pool() - future = thread_pool.submit(run_in_thread) - future.result() # Wait for completion - - # Re-raise any exception that occurred in the thread - if result_container["exception"] is not None: - raise result_container["exception"] - - return result_container["result"] - - @classmethod - def create_sync_class(cls, async_class: Type, thread_pool_size=10) -> Type: - """ - Creates a new class with synchronous versions of all async methods. - - Args: - async_class: The async class to convert - thread_pool_size: Size of thread pool to use - - Returns: - A new class with sync versions of all async methods - """ - sync_class_name = "ComfyAPISyncStub" - cls.get_thread_pool(thread_pool_size) - - # Create a proper class with docstrings and proper base classes - sync_class_dict = { - "__doc__": async_class.__doc__, - "__module__": async_class.__module__, - "__qualname__": sync_class_name, - "__orig_class__": async_class, # Store original class for typing references - } - - # Create __init__ method - def __init__(self, *args, **kwargs): - self._async_instance = async_class(*args, **kwargs) - - # Handle annotated class attributes (like execution: Execution) - # Get all annotations from the class hierarchy - all_annotations = {} - for base_class in reversed(inspect.getmro(async_class)): - if hasattr(base_class, "__annotations__"): - all_annotations.update(base_class.__annotations__) - - # For each annotated attribute, check if it needs to be created or wrapped - for attr_name, attr_type in all_annotations.items(): - if hasattr(self._async_instance, attr_name): - # Attribute exists on the instance - attr = getattr(self._async_instance, attr_name) - # Check if this attribute needs a sync wrapper - if hasattr(attr, "__class__"): - from comfy_api.internal.singleton import ProxiedSingleton - - if isinstance(attr, ProxiedSingleton): - # Create a sync version of this attribute - try: - sync_attr_class = cls.create_sync_class(attr.__class__) - # Create instance of the sync wrapper with the async instance - sync_attr = object.__new__(sync_attr_class) # type: ignore - sync_attr._async_instance = attr - setattr(self, attr_name, sync_attr) - except Exception: - # If we can't create a sync version, keep the original - setattr(self, attr_name, attr) - else: - # Not async, just copy the reference - setattr(self, attr_name, attr) - else: - # Attribute doesn't exist, but is annotated - create it - # This handles cases like execution: Execution - if isinstance(attr_type, type): - # Check if the type is defined as an inner class - if hasattr(async_class, attr_type.__name__): - inner_class = getattr(async_class, attr_type.__name__) - from comfy_api.internal.singleton import ProxiedSingleton - - # Create an instance of the inner class - try: - # For ProxiedSingleton classes, get or create the singleton instance - if issubclass(inner_class, ProxiedSingleton): - async_instance = inner_class.get_instance() - else: - async_instance = inner_class() - - # Create sync wrapper - sync_attr_class = cls.create_sync_class(inner_class) - sync_attr = object.__new__(sync_attr_class) # type: ignore - sync_attr._async_instance = async_instance - setattr(self, attr_name, sync_attr) - # Also set on the async instance for consistency - setattr(self._async_instance, attr_name, async_instance) - except Exception as e: - logging.warning( - f"Failed to create instance for {attr_name}: {e}" - ) - - # Handle other instance attributes that might not be annotated - for name, attr in inspect.getmembers(self._async_instance): - if name.startswith("_") or hasattr(self, name): - continue - - # If attribute is an instance of a class, and that class is defined in the original class - # we need to check if it needs a sync wrapper - if isinstance(attr, object) and not isinstance( - attr, (str, int, float, bool, list, dict, tuple) - ): - from comfy_api.internal.singleton import ProxiedSingleton - - if isinstance(attr, ProxiedSingleton): - # Create a sync version of this nested class - try: - sync_attr_class = cls.create_sync_class(attr.__class__) - # Create instance of the sync wrapper with the async instance - sync_attr = object.__new__(sync_attr_class) # type: ignore - sync_attr._async_instance = attr - setattr(self, name, sync_attr) - except Exception: - # If we can't create a sync version, keep the original - setattr(self, name, attr) - - sync_class_dict["__init__"] = __init__ - - # Process methods from the async class - for name, method in inspect.getmembers( - async_class, predicate=inspect.isfunction - ): - if name.startswith("_"): - continue - - # Extract the actual return type from a coroutine - if inspect.iscoroutinefunction(method): - # Create sync version of async method with proper signature - @functools.wraps(method) - def sync_method(self, *args, _method_name=name, **kwargs): - async_method = getattr(self._async_instance, _method_name) - return AsyncToSyncConverter.run_async_in_thread( - async_method, *args, **kwargs - ) - - # Add to the class dict - sync_class_dict[name] = sync_method - else: - # For regular methods, create a proxy method - @functools.wraps(method) - def proxy_method(self, *args, _method_name=name, **kwargs): - method = getattr(self._async_instance, _method_name) - return method(*args, **kwargs) - - # Add to the class dict - sync_class_dict[name] = proxy_method - - # Handle property access - for name, prop in inspect.getmembers( - async_class, lambda x: isinstance(x, property) - ): - - def make_property(name, prop_obj): - def getter(self): - value = getattr(self._async_instance, name) - if inspect.iscoroutinefunction(value): - - def sync_fn(*args, **kwargs): - return AsyncToSyncConverter.run_async_in_thread( - value, *args, **kwargs - ) - - return sync_fn - return value - - def setter(self, value): - setattr(self._async_instance, name, value) - - return property(getter, setter if prop_obj.fset else None) - - sync_class_dict[name] = make_property(name, prop) - - # Create the class - sync_class = type(sync_class_name, (object,), sync_class_dict) - - return sync_class - - @classmethod - def _format_type_annotation( - cls, annotation, type_tracker: Optional[TypeTracker] = None - ) -> str: - """Convert a type annotation to its string representation for stub files.""" - if ( - annotation is inspect.Parameter.empty - or annotation is inspect.Signature.empty - ): - return "Any" - - # Handle None type - if annotation is type(None): - return "None" - - # Track the type if we have a tracker - if type_tracker: - type_tracker.track_type(annotation) - - # Try using typing.get_origin/get_args for Python 3.8+ - try: - origin = get_origin(annotation) - args = get_args(annotation) - - if origin is not None: - # Track the origin type - if type_tracker: - type_tracker.track_type(origin) - - # Get the origin name - origin_name = getattr(origin, "__name__", str(origin)) - if "." in origin_name: - origin_name = origin_name.split(".")[-1] - - # Special handling for types.UnionType (Python 3.10+ pipe operator) - # Convert to old-style Union for compatibility - if str(origin) == "" or origin_name == "UnionType": - origin_name = "Union" - - # Format arguments recursively - if args: - formatted_args = [] - for arg in args: - # Track each type in the union - if type_tracker: - type_tracker.track_type(arg) - formatted_args.append(cls._format_type_annotation(arg, type_tracker)) - return f"{origin_name}[{', '.join(formatted_args)}]" - else: - return origin_name - except (AttributeError, TypeError): - # Fallback for older Python versions or non-generic types - pass - - # Handle generic types the old way for compatibility - if hasattr(annotation, "__origin__") and hasattr(annotation, "__args__"): - origin = annotation.__origin__ - origin_name = ( - origin.__name__ - if hasattr(origin, "__name__") - else str(origin).split("'")[1] - ) - - # Format each type argument - args = [] - for arg in annotation.__args__: - args.append(cls._format_type_annotation(arg, type_tracker)) - - return f"{origin_name}[{', '.join(args)}]" - - # Handle regular types with __name__ - if hasattr(annotation, "__name__"): - return annotation.__name__ - - # Handle special module types (like types from typing module) - if hasattr(annotation, "__module__") and hasattr(annotation, "__qualname__"): - # For types like typing.Literal, typing.TypedDict, etc. - return annotation.__qualname__ - - # Last resort: string conversion with cleanup - type_str = str(annotation) - - # Clean up common patterns more robustly - if type_str.startswith(""): - type_str = type_str[8:-2] # Remove "" - - # Remove module prefixes for common modules - for prefix in ["typing.", "builtins.", "types."]: - if type_str.startswith(prefix): - type_str = type_str[len(prefix) :] - - # Handle special cases - if type_str in ("_empty", "inspect._empty"): - return "None" - - # Fix NoneType (this should rarely be needed now) - if type_str == "NoneType": - return "None" - - return type_str - - @classmethod - def _extract_coroutine_return_type(cls, annotation): - """Extract the actual return type from a Coroutine annotation.""" - if hasattr(annotation, "__args__") and len(annotation.__args__) > 2: - # Coroutine[Any, Any, ReturnType] -> extract ReturnType - return annotation.__args__[2] - return annotation - - @classmethod - def _format_parameter_default(cls, default_value) -> str: - """Format a parameter's default value for stub files.""" - if default_value is inspect.Parameter.empty: - return "" - elif default_value is None: - return " = None" - elif isinstance(default_value, bool): - return f" = {default_value}" - elif default_value == {}: - return " = {}" - elif default_value == []: - return " = []" - else: - return f" = {default_value}" - - @classmethod - def _format_method_parameters( - cls, - sig: inspect.Signature, - skip_self: bool = True, - type_hints: Optional[dict] = None, - type_tracker: Optional[TypeTracker] = None, - ) -> str: - """Format method parameters for stub files.""" - params = [] - if type_hints is None: - type_hints = {} - - for i, (param_name, param) in enumerate(sig.parameters.items()): - if i == 0 and param_name == "self" and skip_self: - params.append("self") - else: - # Get type annotation from type hints if available, otherwise from signature - annotation = type_hints.get(param_name, param.annotation) - type_str = cls._format_type_annotation(annotation, type_tracker) - - # Get default value - default_str = cls._format_parameter_default(param.default) - - # Combine parameter parts - if annotation is inspect.Parameter.empty: - params.append(f"{param_name}: Any{default_str}") - else: - params.append(f"{param_name}: {type_str}{default_str}") - - return ", ".join(params) - - @classmethod - def _generate_method_signature( - cls, - method_name: str, - method, - is_async: bool = False, - type_tracker: Optional[TypeTracker] = None, - ) -> str: - """Generate a complete method signature for stub files.""" - sig = inspect.signature(method) - - # Try to get evaluated type hints to resolve string annotations - try: - from typing import get_type_hints - type_hints = get_type_hints(method) - except Exception: - # Fallback to empty dict if we can't get type hints - type_hints = {} - - # For async methods, extract the actual return type - return_annotation = type_hints.get('return', sig.return_annotation) - if is_async and inspect.iscoroutinefunction(method): - return_annotation = cls._extract_coroutine_return_type(return_annotation) - - # Format parameters with type hints - params_str = cls._format_method_parameters(sig, type_hints=type_hints, type_tracker=type_tracker) - - # Format return type - return_type = cls._format_type_annotation(return_annotation, type_tracker) - if return_annotation is inspect.Signature.empty: - return_type = "None" - - return f"def {method_name}({params_str}) -> {return_type}: ..." - - @classmethod - def _generate_imports( - cls, async_class: Type, type_tracker: TypeTracker - ) -> list[str]: - """Generate import statements for the stub file.""" - imports = [] - - # Add standard typing imports - imports.append( - "from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple" - ) - - # Add imports from the original module - if async_class.__module__ != "builtins": - module = inspect.getmodule(async_class) - additional_types = [] - - if module: - # Check if module has __all__ defined - module_all = getattr(module, "__all__", None) - - for name, obj in sorted(inspect.getmembers(module)): - if isinstance(obj, type): - # Skip if __all__ is defined and this name isn't in it - # unless it's already been tracked as used in type annotations - if module_all is not None and name not in module_all: - # Check if this type was actually used in annotations - if name not in type_tracker.discovered_types: - continue - - # Check for NamedTuple - if issubclass(obj, tuple) and hasattr(obj, "_fields"): - additional_types.append(name) - # Mark as already imported - type_tracker.already_imported.add(name) - # Check for Enum - elif issubclass(obj, Enum) and name != "Enum": - additional_types.append(name) - # Mark as already imported - type_tracker.already_imported.add(name) - - if additional_types: - type_imports = ", ".join([async_class.__name__] + additional_types) - imports.append(f"from {async_class.__module__} import {type_imports}") - else: - imports.append( - f"from {async_class.__module__} import {async_class.__name__}" - ) - - # Add imports for all discovered types - # Pass the main module name to avoid duplicate imports - imports.extend( - type_tracker.get_imports(main_module_name=async_class.__module__) - ) - - # Add base module import if needed - if hasattr(inspect.getmodule(async_class), "__name__"): - module_name = inspect.getmodule(async_class).__name__ - if "." in module_name: - base_module = module_name.split(".")[0] - # Only add if not already importing from it - if not any(imp.startswith(f"from {base_module}") for imp in imports): - imports.append(f"import {base_module}") - - return imports - - @classmethod - def _get_class_attributes(cls, async_class: Type) -> list[tuple[str, Type]]: - """Extract class attributes that are classes themselves.""" - class_attributes = [] - - # Look for class attributes that are classes - for name, attr in sorted(inspect.getmembers(async_class)): - if isinstance(attr, type) and not name.startswith("_"): - class_attributes.append((name, attr)) - elif ( - hasattr(async_class, "__annotations__") - and name in async_class.__annotations__ - ): - annotation = async_class.__annotations__[name] - if isinstance(annotation, type): - class_attributes.append((name, annotation)) - - return class_attributes - - @classmethod - def _generate_inner_class_stub( - cls, - name: str, - attr: Type, - indent: str = " ", - type_tracker: Optional[TypeTracker] = None, - ) -> list[str]: - """Generate stub for an inner class.""" - stub_lines = [] - stub_lines.append(f"{indent}class {name}Sync:") - - # Add docstring if available - if hasattr(attr, "__doc__") and attr.__doc__: - stub_lines.extend( - cls._format_docstring_for_stub(attr.__doc__, f"{indent} ") - ) - - # Add __init__ if it exists - if hasattr(attr, "__init__"): - try: - init_method = getattr(attr, "__init__") - init_sig = inspect.signature(init_method) - - # Try to get type hints - try: - from typing import get_type_hints - init_hints = get_type_hints(init_method) - except Exception: - init_hints = {} - - # Format parameters - params_str = cls._format_method_parameters( - init_sig, type_hints=init_hints, type_tracker=type_tracker - ) - # Add __init__ docstring if available (before the method) - if hasattr(init_method, "__doc__") and init_method.__doc__: - stub_lines.extend( - cls._format_docstring_for_stub( - init_method.__doc__, f"{indent} " - ) - ) - stub_lines.append( - f"{indent} def __init__({params_str}) -> None: ..." - ) - except (ValueError, TypeError): - stub_lines.append( - f"{indent} def __init__(self, *args, **kwargs) -> None: ..." - ) - - # Add methods to the inner class - has_methods = False - for method_name, method in sorted( - inspect.getmembers(attr, predicate=inspect.isfunction) - ): - if method_name.startswith("_"): - continue - - has_methods = True - try: - # Add method docstring if available (before the method signature) - if method.__doc__: - stub_lines.extend( - cls._format_docstring_for_stub(method.__doc__, f"{indent} ") - ) - - method_sig = cls._generate_method_signature( - method_name, method, is_async=True, type_tracker=type_tracker - ) - stub_lines.append(f"{indent} {method_sig}") - except (ValueError, TypeError): - stub_lines.append( - f"{indent} def {method_name}(self, *args, **kwargs): ..." - ) - - if not has_methods: - stub_lines.append(f"{indent} pass") - - return stub_lines - - @classmethod - def _format_docstring_for_stub( - cls, docstring: str, indent: str = " " - ) -> list[str]: - """Format a docstring for inclusion in a stub file with proper indentation.""" - if not docstring: - return [] - - # First, dedent the docstring to remove any existing indentation - dedented = textwrap.dedent(docstring).strip() - - # Split into lines - lines = dedented.split("\n") - - # Build the properly indented docstring - result = [] - result.append(f'{indent}"""') - - for line in lines: - if line.strip(): # Non-empty line - result.append(f"{indent}{line}") - else: # Empty line - result.append("") - - result.append(f'{indent}"""') - return result - - @classmethod - def _post_process_stub_content(cls, stub_content: list[str]) -> list[str]: - """Post-process stub content to fix any remaining issues.""" - processed = [] - - for line in stub_content: - # Skip processing imports - if line.startswith(("from ", "import ")): - processed.append(line) - continue - - # Fix method signatures missing return types - if ( - line.strip().startswith("def ") - and line.strip().endswith(": ...") - and ") -> " not in line - ): - # Add -> None for methods without return annotation - line = line.replace(": ...", " -> None: ...") - - processed.append(line) - - return processed - - @classmethod - def generate_stub_file(cls, async_class: Type, sync_class: Type) -> None: - """ - Generate a .pyi stub file for the sync class to help IDEs with type checking. - """ - try: - # Only generate stub if we can determine module path - if async_class.__module__ == "__main__": - return - - module = inspect.getmodule(async_class) - if not module: - return - - module_path = module.__file__ - if not module_path: - return - - # Create stub file path in a 'generated' subdirectory - module_dir = os.path.dirname(module_path) - stub_dir = os.path.join(module_dir, "generated") - - # Ensure the generated directory exists - os.makedirs(stub_dir, exist_ok=True) - - module_name = os.path.basename(module_path) - if module_name.endswith(".py"): - module_name = module_name[:-3] - - sync_stub_path = os.path.join(stub_dir, f"{sync_class.__name__}.pyi") - - # Create a type tracker for this stub generation - type_tracker = TypeTracker() - - stub_content = [] - - # We'll generate imports after processing all methods to capture all types - # Leave a placeholder for imports - imports_placeholder_index = len(stub_content) - stub_content.append("") # Will be replaced with imports later - - # Class definition - stub_content.append(f"class {sync_class.__name__}:") - - # Docstring - if async_class.__doc__: - stub_content.extend( - cls._format_docstring_for_stub(async_class.__doc__, " ") - ) - - # Generate __init__ - try: - init_method = async_class.__init__ - init_signature = inspect.signature(init_method) - - # Try to get type hints for __init__ - try: - from typing import get_type_hints - init_hints = get_type_hints(init_method) - except Exception: - init_hints = {} - - # Format parameters - params_str = cls._format_method_parameters( - init_signature, type_hints=init_hints, type_tracker=type_tracker - ) - # Add __init__ docstring if available (before the method) - if hasattr(init_method, "__doc__") and init_method.__doc__: - stub_content.extend( - cls._format_docstring_for_stub(init_method.__doc__, " ") - ) - stub_content.append(f" def __init__({params_str}) -> None: ...") - except (ValueError, TypeError): - stub_content.append( - " def __init__(self, *args, **kwargs) -> None: ..." - ) - - stub_content.append("") # Add newline after __init__ - - # Get class attributes - class_attributes = cls._get_class_attributes(async_class) - - # Generate inner classes - for name, attr in class_attributes: - inner_class_stub = cls._generate_inner_class_stub( - name, attr, type_tracker=type_tracker - ) - stub_content.extend(inner_class_stub) - stub_content.append("") # Add newline after the inner class - - # Add methods to the main class - processed_methods = set() # Keep track of methods we've processed - for name, method in sorted( - inspect.getmembers(async_class, predicate=inspect.isfunction) - ): - if name.startswith("_") or name in processed_methods: - continue - - processed_methods.add(name) - - try: - method_sig = cls._generate_method_signature( - name, method, is_async=True, type_tracker=type_tracker - ) - - # Add docstring if available (before the method signature for proper formatting) - if method.__doc__: - stub_content.extend( - cls._format_docstring_for_stub(method.__doc__, " ") - ) - - stub_content.append(f" {method_sig}") - - stub_content.append("") # Add newline after each method - - except (ValueError, TypeError): - # If we can't get the signature, just add a simple stub - stub_content.append(f" def {name}(self, *args, **kwargs): ...") - stub_content.append("") # Add newline - - # Add properties - for name, prop in sorted( - inspect.getmembers(async_class, lambda x: isinstance(x, property)) - ): - stub_content.append(" @property") - stub_content.append(f" def {name}(self) -> Any: ...") - if prop.fset: - stub_content.append(f" @{name}.setter") - stub_content.append( - f" def {name}(self, value: Any) -> None: ..." - ) - stub_content.append("") # Add newline after each property - - # Add placeholders for the nested class instances - # Check the actual attribute names from class annotations and attributes - attribute_mappings = {} - - # First check annotations for typed attributes (including from parent classes) - # Collect all annotations from the class hierarchy - all_annotations = {} - for base_class in reversed(inspect.getmro(async_class)): - if hasattr(base_class, "__annotations__"): - all_annotations.update(base_class.__annotations__) - - for attr_name, attr_type in sorted(all_annotations.items()): - for class_name, class_type in class_attributes: - # If the class type matches the annotated type - if ( - attr_type == class_type - or (hasattr(attr_type, "__name__") and attr_type.__name__ == class_name) - or (isinstance(attr_type, str) and attr_type == class_name) - ): - attribute_mappings[class_name] = attr_name - - # Remove the extra checking - annotations should be sufficient - - # Add the attribute declarations with proper names - for class_name, class_type in class_attributes: - # Check if there's a mapping from annotation - attr_name = attribute_mappings.get(class_name, class_name) - # Use the annotation name if it exists, even if the attribute doesn't exist yet - # This is because the attribute might be created at runtime - stub_content.append(f" {attr_name}: {class_name}Sync") - - stub_content.append("") # Add a final newline - - # Now generate imports with all discovered types - imports = cls._generate_imports(async_class, type_tracker) - - # Deduplicate imports while preserving order - seen = set() - unique_imports = [] - for imp in imports: - if imp not in seen: - seen.add(imp) - unique_imports.append(imp) - else: - logging.warning(f"Duplicate import detected: {imp}") - - # Replace the placeholder with actual imports - stub_content[imports_placeholder_index : imports_placeholder_index + 1] = ( - unique_imports - ) - - # Post-process stub content - stub_content = cls._post_process_stub_content(stub_content) - - # Write stub file - with open(sync_stub_path, "w") as f: - f.write("\n".join(stub_content)) - - logging.info(f"Generated stub file: {sync_stub_path}") - - except Exception as e: - # If stub generation fails, log the error but don't break the main functionality - logging.error( - f"Error generating stub file for {sync_class.__name__}: {str(e)}" - ) - import traceback - - logging.error(traceback.format_exc()) - - -def create_sync_class(async_class: Type, thread_pool_size=10) -> Type: - """ - Creates a sync version of an async class - - Args: - async_class: The async class to convert - thread_pool_size: Size of thread pool to use - - Returns: - A new class with sync versions of all async methods - """ - return AsyncToSyncConverter.create_sync_class(async_class, thread_pool_size) diff --git a/comfy_api/internal/singleton.py b/comfy_api/internal/singleton.py deleted file mode 100644 index 75f16f98ed7aab3378f55e749b69e806392d5bd7..0000000000000000000000000000000000000000 --- a/comfy_api/internal/singleton.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import Type, TypeVar - -class SingletonMetaclass(type): - T = TypeVar("T", bound="SingletonMetaclass") - _instances = {} - - def __call__(cls, *args, **kwargs): - if cls not in cls._instances: - cls._instances[cls] = super(SingletonMetaclass, cls).__call__( - *args, **kwargs - ) - return cls._instances[cls] - - def inject_instance(cls: Type[T], instance: T) -> None: - assert cls not in SingletonMetaclass._instances, ( - "Cannot inject instance after first instantiation" - ) - SingletonMetaclass._instances[cls] = instance - - def get_instance(cls: Type[T], *args, **kwargs) -> T: - """ - Gets the singleton instance of the class, creating it if it doesn't exist. - """ - if cls not in SingletonMetaclass._instances: - SingletonMetaclass._instances[cls] = super( - SingletonMetaclass, cls - ).__call__(*args, **kwargs) - return cls._instances[cls] - - -class ProxiedSingleton(object, metaclass=SingletonMetaclass): - def __init__(self): - super().__init__() diff --git a/comfy_api/latest/.DS_Store b/comfy_api/latest/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/latest/.DS_Store and /dev/null differ diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py deleted file mode 100644 index 2cee65aa9fb2bb845a9caa2e4fe666924c082b7f..0000000000000000000000000000000000000000 --- a/comfy_api/latest/__init__.py +++ /dev/null @@ -1,124 +0,0 @@ -from __future__ import annotations - -from abc import ABC, abstractmethod -from typing import Type, TYPE_CHECKING -from comfy_api.internal import ComfyAPIBase -from comfy_api.internal.singleton import ProxiedSingleton -from comfy_api.internal.async_to_sync import create_sync_class -from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput -from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents -from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents -from comfy_api.latest._io import _IO as io #noqa: F401 -from comfy_api.latest._ui import _UI as ui #noqa: F401 -# from comfy_api.latest._resources import _RESOURCES as resources #noqa: F401 -from comfy_execution.utils import get_executing_context -from comfy_execution.progress import get_progress_state, PreviewImageTuple -from PIL import Image -from comfy.cli_args import args -import numpy as np - - -class ComfyAPI_latest(ComfyAPIBase): - VERSION = "latest" - STABLE = False - - class Execution(ProxiedSingleton): - async def set_progress( - self, - value: float, - max_value: float, - node_id: str | None = None, - preview_image: Image.Image | ImageInput | None = None, - ignore_size_limit: bool = False, - ) -> None: - """ - Update the progress bar displayed in the ComfyUI interface. - - This function allows custom nodes and API calls to report their progress - back to the user interface, providing visual feedback during long operations. - - Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK - """ - executing_context = get_executing_context() - if node_id is None and executing_context is not None: - node_id = executing_context.node_id - if node_id is None: - raise ValueError("node_id must be provided if not in executing context") - - # Convert preview_image to PreviewImageTuple if needed - to_display: PreviewImageTuple | Image.Image | ImageInput | None = preview_image - if to_display is not None: - # First convert to PIL Image if needed - if isinstance(to_display, ImageInput): - # Convert ImageInput (torch.Tensor) to PIL Image - # Handle tensor shape [B, H, W, C] -> get first image if batch - tensor = to_display - if len(tensor.shape) == 4: - tensor = tensor[0] - - # Convert to numpy array and scale to 0-255 - image_np = (tensor.cpu().numpy() * 255).astype(np.uint8) - to_display = Image.fromarray(image_np) - - if isinstance(to_display, Image.Image): - # Detect image format from PIL Image - image_format = to_display.format if to_display.format else "JPEG" - # Use None for preview_size if ignore_size_limit is True - preview_size = None if ignore_size_limit else args.preview_size - to_display = (image_format, to_display, preview_size) - - get_progress_state().update_progress( - node_id=node_id, - value=value, - max_value=max_value, - image=to_display, - ) - - execution: Execution - -class ComfyExtension(ABC): - async def on_load(self) -> None: - """ - Called when an extension is loaded. - This should be used to initialize any global resources neeeded by the extension. - """ - - @abstractmethod - async def get_node_list(self) -> list[type[io.ComfyNode]]: - """ - Returns a list of nodes that this extension provides. - """ - -class Input: - Image = ImageInput - Audio = AudioInput - Mask = MaskInput - Latent = LatentInput - Video = VideoInput - -class InputImpl: - VideoFromFile = VideoFromFile - VideoFromComponents = VideoFromComponents - -class Types: - VideoCodec = VideoCodec - VideoContainer = VideoContainer - VideoComponents = VideoComponents - -ComfyAPI = ComfyAPI_latest - -# Create a synchronous version of the API -if TYPE_CHECKING: - import comfy_api.latest.generated.ComfyAPISyncStub # type: ignore - - ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub] -ComfyAPISync = create_sync_class(ComfyAPI_latest) - -__all__ = [ - "ComfyAPI", - "ComfyAPISync", - "Input", - "InputImpl", - "Types", - "ComfyExtension", -] diff --git a/comfy_api/latest/_input/.DS_Store b/comfy_api/latest/_input/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/latest/_input/.DS_Store and /dev/null differ diff --git a/comfy_api/latest/_input/__init__.py b/comfy_api/latest/_input/__init__.py deleted file mode 100644 index 14f0e72f46bfb079e85a1c0e56993b5a99425ffe..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_input/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput -from .video_types import VideoInput - -__all__ = [ - "ImageInput", - "AudioInput", - "VideoInput", - "MaskInput", - "LatentInput", -] diff --git a/comfy_api/latest/_input/basic_types.py b/comfy_api/latest/_input/basic_types.py deleted file mode 100644 index 245c6cbb1e775cf3b0d5369ca2f954ab6ca504b3..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_input/basic_types.py +++ /dev/null @@ -1,42 +0,0 @@ -import torch -from typing import TypedDict, List, Optional - -ImageInput = torch.Tensor -""" -An image in format [B, H, W, C] where B is the batch size, C is the number of channels, -""" - -MaskInput = torch.Tensor -""" -A mask in format [B, H, W] where B is the batch size -""" - -class AudioInput(TypedDict): - """ - TypedDict representing audio input. - """ - - waveform: torch.Tensor - """ - Tensor in the format [B, C, T] where B is the batch size, C is the number of channels, - """ - - sample_rate: int - -class LatentInput(TypedDict): - """ - TypedDict representing latent input. - """ - - samples: torch.Tensor - """ - Tensor in the format [B, C, H, W] where B is the batch size, C is the number of channels, - H is the height, and W is the width. - """ - - noise_mask: Optional[MaskInput] - """ - Optional noise mask tensor in the same format as samples. - """ - - batch_index: Optional[List[int]] diff --git a/comfy_api/latest/_input/video_types.py b/comfy_api/latest/_input/video_types.py deleted file mode 100644 index 5d95dc507f80bd83c424db88895ae26738f1c05c..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_input/video_types.py +++ /dev/null @@ -1,85 +0,0 @@ -from __future__ import annotations -from abc import ABC, abstractmethod -from typing import Optional, Union -import io -import av -from comfy_api.util import VideoContainer, VideoCodec, VideoComponents - -class VideoInput(ABC): - """ - Abstract base class for video input types. - """ - - @abstractmethod - def get_components(self) -> VideoComponents: - """ - Abstract method to get the video components (images, audio, and frame rate). - - Returns: - VideoComponents containing images, audio, and frame rate - """ - pass - - @abstractmethod - def save_to( - self, - path: str, - format: VideoContainer = VideoContainer.AUTO, - codec: VideoCodec = VideoCodec.AUTO, - metadata: Optional[dict] = None - ): - """ - Abstract method to save the video input to a file. - """ - pass - - def get_stream_source(self) -> Union[str, io.BytesIO]: - """ - Get a streamable source for the video. This allows processing without - loading the entire video into memory. - - Returns: - Either a file path (str) or a BytesIO object that can be opened with av. - - Default implementation creates a BytesIO buffer, but subclasses should - override this for better performance when possible. - """ - buffer = io.BytesIO() - self.save_to(buffer) - buffer.seek(0) - return buffer - - # Provide a default implementation, but subclasses can provide optimized versions - # if possible. - def get_dimensions(self) -> tuple[int, int]: - """ - Returns the dimensions of the video input. - - Returns: - Tuple of (width, height) - """ - components = self.get_components() - return components.images.shape[2], components.images.shape[1] - - def get_duration(self) -> float: - """ - Returns the duration of the video in seconds. - - Returns: - Duration in seconds - """ - components = self.get_components() - frame_count = components.images.shape[0] - return float(frame_count / components.frame_rate) - - def get_container_format(self) -> str: - """ - Returns the container format of the video (e.g., 'mp4', 'mov', 'avi'). - - Returns: - Container format as string - """ - # Default implementation - subclasses should override for better performance - source = self.get_stream_source() - with av.open(source, mode="r") as container: - return container.format.name diff --git a/comfy_api/latest/_input_impl/.DS_Store b/comfy_api/latest/_input_impl/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/latest/_input_impl/.DS_Store and /dev/null differ diff --git a/comfy_api/latest/_input_impl/__init__.py b/comfy_api/latest/_input_impl/__init__.py deleted file mode 100644 index 02901b8b933caa569cd9dd45eb8e39e7c66e43a1..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_input_impl/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .video_types import VideoFromFile, VideoFromComponents - -__all__ = [ - # Implementations - "VideoFromFile", - "VideoFromComponents", -] diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py deleted file mode 100644 index f646504c80b09cc39044b231afe1aedea8a88446..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_input_impl/video_types.py +++ /dev/null @@ -1,308 +0,0 @@ -from __future__ import annotations -from av.container import InputContainer -from av.subtitles.stream import SubtitleStream -from fractions import Fraction -from typing import Optional -from comfy_api.latest._input import AudioInput, VideoInput -import av -import io -import json -import numpy as np -import math -import torch -from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents - - -def container_to_output_format(container_format: str | None) -> str | None: - """ - A container's `format` may be a comma-separated list of formats. - E.g., iso container's `format` may be `mov,mp4,m4a,3gp,3g2,mj2`. - However, writing to a file/stream with `av.open` requires a single format, - or `None` to auto-detect. - """ - if not container_format: - return None # Auto-detect - - if "," not in container_format: - return container_format - - formats = container_format.split(",") - return formats[0] - - -def get_open_write_kwargs( - dest: str | io.BytesIO, container_format: str, to_format: str | None -) -> dict: - """Get kwargs for writing a `VideoFromFile` to a file/stream with `av.open`""" - open_kwargs = { - "mode": "w", - # If isobmff, preserve custom metadata tags (workflow, prompt, extra_pnginfo) - "options": {"movflags": "use_metadata_tags"}, - } - - is_write_to_buffer = isinstance(dest, io.BytesIO) - if is_write_to_buffer: - # Set output format explicitly, since it cannot be inferred from file extension - if to_format == VideoContainer.AUTO: - to_format = container_format.lower() - elif isinstance(to_format, str): - to_format = to_format.lower() - open_kwargs["format"] = container_to_output_format(to_format) - - return open_kwargs - - -class VideoFromFile(VideoInput): - """ - Class representing video input from a file. - """ - - def __init__(self, file: str | io.BytesIO): - """ - Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object - containing the file contents. - """ - self.__file = file - - def get_stream_source(self) -> str | io.BytesIO: - """ - Return the underlying file source for efficient streaming. - This avoids unnecessary memory copies when the source is already a file path. - """ - if isinstance(self.__file, io.BytesIO): - self.__file.seek(0) - return self.__file - - def get_dimensions(self) -> tuple[int, int]: - """ - Returns the dimensions of the video input. - - Returns: - Tuple of (width, height) - """ - if isinstance(self.__file, io.BytesIO): - self.__file.seek(0) # Reset the BytesIO object to the beginning - with av.open(self.__file, mode='r') as container: - for stream in container.streams: - if stream.type == 'video': - assert isinstance(stream, av.VideoStream) - return stream.width, stream.height - raise ValueError(f"No video stream found in file '{self.__file}'") - - def get_duration(self) -> float: - """ - Returns the duration of the video in seconds. - - Returns: - Duration in seconds - """ - if isinstance(self.__file, io.BytesIO): - self.__file.seek(0) - with av.open(self.__file, mode="r") as container: - if container.duration is not None: - return float(container.duration / av.time_base) - - # Fallback: calculate from frame count and frame rate - video_stream = next( - (s for s in container.streams if s.type == "video"), None - ) - if video_stream and video_stream.frames and video_stream.average_rate: - return float(video_stream.frames / video_stream.average_rate) - - # Last resort: decode frames to count them - if video_stream and video_stream.average_rate: - frame_count = 0 - container.seek(0) - for packet in container.demux(video_stream): - for _ in packet.decode(): - frame_count += 1 - if frame_count > 0: - return float(frame_count / video_stream.average_rate) - - raise ValueError(f"Could not determine duration for file '{self.__file}'") - - def get_container_format(self) -> str: - """ - Returns the container format of the video (e.g., 'mp4', 'mov', 'avi'). - - Returns: - Container format as string - """ - if isinstance(self.__file, io.BytesIO): - self.__file.seek(0) - with av.open(self.__file, mode='r') as container: - return container.format.name - - def get_components_internal(self, container: InputContainer) -> VideoComponents: - # Get video frames - frames = [] - for frame in container.decode(video=0): - img = frame.to_ndarray(format='rgb24') # shape: (H, W, 3) - img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3) - frames.append(img) - - images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0) - - # Get frame rate - video_stream = next(s for s in container.streams if s.type == 'video') - frame_rate = Fraction(video_stream.average_rate) if video_stream and video_stream.average_rate else Fraction(1) - - # Get audio if available - audio = None - try: - container.seek(0) # Reset the container to the beginning - for stream in container.streams: - if stream.type != 'audio': - continue - assert isinstance(stream, av.AudioStream) - audio_frames = [] - for packet in container.demux(stream): - for frame in packet.decode(): - assert isinstance(frame, av.AudioFrame) - audio_frames.append(frame.to_ndarray()) # shape: (channels, samples) - if len(audio_frames) > 0: - audio_data = np.concatenate(audio_frames, axis=1) # shape: (channels, total_samples) - audio_tensor = torch.from_numpy(audio_data).unsqueeze(0) # shape: (1, channels, total_samples) - audio = AudioInput({ - "waveform": audio_tensor, - "sample_rate": int(stream.sample_rate) if stream.sample_rate else 1, - }) - except StopIteration: - pass # No audio stream - - metadata = container.metadata - return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata) - - def get_components(self) -> VideoComponents: - if isinstance(self.__file, io.BytesIO): - self.__file.seek(0) # Reset the BytesIO object to the beginning - with av.open(self.__file, mode='r') as container: - return self.get_components_internal(container) - raise ValueError(f"No video stream found in file '{self.__file}'") - - def save_to( - self, - path: str | io.BytesIO, - format: VideoContainer = VideoContainer.AUTO, - codec: VideoCodec = VideoCodec.AUTO, - metadata: Optional[dict] = None - ): - if isinstance(self.__file, io.BytesIO): - self.__file.seek(0) # Reset the BytesIO object to the beginning - with av.open(self.__file, mode='r') as container: - container_format = container.format.name - video_encoding = container.streams.video[0].codec.name if len(container.streams.video) > 0 else None - reuse_streams = True - if format != VideoContainer.AUTO and format not in container_format.split(","): - reuse_streams = False - if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None: - reuse_streams = False - - if not reuse_streams: - components = self.get_components_internal(container) - video = VideoFromComponents(components) - return video.save_to( - path, - format=format, - codec=codec, - metadata=metadata - ) - - streams = container.streams - - open_kwargs = get_open_write_kwargs(path, container_format, format) - with av.open(path, **open_kwargs) as output_container: - # Copy over the original metadata - for key, value in container.metadata.items(): - if metadata is None or key not in metadata: - output_container.metadata[key] = value - - # Add our new metadata - if metadata is not None: - for key, value in metadata.items(): - if isinstance(value, str): - output_container.metadata[key] = value - else: - output_container.metadata[key] = json.dumps(value) - - # Add streams to the new container - stream_map = {} - for stream in streams: - if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)): - out_stream = output_container.add_stream_from_template(template=stream, opaque=True) - stream_map[stream] = out_stream - - # Write packets to the new container - for packet in container.demux(): - if packet.stream in stream_map and packet.dts is not None: - packet.stream = stream_map[packet.stream] - output_container.mux(packet) - -class VideoFromComponents(VideoInput): - """ - Class representing video input from tensors. - """ - - def __init__(self, components: VideoComponents): - self.__components = components - - def get_components(self) -> VideoComponents: - return VideoComponents( - images=self.__components.images, - audio=self.__components.audio, - frame_rate=self.__components.frame_rate - ) - - def save_to( - self, - path: str, - format: VideoContainer = VideoContainer.AUTO, - codec: VideoCodec = VideoCodec.AUTO, - metadata: Optional[dict] = None - ): - if format != VideoContainer.AUTO and format != VideoContainer.MP4: - raise ValueError("Only MP4 format is supported for now") - if codec != VideoCodec.AUTO and codec != VideoCodec.H264: - raise ValueError("Only H264 codec is supported for now") - with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output: - # Add metadata before writing any streams - if metadata is not None: - for key, value in metadata.items(): - output.metadata[key] = json.dumps(value) - - frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000) - # Create a video stream - video_stream = output.add_stream('h264', rate=frame_rate) - video_stream.width = self.__components.images.shape[2] - video_stream.height = self.__components.images.shape[1] - video_stream.pix_fmt = 'yuv420p' - - # Create an audio stream - audio_sample_rate = 1 - audio_stream: Optional[av.AudioStream] = None - if self.__components.audio: - audio_sample_rate = int(self.__components.audio['sample_rate']) - audio_stream = output.add_stream('aac', rate=audio_sample_rate) - - # Encode video - for i, frame in enumerate(self.__components.images): - img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3) - frame = av.VideoFrame.from_ndarray(img, format='rgb24') - frame = frame.reformat(format='yuv420p') # Convert to YUV420P as required by h264 - packet = video_stream.encode(frame) - output.mux(packet) - - # Flush video - packet = video_stream.encode(None) - output.mux(packet) - - if audio_stream and self.__components.audio: - waveform = self.__components.audio['waveform'] - waveform = waveform[:, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])] - frame = av.AudioFrame.from_ndarray(waveform.movedim(2, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[1] == 1 else 'stereo') - frame.sample_rate = audio_sample_rate - frame.pts = 0 - output.mux(audio_stream.encode(frame)) - - # Flush encoder - output.mux(audio_stream.encode(None)) diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py deleted file mode 100644 index e0ee943a7a53810b4bc394dc755657dde0601883..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_io.py +++ /dev/null @@ -1,1631 +0,0 @@ -from __future__ import annotations - -import copy -import inspect -from abc import ABC, abstractmethod -from collections import Counter -from dataclasses import asdict, dataclass -from enum import Enum -from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING -from typing_extensions import NotRequired, final - -# used for type hinting -import torch - -if TYPE_CHECKING: - from spandrel import ImageModelDescriptor - from comfy.clip_vision import ClipVisionModel - from comfy.clip_vision import Output as ClipVisionOutput_ - from comfy.controlnet import ControlNet - from comfy.hooks import HookGroup, HookKeyframeGroup - from comfy.model_patcher import ModelPatcher - from comfy.samplers import CFGGuider, Sampler - from comfy.sd import CLIP, VAE - from comfy.sd import StyleModel as StyleModel_ - from comfy_api.input import VideoInput -from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class, - prune_dict, shallow_clone_class) -from comfy_api.latest._resources import Resources, ResourcesLocal -from comfy_execution.graph_utils import ExecutionBlocker - -# from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference - -class FolderType(str, Enum): - input = "input" - output = "output" - temp = "temp" - - -class UploadType(str, Enum): - image = "image_upload" - audio = "audio_upload" - video = "video_upload" - model = "file_upload" - - -class RemoteOptions: - def __init__(self, route: str, refresh_button: bool, control_after_refresh: Literal["first", "last"]="first", - timeout: int=None, max_retries: int=None, refresh: int=None): - self.route = route - """The route to the remote source.""" - self.refresh_button = refresh_button - """Specifies whether to show a refresh button in the UI below the widget.""" - self.control_after_refresh = control_after_refresh - """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on.""" - self.timeout = timeout - """The maximum amount of time to wait for a response from the remote source in milliseconds.""" - self.max_retries = max_retries - """The maximum number of retries before aborting the request.""" - self.refresh = refresh - """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.""" - - def as_dict(self): - return prune_dict({ - "route": self.route, - "refresh_button": self.refresh_button, - "control_after_refresh": self.control_after_refresh, - "timeout": self.timeout, - "max_retries": self.max_retries, - "refresh": self.refresh, - }) - - -class NumberDisplay(str, Enum): - number = "number" - slider = "slider" - - -class _StringIOType(str): - def __ne__(self, value: object) -> bool: - if self == "*" or value == "*": - return False - if not isinstance(value, str): - return True - a = frozenset(self.split(",")) - b = frozenset(value.split(",")) - return not (b.issubset(a) or a.issubset(b)) - -class _ComfyType(ABC): - Type = Any - io_type: str = None - -# NOTE: this is a workaround to make the decorator return the correct type -T = TypeVar("T", bound=type) -def comfytype(io_type: str, **kwargs): - ''' - Decorator to mark nested classes as ComfyType; io_type will be bound to the class. - - A ComfyType may have the following attributes: - - Type = - - class Input(Input): ... - - class Output(Output): ... - ''' - def decorator(cls: T) -> T: - if isinstance(cls, _ComfyType) or issubclass(cls, _ComfyType): - # clone Input and Output classes to avoid modifying the original class - new_cls = cls - if hasattr(new_cls, "Input"): - new_cls.Input = copy_class(new_cls.Input) - if hasattr(new_cls, "Output"): - new_cls.Output = copy_class(new_cls.Output) - else: - # copy class attributes except for special ones that shouldn't be in type() - cls_dict = { - k: v for k, v in cls.__dict__.items() - if k not in ('__dict__', '__weakref__', '__module__', '__doc__') - } - # new class - new_cls: ComfyTypeIO = type( - cls.__name__, - (cls, ComfyTypeIO), - cls_dict - ) - # metadata preservation - new_cls.__module__ = cls.__module__ - new_cls.__doc__ = cls.__doc__ - # assign ComfyType attributes, if needed - # NOTE: use __ne__ trick for io_type (see node_typing.IO.__ne__ for details) - new_cls.io_type = _StringIOType(io_type) - if hasattr(new_cls, "Input") and new_cls.Input is not None: - new_cls.Input.Parent = new_cls - if hasattr(new_cls, "Output") and new_cls.Output is not None: - new_cls.Output.Parent = new_cls - return new_cls - return decorator - -def Custom(io_type: str) -> type[ComfyTypeIO]: - '''Create a ComfyType for a custom io_type.''' - @comfytype(io_type=io_type) - class CustomComfyType(ComfyTypeIO): - ... - return CustomComfyType - -class _IO_V3: - ''' - Base class for V3 Inputs and Outputs. - ''' - Parent: _ComfyType = None - - def __init__(self): - pass - - @property - def io_type(self): - return self.Parent.io_type - - @property - def Type(self): - return self.Parent.Type - -class Input(_IO_V3): - ''' - Base class for a V3 Input. - ''' - def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): - super().__init__() - self.id = id - self.display_name = display_name - self.optional = optional - self.tooltip = tooltip - self.lazy = lazy - self.extra_dict = extra_dict if extra_dict is not None else {} - - def as_dict(self): - return prune_dict({ - "display_name": self.display_name, - "optional": self.optional, - "tooltip": self.tooltip, - "lazy": self.lazy, - }) | prune_dict(self.extra_dict) - - def get_io_type(self): - return _StringIOType(self.io_type) - -class WidgetInput(Input): - ''' - Base class for a V3 Input with widget. - ''' - def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - default: Any=None, - socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None): - super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) - self.default = default - self.socketless = socketless - self.widget_type = widget_type - self.force_input = force_input - - def as_dict(self): - return super().as_dict() | prune_dict({ - "default": self.default, - "socketless": self.socketless, - "widgetType": self.widget_type, - "forceInput": self.force_input, - }) - - def get_io_type(self): - return self.widget_type if self.widget_type is not None else super().get_io_type() - - -class Output(_IO_V3): - def __init__(self, id: str=None, display_name: str=None, tooltip: str=None, - is_output_list=False): - self.id = id - self.display_name = display_name - self.tooltip = tooltip - self.is_output_list = is_output_list - - def as_dict(self): - return prune_dict({ - "display_name": self.display_name, - "tooltip": self.tooltip, - "is_output_list": self.is_output_list, - }) - - def get_io_type(self): - return self.io_type - - -class ComfyTypeI(_ComfyType): - '''ComfyType subclass that only has a default Input class - intended for types that only have Inputs.''' - class Input(Input): - ... - -class ComfyTypeIO(ComfyTypeI): - '''ComfyType subclass that has default Input and Output classes; useful for types with both Inputs and Outputs.''' - class Output(Output): - ... - - -@comfytype(io_type="BOOLEAN") -class Boolean(ComfyTypeIO): - Type = bool - - class Input(WidgetInput): - '''Boolean input.''' - def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - default: bool=None, label_on: str=None, label_off: str=None, - socketless: bool=None, force_input: bool=None): - super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) - self.label_on = label_on - self.label_off = label_off - self.default: bool - - def as_dict(self): - return super().as_dict() | prune_dict({ - "label_on": self.label_on, - "label_off": self.label_off, - }) - -@comfytype(io_type="INT") -class Int(ComfyTypeIO): - Type = int - - class Input(WidgetInput): - '''Integer input.''' - def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool=None, - display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None): - super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) - self.min = min - self.max = max - self.step = step - self.control_after_generate = control_after_generate - self.display_mode = display_mode - self.default: int - - def as_dict(self): - return super().as_dict() | prune_dict({ - "min": self.min, - "max": self.max, - "step": self.step, - "control_after_generate": self.control_after_generate, - "display": self.display_mode.value if self.display_mode else None, - }) - -@comfytype(io_type="FLOAT") -class Float(ComfyTypeIO): - Type = float - - class Input(WidgetInput): - '''Float input.''' - def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - default: float=None, min: float=None, max: float=None, step: float=None, round: float=None, - display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None): - super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) - self.min = min - self.max = max - self.step = step - self.round = round - self.display_mode = display_mode - self.default: float - - def as_dict(self): - return super().as_dict() | prune_dict({ - "min": self.min, - "max": self.max, - "step": self.step, - "round": self.round, - "display": self.display_mode, - }) - -@comfytype(io_type="STRING") -class String(ComfyTypeIO): - Type = str - - class Input(WidgetInput): - '''String input.''' - def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - multiline=False, placeholder: str=None, default: str=None, dynamic_prompts: bool=None, - socketless: bool=None, force_input: bool=None): - super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) - self.multiline = multiline - self.placeholder = placeholder - self.dynamic_prompts = dynamic_prompts - self.default: str - - def as_dict(self): - return super().as_dict() | prune_dict({ - "multiline": self.multiline, - "placeholder": self.placeholder, - "dynamicPrompts": self.dynamic_prompts, - }) - -@comfytype(io_type="COMBO") -class Combo(ComfyTypeI): - Type = str - class Input(WidgetInput): - """Combo input (dropdown).""" - Type = str - def __init__(self, id: str, options: list[str]=None, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - default: str=None, control_after_generate: bool=None, - upload: UploadType=None, image_folder: FolderType=None, - remote: RemoteOptions=None, - socketless: bool=None): - super().__init__(id, display_name, optional, tooltip, lazy, default, socketless) - self.multiselect = False - self.options = options - self.control_after_generate = control_after_generate - self.upload = upload - self.image_folder = image_folder - self.remote = remote - self.default: str - - def as_dict(self): - return super().as_dict() | prune_dict({ - "multiselect": self.multiselect, - "options": self.options, - "control_after_generate": self.control_after_generate, - **({self.upload.value: True} if self.upload is not None else {}), - "image_folder": self.image_folder.value if self.image_folder else None, - "remote": self.remote.as_dict() if self.remote else None, - }) - - -@comfytype(io_type="COMBO") -class MultiCombo(ComfyTypeI): - '''Multiselect Combo input (dropdown for selecting potentially more than one value).''' - # TODO: something is wrong with the serialization, frontend does not recognize it as multiselect - Type = list[str] - class Input(Combo.Input): - def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, - default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None, - socketless: bool=None): - super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless) - self.multiselect = True - self.placeholder = placeholder - self.chip = chip - self.default: list[str] - - def as_dict(self): - to_return = super().as_dict() | prune_dict({ - "multi_select": self.multiselect, - "placeholder": self.placeholder, - "chip": self.chip, - }) - return to_return - -@comfytype(io_type="IMAGE") -class Image(ComfyTypeIO): - Type = torch.Tensor - - -@comfytype(io_type="WAN_CAMERA_EMBEDDING") -class WanCameraEmbedding(ComfyTypeIO): - Type = torch.Tensor - - -@comfytype(io_type="WEBCAM") -class Webcam(ComfyTypeIO): - Type = str - - class Input(WidgetInput): - """Webcam input.""" - Type = str - def __init__( - self, id: str, display_name: str=None, optional=False, - tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None - ): - super().__init__(id, display_name, optional, tooltip, lazy, default, socketless) - - -@comfytype(io_type="MASK") -class Mask(ComfyTypeIO): - Type = torch.Tensor - -@comfytype(io_type="LATENT") -class Latent(ComfyTypeIO): - '''Latents are stored as a dictionary.''' - class LatentDict(TypedDict): - samples: torch.Tensor - '''Latent tensors.''' - noise_mask: NotRequired[torch.Tensor] - batch_index: NotRequired[list[int]] - type: NotRequired[str] - '''Only needed if dealing with these types: audio, hunyuan3dv2''' - Type = LatentDict - -@comfytype(io_type="CONDITIONING") -class Conditioning(ComfyTypeIO): - class PooledDict(TypedDict): - pooled_output: torch.Tensor - '''Pooled output from CLIP.''' - control: NotRequired[ControlNet] - '''ControlNet to apply to conditioning.''' - control_apply_to_uncond: NotRequired[bool] - '''Whether to apply ControlNet to matching negative conditioning at sample time, if applicable.''' - cross_attn_controlnet: NotRequired[torch.Tensor] - '''CrossAttn from CLIP to use for controlnet only.''' - pooled_output_controlnet: NotRequired[torch.Tensor] - '''Pooled output from CLIP to use for controlnet only.''' - gligen: NotRequired[tuple[str, Gligen, list[tuple[torch.Tensor, int, ...]]]] - '''GLIGEN to apply to conditioning.''' - area: NotRequired[tuple[int, ...] | tuple[str, float, ...]] - '''Set area of conditioning. First half of values apply to dimensions, the second half apply to coordinates. - By default, the dimensions are based on total pixel amount, but the first value can be set to "percentage" to use a percentage of the image size instead. - - (1024, 1024, 0, 0) would apply conditioning to the top-left 1024x1024 pixels. - - ("percentage", 0.5, 0.5, 0, 0) would apply conditioning to the top-left 50% of the image.''' # TODO: verify its actually top-left - strength: NotRequired[float] - '''Strength of conditioning. Default strength is 1.0.''' - mask: NotRequired[torch.Tensor] - '''Mask to apply conditioning to.''' - mask_strength: NotRequired[float] - '''Strength of conditioning mask. Default strength is 1.0.''' - set_area_to_bounds: NotRequired[bool] - '''Whether conditioning mask should determine bounds of area - if set to false, latents are sampled at full resolution and result is applied in mask.''' - concat_latent_image: NotRequired[torch.Tensor] - '''Used for inpainting and specific models.''' - concat_mask: NotRequired[torch.Tensor] - '''Used for inpainting and specific models.''' - concat_image: NotRequired[torch.Tensor] - '''Used by SD_4XUpscale_Conditioning.''' - noise_augmentation: NotRequired[float] - '''Used by SD_4XUpscale_Conditioning.''' - hooks: NotRequired[HookGroup] - '''Applies hooks to conditioning.''' - default: NotRequired[bool] - '''Whether to this conditioning is 'default'; default conditioning gets applied to any areas of the image that have no masks/areas applied, assuming at least one area/mask is present during sampling.''' - start_percent: NotRequired[float] - '''Determines relative step to begin applying conditioning, expressed as a float between 0.0 and 1.0.''' - end_percent: NotRequired[float] - '''Determines relative step to end applying conditioning, expressed as a float between 0.0 and 1.0.''' - clip_start_percent: NotRequired[float] - '''Internal variable for conditioning scheduling - start of application, expressed as a float between 0.0 and 1.0.''' - clip_end_percent: NotRequired[float] - '''Internal variable for conditioning scheduling - end of application, expressed as a float between 0.0 and 1.0.''' - attention_mask: NotRequired[torch.Tensor] - '''Masks text conditioning; used by StyleModel among others.''' - attention_mask_img_shape: NotRequired[tuple[int, ...]] - '''Masks text conditioning; used by StyleModel among others.''' - unclip_conditioning: NotRequired[list[dict]] - '''Used by unCLIP.''' - conditioning_lyrics: NotRequired[torch.Tensor] - '''Used by AceT5Model.''' - seconds_start: NotRequired[float] - '''Used by StableAudio.''' - seconds_total: NotRequired[float] - '''Used by StableAudio.''' - lyrics_strength: NotRequired[float] - '''Used by AceStepAudio.''' - width: NotRequired[int] - '''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).''' - height: NotRequired[int] - '''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).''' - aesthetic_score: NotRequired[float] - '''Used by CLIPTextEncodeSDXL/Refiner.''' - crop_w: NotRequired[int] - '''Used by CLIPTextEncodeSDXL.''' - crop_h: NotRequired[int] - '''Used by CLIPTextEncodeSDXL.''' - target_width: NotRequired[int] - '''Used by CLIPTextEncodeSDXL.''' - target_height: NotRequired[int] - '''Used by CLIPTextEncodeSDXL.''' - reference_latents: NotRequired[list[torch.Tensor]] - '''Used by ReferenceLatent.''' - guidance: NotRequired[float] - '''Used by Flux-like models with guidance embed.''' - guiding_frame_index: NotRequired[int] - '''Used by Hunyuan ImageToVideo.''' - ref_latent: NotRequired[torch.Tensor] - '''Used by Hunyuan ImageToVideo.''' - keyframe_idxs: NotRequired[list[int]] - '''Used by LTXV.''' - frame_rate: NotRequired[float] - '''Used by LTXV.''' - stable_cascade_prior: NotRequired[torch.Tensor] - '''Used by StableCascade.''' - elevation: NotRequired[list[float]] - '''Used by SV3D.''' - azimuth: NotRequired[list[float]] - '''Used by SV3D.''' - motion_bucket_id: NotRequired[int] - '''Used by SVD-like models.''' - fps: NotRequired[int] - '''Used by SVD-like models.''' - augmentation_level: NotRequired[float] - '''Used by SVD-like models.''' - clip_vision_output: NotRequired[ClipVisionOutput_] - '''Used by WAN-like models.''' - vace_frames: NotRequired[torch.Tensor] - '''Used by WAN VACE.''' - vace_mask: NotRequired[torch.Tensor] - '''Used by WAN VACE.''' - vace_strength: NotRequired[float] - '''Used by WAN VACE.''' - camera_conditions: NotRequired[Any] # TODO: assign proper type once defined - '''Used by WAN Camera.''' - time_dim_concat: NotRequired[torch.Tensor] - '''Used by WAN Phantom Subject.''' - - CondList = list[tuple[torch.Tensor, PooledDict]] - Type = CondList - -@comfytype(io_type="SAMPLER") -class Sampler(ComfyTypeIO): - if TYPE_CHECKING: - Type = Sampler - -@comfytype(io_type="SIGMAS") -class Sigmas(ComfyTypeIO): - Type = torch.Tensor - -@comfytype(io_type="NOISE") -class Noise(ComfyTypeIO): - Type = torch.Tensor - -@comfytype(io_type="GUIDER") -class Guider(ComfyTypeIO): - if TYPE_CHECKING: - Type = CFGGuider - -@comfytype(io_type="CLIP") -class Clip(ComfyTypeIO): - if TYPE_CHECKING: - Type = CLIP - -@comfytype(io_type="CONTROL_NET") -class ControlNet(ComfyTypeIO): - if TYPE_CHECKING: - Type = ControlNet - -@comfytype(io_type="VAE") -class Vae(ComfyTypeIO): - if TYPE_CHECKING: - Type = VAE - -@comfytype(io_type="MODEL") -class Model(ComfyTypeIO): - if TYPE_CHECKING: - Type = ModelPatcher - -@comfytype(io_type="CLIP_VISION") -class ClipVision(ComfyTypeIO): - if TYPE_CHECKING: - Type = ClipVisionModel - -@comfytype(io_type="CLIP_VISION_OUTPUT") -class ClipVisionOutput(ComfyTypeIO): - if TYPE_CHECKING: - Type = ClipVisionOutput_ - -@comfytype(io_type="STYLE_MODEL") -class StyleModel(ComfyTypeIO): - if TYPE_CHECKING: - Type = StyleModel_ - -@comfytype(io_type="GLIGEN") -class Gligen(ComfyTypeIO): - '''ModelPatcher that wraps around a 'Gligen' model.''' - if TYPE_CHECKING: - Type = ModelPatcher - -@comfytype(io_type="UPSCALE_MODEL") -class UpscaleModel(ComfyTypeIO): - if TYPE_CHECKING: - Type = ImageModelDescriptor - -@comfytype(io_type="AUDIO") -class Audio(ComfyTypeIO): - class AudioDict(TypedDict): - waveform: torch.Tensor - sampler_rate: int - Type = AudioDict - -@comfytype(io_type="VIDEO") -class Video(ComfyTypeIO): - if TYPE_CHECKING: - Type = VideoInput - -@comfytype(io_type="SVG") -class SVG(ComfyTypeIO): - Type = Any # TODO: SVG class is defined in comfy_extras/nodes_images.py, causing circular reference; should be moved to somewhere else before referenced directly in v3 - -@comfytype(io_type="LORA_MODEL") -class LoraModel(ComfyTypeIO): - Type = dict[str, torch.Tensor] - -@comfytype(io_type="LOSS_MAP") -class LossMap(ComfyTypeIO): - class LossMapDict(TypedDict): - loss: list[torch.Tensor] - Type = LossMapDict - -@comfytype(io_type="VOXEL") -class Voxel(ComfyTypeIO): - Type = Any # TODO: VOXEL class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3 - -@comfytype(io_type="MESH") -class Mesh(ComfyTypeIO): - Type = Any # TODO: MESH class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3 - -@comfytype(io_type="HOOKS") -class Hooks(ComfyTypeIO): - if TYPE_CHECKING: - Type = HookGroup - -@comfytype(io_type="HOOK_KEYFRAMES") -class HookKeyframes(ComfyTypeIO): - if TYPE_CHECKING: - Type = HookKeyframeGroup - -@comfytype(io_type="TIMESTEPS_RANGE") -class TimestepsRange(ComfyTypeIO): - '''Range defined by start and endpoint, between 0.0 and 1.0.''' - Type = tuple[int, int] - -@comfytype(io_type="LATENT_OPERATION") -class LatentOperation(ComfyTypeIO): - Type = Callable[[torch.Tensor], torch.Tensor] - -@comfytype(io_type="FLOW_CONTROL") -class FlowControl(ComfyTypeIO): - # NOTE: only used in testing_nodes right now - Type = tuple[str, Any] - -@comfytype(io_type="ACCUMULATION") -class Accumulation(ComfyTypeIO): - # NOTE: only used in testing_nodes right now - class AccumulationDict(TypedDict): - accum: list[Any] - Type = AccumulationDict - - -@comfytype(io_type="LOAD3D_CAMERA") -class Load3DCamera(ComfyTypeIO): - class CameraInfo(TypedDict): - position: dict[str, float | int] - target: dict[str, float | int] - zoom: int - cameraType: str - - Type = CameraInfo - - -@comfytype(io_type="LOAD_3D") -class Load3D(ComfyTypeIO): - """3D models are stored as a dictionary.""" - class Model3DDict(TypedDict): - image: str - mask: str - normal: str - camera_info: Load3DCamera.CameraInfo - recording: NotRequired[str] - - Type = Model3DDict - - -@comfytype(io_type="LOAD_3D_ANIMATION") -class Load3DAnimation(Load3D): - ... - - -@comfytype(io_type="PHOTOMAKER") -class Photomaker(ComfyTypeIO): - Type = Any - - -@comfytype(io_type="POINT") -class Point(ComfyTypeIO): - Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? - -@comfytype(io_type="FACE_ANALYSIS") -class FaceAnalysis(ComfyTypeIO): - Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? - -@comfytype(io_type="BBOX") -class BBOX(ComfyTypeIO): - Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? - -@comfytype(io_type="SEGS") -class SEGS(ComfyTypeIO): - Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? - -@comfytype(io_type="*") -class AnyType(ComfyTypeIO): - Type = Any - -@comfytype(io_type="MODEL_PATCH") -class MODEL_PATCH(ComfyTypeIO): - Type = Any - -@comfytype(io_type="AUDIO_ENCODER") -class AudioEncoder(ComfyTypeIO): - Type = Any - -@comfytype(io_type="AUDIO_ENCODER_OUTPUT") -class AudioEncoderOutput(ComfyTypeIO): - Type = Any - -@comfytype(io_type="COMFY_MULTITYPED_V3") -class MultiType: - Type = Any - class Input(Input): - ''' - Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values. - ''' - def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): - # if id is an Input, then use that Input with overridden values - self.input_override = None - if isinstance(id, Input): - self.input_override = copy.copy(id) - optional = id.optional if id.optional is True else optional - tooltip = id.tooltip if id.tooltip is not None else tooltip - display_name = id.display_name if id.display_name is not None else display_name - lazy = id.lazy if id.lazy is not None else lazy - id = id.id - # if is a widget input, make sure widget_type is set appropriately - if isinstance(self.input_override, WidgetInput): - self.input_override.widget_type = self.input_override.get_io_type() - super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) - self._io_types = types - - @property - def io_types(self) -> list[type[Input]]: - ''' - Returns list of Input class types permitted. - ''' - io_types = [] - for x in self._io_types: - if not is_class(x): - io_types.append(type(x)) - else: - io_types.append(x) - return io_types - - def get_io_type(self): - # ensure types are unique and order is preserved - str_types = [x.io_type for x in self.io_types] - if self.input_override is not None: - str_types.insert(0, self.input_override.get_io_type()) - return ",".join(list(dict.fromkeys(str_types))) - - def as_dict(self): - if self.input_override is not None: - return self.input_override.as_dict() | super().as_dict() - else: - return super().as_dict() - -class DynamicInput(Input, ABC): - ''' - Abstract class for dynamic input registration. - ''' - @abstractmethod - def get_dynamic(self) -> list[Input]: - ... - -class DynamicOutput(Output, ABC): - ''' - Abstract class for dynamic output registration. - ''' - def __init__(self, id: str=None, display_name: str=None, tooltip: str=None, - is_output_list=False): - super().__init__(id, display_name, tooltip, is_output_list) - - @abstractmethod - def get_dynamic(self) -> list[Output]: - ... - - -@comfytype(io_type="COMFY_AUTOGROW_V3") -class AutogrowDynamic(ComfyTypeI): - Type = list[Any] - class Input(DynamicInput): - def __init__(self, id: str, template_input: Input, min: int=1, max: int=None, - display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): - super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) - self.template_input = template_input - if min is not None: - assert(min >= 1) - if max is not None: - assert(max >= 1) - self.min = min - self.max = max - - def get_dynamic(self) -> list[Input]: - curr_count = 1 - new_inputs = [] - for i in range(self.min): - new_input = copy.copy(self.template_input) - new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$" - if new_input.display_name is not None: - new_input.display_name = f"{new_input.display_name}{curr_count}" - new_input.optional = self.optional or new_input.optional - if isinstance(self.template_input, WidgetInput): - new_input.force_input = True - new_inputs.append(new_input) - curr_count += 1 - # pretend to expand up to max - for i in range(curr_count-1, self.max): - new_input = copy.copy(self.template_input) - new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$" - if new_input.display_name is not None: - new_input.display_name = f"{new_input.display_name}{curr_count}" - new_input.optional = True - if isinstance(self.template_input, WidgetInput): - new_input.force_input = True - new_inputs.append(new_input) - curr_count += 1 - return new_inputs - -@comfytype(io_type="COMFY_COMBODYNAMIC_V3") -class ComboDynamic(ComfyTypeI): - class Input(DynamicInput): - def __init__(self, id: str): - pass - -@comfytype(io_type="COMFY_MATCHTYPE_V3") -class MatchType(ComfyTypeIO): - class Template: - def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType]): - self.template_id = template_id - self.allowed_types = [allowed_types] if isinstance(allowed_types, _ComfyType) else allowed_types - - def as_dict(self): - return { - "template_id": self.template_id, - "allowed_types": "".join(t.io_type for t in self.allowed_types), - } - - class Input(DynamicInput): - def __init__(self, id: str, template: MatchType.Template, - display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): - super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) - self.template = template - - def get_dynamic(self) -> list[Input]: - return [self] - - def as_dict(self): - return super().as_dict() | prune_dict({ - "template": self.template.as_dict(), - }) - - class Output(DynamicOutput): - def __init__(self, id: str, template: MatchType.Template, display_name: str=None, tooltip: str=None, - is_output_list=False): - super().__init__(id, display_name, tooltip, is_output_list) - self.template = template - - def get_dynamic(self) -> list[Output]: - return [self] - - def as_dict(self): - return super().as_dict() | prune_dict({ - "template": self.template.as_dict(), - }) - - -class HiddenHolder: - def __init__(self, unique_id: str, prompt: Any, - extra_pnginfo: Any, dynprompt: Any, - auth_token_comfy_org: str, api_key_comfy_org: str, **kwargs): - self.unique_id = unique_id - """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" - self.prompt = prompt - """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" - self.extra_pnginfo = extra_pnginfo - """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" - self.dynprompt = dynprompt - """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" - self.auth_token_comfy_org = auth_token_comfy_org - """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend.""" - self.api_key_comfy_org = api_key_comfy_org - """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend.""" - - def __getattr__(self, key: str): - '''If hidden variable not found, return None.''' - return None - - @classmethod - def from_dict(cls, d: dict | None): - if d is None: - d = {} - return cls( - unique_id=d.get(Hidden.unique_id, None), - prompt=d.get(Hidden.prompt, None), - extra_pnginfo=d.get(Hidden.extra_pnginfo, None), - dynprompt=d.get(Hidden.dynprompt, None), - auth_token_comfy_org=d.get(Hidden.auth_token_comfy_org, None), - api_key_comfy_org=d.get(Hidden.api_key_comfy_org, None), - ) - -class Hidden(str, Enum): - ''' - Enumerator for requesting hidden variables in nodes. - ''' - unique_id = "UNIQUE_ID" - """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" - prompt = "PROMPT" - """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" - extra_pnginfo = "EXTRA_PNGINFO" - """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" - dynprompt = "DYNPROMPT" - """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" - auth_token_comfy_org = "AUTH_TOKEN_COMFY_ORG" - """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend.""" - api_key_comfy_org = "API_KEY_COMFY_ORG" - """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend.""" - - -@dataclass -class NodeInfoV1: - input: dict=None - input_order: dict[str, list[str]]=None - output: list[str]=None - output_is_list: list[bool]=None - output_name: list[str]=None - output_tooltips: list[str]=None - name: str=None - display_name: str=None - description: str=None - python_module: Any=None - category: str=None - output_node: bool=None - deprecated: bool=None - experimental: bool=None - api_node: bool=None - -@dataclass -class NodeInfoV3: - input: dict=None - output: dict=None - hidden: list[str]=None - name: str=None - display_name: str=None - description: str=None - category: str=None - output_node: bool=None - deprecated: bool=None - experimental: bool=None - api_node: bool=None - - -@dataclass -class Schema: - """Definition of V3 node properties.""" - - node_id: str - """ID of node - should be globally unique. If this is a custom node, add a prefix or postfix to avoid name clashes.""" - display_name: str = None - """Display name of node.""" - category: str = "sd" - """The category of the node, as per the "Add Node" menu.""" - inputs: list[Input]=None - outputs: list[Output]=None - hidden: list[Hidden]=None - description: str="" - """Node description, shown as a tooltip when hovering over the node.""" - is_input_list: bool = False - """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes. - - All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in. This also affects ``check_lazy_status``. - - From the docs: - - A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing - """ - is_output_node: bool=False - """Flags this node as an output node, causing any inputs it requires to be executed. - - If a node is not connected to any output nodes, that node will not be executed. Usage:: - - From the docs: - - By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is. - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node - """ - is_deprecated: bool=False - """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" - is_experimental: bool=False - """Flags a node as experimental, informing users that it may change or not work as expected.""" - is_api_node: bool=False - """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" - not_idempotent: bool=False - """Flags a node as not idempotent; when True, the node will run and not reuse the cached outputs when identical inputs are provided on a different node in the graph.""" - enable_expand: bool=False - """Flags a node as expandable, allowing NodeOutput to include 'expand' property.""" - - def validate(self): - '''Validate the schema: - - verify ids on inputs and outputs are unique - both internally and in relation to each other - ''' - input_ids = [i.id for i in self.inputs] if self.inputs is not None else [] - output_ids = [o.id for o in self.outputs] if self.outputs is not None else [] - input_set = set(input_ids) - output_set = set(output_ids) - issues = [] - # verify ids are unique per list - if len(input_set) != len(input_ids): - issues.append(f"Input ids must be unique, but {[item for item, count in Counter(input_ids).items() if count > 1]} are not.") - if len(output_set) != len(output_ids): - issues.append(f"Output ids must be unique, but {[item for item, count in Counter(output_ids).items() if count > 1]} are not.") - # verify ids are unique between lists - intersection = input_set & output_set - if len(intersection) > 0: - issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.") - if len(issues) > 0: - raise ValueError("\n".join(issues)) - - def finalize(self): - """Add hidden based on selected schema options, and give outputs without ids default ids.""" - # if is an api_node, will need key-related hidden - if self.is_api_node: - if self.hidden is None: - self.hidden = [] - if Hidden.auth_token_comfy_org not in self.hidden: - self.hidden.append(Hidden.auth_token_comfy_org) - if Hidden.api_key_comfy_org not in self.hidden: - self.hidden.append(Hidden.api_key_comfy_org) - # if is an output_node, will need prompt and extra_pnginfo - if self.is_output_node: - if self.hidden is None: - self.hidden = [] - if Hidden.prompt not in self.hidden: - self.hidden.append(Hidden.prompt) - if Hidden.extra_pnginfo not in self.hidden: - self.hidden.append(Hidden.extra_pnginfo) - # give outputs without ids default ids - if self.outputs is not None: - for i, output in enumerate(self.outputs): - if output.id is None: - output.id = f"_{i}_{output.io_type}_" - - def get_v1_info(self, cls) -> NodeInfoV1: - # get V1 inputs - input = { - "required": {} - } - if self.inputs: - for i in self.inputs: - if isinstance(i, DynamicInput): - dynamic_inputs = i.get_dynamic() - for d in dynamic_inputs: - add_to_dict_v1(d, input) - else: - add_to_dict_v1(i, input) - if self.hidden: - for hidden in self.hidden: - input.setdefault("hidden", {})[hidden.name] = (hidden.value,) - # create separate lists from output fields - output = [] - output_is_list = [] - output_name = [] - output_tooltips = [] - if self.outputs: - for o in self.outputs: - output.append(o.io_type) - output_is_list.append(o.is_output_list) - output_name.append(o.display_name if o.display_name else o.io_type) - output_tooltips.append(o.tooltip if o.tooltip else None) - - info = NodeInfoV1( - input=input, - input_order={key: list(value.keys()) for (key, value) in input.items()}, - output=output, - output_is_list=output_is_list, - output_name=output_name, - output_tooltips=output_tooltips, - name=self.node_id, - display_name=self.display_name, - category=self.category, - description=self.description, - output_node=self.is_output_node, - deprecated=self.is_deprecated, - experimental=self.is_experimental, - api_node=self.is_api_node, - python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes") - ) - return info - - - def get_v3_info(self, cls) -> NodeInfoV3: - input_dict = {} - output_dict = {} - hidden_list = [] - # TODO: make sure dynamic types will be handled correctly - if self.inputs: - for input in self.inputs: - add_to_dict_v3(input, input_dict) - if self.outputs: - for output in self.outputs: - add_to_dict_v3(output, output_dict) - if self.hidden: - for hidden in self.hidden: - hidden_list.append(hidden.value) - - info = NodeInfoV3( - input=input_dict, - output=output_dict, - hidden=hidden_list, - name=self.node_id, - display_name=self.display_name, - description=self.description, - category=self.category, - output_node=self.is_output_node, - deprecated=self.is_deprecated, - experimental=self.is_experimental, - api_node=self.is_api_node, - python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes") - ) - return info - - -def add_to_dict_v1(i: Input, input: dict): - key = "optional" if i.optional else "required" - as_dict = i.as_dict() - # for v1, we don't want to include the optional key - as_dict.pop("optional", None) - input.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict) - -def add_to_dict_v3(io: Input | Output, d: dict): - d[io.id] = (io.get_io_type(), io.as_dict()) - - - -class _ComfyNodeBaseInternal(_ComfyNodeInternal): - """Common base class for storing internal methods and properties; DO NOT USE for defining nodes.""" - - RELATIVE_PYTHON_MODULE = None - SCHEMA = None - - # filled in during execution - resources: Resources = None - hidden: HiddenHolder = None - - @classmethod - @abstractmethod - def define_schema(cls) -> Schema: - """Override this function with one that returns a Schema instance.""" - raise NotImplementedError - - @classmethod - @abstractmethod - def execute(cls, **kwargs) -> NodeOutput: - """Override this function with one that performs node's actions.""" - raise NotImplementedError - - @classmethod - def validate_inputs(cls, **kwargs) -> bool: - """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS.""" - raise NotImplementedError - - @classmethod - def fingerprint_inputs(cls, **kwargs) -> Any: - """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED.""" - raise NotImplementedError - - @classmethod - def check_lazy_status(cls, **kwargs) -> list[str]: - """Optionally, define this function to return a list of input names that should be evaluated. - - This basic mixin impl. requires all inputs. - - :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ - When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. - - Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). - Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status - """ - return [name for name in kwargs if kwargs[name] is None] - - def __init__(self): - self.local_resources: ResourcesLocal = None - self.__class__.VALIDATE_CLASS() - - @classmethod - def GET_BASE_CLASS(cls): - return _ComfyNodeBaseInternal - - @final - @classmethod - def VALIDATE_CLASS(cls): - if first_real_override(cls, "define_schema") is None: - raise Exception(f"No define_schema function was defined for node class {cls.__name__}.") - if first_real_override(cls, "execute") is None: - raise Exception(f"No execute function was defined for node class {cls.__name__}.") - - @classproperty - def FUNCTION(cls): # noqa - if inspect.iscoroutinefunction(cls.execute): - return "EXECUTE_NORMALIZED_ASYNC" - return "EXECUTE_NORMALIZED" - - @final - @classmethod - def EXECUTE_NORMALIZED(cls, *args, **kwargs) -> NodeOutput: - to_return = cls.execute(*args, **kwargs) - if to_return is None: - to_return = NodeOutput() - elif isinstance(to_return, NodeOutput): - pass - elif isinstance(to_return, tuple): - to_return = NodeOutput(*to_return) - elif isinstance(to_return, dict): - to_return = NodeOutput.from_dict(to_return) - elif isinstance(to_return, ExecutionBlocker): - to_return = NodeOutput(block_execution=to_return.message) - else: - raise Exception(f"Invalid return type from node: {type(to_return)}") - if to_return.expand is not None and not cls.SCHEMA.enable_expand: - raise Exception(f"Node {cls.__name__} is not expandable, but expand included in NodeOutput; developer should set enable_expand=True on node's Schema to allow this.") - return to_return - - @final - @classmethod - async def EXECUTE_NORMALIZED_ASYNC(cls, *args, **kwargs) -> NodeOutput: - to_return = await cls.execute(*args, **kwargs) - if to_return is None: - to_return = NodeOutput() - elif isinstance(to_return, NodeOutput): - pass - elif isinstance(to_return, tuple): - to_return = NodeOutput(*to_return) - elif isinstance(to_return, dict): - to_return = NodeOutput.from_dict(to_return) - elif isinstance(to_return, ExecutionBlocker): - to_return = NodeOutput(block_execution=to_return.message) - else: - raise Exception(f"Invalid return type from node: {type(to_return)}") - if to_return.expand is not None and not cls.SCHEMA.enable_expand: - raise Exception(f"Node {cls.__name__} is not expandable, but expand included in NodeOutput; developer should set enable_expand=True on node's Schema to allow this.") - return to_return - - @final - @classmethod - def PREPARE_CLASS_CLONE(cls, hidden_inputs: dict) -> type[ComfyNode]: - """Creates clone of real node class to prevent monkey-patching.""" - c_type: type[ComfyNode] = cls if is_class(cls) else type(cls) - type_clone: type[ComfyNode] = shallow_clone_class(c_type) - # set hidden - type_clone.hidden = HiddenHolder.from_dict(hidden_inputs) - return type_clone - - @final - @classmethod - def GET_NODE_INFO_V3(cls) -> dict[str, Any]: - schema = cls.GET_SCHEMA() - info = schema.get_v3_info(cls) - return asdict(info) - ############################################# - # V1 Backwards Compatibility code - #-------------------------------------------- - @final - @classmethod - def GET_NODE_INFO_V1(cls) -> dict[str, Any]: - schema = cls.GET_SCHEMA() - info = schema.get_v1_info(cls) - return asdict(info) - - _DESCRIPTION = None - @final - @classproperty - def DESCRIPTION(cls): # noqa - if cls._DESCRIPTION is None: - cls.GET_SCHEMA() - return cls._DESCRIPTION - - _CATEGORY = None - @final - @classproperty - def CATEGORY(cls): # noqa - if cls._CATEGORY is None: - cls.GET_SCHEMA() - return cls._CATEGORY - - _EXPERIMENTAL = None - @final - @classproperty - def EXPERIMENTAL(cls): # noqa - if cls._EXPERIMENTAL is None: - cls.GET_SCHEMA() - return cls._EXPERIMENTAL - - _DEPRECATED = None - @final - @classproperty - def DEPRECATED(cls): # noqa - if cls._DEPRECATED is None: - cls.GET_SCHEMA() - return cls._DEPRECATED - - _API_NODE = None - @final - @classproperty - def API_NODE(cls): # noqa - if cls._API_NODE is None: - cls.GET_SCHEMA() - return cls._API_NODE - - _OUTPUT_NODE = None - @final - @classproperty - def OUTPUT_NODE(cls): # noqa - if cls._OUTPUT_NODE is None: - cls.GET_SCHEMA() - return cls._OUTPUT_NODE - - _INPUT_IS_LIST = None - @final - @classproperty - def INPUT_IS_LIST(cls): # noqa - if cls._INPUT_IS_LIST is None: - cls.GET_SCHEMA() - return cls._INPUT_IS_LIST - _OUTPUT_IS_LIST = None - - @final - @classproperty - def OUTPUT_IS_LIST(cls): # noqa - if cls._OUTPUT_IS_LIST is None: - cls.GET_SCHEMA() - return cls._OUTPUT_IS_LIST - - _RETURN_TYPES = None - @final - @classproperty - def RETURN_TYPES(cls): # noqa - if cls._RETURN_TYPES is None: - cls.GET_SCHEMA() - return cls._RETURN_TYPES - - _RETURN_NAMES = None - @final - @classproperty - def RETURN_NAMES(cls): # noqa - if cls._RETURN_NAMES is None: - cls.GET_SCHEMA() - return cls._RETURN_NAMES - - _OUTPUT_TOOLTIPS = None - @final - @classproperty - def OUTPUT_TOOLTIPS(cls): # noqa - if cls._OUTPUT_TOOLTIPS is None: - cls.GET_SCHEMA() - return cls._OUTPUT_TOOLTIPS - - _NOT_IDEMPOTENT = None - @final - @classproperty - def NOT_IDEMPOTENT(cls): # noqa - if cls._NOT_IDEMPOTENT is None: - cls.GET_SCHEMA() - return cls._NOT_IDEMPOTENT - - @final - @classmethod - def INPUT_TYPES(cls, include_hidden=True, return_schema=False) -> dict[str, dict] | tuple[dict[str, dict], Schema]: - schema = cls.FINALIZE_SCHEMA() - info = schema.get_v1_info(cls) - input = info.input - if not include_hidden: - input.pop("hidden", None) - if return_schema: - return input, schema - return input - - @final - @classmethod - def FINALIZE_SCHEMA(cls): - """Call define_schema and finalize it.""" - schema = cls.define_schema() - schema.finalize() - return schema - - @final - @classmethod - def GET_SCHEMA(cls) -> Schema: - """Validate node class, finalize schema, validate schema, and set expected class properties.""" - cls.VALIDATE_CLASS() - schema = cls.FINALIZE_SCHEMA() - schema.validate() - if cls._DESCRIPTION is None: - cls._DESCRIPTION = schema.description - if cls._CATEGORY is None: - cls._CATEGORY = schema.category - if cls._EXPERIMENTAL is None: - cls._EXPERIMENTAL = schema.is_experimental - if cls._DEPRECATED is None: - cls._DEPRECATED = schema.is_deprecated - if cls._API_NODE is None: - cls._API_NODE = schema.is_api_node - if cls._OUTPUT_NODE is None: - cls._OUTPUT_NODE = schema.is_output_node - if cls._INPUT_IS_LIST is None: - cls._INPUT_IS_LIST = schema.is_input_list - if cls._NOT_IDEMPOTENT is None: - cls._NOT_IDEMPOTENT = schema.not_idempotent - - if cls._RETURN_TYPES is None: - output = [] - output_name = [] - output_is_list = [] - output_tooltips = [] - if schema.outputs: - for o in schema.outputs: - output.append(o.io_type) - output_name.append(o.display_name if o.display_name else o.io_type) - output_is_list.append(o.is_output_list) - output_tooltips.append(o.tooltip if o.tooltip else None) - - cls._RETURN_TYPES = output - cls._RETURN_NAMES = output_name - cls._OUTPUT_IS_LIST = output_is_list - cls._OUTPUT_TOOLTIPS = output_tooltips - cls.SCHEMA = schema - return schema - #-------------------------------------------- - ############################################# - - -class ComfyNode(_ComfyNodeBaseInternal): - """Common base class for all V3 nodes.""" - - @classmethod - @abstractmethod - def define_schema(cls) -> Schema: - """Override this function with one that returns a Schema instance.""" - raise NotImplementedError - - @classmethod - @abstractmethod - def execute(cls, **kwargs) -> NodeOutput: - """Override this function with one that performs node's actions.""" - raise NotImplementedError - - @classmethod - def validate_inputs(cls, **kwargs) -> bool: - """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS.""" - raise NotImplementedError - - @classmethod - def fingerprint_inputs(cls, **kwargs) -> Any: - """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED.""" - raise NotImplementedError - - @classmethod - def check_lazy_status(cls, **kwargs) -> list[str]: - """Optionally, define this function to return a list of input names that should be evaluated. - - This basic mixin impl. requires all inputs. - - :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ - When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. - - Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). - Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). - - Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status - """ - return [name for name in kwargs if kwargs[name] is None] - - @final - @classmethod - def GET_BASE_CLASS(cls): - """DO NOT override this class. Will break things in execution.py.""" - return ComfyNode - - -class NodeOutput(_NodeOutputInternal): - ''' - Standardized output of a node; can pass in any number of args and/or a UIOutput into 'ui' kwarg. - ''' - def __init__(self, *args: Any, ui: _UIOutput | dict=None, expand: dict=None, block_execution: str=None): - self.args = args - self.ui = ui - self.expand = expand - self.block_execution = block_execution - - @property - def result(self): - return self.args if len(self.args) > 0 else None - - @classmethod - def from_dict(cls, data: dict[str, Any]) -> "NodeOutput": - args = () - ui = None - expand = None - if "result" in data: - result = data["result"] - if isinstance(result, ExecutionBlocker): - return cls(block_execution=result.message) - args = result - if "ui" in data: - ui = data["ui"] - if "expand" in data: - expand = data["expand"] - return cls(args=args, ui=ui, expand=expand) - - def __getitem__(self, index) -> Any: - return self.args[index] - -class _UIOutput(ABC): - def __init__(self): - pass - - @abstractmethod - def as_dict(self) -> dict: - ... - - -class _IO: - FolderType = FolderType - UploadType = UploadType - RemoteOptions = RemoteOptions - NumberDisplay = NumberDisplay - - comfytype = staticmethod(comfytype) - Custom = staticmethod(Custom) - Input = Input - WidgetInput = WidgetInput - Output = Output - ComfyTypeI = ComfyTypeI - ComfyTypeIO = ComfyTypeIO - #--------------------------------- - # Supported Types - Boolean = Boolean - Int = Int - Float = Float - String = String - Combo = Combo - MultiCombo = MultiCombo - Image = Image - WanCameraEmbedding = WanCameraEmbedding - Webcam = Webcam - Mask = Mask - Latent = Latent - Conditioning = Conditioning - Sampler = Sampler - Sigmas = Sigmas - Noise = Noise - Guider = Guider - Clip = Clip - ControlNet = ControlNet - Vae = Vae - Model = Model - ClipVision = ClipVision - ClipVisionOutput = ClipVisionOutput - AudioEncoderOutput = AudioEncoderOutput - StyleModel = StyleModel - Gligen = Gligen - UpscaleModel = UpscaleModel - Audio = Audio - Video = Video - SVG = SVG - LoraModel = LoraModel - LossMap = LossMap - Voxel = Voxel - Mesh = Mesh - Hooks = Hooks - HookKeyframes = HookKeyframes - TimestepsRange = TimestepsRange - LatentOperation = LatentOperation - FlowControl = FlowControl - Accumulation = Accumulation - Load3DCamera = Load3DCamera - Load3D = Load3D - Load3DAnimation = Load3DAnimation - Photomaker = Photomaker - Point = Point - FaceAnalysis = FaceAnalysis - BBOX = BBOX - SEGS = SEGS - AnyType = AnyType - MultiType = MultiType - #--------------------------------- - HiddenHolder = HiddenHolder - Hidden = Hidden - NodeInfoV1 = NodeInfoV1 - NodeInfoV3 = NodeInfoV3 - Schema = Schema - ComfyNode = ComfyNode - NodeOutput = NodeOutput - add_to_dict_v1 = staticmethod(add_to_dict_v1) - add_to_dict_v3 = staticmethod(add_to_dict_v3) diff --git a/comfy_api/latest/_resources.py b/comfy_api/latest/_resources.py deleted file mode 100644 index a6bdda97204ef1079a679bc6fe5ed49f05b683f6..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_resources.py +++ /dev/null @@ -1,72 +0,0 @@ -from __future__ import annotations -import comfy.utils -import folder_paths -import logging -from abc import ABC, abstractmethod -from typing import Any -import torch - -class ResourceKey(ABC): - Type = Any - def __init__(self): - ... - -class TorchDictFolderFilename(ResourceKey): - '''Key for requesting a torch file via file_name from a folder category.''' - Type = dict[str, torch.Tensor] - def __init__(self, folder_name: str, file_name: str): - self.folder_name = folder_name - self.file_name = file_name - - def __hash__(self): - return hash((self.folder_name, self.file_name)) - - def __eq__(self, other: object) -> bool: - if not isinstance(other, TorchDictFolderFilename): - return False - return self.folder_name == other.folder_name and self.file_name == other.file_name - - def __str__(self): - return f"{self.folder_name} -> {self.file_name}" - -class Resources(ABC): - def __init__(self): - ... - - @abstractmethod - def get(self, key: ResourceKey, default: Any=...) -> Any: - pass - -class ResourcesLocal(Resources): - def __init__(self): - super().__init__() - self.local_resources: dict[ResourceKey, Any] = {} - - def get(self, key: ResourceKey, default: Any=...) -> Any: - cached = self.local_resources.get(key, None) - if cached is not None: - logging.info(f"Using cached resource '{key}'") - return cached - logging.info(f"Loading resource '{key}'") - to_return = None - if isinstance(key, TorchDictFolderFilename): - if default is ...: - to_return = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise(key.folder_name, key.file_name), safe_load=True) - else: - full_path = folder_paths.get_full_path(key.folder_name, key.file_name) - if full_path is not None: - to_return = comfy.utils.load_torch_file(full_path, safe_load=True) - - if to_return is not None: - self.local_resources[key] = to_return - return to_return - if default is not ...: - return default - raise Exception(f"Unsupported resource key type: {type(key)}") - - -class _RESOURCES: - ResourceKey = ResourceKey - TorchDictFolderFilename = TorchDictFolderFilename - Resources = Resources - ResourcesLocal = ResourcesLocal diff --git a/comfy_api/latest/_ui.py b/comfy_api/latest/_ui.py deleted file mode 100644 index 26a55615f8c947f2b6e797a8ab6fd6683103bf70..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_ui.py +++ /dev/null @@ -1,463 +0,0 @@ -from __future__ import annotations - -import json -import os -import random -from io import BytesIO -from typing import Type - -import av -import numpy as np -import torch -try: - import torchaudio - TORCH_AUDIO_AVAILABLE = True -except: - TORCH_AUDIO_AVAILABLE = False -from PIL import Image as PILImage -from PIL.PngImagePlugin import PngInfo - -import folder_paths - -# used for image preview -from comfy.cli_args import args -from comfy_api.latest._io import ComfyNode, FolderType, Image, _UIOutput - - -class SavedResult(dict): - def __init__(self, filename: str, subfolder: str, type: FolderType): - super().__init__(filename=filename, subfolder=subfolder,type=type.value) - - @property - def filename(self) -> str: - return self["filename"] - - @property - def subfolder(self) -> str: - return self["subfolder"] - - @property - def type(self) -> FolderType: - return FolderType(self["type"]) - - -class SavedImages(_UIOutput): - """A UI output class to represent one or more saved images, potentially animated.""" - def __init__(self, results: list[SavedResult], is_animated: bool = False): - super().__init__() - self.results = results - self.is_animated = is_animated - - def as_dict(self) -> dict: - data = {"images": self.results} - if self.is_animated: - data["animated"] = (True,) - return data - - -class SavedAudios(_UIOutput): - """UI wrapper around one or more audio files on disk (FLAC / MP3 / Opus).""" - def __init__(self, results: list[SavedResult]): - super().__init__() - self.results = results - - def as_dict(self) -> dict: - return {"audio": self.results} - - -def _get_directory_by_folder_type(folder_type: FolderType) -> str: - if folder_type == FolderType.input: - return folder_paths.get_input_directory() - if folder_type == FolderType.output: - return folder_paths.get_output_directory() - return folder_paths.get_temp_directory() - - -class ImageSaveHelper: - """A helper class with static methods to handle image saving and metadata.""" - - @staticmethod - def _convert_tensor_to_pil(image_tensor: torch.Tensor) -> PILImage.Image: - """Converts a single torch tensor to a PIL Image.""" - return PILImage.fromarray(np.clip(255.0 * image_tensor.cpu().numpy(), 0, 255).astype(np.uint8)) - - @staticmethod - def _create_png_metadata(cls: Type[ComfyNode] | None) -> PngInfo | None: - """Creates a PngInfo object with prompt and extra_pnginfo.""" - if args.disable_metadata or cls is None or not cls.hidden: - return None - metadata = PngInfo() - if cls.hidden.prompt: - metadata.add_text("prompt", json.dumps(cls.hidden.prompt)) - if cls.hidden.extra_pnginfo: - for x in cls.hidden.extra_pnginfo: - metadata.add_text(x, json.dumps(cls.hidden.extra_pnginfo[x])) - return metadata - - @staticmethod - def _create_animated_png_metadata(cls: Type[ComfyNode] | None) -> PngInfo | None: - """Creates a PngInfo object with prompt and extra_pnginfo for animated PNGs (APNG).""" - if args.disable_metadata or cls is None or not cls.hidden: - return None - metadata = PngInfo() - if cls.hidden.prompt: - metadata.add( - b"comf", - "prompt".encode("latin-1", "strict") - + b"\0" - + json.dumps(cls.hidden.prompt).encode("latin-1", "strict"), - after_idat=True, - ) - if cls.hidden.extra_pnginfo: - for x in cls.hidden.extra_pnginfo: - metadata.add( - b"comf", - x.encode("latin-1", "strict") - + b"\0" - + json.dumps(cls.hidden.extra_pnginfo[x]).encode("latin-1", "strict"), - after_idat=True, - ) - return metadata - - @staticmethod - def _create_webp_metadata(pil_image: PILImage.Image, cls: Type[ComfyNode] | None) -> PILImage.Exif: - """Creates EXIF metadata bytes for WebP images.""" - exif_data = pil_image.getexif() - if args.disable_metadata or cls is None or cls.hidden is None: - return exif_data - if cls.hidden.prompt is not None: - exif_data[0x0110] = "prompt:{}".format(json.dumps(cls.hidden.prompt)) # EXIF 0x0110 = Model - if cls.hidden.extra_pnginfo is not None: - inital_exif_tag = 0x010F # EXIF 0x010f = Make - for key, value in cls.hidden.extra_pnginfo.items(): - exif_data[inital_exif_tag] = "{}:{}".format(key, json.dumps(value)) - inital_exif_tag -= 1 - return exif_data - - @staticmethod - def save_images( - images, filename_prefix: str, folder_type: FolderType, cls: Type[ComfyNode] | None, compress_level = 4, - ) -> list[SavedResult]: - """Saves a batch of images as individual PNG files.""" - full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( - filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0] - ) - results = [] - metadata = ImageSaveHelper._create_png_metadata(cls) - for batch_number, image_tensor in enumerate(images): - img = ImageSaveHelper._convert_tensor_to_pil(image_tensor) - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.png" - img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level) - results.append(SavedResult(file, subfolder, folder_type)) - counter += 1 - return results - - @staticmethod - def get_save_images_ui(images, filename_prefix: str, cls: Type[ComfyNode] | None, compress_level=4) -> SavedImages: - """Saves a batch of images and returns a UI object for the node output.""" - return SavedImages( - ImageSaveHelper.save_images( - images, - filename_prefix=filename_prefix, - folder_type=FolderType.output, - cls=cls, - compress_level=compress_level, - ) - ) - - @staticmethod - def save_animated_png( - images, filename_prefix: str, folder_type: FolderType, cls: Type[ComfyNode] | None, fps: float, compress_level: int - ) -> SavedResult: - """Saves a batch of images as a single animated PNG.""" - full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( - filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0] - ) - pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images] - metadata = ImageSaveHelper._create_animated_png_metadata(cls) - file = f"{filename}_{counter:05}_.png" - save_path = os.path.join(full_output_folder, file) - pil_images[0].save( - save_path, - pnginfo=metadata, - compress_level=compress_level, - save_all=True, - duration=int(1000.0 / fps), - append_images=pil_images[1:], - ) - return SavedResult(file, subfolder, folder_type) - - @staticmethod - def get_save_animated_png_ui( - images, filename_prefix: str, cls: Type[ComfyNode] | None, fps: float, compress_level: int - ) -> SavedImages: - """Saves an animated PNG and returns a UI object for the node output.""" - result = ImageSaveHelper.save_animated_png( - images, - filename_prefix=filename_prefix, - folder_type=FolderType.output, - cls=cls, - fps=fps, - compress_level=compress_level, - ) - return SavedImages([result], is_animated=len(images) > 1) - - @staticmethod - def save_animated_webp( - images, - filename_prefix: str, - folder_type: FolderType, - cls: Type[ComfyNode] | None, - fps: float, - lossless: bool, - quality: int, - method: int, - ) -> SavedResult: - """Saves a batch of images as a single animated WebP.""" - full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( - filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0] - ) - pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images] - pil_exif = ImageSaveHelper._create_webp_metadata(pil_images[0], cls) - file = f"{filename}_{counter:05}_.webp" - pil_images[0].save( - os.path.join(full_output_folder, file), - save_all=True, - duration=int(1000.0 / fps), - append_images=pil_images[1:], - exif=pil_exif, - lossless=lossless, - quality=quality, - method=method, - ) - return SavedResult(file, subfolder, folder_type) - - @staticmethod - def get_save_animated_webp_ui( - images, - filename_prefix: str, - cls: Type[ComfyNode] | None, - fps: float, - lossless: bool, - quality: int, - method: int, - ) -> SavedImages: - """Saves an animated WebP and returns a UI object for the node output.""" - result = ImageSaveHelper.save_animated_webp( - images, - filename_prefix=filename_prefix, - folder_type=FolderType.output, - cls=cls, - fps=fps, - lossless=lossless, - quality=quality, - method=method, - ) - return SavedImages([result], is_animated=len(images) > 1) - - -class AudioSaveHelper: - """A helper class with static methods to handle audio saving and metadata.""" - _OPUS_RATES = [8000, 12000, 16000, 24000, 48000] - - @staticmethod - def save_audio( - audio: dict, - filename_prefix: str, - folder_type: FolderType, - cls: Type[ComfyNode] | None, - format: str = "flac", - quality: str = "128k", - ) -> list[SavedResult]: - full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( - filename_prefix, _get_directory_by_folder_type(folder_type) - ) - - metadata = {} - if not args.disable_metadata and cls is not None: - if cls.hidden.prompt is not None: - metadata["prompt"] = json.dumps(cls.hidden.prompt) - if cls.hidden.extra_pnginfo is not None: - for x in cls.hidden.extra_pnginfo: - metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x]) - - results = [] - for batch_number, waveform in enumerate(audio["waveform"].cpu()): - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.{format}" - output_path = os.path.join(full_output_folder, file) - - # Use original sample rate initially - sample_rate = audio["sample_rate"] - - # Handle Opus sample rate requirements - if format == "opus": - if sample_rate > 48000: - sample_rate = 48000 - elif sample_rate not in AudioSaveHelper._OPUS_RATES: - # Find the next highest supported rate - for rate in sorted(AudioSaveHelper._OPUS_RATES): - if rate > sample_rate: - sample_rate = rate - break - if sample_rate not in AudioSaveHelper._OPUS_RATES: # Fallback if still not supported - sample_rate = 48000 - - # Resample if necessary - if sample_rate != audio["sample_rate"]: - if not TORCH_AUDIO_AVAILABLE: - raise Exception("torchaudio is not available; cannot resample audio.") - waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate) - - # Create output with specified format - output_buffer = BytesIO() - output_container = av.open(output_buffer, mode="w", format=format) - - # Set metadata on the container - for key, value in metadata.items(): - output_container.metadata[key] = value - - # Set up the output stream with appropriate properties - if format == "opus": - out_stream = output_container.add_stream("libopus", rate=sample_rate) - if quality == "64k": - out_stream.bit_rate = 64000 - elif quality == "96k": - out_stream.bit_rate = 96000 - elif quality == "128k": - out_stream.bit_rate = 128000 - elif quality == "192k": - out_stream.bit_rate = 192000 - elif quality == "320k": - out_stream.bit_rate = 320000 - elif format == "mp3": - out_stream = output_container.add_stream("libmp3lame", rate=sample_rate) - if quality == "V0": - # TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool - out_stream.codec_context.qscale = 1 - elif quality == "128k": - out_stream.bit_rate = 128000 - elif quality == "320k": - out_stream.bit_rate = 320000 - else: # format == "flac": - out_stream = output_container.add_stream("flac", rate=sample_rate) - - frame = av.AudioFrame.from_ndarray( - waveform.movedim(0, 1).reshape(1, -1).float().numpy(), - format="flt", - layout="mono" if waveform.shape[0] == 1 else "stereo", - ) - frame.sample_rate = sample_rate - frame.pts = 0 - output_container.mux(out_stream.encode(frame)) - - # Flush encoder - output_container.mux(out_stream.encode(None)) - - # Close containers - output_container.close() - - # Write the output to file - output_buffer.seek(0) - with open(output_path, "wb") as f: - f.write(output_buffer.getbuffer()) - - results.append(SavedResult(file, subfolder, folder_type)) - counter += 1 - - return results - - @staticmethod - def get_save_audio_ui( - audio, filename_prefix: str, cls: Type[ComfyNode] | None, format: str = "flac", quality: str = "128k", - ) -> SavedAudios: - """Save and instantly wrap for UI.""" - return SavedAudios( - AudioSaveHelper.save_audio( - audio, - filename_prefix=filename_prefix, - folder_type=FolderType.output, - cls=cls, - format=format, - quality=quality, - ) - ) - - -class PreviewImage(_UIOutput): - def __init__(self, image: Image.Type, animated: bool = False, cls: Type[ComfyNode] = None, **kwargs): - self.values = ImageSaveHelper.save_images( - image, - filename_prefix="ComfyUI_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for _ in range(5)), - folder_type=FolderType.temp, - cls=cls, - compress_level=1, - ) - self.animated = animated - - def as_dict(self): - return { - "images": self.values, - "animated": (self.animated,) - } - - -class PreviewMask(PreviewImage): - def __init__(self, mask: PreviewMask.Type, animated: bool=False, cls: ComfyNode=None, **kwargs): - preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) - super().__init__(preview, animated, cls, **kwargs) - - -class PreviewAudio(_UIOutput): - def __init__(self, audio: dict, cls: Type[ComfyNode] = None, **kwargs): - self.values = AudioSaveHelper.save_audio( - audio, - filename_prefix="ComfyUI_temp_" + "".join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5)), - folder_type=FolderType.temp, - cls=cls, - format="flac", - quality="128k", - ) - - def as_dict(self) -> dict: - return {"audio": self.values} - - -class PreviewVideo(_UIOutput): - def __init__(self, values: list[SavedResult | dict], **kwargs): - self.values = values - - def as_dict(self): - return {"images": self.values, "animated": (True,)} - - -class PreviewUI3D(_UIOutput): - def __init__(self, model_file, camera_info, **kwargs): - self.model_file = model_file - self.camera_info = camera_info - - def as_dict(self): - return {"result": [self.model_file, self.camera_info]} - - -class PreviewText(_UIOutput): - def __init__(self, value: str, **kwargs): - self.value = value - - def as_dict(self): - return {"text": (self.value,)} - - -class _UI: - SavedResult = SavedResult - SavedImages = SavedImages - SavedAudios = SavedAudios - ImageSaveHelper = ImageSaveHelper - AudioSaveHelper = AudioSaveHelper - PreviewImage = PreviewImage - PreviewMask = PreviewMask - PreviewAudio = PreviewAudio - PreviewVideo = PreviewVideo - PreviewUI3D = PreviewUI3D - PreviewText = PreviewText diff --git a/comfy_api/latest/_util/.DS_Store b/comfy_api/latest/_util/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/latest/_util/.DS_Store and /dev/null differ diff --git a/comfy_api/latest/_util/__init__.py b/comfy_api/latest/_util/__init__.py deleted file mode 100644 index 9019c46dbbcd58a7024301d3bc1bd53570a83bc0..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_util/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .video_types import VideoContainer, VideoCodec, VideoComponents - -__all__ = [ - # Utility Types - "VideoContainer", - "VideoCodec", - "VideoComponents", -] diff --git a/comfy_api/latest/_util/video_types.py b/comfy_api/latest/_util/video_types.py deleted file mode 100644 index c3e3d8e3a5bb25cdfe51309f0b9fdb9a55d97eb6..0000000000000000000000000000000000000000 --- a/comfy_api/latest/_util/video_types.py +++ /dev/null @@ -1,52 +0,0 @@ -from __future__ import annotations -from dataclasses import dataclass -from enum import Enum -from fractions import Fraction -from typing import Optional -from comfy_api.latest._input import ImageInput, AudioInput - -class VideoCodec(str, Enum): - AUTO = "auto" - H264 = "h264" - - @classmethod - def as_input(cls) -> list[str]: - """ - Returns a list of codec names that can be used as node input. - """ - return [member.value for member in cls] - -class VideoContainer(str, Enum): - AUTO = "auto" - MP4 = "mp4" - - @classmethod - def as_input(cls) -> list[str]: - """ - Returns a list of container names that can be used as node input. - """ - return [member.value for member in cls] - - @classmethod - def get_extension(cls, value) -> str: - """ - Returns the file extension for the container. - """ - if isinstance(value, str): - value = cls(value) - if value == VideoContainer.MP4 or value == VideoContainer.AUTO: - return "mp4" - return "" - -@dataclass -class VideoComponents: - """ - Dataclass representing the components of a video. - """ - - images: ImageInput - frame_rate: Fraction - audio: Optional[AudioInput] = None - metadata: Optional[dict] = None - - diff --git a/comfy_api/latest/generated/ComfyAPISyncStub.pyi b/comfy_api/latest/generated/ComfyAPISyncStub.pyi deleted file mode 100644 index 525c074dda49d12f5f9eca7bd3b82abe8799c862..0000000000000000000000000000000000000000 --- a/comfy_api/latest/generated/ComfyAPISyncStub.pyi +++ /dev/null @@ -1,20 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple -from comfy_api.latest import ComfyAPI_latest -from PIL.Image import Image -from torch import Tensor -class ComfyAPISyncStub: - def __init__(self) -> None: ... - - class ExecutionSync: - def __init__(self) -> None: ... - """ - Update the progress bar displayed in the ComfyUI interface. - - This function allows custom nodes and API calls to report their progress - back to the user interface, providing visual feedback during long operations. - - Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK - """ - def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ... - - execution: ExecutionSync diff --git a/comfy_api/torch_helpers/.DS_Store b/comfy_api/torch_helpers/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/torch_helpers/.DS_Store and /dev/null differ diff --git a/comfy_api/torch_helpers/__init__.py b/comfy_api/torch_helpers/__init__.py deleted file mode 100644 index be7ae7a6165bdeed5013806a7adde1a02c0ac93f..0000000000000000000000000000000000000000 --- a/comfy_api/torch_helpers/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .torch_compile import set_torch_compile_wrapper - -__all__ = [ - "set_torch_compile_wrapper", -] diff --git a/comfy_api/torch_helpers/torch_compile.py b/comfy_api/torch_helpers/torch_compile.py deleted file mode 100644 index 9223f58db22415897cbf3d6239511d289f5e1091..0000000000000000000000000000000000000000 --- a/comfy_api/torch_helpers/torch_compile.py +++ /dev/null @@ -1,69 +0,0 @@ -from __future__ import annotations -import torch - -import comfy.utils -from comfy.patcher_extension import WrappersMP -from typing import TYPE_CHECKING, Callable, Optional -if TYPE_CHECKING: - from comfy.model_patcher import ModelPatcher - from comfy.patcher_extension import WrapperExecutor - - -COMPILE_KEY = "torch.compile" -TORCH_COMPILE_KWARGS = "torch_compile_kwargs" - - -def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable: - ''' - Create a wrapper that will refer to the compiled_diffusion_model. - ''' - def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs): - try: - orig_modules = {} - for key, value in compiled_module_dict.items(): - orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key) - comfy.utils.set_attr(executor.class_obj, key, value) - return executor(*args, **kwargs) - finally: - for key, value in orig_modules.items(): - comfy.utils.set_attr(executor.class_obj, key, value) - return apply_torch_compile_wrapper - - -def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None, - mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None, - keys: list[str]=["diffusion_model"], *args, **kwargs): - ''' - Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance. - - When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model. - When a list of keys is provided, it will perform torch.compile on only the selected modules. - ''' - # clear out any other torch.compile wrappers - model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY) - # if no keys, default to 'diffusion_model' - if not keys: - keys = ["diffusion_model"] - # create kwargs dict that can be referenced later - compile_kwargs = { - "backend": backend, - "options": options, - "mode": mode, - "fullgraph": fullgraph, - "dynamic": dynamic, - } - # get a dict of compiled keys - compiled_modules = {} - for key in keys: - compiled_modules[key] = torch.compile( - model=model.get_model_object(key), - **compile_kwargs, - ) - # add torch.compile wrapper - wrapper_func = apply_torch_compile_factory( - compiled_module_dict=compiled_modules, - ) - # store wrapper to run on BaseModel's apply_model function - model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func) - # keep compile kwargs for reference - model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs diff --git a/comfy_api/util.py b/comfy_api/util.py deleted file mode 100644 index 1aa9606d2e4691657ed3580fc7a1e4a55aaf886c..0000000000000000000000000000000000000000 --- a/comfy_api/util.py +++ /dev/null @@ -1,8 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents - -__all__ = [ - "VideoCodec", - "VideoContainer", - "VideoComponents", -] diff --git a/comfy_api/util/.DS_Store b/comfy_api/util/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/util/.DS_Store and /dev/null differ diff --git a/comfy_api/util/__init__.py b/comfy_api/util/__init__.py deleted file mode 100644 index 4c8a89d1e077d0b331de5506ba69b50a99de2bd2..0000000000000000000000000000000000000000 --- a/comfy_api/util/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents - -__all__ = [ - "VideoContainer", - "VideoCodec", - "VideoComponents", -] diff --git a/comfy_api/util/video_types.py b/comfy_api/util/video_types.py deleted file mode 100644 index 68c780d64daffa6fb621821e111330cb388c07e0..0000000000000000000000000000000000000000 --- a/comfy_api/util/video_types.py +++ /dev/null @@ -1,12 +0,0 @@ -# This file only exists for backwards compatibility. -from comfy_api.latest._util.video_types import ( - VideoContainer, - VideoCodec, - VideoComponents, -) - -__all__ = [ - "VideoContainer", - "VideoCodec", - "VideoComponents", -] diff --git a/comfy_api/v0_0_1/.DS_Store b/comfy_api/v0_0_1/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/v0_0_1/.DS_Store and /dev/null differ diff --git a/comfy_api/v0_0_1/__init__.py b/comfy_api/v0_0_1/__init__.py deleted file mode 100644 index 93608771d156017233cadc5af3ba2d52c82f1529..0000000000000000000000000000000000000000 --- a/comfy_api/v0_0_1/__init__.py +++ /dev/null @@ -1,42 +0,0 @@ -from comfy_api.v0_0_2 import ( - ComfyAPIAdapter_v0_0_2, - Input as Input_v0_0_2, - InputImpl as InputImpl_v0_0_2, - Types as Types_v0_0_2, -) -from typing import Type, TYPE_CHECKING -from comfy_api.internal.async_to_sync import create_sync_class - - -# This version only exists to serve as a template for future version adapters. -# There is no reason anyone should ever use it. -class ComfyAPIAdapter_v0_0_1(ComfyAPIAdapter_v0_0_2): - VERSION = "0.0.1" - STABLE = True - -class Input(Input_v0_0_2): - pass - -class InputImpl(InputImpl_v0_0_2): - pass - -class Types(Types_v0_0_2): - pass - -ComfyAPI = ComfyAPIAdapter_v0_0_1 - -# Create a synchronous version of the API -if TYPE_CHECKING: - from comfy_api.v0_0_1.generated.ComfyAPISyncStub import ComfyAPISyncStub # type: ignore - - ComfyAPISync: Type[ComfyAPISyncStub] - -ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_1) - -__all__ = [ - "ComfyAPI", - "ComfyAPISync", - "Input", - "InputImpl", - "Types", -] diff --git a/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi b/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi deleted file mode 100644 index 270030324cd155fdadc0f0d5009bca4be948fc0a..0000000000000000000000000000000000000000 --- a/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi +++ /dev/null @@ -1,20 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple -from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1 -from PIL.Image import Image -from torch import Tensor -class ComfyAPISyncStub: - def __init__(self) -> None: ... - - class ExecutionSync: - def __init__(self) -> None: ... - """ - Update the progress bar displayed in the ComfyUI interface. - - This function allows custom nodes and API calls to report their progress - back to the user interface, providing visual feedback during long operations. - - Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK - """ - def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ... - - execution: ExecutionSync diff --git a/comfy_api/v0_0_2/.DS_Store b/comfy_api/v0_0_2/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api/v0_0_2/.DS_Store and /dev/null differ diff --git a/comfy_api/v0_0_2/__init__.py b/comfy_api/v0_0_2/__init__.py deleted file mode 100644 index de0f95001ba79361ce9f85dff20874351be4e725..0000000000000000000000000000000000000000 --- a/comfy_api/v0_0_2/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ -from comfy_api.latest import ( - ComfyAPI_latest, - Input as Input_latest, - InputImpl as InputImpl_latest, - Types as Types_latest, -) -from typing import Type, TYPE_CHECKING -from comfy_api.internal.async_to_sync import create_sync_class -from comfy_api.latest import io, ui, ComfyExtension #noqa: F401 - - -class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest): - VERSION = "0.0.2" - STABLE = False - - -class Input(Input_latest): - pass - - -class InputImpl(InputImpl_latest): - pass - - -class Types(Types_latest): - pass - - -ComfyAPI = ComfyAPIAdapter_v0_0_2 - -# Create a synchronous version of the API -if TYPE_CHECKING: - from comfy_api.v0_0_2.generated.ComfyAPISyncStub import ComfyAPISyncStub # type: ignore - - ComfyAPISync: Type[ComfyAPISyncStub] -ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_2) - -__all__ = [ - "ComfyAPI", - "ComfyAPISync", - "Input", - "InputImpl", - "Types", - "ComfyExtension", -] diff --git a/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi b/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi deleted file mode 100644 index 7fcec685e880e68106a35f6fd8ebf84ea07769e3..0000000000000000000000000000000000000000 --- a/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi +++ /dev/null @@ -1,20 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple -from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2 -from PIL.Image import Image -from torch import Tensor -class ComfyAPISyncStub: - def __init__(self) -> None: ... - - class ExecutionSync: - def __init__(self) -> None: ... - """ - Update the progress bar displayed in the ComfyUI interface. - - This function allows custom nodes and API calls to report their progress - back to the user interface, providing visual feedback during long operations. - - Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK - """ - def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ... - - execution: ExecutionSync diff --git a/comfy_api/version_list.py b/comfy_api/version_list.py deleted file mode 100644 index 7cb1871d5a1fbce221d100dd63327361343faafe..0000000000000000000000000000000000000000 --- a/comfy_api/version_list.py +++ /dev/null @@ -1,12 +0,0 @@ -from comfy_api.latest import ComfyAPI_latest -from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2 -from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1 -from comfy_api.internal import ComfyAPIBase -from typing import List, Type - -supported_versions: List[Type[ComfyAPIBase]] = [ - ComfyAPI_latest, - ComfyAPIAdapter_v0_0_2, - ComfyAPIAdapter_v0_0_1, -] - diff --git a/comfy_api_nodes/.DS_Store b/comfy_api_nodes/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_api_nodes/.DS_Store and /dev/null differ diff --git a/comfy_api_nodes/README.md b/comfy_api_nodes/README.md deleted file mode 100644 index f56d6c8606346dc4d6c8d688cc8ca4f52cb5c9cd..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/README.md +++ /dev/null @@ -1,65 +0,0 @@ -# ComfyUI API Nodes - -## Introduction - -Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview). - -## Development - -While developing, you should be testing against the Staging environment. To test against staging: - -**Install ComfyUI_frontend** - -Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to start the frontend server. By default, it will connect to Staging authentication. - -> **Hint:** If you use --front-end-version argument for ComfyUI, it will use production authentication. - -```bash -python run main.py --comfy-api-base https://stagingapi.comfy.org -``` - -To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging. - -API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes. - -### Redocly Instructions - -**Tip** -When developing locally, use the `redocly-dev.yaml` file to generate pydantic models. This lets you use stubs for APIs that are not marked `Released` yet. - -Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging. - -```bash -# Download the OpenAPI file from staging server. -curl -o openapi.yaml https://stagingapi.comfy.org/openapi - -# Filter out unneeded API definitions. -npm install -g @redocly/cli -redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly-dev.yaml --remove-unused-components - -# Generate the pydantic datamodels for validation. -datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel - -``` - - -# Merging to Master - -Before merging to comfyanonymous/ComfyUI master, follow these steps: - -1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. -1. Make sure the ComfyUI API is deployed to prod with your changes. -1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file. - -```bash -# Download the OpenAPI file from prod server. -curl -o openapi.yaml https://api.comfy.org/openapi - -# Filter out unneeded API definitions. -npm install -g @redocly/cli -redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components - -# Generate the pydantic datamodels for validation. -datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel - -``` diff --git a/comfy_api_nodes/__init__.py b/comfy_api_nodes/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py deleted file mode 100644 index f953f86df0de34b2376ba72cc9e0667521645062..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apinode_utils.py +++ /dev/null @@ -1,626 +0,0 @@ -from __future__ import annotations -import aiohttp -import io -import logging -import mimetypes -from typing import Optional, Union -from comfy.utils import common_upscale -from comfy_api.input_impl import VideoFromFile -from comfy_api.util import VideoContainer, VideoCodec -from comfy_api.input.video_types import VideoInput -from comfy_api.input.basic_types import AudioInput -from comfy_api_nodes.apis.client import ( - ApiClient, - ApiEndpoint, - HttpMethod, - SynchronousOperation, - UploadRequest, - UploadResponse, -) -from server import PromptServer - - -import numpy as np -from PIL import Image -import torch -import math -import base64 -import uuid -from io import BytesIO -import av - - -async def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile: - """Downloads a video from a URL and returns a `VIDEO` output. - - Args: - video_url: The URL of the video to download. - - Returns: - A Comfy node `VIDEO` output. - """ - video_io = await download_url_to_bytesio(video_url, timeout) - if video_io is None: - error_msg = f"Failed to download video from {video_url}" - logging.error(error_msg) - raise ValueError(error_msg) - return VideoFromFile(video_io) - - -def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor: - """Downscale input image tensor to roughly the specified total pixels.""" - samples = image.movedim(-1, 1) - total = int(total_pixels) - scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) - if scale_by >= 1: - return image - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - - s = common_upscale(samples, width, height, "lanczos", "disabled") - s = s.movedim(1, -1) - return s - - -async def validate_and_cast_response( - response, timeout: int = None, node_id: Union[str, None] = None -) -> torch.Tensor: - """Validates and casts a response to a torch.Tensor. - - Args: - response: The response to validate and cast. - timeout: Request timeout in seconds. Defaults to None (no timeout). - - Returns: - A torch.Tensor representing the image (1, H, W, C). - - Raises: - ValueError: If the response is not valid. - """ - # validate raw JSON response - data = response.data - if not data or len(data) == 0: - raise ValueError("No images returned from API endpoint") - - # Initialize list to store image tensors - image_tensors: list[torch.Tensor] = [] - - # Process each image in the data array - async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout)) as session: - for img_data in data: - img_bytes: bytes - if img_data.b64_json: - img_bytes = base64.b64decode(img_data.b64_json) - elif img_data.url: - if node_id: - PromptServer.instance.send_progress_text(f"Result URL: {img_data.url}", node_id) - async with session.get(img_data.url) as resp: - if resp.status != 200: - raise ValueError("Failed to download generated image") - img_bytes = await resp.read() - else: - raise ValueError("Invalid image payload – neither URL nor base64 data present.") - - pil_img = Image.open(BytesIO(img_bytes)).convert("RGBA") - arr = np.asarray(pil_img).astype(np.float32) / 255.0 - image_tensors.append(torch.from_numpy(arr)) - - return torch.stack(image_tensors, dim=0) - - -def validate_aspect_ratio( - aspect_ratio: str, - minimum_ratio: float, - maximum_ratio: float, - minimum_ratio_str: str, - maximum_ratio_str: str, -) -> float: - """Validates and casts an aspect ratio string to a float. - - Args: - aspect_ratio: The aspect ratio string to validate. - minimum_ratio: The minimum aspect ratio. - maximum_ratio: The maximum aspect ratio. - minimum_ratio_str: The minimum aspect ratio string. - maximum_ratio_str: The maximum aspect ratio string. - - Returns: - The validated and cast aspect ratio. - - Raises: - Exception: If the aspect ratio is not valid. - """ - # get ratio values - numbers = aspect_ratio.split(":") - if len(numbers) != 2: - raise TypeError( - f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}." - ) - try: - numerator = int(numbers[0]) - denominator = int(numbers[1]) - except ValueError as exc: - raise TypeError( - f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}." - ) from exc - calculated_ratio = numerator / denominator - # if not close to minimum and maximum, check bounds - if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose( - calculated_ratio, maximum_ratio - ): - if calculated_ratio < minimum_ratio: - raise TypeError( - f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})." - ) - elif calculated_ratio > maximum_ratio: - raise TypeError( - f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})." - ) - return aspect_ratio - - -def mimetype_to_extension(mime_type: str) -> str: - """Converts a MIME type to a file extension.""" - return mime_type.split("/")[-1].lower() - - -async def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO: - """Downloads content from a URL using requests and returns it as BytesIO. - - Args: - url: The URL to download. - timeout: Request timeout in seconds. Defaults to None (no timeout). - - Returns: - BytesIO object containing the downloaded content. - """ - timeout_cfg = aiohttp.ClientTimeout(total=timeout) if timeout else None - async with aiohttp.ClientSession(timeout=timeout_cfg) as session: - async with session.get(url) as resp: - resp.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX) - return BytesIO(await resp.read()) - - -def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch.Tensor: - """Converts image data from BytesIO to a torch.Tensor. - - Args: - image_bytesio: BytesIO object containing the image data. - mode: The PIL mode to convert the image to (e.g., "RGB", "RGBA"). - - Returns: - A torch.Tensor representing the image (1, H, W, C). - - Raises: - PIL.UnidentifiedImageError: If the image data cannot be identified. - ValueError: If the specified mode is invalid. - """ - image = Image.open(image_bytesio) - image = image.convert(mode) - image_array = np.array(image).astype(np.float32) / 255.0 - return torch.from_numpy(image_array).unsqueeze(0) - - -async def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor: - """Downloads an image from a URL and returns a [B, H, W, C] tensor.""" - image_bytesio = await download_url_to_bytesio(url, timeout) - return bytesio_to_image_tensor(image_bytesio) - - -def process_image_response(response_content: bytes | str) -> torch.Tensor: - """Uses content from a Response object and converts it to a torch.Tensor""" - return bytesio_to_image_tensor(BytesIO(response_content)) - - -def _tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image: - """Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling.""" - if len(image.shape) > 3: - image = image[0] - # TODO: remove alpha if not allowed and present - input_tensor = image.cpu() - input_tensor = downscale_image_tensor( - input_tensor.unsqueeze(0), total_pixels=total_pixels - ).squeeze() - image_np = (input_tensor.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - return img - - -def _pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO: - """Converts a PIL Image to a BytesIO object.""" - if not mime_type: - mime_type = "image/png" - - img_byte_arr = io.BytesIO() - # Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG') - pil_format = mime_type.split("/")[-1].upper() - if pil_format == "JPG": - pil_format = "JPEG" - img.save(img_byte_arr, format=pil_format) - img_byte_arr.seek(0) - return img_byte_arr - - -def tensor_to_bytesio( - image: torch.Tensor, - name: Optional[str] = None, - total_pixels: int = 2048 * 2048, - mime_type: str = "image/png", -) -> BytesIO: - """Converts a torch.Tensor image to a named BytesIO object. - - Args: - image: Input torch.Tensor image. - name: Optional filename for the BytesIO object. - total_pixels: Maximum total pixels for potential downscaling. - mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). - - Returns: - Named BytesIO object containing the image data. - """ - if not mime_type: - mime_type = "image/png" - - pil_image = _tensor_to_pil(image, total_pixels=total_pixels) - img_binary = _pil_to_bytesio(pil_image, mime_type=mime_type) - img_binary.name = ( - f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}" - ) - return img_binary - - -def tensor_to_base64_string( - image_tensor: torch.Tensor, - total_pixels: int = 2048 * 2048, - mime_type: str = "image/png", -) -> str: - """Convert [B, H, W, C] or [H, W, C] tensor to a base64 string. - - Args: - image_tensor: Input torch.Tensor image. - total_pixels: Maximum total pixels for potential downscaling. - mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). - - Returns: - Base64 encoded string of the image. - """ - pil_image = _tensor_to_pil(image_tensor, total_pixels=total_pixels) - img_byte_arr = _pil_to_bytesio(pil_image, mime_type=mime_type) - img_bytes = img_byte_arr.getvalue() - # Encode bytes to base64 string - base64_encoded_string = base64.b64encode(img_bytes).decode("utf-8") - return base64_encoded_string - - -def tensor_to_data_uri( - image_tensor: torch.Tensor, - total_pixels: int = 2048 * 2048, - mime_type: str = "image/png", -) -> str: - """Converts a tensor image to a Data URI string. - - Args: - image_tensor: Input torch.Tensor image. - total_pixels: Maximum total pixels for potential downscaling. - mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp'). - - Returns: - Data URI string (e.g., 'data:image/png;base64,...'). - """ - base64_string = tensor_to_base64_string(image_tensor, total_pixels, mime_type) - return f"data:{mime_type};base64,{base64_string}" - - -def text_filepath_to_base64_string(filepath: str) -> str: - """Converts a text file to a base64 string.""" - with open(filepath, "rb") as f: - file_content = f.read() - return base64.b64encode(file_content).decode("utf-8") - - -def text_filepath_to_data_uri(filepath: str) -> str: - """Converts a text file to a data URI.""" - base64_string = text_filepath_to_base64_string(filepath) - mime_type, _ = mimetypes.guess_type(filepath) - if mime_type is None: - mime_type = "application/octet-stream" - return f"data:{mime_type};base64,{base64_string}" - - -async def upload_file_to_comfyapi( - file_bytes_io: BytesIO, - filename: str, - upload_mime_type: Optional[str], - auth_kwargs: Optional[dict[str, str]] = None, -) -> str: - """ - Uploads a single file to ComfyUI API and returns its download URL. - - Args: - file_bytes_io: BytesIO object containing the file data. - filename: The filename of the file. - upload_mime_type: MIME type of the file. - auth_kwargs: Optional authentication token(s). - - Returns: - The download URL for the uploaded file. - """ - if upload_mime_type is None: - request_object = UploadRequest(file_name=filename) - else: - request_object = UploadRequest(file_name=filename, content_type=upload_mime_type) - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/customers/storage", - method=HttpMethod.POST, - request_model=UploadRequest, - response_model=UploadResponse, - ), - request=request_object, - auth_kwargs=auth_kwargs, - ) - - response: UploadResponse = await operation.execute() - await ApiClient.upload_file(response.upload_url, file_bytes_io, content_type=upload_mime_type) - return response.download_url - - -def video_to_base64_string( - video: VideoInput, - container_format: VideoContainer = None, - codec: VideoCodec = None -) -> str: - """ - Converts a video input to a base64 string. - - Args: - video: The video input to convert - container_format: Optional container format to use (defaults to video.container if available) - codec: Optional codec to use (defaults to video.codec if available) - """ - video_bytes_io = io.BytesIO() - - # Use provided format/codec if specified, otherwise use video's own if available - format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4) - codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264) - - video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use) - video_bytes_io.seek(0) - return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8") - - -async def upload_video_to_comfyapi( - video: VideoInput, - auth_kwargs: Optional[dict[str, str]] = None, - container: VideoContainer = VideoContainer.MP4, - codec: VideoCodec = VideoCodec.H264, - max_duration: Optional[int] = None, -) -> str: - """ - Uploads a single video to ComfyUI API and returns its download URL. - Uses the specified container and codec for saving the video before upload. - - Args: - video: VideoInput object (Comfy VIDEO type). - auth_kwargs: Optional authentication token(s). - container: The video container format to use (default: MP4). - codec: The video codec to use (default: H264). - max_duration: Optional maximum duration of the video in seconds. If the video is longer than this, an error will be raised. - - Returns: - The download URL for the uploaded video file. - """ - if max_duration is not None: - try: - actual_duration = video.duration_seconds - if actual_duration is not None and actual_duration > max_duration: - raise ValueError( - f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)." - ) - except Exception as e: - logging.error(f"Error getting video duration: {e}") - raise ValueError(f"Could not verify video duration from source: {e}") from e - - upload_mime_type = f"video/{container.value.lower()}" - filename = f"uploaded_video.{container.value.lower()}" - - # Convert VideoInput to BytesIO using specified container/codec - video_bytes_io = io.BytesIO() - video.save_to(video_bytes_io, format=container, codec=codec) - video_bytes_io.seek(0) - - return await upload_file_to_comfyapi(video_bytes_io, filename, upload_mime_type, auth_kwargs) - - -def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray: - """ - Prepares audio waveform for av library by converting to a contiguous numpy array. - - Args: - waveform: a tensor of shape (1, channels, samples) derived from a Comfy `AUDIO` type. - - Returns: - Contiguous numpy array of the audio waveform. If the audio was batched, - the first item is taken. - """ - if waveform.ndim != 3 or waveform.shape[0] != 1: - raise ValueError("Expected waveform tensor shape (1, channels, samples)") - - # If batch is > 1, take first item - if waveform.shape[0] > 1: - waveform = waveform[0] - - # Prepare for av: remove batch dim, move to CPU, make contiguous, convert to numpy array - audio_data_np = waveform.squeeze(0).cpu().contiguous().numpy() - if audio_data_np.dtype != np.float32: - audio_data_np = audio_data_np.astype(np.float32) - - return audio_data_np - - -def audio_ndarray_to_bytesio( - audio_data_np: np.ndarray, - sample_rate: int, - container_format: str = "mp4", - codec_name: str = "aac", -) -> BytesIO: - """ - Encodes a numpy array of audio data into a BytesIO object. - """ - audio_bytes_io = io.BytesIO() - with av.open(audio_bytes_io, mode="w", format=container_format) as output_container: - audio_stream = output_container.add_stream(codec_name, rate=sample_rate) - frame = av.AudioFrame.from_ndarray( - audio_data_np, - format="fltp", - layout="stereo" if audio_data_np.shape[0] > 1 else "mono", - ) - frame.sample_rate = sample_rate - frame.pts = 0 - - for packet in audio_stream.encode(frame): - output_container.mux(packet) - - # Flush stream - for packet in audio_stream.encode(None): - output_container.mux(packet) - - audio_bytes_io.seek(0) - return audio_bytes_io - - -async def upload_audio_to_comfyapi( - audio: AudioInput, - auth_kwargs: Optional[dict[str, str]] = None, - container_format: str = "mp4", - codec_name: str = "aac", - mime_type: str = "audio/mp4", - filename: str = "uploaded_audio.mp4", -) -> str: - """ - Uploads a single audio input to ComfyUI API and returns its download URL. - Encodes the raw waveform into the specified format before uploading. - - Args: - audio: a Comfy `AUDIO` type (contains waveform tensor and sample_rate) - auth_kwargs: Optional authentication token(s). - - Returns: - The download URL for the uploaded audio file. - """ - sample_rate: int = audio["sample_rate"] - waveform: torch.Tensor = audio["waveform"] - audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) - audio_bytes_io = audio_ndarray_to_bytesio( - audio_data_np, sample_rate, container_format, codec_name - ) - - return await upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs) - - -def audio_to_base64_string( - audio: AudioInput, container_format: str = "mp4", codec_name: str = "aac" -) -> str: - """Converts an audio input to a base64 string.""" - sample_rate: int = audio["sample_rate"] - waveform: torch.Tensor = audio["waveform"] - audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) - audio_bytes_io = audio_ndarray_to_bytesio( - audio_data_np, sample_rate, container_format, codec_name - ) - audio_bytes = audio_bytes_io.getvalue() - return base64.b64encode(audio_bytes).decode("utf-8") - - -async def upload_images_to_comfyapi( - image: torch.Tensor, - max_images=8, - auth_kwargs: Optional[dict[str, str]] = None, - mime_type: Optional[str] = None, -) -> list[str]: - """ - Uploads images to ComfyUI API and returns download URLs. - To upload multiple images, stack them in the batch dimension first. - - Args: - image: Input torch.Tensor image. - max_images: Maximum number of images to upload. - auth_kwargs: Optional authentication token(s). - mime_type: Optional MIME type for the image. - """ - # if batch, try to upload each file if max_images is greater than 0 - download_urls: list[str] = [] - is_batch = len(image.shape) > 3 - batch_len = image.shape[0] if is_batch else 1 - - for idx in range(min(batch_len, max_images)): - tensor = image[idx] if is_batch else image - img_io = tensor_to_bytesio(tensor, mime_type=mime_type) - url = await upload_file_to_comfyapi(img_io, img_io.name, mime_type, auth_kwargs) - download_urls.append(url) - return download_urls - - -def resize_mask_to_image( - mask: torch.Tensor, - image: torch.Tensor, - upscale_method="nearest-exact", - crop="disabled", - allow_gradient=True, - add_channel_dim=False, -): - """ - Resize mask to be the same dimensions as an image, while maintaining proper format for API calls. - """ - _, H, W, _ = image.shape - mask = mask.unsqueeze(-1) - mask = mask.movedim(-1, 1) - mask = common_upscale( - mask, width=W, height=H, upscale_method=upscale_method, crop=crop - ) - mask = mask.movedim(1, -1) - if not add_channel_dim: - mask = mask.squeeze(-1) - if not allow_gradient: - mask = (mask > 0.5).float() - return mask - - -def validate_string( - string: str, - strip_whitespace=True, - field_name="prompt", - min_length=None, - max_length=None, -): - if string is None: - raise Exception(f"Field '{field_name}' cannot be empty.") - if strip_whitespace: - string = string.strip() - if min_length and len(string) < min_length: - raise Exception( - f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long." - ) - if max_length and len(string) > max_length: - raise Exception( - f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long." - ) - - -def image_tensor_pair_to_batch( - image1: torch.Tensor, image2: torch.Tensor -) -> torch.Tensor: - """ - Converts a pair of image tensors to a batch tensor. - If the images are not the same size, the smaller image is resized to - match the larger image. - """ - if image1.shape[1:] != image2.shape[1:]: - image2 = common_upscale( - image2.movedim(-1, 1), - image1.shape[2], - image1.shape[1], - "bilinear", - "center", - ).movedim(1, -1) - return torch.cat((image1, image2), dim=0) diff --git a/comfy_api_nodes/apis/PixverseController.py b/comfy_api_nodes/apis/PixverseController.py deleted file mode 100644 index 310c0f54655d72a76eb2abe2d843cc47e28bc37a..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/PixverseController.py +++ /dev/null @@ -1,17 +0,0 @@ -# generated by datamodel-codegen: -# filename: filtered-openapi.yaml -# timestamp: 2025-04-29T23:44:54+00:00 - -from __future__ import annotations - -from typing import Optional - -from pydantic import BaseModel - -from . import PixverseDto - - -class ResponseData(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None diff --git a/comfy_api_nodes/apis/PixverseDto.py b/comfy_api_nodes/apis/PixverseDto.py deleted file mode 100644 index 323c38e963903e523431fac19f067b88d051e396..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/PixverseDto.py +++ /dev/null @@ -1,57 +0,0 @@ -# generated by datamodel-codegen: -# filename: filtered-openapi.yaml -# timestamp: 2025-04-29T23:44:54+00:00 - -from __future__ import annotations - -from typing import Optional - -from pydantic import BaseModel, Field - - -class V2OpenAPII2VResp(BaseModel): - video_id: Optional[int] = Field(None, description='Video_id') - - -class V2OpenAPIT2VReq(BaseModel): - aspect_ratio: str = Field( - ..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9'] - ) - duration: int = Field( - ..., - description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)', - examples=[5], - ) - model: str = Field( - ..., description='Model version (only supports v3.5)', examples=['v3.5'] - ) - motion_mode: Optional[str] = Field( - 'normal', - description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)', - examples=['normal'], - ) - negative_prompt: Optional[str] = Field( - None, description='Negative prompt\n', max_length=2048 - ) - prompt: str = Field(..., description='Prompt', max_length=2048) - quality: str = Field( - ..., - description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")', - examples=['540p'], - ) - seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647') - style: Optional[str] = Field( - None, - description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed', - examples=['anime'], - ) - template_id: Optional[int] = Field( - None, - description='Template ID (template_id must be activated before use)', - examples=[302325299692608], - ) - water_mark: Optional[bool] = Field( - False, - description='Watermark (true: add watermark, false: no watermark)', - examples=[False], - ) diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py deleted file mode 100644 index 7a09df55b2164d66230c54f083bc08dae0f2c6ec..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/__init__.py +++ /dev/null @@ -1,6138 +0,0 @@ -# generated by datamodel-codegen: -# filename: filtered-openapi.yaml -# timestamp: 2025-07-30T08:54:00+00:00 - -from __future__ import annotations - -from datetime import date, datetime -from enum import Enum -from typing import Any, Dict, List, Literal, Optional, Union -from uuid import UUID - -from pydantic import AnyUrl, BaseModel, ConfigDict, Field, RootModel, StrictBytes - - -class APIKey(BaseModel): - created_at: Optional[datetime] = None - description: Optional[str] = None - id: Optional[str] = None - key_prefix: Optional[str] = None - name: Optional[str] = None - - -class APIKeyWithPlaintext(APIKey): - plaintext_key: Optional[str] = Field( - None, description='The full API key (only returned at creation)' - ) - - -class AuditLog(BaseModel): - createdAt: Optional[datetime] = Field( - None, description='The date and time the event was created' - ) - event_id: Optional[str] = Field(None, description='the id of the event') - event_type: Optional[str] = Field(None, description='the type of the event') - params: Optional[Dict[str, Any]] = Field( - None, description='data related to the event' - ) - - -class BFLAsyncResponse(BaseModel): - id: str = Field(..., title='Id') - polling_url: str = Field(..., title='Polling Url') - - -class BFLAsyncWebhookResponse(BaseModel): - id: str = Field(..., title='Id') - status: str = Field(..., title='Status') - webhook_url: str = Field(..., title='Webhook Url') - - -class CannyHighThreshold(RootModel[int]): - root: int = Field( - ..., - description='High threshold for Canny edge detection', - ge=0, - le=500, - title='Canny High Threshold', - ) - - -class CannyLowThreshold(RootModel[int]): - root: int = Field( - ..., - description='Low threshold for Canny edge detection', - ge=0, - le=500, - title='Canny Low Threshold', - ) - - -class Guidance(RootModel[float]): - root: float = Field( - ..., - description='Guidance strength for the image generation process', - ge=1.0, - le=100.0, - title='Guidance', - ) - - -class Steps(RootModel[int]): - root: int = Field( - ..., - description='Number of steps for the image generation process', - ge=15, - le=50, - title='Steps', - ) - - -class WebhookUrl(RootModel[AnyUrl]): - root: AnyUrl = Field( - ..., description='URL to receive webhook notifications', title='Webhook Url' - ) - - -class BFLFluxKontextMaxGenerateRequest(BaseModel): - guidance: Optional[float] = Field( - 3, description='The guidance scale for generation', ge=1.0, le=20.0 - ) - input_image: str = Field(..., description='Base64 encoded image to be edited') - prompt: str = Field( - ..., description='The text prompt describing what to edit on the image' - ) - steps: Optional[int] = Field( - 50, description='Number of inference steps', ge=1, le=50 - ) - - -class BFLFluxKontextMaxGenerateResponse(BaseModel): - id: str = Field(..., description='Job ID for tracking') - polling_url: str = Field(..., description='URL to poll for results') - - -class BFLFluxKontextProGenerateRequest(BaseModel): - guidance: Optional[float] = Field( - 3, description='The guidance scale for generation', ge=1.0, le=20.0 - ) - input_image: str = Field(..., description='Base64 encoded image to be edited') - prompt: str = Field( - ..., description='The text prompt describing what to edit on the image' - ) - steps: Optional[int] = Field( - 50, description='Number of inference steps', ge=1, le=50 - ) - - -class BFLFluxKontextProGenerateResponse(BaseModel): - id: str = Field(..., description='Job ID for tracking') - polling_url: str = Field(..., description='URL to poll for results') - - -class OutputFormat(str, Enum): - jpeg = 'jpeg' - png = 'png' - - -class BFLFluxPro11GenerateRequest(BaseModel): - height: int = Field(..., description='Height of the generated image') - image_prompt: Optional[str] = Field(None, description='Optional image prompt') - output_format: Optional[OutputFormat] = Field( - None, description='Output image format' - ) - prompt: str = Field(..., description='The main text prompt for image generation') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to use prompt upsampling' - ) - safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level') - seed: Optional[int] = Field(None, description='Random seed for reproducibility') - webhook_secret: Optional[str] = Field( - None, description='Optional webhook secret for async processing' - ) - webhook_url: Optional[str] = Field( - None, description='Optional webhook URL for async processing' - ) - width: int = Field(..., description='Width of the generated image') - - -class BFLFluxPro11GenerateResponse(BaseModel): - id: str = Field(..., description='Job ID for tracking') - polling_url: str = Field(..., description='URL to poll for results') - - -class Bottom(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand at the bottom of the image', - ge=0, - le=2048, - title='Bottom', - ) - - -class Guidance2(RootModel[float]): - root: float = Field( - ..., - description='Guidance strength for the image generation process', - ge=1.5, - le=100.0, - title='Guidance', - ) - - -class Left(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand on the left side of the image', - ge=0, - le=2048, - title='Left', - ) - - -class Right(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand on the right side of the image', - ge=0, - le=2048, - title='Right', - ) - - -class Steps2(RootModel[int]): - root: int = Field( - ..., - description='Number of steps for the image generation process', - examples=[50], - ge=15, - le=50, - title='Steps', - ) - - -class Top(RootModel[int]): - root: int = Field( - ..., - description='Number of pixels to expand at the top of the image', - ge=0, - le=2048, - title='Top', - ) - - -class BFLFluxProGenerateRequest(BaseModel): - guidance_scale: Optional[float] = Field( - None, description='The guidance scale for generation.', ge=1.0, le=20.0 - ) - height: int = Field( - ..., description='The height of the image to generate.', ge=64, le=2048 - ) - negative_prompt: Optional[str] = Field( - None, description='The negative prompt for image generation.' - ) - num_images: Optional[int] = Field( - None, description='The number of images to generate.', ge=1, le=4 - ) - num_inference_steps: Optional[int] = Field( - None, description='The number of inference steps.', ge=1, le=100 - ) - prompt: str = Field(..., description='The text prompt for image generation.') - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - width: int = Field( - ..., description='The width of the image to generate.', ge=64, le=2048 - ) - - -class BFLFluxProGenerateResponse(BaseModel): - id: str = Field(..., description='The unique identifier for the generation task.') - polling_url: str = Field(..., description='URL to poll for the generation result.') - - -class BFLOutputFormat(str, Enum): - jpeg = 'jpeg' - png = 'png' - - -class BFLValidationError(BaseModel): - loc: List[Union[str, int]] = Field(..., title='Location') - msg: str = Field(..., title='Message') - type: str = Field(..., title='Error Type') - - -class Status(str, Enum): - success = 'success' - not_found = 'not_found' - error = 'error' - - -class ClaimMyNodeRequest(BaseModel): - GH_TOKEN: str = Field( - ..., description='GitHub token to verify if the user owns the repo of the node' - ) - - -class ComfyNode(BaseModel): - category: Optional[str] = Field( - None, - description='UI category where the node is listed, used for grouping nodes.', - ) - comfy_node_name: Optional[str] = Field( - None, description='Unique identifier for the node' - ) - deprecated: Optional[bool] = Field( - None, - description='Indicates if the node is deprecated. Deprecated nodes are hidden in the UI.', - ) - description: Optional[str] = Field( - None, description="Brief description of the node's functionality or purpose." - ) - experimental: Optional[bool] = Field( - None, - description='Indicates if the node is experimental, subject to changes or removal.', - ) - function: Optional[str] = Field( - None, description='Name of the entry-point function to execute the node.' - ) - input_types: Optional[str] = Field(None, description='Defines input parameters') - output_is_list: Optional[List[bool]] = Field( - None, description='Boolean values indicating if each output is a list.' - ) - return_names: Optional[str] = Field( - None, description='Names of the outputs for clarity in workflows.' - ) - return_types: Optional[str] = Field( - None, description='Specifies the types of outputs produced by the node.' - ) - - -class ComfyNodeCloudBuildInfo(BaseModel): - build_id: Optional[str] = None - location: Optional[str] = None - project_id: Optional[str] = None - project_number: Optional[str] = None - - -class Status1(str, Enum): - in_progress = 'in_progress' - completed = 'completed' - incomplete = 'incomplete' - - -class Type(str, Enum): - computer_call = 'computer_call' - - -class ComputerToolCall(BaseModel): - action: Dict[str, Any] - call_id: str = Field( - ..., - description='An identifier used when responding to the tool call with output.\n', - ) - id: str = Field(..., description='The unique ID of the computer call.') - status: Status1 = Field( - ..., - description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', - ) - type: Type = Field( - ..., description='The type of the computer call. Always `computer_call`.' - ) - - -class Environment(str, Enum): - windows = 'windows' - mac = 'mac' - linux = 'linux' - ubuntu = 'ubuntu' - browser = 'browser' - - -class Type1(str, Enum): - computer_use_preview = 'computer_use_preview' - - -class ComputerUsePreviewTool(BaseModel): - display_height: int = Field(..., description='The height of the computer display.') - display_width: int = Field(..., description='The width of the computer display.') - environment: Environment = Field( - ..., description='The type of computer environment to control.' - ) - type: Literal['ComputerUsePreviewTool'] = Field( - ..., - description='The type of the computer use tool. Always `computer_use_preview`.', - ) - - -class CreateAPIKeyRequest(BaseModel): - description: Optional[str] = None - name: str - - -class Customer(BaseModel): - createdAt: Optional[datetime] = Field( - None, description='The date and time the user was created' - ) - email: Optional[str] = Field(None, description='The email address for this user') - has_fund: Optional[bool] = Field(None, description='Whether the user has funds') - id: str = Field(..., description='The firebase UID of the user') - is_admin: Optional[bool] = Field(None, description='Whether the user is an admin') - metronome_id: Optional[str] = Field(None, description='The Metronome customer ID') - name: Optional[str] = Field(None, description='The name for this user') - stripe_id: Optional[str] = Field(None, description='The Stripe customer ID') - updatedAt: Optional[datetime] = Field( - None, description='The date and time the user was last updated' - ) - - -class CustomerStorageResourceResponse(BaseModel): - download_url: Optional[str] = Field( - None, - description='The signed URL to use for downloading the file from the specified path', - ) - existing_file: Optional[bool] = Field( - None, description='Whether an existing file with the same hash was found' - ) - expires_at: Optional[datetime] = Field( - None, description='When the signed URL will expire' - ) - upload_url: Optional[str] = Field( - None, - description='The signed URL to use for uploading the file to the specified path', - ) - - -class Role(str, Enum): - user = 'user' - assistant = 'assistant' - system = 'system' - developer = 'developer' - - -class Type2(str, Enum): - message = 'message' - - -class Error(BaseModel): - details: Optional[List[str]] = Field( - None, - description='Optional detailed information about the error or hints for resolving it.', - ) - message: Optional[str] = Field( - None, description='A clear and concise description of the error.' - ) - - -class ErrorResponse(BaseModel): - error: str - message: str - - -class Type3(str, Enum): - file_search = 'file_search' - - -class FileSearchTool(BaseModel): - type: Literal['FileSearchTool'] = Field(..., description='The type of tool') - vector_store_ids: List[str] = Field( - ..., description='IDs of vector stores to search in' - ) - - -class Result(BaseModel): - file_id: Optional[str] = Field(None, description='The unique ID of the file.\n') - filename: Optional[str] = Field(None, description='The name of the file.\n') - score: Optional[float] = Field( - None, description='The relevance score of the file - a value between 0 and 1.\n' - ) - text: Optional[str] = Field( - None, description='The text that was retrieved from the file.\n' - ) - - -class Status2(str, Enum): - in_progress = 'in_progress' - searching = 'searching' - completed = 'completed' - incomplete = 'incomplete' - failed = 'failed' - - -class Type4(str, Enum): - file_search_call = 'file_search_call' - - -class FileSearchToolCall(BaseModel): - id: str = Field(..., description='The unique ID of the file search tool call.\n') - queries: List[str] = Field( - ..., description='The queries used to search for files.\n' - ) - results: Optional[List[Result]] = Field( - None, description='The results of the file search tool call.\n' - ) - status: Status2 = Field( - ..., - description='The status of the file search tool call. One of `in_progress`, \n`searching`, `incomplete` or `failed`,\n', - ) - type: Type4 = Field( - ..., - description='The type of the file search tool call. Always `file_search_call`.\n', - ) - - -class Type5(str, Enum): - function = 'function' - - -class FunctionTool(BaseModel): - description: Optional[str] = Field( - None, description='Description of what the function does' - ) - name: str = Field(..., description='Name of the function') - parameters: Dict[str, Any] = Field( - ..., description='JSON Schema object describing the function parameters' - ) - type: Literal['FunctionTool'] = Field(..., description='The type of tool') - - -class Status3(str, Enum): - in_progress = 'in_progress' - completed = 'completed' - incomplete = 'incomplete' - - -class Type6(str, Enum): - function_call = 'function_call' - - -class FunctionToolCall(BaseModel): - arguments: str = Field( - ..., description='A JSON string of the arguments to pass to the function.\n' - ) - call_id: str = Field( - ..., - description='The unique ID of the function tool call generated by the model.\n', - ) - id: Optional[str] = Field( - None, description='The unique ID of the function tool call.\n' - ) - name: str = Field(..., description='The name of the function to run.\n') - status: Optional[Status3] = Field( - None, - description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', - ) - type: Type6 = Field( - ..., description='The type of the function tool call. Always `function_call`.\n' - ) - - -class GeminiCitation(BaseModel): - authors: Optional[List[str]] = None - endIndex: Optional[int] = None - license: Optional[str] = None - publicationDate: Optional[date] = None - startIndex: Optional[int] = None - title: Optional[str] = None - uri: Optional[str] = None - - -class GeminiCitationMetadata(BaseModel): - citations: Optional[List[GeminiCitation]] = None - - -class Role1(str, Enum): - user = 'user' - model = 'model' - - -class GeminiFunctionDeclaration(BaseModel): - description: Optional[str] = None - name: str - parameters: Dict[str, Any] = Field( - ..., description='JSON schema for the function parameters' - ) - - -class GeminiGenerationConfig(BaseModel): - maxOutputTokens: Optional[int] = Field( - None, - description='Maximum number of tokens that can be generated in the response. A token is approximately 4 characters. 100 tokens correspond to roughly 60-80 words.\n', - examples=[2048], - ge=16, - le=8192, - ) - seed: Optional[int] = Field( - None, - description="When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used. Available for the following models:, gemini-2.5-flash-preview-04-1, gemini-2.5-pro-preview-05-0, gemini-2.0-flash-lite-00, gemini-2.0-flash-001\n", - examples=[343940597], - ) - stopSequences: Optional[List[str]] = None - temperature: Optional[float] = Field( - 1, - description="The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible. If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature\n", - ge=0.0, - le=2.0, - ) - topK: Optional[int] = Field( - 40, - description="Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary. A top-K of 3 means that the next token is selected from among the 3 most probable tokens by using temperature.\n", - examples=[40], - ge=1, - ) - topP: Optional[float] = Field( - 0.95, - description='If specified, nucleus sampling is used.\nTop-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.\nSpecify a lower value for less random responses and a higher value for more random responses.\n', - ge=0.0, - le=1.0, - ) - - -class GeminiMimeType(str, Enum): - application_pdf = 'application/pdf' - audio_mpeg = 'audio/mpeg' - audio_mp3 = 'audio/mp3' - audio_wav = 'audio/wav' - image_png = 'image/png' - image_jpeg = 'image/jpeg' - image_webp = 'image/webp' - text_plain = 'text/plain' - video_mov = 'video/mov' - video_mpeg = 'video/mpeg' - video_mp4 = 'video/mp4' - video_mpg = 'video/mpg' - video_avi = 'video/avi' - video_wmv = 'video/wmv' - video_mpegps = 'video/mpegps' - video_flv = 'video/flv' - - -class GeminiOffset(BaseModel): - nanos: Optional[int] = Field( - None, - description='Signed fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values.\n', - examples=[0], - ge=0, - le=999999999, - ) - seconds: Optional[int] = Field( - None, - description='Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive.\n', - examples=[60], - ge=-315576000000, - le=315576000000, - ) - - -class GeminiSafetyCategory(str, Enum): - HARM_CATEGORY_SEXUALLY_EXPLICIT = 'HARM_CATEGORY_SEXUALLY_EXPLICIT' - HARM_CATEGORY_HATE_SPEECH = 'HARM_CATEGORY_HATE_SPEECH' - HARM_CATEGORY_HARASSMENT = 'HARM_CATEGORY_HARASSMENT' - HARM_CATEGORY_DANGEROUS_CONTENT = 'HARM_CATEGORY_DANGEROUS_CONTENT' - - -class Probability(str, Enum): - NEGLIGIBLE = 'NEGLIGIBLE' - LOW = 'LOW' - MEDIUM = 'MEDIUM' - HIGH = 'HIGH' - UNKNOWN = 'UNKNOWN' - - -class GeminiSafetyRating(BaseModel): - category: Optional[GeminiSafetyCategory] = None - probability: Optional[Probability] = Field( - None, - description='The probability that the content violates the specified safety category', - ) - - -class GeminiSafetyThreshold(str, Enum): - OFF = 'OFF' - BLOCK_NONE = 'BLOCK_NONE' - BLOCK_LOW_AND_ABOVE = 'BLOCK_LOW_AND_ABOVE' - BLOCK_MEDIUM_AND_ABOVE = 'BLOCK_MEDIUM_AND_ABOVE' - BLOCK_ONLY_HIGH = 'BLOCK_ONLY_HIGH' - - -class GeminiTextPart(BaseModel): - text: Optional[str] = Field( - None, - description='A text prompt or code snippet.', - examples=['Answer as concisely as possible'], - ) - - -class GeminiTool(BaseModel): - functionDeclarations: Optional[List[GeminiFunctionDeclaration]] = None - - -class GeminiVideoMetadata(BaseModel): - endOffset: Optional[GeminiOffset] = None - startOffset: Optional[GeminiOffset] = None - - -class GitCommitSummary(BaseModel): - author: Optional[str] = Field(None, description='The author of the commit') - branch_name: Optional[str] = Field( - None, description='The branch where the commit was made' - ) - commit_hash: Optional[str] = Field(None, description='The hash of the commit') - commit_name: Optional[str] = Field(None, description='The name of the commit') - status_summary: Optional[Dict[str, str]] = Field( - None, description='A map of operating system to status pairs' - ) - timestamp: Optional[datetime] = Field( - None, description='The timestamp when the commit was made' - ) - - -class GithubEnterprise(BaseModel): - avatar_url: str = Field(..., description='URL to the enterprise avatar') - created_at: datetime = Field(..., description='When the enterprise was created') - description: Optional[str] = Field(None, description='The enterprise description') - html_url: str = Field(..., description='The HTML URL of the enterprise') - id: int = Field(..., description='The enterprise ID') - name: str = Field(..., description='The enterprise name') - node_id: str = Field(..., description='The enterprise node ID') - slug: str = Field(..., description='The enterprise slug') - updated_at: datetime = Field( - ..., description='When the enterprise was last updated' - ) - website_url: Optional[str] = Field(None, description='The enterprise website URL') - - -class RepositorySelection(str, Enum): - selected = 'selected' - all = 'all' - - -class GithubOrganization(BaseModel): - avatar_url: str = Field(..., description="URL to the organization's avatar") - description: Optional[str] = Field(None, description='The organization description') - events_url: str = Field(..., description="The API URL of the organization's events") - hooks_url: str = Field(..., description="The API URL of the organization's hooks") - id: int = Field(..., description='The organization ID') - issues_url: str = Field(..., description="The API URL of the organization's issues") - login: str = Field(..., description="The organization's login name") - members_url: str = Field( - ..., description="The API URL of the organization's members" - ) - node_id: str = Field(..., description='The organization node ID') - public_members_url: str = Field( - ..., description="The API URL of the organization's public members" - ) - repos_url: str = Field( - ..., description="The API URL of the organization's repositories" - ) - url: str = Field(..., description='The API URL of the organization') - - -class State(str, Enum): - uploaded = 'uploaded' - open = 'open' - - -class Action(str, Enum): - published = 'published' - unpublished = 'unpublished' - created = 'created' - edited = 'edited' - deleted = 'deleted' - prereleased = 'prereleased' - released = 'released' - - -class Type7(str, Enum): - Bot = 'Bot' - User = 'User' - Organization = 'Organization' - - -class GithubUser(BaseModel): - avatar_url: str = Field(..., description="URL to the user's avatar") - gravatar_id: Optional[str] = Field(None, description="The user's gravatar ID") - html_url: str = Field(..., description='The HTML URL of the user') - id: int = Field(..., description="The user's ID") - login: str = Field(..., description="The user's login name") - node_id: str = Field(..., description="The user's node ID") - site_admin: bool = Field(..., description='Whether the user is a site admin') - type: Type7 = Field(..., description='The type of user') - url: str = Field(..., description='The API URL of the user') - - -class IdeogramColorPalette1(BaseModel): - name: str = Field(..., description='Name of the preset color palette') - - -class Member(BaseModel): - color: Optional[str] = Field( - None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$' - ) - weight: Optional[float] = Field( - None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0 - ) - - -class IdeogramColorPalette2(BaseModel): - members: List[Member] = Field( - ..., description='Array of color definitions with optional weights' - ) - - -class IdeogramColorPalette( - RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]] -): - root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field( - ..., - description='A color palette specification that can either use a preset name or explicit color definitions with weights', - ) - - -class ImageRequest(BaseModel): - aspect_ratio: Optional[str] = Field( - None, - description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.", - ) - color_palette: Optional[Dict[str, Any]] = Field( - None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' - ) - magic_prompt_option: Optional[str] = Field( - None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')." - ) - model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") - negative_prompt: Optional[str] = Field( - None, - description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', - ) - num_images: Optional[int] = Field( - 1, - description='Optional. Number of images to generate (1-8). Defaults to 1.', - ge=1, - le=8, - ) - prompt: str = Field( - ..., description='Required. The prompt to use to generate the image.' - ) - resolution: Optional[str] = Field( - None, - description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", - ) - seed: Optional[int] = Field( - None, - description='Optional. A number between 0 and 2147483647.', - ge=0, - le=2147483647, - ) - style_type: Optional[str] = Field( - None, - description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.", - ) - - -class IdeogramGenerateRequest(BaseModel): - image_request: ImageRequest = Field( - ..., description='The image generation request parameters.' - ) - - -class Datum(BaseModel): - is_image_safe: Optional[bool] = Field( - None, description='Indicates whether the image is considered safe.' - ) - prompt: Optional[str] = Field( - None, description='The prompt used to generate this image.' - ) - resolution: Optional[str] = Field( - None, description="The resolution of the generated image (e.g., '1024x1024')." - ) - seed: Optional[int] = Field( - None, description='The seed value used for this generation.' - ) - style_type: Optional[str] = Field( - None, - description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').", - ) - url: Optional[str] = Field(None, description='URL to the generated image.') - - -class IdeogramGenerateResponse(BaseModel): - created: Optional[datetime] = Field( - None, description='Timestamp when the generation was created.' - ) - data: Optional[List[Datum]] = Field( - None, description='Array of generated image information.' - ) - - -class StyleCode(RootModel[str]): - root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$') - - -class Datum1(BaseModel): - is_image_safe: Optional[bool] = None - prompt: Optional[str] = None - resolution: Optional[str] = None - seed: Optional[int] = None - style_type: Optional[str] = None - url: Optional[str] = None - - -class IdeogramV3IdeogramResponse(BaseModel): - created: Optional[datetime] = None - data: Optional[List[Datum1]] = None - - -class RenderingSpeed1(str, Enum): - TURBO = 'TURBO' - DEFAULT = 'DEFAULT' - QUALITY = 'QUALITY' - - -class IdeogramV3ReframeRequest(BaseModel): - color_palette: Optional[Dict[str, Any]] = None - image: Optional[StrictBytes] = None - num_images: Optional[int] = Field(None, ge=1, le=8) - rendering_speed: Optional[RenderingSpeed1] = None - resolution: str - seed: Optional[int] = Field(None, ge=0, le=2147483647) - style_codes: Optional[List[str]] = None - style_reference_images: Optional[List[StrictBytes]] = None - - -class MagicPrompt(str, Enum): - AUTO = 'AUTO' - ON = 'ON' - OFF = 'OFF' - - -class StyleType(str, Enum): - AUTO = 'AUTO' - GENERAL = 'GENERAL' - REALISTIC = 'REALISTIC' - DESIGN = 'DESIGN' - - -class IdeogramV3RemixRequest(BaseModel): - aspect_ratio: Optional[str] = None - color_palette: Optional[Dict[str, Any]] = None - image: Optional[StrictBytes] = None - image_weight: Optional[int] = Field(50, ge=1, le=100) - magic_prompt: Optional[MagicPrompt] = None - negative_prompt: Optional[str] = None - num_images: Optional[int] = Field(None, ge=1, le=8) - prompt: str - rendering_speed: Optional[RenderingSpeed1] = None - resolution: Optional[str] = None - seed: Optional[int] = Field(None, ge=0, le=2147483647) - style_codes: Optional[List[str]] = None - style_reference_images: Optional[List[StrictBytes]] = None - style_type: Optional[StyleType] = None - - -class IdeogramV3ReplaceBackgroundRequest(BaseModel): - color_palette: Optional[Dict[str, Any]] = None - image: Optional[StrictBytes] = None - magic_prompt: Optional[MagicPrompt] = None - num_images: Optional[int] = Field(None, ge=1, le=8) - prompt: str - rendering_speed: Optional[RenderingSpeed1] = None - seed: Optional[int] = Field(None, ge=0, le=2147483647) - style_codes: Optional[List[str]] = None - style_reference_images: Optional[List[StrictBytes]] = None - - -class ColorPalette(BaseModel): - name: str = Field(..., description='Name of the color palette', examples=['PASTEL']) - - -class MagicPrompt2(str, Enum): - ON = 'ON' - OFF = 'OFF' - - -class StyleType1(str, Enum): - GENERAL = 'GENERAL' - - -class ImagenImageGenerationInstance(BaseModel): - prompt: str = Field(..., description='Text prompt for image generation') - - -class AspectRatio(str, Enum): - field_1_1 = '1:1' - field_9_16 = '9:16' - field_16_9 = '16:9' - field_3_4 = '3:4' - field_4_3 = '4:3' - - -class PersonGeneration(str, Enum): - dont_allow = 'dont_allow' - allow_adult = 'allow_adult' - allow_all = 'allow_all' - - -class SafetySetting(str, Enum): - block_most = 'block_most' - block_some = 'block_some' - block_few = 'block_few' - block_fewest = 'block_fewest' - - -class ImagenImagePrediction(BaseModel): - bytesBase64Encoded: Optional[str] = Field( - None, description='Base64-encoded image content' - ) - mimeType: Optional[str] = Field( - None, description='MIME type of the generated image' - ) - prompt: Optional[str] = Field( - None, description='Enhanced or rewritten prompt used to generate this image' - ) - - -class MimeType(str, Enum): - image_png = 'image/png' - image_jpeg = 'image/jpeg' - - -class ImagenOutputOptions(BaseModel): - compressionQuality: Optional[int] = Field(None, ge=0, le=100) - mimeType: Optional[MimeType] = None - - -class Includable(str, Enum): - file_search_call_results = 'file_search_call.results' - message_input_image_image_url = 'message.input_image.image_url' - computer_call_output_output_image_url = 'computer_call_output.output.image_url' - - -class Type8(str, Enum): - input_file = 'input_file' - - -class InputFileContent(BaseModel): - file_data: Optional[str] = Field( - None, description='The content of the file to be sent to the model.\n' - ) - file_id: Optional[str] = Field( - None, description='The ID of the file to be sent to the model.' - ) - filename: Optional[str] = Field( - None, description='The name of the file to be sent to the model.' - ) - type: Type8 = Field( - ..., description='The type of the input item. Always `input_file`.' - ) - - -class Detail(str, Enum): - low = 'low' - high = 'high' - auto = 'auto' - - -class Type9(str, Enum): - input_image = 'input_image' - - -class InputImageContent(BaseModel): - detail: Detail = Field( - ..., - description='The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.', - ) - file_id: Optional[str] = Field( - None, description='The ID of the file to be sent to the model.' - ) - image_url: Optional[str] = Field( - None, - description='The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.', - ) - type: Type9 = Field( - ..., description='The type of the input item. Always `input_image`.' - ) - - -class Role3(str, Enum): - user = 'user' - system = 'system' - developer = 'developer' - - -class Type10(str, Enum): - message = 'message' - - -class Type11(str, Enum): - input_text = 'input_text' - - -class InputTextContent(BaseModel): - text: str = Field(..., description='The text input to the model.') - type: Type11 = Field( - ..., description='The type of the input item. Always `input_text`.' - ) - - -class KlingAudioUploadType(str, Enum): - file = 'file' - url = 'url' - - -class KlingCameraConfig(BaseModel): - horizontal: Optional[float] = Field( - None, - description="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right.", - ge=-10.0, - le=10.0, - ) - pan: Optional[float] = Field( - None, - description="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", - ge=-10.0, - le=10.0, - ) - roll: Optional[float] = Field( - None, - description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", - ge=-10.0, - le=10.0, - ) - tilt: Optional[float] = Field( - None, - description="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", - ge=-10.0, - le=10.0, - ) - vertical: Optional[float] = Field( - None, - description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", - ge=-10.0, - le=10.0, - ) - zoom: Optional[float] = Field( - None, - description="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", - ge=-10.0, - le=10.0, - ) - - -class KlingCameraControlType(str, Enum): - simple = 'simple' - down_back = 'down_back' - forward_up = 'forward_up' - right_turn_forward = 'right_turn_forward' - left_turn_forward = 'left_turn_forward' - - -class KlingCharacterEffectModelName(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_5 = 'kling-v1-5' - kling_v1_6 = 'kling-v1-6' - - -class KlingDualCharacterEffectsScene(str, Enum): - hug = 'hug' - kiss = 'kiss' - heart_gesture = 'heart_gesture' - - -class KlingDualCharacterImages(RootModel[List[str]]): - root: List[str] = Field(..., max_length=2, min_length=2) - - -class KlingErrorResponse(BaseModel): - code: int = Field( - ..., - description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n', - ) - message: str = Field(..., description='Human-readable error message') - request_id: str = Field( - ..., description='Request ID for tracking and troubleshooting' - ) - - -class Trajectory(BaseModel): - x: Optional[int] = Field( - None, - description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', - ) - y: Optional[int] = Field( - None, - description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', - ) - - -class DynamicMask(BaseModel): - mask: Optional[AnyUrl] = Field( - None, - description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', - ) - trajectories: Optional[List[Trajectory]] = None - - -class TaskInfo(BaseModel): - external_task_id: Optional[str] = None - - -class KlingImageGenAspectRatio(str, Enum): - field_16_9 = '16:9' - field_9_16 = '9:16' - field_1_1 = '1:1' - field_4_3 = '4:3' - field_3_4 = '3:4' - field_3_2 = '3:2' - field_2_3 = '2:3' - field_21_9 = '21:9' - - -class KlingImageGenImageReferenceType(str, Enum): - subject = 'subject' - face = 'face' - - -class KlingImageGenModelName(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_5 = 'kling-v1-5' - kling_v2 = 'kling-v2' - - -class KlingImageGenerationsRequest(BaseModel): - aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9' - callback_url: Optional[AnyUrl] = Field( - None, description='The callback notification address' - ) - human_fidelity: Optional[float] = Field( - 0.45, description='Subject reference similarity', ge=0.0, le=1.0 - ) - image: Optional[str] = Field( - None, description='Reference Image - Base64 encoded string or image URL' - ) - image_fidelity: Optional[float] = Field( - 0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0 - ) - image_reference: Optional[KlingImageGenImageReferenceType] = None - model_name: Optional[KlingImageGenModelName] = 'kling-v1' - n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9) - negative_prompt: Optional[str] = Field( - None, description='Negative text prompt', max_length=200 - ) - prompt: str = Field(..., description='Positive text prompt', max_length=500) - - -class KlingImageResult(BaseModel): - index: Optional[int] = Field(None, description='Image Number (0-9)') - url: Optional[AnyUrl] = Field(None, description='URL for generated image') - - -class KlingLipSyncMode(str, Enum): - text2video = 'text2video' - audio2video = 'audio2video' - - -class KlingLipSyncVoiceLanguage(str, Enum): - zh = 'zh' - en = 'en' - - -class ResourcePackType(str, Enum): - decreasing_total = 'decreasing_total' - constant_period = 'constant_period' - - -class Status5(str, Enum): - toBeOnline = 'toBeOnline' - online = 'online' - expired = 'expired' - runOut = 'runOut' - - -class ResourcePackSubscribeInfo(BaseModel): - effective_time: Optional[int] = Field( - None, description='Effective time, Unix timestamp in ms' - ) - invalid_time: Optional[int] = Field( - None, description='Expiration time, Unix timestamp in ms' - ) - purchase_time: Optional[int] = Field( - None, description='Purchase time, Unix timestamp in ms' - ) - remaining_quantity: Optional[float] = Field( - None, description='Remaining quantity (updated with a 12-hour delay)' - ) - resource_pack_id: Optional[str] = Field(None, description='Resource package ID') - resource_pack_name: Optional[str] = Field(None, description='Resource package name') - resource_pack_type: Optional[ResourcePackType] = Field( - None, - description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', - ) - status: Optional[Status5] = Field(None, description='Resource Package Status') - total_quantity: Optional[float] = Field(None, description='Total quantity') - - -class Data3(BaseModel): - code: Optional[int] = Field(None, description='Error code; 0 indicates success') - msg: Optional[str] = Field(None, description='Error information') - resource_pack_subscribe_infos: Optional[List[ResourcePackSubscribeInfo]] = Field( - None, description='Resource package list' - ) - - -class KlingResourcePackageResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code; 0 indicates success') - data: Optional[Data3] = None - message: Optional[str] = Field(None, description='Error information') - request_id: Optional[str] = Field( - None, - description='Request ID, generated by the system, used to track requests and troubleshoot problems', - ) - - -class KlingSingleImageEffectDuration(str, Enum): - field_5 = '5' - - -class KlingSingleImageEffectModelName(str, Enum): - kling_v1_6 = 'kling-v1-6' - - -class KlingSingleImageEffectsScene(str, Enum): - bloombloom = 'bloombloom' - dizzydizzy = 'dizzydizzy' - fuzzyfuzzy = 'fuzzyfuzzy' - squish = 'squish' - expansion = 'expansion' - - -class KlingTaskStatus(str, Enum): - submitted = 'submitted' - processing = 'processing' - succeed = 'succeed' - failed = 'failed' - - -class KlingTextToVideoModelName(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_6 = 'kling-v1-6' - kling_v2_1_master = 'kling-v2-1-master' - - -class KlingVideoGenAspectRatio(str, Enum): - field_16_9 = '16:9' - field_9_16 = '9:16' - field_1_1 = '1:1' - - -class KlingVideoGenCfgScale(RootModel[float]): - root: float = Field( - ..., - description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.", - ge=0.0, - le=1.0, - ) - - -class KlingVideoGenDuration(str, Enum): - field_5 = '5' - field_10 = '10' - - -class KlingVideoGenMode(str, Enum): - std = 'std' - pro = 'pro' - - -class KlingVideoGenModelName(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_5 = 'kling-v1-5' - kling_v1_6 = 'kling-v1-6' - kling_v2_master = 'kling-v2-master' - kling_v2_1 = 'kling-v2-1' - kling_v2_1_master = 'kling-v2-1-master' - - -class KlingVideoResult(BaseModel): - duration: Optional[str] = Field(None, description='Total video duration') - id: Optional[str] = Field(None, description='Generated video ID') - url: Optional[AnyUrl] = Field(None, description='URL for generated video') - - -class KlingVirtualTryOnModelName(str, Enum): - kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1' - kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5' - - -class KlingVirtualTryOnRequest(BaseModel): - callback_url: Optional[AnyUrl] = Field( - None, description='The callback notification address' - ) - cloth_image: Optional[str] = Field( - None, - description='Reference clothing image - Base64 encoded string or image URL', - ) - human_image: str = Field( - ..., description='Reference human image - Base64 encoded string or image URL' - ) - model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1' - - -class TaskResult6(BaseModel): - images: Optional[List[KlingImageResult]] = None - - -class Data7(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_result: Optional[TaskResult6] = None - task_status: Optional[KlingTaskStatus] = None - task_status_msg: Optional[str] = Field(None, description='Task status information') - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingVirtualTryOnResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data7] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class LumaAspectRatio(str, Enum): - field_1_1 = '1:1' - field_16_9 = '16:9' - field_9_16 = '9:16' - field_4_3 = '4:3' - field_3_4 = '3:4' - field_21_9 = '21:9' - field_9_21 = '9:21' - - -class LumaAssets(BaseModel): - image: Optional[AnyUrl] = Field(None, description='The URL of the image') - progress_video: Optional[AnyUrl] = Field( - None, description='The URL of the progress video' - ) - video: Optional[AnyUrl] = Field(None, description='The URL of the video') - - -class GenerationType(str, Enum): - add_audio = 'add_audio' - - -class LumaAudioGenerationRequest(BaseModel): - callback_url: Optional[AnyUrl] = Field( - None, description='The callback URL for the audio' - ) - generation_type: Optional[GenerationType] = 'add_audio' - negative_prompt: Optional[str] = Field( - None, description='The negative prompt of the audio' - ) - prompt: Optional[str] = Field(None, description='The prompt of the audio') - - -class LumaError(BaseModel): - detail: Optional[str] = Field(None, description='The error message') - - -class Type12(str, Enum): - generation = 'generation' - - -class LumaGenerationReference(BaseModel): - id: UUID = Field(..., description='The ID of the generation') - type: Literal['generation'] - - -class GenerationType1(str, Enum): - video = 'video' - - -class LumaGenerationType(str, Enum): - video = 'video' - image = 'image' - - -class GenerationType2(str, Enum): - image = 'image' - - -class LumaImageIdentity(BaseModel): - images: Optional[List[AnyUrl]] = Field( - None, description='The URLs of the image identity' - ) - - -class LumaImageModel(str, Enum): - photon_1 = 'photon-1' - photon_flash_1 = 'photon-flash-1' - - -class LumaImageRef(BaseModel): - url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') - weight: Optional[float] = Field( - None, description='The weight of the image reference' - ) - - -class Type13(str, Enum): - image = 'image' - - -class LumaImageReference(BaseModel): - type: Literal['image'] - url: AnyUrl = Field(..., description='The URL of the image') - - -class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]): - root: Union[LumaGenerationReference, LumaImageReference] = Field( - ..., - description='A keyframe can be either a Generation reference, an Image, or a Video', - discriminator='type', - ) - - -class LumaKeyframes(BaseModel): - frame0: Optional[LumaKeyframe] = None - frame1: Optional[LumaKeyframe] = None - - -class LumaModifyImageRef(BaseModel): - url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') - weight: Optional[float] = Field( - None, description='The weight of the modify image reference' - ) - - -class LumaState(str, Enum): - queued = 'queued' - dreaming = 'dreaming' - completed = 'completed' - failed = 'failed' - - -class GenerationType3(str, Enum): - upscale_video = 'upscale_video' - - -class LumaVideoModel(str, Enum): - ray_2 = 'ray-2' - ray_flash_2 = 'ray-flash-2' - ray_1_6 = 'ray-1-6' - - -class LumaVideoModelOutputDuration1(str, Enum): - field_5s = '5s' - field_9s = '9s' - - -class LumaVideoModelOutputDuration( - RootModel[Union[LumaVideoModelOutputDuration1, str]] -): - root: Union[LumaVideoModelOutputDuration1, str] - - -class LumaVideoModelOutputResolution1(str, Enum): - field_540p = '540p' - field_720p = '720p' - field_1080p = '1080p' - field_4k = '4k' - - -class LumaVideoModelOutputResolution( - RootModel[Union[LumaVideoModelOutputResolution1, str]] -): - root: Union[LumaVideoModelOutputResolution1, str] - - -class MachineStats(BaseModel): - cpu_capacity: Optional[str] = Field(None, description='Total CPU on the machine.') - disk_capacity: Optional[str] = Field( - None, description='Total disk capacity on the machine.' - ) - gpu_type: Optional[str] = Field( - None, description='The GPU type. eg. NVIDIA Tesla K80' - ) - initial_cpu: Optional[str] = Field( - None, description='Initial CPU available before the job starts.' - ) - initial_disk: Optional[str] = Field( - None, description='Initial disk available before the job starts.' - ) - initial_ram: Optional[str] = Field( - None, description='Initial RAM available before the job starts.' - ) - machine_name: Optional[str] = Field(None, description='Name of the machine.') - memory_capacity: Optional[str] = Field( - None, description='Total memory on the machine.' - ) - os_version: Optional[str] = Field( - None, description='The operating system version. eg. Ubuntu Linux 20.04' - ) - pip_freeze: Optional[str] = Field(None, description='The pip freeze output') - vram_time_series: Optional[Dict[str, Any]] = Field( - None, description='Time series of VRAM usage.' - ) - - -class MinimaxBaseResponse(BaseModel): - status_code: int = Field( - ..., - description='Status code. 0 indicates success, other values indicate errors.', - ) - status_msg: str = Field( - ..., description='Specific error details or success message.' - ) - - -class File(BaseModel): - bytes: Optional[int] = Field(None, description='File size in bytes') - created_at: Optional[int] = Field( - None, description='Unix timestamp when the file was created, in seconds' - ) - download_url: Optional[str] = Field( - None, description='The URL to download the video' - ) - file_id: Optional[int] = Field(None, description='Unique identifier for the file') - filename: Optional[str] = Field(None, description='The name of the file') - purpose: Optional[str] = Field(None, description='The purpose of using the file') - - -class MinimaxFileRetrieveResponse(BaseModel): - base_resp: MinimaxBaseResponse - file: File - - -class Status6(str, Enum): - Queueing = 'Queueing' - Preparing = 'Preparing' - Processing = 'Processing' - Success = 'Success' - Fail = 'Fail' - - -class MinimaxTaskResultResponse(BaseModel): - base_resp: MinimaxBaseResponse - file_id: Optional[str] = Field( - None, - description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.', - ) - status: Status6 = Field( - ..., - description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).", - ) - task_id: str = Field(..., description='The task ID being queried.') - - -class MiniMaxModel(str, Enum): - T2V_01_Director = 'T2V-01-Director' - I2V_01_Director = 'I2V-01-Director' - S2V_01 = 'S2V-01' - I2V_01 = 'I2V-01' - I2V_01_live = 'I2V-01-live' - T2V_01 = 'T2V-01' - Hailuo_02 = 'MiniMax-Hailuo-02' - - -class SubjectReferenceItem(BaseModel): - image: Optional[str] = Field( - None, description='URL or base64 encoding of the subject reference image.' - ) - mask: Optional[str] = Field( - None, - description='URL or base64 encoding of the mask for the subject reference image.', - ) - - -class MinimaxVideoGenerationRequest(BaseModel): - callback_url: Optional[str] = Field( - None, - description='Optional. URL to receive real-time status updates about the video generation task.', - ) - first_frame_image: Optional[str] = Field( - None, - description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.', - ) - model: MiniMaxModel = Field( - ..., - description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01', - ) - prompt: Optional[str] = Field( - None, - description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].', - max_length=2000, - ) - prompt_optimizer: Optional[bool] = Field( - True, - description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.', - ) - subject_reference: Optional[List[SubjectReferenceItem]] = Field( - None, - description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.', - ) - duration: Optional[int] = Field( - None, - description="The length of the output video in seconds." - ) - resolution: Optional[str] = Field( - None, - description="The dimensions of the video display. 1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels." - ) - - -class MinimaxVideoGenerationResponse(BaseModel): - base_resp: MinimaxBaseResponse - task_id: str = Field( - ..., description='The task ID for the asynchronous video generation task.' - ) - - -class Modality(str, Enum): - MODALITY_UNSPECIFIED = 'MODALITY_UNSPECIFIED' - TEXT = 'TEXT' - IMAGE = 'IMAGE' - VIDEO = 'VIDEO' - AUDIO = 'AUDIO' - DOCUMENT = 'DOCUMENT' - - -class ModalityTokenCount(BaseModel): - modality: Optional[Modality] = None - tokenCount: Optional[int] = Field( - None, description='Number of tokens for the given modality.' - ) - - -class Truncation(str, Enum): - disabled = 'disabled' - auto = 'auto' - - -class ModelResponseProperties(BaseModel): - instructions: Optional[str] = Field( - None, description='Instructions for the model on how to generate the response' - ) - max_output_tokens: Optional[int] = Field( - None, description='Maximum number of tokens to generate' - ) - model: Optional[str] = Field( - None, description='The model used to generate the response' - ) - temperature: Optional[float] = Field( - 1, description='Controls randomness in the response', ge=0.0, le=2.0 - ) - top_p: Optional[float] = Field( - 1, - description='Controls diversity of the response via nucleus sampling', - ge=0.0, - le=1.0, - ) - truncation: Optional[Truncation] = Field( - 'disabled', description='How to handle truncation of the response' - ) - - -class Keyframes(BaseModel): - image_url: Optional[str] = None - - -class MoonvalleyPromptResponse(BaseModel): - error: Optional[Dict[str, Any]] = None - frame_conditioning: Optional[Dict[str, Any]] = None - id: Optional[str] = None - inference_params: Optional[Dict[str, Any]] = None - meta: Optional[Dict[str, Any]] = None - model_params: Optional[Dict[str, Any]] = None - output_url: Optional[str] = None - prompt_text: Optional[str] = None - status: Optional[str] = None - - -class MoonvalleyTextToVideoInferenceParams(BaseModel): - add_quality_guidance: Optional[bool] = Field( - True, description='Whether to add quality guidance' - ) - caching_coefficient: Optional[float] = Field( - 0.3, description='Caching coefficient for optimization' - ) - caching_cooldown: Optional[int] = Field( - 3, description='Number of caching cooldown steps' - ) - caching_warmup: Optional[int] = Field( - 3, description='Number of caching warmup steps' - ) - clip_value: Optional[float] = Field( - 3, description='CLIP value for generation control' - ) - conditioning_frame_index: Optional[int] = Field( - 0, description='Index of the conditioning frame' - ) - cooldown_steps: Optional[int] = Field( - 75, description='Number of cooldown steps (calculated based on num_frames)' - ) - fps: Optional[int] = Field( - 24, description='Frames per second of the generated video' - ) - guidance_scale: Optional[float] = Field( - 10, description='Guidance scale for generation control' - ) - height: Optional[int] = Field( - 1080, description='Height of the generated video in pixels' - ) - negative_prompt: Optional[str] = Field(None, description='Negative prompt text') - num_frames: Optional[int] = Field(64, description='Number of frames to generate') - seed: Optional[int] = Field( - None, description='Random seed for generation (default: random)' - ) - shift_value: Optional[float] = Field( - 3, description='Shift value for generation control' - ) - steps: Optional[int] = Field(80, description='Number of denoising steps') - use_guidance_schedule: Optional[bool] = Field( - True, description='Whether to use guidance scheduling' - ) - use_negative_prompts: Optional[bool] = Field( - False, description='Whether to use negative prompts' - ) - use_timestep_transform: Optional[bool] = Field( - True, description='Whether to use timestep transformation' - ) - warmup_steps: Optional[int] = Field( - 0, description='Number of warmup steps (calculated based on num_frames)' - ) - width: Optional[int] = Field( - 1920, description='Width of the generated video in pixels' - ) - - -class MoonvalleyTextToVideoRequest(BaseModel): - image_url: Optional[str] = None - inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None - prompt_text: Optional[str] = None - webhook_url: Optional[str] = None - - -class MoonvalleyUploadFileRequest(BaseModel): - file: Optional[StrictBytes] = None - - -class MoonvalleyUploadFileResponse(BaseModel): - access_url: Optional[str] = None - - -class MoonvalleyVideoToVideoInferenceParams(BaseModel): - add_quality_guidance: Optional[bool] = Field( - True, description='Whether to add quality guidance' - ) - caching_coefficient: Optional[float] = Field( - 0.3, description='Caching coefficient for optimization' - ) - caching_cooldown: Optional[int] = Field( - 3, description='Number of caching cooldown steps' - ) - caching_warmup: Optional[int] = Field( - 3, description='Number of caching warmup steps' - ) - clip_value: Optional[float] = Field( - 3, description='CLIP value for generation control' - ) - conditioning_frame_index: Optional[int] = Field( - 0, description='Index of the conditioning frame' - ) - cooldown_steps: Optional[int] = Field( - 36, description='Number of cooldown steps (calculated based on num_frames)' - ) - guidance_scale: Optional[float] = Field( - 15, description='Guidance scale for generation control' - ) - negative_prompt: Optional[str] = Field(None, description='Negative prompt text') - seed: Optional[int] = Field( - None, description='Random seed for generation (default: random)' - ) - shift_value: Optional[float] = Field( - 3, description='Shift value for generation control' - ) - steps: Optional[int] = Field(80, description='Number of denoising steps') - use_guidance_schedule: Optional[bool] = Field( - True, description='Whether to use guidance scheduling' - ) - use_negative_prompts: Optional[bool] = Field( - False, description='Whether to use negative prompts' - ) - use_timestep_transform: Optional[bool] = Field( - True, description='Whether to use timestep transformation' - ) - warmup_steps: Optional[int] = Field( - 24, description='Number of warmup steps (calculated based on num_frames)' - ) - - -class ControlType(str, Enum): - motion_control = 'motion_control' - pose_control = 'pose_control' - - -class MoonvalleyVideoToVideoRequest(BaseModel): - control_type: ControlType = Field( - ..., description='Supported types for video control' - ) - inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None - prompt_text: str = Field(..., description='Describes the video to generate') - video_url: str = Field(..., description='Url to control video') - webhook_url: Optional[str] = Field( - None, description='Optional webhook URL for notifications' - ) - - -class NodeStatus(str, Enum): - NodeStatusActive = 'NodeStatusActive' - NodeStatusDeleted = 'NodeStatusDeleted' - NodeStatusBanned = 'NodeStatusBanned' - - -class NodeVersionIdentifier(BaseModel): - node_id: str = Field(..., description='The unique identifier of the node') - version: str = Field(..., description='The version of the node') - - -class NodeVersionStatus(str, Enum): - NodeVersionStatusActive = 'NodeVersionStatusActive' - NodeVersionStatusDeleted = 'NodeVersionStatusDeleted' - NodeVersionStatusBanned = 'NodeVersionStatusBanned' - NodeVersionStatusPending = 'NodeVersionStatusPending' - NodeVersionStatusFlagged = 'NodeVersionStatusFlagged' - - -class NodeVersionUpdateRequest(BaseModel): - changelog: Optional[str] = Field( - None, description='The changelog describing the version changes.' - ) - deprecated: Optional[bool] = Field( - None, description='Whether the version is deprecated.' - ) - - -class Moderation(str, Enum): - low = 'low' - auto = 'auto' - - -class OutputFormat1(str, Enum): - png = 'png' - webp = 'webp' - jpeg = 'jpeg' - - -class OpenAIImageEditRequest(BaseModel): - background: Optional[str] = Field( - None, description='Background transparency', examples=['opaque'] - ) - model: str = Field( - ..., description='The model to use for image editing', examples=['gpt-image-1'] - ) - moderation: Optional[Moderation] = Field( - None, description='Content moderation setting', examples=['auto'] - ) - n: Optional[int] = Field( - None, description='The number of images to generate', examples=[1] - ) - output_compression: Optional[int] = Field( - None, description='Compression level for JPEG or WebP (0-100)', examples=[100] - ) - output_format: Optional[OutputFormat1] = Field( - None, description='Format of the output image', examples=['png'] - ) - prompt: str = Field( - ..., - description='A text description of the desired edit', - examples=['Give the rocketship rainbow coloring'], - ) - quality: Optional[str] = Field( - None, description='The quality of the edited image', examples=['low'] - ) - size: Optional[str] = Field( - None, description='Size of the output image', examples=['1024x1024'] - ) - user: Optional[str] = Field( - None, - description='A unique identifier for end-user monitoring', - examples=['user-1234'], - ) - - -class Background(str, Enum): - transparent = 'transparent' - opaque = 'opaque' - - -class Quality(str, Enum): - low = 'low' - medium = 'medium' - high = 'high' - standard = 'standard' - hd = 'hd' - - -class ResponseFormat(str, Enum): - url = 'url' - b64_json = 'b64_json' - - -class Style(str, Enum): - vivid = 'vivid' - natural = 'natural' - - -class OpenAIImageGenerationRequest(BaseModel): - background: Optional[Background] = Field( - None, description='Background transparency', examples=['opaque'] - ) - model: Optional[str] = Field( - None, description='The model to use for image generation', examples=['dall-e-3'] - ) - moderation: Optional[Moderation] = Field( - None, description='Content moderation setting', examples=['auto'] - ) - n: Optional[int] = Field( - None, - description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', - examples=[1], - ) - output_compression: Optional[int] = Field( - None, description='Compression level for JPEG or WebP (0-100)', examples=[100] - ) - output_format: Optional[OutputFormat1] = Field( - None, description='Format of the output image', examples=['png'] - ) - prompt: str = Field( - ..., - description='A text description of the desired image', - examples=['Draw a rocket in front of a blackhole in deep space'], - ) - quality: Optional[Quality] = Field( - None, description='The quality of the generated image', examples=['high'] - ) - response_format: Optional[ResponseFormat] = Field( - None, description='Response format of image data', examples=['b64_json'] - ) - size: Optional[str] = Field( - None, - description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', - examples=['1024x1536'], - ) - style: Optional[Style] = Field( - None, description='Style of the image (only for dall-e-3)', examples=['vivid'] - ) - user: Optional[str] = Field( - None, - description='A unique identifier for end-user monitoring', - examples=['user-1234'], - ) - - -class Datum2(BaseModel): - b64_json: Optional[str] = Field(None, description='Base64 encoded image data') - revised_prompt: Optional[str] = Field(None, description='Revised prompt') - url: Optional[str] = Field(None, description='URL of the image') - - -class InputTokensDetails(BaseModel): - image_tokens: Optional[int] = None - text_tokens: Optional[int] = None - - -class Usage(BaseModel): - input_tokens: Optional[int] = None - input_tokens_details: Optional[InputTokensDetails] = None - output_tokens: Optional[int] = None - total_tokens: Optional[int] = None - - -class OpenAIImageGenerationResponse(BaseModel): - data: Optional[List[Datum2]] = None - usage: Optional[Usage] = None - - -class OpenAIModels(str, Enum): - gpt_4 = 'gpt-4' - gpt_4_0314 = 'gpt-4-0314' - gpt_4_0613 = 'gpt-4-0613' - gpt_4_32k = 'gpt-4-32k' - gpt_4_32k_0314 = 'gpt-4-32k-0314' - gpt_4_32k_0613 = 'gpt-4-32k-0613' - gpt_4_0125_preview = 'gpt-4-0125-preview' - gpt_4_turbo = 'gpt-4-turbo' - gpt_4_turbo_2024_04_09 = 'gpt-4-turbo-2024-04-09' - gpt_4_turbo_preview = 'gpt-4-turbo-preview' - gpt_4_1106_preview = 'gpt-4-1106-preview' - gpt_4_vision_preview = 'gpt-4-vision-preview' - gpt_3_5_turbo = 'gpt-3.5-turbo' - gpt_3_5_turbo_16k = 'gpt-3.5-turbo-16k' - gpt_3_5_turbo_0301 = 'gpt-3.5-turbo-0301' - gpt_3_5_turbo_0613 = 'gpt-3.5-turbo-0613' - gpt_3_5_turbo_1106 = 'gpt-3.5-turbo-1106' - gpt_3_5_turbo_0125 = 'gpt-3.5-turbo-0125' - gpt_3_5_turbo_16k_0613 = 'gpt-3.5-turbo-16k-0613' - gpt_4_1 = 'gpt-4.1' - gpt_4_1_mini = 'gpt-4.1-mini' - gpt_4_1_nano = 'gpt-4.1-nano' - gpt_4_1_2025_04_14 = 'gpt-4.1-2025-04-14' - gpt_4_1_mini_2025_04_14 = 'gpt-4.1-mini-2025-04-14' - gpt_4_1_nano_2025_04_14 = 'gpt-4.1-nano-2025-04-14' - o1 = 'o1' - o1_mini = 'o1-mini' - o1_preview = 'o1-preview' - o1_pro = 'o1-pro' - o1_2024_12_17 = 'o1-2024-12-17' - o1_preview_2024_09_12 = 'o1-preview-2024-09-12' - o1_mini_2024_09_12 = 'o1-mini-2024-09-12' - o1_pro_2025_03_19 = 'o1-pro-2025-03-19' - o3 = 'o3' - o3_mini = 'o3-mini' - o3_2025_04_16 = 'o3-2025-04-16' - o3_mini_2025_01_31 = 'o3-mini-2025-01-31' - o4_mini = 'o4-mini' - o4_mini_2025_04_16 = 'o4-mini-2025-04-16' - gpt_4o = 'gpt-4o' - gpt_4o_mini = 'gpt-4o-mini' - gpt_4o_2024_11_20 = 'gpt-4o-2024-11-20' - gpt_4o_2024_08_06 = 'gpt-4o-2024-08-06' - gpt_4o_2024_05_13 = 'gpt-4o-2024-05-13' - gpt_4o_mini_2024_07_18 = 'gpt-4o-mini-2024-07-18' - gpt_4o_audio_preview = 'gpt-4o-audio-preview' - gpt_4o_audio_preview_2024_10_01 = 'gpt-4o-audio-preview-2024-10-01' - gpt_4o_audio_preview_2024_12_17 = 'gpt-4o-audio-preview-2024-12-17' - gpt_4o_mini_audio_preview = 'gpt-4o-mini-audio-preview' - gpt_4o_mini_audio_preview_2024_12_17 = 'gpt-4o-mini-audio-preview-2024-12-17' - gpt_4o_search_preview = 'gpt-4o-search-preview' - gpt_4o_mini_search_preview = 'gpt-4o-mini-search-preview' - gpt_4o_search_preview_2025_03_11 = 'gpt-4o-search-preview-2025-03-11' - gpt_4o_mini_search_preview_2025_03_11 = 'gpt-4o-mini-search-preview-2025-03-11' - computer_use_preview = 'computer-use-preview' - computer_use_preview_2025_03_11 = 'computer-use-preview-2025-03-11' - chatgpt_4o_latest = 'chatgpt-4o-latest' - - -class Reason(str, Enum): - max_output_tokens = 'max_output_tokens' - content_filter = 'content_filter' - - -class IncompleteDetails(BaseModel): - reason: Optional[Reason] = Field( - None, description='The reason why the response is incomplete.' - ) - - -class Object(str, Enum): - response = 'response' - - -class Status7(str, Enum): - completed = 'completed' - failed = 'failed' - in_progress = 'in_progress' - incomplete = 'incomplete' - - -class Type14(str, Enum): - output_audio = 'output_audio' - - -class OutputAudioContent(BaseModel): - data: str = Field(..., description='Base64-encoded audio data') - transcript: str = Field(..., description='Transcript of the audio') - type: Type14 = Field(..., description='The type of output content') - - -class Role4(str, Enum): - assistant = 'assistant' - - -class Type15(str, Enum): - message = 'message' - - -class Type16(str, Enum): - output_text = 'output_text' - - -class OutputTextContent(BaseModel): - text: str = Field(..., description='The text content') - type: Type16 = Field(..., description='The type of output content') - - -class PersonalAccessToken(BaseModel): - createdAt: Optional[datetime] = Field( - None, description='[Output Only]The date and time the token was created.' - ) - description: Optional[str] = Field( - None, - description="Optional. A more detailed description of the token's intended use.", - ) - id: Optional[UUID] = Field(None, description='Unique identifier for the GitCommit') - name: Optional[str] = Field( - None, - description='Required. The name of the token. Can be a simple description.', - ) - token: Optional[str] = Field( - None, - description='[Output Only]. The personal access token. Only returned during creation.', - ) - - -class AspectRatio1(RootModel[float]): - root: float = Field( - ..., - description='Aspect ratio (width / height)', - ge=0.4, - le=2.5, - title='Aspectratio', - ) - - -class IngredientsMode(str, Enum): - creative = 'creative' - precise = 'precise' - - -class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): - aspectRatio: Optional[AspectRatio1] = Field( - None, description='Aspect ratio (width / height)', title='Aspectratio' - ) - duration: Optional[int] = Field(5, title='Duration') - images: Optional[List[StrictBytes]] = Field(None, title='Images') - ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - promptText: Optional[str] = Field(None, title='Prompttext') - resolution: Optional[str] = Field('1080p', title='Resolution') - seed: Optional[int] = Field(None, title='Seed') - - -class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): - image: Optional[StrictBytes] = Field(None, title='Image') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - promptText: Optional[str] = Field(None, title='Prompttext') - seed: Optional[int] = Field(None, title='Seed') - video: Optional[StrictBytes] = Field(None, title='Video') - - -class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): - image: Optional[StrictBytes] = Field(None, title='Image') - modifyRegionMask: Optional[StrictBytes] = Field( - None, - description='A mask image that specifies the region to modify, where the mask is white and the background is black', - title='Modifyregionmask', - ) - modifyRegionRoi: Optional[str] = Field( - None, - description='Plaintext description of the object / region to modify', - title='Modifyregionroi', - ) - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - promptText: Optional[str] = Field(None, title='Prompttext') - seed: Optional[int] = Field(None, title='Seed') - video: Optional[StrictBytes] = Field(None, title='Video') - - -class PikaDurationEnum(int, Enum): - integer_5 = 5 - integer_10 = 10 - - -class PikaGenerateResponse(BaseModel): - video_id: str = Field(..., title='Video Id') - - -class PikaResolutionEnum(str, Enum): - field_1080p = '1080p' - field_720p = '720p' - - -class PikaStatusEnum(str, Enum): - queued = 'queued' - started = 'started' - finished = 'finished' - - -class PikaValidationError(BaseModel): - loc: List[Union[str, int]] = Field(..., title='Location') - msg: str = Field(..., title='Message') - type: str = Field(..., title='Error Type') - - -class PikaVideoResponse(BaseModel): - id: str = Field(..., title='Id') - progress: Optional[int] = Field(None, title='Progress') - status: PikaStatusEnum - url: Optional[str] = Field(None, title='Url') - - -class Pikaffect(str, Enum): - Cake_ify = 'Cake-ify' - Crumble = 'Crumble' - Crush = 'Crush' - Decapitate = 'Decapitate' - Deflate = 'Deflate' - Dissolve = 'Dissolve' - Explode = 'Explode' - Eye_pop = 'Eye-pop' - Inflate = 'Inflate' - Levitate = 'Levitate' - Melt = 'Melt' - Peel = 'Peel' - Poke = 'Poke' - Squish = 'Squish' - Ta_da = 'Ta-da' - Tear = 'Tear' - - -class Resp(BaseModel): - img_id: Optional[int] = None - - -class PixverseImageUploadResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp_1: Optional[Resp] = Field(None, alias='Resp') - - -class Duration(int, Enum): - integer_5 = 5 - integer_8 = 8 - - -class Model1(str, Enum): - v3_5 = 'v3.5' - - -class MotionMode(str, Enum): - normal = 'normal' - fast = 'fast' - - -class Quality1(str, Enum): - field_360p = '360p' - field_540p = '540p' - field_720p = '720p' - field_1080p = '1080p' - - -class Style1(str, Enum): - anime = 'anime' - field_3d_animation = '3d_animation' - clay = 'clay' - comic = 'comic' - cyberpunk = 'cyberpunk' - - -class PixverseImageVideoRequest(BaseModel): - duration: Duration - img_id: int - model: Model1 - motion_mode: Optional[MotionMode] = None - prompt: str - quality: Quality1 - seed: Optional[int] = None - style: Optional[Style1] = None - template_id: Optional[int] = None - water_mark: Optional[bool] = None - - -class AspectRatio2(str, Enum): - field_16_9 = '16:9' - field_4_3 = '4:3' - field_1_1 = '1:1' - field_3_4 = '3:4' - field_9_16 = '9:16' - - -class PixverseTextVideoRequest(BaseModel): - aspect_ratio: AspectRatio2 - duration: Duration - model: Model1 - motion_mode: Optional[MotionMode] = None - negative_prompt: Optional[str] = None - prompt: str - quality: Quality1 - seed: Optional[int] = None - style: Optional[Style1] = None - template_id: Optional[int] = None - water_mark: Optional[bool] = None - - -class PixverseTransitionVideoRequest(BaseModel): - duration: Duration - first_frame_img: int - last_frame_img: int - model: Model1 - motion_mode: MotionMode - prompt: str - quality: Quality1 - seed: int - style: Optional[Style1] = None - template_id: Optional[int] = None - water_mark: Optional[bool] = None - - -class Resp1(BaseModel): - video_id: Optional[int] = None - - -class PixverseVideoResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp: Optional[Resp1] = None - - -class Status8(int, Enum): - integer_1 = 1 - integer_5 = 5 - integer_6 = 6 - integer_7 = 7 - integer_8 = 8 - - -class Resp2(BaseModel): - create_time: Optional[str] = None - id: Optional[int] = None - modify_time: Optional[str] = None - negative_prompt: Optional[str] = None - outputHeight: Optional[int] = None - outputWidth: Optional[int] = None - prompt: Optional[str] = None - resolution_ratio: Optional[int] = None - seed: Optional[int] = None - size: Optional[int] = None - status: Optional[Status8] = Field( - None, - description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n', - ) - style: Optional[str] = None - url: Optional[str] = None - - -class PixverseVideoResultResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp: Optional[Resp2] = None - - -class PublisherStatus(str, Enum): - PublisherStatusActive = 'PublisherStatusActive' - PublisherStatusBanned = 'PublisherStatusBanned' - - -class PublisherUser(BaseModel): - email: Optional[str] = Field(None, description='The email address for this user.') - id: Optional[str] = Field(None, description='The unique id for this user.') - name: Optional[str] = Field(None, description='The name for this user.') - - -class RgbItem(RootModel[int]): - root: int = Field(..., ge=0, le=255) - - -class RGBColor(BaseModel): - rgb: List[RgbItem] = Field(..., max_length=3, min_length=3) - - -class GenerateSummary(str, Enum): - auto = 'auto' - concise = 'concise' - detailed = 'detailed' - - -class Summary(str, Enum): - auto = 'auto' - concise = 'concise' - detailed = 'detailed' - - -class ReasoningEffort(str, Enum): - low = 'low' - medium = 'medium' - high = 'high' - - -class Status9(str, Enum): - in_progress = 'in_progress' - completed = 'completed' - incomplete = 'incomplete' - - -class Type17(str, Enum): - summary_text = 'summary_text' - - -class SummaryItem(BaseModel): - text: str = Field( - ..., - description='A short summary of the reasoning used by the model when generating\nthe response.\n', - ) - type: Type17 = Field( - ..., description='The type of the object. Always `summary_text`.\n' - ) - - -class Type18(str, Enum): - reasoning = 'reasoning' - - -class ReasoningItem(BaseModel): - id: str = Field( - ..., description='The unique identifier of the reasoning content.\n' - ) - status: Optional[Status9] = Field( - None, - description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', - ) - summary: List[SummaryItem] = Field(..., description='Reasoning text contents.\n') - type: Type18 = Field( - ..., description='The type of the object. Always `reasoning`.\n' - ) - - -class RecraftImageColor(BaseModel): - rgb: Optional[List[int]] = None - std: Optional[List[float]] = None - weight: Optional[float] = None - - -class RecraftImageFeatures(BaseModel): - nsfw_score: Optional[float] = None - - -class RecraftImageFormat(str, Enum): - webp = 'webp' - png = 'png' - - -class Controls(BaseModel): - artistic_level: Optional[int] = Field( - None, - description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.', - ge=0, - le=5, - ) - background_color: Optional[RGBColor] = None - colors: Optional[List[RGBColor]] = Field( - None, description='An array of preferable colors' - ) - no_text: Optional[bool] = Field(None, description='Do not embed text layouts') - - -class RecraftImageGenerationRequest(BaseModel): - controls: Optional[Controls] = Field( - None, description='The controls for the generated image' - ) - model: str = Field( - ..., description='The model to use for generation (e.g., "recraftv3")' - ) - n: int = Field(..., description='The number of images to generate', ge=1, le=4) - prompt: str = Field( - ..., description='The text prompt describing the image to generate' - ) - size: str = Field( - ..., description='The size of the generated image (e.g., "1024x1024")' - ) - style: Optional[str] = Field( - None, - description='The style to apply to the generated image (e.g., "digital_illustration")', - ) - style_id: Optional[str] = Field( - None, - description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.', - ) - - -class Datum3(BaseModel): - image_id: Optional[str] = Field( - None, description='Unique identifier for the generated image' - ) - url: Optional[str] = Field(None, description='URL to access the generated image') - - -class RecraftImageGenerationResponse(BaseModel): - created: int = Field( - ..., description='Unix timestamp when the generation was created' - ) - credits: int = Field(..., description='Number of credits used for the generation') - data: List[Datum3] = Field(..., description='Array of generated image information') - - -class RecraftImageStyle(str, Enum): - digital_illustration = 'digital_illustration' - icon = 'icon' - realistic_image = 'realistic_image' - vector_illustration = 'vector_illustration' - - -class RecraftImageSubStyle(str, Enum): - field_2d_art_poster = '2d_art_poster' - field_3d = '3d' - field_80s = '80s' - glow = 'glow' - grain = 'grain' - hand_drawn = 'hand_drawn' - infantile_sketch = 'infantile_sketch' - kawaii = 'kawaii' - pixel_art = 'pixel_art' - psychedelic = 'psychedelic' - seamless = 'seamless' - voxel = 'voxel' - watercolor = 'watercolor' - broken_line = 'broken_line' - colored_outline = 'colored_outline' - colored_shapes = 'colored_shapes' - colored_shapes_gradient = 'colored_shapes_gradient' - doodle_fill = 'doodle_fill' - doodle_offset_fill = 'doodle_offset_fill' - offset_fill = 'offset_fill' - outline = 'outline' - outline_gradient = 'outline_gradient' - uneven_fill = 'uneven_fill' - field_70s = '70s' - cartoon = 'cartoon' - doodle_line_art = 'doodle_line_art' - engraving = 'engraving' - flat_2 = 'flat_2' - kawaii_1 = 'kawaii' - line_art = 'line_art' - linocut = 'linocut' - seamless_1 = 'seamless' - b_and_w = 'b_and_w' - enterprise = 'enterprise' - hard_flash = 'hard_flash' - hdr = 'hdr' - motion_blur = 'motion_blur' - natural_light = 'natural_light' - studio_portrait = 'studio_portrait' - line_circuit = 'line_circuit' - field_2d_art_poster_2 = '2d_art_poster_2' - engraving_color = 'engraving_color' - flat_air_art = 'flat_air_art' - hand_drawn_outline = 'hand_drawn_outline' - handmade_3d = 'handmade_3d' - stickers_drawings = 'stickers_drawings' - plastic = 'plastic' - pictogram = 'pictogram' - - -class RecraftResponseFormat(str, Enum): - url = 'url' - b64_json = 'b64_json' - - -class RecraftTextLayoutItem(BaseModel): - bbox: List[List[float]] - text: str - - -class RecraftTransformModel(str, Enum): - refm1 = 'refm1' - recraft20b = 'recraft20b' - recraftv2 = 'recraftv2' - recraftv3 = 'recraftv3' - flux1_1pro = 'flux1_1pro' - flux1dev = 'flux1dev' - imagen3 = 'imagen3' - hidream_i1_dev = 'hidream_i1_dev' - - -class RecraftUserControls(BaseModel): - artistic_level: Optional[int] = None - background_color: Optional[RecraftImageColor] = None - colors: Optional[List[RecraftImageColor]] = None - no_text: Optional[bool] = None - - -class Attention(str, Enum): - low = 'low' - medium = 'medium' - high = 'high' - - -class Project(str, Enum): - comfyui = 'comfyui' - comfyui_frontend = 'comfyui_frontend' - desktop = 'desktop' - - -class ReleaseNote(BaseModel): - attention: Attention = Field( - ..., description='The attention level for this release' - ) - content: str = Field( - ..., description='The content of the release note in markdown format' - ) - id: int = Field(..., description='Unique identifier for the release note') - project: Project = Field( - ..., description='The project this release note belongs to' - ) - published_at: datetime = Field( - ..., description='When the release note was published' - ) - version: str = Field(..., description='The version of the release') - - -class RenderingSpeed(str, Enum): - BALANCED = 'BALANCED' - TURBO = 'TURBO' - QUALITY = 'QUALITY' - - -class Type19(str, Enum): - response_completed = 'response.completed' - - -class Type20(str, Enum): - response_content_part_added = 'response.content_part.added' - - -class Type21(str, Enum): - response_content_part_done = 'response.content_part.done' - - -class Type22(str, Enum): - response_created = 'response.created' - - -class ResponseErrorCode(str, Enum): - server_error = 'server_error' - rate_limit_exceeded = 'rate_limit_exceeded' - invalid_prompt = 'invalid_prompt' - vector_store_timeout = 'vector_store_timeout' - invalid_image = 'invalid_image' - invalid_image_format = 'invalid_image_format' - invalid_base64_image = 'invalid_base64_image' - invalid_image_url = 'invalid_image_url' - image_too_large = 'image_too_large' - image_too_small = 'image_too_small' - image_parse_error = 'image_parse_error' - image_content_policy_violation = 'image_content_policy_violation' - invalid_image_mode = 'invalid_image_mode' - image_file_too_large = 'image_file_too_large' - unsupported_image_media_type = 'unsupported_image_media_type' - empty_image_file = 'empty_image_file' - failed_to_download_image = 'failed_to_download_image' - image_file_not_found = 'image_file_not_found' - - -class Type23(str, Enum): - error = 'error' - - -class ResponseErrorEvent(BaseModel): - code: str = Field(..., description='The error code.\n') - message: str = Field(..., description='The error message.\n') - param: str = Field(..., description='The error parameter.\n') - type: Type23 = Field(..., description='The type of the event. Always `error`.\n') - - -class Type24(str, Enum): - response_failed = 'response.failed' - - -class Type25(str, Enum): - json_object = 'json_object' - - -class ResponseFormatJsonObject(BaseModel): - type: Type25 = Field( - ..., - description='The type of response format being defined. Always `json_object`.', - ) - - -class ResponseFormatJsonSchemaSchema(BaseModel): - pass - model_config = ConfigDict( - extra='allow', - ) - - -class Type26(str, Enum): - text = 'text' - - -class ResponseFormatText(BaseModel): - type: Type26 = Field( - ..., description='The type of response format being defined. Always `text`.' - ) - - -class Type27(str, Enum): - response_in_progress = 'response.in_progress' - - -class Type28(str, Enum): - response_incomplete = 'response.incomplete' - - -class Type29(str, Enum): - response_output_item_added = 'response.output_item.added' - - -class Type30(str, Enum): - response_output_item_done = 'response.output_item.done' - - -class Truncation1(str, Enum): - auto = 'auto' - disabled = 'disabled' - - -class InputTokensDetails1(BaseModel): - cached_tokens: int = Field( - ..., - description='The number of tokens that were retrieved from the cache. \n[More on prompt caching](/docs/guides/prompt-caching).\n', - ) - - -class OutputTokensDetails(BaseModel): - reasoning_tokens: int = Field(..., description='The number of reasoning tokens.') - - -class ResponseUsage(BaseModel): - input_tokens: int = Field(..., description='The number of input tokens.') - input_tokens_details: InputTokensDetails1 = Field( - ..., description='A detailed breakdown of the input tokens.' - ) - output_tokens: int = Field(..., description='The number of output tokens.') - output_tokens_details: OutputTokensDetails = Field( - ..., description='A detailed breakdown of the output tokens.' - ) - total_tokens: int = Field(..., description='The total number of tokens used.') - - -class Rodin3DCheckStatusRequest(BaseModel): - subscription_key: str = Field( - ..., description='subscription from generate endpoint' - ) - - -class Rodin3DDownloadRequest(BaseModel): - task_uuid: str = Field(..., description='Task UUID') - - -class RodinGenerateJobsData(BaseModel): - subscription_key: Optional[str] = Field(None, description='Subscription Key.') - uuids: Optional[List[str]] = Field(None, description='subjobs uuid.') - - -class RodinMaterialType(str, Enum): - PBR = 'PBR' - Shaded = 'Shaded' - - -class RodinMeshModeType(str, Enum): - Quad = 'Quad' - Raw = 'Raw' - - -class RodinQualityType(str, Enum): - extra_low = 'extra-low' - low = 'low' - medium = 'medium' - high = 'high' - - -class RodinResourceItem(BaseModel): - name: Optional[str] = Field(None, description='File name') - url: Optional[str] = Field(None, description='Download url') - - -class RodinStatusOptions(str, Enum): - Done = 'Done' - Failed = 'Failed' - Generating = 'Generating' - Waiting = 'Waiting' - - -class RodinTierType(str, Enum): - Regular = 'Regular' - Sketch = 'Sketch' - Detail = 'Detail' - Smooth = 'Smooth' - - -class RunwayAspectRatioEnum(str, Enum): - field_1280_720 = '1280:720' - field_720_1280 = '720:1280' - field_1104_832 = '1104:832' - field_832_1104 = '832:1104' - field_960_960 = '960:960' - field_1584_672 = '1584:672' - field_1280_768 = '1280:768' - field_768_1280 = '768:1280' - - -class RunwayDurationEnum(int, Enum): - integer_5 = 5 - integer_10 = 10 - - -class RunwayImageToVideoResponse(BaseModel): - id: Optional[str] = Field(None, description='Task ID') - - -class RunwayModelEnum(str, Enum): - gen4_turbo = 'gen4_turbo' - gen3a_turbo = 'gen3a_turbo' - - -class Position(str, Enum): - first = 'first' - last = 'last' - - -class RunwayPromptImageDetailedObject(BaseModel): - position: Position = Field( - ..., - description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.", - ) - uri: str = Field( - ..., description='A HTTPS URL or data URI containing an encoded image.' - ) - - -class RunwayPromptImageObject( - RootModel[Union[str, List[RunwayPromptImageDetailedObject]]] -): - root: Union[str, List[RunwayPromptImageDetailedObject]] = Field( - ..., - description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.', - ) - - -class RunwayTaskStatusEnum(str, Enum): - SUCCEEDED = 'SUCCEEDED' - RUNNING = 'RUNNING' - FAILED = 'FAILED' - PENDING = 'PENDING' - CANCELLED = 'CANCELLED' - THROTTLED = 'THROTTLED' - - -class RunwayTaskStatusResponse(BaseModel): - createdAt: datetime = Field(..., description='Task creation timestamp') - id: str = Field(..., description='Task ID') - output: Optional[List[str]] = Field(None, description='Array of output video URLs') - progress: Optional[float] = Field( - None, - description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.', - ge=0.0, - le=1.0, - ) - status: RunwayTaskStatusEnum - - -class RunwayTextToImageAspectRatioEnum(str, Enum): - field_1920_1080 = '1920:1080' - field_1080_1920 = '1080:1920' - field_1024_1024 = '1024:1024' - field_1360_768 = '1360:768' - field_1080_1080 = '1080:1080' - field_1168_880 = '1168:880' - field_1440_1080 = '1440:1080' - field_1080_1440 = '1080:1440' - field_1808_768 = '1808:768' - field_2112_912 = '2112:912' - - -class Model4(str, Enum): - gen4_image = 'gen4_image' - - -class ReferenceImage(BaseModel): - uri: Optional[str] = Field( - None, description='A HTTPS URL or data URI containing an encoded image' - ) - - -class RunwayTextToImageRequest(BaseModel): - model: Model4 = Field(..., description='Model to use for generation') - promptText: str = Field( - ..., description='Text prompt for the image generation', max_length=1000 - ) - ratio: RunwayTextToImageAspectRatioEnum - referenceImages: Optional[List[ReferenceImage]] = Field( - None, description='Array of reference images to guide the generation' - ) - - -class RunwayTextToImageResponse(BaseModel): - id: Optional[str] = Field(None, description='Task ID') - - -class Name(str, Enum): - content_moderation = 'content_moderation' - - -class StabilityContentModerationResponse(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: Name = Field( - ..., - description='Our content moderation system has flagged some part of your request and subsequently denied it. You were not charged for this request. While this may at times be frustrating, it is necessary to maintain the integrity of our platform and ensure a safe experience for all users. If you would like to provide feedback, please use the [Support Form](https://kb.stability.ai/knowledge-base/kb-tickets/new).', - ) - - -class StabilityCreativity(RootModel[float]): - root: float = Field( - ..., - description='Controls the likelihood of creating additional details not heavily conditioned by the init image.', - ge=0.2, - le=0.5, - ) - - -class StabilityError(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[[{'some-field': 'is required'}]], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.\n', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityGenerationID(RootModel[str]): - root: str = Field( - ..., - description='The `id` of a generation, typically used for async generations, that can be used to check the status of the generation or retrieve the result.', - examples=['a6dc6c6e20acda010fe14d71f180658f2896ed9b4ec25aa99a6ff06c796987c4'], - max_length=64, - min_length=64, - ) - - -class Status10(str, Enum): - in_progress = 'in-progress' - - -class StabilityGetResultResponse202(BaseModel): - id: Optional[str] = Field( - None, description='The ID of the generation result.', examples=[1234567890] - ) - status: Optional[Status10] = None - - -class AspectRatio3(str, Enum): - field_21_9 = '21:9' - field_16_9 = '16:9' - field_3_2 = '3:2' - field_5_4 = '5:4' - field_1_1 = '1:1' - field_4_5 = '4:5' - field_2_3 = '2:3' - field_9_16 = '9:16' - field_9_21 = '9:21' - - -class Mode(str, Enum): - text_to_image = 'text-to-image' - image_to_image = 'image-to-image' - - -class Model5(str, Enum): - sd3_5_large = 'sd3.5-large' - sd3_5_large_turbo = 'sd3.5-large-turbo' - sd3_5_medium = 'sd3.5-medium' - - -class OutputFormat3(str, Enum): - png = 'png' - jpeg = 'jpeg' - - -class StylePreset(str, Enum): - enhance = 'enhance' - anime = 'anime' - photographic = 'photographic' - digital_art = 'digital-art' - comic_book = 'comic-book' - fantasy_art = 'fantasy-art' - line_art = 'line-art' - analog_film = 'analog-film' - neon_punk = 'neon-punk' - isometric = 'isometric' - low_poly = 'low-poly' - origami = 'origami' - modeling_compound = 'modeling-compound' - cinematic = 'cinematic' - field_3d_model = '3d-model' - pixel_art = 'pixel-art' - tile_texture = 'tile-texture' - - -class StabilityImageGenerationSD3Request(BaseModel): - aspect_ratio: Optional[AspectRatio3] = Field( - '1:1', - description='Controls the aspect ratio of the generated image. Defaults to 1:1.\n\n> **Important:** This parameter is only valid for **text-to-image** requests.', - ) - cfg_scale: Optional[float] = Field( - None, - description='How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt). The _Large_ and _Medium_ models use a default of `4`. The _Turbo_ model uses a default of `1`.', - ge=1.0, - le=10.0, - ) - image: Optional[StrictBytes] = Field( - None, - description='The image to use as the starting point for the generation.\n\nSupported formats:\n\n\n\n - jpeg\n - png\n - webp\n\nSupported dimensions:\n\n\n\n - Every side must be at least 64 pixels\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', - ) - mode: Optional[Mode] = Field( - 'text-to-image', - description='Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:\n- **text-to-image** requires only the `prompt` parameter\n- **image-to-image** requires the `prompt`, `image`, and `strength` parameters', - title='GenerationMode', - ) - model: Optional[Model5] = Field( - 'sd3.5-large', - description='The model to use for generation.\n\n- `sd3.5-large` requires 6.5 credits per generation\n- `sd3.5-large-turbo` requires 4 credits per generation\n- `sd3.5-medium` requires 3.5 credits per generation\n- As of the April 17, 2025, `sd3-large`, `sd3-large-turbo` and `sd3-medium`\n\n\n\n are re-routed to their `sd3.5-[model version]` equivalent, at the same price.', - ) - negative_prompt: Optional[str] = Field( - None, - description='Keywords of what you **do not** wish to see in the output image.\nThis is an advanced feature.', - max_length=10000, - ) - output_format: Optional[OutputFormat3] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - prompt: str = Field( - ..., - description='What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.', - max_length=10000, - min_length=1, - ) - seed: Optional[float] = Field( - 0, - description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", - ge=0.0, - le=4294967294.0, - ) - strength: Optional[float] = Field( - None, - description='Sometimes referred to as _denoising_, this parameter controls how much influence the\n`image` parameter has on the generated image. A value of 0 would yield an image that\nis identical to the input. A value of 1 would be as if you passed in no image at all.\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', - ge=0.0, - le=1.0, - ) - style_preset: Optional[StylePreset] = Field( - None, description='Guides the image model towards a particular style.' - ) - - -class FinishReason(str, Enum): - SUCCESS = 'SUCCESS' - CONTENT_FILTERED = 'CONTENT_FILTERED' - - -class StabilityImageGenrationSD3Response200(BaseModel): - finish_reason: FinishReason = Field( - ..., - description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', - examples=['SUCCESS'], - ) - image: str = Field( - ..., - description='The generated image, encoded to base64.', - examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], - ) - seed: Optional[float] = Field( - 0, - description='The seed used as random noise for this generation.', - examples=[343940597], - ge=0.0, - le=4294967294.0, - ) - - -class StabilityImageGenrationSD3Response400(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationSD3Response413(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationSD3Response422(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationSD3Response429(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationSD3Response500(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class OutputFormat4(str, Enum): - jpeg = 'jpeg' - png = 'png' - webp = 'webp' - - -class StabilityImageGenrationUpscaleConservativeRequest(BaseModel): - creativity: Optional[StabilityCreativity] = Field( - default_factory=lambda: StabilityCreativity.model_validate(0.35) - ) - image: StrictBytes = Field( - ..., - description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 9,437,184 pixels\n- The aspect ratio must be between 1:2.5 and 2.5:1', - examples=['./some/image.png'], - ) - negative_prompt: Optional[str] = Field( - None, - description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', - max_length=10000, - ) - output_format: Optional[OutputFormat4] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - prompt: str = Field( - ..., - description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", - max_length=10000, - min_length=1, - ) - seed: Optional[float] = Field( - 0, - description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", - ge=0.0, - le=4294967294.0, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse200(BaseModel): - finish_reason: FinishReason = Field( - ..., - description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', - examples=['SUCCESS'], - ) - image: str = Field( - ..., - description='The generated image, encoded to base64.', - examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], - ) - seed: Optional[float] = Field( - 0, - description='The seed used as random noise for this generation.', - examples=[343940597], - ge=0.0, - le=4294967294.0, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse400(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse413(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse422(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse429(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleConservativeResponse500(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeRequest(BaseModel): - creativity: Optional[float] = Field( - 0.3, - description='Indicates how creative the model should be when upscaling an image.\nHigher values will result in more details being added to the image during upscaling.', - ge=0.1, - le=0.5, - ) - image: StrictBytes = Field( - ..., - description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 1,048,576 pixels', - examples=['./some/image.png'], - ) - negative_prompt: Optional[str] = Field( - None, - description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', - max_length=10000, - ) - output_format: Optional[OutputFormat4] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - prompt: str = Field( - ..., - description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", - max_length=10000, - min_length=1, - ) - seed: Optional[float] = Field( - 0, - description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", - ge=0.0, - le=4294967294.0, - ) - style_preset: Optional[StylePreset] = Field( - None, description='Guides the image model towards a particular style.' - ) - - -class StabilityImageGenrationUpscaleCreativeResponse200(BaseModel): - id: StabilityGenerationID - - -class StabilityImageGenrationUpscaleCreativeResponse400(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse413(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse422(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse429(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleCreativeResponse500(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastRequest(BaseModel): - image: StrictBytes = Field( - ..., - description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Width must be between 32 and 1,536 pixels\n- Height must be between 32 and 1,536 pixels\n- Total pixel count must be between 1,024 and 1,048,576 pixels', - examples=['./some/image.png'], - ) - output_format: Optional[OutputFormat4] = Field( - 'png', description='Dictates the `content-type` of the generated image.' - ) - - -class StabilityImageGenrationUpscaleFastResponse200(BaseModel): - finish_reason: FinishReason = Field( - ..., - description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', - examples=['SUCCESS'], - ) - image: str = Field( - ..., - description='The generated image, encoded to base64.', - examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], - ) - seed: Optional[float] = Field( - 0, - description='The seed used as random noise for this generation.', - examples=[343940597], - ge=0.0, - le=4294967294.0, - ) - - -class StabilityImageGenrationUpscaleFastResponse400(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse413(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse422(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse429(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityImageGenrationUpscaleFastResponse500(BaseModel): - errors: List[str] = Field( - ..., - description='One or more error messages indicating what went wrong.', - examples=[['some-field: is required']], - min_length=1, - ) - id: str = Field( - ..., - description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', - examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], - min_length=1, - ) - name: str = Field( - ..., - description='Short-hand name for an error, useful for discriminating between errors with the same status code.', - examples=['bad_request'], - min_length=1, - ) - - -class StabilityStabilityClientID(RootModel[str]): - root: str = Field( - ..., - description='The name of your application, used to help us communicate app-specific debugging or moderation issues to you.', - examples=['my-awesome-app'], - max_length=256, - ) - - -class StabilityStabilityClientUserID(RootModel[str]): - root: str = Field( - ..., - description='A unique identifier for your end user. Used to help us communicate user-specific debugging or moderation issues to you. Feel free to obfuscate this value to protect user privacy.', - examples=['DiscordUser#9999'], - max_length=256, - ) - - -class StabilityStabilityClientVersion(RootModel[str]): - root: str = Field( - ..., - description='The version of your application, used to help us communicate version-specific debugging or moderation issues to you.', - examples=['1.2.1'], - max_length=256, - ) - - -class StorageFile(BaseModel): - file_path: Optional[str] = Field(None, description='Path to the file in storage') - id: Optional[UUID] = Field( - None, description='Unique identifier for the storage file' - ) - public_url: Optional[str] = Field(None, description='Public URL') - - -class StripeAddress(BaseModel): - city: Optional[str] = None - country: Optional[str] = None - line1: Optional[str] = None - line2: Optional[str] = None - postal_code: Optional[str] = None - state: Optional[str] = None - - -class StripeAmountDetails(BaseModel): - tip: Optional[Dict[str, Any]] = None - - -class StripeBillingDetails(BaseModel): - address: Optional[StripeAddress] = None - email: Optional[str] = None - name: Optional[str] = None - phone: Optional[str] = None - tax_id: Optional[Any] = None - - -class Checks(BaseModel): - address_line1_check: Optional[Any] = None - address_postal_code_check: Optional[Any] = None - cvc_check: Optional[str] = None - - -class ExtendedAuthorization(BaseModel): - status: Optional[str] = None - - -class IncrementalAuthorization(BaseModel): - status: Optional[str] = None - - -class Multicapture(BaseModel): - status: Optional[str] = None - - -class NetworkToken(BaseModel): - used: Optional[bool] = None - - -class Overcapture(BaseModel): - maximum_amount_capturable: Optional[int] = None - status: Optional[str] = None - - -class StripeCardDetails(BaseModel): - amount_authorized: Optional[int] = None - authorization_code: Optional[Any] = None - brand: Optional[str] = None - checks: Optional[Checks] = None - country: Optional[str] = None - exp_month: Optional[int] = None - exp_year: Optional[int] = None - extended_authorization: Optional[ExtendedAuthorization] = None - fingerprint: Optional[str] = None - funding: Optional[str] = None - incremental_authorization: Optional[IncrementalAuthorization] = None - installments: Optional[Any] = None - last4: Optional[str] = None - mandate: Optional[Any] = None - multicapture: Optional[Multicapture] = None - network: Optional[str] = None - network_token: Optional[NetworkToken] = None - network_transaction_id: Optional[str] = None - overcapture: Optional[Overcapture] = None - regulated_status: Optional[str] = None - three_d_secure: Optional[Any] = None - wallet: Optional[Any] = None - - -class Object1(str, Enum): - charge = 'charge' - - -class Object2(str, Enum): - event = 'event' - - -class Type31(str, Enum): - payment_intent_succeeded = 'payment_intent.succeeded' - - -class StripeOutcome(BaseModel): - advice_code: Optional[Any] = None - network_advice_code: Optional[Any] = None - network_decline_code: Optional[Any] = None - network_status: Optional[str] = None - reason: Optional[Any] = None - risk_level: Optional[str] = None - risk_score: Optional[int] = None - seller_message: Optional[str] = None - type: Optional[str] = None - - -class Object3(str, Enum): - payment_intent = 'payment_intent' - - -class StripePaymentMethodDetails(BaseModel): - card: Optional[StripeCardDetails] = None - type: Optional[str] = None - - -class Card(BaseModel): - installments: Optional[Any] = None - mandate_options: Optional[Any] = None - network: Optional[Any] = None - request_three_d_secure: Optional[str] = None - - -class StripePaymentMethodOptions(BaseModel): - card: Optional[Card] = None - - -class StripeRefundList(BaseModel): - data: Optional[List[Dict[str, Any]]] = None - has_more: Optional[bool] = None - object: Optional[str] = None - total_count: Optional[int] = None - url: Optional[str] = None - - -class StripeRequestInfo(BaseModel): - id: Optional[str] = None - idempotency_key: Optional[str] = None - - -class StripeShipping(BaseModel): - address: Optional[StripeAddress] = None - carrier: Optional[str] = None - name: Optional[str] = None - phone: Optional[str] = None - tracking_number: Optional[str] = None - - -class Type32(str, Enum): - json_schema = 'json_schema' - - -class TextResponseFormatJsonSchema(BaseModel): - description: Optional[str] = Field( - None, - description='A description of what the response format is for, used by the model to\ndetermine how to respond in the format.\n', - ) - name: str = Field( - ..., - description='The name of the response format. Must be a-z, A-Z, 0-9, or contain\nunderscores and dashes, with a maximum length of 64.\n', - ) - schema_: ResponseFormatJsonSchemaSchema = Field(..., alias='schema') - strict: Optional[bool] = Field( - False, - description='Whether to enable strict schema adherence when generating the output.\nIf set to true, the model will always follow the exact schema defined\nin the `schema` field. Only a subset of JSON Schema is supported when\n`strict` is `true`. To learn more, read the [Structured Outputs\nguide](/docs/guides/structured-outputs).\n', - ) - type: Type32 = Field( - ..., - description='The type of response format being defined. Always `json_schema`.', - ) - - -class Type33(str, Enum): - function = 'function' - - -class ToolChoiceFunction(BaseModel): - name: str = Field(..., description='The name of the function to call.') - type: Type33 = Field( - ..., description='For function calling, the type is always `function`.' - ) - - -class ToolChoiceOptions(str, Enum): - none = 'none' - auto = 'auto' - required = 'required' - - -class Type34(str, Enum): - file_search = 'file_search' - web_search_preview = 'web_search_preview' - computer_use_preview = 'computer_use_preview' - web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' - - -class ToolChoiceTypes(BaseModel): - type: Type34 = Field( - ..., - description='The type of hosted tool the model should to use. Learn more about\n[built-in tools](/docs/guides/tools).\n\nAllowed values are:\n- `file_search`\n- `web_search_preview`\n- `computer_use_preview`\n', - ) - - -class TripoAnimation(str, Enum): - preset_idle = 'preset:idle' - preset_walk = 'preset:walk' - preset_climb = 'preset:climb' - preset_jump = 'preset:jump' - preset_run = 'preset:run' - preset_slash = 'preset:slash' - preset_shoot = 'preset:shoot' - preset_hurt = 'preset:hurt' - preset_fall = 'preset:fall' - preset_turn = 'preset:turn' - - -class TripoBalance(BaseModel): - balance: float - frozen: float - - -class TripoConvertFormat(str, Enum): - GLTF = 'GLTF' - USDZ = 'USDZ' - FBX = 'FBX' - OBJ = 'OBJ' - STL = 'STL' - field_3MF = '3MF' - - -class Code(int, Enum): - integer_1001 = 1001 - integer_2000 = 2000 - integer_2001 = 2001 - integer_2002 = 2002 - integer_2003 = 2003 - integer_2004 = 2004 - integer_2006 = 2006 - integer_2007 = 2007 - integer_2008 = 2008 - integer_2010 = 2010 - - -class TripoErrorResponse(BaseModel): - code: Code - message: str - suggestion: str - - -class TripoImageToModel(str, Enum): - image_to_model = 'image_to_model' - - -class TripoModelStyle(str, Enum): - person_person2cartoon = 'person:person2cartoon' - animal_venom = 'animal:venom' - object_clay = 'object:clay' - object_steampunk = 'object:steampunk' - object_christmas = 'object:christmas' - object_barbie = 'object:barbie' - gold = 'gold' - ancient_bronze = 'ancient_bronze' - - -class TripoModelVersion(str, Enum): - v2_5_20250123 = 'v2.5-20250123' - v2_0_20240919 = 'v2.0-20240919' - v1_4_20240625 = 'v1.4-20240625' - - -class TripoMultiviewMode(str, Enum): - LEFT = 'LEFT' - RIGHT = 'RIGHT' - - -class TripoMultiviewToModel(str, Enum): - multiview_to_model = 'multiview_to_model' - - -class TripoOrientation(str, Enum): - align_image = 'align_image' - default = 'default' - - -class TripoResponseSuccessCode(RootModel[int]): - root: int = Field( - ..., - description='Standard success code for Tripo API responses. Typically 0 for success.', - examples=[0], - ) - - -class TripoSpec(str, Enum): - mixamo = 'mixamo' - tripo = 'tripo' - - -class TripoStandardFormat(str, Enum): - glb = 'glb' - fbx = 'fbx' - - -class TripoStylizeOptions(str, Enum): - lego = 'lego' - voxel = 'voxel' - voronoi = 'voronoi' - minecraft = 'minecraft' - - -class Code1(int, Enum): - integer_0 = 0 - - -class Data9(BaseModel): - task_id: str = Field(..., description='used for getTask') - - -class TripoSuccessTask(BaseModel): - code: Code1 - data: Data9 - - -class Topology(str, Enum): - bip = 'bip' - quad = 'quad' - - -class Output(BaseModel): - base_model: Optional[str] = None - model: Optional[str] = None - pbr_model: Optional[str] = None - rendered_image: Optional[str] = None - riggable: Optional[bool] = None - topology: Optional[Topology] = None - - -class Status11(str, Enum): - queued = 'queued' - running = 'running' - success = 'success' - failed = 'failed' - cancelled = 'cancelled' - unknown = 'unknown' - banned = 'banned' - expired = 'expired' - - -class TripoTask(BaseModel): - create_time: int - input: Dict[str, Any] - output: Output - progress: int = Field(..., ge=0, le=100) - status: Status11 - task_id: str - type: str - - -class TripoTextToModel(str, Enum): - text_to_model = 'text_to_model' - - -class TripoTextureAlignment(str, Enum): - original_image = 'original_image' - geometry = 'geometry' - - -class TripoTextureFormat(str, Enum): - BMP = 'BMP' - DPX = 'DPX' - HDR = 'HDR' - JPEG = 'JPEG' - OPEN_EXR = 'OPEN_EXR' - PNG = 'PNG' - TARGA = 'TARGA' - TIFF = 'TIFF' - WEBP = 'WEBP' - - -class TripoTextureQuality(str, Enum): - standard = 'standard' - detailed = 'detailed' - - -class TripoTopology(str, Enum): - bip = 'bip' - quad = 'quad' - - -class TripoTypeAnimatePrerigcheck(str, Enum): - animate_prerigcheck = 'animate_prerigcheck' - - -class TripoTypeAnimateRetarget(str, Enum): - animate_retarget = 'animate_retarget' - - -class TripoTypeAnimateRig(str, Enum): - animate_rig = 'animate_rig' - - -class TripoTypeConvertModel(str, Enum): - convert_model = 'convert_model' - - -class TripoTypeRefineModel(str, Enum): - refine_model = 'refine_model' - - -class TripoTypeStylizeModel(str, Enum): - stylize_model = 'stylize_model' - - -class TripoTypeTextureModel(str, Enum): - texture_model = 'texture_model' - - -class User(BaseModel): - email: Optional[str] = Field(None, description='The email address for this user.') - id: Optional[str] = Field(None, description='The unique id for this user.') - isAdmin: Optional[bool] = Field( - None, description='Indicates if the user has admin privileges.' - ) - isApproved: Optional[bool] = Field( - None, description='Indicates if the user is approved.' - ) - name: Optional[str] = Field(None, description='The name for this user.') - - -class Veo2GenVidPollRequest(BaseModel): - operationName: str = Field( - ..., - description='Full operation name (from predict response)', - examples=[ - 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' - ], - ) - - -class Error1(BaseModel): - code: Optional[int] = Field(None, description='Error code') - message: Optional[str] = Field(None, description='Error message') - - -class Video(BaseModel): - bytesBase64Encoded: Optional[str] = Field( - None, description='Base64-encoded video content' - ) - gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') - mimeType: Optional[str] = Field(None, description='Video MIME type') - - -class Response(BaseModel): - field_type: Optional[str] = Field( - None, - alias='@type', - examples=[ - 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' - ], - ) - raiMediaFilteredCount: Optional[int] = Field( - None, description='Count of media filtered by responsible AI policies' - ) - raiMediaFilteredReasons: Optional[List[str]] = Field( - None, description='Reasons why media was filtered by responsible AI policies' - ) - videos: Optional[List[Video]] = None - - -class Veo2GenVidPollResponse(BaseModel): - done: Optional[bool] = None - error: Optional[Error1] = Field( - None, description='Error details if operation failed' - ) - name: Optional[str] = None - response: Optional[Response] = Field( - None, description='The actual prediction response if done is true' - ) - - -class Image(BaseModel): - bytesBase64Encoded: str - gcsUri: Optional[str] = None - mimeType: Optional[str] = None - - -class Image1(BaseModel): - bytesBase64Encoded: Optional[str] = None - gcsUri: str - mimeType: Optional[str] = None - - -class Instance(BaseModel): - image: Optional[Union[Image, Image1]] = Field( - None, description='Optional image to guide video generation' - ) - prompt: str = Field(..., description='Text description of the video') - - -class PersonGeneration1(str, Enum): - ALLOW = 'ALLOW' - BLOCK = 'BLOCK' - - -class Parameters(BaseModel): - aspectRatio: Optional[str] = Field(None, examples=['16:9']) - durationSeconds: Optional[int] = None - enhancePrompt: Optional[bool] = None - negativePrompt: Optional[str] = None - personGeneration: Optional[PersonGeneration1] = None - sampleCount: Optional[int] = None - seed: Optional[int] = None - storageUri: Optional[str] = Field( - None, description='Optional Cloud Storage URI to upload the video' - ) - - -class Veo2GenVidRequest(BaseModel): - instances: Optional[List[Instance]] = None - parameters: Optional[Parameters] = None - - -class Veo2GenVidResponse(BaseModel): - name: str = Field( - ..., - description='Operation resource name', - examples=[ - 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' - ], - ) - - -class VeoGenVidPollRequest(BaseModel): - operationName: str = Field( - ..., - description='Full operation name (from predict response)', - examples=[ - 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' - ], - ) - - -class Response1(BaseModel): - field_type: Optional[str] = Field( - None, - alias='@type', - examples=[ - 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' - ], - ) - raiMediaFilteredCount: Optional[int] = Field( - None, description='Count of media filtered by responsible AI policies' - ) - raiMediaFilteredReasons: Optional[List[str]] = Field( - None, description='Reasons why media was filtered by responsible AI policies' - ) - videos: Optional[List[Video]] = None - - -class VeoGenVidPollResponse(BaseModel): - done: Optional[bool] = None - error: Optional[Error1] = Field( - None, description='Error details if operation failed' - ) - name: Optional[str] = None - response: Optional[Response1] = Field( - None, description='The actual prediction response if done is true' - ) - - -class Image2(BaseModel): - bytesBase64Encoded: str - gcsUri: Optional[str] = None - mimeType: Optional[str] = None - - -class Image3(BaseModel): - bytesBase64Encoded: Optional[str] = None - gcsUri: str - mimeType: Optional[str] = None - - -class Instance1(BaseModel): - image: Optional[Union[Image2, Image3]] = Field( - None, description='Optional image to guide video generation' - ) - prompt: str = Field(..., description='Text description of the video') - - -class Parameters1(BaseModel): - aspectRatio: Optional[str] = Field(None, examples=['16:9']) - durationSeconds: Optional[int] = None - enhancePrompt: Optional[bool] = None - generateAudio: Optional[bool] = Field( - None, - description='Generate audio for the video. Only supported by veo 3 models.', - ) - negativePrompt: Optional[str] = None - personGeneration: Optional[PersonGeneration1] = None - sampleCount: Optional[int] = None - seed: Optional[int] = None - storageUri: Optional[str] = Field( - None, description='Optional Cloud Storage URI to upload the video' - ) - - -class VeoGenVidRequest(BaseModel): - instances: Optional[List[Instance1]] = None - parameters: Optional[Parameters1] = None - - -class VeoGenVidResponse(BaseModel): - name: str = Field( - ..., - description='Operation resource name', - examples=[ - 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' - ], - ) - - -class SearchContextSize(str, Enum): - low = 'low' - medium = 'medium' - high = 'high' - - -class Type35(str, Enum): - web_search_preview = 'web_search_preview' - web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' - - -class WebSearchPreviewTool(BaseModel): - search_context_size: Optional[SearchContextSize] = Field( - None, - description='High level guidance for the amount of context window space to use for the search. One of `low`, `medium`, or `high`. `medium` is the default.', - ) - type: Literal['WebSearchPreviewTool'] = Field( - ..., - description='The type of the web search tool. One of `web_search_preview` or `web_search_preview_2025_03_11`.', - ) - - -class Status12(str, Enum): - in_progress = 'in_progress' - searching = 'searching' - completed = 'completed' - failed = 'failed' - - -class Type36(str, Enum): - web_search_call = 'web_search_call' - - -class WebSearchToolCall(BaseModel): - id: str = Field(..., description='The unique ID of the web search tool call.\n') - status: Status12 = Field( - ..., description='The status of the web search tool call.\n' - ) - type: Type36 = Field( - ..., - description='The type of the web search tool call. Always `web_search_call`.\n', - ) - - -class WorkflowRunStatus(str, Enum): - WorkflowRunStatusStarted = 'WorkflowRunStatusStarted' - WorkflowRunStatusFailed = 'WorkflowRunStatusFailed' - WorkflowRunStatusCompleted = 'WorkflowRunStatusCompleted' - - -class ActionJobResult(BaseModel): - action_job_id: Optional[str] = Field( - None, description='Identifier of the job this result belongs to' - ) - action_run_id: Optional[str] = Field( - None, description='Identifier of the run this result belongs to' - ) - author: Optional[str] = Field(None, description='The author of the commit') - avg_vram: Optional[int] = Field( - None, description='The average VRAM used by the job' - ) - branch_name: Optional[str] = Field( - None, description='Name of the relevant git branch' - ) - comfy_run_flags: Optional[str] = Field( - None, description='The comfy run flags. E.g. `--low-vram`' - ) - commit_hash: Optional[str] = Field(None, description='The hash of the commit') - commit_id: Optional[str] = Field(None, description='The ID of the commit') - commit_message: Optional[str] = Field(None, description='The message of the commit') - commit_time: Optional[int] = Field( - None, description='The Unix timestamp when the commit was made' - ) - cuda_version: Optional[str] = Field(None, description='CUDA version used') - end_time: Optional[int] = Field( - None, description='The end time of the job as a Unix timestamp.' - ) - git_repo: Optional[str] = Field(None, description='The repository name') - id: Optional[UUID] = Field(None, description='Unique identifier for the job result') - job_trigger_user: Optional[str] = Field( - None, description='The user who triggered the job.' - ) - machine_stats: Optional[MachineStats] = None - operating_system: Optional[str] = Field(None, description='Operating system used') - peak_vram: Optional[int] = Field(None, description='The peak VRAM used by the job') - pr_number: Optional[str] = Field(None, description='The pull request number') - python_version: Optional[str] = Field(None, description='PyTorch version used') - pytorch_version: Optional[str] = Field(None, description='PyTorch version used') - start_time: Optional[int] = Field( - None, description='The start time of the job as a Unix timestamp.' - ) - status: Optional[WorkflowRunStatus] = None - storage_file: Optional[StorageFile] = None - workflow_name: Optional[str] = Field(None, description='Name of the workflow') - - -class BFLCannyInputs(BaseModel): - canny_high_threshold: Optional[CannyHighThreshold] = Field( - default_factory=lambda: CannyHighThreshold.model_validate(200), - description='High threshold for Canny edge detection', - title='Canny High Threshold', - ) - canny_low_threshold: Optional[CannyLowThreshold] = Field( - default_factory=lambda: CannyLowThreshold.model_validate(50), - description='Low threshold for Canny edge detection', - title='Canny Low Threshold', - ) - control_image: Optional[str] = Field( - None, - description='Base64 encoded image to use as control input if no preprocessed image is provided', - title='Control Image', - ) - guidance: Optional[Guidance] = Field( - default_factory=lambda: Guidance.model_validate(30), - description='Guidance strength for the image generation process', - title='Guidance', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - preprocessed_image: Optional[str] = Field( - None, - description='Optional pre-processed image that will bypass the control preprocessing step', - title='Preprocessed Image', - ) - prompt: str = Field( - ..., - description='Text prompt for image generation', - examples=['ein fantastisches bild'], - title='Prompt', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt', - title='Prompt Upsampling', - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - ge=0, - le=6, - title='Safety Tolerance', - ) - seed: Optional[int] = Field( - None, - description='Optional seed for reproducibility', - examples=[42], - title='Seed', - ) - steps: Optional[Steps] = Field( - default_factory=lambda: Steps.model_validate(50), - description='Number of steps for the image generation process', - title='Steps', - ) - webhook_secret: Optional[str] = Field( - None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - - -class BFLDepthInputs(BaseModel): - control_image: Optional[str] = Field( - None, - description='Base64 encoded image to use as control input', - title='Control Image', - ) - guidance: Optional[Guidance] = Field( - default_factory=lambda: Guidance.model_validate(15), - description='Guidance strength for the image generation process', - title='Guidance', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - preprocessed_image: Optional[str] = Field( - None, - description='Optional pre-processed image that will bypass the control preprocessing step', - title='Preprocessed Image', - ) - prompt: str = Field( - ..., - description='Text prompt for image generation', - examples=['ein fantastisches bild'], - title='Prompt', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt', - title='Prompt Upsampling', - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - ge=0, - le=6, - title='Safety Tolerance', - ) - seed: Optional[int] = Field( - None, - description='Optional seed for reproducibility', - examples=[42], - title='Seed', - ) - steps: Optional[Steps] = Field( - default_factory=lambda: Steps.model_validate(50), - description='Number of steps for the image generation process', - title='Steps', - ) - webhook_secret: Optional[str] = Field( - None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - - -class BFLFluxProExpandInputs(BaseModel): - bottom: Optional[Bottom] = Field( - 0, - description='Number of pixels to expand at the bottom of the image', - title='Bottom', - ) - guidance: Optional[Guidance2] = Field( - default_factory=lambda: Guidance2.model_validate(60), - description='Guidance strength for the image generation process', - title='Guidance', - ) - image: str = Field( - ..., - description='A Base64-encoded string representing the image you wish to expand.', - title='Image', - ) - left: Optional[Left] = Field( - 0, - description='Number of pixels to expand on the left side of the image', - title='Left', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - prompt: Optional[str] = Field( - '', - description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.', - examples=['ein fantastisches bild'], - title='Prompt', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', - title='Prompt Upsampling', - ) - right: Optional[Right] = Field( - 0, - description='Number of pixels to expand on the right side of the image', - title='Right', - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - examples=[2], - ge=0, - le=6, - title='Safety Tolerance', - ) - seed: Optional[int] = Field( - None, description='Optional seed for reproducibility', title='Seed' - ) - steps: Optional[Steps2] = Field( - default_factory=lambda: Steps2.model_validate(50), - description='Number of steps for the image generation process', - examples=[50], - title='Steps', - ) - top: Optional[Top] = Field( - 0, description='Number of pixels to expand at the top of the image', title='Top' - ) - webhook_secret: Optional[str] = Field( - None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - - -class BFLFluxProFillInputs(BaseModel): - guidance: Optional[Guidance2] = Field( - default_factory=lambda: Guidance2.model_validate(60), - description='Guidance strength for the image generation process', - title='Guidance', - ) - image: str = Field( - ..., - description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.', - title='Image', - ) - mask: Optional[str] = Field( - None, - description='A Base64-encoded string representing a mask for the areas you want to modify in the image. The mask should be the same dimensions as the image and in black and white. Black areas (0%) indicate no modification, while white areas (100%) specify areas for inpainting. Optional if you provide an alpha mask in the original image. Validation: The endpoint verifies that the dimensions of the mask match the original image.', - title='Mask', - ) - output_format: Optional[BFLOutputFormat] = Field( - 'jpeg', - description="Output format for the generated image. Can be 'jpeg' or 'png'.", - ) - prompt: Optional[str] = Field( - '', - description='The description of the changes you want to make. This text guides the inpainting process, allowing you to specify features, styles, or modifications for the masked area.', - examples=['ein fantastisches bild'], - title='Prompt', - ) - prompt_upsampling: Optional[bool] = Field( - False, - description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', - title='Prompt Upsampling', - ) - safety_tolerance: Optional[int] = Field( - 2, - description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', - examples=[2], - ge=0, - le=6, - title='Safety Tolerance', - ) - seed: Optional[int] = Field( - None, description='Optional seed for reproducibility', title='Seed' - ) - steps: Optional[Steps2] = Field( - default_factory=lambda: Steps2.model_validate(50), - description='Number of steps for the image generation process', - examples=[50], - title='Steps', - ) - webhook_secret: Optional[str] = Field( - None, - description='Optional secret for webhook signature verification', - title='Webhook Secret', - ) - webhook_url: Optional[WebhookUrl] = Field( - None, description='URL to receive webhook notifications', title='Webhook Url' - ) - - -class BFLHTTPValidationError(BaseModel): - detail: Optional[List[BFLValidationError]] = Field(None, title='Detail') - - -class BulkNodeVersionsRequest(BaseModel): - node_versions: List[NodeVersionIdentifier] = Field( - ..., description='List of node ID and version pairs to retrieve' - ) - - -CreateModelResponseProperties = ModelResponseProperties - - -class GeminiInlineData(BaseModel): - data: Optional[str] = Field( - None, - description='The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB\n', - ) - mimeType: Optional[GeminiMimeType] = None - - -class GeminiPart(BaseModel): - inlineData: Optional[GeminiInlineData] = None - text: Optional[str] = Field( - None, - description='A text prompt or code snippet.', - examples=['Write a story about a robot learning to paint'], - ) - - -class GeminiPromptFeedback(BaseModel): - blockReason: Optional[str] = None - blockReasonMessage: Optional[str] = None - safetyRatings: Optional[List[GeminiSafetyRating]] = None - - -class GeminiSafetySetting(BaseModel): - category: GeminiSafetyCategory - threshold: GeminiSafetyThreshold - - -class GeminiSystemInstructionContent(BaseModel): - parts: List[GeminiTextPart] = Field( - ..., - description='A list of ordered parts that make up a single message. Different parts may have different IANA MIME types. For limits on the inputs, such as the maximum number of tokens or the number of images, see the model specifications on the Google models page.\n', - ) - role: Role1 = Field( - ..., - description='The identity of the entity that creates the message. The following values are supported: user: This indicates that the message is sent by a real person, typically a user-generated message. model: This indicates that the message is generated by the model. The model value is used to insert messages from the model into the conversation during multi-turn conversations. For non-multi-turn conversations, this field can be left blank or unset.\n', - examples=['user'], - ) - - -class GeminiUsageMetadata(BaseModel): - cachedContentTokenCount: Optional[int] = Field( - None, - description='Output only. Number of tokens in the cached part in the input (the cached content).', - ) - candidatesTokenCount: Optional[int] = Field( - None, description='Number of tokens in the response(s).' - ) - candidatesTokensDetails: Optional[List[ModalityTokenCount]] = Field( - None, description='Breakdown of candidate tokens by modality.' - ) - promptTokenCount: Optional[int] = Field( - None, - description='Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.', - ) - promptTokensDetails: Optional[List[ModalityTokenCount]] = Field( - None, description='Breakdown of prompt tokens by modality.' - ) - thoughtsTokenCount: Optional[int] = Field( - None, description='Number of tokens present in thoughts output.' - ) - toolUsePromptTokenCount: Optional[int] = Field( - None, description='Number of tokens present in tool-use prompt(s).' - ) - - -class GithubInstallation(BaseModel): - access_tokens_url: str = Field(..., description='The API URL for access tokens') - account: GithubUser - app_id: int = Field(..., description='The GitHub App ID') - created_at: datetime = Field(..., description='When the installation was created') - events: List[str] = Field( - ..., description='The events the installation subscribes to' - ) - html_url: str = Field(..., description='The HTML URL of the installation') - id: int = Field(..., description='The installation ID') - permissions: Dict[str, Any] = Field(..., description='The installation permissions') - repositories_url: str = Field(..., description='The API URL for repositories') - repository_selection: RepositorySelection = Field( - ..., description='Repository selection for the installation' - ) - single_file_name: Optional[str] = Field( - None, description='The single file name if applicable' - ) - target_id: int = Field(..., description='The target ID') - target_type: str = Field(..., description='The target type') - updated_at: datetime = Field( - ..., description='When the installation was last updated' - ) - - -class GithubReleaseAsset(BaseModel): - browser_download_url: str = Field(..., description='The browser download URL') - content_type: str = Field(..., description='The content type of the asset') - created_at: datetime = Field(..., description='When the asset was created') - download_count: int = Field(..., description='The number of downloads') - id: int = Field(..., description='The asset ID') - label: Optional[str] = Field(None, description='The label of the asset') - name: str = Field(..., description='The name of the asset') - node_id: str = Field(..., description='The asset node ID') - size: int = Field(..., description='The size of the asset in bytes') - state: State = Field(..., description='The state of the asset') - updated_at: datetime = Field(..., description='When the asset was last updated') - uploader: GithubUser - - -class Release(BaseModel): - assets: List[GithubReleaseAsset] = Field(..., description='Array of release assets') - assets_url: Optional[str] = Field(None, description='The URL to the release assets') - author: GithubUser - body: Optional[str] = Field(None, description='The release notes/body') - created_at: datetime = Field(..., description='When the release was created') - draft: bool = Field(..., description='Whether the release is a draft') - html_url: str = Field(..., description='The HTML URL of the release') - id: int = Field(..., description='The ID of the release') - name: Optional[str] = Field(None, description='The name of the release') - node_id: str = Field(..., description='The node ID of the release') - prerelease: bool = Field(..., description='Whether the release is a prerelease') - published_at: Optional[datetime] = Field( - None, description='When the release was published' - ) - tag_name: str = Field(..., description='The tag name of the release') - tarball_url: str = Field(..., description='URL to the tarball') - target_commitish: str = Field( - ..., description='The branch or commit the release was created from' - ) - upload_url: Optional[str] = Field( - None, description='The URL to upload release assets' - ) - url: str = Field(..., description='The API URL of the release') - zipball_url: str = Field(..., description='URL to the zipball') - - -class GithubRepository(BaseModel): - clone_url: str = Field(..., description='The clone URL of the repository') - created_at: datetime = Field(..., description='When the repository was created') - default_branch: str = Field(..., description='The default branch of the repository') - description: Optional[str] = Field(None, description='The repository description') - fork: bool = Field(..., description='Whether the repository is a fork') - full_name: str = Field( - ..., description='The full name of the repository (owner/repo)' - ) - git_url: str = Field(..., description='The git URL of the repository') - html_url: str = Field(..., description='The HTML URL of the repository') - id: int = Field(..., description='The repository ID') - name: str = Field(..., description='The name of the repository') - node_id: str = Field(..., description='The repository node ID') - owner: GithubUser - private: bool = Field(..., description='Whether the repository is private') - pushed_at: datetime = Field( - ..., description='When the repository was last pushed to' - ) - ssh_url: str = Field(..., description='The SSH URL of the repository') - updated_at: datetime = Field( - ..., description='When the repository was last updated' - ) - url: str = Field(..., description='The API URL of the repository') - - -class IdeogramV3EditRequest(BaseModel): - color_palette: Optional[IdeogramColorPalette] = None - image: Optional[StrictBytes] = Field( - None, - description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.', - ) - magic_prompt: Optional[str] = Field( - None, - description='Determine if MagicPrompt should be used in generating the request or not.', - ) - mask: Optional[StrictBytes] = Field( - None, - description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.', - ) - num_images: Optional[int] = Field( - None, description='The number of images to generate.' - ) - prompt: str = Field( - ..., description='The prompt used to describe the edited result.' - ) - rendering_speed: RenderingSpeed - seed: Optional[int] = Field( - None, description='Random seed. Set for reproducible generation.' - ) - style_codes: Optional[List[StyleCode]] = Field( - None, - description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.', - ) - style_reference_images: Optional[List[StrictBytes]] = Field( - None, - description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.', - ) - - -class IdeogramV3Request(BaseModel): - aspect_ratio: Optional[str] = Field( - None, description='Aspect ratio in format WxH', examples=['1x3'] - ) - color_palette: Optional[ColorPalette] = None - magic_prompt: Optional[MagicPrompt2] = Field( - None, description='Whether to enable magic prompt enhancement' - ) - negative_prompt: Optional[str] = Field( - None, description='Text prompt specifying what to avoid in the generation' - ) - num_images: Optional[int] = Field( - None, description='Number of images to generate', ge=1 - ) - prompt: str = Field(..., description='The text prompt for image generation') - rendering_speed: RenderingSpeed - resolution: Optional[str] = Field( - None, description='Image resolution in format WxH', examples=['1280x800'] - ) - seed: Optional[int] = Field( - None, description='Seed value for reproducible generation' - ) - style_codes: Optional[List[StyleCode]] = Field( - None, description='Array of style codes in hexadecimal format' - ) - style_reference_images: Optional[List[str]] = Field( - None, description='Array of reference image URLs or identifiers' - ) - style_type: Optional[StyleType1] = Field( - None, description='The type of style to apply' - ) - - -class ImagenGenerateImageResponse(BaseModel): - predictions: Optional[List[ImagenImagePrediction]] = None - - -class ImagenImageGenerationParameters(BaseModel): - addWatermark: Optional[bool] = None - aspectRatio: Optional[AspectRatio] = None - enhancePrompt: Optional[bool] = None - includeRaiReason: Optional[bool] = None - includeSafetyAttributes: Optional[bool] = None - outputOptions: Optional[ImagenOutputOptions] = None - personGeneration: Optional[PersonGeneration] = None - safetySetting: Optional[SafetySetting] = None - sampleCount: Optional[int] = Field(None, ge=1, le=4) - seed: Optional[int] = None - storageUri: Optional[AnyUrl] = None - - -class InputContent( - RootModel[Union[InputTextContent, InputImageContent, InputFileContent]] -): - root: Union[InputTextContent, InputImageContent, InputFileContent] - - -class InputMessageContentList(RootModel[List[InputContent]]): - root: List[InputContent] = Field( - ..., - description='A list of one or many input items to the model, containing different content \ntypes.\n', - title='Input item content list', - ) - - -class KlingCameraControl(BaseModel): - config: Optional[KlingCameraConfig] = None - type: Optional[KlingCameraControlType] = None - - -class KlingDualCharacterEffectInput(BaseModel): - duration: KlingVideoGenDuration - images: KlingDualCharacterImages - mode: Optional[KlingVideoGenMode] = 'std' - model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1' - - -class KlingImage2VideoRequest(BaseModel): - aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback notification address. Server will notify when the task status changes.', - ) - camera_control: Optional[KlingCameraControl] = None - cfg_scale: Optional[KlingVideoGenCfgScale] = Field( - default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) - ) - duration: Optional[KlingVideoGenDuration] = '5' - dynamic_masks: Optional[List[DynamicMask]] = Field( - None, - description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.', - ) - external_task_id: Optional[str] = Field( - None, - description='Customized Task ID. Must be unique within a single user account.', - ) - image: Optional[str] = Field( - None, - description='Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.', - ) - image_tail: Optional[str] = Field( - None, - description='Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.', - ) - mode: Optional[KlingVideoGenMode] = 'std' - model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' - negative_prompt: Optional[str] = Field( - None, description='Negative text prompt', max_length=2500 - ) - prompt: Optional[str] = Field( - None, description='Positive text prompt', max_length=2500 - ) - static_mask: Optional[str] = Field( - None, - description='Static Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', - ) - - -class TaskResult(BaseModel): - videos: Optional[List[KlingVideoResult]] = None - - -class Data(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult] = None - task_status: Optional[KlingTaskStatus] = None - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingImage2VideoResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class TaskResult1(BaseModel): - images: Optional[List[KlingImageResult]] = None - - -class Data1(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_result: Optional[TaskResult1] = None - task_status: Optional[KlingTaskStatus] = None - task_status_msg: Optional[str] = Field(None, description='Task status information') - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingImageGenerationsResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data1] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class KlingLipSyncInputObject(BaseModel): - audio_file: Optional[str] = Field( - None, - description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.', - ) - audio_type: Optional[KlingAudioUploadType] = None - audio_url: Optional[str] = Field( - None, - description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.', - ) - mode: KlingLipSyncMode - text: Optional[str] = Field( - None, - description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.', - ) - video_id: Optional[str] = Field( - None, - description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.', - ) - video_url: Optional[str] = Field( - None, - description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.', - ) - voice_id: Optional[str] = Field( - None, - description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.', - ) - voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en' - voice_speed: Optional[float] = Field( - 1, - description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.', - ge=0.8, - le=2.0, - ) - - -class KlingLipSyncRequest(BaseModel): - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback notification address. Server will notify when the task status changes.', - ) - input: KlingLipSyncInputObject - - -class TaskResult2(BaseModel): - videos: Optional[List[KlingVideoResult]] = None - - -class Data2(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult2] = None - task_status: Optional[KlingTaskStatus] = None - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingLipSyncResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data2] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class KlingSingleImageEffectInput(BaseModel): - duration: KlingSingleImageEffectDuration - image: str = Field( - ..., - description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.', - ) - model_name: KlingSingleImageEffectModelName - - -class KlingText2VideoRequest(BaseModel): - aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' - callback_url: Optional[AnyUrl] = Field( - None, description='The callback notification address' - ) - camera_control: Optional[KlingCameraControl] = None - cfg_scale: Optional[KlingVideoGenCfgScale] = Field( - default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) - ) - duration: Optional[KlingVideoGenDuration] = '5' - external_task_id: Optional[str] = Field(None, description='Customized Task ID') - mode: Optional[KlingVideoGenMode] = 'std' - model_name: Optional[KlingTextToVideoModelName] = 'kling-v1' - negative_prompt: Optional[str] = Field( - None, description='Negative text prompt', max_length=2500 - ) - prompt: Optional[str] = Field( - None, description='Positive text prompt', max_length=2500 - ) - - -class Data4(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult2] = None - task_status: Optional[KlingTaskStatus] = None - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingText2VideoResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data4] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class KlingVideoEffectsInput( - RootModel[Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput]] -): - root: Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput] - - -class KlingVideoEffectsRequest(BaseModel): - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback notification address for the result of this task.', - ) - effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene] - external_task_id: Optional[str] = Field( - None, - description='Customized Task ID. Must be unique within a single user account.', - ) - input: KlingVideoEffectsInput - - -class Data5(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult2] = None - task_status: Optional[KlingTaskStatus] = None - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingVideoEffectsResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data5] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class KlingVideoExtendRequest(BaseModel): - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback notification address. Server will notify when the task status changes.', - ) - cfg_scale: Optional[KlingVideoGenCfgScale] = Field( - default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) - ) - negative_prompt: Optional[str] = Field( - None, - description='Negative text prompt for elements to avoid in the extended video', - max_length=2500, - ) - prompt: Optional[str] = Field( - None, - description='Positive text prompt for guiding the video extension', - max_length=2500, - ) - video_id: Optional[str] = Field( - None, - description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.', - ) - - -class Data6(BaseModel): - created_at: Optional[int] = Field(None, description='Task creation time') - task_id: Optional[str] = Field(None, description='Task ID') - task_info: Optional[TaskInfo] = None - task_result: Optional[TaskResult2] = None - task_status: Optional[KlingTaskStatus] = None - updated_at: Optional[int] = Field(None, description='Task update time') - - -class KlingVideoExtendResponse(BaseModel): - code: Optional[int] = Field(None, description='Error code') - data: Optional[Data6] = None - message: Optional[str] = Field(None, description='Error message') - request_id: Optional[str] = Field(None, description='Request ID') - - -class LumaGenerationRequest(BaseModel): - aspect_ratio: LumaAspectRatio - callback_url: Optional[AnyUrl] = Field( - None, - description='The callback URL of the generation, a POST request with Generation object will be sent to the callback URL when the generation is dreaming, completed, or failed', - ) - duration: LumaVideoModelOutputDuration - generation_type: Optional[GenerationType1] = 'video' - keyframes: Optional[LumaKeyframes] = None - loop: Optional[bool] = Field(None, description='Whether to loop the video') - model: LumaVideoModel - prompt: str = Field(..., description='The prompt of the generation') - resolution: LumaVideoModelOutputResolution - - -class CharacterRef(BaseModel): - identity0: Optional[LumaImageIdentity] = None - - -class LumaImageGenerationRequest(BaseModel): - aspect_ratio: Optional[LumaAspectRatio] = '16:9' - callback_url: Optional[AnyUrl] = Field( - None, description='The callback URL for the generation' - ) - character_ref: Optional[CharacterRef] = None - generation_type: Optional[GenerationType2] = 'image' - image_ref: Optional[List[LumaImageRef]] = None - model: Optional[LumaImageModel] = 'photon-1' - modify_image_ref: Optional[LumaModifyImageRef] = None - prompt: Optional[str] = Field(None, description='The prompt of the generation') - style_ref: Optional[List[LumaImageRef]] = None - - -class LumaUpscaleVideoGenerationRequest(BaseModel): - callback_url: Optional[AnyUrl] = Field( - None, description='The callback URL for the upscale' - ) - generation_type: Optional[GenerationType3] = 'upscale_video' - resolution: Optional[LumaVideoModelOutputResolution] = None - - -class MoonvalleyImageToVideoRequest(MoonvalleyTextToVideoRequest): - keyframes: Optional[Dict[str, Keyframes]] = None - - -class MoonvalleyResizeVideoRequest(MoonvalleyVideoToVideoRequest): - frame_position: Optional[List[int]] = Field(None, max_length=2, min_length=2) - frame_resolution: Optional[List[int]] = Field(None, max_length=2, min_length=2) - scale: Optional[List[int]] = Field(None, max_length=2, min_length=2) - - -class MoonvalleyTextToImageRequest(BaseModel): - image_url: Optional[str] = None - inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None - prompt_text: Optional[str] = None - webhook_url: Optional[str] = None - - -class NodeVersion(BaseModel): - changelog: Optional[str] = Field( - None, description='Summary of changes made in this version' - ) - comfy_node_extract_status: Optional[str] = Field( - None, description='The status of comfy node extraction process.' - ) - createdAt: Optional[datetime] = Field( - None, description='The date and time the version was created.' - ) - dependencies: Optional[List[str]] = Field( - None, description='A list of pip dependencies required by the node.' - ) - deprecated: Optional[bool] = Field( - None, description='Indicates if this version is deprecated.' - ) - downloadUrl: Optional[str] = Field( - None, description='[Output Only] URL to download this version of the node' - ) - id: Optional[str] = None - node_id: Optional[str] = Field( - None, description='The unique identifier of the node.' - ) - status: Optional[NodeVersionStatus] = None - status_reason: Optional[str] = Field( - None, description='The reason for the status change.' - ) - supported_accelerators: Optional[List[str]] = Field( - None, - description='List of accelerators (e.g. CUDA, DirectML, ROCm) that this node supports', - ) - supported_comfyui_frontend_version: Optional[str] = Field( - None, description='Supported versions of ComfyUI frontend' - ) - supported_comfyui_version: Optional[str] = Field( - None, description='Supported versions of ComfyUI' - ) - supported_os: Optional[List[str]] = Field( - None, description='List of operating systems that this node supports' - ) - version: Optional[str] = Field( - None, - description='The version identifier, following semantic versioning. Must be unique for the node.', - ) - - -class OutputContent(RootModel[Union[OutputTextContent, OutputAudioContent]]): - root: Union[OutputTextContent, OutputAudioContent] - - -class OutputMessage(BaseModel): - content: List[OutputContent] = Field(..., description='The content of the message') - role: Role4 = Field(..., description='The role of the message') - type: Type15 = Field(..., description='The type of output item') - - -class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): - duration: Optional[PikaDurationEnum] = 5 - image: Optional[StrictBytes] = Field(None, title='Image') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - promptText: Optional[str] = Field(None, title='Prompttext') - resolution: Optional[PikaResolutionEnum] = '1080p' - seed: Optional[int] = Field(None, title='Seed') - - -class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): - duration: Optional[int] = Field(None, ge=5, le=10, title='Duration') - keyFrames: Optional[List[StrictBytes]] = Field( - None, description='Array of keyframe images', title='Keyframes' - ) - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - promptText: str = Field(..., title='Prompttext') - resolution: Optional[PikaResolutionEnum] = '1080p' - seed: Optional[int] = Field(None, title='Seed') - - -class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): - aspectRatio: Optional[float] = Field( - 1.7777777777777777, - description='Aspect ratio (width / height)', - ge=0.4, - le=2.5, - title='Aspectratio', - ) - duration: Optional[PikaDurationEnum] = 5 - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - promptText: str = Field(..., title='Prompttext') - resolution: Optional[PikaResolutionEnum] = '1080p' - seed: Optional[int] = Field(None, title='Seed') - - -class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): - image: Optional[StrictBytes] = Field(None, title='Image') - negativePrompt: Optional[str] = Field(None, title='Negativeprompt') - pikaffect: Optional[Pikaffect] = None - promptText: Optional[str] = Field(None, title='Prompttext') - seed: Optional[int] = Field(None, title='Seed') - - -class PikaHTTPValidationError(BaseModel): - detail: Optional[List[PikaValidationError]] = Field(None, title='Detail') - - -class PublisherMember(BaseModel): - id: Optional[str] = Field( - None, description='The unique identifier for the publisher member.' - ) - role: Optional[str] = Field( - None, description='The role of the user in the publisher.' - ) - user: Optional[PublisherUser] = None - - -class Reasoning(BaseModel): - effort: Optional[ReasoningEffort] = 'medium' - generate_summary: Optional[GenerateSummary] = Field( - None, - description="**Deprecated:** use `summary` instead.\n\nA summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", - ) - summary: Optional[Summary] = Field( - None, - description="A summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", - ) - - -class RecraftImage(BaseModel): - b64_json: Optional[str] = None - features: Optional[RecraftImageFeatures] = None - image_id: UUID - revised_prompt: Optional[str] = None - url: Optional[str] = None - - -class RecraftProcessImageRequest(BaseModel): - image: StrictBytes - image_format: Optional[RecraftImageFormat] = None - response_format: Optional[RecraftResponseFormat] = None - - -class RecraftProcessImageResponse(BaseModel): - created: int - credits: int - image: RecraftImage - - -class RecraftTextLayout(RootModel[List[RecraftTextLayoutItem]]): - root: List[RecraftTextLayoutItem] - - -class RecraftTransformImageWithMaskRequest(BaseModel): - block_nsfw: Optional[bool] = None - calculate_features: Optional[bool] = None - image: StrictBytes - image_format: Optional[RecraftImageFormat] = None - mask: StrictBytes - model: Optional[RecraftTransformModel] = None - n: Optional[int] = None - negative_prompt: Optional[str] = None - prompt: str - response_format: Optional[RecraftResponseFormat] = None - style: Optional[RecraftImageStyle] = None - style_id: Optional[UUID] = None - substyle: Optional[RecraftImageSubStyle] = None - text_layout: Optional[RecraftTextLayout] = None - - -class ResponseContentPartAddedEvent(BaseModel): - content_index: int = Field( - ..., description='The index of the content part that was added.' - ) - item_id: str = Field( - ..., description='The ID of the output item that the content part was added to.' - ) - output_index: int = Field( - ..., - description='The index of the output item that the content part was added to.', - ) - part: OutputContent - type: Type20 = Field( - ..., description='The type of the event. Always `response.content_part.added`.' - ) - - -class ResponseContentPartDoneEvent(BaseModel): - content_index: int = Field( - ..., description='The index of the content part that is done.' - ) - item_id: str = Field( - ..., description='The ID of the output item that the content part was added to.' - ) - output_index: int = Field( - ..., - description='The index of the output item that the content part was added to.', - ) - part: OutputContent - type: Type21 = Field( - ..., description='The type of the event. Always `response.content_part.done`.' - ) - - -class ResponseError(BaseModel): - code: ResponseErrorCode - message: str = Field(..., description='A human-readable description of the error.') - - -class Rodin3DDownloadResponse(BaseModel): - list: Optional[List[RodinResourceItem]] = None - - -class Rodin3DGenerateRequest(BaseModel): - images: str = Field(..., description='The reference images to generate 3D Assets.') - material: Optional[RodinMaterialType] = None - mesh_mode: Optional[RodinMeshModeType] = None - quality: Optional[RodinQualityType] = None - seed: Optional[int] = Field(None, description='Seed.') - tier: Optional[RodinTierType] = None - - -class Rodin3DGenerateResponse(BaseModel): - jobs: Optional[RodinGenerateJobsData] = None - message: Optional[str] = Field(None, description='message') - prompt: Optional[str] = Field(None, description='prompt') - submit_time: Optional[str] = Field(None, description='Time') - uuid: Optional[str] = Field(None, description='Task UUID') - - -class RodinCheckStatusJobItem(BaseModel): - status: Optional[RodinStatusOptions] = None - uuid: Optional[str] = Field(None, description='sub uuid') - - -class RunwayImageToVideoRequest(BaseModel): - duration: RunwayDurationEnum - model: RunwayModelEnum - promptImage: RunwayPromptImageObject - promptText: Optional[str] = Field( - None, description='Text prompt for the generation', max_length=1000 - ) - ratio: RunwayAspectRatioEnum - seed: int = Field( - ..., description='Random seed for generation', ge=0, le=4294967295 - ) - - -class StripeCharge(BaseModel): - amount: Optional[int] = None - amount_captured: Optional[int] = None - amount_refunded: Optional[int] = None - application: Optional[str] = None - application_fee: Optional[str] = None - application_fee_amount: Optional[int] = None - balance_transaction: Optional[str] = None - billing_details: Optional[StripeBillingDetails] = None - calculated_statement_descriptor: Optional[str] = None - captured: Optional[bool] = None - created: Optional[int] = None - currency: Optional[str] = None - customer: Optional[str] = None - description: Optional[str] = None - destination: Optional[Any] = None - dispute: Optional[Any] = None - disputed: Optional[bool] = None - failure_balance_transaction: Optional[Any] = None - failure_code: Optional[Any] = None - failure_message: Optional[Any] = None - fraud_details: Optional[Dict[str, Any]] = None - id: Optional[str] = None - invoice: Optional[Any] = None - livemode: Optional[bool] = None - metadata: Optional[Dict[str, Any]] = None - object: Optional[Object1] = None - on_behalf_of: Optional[Any] = None - order: Optional[Any] = None - outcome: Optional[StripeOutcome] = None - paid: Optional[bool] = None - payment_intent: Optional[str] = None - payment_method: Optional[str] = None - payment_method_details: Optional[StripePaymentMethodDetails] = None - radar_options: Optional[Dict[str, Any]] = None - receipt_email: Optional[str] = None - receipt_number: Optional[str] = None - receipt_url: Optional[str] = None - refunded: Optional[bool] = None - refunds: Optional[StripeRefundList] = None - review: Optional[Any] = None - shipping: Optional[StripeShipping] = None - source: Optional[Any] = None - source_transfer: Optional[Any] = None - statement_descriptor: Optional[Any] = None - statement_descriptor_suffix: Optional[Any] = None - status: Optional[str] = None - transfer_data: Optional[Any] = None - transfer_group: Optional[Any] = None - - -class StripeChargeList(BaseModel): - data: Optional[List[StripeCharge]] = None - has_more: Optional[bool] = None - object: Optional[str] = None - total_count: Optional[int] = None - url: Optional[str] = None - - -class StripePaymentIntent(BaseModel): - amount: Optional[int] = None - amount_capturable: Optional[int] = None - amount_details: Optional[StripeAmountDetails] = None - amount_received: Optional[int] = None - application: Optional[str] = None - application_fee_amount: Optional[int] = None - automatic_payment_methods: Optional[Any] = None - canceled_at: Optional[int] = None - cancellation_reason: Optional[str] = None - capture_method: Optional[str] = None - charges: Optional[StripeChargeList] = None - client_secret: Optional[str] = None - confirmation_method: Optional[str] = None - created: Optional[int] = None - currency: Optional[str] = None - customer: Optional[str] = None - description: Optional[str] = None - id: Optional[str] = None - invoice: Optional[str] = None - last_payment_error: Optional[Any] = None - latest_charge: Optional[str] = None - livemode: Optional[bool] = None - metadata: Optional[Dict[str, Any]] = None - next_action: Optional[Any] = None - object: Optional[Object3] = None - on_behalf_of: Optional[Any] = None - payment_method: Optional[str] = None - payment_method_configuration_details: Optional[Any] = None - payment_method_options: Optional[StripePaymentMethodOptions] = None - payment_method_types: Optional[List[str]] = None - processing: Optional[Any] = None - receipt_email: Optional[str] = None - review: Optional[Any] = None - setup_future_usage: Optional[Any] = None - shipping: Optional[StripeShipping] = None - source: Optional[Any] = None - statement_descriptor: Optional[Any] = None - statement_descriptor_suffix: Optional[Any] = None - status: Optional[str] = None - transfer_data: Optional[Any] = None - transfer_group: Optional[Any] = None - - -class TextResponseFormatConfiguration( - RootModel[ - Union[ - ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject - ] - ] -): - root: Union[ - ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject - ] = Field( - ..., - description='An object specifying the format that the model must output.\n\nConfiguring `{ "type": "json_schema" }` enables Structured Outputs, \nwhich ensures the model will match your supplied JSON schema. Learn more in the \n[Structured Outputs guide](/docs/guides/structured-outputs).\n\nThe default format is `{ "type": "text" }` with no additional options.\n\n**Not recommended for gpt-4o and newer models:**\n\nSetting to `{ "type": "json_object" }` enables the older JSON mode, which\nensures the message the model generates is valid JSON. Using `json_schema`\nis preferred for models that support it.\n', - ) - - -class Tool( - RootModel[ - Union[ - FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool - ] - ] -): - root: Union[ - FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool - ] = Field(..., discriminator='type') - - -class BulkNodeVersionResult(BaseModel): - error_message: Optional[str] = Field( - None, - description='Error message if retrieval failed (only present if status is error)', - ) - identifier: NodeVersionIdentifier - node_version: Optional[NodeVersion] = None - status: Status = Field(..., description='Status of the retrieval operation') - - -class BulkNodeVersionsResponse(BaseModel): - node_versions: List[BulkNodeVersionResult] = Field( - ..., description='List of retrieved node versions with their status' - ) - - -class EasyInputMessage(BaseModel): - content: Union[str, InputMessageContentList] = Field( - ..., - description='Text, image, or audio input to the model, used to generate a response.\nCan also contain previous assistant responses.\n', - ) - role: Role = Field( - ..., - description='The role of the message input. One of `user`, `assistant`, `system`, or\n`developer`.\n', - ) - type: Optional[Type2] = Field( - None, description='The type of the message input. Always `message`.\n' - ) - - -class GeminiContent(BaseModel): - parts: List[GeminiPart] - role: Role1 = Field(..., examples=['user']) - - -class GeminiGenerateContentRequest(BaseModel): - contents: List[GeminiContent] - generationConfig: Optional[GeminiGenerationConfig] = None - safetySettings: Optional[List[GeminiSafetySetting]] = None - systemInstruction: Optional[GeminiSystemInstructionContent] = None - tools: Optional[List[GeminiTool]] = None - videoMetadata: Optional[GeminiVideoMetadata] = None - - -class GithubReleaseWebhook(BaseModel): - action: Action = Field(..., description='The action performed on the release') - enterprise: Optional[GithubEnterprise] = None - installation: Optional[GithubInstallation] = None - organization: Optional[GithubOrganization] = None - release: Release = Field(..., description='The release object') - repository: GithubRepository - sender: GithubUser - - -class ImagenGenerateImageRequest(BaseModel): - instances: List[ImagenImageGenerationInstance] - parameters: ImagenImageGenerationParameters - - -class InputMessage(BaseModel): - content: Optional[InputMessageContentList] = None - role: Optional[Role3] = None - status: Optional[Status3] = None - type: Optional[Type10] = None - - -class Item( - RootModel[ - Union[ - InputMessage, - OutputMessage, - FileSearchToolCall, - ComputerToolCall, - WebSearchToolCall, - FunctionToolCall, - ReasoningItem, - ] - ] -): - root: Union[ - InputMessage, - OutputMessage, - FileSearchToolCall, - ComputerToolCall, - WebSearchToolCall, - FunctionToolCall, - ReasoningItem, - ] = Field(..., description='Content item used to generate a response.\n') - - -class LumaGeneration(BaseModel): - assets: Optional[LumaAssets] = None - created_at: Optional[datetime] = Field( - None, description='The date and time when the generation was created' - ) - failure_reason: Optional[str] = Field( - None, description='The reason for the state of the generation' - ) - generation_type: Optional[LumaGenerationType] = None - id: Optional[UUID] = Field(None, description='The ID of the generation') - model: Optional[str] = Field(None, description='The model used for the generation') - request: Optional[ - Union[ - LumaGenerationRequest, - LumaImageGenerationRequest, - LumaUpscaleVideoGenerationRequest, - LumaAudioGenerationRequest, - ] - ] = Field(None, description='The request of the generation') - state: Optional[LumaState] = None - - -class OutputItem( - RootModel[ - Union[ - OutputMessage, - FileSearchToolCall, - FunctionToolCall, - WebSearchToolCall, - ComputerToolCall, - ReasoningItem, - ] - ] -): - root: Union[ - OutputMessage, - FileSearchToolCall, - FunctionToolCall, - WebSearchToolCall, - ComputerToolCall, - ReasoningItem, - ] - - -class Publisher(BaseModel): - createdAt: Optional[datetime] = Field( - None, description='The date and time the publisher was created.' - ) - description: Optional[str] = None - id: Optional[str] = Field( - None, - description="The unique identifier for the publisher. It's akin to a username. Should be lowercase.", - ) - logo: Optional[str] = Field(None, description="URL to the publisher's logo.") - members: Optional[List[PublisherMember]] = Field( - None, description='A list of members in the publisher.' - ) - name: Optional[str] = None - source_code_repo: Optional[str] = None - status: Optional[PublisherStatus] = None - support: Optional[str] = None - website: Optional[str] = None - - -class RecraftGenerateImageResponse(BaseModel): - created: int - credits: int - data: List[RecraftImage] - - -class RecraftImageToImageRequest(BaseModel): - block_nsfw: Optional[bool] = None - calculate_features: Optional[bool] = None - controls: Optional[RecraftUserControls] = None - image: StrictBytes - image_format: Optional[RecraftImageFormat] = None - model: Optional[RecraftTransformModel] = None - n: Optional[int] = None - negative_prompt: Optional[str] = None - prompt: str - response_format: Optional[RecraftResponseFormat] = None - strength: float - style: Optional[RecraftImageStyle] = None - style_id: Optional[UUID] = None - substyle: Optional[RecraftImageSubStyle] = None - text_layout: Optional[RecraftTextLayout] = None - - -class ResponseOutputItemAddedEvent(BaseModel): - item: OutputItem - output_index: int = Field( - ..., description='The index of the output item that was added.\n' - ) - type: Type29 = Field( - ..., description='The type of the event. Always `response.output_item.added`.\n' - ) - - -class ResponseOutputItemDoneEvent(BaseModel): - item: OutputItem - output_index: int = Field( - ..., description='The index of the output item that was marked done.\n' - ) - type: Type30 = Field( - ..., description='The type of the event. Always `response.output_item.done`.\n' - ) - - -class Text(BaseModel): - format: Optional[TextResponseFormatConfiguration] = None - - -class ResponseProperties(BaseModel): - instructions: Optional[str] = Field( - None, - description="Inserts a system (or developer) message as the first item in the model's context.\n\nWhen using along with `previous_response_id`, the instructions from a previous\nresponse will not be carried over to the next response. This makes it simple\nto swap out system (or developer) messages in new responses.\n", - ) - max_output_tokens: Optional[int] = Field( - None, - description='An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](/docs/guides/reasoning).\n', - ) - model: Optional[OpenAIModels] = None - previous_response_id: Optional[str] = Field( - None, - description='The unique ID of the previous response to the model. Use this to\ncreate multi-turn conversations. Learn more about \n[conversation state](/docs/guides/conversation-state).\n', - ) - reasoning: Optional[Reasoning] = None - text: Optional[Text] = None - tool_choice: Optional[ - Union[ToolChoiceOptions, ToolChoiceTypes, ToolChoiceFunction] - ] = Field( - None, - description='How the model should select which tool (or tools) to use when generating\na response. See the `tools` parameter to see how to specify which tools\nthe model can call.\n', - ) - tools: Optional[List[Tool]] = None - truncation: Optional[Truncation1] = Field( - 'disabled', - description="The truncation strategy to use for the model response.\n- `auto`: If the context of this response and previous ones exceeds\n the model's context window size, the model will truncate the \n response to fit the context window by dropping input items in the\n middle of the conversation. \n- `disabled` (default): If a model response will exceed the context window \n size for a model, the request will fail with a 400 error.\n", - ) - - -class Rodin3DCheckStatusResponse(BaseModel): - jobs: Optional[List[RodinCheckStatusJobItem]] = Field( - None, description='Details for the generation status.' - ) - - -class Data8(BaseModel): - object: Optional[StripePaymentIntent] = None - - -class StripeEvent(BaseModel): - api_version: Optional[str] = None - created: Optional[int] = None - data: Data8 - id: str - livemode: Optional[bool] = None - object: Object2 - pending_webhooks: Optional[int] = None - request: Optional[StripeRequestInfo] = None - type: Type31 - - -class GeminiCandidate(BaseModel): - citationMetadata: Optional[GeminiCitationMetadata] = None - content: Optional[GeminiContent] = None - finishReason: Optional[str] = None - safetyRatings: Optional[List[GeminiSafetyRating]] = None - - -class GeminiGenerateContentResponse(BaseModel): - candidates: Optional[List[GeminiCandidate]] = None - promptFeedback: Optional[GeminiPromptFeedback] = None - usageMetadata: Optional[GeminiUsageMetadata] = None - - -class InputItem(RootModel[Union[EasyInputMessage, Item]]): - root: Union[EasyInputMessage, Item] - - -class Node(BaseModel): - author: Optional[str] = None - banner_url: Optional[str] = Field(None, description="URL to the node's banner.") - category: Optional[str] = Field(None, description='The category of the node.') - created_at: Optional[datetime] = Field( - None, description='The date and time when the node was created' - ) - description: Optional[str] = None - downloads: Optional[int] = Field( - None, description='The number of downloads of the node.' - ) - github_stars: Optional[int] = Field( - None, description='Number of stars on the GitHub repository.' - ) - icon: Optional[str] = Field(None, description="URL to the node's icon.") - id: Optional[str] = Field(None, description='The unique identifier of the node.') - latest_version: Optional[NodeVersion] = None - license: Optional[str] = Field( - None, description="The path to the LICENSE file in the node's repository." - ) - name: Optional[str] = Field(None, description='The display name of the node.') - preempted_comfy_node_names: Optional[List[str]] = Field( - None, description='A list of Comfy node names that are preempted by this node.' - ) - publisher: Optional[Publisher] = None - rating: Optional[float] = Field(None, description='The average rating of the node.') - repository: Optional[str] = Field(None, description="URL to the node's repository.") - search_ranking: Optional[int] = Field( - None, - description="A numerical value representing the node's search ranking, used for sorting search results.", - ) - status: Optional[NodeStatus] = None - status_detail: Optional[str] = Field( - None, description='The status detail of the node.' - ) - supported_accelerators: Optional[List[str]] = Field( - None, - description='List of accelerators (e.g. CUDA, DirectML, ROCm) that this node supports', - ) - supported_comfyui_frontend_version: Optional[str] = Field( - None, description='Supported versions of ComfyUI frontend' - ) - supported_comfyui_version: Optional[str] = Field( - None, description='Supported versions of ComfyUI' - ) - supported_os: Optional[List[str]] = Field( - None, description='List of operating systems that this node supports' - ) - tags: Optional[List[str]] = None - translations: Optional[Dict[str, Dict[str, Any]]] = Field( - None, description='Translations of node metadata in different languages.' - ) - - -class OpenAICreateResponse(CreateModelResponseProperties, ResponseProperties): - include: Optional[List[Includable]] = Field( - None, - description='Specify additional output data to include in the model response. Currently\nsupported values are:\n- `file_search_call.results`: Include the search results of\n the file search tool call.\n- `message.input_image.image_url`: Include image urls from the input message.\n- `computer_call_output.output.image_url`: Include image urls from the computer call output.\n', - ) - input: Union[str, List[InputItem]] = Field( - ..., - description='Text, image, or file inputs to the model, used to generate a response.\n\nLearn more:\n- [Text inputs and outputs](/docs/guides/text)\n- [Image inputs](/docs/guides/images)\n- [File inputs](/docs/guides/pdf-files)\n- [Conversation state](/docs/guides/conversation-state)\n- [Function calling](/docs/guides/function-calling)\n', - ) - parallel_tool_calls: Optional[bool] = Field( - True, description='Whether to allow the model to run tool calls in parallel.\n' - ) - store: Optional[bool] = Field( - True, - description='Whether to store the generated model response for later retrieval via\nAPI.\n', - ) - stream: Optional[bool] = Field( - False, - description='If set to true, the model response data will be streamed to the client\nas it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).\nSee the [Streaming section below](/docs/api-reference/responses-streaming)\nfor more information.\n', - ) - usage: Optional[ResponseUsage] = None - - -class OpenAIResponse(ModelResponseProperties, ResponseProperties): - created_at: Optional[float] = Field( - None, - description='Unix timestamp (in seconds) of when this Response was created.', - ) - error: Optional[ResponseError] = None - id: Optional[str] = Field(None, description='Unique identifier for this Response.') - incomplete_details: Optional[IncompleteDetails] = Field( - None, description='Details about why the response is incomplete.\n' - ) - object: Optional[Object] = Field( - None, description='The object type of this resource - always set to `response`.' - ) - output: Optional[List[OutputItem]] = Field( - None, - description="An array of content items generated by the model.\n\n- The length and order of items in the `output` array is dependent\n on the model's response.\n- Rather than accessing the first item in the `output` array and \n assuming it's an `assistant` message with the content generated by\n the model, you might consider using the `output_text` property where\n supported in SDKs.\n", - ) - output_text: Optional[str] = Field( - None, - description='SDK-only convenience property that contains the aggregated text output \nfrom all `output_text` items in the `output` array, if any are present. \nSupported in the Python and JavaScript SDKs.\n', - ) - parallel_tool_calls: Optional[bool] = Field( - True, description='Whether to allow the model to run tool calls in parallel.\n' - ) - status: Optional[Status7] = Field( - None, - description='The status of the response generation. One of `completed`, `failed`, `in_progress`, or `incomplete`.', - ) - usage: Optional[ResponseUsage] = None - - -class ResponseCompletedEvent(BaseModel): - response: OpenAIResponse - type: Type19 = Field( - ..., description='The type of the event. Always `response.completed`.' - ) - - -class ResponseCreatedEvent(BaseModel): - response: OpenAIResponse - type: Type22 = Field( - ..., description='The type of the event. Always `response.created`.' - ) - - -class ResponseFailedEvent(BaseModel): - response: OpenAIResponse - type: Type24 = Field( - ..., description='The type of the event. Always `response.failed`.\n' - ) - - -class ResponseInProgressEvent(BaseModel): - response: OpenAIResponse - type: Type27 = Field( - ..., description='The type of the event. Always `response.in_progress`.\n' - ) - - -class ResponseIncompleteEvent(BaseModel): - response: OpenAIResponse - type: Type28 = Field( - ..., description='The type of the event. Always `response.incomplete`.\n' - ) - - -class OpenAIResponseStreamEvent( - RootModel[ - Union[ - ResponseCreatedEvent, - ResponseInProgressEvent, - ResponseCompletedEvent, - ResponseFailedEvent, - ResponseIncompleteEvent, - ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, - ResponseContentPartAddedEvent, - ResponseContentPartDoneEvent, - ResponseErrorEvent, - ] - ] -): - root: Union[ - ResponseCreatedEvent, - ResponseInProgressEvent, - ResponseCompletedEvent, - ResponseFailedEvent, - ResponseIncompleteEvent, - ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, - ResponseContentPartAddedEvent, - ResponseContentPartDoneEvent, - ResponseErrorEvent, - ] = Field(..., description='Events that can be emitted during response streaming') diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py deleted file mode 100644 index 0e90aef7c6811e2df28a0675563750d35669bf39..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/bfl_api.py +++ /dev/null @@ -1,174 +0,0 @@ -from __future__ import annotations - -from enum import Enum -from typing import Any, Dict, Optional - -from pydantic import BaseModel, Field, confloat, conint - - -class BFLOutputFormat(str, Enum): - png = 'png' - jpeg = 'jpeg' - - -class BFLFluxExpandImageRequest(BaseModel): - prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image') - bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image') - left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image') - right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image') - steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') - guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand') - - -class BFLFluxFillImageRequest(BaseModel): - prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') - guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.') - mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.') - - -class BFLFluxCannyImageRequest(BaseModel): - prompt: str = Field(..., description='Text prompt for image generation') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - canny_low_threshold: Optional[int] = Field(None, description='Low threshold for Canny edge detection') - canny_high_threshold: Optional[int] = Field(None, description='High threshold for Canny edge detection') - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') - guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided') - preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step') - - -class BFLFluxDepthImageRequest(BaseModel): - prompt: str = Field(..., description='Text prompt for image generation') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') - guidance: confloat(ge=1, le=100) = Field(..., description='Guidance strength for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - control_image: Optional[str] = Field(None, description='Base64 encoded image to use as control input if no preprocessed image is provided') - preprocessed_image: Optional[str] = Field(None, description='Optional pre-processed image that will bypass the control preprocessing step') - - -class BFLFluxProGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for image generation.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.') - height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') - # image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( - # None, description='Blend between the prompt and the image prompt.' - # ) - - -class BFLFluxKontextProGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for what you wannt to edit.') - input_image: Optional[str] = Field(None, description='Image to edit in base64 format') - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') - steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=2)] = Field( - 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - - -class BFLFluxProUltraGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for image generation.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.') - image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') - image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( - None, description='Blend between the prompt and the image prompt.' - ) - - -class BFLFluxProGenerateResponse(BaseModel): - id: str = Field(..., description='The unique identifier for the generation task.') - polling_url: str = Field(..., description='URL to poll for the generation result.') - - -class BFLStatus(str, Enum): - task_not_found = "Task not found" - pending = "Pending" - request_moderated = "Request Moderated" - content_moderated = "Content Moderated" - ready = "Ready" - error = "Error" - - -class BFLFluxProStatusResponse(BaseModel): - id: str = Field(..., description="The unique identifier for the generation task.") - status: BFLStatus = Field(..., description="The status of the task.") - result: Optional[Dict[str, Any]] = Field( - None, description="The result of the task (null if not completed)." - ) - progress: confloat(ge=0.0, le=1.0) = Field( - ..., description="The progress of the task (0.0 to 1.0)." - ) - details: Optional[Dict[str, Any]] = Field( - None, description="Additional details about the task (null if not available)." - ) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py deleted file mode 100644 index 4ad0b783bc610fd559169ea2056c8a2dbf42b492..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/client.py +++ /dev/null @@ -1,957 +0,0 @@ -""" -API Client Framework for api.comfy.org. - -This module provides a flexible framework for making API requests from ComfyUI nodes. -It supports both synchronous and asynchronous API operations with proper type validation. - -Key Components: --------------- -1. ApiClient - Handles HTTP requests with authentication and error handling -2. ApiEndpoint - Defines a single HTTP endpoint with its request/response models -3. ApiOperation - Executes a single synchronous API operation - -Usage Examples: --------------- - -# Example 1: Synchronous API Operation -# ------------------------------------ -# For a simple API call that returns the result immediately: - -# 1. Create the API client -api_client = ApiClient( - base_url="https://api.example.com", - auth_token="your_auth_token_here", - comfy_api_key="your_comfy_api_key_here", - timeout=30.0, - verify_ssl=True -) - -# 2. Define the endpoint -user_info_endpoint = ApiEndpoint( - path="/v1/users/me", - method=HttpMethod.GET, - request_model=EmptyRequest, # No request body needed - response_model=UserProfile, # Pydantic model for the response - query_params=None -) - -# 3. Create the request object -request = EmptyRequest() - -# 4. Create and execute the operation -operation = ApiOperation( - endpoint=user_info_endpoint, - request=request -) -user_profile = await operation.execute(client=api_client) # Returns immediately with the result - - -# Example 2: Asynchronous API Operation with Polling -# ------------------------------------------------- -# For an API that starts a task and requires polling for completion: - -# 1. Define the endpoints (initial request and polling) -generate_image_endpoint = ApiEndpoint( - path="/v1/images/generate", - method=HttpMethod.POST, - request_model=ImageGenerationRequest, - response_model=TaskCreatedResponse, - query_params=None -) - -check_task_endpoint = ApiEndpoint( - path="/v1/tasks/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=ImageGenerationResult, - query_params=None -) - -# 2. Create the request object -request = ImageGenerationRequest( - prompt="a beautiful sunset over mountains", - width=1024, - height=1024, - num_images=1 -) - -# 3. Create and execute the polling operation -operation = PollingOperation( - initial_endpoint=generate_image_endpoint, - initial_request=request, - poll_endpoint=check_task_endpoint, - task_id_field="task_id", - status_field="status", - completed_statuses=["completed"], - failed_statuses=["failed", "error"] -) - -# This will make the initial request and then poll until completion -result = await operation.execute(client=api_client) # Returns the final ImageGenerationResult when done -""" - -from __future__ import annotations -import aiohttp -import asyncio -import logging -import io -import socket -from aiohttp.client_exceptions import ClientError, ClientResponseError -from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple -from enum import Enum -import json -from urllib.parse import urljoin, urlparse -from pydantic import BaseModel, Field -import uuid # For generating unique operation IDs - -from server import PromptServer -from comfy.cli_args import args -from comfy import utils -from . import request_logger - -T = TypeVar("T", bound=BaseModel) -R = TypeVar("R", bound=BaseModel) -P = TypeVar("P", bound=BaseModel) # For poll response - -PROGRESS_BAR_MAX = 100 - - -class NetworkError(Exception): - """Base exception for network-related errors with diagnostic information.""" - pass - - -class LocalNetworkError(NetworkError): - """Exception raised when local network connectivity issues are detected.""" - pass - - -class ApiServerError(NetworkError): - """Exception raised when the API server is unreachable but internet is working.""" - pass - - -class EmptyRequest(BaseModel): - """Base class for empty request bodies. - For GET requests, fields will be sent as query parameters.""" - - pass - - -class UploadRequest(BaseModel): - file_name: str = Field(..., description="Filename to upload") - content_type: Optional[str] = Field( - None, - description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.", - ) - - -class UploadResponse(BaseModel): - download_url: str = Field(..., description="URL to GET uploaded file") - upload_url: str = Field(..., description="URL to PUT file to upload") - - -class HttpMethod(str, Enum): - GET = "GET" - POST = "POST" - PUT = "PUT" - DELETE = "DELETE" - PATCH = "PATCH" - - -class ApiClient: - """ - Client for making HTTP requests to an API with authentication, error handling, and retry logic. - """ - - def __init__( - self, - base_url: str, - auth_token: Optional[str] = None, - comfy_api_key: Optional[str] = None, - timeout: float = 3600.0, - verify_ssl: bool = True, - max_retries: int = 3, - retry_delay: float = 1.0, - retry_backoff_factor: float = 2.0, - retry_status_codes: Optional[Tuple[int, ...]] = None, - session: Optional[aiohttp.ClientSession] = None, - ): - self.base_url = base_url - self.auth_token = auth_token - self.comfy_api_key = comfy_api_key - self.timeout = timeout - self.verify_ssl = verify_ssl - self.max_retries = max_retries - self.retry_delay = retry_delay - self.retry_backoff_factor = retry_backoff_factor - # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests), - # 500, 502, 503, 504 (Server Errors) - self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504) - self._session: Optional[aiohttp.ClientSession] = session - self._owns_session = session is None # Track if we have to close it - - @staticmethod - def _generate_operation_id(path: str) -> str: - """Generates a unique operation ID for logging.""" - return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}" - - @staticmethod - def _create_json_payload_args( - data: Optional[Dict[str, Any]] = None, - headers: Optional[Dict[str, str]] = None, - ) -> Dict[str, Any]: - return { - "json": data, - "headers": headers, - } - - def _create_form_data_args( - self, - data: Dict[str, Any] | None, - files: Dict[str, Any] | None, - headers: Optional[Dict[str, str]] = None, - multipart_parser: Callable | None = None, - ) -> Dict[str, Any]: - if headers and "Content-Type" in headers: - del headers["Content-Type"] - - if multipart_parser and data: - data = multipart_parser(data) - - form = aiohttp.FormData(default_to_multipart=True) - if data: # regular text fields - for k, v in data.items(): - if v is None: - continue # aiohttp fails to serialize "None" values - # aiohttp expects strings or bytes; convert enums etc. - form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v) - - if files: - file_iter = files if isinstance(files, list) else files.items() - for field_name, file_obj in file_iter: - if file_obj is None: - continue # aiohttp fails to serialize "None" values - # file_obj can be (filename, bytes/io.BytesIO, content_type) tuple - if isinstance(file_obj, tuple): - filename, file_value, content_type = self._unpack_tuple(file_obj) - else: - file_value = file_obj - filename = getattr(file_obj, "name", field_name) - content_type = "application/octet-stream" - - form.add_field( - name=field_name, - value=file_value, - filename=filename, - content_type=content_type, - ) - return {"data": form, "headers": headers or {}} - - @staticmethod - def _create_urlencoded_form_data_args( - data: Dict[str, Any], - headers: Optional[Dict[str, str]] = None, - ) -> Dict[str, Any]: - headers = headers or {} - headers["Content-Type"] = "application/x-www-form-urlencoded" - return { - "data": data, - "headers": headers, - } - - def get_headers(self) -> Dict[str, str]: - """Get headers for API requests, including authentication if available""" - headers = {"Content-Type": "application/json", "Accept": "application/json"} - - if self.auth_token: - headers["Authorization"] = f"Bearer {self.auth_token}" - elif self.comfy_api_key: - headers["X-API-KEY"] = self.comfy_api_key - - return headers - - async def _check_connectivity(self, target_url: str) -> Dict[str, bool]: - """ - Check connectivity to determine if network issues are local or server-related. - - Args: - target_url: URL to check connectivity to - - Returns: - Dictionary with connectivity status details - """ - results = { - "internet_accessible": False, - "api_accessible": False, - "is_local_issue": False, - "is_api_issue": False, - } - timeout = aiohttp.ClientTimeout(total=5.0) - async with aiohttp.ClientSession(timeout=timeout) as session: - try: - async with session.get("https://www.google.com", ssl=self.verify_ssl) as resp: - results["internet_accessible"] = resp.status < 500 - except (ClientError, asyncio.TimeoutError, socket.gaierror): - results["is_local_issue"] = True - return results # cannot reach the internet – early exit - - # Now check API health endpoint - parsed = urlparse(target_url) - health_url = f"{parsed.scheme}://{parsed.netloc}/health" - try: - async with session.get(health_url, ssl=self.verify_ssl) as resp: - results["api_accessible"] = resp.status < 500 - except ClientError: - pass # leave as False - - results["is_api_issue"] = results["internet_accessible"] and not results["api_accessible"] - return results - - async def request( - self, - method: str, - path: str, - params: Optional[Dict[str, Any]] = None, - data: Optional[Dict[str, Any]] = None, - files: Optional[Dict[str, Any] | list[tuple[str, Any]]] = None, - headers: Optional[Dict[str, str]] = None, - content_type: str = "application/json", - multipart_parser: Callable | None = None, - retry_count: int = 0, # Used internally for tracking retries - ) -> Dict[str, Any]: - """ - Make an HTTP request to the API with automatic retries for transient errors. - - Args: - method: HTTP method (GET, POST, etc.) - path: API endpoint path (will be joined with base_url) - params: Query parameters - data: body data - files: Files to upload - headers: Additional headers - content_type: Content type of the request. Defaults to application/json. - retry_count: Internal parameter for tracking retries, do not set manually - - Returns: - Parsed JSON response - - Raises: - LocalNetworkError: If local network connectivity issues are detected - ApiServerError: If the API server is unreachable but internet is working - Exception: For other request failures - """ - - # Build full URL and merge headers - relative_path = path.lstrip("/") - url = urljoin(self.base_url, relative_path) - self._check_auth(self.auth_token, self.comfy_api_key) - - request_headers = self.get_headers() - if headers: - request_headers.update(headers) - if files: - request_headers.pop("Content-Type", None) - if params: - params = {k: v for k, v in params.items() if v is not None} # aiohttp fails to serialize None values - - logging.debug(f"[DEBUG] Request Headers: {request_headers}") - logging.debug(f"[DEBUG] Files: {files}") - logging.debug(f"[DEBUG] Params: {params}") - logging.debug(f"[DEBUG] Data: {data}") - - if content_type == "application/x-www-form-urlencoded": - payload_args = self._create_urlencoded_form_data_args(data or {}, request_headers) - elif content_type == "multipart/form-data": - payload_args = self._create_form_data_args(data, files, request_headers, multipart_parser) - else: - payload_args = self._create_json_payload_args(data, request_headers) - - operation_id = self._generate_operation_id(path) - request_logger.log_request_response( - operation_id=operation_id, - request_method=method, - request_url=url, - request_headers=request_headers, - request_params=params, - request_data=data if content_type == "application/json" else "[form-data or other]", - ) - - session = await self._get_session() - try: - async with session.request( - method, - url, - params=params, - ssl=self.verify_ssl, - **payload_args, - ) as resp: - if resp.status >= 400: - try: - error_data = await resp.json() - except (aiohttp.ContentTypeError, json.JSONDecodeError): - error_data = await resp.text() - - return await self._handle_http_error( - ClientResponseError(resp.request_info, resp.history, status=resp.status, message=error_data), - operation_id, - method, - url, - params, - data, - files, - headers, - content_type, - multipart_parser, - retry_count=retry_count, - response_content=error_data, - ) - - # Success – parse JSON (safely) and log - try: - payload = await resp.json() - response_content_to_log = payload - except (aiohttp.ContentTypeError, json.JSONDecodeError): - payload = {} - response_content_to_log = await resp.text() - - request_logger.log_request_response( - operation_id=operation_id, - request_method=method, - request_url=url, - response_status_code=resp.status, - response_headers=dict(resp.headers), - response_content=response_content_to_log, - ) - return payload - - except (ClientError, asyncio.TimeoutError, socket.gaierror) as e: - # Treat as *connection* problem – optionally retry, else escalate - if retry_count < self.max_retries: - delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) - logging.warning("Connection error. Retrying in %.2fs (%s/%s): %s", delay, retry_count + 1, - self.max_retries, str(e)) - await asyncio.sleep(delay) - return await self.request( - method, - path, - params=params, - data=data, - files=files, - headers=headers, - content_type=content_type, - multipart_parser=multipart_parser, - retry_count=retry_count + 1, - ) - # One final connectivity check for diagnostics - connectivity = await self._check_connectivity(self.base_url) - if connectivity["is_local_issue"]: - raise LocalNetworkError( - "Unable to connect to the API server due to local network issues. " - "Please check your internet connection and try again." - ) from e - raise ApiServerError( - f"The API server at {self.base_url} is currently unreachable. " - f"The service may be experiencing issues. Please try again later." - ) from e - - @staticmethod - def _check_auth(auth_token, comfy_api_key): - """Verify that an auth token is present or comfy_api_key is present""" - if auth_token is None and comfy_api_key is None: - raise Exception("Unauthorized: Please login first to use this node.") - return auth_token or comfy_api_key - - @staticmethod - async def upload_file( - upload_url: str, - file: io.BytesIO | str, - content_type: str | None = None, - max_retries: int = 3, - retry_delay: float = 1.0, - retry_backoff_factor: float = 2.0, - ) -> aiohttp.ClientResponse: - """Upload a file to the API with retry logic. - - Args: - upload_url: The URL to upload to - file: Either a file path string, BytesIO object, or tuple of (file_path, filename) - content_type: Optional mime type to set for the upload - max_retries: Maximum number of retry attempts - retry_delay: Initial delay between retries in seconds - retry_backoff_factor: Multiplier for the delay after each retry - """ - headers: Dict[str, str] = {} - skip_auto_headers: set[str] = set() - if content_type: - headers["Content-Type"] = content_type - else: - # tell aiohttp not to add Content-Type that will break the request signature and result in a 403 status. - skip_auto_headers.add("Content-Type") - - # Extract file bytes - if isinstance(file, io.BytesIO): - file.seek(0) - data = file.read() - elif isinstance(file, str): - with open(file, "rb") as f: - data = f.read() - else: - raise ValueError("File must be BytesIO or str path") - - operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}" - request_logger.log_request_response( - operation_id=operation_id, - request_method="PUT", - request_url=upload_url, - request_headers=headers, - request_data=f"[File data {len(data)} bytes]", - ) - - delay = retry_delay - for attempt in range(max_retries + 1): - try: - timeout = aiohttp.ClientTimeout(total=None) # honour server side timeouts - async with aiohttp.ClientSession(timeout=timeout) as session: - async with session.put( - upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers, - ) as resp: - resp.raise_for_status() - request_logger.log_request_response( - operation_id=operation_id, - request_method="PUT", - request_url=upload_url, - response_status_code=resp.status, - response_headers=dict(resp.headers), - response_content="File uploaded successfully.", - ) - return resp - except (ClientError, asyncio.TimeoutError) as e: - request_logger.log_request_response( - operation_id=operation_id, - request_method="PUT", - request_url=upload_url, - response_status_code=e.status if hasattr(e, "status") else None, - response_headers=dict(e.headers) if getattr(e, "headers") else None, - response_content=None, - error_message=f"{type(e).__name__}: {str(e)}", - ) - if attempt < max_retries: - logging.warning( - "Upload failed (%s/%s). Retrying in %.2fs. %s", attempt + 1, max_retries, delay, str(e) - ) - await asyncio.sleep(delay) - delay *= retry_backoff_factor - else: - raise NetworkError(f"Failed to upload file after {max_retries + 1} attempts: {e}") from e - - async def _handle_http_error( - self, - exc: ClientResponseError, - operation_id: str, - *req_meta, - retry_count: int, - response_content: dict | str = "", - ) -> Dict[str, Any]: - status_code = exc.status - if status_code == 401: - user_friendly = "Unauthorized: Please login first to use this node." - elif status_code == 402: - user_friendly = "Payment Required: Please add credits to your account to use this node." - elif status_code == 409: - user_friendly = "There is a problem with your account. Please contact support@comfy.org." - elif status_code == 429: - user_friendly = "Rate Limit Exceeded: Please try again later." - else: - if isinstance(response_content, dict): - if "error" in response_content and "message" in response_content["error"]: - user_friendly = f"API Error: {response_content['error']['message']}" - if "type" in response_content["error"]: - user_friendly += f" (Type: {response_content['error']['type']})" - else: # Handle cases where error is just a JSON dict with unknown format - user_friendly = f"API Error: {json.dumps(response_content)}" - else: - if len(response_content) < 200: # Arbitrary limit for display - user_friendly = f"API Error (raw): {response_content}" - else: - user_friendly = f"API Error (raw, status {response_content})" - - request_logger.log_request_response( - operation_id=operation_id, - request_method=req_meta[0], - request_url=req_meta[1], - response_status_code=exc.status, - response_headers=dict(req_meta[5]) if req_meta[5] else None, - response_content=response_content, - error_message=f"HTTP Error {exc.status}", - ) - - logging.debug(f"[DEBUG] API Error: {user_friendly} (Status: {status_code})") - if response_content: - logging.debug(f"[DEBUG] Response content: {response_content}") - - # Retry if eligible - if status_code in self.retry_status_codes and retry_count < self.max_retries: - delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) - logging.warning( - "HTTP error %s. Retrying in %.2fs (%s/%s)", - status_code, - delay, - retry_count + 1, - self.max_retries, - ) - await asyncio.sleep(delay) - return await self.request( - req_meta[0], # method - req_meta[1].replace(self.base_url, ""), # path - params=req_meta[2], - data=req_meta[3], - files=req_meta[4], - headers=req_meta[5], - content_type=req_meta[6], - multipart_parser=req_meta[7], - retry_count=retry_count + 1, - ) - - raise Exception(user_friendly) from exc - - @staticmethod - def _unpack_tuple(t): - """Helper to normalise (filename, file, content_type) tuples.""" - if len(t) == 3: - return t - elif len(t) == 2: - return t[0], t[1], "application/octet-stream" - else: - raise ValueError("files tuple must be (filename, file[, content_type])") - - async def _get_session(self) -> aiohttp.ClientSession: - if self._session is None or self._session.closed: - timeout = aiohttp.ClientTimeout(total=self.timeout) - self._session = aiohttp.ClientSession(timeout=timeout) - self._owns_session = True - return self._session - - async def close(self) -> None: - if self._owns_session and self._session and not self._session.closed: - await self._session.close() - - async def __aenter__(self) -> "ApiClient": - """Allow usage as async‑context‑manager – ensures clean teardown""" - return self - - async def __aexit__(self, exc_type, exc, tb): - await self.close() - - -class ApiEndpoint(Generic[T, R]): - """Defines an API endpoint with its request and response types""" - - def __init__( - self, - path: str, - method: HttpMethod, - request_model: Type[T], - response_model: Type[R], - query_params: Optional[Dict[str, Any]] = None, - ): - """Initialize an API endpoint definition. - - Args: - path: The URL path for this endpoint, can include placeholders like {id} - method: The HTTP method to use (GET, POST, etc.) - request_model: Pydantic model class that defines the structure and validation rules for API requests to this endpoint - response_model: Pydantic model class that defines the structure and validation rules for API responses from this endpoint - query_params: Optional dictionary of query parameters to include in the request - """ - self.path = path - self.method = method - self.request_model = request_model - self.response_model = response_model - self.query_params = query_params or {} - - -class SynchronousOperation(Generic[T, R]): - """Represents a single synchronous API operation.""" - - def __init__( - self, - endpoint: ApiEndpoint[T, R], - request: T, - files: Optional[Dict[str, Any] | list[tuple[str, Any]]] = None, - api_base: str | None = None, - auth_token: Optional[str] = None, - comfy_api_key: Optional[str] = None, - auth_kwargs: Optional[Dict[str, str]] = None, - timeout: float = 604800.0, - verify_ssl: bool = True, - content_type: str = "application/json", - multipart_parser: Callable | None = None, - max_retries: int = 3, - retry_delay: float = 1.0, - retry_backoff_factor: float = 2.0, - ) -> None: - self.endpoint = endpoint - self.request = request - self.files = files - self.api_base: str = api_base or args.comfy_api_base - self.auth_token = auth_token - self.comfy_api_key = comfy_api_key - if auth_kwargs is not None: - self.auth_token = auth_kwargs.get("auth_token", self.auth_token) - self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) - self.timeout = timeout - self.verify_ssl = verify_ssl - self.content_type = content_type - self.multipart_parser = multipart_parser - self.max_retries = max_retries - self.retry_delay = retry_delay - self.retry_backoff_factor = retry_backoff_factor - - async def execute(self, client: Optional[ApiClient] = None) -> R: - owns_client = client is None - if owns_client: - client = ApiClient( - base_url=self.api_base, - auth_token=self.auth_token, - comfy_api_key=self.comfy_api_key, - timeout=self.timeout, - verify_ssl=self.verify_ssl, - max_retries=self.max_retries, - retry_delay=self.retry_delay, - retry_backoff_factor=self.retry_backoff_factor, - ) - - try: - request_dict: Optional[Dict[str, Any]] - if isinstance(self.request, EmptyRequest): - request_dict = None - else: - request_dict = self.request.model_dump(exclude_none=True) - for k, v in list(request_dict.items()): - if isinstance(v, Enum): - request_dict[k] = v.value - - logging.debug( - f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}" - ) - logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}") - logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}") - - response_json = await client.request( - self.endpoint.method.value, - self.endpoint.path, - params=self.endpoint.query_params, - data=request_dict, - files=self.files, - content_type=self.content_type, - multipart_parser=self.multipart_parser, - ) - - logging.debug("=" * 50) - logging.debug("[DEBUG] RESPONSE DETAILS:") - logging.debug("[DEBUG] Status Code: 200 (Success)") - logging.debug(f"[DEBUG] Response Body: {json.dumps(response_json, indent=2)}") - logging.debug("=" * 50) - - parsed_response = self.endpoint.response_model.model_validate(response_json) - logging.debug(f"[DEBUG] Parsed Response: {parsed_response}") - return parsed_response - finally: - if owns_client: - await client.close() - - -class TaskStatus(str, Enum): - """Enum for task status values""" - - COMPLETED = "completed" - FAILED = "failed" - PENDING = "pending" - - -class PollingOperation(Generic[T, R]): - """Represents an asynchronous API operation that requires polling for completion.""" - - def __init__( - self, - poll_endpoint: ApiEndpoint[EmptyRequest, R], - completed_statuses: list[str], - failed_statuses: list[str], - status_extractor: Callable[[R], str], - progress_extractor: Callable[[R], float] | None = None, - result_url_extractor: Callable[[R], str] | None = None, - request: Optional[T] = None, - api_base: str | None = None, - auth_token: Optional[str] = None, - comfy_api_key: Optional[str] = None, - auth_kwargs: Optional[Dict[str, str]] = None, - poll_interval: float = 5.0, - max_poll_attempts: int = 120, # Default max polling attempts (10 minutes with 5s interval) - max_retries: int = 3, # Max retries per individual API call - retry_delay: float = 1.0, - retry_backoff_factor: float = 2.0, - estimated_duration: Optional[float] = None, - node_id: Optional[str] = None, - ) -> None: - self.poll_endpoint = poll_endpoint - self.request = request - self.api_base: str = api_base or args.comfy_api_base - self.auth_token = auth_token - self.comfy_api_key = comfy_api_key - if auth_kwargs is not None: - self.auth_token = auth_kwargs.get("auth_token", self.auth_token) - self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) - self.poll_interval = poll_interval - self.max_poll_attempts = max_poll_attempts - self.max_retries = max_retries - self.retry_delay = retry_delay - self.retry_backoff_factor = retry_backoff_factor - self.estimated_duration = estimated_duration - self.status_extractor = status_extractor or (lambda x: getattr(x, "status", None)) - self.progress_extractor = progress_extractor - self.result_url_extractor = result_url_extractor - self.node_id = node_id - self.completed_statuses = completed_statuses - self.failed_statuses = failed_statuses - self.final_response: Optional[R] = None - - async def execute(self, client: Optional[ApiClient] = None) -> R: - owns_client = client is None - if owns_client: - client = ApiClient( - base_url=self.api_base, - auth_token=self.auth_token, - comfy_api_key=self.comfy_api_key, - max_retries=self.max_retries, - retry_delay=self.retry_delay, - retry_backoff_factor=self.retry_backoff_factor, - ) - try: - return await self._poll_until_complete(client) - finally: - if owns_client: - await client.close() - - def _display_text_on_node(self, text: str): - if not self.node_id: - return - PromptServer.instance.send_progress_text(text, self.node_id) - - def _display_time_progress_on_node(self, time_completed: int | float): - if not self.node_id: - return - if self.estimated_duration is not None: - remaining = max(0, int(self.estimated_duration) - time_completed) - message = f"Task in progress: {time_completed}s (~{remaining}s remaining)" - else: - message = f"Task in progress: {time_completed}s" - self._display_text_on_node(message) - - def _check_task_status(self, response: R) -> TaskStatus: - try: - status = self.status_extractor(response) - if status in self.completed_statuses: - return TaskStatus.COMPLETED - if status in self.failed_statuses: - return TaskStatus.FAILED - return TaskStatus.PENDING - except Exception as e: - logging.error("Error extracting status: %s", e) - return TaskStatus.PENDING - - async def _poll_until_complete(self, client: ApiClient) -> R: - """Poll until the task is complete""" - consecutive_errors = 0 - max_consecutive_errors = min(5, self.max_retries * 2) # Limit consecutive errors - - if self.progress_extractor: - progress = utils.ProgressBar(PROGRESS_BAR_MAX) - - status = TaskStatus.PENDING - for poll_count in range(1, self.max_poll_attempts + 1): - try: - logging.debug(f"[DEBUG] Polling attempt #{poll_count}") - - request_dict = ( - None if self.request is None else self.request.model_dump(exclude_none=True) - ) - - if poll_count == 1: - logging.debug( - f"[DEBUG] Poll Request: {self.poll_endpoint.method.value} {self.poll_endpoint.path}" - ) - logging.debug( - f"[DEBUG] Poll Request Data: {json.dumps(request_dict, indent=2) if request_dict else 'None'}" - ) - - # Query task status - resp = await client.request( - self.poll_endpoint.method.value, - self.poll_endpoint.path, - params=self.poll_endpoint.query_params, - data=request_dict, - ) - consecutive_errors = 0 # reset on success - response_obj: R = self.poll_endpoint.response_model.model_validate(resp) - - # Check if task is complete - status = self._check_task_status(response_obj) - logging.debug(f"[DEBUG] Task Status: {status}") - - # If progress extractor is provided, extract progress - if self.progress_extractor: - new_progress = self.progress_extractor(response_obj) - if new_progress is not None: - progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX) - - if status == TaskStatus.COMPLETED: - message = "Task completed successfully" - if self.result_url_extractor: - result_url = self.result_url_extractor(response_obj) - if result_url: - message = f"Result URL: {result_url}" - logging.debug(f"[DEBUG] {message}") - self._display_text_on_node(message) - self.final_response = response_obj - if self.progress_extractor: - progress.update(100) - return self.final_response - if status == TaskStatus.FAILED: - message = f"Task failed: {json.dumps(resp)}" - logging.error(f"[DEBUG] {message}") - raise Exception(message) - logging.debug("[DEBUG] Task still pending, continuing to poll...") - # Task pending – wait - for i in range(int(self.poll_interval)): - self._display_time_progress_on_node((poll_count - 1) * self.poll_interval + i) - await asyncio.sleep(1) - - except (LocalNetworkError, ApiServerError, NetworkError) as e: - consecutive_errors += 1 - if consecutive_errors >= max_consecutive_errors: - raise Exception( - f"Polling aborted after {consecutive_errors} network errors: {str(e)}" - ) from e - logging.warning("Network error (%s/%s): %s", consecutive_errors, max_consecutive_errors, str(e)) - await asyncio.sleep(self.poll_interval) - except Exception as e: - # For other errors, increment count and potentially abort - consecutive_errors += 1 - if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED: - raise Exception( - f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}" - ) from e - - logging.error(f"[DEBUG] Polling error: {str(e)}") - logging.warning( - f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. " - f"Will retry in {self.poll_interval} seconds." - ) - await asyncio.sleep(self.poll_interval) - - # If we've exhausted all polling attempts - raise Exception( - f"Polling timed out after {self.max_poll_attempts} attempts (" f"{self.max_poll_attempts * self.poll_interval} seconds). " - "The operation may still be running on the server but is taking longer than expected." - ) diff --git a/comfy_api_nodes/apis/gemini_api.py b/comfy_api_nodes/apis/gemini_api.py deleted file mode 100644 index 138bf035d81360a5cecb4de1f745febba88d7f37..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/gemini_api.py +++ /dev/null @@ -1,19 +0,0 @@ -from __future__ import annotations - -from typing import List, Optional - -from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata -from pydantic import BaseModel - - -class GeminiImageGenerationConfig(GeminiGenerationConfig): - responseModalities: Optional[List[str]] = None - - -class GeminiImageGenerateContentRequest(BaseModel): - contents: List[GeminiContent] - generationConfig: Optional[GeminiImageGenerationConfig] = None - safetySettings: Optional[List[GeminiSafetySetting]] = None - systemInstruction: Optional[GeminiSystemInstructionContent] = None - tools: Optional[List[GeminiTool]] = None - videoMetadata: Optional[GeminiVideoMetadata] = None diff --git a/comfy_api_nodes/apis/luma_api.py b/comfy_api_nodes/apis/luma_api.py deleted file mode 100644 index 632c4ab9697a8ab2659dc0171890e739788b286d..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/luma_api.py +++ /dev/null @@ -1,253 +0,0 @@ -from __future__ import annotations - - -import torch - -from enum import Enum -from typing import Optional, Union - -from pydantic import BaseModel, Field, confloat - - - -class LumaIO: - LUMA_REF = "LUMA_REF" - LUMA_CONCEPTS = "LUMA_CONCEPTS" - - -class LumaReference: - def __init__(self, image: torch.Tensor, weight: float): - self.image = image - self.weight = weight - - def create_api_model(self, download_url: str): - return LumaImageRef(url=download_url, weight=self.weight) - -class LumaReferenceChain: - def __init__(self, first_ref: LumaReference=None): - self.refs: list[LumaReference] = [] - if first_ref: - self.refs.append(first_ref) - - def add(self, luma_ref: LumaReference=None): - self.refs.append(luma_ref) - - def create_api_model(self, download_urls: list[str], max_refs=4): - if len(self.refs) == 0: - return None - api_refs: list[LumaImageRef] = [] - for ref, url in zip(self.refs, download_urls): - api_ref = LumaImageRef(url=url, weight=ref.weight) - api_refs.append(api_ref) - return api_refs - - def clone(self): - c = LumaReferenceChain() - for ref in self.refs: - c.add(ref) - return c - - -class LumaConcept: - def __init__(self, key: str): - self.key = key - - -class LumaConceptChain: - def __init__(self, str_list: list[str] = None): - self.concepts: list[LumaConcept] = [] - if str_list is not None: - for c in str_list: - if c != "None": - self.add(LumaConcept(key=c)) - - def add(self, concept: LumaConcept): - self.concepts.append(concept) - - def create_api_model(self): - if len(self.concepts) == 0: - return None - api_concepts: list[LumaConceptObject] = [] - for concept in self.concepts: - if concept.key == "None": - continue - api_concepts.append(LumaConceptObject(key=concept.key)) - if len(api_concepts) == 0: - return None - return api_concepts - - def clone(self): - c = LumaConceptChain() - for concept in self.concepts: - c.add(concept) - return c - - def clone_and_merge(self, other: LumaConceptChain): - c = self.clone() - for concept in other.concepts: - c.add(concept) - return c - - -def get_luma_concepts(include_none=False): - concepts = [] - if include_none: - concepts.append("None") - return concepts + [ - "truck_left", - "pan_right", - "pedestal_down", - "low_angle", - "pedestal_up", - "selfie", - "pan_left", - "roll_right", - "zoom_in", - "over_the_shoulder", - "orbit_right", - "orbit_left", - "static", - "tiny_planet", - "high_angle", - "bolt_cam", - "dolly_zoom", - "overhead", - "zoom_out", - "handheld", - "roll_left", - "pov", - "aerial_drone", - "push_in", - "crane_down", - "truck_right", - "tilt_down", - "elevator_doors", - "tilt_up", - "ground_level", - "pull_out", - "aerial", - "crane_up", - "eye_level" - ] - - -class LumaImageModel(str, Enum): - photon_1 = "photon-1" - photon_flash_1 = "photon-flash-1" - - -class LumaVideoModel(str, Enum): - ray_2 = "ray-2" - ray_flash_2 = "ray-flash-2" - ray_1_6 = "ray-1-6" - - -class LumaAspectRatio(str, Enum): - ratio_1_1 = "1:1" - ratio_16_9 = "16:9" - ratio_9_16 = "9:16" - ratio_4_3 = "4:3" - ratio_3_4 = "3:4" - ratio_21_9 = "21:9" - ratio_9_21 = "9:21" - - -class LumaVideoOutputResolution(str, Enum): - res_540p = "540p" - res_720p = "720p" - res_1080p = "1080p" - res_4k = "4k" - - -class LumaVideoModelOutputDuration(str, Enum): - dur_5s = "5s" - dur_9s = "9s" - - -class LumaGenerationType(str, Enum): - video = 'video' - image = 'image' - - -class LumaState(str, Enum): - queued = "queued" - dreaming = "dreaming" - completed = "completed" - failed = "failed" - - -class LumaAssets(BaseModel): - video: Optional[str] = Field(None, description='The URL of the video') - image: Optional[str] = Field(None, description='The URL of the image') - progress_video: Optional[str] = Field(None, description='The URL of the progress video') - - -class LumaImageRef(BaseModel): - '''Used for image gen''' - url: str = Field(..., description='The URL of the image reference') - weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') - - -class LumaImageReference(BaseModel): - '''Used for video gen''' - type: Optional[str] = Field('image', description='Input type, defaults to image') - url: str = Field(..., description='The URL of the image') - - -class LumaModifyImageRef(BaseModel): - url: str = Field(..., description='The URL of the image reference') - weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') - - -class LumaCharacterRef(BaseModel): - identity0: LumaImageIdentity = Field(..., description='The image identity object') - - -class LumaImageIdentity(BaseModel): - images: list[str] = Field(..., description='The URLs of the image identity') - - -class LumaGenerationReference(BaseModel): - type: str = Field('generation', description='Input type, defaults to generation') - id: str = Field(..., description='The ID of the generation') - - -class LumaKeyframes(BaseModel): - frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') - frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') - - -class LumaConceptObject(BaseModel): - key: str = Field(..., description='Camera Concept name') - - -class LumaImageGenerationRequest(BaseModel): - prompt: str = Field(..., description='The prompt of the generation') - model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation') - aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation') - image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects') - style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects') - character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object') - modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object') - - -class LumaGenerationRequest(BaseModel): - prompt: str = Field(..., description='The prompt of the generation') - model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation') - duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation') - aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation') - resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation') - loop: Optional[bool] = Field(None, description='Whether to loop the video') - keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation') - concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation') - - -class LumaGeneration(BaseModel): - id: str = Field(..., description='The ID of the generation') - generation_type: LumaGenerationType = Field(..., description='Generation type, image or video') - state: LumaState = Field(..., description='The state of the generation') - failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation') - created_at: str = Field(..., description='The date and time when the generation was created') - assets: Optional[LumaAssets] = Field(None, description='The assets of the generation') - model: str = Field(..., description='The model used for the generation') - request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation") diff --git a/comfy_api_nodes/apis/pixverse_api.py b/comfy_api_nodes/apis/pixverse_api.py deleted file mode 100644 index 9bb29c3835104784087d99821c85f9ddf6ba218c..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/pixverse_api.py +++ /dev/null @@ -1,146 +0,0 @@ -from __future__ import annotations - -from enum import Enum -from typing import Optional - -from pydantic import BaseModel, Field - - -pixverse_templates = { - "Microwave": 324641385496960, - "Suit Swagger": 328545151283968, - "Anything, Robot": 313358700761536, - "Subject 3 Fever": 327828816843648, - "kiss kiss": 315446315336768, -} - - -class PixverseIO: - TEMPLATE = "PIXVERSE_TEMPLATE" - - -class PixverseStatus(int, Enum): - successful = 1 - generating = 5 - deleted = 6 - contents_moderation = 7 - failed = 8 - - -class PixverseAspectRatio(str, Enum): - ratio_16_9 = "16:9" - ratio_4_3 = "4:3" - ratio_1_1 = "1:1" - ratio_3_4 = "3:4" - ratio_9_16 = "9:16" - - -class PixverseQuality(str, Enum): - res_360p = "360p" - res_540p = "540p" - res_720p = "720p" - res_1080p = "1080p" - - -class PixverseDuration(int, Enum): - dur_5 = 5 - dur_8 = 8 - - -class PixverseMotionMode(str, Enum): - normal = "normal" - fast = "fast" - - -class PixverseStyle(str, Enum): - anime = "anime" - animation_3d = "3d_animation" - clay = "clay" - comic = "comic" - cyberpunk = "cyberpunk" - - -# NOTE: forgoing descriptions for now in return for dev speed -class PixverseTextVideoRequest(BaseModel): - aspect_ratio: PixverseAspectRatio = Field(...) - quality: PixverseQuality = Field(...) - duration: PixverseDuration = Field(...) - model: Optional[str] = Field("v3.5") - motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) - prompt: str = Field(...) - negative_prompt: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - style: Optional[str] = Field(None) - template_id: Optional[int] = Field(None) - water_mark: Optional[bool] = Field(None) - - -class PixverseImageVideoRequest(BaseModel): - quality: PixverseQuality = Field(...) - duration: PixverseDuration = Field(...) - img_id: int = Field(...) - model: Optional[str] = Field("v3.5") - motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) - prompt: str = Field(...) - negative_prompt: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - style: Optional[str] = Field(None) - template_id: Optional[int] = Field(None) - water_mark: Optional[bool] = Field(None) - - -class PixverseTransitionVideoRequest(BaseModel): - quality: PixverseQuality = Field(...) - duration: PixverseDuration = Field(...) - first_frame_img: int = Field(...) - last_frame_img: int = Field(...) - model: Optional[str] = Field("v3.5") - motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) - prompt: str = Field(...) - # negative_prompt: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - # style: Optional[str] = Field(None) - # template_id: Optional[int] = Field(None) - # water_mark: Optional[bool] = Field(None) - - -class PixverseImageUploadResponse(BaseModel): - ErrCode: Optional[int] = None - ErrMsg: Optional[str] = None - Resp: Optional[PixverseImgIdResponseObject] = Field(None, alias='Resp') - - -class PixverseImgIdResponseObject(BaseModel): - img_id: Optional[int] = None - - -class PixverseVideoResponse(BaseModel): - ErrCode: Optional[int] = Field(None) - ErrMsg: Optional[str] = Field(None) - Resp: Optional[PixverseVideoIdResponseObject] = Field(None) - - -class PixverseVideoIdResponseObject(BaseModel): - video_id: int = Field(..., description='Video_id') - - -class PixverseGenerationStatusResponse(BaseModel): - ErrCode: Optional[int] = Field(None) - ErrMsg: Optional[str] = Field(None) - Resp: Optional[PixverseGenerationStatusResponseObject] = Field(None) - - -class PixverseGenerationStatusResponseObject(BaseModel): - create_time: Optional[str] = Field(None) - id: Optional[int] = Field(None) - modify_time: Optional[str] = Field(None) - negative_prompt: Optional[str] = Field(None) - outputHeight: Optional[int] = Field(None) - outputWidth: Optional[int] = Field(None) - prompt: Optional[str] = Field(None) - resolution_ratio: Optional[int] = Field(None) - seed: Optional[int] = Field(None) - size: Optional[int] = Field(None) - status: Optional[int] = Field(None) - style: Optional[str] = Field(None) - url: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/recraft_api.py b/comfy_api_nodes/apis/recraft_api.py deleted file mode 100644 index c36d95f24e6ab53f5790b0ac97f71a26087a410f..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/recraft_api.py +++ /dev/null @@ -1,262 +0,0 @@ -from __future__ import annotations - - - -from enum import Enum -from typing import Optional - -from pydantic import BaseModel, Field, conint, confloat - - -class RecraftColor: - def __init__(self, r: int, g: int, b: int): - self.color = [r, g, b] - - def create_api_model(self): - return RecraftColorObject(rgb=self.color) - - -class RecraftColorChain: - def __init__(self): - self.colors: list[RecraftColor] = [] - - def get_first(self): - if len(self.colors) > 0: - return self.colors[0] - return None - - def add(self, color: RecraftColor): - self.colors.append(color) - - def create_api_model(self): - if not self.colors: - return None - colors_api = [x.create_api_model() for x in self.colors] - return colors_api - - def clone(self): - c = RecraftColorChain() - for color in self.colors: - c.add(color) - return c - - def clone_and_merge(self, other: RecraftColorChain): - c = self.clone() - for color in other.colors: - c.add(color) - return c - - -class RecraftControls: - def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None, - artistic_level: int=None, no_text: bool=None): - self.colors = colors - self.background_color = background_color - self.artistic_level = artistic_level - self.no_text = no_text - - def create_api_model(self): - if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None: - return None - colors_api = None - background_color_api = None - if self.colors: - colors_api = self.colors.create_api_model() - if self.background_color: - first_background = self.background_color.get_first() - background_color_api = first_background.create_api_model() if first_background else None - - return RecraftControlsObject(colors=colors_api, background_color=background_color_api, - artistic_level=self.artistic_level, no_text=self.no_text) - - -class RecraftStyle: - def __init__(self, style: str=None, substyle: str=None, style_id: str=None): - self.style = style - if substyle == "None": - substyle = None - self.substyle = substyle - self.style_id = style_id - - -class RecraftIO: - STYLEV3 = "RECRAFT_V3_STYLE" - COLOR = "RECRAFT_COLOR" - CONTROLS = "RECRAFT_CONTROLS" - - -class RecraftStyleV3(str, Enum): - #any = 'any' NOTE: this does not work for some reason... why? - realistic_image = 'realistic_image' - digital_illustration = 'digital_illustration' - vector_illustration = 'vector_illustration' - logo_raster = 'logo_raster' - - -def get_v3_substyles(style_v3: str, include_none=True) -> list[str]: - substyles: list[str] = [] - if include_none: - substyles.append("None") - return substyles + dict_recraft_substyles_v3.get(style_v3, []) - - -dict_recraft_substyles_v3 = { - RecraftStyleV3.realistic_image: [ - "b_and_w", - "enterprise", - "evening_light", - "faded_nostalgia", - "forest_life", - "hard_flash", - "hdr", - "motion_blur", - "mystic_naturalism", - "natural_light", - "natural_tones", - "organic_calm", - "real_life_glow", - "retro_realism", - "retro_snapshot", - "studio_portrait", - "urban_drama", - "village_realism", - "warm_folk" - ], - RecraftStyleV3.digital_illustration: [ - "2d_art_poster", - "2d_art_poster_2", - "antiquarian", - "bold_fantasy", - "child_book", - "child_books", - "cover", - "crosshatch", - "digital_engraving", - "engraving_color", - "expressionism", - "freehand_details", - "grain", - "grain_20", - "graphic_intensity", - "hand_drawn", - "hand_drawn_outline", - "handmade_3d", - "hard_comics", - "infantile_sketch", - "long_shadow", - "modern_folk", - "multicolor", - "neon_calm", - "noir", - "nostalgic_pastel", - "outline_details", - "pastel_gradient", - "pastel_sketch", - "pixel_art", - "plastic", - "pop_art", - "pop_renaissance", - "seamless", - "street_art", - "tablet_sketch", - "urban_glow", - "urban_sketching", - "vanilla_dreams", - "young_adult_book", - "young_adult_book_2" - ], - RecraftStyleV3.vector_illustration: [ - "bold_stroke", - "chemistry", - "colored_stencil", - "contour_pop_art", - "cosmics", - "cutout", - "depressive", - "editorial", - "emotional_flat", - "engraving", - "infographical", - "line_art", - "line_circuit", - "linocut", - "marker_outline", - "mosaic", - "naivector", - "roundish_flat", - "seamless", - "segmented_colors", - "sharp_contrast", - "thin", - "vector_photo", - "vivid_shapes" - ], - RecraftStyleV3.logo_raster: [ - "emblem_graffiti", - "emblem_pop_art", - "emblem_punk", - "emblem_stamp", - "emblem_vintage" - ], -} - - -class RecraftModel(str, Enum): - recraftv3 = 'recraftv3' - recraftv2 = 'recraftv2' - - -class RecraftImageSize(str, Enum): - res_1024x1024 = '1024x1024' - res_1365x1024 = '1365x1024' - res_1024x1365 = '1024x1365' - res_1536x1024 = '1536x1024' - res_1024x1536 = '1024x1536' - res_1820x1024 = '1820x1024' - res_1024x1820 = '1024x1820' - res_1024x2048 = '1024x2048' - res_2048x1024 = '2048x1024' - res_1434x1024 = '1434x1024' - res_1024x1434 = '1024x1434' - res_1024x1280 = '1024x1280' - res_1280x1024 = '1280x1024' - res_1024x1707 = '1024x1707' - res_1707x1024 = '1707x1024' - - -class RecraftColorObject(BaseModel): - rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model') - - -class RecraftControlsObject(BaseModel): - colors: Optional[list[RecraftColorObject]] = Field(None, description='An array of preferable colors') - background_color: Optional[RecraftColorObject] = Field(None, description='Use given color as a desired background color') - no_text: Optional[bool] = Field(None, description='Do not embed text layouts') - artistic_level: Optional[conint(ge=0, le=5)] = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].') - - -class RecraftImageGenerationRequest(BaseModel): - prompt: str = Field(..., description='The text prompt describing the image to generate') - size: Optional[RecraftImageSize] = Field(None, description='The size of the generated image (e.g., "1024x1024")') - n: conint(ge=1, le=6) = Field(..., description='The number of images to generate') - negative_prompt: Optional[str] = Field(None, description='A text description of undesired elements on an image') - model: Optional[RecraftModel] = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")') - style: Optional[str] = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")') - substyle: Optional[str] = Field(None, description='The substyle to apply to the generated image, depending on the style input') - controls: Optional[RecraftControlsObject] = Field(None, description='A set of custom parameters to tweak generation process') - style_id: Optional[str] = Field(None, description='Use a previously uploaded style as a reference; UUID') - strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity') - random_seed: Optional[int] = Field(None, description="Seed for video generation") - # text_layout - - -class RecraftReturnedObject(BaseModel): - image_id: str = Field(..., description='Unique identifier for the generated image') - url: str = Field(..., description='URL to access the generated image') - - -class RecraftImageGenerationResponse(BaseModel): - created: int = Field(..., description='Unix timestamp when the generation was created') - credits: int = Field(..., description='Number of credits used for the generation') - data: Optional[list[RecraftReturnedObject]] = Field(None, description='Array of generated image information') - image: Optional[RecraftReturnedObject] = Field(None, description='Single generated image') diff --git a/comfy_api_nodes/apis/request_logger.py b/comfy_api_nodes/apis/request_logger.py deleted file mode 100644 index 42901e1413dab54fe5fa6454878292009582492c..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/request_logger.py +++ /dev/null @@ -1,127 +0,0 @@ -from __future__ import annotations - -import os -import datetime -import json -import logging -import folder_paths - -# Get the logger instance -logger = logging.getLogger(__name__) - -def get_log_directory(): - """ - Ensures the API log directory exists within ComfyUI's temp directory - and returns its path. - """ - base_temp_dir = folder_paths.get_temp_directory() - log_dir = os.path.join(base_temp_dir, "api_logs") - try: - os.makedirs(log_dir, exist_ok=True) - except Exception as e: - logger.error(f"Error creating API log directory {log_dir}: {e}") - # Fallback to base temp directory if sub-directory creation fails - return base_temp_dir - return log_dir - -def _format_data_for_logging(data): - """Helper to format data (dict, str, bytes) for logging.""" - if isinstance(data, bytes): - try: - return data.decode('utf-8') # Try to decode as text - except UnicodeDecodeError: - return f"[Binary data of length {len(data)} bytes]" - elif isinstance(data, (dict, list)): - try: - return json.dumps(data, indent=2, ensure_ascii=False) - except TypeError: - return str(data) # Fallback for non-serializable objects - return str(data) - -def log_request_response( - operation_id: str, - request_method: str, - request_url: str, - request_headers: dict | None = None, - request_params: dict | None = None, - request_data: any = None, - response_status_code: int | None = None, - response_headers: dict | None = None, - response_content: any = None, - error_message: str | None = None -): - """ - Logs API request and response details to a file in the temp/api_logs directory. - """ - log_dir = get_log_directory() - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f") - filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log" - filepath = os.path.join(log_dir, filename) - - log_content = [] - - log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}") - log_content.append(f"Operation ID: {operation_id}") - log_content.append("-" * 30 + " REQUEST " + "-" * 30) - log_content.append(f"Method: {request_method}") - log_content.append(f"URL: {request_url}") - if request_headers: - log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}") - if request_params: - log_content.append(f"Params:\n{_format_data_for_logging(request_params)}") - if request_data: - log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}") - - log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30) - if response_status_code is not None: - log_content.append(f"Status Code: {response_status_code}") - if response_headers: - log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}") - if response_content: - log_content.append(f"Content:\n{_format_data_for_logging(response_content)}") - if error_message: - log_content.append(f"Error:\n{error_message}") - - try: - with open(filepath, "w", encoding="utf-8") as f: - f.write("\n".join(log_content)) - logger.debug(f"API log saved to: {filepath}") - except Exception as e: - logger.error(f"Error writing API log to {filepath}: {e}") - -if __name__ == '__main__': - # Example usage (for testing the logger directly) - logger.setLevel(logging.DEBUG) - # Mock folder_paths for direct execution if not running within ComfyUI full context - if not hasattr(folder_paths, 'get_temp_directory'): - class MockFolderPaths: - def get_temp_directory(self): - # Create a local temp dir for testing if needed - p = os.path.join(os.path.dirname(__file__), 'temp_test_logs') - os.makedirs(p, exist_ok=True) - return p - folder_paths = MockFolderPaths() - - log_request_response( - operation_id="test_operation_get", - request_method="GET", - request_url="https://api.example.com/test", - request_headers={"Authorization": "Bearer testtoken"}, - request_params={"param1": "value1"}, - response_status_code=200, - response_content={"message": "Success!"} - ) - log_request_response( - operation_id="test_operation_post_error", - request_method="POST", - request_url="https://api.example.com/submit", - request_data={"key": "value", "nested": {"num": 123}}, - error_message="Connection timed out" - ) - log_request_response( - operation_id="test_binary_response", - request_method="GET", - request_url="https://api.example.com/image.png", - response_status_code=200, - response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data - ) diff --git a/comfy_api_nodes/apis/rodin_api.py b/comfy_api_nodes/apis/rodin_api.py deleted file mode 100644 index b0cf171fa636a0571f2e771732c0cc7f7536835b..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/rodin_api.py +++ /dev/null @@ -1,57 +0,0 @@ -from __future__ import annotations - -from enum import Enum -from typing import Optional, List -from pydantic import BaseModel, Field - - -class Rodin3DGenerateRequest(BaseModel): - seed: int = Field(..., description="seed_") - tier: str = Field(..., description="Tier of generation.") - material: str = Field(..., description="The material type.") - quality: str = Field(..., description="The generation quality of the mesh.") - mesh_mode: str = Field(..., description="It controls the type of faces of generated models.") - -class GenerateJobsData(BaseModel): - uuids: List[str] = Field(..., description="str LIST") - subscription_key: str = Field(..., description="subscription key") - -class Rodin3DGenerateResponse(BaseModel): - message: Optional[str] = Field(None, description="Return message.") - prompt: Optional[str] = Field(None, description="Generated Prompt from image.") - submit_time: Optional[str] = Field(None, description="Submit Time") - uuid: Optional[str] = Field(None, description="Task str") - jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs") - -class JobStatus(str, Enum): - """ - Status for jobs - """ - Done = "Done" - Failed = "Failed" - Generating = "Generating" - Waiting = "Waiting" - -class Rodin3DCheckStatusRequest(BaseModel): - subscription_key: str = Field(..., description="subscription from generate endpoint") - -class JobItem(BaseModel): - uuid: str = Field(..., description="uuid") - status: JobStatus = Field(...,description="Status Currently") - -class Rodin3DCheckStatusResponse(BaseModel): - jobs: List[JobItem] = Field(..., description="Job status List") - -class Rodin3DDownloadRequest(BaseModel): - task_uuid: str = Field(..., description="Task str") - -class RodinResourceItem(BaseModel): - url: str = Field(..., description="Download Url") - name: str = Field(..., description="File name with ext") - -class Rodin3DDownloadResponse(BaseModel): - list: List[RodinResourceItem] = Field(..., description="Source List") - - - - diff --git a/comfy_api_nodes/apis/stability_api.py b/comfy_api_nodes/apis/stability_api.py deleted file mode 100644 index 47c87daec1bc3d3f5384855c93ad2c4b929227ff..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/stability_api.py +++ /dev/null @@ -1,127 +0,0 @@ -from __future__ import annotations - -from enum import Enum -from typing import Optional - -from pydantic import BaseModel, Field, confloat - - -class StabilityFormat(str, Enum): - png = 'png' - jpeg = 'jpeg' - webp = 'webp' - - -class StabilityAspectRatio(str, Enum): - ratio_1_1 = "1:1" - ratio_16_9 = "16:9" - ratio_9_16 = "9:16" - ratio_3_2 = "3:2" - ratio_2_3 = "2:3" - ratio_5_4 = "5:4" - ratio_4_5 = "4:5" - ratio_21_9 = "21:9" - ratio_9_21 = "9:21" - - -def get_stability_style_presets(include_none=True): - presets = [] - if include_none: - presets.append("None") - return presets + [x.value for x in StabilityStylePreset] - - -class StabilityStylePreset(str, Enum): - _3d_model = "3d-model" - analog_film = "analog-film" - anime = "anime" - cinematic = "cinematic" - comic_book = "comic-book" - digital_art = "digital-art" - enhance = "enhance" - fantasy_art = "fantasy-art" - isometric = "isometric" - line_art = "line-art" - low_poly = "low-poly" - modeling_compound = "modeling-compound" - neon_punk = "neon-punk" - origami = "origami" - photographic = "photographic" - pixel_art = "pixel-art" - tile_texture = "tile-texture" - - -class Stability_SD3_5_Model(str, Enum): - sd3_5_large = "sd3.5-large" - # sd3_5_large_turbo = "sd3.5-large-turbo" - sd3_5_medium = "sd3.5-medium" - - -class Stability_SD3_5_GenerationMode(str, Enum): - text_to_image = "text-to-image" - image_to_image = "image-to-image" - - -class StabilityStable3_5Request(BaseModel): - model: str = Field(...) - mode: str = Field(...) - prompt: str = Field(...) - negative_prompt: Optional[str] = Field(None) - aspect_ratio: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - output_format: Optional[str] = Field(StabilityFormat.png.value) - image: Optional[str] = Field(None) - style_preset: Optional[str] = Field(None) - cfg_scale: float = Field(...) - strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) - - -class StabilityUpscaleConservativeRequest(BaseModel): - prompt: str = Field(...) - negative_prompt: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - output_format: Optional[str] = Field(StabilityFormat.png.value) - image: Optional[str] = Field(None) - creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None) - - -class StabilityUpscaleCreativeRequest(BaseModel): - prompt: str = Field(...) - negative_prompt: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - output_format: Optional[str] = Field(StabilityFormat.png.value) - image: Optional[str] = Field(None) - creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None) - style_preset: Optional[str] = Field(None) - - -class StabilityStableUltraRequest(BaseModel): - prompt: str = Field(...) - negative_prompt: Optional[str] = Field(None) - aspect_ratio: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - output_format: Optional[str] = Field(StabilityFormat.png.value) - image: Optional[str] = Field(None) - style_preset: Optional[str] = Field(None) - strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) - - -class StabilityStableUltraResponse(BaseModel): - image: Optional[str] = Field(None) - finish_reason: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - - -class StabilityResultsGetResponse(BaseModel): - image: Optional[str] = Field(None) - finish_reason: Optional[str] = Field(None) - seed: Optional[int] = Field(None) - id: Optional[str] = Field(None) - name: Optional[str] = Field(None) - errors: Optional[list[str]] = Field(None) - status: Optional[str] = Field(None) - result: Optional[str] = Field(None) - - -class StabilityAsyncResponse(BaseModel): - id: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/tripo_api.py b/comfy_api_nodes/apis/tripo_api.py deleted file mode 100644 index 9f43d4d0901bb24170f9b628b6aea4d157e21deb..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/apis/tripo_api.py +++ /dev/null @@ -1,275 +0,0 @@ -from __future__ import annotations -from comfy_api_nodes.apis import ( - TripoModelVersion, - TripoTextureQuality, -) -from enum import Enum -from typing import Optional, List, Dict, Any, Union - -from pydantic import BaseModel, Field, RootModel - -class TripoStyle(str, Enum): - PERSON_TO_CARTOON = "person:person2cartoon" - ANIMAL_VENOM = "animal:venom" - OBJECT_CLAY = "object:clay" - OBJECT_STEAMPUNK = "object:steampunk" - OBJECT_CHRISTMAS = "object:christmas" - OBJECT_BARBIE = "object:barbie" - GOLD = "gold" - ANCIENT_BRONZE = "ancient_bronze" - NONE = "None" - -class TripoTaskType(str, Enum): - TEXT_TO_MODEL = "text_to_model" - IMAGE_TO_MODEL = "image_to_model" - MULTIVIEW_TO_MODEL = "multiview_to_model" - TEXTURE_MODEL = "texture_model" - REFINE_MODEL = "refine_model" - ANIMATE_PRERIGCHECK = "animate_prerigcheck" - ANIMATE_RIG = "animate_rig" - ANIMATE_RETARGET = "animate_retarget" - STYLIZE_MODEL = "stylize_model" - CONVERT_MODEL = "convert_model" - -class TripoTextureAlignment(str, Enum): - ORIGINAL_IMAGE = "original_image" - GEOMETRY = "geometry" - -class TripoOrientation(str, Enum): - ALIGN_IMAGE = "align_image" - DEFAULT = "default" - -class TripoOutFormat(str, Enum): - GLB = "glb" - FBX = "fbx" - -class TripoTopology(str, Enum): - BIP = "bip" - QUAD = "quad" - -class TripoSpec(str, Enum): - MIXAMO = "mixamo" - TRIPO = "tripo" - -class TripoAnimation(str, Enum): - IDLE = "preset:idle" - WALK = "preset:walk" - CLIMB = "preset:climb" - JUMP = "preset:jump" - RUN = "preset:run" - SLASH = "preset:slash" - SHOOT = "preset:shoot" - HURT = "preset:hurt" - FALL = "preset:fall" - TURN = "preset:turn" - -class TripoStylizeStyle(str, Enum): - LEGO = "lego" - VOXEL = "voxel" - VORONOI = "voronoi" - MINECRAFT = "minecraft" - -class TripoConvertFormat(str, Enum): - GLTF = "GLTF" - USDZ = "USDZ" - FBX = "FBX" - OBJ = "OBJ" - STL = "STL" - _3MF = "3MF" - -class TripoTextureFormat(str, Enum): - BMP = "BMP" - DPX = "DPX" - HDR = "HDR" - JPEG = "JPEG" - OPEN_EXR = "OPEN_EXR" - PNG = "PNG" - TARGA = "TARGA" - TIFF = "TIFF" - WEBP = "WEBP" - -class TripoTaskStatus(str, Enum): - QUEUED = "queued" - RUNNING = "running" - SUCCESS = "success" - FAILED = "failed" - CANCELLED = "cancelled" - UNKNOWN = "unknown" - BANNED = "banned" - EXPIRED = "expired" - -class TripoFileTokenReference(BaseModel): - type: Optional[str] = Field(None, description='The type of the reference') - file_token: str - -class TripoUrlReference(BaseModel): - type: Optional[str] = Field(None, description='The type of the reference') - url: str - -class TripoObjectStorage(BaseModel): - bucket: str - key: str - -class TripoObjectReference(BaseModel): - type: str - object: TripoObjectStorage - -class TripoFileEmptyReference(BaseModel): - pass - -class TripoFileReference(RootModel): - root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference] - -class TripoGetStsTokenRequest(BaseModel): - format: str = Field(..., description='The format of the image') - -class TripoTextToModelRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description='Type of task') - prompt: str = Field(..., description='The text prompt describing the model to generate', max_length=1024) - negative_prompt: Optional[str] = Field(None, description='The negative text prompt', max_length=1024) - model_version: Optional[TripoModelVersion] = TripoModelVersion.v2_5_20250123 - face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') - texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') - pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') - image_seed: Optional[int] = Field(None, description='The seed for the text') - model_seed: Optional[int] = Field(None, description='The seed for the model') - texture_seed: Optional[int] = Field(None, description='The seed for the texture') - texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard - style: Optional[TripoStyle] = None - auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') - quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') - -class TripoImageToModelRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description='Type of task') - file: TripoFileReference = Field(..., description='The file reference to convert to a model') - model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') - face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') - texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') - pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') - model_seed: Optional[int] = Field(None, description='The seed for the model') - texture_seed: Optional[int] = Field(None, description='The seed for the texture') - texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard - texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') - style: Optional[TripoStyle] = Field(None, description='The style to apply to the generated model') - auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') - orientation: Optional[TripoOrientation] = TripoOrientation.DEFAULT - quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') - -class TripoMultiviewToModelRequest(BaseModel): - type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL - files: List[TripoFileReference] = Field(..., description='The file references to convert to a model') - model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') - orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection') - face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') - texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') - pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') - model_seed: Optional[int] = Field(None, description='The seed for the model') - texture_seed: Optional[int] = Field(None, description='The seed for the texture') - texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard - texture_alignment: Optional[TripoTextureAlignment] = TripoTextureAlignment.ORIGINAL_IMAGE - auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') - orientation: Optional[TripoOrientation] = Field(TripoOrientation.DEFAULT, description='The orientation for the model') - quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') - -class TripoTextureModelRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description='Type of task') - original_model_task_id: str = Field(..., description='The task ID of the original model') - texture: Optional[bool] = Field(True, description='Whether to apply texture to the model') - pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the model') - model_seed: Optional[int] = Field(None, description='The seed for the model') - texture_seed: Optional[int] = Field(None, description='The seed for the texture') - texture_quality: Optional[TripoTextureQuality] = Field(None, description='The quality of the texture') - texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') - -class TripoRefineModelRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.REFINE_MODEL, description='Type of task') - draft_model_task_id: str = Field(..., description='The task ID of the draft model') - -class TripoAnimatePrerigcheckRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.ANIMATE_PRERIGCHECK, description='Type of task') - original_model_task_id: str = Field(..., description='The task ID of the original model') - -class TripoAnimateRigRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description='Type of task') - original_model_task_id: str = Field(..., description='The task ID of the original model') - out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') - spec: Optional[TripoSpec] = Field(TripoSpec.TRIPO, description='The specification for rigging') - -class TripoAnimateRetargetRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description='Type of task') - original_model_task_id: str = Field(..., description='The task ID of the original model') - animation: TripoAnimation = Field(..., description='The animation to apply') - out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') - bake_animation: Optional[bool] = Field(True, description='Whether to bake the animation') - -class TripoStylizeModelRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.STYLIZE_MODEL, description='Type of task') - style: TripoStylizeStyle = Field(..., description='The style to apply to the model') - original_model_task_id: str = Field(..., description='The task ID of the original model') - block_size: Optional[int] = Field(80, description='The block size for stylization') - -class TripoConvertModelRequest(BaseModel): - type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description='Type of task') - format: TripoConvertFormat = Field(..., description='The format to convert to') - original_model_task_id: str = Field(..., description='The task ID of the original model') - quad: Optional[bool] = Field(False, description='Whether to apply quad to the model') - force_symmetry: Optional[bool] = Field(False, description='Whether to force symmetry') - face_limit: Optional[int] = Field(10000, description='The number of faces to limit the conversion to') - flatten_bottom: Optional[bool] = Field(False, description='Whether to flatten the bottom of the model') - flatten_bottom_threshold: Optional[float] = Field(0.01, description='The threshold for flattening the bottom') - texture_size: Optional[int] = Field(4096, description='The size of the texture') - texture_format: Optional[TripoTextureFormat] = Field(TripoTextureFormat.JPEG, description='The format of the texture') - pivot_to_center_bottom: Optional[bool] = Field(False, description='Whether to pivot to the center bottom') - -class TripoTaskRequest(RootModel): - root: Union[ - TripoTextToModelRequest, - TripoImageToModelRequest, - TripoMultiviewToModelRequest, - TripoTextureModelRequest, - TripoRefineModelRequest, - TripoAnimatePrerigcheckRequest, - TripoAnimateRigRequest, - TripoAnimateRetargetRequest, - TripoStylizeModelRequest, - TripoConvertModelRequest - ] - -class TripoTaskOutput(BaseModel): - model: Optional[str] = Field(None, description='URL to the model') - base_model: Optional[str] = Field(None, description='URL to the base model') - pbr_model: Optional[str] = Field(None, description='URL to the PBR model') - rendered_image: Optional[str] = Field(None, description='URL to the rendered image') - riggable: Optional[bool] = Field(None, description='Whether the model is riggable') - -class TripoTask(BaseModel): - task_id: str = Field(..., description='The task ID') - type: Optional[str] = Field(None, description='The type of task') - status: Optional[TripoTaskStatus] = Field(None, description='The status of the task') - input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task') - output: Optional[TripoTaskOutput] = Field(None, description='The output of the task') - progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100) - create_time: Optional[int] = Field(None, description='The creation time of the task') - running_left_time: Optional[int] = Field(None, description='The estimated time left for the task') - queue_position: Optional[int] = Field(None, description='The position in the queue') - -class TripoTaskResponse(BaseModel): - code: int = Field(0, description='The response code') - data: TripoTask = Field(..., description='The task data') - -class TripoGeneralResponse(BaseModel): - code: int = Field(0, description='The response code') - data: Dict[str, str] = Field(..., description='The task ID data') - -class TripoBalanceData(BaseModel): - balance: float = Field(..., description='The account balance') - frozen: float = Field(..., description='The frozen balance') - -class TripoBalanceResponse(BaseModel): - code: int = Field(0, description='The response code') - data: TripoBalanceData = Field(..., description='The balance data') - -class TripoErrorResponse(BaseModel): - code: int = Field(..., description='The error code') - message: str = Field(..., description='The error message') - suggestion: str = Field(..., description='The suggestion for fixing the error') diff --git a/comfy_api_nodes/canary.py b/comfy_api_nodes/canary.py deleted file mode 100644 index 4df7590b64fea8f6f3f77fde912cb2f64fdad357..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/canary.py +++ /dev/null @@ -1,10 +0,0 @@ -import av - -ver = av.__version__.split(".") -if int(ver[0]) < 14: - raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") - -if int(ver[0]) == 14 and int(ver[1]) < 2: - raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") - -NODE_CLASS_MAPPINGS = {} diff --git a/comfy_api_nodes/mapper_utils.py b/comfy_api_nodes/mapper_utils.py deleted file mode 100644 index 6fab8f4bbc6ce4495ce41588f2fba8103abeffeb..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/mapper_utils.py +++ /dev/null @@ -1,116 +0,0 @@ -from enum import Enum - -from pydantic.fields import FieldInfo -from pydantic import BaseModel -from pydantic_core import PydanticUndefined - -from comfy.comfy_types.node_typing import IO, InputTypeOptions - -NodeInput = tuple[IO, InputTypeOptions] - - -def _create_base_config(field_info: FieldInfo) -> InputTypeOptions: - config = {} - if hasattr(field_info, "default") and field_info.default is not PydanticUndefined: - config["default"] = field_info.default - if hasattr(field_info, "description") and field_info.description is not None: - config["tooltip"] = field_info.description - return config - - -def _get_number_constraints_config(field_info: FieldInfo) -> dict: - config = {} - if hasattr(field_info, "metadata"): - metadata = field_info.metadata - for constraint in metadata: - if hasattr(constraint, "ge"): - config["min"] = constraint.ge - if hasattr(constraint, "le"): - config["max"] = constraint.le - if hasattr(constraint, "multiple_of"): - config["step"] = constraint.multiple_of - return config - - -def _model_field_to_image_input(field_info: FieldInfo, **kwargs) -> NodeInput: - return IO.IMAGE, { - **_create_base_config(field_info), - **kwargs, - } - - -def _model_field_to_string_input(field_info: FieldInfo, **kwargs) -> NodeInput: - return IO.STRING, { - **_create_base_config(field_info), - **kwargs, - } - - -def _model_field_to_float_input(field_info: FieldInfo, **kwargs) -> NodeInput: - return IO.FLOAT, { - **_create_base_config(field_info), - **_get_number_constraints_config(field_info), - **kwargs, - } - - -def _model_field_to_int_input(field_info: FieldInfo, **kwargs) -> NodeInput: - return IO.INT, { - **_create_base_config(field_info), - **_get_number_constraints_config(field_info), - **kwargs, - } - - -def _model_field_to_combo_input( - field_info: FieldInfo, enum_type: type[Enum] = None, **kwargs -) -> NodeInput: - combo_config = {} - if enum_type is not None: - combo_config["options"] = [option.value for option in enum_type] - combo_config = { - **combo_config, - **_create_base_config(field_info), - **kwargs, - } - return IO.COMBO, combo_config - - -def model_field_to_node_input( - input_type: IO, base_model: type[BaseModel], field_name: str, **kwargs -) -> NodeInput: - """ - Maps a field from a Pydantic model to a Comfy node input. - - Args: - input_type: The type of the input. - base_model: The Pydantic model to map the field from. - field_name: The name of the field to map. - **kwargs: Additional key/values to include in the input options. - - Note: - For combo inputs, pass an `Enum` to the `enum_type` keyword argument to populate the options automatically. - - Example: - >>> model_field_to_node_input(IO.STRING, MyModel, "my_field", multiline=True) - >>> model_field_to_node_input(IO.COMBO, MyModel, "my_field", enum_type=MyEnum) - >>> model_field_to_node_input(IO.FLOAT, MyModel, "my_field", slider=True) - """ - field_info: FieldInfo = base_model.model_fields[field_name] - result: NodeInput - - if input_type == IO.IMAGE: - result = _model_field_to_image_input(field_info, **kwargs) - elif input_type == IO.STRING: - result = _model_field_to_string_input(field_info, **kwargs) - elif input_type == IO.FLOAT: - result = _model_field_to_float_input(field_info, **kwargs) - elif input_type == IO.INT: - result = _model_field_to_int_input(field_info, **kwargs) - elif input_type == IO.COMBO: - result = _model_field_to_combo_input(field_info, **kwargs) - else: - message = f"Invalid input type: {input_type}" - raise ValueError(message) - - return result diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py deleted file mode 100644 index c09be8d5bf22d1e70abc344d20ac3cedabf9aa16..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_bfl.py +++ /dev/null @@ -1,1077 +0,0 @@ -import asyncio -import io -from inspect import cleandoc -from typing import Union, Optional -from comfy.comfy_types.node_typing import IO, ComfyNodeABC -from comfy_api_nodes.apis.bfl_api import ( - BFLStatus, - BFLFluxExpandImageRequest, - BFLFluxFillImageRequest, - BFLFluxCannyImageRequest, - BFLFluxDepthImageRequest, - BFLFluxProGenerateRequest, - BFLFluxKontextProGenerateRequest, - BFLFluxProUltraGenerateRequest, - BFLFluxProGenerateResponse, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, -) -from comfy_api_nodes.apinode_utils import ( - downscale_image_tensor, - validate_aspect_ratio, - process_image_response, - resize_mask_to_image, - validate_string, -) - -import numpy as np -from PIL import Image -import aiohttp -import torch -import base64 -import time -from server import PromptServer - - -def convert_mask_to_image(mask: torch.Tensor): - """ - Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image. - """ - mask = mask.unsqueeze(-1) - mask = torch.cat([mask]*3, dim=-1) - return mask - - -async def handle_bfl_synchronous_operation( - operation: SynchronousOperation, - timeout_bfl_calls=360, - node_id: Union[str, None] = None, -): - response_api: BFLFluxProGenerateResponse = await operation.execute() - return await _poll_until_generated( - response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id - ) - - -async def _poll_until_generated( - polling_url: str, timeout=360, node_id: Union[str, None] = None -): - # used bfl-comfy-nodes to verify code implementation: - # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main - start_time = time.time() - retries_404 = 0 - max_retries_404 = 5 - retry_404_seconds = 2 - retry_202_seconds = 2 - retry_pending_seconds = 1 - - async with aiohttp.ClientSession() as session: - # NOTE: should True loop be replaced with checking if workflow has been interrupted? - while True: - if node_id: - time_elapsed = time.time() - start_time - PromptServer.instance.send_progress_text( - f"Generating ({time_elapsed:.0f}s)", node_id - ) - - async with session.get(polling_url) as response: - if response.status == 200: - result = await response.json() - if result["status"] == BFLStatus.ready: - img_url = result["result"]["sample"] - if node_id: - PromptServer.instance.send_progress_text( - f"Result URL: {img_url}", node_id - ) - async with session.get(img_url) as img_resp: - return process_image_response(await img_resp.content.read()) - elif result["status"] in [ - BFLStatus.request_moderated, - BFLStatus.content_moderated, - ]: - status = result["status"] - raise Exception( - f"BFL API did not return an image due to: {status}." - ) - elif result["status"] == BFLStatus.error: - raise Exception(f"BFL API encountered an error: {result}.") - elif result["status"] == BFLStatus.pending: - await asyncio.sleep(retry_pending_seconds) - continue - elif response.status == 404: - if retries_404 < max_retries_404: - retries_404 += 1 - await asyncio.sleep(retry_404_seconds) - continue - raise Exception( - f"BFL API could not find task after {max_retries_404} tries." - ) - elif response.status == 202: - await asyncio.sleep(retry_202_seconds) - elif time.time() - start_time > timeout: - raise Exception( - f"BFL API experienced a timeout; could not return request under {timeout} seconds." - ) - else: - raise Exception(f"BFL API encountered an error: {response.json()}") - -def convert_image_to_base64(image: torch.Tensor): - scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048) - # remove batch dimension if present - if len(scaled_image.shape) > 3: - scaled_image = scaled_image[0] - image_np = (scaled_image.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format="PNG") - return base64.b64encode(img_byte_arr.getvalue()).decode() - - -class FluxProUltraImageNode(ComfyNodeABC): - """ - Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution. - """ - - MINIMUM_RATIO = 1 / 4 - MAXIMUM_RATIO = 4 / 1 - MINIMUM_RATIO_STR = "1:4" - MAXIMUM_RATIO_STR = "4:1" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - "aspect_ratio": ( - IO.STRING, - { - "default": "16:9", - "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", - }, - ), - "raw": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "When True, generate less processed, more natural-looking images.", - }, - ), - }, - "optional": { - "image_prompt": (IO.IMAGE,), - "image_prompt_strength": ( - IO.FLOAT, - { - "default": 0.1, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Blend between the prompt and the image prompt.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - @classmethod - def VALIDATE_INPUTS(cls, aspect_ratio: str): - try: - validate_aspect_ratio( - aspect_ratio, - minimum_ratio=cls.MINIMUM_RATIO, - maximum_ratio=cls.MAXIMUM_RATIO, - minimum_ratio_str=cls.MINIMUM_RATIO_STR, - maximum_ratio_str=cls.MAXIMUM_RATIO_STR, - ) - except Exception as e: - return str(e) - return True - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - async def api_call( - self, - prompt: str, - aspect_ratio: str, - prompt_upsampling=False, - raw=False, - seed=0, - image_prompt=None, - image_prompt_strength=0.1, - unique_id: Union[str, None] = None, - **kwargs, - ): - if image_prompt is None: - validate_string(prompt, strip_whitespace=False) - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.1-ultra/generate", - method=HttpMethod.POST, - request_model=BFLFluxProUltraGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxProUltraGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - seed=seed, - aspect_ratio=validate_aspect_ratio( - aspect_ratio, - minimum_ratio=self.MINIMUM_RATIO, - maximum_ratio=self.MAXIMUM_RATIO, - minimum_ratio_str=self.MINIMUM_RATIO_STR, - maximum_ratio_str=self.MAXIMUM_RATIO_STR, - ), - raw=raw, - image_prompt=( - image_prompt - if image_prompt is None - else convert_image_to_base64(image_prompt) - ), - image_prompt_strength=( - None if image_prompt is None else round(image_prompt_strength, 2) - ), - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -class FluxKontextProImageNode(ComfyNodeABC): - """ - Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio. - """ - - MINIMUM_RATIO = 1 / 4 - MAXIMUM_RATIO = 4 / 1 - MINIMUM_RATIO_STR = "1:4" - MAXIMUM_RATIO_STR = "4:1" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation - specify what and how to edit.", - }, - ), - "aspect_ratio": ( - IO.STRING, - { - "default": "16:9", - "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 3.0, - "min": 0.1, - "max": 99.0, - "step": 0.1, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 1, - "max": 150, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 1234, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - }, - "optional": { - "input_image": (IO.IMAGE,), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate" - - async def api_call( - self, - prompt: str, - aspect_ratio: str, - guidance: float, - steps: int, - input_image: Optional[torch.Tensor]=None, - seed=0, - prompt_upsampling=False, - unique_id: Union[str, None] = None, - **kwargs, - ): - aspect_ratio = validate_aspect_ratio( - aspect_ratio, - minimum_ratio=self.MINIMUM_RATIO, - maximum_ratio=self.MAXIMUM_RATIO, - minimum_ratio_str=self.MINIMUM_RATIO_STR, - maximum_ratio_str=self.MAXIMUM_RATIO_STR, - ) - if input_image is None: - validate_string(prompt, strip_whitespace=False) - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=self.BFL_PATH, - method=HttpMethod.POST, - request_model=BFLFluxKontextProGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxKontextProGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - guidance=round(guidance, 1), - steps=steps, - seed=seed, - aspect_ratio=aspect_ratio, - input_image=( - input_image - if input_image is None - else convert_image_to_base64(input_image) - ) - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -class FluxKontextMaxImageNode(FluxKontextProImageNode): - """ - Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio. - """ - - DESCRIPTION = cleandoc(__doc__ or "") - BFL_PATH = "/proxy/bfl/flux-kontext-max/generate" - - -class FluxProImageNode(ComfyNodeABC): - """ - Generates images synchronously based on prompt and resolution. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "width": ( - IO.INT, - { - "default": 1024, - "min": 256, - "max": 1440, - "step": 32, - }, - ), - "height": ( - IO.INT, - { - "default": 768, - "min": 256, - "max": 1440, - "step": 32, - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": { - "image_prompt": (IO.IMAGE,), - # "image_prompt_strength": ( - # IO.FLOAT, - # { - # "default": 0.1, - # "min": 0.0, - # "max": 1.0, - # "step": 0.01, - # "tooltip": "Blend between the prompt and the image prompt.", - # }, - # ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - async def api_call( - self, - prompt: str, - prompt_upsampling, - width: int, - height: int, - seed=0, - image_prompt=None, - # image_prompt_strength=0.1, - unique_id: Union[str, None] = None, - **kwargs, - ): - image_prompt = ( - image_prompt - if image_prompt is None - else convert_image_to_base64(image_prompt) - ) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.1/generate", - method=HttpMethod.POST, - request_model=BFLFluxProGenerateRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxProGenerateRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - width=width, - height=height, - seed=seed, - image_prompt=image_prompt, - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -class FluxProExpandNode(ComfyNodeABC): - """ - Outpaints image based on prompt. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "top": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the top of the image" - }, - ), - "bottom": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the bottom of the image" - }, - ), - "left": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the left side of the image" - }, - ), - "right": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2048, - "tooltip": "Number of pixels to expand at the right side of the image" - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 60, - "min": 1.5, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - async def api_call( - self, - image: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - top: int, - bottom: int, - left: int, - right: int, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - image = convert_image_to_base64(image) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-expand/generate", - method=HttpMethod.POST, - request_model=BFLFluxExpandImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxExpandImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - top=top, - bottom=bottom, - left=left, - right=right, - steps=steps, - guidance=guidance, - seed=seed, - image=image, - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - - -class FluxProFillNode(ComfyNodeABC): - """ - Inpaints image based on mask and prompt. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "mask": (IO.MASK,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 60, - "min": 1.5, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - async def api_call( - self, - image: torch.Tensor, - mask: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - # prepare mask - mask = resize_mask_to_image(mask, image) - mask = convert_image_to_base64(convert_mask_to_image(mask)) - # make sure image will have alpha channel removed - image = convert_image_to_base64(image[:, :, :, :3]) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-fill/generate", - method=HttpMethod.POST, - request_model=BFLFluxFillImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxFillImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - steps=steps, - guidance=guidance, - seed=seed, - image=image, - mask=mask, - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -class FluxProCannyNode(ComfyNodeABC): - """ - Generate image using a control image (canny). - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "control_image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "canny_low_threshold": ( - IO.FLOAT, - { - "default": 0.1, - "min": 0.01, - "max": 0.99, - "step": 0.01, - "tooltip": "Low threshold for Canny edge detection; ignored if skip_processing is True" - }, - ), - "canny_high_threshold": ( - IO.FLOAT, - { - "default": 0.4, - "min": 0.01, - "max": 0.99, - "step": 0.01, - "tooltip": "High threshold for Canny edge detection; ignored if skip_processing is True" - }, - ), - "skip_preprocessing": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to skip preprocessing; set to True if control_image already is canny-fied, False if it is a raw image.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 30, - "min": 1, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - async def api_call( - self, - control_image: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - canny_low_threshold: float, - canny_high_threshold: float, - skip_preprocessing: bool, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - control_image = convert_image_to_base64(control_image[:, :, :, :3]) - preprocessed_image = None - - # scale canny threshold between 0-500, to match BFL's API - def scale_value(value: float, min_val=0, max_val=500): - return min_val + value * (max_val - min_val) - canny_low_threshold = int(round(scale_value(canny_low_threshold))) - canny_high_threshold = int(round(scale_value(canny_high_threshold))) - - - if skip_preprocessing: - preprocessed_image = control_image - control_image = None - canny_low_threshold = None - canny_high_threshold = None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-canny/generate", - method=HttpMethod.POST, - request_model=BFLFluxCannyImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxCannyImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - steps=steps, - guidance=guidance, - seed=seed, - control_image=control_image, - canny_low_threshold=canny_low_threshold, - canny_high_threshold=canny_high_threshold, - preprocessed_image=preprocessed_image, - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -class FluxProDepthNode(ComfyNodeABC): - """ - Generate image using a control image (depth). - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "control_image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "prompt_upsampling": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", - }, - ), - "skip_preprocessing": ( - IO.BOOLEAN, - { - "default": False, - "tooltip": "Whether to skip preprocessing; set to True if control_image already is depth-ified, False if it is a raw image.", - }, - ), - "guidance": ( - IO.FLOAT, - { - "default": 15, - "min": 1, - "max": 100, - "tooltip": "Guidance strength for the image generation process" - }, - ), - "steps": ( - IO.INT, - { - "default": 50, - "min": 15, - "max": 50, - "tooltip": "Number of steps for the image generation process" - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/BFL" - - async def api_call( - self, - control_image: torch.Tensor, - prompt: str, - prompt_upsampling: bool, - skip_preprocessing: bool, - steps: int, - guidance: float, - seed=0, - unique_id: Union[str, None] = None, - **kwargs, - ): - control_image = convert_image_to_base64(control_image[:,:,:,:3]) - preprocessed_image = None - - if skip_preprocessing: - preprocessed_image = control_image - control_image = None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/bfl/flux-pro-1.0-depth/generate", - method=HttpMethod.POST, - request_model=BFLFluxDepthImageRequest, - response_model=BFLFluxProGenerateResponse, - ), - request=BFLFluxDepthImageRequest( - prompt=prompt, - prompt_upsampling=prompt_upsampling, - steps=steps, - guidance=guidance, - seed=seed, - control_image=control_image, - preprocessed_image=preprocessed_image, - ), - auth_kwargs=kwargs, - ) - output_image = await handle_bfl_synchronous_operation(operation, node_id=unique_id) - return (output_image,) - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "FluxProUltraImageNode": FluxProUltraImageNode, - # "FluxProImageNode": FluxProImageNode, - "FluxKontextProImageNode": FluxKontextProImageNode, - "FluxKontextMaxImageNode": FluxKontextMaxImageNode, - "FluxProExpandNode": FluxProExpandNode, - "FluxProFillNode": FluxProFillNode, - "FluxProCannyNode": FluxProCannyNode, - "FluxProDepthNode": FluxProDepthNode, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image", - # "FluxProImageNode": "Flux 1.1 [pro] Image", - "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image", - "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image", - "FluxProExpandNode": "Flux.1 Expand Image", - "FluxProFillNode": "Flux.1 Fill Image", - "FluxProCannyNode": "Flux.1 Canny Control Image", - "FluxProDepthNode": "Flux.1 Depth Control Image", -} diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py deleted file mode 100644 index baa379b75448e66e9297de15d4e8315ae470a0ed..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_gemini.py +++ /dev/null @@ -1,673 +0,0 @@ -""" -API Nodes for Gemini Multimodal LLM Usage via Remote API -See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference -""" -from __future__ import annotations - -import json -import time -import os -import uuid -import base64 -from io import BytesIO -from enum import Enum -from typing import Optional, Literal - -import torch - -import folder_paths -from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict -from server import PromptServer -from comfy_api_nodes.apis import ( - GeminiContent, - GeminiGenerateContentRequest, - GeminiGenerateContentResponse, - GeminiInlineData, - GeminiPart, - GeminiMimeType, -) -from comfy_api_nodes.apis.gemini_api import GeminiImageGenerationConfig, GeminiImageGenerateContentRequest -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, -) -from comfy_api_nodes.apinode_utils import ( - validate_string, - audio_to_base64_string, - video_to_base64_string, - tensor_to_base64_string, - bytesio_to_image_tensor, -) - - -GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini" -GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB - - -class GeminiModel(str, Enum): - """ - Gemini Model Names allowed by comfy-api - """ - - gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06" - gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17" - gemini_2_5_pro = "gemini-2.5-pro" - gemini_2_5_flash = "gemini-2.5-flash" - - -class GeminiImageModel(str, Enum): - """ - Gemini Image Model Names allowed by comfy-api - """ - - gemini_2_5_flash_image_preview = "gemini-2.5-flash-image-preview" - - -def get_gemini_endpoint( - model: GeminiModel, -) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]: - """ - Get the API endpoint for a given Gemini model. - - Args: - model: The Gemini model to use, either as enum or string value. - - Returns: - ApiEndpoint configured for the specific Gemini model. - """ - if isinstance(model, str): - model = GeminiModel(model) - return ApiEndpoint( - path=f"{GEMINI_BASE_ENDPOINT}/{model.value}", - method=HttpMethod.POST, - request_model=GeminiGenerateContentRequest, - response_model=GeminiGenerateContentResponse, - ) - - -def get_gemini_image_endpoint( - model: GeminiImageModel, -) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]: - """ - Get the API endpoint for a given Gemini model. - - Args: - model: The Gemini model to use, either as enum or string value. - - Returns: - ApiEndpoint configured for the specific Gemini model. - """ - if isinstance(model, str): - model = GeminiImageModel(model) - return ApiEndpoint( - path=f"{GEMINI_BASE_ENDPOINT}/{model.value}", - method=HttpMethod.POST, - request_model=GeminiImageGenerateContentRequest, - response_model=GeminiGenerateContentResponse, - ) - - -def create_image_parts(image_input: torch.Tensor) -> list[GeminiPart]: - """ - Convert image tensor input to Gemini API compatible parts. - - Args: - image_input: Batch of image tensors from ComfyUI. - - Returns: - List of GeminiPart objects containing the encoded images. - """ - image_parts: list[GeminiPart] = [] - for image_index in range(image_input.shape[0]): - image_as_b64 = tensor_to_base64_string( - image_input[image_index].unsqueeze(0) - ) - image_parts.append( - GeminiPart( - inlineData=GeminiInlineData( - mimeType=GeminiMimeType.image_png, - data=image_as_b64, - ) - ) - ) - return image_parts - - -def create_text_part(text: str) -> GeminiPart: - """ - Create a text part for the Gemini API request. - - Args: - text: The text content to include in the request. - - Returns: - A GeminiPart object with the text content. - """ - return GeminiPart(text=text) - - -def get_parts_from_response( - response: GeminiGenerateContentResponse -) -> list[GeminiPart]: - """ - Extract all parts from the Gemini API response. - - Args: - response: The API response from Gemini. - - Returns: - List of response parts from the first candidate. - """ - return response.candidates[0].content.parts - - -def get_parts_by_type( - response: GeminiGenerateContentResponse, part_type: Literal["text"] | str -) -> list[GeminiPart]: - """ - Filter response parts by their type. - - Args: - response: The API response from Gemini. - part_type: Type of parts to extract ("text" or a MIME type). - - Returns: - List of response parts matching the requested type. - """ - parts = [] - for part in get_parts_from_response(response): - if part_type == "text" and hasattr(part, "text") and part.text: - parts.append(part) - elif ( - hasattr(part, "inlineData") - and part.inlineData - and part.inlineData.mimeType == part_type - ): - parts.append(part) - # Skip parts that don't match the requested type - return parts - - -def get_text_from_response(response: GeminiGenerateContentResponse) -> str: - """ - Extract and concatenate all text parts from the response. - - Args: - response: The API response from Gemini. - - Returns: - Combined text from all text parts in the response. - """ - parts = get_parts_by_type(response, "text") - return "\n".join([part.text for part in parts]) - - -def get_image_from_response(response: GeminiGenerateContentResponse) -> torch.Tensor: - image_tensors: list[torch.Tensor] = [] - parts = get_parts_by_type(response, "image/png") - for part in parts: - image_data = base64.b64decode(part.inlineData.data) - returned_image = bytesio_to_image_tensor(BytesIO(image_data)) - image_tensors.append(returned_image) - if len(image_tensors) == 0: - return torch.zeros((1,1024,1024,4)) - return torch.cat(image_tensors, dim=0) - - -class GeminiNode(ComfyNodeABC): - """ - Node to generate text responses from a Gemini model. - - This node allows users to interact with Google's Gemini AI models, providing - multimodal inputs (text, images, audio, video, files) to generate coherent - text responses. The node works with the latest Gemini models, handling the - API communication and response parsing. - """ - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Text inputs to the model, used to generate a response. You can include detailed instructions, questions, or context for the model.", - }, - ), - "model": ( - IO.COMBO, - { - "tooltip": "The Gemini model to use for generating responses.", - "options": [model.value for model in GeminiModel], - "default": GeminiModel.gemini_2_5_pro.value, - }, - ), - "seed": ( - IO.INT, - { - "default": 42, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.", - }, - ), - }, - "optional": { - "images": ( - IO.IMAGE, - { - "default": None, - "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", - }, - ), - "audio": ( - IO.AUDIO, - { - "tooltip": "Optional audio to use as context for the model.", - "default": None, - }, - ), - "video": ( - IO.VIDEO, - { - "tooltip": "Optional video to use as context for the model.", - "default": None, - }, - ), - "files": ( - "GEMINI_INPUT_FILES", - { - "default": None, - "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Generate text responses with Google's Gemini AI model. You can provide multiple types of inputs (text, images, audio, video) as context for generating more relevant and meaningful responses." - RETURN_TYPES = ("STRING",) - FUNCTION = "api_call" - CATEGORY = "api node/text/Gemini" - API_NODE = True - - def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]: - """ - Convert video input to Gemini API compatible parts. - - Args: - video_input: Video tensor from ComfyUI. - **kwargs: Additional arguments to pass to the conversion function. - - Returns: - List of GeminiPart objects containing the encoded video. - """ - from comfy_api.util import VideoContainer, VideoCodec - base_64_string = video_to_base64_string( - video_input, - container_format=VideoContainer.MP4, - codec=VideoCodec.H264 - ) - return [ - GeminiPart( - inlineData=GeminiInlineData( - mimeType=GeminiMimeType.video_mp4, - data=base_64_string, - ) - ) - ] - - def create_audio_parts(self, audio_input: IO.AUDIO) -> list[GeminiPart]: - """ - Convert audio input to Gemini API compatible parts. - - Args: - audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate. - - Returns: - List of GeminiPart objects containing the encoded audio. - """ - audio_parts: list[GeminiPart] = [] - for batch_index in range(audio_input["waveform"].shape[0]): - # Recreate an IO.AUDIO object for the given batch dimension index - audio_at_index = { - "waveform": audio_input["waveform"][batch_index].unsqueeze(0), - "sample_rate": audio_input["sample_rate"], - } - # Convert to MP3 format for compatibility with Gemini API - audio_bytes = audio_to_base64_string( - audio_at_index, - container_format="mp3", - codec_name="libmp3lame", - ) - audio_parts.append( - GeminiPart( - inlineData=GeminiInlineData( - mimeType=GeminiMimeType.audio_mp3, - data=audio_bytes, - ) - ) - ) - return audio_parts - - async def api_call( - self, - prompt: str, - model: GeminiModel, - images: Optional[IO.IMAGE] = None, - audio: Optional[IO.AUDIO] = None, - video: Optional[IO.VIDEO] = None, - files: Optional[list[GeminiPart]] = None, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[str]: - # Validate inputs - validate_string(prompt, strip_whitespace=False) - - # Create parts list with text prompt as the first part - parts: list[GeminiPart] = [create_text_part(prompt)] - - # Add other modal parts - if images is not None: - image_parts = create_image_parts(images) - parts.extend(image_parts) - if audio is not None: - parts.extend(self.create_audio_parts(audio)) - if video is not None: - parts.extend(self.create_video_parts(video)) - if files is not None: - parts.extend(files) - - # Create response - response = await SynchronousOperation( - endpoint=get_gemini_endpoint(model), - request=GeminiGenerateContentRequest( - contents=[ - GeminiContent( - role="user", - parts=parts, - ) - ] - ), - auth_kwargs=kwargs, - ).execute() - - # Get result output - output_text = get_text_from_response(response) - if unique_id and output_text: - # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. - render_spec = { - "node_id": unique_id, - "component": "ChatHistoryWidget", - "props": { - "history": json.dumps( - [ - { - "prompt": prompt, - "response": output_text, - "response_id": str(uuid.uuid4()), - "timestamp": time.time(), - } - ] - ), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - - return (output_text or "Empty response from Gemini model...",) - - -class GeminiInputFiles(ComfyNodeABC): - """ - Loads and formats input files for use with the Gemini API. - - This node allows users to include text (.txt) and PDF (.pdf) files as input - context for the Gemini model. Files are converted to the appropriate format - required by the API and can be chained together to include multiple files - in a single request. - """ - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - """ - For details about the supported file input types, see: - https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference - """ - input_dir = folder_paths.get_input_directory() - input_files = [ - f - for f in os.scandir(input_dir) - if f.is_file() - and (f.name.endswith(".txt") or f.name.endswith(".pdf")) - and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE - ] - input_files = sorted(input_files, key=lambda x: x.name) - input_files = [f.name for f in input_files] - return { - "required": { - "file": ( - IO.COMBO, - { - "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", - "options": input_files, - "default": input_files[0] if input_files else None, - }, - ), - }, - "optional": { - "GEMINI_INPUT_FILES": ( - "GEMINI_INPUT_FILES", - { - "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", - "default": None, - }, - ), - }, - } - - DESCRIPTION = "Loads and prepares input files to include as inputs for Gemini LLM nodes. The files will be read by the Gemini model when generating a response. The contents of the text file count toward the token limit. 🛈 TIP: Can be chained together with other Gemini Input File nodes." - RETURN_TYPES = ("GEMINI_INPUT_FILES",) - FUNCTION = "prepare_files" - CATEGORY = "api node/text/Gemini" - - def create_file_part(self, file_path: str) -> GeminiPart: - mime_type = ( - GeminiMimeType.application_pdf - if file_path.endswith(".pdf") - else GeminiMimeType.text_plain - ) - # Use base64 string directly, not the data URI - with open(file_path, "rb") as f: - file_content = f.read() - import base64 - base64_str = base64.b64encode(file_content).decode("utf-8") - - return GeminiPart( - inlineData=GeminiInlineData( - mimeType=mime_type, - data=base64_str, - ) - ) - - def prepare_files( - self, file: str, GEMINI_INPUT_FILES: list[GeminiPart] = [] - ) -> tuple[list[GeminiPart]]: - """ - Loads and formats input files for Gemini API. - """ - file_path = folder_paths.get_annotated_filepath(file) - input_file_content = self.create_file_part(file_path) - files = [input_file_content] + GEMINI_INPUT_FILES - return (files,) - - -class GeminiImage(ComfyNodeABC): - """ - Node to generate text and image responses from a Gemini model. - - This node allows users to interact with Google's Gemini AI models, providing - multimodal inputs (text, images, files) to generate coherent - text and image responses. The node works with the latest Gemini models, handling the - API communication and response parsing. - """ - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Text prompt for generation", - }, - ), - "model": ( - IO.COMBO, - { - "tooltip": "The Gemini model to use for generating responses.", - "options": [model.value for model in GeminiImageModel], - "default": GeminiImageModel.gemini_2_5_flash_image_preview.value, - }, - ), - "seed": ( - IO.INT, - { - "default": 42, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.", - }, - ), - }, - "optional": { - "images": ( - IO.IMAGE, - { - "default": None, - "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", - }, - ), - "files": ( - "GEMINI_INPUT_FILES", - { - "default": None, - "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.", - }, - ), - # TODO: later we can add this parameter later - # "n": ( - # IO.INT, - # { - # "default": 1, - # "min": 1, - # "max": 8, - # "step": 1, - # "display": "number", - # "tooltip": "How many images to generate", - # }, - # ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE, IO.STRING) - FUNCTION = "api_call" - CATEGORY = "api node/image/Gemini" - DESCRIPTION = "Edit images synchronously via Google API." - API_NODE = True - - async def api_call( - self, - prompt: str, - model: GeminiImageModel, - images: Optional[IO.IMAGE] = None, - files: Optional[list[GeminiPart]] = None, - n=1, - unique_id: Optional[str] = None, - **kwargs, - ): - # Validate inputs - validate_string(prompt, strip_whitespace=True, min_length=1) - # Create parts list with text prompt as the first part - parts: list[GeminiPart] = [create_text_part(prompt)] - - # Add other modal parts - if images is not None: - image_parts = create_image_parts(images) - parts.extend(image_parts) - if files is not None: - parts.extend(files) - - response = await SynchronousOperation( - endpoint=get_gemini_image_endpoint(model), - request=GeminiImageGenerateContentRequest( - contents=[ - GeminiContent( - role="user", - parts=parts, - ), - ], - generationConfig=GeminiImageGenerationConfig( - responseModalities=["TEXT","IMAGE"] - ) - ), - auth_kwargs=kwargs, - ).execute() - - output_image = get_image_from_response(response) - output_text = get_text_from_response(response) - if unique_id and output_text: - # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. - render_spec = { - "node_id": unique_id, - "component": "ChatHistoryWidget", - "props": { - "history": json.dumps( - [ - { - "prompt": prompt, - "response": output_text, - "response_id": str(uuid.uuid4()), - "timestamp": time.time(), - } - ] - ), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - - output_text = output_text or "Empty response from Gemini model..." - return (output_image, output_text,) - - -NODE_CLASS_MAPPINGS = { - "GeminiNode": GeminiNode, - "GeminiImageNode": GeminiImage, - "GeminiInputFiles": GeminiInputFiles, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "GeminiNode": "Google Gemini", - "GeminiImageNode": "Google Gemini Image", - "GeminiInputFiles": "Gemini Input Files", -} diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py deleted file mode 100644 index d28895f3e10d341a2896cf60f8f0f72d58c21d5f..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_ideogram.py +++ /dev/null @@ -1,779 +0,0 @@ -from io import BytesIO -from typing_extensions import override -from comfy_api.latest import ComfyExtension, io as comfy_io -from PIL import Image -import numpy as np -import torch -from comfy_api_nodes.apis import ( - IdeogramGenerateRequest, - IdeogramGenerateResponse, - ImageRequest, - IdeogramV3Request, - IdeogramV3EditRequest, -) - -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, -) - -from comfy_api_nodes.apinode_utils import ( - download_url_to_bytesio, - bytesio_to_image_tensor, - resize_mask_to_image, -) -from server import PromptServer - -V1_V1_RES_MAP = { - "Auto":"AUTO", - "512 x 1536":"RESOLUTION_512_1536", - "576 x 1408":"RESOLUTION_576_1408", - "576 x 1472":"RESOLUTION_576_1472", - "576 x 1536":"RESOLUTION_576_1536", - "640 x 1024":"RESOLUTION_640_1024", - "640 x 1344":"RESOLUTION_640_1344", - "640 x 1408":"RESOLUTION_640_1408", - "640 x 1472":"RESOLUTION_640_1472", - "640 x 1536":"RESOLUTION_640_1536", - "704 x 1152":"RESOLUTION_704_1152", - "704 x 1216":"RESOLUTION_704_1216", - "704 x 1280":"RESOLUTION_704_1280", - "704 x 1344":"RESOLUTION_704_1344", - "704 x 1408":"RESOLUTION_704_1408", - "704 x 1472":"RESOLUTION_704_1472", - "720 x 1280":"RESOLUTION_720_1280", - "736 x 1312":"RESOLUTION_736_1312", - "768 x 1024":"RESOLUTION_768_1024", - "768 x 1088":"RESOLUTION_768_1088", - "768 x 1152":"RESOLUTION_768_1152", - "768 x 1216":"RESOLUTION_768_1216", - "768 x 1232":"RESOLUTION_768_1232", - "768 x 1280":"RESOLUTION_768_1280", - "768 x 1344":"RESOLUTION_768_1344", - "832 x 960":"RESOLUTION_832_960", - "832 x 1024":"RESOLUTION_832_1024", - "832 x 1088":"RESOLUTION_832_1088", - "832 x 1152":"RESOLUTION_832_1152", - "832 x 1216":"RESOLUTION_832_1216", - "832 x 1248":"RESOLUTION_832_1248", - "864 x 1152":"RESOLUTION_864_1152", - "896 x 960":"RESOLUTION_896_960", - "896 x 1024":"RESOLUTION_896_1024", - "896 x 1088":"RESOLUTION_896_1088", - "896 x 1120":"RESOLUTION_896_1120", - "896 x 1152":"RESOLUTION_896_1152", - "960 x 832":"RESOLUTION_960_832", - "960 x 896":"RESOLUTION_960_896", - "960 x 1024":"RESOLUTION_960_1024", - "960 x 1088":"RESOLUTION_960_1088", - "1024 x 640":"RESOLUTION_1024_640", - "1024 x 768":"RESOLUTION_1024_768", - "1024 x 832":"RESOLUTION_1024_832", - "1024 x 896":"RESOLUTION_1024_896", - "1024 x 960":"RESOLUTION_1024_960", - "1024 x 1024":"RESOLUTION_1024_1024", - "1088 x 768":"RESOLUTION_1088_768", - "1088 x 832":"RESOLUTION_1088_832", - "1088 x 896":"RESOLUTION_1088_896", - "1088 x 960":"RESOLUTION_1088_960", - "1120 x 896":"RESOLUTION_1120_896", - "1152 x 704":"RESOLUTION_1152_704", - "1152 x 768":"RESOLUTION_1152_768", - "1152 x 832":"RESOLUTION_1152_832", - "1152 x 864":"RESOLUTION_1152_864", - "1152 x 896":"RESOLUTION_1152_896", - "1216 x 704":"RESOLUTION_1216_704", - "1216 x 768":"RESOLUTION_1216_768", - "1216 x 832":"RESOLUTION_1216_832", - "1232 x 768":"RESOLUTION_1232_768", - "1248 x 832":"RESOLUTION_1248_832", - "1280 x 704":"RESOLUTION_1280_704", - "1280 x 720":"RESOLUTION_1280_720", - "1280 x 768":"RESOLUTION_1280_768", - "1280 x 800":"RESOLUTION_1280_800", - "1312 x 736":"RESOLUTION_1312_736", - "1344 x 640":"RESOLUTION_1344_640", - "1344 x 704":"RESOLUTION_1344_704", - "1344 x 768":"RESOLUTION_1344_768", - "1408 x 576":"RESOLUTION_1408_576", - "1408 x 640":"RESOLUTION_1408_640", - "1408 x 704":"RESOLUTION_1408_704", - "1472 x 576":"RESOLUTION_1472_576", - "1472 x 640":"RESOLUTION_1472_640", - "1472 x 704":"RESOLUTION_1472_704", - "1536 x 512":"RESOLUTION_1536_512", - "1536 x 576":"RESOLUTION_1536_576", - "1536 x 640":"RESOLUTION_1536_640", -} - -V1_V2_RATIO_MAP = { - "1:1":"ASPECT_1_1", - "4:3":"ASPECT_4_3", - "3:4":"ASPECT_3_4", - "16:9":"ASPECT_16_9", - "9:16":"ASPECT_9_16", - "2:1":"ASPECT_2_1", - "1:2":"ASPECT_1_2", - "3:2":"ASPECT_3_2", - "2:3":"ASPECT_2_3", - "4:5":"ASPECT_4_5", - "5:4":"ASPECT_5_4", -} - -V3_RATIO_MAP = { - "1:3":"1x3", - "3:1":"3x1", - "1:2":"1x2", - "2:1":"2x1", - "9:16":"9x16", - "16:9":"16x9", - "10:16":"10x16", - "16:10":"16x10", - "2:3":"2x3", - "3:2":"3x2", - "3:4":"3x4", - "4:3":"4x3", - "4:5":"4x5", - "5:4":"5x4", - "1:1":"1x1", -} - -V3_RESOLUTIONS= [ - "Auto", - "512x1536", - "576x1408", - "576x1472", - "576x1536", - "640x1344", - "640x1408", - "640x1472", - "640x1536", - "704x1152", - "704x1216", - "704x1280", - "704x1344", - "704x1408", - "704x1472", - "736x1312", - "768x1088", - "768x1216", - "768x1280", - "768x1344", - "800x1280", - "832x960", - "832x1024", - "832x1088", - "832x1152", - "832x1216", - "832x1248", - "864x1152", - "896x960", - "896x1024", - "896x1088", - "896x1120", - "896x1152", - "960x832", - "960x896", - "960x1024", - "960x1088", - "1024x832", - "1024x896", - "1024x960", - "1024x1024", - "1088x768", - "1088x832", - "1088x896", - "1088x960", - "1120x896", - "1152x704", - "1152x832", - "1152x864", - "1152x896", - "1216x704", - "1216x768", - "1216x832", - "1248x832", - "1280x704", - "1280x768", - "1280x800", - "1312x736", - "1344x640", - "1344x704", - "1344x768", - "1408x576", - "1408x640", - "1408x704", - "1472x576", - "1472x640", - "1472x704", - "1536x512", - "1536x576", - "1536x640" -] - -async def download_and_process_images(image_urls): - """Helper function to download and process multiple images from URLs""" - - # Initialize list to store image tensors - image_tensors = [] - - for image_url in image_urls: - # Using functions from apinode_utils.py to handle downloading and processing - image_bytesio = await download_url_to_bytesio(image_url) # Download image content to BytesIO - img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB") # Convert to torch.Tensor with RGB mode - image_tensors.append(img_tensor) - - # Stack tensors to match (N, width, height, channels) - if image_tensors: - stacked_tensors = torch.cat(image_tensors, dim=0) - else: - raise Exception("No valid images were processed") - - return stacked_tensors - - -def display_image_urls_on_node(image_urls, node_id): - if node_id and image_urls: - if len(image_urls) == 1: - PromptServer.instance.send_progress_text( - f"Generated Image URL:\n{image_urls[0]}", node_id - ) - else: - urls_text = "Generated Image URLs:\n" + "\n".join( - f"{i+1}. {url}" for i, url in enumerate(image_urls) - ) - PromptServer.instance.send_progress_text(urls_text, node_id) - - -class IdeogramV1(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="IdeogramV1", - display_name="Ideogram V1", - category="api node/image/Ideogram", - description="Generates images using the Ideogram V1 model.", - inputs=[ - comfy_io.String.Input( - "prompt", - multiline=True, - default="", - tooltip="Prompt for the image generation", - ), - comfy_io.Boolean.Input( - "turbo", - default=False, - tooltip="Whether to use turbo mode (faster generation, potentially lower quality)", - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=list(V1_V2_RATIO_MAP.keys()), - default="1:1", - tooltip="The aspect ratio for image generation.", - optional=True, - ), - comfy_io.Combo.Input( - "magic_prompt_option", - options=["AUTO", "ON", "OFF"], - default="AUTO", - tooltip="Determine if MagicPrompt should be used in generation", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - control_after_generate=True, - display_mode=comfy_io.NumberDisplay.number, - optional=True, - ), - comfy_io.String.Input( - "negative_prompt", - multiline=True, - default="", - tooltip="Description of what to exclude from the image", - optional=True, - ), - comfy_io.Int.Input( - "num_images", - default=1, - min=1, - max=8, - step=1, - display_mode=comfy_io.NumberDisplay.number, - optional=True, - ), - ], - outputs=[ - comfy_io.Image.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - ) - - @classmethod - async def execute( - cls, - prompt, - turbo=False, - aspect_ratio="1:1", - magic_prompt_option="AUTO", - seed=0, - negative_prompt="", - num_images=1, - ): - # Determine the model based on turbo setting - aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) - model = "V_1_TURBO" if turbo else "V_1" - - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/ideogram/generate", - method=HttpMethod.POST, - request_model=IdeogramGenerateRequest, - response_model=IdeogramGenerateResponse, - ), - request=IdeogramGenerateRequest( - image_request=ImageRequest( - prompt=prompt, - model=model, - num_images=num_images, - seed=seed, - aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None, - magic_prompt_option=( - magic_prompt_option if magic_prompt_option != "AUTO" else None - ), - negative_prompt=negative_prompt if negative_prompt else None, - ) - ), - auth_kwargs=auth, - ) - - response = await operation.execute() - - if not response.data or len(response.data) == 0: - raise Exception("No images were generated in the response") - - image_urls = [image_data.url for image_data in response.data if image_data.url] - - if not image_urls: - raise Exception("No image URLs were generated in the response") - - display_image_urls_on_node(image_urls, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_and_process_images(image_urls)) - - -class IdeogramV2(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="IdeogramV2", - display_name="Ideogram V2", - category="api node/image/Ideogram", - description="Generates images using the Ideogram V2 model.", - inputs=[ - comfy_io.String.Input( - "prompt", - multiline=True, - default="", - tooltip="Prompt for the image generation", - ), - comfy_io.Boolean.Input( - "turbo", - default=False, - tooltip="Whether to use turbo mode (faster generation, potentially lower quality)", - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=list(V1_V2_RATIO_MAP.keys()), - default="1:1", - tooltip="The aspect ratio for image generation. Ignored if resolution is not set to AUTO.", - optional=True, - ), - comfy_io.Combo.Input( - "resolution", - options=list(V1_V1_RES_MAP.keys()), - default="Auto", - tooltip="The resolution for image generation. " - "If not set to AUTO, this overrides the aspect_ratio setting.", - optional=True, - ), - comfy_io.Combo.Input( - "magic_prompt_option", - options=["AUTO", "ON", "OFF"], - default="AUTO", - tooltip="Determine if MagicPrompt should be used in generation", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - control_after_generate=True, - display_mode=comfy_io.NumberDisplay.number, - optional=True, - ), - comfy_io.Combo.Input( - "style_type", - options=["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"], - default="NONE", - tooltip="Style type for generation (V2 only)", - optional=True, - ), - comfy_io.String.Input( - "negative_prompt", - multiline=True, - default="", - tooltip="Description of what to exclude from the image", - optional=True, - ), - comfy_io.Int.Input( - "num_images", - default=1, - min=1, - max=8, - step=1, - display_mode=comfy_io.NumberDisplay.number, - optional=True, - ), - #"color_palette": ( - # IO.STRING, - # { - # "multiline": False, - # "default": "", - # "tooltip": "Color palette preset name or hex colors with weights", - # }, - #), - ], - outputs=[ - comfy_io.Image.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - ) - - @classmethod - async def execute( - cls, - prompt, - turbo=False, - aspect_ratio="1:1", - resolution="Auto", - magic_prompt_option="AUTO", - seed=0, - style_type="NONE", - negative_prompt="", - num_images=1, - color_palette="", - ): - aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) - resolution = V1_V1_RES_MAP.get(resolution, None) - # Determine the model based on turbo setting - model = "V_2_TURBO" if turbo else "V_2" - - # Handle resolution vs aspect_ratio logic - # If resolution is not AUTO, it overrides aspect_ratio - final_resolution = None - final_aspect_ratio = None - - if resolution != "AUTO": - final_resolution = resolution - else: - final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None - - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/ideogram/generate", - method=HttpMethod.POST, - request_model=IdeogramGenerateRequest, - response_model=IdeogramGenerateResponse, - ), - request=IdeogramGenerateRequest( - image_request=ImageRequest( - prompt=prompt, - model=model, - num_images=num_images, - seed=seed, - aspect_ratio=final_aspect_ratio, - resolution=final_resolution, - magic_prompt_option=( - magic_prompt_option if magic_prompt_option != "AUTO" else None - ), - style_type=style_type if style_type != "NONE" else None, - negative_prompt=negative_prompt if negative_prompt else None, - color_palette=color_palette if color_palette else None, - ) - ), - auth_kwargs=auth, - ) - - response = await operation.execute() - - if not response.data or len(response.data) == 0: - raise Exception("No images were generated in the response") - - image_urls = [image_data.url for image_data in response.data if image_data.url] - - if not image_urls: - raise Exception("No image URLs were generated in the response") - - display_image_urls_on_node(image_urls, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_and_process_images(image_urls)) - - -class IdeogramV3(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="IdeogramV3", - display_name="Ideogram V3", - category="api node/image/Ideogram", - description="Generates images using the Ideogram V3 model. " - "Supports both regular image generation from text prompts and image editing with mask.", - inputs=[ - comfy_io.String.Input( - "prompt", - multiline=True, - default="", - tooltip="Prompt for the image generation or editing", - ), - comfy_io.Image.Input( - "image", - tooltip="Optional reference image for image editing.", - optional=True, - ), - comfy_io.Mask.Input( - "mask", - tooltip="Optional mask for inpainting (white areas will be replaced)", - optional=True, - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=list(V3_RATIO_MAP.keys()), - default="1:1", - tooltip="The aspect ratio for image generation. Ignored if resolution is not set to Auto.", - optional=True, - ), - comfy_io.Combo.Input( - "resolution", - options=V3_RESOLUTIONS, - default="Auto", - tooltip="The resolution for image generation. " - "If not set to Auto, this overrides the aspect_ratio setting.", - optional=True, - ), - comfy_io.Combo.Input( - "magic_prompt_option", - options=["AUTO", "ON", "OFF"], - default="AUTO", - tooltip="Determine if MagicPrompt should be used in generation", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - control_after_generate=True, - display_mode=comfy_io.NumberDisplay.number, - optional=True, - ), - comfy_io.Int.Input( - "num_images", - default=1, - min=1, - max=8, - step=1, - display_mode=comfy_io.NumberDisplay.number, - optional=True, - ), - comfy_io.Combo.Input( - "rendering_speed", - options=["BALANCED", "TURBO", "QUALITY"], - default="BALANCED", - tooltip="Controls the trade-off between generation speed and quality", - optional=True, - ), - ], - outputs=[ - comfy_io.Image.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - ) - - @classmethod - async def execute( - cls, - prompt, - image=None, - mask=None, - resolution="Auto", - aspect_ratio="1:1", - magic_prompt_option="AUTO", - seed=0, - num_images=1, - rendering_speed="BALANCED", - ): - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - # Check if both image and mask are provided for editing mode - if image is not None and mask is not None: - # Edit mode - path = "/proxy/ideogram/ideogram-v3/edit" - - # Process image and mask - input_tensor = image.squeeze().cpu() - # Resize mask to match image dimension - mask = resize_mask_to_image(mask, image, allow_gradient=False) - # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention). - mask = 1.0 - mask - - # Validate mask dimensions match image - if mask.shape[1:] != image.shape[1:-1]: - raise Exception("Mask and Image must be the same size") - - # Process image - img_np = (input_tensor.numpy() * 255).astype(np.uint8) - img = Image.fromarray(img_np) - img_byte_arr = BytesIO() - img.save(img_byte_arr, format="PNG") - img_byte_arr.seek(0) - img_binary = img_byte_arr - img_binary.name = "image.png" - - # Process mask - white areas will be replaced - mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8) - mask_img = Image.fromarray(mask_np) - mask_byte_arr = BytesIO() - mask_img.save(mask_byte_arr, format="PNG") - mask_byte_arr.seek(0) - mask_binary = mask_byte_arr - mask_binary.name = "mask.png" - - # Create edit request - edit_request = IdeogramV3EditRequest( - prompt=prompt, - rendering_speed=rendering_speed, - ) - - # Add optional parameters - if magic_prompt_option != "AUTO": - edit_request.magic_prompt = magic_prompt_option - if seed != 0: - edit_request.seed = seed - if num_images > 1: - edit_request.num_images = num_images - - # Execute the operation for edit mode - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=IdeogramV3EditRequest, - response_model=IdeogramGenerateResponse, - ), - request=edit_request, - files={ - "image": img_binary, - "mask": mask_binary, - }, - content_type="multipart/form-data", - auth_kwargs=auth, - ) - - elif image is not None or mask is not None: - # If only one of image or mask is provided, raise an error - raise Exception("Ideogram V3 image editing requires both an image AND a mask") - else: - # Generation mode - path = "/proxy/ideogram/ideogram-v3/generate" - - # Create generation request - gen_request = IdeogramV3Request( - prompt=prompt, - rendering_speed=rendering_speed, - ) - - # Handle resolution vs aspect ratio - if resolution != "Auto": - gen_request.resolution = resolution - elif aspect_ratio != "1:1": - v3_aspect = V3_RATIO_MAP.get(aspect_ratio) - if v3_aspect: - gen_request.aspect_ratio = v3_aspect - - # Add optional parameters - if magic_prompt_option != "AUTO": - gen_request.magic_prompt = magic_prompt_option - if seed != 0: - gen_request.seed = seed - if num_images > 1: - gen_request.num_images = num_images - - # Execute the operation for generation mode - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=IdeogramV3Request, - response_model=IdeogramGenerateResponse, - ), - request=gen_request, - auth_kwargs=auth, - ) - - # Execute the operation and process response - response = await operation.execute() - - if not response.data or len(response.data) == 0: - raise Exception("No images were generated in the response") - - image_urls = [image_data.url for image_data in response.data if image_data.url] - - if not image_urls: - raise Exception("No image URLs were generated in the response") - - display_image_urls_on_node(image_urls, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_and_process_images(image_urls)) - - -class IdeogramExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]: - return [ - IdeogramV1, - IdeogramV2, - IdeogramV3, - ] - -async def comfy_entrypoint() -> IdeogramExtension: - return IdeogramExtension() diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py deleted file mode 100644 index 9fa39098516a5a675e92c73acd367b36971a8242..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_kling.py +++ /dev/null @@ -1,1766 +0,0 @@ -"""Kling API Nodes - -For source of truth on the allowed permutations of request fields, please reference: -- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) -""" - -from __future__ import annotations -from typing import Optional, TypeVar, Any -from collections.abc import Callable -import math -import logging - -import torch - -from comfy_api_nodes.apis import ( - KlingTaskStatus, - KlingCameraControl, - KlingCameraConfig, - KlingCameraControlType, - KlingVideoGenDuration, - KlingVideoGenMode, - KlingVideoGenAspectRatio, - KlingVideoGenModelName, - KlingText2VideoRequest, - KlingText2VideoResponse, - KlingImage2VideoRequest, - KlingImage2VideoResponse, - KlingVideoExtendRequest, - KlingVideoExtendResponse, - KlingLipSyncVoiceLanguage, - KlingLipSyncInputObject, - KlingLipSyncRequest, - KlingLipSyncResponse, - KlingVirtualTryOnModelName, - KlingVirtualTryOnRequest, - KlingVirtualTryOnResponse, - KlingVideoResult, - KlingImageResult, - KlingImageGenerationsRequest, - KlingImageGenerationsResponse, - KlingImageGenImageReferenceType, - KlingImageGenModelName, - KlingImageGenAspectRatio, - KlingVideoEffectsRequest, - KlingVideoEffectsResponse, - KlingDualCharacterEffectsScene, - KlingSingleImageEffectsScene, - KlingDualCharacterEffectInput, - KlingSingleImageEffectInput, - KlingCharacterEffectModelName, - KlingSingleImageEffectModelName, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - tensor_to_base64_string, - download_url_to_video_output, - upload_video_to_comfyapi, - upload_audio_to_comfyapi, - download_url_to_image_tensor, -) -from comfy_api_nodes.mapper_utils import model_field_to_node_input -from comfy_api_nodes.util.validation_utils import ( - validate_image_dimensions, - validate_image_aspect_ratio, - validate_video_dimensions, - validate_video_duration, -) -from comfy_api.input.basic_types import AudioInput -from comfy_api.input.video_types import VideoInput -from comfy_api.input_impl import VideoFromFile -from comfy.comfy_types.node_typing import IO, InputTypeOptions, ComfyNodeABC - -KLING_API_VERSION = "v1" -PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video" -PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video" -PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend" -PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync" -PATH_VIDEO_EFFECTS = f"/proxy/kling/{KLING_API_VERSION}/videos/effects" -PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations" -PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on" -PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations" - -MAX_PROMPT_LENGTH_T2V = 2500 -MAX_PROMPT_LENGTH_I2V = 500 -MAX_PROMPT_LENGTH_IMAGE_GEN = 500 -MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200 -MAX_PROMPT_LENGTH_LIP_SYNC = 120 - -AVERAGE_DURATION_T2V = 319 -AVERAGE_DURATION_I2V = 164 -AVERAGE_DURATION_LIP_SYNC = 455 -AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 -AVERAGE_DURATION_IMAGE_GEN = 32 -AVERAGE_DURATION_VIDEO_EFFECTS = 320 -AVERAGE_DURATION_VIDEO_EXTEND = 320 - -R = TypeVar("R") - - -class KlingApiError(Exception): - """Base exception for Kling API errors.""" - - pass - - -async def poll_until_finished( - auth_kwargs: dict[str, str], - api_endpoint: ApiEndpoint[Any, R], - result_url_extractor: Optional[Callable[[R], str]] = None, - estimated_duration: Optional[int] = None, - node_id: Optional[str] = None, -) -> R: - """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response.""" - return await PollingOperation( - poll_endpoint=api_endpoint, - completed_statuses=[ - KlingTaskStatus.succeed.value, - ], - failed_statuses=[KlingTaskStatus.failed.value], - status_extractor=lambda response: ( - response.data.task_status.value - if response.data and response.data.task_status - else None - ), - auth_kwargs=auth_kwargs, - result_url_extractor=result_url_extractor, - estimated_duration=estimated_duration, - node_id=node_id, - poll_interval=16.0, - max_poll_attempts=256, - ).execute() - - -def is_valid_camera_control_configs(configs: list[float]) -> bool: - """Verifies that at least one camera control configuration is non-zero.""" - return any(not math.isclose(value, 0.0) for value in configs) - - -def is_valid_prompt(prompt: str) -> bool: - """Verifies that the prompt is not empty.""" - return bool(prompt) - - -def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool: - """Verifies that the initial response contains a task ID.""" - return bool(response.data.task_id) - - -def is_valid_video_response(response: KlingText2VideoResponse) -> bool: - """Verifies that the response contains a task result with at least one video.""" - return ( - response.data is not None - and response.data.task_result is not None - and response.data.task_result.videos is not None - and len(response.data.task_result.videos) > 0 - ) - - -def is_valid_image_response(response: KlingVirtualTryOnResponse) -> bool: - """Verifies that the response contains a task result with at least one image.""" - return ( - response.data is not None - and response.data.task_result is not None - and response.data.task_result.images is not None - and len(response.data.task_result.images) > 0 - ) - - -def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool: - """Verifies that the positive prompt is not empty and that neither promt is too long.""" - if not prompt: - raise ValueError("Positive prompt is empty") - if len(prompt) > max_length: - raise ValueError(f"Positive prompt is too long: {len(prompt)} characters") - if negative_prompt and len(negative_prompt) > max_length: - raise ValueError( - f"Negative prompt is too long: {len(negative_prompt)} characters" - ) - return True - - -def validate_task_creation_response(response) -> None: - """Validates that the Kling task creation request was successful.""" - if not is_valid_task_creation_response(response): - error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}" - logging.error(error_msg) - raise KlingApiError(error_msg) - - -def validate_video_result_response(response) -> None: - """Validates that the Kling task result contains a video.""" - if not is_valid_video_response(response): - error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response." - logging.error(f"Error: {error_msg}.\nResponse: {response}") - raise KlingApiError(error_msg) - - -def validate_image_result_response(response) -> None: - """Validates that the Kling task result contains an image.""" - if not is_valid_image_response(response): - error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response." - logging.error(f"Error: {error_msg}.\nResponse: {response}") - raise KlingApiError(error_msg) - - -def validate_input_image(image: torch.Tensor) -> None: - """ - Validates the input image adheres to the expectations of the Kling API: - - The image resolution should not be less than 300*300px - - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1 - - See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo - """ - validate_image_dimensions(image, min_width=300, min_height=300) - validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5) - - -def get_camera_control_input_config( - tooltip: str, default: float = 0.0 -) -> tuple[IO, InputTypeOptions]: - """Returns common InputTypeOptions for Kling camera control configurations.""" - input_config = { - "default": default, - "min": -10.0, - "max": 10.0, - "step": 0.25, - "display": "slider", - "tooltip": tooltip, - } - return IO.FLOAT, input_config - - -def get_video_from_response(response) -> KlingVideoResult: - """Returns the first video object from the Kling video generation task result. - Will raise an error if the response is not valid. - """ - video = response.data.task_result.videos[0] - logging.info( - "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url - ) - return video - - -def get_video_url_from_response(response) -> Optional[str]: - """Returns the first video url from the Kling video generation task result. - Will not raise an error if the response is not valid. - """ - if response and is_valid_video_response(response): - return str(get_video_from_response(response).url) - else: - return None - - -def get_images_from_response(response) -> list[KlingImageResult]: - """Returns the list of image objects from the Kling image generation task result. - Will raise an error if the response is not valid. - """ - images = response.data.task_result.images - logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images) - return images - - -def get_images_urls_from_response(response) -> Optional[str]: - """Returns the list of image urls from the Kling image generation task result. - Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls. - """ - if response and is_valid_image_response(response): - images = get_images_from_response(response) - image_urls = [str(image.url) for image in images] - return "\n".join(image_urls) - else: - return None - - -async def video_result_to_node_output( - video: KlingVideoResult, -) -> tuple[VideoFromFile, str, str]: - """Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output.""" - return ( - await download_url_to_video_output(str(video.url)), - str(video.id), - str(video.duration), - ) - - -async def image_result_to_node_output( - images: list[KlingImageResult], -) -> torch.Tensor: - """ - Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor. - If multiple images are returned, they will be stacked along the batch dimension. - """ - if len(images) == 1: - return await download_url_to_image_tensor(str(images[0].url)) - else: - return torch.cat([await download_url_to_image_tensor(str(image.url)) for image in images]) - - -class KlingNodeBase(ComfyNodeABC): - """Base class for Kling nodes.""" - - FUNCTION = "api_call" - CATEGORY = "api node/video/Kling" - API_NODE = True - - -class KlingCameraControls(KlingNodeBase): - """Kling Camera Controls Node""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "camera_control_type": model_field_to_node_input( - IO.COMBO, - KlingCameraControl, - "type", - enum_type=KlingCameraControlType, - ), - "horizontal_movement": get_camera_control_input_config( - "Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right" - ), - "vertical_movement": get_camera_control_input_config( - "Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward." - ), - "pan": get_camera_control_input_config( - "Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", - default=0.5, - ), - "tilt": get_camera_control_input_config( - "Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", - ), - "roll": get_camera_control_input_config( - "Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", - ), - "zoom": get_camera_control_input_config( - "Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", - ), - } - } - - DESCRIPTION = "Allows specifying configuration options for Kling Camera Controls and motion control effects." - RETURN_TYPES = ("CAMERA_CONTROL",) - RETURN_NAMES = ("camera_control",) - FUNCTION = "main" - API_NODE = False # This is just a helper node, it doesn't make an API call - - @classmethod - def VALIDATE_INPUTS( - cls, - horizontal_movement: float, - vertical_movement: float, - pan: float, - tilt: float, - roll: float, - zoom: float, - ) -> bool | str: - if not is_valid_camera_control_configs( - [ - horizontal_movement, - vertical_movement, - pan, - tilt, - roll, - zoom, - ] - ): - return "Invalid camera control configs: at least one of the values must be non-zero" - return True - - def main( - self, - camera_control_type: str, - horizontal_movement: float, - vertical_movement: float, - pan: float, - tilt: float, - roll: float, - zoom: float, - ) -> tuple[KlingCameraControl]: - return ( - KlingCameraControl( - type=KlingCameraControlType(camera_control_type), - config=KlingCameraConfig( - horizontal=horizontal_movement, - vertical=vertical_movement, - pan=pan, - roll=roll, - tilt=tilt, - zoom=zoom, - ), - ), - ) - - -class KlingTextToVideoNode(KlingNodeBase): - """Kling Text to Video Node""" - - @staticmethod - def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]: - """ - Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. - Only includes config combos that support the `image_tail` request field. - - See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) - """ - return { - "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), - "standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"), - "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), - "pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"), - "standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"), - "standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"), - "pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"), - "pro mode / 10s duration / kling-v2-master": ("pro", "10", "kling-v2-master"), - "standard mode / 5s duration / kling-v2-master": ("std", "5", "kling-v2-master"), - "standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"), - "pro mode / 5s duration / kling-v2-1-master": ("pro", "5", "kling-v2-1-master"), - "pro mode / 10s duration / kling-v2-1-master": ("pro", "10", "kling-v2-1-master"), - } - - @classmethod - def INPUT_TYPES(s): - modes = list(KlingTextToVideoNode.get_mode_string_mapping().keys()) - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, KlingText2VideoRequest, "prompt", multiline=True - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, KlingText2VideoRequest, "negative_prompt", multiline=True - ), - "cfg_scale": model_field_to_node_input( - IO.FLOAT, - KlingText2VideoRequest, - "cfg_scale", - default=1.0, - min=0.0, - max=1.0, - ), - "aspect_ratio": model_field_to_node_input( - IO.COMBO, - KlingText2VideoRequest, - "aspect_ratio", - enum_type=KlingVideoGenAspectRatio, - ), - "mode": ( - modes, - { - "default": modes[4], - "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO", "STRING", "STRING") - RETURN_NAMES = ("VIDEO", "video_id", "duration") - DESCRIPTION = "Kling Text to Video Node" - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> KlingText2VideoResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_TEXT_TO_VIDEO}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=KlingText2VideoResponse, - ), - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_T2V, - node_id=node_id, - ) - - async def api_call( - self, - prompt: str, - negative_prompt: str, - cfg_scale: float, - mode: str, - aspect_ratio: str, - camera_control: Optional[KlingCameraControl] = None, - model_name: Optional[str] = None, - duration: Optional[str] = None, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile, str, str]: - validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) - if model_name is None: - mode, duration, model_name = self.get_mode_string_mapping()[mode] - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_TEXT_TO_VIDEO, - method=HttpMethod.POST, - request_model=KlingText2VideoRequest, - response_model=KlingText2VideoResponse, - ), - request=KlingText2VideoRequest( - prompt=prompt if prompt else None, - negative_prompt=negative_prompt if negative_prompt else None, - duration=KlingVideoGenDuration(duration), - mode=KlingVideoGenMode(mode), - model_name=KlingVideoGenModelName(model_name), - cfg_scale=cfg_scale, - aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), - camera_control=camera_control, - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - - task_id = task_creation_response.data.task_id - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_video_result_response(final_response) - - video = get_video_from_response(final_response) - return await video_result_to_node_output(video) - - -class KlingCameraControlT2VNode(KlingTextToVideoNode): - """ - Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera. - Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, KlingText2VideoRequest, "prompt", multiline=True - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - KlingText2VideoRequest, - "negative_prompt", - multiline=True, - ), - "cfg_scale": model_field_to_node_input( - IO.FLOAT, - KlingText2VideoRequest, - "cfg_scale", - default=0.75, - min=0.0, - max=1.0, - ), - "aspect_ratio": model_field_to_node_input( - IO.COMBO, - KlingText2VideoRequest, - "aspect_ratio", - enum_type=KlingVideoGenAspectRatio, - ), - "camera_control": ( - "CAMERA_CONTROL", - { - "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text." - - async def api_call( - self, - prompt: str, - negative_prompt: str, - cfg_scale: float, - aspect_ratio: str, - camera_control: Optional[KlingCameraControl] = None, - unique_id: Optional[str] = None, - **kwargs, - ): - return await super().api_call( - model_name=KlingVideoGenModelName.kling_v1, - cfg_scale=cfg_scale, - mode=KlingVideoGenMode.std, - aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), - duration=KlingVideoGenDuration.field_5, - prompt=prompt, - negative_prompt=negative_prompt, - camera_control=camera_control, - **kwargs, - ) - - -class KlingImage2VideoNode(KlingNodeBase): - """Kling Image to Video Node""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "start_frame": model_field_to_node_input( - IO.IMAGE, - KlingImage2VideoRequest, - "image", - tooltip="The reference image used to generate the video.", - ), - "prompt": model_field_to_node_input( - IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - KlingImage2VideoRequest, - "negative_prompt", - multiline=True, - ), - "model_name": model_field_to_node_input( - IO.COMBO, - KlingImage2VideoRequest, - "model_name", - enum_type=KlingVideoGenModelName, - ), - "cfg_scale": model_field_to_node_input( - IO.FLOAT, - KlingImage2VideoRequest, - "cfg_scale", - default=0.8, - min=0.0, - max=1.0, - ), - "mode": model_field_to_node_input( - IO.COMBO, - KlingImage2VideoRequest, - "mode", - enum_type=KlingVideoGenMode, - ), - "aspect_ratio": model_field_to_node_input( - IO.COMBO, - KlingImage2VideoRequest, - "aspect_ratio", - enum_type=KlingVideoGenAspectRatio, - ), - "duration": model_field_to_node_input( - IO.COMBO, - KlingImage2VideoRequest, - "duration", - enum_type=KlingVideoGenDuration, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO", "STRING", "STRING") - RETURN_NAMES = ("VIDEO", "video_id", "duration") - DESCRIPTION = "Kling Image to Video Node" - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> KlingImage2VideoResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}", - method=HttpMethod.GET, - request_model=KlingImage2VideoRequest, - response_model=KlingImage2VideoResponse, - ), - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_I2V, - node_id=node_id, - ) - - async def api_call( - self, - start_frame: torch.Tensor, - prompt: str, - negative_prompt: str, - model_name: str, - cfg_scale: float, - mode: str, - aspect_ratio: str, - duration: str, - camera_control: Optional[KlingCameraControl] = None, - end_frame: Optional[torch.Tensor] = None, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile]: - validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) - validate_input_image(start_frame) - - if camera_control is not None: - # Camera control type for image 2 video is always `simple` - camera_control.type = KlingCameraControlType.simple - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_IMAGE_TO_VIDEO, - method=HttpMethod.POST, - request_model=KlingImage2VideoRequest, - response_model=KlingImage2VideoResponse, - ), - request=KlingImage2VideoRequest( - model_name=KlingVideoGenModelName(model_name), - image=tensor_to_base64_string(start_frame), - image_tail=( - tensor_to_base64_string(end_frame) - if end_frame is not None - else None - ), - prompt=prompt, - negative_prompt=negative_prompt if negative_prompt else None, - cfg_scale=cfg_scale, - mode=KlingVideoGenMode(mode), - duration=KlingVideoGenDuration(duration), - camera_control=camera_control, - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.data.task_id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_video_result_response(final_response) - - video = get_video_from_response(final_response) - return await video_result_to_node_output(video) - - -class KlingCameraControlI2VNode(KlingImage2VideoNode): - """ - Kling Image to Video Camera Control Node. This node is a image to video node, but it supports controlling the camera. - Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "start_frame": model_field_to_node_input( - IO.IMAGE, KlingImage2VideoRequest, "image" - ), - "prompt": model_field_to_node_input( - IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - KlingImage2VideoRequest, - "negative_prompt", - multiline=True, - ), - "cfg_scale": model_field_to_node_input( - IO.FLOAT, - KlingImage2VideoRequest, - "cfg_scale", - default=0.75, - min=0.0, - max=1.0, - ), - "aspect_ratio": model_field_to_node_input( - IO.COMBO, - KlingImage2VideoRequest, - "aspect_ratio", - enum_type=KlingVideoGenAspectRatio, - ), - "camera_control": ( - "CAMERA_CONTROL", - { - "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image." - - async def api_call( - self, - start_frame: torch.Tensor, - prompt: str, - negative_prompt: str, - cfg_scale: float, - aspect_ratio: str, - camera_control: KlingCameraControl, - unique_id: Optional[str] = None, - **kwargs, - ): - return await super().api_call( - model_name=KlingVideoGenModelName.kling_v1_5, - start_frame=start_frame, - cfg_scale=cfg_scale, - mode=KlingVideoGenMode.pro, - aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), - duration=KlingVideoGenDuration.field_5, - prompt=prompt, - negative_prompt=negative_prompt, - camera_control=camera_control, - unique_id=unique_id, - **kwargs, - ) - - -class KlingStartEndFrameNode(KlingImage2VideoNode): - """ - Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field. - """ - - @staticmethod - def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]: - """ - Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. - Only includes config combos that support the `image_tail` request field. - - See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) - """ - return { - "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), - "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), - "pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"), - "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"), - "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"), - "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"), - } - - @classmethod - def INPUT_TYPES(s): - modes = list(KlingStartEndFrameNode.get_mode_string_mapping().keys()) - return { - "required": { - "start_frame": model_field_to_node_input( - IO.IMAGE, KlingImage2VideoRequest, "image" - ), - "end_frame": model_field_to_node_input( - IO.IMAGE, KlingImage2VideoRequest, "image_tail" - ), - "prompt": model_field_to_node_input( - IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - KlingImage2VideoRequest, - "negative_prompt", - multiline=True, - ), - "cfg_scale": model_field_to_node_input( - IO.FLOAT, - KlingImage2VideoRequest, - "cfg_scale", - default=0.5, - min=0.0, - max=1.0, - ), - "aspect_ratio": model_field_to_node_input( - IO.COMBO, - KlingImage2VideoRequest, - "aspect_ratio", - enum_type=KlingVideoGenAspectRatio, - ), - "mode": ( - modes, - { - "default": modes[2], - "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last." - - async def api_call( - self, - start_frame: torch.Tensor, - end_frame: torch.Tensor, - prompt: str, - negative_prompt: str, - cfg_scale: float, - aspect_ratio: str, - mode: str, - unique_id: Optional[str] = None, - **kwargs, - ): - mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[ - mode - ] - return await super().api_call( - prompt=prompt, - negative_prompt=negative_prompt, - model_name=model_name, - start_frame=start_frame, - cfg_scale=cfg_scale, - mode=mode, - aspect_ratio=aspect_ratio, - duration=duration, - end_frame=end_frame, - unique_id=unique_id, - **kwargs, - ) - - -class KlingVideoExtendNode(KlingNodeBase): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, KlingVideoExtendRequest, "prompt", multiline=True - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - KlingVideoExtendRequest, - "negative_prompt", - multiline=True, - ), - "cfg_scale": model_field_to_node_input( - IO.FLOAT, - KlingVideoExtendRequest, - "cfg_scale", - default=0.5, - min=0.0, - max=1.0, - ), - "video_id": model_field_to_node_input( - IO.STRING, KlingVideoExtendRequest, "video_id", forceInput=True - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO", "STRING", "STRING") - RETURN_NAMES = ("VIDEO", "video_id", "duration") - DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes." - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> KlingVideoExtendResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_VIDEO_EXTEND}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=KlingVideoExtendResponse, - ), - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND, - node_id=node_id, - ) - - async def api_call( - self, - prompt: str, - negative_prompt: str, - cfg_scale: float, - video_id: str, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile, str, str]: - validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_VIDEO_EXTEND, - method=HttpMethod.POST, - request_model=KlingVideoExtendRequest, - response_model=KlingVideoExtendResponse, - ), - request=KlingVideoExtendRequest( - prompt=prompt if prompt else None, - negative_prompt=negative_prompt if negative_prompt else None, - cfg_scale=cfg_scale, - video_id=video_id, - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.data.task_id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_video_result_response(final_response) - - video = get_video_from_response(final_response) - return await video_result_to_node_output(video) - - -class KlingVideoEffectsBase(KlingNodeBase): - """Kling Video Effects Base""" - - RETURN_TYPES = ("VIDEO", "STRING", "STRING") - RETURN_NAMES = ("VIDEO", "video_id", "duration") - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> KlingVideoEffectsResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_VIDEO_EFFECTS}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=KlingVideoEffectsResponse, - ), - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS, - node_id=node_id, - ) - - async def api_call( - self, - dual_character: bool, - effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene, - model_name: str, - duration: KlingVideoGenDuration, - image_1: torch.Tensor, - image_2: Optional[torch.Tensor] = None, - mode: Optional[KlingVideoGenMode] = None, - unique_id: Optional[str] = None, - **kwargs, - ): - if dual_character: - request_input_field = KlingDualCharacterEffectInput( - model_name=model_name, - mode=mode, - images=[ - tensor_to_base64_string(image_1), - tensor_to_base64_string(image_2), - ], - duration=duration, - ) - else: - request_input_field = KlingSingleImageEffectInput( - model_name=model_name, - image=tensor_to_base64_string(image_1), - duration=duration, - ) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_VIDEO_EFFECTS, - method=HttpMethod.POST, - request_model=KlingVideoEffectsRequest, - response_model=KlingVideoEffectsResponse, - ), - request=KlingVideoEffectsRequest( - effect_scene=effect_scene, - input=request_input_field, - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.data.task_id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_video_result_response(final_response) - - video = get_video_from_response(final_response) - return await video_result_to_node_output(video) - - -class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase): - """Kling Dual Character Video Effect Node""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image_left": (IO.IMAGE, {"tooltip": "Left side image"}), - "image_right": (IO.IMAGE, {"tooltip": "Right side image"}), - "effect_scene": model_field_to_node_input( - IO.COMBO, - KlingVideoEffectsRequest, - "effect_scene", - enum_type=KlingDualCharacterEffectsScene, - ), - "model_name": model_field_to_node_input( - IO.COMBO, - KlingDualCharacterEffectInput, - "model_name", - enum_type=KlingCharacterEffectModelName, - ), - "mode": model_field_to_node_input( - IO.COMBO, - KlingDualCharacterEffectInput, - "mode", - enum_type=KlingVideoGenMode, - ), - "duration": model_field_to_node_input( - IO.COMBO, - KlingDualCharacterEffectInput, - "duration", - enum_type=KlingVideoGenDuration, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite." - RETURN_TYPES = ("VIDEO", "STRING") - RETURN_NAMES = ("VIDEO", "duration") - - async def api_call( - self, - image_left: torch.Tensor, - image_right: torch.Tensor, - effect_scene: KlingDualCharacterEffectsScene, - model_name: KlingCharacterEffectModelName, - mode: KlingVideoGenMode, - duration: KlingVideoGenDuration, - unique_id: Optional[str] = None, - **kwargs, - ): - video, _, duration = await super().api_call( - dual_character=True, - effect_scene=effect_scene, - model_name=model_name, - mode=mode, - duration=duration, - image_1=image_left, - image_2=image_right, - unique_id=unique_id, - **kwargs, - ) - return video, duration - - -class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase): - """Kling Single Image Video Effect Node""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ( - IO.IMAGE, - { - "tooltip": " Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1" - }, - ), - "effect_scene": model_field_to_node_input( - IO.COMBO, - KlingVideoEffectsRequest, - "effect_scene", - enum_type=KlingSingleImageEffectsScene, - ), - "model_name": model_field_to_node_input( - IO.COMBO, - KlingSingleImageEffectInput, - "model_name", - enum_type=KlingSingleImageEffectModelName, - ), - "duration": model_field_to_node_input( - IO.COMBO, - KlingSingleImageEffectInput, - "duration", - enum_type=KlingVideoGenDuration, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene." - - async def api_call( - self, - image: torch.Tensor, - effect_scene: KlingSingleImageEffectsScene, - model_name: KlingSingleImageEffectModelName, - duration: KlingVideoGenDuration, - unique_id: Optional[str] = None, - **kwargs, - ): - return await super().api_call( - dual_character=False, - effect_scene=effect_scene, - model_name=model_name, - duration=duration, - image_1=image, - unique_id=unique_id, - **kwargs, - ) - - -class KlingLipSyncBase(KlingNodeBase): - """Kling Lip Sync Base""" - - RETURN_TYPES = ("VIDEO", "STRING", "STRING") - RETURN_NAMES = ("VIDEO", "video_id", "duration") - - def validate_lip_sync_video(self, video: VideoInput): - """ - Validates the input video adheres to the expectations of the Kling Lip Sync API: - - Video length does not exceed 10s and is not shorter than 2s - - Length and width dimensions should both be between 720px and 1920px - - See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip - """ - validate_video_dimensions(video, 720, 1920) - validate_video_duration(video, 2, 10) - - def validate_text(self, text: str): - if not text: - raise ValueError("Text is required") - if len(text) > MAX_PROMPT_LENGTH_LIP_SYNC: - raise ValueError( - f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters." - ) - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> KlingLipSyncResponse: - """Polls the Kling API endpoint until the task reaches a terminal state.""" - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_LIP_SYNC}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=KlingLipSyncResponse, - ), - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_LIP_SYNC, - node_id=node_id, - ) - - async def api_call( - self, - video: VideoInput, - audio: Optional[AudioInput] = None, - voice_language: Optional[str] = None, - mode: Optional[str] = None, - text: Optional[str] = None, - voice_speed: Optional[float] = None, - voice_id: Optional[str] = None, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile, str, str]: - if text: - self.validate_text(text) - self.validate_lip_sync_video(video) - - # Upload video to Comfy API and get download URL - video_url = await upload_video_to_comfyapi(video, auth_kwargs=kwargs) - logging.info("Uploaded video to Comfy API. URL: %s", video_url) - - # Upload the audio file to Comfy API and get download URL - if audio: - audio_url = await upload_audio_to_comfyapi(audio, auth_kwargs=kwargs) - logging.info("Uploaded audio to Comfy API. URL: %s", audio_url) - else: - audio_url = None - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_LIP_SYNC, - method=HttpMethod.POST, - request_model=KlingLipSyncRequest, - response_model=KlingLipSyncResponse, - ), - request=KlingLipSyncRequest( - input=KlingLipSyncInputObject( - video_url=video_url, - mode=mode, - text=text, - voice_language=voice_language, - voice_speed=voice_speed, - audio_type="url", - audio_url=audio_url, - voice_id=voice_id, - ), - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.data.task_id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_video_result_response(final_response) - - video = get_video_from_response(final_response) - return await video_result_to_node_output(video) - - -class KlingLipSyncAudioToVideoNode(KlingLipSyncBase): - """Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file.""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "video": (IO.VIDEO, {}), - "audio": (IO.AUDIO, {}), - "voice_language": model_field_to_node_input( - IO.COMBO, - KlingLipSyncInputObject, - "voice_language", - enum_type=KlingLipSyncVoiceLanguage, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length." - - async def api_call( - self, - video: VideoInput, - audio: AudioInput, - voice_language: str, - unique_id: Optional[str] = None, - **kwargs, - ): - return await super().api_call( - video=video, - audio=audio, - voice_language=voice_language, - mode="audio2video", - unique_id=unique_id, - **kwargs, - ) - - -class KlingLipSyncTextToVideoNode(KlingLipSyncBase): - """Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt.""" - - @staticmethod - def get_voice_config() -> dict[str, tuple[str, str]]: - return { - # English voices - "Melody": ("girlfriend_4_speech02", "en"), - "Sunny": ("genshin_vindi2", "en"), - "Sage": ("zhinen_xuesheng", "en"), - "Ace": ("AOT", "en"), - "Blossom": ("ai_shatang", "en"), - "Peppy": ("genshin_klee2", "en"), - "Dove": ("genshin_kirara", "en"), - "Shine": ("ai_kaiya", "en"), - "Anchor": ("oversea_male1", "en"), - "Lyric": ("ai_chenjiahao_712", "en"), - "Tender": ("chat1_female_new-3", "en"), - "Siren": ("chat_0407_5-1", "en"), - "Zippy": ("cartoon-boy-07", "en"), - "Bud": ("uk_boy1", "en"), - "Sprite": ("cartoon-girl-01", "en"), - "Candy": ("PeppaPig_platform", "en"), - "Beacon": ("ai_huangzhong_712", "en"), - "Rock": ("ai_huangyaoshi_712", "en"), - "Titan": ("ai_laoguowang_712", "en"), - "Grace": ("chengshu_jiejie", "en"), - "Helen": ("you_pingjing", "en"), - "Lore": ("calm_story1", "en"), - "Crag": ("uk_man2", "en"), - "Prattle": ("laopopo_speech02", "en"), - "Hearth": ("heainainai_speech02", "en"), - "The Reader": ("reader_en_m-v1", "en"), - "Commercial Lady": ("commercial_lady_en_f-v1", "en"), - # Chinese voices - "阳光少年": ("genshin_vindi2", "zh"), - "懂事小弟": ("zhinen_xuesheng", "zh"), - "运动少年": ("tiyuxi_xuedi", "zh"), - "青春少女": ("ai_shatang", "zh"), - "温柔小妹": ("genshin_klee2", "zh"), - "元气少女": ("genshin_kirara", "zh"), - "阳光男生": ("ai_kaiya", "zh"), - "幽默小哥": ("tiexin_nanyou", "zh"), - "文艺小哥": ("ai_chenjiahao_712", "zh"), - "甜美邻家": ("girlfriend_1_speech02", "zh"), - "温柔姐姐": ("chat1_female_new-3", "zh"), - "职场女青": ("girlfriend_2_speech02", "zh"), - "活泼男童": ("cartoon-boy-07", "zh"), - "俏皮女童": ("cartoon-girl-01", "zh"), - "稳重老爸": ("ai_huangyaoshi_712", "zh"), - "温柔妈妈": ("you_pingjing", "zh"), - "严肃上司": ("ai_laoguowang_712", "zh"), - "优雅贵妇": ("chengshu_jiejie", "zh"), - "慈祥爷爷": ("zhuxi_speech02", "zh"), - "唠叨爷爷": ("uk_oldman3", "zh"), - "唠叨奶奶": ("laopopo_speech02", "zh"), - "和蔼奶奶": ("heainainai_speech02", "zh"), - "东北老铁": ("dongbeilaotie_speech02", "zh"), - "重庆小伙": ("chongqingxiaohuo_speech02", "zh"), - "四川妹子": ("chuanmeizi_speech02", "zh"), - "潮汕大叔": ("chaoshandashu_speech02", "zh"), - "台湾男生": ("ai_taiwan_man2_speech02", "zh"), - "西安掌柜": ("xianzhanggui_speech02", "zh"), - "天津姐姐": ("tianjinjiejie_speech02", "zh"), - "新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"), - "译制片男": ("yizhipiannan-v1", "zh"), - "撒娇女友": ("tianmeixuemei-v1", "zh"), - "刀片烟嗓": ("daopianyansang-v1", "zh"), - "乖巧正太": ("mengwa-v1", "zh"), - } - - @classmethod - def INPUT_TYPES(s): - voice_options = list(s.get_voice_config().keys()) - return { - "required": { - "video": (IO.VIDEO, {}), - "text": model_field_to_node_input( - IO.STRING, KlingLipSyncInputObject, "text", multiline=True - ), - "voice": (voice_options, {"default": voice_options[0]}), - "voice_speed": model_field_to_node_input( - IO.FLOAT, KlingLipSyncInputObject, "voice_speed", slider=True - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length." - - async def api_call( - self, - video: VideoInput, - text: str, - voice: str, - voice_speed: float, - unique_id: Optional[str] = None, - **kwargs, - ): - voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice] - return await super().api_call( - video=video, - text=text, - voice_language=voice_language, - voice_id=voice_id, - voice_speed=voice_speed, - mode="text2video", - unique_id=unique_id, - **kwargs, - ) - - -class KlingImageGenerationBase(KlingNodeBase): - """Kling Image Generation Base Node.""" - - RETURN_TYPES = ("IMAGE",) - CATEGORY = "api node/image/Kling" - - def validate_prompt(self, prompt: str, negative_prompt: Optional[str] = None): - if not prompt or len(prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN: - raise ValueError( - f"Prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters" - ) - if negative_prompt and len(negative_prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN: - raise ValueError( - f"Negative prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters" - ) - - -class KlingVirtualTryOnNode(KlingImageGenerationBase): - """Kling Virtual Try On Node.""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "human_image": (IO.IMAGE, {}), - "cloth_image": (IO.IMAGE, {}), - "model_name": model_field_to_node_input( - IO.COMBO, - KlingVirtualTryOnRequest, - "model_name", - enum_type=KlingVirtualTryOnModelName, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background." - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> KlingVirtualTryOnResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=KlingVirtualTryOnResponse, - ), - result_url_extractor=get_images_urls_from_response, - estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON, - node_id=node_id, - ) - - async def api_call( - self, - human_image: torch.Tensor, - cloth_image: torch.Tensor, - model_name: KlingVirtualTryOnModelName, - unique_id: Optional[str] = None, - **kwargs, - ): - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_VIRTUAL_TRY_ON, - method=HttpMethod.POST, - request_model=KlingVirtualTryOnRequest, - response_model=KlingVirtualTryOnResponse, - ), - request=KlingVirtualTryOnRequest( - human_image=tensor_to_base64_string(human_image), - cloth_image=tensor_to_base64_string(cloth_image), - model_name=model_name, - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.data.task_id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_image_result_response(final_response) - - images = get_images_from_response(final_response) - return (await image_result_to_node_output(images),) - - -class KlingImageGenerationNode(KlingImageGenerationBase): - """Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, - KlingImageGenerationsRequest, - "prompt", - multiline=True, - max_length=MAX_PROMPT_LENGTH_IMAGE_GEN, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - KlingImageGenerationsRequest, - "negative_prompt", - multiline=True, - ), - "image_type": model_field_to_node_input( - IO.COMBO, - KlingImageGenerationsRequest, - "image_reference", - enum_type=KlingImageGenImageReferenceType, - ), - "image_fidelity": model_field_to_node_input( - IO.FLOAT, - KlingImageGenerationsRequest, - "image_fidelity", - slider=True, - step=0.01, - ), - "human_fidelity": model_field_to_node_input( - IO.FLOAT, - KlingImageGenerationsRequest, - "human_fidelity", - slider=True, - step=0.01, - ), - "model_name": model_field_to_node_input( - IO.COMBO, - KlingImageGenerationsRequest, - "model_name", - enum_type=KlingImageGenModelName, - ), - "aspect_ratio": model_field_to_node_input( - IO.COMBO, - KlingImageGenerationsRequest, - "aspect_ratio", - enum_type=KlingImageGenAspectRatio, - ), - "n": model_field_to_node_input( - IO.INT, - KlingImageGenerationsRequest, - "n", - ), - }, - "optional": { - "image": (IO.IMAGE, {}), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image." - - async def get_response( - self, - task_id: str, - auth_kwargs: Optional[dict[str, str]], - node_id: Optional[str] = None, - ) -> KlingImageGenerationsResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_IMAGE_GENERATIONS}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=KlingImageGenerationsResponse, - ), - result_url_extractor=get_images_urls_from_response, - estimated_duration=AVERAGE_DURATION_IMAGE_GEN, - node_id=node_id, - ) - - async def api_call( - self, - model_name: KlingImageGenModelName, - prompt: str, - negative_prompt: str, - image_type: KlingImageGenImageReferenceType, - image_fidelity: float, - human_fidelity: float, - n: int, - aspect_ratio: KlingImageGenAspectRatio, - image: Optional[torch.Tensor] = None, - unique_id: Optional[str] = None, - **kwargs, - ): - self.validate_prompt(prompt, negative_prompt) - - if image is None: - image_type = None - elif model_name == KlingImageGenModelName.kling_v1: - raise ValueError(f"The model {KlingImageGenModelName.kling_v1.value} does not support reference images.") - else: - image = tensor_to_base64_string(image) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_IMAGE_GENERATIONS, - method=HttpMethod.POST, - request_model=KlingImageGenerationsRequest, - response_model=KlingImageGenerationsResponse, - ), - request=KlingImageGenerationsRequest( - model_name=model_name, - prompt=prompt, - negative_prompt=negative_prompt, - image=image, - image_reference=image_type, - image_fidelity=image_fidelity, - human_fidelity=human_fidelity, - n=n, - aspect_ratio=aspect_ratio, - ), - auth_kwargs=kwargs, - ) - - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.data.task_id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - validate_image_result_response(final_response) - - images = get_images_from_response(final_response) - return (await image_result_to_node_output(images),) - - -NODE_CLASS_MAPPINGS = { - "KlingCameraControls": KlingCameraControls, - "KlingTextToVideoNode": KlingTextToVideoNode, - "KlingImage2VideoNode": KlingImage2VideoNode, - "KlingCameraControlI2VNode": KlingCameraControlI2VNode, - "KlingCameraControlT2VNode": KlingCameraControlT2VNode, - "KlingStartEndFrameNode": KlingStartEndFrameNode, - "KlingVideoExtendNode": KlingVideoExtendNode, - "KlingLipSyncAudioToVideoNode": KlingLipSyncAudioToVideoNode, - "KlingLipSyncTextToVideoNode": KlingLipSyncTextToVideoNode, - "KlingVirtualTryOnNode": KlingVirtualTryOnNode, - "KlingImageGenerationNode": KlingImageGenerationNode, - "KlingSingleImageVideoEffectNode": KlingSingleImageVideoEffectNode, - "KlingDualCharacterVideoEffectNode": KlingDualCharacterVideoEffectNode, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "KlingCameraControls": "Kling Camera Controls", - "KlingTextToVideoNode": "Kling Text to Video", - "KlingImage2VideoNode": "Kling Image to Video", - "KlingCameraControlI2VNode": "Kling Image to Video (Camera Control)", - "KlingCameraControlT2VNode": "Kling Text to Video (Camera Control)", - "KlingStartEndFrameNode": "Kling Start-End Frame to Video", - "KlingVideoExtendNode": "Kling Video Extend", - "KlingLipSyncAudioToVideoNode": "Kling Lip Sync Video with Audio", - "KlingLipSyncTextToVideoNode": "Kling Lip Sync Video with Text", - "KlingVirtualTryOnNode": "Kling Virtual Try On", - "KlingImageGenerationNode": "Kling Image Generation", - "KlingSingleImageVideoEffectNode": "Kling Video Effects", - "KlingDualCharacterVideoEffectNode": "Kling Dual Character Video Effects", -} diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py deleted file mode 100644 index b3c32bed55c547e981aa5d497d4464e36f78b0d8..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_luma.py +++ /dev/null @@ -1,741 +0,0 @@ -from __future__ import annotations -from inspect import cleandoc -from typing import Optional -from comfy.comfy_types.node_typing import IO, ComfyNodeABC -from comfy_api.input_impl.video_types import VideoFromFile -from comfy_api_nodes.apis.luma_api import ( - LumaImageModel, - LumaVideoModel, - LumaVideoOutputResolution, - LumaVideoModelOutputDuration, - LumaAspectRatio, - LumaState, - LumaImageGenerationRequest, - LumaGenerationRequest, - LumaGeneration, - LumaCharacterRef, - LumaModifyImageRef, - LumaImageIdentity, - LumaReference, - LumaReferenceChain, - LumaImageReference, - LumaKeyframes, - LumaConceptChain, - LumaIO, - get_luma_concepts, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - upload_images_to_comfyapi, - process_image_response, - validate_string, -) -from server import PromptServer - -import aiohttp -import torch -from io import BytesIO - -LUMA_T2V_AVERAGE_DURATION = 105 -LUMA_I2V_AVERAGE_DURATION = 100 - -def image_result_url_extractor(response: LumaGeneration): - return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None - -def video_result_url_extractor(response: LumaGeneration): - return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None - -class LumaReferenceNode(ComfyNodeABC): - """ - Holds an image and weight for use with Luma Generate Image node. - """ - - RETURN_TYPES = (LumaIO.LUMA_REF,) - RETURN_NAMES = ("luma_ref",) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "create_luma_reference" - CATEGORY = "api node/image/Luma" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ( - IO.IMAGE, - { - "tooltip": "Image to use as reference.", - }, - ), - "weight": ( - IO.FLOAT, - { - "default": 1.0, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Weight of image reference.", - }, - ), - }, - "optional": {"luma_ref": (LumaIO.LUMA_REF,)}, - } - - def create_luma_reference( - self, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None - ): - if luma_ref is not None: - luma_ref = luma_ref.clone() - else: - luma_ref = LumaReferenceChain() - luma_ref.add(LumaReference(image=image, weight=round(weight, 2))) - return (luma_ref,) - - -class LumaConceptsNode(ComfyNodeABC): - """ - Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes. - """ - - RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,) - RETURN_NAMES = ("luma_concepts",) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "create_concepts" - CATEGORY = "api node/video/Luma" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "concept1": (get_luma_concepts(include_none=True),), - "concept2": (get_luma_concepts(include_none=True),), - "concept3": (get_luma_concepts(include_none=True),), - "concept4": (get_luma_concepts(include_none=True),), - }, - "optional": { - "luma_concepts": ( - LumaIO.LUMA_CONCEPTS, - { - "tooltip": "Optional Camera Concepts to add to the ones chosen here." - }, - ), - }, - } - - def create_concepts( - self, - concept1: str, - concept2: str, - concept3: str, - concept4: str, - luma_concepts: LumaConceptChain = None, - ): - chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4]) - if luma_concepts is not None: - chain = luma_concepts.clone_and_merge(chain) - return (chain,) - - -class LumaImageGenerationNode(ComfyNodeABC): - """ - Generates images synchronously based on prompt and aspect ratio. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Luma" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "model": ([model.value for model in LumaImageModel],), - "aspect_ratio": ( - [ratio.value for ratio in LumaAspectRatio], - { - "default": LumaAspectRatio.ratio_16_9, - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - "style_image_weight": ( - IO.FLOAT, - { - "default": 1.0, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Weight of style image. Ignored if no style_image provided.", - }, - ), - }, - "optional": { - "image_luma_ref": ( - LumaIO.LUMA_REF, - { - "tooltip": "Luma Reference node connection to influence generation with input images; up to 4 images can be considered." - }, - ), - "style_image": ( - IO.IMAGE, - {"tooltip": "Style reference image; only 1 image will be used."}, - ), - "character_image": ( - IO.IMAGE, - { - "tooltip": "Character reference images; can be a batch of multiple, up to 4 images can be considered." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - model: str, - aspect_ratio: str, - seed, - style_image_weight: float, - image_luma_ref: LumaReferenceChain = None, - style_image: torch.Tensor = None, - character_image: torch.Tensor = None, - unique_id: str = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=True, min_length=3) - # handle image_luma_ref - api_image_ref = None - if image_luma_ref is not None: - api_image_ref = await self._convert_luma_refs( - image_luma_ref, max_refs=4, auth_kwargs=kwargs, - ) - # handle style_luma_ref - api_style_ref = None - if style_image is not None: - api_style_ref = await self._convert_style_image( - style_image, weight=style_image_weight, auth_kwargs=kwargs, - ) - # handle character_ref images - character_ref = None - if character_image is not None: - download_urls = await upload_images_to_comfyapi( - character_image, max_images=4, auth_kwargs=kwargs, - ) - character_ref = LumaCharacterRef( - identity0=LumaImageIdentity(images=download_urls) - ) - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/luma/generations/image", - method=HttpMethod.POST, - request_model=LumaImageGenerationRequest, - response_model=LumaGeneration, - ), - request=LumaImageGenerationRequest( - prompt=prompt, - model=model, - aspect_ratio=aspect_ratio, - image_ref=api_image_ref, - style_ref=api_style_ref, - character_ref=character_ref, - ), - auth_kwargs=kwargs, - ) - response_api: LumaGeneration = await operation.execute() - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/luma/generations/{response_api.id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=LumaGeneration, - ), - completed_statuses=[LumaState.completed], - failed_statuses=[LumaState.failed], - status_extractor=lambda x: x.state, - result_url_extractor=image_result_url_extractor, - node_id=unique_id, - auth_kwargs=kwargs, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.assets.image) as img_response: - img = process_image_response(await img_response.content.read()) - return (img,) - - async def _convert_luma_refs( - self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None - ): - luma_urls = [] - ref_count = 0 - for ref in luma_ref.refs: - download_urls = await upload_images_to_comfyapi( - ref.image, max_images=1, auth_kwargs=auth_kwargs - ) - luma_urls.append(download_urls[0]) - ref_count += 1 - if ref_count >= max_refs: - break - return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs) - - async def _convert_style_image( - self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None - ): - chain = LumaReferenceChain( - first_ref=LumaReference(image=style_image, weight=weight) - ) - return await self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs) - - -class LumaImageModifyNode(ComfyNodeABC): - """ - Modifies images synchronously based on prompt and aspect ratio. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Luma" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation", - }, - ), - "image_weight": ( - IO.FLOAT, - { - "default": 0.1, - "min": 0.0, - "max": 0.98, - "step": 0.01, - "tooltip": "Weight of the image; the closer to 1.0, the less the image will be modified.", - }, - ), - "model": ([model.value for model in LumaImageModel],), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": {}, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - model: str, - image: torch.Tensor, - image_weight: float, - seed, - unique_id: str = None, - **kwargs, - ): - # first, upload image - download_urls = await upload_images_to_comfyapi( - image, max_images=1, auth_kwargs=kwargs, - ) - image_url = download_urls[0] - # next, make Luma call with download url provided - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/luma/generations/image", - method=HttpMethod.POST, - request_model=LumaImageGenerationRequest, - response_model=LumaGeneration, - ), - request=LumaImageGenerationRequest( - prompt=prompt, - model=model, - modify_image_ref=LumaModifyImageRef( - url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2) - ), - ), - auth_kwargs=kwargs, - ) - response_api: LumaGeneration = await operation.execute() - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/luma/generations/{response_api.id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=LumaGeneration, - ), - completed_statuses=[LumaState.completed], - failed_statuses=[LumaState.failed], - status_extractor=lambda x: x.state, - result_url_extractor=image_result_url_extractor, - node_id=unique_id, - auth_kwargs=kwargs, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.assets.image) as img_response: - img = process_image_response(await img_response.content.read()) - return (img,) - - -class LumaTextToVideoGenerationNode(ComfyNodeABC): - """ - Generates videos synchronously based on prompt and output_size. - """ - - RETURN_TYPES = (IO.VIDEO,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/video/Luma" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the video generation", - }, - ), - "model": ([model.value for model in LumaVideoModel],), - "aspect_ratio": ( - [ratio.value for ratio in LumaAspectRatio], - { - "default": LumaAspectRatio.ratio_16_9, - }, - ), - "resolution": ( - [resolution.value for resolution in LumaVideoOutputResolution], - { - "default": LumaVideoOutputResolution.res_540p, - }, - ), - "duration": ([dur.value for dur in LumaVideoModelOutputDuration],), - "loop": ( - IO.BOOLEAN, - { - "default": False, - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "luma_concepts": ( - LumaIO.LUMA_CONCEPTS, - { - "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - model: str, - aspect_ratio: str, - resolution: str, - duration: str, - loop: bool, - seed, - luma_concepts: LumaConceptChain = None, - unique_id: str = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False, min_length=3) - duration = duration if model != LumaVideoModel.ray_1_6 else None - resolution = resolution if model != LumaVideoModel.ray_1_6 else None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/luma/generations", - method=HttpMethod.POST, - request_model=LumaGenerationRequest, - response_model=LumaGeneration, - ), - request=LumaGenerationRequest( - prompt=prompt, - model=model, - resolution=resolution, - aspect_ratio=aspect_ratio, - duration=duration, - loop=loop, - concepts=luma_concepts.create_api_model() if luma_concepts else None, - ), - auth_kwargs=kwargs, - ) - response_api: LumaGeneration = await operation.execute() - - if unique_id: - PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id) - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/luma/generations/{response_api.id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=LumaGeneration, - ), - completed_statuses=[LumaState.completed], - failed_statuses=[LumaState.failed], - status_extractor=lambda x: x.state, - result_url_extractor=video_result_url_extractor, - node_id=unique_id, - estimated_duration=LUMA_T2V_AVERAGE_DURATION, - auth_kwargs=kwargs, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.assets.video) as vid_response: - return (VideoFromFile(BytesIO(await vid_response.content.read())),) - - -class LumaImageToVideoGenerationNode(ComfyNodeABC): - """ - Generates videos synchronously based on prompt, input images, and output_size. - """ - - RETURN_TYPES = (IO.VIDEO,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/video/Luma" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the video generation", - }, - ), - "model": ([model.value for model in LumaVideoModel],), - # "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], { - # "default": LumaAspectRatio.ratio_16_9, - # }), - "resolution": ( - [resolution.value for resolution in LumaVideoOutputResolution], - { - "default": LumaVideoOutputResolution.res_540p, - }, - ), - "duration": ([dur.value for dur in LumaVideoModelOutputDuration],), - "loop": ( - IO.BOOLEAN, - { - "default": False, - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "first_image": ( - IO.IMAGE, - {"tooltip": "First frame of generated video."}, - ), - "last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}), - "luma_concepts": ( - LumaIO.LUMA_CONCEPTS, - { - "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - model: str, - resolution: str, - duration: str, - loop: bool, - seed, - first_image: torch.Tensor = None, - last_image: torch.Tensor = None, - luma_concepts: LumaConceptChain = None, - unique_id: str = None, - **kwargs, - ): - if first_image is None and last_image is None: - raise Exception( - "At least one of first_image and last_image requires an input." - ) - keyframes = await self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs) - duration = duration if model != LumaVideoModel.ray_1_6 else None - resolution = resolution if model != LumaVideoModel.ray_1_6 else None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/luma/generations", - method=HttpMethod.POST, - request_model=LumaGenerationRequest, - response_model=LumaGeneration, - ), - request=LumaGenerationRequest( - prompt=prompt, - model=model, - aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason - resolution=resolution, - duration=duration, - loop=loop, - keyframes=keyframes, - concepts=luma_concepts.create_api_model() if luma_concepts else None, - ), - auth_kwargs=kwargs, - ) - response_api: LumaGeneration = await operation.execute() - - if unique_id: - PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id) - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/luma/generations/{response_api.id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=LumaGeneration, - ), - completed_statuses=[LumaState.completed], - failed_statuses=[LumaState.failed], - status_extractor=lambda x: x.state, - result_url_extractor=video_result_url_extractor, - node_id=unique_id, - estimated_duration=LUMA_I2V_AVERAGE_DURATION, - auth_kwargs=kwargs, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.assets.video) as vid_response: - return (VideoFromFile(BytesIO(await vid_response.content.read())),) - - async def _convert_to_keyframes( - self, - first_image: torch.Tensor = None, - last_image: torch.Tensor = None, - auth_kwargs: Optional[dict[str,str]] = None, - ): - if first_image is None and last_image is None: - return None - frame0 = None - frame1 = None - if first_image is not None: - download_urls = await upload_images_to_comfyapi( - first_image, max_images=1, auth_kwargs=auth_kwargs, - ) - frame0 = LumaImageReference(type="image", url=download_urls[0]) - if last_image is not None: - download_urls = await upload_images_to_comfyapi( - last_image, max_images=1, auth_kwargs=auth_kwargs, - ) - frame1 = LumaImageReference(type="image", url=download_urls[0]) - return LumaKeyframes(frame0=frame0, frame1=frame1) - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "LumaImageNode": LumaImageGenerationNode, - "LumaImageModifyNode": LumaImageModifyNode, - "LumaVideoNode": LumaTextToVideoGenerationNode, - "LumaImageToVideoNode": LumaImageToVideoGenerationNode, - "LumaReferenceNode": LumaReferenceNode, - "LumaConceptsNode": LumaConceptsNode, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "LumaImageNode": "Luma Text to Image", - "LumaImageModifyNode": "Luma Image to Image", - "LumaVideoNode": "Luma Text to Video", - "LumaImageToVideoNode": "Luma Image to Video", - "LumaReferenceNode": "Luma Reference", - "LumaConceptsNode": "Luma Concepts", -} diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py deleted file mode 100644 index bb3c9e7104b127f717fd1524a2c4045770bbd278..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_minimax.py +++ /dev/null @@ -1,507 +0,0 @@ -from inspect import cleandoc -from typing import Union -import logging -import torch - -from comfy.comfy_types.node_typing import IO -from comfy_api.input_impl.video_types import VideoFromFile -from comfy_api_nodes.apis import ( - MinimaxVideoGenerationRequest, - MinimaxVideoGenerationResponse, - MinimaxFileRetrieveResponse, - MinimaxTaskResultResponse, - SubjectReferenceItem, - MiniMaxModel -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - download_url_to_bytesio, - upload_images_to_comfyapi, - validate_string, -) -from server import PromptServer - - -I2V_AVERAGE_DURATION = 114 -T2V_AVERAGE_DURATION = 234 - -class MinimaxTextToVideoNode: - """ - Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API. - """ - - AVERAGE_DURATION = T2V_AVERAGE_DURATION - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation", - }, - ), - "model": ( - [ - "T2V-01", - "T2V-01-Director", - ], - { - "default": "T2V-01", - "tooltip": "Model to use for video generation", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = "Generates videos from prompts using MiniMax's API" - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True - - async def generate_video( - self, - prompt_text, - seed=0, - model="T2V-01", - image: torch.Tensor=None, # used for ImageToVideo - subject: torch.Tensor=None, # used for SubjectToVideo - unique_id: Union[str, None]=None, - **kwargs, - ): - ''' - Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments. - ''' - if image is None: - validate_string(prompt_text, field_name="prompt_text") - # upload image, if passed in - image_url = None - if image is not None: - image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs))[0] - - # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model - subject_reference = None - if subject is not None: - subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=kwargs))[0] - subject_reference = [SubjectReferenceItem(image=subject_url)] - - - video_generate_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/minimax/video_generation", - method=HttpMethod.POST, - request_model=MinimaxVideoGenerationRequest, - response_model=MinimaxVideoGenerationResponse, - ), - request=MinimaxVideoGenerationRequest( - model=MiniMaxModel(model), - prompt=prompt_text, - callback_url=None, - first_frame_image=image_url, - subject_reference=subject_reference, - prompt_optimizer=None, - ), - auth_kwargs=kwargs, - ) - response = await video_generate_operation.execute() - - task_id = response.task_id - if not task_id: - raise Exception(f"MiniMax generation failed: {response.base_resp}") - - video_generate_operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path="/proxy/minimax/query/video_generation", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MinimaxTaskResultResponse, - query_params={"task_id": task_id}, - ), - completed_statuses=["Success"], - failed_statuses=["Fail"], - status_extractor=lambda x: x.status.value, - estimated_duration=self.AVERAGE_DURATION, - node_id=unique_id, - auth_kwargs=kwargs, - ) - task_result = await video_generate_operation.execute() - - file_id = task_result.file_id - if file_id is None: - raise Exception("Request was not successful. Missing file ID.") - file_retrieve_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/minimax/files/retrieve", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MinimaxFileRetrieveResponse, - query_params={"file_id": int(file_id)}, - ), - request=EmptyRequest(), - auth_kwargs=kwargs, - ) - file_result = await file_retrieve_operation.execute() - - file_url = file_result.file.download_url - if file_url is None: - raise Exception( - f"No video was found in the response. Full response: {file_result.model_dump()}" - ) - logging.info(f"Generated video URL: {file_url}") - if unique_id: - if hasattr(file_result.file, "backup_download_url"): - message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" - else: - message = f"Result URL: {file_url}" - PromptServer.instance.send_progress_text(message, unique_id) - - video_io = await download_url_to_bytesio(file_url) - if video_io is None: - error_msg = f"Failed to download video from {file_url}" - logging.error(error_msg) - raise Exception(error_msg) - return (VideoFromFile(video_io),) - - -class MinimaxImageToVideoNode(MinimaxTextToVideoNode): - """ - Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. - """ - - AVERAGE_DURATION = I2V_AVERAGE_DURATION - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ( - IO.IMAGE, - { - "tooltip": "Image to use as first frame of video generation" - }, - ), - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation", - }, - ), - "model": ( - [ - "I2V-01-Director", - "I2V-01", - "I2V-01-live", - ], - { - "default": "I2V-01", - "tooltip": "Model to use for video generation", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True - - -class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode): - """ - Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. - """ - - AVERAGE_DURATION = T2V_AVERAGE_DURATION - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "subject": ( - IO.IMAGE, - { - "tooltip": "Image of subject to reference video generation" - }, - ), - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation", - }, - ), - "model": ( - [ - "S2V-01", - ], - { - "default": "S2V-01", - "tooltip": "Model to use for video generation", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API" - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True - - -class MinimaxHailuoVideoNode: - """Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt_text": ( - "STRING", - { - "multiline": True, - "default": "", - "tooltip": "Text prompt to guide the video generation.", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - "first_frame_image": ( - IO.IMAGE, - { - "tooltip": "Optional image to use as the first frame to generate a video." - }, - ), - "prompt_optimizer": ( - IO.BOOLEAN, - { - "tooltip": "Optimize prompt to improve generation quality when needed.", - "default": True, - }, - ), - "duration": ( - IO.COMBO, - { - "tooltip": "The length of the output video in seconds.", - "default": 6, - "options": [6, 10], - }, - ), - "resolution": ( - IO.COMBO, - { - "tooltip": "The dimensions of the video display. " - "1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels.", - "default": "768P", - "options": ["768P", "1080P"], - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("VIDEO",) - DESCRIPTION = cleandoc(__doc__ or "") - FUNCTION = "generate_video" - CATEGORY = "api node/video/MiniMax" - API_NODE = True - - async def generate_video( - self, - prompt_text, - seed=0, - first_frame_image: torch.Tensor=None, # used for ImageToVideo - prompt_optimizer=True, - duration=6, - resolution="768P", - model="MiniMax-Hailuo-02", - unique_id: Union[str, None]=None, - **kwargs, - ): - if first_frame_image is None: - validate_string(prompt_text, field_name="prompt_text") - - if model == "MiniMax-Hailuo-02" and resolution.upper() == "1080P" and duration != 6: - raise Exception( - "When model is MiniMax-Hailuo-02 and resolution is 1080P, duration is limited to 6 seconds." - ) - - # upload image, if passed in - image_url = None - if first_frame_image is not None: - image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=kwargs))[0] - - video_generate_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/minimax/video_generation", - method=HttpMethod.POST, - request_model=MinimaxVideoGenerationRequest, - response_model=MinimaxVideoGenerationResponse, - ), - request=MinimaxVideoGenerationRequest( - model=MiniMaxModel(model), - prompt=prompt_text, - callback_url=None, - first_frame_image=image_url, - prompt_optimizer=prompt_optimizer, - duration=duration, - resolution=resolution, - ), - auth_kwargs=kwargs, - ) - response = await video_generate_operation.execute() - - task_id = response.task_id - if not task_id: - raise Exception(f"MiniMax generation failed: {response.base_resp}") - - average_duration = 120 if resolution == "768P" else 240 - video_generate_operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path="/proxy/minimax/query/video_generation", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MinimaxTaskResultResponse, - query_params={"task_id": task_id}, - ), - completed_statuses=["Success"], - failed_statuses=["Fail"], - status_extractor=lambda x: x.status.value, - estimated_duration=average_duration, - node_id=unique_id, - auth_kwargs=kwargs, - ) - task_result = await video_generate_operation.execute() - - file_id = task_result.file_id - if file_id is None: - raise Exception("Request was not successful. Missing file ID.") - file_retrieve_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/minimax/files/retrieve", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MinimaxFileRetrieveResponse, - query_params={"file_id": int(file_id)}, - ), - request=EmptyRequest(), - auth_kwargs=kwargs, - ) - file_result = await file_retrieve_operation.execute() - - file_url = file_result.file.download_url - if file_url is None: - raise Exception( - f"No video was found in the response. Full response: {file_result.model_dump()}" - ) - logging.info(f"Generated video URL: {file_url}") - if unique_id: - if hasattr(file_result.file, "backup_download_url"): - message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" - else: - message = f"Result URL: {file_url}" - PromptServer.instance.send_progress_text(message, unique_id) - - video_io = await download_url_to_bytesio(file_url) - if video_io is None: - error_msg = f"Failed to download video from {file_url}" - logging.error(error_msg) - raise Exception(error_msg) - return (VideoFromFile(video_io),) - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "MinimaxTextToVideoNode": MinimaxTextToVideoNode, - "MinimaxImageToVideoNode": MinimaxImageToVideoNode, - # "MinimaxSubjectToVideoNode": MinimaxSubjectToVideoNode, - "MinimaxHailuoVideoNode": MinimaxHailuoVideoNode, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "MinimaxTextToVideoNode": "MiniMax Text to Video", - "MinimaxImageToVideoNode": "MiniMax Image to Video", - "MinimaxSubjectToVideoNode": "MiniMax Subject to Video", - "MinimaxHailuoVideoNode": "MiniMax Hailuo Video", -} diff --git a/comfy_api_nodes/nodes_moonvalley.py b/comfy_api_nodes/nodes_moonvalley.py deleted file mode 100644 index 806a70e0697d8775b29b45099eb62f052f1fbf1e..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_moonvalley.py +++ /dev/null @@ -1,797 +0,0 @@ -import logging -from typing import Any, Callable, Optional, TypeVar -import torch -from comfy_api_nodes.util.validation_utils import ( - get_image_dimensions, - validate_image_dimensions, -) - - -from comfy_api_nodes.apis import ( - MoonvalleyTextToVideoRequest, - MoonvalleyTextToVideoInferenceParams, - MoonvalleyVideoToVideoInferenceParams, - MoonvalleyVideoToVideoRequest, - MoonvalleyPromptResponse, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - download_url_to_video_output, - upload_images_to_comfyapi, - upload_video_to_comfyapi, -) -from comfy_api_nodes.mapper_utils import model_field_to_node_input - -from comfy_api.input.video_types import VideoInput -from comfy.comfy_types.node_typing import IO -from comfy_api.input_impl import VideoFromFile -import av -import io - -API_UPLOADS_ENDPOINT = "/proxy/moonvalley/uploads" -API_PROMPTS_ENDPOINT = "/proxy/moonvalley/prompts" -API_VIDEO2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/video-to-video" -API_TXT2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/text-to-video" -API_IMG2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/image-to-video" - -MIN_WIDTH = 300 -MIN_HEIGHT = 300 - -MAX_WIDTH = 10000 -MAX_HEIGHT = 10000 - -MIN_VID_WIDTH = 300 -MIN_VID_HEIGHT = 300 - -MAX_VID_WIDTH = 10000 -MAX_VID_HEIGHT = 10000 - -MAX_VIDEO_SIZE = 1024 * 1024 * 1024 # 1 GB max for in-memory video processing - -MOONVALLEY_MAREY_MAX_PROMPT_LENGTH = 5000 -R = TypeVar("R") - - -class MoonvalleyApiError(Exception): - """Base exception for Moonvalley API errors.""" - - pass - - -def is_valid_task_creation_response(response: MoonvalleyPromptResponse) -> bool: - """Verifies that the initial response contains a task ID.""" - return bool(response.id) - - -def validate_task_creation_response(response) -> None: - if not is_valid_task_creation_response(response): - error_msg = f"Moonvalley Marey API: Initial request failed. Code: {response.code}, Message: {response.message}, Data: {response}" - logging.error(error_msg) - raise MoonvalleyApiError(error_msg) - - -def get_video_from_response(response): - video = response.output_url - logging.info( - "Moonvalley Marey API: Task %s succeeded. Video URL: %s", response.id, video - ) - return video - - -def get_video_url_from_response(response) -> Optional[str]: - """Returns the first video url from the Moonvalley video generation task result. - Will not raise an error if the response is not valid. - """ - if response: - return str(get_video_from_response(response)) - else: - return None - - -async def poll_until_finished( - auth_kwargs: dict[str, str], - api_endpoint: ApiEndpoint[Any, R], - result_url_extractor: Optional[Callable[[R], str]] = None, - node_id: Optional[str] = None, -) -> R: - """Polls the Moonvalley API endpoint until the task reaches a terminal state, then returns the response.""" - return await PollingOperation( - poll_endpoint=api_endpoint, - completed_statuses=[ - "completed", - ], - max_poll_attempts=240, # 64 minutes with 16s interval - poll_interval=16.0, - failed_statuses=["error"], - status_extractor=lambda response: ( - response.status if response and response.status else None - ), - auth_kwargs=auth_kwargs, - result_url_extractor=result_url_extractor, - node_id=node_id, - ).execute() - - -def validate_prompts( - prompt: str, negative_prompt: str, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH -): - """Verifies that the prompt isn't empty and that neither prompt is too long.""" - if not prompt: - raise ValueError("Positive prompt is empty") - if len(prompt) > max_length: - raise ValueError(f"Positive prompt is too long: {len(prompt)} characters") - if negative_prompt and len(negative_prompt) > max_length: - raise ValueError( - f"Negative prompt is too long: {len(negative_prompt)} characters" - ) - return True - - -def validate_input_media(width, height, with_frame_conditioning, num_frames_in=None): - # inference validation - # T = num_frames - # in all cases, the following must be true: T divisible by 16 and H,W by 8. in addition... - # with image conditioning: H*W must be divisible by 8192 - # without image conditioning: T divisible by 32 - if num_frames_in and not num_frames_in % 16 == 0: - return False, ("The input video total frame count must be divisible by 16!") - - if height % 8 != 0 or width % 8 != 0: - return False, ( - f"Height ({height}) and width ({width}) must be " "divisible by 8" - ) - - if with_frame_conditioning: - if (height * width) % 8192 != 0: - return False, ( - f"Height * width ({height * width}) must be " - "divisible by 8192 for frame conditioning" - ) - else: - if num_frames_in and not num_frames_in % 32 == 0: - return False, ("The input video total frame count must be divisible by 32!") - - -def validate_input_image( - image: torch.Tensor, with_frame_conditioning: bool = False -) -> None: - """ - Validates the input image adheres to the expectations of the API: - - The image resolution should not be less than 300*300px - - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1 - - """ - height, width = get_image_dimensions(image) - validate_input_media(width, height, with_frame_conditioning) - validate_image_dimensions( - image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH - ) - - -def validate_video_to_video_input(video: VideoInput) -> VideoInput: - """ - Validates and processes video input for Moonvalley Video-to-Video generation. - - Args: - video: Input video to validate - - Returns: - Validated and potentially trimmed video - - Raises: - ValueError: If video doesn't meet requirements - MoonvalleyApiError: If video duration is too short - """ - width, height = _get_video_dimensions(video) - _validate_video_dimensions(width, height) - _validate_container_format(video) - - return _validate_and_trim_duration(video) - - -def _get_video_dimensions(video: VideoInput) -> tuple[int, int]: - """Extracts video dimensions with error handling.""" - try: - return video.get_dimensions() - except Exception as e: - logging.error("Error getting dimensions of video: %s", e) - raise ValueError(f"Cannot get video dimensions: {e}") from e - - -def _validate_video_dimensions(width: int, height: int) -> None: - """Validates video dimensions meet Moonvalley V2V requirements.""" - supported_resolutions = { - (1920, 1080), - (1080, 1920), - (1152, 1152), - (1536, 1152), - (1152, 1536), - } - - if (width, height) not in supported_resolutions: - supported_list = ", ".join( - [f"{w}x{h}" for w, h in sorted(supported_resolutions)] - ) - raise ValueError( - f"Resolution {width}x{height} not supported. Supported: {supported_list}" - ) - - -def _validate_container_format(video: VideoInput) -> None: - """Validates video container format is MP4.""" - container_format = video.get_container_format() - if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]: - raise ValueError( - f"Only MP4 container format supported. Got: {container_format}" - ) - - -def _validate_and_trim_duration(video: VideoInput) -> VideoInput: - """Validates video duration and trims to 5 seconds if needed.""" - duration = video.get_duration() - _validate_minimum_duration(duration) - return _trim_if_too_long(video, duration) - - -def _validate_minimum_duration(duration: float) -> None: - """Ensures video is at least 5 seconds long.""" - if duration < 5: - raise MoonvalleyApiError("Input video must be at least 5 seconds long.") - - -def _trim_if_too_long(video: VideoInput, duration: float) -> VideoInput: - """Trims video to 5 seconds if longer.""" - if duration > 5: - return trim_video(video, 5) - return video - - -def trim_video(video: VideoInput, duration_sec: float) -> VideoInput: - """ - Returns a new VideoInput object trimmed from the beginning to the specified duration, - using av to avoid loading entire video into memory. - - Args: - video: Input video to trim - duration_sec: Duration in seconds to keep from the beginning - - Returns: - VideoFromFile object that owns the output buffer - """ - output_buffer = io.BytesIO() - - input_container = None - output_container = None - - try: - # Get the stream source - this avoids loading entire video into memory - # when the source is already a file path - input_source = video.get_stream_source() - - # Open containers - input_container = av.open(input_source, mode="r") - output_container = av.open(output_buffer, mode="w", format="mp4") - - # Set up output streams for re-encoding - video_stream = None - audio_stream = None - - for stream in input_container.streams: - logging.info(f"Found stream: type={stream.type}, class={type(stream)}") - if isinstance(stream, av.VideoStream): - # Create output video stream with same parameters - video_stream = output_container.add_stream( - "h264", rate=stream.average_rate - ) - video_stream.width = stream.width - video_stream.height = stream.height - video_stream.pix_fmt = "yuv420p" - logging.info( - f"Added video stream: {stream.width}x{stream.height} @ {stream.average_rate}fps" - ) - elif isinstance(stream, av.AudioStream): - # Create output audio stream with same parameters - audio_stream = output_container.add_stream( - "aac", rate=stream.sample_rate - ) - audio_stream.sample_rate = stream.sample_rate - audio_stream.layout = stream.layout - logging.info( - f"Added audio stream: {stream.sample_rate}Hz, {stream.channels} channels" - ) - - # Calculate target frame count that's divisible by 16 - fps = input_container.streams.video[0].average_rate - estimated_frames = int(duration_sec * fps) - target_frames = ( - estimated_frames // 16 - ) * 16 # Round down to nearest multiple of 16 - - if target_frames == 0: - raise ValueError("Video too short: need at least 16 frames for Moonvalley") - - frame_count = 0 - audio_frame_count = 0 - - # Decode and re-encode video frames - if video_stream: - for frame in input_container.decode(video=0): - if frame_count >= target_frames: - break - - # Re-encode frame - for packet in video_stream.encode(frame): - output_container.mux(packet) - frame_count += 1 - - # Flush encoder - for packet in video_stream.encode(): - output_container.mux(packet) - - logging.info( - f"Encoded {frame_count} video frames (target: {target_frames})" - ) - - # Decode and re-encode audio frames - if audio_stream: - input_container.seek(0) # Reset to beginning for audio - for frame in input_container.decode(audio=0): - if frame.time >= duration_sec: - break - - # Re-encode frame - for packet in audio_stream.encode(frame): - output_container.mux(packet) - audio_frame_count += 1 - - # Flush encoder - for packet in audio_stream.encode(): - output_container.mux(packet) - - logging.info(f"Encoded {audio_frame_count} audio frames") - - # Close containers - output_container.close() - input_container.close() - - # Return as VideoFromFile using the buffer - output_buffer.seek(0) - return VideoFromFile(output_buffer) - - except Exception as e: - # Clean up on error - if input_container is not None: - input_container.close() - if output_container is not None: - output_container.close() - raise RuntimeError(f"Failed to trim video: {str(e)}") from e - - -# --- BaseMoonvalleyVideoNode --- -class BaseMoonvalleyVideoNode: - def parseWidthHeightFromRes(self, resolution: str): - # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict - res_map = { - "16:9 (1920 x 1080)": {"width": 1920, "height": 1080}, - "9:16 (1080 x 1920)": {"width": 1080, "height": 1920}, - "1:1 (1152 x 1152)": {"width": 1152, "height": 1152}, - "4:3 (1536 x 1152)": {"width": 1536, "height": 1152}, - "3:4 (1152 x 1536)": {"width": 1152, "height": 1536}, - "21:9 (2560 x 1080)": {"width": 2560, "height": 1080}, - } - if resolution in res_map: - return res_map[resolution] - else: - # Default to 1920x1080 if unknown - return {"width": 1920, "height": 1080} - - def parseControlParameter(self, value): - control_map = { - "Motion Transfer": "motion_control", - "Canny": "canny_control", - "Pose Transfer": "pose_control", - "Depth": "depth_control", - } - if value in control_map: - return control_map[value] - else: - return control_map["Motion Transfer"] - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> MoonvalleyPromptResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{API_PROMPTS_ENDPOINT}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=MoonvalleyPromptResponse, - ), - result_url_extractor=get_video_url_from_response, - node_id=node_id, - ) - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyTextToVideoRequest, - "prompt_text", - multiline=True, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyTextToVideoInferenceParams, - "negative_prompt", - multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, wobbly, weird, low quality, plastic, stock footage, video camera, boring", - ), - "resolution": ( - IO.COMBO, - { - "options": [ - "16:9 (1920 x 1080)", - "9:16 (1080 x 1920)", - "1:1 (1152 x 1152)", - "4:3 (1440 x 1080)", - "3:4 (1080 x 1440)", - "21:9 (2560 x 1080)", - ], - "default": "16:9 (1920 x 1080)", - "tooltip": "Resolution of the output video", - }, - ), - "prompt_adherence": model_field_to_node_input( - IO.FLOAT, - MoonvalleyTextToVideoInferenceParams, - "guidance_scale", - default=10.0, - step=1, - min=1, - max=20, - ), - "seed": model_field_to_node_input( - IO.INT, - MoonvalleyTextToVideoInferenceParams, - "seed", - default=9, - min=0, - max=4294967295, - step=1, - display="number", - tooltip="Random seed value", - ), - "steps": model_field_to_node_input( - IO.INT, - MoonvalleyTextToVideoInferenceParams, - "steps", - default=100, - min=1, - max=100, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - "optional": { - "image": model_field_to_node_input( - IO.IMAGE, - MoonvalleyTextToVideoRequest, - "image_url", - tooltip="The reference image used to generate the video", - ), - }, - } - - RETURN_TYPES = ("STRING",) - FUNCTION = "generate" - CATEGORY = "api node/video/Moonvalley Marey" - API_NODE = True - - def generate(self, **kwargs): - return None - - -# --- MoonvalleyImg2VideoNode --- -class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode): - - @classmethod - def INPUT_TYPES(cls): - return super().INPUT_TYPES() - - RETURN_TYPES = ("VIDEO",) - RETURN_NAMES = ("video",) - DESCRIPTION = "Moonvalley Marey Image to Video Node" - - async def generate( - self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs - ): - image = kwargs.get("image", None) - if image is None: - raise MoonvalleyApiError("image is required") - - validate_input_image(image, True) - validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - width_height = self.parseWidthHeightFromRes(kwargs.get("resolution")) - - inference_params = MoonvalleyTextToVideoInferenceParams( - negative_prompt=negative_prompt, - steps=kwargs.get("steps"), - seed=kwargs.get("seed"), - guidance_scale=kwargs.get("prompt_adherence"), - num_frames=128, - width=width_height.get("width"), - height=width_height.get("height"), - use_negative_prompts=True, - ) - """Upload image to comfy backend to have a URL available for further processing""" - # Get MIME type from tensor - assuming PNG format for image tensors - mime_type = "image/png" - - image_url = ( - await upload_images_to_comfyapi( - image, max_images=1, auth_kwargs=kwargs, mime_type=mime_type - ) - )[0] - - request = MoonvalleyTextToVideoRequest( - image_url=image_url, prompt_text=prompt, inference_params=inference_params - ) - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=API_IMG2VIDEO_ENDPOINT, - method=HttpMethod.POST, - request_model=MoonvalleyTextToVideoRequest, - response_model=MoonvalleyPromptResponse, - ), - request=request, - auth_kwargs=kwargs, - ) - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - video = await download_url_to_video_output(final_response.output_url) - return (video,) - - -# --- MoonvalleyVid2VidNode --- -class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode): - def __init__(self): - super().__init__() - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyVideoToVideoRequest, - "prompt_text", - multiline=True, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - MoonvalleyVideoToVideoInferenceParams, - "negative_prompt", - multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, wobbly, weird, low quality, plastic, stock footage, video camera, boring", - ), - "seed": model_field_to_node_input( - IO.INT, - MoonvalleyVideoToVideoInferenceParams, - "seed", - default=9, - min=0, - max=4294967295, - step=1, - display="number", - tooltip="Random seed value", - control_after_generate=False, - ), - "prompt_adherence": model_field_to_node_input( - IO.FLOAT, - MoonvalleyVideoToVideoInferenceParams, - "guidance_scale", - default=10.0, - step=1, - min=1, - max=20, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - "optional": { - "video": ( - IO.VIDEO, - { - "default": "", - "multiline": False, - "tooltip": "The reference video used to generate the output video. Must be at least 5 seconds long. Videos longer than 5s will be automatically trimmed. Only MP4 format supported.", - }, - ), - "control_type": ( - ["Motion Transfer", "Pose Transfer"], - {"default": "Motion Transfer"}, - ), - "motion_intensity": ( - "INT", - { - "default": 100, - "step": 1, - "min": 0, - "max": 100, - "tooltip": "Only used if control_type is 'Motion Transfer'", - }, - ), - "image": model_field_to_node_input( - IO.IMAGE, - MoonvalleyTextToVideoRequest, - "image_url", - tooltip="The reference image used to generate the video", - ), - }, - } - - RETURN_TYPES = ("VIDEO",) - RETURN_NAMES = ("video",) - - async def generate( - self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs - ): - video = kwargs.get("video") - image = kwargs.get("image", None) - - if not video: - raise MoonvalleyApiError("video is required") - - video_url = "" - if video: - validated_video = validate_video_to_video_input(video) - video_url = await upload_video_to_comfyapi( - validated_video, auth_kwargs=kwargs - ) - mime_type = "image/png" - - if not image is None: - validate_input_image(image, with_frame_conditioning=True) - image_url = await upload_images_to_comfyapi( - image=image, auth_kwargs=kwargs, max_images=1, mime_type=mime_type - ) - control_type = kwargs.get("control_type") - motion_intensity = kwargs.get("motion_intensity") - - """Validate prompts and inference input""" - validate_prompts(prompt, negative_prompt) - - # Only include motion_intensity for Motion Transfer - control_params = {} - if control_type == "Motion Transfer" and motion_intensity is not None: - control_params["motion_intensity"] = motion_intensity - - inference_params = MoonvalleyVideoToVideoInferenceParams( - negative_prompt=negative_prompt, - seed=kwargs.get("seed"), - control_params=control_params, - ) - - control = self.parseControlParameter(control_type) - - request = MoonvalleyVideoToVideoRequest( - control_type=control, - video_url=video_url, - prompt_text=prompt, - inference_params=inference_params, - ) - request.image_url = image_url if not image is None else None - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=API_VIDEO2VIDEO_ENDPOINT, - method=HttpMethod.POST, - request_model=MoonvalleyVideoToVideoRequest, - response_model=MoonvalleyPromptResponse, - ), - request=request, - auth_kwargs=kwargs, - ) - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - - video = await download_url_to_video_output(final_response.output_url) - - return (video,) - - -# --- MoonvalleyTxt2VideoNode --- -class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode): - def __init__(self): - super().__init__() - - RETURN_TYPES = ("VIDEO",) - RETURN_NAMES = ("video",) - - @classmethod - def INPUT_TYPES(cls): - input_types = super().INPUT_TYPES() - # Remove image-specific parameters - for param in ["image"]: - if param in input_types["optional"]: - del input_types["optional"][param] - return input_types - - async def generate( - self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs - ): - validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - width_height = self.parseWidthHeightFromRes(kwargs.get("resolution")) - - inference_params = MoonvalleyTextToVideoInferenceParams( - negative_prompt=negative_prompt, - steps=kwargs.get("steps"), - seed=kwargs.get("seed"), - guidance_scale=kwargs.get("prompt_adherence"), - num_frames=128, - width=width_height.get("width"), - height=width_height.get("height"), - ) - request = MoonvalleyTextToVideoRequest( - prompt_text=prompt, inference_params=inference_params - ) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=API_TXT2VIDEO_ENDPOINT, - method=HttpMethod.POST, - request_model=MoonvalleyTextToVideoRequest, - response_model=MoonvalleyPromptResponse, - ), - request=request, - auth_kwargs=kwargs, - ) - task_creation_response = await initial_operation.execute() - validate_task_creation_response(task_creation_response) - task_id = task_creation_response.id - - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - - video = await download_url_to_video_output(final_response.output_url) - return (video,) - - -NODE_CLASS_MAPPINGS = { - "MoonvalleyImg2VideoNode": MoonvalleyImg2VideoNode, - "MoonvalleyTxt2VideoNode": MoonvalleyTxt2VideoNode, - "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode, -} - - -NODE_DISPLAY_NAME_MAPPINGS = { - "MoonvalleyImg2VideoNode": "Moonvalley Marey Image to Video", - "MoonvalleyTxt2VideoNode": "Moonvalley Marey Text to Video", - "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video", -} diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py deleted file mode 100644 index e3b81de7599e96bfb1c8828b2cff149bb699d116..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_openai.py +++ /dev/null @@ -1,1004 +0,0 @@ -import io -from typing import TypedDict, Optional -import json -import os -import time -import re -import uuid -from enum import Enum -from inspect import cleandoc -import numpy as np -import torch -from PIL import Image -from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict -from server import PromptServer -import folder_paths - - -from comfy_api_nodes.apis import ( - OpenAIImageGenerationRequest, - OpenAIImageEditRequest, - OpenAIImageGenerationResponse, - OpenAICreateResponse, - OpenAIResponse, - CreateModelResponseProperties, - Item, - Includable, - OutputContent, - InputImageContent, - Detail, - InputTextContent, - InputMessage, - InputMessageContentList, - InputContent, - InputFileContent, -) - -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) - -from comfy_api_nodes.apinode_utils import ( - downscale_image_tensor, - validate_and_cast_response, - validate_string, - tensor_to_base64_string, - text_filepath_to_data_uri, -) -from comfy_api_nodes.mapper_utils import model_field_to_node_input - - -RESPONSES_ENDPOINT = "/proxy/openai/v1/responses" -STARTING_POINT_ID_PATTERN = r"" - - -class HistoryEntry(TypedDict): - """Type definition for a single history entry in the chat.""" - - prompt: str - response: str - response_id: str - timestamp: float - - -class ChatHistory(TypedDict): - """Type definition for the chat history dictionary.""" - - __annotations__: dict[str, list[HistoryEntry]] - - -class SupportedOpenAIModel(str, Enum): - o4_mini = "o4-mini" - o1 = "o1" - o3 = "o3" - o1_pro = "o1-pro" - gpt_4o = "gpt-4o" - gpt_4_1 = "gpt-4.1" - gpt_4_1_mini = "gpt-4.1-mini" - gpt_4_1_nano = "gpt-4.1-nano" - gpt_5 = "gpt-5" - gpt_5_mini = "gpt-5-mini" - gpt_5_nano = "gpt-5-nano" - - -class OpenAIDalle2(ComfyNodeABC): - """ - Generates images synchronously via OpenAI's DALL·E 2 endpoint. - """ - - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Text prompt for DALL·E", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2**31 - 1, - "step": 1, - "display": "number", - "control_after_generate": True, - "tooltip": "not implemented yet in backend", - }, - ), - "size": ( - IO.COMBO, - { - "options": ["256x256", "512x512", "1024x1024"], - "default": "1024x1024", - "tooltip": "Image size", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 8, - "step": 1, - "display": "number", - "tooltip": "How many images to generate", - }, - ), - "image": ( - IO.IMAGE, - { - "default": None, - "tooltip": "Optional reference image for image editing.", - }, - ), - "mask": ( - IO.MASK, - { - "default": None, - "tooltip": "Optional mask for inpainting (white areas will be replaced)", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "api_call" - CATEGORY = "api node/image/OpenAI" - DESCRIPTION = cleandoc(__doc__ or "") - API_NODE = True - - async def api_call( - self, - prompt, - seed=0, - image=None, - mask=None, - n=1, - size="1024x1024", - unique_id=None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False) - model = "dall-e-2" - path = "/proxy/openai/images/generations" - content_type = "application/json" - request_class = OpenAIImageGenerationRequest - img_binary = None - - if image is not None and mask is not None: - path = "/proxy/openai/images/edits" - content_type = "multipart/form-data" - request_class = OpenAIImageEditRequest - - input_tensor = image.squeeze().cpu() - height, width, channels = input_tensor.shape - rgba_tensor = torch.ones(height, width, 4, device="cpu") - rgba_tensor[:, :, :channels] = input_tensor - - if mask.shape[1:] != image.shape[1:-1]: - raise Exception("Mask and Image must be the same size") - rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu() - - rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze() - - image_np = (rgba_tensor.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format="PNG") - img_byte_arr.seek(0) - img_binary = img_byte_arr # .getvalue() - img_binary.name = "image.png" - elif image is not None or mask is not None: - raise Exception("Dall-E 2 image editing requires an image AND a mask") - - # Build the operation - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=request_class, - response_model=OpenAIImageGenerationResponse, - ), - request=request_class( - model=model, - prompt=prompt, - n=n, - size=size, - seed=seed, - ), - files=( - { - "image": img_binary, - } - if img_binary - else None - ), - content_type=content_type, - auth_kwargs=kwargs, - ) - - response = await operation.execute() - - img_tensor = await validate_and_cast_response(response, node_id=unique_id) - return (img_tensor,) - - -class OpenAIDalle3(ComfyNodeABC): - """ - Generates images synchronously via OpenAI's DALL·E 3 endpoint. - """ - - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Text prompt for DALL·E", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2**31 - 1, - "step": 1, - "display": "number", - "control_after_generate": True, - "tooltip": "not implemented yet in backend", - }, - ), - "quality": ( - IO.COMBO, - { - "options": ["standard", "hd"], - "default": "standard", - "tooltip": "Image quality", - }, - ), - "style": ( - IO.COMBO, - { - "options": ["natural", "vivid"], - "default": "natural", - "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.", - }, - ), - "size": ( - IO.COMBO, - { - "options": ["1024x1024", "1024x1792", "1792x1024"], - "default": "1024x1024", - "tooltip": "Image size", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "api_call" - CATEGORY = "api node/image/OpenAI" - DESCRIPTION = cleandoc(__doc__ or "") - API_NODE = True - - async def api_call( - self, - prompt, - seed=0, - style="natural", - quality="standard", - size="1024x1024", - unique_id=None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False) - model = "dall-e-3" - - # build the operation - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/openai/images/generations", - method=HttpMethod.POST, - request_model=OpenAIImageGenerationRequest, - response_model=OpenAIImageGenerationResponse, - ), - request=OpenAIImageGenerationRequest( - model=model, - prompt=prompt, - quality=quality, - size=size, - style=style, - seed=seed, - ), - auth_kwargs=kwargs, - ) - - response = await operation.execute() - - img_tensor = await validate_and_cast_response(response, node_id=unique_id) - return (img_tensor,) - - -class OpenAIGPTImage1(ComfyNodeABC): - """ - Generates images synchronously via OpenAI's GPT Image 1 endpoint. - """ - - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Text prompt for GPT Image 1", - }, - ), - }, - "optional": { - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2**31 - 1, - "step": 1, - "display": "number", - "control_after_generate": True, - "tooltip": "not implemented yet in backend", - }, - ), - "quality": ( - IO.COMBO, - { - "options": ["low", "medium", "high"], - "default": "low", - "tooltip": "Image quality, affects cost and generation time.", - }, - ), - "background": ( - IO.COMBO, - { - "options": ["opaque", "transparent"], - "default": "opaque", - "tooltip": "Return image with or without background", - }, - ), - "size": ( - IO.COMBO, - { - "options": ["auto", "1024x1024", "1024x1536", "1536x1024"], - "default": "auto", - "tooltip": "Image size", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 8, - "step": 1, - "display": "number", - "tooltip": "How many images to generate", - }, - ), - "image": ( - IO.IMAGE, - { - "default": None, - "tooltip": "Optional reference image for image editing.", - }, - ), - "mask": ( - IO.MASK, - { - "default": None, - "tooltip": "Optional mask for inpainting (white areas will be replaced)", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "api_call" - CATEGORY = "api node/image/OpenAI" - DESCRIPTION = cleandoc(__doc__ or "") - API_NODE = True - - async def api_call( - self, - prompt, - seed=0, - quality="low", - background="opaque", - image=None, - mask=None, - n=1, - size="1024x1024", - unique_id=None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False) - model = "gpt-image-1" - path = "/proxy/openai/images/generations" - content_type = "application/json" - request_class = OpenAIImageGenerationRequest - files = [] - - if image is not None: - path = "/proxy/openai/images/edits" - request_class = OpenAIImageEditRequest - content_type = "multipart/form-data" - - batch_size = image.shape[0] - - for i in range(batch_size): - single_image = image[i : i + 1] - scaled_image = downscale_image_tensor(single_image).squeeze() - - image_np = (scaled_image.numpy() * 255).astype(np.uint8) - img = Image.fromarray(image_np) - img_byte_arr = io.BytesIO() - img.save(img_byte_arr, format="PNG") - img_byte_arr.seek(0) - - if batch_size == 1: - files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png"))) - else: - files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png"))) - - if mask is not None: - if image is None: - raise Exception("Cannot use a mask without an input image") - if image.shape[0] != 1: - raise Exception("Cannot use a mask with multiple image") - if mask.shape[1:] != image.shape[1:-1]: - raise Exception("Mask and Image must be the same size") - batch, height, width = mask.shape - rgba_mask = torch.zeros(height, width, 4, device="cpu") - rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu() - - scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0)).squeeze() - - mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) - mask_img = Image.fromarray(mask_np) - mask_img_byte_arr = io.BytesIO() - mask_img.save(mask_img_byte_arr, format="PNG") - mask_img_byte_arr.seek(0) - files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png"))) - - # Build the operation - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=request_class, - response_model=OpenAIImageGenerationResponse, - ), - request=request_class( - model=model, - prompt=prompt, - quality=quality, - background=background, - n=n, - seed=seed, - size=size, - ), - files=files if files else None, - content_type=content_type, - auth_kwargs=kwargs, - ) - - response = await operation.execute() - - img_tensor = await validate_and_cast_response(response, node_id=unique_id) - return (img_tensor,) - - -class OpenAITextNode(ComfyNodeABC): - """ - Base class for OpenAI text generation nodes. - """ - - RETURN_TYPES = (IO.STRING,) - FUNCTION = "api_call" - CATEGORY = "api node/text/OpenAI" - API_NODE = True - - -class OpenAIChatNode(OpenAITextNode): - """ - Node to generate text responses from an OpenAI model. - """ - - def __init__(self) -> None: - """Initialize the chat node with a new session ID and empty history.""" - self.current_session_id: str = str(uuid.uuid4()) - self.history: dict[str, list[HistoryEntry]] = {} - self.previous_response_id: Optional[str] = None - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Text inputs to the model, used to generate a response.", - }, - ), - "persist_context": ( - IO.BOOLEAN, - { - "default": True, - "tooltip": "Persist chat context between calls (multi-turn conversation)", - }, - ), - "model": model_field_to_node_input( - IO.COMBO, - OpenAICreateResponse, - "model", - enum_type=SupportedOpenAIModel, - ), - }, - "optional": { - "images": ( - IO.IMAGE, - { - "default": None, - "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", - }, - ), - "files": ( - "OPENAI_INPUT_FILES", - { - "default": None, - "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.", - }, - ), - "advanced_options": ( - "OPENAI_CHAT_CONFIG", - { - "default": None, - "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Generate text responses from an OpenAI model." - - async def get_result_response( - self, - response_id: str, - include: Optional[list[Includable]] = None, - auth_kwargs: Optional[dict[str, str]] = None, - ) -> OpenAIResponse: - """ - Retrieve a model response with the given ID from the OpenAI API. - - Args: - response_id (str): The ID of the response to retrieve. - include (Optional[List[Includable]]): Additional fields to include - in the response. See the `include` parameter for Response - creation above for more information. - - """ - return await PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"{RESPONSES_ENDPOINT}/{response_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=OpenAIResponse, - query_params={"include": include}, - ), - completed_statuses=["completed"], - failed_statuses=["failed"], - status_extractor=lambda response: response.status, - auth_kwargs=auth_kwargs, - ).execute() - - def get_message_content_from_response( - self, response: OpenAIResponse - ) -> list[OutputContent]: - """Extract message content from the API response.""" - for output in response.output: - if output.root.type == "message": - return output.root.content - raise TypeError("No output message found in response") - - def get_text_from_message_content( - self, message_content: list[OutputContent] - ) -> str: - """Extract text content from message content.""" - for content_item in message_content: - if content_item.root.type == "output_text": - return str(content_item.root.text) - return "No text output found in response" - - def get_history_text(self, session_id: str) -> str: - """Convert the entire history for a given session to JSON string.""" - return json.dumps(self.history[session_id]) - - def display_history_on_node(self, session_id: str, node_id: str) -> None: - """Display formatted chat history on the node UI.""" - render_spec = { - "node_id": node_id, - "component": "ChatHistoryWidget", - "props": { - "history": self.get_history_text(session_id), - }, - } - PromptServer.instance.send_sync( - "display_component", - render_spec, - ) - - def add_to_history( - self, session_id: str, prompt: str, output_text: str, response_id: str - ) -> None: - """Add a new entry to the chat history.""" - if session_id not in self.history: - self.history[session_id] = [] - self.history[session_id].append( - { - "prompt": prompt, - "response": output_text, - "response_id": response_id, - "timestamp": time.time(), - } - ) - - def parse_output_text_from_response(self, response: OpenAIResponse) -> str: - """Extract text output from the API response.""" - message_contents = self.get_message_content_from_response(response) - return self.get_text_from_message_content(message_contents) - - def generate_new_session_id(self) -> str: - """Generate a new unique session ID.""" - return str(uuid.uuid4()) - - def get_session_id(self, persist_context: bool) -> str: - """Get the current or generate a new session ID based on context persistence.""" - return ( - self.current_session_id - if persist_context - else self.generate_new_session_id() - ) - - def tensor_to_input_image_content( - self, image: torch.Tensor, detail_level: Detail = "auto" - ) -> InputImageContent: - """Convert a tensor to an input image content object.""" - return InputImageContent( - detail=detail_level, - image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}", - type="input_image", - ) - - def create_input_message_contents( - self, - prompt: str, - image: Optional[torch.Tensor] = None, - files: Optional[list[InputFileContent]] = None, - ) -> InputMessageContentList: - """Create a list of input message contents from prompt and optional image.""" - content_list: list[InputContent] = [ - InputTextContent(text=prompt, type="input_text"), - ] - if image is not None: - for i in range(image.shape[0]): - content_list.append( - self.tensor_to_input_image_content(image[i].unsqueeze(0)) - ) - if files is not None: - content_list.extend(files) - - return InputMessageContentList( - root=content_list, - ) - - def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]: - """Extract response ID from prompt if it exists.""" - parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt) - return parsed_id.group(1) if parsed_id else None - - def strip_response_tag_from_prompt(self, prompt: str) -> str: - """Remove the response ID tag from the prompt.""" - return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip()) - - def delete_history_after_response_id( - self, new_start_id: str, session_id: str - ) -> None: - """Delete history entries after a specific response ID.""" - if session_id not in self.history: - return - - new_history = [] - i = 0 - while ( - i < len(self.history[session_id]) - and self.history[session_id][i]["response_id"] != new_start_id - ): - new_history.append(self.history[session_id][i]) - i += 1 - - # Since it's the new starting point (not the response being edited), we include it as well - if i < len(self.history[session_id]): - new_history.append(self.history[session_id][i]) - - self.history[session_id] = new_history - - async def api_call( - self, - prompt: str, - persist_context: bool, - model: SupportedOpenAIModel, - unique_id: Optional[str] = None, - images: Optional[torch.Tensor] = None, - files: Optional[list[InputFileContent]] = None, - advanced_options: Optional[CreateModelResponseProperties] = None, - **kwargs, - ) -> tuple[str]: - # Validate inputs - validate_string(prompt, strip_whitespace=False) - - session_id = self.get_session_id(persist_context) - response_id_override = self.parse_response_id_from_prompt(prompt) - if response_id_override: - is_starting_from_beginning = response_id_override == "start" - if is_starting_from_beginning: - self.history[session_id] = [] - previous_response_id = None - else: - previous_response_id = response_id_override - self.delete_history_after_response_id(response_id_override, session_id) - prompt = self.strip_response_tag_from_prompt(prompt) - elif persist_context: - previous_response_id = self.previous_response_id - else: - previous_response_id = None - - # Create response - create_response = await SynchronousOperation( - endpoint=ApiEndpoint( - path=RESPONSES_ENDPOINT, - method=HttpMethod.POST, - request_model=OpenAICreateResponse, - response_model=OpenAIResponse, - ), - request=OpenAICreateResponse( - input=[ - Item( - root=InputMessage( - content=self.create_input_message_contents( - prompt, images, files - ), - role="user", - ) - ), - ], - store=True, - stream=False, - model=model, - previous_response_id=previous_response_id, - **( - advanced_options.model_dump(exclude_none=True) - if advanced_options - else {} - ), - ), - auth_kwargs=kwargs, - ).execute() - response_id = create_response.id - - # Get result output - result_response = await self.get_result_response(response_id, auth_kwargs=kwargs) - output_text = self.parse_output_text_from_response(result_response) - - # Update history - self.add_to_history(session_id, prompt, output_text, response_id) - self.display_history_on_node(session_id, unique_id) - self.previous_response_id = response_id - - return (output_text,) - - -class OpenAIInputFiles(ComfyNodeABC): - """ - Loads and formats input files for OpenAI API. - """ - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - """ - For details about the supported file input types, see: - https://platform.openai.com/docs/guides/pdf-files?api-mode=responses - """ - input_dir = folder_paths.get_input_directory() - input_files = [ - f - for f in os.scandir(input_dir) - if f.is_file() - and (f.name.endswith(".txt") or f.name.endswith(".pdf")) - and f.stat().st_size < 32 * 1024 * 1024 - ] - input_files = sorted(input_files, key=lambda x: x.name) - input_files = [f.name for f in input_files] - return { - "required": { - "file": ( - IO.COMBO, - { - "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", - "options": input_files, - "default": input_files[0] if input_files else None, - }, - ), - }, - "optional": { - "OPENAI_INPUT_FILES": ( - "OPENAI_INPUT_FILES", - { - "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", - "default": None, - }, - ), - }, - } - - DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes." - RETURN_TYPES = ("OPENAI_INPUT_FILES",) - FUNCTION = "prepare_files" - CATEGORY = "api node/text/OpenAI" - - def create_input_file_content(self, file_path: str) -> InputFileContent: - return InputFileContent( - file_data=text_filepath_to_data_uri(file_path), - filename=os.path.basename(file_path), - type="input_file", - ) - - def prepare_files( - self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = [] - ) -> tuple[list[InputFileContent]]: - """ - Loads and formats input files for OpenAI API. - """ - file_path = folder_paths.get_annotated_filepath(file) - input_file_content = self.create_input_file_content(file_path) - files = [input_file_content] + OPENAI_INPUT_FILES - return (files,) - - -class OpenAIChatConfig(ComfyNodeABC): - """Allows setting additional configuration for the OpenAI Chat Node.""" - - RETURN_TYPES = ("OPENAI_CHAT_CONFIG",) - FUNCTION = "configure" - DESCRIPTION = ( - "Allows specifying advanced configuration options for the OpenAI Chat Nodes." - ) - CATEGORY = "api node/text/OpenAI" - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "truncation": ( - IO.COMBO, - { - "options": ["auto", "disabled"], - "default": "auto", - "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error", - }, - ), - }, - "optional": { - "max_output_tokens": model_field_to_node_input( - IO.INT, - OpenAICreateResponse, - "max_output_tokens", - min=16, - default=4096, - max=16384, - tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens", - ), - "instructions": model_field_to_node_input( - IO.STRING, OpenAICreateResponse, "instructions", multiline=True - ), - }, - } - - def configure( - self, - truncation: bool, - instructions: Optional[str] = None, - max_output_tokens: Optional[int] = None, - ) -> tuple[CreateModelResponseProperties]: - """ - Configure advanced options for the OpenAI Chat Node. - - Note: - While `top_p` and `temperature` are listed as properties in the - spec, they are not supported for all models (e.g., o4-mini). - They are not exposed as inputs at all to avoid having to manually - remove depending on model choice. - """ - return ( - CreateModelResponseProperties( - instructions=instructions, - truncation=truncation, - max_output_tokens=max_output_tokens, - ), - ) - - -NODE_CLASS_MAPPINGS = { - "OpenAIDalle2": OpenAIDalle2, - "OpenAIDalle3": OpenAIDalle3, - "OpenAIGPTImage1": OpenAIGPTImage1, - "OpenAIChatNode": OpenAIChatNode, - "OpenAIInputFiles": OpenAIInputFiles, - "OpenAIChatConfig": OpenAIChatConfig, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "OpenAIDalle2": "OpenAI DALL·E 2", - "OpenAIDalle3": "OpenAI DALL·E 3", - "OpenAIGPTImage1": "OpenAI GPT Image 1", - "OpenAIChatNode": "OpenAI ChatGPT", - "OpenAIInputFiles": "OpenAI ChatGPT Input Files", - "OpenAIChatConfig": "OpenAI ChatGPT Advanced Options", -} diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py deleted file mode 100644 index a8dc43cb3ba2dcb0346696a533601d7edf6a0bd6..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_pika.py +++ /dev/null @@ -1,779 +0,0 @@ -""" -Pika x ComfyUI API Nodes - -Pika API docs: https://pika-827374fb.mintlify.app/api-reference -""" -from __future__ import annotations - -import io -import logging -from typing import Optional, TypeVar - -import numpy as np -import torch - -from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions -from comfy_api.input_impl import VideoFromFile -from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput -from comfy_api_nodes.apinode_utils import ( - download_url_to_video_output, - tensor_to_bytesio, -) -from comfy_api_nodes.apis import ( - IngredientsMode, - PikaBodyGenerate22C2vGenerate22PikascenesPost, - PikaBodyGenerate22I2vGenerate22I2vPost, - PikaBodyGenerate22KeyframeGenerate22PikaframesPost, - PikaBodyGenerate22T2vGenerate22T2vPost, - PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - PikaBodyGeneratePikaswapsGeneratePikaswapsPost, - PikaDurationEnum, - Pikaffect, - PikaGenerateResponse, - PikaResolutionEnum, - PikaVideoResponse, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - EmptyRequest, - HttpMethod, - PollingOperation, - SynchronousOperation, -) -from comfy_api_nodes.mapper_utils import model_field_to_node_input - -R = TypeVar("R") - -PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions" -PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps" -PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects" - -PIKA_API_VERSION = "2.2" -PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v" -PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v" -PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes" -PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes" - -PATH_VIDEO_GET = "/proxy/pika/videos" - - -class PikaApiError(Exception): - """Exception for Pika API errors.""" - - pass - - -def is_valid_video_response(response: PikaVideoResponse) -> bool: - """Check if the video response is valid.""" - return hasattr(response, "url") and response.url is not None - - -def is_valid_initial_response(response: PikaGenerateResponse) -> bool: - """Check if the initial response is valid.""" - return hasattr(response, "video_id") and response.video_id is not None - - -class PikaNodeBase(ComfyNodeABC): - """Base class for Pika nodes.""" - - @classmethod - def get_base_inputs_types( - cls, request_model - ) -> dict[str, tuple[IO, InputTypeOptions]]: - """Get the base required inputs types common to all Pika nodes.""" - return { - "prompt_text": model_field_to_node_input( - IO.STRING, - request_model, - "promptText", - multiline=True, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - request_model, - "negativePrompt", - multiline=True, - ), - "seed": model_field_to_node_input( - IO.INT, - request_model, - "seed", - min=0, - max=0xFFFFFFFF, - control_after_generate=True, - ), - "resolution": model_field_to_node_input( - IO.COMBO, - request_model, - "resolution", - enum_type=PikaResolutionEnum, - ), - "duration": model_field_to_node_input( - IO.COMBO, - request_model, - "duration", - enum_type=PikaDurationEnum, - ), - } - - CATEGORY = "api node/video/Pika" - API_NODE = True - FUNCTION = "api_call" - RETURN_TYPES = ("VIDEO",) - - async def poll_for_task_status( - self, - task_id: str, - auth_kwargs: Optional[dict[str, str]] = None, - node_id: Optional[str] = None, - ) -> PikaGenerateResponse: - polling_operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"{PATH_VIDEO_GET}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=PikaVideoResponse, - ), - completed_statuses=[ - "finished", - ], - failed_statuses=["failed", "cancelled"], - status_extractor=lambda response: ( - response.status.value if response.status else None - ), - progress_extractor=lambda response: ( - response.progress if hasattr(response, "progress") else None - ), - auth_kwargs=auth_kwargs, - result_url_extractor=lambda response: ( - response.url if hasattr(response, "url") else None - ), - node_id=node_id, - estimated_duration=60 - ) - return await polling_operation.execute() - - async def execute_task( - self, - initial_operation: SynchronousOperation[R, PikaGenerateResponse], - auth_kwargs: Optional[dict[str, str]] = None, - node_id: Optional[str] = None, - ) -> tuple[VideoFromFile]: - """Executes the initial operation then polls for the task status until it is completed. - - Args: - initial_operation: The initial operation to execute. - auth_kwargs: The authentication token(s) to use for the API call. - - Returns: - A tuple containing the video file as a VIDEO output. - """ - initial_response = await initial_operation.execute() - if not is_valid_initial_response(initial_response): - error_msg = f"Pika initial request failed. Code: {initial_response.code}, Message: {initial_response.message}, Data: {initial_response.data}" - logging.error(error_msg) - raise PikaApiError(error_msg) - - task_id = initial_response.video_id - final_response = await self.poll_for_task_status(task_id, auth_kwargs) - if not is_valid_video_response(final_response): - error_msg = ( - f"Pika task {task_id} succeeded but no video data found in response." - ) - logging.error(error_msg) - raise PikaApiError(error_msg) - - video_url = str(final_response.url) - logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url) - - return (await download_url_to_video_output(video_url),) - - -class PikaImageToVideoV2_2(PikaNodeBase): - """Pika 2.2 Image to Video Node.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ( - IO.IMAGE, - {"tooltip": "The image to convert to video"}, - ), - **cls.get_base_inputs_types(PikaBodyGenerate22I2vGenerate22I2vPost), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Sends an image and prompt to the Pika API v2.2 to generate a video." - - async def api_call( - self, - image: torch.Tensor, - prompt_text: str, - negative_prompt: str, - seed: int, - resolution: str, - duration: int, - unique_id: str, - **kwargs, - ) -> tuple[VideoFromFile]: - # Convert image to BytesIO - image_bytes_io = tensor_to_bytesio(image) - image_bytes_io.seek(0) - - pika_files = {"image": ("image.png", image_bytes_io, "image/png")} - - # Prepare non-file data - pika_request_data = PikaBodyGenerate22I2vGenerate22I2vPost( - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - resolution=resolution, - duration=duration, - ) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_IMAGE_TO_VIDEO, - method=HttpMethod.POST, - request_model=PikaBodyGenerate22I2vGenerate22I2vPost, - response_model=PikaGenerateResponse, - ), - request=pika_request_data, - files=pika_files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -class PikaTextToVideoNodeV2_2(PikaNodeBase): - """Pika Text2Video v2.2 Node.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - **cls.get_base_inputs_types(PikaBodyGenerate22T2vGenerate22T2vPost), - "aspect_ratio": model_field_to_node_input( - IO.FLOAT, - PikaBodyGenerate22T2vGenerate22T2vPost, - "aspectRatio", - step=0.001, - min=0.4, - max=2.5, - default=1.7777777777777777, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Sends a text prompt to the Pika API v2.2 to generate a video." - - async def api_call( - self, - prompt_text: str, - negative_prompt: str, - seed: int, - resolution: str, - duration: int, - aspect_ratio: float, - unique_id: str, - **kwargs, - ) -> tuple[VideoFromFile]: - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_TEXT_TO_VIDEO, - method=HttpMethod.POST, - request_model=PikaBodyGenerate22T2vGenerate22T2vPost, - response_model=PikaGenerateResponse, - ), - request=PikaBodyGenerate22T2vGenerate22T2vPost( - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - resolution=resolution, - duration=duration, - aspectRatio=aspect_ratio, - ), - auth_kwargs=kwargs, - content_type="application/x-www-form-urlencoded", - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -class PikaScenesV2_2(PikaNodeBase): - """PikaScenes v2.2 Node.""" - - @classmethod - def INPUT_TYPES(cls): - image_ingredient_input = ( - IO.IMAGE, - {"tooltip": "Image that will be used as ingredient to create a video."}, - ) - return { - "required": { - **cls.get_base_inputs_types( - PikaBodyGenerate22C2vGenerate22PikascenesPost, - ), - "ingredients_mode": model_field_to_node_input( - IO.COMBO, - PikaBodyGenerate22C2vGenerate22PikascenesPost, - "ingredientsMode", - enum_type=IngredientsMode, - default="creative", - ), - "aspect_ratio": model_field_to_node_input( - IO.FLOAT, - PikaBodyGenerate22C2vGenerate22PikascenesPost, - "aspectRatio", - step=0.001, - min=0.4, - max=2.5, - default=1.7777777777777777, - ), - }, - "optional": { - "image_ingredient_1": image_ingredient_input, - "image_ingredient_2": image_ingredient_input, - "image_ingredient_3": image_ingredient_input, - "image_ingredient_4": image_ingredient_input, - "image_ingredient_5": image_ingredient_input, - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them." - - async def api_call( - self, - prompt_text: str, - negative_prompt: str, - seed: int, - resolution: str, - duration: int, - ingredients_mode: str, - aspect_ratio: float, - unique_id: str, - image_ingredient_1: Optional[torch.Tensor] = None, - image_ingredient_2: Optional[torch.Tensor] = None, - image_ingredient_3: Optional[torch.Tensor] = None, - image_ingredient_4: Optional[torch.Tensor] = None, - image_ingredient_5: Optional[torch.Tensor] = None, - **kwargs, - ) -> tuple[VideoFromFile]: - # Convert all passed images to BytesIO - all_image_bytes_io = [] - for image in [ - image_ingredient_1, - image_ingredient_2, - image_ingredient_3, - image_ingredient_4, - image_ingredient_5, - ]: - if image is not None: - image_bytes_io = tensor_to_bytesio(image) - image_bytes_io.seek(0) - all_image_bytes_io.append(image_bytes_io) - - pika_files = [ - ("images", (f"image_{i}.png", image_bytes_io, "image/png")) - for i, image_bytes_io in enumerate(all_image_bytes_io) - ] - - pika_request_data = PikaBodyGenerate22C2vGenerate22PikascenesPost( - ingredientsMode=ingredients_mode, - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - resolution=resolution, - duration=duration, - aspectRatio=aspect_ratio, - ) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_PIKASCENES, - method=HttpMethod.POST, - request_model=PikaBodyGenerate22C2vGenerate22PikascenesPost, - response_model=PikaGenerateResponse, - ), - request=pika_request_data, - files=pika_files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -class PikAdditionsNode(PikaNodeBase): - """Pika Pikadditions Node. Add an image into a video.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "video": (IO.VIDEO, {"tooltip": "The video to add an image to."}), - "image": (IO.IMAGE, {"tooltip": "The image to add to the video."}), - "prompt_text": model_field_to_node_input( - IO.STRING, - PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - "promptText", - multiline=True, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - "negativePrompt", - multiline=True, - ), - "seed": model_field_to_node_input( - IO.INT, - PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - "seed", - min=0, - max=0xFFFFFFFF, - control_after_generate=True, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result." - - async def api_call( - self, - video: VideoInput, - image: torch.Tensor, - prompt_text: str, - negative_prompt: str, - seed: int, - unique_id: str, - **kwargs, - ) -> tuple[VideoFromFile]: - # Convert video to BytesIO - video_bytes_io = io.BytesIO() - video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) - video_bytes_io.seek(0) - - # Convert image to BytesIO - image_bytes_io = tensor_to_bytesio(image) - image_bytes_io.seek(0) - - pika_files = { - "video": ("video.mp4", video_bytes_io, "video/mp4"), - "image": ("image.png", image_bytes_io, "image/png"), - } - - # Prepare non-file data - pika_request_data = PikaBodyGeneratePikadditionsGeneratePikadditionsPost( - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - ) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_PIKADDITIONS, - method=HttpMethod.POST, - request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - response_model=PikaGenerateResponse, - ), - request=pika_request_data, - files=pika_files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -class PikaSwapsNode(PikaNodeBase): - """Pika Pikaswaps Node.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "video": (IO.VIDEO, {"tooltip": "The video to swap an object in."}), - "image": ( - IO.IMAGE, - { - "tooltip": "The image used to replace the masked object in the video." - }, - ), - "mask": ( - IO.MASK, - {"tooltip": "Use the mask to define areas in the video to replace"}, - ), - "prompt_text": model_field_to_node_input( - IO.STRING, - PikaBodyGeneratePikaswapsGeneratePikaswapsPost, - "promptText", - multiline=True, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - PikaBodyGeneratePikaswapsGeneratePikaswapsPost, - "negativePrompt", - multiline=True, - ), - "seed": model_field_to_node_input( - IO.INT, - PikaBodyGeneratePikaswapsGeneratePikaswapsPost, - "seed", - min=0, - max=0xFFFFFFFF, - control_after_generate=True, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates." - RETURN_TYPES = ("VIDEO",) - - async def api_call( - self, - video: VideoInput, - image: torch.Tensor, - mask: torch.Tensor, - prompt_text: str, - negative_prompt: str, - seed: int, - unique_id: str, - **kwargs, - ) -> tuple[VideoFromFile]: - # Convert video to BytesIO - video_bytes_io = io.BytesIO() - video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) - video_bytes_io.seek(0) - - # Convert mask to binary mask with three channels - mask = torch.round(mask) - mask = mask.repeat(1, 3, 1, 1) - - # Convert 3-channel binary mask to BytesIO - mask_bytes_io = io.BytesIO() - mask_bytes_io.write(mask.numpy().astype(np.uint8)) - mask_bytes_io.seek(0) - - # Convert image to BytesIO - image_bytes_io = tensor_to_bytesio(image) - image_bytes_io.seek(0) - - pika_files = { - "video": ("video.mp4", video_bytes_io, "video/mp4"), - "image": ("image.png", image_bytes_io, "image/png"), - "modifyRegionMask": ("mask.png", mask_bytes_io, "image/png"), - } - - # Prepare non-file data - pika_request_data = PikaBodyGeneratePikaswapsGeneratePikaswapsPost( - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - ) - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_PIKADDITIONS, - method=HttpMethod.POST, - request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost, - response_model=PikaGenerateResponse, - ), - request=pika_request_data, - files=pika_files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -class PikaffectsNode(PikaNodeBase): - """Pika Pikaffects Node.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ( - IO.IMAGE, - {"tooltip": "The reference image to apply the Pikaffect to."}, - ), - "pikaffect": model_field_to_node_input( - IO.COMBO, - PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - "pikaffect", - enum_type=Pikaffect, - default="Cake-ify", - ), - "prompt_text": model_field_to_node_input( - IO.STRING, - PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - "promptText", - multiline=True, - ), - "negative_prompt": model_field_to_node_input( - IO.STRING, - PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - "negativePrompt", - multiline=True, - ), - "seed": model_field_to_node_input( - IO.INT, - PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - "seed", - min=0, - max=0xFFFFFFFF, - control_after_generate=True, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear" - - async def api_call( - self, - image: torch.Tensor, - pikaffect: str, - prompt_text: str, - negative_prompt: str, - seed: int, - unique_id: str, - **kwargs, - ) -> tuple[VideoFromFile]: - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_PIKAFFECTS, - method=HttpMethod.POST, - request_model=PikaBodyGeneratePikaffectsGeneratePikaffectsPost, - response_model=PikaGenerateResponse, - ), - request=PikaBodyGeneratePikaffectsGeneratePikaffectsPost( - pikaffect=pikaffect, - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - ), - files={"image": ("image.png", tensor_to_bytesio(image), "image/png")}, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -class PikaStartEndFrameNode2_2(PikaNodeBase): - """PikaFrames v2.2 Node.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image_start": (IO.IMAGE, {"tooltip": "The first image to combine."}), - "image_end": (IO.IMAGE, {"tooltip": "The last image to combine."}), - **cls.get_base_inputs_types( - PikaBodyGenerate22KeyframeGenerate22PikaframesPost - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them." - - async def api_call( - self, - image_start: torch.Tensor, - image_end: torch.Tensor, - prompt_text: str, - negative_prompt: str, - seed: int, - resolution: str, - duration: int, - unique_id: str, - **kwargs, - ) -> tuple[VideoFromFile]: - - pika_files = [ - ("keyFrames", ("image_start.png", tensor_to_bytesio(image_start), "image/png")), - ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")), - ] - - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_PIKAFRAMES, - method=HttpMethod.POST, - request_model=PikaBodyGenerate22KeyframeGenerate22PikaframesPost, - response_model=PikaGenerateResponse, - ), - request=PikaBodyGenerate22KeyframeGenerate22PikaframesPost( - promptText=prompt_text, - negativePrompt=negative_prompt, - seed=seed, - resolution=resolution, - duration=duration, - ), - files=pika_files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - - return await self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id) - - -NODE_CLASS_MAPPINGS = { - "PikaImageToVideoNode2_2": PikaImageToVideoV2_2, - "PikaTextToVideoNode2_2": PikaTextToVideoNodeV2_2, - "PikaScenesV2_2": PikaScenesV2_2, - "Pikadditions": PikAdditionsNode, - "Pikaswaps": PikaSwapsNode, - "Pikaffects": PikaffectsNode, - "PikaStartEndFrameNode2_2": PikaStartEndFrameNode2_2, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "PikaImageToVideoNode2_2": "Pika Image to Video", - "PikaTextToVideoNode2_2": "Pika Text to Video", - "PikaScenesV2_2": "Pika Scenes (Video Image Composition)", - "Pikadditions": "Pikadditions (Video Object Insertion)", - "Pikaswaps": "Pika Swaps (Video Object Replacement)", - "Pikaffects": "Pikaffects (Video Effects)", - "PikaStartEndFrameNode2_2": "Pika Start and End Frame to Video", -} diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py deleted file mode 100644 index 7c5a52feb42cde96e9c256f581a5715d1d11ea24..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_pixverse.py +++ /dev/null @@ -1,527 +0,0 @@ -from inspect import cleandoc -from typing import Optional -from comfy_api_nodes.apis.pixverse_api import ( - PixverseTextVideoRequest, - PixverseImageVideoRequest, - PixverseTransitionVideoRequest, - PixverseImageUploadResponse, - PixverseVideoResponse, - PixverseGenerationStatusResponse, - PixverseAspectRatio, - PixverseQuality, - PixverseDuration, - PixverseMotionMode, - PixverseStatus, - PixverseIO, - pixverse_templates, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - tensor_to_bytesio, - validate_string, -) -from comfy.comfy_types.node_typing import IO, ComfyNodeABC -from comfy_api.input_impl import VideoFromFile - -import torch -import aiohttp -from io import BytesIO - - -AVERAGE_DURATION_T2V = 32 -AVERAGE_DURATION_I2V = 30 -AVERAGE_DURATION_T2T = 52 - - -def get_video_url_from_response( - response: PixverseGenerationStatusResponse, -) -> Optional[str]: - if response.Resp is None or response.Resp.url is None: - return None - return str(response.Resp.url) - - -async def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None): - # first, upload image to Pixverse and get image id to use in actual generation call - files = {"image": tensor_to_bytesio(image)} - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/pixverse/image/upload", - method=HttpMethod.POST, - request_model=EmptyRequest, - response_model=PixverseImageUploadResponse, - ), - request=EmptyRequest(), - files=files, - content_type="multipart/form-data", - auth_kwargs=auth_kwargs, - ) - response_upload: PixverseImageUploadResponse = await operation.execute() - - if response_upload.Resp is None: - raise Exception( - f"PixVerse image upload request failed: '{response_upload.ErrMsg}'" - ) - - return response_upload.Resp.img_id - - -class PixverseTemplateNode: - """ - Select template for PixVerse Video generation. - """ - - RETURN_TYPES = (PixverseIO.TEMPLATE,) - RETURN_NAMES = ("pixverse_template",) - FUNCTION = "create_template" - CATEGORY = "api node/video/PixVerse" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "template": (list(pixverse_templates.keys()),), - } - } - - def create_template(self, template: str): - template_id = pixverse_templates.get(template, None) - if template_id is None: - raise Exception(f"Template '{template}' is not recognized.") - # just return the integer - return (template_id,) - - -class PixverseTextToVideoNode(ComfyNodeABC): - """ - Generates videos based on prompt and output_size. - """ - - RETURN_TYPES = (IO.VIDEO,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/video/PixVerse" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the video generation", - }, - ), - "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],), - "quality": ( - [resolution.value for resolution in PixverseQuality], - { - "default": PixverseQuality.res_540p, - }, - ), - "duration_seconds": ([dur.value for dur in PixverseDuration],), - "motion_mode": ([mode.value for mode in PixverseMotionMode],), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2147483647, - "control_after_generate": True, - "tooltip": "Seed for video generation.", - }, - ), - }, - "optional": { - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - "pixverse_template": ( - PixverseIO.TEMPLATE, - { - "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - aspect_ratio: str, - quality: str, - duration_seconds: int, - motion_mode: str, - seed, - negative_prompt: str = None, - pixverse_template: int = None, - unique_id: Optional[str] = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False) - # 1080p is limited to 5 seconds duration - # only normal motion_mode supported for 1080p or for non-5 second duration - if quality == PixverseQuality.res_1080p: - motion_mode = PixverseMotionMode.normal - duration_seconds = PixverseDuration.dur_5 - elif duration_seconds != PixverseDuration.dur_5: - motion_mode = PixverseMotionMode.normal - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/pixverse/video/text/generate", - method=HttpMethod.POST, - request_model=PixverseTextVideoRequest, - response_model=PixverseVideoResponse, - ), - request=PixverseTextVideoRequest( - prompt=prompt, - aspect_ratio=aspect_ratio, - quality=quality, - duration=duration_seconds, - motion_mode=motion_mode, - negative_prompt=negative_prompt if negative_prompt else None, - template_id=pixverse_template, - seed=seed, - ), - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.Resp is None: - raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=PixverseGenerationStatusResponse, - ), - completed_statuses=[PixverseStatus.successful], - failed_statuses=[ - PixverseStatus.contents_moderation, - PixverseStatus.failed, - PixverseStatus.deleted, - ], - status_extractor=lambda x: x.Resp.status, - auth_kwargs=kwargs, - node_id=unique_id, - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_T2V, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.Resp.url) as vid_response: - return (VideoFromFile(BytesIO(await vid_response.content.read())),) - - -class PixverseImageToVideoNode(ComfyNodeABC): - """ - Generates videos based on prompt and output_size. - """ - - RETURN_TYPES = (IO.VIDEO,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/video/PixVerse" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the video generation", - }, - ), - "quality": ( - [resolution.value for resolution in PixverseQuality], - { - "default": PixverseQuality.res_540p, - }, - ), - "duration_seconds": ([dur.value for dur in PixverseDuration],), - "motion_mode": ([mode.value for mode in PixverseMotionMode],), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2147483647, - "control_after_generate": True, - "tooltip": "Seed for video generation.", - }, - ), - }, - "optional": { - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - "pixverse_template": ( - PixverseIO.TEMPLATE, - { - "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - image: torch.Tensor, - prompt: str, - quality: str, - duration_seconds: int, - motion_mode: str, - seed, - negative_prompt: str = None, - pixverse_template: int = None, - unique_id: Optional[str] = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False) - img_id = await upload_image_to_pixverse(image, auth_kwargs=kwargs) - - # 1080p is limited to 5 seconds duration - # only normal motion_mode supported for 1080p or for non-5 second duration - if quality == PixverseQuality.res_1080p: - motion_mode = PixverseMotionMode.normal - duration_seconds = PixverseDuration.dur_5 - elif duration_seconds != PixverseDuration.dur_5: - motion_mode = PixverseMotionMode.normal - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/pixverse/video/img/generate", - method=HttpMethod.POST, - request_model=PixverseImageVideoRequest, - response_model=PixverseVideoResponse, - ), - request=PixverseImageVideoRequest( - img_id=img_id, - prompt=prompt, - quality=quality, - duration=duration_seconds, - motion_mode=motion_mode, - negative_prompt=negative_prompt if negative_prompt else None, - template_id=pixverse_template, - seed=seed, - ), - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.Resp is None: - raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=PixverseGenerationStatusResponse, - ), - completed_statuses=[PixverseStatus.successful], - failed_statuses=[ - PixverseStatus.contents_moderation, - PixverseStatus.failed, - PixverseStatus.deleted, - ], - status_extractor=lambda x: x.Resp.status, - auth_kwargs=kwargs, - node_id=unique_id, - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_I2V, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.Resp.url) as vid_response: - return (VideoFromFile(BytesIO(await vid_response.content.read())),) - - -class PixverseTransitionVideoNode(ComfyNodeABC): - """ - Generates videos based on prompt and output_size. - """ - - RETURN_TYPES = (IO.VIDEO,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/video/PixVerse" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "first_frame": (IO.IMAGE,), - "last_frame": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the video generation", - }, - ), - "quality": ( - [resolution.value for resolution in PixverseQuality], - { - "default": PixverseQuality.res_540p, - }, - ), - "duration_seconds": ([dur.value for dur in PixverseDuration],), - "motion_mode": ([mode.value for mode in PixverseMotionMode],), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 2147483647, - "control_after_generate": True, - "tooltip": "Seed for video generation.", - }, - ), - }, - "optional": { - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - first_frame: torch.Tensor, - last_frame: torch.Tensor, - prompt: str, - quality: str, - duration_seconds: int, - motion_mode: str, - seed, - negative_prompt: str = None, - unique_id: Optional[str] = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False) - first_frame_id = await upload_image_to_pixverse(first_frame, auth_kwargs=kwargs) - last_frame_id = await upload_image_to_pixverse(last_frame, auth_kwargs=kwargs) - - # 1080p is limited to 5 seconds duration - # only normal motion_mode supported for 1080p or for non-5 second duration - if quality == PixverseQuality.res_1080p: - motion_mode = PixverseMotionMode.normal - duration_seconds = PixverseDuration.dur_5 - elif duration_seconds != PixverseDuration.dur_5: - motion_mode = PixverseMotionMode.normal - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/pixverse/video/transition/generate", - method=HttpMethod.POST, - request_model=PixverseTransitionVideoRequest, - response_model=PixverseVideoResponse, - ), - request=PixverseTransitionVideoRequest( - first_frame_img=first_frame_id, - last_frame_img=last_frame_id, - prompt=prompt, - quality=quality, - duration=duration_seconds, - motion_mode=motion_mode, - negative_prompt=negative_prompt if negative_prompt else None, - seed=seed, - ), - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.Resp is None: - raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=PixverseGenerationStatusResponse, - ), - completed_statuses=[PixverseStatus.successful], - failed_statuses=[ - PixverseStatus.contents_moderation, - PixverseStatus.failed, - PixverseStatus.deleted, - ], - status_extractor=lambda x: x.Resp.status, - auth_kwargs=kwargs, - node_id=unique_id, - result_url_extractor=get_video_url_from_response, - estimated_duration=AVERAGE_DURATION_T2V, - ) - response_poll = await operation.execute() - - async with aiohttp.ClientSession() as session: - async with session.get(response_poll.Resp.url) as vid_response: - return (VideoFromFile(BytesIO(await vid_response.content.read())),) - - -NODE_CLASS_MAPPINGS = { - "PixverseTextToVideoNode": PixverseTextToVideoNode, - "PixverseImageToVideoNode": PixverseImageToVideoNode, - "PixverseTransitionVideoNode": PixverseTransitionVideoNode, - "PixverseTemplateNode": PixverseTemplateNode, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "PixverseTextToVideoNode": "PixVerse Text to Video", - "PixverseImageToVideoNode": "PixVerse Image to Video", - "PixverseTransitionVideoNode": "PixVerse Transition Video", - "PixverseTemplateNode": "PixVerse Template", -} diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py deleted file mode 100644 index c8516b368208455148c00c523eca12e72be14ec3..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_recraft.py +++ /dev/null @@ -1,1138 +0,0 @@ -from __future__ import annotations -from inspect import cleandoc -from typing import Optional -from comfy.utils import ProgressBar -from comfy_extras.nodes_images import SVG # Added -from comfy.comfy_types.node_typing import IO -from comfy_api_nodes.apis.recraft_api import ( - RecraftImageGenerationRequest, - RecraftImageGenerationResponse, - RecraftImageSize, - RecraftModel, - RecraftStyle, - RecraftStyleV3, - RecraftColor, - RecraftColorChain, - RecraftControls, - RecraftIO, - get_v3_substyles, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - bytesio_to_image_tensor, - download_url_to_bytesio, - tensor_to_bytesio, - resize_mask_to_image, - validate_string, -) -from server import PromptServer - -import torch -from io import BytesIO -from PIL import UnidentifiedImageError - - -async def handle_recraft_file_request( - image: torch.Tensor, - path: str, - mask: torch.Tensor=None, - total_pixels=4096*4096, - timeout=1024, - request=None, - auth_kwargs: dict[str,str] = None, - ) -> list[BytesIO]: - """ - Handle sending common Recraft file-only request to get back file bytes. - """ - if request is None: - request = EmptyRequest() - - files = { - 'image': tensor_to_bytesio(image, total_pixels=total_pixels).read() - } - if mask is not None: - files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read() - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=type(request), - response_model=RecraftImageGenerationResponse, - ), - request=request, - files=files, - content_type="multipart/form-data", - auth_kwargs=auth_kwargs, - multipart_parser=recraft_multipart_parser, - ) - response: RecraftImageGenerationResponse = await operation.execute() - all_bytesio = [] - if response.image is not None: - all_bytesio.append(await download_url_to_bytesio(response.image.url, timeout=timeout)) - else: - for data in response.data: - all_bytesio.append(await download_url_to_bytesio(data.url, timeout=timeout)) - - return all_bytesio - - -def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, converted_to_check: list[list]=None, is_list=False) -> dict: - """ - Formats data such that multipart/form-data will work with requests library - when both files and data are present. - - The OpenAI client that Recraft uses has a bizarre way of serializing lists: - - It does NOT keep track of indeces of each list, so for background_color, that must be serialized as: - 'background_color[rgb][]' = [0, 0, 255] - where the array is assigned to a key that has '[]' at the end, to signal it's an array. - - This has the consequence of nested lists having the exact same key, forcing arrays to merge; all colors inputs fall under the same key: - if 1 color -> 'controls[colors][][rgb][]' = [0, 0, 255] - if 2 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0] - if 3 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0, 0, 255, 0] - etc. - Whoever made this serialization up at OpenAI added the constraint that lists must be of uniform length on objects of same 'type'. - """ - # Modification of a function that handled a different type of multipart parsing, big ups: - # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b - - def handle_converted_lists(data, parent_key, lists_to_check=tuple[list]): - # if list already exists exists, just extend list with data - for check_list in lists_to_check: - for conv_tuple in check_list: - if conv_tuple[0] == parent_key and type(conv_tuple[1]) is list: - conv_tuple[1].append(formatter(data)) - return True - return False - - if converted_to_check is None: - converted_to_check = [] - - - if formatter is None: - formatter = lambda v: v # Multipart representation of value - - if type(data) is not dict: - # if list already exists exists, just extend list with data - added = handle_converted_lists(data, parent_key, converted_to_check) - if added: - return {} - # otherwise if is_list, create new list with data - if is_list: - return {parent_key: [formatter(data)]} - # return new key with data - return {parent_key: formatter(data)} - - converted = [] - next_check = [converted] - next_check.extend(converted_to_check) - - for key, value in data.items(): - current_key = key if parent_key is None else f"{parent_key}[{key}]" - if type(value) is dict: - converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items()) - elif type(value) is list: - for ind, list_value in enumerate(value): - iter_key = f"{current_key}[]" - converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items()) - else: - converted.append((current_key, formatter(value))) - - return dict(converted) - - -class handle_recraft_image_output: - """ - Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use. - """ - def __init__(self): - pass - - def __enter__(self): - pass - - def __exit__(self, exc_type, exc_val, exc_tb): - if exc_type is not None and exc_type is UnidentifiedImageError: - raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.") - - -class RecraftColorRGBNode: - """ - Create Recraft Color by choosing specific RGB values. - """ - - RETURN_TYPES = (RecraftIO.COLOR,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - RETURN_NAMES = ("recraft_color",) - FUNCTION = "create_color" - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "r": (IO.INT, { - "default": 0, - "min": 0, - "max": 255, - "tooltip": "Red value of color." - }), - "g": (IO.INT, { - "default": 0, - "min": 0, - "max": 255, - "tooltip": "Green value of color." - }), - "b": (IO.INT, { - "default": 0, - "min": 0, - "max": 255, - "tooltip": "Blue value of color." - }), - }, - "optional": { - "recraft_color": (RecraftIO.COLOR,), - } - } - - def create_color(self, r: int, g: int, b: int, recraft_color: RecraftColorChain=None): - recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain() - recraft_color.add(RecraftColor(r, g, b)) - return (recraft_color, ) - - -class RecraftControlsNode: - """ - Create Recraft Controls for customizing Recraft generation. - """ - - RETURN_TYPES = (RecraftIO.CONTROLS,) - RETURN_NAMES = ("recraft_controls",) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "create_controls" - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "colors": (RecraftIO.COLOR,), - "background_color": (RecraftIO.COLOR,), - } - } - - def create_controls(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None): - return (RecraftControls(colors=colors, background_color=background_color), ) - - -class RecraftStyleV3RealisticImageNode: - """ - Select realistic_image style and optional substyle. - """ - - RETURN_TYPES = (RecraftIO.STYLEV3,) - RETURN_NAMES = ("recraft_style",) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "create_style" - CATEGORY = "api node/image/Recraft" - - RECRAFT_STYLE = RecraftStyleV3.realistic_image - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "substyle": (get_v3_substyles(s.RECRAFT_STYLE),), - } - } - - def create_style(self, substyle: str): - if substyle == "None": - substyle = None - return (RecraftStyle(self.RECRAFT_STYLE, substyle),) - - -class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode): - """ - Select digital_illustration style and optional substyle. - """ - - RECRAFT_STYLE = RecraftStyleV3.digital_illustration - - -class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode): - """ - Select vector_illustration style and optional substyle. - """ - - RECRAFT_STYLE = RecraftStyleV3.vector_illustration - - -class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode): - """ - Select vector_illustration style and optional substyle. - """ - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "substyle": (get_v3_substyles(s.RECRAFT_STYLE, include_none=False),), - } - } - - RECRAFT_STYLE = RecraftStyleV3.logo_raster - - -class RecraftStyleInfiniteStyleLibrary: - """ - Select style based on preexisting UUID from Recraft's Infinite Style Library. - """ - - RETURN_TYPES = (RecraftIO.STYLEV3,) - RETURN_NAMES = ("recraft_style",) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "create_style" - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "style_id": (IO.STRING, { - "default": "", - "tooltip": "UUID of style from Infinite Style Library.", - }) - } - } - - def create_style(self, style_id: str): - if not style_id: - raise Exception("The style_id input cannot be empty.") - return (RecraftStyle(style_id=style_id),) - - -class RecraftTextToImageNode: - """ - Generates images synchronously based on prompt and resolution. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation.", - }, - ), - "size": ( - [res.value for res in RecraftImageSize], - { - "default": RecraftImageSize.res_1024x1024, - "tooltip": "The size of the generated image.", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 6, - "tooltip": "The number of images to generate.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "recraft_style": (RecraftIO.STYLEV3,), - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - "recraft_controls": ( - RecraftIO.CONTROLS, - { - "tooltip": "Optional additional controls over the generation via the Recraft Controls node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - size: str, - n: int, - seed, - recraft_style: RecraftStyle = None, - negative_prompt: str = None, - recraft_controls: RecraftControls = None, - unique_id: Optional[str] = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False, max_length=1000) - default_style = RecraftStyle(RecraftStyleV3.realistic_image) - if recraft_style is None: - recraft_style = default_style - - controls_api = None - if recraft_controls: - controls_api = recraft_controls.create_api_model() - - if not negative_prompt: - negative_prompt = None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/recraft/image_generation", - method=HttpMethod.POST, - request_model=RecraftImageGenerationRequest, - response_model=RecraftImageGenerationResponse, - ), - request=RecraftImageGenerationRequest( - prompt=prompt, - negative_prompt=negative_prompt, - model=RecraftModel.recraftv3, - size=size, - n=n, - style=recraft_style.style, - substyle=recraft_style.substyle, - style_id=recraft_style.style_id, - controls=controls_api, - ), - auth_kwargs=kwargs, - ) - response: RecraftImageGenerationResponse = await operation.execute() - images = [] - urls = [] - for data in response.data: - with handle_recraft_image_output(): - if unique_id and data.url: - urls.append(data.url) - urls_string = '\n'.join(urls) - PromptServer.instance.send_progress_text( - f"Result URL: {urls_string}", unique_id - ) - image = bytesio_to_image_tensor( - await download_url_to_bytesio(data.url, timeout=1024) - ) - if len(image.shape) < 4: - image = image.unsqueeze(0) - images.append(image) - output_image = torch.cat(images, dim=0) - - return (output_image,) - - -class RecraftImageToImageNode: - """ - Modify image based on prompt and strength. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE, ), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation.", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 6, - "tooltip": "The number of images to generate.", - }, - ), - "strength": ( - IO.FLOAT, - { - "default": 0.5, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity." - } - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "recraft_style": (RecraftIO.STYLEV3,), - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - "recraft_controls": ( - RecraftIO.CONTROLS, - { - "tooltip": "Optional additional controls over the generation via the Recraft Controls node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - image: torch.Tensor, - prompt: str, - n: int, - strength: float, - seed, - recraft_style: RecraftStyle = None, - negative_prompt: str = None, - recraft_controls: RecraftControls = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False, max_length=1000) - default_style = RecraftStyle(RecraftStyleV3.realistic_image) - if recraft_style is None: - recraft_style = default_style - - controls_api = None - if recraft_controls: - controls_api = recraft_controls.create_api_model() - - if not negative_prompt: - negative_prompt = None - - request = RecraftImageGenerationRequest( - prompt=prompt, - negative_prompt=negative_prompt, - model=RecraftModel.recraftv3, - n=n, - strength=round(strength, 2), - style=recraft_style.style, - substyle=recraft_style.substyle, - style_id=recraft_style.style_id, - controls=controls_api, - ) - - images = [] - total = image.shape[0] - pbar = ProgressBar(total) - for i in range(total): - sub_bytes = await handle_recraft_file_request( - image=image[i], - path="/proxy/recraft/images/imageToImage", - request=request, - auth_kwargs=kwargs, - ) - with handle_recraft_image_output(): - images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) - pbar.update(1) - - images_tensor = torch.cat(images, dim=0) - return (images_tensor, ) - - -class RecraftImageInpaintingNode: - """ - Modify image based on prompt and mask. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE, ), - "mask": (IO.MASK, ), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation.", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 6, - "tooltip": "The number of images to generate.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "recraft_style": (RecraftIO.STYLEV3,), - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - image: torch.Tensor, - mask: torch.Tensor, - prompt: str, - n: int, - seed, - recraft_style: RecraftStyle = None, - negative_prompt: str = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False, max_length=1000) - default_style = RecraftStyle(RecraftStyleV3.realistic_image) - if recraft_style is None: - recraft_style = default_style - - if not negative_prompt: - negative_prompt = None - - request = RecraftImageGenerationRequest( - prompt=prompt, - negative_prompt=negative_prompt, - model=RecraftModel.recraftv3, - n=n, - style=recraft_style.style, - substyle=recraft_style.substyle, - style_id=recraft_style.style_id, - ) - - # prepare mask tensor - mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True) - - images = [] - total = image.shape[0] - pbar = ProgressBar(total) - for i in range(total): - sub_bytes = await handle_recraft_file_request( - image=image[i], - mask=mask[i:i+1], - path="/proxy/recraft/images/inpaint", - request=request, - auth_kwargs=kwargs, - ) - with handle_recraft_image_output(): - images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) - pbar.update(1) - - images_tensor = torch.cat(images, dim=0) - return (images_tensor, ) - - -class RecraftTextToVectorNode: - """ - Generates SVG synchronously based on prompt and resolution. - """ - - RETURN_TYPES = ("SVG",) # Changed - DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation.", - }, - ), - "substyle": (get_v3_substyles(RecraftStyleV3.vector_illustration),), - "size": ( - [res.value for res in RecraftImageSize], - { - "default": RecraftImageSize.res_1024x1024, - "tooltip": "The size of the generated image.", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 6, - "tooltip": "The number of images to generate.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - "recraft_controls": ( - RecraftIO.CONTROLS, - { - "tooltip": "Optional additional controls over the generation via the Recraft Controls node." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - substyle: str, - size: str, - n: int, - seed, - negative_prompt: str = None, - recraft_controls: RecraftControls = None, - unique_id: Optional[str] = None, - **kwargs, - ): - validate_string(prompt, strip_whitespace=False, max_length=1000) - # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None) - recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle) - - controls_api = None - if recraft_controls: - controls_api = recraft_controls.create_api_model() - - if not negative_prompt: - negative_prompt = None - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/recraft/image_generation", - method=HttpMethod.POST, - request_model=RecraftImageGenerationRequest, - response_model=RecraftImageGenerationResponse, - ), - request=RecraftImageGenerationRequest( - prompt=prompt, - negative_prompt=negative_prompt, - model=RecraftModel.recraftv3, - size=size, - n=n, - style=recraft_style.style, - substyle=recraft_style.substyle, - controls=controls_api, - ), - auth_kwargs=kwargs, - ) - response: RecraftImageGenerationResponse = await operation.execute() - svg_data = [] - urls = [] - for data in response.data: - if unique_id and data.url: - urls.append(data.url) - # Print result on each iteration in case of error - PromptServer.instance.send_progress_text( - f"Result URL: {' '.join(urls)}", unique_id - ) - svg_data.append(await download_url_to_bytesio(data.url, timeout=1024)) - - return (SVG(svg_data),) - - -class RecraftVectorizeImageNode: - """ - Generates SVG synchronously from an input image. - """ - - RETURN_TYPES = ("SVG",) # Changed - DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE, ), - }, - "optional": { - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - image: torch.Tensor, - **kwargs, - ): - svgs = [] - total = image.shape[0] - pbar = ProgressBar(total) - for i in range(total): - sub_bytes = await handle_recraft_file_request( - image=image[i], - path="/proxy/recraft/images/vectorize", - auth_kwargs=kwargs, - ) - svgs.append(SVG(sub_bytes)) - pbar.update(1) - - return (SVG.combine_all(svgs), ) - - -class RecraftReplaceBackgroundNode: - """ - Replace background on image, based on provided prompt. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE, ), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "Prompt for the image generation.", - }, - ), - "n": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 6, - "tooltip": "The number of images to generate.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "control_after_generate": True, - "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", - }, - ), - }, - "optional": { - "recraft_style": (RecraftIO.STYLEV3,), - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "An optional text description of undesired elements on an image.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - image: torch.Tensor, - prompt: str, - n: int, - seed, - recraft_style: RecraftStyle = None, - negative_prompt: str = None, - **kwargs, - ): - default_style = RecraftStyle(RecraftStyleV3.realistic_image) - if recraft_style is None: - recraft_style = default_style - - if not negative_prompt: - negative_prompt = None - - request = RecraftImageGenerationRequest( - prompt=prompt, - negative_prompt=negative_prompt, - model=RecraftModel.recraftv3, - n=n, - style=recraft_style.style, - substyle=recraft_style.substyle, - style_id=recraft_style.style_id, - ) - - images = [] - total = image.shape[0] - pbar = ProgressBar(total) - for i in range(total): - sub_bytes = await handle_recraft_file_request( - image=image[i], - path="/proxy/recraft/images/replaceBackground", - request=request, - auth_kwargs=kwargs, - ) - images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) - pbar.update(1) - - images_tensor = torch.cat(images, dim=0) - return (images_tensor, ) - - -class RecraftRemoveBackgroundNode: - """ - Remove background from image, and return processed image and mask. - """ - - RETURN_TYPES = (IO.IMAGE, IO.MASK) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE, ), - }, - "optional": { - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - image: torch.Tensor, - **kwargs, - ): - images = [] - total = image.shape[0] - pbar = ProgressBar(total) - for i in range(total): - sub_bytes = await handle_recraft_file_request( - image=image[i], - path="/proxy/recraft/images/removeBackground", - auth_kwargs=kwargs, - ) - images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) - pbar.update(1) - - images_tensor = torch.cat(images, dim=0) - # use alpha channel as masks, in B,H,W format - masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1) - return (images_tensor, masks_tensor) - - -class RecraftCrispUpscaleNode: - """ - Upscale image synchronously. - Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - RECRAFT_PATH = "/proxy/recraft/images/crispUpscale" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE, ), - }, - "optional": { - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - image: torch.Tensor, - **kwargs, - ): - images = [] - total = image.shape[0] - pbar = ProgressBar(total) - for i in range(total): - sub_bytes = await handle_recraft_file_request( - image=image[i], - path=self.RECRAFT_PATH, - auth_kwargs=kwargs, - ) - images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) - pbar.update(1) - - images_tensor = torch.cat(images, dim=0) - return (images_tensor,) - - -class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode): - """ - Upscale image synchronously. - Enhances a given raster image using ‘creative upscale’ tool, boosting resolution with a focus on refining small details and faces. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Recraft" - - RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale" - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "RecraftTextToImageNode": RecraftTextToImageNode, - "RecraftImageToImageNode": RecraftImageToImageNode, - "RecraftImageInpaintingNode": RecraftImageInpaintingNode, - "RecraftTextToVectorNode": RecraftTextToVectorNode, - "RecraftVectorizeImageNode": RecraftVectorizeImageNode, - "RecraftRemoveBackgroundNode": RecraftRemoveBackgroundNode, - "RecraftReplaceBackgroundNode": RecraftReplaceBackgroundNode, - "RecraftCrispUpscaleNode": RecraftCrispUpscaleNode, - "RecraftCreativeUpscaleNode": RecraftCreativeUpscaleNode, - "RecraftStyleV3RealisticImage": RecraftStyleV3RealisticImageNode, - "RecraftStyleV3DigitalIllustration": RecraftStyleV3DigitalIllustrationNode, - "RecraftStyleV3LogoRaster": RecraftStyleV3LogoRasterNode, - "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary, - "RecraftColorRGB": RecraftColorRGBNode, - "RecraftControls": RecraftControlsNode, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "RecraftTextToImageNode": "Recraft Text to Image", - "RecraftImageToImageNode": "Recraft Image to Image", - "RecraftImageInpaintingNode": "Recraft Image Inpainting", - "RecraftTextToVectorNode": "Recraft Text to Vector", - "RecraftVectorizeImageNode": "Recraft Vectorize Image", - "RecraftRemoveBackgroundNode": "Recraft Remove Background", - "RecraftReplaceBackgroundNode": "Recraft Replace Background", - "RecraftCrispUpscaleNode": "Recraft Crisp Upscale Image", - "RecraftCreativeUpscaleNode": "Recraft Creative Upscale Image", - "RecraftStyleV3RealisticImage": "Recraft Style - Realistic Image", - "RecraftStyleV3DigitalIllustration": "Recraft Style - Digital Illustration", - "RecraftStyleV3LogoRaster": "Recraft Style - Logo Raster", - "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library", - "RecraftColorRGB": "Recraft Color RGB", - "RecraftControls": "Recraft Controls", -} diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py deleted file mode 100644 index c89d087e5fd2ab23425c3521a875e22ea0dbab19..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_rodin.py +++ /dev/null @@ -1,475 +0,0 @@ -""" -ComfyUI X Rodin3D(Deemos) API Nodes - -Rodin API docs: https://developer.hyper3d.ai/ - -""" - -from __future__ import annotations -from inspect import cleandoc -from comfy.comfy_types.node_typing import IO -import folder_paths as comfy_paths -import aiohttp -import os -import datetime -import asyncio -import io -import logging -import math -from PIL import Image -from comfy_api_nodes.apis.rodin_api import ( - Rodin3DGenerateRequest, - Rodin3DGenerateResponse, - Rodin3DCheckStatusRequest, - Rodin3DCheckStatusResponse, - Rodin3DDownloadRequest, - Rodin3DDownloadResponse, - JobStatus, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, -) - - -COMMON_PARAMETERS = { - "Seed": ( - IO.INT, - { - "default":0, - "min":0, - "max":65535, - "display":"number" - } - ), - "Material_Type": ( - IO.COMBO, - { - "options": ["PBR", "Shaded"], - "default": "PBR" - } - ), - "Polygon_count": ( - IO.COMBO, - { - "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"], - "default": "18K-Quad" - } - ) -} - -def create_task_error(response: Rodin3DGenerateResponse): - """Check if the response has error""" - return hasattr(response, "error") - - -class Rodin3DAPI: - """ - Generate 3D Assets using Rodin API - """ - RETURN_TYPES = (IO.STRING,) - RETURN_NAMES = ("3D Model Path",) - CATEGORY = "api node/3d/Rodin" - DESCRIPTION = cleandoc(__doc__ or "") - FUNCTION = "api_call" - API_NODE = True - - def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048): - """ - Converts a PyTorch tensor to a file-like object. - - Args: - - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C) - where C is the number of channels (3 for RGB), H is height, and W is width. - - Returns: - - io.BytesIO: A file-like object containing the image data. - """ - array = tensor.cpu().numpy() - array = (array * 255).astype('uint8') - image = Image.fromarray(array, 'RGB') - - original_width, original_height = image.size - original_pixels = original_width * original_height - if original_pixels > max_pixels: - scale = math.sqrt(max_pixels / original_pixels) - new_width = int(original_width * scale) - new_height = int(original_height * scale) - else: - new_width, new_height = original_width, original_height - - if new_width != original_width or new_height != original_height: - image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) - - img_byte_arr = io.BytesIO() - image.save(img_byte_arr, format='PNG') # PNG is used for lossless compression - img_byte_arr.seek(0) - return img_byte_arr - - def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str: - has_failed = any(job.status == JobStatus.Failed for job in response.jobs) - all_done = all(job.status == JobStatus.Done for job in response.jobs) - status_list = [str(job.status) for job in response.jobs] - logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}") - if has_failed: - logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.") - raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.") - elif all_done: - return "DONE" - else: - return "Generating" - - async def create_generate_task(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs): - if images is None: - raise Exception("Rodin 3D generate requires at least 1 image.") - if len(images) >= 5: - raise Exception("Rodin 3D generate requires up to 5 image.") - - path = "/proxy/rodin/api/v2/rodin" - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=Rodin3DGenerateRequest, - response_model=Rodin3DGenerateResponse, - ), - request=Rodin3DGenerateRequest( - seed=seed, - tier=tier, - material=material, - quality=quality, - mesh_mode=mesh_mode - ), - files=[ - ( - "images", - open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image) - ) - for image in images if image is not None - ], - content_type = "multipart/form-data", - auth_kwargs=kwargs, - ) - - response = await operation.execute() - - if create_task_error(response): - error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}" - logging.error(error_message) - raise Exception(error_message) - - logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!") - subscription_key = response.jobs.subscription_key - task_uuid = response.uuid - logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}") - return task_uuid, subscription_key - - async def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse: - - path = "/proxy/rodin/api/v2/status" - - poll_operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path = path, - method=HttpMethod.POST, - request_model=Rodin3DCheckStatusRequest, - response_model=Rodin3DCheckStatusResponse, - ), - request=Rodin3DCheckStatusRequest( - subscription_key = subscription_key - ), - completed_statuses=["DONE"], - failed_statuses=["FAILED"], - status_extractor=self.check_rodin_status, - poll_interval=3.0, - auth_kwargs=kwargs, - ) - - logging.info("[ Rodin3D API - CheckStatus ] Generate Start!") - - return await poll_operation.execute() - - async def get_rodin_download_list(self, uuid, **kwargs) -> Rodin3DDownloadResponse: - logging.info("[ Rodin3D API - Downloading ] Generate Successfully!") - - path = "/proxy/rodin/api/v2/download" - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=path, - method=HttpMethod.POST, - request_model=Rodin3DDownloadRequest, - response_model=Rodin3DDownloadResponse, - ), - request=Rodin3DDownloadRequest( - task_uuid=uuid - ), - auth_kwargs=kwargs - ) - - return await operation.execute() - - def get_quality_mode(self, poly_count): - if poly_count == "200K-Triangle": - mesh_mode = "Raw" - quality = "medium" - else: - mesh_mode = "Quad" - if poly_count == "4K-Quad": - quality = "extra-low" - elif poly_count == "8K-Quad": - quality = "low" - elif poly_count == "18K-Quad": - quality = "medium" - elif poly_count == "50K-Quad": - quality = "high" - else: - quality = "medium" - - return mesh_mode, quality - - async def download_files(self, url_list): - save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) - os.makedirs(save_path, exist_ok=True) - model_file_path = None - async with aiohttp.ClientSession() as session: - for i in url_list.list: - url = i.url - file_name = i.name - file_path = os.path.join(save_path, file_name) - if file_path.endswith(".glb"): - model_file_path = file_path - logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}") - max_retries = 5 - for attempt in range(max_retries): - try: - async with session.get(url) as resp: - resp.raise_for_status() - with open(file_path, "wb") as f: - async for chunk in resp.content.iter_chunked(32 * 1024): - f.write(chunk) - break - except Exception as e: - logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}") - if attempt < max_retries - 1: - logging.info("Retrying...") - await asyncio.sleep(2) - else: - logging.info( - "[ Rodin3D API - download_files ] Failed to download %s after %s attempts.", - file_path, - max_retries, - ) - - return model_file_path - - -class Rodin3D_Regular(Rodin3DAPI): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "Images": - ( - IO.IMAGE, - { - "forceInput":True, - } - ) - }, - "optional": { - **COMMON_PARAMETERS - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - Images, - Seed, - Material_Type, - Polygon_count, - **kwargs - ): - tier = "Regular" - num_images = Images.shape[0] - m_images = [] - for i in range(num_images): - m_images.append(Images[i]) - mesh_mode, quality = self.get_quality_mode(Polygon_count) - task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type, - quality=quality, tier=tier, mesh_mode=mesh_mode, - **kwargs) - await self.poll_for_task_status(subscription_key, **kwargs) - download_list = await self.get_rodin_download_list(task_uuid, **kwargs) - model = await self.download_files(download_list) - - return (model,) - - -class Rodin3D_Detail(Rodin3DAPI): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "Images": - ( - IO.IMAGE, - { - "forceInput":True, - } - ) - }, - "optional": { - **COMMON_PARAMETERS - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - Images, - Seed, - Material_Type, - Polygon_count, - **kwargs - ): - tier = "Detail" - num_images = Images.shape[0] - m_images = [] - for i in range(num_images): - m_images.append(Images[i]) - mesh_mode, quality = self.get_quality_mode(Polygon_count) - task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type, - quality=quality, tier=tier, mesh_mode=mesh_mode, - **kwargs) - await self.poll_for_task_status(subscription_key, **kwargs) - download_list = await self.get_rodin_download_list(task_uuid, **kwargs) - model = await self.download_files(download_list) - - return (model,) - - -class Rodin3D_Smooth(Rodin3DAPI): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "Images": - ( - IO.IMAGE, - { - "forceInput":True, - } - ) - }, - "optional": { - **COMMON_PARAMETERS - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - Images, - Seed, - Material_Type, - Polygon_count, - **kwargs - ): - tier = "Smooth" - num_images = Images.shape[0] - m_images = [] - for i in range(num_images): - m_images.append(Images[i]) - mesh_mode, quality = self.get_quality_mode(Polygon_count) - task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type, - quality=quality, tier=tier, mesh_mode=mesh_mode, - **kwargs) - await self.poll_for_task_status(subscription_key, **kwargs) - download_list = await self.get_rodin_download_list(task_uuid, **kwargs) - model = await self.download_files(download_list) - - return (model,) - - -class Rodin3D_Sketch(Rodin3DAPI): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "Images": - ( - IO.IMAGE, - { - "forceInput":True, - } - ) - }, - "optional": { - "Seed": - ( - IO.INT, - { - "default":0, - "min":0, - "max":65535, - "display":"number" - } - ) - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - Images, - Seed, - **kwargs - ): - tier = "Sketch" - num_images = Images.shape[0] - m_images = [] - for i in range(num_images): - m_images.append(Images[i]) - material_type = "PBR" - quality = "medium" - mesh_mode = "Quad" - task_uuid, subscription_key = await self.create_generate_task( - images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs - ) - await self.poll_for_task_status(subscription_key, **kwargs) - download_list = await self.get_rodin_download_list(task_uuid, **kwargs) - model = await self.download_files(download_list) - - return (model,) - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "Rodin3D_Regular": Rodin3D_Regular, - "Rodin3D_Detail": Rodin3D_Detail, - "Rodin3D_Smooth": Rodin3D_Smooth, - "Rodin3D_Sketch": Rodin3D_Sketch, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate", - "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate", - "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate", - "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate", -} diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py deleted file mode 100644 index 98024a9fa0b8ed7b520ae50f170d3d940c0efac4..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_runway.py +++ /dev/null @@ -1,635 +0,0 @@ -"""Runway API Nodes - -API Docs: - - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete - -User Guides: - - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha - - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video - - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo - - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3 - -""" - -from typing import Union, Optional, Any -from enum import Enum - -import torch - -from comfy_api_nodes.apis import ( - RunwayImageToVideoRequest, - RunwayImageToVideoResponse, - RunwayTaskStatusResponse as TaskStatusResponse, - RunwayTaskStatusEnum as TaskStatus, - RunwayModelEnum as Model, - RunwayDurationEnum as Duration, - RunwayAspectRatioEnum as AspectRatio, - RunwayPromptImageObject, - RunwayPromptImageDetailedObject, - RunwayTextToImageRequest, - RunwayTextToImageResponse, - Model4, - ReferenceImage, - RunwayTextToImageAspectRatioEnum, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - upload_images_to_comfyapi, - download_url_to_video_output, - image_tensor_pair_to_batch, - validate_string, - download_url_to_image_tensor, -) -from comfy_api_nodes.mapper_utils import model_field_to_node_input -from comfy_api.input_impl import VideoFromFile -from comfy.comfy_types.node_typing import IO, ComfyNodeABC - -PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video" -PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image" -PATH_GET_TASK_STATUS = "/proxy/runway/tasks" - -AVERAGE_DURATION_I2V_SECONDS = 64 -AVERAGE_DURATION_FLF_SECONDS = 256 -AVERAGE_DURATION_T2I_SECONDS = 41 - - -class RunwayApiError(Exception): - """Base exception for Runway API errors.""" - - pass - - -class RunwayGen4TurboAspectRatio(str, Enum): - """Aspect ratios supported for Image to Video API when using gen4_turbo model.""" - - field_1280_720 = "1280:720" - field_720_1280 = "720:1280" - field_1104_832 = "1104:832" - field_832_1104 = "832:1104" - field_960_960 = "960:960" - field_1584_672 = "1584:672" - - -class RunwayGen3aAspectRatio(str, Enum): - """Aspect ratios supported for Image to Video API when using gen3a_turbo model.""" - - field_768_1280 = "768:1280" - field_1280_768 = "1280:768" - - -def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: - """Returns the video URL from the task status response if it exists.""" - if response.output and len(response.output) > 0: - return response.output[0] - return None - - -# TODO: replace with updated image validation utils (upstream) -def validate_input_image(image: torch.Tensor) -> bool: - """ - Validate the input image is within the size limits for the Runway API. - See: https://docs.dev.runwayml.com/assets/inputs/#common-error-reasons - """ - return image.shape[2] < 8000 and image.shape[1] < 8000 - - -async def poll_until_finished( - auth_kwargs: dict[str, str], - api_endpoint: ApiEndpoint[Any, TaskStatusResponse], - estimated_duration: Optional[int] = None, - node_id: Optional[str] = None, -) -> TaskStatusResponse: - """Polls the Runway API endpoint until the task reaches a terminal state, then returns the response.""" - return await PollingOperation( - poll_endpoint=api_endpoint, - completed_statuses=[ - TaskStatus.SUCCEEDED.value, - ], - failed_statuses=[ - TaskStatus.FAILED.value, - TaskStatus.CANCELLED.value, - ], - status_extractor=lambda response: response.status.value, - auth_kwargs=auth_kwargs, - result_url_extractor=get_video_url_from_task_status, - estimated_duration=estimated_duration, - node_id=node_id, - progress_extractor=extract_progress_from_task_status, - ).execute() - - -def extract_progress_from_task_status( - response: TaskStatusResponse, -) -> Union[float, None]: - if hasattr(response, "progress") and response.progress is not None: - return response.progress * 100 - return None - - -def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: - """Returns the image URL from the task status response if it exists.""" - if response.output and len(response.output) > 0: - return response.output[0] - return None - - -class RunwayVideoGenNode(ComfyNodeABC): - """Runway Video Node Base.""" - - RETURN_TYPES = ("VIDEO",) - FUNCTION = "api_call" - CATEGORY = "api node/video/Runway" - API_NODE = True - - def validate_task_created(self, response: RunwayImageToVideoResponse) -> bool: - """ - Validate the task creation response from the Runway API matches - expected format. - """ - if not bool(response.id): - raise RunwayApiError("Invalid initial response from Runway API.") - return True - - def validate_response(self, response: RunwayImageToVideoResponse) -> bool: - """ - Validate the successful task status response from the Runway API - matches expected format. - """ - if not response.output or len(response.output) == 0: - raise RunwayApiError( - "Runway task succeeded but no video data found in response." - ) - return True - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> RunwayImageToVideoResponse: - """Poll the task status until it is finished then get the response.""" - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_GET_TASK_STATUS}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=TaskStatusResponse, - ), - estimated_duration=AVERAGE_DURATION_FLF_SECONDS, - node_id=node_id, - ) - - async def generate_video( - self, - request: RunwayImageToVideoRequest, - auth_kwargs: dict[str, str], - node_id: Optional[str] = None, - ) -> tuple[VideoFromFile]: - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_IMAGE_TO_VIDEO, - method=HttpMethod.POST, - request_model=RunwayImageToVideoRequest, - response_model=RunwayImageToVideoResponse, - ), - request=request, - auth_kwargs=auth_kwargs, - ) - - initial_response = await initial_operation.execute() - self.validate_task_created(initial_response) - task_id = initial_response.id - - final_response = await self.get_response(task_id, auth_kwargs, node_id) - self.validate_response(final_response) - - video_url = get_video_url_from_task_status(final_response) - return (await download_url_to_video_output(video_url),) - - -class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode): - """Runway Image to Video Node using Gen3a Turbo model.""" - - DESCRIPTION = "Generate a video from a single starting frame using Gen3a Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo." - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True - ), - "start_frame": ( - IO.IMAGE, - {"tooltip": "Start frame to be used for the video"}, - ), - "duration": model_field_to_node_input( - IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration - ), - "ratio": model_field_to_node_input( - IO.COMBO, - RunwayImageToVideoRequest, - "ratio", - enum_type=RunwayGen3aAspectRatio, - ), - "seed": model_field_to_node_input( - IO.INT, - RunwayImageToVideoRequest, - "seed", - control_after_generate=True, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - start_frame: torch.Tensor, - duration: str, - ratio: str, - seed: int, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile]: - # Validate inputs - validate_string(prompt, min_length=1) - validate_input_image(start_frame) - - # Upload image - download_urls = await upload_images_to_comfyapi( - start_frame, - max_images=1, - mime_type="image/png", - auth_kwargs=kwargs, - ) - if len(download_urls) != 1: - raise RunwayApiError("Failed to upload one or more images to comfy api.") - - return await self.generate_video( - RunwayImageToVideoRequest( - promptText=prompt, - seed=seed, - model=Model("gen3a_turbo"), - duration=Duration(duration), - ratio=AspectRatio(ratio), - promptImage=RunwayPromptImageObject( - root=[ - RunwayPromptImageDetailedObject( - uri=str(download_urls[0]), position="first" - ) - ] - ), - ), - auth_kwargs=kwargs, - node_id=unique_id, - ) - - -class RunwayImageToVideoNodeGen4(RunwayVideoGenNode): - """Runway Image to Video Node using Gen4 Turbo model.""" - - DESCRIPTION = "Generate a video from a single starting frame using Gen4 Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video." - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True - ), - "start_frame": ( - IO.IMAGE, - {"tooltip": "Start frame to be used for the video"}, - ), - "duration": model_field_to_node_input( - IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration - ), - "ratio": model_field_to_node_input( - IO.COMBO, - RunwayImageToVideoRequest, - "ratio", - enum_type=RunwayGen4TurboAspectRatio, - ), - "seed": model_field_to_node_input( - IO.INT, - RunwayImageToVideoRequest, - "seed", - control_after_generate=True, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - async def api_call( - self, - prompt: str, - start_frame: torch.Tensor, - duration: str, - ratio: str, - seed: int, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile]: - # Validate inputs - validate_string(prompt, min_length=1) - validate_input_image(start_frame) - - # Upload image - download_urls = await upload_images_to_comfyapi( - start_frame, - max_images=1, - mime_type="image/png", - auth_kwargs=kwargs, - ) - if len(download_urls) != 1: - raise RunwayApiError("Failed to upload one or more images to comfy api.") - - return await self.generate_video( - RunwayImageToVideoRequest( - promptText=prompt, - seed=seed, - model=Model("gen4_turbo"), - duration=Duration(duration), - ratio=AspectRatio(ratio), - promptImage=RunwayPromptImageObject( - root=[ - RunwayPromptImageDetailedObject( - uri=str(download_urls[0]), position="first" - ) - ] - ), - ), - auth_kwargs=kwargs, - node_id=unique_id, - ) - - -class RunwayFirstLastFrameNode(RunwayVideoGenNode): - """Runway First-Last Frame Node.""" - - DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3." - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> RunwayImageToVideoResponse: - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_GET_TASK_STATUS}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=TaskStatusResponse, - ), - estimated_duration=AVERAGE_DURATION_FLF_SECONDS, - node_id=node_id, - ) - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True - ), - "start_frame": ( - IO.IMAGE, - {"tooltip": "Start frame to be used for the video"}, - ), - "end_frame": ( - IO.IMAGE, - { - "tooltip": "End frame to be used for the video. Supported for gen3a_turbo only." - }, - ), - "duration": model_field_to_node_input( - IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration - ), - "ratio": model_field_to_node_input( - IO.COMBO, - RunwayImageToVideoRequest, - "ratio", - enum_type=RunwayGen3aAspectRatio, - ), - "seed": model_field_to_node_input( - IO.INT, - RunwayImageToVideoRequest, - "seed", - control_after_generate=True, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "unique_id": "UNIQUE_ID", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call( - self, - prompt: str, - start_frame: torch.Tensor, - end_frame: torch.Tensor, - duration: str, - ratio: str, - seed: int, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[VideoFromFile]: - # Validate inputs - validate_string(prompt, min_length=1) - validate_input_image(start_frame) - validate_input_image(end_frame) - - # Upload images - stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame) - download_urls = await upload_images_to_comfyapi( - stacked_input_images, - max_images=2, - mime_type="image/png", - auth_kwargs=kwargs, - ) - if len(download_urls) != 2: - raise RunwayApiError("Failed to upload one or more images to comfy api.") - - return await self.generate_video( - RunwayImageToVideoRequest( - promptText=prompt, - seed=seed, - model=Model("gen3a_turbo"), - duration=Duration(duration), - ratio=AspectRatio(ratio), - promptImage=RunwayPromptImageObject( - root=[ - RunwayPromptImageDetailedObject( - uri=str(download_urls[0]), position="first" - ), - RunwayPromptImageDetailedObject( - uri=str(download_urls[1]), position="last" - ), - ] - ), - ), - auth_kwargs=kwargs, - node_id=unique_id, - ) - - -class RunwayTextToImageNode(ComfyNodeABC): - """Runway Text to Image Node.""" - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "api_call" - CATEGORY = "api node/image/Runway" - API_NODE = True - DESCRIPTION = "Generate an image from a text prompt using Runway's Gen 4 model. You can also include reference images to guide the generation." - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": model_field_to_node_input( - IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True - ), - "ratio": model_field_to_node_input( - IO.COMBO, - RunwayTextToImageRequest, - "ratio", - enum_type=RunwayTextToImageAspectRatioEnum, - ), - }, - "optional": { - "reference_image": ( - IO.IMAGE, - {"tooltip": "Optional reference image to guide the generation"}, - ) - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - def validate_task_created(self, response: RunwayTextToImageResponse) -> bool: - """ - Validate the task creation response from the Runway API matches - expected format. - """ - if not bool(response.id): - raise RunwayApiError("Invalid initial response from Runway API.") - return True - - def validate_response(self, response: TaskStatusResponse) -> bool: - """ - Validate the successful task status response from the Runway API - matches expected format. - """ - if not response.output or len(response.output) == 0: - raise RunwayApiError( - "Runway task succeeded but no image data found in response." - ) - return True - - async def get_response( - self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None - ) -> TaskStatusResponse: - """Poll the task status until it is finished then get the response.""" - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=f"{PATH_GET_TASK_STATUS}/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=TaskStatusResponse, - ), - estimated_duration=AVERAGE_DURATION_T2I_SECONDS, - node_id=node_id, - ) - - async def api_call( - self, - prompt: str, - ratio: str, - reference_image: Optional[torch.Tensor] = None, - unique_id: Optional[str] = None, - **kwargs, - ) -> tuple[torch.Tensor]: - # Validate inputs - validate_string(prompt, min_length=1) - - # Prepare reference images if provided - reference_images = None - if reference_image is not None: - validate_input_image(reference_image) - download_urls = await upload_images_to_comfyapi( - reference_image, - max_images=1, - mime_type="image/png", - auth_kwargs=kwargs, - ) - if len(download_urls) != 1: - raise RunwayApiError("Failed to upload reference image to comfy api.") - - reference_images = [ReferenceImage(uri=str(download_urls[0]))] - - # Create request - request = RunwayTextToImageRequest( - promptText=prompt, - model=Model4.gen4_image, - ratio=ratio, - referenceImages=reference_images, - ) - - # Execute initial request - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=PATH_TEXT_TO_IMAGE, - method=HttpMethod.POST, - request_model=RunwayTextToImageRequest, - response_model=RunwayTextToImageResponse, - ), - request=request, - auth_kwargs=kwargs, - ) - - initial_response = await initial_operation.execute() - self.validate_task_created(initial_response) - task_id = initial_response.id - - # Poll for completion - final_response = await self.get_response( - task_id, auth_kwargs=kwargs, node_id=unique_id - ) - self.validate_response(final_response) - - # Download and return image - image_url = get_image_url_from_task_status(final_response) - return (await download_url_to_image_tensor(image_url),) - - -NODE_CLASS_MAPPINGS = { - "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode, - "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a, - "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4, - "RunwayTextToImageNode": RunwayTextToImageNode, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "RunwayFirstLastFrameNode": "Runway First-Last-Frame to Video", - "RunwayImageToVideoNodeGen3a": "Runway Image to Video (Gen3a Turbo)", - "RunwayImageToVideoNodeGen4": "Runway Image to Video (Gen4 Turbo)", - "RunwayTextToImageNode": "Runway Text to Image", -} diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py deleted file mode 100644 index 31309d831b9d4a36baa447ef90377ab1c7990a82..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_stability.py +++ /dev/null @@ -1,613 +0,0 @@ -from inspect import cleandoc -from comfy.comfy_types.node_typing import IO -from comfy_api_nodes.apis.stability_api import ( - StabilityUpscaleConservativeRequest, - StabilityUpscaleCreativeRequest, - StabilityAsyncResponse, - StabilityResultsGetResponse, - StabilityStable3_5Request, - StabilityStableUltraRequest, - StabilityStableUltraResponse, - StabilityAspectRatio, - Stability_SD3_5_Model, - Stability_SD3_5_GenerationMode, - get_stability_style_presets, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - bytesio_to_image_tensor, - tensor_to_bytesio, - validate_string, -) - -import torch -import base64 -from io import BytesIO -from enum import Enum - - -class StabilityPollStatus(str, Enum): - finished = "finished" - in_progress = "in_progress" - failed = "failed" - - -def get_async_dummy_status(x: StabilityResultsGetResponse): - if x.name is not None or x.errors is not None: - return StabilityPollStatus.failed - elif x.finish_reason is not None: - return StabilityPollStatus.finished - return StabilityPollStatus.in_progress - - -class StabilityStableImageUltraNode: - """ - Generates images synchronously based on prompt and resolution. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Stability AI" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + - "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + - "elements, colors, and subjects will lead to better results. " + - "To control the weight of a given word use the format `(word:weight)`," + - "where `word` is the word you'd like to control the weight of and `weight`" + - "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" + - "would convey a sky that was blue and green, but more green than blue." - }, - ), - "aspect_ratio": ([x.value for x in StabilityAspectRatio], - { - "default": StabilityAspectRatio.ratio_1_1, - "tooltip": "Aspect ratio of generated image.", - }, - ), - "style_preset": (get_stability_style_presets(), - { - "tooltip": "Optional desired style of generated image.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 4294967294, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": { - "image": (IO.IMAGE,), - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "A blurb of text describing what you do not wish to see in the output image. This is an advanced feature." - }, - ), - "image_denoise": ( - IO.FLOAT, - { - "default": 0.5, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int, - negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, - **kwargs): - validate_string(prompt, strip_whitespace=False) - # prepare image binary if image present - image_binary = None - if image is not None: - image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() - else: - image_denoise = None - - if not negative_prompt: - negative_prompt = None - if style_preset == "None": - style_preset = None - - files = { - "image": image_binary - } - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/stability/v2beta/stable-image/generate/ultra", - method=HttpMethod.POST, - request_model=StabilityStableUltraRequest, - response_model=StabilityStableUltraResponse, - ), - request=StabilityStableUltraRequest( - prompt=prompt, - negative_prompt=negative_prompt, - aspect_ratio=aspect_ratio, - seed=seed, - strength=image_denoise, - style_preset=style_preset, - ), - files=files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.finish_reason != "SUCCESS": - raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.") - - image_data = base64.b64decode(response_api.image) - returned_image = bytesio_to_image_tensor(BytesIO(image_data)) - - return (returned_image,) - - -class StabilityStableImageSD_3_5Node: - """ - Generates images synchronously based on prompt and resolution. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Stability AI" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." - }, - ), - "model": ([x.value for x in Stability_SD3_5_Model],), - "aspect_ratio": ([x.value for x in StabilityAspectRatio], - { - "default": StabilityAspectRatio.ratio_1_1, - "tooltip": "Aspect ratio of generated image.", - }, - ), - "style_preset": (get_stability_style_presets(), - { - "tooltip": "Optional desired style of generated image.", - }, - ), - "cfg_scale": ( - IO.FLOAT, - { - "default": 4.0, - "min": 1.0, - "max": 10.0, - "step": 0.1, - "tooltip": "How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 4294967294, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": { - "image": (IO.IMAGE,), - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." - }, - ), - "image_denoise": ( - IO.FLOAT, - { - "default": 0.5, - "min": 0.0, - "max": 1.0, - "step": 0.01, - "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float, - negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None, - **kwargs): - validate_string(prompt, strip_whitespace=False) - # prepare image binary if image present - image_binary = None - mode = Stability_SD3_5_GenerationMode.text_to_image - if image is not None: - image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() - mode = Stability_SD3_5_GenerationMode.image_to_image - aspect_ratio = None - else: - image_denoise = None - - if not negative_prompt: - negative_prompt = None - if style_preset == "None": - style_preset = None - - files = { - "image": image_binary - } - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/stability/v2beta/stable-image/generate/sd3", - method=HttpMethod.POST, - request_model=StabilityStable3_5Request, - response_model=StabilityStableUltraResponse, - ), - request=StabilityStable3_5Request( - prompt=prompt, - negative_prompt=negative_prompt, - aspect_ratio=aspect_ratio, - seed=seed, - strength=image_denoise, - style_preset=style_preset, - cfg_scale=cfg_scale, - model=model, - mode=mode, - ), - files=files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.finish_reason != "SUCCESS": - raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.") - - image_data = base64.b64decode(response_api.image) - returned_image = bytesio_to_image_tensor(BytesIO(image_data)) - - return (returned_image,) - - -class StabilityUpscaleConservativeNode: - """ - Upscale image with minimal alterations to 4K resolution. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Stability AI" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." - }, - ), - "creativity": ( - IO.FLOAT, - { - "default": 0.35, - "min": 0.2, - "max": 0.5, - "step": 0.01, - "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 4294967294, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": { - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None, - **kwargs): - validate_string(prompt, strip_whitespace=False) - image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() - - if not negative_prompt: - negative_prompt = None - - files = { - "image": image_binary - } - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/stability/v2beta/stable-image/upscale/conservative", - method=HttpMethod.POST, - request_model=StabilityUpscaleConservativeRequest, - response_model=StabilityStableUltraResponse, - ), - request=StabilityUpscaleConservativeRequest( - prompt=prompt, - negative_prompt=negative_prompt, - creativity=round(creativity,2), - seed=seed, - ), - files=files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.finish_reason != "SUCCESS": - raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.") - - image_data = base64.b64decode(response_api.image) - returned_image = bytesio_to_image_tensor(BytesIO(image_data)) - - return (returned_image,) - - -class StabilityUpscaleCreativeNode: - """ - Upscale image with minimal alterations to 4K resolution. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Stability AI" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - "prompt": ( - IO.STRING, - { - "multiline": True, - "default": "", - "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results." - }, - ), - "creativity": ( - IO.FLOAT, - { - "default": 0.3, - "min": 0.1, - "max": 0.5, - "step": 0.01, - "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.", - }, - ), - "style_preset": (get_stability_style_presets(), - { - "tooltip": "Optional desired style of generated image.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 4294967294, - "control_after_generate": True, - "tooltip": "The random seed used for creating the noise.", - }, - ), - }, - "optional": { - "negative_prompt": ( - IO.STRING, - { - "default": "", - "forceInput": True, - "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature." - }, - ), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None, - **kwargs): - validate_string(prompt, strip_whitespace=False) - image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() - - if not negative_prompt: - negative_prompt = None - if style_preset == "None": - style_preset = None - - files = { - "image": image_binary - } - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/stability/v2beta/stable-image/upscale/creative", - method=HttpMethod.POST, - request_model=StabilityUpscaleCreativeRequest, - response_model=StabilityAsyncResponse, - ), - request=StabilityUpscaleCreativeRequest( - prompt=prompt, - negative_prompt=negative_prompt, - creativity=round(creativity,2), - style_preset=style_preset, - seed=seed, - ), - files=files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/stability/v2beta/results/{response_api.id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=StabilityResultsGetResponse, - ), - poll_interval=3, - completed_statuses=[StabilityPollStatus.finished], - failed_statuses=[StabilityPollStatus.failed], - status_extractor=lambda x: get_async_dummy_status(x), - auth_kwargs=kwargs, - ) - response_poll: StabilityResultsGetResponse = await operation.execute() - - if response_poll.finish_reason != "SUCCESS": - raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.") - - image_data = base64.b64decode(response_poll.result) - returned_image = bytesio_to_image_tensor(BytesIO(image_data)) - - return (returned_image,) - - -class StabilityUpscaleFastNode: - """ - Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images. - """ - - RETURN_TYPES = (IO.IMAGE,) - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "api_call" - API_NODE = True - CATEGORY = "api node/image/Stability AI" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - }, - "optional": { - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - }, - } - - async def api_call(self, image: torch.Tensor, **kwargs): - image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read() - - files = { - "image": image_binary - } - - operation = SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/stability/v2beta/stable-image/upscale/fast", - method=HttpMethod.POST, - request_model=EmptyRequest, - response_model=StabilityStableUltraResponse, - ), - request=EmptyRequest(), - files=files, - content_type="multipart/form-data", - auth_kwargs=kwargs, - ) - response_api = await operation.execute() - - if response_api.finish_reason != "SUCCESS": - raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.") - - image_data = base64.b64decode(response_api.image) - returned_image = bytesio_to_image_tensor(BytesIO(image_data)) - - return (returned_image,) - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "StabilityStableImageUltraNode": StabilityStableImageUltraNode, - "StabilityStableImageSD_3_5Node": StabilityStableImageSD_3_5Node, - "StabilityUpscaleConservativeNode": StabilityUpscaleConservativeNode, - "StabilityUpscaleCreativeNode": StabilityUpscaleCreativeNode, - "StabilityUpscaleFastNode": StabilityUpscaleFastNode, -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "StabilityStableImageUltraNode": "Stability AI Stable Image Ultra", - "StabilityStableImageSD_3_5Node": "Stability AI Stable Diffusion 3.5 Image", - "StabilityUpscaleConservativeNode": "Stability AI Upscale Conservative", - "StabilityUpscaleCreativeNode": "Stability AI Upscale Creative", - "StabilityUpscaleFastNode": "Stability AI Upscale Fast", -} diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py deleted file mode 100644 index d08cf9007655f1c6a133d3960f3a139a3e62a1a1..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_tripo.py +++ /dev/null @@ -1,581 +0,0 @@ -import os -from folder_paths import get_output_directory -from comfy_api_nodes.mapper_utils import model_field_to_node_input -from comfy.comfy_types.node_typing import IO -from comfy_api_nodes.apis import ( - TripoOrientation, - TripoModelVersion, -) -from comfy_api_nodes.apis.tripo_api import ( - TripoTaskType, - TripoStyle, - TripoFileReference, - TripoFileEmptyReference, - TripoUrlReference, - TripoTaskResponse, - TripoTaskStatus, - TripoTextToModelRequest, - TripoImageToModelRequest, - TripoMultiviewToModelRequest, - TripoTextureModelRequest, - TripoRefineModelRequest, - TripoAnimateRigRequest, - TripoAnimateRetargetRequest, - TripoConvertModelRequest, -) - -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import ( - upload_images_to_comfyapi, - download_url_to_bytesio, -) - - -async def upload_image_to_tripo(image, **kwargs): - urls = await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs) - return TripoFileReference(TripoUrlReference(url=urls[0], type="jpeg")) - -def get_model_url_from_response(response: TripoTaskResponse) -> str: - if response.data is not None: - for key in ["pbr_model", "model", "base_model"]: - if getattr(response.data.output, key, None) is not None: - return getattr(response.data.output, key) - raise RuntimeError(f"Failed to get model url from response: {response}") - - -async def poll_until_finished( - kwargs: dict[str, str], - response: TripoTaskResponse, -) -> tuple[str, str]: - """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response.""" - if response.code != 0: - raise RuntimeError(f"Failed to generate mesh: {response.error}") - task_id = response.data.task_id - response_poll = await PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/tripo/v2/openapi/task/{task_id}", - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=TripoTaskResponse, - ), - completed_statuses=[TripoTaskStatus.SUCCESS], - failed_statuses=[ - TripoTaskStatus.FAILED, - TripoTaskStatus.CANCELLED, - TripoTaskStatus.UNKNOWN, - TripoTaskStatus.BANNED, - TripoTaskStatus.EXPIRED, - ], - status_extractor=lambda x: x.data.status, - auth_kwargs=kwargs, - node_id=kwargs["unique_id"], - result_url_extractor=get_model_url_from_response, - progress_extractor=lambda x: x.data.progress, - ).execute() - if response_poll.data.status == TripoTaskStatus.SUCCESS: - url = get_model_url_from_response(response_poll) - bytesio = await download_url_to_bytesio(url) - # Save the downloaded model file - model_file = f"tripo_model_{task_id}.glb" - with open(os.path.join(get_output_directory(), model_file), "wb") as f: - f.write(bytesio.getvalue()) - return model_file, task_id - raise RuntimeError(f"Failed to generate mesh: {response_poll}") - - -class TripoTextToModelNode: - """ - Generates 3D models synchronously based on a text prompt using Tripo's API. - """ - AVERAGE_DURATION = 80 - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "prompt": ("STRING", {"multiline": True}), - }, - "optional": { - "negative_prompt": ("STRING", {"multiline": True}), - "model_version": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "model_version", enum_type=TripoModelVersion), - "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"), - "texture": ("BOOLEAN", {"default": True}), - "pbr": ("BOOLEAN", {"default": True}), - "image_seed": ("INT", {"default": 42}), - "model_seed": ("INT", {"default": 42}), - "texture_seed": ("INT", {"default": 42}), - "texture_quality": (["standard", "detailed"], {"default": "standard"}), - "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), - "quad": ("BOOLEAN", {"default": False}) - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) - RETURN_NAMES = ("model_file", "model task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - - async def generate_mesh(self, prompt, negative_prompt=None, model_version=None, style=None, texture=None, pbr=None, image_seed=None, model_seed=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs): - style_enum = None if style == "None" else style - if not prompt: - raise RuntimeError("Prompt is required") - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoTextToModelRequest, - response_model=TripoTaskResponse, - ), - request=TripoTextToModelRequest( - type=TripoTaskType.TEXT_TO_MODEL, - prompt=prompt, - negative_prompt=negative_prompt if negative_prompt else None, - model_version=model_version, - style=style_enum, - texture=texture, - pbr=pbr, - image_seed=image_seed, - model_seed=model_seed, - texture_seed=texture_seed, - texture_quality=texture_quality, - face_limit=face_limit, - auto_size=True, - quad=quad - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoImageToModelNode: - """ - Generates 3D models synchronously based on a single image using Tripo's API. - """ - AVERAGE_DURATION = 80 - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - }, - "optional": { - "model_version": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "model_version", enum_type=TripoModelVersion), - "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"), - "texture": ("BOOLEAN", {"default": True}), - "pbr": ("BOOLEAN", {"default": True}), - "model_seed": ("INT", {"default": 42}), - "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation), - "texture_seed": ("INT", {"default": 42}), - "texture_quality": (["standard", "detailed"], {"default": "standard"}), - "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), - "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), - "quad": ("BOOLEAN", {"default": False}) - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) - RETURN_NAMES = ("model_file", "model task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - - async def generate_mesh(self, image, model_version=None, style=None, texture=None, pbr=None, model_seed=None, orientation=None, texture_alignment=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs): - style_enum = None if style == "None" else style - if image is None: - raise RuntimeError("Image is required") - tripo_file = await upload_image_to_tripo(image, **kwargs) - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoImageToModelRequest, - response_model=TripoTaskResponse, - ), - request=TripoImageToModelRequest( - type=TripoTaskType.IMAGE_TO_MODEL, - file=tripo_file, - model_version=model_version, - style=style_enum, - texture=texture, - pbr=pbr, - model_seed=model_seed, - orientation=orientation, - texture_alignment=texture_alignment, - texture_seed=texture_seed, - texture_quality=texture_quality, - face_limit=face_limit, - auto_size=True, - quad=quad - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoMultiviewToModelNode: - """ - Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API. - """ - AVERAGE_DURATION = 80 - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - }, - "optional": { - "image_left": ("IMAGE",), - "image_back": ("IMAGE",), - "image_right": ("IMAGE",), - "model_version": model_field_to_node_input(IO.COMBO, TripoMultiviewToModelRequest, "model_version", enum_type=TripoModelVersion), - "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation), - "texture": ("BOOLEAN", {"default": True}), - "pbr": ("BOOLEAN", {"default": True}), - "model_seed": ("INT", {"default": 42}), - "texture_seed": ("INT", {"default": 42}), - "texture_quality": (["standard", "detailed"], {"default": "standard"}), - "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), - "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), - "quad": ("BOOLEAN", {"default": False}) - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) - RETURN_NAMES = ("model_file", "model task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - - async def generate_mesh(self, image, image_left=None, image_back=None, image_right=None, model_version=None, orientation=None, texture=None, pbr=None, model_seed=None, texture_seed=None, texture_quality=None, texture_alignment=None, face_limit=None, quad=None, **kwargs): - if image is None: - raise RuntimeError("front image for multiview is required") - images = [] - image_dict = { - "image": image, - "image_left": image_left, - "image_back": image_back, - "image_right": image_right - } - if image_left is None and image_back is None and image_right is None: - raise RuntimeError("At least one of left, back, or right image must be provided for multiview") - for image_name in ["image", "image_left", "image_back", "image_right"]: - image_ = image_dict[image_name] - if image_ is not None: - tripo_file = await upload_image_to_tripo(image_, **kwargs) - images.append(tripo_file) - else: - images.append(TripoFileEmptyReference()) - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoMultiviewToModelRequest, - response_model=TripoTaskResponse, - ), - request=TripoMultiviewToModelRequest( - type=TripoTaskType.MULTIVIEW_TO_MODEL, - files=images, - model_version=model_version, - orientation=orientation, - texture=texture, - pbr=pbr, - model_seed=model_seed, - texture_seed=texture_seed, - texture_quality=texture_quality, - texture_alignment=texture_alignment, - face_limit=face_limit, - quad=quad, - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoTextureNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model_task_id": ("MODEL_TASK_ID",), - }, - "optional": { - "texture": ("BOOLEAN", {"default": True}), - "pbr": ("BOOLEAN", {"default": True}), - "texture_seed": ("INT", {"default": 42}), - "texture_quality": (["standard", "detailed"], {"default": "standard"}), - "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) - RETURN_NAMES = ("model_file", "model task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - AVERAGE_DURATION = 80 - - async def generate_mesh(self, model_task_id, texture=None, pbr=None, texture_seed=None, texture_quality=None, texture_alignment=None, **kwargs): - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoTextureModelRequest, - response_model=TripoTaskResponse, - ), - request=TripoTextureModelRequest( - original_model_task_id=model_task_id, - texture=texture, - pbr=pbr, - texture_seed=texture_seed, - texture_quality=texture_quality, - texture_alignment=texture_alignment - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoRefineNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model_task_id": ("MODEL_TASK_ID", { - "tooltip": "Must be a v1.4 Tripo model" - }), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - DESCRIPTION = "Refine a draft model created by v1.4 Tripo models only." - - RETURN_TYPES = ("STRING", "MODEL_TASK_ID",) - RETURN_NAMES = ("model_file", "model task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - AVERAGE_DURATION = 240 - - async def generate_mesh(self, model_task_id, **kwargs): - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoRefineModelRequest, - response_model=TripoTaskResponse, - ), - request=TripoRefineModelRequest( - draft_model_task_id=model_task_id - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoRigNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "original_model_task_id": ("MODEL_TASK_ID",), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("STRING", "RIG_TASK_ID") - RETURN_NAMES = ("model_file", "rig task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - AVERAGE_DURATION = 180 - - async def generate_mesh(self, original_model_task_id, **kwargs): - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoAnimateRigRequest, - response_model=TripoTaskResponse, - ), - request=TripoAnimateRigRequest( - original_model_task_id=original_model_task_id, - out_format="glb", - spec="tripo" - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoRetargetNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "original_model_task_id": ("RIG_TASK_ID",), - "animation": ([ - "preset:idle", - "preset:walk", - "preset:climb", - "preset:jump", - "preset:slash", - "preset:shoot", - "preset:hurt", - "preset:fall", - "preset:turn", - ],), - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("STRING", "RETARGET_TASK_ID") - RETURN_NAMES = ("model_file", "retarget task_id") - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - AVERAGE_DURATION = 30 - - async def generate_mesh(self, animation, original_model_task_id, **kwargs): - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoAnimateRetargetRequest, - response_model=TripoTaskResponse, - ), - request=TripoAnimateRetargetRequest( - original_model_task_id=original_model_task_id, - animation=animation, - out_format="glb", - bake_animation=True - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -class TripoConversionNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "original_model_task_id": ("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID",), - "format": (["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"],), - }, - "optional": { - "quad": ("BOOLEAN", {"default": False}), - "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}), - "texture_size": ("INT", {"min": 128, "max": 4096, "default": 4096}), - "texture_format": (["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], {"default": "JPEG"}) - }, - "hidden": { - "auth_token": "AUTH_TOKEN_COMFY_ORG", - "comfy_api_key": "API_KEY_COMFY_ORG", - "unique_id": "UNIQUE_ID", - }, - } - - @classmethod - def VALIDATE_INPUTS(cls, input_types): - # The min and max of input1 and input2 are still validated because - # we didn't take `input1` or `input2` as arguments - if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"): - return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type" - return True - - RETURN_TYPES = () - FUNCTION = "generate_mesh" - CATEGORY = "api node/3d/Tripo" - API_NODE = True - OUTPUT_NODE = True - AVERAGE_DURATION = 30 - - async def generate_mesh(self, original_model_task_id, format, quad, face_limit, texture_size, texture_format, **kwargs): - if not original_model_task_id: - raise RuntimeError("original_model_task_id is required") - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path="/proxy/tripo/v2/openapi/task", - method=HttpMethod.POST, - request_model=TripoConvertModelRequest, - response_model=TripoTaskResponse, - ), - request=TripoConvertModelRequest( - original_model_task_id=original_model_task_id, - format=format, - quad=quad if quad else None, - face_limit=face_limit if face_limit != -1 else None, - texture_size=texture_size if texture_size != 4096 else None, - texture_format=texture_format if texture_format != "JPEG" else None - ), - auth_kwargs=kwargs, - ).execute() - return await poll_until_finished(kwargs, response) - - -NODE_CLASS_MAPPINGS = { - "TripoTextToModelNode": TripoTextToModelNode, - "TripoImageToModelNode": TripoImageToModelNode, - "TripoMultiviewToModelNode": TripoMultiviewToModelNode, - "TripoTextureNode": TripoTextureNode, - "TripoRefineNode": TripoRefineNode, - "TripoRigNode": TripoRigNode, - "TripoRetargetNode": TripoRetargetNode, - "TripoConversionNode": TripoConversionNode, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "TripoTextToModelNode": "Tripo: Text to Model", - "TripoImageToModelNode": "Tripo: Image to Model", - "TripoMultiviewToModelNode": "Tripo: Multiview to Model", - "TripoTextureNode": "Tripo: Texture model", - "TripoRefineNode": "Tripo: Refine Draft model", - "TripoRigNode": "Tripo: Rig model", - "TripoRetargetNode": "Tripo: Retarget rigged model", - "TripoConversionNode": "Tripo: Convert model", -} diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py deleted file mode 100644 index 251aecd42bf7f8dd36ef74e1104924d9d7719afd..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_veo2.py +++ /dev/null @@ -1,421 +0,0 @@ -import logging -import base64 -import aiohttp -import torch -from io import BytesIO -from typing import Optional -from typing_extensions import override - -from comfy_api.latest import ComfyExtension, io as comfy_io -from comfy_api.input_impl.video_types import VideoFromFile -from comfy_api_nodes.apis import ( - VeoGenVidRequest, - VeoGenVidResponse, - VeoGenVidPollRequest, - VeoGenVidPollResponse, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, -) - -from comfy_api_nodes.apinode_utils import ( - downscale_image_tensor, - tensor_to_base64_string, -) - -AVERAGE_DURATION_VIDEO_GEN = 32 - -def convert_image_to_base64(image: torch.Tensor): - if image is None: - return None - - scaled_image = downscale_image_tensor(image, total_pixels=2048*2048) - return tensor_to_base64_string(scaled_image) - - -def get_video_url_from_response(poll_response: VeoGenVidPollResponse) -> Optional[str]: - if ( - poll_response.response - and hasattr(poll_response.response, "videos") - and poll_response.response.videos - and len(poll_response.response.videos) > 0 - ): - video = poll_response.response.videos[0] - else: - return None - if hasattr(video, "gcsUri") and video.gcsUri: - return str(video.gcsUri) - return None - - -class VeoVideoGenerationNode(comfy_io.ComfyNode): - """ - Generates videos from text prompts using Google's Veo API. - - This node can create videos from text descriptions and optional image inputs, - with control over parameters like aspect ratio, duration, and more. - """ - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="VeoVideoGenerationNode", - display_name="Google Veo 2 Video Generation", - category="api node/video/Veo", - description="Generates videos from text prompts using Google's Veo 2 API", - inputs=[ - comfy_io.String.Input( - "prompt", - multiline=True, - default="", - tooltip="Text description of the video", - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=["16:9", "9:16"], - default="16:9", - tooltip="Aspect ratio of the output video", - ), - comfy_io.String.Input( - "negative_prompt", - multiline=True, - default="", - tooltip="Negative text prompt to guide what to avoid in the video", - optional=True, - ), - comfy_io.Int.Input( - "duration_seconds", - default=5, - min=5, - max=8, - step=1, - display_mode=comfy_io.NumberDisplay.number, - tooltip="Duration of the output video in seconds", - optional=True, - ), - comfy_io.Boolean.Input( - "enhance_prompt", - default=True, - tooltip="Whether to enhance the prompt with AI assistance", - optional=True, - ), - comfy_io.Combo.Input( - "person_generation", - options=["ALLOW", "BLOCK"], - default="ALLOW", - tooltip="Whether to allow generating people in the video", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=0xFFFFFFFF, - step=1, - display_mode=comfy_io.NumberDisplay.number, - control_after_generate=True, - tooltip="Seed for video generation (0 for random)", - optional=True, - ), - comfy_io.Image.Input( - "image", - tooltip="Optional reference image to guide video generation", - optional=True, - ), - comfy_io.Combo.Input( - "model", - options=["veo-2.0-generate-001"], - default="veo-2.0-generate-001", - tooltip="Veo 2 model to use for video generation", - optional=True, - ), - ], - outputs=[ - comfy_io.Video.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - is_api_node=True, - ) - - @classmethod - async def execute( - cls, - prompt, - aspect_ratio="16:9", - negative_prompt="", - duration_seconds=5, - enhance_prompt=True, - person_generation="ALLOW", - seed=0, - image=None, - model="veo-2.0-generate-001", - generate_audio=False, - ): - # Prepare the instances for the request - instances = [] - - instance = { - "prompt": prompt - } - - # Add image if provided - if image is not None: - image_base64 = convert_image_to_base64(image) - if image_base64: - instance["image"] = { - "bytesBase64Encoded": image_base64, - "mimeType": "image/png" - } - - instances.append(instance) - - # Create parameters dictionary - parameters = { - "aspectRatio": aspect_ratio, - "personGeneration": person_generation, - "durationSeconds": duration_seconds, - "enhancePrompt": enhance_prompt, - } - - # Add optional parameters if provided - if negative_prompt: - parameters["negativePrompt"] = negative_prompt - if seed > 0: - parameters["seed"] = seed - # Only add generateAudio for Veo 3 models - if "veo-3.0" in model: - parameters["generateAudio"] = generate_audio - - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - # Initial request to start video generation - initial_operation = SynchronousOperation( - endpoint=ApiEndpoint( - path=f"/proxy/veo/{model}/generate", - method=HttpMethod.POST, - request_model=VeoGenVidRequest, - response_model=VeoGenVidResponse - ), - request=VeoGenVidRequest( - instances=instances, - parameters=parameters - ), - auth_kwargs=auth, - ) - - initial_response = await initial_operation.execute() - operation_name = initial_response.name - - logging.info(f"Veo generation started with operation name: {operation_name}") - - # Define status extractor function - def status_extractor(response): - # Only return "completed" if the operation is done, regardless of success or failure - # We'll check for errors after polling completes - return "completed" if response.done else "pending" - - # Define progress extractor function - def progress_extractor(response): - # Could be enhanced if the API provides progress information - return None - - # Define the polling operation - poll_operation = PollingOperation( - poll_endpoint=ApiEndpoint( - path=f"/proxy/veo/{model}/poll", - method=HttpMethod.POST, - request_model=VeoGenVidPollRequest, - response_model=VeoGenVidPollResponse - ), - completed_statuses=["completed"], - failed_statuses=[], # No failed statuses, we'll handle errors after polling - status_extractor=status_extractor, - progress_extractor=progress_extractor, - request=VeoGenVidPollRequest( - operationName=operation_name - ), - auth_kwargs=auth, - poll_interval=5.0, - result_url_extractor=get_video_url_from_response, - node_id=cls.hidden.unique_id, - estimated_duration=AVERAGE_DURATION_VIDEO_GEN, - ) - - # Execute the polling operation - poll_response = await poll_operation.execute() - - # Now check for errors in the final response - # Check for error in poll response - if hasattr(poll_response, 'error') and poll_response.error: - error_message = f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})" - logging.error(error_message) - raise Exception(error_message) - - # Check for RAI filtered content - if (hasattr(poll_response.response, 'raiMediaFilteredCount') and - poll_response.response.raiMediaFilteredCount > 0): - - # Extract reason message if available - if (hasattr(poll_response.response, 'raiMediaFilteredReasons') and - poll_response.response.raiMediaFilteredReasons): - reason = poll_response.response.raiMediaFilteredReasons[0] - error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)" - else: - error_message = f"Content filtered by Google's Responsible AI practices ({poll_response.response.raiMediaFilteredCount} videos filtered.)" - - logging.error(error_message) - raise Exception(error_message) - - # Extract video data - if poll_response.response and hasattr(poll_response.response, 'videos') and poll_response.response.videos and len(poll_response.response.videos) > 0: - video = poll_response.response.videos[0] - - # Check if video is provided as base64 or URL - if hasattr(video, 'bytesBase64Encoded') and video.bytesBase64Encoded: - # Decode base64 string to bytes - video_data = base64.b64decode(video.bytesBase64Encoded) - elif hasattr(video, 'gcsUri') and video.gcsUri: - # Download from URL - async with aiohttp.ClientSession() as session: - async with session.get(video.gcsUri) as video_response: - video_data = await video_response.content.read() - else: - raise Exception("Video returned but no data or URL was provided") - else: - raise Exception("Video generation completed but no video was returned") - - if not video_data: - raise Exception("No video data was returned") - - logging.info("Video generation completed successfully") - - # Convert video data to BytesIO object - video_io = BytesIO(video_data) - - # Return VideoFromFile object - return comfy_io.NodeOutput(VideoFromFile(video_io)) - - -class Veo3VideoGenerationNode(VeoVideoGenerationNode): - """ - Generates videos from text prompts using Google's Veo 3 API. - - Supported models: - - veo-3.0-generate-001 - - veo-3.0-fast-generate-001 - - This node extends the base Veo node with Veo 3 specific features including - audio generation and fixed 8-second duration. - """ - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="Veo3VideoGenerationNode", - display_name="Google Veo 3 Video Generation", - category="api node/video/Veo", - description="Generates videos from text prompts using Google's Veo 3 API", - inputs=[ - comfy_io.String.Input( - "prompt", - multiline=True, - default="", - tooltip="Text description of the video", - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=["16:9", "9:16"], - default="16:9", - tooltip="Aspect ratio of the output video", - ), - comfy_io.String.Input( - "negative_prompt", - multiline=True, - default="", - tooltip="Negative text prompt to guide what to avoid in the video", - optional=True, - ), - comfy_io.Int.Input( - "duration_seconds", - default=8, - min=8, - max=8, - step=1, - display_mode=comfy_io.NumberDisplay.number, - tooltip="Duration of the output video in seconds (Veo 3 only supports 8 seconds)", - optional=True, - ), - comfy_io.Boolean.Input( - "enhance_prompt", - default=True, - tooltip="Whether to enhance the prompt with AI assistance", - optional=True, - ), - comfy_io.Combo.Input( - "person_generation", - options=["ALLOW", "BLOCK"], - default="ALLOW", - tooltip="Whether to allow generating people in the video", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=0xFFFFFFFF, - step=1, - display_mode=comfy_io.NumberDisplay.number, - control_after_generate=True, - tooltip="Seed for video generation (0 for random)", - optional=True, - ), - comfy_io.Image.Input( - "image", - tooltip="Optional reference image to guide video generation", - optional=True, - ), - comfy_io.Combo.Input( - "model", - options=["veo-3.0-generate-001", "veo-3.0-fast-generate-001"], - default="veo-3.0-generate-001", - tooltip="Veo 3 model to use for video generation", - optional=True, - ), - comfy_io.Boolean.Input( - "generate_audio", - default=False, - tooltip="Generate audio for the video. Supported by all Veo 3 models.", - optional=True, - ), - ], - outputs=[ - comfy_io.Video.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - is_api_node=True, - ) - - -class VeoExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]: - return [ - VeoVideoGenerationNode, - Veo3VideoGenerationNode, - ] - -async def comfy_entrypoint() -> VeoExtension: - return VeoExtension() diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py deleted file mode 100644 index 2f441948c2554d50a13b6894ff18bd038741953a..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/nodes_vidu.py +++ /dev/null @@ -1,622 +0,0 @@ -import logging -from enum import Enum -from typing import Any, Callable, Optional, Literal, TypeVar -from typing_extensions import override - -import torch -from pydantic import BaseModel, Field - -from comfy_api.latest import ComfyExtension, io as comfy_io -from comfy_api_nodes.util.validation_utils import ( - validate_aspect_ratio_closeness, - validate_image_dimensions, - validate_image_aspect_ratio_range, - get_number_of_images, -) -from comfy_api_nodes.apis.client import ( - ApiEndpoint, - HttpMethod, - SynchronousOperation, - PollingOperation, - EmptyRequest, -) -from comfy_api_nodes.apinode_utils import download_url_to_video_output, upload_images_to_comfyapi - - -VIDU_TEXT_TO_VIDEO = "/proxy/vidu/text2video" -VIDU_IMAGE_TO_VIDEO = "/proxy/vidu/img2video" -VIDU_REFERENCE_VIDEO = "/proxy/vidu/reference2video" -VIDU_START_END_VIDEO = "/proxy/vidu/start-end2video" -VIDU_GET_GENERATION_STATUS = "/proxy/vidu/tasks/%s/creations" - -R = TypeVar("R") - -class VideoModelName(str, Enum): - vidu_q1 = 'viduq1' - - -class AspectRatio(str, Enum): - r_16_9 = "16:9" - r_9_16 = "9:16" - r_1_1 = "1:1" - - -class Resolution(str, Enum): - r_1080p = "1080p" - - -class MovementAmplitude(str, Enum): - auto = "auto" - small = "small" - medium = "medium" - large = "large" - - -class TaskCreationRequest(BaseModel): - model: VideoModelName = VideoModelName.vidu_q1 - prompt: Optional[str] = Field(None, max_length=1500) - duration: Optional[Literal[5]] = 5 - seed: Optional[int] = Field(0, ge=0, le=2147483647) - aspect_ratio: Optional[AspectRatio] = AspectRatio.r_16_9 - resolution: Optional[Resolution] = Resolution.r_1080p - movement_amplitude: Optional[MovementAmplitude] = MovementAmplitude.auto - images: Optional[list[str]] = Field(None, description="Base64 encoded string or image URL") - - -class TaskStatus(str, Enum): - created = "created" - queueing = "queueing" - processing = "processing" - success = "success" - failed = "failed" - - -class TaskCreationResponse(BaseModel): - task_id: str = Field(...) - state: TaskStatus = Field(...) - created_at: str = Field(...) - code: Optional[int] = Field(None, description="Error code") - - -class TaskResult(BaseModel): - id: str = Field(..., description="Creation id") - url: str = Field(..., description="The URL of the generated results, valid for one hour") - cover_url: str = Field(..., description="The cover URL of the generated results, valid for one hour") - - -class TaskStatusResponse(BaseModel): - state: TaskStatus = Field(...) - err_code: Optional[str] = Field(None) - creations: list[TaskResult] = Field(..., description="Generated results") - - -async def poll_until_finished( - auth_kwargs: dict[str, str], - api_endpoint: ApiEndpoint[Any, R], - result_url_extractor: Optional[Callable[[R], str]] = None, - estimated_duration: Optional[int] = None, - node_id: Optional[str] = None, -) -> R: - return await PollingOperation( - poll_endpoint=api_endpoint, - completed_statuses=[TaskStatus.success.value], - failed_statuses=[TaskStatus.failed.value], - status_extractor=lambda response: response.state.value, - auth_kwargs=auth_kwargs, - result_url_extractor=result_url_extractor, - estimated_duration=estimated_duration, - node_id=node_id, - poll_interval=16.0, - max_poll_attempts=256, - ).execute() - - -def get_video_url_from_response(response) -> Optional[str]: - if response.creations: - return response.creations[0].url - return None - - -def get_video_from_response(response) -> TaskResult: - if not response.creations: - error_msg = f"Vidu request does not contain results. State: {response.state}, Error Code: {response.err_code}" - logging.info(error_msg) - raise RuntimeError(error_msg) - logging.info("Vidu task %s succeeded. Video URL: %s", response.creations[0].id, response.creations[0].url) - return response.creations[0] - - -async def execute_task( - vidu_endpoint: str, - auth_kwargs: Optional[dict[str, str]], - payload: TaskCreationRequest, - estimated_duration: int, - node_id: str, -) -> R: - response = await SynchronousOperation( - endpoint=ApiEndpoint( - path=vidu_endpoint, - method=HttpMethod.POST, - request_model=TaskCreationRequest, - response_model=TaskCreationResponse, - ), - request=payload, - auth_kwargs=auth_kwargs, - ).execute() - if response.state == TaskStatus.failed: - error_msg = f"Vidu request failed. Code: {response.code}" - logging.error(error_msg) - raise RuntimeError(error_msg) - return await poll_until_finished( - auth_kwargs, - ApiEndpoint( - path=VIDU_GET_GENERATION_STATUS % response.task_id, - method=HttpMethod.GET, - request_model=EmptyRequest, - response_model=TaskStatusResponse, - ), - result_url_extractor=get_video_url_from_response, - estimated_duration=estimated_duration, - node_id=node_id, - ) - - -class ViduTextToVideoNode(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="ViduTextToVideoNode", - display_name="Vidu Text To Video Generation", - category="api node/video/Vidu", - description="Generate video from text prompt", - inputs=[ - comfy_io.Combo.Input( - "model", - options=[model.value for model in VideoModelName], - default=VideoModelName.vidu_q1.value, - tooltip="Model name", - ), - comfy_io.String.Input( - "prompt", - multiline=True, - tooltip="A textual description for video generation", - ), - comfy_io.Int.Input( - "duration", - default=5, - min=5, - max=5, - step=1, - display_mode=comfy_io.NumberDisplay.number, - tooltip="Duration of the output video in seconds", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - display_mode=comfy_io.NumberDisplay.number, - control_after_generate=True, - tooltip="Seed for video generation (0 for random)", - optional=True, - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=[model.value for model in AspectRatio], - default=AspectRatio.r_16_9.value, - tooltip="The aspect ratio of the output video", - optional=True, - ), - comfy_io.Combo.Input( - "resolution", - options=[model.value for model in Resolution], - default=Resolution.r_1080p.value, - tooltip="Supported values may vary by model & duration", - optional=True, - ), - comfy_io.Combo.Input( - "movement_amplitude", - options=[model.value for model in MovementAmplitude], - default=MovementAmplitude.auto.value, - tooltip="The movement amplitude of objects in the frame", - optional=True, - ), - ], - outputs=[ - comfy_io.Video.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - is_api_node=True, - ) - - @classmethod - async def execute( - cls, - model: str, - prompt: str, - duration: int, - seed: int, - aspect_ratio: str, - resolution: str, - movement_amplitude: str, - ) -> comfy_io.NodeOutput: - if not prompt: - raise ValueError("The prompt field is required and cannot be empty.") - payload = TaskCreationRequest( - model_name=model, - prompt=prompt, - duration=duration, - seed=seed, - aspect_ratio=aspect_ratio, - resolution=resolution, - movement_amplitude=movement_amplitude, - ) - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - results = await execute_task(VIDU_TEXT_TO_VIDEO, auth, payload, 320, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) - - -class ViduImageToVideoNode(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="ViduImageToVideoNode", - display_name="Vidu Image To Video Generation", - category="api node/video/Vidu", - description="Generate video from image and optional prompt", - inputs=[ - comfy_io.Combo.Input( - "model", - options=[model.value for model in VideoModelName], - default=VideoModelName.vidu_q1.value, - tooltip="Model name", - ), - comfy_io.Image.Input( - "image", - tooltip="An image to be used as the start frame of the generated video", - ), - comfy_io.String.Input( - "prompt", - multiline=True, - default="", - tooltip="A textual description for video generation", - optional=True, - ), - comfy_io.Int.Input( - "duration", - default=5, - min=5, - max=5, - step=1, - display_mode=comfy_io.NumberDisplay.number, - tooltip="Duration of the output video in seconds", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - display_mode=comfy_io.NumberDisplay.number, - control_after_generate=True, - tooltip="Seed for video generation (0 for random)", - optional=True, - ), - comfy_io.Combo.Input( - "resolution", - options=[model.value for model in Resolution], - default=Resolution.r_1080p.value, - tooltip="Supported values may vary by model & duration", - optional=True, - ), - comfy_io.Combo.Input( - "movement_amplitude", - options=[model.value for model in MovementAmplitude], - default=MovementAmplitude.auto.value, - tooltip="The movement amplitude of objects in the frame", - optional=True, - ), - ], - outputs=[ - comfy_io.Video.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - is_api_node=True, - ) - - @classmethod - async def execute( - cls, - model: str, - image: torch.Tensor, - prompt: str, - duration: int, - seed: int, - resolution: str, - movement_amplitude: str, - ) -> comfy_io.NodeOutput: - if get_number_of_images(image) > 1: - raise ValueError("Only one input image is allowed.") - validate_image_aspect_ratio_range(image, (1, 4), (4, 1)) - payload = TaskCreationRequest( - model_name=model, - prompt=prompt, - duration=duration, - seed=seed, - resolution=resolution, - movement_amplitude=movement_amplitude, - ) - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - payload.images = await upload_images_to_comfyapi( - image, - max_images=1, - mime_type="image/png", - auth_kwargs=auth, - ) - results = await execute_task(VIDU_IMAGE_TO_VIDEO, auth, payload, 120, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) - - -class ViduReferenceVideoNode(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="ViduReferenceVideoNode", - display_name="Vidu Reference To Video Generation", - category="api node/video/Vidu", - description="Generate video from multiple images and prompt", - inputs=[ - comfy_io.Combo.Input( - "model", - options=[model.value for model in VideoModelName], - default=VideoModelName.vidu_q1.value, - tooltip="Model name", - ), - comfy_io.Image.Input( - "images", - tooltip="Images to use as references to generate a video with consistent subjects (max 7 images).", - ), - comfy_io.String.Input( - "prompt", - multiline=True, - tooltip="A textual description for video generation", - ), - comfy_io.Int.Input( - "duration", - default=5, - min=5, - max=5, - step=1, - display_mode=comfy_io.NumberDisplay.number, - tooltip="Duration of the output video in seconds", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - display_mode=comfy_io.NumberDisplay.number, - control_after_generate=True, - tooltip="Seed for video generation (0 for random)", - optional=True, - ), - comfy_io.Combo.Input( - "aspect_ratio", - options=[model.value for model in AspectRatio], - default=AspectRatio.r_16_9.value, - tooltip="The aspect ratio of the output video", - optional=True, - ), - comfy_io.Combo.Input( - "resolution", - options=[model.value for model in Resolution], - default=Resolution.r_1080p.value, - tooltip="Supported values may vary by model & duration", - optional=True, - ), - comfy_io.Combo.Input( - "movement_amplitude", - options=[model.value for model in MovementAmplitude], - default=MovementAmplitude.auto.value, - tooltip="The movement amplitude of objects in the frame", - optional=True, - ), - ], - outputs=[ - comfy_io.Video.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - is_api_node=True, - ) - - @classmethod - async def execute( - cls, - model: str, - images: torch.Tensor, - prompt: str, - duration: int, - seed: int, - aspect_ratio: str, - resolution: str, - movement_amplitude: str, - ) -> comfy_io.NodeOutput: - if not prompt: - raise ValueError("The prompt field is required and cannot be empty.") - a = get_number_of_images(images) - if a > 7: - raise ValueError("Too many images, maximum allowed is 7.") - for image in images: - validate_image_aspect_ratio_range(image, (1, 4), (4, 1)) - validate_image_dimensions(image, min_width=128, min_height=128) - payload = TaskCreationRequest( - model_name=model, - prompt=prompt, - duration=duration, - seed=seed, - aspect_ratio=aspect_ratio, - resolution=resolution, - movement_amplitude=movement_amplitude, - ) - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - payload.images = await upload_images_to_comfyapi( - images, - max_images=7, - mime_type="image/png", - auth_kwargs=auth, - ) - results = await execute_task(VIDU_REFERENCE_VIDEO, auth, payload, 120, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) - - -class ViduStartEndToVideoNode(comfy_io.ComfyNode): - - @classmethod - def define_schema(cls): - return comfy_io.Schema( - node_id="ViduStartEndToVideoNode", - display_name="Vidu Start End To Video Generation", - category="api node/video/Vidu", - description="Generate a video from start and end frames and a prompt", - inputs=[ - comfy_io.Combo.Input( - "model", - options=[model.value for model in VideoModelName], - default=VideoModelName.vidu_q1.value, - tooltip="Model name", - ), - comfy_io.Image.Input( - "first_frame", - tooltip="Start frame", - ), - comfy_io.Image.Input( - "end_frame", - tooltip="End frame", - ), - comfy_io.String.Input( - "prompt", - multiline=True, - tooltip="A textual description for video generation", - optional=True, - ), - comfy_io.Int.Input( - "duration", - default=5, - min=5, - max=5, - step=1, - display_mode=comfy_io.NumberDisplay.number, - tooltip="Duration of the output video in seconds", - optional=True, - ), - comfy_io.Int.Input( - "seed", - default=0, - min=0, - max=2147483647, - step=1, - display_mode=comfy_io.NumberDisplay.number, - control_after_generate=True, - tooltip="Seed for video generation (0 for random)", - optional=True, - ), - comfy_io.Combo.Input( - "resolution", - options=[model.value for model in Resolution], - default=Resolution.r_1080p.value, - tooltip="Supported values may vary by model & duration", - optional=True, - ), - comfy_io.Combo.Input( - "movement_amplitude", - options=[model.value for model in MovementAmplitude], - default=MovementAmplitude.auto.value, - tooltip="The movement amplitude of objects in the frame", - optional=True, - ), - ], - outputs=[ - comfy_io.Video.Output(), - ], - hidden=[ - comfy_io.Hidden.auth_token_comfy_org, - comfy_io.Hidden.api_key_comfy_org, - comfy_io.Hidden.unique_id, - ], - is_api_node=True, - ) - - @classmethod - async def execute( - cls, - model: str, - first_frame: torch.Tensor, - end_frame: torch.Tensor, - prompt: str, - duration: int, - seed: int, - resolution: str, - movement_amplitude: str, - ) -> comfy_io.NodeOutput: - validate_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False) - payload = TaskCreationRequest( - model_name=model, - prompt=prompt, - duration=duration, - seed=seed, - resolution=resolution, - movement_amplitude=movement_amplitude, - ) - auth = { - "auth_token": cls.hidden.auth_token_comfy_org, - "comfy_api_key": cls.hidden.api_key_comfy_org, - } - payload.images = [ - (await upload_images_to_comfyapi(frame, max_images=1, mime_type="image/png", auth_kwargs=auth))[0] - for frame in (first_frame, end_frame) - ] - results = await execute_task(VIDU_START_END_VIDEO, auth, payload, 96, cls.hidden.unique_id) - return comfy_io.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) - - -class ViduExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]: - return [ - ViduTextToVideoNode, - ViduImageToVideoNode, - ViduReferenceVideoNode, - ViduStartEndToVideoNode, - ] - -async def comfy_entrypoint() -> ViduExtension: - return ViduExtension() diff --git a/comfy_api_nodes/redocly-dev.yaml b/comfy_api_nodes/redocly-dev.yaml deleted file mode 100644 index d9e3cab70ff18a924faf3f793a71710846beeae5..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/redocly-dev.yaml +++ /dev/null @@ -1,10 +0,0 @@ -# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. -# This is used for development purposes to generate stubs for unreleased API endpoints. -apis: - filter: - root: openapi.yaml - decorators: - filter-in: - property: tags - value: ['API Nodes'] - matchStrategy: all diff --git a/comfy_api_nodes/redocly.yaml b/comfy_api_nodes/redocly.yaml deleted file mode 100644 index d102345b1ec932577e310f2e07c4e32017c189d5..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/redocly.yaml +++ /dev/null @@ -1,10 +0,0 @@ -# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. - -apis: - filter: - root: openapi.yaml - decorators: - filter-in: - property: tags - value: ['API Nodes', 'Released'] - matchStrategy: all diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py deleted file mode 100644 index 606b794bf4c34fc448efbd139925fe991defef71..0000000000000000000000000000000000000000 --- a/comfy_api_nodes/util/validation_utils.py +++ /dev/null @@ -1,153 +0,0 @@ -import logging -from typing import Optional - -import torch -from comfy_api.input.video_types import VideoInput - - -def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]: - if len(image.shape) == 4: - return image.shape[1], image.shape[2] - elif len(image.shape) == 3: - return image.shape[0], image.shape[1] - else: - raise ValueError("Invalid image tensor shape.") - - -def validate_image_dimensions( - image: torch.Tensor, - min_width: Optional[int] = None, - max_width: Optional[int] = None, - min_height: Optional[int] = None, - max_height: Optional[int] = None, -): - height, width = get_image_dimensions(image) - - if min_width is not None and width < min_width: - raise ValueError(f"Image width must be at least {min_width}px, got {width}px") - if max_width is not None and width > max_width: - raise ValueError(f"Image width must be at most {max_width}px, got {width}px") - if min_height is not None and height < min_height: - raise ValueError( - f"Image height must be at least {min_height}px, got {height}px" - ) - if max_height is not None and height > max_height: - raise ValueError(f"Image height must be at most {max_height}px, got {height}px") - - -def validate_image_aspect_ratio( - image: torch.Tensor, - min_aspect_ratio: Optional[float] = None, - max_aspect_ratio: Optional[float] = None, -): - width, height = get_image_dimensions(image) - aspect_ratio = width / height - - if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio: - raise ValueError( - f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}" - ) - if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio: - raise ValueError( - f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}" - ) - - -def validate_image_aspect_ratio_range( - image: torch.Tensor, - min_ratio: tuple[float, float], # e.g. (1, 4) - max_ratio: tuple[float, float], # e.g. (4, 1) - *, - strict: bool = True, # True -> (min, max); False -> [min, max] -) -> float: - a1, b1 = min_ratio - a2, b2 = max_ratio - if a1 <= 0 or b1 <= 0 or a2 <= 0 or b2 <= 0: - raise ValueError("Ratios must be positive, like (1, 4) or (4, 1).") - lo, hi = (a1 / b1), (a2 / b2) - if lo > hi: - lo, hi = hi, lo - a1, b1, a2, b2 = a2, b2, a1, b1 # swap only for error text - w, h = get_image_dimensions(image) - if w <= 0 or h <= 0: - raise ValueError(f"Invalid image dimensions: {w}x{h}") - ar = w / h - ok = (lo < ar < hi) if strict else (lo <= ar <= hi) - if not ok: - op = "<" if strict else "≤" - raise ValueError(f"Image aspect ratio {ar:.6g} is outside allowed range: {a1}:{b1} {op} ratio {op} {a2}:{b2}") - return ar - - -def validate_aspect_ratio_closeness( - start_img, - end_img, - min_rel: float, - max_rel: float, - *, - strict: bool = False, # True => exclusive, False => inclusive -) -> None: - w1, h1 = get_image_dimensions(start_img) - w2, h2 = get_image_dimensions(end_img) - if min(w1, h1, w2, h2) <= 0: - raise ValueError("Invalid image dimensions") - ar1 = w1 / h1 - ar2 = w2 / h2 - # Normalize so it is symmetric (no need to check both ar1/ar2 and ar2/ar1) - closeness = max(ar1, ar2) / min(ar1, ar2) - limit = max(max_rel, 1.0 / min_rel) # for 0.8..1.25 this is 1.25 - if (closeness >= limit) if strict else (closeness > limit): - raise ValueError(f"Aspect ratios must be close: start/end={ar1/ar2:.4f}, allowed range {min_rel}–{max_rel}.") - - -def validate_video_dimensions( - video: VideoInput, - min_width: Optional[int] = None, - max_width: Optional[int] = None, - min_height: Optional[int] = None, - max_height: Optional[int] = None, -): - try: - width, height = video.get_dimensions() - except Exception as e: - logging.error("Error getting dimensions of video: %s", e) - return - - if min_width is not None and width < min_width: - raise ValueError(f"Video width must be at least {min_width}px, got {width}px") - if max_width is not None and width > max_width: - raise ValueError(f"Video width must be at most {max_width}px, got {width}px") - if min_height is not None and height < min_height: - raise ValueError( - f"Video height must be at least {min_height}px, got {height}px" - ) - if max_height is not None and height > max_height: - raise ValueError(f"Video height must be at most {max_height}px, got {height}px") - - -def validate_video_duration( - video: VideoInput, - min_duration: Optional[float] = None, - max_duration: Optional[float] = None, -): - try: - duration = video.get_duration() - except Exception as e: - logging.error("Error getting duration of video: %s", e) - return - - epsilon = 0.0001 - if min_duration is not None and min_duration - epsilon > duration: - raise ValueError( - f"Video duration must be at least {min_duration}s, got {duration}s" - ) - if max_duration is not None and duration > max_duration + epsilon: - raise ValueError( - f"Video duration must be at most {max_duration}s, got {duration}s" - ) - - -def get_number_of_images(images): - if isinstance(images, torch.Tensor): - return images.shape[0] if images.ndim >= 4 else 1 - return len(images) diff --git a/comfy_config/.DS_Store b/comfy_config/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_config/.DS_Store and /dev/null differ diff --git a/comfy_config/config_parser.py b/comfy_config/config_parser.py deleted file mode 100644 index 8da7bd901f421f3aaa5b620c9595313c30585ae1..0000000000000000000000000000000000000000 --- a/comfy_config/config_parser.py +++ /dev/null @@ -1,152 +0,0 @@ -import os -from pathlib import Path -from typing import Optional - -from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource - -from comfy_config.types import ( - ComfyConfig, - ProjectConfig, - PyProjectConfig, - PyProjectSettings -) - -def validate_and_extract_os_classifiers(classifiers: list) -> list: - os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")] - if not os_classifiers: - return [] - - os_values = [c[len("Operating System :: ") :] for c in os_classifiers] - valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"} - - for os_value in os_values: - if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes): - return [] - - return os_values - - -def validate_and_extract_accelerator_classifiers(classifiers: list) -> list: - accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")] - if not accelerator_classifiers: - return [] - - accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers] - - valid_accelerators = { - "GPU :: NVIDIA CUDA", - "GPU :: AMD ROCm", - "GPU :: Intel Arc", - "NPU :: Huawei Ascend", - "GPU :: Apple Metal", - } - - for accelerator_value in accelerator_values: - if accelerator_value not in valid_accelerators: - return [] - - return accelerator_values - - -""" -Extract configuration from a custom node directory's pyproject.toml file or a Python file. - -This function reads and parses the pyproject.toml file in the specified directory -to extract project and ComfyUI-specific configuration information. If no -pyproject.toml file is found, it creates a minimal configuration using the -folder name as the project name. If a Python file is provided, it uses the -file name (without extension) as the project name. - -Args: - path (str): Path to the directory containing the pyproject.toml file, or - path to a .py file. If pyproject.toml doesn't exist in a directory, - the folder name will be used as the default project name. If a .py - file is provided, the filename (without .py extension) will be used - as the project name. - -Returns: - Optional[PyProjectConfig]: A PyProjectConfig object containing: - - project: Basic project information (name, version, dependencies, etc.) - - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.) - Returns None if configuration extraction fails or if the provided file - is not a Python file. - -Notes: - - If pyproject.toml is missing in a directory, creates a default config with folder name - - If a .py file is provided, creates a default config with filename (without extension) - - Returns None for non-Python files - -Example: - >>> from comfy_config import config_parser - >>> # For directory - >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__)) - >>> project_config = config_parser.extract_node_configuration(custom_node_dir) - >>> print(project_config.project.name) # "my_custom_node" or name from pyproject.toml - >>> - >>> # For single-file Python node file - >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py" - >>> project_config = config_parser.extract_node_configuration(py_file_path) - >>> print(project_config.project.name) # "my_node" -""" -def extract_node_configuration(path) -> Optional[PyProjectConfig]: - if os.path.isfile(path): - file_path = Path(path) - - if file_path.suffix.lower() != '.py': - return None - - project_name = file_path.stem - project = ProjectConfig(name=project_name) - comfy = ComfyConfig() - return PyProjectConfig(project=project, tool_comfy=comfy) - - folder_name = os.path.basename(path) - toml_path = Path(path) / "pyproject.toml" - - if not toml_path.exists(): - project = ProjectConfig(name=folder_name) - comfy = ComfyConfig() - return PyProjectConfig(project=project, tool_comfy=comfy) - - raw_settings = load_pyproject_settings(toml_path) - - project_data = raw_settings.project - - tool_data = raw_settings.tool - comfy_data = tool_data.get("comfy", {}) if tool_data else {} - - dependencies = project_data.get("dependencies", []) - supported_comfyui_frontend_version = "" - for dep in dependencies: - if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"): - supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package") - break - - supported_comfyui_version = comfy_data.get("requires-comfyui", "") - - classifiers = project_data.get('classifiers', []) - supported_os = validate_and_extract_os_classifiers(classifiers) - supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers) - - project_data['supported_os'] = supported_os - project_data['supported_accelerators'] = supported_accelerators - project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version - project_data['supported_comfyui_version'] = supported_comfyui_version - - return PyProjectConfig(project=project_data, tool_comfy=comfy_data) - - -def load_pyproject_settings(toml_path: Path) -> PyProjectSettings: - class PyProjectLoader(PyProjectSettings): - @classmethod - def settings_customise_sources( - cls, - settings_cls, - init_settings: PydanticBaseSettingsSource, - env_settings: PydanticBaseSettingsSource, - dotenv_settings: PydanticBaseSettingsSource, - file_secret_settings: PydanticBaseSettingsSource, - ): - return (TomlConfigSettingsSource(settings_cls, toml_path),) - - return PyProjectLoader() diff --git a/comfy_config/types.py b/comfy_config/types.py deleted file mode 100644 index 59448466b2cb6725c622fea1151f51e521d850fc..0000000000000000000000000000000000000000 --- a/comfy_config/types.py +++ /dev/null @@ -1,97 +0,0 @@ -from pydantic import BaseModel, Field, field_validator -from pydantic_settings import BaseSettings, SettingsConfigDict -from typing import List, Optional - -# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes -# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py. -# Any changes to one must be reflected in the other to maintain consistency. - -class NodeVersion(BaseModel): - changelog: str - dependencies: List[str] - deprecated: bool - id: str - version: str - download_url: str - - -class Node(BaseModel): - id: str - name: str - description: str - author: Optional[str] = None - license: Optional[str] = None - icon: Optional[str] = None - repository: Optional[str] = None - tags: List[str] = Field(default_factory=list) - latest_version: Optional[NodeVersion] = None - - -class PublishNodeVersionResponse(BaseModel): - node_version: NodeVersion - signedUrl: str - - -class URLs(BaseModel): - homepage: str = Field(default="", alias="Homepage") - documentation: str = Field(default="", alias="Documentation") - repository: str = Field(default="", alias="Repository") - issues: str = Field(default="", alias="Issues") - - -class Model(BaseModel): - location: str - model_url: str - - -class ComfyConfig(BaseModel): - publisher_id: str = Field(default="", alias="PublisherId") - display_name: str = Field(default="", alias="DisplayName") - icon: str = Field(default="", alias="Icon") - models: List[Model] = Field(default_factory=list, alias="Models") - includes: List[str] = Field(default_factory=list) - web: Optional[str] = None - banner_url: str = "" - -class License(BaseModel): - file: str = "" - text: str = "" - - -class ProjectConfig(BaseModel): - name: str = "" - description: str = "" - version: str = "1.0.0" - requires_python: str = Field(default=">= 3.9", alias="requires-python") - dependencies: List[str] = Field(default_factory=list) - license: License = Field(default_factory=License) - urls: URLs = Field(default_factory=URLs) - supported_os: List[str] = Field(default_factory=list) - supported_accelerators: List[str] = Field(default_factory=list) - supported_comfyui_version: str = "" - supported_comfyui_frontend_version: str = "" - - @field_validator('license', mode='before') - @classmethod - def validate_license(cls, v): - if isinstance(v, str): - return License(text=v) - elif isinstance(v, dict): - return License(**v) - elif isinstance(v, License): - return v - else: - return License() - - -class PyProjectConfig(BaseModel): - project: ProjectConfig = Field(default_factory=ProjectConfig) - tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig) - - -class PyProjectSettings(BaseSettings): - project: dict = Field(default_factory=dict) - - tool: dict = Field(default_factory=dict) - - model_config = SettingsConfigDict(extra='allow') diff --git a/comfy_execution/.DS_Store b/comfy_execution/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_execution/.DS_Store and /dev/null differ diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py deleted file mode 100644 index 41224ce3b82e0432ed89523101c2b3c8ef5f8f1a..0000000000000000000000000000000000000000 --- a/comfy_execution/caching.py +++ /dev/null @@ -1,472 +0,0 @@ -import itertools -from typing import Sequence, Mapping, Dict -from comfy_execution.graph import DynamicPrompt -from abc import ABC, abstractmethod - -import nodes - -from comfy_execution.graph_utils import is_link - -NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {} - - -def include_unique_id_in_input(class_type: str) -> bool: - if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID: - return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values() - return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] - -class CacheKeySet(ABC): - def __init__(self, dynprompt, node_ids, is_changed_cache): - self.keys = {} - self.subcache_keys = {} - - @abstractmethod - async def add_keys(self, node_ids): - raise NotImplementedError() - - def all_node_ids(self): - return set(self.keys.keys()) - - def get_used_keys(self): - return self.keys.values() - - def get_used_subcache_keys(self): - return self.subcache_keys.values() - - def get_data_key(self, node_id): - return self.keys.get(node_id, None) - - def get_subcache_key(self, node_id): - return self.subcache_keys.get(node_id, None) - -class Unhashable: - def __init__(self): - self.value = float("NaN") - -def to_hashable(obj): - # So that we don't infinitely recurse since frozenset and tuples - # are Sequences. - if isinstance(obj, (int, float, str, bool, type(None))): - return obj - elif isinstance(obj, Mapping): - return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())]) - elif isinstance(obj, Sequence): - return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj])) - else: - # TODO - Support other objects like tensors? - return Unhashable() - -class CacheKeySetID(CacheKeySet): - def __init__(self, dynprompt, node_ids, is_changed_cache): - super().__init__(dynprompt, node_ids, is_changed_cache) - self.dynprompt = dynprompt - - async def add_keys(self, node_ids): - for node_id in node_ids: - if node_id in self.keys: - continue - if not self.dynprompt.has_node(node_id): - continue - node = self.dynprompt.get_node(node_id) - self.keys[node_id] = (node_id, node["class_type"]) - self.subcache_keys[node_id] = (node_id, node["class_type"]) - -class CacheKeySetInputSignature(CacheKeySet): - def __init__(self, dynprompt, node_ids, is_changed_cache): - super().__init__(dynprompt, node_ids, is_changed_cache) - self.dynprompt = dynprompt - self.is_changed_cache = is_changed_cache - - def include_node_id_in_input(self) -> bool: - return False - - async def add_keys(self, node_ids): - for node_id in node_ids: - if node_id in self.keys: - continue - if not self.dynprompt.has_node(node_id): - continue - node = self.dynprompt.get_node(node_id) - self.keys[node_id] = await self.get_node_signature(self.dynprompt, node_id) - self.subcache_keys[node_id] = (node_id, node["class_type"]) - - async def get_node_signature(self, dynprompt, node_id): - signature = [] - ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id) - signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping)) - for ancestor_id in ancestors: - signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping)) - return to_hashable(signature) - - async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping): - if not dynprompt.has_node(node_id): - # This node doesn't exist -- we can't cache it. - return [float("NaN")] - node = dynprompt.get_node(node_id) - class_type = node["class_type"] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - signature = [class_type, await self.is_changed_cache.get(node_id)] - if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type): - signature.append(node_id) - inputs = node["inputs"] - for key in sorted(inputs.keys()): - if is_link(inputs[key]): - (ancestor_id, ancestor_socket) = inputs[key] - ancestor_index = ancestor_order_mapping[ancestor_id] - signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket))) - else: - signature.append((key, inputs[key])) - return signature - - # This function returns a list of all ancestors of the given node. The order of the list is - # deterministic based on which specific inputs the ancestor is connected by. - def get_ordered_ancestry(self, dynprompt, node_id): - ancestors = [] - order_mapping = {} - self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping) - return ancestors, order_mapping - - def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping): - if not dynprompt.has_node(node_id): - return - inputs = dynprompt.get_node(node_id)["inputs"] - input_keys = sorted(inputs.keys()) - for key in input_keys: - if is_link(inputs[key]): - ancestor_id = inputs[key][0] - if ancestor_id not in order_mapping: - ancestors.append(ancestor_id) - order_mapping[ancestor_id] = len(ancestors) - 1 - self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping) - -class BasicCache: - def __init__(self, key_class): - self.key_class = key_class - self.initialized = False - self.dynprompt: DynamicPrompt - self.cache_key_set: CacheKeySet - self.cache = {} - self.subcaches = {} - - async def set_prompt(self, dynprompt, node_ids, is_changed_cache): - self.dynprompt = dynprompt - self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache) - await self.cache_key_set.add_keys(node_ids) - self.is_changed_cache = is_changed_cache - self.initialized = True - - def all_node_ids(self): - assert self.initialized - node_ids = self.cache_key_set.all_node_ids() - for subcache in self.subcaches.values(): - node_ids = node_ids.union(subcache.all_node_ids()) - return node_ids - - def _clean_cache(self): - preserve_keys = set(self.cache_key_set.get_used_keys()) - to_remove = [] - for key in self.cache: - if key not in preserve_keys: - to_remove.append(key) - for key in to_remove: - del self.cache[key] - - def _clean_subcaches(self): - preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys()) - - to_remove = [] - for key in self.subcaches: - if key not in preserve_subcaches: - to_remove.append(key) - for key in to_remove: - del self.subcaches[key] - - def clean_unused(self): - assert self.initialized - self._clean_cache() - self._clean_subcaches() - - def _set_immediate(self, node_id, value): - assert self.initialized - cache_key = self.cache_key_set.get_data_key(node_id) - self.cache[cache_key] = value - - def _get_immediate(self, node_id): - if not self.initialized: - return None - cache_key = self.cache_key_set.get_data_key(node_id) - if cache_key in self.cache: - return self.cache[cache_key] - else: - return None - - async def _ensure_subcache(self, node_id, children_ids): - subcache_key = self.cache_key_set.get_subcache_key(node_id) - subcache = self.subcaches.get(subcache_key, None) - if subcache is None: - subcache = BasicCache(self.key_class) - self.subcaches[subcache_key] = subcache - await subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache) - return subcache - - def _get_subcache(self, node_id): - assert self.initialized - subcache_key = self.cache_key_set.get_subcache_key(node_id) - if subcache_key in self.subcaches: - return self.subcaches[subcache_key] - else: - return None - - def recursive_debug_dump(self): - result = [] - for key in self.cache: - result.append({"key": key, "value": self.cache[key]}) - for key in self.subcaches: - result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()}) - return result - -class HierarchicalCache(BasicCache): - def __init__(self, key_class): - super().__init__(key_class) - - def _get_cache_for(self, node_id): - assert self.dynprompt is not None - parent_id = self.dynprompt.get_parent_node_id(node_id) - if parent_id is None: - return self - - hierarchy = [] - while parent_id is not None: - hierarchy.append(parent_id) - parent_id = self.dynprompt.get_parent_node_id(parent_id) - - cache = self - for parent_id in reversed(hierarchy): - cache = cache._get_subcache(parent_id) - if cache is None: - return None - return cache - - def get(self, node_id): - cache = self._get_cache_for(node_id) - if cache is None: - return None - return cache._get_immediate(node_id) - - def set(self, node_id, value): - cache = self._get_cache_for(node_id) - assert cache is not None - cache._set_immediate(node_id, value) - - async def ensure_subcache_for(self, node_id, children_ids): - cache = self._get_cache_for(node_id) - assert cache is not None - return await cache._ensure_subcache(node_id, children_ids) - -class LRUCache(BasicCache): - def __init__(self, key_class, max_size=100): - super().__init__(key_class) - self.max_size = max_size - self.min_generation = 0 - self.generation = 0 - self.used_generation = {} - self.children = {} - - async def set_prompt(self, dynprompt, node_ids, is_changed_cache): - await super().set_prompt(dynprompt, node_ids, is_changed_cache) - self.generation += 1 - for node_id in node_ids: - self._mark_used(node_id) - - def clean_unused(self): - while len(self.cache) > self.max_size and self.min_generation < self.generation: - self.min_generation += 1 - to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation] - for key in to_remove: - del self.cache[key] - del self.used_generation[key] - if key in self.children: - del self.children[key] - self._clean_subcaches() - - def get(self, node_id): - self._mark_used(node_id) - return self._get_immediate(node_id) - - def _mark_used(self, node_id): - cache_key = self.cache_key_set.get_data_key(node_id) - if cache_key is not None: - self.used_generation[cache_key] = self.generation - - def set(self, node_id, value): - self._mark_used(node_id) - return self._set_immediate(node_id, value) - - async def ensure_subcache_for(self, node_id, children_ids): - # Just uses subcaches for tracking 'live' nodes - await super()._ensure_subcache(node_id, children_ids) - - await self.cache_key_set.add_keys(children_ids) - self._mark_used(node_id) - cache_key = self.cache_key_set.get_data_key(node_id) - self.children[cache_key] = [] - for child_id in children_ids: - self._mark_used(child_id) - self.children[cache_key].append(self.cache_key_set.get_data_key(child_id)) - return self - - -class DependencyAwareCache(BasicCache): - """ - A cache implementation that tracks dependencies between nodes and manages - their execution and caching accordingly. It extends the BasicCache class. - Nodes are removed from this cache once all of their descendants have been - executed. - """ - - def __init__(self, key_class): - """ - Initialize the DependencyAwareCache. - - Args: - key_class: The class used for generating cache keys. - """ - super().__init__(key_class) - self.descendants = {} # Maps node_id -> set of descendant node_ids - self.ancestors = {} # Maps node_id -> set of ancestor node_ids - self.executed_nodes = set() # Tracks nodes that have been executed - - async def set_prompt(self, dynprompt, node_ids, is_changed_cache): - """ - Clear the entire cache and rebuild the dependency graph. - - Args: - dynprompt: The dynamic prompt object containing node information. - node_ids: List of node IDs to initialize the cache for. - is_changed_cache: Flag indicating if the cache has changed. - """ - # Clear all existing cache data - self.cache.clear() - self.subcaches.clear() - self.descendants.clear() - self.ancestors.clear() - self.executed_nodes.clear() - - # Call the parent method to initialize the cache with the new prompt - await super().set_prompt(dynprompt, node_ids, is_changed_cache) - - # Rebuild the dependency graph - self._build_dependency_graph(dynprompt, node_ids) - - def _build_dependency_graph(self, dynprompt, node_ids): - """ - Build the dependency graph for all nodes. - - Args: - dynprompt: The dynamic prompt object containing node information. - node_ids: List of node IDs to build the graph for. - """ - self.descendants.clear() - self.ancestors.clear() - for node_id in node_ids: - self.descendants[node_id] = set() - self.ancestors[node_id] = set() - - for node_id in node_ids: - inputs = dynprompt.get_node(node_id)["inputs"] - for input_data in inputs.values(): - if is_link(input_data): # Check if the input is a link to another node - ancestor_id = input_data[0] - self.descendants[ancestor_id].add(node_id) - self.ancestors[node_id].add(ancestor_id) - - def set(self, node_id, value): - """ - Mark a node as executed and store its value in the cache. - - Args: - node_id: The ID of the node to store. - value: The value to store for the node. - """ - self._set_immediate(node_id, value) - self.executed_nodes.add(node_id) - self._cleanup_ancestors(node_id) - - def get(self, node_id): - """ - Retrieve the cached value for a node. - - Args: - node_id: The ID of the node to retrieve. - - Returns: - The cached value for the node. - """ - return self._get_immediate(node_id) - - async def ensure_subcache_for(self, node_id, children_ids): - """ - Ensure a subcache exists for a node and update dependencies. - - Args: - node_id: The ID of the parent node. - children_ids: List of child node IDs to associate with the parent node. - - Returns: - The subcache object for the node. - """ - subcache = await super()._ensure_subcache(node_id, children_ids) - for child_id in children_ids: - self.descendants[node_id].add(child_id) - self.ancestors[child_id].add(node_id) - return subcache - - def _cleanup_ancestors(self, node_id): - """ - Check if ancestors of a node can be removed from the cache. - - Args: - node_id: The ID of the node whose ancestors are to be checked. - """ - for ancestor_id in self.ancestors.get(node_id, []): - if ancestor_id in self.executed_nodes: - # Remove ancestor if all its descendants have been executed - if all(descendant in self.executed_nodes for descendant in self.descendants[ancestor_id]): - self._remove_node(ancestor_id) - - def _remove_node(self, node_id): - """ - Remove a node from the cache. - - Args: - node_id: The ID of the node to remove. - """ - cache_key = self.cache_key_set.get_data_key(node_id) - if cache_key in self.cache: - del self.cache[cache_key] - subcache_key = self.cache_key_set.get_subcache_key(node_id) - if subcache_key in self.subcaches: - del self.subcaches[subcache_key] - - def clean_unused(self): - """ - Clean up unused nodes. This is a no-op for this cache implementation. - """ - pass - - def recursive_debug_dump(self): - """ - Dump the cache and dependency graph for debugging. - - Returns: - A list containing the cache state and dependency graph. - """ - result = super().recursive_debug_dump() - result.append({ - "descendants": self.descendants, - "ancestors": self.ancestors, - "executed_nodes": list(self.executed_nodes), - }) - return result diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py deleted file mode 100644 index f4b427265da7c50684dc66bb6f15682b4685bd0d..0000000000000000000000000000000000000000 --- a/comfy_execution/graph.py +++ /dev/null @@ -1,299 +0,0 @@ -from __future__ import annotations -from typing import Type, Literal - -import nodes -import asyncio -import inspect -from comfy_execution.graph_utils import is_link, ExecutionBlocker -from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions - -# NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests -ExecutionBlocker = ExecutionBlocker - -class DependencyCycleError(Exception): - pass - -class NodeInputError(Exception): - pass - -class NodeNotFoundError(Exception): - pass - -class DynamicPrompt: - def __init__(self, original_prompt): - # The original prompt provided by the user - self.original_prompt = original_prompt - # Any extra pieces of the graph created during execution - self.ephemeral_prompt = {} - self.ephemeral_parents = {} - self.ephemeral_display = {} - - def get_node(self, node_id): - if node_id in self.ephemeral_prompt: - return self.ephemeral_prompt[node_id] - if node_id in self.original_prompt: - return self.original_prompt[node_id] - raise NodeNotFoundError(f"Node {node_id} not found") - - def has_node(self, node_id): - return node_id in self.original_prompt or node_id in self.ephemeral_prompt - - def add_ephemeral_node(self, node_id, node_info, parent_id, display_id): - self.ephemeral_prompt[node_id] = node_info - self.ephemeral_parents[node_id] = parent_id - self.ephemeral_display[node_id] = display_id - - def get_real_node_id(self, node_id): - while node_id in self.ephemeral_parents: - node_id = self.ephemeral_parents[node_id] - return node_id - - def get_parent_node_id(self, node_id): - return self.ephemeral_parents.get(node_id, None) - - def get_display_node_id(self, node_id): - while node_id in self.ephemeral_display: - node_id = self.ephemeral_display[node_id] - return node_id - - def all_node_ids(self): - return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys())) - - def get_original_prompt(self): - return self.original_prompt - -def get_input_info( - class_def: Type[ComfyNodeABC], - input_name: str, - valid_inputs: InputTypeDict | None = None -) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]: - """Get the input type, category, and extra info for a given input name. - - Arguments: - class_def: The class definition of the node. - input_name: The name of the input to get info for. - valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES(). - - Returns: - tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name. - """ - - valid_inputs = valid_inputs or class_def.INPUT_TYPES() - input_info = None - input_category = None - if "required" in valid_inputs and input_name in valid_inputs["required"]: - input_category = "required" - input_info = valid_inputs["required"][input_name] - elif "optional" in valid_inputs and input_name in valid_inputs["optional"]: - input_category = "optional" - input_info = valid_inputs["optional"][input_name] - elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]: - input_category = "hidden" - input_info = valid_inputs["hidden"][input_name] - if input_info is None: - return None, None, None - input_type = input_info[0] - if len(input_info) > 1: - extra_info = input_info[1] - else: - extra_info = {} - return input_type, input_category, extra_info - -class TopologicalSort: - def __init__(self, dynprompt): - self.dynprompt = dynprompt - self.pendingNodes = {} - self.blockCount = {} # Number of nodes this node is directly blocked by - self.blocking = {} # Which nodes are blocked by this node - self.externalBlocks = 0 - self.unblockedEvent = asyncio.Event() - - def get_input_info(self, unique_id, input_name): - class_type = self.dynprompt.get_node(unique_id)["class_type"] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - return get_input_info(class_def, input_name) - - def make_input_strong_link(self, to_node_id, to_input): - inputs = self.dynprompt.get_node(to_node_id)["inputs"] - if to_input not in inputs: - raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all") - value = inputs[to_input] - if not is_link(value): - raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant") - from_node_id, from_socket = value - self.add_strong_link(from_node_id, from_socket, to_node_id) - - def add_strong_link(self, from_node_id, from_socket, to_node_id): - if not self.is_cached(from_node_id): - self.add_node(from_node_id) - if to_node_id not in self.blocking[from_node_id]: - self.blocking[from_node_id][to_node_id] = {} - self.blockCount[to_node_id] += 1 - self.blocking[from_node_id][to_node_id][from_socket] = True - - def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None): - node_ids = [node_unique_id] - links = [] - - while len(node_ids) > 0: - unique_id = node_ids.pop() - if unique_id in self.pendingNodes: - continue - - self.pendingNodes[unique_id] = True - self.blockCount[unique_id] = 0 - self.blocking[unique_id] = {} - - inputs = self.dynprompt.get_node(unique_id)["inputs"] - for input_name in inputs: - value = inputs[input_name] - if is_link(value): - from_node_id, from_socket = value - if subgraph_nodes is not None and from_node_id not in subgraph_nodes: - continue - _, _, input_info = self.get_input_info(unique_id, input_name) - is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"] - if (include_lazy or not is_lazy) and not self.is_cached(from_node_id): - node_ids.append(from_node_id) - links.append((from_node_id, from_socket, unique_id)) - - for link in links: - self.add_strong_link(*link) - - def add_external_block(self, node_id): - assert node_id in self.blockCount, "Can't add external block to a node that isn't pending" - self.externalBlocks += 1 - self.blockCount[node_id] += 1 - def unblock(): - self.externalBlocks -= 1 - self.blockCount[node_id] -= 1 - self.unblockedEvent.set() - return unblock - - def is_cached(self, node_id): - return False - - def get_ready_nodes(self): - return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0] - - def pop_node(self, unique_id): - del self.pendingNodes[unique_id] - for blocked_node_id in self.blocking[unique_id]: - self.blockCount[blocked_node_id] -= 1 - del self.blocking[unique_id] - - def is_empty(self): - return len(self.pendingNodes) == 0 - -class ExecutionList(TopologicalSort): - """ - ExecutionList implements a topological dissolve of the graph. After a node is staged for execution, - it can still be returned to the graph after having further dependencies added. - """ - def __init__(self, dynprompt, output_cache): - super().__init__(dynprompt) - self.output_cache = output_cache - self.staged_node_id = None - - def is_cached(self, node_id): - return self.output_cache.get(node_id) is not None - - async def stage_node_execution(self): - assert self.staged_node_id is None - if self.is_empty(): - return None, None, None - available = self.get_ready_nodes() - while len(available) == 0 and self.externalBlocks > 0: - # Wait for an external block to be released - await self.unblockedEvent.wait() - self.unblockedEvent.clear() - available = self.get_ready_nodes() - if len(available) == 0: - cycled_nodes = self.get_nodes_in_cycle() - # Because cycles composed entirely of static nodes are caught during initial validation, - # we will 'blame' the first node in the cycle that is not a static node. - blamed_node = cycled_nodes[0] - for node_id in cycled_nodes: - display_node_id = self.dynprompt.get_display_node_id(node_id) - if display_node_id != node_id: - blamed_node = display_node_id - break - ex = DependencyCycleError("Dependency cycle detected") - error_details = { - "node_id": blamed_node, - "exception_message": str(ex), - "exception_type": "graph.DependencyCycleError", - "traceback": [], - "current_inputs": [] - } - return None, error_details, ex - - self.staged_node_id = self.ux_friendly_pick_node(available) - return self.staged_node_id, None, None - - def ux_friendly_pick_node(self, node_list): - # If an output node is available, do that first. - # Technically this has no effect on the overall length of execution, but it feels better as a user - # for a PreviewImage to display a result as soon as it can - # Some other heuristics could probably be used here to improve the UX further. - def is_output(node_id): - class_type = self.dynprompt.get_node(node_id)["class_type"] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: - return True - return False - - # If an available node is async, do that first. - # This will execute the asynchronous function earlier, reducing the overall time. - def is_async(node_id): - class_type = self.dynprompt.get_node(node_id)["class_type"] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION)) - - for node_id in node_list: - if is_output(node_id) or is_async(node_id): - return node_id - - #This should handle the VAEDecode -> preview case - for node_id in node_list: - for blocked_node_id in self.blocking[node_id]: - if is_output(blocked_node_id): - return node_id - - #This should handle the VAELoader -> VAEDecode -> preview case - for node_id in node_list: - for blocked_node_id in self.blocking[node_id]: - for blocked_node_id1 in self.blocking[blocked_node_id]: - if is_output(blocked_node_id1): - return node_id - - #TODO: this function should be improved - return node_list[0] - - def unstage_node_execution(self): - assert self.staged_node_id is not None - self.staged_node_id = None - - def complete_node_execution(self): - node_id = self.staged_node_id - self.pop_node(node_id) - self.staged_node_id = None - - def get_nodes_in_cycle(self): - # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle. - # We're skipping some of the performance optimizations from the original TopologicalSort to keep - # the code simple (and because having a cycle in the first place is a catastrophic error) - blocked_by = { node_id: {} for node_id in self.pendingNodes } - for from_node_id in self.blocking: - for to_node_id in self.blocking[from_node_id]: - if True in self.blocking[from_node_id][to_node_id].values(): - blocked_by[to_node_id][from_node_id] = True - to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] - while len(to_remove) > 0: - for node_id in to_remove: - for to_node_id in blocked_by: - if node_id in blocked_by[to_node_id]: - del blocked_by[to_node_id][node_id] - del blocked_by[node_id] - to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] - return list(blocked_by.keys()) diff --git a/comfy_execution/graph_utils.py b/comfy_execution/graph_utils.py deleted file mode 100644 index 496d2c634982aa5afc8d58b9db758f97b3b5c2d8..0000000000000000000000000000000000000000 --- a/comfy_execution/graph_utils.py +++ /dev/null @@ -1,155 +0,0 @@ -def is_link(obj): - if not isinstance(obj, list): - return False - if len(obj) != 2: - return False - if not isinstance(obj[0], str): - return False - if not isinstance(obj[1], int) and not isinstance(obj[1], float): - return False - return True - -# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end -class GraphBuilder: - _default_prefix_root = "" - _default_prefix_call_index = 0 - _default_prefix_graph_index = 0 - - def __init__(self, prefix = None): - if prefix is None: - self.prefix = GraphBuilder.alloc_prefix() - else: - self.prefix = prefix - self.nodes = {} - self.id_gen = 1 - - @classmethod - def set_default_prefix(cls, prefix_root, call_index, graph_index = 0): - cls._default_prefix_root = prefix_root - cls._default_prefix_call_index = call_index - cls._default_prefix_graph_index = graph_index - - @classmethod - def alloc_prefix(cls, root=None, call_index=None, graph_index=None): - if root is None: - root = GraphBuilder._default_prefix_root - if call_index is None: - call_index = GraphBuilder._default_prefix_call_index - if graph_index is None: - graph_index = GraphBuilder._default_prefix_graph_index - result = f"{root}.{call_index}.{graph_index}." - GraphBuilder._default_prefix_graph_index += 1 - return result - - def node(self, class_type, id=None, **kwargs): - if id is None: - id = str(self.id_gen) - self.id_gen += 1 - id = self.prefix + id - if id in self.nodes: - return self.nodes[id] - - node = Node(id, class_type, kwargs) - self.nodes[id] = node - return node - - def lookup_node(self, id): - id = self.prefix + id - return self.nodes.get(id) - - def finalize(self): - output = {} - for node_id, node in self.nodes.items(): - output[node_id] = node.serialize() - return output - - def replace_node_output(self, node_id, index, new_value): - node_id = self.prefix + node_id - to_remove = [] - for node in self.nodes.values(): - for key, value in node.inputs.items(): - if is_link(value) and value[0] == node_id and value[1] == index: - if new_value is None: - to_remove.append((node, key)) - else: - node.inputs[key] = new_value - for node, key in to_remove: - del node.inputs[key] - - def remove_node(self, id): - id = self.prefix + id - del self.nodes[id] - -class Node: - def __init__(self, id, class_type, inputs): - self.id = id - self.class_type = class_type - self.inputs = inputs - self.override_display_id = None - - def out(self, index): - return [self.id, index] - - def set_input(self, key, value): - if value is None: - if key in self.inputs: - del self.inputs[key] - else: - self.inputs[key] = value - - def get_input(self, key): - return self.inputs.get(key) - - def set_override_display_id(self, override_display_id): - self.override_display_id = override_display_id - - def serialize(self): - serialized = { - "class_type": self.class_type, - "inputs": self.inputs - } - if self.override_display_id is not None: - serialized["override_display_id"] = self.override_display_id - return serialized - -def add_graph_prefix(graph, outputs, prefix): - # Change the node IDs and any internal links - new_graph = {} - for node_id, node_info in graph.items(): - # Make sure the added nodes have unique IDs - new_node_id = prefix + node_id - new_node = { "class_type": node_info["class_type"], "inputs": {} } - for input_name, input_value in node_info.get("inputs", {}).items(): - if is_link(input_value): - new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]] - else: - new_node["inputs"][input_name] = input_value - new_graph[new_node_id] = new_node - - # Change the node IDs in the outputs - new_outputs = [] - for n in range(len(outputs)): - output = outputs[n] - if is_link(output): - new_outputs.append([prefix + output[0], output[1]]) - else: - new_outputs.append(output) - - return new_graph, tuple(new_outputs) - -class ExecutionBlocker: - """ - Return this from a node and any users will be blocked with the given error message. - If the message is None, execution will be blocked silently instead. - Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's - possible, a lazy input will be more efficient and have a better user experience. - This functionality is useful in two cases: - 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node - like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using - lazy evaluation to let it conditionally disable itself.) - 2. You have a node with multiple possible outputs, some of which are invalid and should not be used. - (I would recommend not making nodes like this in the future -- instead, make multiple nodes with - different outputs. Unfortunately, there are several popular existing nodes using this pattern.) - """ - def __init__(self, message): - self.message = message diff --git a/comfy_execution/progress.py b/comfy_execution/progress.py deleted file mode 100644 index e8f5ede1ee5b68c50e2af903fd4ba5456c45f7f7..0000000000000000000000000000000000000000 --- a/comfy_execution/progress.py +++ /dev/null @@ -1,349 +0,0 @@ -from __future__ import annotations - -from typing import TypedDict, Dict, Optional, Tuple -from typing_extensions import override -from PIL import Image -from enum import Enum -from abc import ABC -from tqdm import tqdm -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from comfy_execution.graph import DynamicPrompt -from protocol import BinaryEventTypes -from comfy_api import feature_flags - -PreviewImageTuple = Tuple[str, Image.Image, Optional[int]] - -class NodeState(Enum): - Pending = "pending" - Running = "running" - Finished = "finished" - Error = "error" - - -class NodeProgressState(TypedDict): - """ - A class to represent the state of a node's progress. - """ - - state: NodeState - value: float - max: float - - -class ProgressHandler(ABC): - """ - Abstract base class for progress handlers. - Progress handlers receive progress updates and display them in various ways. - """ - - def __init__(self, name: str): - self.name = name - self.enabled = True - - def set_registry(self, registry: "ProgressRegistry"): - pass - - def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): - """Called when a node starts processing""" - pass - - def update_handler( - self, - node_id: str, - value: float, - max_value: float, - state: NodeProgressState, - prompt_id: str, - image: PreviewImageTuple | None = None, - ): - """Called when a node's progress is updated""" - pass - - def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): - """Called when a node finishes processing""" - pass - - def reset(self): - """Called when the progress registry is reset""" - pass - - def enable(self): - """Enable this handler""" - self.enabled = True - - def disable(self): - """Disable this handler""" - self.enabled = False - - -class CLIProgressHandler(ProgressHandler): - """ - Handler that displays progress using tqdm progress bars in the CLI. - """ - - def __init__(self): - super().__init__("cli") - self.progress_bars: Dict[str, tqdm] = {} - - @override - def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): - # Create a new tqdm progress bar - if node_id not in self.progress_bars: - self.progress_bars[node_id] = tqdm( - total=state["max"], - desc=f"Node {node_id}", - unit="steps", - leave=True, - position=len(self.progress_bars), - ) - - @override - def update_handler( - self, - node_id: str, - value: float, - max_value: float, - state: NodeProgressState, - prompt_id: str, - image: PreviewImageTuple | None = None, - ): - # Handle case where start_handler wasn't called - if node_id not in self.progress_bars: - self.progress_bars[node_id] = tqdm( - total=max_value, - desc=f"Node {node_id}", - unit="steps", - leave=True, - position=len(self.progress_bars), - ) - self.progress_bars[node_id].update(value) - else: - # Update existing progress bar - if max_value != self.progress_bars[node_id].total: - self.progress_bars[node_id].total = max_value - # Calculate the update amount (difference from current position) - current_position = self.progress_bars[node_id].n - update_amount = value - current_position - if update_amount > 0: - self.progress_bars[node_id].update(update_amount) - - @override - def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): - # Complete and close the progress bar if it exists - if node_id in self.progress_bars: - # Ensure the bar shows 100% completion - remaining = state["max"] - self.progress_bars[node_id].n - if remaining > 0: - self.progress_bars[node_id].update(remaining) - self.progress_bars[node_id].close() - del self.progress_bars[node_id] - - @override - def reset(self): - # Close all progress bars - for bar in self.progress_bars.values(): - bar.close() - self.progress_bars.clear() - - -class WebUIProgressHandler(ProgressHandler): - """ - Handler that sends progress updates to the WebUI via WebSockets. - """ - - def __init__(self, server_instance): - super().__init__("webui") - self.server_instance = server_instance - - def set_registry(self, registry: "ProgressRegistry"): - self.registry = registry - - def _send_progress_state(self, prompt_id: str, nodes: Dict[str, NodeProgressState]): - """Send the current progress state to the client""" - if self.server_instance is None: - return - - # Only send info for non-pending nodes - active_nodes = { - node_id: { - "value": state["value"], - "max": state["max"], - "state": state["state"].value, - "node_id": node_id, - "prompt_id": prompt_id, - "display_node_id": self.registry.dynprompt.get_display_node_id(node_id), - "parent_node_id": self.registry.dynprompt.get_parent_node_id(node_id), - "real_node_id": self.registry.dynprompt.get_real_node_id(node_id), - } - for node_id, state in nodes.items() - if state["state"] != NodeState.Pending - } - - # Send a combined progress_state message with all node states - self.server_instance.send_sync( - "progress_state", {"prompt_id": prompt_id, "nodes": active_nodes} - ) - - @override - def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): - # Send progress state of all nodes - if self.registry: - self._send_progress_state(prompt_id, self.registry.nodes) - - @override - def update_handler( - self, - node_id: str, - value: float, - max_value: float, - state: NodeProgressState, - prompt_id: str, - image: PreviewImageTuple | None = None, - ): - # Send progress state of all nodes - if self.registry: - self._send_progress_state(prompt_id, self.registry.nodes) - if image: - # Only send new format if client supports it - if feature_flags.supports_feature( - self.server_instance.sockets_metadata, - self.server_instance.client_id, - "supports_preview_metadata", - ): - metadata = { - "node_id": node_id, - "prompt_id": prompt_id, - "display_node_id": self.registry.dynprompt.get_display_node_id( - node_id - ), - "parent_node_id": self.registry.dynprompt.get_parent_node_id( - node_id - ), - "real_node_id": self.registry.dynprompt.get_real_node_id(node_id), - } - self.server_instance.send_sync( - BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA, - (image, metadata), - self.server_instance.client_id, - ) - - @override - def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): - # Send progress state of all nodes - if self.registry: - self._send_progress_state(prompt_id, self.registry.nodes) - -class ProgressRegistry: - """ - Registry that maintains node progress state and notifies registered handlers. - """ - - def __init__(self, prompt_id: str, dynprompt: "DynamicPrompt"): - self.prompt_id = prompt_id - self.dynprompt = dynprompt - self.nodes: Dict[str, NodeProgressState] = {} - self.handlers: Dict[str, ProgressHandler] = {} - - def register_handler(self, handler: ProgressHandler) -> None: - """Register a progress handler""" - self.handlers[handler.name] = handler - - def unregister_handler(self, handler_name: str) -> None: - """Unregister a progress handler""" - if handler_name in self.handlers: - # Allow handler to clean up resources - self.handlers[handler_name].reset() - del self.handlers[handler_name] - - def enable_handler(self, handler_name: str) -> None: - """Enable a progress handler""" - if handler_name in self.handlers: - self.handlers[handler_name].enable() - - def disable_handler(self, handler_name: str) -> None: - """Disable a progress handler""" - if handler_name in self.handlers: - self.handlers[handler_name].disable() - - def ensure_entry(self, node_id: str) -> NodeProgressState: - """Ensure a node entry exists""" - if node_id not in self.nodes: - self.nodes[node_id] = NodeProgressState( - state=NodeState.Pending, value=0, max=1 - ) - return self.nodes[node_id] - - def start_progress(self, node_id: str) -> None: - """Start progress tracking for a node""" - entry = self.ensure_entry(node_id) - entry["state"] = NodeState.Running - entry["value"] = 0.0 - entry["max"] = 1.0 - - # Notify all enabled handlers - for handler in self.handlers.values(): - if handler.enabled: - handler.start_handler(node_id, entry, self.prompt_id) - - def update_progress( - self, node_id: str, value: float, max_value: float, image: PreviewImageTuple | None = None - ) -> None: - """Update progress for a node""" - entry = self.ensure_entry(node_id) - entry["state"] = NodeState.Running - entry["value"] = value - entry["max"] = max_value - - # Notify all enabled handlers - for handler in self.handlers.values(): - if handler.enabled: - handler.update_handler( - node_id, value, max_value, entry, self.prompt_id, image - ) - - def finish_progress(self, node_id: str) -> None: - """Finish progress tracking for a node""" - entry = self.ensure_entry(node_id) - entry["state"] = NodeState.Finished - entry["value"] = entry["max"] - - # Notify all enabled handlers - for handler in self.handlers.values(): - if handler.enabled: - handler.finish_handler(node_id, entry, self.prompt_id) - - def reset_handlers(self) -> None: - """Reset all handlers""" - for handler in self.handlers.values(): - handler.reset() - -# Global registry instance -global_progress_registry: ProgressRegistry | None = None - -def reset_progress_state(prompt_id: str, dynprompt: "DynamicPrompt") -> None: - global global_progress_registry - - # Reset existing handlers if registry exists - if global_progress_registry is not None: - global_progress_registry.reset_handlers() - - # Create new registry - global_progress_registry = ProgressRegistry(prompt_id, dynprompt) - - -def add_progress_handler(handler: ProgressHandler) -> None: - registry = get_progress_state() - handler.set_registry(registry) - registry.register_handler(handler) - - -def get_progress_state() -> ProgressRegistry: - global global_progress_registry - if global_progress_registry is None: - from comfy_execution.graph import DynamicPrompt - - global_progress_registry = ProgressRegistry( - prompt_id="", dynprompt=DynamicPrompt({}) - ) - return global_progress_registry diff --git a/comfy_execution/utils.py b/comfy_execution/utils.py deleted file mode 100644 index 62d32f1013df633889b59d2207154d1cbbbfe347..0000000000000000000000000000000000000000 --- a/comfy_execution/utils.py +++ /dev/null @@ -1,46 +0,0 @@ -import contextvars -from typing import Optional, NamedTuple - -class ExecutionContext(NamedTuple): - """ - Context information about the currently executing node. - - Attributes: - node_id: The ID of the currently executing node - list_index: The index in a list being processed (for operations on batches/lists) - """ - prompt_id: str - node_id: str - list_index: Optional[int] - -current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None) - -def get_executing_context() -> Optional[ExecutionContext]: - return current_executing_context.get(None) - -class CurrentNodeContext: - """ - Context manager for setting the current executing node context. - - Sets the current_executing_context on enter and resets it on exit. - - Example: - with CurrentNodeContext(node_id="123", list_index=0): - # Code that should run with the current node context set - process_image() - """ - def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None): - self.context = ExecutionContext( - prompt_id= prompt_id, - node_id= node_id, - list_index= list_index - ) - self.token = None - - def __enter__(self): - self.token = current_executing_context.set(self.context) - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - if self.token is not None: - current_executing_context.reset(self.token) diff --git a/comfy_execution/validation.py b/comfy_execution/validation.py deleted file mode 100644 index cec105fc9f1bfbecdc17c37cfec9ecf60f188814..0000000000000000000000000000000000000000 --- a/comfy_execution/validation.py +++ /dev/null @@ -1,39 +0,0 @@ -from __future__ import annotations - - -def validate_node_input( - received_type: str, input_type: str, strict: bool = False -) -> bool: - """ - received_type and input_type are both strings of the form "T1,T2,...". - - If strict is True, the input_type must contain the received_type. - For example, if received_type is "STRING" and input_type is "STRING,INT", - this will return True. But if received_type is "STRING,INT" and input_type is - "INT", this will return False. - - If strict is False, the input_type must have overlap with the received_type. - For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT", - this will return True. - - Supports pre-union type extension behaviour of ``__ne__`` overrides. - """ - # If the types are exactly the same, we can return immediately - # Use pre-union behaviour: inverse of `__ne__` - if not received_type != input_type: - return True - - # Not equal, and not strings - if not isinstance(received_type, str) or not isinstance(input_type, str): - return False - - # Split the type strings into sets for comparison - received_types = set(t.strip() for t in received_type.split(",")) - input_types = set(t.strip() for t in input_type.split(",")) - - if strict: - # In strict mode, all received types must be in the input types - return received_types.issubset(input_types) - else: - # In non-strict mode, there must be at least one type in common - return len(received_types.intersection(input_types)) > 0 diff --git a/comfy_extras/.DS_Store b/comfy_extras/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/comfy_extras/.DS_Store and /dev/null differ diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py deleted file mode 100644 index 1bec4476f6171e9f1b2a4a3b967fc63bbf9e9c3c..0000000000000000000000000000000000000000 --- a/comfy_extras/chainner_models/model_loading.py +++ /dev/null @@ -1,6 +0,0 @@ -import logging -from spandrel import ModelLoader - -def load_state_dict(state_dict): - logging.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.") - return ModelLoader().load_from_state_dict(state_dict).eval() diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py deleted file mode 100644 index 1409233c924f6828c4d8467a57605318b810b5d0..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_ace.py +++ /dev/null @@ -1,63 +0,0 @@ -import torch -from typing_extensions import override - -import comfy.model_management -import node_helpers -from comfy_api.latest import ComfyExtension, io - - -class TextEncodeAceStepAudio(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="TextEncodeAceStepAudio", - category="conditioning", - inputs=[ - io.Clip.Input("clip"), - io.String.Input("tags", multiline=True, dynamic_prompts=True), - io.String.Input("lyrics", multiline=True, dynamic_prompts=True), - io.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01), - ], - outputs=[io.Conditioning.Output()], - ) - - @classmethod - def execute(cls, clip, tags, lyrics, lyrics_strength) -> io.NodeOutput: - tokens = clip.tokenize(tags, lyrics=lyrics) - conditioning = clip.encode_from_tokens_scheduled(tokens) - conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength}) - return io.NodeOutput(conditioning) - - -class EmptyAceStepLatentAudio(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="EmptyAceStepLatentAudio", - category="latent/audio", - inputs=[ - io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1), - io.Int.Input( - "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch." - ), - ], - outputs=[io.Latent.Output()], - ) - - @classmethod - def execute(cls, seconds, batch_size) -> io.NodeOutput: - length = int(seconds * 44100 / 512 / 8) - latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device()) - return io.NodeOutput({"samples": latent, "type": "audio"}) - - -class AceExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - TextEncodeAceStepAudio, - EmptyAceStepLatentAudio, - ] - -async def comfy_entrypoint() -> AceExtension: - return AceExtension() diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py deleted file mode 100644 index 5532ffe6a241344abd6d3c3b62cc0104c67316d3..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_advanced_samplers.py +++ /dev/null @@ -1,121 +0,0 @@ -import numpy as np -import torch -from tqdm.auto import trange -from typing_extensions import override - -import comfy.model_patcher -import comfy.samplers -import comfy.utils -from comfy.k_diffusion.sampling import to_d -from comfy_api.latest import ComfyExtension, io - - -@torch.no_grad() -def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None): - extra_args = {} if extra_args is None else extra_args - - if upscale_steps is None: - upscale_steps = max(len(sigmas) // 2 + 1, 2) - else: - upscale_steps += 1 - upscale_steps = min(upscale_steps, len(sigmas) + 1) - - upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:] - - orig_shape = x.size() - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - denoised = model(x, sigmas[i] * s_in, **extra_args) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - - x = denoised - if i < len(upscales): - x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled") - - if sigmas[i + 1] > 0: - x += sigmas[i + 1] * torch.randn_like(x) - return x - - -class SamplerLCMUpscale(io.ComfyNode): - UPSCALE_METHODS = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] - - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="SamplerLCMUpscale", - category="sampling/custom_sampling/samplers", - inputs=[ - io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01), - io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1), - io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS), - ], - outputs=[io.Sampler.Output()], - ) - - @classmethod - def execute(cls, scale_ratio, scale_steps, upscale_method) -> io.NodeOutput: - if scale_steps < 0: - scale_steps = None - sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) - return io.NodeOutput(sampler) - - -@torch.no_grad() -def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): - extra_args = {} if extra_args is None else extra_args - - temp = [0] - def post_cfg_function(args): - temp[0] = args["uncond_denoised"] - return args["denoised"] - - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - sigma_hat = sigmas[i] - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x - denoised + temp[0], sigmas[i], denoised) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - dt = sigmas[i + 1] - sigma_hat - x = x + d * dt - return x - - -class SamplerEulerCFGpp(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="SamplerEulerCFGpp", - display_name="SamplerEulerCFG++", - category="_for_testing", # "sampling/custom_sampling/samplers" - inputs=[ - io.Combo.Input("version", options=["regular", "alternative"]), - ], - outputs=[io.Sampler.Output()], - is_experimental=True, - ) - - @classmethod - def execute(cls, version) -> io.NodeOutput: - if version == "alternative": - sampler = comfy.samplers.KSAMPLER(sample_euler_pp) - else: - sampler = comfy.samplers.ksampler("euler_cfg_pp") - return io.NodeOutput(sampler) - - -class AdvancedSamplersExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - SamplerLCMUpscale, - SamplerEulerCFGpp, - ] - -async def comfy_entrypoint() -> AdvancedSamplersExtension: - return AdvancedSamplersExtension() diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py deleted file mode 100644 index 8d856d0e8592414df823af27d53d421af7753f27..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_align_your_steps.py +++ /dev/null @@ -1,53 +0,0 @@ -#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html -import numpy as np -import torch - -def loglinear_interp(t_steps, num_steps): - """ - Performs log-linear interpolation of a given array of decreasing numbers. - """ - xs = np.linspace(0, 1, len(t_steps)) - ys = np.log(t_steps[::-1]) - - new_xs = np.linspace(0, 1, num_steps) - new_ys = np.interp(new_xs, xs, ys) - - interped_ys = np.exp(new_ys)[::-1].copy() - return interped_ys - -NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], - "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], - "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} - -class AlignYourStepsScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model_type": (["SD1", "SDXL", "SVD"], ), - "steps": ("INT", {"default": 10, "min": 1, "max": 10000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, model_type, steps, denoise): - total_steps = steps - if denoise < 1.0: - if denoise <= 0.0: - return (torch.FloatTensor([]),) - total_steps = round(steps * denoise) - - sigmas = NOISE_LEVELS[model_type][:] - if (steps + 1) != len(sigmas): - sigmas = loglinear_interp(sigmas, steps + 1) - - sigmas = sigmas[-(total_steps + 1):] - sigmas[-1] = 0 - return (torch.FloatTensor(sigmas), ) - -NODE_CLASS_MAPPINGS = { - "AlignYourStepsScheduler": AlignYourStepsScheduler, -} diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py deleted file mode 100644 index f27ae7da8ce795d1148e12e43016edaefabcb151..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_apg.py +++ /dev/null @@ -1,106 +0,0 @@ -import torch -from typing_extensions import override - -from comfy_api.latest import ComfyExtension, io - - -def project(v0, v1): - v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3]) - v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1 - v0_orthogonal = v0 - v0_parallel - return v0_parallel, v0_orthogonal - -class APG(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="APG", - display_name="Adaptive Projected Guidance", - category="sampling/custom_sampling", - inputs=[ - io.Model.Input("model"), - io.Float.Input( - "eta", - default=1.0, - min=-10.0, - max=10.0, - step=0.01, - tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.", - ), - io.Float.Input( - "norm_threshold", - default=5.0, - min=0.0, - max=50.0, - step=0.1, - tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.", - ), - io.Float.Input( - "momentum", - default=0.0, - min=-5.0, - max=1.0, - step=0.01, - tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.", - ), - ], - outputs=[io.Model.Output()], - ) - - @classmethod - def execute(cls, model, eta, norm_threshold, momentum) -> io.NodeOutput: - running_avg = 0 - prev_sigma = None - - def pre_cfg_function(args): - nonlocal running_avg, prev_sigma - - if len(args["conds_out"]) == 1: return args["conds_out"] - - cond = args["conds_out"][0] - uncond = args["conds_out"][1] - sigma = args["sigma"][0] - cond_scale = args["cond_scale"] - - if prev_sigma is not None and sigma > prev_sigma: - running_avg = 0 - prev_sigma = sigma - - guidance = cond - uncond - - if momentum != 0: - if not torch.is_tensor(running_avg): - running_avg = guidance - else: - running_avg = momentum * running_avg + guidance - guidance = running_avg - - if norm_threshold > 0: - guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True) - scale = torch.minimum( - torch.ones_like(guidance_norm), - norm_threshold / guidance_norm - ) - guidance = guidance * scale - - guidance_parallel, guidance_orthogonal = project(guidance, cond) - modified_guidance = guidance_orthogonal + eta * guidance_parallel - - modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale - - return [modified_cond, uncond] + args["conds_out"][2:] - - m = model.clone() - m.set_model_sampler_pre_cfg_function(pre_cfg_function) - return io.NodeOutput(m) - - -class ApgExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - APG, - ] - -async def comfy_entrypoint() -> ApgExtension: - return ApgExtension() diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py deleted file mode 100644 index c0e494c2ad191f63f6e3e9ccf58eced0a203aed5..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_attention_multiply.py +++ /dev/null @@ -1,150 +0,0 @@ -from typing_extensions import override - -from comfy_api.latest import ComfyExtension, io - - -def attention_multiply(attn, model, q, k, v, out): - m = model.clone() - sd = model.model_state_dict() - - for key in sd: - if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)): - m.add_patches({key: (None,)}, 0.0, q) - if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)): - m.add_patches({key: (None,)}, 0.0, k) - if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)): - m.add_patches({key: (None,)}, 0.0, v) - if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)): - m.add_patches({key: (None,)}, 0.0, out) - - return m - - -class UNetSelfAttentionMultiply(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="UNetSelfAttentionMultiply", - category="_for_testing/attention_experiments", - inputs=[ - io.Model.Input("model"), - io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01), - ], - outputs=[io.Model.Output()], - is_experimental=True, - ) - - @classmethod - def execute(cls, model, q, k, v, out) -> io.NodeOutput: - m = attention_multiply("attn1", model, q, k, v, out) - return io.NodeOutput(m) - - -class UNetCrossAttentionMultiply(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="UNetCrossAttentionMultiply", - category="_for_testing/attention_experiments", - inputs=[ - io.Model.Input("model"), - io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01), - ], - outputs=[io.Model.Output()], - is_experimental=True, - ) - - @classmethod - def execute(cls, model, q, k, v, out) -> io.NodeOutput: - m = attention_multiply("attn2", model, q, k, v, out) - return io.NodeOutput(m) - - -class CLIPAttentionMultiply(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="CLIPAttentionMultiply", - category="_for_testing/attention_experiments", - inputs=[ - io.Clip.Input("clip"), - io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01), - ], - outputs=[io.Clip.Output()], - is_experimental=True, - ) - - @classmethod - def execute(cls, clip, q, k, v, out) -> io.NodeOutput: - m = clip.clone() - sd = m.patcher.model_state_dict() - - for key in sd: - if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"): - m.add_patches({key: (None,)}, 0.0, q) - if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"): - m.add_patches({key: (None,)}, 0.0, k) - if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"): - m.add_patches({key: (None,)}, 0.0, v) - if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"): - m.add_patches({key: (None,)}, 0.0, out) - return io.NodeOutput(m) - - -class UNetTemporalAttentionMultiply(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="UNetTemporalAttentionMultiply", - category="_for_testing/attention_experiments", - inputs=[ - io.Model.Input("model"), - io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01), - io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01), - ], - outputs=[io.Model.Output()], - is_experimental=True, - ) - - @classmethod - def execute(cls, model, self_structural, self_temporal, cross_structural, cross_temporal) -> io.NodeOutput: - m = model.clone() - sd = model.model_state_dict() - - for k in sd: - if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")): - if '.time_stack.' in k: - m.add_patches({k: (None,)}, 0.0, self_temporal) - else: - m.add_patches({k: (None,)}, 0.0, self_structural) - elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")): - if '.time_stack.' in k: - m.add_patches({k: (None,)}, 0.0, cross_temporal) - else: - m.add_patches({k: (None,)}, 0.0, cross_structural) - return io.NodeOutput(m) - - -class AttentionMultiplyExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - UNetSelfAttentionMultiply, - UNetCrossAttentionMultiply, - CLIPAttentionMultiply, - UNetTemporalAttentionMultiply, - ] - -async def comfy_entrypoint() -> AttentionMultiplyExtension: - return AttentionMultiplyExtension() diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py deleted file mode 100644 index 3b23f65d8aa30e7bdfe0fffa8adae08885f8127f..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_audio.py +++ /dev/null @@ -1,390 +0,0 @@ -from __future__ import annotations - -import av -import torchaudio -import torch -import comfy.model_management -import folder_paths -import os -import io -import json -import random -import hashlib -import node_helpers -from comfy.cli_args import args -from comfy.comfy_types import FileLocator - -class EmptyLatentAudio: - def __init__(self): - self.device = comfy.model_management.intermediate_device() - - @classmethod - def INPUT_TYPES(s): - return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/audio" - - def generate(self, seconds, batch_size): - length = round((seconds * 44100 / 2048) / 2) * 2 - latent = torch.zeros([batch_size, 64, length], device=self.device) - return ({"samples":latent, "type": "audio"}, ) - -class ConditioningStableAudio: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "seconds_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.1}), - "seconds_total": ("FLOAT", {"default": 47.0, "min": 0.0, "max": 1000.0, "step": 0.1}), - }} - - RETURN_TYPES = ("CONDITIONING","CONDITIONING") - RETURN_NAMES = ("positive", "negative") - - FUNCTION = "append" - - CATEGORY = "conditioning" - - def append(self, positive, negative, seconds_start, seconds_total): - positive = node_helpers.conditioning_set_values(positive, {"seconds_start": seconds_start, "seconds_total": seconds_total}) - negative = node_helpers.conditioning_set_values(negative, {"seconds_start": seconds_start, "seconds_total": seconds_total}) - return (positive, negative) - -class VAEEncodeAudio: - @classmethod - def INPUT_TYPES(s): - return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "latent/audio" - - def encode(self, vae, audio): - sample_rate = audio["sample_rate"] - if 44100 != sample_rate: - waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100) - else: - waveform = audio["waveform"] - - t = vae.encode(waveform.movedim(1, -1)) - return ({"samples":t}, ) - -class VAEDecodeAudio: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} - RETURN_TYPES = ("AUDIO",) - FUNCTION = "decode" - - CATEGORY = "latent/audio" - - def decode(self, vae, samples): - audio = vae.decode(samples["samples"]).movedim(-1, 1) - std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0 - std[std < 1.0] = 1.0 - audio /= std - return ({"waveform": audio, "sample_rate": 44100}, ) - - -def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"): - - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results: list[FileLocator] = [] - - # Prepare metadata dictionary - metadata = {} - if not args.disable_metadata: - if prompt is not None: - metadata["prompt"] = json.dumps(prompt) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) - - # Opus supported sample rates - OPUS_RATES = [8000, 12000, 16000, 24000, 48000] - - for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.{format}" - output_path = os.path.join(full_output_folder, file) - - # Use original sample rate initially - sample_rate = audio["sample_rate"] - - # Handle Opus sample rate requirements - if format == "opus": - if sample_rate > 48000: - sample_rate = 48000 - elif sample_rate not in OPUS_RATES: - # Find the next highest supported rate - for rate in sorted(OPUS_RATES): - if rate > sample_rate: - sample_rate = rate - break - if sample_rate not in OPUS_RATES: # Fallback if still not supported - sample_rate = 48000 - - # Resample if necessary - if sample_rate != audio["sample_rate"]: - waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate) - - # Create output with specified format - output_buffer = io.BytesIO() - output_container = av.open(output_buffer, mode='w', format=format) - - # Set metadata on the container - for key, value in metadata.items(): - output_container.metadata[key] = value - - # Set up the output stream with appropriate properties - if format == "opus": - out_stream = output_container.add_stream("libopus", rate=sample_rate) - if quality == "64k": - out_stream.bit_rate = 64000 - elif quality == "96k": - out_stream.bit_rate = 96000 - elif quality == "128k": - out_stream.bit_rate = 128000 - elif quality == "192k": - out_stream.bit_rate = 192000 - elif quality == "320k": - out_stream.bit_rate = 320000 - elif format == "mp3": - out_stream = output_container.add_stream("libmp3lame", rate=sample_rate) - if quality == "V0": - #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool - out_stream.codec_context.qscale = 1 - elif quality == "128k": - out_stream.bit_rate = 128000 - elif quality == "320k": - out_stream.bit_rate = 320000 - else: #format == "flac": - out_stream = output_container.add_stream("flac", rate=sample_rate) - - frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[0] == 1 else 'stereo') - frame.sample_rate = sample_rate - frame.pts = 0 - output_container.mux(out_stream.encode(frame)) - - # Flush encoder - output_container.mux(out_stream.encode(None)) - - # Close containers - output_container.close() - - # Write the output to file - output_buffer.seek(0) - with open(output_path, 'wb') as f: - f.write(output_buffer.getbuffer()) - - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - - return { "ui": { "audio": results } } - -class SaveAudio: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(s): - return {"required": { "audio": ("AUDIO", ), - "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_flac" - - OUTPUT_NODE = True - - CATEGORY = "audio" - - def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None): - return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo) - -class SaveAudioMP3: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(s): - return {"required": { "audio": ("AUDIO", ), - "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), - "quality": (["V0", "128k", "320k"], {"default": "V0"}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_mp3" - - OUTPUT_NODE = True - - CATEGORY = "audio" - - def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"): - return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) - -class SaveAudioOpus: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(s): - return {"required": { "audio": ("AUDIO", ), - "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), - "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_opus" - - OUTPUT_NODE = True - - CATEGORY = "audio" - - def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"): - return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) - -class PreviewAudio(SaveAudio): - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() - self.type = "temp" - self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) - - @classmethod - def INPUT_TYPES(s): - return {"required": - {"audio": ("AUDIO", ), }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - -def f32_pcm(wav: torch.Tensor) -> torch.Tensor: - """Convert audio to float 32 bits PCM format.""" - if wav.dtype.is_floating_point: - return wav - elif wav.dtype == torch.int16: - return wav.float() / (2 ** 15) - elif wav.dtype == torch.int32: - return wav.float() / (2 ** 31) - raise ValueError(f"Unsupported wav dtype: {wav.dtype}") - -def load(filepath: str) -> tuple[torch.Tensor, int]: - with av.open(filepath) as af: - if not af.streams.audio: - raise ValueError("No audio stream found in the file.") - - stream = af.streams.audio[0] - sr = stream.codec_context.sample_rate - n_channels = stream.channels - - frames = [] - length = 0 - for frame in af.decode(streams=stream.index): - buf = torch.from_numpy(frame.to_ndarray()) - if buf.shape[0] != n_channels: - buf = buf.view(-1, n_channels).t() - - frames.append(buf) - length += buf.shape[1] - - if not frames: - raise ValueError("No audio frames decoded.") - - wav = torch.cat(frames, dim=1) - wav = f32_pcm(wav) - return wav, sr - -class LoadAudio: - @classmethod - def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() - files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"]) - return {"required": {"audio": (sorted(files), {"audio_upload": True})}} - - CATEGORY = "audio" - - RETURN_TYPES = ("AUDIO", ) - FUNCTION = "load" - - def load(self, audio): - audio_path = folder_paths.get_annotated_filepath(audio) - waveform, sample_rate = load(audio_path) - audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} - return (audio, ) - - @classmethod - def IS_CHANGED(s, audio): - image_path = folder_paths.get_annotated_filepath(audio) - m = hashlib.sha256() - with open(image_path, 'rb') as f: - m.update(f.read()) - return m.digest().hex() - - @classmethod - def VALIDATE_INPUTS(s, audio): - if not folder_paths.exists_annotated_filepath(audio): - return "Invalid audio file: {}".format(audio) - return True - -class RecordAudio: - @classmethod - def INPUT_TYPES(s): - return {"required": {"audio": ("AUDIO_RECORD", {})}} - - CATEGORY = "audio" - - RETURN_TYPES = ("AUDIO", ) - FUNCTION = "load" - - def load(self, audio): - audio_path = folder_paths.get_annotated_filepath(audio) - - waveform, sample_rate = torchaudio.load(audio_path) - audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} - return (audio, ) - - -NODE_CLASS_MAPPINGS = { - "EmptyLatentAudio": EmptyLatentAudio, - "VAEEncodeAudio": VAEEncodeAudio, - "VAEDecodeAudio": VAEDecodeAudio, - "SaveAudio": SaveAudio, - "SaveAudioMP3": SaveAudioMP3, - "SaveAudioOpus": SaveAudioOpus, - "LoadAudio": LoadAudio, - "PreviewAudio": PreviewAudio, - "ConditioningStableAudio": ConditioningStableAudio, - "RecordAudio": RecordAudio, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "EmptyLatentAudio": "Empty Latent Audio", - "VAEEncodeAudio": "VAE Encode Audio", - "VAEDecodeAudio": "VAE Decode Audio", - "PreviewAudio": "Preview Audio", - "LoadAudio": "Load Audio", - "SaveAudio": "Save Audio (FLAC)", - "SaveAudioMP3": "Save Audio (MP3)", - "SaveAudioOpus": "Save Audio (Opus)", - "RecordAudio": "Record Audio", -} diff --git a/comfy_extras/nodes_audio_encoder.py b/comfy_extras/nodes_audio_encoder.py deleted file mode 100644 index 39a140fef1820c3a4c67ddbe7df4db092748ca15..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_audio_encoder.py +++ /dev/null @@ -1,44 +0,0 @@ -import folder_paths -import comfy.audio_encoders.audio_encoders -import comfy.utils - - -class AudioEncoderLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "audio_encoder_name": (folder_paths.get_filename_list("audio_encoders"), ), - }} - RETURN_TYPES = ("AUDIO_ENCODER",) - FUNCTION = "load_model" - - CATEGORY = "loaders" - - def load_model(self, audio_encoder_name): - audio_encoder_name = folder_paths.get_full_path_or_raise("audio_encoders", audio_encoder_name) - sd = comfy.utils.load_torch_file(audio_encoder_name, safe_load=True) - audio_encoder = comfy.audio_encoders.audio_encoders.load_audio_encoder_from_sd(sd) - if audio_encoder is None: - raise RuntimeError("ERROR: audio encoder file is invalid and does not contain a valid model.") - return (audio_encoder,) - - -class AudioEncoderEncode: - @classmethod - def INPUT_TYPES(s): - return {"required": { "audio_encoder": ("AUDIO_ENCODER",), - "audio": ("AUDIO",), - }} - RETURN_TYPES = ("AUDIO_ENCODER_OUTPUT",) - FUNCTION = "encode" - - CATEGORY = "conditioning" - - def encode(self, audio_encoder, audio): - output = audio_encoder.encode_audio(audio["waveform"], audio["sample_rate"]) - return (output,) - - -NODE_CLASS_MAPPINGS = { - "AudioEncoderLoader": AudioEncoderLoader, - "AudioEncoderEncode": AudioEncoderEncode, -} diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py deleted file mode 100644 index 5e0e39f914d8ad23eca3b04a433c35decf527546..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_camera_trajectory.py +++ /dev/null @@ -1,218 +0,0 @@ -import nodes -import torch -import numpy as np -from einops import rearrange -import comfy.model_management - - - -MAX_RESOLUTION = nodes.MAX_RESOLUTION - -CAMERA_DICT = { - "base_T_norm": 1.5, - "base_angle": np.pi/3, - "Static": { "angle":[0., 0., 0.], "T":[0., 0., 0.]}, - "Pan Up": { "angle":[0., 0., 0.], "T":[0., -1., 0.]}, - "Pan Down": { "angle":[0., 0., 0.], "T":[0.,1.,0.]}, - "Pan Left": { "angle":[0., 0., 0.], "T":[-1.,0.,0.]}, - "Pan Right": { "angle":[0., 0., 0.], "T": [1.,0.,0.]}, - "Zoom In": { "angle":[0., 0., 0.], "T": [0.,0.,2.]}, - "Zoom Out": { "angle":[0., 0., 0.], "T": [0.,0.,-2.]}, - "Anti Clockwise (ACW)": { "angle": [0., 0., -1.], "T":[0., 0., 0.]}, - "ClockWise (CW)": { "angle": [0., 0., 1.], "T":[0., 0., 0.]}, -} - - -def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'): - - def get_relative_pose(cam_params): - """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py - """ - abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] - abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] - cam_to_origin = 0 - target_cam_c2w = np.array([ - [1, 0, 0, 0], - [0, 1, 0, -cam_to_origin], - [0, 0, 1, 0], - [0, 0, 0, 1] - ]) - abs2rel = target_cam_c2w @ abs_w2cs[0] - ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] - ret_poses = np.array(ret_poses, dtype=np.float32) - return ret_poses - - """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py - """ - cam_params = [Camera(cam_param) for cam_param in cam_params] - - sample_wh_ratio = width / height - pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed - - if pose_wh_ratio > sample_wh_ratio: - resized_ori_w = height * pose_wh_ratio - for cam_param in cam_params: - cam_param.fx = resized_ori_w * cam_param.fx / width - else: - resized_ori_h = width / pose_wh_ratio - for cam_param in cam_params: - cam_param.fy = resized_ori_h * cam_param.fy / height - - intrinsic = np.asarray([[cam_param.fx * width, - cam_param.fy * height, - cam_param.cx * width, - cam_param.cy * height] - for cam_param in cam_params], dtype=np.float32) - - K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] - c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere - c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] - plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W - plucker_embedding = plucker_embedding[None] - plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] - return plucker_embedding - -class Camera(object): - """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py - """ - def __init__(self, entry): - fx, fy, cx, cy = entry[1:5] - self.fx = fx - self.fy = fy - self.cx = cx - self.cy = cy - c2w_mat = np.array(entry[7:]).reshape(4, 4) - self.c2w_mat = c2w_mat - self.w2c_mat = np.linalg.inv(c2w_mat) - -def ray_condition(K, c2w, H, W, device): - """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py - """ - # c2w: B, V, 4, 4 - # K: B, V, 4 - - B = K.shape[0] - - j, i = torch.meshgrid( - torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), - torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), - indexing='ij' - ) - i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] - j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] - - fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 - - zs = torch.ones_like(i) # [B, HxW] - xs = (i - cx) / fx * zs - ys = (j - cy) / fy * zs - zs = zs.expand_as(ys) - - directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 - directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 - - rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW - rays_o = c2w[..., :3, 3] # B, V, 3 - rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW - # c2w @ dirctions - rays_dxo = torch.cross(rays_o, rays_d) - plucker = torch.cat([rays_dxo, rays_d], dim=-1) - plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 - # plucker = plucker.permute(0, 1, 4, 2, 3) - return plucker - -def get_camera_motion(angle, T, speed, n=81): - def compute_R_form_rad_angle(angles): - theta_x, theta_y, theta_z = angles - Rx = np.array([[1, 0, 0], - [0, np.cos(theta_x), -np.sin(theta_x)], - [0, np.sin(theta_x), np.cos(theta_x)]]) - - Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], - [0, 1, 0], - [-np.sin(theta_y), 0, np.cos(theta_y)]]) - - Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], - [np.sin(theta_z), np.cos(theta_z), 0], - [0, 0, 1]]) - - R = np.dot(Rz, np.dot(Ry, Rx)) - return R - RT = [] - for i in range(n): - _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle - R = compute_R_form_rad_angle(_angle) - _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1)) - _RT = np.concatenate([R,_T], axis=1) - RT.append(_RT) - RT = np.stack(RT) - return RT - -class WanCameraEmbedding: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}), - "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}), - }, - "optional":{ - "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}), - "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), - "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), - "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), - "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), - } - - } - - RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT") - RETURN_NAMES = ("camera_embedding","width","height","length") - FUNCTION = "run" - CATEGORY = "camera" - - def run(self, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5): - """ - Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021) - Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py - """ - motion_list = [camera_pose] - speed = speed - angle = np.array(CAMERA_DICT[motion_list[0]]["angle"]) - T = np.array(CAMERA_DICT[motion_list[0]]["T"]) - RT = get_camera_motion(angle, T, speed, length) - - trajs=[] - for cp in RT.tolist(): - traj=[fx,fy,cx,cy,0,0] - traj.extend(cp[0]) - traj.extend(cp[1]) - traj.extend(cp[2]) - traj.extend([0,0,0,1]) - trajs.append(traj) - - cam_params = np.array([[float(x) for x in pose] for pose in trajs]) - cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1) - control_camera_video = process_pose_params(cam_params, width=width, height=height) - control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device()) - - control_camera_video = torch.concat( - [ - torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), - control_camera_video[:, :, 1:] - ], dim=2 - ).transpose(1, 2) - - # Reshape, transpose, and view into desired shape - b, f, c, h, w = control_camera_video.shape - control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) - control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) - - return (control_camera_video, width, height, length) - - -NODE_CLASS_MAPPINGS = { - "WanCameraEmbedding": WanCameraEmbedding, -} diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py deleted file mode 100644 index d85e6b85691dcdcc55e31705039934846d466fc1..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_canny.py +++ /dev/null @@ -1,25 +0,0 @@ -from kornia.filters import canny -import comfy.model_management - - -class Canny: - @classmethod - def INPUT_TYPES(s): - return {"required": {"image": ("IMAGE",), - "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), - "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "detect_edge" - - CATEGORY = "image/preprocessors" - - def detect_edge(self, image, low_threshold, high_threshold): - output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) - img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) - return (img_out,) - -NODE_CLASS_MAPPINGS = { - "Canny": Canny, -} diff --git a/comfy_extras/nodes_cfg.py b/comfy_extras/nodes_cfg.py deleted file mode 100644 index 5abdc115ab4f9e82ca43151ec8826d234a16b172..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_cfg.py +++ /dev/null @@ -1,72 +0,0 @@ -import torch - -# https://github.com/WeichenFan/CFG-Zero-star -def optimized_scale(positive, negative): - positive_flat = positive.reshape(positive.shape[0], -1) - negative_flat = negative.reshape(negative.shape[0], -1) - - # Calculate dot production - dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) - - # Squared norm of uncondition - squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8 - - # st_star = v_cond^T * v_uncond / ||v_uncond||^2 - st_star = dot_product / squared_norm - - return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1)) - -class CFGZeroStar: - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL",), - }} - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - CATEGORY = "advanced/guidance" - - def patch(self, model): - m = model.clone() - def cfg_zero_star(args): - guidance_scale = args['cond_scale'] - x = args['input'] - cond_p = args['cond_denoised'] - uncond_p = args['uncond_denoised'] - out = args["denoised"] - alpha = optimized_scale(x - cond_p, x - uncond_p) - - return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha) - m.set_model_sampler_post_cfg_function(cfg_zero_star) - return (m, ) - -class CFGNorm: - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL",), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - CATEGORY = "advanced/guidance" - EXPERIMENTAL = True - - def patch(self, model, strength): - m = model.clone() - def cfg_norm(args): - cond_p = args['cond_denoised'] - pred_text_ = args["denoised"] - - norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True) - norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True) - scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0) - return pred_text_ * scale * strength - - m.set_model_sampler_post_cfg_function(cfg_norm) - return (m, ) - -NODE_CLASS_MAPPINGS = { - "CFGZeroStar": CFGZeroStar, - "CFGNorm": CFGNorm, -} diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py deleted file mode 100644 index 14269caf352edee45d9f1ca49db3effd5b60b7fb..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_clip_sdxl.py +++ /dev/null @@ -1,54 +0,0 @@ -from nodes import MAX_RESOLUTION - -class CLIPTextEncodeSDXLRefiner: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), - "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, ascore, width, height, text): - tokens = clip.tokenize(text) - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}), ) - -class CLIPTextEncodeSDXL: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), - "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), - "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): - tokens = clip.tokenize(text_g) - tokens["l"] = clip.tokenize(text_l)["l"] - if len(tokens["l"]) != len(tokens["g"]): - empty = clip.tokenize("") - while len(tokens["l"]) < len(tokens["g"]): - tokens["l"] += empty["l"] - while len(tokens["l"]) > len(tokens["g"]): - tokens["g"] += empty["g"] - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}), ) - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, - "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, -} diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py deleted file mode 100644 index 2f994fa11d370d31dc5412da90d0582fe6bd6159..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_compositing.py +++ /dev/null @@ -1,214 +0,0 @@ -import torch -import comfy.utils -from enum import Enum - -def resize_mask(mask, shape): - return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) - -class PorterDuffMode(Enum): - ADD = 0 - CLEAR = 1 - DARKEN = 2 - DST = 3 - DST_ATOP = 4 - DST_IN = 5 - DST_OUT = 6 - DST_OVER = 7 - LIGHTEN = 8 - MULTIPLY = 9 - OVERLAY = 10 - SCREEN = 11 - SRC = 12 - SRC_ATOP = 13 - SRC_IN = 14 - SRC_OUT = 15 - SRC_OVER = 16 - XOR = 17 - - -def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): - # convert mask to alpha - src_alpha = 1 - src_alpha - dst_alpha = 1 - dst_alpha - # premultiply alpha - src_image = src_image * src_alpha - dst_image = dst_image * dst_alpha - - # composite ops below assume alpha-premultiplied images - if mode == PorterDuffMode.ADD: - out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) - out_image = torch.clamp(src_image + dst_image, 0, 1) - elif mode == PorterDuffMode.CLEAR: - out_alpha = torch.zeros_like(dst_alpha) - out_image = torch.zeros_like(dst_image) - elif mode == PorterDuffMode.DARKEN: - out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha - out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) - elif mode == PorterDuffMode.DST: - out_alpha = dst_alpha - out_image = dst_image - elif mode == PorterDuffMode.DST_ATOP: - out_alpha = src_alpha - out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image - elif mode == PorterDuffMode.DST_IN: - out_alpha = src_alpha * dst_alpha - out_image = dst_image * src_alpha - elif mode == PorterDuffMode.DST_OUT: - out_alpha = (1 - src_alpha) * dst_alpha - out_image = (1 - src_alpha) * dst_image - elif mode == PorterDuffMode.DST_OVER: - out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha - out_image = dst_image + (1 - dst_alpha) * src_image - elif mode == PorterDuffMode.LIGHTEN: - out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha - out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image) - elif mode == PorterDuffMode.MULTIPLY: - out_alpha = src_alpha * dst_alpha - out_image = src_image * dst_image - elif mode == PorterDuffMode.OVERLAY: - out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha - out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image, - src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image)) - elif mode == PorterDuffMode.SCREEN: - out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha - out_image = src_image + dst_image - src_image * dst_image - elif mode == PorterDuffMode.SRC: - out_alpha = src_alpha - out_image = src_image - elif mode == PorterDuffMode.SRC_ATOP: - out_alpha = dst_alpha - out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image - elif mode == PorterDuffMode.SRC_IN: - out_alpha = src_alpha * dst_alpha - out_image = src_image * dst_alpha - elif mode == PorterDuffMode.SRC_OUT: - out_alpha = (1 - dst_alpha) * src_alpha - out_image = (1 - dst_alpha) * src_image - elif mode == PorterDuffMode.SRC_OVER: - out_alpha = src_alpha + (1 - src_alpha) * dst_alpha - out_image = src_image + (1 - src_alpha) * dst_image - elif mode == PorterDuffMode.XOR: - out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha - out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image - else: - return None, None - - # back to non-premultiplied alpha - out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image)) - out_image = torch.clamp(out_image, 0, 1) - # convert alpha to mask - out_alpha = 1 - out_alpha - return out_image, out_alpha - - -class PorterDuffImageComposite: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "source": ("IMAGE",), - "source_alpha": ("MASK",), - "destination": ("IMAGE",), - "destination_alpha": ("MASK",), - "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), - }, - } - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "composite" - CATEGORY = "mask/compositing" - - def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): - batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) - out_images = [] - out_alphas = [] - - for i in range(batch_size): - src_image = source[i] - dst_image = destination[i] - - assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels - - src_alpha = source_alpha[i].unsqueeze(2) - dst_alpha = destination_alpha[i].unsqueeze(2) - - if dst_alpha.shape[:2] != dst_image.shape[:2]: - upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) - upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') - dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) - if src_image.shape != dst_image.shape: - upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) - upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') - src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) - if src_alpha.shape != dst_alpha.shape: - upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) - upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') - src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) - - out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) - - out_images.append(out_image) - out_alphas.append(out_alpha.squeeze(2)) - - result = (torch.stack(out_images), torch.stack(out_alphas)) - return result - - -class SplitImageWithAlpha: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - } - } - - CATEGORY = "mask/compositing" - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "split_image_with_alpha" - - def split_image_with_alpha(self, image: torch.Tensor): - out_images = [i[:,:,:3] for i in image] - out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] - result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) - return result - - -class JoinImageWithAlpha: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "alpha": ("MASK",), - } - } - - CATEGORY = "mask/compositing" - RETURN_TYPES = ("IMAGE",) - FUNCTION = "join_image_with_alpha" - - def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): - batch_size = min(len(image), len(alpha)) - out_images = [] - - alpha = 1.0 - resize_mask(alpha, image.shape[1:]) - for i in range(batch_size): - out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) - - result = (torch.stack(out_images),) - return result - - -NODE_CLASS_MAPPINGS = { - "PorterDuffImageComposite": PorterDuffImageComposite, - "SplitImageWithAlpha": SplitImageWithAlpha, - "JoinImageWithAlpha": JoinImageWithAlpha, -} - - -NODE_DISPLAY_NAME_MAPPINGS = { - "PorterDuffImageComposite": "Porter-Duff Image Composite", - "SplitImageWithAlpha": "Split Image with Alpha", - "JoinImageWithAlpha": "Join Image with Alpha", -} diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py deleted file mode 100644 index 58c16f621cd1c5f5abd66dba639ebf5740862789..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_cond.py +++ /dev/null @@ -1,49 +0,0 @@ - - -class CLIPTextEncodeControlnet: - @classmethod - def INPUT_TYPES(s): - return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "_for_testing/conditioning" - - def encode(self, clip, conditioning, text): - tokens = clip.tokenize(text) - cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) - c = [] - for t in conditioning: - n = [t[0], t[1].copy()] - n[1]['cross_attn_controlnet'] = cond - n[1]['pooled_output_controlnet'] = pooled - c.append(n) - return (c, ) - -class T5TokenizerOptions: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "clip": ("CLIP", ), - "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), - "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), - } - } - - CATEGORY = "_for_testing/conditioning" - RETURN_TYPES = ("CLIP",) - FUNCTION = "set_options" - - def set_options(self, clip, min_padding, min_length): - clip = clip.clone() - for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]: - clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding) - clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length) - - return (clip, ) - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet, - "T5TokenizerOptions": T5TokenizerOptions, -} diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py deleted file mode 100644 index 1c3d9e697d6b3a0352e72403bd657d154b8421b4..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_context_windows.py +++ /dev/null @@ -1,89 +0,0 @@ -from __future__ import annotations -from comfy_api.latest import ComfyExtension, io -import comfy.context_windows -import nodes - - -class ContextWindowsManualNode(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="ContextWindowsManual", - display_name="Context Windows (Manual)", - category="context", - description="Manually set context windows.", - inputs=[ - io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), - io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."), - io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."), - io.Combo.Input("context_schedule", options=[ - comfy.context_windows.ContextSchedules.STATIC_STANDARD, - comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, - comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, - comfy.context_windows.ContextSchedules.BATCHED, - ], tooltip="The stride of the context window."), - io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."), - io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."), - io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), - io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."), - ], - outputs=[ - io.Model.Output(tooltip="The model with context windows applied during sampling."), - ], - is_experimental=True, - ) - - @classmethod - def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int) -> io.Model: - model = model.clone() - model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler( - context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule), - fuse_method=comfy.context_windows.get_matching_fuse_method(fuse_method), - context_length=context_length, - context_overlap=context_overlap, - context_stride=context_stride, - closed_loop=closed_loop, - dim=dim) - # make memory usage calculation only take into account the context window latents - comfy.context_windows.create_prepare_sampling_wrapper(model) - return io.NodeOutput(model) - -class WanContextWindowsManualNode(ContextWindowsManualNode): - @classmethod - def define_schema(cls) -> io.Schema: - schema = super().define_schema() - schema.node_id = "WanContextWindowsManual" - schema.display_name = "WAN Context Windows (Manual)" - schema.description = "Manually set context windows for WAN-like models (dim=2)." - schema.inputs = [ - io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), - io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window."), - io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window."), - io.Combo.Input("context_schedule", options=[ - comfy.context_windows.ContextSchedules.STATIC_STANDARD, - comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, - comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, - comfy.context_windows.ContextSchedules.BATCHED, - ], tooltip="The stride of the context window."), - io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."), - io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."), - io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), - ] - return schema - - @classmethod - def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str) -> io.Model: - context_length = max(((context_length - 1) // 4) + 1, 1) # at least length 1 - context_overlap = max(((context_overlap - 1) // 4) + 1, 0) # at least overlap 0 - return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2) - - -class ContextWindowsExtension(ComfyExtension): - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - ContextWindowsManualNode, - WanContextWindowsManualNode, - ] - -def comfy_entrypoint(): - return ContextWindowsExtension() diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py deleted file mode 100644 index 2d20e1fed7c26c8115f4b9878b0b54911e7a2e7f..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_controlnet.py +++ /dev/null @@ -1,60 +0,0 @@ -from comfy.cldm.control_types import UNION_CONTROLNET_TYPES -import nodes -import comfy.utils - -class SetUnionControlNetType: - @classmethod - def INPUT_TYPES(s): - return {"required": {"control_net": ("CONTROL_NET", ), - "type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),) - }} - - CATEGORY = "conditioning/controlnet" - RETURN_TYPES = ("CONTROL_NET",) - - FUNCTION = "set_controlnet_type" - - def set_controlnet_type(self, control_net, type): - control_net = control_net.copy() - type_number = UNION_CONTROLNET_TYPES.get(type, -1) - if type_number >= 0: - control_net.set_extra_arg("control_type", [type_number]) - else: - control_net.set_extra_arg("control_type", []) - - return (control_net,) - -class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced): - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "control_net": ("CONTROL_NET", ), - "vae": ("VAE", ), - "image": ("IMAGE", ), - "mask": ("MASK", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }} - - FUNCTION = "apply_inpaint_controlnet" - - CATEGORY = "conditioning/controlnet" - - def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent): - extra_concat = [] - if control_net.concat_mask: - mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) - mask_apply = comfy.utils.common_upscale(mask, image.shape[2], image.shape[1], "bilinear", "center").round() - image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3]) - extra_concat = [mask] - - return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat) - - - -NODE_CLASS_MAPPINGS = { - "SetUnionControlNetType": SetUnionControlNetType, - "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply, -} diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py deleted file mode 100644 index 4f49605510e4986f5d030d7bbde14fbf2f7a9c2d..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_cosmos.py +++ /dev/null @@ -1,128 +0,0 @@ -import nodes -import torch -import comfy.model_management -import comfy.utils -import comfy.latent_formats - - -class EmptyCosmosLatentVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/video" - - def generate(self, width, height, length, batch_size=1): - latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - return ({"samples": latent}, ) - - -def vae_encode_with_padding(vae, image, width, height, length, padding=0): - pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - pixel_len = min(pixels.shape[0], length) - padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7) - padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5 - padded_pixels[:pixel_len] = pixels[:pixel_len] - latent_len = ((pixel_len - 1) // 8) + 1 - latent_temp = vae.encode(padded_pixels) - return latent_temp[:, :, :latent_len] - - -class CosmosImageToVideoLatent: - @classmethod - def INPUT_TYPES(s): - return {"required": {"vae": ("VAE", ), - "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }, - "optional": {"start_image": ("IMAGE", ), - "end_image": ("IMAGE", ), - }} - - - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "conditioning/inpaint" - - def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None): - latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - if start_image is None and end_image is None: - out_latent = {} - out_latent["samples"] = latent - return (out_latent,) - - mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) - - if start_image is not None: - latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1) - latent[:, :, :latent_temp.shape[-3]] = latent_temp - mask[:, :, :latent_temp.shape[-3]] *= 0.0 - - if end_image is not None: - latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0) - latent[:, :, -latent_temp.shape[-3]:] = latent_temp - mask[:, :, -latent_temp.shape[-3]:] *= 0.0 - - out_latent = {} - out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) - out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) - return (out_latent,) - -class CosmosPredict2ImageToVideoLatent: - @classmethod - def INPUT_TYPES(s): - return {"required": {"vae": ("VAE", ), - "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 93, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }, - "optional": {"start_image": ("IMAGE", ), - "end_image": ("IMAGE", ), - }} - - - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "conditioning/inpaint" - - def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None): - latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - if start_image is None and end_image is None: - out_latent = {} - out_latent["samples"] = latent - return (out_latent,) - - mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) - - if start_image is not None: - latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1) - latent[:, :, :latent_temp.shape[-3]] = latent_temp - mask[:, :, :latent_temp.shape[-3]] *= 0.0 - - if end_image is not None: - latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0) - latent[:, :, -latent_temp.shape[-3]:] = latent_temp - mask[:, :, -latent_temp.shape[-3]:] *= 0.0 - - out_latent = {} - latent_format = comfy.latent_formats.Wan21() - latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) - out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) - out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) - return (out_latent,) - -NODE_CLASS_MAPPINGS = { - "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo, - "CosmosImageToVideoLatent": CosmosImageToVideoLatent, - "CosmosPredict2ImageToVideoLatent": CosmosPredict2ImageToVideoLatent, -} diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py deleted file mode 100644 index d011f433b5db84d665dc20349b9b9aa75a703df9..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_custom_sampler.py +++ /dev/null @@ -1,932 +0,0 @@ -import math -import comfy.samplers -import comfy.sample -from comfy.k_diffusion import sampling as k_diffusion_sampling -from comfy.k_diffusion import sa_solver -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict -import latent_preview -import torch -import comfy.utils -import node_helpers - - -class BasicScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "scheduler": (comfy.samplers.SCHEDULER_NAMES, ), - "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, model, scheduler, steps, denoise): - total_steps = steps - if denoise < 1.0: - if denoise <= 0.0: - return (torch.FloatTensor([]),) - total_steps = int(steps/denoise) - - sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu() - sigmas = sigmas[-(steps + 1):] - return (sigmas, ) - - -class KarrasScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min, rho): - sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) - return (sigmas, ) - -class ExponentialScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min): - sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) - return (sigmas, ) - -class PolyexponentialScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min, rho): - sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) - return (sigmas, ) - -class LaplaceScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}), - "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta): - sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta) - return (sigmas, ) - - -class SDTurboScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "steps": ("INT", {"default": 1, "min": 1, "max": 10}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, model, steps, denoise): - start_step = 10 - int(10 * denoise) - timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] - sigmas = model.get_model_object("model_sampling").sigma(timesteps) - sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) - return (sigmas, ) - -class BetaSamplingScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), - "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, model, steps, alpha, beta): - sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta) - return (sigmas, ) - -class VPScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values - "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), - "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, beta_d, beta_min, eps_s): - sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) - return (sigmas, ) - -class SplitSigmas: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "step": ("INT", {"default": 0, "min": 0, "max": 10000}), - } - } - RETURN_TYPES = ("SIGMAS","SIGMAS") - RETURN_NAMES = ("high_sigmas", "low_sigmas") - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, sigmas, step): - sigmas1 = sigmas[:step + 1] - sigmas2 = sigmas[step:] - return (sigmas1, sigmas2) - -class SplitSigmasDenoise: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS","SIGMAS") - RETURN_NAMES = ("high_sigmas", "low_sigmas") - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, sigmas, denoise): - steps = max(sigmas.shape[-1] - 1, 0) - total_steps = round(steps * denoise) - sigmas1 = sigmas[:-(total_steps)] - sigmas2 = sigmas[-(total_steps + 1):] - return (sigmas1, sigmas2) - -class FlipSigmas: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, sigmas): - if len(sigmas) == 0: - return (sigmas,) - - sigmas = sigmas.flip(0) - if sigmas[0] == 0: - sigmas[0] = 0.0001 - return (sigmas,) - -class SetFirstSigma: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "set_first_sigma" - - def set_first_sigma(self, sigmas, sigma): - sigmas = sigmas.clone() - sigmas[0] = sigma - return (sigmas, ) - -class ExtendIntermediateSigmas: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "steps": ("INT", {"default": 2, "min": 1, "max": 100}), - "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}), - "end_at_sigma": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 20000.0, "step": 0.01, "round": False}), - "spacing": (['linear', 'cosine', 'sine'],), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "extend" - - def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str): - if start_at_sigma < 0: - start_at_sigma = float("inf") - - interpolator = { - 'linear': lambda x: x, - 'cosine': lambda x: torch.sin(x*math.pi/2), - 'sine': lambda x: 1 - torch.cos(x*math.pi/2) - }[spacing] - - # linear space for our interpolation function - x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1] - computed_spacing = interpolator(x) - - extended_sigmas = [] - for i in range(len(sigmas) - 1): - sigma_current = sigmas[i] - sigma_next = sigmas[i+1] - - extended_sigmas.append(sigma_current) - - if end_at_sigma <= sigma_current <= start_at_sigma: - interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current - extended_sigmas.extend(interpolated_steps.tolist()) - - # Add the last sigma value - if len(sigmas) > 0: - extended_sigmas.append(sigmas[-1]) - - extended_sigmas = torch.FloatTensor(extended_sigmas) - - return (extended_sigmas,) - - -class SamplingPercentToSigma: - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "model": (IO.MODEL, {}), - "sampling_percent": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001}), - "return_actual_sigma": (IO.BOOLEAN, {"default": False, "tooltip": "Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."}), - } - } - - RETURN_TYPES = (IO.FLOAT,) - RETURN_NAMES = ("sigma_value",) - CATEGORY = "sampling/custom_sampling/sigmas" - - FUNCTION = "get_sigma" - - def get_sigma(self, model, sampling_percent, return_actual_sigma): - model_sampling = model.get_model_object("model_sampling") - sigma_val = model_sampling.percent_to_sigma(sampling_percent) - if return_actual_sigma: - if sampling_percent == 0.0: - sigma_val = model_sampling.sigma_max.item() - elif sampling_percent == 1.0: - sigma_val = model_sampling.sigma_min.item() - return (sigma_val,) - - -class KSamplerSelect: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, sampler_name): - sampler = comfy.samplers.sampler_object(sampler_name) - return (sampler, ) - -class SamplerDPMPP_3M_SDE: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "noise_device": (['gpu', 'cpu'], ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise, noise_device): - if noise_device == 'cpu': - sampler_name = "dpmpp_3m_sde" - else: - sampler_name = "dpmpp_3m_sde_gpu" - sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise}) - return (sampler, ) - -class SamplerDPMPP_2M_SDE: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"solver_type": (['midpoint', 'heun'], ), - "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "noise_device": (['gpu', 'cpu'], ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, solver_type, eta, s_noise, noise_device): - if noise_device == 'cpu': - sampler_name = "dpmpp_2m_sde" - else: - sampler_name = "dpmpp_2m_sde_gpu" - sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) - return (sampler, ) - - -class SamplerDPMPP_SDE: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "noise_device": (['gpu', 'cpu'], ), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise, r, noise_device): - if noise_device == 'cpu': - sampler_name = "dpmpp_sde" - else: - sampler_name = "dpmpp_sde_gpu" - sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) - return (sampler, ) - -class SamplerDPMPP_2S_Ancestral: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise): - sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise}) - return (sampler, ) - -class SamplerEulerAncestral: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise): - sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise}) - return (sampler, ) - -class SamplerEulerAncestralCFGPP: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}), - }} - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, eta, s_noise): - sampler = comfy.samplers.ksampler( - "euler_ancestral_cfg_pp", - {"eta": eta, "s_noise": s_noise}) - return (sampler, ) - -class SamplerLMS: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"order": ("INT", {"default": 4, "min": 1, "max": 100}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, order): - sampler = comfy.samplers.ksampler("lms", {"order": order}) - return (sampler, ) - -class SamplerDPMAdaptative: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"order": ("INT", {"default": 3, "min": 2, "max": 3}), - "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise): - sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff, - "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta, - "s_noise":s_noise }) - return (sampler, ) - - -class SamplerER_SDE(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "solver_type": (IO.COMBO, {"options": ["ER-SDE", "Reverse-time SDE", "ODE"]}), - "max_stage": (IO.INT, {"default": 3, "min": 1, "max": 3}), - "eta": ( - IO.FLOAT, - {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False, "tooltip": "Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."}, - ), - "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False}), - } - } - - RETURN_TYPES = (IO.SAMPLER,) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, solver_type, max_stage, eta, s_noise): - if solver_type == "ODE" or (solver_type == "Reverse-time SDE" and eta == 0): - eta = 0 - s_noise = 0 - - def reverse_time_sde_noise_scaler(x): - return x ** (eta + 1) - - if solver_type == "ER-SDE": - # Use the default one in sample_er_sde() - noise_scaler = None - else: - noise_scaler = reverse_time_sde_noise_scaler - - sampler_name = "er_sde" - sampler = comfy.samplers.ksampler(sampler_name, {"s_noise": s_noise, "noise_scaler": noise_scaler, "max_stage": max_stage}) - return (sampler,) - - -class SamplerSASolver(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "model": (IO.MODEL, {}), - "eta": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False},), - "sde_start_percent": (IO.FLOAT, {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001},), - "sde_end_percent": (IO.FLOAT, {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.001},), - "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False},), - "predictor_order": (IO.INT, {"default": 3, "min": 1, "max": 6}), - "corrector_order": (IO.INT, {"default": 4, "min": 0, "max": 6}), - "use_pece": (IO.BOOLEAN, {}), - "simple_order_2": (IO.BOOLEAN, {}), - } - } - - RETURN_TYPES = (IO.SAMPLER,) - CATEGORY = "sampling/custom_sampling/samplers" - - FUNCTION = "get_sampler" - - def get_sampler(self, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2): - model_sampling = model.get_model_object("model_sampling") - start_sigma = model_sampling.percent_to_sigma(sde_start_percent) - end_sigma = model_sampling.percent_to_sigma(sde_end_percent) - tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=eta) - - sampler_name = "sa_solver" - sampler = comfy.samplers.ksampler( - sampler_name, - { - "tau_func": tau_func, - "s_noise": s_noise, - "predictor_order": predictor_order, - "corrector_order": corrector_order, - "use_pece": use_pece, - "simple_order_2": simple_order_2, - }, - ) - return (sampler,) - - -class Noise_EmptyNoise: - def __init__(self): - self.seed = 0 - - def generate_noise(self, input_latent): - latent_image = input_latent["samples"] - return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") - - -class Noise_RandomNoise: - def __init__(self, seed): - self.seed = seed - - def generate_noise(self, input_latent): - latent_image = input_latent["samples"] - batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None - return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds) - -class SamplerCustom: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "add_noise": ("BOOLEAN", {"default": True}), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "sampler": ("SAMPLER", ), - "sigmas": ("SIGMAS", ), - "latent_image": ("LATENT", ), - } - } - - RETURN_TYPES = ("LATENT","LATENT") - RETURN_NAMES = ("output", "denoised_output") - - FUNCTION = "sample" - - CATEGORY = "sampling/custom_sampling" - - def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): - latent = latent_image - latent_image = latent["samples"] - latent = latent.copy() - latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) - latent["samples"] = latent_image - - if not add_noise: - noise = Noise_EmptyNoise().generate_noise(latent) - else: - noise = Noise_RandomNoise(noise_seed).generate_noise(latent) - - noise_mask = None - if "noise_mask" in latent: - noise_mask = latent["noise_mask"] - - x0_output = {} - callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) - - disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED - samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) - - out = latent.copy() - out["samples"] = samples - if "x0" in x0_output: - out_denoised = latent.copy() - out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) - else: - out_denoised = out - return (out, out_denoised) - -class Guider_Basic(comfy.samplers.CFGGuider): - def set_conds(self, positive): - self.inner_set_conds({"positive": positive}) - -class BasicGuider: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "conditioning": ("CONDITIONING", ), - } - } - - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "sampling/custom_sampling/guiders" - - def get_guider(self, model, conditioning): - guider = Guider_Basic(model) - guider.set_conds(conditioning) - return (guider,) - -class CFGGuider: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - } - } - - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "sampling/custom_sampling/guiders" - - def get_guider(self, model, positive, negative, cfg): - guider = comfy.samplers.CFGGuider(model) - guider.set_conds(positive, negative) - guider.set_cfg(cfg) - return (guider,) - -class Guider_DualCFG(comfy.samplers.CFGGuider): - def set_cfg(self, cfg1, cfg2, nested=False): - self.cfg1 = cfg1 - self.cfg2 = cfg2 - self.nested = nested - - def set_conds(self, positive, middle, negative): - middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"}) - self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative}) - - def predict_noise(self, x, timestep, model_options={}, seed=None): - negative_cond = self.conds.get("negative", None) - middle_cond = self.conds.get("middle", None) - positive_cond = self.conds.get("positive", None) - - if self.nested: - out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options) - pred_text = comfy.samplers.cfg_function(self.inner_model, out[2], out[1], self.cfg1, x, timestep, model_options=model_options, cond=positive_cond, uncond=middle_cond) - return out[0] + self.cfg2 * (pred_text - out[0]) - else: - if model_options.get("disable_cfg1_optimization", False) == False: - if math.isclose(self.cfg2, 1.0): - negative_cond = None - if math.isclose(self.cfg1, 1.0): - middle_cond = None - - out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options) - return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 - -class DualCFGGuider: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "cond1": ("CONDITIONING", ), - "cond2": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "style": (["regular", "nested"],), - } - } - - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "sampling/custom_sampling/guiders" - - def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style): - guider = Guider_DualCFG(model) - guider.set_conds(cond1, cond2, negative) - guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested")) - return (guider,) - -class DisableNoise: - @classmethod - def INPUT_TYPES(s): - return {"required":{ - } - } - - RETURN_TYPES = ("NOISE",) - FUNCTION = "get_noise" - CATEGORY = "sampling/custom_sampling/noise" - - def get_noise(self): - return (Noise_EmptyNoise(),) - - -class RandomNoise(DisableNoise): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "noise_seed": ("INT", { - "default": 0, - "min": 0, - "max": 0xffffffffffffffff, - "control_after_generate": True, - }), - } - } - - def get_noise(self, noise_seed): - return (Noise_RandomNoise(noise_seed),) - - -class SamplerCustomAdvanced: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"noise": ("NOISE", ), - "guider": ("GUIDER", ), - "sampler": ("SAMPLER", ), - "sigmas": ("SIGMAS", ), - "latent_image": ("LATENT", ), - } - } - - RETURN_TYPES = ("LATENT","LATENT") - RETURN_NAMES = ("output", "denoised_output") - - FUNCTION = "sample" - - CATEGORY = "sampling/custom_sampling" - - def sample(self, noise, guider, sampler, sigmas, latent_image): - latent = latent_image - latent_image = latent["samples"] - latent = latent.copy() - latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) - latent["samples"] = latent_image - - noise_mask = None - if "noise_mask" in latent: - noise_mask = latent["noise_mask"] - - x0_output = {} - callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) - - disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED - samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed) - samples = samples.to(comfy.model_management.intermediate_device()) - - out = latent.copy() - out["samples"] = samples - if "x0" in x0_output: - out_denoised = latent.copy() - out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) - else: - out_denoised = out - return (out, out_denoised) - -class AddNoise: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "noise": ("NOISE", ), - "sigmas": ("SIGMAS", ), - "latent_image": ("LATENT", ), - } - } - - RETURN_TYPES = ("LATENT",) - - FUNCTION = "add_noise" - - CATEGORY = "_for_testing/custom_sampling/noise" - - def add_noise(self, model, noise, sigmas, latent_image): - if len(sigmas) == 0: - return latent_image - - latent = latent_image - latent_image = latent["samples"] - - noisy = noise.generate_noise(latent) - - model_sampling = model.get_model_object("model_sampling") - process_latent_out = model.get_model_object("process_latent_out") - process_latent_in = model.get_model_object("process_latent_in") - - if len(sigmas) > 1: - scale = torch.abs(sigmas[0] - sigmas[-1]) - else: - scale = sigmas[0] - - if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. - latent_image = process_latent_in(latent_image) - noisy = model_sampling.noise_scaling(scale, noisy, latent_image) - noisy = process_latent_out(noisy) - noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0) - - out = latent.copy() - out["samples"] = noisy - return (out,) - - -NODE_CLASS_MAPPINGS = { - "SamplerCustom": SamplerCustom, - "BasicScheduler": BasicScheduler, - "KarrasScheduler": KarrasScheduler, - "ExponentialScheduler": ExponentialScheduler, - "PolyexponentialScheduler": PolyexponentialScheduler, - "LaplaceScheduler": LaplaceScheduler, - "VPScheduler": VPScheduler, - "BetaSamplingScheduler": BetaSamplingScheduler, - "SDTurboScheduler": SDTurboScheduler, - "KSamplerSelect": KSamplerSelect, - "SamplerEulerAncestral": SamplerEulerAncestral, - "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP, - "SamplerLMS": SamplerLMS, - "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE, - "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, - "SamplerDPMPP_SDE": SamplerDPMPP_SDE, - "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral, - "SamplerDPMAdaptative": SamplerDPMAdaptative, - "SamplerER_SDE": SamplerER_SDE, - "SamplerSASolver": SamplerSASolver, - "SplitSigmas": SplitSigmas, - "SplitSigmasDenoise": SplitSigmasDenoise, - "FlipSigmas": FlipSigmas, - "SetFirstSigma": SetFirstSigma, - "ExtendIntermediateSigmas": ExtendIntermediateSigmas, - "SamplingPercentToSigma": SamplingPercentToSigma, - - "CFGGuider": CFGGuider, - "DualCFGGuider": DualCFGGuider, - "BasicGuider": BasicGuider, - "RandomNoise": RandomNoise, - "DisableNoise": DisableNoise, - "AddNoise": AddNoise, - "SamplerCustomAdvanced": SamplerCustomAdvanced, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++", -} diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py deleted file mode 100644 index 98dbbf102dac861cfb65ed19ad1af499abf7465d..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_differential_diffusion.py +++ /dev/null @@ -1,42 +0,0 @@ -# code adapted from https://github.com/exx8/differential-diffusion - -import torch - -class DifferentialDiffusion(): - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "apply" - CATEGORY = "_for_testing" - INIT = False - - def apply(self, model): - model = model.clone() - model.set_model_denoise_mask_function(self.forward) - return (model,) - - def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): - model = extra_options["model"] - step_sigmas = extra_options["sigmas"] - sigma_to = model.inner_model.model_sampling.sigma_min - if step_sigmas[-1] > sigma_to: - sigma_to = step_sigmas[-1] - sigma_from = step_sigmas[0] - - ts_from = model.inner_model.model_sampling.timestep(sigma_from) - ts_to = model.inner_model.model_sampling.timestep(sigma_to) - current_ts = model.inner_model.model_sampling.timestep(sigma[0]) - - threshold = (current_ts - ts_to) / (ts_from - ts_to) - - return (denoise_mask >= threshold).to(denoise_mask.dtype) - - -NODE_CLASS_MAPPINGS = { - "DifferentialDiffusion": DifferentialDiffusion, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "DifferentialDiffusion": "Differential Diffusion", -} diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py deleted file mode 100644 index 9d2988f5f642de445cd6dbddf642518e9d54ff1c..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_easycache.py +++ /dev/null @@ -1,493 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING, Union -from comfy_api.latest import io, ComfyExtension -import comfy.patcher_extension -import logging -import torch -import comfy.model_patcher -if TYPE_CHECKING: - from uuid import UUID - - -def easycache_forward_wrapper(executor, *args, **kwargs): - # get values from args - x: torch.Tensor = args[0] - transformer_options: dict[str] = args[-1] - if not isinstance(transformer_options, dict): - transformer_options = kwargs.get("transformer_options") - if not transformer_options: - transformer_options = args[-2] - easycache: EasyCacheHolder = transformer_options["easycache"] - sigmas = transformer_options["sigmas"] - uuids = transformer_options["uuids"] - if sigmas is not None and easycache.is_past_end_timestep(sigmas): - return executor(*args, **kwargs) - # prepare next x_prev - has_first_cond_uuid = easycache.has_first_cond_uuid(uuids) - next_x_prev = x - input_change = None - do_easycache = easycache.should_do_easycache(sigmas) - if do_easycache: - easycache.check_metadata(x) - # if first cond marked this step for skipping, skip it and use appropriate cached values - if easycache.skip_current_step: - if easycache.verbose: - logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}") - return easycache.apply_cache_diff(x, uuids) - if easycache.initial_step: - easycache.first_cond_uuid = uuids[0] - has_first_cond_uuid = easycache.has_first_cond_uuid(uuids) - easycache.initial_step = False - if has_first_cond_uuid: - if easycache.has_x_prev_subsampled(): - input_change = (easycache.subsample(x, uuids, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean() - if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate(): - approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm - easycache.cumulative_change_rate += approx_output_change_rate - if easycache.cumulative_change_rate < easycache.reuse_threshold: - if easycache.verbose: - logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") - # other conds should also skip this step, and instead use their cached values - easycache.skip_current_step = True - return easycache.apply_cache_diff(x, uuids) - else: - if easycache.verbose: - logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") - easycache.cumulative_change_rate = 0.0 - - output: torch.Tensor = executor(*args, **kwargs) - if has_first_cond_uuid and easycache.has_output_prev_norm(): - output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean() - if easycache.verbose: - output_change_rate = output_change / easycache.output_prev_norm - easycache.output_change_rates.append(output_change_rate.item()) - if easycache.has_relative_transformation_rate(): - approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm - easycache.approx_output_change_rates.append(approx_output_change_rate.item()) - if easycache.verbose: - logging.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}") - if input_change is not None: - easycache.relative_transformation_rate = output_change / input_change - if easycache.verbose: - logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}") - # TODO: allow cache_diff to be offloaded - easycache.update_cache_diff(output, next_x_prev, uuids) - if has_first_cond_uuid: - easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids) - easycache.output_prev_subsampled = easycache.subsample(output, uuids) - easycache.output_prev_norm = output.flatten().abs().mean() - if easycache.verbose: - logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}") - return output - -def lazycache_predict_noise_wrapper(executor, *args, **kwargs): - # get values from args - x: torch.Tensor = args[0] - timestep: float = args[1] - model_options: dict[str] = args[2] - easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"] - if easycache.is_past_end_timestep(timestep): - return executor(*args, **kwargs) - # prepare next x_prev - next_x_prev = x - input_change = None - do_easycache = easycache.should_do_easycache(timestep) - if do_easycache: - easycache.check_metadata(x) - if easycache.has_x_prev_subsampled(): - if easycache.has_x_prev_subsampled(): - input_change = (easycache.subsample(x, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean() - if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate(): - approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm - easycache.cumulative_change_rate += approx_output_change_rate - if easycache.cumulative_change_rate < easycache.reuse_threshold: - if easycache.verbose: - logging.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") - # other conds should also skip this step, and instead use their cached values - easycache.skip_current_step = True - return easycache.apply_cache_diff(x) - else: - if easycache.verbose: - logging.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") - easycache.cumulative_change_rate = 0.0 - output: torch.Tensor = executor(*args, **kwargs) - if easycache.has_output_prev_norm(): - output_change = (easycache.subsample(output, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean() - if easycache.verbose: - output_change_rate = output_change / easycache.output_prev_norm - easycache.output_change_rates.append(output_change_rate.item()) - if easycache.has_relative_transformation_rate(): - approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm - easycache.approx_output_change_rates.append(approx_output_change_rate.item()) - if easycache.verbose: - logging.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}") - if input_change is not None: - easycache.relative_transformation_rate = output_change / input_change - if easycache.verbose: - logging.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}") - # TODO: allow cache_diff to be offloaded - easycache.update_cache_diff(output, next_x_prev) - easycache.x_prev_subsampled = easycache.subsample(next_x_prev) - easycache.output_prev_subsampled = easycache.subsample(output) - easycache.output_prev_norm = output.flatten().abs().mean() - if easycache.verbose: - logging.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}") - return output - -def easycache_calc_cond_batch_wrapper(executor, *args, **kwargs): - model_options = args[-1] - easycache: EasyCacheHolder = model_options["transformer_options"]["easycache"] - easycache.skip_current_step = False - # TODO: check if first_cond_uuid is active at this timestep; otherwise, EasyCache needs to be partially reset - return executor(*args, **kwargs) - -def easycache_sample_wrapper(executor, *args, **kwargs): - """ - This OUTER_SAMPLE wrapper makes sure easycache is prepped for current run, and all memory usage is cleared at the end. - """ - try: - guider = executor.class_obj - orig_model_options = guider.model_options - guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options) - # clone and prepare timesteps - guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling) - easycache: Union[EasyCacheHolder, LazyCacheHolder] = guider.model_options['transformer_options']['easycache'] - logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}") - return executor(*args, **kwargs) - finally: - easycache = guider.model_options['transformer_options']['easycache'] - output_change_rates = easycache.output_change_rates - approx_output_change_rates = easycache.approx_output_change_rates - if easycache.verbose: - logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}") - logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}") - total_steps = len(args[3])-1 - logging.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({total_steps/(total_steps-easycache.total_steps_skipped):.2f}x speedup).") - easycache.reset() - guider.model_options = orig_model_options - - -class EasyCacheHolder: - def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): - self.name = "EasyCache" - self.reuse_threshold = reuse_threshold - self.start_percent = start_percent - self.end_percent = end_percent - self.subsample_factor = subsample_factor - self.offload_cache_diff = offload_cache_diff - self.verbose = verbose - # timestep values - self.start_t = 0.0 - self.end_t = 0.0 - # control values - self.relative_transformation_rate: float = None - self.cumulative_change_rate = 0.0 - self.initial_step = True - self.skip_current_step = False - # cache values - self.first_cond_uuid = None - self.x_prev_subsampled: torch.Tensor = None - self.output_prev_subsampled: torch.Tensor = None - self.output_prev_norm: torch.Tensor = None - self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {} - self.output_change_rates = [] - self.approx_output_change_rates = [] - self.total_steps_skipped = 0 - # how to deal with mismatched dims - self.allow_mismatch = True - self.cut_from_start = True - self.state_metadata = None - - def is_past_end_timestep(self, timestep: float) -> bool: - return not (timestep[0] > self.end_t).item() - - def should_do_easycache(self, timestep: float) -> bool: - return (timestep[0] <= self.start_t).item() - - def has_x_prev_subsampled(self) -> bool: - return self.x_prev_subsampled is not None - - def has_output_prev_subsampled(self) -> bool: - return self.output_prev_subsampled is not None - - def has_output_prev_norm(self) -> bool: - return self.output_prev_norm is not None - - def has_relative_transformation_rate(self) -> bool: - return self.relative_transformation_rate is not None - - def prepare_timesteps(self, model_sampling): - self.start_t = model_sampling.percent_to_sigma(self.start_percent) - self.end_t = model_sampling.percent_to_sigma(self.end_percent) - return self - - def subsample(self, x: torch.Tensor, uuids: list[UUID], clone: bool = True) -> torch.Tensor: - batch_offset = x.shape[0] // len(uuids) - uuid_idx = uuids.index(self.first_cond_uuid) - if self.subsample_factor > 1: - to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ..., ::self.subsample_factor, ::self.subsample_factor] - if clone: - return to_return.clone() - return to_return - to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ...] - if clone: - return to_return.clone() - return to_return - - def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]): - if self.first_cond_uuid in uuids: - self.total_steps_skipped += 1 - batch_offset = x.shape[0] // len(uuids) - for i, uuid in enumerate(uuids): - # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video) - if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]: - if not self.allow_mismatch: - raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good") - slicing = [] - skip_this_dim = True - for dim_u, dim_x in zip(self.uuid_cache_diffs[uuid].shape, x.shape): - if skip_this_dim: - skip_this_dim = False - continue - if dim_u != dim_x: - if self.cut_from_start: - slicing.append(slice(dim_x-dim_u, None)) - else: - slicing.append(slice(None, dim_u)) - else: - slicing.append(slice(None)) - slicing = [slice(i*batch_offset,(i+1)*batch_offset)] + slicing - x = x[slicing] - x += self.uuid_cache_diffs[uuid].to(x.device) - return x - - def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]): - # if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video) - if output.shape[1:] != x.shape[1:]: - if not self.allow_mismatch: - raise ValueError(f"Output dims {output.shape} don't match x dims {x.shape} - this is no good") - slicing = [] - skip_dim = True - for dim_o, dim_x in zip(output.shape, x.shape): - if not skip_dim and dim_o != dim_x: - if self.cut_from_start: - slicing.append(slice(dim_x-dim_o, None)) - else: - slicing.append(slice(None, dim_o)) - else: - slicing.append(slice(None)) - skip_dim = False - x = x[slicing] - diff = output - x - batch_offset = diff.shape[0] // len(uuids) - for i, uuid in enumerate(uuids): - self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...] - - def has_first_cond_uuid(self, uuids: list[UUID]) -> bool: - return self.first_cond_uuid in uuids - - def check_metadata(self, x: torch.Tensor) -> bool: - metadata = (x.device, x.dtype, x.shape[1:]) - if self.state_metadata is None: - self.state_metadata = metadata - return True - if metadata == self.state_metadata: - return True - logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state") - self.reset() - return False - - def reset(self): - self.relative_transformation_rate = 0.0 - self.cumulative_change_rate = 0.0 - self.initial_step = True - self.skip_current_step = False - self.output_change_rates = [] - self.first_cond_uuid = None - del self.x_prev_subsampled - self.x_prev_subsampled = None - del self.output_prev_subsampled - self.output_prev_subsampled = None - del self.output_prev_norm - self.output_prev_norm = None - del self.uuid_cache_diffs - self.uuid_cache_diffs = {} - self.total_steps_skipped = 0 - self.state_metadata = None - return self - - def clone(self): - return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) - - -class EasyCacheNode(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="EasyCache", - display_name="EasyCache", - description="Native EasyCache implementation.", - category="advanced/debug/model", - is_experimental=True, - inputs=[ - io.Model.Input("model", tooltip="The model to add EasyCache to."), - io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."), - io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."), - io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."), - io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."), - ], - outputs=[ - io.Model.Output(tooltip="The model with EasyCache."), - ], - ) - - @classmethod - def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: - model = model.clone() - model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) - model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper) - model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper) - model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper) - return io.NodeOutput(model) - - -class LazyCacheHolder: - def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): - self.name = "LazyCache" - self.reuse_threshold = reuse_threshold - self.start_percent = start_percent - self.end_percent = end_percent - self.subsample_factor = subsample_factor - self.offload_cache_diff = offload_cache_diff - self.verbose = verbose - # timestep values - self.start_t = 0.0 - self.end_t = 0.0 - # control values - self.relative_transformation_rate: float = None - self.cumulative_change_rate = 0.0 - self.initial_step = True - # cache values - self.x_prev_subsampled: torch.Tensor = None - self.output_prev_subsampled: torch.Tensor = None - self.output_prev_norm: torch.Tensor = None - self.cache_diff: torch.Tensor = None - self.output_change_rates = [] - self.approx_output_change_rates = [] - self.total_steps_skipped = 0 - self.state_metadata = None - - def has_cache_diff(self) -> bool: - return self.cache_diff is not None - - def is_past_end_timestep(self, timestep: float) -> bool: - return not (timestep[0] > self.end_t).item() - - def should_do_easycache(self, timestep: float) -> bool: - return (timestep[0] <= self.start_t).item() - - def has_x_prev_subsampled(self) -> bool: - return self.x_prev_subsampled is not None - - def has_output_prev_subsampled(self) -> bool: - return self.output_prev_subsampled is not None - - def has_output_prev_norm(self) -> bool: - return self.output_prev_norm is not None - - def has_relative_transformation_rate(self) -> bool: - return self.relative_transformation_rate is not None - - def prepare_timesteps(self, model_sampling): - self.start_t = model_sampling.percent_to_sigma(self.start_percent) - self.end_t = model_sampling.percent_to_sigma(self.end_percent) - return self - - def subsample(self, x: torch.Tensor, clone: bool = True) -> torch.Tensor: - if self.subsample_factor > 1: - to_return = x[..., ::self.subsample_factor, ::self.subsample_factor] - if clone: - return to_return.clone() - return to_return - if clone: - return x.clone() - return x - - def apply_cache_diff(self, x: torch.Tensor): - self.total_steps_skipped += 1 - return x + self.cache_diff.to(x.device) - - def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor): - self.cache_diff = output - x - - def check_metadata(self, x: torch.Tensor) -> bool: - metadata = (x.device, x.dtype, x.shape) - if self.state_metadata is None: - self.state_metadata = metadata - return True - if metadata == self.state_metadata: - return True - logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state") - self.reset() - return False - - def reset(self): - self.relative_transformation_rate = 0.0 - self.cumulative_change_rate = 0.0 - self.initial_step = True - self.output_change_rates = [] - self.approx_output_change_rates = [] - del self.cache_diff - self.cache_diff = None - del self.x_prev_subsampled - self.x_prev_subsampled = None - del self.output_prev_subsampled - self.output_prev_subsampled = None - del self.output_prev_norm - self.output_prev_norm = None - self.total_steps_skipped = 0 - self.state_metadata = None - return self - - def clone(self): - return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) - -class LazyCacheNode(io.ComfyNode): - @classmethod - def define_schema(cls) -> io.Schema: - return io.Schema( - node_id="LazyCache", - display_name="LazyCache", - description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.", - category="advanced/debug/model", - is_experimental=True, - inputs=[ - io.Model.Input("model", tooltip="The model to add LazyCache to."), - io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."), - io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache."), - io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache."), - io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."), - ], - outputs=[ - io.Model.Output(tooltip="The model with LazyCache."), - ], - ) - - @classmethod - def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: - model = model.clone() - model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) - model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper) - model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper) - return io.NodeOutput(model) - - -class EasyCacheExtension(ComfyExtension): - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - EasyCacheNode, - LazyCacheNode, - ] - -def comfy_entrypoint(): - return EasyCacheExtension() diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py deleted file mode 100644 index b69f7971591b383774d322b022e3b3b39ec0d704..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_edit_model.py +++ /dev/null @@ -1,26 +0,0 @@ -import node_helpers - - -class ReferenceLatent: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - }, - "optional": {"latent": ("LATENT", ),} - } - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "advanced/conditioning/edit_models" - DESCRIPTION = "This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images." - - def append(self, conditioning, latent=None): - if latent is not None: - conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True) - return (conditioning, ) - - -NODE_CLASS_MAPPINGS = { - "ReferenceLatent": ReferenceLatent, -} diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py deleted file mode 100644 index c8db75bb39df365aedc5af56a7324bd305fb639c..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_flux.py +++ /dev/null @@ -1,127 +0,0 @@ -import node_helpers -import comfy.utils - -class CLIPTextEncodeFlux: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning/flux" - - def encode(self, clip, clip_l, t5xxl, guidance): - tokens = clip.tokenize(clip_l) - tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] - - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}), ) - -class FluxGuidance: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "conditioning": ("CONDITIONING", ), - "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), - }} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "advanced/conditioning/flux" - - def append(self, conditioning, guidance): - c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance}) - return (c, ) - - -class FluxDisableGuidance: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "conditioning": ("CONDITIONING", ), - }} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "advanced/conditioning/flux" - DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models" - - def append(self, conditioning): - c = node_helpers.conditioning_set_values(conditioning, {"guidance": None}) - return (c, ) - - -PREFERED_KONTEXT_RESOLUTIONS = [ - (672, 1568), - (688, 1504), - (720, 1456), - (752, 1392), - (800, 1328), - (832, 1248), - (880, 1184), - (944, 1104), - (1024, 1024), - (1104, 944), - (1184, 880), - (1248, 832), - (1328, 800), - (1392, 752), - (1456, 720), - (1504, 688), - (1568, 672), -] - - -class FluxKontextImageScale: - @classmethod - def INPUT_TYPES(s): - return {"required": {"image": ("IMAGE", ), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "scale" - - CATEGORY = "advanced/conditioning/flux" - DESCRIPTION = "This node resizes the image to one that is more optimal for flux kontext." - - def scale(self, image): - width = image.shape[2] - height = image.shape[1] - aspect_ratio = width / height - _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS) - image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1) - return (image, ) - - -class FluxKontextMultiReferenceLatentMethod: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "conditioning": ("CONDITIONING", ), - "reference_latents_method": (("offset", "index"), ), - }} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - EXPERIMENTAL = True - - CATEGORY = "advanced/conditioning/flux" - - def append(self, conditioning, reference_latents_method): - c = node_helpers.conditioning_set_values(conditioning, {"reference_latents_method": reference_latents_method}) - return (c, ) - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeFlux": CLIPTextEncodeFlux, - "FluxGuidance": FluxGuidance, - "FluxDisableGuidance": FluxDisableGuidance, - "FluxKontextImageScale": FluxKontextImageScale, - "FluxKontextMultiReferenceLatentMethod": FluxKontextMultiReferenceLatentMethod, -} diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py deleted file mode 100644 index e3ac58447b29f604debb5bfc0aed3a5f100a4ae9..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_freelunch.py +++ /dev/null @@ -1,113 +0,0 @@ -#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) - -import torch -import logging - -def Fourier_filter(x, threshold, scale): - # FFT - x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) - x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) - - B, C, H, W = x_freq.shape - mask = torch.ones((B, C, H, W), device=x.device) - - crow, ccol = H // 2, W //2 - mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale - x_freq = x_freq * mask - - # IFFT - x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) - x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real - - return x_filtered.to(x.dtype) - - -class FreeU: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), - "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), - "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), - "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, b1, b2, s1, s2): - model_channels = model.model.model_config.unet_config["model_channels"] - scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} - on_cpu_devices = {} - - def output_block_patch(h, hsp, transformer_options): - scale = scale_dict.get(int(h.shape[1]), None) - if scale is not None: - h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] - if hsp.device not in on_cpu_devices: - try: - hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) - except: - logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) - on_cpu_devices[hsp.device] = True - hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) - else: - hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) - - return h, hsp - - m = model.clone() - m.set_model_output_block_patch(output_block_patch) - return (m, ) - -class FreeU_V2: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), - "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), - "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), - "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, b1, b2, s1, s2): - model_channels = model.model.model_config.unet_config["model_channels"] - scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} - on_cpu_devices = {} - - def output_block_patch(h, hsp, transformer_options): - scale = scale_dict.get(int(h.shape[1]), None) - if scale is not None: - hidden_mean = h.mean(1).unsqueeze(1) - B = hidden_mean.shape[0] - hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) - hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) - hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) - - h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) - - if hsp.device not in on_cpu_devices: - try: - hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) - except: - logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device)) - on_cpu_devices[hsp.device] = True - hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) - else: - hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) - - return h, hsp - - m = model.clone() - m.set_model_output_block_patch(output_block_patch) - return (m, ) - -NODE_CLASS_MAPPINGS = { - "FreeU": FreeU, - "FreeU_V2": FreeU_V2, -} diff --git a/comfy_extras/nodes_fresca.py b/comfy_extras/nodes_fresca.py deleted file mode 100644 index 65c2d0d0ea3f35b2795ac92a208424078085c5b2..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_fresca.py +++ /dev/null @@ -1,103 +0,0 @@ -# Code based on https://github.com/WikiChao/FreSca (MIT License) -import torch -import torch.fft as fft - - -def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20): - """ - Apply frequency-dependent scaling to an image tensor using Fourier transforms. - - Parameters: - x: Input tensor of shape (B, C, H, W) - scale_low: Scaling factor for low-frequency components (default: 1.0) - scale_high: Scaling factor for high-frequency components (default: 1.5) - freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20) - - Returns: - x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied. - """ - # Preserve input dtype and device - dtype, device = x.dtype, x.device - - # Convert to float32 for FFT computations - x = x.to(torch.float32) - - # 1) Apply FFT and shift low frequencies to center - x_freq = fft.fftn(x, dim=(-2, -1)) - x_freq = fft.fftshift(x_freq, dim=(-2, -1)) - - # Initialize mask with high-frequency scaling factor - mask = torch.ones(x_freq.shape, device=device) * scale_high - m = mask - for d in range(len(x_freq.shape) - 2): - dim = d + 2 - cc = x_freq.shape[dim] // 2 - f_c = min(freq_cutoff, cc) - m = m.narrow(dim, cc - f_c, f_c * 2) - - # Apply low-frequency scaling factor to center region - m[:] = scale_low - - # 3) Apply frequency-specific scaling - x_freq = x_freq * mask - - # 4) Convert back to spatial domain - x_freq = fft.ifftshift(x_freq, dim=(-2, -1)) - x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real - - # 5) Restore original dtype - x_filtered = x_filtered.to(dtype) - - return x_filtered - - -class FreSca: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01, - "tooltip": "Scaling factor for low-frequency components"}), - "scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01, - "tooltip": "Scaling factor for high-frequency components"}), - "freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1, - "tooltip": "Number of frequency indices around center to consider as low-frequency"}), - } - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - CATEGORY = "_for_testing" - DESCRIPTION = "Applies frequency-dependent scaling to the guidance" - def patch(self, model, scale_low, scale_high, freq_cutoff): - def custom_cfg_function(args): - conds_out = args["conds_out"] - if len(conds_out) <= 1 or None in args["conds"][:2]: - return conds_out - cond = conds_out[0] - uncond = conds_out[1] - - guidance = cond - uncond - filtered_guidance = Fourier_filter( - guidance, - scale_low=scale_low, - scale_high=scale_high, - freq_cutoff=freq_cutoff, - ) - filtered_cond = filtered_guidance + uncond - - return [filtered_cond, uncond] + conds_out[2:] - - m = model.clone() - m.set_model_sampler_pre_cfg_function(custom_cfg_function) - - return (m,) - - -NODE_CLASS_MAPPINGS = { - "FreSca": FreSca, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "FreSca": "FreSca", -} diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py deleted file mode 100644 index 47b1dd049702cc481550dd04d2c4edebfdcf7a0e..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_gits.py +++ /dev/null @@ -1,369 +0,0 @@ -# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main -import numpy as np -import torch - -def loglinear_interp(t_steps, num_steps): - """ - Performs log-linear interpolation of a given array of decreasing numbers. - """ - xs = np.linspace(0, 1, len(t_steps)) - ys = np.log(t_steps[::-1]) - - new_xs = np.linspace(0, 1, num_steps) - new_ys = np.interp(new_xs, xs, ys) - - interped_ys = np.exp(new_ys)[::-1].copy() - return interped_ys - -NOISE_LEVELS = { - 0.80: [ - [14.61464119, 7.49001646, 0.02916753], - [14.61464119, 11.54541874, 6.77309084, 0.02916753], - [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], - [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], - [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], - [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753], - 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[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753], - [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753], - 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], -} - -class GITSScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), - "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, coeff, steps, denoise): - total_steps = steps - if denoise < 1.0: - if denoise <= 0.0: - return (torch.FloatTensor([]),) - total_steps = round(steps * denoise) - - if steps <= 20: - sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:] - else: - sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:] - sigmas = loglinear_interp(sigmas, steps + 1) - - sigmas = sigmas[-(total_steps + 1):] - sigmas[-1] = 0 - return (torch.FloatTensor(sigmas), ) - -NODE_CLASS_MAPPINGS = { - "GITSScheduler": GITSScheduler, -} diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py deleted file mode 100644 index dfb98597b8427622360588994c5fc8f75c3e6a1e..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_hidream.py +++ /dev/null @@ -1,55 +0,0 @@ -import folder_paths -import comfy.sd -import comfy.model_management - - -class QuadrupleCLIPLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), - "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), - "clip_name3": (folder_paths.get_filename_list("text_encoders"), ), - "clip_name4": (folder_paths.get_filename_list("text_encoders"), ) - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "load_clip" - - CATEGORY = "advanced/loaders" - - DESCRIPTION = "[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct" - - def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4): - clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) - clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) - clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) - clip_path4 = folder_paths.get_full_path_or_raise("text_encoders", clip_name4) - clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3, clip_path4], embedding_directory=folder_paths.get_folder_paths("embeddings")) - return (clip,) - -class CLIPTextEncodeHiDream: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "llama": ("STRING", {"multiline": True, "dynamicPrompts": True}) - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, clip_l, clip_g, t5xxl, llama): - - tokens = clip.tokenize(clip_g) - tokens["l"] = clip.tokenize(clip_l)["l"] - tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] - tokens["llama"] = clip.tokenize(llama)["llama"] - return (clip.encode_from_tokens_scheduled(tokens), ) - -NODE_CLASS_MAPPINGS = { - "QuadrupleCLIPLoader": QuadrupleCLIPLoader, - "CLIPTextEncodeHiDream": CLIPTextEncodeHiDream, -} diff --git a/comfy_extras/nodes_hooks.py b/comfy_extras/nodes_hooks.py deleted file mode 100644 index 1edc06f3d7ae6b0682b03afe666ef936b16f2f28..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_hooks.py +++ /dev/null @@ -1,745 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING, Union -import logging -import torch -from collections.abc import Iterable - -if TYPE_CHECKING: - from comfy.sd import CLIP - -import comfy.hooks -import comfy.sd -import comfy.utils -import folder_paths - -########################################### -# Mask, Combine, and Hook Conditioning -#------------------------------------------ -class PairConditioningSetProperties: - NodeId = 'PairConditioningSetProperties' - NodeName = 'Cond Pair Set Props' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "positive_NEW": ("CONDITIONING", ), - "negative_NEW": ("CONDITIONING", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - }, - "optional": { - "mask": ("MASK", ), - "hooks": ("HOOKS",), - "timesteps": ("TIMESTEPS_RANGE",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - CATEGORY = "advanced/hooks/cond pair" - FUNCTION = "set_properties" - - def set_properties(self, positive_NEW, negative_NEW, - strength: float, set_cond_area: str, - mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): - final_positive, final_negative = comfy.hooks.set_conds_props(conds=[positive_NEW, negative_NEW], - strength=strength, set_cond_area=set_cond_area, - mask=mask, hooks=hooks, timesteps_range=timesteps) - return (final_positive, final_negative) - -class PairConditioningSetPropertiesAndCombine: - NodeId = 'PairConditioningSetPropertiesAndCombine' - NodeName = 'Cond Pair Set Props Combine' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "positive_NEW": ("CONDITIONING", ), - "negative_NEW": ("CONDITIONING", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - }, - "optional": { - "mask": ("MASK", ), - "hooks": ("HOOKS",), - "timesteps": ("TIMESTEPS_RANGE",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - CATEGORY = "advanced/hooks/cond pair" - FUNCTION = "set_properties" - - def set_properties(self, positive, negative, positive_NEW, negative_NEW, - strength: float, set_cond_area: str, - mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): - final_positive, final_negative = comfy.hooks.set_conds_props_and_combine(conds=[positive, negative], new_conds=[positive_NEW, negative_NEW], - strength=strength, set_cond_area=set_cond_area, - mask=mask, hooks=hooks, timesteps_range=timesteps) - return (final_positive, final_negative) - -class ConditioningSetProperties: - NodeId = 'ConditioningSetProperties' - NodeName = 'Cond Set Props' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond_NEW": ("CONDITIONING", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - }, - "optional": { - "mask": ("MASK", ), - "hooks": ("HOOKS",), - "timesteps": ("TIMESTEPS_RANGE",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING",) - CATEGORY = "advanced/hooks/cond single" - FUNCTION = "set_properties" - - def set_properties(self, cond_NEW, - strength: float, set_cond_area: str, - mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): - (final_cond,) = comfy.hooks.set_conds_props(conds=[cond_NEW], - strength=strength, set_cond_area=set_cond_area, - mask=mask, hooks=hooks, timesteps_range=timesteps) - return (final_cond,) - -class ConditioningSetPropertiesAndCombine: - NodeId = 'ConditioningSetPropertiesAndCombine' - NodeName = 'Cond Set Props Combine' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond": ("CONDITIONING", ), - "cond_NEW": ("CONDITIONING", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - }, - "optional": { - "mask": ("MASK", ), - "hooks": ("HOOKS",), - "timesteps": ("TIMESTEPS_RANGE",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING",) - CATEGORY = "advanced/hooks/cond single" - FUNCTION = "set_properties" - - def set_properties(self, cond, cond_NEW, - strength: float, set_cond_area: str, - mask: torch.Tensor=None, hooks: comfy.hooks.HookGroup=None, timesteps: tuple=None): - (final_cond,) = comfy.hooks.set_conds_props_and_combine(conds=[cond], new_conds=[cond_NEW], - strength=strength, set_cond_area=set_cond_area, - mask=mask, hooks=hooks, timesteps_range=timesteps) - return (final_cond,) - -class PairConditioningCombine: - NodeId = 'PairConditioningCombine' - NodeName = 'Cond Pair Combine' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "positive_A": ("CONDITIONING",), - "negative_A": ("CONDITIONING",), - "positive_B": ("CONDITIONING",), - "negative_B": ("CONDITIONING",), - }, - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - CATEGORY = "advanced/hooks/cond pair" - FUNCTION = "combine" - - def combine(self, positive_A, negative_A, positive_B, negative_B): - final_positive, final_negative = comfy.hooks.set_conds_props_and_combine(conds=[positive_A, negative_A], new_conds=[positive_B, negative_B],) - return (final_positive, final_negative,) - -class PairConditioningSetDefaultAndCombine: - NodeId = 'PairConditioningSetDefaultCombine' - NodeName = 'Cond Pair Set Default Combine' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "positive": ("CONDITIONING",), - "negative": ("CONDITIONING",), - "positive_DEFAULT": ("CONDITIONING",), - "negative_DEFAULT": ("CONDITIONING",), - }, - "optional": { - "hooks": ("HOOKS",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - CATEGORY = "advanced/hooks/cond pair" - FUNCTION = "set_default_and_combine" - - def set_default_and_combine(self, positive, negative, positive_DEFAULT, negative_DEFAULT, - hooks: comfy.hooks.HookGroup=None): - final_positive, final_negative = comfy.hooks.set_default_conds_and_combine(conds=[positive, negative], new_conds=[positive_DEFAULT, negative_DEFAULT], - hooks=hooks) - return (final_positive, final_negative) - -class ConditioningSetDefaultAndCombine: - NodeId = 'ConditioningSetDefaultCombine' - NodeName = 'Cond Set Default Combine' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond": ("CONDITIONING",), - "cond_DEFAULT": ("CONDITIONING",), - }, - "optional": { - "hooks": ("HOOKS",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING",) - CATEGORY = "advanced/hooks/cond single" - FUNCTION = "set_default_and_combine" - - def set_default_and_combine(self, cond, cond_DEFAULT, - hooks: comfy.hooks.HookGroup=None): - (final_conditioning,) = comfy.hooks.set_default_conds_and_combine(conds=[cond], new_conds=[cond_DEFAULT], - hooks=hooks) - return (final_conditioning,) - -class SetClipHooks: - NodeId = 'SetClipHooks' - NodeName = 'Set CLIP Hooks' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "clip": ("CLIP",), - "apply_to_conds": ("BOOLEAN", {"default": True}), - "schedule_clip": ("BOOLEAN", {"default": False}) - }, - "optional": { - "hooks": ("HOOKS",) - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CLIP",) - CATEGORY = "advanced/hooks/clip" - FUNCTION = "apply_hooks" - - def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None): - if hooks is not None: - clip = clip.clone() - if apply_to_conds: - clip.apply_hooks_to_conds = hooks - clip.patcher.forced_hooks = hooks.clone() - clip.use_clip_schedule = schedule_clip - if not clip.use_clip_schedule: - clip.patcher.forced_hooks.set_keyframes_on_hooks(None) - clip.patcher.register_all_hook_patches(hooks, comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Clip)) - return (clip,) - -class ConditioningTimestepsRange: - NodeId = 'ConditioningTimestepsRange' - NodeName = 'Timesteps Range' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }, - } - - EXPERIMENTAL = True - RETURN_TYPES = ("TIMESTEPS_RANGE", "TIMESTEPS_RANGE", "TIMESTEPS_RANGE") - RETURN_NAMES = ("TIMESTEPS_RANGE", "BEFORE_RANGE", "AFTER_RANGE") - CATEGORY = "advanced/hooks" - FUNCTION = "create_range" - - def create_range(self, start_percent: float, end_percent: float): - return ((start_percent, end_percent), (0.0, start_percent), (end_percent, 1.0)) -#------------------------------------------ -########################################### - - -########################################### -# Create Hooks -#------------------------------------------ -class CreateHookLora: - NodeId = 'CreateHookLora' - NodeName = 'Create Hook LoRA' - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "lora_name": (folder_paths.get_filename_list("loras"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - }, - "optional": { - "prev_hooks": ("HOOKS",) - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/create" - FUNCTION = "create_hook" - - def create_hook(self, lora_name: str, strength_model: float, strength_clip: float, prev_hooks: comfy.hooks.HookGroup=None): - if prev_hooks is None: - prev_hooks = comfy.hooks.HookGroup() - prev_hooks.clone() - - if strength_model == 0 and strength_clip == 0: - return (prev_hooks,) - - lora_path = folder_paths.get_full_path("loras", lora_name) - lora = None - if self.loaded_lora is not None: - if self.loaded_lora[0] == lora_path: - lora = self.loaded_lora[1] - else: - temp = self.loaded_lora - self.loaded_lora = None - del temp - - if lora is None: - lora = comfy.utils.load_torch_file(lora_path, safe_load=True) - self.loaded_lora = (lora_path, lora) - - hooks = comfy.hooks.create_hook_lora(lora=lora, strength_model=strength_model, strength_clip=strength_clip) - return (prev_hooks.clone_and_combine(hooks),) - -class CreateHookLoraModelOnly(CreateHookLora): - NodeId = 'CreateHookLoraModelOnly' - NodeName = 'Create Hook LoRA (MO)' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "lora_name": (folder_paths.get_filename_list("loras"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - }, - "optional": { - "prev_hooks": ("HOOKS",) - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/create" - FUNCTION = "create_hook_model_only" - - def create_hook_model_only(self, lora_name: str, strength_model: float, prev_hooks: comfy.hooks.HookGroup=None): - return self.create_hook(lora_name=lora_name, strength_model=strength_model, strength_clip=0, prev_hooks=prev_hooks) - -class CreateHookModelAsLora: - NodeId = 'CreateHookModelAsLora' - NodeName = 'Create Hook Model as LoRA' - - def __init__(self): - # when not None, will be in following format: - # (ckpt_path: str, weights_model: dict, weights_clip: dict) - self.loaded_weights = None - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - }, - "optional": { - "prev_hooks": ("HOOKS",) - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/create" - FUNCTION = "create_hook" - - def create_hook(self, ckpt_name: str, strength_model: float, strength_clip: float, - prev_hooks: comfy.hooks.HookGroup=None): - if prev_hooks is None: - prev_hooks = comfy.hooks.HookGroup() - prev_hooks.clone() - - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - weights_model = None - weights_clip = None - if self.loaded_weights is not None: - if self.loaded_weights[0] == ckpt_path: - weights_model = self.loaded_weights[1] - weights_clip = self.loaded_weights[2] - else: - temp = self.loaded_weights - self.loaded_weights = None - del temp - - if weights_model is None: - out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) - weights_model = comfy.hooks.get_patch_weights_from_model(out[0]) - weights_clip = comfy.hooks.get_patch_weights_from_model(out[1].patcher if out[1] else out[1]) - self.loaded_weights = (ckpt_path, weights_model, weights_clip) - - hooks = comfy.hooks.create_hook_model_as_lora(weights_model=weights_model, weights_clip=weights_clip, - strength_model=strength_model, strength_clip=strength_clip) - return (prev_hooks.clone_and_combine(hooks),) - -class CreateHookModelAsLoraModelOnly(CreateHookModelAsLora): - NodeId = 'CreateHookModelAsLoraModelOnly' - NodeName = 'Create Hook Model as LoRA (MO)' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - }, - "optional": { - "prev_hooks": ("HOOKS",) - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/create" - FUNCTION = "create_hook_model_only" - - def create_hook_model_only(self, ckpt_name: str, strength_model: float, - prev_hooks: comfy.hooks.HookGroup=None): - return self.create_hook(ckpt_name=ckpt_name, strength_model=strength_model, strength_clip=0.0, prev_hooks=prev_hooks) -#------------------------------------------ -########################################### - - -########################################### -# Schedule Hooks -#------------------------------------------ -class SetHookKeyframes: - NodeId = 'SetHookKeyframes' - NodeName = 'Set Hook Keyframes' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "hooks": ("HOOKS",), - }, - "optional": { - "hook_kf": ("HOOK_KEYFRAMES",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/scheduling" - FUNCTION = "set_hook_keyframes" - - def set_hook_keyframes(self, hooks: comfy.hooks.HookGroup, hook_kf: comfy.hooks.HookKeyframeGroup=None): - if hook_kf is not None: - hooks = hooks.clone() - hooks.set_keyframes_on_hooks(hook_kf=hook_kf) - return (hooks,) - -class CreateHookKeyframe: - NodeId = 'CreateHookKeyframe' - NodeName = 'Create Hook Keyframe' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "strength_mult": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - }, - "optional": { - "prev_hook_kf": ("HOOK_KEYFRAMES",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOK_KEYFRAMES",) - RETURN_NAMES = ("HOOK_KF",) - CATEGORY = "advanced/hooks/scheduling" - FUNCTION = "create_hook_keyframe" - - def create_hook_keyframe(self, strength_mult: float, start_percent: float, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None): - if prev_hook_kf is None: - prev_hook_kf = comfy.hooks.HookKeyframeGroup() - prev_hook_kf = prev_hook_kf.clone() - keyframe = comfy.hooks.HookKeyframe(strength=strength_mult, start_percent=start_percent) - prev_hook_kf.add(keyframe) - return (prev_hook_kf,) - -class CreateHookKeyframesInterpolated: - NodeId = 'CreateHookKeyframesInterpolated' - NodeName = 'Create Hook Keyframes Interp.' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), - "strength_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ), - "interpolation": (comfy.hooks.InterpolationMethod._LIST, ), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "keyframes_count": ("INT", {"default": 5, "min": 2, "max": 100, "step": 1}), - "print_keyframes": ("BOOLEAN", {"default": False}), - }, - "optional": { - "prev_hook_kf": ("HOOK_KEYFRAMES",), - }, - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOK_KEYFRAMES",) - RETURN_NAMES = ("HOOK_KF",) - CATEGORY = "advanced/hooks/scheduling" - FUNCTION = "create_hook_keyframes" - - def create_hook_keyframes(self, strength_start: float, strength_end: float, interpolation: str, - start_percent: float, end_percent: float, keyframes_count: int, - print_keyframes=False, prev_hook_kf: comfy.hooks.HookKeyframeGroup=None): - if prev_hook_kf is None: - prev_hook_kf = comfy.hooks.HookKeyframeGroup() - prev_hook_kf = prev_hook_kf.clone() - percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=keyframes_count, - method=comfy.hooks.InterpolationMethod.LINEAR) - strengths = comfy.hooks.InterpolationMethod.get_weights(num_from=strength_start, num_to=strength_end, length=keyframes_count, method=interpolation) - - is_first = True - for percent, strength in zip(percents, strengths): - guarantee_steps = 0 - if is_first: - guarantee_steps = 1 - is_first = False - prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps)) - if print_keyframes: - logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}") - return (prev_hook_kf,) - -class CreateHookKeyframesFromFloats: - NodeId = 'CreateHookKeyframesFromFloats' - NodeName = 'Create Hook Keyframes From Floats' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "floats_strength": ("FLOATS", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "print_keyframes": ("BOOLEAN", {"default": False}), - }, - "optional": { - "prev_hook_kf": ("HOOK_KEYFRAMES",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOK_KEYFRAMES",) - RETURN_NAMES = ("HOOK_KF",) - CATEGORY = "advanced/hooks/scheduling" - FUNCTION = "create_hook_keyframes" - - def create_hook_keyframes(self, floats_strength: Union[float, list[float]], - start_percent: float, end_percent: float, - prev_hook_kf: comfy.hooks.HookKeyframeGroup=None, print_keyframes=False): - if prev_hook_kf is None: - prev_hook_kf = comfy.hooks.HookKeyframeGroup() - prev_hook_kf = prev_hook_kf.clone() - if type(floats_strength) in (float, int): - floats_strength = [float(floats_strength)] - elif isinstance(floats_strength, Iterable): - pass - else: - raise Exception(f"floats_strength must be either an iterable input or a float, but was{type(floats_strength).__repr__}.") - percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=len(floats_strength), - method=comfy.hooks.InterpolationMethod.LINEAR) - - is_first = True - for percent, strength in zip(percents, floats_strength): - guarantee_steps = 0 - if is_first: - guarantee_steps = 1 - is_first = False - prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps)) - if print_keyframes: - logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}") - return (prev_hook_kf,) -#------------------------------------------ -########################################### - - -class SetModelHooksOnCond: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "conditioning": ("CONDITIONING",), - "hooks": ("HOOKS",), - }, - } - - EXPERIMENTAL = True - RETURN_TYPES = ("CONDITIONING",) - CATEGORY = "advanced/hooks/manual" - FUNCTION = "attach_hook" - - def attach_hook(self, conditioning, hooks: comfy.hooks.HookGroup): - return (comfy.hooks.set_hooks_for_conditioning(conditioning, hooks),) - - -########################################### -# Combine Hooks -#------------------------------------------ -class CombineHooks: - NodeId = 'CombineHooks2' - NodeName = 'Combine Hooks [2]' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "hooks_A": ("HOOKS",), - "hooks_B": ("HOOKS",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/combine" - FUNCTION = "combine_hooks" - - def combine_hooks(self, - hooks_A: comfy.hooks.HookGroup=None, - hooks_B: comfy.hooks.HookGroup=None): - candidates = [hooks_A, hooks_B] - return (comfy.hooks.HookGroup.combine_all_hooks(candidates),) - -class CombineHooksFour: - NodeId = 'CombineHooks4' - NodeName = 'Combine Hooks [4]' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "hooks_A": ("HOOKS",), - "hooks_B": ("HOOKS",), - "hooks_C": ("HOOKS",), - "hooks_D": ("HOOKS",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/combine" - FUNCTION = "combine_hooks" - - def combine_hooks(self, - hooks_A: comfy.hooks.HookGroup=None, - hooks_B: comfy.hooks.HookGroup=None, - hooks_C: comfy.hooks.HookGroup=None, - hooks_D: comfy.hooks.HookGroup=None): - candidates = [hooks_A, hooks_B, hooks_C, hooks_D] - return (comfy.hooks.HookGroup.combine_all_hooks(candidates),) - -class CombineHooksEight: - NodeId = 'CombineHooks8' - NodeName = 'Combine Hooks [8]' - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "hooks_A": ("HOOKS",), - "hooks_B": ("HOOKS",), - "hooks_C": ("HOOKS",), - "hooks_D": ("HOOKS",), - "hooks_E": ("HOOKS",), - "hooks_F": ("HOOKS",), - "hooks_G": ("HOOKS",), - "hooks_H": ("HOOKS",), - } - } - - EXPERIMENTAL = True - RETURN_TYPES = ("HOOKS",) - CATEGORY = "advanced/hooks/combine" - FUNCTION = "combine_hooks" - - def combine_hooks(self, - hooks_A: comfy.hooks.HookGroup=None, - hooks_B: comfy.hooks.HookGroup=None, - hooks_C: comfy.hooks.HookGroup=None, - hooks_D: comfy.hooks.HookGroup=None, - hooks_E: comfy.hooks.HookGroup=None, - hooks_F: comfy.hooks.HookGroup=None, - hooks_G: comfy.hooks.HookGroup=None, - hooks_H: comfy.hooks.HookGroup=None): - candidates = [hooks_A, hooks_B, hooks_C, hooks_D, hooks_E, hooks_F, hooks_G, hooks_H] - return (comfy.hooks.HookGroup.combine_all_hooks(candidates),) -#------------------------------------------ -########################################### - -node_list = [ - # Create - CreateHookLora, - CreateHookLoraModelOnly, - CreateHookModelAsLora, - CreateHookModelAsLoraModelOnly, - # Scheduling - SetHookKeyframes, - CreateHookKeyframe, - CreateHookKeyframesInterpolated, - CreateHookKeyframesFromFloats, - # Combine - CombineHooks, - CombineHooksFour, - CombineHooksEight, - # Attach - ConditioningSetProperties, - ConditioningSetPropertiesAndCombine, - PairConditioningSetProperties, - PairConditioningSetPropertiesAndCombine, - ConditioningSetDefaultAndCombine, - PairConditioningSetDefaultAndCombine, - PairConditioningCombine, - SetClipHooks, - # Other - ConditioningTimestepsRange, -] -NODE_CLASS_MAPPINGS = {} -NODE_DISPLAY_NAME_MAPPINGS = {} - -for node in node_list: - NODE_CLASS_MAPPINGS[node.NodeId] = node - NODE_DISPLAY_NAME_MAPPINGS[node.NodeId] = node.NodeName diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py deleted file mode 100644 index d7278e7a7d866dcc0519c66f1f5894d8b7344e1c..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_hunyuan.py +++ /dev/null @@ -1,123 +0,0 @@ -import nodes -import node_helpers -import torch -import comfy.model_management - - -class CLIPTextEncodeHunyuanDiT: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, bert, mt5xl): - tokens = clip.tokenize(bert) - tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"] - - return (clip.encode_from_tokens_scheduled(tokens), ) - -class EmptyHunyuanLatentVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 25, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/video" - - def generate(self, width, height, length, batch_size=1): - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - return ({"samples":latent}, ) - -PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( - "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " - "1. The main content and theme of the video." - "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." - "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." - "4. background environment, light, style and atmosphere." - "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" - "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" - "<|start_header_id|>assistant<|end_header_id|>\n\n" -) - -class TextEncodeHunyuanVideo_ImageToVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "clip_vision_output": ("CLIP_VISION_OUTPUT", ), - "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "image_interleave": ("INT", {"default": 2, "min": 1, "max": 512, "tooltip": "How much the image influences things vs the text prompt. Higher number means more influence from the text prompt."}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, clip_vision_output, prompt, image_interleave): - tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave) - return (clip.encode_from_tokens_scheduled(tokens), ) - -class HunyuanImageToVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "vae": ("VAE", ), - "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], ) - }, - "optional": {"start_image": ("IMAGE", ), - }} - - RETURN_TYPES = ("CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "latent") - FUNCTION = "encode" - - CATEGORY = "conditioning/video_models" - - def encode(self, positive, vae, width, height, length, batch_size, guidance_type, start_image=None): - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - out_latent = {} - - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - - concat_latent_image = vae.encode(start_image) - mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) - mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 - - if guidance_type == "v1 (concat)": - cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask} - elif guidance_type == "v2 (replace)": - cond = {'guiding_frame_index': 0} - latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image - out_latent["noise_mask"] = mask - elif guidance_type == "custom": - cond = {"ref_latent": concat_latent_image} - - positive = node_helpers.conditioning_set_values(positive, cond) - - out_latent["samples"] = latent - return (positive, out_latent) - - - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT, - "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo, - "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo, - "HunyuanImageToVideo": HunyuanImageToVideo, -} diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py deleted file mode 100644 index 51e45336ad4a450b8f83be1e22f0033ad3cd4433..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_hunyuan3d.py +++ /dev/null @@ -1,634 +0,0 @@ -import torch -import os -import json -import struct -import numpy as np -from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_from_grid_torch -import folder_paths -import comfy.model_management -from comfy.cli_args import args - - -class EmptyLatentHunyuan3Dv2: - @classmethod - def INPUT_TYPES(s): - return {"required": {"resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/3d" - - def generate(self, resolution, batch_size): - latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device()) - return ({"samples": latent, "type": "hunyuan3dv2"}, ) - - -class Hunyuan3Dv2Conditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": {"clip_vision_output": ("CLIP_VISION_OUTPUT",), - }} - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - - FUNCTION = "encode" - - CATEGORY = "conditioning/video_models" - - def encode(self, clip_vision_output): - embeds = clip_vision_output.last_hidden_state - positive = [[embeds, {}]] - negative = [[torch.zeros_like(embeds), {}]] - return (positive, negative) - - -class Hunyuan3Dv2ConditioningMultiView: - @classmethod - def INPUT_TYPES(s): - return {"required": {}, - "optional": {"front": ("CLIP_VISION_OUTPUT",), - "left": ("CLIP_VISION_OUTPUT",), - "back": ("CLIP_VISION_OUTPUT",), - "right": ("CLIP_VISION_OUTPUT",), }} - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - - FUNCTION = "encode" - - CATEGORY = "conditioning/video_models" - - def encode(self, front=None, left=None, back=None, right=None): - all_embeds = [front, left, back, right] - out = [] - pos_embeds = None - for i, e in enumerate(all_embeds): - if e is not None: - if pos_embeds is None: - pos_embeds = get_1d_sincos_pos_embed_from_grid_torch(e.last_hidden_state.shape[-1], torch.arange(4)) - out.append(e.last_hidden_state + pos_embeds[i].reshape(1, 1, -1)) - - embeds = torch.cat(out, dim=1) - positive = [[embeds, {}]] - negative = [[torch.zeros_like(embeds), {}]] - return (positive, negative) - - -class VOXEL: - def __init__(self, data): - self.data = data - - -class VAEDecodeHunyuan3D: - @classmethod - def INPUT_TYPES(s): - return {"required": {"samples": ("LATENT", ), - "vae": ("VAE", ), - "num_chunks": ("INT", {"default": 8000, "min": 1000, "max": 500000}), - "octree_resolution": ("INT", {"default": 256, "min": 16, "max": 512}), - }} - RETURN_TYPES = ("VOXEL",) - FUNCTION = "decode" - - CATEGORY = "latent/3d" - - def decode(self, vae, samples, num_chunks, octree_resolution): - voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution})) - return (voxels, ) - - -def voxel_to_mesh(voxels, threshold=0.5, device=None): - if device is None: - device = torch.device("cpu") - voxels = voxels.to(device) - - binary = (voxels > threshold).float() - padded = torch.nn.functional.pad(binary, (1, 1, 1, 1, 1, 1), 'constant', 0) - - D, H, W = binary.shape - - neighbors = torch.tensor([ - [0, 0, 1], - [0, 0, -1], - [0, 1, 0], - [0, -1, 0], - [1, 0, 0], - [-1, 0, 0] - ], device=device) - - z, y, x = torch.meshgrid( - torch.arange(D, device=device), - torch.arange(H, device=device), - torch.arange(W, device=device), - indexing='ij' - ) - voxel_indices = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1) - - solid_mask = binary.flatten() > 0 - solid_indices = voxel_indices[solid_mask] - - corner_offsets = [ - torch.tensor([ - [0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1] - ], device=device), - torch.tensor([ - [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0] - ], device=device), - torch.tensor([ - [0, 1, 0], [1, 1, 0], [1, 1, 1], [0, 1, 1] - ], device=device), - torch.tensor([ - [0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0] - ], device=device), - torch.tensor([ - [1, 0, 1], [1, 1, 1], [1, 1, 0], [1, 0, 0] - ], device=device), - torch.tensor([ - [0, 1, 0], [0, 1, 1], [0, 0, 1], [0, 0, 0] - ], device=device) - ] - - all_vertices = [] - all_indices = [] - - vertex_count = 0 - - for face_idx, offset in enumerate(neighbors): - neighbor_indices = solid_indices + offset - - padded_indices = neighbor_indices + 1 - - is_exposed = padded[ - padded_indices[:, 0], - padded_indices[:, 1], - padded_indices[:, 2] - ] == 0 - - if not is_exposed.any(): - continue - - exposed_indices = solid_indices[is_exposed] - - corners = corner_offsets[face_idx].unsqueeze(0) - - face_vertices = exposed_indices.unsqueeze(1) + corners - - all_vertices.append(face_vertices.reshape(-1, 3)) - - num_faces = exposed_indices.shape[0] - face_indices = torch.arange( - vertex_count, - vertex_count + 4 * num_faces, - device=device - ).reshape(-1, 4) - - all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 1], face_indices[:, 2]], dim=1)) - all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 2], face_indices[:, 3]], dim=1)) - - vertex_count += 4 * num_faces - - if len(all_vertices) > 0: - vertices = torch.cat(all_vertices, dim=0) - faces = torch.cat(all_indices, dim=0) - else: - vertices = torch.zeros((1, 3)) - faces = torch.zeros((1, 3)) - - v_min = 0 - v_max = max(voxels.shape) - - vertices = vertices - (v_min + v_max) / 2 - - scale = (v_max - v_min) / 2 - if scale > 0: - vertices = vertices / scale - - vertices = torch.fliplr(vertices) - return vertices, faces - -def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None): - if device is None: - device = torch.device("cpu") - voxels = voxels.to(device) - - D, H, W = voxels.shape - - padded = torch.nn.functional.pad(voxels, (1, 1, 1, 1, 1, 1), 'constant', 0) - z, y, x = torch.meshgrid( - torch.arange(D, device=device), - torch.arange(H, device=device), - torch.arange(W, device=device), - indexing='ij' - ) - cell_positions = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1) - - corner_offsets = torch.tensor([ - [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], - [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1] - ], device=device) - - corner_values = torch.zeros((cell_positions.shape[0], 8), device=device) - for c, (dz, dy, dx) in enumerate(corner_offsets): - corner_values[:, c] = padded[ - cell_positions[:, 0] + dz, - cell_positions[:, 1] + dy, - cell_positions[:, 2] + dx - ] - - corner_signs = corner_values > threshold - has_inside = torch.any(corner_signs, dim=1) - has_outside = torch.any(~corner_signs, dim=1) - contains_surface = has_inside & has_outside - - active_cells = cell_positions[contains_surface] - active_signs = corner_signs[contains_surface] - active_values = corner_values[contains_surface] - - if active_cells.shape[0] == 0: - return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device) - - edges = torch.tensor([ - [0, 1], [0, 2], [0, 4], [1, 3], - [1, 5], [2, 3], [2, 6], [3, 7], - [4, 5], [4, 6], [5, 7], [6, 7] - ], device=device) - - cell_vertices = {} - progress = comfy.utils.ProgressBar(100) - - for edge_idx, (e1, e2) in enumerate(edges): - progress.update(1) - crossing = active_signs[:, e1] != active_signs[:, e2] - if not crossing.any(): - continue - - cell_indices = torch.nonzero(crossing, as_tuple=True)[0] - - v1 = active_values[cell_indices, e1] - v2 = active_values[cell_indices, e2] - - t = torch.zeros_like(v1, device=device) - denom = v2 - v1 - valid = denom != 0 - t[valid] = (threshold - v1[valid]) / denom[valid] - t[~valid] = 0.5 - - p1 = corner_offsets[e1].float() - p2 = corner_offsets[e2].float() - - intersection = p1.unsqueeze(0) + t.unsqueeze(1) * (p2.unsqueeze(0) - p1.unsqueeze(0)) - - for i, point in zip(cell_indices.tolist(), intersection): - if i not in cell_vertices: - cell_vertices[i] = [] - cell_vertices[i].append(point) - - # Calculate the final vertices as the average of intersection points for each cell - vertices = [] - vertex_lookup = {} - - vert_progress_mod = round(len(cell_vertices)/50) - - for i, points in cell_vertices.items(): - if not i % vert_progress_mod: - progress.update(1) - - if points: - vertex = torch.stack(points).mean(dim=0) - vertex = vertex + active_cells[i].float() - vertex_lookup[tuple(active_cells[i].tolist())] = len(vertices) - vertices.append(vertex) - - if not vertices: - return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device) - - final_vertices = torch.stack(vertices) - - inside_corners_mask = active_signs - outside_corners_mask = ~active_signs - - inside_counts = inside_corners_mask.sum(dim=1, keepdim=True).float() - outside_counts = outside_corners_mask.sum(dim=1, keepdim=True).float() - - inside_pos = torch.zeros((active_cells.shape[0], 3), device=device) - outside_pos = torch.zeros((active_cells.shape[0], 3), device=device) - - for i in range(8): - mask_inside = inside_corners_mask[:, i].unsqueeze(1) - mask_outside = outside_corners_mask[:, i].unsqueeze(1) - inside_pos += corner_offsets[i].float().unsqueeze(0) * mask_inside - outside_pos += corner_offsets[i].float().unsqueeze(0) * mask_outside - - inside_pos /= inside_counts - outside_pos /= outside_counts - gradients = inside_pos - outside_pos - - pos_dirs = torch.tensor([ - [1, 0, 0], - [0, 1, 0], - [0, 0, 1] - ], device=device) - - cross_products = [ - torch.linalg.cross(pos_dirs[i].float(), pos_dirs[j].float()) - for i in range(3) for j in range(i+1, 3) - ] - - faces = [] - all_keys = set(vertex_lookup.keys()) - - face_progress_mod = round(len(active_cells)/38*3) - - for pair_idx, (i, j) in enumerate([(0,1), (0,2), (1,2)]): - dir_i = pos_dirs[i] - dir_j = pos_dirs[j] - cross_product = cross_products[pair_idx] - - ni_positions = active_cells + dir_i - nj_positions = active_cells + dir_j - diag_positions = active_cells + dir_i + dir_j - - alignments = torch.matmul(gradients, cross_product) - - valid_quads = [] - quad_indices = [] - - for idx, active_cell in enumerate(active_cells): - if not idx % face_progress_mod: - progress.update(1) - cell_key = tuple(active_cell.tolist()) - ni_key = tuple(ni_positions[idx].tolist()) - nj_key = tuple(nj_positions[idx].tolist()) - diag_key = tuple(diag_positions[idx].tolist()) - - if cell_key in all_keys and ni_key in all_keys and nj_key in all_keys and diag_key in all_keys: - v0 = vertex_lookup[cell_key] - v1 = vertex_lookup[ni_key] - v2 = vertex_lookup[nj_key] - v3 = vertex_lookup[diag_key] - - valid_quads.append((v0, v1, v2, v3)) - quad_indices.append(idx) - - for q_idx, (v0, v1, v2, v3) in enumerate(valid_quads): - cell_idx = quad_indices[q_idx] - if alignments[cell_idx] > 0: - faces.append(torch.tensor([v0, v1, v3], device=device, dtype=torch.long)) - faces.append(torch.tensor([v0, v3, v2], device=device, dtype=torch.long)) - else: - faces.append(torch.tensor([v0, v3, v1], device=device, dtype=torch.long)) - faces.append(torch.tensor([v0, v2, v3], device=device, dtype=torch.long)) - - if faces: - faces = torch.stack(faces) - else: - faces = torch.zeros((0, 3), dtype=torch.long, device=device) - - v_min = 0 - v_max = max(D, H, W) - - final_vertices = final_vertices - (v_min + v_max) / 2 - - scale = (v_max - v_min) / 2 - if scale > 0: - final_vertices = final_vertices / scale - - final_vertices = torch.fliplr(final_vertices) - - return final_vertices, faces - -class MESH: - def __init__(self, vertices, faces): - self.vertices = vertices - self.faces = faces - - -class VoxelToMeshBasic: - @classmethod - def INPUT_TYPES(s): - return {"required": {"voxel": ("VOXEL", ), - "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MESH",) - FUNCTION = "decode" - - CATEGORY = "3d" - - def decode(self, voxel, threshold): - vertices = [] - faces = [] - for x in voxel.data: - v, f = voxel_to_mesh(x, threshold=threshold, device=None) - vertices.append(v) - faces.append(f) - - return (MESH(torch.stack(vertices), torch.stack(faces)), ) - -class VoxelToMesh: - @classmethod - def INPUT_TYPES(s): - return {"required": {"voxel": ("VOXEL", ), - "algorithm": (["surface net", "basic"], ), - "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MESH",) - FUNCTION = "decode" - - CATEGORY = "3d" - - def decode(self, voxel, algorithm, threshold): - vertices = [] - faces = [] - - if algorithm == "basic": - mesh_function = voxel_to_mesh - elif algorithm == "surface net": - mesh_function = voxel_to_mesh_surfnet - - for x in voxel.data: - v, f = mesh_function(x, threshold=threshold, device=None) - vertices.append(v) - faces.append(f) - - return (MESH(torch.stack(vertices), torch.stack(faces)), ) - - -def save_glb(vertices, faces, filepath, metadata=None): - """ - Save PyTorch tensor vertices and faces as a GLB file without external dependencies. - - Parameters: - vertices: torch.Tensor of shape (N, 3) - The vertex coordinates - faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces) - filepath: str - Output filepath (should end with .glb) - """ - - # Convert tensors to numpy arrays - vertices_np = vertices.cpu().numpy().astype(np.float32) - faces_np = faces.cpu().numpy().astype(np.uint32) - - vertices_buffer = vertices_np.tobytes() - indices_buffer = faces_np.tobytes() - - def pad_to_4_bytes(buffer): - padding_length = (4 - (len(buffer) % 4)) % 4 - return buffer + b'\x00' * padding_length - - vertices_buffer_padded = pad_to_4_bytes(vertices_buffer) - indices_buffer_padded = pad_to_4_bytes(indices_buffer) - - buffer_data = vertices_buffer_padded + indices_buffer_padded - - vertices_byte_length = len(vertices_buffer) - vertices_byte_offset = 0 - indices_byte_length = len(indices_buffer) - indices_byte_offset = len(vertices_buffer_padded) - - gltf = { - "asset": {"version": "2.0", "generator": "ComfyUI"}, - "buffers": [ - { - "byteLength": len(buffer_data) - } - ], - "bufferViews": [ - { - "buffer": 0, - "byteOffset": vertices_byte_offset, - "byteLength": vertices_byte_length, - "target": 34962 # ARRAY_BUFFER - }, - { - "buffer": 0, - "byteOffset": indices_byte_offset, - "byteLength": indices_byte_length, - "target": 34963 # ELEMENT_ARRAY_BUFFER - } - ], - "accessors": [ - { - "bufferView": 0, - "byteOffset": 0, - "componentType": 5126, # FLOAT - "count": len(vertices_np), - "type": "VEC3", - "max": vertices_np.max(axis=0).tolist(), - "min": vertices_np.min(axis=0).tolist() - }, - { - "bufferView": 1, - "byteOffset": 0, - "componentType": 5125, # UNSIGNED_INT - "count": faces_np.size, - "type": "SCALAR" - } - ], - "meshes": [ - { - "primitives": [ - { - "attributes": { - "POSITION": 0 - }, - "indices": 1, - "mode": 4 # TRIANGLES - } - ] - } - ], - "nodes": [ - { - "mesh": 0 - } - ], - "scenes": [ - { - "nodes": [0] - } - ], - "scene": 0 - } - - if metadata is not None: - gltf["asset"]["extras"] = metadata - - # Convert the JSON to bytes - gltf_json = json.dumps(gltf).encode('utf8') - - def pad_json_to_4_bytes(buffer): - padding_length = (4 - (len(buffer) % 4)) % 4 - return buffer + b' ' * padding_length - - gltf_json_padded = pad_json_to_4_bytes(gltf_json) - - # Create the GLB header - # Magic glTF - glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data)) - - # Create JSON chunk header (chunk type 0) - json_chunk_header = struct.pack(' int: - min_value = min(min_value, value) - - # All big divisors of value (inclusive) - divisors = [i for i in range(min_value, value + 1) if value % i == 0] - - ns = [value // i for i in divisors[:max_options]] # has at least 1 element - - if len(ns) - 1 > 0: - idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() - else: - idx = 0 - - return ns[idx] - -class HyperTile: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), - "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), - "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), - "scale_depth": ("BOOLEAN", {"default": False}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, tile_size, swap_size, max_depth, scale_depth): - latent_tile_size = max(32, tile_size) // 8 - self.temp = None - - def hypertile_in(q, k, v, extra_options): - model_chans = q.shape[-2] - orig_shape = extra_options['original_shape'] - apply_to = [] - for i in range(max_depth + 1): - apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) - - if model_chans in apply_to: - shape = extra_options["original_shape"] - aspect_ratio = shape[-1] / shape[-2] - - hw = q.size(1) - h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) - - factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 - nh = random_divisor(h, latent_tile_size * factor, swap_size) - nw = random_divisor(w, latent_tile_size * factor, swap_size) - - if nh * nw > 1: - q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) - self.temp = (nh, nw, h, w) - return q, k, v - - return q, k, v - def hypertile_out(out, extra_options): - if self.temp is not None: - nh, nw, h, w = self.temp - self.temp = None - out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) - out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) - return out - - - m = model.clone() - m.set_model_attn1_patch(hypertile_in) - m.set_model_attn1_output_patch(hypertile_out) - return (m, ) - -NODE_CLASS_MAPPINGS = { - "HyperTile": HyperTile, -} diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py deleted file mode 100644 index fba80e2aeafe88a6f3d68c1d0715312b34f25b7f..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_images.py +++ /dev/null @@ -1,642 +0,0 @@ -from __future__ import annotations - -import nodes -import folder_paths -from comfy.cli_args import args - -from PIL import Image -from PIL.PngImagePlugin import PngInfo - -import numpy as np -import json -import os -import re -from io import BytesIO -from inspect import cleandoc -import torch -import comfy.utils - -from comfy.comfy_types import FileLocator, IO -from server import PromptServer - -MAX_RESOLUTION = nodes.MAX_RESOLUTION - -class ImageCrop: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), - "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "crop" - - CATEGORY = "image/transform" - - def crop(self, image, width, height, x, y): - x = min(x, image.shape[2] - 1) - y = min(y, image.shape[1] - 1) - to_x = width + x - to_y = height + y - img = image[:,y:to_y, x:to_x, :] - return (img,) - -class RepeatImageBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), - "amount": ("INT", {"default": 1, "min": 1, "max": 4096}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "repeat" - - CATEGORY = "image/batch" - - def repeat(self, image, amount): - s = image.repeat((amount, 1,1,1)) - return (s,) - -class ImageFromBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), - "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}), - "length": ("INT", {"default": 1, "min": 1, "max": 4096}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "frombatch" - - CATEGORY = "image/batch" - - def frombatch(self, image, batch_index, length): - s_in = image - batch_index = min(s_in.shape[0] - 1, batch_index) - length = min(s_in.shape[0] - batch_index, length) - s = s_in[batch_index:batch_index + length].clone() - return (s,) - - -class ImageAddNoise: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), - "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "repeat" - - CATEGORY = "image" - - def repeat(self, image, seed, strength): - generator = torch.manual_seed(seed) - s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0) - return (s,) - -class SaveAnimatedWEBP: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - methods = {"default": 4, "fastest": 0, "slowest": 6} - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ), - "filename_prefix": ("STRING", {"default": "ComfyUI"}), - "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), - "lossless": ("BOOLEAN", {"default": True}), - "quality": ("INT", {"default": 80, "min": 0, "max": 100}), - "method": (list(s.methods.keys()),), - # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "image/animation" - - def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): - method = self.methods.get(method) - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - results: list[FileLocator] = [] - pil_images = [] - for image in images: - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - pil_images.append(img) - - metadata = pil_images[0].getexif() - if not args.disable_metadata: - if prompt is not None: - metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) - if extra_pnginfo is not None: - inital_exif = 0x010f - for x in extra_pnginfo: - metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) - inital_exif -= 1 - - if num_frames == 0: - num_frames = len(pil_images) - - c = len(pil_images) - for i in range(0, c, num_frames): - file = f"{filename}_{counter:05}_.webp" - pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - - animated = num_frames != 1 - return { "ui": { "images": results, "animated": (animated,) } } - -class SaveAnimatedPNG: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ), - "filename_prefix": ("STRING", {"default": "ComfyUI"}), - "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), - "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "image/animation" - - def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - results = list() - pil_images = [] - for image in images: - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - pil_images.append(img) - - metadata = None - if not args.disable_metadata: - metadata = PngInfo() - if prompt is not None: - metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) - - file = f"{filename}_{counter:05}_.png" - pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - - return { "ui": { "images": results, "animated": (True,)} } - -class SVG: - """ - Stores SVG representations via a list of BytesIO objects. - """ - def __init__(self, data: list[BytesIO]): - self.data = data - - def combine(self, other: 'SVG') -> 'SVG': - return SVG(self.data + other.data) - - @staticmethod - def combine_all(svgs: list['SVG']) -> 'SVG': - all_svgs_list: list[BytesIO] = [] - for svg_item in svgs: - all_svgs_list.extend(svg_item.data) - return SVG(all_svgs_list) - - -class ImageStitch: - """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image1": ("IMAGE",), - "direction": (["right", "down", "left", "up"], {"default": "right"}), - "match_image_size": ("BOOLEAN", {"default": True}), - "spacing_width": ( - "INT", - {"default": 0, "min": 0, "max": 1024, "step": 2}, - ), - "spacing_color": ( - ["white", "black", "red", "green", "blue"], - {"default": "white"}, - ), - }, - "optional": { - "image2": ("IMAGE",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "stitch" - CATEGORY = "image/transform" - DESCRIPTION = """ -Stitches image2 to image1 in the specified direction. -If image2 is not provided, returns image1 unchanged. -Optional spacing can be added between images. -""" - - def stitch( - self, - image1, - direction, - match_image_size, - spacing_width, - spacing_color, - image2=None, - ): - if image2 is None: - return (image1,) - - # Handle batch size differences - if image1.shape[0] != image2.shape[0]: - max_batch = max(image1.shape[0], image2.shape[0]) - if image1.shape[0] < max_batch: - image1 = torch.cat( - [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)] - ) - if image2.shape[0] < max_batch: - image2 = torch.cat( - [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)] - ) - - # Match image sizes if requested - if match_image_size: - h1, w1 = image1.shape[1:3] - h2, w2 = image2.shape[1:3] - aspect_ratio = w2 / h2 - - if direction in ["left", "right"]: - target_h, target_w = h1, int(h1 * aspect_ratio) - else: # up, down - target_w, target_h = w1, int(w1 / aspect_ratio) - - image2 = comfy.utils.common_upscale( - image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled" - ).movedim(1, -1) - - color_map = { - "white": 1.0, - "black": 0.0, - "red": (1.0, 0.0, 0.0), - "green": (0.0, 1.0, 0.0), - "blue": (0.0, 0.0, 1.0), - } - - color_val = color_map[spacing_color] - - # When not matching sizes, pad to align non-concat dimensions - if not match_image_size: - h1, w1 = image1.shape[1:3] - h2, w2 = image2.shape[1:3] - pad_value = 0.0 - if not isinstance(color_val, tuple): - pad_value = color_val - - if direction in ["left", "right"]: - # For horizontal concat, pad heights to match - if h1 != h2: - target_h = max(h1, h2) - if h1 < target_h: - pad_h = target_h - h1 - pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 - image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value) - if h2 < target_h: - pad_h = target_h - h2 - pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 - image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value) - else: # up, down - # For vertical concat, pad widths to match - if w1 != w2: - target_w = max(w1, w2) - if w1 < target_w: - pad_w = target_w - w1 - pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 - image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value) - if w2 < target_w: - pad_w = target_w - w2 - pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 - image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value) - - # Ensure same number of channels - if image1.shape[-1] != image2.shape[-1]: - max_channels = max(image1.shape[-1], image2.shape[-1]) - if image1.shape[-1] < max_channels: - image1 = torch.cat( - [ - image1, - torch.ones( - *image1.shape[:-1], - max_channels - image1.shape[-1], - device=image1.device, - ), - ], - dim=-1, - ) - if image2.shape[-1] < max_channels: - image2 = torch.cat( - [ - image2, - torch.ones( - *image2.shape[:-1], - max_channels - image2.shape[-1], - device=image2.device, - ), - ], - dim=-1, - ) - - # Add spacing if specified - if spacing_width > 0: - spacing_width = spacing_width + (spacing_width % 2) # Ensure even - - if direction in ["left", "right"]: - spacing_shape = ( - image1.shape[0], - max(image1.shape[1], image2.shape[1]), - spacing_width, - image1.shape[-1], - ) - else: - spacing_shape = ( - image1.shape[0], - spacing_width, - max(image1.shape[2], image2.shape[2]), - image1.shape[-1], - ) - - spacing = torch.full(spacing_shape, 0.0, device=image1.device) - if isinstance(color_val, tuple): - for i, c in enumerate(color_val): - if i < spacing.shape[-1]: - spacing[..., i] = c - if spacing.shape[-1] == 4: # Add alpha - spacing[..., 3] = 1.0 - else: - spacing[..., : min(3, spacing.shape[-1])] = color_val - if spacing.shape[-1] == 4: - spacing[..., 3] = 1.0 - - # Concatenate images - images = [image2, image1] if direction in ["left", "up"] else [image1, image2] - if spacing_width > 0: - images.insert(1, spacing) - - concat_dim = 2 if direction in ["left", "right"] else 1 - return (torch.cat(images, dim=concat_dim),) - -class ResizeAndPadImage: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ("IMAGE",), - "target_width": ("INT", { - "default": 512, - "min": 1, - "max": MAX_RESOLUTION, - "step": 1 - }), - "target_height": ("INT", { - "default": 512, - "min": 1, - "max": MAX_RESOLUTION, - "step": 1 - }), - "padding_color": (["white", "black"],), - "interpolation": (["area", "bicubic", "nearest-exact", "bilinear", "lanczos"],), - } - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "resize_and_pad" - CATEGORY = "image/transform" - - def resize_and_pad(self, image, target_width, target_height, padding_color, interpolation): - batch_size, orig_height, orig_width, channels = image.shape - - scale_w = target_width / orig_width - scale_h = target_height / orig_height - scale = min(scale_w, scale_h) - - new_width = int(orig_width * scale) - new_height = int(orig_height * scale) - - image_permuted = image.permute(0, 3, 1, 2) - - resized = comfy.utils.common_upscale(image_permuted, new_width, new_height, interpolation, "disabled") - - pad_value = 0.0 if padding_color == "black" else 1.0 - padded = torch.full( - (batch_size, channels, target_height, target_width), - pad_value, - dtype=image.dtype, - device=image.device - ) - - y_offset = (target_height - new_height) // 2 - x_offset = (target_width - new_width) // 2 - - padded[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized - - output = padded.permute(0, 2, 3, 1) - return (output,) - -class SaveSVGNode: - """ - Save SVG files on disk. - """ - - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - RETURN_TYPES = () - DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value - FUNCTION = "save_svg" - CATEGORY = "image/save" # Changed - OUTPUT_NODE = True - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "svg": ("SVG",), # Changed - "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) - }, - "hidden": { - "prompt": "PROMPT", - "extra_pnginfo": "EXTRA_PNGINFO" - } - } - - def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results = list() - - # Prepare metadata JSON - metadata_dict = {} - if prompt is not None: - metadata_dict["prompt"] = prompt - if extra_pnginfo is not None: - metadata_dict.update(extra_pnginfo) - - # Convert metadata to JSON string - metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None - - for batch_number, svg_bytes in enumerate(svg.data): - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.svg" - - # Read SVG content - svg_bytes.seek(0) - svg_content = svg_bytes.read().decode('utf-8') - - # Inject metadata if available - if metadata_json: - # Create metadata element with CDATA section - metadata_element = f""" - - - """ - # Insert metadata after opening svg tag using regex with a replacement function - def replacement(match): - # match.group(1) contains the captured tag - return match.group(1) + '\n' + metadata_element - - # Apply the substitution - svg_content = re.sub(r'(]*>)', replacement, svg_content, flags=re.UNICODE) - - # Write the modified SVG to file - with open(os.path.join(full_output_folder, file), 'wb') as svg_file: - svg_file.write(svg_content.encode('utf-8')) - - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - return { "ui": { "images": results } } - -class GetImageSize: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": (IO.IMAGE,), - }, - "hidden": { - "unique_id": "UNIQUE_ID", - } - } - - RETURN_TYPES = (IO.INT, IO.INT, IO.INT) - RETURN_NAMES = ("width", "height", "batch_size") - FUNCTION = "get_size" - - CATEGORY = "image" - DESCRIPTION = """Returns width and height of the image, and passes it through unchanged.""" - - def get_size(self, image, unique_id=None) -> tuple[int, int]: - height = image.shape[1] - width = image.shape[2] - batch_size = image.shape[0] - - # Send progress text to display size on the node - if unique_id: - PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id) - - return width, height, batch_size - -class ImageRotate: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": (IO.IMAGE,), - "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), - }} - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "rotate" - - CATEGORY = "image/transform" - - def rotate(self, image, rotation): - rotate_by = 0 - if rotation.startswith("90"): - rotate_by = 1 - elif rotation.startswith("180"): - rotate_by = 2 - elif rotation.startswith("270"): - rotate_by = 3 - - image = torch.rot90(image, k=rotate_by, dims=[2, 1]) - return (image,) - -class ImageFlip: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": (IO.IMAGE,), - "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), - }} - RETURN_TYPES = (IO.IMAGE,) - FUNCTION = "flip" - - CATEGORY = "image/transform" - - def flip(self, image, flip_method): - if flip_method.startswith("x"): - image = torch.flip(image, dims=[1]) - elif flip_method.startswith("y"): - image = torch.flip(image, dims=[2]) - - return (image,) - - -NODE_CLASS_MAPPINGS = { - "ImageCrop": ImageCrop, - "RepeatImageBatch": RepeatImageBatch, - "ImageFromBatch": ImageFromBatch, - "ImageAddNoise": ImageAddNoise, - "SaveAnimatedWEBP": SaveAnimatedWEBP, - "SaveAnimatedPNG": SaveAnimatedPNG, - "SaveSVGNode": SaveSVGNode, - "ImageStitch": ImageStitch, - "ResizeAndPadImage": ResizeAndPadImage, - "GetImageSize": GetImageSize, - "ImageRotate": ImageRotate, - "ImageFlip": ImageFlip, -} diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py deleted file mode 100644 index c2e70a84c10ca5cc1b3ca853a97adc3c64fbb315..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_ip2p.py +++ /dev/null @@ -1,45 +0,0 @@ -import torch - -class InstructPixToPixConditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "vae": ("VAE", ), - "pixels": ("IMAGE", ), - }} - - RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - FUNCTION = "encode" - - CATEGORY = "conditioning/instructpix2pix" - - def encode(self, positive, negative, pixels, vae): - x = (pixels.shape[1] // 8) * 8 - y = (pixels.shape[2] // 8) * 8 - - if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % 8) // 2 - y_offset = (pixels.shape[2] % 8) // 2 - pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] - - concat_latent = vae.encode(pixels) - - out_latent = {} - out_latent["samples"] = torch.zeros_like(concat_latent) - - out = [] - for conditioning in [positive, negative]: - c = [] - for t in conditioning: - d = t[1].copy() - d["concat_latent_image"] = concat_latent - n = [t[0], d] - c.append(n) - out.append(c) - return (out[0], out[1], out_latent) - -NODE_CLASS_MAPPINGS = { - "InstructPixToPixConditioning": InstructPixToPixConditioning, -} diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py deleted file mode 100644 index 0f90cf60c9def2c56ac4613cba5b104873125d1f..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_latent.py +++ /dev/null @@ -1,358 +0,0 @@ -import comfy.utils -import comfy_extras.nodes_post_processing -import torch -import nodes - - -def reshape_latent_to(target_shape, latent, repeat_batch=True): - if latent.shape[1:] != target_shape[1:]: - latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center") - if repeat_batch: - return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) - else: - return latent - - -class LatentAdd: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2): - samples_out = samples1.copy() - - s1 = samples1["samples"] - s2 = samples2["samples"] - - s2 = reshape_latent_to(s1.shape, s2) - samples_out["samples"] = s1 + s2 - return (samples_out,) - -class LatentSubtract: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2): - samples_out = samples1.copy() - - s1 = samples1["samples"] - s2 = samples2["samples"] - - s2 = reshape_latent_to(s1.shape, s2) - samples_out["samples"] = s1 - s2 - return (samples_out,) - -class LatentMultiply: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), - }} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples, multiplier): - samples_out = samples.copy() - - s1 = samples["samples"] - samples_out["samples"] = s1 * multiplier - return (samples_out,) - -class LatentInterpolate: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), - "samples2": ("LATENT",), - "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2, ratio): - samples_out = samples1.copy() - - s1 = samples1["samples"] - s2 = samples2["samples"] - - s2 = reshape_latent_to(s1.shape, s2) - - m1 = torch.linalg.vector_norm(s1, dim=(1)) - m2 = torch.linalg.vector_norm(s2, dim=(1)) - - s1 = torch.nan_to_num(s1 / m1) - s2 = torch.nan_to_num(s2 / m2) - - t = (s1 * ratio + s2 * (1.0 - ratio)) - mt = torch.linalg.vector_norm(t, dim=(1)) - st = torch.nan_to_num(t / mt) - - samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) - return (samples_out,) - -class LatentConcat: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "dim": (["x", "-x", "y", "-y", "t", "-t"], )}} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2, dim): - samples_out = samples1.copy() - - s1 = samples1["samples"] - s2 = samples2["samples"] - s2 = comfy.utils.repeat_to_batch_size(s2, s1.shape[0]) - - if "-" in dim: - c = (s2, s1) - else: - c = (s1, s2) - - if "x" in dim: - dim = -1 - elif "y" in dim: - dim = -2 - elif "t" in dim: - dim = -3 - - samples_out["samples"] = torch.cat(c, dim=dim) - return (samples_out,) - -class LatentCut: - @classmethod - def INPUT_TYPES(s): - return {"required": {"samples": ("LATENT",), - "dim": (["x", "y", "t"], ), - "index": ("INT", {"default": 0, "min": -nodes.MAX_RESOLUTION, "max": nodes.MAX_RESOLUTION, "step": 1}), - "amount": ("INT", {"default": 1, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 1})}} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples, dim, index, amount): - samples_out = samples.copy() - - s1 = samples["samples"] - - if "x" in dim: - dim = s1.ndim - 1 - elif "y" in dim: - dim = s1.ndim - 2 - elif "t" in dim: - dim = s1.ndim - 3 - - if index >= 0: - index = min(index, s1.shape[dim] - 1) - amount = min(s1.shape[dim] - index, amount) - else: - index = max(index, -s1.shape[dim]) - amount = min(-index, amount) - - samples_out["samples"] = torch.narrow(s1, dim, index, amount) - return (samples_out,) - -class LatentBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "batch" - - CATEGORY = "latent/batch" - - def batch(self, samples1, samples2): - samples_out = samples1.copy() - s1 = samples1["samples"] - s2 = samples2["samples"] - - s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False) - s = torch.cat((s1, s2), dim=0) - samples_out["samples"] = s - samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) - return (samples_out,) - -class LatentBatchSeedBehavior: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples, seed_behavior): - samples_out = samples.copy() - latent = samples["samples"] - if seed_behavior == "random": - if 'batch_index' in samples_out: - samples_out.pop('batch_index') - elif seed_behavior == "fixed": - batch_number = samples_out.get("batch_index", [0])[0] - samples_out["batch_index"] = [batch_number] * latent.shape[0] - - return (samples_out,) - -class LatentApplyOperation: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "operation": ("LATENT_OPERATION",), - }} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def op(self, samples, operation): - samples_out = samples.copy() - - s1 = samples["samples"] - samples_out["samples"] = operation(latent=s1) - return (samples_out,) - -class LatentApplyOperationCFG: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "operation": ("LATENT_OPERATION",), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def patch(self, model, operation): - m = model.clone() - - def pre_cfg_function(args): - conds_out = args["conds_out"] - if len(conds_out) == 2: - conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1] - else: - conds_out[0] = operation(latent=conds_out[0]) - return conds_out - - m.set_model_sampler_pre_cfg_function(pre_cfg_function) - return (m, ) - -class LatentOperationTonemapReinhard: - @classmethod - def INPUT_TYPES(s): - return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} - - RETURN_TYPES = ("LATENT_OPERATION",) - FUNCTION = "op" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def op(self, multiplier): - def tonemap_reinhard(latent, **kwargs): - latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None] - normalized_latent = latent / latent_vector_magnitude - - mean = torch.mean(latent_vector_magnitude, dim=(1,2,3), keepdim=True) - std = torch.std(latent_vector_magnitude, dim=(1,2,3), keepdim=True) - - top = (std * 5 + mean) * multiplier - - #reinhard - latent_vector_magnitude *= (1.0 / top) - new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0) - new_magnitude *= top - - return normalized_latent * new_magnitude - return (tonemap_reinhard,) - -class LatentOperationSharpen: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "sharpen_radius": ("INT", { - "default": 9, - "min": 1, - "max": 31, - "step": 1 - }), - "sigma": ("FLOAT", { - "default": 1.0, - "min": 0.1, - "max": 10.0, - "step": 0.1 - }), - "alpha": ("FLOAT", { - "default": 0.1, - "min": 0.0, - "max": 5.0, - "step": 0.01 - }), - }} - - RETURN_TYPES = ("LATENT_OPERATION",) - FUNCTION = "op" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def op(self, sharpen_radius, sigma, alpha): - def sharpen(latent, **kwargs): - luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None] - normalized_latent = latent / luminance - channels = latent.shape[1] - - kernel_size = sharpen_radius * 2 + 1 - kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device) - center = kernel_size // 2 - - kernel *= alpha * -10 - kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 - - padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') - sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] - - return luminance * sharpened - return (sharpen,) - -NODE_CLASS_MAPPINGS = { - "LatentAdd": LatentAdd, - "LatentSubtract": LatentSubtract, - "LatentMultiply": LatentMultiply, - "LatentInterpolate": LatentInterpolate, - "LatentConcat": LatentConcat, - "LatentCut": LatentCut, - "LatentBatch": LatentBatch, - "LatentBatchSeedBehavior": LatentBatchSeedBehavior, - "LatentApplyOperation": LatentApplyOperation, - "LatentApplyOperationCFG": LatentApplyOperationCFG, - "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard, - "LatentOperationSharpen": LatentOperationSharpen, -} diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py deleted file mode 100644 index 899608149aba1861b9627f22d517dc0feb41d9cc..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_load_3d.py +++ /dev/null @@ -1,182 +0,0 @@ -import nodes -import folder_paths -import os - -from comfy.comfy_types import IO -from comfy_api.input_impl import VideoFromFile - -from pathlib import Path - - -def normalize_path(path): - return path.replace('\\', '/') - -class Load3D(): - @classmethod - def INPUT_TYPES(s): - input_dir = os.path.join(folder_paths.get_input_directory(), "3d") - - os.makedirs(input_dir, exist_ok=True) - - input_path = Path(input_dir) - base_path = Path(folder_paths.get_input_directory()) - - files = [ - normalize_path(str(file_path.relative_to(base_path))) - for file_path in input_path.rglob("*") - if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'} - ] - - return {"required": { - "model_file": (sorted(files), {"file_upload": True}), - "image": ("LOAD_3D", {}), - "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - }} - - RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) - RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video") - - FUNCTION = "process" - EXPERIMENTAL = True - - CATEGORY = "3d" - - def process(self, model_file, image, **kwargs): - image_path = folder_paths.get_annotated_filepath(image['image']) - mask_path = folder_paths.get_annotated_filepath(image['mask']) - normal_path = folder_paths.get_annotated_filepath(image['normal']) - lineart_path = folder_paths.get_annotated_filepath(image['lineart']) - - load_image_node = nodes.LoadImage() - output_image, ignore_mask = load_image_node.load_image(image=image_path) - ignore_image, output_mask = load_image_node.load_image(image=mask_path) - normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) - lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path) - - video = None - - if image['recording'] != "": - recording_video_path = folder_paths.get_annotated_filepath(image['recording']) - - video = VideoFromFile(recording_video_path) - - return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video - -class Load3DAnimation(): - @classmethod - def INPUT_TYPES(s): - input_dir = os.path.join(folder_paths.get_input_directory(), "3d") - - os.makedirs(input_dir, exist_ok=True) - - input_path = Path(input_dir) - base_path = Path(folder_paths.get_input_directory()) - - files = [ - normalize_path(str(file_path.relative_to(base_path))) - for file_path in input_path.rglob("*") - if file_path.suffix.lower() in {'.gltf', '.glb', '.fbx'} - ] - - return {"required": { - "model_file": (sorted(files), {"file_upload": True}), - "image": ("LOAD_3D_ANIMATION", {}), - "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - }} - - RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) - RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video") - - FUNCTION = "process" - EXPERIMENTAL = True - - CATEGORY = "3d" - - def process(self, model_file, image, **kwargs): - image_path = folder_paths.get_annotated_filepath(image['image']) - mask_path = folder_paths.get_annotated_filepath(image['mask']) - normal_path = folder_paths.get_annotated_filepath(image['normal']) - - load_image_node = nodes.LoadImage() - output_image, ignore_mask = load_image_node.load_image(image=image_path) - ignore_image, output_mask = load_image_node.load_image(image=mask_path) - normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) - - video = None - - if image['recording'] != "": - recording_video_path = folder_paths.get_annotated_filepath(image['recording']) - - video = VideoFromFile(recording_video_path) - - return output_image, output_mask, model_file, normal_image, image['camera_info'], video - -class Preview3D(): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model_file": ("STRING", {"default": "", "multiline": False}), - }, - "optional": { - "camera_info": ("LOAD3D_CAMERA", {}) - }} - - OUTPUT_NODE = True - RETURN_TYPES = () - - CATEGORY = "3d" - - FUNCTION = "process" - EXPERIMENTAL = True - - def process(self, model_file, **kwargs): - camera_info = kwargs.get("camera_info", None) - - return { - "ui": { - "result": [model_file, camera_info] - } - } - -class Preview3DAnimation(): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model_file": ("STRING", {"default": "", "multiline": False}), - }, - "optional": { - "camera_info": ("LOAD3D_CAMERA", {}) - }} - - OUTPUT_NODE = True - RETURN_TYPES = () - - CATEGORY = "3d" - - FUNCTION = "process" - EXPERIMENTAL = True - - def process(self, model_file, **kwargs): - camera_info = kwargs.get("camera_info", None) - - return { - "ui": { - "result": [model_file, camera_info] - } - } - -NODE_CLASS_MAPPINGS = { - "Load3D": Load3D, - "Load3DAnimation": Load3DAnimation, - "Preview3D": Preview3D, - "Preview3DAnimation": Preview3DAnimation -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "Load3D": "Load 3D", - "Load3DAnimation": "Load 3D - Animation", - "Preview3D": "Preview 3D", - "Preview3DAnimation": "Preview 3D - Animation" -} diff --git a/comfy_extras/nodes_lora_extract.py b/comfy_extras/nodes_lora_extract.py deleted file mode 100644 index dfd4fe9f4a5c4b7aff37d244fc25033e9a286119..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_lora_extract.py +++ /dev/null @@ -1,119 +0,0 @@ -import torch -import comfy.model_management -import comfy.utils -import folder_paths -import os -import logging -from enum import Enum - -CLAMP_QUANTILE = 0.99 - -def extract_lora(diff, rank): - conv2d = (len(diff.shape) == 4) - kernel_size = None if not conv2d else diff.size()[2:4] - conv2d_3x3 = conv2d and kernel_size != (1, 1) - out_dim, in_dim = diff.size()[0:2] - rank = min(rank, in_dim, out_dim) - - if conv2d: - if conv2d_3x3: - diff = diff.flatten(start_dim=1) - else: - diff = diff.squeeze() - - - U, S, Vh = torch.linalg.svd(diff.float()) - U = U[:, :rank] - S = S[:rank] - U = U @ torch.diag(S) - Vh = Vh[:rank, :] - - dist = torch.cat([U.flatten(), Vh.flatten()]) - hi_val = torch.quantile(dist, CLAMP_QUANTILE) - low_val = -hi_val - - U = U.clamp(low_val, hi_val) - Vh = Vh.clamp(low_val, hi_val) - if conv2d: - U = U.reshape(out_dim, rank, 1, 1) - Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) - return (U, Vh) - -class LORAType(Enum): - STANDARD = 0 - FULL_DIFF = 1 - -LORA_TYPES = {"standard": LORAType.STANDARD, - "full_diff": LORAType.FULL_DIFF} - -def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, bias_diff=False): - comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True) - sd = model_diff.model_state_dict(filter_prefix=prefix_model) - - for k in sd: - if k.endswith(".weight"): - weight_diff = sd[k] - if lora_type == LORAType.STANDARD: - if weight_diff.ndim < 2: - if bias_diff: - output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu() - continue - try: - out = extract_lora(weight_diff, rank) - output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().half().cpu() - output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().half().cpu() - except: - logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k)) - elif lora_type == LORAType.FULL_DIFF: - output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().half().cpu() - - elif bias_diff and k.endswith(".bias"): - output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu() - return output_sd - -class LoraSave: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}), - "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}), - "lora_type": (tuple(LORA_TYPES.keys()),), - "bias_diff": ("BOOLEAN", {"default": True}), - }, - "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}), - "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})}, - } - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "_for_testing" - - def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None): - if model_diff is None and text_encoder_diff is None: - return {} - - lora_type = LORA_TYPES.get(lora_type) - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - - output_sd = {} - if model_diff is not None: - output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, bias_diff=bias_diff) - if text_encoder_diff is not None: - output_sd = calc_lora_model(text_encoder_diff.patcher, rank, "", "text_encoders.", output_sd, lora_type, bias_diff=bias_diff) - - output_checkpoint = f"{filename}_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - - comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None) - return {} - -NODE_CLASS_MAPPINGS = { - "LoraSave": LoraSave -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "LoraSave": "Extract and Save Lora" -} diff --git a/comfy_extras/nodes_lotus.py b/comfy_extras/nodes_lotus.py deleted file mode 100644 index 739dbdd3dd49d1fd007c511e0f6d0da5bb619550..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_lotus.py +++ /dev/null @@ -1,29 +0,0 @@ -import torch -import comfy.model_management as mm - -class LotusConditioning: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - } - - RETURN_TYPES = ("CONDITIONING",) - RETURN_NAMES = ("conditioning",) - FUNCTION = "conditioning" - CATEGORY = "conditioning/lotus" - - def conditioning(self): - device = mm.get_torch_device() - #lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change - #and 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{"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "length": ("INT", {"default": 97, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/video/ltxv" - - def generate(self, width, height, length, batch_size=1): - latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) - return ({"samples": latent}, ) - - -class LTXVImgToVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "vae": ("VAE",), - "image": ("IMAGE",), - "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}), - }} - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - CATEGORY = "conditioning/video_models" - FUNCTION = "generate" - - def generate(self, positive, negative, image, vae, width, height, length, batch_size, strength): - pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - encode_pixels = pixels[:, :, :, :3] - t = vae.encode(encode_pixels) - - latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) - latent[:, :, :t.shape[2]] = t - - conditioning_latent_frames_mask = torch.ones( - (batch_size, 1, latent.shape[2], 1, 1), - dtype=torch.float32, - device=latent.device, - ) - conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength - - return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, ) - - -def conditioning_get_any_value(conditioning, key, default=None): - for t in conditioning: - if key in t[1]: - return t[1][key] - return default - - -def get_noise_mask(latent): - noise_mask = latent.get("noise_mask", None) - latent_image = latent["samples"] - if noise_mask is None: - batch_size, _, latent_length, _, _ = latent_image.shape - noise_mask = torch.ones( - (batch_size, 1, latent_length, 1, 1), - dtype=torch.float32, - device=latent_image.device, - ) - else: - noise_mask = noise_mask.clone() - return noise_mask - -def get_keyframe_idxs(cond): - keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None) - if keyframe_idxs is None: - return None, 0 - num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0] - return keyframe_idxs, num_keyframes - -class LTXVAddGuide: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "vae": ("VAE",), - "latent": ("LATENT",), - "image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames." - "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames."}), - "frame_idx": ("INT", {"default": 0, "min": -9999, "max": 9999, - "tooltip": "Frame index to start the conditioning at. For single-frame images or " - "videos with 1-8 frames, any frame_idx value is acceptable. For videos with 9+ " - "frames, frame_idx must be divisible by 8, otherwise it will be rounded down to " - "the nearest multiple of 8. Negative values are counted from the end of the video."}), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - CATEGORY = "conditioning/video_models" - FUNCTION = "generate" - - def __init__(self): - self._num_prefix_frames = 2 - self._patchifier = SymmetricPatchifier(1) - - def encode(self, vae, latent_width, latent_height, images, scale_factors): - time_scale_factor, width_scale_factor, height_scale_factor = scale_factors - images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1] - pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1) - encode_pixels = pixels[:, :, :, :3] - t = vae.encode(encode_pixels) - return encode_pixels, t - - def get_latent_index(self, cond, latent_length, guide_length, frame_idx, scale_factors): - time_scale_factor, _, _ = scale_factors - _, num_keyframes = get_keyframe_idxs(cond) - latent_count = latent_length - num_keyframes - frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0) - if guide_length > 1 and frame_idx != 0: - frame_idx = (frame_idx - 1) // time_scale_factor * time_scale_factor + 1 # frame index - 1 must be divisible by 8 or frame_idx == 0 - - latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor - - return frame_idx, latent_idx - - def add_keyframe_index(self, cond, frame_idx, guiding_latent, scale_factors): - keyframe_idxs, _ = get_keyframe_idxs(cond) - _, latent_coords = self._patchifier.patchify(guiding_latent) - pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=frame_idx == 0) # we need the causal fix only if we're placing the new latents at index 0 - pixel_coords[:, 0] += frame_idx - if keyframe_idxs is None: - keyframe_idxs = pixel_coords - else: - keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2) - return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs}) - - def append_keyframe(self, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors): - _, latent_idx = self.get_latent_index( - cond=positive, - latent_length=latent_image.shape[2], - guide_length=guiding_latent.shape[2], - frame_idx=frame_idx, - scale_factors=scale_factors, - ) - noise_mask[:, :, latent_idx:latent_idx + guiding_latent.shape[2]] = 1.0 - - positive = self.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors) - negative = self.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors) - - mask = torch.full( - (noise_mask.shape[0], 1, guiding_latent.shape[2], noise_mask.shape[3], noise_mask.shape[4]), - 1.0 - strength, - dtype=noise_mask.dtype, - device=noise_mask.device, - ) - - latent_image = torch.cat([latent_image, guiding_latent], dim=2) - noise_mask = torch.cat([noise_mask, mask], dim=2) - return positive, negative, latent_image, noise_mask - - def replace_latent_frames(self, latent_image, noise_mask, guiding_latent, latent_idx, strength): - cond_length = guiding_latent.shape[2] - assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence." - - mask = torch.full( - (noise_mask.shape[0], 1, cond_length, 1, 1), - 1.0 - strength, - dtype=noise_mask.dtype, - device=noise_mask.device, - ) - - latent_image = latent_image.clone() - noise_mask = noise_mask.clone() - - latent_image[:, :, latent_idx : latent_idx + cond_length] = guiding_latent - noise_mask[:, :, latent_idx : latent_idx + cond_length] = mask - - return latent_image, noise_mask - - def generate(self, positive, negative, vae, latent, image, frame_idx, strength): - scale_factors = vae.downscale_index_formula - latent_image = latent["samples"] - noise_mask = get_noise_mask(latent) - - _, _, latent_length, latent_height, latent_width = latent_image.shape - image, t = self.encode(vae, latent_width, latent_height, image, scale_factors) - - frame_idx, latent_idx = self.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors) - assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence." - - num_prefix_frames = min(self._num_prefix_frames, t.shape[2]) - - positive, negative, latent_image, noise_mask = self.append_keyframe( - positive, - negative, - frame_idx, - latent_image, - noise_mask, - t[:, :, :num_prefix_frames], - strength, - scale_factors, - ) - - latent_idx += num_prefix_frames - - t = t[:, :, num_prefix_frames:] - if t.shape[2] == 0: - return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) - - latent_image, noise_mask = self.replace_latent_frames( - latent_image, - noise_mask, - t, - latent_idx, - strength, - ) - - return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) - - -class LTXVCropGuides: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "latent": ("LATENT",), - } - } - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - CATEGORY = "conditioning/video_models" - FUNCTION = "crop" - - def __init__(self): - self._patchifier = SymmetricPatchifier(1) - - def crop(self, positive, negative, latent): - latent_image = latent["samples"].clone() - noise_mask = get_noise_mask(latent) - - _, num_keyframes = get_keyframe_idxs(positive) - if num_keyframes == 0: - return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) - - latent_image = latent_image[:, :, :-num_keyframes] - noise_mask = noise_mask[:, :, :-num_keyframes] - - positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None}) - negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None}) - - return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) - - -class LTXVConditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - FUNCTION = "append" - - CATEGORY = "conditioning/video_models" - - def append(self, positive, negative, frame_rate): - positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate}) - negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate}) - return (positive, negative) - - -class ModelSamplingLTXV: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), - "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), - }, - "optional": {"latent": ("LATENT",), } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, max_shift, base_shift, latent=None): - m = model.clone() - - if latent is None: - tokens = 4096 - else: - tokens = math.prod(latent["samples"].shape[2:]) - - x1 = 1024 - x2 = 4096 - mm = (max_shift - base_shift) / (x2 - x1) - b = base_shift - mm * x1 - shift = (tokens) * mm + b - - sampling_base = comfy.model_sampling.ModelSamplingFlux - sampling_type = comfy.model_sampling.CONST - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_parameters(shift=shift) - m.add_object_patch("model_sampling", model_sampling) - - return (m, ) - - -class LTXVScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), - "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), - "stretch": ("BOOLEAN", { - "default": True, - "tooltip": "Stretch the sigmas to be in the range [terminal, 1]." - }), - "terminal": ( - "FLOAT", - { - "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01, - "tooltip": "The terminal value of the sigmas after stretching." - }, - ), - }, - "optional": {"latent": ("LATENT",), } - } - - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, max_shift, base_shift, stretch, terminal, latent=None): - if latent is None: - tokens = 4096 - else: - tokens = math.prod(latent["samples"].shape[2:]) - - sigmas = torch.linspace(1.0, 0.0, steps + 1) - - x1 = 1024 - x2 = 4096 - mm = (max_shift - base_shift) / (x2 - x1) - b = base_shift - mm * x1 - sigma_shift = (tokens) * mm + b - - power = 1 - sigmas = torch.where( - sigmas != 0, - math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power), - 0, - ) - - # Stretch sigmas so that its final value matches the given terminal value. - if stretch: - non_zero_mask = sigmas != 0 - non_zero_sigmas = sigmas[non_zero_mask] - one_minus_z = 1.0 - non_zero_sigmas - scale_factor = one_minus_z[-1] / (1.0 - terminal) - stretched = 1.0 - (one_minus_z / scale_factor) - sigmas[non_zero_mask] = stretched - - return (sigmas,) - -def encode_single_frame(output_file, image_array: np.ndarray, crf): - container = av.open(output_file, "w", format="mp4") - try: - stream = container.add_stream( - "libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"} - ) - stream.height = image_array.shape[0] - stream.width = image_array.shape[1] - av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat( - format="yuv420p" - ) - container.mux(stream.encode(av_frame)) - container.mux(stream.encode()) - finally: - container.close() - - -def decode_single_frame(video_file): - container = av.open(video_file) - try: - stream = next(s for s in container.streams if s.type == "video") - frame = next(container.decode(stream)) - finally: - container.close() - return frame.to_ndarray(format="rgb24") - - -def preprocess(image: torch.Tensor, crf=29): - if crf == 0: - return image - - image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy() - with io.BytesIO() as output_file: - encode_single_frame(output_file, image_array, crf) - video_bytes = output_file.getvalue() - with io.BytesIO(video_bytes) as video_file: - image_array = decode_single_frame(video_file) - tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0 - return tensor - - -class LTXVPreprocess: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "img_compression": ( - "INT", - { - "default": 35, - "min": 0, - "max": 100, - "tooltip": "Amount of compression to apply on image.", - }, - ), - } - } - - FUNCTION = "preprocess" - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("output_image",) - CATEGORY = "image" - - def preprocess(self, image, img_compression): - output_images = [] - for i in range(image.shape[0]): - output_images.append(preprocess(image[i], img_compression)) - return (torch.stack(output_images),) - - -NODE_CLASS_MAPPINGS = { - "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo, - "LTXVImgToVideo": LTXVImgToVideo, - "ModelSamplingLTXV": ModelSamplingLTXV, - "LTXVConditioning": LTXVConditioning, - "LTXVScheduler": LTXVScheduler, - "LTXVAddGuide": LTXVAddGuide, - "LTXVPreprocess": LTXVPreprocess, - "LTXVCropGuides": LTXVCropGuides, -} diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py deleted file mode 100644 index 275189785dca4dd6a9c56b58e87a0ab93342d9ec..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_lumina2.py +++ /dev/null @@ -1,104 +0,0 @@ -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict -import torch - - -class RenormCFG: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}), - "renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, cfg_trunc, renorm_cfg): - def renorm_cfg_func(args): - cond_denoised = args["cond_denoised"] - uncond_denoised = args["uncond_denoised"] - cond_scale = args["cond_scale"] - timestep = args["timestep"] - x_orig = args["input"] - in_channels = model.model.diffusion_model.in_channels - - if timestep[0] < cfg_trunc: - cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels] - cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:] - half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps) - half_rest = cond_rest - - if float(renorm_cfg) > 0.0: - ori_pos_norm = torch.linalg.vector_norm(cond_eps - , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True - ) - max_new_norm = ori_pos_norm * float(renorm_cfg) - new_pos_norm = torch.linalg.vector_norm( - half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True - ) - if new_pos_norm >= max_new_norm: - half_eps = half_eps * (max_new_norm / new_pos_norm) - else: - cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels] - cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:] - half_eps = cond_eps - half_rest = cond_rest - - cfg_result = torch.cat([half_eps, half_rest], dim=1) - - # cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale - - return x_orig - cfg_result - - m = model.clone() - m.set_model_sampler_cfg_function(renorm_cfg_func) - return (m, ) - - -class CLIPTextEncodeLumina2(ComfyNodeABC): - SYSTEM_PROMPT = { - "superior": "You are an assistant designed to generate superior images with the superior "\ - "degree of image-text alignment based on textual prompts or user prompts.", - "alignment": "You are an assistant designed to generate high-quality images with the "\ - "highest degree of image-text alignment based on textual prompts." - } - SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \ - "Superior: You are an assistant designed to generate superior images with the superior "\ - "degree of image-text alignment based on textual prompts or user prompts. "\ - "Alignment: You are an assistant designed to generate high-quality images with the highest "\ - "degree of image-text alignment based on textual prompts." - @classmethod - def INPUT_TYPES(s) -> InputTypeDict: - return { - "required": { - "system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}), - "user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}), - "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}) - } - } - RETURN_TYPES = (IO.CONDITIONING,) - OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",) - FUNCTION = "encode" - - CATEGORY = "conditioning" - DESCRIPTION = "Encodes a system prompt and a user prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." - - def encode(self, clip, user_prompt, system_prompt): - if clip is None: - raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") - system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt] - prompt = f'{system_prompt} {user_prompt}' - tokens = clip.tokenize(prompt) - return (clip.encode_from_tokens_scheduled(tokens), ) - - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeLumina2": CLIPTextEncodeLumina2, - "RenormCFG": RenormCFG -} - - -NODE_DISPLAY_NAME_MAPPINGS = { - "CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2", -} diff --git a/comfy_extras/nodes_mahiro.py b/comfy_extras/nodes_mahiro.py deleted file mode 100644 index 8fcdfba759f97515e5f64d3a130b0d892005cff2..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_mahiro.py +++ /dev/null @@ -1,41 +0,0 @@ -import torch -import torch.nn.functional as F - -class Mahiro: - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL",), - }} - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - CATEGORY = "_for_testing" - DESCRIPTION = "Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt." - def patch(self, model): - m = model.clone() - def mahiro_normd(args): - scale: float = args['cond_scale'] - cond_p: torch.Tensor = args['cond_denoised'] - uncond_p: torch.Tensor = args['uncond_denoised'] - #naive leap - leap = cond_p * scale - #sim with uncond leap - u_leap = uncond_p * scale - cfg = args["denoised"] - merge = (leap + cfg) / 2 - normu = torch.sqrt(u_leap.abs()) * u_leap.sign() - normm = torch.sqrt(merge.abs()) * merge.sign() - sim = F.cosine_similarity(normu, normm).mean() - simsc = 2 * (sim+1) - wm = (simsc*cfg + (4-simsc)*leap) / 4 - return wm - m.set_model_sampler_post_cfg_function(mahiro_normd) - return (m, ) - -NODE_CLASS_MAPPINGS = { - "Mahiro": Mahiro -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "Mahiro": "Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)", -} diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py deleted file mode 100644 index 2b0f8dd5d72b121532ea1c8b595081210fe4779e..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_mask.py +++ /dev/null @@ -1,412 +0,0 @@ -import numpy as np -import scipy.ndimage -import torch -import comfy.utils -import node_helpers -import folder_paths -import random - -import nodes -from nodes import MAX_RESOLUTION - -def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): - source = source.to(destination.device) - if resize_source: - source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") - - source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) - - x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) - y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) - - left, top = (x // multiplier, y // multiplier) - right, bottom = (left + source.shape[3], top + source.shape[2],) - - if mask is None: - mask = torch.ones_like(source) - else: - mask = mask.to(destination.device, copy=True) - mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") - mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) - - # calculate the bounds of the source that will be overlapping the destination - # this prevents the source trying to overwrite latent pixels that are out of bounds - # of the destination - visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) - - mask = mask[:, :, :visible_height, :visible_width] - inverse_mask = torch.ones_like(mask) - mask - - source_portion = mask * source[:, :, :visible_height, :visible_width] - destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] - - destination[:, :, top:bottom, left:right] = source_portion + destination_portion - return destination - -class LatentCompositeMasked: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "destination": ("LATENT",), - "source": ("LATENT",), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "resize_source": ("BOOLEAN", {"default": False}), - }, - "optional": { - "mask": ("MASK",), - } - } - RETURN_TYPES = ("LATENT",) - FUNCTION = "composite" - - CATEGORY = "latent" - - def composite(self, destination, source, x, y, resize_source, mask = None): - output = destination.copy() - destination = destination["samples"].clone() - source = source["samples"] - output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) - return (output,) - -class ImageCompositeMasked: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "destination": ("IMAGE",), - "source": ("IMAGE",), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "resize_source": ("BOOLEAN", {"default": False}), - }, - "optional": { - "mask": ("MASK",), - } - } - RETURN_TYPES = ("IMAGE",) - FUNCTION = "composite" - - CATEGORY = "image" - - def composite(self, destination, source, x, y, resize_source, mask = None): - destination, source = node_helpers.image_alpha_fix(destination, source) - destination = destination.clone().movedim(-1, 1) - output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) - return (output,) - -class MaskToImage: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "mask": ("MASK",), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "mask_to_image" - - def mask_to_image(self, mask): - result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) - return (result,) - -class ImageToMask: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "channel": (["red", "green", "blue", "alpha"],), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - FUNCTION = "image_to_mask" - - def image_to_mask(self, image, channel): - channels = ["red", "green", "blue", "alpha"] - mask = image[:, :, :, channels.index(channel)] - return (mask,) - -class ImageColorToMask: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - FUNCTION = "image_to_mask" - - def image_to_mask(self, image, color): - temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) - temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] - mask = torch.where(temp == color, 1.0, 0).float() - return (mask,) - -class SolidMask: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - - FUNCTION = "solid" - - def solid(self, value, width, height): - out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") - return (out,) - -class InvertMask: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mask": ("MASK",), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - - FUNCTION = "invert" - - def invert(self, mask): - out = 1.0 - mask - return (out,) - -class CropMask: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mask": ("MASK",), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - - FUNCTION = "crop" - - def crop(self, mask, x, y, width, height): - mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) - out = mask[:, y:y + height, x:x + width] - return (out,) - -class MaskComposite: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "destination": ("MASK",), - "source": ("MASK",), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - - FUNCTION = "combine" - - def combine(self, destination, source, x, y, operation): - output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() - source = source.reshape((-1, source.shape[-2], source.shape[-1])) - - left, top = (x, y,) - right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) - visible_width, visible_height = (right - left, bottom - top,) - - source_portion = source[:, :visible_height, :visible_width] - destination_portion = output[:, top:bottom, left:right] - - if operation == "multiply": - output[:, top:bottom, left:right] = destination_portion * source_portion - elif operation == "add": - output[:, top:bottom, left:right] = destination_portion + source_portion - elif operation == "subtract": - output[:, top:bottom, left:right] = destination_portion - source_portion - elif operation == "and": - output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() - elif operation == "or": - output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() - elif operation == "xor": - output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() - - output = torch.clamp(output, 0.0, 1.0) - - return (output,) - -class FeatherMask: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mask": ("MASK",), - "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - - FUNCTION = "feather" - - def feather(self, mask, left, top, right, bottom): - output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() - - left = min(left, output.shape[-1]) - right = min(right, output.shape[-1]) - top = min(top, output.shape[-2]) - bottom = min(bottom, output.shape[-2]) - - for x in range(left): - feather_rate = (x + 1.0) / left - output[:, :, x] *= feather_rate - - for x in range(right): - feather_rate = (x + 1) / right - output[:, :, -x] *= feather_rate - - for y in range(top): - feather_rate = (y + 1) / top - output[:, y, :] *= feather_rate - - for y in range(bottom): - feather_rate = (y + 1) / bottom - output[:, -y, :] *= feather_rate - - return (output,) - -class GrowMask: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mask": ("MASK",), - "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), - "tapered_corners": ("BOOLEAN", {"default": True}), - }, - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - - FUNCTION = "expand_mask" - - def expand_mask(self, mask, expand, tapered_corners): - c = 0 if tapered_corners else 1 - kernel = np.array([[c, 1, c], - [1, 1, 1], - [c, 1, c]]) - mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) - out = [] - for m in mask: - output = m.numpy() - for _ in range(abs(expand)): - if expand < 0: - output = scipy.ndimage.grey_erosion(output, footprint=kernel) - else: - output = scipy.ndimage.grey_dilation(output, footprint=kernel) - output = torch.from_numpy(output) - out.append(output) - return (torch.stack(out, dim=0),) - -class ThresholdMask: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "mask": ("MASK",), - "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - FUNCTION = "image_to_mask" - - def image_to_mask(self, mask, value): - mask = (mask > value).float() - return (mask,) - -# Mask Preview - original implement from -# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81 -# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes -class MaskPreview(nodes.SaveImage): - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() - self.type = "temp" - self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) - self.compress_level = 4 - - @classmethod - def INPUT_TYPES(s): - return { - "required": {"mask": ("MASK",), }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - FUNCTION = "execute" - CATEGORY = "mask" - - def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): - preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) - return self.save_images(preview, filename_prefix, prompt, extra_pnginfo) - - -NODE_CLASS_MAPPINGS = { - "LatentCompositeMasked": LatentCompositeMasked, - "ImageCompositeMasked": ImageCompositeMasked, - "MaskToImage": MaskToImage, - "ImageToMask": ImageToMask, - "ImageColorToMask": ImageColorToMask, - "SolidMask": SolidMask, - "InvertMask": InvertMask, - "CropMask": CropMask, - "MaskComposite": MaskComposite, - "FeatherMask": FeatherMask, - "GrowMask": GrowMask, - "ThresholdMask": ThresholdMask, - "MaskPreview": MaskPreview -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "ImageToMask": "Convert Image to Mask", - "MaskToImage": "Convert Mask to Image", -} diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py deleted file mode 100644 index 1c474faa94eac8dc48f778460c0d833bee94dcf9..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_mochi.py +++ /dev/null @@ -1,23 +0,0 @@ -import nodes -import torch -import comfy.model_management - -class EmptyMochiLatentVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/video" - - def generate(self, width, height, length, batch_size=1): - latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - return ({"samples":latent}, ) - -NODE_CLASS_MAPPINGS = { - "EmptyMochiLatentVideo": EmptyMochiLatentVideo, -} diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py deleted file mode 100644 index ae5d2c563183262456fd6a9668445b6b798108ac..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_model_advanced.py +++ /dev/null @@ -1,329 +0,0 @@ -import comfy.sd -import comfy.model_sampling -import comfy.latent_formats -import nodes -import torch -import node_helpers - - -class LCM(comfy.model_sampling.EPS): - def calculate_denoised(self, sigma, model_output, model_input): - timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) - x0 = model_input - model_output * sigma - - sigma_data = 0.5 - scaled_timestep = timestep * 10.0 #timestep_scaling - - c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) - c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 - - return c_out * x0 + c_skip * model_input - -class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): - original_timesteps = 50 - - def __init__(self, model_config=None, zsnr=None): - super().__init__(model_config, zsnr=zsnr) - - self.skip_steps = self.num_timesteps // self.original_timesteps - - sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) - for x in range(self.original_timesteps): - sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] - - self.set_sigmas(sigmas_valid) - - def timestep(self, sigma): - log_sigma = sigma.log() - dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] - return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) - - def sigma(self, timestep): - t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) - low_idx = t.floor().long() - high_idx = t.ceil().long() - w = t.frac() - log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] - return log_sigma.exp().to(timestep.device) - - -class ModelSamplingDiscrete: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],), - "zsnr": ("BOOLEAN", {"default": False}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, sampling, zsnr): - m = model.clone() - - sampling_base = comfy.model_sampling.ModelSamplingDiscrete - if sampling == "eps": - sampling_type = comfy.model_sampling.EPS - elif sampling == "v_prediction": - sampling_type = comfy.model_sampling.V_PREDICTION - elif sampling == "lcm": - sampling_type = LCM - sampling_base = ModelSamplingDiscreteDistilled - elif sampling == "x0": - sampling_type = comfy.model_sampling.X0 - elif sampling == "img_to_img": - sampling_type = comfy.model_sampling.IMG_TO_IMG - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config, zsnr=zsnr) - - m.add_object_patch("model_sampling", model_sampling) - return (m, ) - -class ModelSamplingStableCascade: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, shift): - m = model.clone() - - sampling_base = comfy.model_sampling.StableCascadeSampling - sampling_type = comfy.model_sampling.EPS - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_parameters(shift) - m.add_object_patch("model_sampling", model_sampling) - return (m, ) - -class ModelSamplingSD3: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, shift, multiplier=1000): - m = model.clone() - - sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow - sampling_type = comfy.model_sampling.CONST - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_parameters(shift=shift, multiplier=multiplier) - m.add_object_patch("model_sampling", model_sampling) - return (m, ) - -class ModelSamplingAuraFlow(ModelSamplingSD3): - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}), - }} - - FUNCTION = "patch_aura" - - def patch_aura(self, model, shift): - return self.patch(model, shift, multiplier=1.0) - -class ModelSamplingFlux: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}), - "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}), - "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, max_shift, base_shift, width, height): - m = model.clone() - - x1 = 256 - x2 = 4096 - mm = (max_shift - base_shift) / (x2 - x1) - b = base_shift - mm * x1 - shift = (width * height / (8 * 8 * 2 * 2)) * mm + b - - sampling_base = comfy.model_sampling.ModelSamplingFlux - sampling_type = comfy.model_sampling.CONST - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_parameters(shift=shift) - m.add_object_patch("model_sampling", model_sampling) - return (m, ) - - -class ModelSamplingContinuousEDM: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],), - "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, sampling, sigma_max, sigma_min): - m = model.clone() - - sampling_base = comfy.model_sampling.ModelSamplingContinuousEDM - latent_format = None - sigma_data = 1.0 - if sampling == "eps": - sampling_type = comfy.model_sampling.EPS - elif sampling == "edm": - sampling_type = comfy.model_sampling.EDM - sigma_data = 0.5 - elif sampling == "v_prediction": - sampling_type = comfy.model_sampling.V_PREDICTION - elif sampling == "edm_playground_v2.5": - sampling_type = comfy.model_sampling.EDM - sigma_data = 0.5 - latent_format = comfy.latent_formats.SDXL_Playground_2_5() - elif sampling == "cosmos_rflow": - sampling_type = comfy.model_sampling.COSMOS_RFLOW - sampling_base = comfy.model_sampling.ModelSamplingCosmosRFlow - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) - m.add_object_patch("model_sampling", model_sampling) - if latent_format is not None: - m.add_object_patch("latent_format", latent_format) - return (m, ) - -class ModelSamplingContinuousV: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "sampling": (["v_prediction"],), - "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), - "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, sampling, sigma_max, sigma_min): - m = model.clone() - - sigma_data = 1.0 - if sampling == "v_prediction": - sampling_type = comfy.model_sampling.V_PREDICTION - - class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type): - pass - - model_sampling = ModelSamplingAdvanced(model.model.model_config) - model_sampling.set_parameters(sigma_min, sigma_max, sigma_data) - m.add_object_patch("model_sampling", model_sampling) - return (m, ) - -class RescaleCFG: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, multiplier): - def rescale_cfg(args): - cond = args["cond"] - uncond = args["uncond"] - cond_scale = args["cond_scale"] - sigma = args["sigma"] - sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) - x_orig = args["input"] - - #rescale cfg has to be done on v-pred model output - x = x_orig / (sigma * sigma + 1.0) - cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) - uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) - - #rescalecfg - x_cfg = uncond + cond_scale * (cond - uncond) - ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) - ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) - - x_rescaled = x_cfg * (ro_pos / ro_cfg) - x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg - - return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) - - m = model.clone() - m.set_model_sampler_cfg_function(rescale_cfg) - return (m, ) - -class ModelComputeDtype: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "dtype": (["default", "fp32", "fp16", "bf16"],), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/debug/model" - - def patch(self, model, dtype): - m = model.clone() - m.set_model_compute_dtype(node_helpers.string_to_torch_dtype(dtype)) - return (m, ) - - -NODE_CLASS_MAPPINGS = { - "ModelSamplingDiscrete": ModelSamplingDiscrete, - "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, - "ModelSamplingContinuousV": ModelSamplingContinuousV, - "ModelSamplingStableCascade": ModelSamplingStableCascade, - "ModelSamplingSD3": ModelSamplingSD3, - "ModelSamplingAuraFlow": ModelSamplingAuraFlow, - "ModelSamplingFlux": ModelSamplingFlux, - "RescaleCFG": RescaleCFG, - "ModelComputeDtype": ModelComputeDtype, -} diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py deleted file mode 100644 index 49420dee9260ced4e0d08c196be937354c5e1d4e..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_model_downscale.py +++ /dev/null @@ -1,53 +0,0 @@ -import comfy.utils - -class PatchModelAddDownscale: - upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), - "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), - "downscale_after_skip": ("BOOLEAN", {"default": True}), - "downscale_method": (s.upscale_methods,), - "upscale_method": (s.upscale_methods,), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): - model_sampling = model.get_model_object("model_sampling") - sigma_start = model_sampling.percent_to_sigma(start_percent) - sigma_end = model_sampling.percent_to_sigma(end_percent) - - def input_block_patch(h, transformer_options): - if transformer_options["block"][1] == block_number: - sigma = transformer_options["sigmas"][0].item() - if sigma <= sigma_start and sigma >= sigma_end: - h = comfy.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") - return h - - def output_block_patch(h, hsp, transformer_options): - if h.shape[2] != hsp.shape[2]: - h = comfy.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") - return h, hsp - - m = model.clone() - if downscale_after_skip: - m.set_model_input_block_patch_after_skip(input_block_patch) - else: - m.set_model_input_block_patch(input_block_patch) - m.set_model_output_block_patch(output_block_patch) - return (m, ) - -NODE_CLASS_MAPPINGS = { - "PatchModelAddDownscale": PatchModelAddDownscale, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - # Sampling - "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", -} diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py deleted file mode 100644 index f20beab7d480391cbd4deb2f855a43c18d19ae33..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_model_merging.py +++ /dev/null @@ -1,374 +0,0 @@ -import comfy.sd -import comfy.utils -import comfy.model_base -import comfy.model_management -import comfy.model_sampling - -import torch -import folder_paths -import json -import os - -from comfy.cli_args import args - -class ModelMergeSimple: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model1": ("MODEL",), - "model2": ("MODEL",), - "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, model1, model2, ratio): - m = model1.clone() - kp = model2.get_key_patches("diffusion_model.") - for k in kp: - m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) - return (m, ) - -class ModelSubtract: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model1": ("MODEL",), - "model2": ("MODEL",), - "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, model1, model2, multiplier): - m = model1.clone() - kp = model2.get_key_patches("diffusion_model.") - for k in kp: - m.add_patches({k: kp[k]}, - multiplier, multiplier) - return (m, ) - -class ModelAdd: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model1": ("MODEL",), - "model2": ("MODEL",), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, model1, model2): - m = model1.clone() - kp = model2.get_key_patches("diffusion_model.") - for k in kp: - m.add_patches({k: kp[k]}, 1.0, 1.0) - return (m, ) - - -class CLIPMergeSimple: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip1": ("CLIP",), - "clip2": ("CLIP",), - "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, clip1, clip2, ratio): - m = clip1.clone() - kp = clip2.get_key_patches() - for k in kp: - if k.endswith(".position_ids") or k.endswith(".logit_scale"): - continue - m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) - return (m, ) - - -class CLIPSubtract: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip1": ("CLIP",), - "clip2": ("CLIP",), - "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, clip1, clip2, multiplier): - m = clip1.clone() - kp = clip2.get_key_patches() - for k in kp: - if k.endswith(".position_ids") or k.endswith(".logit_scale"): - continue - m.add_patches({k: kp[k]}, - multiplier, multiplier) - return (m, ) - - -class CLIPAdd: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip1": ("CLIP",), - "clip2": ("CLIP",), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, clip1, clip2): - m = clip1.clone() - kp = clip2.get_key_patches() - for k in kp: - if k.endswith(".position_ids") or k.endswith(".logit_scale"): - continue - m.add_patches({k: kp[k]}, 1.0, 1.0) - return (m, ) - - -class ModelMergeBlocks: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model1": ("MODEL",), - "model2": ("MODEL",), - "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "merge" - - CATEGORY = "advanced/model_merging" - - def merge(self, model1, model2, **kwargs): - m = model1.clone() - kp = model2.get_key_patches("diffusion_model.") - default_ratio = next(iter(kwargs.values())) - - for k in kp: - ratio = default_ratio - k_unet = k[len("diffusion_model."):] - - last_arg_size = 0 - for arg in kwargs: - if k_unet.startswith(arg) and last_arg_size < len(arg): - ratio = kwargs[arg] - last_arg_size = len(arg) - - m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) - return (m, ) - -def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_dir) - prompt_info = "" - if prompt is not None: - prompt_info = json.dumps(prompt) - - metadata = {} - - enable_modelspec = True - if isinstance(model.model, comfy.model_base.SDXL): - if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix): - metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit" - else: - metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" - elif isinstance(model.model, comfy.model_base.SDXLRefiner): - metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" - elif isinstance(model.model, comfy.model_base.SVD_img2vid): - metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1" - elif isinstance(model.model, comfy.model_base.SD3): - metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants - else: - enable_modelspec = False - - if enable_modelspec: - metadata["modelspec.sai_model_spec"] = "1.0.0" - metadata["modelspec.implementation"] = "sgm" - metadata["modelspec.title"] = "{} {}".format(filename, counter) - - #TODO: - # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512", - # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", - # "v2-inpainting" - - extra_keys = {} - model_sampling = model.get_model_object("model_sampling") - if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM): - if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION): - extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float() - extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float() - - if model.model.model_type == comfy.model_base.ModelType.EPS: - metadata["modelspec.predict_key"] = "epsilon" - elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: - metadata["modelspec.predict_key"] = "v" - extra_keys["v_pred"] = torch.tensor([]) - if getattr(model_sampling, "zsnr", False): - extra_keys["ztsnr"] = torch.tensor([]) - - if not args.disable_metadata: - metadata["prompt"] = prompt_info - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) - - output_checkpoint = f"{filename}_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - - comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys) - -class CheckpointSave: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "clip": ("CLIP",), - "vae": ("VAE",), - "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "advanced/model_merging" - - def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): - save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) - return {} - -class CLIPSave: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip": ("CLIP",), - "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "advanced/model_merging" - - def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): - prompt_info = "" - if prompt is not None: - prompt_info = json.dumps(prompt) - - metadata = {} - if not args.disable_metadata: - metadata["format"] = "pt" - metadata["prompt"] = prompt_info - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) - - comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) - clip_sd = clip.get_sd() - - for prefix in ["clip_l.", "clip_g.", "clip_h.", "t5xxl.", "pile_t5xl.", "mt5xl.", "umt5xxl.", "t5base.", "gemma2_2b.", "llama.", "hydit_clip.", ""]: - k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) - current_clip_sd = {} - for x in k: - current_clip_sd[x] = clip_sd.pop(x) - if len(current_clip_sd) == 0: - continue - - p = prefix[:-1] - replace_prefix = {} - filename_prefix_ = filename_prefix - if len(p) > 0: - filename_prefix_ = "{}_{}".format(filename_prefix_, p) - replace_prefix[prefix] = "" - replace_prefix["transformer."] = "" - - full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir) - - output_checkpoint = f"{filename}_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - - current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) - - comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) - return {} - -class VAESave: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "vae": ("VAE",), - "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "advanced/model_merging" - - def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - prompt_info = "" - if prompt is not None: - prompt_info = json.dumps(prompt) - - metadata = {} - if not args.disable_metadata: - metadata["prompt"] = prompt_info - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) - - output_checkpoint = f"{filename}_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - - comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) - return {} - -class ModelSave: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}),}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "advanced/model_merging" - - def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None): - save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) - return {} - -NODE_CLASS_MAPPINGS = { - "ModelMergeSimple": ModelMergeSimple, - "ModelMergeBlocks": ModelMergeBlocks, - "ModelMergeSubtract": ModelSubtract, - "ModelMergeAdd": ModelAdd, - "CheckpointSave": CheckpointSave, - "CLIPMergeSimple": CLIPMergeSimple, - "CLIPMergeSubtract": CLIPSubtract, - "CLIPMergeAdd": CLIPAdd, - "CLIPSave": CLIPSave, - "VAESave": VAESave, - "ModelSave": ModelSave, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "CheckpointSave": "Save Checkpoint", -} diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py deleted file mode 100644 index 55eb3ccfeadd4dd824932503ce244ecedc815f26..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_model_merging_model_specific.py +++ /dev/null @@ -1,356 +0,0 @@ -import comfy_extras.nodes_model_merging - -class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["time_embed."] = argument - arg_dict["label_emb."] = argument - - for i in range(12): - arg_dict["input_blocks.{}.".format(i)] = argument - - for i in range(3): - arg_dict["middle_block.{}.".format(i)] = argument - - for i in range(12): - arg_dict["output_blocks.{}.".format(i)] = argument - - arg_dict["out."] = argument - - return {"required": arg_dict} - - -class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["time_embed."] = argument - arg_dict["label_emb."] = argument - - for i in range(9): - arg_dict["input_blocks.{}".format(i)] = argument - - for i in range(3): - arg_dict["middle_block.{}".format(i)] = argument - - for i in range(9): - arg_dict["output_blocks.{}".format(i)] = argument - - arg_dict["out."] = argument - - return {"required": arg_dict} - -class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embed."] = argument - arg_dict["x_embedder."] = argument - arg_dict["context_embedder."] = argument - arg_dict["y_embedder."] = argument - arg_dict["t_embedder."] = argument - - for i in range(24): - arg_dict["joint_blocks.{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - - -class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["init_x_linear."] = argument - arg_dict["positional_encoding"] = argument - arg_dict["cond_seq_linear."] = argument - arg_dict["register_tokens"] = argument - arg_dict["t_embedder."] = argument - - for i in range(4): - arg_dict["double_layers.{}.".format(i)] = argument - - for i in range(32): - arg_dict["single_layers.{}.".format(i)] = argument - - arg_dict["modF."] = argument - arg_dict["final_linear."] = argument - - return {"required": arg_dict} - -class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["img_in."] = argument - arg_dict["time_in."] = argument - arg_dict["guidance_in"] = argument - arg_dict["vector_in."] = argument - arg_dict["txt_in."] = argument - - for i in range(19): - arg_dict["double_blocks.{}.".format(i)] = argument - - for i in range(38): - arg_dict["single_blocks.{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embed."] = argument - arg_dict["x_embedder."] = argument - arg_dict["context_embedder."] = argument - arg_dict["y_embedder."] = argument - arg_dict["t_embedder."] = argument - - for i in range(38): - arg_dict["joint_blocks.{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_frequencies."] = argument - arg_dict["t_embedder."] = argument - arg_dict["t5_y_embedder."] = argument - arg_dict["t5_yproj."] = argument - - for i in range(48): - arg_dict["blocks.{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["patchify_proj."] = argument - arg_dict["adaln_single."] = argument - arg_dict["caption_projection."] = argument - - for i in range(28): - arg_dict["transformer_blocks.{}.".format(i)] = argument - - arg_dict["scale_shift_table"] = argument - arg_dict["proj_out."] = argument - - return {"required": arg_dict} - -class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embedder."] = argument - arg_dict["extra_pos_embedder."] = argument - arg_dict["x_embedder."] = argument - arg_dict["t_embedder."] = argument - arg_dict["affline_norm."] = argument - - - for i in range(28): - arg_dict["blocks.block{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embedder."] = argument - arg_dict["extra_pos_embedder."] = argument - arg_dict["x_embedder."] = argument - arg_dict["t_embedder."] = argument - arg_dict["affline_norm."] = argument - - - for i in range(36): - arg_dict["blocks.block{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb." - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["patch_embedding."] = argument - arg_dict["time_embedding."] = argument - arg_dict["time_projection."] = argument - arg_dict["text_embedding."] = argument - arg_dict["img_emb."] = argument - - for i in range(40): - arg_dict["blocks.{}.".format(i)] = argument - - arg_dict["head."] = argument - - return {"required": arg_dict} - -class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embedder."] = argument - arg_dict["x_embedder."] = argument - arg_dict["t_embedder."] = argument - arg_dict["t_embedding_norm."] = argument - - - for i in range(28): - arg_dict["blocks.{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embedder."] = argument - arg_dict["x_embedder."] = argument - arg_dict["t_embedder."] = argument - arg_dict["t_embedding_norm."] = argument - - - for i in range(36): - arg_dict["blocks.{}.".format(i)] = argument - - arg_dict["final_layer."] = argument - - return {"required": arg_dict} - -class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" - - @classmethod - def INPUT_TYPES(s): - arg_dict = { "model1": ("MODEL",), - "model2": ("MODEL",)} - - argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - - arg_dict["pos_embeds."] = argument - arg_dict["img_in."] = argument - arg_dict["txt_norm."] = argument - arg_dict["txt_in."] = argument - arg_dict["time_text_embed."] = argument - - for i in range(60): - arg_dict["transformer_blocks.{}.".format(i)] = argument - - arg_dict["proj_out."] = argument - - return {"required": arg_dict} - -NODE_CLASS_MAPPINGS = { - "ModelMergeSD1": ModelMergeSD1, - "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks - "ModelMergeSDXL": ModelMergeSDXL, - "ModelMergeSD3_2B": ModelMergeSD3_2B, - "ModelMergeAuraflow": ModelMergeAuraflow, - "ModelMergeFlux1": ModelMergeFlux1, - "ModelMergeSD35_Large": ModelMergeSD35_Large, - "ModelMergeMochiPreview": ModelMergeMochiPreview, - "ModelMergeLTXV": ModelMergeLTXV, - "ModelMergeCosmos7B": ModelMergeCosmos7B, - "ModelMergeCosmos14B": ModelMergeCosmos14B, - "ModelMergeWAN2_1": ModelMergeWAN2_1, - "ModelMergeCosmosPredict2_2B": ModelMergeCosmosPredict2_2B, - "ModelMergeCosmosPredict2_14B": ModelMergeCosmosPredict2_14B, - "ModelMergeQwenImage": ModelMergeQwenImage, -} diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py deleted file mode 100644 index 65e766b522e237802718c467dfe6f1782d7c61ad..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_model_patch.py +++ /dev/null @@ -1,163 +0,0 @@ -import torch -import folder_paths -import comfy.utils -import comfy.ops -import comfy.model_management -import comfy.ldm.common_dit -import comfy.latent_formats - - -class BlockWiseControlBlock(torch.nn.Module): - # [linear, gelu, linear] - def __init__(self, dim: int = 3072, device=None, dtype=None, operations=None): - super().__init__() - self.x_rms = operations.RMSNorm(dim, eps=1e-6) - self.y_rms = operations.RMSNorm(dim, eps=1e-6) - self.input_proj = operations.Linear(dim, dim) - self.act = torch.nn.GELU() - self.output_proj = operations.Linear(dim, dim) - - def forward(self, x, y): - x, y = self.x_rms(x), self.y_rms(y) - x = self.input_proj(x + y) - x = self.act(x) - x = self.output_proj(x) - return x - - -class QwenImageBlockWiseControlNet(torch.nn.Module): - def __init__( - self, - num_layers: int = 60, - in_dim: int = 64, - additional_in_dim: int = 0, - dim: int = 3072, - device=None, dtype=None, operations=None - ): - super().__init__() - self.additional_in_dim = additional_in_dim - self.img_in = operations.Linear(in_dim + additional_in_dim, dim, device=device, dtype=dtype) - self.controlnet_blocks = torch.nn.ModuleList( - [ - BlockWiseControlBlock(dim, device=device, dtype=dtype, operations=operations) - for _ in range(num_layers) - ] - ) - - def process_input_latent_image(self, latent_image): - latent_image[:, :16] = comfy.latent_formats.Wan21().process_in(latent_image[:, :16]) - patch_size = 2 - hidden_states = comfy.ldm.common_dit.pad_to_patch_size(latent_image, (1, patch_size, patch_size)) - orig_shape = hidden_states.shape - hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2) - hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5) - hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4) - return self.img_in(hidden_states) - - def control_block(self, img, controlnet_conditioning, block_id): - return self.controlnet_blocks[block_id](img, controlnet_conditioning) - - -class ModelPatchLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "name": (folder_paths.get_filename_list("model_patches"), ), - }} - RETURN_TYPES = ("MODEL_PATCH",) - FUNCTION = "load_model_patch" - EXPERIMENTAL = True - - CATEGORY = "advanced/loaders" - - def load_model_patch(self, name): - model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name) - sd = comfy.utils.load_torch_file(model_patch_path, safe_load=True) - dtype = comfy.utils.weight_dtype(sd) - # TODO: this node will work with more types of model patches - additional_in_dim = sd["img_in.weight"].shape[1] - 64 - model = QwenImageBlockWiseControlNet(additional_in_dim=additional_in_dim, device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast) - model.load_state_dict(sd) - model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) - return (model,) - - -class DiffSynthCnetPatch: - def __init__(self, model_patch, vae, image, strength, mask=None): - self.model_patch = model_patch - self.vae = vae - self.image = image - self.strength = strength - self.mask = mask - self.encoded_image = model_patch.model.process_input_latent_image(self.encode_latent_cond(image)) - self.encoded_image_size = (image.shape[1], image.shape[2]) - - def encode_latent_cond(self, image): - latent_image = self.vae.encode(image) - if self.model_patch.model.additional_in_dim > 0: - if self.mask is None: - mask_ = torch.ones_like(latent_image)[:, :self.model_patch.model.additional_in_dim // 4] - else: - mask_ = comfy.utils.common_upscale(self.mask.mean(dim=1, keepdim=True), latent_image.shape[-1], latent_image.shape[-2], "bilinear", "none") - - return torch.cat([latent_image, mask_], dim=1) - else: - return latent_image - - def __call__(self, kwargs): - x = kwargs.get("x") - img = kwargs.get("img") - block_index = kwargs.get("block_index") - spacial_compression = self.vae.spacial_compression_encode() - if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression): - image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center") - loaded_models = comfy.model_management.loaded_models(only_currently_used=True) - self.encoded_image = self.model_patch.model.process_input_latent_image(self.encode_latent_cond(image_scaled.movedim(1, -1))) - self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1]) - comfy.model_management.load_models_gpu(loaded_models) - - img[:, :self.encoded_image.shape[1]] += (self.model_patch.model.control_block(img[:, :self.encoded_image.shape[1]], self.encoded_image.to(img.dtype), block_index) * self.strength) - kwargs['img'] = img - return kwargs - - def to(self, device_or_dtype): - if isinstance(device_or_dtype, torch.device): - self.encoded_image = self.encoded_image.to(device_or_dtype) - return self - - def models(self): - return [self.model_patch] - -class QwenImageDiffsynthControlnet: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "model_patch": ("MODEL_PATCH",), - "vae": ("VAE",), - "image": ("IMAGE",), - "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), - }, - "optional": {"mask": ("MASK",)}} - RETURN_TYPES = ("MODEL",) - FUNCTION = "diffsynth_controlnet" - EXPERIMENTAL = True - - CATEGORY = "advanced/loaders/qwen" - - def diffsynth_controlnet(self, model, model_patch, vae, image, strength, mask=None): - model_patched = model.clone() - image = image[:, :, :, :3] - if mask is not None: - if mask.ndim == 3: - mask = mask.unsqueeze(1) - if mask.ndim == 4: - mask = mask.unsqueeze(2) - mask = 1.0 - mask - - model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask)) - return (model_patched,) - - -NODE_CLASS_MAPPINGS = { - "ModelPatchLoader": ModelPatchLoader, - "QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet, -} diff --git a/comfy_extras/nodes_morphology.py b/comfy_extras/nodes_morphology.py deleted file mode 100644 index 075b26c4024bccb510b9052f40f51eb88382ab30..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_morphology.py +++ /dev/null @@ -1,87 +0,0 @@ -import torch -import comfy.model_management - -from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat -import kornia.color - - -class Morphology: - @classmethod - def INPUT_TYPES(s): - return {"required": {"image": ("IMAGE",), - "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), - "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process" - - CATEGORY = "image/postprocessing" - - def process(self, image, operation, kernel_size): - device = comfy.model_management.get_torch_device() - kernel = torch.ones(kernel_size, kernel_size, device=device) - image_k = image.to(device).movedim(-1, 1) - if operation == "erode": - output = erosion(image_k, kernel) - elif operation == "dilate": - output = dilation(image_k, kernel) - elif operation == "open": - output = opening(image_k, kernel) - elif operation == "close": - output = closing(image_k, kernel) - elif operation == "gradient": - output = gradient(image_k, kernel) - elif operation == "top_hat": - output = top_hat(image_k, kernel) - elif operation == "bottom_hat": - output = bottom_hat(image_k, kernel) - else: - raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") - img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) - return (img_out,) - - -class ImageRGBToYUV: - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE") - RETURN_NAMES = ("Y", "U", "V") - FUNCTION = "execute" - - CATEGORY = "image/batch" - - def execute(self, image): - out = kornia.color.rgb_to_ycbcr(image.movedim(-1, 1)).movedim(1, -1) - return (out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image)) - -class ImageYUVToRGB: - @classmethod - def INPUT_TYPES(s): - return {"required": {"Y": ("IMAGE",), - "U": ("IMAGE",), - "V": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "execute" - - CATEGORY = "image/batch" - - def execute(self, Y, U, V): - image = torch.cat([torch.mean(Y, dim=-1, keepdim=True), torch.mean(U, dim=-1, keepdim=True), torch.mean(V, dim=-1, keepdim=True)], dim=-1) - out = kornia.color.ycbcr_to_rgb(image.movedim(-1, 1)).movedim(1, -1) - return (out,) - -NODE_CLASS_MAPPINGS = { - "Morphology": Morphology, - "ImageRGBToYUV": ImageRGBToYUV, - "ImageYUVToRGB": ImageYUVToRGB, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "Morphology": "ImageMorphology", -} diff --git a/comfy_extras/nodes_optimalsteps.py b/comfy_extras/nodes_optimalsteps.py deleted file mode 100644 index e7c851ca211c923f48506fd01f5a643f60dfaf62..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_optimalsteps.py +++ /dev/null @@ -1,57 +0,0 @@ -# from https://github.com/bebebe666/OptimalSteps - - -import numpy as np -import torch - -def loglinear_interp(t_steps, num_steps): - """ - Performs log-linear interpolation of a given array of decreasing numbers. - """ - xs = np.linspace(0, 1, len(t_steps)) - ys = np.log(t_steps[::-1]) - - new_xs = np.linspace(0, 1, num_steps) - new_ys = np.interp(new_xs, xs, ys) - - interped_ys = np.exp(new_ys)[::-1].copy() - return interped_ys - - -NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001], -"Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001], -"Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001], -} - -class OptimalStepsScheduler: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model_type": (["FLUX", "Wan", "Chroma"], ), - "steps": ("INT", {"default": 20, "min": 3, "max": 1000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, model_type, steps, denoise): - total_steps = steps - if denoise < 1.0: - if denoise <= 0.0: - return (torch.FloatTensor([]),) - total_steps = round(steps * denoise) - - sigmas = NOISE_LEVELS[model_type][:] - if (steps + 1) != len(sigmas): - sigmas = loglinear_interp(sigmas, steps + 1) - - sigmas = sigmas[-(total_steps + 1):] - sigmas[-1] = 0 - return (torch.FloatTensor(sigmas), ) - -NODE_CLASS_MAPPINGS = { - "OptimalStepsScheduler": OptimalStepsScheduler, -} diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py deleted file mode 100644 index eb28196f41c56fd45fda051a42d0814b96558fb8..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_pag.py +++ /dev/null @@ -1,56 +0,0 @@ -#Modified/simplified version of the node from: https://github.com/pamparamm/sd-perturbed-attention -#If you want the one with more options see the above repo. - -#My modified one here is more basic but has less chances of breaking with ComfyUI updates. - -import comfy.model_patcher -import comfy.samplers - -class PerturbedAttentionGuidance: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}), - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, scale): - unet_block = "middle" - unet_block_id = 0 - m = model.clone() - - def perturbed_attention(q, k, v, extra_options, mask=None): - return v - - def post_cfg_function(args): - model = args["model"] - cond_pred = args["cond_denoised"] - cond = args["cond"] - cfg_result = args["denoised"] - sigma = args["sigma"] - model_options = args["model_options"].copy() - x = args["input"] - - if scale == 0: - return cfg_result - - # Replace Self-attention with PAG - model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id) - (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) - - return cfg_result + (cond_pred - pag) * scale - - m.set_model_sampler_post_cfg_function(post_cfg_function) - - return (m,) - -NODE_CLASS_MAPPINGS = { - "PerturbedAttentionGuidance": PerturbedAttentionGuidance, -} diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py deleted file mode 100644 index 89e5eef905ac5a417b850da7c2f5693915117ec5..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_perpneg.py +++ /dev/null @@ -1,146 +0,0 @@ -import torch -import comfy.model_management -import comfy.sampler_helpers -import comfy.samplers -import comfy.utils -import node_helpers -import math - -def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): - pos = noise_pred_pos - noise_pred_nocond - neg = noise_pred_neg - noise_pred_nocond - - perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos - perp_neg = perp * neg_scale - cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) - return cfg_result - -#TODO: This node should be removed, it has been replaced with PerpNegGuider -class PerpNeg: - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "empty_conditioning": ("CONDITIONING", ), - "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "_for_testing" - DEPRECATED = True - - def patch(self, model, empty_conditioning, neg_scale): - m = model.clone() - nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) - - def cfg_function(args): - model = args["model"] - noise_pred_pos = args["cond_denoised"] - noise_pred_neg = args["uncond_denoised"] - cond_scale = args["cond_scale"] - x = args["input"] - sigma = args["sigma"] - model_options = args["model_options"] - nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") - - (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options) - - cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale) - return cfg_result - - m.set_model_sampler_cfg_function(cfg_function) - - return (m, ) - - -class Guider_PerpNeg(comfy.samplers.CFGGuider): - def set_conds(self, positive, negative, empty_negative_prompt): - empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"}) - self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative}) - - def set_cfg(self, cfg, neg_scale): - self.cfg = cfg - self.neg_scale = neg_scale - - def predict_noise(self, x, timestep, model_options={}, seed=None): - # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg - # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly - - positive_cond = self.conds.get("positive", None) - negative_cond = self.conds.get("negative", None) - empty_cond = self.conds.get("empty_negative_prompt", None) - - if model_options.get("disable_cfg1_optimization", False) == False: - if math.isclose(self.neg_scale, 0.0): - negative_cond = None - if math.isclose(self.cfg, 1.0): - empty_cond = None - - conds = [positive_cond, negative_cond, empty_cond] - - out = comfy.samplers.calc_cond_batch(self.inner_model, conds, x, timestep, model_options) - - # Apply pre_cfg_functions since sampling_function() is skipped - for fn in model_options.get("sampler_pre_cfg_function", []): - args = {"conds":conds, "conds_out": out, "cond_scale": self.cfg, "timestep": timestep, - "input": x, "sigma": timestep, "model": self.inner_model, "model_options": model_options} - out = fn(args) - - noise_pred_pos, noise_pred_neg, noise_pred_empty = out - cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) - - # normally this would be done in cfg_function, but we skipped - # that for efficiency: we can compute the noise predictions in - # a single call to calc_cond_batch() (rather than two) - # so we replicate the hook here - for fn in model_options.get("sampler_post_cfg_function", []): - args = { - "denoised": cfg_result, - "cond": positive_cond, - "uncond": negative_cond, - "cond_scale": self.cfg, - "model": self.inner_model, - "uncond_denoised": noise_pred_neg, - "cond_denoised": noise_pred_pos, - "sigma": timestep, - "model_options": model_options, - "input": x, - # not in the original call in samplers.py:cfg_function, but made available for future hooks - "empty_cond": empty_cond, - "empty_cond_denoised": noise_pred_empty,} - cfg_result = fn(args) - - return cfg_result - -class PerpNegGuider: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "empty_conditioning": ("CONDITIONING", ), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - } - } - - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "_for_testing" - - def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): - guider = Guider_PerpNeg(model) - guider.set_conds(positive, negative, empty_conditioning) - guider.set_cfg(cfg, neg_scale) - return (guider,) - -NODE_CLASS_MAPPINGS = { - "PerpNeg": PerpNeg, - "PerpNegGuider": PerpNegGuider, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", -} diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py deleted file mode 100644 index d358ed6d5b75a37f9195e1f0c663a188eb8aedc3..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_photomaker.py +++ /dev/null @@ -1,188 +0,0 @@ -import torch -import torch.nn as nn -import folder_paths -import comfy.clip_model -import comfy.clip_vision -import comfy.ops - -# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0 -VISION_CONFIG_DICT = { - "hidden_size": 1024, - "image_size": 224, - "intermediate_size": 4096, - "num_attention_heads": 16, - "num_channels": 3, - "num_hidden_layers": 24, - "patch_size": 14, - "projection_dim": 768, - "hidden_act": "quick_gelu", - "model_type": "clip_vision_model", -} - -class MLP(nn.Module): - def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops): - super().__init__() - if use_residual: - assert in_dim == out_dim - self.layernorm = operations.LayerNorm(in_dim) - self.fc1 = operations.Linear(in_dim, hidden_dim) - self.fc2 = operations.Linear(hidden_dim, out_dim) - self.use_residual = use_residual - self.act_fn = nn.GELU() - - def forward(self, x): - residual = x - x = self.layernorm(x) - x = self.fc1(x) - x = self.act_fn(x) - x = self.fc2(x) - if self.use_residual: - x = x + residual - return x - - -class FuseModule(nn.Module): - def __init__(self, embed_dim, operations): - super().__init__() - self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) - self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) - self.layer_norm = operations.LayerNorm(embed_dim) - - def fuse_fn(self, prompt_embeds, id_embeds): - stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) - stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds - stacked_id_embeds = self.mlp2(stacked_id_embeds) - stacked_id_embeds = self.layer_norm(stacked_id_embeds) - return stacked_id_embeds - - def forward( - self, - prompt_embeds, - id_embeds, - class_tokens_mask, - ) -> torch.Tensor: - # id_embeds shape: [b, max_num_inputs, 1, 2048] - id_embeds = id_embeds.to(prompt_embeds.dtype) - num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case - batch_size, max_num_inputs = id_embeds.shape[:2] - # seq_length: 77 - seq_length = prompt_embeds.shape[1] - # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] - flat_id_embeds = id_embeds.view( - -1, id_embeds.shape[-2], id_embeds.shape[-1] - ) - # valid_id_mask [b*max_num_inputs] - valid_id_mask = ( - torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] - < num_inputs[:, None] - ) - valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] - - prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) - class_tokens_mask = class_tokens_mask.view(-1) - valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) - # slice out the image token embeddings - image_token_embeds = prompt_embeds[class_tokens_mask] - stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) - assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" - prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) - updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) - return updated_prompt_embeds - -class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection): - def __init__(self): - self.load_device = comfy.model_management.text_encoder_device() - offload_device = comfy.model_management.text_encoder_offload_device() - dtype = comfy.model_management.text_encoder_dtype(self.load_device) - - super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast) - self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False) - self.fuse_module = FuseModule(2048, comfy.ops.manual_cast) - - def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): - b, num_inputs, c, h, w = id_pixel_values.shape - id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) - - shared_id_embeds = self.vision_model(id_pixel_values)[2] - id_embeds = self.visual_projection(shared_id_embeds) - id_embeds_2 = self.visual_projection_2(shared_id_embeds) - - id_embeds = id_embeds.view(b, num_inputs, 1, -1) - id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) - - id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) - updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) - - return updated_prompt_embeds - - -class PhotoMakerLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}} - - RETURN_TYPES = ("PHOTOMAKER",) - FUNCTION = "load_photomaker_model" - - CATEGORY = "_for_testing/photomaker" - - def load_photomaker_model(self, photomaker_model_name): - photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name) - photomaker_model = PhotoMakerIDEncoder() - data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True) - if "id_encoder" in data: - data = data["id_encoder"] - photomaker_model.load_state_dict(data) - return (photomaker_model,) - - -class PhotoMakerEncode: - @classmethod - def INPUT_TYPES(s): - return {"required": { "photomaker": ("PHOTOMAKER",), - "image": ("IMAGE",), - "clip": ("CLIP", ), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), - }} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "apply_photomaker" - - CATEGORY = "_for_testing/photomaker" - - def apply_photomaker(self, photomaker, image, clip, text): - special_token = "photomaker" - pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() - try: - index = text.split(" ").index(special_token) + 1 - except ValueError: - index = -1 - tokens = clip.tokenize(text, return_word_ids=True) - out_tokens = {} - for k in tokens: - out_tokens[k] = [] - for t in tokens[k]: - f = list(filter(lambda x: x[2] != index, t)) - while len(f) < len(t): - f.append(t[-1]) - out_tokens[k].append(f) - - cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) - - if index > 0: - token_index = index - 1 - num_id_images = 1 - class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] - out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), - class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) - else: - out = cond - - return ([[out, {"pooled_output": pooled}]], ) - - -NODE_CLASS_MAPPINGS = { - "PhotoMakerLoader": PhotoMakerLoader, - "PhotoMakerEncode": PhotoMakerEncode, -} - diff --git a/comfy_extras/nodes_pixart.py b/comfy_extras/nodes_pixart.py deleted file mode 100644 index 8d9276afe4b12c51c818a879cce1cd5453895552..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_pixart.py +++ /dev/null @@ -1,24 +0,0 @@ -from nodes import MAX_RESOLUTION - -class CLIPTextEncodePixArtAlpha: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - # "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), - }} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - CATEGORY = "advanced/conditioning" - DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma." - - def encode(self, clip, width, height, text): - tokens = clip.tokenize(text) - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),) - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha, -} diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py deleted file mode 100644 index cb1a0d88303eef19fff34ce2b19611cfcb162e91..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_post_processing.py +++ /dev/null @@ -1,281 +0,0 @@ -import numpy as np -import torch -import torch.nn.functional as F -from PIL import Image -import math - -import comfy.utils -import comfy.model_management -import node_helpers - -class Blend: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - "blend_factor": ("FLOAT", { - "default": 0.5, - "min": 0.0, - "max": 1.0, - "step": 0.01 - }), - "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "blend_images" - - CATEGORY = "image/postprocessing" - - def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): - image1, image2 = node_helpers.image_alpha_fix(image1, image2) - image2 = image2.to(image1.device) - if image1.shape != image2.shape: - image2 = image2.permute(0, 3, 1, 2) - image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') - image2 = image2.permute(0, 2, 3, 1) - - blended_image = self.blend_mode(image1, image2, blend_mode) - blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor - blended_image = torch.clamp(blended_image, 0, 1) - return (blended_image,) - - def blend_mode(self, img1, img2, mode): - if mode == "normal": - return img2 - elif mode == "multiply": - return img1 * img2 - elif mode == "screen": - return 1 - (1 - img1) * (1 - img2) - elif mode == "overlay": - return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) - elif mode == "soft_light": - return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) - elif mode == "difference": - return img1 - img2 - else: - raise ValueError(f"Unsupported blend mode: {mode}") - - def g(self, x): - return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) - -def gaussian_kernel(kernel_size: int, sigma: float, device=None): - x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") - d = torch.sqrt(x * x + y * y) - g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) - return g / g.sum() - -class Blur: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "blur_radius": ("INT", { - "default": 1, - "min": 1, - "max": 31, - "step": 1 - }), - "sigma": ("FLOAT", { - "default": 1.0, - "min": 0.1, - "max": 10.0, - "step": 0.1 - }), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "blur" - - CATEGORY = "image/postprocessing" - - def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): - if blur_radius == 0: - return (image,) - - image = image.to(comfy.model_management.get_torch_device()) - batch_size, height, width, channels = image.shape - - kernel_size = blur_radius * 2 + 1 - kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) - - image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) - padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') - blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] - blurred = blurred.permute(0, 2, 3, 1) - - return (blurred.to(comfy.model_management.intermediate_device()),) - -class Quantize: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "colors": ("INT", { - "default": 256, - "min": 1, - "max": 256, - "step": 1 - }), - "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "quantize" - - CATEGORY = "image/postprocessing" - - @staticmethod - def bayer(im, pal_im, order): - def normalized_bayer_matrix(n): - if n == 0: - return np.zeros((1,1), "float32") - else: - q = 4 ** n - m = q * normalized_bayer_matrix(n - 1) - return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q - - num_colors = len(pal_im.getpalette()) // 3 - spread = 2 * 256 / num_colors - bayer_n = int(math.log2(order)) - bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) - - result = torch.from_numpy(np.array(im).astype(np.float32)) - tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) - th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) - tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) - result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) - result = result.to(dtype=torch.uint8) - - im = Image.fromarray(result.cpu().numpy()) - im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) - return im - - def quantize(self, image: torch.Tensor, colors: int, dither: str): - batch_size, height, width, _ = image.shape - result = torch.zeros_like(image) - - for b in range(batch_size): - im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') - - pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 - - if dither == "none": - quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) - elif dither == "floyd-steinberg": - quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) - elif dither.startswith("bayer"): - order = int(dither.split('-')[-1]) - quantized_image = Quantize.bayer(im, pal_im, order) - - quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 - result[b] = quantized_array - - return (result,) - -class Sharpen: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "sharpen_radius": ("INT", { - "default": 1, - "min": 1, - "max": 31, - "step": 1 - }), - "sigma": ("FLOAT", { - "default": 1.0, - "min": 0.1, - "max": 10.0, - "step": 0.01 - }), - "alpha": ("FLOAT", { - "default": 1.0, - "min": 0.0, - "max": 5.0, - "step": 0.01 - }), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "sharpen" - - CATEGORY = "image/postprocessing" - - def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): - if sharpen_radius == 0: - return (image,) - - batch_size, height, width, channels = image.shape - image = image.to(comfy.model_management.get_torch_device()) - - kernel_size = sharpen_radius * 2 + 1 - kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) - center = kernel_size // 2 - kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 - kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) - - tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) - tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') - sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] - sharpened = sharpened.permute(0, 2, 3, 1) - - result = torch.clamp(sharpened, 0, 1) - - return (result.to(comfy.model_management.intermediate_device()),) - -class ImageScaleToTotalPixels: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - crop_methods = ["disabled", "center"] - - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), - "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" - - CATEGORY = "image/upscaling" - - def upscale(self, image, upscale_method, megapixels): - samples = image.movedim(-1,1) - total = int(megapixels * 1024 * 1024) - - scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - - s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") - s = s.movedim(1,-1) - return (s,) - -NODE_CLASS_MAPPINGS = { - "ImageBlend": Blend, - "ImageBlur": Blur, - "ImageQuantize": Quantize, - "ImageSharpen": Sharpen, - "ImageScaleToTotalPixels": ImageScaleToTotalPixels, -} diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py deleted file mode 100644 index e6805696f302e785f0ea4afe4ca82aa2eeca0495..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_preview_any.py +++ /dev/null @@ -1,43 +0,0 @@ -import json -from comfy.comfy_types.node_typing import IO - -# Preview Any - original implement from -# https://github.com/rgthree/rgthree-comfy/blob/main/py/display_any.py -# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes -class PreviewAny(): - @classmethod - def INPUT_TYPES(cls): - return { - "required": {"source": (IO.ANY, {})}, - } - - RETURN_TYPES = () - FUNCTION = "main" - OUTPUT_NODE = True - - CATEGORY = "utils" - - def main(self, source=None): - value = 'None' - if isinstance(source, str): - value = source - elif isinstance(source, (int, float, bool)): - value = str(source) - elif source is not None: - try: - value = json.dumps(source) - except Exception: - try: - value = str(source) - except Exception: - value = 'source exists, but could not be serialized.' - - return {"ui": {"text": (value,)}} - -NODE_CLASS_MAPPINGS = { - "PreviewAny": PreviewAny, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "PreviewAny": "Preview Any", -} diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py deleted file mode 100644 index 1f93f87a79531b3650981a651d355466db518ede..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_primitive.py +++ /dev/null @@ -1,98 +0,0 @@ -# Primitive nodes that are evaluated at backend. -from __future__ import annotations - -import sys - -from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO - - -class String(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": {"value": (IO.STRING, {})}, - } - - RETURN_TYPES = (IO.STRING,) - FUNCTION = "execute" - CATEGORY = "utils/primitive" - - def execute(self, value: str) -> tuple[str]: - return (value,) - - -class StringMultiline(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": {"value": (IO.STRING, {"multiline": True,},)}, - } - - RETURN_TYPES = (IO.STRING,) - FUNCTION = "execute" - CATEGORY = "utils/primitive" - - def execute(self, value: str) -> tuple[str]: - return (value,) - - -class Int(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": {"value": (IO.INT, {"min": -sys.maxsize, "max": sys.maxsize, "control_after_generate": True})}, - } - - RETURN_TYPES = (IO.INT,) - FUNCTION = "execute" - CATEGORY = "utils/primitive" - - def execute(self, value: int) -> tuple[int]: - return (value,) - - -class Float(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": {"value": (IO.FLOAT, {"min": -sys.maxsize, "max": sys.maxsize})}, - } - - RETURN_TYPES = (IO.FLOAT,) - FUNCTION = "execute" - CATEGORY = "utils/primitive" - - def execute(self, value: float) -> tuple[float]: - return (value,) - - -class Boolean(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": {"value": (IO.BOOLEAN, {})}, - } - - RETURN_TYPES = (IO.BOOLEAN,) - FUNCTION = "execute" - CATEGORY = "utils/primitive" - - def execute(self, value: bool) -> tuple[bool]: - return (value,) - - -NODE_CLASS_MAPPINGS = { - "PrimitiveString": String, - "PrimitiveStringMultiline": StringMultiline, - "PrimitiveInt": Int, - "PrimitiveFloat": Float, - "PrimitiveBoolean": Boolean, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "PrimitiveString": "String", - "PrimitiveStringMultiline": "String (Multiline)", - "PrimitiveInt": "Int", - "PrimitiveFloat": "Float", - "PrimitiveBoolean": "Boolean", -} diff --git a/comfy_extras/nodes_qwen.py b/comfy_extras/nodes_qwen.py deleted file mode 100644 index fff89556f4fb7331dd4eaeee162ae129e0321b06..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_qwen.py +++ /dev/null @@ -1,48 +0,0 @@ -import node_helpers -import comfy.utils -import math - - -class TextEncodeQwenImageEdit: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}), - }, - "optional": {"vae": ("VAE", ), - "image": ("IMAGE", ),}} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, prompt, vae=None, image=None): - ref_latent = None - if image is None: - images = [] - else: - samples = image.movedim(-1, 1) - total = int(1024 * 1024) - - scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - - s = comfy.utils.common_upscale(samples, width, height, "area", "disabled") - image = s.movedim(1, -1) - images = [image[:, :, :, :3]] - if vae is not None: - ref_latent = vae.encode(image[:, :, :, :3]) - - tokens = clip.tokenize(prompt, images=images) - conditioning = clip.encode_from_tokens_scheduled(tokens) - if ref_latent is not None: - conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True) - return (conditioning, ) - - -NODE_CLASS_MAPPINGS = { - "TextEncodeQwenImageEdit": TextEncodeQwenImageEdit, -} diff --git a/comfy_extras/nodes_rebatch.py b/comfy_extras/nodes_rebatch.py deleted file mode 100644 index e29cb9ed10dae329ac2bea313df72f2387bd7ade..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_rebatch.py +++ /dev/null @@ -1,138 +0,0 @@ -import torch - -class LatentRebatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "latents": ("LATENT",), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }} - RETURN_TYPES = ("LATENT",) - INPUT_IS_LIST = True - OUTPUT_IS_LIST = (True, ) - - FUNCTION = "rebatch" - - CATEGORY = "latent/batch" - - @staticmethod - def get_batch(latents, list_ind, offset): - '''prepare a batch out of the list of latents''' - samples = latents[list_ind]['samples'] - shape = samples.shape - mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') - if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: - torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") - if mask.shape[0] < samples.shape[0]: - mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] - if 'batch_index' in latents[list_ind]: - batch_inds = latents[list_ind]['batch_index'] - else: - batch_inds = [x+offset for x in range(shape[0])] - return samples, mask, batch_inds - - @staticmethod - def get_slices(indexable, num, batch_size): - '''divides an indexable object into num slices of length batch_size, and a remainder''' - slices = [] - for i in range(num): - slices.append(indexable[i*batch_size:(i+1)*batch_size]) - if num * batch_size < len(indexable): - return slices, indexable[num * batch_size:] - else: - return slices, None - - @staticmethod - def slice_batch(batch, num, batch_size): - result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] - return list(zip(*result)) - - @staticmethod - def cat_batch(batch1, batch2): - if batch1[0] is None: - return batch2 - result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] - return result - - def rebatch(self, latents, batch_size): - batch_size = batch_size[0] - - output_list = [] - current_batch = (None, None, None) - processed = 0 - - for i in range(len(latents)): - # fetch new entry of list - #samples, masks, indices = self.get_batch(latents, i) - next_batch = self.get_batch(latents, i, processed) - processed += len(next_batch[2]) - # set to current if current is None - if current_batch[0] is None: - current_batch = next_batch - # add previous to list if dimensions do not match - elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: - sliced, _ = self.slice_batch(current_batch, 1, batch_size) - output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) - current_batch = next_batch - # cat if everything checks out - else: - current_batch = self.cat_batch(current_batch, next_batch) - - # add to list if dimensions gone above target batch size - if current_batch[0].shape[0] > batch_size: - num = current_batch[0].shape[0] // batch_size - sliced, remainder = self.slice_batch(current_batch, num, batch_size) - - for i in range(num): - output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) - - current_batch = remainder - - #add remainder - if current_batch[0] is not None: - sliced, _ = self.slice_batch(current_batch, 1, batch_size) - output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) - - #get rid of empty masks - for s in output_list: - if s['noise_mask'].mean() == 1.0: - del s['noise_mask'] - - return (output_list,) - -class ImageRebatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "images": ("IMAGE",), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }} - RETURN_TYPES = ("IMAGE",) - INPUT_IS_LIST = True - OUTPUT_IS_LIST = (True, ) - - FUNCTION = "rebatch" - - CATEGORY = "image/batch" - - def rebatch(self, images, batch_size): - batch_size = batch_size[0] - - output_list = [] - all_images = [] - for img in images: - for i in range(img.shape[0]): - all_images.append(img[i:i+1]) - - for i in range(0, len(all_images), batch_size): - output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) - - return (output_list,) - -NODE_CLASS_MAPPINGS = { - "RebatchLatents": LatentRebatch, - "RebatchImages": ImageRebatch, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "RebatchLatents": "Rebatch Latents", - "RebatchImages": "Rebatch Images", -} diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py deleted file mode 100644 index 1bd8d7364fdde4bd8ce9b11f342dfce71d3a4214..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_sag.py +++ /dev/null @@ -1,181 +0,0 @@ -import torch -from torch import einsum -import torch.nn.functional as F -import math - -from einops import rearrange, repeat -from comfy.ldm.modules.attention import optimized_attention -import comfy.samplers - -# from comfy/ldm/modules/attention.py -# but modified to return attention scores as well as output -def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None): - b, _, dim_head = q.shape - dim_head //= heads - scale = dim_head ** -0.5 - - h = heads - q, k, v = map( - lambda t: t.unsqueeze(3) - .reshape(b, -1, heads, dim_head) - .permute(0, 2, 1, 3) - .reshape(b * heads, -1, dim_head) - .contiguous(), - (q, k, v), - ) - - # force cast to fp32 to avoid overflowing - if attn_precision == torch.float32: - sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale - else: - sim = einsum('b i d, b j d -> b i j', q, k) * scale - - del q, k - - if mask is not None: - mask = rearrange(mask, 'b ... -> b (...)') - max_neg_value = -torch.finfo(sim.dtype).max - mask = repeat(mask, 'b j -> (b h) () j', h=h) - sim.masked_fill_(~mask, max_neg_value) - - # attention, what we cannot get enough of - sim = sim.softmax(dim=-1) - - out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) - out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) - return (out, sim) - -def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): - # reshape and GAP the attention map - _, hw1, hw2 = attn.shape - b, _, lh, lw = x0.shape - attn = attn.reshape(b, -1, hw1, hw2) - # Global Average Pool - mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold - - total = mask.shape[-1] - x = round(math.sqrt((lh / lw) * total)) - xx = None - for i in range(0, math.floor(math.sqrt(total) / 2)): - for j in [(x + i), max(1, x - i)]: - if total % j == 0: - xx = j - break - if xx is not None: - break - - x = xx - y = total // x - - # Reshape - mask = ( - mask.reshape(b, x, y) - .unsqueeze(1) - .type(attn.dtype) - ) - # Upsample - mask = F.interpolate(mask, (lh, lw)) - - blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) - blurred = blurred * mask + x0 * (1 - mask) - return blurred - -def gaussian_blur_2d(img, kernel_size, sigma): - ksize_half = (kernel_size - 1) * 0.5 - - x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) - - pdf = torch.exp(-0.5 * (x / sigma).pow(2)) - - x_kernel = pdf / pdf.sum() - x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) - - kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) - kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) - - padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] - - img = F.pad(img, padding, mode="reflect") - img = F.conv2d(img, kernel2d, groups=img.shape[-3]) - return img - -class SelfAttentionGuidance: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}), - "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "_for_testing" - - def patch(self, model, scale, blur_sigma): - m = model.clone() - - attn_scores = None - - # TODO: make this work properly with chunked batches - # currently, we can only save the attn from one UNet call - def attn_and_record(q, k, v, extra_options): - nonlocal attn_scores - # if uncond, save the attention scores - heads = extra_options["n_heads"] - cond_or_uncond = extra_options["cond_or_uncond"] - b = q.shape[0] // len(cond_or_uncond) - if 1 in cond_or_uncond: - uncond_index = cond_or_uncond.index(1) - # do the entire attention operation, but save the attention scores to attn_scores - (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) - # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] - n_slices = heads * b - attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] - return out - else: - return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"]) - - def post_cfg_function(args): - nonlocal attn_scores - uncond_attn = attn_scores - - sag_scale = scale - sag_sigma = blur_sigma - sag_threshold = 1.0 - model = args["model"] - uncond_pred = args["uncond_denoised"] - uncond = args["uncond"] - cfg_result = args["denoised"] - sigma = args["sigma"] - model_options = args["model_options"] - x = args["input"] - if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding - return cfg_result - - # create the adversarially blurred image - degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) - degraded_noised = degraded + x - uncond_pred - # call into the UNet - (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options) - return cfg_result + (degraded - sag) * sag_scale - - m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) - - # from diffusers: - # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch - m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) - - return (m, ) - -NODE_CLASS_MAPPINGS = { - "SelfAttentionGuidance": SelfAttentionGuidance, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "SelfAttentionGuidance": "Self-Attention Guidance", -} diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py deleted file mode 100644 index d75b29e606feaf1e0eb125bd63f4abe2bc03b2f5..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_sd3.py +++ /dev/null @@ -1,138 +0,0 @@ -import folder_paths -import comfy.sd -import comfy.model_management -import nodes -import torch -import comfy_extras.nodes_slg - - -class TripleCLIPLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), ) - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "load_clip" - - CATEGORY = "advanced/loaders" - - DESCRIPTION = "[Recipes]\n\nsd3: clip-l, clip-g, t5" - - def load_clip(self, clip_name1, clip_name2, clip_name3): - clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) - clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) - clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) - clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) - return (clip,) - - -class EmptySD3LatentImage: - def __init__(self): - self.device = comfy.model_management.intermediate_device() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" - - CATEGORY = "latent/sd3" - - def generate(self, width, height, batch_size=1): - latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device) - return ({"samples":latent}, ) - - -class CLIPTextEncodeSD3: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "empty_padding": (["none", "empty_prompt"], ) - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - - CATEGORY = "advanced/conditioning" - - def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): - no_padding = empty_padding == "none" - - tokens = clip.tokenize(clip_g) - if len(clip_g) == 0 and no_padding: - tokens["g"] = [] - - if len(clip_l) == 0 and no_padding: - tokens["l"] = [] - else: - tokens["l"] = clip.tokenize(clip_l)["l"] - - if len(t5xxl) == 0 and no_padding: - tokens["t5xxl"] = [] - else: - tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] - if len(tokens["l"]) != len(tokens["g"]): - empty = clip.tokenize("") - while len(tokens["l"]) < len(tokens["g"]): - tokens["l"] += empty["l"] - while len(tokens["l"]) > len(tokens["g"]): - tokens["g"] += empty["g"] - return (clip.encode_from_tokens_scheduled(tokens), ) - - -class ControlNetApplySD3(nodes.ControlNetApplyAdvanced): - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "control_net": ("CONTROL_NET", ), - "vae": ("VAE", ), - "image": ("IMAGE", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }} - CATEGORY = "conditioning/controlnet" - DEPRECATED = True - - -class SkipLayerGuidanceSD3(comfy_extras.nodes_slg.SkipLayerGuidanceDiT): - ''' - Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. - Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) - Experimental implementation by Dango233@StabilityAI. - ''' - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), - "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}) - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "skip_guidance_sd3" - - CATEGORY = "advanced/guidance" - - def skip_guidance_sd3(self, model, layers, scale, start_percent, end_percent): - return self.skip_guidance(model=model, scale=scale, start_percent=start_percent, end_percent=end_percent, double_layers=layers) - - -NODE_CLASS_MAPPINGS = { - "TripleCLIPLoader": TripleCLIPLoader, - "EmptySD3LatentImage": EmptySD3LatentImage, - "CLIPTextEncodeSD3": CLIPTextEncodeSD3, - "ControlNetApplySD3": ControlNetApplySD3, - "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - # Sampling - "ControlNetApplySD3": "Apply Controlnet with VAE", -} diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py deleted file mode 100644 index bba67e8ddff8064a90ec0f8e71e953ca4e56c4c6..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_sdupscale.py +++ /dev/null @@ -1,46 +0,0 @@ -import torch -import comfy.utils - -class SD_4XUpscale_Conditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": { "images": ("IMAGE",), - "positive": ("CONDITIONING",), - "negative": ("CONDITIONING",), - "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - FUNCTION = "encode" - - CATEGORY = "conditioning/upscale_diffusion" - - def encode(self, images, positive, negative, scale_ratio, noise_augmentation): - width = max(1, round(images.shape[-2] * scale_ratio)) - height = max(1, round(images.shape[-3] * scale_ratio)) - - pixels = comfy.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center") - - out_cp = [] - out_cn = [] - - for t in positive: - n = [t[0], t[1].copy()] - n[1]['concat_image'] = pixels - n[1]['noise_augmentation'] = noise_augmentation - out_cp.append(n) - - for t in negative: - n = [t[0], t[1].copy()] - n[1]['concat_image'] = pixels - n[1]['noise_augmentation'] = noise_augmentation - out_cn.append(n) - - latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) - return (out_cp, out_cn, {"samples":latent}) - -NODE_CLASS_MAPPINGS = { - "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, -} diff --git a/comfy_extras/nodes_slg.py b/comfy_extras/nodes_slg.py deleted file mode 100644 index 7adff202eb2fcea8b8d3b57e51e24725859d551c..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_slg.py +++ /dev/null @@ -1,152 +0,0 @@ -import comfy.model_patcher -import comfy.samplers -import re - - -class SkipLayerGuidanceDiT: - ''' - Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. - Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) - Original experimental implementation for SD3 by Dango233@StabilityAI. - ''' - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), - "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}), - "rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "skip_guidance" - EXPERIMENTAL = True - - DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model." - - CATEGORY = "advanced/guidance" - - def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0): - # check if layer is comma separated integers - def skip(args, extra_args): - return args - - model_sampling = model.get_model_object("model_sampling") - sigma_start = model_sampling.percent_to_sigma(start_percent) - sigma_end = model_sampling.percent_to_sigma(end_percent) - - double_layers = re.findall(r'\d+', double_layers) - double_layers = [int(i) for i in double_layers] - - single_layers = re.findall(r'\d+', single_layers) - single_layers = [int(i) for i in single_layers] - - if len(double_layers) == 0 and len(single_layers) == 0: - return (model, ) - - def post_cfg_function(args): - model = args["model"] - cond_pred = args["cond_denoised"] - cond = args["cond"] - cfg_result = args["denoised"] - sigma = args["sigma"] - x = args["input"] - model_options = args["model_options"].copy() - - for layer in double_layers: - model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer) - - for layer in single_layers: - model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "single_block", layer) - - model_sampling.percent_to_sigma(start_percent) - - sigma_ = sigma[0].item() - if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start: - (slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options) - cfg_result = cfg_result + (cond_pred - slg) * scale - if rescaling_scale != 0: - factor = cond_pred.std() / cfg_result.std() - factor = rescaling_scale * factor + (1 - rescaling_scale) - cfg_result *= factor - - return cfg_result - - m = model.clone() - m.set_model_sampler_post_cfg_function(post_cfg_function) - - return (m, ) - -class SkipLayerGuidanceDiTSimple: - ''' - Simple version of the SkipLayerGuidanceDiT node that only modifies the uncond pass. - ''' - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "skip_guidance" - EXPERIMENTAL = True - - DESCRIPTION = "Simple version of the SkipLayerGuidanceDiT node that only modifies the uncond pass." - - CATEGORY = "advanced/guidance" - - def skip_guidance(self, model, start_percent, end_percent, double_layers="", single_layers=""): - def skip(args, extra_args): - return args - - model_sampling = model.get_model_object("model_sampling") - sigma_start = model_sampling.percent_to_sigma(start_percent) - sigma_end = model_sampling.percent_to_sigma(end_percent) - - double_layers = re.findall(r'\d+', double_layers) - double_layers = [int(i) for i in double_layers] - - single_layers = re.findall(r'\d+', single_layers) - single_layers = [int(i) for i in single_layers] - - if len(double_layers) == 0 and len(single_layers) == 0: - return (model, ) - - def calc_cond_batch_function(args): - x = args["input"] - model = args["model"] - conds = args["conds"] - sigma = args["sigma"] - - model_options = args["model_options"] - slg_model_options = model_options.copy() - - for layer in double_layers: - slg_model_options = comfy.model_patcher.set_model_options_patch_replace(slg_model_options, skip, "dit", "double_block", layer) - - for layer in single_layers: - slg_model_options = comfy.model_patcher.set_model_options_patch_replace(slg_model_options, skip, "dit", "single_block", layer) - - cond, uncond = conds - sigma_ = sigma[0].item() - if sigma_ >= sigma_end and sigma_ <= sigma_start and uncond is not None: - cond_out, _ = comfy.samplers.calc_cond_batch(model, [cond, None], x, sigma, model_options) - _, uncond_out = comfy.samplers.calc_cond_batch(model, [None, uncond], x, sigma, slg_model_options) - out = [cond_out, uncond_out] - else: - out = comfy.samplers.calc_cond_batch(model, conds, x, sigma, model_options) - - return out - - m = model.clone() - m.set_model_sampler_calc_cond_batch_function(calc_cond_batch_function) - - return (m, ) - -NODE_CLASS_MAPPINGS = { - "SkipLayerGuidanceDiT": SkipLayerGuidanceDiT, - "SkipLayerGuidanceDiTSimple": SkipLayerGuidanceDiTSimple, -} diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py deleted file mode 100644 index be2e34c28f49f160a21703c313305193ed00546f..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_stable3d.py +++ /dev/null @@ -1,143 +0,0 @@ -import torch -import nodes -import comfy.utils - -def camera_embeddings(elevation, azimuth): - elevation = torch.as_tensor([elevation]) - azimuth = torch.as_tensor([azimuth]) - embeddings = torch.stack( - [ - torch.deg2rad( - (90 - elevation) - (90) - ), # Zero123 polar is 90-elevation - torch.sin(torch.deg2rad(azimuth)), - torch.cos(torch.deg2rad(azimuth)), - torch.deg2rad( - 90 - torch.full_like(elevation, 0) - ), - ], dim=-1).unsqueeze(1) - - return embeddings - - -class StableZero123_Conditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - FUNCTION = "encode" - - CATEGORY = "conditioning/3d_models" - - def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): - output = clip_vision.encode_image(init_image) - pooled = output.image_embeds.unsqueeze(0) - pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) - encode_pixels = pixels[:,:,:,:3] - t = vae.encode(encode_pixels) - cam_embeds = camera_embeddings(elevation, azimuth) - cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) - - positive = [[cond, {"concat_latent_image": t}]] - negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] - latent = torch.zeros([batch_size, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent}) - -class StableZero123_Conditioning_Batched: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - FUNCTION = "encode" - - CATEGORY = "conditioning/3d_models" - - def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): - output = clip_vision.encode_image(init_image) - pooled = output.image_embeds.unsqueeze(0) - pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) - encode_pixels = pixels[:,:,:,:3] - t = vae.encode(encode_pixels) - - cam_embeds = [] - for i in range(batch_size): - cam_embeds.append(camera_embeddings(elevation, azimuth)) - elevation += elevation_batch_increment - azimuth += azimuth_batch_increment - - cam_embeds = torch.cat(cam_embeds, dim=0) - cond = torch.cat([comfy.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) - - positive = [[cond, {"concat_latent_image": t}]] - negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] - latent = torch.zeros([batch_size, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) - -class SV3D_Conditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - FUNCTION = "encode" - - CATEGORY = "conditioning/3d_models" - - def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): - output = clip_vision.encode_image(init_image) - pooled = output.image_embeds.unsqueeze(0) - pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) - encode_pixels = pixels[:,:,:,:3] - t = vae.encode(encode_pixels) - - azimuth = 0 - azimuth_increment = 360 / (max(video_frames, 2) - 1) - - elevations = [] - azimuths = [] - for i in range(video_frames): - elevations.append(elevation) - azimuths.append(azimuth) - azimuth += azimuth_increment - - positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] - negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] - latent = torch.zeros([video_frames, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent}) - - -NODE_CLASS_MAPPINGS = { - "StableZero123_Conditioning": StableZero123_Conditioning, - "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, - "SV3D_Conditioning": SV3D_Conditioning, -} diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py deleted file mode 100644 index 0034032150e6eac48d18bb6bd35819114a01fe8d..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_stable_cascade.py +++ /dev/null @@ -1,141 +0,0 @@ -""" - This file is part of ComfyUI. - Copyright (C) 2024 Stability AI - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . -""" - -import torch -import nodes -import comfy.utils - - -class StableCascade_EmptyLatentImage: - def __init__(self, device="cpu"): - self.device = device - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), - "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) - }} - RETURN_TYPES = ("LATENT", "LATENT") - RETURN_NAMES = ("stage_c", "stage_b") - FUNCTION = "generate" - - CATEGORY = "latent/stable_cascade" - - def generate(self, width, height, compression, batch_size=1): - c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) - b_latent = torch.zeros([batch_size, 4, height // 4, width // 4]) - return ({ - "samples": c_latent, - }, { - "samples": b_latent, - }) - -class StableCascade_StageC_VAEEncode: - def __init__(self, device="cpu"): - self.device = device - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - "vae": ("VAE", ), - "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), - }} - RETURN_TYPES = ("LATENT", "LATENT") - RETURN_NAMES = ("stage_c", "stage_b") - FUNCTION = "generate" - - CATEGORY = "latent/stable_cascade" - - def generate(self, image, vae, compression): - width = image.shape[-2] - height = image.shape[-3] - out_width = (width // compression) * vae.downscale_ratio - out_height = (height // compression) * vae.downscale_ratio - - s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1) - - c_latent = vae.encode(s[:,:,:,:3]) - b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2]) - return ({ - "samples": c_latent, - }, { - "samples": b_latent, - }) - -class StableCascade_StageB_Conditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": { "conditioning": ("CONDITIONING",), - "stage_c": ("LATENT",), - }} - RETURN_TYPES = ("CONDITIONING",) - - FUNCTION = "set_prior" - - CATEGORY = "conditioning/stable_cascade" - - def set_prior(self, conditioning, stage_c): - c = [] - for t in conditioning: - d = t[1].copy() - d['stable_cascade_prior'] = stage_c['samples'] - n = [t[0], d] - c.append(n) - return (c, ) - -class StableCascade_SuperResolutionControlnet: - def __init__(self, device="cpu"): - self.device = device - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - "vae": ("VAE", ), - }} - RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") - RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") - FUNCTION = "generate" - - EXPERIMENTAL = True - CATEGORY = "_for_testing/stable_cascade" - - def generate(self, image, vae): - width = image.shape[-2] - height = image.shape[-3] - batch_size = image.shape[0] - controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1) - - c_latent = torch.zeros([batch_size, 16, height // 16, width // 16]) - b_latent = torch.zeros([batch_size, 4, height // 2, width // 2]) - return (controlnet_input, { - "samples": c_latent, - }, { - "samples": b_latent, - }) - -NODE_CLASS_MAPPINGS = { - "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, - "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, - "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, - "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, -} diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py deleted file mode 100644 index 571d89f6268f0850212208379808eb2b81fe3df6..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_string.py +++ /dev/null @@ -1,385 +0,0 @@ -import re -from typing_extensions import override - -from comfy_api.latest import ComfyExtension, io - - -class StringConcatenate(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringConcatenate", - display_name="Concatenate", - category="utils/string", - inputs=[ - io.String.Input("string_a", multiline=True), - io.String.Input("string_b", multiline=True), - io.String.Input("delimiter", multiline=False, default=""), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string_a, string_b, delimiter): - return io.NodeOutput(delimiter.join((string_a, string_b))) - - -class StringSubstring(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringSubstring", - display_name="Substring", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.Int.Input("start"), - io.Int.Input("end"), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string, start, end): - return io.NodeOutput(string[start:end]) - - -class StringLength(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringLength", - display_name="Length", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - ], - outputs=[ - io.Int.Output(display_name="length"), - ] - ) - - @classmethod - def execute(cls, string): - return io.NodeOutput(len(string)) - - -class CaseConverter(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="CaseConverter", - display_name="Case Converter", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.Combo.Input("mode", options=["UPPERCASE", "lowercase", "Capitalize", "Title Case"]), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string, mode): - if mode == "UPPERCASE": - result = string.upper() - elif mode == "lowercase": - result = string.lower() - elif mode == "Capitalize": - result = string.capitalize() - elif mode == "Title Case": - result = string.title() - else: - result = string - - return io.NodeOutput(result) - - -class StringTrim(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringTrim", - display_name="Trim", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.Combo.Input("mode", options=["Both", "Left", "Right"]), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string, mode): - if mode == "Both": - result = string.strip() - elif mode == "Left": - result = string.lstrip() - elif mode == "Right": - result = string.rstrip() - else: - result = string - - return io.NodeOutput(result) - - -class StringReplace(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringReplace", - display_name="Replace", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.String.Input("find", multiline=True), - io.String.Input("replace", multiline=True), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string, find, replace): - return io.NodeOutput(string.replace(find, replace)) - - -class StringContains(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringContains", - display_name="Contains", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.String.Input("substring", multiline=True), - io.Boolean.Input("case_sensitive", default=True), - ], - outputs=[ - io.Boolean.Output(display_name="contains"), - ] - ) - - @classmethod - def execute(cls, string, substring, case_sensitive): - if case_sensitive: - contains = substring in string - else: - contains = substring.lower() in string.lower() - - return io.NodeOutput(contains) - - -class StringCompare(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="StringCompare", - display_name="Compare", - category="utils/string", - inputs=[ - io.String.Input("string_a", multiline=True), - io.String.Input("string_b", multiline=True), - io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]), - io.Boolean.Input("case_sensitive", default=True), - ], - outputs=[ - io.Boolean.Output(), - ] - ) - - @classmethod - def execute(cls, string_a, string_b, mode, case_sensitive): - if case_sensitive: - a = string_a - b = string_b - else: - a = string_a.lower() - b = string_b.lower() - - if mode == "Equal": - return io.NodeOutput(a == b) - elif mode == "Starts With": - return io.NodeOutput(a.startswith(b)) - elif mode == "Ends With": - return io.NodeOutput(a.endswith(b)) - - -class RegexMatch(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="RegexMatch", - display_name="Regex Match", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.String.Input("regex_pattern", multiline=True), - io.Boolean.Input("case_insensitive", default=True), - io.Boolean.Input("multiline", default=False), - io.Boolean.Input("dotall", default=False), - ], - outputs=[ - io.Boolean.Output(display_name="matches"), - ] - ) - - @classmethod - def execute(cls, string, regex_pattern, case_insensitive, multiline, dotall): - flags = 0 - - if case_insensitive: - flags |= re.IGNORECASE - if multiline: - flags |= re.MULTILINE - if dotall: - flags |= re.DOTALL - - try: - match = re.search(regex_pattern, string, flags) - result = match is not None - - except re.error: - result = False - - return io.NodeOutput(result) - - -class RegexExtract(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="RegexExtract", - display_name="Regex Extract", - category="utils/string", - inputs=[ - io.String.Input("string", multiline=True), - io.String.Input("regex_pattern", multiline=True), - io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]), - io.Boolean.Input("case_insensitive", default=True), - io.Boolean.Input("multiline", default=False), - io.Boolean.Input("dotall", default=False), - io.Int.Input("group_index", default=1, min=0, max=100), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index): - join_delimiter = "\n" - - flags = 0 - if case_insensitive: - flags |= re.IGNORECASE - if multiline: - flags |= re.MULTILINE - if dotall: - flags |= re.DOTALL - - try: - if mode == "First Match": - match = re.search(regex_pattern, string, flags) - if match: - result = match.group(0) - else: - result = "" - - elif mode == "All Matches": - matches = re.findall(regex_pattern, string, flags) - if matches: - if isinstance(matches[0], tuple): - result = join_delimiter.join([m[0] for m in matches]) - else: - result = join_delimiter.join(matches) - else: - result = "" - - elif mode == "First Group": - match = re.search(regex_pattern, string, flags) - if match and len(match.groups()) >= group_index: - result = match.group(group_index) - else: - result = "" - - elif mode == "All Groups": - matches = re.finditer(regex_pattern, string, flags) - results = [] - for match in matches: - if match.groups() and len(match.groups()) >= group_index: - results.append(match.group(group_index)) - result = join_delimiter.join(results) - else: - result = "" - - except re.error: - result = "" - - return io.NodeOutput(result) - - -class RegexReplace(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="RegexReplace", - display_name="Regex Replace", - category="utils/string", - description="Find and replace text using regex patterns.", - inputs=[ - io.String.Input("string", multiline=True), - io.String.Input("regex_pattern", multiline=True), - io.String.Input("replace", multiline=True), - io.Boolean.Input("case_insensitive", default=True, optional=True), - io.Boolean.Input("multiline", default=False, optional=True), - io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."), - io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."), - ], - outputs=[ - io.String.Output(), - ] - ) - - @classmethod - def execute(cls, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0): - flags = 0 - - if case_insensitive: - flags |= re.IGNORECASE - if multiline: - flags |= re.MULTILINE - if dotall: - flags |= re.DOTALL - result = re.sub(regex_pattern, replace, string, count=count, flags=flags) - return io.NodeOutput(result) - - -class StringExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - StringConcatenate, - StringSubstring, - StringLength, - CaseConverter, - StringTrim, - StringReplace, - StringContains, - StringCompare, - RegexMatch, - RegexExtract, - RegexReplace, - ] - -async def comfy_entrypoint() -> StringExtension: - return StringExtension() diff --git a/comfy_extras/nodes_tcfg.py b/comfy_extras/nodes_tcfg.py deleted file mode 100644 index 35b89a73f7fbab0d4ad5e922997a88f3ef04ec7f..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_tcfg.py +++ /dev/null @@ -1,71 +0,0 @@ -# TCFG: Tangential Damping Classifier-free Guidance - (arXiv: https://arxiv.org/abs/2503.18137) - -import torch - -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict - - -def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tensor) -> torch.Tensor: - """Drop tangential components from uncond score to align with cond score.""" - # (B, 1, ...) - batch_num = cond_score.shape[0] - cond_score_flat = cond_score.reshape(batch_num, 1, -1).float() - uncond_score_flat = uncond_score.reshape(batch_num, 1, -1).float() - - # Score matrix A (B, 2, ...) - score_matrix = torch.cat((uncond_score_flat, cond_score_flat), dim=1) - try: - _, _, Vh = torch.linalg.svd(score_matrix, full_matrices=False) - except RuntimeError: - # Fallback to CPU - _, _, Vh = torch.linalg.svd(score_matrix.cpu(), full_matrices=False) - - # Drop the tangential components - v1 = Vh[:, 0:1, :].to(uncond_score_flat.device) # (B, 1, ...) - uncond_score_td = (uncond_score_flat @ v1.transpose(-2, -1)) * v1 - return uncond_score_td.reshape_as(uncond_score).to(uncond_score.dtype) - - -class TCFG(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "model": (IO.MODEL, {}), - } - } - - RETURN_TYPES = (IO.MODEL,) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - - CATEGORY = "advanced/guidance" - DESCRIPTION = "TCFG – Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality." - - def patch(self, model): - m = model.clone() - - def tangential_damping_cfg(args): - # Assume [cond, uncond, ...] - x = args["input"] - conds_out = args["conds_out"] - if len(conds_out) <= 1 or None in args["conds"][:2]: - # Skip when either cond or uncond is None - return conds_out - cond_pred = conds_out[0] - uncond_pred = conds_out[1] - uncond_td = score_tangential_damping(x - cond_pred, x - uncond_pred) - uncond_pred_td = x - uncond_td - return [cond_pred, uncond_pred_td] + conds_out[2:] - - m.set_model_sampler_pre_cfg_function(tangential_damping_cfg) - return (m,) - - -NODE_CLASS_MAPPINGS = { - "TCFG": TCFG, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "TCFG": "Tangential Damping CFG", -} diff --git a/comfy_extras/nodes_tomesd.py b/comfy_extras/nodes_tomesd.py deleted file mode 100644 index 9f77c06fcb12a2dafbb891cbceb50ba8addaa81a..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_tomesd.py +++ /dev/null @@ -1,176 +0,0 @@ -#Taken from: https://github.com/dbolya/tomesd - -import torch -from typing import Tuple, Callable -import math - -def do_nothing(x: torch.Tensor, mode:str=None): - return x - - -def mps_gather_workaround(input, dim, index): - if input.shape[-1] == 1: - return torch.gather( - input.unsqueeze(-1), - dim - 1 if dim < 0 else dim, - index.unsqueeze(-1) - ).squeeze(-1) - else: - return torch.gather(input, dim, index) - - -def bipartite_soft_matching_random2d(metric: torch.Tensor, - w: int, h: int, sx: int, sy: int, r: int, - no_rand: bool = False) -> Tuple[Callable, Callable]: - """ - Partitions the tokens into src and dst and merges r tokens from src to dst. - Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. - Args: - - metric [B, N, C]: metric to use for similarity - - w: image width in tokens - - h: image height in tokens - - sx: stride in the x dimension for dst, must divide w - - sy: stride in the y dimension for dst, must divide h - - r: number of tokens to remove (by merging) - - no_rand: if true, disable randomness (use top left corner only) - """ - B, N, _ = metric.shape - - if r <= 0 or w == 1 or h == 1: - return do_nothing, do_nothing - - gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather - - with torch.no_grad(): - hsy, wsx = h // sy, w // sx - - # For each sy by sx kernel, randomly assign one token to be dst and the rest src - if no_rand: - rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) - else: - rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) - - # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead - idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) - idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) - idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) - - # Image is not divisible by sx or sy so we need to move it into a new buffer - if (hsy * sy) < h or (wsx * sx) < w: - idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) - idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view - else: - idx_buffer = idx_buffer_view - - # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices - rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) - - # We're finished with these - del idx_buffer, idx_buffer_view - - # rand_idx is currently dst|src, so split them - num_dst = hsy * wsx - a_idx = rand_idx[:, num_dst:, :] # src - b_idx = rand_idx[:, :num_dst, :] # dst - - def split(x): - C = x.shape[-1] - src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) - dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) - return src, dst - - # Cosine similarity between A and B - metric = metric / metric.norm(dim=-1, keepdim=True) - a, b = split(metric) - scores = a @ b.transpose(-1, -2) - - # Can't reduce more than the # tokens in src - r = min(a.shape[1], r) - - # Find the most similar greedily - node_max, node_idx = scores.max(dim=-1) - edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] - - unm_idx = edge_idx[..., r:, :] # Unmerged Tokens - src_idx = edge_idx[..., :r, :] # Merged Tokens - dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) - - def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: - src, dst = split(x) - n, t1, c = src.shape - - unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) - src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) - dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) - - return torch.cat([unm, dst], dim=1) - - def unmerge(x: torch.Tensor) -> torch.Tensor: - unm_len = unm_idx.shape[1] - unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] - _, _, c = unm.shape - - src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) - - # Combine back to the original shape - out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) - out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) - out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) - out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) - - return out - - return merge, unmerge - - -def get_functions(x, ratio, original_shape): - b, c, original_h, original_w = original_shape - original_tokens = original_h * original_w - downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) - stride_x = 2 - stride_y = 2 - max_downsample = 1 - - if downsample <= max_downsample: - w = int(math.ceil(original_w / downsample)) - h = int(math.ceil(original_h / downsample)) - r = int(x.shape[1] * ratio) - no_rand = False - m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) - return m, u - - nothing = lambda y: y - return nothing, nothing - - - -class TomePatchModel: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, ratio): - self.u = None - def tomesd_m(q, k, v, extra_options): - #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q - #however from my basic testing it seems that using q instead gives better results - m, self.u = get_functions(q, ratio, extra_options["original_shape"]) - return m(q), k, v - def tomesd_u(n, extra_options): - return self.u(n) - - m = model.clone() - m.set_model_attn1_patch(tomesd_m) - m.set_model_attn1_output_patch(tomesd_u) - return (m, ) - - -NODE_CLASS_MAPPINGS = { - "TomePatchModel": TomePatchModel, -} diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py deleted file mode 100644 index 6055366784d0d46de863d8278b0bb2979e904d2a..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_torch_compile.py +++ /dev/null @@ -1,23 +0,0 @@ -from comfy_api.torch_helpers import set_torch_compile_wrapper - - -class TorchCompileModel: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "backend": (["inductor", "cudagraphs"],), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "_for_testing" - EXPERIMENTAL = True - - def patch(self, model, backend): - m = model.clone() - set_torch_compile_wrapper(model=m, backend=backend) - return (m, ) - -NODE_CLASS_MAPPINGS = { - "TorchCompileModel": TorchCompileModel, -} diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py deleted file mode 100644 index c3aaaee9b03ae7f33c99260851d03b396eee4403..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_train.py +++ /dev/null @@ -1,877 +0,0 @@ -import datetime -import json -import logging -import os - -import numpy as np -import safetensors -import torch -from PIL import Image, ImageDraw, ImageFont -from PIL.PngImagePlugin import PngInfo -import torch.utils.checkpoint -import tqdm - -import comfy.samplers -import comfy.sd -import comfy.utils -import comfy.model_management -import comfy_extras.nodes_custom_sampler -import folder_paths -import node_helpers -from comfy.cli_args import args -from comfy.comfy_types.node_typing import IO -from comfy.weight_adapter import adapters, adapter_maps - - -def make_batch_extra_option_dict(d, indicies, full_size=None): - new_dict = {} - for k, v in d.items(): - newv = v - if isinstance(v, dict): - newv = make_batch_extra_option_dict(v, indicies, full_size=full_size) - elif isinstance(v, torch.Tensor): - if full_size is None or v.size(0) == full_size: - newv = v[indicies] - elif isinstance(v, (list, tuple)) and len(v) == full_size: - newv = [v[i] for i in indicies] - new_dict[k] = newv - return new_dict - - -class TrainSampler(comfy.samplers.Sampler): - def __init__(self, loss_fn, optimizer, loss_callback=None, batch_size=1, grad_acc=1, total_steps=1, seed=0, training_dtype=torch.bfloat16): - self.loss_fn = loss_fn - self.optimizer = optimizer - self.loss_callback = loss_callback - self.batch_size = batch_size - self.total_steps = total_steps - self.grad_acc = grad_acc - self.seed = seed - self.training_dtype = training_dtype - - def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): - cond = model_wrap.conds["positive"] - dataset_size = sigmas.size(0) - torch.cuda.empty_cache() - for i in (pbar:=tqdm.trange(self.total_steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)): - noisegen = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(self.seed + i * 1000) - indicies = torch.randperm(dataset_size)[:self.batch_size].tolist() - - batch_latent = torch.stack([latent_image[i] for i in indicies]) - batch_noise = noisegen.generate_noise({"samples": batch_latent}).to(batch_latent.device) - batch_sigmas = [ - model_wrap.inner_model.model_sampling.percent_to_sigma( - torch.rand((1,)).item() - ) for _ in range(min(self.batch_size, dataset_size)) - ] - batch_sigmas = torch.tensor(batch_sigmas).to(batch_latent.device) - - xt = model_wrap.inner_model.model_sampling.noise_scaling( - batch_sigmas, - batch_noise, - batch_latent, - False - ) - x0 = model_wrap.inner_model.model_sampling.noise_scaling( - torch.zeros_like(batch_sigmas), - torch.zeros_like(batch_noise), - batch_latent, - False - ) - - model_wrap.conds["positive"] = [ - cond[i] for i in indicies - ] - batch_extra_args = make_batch_extra_option_dict(extra_args, indicies, full_size=dataset_size) - - with torch.autocast(xt.device.type, dtype=self.training_dtype): - x0_pred = model_wrap(xt, batch_sigmas, **batch_extra_args) - loss = self.loss_fn(x0_pred, x0) - loss.backward() - if self.loss_callback: - self.loss_callback(loss.item()) - pbar.set_postfix({"loss": f"{loss.item():.4f}"}) - - if (i+1) % self.grad_acc == 0: - self.optimizer.step() - self.optimizer.zero_grad() - torch.cuda.empty_cache() - return torch.zeros_like(latent_image) - - -class BiasDiff(torch.nn.Module): - def __init__(self, bias): - super().__init__() - self.bias = bias - - def __call__(self, b): - org_dtype = b.dtype - return (b.to(self.bias) + self.bias).to(org_dtype) - - def passive_memory_usage(self): - return self.bias.nelement() * self.bias.element_size() - - def move_to(self, device): - self.to(device=device) - return self.passive_memory_usage() - - -def load_and_process_images(image_files, input_dir, resize_method="None", w=None, h=None): - """Utility function to load and process a list of images. - - Args: - image_files: List of image filenames - input_dir: Base directory containing the images - resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad") - - Returns: - torch.Tensor: Batch of processed images - """ - if not image_files: - raise ValueError("No valid images found in input") - - output_images = [] - - for file in image_files: - image_path = os.path.join(input_dir, file) - img = node_helpers.pillow(Image.open, image_path) - - if img.mode == "I": - img = img.point(lambda i: i * (1 / 255)) - img = img.convert("RGB") - - if w is None and h is None: - w, h = img.size[0], img.size[1] - - # Resize image to first image - if img.size[0] != w or img.size[1] != h: - if resize_method == "Stretch": - img = img.resize((w, h), Image.Resampling.LANCZOS) - elif resize_method == "Crop": - img = img.crop((0, 0, w, h)) - elif resize_method == "Pad": - img = img.resize((w, h), Image.Resampling.LANCZOS) - elif resize_method == "None": - raise ValueError( - "Your input image size does not match the first image in the dataset. Either select a valid resize method or use the same size for all images." - ) - - img_array = np.array(img).astype(np.float32) / 255.0 - img_tensor = torch.from_numpy(img_array)[None,] - output_images.append(img_tensor) - - return torch.cat(output_images, dim=0) - - -class LoadImageSetNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ( - [ - f - for f in os.listdir(folder_paths.get_input_directory()) - if f.endswith((".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff")) - ], - {"image_upload": True, "allow_batch": True}, - ) - }, - "optional": { - "resize_method": ( - ["None", "Stretch", "Crop", "Pad"], - {"default": "None"}, - ), - }, - } - - INPUT_IS_LIST = True - RETURN_TYPES = ("IMAGE",) - FUNCTION = "load_images" - CATEGORY = "loaders" - EXPERIMENTAL = True - DESCRIPTION = "Loads a batch of images from a directory for training." - - @classmethod - def VALIDATE_INPUTS(s, images, resize_method): - filenames = images[0] if isinstance(images[0], list) else images - - for image in filenames: - if not folder_paths.exists_annotated_filepath(image): - return "Invalid image file: {}".format(image) - return True - - def load_images(self, input_files, resize_method): - input_dir = folder_paths.get_input_directory() - valid_extensions = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff"] - image_files = [ - f - for f in input_files - if any(f.lower().endswith(ext) for ext in valid_extensions) - ] - output_tensor = load_and_process_images(image_files, input_dir, resize_method) - return (output_tensor,) - - -class LoadImageSetFromFolderNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}) - }, - "optional": { - "resize_method": ( - ["None", "Stretch", "Crop", "Pad"], - {"default": "None"}, - ), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "load_images" - CATEGORY = "loaders" - EXPERIMENTAL = True - DESCRIPTION = "Loads a batch of images from a directory for training." - - def load_images(self, folder, resize_method): - sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) - valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] - image_files = [ - f - for f in os.listdir(sub_input_dir) - if any(f.lower().endswith(ext) for ext in valid_extensions) - ] - output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method) - return (output_tensor,) - - -class LoadImageTextSetFromFolderNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}), - "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}), - }, - "optional": { - "resize_method": ( - ["None", "Stretch", "Crop", "Pad"], - {"default": "None"}, - ), - "width": ( - IO.INT, - { - "default": -1, - "min": -1, - "max": 10000, - "step": 1, - "tooltip": "The width to resize the images to. -1 means use the original width.", - }, - ), - "height": ( - IO.INT, - { - "default": -1, - "min": -1, - "max": 10000, - "step": 1, - "tooltip": "The height to resize the images to. -1 means use the original height.", - }, - ) - }, - } - - RETURN_TYPES = ("IMAGE", IO.CONDITIONING,) - FUNCTION = "load_images" - CATEGORY = "loaders" - EXPERIMENTAL = True - DESCRIPTION = "Loads a batch of images and caption from a directory for training." - - def load_images(self, folder, clip, resize_method, width=None, height=None): - if clip is None: - raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") - - logging.info(f"Loading images from folder: {folder}") - - sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) - valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] - - image_files = [] - for item in os.listdir(sub_input_dir): - path = os.path.join(sub_input_dir, item) - if any(item.lower().endswith(ext) for ext in valid_extensions): - image_files.append(path) - elif os.path.isdir(path): - # Support kohya-ss/sd-scripts folder structure - repeat = 1 - if item.split("_")[0].isdigit(): - repeat = int(item.split("_")[0]) - image_files.extend([ - os.path.join(path, f) for f in os.listdir(path) if any(f.lower().endswith(ext) for ext in valid_extensions) - ] * repeat) - - caption_file_path = [ - f.replace(os.path.splitext(f)[1], ".txt") - for f in image_files - ] - captions = [] - for caption_file in caption_file_path: - caption_path = os.path.join(sub_input_dir, caption_file) - if os.path.exists(caption_path): - with open(caption_path, "r", encoding="utf-8") as f: - caption = f.read().strip() - captions.append(caption) - else: - captions.append("") - - width = width if width != -1 else None - height = height if height != -1 else None - output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method, width, height) - - logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.") - - logging.info(f"Encoding captions from {sub_input_dir}.") - conditions = [] - empty_cond = clip.encode_from_tokens_scheduled(clip.tokenize("")) - for text in captions: - if text == "": - conditions.append(empty_cond) - tokens = clip.tokenize(text) - conditions.extend(clip.encode_from_tokens_scheduled(tokens)) - logging.info(f"Encoded {len(conditions)} captions from {sub_input_dir}.") - return (output_tensor, conditions) - - -def draw_loss_graph(loss_map, steps): - width, height = 500, 300 - img = Image.new("RGB", (width, height), "white") - draw = ImageDraw.Draw(img) - - min_loss, max_loss = min(loss_map.values()), max(loss_map.values()) - scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_map.values()] - - prev_point = (0, height - int(scaled_loss[0] * height)) - for i, l in enumerate(scaled_loss[1:], start=1): - x = int(i / (steps - 1) * width) - y = height - int(l * height) - draw.line([prev_point, (x, y)], fill="blue", width=2) - prev_point = (x, y) - - return img - - -def find_all_highest_child_module_with_forward(model: torch.nn.Module, result = None, name = None): - if result is None: - result = [] - elif hasattr(model, "forward") and not isinstance(model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)): - result.append(model) - logging.debug(f"Found module with forward: {name} ({model.__class__.__name__})") - return result - name = name or "root" - for next_name, child in model.named_children(): - find_all_highest_child_module_with_forward(child, result, f"{name}.{next_name}") - return result - - -def patch(m): - if not hasattr(m, "forward"): - return - org_forward = m.forward - def fwd(args, kwargs): - return org_forward(*args, **kwargs) - def checkpointing_fwd(*args, **kwargs): - return torch.utils.checkpoint.checkpoint( - fwd, args, kwargs, use_reentrant=False - ) - m.org_forward = org_forward - m.forward = checkpointing_fwd - - -def unpatch(m): - if hasattr(m, "org_forward"): - m.forward = m.org_forward - del m.org_forward - - -class TrainLoraNode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": (IO.MODEL, {"tooltip": "The model to train the LoRA on."}), - "latents": ( - "LATENT", - { - "tooltip": "The Latents to use for training, serve as dataset/input of the model." - }, - ), - "positive": ( - IO.CONDITIONING, - {"tooltip": "The positive conditioning to use for training."}, - ), - "batch_size": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 10000, - "step": 1, - "tooltip": "The batch size to use for training.", - }, - ), - "grad_accumulation_steps": ( - IO.INT, - { - "default": 1, - "min": 1, - "max": 1024, - "step": 1, - "tooltip": "The number of gradient accumulation steps to use for training.", - } - ), - "steps": ( - IO.INT, - { - "default": 16, - "min": 1, - "max": 100000, - "tooltip": "The number of steps to train the LoRA for.", - }, - ), - "learning_rate": ( - IO.FLOAT, - { - "default": 0.0005, - "min": 0.0000001, - "max": 1.0, - "step": 0.000001, - "tooltip": "The learning rate to use for training.", - }, - ), - "rank": ( - IO.INT, - { - "default": 8, - "min": 1, - "max": 128, - "tooltip": "The rank of the LoRA layers.", - }, - ), - "optimizer": ( - ["AdamW", "Adam", "SGD", "RMSprop"], - { - "default": "AdamW", - "tooltip": "The optimizer to use for training.", - }, - ), - "loss_function": ( - ["MSE", "L1", "Huber", "SmoothL1"], - { - "default": "MSE", - "tooltip": "The loss function to use for training.", - }, - ), - "seed": ( - IO.INT, - { - "default": 0, - "min": 0, - "max": 0xFFFFFFFFFFFFFFFF, - "tooltip": "The seed to use for training (used in generator for LoRA weight initialization and noise sampling)", - }, - ), - "training_dtype": ( - ["bf16", "fp32"], - {"default": "bf16", "tooltip": "The dtype to use for training."}, - ), - "lora_dtype": ( - ["bf16", "fp32"], - {"default": "bf16", "tooltip": "The dtype to use for lora."}, - ), - "algorithm": ( - list(adapter_maps.keys()), - {"default": list(adapter_maps.keys())[0], "tooltip": "The algorithm to use for training."}, - ), - "gradient_checkpointing": ( - IO.BOOLEAN, - { - "default": True, - "tooltip": "Use gradient checkpointing for training.", - } - ), - "existing_lora": ( - folder_paths.get_filename_list("loras") + ["[None]"], - { - "default": "[None]", - "tooltip": "The existing LoRA to append to. Set to None for new LoRA.", - }, - ), - }, - } - - RETURN_TYPES = (IO.MODEL, IO.LORA_MODEL, IO.LOSS_MAP, IO.INT) - RETURN_NAMES = ("model_with_lora", "lora", "loss", "steps") - FUNCTION = "train" - CATEGORY = "training" - EXPERIMENTAL = True - - def train( - self, - model, - latents, - positive, - batch_size, - steps, - grad_accumulation_steps, - learning_rate, - rank, - optimizer, - loss_function, - seed, - training_dtype, - lora_dtype, - algorithm, - gradient_checkpointing, - existing_lora, - ): - mp = model.clone() - dtype = node_helpers.string_to_torch_dtype(training_dtype) - lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) - mp.set_model_compute_dtype(dtype) - - latents = latents["samples"].to(dtype) - num_images = latents.shape[0] - logging.info(f"Total Images: {num_images}, Total Captions: {len(positive)}") - if len(positive) == 1 and num_images > 1: - positive = positive * num_images - elif len(positive) != num_images: - raise ValueError( - f"Number of positive conditions ({len(positive)}) does not match number of images ({num_images})." - ) - - with torch.inference_mode(False): - lora_sd = {} - generator = torch.Generator() - generator.manual_seed(seed) - - # Load existing LoRA weights if provided - existing_weights = {} - existing_steps = 0 - if existing_lora != "[None]": - lora_path = folder_paths.get_full_path_or_raise("loras", existing_lora) - # Extract steps from filename like "trained_lora_10_steps_20250225_203716" - existing_steps = int(existing_lora.split("_steps_")[0].split("_")[-1]) - if lora_path: - existing_weights = comfy.utils.load_torch_file(lora_path) - - all_weight_adapters = [] - for n, m in mp.model.named_modules(): - if hasattr(m, "weight_function"): - if m.weight is not None: - key = "{}.weight".format(n) - shape = m.weight.shape - if len(shape) >= 2: - alpha = float(existing_weights.get(f"{key}.alpha", 1.0)) - dora_scale = existing_weights.get( - f"{key}.dora_scale", None - ) - for adapter_cls in adapters: - existing_adapter = adapter_cls.load( - n, existing_weights, alpha, dora_scale - ) - if existing_adapter is not None: - break - else: - existing_adapter = None - adapter_cls = adapter_maps[algorithm] - - if existing_adapter is not None: - train_adapter = existing_adapter.to_train().to(lora_dtype) - else: - # Use LoRA with alpha=1.0 by default - train_adapter = adapter_cls.create_train( - m.weight, rank=rank, alpha=1.0 - ).to(lora_dtype) - for name, parameter in train_adapter.named_parameters(): - lora_sd[f"{n}.{name}"] = parameter - - mp.add_weight_wrapper(key, train_adapter) - all_weight_adapters.append(train_adapter) - else: - diff = torch.nn.Parameter( - torch.zeros( - m.weight.shape, dtype=lora_dtype, requires_grad=True - ) - ) - diff_module = BiasDiff(diff) - mp.add_weight_wrapper(key, BiasDiff(diff)) - all_weight_adapters.append(diff_module) - lora_sd["{}.diff".format(n)] = diff - if hasattr(m, "bias") and m.bias is not None: - key = "{}.bias".format(n) - bias = torch.nn.Parameter( - torch.zeros(m.bias.shape, dtype=lora_dtype, requires_grad=True) - ) - bias_module = BiasDiff(bias) - lora_sd["{}.diff_b".format(n)] = bias - mp.add_weight_wrapper(key, BiasDiff(bias)) - all_weight_adapters.append(bias_module) - - if optimizer == "Adam": - optimizer = torch.optim.Adam(lora_sd.values(), lr=learning_rate) - elif optimizer == "AdamW": - optimizer = torch.optim.AdamW(lora_sd.values(), lr=learning_rate) - elif optimizer == "SGD": - optimizer = torch.optim.SGD(lora_sd.values(), lr=learning_rate) - elif optimizer == "RMSprop": - optimizer = torch.optim.RMSprop(lora_sd.values(), lr=learning_rate) - - # Setup loss function based on selection - if loss_function == "MSE": - criterion = torch.nn.MSELoss() - elif loss_function == "L1": - criterion = torch.nn.L1Loss() - elif loss_function == "Huber": - criterion = torch.nn.HuberLoss() - elif loss_function == "SmoothL1": - criterion = torch.nn.SmoothL1Loss() - - # setup models - if gradient_checkpointing: - for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model): - patch(m) - mp.model.requires_grad_(False) - comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True) - - # Setup sampler and guider like in test script - loss_map = {"loss": []} - def loss_callback(loss): - loss_map["loss"].append(loss) - train_sampler = TrainSampler( - criterion, - optimizer, - loss_callback=loss_callback, - batch_size=batch_size, - grad_acc=grad_accumulation_steps, - total_steps=steps*grad_accumulation_steps, - seed=seed, - training_dtype=dtype - ) - guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp) - guider.set_conds(positive) # Set conditioning from input - - # Training loop - try: - # Generate dummy sigmas and noise - sigmas = torch.tensor(range(num_images)) - noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(seed) - guider.sample( - noise.generate_noise({"samples": latents}), - latents, - train_sampler, - sigmas, - seed=noise.seed - ) - finally: - for m in mp.model.modules(): - unpatch(m) - del train_sampler, optimizer - - for adapter in all_weight_adapters: - adapter.requires_grad_(False) - - for param in lora_sd: - lora_sd[param] = lora_sd[param].to(lora_dtype) - - return (mp, lora_sd, loss_map, steps + existing_steps) - - -class LoraModelLoader: - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), - "lora": (IO.LORA_MODEL, {"tooltip": "The LoRA model to apply to the diffusion model."}), - "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), - } - } - - RETURN_TYPES = ("MODEL",) - OUTPUT_TOOLTIPS = ("The modified diffusion model.",) - FUNCTION = "load_lora_model" - - CATEGORY = "loaders" - DESCRIPTION = "Load Trained LoRA weights from Train LoRA node." - EXPERIMENTAL = True - - def load_lora_model(self, model, lora, strength_model): - if strength_model == 0: - return (model, ) - - model_lora, _ = comfy.sd.load_lora_for_models(model, None, lora, strength_model, 0) - return (model_lora, ) - - -class SaveLoRA: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "lora": ( - IO.LORA_MODEL, - { - "tooltip": "The LoRA model to save. Do not use the model with LoRA layers." - }, - ), - "prefix": ( - "STRING", - { - "default": "loras/ComfyUI_trained_lora", - "tooltip": "The prefix to use for the saved LoRA file.", - }, - ), - }, - "optional": { - "steps": ( - IO.INT, - { - "forceInput": True, - "tooltip": "Optional: The number of steps to LoRA has been trained for, used to name the saved file.", - }, - ), - }, - } - - RETURN_TYPES = () - FUNCTION = "save" - CATEGORY = "loaders" - EXPERIMENTAL = True - OUTPUT_NODE = True - - def save(self, lora, prefix, steps=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(prefix, self.output_dir) - if steps is None: - output_checkpoint = f"{filename}_{counter:05}_.safetensors" - else: - output_checkpoint = f"{filename}_{steps}_steps_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - safetensors.torch.save_file(lora, output_checkpoint) - return {} - - -class LossGraphNode: - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "loss": (IO.LOSS_MAP, {"default": {}}), - "filename_prefix": (IO.STRING, {"default": "loss_graph"}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "plot_loss" - OUTPUT_NODE = True - CATEGORY = "training" - EXPERIMENTAL = True - DESCRIPTION = "Plots the loss graph and saves it to the output directory." - - def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None): - loss_values = loss["loss"] - width, height = 800, 480 - margin = 40 - - img = Image.new( - "RGB", (width + margin, height + margin), "white" - ) # Extend canvas - draw = ImageDraw.Draw(img) - - min_loss, max_loss = min(loss_values), max(loss_values) - scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_values] - - steps = len(loss_values) - - prev_point = (margin, height - int(scaled_loss[0] * height)) - for i, l in enumerate(scaled_loss[1:], start=1): - x = margin + int(i / steps * width) # Scale X properly - y = height - int(l * height) - draw.line([prev_point, (x, y)], fill="blue", width=2) - prev_point = (x, y) - - draw.line([(margin, 0), (margin, height)], fill="black", width=2) # Y-axis - draw.line( - [(margin, height), (width + margin, height)], fill="black", width=2 - ) # X-axis - - font = None - try: - font = ImageFont.truetype("arial.ttf", 12) - except IOError: - font = ImageFont.load_default() - - # Add axis labels - draw.text((5, height // 2), "Loss", font=font, fill="black") - draw.text((width // 2, height + 10), "Steps", font=font, fill="black") - - # Add min/max loss values - draw.text((margin - 30, 0), f"{max_loss:.2f}", font=font, fill="black") - draw.text( - (margin - 30, height - 10), f"{min_loss:.2f}", font=font, fill="black" - ) - - metadata = None - if not args.disable_metadata: - metadata = PngInfo() - if prompt is not None: - metadata.add_text("prompt", json.dumps(prompt)) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata.add_text(x, json.dumps(extra_pnginfo[x])) - - date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - img.save( - os.path.join(self.output_dir, f"{filename_prefix}_{date}.png"), - pnginfo=metadata, - ) - return { - "ui": { - "images": [ - { - "filename": f"{filename_prefix}_{date}.png", - "subfolder": "", - "type": "temp", - } - ] - } - } - - -NODE_CLASS_MAPPINGS = { - "TrainLoraNode": TrainLoraNode, - "SaveLoRANode": SaveLoRA, - "LoraModelLoader": LoraModelLoader, - "LoadImageSetFromFolderNode": LoadImageSetFromFolderNode, - "LoadImageTextSetFromFolderNode": LoadImageTextSetFromFolderNode, - "LossGraphNode": LossGraphNode, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "TrainLoraNode": "Train LoRA", - "SaveLoRANode": "Save LoRA Weights", - "LoraModelLoader": "Load LoRA Model", - "LoadImageSetFromFolderNode": "Load Image Dataset from Folder", - "LoadImageTextSetFromFolderNode": "Load Image and Text Dataset from Folder", - "LossGraphNode": "Plot Loss Graph", -} diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py deleted file mode 100644 index 04c948341296dfa7e385141498973977c17c906a..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_upscale_model.py +++ /dev/null @@ -1,83 +0,0 @@ -import logging -from spandrel import ModelLoader, ImageModelDescriptor -from comfy import model_management -import torch -import comfy.utils -import folder_paths - -try: - from spandrel_extra_arches import EXTRA_REGISTRY - from spandrel import MAIN_REGISTRY - MAIN_REGISTRY.add(*EXTRA_REGISTRY) - logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.") -except: - pass - -class UpscaleModelLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), - }} - RETURN_TYPES = ("UPSCALE_MODEL",) - FUNCTION = "load_model" - - CATEGORY = "loaders" - - def load_model(self, model_name): - model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name) - sd = comfy.utils.load_torch_file(model_path, safe_load=True) - if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: - sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""}) - out = ModelLoader().load_from_state_dict(sd).eval() - - if not isinstance(out, ImageModelDescriptor): - raise Exception("Upscale model must be a single-image model.") - - return (out, ) - - -class ImageUpscaleWithModel: - @classmethod - def INPUT_TYPES(s): - return {"required": { "upscale_model": ("UPSCALE_MODEL",), - "image": ("IMAGE",), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" - - CATEGORY = "image/upscaling" - - def upscale(self, upscale_model, image): - device = model_management.get_torch_device() - - memory_required = model_management.module_size(upscale_model.model) - memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate - memory_required += image.nelement() * image.element_size() - model_management.free_memory(memory_required, device) - - upscale_model.to(device) - in_img = image.movedim(-1,-3).to(device) - - tile = 512 - overlap = 32 - - oom = True - while oom: - try: - steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) - pbar = comfy.utils.ProgressBar(steps) - s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) - oom = False - except model_management.OOM_EXCEPTION as e: - tile //= 2 - if tile < 128: - raise e - - upscale_model.to("cpu") - s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) - return (s,) - -NODE_CLASS_MAPPINGS = { - "UpscaleModelLoader": UpscaleModelLoader, - "ImageUpscaleWithModel": ImageUpscaleWithModel -} diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py deleted file mode 100644 index 969f888b933b2d75774e8ef8dc9e6e0f2541d81a..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_video.py +++ /dev/null @@ -1,240 +0,0 @@ -from __future__ import annotations - -import os -import av -import torch -import folder_paths -import json -from typing import Optional, Literal -from fractions import Fraction -from comfy.comfy_types import IO, FileLocator, ComfyNodeABC -from comfy_api.latest import Input, InputImpl, Types -from comfy.cli_args import args - -class SaveWEBM: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ), - "filename_prefix": ("STRING", {"default": "ComfyUI"}), - "codec": (["vp9", "av1"],), - "fps": ("FLOAT", {"default": 24.0, "min": 0.01, "max": 1000.0, "step": 0.01}), - "crf": ("FLOAT", {"default": 32.0, "min": 0, "max": 63.0, "step": 1, "tooltip": "Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "image/video" - - EXPERIMENTAL = True - - def save_images(self, images, codec, fps, filename_prefix, crf, prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - - file = f"{filename}_{counter:05}_.webm" - container = av.open(os.path.join(full_output_folder, file), mode="w") - - if prompt is not None: - container.metadata["prompt"] = json.dumps(prompt) - - if extra_pnginfo is not None: - for x in extra_pnginfo: - container.metadata[x] = json.dumps(extra_pnginfo[x]) - - codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"} - stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000)) - stream.width = images.shape[-2] - stream.height = images.shape[-3] - stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p" - stream.bit_rate = 0 - stream.options = {'crf': str(crf)} - if codec == "av1": - stream.options["preset"] = "6" - - for frame in images: - frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24") - for packet in stream.encode(frame): - container.mux(packet) - container.mux(stream.encode()) - container.close() - - results: list[FileLocator] = [{ - "filename": file, - "subfolder": subfolder, - "type": self.type - }] - - return {"ui": {"images": results, "animated": (True,)}} # TODO: frontend side - -class SaveVideo(ComfyNodeABC): - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type: Literal["output"] = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "video": (IO.VIDEO, {"tooltip": "The video to save."}), - "filename_prefix": ("STRING", {"default": "video/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), - "format": (Types.VideoContainer.as_input(), {"default": "auto", "tooltip": "The format to save the video as."}), - "codec": (Types.VideoCodec.as_input(), {"default": "auto", "tooltip": "The codec to use for the video."}), - }, - "hidden": { - "prompt": "PROMPT", - "extra_pnginfo": "EXTRA_PNGINFO" - }, - } - - RETURN_TYPES = () - FUNCTION = "save_video" - - OUTPUT_NODE = True - - CATEGORY = "image/video" - DESCRIPTION = "Saves the input images to your ComfyUI output directory." - - def save_video(self, video: Input.Video, filename_prefix, format, codec, prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - width, height = video.get_dimensions() - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( - filename_prefix, - self.output_dir, - width, - height - ) - results: list[FileLocator] = list() - saved_metadata = None - if not args.disable_metadata: - metadata = {} - if extra_pnginfo is not None: - metadata.update(extra_pnginfo) - if prompt is not None: - metadata["prompt"] = prompt - if len(metadata) > 0: - saved_metadata = metadata - file = f"{filename}_{counter:05}_.{Types.VideoContainer.get_extension(format)}" - video.save_to( - os.path.join(full_output_folder, file), - format=format, - codec=codec, - metadata=saved_metadata - ) - - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - - return { "ui": { "images": results, "animated": (True,) } } - -class CreateVideo(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images": (IO.IMAGE, {"tooltip": "The images to create a video from."}), - "fps": ("FLOAT", {"default": 30.0, "min": 1.0, "max": 120.0, "step": 1.0}), - }, - "optional": { - "audio": (IO.AUDIO, {"tooltip": "The audio to add to the video."}), - } - } - - RETURN_TYPES = (IO.VIDEO,) - FUNCTION = "create_video" - - CATEGORY = "image/video" - DESCRIPTION = "Create a video from images." - - def create_video(self, images: Input.Image, fps: float, audio: Optional[Input.Audio] = None): - return (InputImpl.VideoFromComponents( - Types.VideoComponents( - images=images, - audio=audio, - frame_rate=Fraction(fps), - ) - ),) - -class GetVideoComponents(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "video": (IO.VIDEO, {"tooltip": "The video to extract components from."}), - } - } - RETURN_TYPES = (IO.IMAGE, IO.AUDIO, IO.FLOAT) - RETURN_NAMES = ("images", "audio", "fps") - FUNCTION = "get_components" - - CATEGORY = "image/video" - DESCRIPTION = "Extracts all components from a video: frames, audio, and framerate." - - def get_components(self, video: Input.Video): - components = video.get_components() - - return (components.images, components.audio, float(components.frame_rate)) - -class LoadVideo(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls): - input_dir = folder_paths.get_input_directory() - files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] - files = folder_paths.filter_files_content_types(files, ["video"]) - return {"required": - {"file": (sorted(files), {"video_upload": True})}, - } - - CATEGORY = "image/video" - - RETURN_TYPES = (IO.VIDEO,) - FUNCTION = "load_video" - def load_video(self, file): - video_path = folder_paths.get_annotated_filepath(file) - return (InputImpl.VideoFromFile(video_path),) - - @classmethod - def IS_CHANGED(cls, file): - video_path = folder_paths.get_annotated_filepath(file) - mod_time = os.path.getmtime(video_path) - # Instead of hashing the file, we can just use the modification time to avoid - # rehashing large files. - return mod_time - - @classmethod - def VALIDATE_INPUTS(cls, file): - if not folder_paths.exists_annotated_filepath(file): - return "Invalid video file: {}".format(file) - - return True - -NODE_CLASS_MAPPINGS = { - "SaveWEBM": SaveWEBM, - "SaveVideo": SaveVideo, - "CreateVideo": CreateVideo, - "GetVideoComponents": GetVideoComponents, - "LoadVideo": LoadVideo, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "SaveVideo": "Save Video", - "CreateVideo": "Create Video", - "GetVideoComponents": "Get Video Components", - "LoadVideo": "Load Video", -} - diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py deleted file mode 100644 index 0f760aa26627f7dcc9e0d4483e05dae1326a4c66..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_video_model.py +++ /dev/null @@ -1,161 +0,0 @@ -import nodes -import torch -import comfy.utils -import comfy.sd -import folder_paths -import comfy_extras.nodes_model_merging -import node_helpers - - -class ImageOnlyCheckpointLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), - }} - RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") - FUNCTION = "load_checkpoint" - - CATEGORY = "loaders/video_models" - - def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): - ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) - out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) - return (out[0], out[3], out[2]) - - -class SVD_img2vid_Conditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), - "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), - "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), - "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - FUNCTION = "encode" - - CATEGORY = "conditioning/video_models" - - def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): - output = clip_vision.encode_image(init_image) - pooled = output.image_embeds.unsqueeze(0) - pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) - encode_pixels = pixels[:,:,:,:3] - if augmentation_level > 0: - encode_pixels += torch.randn_like(pixels) * augmentation_level - t = vae.encode(encode_pixels) - positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] - negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] - latent = torch.zeros([video_frames, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent}) - -class VideoLinearCFGGuidance: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "sampling/video_models" - - def patch(self, model, min_cfg): - def linear_cfg(args): - cond = args["cond"] - uncond = args["uncond"] - cond_scale = args["cond_scale"] - - scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) - return uncond + scale * (cond - uncond) - - m = model.clone() - m.set_model_sampler_cfg_function(linear_cfg) - return (m, ) - -class VideoTriangleCFGGuidance: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "sampling/video_models" - - def patch(self, model, min_cfg): - def linear_cfg(args): - cond = args["cond"] - uncond = args["uncond"] - cond_scale = args["cond_scale"] - period = 1.0 - values = torch.linspace(0, 1, cond.shape[0], device=cond.device) - values = 2 * (values / period - torch.floor(values / period + 0.5)).abs() - scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1)) - - return uncond + scale * (cond - uncond) - - m = model.clone() - m.set_model_sampler_cfg_function(linear_cfg) - return (m, ) - -class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): - CATEGORY = "advanced/model_merging" - - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "clip_vision": ("CLIP_VISION",), - "vae": ("VAE",), - "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} - - def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): - comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) - return {} - - -class ConditioningSetAreaPercentageVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - "temporal": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), - "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), - "z": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "conditioning" - - def append(self, conditioning, width, height, temporal, x, y, z, strength): - c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x), - "strength": strength, - "set_area_to_bounds": False}) - return (c, ) - - -NODE_CLASS_MAPPINGS = { - "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, - "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, - "VideoLinearCFGGuidance": VideoLinearCFGGuidance, - "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, - "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, - "ConditioningSetAreaPercentageVideo": ConditioningSetAreaPercentageVideo, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", -} diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py deleted file mode 100644 index 4f73369f5b0342f6fb244c838c85d100c22cda89..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_wan.py +++ /dev/null @@ -1,1082 +0,0 @@ -import math -import nodes -import node_helpers -import torch -import comfy.model_management -import comfy.utils -import comfy.latent_formats -import comfy.clip_vision -import json -import numpy as np -from typing import Tuple -from typing_extensions import override -from comfy_api.latest import ComfyExtension, io - -class WanImageToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanImageToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.ClipVisionOutput.Input("clip_vision_output", optional=True), - io.Image.Input("start_image", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5 - image[:start_image.shape[0]] = start_image - - concat_latent_image = vae.encode(image[:, :, :, :3]) - mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) - mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 - - positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) - negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) - - if clip_vision_output is not None: - positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) - negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - - -class WanFunControlToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanFunControlToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.ClipVisionOutput.Input("clip_vision_output", optional=True), - io.Image.Input("start_image", optional=True), - io.Image.Input("control_video", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, control_video=None) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) - concat_latent = concat_latent.repeat(1, 2, 1, 1, 1) - - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - concat_latent_image = vae.encode(start_image[:, :, :, :3]) - concat_latent[:,16:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] - - if control_video is not None: - control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - concat_latent_image = vae.encode(control_video[:, :, :, :3]) - concat_latent[:,:16,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] - - positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent}) - negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent}) - - if clip_vision_output is not None: - positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) - negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - -class Wan22FunControlToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="Wan22FunControlToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.Image.Input("ref_image", optional=True), - io.Image.Input("control_video", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, ref_image=None, start_image=None, control_video=None) -> io.NodeOutput: - spacial_scale = vae.spacial_compression_encode() - latent_channels = vae.latent_channels - latent = torch.zeros([batch_size, latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale], device=comfy.model_management.intermediate_device()) - concat_latent = torch.zeros([batch_size, latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale], device=comfy.model_management.intermediate_device()) - if latent_channels == 48: - concat_latent = comfy.latent_formats.Wan22().process_out(concat_latent) - else: - concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) - concat_latent = concat_latent.repeat(1, 2, 1, 1, 1) - mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) - - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - concat_latent_image = vae.encode(start_image[:, :, :, :3]) - concat_latent[:,latent_channels:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] - mask[:, :, :start_image.shape[0] + 3] = 0.0 - - ref_latent = None - if ref_image is not None: - ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - ref_latent = vae.encode(ref_image[:, :, :, :3]) - - if control_video is not None: - control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - concat_latent_image = vae.encode(control_video[:, :, :, :3]) - concat_latent[:,:latent_channels,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] - - mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) - positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, "concat_mask": mask, "concat_mask_index": latent_channels}) - negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, "concat_mask": mask, "concat_mask_index": latent_channels}) - - if ref_latent is not None: - positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) - negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - -class WanFirstLastFrameToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanFirstLastFrameToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.ClipVisionOutput.Input("clip_vision_start_image", optional=True), - io.ClipVisionOutput.Input("clip_vision_end_image", optional=True), - io.Image.Input("start_image", optional=True), - io.Image.Input("end_image", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_start_image=None, clip_vision_end_image=None) -> io.NodeOutput: - spacial_scale = vae.spacial_compression_encode() - latent = torch.zeros([batch_size, vae.latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale], device=comfy.model_management.intermediate_device()) - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - if end_image is not None: - end_image = comfy.utils.common_upscale(end_image[-length:].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - - image = torch.ones((length, height, width, 3)) * 0.5 - mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) - - if start_image is not None: - image[:start_image.shape[0]] = start_image - mask[:, :, :start_image.shape[0] + 3] = 0.0 - - if end_image is not None: - image[-end_image.shape[0]:] = end_image - mask[:, :, -end_image.shape[0]:] = 0.0 - - concat_latent_image = vae.encode(image[:, :, :, :3]) - mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) - positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) - negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) - - clip_vision_output = None - if clip_vision_start_image is not None: - clip_vision_output = clip_vision_start_image - - if clip_vision_end_image is not None: - if clip_vision_output is not None: - states = torch.cat([clip_vision_output.penultimate_hidden_states, clip_vision_end_image.penultimate_hidden_states], dim=-2) - clip_vision_output = comfy.clip_vision.Output() - clip_vision_output.penultimate_hidden_states = states - else: - clip_vision_output = clip_vision_end_image - - if clip_vision_output is not None: - positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) - negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - - -class WanFunInpaintToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanFunInpaintToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.ClipVisionOutput.Input("clip_vision_output", optional=True), - io.Image.Input("start_image", optional=True), - io.Image.Input("end_image", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_output=None) -> io.NodeOutput: - flfv = WanFirstLastFrameToVideo() - return flfv.execute(positive, negative, vae, width, height, length, batch_size, start_image=start_image, end_image=end_image, clip_vision_start_image=clip_vision_output) - - -class WanVaceToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanVaceToVideo", - category="conditioning/video_models", - is_experimental=True, - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.Float.Input("strength", default=1.0, min=0.0, max=1000.0, step=0.01), - io.Image.Input("control_video", optional=True), - io.Mask.Input("control_masks", optional=True), - io.Image.Input("reference_image", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - io.Int.Output(display_name="trim_latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None) -> io.NodeOutput: - latent_length = ((length - 1) // 4) + 1 - if control_video is not None: - control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - if control_video.shape[0] < length: - control_video = torch.nn.functional.pad(control_video, (0, 0, 0, 0, 0, 0, 0, length - control_video.shape[0]), value=0.5) - else: - control_video = torch.ones((length, height, width, 3)) * 0.5 - - if reference_image is not None: - reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - reference_image = vae.encode(reference_image[:, :, :, :3]) - reference_image = torch.cat([reference_image, comfy.latent_formats.Wan21().process_out(torch.zeros_like(reference_image))], dim=1) - - if control_masks is None: - mask = torch.ones((length, height, width, 1)) - else: - mask = control_masks - if mask.ndim == 3: - mask = mask.unsqueeze(1) - mask = comfy.utils.common_upscale(mask[:length], width, height, "bilinear", "center").movedim(1, -1) - if mask.shape[0] < length: - mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, 0, 0, length - mask.shape[0]), value=1.0) - - control_video = control_video - 0.5 - inactive = (control_video * (1 - mask)) + 0.5 - reactive = (control_video * mask) + 0.5 - - inactive = vae.encode(inactive[:, :, :, :3]) - reactive = vae.encode(reactive[:, :, :, :3]) - control_video_latent = torch.cat((inactive, reactive), dim=1) - if reference_image is not None: - control_video_latent = torch.cat((reference_image, control_video_latent), dim=2) - - vae_stride = 8 - height_mask = height // vae_stride - width_mask = width // vae_stride - mask = mask.view(length, height_mask, vae_stride, width_mask, vae_stride) - mask = mask.permute(2, 4, 0, 1, 3) - mask = mask.reshape(vae_stride * vae_stride, length, height_mask, width_mask) - mask = torch.nn.functional.interpolate(mask.unsqueeze(0), size=(latent_length, height_mask, width_mask), mode='nearest-exact').squeeze(0) - - trim_latent = 0 - if reference_image is not None: - mask_pad = torch.zeros_like(mask[:, :reference_image.shape[2], :, :]) - mask = torch.cat((mask_pad, mask), dim=1) - latent_length += reference_image.shape[2] - trim_latent = reference_image.shape[2] - - mask = mask.unsqueeze(0) - - positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) - negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) - - latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent, trim_latent) - -class TrimVideoLatent(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="TrimVideoLatent", - category="latent/video", - is_experimental=True, - inputs=[ - io.Latent.Input("samples"), - io.Int.Input("trim_amount", default=0, min=0, max=99999), - ], - outputs=[ - io.Latent.Output(), - ], - ) - - @classmethod - def execute(cls, samples, trim_amount) -> io.NodeOutput: - samples_out = samples.copy() - - s1 = samples["samples"] - samples_out["samples"] = s1[:, :, trim_amount:] - return io.NodeOutput(samples_out) - -class WanCameraImageToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanCameraImageToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.ClipVisionOutput.Input("clip_vision_output", optional=True), - io.Image.Input("start_image", optional=True), - io.WanCameraEmbedding.Input("camera_conditions", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) - - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - concat_latent_image = vae.encode(start_image[:, :, :, :3]) - concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] - mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) - mask[:, :, :start_image.shape[0] + 3] = 0.0 - mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) - - positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, "concat_mask": mask}) - negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, "concat_mask": mask}) - - if camera_conditions is not None: - positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions}) - negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions}) - - if clip_vision_output is not None: - positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) - negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - -class WanPhantomSubjectToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanPhantomSubjectToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.Image.Input("images", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative_text"), - io.Conditioning.Output(display_name="negative_img_text"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, images) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - cond2 = negative - if images is not None: - images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - latent_images = [] - for i in images: - latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])] - concat_latent_image = torch.cat(latent_images, dim=2) - - positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image}) - cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image}) - negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))}) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, cond2, negative, out_latent) - -def parse_json_tracks(tracks): - """Parse JSON track data into a standardized format""" - tracks_data = [] - try: - # If tracks is a string, try to parse it as JSON - if isinstance(tracks, str): - parsed = json.loads(tracks.replace("'", '"')) - tracks_data.extend(parsed) - else: - # If tracks is a list of strings, parse each one - for track_str in tracks: - parsed = json.loads(track_str.replace("'", '"')) - tracks_data.append(parsed) - - # Check if we have a single track (dict with x,y) or a list of tracks - if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]: - # Single track detected, wrap it in a list - tracks_data = [tracks_data] - elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]: - # Already a list of tracks, nothing to do - pass - else: - # Unexpected format - pass - - except json.JSONDecodeError: - tracks_data = [] - return tracks_data - -def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], num_frames, quant_multi: int = 8, **kwargs): - # tracks: shape [t, h, w, 3] => samples align with 24 fps, model trained with 16 fps. - # frame_size: tuple (W, H) - tracks = torch.from_numpy(tracks_np).float() - - if tracks.shape[1] == 121: - tracks = torch.permute(tracks, (1, 0, 2, 3)) - - tracks, visibles = tracks[..., :2], tracks[..., 2:3] - - short_edge = min(*frame_size) - - frame_center = torch.tensor([*frame_size]).type_as(tracks) / 2 - tracks = tracks - frame_center - - tracks = tracks / short_edge * 2 - - visibles = visibles * 2 - 1 - - trange = torch.linspace(-1, 1, tracks.shape[0]).view(-1, 1, 1, 1).expand(*visibles.shape) - - out_ = torch.cat([trange, tracks, visibles], dim=-1).view(121, -1, 4) - - out_0 = out_[:1] - - out_l = out_[1:] # 121 => 120 | 1 - a = 120 // math.gcd(120, num_frames) - b = num_frames // math.gcd(120, num_frames) - out_l = torch.repeat_interleave(out_l, b, dim=0)[1::a] # 120 => 120 * b => 120 * b / a == F - - final_result = torch.cat([out_0, out_l], dim=0) - - return final_result - -FIXED_LENGTH = 121 -def pad_pts(tr): - """Convert list of {x,y} to (FIXED_LENGTH,1,3) array, padding/truncating.""" - pts = np.array([[p['x'], p['y'], 1] for p in tr], dtype=np.float32) - n = pts.shape[0] - if n < FIXED_LENGTH: - pad = np.zeros((FIXED_LENGTH - n, 3), dtype=np.float32) - pts = np.vstack((pts, pad)) - else: - pts = pts[:FIXED_LENGTH] - return pts.reshape(FIXED_LENGTH, 1, 3) - -def ind_sel(target: torch.Tensor, ind: torch.Tensor, dim: int = 1): - """Index selection utility function""" - assert ( - len(ind.shape) > dim - ), "Index must have the target dim, but get dim: %d, ind shape: %s" % (dim, str(ind.shape)) - - target = target.expand( - *tuple( - [ind.shape[k] if target.shape[k] == 1 else -1 for k in range(dim)] - + [ - -1, - ] - * (len(target.shape) - dim) - ) - ) - - ind_pad = ind - - if len(target.shape) > dim + 1: - for _ in range(len(target.shape) - (dim + 1)): - ind_pad = ind_pad.unsqueeze(-1) - ind_pad = ind_pad.expand(*(-1,) * (dim + 1), *target.shape[(dim + 1) : :]) - - return torch.gather(target, dim=dim, index=ind_pad) - -def merge_final(vert_attr: torch.Tensor, weight: torch.Tensor, vert_assign: torch.Tensor): - """Merge vertex attributes with weights""" - target_dim = len(vert_assign.shape) - 1 - if len(vert_attr.shape) == 2: - assert vert_attr.shape[0] > vert_assign.max() - new_shape = [1] * target_dim + list(vert_attr.shape) - tensor = vert_attr.reshape(new_shape) - sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim) - else: - assert vert_attr.shape[1] > vert_assign.max() - new_shape = [vert_attr.shape[0]] + [1] * (target_dim - 1) + list(vert_attr.shape[1:]) - tensor = vert_attr.reshape(new_shape) - sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim) - - final_attr = torch.sum(sel_attr * weight.unsqueeze(-1), dim=-2) - return final_attr - - -def _patch_motion_single( - tracks: torch.FloatTensor, # (B, T, N, 4) - vid: torch.FloatTensor, # (C, T, H, W) - temperature: float, - vae_divide: tuple, - topk: int, -): - """Apply motion patching based on tracks""" - _, T, H, W = vid.shape - N = tracks.shape[2] - _, tracks_xy, visible = torch.split( - tracks, [1, 2, 1], dim=-1 - ) # (B, T, N, 2) | (B, T, N, 1) - tracks_n = tracks_xy / torch.tensor([W / min(H, W), H / min(H, W)], device=tracks_xy.device) - tracks_n = tracks_n.clamp(-1, 1) - visible = visible.clamp(0, 1) - - xx = torch.linspace(-W / min(H, W), W / min(H, W), W) - yy = torch.linspace(-H / min(H, W), H / min(H, W), H) - - grid = torch.stack(torch.meshgrid(yy, xx, indexing="ij")[::-1], dim=-1).to( - tracks_xy.device - ) - - tracks_pad = tracks_xy[:, 1:] - visible_pad = visible[:, 1:] - - visible_align = visible_pad.view(T - 1, 4, *visible_pad.shape[2:]).sum(1) - tracks_align = (tracks_pad * visible_pad).view(T - 1, 4, *tracks_pad.shape[2:]).sum( - 1 - ) / (visible_align + 1e-5) - dist_ = ( - (tracks_align[:, None, None] - grid[None, :, :, None]).pow(2).sum(-1) - ) # T, H, W, N - weight = torch.exp(-dist_ * temperature) * visible_align.clamp(0, 1).view( - T - 1, 1, 1, N - ) - vert_weight, vert_index = torch.topk( - weight, k=min(topk, weight.shape[-1]), dim=-1 - ) - - grid_mode = "bilinear" - point_feature = torch.nn.functional.grid_sample( - vid.permute(1, 0, 2, 3)[:1], - tracks_n[:, :1].type(vid.dtype), - mode=grid_mode, - padding_mode="zeros", - align_corners=False, - ) - point_feature = point_feature.squeeze(0).squeeze(1).permute(1, 0) # N, C=16 - - out_feature = merge_final(point_feature, vert_weight, vert_index).permute(3, 0, 1, 2) # T - 1, H, W, C => C, T - 1, H, W - out_weight = vert_weight.sum(-1) # T - 1, H, W - - # out feature -> already soft weighted - mix_feature = out_feature + vid[:, 1:] * (1 - out_weight.clamp(0, 1)) - - out_feature_full = torch.cat([vid[:, :1], mix_feature], dim=1) # C, T, H, W - out_mask_full = torch.cat([torch.ones_like(out_weight[:1]), out_weight], dim=0) # T, H, W - - return out_mask_full[None].expand(vae_divide[0], -1, -1, -1), out_feature_full - - -def patch_motion( - tracks: torch.FloatTensor, # (B, TB, T, N, 4) - vid: torch.FloatTensor, # (C, T, H, W) - temperature: float = 220.0, - vae_divide: tuple = (4, 16), - topk: int = 2, -): - B = len(tracks) - - # Process each batch separately - out_masks = [] - out_features = [] - - for b in range(B): - mask, feature = _patch_motion_single( - tracks[b], # (T, N, 4) - vid[b], # (C, T, H, W) - temperature, - vae_divide, - topk - ) - out_masks.append(mask) - out_features.append(feature) - - # Stack results: (B, C, T, H, W) - out_mask_full = torch.stack(out_masks, dim=0) - out_feature_full = torch.stack(out_features, dim=0) - - return out_mask_full, out_feature_full - -class WanTrackToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanTrackToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.String.Input("tracks", multiline=True, default="[]"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1), - io.Int.Input("topk", default=2, min=1, max=10), - io.Image.Input("start_image"), - io.ClipVisionOutput.Input("clip_vision_output", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - ) - - @classmethod - def execute(cls, positive, negative, vae, tracks, width, height, length, batch_size, - temperature, topk, start_image=None, clip_vision_output=None) -> io.NodeOutput: - - tracks_data = parse_json_tracks(tracks) - - if not tracks_data: - return WanImageToVideo().execute(positive, negative, vae, width, height, length, batch_size, start_image=start_image, clip_vision_output=clip_vision_output) - - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], - device=comfy.model_management.intermediate_device()) - - if isinstance(tracks_data[0][0], dict): - tracks_data = [tracks_data] - - processed_tracks = [] - for batch in tracks_data: - arrs = [] - for track in batch: - pts = pad_pts(track) - arrs.append(pts) - - tracks_np = np.stack(arrs, axis=0) - processed_tracks.append(process_tracks(tracks_np, (width, height), length - 1).unsqueeze(0)) - - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:batch_size].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - videos = torch.ones((start_image.shape[0], length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5 - for i in range(start_image.shape[0]): - videos[i, 0] = start_image[i] - - latent_videos = [] - videos = comfy.utils.resize_to_batch_size(videos, batch_size) - for i in range(batch_size): - latent_videos += [vae.encode(videos[i, :, :, :, :3])] - y = torch.cat(latent_videos, dim=0) - - # Scale latent since patch_motion is non-linear - y = comfy.latent_formats.Wan21().process_in(y) - - processed_tracks = comfy.utils.resize_list_to_batch_size(processed_tracks, batch_size) - res = patch_motion( - processed_tracks, y, temperature=temperature, topk=topk, vae_divide=(4, 16) - ) - - mask, concat_latent_image = res - concat_latent_image = comfy.latent_formats.Wan21().process_out(concat_latent_image) - mask = -mask + 1.0 # Invert mask to match expected format - positive = node_helpers.conditioning_set_values(positive, - {"concat_mask": mask, - "concat_latent_image": concat_latent_image}) - negative = node_helpers.conditioning_set_values(negative, - {"concat_mask": mask, - "concat_latent_image": concat_latent_image}) - - if clip_vision_output is not None: - positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) - negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - - -def linear_interpolation(features, input_fps, output_fps, output_len=None): - """ - features: shape=[1, T, 512] - input_fps: fps for audio, f_a - output_fps: fps for video, f_m - output_len: video length - """ - features = features.transpose(1, 2) # [1, 512, T] - seq_len = features.shape[2] / float(input_fps) # T/f_a - if output_len is None: - output_len = int(seq_len * output_fps) # f_m*T/f_a - output_features = torch.nn.functional.interpolate( - features, size=output_len, align_corners=True, - mode='linear') # [1, 512, output_len] - return output_features.transpose(1, 2) # [1, output_len, 512] - - -def get_sample_indices(original_fps, - total_frames, - target_fps, - num_sample, - fixed_start=None): - required_duration = num_sample / target_fps - required_origin_frames = int(np.ceil(required_duration * original_fps)) - if required_duration > total_frames / original_fps: - raise ValueError("required_duration must be less than video length") - - if not fixed_start is None and fixed_start >= 0: - start_frame = fixed_start - else: - max_start = total_frames - required_origin_frames - if max_start < 0: - raise ValueError("video length is too short") - start_frame = np.random.randint(0, max_start + 1) - start_time = start_frame / original_fps - - end_time = start_time + required_duration - time_points = np.linspace(start_time, end_time, num_sample, endpoint=False) - - frame_indices = np.round(np.array(time_points) * original_fps).astype(int) - frame_indices = np.clip(frame_indices, 0, total_frames - 1) - return frame_indices - - -def get_audio_embed_bucket_fps(audio_embed, fps=16, batch_frames=81, m=0, video_rate=30): - num_layers, audio_frame_num, audio_dim = audio_embed.shape - - if num_layers > 1: - return_all_layers = True - else: - return_all_layers = False - - scale = video_rate / fps - - min_batch_num = int(audio_frame_num / (batch_frames * scale)) + 1 - - bucket_num = min_batch_num * batch_frames - padd_audio_num = math.ceil(min_batch_num * batch_frames / fps * video_rate) - audio_frame_num - batch_idx = get_sample_indices( - original_fps=video_rate, - total_frames=audio_frame_num + padd_audio_num, - target_fps=fps, - num_sample=bucket_num, - fixed_start=0) - batch_audio_eb = [] - audio_sample_stride = int(video_rate / fps) - for bi in batch_idx: - if bi < audio_frame_num: - - chosen_idx = list( - range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride)) - chosen_idx = [0 if c < 0 else c for c in chosen_idx] - chosen_idx = [ - audio_frame_num - 1 if c >= audio_frame_num else c - for c in chosen_idx - ] - - if return_all_layers: - frame_audio_embed = audio_embed[:, chosen_idx].flatten( - start_dim=-2, end_dim=-1) - else: - frame_audio_embed = audio_embed[0][chosen_idx].flatten() - else: - frame_audio_embed = torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \ - else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device) - batch_audio_eb.append(frame_audio_embed) - batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0) - - return batch_audio_eb, min_batch_num - - -def wan_sound_to_video(positive, negative, vae, width, height, length, batch_size, frame_offset=0, ref_image=None, audio_encoder_output=None, control_video=None, ref_motion=None, ref_motion_latent=None): - latent_t = ((length - 1) // 4) + 1 - if audio_encoder_output is not None: - feat = torch.cat(audio_encoder_output["encoded_audio_all_layers"]) - video_rate = 30 - fps = 16 - feat = linear_interpolation(feat, input_fps=50, output_fps=video_rate) - batch_frames = latent_t * 4 - audio_embed_bucket, num_repeat = get_audio_embed_bucket_fps(feat, fps=fps, batch_frames=batch_frames, m=0, video_rate=video_rate) - audio_embed_bucket = audio_embed_bucket.unsqueeze(0) - if len(audio_embed_bucket.shape) == 3: - audio_embed_bucket = audio_embed_bucket.permute(0, 2, 1) - elif len(audio_embed_bucket.shape) == 4: - audio_embed_bucket = audio_embed_bucket.permute(0, 2, 3, 1) - - audio_embed_bucket = audio_embed_bucket[:, :, :, frame_offset:frame_offset + batch_frames] - if audio_embed_bucket.shape[3] > 0: - positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_embed_bucket}) - negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_embed_bucket * 0.0}) - frame_offset += batch_frames - - if ref_image is not None: - ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - ref_latent = vae.encode(ref_image[:, :, :, :3]) - positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) - negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True) - - if ref_motion is not None: - if ref_motion.shape[0] > 73: - ref_motion = ref_motion[-73:] - - ref_motion = comfy.utils.common_upscale(ref_motion.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - - if ref_motion.shape[0] < 73: - r = torch.ones([73, height, width, 3]) * 0.5 - r[-ref_motion.shape[0]:] = ref_motion - ref_motion = r - - ref_motion_latent = vae.encode(ref_motion[:, :, :, :3]) - - if ref_motion_latent is not None: - ref_motion_latent = ref_motion_latent[:, :, -19:] - positive = node_helpers.conditioning_set_values(positive, {"reference_motion": ref_motion_latent}) - negative = node_helpers.conditioning_set_values(negative, {"reference_motion": ref_motion_latent}) - - latent = torch.zeros([batch_size, 16, latent_t, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - - control_video_out = comfy.latent_formats.Wan21().process_out(torch.zeros_like(latent)) - if control_video is not None: - control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - control_video = vae.encode(control_video[:, :, :, :3]) - control_video_out[:, :, :control_video.shape[2]] = control_video - - # TODO: check if zero is better than none if none provided - positive = node_helpers.conditioning_set_values(positive, {"control_video": control_video_out}) - negative = node_helpers.conditioning_set_values(negative, {"control_video": control_video_out}) - - out_latent = {} - out_latent["samples"] = latent - return positive, negative, out_latent, frame_offset - - -class WanSoundImageToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanSoundImageToVideo", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), - io.Int.Input("length", default=77, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.AudioEncoderOutput.Input("audio_encoder_output", optional=True), - io.Image.Input("ref_image", optional=True), - io.Image.Input("control_video", optional=True), - io.Image.Input("ref_motion", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - is_experimental=True, - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, ref_image=None, audio_encoder_output=None, control_video=None, ref_motion=None) -> io.NodeOutput: - positive, negative, out_latent, frame_offset = wan_sound_to_video(positive, negative, vae, width, height, length, batch_size, ref_image=ref_image, audio_encoder_output=audio_encoder_output, - control_video=control_video, ref_motion=ref_motion) - return io.NodeOutput(positive, negative, out_latent) - - -class WanSoundImageToVideoExtend(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanSoundImageToVideoExtend", - category="conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("length", default=77, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Latent.Input("video_latent"), - io.AudioEncoderOutput.Input("audio_encoder_output", optional=True), - io.Image.Input("ref_image", optional=True), - io.Image.Input("control_video", optional=True), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent"), - ], - is_experimental=True, - ) - - @classmethod - def execute(cls, positive, negative, vae, length, video_latent, ref_image=None, audio_encoder_output=None, control_video=None) -> io.NodeOutput: - video_latent = video_latent["samples"] - width = video_latent.shape[-1] * 8 - height = video_latent.shape[-2] * 8 - batch_size = video_latent.shape[0] - frame_offset = video_latent.shape[-3] * 4 - positive, negative, out_latent, frame_offset = wan_sound_to_video(positive, negative, vae, width, height, length, batch_size, frame_offset=frame_offset, ref_image=ref_image, audio_encoder_output=audio_encoder_output, - control_video=control_video, ref_motion=None, ref_motion_latent=video_latent) - return io.NodeOutput(positive, negative, out_latent) - - -class Wan22ImageToVideoLatent(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="Wan22ImageToVideoLatent", - category="conditioning/inpaint", - inputs=[ - io.Vae.Input("vae"), - io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32), - io.Int.Input("height", default=704, min=32, max=nodes.MAX_RESOLUTION, step=32), - io.Int.Input("length", default=49, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.Image.Input("start_image", optional=True), - ], - outputs=[ - io.Latent.Output(), - ], - ) - - @classmethod - def execute(cls, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput: - latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device()) - - if start_image is None: - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(out_latent) - - mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) - - if start_image is not None: - start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - latent_temp = vae.encode(start_image) - latent[:, :, :latent_temp.shape[-3]] = latent_temp - mask[:, :, :latent_temp.shape[-3]] *= 0.0 - - out_latent = {} - latent_format = comfy.latent_formats.Wan22() - latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) - out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) - out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) - return io.NodeOutput(out_latent) - - -class WanExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - WanTrackToVideo, - WanImageToVideo, - WanFunControlToVideo, - Wan22FunControlToVideo, - WanFunInpaintToVideo, - WanFirstLastFrameToVideo, - WanVaceToVideo, - TrimVideoLatent, - WanCameraImageToVideo, - WanPhantomSubjectToVideo, - WanSoundImageToVideo, - WanSoundImageToVideoExtend, - Wan22ImageToVideoLatent, - ] - -async def comfy_entrypoint() -> WanExtension: - return WanExtension() diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py deleted file mode 100644 index 5bf80b4c6e055f70ed277ec0a9bbbeb180151a3d..0000000000000000000000000000000000000000 --- a/comfy_extras/nodes_webcam.py +++ /dev/null @@ -1,37 +0,0 @@ -import nodes -import folder_paths - -MAX_RESOLUTION = nodes.MAX_RESOLUTION - - -class WebcamCapture(nodes.LoadImage): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("WEBCAM", {}), - "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "capture_on_queue": ("BOOLEAN", {"default": True}), - } - } - RETURN_TYPES = ("IMAGE",) - FUNCTION = "load_capture" - - CATEGORY = "image" - - def load_capture(self, image, **kwargs): - return super().load_image(folder_paths.get_annotated_filepath(image)) - - @classmethod - def IS_CHANGED(cls, image, width, height, capture_on_queue): - return super().IS_CHANGED(image) - - -NODE_CLASS_MAPPINGS = { - "WebcamCapture": WebcamCapture, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "WebcamCapture": "Webcam Capture", -} diff --git a/comfyui_version.py b/comfyui_version.py deleted file mode 100644 index e8e0393737f684ec2da92f5dbfde68a814683a5d..0000000000000000000000000000000000000000 --- a/comfyui_version.py +++ /dev/null @@ -1,3 +0,0 @@ -# This file is automatically generated by the build process when version is -# updated in pyproject.toml. -__version__ = "0.3.56" diff --git a/cuda_malloc.py b/cuda_malloc.py deleted file mode 100644 index c1d9ae3cab5cd664b0bd8797e9e07117bc7962f7..0000000000000000000000000000000000000000 --- a/cuda_malloc.py +++ /dev/null @@ -1,91 +0,0 @@ -import os -import importlib.util -from comfy.cli_args import args -import subprocess - -#Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import. -def get_gpu_names(): - if os.name == 'nt': - import ctypes - - # Define necessary C structures and types - class DISPLAY_DEVICEA(ctypes.Structure): - _fields_ = [ - ('cb', ctypes.c_ulong), - ('DeviceName', ctypes.c_char * 32), - ('DeviceString', ctypes.c_char * 128), - ('StateFlags', ctypes.c_ulong), - ('DeviceID', ctypes.c_char * 128), - ('DeviceKey', ctypes.c_char * 128) - ] - - # Load user32.dll - user32 = ctypes.windll.user32 - - # Call EnumDisplayDevicesA - def enum_display_devices(): - device_info = DISPLAY_DEVICEA() - device_info.cb = ctypes.sizeof(device_info) - device_index = 0 - gpu_names = set() - - while user32.EnumDisplayDevicesA(None, device_index, ctypes.byref(device_info), 0): - device_index += 1 - gpu_names.add(device_info.DeviceString.decode('utf-8')) - return gpu_names - return enum_display_devices() - else: - gpu_names = set() - out = subprocess.check_output(['nvidia-smi', '-L']) - for l in out.split(b'\n'): - if len(l) > 0: - gpu_names.add(l.decode('utf-8').split(' (UUID')[0]) - return gpu_names - -blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M", - "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620", - "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000", - "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000", - "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M", - "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60" - } - -def cuda_malloc_supported(): - try: - names = get_gpu_names() - except: - names = set() - for x in names: - if "NVIDIA" in x: - for b in blacklist: - if b in x: - return False - return True - - -if not args.cuda_malloc: - try: - version = "" - torch_spec = importlib.util.find_spec("torch") - for folder in torch_spec.submodule_search_locations: - ver_file = os.path.join(folder, "version.py") - if os.path.isfile(ver_file): - spec = importlib.util.spec_from_file_location("torch_version_import", ver_file) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - version = module.__version__ - - if int(version[0]) >= 2 and "+cu" in version: #enable by default for torch version 2.0 and up only on cuda torch - args.cuda_malloc = cuda_malloc_supported() - except: - pass - - -if args.cuda_malloc and not args.disable_cuda_malloc: - env_var = os.environ.get('PYTORCH_CUDA_ALLOC_CONF', None) - if env_var is None: - env_var = "backend:cudaMallocAsync" - else: - env_var += ",backend:cudaMallocAsync" - - os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var diff --git a/custom_node_manager.py b/custom_node_manager.py deleted file mode 100644 index 281febca952363659eb3925280fab00f07c986d0..0000000000000000000000000000000000000000 --- a/custom_node_manager.py +++ /dev/null @@ -1,145 +0,0 @@ -from __future__ import annotations - -import os -import folder_paths -import glob -from aiohttp import web -import json -import logging -from functools import lru_cache - -from utils.json_util import merge_json_recursive - - -# Extra locale files to load into main.json -EXTRA_LOCALE_FILES = [ - "nodeDefs.json", - "commands.json", - "settings.json", -] - - -def safe_load_json_file(file_path: str) -> dict: - if not os.path.exists(file_path): - return {} - - try: - with open(file_path, "r", encoding="utf-8") as f: - return json.load(f) - except json.JSONDecodeError: - logging.error(f"Error loading {file_path}") - return {} - - -class CustomNodeManager: - @lru_cache(maxsize=1) - def build_translations(self): - """Load all custom nodes translations during initialization. Translations are - expected to be loaded from `locales/` folder. - - The folder structure is expected to be the following: - - custom_nodes/ - - custom_node_1/ - - locales/ - - en/ - - main.json - - commands.json - - settings.json - - returned translations are expected to be in the following format: - { - "en": { - "nodeDefs": {...}, - "commands": {...}, - "settings": {...}, - ...{other main.json keys} - } - } - """ - - translations = {} - - for folder in folder_paths.get_folder_paths("custom_nodes"): - # Sort glob results for deterministic ordering - for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))): - locales_dir = os.path.join(custom_node_dir, "locales") - if not os.path.exists(locales_dir): - continue - - for lang_dir in glob.glob(os.path.join(locales_dir, "*/")): - lang_code = os.path.basename(os.path.dirname(lang_dir)) - - if lang_code not in translations: - translations[lang_code] = {} - - # Load main.json - main_file = os.path.join(lang_dir, "main.json") - node_translations = safe_load_json_file(main_file) - - # Load extra locale files - for extra_file in EXTRA_LOCALE_FILES: - extra_file_path = os.path.join(lang_dir, extra_file) - key = extra_file.split(".")[0] - json_data = safe_load_json_file(extra_file_path) - if json_data: - node_translations[key] = json_data - - if node_translations: - translations[lang_code] = merge_json_recursive( - translations[lang_code], node_translations - ) - - return translations - - def add_routes(self, routes, webapp, loadedModules): - - example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"] - - @routes.get("/workflow_templates") - async def get_workflow_templates(request): - """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.""" - - files = [] - - for folder in folder_paths.get_folder_paths("custom_nodes"): - for folder_name in example_workflow_folder_names: - pattern = os.path.join(folder, f"*/{folder_name}/*.json") - matched_files = glob.glob(pattern) - files.extend(matched_files) - - workflow_templates_dict = ( - {} - ) # custom_nodes folder name -> example workflow names - for file in files: - custom_nodes_name = os.path.basename( - os.path.dirname(os.path.dirname(file)) - ) - workflow_name = os.path.splitext(os.path.basename(file))[0] - workflow_templates_dict.setdefault(custom_nodes_name, []).append( - workflow_name - ) - return web.json_response(workflow_templates_dict) - - # Serve workflow templates from custom nodes. - for module_name, module_dir in loadedModules: - for folder_name in example_workflow_folder_names: - workflows_dir = os.path.join(module_dir, folder_name) - - if os.path.exists(workflows_dir): - if folder_name != "example_workflows": - logging.debug( - "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", - folder_name, module_name) - - webapp.add_routes( - [ - web.static( - "/api/workflow_templates/" + module_name, workflows_dir - ) - ] - ) - - @routes.get("/i18n") - async def get_i18n(request): - """Returns translations from all custom nodes' locales folders.""" - return web.json_response(self.build_translations()) diff --git a/custom_nodes/.DS_Store b/custom_nodes/.DS_Store deleted file mode 100644 index cd8407d0e1dd11f197b605bbbecd44e7d9fddded..0000000000000000000000000000000000000000 Binary files a/custom_nodes/.DS_Store and /dev/null differ diff --git a/custom_nodes/ComfyUI-GGUF/.DS_Store b/custom_nodes/ComfyUI-GGUF/.DS_Store deleted file mode 100644 index 38734ca2de71d90578b12a191d5ff30a57f26d5c..0000000000000000000000000000000000000000 Binary files a/custom_nodes/ComfyUI-GGUF/.DS_Store and /dev/null differ diff --git a/custom_nodes/ComfyUI-GGUF/.github/workflows/registry.yaml b/custom_nodes/ComfyUI-GGUF/.github/workflows/registry.yaml deleted file mode 100644 index aaed69c2aa9af79a0c9a9459d971cd36bcfddc6e..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/.github/workflows/registry.yaml +++ /dev/null @@ -1,21 +0,0 @@ -name: ComfyUI Registry publish -on: - workflow_dispatch: - push: - branches: - - stable - paths: - - "pyproject.toml" - -jobs: - publish-node: - name: ComfyUI Registry publish - runs-on: ubuntu-latest - if: github.event.repository.fork == false - steps: - - name: Check out code - uses: actions/checkout@v4 - - name: Publish Custom Node - uses: Comfy-Org/publish-node-action@main - with: - personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} diff --git a/custom_nodes/ComfyUI-GGUF/.gitignore b/custom_nodes/ComfyUI-GGUF/.gitignore deleted file mode 100644 index 44fe82fdbad306e07e27f784d6c906f0708956e2..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/.gitignore +++ /dev/null @@ -1,167 +0,0 @@ -*.bin -*.gguf -*.safetensors -tools/llama.cpp* - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control -.pdm.toml -.pdm-python -.pdm-build/ - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ diff --git a/custom_nodes/ComfyUI-GGUF/.tracking b/custom_nodes/ComfyUI-GGUF/.tracking deleted file mode 100644 index 73b53b894d5e4f9e26bb1f191114e7222afcb156..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/.tracking +++ /dev/null @@ -1,17 +0,0 @@ -.github/workflows/registry.yaml -.gitignore -LICENSE -README.md -__init__.py -dequant.py -loader.py -nodes.py -ops.py -pyproject.toml -requirements.txt -tools/README.md -tools/convert.py -tools/fix_5d_tensors.py -tools/fix_lines_ending.py -tools/lcpp.patch -tools/read_tensors.py \ No newline at end of file diff --git a/custom_nodes/ComfyUI-GGUF/LICENSE b/custom_nodes/ComfyUI-GGUF/LICENSE deleted file mode 100644 index 261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/custom_nodes/ComfyUI-GGUF/README.md b/custom_nodes/ComfyUI-GGUF/README.md deleted file mode 100644 index 6915927e1b61cc3c1113732e4e581e2af627a791..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/README.md +++ /dev/null @@ -1,49 +0,0 @@ -# ComfyUI-GGUF -GGUF Quantization support for native ComfyUI models - -This is currently very much WIP. These custom nodes provide support for model files stored in the GGUF format popularized by [llama.cpp](https://github.com/ggerganov/llama.cpp). - -While quantization wasn't feasible for regular UNET models (conv2d), transformer/DiT models such as flux seem less affected by quantization. This allows running it in much lower bits per weight variable bitrate quants on low-end GPUs. For further VRAM savings, a node to load a quantized version of the T5 text encoder is also included. - -![Comfy_Flux1_dev_Q4_0_GGUF_1024](https://github.com/user-attachments/assets/70d16d97-c522-4ef4-9435-633f128644c8) - -Note: The "Force/Set CLIP Device" is **NOT** part of this node pack. Do not install it if you only have one GPU. Do not set it to cuda:0 then complain about OOM errors if you do not undestand what it is for. There is not need to copy the workflow above, just use your own workflow and replace the stock "Load Diffusion Model" with the "Unet Loader (GGUF)" node. - -## Installation - -> [!IMPORTANT] -> Make sure your ComfyUI is on a recent-enough version to support custom ops when loading the UNET-only. - -To install the custom node normally, git clone this repository into your custom nodes folder (`ComfyUI/custom_nodes`) and install the only dependency for inference (`pip install --upgrade gguf`) - -``` -git clone https://github.com/city96/ComfyUI-GGUF -``` - -To install the custom node on a standalone ComfyUI release, open a CMD inside the "ComfyUI_windows_portable" folder (where your `run_nvidia_gpu.bat` file is) and use the following commands: - -``` -git clone https://github.com/city96/ComfyUI-GGUF ComfyUI/custom_nodes/ComfyUI-GGUF -.\python_embeded\python.exe -s -m pip install -r .\ComfyUI\custom_nodes\ComfyUI-GGUF\requirements.txt -``` - -On MacOS sequoia, torch 2.4.1 seems to be required, as 2.6.X nightly versions cause a "M1 buffer is not large enough" error. See [this issue](https://github.com/city96/ComfyUI-GGUF/issues/107) for more information/workarounds. - -## Usage - -Simply use the GGUF Unet loader found under the `bootleg` category. Place the .gguf model files in your `ComfyUI/models/unet` folder. - -LoRA loading is experimental but it should work with just the built-in LoRA loader node(s). - -Pre-quantized models: - -- [flux1-dev GGUF](https://huggingface.co/city96/FLUX.1-dev-gguf) -- [flux1-schnell GGUF](https://huggingface.co/city96/FLUX.1-schnell-gguf) -- [stable-diffusion-3.5-large GGUF](https://huggingface.co/city96/stable-diffusion-3.5-large-gguf) -- [stable-diffusion-3.5-large-turbo GGUF](https://huggingface.co/city96/stable-diffusion-3.5-large-turbo-gguf) - -Initial support for quantizing T5 has also been added recently, these can be used using the various `*CLIPLoader (gguf)` nodes which can be used inplace of the regular ones. For the CLIP model, use whatever model you were using before for CLIP. The loader can handle both types of files - `gguf` and regular `safetensors`/`bin`. - -- [t5_v1.1-xxl GGUF](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf) - -See the instructions in the [tools](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) folder for how to create your own quants. diff --git a/custom_nodes/ComfyUI-GGUF/__init__.py b/custom_nodes/ComfyUI-GGUF/__init__.py deleted file mode 100644 index a03726e3b0a08957ded67cdd21beb9544a3f6e4d..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# only import if running as a custom node -try: - import comfy.utils -except ImportError: - pass -else: - from .nodes import NODE_CLASS_MAPPINGS - NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()} - __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/custom_nodes/ComfyUI-GGUF/dequant.py b/custom_nodes/ComfyUI-GGUF/dequant.py deleted file mode 100644 index 9e545b72c9bc2209c5ee0ae1cc687a905f4a3dc8..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/dequant.py +++ /dev/null @@ -1,248 +0,0 @@ -# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0) -import gguf -import torch -from tqdm import tqdm - - -TORCH_COMPATIBLE_QTYPES = (None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16) - -def is_torch_compatible(tensor): - return tensor is None or getattr(tensor, "tensor_type", None) in TORCH_COMPATIBLE_QTYPES - -def is_quantized(tensor): - return not is_torch_compatible(tensor) - -def dequantize_tensor(tensor, dtype=None, dequant_dtype=None): - qtype = getattr(tensor, "tensor_type", None) - oshape = getattr(tensor, "tensor_shape", tensor.shape) - - if qtype in TORCH_COMPATIBLE_QTYPES: - return tensor.to(dtype) - elif qtype in dequantize_functions: - dequant_dtype = dtype if dequant_dtype == "target" else dequant_dtype - return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype) - else: - # this is incredibly slow - tqdm.write(f"Falling back to numpy dequant for qtype: {qtype}") - new = gguf.quants.dequantize(tensor.cpu().numpy(), qtype) - return torch.from_numpy(new).to(tensor.device, dtype=dtype) - -def dequantize(data, qtype, oshape, dtype=None): - """ - Dequantize tensor back to usable shape/dtype - """ - block_size, type_size = gguf.GGML_QUANT_SIZES[qtype] - dequantize_blocks = dequantize_functions[qtype] - - rows = data.reshape( - (-1, data.shape[-1]) - ).view(torch.uint8) - - n_blocks = rows.numel() // type_size - blocks = rows.reshape((n_blocks, type_size)) - blocks = dequantize_blocks(blocks, block_size, type_size, dtype) - return blocks.reshape(oshape) - -def to_uint32(x): - # no uint32 :( - x = x.view(torch.uint8).to(torch.int32) - return (x[:, 0] | x[:, 1] << 8 | x[:, 2] << 16 | x[:, 3] << 24).unsqueeze(1) - -def split_block_dims(blocks, *args): - n_max = blocks.shape[1] - dims = list(args) + [n_max - sum(args)] - return torch.split(blocks, dims, dim=1) - -# Full weights # -def dequantize_blocks_BF16(blocks, block_size, type_size, dtype=None): - return (blocks.view(torch.int16).to(torch.int32) << 16).view(torch.float32) - -# Legacy Quants # -def dequantize_blocks_Q8_0(blocks, block_size, type_size, dtype=None): - d, x = split_block_dims(blocks, 2) - d = d.view(torch.float16).to(dtype) - x = x.view(torch.int8) - return (d * x) - -def dequantize_blocks_Q5_1(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - d, m, qh, qs = split_block_dims(blocks, 2, 2, 4) - d = d.view(torch.float16).to(dtype) - m = m.view(torch.float16).to(dtype) - qh = to_uint32(qh) - - qh = qh.reshape((n_blocks, 1)) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32) - ql = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1) - qh = (qh & 1).to(torch.uint8) - ql = (ql & 0x0F).reshape((n_blocks, -1)) - - qs = (ql | (qh << 4)) - return (d * qs) + m - -def dequantize_blocks_Q5_0(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - d, qh, qs = split_block_dims(blocks, 2, 4) - d = d.view(torch.float16).to(dtype) - qh = to_uint32(qh) - - qh = qh.reshape(n_blocks, 1) >> torch.arange(32, device=d.device, dtype=torch.int32).reshape(1, 32) - ql = qs.reshape(n_blocks, -1, 1, block_size // 2) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1) - - qh = (qh & 1).to(torch.uint8) - ql = (ql & 0x0F).reshape(n_blocks, -1) - - qs = (ql | (qh << 4)).to(torch.int8) - 16 - return (d * qs) - -def dequantize_blocks_Q4_1(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - d, m, qs = split_block_dims(blocks, 2, 2) - d = d.view(torch.float16).to(dtype) - m = m.view(torch.float16).to(dtype) - - qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape(1, 1, 2, 1) - qs = (qs & 0x0F).reshape(n_blocks, -1) - - return (d * qs) + m - -def dequantize_blocks_Q4_0(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - d, qs = split_block_dims(blocks, 2) - d = d.view(torch.float16).to(dtype) - - qs = qs.reshape((n_blocks, -1, 1, block_size // 2)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1)) - qs = (qs & 0x0F).reshape((n_blocks, -1)).to(torch.int8) - 8 - return (d * qs) - -# K Quants # -QK_K = 256 -K_SCALE_SIZE = 12 - -def get_scale_min(scales): - n_blocks = scales.shape[0] - scales = scales.view(torch.uint8) - scales = scales.reshape((n_blocks, 3, 4)) - - d, m, m_d = torch.split(scales, scales.shape[-2] // 3, dim=-2) - - sc = torch.cat([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], dim=-1) - min = torch.cat([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], dim=-1) - - return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8))) - -def dequantize_blocks_Q6_K(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - ql, qh, scales, d, = split_block_dims(blocks, QK_K // 2, QK_K // 4, QK_K // 16) - - scales = scales.view(torch.int8).to(dtype) - d = d.view(torch.float16).to(dtype) - d = (d * scales).reshape((n_blocks, QK_K // 16, 1)) - - ql = ql.reshape((n_blocks, -1, 1, 64)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1)) - ql = (ql & 0x0F).reshape((n_blocks, -1, 32)) - qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1)) - qh = (qh & 0x03).reshape((n_blocks, -1, 32)) - q = (ql | (qh << 4)).to(torch.int8) - 32 - q = q.reshape((n_blocks, QK_K // 16, -1)) - - return (d * q).reshape((n_blocks, QK_K)) - -def dequantize_blocks_Q5_K(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - d, dmin, scales, qh, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE, QK_K // 8) - - d = d.view(torch.float16).to(dtype) - dmin = dmin.view(torch.float16).to(dtype) - - sc, m = get_scale_min(scales) - - d = (d * sc).reshape((n_blocks, -1, 1)) - dm = (dmin * m).reshape((n_blocks, -1, 1)) - - ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1)) - qh = qh.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([i for i in range(8)], device=d.device, dtype=torch.uint8).reshape((1, 1, 8, 1)) - ql = (ql & 0x0F).reshape((n_blocks, -1, 32)) - qh = (qh & 0x01).reshape((n_blocks, -1, 32)) - q = (ql | (qh << 4)) - - return (d * q - dm).reshape((n_blocks, QK_K)) - -def dequantize_blocks_Q4_K(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - d, dmin, scales, qs = split_block_dims(blocks, 2, 2, K_SCALE_SIZE) - d = d.view(torch.float16).to(dtype) - dmin = dmin.view(torch.float16).to(dtype) - - sc, m = get_scale_min(scales) - - d = (d * sc).reshape((n_blocks, -1, 1)) - dm = (dmin * m).reshape((n_blocks, -1, 1)) - - qs = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 1, 2, 1)) - qs = (qs & 0x0F).reshape((n_blocks, -1, 32)) - - return (d * qs - dm).reshape((n_blocks, QK_K)) - -def dequantize_blocks_Q3_K(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - hmask, qs, scales, d = split_block_dims(blocks, QK_K // 8, QK_K // 4, 12) - d = d.view(torch.float16).to(dtype) - - lscales, hscales = scales[:, :8], scales[:, 8:] - lscales = lscales.reshape((n_blocks, 1, 8)) >> torch.tensor([0, 4], device=d.device, dtype=torch.uint8).reshape((1, 2, 1)) - lscales = lscales.reshape((n_blocks, 16)) - hscales = hscales.reshape((n_blocks, 1, 4)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 4, 1)) - hscales = hscales.reshape((n_blocks, 16)) - scales = (lscales & 0x0F) | ((hscales & 0x03) << 4) - scales = (scales.to(torch.int8) - 32) - - dl = (d * scales).reshape((n_blocks, 16, 1)) - - ql = qs.reshape((n_blocks, -1, 1, 32)) >> torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1)) - qh = hmask.reshape(n_blocks, -1, 1, 32) >> torch.tensor([i for i in range(8)], device=d.device, dtype=torch.uint8).reshape((1, 1, 8, 1)) - ql = ql.reshape((n_blocks, 16, QK_K // 16)) & 3 - qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & 1) ^ 1 - q = (ql.to(torch.int8) - (qh << 2).to(torch.int8)) - - return (dl * q).reshape((n_blocks, QK_K)) - -def dequantize_blocks_Q2_K(blocks, block_size, type_size, dtype=None): - n_blocks = blocks.shape[0] - - scales, qs, d, dmin = split_block_dims(blocks, QK_K // 16, QK_K // 4, 2) - d = d.view(torch.float16).to(dtype) - dmin = dmin.view(torch.float16).to(dtype) - - # (n_blocks, 16, 1) - dl = (d * (scales & 0xF)).reshape((n_blocks, QK_K // 16, 1)) - ml = (dmin * (scales >> 4)).reshape((n_blocks, QK_K // 16, 1)) - - shift = torch.tensor([0, 2, 4, 6], device=d.device, dtype=torch.uint8).reshape((1, 1, 4, 1)) - - qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & 3 - qs = qs.reshape((n_blocks, QK_K // 16, 16)) - qs = dl * qs - ml - - return qs.reshape((n_blocks, -1)) - -dequantize_functions = { - gguf.GGMLQuantizationType.BF16: dequantize_blocks_BF16, - gguf.GGMLQuantizationType.Q8_0: dequantize_blocks_Q8_0, - gguf.GGMLQuantizationType.Q5_1: dequantize_blocks_Q5_1, - gguf.GGMLQuantizationType.Q5_0: dequantize_blocks_Q5_0, - gguf.GGMLQuantizationType.Q4_1: dequantize_blocks_Q4_1, - gguf.GGMLQuantizationType.Q4_0: dequantize_blocks_Q4_0, - gguf.GGMLQuantizationType.Q6_K: dequantize_blocks_Q6_K, - gguf.GGMLQuantizationType.Q5_K: dequantize_blocks_Q5_K, - gguf.GGMLQuantizationType.Q4_K: dequantize_blocks_Q4_K, - gguf.GGMLQuantizationType.Q3_K: dequantize_blocks_Q3_K, - gguf.GGMLQuantizationType.Q2_K: dequantize_blocks_Q2_K, -} diff --git a/custom_nodes/ComfyUI-GGUF/loader.py b/custom_nodes/ComfyUI-GGUF/loader.py deleted file mode 100644 index fd35e13441d87c0f7a3637d6f0581d4e8f0fc1c1..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/loader.py +++ /dev/null @@ -1,353 +0,0 @@ -# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0) -import warnings -import logging -import torch -import gguf -import re -import os - -from .ops import GGMLTensor -from .dequant import is_quantized, dequantize_tensor - -IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image"} -TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl"} -VIS_TYPE_LIST = {"clip-vision"} - -def get_orig_shape(reader, tensor_name): - field_key = f"comfy.gguf.orig_shape.{tensor_name}" - field = reader.get_field(field_key) - if field is None: - return None - # Has original shape metadata, so we try to decode it. - if len(field.types) != 2 or field.types[0] != gguf.GGUFValueType.ARRAY or field.types[1] != gguf.GGUFValueType.INT32: - raise TypeError(f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}") - return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data)) - -def get_field(reader, field_name, field_type): - field = reader.get_field(field_name) - if field is None: - return None - elif field_type == str: - # extra check here as this is used for checking arch string - if len(field.types) != 1 or field.types[0] != gguf.GGUFValueType.STRING: - raise TypeError(f"Bad type for GGUF {field_name} key: expected string, got {field.types!r}") - return str(field.parts[field.data[-1]], encoding="utf-8") - elif field_type in [int, float, bool]: - return field_type(field.parts[field.data[-1]]) - else: - raise TypeError(f"Unknown field type {field_type}") - -def get_list_field(reader, field_name, field_type): - field = reader.get_field(field_name) - if field is None: - return None - elif field_type == str: - return tuple(str(field.parts[part_idx], encoding="utf-8") for part_idx in field.data) - elif field_type in [int, float, bool]: - return tuple(field_type(field.parts[part_idx][0]) for part_idx in field.data) - else: - raise TypeError(f"Unknown field type {field_type}") - -def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", return_arch=False, is_text_model=False): - """ - Read state dict as fake tensors - """ - reader = gguf.GGUFReader(path) - - # filter and strip prefix - has_prefix = False - if handle_prefix is not None: - prefix_len = len(handle_prefix) - tensor_names = set(tensor.name for tensor in reader.tensors) - has_prefix = any(s.startswith(handle_prefix) for s in tensor_names) - - tensors = [] - for tensor in reader.tensors: - sd_key = tensor_name = tensor.name - if has_prefix: - if not tensor_name.startswith(handle_prefix): - continue - sd_key = tensor_name[prefix_len:] - tensors.append((sd_key, tensor)) - - # detect and verify architecture - compat = None - arch_str = get_field(reader, "general.architecture", str) - type_str = get_field(reader, "general.type", str) - if arch_str in [None, "pig"]: - if is_text_model: - raise ValueError(f"This text model is incompatible with llama.cpp!\nConsider using the safetensors version\n({path})") - compat = "sd.cpp" if arch_str is None else arch_str - # import here to avoid changes to convert.py breaking regular models - from .tools.convert import detect_arch - try: - arch_str = detect_arch(set(val[0] for val in tensors)).arch - except Exception as e: - raise ValueError(f"This model is not currently supported - ({e})") - elif arch_str not in TXT_ARCH_LIST and is_text_model: - if type_str not in VIS_TYPE_LIST: - raise ValueError(f"Unexpected text model architecture type in GGUF file: {arch_str!r}") - elif arch_str not in IMG_ARCH_LIST and not is_text_model: - raise ValueError(f"Unexpected architecture type in GGUF file: {arch_str!r}") - - if compat: - logging.warning(f"Warning: This gguf model file is loaded in compatibility mode '{compat}' [arch:{arch_str}]") - - # main loading loop - state_dict = {} - qtype_dict = {} - for sd_key, tensor in tensors: - tensor_name = tensor.name - # torch_tensor = torch.from_numpy(tensor.data) # mmap - - # NOTE: line above replaced with this block to avoid persistent numpy warning about mmap - with warnings.catch_warnings(): - warnings.filterwarnings("ignore", message="The given NumPy array is not writable") - torch_tensor = torch.from_numpy(tensor.data) # mmap - - shape = get_orig_shape(reader, tensor_name) - if shape is None: - shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape))) - # Workaround for stable-diffusion.cpp SDXL detection. - if compat == "sd.cpp" and arch_str == "sdxl": - if any([tensor_name.endswith(x) for x in (".proj_in.weight", ".proj_out.weight")]): - while len(shape) > 2 and shape[-1] == 1: - shape = shape[:-1] - - # add to state dict - if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}: - torch_tensor = torch_tensor.view(*shape) - state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape) - - # keep track of loaded tensor types - tensor_type_str = getattr(tensor.tensor_type, "name", repr(tensor.tensor_type)) - qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1 - - # print loaded tensor type counts - logging.info("gguf qtypes: " + ", ".join(f"{k} ({v})" for k, v in qtype_dict.items())) - - # mark largest tensor for vram estimation - qsd = {k:v for k,v in state_dict.items() if is_quantized(v)} - if len(qsd) > 0: - max_key = max(qsd.keys(), key=lambda k: qsd[k].numel()) - state_dict[max_key].is_largest_weight = True - - if return_arch: - return (state_dict, arch_str) - return state_dict - -# for remapping llama.cpp -> original key names -T5_SD_MAP = { - "enc.": "encoder.", - ".blk.": ".block.", - "token_embd": "shared", - "output_norm": "final_layer_norm", - "attn_q": "layer.0.SelfAttention.q", - "attn_k": "layer.0.SelfAttention.k", - "attn_v": "layer.0.SelfAttention.v", - "attn_o": "layer.0.SelfAttention.o", - "attn_norm": "layer.0.layer_norm", - "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias", - "ffn_up": "layer.1.DenseReluDense.wi_1", - "ffn_down": "layer.1.DenseReluDense.wo", - "ffn_gate": "layer.1.DenseReluDense.wi_0", - "ffn_norm": "layer.1.layer_norm", -} - -LLAMA_SD_MAP = { - "blk.": "model.layers.", - "attn_norm": "input_layernorm", - "attn_q": "self_attn.q_proj", - "attn_k": "self_attn.k_proj", - "attn_v": "self_attn.v_proj", - "attn_output": "self_attn.o_proj", - "ffn_up": "mlp.up_proj", - "ffn_down": "mlp.down_proj", - "ffn_gate": "mlp.gate_proj", - "ffn_norm": "post_attention_layernorm", - "token_embd": "model.embed_tokens", - "output_norm": "model.norm", - "output.weight": "lm_head.weight", -} - -CLIP_VISION_SD_MAP = { - "mm.": "visual.merger.mlp.", - "v.post_ln.": "visual.merger.ln_q.", - "v.patch_embd": "visual.patch_embed.proj", - "v.blk.": "visual.blocks.", - "ffn_up": "mlp.up_proj", - "ffn_down": "mlp.down_proj", - "ffn_gate": "mlp.gate_proj", - "attn_out.": "attn.proj.", - "ln1.": "norm1.", - "ln2.": "norm2.", -} - -def sd_map_replace(raw_sd, key_map): - sd = {} - for k,v in raw_sd.items(): - for s,d in key_map.items(): - k = k.replace(s,d) - sd[k] = v - return sd - -def llama_permute(raw_sd, n_head, n_head_kv): - # Reverse version of LlamaModel.permute in llama.cpp convert script - sd = {} - permute = lambda x,h: x.reshape(h, x.shape[0] // h // 2, 2, *x.shape[1:]).swapaxes(1, 2).reshape(x.shape) - for k,v in raw_sd.items(): - if k.endswith(("q_proj.weight", "q_proj.bias")): - v.data = permute(v.data, n_head) - if k.endswith(("k_proj.weight", "k_proj.bias")): - v.data = permute(v.data, n_head_kv) - sd[k] = v - return sd - -def strip_quant_suffix(name): - pattern = r"[-_]?(?:ud-)?i?q[0-9]_[a-z0-9_\-]{1,8}$" - match = re.search(pattern, name, re.IGNORECASE) - if match: - name = name[:match.start()] - return name - -def gguf_mmproj_loader(path): - # Reverse version of Qwen2VLVisionModel.modify_tensors - logging.info("Attenpting to find mmproj file for text encoder...") - - # get name to match w/o quant suffix - tenc_fname = os.path.basename(path) - tenc = os.path.splitext(tenc_fname)[0].lower() - tenc = strip_quant_suffix(tenc) - - # try and find matching mmproj - target = [] - root = os.path.dirname(path) - for fname in os.listdir(root): - name, ext = os.path.splitext(fname) - if ext.lower() != ".gguf": - continue - if "mmproj" not in name.lower(): - continue - if tenc in name.lower(): - target.append(fname) - - if len(target) == 0: - logging.error(f"Error: Can't find mmproj file for '{tenc_fname}' (matching:'{tenc}')! Qwen-Image-Edit will be broken!") - return {} - if len(target) > 1: - logging.error(f"Ambiguous mmproj for text encoder '{tenc_fname}', will use first match.") - - logging.info(f"Using mmproj '{target[0]}' for text encoder '{tenc_fname}'.") - target = os.path.join(root, target[0]) - vsd = gguf_sd_loader(target, is_text_model=True) - - # concat 4D to 5D - if "v.patch_embd.weight.1" in vsd: - w1 = dequantize_tensor(vsd.pop("v.patch_embd.weight"), dtype=torch.float32) - w2 = dequantize_tensor(vsd.pop("v.patch_embd.weight.1"), dtype=torch.float32) - vsd["v.patch_embd.weight"] = torch.stack([w1, w2], dim=2) - - # run main replacement - vsd = sd_map_replace(vsd, CLIP_VISION_SD_MAP) - - # handle split Q/K/V - if "visual.blocks.0.attn_q.weight" in vsd: - attns = {} - # filter out attentions + group - for k,v in vsd.items(): - if any(x in k for x in ["attn_q", "attn_k", "attn_v"]): - k_attn, k_name = k.rsplit(".attn_", 1) - k_attn += ".attn.qkv." + k_name.split(".")[-1] - if k_attn not in attns: - attns[k_attn] = {} - attns[k_attn][k_name] = dequantize_tensor( - v, dtype=(torch.bfloat16 if is_quantized(v) else torch.float16) - ) - - # recombine - for k,v in attns.items(): - suffix = k.split(".")[-1] - vsd[k] = torch.cat([ - v[f"q.{suffix}"], - v[f"k.{suffix}"], - v[f"v.{suffix}"], - ], dim=0) - del attns - - return vsd - -def gguf_tokenizer_loader(path, temb_shape): - # convert gguf tokenizer to spiece - logging.info("Attempting to recreate sentencepiece tokenizer from GGUF file metadata...") - try: - from sentencepiece import sentencepiece_model_pb2 as model - except ImportError: - raise ImportError("Please make sure sentencepiece and protobuf are installed.\npip install sentencepiece protobuf") - spm = model.ModelProto() - - reader = gguf.GGUFReader(path) - - if get_field(reader, "tokenizer.ggml.model", str) == "t5": - if temb_shape == (256384, 4096): # probably UMT5 - spm.trainer_spec.model_type == 1 # Unigram (do we have a T5 w/ BPE?) - else: - raise NotImplementedError("Unknown model, can't set tokenizer!") - else: - raise NotImplementedError("Unknown model, can't set tokenizer!") - - spm.normalizer_spec.add_dummy_prefix = get_field(reader, "tokenizer.ggml.add_space_prefix", bool) - spm.normalizer_spec.remove_extra_whitespaces = get_field(reader, "tokenizer.ggml.remove_extra_whitespaces", bool) - - tokens = get_list_field(reader, "tokenizer.ggml.tokens", str) - scores = get_list_field(reader, "tokenizer.ggml.scores", float) - toktypes = get_list_field(reader, "tokenizer.ggml.token_type", int) - - for idx, (token, score, toktype) in enumerate(zip(tokens, scores, toktypes)): - # # These aren't present in the original? - # if toktype == 5 and idx >= temb_shape[0]%1000): - # continue - - piece = spm.SentencePiece() - piece.piece = token - piece.score = score - piece.type = toktype - spm.pieces.append(piece) - - # unsure if any of these are correct - spm.trainer_spec.byte_fallback = True - spm.trainer_spec.vocab_size = len(tokens) # split off unused? - spm.trainer_spec.max_sentence_length = 4096 - spm.trainer_spec.eos_id = get_field(reader, "tokenizer.ggml.eos_token_id", int) - spm.trainer_spec.pad_id = get_field(reader, "tokenizer.ggml.padding_token_id", int) - - logging.info(f"Created tokenizer with vocab size of {len(spm.pieces)}") - del reader - return torch.ByteTensor(list(spm.SerializeToString())) - -def gguf_clip_loader(path): - sd, arch = gguf_sd_loader(path, return_arch=True, is_text_model=True) - if arch in {"t5", "t5encoder"}: - temb_key = "token_embd.weight" - if temb_key in sd and sd[temb_key].shape == (256384, 4096): - # non-standard Comfy-Org tokenizer - sd["spiece_model"] = gguf_tokenizer_loader(path, sd[temb_key].shape) - # TODO: dequantizing token embed here is janky but otherwise we OOM due to tensor being massive. - logging.warning(f"Dequantizing {temb_key} to prevent runtime OOM.") - sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16) - sd = sd_map_replace(sd, T5_SD_MAP) - elif arch in {"llama", "qwen2vl"}: - # TODO: pass model_options["vocab_size"] to loader somehow - temb_key = "token_embd.weight" - if temb_key in sd and sd[temb_key].shape[0] >= (64 * 1024): - # See note above for T5. - logging.warning(f"Dequantizing {temb_key} to prevent runtime OOM.") - sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16) - sd = sd_map_replace(sd, LLAMA_SD_MAP) - if arch == "llama": - sd = llama_permute(sd, 32, 8) # L3 - if arch == "qwen2vl": - vsd = gguf_mmproj_loader(path) - sd.update(vsd) - else: - pass - return sd diff --git a/custom_nodes/ComfyUI-GGUF/nodes.py b/custom_nodes/ComfyUI-GGUF/nodes.py deleted file mode 100644 index 415914234340c2eba5913bf1fdab934df72dc05f..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/nodes.py +++ /dev/null @@ -1,305 +0,0 @@ -# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0) -import torch -import logging -import collections - -import nodes -import comfy.sd -import comfy.lora -import comfy.float -import comfy.utils -import comfy.model_patcher -import comfy.model_management -import folder_paths - -from .ops import GGMLOps, move_patch_to_device -from .loader import gguf_sd_loader, gguf_clip_loader -from .dequant import is_quantized, is_torch_compatible - -def update_folder_names_and_paths(key, targets=[]): - # check for existing key - base = folder_paths.folder_names_and_paths.get(key, ([], {})) - base = base[0] if isinstance(base[0], (list, set, tuple)) else [] - # find base key & add w/ fallback, sanity check + warning - target = next((x for x in targets if x in folder_paths.folder_names_and_paths), targets[0]) - orig, _ = folder_paths.folder_names_and_paths.get(target, ([], {})) - folder_paths.folder_names_and_paths[key] = (orig or base, {".gguf"}) - if base and base != orig: - logging.warning(f"Unknown file list already present on key {key}: {base}") - -# Add a custom keys for files ending in .gguf -update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"]) -update_folder_names_and_paths("clip_gguf", ["text_encoders", "clip"]) - -class GGUFModelPatcher(comfy.model_patcher.ModelPatcher): - patch_on_device = False - - def patch_weight_to_device(self, key, device_to=None, inplace_update=False): - if key not in self.patches: - return - weight = comfy.utils.get_attr(self.model, key) - - patches = self.patches[key] - if is_quantized(weight): - out_weight = weight.to(device_to) - patches = move_patch_to_device(patches, self.load_device if self.patch_on_device else self.offload_device) - # TODO: do we ever have legitimate duplicate patches? (i.e. patch on top of patched weight) - out_weight.patches = [(patches, key)] - else: - inplace_update = self.weight_inplace_update or inplace_update - if key not in self.backup: - self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])( - weight.to(device=self.offload_device, copy=inplace_update), inplace_update - ) - - if device_to is not None: - temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) - else: - temp_weight = weight.to(torch.float32, copy=True) - - out_weight = comfy.lora.calculate_weight(patches, temp_weight, key) - out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype) - - if inplace_update: - comfy.utils.copy_to_param(self.model, key, out_weight) - else: - comfy.utils.set_attr_param(self.model, key, out_weight) - - def unpatch_model(self, device_to=None, unpatch_weights=True): - if unpatch_weights: - for p in self.model.parameters(): - if is_torch_compatible(p): - continue - patches = getattr(p, "patches", []) - if len(patches) > 0: - p.patches = [] - # TODO: Find another way to not unload after patches - return super().unpatch_model(device_to=device_to, unpatch_weights=unpatch_weights) - - mmap_released = False - def load(self, *args, force_patch_weights=False, **kwargs): - # always call `patch_weight_to_device` even for lowvram - super().load(*args, force_patch_weights=True, **kwargs) - - # make sure nothing stays linked to mmap after first load - if not self.mmap_released: - linked = [] - if kwargs.get("lowvram_model_memory", 0) > 0: - for n, m in self.model.named_modules(): - if hasattr(m, "weight"): - device = getattr(m.weight, "device", None) - if device == self.offload_device: - linked.append((n, m)) - continue - if hasattr(m, "bias"): - device = getattr(m.bias, "device", None) - if device == self.offload_device: - linked.append((n, m)) - continue - if linked and self.load_device != self.offload_device: - logging.info(f"Attempting to release mmap ({len(linked)})") - for n, m in linked: - # TODO: possible to OOM, find better way to detach - m.to(self.load_device).to(self.offload_device) - self.mmap_released = True - - def clone(self, *args, **kwargs): - src_cls = self.__class__ - self.__class__ = GGUFModelPatcher - n = super().clone(*args, **kwargs) - n.__class__ = GGUFModelPatcher - self.__class__ = src_cls - # GGUF specific clone values below - n.patch_on_device = getattr(self, "patch_on_device", False) - if src_cls != GGUFModelPatcher: - n.size = 0 # force recalc - return n - -class UnetLoaderGGUF: - @classmethod - def INPUT_TYPES(s): - unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")] - return { - "required": { - "unet_name": (unet_names,), - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "load_unet" - CATEGORY = "bootleg" - TITLE = "Unet Loader (GGUF)" - - def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None): - ops = GGMLOps() - - if dequant_dtype in ("default", None): - ops.Linear.dequant_dtype = None - elif dequant_dtype in ["target"]: - ops.Linear.dequant_dtype = dequant_dtype - else: - ops.Linear.dequant_dtype = getattr(torch, dequant_dtype) - - if patch_dtype in ("default", None): - ops.Linear.patch_dtype = None - elif patch_dtype in ["target"]: - ops.Linear.patch_dtype = patch_dtype - else: - ops.Linear.patch_dtype = getattr(torch, patch_dtype) - - # init model - unet_path = folder_paths.get_full_path("unet", unet_name) - sd = gguf_sd_loader(unet_path) - model = comfy.sd.load_diffusion_model_state_dict( - sd, model_options={"custom_operations": ops} - ) - if model is None: - logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) - raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) - model = GGUFModelPatcher.clone(model) - model.patch_on_device = patch_on_device - return (model,) - -class UnetLoaderGGUFAdvanced(UnetLoaderGGUF): - @classmethod - def INPUT_TYPES(s): - unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")] - return { - "required": { - "unet_name": (unet_names,), - "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}), - "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}), - "patch_on_device": ("BOOLEAN", {"default": False}), - } - } - TITLE = "Unet Loader (GGUF/Advanced)" - -class CLIPLoaderGGUF: - @classmethod - def INPUT_TYPES(s): - base = nodes.CLIPLoader.INPUT_TYPES() - return { - "required": { - "clip_name": (s.get_filename_list(),), - "type": base["required"]["type"], - } - } - - RETURN_TYPES = ("CLIP",) - FUNCTION = "load_clip" - CATEGORY = "bootleg" - TITLE = "CLIPLoader (GGUF)" - - @classmethod - def get_filename_list(s): - files = [] - files += folder_paths.get_filename_list("clip") - files += folder_paths.get_filename_list("clip_gguf") - return sorted(files) - - def load_data(self, ckpt_paths): - clip_data = [] - for p in ckpt_paths: - if p.endswith(".gguf"): - sd = gguf_clip_loader(p) - else: - sd = comfy.utils.load_torch_file(p, safe_load=True) - if "scaled_fp8" in sd: # NOTE: Scaled FP8 would require different custom ops, but only one can be active - raise NotImplementedError(f"Mixing scaled FP8 with GGUF is not supported! Use regular CLIP loader or switch model(s)\n({p})") - clip_data.append(sd) - return clip_data - - def load_patcher(self, clip_paths, clip_type, clip_data): - clip = comfy.sd.load_text_encoder_state_dicts( - clip_type = clip_type, - state_dicts = clip_data, - model_options = { - "custom_operations": GGMLOps, - "initial_device": comfy.model_management.text_encoder_offload_device() - }, - embedding_directory = folder_paths.get_folder_paths("embeddings"), - ) - clip.patcher = GGUFModelPatcher.clone(clip.patcher) - return clip - - def load_clip(self, clip_name, type="stable_diffusion"): - clip_path = folder_paths.get_full_path("clip", clip_name) - clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) - return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),) - -class DualCLIPLoaderGGUF(CLIPLoaderGGUF): - @classmethod - def INPUT_TYPES(s): - base = nodes.DualCLIPLoader.INPUT_TYPES() - file_options = (s.get_filename_list(), ) - return { - "required": { - "clip_name1": file_options, - "clip_name2": file_options, - "type": base["required"]["type"], - } - } - - TITLE = "DualCLIPLoader (GGUF)" - - def load_clip(self, clip_name1, clip_name2, type): - clip_path1 = folder_paths.get_full_path("clip", clip_name1) - clip_path2 = folder_paths.get_full_path("clip", clip_name2) - clip_paths = (clip_path1, clip_path2) - clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) - return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),) - -class TripleCLIPLoaderGGUF(CLIPLoaderGGUF): - @classmethod - def INPUT_TYPES(s): - file_options = (s.get_filename_list(), ) - return { - "required": { - "clip_name1": file_options, - "clip_name2": file_options, - "clip_name3": file_options, - } - } - - TITLE = "TripleCLIPLoader (GGUF)" - - def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"): - clip_path1 = folder_paths.get_full_path("clip", clip_name1) - clip_path2 = folder_paths.get_full_path("clip", clip_name2) - clip_path3 = folder_paths.get_full_path("clip", clip_name3) - clip_paths = (clip_path1, clip_path2, clip_path3) - clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) - return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),) - -class QuadrupleCLIPLoaderGGUF(CLIPLoaderGGUF): - @classmethod - def INPUT_TYPES(s): - file_options = (s.get_filename_list(), ) - return { - "required": { - "clip_name1": file_options, - "clip_name2": file_options, - "clip_name3": file_options, - "clip_name4": file_options, - } - } - - TITLE = "QuadrupleCLIPLoader (GGUF)" - - def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable_diffusion"): - clip_path1 = folder_paths.get_full_path("clip", clip_name1) - clip_path2 = folder_paths.get_full_path("clip", clip_name2) - clip_path3 = folder_paths.get_full_path("clip", clip_name3) - clip_path4 = folder_paths.get_full_path("clip", clip_name4) - clip_paths = (clip_path1, clip_path2, clip_path3, clip_path4) - clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) - return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),) - -NODE_CLASS_MAPPINGS = { - "UnetLoaderGGUF": UnetLoaderGGUF, - "CLIPLoaderGGUF": CLIPLoaderGGUF, - "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF, - "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF, - "QuadrupleCLIPLoaderGGUF": QuadrupleCLIPLoaderGGUF, - "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced, -} diff --git a/custom_nodes/ComfyUI-GGUF/ops.py b/custom_nodes/ComfyUI-GGUF/ops.py deleted file mode 100644 index 41d42f5549b8789dac470bc846651833b600056b..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/ops.py +++ /dev/null @@ -1,281 +0,0 @@ -# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0) -import gguf -import torch -import logging - -import comfy.ops -import comfy.lora -import comfy.model_management -from .dequant import dequantize_tensor, is_quantized - -def chained_hasattr(obj, chained_attr): - probe = obj - for attr in chained_attr.split('.'): - if hasattr(probe, attr): - probe = getattr(probe, attr) - else: - return False - return True - -# A bakcward and forward compatible way to get `torch.compiler.disable`. -def get_torch_compiler_disable_decorator(): - def dummy_decorator(*args, **kwargs): - def noop(x): - return x - return noop - - from packaging import version - - if not chained_hasattr(torch, "compiler.disable"): - logging.info("ComfyUI-GGUF: Torch too old for torch.compile - bypassing") - return dummy_decorator # torch too old - elif version.parse(torch.__version__) >= version.parse("2.8"): - logging.info("ComfyUI-GGUF: Allowing full torch compile") - return dummy_decorator # torch compile works - if chained_hasattr(torch, "_dynamo.config.nontraceable_tensor_subclasses"): - logging.info("ComfyUI-GGUF: Allowing full torch compile (nightly)") - return dummy_decorator # torch compile works, nightly before 2.8 release - else: - logging.info("ComfyUI-GGUF: Partial torch compile only, consider updating pytorch") - return torch.compiler.disable - -torch_compiler_disable = get_torch_compiler_disable_decorator() - -class GGMLTensor(torch.Tensor): - """ - Main tensor-like class for storing quantized weights - """ - def __init__(self, *args, tensor_type, tensor_shape, patches=[], **kwargs): - super().__init__() - self.tensor_type = tensor_type - self.tensor_shape = tensor_shape - self.patches = patches - - def __new__(cls, *args, tensor_type, tensor_shape, patches=[], **kwargs): - return super().__new__(cls, *args, **kwargs) - - def to(self, *args, **kwargs): - new = super().to(*args, **kwargs) - new.tensor_type = getattr(self, "tensor_type", None) - new.tensor_shape = getattr(self, "tensor_shape", new.data.shape) - new.patches = getattr(self, "patches", []).copy() - return new - - def clone(self, *args, **kwargs): - return self - - def detach(self, *args, **kwargs): - return self - - def copy_(self, *args, **kwargs): - # fixes .weight.copy_ in comfy/clip_model/CLIPTextModel - try: - return super().copy_(*args, **kwargs) - except Exception as e: - logging.warning(f"ignoring 'copy_' on tensor: {e}") - - def new_empty(self, size, *args, **kwargs): - # Intel Arc fix, ref#50 - new_tensor = super().new_empty(size, *args, **kwargs) - return GGMLTensor( - new_tensor, - tensor_type = getattr(self, "tensor_type", None), - tensor_shape = size, - patches = getattr(self, "patches", []).copy() - ) - - @property - def shape(self): - if not hasattr(self, "tensor_shape"): - self.tensor_shape = self.size() - return self.tensor_shape - -class GGMLLayer(torch.nn.Module): - """ - This (should) be responsible for de-quantizing on the fly - """ - comfy_cast_weights = True - dequant_dtype = None - patch_dtype = None - largest_layer = False - torch_compatible_tensor_types = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16} - - def is_ggml_quantized(self, *, weight=None, bias=None): - if weight is None: - weight = self.weight - if bias is None: - bias = self.bias - return is_quantized(weight) or is_quantized(bias) - - def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): - weight, bias = state_dict.get(f"{prefix}weight"), state_dict.get(f"{prefix}bias") - # NOTE: using modified load for linear due to not initializing on creation, see GGMLOps todo - if self.is_ggml_quantized(weight=weight, bias=bias) or isinstance(self, torch.nn.Linear): - return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs) - # Not strictly required, but fixes embedding shape mismatch. Threshold set in loader.py - if isinstance(self, torch.nn.Embedding) and self.weight.shape[0] >= (64 * 1024): - return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs) - return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) - - def ggml_load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): - prefix_len = len(prefix) - for k,v in state_dict.items(): - if k[prefix_len:] == "weight": - self.weight = torch.nn.Parameter(v, requires_grad=False) - elif k[prefix_len:] == "bias" and v is not None: - self.bias = torch.nn.Parameter(v, requires_grad=False) - else: - unexpected_keys.append(k) - - # For Linear layer with missing weight - if self.weight is None and isinstance(self, torch.nn.Linear): - v = torch.zeros(self.in_features, self.out_features) - self.weight = torch.nn.Parameter(v, requires_grad=False) - missing_keys.append(prefix+"weight") - - # for vram estimation (TODO: less fragile logic?) - if getattr(self.weight, "is_largest_weight", False): - self.largest_layer = True - - def _save_to_state_dict(self, *args, **kwargs): - if self.is_ggml_quantized(): - return self.ggml_save_to_state_dict(*args, **kwargs) - return super()._save_to_state_dict(*args, **kwargs) - - def ggml_save_to_state_dict(self, destination, prefix, keep_vars): - # This is a fake state dict for vram estimation - weight = torch.zeros_like(self.weight, device=torch.device("meta")) - destination[prefix + "weight"] = weight - if self.bias is not None: - bias = torch.zeros_like(self.bias, device=torch.device("meta")) - destination[prefix + "bias"] = bias - - # Take into account space required for dequantizing the largest tensor - if self.largest_layer: - shape = getattr(self.weight, "tensor_shape", self.weight.shape) - dtype = self.dequant_dtype or torch.float16 - temp = torch.empty(*shape, device=torch.device("meta"), dtype=dtype) - destination[prefix + "temp.weight"] = temp - - return - # This would return the dequantized state dict - destination[prefix + "weight"] = self.get_weight(self.weight) - if bias is not None: - destination[prefix + "bias"] = self.get_weight(self.bias) - - def get_weight(self, tensor, dtype): - if tensor is None: - return - - # consolidate and load patches to GPU in async - patch_list = [] - device = tensor.device - for patches, key in getattr(tensor, "patches", []): - patch_list += move_patch_to_device(patches, device) - - # dequantize tensor while patches load - weight = dequantize_tensor(tensor, dtype, self.dequant_dtype) - - # prevent propagating custom tensor class - if isinstance(weight, GGMLTensor): - weight = torch.Tensor(weight) - - # apply patches - if len(patch_list) > 0: - if self.patch_dtype is None: - weight = comfy.lora.calculate_weight(patch_list, weight, key) - else: - # for testing, may degrade image quality - patch_dtype = dtype if self.patch_dtype == "target" else self.patch_dtype - weight = comfy.lora.calculate_weight(patch_list, weight, key, patch_dtype) - return weight - - @torch_compiler_disable() - def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): - if input is not None: - if dtype is None: - dtype = getattr(input, "dtype", torch.float32) - if bias_dtype is None: - bias_dtype = dtype - if device is None: - device = input.device - - bias = None - non_blocking = comfy.model_management.device_supports_non_blocking(device) - if s.bias is not None: - bias = s.get_weight(s.bias.to(device), dtype) - bias = comfy.ops.cast_to(bias, bias_dtype, device, non_blocking=non_blocking, copy=False) - - weight = s.get_weight(s.weight.to(device), dtype) - weight = comfy.ops.cast_to(weight, dtype, device, non_blocking=non_blocking, copy=False) - return weight, bias - - def forward_comfy_cast_weights(self, input, *args, **kwargs): - if self.is_ggml_quantized(): - out = self.forward_ggml_cast_weights(input, *args, **kwargs) - else: - out = super().forward_comfy_cast_weights(input, *args, **kwargs) - - # non-ggml forward might still propagate custom tensor class - if isinstance(out, GGMLTensor): - out = torch.Tensor(out) - return out - - def forward_ggml_cast_weights(self, input): - raise NotImplementedError - -class GGMLOps(comfy.ops.manual_cast): - """ - Dequantize weights on the fly before doing the compute - """ - class Linear(GGMLLayer, comfy.ops.manual_cast.Linear): - def __init__(self, in_features, out_features, bias=True, device=None, dtype=None): - torch.nn.Module.__init__(self) - # TODO: better workaround for reserved memory spike on windows - # Issue is with `torch.empty` still reserving the full memory for the layer - # Windows doesn't over-commit memory so without this 24GB+ of pagefile is used - self.in_features = in_features - self.out_features = out_features - self.weight = None - self.bias = None - - def forward_ggml_cast_weights(self, input): - weight, bias = self.cast_bias_weight(input) - return torch.nn.functional.linear(input, weight, bias) - - class Conv2d(GGMLLayer, comfy.ops.manual_cast.Conv2d): - def forward_ggml_cast_weights(self, input): - weight, bias = self.cast_bias_weight(input) - return self._conv_forward(input, weight, bias) - - class Embedding(GGMLLayer, comfy.ops.manual_cast.Embedding): - def forward_ggml_cast_weights(self, input, out_dtype=None): - output_dtype = out_dtype - if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16: - out_dtype = None - weight, _bias = self.cast_bias_weight(self, device=input.device, dtype=out_dtype) - return torch.nn.functional.embedding( - input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse - ).to(dtype=output_dtype) - - class LayerNorm(GGMLLayer, comfy.ops.manual_cast.LayerNorm): - def forward_ggml_cast_weights(self, input): - if self.weight is None: - return super().forward_comfy_cast_weights(input) - weight, bias = self.cast_bias_weight(input) - return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) - - class GroupNorm(GGMLLayer, comfy.ops.manual_cast.GroupNorm): - def forward_ggml_cast_weights(self, input): - weight, bias = self.cast_bias_weight(input) - return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) - -def move_patch_to_device(item, device): - if isinstance(item, torch.Tensor): - return item.to(device, non_blocking=True) - elif isinstance(item, tuple): - return tuple(move_patch_to_device(x, device) for x in item) - elif isinstance(item, list): - return [move_patch_to_device(x, device) for x in item] - else: - return item diff --git a/custom_nodes/ComfyUI-GGUF/pyproject.toml b/custom_nodes/ComfyUI-GGUF/pyproject.toml deleted file mode 100644 index 8abefe84ff2a096d0cb335e634a8a239241b9a01..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/pyproject.toml +++ /dev/null @@ -1,14 +0,0 @@ -[project] -name = "ComfyUI-GGUF" -description = "GGUF Quantization support for native ComfyUI models." -version = "1.1.4" # 2.0.0 = GitHub main, 1.X.X = ComfyUI Registry -license = { file = "LICENSE" } -dependencies = ["gguf>=0.13.0", "sentencepiece", "protobuf"] - -[project.urls] -Repository = "https://github.com/city96/ComfyUI-GGUF" - -[tool.comfy] -PublisherId = "city96" -DisplayName = "ComfyUI-GGUF" -Icon = "" diff --git a/custom_nodes/ComfyUI-GGUF/requirements.txt b/custom_nodes/ComfyUI-GGUF/requirements.txt deleted file mode 100644 index f49905c79e1ea579a8e4fde0fbec966dcff2f2f8..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -# main -gguf>=0.13.0 -# optional - tokenizer -sentencepiece -protobuf diff --git a/custom_nodes/ComfyUI-GGUF/tools/README.md b/custom_nodes/ComfyUI-GGUF/tools/README.md deleted file mode 100644 index 228bc220593d7878ca854032638bdc2682c76a71..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/tools/README.md +++ /dev/null @@ -1,93 +0,0 @@ -## Converting initial model - -To convert your initial safetensors/ckpt model to FP16/BF16 GGUF, run the following command: - -``` -python convert.py --src E:\models\unet\flux1-dev.safetensors -``` -Make sure `gguf>=0.13.0` is installed for this step. Optionally, specify the output gguf file with the `--dst` arg. - -> [!NOTE] -> Do not use the diffusers UNET format for flux, it won't work, use the default/reference checkpoint key format. This is due to q/k/v being merged into one qkv key. -> You can convert it by loading it in ComfyUI and saving it using the built-in "ModelSave" node. - -> [!WARNING] -> For hunyuan video/wan 2.1, you will see a warning about 5D tensors. This means the script will save a **non functional** model to disk first, that you can quantize. I recommend saving these in a separate `raw` folder to avoid confusion. -> -> After quantization, you will have to run `fix_5d_tensor.py` manually to add back the missing key that was saved by the conversion code. - -## Quantizing using custom llama.cpp - -Depending on your git settings, you may need to run the following script first in order to make sure the patch file is valid. It will convert Windows (CRLF) line endings to Unix (LF) ones. - -``` -python fix_lines_ending.py -``` - -Git clone llama.cpp into the current folder: - -``` -git clone https://github.com/ggerganov/llama.cpp -``` - -Check out the correct branch, then apply the custom patch needed to add image model support to the repo you just cloned. - -``` -cd llama.cpp -git checkout tags/b3962 -git apply ..\lcpp.patch -``` - -Compile the llama-quantize binary. This example uses cmake, on linux you can just use make. - -### Visual Studio 2019, Linux, etc... - -``` -mkdir build -cmake -B build -cmake --build build --config Debug -j10 --target llama-quantize -cd .. -``` - -### Visual Studio 2022 - -``` -mkdir build -cmake -B build -DCMAKE_CXX_STANDARD=17 -DCMAKE_CXX_STANDARD_REQUIRED=ON -DCMAKE_CXX_FLAGS="-std=c++17" -``` - -Edit the `llama.cpp\common\log.cpp` file, inserts two lines after the existing first line: - -``` -#include "log.h" - -#define _SILENCE_CXX23_CHRONO_DEPRECATION_WARNING -#include -``` - -Then you can build the project: -``` -cmake --build build --config Debug -j10 --target llama-quantize -cd .. -``` - -### Quantize your model - - -Now you can use the newly build binary to quantize your model to the desired format: -``` -llama.cpp\build\bin\Debug\llama-quantize.exe E:\models\unet\flux1-dev-BF16.gguf E:\models\unet\flux1-dev-Q4_K_S.gguf Q4_K_S -``` - -You can extract the patch again with `git diff src\llama.cpp > lcpp.patch` if you wish to change something and contribute back. - -> [!WARNING] -> For hunyuan video/wan 2.1, you will have to run `fix_5d_tensor.py` after the quantization step is done. -> -> Example usage: `fix_5d_tensors.py --src E:\models\video\raw\wan2.1-t2v-1.3b-Q8_0.gguf --dst E:\models\video\wan2.1-t2v-1.3b-Q8_0.gguf` -> -> By default, this also saves a `fix_5d_tensors_[arch].safetensors` file in the `ComfyUI-GGUF/tools` folder, it's recommended to delete this after all models have been converted. - -> [!NOTE] -> Do not quantize SDXL / SD1 / other Conv2D heavy models. If you do, make sure to **extract the UNET model first**. ->This should be obvious, but also don't use the resulting llama-quantize binary with LLMs. diff --git a/custom_nodes/ComfyUI-GGUF/tools/convert.py b/custom_nodes/ComfyUI-GGUF/tools/convert.py deleted file mode 100644 index 5029c874277358559f8855d3f1032963437a3e91..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/tools/convert.py +++ /dev/null @@ -1,365 +0,0 @@ -# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0) -import os -import gguf -import torch -import logging -import argparse -from tqdm import tqdm -from safetensors.torch import load_file, save_file - -QUANTIZATION_THRESHOLD = 1024 -REARRANGE_THRESHOLD = 512 -MAX_TENSOR_NAME_LENGTH = 127 -MAX_TENSOR_DIMS = 4 - -class ModelTemplate: - arch = "invalid" # string describing architecture - shape_fix = False # whether to reshape tensors - keys_detect = [] # list of lists to match in state dict - keys_banned = [] # list of keys that should mark model as invalid for conversion - keys_hiprec = [] # list of keys that need to be kept in fp32 for some reason - keys_ignore = [] # list of strings to ignore keys by when found - - def handle_nd_tensor(self, key, data): - raise NotImplementedError(f"Tensor detected that exceeds dims supported by C++ code! ({key} @ {data.shape})") - -class ModelFlux(ModelTemplate): - arch = "flux" - keys_detect = [ - ("transformer_blocks.0.attn.norm_added_k.weight",), - ("double_blocks.0.img_attn.proj.weight",), - ] - keys_banned = ["transformer_blocks.0.attn.norm_added_k.weight",] - -class ModelSD3(ModelTemplate): - arch = "sd3" - keys_detect = [ - ("transformer_blocks.0.attn.add_q_proj.weight",), - ("joint_blocks.0.x_block.attn.qkv.weight",), - ] - keys_banned = ["transformer_blocks.0.attn.add_q_proj.weight",] - -class ModelAura(ModelTemplate): - arch = "aura" - keys_detect = [ - ("double_layers.3.modX.1.weight",), - ("joint_transformer_blocks.3.ff_context.out_projection.weight",), - ] - keys_banned = ["joint_transformer_blocks.3.ff_context.out_projection.weight",] - -class ModelHiDream(ModelTemplate): - arch = "hidream" - keys_detect = [ - ( - "caption_projection.0.linear.weight", - "double_stream_blocks.0.block.ff_i.shared_experts.w3.weight" - ) - ] - keys_hiprec = [ - # nn.parameter, can't load from BF16 ver - ".ff_i.gate.weight", - "img_emb.emb_pos" - ] - -class CosmosPredict2(ModelTemplate): - arch = "cosmos" - keys_detect = [ - ( - "blocks.0.mlp.layer1.weight", - "blocks.0.adaln_modulation_cross_attn.1.weight", - ) - ] - keys_hiprec = ["pos_embedder"] - keys_ignore = ["_extra_state", "accum_"] - -class ModelHyVid(ModelTemplate): - arch = "hyvid" - keys_detect = [ - ( - "double_blocks.0.img_attn_proj.weight", - "txt_in.individual_token_refiner.blocks.1.self_attn_qkv.weight", - ) - ] - - def handle_nd_tensor(self, key, data): - # hacky but don't have any better ideas - path = f"./fix_5d_tensors_{self.arch}.safetensors" # TODO: somehow get a path here?? - if os.path.isfile(path): - raise RuntimeError(f"5D tensor fix file already exists! {path}") - fsd = {key: torch.from_numpy(data)} - tqdm.write(f"5D key found in state dict! Manual fix required! - {key} {data.shape}") - save_file(fsd, path) - -class ModelWan(ModelHyVid): - arch = "wan" - keys_detect = [ - ( - "blocks.0.self_attn.norm_q.weight", - "text_embedding.2.weight", - "head.modulation", - ) - ] - keys_hiprec = [ - ".modulation" # nn.parameter, can't load from BF16 ver - ] - -class ModelLTXV(ModelTemplate): - arch = "ltxv" - keys_detect = [ - ( - "adaln_single.emb.timestep_embedder.linear_2.weight", - "transformer_blocks.27.scale_shift_table", - "caption_projection.linear_2.weight", - ) - ] - keys_hiprec = [ - "scale_shift_table" # nn.parameter, can't load from BF16 base quant - ] - -class ModelSDXL(ModelTemplate): - arch = "sdxl" - shape_fix = True - keys_detect = [ - ("down_blocks.0.downsamplers.0.conv.weight", "add_embedding.linear_1.weight",), - ( - "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight", - "output_blocks.2.2.conv.weight", "output_blocks.5.2.conv.weight", - ), # Non-diffusers - ("label_emb.0.0.weight",), - ] - -class ModelSD1(ModelTemplate): - arch = "sd1" - shape_fix = True - keys_detect = [ - ("down_blocks.0.downsamplers.0.conv.weight",), - ( - "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight", "input_blocks.9.0.op.weight", - "output_blocks.2.1.conv.weight", "output_blocks.5.2.conv.weight", "output_blocks.8.2.conv.weight" - ), # Non-diffusers - ] - -class ModelLumina2(ModelTemplate): - arch = "lumina2" - keys_detect = [ - ("cap_embedder.1.weight", "context_refiner.0.attention.qkv.weight") - ] - -arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2, - ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2] - -def is_model_arch(model, state_dict): - # check if model is correct - matched = False - invalid = False - for match_list in model.keys_detect: - if all(key in state_dict for key in match_list): - matched = True - invalid = any(key in state_dict for key in model.keys_banned) - break - assert not invalid, "Model architecture not allowed for conversion! (i.e. reference VS diffusers format)" - return matched - -def detect_arch(state_dict): - model_arch = None - for arch in arch_list: - if is_model_arch(arch, state_dict): - model_arch = arch() - break - assert model_arch is not None, "Unknown model architecture!" - return model_arch - -def parse_args(): - parser = argparse.ArgumentParser(description="Generate F16 GGUF files from single UNET") - parser.add_argument("--src", required=True, help="Source model ckpt file.") - parser.add_argument("--dst", help="Output unet gguf file.") - args = parser.parse_args() - - if not os.path.isfile(args.src): - parser.error("No input provided!") - - return args - -def strip_prefix(state_dict): - # prefix for mixed state dict - prefix = None - for pfx in ["model.diffusion_model.", "model."]: - if any([x.startswith(pfx) for x in state_dict.keys()]): - prefix = pfx - break - - # prefix for uniform state dict - if prefix is None: - for pfx in ["net."]: - if all([x.startswith(pfx) for x in state_dict.keys()]): - prefix = pfx - break - - # strip prefix if found - if prefix is not None: - logging.info(f"State dict prefix found: '{prefix}'") - sd = {} - for k, v in state_dict.items(): - if prefix not in k: - continue - k = k.replace(prefix, "") - sd[k] = v - else: - logging.debug("State dict has no prefix") - sd = state_dict - - return sd - -def load_state_dict(path): - if any(path.endswith(x) for x in [".ckpt", ".pt", ".bin", ".pth"]): - state_dict = torch.load(path, map_location="cpu", weights_only=True) - for subkey in ["model", "module"]: - if subkey in state_dict: - state_dict = state_dict[subkey] - break - if len(state_dict) < 20: - raise RuntimeError(f"pt subkey load failed: {state_dict.keys()}") - else: - state_dict = load_file(path) - - return strip_prefix(state_dict) - -def handle_tensors(writer, state_dict, model_arch): - name_lengths = tuple(sorted( - ((key, len(key)) for key in state_dict.keys()), - key=lambda item: item[1], - reverse=True, - )) - if not name_lengths: - return - max_name_len = name_lengths[0][1] - if max_name_len > MAX_TENSOR_NAME_LENGTH: - bad_list = ", ".join(f"{key!r} ({namelen})" for key, namelen in name_lengths if namelen > MAX_TENSOR_NAME_LENGTH) - raise ValueError(f"Can only handle tensor names up to {MAX_TENSOR_NAME_LENGTH} characters. Tensors exceeding the limit: {bad_list}") - for key, data in tqdm(state_dict.items()): - old_dtype = data.dtype - - if any(x in key for x in model_arch.keys_ignore): - tqdm.write(f"Filtering ignored key: '{key}'") - continue - - if data.dtype == torch.bfloat16: - data = data.to(torch.float32).numpy() - # this is so we don't break torch 2.0.X - elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]: - data = data.to(torch.float16).numpy() - else: - data = data.numpy() - - n_dims = len(data.shape) - data_shape = data.shape - if old_dtype == torch.bfloat16: - data_qtype = gguf.GGMLQuantizationType.BF16 - # elif old_dtype == torch.float32: - # data_qtype = gguf.GGMLQuantizationType.F32 - else: - data_qtype = gguf.GGMLQuantizationType.F16 - - # The max no. of dimensions that can be handled by the quantization code is 4 - if len(data.shape) > MAX_TENSOR_DIMS: - model_arch.handle_nd_tensor(key, data) - continue # needs to be added back later - - # get number of parameters (AKA elements) in this tensor - n_params = 1 - for dim_size in data_shape: - n_params *= dim_size - - if old_dtype in (torch.float32, torch.bfloat16): - if n_dims == 1: - # one-dimensional tensors should be kept in F32 - # also speeds up inference due to not dequantizing - data_qtype = gguf.GGMLQuantizationType.F32 - - elif n_params <= QUANTIZATION_THRESHOLD: - # very small tensors - data_qtype = gguf.GGMLQuantizationType.F32 - - elif any(x in key for x in model_arch.keys_hiprec): - # tensors that require max precision - data_qtype = gguf.GGMLQuantizationType.F32 - - if (model_arch.shape_fix # NEVER reshape for models such as flux - and n_dims > 1 # Skip one-dimensional tensors - and n_params >= REARRANGE_THRESHOLD # Only rearrange tensors meeting the size requirement - and (n_params / 256).is_integer() # Rearranging only makes sense if total elements is divisible by 256 - and not (data.shape[-1] / 256).is_integer() # Only need to rearrange if the last dimension is not divisible by 256 - ): - orig_shape = data.shape - data = data.reshape(n_params // 256, 256) - writer.add_array(f"comfy.gguf.orig_shape.{key}", tuple(int(dim) for dim in orig_shape)) - - try: - data = gguf.quants.quantize(data, data_qtype) - except (AttributeError, gguf.QuantError) as e: - tqdm.write(f"falling back to F16: {e}") - data_qtype = gguf.GGMLQuantizationType.F16 - data = gguf.quants.quantize(data, data_qtype) - - new_name = key # do we need to rename? - - shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}" - tqdm.write(f"{f'%-{max_name_len + 4}s' % f'{new_name}'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") - - writer.add_tensor(new_name, data, raw_dtype=data_qtype) - -def convert_file(path, dst_path=None, interact=True, overwrite=False): - # load & run model detection logic - state_dict = load_state_dict(path) - model_arch = detect_arch(state_dict) - logging.info(f"* Architecture detected from input: {model_arch.arch}") - - # detect & set dtype for output file - dtypes = [x.dtype for x in state_dict.values()] - dtypes = {x:dtypes.count(x) for x in set(dtypes)} - main_dtype = max(dtypes, key=dtypes.get) - - if main_dtype == torch.bfloat16: - ftype_name = "BF16" - ftype_gguf = gguf.LlamaFileType.MOSTLY_BF16 - # elif main_dtype == torch.float32: - # ftype_name = "F32" - # ftype_gguf = None - else: - ftype_name = "F16" - ftype_gguf = gguf.LlamaFileType.MOSTLY_F16 - - if dst_path is None: - dst_path = f"{os.path.splitext(path)[0]}-{ftype_name}.gguf" - elif "{ftype}" in dst_path: # lcpp logic - dst_path = dst_path.replace("{ftype}", ftype_name) - - if os.path.isfile(dst_path) and not overwrite: - if interact: - input("Output exists enter to continue or ctrl+c to abort!") - else: - raise OSError("Output exists and overwriting is disabled!") - - # handle actual file - writer = gguf.GGUFWriter(path=None, arch=model_arch.arch) - writer.add_quantization_version(gguf.GGML_QUANT_VERSION) - if ftype_gguf is not None: - writer.add_file_type(ftype_gguf) - - handle_tensors(writer, state_dict, model_arch) - writer.write_header_to_file(path=dst_path) - writer.write_kv_data_to_file() - writer.write_tensors_to_file(progress=True) - writer.close() - - fix = f"./fix_5d_tensors_{model_arch.arch}.safetensors" - if os.path.isfile(fix): - logging.warning(f"\n### Warning! Fix file found at '{fix}'") - logging.warning(" you most likely need to run 'fix_5d_tensors.py' after quantization.") - - return dst_path, model_arch - -if __name__ == "__main__": - args = parse_args() - convert_file(args.src, args.dst) - diff --git a/custom_nodes/ComfyUI-GGUF/tools/fix_5d_tensors.py b/custom_nodes/ComfyUI-GGUF/tools/fix_5d_tensors.py deleted file mode 100644 index 0e61d1c2e5f2572c3d9fa12eba38c84f13689a53..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/tools/fix_5d_tensors.py +++ /dev/null @@ -1,82 +0,0 @@ -# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0) -import os -import gguf -import torch -import argparse -from tqdm import tqdm -from safetensors.torch import load_file - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--src", required=True) - parser.add_argument("--dst", required=True) - parser.add_argument("--fix", required=False, help="Defaults to ./fix_5d_tensors_[arch].pt") - parser.add_argument("--overwrite", action="store_true") - args = parser.parse_args() - - if not os.path.isfile(args.src): - parser.error(f"Invalid source file '{args.src}'") - if not args.overwrite and os.path.exists(args.dst): - parser.error(f"Output exists, use '--overwrite' ({args.dst})") - - return args - -def get_arch_str(reader): - field = reader.get_field("general.architecture") - return str(field.parts[field.data[-1]], encoding="utf-8") - -def get_file_type(reader): - field = reader.get_field("general.file_type") - ft = int(field.parts[field.data[-1]]) - return gguf.LlamaFileType(ft) - -if __name__ == "__main__": - args = get_args() - - # read existing - reader = gguf.GGUFReader(args.src) - arch = get_arch_str(reader) - file_type = get_file_type(reader) - print(f"Detected arch: '{arch}' (ftype: {str(file_type)})") - - # prep fix - if args.fix is None: - args.fix = f"./fix_5d_tensors_{arch}.safetensors" - - if not os.path.isfile(args.fix): - raise OSError(f"No 5D tensor fix file: {args.fix}") - - sd5d = load_file(args.fix) - sd5d = {k:v.numpy() for k,v in sd5d.items()} - print("5D tensors:", sd5d.keys()) - - # prep output - writer = gguf.GGUFWriter(path=None, arch=arch) - writer.add_quantization_version(gguf.GGML_QUANT_VERSION) - writer.add_file_type(file_type) - - added = [] - def add_extra_key(writer, key, data): - global added - data_qtype = gguf.GGMLQuantizationType.F32 - data = gguf.quants.quantize(data, data_qtype) - tqdm.write(f"Adding key {key} ({data.shape})") - writer.add_tensor(key, data, raw_dtype=data_qtype) - added.append(key) - - # main loop to add missing 5D tensor(s) - for tensor in tqdm(reader.tensors): - writer.add_tensor(tensor.name, tensor.data, raw_dtype=tensor.tensor_type) - key5d = tensor.name.replace(".bias", ".weight") - if key5d in sd5d.keys(): - add_extra_key(writer, key5d, sd5d[key5d]) - - # brute force for any missed - for key, data in sd5d.items(): - if key not in added: - add_extra_key(writer, key, data) - - writer.write_header_to_file(path=args.dst) - writer.write_kv_data_to_file() - writer.write_tensors_to_file(progress=True) - writer.close() diff --git a/custom_nodes/ComfyUI-GGUF/tools/fix_lines_ending.py b/custom_nodes/ComfyUI-GGUF/tools/fix_lines_ending.py deleted file mode 100644 index 346e3501fb7682fa3754f175965aa750241fe4ac..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/tools/fix_lines_ending.py +++ /dev/null @@ -1,31 +0,0 @@ -import os - -files = ["lcpp.patch", "lcpp_sd3.patch"] - -def has_unix_line_endings(file_path): - try: - with open(file_path, 'rb') as file: - content = file.read() - return b'\r\n' not in content - except Exception as e: - print(f"Error checking '{file_path}': {e}") - return False - -def convert_to_linux_format(file_path): - try: - with open(file_path, 'rb') as file: - content = file.read().replace(b'\r\n', b'\n') - with open(file_path, 'wb') as file: - file.write(content) - print(f"'{file_path}' converted to Linux line endings (LF).") - except Exception as e: - print(f"Error processing '{file_path}': {e}") - -for file in files: - if os.path.exists(file): - if has_unix_line_endings(file): - print(f"'{file}' already has Unix line endings (LF). No conversion needed.") - else: - convert_to_linux_format(file) - else: - print(f"File '{file}' does not exist.") diff --git a/custom_nodes/ComfyUI-GGUF/tools/lcpp.patch b/custom_nodes/ComfyUI-GGUF/tools/lcpp.patch deleted file mode 100644 index 92396e17b4ecf281265f07603232d23111ee9baa..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/tools/lcpp.patch +++ /dev/null @@ -1,451 +0,0 @@ -diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h -index de3c706f..0267c1fa 100644 ---- a/ggml/include/ggml.h -+++ b/ggml/include/ggml.h -@@ -223,7 +223,7 @@ - #define GGML_MAX_OP_PARAMS 64 - - #ifndef GGML_MAX_NAME --# define GGML_MAX_NAME 64 -+# define GGML_MAX_NAME 128 - #endif - - #define GGML_DEFAULT_N_THREADS 4 -@@ -2449,6 +2449,7 @@ extern "C" { - - // manage tensor info - GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor); -+ GGML_API void gguf_set_tensor_ndim(struct gguf_context * ctx, const char * name, int n_dim); - GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type); - GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size); - -diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c -index b16c462f..6d1568f1 100644 ---- a/ggml/src/ggml.c -+++ b/ggml/src/ggml.c -@@ -22960,6 +22960,14 @@ void gguf_add_tensor( - ctx->header.n_tensors++; - } - -+void gguf_set_tensor_ndim(struct gguf_context * ctx, const char * name, const int n_dim) { -+ const int idx = gguf_find_tensor(ctx, name); -+ if (idx < 0) { -+ GGML_ABORT("tensor not found"); -+ } -+ ctx->infos[idx].n_dims = n_dim; -+} -+ - void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) { - const int idx = gguf_find_tensor(ctx, name); - if (idx < 0) { -diff --git a/src/llama.cpp b/src/llama.cpp -index 24e1f1f0..25db4c69 100644 ---- a/src/llama.cpp -+++ b/src/llama.cpp -@@ -205,6 +205,17 @@ enum llm_arch { - LLM_ARCH_GRANITE, - LLM_ARCH_GRANITE_MOE, - LLM_ARCH_CHAMELEON, -+ LLM_ARCH_FLUX, -+ LLM_ARCH_SD1, -+ LLM_ARCH_SDXL, -+ LLM_ARCH_SD3, -+ LLM_ARCH_AURA, -+ LLM_ARCH_LTXV, -+ LLM_ARCH_HYVID, -+ LLM_ARCH_WAN, -+ LLM_ARCH_HIDREAM, -+ LLM_ARCH_COSMOS, -+ LLM_ARCH_LUMINA2, - LLM_ARCH_UNKNOWN, - }; - -@@ -258,6 +269,17 @@ static const std::map LLM_ARCH_NAMES = { - { LLM_ARCH_GRANITE, "granite" }, - { LLM_ARCH_GRANITE_MOE, "granitemoe" }, - { LLM_ARCH_CHAMELEON, "chameleon" }, -+ { LLM_ARCH_FLUX, "flux" }, -+ { LLM_ARCH_SD1, "sd1" }, -+ { LLM_ARCH_SDXL, "sdxl" }, -+ { LLM_ARCH_SD3, "sd3" }, -+ { LLM_ARCH_AURA, "aura" }, -+ { LLM_ARCH_LTXV, "ltxv" }, -+ { LLM_ARCH_HYVID, "hyvid" }, -+ { LLM_ARCH_WAN, "wan" }, -+ { LLM_ARCH_HIDREAM, "hidream" }, -+ { LLM_ARCH_COSMOS, "cosmos" }, -+ { LLM_ARCH_LUMINA2, "lumina2" }, - { LLM_ARCH_UNKNOWN, "(unknown)" }, - }; - -@@ -1531,6 +1553,17 @@ static const std::map> LLM_TENSOR_N - { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, - }, - }, -+ { LLM_ARCH_FLUX, {}}, -+ { LLM_ARCH_SD1, {}}, -+ { LLM_ARCH_SDXL, {}}, -+ { LLM_ARCH_SD3, {}}, -+ { LLM_ARCH_AURA, {}}, -+ { LLM_ARCH_LTXV, {}}, -+ { LLM_ARCH_HYVID, {}}, -+ { LLM_ARCH_WAN, {}}, -+ { LLM_ARCH_HIDREAM, {}}, -+ { LLM_ARCH_COSMOS, {}}, -+ { LLM_ARCH_LUMINA2, {}}, - { - LLM_ARCH_UNKNOWN, - { -@@ -5403,6 +5436,25 @@ static void llm_load_hparams( - // get general kv - ml.get_key(LLM_KV_GENERAL_NAME, model.name, false); - -+ // Disable LLM metadata for image models -+ switch (model.arch) { -+ case LLM_ARCH_FLUX: -+ case LLM_ARCH_SD1: -+ case LLM_ARCH_SDXL: -+ case LLM_ARCH_SD3: -+ case LLM_ARCH_AURA: -+ case LLM_ARCH_LTXV: -+ case LLM_ARCH_HYVID: -+ case LLM_ARCH_WAN: -+ case LLM_ARCH_HIDREAM: -+ case LLM_ARCH_COSMOS: -+ case LLM_ARCH_LUMINA2: -+ model.ftype = ml.ftype; -+ return; -+ default: -+ break; -+ } -+ - // get hparams kv - ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); - -@@ -18016,6 +18068,134 @@ static void llama_tensor_dequantize_internal( - workers.clear(); - } - -+static ggml_type img_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) { -+ // Special function for quantizing image model tensors -+ const std::string name = ggml_get_name(tensor); -+ const llm_arch arch = qs.model.arch; -+ -+ // Sanity check -+ if ( -+ (name.find("model.diffusion_model.") != std::string::npos) || -+ (name.find("first_stage_model.") != std::string::npos) || -+ (name.find("single_transformer_blocks.") != std::string::npos) || -+ (name.find("joint_transformer_blocks.") != std::string::npos) -+ ) { -+ throw std::runtime_error("Invalid input GGUF file. This is not a supported UNET model"); -+ } -+ -+ // Unsupported quant types - exclude all IQ quants for now -+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || -+ ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || -+ ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || -+ ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || -+ ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S || -+ ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_Q4_0_4_4 || -+ ftype == LLAMA_FTYPE_MOSTLY_Q4_0_4_8 || ftype == LLAMA_FTYPE_MOSTLY_Q4_0_8_8) { -+ throw std::runtime_error("Invalid quantization type for image model (Not supported)"); -+ } -+ -+ if ( // Rules for to_v attention -+ (name.find("attn_v.weight") != std::string::npos) || -+ (name.find(".to_v.weight") != std::string::npos) || -+ (name.find(".v.weight") != std::string::npos) || -+ (name.find(".attn.w1v.weight") != std::string::npos) || -+ (name.find(".attn.w2v.weight") != std::string::npos) || -+ (name.find("_attn.v_proj.weight") != std::string::npos) -+ ){ -+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) { -+ new_type = GGML_TYPE_Q3_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { -+ new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { -+ new_type = GGML_TYPE_Q5_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) { -+ new_type = GGML_TYPE_Q6_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) { -+ new_type = GGML_TYPE_Q5_K; -+ } -+ ++qs.i_attention_wv; -+ } else if ( // Rules for fused qkv attention -+ (name.find("attn_qkv.weight") != std::string::npos) || -+ (name.find("attn.qkv.weight") != std::string::npos) || -+ (name.find("attention.qkv.weight") != std::string::npos) -+ ) { -+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { -+ new_type = GGML_TYPE_Q4_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { -+ new_type = GGML_TYPE_Q5_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) { -+ new_type = GGML_TYPE_Q6_K; -+ } -+ } else if ( // Rules for ffn -+ (name.find("ffn_down") != std::string::npos) || -+ ((name.find("experts.") != std::string::npos) && (name.find(".w2.weight") != std::string::npos)) || -+ (name.find(".ffn.2.weight") != std::string::npos) || // is this even the right way around? -+ (name.find(".ff.net.2.weight") != std::string::npos) || -+ (name.find(".mlp.layer2.weight") != std::string::npos) || -+ (name.find(".adaln_modulation_mlp.2.weight") != std::string::npos) || -+ (name.find(".feed_forward.w2.weight") != std::string::npos) -+ ) { -+ // TODO: add back `layer_info` with some model specific logic + logic further down -+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { -+ new_type = GGML_TYPE_Q4_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { -+ new_type = GGML_TYPE_Q5_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S) { -+ new_type = GGML_TYPE_Q5_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { -+ new_type = GGML_TYPE_Q6_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) { -+ new_type = GGML_TYPE_Q6_K; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_0) { -+ new_type = GGML_TYPE_Q4_1; -+ } -+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_0) { -+ new_type = GGML_TYPE_Q5_1; -+ } -+ ++qs.i_ffn_down; -+ } -+ -+ // Sanity check for row shape -+ bool convert_incompatible_tensor = false; -+ if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || -+ new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) { -+ int nx = tensor->ne[0]; -+ int ny = tensor->ne[1]; -+ if (nx % QK_K != 0) { -+ LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type)); -+ convert_incompatible_tensor = true; -+ } else { -+ ++qs.n_k_quantized; -+ } -+ } -+ if (convert_incompatible_tensor) { -+ // TODO: Possibly reenable this in the future -+ // switch (new_type) { -+ // case GGML_TYPE_Q2_K: -+ // case GGML_TYPE_Q3_K: -+ // case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; -+ // case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break; -+ // case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break; -+ // default: throw std::runtime_error("\nUnsupported tensor size encountered\n"); -+ // } -+ new_type = GGML_TYPE_F16; -+ LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type)); -+ ++qs.n_fallback; -+ } -+ return new_type; -+} -+ - static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) { - const std::string name = ggml_get_name(tensor); - -@@ -18513,7 +18693,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s - if (llama_model_has_encoder(&model)) { - n_attn_layer *= 3; - } -- GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected"); -+ if (model.arch != LLM_ARCH_HYVID) { // TODO: Check why this fails -+ GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected"); -+ } - } - - size_t total_size_org = 0; -@@ -18547,6 +18729,51 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s - ctx_outs[i_split] = gguf_init_empty(); - } - gguf_add_tensor(ctx_outs[i_split], tensor); -+ // SD3 pos_embed needs special fix as first dim is 1, which gets truncated here -+ if (model.arch == LLM_ARCH_SD3) { -+ const std::string name = ggml_get_name(tensor); -+ if (name == "pos_embed" && tensor->ne[2] == 1) { -+ const int n_dim = 3; -+ gguf_set_tensor_ndim(ctx_outs[i_split], "pos_embed", n_dim); -+ LLAMA_LOG_INFO("\n%s: Correcting pos_embed shape for SD3: [key:%s]\n", __func__, tensor->name); -+ } -+ } -+ // same goes for auraflow -+ if (model.arch == LLM_ARCH_AURA) { -+ const std::string name = ggml_get_name(tensor); -+ if (name == "positional_encoding" && tensor->ne[2] == 1) { -+ const int n_dim = 3; -+ gguf_set_tensor_ndim(ctx_outs[i_split], "positional_encoding", n_dim); -+ LLAMA_LOG_INFO("\n%s: Correcting positional_encoding shape for AuraFlow: [key:%s]\n", __func__, tensor->name); -+ } -+ if (name == "register_tokens" && tensor->ne[2] == 1) { -+ const int n_dim = 3; -+ gguf_set_tensor_ndim(ctx_outs[i_split], "register_tokens", n_dim); -+ LLAMA_LOG_INFO("\n%s: Correcting register_tokens shape for AuraFlow: [key:%s]\n", __func__, tensor->name); -+ } -+ } -+ // conv3d fails due to max dims - unsure what to do here as we never even reach this check -+ if (model.arch == LLM_ARCH_HYVID) { -+ const std::string name = ggml_get_name(tensor); -+ if (name == "img_in.proj.weight" && tensor->ne[5] != 1 ) { -+ throw std::runtime_error("img_in.proj.weight size failed for HyVid"); -+ } -+ } -+ // All the modulation layers also have dim1, and I think conv3d fails here too but we segfaul way before that... -+ if (model.arch == LLM_ARCH_WAN) { -+ const std::string name = ggml_get_name(tensor); -+ if (name.find(".modulation") != std::string::npos && tensor->ne[2] == 1) { -+ const int n_dim = 3; -+ gguf_set_tensor_ndim(ctx_outs[i_split], tensor->name, n_dim); -+ LLAMA_LOG_INFO("\n%s: Correcting shape for Wan: [key:%s]\n", __func__, tensor->name); -+ } -+ // FLF2V model only -+ if (name == "img_emb.emb_pos") { -+ const int n_dim = 3; -+ gguf_set_tensor_ndim(ctx_outs[i_split], tensor->name, n_dim); -+ LLAMA_LOG_INFO("\n%s: Correcting shape for Wan FLF2V: [key:%s]\n", __func__, tensor->name); -+ } -+ } - } - - // Set split info if needed -@@ -18647,6 +18874,110 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s - // do not quantize relative position bias (T5) - quantize &= name.find("attn_rel_b.weight") == std::string::npos; - -+ // rules for image models -+ bool image_model = false; -+ if (model.arch == LLM_ARCH_FLUX) { -+ image_model = true; -+ quantize &= name.find("txt_in.") == std::string::npos; -+ quantize &= name.find("img_in.") == std::string::npos; -+ quantize &= name.find("time_in.") == std::string::npos; -+ quantize &= name.find("vector_in.") == std::string::npos; -+ quantize &= name.find("guidance_in.") == std::string::npos; -+ quantize &= name.find("final_layer.") == std::string::npos; -+ } -+ if (model.arch == LLM_ARCH_SD1 || model.arch == LLM_ARCH_SDXL) { -+ image_model = true; -+ quantize &= name.find("class_embedding.") == std::string::npos; -+ quantize &= name.find("time_embedding.") == std::string::npos; -+ quantize &= name.find("add_embedding.") == std::string::npos; -+ quantize &= name.find("time_embed.") == std::string::npos; -+ quantize &= name.find("label_emb.") == std::string::npos; -+ quantize &= name.find("conv_in.") == std::string::npos; -+ quantize &= name.find("conv_out.") == std::string::npos; -+ quantize &= name != "input_blocks.0.0.weight"; -+ quantize &= name != "out.2.weight"; -+ } -+ if (model.arch == LLM_ARCH_SD3) { -+ image_model = true; -+ quantize &= name.find("final_layer.") == std::string::npos; -+ quantize &= name.find("time_text_embed.") == std::string::npos; -+ quantize &= name.find("context_embedder.") == std::string::npos; -+ quantize &= name.find("t_embedder.") == std::string::npos; -+ quantize &= name.find("y_embedder.") == std::string::npos; -+ quantize &= name.find("x_embedder.") == std::string::npos; -+ quantize &= name != "proj_out.weight"; -+ quantize &= name != "pos_embed"; -+ } -+ if (model.arch == LLM_ARCH_AURA) { -+ image_model = true; -+ quantize &= name.find("t_embedder.") == std::string::npos; -+ quantize &= name.find("init_x_linear.") == std::string::npos; -+ quantize &= name != "modF.1.weight"; -+ quantize &= name != "cond_seq_linear.weight"; -+ quantize &= name != "final_linear.weight"; -+ quantize &= name != "final_linear.weight"; -+ quantize &= name != "positional_encoding"; -+ quantize &= name != "register_tokens"; -+ } -+ if (model.arch == LLM_ARCH_LTXV) { -+ image_model = true; -+ quantize &= name.find("adaln_single.") == std::string::npos; -+ quantize &= name.find("caption_projection.") == std::string::npos; -+ quantize &= name.find("patchify_proj.") == std::string::npos; -+ quantize &= name.find("proj_out.") == std::string::npos; -+ quantize &= name.find("scale_shift_table") == std::string::npos; // last block too -+ } -+ if (model.arch == LLM_ARCH_HYVID) { -+ image_model = true; -+ quantize &= name.find("txt_in.") == std::string::npos; -+ quantize &= name.find("img_in.") == std::string::npos; -+ quantize &= name.find("time_in.") == std::string::npos; -+ quantize &= name.find("vector_in.") == std::string::npos; -+ quantize &= name.find("guidance_in.") == std::string::npos; -+ quantize &= name.find("final_layer.") == std::string::npos; -+ } -+ if (model.arch == LLM_ARCH_WAN) { -+ image_model = true; -+ quantize &= name.find("modulation.") == std::string::npos; -+ quantize &= name.find("patch_embedding.") == std::string::npos; -+ quantize &= name.find("text_embedding.") == std::string::npos; -+ quantize &= name.find("time_projection.") == std::string::npos; -+ quantize &= name.find("time_embedding.") == std::string::npos; -+ quantize &= name.find("img_emb.") == std::string::npos; -+ quantize &= name.find("head.") == std::string::npos; -+ } -+ if (model.arch == LLM_ARCH_HIDREAM) { -+ image_model = true; -+ quantize &= name.find("p_embedder.") == std::string::npos; -+ quantize &= name.find("t_embedder.") == std::string::npos; -+ quantize &= name.find("x_embedder.") == std::string::npos; -+ quantize &= name.find("final_layer.") == std::string::npos; -+ quantize &= name.find(".ff_i.gate.weight") == std::string::npos; -+ quantize &= name.find("caption_projection.") == std::string::npos; -+ } -+ if (model.arch == LLM_ARCH_COSMOS) { -+ image_model = true; -+ quantize &= name.find("p_embedder.") == std::string::npos; -+ quantize &= name.find("t_embedder.") == std::string::npos; -+ quantize &= name.find("t_embedding_norm.") == std::string::npos; -+ quantize &= name.find("x_embedder.") == std::string::npos; -+ quantize &= name.find("pos_embedder.") == std::string::npos; -+ quantize &= name.find("final_layer.") == std::string::npos; -+ } -+ if (model.arch == LLM_ARCH_LUMINA2) { -+ image_model = true; -+ quantize &= name.find("t_embedder.") == std::string::npos; -+ quantize &= name.find("x_embedder.") == std::string::npos; -+ quantize &= name.find("final_layer.") == std::string::npos; -+ quantize &= name.find("cap_embedder.") == std::string::npos; -+ quantize &= name.find("context_refiner.") == std::string::npos; -+ quantize &= name.find("noise_refiner.") == std::string::npos; -+ } -+ // ignore 3D/4D tensors for image models as the code was never meant to handle these -+ if (image_model) { -+ quantize &= ggml_n_dims(tensor) == 2; -+ } -+ - enum ggml_type new_type; - void * new_data; - size_t new_size; -@@ -18655,6 +18986,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s - new_type = default_type; - - // get more optimal quantization type based on the tensor shape, layer, etc. -+ if (image_model) { -+ new_type = img_tensor_get_type(qs, new_type, tensor, ftype); -+ } else { - if (!params->pure && ggml_is_quantized(default_type)) { - new_type = llama_tensor_get_type(qs, new_type, tensor, ftype); - } -@@ -18664,6 +18998,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s - if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) { - new_type = params->output_tensor_type; - } -+ } - - // If we've decided to quantize to the same type the tensor is already - // in then there's nothing to do. diff --git a/custom_nodes/ComfyUI-GGUF/tools/read_tensors.py b/custom_nodes/ComfyUI-GGUF/tools/read_tensors.py deleted file mode 100644 index 1bdff028a787c09b38e5616ef75a2f070c672445..0000000000000000000000000000000000000000 --- a/custom_nodes/ComfyUI-GGUF/tools/read_tensors.py +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/python3 -import os -import sys -import gguf - -def read_tensors(path): - reader = gguf.GGUFReader(path) - for tensor in reader.tensors: - if tensor.tensor_type == gguf.GGMLQuantizationType.F32: - continue - print(f"{str(tensor.tensor_type):32}: {tensor.name}") - -try: - path = sys.argv[1] - assert os.path.isfile(path), "Invalid path" - print(f"input: {path}") -except Exception as e: - input(f"failed: {e}") -else: - read_tensors(path) - input() diff --git a/custom_nodes/comfyui-kjnodes/.DS_Store b/custom_nodes/comfyui-kjnodes/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/.DS_Store and /dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/.github/FUNDING.yml b/custom_nodes/comfyui-kjnodes/.github/FUNDING.yml deleted file mode 100644 index f475f425424d9172d049b4c016ebe8817987149e..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/.github/FUNDING.yml +++ /dev/null @@ -1,2 +0,0 @@ -github: [kijai] -custom: ["https://www.paypal.me/kijaidesign"] diff --git a/custom_nodes/comfyui-kjnodes/.github/workflows/publish.yml b/custom_nodes/comfyui-kjnodes/.github/workflows/publish.yml deleted file mode 100644 index e155f5f40e46fa83942dee5a9460e8093f3b4208..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/.github/workflows/publish.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: Publish to Comfy registry -on: - workflow_dispatch: - push: - branches: - - main - paths: - - "pyproject.toml" - -permissions: - issues: write - -jobs: - publish-node: - name: Publish Custom Node to registry - runs-on: ubuntu-latest - if: ${{ github.repository_owner == 'kijai' }} - steps: - - name: Check out code - uses: actions/checkout@v4 - - name: Publish Custom Node - uses: Comfy-Org/publish-node-action@v1 - with: - ## Add your own personal access token to your Github Repository secrets and reference it here. - personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} diff --git a/custom_nodes/comfyui-kjnodes/.gitignore b/custom_nodes/comfyui-kjnodes/.gitignore deleted file mode 100644 index d462a62fdd67858a783934170db4091ea95eb18f..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/.gitignore +++ /dev/null @@ -1,11 +0,0 @@ -__pycache__ -/venv -*.code-workspace -.history -.vscode -*.ckpt -*.pth -types -models -jsconfig.json -custom_dimensions.json diff --git a/custom_nodes/comfyui-kjnodes/.tracking b/custom_nodes/comfyui-kjnodes/.tracking deleted file mode 100644 index b99092643f12e91e7a785bfb3a24e47440cd6ac9..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/.tracking +++ /dev/null @@ -1,49 +0,0 @@ -.github/FUNDING.yml -.github/workflows/publish.yml -.gitignore -LICENSE -README.md -__init__.py -custom_dimensions_example.json -docs/images/2024-04-03_20_49_29-ComfyUI.png -docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png -docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png -example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json -fonts/FreeMono.ttf -fonts/FreeMonoBoldOblique.otf -fonts/TTNorms-Black.otf -intrinsic_loras/intrinsic_lora_sd15_albedo.safetensors -intrinsic_loras/intrinsic_lora_sd15_depth.safetensors -intrinsic_loras/intrinsic_lora_sd15_normal.safetensors -intrinsic_loras/intrinsic_lora_sd15_shading.safetensors -intrinsic_loras/intrinsic_loras.txt -kjweb_async/marked.min.js -kjweb_async/protovis.min.js -kjweb_async/purify.min.js -kjweb_async/svg-path-properties.min.js -nodes/audioscheduler_nodes.py -nodes/batchcrop_nodes.py -nodes/curve_nodes.py -nodes/image_nodes.py -nodes/intrinsic_lora_nodes.py -nodes/lora_nodes.py -nodes/mask_nodes.py -nodes/model_optimization_nodes.py -nodes/nodes.py -pyproject.toml -requirements.txt -utility/fluid.py -utility/magictex.py -utility/numerical.py -utility/utility.py -web/green.png -web/js/appearance.js -web/js/browserstatus.js -web/js/contextmenu.js -web/js/fast_preview.js -web/js/help_popup.js -web/js/jsnodes.js -web/js/point_editor.js -web/js/setgetnodes.js -web/js/spline_editor.js -web/red.png \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/LICENSE b/custom_nodes/comfyui-kjnodes/LICENSE deleted file mode 100644 index f288702d2fa16d3cdf0035b15a9fcbc552cd88e7..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/custom_nodes/comfyui-kjnodes/README.md b/custom_nodes/comfyui-kjnodes/README.md deleted file mode 100644 index 6371f5014823bc66ffd1f378fea8715a7dd590ff..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/README.md +++ /dev/null @@ -1,65 +0,0 @@ -# KJNodes for ComfyUI - -Various quality of life and masking related -nodes and scripts made by combining functionality of existing nodes for ComfyUI. - -I know I'm bad at documentation, especially this project that has grown from random practice nodes to... too many lines in one file. -I have however started to add descriptions to the nodes themselves, there's a small ? you can click for info what the node does. -This is still work in progress, like everything else. - -# Installation -1. Clone this repo into `custom_nodes` folder. -2. Install dependencies: `pip install -r requirements.txt` - or if you use the portable install, run this in ComfyUI_windows_portable -folder: - - `python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-KJNodes\requirements.txt` - - -## Javascript - -### browserstatus.js -Sets the favicon to green circle when not processing anything, sets it to red when processing and shows progress percentage and the length of your queue. -Default off, needs to be enabled from options, overrides Custom-Scripts favicon when enabled. - -## Nodes: - -### Set/Get - -Javascript nodes to set and get constants to reduce unnecessary lines. Takes in and returns anything, purely visual nodes. -On the right click menu of these nodes there's now an options to visualize the paths, as well as option to jump to the corresponding node on the other end. - -**Known limitations**: - - Will not work with any node that dynamically sets it's outpute, such as reroute or other Set/Get node - - Will not work when directly connected to a bypassed node - - Other possible conflicts with javascript based nodes. - -### ColorToMask - -RBG color value to mask, works with batches and AnimateDiff. - -### ConditioningMultiCombine - -Combine any number of conditions, saves space. - -### ConditioningSetMaskAndCombine - -Mask and combine two sets of conditions, saves space. - -### GrowMaskWithBlur - -Grows or shrinks (with negative values) mask, option to invert input, returns mask and inverted mask. Additionally Blurs the mask, this is a slow operation especially with big batches. - -### RoundMask - -![image](https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40) - -### WidgetToString -Outputs the value of a widget on any node as a string -![example of use](docs/images/2024-04-03_20_49_29-ComfyUI.png) - -Enable node id display from Manager menu, to get the ID of the node you want to read a widget from: -![enable node id display](docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png) - -Use the node id of the target node, and add the name of the widget to read from -![use node id and widget name](docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png) - -Recreating or reloading the target node will change its id, and the WidgetToString node will no longer be able to find it until you update the node id value with the new id. diff --git a/custom_nodes/comfyui-kjnodes/__init__.py b/custom_nodes/comfyui-kjnodes/__init__.py deleted file mode 100644 index d3d1325ce6118a0a9468f46de1366da51d9f71b5..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/__init__.py +++ /dev/null @@ -1,245 +0,0 @@ -from .nodes.nodes import * -from .nodes.curve_nodes import * -from .nodes.batchcrop_nodes import * -from .nodes.audioscheduler_nodes import * -from .nodes.image_nodes import * -from .nodes.intrinsic_lora_nodes import * -from .nodes.mask_nodes import * -from .nodes.model_optimization_nodes import * -from .nodes.lora_nodes import * -NODE_CONFIG = { - #constants - "BOOLConstant": {"class": BOOLConstant, "name": "BOOL Constant"}, - "INTConstant": {"class": INTConstant, "name": "INT Constant"}, - "FloatConstant": {"class": FloatConstant, "name": "Float Constant"}, - "StringConstant": {"class": StringConstant, "name": "String Constant"}, - "StringConstantMultiline": {"class": StringConstantMultiline, "name": "String Constant Multiline"}, - #conditioning - "ConditioningMultiCombine": {"class": ConditioningMultiCombine, "name": "Conditioning Multi Combine"}, - "ConditioningSetMaskAndCombine": {"class": ConditioningSetMaskAndCombine, "name": "ConditioningSetMaskAndCombine"}, - "ConditioningSetMaskAndCombine3": {"class": ConditioningSetMaskAndCombine3, "name": "ConditioningSetMaskAndCombine3"}, - "ConditioningSetMaskAndCombine4": {"class": ConditioningSetMaskAndCombine4, "name": "ConditioningSetMaskAndCombine4"}, - "ConditioningSetMaskAndCombine5": {"class": ConditioningSetMaskAndCombine5, "name": "ConditioningSetMaskAndCombine5"}, - "CondPassThrough": {"class": CondPassThrough}, - #masking - "DownloadAndLoadCLIPSeg": {"class": DownloadAndLoadCLIPSeg, "name": "(Down)load CLIPSeg"}, - "BatchCLIPSeg": {"class": BatchCLIPSeg, "name": "Batch CLIPSeg"}, - "ColorToMask": {"class": ColorToMask, "name": "Color To Mask"}, - "CreateGradientMask": {"class": CreateGradientMask, "name": "Create Gradient Mask"}, - "CreateTextMask": {"class": CreateTextMask, "name": "Create Text Mask"}, - "CreateAudioMask": {"class": CreateAudioMask, "name": "Create Audio Mask"}, - "CreateFadeMask": {"class": CreateFadeMask, "name": "Create Fade Mask"}, - "CreateFadeMaskAdvanced": {"class": CreateFadeMaskAdvanced, "name": "Create Fade Mask Advanced"}, - "CreateFluidMask": {"class": CreateFluidMask, "name": "Create Fluid Mask"}, - "CreateShapeMask": {"class": CreateShapeMask, "name": "Create Shape Mask"}, - "CreateVoronoiMask": {"class": CreateVoronoiMask, "name": "Create Voronoi Mask"}, - "CreateMagicMask": {"class": CreateMagicMask, "name": "Create Magic Mask"}, - "GetMaskSizeAndCount": {"class": GetMaskSizeAndCount, "name": "Get Mask Size & Count"}, - "GrowMaskWithBlur": {"class": GrowMaskWithBlur, "name": "Grow Mask With Blur"}, - "MaskBatchMulti": {"class": MaskBatchMulti, "name": "Mask Batch Multi"}, - "OffsetMask": {"class": OffsetMask, "name": "Offset Mask"}, - "RemapMaskRange": {"class": RemapMaskRange, "name": "Remap Mask Range"}, - "ResizeMask": {"class": ResizeMask, "name": "Resize Mask"}, - "RoundMask": {"class": RoundMask, "name": "Round Mask"}, - "SeparateMasks": {"class": SeparateMasks, "name": "Separate Masks"}, - #images - "AddLabel": {"class": AddLabel, "name": "Add Label"}, - "ColorMatch": {"class": ColorMatch, "name": "Color Match"}, - "ImageTensorList": {"class": ImageTensorList, "name": "Image Tensor List"}, - "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"}, - "CrossFadeImagesMulti": {"class": CrossFadeImagesMulti, "name": "Cross Fade Images Multi"}, - "GetImagesFromBatchIndexed": {"class": GetImagesFromBatchIndexed, "name": "Get Images From Batch Indexed"}, - "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image or Mask Range From Batch"}, - "GetLatentRangeFromBatch": {"class": GetLatentRangeFromBatch, "name": "Get Latent Range From Batch"}, - "GetLatentSizeAndCount": {"class": GetLatentSizeAndCount, "name": "Get Latent Size & Count"}, - "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"}, - "FastPreview": {"class": FastPreview, "name": "Fast Preview"}, - "ImageBatchFilter": {"class": ImageBatchFilter, "name": "Image Batch Filter"}, - "ImageAndMaskPreview": {"class": ImageAndMaskPreview}, - "ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"}, - "ImageBatchJoinWithTransition": {"class": ImageBatchJoinWithTransition, "name": "Image Batch Join With Transition"}, - "ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"}, - "ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving}, - "ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"}, - "ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"}, - "ImageConcatFromBatch": {"class": ImageConcatFromBatch, "name": "Image Concatenate From Batch"}, - "ImageConcatMulti": {"class": ImageConcatMulti, "name": "Image Concatenate Multi"}, - "ImageCropByMask": {"class": ImageCropByMask, "name": "Image Crop By Mask"}, - "ImageCropByMaskAndResize": {"class": ImageCropByMaskAndResize, "name": "Image Crop By Mask And Resize"}, - "ImageCropByMaskBatch": {"class": ImageCropByMaskBatch, "name": "Image Crop By Mask Batch"}, - "ImageUncropByMask": {"class": ImageUncropByMask, "name": "Image Uncrop By Mask"}, - "ImageGrabPIL": {"class": ImageGrabPIL, "name": "Image Grab PIL"}, - "ImageGridComposite2x2": {"class": ImageGridComposite2x2, "name": "Image Grid Composite 2x2"}, - "ImageGridComposite3x3": {"class": ImageGridComposite3x3, "name": "Image Grid Composite 3x3"}, - "ImageGridtoBatch": {"class": ImageGridtoBatch, "name": "Image Grid To Batch"}, - "ImageNoiseAugmentation": {"class": ImageNoiseAugmentation, "name": "Image Noise Augmentation"}, - "ImageNormalize_Neg1_To_1": {"class": ImageNormalize_Neg1_To_1, "name": "Image Normalize -1 to 1"}, - "ImagePass": {"class": ImagePass}, - "ImagePadKJ": {"class": ImagePadKJ, "name": "ImagePad KJ"}, - "ImagePadForOutpaintMasked": {"class": ImagePadForOutpaintMasked, "name": "Image Pad For Outpaint Masked"}, - "ImagePadForOutpaintTargetSize": {"class": ImagePadForOutpaintTargetSize, "name": "Image Pad For Outpaint Target Size"}, - "ImagePrepForICLora": {"class": ImagePrepForICLora, "name": "Image Prep For ICLora"}, - "ImageResizeKJ": {"class": ImageResizeKJ, "name": "Resize Image (deprecated)"}, - "ImageResizeKJv2": {"class": ImageResizeKJv2, "name": "Resize Image v2"}, - "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"}, - "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"}, - "InsertLatentToIndexed": {"class": InsertLatentToIndex, "name": "Insert Latent To Index"}, - "LoadAndResizeImage": {"class": LoadAndResizeImage, "name": "Load & Resize Image"}, - "LoadImagesFromFolderKJ": {"class": LoadImagesFromFolderKJ, "name": "Load Images From Folder (KJ)"}, - "LoadVideosFromFolder": {"class": LoadVideosFromFolder, "name": "Load Videos From Folder"}, - "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"}, - "PadImageBatchInterleaved": {"class": PadImageBatchInterleaved, "name": "Pad Image Batch Interleaved"}, - "PreviewAnimation": {"class": PreviewAnimation, "name": "Preview Animation"}, - "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"}, - "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"}, - "ReplaceImagesInBatch": {"class": ReplaceImagesInBatch, "name": "Replace Images In Batch"}, - "SaveImageWithAlpha": {"class": SaveImageWithAlpha, "name": "Save Image With Alpha"}, - "SaveImageKJ": {"class": SaveImageKJ, "name": "Save Image KJ"}, - "ShuffleImageBatch": {"class": ShuffleImageBatch, "name": "Shuffle Image Batch"}, - "SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"}, - "TransitionImagesMulti": {"class": TransitionImagesMulti, "name": "Transition Images Multi"}, - "TransitionImagesInBatch": {"class": TransitionImagesInBatch, "name": "Transition Images In Batch"}, - #batch cropping - "BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"}, - "BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"}, - "FilterZeroMasksAndCorrespondingImages": {"class": FilterZeroMasksAndCorrespondingImages}, - "InsertImageBatchByIndexes": {"class": InsertImageBatchByIndexes, "name": "Insert Image Batch By Indexes"}, - "BatchUncrop": {"class": BatchUncrop, "name": "Batch Uncrop"}, - "BatchUncropAdvanced": {"class": BatchUncropAdvanced, "name": "Batch Uncrop Advanced"}, - "SplitBboxes": {"class": SplitBboxes, "name": "Split Bboxes"}, - "BboxToInt": {"class": BboxToInt, "name": "Bbox To Int"}, - "BboxVisualize": {"class": BboxVisualize, "name": "Bbox Visualize"}, - #noise - "GenerateNoise": {"class": GenerateNoise, "name": "Generate Noise"}, - "FlipSigmasAdjusted": {"class": FlipSigmasAdjusted, "name": "Flip Sigmas Adjusted"}, - "InjectNoiseToLatent": {"class": InjectNoiseToLatent, "name": "Inject Noise To Latent"}, - "CustomSigmas": {"class": CustomSigmas, "name": "Custom Sigmas"}, - #utility - "StringToFloatList": {"class": StringToFloatList, "name": "String to Float List"}, - "WidgetToString": {"class": WidgetToString, "name": "Widget To String"}, - "SaveStringKJ": {"class": SaveStringKJ, "name": "Save String KJ"}, - "DummyOut": {"class": DummyOut, "name": "Dummy Out"}, - "GetLatentsFromBatchIndexed": {"class": GetLatentsFromBatchIndexed, "name": "Get Latents From Batch Indexed"}, - "ScaleBatchPromptSchedule": {"class": ScaleBatchPromptSchedule, "name": "Scale Batch Prompt Schedule"}, - "CameraPoseVisualizer": {"class": CameraPoseVisualizer, "name": "Camera Pose Visualizer"}, - "AppendStringsToList": {"class": AppendStringsToList, "name": "Append Strings To List"}, - "JoinStrings": {"class": JoinStrings, "name": "Join Strings"}, - "JoinStringMulti": {"class": JoinStringMulti, "name": "Join String Multi"}, - "SomethingToString": {"class": SomethingToString, "name": "Something To String"}, - "Sleep": {"class": Sleep, "name": "Sleep"}, - "VRAM_Debug": {"class": VRAM_Debug, "name": "VRAM Debug"}, - "SomethingToString": {"class": SomethingToString, "name": "Something To String"}, - "EmptyLatentImagePresets": {"class": EmptyLatentImagePresets, "name": "Empty Latent Image Presets"}, - "EmptyLatentImageCustomPresets": {"class": EmptyLatentImageCustomPresets, "name": "Empty Latent Image Custom Presets"}, - "ModelPassThrough": {"class": ModelPassThrough, "name": "ModelPass"}, - "ModelSaveKJ": {"class": ModelSaveKJ, "name": "Model Save KJ"}, - "SetShakkerLabsUnionControlNetType": {"class": SetShakkerLabsUnionControlNetType, "name": "Set Shakker Labs Union ControlNet Type"}, - "StyleModelApplyAdvanced": {"class": StyleModelApplyAdvanced, "name": "Style Model Apply Advanced"}, - "DiffusionModelSelector": {"class": DiffusionModelSelector, "name": "Diffusion Model Selector"}, - "LazySwitchKJ": {"class": LazySwitchKJ, "name": "Lazy Switch KJ"}, - #audioscheduler stuff - "NormalizedAmplitudeToMask": {"class": NormalizedAmplitudeToMask}, - "NormalizedAmplitudeToFloatList": {"class": NormalizedAmplitudeToFloatList}, - "OffsetMaskByNormalizedAmplitude": {"class": OffsetMaskByNormalizedAmplitude}, - "ImageTransformByNormalizedAmplitude": {"class": ImageTransformByNormalizedAmplitude}, - "AudioConcatenate": {"class": AudioConcatenate}, - #curve nodes - "SplineEditor": {"class": SplineEditor, "name": "Spline Editor"}, - "CreateShapeImageOnPath": {"class": CreateShapeImageOnPath, "name": "Create Shape Image On Path"}, - "CreateShapeMaskOnPath": {"class": CreateShapeMaskOnPath, "name": "Create Shape Mask On Path"}, - "CreateTextOnPath": {"class": CreateTextOnPath, "name": "Create Text On Path"}, - "CreateGradientFromCoords": {"class": CreateGradientFromCoords, "name": "Create Gradient From Coords"}, - "CutAndDragOnPath": {"class": CutAndDragOnPath, "name": "Cut And Drag On Path"}, - "GradientToFloat": {"class": GradientToFloat, "name": "Gradient To Float"}, - "WeightScheduleExtend": {"class": WeightScheduleExtend, "name": "Weight Schedule Extend"}, - "MaskOrImageToWeight": {"class": MaskOrImageToWeight, "name": "Mask Or Image To Weight"}, - "WeightScheduleConvert": {"class": WeightScheduleConvert, "name": "Weight Schedule Convert"}, - "FloatToMask": {"class": FloatToMask, "name": "Float To Mask"}, - "FloatToSigmas": {"class": FloatToSigmas, "name": "Float To Sigmas"}, - "SigmasToFloat": {"class": SigmasToFloat, "name": "Sigmas To Float"}, - "PlotCoordinates": {"class": PlotCoordinates, "name": "Plot Coordinates"}, - "InterpolateCoords": {"class": InterpolateCoords, "name": "Interpolate Coords"}, - "PointsEditor": {"class": PointsEditor, "name": "Points Editor"}, - #experimental - "SoundReactive": {"class": SoundReactive, "name": "Sound Reactive"}, - "StableZero123_BatchSchedule": {"class": StableZero123_BatchSchedule, "name": "Stable Zero123 Batch Schedule"}, - "SV3D_BatchSchedule": {"class": SV3D_BatchSchedule, "name": "SV3D Batch Schedule"}, - "LoadResAdapterNormalization": {"class": LoadResAdapterNormalization}, - "Superprompt": {"class": Superprompt, "name": "Superprompt"}, - "GLIGENTextBoxApplyBatchCoords": {"class": GLIGENTextBoxApplyBatchCoords}, - "Intrinsic_lora_sampling": {"class": Intrinsic_lora_sampling, "name": "Intrinsic Lora Sampling"}, - "CheckpointPerturbWeights": {"class": CheckpointPerturbWeights, "name": "CheckpointPerturbWeights"}, - "Screencap_mss": {"class": Screencap_mss, "name": "Screencap mss"}, - "WebcamCaptureCV2": {"class": WebcamCaptureCV2, "name": "Webcam Capture CV2"}, - "DifferentialDiffusionAdvanced": {"class": DifferentialDiffusionAdvanced, "name": "Differential Diffusion Advanced"}, - "DiTBlockLoraLoader": {"class": DiTBlockLoraLoader, "name": "DiT Block Lora Loader"}, - "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"}, - "HunyuanVideoBlockLoraSelect": {"class": HunyuanVideoBlockLoraSelect, "name": "Hunyuan Video Block Lora Select"}, - "Wan21BlockLoraSelect": {"class": Wan21BlockLoraSelect, "name": "Wan21 Block Lora Select"}, - "CustomControlNetWeightsFluxFromList": {"class": CustomControlNetWeightsFluxFromList, "name": "Custom ControlNet Weights Flux From List"}, - "CheckpointLoaderKJ": {"class": CheckpointLoaderKJ, "name": "CheckpointLoaderKJ"}, - "DiffusionModelLoaderKJ": {"class": DiffusionModelLoaderKJ, "name": "Diffusion Model Loader KJ"}, - "TorchCompileModelFluxAdvanced": {"class": TorchCompileModelFluxAdvanced, "name": "TorchCompileModelFluxAdvanced"}, - "TorchCompileModelFluxAdvancedV2": {"class": TorchCompileModelFluxAdvancedV2, "name": "TorchCompileModelFluxAdvancedV2"}, - "TorchCompileModelHyVideo": {"class": TorchCompileModelHyVideo, "name": "TorchCompileModelHyVideo"}, - "TorchCompileVAE": {"class": TorchCompileVAE, "name": "TorchCompileVAE"}, - "TorchCompileControlNet": {"class": TorchCompileControlNet, "name": "TorchCompileControlNet"}, - "PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"}, - "TorchCompileLTXModel": {"class": TorchCompileLTXModel, "name": "TorchCompileLTXModel"}, - "TorchCompileCosmosModel": {"class": TorchCompileCosmosModel, "name": "TorchCompileCosmosModel"}, - "TorchCompileModelQwenImage": {"class": TorchCompileModelQwenImage, "name": "TorchCompileModelQwenImage"}, - "TorchCompileModelWanVideo": {"class": TorchCompileModelWanVideo, "name": "TorchCompileModelWanVideo"}, - "TorchCompileModelWanVideoV2": {"class": TorchCompileModelWanVideoV2, "name": "TorchCompileModelWanVideoV2"}, - "PathchSageAttentionKJ": {"class": PathchSageAttentionKJ, "name": "Patch Sage Attention KJ"}, - "LeapfusionHunyuanI2VPatcher": {"class": LeapfusionHunyuanI2V, "name": "Leapfusion Hunyuan I2V Patcher"}, - "VAELoaderKJ": {"class": VAELoaderKJ, "name": "VAELoader KJ"}, - "ScheduledCFGGuidance": {"class": ScheduledCFGGuidance, "name": "Scheduled CFG Guidance"}, - "ApplyRifleXRoPE_HunuyanVideo": {"class": ApplyRifleXRoPE_HunuyanVideo, "name": "Apply RifleXRoPE HunuyanVideo"}, - "ApplyRifleXRoPE_WanVideo": {"class": ApplyRifleXRoPE_WanVideo, "name": "Apply RifleXRoPE WanVideo"}, - "WanVideoTeaCacheKJ": {"class": WanVideoTeaCacheKJ, "name": "WanVideo Tea Cache (native)"}, - "WanVideoEnhanceAVideoKJ": {"class": WanVideoEnhanceAVideoKJ, "name": "WanVideo Enhance A Video (native)"}, - "SkipLayerGuidanceWanVideo": {"class": SkipLayerGuidanceWanVideo, "name": "Skip Layer Guidance WanVideo"}, - "TimerNodeKJ": {"class": TimerNodeKJ, "name": "Timer Node KJ"}, - "HunyuanVideoEncodeKeyframesToCond": {"class": HunyuanVideoEncodeKeyframesToCond, "name": "HunyuanVideo Encode Keyframes To Cond"}, - "CFGZeroStarAndInit": {"class": CFGZeroStarAndInit, "name": "CFG Zero Star/Init"}, - "ModelPatchTorchSettings": {"class": ModelPatchTorchSettings, "name": "Model Patch Torch Settings"}, - "WanVideoNAG": {"class": WanVideoNAG, "name": "WanVideoNAG"}, - - #instance diffusion - "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking}, - "AppendInstanceDiffusionTracking": {"class": AppendInstanceDiffusionTracking}, - "DrawInstanceDiffusionTracking": {"class": DrawInstanceDiffusionTracking}, - - #lora - "LoraExtractKJ": {"class": LoraExtractKJ, "name": "LoraExtractKJ"}, -} - -def generate_node_mappings(node_config): - node_class_mappings = {} - node_display_name_mappings = {} - - for node_name, node_info in node_config.items(): - node_class_mappings[node_name] = node_info["class"] - node_display_name_mappings[node_name] = node_info.get("name", node_info["class"].__name__) - - return node_class_mappings, node_display_name_mappings - -NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS = generate_node_mappings(NODE_CONFIG) - -__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] - -WEB_DIRECTORY = "./web" - -from aiohttp import web -from server import PromptServer -from pathlib import Path - -if hasattr(PromptServer, "instance"): - try: - # NOTE: we add an extra static path to avoid comfy mechanism - # that loads every script in web. - PromptServer.instance.app.add_routes( - [web.static("/kjweb_async", (Path(__file__).parent.absolute() / "kjweb_async").as_posix())] - ) - except: - pass \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/custom_dimensions_example.json b/custom_nodes/comfyui-kjnodes/custom_dimensions_example.json deleted file mode 100644 index 5c4814d377d44916a14b4d4a83b7cba72ae2958b..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/custom_dimensions_example.json +++ /dev/null @@ -1,22 +0,0 @@ -[ - { - "label": "SD", - "value": "512x512" - }, - { - "label": "HD", - "value": "768x768" - }, - { - "label": "Full HD", - "value": "1024x1024" - }, - { - "label": "4k", - "value": "2048x2048" - }, - { - "label": "SVD", - "value": "1024x576" - } -] diff --git a/custom_nodes/comfyui-kjnodes/docs/images/2024-04-03_20_49_29-ComfyUI.png b/custom_nodes/comfyui-kjnodes/docs/images/2024-04-03_20_49_29-ComfyUI.png deleted file mode 100644 index b42cfe56374c6db142326761a2a3d96211519664..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/docs/images/2024-04-03_20_49_29-ComfyUI.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:85805d3c7ca8f5d281886ea0ad61f9a78edad755ef8014b3870f91b871807ac9 -size 176158 diff --git a/custom_nodes/comfyui-kjnodes/docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png b/custom_nodes/comfyui-kjnodes/docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png deleted file mode 100644 index e749239c1c4ffd5ab29b51695dd8d8b51ed3597f..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png and /dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png b/custom_nodes/comfyui-kjnodes/docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png deleted file mode 100644 index b53ad666ff060d87971f3962e74101f0cb2a5c3f..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png and /dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json b/custom_nodes/comfyui-kjnodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json deleted file mode 100644 index 134a83788815d04b5574a807db67eb6e45bf9263..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json +++ /dev/null @@ -1,1188 +0,0 @@ -{ - "last_node_id": 86, - "last_link_id": 144, - "nodes": [ - { - "id": 62, - "type": "FluxGuidance", - "pos": [ - -630, - -170 - ], - "size": [ - 317.4000244140625, - 58 - ], - "flags": {}, - "order": 13, - "mode": 0, - "inputs": [ - { - "name": "conditioning", - "type": "CONDITIONING", - "link": 82 - } - ], - "outputs": [ - { - "name": "CONDITIONING", - "type": "CONDITIONING", - "links": [ - 83 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "FluxGuidance" - }, - "widgets_values": [ - 6 - ] - }, - { - "id": 51, - "type": "KSamplerSelect", - "pos": [ - -610, - -480 - ], - "size": [ - 315, - 58 - ], - "flags": {}, - "order": 0, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "SAMPLER", - "type": "SAMPLER", - "links": [ - 61 - ] - } - ], - "properties": { - "Node name for S&R": "KSamplerSelect" - }, - "widgets_values": [ - "euler" - ] - }, - { - "id": 57, - "type": "VAEDecodeTiled", - "pos": [ - -200, - 90 - ], - "size": [ - 315, - 150 - ], - "flags": {}, - "order": 20, - "mode": 0, - "inputs": [ - { - "name": "samples", - "type": "LATENT", - "link": 142 - }, - { - "name": "vae", - "type": "VAE", - "link": 74 - } - ], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "links": [ - 105 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "VAEDecodeTiled" - }, - "widgets_values": [ - 128, - 64, - 64, - 8 - ] - }, - { - "id": 65, - "type": "LoadImage", - "pos": [ - -2212.498779296875, - -632.4085083007812 - ], - "size": [ - 315, - 314 - ], - "flags": {}, - "order": 1, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "links": [ - 86 - ], - "slot_index": 0 - }, - { - "name": "MASK", - "type": "MASK", - "links": null - } - ], - "properties": { - "Node name for S&R": "LoadImage" - }, - "widgets_values": [ - "Mona-Lisa-oil-wood-panel-Leonardo-da.webp", - "image" - ] - }, - { - "id": 64, - "type": "VAEEncode", - "pos": [ - -1336.7884521484375, - -492.5806884765625 - ], - "size": [ - 210, - 46 - ], - "flags": {}, - "order": 14, - "mode": 0, - "inputs": [ - { - "name": "pixels", - "type": "IMAGE", - "link": 144 - }, - { - "name": "vae", - "type": "VAE", - "link": 88 - } - ], - "outputs": [ - { - "name": "LATENT", - "type": "LATENT", - "links": [ - 137 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "VAEEncode" - }, - "widgets_values": [] - }, - { - "id": 44, - "type": "UNETLoader", - "pos": [ - -2373.55029296875, - -193.91510009765625 - ], - "size": [ - 459.56060791015625, - 82 - ], - "flags": {}, - "order": 2, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "MODEL", - "type": "MODEL", - "links": [ - 135 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "UNETLoader" - }, - "widgets_values": [ - "hyvideo\\hunyuan_video_720_fp8_e4m3fn.safetensors", - "fp8_e4m3fn_fast" - ] - }, - { - "id": 49, - "type": "VAELoader", - "pos": [ - -1876.39306640625, - -35.19633865356445 - ], - "size": [ - 433.7603454589844, - 58.71116256713867 - ], - "flags": {}, - "order": 3, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "VAE", - "type": "VAE", - "links": [ - 74, - 88 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "VAELoader" - }, - "widgets_values": [ - "hyvid\\hunyuan_video_vae_bf16.safetensors" - ] - }, - { - "id": 47, - "type": "DualCLIPLoader", - "pos": [ - -2284.893798828125, - 150.4042205810547 - ], - "size": [ - 343.3958435058594, - 106.86042785644531 - ], - "flags": {}, - "order": 4, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "CLIP", - "type": "CLIP", - "links": [ - 56 - ], - "slot_index": 0 - } - ], - "properties": { - "Node name for S&R": "DualCLIPLoader" - }, - "widgets_values": [ - "clip_l.safetensors", - "llava_llama3_fp16.safetensors", - "hunyuan_video", - "default" - ] - }, - { - "id": 45, - "type": "CLIPTextEncode", - "pos": [ - -1839.1649169921875, - 143.5203094482422 - ], - "size": [ - 400, - 200 - ], - "flags": {}, - "order": 8, - "mode": 0, - "inputs": [ - 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this.scene[0]};a.last=function(){return this.scene[this.scene.length-1]};a.sibling=function(){return this.index==0?null:this.scene[this.index-1]}; -a.cousin=function(){var b=this.parent;return(b=b&&b.sibling())&&b.children?b.children[this.childIndex][this.index]:null}; -a.render=function(){function b(i,j,k){i.scale=k;if(j=0;l--){var q=k[l];if(!(q.name in c)){c[q.name]=q;switch(q.name){case "data":f=q;break;case "visible":g=q;break;default:d[q.type].push(q);break}}}while(j=j.proto)}var c={},d=[[],[],[],[]],f,g;b(this);b(this.defaults);d[1].reverse();d[3].reverse();var h=this;do for(var i in h.properties)i in c||d[2].push(c[i]={name:i,type:2,value:null});while(h=h.proto);h=d[0].concat(d[1]);for(i=0;ih.id)d[g.name]={id:0,value:g.type&1?g.value.apply(this,c):g.value}}}d=this.binds.data;d=d.type& -1?d.value.apply(this,c):d.value;c.unshift(null);b.length=d.length;for(f=0;f0;l--){p=m[l];p.scale=q;q*=p.scene[p.index].transform.k}if(n.children){l=0;for(m=n.children.length;l=3*Math.PI/2};pv.Wedge.prototype.buildImplied=function(b){if(b.angle==null)b.angle=b.endAngle-b.startAngle;else if(b.endAngle==null)b.endAngle=b.startAngle+b.angle;pv.Mark.prototype.buildImplied.call(this,b)};pv.simulation=function(b){return new pv.Simulation(b)};pv.Simulation=function(b){for(var c=0;c=s,u=q.y>=t;l.leaf=false;switch((u<<1)+x){case 0:l=l.c1||(l.c1=new pv.Quadtree.Node);break;case 1:l=l.c2||(l.c2=new pv.Quadtree.Node);break;case 2:l=l.c3||(l.c3=new pv.Quadtree.Node);break;case 3:l=l.c4||(l.c4=new pv.Quadtree.Node); -break}if(x)n=s;else m=s;if(u)p=t;else r=t;c(l,q,n,p,m,r)}var f,g=Number.POSITIVE_INFINITY,h=g,i=Number.NEGATIVE_INFINITY,j=i;for(f=b;f;f=f.next){if(f.xi)i=f.x;if(f.y>j)j=f.y}f=i-g;var k=j-h;if(f>k)j=h+f;else i=g+k;this.xMin=g;this.yMin=h;this.xMax=i;this.yMax=j;this.root=new pv.Quadtree.Node;for(f=b;f;f=f.next)c(this.root,f,g,h,i,j)};pv.Quadtree.Node=function(){this.leaf=true;this.p=this.c4=this.c3=this.c2=this.c1=null};pv.Force={}; -pv.Force.charge=function(b){function c(l){function q(m){c(m);l.cn+=m.cn;n+=m.cn*m.cx;p+=m.cn*m.cy}var n=0,p=0;l.cn=0;if(!l.leaf){l.c1&&q(l.c1);l.c2&&q(l.c2);l.c3&&q(l.c3);l.c4&&q(l.c4)}if(l.p){l.cn+=b;n+=b*l.p.x;p+=b*l.p.y}l.cx=n/l.cn;l.cy=p/l.cn}function d(l,q,n,p,m,r){var s=l.cx-q.x,t=l.cy-q.y,x=1/Math.sqrt(s*s+t*t);if(l.leaf&&l.p!=q||(m-n)*xg)x=g;l=l.cn*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}else if(!l.leaf){var u=(n+m)*0.5,o=(p+r)*0.5;l.c1&&d(l.c1,q,n,p,u,o);l.c2&&d(l.c2,q,u,p, -m,o);l.c3&&d(l.c3,q,n,o,u,r);l.c4&&d(l.c4,q,u,o,m,r);if(!(xg)x=g;if(l.p&&l.p!=q){l=b*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}}}var f=2,g=1/f,h=500,i=1/h,j=0.9,k={};arguments.length||(b=-40);k.constant=function(l){if(arguments.length){b=Number(l);return k}return b};k.domain=function(l,q){if(arguments.length){f=Number(l);g=1/f;h=Number(q);i=1/h;return k}return[f,h]};k.theta=function(l){if(arguments.length){j=Number(l);return k}return j};k.apply=function(l,q){c(q.root);for(l=l;l;l=l.next)d(q.root, -l,q.xMin,q.yMin,q.xMax,q.yMax)};return k};pv.Force.drag=function(b){var c={};arguments.length||(b=0.1);c.constant=function(d){if(arguments.length){b=d;return c}return b};c.apply=function(d){if(b)for(d=d;d;d=d.next){d.fx-=b*d.vx;d.fy-=b*d.vy}};return c}; -pv.Force.spring=function(b){var c=0.1,d=20,f,g,h={};arguments.length||(b=0.1);h.links=function(i){if(arguments.length){f=i;g=i.map(function(j){return 1/Math.sqrt(Math.max(j.sourceNode.linkDegree,j.targetNode.linkDegree))});return h}return f};h.constant=function(i){if(arguments.length){b=Number(i);return h}return b};h.damping=function(i){if(arguments.length){c=Number(i);return h}return c};h.length=function(i){if(arguments.length){d=Number(i);return h}return d};h.apply=function(){for(var i=0;ig,o=sh){l.c1&&u&&c(l.c1,q,n,p,s,t);l.c2&&o&&c(l.c2,q,s,p,m,t)}if(x){l.c3&&u&&c(l.c3,q,n,t,s,r);l.c4&&o&&c(l.c4,q,s,t,m,r)}}if(l.p&&l.p!=q){n=q.x-l.p.x;p=q.y-l.p.y;m=Math.sqrt(n*n+p*p);r=f+b(l.p);if(mm)m=p}for(var r=0;rc.max?c.max:g.x;if(d)for(g=f;g;g=g.next)g.y=g.yd.max?d.max:g.y};return b};pv.Layout=function(){pv.Panel.call(this)};pv.Layout.prototype=pv.extend(pv.Panel); -pv.Layout.prototype.property=function(b,c){if(!this.hasOwnProperty("properties"))this.properties=pv.extend(this.properties);this.properties[b]=true;this.propertyMethod(b,false,pv.Mark.cast[b]=c);return this}; -pv.Layout.Network=function(){pv.Layout.call(this);var b=this;this.$id=pv.id();(this.node=(new pv.Mark).data(function(){return b.nodes()}).strokeStyle("#1f77b4").fillStyle("#fff").left(function(c){return c.x}).top(function(c){return c.y})).parent=this;this.link=(new pv.Mark).extend(this.node).data(function(c){return[c.sourceNode,c.targetNode]}).fillStyle(null).lineWidth(function(c,d){return d.linkValue*1.5}).strokeStyle("rgba(0,0,0,.2)");this.link.add=function(c){return b.add(pv.Panel).data(function(){return b.links()}).add(c).extend(this)}; -(this.label=(new pv.Mark).extend(this.node).textMargin(7).textBaseline("middle").text(function(c){return c.nodeName||c.nodeValue}).textAngle(function(c){c=c.midAngle;return pv.Wedge.upright(c)?c:c+Math.PI}).textAlign(function(c){return pv.Wedge.upright(c.midAngle)?"left":"right"})).parent=this}; -pv.Layout.Network.prototype=pv.extend(pv.Layout).property("nodes",function(b){return b.map(function(c,d){if(typeof c!="object")c={nodeValue:c};c.index=d;return c})}).property("links",function(b){return b.map(function(c){if(isNaN(c.linkValue))c.linkValue=isNaN(c.value)?1:c.value;return c})});pv.Layout.Network.prototype.reset=function(){this.$id=pv.id();return this};pv.Layout.Network.prototype.buildProperties=function(b,c){if((b.$id||0)=this.$id)return true;b.$id=this.$id;b.nodes.forEach(function(c){c.linkDegree=0});b.links.forEach(function(c){var d=c.linkValue;(c.sourceNode||(c.sourceNode=b.nodes[c.source])).linkDegree+=d;(c.targetNode||(c.targetNode=b.nodes[c.target])).linkDegree+=d})};pv.Layout.Hierarchy=function(){pv.Layout.Network.call(this);this.link.strokeStyle("#ccc")};pv.Layout.Hierarchy.prototype=pv.extend(pv.Layout.Network); -pv.Layout.Hierarchy.prototype.buildImplied=function(b){if(!b.links)b.links=pv.Layout.Hierarchy.links.call(this);pv.Layout.Network.prototype.buildImplied.call(this,b)};pv.Layout.Hierarchy.links=function(){return this.nodes().filter(function(b){return b.parentNode}).map(function(b){return{sourceNode:b,targetNode:b.parentNode,linkValue:1}})}; -pv.Layout.Hierarchy.NodeLink={buildImplied:function(b){function c(m){return m.parentNode?m.depth*(n-q)+q:0}function d(m){return m.parentNode?(m.breadth-0.25)*2*Math.PI:0}function f(m){switch(i){case "left":return m.depth*k;case "right":return k-m.depth*k;case "top":return m.breadth*k;case "bottom":return k-m.breadth*k;case "radial":return k/2+c(m)*Math.cos(m.midAngle)}}function g(m){switch(i){case "left":return m.breadth*l;case "right":return l-m.breadth*l;case "top":return m.depth*l;case "bottom":return l- -m.depth*l;case "radial":return l/2+c(m)*Math.sin(m.midAngle)}}var h=b.nodes,i=b.orient,j=/^(top|bottom)$/.test(i),k=b.width,l=b.height;if(i=="radial"){var q=b.innerRadius,n=b.outerRadius;if(q==null)q=0;if(n==null)n=Math.min(k,l)/2}for(b=0;bb.dy?0:-Math.PI/2});(this.leaf=(new pv.Mark).extend(this.node).fillStyle(null).strokeStyle(null).visible(function(b){return!b.firstChild})).parent= -this;delete this.link};pv.Layout.Treemap.prototype=pv.extend(pv.Layout.Hierarchy).property("round",Boolean).property("paddingLeft",Number).property("paddingRight",Number).property("paddingTop",Number).property("paddingBottom",Number).property("mode",String).property("order",String);a=pv.Layout.Treemap.prototype;a.defaults=(new pv.Layout.Treemap).extend(pv.Layout.Hierarchy.prototype.defaults).mode("squarify").order("ascending");a.padding=function(b){return this.paddingLeft(b).paddingRight(b).paddingTop(b).paddingBottom(b)}; -a.$size=function(b){return Number(b.nodeValue)};a.size=function(b){this.$size=pv.functor(b);return this}; -a.buildImplied=function(b){function c(r,s,t,x,u,o,v){for(var w=0,y=0;wt)t=v;u+=v}u*=u;s*=s;return Math.max(s*t/u,u/(s*x))}function f(r,s){function t(A){var D=o==y,G=pv.sum(A,n),E=y?p(G/y):0;c(A,G,D,x,u,D?o:E,D?E:v);if(D){u+=E;v-=E}else{x+= -E;o-=E}y=Math.min(o,v);return D}var x=r.x+j,u=r.y+l,o=r.dx-j-k,v=r.dy-l-q;if(m!="squarify")c(r.childNodes,r.size,m=="slice"?true:m=="dice"?false:s&1,x,u,o,v);else{var w=[];s=Infinity;var y=Math.min(o,v),z=o*v/r.size;if(!(r.size<=0)){r.visitBefore(function(A){A.size*=z});for(r=r.childNodes.slice();r.length;){var C=r[r.length-1];if(C.size){w.push(C);z=d(w,y);if(z<=s){r.pop();s=z}else{w.pop();t(w);w.length=0;s=Infinity}}else r.pop()}if(t(w))for(s=0;s0){i(k(C,o,v),o,B);A+=B;D+=B}G+=C.mod;A+=y.mod;E+=w.mod;D+=z.mod;C=h(C);y=g(y)}if(C&&!h(z)){z.thread=C;z.mod+=G-D}if(y&&!g(w)){w.thread=y;w.mod+=A-E;v=o}}return v}function g(o){return o.firstChild||o.thread}function h(o){return o.lastChild||o.thread}function i(o,v,w){var y=v.number-o.number;v.change-=w/y;v.shift+=w;o.change+= -w/y;v.prelim+=w;v.mod+=w}function j(o){var v=0,w=0;for(o=o.lastChild;o;o=o.previousSibling){o.prelim+=v;o.mod+=v;w+=o.change;v+=o.shift+w}}function k(o,v,w){return o.ancestor.parentNode==v.parentNode?o.ancestor:w}function l(o,v){return(v?1:t+1)/(m=="radial"?o:1)}function q(o){return m=="radial"?o.breadth/r:0}function n(o){switch(m){case "left":return o.depth;case "right":return x-o.depth;case "top":case "bottom":return o.breadth+x/2;case "radial":return x/2+o.depth*Math.cos(q(o))}}function p(o){switch(m){case "left":case "right":return o.breadth+ -u/2;case "top":return o.depth;case "bottom":return u-o.depth;case "radial":return u/2+o.depth*Math.sin(q(o))}}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var m=b.orient,r=b.depth,s=b.breadth,t=b.group,x=b.width,u=b.height;b=b.nodes[0];b.visitAfter(function(o,v){o.ancestor=o;o.prelim=0;o.mod=0;o.change=0;o.shift=0;o.number=o.previousSibling?o.previousSibling.number+1:0;o.depth=v});c(b);d(b,-b.prelim,0);b.visitAfter(function(o){o.breadth*=s;o.depth*=r;o.midAngle=q(o);o.x=n(o);o.y=p(o); -if(o.firstChild)o.midAngle+=Math.PI;delete o.breadth;delete o.depth;delete o.ancestor;delete o.prelim;delete o.mod;delete o.change;delete o.shift;delete o.number;delete o.thread})}};pv.Layout.Indent=function(){pv.Layout.Hierarchy.call(this);this.link.interpolate("step-after")};pv.Layout.Indent.prototype=pv.extend(pv.Layout.Hierarchy).property("depth",Number).property("breadth",Number);pv.Layout.Indent.prototype.defaults=(new pv.Layout.Indent).extend(pv.Layout.Hierarchy.prototype.defaults).depth(15).breadth(15); -pv.Layout.Indent.prototype.buildImplied=function(b){function c(i,j,k){i.x=g+k++*f;i.y=h+j++*d;i.midAngle=0;for(i=i.firstChild;i;i=i.nextSibling)j=c(i,j,k);return j}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var d=b.breadth,f=b.depth,g=0,h=0;c(b.nodes[0],1,1)}};pv.Layout.Pack=function(){pv.Layout.Hierarchy.call(this);this.node.radius(function(b){return b.radius}).strokeStyle("rgb(31, 119, 180)").fillStyle("rgba(31, 119, 180, .25)");this.label.textAlign("center");delete this.link}; -pv.Layout.Pack.prototype=pv.extend(pv.Layout.Hierarchy).property("spacing",Number).property("order",String);pv.Layout.Pack.prototype.defaults=(new pv.Layout.Pack).extend(pv.Layout.Hierarchy.prototype.defaults).spacing(1).order("ascending");pv.Layout.Pack.prototype.$radius=function(){return 1};pv.Layout.Pack.prototype.size=function(b){this.$radius=typeof b=="function"?function(){return Math.sqrt(b.apply(this,arguments))}:(b=Math.sqrt(b),function(){return b});return this}; -pv.Layout.Pack.prototype.buildImplied=function(b){function c(n){var p=pv.Mark.stack;p.unshift(null);for(var m=0,r=n.length;m0.0010}var t=Infinity,x=-Infinity,u=Infinity,o=-Infinity,v,w,y,z,C;v=n[0];v.x=-v.radius;v.y=0;p(v);if(n.length>1){w=n[1];w.x=w.radius;w.y=0;p(w);if(n.length>2){y=n[2];g(v,w,y);p(y);m(v,y);v.p= -y;m(y,w);w=v.n;for(var A=3;A0){r(v,z);w=z;A--}else if(D<0){r(z,w);v=z;A--}}}}v=(t+x)/2;w=(u+o)/2;for(A=y=0;An.min){n.sim.step(); -q=true}q&&d.render()},42)}else for(k=0;kg)g=j;i.size=i.firstChild?pv.sum(i.childNodes,function(k){return k.size}):c.$size.apply(c,(f[0]=i,f))});f.shift();switch(b.order){case "ascending":d.sort(function(i,j){return i.size-j.size});break;case "descending":d.sort(function(i,j){return j.size-i.size});break}var h=1/g;d.minBreadth=0;d.breadth= -0.5;d.maxBreadth=1;d.visitBefore(function(i){for(var j=i.minBreadth,k=i.maxBreadth-j,l=i.firstChild;l;l=l.nextSibling){l.minBreadth=j;l.maxBreadth=j+=l.size/i.size*k;l.breadth=(j+l.minBreadth)/2}});d.visitAfter(function(i,j){i.minDepth=(j-1)*h;i.maxDepth=i.depth=j*h});pv.Layout.Hierarchy.NodeLink.buildImplied.call(this,b)}};pv.Layout.Partition.Fill=function(){pv.Layout.Partition.call(this);pv.Layout.Hierarchy.Fill.constructor.call(this)};pv.Layout.Partition.Fill.prototype=pv.extend(pv.Layout.Partition); -pv.Layout.Partition.Fill.prototype.buildImplied=function(b){pv.Layout.Partition.prototype.buildImplied.call(this,b)||pv.Layout.Hierarchy.Fill.buildImplied.call(this,b)};pv.Layout.Arc=function(){pv.Layout.Network.call(this);var b,c,d,f=this.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.directed;b=g.orient=="radial"?"linear":"polar";d=g.orient=="right"||g.orient=="top"};this.link.data(function(g){var h=g.sourceNode;g=g.targetNode;return d!=(c||h.breadth>1)*f:null}).bottom(function(k,l){return d=="mirror"?l&1?null:(l+1>>1)*-f:(l&1||-1)*(l+1>>1)*f}).fillStyle(function(k,l){return(l&1?h:i)((l>>1)+1)});this.band.add=function(k){return b.add(pv.Panel).extend(c).add(k).extend(this)}};pv.Layout.Horizon.prototype=pv.extend(pv.Layout).property("bands",Number).property("mode",String).property("backgroundStyle",pv.color).property("positiveStyle",pv.color).property("negativeStyle",pv.color); -pv.Layout.Horizon.prototype.defaults=(new pv.Layout.Horizon).extend(pv.Layout.prototype.defaults).bands(2).mode("offset").backgroundStyle("white").positiveStyle("#1f77b4").negativeStyle("#d62728"); -pv.Layout.Rollup=function(){pv.Layout.Network.call(this);var b=this,c,d,f=b.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.$rollup.nodes;d=g.$rollup.links};this.node.data(function(){return c}).size(function(g){return g.nodes.length*20});this.link.interpolate("polar").eccentricity(0.8);this.link.add=function(g){return b.add(pv.Panel).data(function(){return d}).add(g).extend(this)}};pv.Layout.Rollup.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean); -pv.Layout.Rollup.prototype.x=function(b){this.$x=pv.functor(b);return this};pv.Layout.Rollup.prototype.y=function(b){this.$y=pv.functor(b);return this}; -pv.Layout.Rollup.prototype.buildImplied=function(b){function c(r){return i[r]+","+j[r]}if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var d=b.nodes,f=b.links,g=b.directed,h=d.length,i=[],j=[],k=0,l={},q={},n=pv.Mark.stack,p={parent:this};n.unshift(null);for(var m=0;mk.index?k.index+","+d.index:d.index+","+k.index;(n=q[h])||(n=q[h]={sourceNode:d,targetNode:k,linkValue:0,links:[]});n.links.push(f[m]);n.linkValue+=f[m].linkValue}b.$rollup={nodes:pv.values(l),links:pv.values(q)}}}; -pv.Layout.Matrix=function(){pv.Layout.Network.call(this);var b,c,d,f,g,h=this.buildImplied;this.buildImplied=function(i){h.call(this,i);b=i.nodes.length;c=i.width/b;d=i.height/b;f=i.$matrix.labels;g=i.$matrix.pairs};this.link.data(function(){return g}).left(function(){return c*(this.index%b)}).top(function(){return d*Math.floor(this.index/b)}).width(function(){return c}).height(function(){return d}).lineWidth(1.5).strokeStyle("#fff").fillStyle(function(i){return i.linkValue?"#555":"#eee"}).parent= -this;delete this.link.add;this.label.data(function(){return f}).left(function(){return this.index&1?c*((this.index>>1)+0.5):0}).top(function(){return this.index&1?0:d*((this.index>>1)+0.5)}).textMargin(4).textAlign(function(){return this.index&1?"left":"right"}).textAngle(function(){return this.index&1?-Math.PI/2:0});delete this.node};pv.Layout.Matrix.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean);pv.Layout.Matrix.prototype.sort=function(b){this.$sort=b;return this}; -pv.Layout.Matrix.prototype.buildImplied=function(b){if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var c=b.nodes,d=b.links,f=this.$sort,g=c.length,h=pv.range(g),i=[],j=[],k={};b.$matrix={labels:i,pairs:j};f&&h.sort(function(m,r){return f(c[m],c[r])});for(var l=0;lk)l=null;if(g){if(l&&g.scene==l.scene&&g.index==l.index)return;pv.Mark.dispatch("unpoint",g.scene,g.index)}if(g=l){pv.Mark.dispatch("point",l.scene,l.index);pv.listen(this.root.canvas(),"mouseout",f)}}function f(l){if(g&&!pv.ancestor(this,l.relatedTarget)){pv.Mark.dispatch("unpoint",g.scene,g.index);g=null}}var g,h=null,i=1,j=1,k=arguments.length?b*b:900;d.collapse=function(l){if(arguments.length){h=String(l);switch(h){case "y":i= -1;j=0;break;case "x":i=0;j=1;break;default:j=i=1;break}return d}return h};return d}; -pv.Behavior.select=function(){function b(j){g=this.index;f=this.scene;i=this.mouse();h=j;h.x=i.x;h.y=i.y;h.dx=h.dy=0;pv.Mark.dispatch("selectstart",f,g)}function c(){if(f){f.mark.context(f,g,function(){var j=this.mouse();h.x=Math.max(0,Math.min(i.x,j.x));h.y=Math.max(0,Math.min(i.y,j.y));h.dx=Math.min(this.width(),Math.max(j.x,i.x))-h.x;h.dy=Math.min(this.height(),Math.max(j.y,i.y))-h.y;this.render()});pv.Mark.dispatch("select",f,g)}}function d(){if(f){pv.Mark.dispatch("selectend",f,g);f=null}}var f, -g,h,i;pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b}; -pv.Behavior.resize=function(b){function c(k){h=this.index;g=this.scene;j=this.mouse();i=k;switch(b){case "left":j.x=i.x+i.dx;break;case "right":j.x=i.x;break;case "top":j.y=i.y+i.dy;break;case "bottom":j.y=i.y;break}pv.Mark.dispatch("resizestart",g,h)}function d(){if(g){g.mark.context(g,h,function(){var k=this.mouse();i.x=Math.max(0,Math.min(j.x,k.x));i.y=Math.max(0,Math.min(j.y,k.y));i.dx=Math.min(this.parent.width(),Math.max(k.x,j.x))-i.x;i.dy=Math.min(this.parent.height(),Math.max(k.y,j.y))-i.y; -this.render()});pv.Mark.dispatch("resize",g,h)}}function 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/dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/nodes/__pycache__/nodes.cpython-310.pyc b/custom_nodes/comfyui-kjnodes/nodes/__pycache__/nodes.cpython-310.pyc deleted file mode 100644 index 527cbde686091ac4cf0a111e22366b25b71ceb37..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/nodes/__pycache__/nodes.cpython-310.pyc and /dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/nodes/audioscheduler_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/audioscheduler_nodes.py deleted file mode 100644 index 69d0422e7da875298f87fe60a7f6d1494530dca2..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/audioscheduler_nodes.py +++ /dev/null @@ -1,251 +0,0 @@ -# to be used with https://github.com/a1lazydog/ComfyUI-AudioScheduler -import torch -from torchvision.transforms import functional as TF -from PIL import Image, ImageDraw -import numpy as np -from ..utility.utility import pil2tensor -from nodes import MAX_RESOLUTION - -class NormalizedAmplitudeToMask: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "normalized_amp": ("NORMALIZED_AMPLITUDE",), - "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_offset": ("INT", {"default": 0,"min": -255, "max": 255, "step": 1}), - "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), - "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), - "size": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), - "shape": ( - [ - 'none', - 'circle', - 'square', - 'triangle', - ], - { - "default": 'none' - }), - "color": ( - [ - 'white', - 'amplitude', - ], - { - "default": 'amplitude' - }), - },} - - CATEGORY = "KJNodes/audio" - RETURN_TYPES = ("MASK",) - FUNCTION = "convert" - DESCRIPTION = """ -Works as a bridge to the AudioScheduler -nodes: -https://github.com/a1lazydog/ComfyUI-AudioScheduler -Creates masks based on the normalized amplitude. -""" - - def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color): - # Ensure normalized_amp is an array and within the range [0, 1] - normalized_amp = np.clip(normalized_amp, 0.0, 1.0) - - # Offset the amplitude values by rolling the array - normalized_amp = np.roll(normalized_amp, frame_offset) - - # Initialize an empty list to hold the image tensors - out = [] - # Iterate over each amplitude value to create an image - for amp in normalized_amp: - # Scale the amplitude value to cover the full range of grayscale values - if color == 'amplitude': - grayscale_value = int(amp * 255) - elif color == 'white': - grayscale_value = 255 - # Convert the grayscale value to an RGB format - gray_color = (grayscale_value, grayscale_value, grayscale_value) - finalsize = size * amp - - if shape == 'none': - shapeimage = Image.new("RGB", (width, height), gray_color) - else: - shapeimage = Image.new("RGB", (width, height), "black") - - draw = ImageDraw.Draw(shapeimage) - if shape == 'circle' or shape == 'square': - # Define the bounding box for the shape - left_up_point = (location_x - finalsize, location_y - finalsize) - right_down_point = (location_x + finalsize,location_y + finalsize) - two_points = [left_up_point, right_down_point] - - if shape == 'circle': - draw.ellipse(two_points, fill=gray_color) - elif shape == 'square': - draw.rectangle(two_points, fill=gray_color) - - elif shape == 'triangle': - # Define the points for the triangle - left_up_point = (location_x - finalsize, location_y + finalsize) # bottom left - right_down_point = (location_x + finalsize, location_y + finalsize) # bottom right - top_point = (location_x, location_y) # top point - draw.polygon([top_point, left_up_point, right_down_point], fill=gray_color) - - shapeimage = pil2tensor(shapeimage) - mask = shapeimage[:, :, :, 0] - out.append(mask) - - return (torch.cat(out, dim=0),) - -class NormalizedAmplitudeToFloatList: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "normalized_amp": ("NORMALIZED_AMPLITUDE",), - },} - - CATEGORY = "KJNodes/audio" - RETURN_TYPES = ("FLOAT",) - FUNCTION = "convert" - DESCRIPTION = """ -Works as a bridge to the AudioScheduler -nodes: -https://github.com/a1lazydog/ComfyUI-AudioScheduler -Creates a list of floats from the normalized amplitude. -""" - - def convert(self, normalized_amp): - # Ensure normalized_amp is an array and within the range [0, 1] - normalized_amp = np.clip(normalized_amp, 0.0, 1.0) - return (normalized_amp.tolist(),) - -class OffsetMaskByNormalizedAmplitude: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "normalized_amp": ("NORMALIZED_AMPLITUDE",), - "mask": ("MASK",), - "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "rotate": ("BOOLEAN", { "default": False }), - "angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }), - } - } - - RETURN_TYPES = ("MASK",) - RETURN_NAMES = ("mask",) - FUNCTION = "offset" - CATEGORY = "KJNodes/audio" - DESCRIPTION = """ -Works as a bridge to the AudioScheduler -nodes: -https://github.com/a1lazydog/ComfyUI-AudioScheduler -Offsets masks based on the normalized amplitude. -""" - - def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp): - - # Ensure normalized_amp is an array and within the range [0, 1] - offsetmask = mask.clone() - normalized_amp = np.clip(normalized_amp, 0.0, 1.0) - - batch_size, height, width = mask.shape - - if rotate: - for i in range(batch_size): - rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier)) - rotation_angle = rotation_amp - offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0) - if x != 0 or y != 0: - for i in range(batch_size): - offset_amp = normalized_amp[i] * 10 - shift_x = min(x*offset_amp, width-1) - shift_y = min(y*offset_amp, height-1) - if shift_x != 0: - offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1) - if shift_y != 0: - offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0) - - return offsetmask, - -class ImageTransformByNormalizedAmplitude: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "normalized_amp": ("NORMALIZED_AMPLITUDE",), - "zoom_scale": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }), - "x_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "y_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "cumulative": ("BOOLEAN", { "default": False }), - "image": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "amptransform" - CATEGORY = "KJNodes/audio" - DESCRIPTION = """ -Works as a bridge to the AudioScheduler -nodes: -https://github.com/a1lazydog/ComfyUI-AudioScheduler -Transforms image based on the normalized amplitude. -""" - - def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset): - # Ensure normalized_amp is an array and within the range [0, 1] - normalized_amp = np.clip(normalized_amp, 0.0, 1.0) - transformed_images = [] - - # Initialize the cumulative zoom factor - prev_amp = 0.0 - - for i in range(image.shape[0]): - img = image[i] # Get the i-th image in the batch - amp = normalized_amp[i] # Get the corresponding amplitude value - - # Incrementally increase the cumulative zoom factor - if cumulative: - prev_amp += amp - amp += prev_amp - - # Convert the image tensor from BxHxWxC to CxHxW format expected by torchvision - img = img.permute(2, 0, 1) - - # Convert PyTorch tensor to PIL Image for processing - pil_img = TF.to_pil_image(img) - - # Calculate the crop size based on the amplitude - width, height = pil_img.size - crop_size = int(min(width, height) * (1 - amp * zoom_scale)) - crop_size = max(crop_size, 1) - - # Calculate the crop box coordinates (centered crop) - left = (width - crop_size) // 2 - top = (height - crop_size) // 2 - right = (width + crop_size) // 2 - bottom = (height + crop_size) // 2 - - # Crop and resize back to original size - cropped_img = TF.crop(pil_img, top, left, crop_size, crop_size) - resized_img = TF.resize(cropped_img, (height, width)) - - # Convert back to tensor in CxHxW format - tensor_img = TF.to_tensor(resized_img) - - # Convert the tensor back to BxHxWxC format - tensor_img = tensor_img.permute(1, 2, 0) - - # Offset the image based on the amplitude - offset_amp = amp * 10 # Calculate the offset magnitude based on the amplitude - shift_x = min(x_offset * offset_amp, img.shape[1] - 1) # Calculate the shift in x direction - shift_y = min(y_offset * offset_amp, img.shape[0] - 1) # Calculate the shift in y direction - - # Apply the offset to the image tensor - if shift_x != 0: - tensor_img = torch.roll(tensor_img, shifts=int(shift_x), dims=1) - if shift_y != 0: - tensor_img = torch.roll(tensor_img, shifts=int(shift_y), dims=0) - - # Add to the list - transformed_images.append(tensor_img) - - # Stack all transformed images into a batch - transformed_batch = torch.stack(transformed_images) - - return (transformed_batch,) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/batchcrop_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/batchcrop_nodes.py deleted file mode 100644 index 3b8cd3aa39a2b14662aa323a86005a68579f4b04..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/batchcrop_nodes.py +++ /dev/null @@ -1,763 +0,0 @@ -from ..utility.utility import tensor2pil, pil2tensor -from PIL import Image, ImageDraw, ImageFilter -import numpy as np -import torch -from torchvision.transforms import Resize, CenterCrop, InterpolationMode -import math - -#based on nodes from mtb https://github.com/melMass/comfy_mtb - -def bbox_to_region(bbox, target_size=None): - bbox = bbox_check(bbox, target_size) - return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]) - -def bbox_check(bbox, target_size=None): - if not target_size: - return bbox - - new_bbox = ( - bbox[0], - bbox[1], - min(target_size[0] - bbox[0], bbox[2]), - min(target_size[1] - bbox[1], bbox[3]), - ) - return new_bbox - -class BatchCropFromMask: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "original_images": ("IMAGE",), - "masks": ("MASK",), - "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), - "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - }, - } - - RETURN_TYPES = ( - "IMAGE", - "IMAGE", - "BBOX", - "INT", - "INT", - ) - RETURN_NAMES = ( - "original_images", - "cropped_images", - "bboxes", - "width", - "height", - ) - FUNCTION = "crop" - CATEGORY = "KJNodes/masking" - - def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): - if alpha == 0: - return prev_bbox_size - return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) - - def smooth_center(self, prev_center, curr_center, alpha=0.5): - if alpha == 0: - return prev_center - return ( - round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), - round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]) - ) - - def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha): - - bounding_boxes = [] - cropped_images = [] - - self.max_bbox_width = 0 - self.max_bbox_height = 0 - - # First, calculate the maximum bounding box size across all masks - curr_max_bbox_width = 0 - curr_max_bbox_height = 0 - for mask in masks: - _mask = tensor2pil(mask)[0] - non_zero_indices = np.nonzero(np.array(_mask)) - min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) - min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) - width = max_x - min_x - height = max_y - min_y - curr_max_bbox_width = max(curr_max_bbox_width, width) - curr_max_bbox_height = max(curr_max_bbox_height, height) - - # Smooth the changes in the bounding box size - self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha) - self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha) - - # Apply the crop size multiplier - self.max_bbox_width = round(self.max_bbox_width * crop_size_mult) - self.max_bbox_height = round(self.max_bbox_height * crop_size_mult) - bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height - - # Then, for each mask and corresponding image... - for i, (mask, img) in enumerate(zip(masks, original_images)): - _mask = tensor2pil(mask)[0] - non_zero_indices = np.nonzero(np.array(_mask)) - min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) - min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) - - # Calculate center of bounding box - center_x = np.mean(non_zero_indices[1]) - center_y = np.mean(non_zero_indices[0]) - curr_center = (round(center_x), round(center_y)) - - # If this is the first frame, initialize prev_center with curr_center - if not hasattr(self, 'prev_center'): - self.prev_center = curr_center - - # Smooth the changes in the center coordinates from the second frame onwards - if i > 0: - center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha) - else: - center = curr_center - - # Update prev_center for the next frame - self.prev_center = center - - # Create bounding box using max_bbox_width and max_bbox_height - half_box_width = round(self.max_bbox_width / 2) - half_box_height = round(self.max_bbox_height / 2) - min_x = max(0, center[0] - half_box_width) - max_x = min(img.shape[1], center[0] + half_box_width) - min_y = max(0, center[1] - half_box_height) - max_y = min(img.shape[0], center[1] + half_box_height) - - # Append bounding box coordinates - bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y)) - - # Crop the image from the bounding box - cropped_img = img[min_y:max_y, min_x:max_x, :] - - # Calculate the new dimensions while maintaining the aspect ratio - new_height = min(cropped_img.shape[0], self.max_bbox_height) - new_width = round(new_height * bbox_aspect_ratio) - - # Resize the image - resize_transform = Resize((new_height, new_width)) - resized_img = resize_transform(cropped_img.permute(2, 0, 1)) - - # Perform the center crop to the desired size - crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary - cropped_resized_img = crop_transform(resized_img) - - cropped_images.append(cropped_resized_img.permute(1, 2, 0)) - - cropped_out = torch.stack(cropped_images, dim=0) - - return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, ) - -class BatchUncrop: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "original_images": ("IMAGE",), - "cropped_images": ("IMAGE",), - "bboxes": ("BBOX",), - "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ), - "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "border_top": ("BOOLEAN", {"default": True}), - "border_bottom": ("BOOLEAN", {"default": True}), - "border_left": ("BOOLEAN", {"default": True}), - "border_right": ("BOOLEAN", {"default": True}), - } - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "uncrop" - - CATEGORY = "KJNodes/masking" - - def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale, border_top, border_bottom, border_left, border_right): - def inset_border(image, border_width, border_color, border_top, border_bottom, border_left, border_right): - draw = ImageDraw.Draw(image) - width, height = image.size - if border_top: - draw.rectangle((0, 0, width, border_width), fill=border_color) - if border_bottom: - draw.rectangle((0, height - border_width, width, height), fill=border_color) - if border_left: - draw.rectangle((0, 0, border_width, height), fill=border_color) - if border_right: - draw.rectangle((width - border_width, 0, width, height), fill=border_color) - return image - - if len(original_images) != len(cropped_images): - raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same") - - # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images - if len(bboxes) > len(original_images): - print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}") - bboxes = bboxes[:len(original_images)] - elif len(bboxes) < len(original_images): - raise ValueError("There should be at least as many bboxes as there are original and cropped images") - - input_images = tensor2pil(original_images) - crop_imgs = tensor2pil(cropped_images) - - out_images = [] - for i in range(len(input_images)): - img = input_images[i] - crop = crop_imgs[i] - bbox = bboxes[i] - - # uncrop the image based on the bounding box - bb_x, bb_y, bb_width, bb_height = bbox - - paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) - - # scale factors - scale_x = crop_rescale - scale_y = crop_rescale - - # scaled paste_region - paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y)) - - # rescale the crop image to fit the paste_region - crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1]))) - crop_img = crop.convert("RGB") - - if border_blending > 1.0: - border_blending = 1.0 - elif border_blending < 0.0: - border_blending = 0.0 - - blend_ratio = (max(crop_img.size) / 2) * float(border_blending) - - blend = img.convert("RGBA") - mask = Image.new("L", img.size, 0) - - mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255) - mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right) - - mask.paste(mask_block, paste_region) - blend.paste(crop_img, paste_region) - - mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4)) - mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4)) - - blend.putalpha(mask) - img = Image.alpha_composite(img.convert("RGBA"), blend) - out_images.append(img.convert("RGB")) - - return (pil2tensor(out_images),) - -class BatchCropFromMaskAdvanced: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "original_images": ("IMAGE",), - "masks": ("MASK",), - "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - }, - } - - RETURN_TYPES = ( - "IMAGE", - "IMAGE", - "MASK", - "IMAGE", - "MASK", - "BBOX", - "BBOX", - "INT", - "INT", - ) - RETURN_NAMES = ( - "original_images", - "cropped_images", - "cropped_masks", - "combined_crop_image", - "combined_crop_masks", - "bboxes", - "combined_bounding_box", - "bbox_width", - "bbox_height", - ) - FUNCTION = "crop" - CATEGORY = "KJNodes/masking" - - def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): - return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) - - def smooth_center(self, prev_center, curr_center, alpha=0.5): - return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), - round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])) - - def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha): - bounding_boxes = [] - combined_bounding_box = [] - cropped_images = [] - cropped_masks = [] - cropped_masks_out = [] - combined_crop_out = [] - combined_cropped_images = [] - combined_cropped_masks = [] - - def calculate_bbox(mask): - non_zero_indices = np.nonzero(np.array(mask)) - - # handle empty masks - min_x, max_x, min_y, max_y = 0, 0, 0, 0 - if len(non_zero_indices[1]) > 0 and len(non_zero_indices[0]) > 0: - min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) - min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) - - width = max_x - min_x - height = max_y - min_y - bbox_size = max(width, height) - return min_x, max_x, min_y, max_y, bbox_size - - combined_mask = torch.max(masks, dim=0)[0] - _mask = tensor2pil(combined_mask)[0] - new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask) - center_x = (new_min_x + new_max_x) / 2 - center_y = (new_min_y + new_max_y) / 2 - half_box_size = round(combined_bbox_size // 2) - new_min_x = max(0, round(center_x - half_box_size)) - new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size)) - new_min_y = max(0, round(center_y - half_box_size)) - new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size)) - - combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y)) - - self.max_bbox_size = 0 - - # First, calculate the maximum bounding box size across all masks - curr_max_bbox_size = max(calculate_bbox(tensor2pil(mask)[0])[-1] for mask in masks) - # Smooth the changes in the bounding box size - self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha) - # Apply the crop size multiplier - self.max_bbox_size = round(self.max_bbox_size * crop_size_mult) - # Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is - self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16 - - if self.max_bbox_size > original_images[0].shape[0] or self.max_bbox_size > original_images[0].shape[1]: - # max_bbox_size can only be as big as our input's width or height, and it has to be even - self.max_bbox_size = math.floor(min(original_images[0].shape[0], original_images[0].shape[1]) / 2) * 2 - - # Then, for each mask and corresponding image... - for i, (mask, img) in enumerate(zip(masks, original_images)): - _mask = tensor2pil(mask)[0] - non_zero_indices = np.nonzero(np.array(_mask)) - - # check for empty masks - if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0: - min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1]) - min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0]) - - # Calculate center of bounding box - center_x = np.mean(non_zero_indices[1]) - center_y = np.mean(non_zero_indices[0]) - curr_center = (round(center_x), round(center_y)) - - # If this is the first frame, initialize prev_center with curr_center - if not hasattr(self, 'prev_center'): - self.prev_center = curr_center - - # Smooth the changes in the center coordinates from the second frame onwards - if i > 0: - center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha) - else: - center = curr_center - - # Update prev_center for the next frame - self.prev_center = center - - # Create bounding box using max_bbox_size - half_box_size = self.max_bbox_size // 2 - min_x = max(0, center[0] - half_box_size) - max_x = min(img.shape[1], center[0] + half_box_size) - min_y = max(0, center[1] - half_box_size) - max_y = min(img.shape[0], center[1] + half_box_size) - - # Append bounding box coordinates - bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y)) - - # Crop the image from the bounding box - cropped_img = img[min_y:max_y, min_x:max_x, :] - cropped_mask = mask[min_y:max_y, min_x:max_x] - - # Resize the cropped image to a fixed size - new_size = max(cropped_img.shape[0], cropped_img.shape[1]) - resize_transform = Resize(new_size, interpolation=InterpolationMode.NEAREST, max_size=max(img.shape[0], img.shape[1])) - resized_mask = resize_transform(cropped_mask.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0) - resized_img = resize_transform(cropped_img.permute(2, 0, 1)) - # Perform the center crop to the desired size - # Constrain the crop to the smaller of our bbox or our image so we don't expand past the image dimensions. - crop_transform = CenterCrop((min(self.max_bbox_size, resized_img.shape[1]), min(self.max_bbox_size, resized_img.shape[2]))) - - cropped_resized_img = crop_transform(resized_img) - cropped_images.append(cropped_resized_img.permute(1, 2, 0)) - - cropped_resized_mask = crop_transform(resized_mask) - cropped_masks.append(cropped_resized_mask) - - combined_cropped_img = original_images[i][new_min_y:new_max_y, new_min_x:new_max_x, :] - combined_cropped_images.append(combined_cropped_img) - - combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x] - combined_cropped_masks.append(combined_cropped_mask) - else: - bounding_boxes.append((0, 0, img.shape[1], img.shape[0])) - cropped_images.append(img) - cropped_masks.append(mask) - combined_cropped_images.append(img) - combined_cropped_masks.append(mask) - - cropped_out = torch.stack(cropped_images, dim=0) - combined_crop_out = torch.stack(combined_cropped_images, dim=0) - cropped_masks_out = torch.stack(cropped_masks, dim=0) - combined_crop_mask_out = torch.stack(combined_cropped_masks, dim=0) - - return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size) - -class FilterZeroMasksAndCorrespondingImages: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "masks": ("MASK",), - }, - "optional": { - "original_images": ("IMAGE",), - }, - } - - RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",) - RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",) - FUNCTION = "filter" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Filter out all the empty (i.e. all zero) mask in masks -Also filter out all the corresponding images in original_images by indexes if provide - -original_images (optional): If provided, need have same length as masks. -""" - - def filter(self, masks, original_images=None): - non_zero_masks = [] - non_zero_mask_images = [] - zero_mask_images = [] - zero_mask_images_indexes = [] - - masks_num = len(masks) - also_process_images = False - if original_images is not None: - imgs_num = len(original_images) - if len(original_images) == masks_num: - also_process_images = True - else: - print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})") - - for i in range(masks_num): - non_zero_num = np.count_nonzero(np.array(masks[i])) - if non_zero_num > 0: - non_zero_masks.append(masks[i]) - if also_process_images: - non_zero_mask_images.append(original_images[i]) - else: - zero_mask_images.append(original_images[i]) - zero_mask_images_indexes.append(i) - - non_zero_masks_out = torch.stack(non_zero_masks, dim=0) - non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None - - if also_process_images: - non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0) - if len(zero_mask_images) > 0: - zero_mask_images_out = torch.stack(zero_mask_images, dim=0) - zero_mask_images_out_indexes = zero_mask_images_indexes - - return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes) - -class InsertImageBatchByIndexes: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images": ("IMAGE",), - "images_to_insert": ("IMAGE",), - "insert_indexes": ("INDEXES",), - }, - } - - RETURN_TYPES = ("IMAGE", ) - RETURN_NAMES = ("images_after_insert", ) - FUNCTION = "insert" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -This node is designed to be use with node FilterZeroMasksAndCorrespondingImages -It inserts the images_to_insert into images according to insert_indexes - -Returns: - images_after_insert: updated original images with origonal sequence order -""" - - def insert(self, images, images_to_insert, insert_indexes): - images_after_insert = images - - if images_to_insert is not None and insert_indexes is not None: - images_to_insert_num = len(images_to_insert) - insert_indexes_num = len(insert_indexes) - if images_to_insert_num == insert_indexes_num: - images_after_insert = [] - - i_images = 0 - for i in range(len(images) + images_to_insert_num): - if i in insert_indexes: - images_after_insert.append(images_to_insert[insert_indexes.index(i)]) - else: - images_after_insert.append(images[i_images]) - i_images += 1 - - images_after_insert = torch.stack(images_after_insert, dim=0) - - else: - print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})") - - - return (images_after_insert, ) - -class BatchUncropAdvanced: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "original_images": ("IMAGE",), - "cropped_images": ("IMAGE",), - "cropped_masks": ("MASK",), - "combined_crop_mask": ("MASK",), - "bboxes": ("BBOX",), - "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ), - "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "use_combined_mask": ("BOOLEAN", {"default": False}), - "use_square_mask": ("BOOLEAN", {"default": True}), - }, - "optional": { - "combined_bounding_box": ("BBOX", {"default": None}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "uncrop" - CATEGORY = "KJNodes/masking" - - - def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None): - - def inset_border(image, border_width=20, border_color=(0)): - width, height = image.size - bordered_image = Image.new(image.mode, (width, height), border_color) - bordered_image.paste(image, (0, 0)) - draw = ImageDraw.Draw(bordered_image) - draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width) - return bordered_image - - if len(original_images) != len(cropped_images): - raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same") - - # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images - if len(bboxes) > len(original_images): - print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}") - bboxes = bboxes[:len(original_images)] - elif len(bboxes) < len(original_images): - raise ValueError("There should be at least as many bboxes as there are original and cropped images") - - crop_imgs = tensor2pil(cropped_images) - input_images = tensor2pil(original_images) - out_images = [] - - for i in range(len(input_images)): - img = input_images[i] - crop = crop_imgs[i] - bbox = bboxes[i] - - if use_combined_mask: - bb_x, bb_y, bb_width, bb_height = combined_bounding_box[0] - paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) - mask = combined_crop_mask[i] - else: - bb_x, bb_y, bb_width, bb_height = bbox - paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size) - mask = cropped_masks[i] - - # scale paste_region - scale_x = scale_y = crop_rescale - paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y)) - - # rescale the crop image to fit the paste_region - crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1]))) - crop_img = crop.convert("RGB") - - #border blending - if border_blending > 1.0: - border_blending = 1.0 - elif border_blending < 0.0: - border_blending = 0.0 - - blend_ratio = (max(crop_img.size) / 2) * float(border_blending) - blend = img.convert("RGBA") - - if use_square_mask: - mask = Image.new("L", img.size, 0) - mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255) - mask_block = inset_border(mask_block, round(blend_ratio / 2), (0)) - mask.paste(mask_block, paste_region) - else: - original_mask = tensor2pil(mask)[0] - original_mask = original_mask.resize((paste_region[2]-paste_region[0], paste_region[3]-paste_region[1])) - mask = Image.new("L", img.size, 0) - mask.paste(original_mask, paste_region) - - mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4)) - mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4)) - - blend.paste(crop_img, paste_region) - blend.putalpha(mask) - - img = Image.alpha_composite(img.convert("RGBA"), blend) - out_images.append(img.convert("RGB")) - - return (pil2tensor(out_images),) - -class SplitBboxes: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "bboxes": ("BBOX",), - "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}), - }, - } - - RETURN_TYPES = ("BBOX","BBOX",) - RETURN_NAMES = ("bboxes_a","bboxes_b",) - FUNCTION = "splitbbox" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Splits the specified bbox list at the given index into two lists. -""" - - def splitbbox(self, bboxes, index): - bboxes_a = bboxes[:index] # Sub-list from the start of bboxes up to (but not including) the index - bboxes_b = bboxes[index:] # Sub-list from the index to the end of bboxes - - return (bboxes_a, bboxes_b,) - -class BboxToInt: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "bboxes": ("BBOX",), - "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}), - }, - } - - RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",) - RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",) - FUNCTION = "bboxtoint" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Returns selected index from bounding box list as integers. -""" - def bboxtoint(self, bboxes, index): - x_min, y_min, width, height = bboxes[index] - center_x = int(x_min + width / 2) - center_y = int(y_min + height / 2) - - return (x_min, y_min, width, height, center_x, center_y,) - -class BboxVisualize: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images": ("IMAGE",), - "bboxes": ("BBOX",), - "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}), - "bbox_format": (["xywh", "xyxy"], {"default": "xywh"}), - }, - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("images",) - FUNCTION = "visualizebbox" - DESCRIPTION = """ -Visualizes the specified bbox on the image. -""" - - CATEGORY = "KJNodes/masking" - - def visualizebbox(self, bboxes, images, line_width, bbox_format): - image_list = [] - for image, bbox in zip(images, bboxes): - if bbox_format == "xywh": - x_min, y_min, width, height = bbox - elif bbox_format == "xyxy": - x_min, y_min, x_max, y_max = bbox - width = x_max - x_min - height = y_max - y_min - else: - raise ValueError(f"Unknown bbox_format: {bbox_format}") - - # Ensure bbox coordinates are integers - x_min = int(x_min) - y_min = int(y_min) - width = int(width) - height = int(height) - - # Permute the image dimensions - image = image.permute(2, 0, 1) - - # Clone the image to draw bounding boxes - img_with_bbox = image.clone() - - # Define the color for the bbox, e.g., red - color = torch.tensor([1, 0, 0], dtype=torch.float32) - - # Ensure color tensor matches the image channels - if color.shape[0] != img_with_bbox.shape[0]: - color = color.unsqueeze(1).expand(-1, line_width) - - # Draw lines for each side of the bbox with the specified line width - for lw in range(line_width): - # Top horizontal line - if y_min + lw < img_with_bbox.shape[1]: - img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None] - - # Bottom horizontal line - if y_min + height - lw < img_with_bbox.shape[1]: - img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None] - - # Left vertical line - if x_min + lw < img_with_bbox.shape[2]: - img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None] - - # Right vertical line - if x_min + width - lw < img_with_bbox.shape[2]: - img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None] - - # Permute the image dimensions back - img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0) - image_list.append(img_with_bbox) - - return (torch.cat(image_list, dim=0),) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/curve_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/curve_nodes.py deleted file mode 100644 index 77be51f9a8a7a7fcc78b8fdc8ea6c423ed2a185c..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/curve_nodes.py +++ /dev/null @@ -1,1636 +0,0 @@ -import torch -from torchvision import transforms -import json -from PIL import Image, ImageDraw, ImageFont, ImageColor, ImageFilter, ImageChops -import numpy as np -from ..utility.utility import pil2tensor, tensor2pil -import folder_paths -import io -import base64 - -from comfy.utils import common_upscale - -def parse_color(color): - if isinstance(color, str) and ',' in color: - return tuple(int(c.strip()) for c in color.split(',')) - return color - -def parse_json_tracks(tracks): - tracks_data = [] - try: - # If tracks is a string, try to parse it as JSON - if isinstance(tracks, str): - parsed = json.loads(tracks.replace("'", '"')) - tracks_data.extend(parsed) - else: - # If tracks is a list of strings, parse each one - for track_str in tracks: - parsed = json.loads(track_str.replace("'", '"')) - tracks_data.append(parsed) - - # Check if we have a single track (dict with x,y) or a list of tracks - if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]: - # Single track detected, wrap it in a list - tracks_data = [tracks_data] - elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]: - # Already a list of tracks, nothing to do - pass - else: - # Unexpected format - print(f"Warning: Unexpected track format: {type(tracks_data[0])}") - - except json.JSONDecodeError as e: - print(f"Error parsing tracks JSON: {e}") - tracks_data = [] - - return tracks_data - -def plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, prompt): - import matplotlib - matplotlib.use('Agg') - from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas - text_color = '#999999' - bg_color = '#353535' - matplotlib.pyplot.rcParams['text.color'] = text_color - fig, ax = matplotlib.pyplot.subplots(figsize=(width/100, height/100), dpi=100) - fig.patch.set_facecolor(bg_color) - ax.set_facecolor(bg_color) - ax.grid(color=text_color, linestyle='-', linewidth=0.5) - ax.set_xlabel('x', color=text_color) - ax.set_ylabel('y', color=text_color) - for text in ax.get_xticklabels() + ax.get_yticklabels(): - text.set_color(text_color) - ax.set_title('position for: ' + prompt) - ax.set_xlabel('X Coordinate') - ax.set_ylabel('Y Coordinate') - #ax.legend().remove() - ax.set_xlim(0, width) # Set the x-axis to match the input latent width - ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left - # Adjust the margins of the subplot - matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2) - - cmap = matplotlib.pyplot.get_cmap('rainbow') - image_batch = [] - canvas = FigureCanvas(fig) - width, height = fig.get_size_inches() * fig.get_dpi() - # Draw a box at each coordinate - for i, ((x, y), size) in enumerate(zip(coordinates, size_multiplier)): - color_index = i / (len(coordinates) - 1) - color = cmap(color_index) - draw_height = bbox_height * size - draw_width = bbox_width * size - rect = matplotlib.patches.Rectangle((x - draw_width/2, y - draw_height/2), draw_width, draw_height, - linewidth=1, edgecolor=color, facecolor='none', alpha=0.5) - ax.add_patch(rect) - - # Check if there is a next coordinate to draw an arrow to - if i < len(coordinates) - 1: - x1, y1 = coordinates[i] - x2, y2 = coordinates[i + 1] - ax.annotate("", xy=(x2, y2), xytext=(x1, y1), - arrowprops=dict(arrowstyle="->", - linestyle="-", - lw=1, - color=color, - mutation_scale=20)) - canvas.draw() - image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3).copy() - image_tensor = torch.from_numpy(image_np).float() / 255.0 - image_tensor = image_tensor.unsqueeze(0) - image_batch.append(image_tensor) - - matplotlib.pyplot.close(fig) - image_batch_tensor = torch.cat(image_batch, dim=0) - - return image_batch_tensor - -class PlotCoordinates: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "coordinates": ("STRING", {"forceInput": True}), - "text": ("STRING", {"default": 'title', "multiline": False}), - "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), - "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), - "bbox_width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), - "bbox_height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), - }, - "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})}, - } - RETURN_TYPES = ("IMAGE", "INT", "INT", "INT", "INT",) - RETURN_NAMES = ("images", "width", "height", "bbox_width", "bbox_height",) - FUNCTION = "append" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = """ -Plots coordinates to sequence of images using Matplotlib. - -""" - - def append(self, coordinates, text, width, height, bbox_width, bbox_height, size_multiplier=[1.0]): - coordinates = json.loads(coordinates.replace("'", '"')) - coordinates = [(coord['x'], coord['y']) for coord in coordinates] - batch_size = len(coordinates) - if not size_multiplier or len(size_multiplier) != batch_size: - size_multiplier = [0] * batch_size - else: - size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] - - plot_image_tensor = plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, text) - - return (plot_image_tensor, width, height, bbox_width, bbox_height) - -class SplineEditor: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "points_store": ("STRING", {"multiline": False}), - "coordinates": ("STRING", {"multiline": False}), - "mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), - "mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), - "points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}), - "sampling_method": ( - [ - 'path', - 'time', - 'controlpoints', - 'speed' - ], - { - "default": 'time' - }), - "interpolation": ( - [ - 'cardinal', - 'monotone', - 'basis', - 'linear', - 'step-before', - 'step-after', - 'polar', - 'polar-reverse', - ], - { - "default": 'cardinal' - }), - "tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - "repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}), - "float_output_type": ( - [ - 'list', - 'pandas series', - 'tensor', - ], - { - "default": 'list' - }), - }, - "optional": { - "min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}), - "max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}), - "bg_image": ("IMAGE", ), - } - } - - RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT", "STRING",) - RETURN_NAMES = ("mask", "coord_str", "float", "count", "normalized_str",) - FUNCTION = "splinedata" - CATEGORY = "KJNodes/weights" - DESCRIPTION = """ -# WORK IN PROGRESS -Do not count on this as part of your workflow yet, -probably contains lots of bugs and stability is not -guaranteed!! - -## Graphical editor to create values for various -## schedules and/or mask batches. - -**Shift + click** to add control point at end. -**Ctrl + click** to add control point (subdivide) between two points. -**Right click on a point** to delete it. -Note that you can't delete from start/end. - -Right click on canvas for context menu: -NEW!: -- Add new spline - - Creates a new spline on same canvas, currently these paths are only outputed - as coordinates. -- Add single point - - Creates a single point that only returns it's current position coords -- Delete spline - - Deletes the currently selected spline, you can select a spline by clicking on - it's path, or cycle through them with the 'Next spline' -option. - -These are purely visual options, doesn't affect the output: - - Toggle handles visibility - - Display sample points: display the points to be returned. - -**points_to_sample** value sets the number of samples -returned from the **drawn spline itself**, this is independent from the -actual control points, so the interpolation type matters. -sampling_method: - - time: samples along the time axis, used for schedules - - path: samples along the path itself, useful for coordinates - - controlpoints: samples only the control points themselves - -output types: - - mask batch - example compatible nodes: anything that takes masks - - list of floats - example compatible nodes: IPAdapter weights - - pandas series - example compatible nodes: anything that takes Fizz' - nodes Batch Value Schedule - - torch tensor - example compatible nodes: unknown -""" - - def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation, - points_to_sample, sampling_method, points_store, tension, repeat_output, - min_value=0.0, max_value=1.0, bg_image=None): - - coordinates = json.loads(coordinates) - - # Handle nested list structure if present - all_normalized = [] - all_normalized_y_values = [] - - # Check if we have a nested list structure - if isinstance(coordinates, list) and len(coordinates) > 0 and isinstance(coordinates[0], list): - # Process each list of coordinates in the nested structure - coordinate_sets = coordinates - else: - # If not nested, treat as a single list of coordinates - coordinate_sets = [coordinates] - - # Process each set of coordinates - for coord_set in coordinate_sets: - normalized = [] - normalized_y_values = [] - - for coord in coord_set: - coord['x'] = int(round(coord['x'])) - coord['y'] = int(round(coord['y'])) - norm_x = (1.0 - (coord['x'] / mask_height) - 0.0) * (max_value - min_value) + min_value - norm_y = (1.0 - (coord['y'] / mask_height) - 0.0) * (max_value - min_value) + min_value - normalized_y_values.append(norm_y) - normalized.append({'x':norm_x, 'y':norm_y}) - - all_normalized.extend(normalized) - all_normalized_y_values.extend(normalized_y_values) - - # Use the combined normalized values for output - if float_output_type == 'list': - out_floats = all_normalized_y_values * repeat_output - elif float_output_type == 'pandas series': - try: - import pandas as pd - except: - raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type") - out_floats = pd.Series(all_normalized_y_values * repeat_output), - elif float_output_type == 'tensor': - out_floats = torch.tensor(all_normalized_y_values * repeat_output, dtype=torch.float32) - - # Create a color map for grayscale intensities - color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32) - - # Create image tensors for each normalized y value - mask_tensors = [color_map(y) for y in all_normalized_y_values] - masks_out = torch.stack(mask_tensors) - masks_out = masks_out.repeat(repeat_output, 1, 1, 1) - masks_out = masks_out.mean(dim=-1) - - if bg_image is None: - return (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized)) - else: - transform = transforms.ToPILImage() - image = transform(bg_image[0].permute(2, 0, 1)) - buffered = io.BytesIO() - image.save(buffered, format="JPEG", quality=75) - - # Encode the image bytes to a Base64 string - img_bytes = buffered.getvalue() - img_base64 = base64.b64encode(img_bytes).decode('utf-8') - - return { - "ui": {"bg_image": [img_base64]}, - "result": (masks_out, json.dumps(coordinates if len(coordinates) > 1 else coordinates[0]), out_floats, len(out_floats), json.dumps(all_normalized)) - } - - -class CreateShapeMaskOnPath: - - RETURN_TYPES = ("MASK", "MASK",) - RETURN_NAMES = ("mask", "mask_inverted",) - FUNCTION = "createshapemask" - CATEGORY = "KJNodes/masking/generate" - DESCRIPTION = """ -Creates a mask or batch of masks with the specified shape. -Locations are center locations. -""" - DEPRECATED = True - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "shape": ( - [ 'circle', - 'square', - 'triangle', - ], - { - "default": 'circle' - }), - "coordinates": ("STRING", {"forceInput": True}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), - "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), - }, - "optional": { - "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), - } - } - - def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape, size_multiplier=[1.0]): - # Define the number of images in the batch - coordinates = coordinates.replace("'", '"') - coordinates = json.loads(coordinates) - - batch_size = len(coordinates) - out = [] - color = "white" - if not size_multiplier or len(size_multiplier) != batch_size: - size_multiplier = [0] * batch_size - else: - size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] - for i, coord in enumerate(coordinates): - image = Image.new("RGB", (frame_width, frame_height), "black") - draw = ImageDraw.Draw(image) - - # Calculate the size for this frame and ensure it's not less than 0 - current_width = max(0, shape_width + i * size_multiplier[i]) - current_height = max(0, shape_height + i * size_multiplier[i]) - - location_x = coord['x'] - location_y = coord['y'] - - if shape == 'circle' or shape == 'square': - # Define the bounding box for the shape - left_up_point = (location_x - current_width // 2, location_y - current_height // 2) - right_down_point = (location_x + current_width // 2, location_y + current_height // 2) - two_points = [left_up_point, right_down_point] - - if shape == 'circle': - draw.ellipse(two_points, fill=color) - elif shape == 'square': - draw.rectangle(two_points, fill=color) - - elif shape == 'triangle': - # Define the points for the triangle - left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left - right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right - top_point = (location_x, location_y - current_height // 2) # top point - draw.polygon([top_point, left_up_point, right_down_point], fill=color) - - image = pil2tensor(image) - mask = image[:, :, :, 0] - out.append(mask) - outstack = torch.cat(out, dim=0) - return (outstack, 1.0 - outstack,) - - - -class CreateShapeImageOnPath: - - RETURN_TYPES = ("IMAGE", "MASK",) - RETURN_NAMES = ("image","mask", ) - FUNCTION = "createshapemask" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates an image or batch of images with the specified shape. -Locations are center locations. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "shape": ( - [ 'circle', - 'square', - 'triangle', - ], - { - "default": 'circle' - }), - "coordinates": ("STRING", {"forceInput": True}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "shape_width": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}), - "shape_height": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}), - "shape_color": ("STRING", {"default": 'white'}), - "bg_color": ("STRING", {"default": 'black'}), - "blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}), - "intensity": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}), - }, - "optional": { - "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), - "trailing": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "border_width": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), - "border_color": ("STRING", {"default": 'black'}), - } - } - - def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape_color, - bg_color, blur_radius, shape, intensity, size_multiplier=[1.0], trailing=1.0, border_width=0, border_color='black'): - - shape_color = parse_color(shape_color) - border_color = parse_color(border_color) - bg_color = parse_color(bg_color) - coords_list = parse_json_tracks(coordinates) - - batch_size = len(coords_list[0]) - images_list = [] - masks_list = [] - - if not size_multiplier or len(size_multiplier) != batch_size: - size_multiplier = [1] * batch_size - else: - size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] - - previous_output = None - - for i in range(batch_size): - image = Image.new("RGB", (frame_width, frame_height), bg_color) - draw = ImageDraw.Draw(image) - - # Calculate the size for this frame and ensure it's not less than 0 - current_width = shape_width * size_multiplier[i] - current_height = shape_height * size_multiplier[i] - - for coords in coords_list: - location_x = coords[i]['x'] - location_y = coords[i]['y'] - - if shape == 'circle' or shape == 'square': - # Define the bounding box for the shape - left_up_point = (location_x - current_width // 2, location_y - current_height // 2) - right_down_point = (location_x + current_width // 2, location_y + current_height // 2) - two_points = [left_up_point, right_down_point] - - if shape == 'circle': - if border_width > 0: - draw.ellipse(two_points, fill=shape_color, outline=border_color, width=border_width) - else: - draw.ellipse(two_points, fill=shape_color) - elif shape == 'square': - if border_width > 0: - draw.rectangle(two_points, fill=shape_color, outline=border_color, width=border_width) - else: - draw.rectangle(two_points, fill=shape_color) - - elif shape == 'triangle': - # Define the points for the triangle - left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left - right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right - top_point = (location_x, location_y - current_height // 2) # top point - - if border_width > 0: - draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color, outline=border_color, width=border_width) - else: - draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color) - - if blur_radius != 0: - image = image.filter(ImageFilter.GaussianBlur(blur_radius)) - # Blend the current image with the accumulated image - - image = pil2tensor(image) - if trailing != 1.0 and previous_output is not None: - # Add the decayed previous output to the current frame - image += trailing * previous_output - image = image / image.max() - previous_output = image - image = image * intensity - mask = image[:, :, :, 0] - masks_list.append(mask) - images_list.append(image) - out_images = torch.cat(images_list, dim=0).cpu().float() - out_masks = torch.cat(masks_list, dim=0) - return (out_images, out_masks) - -class CreateTextOnPath: - - RETURN_TYPES = ("IMAGE", "MASK", "MASK",) - RETURN_NAMES = ("image", "mask", "mask_inverted",) - FUNCTION = "createtextmask" - CATEGORY = "KJNodes/masking/generate" - DESCRIPTION = """ -Creates a mask or batch of masks with the specified text. -Locations are center locations. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "coordinates": ("STRING", {"forceInput": True}), - "text": ("STRING", {"default": 'text', "multiline": True}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), - "font_size": ("INT", {"default": 42}), - "alignment": ( - [ 'left', - 'center', - 'right' - ], - {"default": 'center'} - ), - "text_color": ("STRING", {"default": 'white'}), - }, - "optional": { - "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), - } - } - - def createtextmask(self, coordinates, frame_width, frame_height, font, font_size, text, text_color, alignment, size_multiplier=[1.0]): - coordinates = coordinates.replace("'", '"') - coordinates = json.loads(coordinates) - - batch_size = len(coordinates) - mask_list = [] - image_list = [] - color = parse_color(text_color) - font_path = folder_paths.get_full_path("kjnodes_fonts", font) - - if len(size_multiplier) != batch_size: - size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] - - for i, coord in enumerate(coordinates): - image = Image.new("RGB", (frame_width, frame_height), "black") - draw = ImageDraw.Draw(image) - lines = text.split('\n') # Split the text into lines - # Apply the size multiplier to the font size for this iteration - current_font_size = int(font_size * size_multiplier[i]) - current_font = ImageFont.truetype(font_path, current_font_size) - line_heights = [current_font.getbbox(line)[3] for line in lines] # List of line heights - total_text_height = sum(line_heights) # Total height of text block - - # Calculate the starting Y position to center the block of text - start_y = coord['y'] - total_text_height // 2 - for j, line in enumerate(lines): - text_width, text_height = current_font.getbbox(line)[2], line_heights[j] - if alignment == 'left': - location_x = coord['x'] - elif alignment == 'center': - location_x = int(coord['x'] - text_width // 2) - elif alignment == 'right': - location_x = int(coord['x'] - text_width) - - location_y = int(start_y + sum(line_heights[:j])) - text_position = (location_x, location_y) - # Draw the text - try: - draw.text(text_position, line, fill=color, font=current_font, features=['-liga']) - except: - draw.text(text_position, line, fill=color, font=current_font) - - image = pil2tensor(image) - non_black_pixels = (image > 0).any(dim=-1) - mask = non_black_pixels.to(image.dtype) - mask_list.append(mask) - image_list.append(image) - - out_images = torch.cat(image_list, dim=0).cpu().float() - out_masks = torch.cat(mask_list, dim=0) - return (out_images, out_masks, 1.0 - out_masks,) - -class CreateGradientFromCoords: - - RETURN_TYPES = ("IMAGE", ) - RETURN_NAMES = ("image", ) - FUNCTION = "generate" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates a gradient image from coordinates. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "coordinates": ("STRING", {"forceInput": True}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "start_color": ("STRING", {"default": 'white'}), - "end_color": ("STRING", {"default": 'black'}), - "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}), - }, - } - - def generate(self, coordinates, frame_width, frame_height, start_color, end_color, multiplier): - # Parse the coordinates - coordinates = json.loads(coordinates.replace("'", '"')) - - # Create an image - image = Image.new("RGB", (frame_width, frame_height)) - draw = ImageDraw.Draw(image) - - # Extract start and end points for the gradient - start_coord = coordinates[0] - end_coord = coordinates[1] - - start_color = parse_color(start_color) - end_color = parse_color(end_color) - - # Calculate the gradient direction (vector) - gradient_direction = (end_coord['x'] - start_coord['x'], end_coord['y'] - start_coord['y']) - gradient_length = (gradient_direction[0] ** 2 + gradient_direction[1] ** 2) ** 0.5 - - # Iterate over each pixel in the image - for y in range(frame_height): - for x in range(frame_width): - # Calculate the projection of the point on the gradient line - point_vector = (x - start_coord['x'], y - start_coord['y']) - projection = (point_vector[0] * gradient_direction[0] + point_vector[1] * gradient_direction[1]) / gradient_length - projection = max(min(projection, gradient_length), 0) # Clamp the projection value - - # Calculate the blend factor for the current pixel - blend = projection * multiplier / gradient_length - - # Determine the color of the current pixel - color = ( - int(start_color[0] + (end_color[0] - start_color[0]) * blend), - int(start_color[1] + (end_color[1] - start_color[1]) * blend), - int(start_color[2] + (end_color[2] - start_color[2]) * blend) - ) - - # Set the pixel color - draw.point((x, y), fill=color) - - # Convert the PIL image to a tensor (assuming such a function exists in your context) - image_tensor = pil2tensor(image) - - return (image_tensor,) - -class GradientToFloat: - - RETURN_TYPES = ("FLOAT", "FLOAT",) - RETURN_NAMES = ("float_x", "float_y", ) - FUNCTION = "sample" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Calculates list of floats from image. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE", ), - "steps": ("INT", {"default": 10, "min": 2, "max": 10000, "step": 1}), - }, - } - - def sample(self, image, steps): - # Assuming image is a tensor with shape [B, H, W, C] - B, H, W, C = image.shape - - # Sample along the width axis (W) - w_intervals = torch.linspace(0, W - 1, steps=steps, dtype=torch.int64) - # Assuming we're sampling from the first batch and the first channel - w_sampled = image[0, :, w_intervals, 0] - - # Sample along the height axis (H) - h_intervals = torch.linspace(0, H - 1, steps=steps, dtype=torch.int64) - # Assuming we're sampling from the first batch and the first channel - h_sampled = image[0, h_intervals, :, 0] - - # Taking the mean across the height for width sampling, and across the width for height sampling - w_values = w_sampled.mean(dim=0).tolist() - h_values = h_sampled.mean(dim=1).tolist() - - return (w_values, h_values) - -class MaskOrImageToWeight: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "output_type": ( - [ - 'list', - 'pandas series', - 'tensor', - 'string' - ], - { - "default": 'list' - }), - }, - "optional": { - "images": ("IMAGE",), - "masks": ("MASK",), - }, - - } - RETURN_TYPES = ("FLOAT", "STRING",) - FUNCTION = "execute" - CATEGORY = "KJNodes/weights" - DESCRIPTION = """ -Gets the mean values from mask or image batch -and returns that as the selected output type. -""" - - def execute(self, output_type, images=None, masks=None): - mean_values = [] - if masks is not None and images is None: - for mask in masks: - mean_values.append(mask.mean().item()) - elif masks is None and images is not None: - for image in images: - mean_values.append(image.mean().item()) - elif masks is not None and images is not None: - raise Exception("MaskOrImageToWeight: Use either mask or image input only.") - - # Convert mean_values to the specified output_type - if output_type == 'list': - out = mean_values - elif output_type == 'pandas series': - try: - import pandas as pd - except: - raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type") - out = pd.Series(mean_values), - elif output_type == 'tensor': - out = torch.tensor(mean_values, dtype=torch.float32), - return (out, [str(value) for value in mean_values],) - -class WeightScheduleConvert: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "input_values": ("FLOAT", {"default": 0.0, "forceInput": True}), - "output_type": ( - [ - 'match_input', - 'list', - 'pandas series', - 'tensor', - ], - { - "default": 'list' - }), - "invert": ("BOOLEAN", {"default": False}), - "repeat": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), - }, - "optional": { - "remap_to_frames": ("INT", {"default": 0}), - "interpolation_curve": ("FLOAT", {"forceInput": True}), - "remap_values": ("BOOLEAN", {"default": False}), - "remap_min": ("FLOAT", {"default": 0.0, "min": -100000, "max": 100000.0, "step": 0.01}), - "remap_max": ("FLOAT", {"default": 1.0, "min": -100000, "max": 100000.0, "step": 0.01}), - }, - - } - RETURN_TYPES = ("FLOAT", "STRING", "INT",) - FUNCTION = "execute" - CATEGORY = "KJNodes/weights" - DESCRIPTION = """ -Converts different value lists/series to another type. -""" - - def detect_input_type(self, input_values): - import pandas as pd - if isinstance(input_values, list): - return 'list' - elif isinstance(input_values, pd.Series): - return 'pandas series' - elif isinstance(input_values, torch.Tensor): - return 'tensor' - else: - raise ValueError("Unsupported input type") - - def execute(self, input_values, output_type, invert, repeat, remap_to_frames=0, interpolation_curve=None, remap_min=0.0, remap_max=1.0, remap_values=False): - import pandas as pd - input_type = self.detect_input_type(input_values) - - if input_type == 'pandas series': - float_values = input_values.tolist() - elif input_type == 'tensor': - float_values = input_values - else: - float_values = input_values - - if invert: - float_values = [1 - value for value in float_values] - - if interpolation_curve is not None: - interpolated_pattern = [] - orig_float_values = float_values - for value in interpolation_curve: - min_val = min(orig_float_values) - max_val = max(orig_float_values) - # Normalize the values to [0, 1] - normalized_values = [(value - min_val) / (max_val - min_val) for value in orig_float_values] - # Interpolate the normalized values to the new frame count - remapped_float_values = np.interp(np.linspace(0, 1, int(remap_to_frames * value)), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist() - interpolated_pattern.extend(remapped_float_values) - float_values = interpolated_pattern - else: - # Remap float_values to match target_frame_amount - if remap_to_frames > 0 and remap_to_frames != len(float_values): - min_val = min(float_values) - max_val = max(float_values) - # Normalize the values to [0, 1] - normalized_values = [(value - min_val) / (max_val - min_val) for value in float_values] - # Interpolate the normalized values to the new frame count - float_values = np.interp(np.linspace(0, 1, remap_to_frames), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist() - - float_values = float_values * repeat - if remap_values: - float_values = self.remap_values(float_values, remap_min, remap_max) - - if output_type == 'list': - out = float_values, - elif output_type == 'pandas series': - out = pd.Series(float_values), - elif output_type == 'tensor': - if input_type == 'pandas series': - out = torch.tensor(float_values.values, dtype=torch.float32), - else: - out = torch.tensor(float_values, dtype=torch.float32), - elif output_type == 'match_input': - out = float_values, - return (out, [str(value) for value in float_values], [int(value) for value in float_values]) - - def remap_values(self, values, target_min, target_max): - # Determine the current range - current_min = min(values) - current_max = max(values) - current_range = current_max - current_min - - # Determine the target range - target_range = target_max - target_min - - # Perform the linear interpolation for each value - remapped_values = [(value - current_min) / current_range * target_range + target_min for value in values] - - return remapped_values - - -class FloatToMask: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "input_values": ("FLOAT", {"forceInput": True, "default": 0}), - "width": ("INT", {"default": 100, "min": 1}), - "height": ("INT", {"default": 100, "min": 1}), - }, - } - RETURN_TYPES = ("MASK",) - FUNCTION = "execute" - CATEGORY = "KJNodes/masking/generate" - DESCRIPTION = """ -Generates a batch of masks based on the input float values. -The batch size is determined by the length of the input float values. -Each mask is generated with the specified width and height. -""" - - def execute(self, input_values, width, height): - import pandas as pd - # Ensure input_values is a list - if isinstance(input_values, (float, int)): - input_values = [input_values] - elif isinstance(input_values, pd.Series): - input_values = input_values.tolist() - elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values): - input_values = [item for sublist in input_values for item in sublist] - - # Generate a batch of masks based on the input_values - masks = [] - for value in input_values: - # Assuming value is a float between 0 and 1 representing the mask's intensity - mask = torch.ones((height, width), dtype=torch.float32) * value - masks.append(mask) - masks_out = torch.stack(masks, dim=0) - - return(masks_out,) -class WeightScheduleExtend: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "input_values_1": ("FLOAT", {"default": 0.0, "forceInput": True}), - "input_values_2": ("FLOAT", {"default": 0.0, "forceInput": True}), - "output_type": ( - [ - 'match_input', - 'list', - 'pandas series', - 'tensor', - ], - { - "default": 'match_input' - }), - }, - - } - RETURN_TYPES = ("FLOAT",) - FUNCTION = "execute" - CATEGORY = "KJNodes/weights" - DESCRIPTION = """ -Extends, and converts if needed, different value lists/series -""" - - def detect_input_type(self, input_values): - import pandas as pd - if isinstance(input_values, list): - return 'list' - elif isinstance(input_values, pd.Series): - return 'pandas series' - elif isinstance(input_values, torch.Tensor): - return 'tensor' - else: - raise ValueError("Unsupported input type") - - def execute(self, input_values_1, input_values_2, output_type): - import pandas as pd - input_type_1 = self.detect_input_type(input_values_1) - input_type_2 = self.detect_input_type(input_values_2) - # Convert input_values_2 to the same format as input_values_1 if they do not match - if not input_type_1 == input_type_2: - print("Converting input_values_2 to the same format as input_values_1") - if input_type_1 == 'pandas series': - # Convert input_values_2 to a pandas Series - float_values_2 = pd.Series(input_values_2) - elif input_type_1 == 'tensor': - # Convert input_values_2 to a tensor - float_values_2 = torch.tensor(input_values_2, dtype=torch.float32) - else: - print("Input types match, no conversion needed") - # If the types match, no conversion is needed - float_values_2 = input_values_2 - - float_values = input_values_1 + float_values_2 - - if output_type == 'list': - return float_values, - elif output_type == 'pandas series': - return pd.Series(float_values), - elif output_type == 'tensor': - if input_type_1 == 'pandas series': - return torch.tensor(float_values.values, dtype=torch.float32), - else: - return torch.tensor(float_values, dtype=torch.float32), - elif output_type == 'match_input': - return float_values, - else: - raise ValueError(f"Unsupported output_type: {output_type}") - -class FloatToSigmas: - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "float_list": ("FLOAT", {"default": 0.0, "forceInput": True}), - } - } - RETURN_TYPES = ("SIGMAS",) - RETURN_NAMES = ("SIGMAS",) - CATEGORY = "KJNodes/noise" - FUNCTION = "customsigmas" - DESCRIPTION = """ -Creates a sigmas tensor from list of float values. - -""" - def customsigmas(self, float_list): - return torch.tensor(float_list, dtype=torch.float32), - -class SigmasToFloat: - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "sigmas": ("SIGMAS",), - } - } - RETURN_TYPES = ("FLOAT",) - RETURN_NAMES = ("float",) - CATEGORY = "KJNodes/noise" - FUNCTION = "customsigmas" - DESCRIPTION = """ -Creates a float list from sigmas tensors. - -""" - def customsigmas(self, sigmas): - return sigmas.tolist(), - -class GLIGENTextBoxApplyBatchCoords: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning_to": ("CONDITIONING", ), - "latents": ("LATENT", ), - "clip": ("CLIP", ), - "gligen_textbox_model": ("GLIGEN", ), - "coordinates": ("STRING", {"forceInput": True}), - "text": ("STRING", {"multiline": True}), - "width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), - "height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}), - }, - "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})}, - } - RETURN_TYPES = ("CONDITIONING", "IMAGE", ) - RETURN_NAMES = ("conditioning", "coord_preview", ) - FUNCTION = "append" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = """ -This node allows scheduling GLIGEN text box positions in a batch, -to be used with AnimateDiff-Evolved. Intended to pair with the -Spline Editor -node. - -GLIGEN model can be downloaded through the Manage's "Install Models" menu. -Or directly from here: -https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/tree/main - -Inputs: -- **latents** input is used to calculate batch size -- **clip** is your standard text encoder, use same as for the main prompt -- **gligen_textbox_model** connects to GLIGEN Loader -- **coordinates** takes a json string of points, directly compatible -with the spline editor node. -- **text** is the part of the prompt to set position for -- **width** and **height** are the size of the GLIGEN bounding box - -Outputs: -- **conditioning** goes between to clip text encode and the sampler -- **coord_preview** is an optional preview of the coordinates and -bounding boxes. - -""" - - def append(self, latents, coordinates, conditioning_to, clip, gligen_textbox_model, text, width, height, size_multiplier=[1.0]): - coordinates = json.loads(coordinates.replace("'", '"')) - coordinates = [(coord['x'], coord['y']) for coord in coordinates] - - batch_size = sum(tensor.size(0) for tensor in latents.values()) - if len(coordinates) != batch_size: - print("GLIGENTextBoxApplyBatchCoords WARNING: The number of coordinates does not match the number of latents") - - c = [] - _, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True) - - for t in conditioning_to: - n = [t[0], t[1].copy()] - - position_params_batch = [[] for _ in range(batch_size)] # Initialize a list of empty lists for each batch item - if len(size_multiplier) != batch_size: - size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] - - for i in range(batch_size): - x_position, y_position = coordinates[i] - position_param = (cond_pooled, int((height // 8) * size_multiplier[i]), int((width // 8) * size_multiplier[i]), (y_position - height // 2) // 8, (x_position - width // 2) // 8) - position_params_batch[i].append(position_param) # Append position_param to the correct sublist - - prev = [] - if "gligen" in n[1]: - prev = n[1]['gligen'][2] - else: - prev = [[] for _ in range(batch_size)] - # Concatenate prev and position_params_batch, ensuring both are lists of lists - # and each sublist corresponds to a batch item - combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)] - n[1]['gligen'] = ("position_batched", gligen_textbox_model, combined_position_params) - c.append(n) - - image_height = latents['samples'].shape[-2] * 8 - image_width = latents['samples'].shape[-1] * 8 - plot_image_tensor = plot_coordinates_to_tensor(coordinates, image_height, image_width, height, width, size_multiplier, text) - - return (c, plot_image_tensor,) - -class CreateInstanceDiffusionTracking: - - RETURN_TYPES = ("TRACKING", "STRING", "INT", "INT", "INT", "INT",) - RETURN_NAMES = ("tracking", "prompt", "width", "height", "bbox_width", "bbox_height",) - FUNCTION = "tracking" - CATEGORY = "KJNodes/InstanceDiffusion" - DESCRIPTION = """ -Creates tracking data to be used with InstanceDiffusion: -https://github.com/logtd/ComfyUI-InstanceDiffusion - -InstanceDiffusion prompt format: -"class_id.class_name": "prompt", -for example: -"1.head": "((head))", -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "coordinates": ("STRING", {"forceInput": True}), - "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "bbox_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "bbox_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "class_name": ("STRING", {"default": "class_name"}), - "class_id": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "prompt": ("STRING", {"default": "prompt", "multiline": True}), - }, - "optional": { - "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}), - "fit_in_frame": ("BOOLEAN", {"default": True}), - } - } - - def tracking(self, coordinates, class_name, class_id, width, height, bbox_width, bbox_height, prompt, size_multiplier=[1.0], fit_in_frame=True): - # Define the number of images in the batch - coordinates = coordinates.replace("'", '"') - coordinates = json.loads(coordinates) - - tracked = {} - tracked[class_name] = {} - batch_size = len(coordinates) - # Initialize a list to hold the coordinates for the current ID - id_coordinates = [] - if not size_multiplier or len(size_multiplier) != batch_size: - size_multiplier = [0] * batch_size - else: - size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)] - for i, coord in enumerate(coordinates): - x = coord['x'] - y = coord['y'] - adjusted_bbox_width = bbox_width * size_multiplier[i] - adjusted_bbox_height = bbox_height * size_multiplier[i] - # Calculate the top left and bottom right coordinates - top_left_x = x - adjusted_bbox_width // 2 - top_left_y = y - adjusted_bbox_height // 2 - bottom_right_x = x + adjusted_bbox_width // 2 - bottom_right_y = y + adjusted_bbox_height // 2 - - if fit_in_frame: - # Clip the coordinates to the frame boundaries - top_left_x = max(0, top_left_x) - top_left_y = max(0, top_left_y) - bottom_right_x = min(width, bottom_right_x) - bottom_right_y = min(height, bottom_right_y) - # Ensure width and height are positive - adjusted_bbox_width = max(1, bottom_right_x - top_left_x) - adjusted_bbox_height = max(1, bottom_right_y - top_left_y) - - # Update the coordinates with the new width and height - bottom_right_x = top_left_x + adjusted_bbox_width - bottom_right_y = top_left_y + adjusted_bbox_height - - # Append the top left and bottom right coordinates to the list for the current ID - id_coordinates.append([top_left_x, top_left_y, bottom_right_x, bottom_right_y, width, height]) - - class_id = int(class_id) - # Assign the list of coordinates to the specified ID within the class_id dictionary - tracked[class_name][class_id] = id_coordinates - - prompt_string = "" - for class_name, class_data in tracked.items(): - for class_id in class_data.keys(): - class_id_str = str(class_id) - # Use the incoming prompt for each class name and ID - prompt_string += f'"{class_id_str}.{class_name}": "({prompt})",\n' - - # Remove the last comma and newline - prompt_string = prompt_string.rstrip(",\n") - - return (tracked, prompt_string, width, height, bbox_width, bbox_height) - -class AppendInstanceDiffusionTracking: - - RETURN_TYPES = ("TRACKING", "STRING",) - RETURN_NAMES = ("tracking", "prompt",) - FUNCTION = "append" - CATEGORY = "KJNodes/InstanceDiffusion" - DESCRIPTION = """ -Appends tracking data to be used with InstanceDiffusion: -https://github.com/logtd/ComfyUI-InstanceDiffusion - -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "tracking_1": ("TRACKING", {"forceInput": True}), - "tracking_2": ("TRACKING", {"forceInput": True}), - }, - "optional": { - "prompt_1": ("STRING", {"default": "", "forceInput": True}), - "prompt_2": ("STRING", {"default": "", "forceInput": True}), - } - } - - def append(self, tracking_1, tracking_2, prompt_1="", prompt_2=""): - tracking_copy = tracking_1.copy() - # Check for existing class names and class IDs, and raise an error if they exist - for class_name, class_data in tracking_2.items(): - if class_name not in tracking_copy: - tracking_copy[class_name] = class_data - else: - # If the class name exists, merge the class data from tracking_2 into tracking_copy - # This will add new class IDs under the same class name without raising an error - tracking_copy[class_name].update(class_data) - prompt_string = prompt_1 + "," + prompt_2 - return (tracking_copy, prompt_string) - -class InterpolateCoords: - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("coordinates",) - FUNCTION = "interpolate" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = """ -Interpolates coordinates based on a curve. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "coordinates": ("STRING", {"forceInput": True}), - "interpolation_curve": ("FLOAT", {"forceInput": True}), - - }, - } - - def interpolate(self, coordinates, interpolation_curve): - # Parse the JSON string to get the list of coordinates - coordinates = json.loads(coordinates.replace("'", '"')) - - # Convert the list of dictionaries to a list of (x, y) tuples for easier processing - coordinates = [(coord['x'], coord['y']) for coord in coordinates] - - # Calculate the total length of the original path - path_length = sum(np.linalg.norm(np.array(coordinates[i]) - np.array(coordinates[i-1])) - for i in range(1, len(coordinates))) - - # Initialize variables for interpolation - interpolated_coords = [] - current_length = 0 - current_index = 0 - - # Iterate over the normalized curve - for normalized_length in interpolation_curve: - target_length = normalized_length * path_length # Convert to the original scale - while current_index < len(coordinates) - 1: - segment_start, segment_end = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1]) - segment_length = np.linalg.norm(segment_end - segment_start) - if current_length + segment_length >= target_length: - break - current_length += segment_length - current_index += 1 - - # Interpolate between the last two points - if current_index < len(coordinates) - 1: - p1, p2 = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1]) - segment_length = np.linalg.norm(p2 - p1) - if segment_length > 0: - t = (target_length - current_length) / segment_length - interpolated_point = p1 + t * (p2 - p1) - interpolated_coords.append(interpolated_point.tolist()) - else: - interpolated_coords.append(p1.tolist()) - else: - # If the target_length is at or beyond the end of the path, add the last coordinate - interpolated_coords.append(coordinates[-1]) - - # Convert back to string format if necessary - interpolated_coords_str = "[" + ", ".join([f"{{'x': {round(coord[0])}, 'y': {round(coord[1])}}}" for coord in interpolated_coords]) + "]" - print(interpolated_coords_str) - - return (interpolated_coords_str,) - -class DrawInstanceDiffusionTracking: - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image", ) - FUNCTION = "draw" - CATEGORY = "KJNodes/InstanceDiffusion" - DESCRIPTION = """ -Draws the tracking data from -CreateInstanceDiffusionTracking -node. - -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE", ), - "tracking": ("TRACKING", {"forceInput": True}), - "box_line_width": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}), - "draw_text": ("BOOLEAN", {"default": True}), - "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), - "font_size": ("INT", {"default": 20}), - }, - } - - def draw(self, image, tracking, box_line_width, draw_text, font, font_size): - import matplotlib.cm as cm - - modified_images = [] - - colormap = cm.get_cmap('rainbow', len(tracking)) - if draw_text: - font_path = folder_paths.get_full_path("kjnodes_fonts", font) - font = ImageFont.truetype(font_path, font_size) - - # Iterate over each image in the batch - for i in range(image.shape[0]): - # Extract the current image and convert it to a PIL image - current_image = image[i, :, :, :].permute(2, 0, 1) - pil_image = transforms.ToPILImage()(current_image) - - draw = ImageDraw.Draw(pil_image) - - # Iterate over the bounding boxes for the current image - for j, (class_name, class_data) in enumerate(tracking.items()): - for class_id, bbox_list in class_data.items(): - # Check if the current index is within the bounds of the bbox_list - if i < len(bbox_list): - bbox = bbox_list[i] - # Ensure bbox is a list or tuple before unpacking - if isinstance(bbox, (list, tuple)): - x1, y1, x2, y2, _, _ = bbox - # Convert coordinates to integers - x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) - # Generate a color from the rainbow colormap - color = tuple(int(255 * x) for x in colormap(j / len(tracking)))[:3] - # Draw the bounding box on the image with the generated color - draw.rectangle([x1, y1, x2, y2], outline=color, width=box_line_width) - if draw_text: - # Draw the class name and ID as text above the box with the generated color - text = f"{class_id}.{class_name}" - # Calculate the width and height of the text - _, _, text_width, text_height = draw.textbbox((0, 0), text=text, font=font) - # Position the text above the top-left corner of the box - text_position = (x1, y1 - text_height) - draw.text(text_position, text, fill=color, font=font) - else: - print(f"Unexpected data type for bbox: {type(bbox)}") - - # Convert the drawn image back to a torch tensor and adjust back to (H, W, C) - modified_image_tensor = transforms.ToTensor()(pil_image).permute(1, 2, 0) - modified_images.append(modified_image_tensor) - - # Stack the modified images back into a batch - image_tensor_batch = torch.stack(modified_images).cpu().float() - - return image_tensor_batch, - -class PointsEditor: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "points_store": ("STRING", {"multiline": False}), - "coordinates": ("STRING", {"multiline": False}), - "neg_coordinates": ("STRING", {"multiline": False}), - "bbox_store": ("STRING", {"multiline": False}), - "bboxes": ("STRING", {"multiline": False}), - "bbox_format": ( - [ - 'xyxy', - 'xywh', - ], - ), - "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), - "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}), - "normalize": ("BOOLEAN", {"default": False}), - }, - "optional": { - "bg_image": ("IMAGE", ), - }, - } - - RETURN_TYPES = ("STRING", "STRING", "BBOX", "MASK", "IMAGE") - RETURN_NAMES = ("positive_coords", "negative_coords", "bbox", "bbox_mask", "cropped_image") - FUNCTION = "pointdata" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = """ -# WORK IN PROGRESS -Do not count on this as part of your workflow yet, -probably contains lots of bugs and stability is not -guaranteed!! - -## Graphical editor to create coordinates - -**Shift + click** to add a positive (green) point. -**Shift + right click** to add a negative (red) point. -**Ctrl + click** to draw a box. -**Right click on a point** to delete it. -Note that you can't delete from start/end of the points array. - -To add an image select the node and copy/paste or drag in the image. -Or from the bg_image input on queue (first frame of the batch). - -**THE IMAGE IS SAVED TO THE NODE AND WORKFLOW METADATA** -you can clear the image from the context menu by right clicking on the canvas - -""" - - def pointdata(self, points_store, bbox_store, width, height, coordinates, neg_coordinates, normalize, bboxes, bbox_format="xyxy", bg_image=None): - coordinates = json.loads(coordinates) - pos_coordinates = [] - for coord in coordinates: - coord['x'] = int(round(coord['x'])) - coord['y'] = int(round(coord['y'])) - if normalize: - norm_x = coord['x'] / width - norm_y = coord['y'] / height - pos_coordinates.append({'x': norm_x, 'y': norm_y}) - else: - pos_coordinates.append({'x': coord['x'], 'y': coord['y']}) - - if neg_coordinates: - coordinates = json.loads(neg_coordinates) - neg_coordinates = [] - for coord in coordinates: - coord['x'] = int(round(coord['x'])) - coord['y'] = int(round(coord['y'])) - if normalize: - norm_x = coord['x'] / width - norm_y = coord['y'] / height - neg_coordinates.append({'x': norm_x, 'y': norm_y}) - else: - neg_coordinates.append({'x': coord['x'], 'y': coord['y']}) - - # Create a blank mask - mask = np.zeros((height, width), dtype=np.uint8) - bboxes = json.loads(bboxes) - print(bboxes) - valid_bboxes = [] - for bbox in bboxes: - if (bbox.get("startX") is None or - bbox.get("startY") is None or - bbox.get("endX") is None or - bbox.get("endY") is None): - continue # Skip this bounding box if any value is None - else: - # Ensure that endX and endY are greater than startX and startY - x_min = min(int(bbox["startX"]), int(bbox["endX"])) - y_min = min(int(bbox["startY"]), int(bbox["endY"])) - x_max = max(int(bbox["startX"]), int(bbox["endX"])) - y_max = max(int(bbox["startY"]), int(bbox["endY"])) - - valid_bboxes.append((x_min, y_min, x_max, y_max)) - - bboxes_xyxy = [] - for bbox in valid_bboxes: - x_min, y_min, x_max, y_max = bbox - bboxes_xyxy.append((x_min, y_min, x_max, y_max)) - mask[y_min:y_max, x_min:x_max] = 1 # Fill the bounding box area with 1s - - if bbox_format == "xywh": - bboxes_xywh = [] - for bbox in valid_bboxes: - x_min, y_min, x_max, y_max = bbox - width = x_max - x_min - height = y_max - y_min - bboxes_xywh.append((x_min, y_min, width, height)) - bboxes = bboxes_xywh - else: - bboxes = bboxes_xyxy - - mask_tensor = torch.from_numpy(mask) - mask_tensor = mask_tensor.unsqueeze(0).float().cpu() - - if bg_image is not None and len(valid_bboxes) > 0: - x_min, y_min, x_max, y_max = bboxes[0] - cropped_image = bg_image[:, y_min:y_max, x_min:x_max, :] - - elif bg_image is not None: - cropped_image = bg_image - - if bg_image is None: - return (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor) - else: - transform = transforms.ToPILImage() - image = transform(bg_image[0].permute(2, 0, 1)) - buffered = io.BytesIO() - image.save(buffered, format="JPEG", quality=75) - - # Step 3: Encode the image bytes to a Base64 string - img_bytes = buffered.getvalue() - img_base64 = base64.b64encode(img_bytes).decode('utf-8') - - return { - "ui": {"bg_image": [img_base64]}, - "result": (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor, cropped_image) - } - -class CutAndDragOnPath: - RETURN_TYPES = ("IMAGE", "MASK",) - RETURN_NAMES = ("image","mask", ) - FUNCTION = "cutanddrag" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Cuts the masked area from the image, and drags it along the path. If inpaint is enabled, and no bg_image is provided, the cut area is filled using cv2 TELEA algorithm. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "coordinates": ("STRING", {"forceInput": True}), - "mask": ("MASK",), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "inpaint": ("BOOLEAN", {"default": True}), - }, - "optional": { - "bg_image": ("IMAGE",), - } - } - - def cutanddrag(self, image, coordinates, mask, frame_width, frame_height, inpaint, bg_image=None): - # Parse coordinates - coords_list = parse_json_tracks(coordinates) - - batch_size = len(coords_list[0]) - images_list = [] - masks_list = [] - - # Convert input image and mask to PIL - input_image = tensor2pil(image)[0] - input_mask = tensor2pil(mask)[0] - - # Find masked region bounds - mask_array = np.array(input_mask) - y_indices, x_indices = np.where(mask_array > 0) - if len(x_indices) == 0 or len(y_indices) == 0: - return (image, mask) - - x_min, x_max = x_indices.min(), x_indices.max() - y_min, y_max = y_indices.min(), y_indices.max() - - # Cut out the masked region - cut_width = x_max - x_min - cut_height = y_max - y_min - cut_image = input_image.crop((x_min, y_min, x_max, y_max)) - cut_mask = input_mask.crop((x_min, y_min, x_max, y_max)) - - # Create inpainted background - if bg_image is None: - background = input_image.copy() - # Inpaint the cut area - if inpaint: - import cv2 - border = 5 # Create small border around cut area for better inpainting - fill_mask = Image.new("L", background.size, 0) - draw = ImageDraw.Draw(fill_mask) - draw.rectangle([x_min-border, y_min-border, x_max+border, y_max+border], fill=255) - background = cv2.inpaint( - np.array(background), - np.array(fill_mask), - inpaintRadius=3, - flags=cv2.INPAINT_TELEA - ) - background = Image.fromarray(background) - else: - background = tensor2pil(bg_image)[0] - - # Create batch of images with cut region at different positions - for i in range(batch_size): - # Create new image - new_image = background.copy() - new_mask = Image.new("L", (frame_width, frame_height), 0) - - # Get target position from coordinates - for coords in coords_list: - target_x = int(coords[i]['x'] - cut_width/2) - target_y = int(coords[i]['y'] - cut_height/2) - - # Paste cut region at new position - new_image.paste(cut_image, (target_x, target_y), cut_mask) - new_mask.paste(cut_mask, (target_x, target_y)) - - # Convert to tensor and append - image_tensor = pil2tensor(new_image) - mask_tensor = pil2tensor(new_mask) - - images_list.append(image_tensor) - masks_list.append(mask_tensor) - - # Stack tensors into batches - out_images = torch.cat(images_list, dim=0).cpu().float() - out_masks = torch.cat(masks_list, dim=0) - - return (out_images, out_masks) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/image_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/image_nodes.py deleted file mode 100644 index 7cb9cae1189534ed1d35bd4d018c85a28a5f29d1..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/image_nodes.py +++ /dev/null @@ -1,3797 +0,0 @@ -import numpy as np -import time -import torch -import torch.nn.functional as F -import torchvision.transforms as T -import io -import base64 -import random -import math -import os -import re -import json -import importlib -from PIL.PngImagePlugin import PngInfo -try: - import cv2 -except: - print("OpenCV not installed") - pass -from PIL import ImageGrab, ImageDraw, ImageFont, Image, ImageOps - -from nodes import MAX_RESOLUTION, SaveImage -from comfy_extras.nodes_mask import ImageCompositeMasked -from comfy.cli_args import args -from comfy.utils import ProgressBar, common_upscale -import folder_paths -from comfy import model_management -try: - from server import PromptServer -except: - PromptServer = None -from concurrent.futures import ThreadPoolExecutor - -script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - -class ImagePass: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "image": ("IMAGE",), - }, - } - RETURN_TYPES = ("IMAGE",) - FUNCTION = "passthrough" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Passes the image through without modifying it. -""" - - def passthrough(self, image=None): - return image, - -class ColorMatch: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image_ref": ("IMAGE",), - "image_target": ("IMAGE",), - "method": ( - [ - 'mkl', - 'hm', - 'reinhard', - 'mvgd', - 'hm-mvgd-hm', - 'hm-mkl-hm', - ], { - "default": 'mkl' - }), - }, - "optional": { - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "multithread": ("BOOLEAN", {"default": True}), - } - } - - CATEGORY = "KJNodes/image" - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "colormatch" - DESCRIPTION = """ -color-matcher enables color transfer across images which comes in handy for automatic -color-grading of photographs, paintings and film sequences as well as light-field -and stopmotion corrections. - -The methods behind the mappings are based on the approach from Reinhard et al., -the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution -to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram -matching. As shown below our HM-MVGD-HM compound outperforms existing methods. -https://github.com/hahnec/color-matcher/ - -""" - - def colormatch(self, image_ref, image_target, method, strength=1.0, multithread=True): - try: - from color_matcher import ColorMatcher - except: - raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher") - - image_ref = image_ref.cpu() - image_target = image_target.cpu() - batch_size = image_target.size(0) - - images_target = image_target.squeeze() - images_ref = image_ref.squeeze() - - image_ref_np = images_ref.numpy() - images_target_np = images_target.numpy() - - def process(i): - cm = ColorMatcher() - image_target_np_i = images_target_np if batch_size == 1 else images_target[i].numpy() - image_ref_np_i = image_ref_np if image_ref.size(0) == 1 else images_ref[i].numpy() - try: - image_result = cm.transfer(src=image_target_np_i, ref=image_ref_np_i, method=method) - image_result = image_target_np_i + strength * (image_result - image_target_np_i) - return torch.from_numpy(image_result) - except Exception as e: - print(f"Thread {i} error: {e}") - return torch.from_numpy(image_target_np_i) # fallback - - if multithread and batch_size > 1: - max_threads = min(os.cpu_count() or 1, batch_size) - with ThreadPoolExecutor(max_workers=max_threads) as executor: - out = list(executor.map(process, range(batch_size))) - else: - out = [process(i) for i in range(batch_size)] - - out = torch.stack(out, dim=0).to(torch.float32) - out.clamp_(0, 1) - return (out,) - -class SaveImageWithAlpha: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ), - "mask": ("MASK", ), - "filename_prefix": ("STRING", {"default": "ComfyUI"})}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - - RETURN_TYPES = () - FUNCTION = "save_images_alpha" - OUTPUT_NODE = True - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Saves an image and mask as .PNG with the mask as the alpha channel. -""" - - def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None): - from PIL.PngImagePlugin import PngInfo - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - results = list() - if mask.dtype == torch.float16: - mask = mask.to(torch.float32) - def file_counter(): - max_counter = 0 - # Loop through the existing files - for existing_file in os.listdir(full_output_folder): - # Check if the file matches the expected format - match = re.fullmatch(fr"{filename}_(\d+)_?\.[a-zA-Z0-9]+", existing_file) - if match: - # Extract the numeric portion of the filename - file_counter = int(match.group(1)) - # Update the maximum counter value if necessary - if file_counter > max_counter: - max_counter = file_counter - return max_counter - - for image, alpha in zip(images, mask): - i = 255. * image.cpu().numpy() - a = 255. * alpha.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - - # Resize the mask to match the image size - a_resized = Image.fromarray(a).resize(img.size, Image.LANCZOS) - a_resized = np.clip(a_resized, 0, 255).astype(np.uint8) - img.putalpha(Image.fromarray(a_resized, mode='L')) - metadata = None - if not args.disable_metadata: - metadata = PngInfo() - if prompt is not None: - metadata.add_text("prompt", json.dumps(prompt)) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata.add_text(x, json.dumps(extra_pnginfo[x])) - - # Increment the counter by 1 to get the next available value - counter = file_counter() + 1 - file = f"{filename}_{counter:05}.png" - img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4) - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - - return { "ui": { "images": results } } - -class ImageConcanate: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - "direction": ( - [ 'right', - 'down', - 'left', - 'up', - ], - { - "default": 'right' - }), - "match_image_size": ("BOOLEAN", {"default": True}), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "concatenate" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Concatenates the image2 to image1 in the specified direction. -""" - - def concatenate(self, image1, image2, direction, match_image_size, first_image_shape=None): - # Check if the batch sizes are different - batch_size1 = image1.shape[0] - batch_size2 = image2.shape[0] - - if batch_size1 != batch_size2: - # Calculate the number of repetitions needed - max_batch_size = max(batch_size1, batch_size2) - repeats1 = max_batch_size - batch_size1 - repeats2 = max_batch_size - batch_size2 - - # Repeat the last image to match the largest batch size - if repeats1 > 0: - last_image1 = image1[-1].unsqueeze(0).repeat(repeats1, 1, 1, 1) - image1 = torch.cat([image1.clone(), last_image1], dim=0) - if repeats2 > 0: - last_image2 = image2[-1].unsqueeze(0).repeat(repeats2, 1, 1, 1) - image2 = torch.cat([image2.clone(), last_image2], dim=0) - - if match_image_size: - # Use first_image_shape if provided; otherwise, default to image1's shape - target_shape = first_image_shape if first_image_shape is not None else image1.shape - - original_height = image2.shape[1] - original_width = image2.shape[2] - original_aspect_ratio = original_width / original_height - - if direction in ['left', 'right']: - # Match the height and adjust the width to preserve aspect ratio - target_height = target_shape[1] # B, H, W, C format - target_width = int(target_height * original_aspect_ratio) - elif direction in ['up', 'down']: - # Match the width and adjust the height to preserve aspect ratio - target_width = target_shape[2] # B, H, W, C format - target_height = int(target_width / original_aspect_ratio) - - # Adjust image2 to the expected format for common_upscale - image2_for_upscale = image2.movedim(-1, 1) # Move C to the second position (B, C, H, W) - - # Resize image2 to match the target size while preserving aspect ratio - image2_resized = common_upscale(image2_for_upscale, target_width, target_height, "lanczos", "disabled") - - # Adjust image2 back to the original format (B, H, W, C) after resizing - image2_resized = image2_resized.movedim(1, -1) - else: - image2_resized = image2 - - # Ensure both images have the same number of channels - channels_image1 = image1.shape[-1] - channels_image2 = image2_resized.shape[-1] - - if channels_image1 != channels_image2: - if channels_image1 < channels_image2: - # Add alpha channel to image1 if image2 has it - alpha_channel = torch.ones((*image1.shape[:-1], channels_image2 - channels_image1), device=image1.device) - image1 = torch.cat((image1, alpha_channel), dim=-1) - else: - # Add alpha channel to image2 if image1 has it - alpha_channel = torch.ones((*image2_resized.shape[:-1], channels_image1 - channels_image2), device=image2_resized.device) - image2_resized = torch.cat((image2_resized, alpha_channel), dim=-1) - - - # Concatenate based on the specified direction - if direction == 'right': - concatenated_image = torch.cat((image1, image2_resized), dim=2) # Concatenate along width - elif direction == 'down': - concatenated_image = torch.cat((image1, image2_resized), dim=1) # Concatenate along height - elif direction == 'left': - concatenated_image = torch.cat((image2_resized, image1), dim=2) # Concatenate along width - elif direction == 'up': - concatenated_image = torch.cat((image2_resized, image1), dim=1) # Concatenate along height - return concatenated_image, - -import torch # Make sure you have PyTorch installed - -class ImageConcatFromBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "images": ("IMAGE",), - "num_columns": ("INT", {"default": 3, "min": 1, "max": 255, "step": 1}), - "match_image_size": ("BOOLEAN", {"default": False}), - "max_resolution": ("INT", {"default": 4096}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "concat" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ - Concatenates images from a batch into a grid with a specified number of columns. - """ - - def concat(self, images, num_columns, match_image_size, max_resolution): - # Assuming images is a batch of images (B, H, W, C) - batch_size, height, width, channels = images.shape - num_rows = (batch_size + num_columns - 1) // num_columns # Calculate number of rows - - print(f"Initial dimensions: batch_size={batch_size}, height={height}, width={width}, channels={channels}") - print(f"num_rows={num_rows}, num_columns={num_columns}") - - if match_image_size: - target_shape = images[0].shape - - resized_images = [] - for image in images: - original_height = image.shape[0] - original_width = image.shape[1] - original_aspect_ratio = original_width / original_height - - if original_aspect_ratio > 1: - target_height = target_shape[0] - target_width = int(target_height * original_aspect_ratio) - else: - target_width = target_shape[1] - target_height = int(target_width / original_aspect_ratio) - - print(f"Resizing image from ({original_height}, {original_width}) to ({target_height}, {target_width})") - - # Resize the image to match the target size while preserving aspect ratio - resized_image = common_upscale(image.movedim(-1, 0), target_width, target_height, "lanczos", "disabled") - resized_image = resized_image.movedim(0, -1) # Move channels back to the last dimension - resized_images.append(resized_image) - - # Convert the list of resized images back to a tensor - images = torch.stack(resized_images) - - height, width = target_shape[:2] # Update height and width - - # Initialize an empty grid - grid_height = num_rows * height - grid_width = num_columns * width - - print(f"Grid dimensions before scaling: grid_height={grid_height}, grid_width={grid_width}") - - # Original scale factor calculation remains unchanged - scale_factor = min(max_resolution / grid_height, max_resolution / grid_width, 1.0) - - # Apply scale factor to height and width - scaled_height = height * scale_factor - scaled_width = width * scale_factor - - # Round scaled dimensions to the nearest number divisible by 8 - height = max(1, int(round(scaled_height / 8) * 8)) - width = max(1, int(round(scaled_width / 8) * 8)) - - if abs(scaled_height - height) > 4: - height = max(1, int(round((scaled_height + 4) / 8) * 8)) - if abs(scaled_width - width) > 4: - width = max(1, int(round((scaled_width + 4) / 8) * 8)) - - # Recalculate grid dimensions with adjusted height and width - grid_height = num_rows * height - grid_width = num_columns * width - print(f"Grid dimensions after scaling: grid_height={grid_height}, grid_width={grid_width}") - print(f"Final image dimensions: height={height}, width={width}") - - grid = torch.zeros((grid_height, grid_width, channels), dtype=images.dtype) - - for idx, image in enumerate(images): - resized_image = torch.nn.functional.interpolate(image.unsqueeze(0).permute(0, 3, 1, 2), size=(height, width), mode="bilinear").squeeze().permute(1, 2, 0) - row = idx // num_columns - col = idx % num_columns - grid[row*height:(row+1)*height, col*width:(col+1)*width, :] = resized_image - - return grid.unsqueeze(0), - - -class ImageGridComposite2x2: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - "image3": ("IMAGE",), - "image4": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "compositegrid" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Concatenates the 4 input images into a 2x2 grid. -""" - - def compositegrid(self, image1, image2, image3, image4): - top_row = torch.cat((image1, image2), dim=2) - bottom_row = torch.cat((image3, image4), dim=2) - grid = torch.cat((top_row, bottom_row), dim=1) - return (grid,) - -class ImageGridComposite3x3: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - "image3": ("IMAGE",), - "image4": ("IMAGE",), - "image5": ("IMAGE",), - "image6": ("IMAGE",), - "image7": ("IMAGE",), - "image8": ("IMAGE",), - "image9": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "compositegrid" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Concatenates the 9 input images into a 3x3 grid. -""" - - def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9): - top_row = torch.cat((image1, image2, image3), dim=2) - mid_row = torch.cat((image4, image5, image6), dim=2) - bottom_row = torch.cat((image7, image8, image9), dim=2) - grid = torch.cat((top_row, mid_row, bottom_row), dim=1) - return (grid,) - -class ImageBatchTestPattern: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "batch_size": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), - "start_from": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "text_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), - "text_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), - "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), - "font_size": ("INT", {"default": 255,"min": 8, "max": 4096, "step": 1}), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "generatetestpattern" - CATEGORY = "KJNodes/text" - - def generatetestpattern(self, batch_size, font, font_size, start_from, width, height, text_x, text_y): - out = [] - # Generate the sequential numbers for each image - numbers = np.arange(start_from, start_from + batch_size) - font_path = folder_paths.get_full_path("kjnodes_fonts", font) - - for number in numbers: - # Create a black image with the number as a random color text - image = Image.new("RGB", (width, height), color='black') - draw = ImageDraw.Draw(image) - - # Generate a random color for the text - font_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) - - font = ImageFont.truetype(font_path, font_size) - - # Get the size of the text and position it in the center - text = str(number) - - try: - draw.text((text_x, text_y), text, font=font, fill=font_color, features=['-liga']) - except: - draw.text((text_x, text_y), text, font=font, fill=font_color,) - - # Convert the image to a numpy array and normalize the pixel values - image_np = np.array(image).astype(np.float32) / 255.0 - image_tensor = torch.from_numpy(image_np).unsqueeze(0) - out.append(image_tensor) - out_tensor = torch.cat(out, dim=0) - - return (out_tensor,) - -class ImageGrabPIL: - - @classmethod - def IS_CHANGED(cls): - - return - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "screencap" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Captures an area specified by screen coordinates. -Can be used for realtime diffusion with autoqueue. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), - "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), - "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}), - }, - } - - def screencap(self, x, y, width, height, num_frames, delay): - start_time = time.time() - captures = [] - bbox = (x, y, x + width, y + height) - - for _ in range(num_frames): - # Capture screen - screen_capture = ImageGrab.grab(bbox=bbox) - screen_capture_torch = torch.from_numpy(np.array(screen_capture, dtype=np.float32) / 255.0).unsqueeze(0) - captures.append(screen_capture_torch) - - # Wait for a short delay if more than one frame is to be captured - if num_frames > 1: - time.sleep(delay) - - elapsed_time = time.time() - start_time - print(f"screengrab took {elapsed_time} seconds.") - - return (torch.cat(captures, dim=0),) - -class Screencap_mss: - - @classmethod - def IS_CHANGED(s, **kwargs): - return float("NaN") - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "screencap" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Captures an area specified by screen coordinates. -Can be used for realtime diffusion with autoqueue. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "x": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}), - "y": ("INT", {"default": 0,"min": 0, "max": 10000, "step": 1}), - "width": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}), - "height": ("INT", {"default": 512,"min": 0, "max": 10000, "step": 1}), - "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}), - "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}), - }, - } - - def screencap(self, x, y, width, height, num_frames, delay): - from mss import mss - captures = [] - with mss() as sct: - bbox = {'top': y, 'left': x, 'width': width, 'height': height} - - for _ in range(num_frames): - sct_img = sct.grab(bbox) - img_np = np.array(sct_img) - img_torch = torch.from_numpy(img_np[..., [2, 1, 0]]).float() / 255.0 - captures.append(img_torch) - - if num_frames > 1: - time.sleep(delay) - - return (torch.stack(captures, 0),) - -class WebcamCaptureCV2: - - @classmethod - def IS_CHANGED(cls): - return - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "capture" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = """ -Captures a frame from a webcam using CV2. -Can be used for realtime diffusion with autoqueue. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}), - "cam_index": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "release": ("BOOLEAN", {"default": False}), - }, - } - - def capture(self, x, y, cam_index, width, height, release): - # Check if the camera index has changed or the capture object doesn't exist - if not hasattr(self, "cap") or self.cap is None or self.current_cam_index != cam_index: - if hasattr(self, "cap") and self.cap is not None: - self.cap.release() - self.current_cam_index = cam_index - self.cap = cv2.VideoCapture(cam_index) - try: - self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width) - self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height) - except: - pass - if not self.cap.isOpened(): - raise Exception("Could not open webcam") - - ret, frame = self.cap.read() - if not ret: - raise Exception("Failed to capture image from webcam") - - # Crop the frame to the specified bbox - frame = frame[y:y+height, x:x+width] - img_torch = torch.from_numpy(frame[..., [2, 1, 0]]).float() / 255.0 - - if release: - self.cap.release() - self.cap = None - - return (img_torch.unsqueeze(0),) - -class AddLabel: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image":("IMAGE",), - "text_x": ("INT", {"default": 10, "min": 0, "max": 4096, "step": 1}), - "text_y": ("INT", {"default": 2, "min": 0, "max": 4096, "step": 1}), - "height": ("INT", {"default": 48, "min": -1, "max": 4096, "step": 1}), - "font_size": ("INT", {"default": 32, "min": 0, "max": 4096, "step": 1}), - "font_color": ("STRING", {"default": "white"}), - "label_color": ("STRING", {"default": "black"}), - "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), - "text": ("STRING", {"default": "Text"}), - "direction": ( - [ 'up', - 'down', - 'left', - 'right', - 'overlay' - ], - { - "default": 'up' - }), - }, - "optional":{ - "caption": ("STRING", {"default": "", "forceInput": True}), - } - } - RETURN_TYPES = ("IMAGE",) - FUNCTION = "addlabel" - CATEGORY = "KJNodes/text" - DESCRIPTION = """ -Creates a new with the given text, and concatenates it to -either above or below the input image. -Note that this changes the input image's height! -Fonts are loaded from this folder: -ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts -""" - - def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction, caption=""): - batch_size = image.shape[0] - width = image.shape[2] - - font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf") if font == "TTNorms-Black.otf" else folder_paths.get_full_path("kjnodes_fonts", font) - - def process_image(input_image, caption_text): - font = ImageFont.truetype(font_path, font_size) - words = caption_text.split() - lines = [] - current_line = [] - current_line_width = 0 - - for word in words: - word_width = font.getbbox(word)[2] - if current_line_width + word_width <= width - 2 * text_x: - current_line.append(word) - current_line_width += word_width + font.getbbox(" ")[2] # Add space width - else: - lines.append(" ".join(current_line)) - current_line = [word] - current_line_width = word_width - - if current_line: - lines.append(" ".join(current_line)) - - if direction == 'overlay': - pil_image = Image.fromarray((input_image.cpu().numpy() * 255).astype(np.uint8)) - else: - if height == -1: - # Adjust the image height automatically - margin = 8 - required_height = (text_y + len(lines) * font_size) + margin # Calculate required height - pil_image = Image.new("RGB", (width, required_height), label_color) - else: - # Initialize with a minimal height - label_image = Image.new("RGB", (width, height), label_color) - pil_image = label_image - - draw = ImageDraw.Draw(pil_image) - - - y_offset = text_y - for line in lines: - try: - draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga']) - except: - draw.text((text_x, y_offset), line, font=font, fill=font_color) - y_offset += font_size - - processed_image = torch.from_numpy(np.array(pil_image).astype(np.float32) / 255.0).unsqueeze(0) - return processed_image - - if caption == "": - processed_images = [process_image(img, text) for img in image] - else: - assert len(caption) == batch_size, f"Number of captions {(len(caption))} does not match number of images" - processed_images = [process_image(img, cap) for img, cap in zip(image, caption)] - processed_batch = torch.cat(processed_images, dim=0) - - # Combine images based on direction - if direction == 'down': - combined_images = torch.cat((image, processed_batch), dim=1) - elif direction == 'up': - combined_images = torch.cat((processed_batch, image), dim=1) - elif direction == 'left': - processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2) - combined_images = torch.cat((processed_batch, image), dim=2) - elif direction == 'right': - processed_batch = torch.rot90(processed_batch, 3, (2, 3)).permute(0, 3, 1, 2) - combined_images = torch.cat((image, processed_batch), dim=2) - else: - combined_images = processed_batch - - return (combined_images,) - -class GetImageSizeAndCount: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE","INT", "INT", "INT",) - RETURN_NAMES = ("image", "width", "height", "count",) - FUNCTION = "getsize" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Returns width, height and batch size of the image, -and passes it through unchanged. - -""" - - def getsize(self, image): - width = image.shape[2] - height = image.shape[1] - count = image.shape[0] - return {"ui": { - "text": [f"{count}x{width}x{height}"]}, - "result": (image, width, height, count) - } - -class GetLatentSizeAndCount: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "latent": ("LATENT",), - }} - - RETURN_TYPES = ("LATENT","INT", "INT", "INT", "INT", "INT") - RETURN_NAMES = ("latent", "batch_size", "channels", "frames", "width", "height") - FUNCTION = "getsize" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Returns latent tensor dimensions, -and passes the latent through unchanged. - -""" - def getsize(self, latent): - if len(latent["samples"].shape) == 5: - B, C, T, H, W = latent["samples"].shape - elif len(latent["samples"].shape) == 4: - B, C, H, W = latent["samples"].shape - T = 0 - else: - raise ValueError("Invalid latent shape") - - return {"ui": { - "text": [f"{B}x{C}x{T}x{H}x{W}"]}, - "result": (latent, B, C, T, H, W) - } - -class ImageBatchRepeatInterleaving: - - RETURN_TYPES = ("IMAGE", "MASK",) - FUNCTION = "repeat" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Repeats each image in a batch by the specified number of times. -Example batch of 5 images: 0, 1 ,2, 3, 4 -with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4 -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "repeats": ("INT", {"default": 1, "min": 1, "max": 4096}), - }, - "optional": { - "mask": ("MASK",), - } - } - - def repeat(self, images, repeats, mask=None): - original_count = images.shape[0] - total_count = original_count * repeats - - repeated_images = torch.repeat_interleave(images, repeats=repeats, dim=0) - if mask is not None: - mask = torch.repeat_interleave(mask, repeats=repeats, dim=0) - else: - mask = torch.zeros((total_count, images.shape[1], images.shape[2]), - device=images.device, dtype=images.dtype) - for i in range(original_count): - mask[i * repeats] = 1.0 - - print("mask shape", mask.shape) - return (repeated_images, mask) - -class ImageUpscaleWithModelBatched: - @classmethod - def INPUT_TYPES(s): - return {"required": { "upscale_model": ("UPSCALE_MODEL",), - "images": ("IMAGE",), - "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Same as ComfyUI native model upscaling node, -but allows setting sub-batches for reduced VRAM usage. -""" - def upscale(self, upscale_model, images, per_batch): - - device = model_management.get_torch_device() - upscale_model.to(device) - in_img = images.movedim(-1,-3) - - steps = in_img.shape[0] - pbar = ProgressBar(steps) - t = [] - - for start_idx in range(0, in_img.shape[0], per_batch): - sub_images = upscale_model(in_img[start_idx:start_idx+per_batch].to(device)) - t.append(sub_images.cpu()) - # Calculate the number of images processed in this batch - batch_count = sub_images.shape[0] - # Update the progress bar by the number of images processed in this batch - pbar.update(batch_count) - upscale_model.cpu() - - t = torch.cat(t, dim=0).permute(0, 2, 3, 1).cpu() - - return (t,) - -class ImageNormalize_Neg1_To_1: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "images": ("IMAGE",), - - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "normalize" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Normalize the images to be in the range [-1, 1] -""" - - def normalize(self,images): - images = images * 2.0 - 1.0 - return (images,) - -class RemapImageRange: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}), - "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}), - "clamp": ("BOOLEAN", {"default": True}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "remap" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Remaps the image values to the specified range. -""" - - def remap(self, image, min, max, clamp): - if image.dtype == torch.float16: - image = image.to(torch.float32) - image = min + image * (max - min) - if clamp: - image = torch.clamp(image, min=0.0, max=1.0) - return (image, ) - -class SplitImageChannels: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - }, - } - - RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK") - RETURN_NAMES = ("red", "green", "blue", "mask") - FUNCTION = "split" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Splits image channels into images where the selected channel -is repeated for all channels, and the alpha as a mask. -""" - - def split(self, image): - red = image[:, :, :, 0:1] # Red channel - green = image[:, :, :, 1:2] # Green channel - blue = image[:, :, :, 2:3] # Blue channel - alpha = image[:, :, :, 3:4] # Alpha channel - alpha = alpha.squeeze(-1) - - # Repeat the selected channel for all channels - red = torch.cat([red, red, red], dim=3) - green = torch.cat([green, green, green], dim=3) - blue = torch.cat([blue, blue, blue], dim=3) - return (red, green, blue, alpha) - -class MergeImageChannels: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "red": ("IMAGE",), - "green": ("IMAGE",), - "blue": ("IMAGE",), - - }, - "optional": { - "alpha": ("MASK", {"default": None}), - }, - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "merge" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Merges channel data into an image. -""" - - def merge(self, red, green, blue, alpha=None): - image = torch.stack([ - red[..., 0, None], # Red channel - green[..., 1, None], # Green channel - blue[..., 2, None] # Blue channel - ], dim=-1) - image = image.squeeze(-2) - if alpha is not None: - image = torch.cat([image, alpha.unsqueeze(-1)], dim=-1) - return (image,) - -class ImagePadForOutpaintMasked: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - }, - "optional": { - "mask": ("MASK",), - } - } - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "expand_image" - - CATEGORY = "image" - - def expand_image(self, image, left, top, right, bottom, feathering, mask=None): - if mask is not None: - if torch.allclose(mask, torch.zeros_like(mask)): - print("Warning: The incoming mask is fully black. Handling it as None.") - mask = None - B, H, W, C = image.size() - - new_image = torch.ones( - (B, H + top + bottom, W + left + right, C), - dtype=torch.float32, - ) * 0.5 - - new_image[:, top:top + H, left:left + W, :] = image - - if mask is None: - new_mask = torch.ones( - (B, H + top + bottom, W + left + right), - dtype=torch.float32, - ) - - t = torch.zeros( - (B, H, W), - dtype=torch.float32 - ) - else: - # If a mask is provided, pad it to fit the new image size - mask = F.pad(mask, (left, right, top, bottom), mode='constant', value=0) - mask = 1 - mask - t = torch.zeros_like(mask) - - if feathering > 0 and feathering * 2 < H and feathering * 2 < W: - - for i in range(H): - for j in range(W): - dt = i if top != 0 else H - db = H - i if bottom != 0 else H - - dl = j if left != 0 else W - dr = W - j if right != 0 else W - - d = min(dt, db, dl, dr) - - if d >= feathering: - continue - - v = (feathering - d) / feathering - - if mask is None: - t[:, i, j] = v * v - else: - t[:, top + i, left + j] = v * v - - if mask is None: - new_mask[:, top:top + H, left:left + W] = t - return (new_image, new_mask,) - else: - return (new_image, mask,) - -class ImagePadForOutpaintTargetSize: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "target_width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "target_height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "upscale_method": (s.upscale_methods,), - }, - "optional": { - "mask": ("MASK",), - } - } - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "expand_image" - - CATEGORY = "image" - - def expand_image(self, image, target_width, target_height, feathering, upscale_method, mask=None): - B, H, W, C = image.size() - new_height = H - new_width = W - # Calculate the scaling factor while maintaining aspect ratio - scaling_factor = min(target_width / W, target_height / H) - - # Check if the image needs to be downscaled - if scaling_factor < 1: - image = image.movedim(-1,1) - # Calculate the new width and height after downscaling - new_width = int(W * scaling_factor) - new_height = int(H * scaling_factor) - - # Downscale the image - image_scaled = common_upscale(image, new_width, new_height, upscale_method, "disabled").movedim(1,-1) - if mask is not None: - mask_scaled = mask.unsqueeze(0) # Add an extra dimension for batch size - mask_scaled = F.interpolate(mask_scaled, size=(new_height, new_width), mode="nearest") - mask_scaled = mask_scaled.squeeze(0) # Remove the extra dimension after interpolation - else: - mask_scaled = mask - else: - # If downscaling is not needed, use the original image dimensions - image_scaled = image - mask_scaled = mask - - # Calculate how much padding is needed to reach the target dimensions - pad_top = max(0, (target_height - new_height) // 2) - pad_bottom = max(0, target_height - new_height - pad_top) - pad_left = max(0, (target_width - new_width) // 2) - pad_right = max(0, target_width - new_width - pad_left) - - # Now call the original expand_image with the calculated padding - return ImagePadForOutpaintMasked.expand_image(self, image_scaled, pad_left, pad_top, pad_right, pad_bottom, feathering, mask_scaled) - -class ImagePrepForICLora: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "reference_image": ("IMAGE",), - "output_width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "output_height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "border_width": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}), - }, - "optional": { - "latent_image": ("IMAGE",), - "latent_mask": ("MASK",), - "reference_mask": ("MASK",), - } - } - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "expand_image" - - CATEGORY = "image" - - def expand_image(self, reference_image, output_width, output_height, border_width, latent_image=None, reference_mask=None, latent_mask=None): - - if reference_mask is not None: - if torch.allclose(reference_mask, torch.zeros_like(reference_mask)): - print("Warning: The incoming mask is fully black. Handling it as None.") - reference_mask = None - image = reference_image - if latent_image is not None: - if image.shape[0] != latent_image.shape[0]: - image = image.repeat(latent_image.shape[0], 1, 1, 1) - B, H, W, C = image.size() - - # Handle mask - if reference_mask is not None: - resized_mask = torch.nn.functional.interpolate( - reference_mask.unsqueeze(1), - size=(H, W), - mode='nearest' - ).squeeze(1) - print(resized_mask.shape) - image = image * resized_mask.unsqueeze(-1) - - # Calculate new width maintaining aspect ratio - new_width = int((W / H) * output_height) - - # Resize image to new height while maintaining aspect ratio - resized_image = common_upscale(image.movedim(-1,1), new_width, output_height, "lanczos", "disabled").movedim(1,-1) - - # Create padded image - if latent_image is None: - pad_image = torch.zeros((B, output_height, output_width, C), device=image.device) - else: - resized_latent_image = common_upscale(latent_image.movedim(-1,1), output_width, output_height, "lanczos", "disabled").movedim(1,-1) - pad_image = resized_latent_image - if latent_mask is not None: - resized_latent_mask = torch.nn.functional.interpolate( - latent_mask.unsqueeze(1), - size=(pad_image.shape[1], pad_image.shape[2]), - mode='nearest' - ).squeeze(1) - - if border_width > 0: - border = torch.zeros((B, output_height, border_width, C), device=image.device) - padded_image = torch.cat((resized_image, border, pad_image), dim=2) - if latent_mask is not None: - padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) - padded_mask[:, :, (new_width + border_width):] = resized_latent_mask - else: - padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) - padded_mask[:, :, :new_width + border_width] = 0 - else: - padded_image = torch.cat((resized_image, pad_image), dim=2) - if latent_mask is not None: - padded_mask = torch.zeros((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) - padded_mask[:, :, new_width:] = resized_latent_mask - else: - padded_mask = torch.ones((B, padded_image.shape[1], padded_image.shape[2]), device=image.device) - padded_mask[:, :, :new_width] = 0 - - return (padded_image, padded_mask) - - -class ImageAndMaskPreview(SaveImage): - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() - self.type = "temp" - self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) - self.compress_level = 4 - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "mask_opacity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - "mask_color": ("STRING", {"default": "255, 255, 255"}), - "pass_through": ("BOOLEAN", {"default": False}), - }, - "optional": { - "image": ("IMAGE",), - "mask": ("MASK",), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("composite",) - FUNCTION = "execute" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Preview an image or a mask, when both inputs are used -composites the mask on top of the image. -with pass_through on the preview is disabled and the -composite is returned from the composite slot instead, -this allows for the preview to be passed for video combine -nodes for example. -""" - - def execute(self, mask_opacity, mask_color, pass_through, filename_prefix="ComfyUI", image=None, mask=None, prompt=None, extra_pnginfo=None): - if mask is not None and image is None: - preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) - elif mask is None and image is not None: - preview = image - elif mask is not None and image is not None: - mask_adjusted = mask * mask_opacity - mask_image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3).clone() - - if ',' in mask_color: - color_list = np.clip([int(channel) for channel in mask_color.split(',')], 0, 255) # RGB format - else: - mask_color = mask_color.lstrip('#') - color_list = [int(mask_color[i:i+2], 16) for i in (0, 2, 4)] # Hex format - mask_image[:, :, :, 0] = color_list[0] / 255 # Red channel - mask_image[:, :, :, 1] = color_list[1] / 255 # Green channel - mask_image[:, :, :, 2] = color_list[2] / 255 # Blue channel - - preview, = ImageCompositeMasked.composite(self, image, mask_image, 0, 0, True, mask_adjusted) - if pass_through: - return (preview, ) - return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo)) - -def crossfade(images_1, images_2, alpha): - crossfade = (1 - alpha) * images_1 + alpha * images_2 - return crossfade -def ease_in(t): - return t * t -def ease_out(t): - return 1 - (1 - t) * (1 - t) -def ease_in_out(t): - return 3 * t * t - 2 * t * t * t -def bounce(t): - if t < 0.5: - return ease_out(t * 2) * 0.5 - else: - return ease_in((t - 0.5) * 2) * 0.5 + 0.5 -def elastic(t): - return math.sin(13 * math.pi / 2 * t) * math.pow(2, 10 * (t - 1)) -def glitchy(t): - return t + 0.1 * math.sin(40 * t) -def exponential_ease_out(t): - return 1 - (1 - t) ** 4 - -easing_functions = { - "linear": lambda t: t, - "ease_in": ease_in, - "ease_out": ease_out, - "ease_in_out": ease_in_out, - "bounce": bounce, - "elastic": elastic, - "glitchy": glitchy, - "exponential_ease_out": exponential_ease_out, -} - -class CrossFadeImages: - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "crossfadeimages" - CATEGORY = "KJNodes/image" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images_1": ("IMAGE",), - "images_2": ("IMAGE",), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), - "transition_start_index": ("INT", {"default": 1,"min": -4096, "max": 4096, "step": 1}), - "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), - "start_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), - "end_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), - }, - } - - def crossfadeimages(self, images_1, images_2, transition_start_index, transitioning_frames, interpolation, start_level, end_level): - - crossfade_images = [] - - if transition_start_index < 0: - transition_start_index = len(images_1) + transition_start_index - if transition_start_index < 0: - raise ValueError("Transition start index is out of range for images_1.") - - transitioning_frames = min(transitioning_frames, len(images_1) - transition_start_index, len(images_2)) - - alphas = torch.linspace(start_level, end_level, transitioning_frames) - for i in range(transitioning_frames): - alpha = alphas[i] - image1 = images_1[transition_start_index + i] - image2 = images_2[i] - easing_function = easing_functions.get(interpolation) - alpha = easing_function(alpha) # Apply the easing function to the alpha value - - crossfade_image = crossfade(image1, image2, alpha) - crossfade_images.append(crossfade_image) - - # Convert crossfade_images to tensor - crossfade_images = torch.stack(crossfade_images, dim=0) - - # Append the beginning of images_1 (before the transition) - beginning_images_1 = images_1[:transition_start_index] - crossfade_images = torch.cat([beginning_images_1, crossfade_images], dim=0) - - # Append the remaining frames of images_2 (after the transition) - remaining_images_2 = images_2[transitioning_frames:] - if len(remaining_images_2) > 0: - crossfade_images = torch.cat([crossfade_images, remaining_images_2], dim=0) - - return (crossfade_images, ) - -class CrossFadeImagesMulti: - RETURN_TYPES = ("IMAGE",) - FUNCTION = "crossfadeimages" - CATEGORY = "KJNodes/image" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "image_1": ("IMAGE",), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), - "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), - }, - "optional": { - "image_2": ("IMAGE",), - } - } - - def crossfadeimages(self, inputcount, transitioning_frames, interpolation, **kwargs): - - image_1 = kwargs["image_1"] - first_image_shape = image_1.shape - first_image_device = image_1.device - height = image_1.shape[1] - width = image_1.shape[2] - - easing_function = easing_functions[interpolation] - - for c in range(1, inputcount): - frames = [] - new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device) - new_image_height = new_image.shape[1] - new_image_width = new_image.shape[2] - - if new_image_height != height or new_image_width != width: - new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled") - new_image = new_image.movedim(1, -1) # Move channels back to the last dimension - - last_frame_image_1 = image_1[-1] - first_frame_image_2 = new_image[0] - - for frame in range(transitioning_frames): - t = frame / (transitioning_frames - 1) - alpha = easing_function(t) - alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device) - frame_image = crossfade(last_frame_image_1, first_frame_image_2, alpha_tensor) - frames.append(frame_image) - - frames = torch.stack(frames) - image_1 = torch.cat((image_1, frames, new_image), dim=0) - - return image_1, - -def transition_images(images_1, images_2, alpha, transition_type, blur_radius, reverse): - width = images_1.shape[1] - height = images_1.shape[0] - - mask = torch.zeros_like(images_1, device=images_1.device) - - alpha = alpha.item() - if reverse: - alpha = 1 - alpha - - #transitions from matteo's essential nodes - if "horizontal slide" in transition_type: - pos = round(width * alpha) - mask[:, :pos, :] = 1.0 - elif "vertical slide" in transition_type: - pos = round(height * alpha) - mask[:pos, :, :] = 1.0 - elif "box" in transition_type: - box_w = round(width * alpha) - box_h = round(height * alpha) - x1 = (width - box_w) // 2 - y1 = (height - box_h) // 2 - x2 = x1 + box_w - y2 = y1 + box_h - mask[y1:y2, x1:x2, :] = 1.0 - elif "circle" in transition_type: - radius = math.ceil(math.sqrt(pow(width, 2) + pow(height, 2)) * alpha / 2) - c_x = width // 2 - c_y = height // 2 - x = torch.arange(0, width, dtype=torch.float32, device="cpu") - y = torch.arange(0, height, dtype=torch.float32, device="cpu") - y, x = torch.meshgrid((y, x), indexing="ij") - circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2) - mask[circle] = 1.0 - elif "horizontal door" in transition_type: - bar = math.ceil(height * alpha / 2) - if bar > 0: - mask[:bar, :, :] = 1.0 - mask[-bar:,:, :] = 1.0 - elif "vertical door" in transition_type: - bar = math.ceil(width * alpha / 2) - if bar > 0: - mask[:, :bar,:] = 1.0 - mask[:, -bar:,:] = 1.0 - elif "fade" in transition_type: - mask[:, :, :] = alpha - - mask = gaussian_blur(mask, blur_radius) - - return images_1 * (1 - mask) + images_2 * mask - -def gaussian_blur(mask, blur_radius): - if blur_radius > 0: - kernel_size = int(blur_radius * 2) + 1 - if kernel_size % 2 == 0: - kernel_size += 1 # Ensure kernel size is odd - sigma = blur_radius / 3 - x = torch.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1, dtype=torch.float32) - x = torch.exp(-0.5 * (x / sigma) ** 2) - kernel1d = x / x.sum() - kernel2d = kernel1d[:, None] * kernel1d[None, :] - kernel2d = kernel2d.to(mask.device) - kernel2d = kernel2d.expand(mask.shape[2], 1, kernel2d.shape[0], kernel2d.shape[1]) - mask = mask.permute(2, 0, 1).unsqueeze(0) # Change to [C, H, W] and add batch dimension - mask = F.conv2d(mask, kernel2d, padding=kernel_size // 2, groups=mask.shape[1]) - mask = mask.squeeze(0).permute(1, 2, 0) # Change back to [H, W, C] - return mask - -easing_functions = { - "linear": lambda t: t, - "ease_in": ease_in, - "ease_out": ease_out, - "ease_in_out": ease_in_out, - "bounce": bounce, - "elastic": elastic, - "glitchy": glitchy, - "exponential_ease_out": exponential_ease_out, -} - -class TransitionImagesMulti: - RETURN_TYPES = ("IMAGE",) - FUNCTION = "transition" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates transitions between images. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "image_1": ("IMAGE",), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), - "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],), - "transitioning_frames": ("INT", {"default": 2,"min": 2, "max": 4096, "step": 1}), - "blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}), - "reverse": ("BOOLEAN", {"default": False}), - "device": (["CPU", "GPU"], {"default": "CPU"}), - }, - "optional": { - "image_2": ("IMAGE",), - } - } - - def transition(self, inputcount, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse, **kwargs): - - gpu = model_management.get_torch_device() - - image_1 = kwargs["image_1"] - height = image_1.shape[1] - width = image_1.shape[2] - first_image_shape = image_1.shape - first_image_device = image_1.device - - easing_function = easing_functions[interpolation] - - for c in range(1, inputcount): - frames = [] - new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).to(first_image_device) - new_image_height = new_image.shape[1] - new_image_width = new_image.shape[2] - - if new_image_height != height or new_image_width != width: - new_image = common_upscale(new_image.movedim(-1, 1), width, height, "lanczos", "disabled") - new_image = new_image.movedim(1, -1) # Move channels back to the last dimension - - last_frame_image_1 = image_1[-1] - first_frame_image_2 = new_image[0] - if device == "GPU": - last_frame_image_1 = last_frame_image_1.to(gpu) - first_frame_image_2 = first_frame_image_2.to(gpu) - - if reverse: - last_frame_image_1, first_frame_image_2 = first_frame_image_2, last_frame_image_1 - - for frame in range(transitioning_frames): - t = frame / (transitioning_frames - 1) - alpha = easing_function(t) - alpha_tensor = torch.tensor(alpha, dtype=last_frame_image_1.dtype, device=last_frame_image_1.device) - frame_image = transition_images(last_frame_image_1, first_frame_image_2, alpha_tensor, transition_type, blur_radius, reverse) - frames.append(frame_image) - - frames = torch.stack(frames).cpu() - image_1 = torch.cat((image_1, frames, new_image), dim=0) - - return image_1.cpu(), - -class TransitionImagesInBatch: - RETURN_TYPES = ("IMAGE",) - FUNCTION = "transition" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates transitions between images in a batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), - "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],), - "transitioning_frames": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), - "blur_radius": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 100.0, "step": 0.1}), - "reverse": ("BOOLEAN", {"default": False}), - "device": (["CPU", "GPU"], {"default": "CPU"}), - }, - } - - #transitions from matteo's essential nodes - def transition(self, images, transitioning_frames, transition_type, interpolation, device, blur_radius, reverse): - if images.shape[0] == 1: - return images, - - gpu = model_management.get_torch_device() - - easing_function = easing_functions[interpolation] - - images_list = [] - pbar = ProgressBar(images.shape[0] - 1) - for i in range(images.shape[0] - 1): - frames = [] - image_1 = images[i] - image_2 = images[i + 1] - - if device == "GPU": - image_1 = image_1.to(gpu) - image_2 = image_2.to(gpu) - - if reverse: - image_1, image_2 = image_2, image_1 - - for frame in range(transitioning_frames): - t = frame / (transitioning_frames - 1) - alpha = easing_function(t) - alpha_tensor = torch.tensor(alpha, dtype=image_1.dtype, device=image_1.device) - frame_image = transition_images(image_1, image_2, alpha_tensor, transition_type, blur_radius, reverse) - frames.append(frame_image) - pbar.update(1) - - frames = torch.stack(frames).cpu() - images_list.append(frames) - images = torch.cat(images_list, dim=0) - - return images.cpu(), - -class ImageBatchJoinWithTransition: - RETURN_TYPES = ("IMAGE",) - FUNCTION = "transition_batches" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Transitions between two batches of images, starting at a specified index in the first batch. -During the transition, frames from both batches are blended frame-by-frame, so the video keeps playing. -""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images_1": ("IMAGE",), - "images_2": ("IMAGE",), - "start_index": ("INT", {"default": 0, "min": -10000, "max": 10000, "step": 1}), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "bounce", "elastic", "glitchy", "exponential_ease_out"],), - "transition_type": (["horizontal slide", "vertical slide", "box", "circle", "horizontal door", "vertical door", "fade"],), - "transitioning_frames": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}), - "blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), - "reverse": ("BOOLEAN", {"default": False}), - "device": (["CPU", "GPU"], {"default": "CPU"}), - }, - } - - def transition_batches(self, images_1, images_2, start_index, interpolation, transition_type, transitioning_frames, blur_radius, reverse, device): - if images_1.shape[0] == 0 or images_2.shape[0] == 0: - raise ValueError("Both input batches must have at least one image.") - - if start_index < 0: - start_index = images_1.shape[0] + start_index - if start_index < 0 or start_index > images_1.shape[0]: - raise ValueError("start_index is out of range.") - - gpu = model_management.get_torch_device() - easing_function = easing_functions[interpolation] - out_frames = [] - - # Add images from images_1 up to start_index - if start_index > 0: - out_frames.append(images_1[:start_index]) - - # Determine how many frames we can blend - max_transition = min(transitioning_frames, images_1.shape[0] - start_index, images_2.shape[0]) - - # Blend corresponding frames from both batches - for i in range(max_transition): - img1 = images_1[start_index + i] - img2 = images_2[i] - if device == "GPU": - img1 = img1.to(gpu) - img2 = img2.to(gpu) - if reverse: - img1, img2 = img2, img1 - t = i / (max_transition - 1) if max_transition > 1 else 1.0 - alpha = easing_function(t) - alpha_tensor = torch.tensor(alpha, dtype=img1.dtype, device=img1.device) - frame_image = transition_images(img1, img2, alpha_tensor, transition_type, blur_radius, reverse) - out_frames.append(frame_image.cpu().unsqueeze(0)) - - # Add remaining images from images_2 after transition - if images_2.shape[0] > max_transition: - out_frames.append(images_2[max_transition:]) - - # Concatenate all frames - out = torch.cat(out_frames, dim=0) - return (out.cpu(),) - -class ShuffleImageBatch: - RETURN_TYPES = ("IMAGE",) - FUNCTION = "shuffle" - CATEGORY = "KJNodes/image" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), - }, - } - - def shuffle(self, images, seed): - torch.manual_seed(seed) - B, H, W, C = images.shape - indices = torch.randperm(B) - shuffled_images = images[indices] - - return shuffled_images, - -class GetImageRangeFromBatch: - - RETURN_TYPES = ("IMAGE", "MASK", ) - FUNCTION = "imagesfrombatch" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Returns a range of images from a batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}), - "num_frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), - }, - "optional": { - "images": ("IMAGE",), - "masks": ("MASK",), - } - } - - def imagesfrombatch(self, start_index, num_frames, images=None, masks=None): - chosen_images = None - chosen_masks = None - - # Process images if provided - if images is not None: - if start_index == -1: - start_index = max(0, len(images) - num_frames) - if start_index < 0 or start_index >= len(images): - raise ValueError("Start index is out of range") - end_index = min(start_index + num_frames, len(images)) - chosen_images = images[start_index:end_index] - - # Process masks if provided - if masks is not None: - if start_index == -1: - start_index = max(0, len(masks) - num_frames) - if start_index < 0 or start_index >= len(masks): - raise ValueError("Start index is out of range for masks") - end_index = min(start_index + num_frames, len(masks)) - chosen_masks = masks[start_index:end_index] - - return (chosen_images, chosen_masks,) - -class GetLatentRangeFromBatch: - - RETURN_TYPES = ("LATENT", ) - FUNCTION = "latentsfrombatch" - CATEGORY = "KJNodes/latents" - DESCRIPTION = """ -Returns a range of latents from a batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "latents": ("LATENT",), - "start_index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}), - "num_frames": ("INT", {"default": 1,"min": -1, "max": 4096, "step": 1}), - }, - } - - def latentsfrombatch(self, latents, start_index, num_frames): - chosen_latents = None - samples = latents["samples"] - if len(samples.shape) == 4: - B, C, H, W = samples.shape - num_latents = B - elif len(samples.shape) == 5: - B, C, T, H, W = samples.shape - num_latents = T - - if start_index == -1: - start_index = max(0, num_latents - num_frames) - if start_index < 0 or start_index >= num_latents: - raise ValueError("Start index is out of range") - - end_index = num_latents if num_frames == -1 else min(start_index + num_frames, num_latents) - - if len(samples.shape) == 4: - chosen_latents = samples[start_index:end_index] - elif len(samples.shape) == 5: - chosen_latents = samples[:, :, start_index:end_index] - - return ({"samples": chosen_latents.contiguous(),},) - -class InsertLatentToIndex: - - RETURN_TYPES = ("LATENT", ) - FUNCTION = "insert" - CATEGORY = "KJNodes/latents" - DESCRIPTION = """ -Inserts a latent at the specified index into the original latent batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "source": ("LATENT",), - "destination": ("LATENT",), - "index": ("INT", {"default": 0,"min": -1, "max": 4096, "step": 1}), - }, - } - - def insert(self, source, destination, index): - samples_destination = destination["samples"] - samples_source = source["samples"].to(samples_destination) - - if len(samples_source.shape) == 4: - B, C, H, W = samples_source.shape - num_latents = B - elif len(samples_source.shape) == 5: - B, C, T, H, W = samples_source.shape - num_latents = T - - if index >= num_latents or index < 0: - raise ValueError(f"Index {index} out of bounds for tensor with {num_latents} latents") - - if len(samples_source.shape) == 4: - joined_latents = torch.cat([ - samples_destination[:index], - samples_source, - samples_destination[index+1:] - ], dim=0) - else: - joined_latents = torch.cat([ - samples_destination[:, :, :index], - samples_source, - samples_destination[:, :, index+1:] - ], dim=2) - - return ({"samples": joined_latents,},) - -class ImageBatchFilter: - - RETURN_TYPES = ("IMAGE", "STRING",) - RETURN_NAMES = ("images", "removed_indices",) - FUNCTION = "filter" - CATEGORY = "KJNodes/image" - DESCRIPTION = "Removes empty images from a batch" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "empty_color": ("STRING", {"default": "0, 0, 0"}), - "empty_threshold": ("FLOAT", {"default": 0.01,"min": 0.0, "max": 1.0, "step": 0.01}), - }, - "optional": { - "replacement_image": ("IMAGE",), - } - } - - def filter(self, images, empty_color, empty_threshold, replacement_image=None): - B, H, W, C = images.shape - - input_images = images.clone() - - empty_color_list = [int(color.strip()) for color in empty_color.split(',')] - empty_color_tensor = torch.tensor(empty_color_list, dtype=torch.float32).to(input_images.device) - - color_diff = torch.abs(input_images - empty_color_tensor) - mean_diff = color_diff.mean(dim=(1, 2, 3)) - - empty_indices = mean_diff <= empty_threshold - empty_indices_string = ', '.join([str(i) for i in range(B) if empty_indices[i]]) - - if replacement_image is not None: - B_rep, H_rep, W_rep, C_rep = replacement_image.shape - replacement = replacement_image.clone() - if (H_rep != images.shape[1]) or (W_rep != images.shape[2]) or (C_rep != images.shape[3]): - replacement = common_upscale(replacement.movedim(-1, 1), W, H, "lanczos", "center").movedim(1, -1) - input_images[empty_indices] = replacement[0] - - return (input_images, empty_indices_string,) - else: - non_empty_images = input_images[~empty_indices] - return (non_empty_images, empty_indices_string,) - -class GetImagesFromBatchIndexed: - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "indexedimagesfrombatch" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Selects and returns the images at the specified indices as an image batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), - }, - } - - def indexedimagesfrombatch(self, images, indexes): - - # Parse the indexes string into a list of integers - index_list = [int(index.strip()) for index in indexes.split(',')] - - # Convert list of indices to a PyTorch tensor - indices_tensor = torch.tensor(index_list, dtype=torch.long) - - # Select the images at the specified indices - chosen_images = images[indices_tensor] - - return (chosen_images,) - -class InsertImagesToBatchIndexed: - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "insertimagesfrombatch" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Inserts images at the specified indices into the original image batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "original_images": ("IMAGE",), - "images_to_insert": ("IMAGE",), - "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), - }, - "optional": { - "mode": (["replace", "insert"],), - } - } - - def insertimagesfrombatch(self, original_images, images_to_insert, indexes, mode="replace"): - if indexes == "": - return (original_images,) - - input_images = original_images.clone() - - # Parse the indexes string into a list of integers - index_list = [int(index.strip()) for index in indexes.split(',')] - - # Convert list of indices to a PyTorch tensor - indices_tensor = torch.tensor(index_list, dtype=torch.long) - - # Ensure the images_to_insert is a tensor - if not isinstance(images_to_insert, torch.Tensor): - images_to_insert = torch.tensor(images_to_insert) - - if mode == "replace": - # Replace the images at the specified indices - for index, image in zip(indices_tensor, images_to_insert): - input_images[index] = image - else: - # Create a list to hold the new image sequence - new_images = [] - insert_offset = 0 - - for i in range(len(input_images) + len(indices_tensor)): - if insert_offset < len(indices_tensor) and i == indices_tensor[insert_offset]: - # Use modulo to cycle through images_to_insert - new_images.append(images_to_insert[insert_offset % len(images_to_insert)]) - insert_offset += 1 - else: - new_images.append(input_images[i - insert_offset]) - - # Convert the list back to a tensor - input_images = torch.stack(new_images, dim=0) - - return (input_images,) - -class PadImageBatchInterleaved: - - RETURN_TYPES = ("IMAGE", "MASK",) - RETURN_NAMES = ("images", "masks",) - FUNCTION = "pad" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Inserts empty frames between the images in a batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "empty_frames_per_image": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), - "pad_frame_value": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), - "add_after_last": ("BOOLEAN", {"default": False}), - }, - } - - def pad(self, images, empty_frames_per_image, pad_frame_value, add_after_last): - B, H, W, C = images.shape - - # Handle single frame case specifically - if B == 1: - total_frames = 1 + empty_frames_per_image if add_after_last else 1 - else: - # Original B images + (B-1) sets of empty frames between them - total_frames = B + (B-1) * empty_frames_per_image - # Add additional empty frames after the last image if requested - if add_after_last: - total_frames += empty_frames_per_image - - # Create new tensor with zeros (empty frames) - padded_batch = torch.ones((total_frames, H, W, C), - dtype=images.dtype, - device=images.device) * pad_frame_value - # Create mask tensor (1 for original frames, 0 for empty frames) - mask = torch.zeros((total_frames, H, W), - dtype=images.dtype, - device=images.device) - - # Fill in original images at their new positions - for i in range(B): - if B == 1: - # For single frame, just place it at the beginning - new_pos = 0 - else: - # Each image is separated by empty_frames_per_image blank frames - new_pos = i * (empty_frames_per_image + 1) - - padded_batch[new_pos] = images[i] - mask[new_pos] = 1.0 # Mark this as an original frame - - return (padded_batch, mask) - -class ReplaceImagesInBatch: - - RETURN_TYPES = ("IMAGE", "MASK",) - FUNCTION = "replace" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Replaces the images in a batch, starting from the specified start index, -with the replacement images. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "start_index": ("INT", {"default": 1,"min": 0, "max": 4096, "step": 1}), - }, - "optional": { - "original_images": ("IMAGE",), - "replacement_images": ("IMAGE",), - "original_masks": ("MASK",), - "replacement_masks": ("MASK",), - } - } - - def replace(self, original_images=None, replacement_images=None, start_index=1, original_masks=None, replacement_masks=None): - images = None - masks = None - - if original_images is not None and replacement_images is not None: - if start_index >= len(original_images): - raise ValueError("ReplaceImagesInBatch: Start index is out of range") - end_index = start_index + len(replacement_images) - if end_index > len(original_images): - raise ValueError("ReplaceImagesInBatch: End index is out of range") - - original_images_copy = original_images.clone() - if original_images_copy.shape[2] != replacement_images.shape[2] or original_images_copy.shape[3] != replacement_images.shape[3]: - replacement_images = common_upscale(replacement_images.movedim(-1, 1), original_images_copy.shape[1], original_images_copy.shape[2], "lanczos", "center").movedim(1, -1) - - original_images_copy[start_index:end_index] = replacement_images - images = original_images_copy - else: - images = torch.zeros((1, 64, 64, 3)) - - if original_masks is not None and replacement_masks is not None: - if start_index >= len(original_masks): - raise ValueError("ReplaceImagesInBatch: Start index is out of range") - end_index = start_index + len(replacement_masks) - if end_index > len(original_masks): - raise ValueError("ReplaceImagesInBatch: End index is out of range") - - original_masks_copy = original_masks.clone() - if original_masks_copy.shape[1] != replacement_masks.shape[1] or original_masks_copy.shape[2] != replacement_masks.shape[2]: - replacement_masks = common_upscale(replacement_masks.unsqueeze(1), original_masks_copy.shape[1], original_masks_copy.shape[2], "nearest-exact", "center").squeeze(0) - - original_masks_copy[start_index:end_index] = replacement_masks - masks = original_masks_copy - else: - masks = torch.zeros((1, 64, 64)) - - return (images, masks) - - -class ReverseImageBatch: - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "reverseimagebatch" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Reverses the order of the images in a batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - }, - } - - def reverseimagebatch(self, images): - reversed_images = torch.flip(images, [0]) - return (reversed_images, ) - -class ImageBatchMulti: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "image_1": ("IMAGE", ), - - }, - "optional": { - "image_2": ("IMAGE", ), - } - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("images",) - FUNCTION = "combine" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates an image batch from multiple images. -You can set how many inputs the node has, -with the **inputcount** and clicking update. -""" - - def combine(self, inputcount, **kwargs): - from nodes import ImageBatch - image_batch_node = ImageBatch() - image = kwargs["image_1"].cpu() - first_image_shape = image.shape - for c in range(1, inputcount): - new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)).cpu() - image, = image_batch_node.batch(image, new_image) - return (image,) - - -class ImageTensorList: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - }} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "append" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates an image list from the input images. -""" - - def append(self, image1, image2): - image_list = [] - if isinstance(image1, torch.Tensor) and isinstance(image2, torch.Tensor): - image_list = [image1, image2] - elif isinstance(image1, list) and isinstance(image2, torch.Tensor): - image_list = image1 + [image2] - elif isinstance(image1, torch.Tensor) and isinstance(image2, list): - image_list = [image1] + image2 - elif isinstance(image1, list) and isinstance(image2, list): - image_list = image1 + image2 - return image_list, - -class ImageAddMulti: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "image_1": ("IMAGE", ), - "image_2": ("IMAGE", ), - "blending": ( - [ 'add', - 'subtract', - 'multiply', - 'difference', - ], - { - "default": 'add' - }), - "blend_amount": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}), - }, - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("images",) - FUNCTION = "add" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Add blends multiple images together. -You can set how many inputs the node has, -with the **inputcount** and clicking update. -""" - - def add(self, inputcount, blending, blend_amount, **kwargs): - image = kwargs["image_1"] - for c in range(1, inputcount): - new_image = kwargs[f"image_{c + 1}"] - if blending == "add": - image = torch.add(image * blend_amount, new_image * blend_amount) - elif blending == "subtract": - image = torch.sub(image * blend_amount, new_image * blend_amount) - elif blending == "multiply": - image = torch.mul(image * blend_amount, new_image * blend_amount) - elif blending == "difference": - image = torch.sub(image, new_image) - return (image,) - -class ImageConcatMulti: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "image_1": ("IMAGE", ), - - "direction": ( - [ 'right', - 'down', - 'left', - 'up', - ], - { - "default": 'right' - }), - "match_image_size": ("BOOLEAN", {"default": False}), - }, - "optional": { - "image_2": ("IMAGE", ), - }, - } - - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("images",) - FUNCTION = "combine" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Creates an image from multiple images. -You can set how many inputs the node has, -with the **inputcount** and clicking update. -""" - - def combine(self, inputcount, direction, match_image_size, **kwargs): - image = kwargs["image_1"] - first_image_shape = None - if first_image_shape is None: - first_image_shape = image.shape - for c in range(1, inputcount): - new_image = kwargs.get(f"image_{c + 1}", torch.zeros(first_image_shape)) - image, = ImageConcanate.concatenate(self, image, new_image, direction, match_image_size, first_image_shape=first_image_shape) - first_image_shape = None - return (image,) - -class PreviewAnimation: - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() - self.type = "temp" - self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) - self.compress_level = 1 - - methods = {"default": 4, "fastest": 0, "slowest": 6} - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "fps": ("FLOAT", {"default": 8.0, "min": 0.01, "max": 1000.0, "step": 0.01}), - }, - "optional": { - "images": ("IMAGE", ), - "masks": ("MASK", ), - }, - } - - RETURN_TYPES = () - FUNCTION = "preview" - OUTPUT_NODE = True - CATEGORY = "KJNodes/image" - - def preview(self, fps, images=None, masks=None): - filename_prefix = "AnimPreview" - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results = list() - - pil_images = [] - - if images is not None and masks is not None: - for image in images: - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - pil_images.append(img) - for mask in masks: - if pil_images: - mask_np = mask.cpu().numpy() - mask_np = np.clip(mask_np * 255, 0, 255).astype(np.uint8) # Convert to values between 0 and 255 - mask_img = Image.fromarray(mask_np, mode='L') - img = pil_images.pop(0) # Remove and get the first image - img = img.convert("RGBA") # Convert base image to RGBA - - # Create a new RGBA image based on the grayscale mask - rgba_mask_img = Image.new("RGBA", img.size, (255, 255, 255, 255)) - rgba_mask_img.putalpha(mask_img) # Use the mask image as the alpha channel - - # Composite the RGBA mask onto the base image - composited_img = Image.alpha_composite(img, rgba_mask_img) - pil_images.append(composited_img) # Add the composited image back - - elif images is not None and masks is None: - for image in images: - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - pil_images.append(img) - - elif masks is not None and images is None: - for mask in masks: - mask_np = 255. * mask.cpu().numpy() - mask_img = Image.fromarray(np.clip(mask_np, 0, 255).astype(np.uint8)) - pil_images.append(mask_img) - else: - print("PreviewAnimation: No images or masks provided") - return { "ui": { "images": results, "animated": (None,), "text": "empty" }} - - num_frames = len(pil_images) - - c = len(pil_images) - for i in range(0, c, num_frames): - file = f"{filename}_{counter:05}_.webp" - pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], lossless=False, quality=50, method=0) - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - - animated = num_frames != 1 - return { "ui": { "images": results, "animated": (animated,), "text": [f"{num_frames}x{pil_images[0].size[0]}x{pil_images[0].size[1]}"] } } - -class ImageResizeKJ: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "upscale_method": (s.upscale_methods,), - "keep_proportion": ("BOOLEAN", { "default": False }), - "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }), - }, - "optional" : { - #"width_input": ("INT", { "forceInput": True}), - #"height_input": ("INT", { "forceInput": True}), - "get_image_size": ("IMAGE",), - "crop": (["disabled","center", 0], { "tooltip": "0 will do the default center crop, this is a workaround for the widget order changing with the new frontend, as in old workflows the value of this widget becomes 0 automatically" }), - } - } - - RETURN_TYPES = ("IMAGE", "INT", "INT",) - RETURN_NAMES = ("IMAGE", "width", "height",) - FUNCTION = "resize" - CATEGORY = "KJNodes/image" - DEPRECATED = True - DESCRIPTION = """ -DEPRECATED! - -Due to ComfyUI frontend changes, this node should no longer be used, please check the -v2 of the node. This node is only kept to not completely break older workflows. - -""" - - def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, - width_input=None, height_input=None, get_image_size=None, crop="disabled"): - B, H, W, C = image.shape - - if width_input: - width = width_input - if height_input: - height = height_input - if get_image_size is not None: - _, height, width, _ = get_image_size.shape - - if keep_proportion and get_image_size is None: - # If one of the dimensions is zero, calculate it to maintain the aspect ratio - if width == 0 and height != 0: - ratio = height / H - width = round(W * ratio) - elif height == 0 and width != 0: - ratio = width / W - height = round(H * ratio) - elif width != 0 and height != 0: - # Scale based on which dimension is smaller in proportion to the desired dimensions - ratio = min(width / W, height / H) - width = round(W * ratio) - height = round(H * ratio) - else: - if width == 0: - width = W - if height == 0: - height = H - - if divisible_by > 1 and get_image_size is None: - width = width - (width % divisible_by) - height = height - (height % divisible_by) - - if crop == 0: #workaround for old workflows - crop = "center" - - image = image.movedim(-1,1) - image = common_upscale(image, width, height, upscale_method, crop) - image = image.movedim(1,-1) - - return(image, image.shape[2], image.shape[1],) - -class ImageResizeKJv2: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "upscale_method": (s.upscale_methods,), - "keep_proportion": (["stretch", "resize", "pad", "pad_edge", "crop"], { "default": False }), - "pad_color": ("STRING", { "default": "0, 0, 0", "tooltip": "Color to use for padding."}), - "crop_position": (["center", "top", "bottom", "left", "right"], { "default": "center" }), - "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }), - }, - "optional" : { - "mask": ("MASK",), - "device": (["cpu", "gpu"],), - }, - "hidden": { - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK",) - RETURN_NAMES = ("IMAGE", "width", "height", "mask",) - FUNCTION = "resize" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ -Resizes the image to the specified width and height. -Size can be retrieved from the input. - -Keep proportions keeps the aspect ratio of the image, by -highest dimension. -""" - - def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position, unique_id, device="cpu", mask=None): - B, H, W, C = image.shape - - if device == "gpu": - if upscale_method == "lanczos": - raise Exception("Lanczos is not supported on the GPU") - device = model_management.get_torch_device() - else: - device = torch.device("cpu") - - if width == 0: - width = W - if height == 0: - height = H - - if keep_proportion == "resize" or keep_proportion.startswith("pad"): - # If one of the dimensions is zero, calculate it to maintain the aspect ratio - if width == 0 and height != 0: - ratio = height / H - new_width = round(W * ratio) - elif height == 0 and width != 0: - ratio = width / W - new_height = round(H * ratio) - elif width != 0 and height != 0: - # Scale based on which dimension is smaller in proportion to the desired dimensions - ratio = min(width / W, height / H) - new_width = round(W * ratio) - new_height = round(H * ratio) - - if keep_proportion.startswith("pad"): - # Calculate padding based on position - if crop_position == "center": - pad_left = (width - new_width) // 2 - pad_right = width - new_width - pad_left - pad_top = (height - new_height) // 2 - pad_bottom = height - new_height - pad_top - elif crop_position == "top": - pad_left = (width - new_width) // 2 - pad_right = width - new_width - pad_left - pad_top = 0 - pad_bottom = height - new_height - elif crop_position == "bottom": - pad_left = (width - new_width) // 2 - pad_right = width - new_width - pad_left - pad_top = height - new_height - pad_bottom = 0 - elif crop_position == "left": - pad_left = 0 - pad_right = width - new_width - pad_top = (height - new_height) // 2 - pad_bottom = height - new_height - pad_top - elif crop_position == "right": - pad_left = width - new_width - pad_right = 0 - pad_top = (height - new_height) // 2 - pad_bottom = height - new_height - pad_top - - width = new_width - height = new_height - - if divisible_by > 1: - width = width - (width % divisible_by) - height = height - (height % divisible_by) - - out_image = image.clone().to(device) - - if mask is not None: - out_mask = mask.clone().to(device) - else: - out_mask = None - - if keep_proportion == "crop": - old_width = W - old_height = H - old_aspect = old_width / old_height - new_aspect = width / height - - # Calculate dimensions to keep - if old_aspect > new_aspect: # Image is wider than target - crop_w = round(old_height * new_aspect) - crop_h = old_height - else: # Image is taller than target - crop_w = old_width - crop_h = round(old_width / new_aspect) - - # Calculate crop position - if crop_position == "center": - x = (old_width - crop_w) // 2 - y = (old_height - crop_h) // 2 - elif crop_position == "top": - x = (old_width - crop_w) // 2 - y = 0 - elif crop_position == "bottom": - x = (old_width - crop_w) // 2 - y = old_height - crop_h - elif crop_position == "left": - x = 0 - y = (old_height - crop_h) // 2 - elif crop_position == "right": - x = old_width - crop_w - y = (old_height - crop_h) // 2 - - # Apply crop - out_image = out_image.narrow(-2, x, crop_w).narrow(-3, y, crop_h) - if mask is not None: - out_mask = out_mask.narrow(-1, x, crop_w).narrow(-2, y, crop_h) - - out_image = common_upscale(out_image.movedim(-1,1), width, height, upscale_method, crop="disabled").movedim(1,-1) - - if mask is not None: - if upscale_method == "lanczos": - out_mask = common_upscale(out_mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop="disabled").movedim(1,-1)[:, :, :, 0] - else: - out_mask = common_upscale(out_mask.unsqueeze(1), width, height, upscale_method, crop="disabled").squeeze(1) - - if keep_proportion.startswith("pad"): - if pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0: - padded_width = width + pad_left + pad_right - padded_height = height + pad_top + pad_bottom - if divisible_by > 1: - width_remainder = padded_width % divisible_by - height_remainder = padded_height % divisible_by - if width_remainder > 0: - extra_width = divisible_by - width_remainder - pad_right += extra_width - if height_remainder > 0: - extra_height = divisible_by - height_remainder - pad_bottom += extra_height - out_image, _ = ImagePadKJ.pad(self, out_image, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, "edge" if keep_proportion == "pad_edge" else "color") - if mask is not None: - out_mask = out_mask.unsqueeze(1).repeat(1, 3, 1, 1).movedim(1,-1) - out_mask, _ = ImagePadKJ.pad(self, out_mask, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color, "edge" if keep_proportion == "pad_edge" else "color") - out_mask = out_mask[:, :, :, 0] - else: - B, H_pad, W_pad, _ = out_image.shape - out_mask = torch.ones((B, H_pad, W_pad), dtype=out_image.dtype, device=out_image.device) - out_mask[:, pad_top:pad_top+height, pad_left:pad_left+width] = 0.0 - - - if unique_id and PromptServer is not None: - try: - num_elements = out_image.numel() - element_size = out_image.element_size() - memory_size_mb = (num_elements * element_size) / (1024 * 1024) - - PromptServer.instance.send_progress_text( - f"Output: {out_image.shape[0]} x {out_image.shape[2]} x {out_image.shape[1]} | {memory_size_mb:.2f}MB", - unique_id - ) - except: - pass - - return(out_image.cpu(), out_image.shape[2], out_image.shape[1], out_mask.cpu() if out_mask is not None else torch.zeros(64,64, device=torch.device("cpu"), dtype=torch.float32)) - -import pathlib -class LoadAndResizeImage: - _color_channels = ["alpha", "red", "green", "blue"] - @classmethod - def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() - files = [f.name for f in pathlib.Path(input_dir).iterdir() if f.is_file()] - return {"required": - { - "image": (sorted(files), {"image_upload": True}), - "resize": ("BOOLEAN", { "default": False }), - "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - "repeat": ("INT", { "default": 1, "min": 1, "max": 4096, "step": 1, }), - "keep_proportion": ("BOOLEAN", { "default": False }), - "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }), - "mask_channel": (s._color_channels, {"tooltip": "Channel to use for the mask output"}), - "background_color": ("STRING", { "default": "", "tooltip": "Fills the alpha channel with the specified color."}), - }, - } - - CATEGORY = "KJNodes/image" - RETURN_TYPES = ("IMAGE", "MASK", "INT", "INT", "STRING",) - RETURN_NAMES = ("image", "mask", "width", "height","image_path",) - FUNCTION = "load_image" - - def load_image(self, image, resize, width, height, repeat, keep_proportion, divisible_by, mask_channel, background_color): - from PIL import ImageColor, Image, ImageOps, ImageSequence - import numpy as np - import torch - image_path = folder_paths.get_annotated_filepath(image) - - import node_helpers - img = node_helpers.pillow(Image.open, image_path) - - # Process the background_color - if background_color: - try: - # Try to parse as RGB tuple - bg_color_rgba = tuple(int(x.strip()) for x in background_color.split(',')) - except ValueError: - # If parsing fails, it might be a hex color or named color - if background_color.startswith('#') or background_color.lower() in ImageColor.colormap: - bg_color_rgba = ImageColor.getrgb(background_color) - else: - raise ValueError(f"Invalid background color: {background_color}") - - bg_color_rgba += (255,) # Add alpha channel - else: - bg_color_rgba = None # No background color specified - - output_images = [] - output_masks = [] - w, h = None, None - - excluded_formats = ['MPO'] - - W, H = img.size - if resize: - if keep_proportion: - ratio = min(width / W, height / H) - width = round(W * ratio) - height = round(H * ratio) - else: - if width == 0: - width = W - if height == 0: - height = H - - if divisible_by > 1: - width = width - (width % divisible_by) - height = height - (height % divisible_by) - else: - width, height = W, H - - for frame in ImageSequence.Iterator(img): - frame = node_helpers.pillow(ImageOps.exif_transpose, frame) - - if frame.mode == 'I': - frame = frame.point(lambda i: i * (1 / 255)) - - if frame.mode == 'P': - frame = frame.convert("RGBA") - elif 'A' in frame.getbands(): - frame = frame.convert("RGBA") - - # Extract alpha channel if it exists - if 'A' in frame.getbands() and bg_color_rgba: - alpha_mask = np.array(frame.getchannel('A')).astype(np.float32) / 255.0 - alpha_mask = 1. - torch.from_numpy(alpha_mask) - bg_image = Image.new("RGBA", frame.size, bg_color_rgba) - # Composite the frame onto the background - frame = Image.alpha_composite(bg_image, frame) - else: - alpha_mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") - - image = frame.convert("RGB") - - if len(output_images) == 0: - w = image.size[0] - h = image.size[1] - - if image.size[0] != w or image.size[1] != h: - continue - if resize: - image = image.resize((width, height), Image.Resampling.BILINEAR) - - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - - c = mask_channel[0].upper() - if c in frame.getbands(): - if resize: - frame = frame.resize((width, height), Image.Resampling.BILINEAR) - mask = np.array(frame.getchannel(c)).astype(np.float32) / 255.0 - mask = torch.from_numpy(mask) - if c == 'A' and bg_color_rgba: - mask = alpha_mask - elif c == 'A': - mask = 1. - mask - else: - mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") - - output_images.append(image) - output_masks.append(mask.unsqueeze(0)) - - if len(output_images) > 1 and img.format not in excluded_formats: - output_image = torch.cat(output_images, dim=0) - output_mask = torch.cat(output_masks, dim=0) - else: - output_image = output_images[0] - output_mask = output_masks[0] - if repeat > 1: - output_image = output_image.repeat(repeat, 1, 1, 1) - output_mask = output_mask.repeat(repeat, 1, 1) - - return (output_image, output_mask, width, height, image_path) - - - # @classmethod - # def IS_CHANGED(s, image, **kwargs): - # image_path = folder_paths.get_annotated_filepath(image) - # m = hashlib.sha256() - # with open(image_path, 'rb') as f: - # m.update(f.read()) - # return m.digest().hex() - - @classmethod - def VALIDATE_INPUTS(s, image): - if not folder_paths.exists_annotated_filepath(image): - return "Invalid image file: {}".format(image) - - return True - -import hashlib -class LoadImagesFromFolderKJ: - # Dictionary to store folder hashes - folder_hashes = {} - - @classmethod - def IS_CHANGED(cls, folder, **kwargs): - if not os.path.isdir(folder): - return float("NaN") - - valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga'] - include_subfolders = kwargs.get('include_subfolders', False) - - file_data = [] - if include_subfolders: - for root, _, files in os.walk(folder): - for file in files: - if any(file.lower().endswith(ext) for ext in valid_extensions): - path = os.path.join(root, file) - try: - mtime = os.path.getmtime(path) - file_data.append((path, mtime)) - except OSError: - pass - else: - for file in os.listdir(folder): - if any(file.lower().endswith(ext) for ext in valid_extensions): - path = os.path.join(folder, file) - try: - mtime = os.path.getmtime(path) - file_data.append((path, mtime)) - except OSError: - pass - - file_data.sort() - - combined_hash = hashlib.md5() - combined_hash.update(folder.encode('utf-8')) - combined_hash.update(str(len(file_data)).encode('utf-8')) - - for path, mtime in file_data: - combined_hash.update(f"{path}:{mtime}".encode('utf-8')) - - current_hash = combined_hash.hexdigest() - - old_hash = cls.folder_hashes.get(folder) - cls.folder_hashes[folder] = current_hash - - if old_hash == current_hash: - return old_hash - - return current_hash - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "folder": ("STRING", {"default": ""}), - "width": ("INT", {"default": 1024, "min": -1, "step": 1}), - "height": ("INT", {"default": 1024, "min": -1, "step": 1}), - "keep_aspect_ratio": (["crop", "pad", "stretch",],), - }, - "optional": { - "image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}), - "start_index": ("INT", {"default": 0, "min": 0, "step": 1}), - "include_subfolders": ("BOOLEAN", {"default": False}), - } - } - - RETURN_TYPES = ("IMAGE", "MASK", "INT", "STRING",) - RETURN_NAMES = ("image", "mask", "count", "image_path",) - FUNCTION = "load_images" - CATEGORY = "KJNodes/image" - DESCRIPTION = """Loads images from a folder into a batch, images are resized and loaded into a batch.""" - - def load_images(self, folder, width, height, image_load_cap, start_index, keep_aspect_ratio, include_subfolders=False): - if not os.path.isdir(folder): - raise FileNotFoundError(f"Folder '{folder} cannot be found.'") - - valid_extensions = ['.jpg', '.jpeg', '.png', '.webp', '.tga'] - image_paths = [] - if include_subfolders: - for root, _, files in os.walk(folder): - for file in files: - if any(file.lower().endswith(ext) for ext in valid_extensions): - image_paths.append(os.path.join(root, file)) - else: - for file in os.listdir(folder): - if any(file.lower().endswith(ext) for ext in valid_extensions): - image_paths.append(os.path.join(folder, file)) - - dir_files = sorted(image_paths) - - if len(dir_files) == 0: - raise FileNotFoundError(f"No files in directory '{folder}'.") - - # start at start_index - dir_files = dir_files[start_index:] - - images = [] - masks = [] - image_path_list = [] - - limit_images = False - if image_load_cap > 0: - limit_images = True - image_count = 0 - - for image_path in dir_files: - if os.path.isdir(image_path): - continue - if limit_images and image_count >= image_load_cap: - break - i = Image.open(image_path) - i = ImageOps.exif_transpose(i) - - # Resize image to maximum dimensions - if width == -1 and height == -1: - width = i.size[0] - height = i.size[1] - if i.size != (width, height): - i = self.resize_with_aspect_ratio(i, width, height, keep_aspect_ratio) - - - image = i.convert("RGB") - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - - if 'A' in i.getbands(): - mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 - mask = 1. - torch.from_numpy(mask) - if mask.shape != (height, width): - mask = torch.nn.functional.interpolate(mask.unsqueeze(0).unsqueeze(0), - size=(height, width), - mode='bilinear', - align_corners=False).squeeze() - else: - mask = torch.zeros((height, width), dtype=torch.float32, device="cpu") - - images.append(image) - masks.append(mask) - image_path_list.append(image_path) - image_count += 1 - - if len(images) == 1: - return (images[0], masks[0], 1, image_path_list) - - elif len(images) > 1: - image1 = images[0] - mask1 = masks[0].unsqueeze(0) - - for image2 in images[1:]: - image1 = torch.cat((image1, image2), dim=0) - - for mask2 in masks[1:]: - mask1 = torch.cat((mask1, mask2.unsqueeze(0)), dim=0) - - return (image1, mask1, len(images), image_path_list) - def resize_with_aspect_ratio(self, img, width, height, mode): - if mode == "stretch": - return img.resize((width, height), Image.Resampling.LANCZOS) - - img_width, img_height = img.size - aspect_ratio = img_width / img_height - target_ratio = width / height - - if mode == "crop": - # Calculate dimensions for center crop - if aspect_ratio > target_ratio: - # Image is wider - crop width - new_width = int(height * aspect_ratio) - img = img.resize((new_width, height), Image.Resampling.LANCZOS) - left = (new_width - width) // 2 - return img.crop((left, 0, left + width, height)) - else: - # Image is taller - crop height - new_height = int(width / aspect_ratio) - img = img.resize((width, new_height), Image.Resampling.LANCZOS) - top = (new_height - height) // 2 - return img.crop((0, top, width, top + height)) - - elif mode == "pad": - pad_color = self.get_edge_color(img) - # Calculate dimensions for padding - if aspect_ratio > target_ratio: - # Image is wider - pad height - new_height = int(width / aspect_ratio) - img = img.resize((width, new_height), Image.Resampling.LANCZOS) - padding = (height - new_height) // 2 - padded = Image.new('RGBA', (width, height), pad_color) - padded.paste(img, (0, padding)) - return padded - else: - # Image is taller - pad width - new_width = int(height * aspect_ratio) - img = img.resize((new_width, height), Image.Resampling.LANCZOS) - padding = (width - new_width) // 2 - padded = Image.new('RGBA', (width, height), pad_color) - padded.paste(img, (padding, 0)) - return padded - def get_edge_color(self, img): - from PIL import ImageStat - """Sample edges and return dominant color""" - width, height = img.size - img = img.convert('RGBA') - - # Create 1-pixel high/wide images from edges - top = img.crop((0, 0, width, 1)) - bottom = img.crop((0, height-1, width, height)) - left = img.crop((0, 0, 1, height)) - right = img.crop((width-1, 0, width, height)) - - # Combine edges into single image - edges = Image.new('RGBA', (width*2 + height*2, 1)) - edges.paste(top, (0, 0)) - edges.paste(bottom, (width, 0)) - edges.paste(left.resize((height, 1)), (width*2, 0)) - edges.paste(right.resize((height, 1)), (width*2 + height, 0)) - - # Get median color - stat = ImageStat.Stat(edges) - median = tuple(map(int, stat.median)) - return median - - -class ImageGridtoBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE", ), - "columns": ("INT", {"default": 3, "min": 1, "max": 8, "tooltip": "The number of columns in the grid."}), - "rows": ("INT", {"default": 0, "min": 1, "max": 8, "tooltip": "The number of rows in the grid. Set to 0 for automatic calculation."}), - } - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "decompose" - CATEGORY = "KJNodes/image" - DESCRIPTION = "Converts a grid of images to a batch of images." - - def decompose(self, image, columns, rows): - B, H, W, C = image.shape - print("input size: ", image.shape) - - # Calculate cell width, rounding down - cell_width = W // columns - - if rows == 0: - # If rows is 0, calculate number of full rows - rows = H // cell_height - else: - # If rows is specified, adjust cell_height - cell_height = H // rows - - # Crop the image to fit full cells - image = image[:, :rows*cell_height, :columns*cell_width, :] - - # Reshape and permute the image to get the grid - image = image.view(B, rows, cell_height, columns, cell_width, C) - image = image.permute(0, 1, 3, 2, 4, 5).contiguous() - image = image.view(B, rows * columns, cell_height, cell_width, C) - - # Reshape to the final batch tensor - img_tensor = image.view(-1, cell_height, cell_width, C) - - return (img_tensor,) - -class SaveImageKJ: - def __init__(self): - self.type = "output" - self.prefix_append = "" - self.compress_level = 4 - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE", {"tooltip": "The images to save."}), - "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), - "output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}), - }, - "optional": { - "caption_file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file."}), - "caption": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}), - }, - "hidden": { - "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("filename",) - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "KJNodes/image" - DESCRIPTION = "Saves the input images to your ComfyUI output directory." - - def save_images(self, images, output_folder, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, caption=None, caption_file_extension=".txt"): - filename_prefix += self.prefix_append - - if os.path.isabs(output_folder): - if not os.path.exists(output_folder): - os.makedirs(output_folder, exist_ok=True) - full_output_folder = output_folder - _, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, output_folder, images[0].shape[1], images[0].shape[0]) - else: - self.output_dir = folder_paths.get_output_directory() - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - - results = list() - for (batch_number, image) in enumerate(images): - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - metadata = None - if not args.disable_metadata: - metadata = PngInfo() - if prompt is not None: - metadata.add_text("prompt", json.dumps(prompt)) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata.add_text(x, json.dumps(extra_pnginfo[x])) - - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - base_file_name = f"{filename_with_batch_num}_{counter:05}_" - file = f"{base_file_name}.png" - img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - if caption is not None: - txt_file = base_file_name + caption_file_extension - file_path = os.path.join(full_output_folder, txt_file) - with open(file_path, 'w') as f: - f.write(caption) - - counter += 1 - - return file, - -class SaveStringKJ: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - self.compress_level = 4 - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "string": ("STRING", {"forceInput": True, "tooltip": "string to save as .txt file"}), - "filename_prefix": ("STRING", {"default": "text", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}), - "output_folder": ("STRING", {"default": "output", "tooltip": "The folder to save the images to."}), - }, - "optional": { - "file_extension": ("STRING", {"default": ".txt", "tooltip": "The extension for the caption file."}), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("filename",) - FUNCTION = "save_string" - - OUTPUT_NODE = True - - CATEGORY = "KJNodes/misc" - DESCRIPTION = "Saves the input string to your ComfyUI output directory." - - def save_string(self, string, output_folder, filename_prefix="text", file_extension=".txt"): - filename_prefix += self.prefix_append - - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - if output_folder != "output": - if not os.path.exists(output_folder): - os.makedirs(output_folder, exist_ok=True) - full_output_folder = output_folder - - base_file_name = f"{filename_prefix}_{counter:05}_" - results = list() - - txt_file = base_file_name + file_extension - file_path = os.path.join(full_output_folder, txt_file) - with open(file_path, 'w') as f: - f.write(string) - - return results, - -to_pil_image = T.ToPILImage() - -class FastPreview: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ("IMAGE", ), - "format": (["JPEG", "PNG", "WEBP"], {"default": "JPEG"}), - "quality" : ("INT", {"default": 75, "min": 1, "max": 100, "step": 1}), - }, - } - - RETURN_TYPES = () - FUNCTION = "preview" - CATEGORY = "KJNodes/experimental" - OUTPUT_NODE = True - DESCRIPTION = "Experimental node for faster image previews by displaying through base64 it without saving to disk." - - def preview(self, image, format, quality): - pil_image = to_pil_image(image[0].permute(2, 0, 1)) - - with io.BytesIO() as buffered: - pil_image.save(buffered, format=format, quality=quality) - img_bytes = buffered.getvalue() - - img_base64 = base64.b64encode(img_bytes).decode('utf-8') - - return { - "ui": {"bg_image": [img_base64]}, - "result": () - } - -class ImageCropByMaskAndResize: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE", ), - "mask": ("MASK", ), - "base_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - "padding": ("INT", { "default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "min_crop_resolution": ("INT", { "default": 128, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - "max_crop_resolution": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - - }, - } - - RETURN_TYPES = ("IMAGE", "MASK", "BBOX", ) - RETURN_NAMES = ("images", "masks", "bbox",) - FUNCTION = "crop" - CATEGORY = "KJNodes/image" - - def crop_by_mask(self, mask, padding=0, min_crop_resolution=None, max_crop_resolution=None): - iy, ix = (mask == 1).nonzero(as_tuple=True) - h0, w0 = mask.shape - - if iy.numel() == 0: - x_c = w0 / 2.0 - y_c = h0 / 2.0 - width = 0 - height = 0 - else: - x_min = ix.min().item() - x_max = ix.max().item() - y_min = iy.min().item() - y_max = iy.max().item() - - width = x_max - x_min - height = y_max - y_min - - if width > w0 or height > h0: - raise Exception("Masked area out of bounds") - - x_c = (x_min + x_max) / 2.0 - y_c = (y_min + y_max) / 2.0 - - if min_crop_resolution: - width = max(width, min_crop_resolution) - height = max(height, min_crop_resolution) - - if max_crop_resolution: - width = min(width, max_crop_resolution) - height = min(height, max_crop_resolution) - - if w0 <= width: - x0 = 0 - w = w0 - else: - x0 = max(0, x_c - width / 2 - padding) - w = width + 2 * padding - if x0 + w > w0: - x0 = w0 - w - - if h0 <= height: - y0 = 0 - h = h0 - else: - y0 = max(0, y_c - height / 2 - padding) - h = height + 2 * padding - if y0 + h > h0: - y0 = h0 - h - - return (int(x0), int(y0), int(w), int(h)) - - def crop(self, image, mask, base_resolution, padding=0, min_crop_resolution=128, max_crop_resolution=512): - mask = mask.round() - image_list = [] - mask_list = [] - bbox_list = [] - - # First, collect all bounding boxes - bbox_params = [] - aspect_ratios = [] - for i in range(image.shape[0]): - x0, y0, w, h = self.crop_by_mask(mask[i], padding, min_crop_resolution, max_crop_resolution) - bbox_params.append((x0, y0, w, h)) - aspect_ratios.append(w / h) - - # Find maximum width and height - max_w = max([w for x0, y0, w, h in bbox_params]) - max_h = max([h for x0, y0, w, h in bbox_params]) - max_aspect_ratio = max(aspect_ratios) - - # Ensure dimensions are divisible by 16 - max_w = (max_w + 15) // 16 * 16 - max_h = (max_h + 15) // 16 * 16 - # Calculate common target dimensions - if max_aspect_ratio > 1: - target_width = base_resolution - target_height = int(base_resolution / max_aspect_ratio) - else: - target_height = base_resolution - target_width = int(base_resolution * max_aspect_ratio) - - for i in range(image.shape[0]): - x0, y0, w, h = bbox_params[i] - - # Adjust cropping to use maximum width and height - x_center = x0 + w / 2 - y_center = y0 + h / 2 - - x0_new = int(max(0, x_center - max_w / 2)) - y0_new = int(max(0, y_center - max_h / 2)) - x1_new = int(min(x0_new + max_w, image.shape[2])) - y1_new = int(min(y0_new + max_h, image.shape[1])) - x0_new = x1_new - max_w - y0_new = y1_new - max_h - - cropped_image = image[i][y0_new:y1_new, x0_new:x1_new, :] - cropped_mask = mask[i][y0_new:y1_new, x0_new:x1_new] - - # Ensure dimensions are divisible by 16 - target_width = (target_width + 15) // 16 * 16 - target_height = (target_height + 15) // 16 * 16 - - cropped_image = cropped_image.unsqueeze(0).movedim(-1, 1) # Move C to the second position (B, C, H, W) - cropped_image = common_upscale(cropped_image, target_width, target_height, "lanczos", "disabled") - cropped_image = cropped_image.movedim(1, -1).squeeze(0) - - cropped_mask = cropped_mask.unsqueeze(0).unsqueeze(0) - cropped_mask = common_upscale(cropped_mask, target_width, target_height, 'bilinear', "disabled") - cropped_mask = cropped_mask.squeeze(0).squeeze(0) - - image_list.append(cropped_image) - mask_list.append(cropped_mask) - bbox_list.append((x0_new, y0_new, x1_new, y1_new)) - - - return (torch.stack(image_list), torch.stack(mask_list), bbox_list) - -class ImageCropByMask: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE", ), - "mask": ("MASK", ), - }, - } - - RETURN_TYPES = ("IMAGE", ) - RETURN_NAMES = ("image", ) - FUNCTION = "crop" - CATEGORY = "KJNodes/image" - DESCRIPTION = "Crops the input images based on the provided mask." - - def crop(self, image, mask): - B, H, W, C = image.shape - mask = mask.round() - - # Find bounding box for each batch - crops = [] - - for b in range(B): - # Get coordinates of non-zero elements - rows = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=1) - cols = torch.any(mask[min(b, mask.shape[0]-1)] > 0, dim=0) - - # Find boundaries - y_min, y_max = torch.where(rows)[0][[0, -1]] - x_min, x_max = torch.where(cols)[0][[0, -1]] - - # Crop image and mask - crop = image[b:b+1, y_min:y_max+1, x_min:x_max+1, :] - crops.append(crop) - - # Stack results back together - cropped_images = torch.cat(crops, dim=0) - - return (cropped_images, ) - - - -class ImageUncropByMask: - - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "destination": ("IMAGE",), - "source": ("IMAGE",), - "mask": ("MASK",), - "bbox": ("BBOX",), - }, - } - - CATEGORY = "KJNodes/image" - RETURN_TYPES = ("IMAGE",) - RETURN_NAMES = ("image",) - FUNCTION = "uncrop" - - def uncrop(self, destination, source, mask, bbox=None): - - output_list = [] - - B, H, W, C = destination.shape - - for i in range(source.shape[0]): - x0, y0, x1, y1 = bbox[i] - bbox_height = y1 - y0 - bbox_width = x1 - x0 - - # Resize source image to match the bounding box dimensions - #resized_source = F.interpolate(source[i].unsqueeze(0).movedim(-1, 1), size=(bbox_height, bbox_width), mode='bilinear', align_corners=False) - resized_source = common_upscale(source[i].unsqueeze(0).movedim(-1, 1), bbox_width, bbox_height, "lanczos", "disabled") - resized_source = resized_source.movedim(1, -1).squeeze(0) - - # Resize mask to match the bounding box dimensions - resized_mask = common_upscale(mask[i].unsqueeze(0).unsqueeze(0), bbox_width, bbox_height, "bilinear", "disabled") - resized_mask = resized_mask.squeeze(0).squeeze(0) - - # Calculate padding values - pad_left = x0 - pad_right = W - x1 - pad_top = y0 - pad_bottom = H - y1 - - # Pad the resized source image and mask to fit the destination dimensions - padded_source = F.pad(resized_source, pad=(0, 0, pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0) - padded_mask = F.pad(resized_mask, pad=(pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=0) - - # Ensure the padded mask has the correct shape - padded_mask = padded_mask.unsqueeze(2).expand(-1, -1, destination[i].shape[2]) - # Ensure the padded source has the correct shape - padded_source = padded_source.unsqueeze(2).expand(-1, -1, -1, destination[i].shape[2]).squeeze(2) - - # Combine the destination and padded source images using the mask - result = destination[i] * (1.0 - padded_mask) + padded_source * padded_mask - - output_list.append(result) - - - return (torch.stack(output_list),) - -class ImageCropByMaskBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE", ), - "masks": ("MASK", ), - "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8, }), - "padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1, }), - "preserve_size": ("BOOLEAN", {"default": False}), - "bg_color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}), - } - } - - RETURN_TYPES = ("IMAGE", "MASK", ) - RETURN_NAMES = ("images", "masks",) - FUNCTION = "crop" - CATEGORY = "KJNodes/image" - DESCRIPTION = "Crops the input images based on the provided masks." - - def crop(self, image, masks, width, height, bg_color, padding, preserve_size): - B, H, W, C = image.shape - BM, HM, WM = masks.shape - mask_count = BM - if HM != H or WM != W: - masks = F.interpolate(masks.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1) - print(masks.shape) - output_images = [] - output_masks = [] - - bg_color = [int(x.strip())/255.0 for x in bg_color.split(",")] - - # For each mask - for i in range(mask_count): - curr_mask = masks[i] - - # Find bounds - y_indices, x_indices = torch.nonzero(curr_mask, as_tuple=True) - if len(y_indices) == 0 or len(x_indices) == 0: - continue - - # Get exact bounds with padding - min_y = max(0, y_indices.min().item() - padding) - max_y = min(H, y_indices.max().item() + 1 + padding) - min_x = max(0, x_indices.min().item() - padding) - max_x = min(W, x_indices.max().item() + 1 + padding) - - # Ensure mask has correct shape for multiplication - curr_mask = curr_mask.unsqueeze(-1).expand(-1, -1, C) - - # Crop image and mask together - cropped_img = image[0, min_y:max_y, min_x:max_x, :] - cropped_mask = curr_mask[min_y:max_y, min_x:max_x, :] - - crop_h, crop_w = cropped_img.shape[0:2] - new_w = crop_w - new_h = crop_h - - if not preserve_size or crop_w > width or crop_h > height: - scale = min(width/crop_w, height/crop_h) - new_w = int(crop_w * scale) - new_h = int(crop_h * scale) - - # Resize RGB - resized_img = common_upscale(cropped_img.permute(2,0,1).unsqueeze(0), new_w, new_h, "lanczos", "disabled").squeeze(0).permute(1,2,0) - resized_mask = torch.nn.functional.interpolate( - cropped_mask.permute(2,0,1).unsqueeze(0), - size=(new_h, new_w), - mode='nearest' - ).squeeze(0).permute(1,2,0) - else: - resized_img = cropped_img - resized_mask = cropped_mask - - # Create empty tensors - new_img = torch.zeros((height, width, 3), dtype=image.dtype) - new_mask = torch.zeros((height, width), dtype=image.dtype) - - # Pad both - pad_x = (width - new_w) // 2 - pad_y = (height - new_h) // 2 - new_img[pad_y:pad_y+new_h, pad_x:pad_x+new_w, :] = resized_img - if len(resized_mask.shape) == 3: - resized_mask = resized_mask[:,:,0] # Take first channel if 3D - new_mask[pad_y:pad_y+new_h, pad_x:pad_x+new_w] = resized_mask - - output_images.append(new_img) - output_masks.append(new_mask) - - if not output_images: - return (torch.zeros((0, height, width, 3), dtype=image.dtype),) - - out_rgb = torch.stack(output_images, dim=0) - out_masks = torch.stack(output_masks, dim=0) - - # Apply mask to RGB - mask_expanded = out_masks.unsqueeze(-1).expand(-1, -1, -1, 3) - background_color = torch.tensor(bg_color, dtype=torch.float32, device=image.device) - out_rgb = out_rgb * mask_expanded + background_color * (1 - mask_expanded) - - return (out_rgb, out_masks) - -class ImagePadKJ: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE", ), - "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "extra_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }), - "pad_mode": (["edge", "color"],), - "color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}), - }, - "optional": { - "mask": ("MASK", ), - "target_width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}), - "target_height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "forceInput": True}), - } - } - - RETURN_TYPES = ("IMAGE", "MASK", ) - RETURN_NAMES = ("images", "masks",) - FUNCTION = "pad" - CATEGORY = "KJNodes/image" - DESCRIPTION = "Pad the input image and optionally mask with the specified padding." - - def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None, target_height=None): - B, H, W, C = image.shape - - # Resize masks to image dimensions if necessary - if mask is not None: - BM, HM, WM = mask.shape - if HM != H or WM != W: - mask = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1) - - # Parse background color - bg_color = [int(x.strip())/255.0 for x in color.split(",")] - if len(bg_color) == 1: - bg_color = bg_color * 3 # Grayscale to RGB - bg_color = torch.tensor(bg_color, dtype=image.dtype, device=image.device) - - # Calculate padding sizes with extra padding - if target_width is not None and target_height is not None: - if extra_padding > 0: - image = common_upscale(image.movedim(-1, 1), W - extra_padding, H - extra_padding, "lanczos", "disabled").movedim(1, -1) - B, H, W, C = image.shape - - padded_width = target_width - padded_height = target_height - pad_left = (padded_width - W) // 2 - pad_right = padded_width - W - pad_left - pad_top = (padded_height - H) // 2 - pad_bottom = padded_height - H - pad_top - else: - pad_left = left + extra_padding - pad_right = right + extra_padding - pad_top = top + extra_padding - pad_bottom = bottom + extra_padding - - padded_width = W + pad_left + pad_right - padded_height = H + pad_top + pad_bottom - out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device) - - # Fill padded areas - for b in range(B): - if pad_mode == "edge": - # Pad with edge color - # Define edge pixels - top_edge = image[b, 0, :, :] - bottom_edge = image[b, H-1, :, :] - left_edge = image[b, :, 0, :] - right_edge = image[b, :, W-1, :] - - # Fill borders with edge colors - out_image[b, :pad_top, :, :] = top_edge.mean(dim=0) - out_image[b, pad_top+H:, :, :] = bottom_edge.mean(dim=0) - out_image[b, :, :pad_left, :] = left_edge.mean(dim=0) - out_image[b, :, pad_left+W:, :] = right_edge.mean(dim=0) - out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b] - else: - # Pad with specified background color - out_image[b, :, :, :] = bg_color.unsqueeze(0).unsqueeze(0) # Expand for H and W dimensions - out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b] - - - if mask is not None: - out_masks = torch.nn.functional.pad( - mask, - (pad_left, pad_right, pad_top, pad_bottom), - mode='replicate' - ) - else: - out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device) - for m in range(B): - out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0 - - return (out_image, out_masks) - -# extends https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite -class LoadVideosFromFolder: - @classmethod - def __init__(cls): - try: - cls.vhs_nodes = importlib.import_module("ComfyUI-VideoHelperSuite.videohelpersuite") - except ImportError: - try: - cls.vhs_nodes = importlib.import_module("comfyui-videohelpersuite.videohelpersuite") - except ImportError: - raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.") - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "video": ("STRING", {"default": "X://insert/path/"},), - "force_rate": ("FLOAT", {"default": 0, "min": 0, "max": 60, "step": 1, "disable": 0}), - "custom_width": ("INT", {"default": 0, "min": 0, "max": 4096, 'disable': 0}), - "custom_height": ("INT", {"default": 0, "min": 0, "max": 4096, 'disable': 0}), - "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "disable": 0}), - "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}), - "select_every_nth": ("INT", {"default": 1, "min": 1, "max": 1000, "step": 1}), - "output_type": (["batch", "grid"], {"default": "batch"}), - "grid_max_columns": ("INT", {"default": 4, "min": 1, "max": 16, "step": 1, "disable": 1}), - "add_label": ( "BOOLEAN", {"default": False} ), - }, - "hidden": { - "force_size": "STRING", - "unique_id": "UNIQUE_ID" - }, - } - - CATEGORY = "KJNodes/misc" - - RETURN_TYPES = ("IMAGE", ) - RETURN_NAMES = ("IMAGE", ) - - FUNCTION = "load_video" - - def load_video(self, output_type, grid_max_columns, add_label=False, **kwargs): - if self.vhs_nodes is None: - raise ImportError("This node requires ComfyUI-VideoHelperSuite to be installed.") - videos_list = [] - filenames = [] - for f in os.listdir(kwargs['video']): - if os.path.isfile(os.path.join(kwargs['video'], f)): - file_parts = f.split('.') - if len(file_parts) > 1 and (file_parts[-1].lower() in ['webm', 'mp4', 'mkv', 'gif', 'mov']): - videos_list.append(os.path.join(kwargs['video'], f)) - filenames.append(f) - print(videos_list) - kwargs.pop('video') - loaded_videos = [] - for idx, video in enumerate(videos_list): - video_tensor = self.vhs_nodes.load_video_nodes.load_video(video=video, **kwargs)[0] - if add_label: - # Add filename label above video (without extension) - if video_tensor.dim() == 4: - _, h, w, c = video_tensor.shape - else: - h, w, c = video_tensor.shape - # Remove extension from filename - label_text = filenames[idx].rsplit('.', 1)[0] - font_size = max(16, w // 20) - try: - font = ImageFont.truetype("arial.ttf", font_size) - except: - font = ImageFont.load_default() - dummy_img = Image.new("RGB", (w, 10), (0,0,0)) - draw = ImageDraw.Draw(dummy_img) - text_bbox = draw.textbbox((0,0), label_text, font=font) - extra_padding = max(12, font_size // 2) # More padding under the font - label_height = text_bbox[3] - text_bbox[1] + extra_padding - label_img = Image.new("RGB", (w, label_height), (0,0,0)) - draw = ImageDraw.Draw(label_img) - draw.text((w//2 - (text_bbox[2]-text_bbox[0])//2, 4), label_text, font=font, fill=(255,255,255)) - label_np = np.asarray(label_img).astype(np.float32) / 255.0 - label_tensor = torch.from_numpy(label_np) - if c == 1: - label_tensor = label_tensor.mean(dim=2, keepdim=True) - elif c == 4: - alpha = torch.ones((label_height, w, 1), dtype=label_tensor.dtype) - label_tensor = torch.cat([label_tensor, alpha], dim=2) - if video_tensor.dim() == 4: - label_tensor = label_tensor.unsqueeze(0).expand(video_tensor.shape[0], -1, -1, -1) - video_tensor = torch.cat([label_tensor, video_tensor], dim=1) - else: - video_tensor = torch.cat([label_tensor, video_tensor], dim=0) - loaded_videos.append(video_tensor) - if output_type == "batch": - out_tensor = torch.cat(loaded_videos) - elif output_type == "grid": - rows = (len(loaded_videos) + grid_max_columns - 1) // grid_max_columns - # Pad the last row if needed - total_slots = rows * grid_max_columns - while len(loaded_videos) < total_slots: - loaded_videos.append(torch.zeros_like(loaded_videos[0])) - # Create grid by rows - row_tensors = [] - for row_idx in range(rows): - start_idx = row_idx * grid_max_columns - end_idx = start_idx + grid_max_columns - row_videos = loaded_videos[start_idx:end_idx] - # Pad all videos in this row to the same height - heights = [v.shape[1] for v in row_videos] - max_height = max(heights) - padded_row_videos = [] - for v in row_videos: - pad_height = max_height - v.shape[1] - if pad_height > 0: - # Pad (frames, H, W, C) or (H, W, C) - if v.dim() == 4: - pad = (0,0, 0,0, 0,pad_height, 0,0) # (C,W,H,F) - v = torch.nn.functional.pad(v, (0,0,0,0,0,pad_height,0,0)) - else: - v = torch.nn.functional.pad(v, (0,0,0,0,pad_height,0)) - padded_row_videos.append(v) - row_tensor = torch.cat(padded_row_videos, dim=2) # Concatenate horizontally - row_tensors.append(row_tensor) - out_tensor = torch.cat(row_tensors, dim=1) # Concatenate rows vertically - print(out_tensor.shape) - return out_tensor, - - @classmethod - def IS_CHANGED(s, video, **kwargs): - if s.vhs_nodes is not None: - return s.vhs_nodes.utils.hash_path(video) - return None \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/intrinsic_lora_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/intrinsic_lora_nodes.py deleted file mode 100644 index c8f125363836cc7721b4b61d100702594522d389..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/intrinsic_lora_nodes.py +++ /dev/null @@ -1,115 +0,0 @@ -import folder_paths -import os -import torch -import torch.nn.functional as F -from comfy.utils import ProgressBar, load_torch_file -import comfy.sample -from nodes import CLIPTextEncode - -script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -folder_paths.add_model_folder_path("intrinsic_loras", os.path.join(script_directory, "intrinsic_loras")) - -class Intrinsic_lora_sampling: - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "lora_name": (folder_paths.get_filename_list("intrinsic_loras"), ), - "task": ( - [ - 'depth map', - 'surface normals', - 'albedo', - 'shading', - ], - { - "default": 'depth map' - }), - "text": ("STRING", {"multiline": True, "default": ""}), - "clip": ("CLIP", ), - "vae": ("VAE", ), - "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), - }, - "optional": { - "image": ("IMAGE",), - "optional_latent": ("LATENT",), - }, - } - - RETURN_TYPES = ("IMAGE", "LATENT",) - FUNCTION = "onestepsample" - CATEGORY = "KJNodes" - DESCRIPTION = """ -Sampler to use the intrinsic loras: -https://github.com/duxiaodan/intrinsic-lora -These LoRAs are tiny and thus included -with this node pack. -""" - - def onestepsample(self, model, lora_name, clip, vae, text, task, per_batch, image=None, optional_latent=None): - pbar = ProgressBar(3) - - if optional_latent is None: - image_list = [] - for start_idx in range(0, image.shape[0], per_batch): - sub_pixels = vae.vae_encode_crop_pixels(image[start_idx:start_idx+per_batch]) - image_list.append(vae.encode(sub_pixels[:,:,:,:3])) - sample = torch.cat(image_list, dim=0) - else: - sample = optional_latent["samples"] - noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu") - prompt = task + "," + text - positive, = CLIPTextEncode.encode(self, clip, prompt) - negative = positive #negative shouldn't do anything in this scenario - - pbar.update(1) - - #custom model sampling to pass latent through as it is - class X0_PassThrough(comfy.model_sampling.EPS): - def calculate_denoised(self, sigma, model_output, model_input): - return model_output - def calculate_input(self, sigma, noise): - return noise - sampling_base = comfy.model_sampling.ModelSamplingDiscrete - sampling_type = X0_PassThrough - - class ModelSamplingAdvanced(sampling_base, sampling_type): - pass - model_sampling = ModelSamplingAdvanced(model.model.model_config) - - #load lora - model_clone = model.clone() - lora_path = folder_paths.get_full_path("intrinsic_loras", lora_name) - lora = load_torch_file(lora_path, safe_load=True) - self.loaded_lora = (lora_path, lora) - - model_clone_with_lora = comfy.sd.load_lora_for_models(model_clone, None, lora, 1.0, 0)[0] - - model_clone_with_lora.add_object_patch("model_sampling", model_sampling) - - samples = {"samples": comfy.sample.sample(model_clone_with_lora, noise, 1, 1.0, "euler", "simple", positive, negative, sample, - denoise=1.0, disable_noise=True, start_step=0, last_step=1, - force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)} - pbar.update(1) - - decoded = [] - for start_idx in range(0, samples["samples"].shape[0], per_batch): - decoded.append(vae.decode(samples["samples"][start_idx:start_idx+per_batch])) - image_out = torch.cat(decoded, dim=0) - - pbar.update(1) - - if task == 'depth map': - imax = image_out.max() - imin = image_out.min() - image_out = (image_out-imin)/(imax-imin) - image_out = torch.max(image_out, dim=3, keepdim=True)[0].repeat(1, 1, 1, 3) - elif task == 'surface normals': - image_out = F.normalize(image_out * 2 - 1, dim=3) / 2 + 0.5 - image_out = 1.0 - image_out - else: - image_out = image_out.clamp(-1.,1.) - - return (image_out, samples,) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/lora_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/lora_nodes.py deleted file mode 100644 index fe9959c01bcad0a27641b0cd3ba13b53cfa9434e..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/lora_nodes.py +++ /dev/null @@ -1,190 +0,0 @@ -import torch -import comfy.model_management -import comfy.utils -import folder_paths -import os -import logging -from tqdm import tqdm - -device = comfy.model_management.get_torch_device() - -CLAMP_QUANTILE = 0.99 - -def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptive_param=1.0): - """ - Extracts LoRA weights from a weight difference tensor using SVD. - """ - conv2d = (len(diff.shape) == 4) - kernel_size = None if not conv2d else diff.size()[2:4] - conv2d_3x3 = conv2d and kernel_size != (1, 1) - out_dim, in_dim = diff.size()[0:2] - - if conv2d: - if conv2d_3x3: - diff = diff.flatten(start_dim=1) - else: - diff = diff.squeeze() - - diff_float = diff.float() - if algorithm == "svd_lowrank": - U, S, V = torch.svd_lowrank(diff_float, q=min(rank, in_dim, out_dim), niter=lowrank_iters) - U = U @ torch.diag(S) - Vh = V.t() - else: - #torch.linalg.svdvals() - U, S, Vh = torch.linalg.svd(diff_float) - # Flexible rank selection logic like locon: https://github.com/KohakuBlueleaf/LyCORIS/blob/main/tools/extract_locon.py - if "adaptive" in lora_type: - if lora_type == "adaptive_ratio": - min_s = torch.max(S) * adaptive_param - lora_rank = torch.sum(S > min_s).item() - elif lora_type == "adaptive_energy": - energy = torch.cumsum(S**2, dim=0) - total_energy = torch.sum(S**2) - threshold = adaptive_param * total_energy # e.g., adaptive_param=0.95 for 95% - lora_rank = torch.sum(energy < threshold).item() + 1 - elif lora_type == "adaptive_quantile": - s_cum = torch.cumsum(S, dim=0) - min_cum_sum = adaptive_param * torch.sum(S) - lora_rank = torch.sum(s_cum < min_cum_sum).item() - print(f"{key} Extracted LoRA rank: {lora_rank}") - else: - lora_rank = rank - - lora_rank = max(1, lora_rank) - lora_rank = min(out_dim, in_dim, lora_rank) - - U = U[:, :lora_rank] - S = S[:lora_rank] - U = U @ torch.diag(S) - Vh = Vh[:lora_rank, :] - - dist = torch.cat([U.flatten(), Vh.flatten()]) - if dist.numel() > 100_000: - # Sample 100,000 elements for quantile estimation - idx = torch.randperm(dist.numel(), device=dist.device)[:100_000] - dist_sample = dist[idx] - hi_val = torch.quantile(dist_sample, CLAMP_QUANTILE) - else: - hi_val = torch.quantile(dist, CLAMP_QUANTILE) - low_val = -hi_val - - U = U.clamp(low_val, hi_val) - Vh = Vh.clamp(low_val, hi_val) - if conv2d: - U = U.reshape(out_dim, lora_rank, 1, 1) - Vh = Vh.reshape(lora_rank, in_dim, kernel_size[0], kernel_size[1]) - return (U, Vh) - - -def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, algorithm, lowrank_iters, out_dtype, bias_diff=False, adaptive_param=1.0): - comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True) - model_diff.model.diffusion_model.cpu() - sd = model_diff.model_state_dict(filter_prefix=prefix_model) - del model_diff - comfy.model_management.soft_empty_cache() - for k, v in sd.items(): - if isinstance(v, torch.Tensor): - sd[k] = v.cpu() - - # Get total number of keys to process for progress bar - total_keys = len([k for k in sd if k.endswith(".weight") or (bias_diff and k.endswith(".bias"))]) - - # Create progress bar - progress_bar = tqdm(total=total_keys, desc=f"Extracting LoRA ({prefix_lora.strip('.')})") - comfy_pbar = comfy.utils.ProgressBar(total_keys) - - for k in sd: - if k.endswith(".weight"): - weight_diff = sd[k] - if weight_diff.ndim == 5: - logging.info(f"Skipping 5D tensor for key {k}") #skip patch embed - progress_bar.update(1) - comfy_pbar.update(1) - continue - if lora_type != "full": - if weight_diff.ndim < 2: - if bias_diff: - output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu() - progress_bar.update(1) - comfy_pbar.update(1) - continue - try: - out = extract_lora(weight_diff.to(device), k, rank, algorithm, lora_type, lowrank_iters=lowrank_iters, adaptive_param=adaptive_param) - output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().to(out_dtype).cpu() - output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().to(out_dtype).cpu() - except Exception as e: - logging.warning(f"Could not generate lora weights for key {k}, error {e}") - else: - output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu() - - progress_bar.update(1) - comfy_pbar.update(1) - - elif bias_diff and k.endswith(".bias"): - output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().to(out_dtype).cpu() - progress_bar.update(1) - comfy_pbar.update(1) - progress_bar.close() - return output_sd - -class LoraExtractKJ: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "finetuned_model": ("MODEL",), - "original_model": ("MODEL",), - "filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}), - "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}), - "lora_type": (["standard", "full", "adaptive_ratio", "adaptive_quantile", "adaptive_energy"],), - "algorithm": (["svd_linalg", "svd_lowrank"], {"default": "svd_linalg", "tooltip": "SVD algorithm to use, svd_lowrank is faster but less accurate."}), - "lowrank_iters": ("INT", {"default": 7, "min": 1, "max": 100, "step": 1, "tooltip": "The number of subspace iterations for lowrank SVD algorithm."}), - "output_dtype": (["fp16", "bf16", "fp32"], {"default": "fp16"}), - "bias_diff": ("BOOLEAN", {"default": True}), - "adaptive_param": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "For ratio mode, this is the ratio of the maximum singular value. For quantile mode, this is the quantile of the singular values."}), - }, - - } - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "KJNodes/lora" - - def save(self, finetuned_model, original_model, filename_prefix, rank, lora_type, algorithm, lowrank_iters, output_dtype, bias_diff, adaptive_param): - if algorithm == "svd_lowrank" and lora_type != "standard": - raise ValueError("svd_lowrank algorithm is only supported for standard LoRA extraction.") - - dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[output_dtype] - m = finetuned_model.clone() - kp = original_model.get_key_patches("diffusion_model.") - for k in kp: - m.add_patches({k: kp[k]}, - 1.0, 1.0) - model_diff = m - - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - - output_sd = {} - if model_diff is not None: - output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param) - if "adaptive" in lora_type: - rank_str = f"{lora_type}_{adaptive_param:.2f}" - else: - rank_str = rank - output_checkpoint = f"{filename}_rank_{rank_str}_{output_dtype}_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - - comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None) - return {} - -NODE_CLASS_MAPPINGS = { - "LoraExtractKJ": LoraExtractKJ -} - -NODE_DISPLAY_NAME_MAPPINGS = { - "LoraExtractKJ": "LoraExtractKJ" -} diff --git a/custom_nodes/comfyui-kjnodes/nodes/mask_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/mask_nodes.py deleted file mode 100644 index 9a84a127dd618ebaabb2e9e522bf84594d9549e1..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/mask_nodes.py +++ /dev/null @@ -1,1427 +0,0 @@ -import torch -import torch.nn.functional as F -from torchvision.transforms import functional as TF -from PIL import Image, ImageDraw, ImageFilter, ImageFont -import scipy.ndimage -import numpy as np -from contextlib import nullcontext -import os - -from comfy import model_management -from comfy.utils import ProgressBar -from comfy.utils import common_upscale -from nodes import MAX_RESOLUTION - -import folder_paths - -from ..utility.utility import tensor2pil, pil2tensor - -script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - -class BatchCLIPSeg: - - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - - return {"required": - { - "images": ("IMAGE",), - "text": ("STRING", {"multiline": False}), - "threshold": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 10.0, "step": 0.001}), - "binary_mask": ("BOOLEAN", {"default": True}), - "combine_mask": ("BOOLEAN", {"default": False}), - "use_cuda": ("BOOLEAN", {"default": True}), - }, - "optional": - { - "blur_sigma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), - "opt_model": ("CLIPSEGMODEL", ), - "prev_mask": ("MASK", {"default": None}), - "image_bg_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - "invert": ("BOOLEAN", {"default": False}), - } - } - - CATEGORY = "KJNodes/masking" - RETURN_TYPES = ("MASK", "IMAGE", ) - RETURN_NAMES = ("Mask", "Image", ) - FUNCTION = "segment_image" - DESCRIPTION = """ -Segments an image or batch of images using CLIPSeg. -""" - - def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda, blur_sigma=0.0, opt_model=None, prev_mask=None, invert= False, image_bg_level=0.5): - from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation - import torchvision.transforms as transforms - offload_device = model_management.unet_offload_device() - device = model_management.get_torch_device() - if not use_cuda: - device = torch.device("cpu") - dtype = model_management.unet_dtype() - - if opt_model is None: - checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', 'clipseg-rd64-refined-fp16') - if not hasattr(self, "model"): - try: - if not os.path.exists(checkpoint_path): - from huggingface_hub import snapshot_download - snapshot_download(repo_id="Kijai/clipseg-rd64-refined-fp16", local_dir=checkpoint_path, local_dir_use_symlinks=False) - self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) - except: - checkpoint_path = "CIDAS/clipseg-rd64-refined" - self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) - processor = CLIPSegProcessor.from_pretrained(checkpoint_path) - - else: - self.model = opt_model['model'] - processor = opt_model['processor'] - - self.model.to(dtype).to(device) - - B, H, W, C = images.shape - images = images.to(device) - - autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device) - with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): - - PIL_images = [Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) for image in images ] - prompt = [text] * len(images) - input_prc = processor(text=prompt, images=PIL_images, return_tensors="pt") - - for key in input_prc: - input_prc[key] = input_prc[key].to(device) - outputs = self.model(**input_prc) - - mask_tensor = torch.sigmoid(outputs.logits) - mask_tensor = (mask_tensor - mask_tensor.min()) / (mask_tensor.max() - mask_tensor.min()) - mask_tensor = torch.where(mask_tensor > (threshold), mask_tensor, torch.tensor(0, dtype=torch.float)) - print(mask_tensor.shape) - if len(mask_tensor.shape) == 2: - mask_tensor = mask_tensor.unsqueeze(0) - mask_tensor = F.interpolate(mask_tensor.unsqueeze(1), size=(H, W), mode='nearest') - mask_tensor = mask_tensor.squeeze(1) - - self.model.to(offload_device) - - if binary_mask: - mask_tensor = (mask_tensor > 0).float() - if blur_sigma > 0: - kernel_size = int(6 * int(blur_sigma) + 1) - blur = transforms.GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma)) - mask_tensor = blur(mask_tensor) - - if combine_mask: - mask_tensor = torch.max(mask_tensor, dim=0)[0] - mask_tensor = mask_tensor.unsqueeze(0).repeat(len(images),1,1) - - del outputs - model_management.soft_empty_cache() - - if prev_mask is not None: - if prev_mask.shape != mask_tensor.shape: - prev_mask = F.interpolate(prev_mask.unsqueeze(1), size=(H, W), mode='nearest') - mask_tensor = mask_tensor + prev_mask.to(device) - torch.clamp(mask_tensor, min=0.0, max=1.0) - - if invert: - mask_tensor = 1 - mask_tensor - - image_tensor = images * mask_tensor.unsqueeze(-1) + (1 - mask_tensor.unsqueeze(-1)) * image_bg_level - image_tensor = torch.clamp(image_tensor, min=0.0, max=1.0).cpu().float() - - mask_tensor = mask_tensor.cpu().float() - - return mask_tensor, image_tensor, - -class DownloadAndLoadCLIPSeg: - - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - - return {"required": - { - "model": ( - [ 'Kijai/clipseg-rd64-refined-fp16', - 'CIDAS/clipseg-rd64-refined', - ], - ), - }, - } - - CATEGORY = "KJNodes/masking" - RETURN_TYPES = ("CLIPSEGMODEL",) - RETURN_NAMES = ("clipseg_model",) - FUNCTION = "segment_image" - DESCRIPTION = """ -Downloads and loads CLIPSeg model with huggingface_hub, -to ComfyUI/models/clip_seg -""" - - def segment_image(self, model): - from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation - checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', os.path.basename(model)) - if not hasattr(self, "model"): - if not os.path.exists(checkpoint_path): - from huggingface_hub import snapshot_download - snapshot_download(repo_id=model, local_dir=checkpoint_path, local_dir_use_symlinks=False) - self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path) - - processor = CLIPSegProcessor.from_pretrained(checkpoint_path) - - clipseg_model = {} - clipseg_model['model'] = self.model - clipseg_model['processor'] = processor - - return clipseg_model, - -class CreateTextMask: - - RETURN_TYPES = ("IMAGE", "MASK",) - FUNCTION = "createtextmask" - CATEGORY = "KJNodes/text" - DESCRIPTION = """ -Creates a text image and mask. -Looks for fonts from this folder: -ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts - -If start_rotation and/or end_rotation are different values, -creates animation between them. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "invert": ("BOOLEAN", {"default": False}), - "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), - "text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - "text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - "font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}), - "font_color": ("STRING", {"default": "white"}), - "text": ("STRING", {"default": "HELLO!", "multiline": True}), - "font": (folder_paths.get_filename_list("kjnodes_fonts"), ), - "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}), - "end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}), - }, - } - - def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation): - # Define the number of images in the batch - batch_size = frames - out = [] - masks = [] - rotation = start_rotation - if start_rotation != end_rotation: - rotation_increment = (end_rotation - start_rotation) / (batch_size - 1) - - font_path = folder_paths.get_full_path("kjnodes_fonts", font) - # Generate the text - for i in range(batch_size): - image = Image.new("RGB", (width, height), "black") - draw = ImageDraw.Draw(image) - font = ImageFont.truetype(font_path, font_size) - - # Split the text into words - words = text.split() - - # Initialize variables for line creation - lines = [] - current_line = [] - current_line_width = 0 - try: #new pillow - # Iterate through words to create lines - for word in words: - word_width = font.getbbox(word)[2] - if current_line_width + word_width <= width - 2 * text_x: - current_line.append(word) - current_line_width += word_width + font.getbbox(" ")[2] # Add space width - else: - lines.append(" ".join(current_line)) - current_line = [word] - current_line_width = word_width - except: #old pillow - for word in words: - word_width = font.getsize(word)[0] - if current_line_width + word_width <= width - 2 * text_x: - current_line.append(word) - current_line_width += word_width + font.getsize(" ")[0] # Add space width - else: - lines.append(" ".join(current_line)) - current_line = [word] - current_line_width = word_width - - # Add the last line if it's not empty - if current_line: - lines.append(" ".join(current_line)) - - # Draw each line of text separately - y_offset = text_y - for line in lines: - text_width = font.getlength(line) - text_height = font_size - text_center_x = text_x + text_width / 2 - text_center_y = y_offset + text_height / 2 - try: - draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga']) - except: - draw.text((text_x, y_offset), line, font=font, fill=font_color) - y_offset += text_height # Move to the next line - - if start_rotation != end_rotation: - image = image.rotate(rotation, center=(text_center_x, text_center_y)) - rotation += rotation_increment - - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - mask = image[:, :, :, 0] - masks.append(mask) - out.append(image) - - if invert: - return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),) - return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) - -class ColorToMask: - - RETURN_TYPES = ("MASK",) - FUNCTION = "clip" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Converts chosen RGB value to a mask. -With batch inputs, the **per_batch** -controls the number of images processed at once. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE",), - "invert": ("BOOLEAN", {"default": False}), - "red": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}), - "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}), - }, - } - - def clip(self, images, red, green, blue, threshold, invert, per_batch): - - color = torch.tensor([red, green, blue], dtype=torch.uint8) - black = torch.tensor([0, 0, 0], dtype=torch.uint8) - white = torch.tensor([255, 255, 255], dtype=torch.uint8) - - if invert: - black, white = white, black - - steps = images.shape[0] - pbar = ProgressBar(steps) - tensors_out = [] - - for start_idx in range(0, images.shape[0], per_batch): - - # Calculate color distances - color_distances = torch.norm(images[start_idx:start_idx+per_batch] * 255 - color, dim=-1) - - # Create a mask based on the threshold - mask = color_distances <= threshold - - # Apply the mask to create new images - mask_out = torch.where(mask.unsqueeze(-1), white, black).float() - mask_out = mask_out.mean(dim=-1) - - tensors_out.append(mask_out.cpu()) - batch_count = mask_out.shape[0] - pbar.update(batch_count) - - tensors_out = torch.cat(tensors_out, dim=0) - tensors_out = torch.clamp(tensors_out, min=0.0, max=1.0) - return tensors_out, - -class CreateFluidMask: - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "createfluidmask" - CATEGORY = "KJNodes/masking/generate" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "invert": ("BOOLEAN", {"default": False}), - "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), - "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - "inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}), - "inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}), - "inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}), - "inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}), - "inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}), - }, - } - #using code from https://github.com/GregTJ/stable-fluids - def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration): - from ..utility.fluid import Fluid - try: - from scipy.special import erf - except: - from scipy.spatial import erf - out = [] - masks = [] - RESOLUTION = width, height - DURATION = frames - - INFLOW_PADDING = inflow_padding - INFLOW_DURATION = inflow_duration - INFLOW_RADIUS = inflow_radius - INFLOW_VELOCITY = inflow_velocity - INFLOW_COUNT = inflow_count - - print('Generating fluid solver, this may take some time.') - fluid = Fluid(RESOLUTION, 'dye') - - center = np.floor_divide(RESOLUTION, 2) - r = np.min(center) - INFLOW_PADDING - - points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False) - points = tuple(np.array((np.cos(p), np.sin(p))) for p in points) - normals = tuple(-p for p in points) - points = tuple(r * p + center for p in points) - - inflow_velocity = np.zeros_like(fluid.velocity) - inflow_dye = np.zeros(fluid.shape) - for p, n in zip(points, normals): - mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS - inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY - inflow_dye[mask] = 1 - - - for f in range(DURATION): - print(f'Computing frame {f + 1} of {DURATION}.') - if f <= INFLOW_DURATION: - fluid.velocity += inflow_velocity - fluid.dye += inflow_dye - - curl = fluid.step()[1] - # Using the error function to make the contrast a bit higher. - # Any other sigmoid function e.g. smoothstep would work. - curl = (erf(curl * 2) + 1) / 4 - - color = np.dstack((curl, np.ones(fluid.shape), fluid.dye)) - color = (np.clip(color, 0, 1) * 255).astype('uint8') - image = np.array(color).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - mask = image[:, :, :, 0] - masks.append(mask) - out.append(image) - - if invert: - return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),) - return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) - -class CreateAudioMask: - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "createaudiomask" - CATEGORY = "KJNodes/deprecated" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "invert": ("BOOLEAN", {"default": False}), - "frames": ("INT", {"default": 16,"min": 1, "max": 255, "step": 1}), - "scale": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 2.0, "step": 0.01}), - "audio_path": ("STRING", {"default": "audio.wav"}), - "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - }, - } - - def createaudiomask(self, frames, width, height, invert, audio_path, scale): - try: - import librosa - except ImportError: - raise Exception("Can not import librosa. Install it with 'pip install librosa'") - batch_size = frames - out = [] - masks = [] - if audio_path == "audio.wav": #I don't know why relative path won't work otherwise... - audio_path = os.path.join(script_directory, audio_path) - audio, sr = librosa.load(audio_path) - spectrogram = np.abs(librosa.stft(audio)) - - for i in range(batch_size): - image = Image.new("RGB", (width, height), "black") - draw = ImageDraw.Draw(image) - frame = spectrogram[:, i] - circle_radius = int(height * np.mean(frame)) - circle_radius *= scale - circle_center = (width // 2, height // 2) # Calculate the center of the image - - draw.ellipse([(circle_center[0] - circle_radius, circle_center[1] - circle_radius), - (circle_center[0] + circle_radius, circle_center[1] + circle_radius)], - fill='white') - - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - mask = image[:, :, :, 0] - masks.append(mask) - out.append(image) - - if invert: - return (1.0 - torch.cat(out, dim=0),) - return (torch.cat(out, dim=0),torch.cat(masks, dim=0),) - -class CreateGradientMask: - - RETURN_TYPES = ("MASK",) - FUNCTION = "createmask" - CATEGORY = "KJNodes/masking/generate" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "invert": ("BOOLEAN", {"default": False}), - "frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}), - "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - }, - } - def createmask(self, frames, width, height, invert): - # Define the number of images in the batch - batch_size = frames - out = [] - # Create an empty array to store the image batch - image_batch = np.zeros((batch_size, height, width), dtype=np.float32) - # Generate the black to white gradient for each image - for i in range(batch_size): - gradient = np.linspace(1.0, 0.0, width, dtype=np.float32) - time = i / frames # Calculate the time variable - offset_gradient = gradient - time # Offset the gradient values based on time - image_batch[i] = offset_gradient.reshape(1, -1) - output = torch.from_numpy(image_batch) - mask = output - out.append(mask) - if invert: - return (1.0 - torch.cat(out, dim=0),) - return (torch.cat(out, dim=0),) - -class CreateFadeMask: - - RETURN_TYPES = ("MASK",) - FUNCTION = "createfademask" - CATEGORY = "KJNodes/deprecated" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "invert": ("BOOLEAN", {"default": False}), - "frames": ("INT", {"default": 2,"min": 2, "max": 10000, "step": 1}), - "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), - "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), - "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), - "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}), - "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}), - }, - } - - def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame): - def ease_in(t): - return t * t - - def ease_out(t): - return 1 - (1 - t) * (1 - t) - - def ease_in_out(t): - return 3 * t * t - 2 * t * t * t - - batch_size = frames - out = [] - image_batch = np.zeros((batch_size, height, width), dtype=np.float32) - - if midpoint_frame == 0: - midpoint_frame = batch_size // 2 - - for i in range(batch_size): - if i <= midpoint_frame: - t = i / midpoint_frame - if interpolation == "ease_in": - t = ease_in(t) - elif interpolation == "ease_out": - t = ease_out(t) - elif interpolation == "ease_in_out": - t = ease_in_out(t) - color = start_level - t * (start_level - midpoint_level) - else: - t = (i - midpoint_frame) / (batch_size - midpoint_frame) - if interpolation == "ease_in": - t = ease_in(t) - elif interpolation == "ease_out": - t = ease_out(t) - elif interpolation == "ease_in_out": - t = ease_in_out(t) - color = midpoint_level - t * (midpoint_level - end_level) - - color = np.clip(color, 0, 255) - image = np.full((height, width), color, dtype=np.float32) - image_batch[i] = image - - output = torch.from_numpy(image_batch) - mask = output - out.append(mask) - - if invert: - return (1.0 - torch.cat(out, dim=0),) - return (torch.cat(out, dim=0),) - -class CreateFadeMaskAdvanced: - - RETURN_TYPES = ("MASK",) - FUNCTION = "createfademask" - CATEGORY = "KJNodes/masking/generate" - DESCRIPTION = """ -Create a batch of masks interpolated between given frames and values. -Uses same syntax as Fizz' BatchValueSchedule. -First value is the frame index (not that this starts from 0, not 1) -and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0 - -For example the default values: -0:(0.0) -7:(1.0) -15:(0.0) - -Would create a mask batch fo 16 frames, starting from black, -interpolating with the chosen curve to fully white at the 8th frame, -and interpolating from that to fully black at the 16th frame. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), - "invert": ("BOOLEAN", {"default": False}), - "frames": ("INT", {"default": 16,"min": 2, "max": 10000, "step": 1}), - "width": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out", "none", "default_to_black"],), - }, - } - - def createfademask(self, frames, width, height, invert, points_string, interpolation): - def ease_in(t): - return t * t - - def ease_out(t): - return 1 - (1 - t) * (1 - t) - - def ease_in_out(t): - return 3 * t * t - 2 * t * t * t - - # Parse the input string into a list of tuples - points = [] - points_string = points_string.rstrip(',\n') - for point_str in points_string.split(','): - frame_str, color_str = point_str.split(':') - frame = int(frame_str.strip()) - color = float(color_str.strip()[1:-1]) # Remove parentheses around color - points.append((frame, color)) - - # Check if the last frame is already in the points - if (interpolation != "default_to_black") and (len(points) == 0 or points[-1][0] != frames - 1): - # If not, add it with the color of the last specified frame - points.append((frames - 1, points[-1][1] if points else 0)) - - # Sort the points by frame number - points.sort(key=lambda x: x[0]) - - batch_size = frames - out = [] - image_batch = np.zeros((batch_size, height, width), dtype=np.float32) - - # Index of the next point to interpolate towards - next_point = 1 - - for i in range(batch_size): - while next_point < len(points) and i > points[next_point][0]: - next_point += 1 - - # Interpolate between the previous point and the next point - prev_point = next_point - 1 - - if interpolation == "none": - exact_match = False - for p in points: - if p[0] == i: # Exact frame match - color = p[1] - exact_match = True - break - if not exact_match: - color = points[prev_point][1] - - elif interpolation == "default_to_black": - exact_match = False - for p in points: - if p[0] == i: # Exact frame match - color = p[1] - exact_match = True - break - if not exact_match: - color = 0 - else: - t = (i - points[prev_point][0]) / (points[next_point][0] - points[prev_point][0]) - if interpolation == "ease_in": - t = ease_in(t) - elif interpolation == "ease_out": - t = ease_out(t) - elif interpolation == "ease_in_out": - t = ease_in_out(t) - elif interpolation == "linear": - pass # No need to modify `t` for linear interpolation - - color = points[prev_point][1] - t * (points[prev_point][1] - points[next_point][1]) - - color = np.clip(color, 0, 255) - image = np.full((height, width), color, dtype=np.float32) - image_batch[i] = image - - output = torch.from_numpy(image_batch) - mask = output - out.append(mask) - - if invert: - return (1.0 - torch.cat(out, dim=0),) - return (torch.cat(out, dim=0),) - -class CreateMagicMask: - - RETURN_TYPES = ("MASK", "MASK",) - RETURN_NAMES = ("mask", "mask_inverted",) - FUNCTION = "createmagicmask" - CATEGORY = "KJNodes/masking/generate" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}), - "depth": ("INT", {"default": 12,"min": 1, "max": 500, "step": 1}), - "distortion": ("FLOAT", {"default": 1.5,"min": 0.0, "max": 100.0, "step": 0.01}), - "seed": ("INT", {"default": 123,"min": 0, "max": 99999999, "step": 1}), - "transitions": ("INT", {"default": 1,"min": 1, "max": 20, "step": 1}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - }, - } - - def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height): - from ..utility.magictex import coordinate_grid, random_transform, magic - import matplotlib.pyplot as plt - rng = np.random.default_rng(seed) - out = [] - coords = coordinate_grid((frame_width, frame_height)) - - # Calculate the number of frames for each transition - frames_per_transition = frames // transitions - - # Generate a base set of parameters - base_params = { - "coords": random_transform(coords, rng), - "depth": depth, - "distortion": distortion, - } - for t in range(transitions): - # Generate a second set of parameters that is at most max_diff away from the base parameters - params1 = base_params.copy() - params2 = base_params.copy() - - params1['coords'] = random_transform(coords, rng) - params2['coords'] = random_transform(coords, rng) - - for i in range(frames_per_transition): - # Compute the interpolation factor - alpha = i / frames_per_transition - - # Interpolate between the two sets of parameters - params = params1.copy() - params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords'] - - tex = magic(**params) - - dpi = frame_width / 10 - fig = plt.figure(figsize=(10, 10), dpi=dpi) - - ax = fig.add_subplot(111) - plt.subplots_adjust(left=0, right=1, bottom=0, top=1) - - ax.get_yaxis().set_ticks([]) - ax.get_xaxis().set_ticks([]) - ax.imshow(tex, aspect='auto') - - fig.canvas.draw() - img = np.array(fig.canvas.renderer._renderer) - - plt.close(fig) - - pil_img = Image.fromarray(img).convert("L") - mask = torch.tensor(np.array(pil_img)) / 255.0 - - out.append(mask) - - return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) - -class CreateShapeMask: - - RETURN_TYPES = ("MASK", "MASK",) - RETURN_NAMES = ("mask", "mask_inverted",) - FUNCTION = "createshapemask" - CATEGORY = "KJNodes/masking/generate" - DESCRIPTION = """ -Creates a mask or batch of masks with the specified shape. -Locations are center locations. -Grow value is the amount to grow the shape on each frame, creating animated masks. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "shape": ( - [ 'circle', - 'square', - 'triangle', - ], - { - "default": 'circle' - }), - "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}), - "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), - "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}), - "grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), - "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}), - }, - } - - def createshapemask(self, frames, frame_width, frame_height, location_x, location_y, shape_width, shape_height, grow, shape): - # Define the number of images in the batch - batch_size = frames - out = [] - color = "white" - for i in range(batch_size): - image = Image.new("RGB", (frame_width, frame_height), "black") - draw = ImageDraw.Draw(image) - - # Calculate the size for this frame and ensure it's not less than 0 - current_width = max(0, shape_width + i*grow) - current_height = max(0, shape_height + i*grow) - - if shape == 'circle' or shape == 'square': - # Define the bounding box for the shape - left_up_point = (location_x - current_width // 2, location_y - current_height // 2) - right_down_point = (location_x + current_width // 2, location_y + current_height // 2) - two_points = [left_up_point, right_down_point] - - if shape == 'circle': - draw.ellipse(two_points, fill=color) - elif shape == 'square': - draw.rectangle(two_points, fill=color) - - elif shape == 'triangle': - # Define the points for the triangle - left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left - right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right - top_point = (location_x, location_y - current_height // 2) # top point - draw.polygon([top_point, left_up_point, right_down_point], fill=color) - - image = pil2tensor(image) - mask = image[:, :, :, 0] - out.append(mask) - outstack = torch.cat(out, dim=0) - return (outstack, 1.0 - outstack,) - -class CreateVoronoiMask: - - RETURN_TYPES = ("MASK", "MASK",) - RETURN_NAMES = ("mask", "mask_inverted",) - FUNCTION = "createvoronoi" - CATEGORY = "KJNodes/masking/generate" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}), - "num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}), - "line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}), - "speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}), - "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - }, - } - - def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height): - from scipy.spatial import Voronoi - # Define the number of images in the batch - batch_size = frames - out = [] - - # Calculate aspect ratio - aspect_ratio = frame_width / frame_height - - # Create start and end points for each point, considering the aspect ratio - start_points = np.random.rand(num_points, 2) - start_points[:, 0] *= aspect_ratio - - end_points = np.random.rand(num_points, 2) - end_points[:, 0] *= aspect_ratio - - for i in range(batch_size): - # Interpolate the points' positions based on the current frame - t = (i * speed) / (batch_size - 1) # normalize to [0, 1] over the frames - t = np.clip(t, 0, 1) # ensure t is in [0, 1] - points = (1 - t) * start_points + t * end_points # lerp - - # Adjust points for aspect ratio - points[:, 0] *= aspect_ratio - - vor = Voronoi(points) - - # Create a blank image with a white background - fig, ax = plt.subplots() - plt.subplots_adjust(left=0, right=1, bottom=0, top=1) - ax.set_xlim([0, aspect_ratio]); ax.set_ylim([0, 1]) # adjust x limits - ax.axis('off') - ax.margins(0, 0) - fig.set_size_inches(aspect_ratio * frame_height/100, frame_height/100) # adjust figure size - ax.fill_between([0, 1], [0, 1], color='white') - - # Plot each Voronoi ridge - for simplex in vor.ridge_vertices: - simplex = np.asarray(simplex) - if np.all(simplex >= 0): - plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width) - - fig.canvas.draw() - img = np.array(fig.canvas.renderer._renderer) - - plt.close(fig) - - pil_img = Image.fromarray(img).convert("L") - mask = torch.tensor(np.array(pil_img)) / 255.0 - - out.append(mask) - - return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) - -class GetMaskSizeAndCount: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "mask": ("MASK",), - }} - - RETURN_TYPES = ("MASK","INT", "INT", "INT",) - RETURN_NAMES = ("mask", "width", "height", "count",) - FUNCTION = "getsize" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Returns the width, height and batch size of the mask, -and passes it through unchanged. - -""" - - def getsize(self, mask): - width = mask.shape[2] - height = mask.shape[1] - count = mask.shape[0] - return {"ui": { - "text": [f"{count}x{width}x{height}"]}, - "result": (mask, width, height, count) - } - -class GrowMaskWithBlur: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mask": ("MASK",), - "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), - "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), - "tapered_corners": ("BOOLEAN", {"default": True}), - "flip_input": ("BOOLEAN", {"default": False}), - "blur_radius": ("FLOAT", { - "default": 0.0, - "min": 0.0, - "max": 100, - "step": 0.1 - }), - "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - }, - "optional": { - "fill_holes": ("BOOLEAN", {"default": False}), - }, - } - - CATEGORY = "KJNodes/masking" - RETURN_TYPES = ("MASK", "MASK",) - RETURN_NAMES = ("mask", "mask_inverted",) - FUNCTION = "expand_mask" - DESCRIPTION = """ -# GrowMaskWithBlur -- mask: Input mask or mask batch -- expand: Expand or contract mask or mask batch by a given amount -- incremental_expandrate: increase expand rate by a given amount per frame -- tapered_corners: use tapered corners -- flip_input: flip input mask -- blur_radius: value higher than 0 will blur the mask -- lerp_alpha: alpha value for interpolation between frames -- decay_factor: decay value for interpolation between frames -- fill_holes: fill holes in the mask (slow)""" - - def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False): - alpha = lerp_alpha - decay = decay_factor - if flip_input: - mask = 1.0 - mask - c = 0 if tapered_corners else 1 - kernel = np.array([[c, 1, c], - [1, 1, 1], - [c, 1, c]]) - growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).cpu() - out = [] - previous_output = None - current_expand = expand - for m in growmask: - output = m.numpy().astype(np.float32) - for _ in range(abs(round(current_expand))): - if current_expand < 0: - output = scipy.ndimage.grey_erosion(output, footprint=kernel) - else: - output = scipy.ndimage.grey_dilation(output, footprint=kernel) - if current_expand < 0: - current_expand -= abs(incremental_expandrate) - else: - current_expand += abs(incremental_expandrate) - if fill_holes: - binary_mask = output > 0 - output = scipy.ndimage.binary_fill_holes(binary_mask) - output = output.astype(np.float32) * 255 - output = torch.from_numpy(output) - if alpha < 1.0 and previous_output is not None: - # Interpolate between the previous and current frame - output = alpha * output + (1 - alpha) * previous_output - if decay < 1.0 and previous_output is not None: - # Add the decayed previous output to the current frame - output += decay * previous_output - output = output / output.max() - previous_output = output - out.append(output) - - if blur_radius != 0: - # Convert the tensor list to PIL images, apply blur, and convert back - for idx, tensor in enumerate(out): - # Convert tensor to PIL image - pil_image = tensor2pil(tensor.cpu().detach())[0] - # Apply Gaussian blur - pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius)) - # Convert back to tensor - out[idx] = pil2tensor(pil_image) - blurred = torch.cat(out, dim=0) - return (blurred, 1.0 - blurred) - else: - return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) - -class MaskBatchMulti: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "mask_1": ("MASK", ), - "mask_2": ("MASK", ), - }, - } - - RETURN_TYPES = ("MASK",) - RETURN_NAMES = ("masks",) - FUNCTION = "combine" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Creates an image batch from multiple masks. -You can set how many inputs the node has, -with the **inputcount** and clicking update. -""" - - def combine(self, inputcount, **kwargs): - mask = kwargs["mask_1"] - for c in range(1, inputcount): - new_mask = kwargs[f"mask_{c + 1}"] - if mask.shape[1:] != new_mask.shape[1:]: - new_mask = F.interpolate(new_mask.unsqueeze(1), size=(mask.shape[1], mask.shape[2]), mode="bicubic").squeeze(1) - mask = torch.cat((mask, new_mask), dim=0) - return (mask,) - -class OffsetMask: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "mask": ("MASK",), - "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }), - "duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }), - "roll": ("BOOLEAN", { "default": False }), - "incremental": ("BOOLEAN", { "default": False }), - "padding_mode": ( - [ - 'empty', - 'border', - 'reflection', - - ], { - "default": 'empty' - }), - } - } - - RETURN_TYPES = ("MASK",) - RETURN_NAMES = ("mask",) - FUNCTION = "offset" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Offsets the mask by the specified amount. - - mask: Input mask or mask batch - - x: Horizontal offset - - y: Vertical offset - - angle: Angle in degrees - - roll: roll edge wrapping - - duplication_factor: Number of times to duplicate the mask to form a batch - - border padding_mode: Padding mode for the mask -""" - - def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"): - # Create duplicates of the mask batch - mask = mask.repeat(duplication_factor, 1, 1).clone() - - batch_size, height, width = mask.shape - - if angle != 0 and incremental: - for i in range(batch_size): - rotation_angle = angle * (i+1) - mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0) - elif angle > 0: - for i in range(batch_size): - mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0) - - if roll: - if incremental: - for i in range(batch_size): - shift_x = min(x*(i+1), width-1) - shift_y = min(y*(i+1), height-1) - if shift_x != 0: - mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1) - if shift_y != 0: - mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0) - else: - shift_x = min(x, width-1) - shift_y = min(y, height-1) - if shift_x != 0: - mask = torch.roll(mask, shifts=shift_x, dims=2) - if shift_y != 0: - mask = torch.roll(mask, shifts=shift_y, dims=1) - else: - - for i in range(batch_size): - if incremental: - temp_x = min(x * (i+1), width-1) - temp_y = min(y * (i+1), height-1) - else: - temp_x = min(x, width-1) - temp_y = min(y, height-1) - if temp_x > 0: - if padding_mode == 'empty': - mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1) - elif padding_mode in ['replicate', 'reflect']: - mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode) - elif temp_x < 0: - if padding_mode == 'empty': - mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1) - elif padding_mode in ['replicate', 'reflect']: - mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode) - - if temp_y > 0: - if padding_mode == 'empty': - mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0) - elif padding_mode in ['replicate', 'reflect']: - mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode) - elif temp_y < 0: - if padding_mode == 'empty': - mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0) - elif padding_mode in ['replicate', 'reflect']: - mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode) - - return mask, - -class RoundMask: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "mask": ("MASK",), - }} - - RETURN_TYPES = ("MASK",) - FUNCTION = "round" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Rounds the mask or batch of masks to a binary mask. -RoundMask example - -""" - - def round(self, mask): - mask = mask.round() - return (mask,) - -class ResizeMask: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "mask": ("MASK",), - "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }), - "keep_proportions": ("BOOLEAN", { "default": False }), - "upscale_method": (s.upscale_methods,), - "crop": (["disabled","center"],), - } - } - - RETURN_TYPES = ("MASK", "INT", "INT",) - RETURN_NAMES = ("mask", "width", "height",) - FUNCTION = "resize" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Resizes the mask or batch of masks to the specified width and height. -""" - - def resize(self, mask, width, height, keep_proportions, upscale_method,crop): - if keep_proportions: - _, oh, ow = mask.shape - width = ow if width == 0 else width - height = oh if height == 0 else height - ratio = min(width / ow, height / oh) - width = round(ow*ratio) - height = round(oh*ratio) - - if upscale_method == "lanczos": - out_mask = common_upscale(mask.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop=crop).movedim(1,-1)[:, :, :, 0] - else: - out_mask = common_upscale(mask.unsqueeze(1), width, height, upscale_method, crop=crop).squeeze(1) - - return(out_mask, out_mask.shape[2], out_mask.shape[1],) - -class RemapMaskRange: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "mask": ("MASK",), - "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}), - "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}), - } - } - - RETURN_TYPES = ("MASK",) - RETURN_NAMES = ("mask",) - FUNCTION = "remap" - CATEGORY = "KJNodes/masking" - DESCRIPTION = """ -Sets new min and max values for the mask. -""" - - def remap(self, mask, min, max): - - # Find the maximum value in the mask - mask_max = torch.max(mask) - - # If the maximum mask value is zero, avoid division by zero by setting it to 1 - mask_max = mask_max if mask_max > 0 else 1 - - # Scale the mask values to the new range defined by min and max - # The highest pixel value in the mask will be scaled to max - scaled_mask = (mask / mask_max) * (max - min) + min - - # Clamp the values to ensure they are within [0.0, 1.0] - scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0) - - return (scaled_mask, ) - - -def get_mask_polygon(self, mask_np): - import cv2 - """Helper function to get polygon points from mask""" - # Find contours - contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - - if not contours: - return None - - # Get the largest contour - largest_contour = max(contours, key=cv2.contourArea) - - # Approximate polygon - epsilon = 0.02 * cv2.arcLength(largest_contour, True) - polygon = cv2.approxPolyDP(largest_contour, epsilon, True) - - return polygon.squeeze() - -import cv2 -class SeparateMasks: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "mask": ("MASK", ), - "size_threshold_width" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}), - "size_threshold_height" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}), - "mode": (["convex_polygons", "area", "box"],), - "max_poly_points": ("INT", {"default": 8, "min": 3, "max": 32, "step": 1}), - - }, - } - - RETURN_TYPES = ("MASK",) - RETURN_NAMES = ("mask",) - FUNCTION = "separate" - CATEGORY = "KJNodes/masking" - OUTPUT_NODE = True - DESCRIPTION = "Separates a mask into multiple masks based on the size of the connected components." - - def polygon_to_mask(self, polygon, shape): - mask = np.zeros((shape[0], shape[1]), dtype=np.uint8) # Fixed shape handling - - if len(polygon.shape) == 2: # Check if polygon points are valid - polygon = polygon.astype(np.int32) - cv2.fillPoly(mask, [polygon], 1) - return mask - - def get_mask_polygon(self, mask_np, max_points): - contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - if not contours: - return None - - largest_contour = max(contours, key=cv2.contourArea) - hull = cv2.convexHull(largest_contour) - - # Initialize with smaller epsilon for more points - perimeter = cv2.arcLength(hull, True) - epsilon = perimeter * 0.01 # Start smaller - - min_eps = perimeter * 0.001 # Much smaller minimum - max_eps = perimeter * 0.2 # Smaller maximum - - best_approx = None - best_diff = float('inf') - max_iterations = 20 - - #print(f"Target points: {max_points}, Perimeter: {perimeter}") - - for i in range(max_iterations): - curr_eps = (min_eps + max_eps) / 2 - approx = cv2.approxPolyDP(hull, curr_eps, True) - points_diff = len(approx) - max_points - - #print(f"Iteration {i}: points={len(approx)}, eps={curr_eps:.4f}") - - if abs(points_diff) < best_diff: - best_approx = approx - best_diff = abs(points_diff) - - if len(approx) > max_points: - min_eps = curr_eps * 1.1 # More gradual adjustment - elif len(approx) < max_points: - max_eps = curr_eps * 0.9 # More gradual adjustment - else: - return approx.squeeze() - - if abs(max_eps - min_eps) < perimeter * 0.0001: # Relative tolerance - break - - # If we didn't find exact match, return best approximation - return best_approx.squeeze() if best_approx is not None else hull.squeeze() - - def separate(self, mask: torch.Tensor, size_threshold_width: int, size_threshold_height: int, max_poly_points: int, mode: str): - from scipy.ndimage import label, center_of_mass - import numpy as np - - B, H, W = mask.shape - separated = [] - - mask = mask.round() - - for b in range(B): - mask_np = mask[b].cpu().numpy().astype(np.uint8) - structure = np.ones((3, 3), dtype=np.int8) - labeled, ncomponents = label(mask_np, structure=structure) - pbar = ProgressBar(ncomponents) - - for component in range(1, ncomponents + 1): - component_mask_np = (labeled == component).astype(np.uint8) - - rows = np.any(component_mask_np, axis=1) - cols = np.any(component_mask_np, axis=0) - y_min, y_max = np.where(rows)[0][[0, -1]] - x_min, x_max = np.where(cols)[0][[0, -1]] - - width = x_max - x_min + 1 - height = y_max - y_min + 1 - centroid_x = (x_min + x_max) / 2 # Calculate x centroid - print(f"Component {component}: width={width}, height={height}, x_pos={centroid_x}") - - if width >= size_threshold_width and height >= size_threshold_height: - if mode == "convex_polygons": - polygon = self.get_mask_polygon(component_mask_np, max_poly_points) - if polygon is not None: - poly_mask = self.polygon_to_mask(polygon, (H, W)) - poly_mask = torch.tensor(poly_mask, device=mask.device) - separated.append((centroid_x, poly_mask)) - elif mode == "box": - # Create bounding box mask - box_mask = np.zeros((H, W), dtype=np.uint8) - box_mask[y_min:y_max+1, x_min:x_max+1] = 1 - box_mask = torch.tensor(box_mask, device=mask.device) - separated.append((centroid_x, box_mask)) - else: - area_mask = torch.tensor(component_mask_np, device=mask.device) - separated.append((centroid_x, area_mask)) - pbar.update(1) - - if len(separated) > 0: - # Sort by x position and extract only the masks - separated.sort(key=lambda x: x[0]) - separated = [x[1] for x in separated] - out_masks = torch.stack(separated, dim=0) - return out_masks, - else: - return torch.empty((1, 64, 64), device=mask.device), - \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/model_optimization_nodes.py b/custom_nodes/comfyui-kjnodes/nodes/model_optimization_nodes.py deleted file mode 100644 index c1f2e88d276d177a6db75dc53cff14e5b63b4542..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/model_optimization_nodes.py +++ /dev/null @@ -1,1885 +0,0 @@ -from comfy.ldm.modules import attention as comfy_attention -import logging -import comfy.model_patcher -import comfy.utils -import comfy.sd -import torch -import folder_paths -import comfy.model_management as mm -from comfy.cli_args import args -from typing import Optional, Tuple - - -sageattn_modes = ["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda", "sageattn_qk_int8_pv_fp8_cuda++"] - -_initialized = False -_original_functions = {} - -if not _initialized: - _original_functions["orig_attention"] = comfy_attention.optimized_attention - _original_functions["original_patch_model"] = comfy.model_patcher.ModelPatcher.patch_model - _original_functions["original_load_lora_for_models"] = comfy.sd.load_lora_for_models - try: - _original_functions["original_qwen_forward"] = comfy.ldm.qwen_image.model.Attention.forward - except: - pass - _initialized = True - -class BaseLoaderKJ: - original_linear = None - cublas_patched = False - - @torch.compiler.disable() - def _patch_modules(self, patch_cublaslinear, sage_attention): - try: - from comfy.ldm.qwen_image.model import apply_rotary_emb - def qwen_sage_forward( - self, - hidden_states: torch.FloatTensor, # Image stream - encoder_hidden_states: torch.FloatTensor = None, # Text stream - encoder_hidden_states_mask: torch.FloatTensor = None, - attention_mask: Optional[torch.FloatTensor] = None, - image_rotary_emb: Optional[torch.Tensor] = None, - ) -> Tuple[torch.Tensor, torch.Tensor]: - seq_txt = encoder_hidden_states.shape[1] - - img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1)) - img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1)) - img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1)) - - txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) - txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) - txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) - - img_query = self.norm_q(img_query) - img_key = self.norm_k(img_key) - txt_query = self.norm_added_q(txt_query) - txt_key = self.norm_added_k(txt_key) - - joint_query = torch.cat([txt_query, img_query], dim=1) - joint_key = torch.cat([txt_key, img_key], dim=1) - joint_value = torch.cat([txt_value, img_value], dim=1) - - joint_query = apply_rotary_emb(joint_query, image_rotary_emb) - joint_key = apply_rotary_emb(joint_key, image_rotary_emb) - - joint_query = joint_query.flatten(start_dim=2) - joint_key = joint_key.flatten(start_dim=2) - joint_value = joint_value.flatten(start_dim=2) - - joint_hidden_states = attention_sage(joint_query, joint_key, joint_value, self.heads, attention_mask) - - txt_attn_output = joint_hidden_states[:, :seq_txt, :] - img_attn_output = joint_hidden_states[:, seq_txt:, :] - - img_attn_output = self.to_out[0](img_attn_output) - img_attn_output = self.to_out[1](img_attn_output) - txt_attn_output = self.to_add_out(txt_attn_output) - - return img_attn_output, txt_attn_output - except: - print("Failed to patch QwenImage attention, Comfy not updated, skipping") - - from comfy.ops import disable_weight_init, CastWeightBiasOp, cast_bias_weight - - if sage_attention != "disabled": - print("Patching comfy attention to use sageattn") - from sageattention import sageattn - def set_sage_func(sage_attention): - if sage_attention == "auto": - def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): - return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout) - return func - elif sage_attention == "sageattn_qk_int8_pv_fp16_cuda": - from sageattention import sageattn_qk_int8_pv_fp16_cuda - def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): - return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32", tensor_layout=tensor_layout) - return func - elif sage_attention == "sageattn_qk_int8_pv_fp16_triton": - from sageattention import sageattn_qk_int8_pv_fp16_triton - def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): - return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout) - return func - elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda": - from sageattention import sageattn_qk_int8_pv_fp8_cuda - def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): - return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32", tensor_layout=tensor_layout) - return func - elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda++": - from sageattention import sageattn_qk_int8_pv_fp8_cuda - def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"): - return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp16", tensor_layout=tensor_layout) - return func - - sage_func = set_sage_func(sage_attention) - - @torch.compiler.disable() - def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False): - if skip_reshape: - b, _, _, dim_head = q.shape - tensor_layout="HND" - else: - b, _, dim_head = q.shape - dim_head //= heads - q, k, v = map( - lambda t: t.view(b, -1, heads, dim_head), - (q, k, v), - ) - tensor_layout="NHD" - if mask is not None: - # add a batch dimension if there isn't already one - if mask.ndim == 2: - mask = mask.unsqueeze(0) - # add a heads dimension if there isn't already one - if mask.ndim == 3: - mask = mask.unsqueeze(1) - out = sage_func(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) - if tensor_layout == "HND": - if not skip_output_reshape: - out = ( - out.transpose(1, 2).reshape(b, -1, heads * dim_head) - ) - else: - if skip_output_reshape: - out = out.transpose(1, 2) - else: - out = out.reshape(b, -1, heads * dim_head) - return out - - comfy_attention.optimized_attention = attention_sage - comfy.ldm.hunyuan_video.model.optimized_attention = attention_sage - comfy.ldm.flux.math.optimized_attention = attention_sage - comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = attention_sage - comfy.ldm.cosmos.blocks.optimized_attention = attention_sage - comfy.ldm.wan.model.optimized_attention = attention_sage - try: - comfy.ldm.qwen_image.model.Attention.forward = qwen_sage_forward - except: - pass - - else: - print("Restoring initial comfy attention") - comfy_attention.optimized_attention = _original_functions.get("orig_attention") - comfy.ldm.hunyuan_video.model.optimized_attention = _original_functions.get("orig_attention") - comfy.ldm.flux.math.optimized_attention = _original_functions.get("orig_attention") - comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = _original_functions.get("orig_attention") - comfy.ldm.cosmos.blocks.optimized_attention = _original_functions.get("orig_attention") - comfy.ldm.wan.model.optimized_attention = _original_functions.get("orig_attention") - try: - comfy.ldm.qwen_image.model.Attention.forward = _original_functions.get("original_qwen_forward") - except: - pass - - if patch_cublaslinear: - if not BaseLoaderKJ.cublas_patched: - BaseLoaderKJ.original_linear = disable_weight_init.Linear - try: - from cublas_ops import CublasLinear - except ImportError: - raise Exception("Can't import 'torch-cublas-hgemm', install it from here https://github.com/aredden/torch-cublas-hgemm") - - class PatchedLinear(CublasLinear, CastWeightBiasOp): - def reset_parameters(self): - pass - - def forward_comfy_cast_weights(self, input): - weight, bias = cast_bias_weight(self, input) - return torch.nn.functional.linear(input, weight, bias) - - def forward(self, *args, **kwargs): - if self.comfy_cast_weights: - return self.forward_comfy_cast_weights(*args, **kwargs) - else: - return super().forward(*args, **kwargs) - - disable_weight_init.Linear = PatchedLinear - BaseLoaderKJ.cublas_patched = True - else: - if BaseLoaderKJ.cublas_patched: - disable_weight_init.Linear = BaseLoaderKJ.original_linear - BaseLoaderKJ.cublas_patched = False - - -from comfy.patcher_extension import CallbacksMP -class PathchSageAttentionKJ(BaseLoaderKJ): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Global patch comfy attention to use sageattn, once patched to revert back to normal you would need to run this node again with disabled option."}), - }} - - RETURN_TYPES = ("MODEL", ) - FUNCTION = "patch" - DESCRIPTION = "Experimental node for patching attention mode. This doesn't use the model patching system and thus can't be disabled without running the node again with 'disabled' option." - EXPERIMENTAL = True - CATEGORY = "KJNodes/experimental" - - def patch(self, model, sage_attention): - model_clone = model.clone() - @torch.compiler.disable() - def patch_attention_enable(model): - self._patch_modules(False, sage_attention) - @torch.compiler.disable() - def patch_attention_disable(model): - self._patch_modules(False, "disabled") - - model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_attention_enable) - model_clone.add_callback(CallbacksMP.ON_CLEANUP, patch_attention_disable) - - return model_clone, - -class CheckpointLoaderKJ(BaseLoaderKJ): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}), - "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],), - "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}), - "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}), - "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}), - "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}), - }} - - RETURN_TYPES = ("MODEL", "CLIP", "VAE") - FUNCTION = "patch" - DESCRIPTION = "Experimental node for patching torch.nn.Linear with CublasLinear." - EXPERIMENTAL = True - CATEGORY = "KJNodes/experimental" - - def patch(self, ckpt_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation): - DTYPE_MAP = { - "fp8_e4m3fn": torch.float8_e4m3fn, - "fp8_e5m2": torch.float8_e5m2, - "fp16": torch.float16, - "bf16": torch.bfloat16, - "fp32": torch.float32 - } - model_options = {} - if dtype := DTYPE_MAP.get(weight_dtype): - model_options["dtype"] = dtype - print(f"Setting {ckpt_name} weight dtype to {dtype}") - - if weight_dtype == "fp8_e4m3fn_fast": - model_options["dtype"] = torch.float8_e4m3fn - model_options["fp8_optimizations"] = True - - ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) - sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) - - model, clip, vae = self.load_state_dict_guess_config( - sd, - output_vae=True, - output_clip=True, - embedding_directory=folder_paths.get_folder_paths("embeddings"), - metadata=metadata, - model_options=model_options) - - if dtype := DTYPE_MAP.get(compute_dtype): - model.set_model_compute_dtype(dtype) - model.force_cast_weights = False - print(f"Setting {ckpt_name} compute dtype to {dtype}") - - if enable_fp16_accumulation: - if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): - torch.backends.cuda.matmul.allow_fp16_accumulation = True - else: - raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") - else: - if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): - torch.backends.cuda.matmul.allow_fp16_accumulation = False - - def patch_attention(model): - self._patch_modules(patch_cublaslinear, sage_attention) - model.add_callback(CallbacksMP.ON_PRE_RUN,patch_attention) - return model, clip, vae - - def load_state_dict_guess_config(self, sd, output_vae=True, output_clip=True, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None): - from comfy.sd import load_diffusion_model_state_dict, model_detection, VAE, CLIP - clip = None - vae = None - model = None - model_patcher = None - - diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd) - parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix) - weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) - load_device = mm.get_torch_device() - - model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) - if model_config is None: - logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.") - diffusion_model = load_diffusion_model_state_dict(sd, model_options={}) - if diffusion_model is None: - return None - return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used' - - - unet_weight_dtype = list(model_config.supported_inference_dtypes) - if model_config.scaled_fp8 is not None: - weight_dtype = None - - model_config.custom_operations = model_options.get("custom_operations", None) - unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) - - if unet_dtype is None: - unet_dtype = mm.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) - - manual_cast_dtype = mm.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) - model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) - - if output_model: - inital_load_device = mm.unet_inital_load_device(parameters, unet_dtype) - model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device) - model.load_model_weights(sd, diffusion_model_prefix) - - if output_vae: - vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) - vae_sd = model_config.process_vae_state_dict(vae_sd) - vae = VAE(sd=vae_sd, metadata=metadata) - - if output_clip: - clip_target = model_config.clip_target(state_dict=sd) - if clip_target is not None: - clip_sd = model_config.process_clip_state_dict(sd) - if len(clip_sd) > 0: - parameters = comfy.utils.calculate_parameters(clip_sd) - clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options) - m, u = clip.load_sd(clip_sd, full_model=True) - if len(m) > 0: - m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m)) - if len(m_filter) > 0: - logging.warning("clip missing: {}".format(m)) - else: - logging.debug("clip missing: {}".format(m)) - - if len(u) > 0: - logging.debug("clip unexpected {}:".format(u)) - else: - logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.") - - left_over = sd.keys() - if len(left_over) > 0: - logging.debug("left over keys: {}".format(left_over)) - - if output_model: - model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=mm.unet_offload_device()) - if inital_load_device != torch.device("cpu"): - logging.info("loaded diffusion model directly to GPU") - mm.load_models_gpu([model_patcher], force_full_load=True) - - return (model_patcher, clip, vae) - -class DiffusionModelSelector(): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load."}), - }, - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("model_path",) - FUNCTION = "get_path" - DESCRIPTION = "Returns the path to the model as a string." - EXPERIMENTAL = True - CATEGORY = "KJNodes/experimental" - - def get_path(self, model_name): - model_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name) - return (model_path,) - -class DiffusionModelLoaderKJ(BaseLoaderKJ): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load."}), - "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],), - "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "default", "tooltip": "The compute dtype to use for the model."}), - "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}), - "sage_attention": (sageattn_modes, {"default": False, "tooltip": "Patch comfy attention to use sageattn."}), - "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}), - }, - "optional": { - "extra_state_dict": ("STRING", {"forceInput": True, "tooltip": "The full path to an additional state dict to load, this will be merged with the main state dict. Useful for example to add VACE module to a WanVideoModel. You can use DiffusionModelSelector to easily get the path."}), - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch_and_load" - DESCRIPTION = "Node for patching torch.nn.Linear with CublasLinear." - EXPERIMENTAL = True - CATEGORY = "KJNodes/experimental" - - def patch_and_load(self, model_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation, extra_state_dict=None): - DTYPE_MAP = { - "fp8_e4m3fn": torch.float8_e4m3fn, - "fp8_e5m2": torch.float8_e5m2, - "fp16": torch.float16, - "bf16": torch.bfloat16, - "fp32": torch.float32 - } - model_options = {} - if dtype := DTYPE_MAP.get(weight_dtype): - model_options["dtype"] = dtype - print(f"Setting {model_name} weight dtype to {dtype}") - - if weight_dtype == "fp8_e4m3fn_fast": - model_options["dtype"] = torch.float8_e4m3fn - model_options["fp8_optimizations"] = True - - if enable_fp16_accumulation: - if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): - torch.backends.cuda.matmul.allow_fp16_accumulation = True - else: - raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") - else: - if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): - torch.backends.cuda.matmul.allow_fp16_accumulation = False - - unet_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name) - - sd = comfy.utils.load_torch_file(unet_path) - if extra_state_dict is not None: - extra_sd = comfy.utils.load_torch_file(extra_state_dict) - sd.update(extra_sd) - del extra_sd - - model = comfy.sd.load_diffusion_model_state_dict(sd, model_options=model_options) - if dtype := DTYPE_MAP.get(compute_dtype): - model.set_model_compute_dtype(dtype) - model.force_cast_weights = False - print(f"Setting {model_name} compute dtype to {dtype}") - - def patch_attention(model): - self._patch_modules(patch_cublaslinear, sage_attention) - model.add_callback(CallbacksMP.ON_PRE_RUN,patch_attention) - - return (model,) - -class ModelPatchTorchSettings: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}), - }} - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - DESCRIPTION = "Adds callbacks to model to set torch settings before and after running the model." - EXPERIMENTAL = True - CATEGORY = "KJNodes/experimental" - - def patch(self, model, enable_fp16_accumulation): - model_clone = model.clone() - - def patch_enable_fp16_accum(model): - print("Patching torch settings: torch.backends.cuda.matmul.allow_fp16_accumulation = True") - torch.backends.cuda.matmul.allow_fp16_accumulation = True - def patch_disable_fp16_accum(model): - print("Patching torch settings: torch.backends.cuda.matmul.allow_fp16_accumulation = False") - torch.backends.cuda.matmul.allow_fp16_accumulation = False - - if enable_fp16_accumulation: - if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): - model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_enable_fp16_accum) - model_clone.add_callback(CallbacksMP.ON_CLEANUP, patch_disable_fp16_accum) - else: - raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") - else: - if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"): - model_clone.add_callback(CallbacksMP.ON_PRE_RUN, patch_disable_fp16_accum) - else: - raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently") - - return (model_clone,) - -def patched_patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False): - with self.use_ejected(): - - device_to = mm.get_torch_device() - - full_load_override = getattr(self.model, "full_load_override", "auto") - if full_load_override in ["enabled", "disabled"]: - full_load = full_load_override == "enabled" - else: - full_load = lowvram_model_memory == 0 - - self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load) - - for k in self.object_patches: - old = comfy.utils.set_attr(self.model, k, self.object_patches[k]) - if k not in self.object_patches_backup: - self.object_patches_backup[k] = old - - self.inject_model() - return self.model - -def patched_load_lora_for_models(model, clip, lora, strength_model, strength_clip): - - patch_keys = list(model.object_patches_backup.keys()) - for k in patch_keys: - #print("backing up object patch: ", k) - comfy.utils.set_attr(model.model, k, model.object_patches_backup[k]) - - key_map = {} - if model is not None: - key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) - if clip is not None: - key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) - - lora = comfy.lora_convert.convert_lora(lora) - loaded = comfy.lora.load_lora(lora, key_map) - #print(temp_object_patches_backup) - - if model is not None: - new_modelpatcher = model.clone() - k = new_modelpatcher.add_patches(loaded, strength_model) - else: - k = () - new_modelpatcher = None - - if clip is not None: - new_clip = clip.clone() - k1 = new_clip.add_patches(loaded, strength_clip) - else: - k1 = () - new_clip = None - k = set(k) - k1 = set(k1) - for x in loaded: - if (x not in k) and (x not in k1): - print("NOT LOADED {}".format(x)) - - if patch_keys: - if hasattr(model.model, "compile_settings"): - compile_settings = getattr(model.model, "compile_settings") - print("compile_settings: ", compile_settings) - for k in patch_keys: - if "diffusion_model." in k: - # Remove the prefix to get the attribute path - key = k.replace('diffusion_model.', '') - attributes = key.split('.') - # Start with the diffusion_model object - block = model.get_model_object("diffusion_model") - # Navigate through the attributes to get to the block - for attr in attributes: - if attr.isdigit(): - block = block[int(attr)] - else: - block = getattr(block, attr) - # Compile the block - compiled_block = torch.compile(block, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"]) - # Add the compiled block back as an object patch - model.add_object_patch(k, compiled_block) - return (new_modelpatcher, new_clip) - -class PatchModelPatcherOrder: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "patch_order": (["object_patch_first", "weight_patch_first"], {"default": "weight_patch_first", "tooltip": "Patch the comfy patch_model function to load weight patches (LoRAs) before compiling the model"}), - "full_load": (["enabled", "disabled", "auto"], {"default": "auto", "tooltip": "Disabling may help with memory issues when loading large models, when changing this you should probably force model reload to avoid issues!"}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "Patch the comfy patch_model function patching order, useful for torch.compile (used as object_patch) as it should come last if you want to use LoRAs with compile" - EXPERIMENTAL = True - - def patch(self, model, patch_order, full_load): - comfy.model_patcher.ModelPatcher.temp_object_patches_backup = {} - setattr(model.model, "full_load_override", full_load) - if patch_order == "weight_patch_first": - comfy.model_patcher.ModelPatcher.patch_model = patched_patch_model - comfy.sd.load_lora_for_models = patched_load_lora_for_models - else: - comfy.model_patcher.ModelPatcher.patch_model = _original_functions.get("original_patch_model") - comfy.sd.load_lora_for_models = _original_functions.get("original_load_lora_for_models") - - return model, - -class TorchCompileModelFluxAdvanced: - def __init__(self): - self._compiled = False - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "backend": (["inductor", "cudagraphs"],), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "double_blocks": ("STRING", {"default": "0-18", "multiline": True}), - "single_blocks": ("STRING", {"default": "0-37", "multiline": True}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - }, - "optional": { - "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), - } - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - DEPRECATED = True - - def parse_blocks(self, blocks_str): - blocks = [] - for part in blocks_str.split(','): - part = part.strip() - if '-' in part: - start, end = map(int, part.split('-')) - blocks.extend(range(start, end + 1)) - else: - blocks.append(int(part)) - return blocks - - def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit): - single_block_list = self.parse_blocks(single_blocks) - double_block_list = self.parse_blocks(double_blocks) - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - - if not self._compiled: - try: - for i, block in enumerate(diffusion_model.double_blocks): - if i in double_block_list: - #print("Compiling double_block", i) - m.add_object_patch(f"diffusion_model.double_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)) - for i, block in enumerate(diffusion_model.single_blocks): - if i in single_block_list: - #print("Compiling single block", i) - m.add_object_patch(f"diffusion_model.single_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)) - self._compiled = True - compile_settings = { - "backend": backend, - "mode": mode, - "fullgraph": fullgraph, - "dynamic": dynamic, - } - setattr(m.model, "compile_settings", compile_settings) - except: - raise RuntimeError("Failed to compile model") - - return (m, ) - # rest of the layers that are not patched - # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend) - -class TorchCompileModelFluxAdvancedV2: - def __init__(self): - self._compiled = False - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "backend": (["inductor", "cudagraphs"],), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}), - "single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - }, - "optional": { - "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), - } - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit): - from comfy_api.torch_helpers import set_torch_compile_wrapper - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - - compile_key_list = [] - - try: - if double_blocks: - for i, block in enumerate(diffusion_model.double_blocks): - compile_key_list.append(f"diffusion_model.double_blocks.{i}") - if single_blocks: - for i, block in enumerate(diffusion_model.single_blocks): - compile_key_list.append(f"diffusion_model.single_blocks.{i}") - - set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph) - except: - raise RuntimeError("Failed to compile model") - - return (m, ) - # rest of the layers that are not patched - # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend) - # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend) - - -class TorchCompileModelHyVideo: - def __init__(self): - self._compiled = False - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "backend": (["inductor","cudagraphs"], {"default": "inductor"}), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), - "compile_single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}), - "compile_double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}), - "compile_txt_in": ("BOOLEAN", {"default": False, "tooltip": "Compile txt_in layers"}), - "compile_vector_in": ("BOOLEAN", {"default": False, "tooltip": "Compile vector_in layers"}), - "compile_final_layer": ("BOOLEAN", {"default": False, "tooltip": "Compile final layer"}), - - }, - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_single_blocks, compile_double_blocks, compile_txt_in, compile_vector_in, compile_final_layer): - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - if not self._compiled: - try: - if compile_single_blocks: - for i, block in enumerate(diffusion_model.single_blocks): - compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch(f"diffusion_model.single_blocks.{i}", compiled_block) - if compile_double_blocks: - for i, block in enumerate(diffusion_model.double_blocks): - compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch(f"diffusion_model.double_blocks.{i}", compiled_block) - if compile_txt_in: - compiled_block = torch.compile(diffusion_model.txt_in, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch("diffusion_model.txt_in", compiled_block) - if compile_vector_in: - compiled_block = torch.compile(diffusion_model.vector_in, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch("diffusion_model.vector_in", compiled_block) - if compile_final_layer: - compiled_block = torch.compile(diffusion_model.final_layer, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch("diffusion_model.final_layer", compiled_block) - self._compiled = True - compile_settings = { - "backend": backend, - "mode": mode, - "fullgraph": fullgraph, - "dynamic": dynamic, - } - setattr(m.model, "compile_settings", compile_settings) - except: - raise RuntimeError("Failed to compile model") - return (m, ) - -class TorchCompileModelWanVideo: - def __init__(self): - self._compiled = False - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "backend": (["inductor","cudagraphs"], {"default": "inductor"}), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), - "compile_transformer_blocks_only": ("BOOLEAN", {"default": False, "tooltip": "Compile only transformer blocks"}), - }, - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - DEPRECATED = True - - def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only): - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - try: - if compile_transformer_blocks_only: - for i, block in enumerate(diffusion_model.blocks): - if hasattr(block, "_orig_mod"): - block = block._orig_mod - compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch(f"diffusion_model.blocks.{i}", compiled_block) - else: - compiled_model = torch.compile(diffusion_model, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode) - m.add_object_patch("diffusion_model", compiled_model) - - compile_settings = { - "backend": backend, - "mode": mode, - "fullgraph": fullgraph, - "dynamic": dynamic, - } - setattr(m.model, "compile_settings", compile_settings) - except: - raise RuntimeError("Failed to compile model") - return (m, ) - -class TorchCompileModelWanVideoV2: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "backend": (["inductor","cudagraphs"], {"default": "inductor"}), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}), - "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), - }, - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only): - from comfy_api.torch_helpers import set_torch_compile_wrapper - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - try: - if compile_transformer_blocks_only: - compile_key_list = [] - for i, block in enumerate(diffusion_model.blocks): - compile_key_list.append(f"diffusion_model.blocks.{i}") - else: - compile_key_list =["diffusion_model"] - - set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph) - except: - raise RuntimeError("Failed to compile model") - - return (m, ) - -class TorchCompileModelQwenImage: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "backend": (["inductor","cudagraphs"], {"default": "inductor"}), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}), - "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}), - }, - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only): - from comfy_api.torch_helpers import set_torch_compile_wrapper - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - try: - if compile_transformer_blocks_only: - compile_key_list = [] - for i, block in enumerate(diffusion_model.transformer_blocks): - compile_key_list.append(f"diffusion_model.transformer_blocks.{i}") - else: - compile_key_list =["diffusion_model"] - - set_torch_compile_wrapper(model=m, keys=compile_key_list, backend=backend, mode=mode, dynamic=dynamic, fullgraph=fullgraph) - except: - raise RuntimeError("Failed to compile model") - - return (m, ) - -class TorchCompileVAE: - def __init__(self): - self._compiled_encoder = False - self._compiled_decoder = False - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "vae": ("VAE",), - "backend": (["inductor", "cudagraphs"],), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "compile_encoder": ("BOOLEAN", {"default": True, "tooltip": "Compile encoder"}), - "compile_decoder": ("BOOLEAN", {"default": True, "tooltip": "Compile decoder"}), - }} - RETURN_TYPES = ("VAE",) - FUNCTION = "compile" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def compile(self, vae, backend, mode, fullgraph, compile_encoder, compile_decoder): - if compile_encoder: - if not self._compiled_encoder: - encoder_name = "encoder" - if hasattr(vae.first_stage_model, "taesd_encoder"): - encoder_name = "taesd_encoder" - - try: - setattr( - vae.first_stage_model, - encoder_name, - torch.compile( - getattr(vae.first_stage_model, encoder_name), - mode=mode, - fullgraph=fullgraph, - backend=backend, - ), - ) - self._compiled_encoder = True - except: - raise RuntimeError("Failed to compile model") - if compile_decoder: - if not self._compiled_decoder: - decoder_name = "decoder" - if hasattr(vae.first_stage_model, "taesd_decoder"): - decoder_name = "taesd_decoder" - - try: - setattr( - vae.first_stage_model, - decoder_name, - torch.compile( - getattr(vae.first_stage_model, decoder_name), - mode=mode, - fullgraph=fullgraph, - backend=backend, - ), - ) - self._compiled_decoder = True - except: - raise RuntimeError("Failed to compile model") - return (vae, ) - -class TorchCompileControlNet: - def __init__(self): - self._compiled= False - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "controlnet": ("CONTROL_NET",), - "backend": (["inductor", "cudagraphs"],), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - }} - RETURN_TYPES = ("CONTROL_NET",) - FUNCTION = "compile" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def compile(self, controlnet, backend, mode, fullgraph): - if not self._compiled: - try: - # for i, block in enumerate(controlnet.control_model.double_blocks): - # print("Compiling controlnet double_block", i) - # controlnet.control_model.double_blocks[i] = torch.compile(block, mode=mode, fullgraph=fullgraph, backend=backend) - controlnet.control_model = torch.compile(controlnet.control_model, mode=mode, fullgraph=fullgraph, backend=backend) - self._compiled = True - except: - self._compiled = False - raise RuntimeError("Failed to compile model") - - return (controlnet, ) - -class TorchCompileLTXModel: - def __init__(self): - self._compiled = False - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "backend": (["inductor", "cudagraphs"],), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def patch(self, model, backend, mode, fullgraph, dynamic): - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - - if not self._compiled: - try: - for i, block in enumerate(diffusion_model.transformer_blocks): - compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend) - m.add_object_patch(f"diffusion_model.transformer_blocks.{i}", compiled_block) - self._compiled = True - compile_settings = { - "backend": backend, - "mode": mode, - "fullgraph": fullgraph, - "dynamic": dynamic, - } - setattr(m.model, "compile_settings", compile_settings) - - except: - raise RuntimeError("Failed to compile model") - - return (m, ) - -class TorchCompileCosmosModel: - def __init__(self): - self._compiled = False - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "backend": (["inductor", "cudagraphs"],), - "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}), - "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}), - "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}), - "dynamo_cache_size_limit": ("INT", {"default": 64, "tooltip": "Set the dynamo cache size limit"}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/torchcompile" - EXPERIMENTAL = True - - def patch(self, model, backend, mode, fullgraph, dynamic, dynamo_cache_size_limit): - - m = model.clone() - diffusion_model = m.get_model_object("diffusion_model") - torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit - - if not self._compiled: - try: - for name, block in diffusion_model.blocks.items(): - #print(f"Compiling block {name}") - compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend) - m.add_object_patch(f"diffusion_model.blocks.{name}", compiled_block) - #diffusion_model.blocks[name] = compiled_block - - self._compiled = True - compile_settings = { - "backend": backend, - "mode": mode, - "fullgraph": fullgraph, - "dynamic": dynamic, - } - setattr(m.model, "compile_settings", compile_settings) - - except: - raise RuntimeError("Failed to compile model") - - return (m, ) - - -#teacache - -try: - from comfy.ldm.wan.model import sinusoidal_embedding_1d -except: - pass -from einops import repeat -from unittest.mock import patch -from contextlib import nullcontext -import numpy as np - -def relative_l1_distance(last_tensor, current_tensor): - l1_distance = torch.abs(last_tensor - current_tensor).mean() - norm = torch.abs(last_tensor).mean() - relative_l1_distance = l1_distance / norm - return relative_l1_distance.to(torch.float32) - -@torch.compiler.disable() -def tea_cache(self, x, e0, e, transformer_options): - #teacache for cond and uncond separately - rel_l1_thresh = transformer_options["rel_l1_thresh"] - - is_cond = True if transformer_options["cond_or_uncond"] == [0] else False - - should_calc = True - suffix = "cond" if is_cond else "uncond" - - # Init cache dict if not exists - if not hasattr(self, 'teacache_state'): - self.teacache_state = { - 'cond': {'accumulated_rel_l1_distance': 0, 'prev_input': None, - 'teacache_skipped_steps': 0, 'previous_residual': None}, - 'uncond': {'accumulated_rel_l1_distance': 0, 'prev_input': None, - 'teacache_skipped_steps': 0, 'previous_residual': None} - } - logging.info("\nTeaCache: Initialized") - - cache = self.teacache_state[suffix] - - if cache['prev_input'] is not None: - if transformer_options["coefficients"] == []: - temb_relative_l1 = relative_l1_distance(cache['prev_input'], e0) - curr_acc_dist = cache['accumulated_rel_l1_distance'] + temb_relative_l1 - else: - rescale_func = np.poly1d(transformer_options["coefficients"]) - curr_acc_dist = cache['accumulated_rel_l1_distance'] + rescale_func(((e-cache['prev_input']).abs().mean() / cache['prev_input'].abs().mean()).cpu().item()) - try: - if curr_acc_dist < rel_l1_thresh: - should_calc = False - cache['accumulated_rel_l1_distance'] = curr_acc_dist - else: - should_calc = True - cache['accumulated_rel_l1_distance'] = 0 - except: - should_calc = True - cache['accumulated_rel_l1_distance'] = 0 - - if transformer_options["coefficients"] == []: - cache['prev_input'] = e0.clone().detach() - else: - cache['prev_input'] = e.clone().detach() - - if not should_calc: - x += cache['previous_residual'].to(x.device) - cache['teacache_skipped_steps'] += 1 - #print(f"TeaCache: Skipping {suffix} step") - return should_calc, cache - -def teacache_wanvideo_vace_forward_orig(self, x, t, context, vace_context, vace_strength, clip_fea=None, freqs=None, transformer_options={}, **kwargs): - # embeddings - x = self.patch_embedding(x.float()).to(x.dtype) - grid_sizes = x.shape[2:] - x = x.flatten(2).transpose(1, 2) - - # time embeddings - e = self.time_embedding( - sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) - e0 = self.time_projection(e).unflatten(1, (6, self.dim)) - - # context - context = self.text_embedding(context) - - context_img_len = None - if clip_fea is not None: - if self.img_emb is not None: - context_clip = self.img_emb(clip_fea) # bs x 257 x dim - context = torch.concat([context_clip, context], dim=1) - context_img_len = clip_fea.shape[-2] - - orig_shape = list(vace_context.shape) - vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) - c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) - c = c.flatten(2).transpose(1, 2) - c = list(c.split(orig_shape[0], dim=0)) - - if not transformer_options: - raise RuntimeError("Can't access transformer_options, this requires ComfyUI nightly version from Mar 14, 2025 or later") - - teacache_enabled = transformer_options.get("teacache_enabled", False) - if not teacache_enabled: - should_calc = True - else: - should_calc, cache = tea_cache(self, x, e0, e, transformer_options) - - if should_calc: - original_x = x.clone().detach() - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap, "transformer_options": transformer_options}) - x = out["img"] - else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - - ii = self.vace_layers_mapping.get(i, None) - if ii is not None: - for iii in range(len(c)): - c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=original_x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - x += c_skip * vace_strength[iii] - del c_skip - - if teacache_enabled: - cache['previous_residual'] = (x - original_x).to(transformer_options["teacache_device"]) - - # head - x = self.head(x, e) - - # unpatchify - x = self.unpatchify(x, grid_sizes) - return x - -def teacache_wanvideo_forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, **kwargs): - # embeddings - x = self.patch_embedding(x.float()).to(x.dtype) - grid_sizes = x.shape[2:] - x = x.flatten(2).transpose(1, 2) - - # time embeddings - e = self.time_embedding( - sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) - e0 = self.time_projection(e).unflatten(1, (6, self.dim)) - - # context - context = self.text_embedding(context) - - context_img_len = None - if clip_fea is not None: - if self.img_emb is not None: - context_clip = self.img_emb(clip_fea) # bs x 257 x dim - context = torch.concat([context_clip, context], dim=1) - context_img_len = clip_fea.shape[-2] - - - teacache_enabled = transformer_options.get("teacache_enabled", False) - if not teacache_enabled: - should_calc = True - else: - should_calc, cache = tea_cache(self, x, e0, e, transformer_options) - - if should_calc: - original_x = x.clone().detach() - patches_replace = transformer_options.get("patches_replace", {}) - blocks_replace = patches_replace.get("dit", {}) - for i, block in enumerate(self.blocks): - if ("double_block", i) in blocks_replace: - def block_wrap(args): - out = {} - out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len) - return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap, "transformer_options": transformer_options}) - x = out["img"] - else: - x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len) - - if teacache_enabled: - cache['previous_residual'] = (x - original_x).to(transformer_options["teacache_device"]) - - # head - x = self.head(x, e) - - # unpatchify - x = self.unpatchify(x, grid_sizes) - return x - -class WanVideoTeaCacheKJ: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4 for all but 1.3B model, which should be about 10 times smaller, same as when not using coefficients."}), - "start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of the steps to use with TeaCache."}), - "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}), - "coefficients": (["disabled", "1.3B", "14B", "i2v_480", "i2v_720"], {"default": "i2v_480", "tooltip": "Coefficients for rescaling the relative l1 distance, if disabled the threshold value should be about 10 times smaller than the value used with coefficients."}), - } - } - - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("model",) - FUNCTION = "patch_teacache" - CATEGORY = "KJNodes/teacache" - DESCRIPTION = """ -Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and -applying it instead of doing the step. Best results are achieved by choosing the -appropriate coefficients for the model. Early steps should never be skipped, with too -aggressive values this can happen and the motion suffers. Starting later can help with that too. -When NOT using coefficients, the threshold value should be -about 10 times smaller than the value used with coefficients. - -Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1: - - -
    -+-------------------+--------+---------+--------+
-|       Model       |  Low   | Medium  |  High  |
-+-------------------+--------+---------+--------+
-| Wan2.1 t2v 1.3B  |  0.05  |  0.07   |  0.08  |
-| Wan2.1 t2v 14B   |  0.14  |  0.15   |  0.20  |
-| Wan2.1 i2v 480P  |  0.13  |  0.19   |  0.26  |
-| Wan2.1 i2v 720P  |  0.18  |  0.20   |  0.30  |
-+-------------------+--------+---------+--------+
-
    -""" - EXPERIMENTAL = True - - def patch_teacache(self, model, rel_l1_thresh, start_percent, end_percent, cache_device, coefficients): - if rel_l1_thresh == 0: - return (model,) - - if coefficients == "disabled" and rel_l1_thresh > 0.1: - logging.warning("Threshold value is too high for TeaCache without coefficients, consider using coefficients for better results.") - if coefficients != "disabled" and rel_l1_thresh < 0.1 and "1.3B" not in coefficients: - logging.warning("Threshold value is too low for TeaCache with coefficients, consider using higher threshold value for better results.") - - # type_str = str(type(model.model.model_config).__name__) - #if model.model.diffusion_model.dim == 1536: - # model_type ="1.3B" - # else: - # if "WAN21_T2V" in type_str: - # model_type = "14B" - # elif "WAN21_I2V" in type_str: - # model_type = "i2v_480" - # else: - # model_type = "i2v_720" #how to detect this? - - - teacache_coefficients_map = { - "disabled": [], - "1.3B": [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01], - "14B": [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404], - "i2v_480": [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01], - "i2v_720": [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683], - } - coefficients = teacache_coefficients_map[coefficients] - - teacache_device = mm.get_torch_device() if cache_device == "main_device" else mm.unet_offload_device() - - model_clone = model.clone() - if 'transformer_options' not in model_clone.model_options: - model_clone.model_options['transformer_options'] = {} - model_clone.model_options["transformer_options"]["rel_l1_thresh"] = rel_l1_thresh - model_clone.model_options["transformer_options"]["teacache_device"] = teacache_device - model_clone.model_options["transformer_options"]["coefficients"] = coefficients - diffusion_model = model_clone.get_model_object("diffusion_model") - - def outer_wrapper(start_percent, end_percent): - def unet_wrapper_function(model_function, kwargs): - input = kwargs["input"] - timestep = kwargs["timestep"] - c = kwargs["c"] - sigmas = c["transformer_options"]["sample_sigmas"] - cond_or_uncond = kwargs["cond_or_uncond"] - last_step = (len(sigmas) - 1) - - matched_step_index = (sigmas == timestep[0] ).nonzero() - if len(matched_step_index) > 0: - current_step_index = matched_step_index.item() - else: - for i in range(len(sigmas) - 1): - # walk from beginning of steps until crossing the timestep - if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0: - current_step_index = i - break - else: - current_step_index = 0 - - if current_step_index == 0: - if (len(cond_or_uncond) == 1 and cond_or_uncond[0] == 1) or len(cond_or_uncond) == 2: - if hasattr(diffusion_model, "teacache_state"): - delattr(diffusion_model, "teacache_state") - logging.info("\nResetting TeaCache state") - - current_percent = current_step_index / (len(sigmas) - 1) - c["transformer_options"]["current_percent"] = current_percent - if start_percent <= current_percent <= end_percent: - c["transformer_options"]["teacache_enabled"] = True - - forward_function = teacache_wanvideo_vace_forward_orig if hasattr(diffusion_model, "vace_layers") else teacache_wanvideo_forward_orig - context = patch.multiple( - diffusion_model, - forward_orig=forward_function.__get__(diffusion_model, diffusion_model.__class__) - ) - - with context: - out = model_function(input, timestep, **c) - if current_step_index+1 == last_step and hasattr(diffusion_model, "teacache_state"): - if len(cond_or_uncond) == 1 and cond_or_uncond[0] == 0: - skipped_steps_cond = diffusion_model.teacache_state["cond"]["teacache_skipped_steps"] - skipped_steps_uncond = diffusion_model.teacache_state["uncond"]["teacache_skipped_steps"] - logging.info("-----------------------------------") - logging.info(f"TeaCache skipped:") - logging.info(f"{skipped_steps_cond} cond steps") - logging.info(f"{skipped_steps_uncond} uncond step") - logging.info(f"out of {last_step} steps") - logging.info("-----------------------------------") - elif len(cond_or_uncond) == 2: - skipped_steps_cond = diffusion_model.teacache_state["uncond"]["teacache_skipped_steps"] - logging.info("-----------------------------------") - logging.info(f"TeaCache skipped:") - logging.info(f"{skipped_steps_cond} cond steps") - logging.info(f"out of {last_step} steps") - logging.info("-----------------------------------") - - return out - return unet_wrapper_function - - model_clone.set_model_unet_function_wrapper(outer_wrapper(start_percent=start_percent, end_percent=end_percent)) - - return (model_clone,) - - - - -from comfy.ldm.flux.math import apply_rope - -def modified_wan_self_attention_forward(self, x, freqs): - r""" - Args: - x(Tensor): Shape [B, L, num_heads, C / num_heads] - freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] - """ - b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim - - # query, key, value function - def qkv_fn(x): - q = self.norm_q(self.q(x)).view(b, s, n, d) - k = self.norm_k(self.k(x)).view(b, s, n, d) - v = self.v(x).view(b, s, n * d) - return q, k, v - - q, k, v = qkv_fn(x) - - q, k = apply_rope(q, k, freqs) - - feta_scores = get_feta_scores(q, k, self.num_frames, self.enhance_weight) - - x = comfy.ldm.modules.attention.optimized_attention( - q.view(b, s, n * d), - k.view(b, s, n * d), - v, - heads=self.num_heads, - ) - - x = self.o(x) - - x *= feta_scores - - return x - -from einops import rearrange -def get_feta_scores(query, key, num_frames, enhance_weight): - img_q, img_k = query, key #torch.Size([2, 9216, 12, 128]) - - _, ST, num_heads, head_dim = img_q.shape - spatial_dim = ST / num_frames - spatial_dim = int(spatial_dim) - - query_image = rearrange( - img_q, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim - ) - key_image = rearrange( - img_k, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim - ) - - return feta_score(query_image, key_image, head_dim, num_frames, enhance_weight) - -def feta_score(query_image, key_image, head_dim, num_frames, enhance_weight): - scale = head_dim**-0.5 - query_image = query_image * scale - attn_temp = query_image @ key_image.transpose(-2, -1) # translate attn to float32 - attn_temp = attn_temp.to(torch.float32) - attn_temp = attn_temp.softmax(dim=-1) - - # Reshape to [batch_size * num_tokens, num_frames, num_frames] - attn_temp = attn_temp.reshape(-1, num_frames, num_frames) - - # Create a mask for diagonal elements - diag_mask = torch.eye(num_frames, device=attn_temp.device).bool() - diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1) - - # Zero out diagonal elements - attn_wo_diag = attn_temp.masked_fill(diag_mask, 0) - - # Calculate mean for each token's attention matrix - # Number of off-diagonal elements per matrix is n*n - n - num_off_diag = num_frames * num_frames - num_frames - mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag - - enhance_scores = mean_scores.mean() * (num_frames + enhance_weight) - enhance_scores = enhance_scores.clamp(min=1) - return enhance_scores - -import types -class WanAttentionPatch: - def __init__(self, num_frames, weight): - self.num_frames = num_frames - self.enhance_weight = weight - - def __get__(self, obj, objtype=None): - # Create bound method with stored parameters - def wrapped_attention(self_module, *args, **kwargs): - self_module.num_frames = self.num_frames - self_module.enhance_weight = self.enhance_weight - return modified_wan_self_attention_forward(self_module, *args, **kwargs) - return types.MethodType(wrapped_attention, obj) - -class WanVideoEnhanceAVideoKJ: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), - "weight": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of the enhance effect"}), - } - } - - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("model",) - FUNCTION = "enhance" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video" - EXPERIMENTAL = True - - def enhance(self, model, weight, latent): - if weight == 0: - return (model,) - - num_frames = latent["samples"].shape[2] - - model_clone = model.clone() - if 'transformer_options' not in model_clone.model_options: - model_clone.model_options['transformer_options'] = {} - model_clone.model_options["transformer_options"]["enhance_weight"] = weight - diffusion_model = model_clone.get_model_object("diffusion_model") - - compile_settings = getattr(model.model, "compile_settings", None) - for idx, block in enumerate(diffusion_model.blocks): - patched_attn = WanAttentionPatch(num_frames, weight).__get__(block.self_attn, block.__class__) - if compile_settings is not None: - patched_attn = torch.compile(patched_attn, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"]) - - model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.self_attn.forward", patched_attn) - - return (model_clone,) - -def normalized_attention_guidance(self, query, context_positive, context_negative): - k_positive = self.norm_k(self.k(context_positive)) - v_positive = self.v(context_positive) - k_negative = self.norm_k(self.k(context_negative)) - v_negative = self.v(context_negative) - - x_positive = comfy.ldm.modules.attention.optimized_attention(query, k_positive, v_positive, heads=self.num_heads).flatten(2) - x_negative = comfy.ldm.modules.attention.optimized_attention(query, k_negative, v_negative, heads=self.num_heads).flatten(2) - - nag_guidance = x_positive * self.nag_scale - x_negative * (self.nag_scale - 1) - - norm_positive = torch.norm(x_positive, p=1, dim=-1, keepdim=True).expand_as(x_positive) - norm_guidance = torch.norm(nag_guidance, p=1, dim=-1, keepdim=True).expand_as(nag_guidance) - - scale = torch.nan_to_num(norm_guidance / norm_positive, nan=10.0) - - mask = scale > self.nag_tau - adjustment = (norm_positive * self.nag_tau) / (norm_guidance + 1e-7) - nag_guidance = torch.where(mask, nag_guidance * adjustment, nag_guidance) - - x = nag_guidance * self.nag_alpha + x_positive * (1 - self.nag_alpha) - del nag_guidance - - return x - -#region NAG -def wan_crossattn_forward_nag(self, x, context, **kwargs): - r""" - Args: - x(Tensor): Shape [B, L1, C] - context(Tensor): Shape [B, L2, C] - """ - # Determine batch splitting and context handling - if self.input_type == "default": - # Single or [pos, neg] pair - if context.shape[0] == 1: - x_pos, context_pos = x, context - x_neg, context_neg = None, None - else: - x_pos, x_neg = torch.chunk(x, 2, dim=0) - context_pos, context_neg = torch.chunk(context, 2, dim=0) - elif self.input_type == "batch": - # Standard batch, no CFG - x_pos, context_pos = x, context - x_neg, context_neg = None, None - - # Positive branch - q_pos = self.norm_q(self.q(x_pos)) - nag_context = self.nag_context - if self.input_type == "batch": - nag_context = nag_context.repeat(x_pos.shape[0], 1, 1) - x_pos_out = normalized_attention_guidance(self, q_pos, context_pos, nag_context) - - # Negative branch - if x_neg is not None and context_neg is not None: - q_neg = self.norm_q(self.q(x_neg)) - k_neg = self.norm_k(self.k(context_neg)) - v_neg = self.v(context_neg) - x_neg_out = comfy.ldm.modules.attention.optimized_attention(q_neg, k_neg, v_neg, heads=self.num_heads) - x = torch.cat([x_pos_out, x_neg_out], dim=0) - else: - x = x_pos_out - - return self.o(x) - - -def wan_i2v_crossattn_forward_nag(self, x, context, context_img_len): - r""" - Args: - x(Tensor): Shape [B, L1, C] - context(Tensor): Shape [B, L2, C] - """ - context_img = context[:, :context_img_len] - context = context[:, context_img_len:] - - q_img = self.norm_q(self.q(x)) - k_img = self.norm_k_img(self.k_img(context_img)) - v_img = self.v_img(context_img) - img_x = comfy.ldm.modules.attention.optimized_attention(q_img, k_img, v_img, heads=self.num_heads) - - if context.shape[0] == 2: - x, x_real_negative = torch.chunk(x, 2, dim=0) - context_positive, context_negative = torch.chunk(context, 2, dim=0) - else: - context_positive = context - context_negative = None - - q = self.norm_q(self.q(x)) - - x = normalized_attention_guidance(self, q, context_positive, self.nag_context) - - if context_negative is not None: - q_real_negative = self.norm_q(self.q(x_real_negative)) - k_real_negative = self.norm_k(self.k(context_negative)) - v_real_negative = self.v(context_negative) - x_real_negative = comfy.ldm.modules.attention.optimized_attention(q_real_negative, k_real_negative, v_real_negative, heads=self.num_heads) - x = torch.cat([x, x_real_negative], dim=0) - - # output - x = x + img_x - x = self.o(x) - return x - -class WanCrossAttentionPatch: - def __init__(self, context, nag_scale, nag_alpha, nag_tau, i2v=False, input_type="default"): - self.nag_context = context - self.nag_scale = nag_scale - self.nag_alpha = nag_alpha - self.nag_tau = nag_tau - self.i2v = i2v - self.input_type = input_type - def __get__(self, obj, objtype=None): - # Create bound method with stored parameters - def wrapped_attention(self_module, *args, **kwargs): - self_module.nag_context = self.nag_context - self_module.nag_scale = self.nag_scale - self_module.nag_alpha = self.nag_alpha - self_module.nag_tau = self.nag_tau - self_module.input_type = self.input_type - if self.i2v: - return wan_i2v_crossattn_forward_nag(self_module, *args, **kwargs) - else: - return wan_crossattn_forward_nag(self_module, *args, **kwargs) - return types.MethodType(wrapped_attention, obj) - -class WanVideoNAG: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "conditioning": ("CONDITIONING",), - "nag_scale": ("FLOAT", {"default": 11.0, "min": 0.0, "max": 100.0, "step": 0.001, "tooltip": "Strength of negative guidance effect"}), - "nag_alpha": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Mixing coefficient in that controls the balance between the normalized guided representation and the original positive representation."}), - "nag_tau": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Clipping threshold that controls how much the guided attention can deviate from the positive attention."}), - }, - "optional": { - "input_type": (["default", "batch"], {"tooltip": "Type of the model input"}), - }, - - } - - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("model",) - FUNCTION = "patch" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "https://github.com/ChenDarYen/Normalized-Attention-Guidance" - EXPERIMENTAL = True - - def patch(self, model, conditioning, nag_scale, nag_alpha, nag_tau, input_type="default"): - if nag_scale == 0: - return (model,) - - device = mm.get_torch_device() - dtype = mm.unet_dtype() - - model_clone = model.clone() - - diffusion_model = model_clone.get_model_object("diffusion_model") - - diffusion_model.text_embedding.to(device) - context = diffusion_model.text_embedding(conditioning[0][0].to(device, dtype)) - - type_str = str(type(model.model.model_config).__name__) - i2v = True if "WAN21_I2V" in type_str else False - - for idx, block in enumerate(diffusion_model.blocks): - patched_attn = WanCrossAttentionPatch(context, nag_scale, nag_alpha, nag_tau, i2v, input_type=input_type).__get__(block.cross_attn, block.__class__) - - model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.cross_attn.forward", patched_attn) - - return (model_clone,) - -class SkipLayerGuidanceWanVideo: - @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "blocks": ("STRING", {"default": "10", "multiline": False}), - "start_percent": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "slg" - EXPERIMENTAL = True - DESCRIPTION = "Simplified skip layer guidance that only skips the uncond on selected blocks" - - CATEGORY = "advanced/guidance" - - def slg(self, model, start_percent, end_percent, blocks): - def skip(args, extra_args): - transformer_options = extra_args.get("transformer_options", {}) - original_block = extra_args["original_block"] - - if not transformer_options: - raise ValueError("transformer_options not found in extra_args, currently SkipLayerGuidanceWanVideo only works with TeaCacheKJ") - if start_percent <= transformer_options["current_percent"] <= end_percent: - if args["img"].shape[0] == 2: - prev_img_uncond = args["img"][0].unsqueeze(0) - - new_args = { - "img": args["img"][1].unsqueeze(0), - "txt": args["txt"][1].unsqueeze(0), - "vec": args["vec"][1].unsqueeze(0), - "pe": args["pe"][1].unsqueeze(0) - } - - block_out = original_block(new_args) - - out = { - "img": torch.cat([prev_img_uncond, block_out["img"]], dim=0), - "txt": args["txt"], - "vec": args["vec"], - "pe": args["pe"] - } - else: - if transformer_options.get("cond_or_uncond") == [0]: - out = original_block(args) - else: - out = args - else: - out = original_block(args) - return out - - block_list = [int(x.strip()) for x in blocks.split(",")] - blocks = [int(i) for i in block_list] - logging.info(f"Selected blocks to skip uncond on: {blocks}") - - m = model.clone() - - for b in blocks: - #m.set_model_patch_replace(skip, "dit", "double_block", b) - model_options = m.model_options["transformer_options"].copy() - if "patches_replace" not in model_options: - model_options["patches_replace"] = {} - else: - model_options["patches_replace"] = model_options["patches_replace"].copy() - - if "dit" not in model_options["patches_replace"]: - model_options["patches_replace"]["dit"] = {} - else: - model_options["patches_replace"]["dit"] = model_options["patches_replace"]["dit"].copy() - - block = ("double_block", b) - - model_options["patches_replace"]["dit"][block] = skip - m.model_options["transformer_options"] = model_options - - - return (m, ) - -class CFGZeroStarAndInit: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "use_zero_init": ("BOOLEAN", {"default": True}), - "zero_init_steps": ("INT", {"default": 0, "min": 0, "tooltip": "for zero init, starts from 0 so first step is always zeroed out if use_zero_init enabled"}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - DESCRIPTION = "https://github.com/WeichenFan/CFG-Zero-star" - CATEGORY = "KJNodes/experimental" - EXPERIMENTAL = True - - def patch(self, model, use_zero_init, zero_init_steps): - def cfg_zerostar(args): - #zero init - cond = args["cond"] - timestep = args["timestep"] - sigmas = args["model_options"]["transformer_options"]["sample_sigmas"] - matched_step_index = (sigmas == timestep[0]).nonzero() - if len(matched_step_index) > 0: - current_step_index = matched_step_index.item() - else: - for i in range(len(sigmas) - 1): - if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0: - current_step_index = i - break - else: - current_step_index = 0 - - if (current_step_index <= zero_init_steps) and use_zero_init: - return cond * 0 - - uncond = args["uncond"] - cond_scale = args["cond_scale"] - - batch_size = cond.shape[0] - - positive_flat = cond.view(batch_size, -1) - negative_flat = uncond.view(batch_size, -1) - - dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) - squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8 - alpha = dot_product / squared_norm - alpha = alpha.view(batch_size, *([1] * (len(cond.shape) - 1))) - - noise_pred = uncond * alpha + cond_scale * (cond - uncond * alpha) - return noise_pred - - m = model.clone() - m.set_model_sampler_cfg_function(cfg_zerostar) - return (m, ) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/nodes/nodes.py b/custom_nodes/comfyui-kjnodes/nodes/nodes.py deleted file mode 100644 index afc793f36035c5308891ed9f91887ba5a6a1fba0..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/nodes/nodes.py +++ /dev/null @@ -1,2625 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np -from PIL import Image -import json, re, os, io, time -import re -import importlib - -from comfy import model_management -import folder_paths -from nodes import MAX_RESOLUTION -from comfy.utils import common_upscale, ProgressBar, load_torch_file -from comfy.comfy_types.node_typing import IO - -script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts")) - -class BOOLConstant: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "value": ("BOOLEAN", {"default": True}), - }, - } - RETURN_TYPES = ("BOOLEAN",) - RETURN_NAMES = ("value",) - FUNCTION = "get_value" - CATEGORY = "KJNodes/constants" - - def get_value(self, value): - return (value,) - -class INTConstant: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "value": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff}), - }, - } - RETURN_TYPES = ("INT",) - RETURN_NAMES = ("value",) - FUNCTION = "get_value" - CATEGORY = "KJNodes/constants" - - def get_value(self, value): - return (value,) - -class FloatConstant: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "value": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.00001}), - }, - } - - RETURN_TYPES = ("FLOAT",) - RETURN_NAMES = ("value",) - FUNCTION = "get_value" - CATEGORY = "KJNodes/constants" - - def get_value(self, value): - return (round(value, 6),) - -class StringConstant: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "string": ("STRING", {"default": '', "multiline": False}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "passtring" - CATEGORY = "KJNodes/constants" - - def passtring(self, string): - return (string, ) - -class StringConstantMultiline: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "string": ("STRING", {"default": "", "multiline": True}), - "strip_newlines": ("BOOLEAN", {"default": True}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "stringify" - CATEGORY = "KJNodes/constants" - - def stringify(self, string, strip_newlines): - new_string = [] - for line in io.StringIO(string): - if not line.strip().startswith("\n") and strip_newlines: - line = line.replace("\n", '') - new_string.append(line) - new_string = "\n".join(new_string) - - return (new_string, ) - - - -class ScaleBatchPromptSchedule: - - RETURN_TYPES = ("STRING",) - FUNCTION = "scaleschedule" - CATEGORY = "KJNodes/misc" - DESCRIPTION = """ -Scales a batch schedule from Fizz' nodes BatchPromptSchedule -to a different frame count. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "input_str": ("STRING", {"forceInput": True,"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n"}), - "old_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}), - "new_frame_count": ("INT", {"forceInput": True,"default": 1,"min": 1, "max": 4096, "step": 1}), - - }, - } - - def scaleschedule(self, old_frame_count, input_str, new_frame_count): - pattern = r'"(\d+)"\s*:\s*"(.*?)"(?:,|\Z)' - frame_strings = dict(re.findall(pattern, input_str)) - - # Calculate the scaling factor - scaling_factor = (new_frame_count - 1) / (old_frame_count - 1) - - # Initialize a dictionary to store the new frame numbers and strings - new_frame_strings = {} - - # Iterate over the frame numbers and strings - for old_frame, string in frame_strings.items(): - # Calculate the new frame number - new_frame = int(round(int(old_frame) * scaling_factor)) - - # Store the new frame number and corresponding string - new_frame_strings[new_frame] = string - - # Format the output string - output_str = ', '.join([f'"{k}":"{v}"' for k, v in sorted(new_frame_strings.items())]) - return (output_str,) - - -class GetLatentsFromBatchIndexed: - - RETURN_TYPES = ("LATENT",) - FUNCTION = "indexedlatentsfrombatch" - CATEGORY = "KJNodes/latents" - DESCRIPTION = """ -Selects and returns the latents at the specified indices as an latent batch. -""" - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "latents": ("LATENT",), - "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}), - "latent_format": (["BCHW", "BTCHW", "BCTHW"], {"default": "BCHW"}), - }, - } - - def indexedlatentsfrombatch(self, latents, indexes, latent_format): - - samples = latents.copy() - latent_samples = samples["samples"] - - # Parse the indexes string into a list of integers - index_list = [int(index.strip()) for index in indexes.split(',')] - - # Convert list of indices to a PyTorch tensor - indices_tensor = torch.tensor(index_list, dtype=torch.long) - - # Select the latents at the specified indices - if latent_format == "BCHW": - chosen_latents = latent_samples[indices_tensor] - elif latent_format == "BTCHW": - chosen_latents = latent_samples[:, indices_tensor] - elif latent_format == "BCTHW": - chosen_latents = latent_samples[:, :, indices_tensor] - - samples["samples"] = chosen_latents - return (samples,) - - -class ConditioningMultiCombine: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 20, "step": 1}), - "operation": (["combine", "concat"], {"default": "combine"}), - "conditioning_1": ("CONDITIONING", ), - "conditioning_2": ("CONDITIONING", ), - }, - } - - RETURN_TYPES = ("CONDITIONING", "INT") - RETURN_NAMES = ("combined", "inputcount") - FUNCTION = "combine" - CATEGORY = "KJNodes/masking/conditioning" - DESCRIPTION = """ -Combines multiple conditioning nodes into one -""" - - def combine(self, inputcount, operation, **kwargs): - from nodes import ConditioningCombine - from nodes import ConditioningConcat - cond_combine_node = ConditioningCombine() - cond_concat_node = ConditioningConcat() - cond = kwargs["conditioning_1"] - for c in range(1, inputcount): - new_cond = kwargs[f"conditioning_{c + 1}"] - if operation == "combine": - cond = cond_combine_node.combine(new_cond, cond)[0] - elif operation == "concat": - cond = cond_concat_node.concat(cond, new_cond)[0] - return (cond, inputcount,) - -class AppendStringsToList: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "string1": ("STRING", {"default": '', "forceInput": True}), - "string2": ("STRING", {"default": '', "forceInput": True}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "joinstring" - CATEGORY = "KJNodes/text" - - def joinstring(self, string1, string2): - if not isinstance(string1, list): - string1 = [string1] - if not isinstance(string2, list): - string2 = [string2] - - joined_string = string1 + string2 - return (joined_string, ) - -class JoinStrings: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "delimiter": ("STRING", {"default": ' ', "multiline": False}), - }, - "optional": { - "string1": ("STRING", {"default": '', "forceInput": True}), - "string2": ("STRING", {"default": '', "forceInput": True}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "joinstring" - CATEGORY = "KJNodes/text" - - def joinstring(self, delimiter, string1="", string2=""): - joined_string = string1 + delimiter + string2 - return (joined_string, ) - -class JoinStringMulti: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}), - "string_1": ("STRING", {"default": '', "forceInput": True}), - "delimiter": ("STRING", {"default": ' ', "multiline": False}), - "return_list": ("BOOLEAN", {"default": False}), - }, - "optional": { - "string_2": ("STRING", {"default": '', "forceInput": True}), - } - } - - RETURN_TYPES = ("STRING",) - RETURN_NAMES = ("string",) - FUNCTION = "combine" - CATEGORY = "KJNodes/text" - DESCRIPTION = """ -Creates single string, or a list of strings, from -multiple input strings. -You can set how many inputs the node has, -with the **inputcount** and clicking update. -""" - - def combine(self, inputcount, delimiter, **kwargs): - string = kwargs["string_1"] - return_list = kwargs["return_list"] - strings = [string] # Initialize a list with the first string - for c in range(1, inputcount): - new_string = kwargs.get(f"string_{c + 1}", "") - if not new_string: - continue - if return_list: - strings.append(new_string) # Add new string to the list - else: - string = string + delimiter + new_string - if return_list: - return (strings,) # Return the list of strings - else: - return (string,) # Return the combined string - -class CondPassThrough: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - }, - } - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING",) - RETURN_NAMES = ("positive", "negative") - FUNCTION = "passthrough" - CATEGORY = "KJNodes/misc" - DESCRIPTION = """ - Simply passes through the positive and negative conditioning, - workaround for Set node not allowing bypassed inputs. -""" - - def passthrough(self, positive=None, negative=None): - return (positive, negative,) - -class ModelPassThrough: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - }, - "optional": { - "model": ("MODEL", ), - }, - } - - RETURN_TYPES = ("MODEL", ) - RETURN_NAMES = ("model",) - FUNCTION = "passthrough" - CATEGORY = "KJNodes/misc" - DESCRIPTION = """ - Simply passes through the model, - workaround for Set node not allowing bypassed inputs. -""" - - def passthrough(self, model=None): - return (model,) - -def append_helper(t, mask, c, set_area_to_bounds, strength): - n = [t[0], t[1].copy()] - _, h, w = mask.shape - n[1]['mask'] = mask - n[1]['set_area_to_bounds'] = set_area_to_bounds - n[1]['mask_strength'] = strength - c.append(n) - -class ConditioningSetMaskAndCombine: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "positive_1": ("CONDITIONING", ), - "negative_1": ("CONDITIONING", ), - "positive_2": ("CONDITIONING", ), - "negative_2": ("CONDITIONING", ), - "mask_1": ("MASK", ), - "mask_2": ("MASK", ), - "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - } - } - - RETURN_TYPES = ("CONDITIONING","CONDITIONING",) - RETURN_NAMES = ("combined_positive", "combined_negative",) - FUNCTION = "append" - CATEGORY = "KJNodes/masking/conditioning" - DESCRIPTION = """ -Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes -""" - - def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength): - c = [] - c2 = [] - set_area_to_bounds = False - if set_cond_area != "default": - set_area_to_bounds = True - if len(mask_1.shape) < 3: - mask_1 = mask_1.unsqueeze(0) - if len(mask_2.shape) < 3: - mask_2 = mask_2.unsqueeze(0) - for t in positive_1: - append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) - for t in positive_2: - append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) - for t in negative_1: - append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) - for t in negative_2: - append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) - return (c, c2) - -class ConditioningSetMaskAndCombine3: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "positive_1": ("CONDITIONING", ), - "negative_1": ("CONDITIONING", ), - "positive_2": ("CONDITIONING", ), - "negative_2": ("CONDITIONING", ), - "positive_3": ("CONDITIONING", ), - "negative_3": ("CONDITIONING", ), - "mask_1": ("MASK", ), - "mask_2": ("MASK", ), - "mask_3": ("MASK", ), - "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - } - } - - RETURN_TYPES = ("CONDITIONING","CONDITIONING",) - RETURN_NAMES = ("combined_positive", "combined_negative",) - FUNCTION = "append" - CATEGORY = "KJNodes/masking/conditioning" - DESCRIPTION = """ -Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes -""" - - def append(self, positive_1, negative_1, positive_2, positive_3, negative_2, negative_3, mask_1, mask_2, mask_3, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength): - c = [] - c2 = [] - set_area_to_bounds = False - if set_cond_area != "default": - set_area_to_bounds = True - if len(mask_1.shape) < 3: - mask_1 = mask_1.unsqueeze(0) - if len(mask_2.shape) < 3: - mask_2 = mask_2.unsqueeze(0) - if len(mask_3.shape) < 3: - mask_3 = mask_3.unsqueeze(0) - for t in positive_1: - append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) - for t in positive_2: - append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) - for t in positive_3: - append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) - for t in negative_1: - append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) - for t in negative_2: - append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) - for t in negative_3: - append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) - return (c, c2) - -class ConditioningSetMaskAndCombine4: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "positive_1": ("CONDITIONING", ), - "negative_1": ("CONDITIONING", ), - "positive_2": ("CONDITIONING", ), - "negative_2": ("CONDITIONING", ), - "positive_3": ("CONDITIONING", ), - "negative_3": ("CONDITIONING", ), - "positive_4": ("CONDITIONING", ), - "negative_4": ("CONDITIONING", ), - "mask_1": ("MASK", ), - "mask_2": ("MASK", ), - "mask_3": ("MASK", ), - "mask_4": ("MASK", ), - "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - } - } - - RETURN_TYPES = ("CONDITIONING","CONDITIONING",) - RETURN_NAMES = ("combined_positive", "combined_negative",) - FUNCTION = "append" - CATEGORY = "KJNodes/masking/conditioning" - DESCRIPTION = """ -Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes -""" - - def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, negative_2, negative_3, negative_4, mask_1, mask_2, mask_3, mask_4, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength): - c = [] - c2 = [] - set_area_to_bounds = False - if set_cond_area != "default": - set_area_to_bounds = True - if len(mask_1.shape) < 3: - mask_1 = mask_1.unsqueeze(0) - if len(mask_2.shape) < 3: - mask_2 = mask_2.unsqueeze(0) - if len(mask_3.shape) < 3: - mask_3 = mask_3.unsqueeze(0) - if len(mask_4.shape) < 3: - mask_4 = mask_4.unsqueeze(0) - for t in positive_1: - append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) - for t in positive_2: - append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) - for t in positive_3: - append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) - for t in positive_4: - append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength) - for t in negative_1: - append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) - for t in negative_2: - append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) - for t in negative_3: - append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) - for t in negative_4: - append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength) - return (c, c2) - -class ConditioningSetMaskAndCombine5: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "positive_1": ("CONDITIONING", ), - "negative_1": ("CONDITIONING", ), - "positive_2": ("CONDITIONING", ), - "negative_2": ("CONDITIONING", ), - "positive_3": ("CONDITIONING", ), - "negative_3": ("CONDITIONING", ), - "positive_4": ("CONDITIONING", ), - "negative_4": ("CONDITIONING", ), - "positive_5": ("CONDITIONING", ), - "negative_5": ("CONDITIONING", ), - "mask_1": ("MASK", ), - "mask_2": ("MASK", ), - "mask_3": ("MASK", ), - "mask_4": ("MASK", ), - "mask_5": ("MASK", ), - "mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "mask_5_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - } - } - - RETURN_TYPES = ("CONDITIONING","CONDITIONING",) - RETURN_NAMES = ("combined_positive", "combined_negative",) - FUNCTION = "append" - CATEGORY = "KJNodes/masking/conditioning" - DESCRIPTION = """ -Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes -""" - - def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength): - c = [] - c2 = [] - set_area_to_bounds = False - if set_cond_area != "default": - set_area_to_bounds = True - if len(mask_1.shape) < 3: - mask_1 = mask_1.unsqueeze(0) - if len(mask_2.shape) < 3: - mask_2 = mask_2.unsqueeze(0) - if len(mask_3.shape) < 3: - mask_3 = mask_3.unsqueeze(0) - if len(mask_4.shape) < 3: - mask_4 = mask_4.unsqueeze(0) - if len(mask_5.shape) < 3: - mask_5 = mask_5.unsqueeze(0) - for t in positive_1: - append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength) - for t in positive_2: - append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength) - for t in positive_3: - append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength) - for t in positive_4: - append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength) - for t in positive_5: - append_helper(t, mask_5, c, set_area_to_bounds, mask_5_strength) - for t in negative_1: - append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength) - for t in negative_2: - append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength) - for t in negative_3: - append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength) - for t in negative_4: - append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength) - for t in negative_5: - append_helper(t, mask_5, c2, set_area_to_bounds, mask_5_strength) - return (c, c2) - -class VRAM_Debug: - - @classmethod - - def INPUT_TYPES(s): - return { - "required": { - - "empty_cache": ("BOOLEAN", {"default": True}), - "gc_collect": ("BOOLEAN", {"default": True}), - "unload_all_models": ("BOOLEAN", {"default": False}), - }, - "optional": { - "any_input": (IO.ANY,), - "image_pass": ("IMAGE",), - "model_pass": ("MODEL",), - } - } - - RETURN_TYPES = (IO.ANY, "IMAGE","MODEL","INT", "INT",) - RETURN_NAMES = ("any_output", "image_pass", "model_pass", "freemem_before", "freemem_after") - FUNCTION = "VRAMdebug" - CATEGORY = "KJNodes/misc" - DESCRIPTION = """ -Returns the inputs unchanged, they are only used as triggers, -and performs comfy model management functions and garbage collection, -reports free VRAM before and after the operations. -""" - - def VRAMdebug(self, gc_collect, empty_cache, unload_all_models, image_pass=None, model_pass=None, any_input=None): - freemem_before = model_management.get_free_memory() - print("VRAMdebug: free memory before: ", f"{freemem_before:,.0f}") - if empty_cache: - model_management.soft_empty_cache() - if unload_all_models: - model_management.unload_all_models() - if gc_collect: - import gc - gc.collect() - freemem_after = model_management.get_free_memory() - print("VRAMdebug: free memory after: ", f"{freemem_after:,.0f}") - print("VRAMdebug: freed memory: ", f"{freemem_after - freemem_before:,.0f}") - return {"ui": { - "text": [f"{freemem_before:,.0f}x{freemem_after:,.0f}"]}, - "result": (any_input, image_pass, model_pass, freemem_before, freemem_after) - } - -class SomethingToString: - @classmethod - - def INPUT_TYPES(s): - return { - "required": { - "input": (IO.ANY, ), - }, - "optional": { - "prefix": ("STRING", {"default": ""}), - "suffix": ("STRING", {"default": ""}), - } - } - RETURN_TYPES = ("STRING",) - FUNCTION = "stringify" - CATEGORY = "KJNodes/text" - DESCRIPTION = """ -Converts any type to a string. -""" - - def stringify(self, input, prefix="", suffix=""): - if isinstance(input, (int, float, bool)): - stringified = str(input) - elif isinstance(input, list): - stringified = ', '.join(str(item) for item in input) - else: - return - if prefix: # Check if prefix is not empty - stringified = prefix + stringified # Add the prefix - if suffix: # Check if suffix is not empty - stringified = stringified + suffix # Add the suffix - - return (stringified,) - -class Sleep: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "input": (IO.ANY, ), - "minutes": ("INT", {"default": 0, "min": 0, "max": 1439}), - "seconds": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 59.99, "step": 0.01}), - }, - } - RETURN_TYPES = (IO.ANY,) - FUNCTION = "sleepdelay" - CATEGORY = "KJNodes/misc" - DESCRIPTION = """ -Delays the execution for the input amount of time. -""" - - def sleepdelay(self, input, minutes, seconds): - total_seconds = minutes * 60 + seconds - time.sleep(total_seconds) - return input, - -class EmptyLatentImagePresets: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "dimensions": ( - [ - '512 x 512 (1:1)', - '768 x 512 (1.5:1)', - '960 x 512 (1.875:1)', - '1024 x 512 (2:1)', - '1024 x 576 (1.778:1)', - '1536 x 640 (2.4:1)', - '1344 x 768 (1.75:1)', - '1216 x 832 (1.46:1)', - '1152 x 896 (1.286:1)', - '1024 x 1024 (1:1)', - ], - { - "default": '512 x 512 (1:1)' - }), - - "invert": ("BOOLEAN", {"default": False}), - "batch_size": ("INT", { - "default": 1, - "min": 1, - "max": 4096 - }), - }, - } - - RETURN_TYPES = ("LATENT", "INT", "INT") - RETURN_NAMES = ("Latent", "Width", "Height") - FUNCTION = "generate" - CATEGORY = "KJNodes/latents" - - def generate(self, dimensions, invert, batch_size): - from nodes import EmptyLatentImage - result = [x.strip() for x in dimensions.split('x')] - - # Remove the aspect ratio part - result[0] = result[0].split('(')[0].strip() - result[1] = result[1].split('(')[0].strip() - - if invert: - width = int(result[1].split(' ')[0]) - height = int(result[0]) - else: - width = int(result[0]) - height = int(result[1].split(' ')[0]) - latent = EmptyLatentImage().generate(width, height, batch_size)[0] - - return (latent, int(width), int(height),) - -class EmptyLatentImageCustomPresets: - @classmethod - def INPUT_TYPES(cls): - try: - with open(os.path.join(script_directory, 'custom_dimensions.json')) as f: - dimensions_dict = json.load(f) - except FileNotFoundError: - dimensions_dict = [] - return { - "required": { - "dimensions": ( - [f"{d['label']} - {d['value']}" for d in dimensions_dict], - ), - - "invert": ("BOOLEAN", {"default": False}), - "batch_size": ("INT", { - "default": 1, - "min": 1, - "max": 4096 - }), - }, - } - - RETURN_TYPES = ("LATENT", "INT", "INT") - RETURN_NAMES = ("Latent", "Width", "Height") - FUNCTION = "generate" - CATEGORY = "KJNodes/latents" - DESCRIPTION = """ -Generates an empty latent image with the specified dimensions. -The choices are loaded from 'custom_dimensions.json' in the nodes folder. -""" - - def generate(self, dimensions, invert, batch_size): - from nodes import EmptyLatentImage - # Split the string into label and value - label, value = dimensions.split(' - ') - # Split the value into width and height - width, height = [x.strip() for x in value.split('x')] - - if invert: - width, height = height, width - - latent = EmptyLatentImage().generate(int(width), int(height), batch_size)[0] - - return (latent, int(width), int(height),) - -class WidgetToString: - @classmethod - def IS_CHANGED(cls,*,id,node_title,any_input,**kwargs): - if any_input is not None and (id != 0 or node_title != ""): - return float("NaN") - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "id": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}), - "widget_name": ("STRING", {"multiline": False}), - "return_all": ("BOOLEAN", {"default": False}), - }, - "optional": { - "any_input": (IO.ANY, ), - "node_title": ("STRING", {"multiline": False}), - "allowed_float_decimals": ("INT", {"default": 2, "min": 0, "max": 10, "tooltip": "Number of decimal places to display for float values"}), - - }, - "hidden": {"extra_pnginfo": "EXTRA_PNGINFO", - "prompt": "PROMPT", - "unique_id": "UNIQUE_ID",}, - } - - RETURN_TYPES = ("STRING", ) - FUNCTION = "get_widget_value" - CATEGORY = "KJNodes/text" - DESCRIPTION = """ -Selects a node and it's specified widget and outputs the value as a string. -If no node id or title is provided it will use the 'any_input' link and use that node. -To see node id's, enable node id display from Manager badge menu. -Alternatively you can search with the node title. Node titles ONLY exist if they -are manually edited! -The 'any_input' is required for making sure the node you want the value from exists in the workflow. -""" - - def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, unique_id, return_all=False, any_input=None, node_title="", allowed_float_decimals=2): - workflow = extra_pnginfo["workflow"] - #print(json.dumps(workflow, indent=4)) - results = [] - node_id = None # Initialize node_id to handle cases where no match is found - link_id = None - link_to_node_map = {} - - for node in workflow["nodes"]: - if node_title: - if "title" in node: - if node["title"] == node_title: - node_id = node["id"] - break - else: - print("Node title not found.") - elif id != 0: - if node["id"] == id: - node_id = id - break - elif any_input is not None: - if node["type"] == "WidgetToString" and node["id"] == int(unique_id) and not link_id: - for node_input in node["inputs"]: - if node_input["name"] == "any_input": - link_id = node_input["link"] - - # Construct a map of links to node IDs for future reference - node_outputs = node.get("outputs", None) - if not node_outputs: - continue - for output in node_outputs: - node_links = output.get("links", None) - if not node_links: - continue - for link in node_links: - link_to_node_map[link] = node["id"] - if link_id and link == link_id: - break - - if link_id: - node_id = link_to_node_map.get(link_id, None) - - if node_id is None: - raise ValueError("No matching node found for the given title or id") - - values = prompt[str(node_id)] - if "inputs" in values: - if return_all: - # Format items based on type - formatted_items = [] - for k, v in values["inputs"].items(): - if isinstance(v, float): - item = f"{k}: {v:.{allowed_float_decimals}f}" - else: - item = f"{k}: {str(v)}" - formatted_items.append(item) - results.append(', '.join(formatted_items)) - elif widget_name in values["inputs"]: - v = values["inputs"][widget_name] - if isinstance(v, float): - v = f"{v:.{allowed_float_decimals}f}" - else: - v = str(v) - return (v, ) - else: - raise NameError(f"Widget not found: {node_id}.{widget_name}") - return (', '.join(results).strip(', '), ) - -class DummyOut: - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "any_input": (IO.ANY, ), - } - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "dummy" - CATEGORY = "KJNodes/misc" - OUTPUT_NODE = True - DESCRIPTION = """ -Does nothing, used to trigger generic workflow output. -A way to get previews in the UI without saving anything to disk. -""" - - def dummy(self, any_input): - return (any_input,) - -class FlipSigmasAdjusted: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"sigmas": ("SIGMAS", ), - "divide_by_last_sigma": ("BOOLEAN", {"default": False}), - "divide_by": ("FLOAT", {"default": 1,"min": 1, "max": 255, "step": 0.01}), - "offset_by": ("INT", {"default": 1,"min": -100, "max": 100, "step": 1}), - } - } - RETURN_TYPES = ("SIGMAS", "STRING",) - RETURN_NAMES = ("SIGMAS", "sigmas_string",) - CATEGORY = "KJNodes/noise" - FUNCTION = "get_sigmas_adjusted" - - def get_sigmas_adjusted(self, sigmas, divide_by_last_sigma, divide_by, offset_by): - - sigmas = sigmas.flip(0) - if sigmas[0] == 0: - sigmas[0] = 0.0001 - adjusted_sigmas = sigmas.clone() - #offset sigma - for i in range(1, len(sigmas)): - offset_index = i - offset_by - if 0 <= offset_index < len(sigmas): - adjusted_sigmas[i] = sigmas[offset_index] - else: - adjusted_sigmas[i] = 0.0001 - if adjusted_sigmas[0] == 0: - adjusted_sigmas[0] = 0.0001 - if divide_by_last_sigma: - adjusted_sigmas = adjusted_sigmas / adjusted_sigmas[-1] - - sigma_np_array = adjusted_sigmas.numpy() - array_string = np.array2string(sigma_np_array, precision=2, separator=', ', threshold=np.inf) - adjusted_sigmas = adjusted_sigmas / divide_by - return (adjusted_sigmas, array_string,) - -class CustomSigmas: - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "sigmas_string" :("STRING", {"default": "14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029","multiline": True}), - "interpolate_to_steps": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}), - } - } - RETURN_TYPES = ("SIGMAS",) - RETURN_NAMES = ("SIGMAS",) - CATEGORY = "KJNodes/noise" - FUNCTION = "customsigmas" - DESCRIPTION = """ -Creates a sigmas tensor from a string of comma separated values. -Examples: - -Nvidia's optimized AYS 10 step schedule for SD 1.5: -14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029 -SDXL: -14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029 -SVD: -700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002 -""" - def customsigmas(self, sigmas_string, interpolate_to_steps): - sigmas_list = sigmas_string.split(', ') - sigmas_float_list = [float(sigma) for sigma in sigmas_list] - sigmas_tensor = torch.FloatTensor(sigmas_float_list) - if len(sigmas_tensor) != interpolate_to_steps + 1: - sigmas_tensor = self.loglinear_interp(sigmas_tensor, interpolate_to_steps + 1) - sigmas_tensor[-1] = 0 - return (sigmas_tensor.float(),) - - def loglinear_interp(self, t_steps, num_steps): - """ - Performs log-linear interpolation of a given array of decreasing numbers. - """ - t_steps_np = t_steps.numpy() - - xs = np.linspace(0, 1, len(t_steps_np)) - ys = np.log(t_steps_np[::-1]) - - new_xs = np.linspace(0, 1, num_steps) - new_ys = np.interp(new_xs, xs, ys) - - interped_ys = np.exp(new_ys)[::-1].copy() - interped_ys_tensor = torch.tensor(interped_ys) - return interped_ys_tensor - -class StringToFloatList: - @classmethod - def INPUT_TYPES(s): - return {"required": - { - "string" :("STRING", {"default": "1, 2, 3", "multiline": True}), - } - } - RETURN_TYPES = ("FLOAT",) - RETURN_NAMES = ("FLOAT",) - CATEGORY = "KJNodes/misc" - FUNCTION = "createlist" - - def createlist(self, string): - float_list = [float(x.strip()) for x in string.split(',')] - return (float_list,) - - -class InjectNoiseToLatent: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "latents":("LATENT",), - "strength": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 200.0, "step": 0.0001}), - "noise": ("LATENT",), - "normalize": ("BOOLEAN", {"default": False}), - "average": ("BOOLEAN", {"default": False}), - }, - "optional":{ - "mask": ("MASK", ), - "mix_randn_amount": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.001}), - "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), - } - } - - RETURN_TYPES = ("LATENT",) - FUNCTION = "injectnoise" - CATEGORY = "KJNodes/noise" - - def injectnoise(self, latents, strength, noise, normalize, average, mix_randn_amount=0, seed=None, mask=None): - samples = latents["samples"].clone().cpu() - noise = noise["samples"].clone().cpu() - if samples.shape != samples.shape: - raise ValueError("InjectNoiseToLatent: Latent and noise must have the same shape") - if average: - noised = (samples + noise) / 2 - else: - noised = samples + noise * strength - if normalize: - noised = noised / noised.std() - if mask is not None: - mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(noised.shape[2], noised.shape[3]), mode="bilinear") - mask = mask.expand((-1,noised.shape[1],-1,-1)) - if mask.shape[0] < noised.shape[0]: - mask = mask.repeat((noised.shape[0] -1) // mask.shape[0] + 1, 1, 1, 1)[:noised.shape[0]] - noised = mask * noised + (1-mask) * samples - if mix_randn_amount > 0: - if seed is not None: - generator = torch.manual_seed(seed) - rand_noise = torch.randn(noised.size(), dtype=noised.dtype, layout=noised.layout, generator=generator, device="cpu") - noised = noised + (mix_randn_amount * rand_noise) - - return ({"samples":noised},) - -class SoundReactive: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "sound_level": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 99999, "step": 0.01}), - "start_range_hz": ("INT", {"default": 150, "min": 0, "max": 9999, "step": 1}), - "end_range_hz": ("INT", {"default": 2000, "min": 0, "max": 9999, "step": 1}), - "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 99999, "step": 0.01}), - "smoothing_factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - "normalize": ("BOOLEAN", {"default": False}), - }, - } - - RETURN_TYPES = ("FLOAT","INT",) - RETURN_NAMES =("sound_level", "sound_level_int",) - FUNCTION = "react" - CATEGORY = "KJNodes/audio" - DESCRIPTION = """ -Reacts to the sound level of the input. -Uses your browsers sound input options and requires. -Meant to be used with realtime diffusion with autoqueue. -""" - - def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize): - - sound_level *= multiplier - - if normalize: - sound_level /= 255 - - sound_level_int = int(sound_level) - return (sound_level, sound_level_int, ) - -class GenerateNoise: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), - "multiplier": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 4096, "step": 0.01}), - "constant_batch_noise": ("BOOLEAN", {"default": False}), - "normalize": ("BOOLEAN", {"default": False}), - }, - "optional": { - "model": ("MODEL", ), - "sigmas": ("SIGMAS", ), - "latent_channels": (['4', '16', ],), - "shape": (["BCHW", "BCTHW","BTCHW",],), - } - } - - RETURN_TYPES = ("LATENT",) - FUNCTION = "generatenoise" - CATEGORY = "KJNodes/noise" - DESCRIPTION = """ -Generates noise for injection or to be used as empty latents on samplers with add_noise off. -""" - - def generatenoise(self, batch_size, width, height, seed, multiplier, constant_batch_noise, normalize, sigmas=None, model=None, latent_channels=4, shape="BCHW"): - - generator = torch.manual_seed(seed) - if shape == "BCHW": - noise = torch.randn([batch_size, int(latent_channels), height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") - elif shape == "BCTHW": - noise = torch.randn([1, int(latent_channels), batch_size,height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") - elif shape == "BTCHW": - noise = torch.randn([1, batch_size, int(latent_channels), height // 8, width // 8], dtype=torch.float32, layout=torch.strided, generator=generator, device="cpu") - if sigmas is not None: - sigma = sigmas[0] - sigmas[-1] - sigma /= model.model.latent_format.scale_factor - noise *= sigma - - noise *=multiplier - - if normalize: - noise = noise / noise.std() - if constant_batch_noise: - noise = noise[0].repeat(batch_size, 1, 1, 1) - - - return ({"samples":noise}, ) - -def camera_embeddings(elevation, azimuth): - elevation = torch.as_tensor([elevation]) - azimuth = torch.as_tensor([azimuth]) - embeddings = torch.stack( - [ - torch.deg2rad( - (90 - elevation) - (90) - ), # Zero123 polar is 90-elevation - torch.sin(torch.deg2rad(azimuth)), - torch.cos(torch.deg2rad(azimuth)), - torch.deg2rad( - 90 - torch.full_like(elevation, 0) - ), - ], dim=-1).unsqueeze(1) - - return embeddings - -def interpolate_angle(start, end, fraction): - # Calculate the difference in angles and adjust for wraparound if necessary - diff = (end - start + 540) % 360 - 180 - # Apply fraction to the difference - interpolated = start + fraction * diff - # Normalize the result to be within the range of -180 to 180 - return (interpolated + 180) % 360 - 180 - - -class StableZero123_BatchSchedule: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), - "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}), - "elevation_points_string": ("STRING", {"default": "0:(0.0),\n7:(0.0),\n15:(0.0)\n", "multiline": True}), - }} - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - FUNCTION = "encode" - CATEGORY = "KJNodes/experimental" - - def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation): - output = clip_vision.encode_image(init_image) - pooled = output.image_embeds.unsqueeze(0) - pixels = common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) - encode_pixels = pixels[:,:,:,:3] - t = vae.encode(encode_pixels) - - def ease_in(t): - return t * t - def ease_out(t): - return 1 - (1 - t) * (1 - t) - def ease_in_out(t): - return 3 * t * t - 2 * t * t * t - - # Parse the azimuth input string into a list of tuples - azimuth_points = [] - azimuth_points_string = azimuth_points_string.rstrip(',\n') - for point_str in azimuth_points_string.split(','): - frame_str, azimuth_str = point_str.split(':') - frame = int(frame_str.strip()) - azimuth = float(azimuth_str.strip()[1:-1]) - azimuth_points.append((frame, azimuth)) - # Sort the points by frame number - azimuth_points.sort(key=lambda x: x[0]) - - # Parse the elevation input string into a list of tuples - elevation_points = [] - elevation_points_string = elevation_points_string.rstrip(',\n') - for point_str in elevation_points_string.split(','): - frame_str, elevation_str = point_str.split(':') - frame = int(frame_str.strip()) - elevation_val = float(elevation_str.strip()[1:-1]) - elevation_points.append((frame, elevation_val)) - # Sort the points by frame number - elevation_points.sort(key=lambda x: x[0]) - - # Index of the next point to interpolate towards - next_point = 1 - next_elevation_point = 1 - - positive_cond_out = [] - positive_pooled_out = [] - negative_cond_out = [] - negative_pooled_out = [] - - #azimuth interpolation - for i in range(batch_size): - # Find the interpolated azimuth for the current frame - while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: - next_point += 1 - # If next_point is equal to the length of points, we've gone past the last point - if next_point == len(azimuth_points): - next_point -= 1 # Set next_point to the last index of points - prev_point = max(next_point - 1, 0) # Ensure prev_point is not less than 0 - - # Calculate fraction - if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: # Prevent division by zero - fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0]) - if interpolation == "ease_in": - fraction = ease_in(fraction) - elif interpolation == "ease_out": - fraction = ease_out(fraction) - elif interpolation == "ease_in_out": - fraction = ease_in_out(fraction) - - # Use the new interpolate_angle function - interpolated_azimuth = interpolate_angle(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction) - else: - interpolated_azimuth = azimuth_points[prev_point][1] - # Interpolate the elevation - next_elevation_point = 1 - while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: - next_elevation_point += 1 - if next_elevation_point == len(elevation_points): - next_elevation_point -= 1 - prev_elevation_point = max(next_elevation_point - 1, 0) - - if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: - fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) - if interpolation == "ease_in": - fraction = ease_in(fraction) - elif interpolation == "ease_out": - fraction = ease_out(fraction) - elif interpolation == "ease_in_out": - fraction = ease_in_out(fraction) - - interpolated_elevation = interpolate_angle(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction) - else: - interpolated_elevation = elevation_points[prev_elevation_point][1] - - cam_embeds = camera_embeddings(interpolated_elevation, interpolated_azimuth) - cond = torch.cat([pooled, cam_embeds.repeat((pooled.shape[0], 1, 1))], dim=-1) - - positive_pooled_out.append(t) - positive_cond_out.append(cond) - negative_pooled_out.append(torch.zeros_like(t)) - negative_cond_out.append(torch.zeros_like(pooled)) - - # Concatenate the conditions and pooled outputs - final_positive_cond = torch.cat(positive_cond_out, dim=0) - final_positive_pooled = torch.cat(positive_pooled_out, dim=0) - final_negative_cond = torch.cat(negative_cond_out, dim=0) - final_negative_pooled = torch.cat(negative_pooled_out, dim=0) - - # Structure the final output - final_positive = [[final_positive_cond, {"concat_latent_image": final_positive_pooled}]] - final_negative = [[final_negative_cond, {"concat_latent_image": final_negative_pooled}]] - - latent = torch.zeros([batch_size, 4, height // 8, width // 8]) - return (final_positive, final_negative, {"samples": latent}) - -def linear_interpolate(start, end, fraction): - return start + (end - start) * fraction - -class SV3D_BatchSchedule: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}), - "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), - "azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}), - "elevation_points_string": ("STRING", {"default": "0:(0.0),\n9:(0.0),\n20:(0.0)\n", "multiline": True}), - }} - - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - FUNCTION = "encode" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = """ -Allow scheduling of the azimuth and elevation conditions for SV3D. -Note that SV3D is still a video model and the schedule needs to always go forward -https://huggingface.co/stabilityai/sv3d -""" - - def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation): - output = clip_vision.encode_image(init_image) - pooled = output.image_embeds.unsqueeze(0) - pixels = common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) - encode_pixels = pixels[:,:,:,:3] - t = vae.encode(encode_pixels) - - def ease_in(t): - return t * t - def ease_out(t): - return 1 - (1 - t) * (1 - t) - def ease_in_out(t): - return 3 * t * t - 2 * t * t * t - - # Parse the azimuth input string into a list of tuples - azimuth_points = [] - azimuth_points_string = azimuth_points_string.rstrip(',\n') - for point_str in azimuth_points_string.split(','): - frame_str, azimuth_str = point_str.split(':') - frame = int(frame_str.strip()) - azimuth = float(azimuth_str.strip()[1:-1]) - azimuth_points.append((frame, azimuth)) - # Sort the points by frame number - azimuth_points.sort(key=lambda x: x[0]) - - # Parse the elevation input string into a list of tuples - elevation_points = [] - elevation_points_string = elevation_points_string.rstrip(',\n') - for point_str in elevation_points_string.split(','): - frame_str, elevation_str = point_str.split(':') - frame = int(frame_str.strip()) - elevation_val = float(elevation_str.strip()[1:-1]) - elevation_points.append((frame, elevation_val)) - # Sort the points by frame number - elevation_points.sort(key=lambda x: x[0]) - - # Index of the next point to interpolate towards - next_point = 1 - next_elevation_point = 1 - elevations = [] - azimuths = [] - # For azimuth interpolation - for i in range(batch_size): - # Find the interpolated azimuth for the current frame - while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]: - next_point += 1 - if next_point == len(azimuth_points): - next_point -= 1 - prev_point = max(next_point - 1, 0) - - if azimuth_points[next_point][0] != azimuth_points[prev_point][0]: - fraction = (i - azimuth_points[prev_point][0]) / (azimuth_points[next_point][0] - azimuth_points[prev_point][0]) - # Apply the ease function to the fraction - if interpolation == "ease_in": - fraction = ease_in(fraction) - elif interpolation == "ease_out": - fraction = ease_out(fraction) - elif interpolation == "ease_in_out": - fraction = ease_in_out(fraction) - - interpolated_azimuth = linear_interpolate(azimuth_points[prev_point][1], azimuth_points[next_point][1], fraction) - else: - interpolated_azimuth = azimuth_points[prev_point][1] - - # Interpolate the elevation - next_elevation_point = 1 - while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]: - next_elevation_point += 1 - if next_elevation_point == len(elevation_points): - next_elevation_point -= 1 - prev_elevation_point = max(next_elevation_point - 1, 0) - - if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]: - fraction = (i - elevation_points[prev_elevation_point][0]) / (elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0]) - # Apply the ease function to the fraction - if interpolation == "ease_in": - fraction = ease_in(fraction) - elif interpolation == "ease_out": - fraction = ease_out(fraction) - elif interpolation == "ease_in_out": - fraction = ease_in_out(fraction) - - interpolated_elevation = linear_interpolate(elevation_points[prev_elevation_point][1], elevation_points[next_elevation_point][1], fraction) - else: - interpolated_elevation = elevation_points[prev_elevation_point][1] - - azimuths.append(interpolated_azimuth) - elevations.append(interpolated_elevation) - - #print("azimuths", azimuths) - #print("elevations", elevations) - - # Structure the final output - final_positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] - final_negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t),"elevation": elevations, "azimuth": azimuths}]] - - latent = torch.zeros([batch_size, 4, height // 8, width // 8]) - return (final_positive, final_negative, {"samples": latent}) - -class LoadResAdapterNormalization: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "resadapter_path": (folder_paths.get_filename_list("checkpoints"), ) - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "load_res_adapter" - CATEGORY = "KJNodes/experimental" - - def load_res_adapter(self, model, resadapter_path): - print("ResAdapter: Checking ResAdapter path") - resadapter_full_path = folder_paths.get_full_path("checkpoints", resadapter_path) - if not os.path.exists(resadapter_full_path): - raise Exception("Invalid model path") - else: - print("ResAdapter: Loading ResAdapter normalization weights") - from comfy.utils import load_torch_file - prefix_to_remove = 'diffusion_model.' - model_clone = model.clone() - norm_state_dict = load_torch_file(resadapter_full_path) - new_values = {key[len(prefix_to_remove):]: value for key, value in norm_state_dict.items() if key.startswith(prefix_to_remove)} - print("ResAdapter: Attempting to add patches with ResAdapter weights") - try: - for key in model.model.diffusion_model.state_dict().keys(): - if key in new_values: - original_tensor = model.model.diffusion_model.state_dict()[key] - new_tensor = new_values[key].to(model.model.diffusion_model.dtype) - if original_tensor.shape == new_tensor.shape: - model_clone.add_object_patch(f"diffusion_model.{key}.data", new_tensor) - else: - print("ResAdapter: No match for key: ",key) - except: - raise Exception("Could not patch model, this way of patching was added to ComfyUI on March 3rd 2024, is your ComfyUI up to date?") - print("ResAdapter: Added resnet normalization patches") - return (model_clone, ) - -class Superprompt: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "instruction_prompt": ("STRING", {"default": 'Expand the following prompt to add more detail', "multiline": True}), - "prompt": ("STRING", {"default": '', "multiline": True, "forceInput": True}), - "max_new_tokens": ("INT", {"default": 128, "min": 1, "max": 4096, "step": 1}), - } - } - - RETURN_TYPES = ("STRING",) - FUNCTION = "process" - CATEGORY = "KJNodes/text" - DESCRIPTION = """ -# SuperPrompt -A T5 model fine-tuned on the SuperPrompt dataset for -upsampling text prompts to more detailed descriptions. -Meant to be used as a pre-generation step for text-to-image -models that benefit from more detailed prompts. -https://huggingface.co/roborovski/superprompt-v1 -""" - - def process(self, instruction_prompt, prompt, max_new_tokens): - device = model_management.get_torch_device() - from transformers import T5Tokenizer, T5ForConditionalGeneration - - checkpoint_path = os.path.join(script_directory, "models","superprompt-v1") - if not os.path.exists(checkpoint_path): - print(f"Downloading model to: {checkpoint_path}") - from huggingface_hub import snapshot_download - snapshot_download(repo_id="roborovski/superprompt-v1", - local_dir=checkpoint_path, - local_dir_use_symlinks=False) - tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False) - - model = T5ForConditionalGeneration.from_pretrained(checkpoint_path, device_map=device) - model.to(device) - input_text = instruction_prompt + ": " + prompt - - input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device) - outputs = model.generate(input_ids, max_new_tokens=max_new_tokens) - out = (tokenizer.decode(outputs[0])) - out = out.replace('', '') - out = out.replace('    ', '') - - return (out, ) - - -class CameraPoseVisualizer: - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "pose_file_path": ("STRING", {"default": '', "multiline": False}), - "base_xval": ("FLOAT", {"default": 0.2,"min": 0, "max": 100, "step": 0.01}), - "zval": ("FLOAT", {"default": 0.3,"min": 0, "max": 100, "step": 0.01}), - "scale": ("FLOAT", {"default": 1.0,"min": 0.01, "max": 10.0, "step": 0.01}), - "use_exact_fx": ("BOOLEAN", {"default": False}), - "relative_c2w": ("BOOLEAN", {"default": True}), - "use_viewer": ("BOOLEAN", {"default": False}), - }, - "optional": { - "cameractrl_poses": ("CAMERACTRL_POSES", {"default": None}), - } - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "plot" - CATEGORY = "KJNodes/misc" - DESCRIPTION = """ -Visualizes the camera poses, from Animatediff-Evolved CameraCtrl Pose -or a .txt file with RealEstate camera intrinsics and coordinates, in a 3D plot. -""" - - def plot(self, pose_file_path, scale, base_xval, zval, use_exact_fx, relative_c2w, use_viewer, cameractrl_poses=None): - import matplotlib as mpl - import matplotlib.pyplot as plt - from torchvision.transforms import ToTensor - - x_min = -2.0 * scale - x_max = 2.0 * scale - y_min = -2.0 * scale - y_max = 2.0 * scale - z_min = -2.0 * scale - z_max = 2.0 * scale - plt.rcParams['text.color'] = '#999999' - self.fig = plt.figure(figsize=(18, 7)) - self.fig.patch.set_facecolor('#353535') - self.ax = self.fig.add_subplot(projection='3d') - self.ax.set_facecolor('#353535') # Set the background color here - self.ax.grid(color='#999999', linestyle='-', linewidth=0.5) - self.plotly_data = None # plotly data traces - self.ax.set_aspect("auto") - self.ax.set_xlim(x_min, x_max) - self.ax.set_ylim(y_min, y_max) - self.ax.set_zlim(z_min, z_max) - self.ax.set_xlabel('x', color='#999999') - self.ax.set_ylabel('y', color='#999999') - self.ax.set_zlabel('z', color='#999999') - for text in self.ax.get_xticklabels() + self.ax.get_yticklabels() + self.ax.get_zticklabels(): - text.set_color('#999999') - print('initialize camera pose visualizer') - - if pose_file_path != "": - with open(pose_file_path, 'r') as f: - poses = f.readlines() - w2cs = [np.asarray([float(p) for p in pose.strip().split(' ')[7:]]).reshape(3, 4) for pose in poses[1:]] - fxs = [float(pose.strip().split(' ')[1]) for pose in poses[1:]] - #print(poses) - elif cameractrl_poses is not None: - poses = cameractrl_poses - w2cs = [np.array(pose[7:]).reshape(3, 4) for pose in cameractrl_poses] - fxs = [pose[1] for pose in cameractrl_poses] - else: - raise ValueError("Please provide either pose_file_path or cameractrl_poses") - - total_frames = len(w2cs) - transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4) - last_row = np.zeros((1, 4)) - last_row[0, -1] = 1.0 - - w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs] - c2ws = self.get_c2w(w2cs, transform_matrix, relative_c2w) - - for frame_idx, c2w in enumerate(c2ws): - self.extrinsic2pyramid(c2w, frame_idx / total_frames, hw_ratio=1/1, base_xval=base_xval, - zval=(fxs[frame_idx] if use_exact_fx else zval)) - - # Create the colorbar - cmap = mpl.cm.rainbow - norm = mpl.colors.Normalize(vmin=0, vmax=total_frames) - colorbar = self.fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=self.ax, orientation='vertical') - - # Change the colorbar label - colorbar.set_label('Frame', color='#999999') # Change the label and its color - - # Change the tick colors - colorbar.ax.yaxis.set_tick_params(colors='#999999') # Change the tick color - - # Change the tick frequency - # Assuming you want to set the ticks at every 10th frame - ticks = np.arange(0, total_frames, 10) - colorbar.ax.yaxis.set_ticks(ticks) - - plt.title('') - plt.draw() - buf = io.BytesIO() - plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0) - buf.seek(0) - img = Image.open(buf) - tensor_img = ToTensor()(img) - buf.close() - tensor_img = tensor_img.permute(1, 2, 0).unsqueeze(0) - if use_viewer: - time.sleep(1) - plt.show() - return (tensor_img,) - - def extrinsic2pyramid(self, extrinsic, color_map='red', hw_ratio=1/1, base_xval=1, zval=3): - import matplotlib.pyplot as plt - from mpl_toolkits.mplot3d.art3d import Poly3DCollection - vertex_std = np.array([[0, 0, 0, 1], - [base_xval, -base_xval * hw_ratio, zval, 1], - [base_xval, base_xval * hw_ratio, zval, 1], - [-base_xval, base_xval * hw_ratio, zval, 1], - [-base_xval, -base_xval * hw_ratio, zval, 1]]) - vertex_transformed = vertex_std @ extrinsic.T - meshes = [[vertex_transformed[0, :-1], vertex_transformed[1][:-1], vertex_transformed[2, :-1]], - [vertex_transformed[0, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1]], - [vertex_transformed[0, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]], - [vertex_transformed[0, :-1], vertex_transformed[4, :-1], vertex_transformed[1, :-1]], - [vertex_transformed[1, :-1], vertex_transformed[2, :-1], vertex_transformed[3, :-1], vertex_transformed[4, :-1]]] - - color = color_map if isinstance(color_map, str) else plt.cm.rainbow(color_map) - - self.ax.add_collection3d( - Poly3DCollection(meshes, facecolors=color, linewidths=0.3, edgecolors=color, alpha=0.25)) - - def customize_legend(self, list_label): - from matplotlib.patches import Patch - import matplotlib.pyplot as plt - list_handle = [] - for idx, label in enumerate(list_label): - color = plt.cm.rainbow(idx / len(list_label)) - patch = Patch(color=color, label=label) - list_handle.append(patch) - plt.legend(loc='right', bbox_to_anchor=(1.8, 0.5), handles=list_handle) - - def get_c2w(self, w2cs, transform_matrix, relative_c2w): - if relative_c2w: - target_cam_c2w = np.array([ - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0], - [0, 0, 0, 1] - ]) - abs2rel = target_cam_c2w @ w2cs[0] - ret_poses = [target_cam_c2w, ] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]] - else: - ret_poses = [np.linalg.inv(w2c) for w2c in w2cs] - ret_poses = [transform_matrix @ x for x in ret_poses] - return np.array(ret_poses, dtype=np.float32) - - - -class CheckpointPerturbWeights: - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "joint_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), - "final_layer": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), - "rest_of_the_blocks": ("FLOAT", {"default": 0.02, "min": 0.001, "max": 10.0, "step": 0.001}), - "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), - } - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "mod" - OUTPUT_NODE = True - - CATEGORY = "KJNodes/experimental" - - def mod(self, seed, model, joint_blocks, final_layer, rest_of_the_blocks): - import copy - torch.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - device = model_management.get_torch_device() - model_copy = copy.deepcopy(model) - model_copy.model.to(device) - keys = model_copy.model.diffusion_model.state_dict().keys() - - dict = {} - for key in keys: - dict[key] = model_copy.model.diffusion_model.state_dict()[key] - - pbar = ProgressBar(len(keys)) - for k in keys: - v = dict[k] - print(f'{k}: {v.std()}') - if k.startswith('joint_blocks'): - multiplier = joint_blocks - elif k.startswith('final_layer'): - multiplier = final_layer - else: - multiplier = rest_of_the_blocks - dict[k] += torch.normal(torch.zeros_like(v) * v.mean(), torch.ones_like(v) * v.std() * multiplier).to(device) - pbar.update(1) - model_copy.model.diffusion_model.load_state_dict(dict) - return model_copy, - -class DifferentialDiffusionAdvanced(): - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL", ), - "samples": ("LATENT",), - "mask": ("MASK",), - "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), - }} - RETURN_TYPES = ("MODEL", "LATENT") - FUNCTION = "apply" - CATEGORY = "_for_testing" - INIT = False - - def apply(self, model, samples, mask, multiplier): - self.multiplier = multiplier - model = model.clone() - model.set_model_denoise_mask_function(self.forward) - s = samples.copy() - s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) - return (model, s) - - def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): - model = extra_options["model"] - step_sigmas = extra_options["sigmas"] - sigma_to = model.inner_model.model_sampling.sigma_min - if step_sigmas[-1] > sigma_to: - sigma_to = step_sigmas[-1] - sigma_from = step_sigmas[0] - - ts_from = model.inner_model.model_sampling.timestep(sigma_from) - ts_to = model.inner_model.model_sampling.timestep(sigma_to) - current_ts = model.inner_model.model_sampling.timestep(sigma[0]) - - threshold = (current_ts - ts_to) / (ts_from - ts_to) / self.multiplier - - return (denoise_mask >= threshold).to(denoise_mask.dtype) - -class FluxBlockLoraSelect: - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - arg_dict = {} - argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) - - for i in range(19): - arg_dict["double_blocks.{}.".format(i)] = argument - - for i in range(38): - arg_dict["single_blocks.{}.".format(i)] = argument - - return {"required": arg_dict} - - RETURN_TYPES = ("SELECTEDDITBLOCKS", ) - RETURN_NAMES = ("blocks", ) - OUTPUT_TOOLTIPS = ("The modified diffusion model.",) - FUNCTION = "load_lora" - - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" - - def load_lora(self, **kwargs): - return (kwargs,) - -class HunyuanVideoBlockLoraSelect: - @classmethod - def INPUT_TYPES(s): - arg_dict = {} - argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) - - for i in range(20): - arg_dict["double_blocks.{}.".format(i)] = argument - - for i in range(40): - arg_dict["single_blocks.{}.".format(i)] = argument - - return {"required": arg_dict} - - RETURN_TYPES = ("SELECTEDDITBLOCKS", ) - RETURN_NAMES = ("blocks", ) - OUTPUT_TOOLTIPS = ("The modified diffusion model.",) - FUNCTION = "load_lora" - - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" - - def load_lora(self, **kwargs): - return (kwargs,) - -class Wan21BlockLoraSelect: - @classmethod - def INPUT_TYPES(s): - arg_dict = {} - argument = ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.01}) - - for i in range(40): - arg_dict["blocks.{}.".format(i)] = argument - - return {"required": arg_dict} - - RETURN_TYPES = ("SELECTEDDITBLOCKS", ) - RETURN_NAMES = ("blocks", ) - OUTPUT_TOOLTIPS = ("The modified diffusion model.",) - FUNCTION = "load_lora" - - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "Select individual block alpha values, value of 0 removes the block altogether" - - def load_lora(self, **kwargs): - return (kwargs,) - -class DiTBlockLoraLoader: - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), - "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), - - }, - "optional": { - "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}), - "opt_lora_path": ("STRING", {"forceInput": True, "tooltip": "Absolute path of the LoRA."}), - "blocks": ("SELECTEDDITBLOCKS",), - } - } - - RETURN_TYPES = ("MODEL", "STRING", ) - RETURN_NAMES = ("model", "rank", ) - OUTPUT_TOOLTIPS = ("The modified diffusion model.", "possible rank of the LoRA.") - FUNCTION = "load_lora" - CATEGORY = "KJNodes/experimental" - - def load_lora(self, model, strength_model, lora_name=None, opt_lora_path=None, blocks=None): - - import comfy.lora - - if opt_lora_path: - lora_path = opt_lora_path - else: - lora_path = folder_paths.get_full_path("loras", lora_name) - - lora = None - if self.loaded_lora is not None: - if self.loaded_lora[0] == lora_path: - lora = self.loaded_lora[1] - else: - self.loaded_lora = None - - if lora is None: - lora = load_torch_file(lora_path, safe_load=True) - self.loaded_lora = (lora_path, lora) - - # Find the first key that ends with "weight" - rank = "unknown" - weight_key = next((key for key in lora.keys() if key.endswith('weight')), None) - # Print the shape of the value corresponding to the key - if weight_key: - print(f"Shape of the first 'weight' key ({weight_key}): {lora[weight_key].shape}") - rank = str(lora[weight_key].shape[0]) - else: - print("No key ending with 'weight' found.") - rank = "Couldn't find rank" - self.loaded_lora = (lora_path, lora) - - key_map = {} - if model is not None: - key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) - - loaded = comfy.lora.load_lora(lora, key_map) - - if blocks is not None: - keys_to_delete = [] - - for block in blocks: - for key in list(loaded.keys()): - match = False - if isinstance(key, str) and block in key: - match = True - elif isinstance(key, tuple): - for k in key: - if block in k: - match = True - break - - if match: - ratio = blocks[block] - if ratio == 0: - keys_to_delete.append(key) - else: - # Only modify LoRA adapters, skip diff tuples - value = loaded[key] - if hasattr(value, 'weights'): - print(f"Modifying LoRA adapter for key: {key}") - weights_list = list(value.weights) - weights_list[2] = ratio - loaded[key].weights = tuple(weights_list) - else: - print(f"Skipping non-LoRA entry for key: {key}") - - for key in keys_to_delete: - del loaded[key] - - print("loading lora keys:") - for key, value in loaded.items(): - if hasattr(value, 'weights'): - print(f"Key: {key}, Alpha: {value.weights[2]}") - else: - print(f"Key: {key}, Type: {type(value)}") - - if model is not None: - new_modelpatcher = model.clone() - k = new_modelpatcher.add_patches(loaded, strength_model) - - k = set(k) - for x in loaded: - if (x not in k): - print("NOT LOADED {}".format(x)) - - return (new_modelpatcher, rank) - -class CustomControlNetWeightsFluxFromList: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "list_of_floats": ("FLOAT", {"forceInput": True}, ), - }, - "optional": { - "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ), - "cn_extras": ("CN_WEIGHTS_EXTRAS",), - "autosize": ("ACNAUTOSIZE", {"padding": 0}), - } - } - - RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",) - RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT") - FUNCTION = "load_weights" - DESCRIPTION = "Creates controlnet weights from a list of floats for Advanced-ControlNet" - - CATEGORY = "KJNodes/controlnet" - - def load_weights(self, list_of_floats: list[float], - uncond_multiplier: float=1.0, cn_extras: dict[str]={}): - - adv_control = importlib.import_module("ComfyUI-Advanced-ControlNet.adv_control") - ControlWeights = adv_control.utils.ControlWeights - TimestepKeyframeGroup = adv_control.utils.TimestepKeyframeGroup - TimestepKeyframe = adv_control.utils.TimestepKeyframe - - weights = ControlWeights.controlnet(weights_input=list_of_floats, uncond_multiplier=uncond_multiplier, extras=cn_extras) - print(weights.weights_input) - return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) - -SHAKKERLABS_UNION_CONTROLNET_TYPES = { - "canny": 0, - "tile": 1, - "depth": 2, - "blur": 3, - "pose": 4, - "gray": 5, - "low quality": 6, -} - -class SetShakkerLabsUnionControlNetType: - @classmethod - def INPUT_TYPES(s): - return {"required": {"control_net": ("CONTROL_NET", ), - "type": (["auto"] + list(SHAKKERLABS_UNION_CONTROLNET_TYPES.keys()),) - }} - - CATEGORY = "conditioning/controlnet" - RETURN_TYPES = ("CONTROL_NET",) - - FUNCTION = "set_controlnet_type" - - def set_controlnet_type(self, control_net, type): - control_net = control_net.copy() - type_number = SHAKKERLABS_UNION_CONTROLNET_TYPES.get(type, -1) - if type_number >= 0: - control_net.set_extra_arg("control_type", [type_number]) - else: - control_net.set_extra_arg("control_type", []) - - return (control_net,) - -class ModelSaveKJ: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "filename_prefix": ("STRING", {"default": "diffusion_models/ComfyUI"}), - "model_key_prefix": ("STRING", {"default": "model.diffusion_model."}), - }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "advanced/model_merging" - - def save(self, model, filename_prefix, model_key_prefix, prompt=None, extra_pnginfo=None): - from comfy.utils import save_torch_file - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - - output_checkpoint = f"{filename}_{counter:05}_.safetensors" - output_checkpoint = os.path.join(full_output_folder, output_checkpoint) - - load_models = [model] - - model_management.load_models_gpu(load_models, force_patch_weights=True) - default_prefix = "model.diffusion_model." - - sd = model.model.state_dict_for_saving(None, None, None) - - new_sd = {} - for k in sd: - if k.startswith(default_prefix): - new_key = model_key_prefix + k[len(default_prefix):] - else: - new_key = k # In case the key doesn't start with the default prefix, keep it unchanged - t = sd[k] - if not t.is_contiguous(): - t = t.contiguous() - new_sd[new_key] = t - print(full_output_folder) - if not os.path.exists(full_output_folder): - os.makedirs(full_output_folder) - save_torch_file(new_sd, os.path.join(full_output_folder, output_checkpoint)) - return {} - -class StyleModelApplyAdvanced: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "style_model": ("STYLE_MODEL", ), - "clip_vision_output": ("CLIP_VISION_OUTPUT", ), - "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "apply_stylemodel" - CATEGORY = "KJNodes/experimental" - DESCRIPTION = "StyleModelApply but with strength parameter" - - def apply_stylemodel(self, clip_vision_output, style_model, conditioning, strength=1.0): - cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) - cond = strength * cond - c = [] - for t in conditioning: - n = [torch.cat((t[0], cond), dim=1), t[1].copy()] - c.append(n) - return (c, ) - -class AudioConcatenate: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "audio1": ("AUDIO",), - "audio2": ("AUDIO",), - "direction": ( - [ 'right', - 'left', - ], - { - "default": 'right' - }), - }} - - RETURN_TYPES = ("AUDIO",) - FUNCTION = "concanate" - CATEGORY = "KJNodes/audio" - DESCRIPTION = """ -Concatenates the audio1 to audio2 in the specified direction. -""" - - def concanate(self, audio1, audio2, direction): - sample_rate_1 = audio1["sample_rate"] - sample_rate_2 = audio2["sample_rate"] - if sample_rate_1 != sample_rate_2: - raise Exception("Sample rates of the two audios do not match") - - waveform_1 = audio1["waveform"] - print(waveform_1.shape) - waveform_2 = audio2["waveform"] - - # Concatenate based on the specified direction - if direction == 'right': - concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2) # Concatenate along width - elif direction == 'left': - concatenated_audio= torch.cat((waveform_2, waveform_1), dim=2) # Concatenate along width - return ({"waveform": concatenated_audio, "sample_rate": sample_rate_1},) - -class LeapfusionHunyuanI2V: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "latent": ("LATENT",), - "index": ("INT", {"default": 0, "min": -1, "max": 1000, "step": 1,"tooltip": "The index of the latent to be replaced. 0 for first frame and -1 for last"}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of steps to apply"}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of steps to apply"}), - "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.001}), - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "KJNodes/experimental" - - def patch(self, model, latent, index, strength, start_percent, end_percent): - - def outer_wrapper(samples, index, start_percent, end_percent): - def unet_wrapper(apply_model, args): - steps = args["c"]["transformer_options"]["sample_sigmas"] - inp, timestep, c = args["input"], args["timestep"], args["c"] - matched_step_index = (steps == timestep).nonzero() - if len(matched_step_index) > 0: - current_step_index = matched_step_index.item() - else: - for i in range(len(steps) - 1): - # walk from beginning of steps until crossing the timestep - if (steps[i] - timestep[0]) * (steps[i + 1] - timestep[0]) <= 0: - current_step_index = i - break - else: - current_step_index = 0 - current_percent = current_step_index / (len(steps) - 1) - if samples is not None: - if start_percent <= current_percent <= end_percent: - inp[:, :, [index], :, :] = samples[:, :, [0], :, :].to(inp) - else: - inp[:, :, [index], :, :] = torch.zeros(1) - return apply_model(inp, timestep, **c) - return unet_wrapper - - samples = latent["samples"] * 0.476986 * strength - m = model.clone() - m.set_model_unet_function_wrapper(outer_wrapper(samples, index, start_percent, end_percent)) - - return (m,) - -class ImageNoiseAugmentation: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "noise_aug_strength": ("FLOAT", {"default": None, "min": 0.0, "max": 100.0, "step": 0.001}), - "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}), - } - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "add_noise" - CATEGORY = "KJNodes/image" - DESCRIPTION = """ - Add noise to an image. - """ - - def add_noise(self, image, noise_aug_strength, seed): - torch.manual_seed(seed) - sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * noise_aug_strength - image_noise = torch.randn_like(image) * sigma[:, None, None, None] - image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise) - image_out = image + image_noise - return image_out, - -class VAELoaderKJ: - @staticmethod - def vae_list(): - vaes = folder_paths.get_filename_list("vae") - approx_vaes = folder_paths.get_filename_list("vae_approx") - sdxl_taesd_enc = False - sdxl_taesd_dec = False - sd1_taesd_enc = False - sd1_taesd_dec = False - sd3_taesd_enc = False - sd3_taesd_dec = False - f1_taesd_enc = False - f1_taesd_dec = False - - for v in approx_vaes: - if v.startswith("taesd_decoder."): - sd1_taesd_dec = True - elif v.startswith("taesd_encoder."): - sd1_taesd_enc = True - elif v.startswith("taesdxl_decoder."): - sdxl_taesd_dec = True - elif v.startswith("taesdxl_encoder."): - sdxl_taesd_enc = True - elif v.startswith("taesd3_decoder."): - sd3_taesd_dec = True - elif v.startswith("taesd3_encoder."): - sd3_taesd_enc = True - elif v.startswith("taef1_encoder."): - f1_taesd_dec = True - elif v.startswith("taef1_decoder."): - f1_taesd_enc = True - if sd1_taesd_dec and sd1_taesd_enc: - vaes.append("taesd") - if sdxl_taesd_dec and sdxl_taesd_enc: - vaes.append("taesdxl") - if sd3_taesd_dec and sd3_taesd_enc: - vaes.append("taesd3") - if f1_taesd_dec and f1_taesd_enc: - vaes.append("taef1") - return vaes - - @staticmethod - def load_taesd(name): - sd = {} - approx_vaes = folder_paths.get_filename_list("vae_approx") - - encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) - decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) - - enc = load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder)) - for k in enc: - sd["taesd_encoder.{}".format(k)] = enc[k] - - dec = load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder)) - for k in dec: - sd["taesd_decoder.{}".format(k)] = dec[k] - - if name == "taesd": - sd["vae_scale"] = torch.tensor(0.18215) - sd["vae_shift"] = torch.tensor(0.0) - elif name == "taesdxl": - sd["vae_scale"] = torch.tensor(0.13025) - sd["vae_shift"] = torch.tensor(0.0) - elif name == "taesd3": - sd["vae_scale"] = torch.tensor(1.5305) - sd["vae_shift"] = torch.tensor(0.0609) - elif name == "taef1": - sd["vae_scale"] = torch.tensor(0.3611) - sd["vae_shift"] = torch.tensor(0.1159) - return sd - - @classmethod - def INPUT_TYPES(s): - return { - "required": { "vae_name": (s.vae_list(), ), - "device": (["main_device", "cpu"],), - "weight_dtype": (["bf16", "fp16", "fp32" ],), - } - } - - RETURN_TYPES = ("VAE",) - FUNCTION = "load_vae" - CATEGORY = "KJNodes/vae" - - def load_vae(self, vae_name, device, weight_dtype): - from comfy.sd import VAE - dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[weight_dtype] - if device == "main_device": - device = model_management.get_torch_device() - elif device == "cpu": - device = torch.device("cpu") - if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: - sd = self.load_taesd(vae_name) - else: - vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) - sd = load_torch_file(vae_path) - vae = VAE(sd=sd, device=device, dtype=dtype) - return (vae,) - -from comfy.samplers import sampling_function, CFGGuider -class Guider_ScheduledCFG(CFGGuider): - - def set_cfg(self, cfg, start_percent, end_percent): - self.cfg = cfg - self.start_percent = start_percent - self.end_percent = end_percent - - def predict_noise(self, x, timestep, model_options={}, seed=None): - steps = model_options["transformer_options"]["sample_sigmas"] - matched_step_index = (steps == timestep).nonzero() - assert not (isinstance(self.cfg, list) and len(self.cfg) != (len(steps) - 1)), "cfg list length must match step count" - if len(matched_step_index) > 0: - current_step_index = matched_step_index.item() - else: - for i in range(len(steps) - 1): - # walk from beginning of steps until crossing the timestep - if (steps[i] - timestep[0]) * (steps[i + 1] - timestep[0]) <= 0: - current_step_index = i - break - else: - current_step_index = 0 - current_percent = current_step_index / (len(steps) - 1) - - if self.start_percent <= current_percent <= self.end_percent: - if isinstance(self.cfg, list): - cfg = self.cfg[current_step_index] - else: - cfg = self.cfg - uncond = self.conds.get("negative", None) - else: - uncond = None - cfg = 1.0 - - return sampling_function(self.inner_model, x, timestep, uncond, self.conds.get("positive", None), cfg, model_options=model_options, seed=seed) - -class ScheduledCFGGuidance: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 100.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step":0.01}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01}), - }, - } - RETURN_TYPES = ("GUIDER",) - FUNCTION = "get_guider" - CATEGORY = "KJNodes/experimental" - DESCRiPTION = """ -CFG Guider that allows for scheduled CFG changes over steps, the steps outside the range will use CFG 1.0 thus being processed faster. -cfg input can be a list of floats matching step count, or a single float for all steps. -""" - - def get_guider(self, model, cfg, positive, negative, start_percent, end_percent): - guider = Guider_ScheduledCFG(model) - guider.set_conds(positive, negative) - guider.set_cfg(cfg, start_percent, end_percent) - return (guider, ) - - -class ApplyRifleXRoPE_WanVideo: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), - "k": ("INT", {"default": 6, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}), - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - CATEGORY = "KJNodes/experimental" - EXPERIMENTAL = True - DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx" - - def patch(self, model, latent, k): - model_class = model.model.diffusion_model - - model_clone = model.clone() - num_frames = latent["samples"].shape[2] - d = model_class.dim // model_class.num_heads - - rope_embedder = EmbedND_RifleX( - d, - 10000.0, - [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)], - num_frames, - k - ) - - model_clone.add_object_patch(f"diffusion_model.rope_embedder", rope_embedder) - - return (model_clone, ) - -class ApplyRifleXRoPE_HunuyanVideo: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}), - "k": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}), - } - } - - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - CATEGORY = "KJNodes/experimental" - EXPERIMENTAL = True - DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx" - - def patch(self, model, latent, k): - model_class = model.model.diffusion_model - - model_clone = model.clone() - num_frames = latent["samples"].shape[2] - - pe_embedder = EmbedND_RifleX( - model_class.params.hidden_size // model_class.params.num_heads, - model_class.params.theta, - model_class.params.axes_dim, - num_frames, - k - ) - - model_clone.add_object_patch(f"diffusion_model.pe_embedder", pe_embedder) - - return (model_clone, ) - -def rope_riflex(pos, dim, theta, L_test, k): - from einops import rearrange - assert dim % 2 == 0 - if model_management.is_device_mps(pos.device) or model_management.is_intel_xpu() or model_management.is_directml_enabled(): - device = torch.device("cpu") - else: - device = pos.device - - scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device) - omega = 1.0 / (theta**scale) - - # RIFLEX modification - adjust last frequency component if L_test and k are provided - if k and L_test: - omega[k-1] = 0.9 * 2 * torch.pi / L_test - - out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega) - out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) - out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) - return out.to(dtype=torch.float32, device=pos.device) - -class EmbedND_RifleX(nn.Module): - def __init__(self, dim, theta, axes_dim, num_frames, k): - super().__init__() - self.dim = dim - self.theta = theta - self.axes_dim = axes_dim - self.num_frames = num_frames - self.k = k - - def forward(self, ids): - n_axes = ids.shape[-1] - emb = torch.cat( - [rope_riflex(ids[..., i], self.axes_dim[i], self.theta, self.num_frames, self.k if i == 0 else 0) for i in range(n_axes)], - dim=-3, - ) - return emb.unsqueeze(1) - - -class Timer: - def __init__(self, name): - self.name = name - self.start_time = None - self.elapsed = 0 - -class TimerNodeKJ: - @classmethod - - def INPUT_TYPES(s): - return { - "required": { - "any_input": (IO.ANY, ), - "mode": (["start", "stop"],), - "name": ("STRING", {"default": "Timer"}), - }, - "optional": { - "timer": ("TIMER",), - }, - } - - RETURN_TYPES = (IO.ANY, "TIMER", "INT", ) - RETURN_NAMES = ("any_output", "timer", "time") - FUNCTION = "timer" - CATEGORY = "KJNodes/misc" - - def timer(self, mode, name, any_input=None, timer=None): - if timer is None: - if mode == "start": - timer = Timer(name=name) - timer.start_time = time.time() - return {"ui": { - "text": [f"{timer.start_time}"]}, - "result": (any_input, timer, 0) - } - elif mode == "stop" and timer is not None: - end_time = time.time() - timer.elapsed = int((end_time - timer.start_time) * 1000) - timer.start_time = None - return (any_input, timer, timer.elapsed) - -class HunyuanVideoEncodeKeyframesToCond: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "model": ("MODEL",), - "positive": ("CONDITIONING", ), - "vae": ("VAE", ), - "start_frame": ("IMAGE", ), - "end_frame": ("IMAGE", ), - "num_frames": ("INT", {"default": 33, "min": 2, "max": 4096, "step": 1}), - "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), - "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), - "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}), - "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), - }, - "optional": { - "negative": ("CONDITIONING", ), - } - } - - RETURN_TYPES = ("MODEL", "CONDITIONING","CONDITIONING","LATENT") - RETURN_NAMES = ("model", "positive", "negative", "latent") - FUNCTION = "encode" - - CATEGORY = "KJNodes/videomodels" - - def encode(self, model, positive, start_frame, end_frame, num_frames, vae, tile_size, overlap, temporal_size, temporal_overlap, negative=None): - - model_clone = model.clone() - - model_clone.add_object_patch("concat_keys", ("concat_image",)) - - - x = (start_frame.shape[1] // 8) * 8 - y = (start_frame.shape[2] // 8) * 8 - - if start_frame.shape[1] != x or start_frame.shape[2] != y: - x_offset = (start_frame.shape[1] % 8) // 2 - y_offset = (start_frame.shape[2] % 8) // 2 - start_frame = start_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:] - if end_frame.shape[1] != x or end_frame.shape[2] != y: - x_offset = (start_frame.shape[1] % 8) // 2 - y_offset = (start_frame.shape[2] % 8) // 2 - end_frame = end_frame[:,x_offset:x + x_offset, y_offset:y + y_offset,:] - - video_frames = torch.zeros(num_frames-2, start_frame.shape[1], start_frame.shape[2], start_frame.shape[3], device=start_frame.device, dtype=start_frame.dtype) - video_frames = torch.cat([start_frame, video_frames, end_frame], dim=0) - - concat_latent = vae.encode_tiled(video_frames[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap) - - out_latent = {} - out_latent["samples"] = torch.zeros_like(concat_latent) - - out = [] - for conditioning in [positive, negative if negative is not None else []]: - c = [] - for t in conditioning: - d = t[1].copy() - d["concat_latent_image"] = concat_latent - n = [t[0], d] - c.append(n) - out.append(c) - if len(out) == 1: - out.append(out[0]) - return (model_clone, out[0], out[1], out_latent) - - -class LazySwitchKJ: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "switch": ("BOOLEAN",), - "on_false": (IO.ANY, {"lazy": True}), - "on_true": (IO.ANY, {"lazy": True}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "switch" - CATEGORY = "KJNodes/misc" - DESCRIPTION = "Controls flow of execution based on a boolean switch." - - def check_lazy_status(self, switch, on_false=None, on_true=None): - if switch and on_true is None: - return ["on_true"] - if not switch and on_false is None: - return ["on_false"] - - def switch(self, switch, on_false = None, on_true=None): - value = on_true if switch else on_false - return (value,) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/pyproject.toml b/custom_nodes/comfyui-kjnodes/pyproject.toml deleted file mode 100644 index f2d41e3eb46617cd61cdbd6138f191ef92f38236..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/pyproject.toml +++ /dev/null @@ -1,15 +0,0 @@ -[project] -name = "comfyui-kjnodes" -description = "Various quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability." -version = "1.1.4" -license = {file = "LICENSE"} -dependencies = ["librosa", "numpy", "pillow>=10.3.0", "scipy", "color-matcher", "matplotlib", "huggingface_hub"] - -[project.urls] -Repository = "https://github.com/kijai/ComfyUI-KJNodes" -# Used by Comfy Registry https://comfyregistry.org - -[tool.comfy] -PublisherId = "kijai" -DisplayName = "ComfyUI-KJNodes" -Icon = "https://avatars.githubusercontent.com/u/40791699" diff --git a/custom_nodes/comfyui-kjnodes/requirements.txt b/custom_nodes/comfyui-kjnodes/requirements.txt deleted file mode 100644 index 5bc18ca95b226298cbb88bd4de3307c157e0a88b..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -pillow>=10.3.0 -scipy -color-matcher -matplotlib -huggingface_hub -mss -opencv-python \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/utility/__pycache__/utility.cpython-310.pyc b/custom_nodes/comfyui-kjnodes/utility/__pycache__/utility.cpython-310.pyc deleted file mode 100644 index 536023f264b00aaff0aa103f593a0e271322e388..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/utility/__pycache__/utility.cpython-310.pyc and /dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/utility/fluid.py b/custom_nodes/comfyui-kjnodes/utility/fluid.py deleted file mode 100644 index c0691987f5249a031ecbb74329ba513d5788b691..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/utility/fluid.py +++ /dev/null @@ -1,67 +0,0 @@ -import numpy as np -from scipy.ndimage import map_coordinates, spline_filter -from scipy.sparse.linalg import factorized - -from .numerical import difference, operator - - -class Fluid: - def __init__(self, shape, *quantities, pressure_order=1, advect_order=3): - self.shape = shape - self.dimensions = len(shape) - - # Prototyping is simplified by dynamically - # creating advected quantities as needed. - self.quantities = quantities - for q in quantities: - setattr(self, q, np.zeros(shape)) - - self.indices = np.indices(shape) - self.velocity = np.zeros((self.dimensions, *shape)) - - laplacian = operator(shape, difference(2, pressure_order)) - self.pressure_solver = factorized(laplacian) - - self.advect_order = advect_order - - def step(self): - # Advection is computed backwards in time as described in Stable Fluids. - advection_map = self.indices - self.velocity - - # SciPy's spline filter introduces checkerboard divergence. - # A linear blend of the filtered and unfiltered fields based - # on some value epsilon eliminates this error. - def advect(field, filter_epsilon=10e-2, mode='constant'): - filtered = spline_filter(field, order=self.advect_order, mode=mode) - field = filtered * (1 - filter_epsilon) + field * filter_epsilon - return map_coordinates(field, advection_map, prefilter=False, order=self.advect_order, mode=mode) - - # Apply advection to each axis of the - # velocity field and each user-defined quantity. - for d in range(self.dimensions): - self.velocity[d] = advect(self.velocity[d]) - - for q in self.quantities: - setattr(self, q, advect(getattr(self, q))) - - # Compute the jacobian at each point in the - # velocity field to extract curl and divergence. - jacobian_shape = (self.dimensions,) * 2 - partials = tuple(np.gradient(d) for d in self.velocity) - jacobian = np.stack(partials).reshape(*jacobian_shape, *self.shape) - - divergence = jacobian.trace() - - # If this curl calculation is extended to 3D, the y-axis value must be negated. - # This corresponds to the coefficients of the levi-civita symbol in that dimension. - # Higher dimensions do not have a vector -> scalar, or vector -> vector, - # correspondence between velocity and curl due to differing isomorphisms - # between exterior powers in dimensions != 2 or 3 respectively. - curl_mask = np.triu(np.ones(jacobian_shape, dtype=bool), k=1) - curl = (jacobian[curl_mask] - jacobian[curl_mask.T]).squeeze() - - # Apply the pressure correction to the fluid's velocity field. - pressure = self.pressure_solver(divergence.flatten()).reshape(self.shape) - self.velocity -= np.gradient(pressure) - - return divergence, curl, pressure \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/utility/magictex.py b/custom_nodes/comfyui-kjnodes/utility/magictex.py deleted file mode 100644 index e6d426f7deb3deb977604dd37581eb4e9fe9e6a9..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/utility/magictex.py +++ /dev/null @@ -1,95 +0,0 @@ -"""Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy - -https://github.com/cheind/magic-texture -""" -from typing import Tuple, Optional -import numpy as np - - -def coordinate_grid(shape: Tuple[int, int], dtype=np.float32): - """Returns a three-dimensional coordinate grid of given shape for use in `magic`.""" - x = np.linspace(-1, 1, shape[1], endpoint=True, dtype=dtype) - y = np.linspace(-1, 1, shape[0], endpoint=True, dtype=dtype) - X, Y = np.meshgrid(x, y) - XYZ = np.stack((X, Y, np.ones_like(X)), -1) - return XYZ - - -def random_transform(coords: np.ndarray, rng: np.random.Generator = None): - """Returns randomly transformed coordinates""" - H, W = coords.shape[:2] - rng = rng or np.random.default_rng() - m = rng.uniform(-1.0, 1.0, size=(3, 3)).astype(coords.dtype) - return (coords.reshape(-1, 3) @ m.T).reshape(H, W, 3) - - -def magic( - coords: np.ndarray, - depth: Optional[int] = None, - distortion: Optional[int] = None, - rng: np.random.Generator = None, -): - """Returns color magic color texture. - - The implementation is based on Blender's (https://www.blender.org/) magic - texture shader. The following adaptions have been made: - - we exchange the nested if-cascade by a probabilistic iterative approach - - Kwargs - ------ - coords: HxWx3 array - Coordinates transformed into colors by this method. See - `magictex.coordinate_grid` to generate the default. - depth: int (optional) - Number of transformations applied. Higher numbers lead to more - nested patterns. If not specified, randomly sampled. - distortion: float (optional) - Distortion of patterns. Larger values indicate more distortion, - lower values tend to generate smoother patterns. If not specified, - randomly sampled. - rng: np.random.Generator - Optional random generator to draw samples from. - - Returns - ------- - colors: HxWx3 array - Three channel color image in range [0,1] - """ - rng = rng or np.random.default_rng() - if distortion is None: - distortion = rng.uniform(1, 4) - if depth is None: - depth = rng.integers(1, 5) - - H, W = coords.shape[:2] - XYZ = coords - x = np.sin((XYZ[..., 0] + XYZ[..., 1] + XYZ[..., 2]) * distortion) - y = np.cos((-XYZ[..., 0] + XYZ[..., 1] - XYZ[..., 2]) * distortion) - z = -np.cos((-XYZ[..., 0] - XYZ[..., 1] + XYZ[..., 2]) * distortion) - - if depth > 0: - x *= distortion - y *= distortion - z *= distortion - y = -np.cos(x - y + z) - y *= distortion - - xyz = [x, y, z] - fns = [np.cos, np.sin] - for _ in range(1, depth): - axis = rng.choice(3) - fn = fns[rng.choice(2)] - signs = rng.binomial(n=1, p=0.5, size=4) * 2 - 1 - - xyz[axis] = signs[-1] * fn( - signs[0] * xyz[0] + signs[1] * xyz[1] + signs[2] * xyz[2] - ) - xyz[axis] *= distortion - - x, y, z = xyz - x /= 2 * distortion - y /= 2 * distortion - z /= 2 * distortion - c = 0.5 - np.stack((x, y, z), -1) - np.clip(c, 0, 1.0) - return c \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/utility/numerical.py b/custom_nodes/comfyui-kjnodes/utility/numerical.py deleted file mode 100644 index b5b88bc63c45d63d8913e56cbd06eb7ab413fe4f..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/utility/numerical.py +++ /dev/null @@ -1,25 +0,0 @@ -from functools import reduce -from itertools import cycle -from math import factorial - -import numpy as np -import scipy.sparse as sp - - -def difference(derivative, accuracy=1): - # Central differences implemented based on the article here: - # http://web.media.mit.edu/~crtaylor/calculator.html - derivative += 1 - radius = accuracy + derivative // 2 - 1 - points = range(-radius, radius + 1) - coefficients = np.linalg.inv(np.vander(points)) - return coefficients[-derivative] * factorial(derivative - 1), points - - -def operator(shape, *differences): - # Credit to Philip Zucker for figuring out - # that kronsum's argument order is reversed. - # Without that bit of wisdom I'd have lost it. - differences = zip(shape, cycle(differences)) - factors = (sp.diags(*diff, shape=(dim,) * 2) for dim, diff in differences) - return reduce(lambda a, f: sp.kronsum(f, a, format='csc'), factors) \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/utility/utility.py b/custom_nodes/comfyui-kjnodes/utility/utility.py deleted file mode 100644 index f3b5c425922784522791e33c225c29be1e8249e0..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/utility/utility.py +++ /dev/null @@ -1,39 +0,0 @@ -import torch -import numpy as np -from PIL import Image -from typing import Union, List - -# Utility functions from mtb nodes: https://github.com/melMass/comfy_mtb -def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor: - if isinstance(image, list): - return torch.cat([pil2tensor(img) for img in image], dim=0) - - return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) - - -def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor: - if isinstance(img_np, list): - return torch.cat([np2tensor(img) for img in img_np], dim=0) - - return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0) - - -def tensor2np(tensor: torch.Tensor): - if len(tensor.shape) == 3: # Single image - return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8) - else: # Batch of images - return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor] - -def tensor2pil(image: torch.Tensor) -> List[Image.Image]: - batch_count = image.size(0) if len(image.shape) > 3 else 1 - if batch_count > 1: - out = [] - for i in range(batch_count): - out.extend(tensor2pil(image[i])) - return out - - return [ - Image.fromarray( - np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8) - ) - ] \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/green.png b/custom_nodes/comfyui-kjnodes/web/green.png deleted file mode 100644 index 900964e4b3907145fe1e75a5b58473567450e16d..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/web/green.png and /dev/null differ diff --git a/custom_nodes/comfyui-kjnodes/web/js/appearance.js b/custom_nodes/comfyui-kjnodes/web/js/appearance.js deleted file mode 100644 index d90b4aa34d4c52b22a4411194100972c83eed88d..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/appearance.js +++ /dev/null @@ -1,23 +0,0 @@ -import { app } from "../../../scripts/app.js"; - -app.registerExtension({ - name: "KJNodes.appearance", - nodeCreated(node) { - switch (node.comfyClass) { - case "INTConstant": - node.setSize([200, 58]); - node.color = "#1b4669"; - node.bgcolor = "#29699c"; - break; - case "FloatConstant": - node.setSize([200, 58]); - node.color = LGraphCanvas.node_colors.green.color; - node.bgcolor = LGraphCanvas.node_colors.green.bgcolor; - break; - case "ConditioningMultiCombine": - node.color = LGraphCanvas.node_colors.brown.color; - node.bgcolor = LGraphCanvas.node_colors.brown.bgcolor; - break; - } - } -}); diff --git a/custom_nodes/comfyui-kjnodes/web/js/browserstatus.js b/custom_nodes/comfyui-kjnodes/web/js/browserstatus.js deleted file mode 100644 index 45abafb163481d9d760c8b273aad4d2a00db1e92..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/browserstatus.js +++ /dev/null @@ -1,55 +0,0 @@ -import { api } from "../../../scripts/api.js"; -import { app } from "../../../scripts/app.js"; - -app.registerExtension({ - name: "KJNodes.browserstatus", - setup() { - if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) { - return; - } - api.addEventListener("status", ({ detail }) => { - let title = "ComfyUI"; - let favicon = "green"; - let queueRemaining = detail && detail.exec_info.queue_remaining; - - if (queueRemaining) { - favicon = "red"; - title = `00% - ${queueRemaining} | ${title}`; - } - let link = document.querySelector("link[rel~='icon']"); - if (!link) { - link = document.createElement("link"); - link.rel = "icon"; - document.head.appendChild(link); - } - link.href = new URL(`../${favicon}.png`, import.meta.url); - document.title = title; - }); - //add progress to the title - api.addEventListener("progress", ({ detail }) => { - const { value, max } = detail; - const progress = Math.floor((value / max) * 100); - let title = document.title; - - if (!isNaN(progress) && progress >= 0 && progress <= 100) { - const paddedProgress = String(progress).padStart(2, '0'); - title = `${paddedProgress}% ${title.replace(/^\d+%\s/, '')}`; - } - document.title = title; - }); - }, - init() { - if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) { - return; - } - const pythongossFeed = app.extensions.find( - (e) => e.name === 'pysssss.FaviconStatus', - ) - if (pythongossFeed) { - console.warn("KJNodes - Overriding pysssss.FaviconStatus") - pythongossFeed.setup = function() { - console.warn("Disabled by KJNodes") - }; - } - }, -}); \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/js/contextmenu.js b/custom_nodes/comfyui-kjnodes/web/js/contextmenu.js deleted file mode 100644 index 8485658ef819722280160b124a2acf39353bb96d..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/contextmenu.js +++ /dev/null @@ -1,147 +0,0 @@ -import { app } from "../../../scripts/app.js"; - -// Adds context menu entries, code partly from pyssssscustom-scripts - -function addMenuHandler(nodeType, cb) { - const getOpts = nodeType.prototype.getExtraMenuOptions; - nodeType.prototype.getExtraMenuOptions = function () { - const r = getOpts.apply(this, arguments); - cb.apply(this, arguments); - return r; - }; -} - -function addNode(name, nextTo, options) { - console.log("name:", name); - console.log("nextTo:", nextTo); - options = { side: "left", select: true, shiftY: 0, shiftX: 0, ...(options || {}) }; - const node = LiteGraph.createNode(name); - app.graph.add(node); - - node.pos = [ - options.side === "left" ? nextTo.pos[0] - (node.size[0] + options.offset): nextTo.pos[0] + nextTo.size[0] + options.offset, - - nextTo.pos[1] + options.shiftY, - ]; - if (options.select) { - app.canvas.selectNode(node, false); - } - return node; -} - -app.registerExtension({ - name: "KJNodesContextmenu", - async beforeRegisterNodeDef(nodeType, nodeData, app) { - if (nodeData.input && nodeData.input.required) { - addMenuHandler(nodeType, function (_, options) { - options.unshift( - { - content: "Add GetNode", - callback: () => {addNode("GetNode", this, { side:"left", offset: 30});} - }, - { - content: "Add SetNode", - callback: () => {addNode("SetNode", this, { side:"right", offset: 30 }); - }, - }); - }); - } - }, - async setup(app) { - const updateSlots = (value) => { - const valuesToAddToIn = ["GetNode"]; - const valuesToAddToOut = ["SetNode"]; - // Remove entries if they exist - for (const arr of Object.values(LiteGraph.slot_types_default_in)) { - for (const valueToAdd of valuesToAddToIn) { - const idx = arr.indexOf(valueToAdd); - if (idx !== -1) { - arr.splice(idx, 1); - } - } - } - - for (const arr of Object.values(LiteGraph.slot_types_default_out)) { - for (const valueToAdd of valuesToAddToOut) { - const idx = arr.indexOf(valueToAdd); - if (idx !== -1) { - arr.splice(idx, 1); - } - } - } - if (value!="disabled") { - for (const arr of Object.values(LiteGraph.slot_types_default_in)) { - for (const valueToAdd of valuesToAddToIn) { - const idx = arr.indexOf(valueToAdd); - if (idx !== -1) { - arr.splice(idx, 1); - } - if (value === "top") { - arr.unshift(valueToAdd); - } else { - arr.push(valueToAdd); - } - } - } - - for (const arr of Object.values(LiteGraph.slot_types_default_out)) { - for (const valueToAdd of valuesToAddToOut) { - const idx = arr.indexOf(valueToAdd); - if (idx !== -1) { - arr.splice(idx, 1); - } - if (value === "top") { - arr.unshift(valueToAdd); - } else { - arr.push(valueToAdd); - } - } - } - } - }; - - app.ui.settings.addSetting({ - id: "KJNodes.SetGetMenu", - name: "KJNodes: Make Set/Get -nodes defaults", - tooltip: 'Adds Set/Get nodes to the top or bottom of the list of available node suggestions.', - options: ['disabled', 'top', 'bottom'], - defaultValue: 'disabled', - type: "combo", - onChange: updateSlots, - - }); - app.ui.settings.addSetting({ - id: "KJNodes.MiddleClickDefault", - name: "KJNodes: Middle click default node adding", - defaultValue: false, - type: "boolean", - onChange: (value) => { - LiteGraph.middle_click_slot_add_default_node = value; - }, - }); - app.ui.settings.addSetting({ - id: "KJNodes.nodeAutoColor", - name: "KJNodes: Automatically set node colors", - type: "boolean", - defaultValue: true, - }); - app.ui.settings.addSetting({ - id: "KJNodes.helpPopup", - name: "KJNodes: Help popups", - defaultValue: true, - type: "boolean", - }); - app.ui.settings.addSetting({ - id: "KJNodes.disablePrefix", - name: "KJNodes: Disable automatic Set_ and Get_ prefix", - defaultValue: true, - type: "boolean", - }); - app.ui.settings.addSetting({ - id: "KJNodes.browserStatus", - name: "KJNodes: 🟢 Stoplight browser status icon 🔴", - defaultValue: false, - type: "boolean", - }); -} -}); diff --git a/custom_nodes/comfyui-kjnodes/web/js/fast_preview.js b/custom_nodes/comfyui-kjnodes/web/js/fast_preview.js deleted file mode 100644 index 822c1f745ba4e895364664f7dbed3d225f0430b2..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/fast_preview.js +++ /dev/null @@ -1,95 +0,0 @@ -import { app } from '../../../scripts/app.js' - -//from melmass -export function makeUUID() { - let dt = new Date().getTime() - const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { - const r = ((dt + Math.random() * 16) % 16) | 0 - dt = Math.floor(dt / 16) - return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) - }) - return uuid -} - -function chainCallback(object, property, callback) { - if (object == undefined) { - //This should not happen. - console.error("Tried to add callback to non-existant object") - return; - } - if (property in object) { - const callback_orig = object[property] - object[property] = function () { - const r = callback_orig.apply(this, arguments); - callback.apply(this, arguments); - return r - }; - } else { - object[property] = callback; - } -} -app.registerExtension({ - name: 'KJNodes.FastPreview', - - async beforeRegisterNodeDef(nodeType, nodeData) { - if (nodeData?.name === 'FastPreview') { - chainCallback(nodeType.prototype, "onNodeCreated", function () { - - var element = document.createElement("div"); - this.uuid = makeUUID() - element.id = `fast-preview-${this.uuid}` - - this.previewWidget = this.addDOMWidget(nodeData.name, "FastPreviewWidget", element, { - serialize: false, - hideOnZoom: false, - }); - - this.previewer = new Previewer(this); - - this.setSize([550, 550]); - this.resizable = false; - this.previewWidget.parentEl = document.createElement("div"); - this.previewWidget.parentEl.className = "fast-preview"; - this.previewWidget.parentEl.id = `fast-preview-${this.uuid}` - element.appendChild(this.previewWidget.parentEl); - - chainCallback(this, "onExecuted", function (message) { - let bg_image = message["bg_image"]; - this.properties.imgData = { - name: "bg_image", - base64: bg_image - }; - this.previewer.refreshBackgroundImage(this); - }); - - - }); // onAfterGraphConfigured - }//node created - } //before register -})//register - -class Previewer { - constructor(context) { - this.node = context; - this.previousWidth = null; - this.previousHeight = null; - } - refreshBackgroundImage = () => { - const imgData = this.node?.properties?.imgData; - if (imgData?.base64) { - const base64String = imgData.base64; - const imageUrl = `data:${imgData.type};base64,${base64String}`; - const img = new Image(); - img.src = imageUrl; - img.onload = () => { - const { width, height } = img; - if (width !== this.previousWidth || height !== this.previousHeight) { - this.node.setSize([width, height]); - this.previousWidth = width; - this.previousHeight = height; - } - this.node.previewWidget.element.style.backgroundImage = `url(${imageUrl})`; - }; - } - }; - } \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/js/help_popup.js b/custom_nodes/comfyui-kjnodes/web/js/help_popup.js deleted file mode 100644 index c4734befd7726d6190b246e66140e99dfb9f7e65..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/help_popup.js +++ /dev/null @@ -1,326 +0,0 @@ -import { app } from "../../../scripts/app.js"; - -// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/ -export const loadScript = ( - FILE_URL, - async = true, - type = 'text/javascript', -) => { - return new Promise((resolve, reject) => { - try { - // Check if the script already exists - const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) - if (existingScript) { - resolve({ status: true, message: 'Script already loaded' }) - return - } - - const scriptEle = document.createElement('script') - scriptEle.type = type - scriptEle.async = async - scriptEle.src = FILE_URL - - scriptEle.addEventListener('load', (ev) => { - resolve({ status: true }) - }) - - scriptEle.addEventListener('error', (ev) => { - reject({ - status: false, - message: `Failed to load the script ${FILE_URL}`, - }) - }) - - document.body.appendChild(scriptEle) - } catch (error) { - reject(error) - } - }) -} - -loadScript('kjweb_async/marked.min.js').catch((e) => { - console.log(e) -}) -loadScript('kjweb_async/purify.min.js').catch((e) => { - console.log(e) -}) - -const categories = ["KJNodes", "SUPIR", "VoiceCraft", "Marigold", "IC-Light", "WanVideoWrapper"]; -app.registerExtension({ - name: "KJNodes.HelpPopup", - async beforeRegisterNodeDef(nodeType, nodeData) { - - if (app.ui.settings.getSettingValue("KJNodes.helpPopup") === false) { - return; - } - try { - categories.forEach(category => { - if (nodeData?.category?.startsWith(category)) { - addDocumentation(nodeData, nodeType); - } - else return - }); - } catch (error) { - console.error("Error in registering KJNodes.HelpPopup", error); - } - }, -}); - -const create_documentation_stylesheet = () => { - const tag = 'kj-documentation-stylesheet' - - let styleTag = document.head.querySelector(tag) - - if (!styleTag) { - styleTag = document.createElement('style') - styleTag.type = 'text/css' - styleTag.id = tag - styleTag.innerHTML = ` - .kj-documentation-popup { - background: var(--comfy-menu-bg); - position: absolute; - color: var(--fg-color); - font: 12px monospace; - line-height: 1.5em; - padding: 10px; - border-radius: 10px; - border-style: solid; - border-width: medium; - border-color: var(--border-color); - z-index: 5; - overflow: hidden; - } - .content-wrapper { - overflow: auto; - max-height: 100%; - /* Scrollbar styling for Chrome */ - &::-webkit-scrollbar { - width: 6px; - } - &::-webkit-scrollbar-track { - background: var(--bg-color); - } - &::-webkit-scrollbar-thumb { - background-color: var(--fg-color); - border-radius: 6px; - border: 3px solid var(--bg-color); - } - - /* Scrollbar styling for Firefox */ - scrollbar-width: thin; - scrollbar-color: var(--fg-color) var(--bg-color); - a { - color: yellow; - } - a:visited { - color: orange; - } - a:hover { - color: red; - } - } - ` - document.head.appendChild(styleTag) - } - } - - /** Add documentation widget to the selected node */ - export const addDocumentation = ( - nodeData, - nodeType, - opts = { icon_size: 14, icon_margin: 4 },) => { - - opts = opts || {} - const iconSize = opts.icon_size ? opts.icon_size : 14 - const iconMargin = opts.icon_margin ? opts.icon_margin : 4 - let docElement = null - let contentWrapper = null - //if no description in the node python code, don't do anything - if (!nodeData.description) { - return - } - - const drawFg = nodeType.prototype.onDrawForeground - nodeType.prototype.onDrawForeground = function (ctx) { - const r = drawFg ? drawFg.apply(this, arguments) : undefined - if (this.flags.collapsed) return r - - // icon position - const x = this.size[0] - iconSize - iconMargin - - // create the popup - if (this.show_doc && docElement === null) { - docElement = document.createElement('div') - contentWrapper = document.createElement('div'); - docElement.appendChild(contentWrapper); - - create_documentation_stylesheet() - contentWrapper.classList.add('content-wrapper'); - docElement.classList.add('kj-documentation-popup') - - //parse the string from the python node code to html with marked, and sanitize the html with DOMPurify - contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(nodeData.description,)) - - // resize handle - const resizeHandle = document.createElement('div'); - resizeHandle.style.width = '0'; - resizeHandle.style.height = '0'; - resizeHandle.style.position = 'absolute'; - resizeHandle.style.bottom = '0'; - resizeHandle.style.right = '0'; - resizeHandle.style.cursor = 'se-resize'; - - // Add pseudo-elements to create a triangle shape - const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim(); - resizeHandle.style.borderTop = '10px solid transparent'; - resizeHandle.style.borderLeft = '10px solid transparent'; - resizeHandle.style.borderBottom = `10px solid ${borderColor}`; - resizeHandle.style.borderRight = `10px solid ${borderColor}`; - - docElement.appendChild(resizeHandle) - let isResizing = false - let startX, startY, startWidth, startHeight - - resizeHandle.addEventListener('mousedown', function (e) { - e.preventDefault(); - e.stopPropagation(); - isResizing = true; - startX = e.clientX; - startY = e.clientY; - startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10); - startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10); - }, - { signal: this.docCtrl.signal }, - ); - - // close button - const closeButton = document.createElement('div'); - closeButton.textContent = '❌'; - closeButton.style.position = 'absolute'; - closeButton.style.top = '0'; - closeButton.style.right = '0'; - closeButton.style.cursor = 'pointer'; - closeButton.style.padding = '5px'; - closeButton.style.color = 'red'; - closeButton.style.fontSize = '12px'; - - docElement.appendChild(closeButton) - - closeButton.addEventListener('mousedown', (e) => { - e.stopPropagation(); - this.show_doc = !this.show_doc - docElement.parentNode.removeChild(docElement) - docElement = null - if (contentWrapper) { - contentWrapper.remove() - contentWrapper = null - } - }, - { signal: this.docCtrl.signal }, - ); - - document.addEventListener('mousemove', function (e) { - if (!isResizing) return; - const scale = app.canvas.ds.scale; - const newWidth = startWidth + (e.clientX - startX) / scale; - const newHeight = startHeight + (e.clientY - startY) / scale;; - docElement.style.width = `${newWidth}px`; - docElement.style.height = `${newHeight}px`; - }, - { signal: this.docCtrl.signal }, - ); - - document.addEventListener('mouseup', function () { - isResizing = false - }, - { signal: this.docCtrl.signal }, - ) - - document.body.appendChild(docElement) - } - // close the popup - else if (!this.show_doc && docElement !== null) { - docElement.parentNode.removeChild(docElement) - docElement = null - } - // update position of the popup - if (this.show_doc && docElement !== null) { - const rect = ctx.canvas.getBoundingClientRect() - const scaleX = rect.width / ctx.canvas.width - const scaleY = rect.height / ctx.canvas.height - - const transform = new DOMMatrix() - .scaleSelf(scaleX, scaleY) - .multiplySelf(ctx.getTransform()) - .translateSelf(this.size[0] * scaleX * Math.max(1.0,window.devicePixelRatio) , 0) - .translateSelf(10, -32) - - const scale = new DOMMatrix() - .scaleSelf(transform.a, transform.d); - const bcr = app.canvas.canvas.getBoundingClientRect() - - const styleObject = { - transformOrigin: '0 0', - transform: scale, - left: `${transform.a + bcr.x + transform.e}px`, - top: `${transform.d + bcr.y + transform.f}px`, - }; - Object.assign(docElement.style, styleObject); - } - - ctx.save() - ctx.translate(x - 2, iconSize - 34) - ctx.scale(iconSize / 32, iconSize / 32) - ctx.strokeStyle = 'rgba(255,255,255,0.3)' - ctx.lineCap = 'round' - ctx.lineJoin = 'round' - ctx.lineWidth = 2.4 - ctx.font = 'bold 36px monospace' - ctx.fillStyle = 'orange'; - ctx.fillText('?', 0, 24) - ctx.restore() - return r - } - // handle clicking of the icon - const mouseDown = nodeType.prototype.onMouseDown - nodeType.prototype.onMouseDown = function (e, localPos, canvas) { - const r = mouseDown ? mouseDown.apply(this, arguments) : undefined - const iconX = this.size[0] - iconSize - iconMargin - const iconY = iconSize - 34 - if ( - localPos[0] > iconX && - localPos[0] < iconX + iconSize && - localPos[1] > iconY && - localPos[1] < iconY + iconSize - ) { - if (this.show_doc === undefined) { - this.show_doc = true - } else { - this.show_doc = !this.show_doc - } - if (this.show_doc) { - this.docCtrl = new AbortController() - } else { - this.docCtrl.abort() - } - return true; - } - return r; - } - const onRem = nodeType.prototype.onRemoved - - nodeType.prototype.onRemoved = function () { - const r = onRem ? onRem.apply(this, []) : undefined - - if (docElement) { - docElement.remove() - docElement = null - } - - if (contentWrapper) { - contentWrapper.remove() - contentWrapper = null - } - return r - } -} \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/js/jsnodes.js b/custom_nodes/comfyui-kjnodes/web/js/jsnodes.js deleted file mode 100644 index 2e676935b83054b693fb6394b54e33a22ed351c2..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/jsnodes.js +++ /dev/null @@ -1,413 +0,0 @@ -import { app } from "../../../scripts/app.js"; -import { applyTextReplacements } from "../../../scripts/utils.js"; - -app.registerExtension({ - name: "KJNodes.jsnodes", - async beforeRegisterNodeDef(nodeType, nodeData, app) { - if(!nodeData?.category?.startsWith("KJNodes")) { - return; - } - switch (nodeData.name) { - case "ConditioningMultiCombine": - nodeType.prototype.onNodeCreated = function () { - this._type = "CONDITIONING" - this.inputs_offset = nodeData.name.includes("selective")?1:0 - this.addWidget("button", "Update inputs", null, () => { - if (!this.inputs) { - this.inputs = []; - } - const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; - const num_inputs = this.inputs.filter(input => input.type === this._type).length - if(target_number_of_inputs===num_inputs)return; // already set, do nothing - - if(target_number_of_inputs < num_inputs){ - const inputs_to_remove = num_inputs - target_number_of_inputs; - for(let i = 0; i < inputs_to_remove; i++) { - this.removeInput(this.inputs.length - 1); - } - } - else{ - for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) - this.addInput(`conditioning_${i}`, this._type) - } - }); - } - break; - case "ImageBatchMulti": - case "ImageAddMulti": - case "ImageConcatMulti": - case "CrossFadeImagesMulti": - case "TransitionImagesMulti": - nodeType.prototype.onNodeCreated = function () { - this._type = "IMAGE" - this.addWidget("button", "Update inputs", null, () => { - if (!this.inputs) { - this.inputs = []; - } - const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; - const num_inputs = this.inputs.filter(input => input.type === this._type).length - if(target_number_of_inputs===num_inputs)return; // already set, do nothing - - if(target_number_of_inputs < num_inputs){ - const inputs_to_remove = num_inputs - target_number_of_inputs; - for(let i = 0; i < inputs_to_remove; i++) { - this.removeInput(this.inputs.length - 1); - } - } - else{ - for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) - this.addInput(`image_${i}`, this._type, {shape: 7}); - } - - }); - } - break; - case "MaskBatchMulti": - nodeType.prototype.onNodeCreated = function () { - this._type = "MASK" - this.addWidget("button", "Update inputs", null, () => { - if (!this.inputs) { - this.inputs = []; - } - const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; - const num_inputs = this.inputs.filter(input => input.type === this._type).length - if(target_number_of_inputs===num_inputs)return; // already set, do nothing - - if(target_number_of_inputs < num_inputs){ - const inputs_to_remove = num_inputs - target_number_of_inputs; - for(let i = 0; i < inputs_to_remove; i++) { - this.removeInput(this.inputs.length - 1); - } - } - else{ - for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) - this.addInput(`mask_${i}`, this._type) - } - }); - } - break; - - case "FluxBlockLoraSelect": - case "HunyuanVideoBlockLoraSelect": - case "Wan21BlockLoraSelect": - nodeType.prototype.onNodeCreated = function () { - this.addWidget("button", "Set all", null, () => { - const userInput = prompt("Enter the values to set for widgets (e.g., s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0):", ""); - if (userInput) { - const regex = /([sd])?(\d+(?:,\d+|-?\d+)*?)?=(\d+(\.\d+)?)/; - const match = userInput.match(regex); - if (match) { - const type = match[1]; - const indicesPart = match[2]; - const value = parseFloat(match[3]); - - let targetWidgets = []; - if (type === 's') { - targetWidgets = this.widgets.filter(widget => widget.name.includes("single")); - } else if (type === 'd') { - targetWidgets = this.widgets.filter(widget => widget.name.includes("double")); - } else { - targetWidgets = this.widgets; // No type specified, all widgets - } - - if (indicesPart) { - const indices = indicesPart.split(',').flatMap(part => { - if (part.includes('-')) { - const [start, end] = part.split('-').map(Number); - return Array.from({ length: end - start + 1 }, (_, i) => start + i); - } - return Number(part); - }); - - for (const index of indices) { - if (index < targetWidgets.length) { - targetWidgets[index].value = value; - } - } - } else { - // No indices provided, set value for all target widgets - for (const widget of targetWidgets) { - widget.value = value; - } - } - } else if (!isNaN(parseFloat(userInput))) { - // Single value provided, set it for all widgets - const value = parseFloat(userInput); - for (const widget of this.widgets) { - widget.value = value; - } - } else { - alert("Invalid input format. Please use the format s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0"); - } - } else { - alert("Invalid input. Please enter a value."); - } - }); - }; - break; - - case "GetMaskSizeAndCount": - const onGetMaskSizeConnectInput = nodeType.prototype.onConnectInput; - nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { - const v = onGetMaskSizeConnectInput? onGetMaskSizeConnectInput.apply(this, arguments): undefined - this.outputs[1]["label"] = "width" - this.outputs[2]["label"] = "height" - this.outputs[3]["label"] = "count" - return v; - } - const onGetMaskSizeExecuted = nodeType.prototype.onAfterExecuteNode; - nodeType.prototype.onExecuted = function(message) { - const r = onGetMaskSizeExecuted? onGetMaskSizeExecuted.apply(this,arguments): undefined - let values = message["text"].toString().split('x').map(Number); - this.outputs[1]["label"] = values[1] + " width" - this.outputs[2]["label"] = values[2] + " height" - this.outputs[3]["label"] = values[0] + " count" - return r - } - break; - - case "GetImageSizeAndCount": - const onGetImageSizeConnectInput = nodeType.prototype.onConnectInput; - nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { - console.log(this) - const v = onGetImageSizeConnectInput? onGetImageSizeConnectInput.apply(this, arguments): undefined - //console.log(this) - this.outputs[1]["label"] = "width" - this.outputs[2]["label"] = "height" - this.outputs[3]["label"] = "count" - return v; - } - //const onGetImageSizeExecuted = nodeType.prototype.onExecuted; - const onGetImageSizeExecuted = nodeType.prototype.onAfterExecuteNode; - nodeType.prototype.onExecuted = function(message) { - console.log(this) - const r = onGetImageSizeExecuted? onGetImageSizeExecuted.apply(this,arguments): undefined - let values = message["text"].toString().split('x').map(Number); - console.log(values) - this.outputs[1]["label"] = values[1] + " width" - this.outputs[2]["label"] = values[2] + " height" - this.outputs[3]["label"] = values[0] + " count" - return r - } - break; - - case "GetLatentSizeAndCount": - const onGetLatentConnectInput = nodeType.prototype.onConnectInput; - nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { - console.log(this) - const v = onGetLatentConnectInput? onGetLatentConnectInput.apply(this, arguments): undefined - //console.log(this) - this.outputs[1]["label"] = "width" - this.outputs[2]["label"] = "height" - this.outputs[3]["label"] = "count" - return v; - } - //const onGetImageSizeExecuted = nodeType.prototype.onExecuted; - const onGetLatentSizeExecuted = nodeType.prototype.onAfterExecuteNode; - nodeType.prototype.onExecuted = function(message) { - console.log(this) - const r = onGetLatentSizeExecuted? onGetLatentSizeExecuted.apply(this,arguments): undefined - let values = message["text"].toString().split('x').map(Number); - console.log(values) - this.outputs[1]["label"] = values[0] + " batch" - this.outputs[2]["label"] = values[1] + " channels" - this.outputs[3]["label"] = values[2] + " frames" - this.outputs[4]["label"] = values[3] + " height" - this.outputs[5]["label"] = values[4] + " width" - return r - } - break; - - case "PreviewAnimation": - const onPreviewAnimationConnectInput = nodeType.prototype.onConnectInput; - nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { - const v = onPreviewAnimationConnectInput? onPreviewAnimationConnectInput.apply(this, arguments): undefined - this.title = "Preview Animation" - return v; - } - const onPreviewAnimationExecuted = nodeType.prototype.onAfterExecuteNode; - nodeType.prototype.onExecuted = function(message) { - const r = onPreviewAnimationExecuted? onPreviewAnimationExecuted.apply(this,arguments): undefined - let values = message["text"].toString(); - this.title = "Preview Animation " + values - return r - } - break; - - case "VRAM_Debug": - const onVRAM_DebugConnectInput = nodeType.prototype.onConnectInput; - nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) { - const v = onVRAM_DebugConnectInput? onVRAM_DebugConnectInput.apply(this, arguments): undefined - this.outputs[3]["label"] = "freemem_before" - this.outputs[4]["label"] = "freemem_after" - return v; - } - const onVRAM_DebugExecuted = nodeType.prototype.onAfterExecuteNode; - nodeType.prototype.onExecuted = function(message) { - const r = onVRAM_DebugExecuted? onVRAM_DebugExecuted.apply(this,arguments): undefined - let values = message["text"].toString().split('x'); - this.outputs[3]["label"] = values[0] + " freemem_before" - this.outputs[4]["label"] = values[1] + " freemem_after" - return r - } - break; - - case "JoinStringMulti": - const originalOnNodeCreated = nodeType.prototype.onNodeCreated || function() {}; - nodeType.prototype.onNodeCreated = function () { - originalOnNodeCreated.apply(this, arguments); - - this._type = "STRING"; - this.addWidget("button", "Update inputs", null, () => { - if (!this.inputs) { - this.inputs = []; - } - const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"]; - const num_inputs = this.inputs.filter(input => input.name && input.name.toLowerCase().includes("string_")).length - if (target_number_of_inputs === num_inputs) return; // already set, do nothing - - if(target_number_of_inputs < num_inputs){ - const inputs_to_remove = num_inputs - target_number_of_inputs; - for(let i = 0; i < inputs_to_remove; i++) { - this.removeInput(this.inputs.length - 1); - } - } - else{ - for(let i = num_inputs+1; i <= target_number_of_inputs; ++i) - this.addInput(`string_${i}`, this._type, {shape: 7}); - } - }); - } - break; - case "SoundReactive": - nodeType.prototype.onNodeCreated = function () { - let audioContext; - let microphoneStream; - let animationFrameId; - let analyser; - let dataArray; - let startRangeHz; - let endRangeHz; - let smoothingFactor = 0.5; - let smoothedSoundLevel = 0; - - // Function to update the widget value in real-time - const updateWidgetValueInRealTime = () => { - // Ensure analyser and dataArray are defined before using them - if (analyser && dataArray) { - analyser.getByteFrequencyData(dataArray); - - const startRangeHzWidget = this.widgets.find(w => w.name === "start_range_hz"); - if (startRangeHzWidget) startRangeHz = startRangeHzWidget.value; - const endRangeHzWidget = this.widgets.find(w => w.name === "end_range_hz"); - if (endRangeHzWidget) endRangeHz = endRangeHzWidget.value; - const smoothingFactorWidget = this.widgets.find(w => w.name === "smoothing_factor"); - if (smoothingFactorWidget) smoothingFactor = smoothingFactorWidget.value; - - // Calculate frequency bin width (frequency resolution) - const frequencyBinWidth = audioContext.sampleRate / analyser.fftSize; - // Convert the widget values from Hz to indices - const startRangeIndex = Math.floor(startRangeHz / frequencyBinWidth); - const endRangeIndex = Math.floor(endRangeHz / frequencyBinWidth); - - // Function to calculate the average value for a frequency range - const calculateAverage = (start, end) => { - const sum = dataArray.slice(start, end).reduce((acc, val) => acc + val, 0); - const average = sum / (end - start); - - // Apply exponential moving average smoothing - smoothedSoundLevel = (average * (1 - smoothingFactor)) + (smoothedSoundLevel * smoothingFactor); - return smoothedSoundLevel; - }; - // Calculate the average levels for each frequency range - const soundLevel = calculateAverage(startRangeIndex, endRangeIndex); - - // Update the widget values - - const lowLevelWidget = this.widgets.find(w => w.name === "sound_level"); - if (lowLevelWidget) lowLevelWidget.value = soundLevel; - - animationFrameId = requestAnimationFrame(updateWidgetValueInRealTime); - } - }; - - // Function to start capturing audio from the microphone - const startMicrophoneCapture = () => { - // Only create the audio context and analyser once - if (!audioContext) { - audioContext = new (window.AudioContext || window.webkitAudioContext)(); - // Access the sample rate of the audio context - console.log(`Sample rate: ${audioContext.sampleRate}Hz`); - analyser = audioContext.createAnalyser(); - analyser.fftSize = 2048; - dataArray = new Uint8Array(analyser.frequencyBinCount); - // Get the range values from widgets (assumed to be in Hz) - const lowRangeWidget = this.widgets.find(w => w.name === "low_range_hz"); - if (lowRangeWidget) startRangeHz = lowRangeWidget.value; - - const midRangeWidget = this.widgets.find(w => w.name === "mid_range_hz"); - if (midRangeWidget) endRangeHz = midRangeWidget.value; - } - - navigator.mediaDevices.getUserMedia({ audio: true }).then(stream => { - microphoneStream = stream; - const microphone = audioContext.createMediaStreamSource(stream); - microphone.connect(analyser); - updateWidgetValueInRealTime(); - }).catch(error => { - console.error('Access to microphone was denied or an error occurred:', error); - }); - }; - - // Function to stop capturing audio from the microphone - const stopMicrophoneCapture = () => { - if (animationFrameId) { - cancelAnimationFrame(animationFrameId); - } - if (microphoneStream) { - microphoneStream.getTracks().forEach(track => track.stop()); - } - if (audioContext) { - audioContext.close(); - // Reset audioContext to ensure it can be created again when starting - audioContext = null; - } - }; - - // Add start button - this.addWidget("button", "Start mic capture", null, startMicrophoneCapture); - - // Add stop button - this.addWidget("button", "Stop mic capture", null, stopMicrophoneCapture); - }; - break; - case "SaveImageKJ": - const onNodeCreated = nodeType.prototype.onNodeCreated; - nodeType.prototype.onNodeCreated = function() { - const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0; - const widget = this.widgets.find((w) => w.name === "filename_prefix"); - widget.serializeValue = () => { - return applyTextReplacements(app, widget.value); - }; - return r; - }; - break; - - } - - }, - async setup() { - // to keep Set/Get node virtual connections visible when offscreen - const originalComputeVisibleNodes = LGraphCanvas.prototype.computeVisibleNodes; - LGraphCanvas.prototype.computeVisibleNodes = function () { - const visibleNodesSet = new Set(originalComputeVisibleNodes.apply(this, arguments)); - for (const node of this.graph._nodes) { - if ((node.type === "SetNode" || node.type === "GetNode") && node.drawConnection) { - visibleNodesSet.add(node); - } - } - return Array.from(visibleNodesSet); - }; - - } -}); \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/js/point_editor.js b/custom_nodes/comfyui-kjnodes/web/js/point_editor.js deleted file mode 100644 index 6baa10830d5cba754eaa440685fdabc0c9d9f8e8..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/point_editor.js +++ /dev/null @@ -1,734 +0,0 @@ -import { app } from '../../../scripts/app.js' - -//from melmass -export function makeUUID() { - let dt = new Date().getTime() - const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { - const r = ((dt + Math.random() * 16) % 16) | 0 - dt = Math.floor(dt / 16) - return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) - }) - return uuid -} - -export const loadScript = ( - FILE_URL, - async = true, - type = 'text/javascript', -) => { - return new Promise((resolve, reject) => { - try { - // Check if the script already exists - const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) - if (existingScript) { - resolve({ status: true, message: 'Script already loaded' }) - return - } - - const scriptEle = document.createElement('script') - scriptEle.type = type - scriptEle.async = async - scriptEle.src = FILE_URL - - scriptEle.addEventListener('load', (ev) => { - resolve({ status: true }) - }) - - scriptEle.addEventListener('error', (ev) => { - reject({ - status: false, - message: `Failed to load the script ${FILE_URL}`, - }) - }) - - document.body.appendChild(scriptEle) - } catch (error) { - reject(error) - } - }) -} -const create_documentation_stylesheet = () => { - const tag = 'kj-pointseditor-stylesheet' - - let styleTag = document.head.querySelector(tag) - - if (!styleTag) { - styleTag = document.createElement('style') - styleTag.type = 'text/css' - styleTag.id = tag - styleTag.innerHTML = ` - .points-editor { - - position: absolute; - - font: 12px monospace; - line-height: 1.5em; - padding: 10px; - z-index: 0; - overflow: hidden; - } - ` - document.head.appendChild(styleTag) - } -} - -loadScript('kjweb_async/svg-path-properties.min.js').catch((e) => { - console.log(e) -}) -loadScript('kjweb_async/protovis.min.js').catch((e) => { - console.log(e) -}) -create_documentation_stylesheet() - -function chainCallback(object, property, callback) { - if (object == undefined) { - //This should not happen. - console.error("Tried to add callback to non-existant object") - return; - } - if (property in object) { - const callback_orig = object[property] - object[property] = function () { - const r = callback_orig.apply(this, arguments); - callback.apply(this, arguments); - return r - }; - } else { - object[property] = callback; - } -} -app.registerExtension({ - name: 'KJNodes.PointEditor', - - async beforeRegisterNodeDef(nodeType, nodeData) { - if (nodeData?.name === 'PointsEditor') { - chainCallback(nodeType.prototype, "onNodeCreated", function () { - - hideWidgetForGood(this, this.widgets.find(w => w.name === "coordinates")) - hideWidgetForGood(this, this.widgets.find(w => w.name === "neg_coordinates")) - hideWidgetForGood(this, this.widgets.find(w => w.name === "bboxes")) - - var element = document.createElement("div"); - this.uuid = makeUUID() - element.id = `points-editor-${this.uuid}` - - this.previewMediaType = 'image' - - this.pointsEditor = this.addDOMWidget(nodeData.name, "PointsEditorWidget", element, { - serialize: false, - hideOnZoom: false, - }); - - // context menu - this.contextMenu = document.createElement("div"); - this.contextMenu.id = "context-menu"; - this.contextMenu.style.display = "none"; - this.contextMenu.style.position = "absolute"; - this.contextMenu.style.backgroundColor = "#202020"; - this.contextMenu.style.minWidth = "100px"; - this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)"; - this.contextMenu.style.zIndex = "100"; - this.contextMenu.style.padding = "5px"; - - function styleMenuItem(menuItem) { - menuItem.style.display = "block"; - menuItem.style.padding = "5px"; - menuItem.style.color = "#FFF"; - menuItem.style.fontFamily = "Arial, sans-serif"; - menuItem.style.fontSize = "16px"; - menuItem.style.textDecoration = "none"; - menuItem.style.marginBottom = "5px"; - } - function createMenuItem(id, textContent) { - let menuItem = document.createElement("a"); - menuItem.href = "#"; - menuItem.id = `menu-item-${id}`; - menuItem.textContent = textContent; - styleMenuItem(menuItem); - return menuItem; - } - - // Create an array of menu items using the createMenuItem function - this.menuItems = [ - createMenuItem(0, "Load Image"), - createMenuItem(1, "Clear Image"), - ]; - - // Add mouseover and mouseout event listeners to each menu item for styling - this.menuItems.forEach(menuItem => { - menuItem.addEventListener('mouseover', function () { - this.style.backgroundColor = "gray"; - }); - - menuItem.addEventListener('mouseout', function () { - this.style.backgroundColor = "#202020"; - }); - }); - - // Append each menu item to the context menu - this.menuItems.forEach(menuItem => { - this.contextMenu.appendChild(menuItem); - }); - - document.body.appendChild(this.contextMenu); - - this.addWidget("button", "New canvas", null, () => { - if (!this.properties || !("points" in this.properties)) { - this.editor = new PointsEditor(this); - this.addProperty("points", this.constructor.type, "string"); - this.addProperty("neg_points", this.constructor.type, "string"); - - } - else { - this.editor = new PointsEditor(this, true); - } - }); - - this.setSize([550, 550]); - this.resizable = false; - this.pointsEditor.parentEl = document.createElement("div"); - this.pointsEditor.parentEl.className = "points-editor"; - this.pointsEditor.parentEl.id = `points-editor-${this.uuid}` - element.appendChild(this.pointsEditor.parentEl); - - chainCallback(this, "onConfigure", function () { - try { - this.editor = new PointsEditor(this); - } catch (error) { - console.error("An error occurred while configuring the editor:", error); - } - }); - chainCallback(this, "onExecuted", function (message) { - let bg_image = message["bg_image"]; - this.properties.imgData = { - name: "bg_image", - base64: bg_image - }; - this.editor.refreshBackgroundImage(this); - }); - - }); // onAfterGraphConfigured - }//node created - } //before register -})//register - -class PointsEditor { - constructor(context, reset = false) { - this.node = context; - this.reset = reset; - const self = this; // Keep a reference to the main class context - - console.log("creatingPointEditor") - - this.node.pasteFile = (file) => { - if (file.type.startsWith("image/")) { - this.handleImageFile(file); - return true; - } - return false; - }; - - this.node.onDragOver = function (e) { - if (e.dataTransfer && e.dataTransfer.items) { - return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/")); - } - return false; - }; - - // On drop upload files - this.node.onDragDrop = (e) => { - console.log("onDragDrop called"); - let handled = false; - for (const file of e.dataTransfer.files) { - if (file.type.startsWith("image/")) { - this.handleImageFile(file); - handled = true; - } - } - return handled; - }; - - // context menu - this.createContextMenu(); - - if (reset && context.pointsEditor.element) { - context.pointsEditor.element.innerHTML = ''; // Clear the container - } - this.pos_coordWidget = context.widgets.find(w => w.name === "coordinates"); - this.neg_coordWidget = context.widgets.find(w => w.name === "neg_coordinates"); - this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store"); - this.widthWidget = context.widgets.find(w => w.name === "width"); - this.heightWidget = context.widgets.find(w => w.name === "height"); - this.bboxStoreWidget = context.widgets.find(w => w.name === "bbox_store"); - this.bboxWidget = context.widgets.find(w => w.name === "bboxes"); - - //widget callbacks - this.widthWidget.callback = () => { - this.width = this.widthWidget.value; - if (this.width > 256) { - context.setSize([this.width + 45, context.size[1]]); - } - this.vis.width(this.width); - this.updateData(); - } - this.heightWidget.callback = () => { - this.height = this.heightWidget.value - this.vis.height(this.height) - context.setSize([context.size[0], this.height + 300]); - this.updateData(); - } - this.pointsStoreWidget.callback = () => { - this.points = JSON.parse(pointsStoreWidget.value).positive; - this.neg_points = JSON.parse(pointsStoreWidget.value).negative; - this.updateData(); - } - this.bboxStoreWidget.callback = () => { - this.bbox = JSON.parse(bboxStoreWidget.value) - this.updateData(); - } - - this.width = this.widthWidget.value; - this.height = this.heightWidget.value; - var i = 3; - this.points = []; - this.neg_points = []; - this.bbox = [{}]; - var drawing = false; - - // Initialize or reset points array - if (!reset && this.pointsStoreWidget.value != "") { - this.points = JSON.parse(this.pointsStoreWidget.value).positive; - this.neg_points = JSON.parse(this.pointsStoreWidget.value).negative; - this.bbox = JSON.parse(this.bboxStoreWidget.value); - console.log(this.bbox) - } else { - this.points = [ - { - x: this.width / 2, // Middle point horizontally centered - y: this.height / 2 // Middle point vertically centered - } - ]; - this.neg_points = [ - { - x: 0, // Middle point horizontally centered - y: 0 // Middle point vertically centered - } - ]; - const combinedPoints = { - positive: this.points, - negative: this.neg_points, - }; - this.pointsStoreWidget.value = JSON.stringify(combinedPoints); - this.bboxStoreWidget.value = JSON.stringify(this.bbox); - } - - //create main canvas panel - this.vis = new pv.Panel() - .width(this.width) - .height(this.height) - .fillStyle("#222") - .strokeStyle("gray") - .lineWidth(2) - .antialias(false) - .margin(10) - .event("mousedown", function () { - if (pv.event.shiftKey && pv.event.button === 2) { // Use pv.event to access the event object - let scaledMouse = { - x: this.mouse().x / app.canvas.ds.scale, - y: this.mouse().y / app.canvas.ds.scale - }; - i = self.neg_points.push(scaledMouse) - 1; - self.updateData(); - return this; - } - else if (pv.event.shiftKey) { - let scaledMouse = { - x: this.mouse().x / app.canvas.ds.scale, - y: this.mouse().y / app.canvas.ds.scale - }; - i = self.points.push(scaledMouse) - 1; - self.updateData(); - return this; - } - else if (pv.event.ctrlKey) { - console.log("start drawing at " + this.mouse().x / app.canvas.ds.scale + ", " + this.mouse().y / app.canvas.ds.scale); - drawing = true; - self.bbox[0].startX = this.mouse().x / app.canvas.ds.scale; - self.bbox[0].startY = this.mouse().y / app.canvas.ds.scale; - } - else if (pv.event.button === 2) { - self.node.contextMenu.style.display = 'block'; - self.node.contextMenu.style.left = `${pv.event.clientX}px`; - self.node.contextMenu.style.top = `${pv.event.clientY}px`; - } - }) - .event("mousemove", function () { - if (drawing) { - self.bbox[0].endX = this.mouse().x / app.canvas.ds.scale; - self.bbox[0].endY = this.mouse().y / app.canvas.ds.scale; - self.vis.render(); - } - }) - .event("mouseup", function () { - console.log("end drawing at " + this.mouse().x / app.canvas.ds.scale + ", " + this.mouse().y / app.canvas.ds.scale); - drawing = false; - self.updateData(); - }); - - this.backgroundImage = this.vis.add(pv.Image).visible(false) - - //create bounding box - this.bounding_box = this.vis.add(pv.Area) - .data(function () { - if (drawing || (self.bbox && self.bbox[0] && Object.keys(self.bbox[0]).length > 0)) { - return [self.bbox[0].startX, self.bbox[0].endX]; - } else { - return []; - } - }) - .bottom(function () {return self.height - Math.max(self.bbox[0].startY, self.bbox[0].endY); }) - .left(function (d) {return d; }) - .height(function () {return Math.abs(self.bbox[0].startY - self.bbox[0].endY);}) - .fillStyle("rgba(70, 130, 180, 0.5)") - .strokeStyle("steelblue") - .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; }) - .add(pv.Dot) - .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; }) - .data(() => { - if (self.bbox && Object.keys(self.bbox[0]).length > 0) { - return [{ - x: self.bbox[0].endX, - y: self.bbox[0].endY - }]; - } else { - return []; - } - }) - .left(d => d.x) - .top(d => d.y) - .radius(Math.log(Math.min(self.width, self.height)) * 1) - .shape("square") - .cursor("move") - .strokeStyle("steelblue") - .lineWidth(2) - .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) - .event("mousedown", pv.Behavior.drag()) - .event("drag", function () { - let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new position by the inverse of the scale factor - let adjustedY = this.mouse().y / app.canvas.ds.scale; - - // Adjust the new position if it would place the dot outside the bounds of the vis.Panel - adjustedX = Math.max(0, Math.min(self.vis.width(), adjustedX)); - adjustedY = Math.max(0, Math.min(self.vis.height(), adjustedY)); - self.bbox[0].endX = this.mouse().x / app.canvas.ds.scale; - self.bbox[0].endY = this.mouse().y / app.canvas.ds.scale; - self.vis.render(); - }) - .event("dragend", function () { - self.updateData(); - }); - - //create positive points - this.vis.add(pv.Dot) - .data(() => this.points) - .left(d => d.x) - .top(d => d.y) - .radius(Math.log(Math.min(self.width, self.height)) * 4) - .shape("circle") - .cursor("move") - .strokeStyle(function () { return i == this.index ? "#07f907" : "#139613"; }) - .lineWidth(4) - .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) - .event("mousedown", pv.Behavior.drag()) - .event("dragstart", function () { - i = this.index; - }) - .event("dragend", function () { - if (pv.event.button === 2 && i !== 0 && i !== self.points.length - 1) { - this.index = i; - self.points.splice(i--, 1); - } - self.updateData(); - - }) - .event("drag", function () { - let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor - let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor - // Determine the bounds of the vis.Panel - const panelWidth = self.vis.width(); - const panelHeight = self.vis.height(); - - // Adjust the new position if it would place the dot outside the bounds of the vis.Panel - adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); - adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); - self.points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position - self.vis.render(); // Re-render the visualization to reflect the new position - }) - - .anchor("center") - .add(pv.Label) - .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left - .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25) // Shift label down if on top half, otherwise shift up - .font(25 + "px sans-serif") - .text(d => {return this.points.indexOf(d); }) - .textStyle("#139613") - .textShadow("2px 2px 2px black") - .add(pv.Dot) // Add smaller point in the center - .data(() => this.points) - .left(d => d.x) - .top(d => d.y) - .radius(2) // Smaller radius for the center point - .shape("circle") - .fillStyle("red") // Color for the center point - .lineWidth(1); // Stroke thickness for the center point - - //create negative points - this.vis.add(pv.Dot) - .data(() => this.neg_points) - .left(d => d.x) - .top(d => d.y) - .radius(Math.log(Math.min(self.width, self.height)) * 4) - .shape("circle") - .cursor("move") - .strokeStyle(function () { return i == this.index ? "#f91111" : "#891616"; }) - .lineWidth(4) - .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; }) - .event("mousedown", pv.Behavior.drag()) - .event("dragstart", function () { - i = this.index; - }) - .event("dragend", function () { - if (pv.event.button === 2 && i !== 0 && i !== self.neg_points.length - 1) { - this.index = i; - self.neg_points.splice(i--, 1); - } - self.updateData(); - - }) - .event("drag", function () { - let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor - let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor - // Determine the bounds of the vis.Panel - const panelWidth = self.vis.width(); - const panelHeight = self.vis.height(); - - // Adjust the new position if it would place the dot outside the bounds of the vis.Panel - adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); - adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); - self.neg_points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position - self.vis.render(); // Re-render the visualization to reflect the new position - }) - .anchor("center") - .add(pv.Label) - .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left - .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25) // Shift label down if on top half, otherwise shift up - .font(25 + "px sans-serif") - .text(d => {return this.neg_points.indexOf(d); }) - .textStyle("red") - .textShadow("2px 2px 2px black") - .add(pv.Dot) // Add smaller point in the center - .data(() => this.neg_points) - .left(d => d.x) - .top(d => d.y) - .radius(2) // Smaller radius for the center point - .shape("circle") - .fillStyle("red") // Color for the center point - .lineWidth(1); // Stroke thickness for the center point - - if (this.points.length != 0) { - this.vis.render(); - } - - var svgElement = this.vis.canvas(); - svgElement.style['zIndex'] = "2" - svgElement.style['position'] = "relative" - this.node.pointsEditor.element.appendChild(svgElement); - - if (this.width > 256) { - this.node.setSize([this.width + 45, this.node.size[1]]); - } - this.node.setSize([this.node.size[0], this.height + 300]); - this.updateData(); - this.refreshBackgroundImage(); - - }//end constructor - - updateData = () => { - if (!this.points || this.points.length === 0) { - console.log("no points"); - return; - } - const combinedPoints = { - positive: this.points, - negative: this.neg_points, - }; - this.pointsStoreWidget.value = JSON.stringify(combinedPoints); - this.pos_coordWidget.value = JSON.stringify(this.points); - this.neg_coordWidget.value = JSON.stringify(this.neg_points); - - if (this.bbox.length != 0) { - let bboxString = JSON.stringify(this.bbox); - this.bboxStoreWidget.value = bboxString; - this.bboxWidget.value = bboxString; - } - - this.vis.render(); - }; - - handleImageLoad = (img, file, base64String) => { - console.log(img.width, img.height); // Access width and height here - this.widthWidget.value = img.width; - this.heightWidget.value = img.height; - - if (img.width != this.vis.width() || img.height != this.vis.height()) { - if (img.width > 256) { - this.node.setSize([img.width + 45, this.node.size[1]]); - } - this.node.setSize([this.node.size[0], img.height + 300]); - this.vis.width(img.width); - this.vis.height(img.height); - this.height = img.height; - this.width = img.width; - this.updateData(); - } - this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render(); - }; - - processImage = (img, file) => { - const canvas = document.createElement('canvas'); - const ctx = canvas.getContext('2d'); - - const maxWidth = 800; // maximum width - const maxHeight = 600; // maximum height - let width = img.width; - let height = img.height; - - // Calculate the new dimensions while preserving the aspect ratio - if (width > height) { - if (width > maxWidth) { - height *= maxWidth / width; - width = maxWidth; - } - } else { - if (height > maxHeight) { - width *= maxHeight / height; - height = maxHeight; - } - } - - canvas.width = width; - canvas.height = height; - ctx.drawImage(img, 0, 0, width, height); - - // Get the compressed image data as a Base64 string - const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1 - - this.node.properties.imgData = { - name: file.name, - lastModified: file.lastModified, - size: file.size, - type: file.type, - base64: base64String - }; - handleImageLoad(img, file, base64String); -}; - - handleImageFile = (file) => { - const reader = new FileReader(); - reader.onloadend = () => { - const img = new Image(); - img.src = reader.result; - img.onload = () => processImage(img, file); - }; - reader.readAsDataURL(file); - - const imageUrl = URL.createObjectURL(file); - const img = new Image(); - img.src = imageUrl; - img.onload = () => this.handleImageLoad(img, file, null); - }; - - refreshBackgroundImage = () => { - if (this.node.properties.imgData && this.node.properties.imgData.base64) { - const base64String = this.node.properties.imgData.base64; - const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`; - const img = new Image(); - img.src = imageUrl; - img.onload = () => this.handleImageLoad(img, null, base64String); - } - }; - - createContextMenu = () => { - self = this; - document.addEventListener('contextmenu', function (e) { - e.preventDefault(); - }); - - document.addEventListener('click', function (e) { - if (!self.node.contextMenu.contains(e.target)) { - self.node.contextMenu.style.display = 'none'; - } - }); - - this.node.menuItems.forEach((menuItem, index) => { - self = this; - menuItem.addEventListener('click', function (e) { - e.preventDefault(); - switch (index) { - case 0: - // Create file input element - const fileInput = document.createElement('input'); - fileInput.type = 'file'; - fileInput.accept = 'image/*'; // Accept only image files - - // Listen for file selection - fileInput.addEventListener('change', function (event) { - const file = event.target.files[0]; // Get the selected file - - if (file) { - const imageUrl = URL.createObjectURL(file); - let img = new Image(); - img.src = imageUrl; - img.onload = () => self.handleImageLoad(img, file, null); - } - }); - - fileInput.click(); - - self.node.contextMenu.style.display = 'none'; - break; - case 1: - self.backgroundImage.visible(false).root.render(); - self.node.properties.imgData = null; - self.node.contextMenu.style.display = 'none'; - break; - } - }); - }); - }//end createContextMenu -}//end class - - -//from melmass -export function hideWidgetForGood(node, widget, suffix = '') { - widget.origType = widget.type - widget.origComputeSize = widget.computeSize - widget.origSerializeValue = widget.serializeValue - widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically - widget.type = "converted-widget" + suffix - // widget.serializeValue = () => { - // // Prevent serializing the widget if we have no input linked - // const w = node.inputs?.find((i) => i.widget?.name === widget.name); - // if (w?.link == null) { - // return undefined; - // } - // return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; - // }; - - // Hide any linked widgets, e.g. seed+seedControl - if (widget.linkedWidgets) { - for (const w of widget.linkedWidgets) { - hideWidgetForGood(node, w, ':' + widget.name) - } - } -} \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/js/setgetnodes.js b/custom_nodes/comfyui-kjnodes/web/js/setgetnodes.js deleted file mode 100644 index c4531881378c51128b1885b212aabc70d2a8c602..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/setgetnodes.js +++ /dev/null @@ -1,565 +0,0 @@ -import { app } from "../../../scripts/app.js"; - -//based on diffus3's SetGet: https://github.com/diffus3/ComfyUI-extensions - -// Nodes that allow you to tunnel connections for cleaner graphs -function setColorAndBgColor(type) { - const colorMap = { - "MODEL": LGraphCanvas.node_colors.blue, - "LATENT": LGraphCanvas.node_colors.purple, - "VAE": LGraphCanvas.node_colors.red, - "CONDITIONING": LGraphCanvas.node_colors.brown, - "IMAGE": LGraphCanvas.node_colors.pale_blue, - "CLIP": LGraphCanvas.node_colors.yellow, - "FLOAT": LGraphCanvas.node_colors.green, - "MASK": { color: "#1c5715", bgcolor: "#1f401b"}, - "INT": { color: "#1b4669", bgcolor: "#29699c"}, - "CONTROL_NET": { color: "#156653", bgcolor: "#1c453b"}, - "NOISE": { color: "#2e2e2e", bgcolor: "#242121"}, - "GUIDER": { color: "#3c7878", bgcolor: "#1c453b"}, - "SAMPLER": { color: "#614a4a", bgcolor: "#3b2c2c"}, - "SIGMAS": { color: "#485248", bgcolor: "#272e27"}, - - }; - - const colors = colorMap[type]; - if (colors) { - this.color = colors.color; - this.bgcolor = colors.bgcolor; - } -} -let disablePrefix = app.ui.settings.getSettingValue("KJNodes.disablePrefix") -const LGraphNode = LiteGraph.LGraphNode - -function showAlert(message) { - app.extensionManager.toast.add({ - severity: 'warn', - summary: "KJ Get/Set", - detail: `${message}. Most likely you're missing custom nodes`, - life: 5000, - }) -} -app.registerExtension({ - name: "SetNode", - registerCustomNodes() { - class SetNode extends LGraphNode { - defaultVisibility = true; - serialize_widgets = true; - drawConnection = false; - currentGetters = null; - slotColor = "#FFF"; - canvas = app.canvas; - menuEntry = "Show connections"; - - constructor(title) { - super(title) - if (!this.properties) { - this.properties = { - "previousName": "" - }; - } - this.properties.showOutputText = SetNode.defaultVisibility; - - const node = this; - - this.addWidget( - "text", - "Constant", - '', - (s, t, u, v, x) => { - node.validateName(node.graph); - if(this.widgets[0].value !== ''){ - this.title = (!disablePrefix ? "Set_" : "") + this.widgets[0].value; - } - this.update(); - this.properties.previousName = this.widgets[0].value; - }, - {} - ) - - this.addInput("*", "*"); - this.addOutput("*", '*'); - - this.onConnectionsChange = function( - slotType, //1 = input, 2 = output - slot, - isChangeConnect, - link_info, - output - ) { - //On Disconnect - if (slotType == 1 && !isChangeConnect) { - if(this.inputs[slot].name === ''){ - this.inputs[slot].type = '*'; - this.inputs[slot].name = '*'; - this.title = "Set" - } - } - if (slotType == 2 && !isChangeConnect) { - if (this.outputs && this.outputs[slot]) { - this.outputs[slot].type = '*'; - this.outputs[slot].name = '*'; - } - } - //On Connect - if (link_info && node.graph && slotType == 1 && isChangeConnect) { - const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id); - - if (fromNode && fromNode.outputs && fromNode.outputs[link_info.origin_slot]) { - const type = fromNode.outputs[link_info.origin_slot].type; - - if (this.title === "Set"){ - this.title = (!disablePrefix ? "Set_" : "") + type; - } - if (this.widgets[0].value === '*'){ - this.widgets[0].value = type - } - - this.validateName(node.graph); - this.inputs[0].type = type; - this.inputs[0].name = type; - - if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){ - setColorAndBgColor.call(this, type); - } - } else { - showAlert("node input undefined.") - } - } - if (link_info && node.graph && slotType == 2 && isChangeConnect) { - const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id); - - if (fromNode && fromNode.inputs && fromNode.inputs[link_info.origin_slot]) { - const type = fromNode.inputs[link_info.origin_slot].type; - - this.outputs[0].type = type; - this.outputs[0].name = type; - } else { - showAlert('node output undefined'); - } - } - - - //Update either way - this.update(); - } - - this.validateName = function(graph) { - let widgetValue = node.widgets[0].value; - - if (widgetValue !== '') { - let tries = 0; - const existingValues = new Set(); - - graph._nodes.forEach(otherNode => { - if (otherNode !== this && otherNode.type === 'SetNode') { - existingValues.add(otherNode.widgets[0].value); - } - }); - - while (existingValues.has(widgetValue)) { - widgetValue = node.widgets[0].value + "_" + tries; - tries++; - } - - node.widgets[0].value = widgetValue; - this.update(); - } - } - - this.clone = function () { - const cloned = SetNode.prototype.clone.apply(this); - cloned.inputs[0].name = '*'; - cloned.inputs[0].type = '*'; - cloned.value = ''; - cloned.properties.previousName = ''; - cloned.size = cloned.computeSize(); - return cloned; - }; - - this.onAdded = function(graph) { - this.validateName(graph); - } - - - this.update = function() { - if (!node.graph) { - return; - } - - const getters = this.findGetters(node.graph); - getters.forEach(getter => { - getter.setType(this.inputs[0].type); - }); - - if (this.widgets[0].value) { - const gettersWithPreviousName = this.findGetters(node.graph, true); - gettersWithPreviousName.forEach(getter => { - getter.setName(this.widgets[0].value); - }); - } - - const allGetters = node.graph._nodes.filter(otherNode => otherNode.type === "GetNode"); - allGetters.forEach(otherNode => { - if (otherNode.setComboValues) { - otherNode.setComboValues(); - } - }); - } - - - this.findGetters = function(graph, checkForPreviousName) { - const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value; - return graph._nodes.filter(otherNode => otherNode.type === 'GetNode' && otherNode.widgets[0].value === name && name !== ''); - } - - - // This node is purely frontend and does not impact the resulting prompt so should not be serialized - this.isVirtualNode = true; - } - - - onRemoved() { - const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "GetNode"); - allGetters.forEach((otherNode) => { - if (otherNode.setComboValues) { - otherNode.setComboValues([this]); - } - }) - } - getExtraMenuOptions(_, options) { - this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; - options.unshift( - { - content: this.menuEntry, - callback: () => { - this.currentGetters = this.findGetters(this.graph); - if (this.currentGetters.length == 0) return; - let linkType = (this.currentGetters[0].outputs[0].type); - this.slotColor = this.canvas.default_connection_color_byType[linkType] - this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; - this.drawConnection = !this.drawConnection; - this.canvas.setDirty(true, true); - - }, - has_submenu: true, - submenu: { - title: "Color", - options: [ - { - content: "Highlight", - callback: () => { - this.slotColor = "orange" - this.canvas.setDirty(true, true); - } - } - ], - }, - }, - { - content: "Hide all connections", - callback: () => { - const allGetters = this.graph._nodes.filter(otherNode => otherNode.type === "GetNode" || otherNode.type === "SetNode"); - allGetters.forEach(otherNode => { - otherNode.drawConnection = false; - console.log(otherNode); - }); - - this.menuEntry = "Show connections"; - this.drawConnection = false - this.canvas.setDirty(true, true); - - }, - - }, - ); - // Dynamically add a submenu for all getters - this.currentGetters = this.findGetters(this.graph); - if (this.currentGetters) { - - let gettersSubmenu = this.currentGetters.map(getter => ({ - - content: `${getter.title} id: ${getter.id}`, - callback: () => { - this.canvas.centerOnNode(getter); - this.canvas.selectNode(getter, false); - this.canvas.setDirty(true, true); - - }, - })); - - options.unshift({ - content: "Getters", - has_submenu: true, - submenu: { - title: "GetNodes", - options: gettersSubmenu, - } - }); - } - } - - - onDrawForeground(ctx, lGraphCanvas) { - if (this.drawConnection) { - this._drawVirtualLinks(lGraphCanvas, ctx); - } - } - // onDrawCollapsed(ctx, lGraphCanvas) { - // if (this.drawConnection) { - // this._drawVirtualLinks(lGraphCanvas, ctx); - // } - // } - _drawVirtualLinks(lGraphCanvas, ctx) { - if (!this.currentGetters?.length) return; - var title = this.getTitle ? this.getTitle() : this.title; - var title_width = ctx.measureText(title).width; - if (!this.flags.collapsed) { - var start_node_slotpos = [ - this.size[0], - LiteGraph.NODE_TITLE_HEIGHT * 0.5, - ]; - } - else { - - var start_node_slotpos = [ - title_width + 55, - -15, - - ]; - } - // Provide a default link object with necessary properties, to avoid errors as link can't be null anymore - const defaultLink = { type: 'default', color: this.slotColor }; - - for (const getter of this.currentGetters) { - if (!this.flags.collapsed) { - var end_node_slotpos = this.getConnectionPos(false, 0); - end_node_slotpos = [ - getter.pos[0] - end_node_slotpos[0] + this.size[0], - getter.pos[1] - end_node_slotpos[1] - ]; - } - else { - var end_node_slotpos = this.getConnectionPos(false, 0); - end_node_slotpos = [ - getter.pos[0] - end_node_slotpos[0] + title_width + 50, - getter.pos[1] - end_node_slotpos[1] - 30 - ]; - } - lGraphCanvas.renderLink( - ctx, - start_node_slotpos, - end_node_slotpos, - defaultLink, - false, - null, - this.slotColor, - LiteGraph.RIGHT, - LiteGraph.LEFT - ); - } - } - } - - LiteGraph.registerNodeType( - "SetNode", - Object.assign(SetNode, { - title: "Set", - }) - ); - - SetNode.category = "KJNodes"; - }, -}); - -app.registerExtension({ - name: "GetNode", - registerCustomNodes() { - class GetNode extends LGraphNode { - - defaultVisibility = true; - serialize_widgets = true; - drawConnection = false; - slotColor = "#FFF"; - currentSetter = null; - canvas = app.canvas; - - constructor(title) { - super(title) - if (!this.properties) { - this.properties = {}; - } - this.properties.showOutputText = GetNode.defaultVisibility; - const node = this; - this.addWidget( - "combo", - "Constant", - "", - (e) => { - this.onRename(); - }, - { - values: () => { - const setterNodes = node.graph._nodes.filter((otherNode) => otherNode.type == 'SetNode'); - return setterNodes.map((otherNode) => otherNode.widgets[0].value).sort(); - } - } - ) - - this.addOutput("*", '*'); - this.onConnectionsChange = function( - slotType, //0 = output, 1 = input - slot, //self-explanatory - isChangeConnect, - link_info, - output - ) { - this.validateLinks(); - } - - this.setName = function(name) { - node.widgets[0].value = name; - node.onRename(); - node.serialize(); - } - - this.onRename = function() { - const setter = this.findSetter(node.graph); - if (setter) { - let linkType = (setter.inputs[0].type); - - this.setType(linkType); - this.title = (!disablePrefix ? "Get_" : "") + setter.widgets[0].value; - - if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){ - setColorAndBgColor.call(this, linkType); - } - - } else { - this.setType('*'); - } - } - - this.clone = function () { - const cloned = GetNode.prototype.clone.apply(this); - cloned.size = cloned.computeSize(); - return cloned; - }; - - this.validateLinks = function() { - if (this.outputs[0].type !== '*' && this.outputs[0].links) { - this.outputs[0].links.filter(linkId => { - const link = node.graph.links[linkId]; - return link && (!link.type.split(",").includes(this.outputs[0].type) && link.type !== '*'); - }).forEach(linkId => { - node.graph.removeLink(linkId); - }); - } - }; - - this.setType = function(type) { - this.outputs[0].name = type; - this.outputs[0].type = type; - this.validateLinks(); - } - - this.findSetter = function(graph) { - const name = this.widgets[0].value; - const foundNode = graph._nodes.find(otherNode => otherNode.type === 'SetNode' && otherNode.widgets[0].value === name && name !== ''); - return foundNode; - }; - - this.goToSetter = function() { - const setter = this.findSetter(this.graph); - this.canvas.centerOnNode(setter); - this.canvas.selectNode(setter, false); - }; - - // This node is purely frontend and does not impact the resulting prompt so should not be serialized - this.isVirtualNode = true; - } - - getInputLink(slot) { - const setter = this.findSetter(this.graph); - - if (setter) { - const slotInfo = setter.inputs[slot]; - const link = this.graph.links[slotInfo.link]; - return link; - } else { - const errorMessage = "No SetNode found for " + this.widgets[0].value + "(" + this.type + ")"; - showAlert(errorMessage); - //throw new Error(errorMessage); - } - } - onAdded(graph) { - } - getExtraMenuOptions(_, options) { - let menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; - - options.unshift( - { - content: "Go to setter", - callback: () => { - this.goToSetter(); - }, - }, - { - content: menuEntry, - callback: () => { - this.currentSetter = this.findSetter(this.graph); - if (this.currentSetter.length == 0) return; - let linkType = (this.currentSetter.inputs[0].type); - this.drawConnection = !this.drawConnection; - this.slotColor = this.canvas.default_connection_color_byType[linkType] - menuEntry = this.drawConnection ? "Hide connections" : "Show connections"; - this.canvas.setDirty(true, true); - }, - }, - ); - } - - onDrawForeground(ctx, lGraphCanvas) { - if (this.drawConnection) { - this._drawVirtualLink(lGraphCanvas, ctx); - } - } - // onDrawCollapsed(ctx, lGraphCanvas) { - // if (this.drawConnection) { - // this._drawVirtualLink(lGraphCanvas, ctx); - // } - // } - _drawVirtualLink(lGraphCanvas, ctx) { - if (!this.currentSetter) return; - - // Provide a default link object with necessary properties, to avoid errors as link can't be null anymore - const defaultLink = { type: 'default', color: this.slotColor }; - - let start_node_slotpos = this.currentSetter.getConnectionPos(false, 0); - start_node_slotpos = [ - start_node_slotpos[0] - this.pos[0], - start_node_slotpos[1] - this.pos[1], - ]; - let end_node_slotpos = [0, -LiteGraph.NODE_TITLE_HEIGHT * 0.5]; - lGraphCanvas.renderLink( - ctx, - start_node_slotpos, - end_node_slotpos, - defaultLink, - false, - null, - this.slotColor - ); - } - } - - LiteGraph.registerNodeType( - "GetNode", - Object.assign(GetNode, { - title: "Get", - }) - ); - - GetNode.category = "KJNodes"; - }, -}); diff --git a/custom_nodes/comfyui-kjnodes/web/js/spline_editor.js b/custom_nodes/comfyui-kjnodes/web/js/spline_editor.js deleted file mode 100644 index cc095990a7ab48b4ea930563e0755982ddc39cf5..0000000000000000000000000000000000000000 --- a/custom_nodes/comfyui-kjnodes/web/js/spline_editor.js +++ /dev/null @@ -1,1379 +0,0 @@ -import { app } from '../../../scripts/app.js' - -//from melmass -export function makeUUID() { - let dt = new Date().getTime() - const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => { - const r = ((dt + Math.random() * 16) % 16) | 0 - dt = Math.floor(dt / 16) - return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16) - }) - return uuid -} - -export const loadScript = ( - FILE_URL, - async = true, - type = 'text/javascript', - ) => { - return new Promise((resolve, reject) => { - try { - // Check if the script already exists - const existingScript = document.querySelector(`script[src="${FILE_URL}"]`) - if (existingScript) { - resolve({ status: true, message: 'Script already loaded' }) - return - } - - const scriptEle = document.createElement('script') - scriptEle.type = type - scriptEle.async = async - scriptEle.src = FILE_URL - - scriptEle.addEventListener('load', (ev) => { - resolve({ status: true }) - }) - - scriptEle.addEventListener('error', (ev) => { - reject({ - status: false, - message: `Failed to load the script ${FILE_URL}`, - }) - }) - - document.body.appendChild(scriptEle) - } catch (error) { - reject(error) - } - }) - } - const create_documentation_stylesheet = () => { - const tag = 'kj-splineditor-stylesheet' - - let styleTag = document.head.querySelector(tag) - - if (!styleTag) { - styleTag = document.createElement('style') - styleTag.type = 'text/css' - styleTag.id = tag - styleTag.innerHTML = ` - .spline-editor { - - position: absolute; - - font: 12px monospace; - line-height: 1.5em; - padding: 10px; - z-index: 0; - overflow: hidden; - } - ` - document.head.appendChild(styleTag) - } - } - -loadScript('kjweb_async/svg-path-properties.min.js').catch((e) => { - console.log(e) -}) -loadScript('kjweb_async/protovis.min.js').catch((e) => { - console.log(e) -}) -create_documentation_stylesheet() - -function chainCallback(object, property, callback) { - if (object == undefined) { - //This should not happen. - console.error("Tried to add callback to non-existant object") - return; - } - if (property in object) { - const callback_orig = object[property] - object[property] = function () { - const r = callback_orig.apply(this, arguments); - callback.apply(this, arguments); - return r - }; - } else { - object[property] = callback; - } -} -app.registerExtension({ - name: 'KJNodes.SplineEditor', - - async beforeRegisterNodeDef(nodeType, nodeData) { - if (nodeData?.name === 'SplineEditor') { - chainCallback(nodeType.prototype, "onNodeCreated", function () { - - this.widgets.find(w => w.name === "coordinates").hidden = true - - var element = document.createElement("div"); - this.uuid = makeUUID() - element.id = `spline-editor-${this.uuid}` - - this.previewMediaType = 'image' - - this.splineEditor = this.addDOMWidget(nodeData.name, "SplineEditorWidget", element, { - serialize: false, - hideOnZoom: false, - }); - - // context menu - this.contextMenu = document.createElement("div"); - this.contextMenu.className = 'spline-editor-context-menu'; - this.contextMenu.id = "context-menu"; - this.contextMenu.style.display = "none"; - this.contextMenu.style.position = "absolute"; - this.contextMenu.style.backgroundColor = "#202020"; - this.contextMenu.style.minWidth = "100px"; - this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)"; - this.contextMenu.style.zIndex = "100"; - this.contextMenu.style.padding = "5px"; - - function styleMenuItem(menuItem) { - menuItem.style.display = "block"; - menuItem.style.padding = "5px"; - menuItem.style.color = "#FFF"; - menuItem.style.fontFamily = "Arial, sans-serif"; - menuItem.style.fontSize = "16px"; - menuItem.style.textDecoration = "none"; - menuItem.style.marginBottom = "5px"; - } - function createMenuItem(id, textContent) { - let menuItem = document.createElement("a"); - menuItem.href = "#"; - menuItem.id = `menu-item-${id}`; - menuItem.textContent = textContent; - styleMenuItem(menuItem); - return menuItem; - } - - // Create an array of menu items using the createMenuItem function - this.menuItems = [ - createMenuItem(0, "Toggle handles"), - createMenuItem(1, "Display sample points"), - createMenuItem(2, "Switch point shape"), - createMenuItem(3, "Background image"), - createMenuItem(4, "Invert point order"), - createMenuItem(5, "Clear Image"), - createMenuItem(6, "Add new spline"), - createMenuItem(7, "Add new single point"), - createMenuItem(8, "Delete current spline"), - createMenuItem(9, "Next spline"), - ]; - - // Add mouseover and mouseout event listeners to each menu item for styling - this.menuItems.forEach(menuItem => { - menuItem.addEventListener('mouseover', function() { - this.style.backgroundColor = "gray"; - }); - - menuItem.addEventListener('mouseout', function() { - this.style.backgroundColor = "#202020"; - }); - }); - - // Append each menu item to the context menu - this.menuItems.forEach(menuItem => { - this.contextMenu.appendChild(menuItem); - }); - - document.body.appendChild(this.contextMenu); - - this.addWidget("button", "New canvas", null, () => { - if (!this.properties || !("points" in this.properties)) { - this.editor = new SplineEditor(this); - this.addProperty("points", this.constructor.type, "string"); - } - else { - this.editor = new SplineEditor(this, true); - } - }); - - this.setSize([550, 1000]); - this.resizable = false; - this.splineEditor.parentEl = document.createElement("div"); - this.splineEditor.parentEl.className = "spline-editor"; - this.splineEditor.parentEl.id = `spline-editor-${this.uuid}` - element.appendChild(this.splineEditor.parentEl); - - chainCallback(this, "onConfigure", function () { - try { - this.editor = new SplineEditor(this); - } catch (error) { - console.error("An error occurred while configuring the editor:", error); - } - }); - chainCallback(this, "onExecuted", function (message) { - let bg_image = message["bg_image"]; - this.properties.imgData = { - name: "bg_image", - base64: bg_image - }; - this.editor.refreshBackgroundImage(this); - }); - - }); // onAfterGraphConfigured - }//node created - } //before register -})//register - - -class SplineEditor{ - constructor(context, reset = false) { - this.node = context; - this.reset=reset; - const self = this; - console.log("creatingSplineEditor") - - this.node.pasteFile = (file) => { - if (file.type.startsWith("image/")) { - this.handleImageFile(file); - return true; - } - return false; - }; - - this.node.onDragOver = function (e) { - if (e.dataTransfer && e.dataTransfer.items) { - return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/")); - } - return false; - }; - - // On drop upload files - this.node.onDragDrop = (e) => { - console.log("onDragDrop called"); - let handled = false; - for (const file of e.dataTransfer.files) { - if (file.type.startsWith("image/")) { - this.handleImageFile(file); - handled = true; - } - } - return handled; - }; - - // context menu - this.createContextMenu(); - - - this.dotShape = "circle"; - this.drawSamplePoints = false; - - if (reset && context.splineEditor.element) { - context.splineEditor.element.innerHTML = ''; // Clear the container - } - this.coordWidget = context.widgets.find(w => w.name === "coordinates"); - this.interpolationWidget = context.widgets.find(w => w.name === "interpolation"); - this.pointsWidget = context.widgets.find(w => w.name === "points_to_sample"); - this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store"); - this.tensionWidget = context.widgets.find(w => w.name === "tension"); - this.minValueWidget = context.widgets.find(w => w.name === "min_value"); - this.maxValueWidget = context.widgets.find(w => w.name === "max_value"); - this.samplingMethodWidget = context.widgets.find(w => w.name === "sampling_method"); - this.widthWidget = context.widgets.find(w => w.name === "mask_width"); - this.heightWidget = context.widgets.find(w => w.name === "mask_height"); - - this.interpolation = this.interpolationWidget.value - this.tension = this.tensionWidget.value - this.points_to_sample = this.pointsWidget.value - this.rangeMin = this.minValueWidget.value - this.rangeMax = this.maxValueWidget.value - this.pointsLayer = null; - this.samplingMethod = this.samplingMethodWidget.value - - if (this.samplingMethod == "path"||this.samplingMethod == "speed") { - this.dotShape = "triangle" - } - - - this.interpolationWidget.callback = () => { - this.interpolation = this.interpolationWidget.value - this.updatePath(); - } - this.samplingMethodWidget.callback = () => { - this.samplingMethod = this.samplingMethodWidget.value - if (this.samplingMethod == "path") { - this.dotShape = "triangle" - } - else if (this.samplingMethod == "controlpoints") { - this.dotShape = "circle" - this.drawSamplePoints = true; - } - this.updatePath(); - } - this.tensionWidget.callback = () => { - this.tension = this.tensionWidget.value - this.updatePath(); - } - this.pointsWidget.callback = () => { - this.points_to_sample = this.pointsWidget.value - this.updatePath(); - } - this.minValueWidget.callback = () => { - this.rangeMin = this.minValueWidget.value - this.updatePath(); - } - this.maxValueWidget.callback = () => { - this.rangeMax = this.maxValueWidget.value - this.updatePath(); - } - this.widthWidget.callback = () => { - this.width = this.widthWidget.value; - if (this.width > 256) { - context.setSize([this.width + 45, context.size[1]]); - } - this.vis.width(this.width); - this.updatePath(); -} -this.heightWidget.callback = () => { - this.height = this.heightWidget.value - this.vis.height(this.height) - context.setSize([context.size[0], this.height + 450]); - this.updatePath(); - } - this.pointsStoreWidget.callback = () => { - points = JSON.parse(this.pointsStoreWidget.value); - this.updatePath(); - } - - // Initialize or reset points array - this.drawHandles = false; - this.drawRuler = true; - var hoverIndex = -1; - var isDragging = false; - this.width = this.widthWidget.value; - this.height = this.heightWidget.value; - var i = 3; - this.splines = []; - this.activeSplineIndex = 0; // Track which spline is being edited - // init mouse position -this.lastMousePosition = { x: this.width/2, y: this.height/2 }; - - if (!reset && this.pointsStoreWidget.value != "") { - try { - const parsedData = JSON.parse(this.pointsStoreWidget.value); - // Check if it's already in the new format (array of splines) - if (Array.isArray(parsedData) && parsedData.length > 0 && parsedData[0].hasOwnProperty('points')) { - this.splines = parsedData; - } else { - // Convert old format (single array of points) to new format - this.splines = [{ - points: parsedData, - color: "#1f77b4", - name: "Spline 1" - }]; - } - } catch (e) { - console.error("Error parsing spline data:", e); - this.initializeDefaultSplines(); - } -} else { - this.initializeDefaultSplines(); - this.pointsStoreWidget.value = JSON.stringify(this.splines); -} - - this.vis = new pv.Panel() - .width(this.width) - .height(this.height) - .fillStyle("#222") - .strokeStyle("gray") - .lineWidth(2) - .antialias(false) - .margin(10) - .event("mousedown", function () { - if (pv.event.shiftKey) { // Use pv.event to access the event object - let scaledMouse = { - x: this.mouse().x / app.canvas.ds.scale, - y: this.mouse().y / app.canvas.ds.scale - }; - i = self.splines[self.activeSplineIndex].points.push(scaledMouse) - 1; - self.updatePath(); - return this; - } - else if (pv.event.ctrlKey) { - // Capture the clicked location - let clickedPoint = { - x: this.mouse().x / app.canvas.ds.scale, - y: this.mouse().y / app.canvas.ds.scale - }; - - // Find the two closest points to the clicked location - const activePoints = self.splines[self.activeSplineIndex].points; - let { point1Index, point2Index } = self.findClosestPoints(self.splines[self.activeSplineIndex].points, clickedPoint); - - // Calculate the midpoint between the two closest points - let midpoint = { - x: (activePoints[point1Index].x + activePoints[point2Index].x) / 2, - y: (activePoints[point1Index].y + activePoints[point2Index].y) / 2 - }; - - // Insert the midpoint into the array - activePoints.splice(point2Index, 0, midpoint); - i = point2Index; - self.updatePath(); - } - else if (pv.event.button === 2) { - // Store the current mouse position adjusted for scale - self.lastMousePosition = { - x: this.mouse().x / app.canvas.ds.scale, - y: this.mouse().y / app.canvas.ds.scale - }; - - self.node.contextMenu.style.display = 'block'; - self.node.contextMenu.style.left = `${pv.event.clientX}px`; - self.node.contextMenu.style.top = `${pv.event.clientY}px`; - } - }) - this.backgroundImage = this.vis.add(pv.Image).visible(false) - - this.vis.add(pv.Rule) - .data(pv.range(0, this.height, 64)) - .bottom(d => d) - .strokeStyle("gray") - .lineWidth(3) - .visible(() => self.drawRuler) - - this.hoverSplineIndex = -1; - - this.splines.forEach((spline, splineIndex) => { - const strokeObj = this.vis.add(pv.Line) - .data(() => spline.points) - .left(d => d.x) - .top(d => d.y) - .interpolate(() => this.interpolation) - .tension(() => this.tension) - .segmented(() => false) - .strokeStyle("black") // Stroke color - .lineWidth(() => { - // Make stroke slightly wider than the main line - if (splineIndex === this.activeSplineIndex) return 5; - if (splineIndex === this.hoverSplineIndex) return 4; - return 3.5; - }); - - this.vis.add(pv.Line) - .data(() => spline.points) - .left(d => d.x) - .top(d => d.y) - .interpolate(() => this.interpolation) - .tension(() => this.tension) - .segmented(() => false) - .strokeStyle(spline.color) - .lineWidth(() => { - // Change line width based on active or hover state - if (splineIndex === this.activeSplineIndex) return 3; - if (splineIndex === this.hoverSplineIndex) return 2; - return 1.5; - }) - .event("mouseover", () => { - this.hoverSplineIndex = splineIndex; - this.vis.render(); - }) - .event("mouseout", () => { - this.hoverSplineIndex = -1; - this.vis.render(); - }) - .event("mousedown", () => { - if (this.activeSplineIndex !== splineIndex) { - this.activeSplineIndex = splineIndex; - this.refreshSplineElements(); - } - }); - }); - - this.vis.add(pv.Dot) - .data(() => { - const activeSpline = this.splines[this.activeSplineIndex]; - // If this is a single point, don't show it in the main visualization - if (activeSpline.isSinglePoint || (activeSpline.points && activeSpline.points.length === 1)) { - return []; // Return empty array to hide in main visualization - } - return activeSpline.points; - }) - .left(d => d.x) - .top(d => d.y) - .radius(12) - .shape(function() { - return self.dotShape; - }) - .angle(function() { - const index = this.index; - let angle = 0; - - if (self.dotShape === "triangle") { - const activePoints = self.splines[self.activeSplineIndex].points; - let dxNext = 0, dyNext = 0; - if (index < activePoints.length - 1) { - dxNext = activePoints[index + 1].x - activePoints[index].x; - dyNext = activePoints[index + 1].y - activePoints[index].y; - } - - let dxPrev = 0, dyPrev = 0; - if (index > 0) { - dxPrev = activePoints[index].x - activePoints[index - 1].x; - dyPrev = activePoints[index].y - activePoints[index - 1].y; - } - - const dx = (dxNext + dxPrev) / 2; - const dy = (dyNext + dyPrev) / 2; - - angle = Math.atan2(dy, dx); - angle -= Math.PI / 2; - angle = (angle + 2 * Math.PI) % (2 * Math.PI); - } - - return angle; - }) - .cursor("move") - .strokeStyle(function () { return i == this.index ? "#ff7f0e" : "#1f77b4"; }) - .fillStyle(function () { return "rgba(100, 100, 100, 0.3)"; }) - .event("mousedown", pv.Behavior.drag()) - .event("dragstart", function () { - i = this.index; - hoverIndex = this.index; - isDragging = true; - const activePoints = self.splines[self.activeSplineIndex].points; - if (pv.event.button === 2 && i !== 0 && i !== activePoints.length - 1) { - activePoints.splice(i--, 1); - self.vis.render(); - } - return this; - }) - .event("dragend", function() { - if (this.pathElements !== null) { - self.updatePath(); - } - isDragging = false; - }) - .event("drag", function () { - let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor - let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor - // Determine the bounds of the vis.Panel - const panelWidth = self.vis.width(); - const panelHeight = self.vis.height(); - - // Adjust the new position if it would place the dot outside the bounds of the vis.Panel - adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); - adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); - self.splines[self.activeSplineIndex].points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position - self.vis.render(); // Re-render the visualization to reflect the new position - }) - .event("mouseover", function() { - hoverIndex = this.index; // Set the hover index to the index of the hovered dot - self.vis.render(); // Re-render the visualization - }) - .event("mouseout", function() { - !isDragging && (hoverIndex = -1); // Reset the hover index when the mouse leaves the dot - self.vis.render(); // Re-render the visualization - }) - .anchor("center") - .add(pv.Label) - .visible(function() { - return hoverIndex === this.index; // Only show the label for the hovered dot - }) - .left(d => d.x < this.width / 2 ? d.x + 80 : d.x - 70) // Shift label to right if on left half, otherwise shift to left - .top(d => d.y < this.height / 2 ? d.y + 20 : d.y - 20) // Shift label down if on top half, otherwise shift up - .font(12 + "px sans-serif") - .text(d => { - if (this.samplingMethod == "path") { - return `X: ${Math.round(d.x)}, Y: ${Math.round(d.y)}`; - } else { - let frame = Math.round((d.x / self.width) * self.points_to_sample); - let normalizedY = (1.0 - (d.y / self.height) - 0.0) * (self.rangeMax - self.rangeMin) + self.rangeMin; - let normalizedX = (d.x / self.width); - return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`; - } - }) - .textStyle("orange") - - // single points - this.vis.add(pv.Dot) - .data(() => { - // Collect all single points from all splines - const singlePoints = []; - this.splines.forEach((spline, splineIndex) => { - if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) { - singlePoints.push({ - x: spline.points[0].x, - y: spline.points[0].y, - splineIndex: splineIndex, - color: spline.color - }); - } - }); - return singlePoints; - }) - .left(d => d.x) - .top(d => d.y) - .radius(6) - .shape("square") - .strokeStyle(d => d.splineIndex === this.activeSplineIndex ? "#ff7f0e" : d.color) - .fillStyle(d => "rgba(100, 100, 100, 0.9)") - .lineWidth(d => d.splineIndex === this.activeSplineIndex ? 3 : 1.5) - .cursor("move") - .event("mousedown", pv.Behavior.drag()) - .event("dragstart", function(d) { - self.activeSplineIndex = d.splineIndex; - self.refreshSplineElements(); - return this; - }) - .event("drag", function(d) { - let adjustedX = this.mouse().x / app.canvas.ds.scale; - let adjustedY = this.mouse().y / app.canvas.ds.scale; - - // Determine the bounds of the vis.Panel - const panelWidth = self.vis.width(); - const panelHeight = self.vis.height(); - - // Adjust the new position if it would place the dot outside the bounds - adjustedX = Math.max(0, Math.min(panelWidth, adjustedX)); - adjustedY = Math.max(0, Math.min(panelHeight, adjustedY)); - - // Update the point position - const spline = self.splines[d.splineIndex]; - spline.points[0] = { x: adjustedX, y: adjustedY }; - - // For single points, we need to refresh the entire spline element - // to prevent the line-drawing effect - - }) - .event("dragend", function(d) { - self.refreshSplineElements(); - self.updatePath(); - }) - .visible(d => true); // Make always visible - - if (this.splines.length != 0) { - this.vis.render(); - } - var svgElement = this.vis.canvas(); - svgElement.style['zIndex'] = "2" - svgElement.style['position'] = "relative" - this.node.splineEditor.element.appendChild(svgElement); - this.pathElements = svgElement.getElementsByTagName('path'); // Get all path elements - - if (this.width > 256) { - this.node.setSize([this.width + 45, this.node.size[1]]); - } - this.node.setSize([this.node.size[0], this.height + 450]); - this.updatePath(); - this.refreshBackgroundImage(); - } - - updatePath = () => { - if (!this.splines || this.splines.length === 0) { - console.log("no splines"); - return; - } - // Get active spline points - console.log("this.activeSplineIndex", this.activeSplineIndex); - const activeSpline = this.splines[this.activeSplineIndex]; - const activePoints = activeSpline.points; - - if (!activePoints || activePoints.length === 0) { - console.log("no points in active spline"); - return; - } - - - let coords; - if (this.samplingMethod != "controlpoints") { - coords = this.samplePoints(this.pathElements[this.activeSplineIndex], this.points_to_sample, this.samplingMethod, this.width, this.activeSplineIndex); - } else { - coords = activePoints; - } - - let allSplineCoords = []; - for (let i = 0; i < this.splines.length; i++) { - // Use the same sampling method for all splines - let splineCoords; - const pathElement = this.pathElements[i]; - - if (this.samplingMethod != "controlpoints" && pathElement) { - splineCoords = this.samplePoints(pathElement, this.points_to_sample, this.samplingMethod, this.width, i); - } else { - // Fall back to control points if no path element or sampling method is "controlpoints" - splineCoords = this.splines[i].points; - } - - allSplineCoords.push(splineCoords); - } - - if (this.drawSamplePoints) { - if (this.pointsLayer) { - // Update the data of the existing points layer - this.pointsLayer.data(coords); - } else { - // Create the points layer if it doesn't exist - this.pointsLayer = this.vis.add(pv.Dot) - .data(coords) - .left(function(d) { return d.x; }) - .top(function(d) { return d.y; }) - .radius(5) // Adjust the radius as needed - .fillStyle("red") // Change the color as needed - .strokeStyle("black") // Change the stroke color as needed - .lineWidth(1); // Adjust the line width as needed - } - } else { - if (this.pointsLayer) { - // Remove the points layer - this.pointsLayer.data([]); - this.vis.render(); - } - } - this.pointsStoreWidget.value = JSON.stringify(this.splines); - if (this.coordWidget) { - this.coordWidget.value = JSON.stringify(allSplineCoords); - } - this.vis.render(); - }; - - handleImageLoad = (img, file, base64String) => { - //console.log(img.width, img.height); // Access width and height here - this.widthWidget.value = img.width; - this.heightWidget.value = img.height; - this.drawRuler = false; - - if (img.width != this.vis.width() || img.height != this.vis.height()) { - if (img.width > 256) { - this.node.setSize([img.width + 45, this.node.size[1]]); - } - this.node.setSize([this.node.size[0], img.height + 520]); - this.vis.width(img.width); - this.vis.height(img.height); - this.height = img.height; - this.width = img.width; - - this.updatePath(); - } - this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render(); - }; - - processImage = (img, file) => { - const canvas = document.createElement('canvas'); - const ctx = canvas.getContext('2d'); - - const maxWidth = 800; // maximum width - const maxHeight = 600; // maximum height - let width = img.width; - let height = img.height; - - // Calculate the new dimensions while preserving the aspect ratio - if (width > height) { - if (width > maxWidth) { - height *= maxWidth / width; - width = maxWidth; - } - } else { - if (height > maxHeight) { - width *= maxHeight / height; - height = maxHeight; - } - } - - canvas.width = width; - canvas.height = height; - ctx.drawImage(img, 0, 0, width, height); - - // Get the compressed image data as a Base64 string - const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1 - - this.node.properties.imgData = { - name: file.name, - lastModified: file.lastModified, - size: file.size, - type: file.type, - base64: base64String - }; - handleImageLoad(img, file, base64String); - }; - - handleImageFile = (file) => { - const reader = new FileReader(); - reader.onloadend = () => { - const img = new Image(); - img.src = reader.result; - img.onload = () => processImage(img, file); - }; - reader.readAsDataURL(file); - - const imageUrl = URL.createObjectURL(file); - const img = new Image(); - img.src = imageUrl; - img.onload = () => this.handleImageLoad(img, file, null); - }; - - refreshBackgroundImage = () => { - if (this.node.properties.imgData && this.node.properties.imgData.base64) { - const base64String = this.node.properties.imgData.base64; - const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`; - const img = new Image(); - img.src = imageUrl; - img.onload = () => this.handleImageLoad(img, null, base64String); - } - }; - - refreshSplineElements = () => { - // Clear existing line elements and recreate them - const svgElement = this.vis.canvas(); - - // Remove all existing line elements - const oldLines = svgElement.querySelectorAll('path'); - oldLines.forEach(line => line.remove()); - - this.pathElements = []; - this.lineObjects = []; - - const originalChildren = [...this.vis.children]; - - // Find line objects to remove (those that represent splines) - const linesToRemove = originalChildren.filter(child => - child instanceof pv.Line - ); - linesToRemove.forEach(line => line.visible(false)); - - // Re-add all spline lines and store references to them - this.splines.forEach((spline, splineIndex) => { - // For single points, we need a special handling - if (spline.isSinglePoint || (spline.points && spline.points.length === 1)) { - const point = spline.points[0]; - // For single points, create a tiny line at the same point - // This ensures we have a path element for the point - const lineObj = this.vis.add(pv.Line) - .data([point, {x: point.x + 0.001, y: point.y + 0.001}]) - .left(d => d.x) - .top(d => d.y) - .strokeStyle(spline.color) - .lineWidth(() => { - if (splineIndex === this.activeSplineIndex) return 3; - if (splineIndex === this.hoverSplineIndex) return 2; - return 1.5; - }) - .event("mouseover", () => { - this.hoverSplineIndex = splineIndex; - this.vis.render(); - }) - .event("mouseout", () => { - this.hoverSplineIndex = -1; - this.vis.render(); - }) - .event("mousedown", () => { - if (this.activeSplineIndex !== splineIndex) { - this.activeSplineIndex = splineIndex; - this.refreshSplineElements(); - } - }); - this.lineObjects.push(lineObj); - } else { - // For normal multi-point splines - const strokeObj = this.vis.add(pv.Line) - .data(() => spline.points) - .left(d => d.x) - .top(d => d.y) - .interpolate(() => this.interpolation) - .tension(() => this.tension) - .segmented(() => false) - .strokeStyle("black") // Stroke color - .lineWidth(() => { - // Make stroke slightly wider than the main line - if (splineIndex === this.activeSplineIndex) return 5; - if (splineIndex === this.hoverSplineIndex) return 4; - return 3.5; - }); - const lineObj = this.vis.add(pv.Line) - .data(() => spline.points) - .left(d => d.x) - .top(d => d.y) - .interpolate(() => this.interpolation) - .tension(() => this.tension) - .segmented(() => false) - .strokeStyle(spline.color) - .lineWidth(() => { - if (splineIndex === this.activeSplineIndex) return 3; - if (splineIndex === this.hoverSplineIndex) return 2; - return 1.5; - }) - .event("mouseover", () => { - this.hoverSplineIndex = splineIndex; - this.vis.render(); - }) - .event("mouseout", () => { - this.hoverSplineIndex = -1; - this.vis.render(); - }) - .event("mousedown", () => { - if (this.activeSplineIndex !== splineIndex) { - this.activeSplineIndex = splineIndex; - this.refreshSplineElements(); - } - }); - - // // Add invisible wider hit area for easier selection - // this.vis.add(pv.Line) - // .data(() => spline.points) - // .left(d => d.x) - // .top(d => d.y) - // .interpolate(() => this.interpolation) - // .tension(() => this.tension) - // .segmented(() => false) - // .strokeStyle("rgba(0,0,0,0.01)") // Nearly invisible - // .lineWidth(15) // Much wider hit area - // .event("mouseover", () => { - // this.hoverSplineIndex = splineIndex; - // this.vis.render(); - // }) - // .event("mouseout", () => { - // this.hoverSplineIndex = -1; - // this.vis.render(); - // }) - // .event("mousedown", () => { - // if (pv.event.shiftKey) { - // if (this.activeSplineIndex !== splineIndex) { - // this.activeSplineIndex = splineIndex; - // this.refreshSplineElements(); - // } - // }} - // ); - - this.lineObjects.push(lineObj); - } - }); - - this.vis.render(); - - requestAnimationFrame(() => { - const allPaths = Array.from(svgElement.querySelectorAll('path')); - this.pathElements = []; - - // First try: look at paths with specific childIndex values - this.lineObjects.forEach((lineObj, i) => { - // Find paths that correspond to our line objects - const childIndex = lineObj.childIndex; - const matchingPath = allPaths.find(path => - path.$scene && path.$scene.scenes && - path.$scene.scenes.childIndex === childIndex - ); - - if (matchingPath) { - //console.log("matchingPath:", matchingPath); - this.pathElements[i] = matchingPath; - } - }); - - // Check if we found all paths - if (this.pathElements.filter(p => p).length !== this.splines.length) { - // Fallback to color matching - this.pathElements = []; - for (let i = 0; i < this.splines.length; i++) { - const color = this.splines[i].color; - const matchingPath = allPaths.find(p => - p.getAttribute('style')?.includes(color) && - !this.pathElements.includes(p) - ); - - if (matchingPath) { - this.pathElements[i] = matchingPath; - } - } - } - - // If we still don't have the right number of paths, use the first N paths - if (this.pathElements.filter(p => p).length !== this.splines.length) { - this.pathElements = allPaths.slice(0, this.splines.length); - } - - this.updatePath(); - }); - }; - - - initializeDefaultSplines() { - this.splines = [{ - points: pv.range(1, 4).map((i, index) => { - if (index === 0) { - return { x: 0, y: this.height }; - } else if (index === 2) { - return { x: this.width, y: 0 }; - } else { - return { - x: i * this.width / 5, - y: 50 + Math.random() * (this.height - 100) - }; - } - }), - color: this.getSplineColor(0), - name: "Spline 1" - }]; - } - - getSplineColor(index) { - const colors = [ - "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", - "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", - "#bcbd22", "#17becf" - ]; - return colors[index % colors.length]; - } - - createContextMenu = () => { - const self = this; - const oldMenu = this.node.contextMenu; - const newMenu = oldMenu.cloneNode(true); - oldMenu.parentNode.replaceChild(newMenu, oldMenu); - this.node.contextMenu = newMenu; - - document.addEventListener('contextmenu', function (e) { - e.preventDefault(); - }); - - document.addEventListener('click', function (e) { - document.querySelectorAll('.spline-editor-context-menu').forEach(menu => { - menu.style.display = 'none'; - }); - }); - - this.node.contextMenu.addEventListener('click', function(e) { - e.preventDefault(); - if (e.target.tagName === 'A') { - const id = parseInt(e.target.id.split('-')[2]); - - switch(id) { - case 0: - e.preventDefault(); - if (!self.drawHandles) { - self.drawHandles = true - self.vis.add(pv.Line) - .data(() => self.splines[self.activeSplineIndex].points.map((point, index) => ({ - start: point, - end: [index] - }))) - .left(d => d.start.x) - .top(d => d.start.y) - .interpolate("linear") - .tension(0) // Straight lines - .strokeStyle("#ff7f0e") // Same color as control points - .lineWidth(1) - .visible(() => self.drawHandles); - self.vis.render(); - } else { - self.drawHandles = false - self.vis.render(); - } - self.node.contextMenu.style.display = 'none'; - break; - case 1: - - self.drawSamplePoints = !self.drawSamplePoints; - self.updatePath(); - break; - case 2: - if (self.dotShape == "circle"){ - self.dotShape = "triangle" - } - else { - self.dotShape = "circle" - } - self.updatePath(); - break; - case 3: - // Create file input element - const fileInput = document.createElement('input'); - fileInput.type = 'file'; - fileInput.accept = 'image/*'; // Accept only image files - - // Listen for file selection - fileInput.addEventListener('change', function (event) { - const file = event.target.files[0]; // Get the selected file - - if (file) { - const imageUrl = URL.createObjectURL(file); - let img = new Image(); - img.src = imageUrl; - img.onload = () => self.handleImageLoad(img, file, null); - } - }); - - fileInput.click(); - - self.node.contextMenu.style.display = 'none'; - break; - case 4: - self.splines[self.activeSplineIndex].points.reverse(); - self.updatePath(); - break; - case 5: - self.backgroundImage.visible(false).root.render(); - self.node.properties.imgData = null; - self.node.contextMenu.style.display = 'none'; - break; - case 6: // Add new spline - const newSplineIndex = self.splines.length; - self.splines.push({ - points: [ - // Create default points for the new spline - { x: 0, y: self.height }, - { x: self.width/2, y: self.height/2 }, - { x: self.width, y: 0 } - ], - color: self.getSplineColor(newSplineIndex), - name: `Spline ${newSplineIndex + 1}` - }); - self.activeSplineIndex = newSplineIndex; - self.refreshSplineElements(); - self.node.contextMenu.style.display = 'none'; - break; - case 7: // Add new single point - const newSingleSplineIndex = self.splines.length; - self.splines.push({ - points: [ - { x: self.lastMousePosition.x, y: self.lastMousePosition.y }, - ], - color: self.getSplineColor(newSingleSplineIndex), - name: `Spline ${newSingleSplineIndex + 1}`, - isSinglePoint: true - }); - self.activeSplineIndex = newSingleSplineIndex; - self.refreshSplineElements(); - self.node.contextMenu.style.display = 'none'; - break; - case 8: // Delete current spline - if (self.splines.length > 1) { - self.splines.splice(self.activeSplineIndex, 1); - self.activeSplineIndex = Math.min(self.activeSplineIndex, self.splines.length - 1); - self.refreshSplineElements(); - } - self.node.contextMenu.style.display = 'none'; - break; - case 9: // Next spline - self.activeSplineIndex = (self.activeSplineIndex + 1) % self.splines.length; - self.refreshSplineElements(); - self.node.contextMenu.style.display = 'none'; - break; - } - } - }); - } - - samplePoints(svgPathElement, numSamples, samplingMethod, width, splineIndex) { - const spline = this.splines[splineIndex]; - - // Check if this is a single point spline - if (spline && (spline.isSinglePoint || (spline.points && spline.points.length === 1))) { - // For a single point, return an array with the same coordinates repeated - const point = spline.points[0]; - return Array(numSamples).fill().map(() => ({ x: point.x, y: point.y })); - } - - if (!svgPathElement) { - console.warn(`Path element not found for spline index: ${splineIndex}. Available paths: ${this.pathElements.length}`); - - - const splinePoints = this.splines[splineIndex].points; - - // If we have no points, return an empty array - if (!splinePoints || splinePoints.length === 0) { - return []; - } - - // Create a simple interpolation between control points - const result = []; - for (let i = 0; i < numSamples; i++) { - const t = i / (numSamples - 1); - const idx = Math.min( - Math.floor(t * (splinePoints.length - 1)), - splinePoints.length - 2 - ); - const fraction = (t * (splinePoints.length - 1)) - idx; - - const x = splinePoints[idx].x + fraction * (splinePoints[idx + 1].x - splinePoints[idx].x); - const y = splinePoints[idx].y + fraction * (splinePoints[idx + 1].y - splinePoints[idx].y); - - result.push({ x, y }); - } - return result; - } - - var svgWidth = width; // Fixed width of the SVG element - var pathLength = svgPathElement.getTotalLength(); - var points = []; - - if (samplingMethod === "speed") { - // Calculate control point distances along the path - const controlPoints = this.splines[splineIndex].points; - const pathPositions = []; - - // Find approximate path positions for each control point - for (const cp of controlPoints) { - let bestDist = Infinity; - let bestPos = 0; - - // Sample the path to find closest point to each control point - for (let pos = 0; pos <= pathLength; pos += pathLength / 100) { - const pt = svgPathElement.getPointAtLength(pos); - const dist = Math.sqrt(Math.pow(pt.x - cp.x, 2) + Math.pow(pt.y - cp.y, 2)); - - if (dist < bestDist) { - bestDist = dist; - bestPos = pos; - } - } - pathPositions.push(bestPos); - } - - // Sort positions along path - pathPositions.sort((a, b) => a - b); - - // Create a smooth speed mapping function with synchronization - const createSynchronizedMapping = () => { - // Calculate segment lengths and densities - const segments = []; - let totalLength = pathPositions[pathPositions.length - 1] - pathPositions[0]; - - for (let i = 0; i < pathPositions.length - 1; i++) { - const segLength = pathPositions[i+1] - pathPositions[i]; - // Inverse relationship - shorter segments = higher density = slower speed - const density = 1 / Math.max(segLength, 0.0001); - segments.push({ - position: pathPositions[i], - length: segLength, - density: density - }); - } - - // Create mapping function with forced synchronization at endpoints - return t => { - // Force synchronization at t=0 and t=1 - if (t === 0) return 0; - if (t === 1) return pathLength; - - // For intermediate points, use the speed control - // Scale t to fit between first and last control points - const firstPos = pathPositions[0]; - const lastPos = pathPositions[pathPositions.length - 1]; - - // Create a density-weighted position mapping - let totalWeight = 0; - let weights = []; - - for (let i = 0; i < segments.length; i++) { - totalWeight += segments[i].density; - weights.push(segments[i].density); - } - - // Normalize weights - const normalizedWeights = weights.map(w => w / totalWeight); - - // Calculate cumulative weights - let cumulativeWeight = 0; - const cumulativeWeights = normalizedWeights.map(w => { - cumulativeWeight += w; - return cumulativeWeight; - }); - - // Find the segment for this t value - let segmentIndex = 0; - for (let i = 0; i < cumulativeWeights.length; i++) { - if (t <= cumulativeWeights[i]) { - segmentIndex = i; - break; - } - } - - // Calculate position within segment - const segmentStart = segmentIndex > 0 ? cumulativeWeights[segmentIndex - 1] : 0; - const segmentEnd = cumulativeWeights[segmentIndex]; - const segmentT = (t - segmentStart) / (segmentEnd - segmentStart); - - // Map to path position - const pathStart = pathPositions[segmentIndex]; - const pathEnd = pathPositions[segmentIndex + 1]; - const pos = pathStart + segmentT * (pathEnd - pathStart); - - // Scale to fill entire path - return pos; - }; - }; - - const mapToPath = createSynchronizedMapping(); - - // Sample using the synchronized mapping function - for (let i = 0; i < numSamples; i++) { - const t = i / (numSamples - 1); - const pathPos = mapToPath(t); - const point = svgPathElement.getPointAtLength(pathPos); - points.push({ x: point.x, y: point.y }); - } - - return points; - - } - else{ - for (var i = 0; i < numSamples; i++) { - if (samplingMethod === "time") { - // Calculate the x-coordinate for the current sample based on the SVG's width - var x = (svgWidth / (numSamples - 1)) * i; - // Find the point on the path that intersects the vertical line at the calculated x-coordinate - var point = this.findPointAtX(svgPathElement, x, pathLength); - } - else if (samplingMethod === "path") { - // Calculate the distance along the path for the current sample - var distance = (pathLength / (numSamples - 1)) * i; - // Get the point at the current distance - var point = svgPathElement.getPointAtLength(distance); - } - - // Add the point to the array of points - points.push({ x: point.x, y: point.y }); - } - return points; - } - } - - findClosestPoints(points, clickedPoint) { - // Calculate distances from clickedPoint to each point in the array - let distances = points.map(point => { - let dx = clickedPoint.x - point.x; - let dy = clickedPoint.y - point.y; - return { index: points.indexOf(point), distance: Math.sqrt(dx * dx + dy * dy) }; - }); - // Sort distances and get the indices of the two closest points - let sortedDistances = distances.sort((a, b) => a.distance - b.distance); - let closestPoint1Index = sortedDistances[0].index; - let closestPoint2Index = sortedDistances[1].index; - // Ensure point1Index is always the smaller index - if (closestPoint1Index > closestPoint2Index) { - [closestPoint1Index, closestPoint2Index] = [closestPoint2Index, closestPoint1Index]; - } - return { point1Index: closestPoint1Index, point2Index: closestPoint2Index }; - } - - findPointAtX(svgPathElement, targetX, pathLength) { - let low = 0; - let high = pathLength; - let bestPoint = svgPathElement.getPointAtLength(0); - - while (low <= high) { - let mid = low + (high - low) / 2; - let point = svgPathElement.getPointAtLength(mid); - - if (Math.abs(point.x - targetX) < 1) { - return point; // The point is close enough to the target - } - - if (point.x < targetX) { - low = mid + 1; - } else { - high = mid - 1; - } - - // Keep track of the closest point found so far - if (Math.abs(point.x - targetX) < Math.abs(bestPoint.x - targetX)) { - bestPoint = point; - } - } - - // Return the closest point found - return bestPoint; - } -} \ No newline at end of file diff --git a/custom_nodes/comfyui-kjnodes/web/red.png b/custom_nodes/comfyui-kjnodes/web/red.png deleted file mode 100644 index 4352c118b2c5fa6f33edc4d99a5e4d22649ff827..0000000000000000000000000000000000000000 Binary files a/custom_nodes/comfyui-kjnodes/web/red.png and /dev/null differ diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example deleted file mode 100644 index 29ab2aa72319354b147b7dd79e1c3179e54d3d06..0000000000000000000000000000000000000000 --- a/custom_nodes/example_node.py.example +++ /dev/null @@ -1,155 +0,0 @@ -class Example: - """ - A example node - - Class methods - ------------- - INPUT_TYPES (dict): - Tell the main program input parameters of nodes. - IS_CHANGED: - optional method to control when the node is re executed. - - Attributes - ---------- - RETURN_TYPES (`tuple`): - The type of each element in the output tuple. - RETURN_NAMES (`tuple`): - Optional: The name of each output in the output tuple. - FUNCTION (`str`): - The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() - OUTPUT_NODE ([`bool`]): - If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example. - The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected. - Assumed to be False if not present. - CATEGORY (`str`): - The category the node should appear in the UI. - DEPRECATED (`bool`): - Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain - functional in existing workflows that use them. - EXPERIMENTAL (`bool`): - Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to - significant changes or removal in future versions. Use with caution in production workflows. - execute(s) -> tuple || None: - The entry point method. The name of this method must be the same as the value of property `FUNCTION`. - For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`. - """ - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(s): - """ - Return a dictionary which contains config for all input fields. - Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT". - Input types "INT", "STRING" or "FLOAT" are special values for fields on the node. - The type can be a list for selection. - - Returns: `dict`: - - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required` - - Value input_fields (`dict`): Contains input fields config: - * Key field_name (`string`): Name of a entry-point method's argument - * Value field_config (`tuple`): - + First value is a string indicate the type of field or a list for selection. - + Second value is a config for type "INT", "STRING" or "FLOAT". - """ - return { - "required": { - "image": ("IMAGE",), - "int_field": ("INT", { - "default": 0, - "min": 0, #Minimum value - "max": 4096, #Maximum value - "step": 64, #Slider's step - "display": "number", # Cosmetic only: display as "number" or "slider" - "lazy": True # Will only be evaluated if check_lazy_status requires it - }), - "float_field": ("FLOAT", { - "default": 1.0, - "min": 0.0, - "max": 10.0, - "step": 0.01, - "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. - "display": "number", - "lazy": True - }), - "print_to_screen": (["enable", "disable"],), - "string_field": ("STRING", { - "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node - "default": "Hello World!", - "lazy": True - }), - }, - } - - RETURN_TYPES = ("IMAGE",) - #RETURN_NAMES = ("image_output_name",) - - FUNCTION = "test" - - #OUTPUT_NODE = False - - CATEGORY = "Example" - - def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen): - """ - Return a list of input names that need to be evaluated. - - This function will be called if there are any lazy inputs which have not yet been - evaluated. As long as you return at least one field which has not yet been evaluated - (and more exist), this function will be called again once the value of the requested - field is available. - - Any evaluated inputs will be passed as arguments to this function. Any unevaluated - inputs will have the value None. - """ - if print_to_screen == "enable": - return ["int_field", "float_field", "string_field"] - else: - return [] - - def test(self, image, string_field, int_field, float_field, print_to_screen): - if print_to_screen == "enable": - print(f"""Your input contains: - string_field aka input text: {string_field} - int_field: {int_field} - float_field: {float_field} - """) - #do some processing on the image, in this example I just invert it - image = 1.0 - image - return (image,) - - """ - The node will always be re executed if any of the inputs change but - this method can be used to force the node to execute again even when the inputs don't change. - You can make this node return a number or a string. This value will be compared to the one returned the last time the node was - executed, if it is different the node will be executed again. - This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash - changes between executions the LoadImage node is executed again. - """ - #@classmethod - #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen): - # return "" - -# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension -# WEB_DIRECTORY = "./somejs" - - -# Add custom API routes, using router -from aiohttp import web -from server import PromptServer - -@PromptServer.instance.routes.get("/hello") -async def get_hello(request): - return web.json_response("hello") - - -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "Example": Example -} - -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "Example": "Example Node" -} diff --git a/custom_nodes/websocket_image_save.py b/custom_nodes/websocket_image_save.py deleted file mode 100644 index 15f87f9f56175f33df18c6142f9e13c4503b1186..0000000000000000000000000000000000000000 --- a/custom_nodes/websocket_image_save.py +++ /dev/null @@ -1,44 +0,0 @@ -from PIL import Image -import numpy as np -import comfy.utils -import time - -#You can use this node to save full size images through the websocket, the -#images will be sent in exactly the same format as the image previews: as -#binary images on the websocket with a 8 byte header indicating the type -#of binary message (first 4 bytes) and the image format (next 4 bytes). - -#Note that no metadata will be put in the images saved with this node. - -class SaveImageWebsocket: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ),} - } - - RETURN_TYPES = () - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "api/image" - - def save_images(self, images): - pbar = comfy.utils.ProgressBar(images.shape[0]) - step = 0 - for image in images: - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) - step += 1 - - return {} - - @classmethod - def IS_CHANGED(s, images): - return time.time() - -NODE_CLASS_MAPPINGS = { - "SaveImageWebsocket": SaveImageWebsocket, -} diff --git a/database/.DS_Store b/database/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/database/.DS_Store and /dev/null differ diff --git a/database/db.py b/database/db.py deleted file mode 100644 index 1de8b80edd8a5761039c7314c6387d2184a15e14..0000000000000000000000000000000000000000 --- a/database/db.py +++ /dev/null @@ -1,112 +0,0 @@ -import logging -import os -import shutil -from app.logger import log_startup_warning -from utils.install_util import get_missing_requirements_message -from comfy.cli_args import args - -_DB_AVAILABLE = False -Session = None - - -try: - from alembic import command - from alembic.config import Config - from alembic.runtime.migration import MigrationContext - from alembic.script import ScriptDirectory - from sqlalchemy import create_engine - from sqlalchemy.orm import sessionmaker - - _DB_AVAILABLE = True -except ImportError as e: - log_startup_warning( - f""" ------------------------------------------------------------------------- -Error importing dependencies: {e} -{get_missing_requirements_message()} -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -""".strip() - ) - - -def dependencies_available(): - """ - Temporary function to check if the dependencies are available - """ - return _DB_AVAILABLE - - -def can_create_session(): - """ - Temporary function to check if the database is available to create a session - During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created - """ - return dependencies_available() and Session is not None - - -def get_alembic_config(): - root_path = os.path.join(os.path.dirname(__file__), "../..") - config_path = os.path.abspath(os.path.join(root_path, "alembic.ini")) - scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db")) - - config = Config(config_path) - config.set_main_option("script_location", scripts_path) - config.set_main_option("sqlalchemy.url", args.database_url) - - return config - - -def get_db_path(): - url = args.database_url - if url.startswith("sqlite:///"): - return url.split("///")[1] - else: - raise ValueError(f"Unsupported database URL '{url}'.") - - -def init_db(): - db_url = args.database_url - logging.debug(f"Database URL: {db_url}") - db_path = get_db_path() - db_exists = os.path.exists(db_path) - - config = get_alembic_config() - - # Check if we need to upgrade - engine = create_engine(db_url) - conn = engine.connect() - - context = MigrationContext.configure(conn) - current_rev = context.get_current_revision() - - script = ScriptDirectory.from_config(config) - target_rev = script.get_current_head() - - if target_rev is None: - logging.warning("No target revision found.") - elif current_rev != target_rev: - # Backup the database pre upgrade - backup_path = db_path + ".bkp" - if db_exists: - shutil.copy(db_path, backup_path) - else: - backup_path = None - - try: - command.upgrade(config, target_rev) - logging.info(f"Database upgraded from {current_rev} to {target_rev}") - except Exception as e: - if backup_path: - # Restore the database from backup if upgrade fails - shutil.copy(backup_path, db_path) - os.remove(backup_path) - logging.exception("Error upgrading database: ") - raise e - - global Session - Session = sessionmaker(bind=engine) - - -def create_session(): - return Session() diff --git a/database/models.py b/database/models.py deleted file mode 100644 index 6facfb8f2b5e7382274021579a453ad3b7853bf7..0000000000000000000000000000000000000000 --- a/database/models.py +++ /dev/null @@ -1,14 +0,0 @@ -from sqlalchemy.orm import declarative_base - -Base = declarative_base() - - -def to_dict(obj): - fields = obj.__table__.columns.keys() - return { - field: (val.to_dict() if hasattr(val, "to_dict") else val) - for field in fields - if (val := getattr(obj, field)) - } - -# TODO: Define models here diff --git a/execution.py b/execution.py deleted file mode 100644 index 1dc35738b823e84bdbee10806798e98dee35dc22..0000000000000000000000000000000000000000 --- a/execution.py +++ /dev/null @@ -1,1220 +0,0 @@ -import copy -import heapq -import inspect -import logging -import sys -import threading -import time -import traceback -from enum import Enum -from typing import List, Literal, NamedTuple, Optional, Union -import asyncio - -import torch - -import comfy.model_management -import nodes -from comfy_execution.caching import ( - BasicCache, - CacheKeySetID, - CacheKeySetInputSignature, - DependencyAwareCache, - HierarchicalCache, - LRUCache, -) -from comfy_execution.graph import ( - DynamicPrompt, - ExecutionBlocker, - ExecutionList, - get_input_info, -) -from comfy_execution.graph_utils import GraphBuilder, is_link -from comfy_execution.validation import validate_node_input -from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler -from comfy_execution.utils import CurrentNodeContext -from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func -from comfy_api.latest import io - - -class ExecutionResult(Enum): - SUCCESS = 0 - FAILURE = 1 - PENDING = 2 - -class DuplicateNodeError(Exception): - pass - -class IsChangedCache: - def __init__(self, prompt_id: str, dynprompt: DynamicPrompt, outputs_cache: BasicCache): - self.prompt_id = prompt_id - self.dynprompt = dynprompt - self.outputs_cache = outputs_cache - self.is_changed = {} - - async def get(self, node_id): - if node_id in self.is_changed: - return self.is_changed[node_id] - - node = self.dynprompt.get_node(node_id) - class_type = node["class_type"] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - has_is_changed = False - is_changed_name = None - if issubclass(class_def, _ComfyNodeInternal) and first_real_override(class_def, "fingerprint_inputs") is not None: - has_is_changed = True - is_changed_name = "fingerprint_inputs" - elif hasattr(class_def, "IS_CHANGED"): - has_is_changed = True - is_changed_name = "IS_CHANGED" - if not has_is_changed: - self.is_changed[node_id] = False - return self.is_changed[node_id] - - if "is_changed" in node: - self.is_changed[node_id] = node["is_changed"] - return self.is_changed[node_id] - - # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED - input_data_all, _, hidden_inputs = get_input_data(node["inputs"], class_def, node_id, None) - try: - is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name) - is_changed = await resolve_map_node_over_list_results(is_changed) - node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed] - except Exception as e: - logging.warning("WARNING: {}".format(e)) - node["is_changed"] = float("NaN") - finally: - self.is_changed[node_id] = node["is_changed"] - return self.is_changed[node_id] - - -class CacheType(Enum): - CLASSIC = 0 - LRU = 1 - DEPENDENCY_AWARE = 2 - - -class CacheSet: - def __init__(self, cache_type=None, cache_size=None): - if cache_type == CacheType.DEPENDENCY_AWARE: - self.init_dependency_aware_cache() - logging.info("Disabling intermediate node cache.") - elif cache_type == CacheType.LRU: - if cache_size is None: - cache_size = 0 - self.init_lru_cache(cache_size) - logging.info("Using LRU cache") - else: - self.init_classic_cache() - - self.all = [self.outputs, self.ui, self.objects] - - # Performs like the old cache -- dump data ASAP - def init_classic_cache(self): - self.outputs = HierarchicalCache(CacheKeySetInputSignature) - self.ui = HierarchicalCache(CacheKeySetInputSignature) - self.objects = HierarchicalCache(CacheKeySetID) - - def init_lru_cache(self, cache_size): - self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size) - self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size) - self.objects = HierarchicalCache(CacheKeySetID) - - # only hold cached items while the decendents have not executed - def init_dependency_aware_cache(self): - self.outputs = DependencyAwareCache(CacheKeySetInputSignature) - self.ui = DependencyAwareCache(CacheKeySetInputSignature) - self.objects = DependencyAwareCache(CacheKeySetID) - - def recursive_debug_dump(self): - result = { - "outputs": self.outputs.recursive_debug_dump(), - "ui": self.ui.recursive_debug_dump(), - } - return result - -SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org") - -def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}): - is_v3 = issubclass(class_def, _ComfyNodeInternal) - if is_v3: - valid_inputs, schema = class_def.INPUT_TYPES(include_hidden=False, return_schema=True) - else: - valid_inputs = class_def.INPUT_TYPES() - input_data_all = {} - missing_keys = {} - hidden_inputs_v3 = {} - for x in inputs: - input_data = inputs[x] - _, input_category, input_info = get_input_info(class_def, x, valid_inputs) - def mark_missing(): - missing_keys[x] = True - input_data_all[x] = (None,) - if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)): - input_unique_id = input_data[0] - output_index = input_data[1] - if outputs is None: - mark_missing() - continue # This might be a lazily-evaluated input - cached_output = outputs.get(input_unique_id) - if cached_output is None: - mark_missing() - continue - if output_index >= len(cached_output): - mark_missing() - continue - obj = cached_output[output_index] - input_data_all[x] = obj - elif input_category is not None: - input_data_all[x] = [input_data] - - if is_v3: - if schema.hidden: - if io.Hidden.prompt in schema.hidden: - hidden_inputs_v3[io.Hidden.prompt] = dynprompt.get_original_prompt() if dynprompt is not None else {} - if io.Hidden.dynprompt in schema.hidden: - hidden_inputs_v3[io.Hidden.dynprompt] = dynprompt - if io.Hidden.extra_pnginfo in schema.hidden: - hidden_inputs_v3[io.Hidden.extra_pnginfo] = extra_data.get('extra_pnginfo', None) - if io.Hidden.unique_id in schema.hidden: - hidden_inputs_v3[io.Hidden.unique_id] = unique_id - if io.Hidden.auth_token_comfy_org in schema.hidden: - hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None) - if io.Hidden.api_key_comfy_org in schema.hidden: - hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None) - else: - if "hidden" in valid_inputs: - h = valid_inputs["hidden"] - for x in h: - if h[x] == "PROMPT": - input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] - if h[x] == "DYNPROMPT": - input_data_all[x] = [dynprompt] - if h[x] == "EXTRA_PNGINFO": - input_data_all[x] = [extra_data.get('extra_pnginfo', None)] - if h[x] == "UNIQUE_ID": - input_data_all[x] = [unique_id] - if h[x] == "AUTH_TOKEN_COMFY_ORG": - input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)] - if h[x] == "API_KEY_COMFY_ORG": - input_data_all[x] = [extra_data.get("api_key_comfy_org", None)] - return input_data_all, missing_keys, hidden_inputs_v3 - -map_node_over_list = None #Don't hook this please - -async def resolve_map_node_over_list_results(results): - remaining = [x for x in results if isinstance(x, asyncio.Task) and not x.done()] - if len(remaining) == 0: - return [x.result() if isinstance(x, asyncio.Task) else x for x in results] - else: - done, pending = await asyncio.wait(remaining) - for task in done: - exc = task.exception() - if exc is not None: - raise exc - return [x.result() if isinstance(x, asyncio.Task) else x for x in results] - -async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None): - # check if node wants the lists - input_is_list = getattr(obj, "INPUT_IS_LIST", False) - - if len(input_data_all) == 0: - max_len_input = 0 - else: - max_len_input = max(len(x) for x in input_data_all.values()) - - # get a slice of inputs, repeat last input when list isn't long enough - def slice_dict(d, i): - return {k: v[i if len(v) > i else -1] for k, v in d.items()} - - results = [] - async def process_inputs(inputs, index=None, input_is_list=False): - if allow_interrupt: - nodes.before_node_execution() - execution_block = None - for k, v in inputs.items(): - if input_is_list: - for e in v: - if isinstance(e, ExecutionBlocker): - v = e - break - if isinstance(v, ExecutionBlocker): - execution_block = execution_block_cb(v) if execution_block_cb else v - break - if execution_block is None: - if pre_execute_cb is not None and index is not None: - pre_execute_cb(index) - # V3 - if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)): - # if is just a class, then assign no resources or state, just create clone - if is_class(obj): - type_obj = obj - obj.VALIDATE_CLASS() - class_clone = obj.PREPARE_CLASS_CLONE(hidden_inputs) - # otherwise, use class instance to populate/reuse some fields - else: - type_obj = type(obj) - type_obj.VALIDATE_CLASS() - class_clone = type_obj.PREPARE_CLASS_CLONE(hidden_inputs) - f = make_locked_method_func(type_obj, func, class_clone) - # V1 - else: - f = getattr(obj, func) - if inspect.iscoroutinefunction(f): - async def async_wrapper(f, prompt_id, unique_id, list_index, args): - with CurrentNodeContext(prompt_id, unique_id, list_index): - return await f(**args) - task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs)) - # Give the task a chance to execute without yielding - await asyncio.sleep(0) - if task.done(): - result = task.result() - results.append(result) - else: - results.append(task) - else: - with CurrentNodeContext(prompt_id, unique_id, index): - result = f(**inputs) - results.append(result) - else: - results.append(execution_block) - - if input_is_list: - await process_inputs(input_data_all, 0, input_is_list=input_is_list) - elif max_len_input == 0: - await process_inputs({}) - else: - for i in range(max_len_input): - input_dict = slice_dict(input_data_all, i) - await process_inputs(input_dict, i) - return results - - -def merge_result_data(results, obj): - # check which outputs need concatenating - output = [] - output_is_list = [False] * len(results[0]) - if hasattr(obj, "OUTPUT_IS_LIST"): - output_is_list = obj.OUTPUT_IS_LIST - - # merge node execution results - for i, is_list in zip(range(len(results[0])), output_is_list): - if is_list: - value = [] - for o in results: - if isinstance(o[i], ExecutionBlocker): - value.append(o[i]) - else: - value.extend(o[i]) - output.append(value) - else: - output.append([o[i] for o in results]) - return output - -async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None): - return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs) - has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values) - if has_pending_task: - return return_values, {}, False, has_pending_task - output, ui, has_subgraph = get_output_from_returns(return_values, obj) - return output, ui, has_subgraph, False - -def get_output_from_returns(return_values, obj): - results = [] - uis = [] - subgraph_results = [] - has_subgraph = False - for i in range(len(return_values)): - r = return_values[i] - if isinstance(r, dict): - if 'ui' in r: - uis.append(r['ui']) - if 'expand' in r: - # Perform an expansion, but do not append results - has_subgraph = True - new_graph = r['expand'] - result = r.get("result", None) - if isinstance(result, ExecutionBlocker): - result = tuple([result] * len(obj.RETURN_TYPES)) - subgraph_results.append((new_graph, result)) - elif 'result' in r: - result = r.get("result", None) - if isinstance(result, ExecutionBlocker): - result = tuple([result] * len(obj.RETURN_TYPES)) - results.append(result) - subgraph_results.append((None, result)) - elif isinstance(r, _NodeOutputInternal): - # V3 - if r.ui is not None: - if isinstance(r.ui, dict): - uis.append(r.ui) - else: - uis.append(r.ui.as_dict()) - if r.expand is not None: - has_subgraph = True - new_graph = r.expand - result = r.result - if r.block_execution is not None: - result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES)) - subgraph_results.append((new_graph, result)) - elif r.result is not None: - result = r.result - if r.block_execution is not None: - result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES)) - results.append(result) - subgraph_results.append((None, result)) - else: - if isinstance(r, ExecutionBlocker): - r = tuple([r] * len(obj.RETURN_TYPES)) - results.append(r) - subgraph_results.append((None, r)) - - if has_subgraph: - output = subgraph_results - elif len(results) > 0: - output = merge_result_data(results, obj) - else: - output = [] - ui = dict() - # TODO: Think there's an existing bug here - # If we're performing a subgraph expansion, we probably shouldn't be returning UI values yet. - # They'll get cached without the completed subgraphs. It's an edge case and I'm not aware of - # any nodes that use both subgraph expansion and custom UI outputs, but might be a problem in the future. - if len(uis) > 0: - ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} - return output, ui, has_subgraph - -def format_value(x): - if x is None: - return None - elif isinstance(x, (int, float, bool, str)): - return x - else: - return str(x) - -async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes): - unique_id = current_item - real_node_id = dynprompt.get_real_node_id(unique_id) - display_node_id = dynprompt.get_display_node_id(unique_id) - parent_node_id = dynprompt.get_parent_node_id(unique_id) - inputs = dynprompt.get_node(unique_id)['inputs'] - class_type = dynprompt.get_node(unique_id)['class_type'] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - if caches.outputs.get(unique_id) is not None: - if server.client_id is not None: - cached_output = caches.ui.get(unique_id) or {} - server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id) - get_progress_state().finish_progress(unique_id) - return (ExecutionResult.SUCCESS, None, None) - - input_data_all = None - try: - if unique_id in pending_async_nodes: - results = [] - for r in pending_async_nodes[unique_id]: - if isinstance(r, asyncio.Task): - try: - results.append(r.result()) - except Exception as ex: - # An async task failed - propagate the exception up - del pending_async_nodes[unique_id] - raise ex - else: - results.append(r) - del pending_async_nodes[unique_id] - output_data, output_ui, has_subgraph = get_output_from_returns(results, class_def) - elif unique_id in pending_subgraph_results: - cached_results = pending_subgraph_results[unique_id] - resolved_outputs = [] - for is_subgraph, result in cached_results: - if not is_subgraph: - resolved_outputs.append(result) - else: - resolved_output = [] - for r in result: - if is_link(r): - source_node, source_output = r[0], r[1] - node_output = caches.outputs.get(source_node)[source_output] - for o in node_output: - resolved_output.append(o) - - else: - resolved_output.append(r) - resolved_outputs.append(tuple(resolved_output)) - output_data = merge_result_data(resolved_outputs, class_def) - output_ui = [] - has_subgraph = False - else: - get_progress_state().start_progress(unique_id) - input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data) - if server.client_id is not None: - server.last_node_id = display_node_id - server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id) - - obj = caches.objects.get(unique_id) - if obj is None: - obj = class_def() - caches.objects.set(unique_id, obj) - - if issubclass(class_def, _ComfyNodeInternal): - lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None - else: - lazy_status_present = getattr(obj, "check_lazy_status", None) is not None - if lazy_status_present: - required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, hidden_inputs=hidden_inputs) - required_inputs = await resolve_map_node_over_list_results(required_inputs) - required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], [])) - required_inputs = [x for x in required_inputs if isinstance(x,str) and ( - x not in input_data_all or x in missing_keys - )] - if len(required_inputs) > 0: - for i in required_inputs: - execution_list.make_input_strong_link(unique_id, i) - return (ExecutionResult.PENDING, None, None) - - def execution_block_cb(block): - if block.message is not None: - mes = { - "prompt_id": prompt_id, - "node_id": unique_id, - "node_type": class_type, - "executed": list(executed), - - "exception_message": f"Execution Blocked: {block.message}", - "exception_type": "ExecutionBlocked", - "traceback": [], - "current_inputs": [], - "current_outputs": [], - } - server.send_sync("execution_error", mes, server.client_id) - return ExecutionBlocker(None) - else: - return block - def pre_execute_cb(call_index): - # TODO - How to handle this with async functions without contextvars (which requires Python 3.12)? - GraphBuilder.set_default_prefix(unique_id, call_index, 0) - output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs) - if has_pending_tasks: - pending_async_nodes[unique_id] = output_data - unblock = execution_list.add_external_block(unique_id) - async def await_completion(): - tasks = [x for x in output_data if isinstance(x, asyncio.Task)] - await asyncio.gather(*tasks, return_exceptions=True) - unblock() - asyncio.create_task(await_completion()) - return (ExecutionResult.PENDING, None, None) - if len(output_ui) > 0: - caches.ui.set(unique_id, { - "meta": { - "node_id": unique_id, - "display_node": display_node_id, - "parent_node": parent_node_id, - "real_node_id": real_node_id, - }, - "output": output_ui - }) - if server.client_id is not None: - server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) - if has_subgraph: - cached_outputs = [] - new_node_ids = [] - new_output_ids = [] - new_output_links = [] - for i in range(len(output_data)): - new_graph, node_outputs = output_data[i] - if new_graph is None: - cached_outputs.append((False, node_outputs)) - else: - # Check for conflicts - for node_id in new_graph.keys(): - if dynprompt.has_node(node_id): - raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.") - for node_id, node_info in new_graph.items(): - new_node_ids.append(node_id) - display_id = node_info.get("override_display_id", unique_id) - dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id) - # Figure out if the newly created node is an output node - class_type = node_info["class_type"] - class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: - new_output_ids.append(node_id) - for i in range(len(node_outputs)): - if is_link(node_outputs[i]): - from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1] - new_output_links.append((from_node_id, from_socket)) - cached_outputs.append((True, node_outputs)) - new_node_ids = set(new_node_ids) - for cache in caches.all: - subcache = await cache.ensure_subcache_for(unique_id, new_node_ids) - subcache.clean_unused() - for node_id in new_output_ids: - execution_list.add_node(node_id) - for link in new_output_links: - execution_list.add_strong_link(link[0], link[1], unique_id) - pending_subgraph_results[unique_id] = cached_outputs - return (ExecutionResult.PENDING, None, None) - caches.outputs.set(unique_id, output_data) - except comfy.model_management.InterruptProcessingException as iex: - logging.info("Processing interrupted") - - # skip formatting inputs/outputs - error_details = { - "node_id": real_node_id, - } - - return (ExecutionResult.FAILURE, error_details, iex) - except Exception as ex: - typ, _, tb = sys.exc_info() - exception_type = full_type_name(typ) - input_data_formatted = {} - if input_data_all is not None: - input_data_formatted = {} - for name, inputs in input_data_all.items(): - input_data_formatted[name] = [format_value(x) for x in inputs] - - logging.error(f"!!! Exception during processing !!! {ex}") - logging.error(traceback.format_exc()) - tips = "" - - if isinstance(ex, comfy.model_management.OOM_EXCEPTION): - tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number." - logging.error("Got an OOM, unloading all loaded models.") - comfy.model_management.unload_all_models() - - error_details = { - "node_id": real_node_id, - "exception_message": "{}\n{}".format(ex, tips), - "exception_type": exception_type, - "traceback": traceback.format_tb(tb), - "current_inputs": input_data_formatted - } - - return (ExecutionResult.FAILURE, error_details, ex) - - get_progress_state().finish_progress(unique_id) - executed.add(unique_id) - - return (ExecutionResult.SUCCESS, None, None) - -class PromptExecutor: - def __init__(self, server, cache_type=False, cache_size=None): - self.cache_size = cache_size - self.cache_type = cache_type - self.server = server - self.reset() - - def reset(self): - self.caches = CacheSet(cache_type=self.cache_type, cache_size=self.cache_size) - self.status_messages = [] - self.success = True - - def add_message(self, event, data: dict, broadcast: bool): - data = { - **data, - "timestamp": int(time.time() * 1000), - } - self.status_messages.append((event, data)) - if self.server.client_id is not None or broadcast: - self.server.send_sync(event, data, self.server.client_id) - - def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex): - node_id = error["node_id"] - class_type = prompt[node_id]["class_type"] - - # First, send back the status to the frontend depending - # on the exception type - if isinstance(ex, comfy.model_management.InterruptProcessingException): - mes = { - "prompt_id": prompt_id, - "node_id": node_id, - "node_type": class_type, - "executed": list(executed), - } - self.add_message("execution_interrupted", mes, broadcast=True) - else: - mes = { - "prompt_id": prompt_id, - "node_id": node_id, - "node_type": class_type, - "executed": list(executed), - "exception_message": error["exception_message"], - "exception_type": error["exception_type"], - "traceback": error["traceback"], - "current_inputs": error["current_inputs"], - "current_outputs": list(current_outputs), - } - self.add_message("execution_error", mes, broadcast=False) - - def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): - asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs)) - - async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): - nodes.interrupt_processing(False) - - if "client_id" in extra_data: - self.server.client_id = extra_data["client_id"] - else: - self.server.client_id = None - - self.status_messages = [] - self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False) - - with torch.inference_mode(): - dynamic_prompt = DynamicPrompt(prompt) - reset_progress_state(prompt_id, dynamic_prompt) - add_progress_handler(WebUIProgressHandler(self.server)) - is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs) - for cache in self.caches.all: - await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) - cache.clean_unused() - - cached_nodes = [] - for node_id in prompt: - if self.caches.outputs.get(node_id) is not None: - cached_nodes.append(node_id) - - comfy.model_management.cleanup_models_gc() - self.add_message("execution_cached", - { "nodes": cached_nodes, "prompt_id": prompt_id}, - broadcast=False) - pending_subgraph_results = {} - pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results - executed = set() - execution_list = ExecutionList(dynamic_prompt, self.caches.outputs) - current_outputs = self.caches.outputs.all_node_ids() - for node_id in list(execute_outputs): - execution_list.add_node(node_id) - - while not execution_list.is_empty(): - node_id, error, ex = await execution_list.stage_node_execution() - if error is not None: - self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) - break - - assert node_id is not None, "Node ID should not be None at this point" - result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes) - self.success = result != ExecutionResult.FAILURE - if result == ExecutionResult.FAILURE: - self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) - break - elif result == ExecutionResult.PENDING: - execution_list.unstage_node_execution() - else: # result == ExecutionResult.SUCCESS: - execution_list.complete_node_execution() - else: - # Only execute when the while-loop ends without break - self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False) - - ui_outputs = {} - meta_outputs = {} - all_node_ids = self.caches.ui.all_node_ids() - for node_id in all_node_ids: - ui_info = self.caches.ui.get(node_id) - if ui_info is not None: - ui_outputs[node_id] = ui_info["output"] - meta_outputs[node_id] = ui_info["meta"] - self.history_result = { - "outputs": ui_outputs, - "meta": meta_outputs, - } - self.server.last_node_id = None - if comfy.model_management.DISABLE_SMART_MEMORY: - comfy.model_management.unload_all_models() - - -async def validate_inputs(prompt_id, prompt, item, validated): - unique_id = item - if unique_id in validated: - return validated[unique_id] - - inputs = prompt[unique_id]['inputs'] - class_type = prompt[unique_id]['class_type'] - obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] - - class_inputs = obj_class.INPUT_TYPES() - valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{}))) - - errors = [] - valid = True - - validate_function_inputs = [] - validate_has_kwargs = False - if issubclass(obj_class, _ComfyNodeInternal): - validate_function_name = "validate_inputs" - validate_function = first_real_override(obj_class, validate_function_name) - else: - validate_function_name = "VALIDATE_INPUTS" - validate_function = getattr(obj_class, validate_function_name, None) - if validate_function is not None: - argspec = inspect.getfullargspec(validate_function) - validate_function_inputs = argspec.args - validate_has_kwargs = argspec.varkw is not None - received_types = {} - - for x in valid_inputs: - input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs) - assert extra_info is not None - if x not in inputs: - if input_category == "required": - error = { - "type": "required_input_missing", - "message": "Required input is missing", - "details": f"{x}", - "extra_info": { - "input_name": x - } - } - errors.append(error) - continue - - val = inputs[x] - info = (input_type, extra_info) - if isinstance(val, list): - if len(val) != 2: - error = { - "type": "bad_linked_input", - "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]", - "details": f"{x}", - "extra_info": { - "input_name": x, - "input_config": info, - "received_value": val - } - } - errors.append(error) - continue - - o_id = val[0] - o_class_type = prompt[o_id]['class_type'] - r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES - received_type = r[val[1]] - received_types[x] = received_type - if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type): - details = f"{x}, received_type({received_type}) mismatch input_type({input_type})" - error = { - "type": "return_type_mismatch", - "message": "Return type mismatch between linked nodes", - "details": details, - "extra_info": { - "input_name": x, - "input_config": info, - "received_type": received_type, - "linked_node": val - } - } - errors.append(error) - continue - try: - r = await validate_inputs(prompt_id, prompt, o_id, validated) - if r[0] is False: - # `r` will be set in `validated[o_id]` already - valid = False - continue - except Exception as ex: - typ, _, tb = sys.exc_info() - valid = False - exception_type = full_type_name(typ) - reasons = [{ - "type": "exception_during_inner_validation", - "message": "Exception when validating inner node", - "details": str(ex), - "extra_info": { - "input_name": x, - "input_config": info, - "exception_message": str(ex), - "exception_type": exception_type, - "traceback": traceback.format_tb(tb), - "linked_node": val - } - }] - validated[o_id] = (False, reasons, o_id) - continue - else: - try: - # Unwraps values wrapped in __value__ key. This is used to pass - # list widget value to execution, as by default list value is - # reserved to represent the connection between nodes. - if isinstance(val, dict) and "__value__" in val: - val = val["__value__"] - inputs[x] = val - - if input_type == "INT": - val = int(val) - inputs[x] = val - if input_type == "FLOAT": - val = float(val) - inputs[x] = val - if input_type == "STRING": - val = str(val) - inputs[x] = val - if input_type == "BOOLEAN": - val = bool(val) - inputs[x] = val - except Exception as ex: - error = { - "type": "invalid_input_type", - "message": f"Failed to convert an input value to a {input_type} value", - "details": f"{x}, {val}, {ex}", - "extra_info": { - "input_name": x, - "input_config": info, - "received_value": val, - "exception_message": str(ex) - } - } - errors.append(error) - continue - - if x not in validate_function_inputs and not validate_has_kwargs: - if "min" in extra_info and val < extra_info["min"]: - error = { - "type": "value_smaller_than_min", - "message": "Value {} smaller than min of {}".format(val, extra_info["min"]), - "details": f"{x}", - "extra_info": { - "input_name": x, - "input_config": info, - "received_value": val, - } - } - errors.append(error) - continue - if "max" in extra_info and val > extra_info["max"]: - error = { - "type": "value_bigger_than_max", - "message": "Value {} bigger than max of {}".format(val, extra_info["max"]), - "details": f"{x}", - "extra_info": { - "input_name": x, - "input_config": info, - "received_value": val, - } - } - errors.append(error) - continue - - if isinstance(input_type, list): - combo_options = input_type - if val not in combo_options: - input_config = info - list_info = "" - - # Don't send back gigantic lists like if they're lots of - # scanned model filepaths - if len(combo_options) > 20: - list_info = f"(list of length {len(combo_options)})" - input_config = None - else: - list_info = str(combo_options) - - error = { - "type": "value_not_in_list", - "message": "Value not in list", - "details": f"{x}: '{val}' not in {list_info}", - "extra_info": { - "input_name": x, - "input_config": input_config, - "received_value": val, - } - } - errors.append(error) - continue - - if len(validate_function_inputs) > 0 or validate_has_kwargs: - input_data_all, _, hidden_inputs = get_input_data(inputs, obj_class, unique_id) - input_filtered = {} - for x in input_data_all: - if x in validate_function_inputs or validate_has_kwargs: - input_filtered[x] = input_data_all[x] - if 'input_types' in validate_function_inputs: - input_filtered['input_types'] = [received_types] - - ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, hidden_inputs=hidden_inputs) - ret = await resolve_map_node_over_list_results(ret) - for x in input_filtered: - for i, r in enumerate(ret): - if r is not True and not isinstance(r, ExecutionBlocker): - details = f"{x}" - if r is not False: - details += f" - {str(r)}" - - error = { - "type": "custom_validation_failed", - "message": "Custom validation failed for node", - "details": details, - "extra_info": { - "input_name": x, - } - } - errors.append(error) - continue - - if len(errors) > 0 or valid is not True: - ret = (False, errors, unique_id) - else: - ret = (True, [], unique_id) - - validated[unique_id] = ret - return ret - -def full_type_name(klass): - module = klass.__module__ - if module == 'builtins': - return klass.__qualname__ - return module + '.' + klass.__qualname__ - -async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[str], None]): - outputs = set() - for x in prompt: - if 'class_type' not in prompt[x]: - error = { - "type": "invalid_prompt", - "message": "Cannot execute because a node is missing the class_type property.", - "details": f"Node ID '#{x}'", - "extra_info": {} - } - return (False, error, [], {}) - - class_type = prompt[x]['class_type'] - class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) - if class_ is None: - error = { - "type": "invalid_prompt", - "message": f"Cannot execute because node {class_type} does not exist.", - "details": f"Node ID '#{x}'", - "extra_info": {} - } - return (False, error, [], {}) - - if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: - if partial_execution_list is None or x in partial_execution_list: - outputs.add(x) - - if len(outputs) == 0: - error = { - "type": "prompt_no_outputs", - "message": "Prompt has no outputs", - "details": "", - "extra_info": {} - } - return (False, error, [], {}) - - good_outputs = set() - errors = [] - node_errors = {} - validated = {} - for o in outputs: - valid = False - reasons = [] - try: - m = await validate_inputs(prompt_id, prompt, o, validated) - valid = m[0] - reasons = m[1] - except Exception as ex: - typ, _, tb = sys.exc_info() - valid = False - exception_type = full_type_name(typ) - reasons = [{ - "type": "exception_during_validation", - "message": "Exception when validating node", - "details": str(ex), - "extra_info": { - "exception_type": exception_type, - "traceback": traceback.format_tb(tb) - } - }] - validated[o] = (False, reasons, o) - - if valid is True: - good_outputs.add(o) - else: - logging.error(f"Failed to validate prompt for output {o}:") - if len(reasons) > 0: - logging.error("* (prompt):") - for reason in reasons: - logging.error(f" - {reason['message']}: {reason['details']}") - errors += [(o, reasons)] - for node_id, result in validated.items(): - valid = result[0] - reasons = result[1] - # If a node upstream has errors, the nodes downstream will also - # be reported as invalid, but there will be no errors attached. - # So don't return those nodes as having errors in the response. - if valid is not True and len(reasons) > 0: - if node_id not in node_errors: - class_type = prompt[node_id]['class_type'] - node_errors[node_id] = { - "errors": reasons, - "dependent_outputs": [], - "class_type": class_type - } - logging.error(f"* {class_type} {node_id}:") - for reason in reasons: - logging.error(f" - {reason['message']}: {reason['details']}") - node_errors[node_id]["dependent_outputs"].append(o) - logging.error("Output will be ignored") - - if len(good_outputs) == 0: - errors_list = [] - for o, errors in errors: - for error in errors: - errors_list.append(f"{error['message']}: {error['details']}") - errors_list = "\n".join(errors_list) - - error = { - "type": "prompt_outputs_failed_validation", - "message": "Prompt outputs failed validation", - "details": errors_list, - "extra_info": {} - } - - return (False, error, list(good_outputs), node_errors) - - return (True, None, list(good_outputs), node_errors) - -MAXIMUM_HISTORY_SIZE = 10000 - -class PromptQueue: - def __init__(self, server): - self.server = server - self.mutex = threading.RLock() - self.not_empty = threading.Condition(self.mutex) - self.task_counter = 0 - self.queue = [] - self.currently_running = {} - self.history = {} - self.flags = {} - - def put(self, item): - with self.mutex: - heapq.heappush(self.queue, item) - self.server.queue_updated() - self.not_empty.notify() - - def get(self, timeout=None): - with self.not_empty: - while len(self.queue) == 0: - self.not_empty.wait(timeout=timeout) - if timeout is not None and len(self.queue) == 0: - return None - item = heapq.heappop(self.queue) - i = self.task_counter - self.currently_running[i] = copy.deepcopy(item) - self.task_counter += 1 - self.server.queue_updated() - return (item, i) - - class ExecutionStatus(NamedTuple): - status_str: Literal['success', 'error'] - completed: bool - messages: List[str] - - def task_done(self, item_id, history_result, - status: Optional['PromptQueue.ExecutionStatus']): - with self.mutex: - prompt = self.currently_running.pop(item_id) - if len(self.history) > MAXIMUM_HISTORY_SIZE: - self.history.pop(next(iter(self.history))) - - status_dict: Optional[dict] = None - if status is not None: - status_dict = copy.deepcopy(status._asdict()) - - # Remove sensitive data from extra_data before storing in history - for sensitive_val in SENSITIVE_EXTRA_DATA_KEYS: - if sensitive_val in prompt[3]: - prompt[3].pop(sensitive_val) - - self.history[prompt[1]] = { - "prompt": prompt, - "outputs": {}, - 'status': status_dict, - } - self.history[prompt[1]].update(history_result) - self.server.queue_updated() - - # Note: slow - def get_current_queue(self): - with self.mutex: - out = [] - for x in self.currently_running.values(): - out += [x] - return (out, copy.deepcopy(self.queue)) - - # read-safe as long as queue items are immutable - def get_current_queue_volatile(self): - with self.mutex: - running = [x for x in self.currently_running.values()] - queued = copy.copy(self.queue) - return (running, queued) - - def get_tasks_remaining(self): - with self.mutex: - return len(self.queue) + len(self.currently_running) - - def wipe_queue(self): - with self.mutex: - self.queue = [] - self.server.queue_updated() - - def delete_queue_item(self, function): - with self.mutex: - for x in range(len(self.queue)): - if function(self.queue[x]): - if len(self.queue) == 1: - self.wipe_queue() - else: - self.queue.pop(x) - heapq.heapify(self.queue) - self.server.queue_updated() - return True - return False - - def get_history(self, prompt_id=None, max_items=None, offset=-1, map_function=None): - with self.mutex: - if prompt_id is None: - out = {} - i = 0 - if offset < 0 and max_items is not None: - offset = len(self.history) - max_items - for k in self.history: - if i >= offset: - p = self.history[k] - if map_function is not None: - p = map_function(p) - out[k] = p - if max_items is not None and len(out) >= max_items: - break - i += 1 - return out - elif prompt_id in self.history: - p = self.history[prompt_id] - if map_function is None: - p = copy.deepcopy(p) - else: - p = map_function(p) - return {prompt_id: p} - else: - return {} - - def wipe_history(self): - with self.mutex: - self.history = {} - - def delete_history_item(self, id_to_delete): - with self.mutex: - self.history.pop(id_to_delete, None) - - def set_flag(self, name, data): - with self.mutex: - self.flags[name] = data - self.not_empty.notify() - - def get_flags(self, reset=True): - with self.mutex: - if reset: - ret = self.flags - self.flags = {} - return ret - else: - return self.flags.copy() diff --git a/extra_model_paths.yaml.example b/extra_model_paths.yaml.example deleted file mode 100644 index b55913a5a9bcbd46b490e7f560b6632e5c780881..0000000000000000000000000000000000000000 --- a/extra_model_paths.yaml.example +++ /dev/null @@ -1,47 +0,0 @@ -#Rename this to extra_model_paths.yaml and ComfyUI will load it - - -#config for a1111 ui -#all you have to do is change the base_path to where yours is installed -a111: - base_path: path/to/stable-diffusion-webui/ - - checkpoints: models/Stable-diffusion - configs: models/Stable-diffusion - vae: models/VAE - loras: | - models/Lora - models/LyCORIS - upscale_models: | - models/ESRGAN - models/RealESRGAN - models/SwinIR - embeddings: embeddings - hypernetworks: models/hypernetworks - controlnet: models/ControlNet - -#config for comfyui -#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc. - -#comfyui: -# base_path: path/to/comfyui/ -# # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads -# #is_default: true -# checkpoints: models/checkpoints/ -# clip: models/clip/ -# clip_vision: models/clip_vision/ -# configs: models/configs/ -# controlnet: models/controlnet/ -# diffusion_models: | -# models/diffusion_models -# models/unet -# embeddings: models/embeddings/ -# loras: models/loras/ -# upscale_models: models/upscale_models/ -# vae: models/vae/ - -#other_ui: -# base_path: path/to/ui -# checkpoints: models/checkpoints -# gligen: models/gligen -# custom_nodes: path/custom_nodes diff --git a/folder_paths.py b/folder_paths.py deleted file mode 100644 index f110d832bb23c84ec27fa3135eb958cb66acb069..0000000000000000000000000000000000000000 --- a/folder_paths.py +++ /dev/null @@ -1,429 +0,0 @@ -from __future__ import annotations - -import os -import time -import mimetypes -import logging -from typing import Literal, List -from collections.abc import Collection - -from comfy.cli_args import args - -supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'} - -folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {} - -# --base-directory - Resets all default paths configured in folder_paths with a new base path -if args.base_directory: - base_path = os.path.abspath(args.base_directory) -else: - base_path = os.path.dirname(os.path.realpath(__file__)) - -models_dir = os.path.join(base_path, "models") -folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) -folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) - -folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) -folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) -folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions) -folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions) -folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) -folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) -folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) -folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) -folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) - -folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) -folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) - -folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) - -folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set()) - -folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) - -folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) - -folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) - -folder_names_and_paths["model_patches"] = ([os.path.join(models_dir, "model_patches")], supported_pt_extensions) - -folder_names_and_paths["audio_encoders"] = ([os.path.join(models_dir, "audio_encoders")], supported_pt_extensions) - -output_directory = os.path.join(base_path, "output") -temp_directory = os.path.join(base_path, "temp") -input_directory = os.path.join(base_path, "input") -user_directory = os.path.join(base_path, "user") - -filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {} - -class CacheHelper: - """ - Helper class for managing file list cache data. - """ - def __init__(self): - self.cache: dict[str, tuple[list[str], dict[str, float], float]] = {} - self.active = False - - def get(self, key: str, default=None) -> tuple[list[str], dict[str, float], float]: - if not self.active: - return default - return self.cache.get(key, default) - - def set(self, key: str, value: tuple[list[str], dict[str, float], float]) -> None: - if self.active: - self.cache[key] = value - - def clear(self): - self.cache.clear() - - def __enter__(self): - self.active = True - return self - - def __exit__(self, exc_type, exc_value, traceback): - self.active = False - self.clear() - -cache_helper = CacheHelper() - -extension_mimetypes_cache = { - "webp" : "image", - "fbx" : "model", -} - -def map_legacy(folder_name: str) -> str: - legacy = {"unet": "diffusion_models", - "clip": "text_encoders"} - return legacy.get(folder_name, folder_name) - -if not os.path.exists(input_directory): - try: - os.makedirs(input_directory) - except: - logging.error("Failed to create input directory") - -def set_output_directory(output_dir: str) -> None: - global output_directory - output_directory = output_dir - -def set_temp_directory(temp_dir: str) -> None: - global temp_directory - temp_directory = temp_dir - -def set_input_directory(input_dir: str) -> None: - global input_directory - input_directory = input_dir - -def get_output_directory() -> str: - global output_directory - return output_directory - -def get_temp_directory() -> str: - global temp_directory - return temp_directory - -def get_input_directory() -> str: - global input_directory - return input_directory - -def get_user_directory() -> str: - return user_directory - -def set_user_directory(user_dir: str) -> None: - global user_directory - user_directory = user_dir - - -#NOTE: used in http server so don't put folders that should not be accessed remotely -def get_directory_by_type(type_name: str) -> str | None: - if type_name == "output": - return get_output_directory() - if type_name == "temp": - return get_temp_directory() - if type_name == "input": - return get_input_directory() - return None - -def filter_files_content_types(files: list[str], content_types: List[Literal["image", "video", "audio", "model"]]) -> list[str]: - """ - Example: - files = os.listdir(folder_paths.get_input_directory()) - videos = filter_files_content_types(files, ["video"]) - - Note: - - 'model' in MIME context refers to 3D models, not files containing trained weights and parameters - """ - global extension_mimetypes_cache - result = [] - for file in files: - extension = file.split('.')[-1] - if extension not in extension_mimetypes_cache: - mime_type, _ = mimetypes.guess_type(file, strict=False) - if not mime_type: - continue - content_type = mime_type.split('/')[0] - extension_mimetypes_cache[extension] = content_type - else: - content_type = extension_mimetypes_cache[extension] - - if content_type in content_types: - result.append(file) - return result - -# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format -# otherwise use default_path as base_dir -def annotated_filepath(name: str) -> tuple[str, str | None]: - if name.endswith("[output]"): - base_dir = get_output_directory() - name = name[:-9] - elif name.endswith("[input]"): - base_dir = get_input_directory() - name = name[:-8] - elif name.endswith("[temp]"): - base_dir = get_temp_directory() - name = name[:-7] - else: - return name, None - - return name, base_dir - - -def get_annotated_filepath(name: str, default_dir: str | None=None) -> str: - name, base_dir = annotated_filepath(name) - - if base_dir is None: - if default_dir is not None: - base_dir = default_dir - else: - base_dir = get_input_directory() # fallback path - - return os.path.join(base_dir, name) - - -def exists_annotated_filepath(name) -> bool: - name, base_dir = annotated_filepath(name) - - if base_dir is None: - base_dir = get_input_directory() # fallback path - - filepath = os.path.join(base_dir, name) - return os.path.exists(filepath) - - -def add_model_folder_path(folder_name: str, full_folder_path: str, is_default: bool = False) -> None: - global folder_names_and_paths - folder_name = map_legacy(folder_name) - if folder_name in folder_names_and_paths: - paths, _exts = folder_names_and_paths[folder_name] - if full_folder_path in paths: - if is_default and paths[0] != full_folder_path: - # If the path to the folder is not the first in the list, move it to the beginning. - paths.remove(full_folder_path) - paths.insert(0, full_folder_path) - else: - if is_default: - paths.insert(0, full_folder_path) - else: - paths.append(full_folder_path) - else: - folder_names_and_paths[folder_name] = ([full_folder_path], set()) - -def get_folder_paths(folder_name: str) -> list[str]: - folder_name = map_legacy(folder_name) - return folder_names_and_paths[folder_name][0][:] - -def recursive_search(directory: str, excluded_dir_names: list[str] | None=None) -> tuple[list[str], dict[str, float]]: - if not os.path.isdir(directory): - return [], {} - - if excluded_dir_names is None: - excluded_dir_names = [] - - result = [] - dirs = {} - - # Attempt to add the initial directory to dirs with error handling - try: - dirs[directory] = os.path.getmtime(directory) - except FileNotFoundError: - logging.warning(f"Warning: Unable to access {directory}. Skipping this path.") - - logging.debug("recursive file list on directory {}".format(directory)) - dirpath: str - subdirs: list[str] - filenames: list[str] - - for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): - subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] - for file_name in filenames: - try: - relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) - result.append(relative_path) - except: - logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.") - continue - - for d in subdirs: - path: str = os.path.join(dirpath, d) - try: - dirs[path] = os.path.getmtime(path) - except FileNotFoundError: - logging.warning(f"Warning: Unable to access {path}. Skipping this path.") - continue - logging.debug("found {} files".format(len(result))) - return result, dirs - -def filter_files_extensions(files: Collection[str], extensions: Collection[str]) -> list[str]: - return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) - - - -def get_full_path(folder_name: str, filename: str) -> str | None: - """ - Get the full path of a file in a folder, has to be a file - """ - global folder_names_and_paths - folder_name = map_legacy(folder_name) - if folder_name not in folder_names_and_paths: - return None - folders = folder_names_and_paths[folder_name] - filename = os.path.relpath(os.path.join("/", filename), "/") - for x in folders[0]: - full_path = os.path.join(x, filename) - if os.path.isfile(full_path): - return full_path - elif os.path.islink(full_path): - logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path)) - - return None - - -def get_full_path_or_raise(folder_name: str, filename: str) -> str: - """ - Get the full path of a file in a folder, has to be a file - """ - full_path = get_full_path(folder_name, filename) - if full_path is None: - raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.") - return full_path - - -def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]: - folder_name = map_legacy(folder_name) - global folder_names_and_paths - output_list = set() - folders = folder_names_and_paths[folder_name] - output_folders = {} - for x in folders[0]: - files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) - output_list.update(filter_files_extensions(files, folders[1])) - output_folders = {**output_folders, **folders_all} - - return sorted(list(output_list)), output_folders, time.perf_counter() - -def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None: - strong_cache = cache_helper.get(folder_name) - if strong_cache is not None: - return strong_cache - - global filename_list_cache - global folder_names_and_paths - folder_name = map_legacy(folder_name) - if folder_name not in filename_list_cache: - return None - out = filename_list_cache[folder_name] - - for x in out[1]: - time_modified = out[1][x] - folder = x - if os.path.getmtime(folder) != time_modified: - return None - - folders = folder_names_and_paths[folder_name] - for x in folders[0]: - if os.path.isdir(x): - if x not in out[1]: - return None - - return out - -def get_filename_list(folder_name: str) -> list[str]: - folder_name = map_legacy(folder_name) - out = cached_filename_list_(folder_name) - if out is None: - out = get_filename_list_(folder_name) - global filename_list_cache - filename_list_cache[folder_name] = out - cache_helper.set(folder_name, out) - return list(out[0]) - -def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]: - def map_filename(filename: str) -> tuple[int, str]: - prefix_len = len(os.path.basename(filename_prefix)) - prefix = filename[:prefix_len + 1] - try: - digits = int(filename[prefix_len + 1:].split('_')[0]) - except: - digits = 0 - return digits, prefix - - def compute_vars(input: str, image_width: int, image_height: int) -> str: - input = input.replace("%width%", str(image_width)) - input = input.replace("%height%", str(image_height)) - now = time.localtime() - input = input.replace("%year%", str(now.tm_year)) - input = input.replace("%month%", str(now.tm_mon).zfill(2)) - input = input.replace("%day%", str(now.tm_mday).zfill(2)) - input = input.replace("%hour%", str(now.tm_hour).zfill(2)) - input = input.replace("%minute%", str(now.tm_min).zfill(2)) - input = input.replace("%second%", str(now.tm_sec).zfill(2)) - return input - - if "%" in filename_prefix: - filename_prefix = compute_vars(filename_prefix, image_width, image_height) - - subfolder = os.path.dirname(os.path.normpath(filename_prefix)) - filename = os.path.basename(os.path.normpath(filename_prefix)) - - full_output_folder = os.path.join(output_dir, subfolder) - - if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: - err = "**** ERROR: Saving image outside the output folder is not allowed." + \ - "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ - "\n output_dir: " + output_dir + \ - "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) - logging.error(err) - raise Exception(err) - - try: - counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 - except ValueError: - counter = 1 - except FileNotFoundError: - os.makedirs(full_output_folder, exist_ok=True) - counter = 1 - return full_output_folder, filename, counter, subfolder, filename_prefix - -def get_input_subfolders() -> list[str]: - """Returns a list of all subfolder paths in the input directory, recursively. - - Returns: - List of folder paths relative to the input directory, excluding the root directory - """ - input_dir = get_input_directory() - folders = [] - - try: - if not os.path.exists(input_dir): - return [] - - for root, dirs, _ in os.walk(input_dir): - rel_path = os.path.relpath(root, input_dir) - if rel_path != ".": # Only include non-root directories - # Normalize path separators to forward slashes - folders.append(rel_path.replace(os.sep, '/')) - - return sorted(folders) - except FileNotFoundError: - return [] diff --git a/frontend_management.py b/frontend_management.py deleted file mode 100644 index 0bee73685b931bc1eddef79c7acafc2d0958098b..0000000000000000000000000000000000000000 --- a/frontend_management.py +++ /dev/null @@ -1,361 +0,0 @@ -from __future__ import annotations -import argparse -import logging -import os -import re -import sys -import tempfile -import zipfile -import importlib -from dataclasses import dataclass -from functools import cached_property -from pathlib import Path -from typing import TypedDict, Optional -from importlib.metadata import version - -import requests -from typing_extensions import NotRequired - -from utils.install_util import get_missing_requirements_message, requirements_path - -from comfy.cli_args import DEFAULT_VERSION_STRING -import app.logger - - -def frontend_install_warning_message(): - return f""" -{get_missing_requirements_message()} - -This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead. -""".strip() - -def parse_version(version: str) -> tuple[int, int, int]: - return tuple(map(int, version.split("."))) - -def is_valid_version(version: str) -> bool: - """Validate if a string is a valid semantic version (X.Y.Z format).""" - pattern = r"^(\d+)\.(\d+)\.(\d+)$" - return bool(re.match(pattern, version)) - -def get_installed_frontend_version(): - """Get the currently installed frontend package version.""" - frontend_version_str = version("comfyui-frontend-package") - return frontend_version_str - -def get_required_frontend_version(): - """Get the required frontend version from requirements.txt.""" - try: - with open(requirements_path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if line.startswith("comfyui-frontend-package=="): - version_str = line.split("==")[-1] - if not is_valid_version(version_str): - logging.error(f"Invalid version format in requirements.txt: {version_str}") - return None - return version_str - logging.error("comfyui-frontend-package not found in requirements.txt") - return None - except FileNotFoundError: - logging.error("requirements.txt not found. Cannot determine required frontend version.") - return None - except Exception as e: - logging.error(f"Error reading requirements.txt: {e}") - return None - -def check_frontend_version(): - """Check if the frontend version is up to date.""" - - try: - frontend_version_str = get_installed_frontend_version() - frontend_version = parse_version(frontend_version_str) - required_frontend_str = get_required_frontend_version() - required_frontend = parse_version(required_frontend_str) - if frontend_version < required_frontend: - app.logger.log_startup_warning( - f""" -________________________________________________________________________ -WARNING WARNING WARNING WARNING WARNING - -Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}. - -{frontend_install_warning_message()} -________________________________________________________________________ -""".strip() - ) - else: - logging.info("ComfyUI frontend version: {}".format(frontend_version_str)) - except Exception as e: - logging.error(f"Failed to check frontend version: {e}") - - -REQUEST_TIMEOUT = 10 # seconds - - -class Asset(TypedDict): - url: str - - -class Release(TypedDict): - id: int - tag_name: str - name: str - prerelease: bool - created_at: str - published_at: str - body: str - assets: NotRequired[list[Asset]] - - -@dataclass -class FrontEndProvider: - owner: str - repo: str - - @property - def folder_name(self) -> str: - return f"{self.owner}_{self.repo}" - - @property - def release_url(self) -> str: - return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases" - - @cached_property - def all_releases(self) -> list[Release]: - releases = [] - api_url = self.release_url - while api_url: - response = requests.get(api_url, timeout=REQUEST_TIMEOUT) - response.raise_for_status() # Raises an HTTPError if the response was an error - releases.extend(response.json()) - # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly. - if "next" in response.links: - api_url = response.links["next"]["url"] - else: - api_url = None - return releases - - @cached_property - def latest_release(self) -> Release: - latest_release_url = f"{self.release_url}/latest" - response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT) - response.raise_for_status() # Raises an HTTPError if the response was an error - return response.json() - - @cached_property - def latest_prerelease(self) -> Release: - """Get the latest pre-release version - even if it's older than the latest release""" - release = [release for release in self.all_releases if release["prerelease"]] - - if not release: - raise ValueError("No pre-releases found") - - # GitHub returns releases in reverse chronological order, so first is latest - return release[0] - - def get_release(self, version: str) -> Release: - if version == "latest": - return self.latest_release - elif version == "prerelease": - return self.latest_prerelease - else: - for release in self.all_releases: - if release["tag_name"] in [version, f"v{version}"]: - return release - raise ValueError(f"Version {version} not found in releases") - - -def download_release_asset_zip(release: Release, destination_path: str) -> None: - """Download dist.zip from github release.""" - asset_url = None - for asset in release.get("assets", []): - if asset["name"] == "dist.zip": - asset_url = asset["url"] - break - - if not asset_url: - raise ValueError("dist.zip not found in the release assets") - - # Use a temporary file to download the zip content - with tempfile.TemporaryFile() as tmp_file: - headers = {"Accept": "application/octet-stream"} - response = requests.get( - asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT - ) - response.raise_for_status() # Ensure we got a successful response - - # Write the content to the temporary file - tmp_file.write(response.content) - - # Go back to the beginning of the temporary file - tmp_file.seek(0) - - # Extract the zip file content to the destination path - with zipfile.ZipFile(tmp_file, "r") as zip_ref: - zip_ref.extractall(destination_path) - - -class FrontendManager: - CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions") - - @classmethod - def get_required_frontend_version(cls) -> str: - """Get the required frontend package version.""" - return get_required_frontend_version() - - @classmethod - def default_frontend_path(cls) -> str: - try: - import comfyui_frontend_package - - return str(importlib.resources.files(comfyui_frontend_package) / "static") - except ImportError: - logging.error( - f""" -********** ERROR *********** - -comfyui-frontend-package is not installed. - -{frontend_install_warning_message()} - -********** ERROR *********** -""".strip() - ) - sys.exit(-1) - - @classmethod - def templates_path(cls) -> str: - try: - import comfyui_workflow_templates - - return str( - importlib.resources.files(comfyui_workflow_templates) / "templates" - ) - except ImportError: - logging.error( - f""" -********** ERROR *********** - -comfyui-workflow-templates is not installed. - -{frontend_install_warning_message()} - -********** ERROR *********** -""".strip() - ) - - @classmethod - def embedded_docs_path(cls) -> str: - """Get the path to embedded documentation""" - try: - import comfyui_embedded_docs - - return str( - importlib.resources.files(comfyui_embedded_docs) / "docs" - ) - except ImportError: - logging.info("comfyui-embedded-docs package not found") - return None - - @classmethod - def parse_version_string(cls, value: str) -> tuple[str, str, str]: - """ - Args: - value (str): The version string to parse. - - Returns: - tuple[str, str]: A tuple containing provider name and version. - - Raises: - argparse.ArgumentTypeError: If the version string is invalid. - """ - VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$" - match_result = re.match(VERSION_PATTERN, value) - if match_result is None: - raise argparse.ArgumentTypeError(f"Invalid version string: {value}") - - return match_result.group(1), match_result.group(2), match_result.group(3) - - @classmethod - def init_frontend_unsafe( - cls, version_string: str, provider: Optional[FrontEndProvider] = None - ) -> str: - """ - Initializes the frontend for the specified version. - - Args: - version_string (str): The version string. - provider (FrontEndProvider, optional): The provider to use. Defaults to None. - - Returns: - str: The path to the initialized frontend. - - Raises: - Exception: If there is an error during the initialization process. - main error source might be request timeout or invalid URL. - """ - if version_string == DEFAULT_VERSION_STRING: - check_frontend_version() - return cls.default_frontend_path() - - repo_owner, repo_name, version = cls.parse_version_string(version_string) - - if version.startswith("v"): - expected_path = str( - Path(cls.CUSTOM_FRONTENDS_ROOT) - / f"{repo_owner}_{repo_name}" - / version.lstrip("v") - ) - if os.path.exists(expected_path): - logging.info( - f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}" - ) - return expected_path - - logging.info( - f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..." - ) - - provider = provider or FrontEndProvider(repo_owner, repo_name) - release = provider.get_release(version) - - semantic_version = release["tag_name"].lstrip("v") - web_root = str( - Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version - ) - if not os.path.exists(web_root): - try: - os.makedirs(web_root, exist_ok=True) - logging.info( - "Downloading frontend(%s) version(%s) to (%s)", - provider.folder_name, - semantic_version, - web_root, - ) - logging.debug(release) - download_release_asset_zip(release, destination_path=web_root) - finally: - # Clean up the directory if it is empty, i.e. the download failed - if not os.listdir(web_root): - os.rmdir(web_root) - - return web_root - - @classmethod - def init_frontend(cls, version_string: str) -> str: - """ - Initializes the frontend with the specified version string. - - Args: - version_string (str): The version string to initialize the frontend with. - - Returns: - str: The path of the initialized frontend. - """ - try: - return cls.init_frontend_unsafe(version_string) - except Exception as e: - logging.error("Failed to initialize frontend: %s", e) - logging.info("Falling back to the default frontend.") - check_frontend_version() - return cls.default_frontend_path() diff --git a/hook_breaker_ac10a0.py b/hook_breaker_ac10a0.py deleted file mode 100644 index c3e1c063382760535cee82e8bfcbc85d17e2cce4..0000000000000000000000000000000000000000 --- a/hook_breaker_ac10a0.py +++ /dev/null @@ -1,17 +0,0 @@ -# Prevent custom nodes from hooking anything important -import comfy.model_management - -HOOK_BREAK = [(comfy.model_management, "cast_to")] - - -SAVED_FUNCTIONS = [] - - -def save_functions(): - for f in HOOK_BREAK: - SAVED_FUNCTIONS.append((f[0], f[1], getattr(f[0], f[1]))) - - -def restore_functions(): - for f in SAVED_FUNCTIONS: - setattr(f[0], f[1], f[2]) diff --git a/input/example.png b/input/example.png deleted file mode 100644 index 7b7f3c9cbbe6d8750c4a9eaf65d6ae4d2f108f79..0000000000000000000000000000000000000000 Binary files a/input/example.png and /dev/null differ diff --git a/input/tmpip5bak5z.png b/input/tmpip5bak5z.png deleted file mode 100644 index 6f360a4fee27f966b6d9802c725bc2a4705aeed4..0000000000000000000000000000000000000000 --- a/input/tmpip5bak5z.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e2b91ff26190ce546ad8f3fd0d1f49bf9cb5136d488431d9aaf12d3aede52d6e -size 1226458 diff --git a/input/tmpvhslhwc_.png b/input/tmpvhslhwc_.png deleted file mode 100644 index 0e6ae07a480d4e5c1eebe0807b039443bc686a29..0000000000000000000000000000000000000000 --- a/input/tmpvhslhwc_.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5291afc856b82dc284e7a9e6269973a75b2593b9fb92e689f9bec9ac2a6a3885 -size 1254225 diff --git a/latent_preview.py b/latent_preview.py deleted file mode 100644 index 95d3cb7338eafc63ce060b43528c6017d8d55953..0000000000000000000000000000000000000000 --- a/latent_preview.py +++ /dev/null @@ -1,108 +0,0 @@ -import torch -from PIL import Image -from comfy.cli_args import args, LatentPreviewMethod -from comfy.taesd.taesd import TAESD -import comfy.model_management -import folder_paths -import comfy.utils -import logging - -MAX_PREVIEW_RESOLUTION = args.preview_size - -def preview_to_image(latent_image): - latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1 - .mul(0xFF) # to 0..255 - ) - if comfy.model_management.directml_enabled: - latents_ubyte = latents_ubyte.to(dtype=torch.uint8) - latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) - - return Image.fromarray(latents_ubyte.numpy()) - -class LatentPreviewer: - def decode_latent_to_preview(self, x0): - pass - - def decode_latent_to_preview_image(self, preview_format, x0): - preview_image = self.decode_latent_to_preview(x0) - return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) - -class TAESDPreviewerImpl(LatentPreviewer): - def __init__(self, taesd): - self.taesd = taesd - - def decode_latent_to_preview(self, x0): - x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2) - return preview_to_image(x_sample) - - -class Latent2RGBPreviewer(LatentPreviewer): - def __init__(self, latent_rgb_factors, latent_rgb_factors_bias=None): - self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1) - self.latent_rgb_factors_bias = None - if latent_rgb_factors_bias is not None: - self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu") - - def decode_latent_to_preview(self, x0): - self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) - if self.latent_rgb_factors_bias is not None: - self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device) - - if x0.ndim == 5: - x0 = x0[0, :, 0] - else: - x0 = x0[0] - - latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias) - # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors - - return preview_to_image(latent_image) - - -def get_previewer(device, latent_format): - previewer = None - method = args.preview_method - if method != LatentPreviewMethod.NoPreviews: - # TODO previewer methods - taesd_decoder_path = None - if latent_format.taesd_decoder_name is not None: - taesd_decoder_path = next( - (fn for fn in folder_paths.get_filename_list("vae_approx") - if fn.startswith(latent_format.taesd_decoder_name)), - "" - ) - taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path) - - if method == LatentPreviewMethod.Auto: - method = LatentPreviewMethod.Latent2RGB - - if method == LatentPreviewMethod.TAESD: - if taesd_decoder_path: - taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device) - previewer = TAESDPreviewerImpl(taesd) - else: - logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) - - if previewer is None: - if latent_format.latent_rgb_factors is not None: - previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors, latent_format.latent_rgb_factors_bias) - return previewer - -def prepare_callback(model, steps, x0_output_dict=None): - preview_format = "JPEG" - if preview_format not in ["JPEG", "PNG"]: - preview_format = "JPEG" - - previewer = get_previewer(model.load_device, model.model.latent_format) - - pbar = comfy.utils.ProgressBar(steps) - def callback(step, x0, x, total_steps): - if x0_output_dict is not None: - x0_output_dict["x0"] = x0 - - preview_bytes = None - if previewer: - preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) - pbar.update_absolute(step + 1, total_steps, preview_bytes) - return callback - diff --git a/logger.py b/logger.py deleted file mode 100644 index 3d26d98fe28005a3ed8b6deb7da34823b98b5696..0000000000000000000000000000000000000000 --- a/logger.py +++ /dev/null @@ -1,98 +0,0 @@ -from collections import deque -from datetime import datetime -import io -import logging -import sys -import threading - -logs = None -stdout_interceptor = None -stderr_interceptor = None - - -class LogInterceptor(io.TextIOWrapper): - def __init__(self, stream, *args, **kwargs): - buffer = stream.buffer - encoding = stream.encoding - super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering) - self._lock = threading.Lock() - self._flush_callbacks = [] - self._logs_since_flush = [] - - def write(self, data): - entry = {"t": datetime.now().isoformat(), "m": data} - with self._lock: - self._logs_since_flush.append(entry) - - # Simple handling for cr to overwrite the last output if it isnt a full line - # else logs just get full of progress messages - if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"): - logs.pop() - logs.append(entry) - super().write(data) - - def flush(self): - super().flush() - for cb in self._flush_callbacks: - cb(self._logs_since_flush) - self._logs_since_flush = [] - - def on_flush(self, callback): - self._flush_callbacks.append(callback) - - -def get_logs(): - return logs - - -def on_flush(callback): - if stdout_interceptor is not None: - stdout_interceptor.on_flush(callback) - if stderr_interceptor is not None: - stderr_interceptor.on_flush(callback) - -def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False): - global logs - if logs: - return - - # Override output streams and log to buffer - logs = deque(maxlen=capacity) - - global stdout_interceptor - global stderr_interceptor - stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout) - stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr) - - # Setup default global logger - logger = logging.getLogger() - logger.setLevel(log_level) - - stream_handler = logging.StreamHandler() - stream_handler.setFormatter(logging.Formatter("%(message)s")) - - if use_stdout: - # Only errors and critical to stderr - stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR) - - # Lesser to stdout - stdout_handler = logging.StreamHandler(sys.stdout) - stdout_handler.setFormatter(logging.Formatter("%(message)s")) - stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR) - logger.addHandler(stdout_handler) - - logger.addHandler(stream_handler) - - -STARTUP_WARNINGS = [] - - -def log_startup_warning(msg): - logging.warning(msg) - STARTUP_WARNINGS.append(msg) - - -def print_startup_warnings(): - for s in STARTUP_WARNINGS: - logging.warning(s) - STARTUP_WARNINGS.clear() diff --git a/main.py b/main.py deleted file mode 100644 index b23d508168bd38a9ffe50069ee59782b87aa5ad3..0000000000000000000000000000000000000000 --- a/main.py +++ /dev/null @@ -1,370 +0,0 @@ -import comfy.options -comfy.options.enable_args_parsing() - -import os -import importlib.util -import folder_paths -import time -from comfy.cli_args import args -from app.logger import setup_logger -import itertools -import utils.extra_config -import logging -import sys -from comfy_execution.progress import get_progress_state -from comfy_execution.utils import get_executing_context -from comfy_api import feature_flags - -if __name__ == "__main__": - #NOTE: These do not do anything on core ComfyUI, they are for custom nodes. - os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' - os.environ['DO_NOT_TRACK'] = '1' - -setup_logger(log_level=args.verbose, use_stdout=args.log_stdout) - -def apply_custom_paths(): - # extra model paths - extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml") - if os.path.isfile(extra_model_paths_config_path): - utils.extra_config.load_extra_path_config(extra_model_paths_config_path) - - if args.extra_model_paths_config: - for config_path in itertools.chain(*args.extra_model_paths_config): - utils.extra_config.load_extra_path_config(config_path) - - # --output-directory, --input-directory, --user-directory - if args.output_directory: - output_dir = os.path.abspath(args.output_directory) - logging.info(f"Setting output directory to: {output_dir}") - folder_paths.set_output_directory(output_dir) - - # These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes - folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints")) - folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip")) - folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae")) - folder_paths.add_model_folder_path("diffusion_models", - os.path.join(folder_paths.get_output_directory(), "diffusion_models")) - folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras")) - - if args.input_directory: - input_dir = os.path.abspath(args.input_directory) - logging.info(f"Setting input directory to: {input_dir}") - folder_paths.set_input_directory(input_dir) - - if args.user_directory: - user_dir = os.path.abspath(args.user_directory) - logging.info(f"Setting user directory to: {user_dir}") - folder_paths.set_user_directory(user_dir) - - -def execute_prestartup_script(): - if args.disable_all_custom_nodes and len(args.whitelist_custom_nodes) == 0: - return - - def execute_script(script_path): - module_name = os.path.splitext(script_path)[0] - try: - spec = importlib.util.spec_from_file_location(module_name, script_path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return True - except Exception as e: - logging.error(f"Failed to execute startup-script: {script_path} / {e}") - return False - - node_paths = folder_paths.get_folder_paths("custom_nodes") - for custom_node_path in node_paths: - possible_modules = os.listdir(custom_node_path) - node_prestartup_times = [] - - for possible_module in possible_modules: - module_path = os.path.join(custom_node_path, possible_module) - if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__": - continue - - script_path = os.path.join(module_path, "prestartup_script.py") - if os.path.exists(script_path): - if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes: - logging.info(f"Prestartup Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes") - continue - time_before = time.perf_counter() - success = execute_script(script_path) - node_prestartup_times.append((time.perf_counter() - time_before, module_path, success)) - if len(node_prestartup_times) > 0: - logging.info("\nPrestartup times for custom nodes:") - for n in sorted(node_prestartup_times): - if n[2]: - import_message = "" - else: - import_message = " (PRESTARTUP FAILED)" - logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) - logging.info("") - -apply_custom_paths() -execute_prestartup_script() - - -# Main code -import asyncio -import shutil -import threading -import gc - - -if os.name == "nt": - os.environ['MIMALLOC_PURGE_DELAY'] = '0' - logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) - -if __name__ == "__main__": - if args.default_device is not None: - default_dev = args.default_device - devices = list(range(32)) - devices.remove(default_dev) - devices.insert(0, default_dev) - devices = ','.join(map(str, devices)) - os.environ['CUDA_VISIBLE_DEVICES'] = str(devices) - os.environ['HIP_VISIBLE_DEVICES'] = str(devices) - - if args.cuda_device is not None: - os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) - os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device) - logging.info("Set cuda device to: {}".format(args.cuda_device)) - - if args.oneapi_device_selector is not None: - os.environ['ONEAPI_DEVICE_SELECTOR'] = args.oneapi_device_selector - logging.info("Set oneapi device selector to: {}".format(args.oneapi_device_selector)) - - if args.deterministic: - if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ: - os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8" - - import cuda_malloc - -if 'torch' in sys.modules: - logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.") - -import comfy.utils - -import execution -import server -from protocol import BinaryEventTypes -import nodes -import comfy.model_management -import comfyui_version -import app.logger -import hook_breaker_ac10a0 - -def cuda_malloc_warning(): - device = comfy.model_management.get_torch_device() - device_name = comfy.model_management.get_torch_device_name(device) - cuda_malloc_warning = False - if "cudaMallocAsync" in device_name: - for b in cuda_malloc.blacklist: - if b in device_name: - cuda_malloc_warning = True - if cuda_malloc_warning: - logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n") - - -def prompt_worker(q, server_instance): - current_time: float = 0.0 - cache_type = execution.CacheType.CLASSIC - if args.cache_lru > 0: - cache_type = execution.CacheType.LRU - elif args.cache_none: - cache_type = execution.CacheType.DEPENDENCY_AWARE - - e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru) - last_gc_collect = 0 - need_gc = False - gc_collect_interval = 10.0 - - while True: - timeout = 1000.0 - if need_gc: - timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0) - - queue_item = q.get(timeout=timeout) - if queue_item is not None: - item, item_id = queue_item - execution_start_time = time.perf_counter() - prompt_id = item[1] - server_instance.last_prompt_id = prompt_id - - e.execute(item[2], prompt_id, item[3], item[4]) - need_gc = True - q.task_done(item_id, - e.history_result, - status=execution.PromptQueue.ExecutionStatus( - status_str='success' if e.success else 'error', - completed=e.success, - messages=e.status_messages)) - if server_instance.client_id is not None: - server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id) - - current_time = time.perf_counter() - execution_time = current_time - execution_start_time - - # Log Time in a more readable way after 10 minutes - if execution_time > 600: - execution_time = time.strftime("%H:%M:%S", time.gmtime(execution_time)) - logging.info(f"Prompt executed in {execution_time}") - else: - logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) - - flags = q.get_flags() - free_memory = flags.get("free_memory", False) - - if flags.get("unload_models", free_memory): - comfy.model_management.unload_all_models() - need_gc = True - last_gc_collect = 0 - - if free_memory: - e.reset() - need_gc = True - last_gc_collect = 0 - - if need_gc: - current_time = time.perf_counter() - if (current_time - last_gc_collect) > gc_collect_interval: - gc.collect() - comfy.model_management.soft_empty_cache() - last_gc_collect = current_time - need_gc = False - hook_breaker_ac10a0.restore_functions() - - -async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None): - addresses = [] - for addr in address.split(","): - addresses.append((addr, port)) - await asyncio.gather( - server_instance.start_multi_address(addresses, call_on_start, verbose), server_instance.publish_loop() - ) - -def hijack_progress(server_instance): - def hook(value, total, preview_image, prompt_id=None, node_id=None): - executing_context = get_executing_context() - if prompt_id is None and executing_context is not None: - prompt_id = executing_context.prompt_id - if node_id is None and executing_context is not None: - node_id = executing_context.node_id - comfy.model_management.throw_exception_if_processing_interrupted() - if prompt_id is None: - prompt_id = server_instance.last_prompt_id - if node_id is None: - node_id = server_instance.last_node_id - progress = {"value": value, "max": total, "prompt_id": prompt_id, "node": node_id} - get_progress_state().update_progress(node_id, value, total, preview_image) - - server_instance.send_sync("progress", progress, server_instance.client_id) - if preview_image is not None: - # Only send old method if client doesn't support preview metadata - if not feature_flags.supports_feature( - server_instance.sockets_metadata, - server_instance.client_id, - "supports_preview_metadata", - ): - server_instance.send_sync( - BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, - preview_image, - server_instance.client_id, - ) - - comfy.utils.set_progress_bar_global_hook(hook) - - -def cleanup_temp(): - temp_dir = folder_paths.get_temp_directory() - if os.path.exists(temp_dir): - shutil.rmtree(temp_dir, ignore_errors=True) - - -def setup_database(): - try: - from app.database.db import init_db, dependencies_available - if dependencies_available(): - init_db() - except Exception as e: - logging.error(f"Failed to initialize database. Please ensure you have installed the latest requirements. If the error persists, please report this as in future the database will be required: {e}") - - -def start_comfyui(asyncio_loop=None): - """ - Starts the ComfyUI server using the provided asyncio event loop or creates a new one. - Returns the event loop, server instance, and a function to start the server asynchronously. - """ - if args.temp_directory: - temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp") - logging.info(f"Setting temp directory to: {temp_dir}") - folder_paths.set_temp_directory(temp_dir) - cleanup_temp() - - if args.windows_standalone_build: - try: - import new_updater - new_updater.update_windows_updater() - except: - pass - - if not asyncio_loop: - asyncio_loop = asyncio.new_event_loop() - asyncio.set_event_loop(asyncio_loop) - prompt_server = server.PromptServer(asyncio_loop) - - hook_breaker_ac10a0.save_functions() - asyncio_loop.run_until_complete(nodes.init_extra_nodes( - init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0, - init_api_nodes=not args.disable_api_nodes - )) - hook_breaker_ac10a0.restore_functions() - - cuda_malloc_warning() - setup_database() - - prompt_server.add_routes() - hijack_progress(prompt_server) - - threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start() - - if args.quick_test_for_ci: - exit(0) - - os.makedirs(folder_paths.get_temp_directory(), exist_ok=True) - call_on_start = None - if args.auto_launch: - def startup_server(scheme, address, port): - import webbrowser - if os.name == 'nt' and address == '0.0.0.0': - address = '127.0.0.1' - if ':' in address: - address = "[{}]".format(address) - webbrowser.open(f"{scheme}://{address}:{port}") - call_on_start = startup_server - - async def start_all(): - await prompt_server.setup() - await run(prompt_server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start) - - # Returning these so that other code can integrate with the ComfyUI loop and server - return asyncio_loop, prompt_server, start_all - - -if __name__ == "__main__": - # Running directly, just start ComfyUI. - logging.info("Python version: {}".format(sys.version)) - logging.info("ComfyUI version: {}".format(comfyui_version.__version__)) - - if sys.version_info.major == 3 and sys.version_info.minor < 10: - logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.") - - event_loop, _, start_all_func = start_comfyui() - try: - x = start_all_func() - app.logger.print_startup_warnings() - event_loop.run_until_complete(x) - except KeyboardInterrupt: - logging.info("\nStopped server") - - cleanup_temp() diff --git a/model_manager.py b/model_manager.py deleted file mode 100644 index ab36bca7441468ef3683f39c16fc6cec85d9740a..0000000000000000000000000000000000000000 --- a/model_manager.py +++ /dev/null @@ -1,195 +0,0 @@ -from __future__ import annotations - -import os -import base64 -import json -import time -import logging -import folder_paths -import glob -import comfy.utils -from aiohttp import web -from PIL import Image -from io import BytesIO -from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types - - -class ModelFileManager: - def __init__(self) -> None: - self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {} - - def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None: - return self.cache.get(key, default) - - def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]): - self.cache[key] = value - - def clear_cache(self): - self.cache.clear() - - def add_routes(self, routes): - # NOTE: This is an experiment to replace `/models` - @routes.get("/experiment/models") - async def get_model_folders(request): - model_types = list(folder_paths.folder_names_and_paths.keys()) - folder_black_list = ["configs", "custom_nodes"] - output_folders: list[dict] = [] - for folder in model_types: - if folder in folder_black_list: - continue - output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)}) - return web.json_response(output_folders) - - # NOTE: This is an experiment to replace `/models/{folder}` - @routes.get("/experiment/models/{folder}") - async def get_all_models(request): - folder = request.match_info.get("folder", None) - if not folder in folder_paths.folder_names_and_paths: - return web.Response(status=404) - files = self.get_model_file_list(folder) - return web.json_response(files) - - @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}") - async def get_model_preview(request): - folder_name = request.match_info.get("folder", None) - path_index = int(request.match_info.get("path_index", None)) - filename = request.match_info.get("filename", None) - - if not folder_name in folder_paths.folder_names_and_paths: - return web.Response(status=404) - - folders = folder_paths.folder_names_and_paths[folder_name] - folder = folders[0][path_index] - full_filename = os.path.join(folder, filename) - - previews = self.get_model_previews(full_filename) - default_preview = previews[0] if len(previews) > 0 else None - if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)): - return web.Response(status=404) - - try: - with Image.open(default_preview) as img: - img_bytes = BytesIO() - img.save(img_bytes, format="WEBP") - img_bytes.seek(0) - return web.Response(body=img_bytes.getvalue(), content_type="image/webp") - except: - return web.Response(status=404) - - def get_model_file_list(self, folder_name: str): - folder_name = map_legacy(folder_name) - folders = folder_paths.folder_names_and_paths[folder_name] - output_list: list[dict] = [] - - for index, folder in enumerate(folders[0]): - if not os.path.isdir(folder): - continue - out = self.cache_model_file_list_(folder) - if out is None: - out = self.recursive_search_models_(folder, index) - self.set_cache(folder, out) - output_list.extend(out[0]) - - return output_list - - def cache_model_file_list_(self, folder: str): - model_file_list_cache = self.get_cache(folder) - - if model_file_list_cache is None: - return None - if not os.path.isdir(folder): - return None - if os.path.getmtime(folder) != model_file_list_cache[1]: - return None - for x in model_file_list_cache[1]: - time_modified = model_file_list_cache[1][x] - folder = x - if os.path.getmtime(folder) != time_modified: - return None - - return model_file_list_cache - - def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]: - if not os.path.isdir(directory): - return [], {}, time.perf_counter() - - excluded_dir_names = [".git"] - # TODO use settings - include_hidden_files = False - - result: list[str] = [] - dirs: dict[str, float] = {} - - for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): - subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] - if not include_hidden_files: - subdirs[:] = [d for d in subdirs if not d.startswith(".")] - filenames = [f for f in filenames if not f.startswith(".")] - - filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions) - - for file_name in filenames: - try: - full_path = os.path.join(dirpath, file_name) - relative_path = os.path.relpath(full_path, directory) - - # Get file metadata - file_info = { - "name": relative_path, - "pathIndex": pathIndex, - "modified": os.path.getmtime(full_path), # Add modification time - "created": os.path.getctime(full_path), # Add creation time - "size": os.path.getsize(full_path) # Add file size - } - result.append(file_info) - - except Exception as e: - logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.") - continue - - for d in subdirs: - path: str = os.path.join(dirpath, d) - try: - dirs[path] = os.path.getmtime(path) - except FileNotFoundError: - logging.warning(f"Warning: Unable to access {path}. Skipping this path.") - continue - - return result, dirs, time.perf_counter() - - def get_model_previews(self, filepath: str) -> list[str | BytesIO]: - dirname = os.path.dirname(filepath) - - if not os.path.exists(dirname): - return [] - - basename = os.path.splitext(filepath)[0] - match_files = glob.glob(f"{basename}.*", recursive=False) - image_files = filter_files_content_types(match_files, "image") - safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None) - safetensors_metadata = {} - - result: list[str | BytesIO] = [] - - for filename in image_files: - _basename = os.path.splitext(filename)[0] - if _basename == basename: - result.append(filename) - if _basename == f"{basename}.preview": - result.append(filename) - - if safetensors_file: - safetensors_filepath = os.path.join(dirname, safetensors_file) - header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024) - if header: - safetensors_metadata = json.loads(header) - safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None) - if safetensors_images: - safetensors_images = json.loads(safetensors_images) - for image in safetensors_images: - result.append(BytesIO(base64.b64decode(image))) - - return result - - def __exit__(self, exc_type, exc_value, traceback): - self.clear_cache() diff --git a/new_updater.py b/new_updater.py deleted file mode 100644 index 9a203acdd71f62b3b1122cb879c75344f5859c5d..0000000000000000000000000000000000000000 --- a/new_updater.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import shutil - -base_path = os.path.dirname(os.path.realpath(__file__)) - - -def update_windows_updater(): - top_path = os.path.dirname(base_path) - updater_path = os.path.join(base_path, ".ci/update_windows/update.py") - bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") - - dest_updater_path = os.path.join(top_path, "update/update.py") - dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") - dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") - - try: - with open(dest_bat_path, 'rb') as f: - contents = f.read() - except: - return - - if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): - return - - shutil.copy(updater_path, dest_updater_path) - try: - with open(dest_bat_deps_path, 'rb') as f: - contents = f.read() - contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') - with open(dest_bat_deps_path, 'wb') as f: - f.write(contents) - except: - pass - shutil.copy(bat_path, dest_bat_path) - print("Updated the windows standalone package updater.") # noqa: T201 diff --git a/node_helpers.py b/node_helpers.py deleted file mode 100644 index 4ff960ef8afeadd935b6e48a9a683b7ef6970b2f..0000000000000000000000000000000000000000 --- a/node_helpers.py +++ /dev/null @@ -1,60 +0,0 @@ -import hashlib -import torch - -from comfy.cli_args import args - -from PIL import ImageFile, UnidentifiedImageError - -def conditioning_set_values(conditioning, values={}, append=False): - c = [] - for t in conditioning: - n = [t[0], t[1].copy()] - for k in values: - val = values[k] - if append: - old_val = n[1].get(k, None) - if old_val is not None: - val = old_val + val - - n[1][k] = val - c.append(n) - - return c - -def pillow(fn, arg): - prev_value = None - try: - x = fn(arg) - except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416 - prev_value = ImageFile.LOAD_TRUNCATED_IMAGES - ImageFile.LOAD_TRUNCATED_IMAGES = True - x = fn(arg) - finally: - if prev_value is not None: - ImageFile.LOAD_TRUNCATED_IMAGES = prev_value - return x - -def hasher(): - hashfuncs = { - "md5": hashlib.md5, - "sha1": hashlib.sha1, - "sha256": hashlib.sha256, - "sha512": hashlib.sha512 - } - return hashfuncs[args.default_hashing_function] - -def string_to_torch_dtype(string): - if string == "fp32": - return torch.float32 - if string == "fp16": - return torch.float16 - if string == "bf16": - return torch.bfloat16 - -def image_alpha_fix(destination, source): - if destination.shape[-1] < source.shape[-1]: - source = source[...,:destination.shape[-1]] - elif destination.shape[-1] > source.shape[-1]: - destination = torch.nn.functional.pad(destination, (0, 1)) - destination[..., -1] = 1.0 - return destination, source diff --git a/nodes.py b/nodes.py deleted file mode 100644 index 0aff6b14af501820c6ab0815dbc00d0549f7b8d1..0000000000000000000000000000000000000000 --- a/nodes.py +++ /dev/null @@ -1,2414 +0,0 @@ -from __future__ import annotations -import torch - - -import os -import sys -import json -import hashlib -import inspect -import traceback -import math -import time -import random -import logging - -from PIL import Image, ImageOps, ImageSequence -from PIL.PngImagePlugin import PngInfo - -import numpy as np -import safetensors.torch - -sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) - -import comfy.diffusers_load -import comfy.samplers -import comfy.sample -import comfy.sd -import comfy.utils -import comfy.controlnet -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator -from comfy_api.internal import register_versions, ComfyAPIWithVersion -from comfy_api.version_list import supported_versions -from comfy_api.latest import io, ComfyExtension - -import comfy.clip_vision - -import comfy.model_management -from comfy.cli_args import args - -import importlib - -import folder_paths -import latent_preview -import node_helpers - -def before_node_execution(): - comfy.model_management.throw_exception_if_processing_interrupted() - -def interrupt_processing(value=True): - comfy.model_management.interrupt_current_processing(value) - -MAX_RESOLUTION=16384 - -class CLIPTextEncode(ComfyNodeABC): - @classmethod - def INPUT_TYPES(s) -> InputTypeDict: - return { - "required": { - "text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}), - "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}) - } - } - RETURN_TYPES = (IO.CONDITIONING,) - OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",) - FUNCTION = "encode" - - CATEGORY = "conditioning" - DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." - - def encode(self, clip, text): - if clip is None: - raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") - tokens = clip.tokenize(text) - return (clip.encode_from_tokens_scheduled(tokens), ) - - -class ConditioningCombine: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "combine" - - CATEGORY = "conditioning" - - def combine(self, conditioning_1, conditioning_2): - return (conditioning_1 + conditioning_2, ) - -class ConditioningAverage : - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), - "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "addWeighted" - - CATEGORY = "conditioning" - - def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): - out = [] - - if len(conditioning_from) > 1: - logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") - - cond_from = conditioning_from[0][0] - pooled_output_from = conditioning_from[0][1].get("pooled_output", None) - - for i in range(len(conditioning_to)): - t1 = conditioning_to[i][0] - pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) - t0 = cond_from[:,:t1.shape[1]] - if t0.shape[1] < t1.shape[1]: - t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) - - tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) - t_to = conditioning_to[i][1].copy() - if pooled_output_from is not None and pooled_output_to is not None: - t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) - elif pooled_output_from is not None: - t_to["pooled_output"] = pooled_output_from - - n = [tw, t_to] - out.append(n) - return (out, ) - -class ConditioningConcat: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "conditioning_to": ("CONDITIONING",), - "conditioning_from": ("CONDITIONING",), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "concat" - - CATEGORY = "conditioning" - - def concat(self, conditioning_to, conditioning_from): - out = [] - - if len(conditioning_from) > 1: - logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") - - cond_from = conditioning_from[0][0] - - for i in range(len(conditioning_to)): - t1 = conditioning_to[i][0] - tw = torch.cat((t1, cond_from),1) - n = [tw, conditioning_to[i][1].copy()] - out.append(n) - - return (out, ) - -class ConditioningSetArea: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "conditioning" - - def append(self, conditioning, width, height, x, y, strength): - c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), - "strength": strength, - "set_area_to_bounds": False}) - return (c, ) - -class ConditioningSetAreaPercentage: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), - "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), - "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "conditioning" - - def append(self, conditioning, width, height, x, y, strength): - c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), - "strength": strength, - "set_area_to_bounds": False}) - return (c, ) - -class ConditioningSetAreaStrength: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "conditioning" - - def append(self, conditioning, strength): - c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) - return (c, ) - - -class ConditioningSetMask: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "mask": ("MASK", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "set_cond_area": (["default", "mask bounds"],), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "conditioning" - - def append(self, conditioning, mask, set_cond_area, strength): - set_area_to_bounds = False - if set_cond_area != "default": - set_area_to_bounds = True - if len(mask.shape) < 3: - mask = mask.unsqueeze(0) - - c = node_helpers.conditioning_set_values(conditioning, {"mask": mask, - "set_area_to_bounds": set_area_to_bounds, - "mask_strength": strength}) - return (c, ) - -class ConditioningZeroOut: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", )}} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "zero_out" - - CATEGORY = "advanced/conditioning" - - def zero_out(self, conditioning): - c = [] - for t in conditioning: - d = t[1].copy() - pooled_output = d.get("pooled_output", None) - if pooled_output is not None: - d["pooled_output"] = torch.zeros_like(pooled_output) - conditioning_lyrics = d.get("conditioning_lyrics", None) - if conditioning_lyrics is not None: - d["conditioning_lyrics"] = torch.zeros_like(conditioning_lyrics) - n = [torch.zeros_like(t[0]), d] - c.append(n) - return (c, ) - -class ConditioningSetTimestepRange: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "set_range" - - CATEGORY = "advanced/conditioning" - - def set_range(self, conditioning, start, end): - c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, - "end_percent": end}) - return (c, ) - -class VAEDecode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "samples": ("LATENT", {"tooltip": "The latent to be decoded."}), - "vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."}) - } - } - RETURN_TYPES = ("IMAGE",) - OUTPUT_TOOLTIPS = ("The decoded image.",) - FUNCTION = "decode" - - CATEGORY = "latent" - DESCRIPTION = "Decodes latent images back into pixel space images." - - def decode(self, vae, samples): - images = vae.decode(samples["samples"]) - if len(images.shape) == 5: #Combine batches - images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) - return (images, ) - -class VAEDecodeTiled: - @classmethod - def INPUT_TYPES(s): - return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 32}), - "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), - "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to decode at a time."}), - "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "decode" - - CATEGORY = "_for_testing" - - def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8): - if tile_size < overlap * 4: - overlap = tile_size // 4 - if temporal_size < temporal_overlap * 2: - temporal_overlap = temporal_overlap // 2 - temporal_compression = vae.temporal_compression_decode() - if temporal_compression is not None: - temporal_size = max(2, temporal_size // temporal_compression) - temporal_overlap = max(1, min(temporal_size // 2, temporal_overlap // temporal_compression)) - else: - temporal_size = None - temporal_overlap = None - - compression = vae.spacial_compression_decode() - images = vae.decode_tiled(samples["samples"], tile_x=tile_size // compression, tile_y=tile_size // compression, overlap=overlap // compression, tile_t=temporal_size, overlap_t=temporal_overlap) - if len(images.shape) == 5: #Combine batches - images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1]) - return (images, ) - -class VAEEncode: - @classmethod - def INPUT_TYPES(s): - return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "latent" - - def encode(self, vae, pixels): - t = vae.encode(pixels[:,:,:,:3]) - return ({"samples":t}, ) - -class VAEEncodeTiled: - @classmethod - def INPUT_TYPES(s): - return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}), - "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}), - "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}), - "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "_for_testing" - - def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8): - t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap) - return ({"samples": t}, ) - -class VAEEncodeForInpaint: - @classmethod - def INPUT_TYPES(s): - return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "encode" - - CATEGORY = "latent/inpaint" - - def encode(self, vae, pixels, mask, grow_mask_by=6): - x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio - y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio - mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") - - pixels = pixels.clone() - if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2 - y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2 - pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] - mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] - - #grow mask by a few pixels to keep things seamless in latent space - if grow_mask_by == 0: - mask_erosion = mask - else: - kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) - padding = math.ceil((grow_mask_by - 1) / 2) - - mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) - - m = (1.0 - mask.round()).squeeze(1) - for i in range(3): - pixels[:,:,:,i] -= 0.5 - pixels[:,:,:,i] *= m - pixels[:,:,:,i] += 0.5 - t = vae.encode(pixels) - - return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) - - -class InpaintModelConditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "vae": ("VAE", ), - "pixels": ("IMAGE", ), - "mask": ("MASK", ), - "noise_mask": ("BOOLEAN", {"default": True, "tooltip": "Add a noise mask to the latent so sampling will only happen within the mask. Might improve results or completely break things depending on the model."}), - }} - - RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - FUNCTION = "encode" - - CATEGORY = "conditioning/inpaint" - - def encode(self, positive, negative, pixels, vae, mask, noise_mask=True): - x = (pixels.shape[1] // 8) * 8 - y = (pixels.shape[2] // 8) * 8 - mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") - - orig_pixels = pixels - pixels = orig_pixels.clone() - if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % 8) // 2 - y_offset = (pixels.shape[2] % 8) // 2 - pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] - mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] - - m = (1.0 - mask.round()).squeeze(1) - for i in range(3): - pixels[:,:,:,i] -= 0.5 - pixels[:,:,:,i] *= m - pixels[:,:,:,i] += 0.5 - concat_latent = vae.encode(pixels) - orig_latent = vae.encode(orig_pixels) - - out_latent = {} - - out_latent["samples"] = orig_latent - if noise_mask: - out_latent["noise_mask"] = mask - - out = [] - for conditioning in [positive, negative]: - c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent, - "concat_mask": mask}) - out.append(c) - return (out[0], out[1], out_latent) - - -class SaveLatent: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT", ), - "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})}, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - RETURN_TYPES = () - FUNCTION = "save" - - OUTPUT_NODE = True - - CATEGORY = "_for_testing" - - def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - - # support save metadata for latent sharing - prompt_info = "" - if prompt is not None: - prompt_info = json.dumps(prompt) - - metadata = None - if not args.disable_metadata: - metadata = {"prompt": prompt_info} - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) - - file = f"{filename}_{counter:05}_.latent" - - results: list[FileLocator] = [] - results.append({ - "filename": file, - "subfolder": subfolder, - "type": "output" - }) - - file = os.path.join(full_output_folder, file) - - output = {} - output["latent_tensor"] = samples["samples"].contiguous() - output["latent_format_version_0"] = torch.tensor([]) - - comfy.utils.save_torch_file(output, file, metadata=metadata) - return { "ui": { "latents": results } } - - -class LoadLatent: - @classmethod - def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() - files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] - return {"required": {"latent": [sorted(files), ]}, } - - CATEGORY = "_for_testing" - - RETURN_TYPES = ("LATENT", ) - FUNCTION = "load" - - def load(self, latent): - latent_path = folder_paths.get_annotated_filepath(latent) - latent = safetensors.torch.load_file(latent_path, device="cpu") - multiplier = 1.0 - if "latent_format_version_0" not in latent: - multiplier = 1.0 / 0.18215 - samples = {"samples": latent["latent_tensor"].float() * multiplier} - return (samples, ) - - @classmethod - def IS_CHANGED(s, latent): - image_path = folder_paths.get_annotated_filepath(latent) - m = hashlib.sha256() - with open(image_path, 'rb') as f: - m.update(f.read()) - return m.digest().hex() - - @classmethod - def VALIDATE_INPUTS(s, latent): - if not folder_paths.exists_annotated_filepath(latent): - return "Invalid latent file: {}".format(latent) - return True - - -class CheckpointLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "config_name": (folder_paths.get_filename_list("configs"), ), - "ckpt_name": (folder_paths.get_filename_list("checkpoints"), )}} - RETURN_TYPES = ("MODEL", "CLIP", "VAE") - FUNCTION = "load_checkpoint" - - CATEGORY = "advanced/loaders" - DEPRECATED = True - - def load_checkpoint(self, config_name, ckpt_name): - config_path = folder_paths.get_full_path("configs", config_name) - ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) - return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) - -class CheckpointLoaderSimple: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}), - } - } - RETURN_TYPES = ("MODEL", "CLIP", "VAE") - OUTPUT_TOOLTIPS = ("The model used for denoising latents.", - "The CLIP model used for encoding text prompts.", - "The VAE model used for encoding and decoding images to and from latent space.") - FUNCTION = "load_checkpoint" - - CATEGORY = "loaders" - DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents." - - def load_checkpoint(self, ckpt_name): - ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) - out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) - return out[:3] - -class DiffusersLoader: - @classmethod - def INPUT_TYPES(cls): - paths = [] - for search_path in folder_paths.get_folder_paths("diffusers"): - if os.path.exists(search_path): - for root, subdir, files in os.walk(search_path, followlinks=True): - if "model_index.json" in files: - paths.append(os.path.relpath(root, start=search_path)) - - return {"required": {"model_path": (paths,), }} - RETURN_TYPES = ("MODEL", "CLIP", "VAE") - FUNCTION = "load_checkpoint" - - CATEGORY = "advanced/loaders/deprecated" - - def load_checkpoint(self, model_path, output_vae=True, output_clip=True): - for search_path in folder_paths.get_folder_paths("diffusers"): - if os.path.exists(search_path): - path = os.path.join(search_path, model_path) - if os.path.exists(path): - model_path = path - break - - return comfy.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) - - -class unCLIPCheckpointLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), - }} - RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") - FUNCTION = "load_checkpoint" - - CATEGORY = "loaders" - - def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): - ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) - out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings")) - return out - -class CLIPSetLastLayer: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip": ("CLIP", ), - "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "set_last_layer" - - CATEGORY = "conditioning" - - def set_last_layer(self, clip, stop_at_clip_layer): - clip = clip.clone() - clip.clip_layer(stop_at_clip_layer) - return (clip,) - -class LoraLoader: - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), - "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}), - "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}), - "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), - "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}), - } - } - - RETURN_TYPES = ("MODEL", "CLIP") - OUTPUT_TOOLTIPS = ("The modified diffusion model.", "The modified CLIP model.") - FUNCTION = "load_lora" - - CATEGORY = "loaders" - DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together." - - def load_lora(self, model, clip, lora_name, strength_model, strength_clip): - if strength_model == 0 and strength_clip == 0: - return (model, clip) - - lora_path = folder_paths.get_full_path_or_raise("loras", lora_name) - lora = None - if self.loaded_lora is not None: - if self.loaded_lora[0] == lora_path: - lora = self.loaded_lora[1] - else: - self.loaded_lora = None - - if lora is None: - lora = comfy.utils.load_torch_file(lora_path, safe_load=True) - self.loaded_lora = (lora_path, lora) - - model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) - return (model_lora, clip_lora) - -class LoraLoaderModelOnly(LoraLoader): - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "lora_name": (folder_paths.get_filename_list("loras"), ), - "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "load_lora_model_only" - - def load_lora_model_only(self, model, lora_name, strength_model): - return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) - -class VAELoader: - @staticmethod - def vae_list(): - vaes = folder_paths.get_filename_list("vae") - approx_vaes = folder_paths.get_filename_list("vae_approx") - sdxl_taesd_enc = False - sdxl_taesd_dec = False - sd1_taesd_enc = False - sd1_taesd_dec = False - sd3_taesd_enc = False - sd3_taesd_dec = False - f1_taesd_enc = False - f1_taesd_dec = False - - for v in approx_vaes: - if v.startswith("taesd_decoder."): - sd1_taesd_dec = True - elif v.startswith("taesd_encoder."): - sd1_taesd_enc = True - elif v.startswith("taesdxl_decoder."): - sdxl_taesd_dec = True - elif v.startswith("taesdxl_encoder."): - sdxl_taesd_enc = True - elif v.startswith("taesd3_decoder."): - sd3_taesd_dec = True - elif v.startswith("taesd3_encoder."): - sd3_taesd_enc = True - elif v.startswith("taef1_encoder."): - f1_taesd_dec = True - elif v.startswith("taef1_decoder."): - f1_taesd_enc = True - if sd1_taesd_dec and sd1_taesd_enc: - vaes.append("taesd") - if sdxl_taesd_dec and sdxl_taesd_enc: - vaes.append("taesdxl") - if sd3_taesd_dec and sd3_taesd_enc: - vaes.append("taesd3") - if f1_taesd_dec and f1_taesd_enc: - vaes.append("taef1") - return vaes - - @staticmethod - def load_taesd(name): - sd = {} - approx_vaes = folder_paths.get_filename_list("vae_approx") - - encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) - decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) - - enc = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", encoder)) - for k in enc: - sd["taesd_encoder.{}".format(k)] = enc[k] - - dec = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("vae_approx", decoder)) - for k in dec: - sd["taesd_decoder.{}".format(k)] = dec[k] - - if name == "taesd": - sd["vae_scale"] = torch.tensor(0.18215) - sd["vae_shift"] = torch.tensor(0.0) - elif name == "taesdxl": - sd["vae_scale"] = torch.tensor(0.13025) - sd["vae_shift"] = torch.tensor(0.0) - elif name == "taesd3": - sd["vae_scale"] = torch.tensor(1.5305) - sd["vae_shift"] = torch.tensor(0.0609) - elif name == "taef1": - sd["vae_scale"] = torch.tensor(0.3611) - sd["vae_shift"] = torch.tensor(0.1159) - return sd - - @classmethod - def INPUT_TYPES(s): - return {"required": { "vae_name": (s.vae_list(), )}} - RETURN_TYPES = ("VAE",) - FUNCTION = "load_vae" - - CATEGORY = "loaders" - - #TODO: scale factor? - def load_vae(self, vae_name): - if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: - sd = self.load_taesd(vae_name) - else: - vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) - sd = comfy.utils.load_torch_file(vae_path) - vae = comfy.sd.VAE(sd=sd) - vae.throw_exception_if_invalid() - return (vae,) - -class ControlNetLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} - - RETURN_TYPES = ("CONTROL_NET",) - FUNCTION = "load_controlnet" - - CATEGORY = "loaders" - - def load_controlnet(self, control_net_name): - controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) - controlnet = comfy.controlnet.load_controlnet(controlnet_path) - if controlnet is None: - raise RuntimeError("ERROR: controlnet file is invalid and does not contain a valid controlnet model.") - return (controlnet,) - -class DiffControlNetLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "control_net_name": (folder_paths.get_filename_list("controlnet"), )}} - - RETURN_TYPES = ("CONTROL_NET",) - FUNCTION = "load_controlnet" - - CATEGORY = "loaders" - - def load_controlnet(self, model, control_net_name): - controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) - controlnet = comfy.controlnet.load_controlnet(controlnet_path, model) - return (controlnet,) - - -class ControlNetApply: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "control_net": ("CONTROL_NET", ), - "image": ("IMAGE", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "apply_controlnet" - - DEPRECATED = True - CATEGORY = "conditioning/controlnet" - - def apply_controlnet(self, conditioning, control_net, image, strength): - if strength == 0: - return (conditioning, ) - - c = [] - control_hint = image.movedim(-1,1) - for t in conditioning: - n = [t[0], t[1].copy()] - c_net = control_net.copy().set_cond_hint(control_hint, strength) - if 'control' in t[1]: - c_net.set_previous_controlnet(t[1]['control']) - n[1]['control'] = c_net - n[1]['control_apply_to_uncond'] = True - c.append(n) - return (c, ) - - -class ControlNetApplyAdvanced: - @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "control_net": ("CONTROL_NET", ), - "image": ("IMAGE", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }, - "optional": {"vae": ("VAE", ), - } - } - - RETURN_TYPES = ("CONDITIONING","CONDITIONING") - RETURN_NAMES = ("positive", "negative") - FUNCTION = "apply_controlnet" - - CATEGORY = "conditioning/controlnet" - - def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]): - if strength == 0: - return (positive, negative) - - control_hint = image.movedim(-1,1) - cnets = {} - - out = [] - for conditioning in [positive, negative]: - c = [] - for t in conditioning: - d = t[1].copy() - - prev_cnet = d.get('control', None) - if prev_cnet in cnets: - c_net = cnets[prev_cnet] - else: - c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat) - c_net.set_previous_controlnet(prev_cnet) - cnets[prev_cnet] = c_net - - d['control'] = c_net - d['control_apply_to_uncond'] = False - n = [t[0], d] - c.append(n) - out.append(c) - return (out[0], out[1]) - - -class UNETLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "unet_name": (folder_paths.get_filename_list("diffusion_models"), ), - "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],) - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "load_unet" - - CATEGORY = "advanced/loaders" - - def load_unet(self, unet_name, weight_dtype): - model_options = {} - if weight_dtype == "fp8_e4m3fn": - model_options["dtype"] = torch.float8_e4m3fn - elif weight_dtype == "fp8_e4m3fn_fast": - model_options["dtype"] = torch.float8_e4m3fn - model_options["fp8_optimizations"] = True - elif weight_dtype == "fp8_e5m2": - model_options["dtype"] = torch.float8_e5m2 - - unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name) - model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options) - return (model,) - -class CLIPLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image"], ), - }, - "optional": { - "device": (["default", "cpu"], {"advanced": True}), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "load_clip" - - CATEGORY = "advanced/loaders" - - DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B" - - def load_clip(self, clip_name, type="stable_diffusion", device="default"): - clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) - - model_options = {} - if device == "cpu": - model_options["load_device"] = model_options["offload_device"] = torch.device("cpu") - - clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name) - clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options) - return (clip,) - -class DualCLIPLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), - "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream"], ), - }, - "optional": { - "device": (["default", "cpu"], {"advanced": True}), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "load_clip" - - CATEGORY = "advanced/loaders" - - DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama" - - def load_clip(self, clip_name1, clip_name2, type, device="default"): - clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) - - clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) - clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) - - model_options = {} - if device == "cpu": - model_options["load_device"] = model_options["offload_device"] = torch.device("cpu") - - clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options) - return (clip,) - -class CLIPVisionLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_name": (folder_paths.get_filename_list("clip_vision"), ), - }} - RETURN_TYPES = ("CLIP_VISION",) - FUNCTION = "load_clip" - - CATEGORY = "loaders" - - def load_clip(self, clip_name): - clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name) - clip_vision = comfy.clip_vision.load(clip_path) - if clip_vision is None: - raise RuntimeError("ERROR: clip vision file is invalid and does not contain a valid vision model.") - return (clip_vision,) - -class CLIPVisionEncode: - @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "image": ("IMAGE",), - "crop": (["center", "none"],) - }} - RETURN_TYPES = ("CLIP_VISION_OUTPUT",) - FUNCTION = "encode" - - CATEGORY = "conditioning" - - def encode(self, clip_vision, image, crop): - crop_image = True - if crop != "center": - crop_image = False - output = clip_vision.encode_image(image, crop=crop_image) - return (output,) - -class StyleModelLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "style_model_name": (folder_paths.get_filename_list("style_models"), )}} - - RETURN_TYPES = ("STYLE_MODEL",) - FUNCTION = "load_style_model" - - CATEGORY = "loaders" - - def load_style_model(self, style_model_name): - style_model_path = folder_paths.get_full_path_or_raise("style_models", style_model_name) - style_model = comfy.sd.load_style_model(style_model_path) - return (style_model,) - - -class StyleModelApply: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "style_model": ("STYLE_MODEL", ), - "clip_vision_output": ("CLIP_VISION_OUTPUT", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}), - "strength_type": (["multiply", "attn_bias"], ), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "apply_stylemodel" - - CATEGORY = "conditioning/style_model" - - def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type): - cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) - if strength_type == "multiply": - cond *= strength - - n = cond.shape[1] - c_out = [] - for t in conditioning: - (txt, keys) = t - keys = keys.copy() - # even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it - if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0): - # math.log raises an error if the argument is zero - # torch.log returns -inf, which is what we want - attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0])) - # get the size of the mask image - mask_ref_size = keys.get("attention_mask_img_shape", (1, 1)) - n_ref = mask_ref_size[0] * mask_ref_size[1] - n_txt = txt.shape[1] - # grab the existing mask - mask = keys.get("attention_mask", None) - # create a default mask if it doesn't exist - if mask is None: - mask = torch.zeros((txt.shape[0], n_txt + n_ref, n_txt + n_ref), dtype=torch.float16) - # convert the mask dtype, because it might be boolean - # we want it to be interpreted as a bias - if mask.dtype == torch.bool: - # log(True) = log(1) = 0 - # log(False) = log(0) = -inf - mask = torch.log(mask.to(dtype=torch.float16)) - # now we make the mask bigger to add space for our new tokens - new_mask = torch.zeros((txt.shape[0], n_txt + n + n_ref, n_txt + n + n_ref), dtype=torch.float16) - # copy over the old mask, in quandrants - new_mask[:, :n_txt, :n_txt] = mask[:, :n_txt, :n_txt] - new_mask[:, :n_txt, n_txt+n:] = mask[:, :n_txt, n_txt:] - new_mask[:, n_txt+n:, :n_txt] = mask[:, n_txt:, :n_txt] - new_mask[:, n_txt+n:, n_txt+n:] = mask[:, n_txt:, n_txt:] - # now fill in the attention bias to our redux tokens - new_mask[:, :n_txt, n_txt:n_txt+n] = attn_bias - new_mask[:, n_txt+n:, n_txt:n_txt+n] = attn_bias - keys["attention_mask"] = new_mask.to(txt.device) - keys["attention_mask_img_shape"] = mask_ref_size - - c_out.append([torch.cat((txt, cond), dim=1), keys]) - - return (c_out,) - -class unCLIPConditioning: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning": ("CONDITIONING", ), - "clip_vision_output": ("CLIP_VISION_OUTPUT", ), - "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), - "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "apply_adm" - - CATEGORY = "conditioning" - - def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): - if strength == 0: - return (conditioning, ) - - c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True) - return (c, ) - -class GLIGENLoader: - @classmethod - def INPUT_TYPES(s): - return {"required": { "gligen_name": (folder_paths.get_filename_list("gligen"), )}} - - RETURN_TYPES = ("GLIGEN",) - FUNCTION = "load_gligen" - - CATEGORY = "loaders" - - def load_gligen(self, gligen_name): - gligen_path = folder_paths.get_full_path_or_raise("gligen", gligen_name) - gligen = comfy.sd.load_gligen(gligen_path) - return (gligen,) - -class GLIGENTextBoxApply: - @classmethod - def INPUT_TYPES(s): - return {"required": {"conditioning_to": ("CONDITIONING", ), - "clip": ("CLIP", ), - "gligen_textbox_model": ("GLIGEN", ), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "conditioning/gligen" - - def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): - c = [] - cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected") - for t in conditioning_to: - n = [t[0], t[1].copy()] - position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] - prev = [] - if "gligen" in n[1]: - prev = n[1]['gligen'][2] - - n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) - c.append(n) - return (c, ) - -class EmptyLatentImage: - def __init__(self): - self.device = comfy.model_management.intermediate_device() - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the latent images in pixels."}), - "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of the latent images in pixels."}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}) - } - } - RETURN_TYPES = ("LATENT",) - OUTPUT_TOOLTIPS = ("The empty latent image batch.",) - FUNCTION = "generate" - - CATEGORY = "latent" - DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling." - - def generate(self, width, height, batch_size=1): - latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) - return ({"samples":latent}, ) - - -class LatentFromBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), - "length": ("INT", {"default": 1, "min": 1, "max": 64}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "frombatch" - - CATEGORY = "latent/batch" - - def frombatch(self, samples, batch_index, length): - s = samples.copy() - s_in = samples["samples"] - batch_index = min(s_in.shape[0] - 1, batch_index) - length = min(s_in.shape[0] - batch_index, length) - s["samples"] = s_in[batch_index:batch_index + length].clone() - if "noise_mask" in samples: - masks = samples["noise_mask"] - if masks.shape[0] == 1: - s["noise_mask"] = masks.clone() - else: - if masks.shape[0] < s_in.shape[0]: - masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] - s["noise_mask"] = masks[batch_index:batch_index + length].clone() - if "batch_index" not in s: - s["batch_index"] = [x for x in range(batch_index, batch_index+length)] - else: - s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] - return (s,) - -class RepeatLatentBatch: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "amount": ("INT", {"default": 1, "min": 1, "max": 64}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "repeat" - - CATEGORY = "latent/batch" - - def repeat(self, samples, amount): - s = samples.copy() - s_in = samples["samples"] - - s["samples"] = s_in.repeat((amount,) + ((1,) * (s_in.ndim - 1))) - if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: - masks = samples["noise_mask"] - if masks.shape[0] < s_in.shape[0]: - masks = masks.repeat((math.ceil(s_in.shape[0] / masks.shape[0]),) + ((1,) * (masks.ndim - 1)))[:s_in.shape[0]] - s["noise_mask"] = samples["noise_mask"].repeat((amount,) + ((1,) * (samples["noise_mask"].ndim - 1))) - if "batch_index" in s: - offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 - s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] - return (s,) - -class LatentUpscale: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] - crop_methods = ["disabled", "center"] - - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), - "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "crop": (s.crop_methods,)}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "upscale" - - CATEGORY = "latent" - - def upscale(self, samples, upscale_method, width, height, crop): - if width == 0 and height == 0: - s = samples - else: - s = samples.copy() - - if width == 0: - height = max(64, height) - width = max(64, round(samples["samples"].shape[-1] * height / samples["samples"].shape[-2])) - elif height == 0: - width = max(64, width) - height = max(64, round(samples["samples"].shape[-2] * width / samples["samples"].shape[-1])) - else: - width = max(64, width) - height = max(64, height) - - s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) - return (s,) - -class LatentUpscaleBy: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] - - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), - "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "upscale" - - CATEGORY = "latent" - - def upscale(self, samples, upscale_method, scale_by): - s = samples.copy() - width = round(samples["samples"].shape[-1] * scale_by) - height = round(samples["samples"].shape[-2] * scale_by) - s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") - return (s,) - -class LatentRotate: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "rotate" - - CATEGORY = "latent/transform" - - def rotate(self, samples, rotation): - s = samples.copy() - rotate_by = 0 - if rotation.startswith("90"): - rotate_by = 1 - elif rotation.startswith("180"): - rotate_by = 2 - elif rotation.startswith("270"): - rotate_by = 3 - - s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) - return (s,) - -class LatentFlip: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "flip" - - CATEGORY = "latent/transform" - - def flip(self, samples, flip_method): - s = samples.copy() - if flip_method.startswith("x"): - s["samples"] = torch.flip(samples["samples"], dims=[2]) - elif flip_method.startswith("y"): - s["samples"] = torch.flip(samples["samples"], dims=[3]) - - return (s,) - -class LatentComposite: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples_to": ("LATENT",), - "samples_from": ("LATENT",), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "composite" - - CATEGORY = "latent" - - def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): - x = x // 8 - y = y // 8 - feather = feather // 8 - samples_out = samples_to.copy() - s = samples_to["samples"].clone() - samples_to = samples_to["samples"] - samples_from = samples_from["samples"] - if feather == 0: - s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] - else: - samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] - mask = torch.ones_like(samples_from) - for t in range(feather): - if y != 0: - mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) - - if y + samples_from.shape[2] < samples_to.shape[2]: - mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) - if x != 0: - mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) - if x + samples_from.shape[3] < samples_to.shape[3]: - mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) - rev_mask = torch.ones_like(mask) - mask - s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask - samples_out["samples"] = s - return (samples_out,) - -class LatentBlend: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "samples1": ("LATENT",), - "samples2": ("LATENT",), - "blend_factor": ("FLOAT", { - "default": 0.5, - "min": 0, - "max": 1, - "step": 0.01 - }), - }} - - RETURN_TYPES = ("LATENT",) - FUNCTION = "blend" - - CATEGORY = "_for_testing" - - def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): - - samples_out = samples1.copy() - samples1 = samples1["samples"] - samples2 = samples2["samples"] - - if samples1.shape != samples2.shape: - samples2.permute(0, 3, 1, 2) - samples2 = comfy.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') - samples2.permute(0, 2, 3, 1) - - samples_blended = self.blend_mode(samples1, samples2, blend_mode) - samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) - samples_out["samples"] = samples_blended - return (samples_out,) - - def blend_mode(self, img1, img2, mode): - if mode == "normal": - return img2 - else: - raise ValueError(f"Unsupported blend mode: {mode}") - -class LatentCrop: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), - "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "crop" - - CATEGORY = "latent/transform" - - def crop(self, samples, width, height, x, y): - s = samples.copy() - samples = samples['samples'] - x = x // 8 - y = y // 8 - - #enfonce minimum size of 64 - if x > (samples.shape[3] - 8): - x = samples.shape[3] - 8 - if y > (samples.shape[2] - 8): - y = samples.shape[2] - 8 - - new_height = height // 8 - new_width = width // 8 - to_x = new_width + x - to_y = new_height + y - s['samples'] = samples[:,:,y:to_y, x:to_x] - return (s,) - -class SetLatentNoiseMask: - @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "mask": ("MASK",), - }} - RETURN_TYPES = ("LATENT",) - FUNCTION = "set_mask" - - CATEGORY = "latent/inpaint" - - def set_mask(self, samples, mask): - s = samples.copy() - s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) - return (s,) - -def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): - latent_image = latent["samples"] - latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) - - if disable_noise: - noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") - else: - batch_inds = latent["batch_index"] if "batch_index" in latent else None - noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) - - noise_mask = None - if "noise_mask" in latent: - noise_mask = latent["noise_mask"] - - callback = latent_preview.prepare_callback(model, steps) - disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED - samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, - denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, - force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) - out = latent.copy() - out["samples"] = samples - return (out, ) - -class KSampler: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), - "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), - "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}), - "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}), - "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}), - "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}), - "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}), - } - } - - RETURN_TYPES = ("LATENT",) - OUTPUT_TOOLTIPS = ("The denoised latent.",) - FUNCTION = "sample" - - CATEGORY = "sampling" - DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image." - - def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): - return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) - -class KSamplerAdvanced: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "add_noise": (["enable", "disable"], ), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), - "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), - "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "latent_image": ("LATENT", ), - "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), - "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), - "return_with_leftover_noise": (["disable", "enable"], ), - } - } - - RETURN_TYPES = ("LATENT",) - FUNCTION = "sample" - - CATEGORY = "sampling" - - def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): - force_full_denoise = True - if return_with_leftover_noise == "enable": - force_full_denoise = False - disable_noise = False - if add_noise == "disable": - disable_noise = True - return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) - -class SaveImage: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() - self.type = "output" - self.prefix_append = "" - self.compress_level = 4 - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "images": ("IMAGE", {"tooltip": "The images to save."}), - "filename_prefix": ("STRING", {"default": "ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) - }, - "hidden": { - "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO" - }, - } - - RETURN_TYPES = () - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "image" - DESCRIPTION = "Saves the input images to your ComfyUI output directory." - - def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - results = list() - for (batch_number, image) in enumerate(images): - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - metadata = None - if not args.disable_metadata: - metadata = PngInfo() - if prompt is not None: - metadata.add_text("prompt", json.dumps(prompt)) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata.add_text(x, json.dumps(extra_pnginfo[x])) - - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.png" - img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 - - return { "ui": { "images": results } } - -class PreviewImage(SaveImage): - def __init__(self): - self.output_dir = folder_paths.get_temp_directory() - self.type = "temp" - self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) - self.compress_level = 1 - - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ), }, - "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, - } - -class LoadImage: - @classmethod - def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() - files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] - files = folder_paths.filter_files_content_types(files, ["image"]) - return {"required": - {"image": (sorted(files), {"image_upload": True})}, - } - - CATEGORY = "image" - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "load_image" - def load_image(self, image): - image_path = folder_paths.get_annotated_filepath(image) - - img = node_helpers.pillow(Image.open, image_path) - - output_images = [] - output_masks = [] - w, h = None, None - - excluded_formats = ['MPO'] - - for i in ImageSequence.Iterator(img): - i = node_helpers.pillow(ImageOps.exif_transpose, i) - - if i.mode == 'I': - i = i.point(lambda i: i * (1 / 255)) - image = i.convert("RGB") - - if len(output_images) == 0: - w = image.size[0] - h = image.size[1] - - if image.size[0] != w or image.size[1] != h: - continue - - image = np.array(image).astype(np.float32) / 255.0 - image = torch.from_numpy(image)[None,] - if 'A' in i.getbands(): - mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 - mask = 1. - torch.from_numpy(mask) - elif i.mode == 'P' and 'transparency' in i.info: - mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0 - mask = 1. - torch.from_numpy(mask) - else: - mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") - output_images.append(image) - output_masks.append(mask.unsqueeze(0)) - - if len(output_images) > 1 and img.format not in excluded_formats: - output_image = torch.cat(output_images, dim=0) - output_mask = torch.cat(output_masks, dim=0) - else: - output_image = output_images[0] - output_mask = output_masks[0] - - return (output_image, output_mask) - - @classmethod - def IS_CHANGED(s, image): - image_path = folder_paths.get_annotated_filepath(image) - m = hashlib.sha256() - with open(image_path, 'rb') as f: - m.update(f.read()) - return m.digest().hex() - - @classmethod - def VALIDATE_INPUTS(s, image): - if not folder_paths.exists_annotated_filepath(image): - return "Invalid image file: {}".format(image) - - return True - -class LoadImageMask: - _color_channels = ["alpha", "red", "green", "blue"] - @classmethod - def INPUT_TYPES(s): - input_dir = folder_paths.get_input_directory() - files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] - return {"required": - {"image": (sorted(files), {"image_upload": True}), - "channel": (s._color_channels, ), } - } - - CATEGORY = "mask" - - RETURN_TYPES = ("MASK",) - FUNCTION = "load_image" - def load_image(self, image, channel): - image_path = folder_paths.get_annotated_filepath(image) - i = node_helpers.pillow(Image.open, image_path) - i = node_helpers.pillow(ImageOps.exif_transpose, i) - if i.getbands() != ("R", "G", "B", "A"): - if i.mode == 'I': - i = i.point(lambda i: i * (1 / 255)) - i = i.convert("RGBA") - mask = None - c = channel[0].upper() - if c in i.getbands(): - mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 - mask = torch.from_numpy(mask) - if c == 'A': - mask = 1. - mask - else: - mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") - return (mask.unsqueeze(0),) - - @classmethod - def IS_CHANGED(s, image, channel): - image_path = folder_paths.get_annotated_filepath(image) - m = hashlib.sha256() - with open(image_path, 'rb') as f: - m.update(f.read()) - return m.digest().hex() - - @classmethod - def VALIDATE_INPUTS(s, image): - if not folder_paths.exists_annotated_filepath(image): - return "Invalid image file: {}".format(image) - - return True - - -class LoadImageOutput(LoadImage): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("COMBO", { - "image_upload": True, - "image_folder": "output", - "remote": { - "route": "/internal/files/output", - "refresh_button": True, - "control_after_refresh": "first", - }, - }), - } - } - - DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration." - EXPERIMENTAL = True - FUNCTION = "load_image" - - -class ImageScale: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - crop_methods = ["disabled", "center"] - - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), - "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - "crop": (s.crop_methods,)}} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" - - CATEGORY = "image/upscaling" - - def upscale(self, image, upscale_method, width, height, crop): - if width == 0 and height == 0: - s = image - else: - samples = image.movedim(-1,1) - - if width == 0: - width = max(1, round(samples.shape[3] * height / samples.shape[2])) - elif height == 0: - height = max(1, round(samples.shape[2] * width / samples.shape[3])) - - s = comfy.utils.common_upscale(samples, width, height, upscale_method, crop) - s = s.movedim(1,-1) - return (s,) - -class ImageScaleBy: - upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] - - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), - "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" - - CATEGORY = "image/upscaling" - - def upscale(self, image, upscale_method, scale_by): - samples = image.movedim(-1,1) - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") - s = s.movedim(1,-1) - return (s,) - -class ImageInvert: - - @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",)}} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "invert" - - CATEGORY = "image" - - def invert(self, image): - s = 1.0 - image - return (s,) - -class ImageBatch: - - @classmethod - def INPUT_TYPES(s): - return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "batch" - - CATEGORY = "image" - - def batch(self, image1, image2): - if image1.shape[1:] != image2.shape[1:]: - image2 = comfy.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) - s = torch.cat((image1, image2), dim=0) - return (s,) - -class EmptyImage: - def __init__(self, device="cpu"): - self.device = device - - @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "generate" - - CATEGORY = "image" - - def generate(self, width, height, batch_size=1, color=0): - r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) - g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) - b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) - return (torch.cat((r, g, b), dim=-1), ) - -class ImagePadForOutpaint: - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), - "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), - } - } - - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "expand_image" - - CATEGORY = "image" - - def expand_image(self, image, left, top, right, bottom, feathering): - d1, d2, d3, d4 = image.size() - - new_image = torch.ones( - (d1, d2 + top + bottom, d3 + left + right, d4), - dtype=torch.float32, - ) * 0.5 - - new_image[:, top:top + d2, left:left + d3, :] = image - - mask = torch.ones( - (d2 + top + bottom, d3 + left + right), - dtype=torch.float32, - ) - - t = torch.zeros( - (d2, d3), - dtype=torch.float32 - ) - - if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: - - for i in range(d2): - for j in range(d3): - dt = i if top != 0 else d2 - db = d2 - i if bottom != 0 else d2 - - dl = j if left != 0 else d3 - dr = d3 - j if right != 0 else d3 - - d = min(dt, db, dl, dr) - - if d >= feathering: - continue - - v = (feathering - d) / feathering - - t[i, j] = v * v - - mask[top:top + d2, left:left + d3] = t - - return (new_image, mask.unsqueeze(0)) - - -NODE_CLASS_MAPPINGS = { - "KSampler": KSampler, - "CheckpointLoaderSimple": CheckpointLoaderSimple, - "CLIPTextEncode": CLIPTextEncode, - "CLIPSetLastLayer": CLIPSetLastLayer, - "VAEDecode": VAEDecode, - "VAEEncode": VAEEncode, - "VAEEncodeForInpaint": VAEEncodeForInpaint, - "VAELoader": VAELoader, - "EmptyLatentImage": EmptyLatentImage, - "LatentUpscale": LatentUpscale, - "LatentUpscaleBy": LatentUpscaleBy, - "LatentFromBatch": LatentFromBatch, - "RepeatLatentBatch": RepeatLatentBatch, - "SaveImage": SaveImage, - "PreviewImage": PreviewImage, - "LoadImage": LoadImage, - "LoadImageMask": LoadImageMask, - "LoadImageOutput": LoadImageOutput, - "ImageScale": ImageScale, - "ImageScaleBy": ImageScaleBy, - "ImageInvert": ImageInvert, - "ImageBatch": ImageBatch, - "ImagePadForOutpaint": ImagePadForOutpaint, - "EmptyImage": EmptyImage, - "ConditioningAverage": ConditioningAverage , - "ConditioningCombine": ConditioningCombine, - "ConditioningConcat": ConditioningConcat, - "ConditioningSetArea": ConditioningSetArea, - "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, - "ConditioningSetAreaStrength": ConditioningSetAreaStrength, - "ConditioningSetMask": ConditioningSetMask, - "KSamplerAdvanced": KSamplerAdvanced, - "SetLatentNoiseMask": SetLatentNoiseMask, - "LatentComposite": LatentComposite, - "LatentBlend": LatentBlend, - "LatentRotate": LatentRotate, - "LatentFlip": LatentFlip, - "LatentCrop": LatentCrop, - "LoraLoader": LoraLoader, - "CLIPLoader": CLIPLoader, - "UNETLoader": UNETLoader, - "DualCLIPLoader": DualCLIPLoader, - "CLIPVisionEncode": CLIPVisionEncode, - "StyleModelApply": StyleModelApply, - "unCLIPConditioning": unCLIPConditioning, - "ControlNetApply": ControlNetApply, - "ControlNetApplyAdvanced": ControlNetApplyAdvanced, - "ControlNetLoader": ControlNetLoader, - "DiffControlNetLoader": DiffControlNetLoader, - "StyleModelLoader": StyleModelLoader, - "CLIPVisionLoader": CLIPVisionLoader, - "VAEDecodeTiled": VAEDecodeTiled, - "VAEEncodeTiled": VAEEncodeTiled, - "unCLIPCheckpointLoader": unCLIPCheckpointLoader, - "GLIGENLoader": GLIGENLoader, - "GLIGENTextBoxApply": GLIGENTextBoxApply, - "InpaintModelConditioning": InpaintModelConditioning, - - "CheckpointLoader": CheckpointLoader, - "DiffusersLoader": DiffusersLoader, - - "LoadLatent": LoadLatent, - "SaveLatent": SaveLatent, - - "ConditioningZeroOut": ConditioningZeroOut, - "ConditioningSetTimestepRange": ConditioningSetTimestepRange, - "LoraLoaderModelOnly": LoraLoaderModelOnly, -} - -NODE_DISPLAY_NAME_MAPPINGS = { - # Sampling - "KSampler": "KSampler", - "KSamplerAdvanced": "KSampler (Advanced)", - # Loaders - "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", - "CheckpointLoaderSimple": "Load Checkpoint", - "VAELoader": "Load VAE", - "LoraLoader": "Load LoRA", - "CLIPLoader": "Load CLIP", - "ControlNetLoader": "Load ControlNet Model", - "DiffControlNetLoader": "Load ControlNet Model (diff)", - "StyleModelLoader": "Load Style Model", - "CLIPVisionLoader": "Load CLIP Vision", - "UpscaleModelLoader": "Load Upscale Model", - "UNETLoader": "Load Diffusion Model", - # Conditioning - "CLIPVisionEncode": "CLIP Vision Encode", - "StyleModelApply": "Apply Style Model", - "CLIPTextEncode": "CLIP Text Encode (Prompt)", - "CLIPSetLastLayer": "CLIP Set Last Layer", - "ConditioningCombine": "Conditioning (Combine)", - "ConditioningAverage ": "Conditioning (Average)", - "ConditioningConcat": "Conditioning (Concat)", - "ConditioningSetArea": "Conditioning (Set Area)", - "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", - "ConditioningSetMask": "Conditioning (Set Mask)", - "ControlNetApply": "Apply ControlNet (OLD)", - "ControlNetApplyAdvanced": "Apply ControlNet", - # Latent - "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", - "SetLatentNoiseMask": "Set Latent Noise Mask", - "VAEDecode": "VAE Decode", - "VAEEncode": "VAE Encode", - "LatentRotate": "Rotate Latent", - "LatentFlip": "Flip Latent", - "LatentCrop": "Crop Latent", - "EmptyLatentImage": "Empty Latent Image", - "LatentUpscale": "Upscale Latent", - "LatentUpscaleBy": "Upscale Latent By", - "LatentComposite": "Latent Composite", - "LatentBlend": "Latent Blend", - "LatentFromBatch" : "Latent From Batch", - "RepeatLatentBatch": "Repeat Latent Batch", - # Image - "SaveImage": "Save Image", - "PreviewImage": "Preview Image", - "LoadImage": "Load Image", - "LoadImageMask": "Load Image (as Mask)", - "LoadImageOutput": "Load Image (from Outputs)", - "ImageScale": "Upscale Image", - "ImageScaleBy": "Upscale Image By", - "ImageUpscaleWithModel": "Upscale Image (using Model)", - "ImageInvert": "Invert Image", - "ImagePadForOutpaint": "Pad Image for Outpainting", - "ImageBatch": "Batch Images", - "ImageCrop": "Image Crop", - "ImageStitch": "Image Stitch", - "ImageBlend": "Image Blend", - "ImageBlur": "Image Blur", - "ImageQuantize": "Image Quantize", - "ImageSharpen": "Image Sharpen", - "ImageScaleToTotalPixels": "Scale Image to Total Pixels", - "GetImageSize": "Get Image Size", - # _for_testing - "VAEDecodeTiled": "VAE Decode (Tiled)", - "VAEEncodeTiled": "VAE Encode (Tiled)", -} - -EXTENSION_WEB_DIRS = {} - -# Dictionary of successfully loaded module names and associated directories. -LOADED_MODULE_DIRS = {} - - -def get_module_name(module_path: str) -> str: - """ - Returns the module name based on the given module path. - Examples: - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "my_custom_node" - get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes - Args: - module_path (str): The path of the module. - Returns: - str: The module name. - """ - base_path = os.path.basename(module_path) - if os.path.isfile(module_path): - base_path = os.path.splitext(base_path)[0] - return base_path - - -async def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool: - module_name = get_module_name(module_path) - if os.path.isfile(module_path): - sp = os.path.splitext(module_path) - module_name = sp[0] - sys_module_name = module_name - elif os.path.isdir(module_path): - sys_module_name = module_path.replace(".", "_x_") - - try: - logging.debug("Trying to load custom node {}".format(module_path)) - if os.path.isfile(module_path): - module_spec = importlib.util.spec_from_file_location(sys_module_name, module_path) - module_dir = os.path.split(module_path)[0] - else: - module_spec = importlib.util.spec_from_file_location(sys_module_name, os.path.join(module_path, "__init__.py")) - module_dir = module_path - - module = importlib.util.module_from_spec(module_spec) - sys.modules[sys_module_name] = module - module_spec.loader.exec_module(module) - - LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir) - - try: - from comfy_config import config_parser - - project_config = config_parser.extract_node_configuration(module_path) - - web_dir_name = project_config.tool_comfy.web - - if web_dir_name: - web_dir_path = os.path.join(module_path, web_dir_name) - - if os.path.isdir(web_dir_path): - project_name = project_config.project.name - - EXTENSION_WEB_DIRS[project_name] = web_dir_path - - logging.info("Automatically register web folder {} for {}".format(web_dir_name, project_name)) - except Exception as e: - logging.warning(f"Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': {e}") - - if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: - web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) - if os.path.isdir(web_dir): - EXTENSION_WEB_DIRS[module_name] = web_dir - - # V1 node definition - if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: - for name, node_cls in module.NODE_CLASS_MAPPINGS.items(): - if name not in ignore: - NODE_CLASS_MAPPINGS[name] = node_cls - node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path)) - if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: - NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) - return True - # V3 Extension Definition - elif hasattr(module, "comfy_entrypoint"): - entrypoint = getattr(module, "comfy_entrypoint") - if not callable(entrypoint): - logging.warning(f"comfy_entrypoint in {module_path} is not callable, skipping.") - return False - try: - if inspect.iscoroutinefunction(entrypoint): - extension = await entrypoint() - else: - extension = entrypoint() - if not isinstance(extension, ComfyExtension): - logging.warning(f"comfy_entrypoint in {module_path} did not return a ComfyExtension, skipping.") - return False - node_list = await extension.get_node_list() - if not isinstance(node_list, list): - logging.warning(f"comfy_entrypoint in {module_path} did not return a list of nodes, skipping.") - return False - for node_cls in node_list: - node_cls: io.ComfyNode - schema = node_cls.GET_SCHEMA() - if schema.node_id not in ignore: - NODE_CLASS_MAPPINGS[schema.node_id] = node_cls - node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path)) - if schema.display_name is not None: - NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name - return True - except Exception as e: - logging.warning(f"Error while calling comfy_entrypoint in {module_path}: {e}") - return False - else: - logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or NODES_LIST (need one).") - return False - except Exception as e: - logging.warning(traceback.format_exc()) - logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") - return False - -async def init_external_custom_nodes(): - """ - Initializes the external custom nodes. - - This function loads custom nodes from the specified folder paths and imports them into the application. - It measures the import times for each custom node and logs the results. - - Returns: - None - """ - base_node_names = set(NODE_CLASS_MAPPINGS.keys()) - node_paths = folder_paths.get_folder_paths("custom_nodes") - node_import_times = [] - for custom_node_path in node_paths: - possible_modules = os.listdir(os.path.realpath(custom_node_path)) - if "__pycache__" in possible_modules: - possible_modules.remove("__pycache__") - - for possible_module in possible_modules: - module_path = os.path.join(custom_node_path, possible_module) - if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue - if module_path.endswith(".disabled"): continue - if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes: - logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes") - continue - time_before = time.perf_counter() - success = await load_custom_node(module_path, base_node_names, module_parent="custom_nodes") - node_import_times.append((time.perf_counter() - time_before, module_path, success)) - - if len(node_import_times) > 0: - logging.info("\nImport times for custom nodes:") - for n in sorted(node_import_times): - if n[2]: - import_message = "" - else: - import_message = " (IMPORT FAILED)" - logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) - logging.info("") - -async def init_builtin_extra_nodes(): - """ - Initializes the built-in extra nodes in ComfyUI. - - This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI. - If any of the extra node files fail to import, a warning message is logged. - - Returns: - None - """ - extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras") - extras_files = [ - "nodes_latent.py", - "nodes_hypernetwork.py", - "nodes_upscale_model.py", - "nodes_post_processing.py", - "nodes_mask.py", - "nodes_compositing.py", - "nodes_rebatch.py", - "nodes_model_merging.py", - "nodes_tomesd.py", - "nodes_clip_sdxl.py", - "nodes_canny.py", - "nodes_freelunch.py", - "nodes_custom_sampler.py", - "nodes_hypertile.py", - "nodes_model_advanced.py", - "nodes_model_downscale.py", - "nodes_images.py", - "nodes_video_model.py", - "nodes_train.py", - "nodes_sag.py", - "nodes_perpneg.py", - "nodes_stable3d.py", - "nodes_sdupscale.py", - "nodes_photomaker.py", - "nodes_pixart.py", - "nodes_cond.py", - "nodes_morphology.py", - "nodes_stable_cascade.py", - "nodes_differential_diffusion.py", - "nodes_ip2p.py", - "nodes_model_merging_model_specific.py", - "nodes_pag.py", - "nodes_align_your_steps.py", - "nodes_attention_multiply.py", - "nodes_advanced_samplers.py", - "nodes_webcam.py", - "nodes_audio.py", - "nodes_sd3.py", - "nodes_gits.py", - "nodes_controlnet.py", - "nodes_hunyuan.py", - "nodes_flux.py", - "nodes_lora_extract.py", - "nodes_torch_compile.py", - "nodes_mochi.py", - "nodes_slg.py", - "nodes_mahiro.py", - "nodes_lt.py", - "nodes_hooks.py", - "nodes_load_3d.py", - "nodes_cosmos.py", - "nodes_video.py", - "nodes_lumina2.py", - "nodes_wan.py", - "nodes_lotus.py", - "nodes_hunyuan3d.py", - "nodes_primitive.py", - "nodes_cfg.py", - "nodes_optimalsteps.py", - "nodes_hidream.py", - "nodes_fresca.py", - "nodes_apg.py", - "nodes_preview_any.py", - "nodes_ace.py", - "nodes_string.py", - "nodes_camera_trajectory.py", - "nodes_edit_model.py", - "nodes_tcfg.py", - "nodes_context_windows.py", - "nodes_qwen.py", - "nodes_model_patch.py", - "nodes_easycache.py", - "nodes_audio_encoder.py", - ] - - import_failed = [] - for node_file in extras_files: - if not await load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): - import_failed.append(node_file) - - return import_failed - - -async def init_builtin_api_nodes(): - api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") - api_nodes_files = [ - "nodes_ideogram.py", - "nodes_openai.py", - "nodes_minimax.py", - "nodes_veo2.py", - "nodes_kling.py", - "nodes_bfl.py", - "nodes_luma.py", - "nodes_recraft.py", - "nodes_pixverse.py", - "nodes_stability.py", - "nodes_pika.py", - "nodes_runway.py", - "nodes_tripo.py", - "nodes_moonvalley.py", - "nodes_rodin.py", - "nodes_gemini.py", - "nodes_vidu.py", - ] - - if not await load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"): - return api_nodes_files - - import_failed = [] - for node_file in api_nodes_files: - if not await load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"): - import_failed.append(node_file) - - return import_failed - -async def init_public_apis(): - register_versions([ - ComfyAPIWithVersion( - version=getattr(v, "VERSION"), - api_class=v - ) for v in supported_versions - ]) - -async def init_extra_nodes(init_custom_nodes=True, init_api_nodes=True): - await init_public_apis() - - import_failed = await init_builtin_extra_nodes() - - import_failed_api = [] - if init_api_nodes: - import_failed_api = await init_builtin_api_nodes() - - if init_custom_nodes: - await init_external_custom_nodes() - else: - logging.info("Skipping loading of custom nodes") - - if len(import_failed_api) > 0: - logging.warning("WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies.\n") - for node in import_failed_api: - logging.warning("IMPORT FAILED: {}".format(node)) - logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") - if args.windows_standalone_build: - logging.warning("Please run the update script: update/update_comfyui.bat") - else: - logging.warning("Please do a: pip install -r requirements.txt") - logging.warning("") - - if len(import_failed) > 0: - logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") - for node in import_failed: - logging.warning("IMPORT FAILED: {}".format(node)) - logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") - if args.windows_standalone_build: - logging.warning("Please run the update script: update/update_comfyui.bat") - else: - logging.warning("Please do a: pip install -r requirements.txt") - logging.warning("") - - return import_failed diff --git a/optimization.py b/optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..a241cf345d413a73a4c365fc76747430eb9dd3dd --- /dev/null +++ b/optimization.py @@ -0,0 +1,133 @@ +""" +""" + +from typing import Any +from typing import Callable +from typing import ParamSpec + +import spaces +import torch +from torch.utils._pytree import tree_map_only +from torchao.quantization import quantize_ +from torchao.quantization import Float8DynamicActivationFloat8WeightConfig +from torchao.quantization import Int8WeightOnlyConfig + +from optimization_utils import capture_component_call +from optimization_utils import aoti_compile +from optimization_utils import drain_module_parameters + + +P = ParamSpec('P') + +# --- CORRECTED DYNAMIC SHAPING --- + +# VAE temporal scale factor is 1, latent_frames = num_frames. Range is [8, 81]. +LATENT_FRAMES_DIM = torch.export.Dim('num_latent_frames', min=8, max=81) + +# The transformer has a patch_size of (1, 2, 2), which means the input latent height and width +# are effectively divided by 2. This creates constraints that fail if the symbolic tracer +# assumes odd numbers are possible. +# +# To solve this, we define the dynamic dimension for the *patched* (i.e., post-division) size, +# and then express the input shape as 2 * this dimension. This mathematically guarantees +# to the compiler that the input latent dimensions are always even, satisfying the constraints. + +# App range for pixel dimensions: [480, 832]. VAE scale factor is 8. +# Latent dimension range: [480/8, 832/8] = [60, 104]. +# Patched latent dimension range: [60/2, 104/2] = [30, 52]. +LATENT_PATCHED_HEIGHT_DIM = torch.export.Dim('latent_patched_height', min=30, max=52) +LATENT_PATCHED_WIDTH_DIM = torch.export.Dim('latent_patched_width', min=30, max=52) + +# Now, we define the dynamic shapes for the transformer's `hidden_states` input, +# which has the shape (batch_size, channels, num_frames, height, width). +TRANSFORMER_DYNAMIC_SHAPES = { + 'hidden_states': { + 2: LATENT_FRAMES_DIM, + 3: 2 * LATENT_PATCHED_HEIGHT_DIM, # Guarantees even height + 4: 2 * LATENT_PATCHED_WIDTH_DIM, # Guarantees even width + }, +} + +# --- END OF CORRECTION --- + + +INDUCTOR_CONFIGS = { + 'conv_1x1_as_mm': True, + 'epilogue_fusion': False, + 'coordinate_descent_tuning': True, + 'coordinate_descent_check_all_directions': True, + 'max_autotune': True, + 'triton.cudagraphs': True, +} + + +def optimize_pipeline_(pipeline: Callable[P, Any], *args: P.args, **kwargs: P.kwargs): + + @spaces.GPU(duration=1500) + def compile_transformer(): + + # This LoRA fusion part remains the same + pipeline.load_lora_weights( + "Kijai/WanVideo_comfy", + weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", + adapter_name="lightx2v" + ) + kwargs_lora = {} + kwargs_lora["load_into_transformer_2"] = True + pipeline.load_lora_weights( + "Kijai/WanVideo_comfy", + weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors", + adapter_name="lightx2v_2", **kwargs_lora + ) + pipeline.set_adapters(["lightx2v", "lightx2v_2"], adapter_weights=[1., 1.]) + pipeline.fuse_lora(adapter_names=["lightx2v"], lora_scale=3., components=["transformer"]) + pipeline.fuse_lora(adapter_names=["lightx2v_2"], lora_scale=1., components=["transformer_2"]) + pipeline.unload_lora_weights() + + # Capture a single call to get the args/kwargs structure + with capture_component_call(pipeline, 'transformer') as call: + pipeline(*args, **kwargs) + + dynamic_shapes = tree_map_only((torch.Tensor, bool), lambda t: None, call.kwargs) + dynamic_shapes |= TRANSFORMER_DYNAMIC_SHAPES + + # Quantization remains the same + quantize_(pipeline.transformer, Float8DynamicActivationFloat8WeightConfig()) + quantize_(pipeline.transformer_2, Float8DynamicActivationFloat8WeightConfig()) + + # --- SIMPLIFIED COMPILATION --- + + exported_1 = torch.export.export( + mod=pipeline.transformer, + args=call.args, + kwargs=call.kwargs, + dynamic_shapes=dynamic_shapes, + ) + + exported_2 = torch.export.export( + mod=pipeline.transformer_2, + args=call.args, + kwargs=call.kwargs, + dynamic_shapes=dynamic_shapes, + ) + + compiled_1 = aoti_compile(exported_1, INDUCTOR_CONFIGS) + compiled_2 = aoti_compile(exported_2, INDUCTOR_CONFIGS) + + # Return the two compiled models + return compiled_1, compiled_2 + + + # Quantize text encoder (same as before) + quantize_(pipeline.text_encoder, Int8WeightOnlyConfig()) + + # Get the two dynamically-shaped compiled models + compiled_transformer_1, compiled_transformer_2 = compile_transformer() + + # --- SIMPLIFIED ASSIGNMENT --- + + pipeline.transformer.forward = compiled_transformer_1 + drain_module_parameters(pipeline.transformer) + + pipeline.transformer_2.forward = compiled_transformer_2 + drain_module_parameters(pipeline.transformer_2) \ No newline at end of file diff --git a/optimization_utils.py b/optimization_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cf6a1183c7a939be47032905a15e2dd0a238fbb8 --- /dev/null +++ b/optimization_utils.py @@ -0,0 +1,107 @@ +""" +""" +import contextlib +from contextvars import ContextVar +from io import BytesIO +from typing import Any +from typing import cast +from unittest.mock import patch + +import torch +from torch._inductor.package.package import package_aoti +from torch.export.pt2_archive._package import AOTICompiledModel +from torch.export.pt2_archive._package_weights import Weights + + +INDUCTOR_CONFIGS_OVERRIDES = { + 'aot_inductor.package_constants_in_so': False, + 'aot_inductor.package_constants_on_disk': True, + 'aot_inductor.package': True, +} + + +class ZeroGPUWeights: + def __init__(self, constants_map: dict[str, torch.Tensor], to_cuda: bool = False): + if to_cuda: + self.constants_map = {name: tensor.to('cuda') for name, tensor in constants_map.items()} + else: + self.constants_map = constants_map + def __reduce__(self): + constants_map: dict[str, torch.Tensor] = {} + for name, tensor in self.constants_map.items(): + tensor_ = torch.empty_like(tensor, device='cpu').pin_memory() + constants_map[name] = tensor_.copy_(tensor).detach().share_memory_() + return ZeroGPUWeights, (constants_map, True) + + +class ZeroGPUCompiledModel: + def __init__(self, archive_file: torch.types.FileLike, weights: ZeroGPUWeights): + self.archive_file = archive_file + self.weights = weights + self.compiled_model: ContextVar[AOTICompiledModel | None] = ContextVar('compiled_model', default=None) + def __call__(self, *args, **kwargs): + if (compiled_model := self.compiled_model.get()) is None: + compiled_model = cast(AOTICompiledModel, torch._inductor.aoti_load_package(self.archive_file)) + compiled_model.load_constants(self.weights.constants_map, check_full_update=True, user_managed=True) + self.compiled_model.set(compiled_model) + return compiled_model(*args, **kwargs) + def __reduce__(self): + return ZeroGPUCompiledModel, (self.archive_file, self.weights) + + +def aoti_compile( + exported_program: torch.export.ExportedProgram, + inductor_configs: dict[str, Any] | None = None, +): + inductor_configs = (inductor_configs or {}) | INDUCTOR_CONFIGS_OVERRIDES + gm = cast(torch.fx.GraphModule, exported_program.module()) + assert exported_program.example_inputs is not None + args, kwargs = exported_program.example_inputs + artifacts = torch._inductor.aot_compile(gm, args, kwargs, options=inductor_configs) + archive_file = BytesIO() + files: list[str | Weights] = [file for file in artifacts if isinstance(file, str)] + package_aoti(archive_file, files) + weights, = (artifact for artifact in artifacts if isinstance(artifact, Weights)) + zerogpu_weights = ZeroGPUWeights({name: weights.get_weight(name)[0] for name in weights}) + return ZeroGPUCompiledModel(archive_file, zerogpu_weights) + + +@contextlib.contextmanager +def capture_component_call( + pipeline: Any, + component_name: str, + component_method='forward', +): + + class CapturedCallException(Exception): + def __init__(self, *args, **kwargs): + super().__init__() + self.args = args + self.kwargs = kwargs + + class CapturedCall: + def __init__(self): + self.args: tuple[Any, ...] = () + self.kwargs: dict[str, Any] = {} + + component = getattr(pipeline, component_name) + captured_call = CapturedCall() + + def capture_call(*args, **kwargs): + raise CapturedCallException(*args, **kwargs) + + with patch.object(component, component_method, new=capture_call): + try: + yield captured_call + except CapturedCallException as e: + captured_call.args = e.args + captured_call.kwargs = e.kwargs + + +def drain_module_parameters(module: torch.nn.Module): + state_dict_meta = {name: {'device': tensor.device, 'dtype': tensor.dtype} for name, tensor in module.state_dict().items()} + state_dict = {name: torch.nn.Parameter(torch.empty_like(tensor, device='cpu')) for name, tensor in module.state_dict().items()} + module.load_state_dict(state_dict, assign=True) + for name, param in state_dict.items(): + meta = state_dict_meta[name] + param.data = torch.Tensor([]).to(**meta) diff --git a/output/.DS_Store b/output/.DS_Store deleted file mode 100644 index cdb5860606b40e02ab489cd463d1bd9faec4b2b4..0000000000000000000000000000000000000000 Binary files a/output/.DS_Store and /dev/null differ diff --git a/output/_output_images_will_be_put_here b/output/_output_images_will_be_put_here deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/protocol.py b/protocol.py deleted file mode 100644 index 038a0a840d4efba202b54eabeeacc01f0eba0ed3..0000000000000000000000000000000000000000 --- a/protocol.py +++ /dev/null @@ -1,7 +0,0 @@ - -class BinaryEventTypes: - PREVIEW_IMAGE = 1 - UNENCODED_PREVIEW_IMAGE = 2 - TEXT = 3 - PREVIEW_IMAGE_WITH_METADATA = 4 - diff --git a/pyproject.toml b/pyproject.toml deleted file mode 100644 index cfd5d45ef761d46b7a4ada679d30eaad3f41e521..0000000000000000000000000000000000000000 --- a/pyproject.toml +++ /dev/null @@ -1,24 +0,0 @@ -[project] -name = "ComfyUI" -version = "0.3.56" -readme = "README.md" -license = { file = "LICENSE" } -requires-python = ">=3.9" - -[project.urls] -homepage = "https://www.comfy.org/" -repository = "https://github.com/comfyanonymous/ComfyUI" -documentation = "https://docs.comfy.org/" - -[tool.ruff] -lint.select = [ - "N805", # invalid-first-argument-name-for-method - "S307", # suspicious-eval-usage - "S102", # exec - "T", # print-usage - "W", - # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names. - # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f - "F", -] -exclude = ["*.ipynb", "**/generated/*.pyi"] diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index a224d8cbb5511cf6be0c0cf75ae92268339e11d6..0000000000000000000000000000000000000000 --- a/pytest.ini +++ /dev/null @@ -1,9 +0,0 @@ -[pytest] -markers = - inference: mark as inference test (deselect with '-m "not inference"') - execution: mark as execution test (deselect with '-m "not execution"') -testpaths = - tests - tests-unit -addopts = -s -pythonpath = . diff --git a/requirements.txt b/requirements.txt index f4dd3e3368daa70edb4b6c817bfbcb71c010aae9..b380162e74f041c13f1eabcbda29cb4da4d4e5b4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,39 +1,11 @@ -torchvision -torchaudio -aiohttp>=3.11.8 -alembic -av>=14.2.0 -black -chardet -color-matcher -comfyui-embedded-docs==0.2.6 -comfyui-frontend-package==1.25.11 -comfyui-workflow-templates==0.1.70 -einops -gguf>=0.13.0 -GitPython -matplotlib -matrix-nio -mss -numpy>=1.25.0 -opencv-python -pillow>=10.3.0 -protobuf -psutil -PyGithub -pyyaml -rich -safetensors>=0.4.2 -scipy +git+https://github.com/linoytsaban/diffusers.git@wan22-loras + +transformers +accelerate +safetensors sentencepiece -SQLAlchemy -tokenizers>=0.13.3 -toml -torchsde -tqdm -transformers>=4.37.2 -typer -typing-extensions -uv -yarl>=1.18.0 -https://huggingface.co/datasets/multimodalart/sageattention-zerogpu/resolve/main/sageattention-2.2.0-cp310-cp310-linux_x86_64.whl \ No newline at end of file +peft +ftfy +imageio-ffmpeg +opencv-python +torchao==0.11.0 \ No newline at end of file diff --git a/script_examples/basic_api_example.py b/script_examples/basic_api_example.py deleted file mode 100644 index 9128420c4ab1f188b00d7d42a9135029b92e84f7..0000000000000000000000000000000000000000 --- a/script_examples/basic_api_example.py +++ /dev/null @@ -1,127 +0,0 @@ -import json -from urllib import request - -#This is the ComfyUI api prompt format. - -#If you want it for a specific workflow you can "enable dev mode options" -#in the settings of the UI (gear beside the "Queue Size: ") this will enable -#a button on the UI to save workflows in api format. - -#keep in mind ComfyUI is pre alpha software so this format will change a bit. - -#this is the one for the default workflow -prompt_text = """ -{ - "3": { - "class_type": "KSampler", - "inputs": { - "cfg": 8, - "denoise": 1, - "latent_image": [ - "5", - 0 - ], - "model": [ - "4", - 0 - ], - "negative": [ - "7", - 0 - ], - "positive": [ - "6", - 0 - ], - "sampler_name": "euler", - "scheduler": "normal", - "seed": 8566257, - "steps": 20 - } - }, - "4": { - "class_type": "CheckpointLoaderSimple", - "inputs": { - "ckpt_name": "v1-5-pruned-emaonly.safetensors" - } - }, - "5": { - "class_type": "EmptyLatentImage", - "inputs": { - "batch_size": 1, - "height": 512, - "width": 512 - } - }, - "6": { - "class_type": "CLIPTextEncode", - "inputs": { - "clip": [ - "4", - 1 - ], - "text": "masterpiece best quality girl" - } - }, - "7": { - "class_type": "CLIPTextEncode", - "inputs": { - "clip": [ - "4", - 1 - ], - "text": "bad hands" - } - }, - "8": { - "class_type": "VAEDecode", - "inputs": { - "samples": [ - "3", - 0 - ], - "vae": [ - "4", - 2 - ] - } - }, - "9": { - "class_type": "SaveImage", - "inputs": { - "filename_prefix": "ComfyUI", - "images": [ - "8", - 0 - ] - } - } -} -""" - -def queue_prompt(prompt): - p = {"prompt": prompt} - - # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload. - # p["extra_data"] = { - # "api_key_comfy_org": "comfyui-87d01e28d*******************************************************" # replace with real key - # } - # See: https://docs.comfy.org/tutorials/api-nodes/overview - # Generate a key here: https://platform.comfy.org/login - - data = json.dumps(p).encode('utf-8') - req = request.Request("http://127.0.0.1:8188/prompt", data=data) - request.urlopen(req) - - -prompt = json.loads(prompt_text) -#set the text prompt for our positive CLIPTextEncode -prompt["6"]["inputs"]["text"] = "masterpiece best quality man" - -#set the seed for our KSampler node -prompt["3"]["inputs"]["seed"] = 5 - - -queue_prompt(prompt) - - diff --git a/script_examples/websockets_api_example.py b/script_examples/websockets_api_example.py deleted file mode 100644 index 58f26cfb696c5012a36af56e91d339b480359bb5..0000000000000000000000000000000000000000 --- a/script_examples/websockets_api_example.py +++ /dev/null @@ -1,167 +0,0 @@ -#This is an example that uses the websockets api to know when a prompt execution is done -#Once the prompt execution is done it downloads the images using the /history endpoint - -import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) -import uuid -import json -import urllib.request -import urllib.parse - -server_address = "127.0.0.1:8188" -client_id = str(uuid.uuid4()) - -def queue_prompt(prompt, prompt_id): - p = {"prompt": prompt, "client_id": client_id, "prompt_id": prompt_id} - data = json.dumps(p).encode('utf-8') - req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) - urllib.request.urlopen(req).read() - -def get_image(filename, subfolder, folder_type): - data = {"filename": filename, "subfolder": subfolder, "type": folder_type} - url_values = urllib.parse.urlencode(data) - with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: - return response.read() - -def get_history(prompt_id): - with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: - return json.loads(response.read()) - -def get_images(ws, prompt): - prompt_id = str(uuid.uuid4()) - queue_prompt(prompt, prompt_id) - output_images = {} - while True: - out = ws.recv() - if isinstance(out, str): - message = json.loads(out) - if message['type'] == 'executing': - data = message['data'] - if data['node'] is None and data['prompt_id'] == prompt_id: - break #Execution is done - else: - # If you want to be able to decode the binary stream for latent previews, here is how you can do it: - # bytesIO = BytesIO(out[8:]) - # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global - continue #previews are binary data - - history = get_history(prompt_id)[prompt_id] - for node_id in history['outputs']: - node_output = history['outputs'][node_id] - images_output = [] - if 'images' in node_output: - for image in node_output['images']: - image_data = get_image(image['filename'], image['subfolder'], image['type']) - images_output.append(image_data) - output_images[node_id] = images_output - - return output_images - -prompt_text = """ -{ - "3": { - "class_type": "KSampler", - "inputs": { - "cfg": 8, - "denoise": 1, - "latent_image": [ - "5", - 0 - ], - "model": [ - "4", - 0 - ], - "negative": [ - "7", - 0 - ], - "positive": [ - "6", - 0 - ], - "sampler_name": "euler", - "scheduler": "normal", - "seed": 8566257, - "steps": 20 - } - }, - "4": { - "class_type": "CheckpointLoaderSimple", - "inputs": { - "ckpt_name": "v1-5-pruned-emaonly.safetensors" - } - }, - "5": { - "class_type": "EmptyLatentImage", - "inputs": { - "batch_size": 1, - "height": 512, - "width": 512 - } - }, - "6": { - "class_type": "CLIPTextEncode", - "inputs": { - "clip": [ - "4", - 1 - ], - "text": "masterpiece best quality girl" - } - }, - "7": { - "class_type": "CLIPTextEncode", - "inputs": { - "clip": [ - "4", - 1 - ], - "text": "bad hands" - } - }, - "8": { - "class_type": "VAEDecode", - "inputs": { - "samples": [ - "3", - 0 - ], - "vae": [ - "4", - 2 - ] - } - }, - "9": { - "class_type": "SaveImage", - "inputs": { - "filename_prefix": "ComfyUI", - "images": [ - "8", - 0 - ] - } - } -} -""" - -prompt = json.loads(prompt_text) -#set the text prompt for our positive CLIPTextEncode -prompt["6"]["inputs"]["text"] = "masterpiece best quality man" - -#set the seed for our KSampler node -prompt["3"]["inputs"]["seed"] = 5 - -ws = websocket.WebSocket() -ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) -images = get_images(ws, prompt) -ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts -#Commented out code to display the output images: - -# for node_id in images: -# for image_data in images[node_id]: -# from PIL import Image -# import io -# image = Image.open(io.BytesIO(image_data)) -# image.show() - diff --git a/script_examples/websockets_api_example_ws_images.py b/script_examples/websockets_api_example_ws_images.py deleted file mode 100644 index 6508ecc99e732f75c2489e3cddb42229de2d356a..0000000000000000000000000000000000000000 --- a/script_examples/websockets_api_example_ws_images.py +++ /dev/null @@ -1,159 +0,0 @@ -#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without -#them being saved to disk - -import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) -import uuid -import json -import urllib.request -import urllib.parse - -server_address = "127.0.0.1:8188" -client_id = str(uuid.uuid4()) - -def queue_prompt(prompt): - p = {"prompt": prompt, "client_id": client_id} - data = json.dumps(p).encode('utf-8') - req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) - return json.loads(urllib.request.urlopen(req).read()) - -def get_image(filename, subfolder, folder_type): - data = {"filename": filename, "subfolder": subfolder, "type": folder_type} - url_values = urllib.parse.urlencode(data) - with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: - return response.read() - -def get_history(prompt_id): - with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: - return json.loads(response.read()) - -def get_images(ws, prompt): - prompt_id = queue_prompt(prompt)['prompt_id'] - output_images = {} - current_node = "" - while True: - out = ws.recv() - if isinstance(out, str): - message = json.loads(out) - if message['type'] == 'executing': - data = message['data'] - if data['prompt_id'] == prompt_id: - if data['node'] is None: - break #Execution is done - else: - current_node = data['node'] - else: - if current_node == 'save_image_websocket_node': - images_output = output_images.get(current_node, []) - images_output.append(out[8:]) - output_images[current_node] = images_output - - return output_images - -prompt_text = """ -{ - "3": { - "class_type": "KSampler", - "inputs": { - "cfg": 8, - "denoise": 1, - "latent_image": [ - "5", - 0 - ], - "model": [ - "4", - 0 - ], - "negative": [ - "7", - 0 - ], - "positive": [ - "6", - 0 - ], - "sampler_name": "euler", - "scheduler": "normal", - "seed": 8566257, - "steps": 20 - } - }, - "4": { - "class_type": "CheckpointLoaderSimple", - "inputs": { - "ckpt_name": "v1-5-pruned-emaonly.safetensors" - } - }, - "5": { - "class_type": "EmptyLatentImage", - "inputs": { - "batch_size": 1, - "height": 512, - "width": 512 - } - }, - "6": { - "class_type": "CLIPTextEncode", - "inputs": { - "clip": [ - "4", - 1 - ], - "text": "masterpiece best quality girl" - } - }, - "7": { - "class_type": "CLIPTextEncode", - "inputs": { - "clip": [ - "4", - 1 - ], - "text": "bad hands" - } - }, - "8": { - "class_type": "VAEDecode", - "inputs": { - "samples": [ - "3", - 0 - ], - "vae": [ - "4", - 2 - ] - } - }, - "save_image_websocket_node": { - "class_type": "SaveImageWebsocket", - "inputs": { - "images": [ - "8", - 0 - ] - } - } -} -""" - -prompt = json.loads(prompt_text) -#set the text prompt for our positive CLIPTextEncode -prompt["6"]["inputs"]["text"] = "masterpiece best quality man" - -#set the seed for our KSampler node -prompt["3"]["inputs"]["seed"] = 5 - -ws = websocket.WebSocket() -ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) -images = get_images(ws, prompt) -ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts -#Commented out code to display the output images: - -# for node_id in images: -# for image_data in images[node_id]: -# from PIL import Image -# import io -# image = Image.open(io.BytesIO(image_data)) -# image.show() - diff --git a/server.py b/server.py deleted file mode 100644 index 8f9c88ebf7714284ba89077e3696182aa6411779..0000000000000000000000000000000000000000 --- a/server.py +++ /dev/null @@ -1,985 +0,0 @@ -import os -import sys -import asyncio -import traceback - -import nodes -import folder_paths -import execution -import uuid -import urllib -import json -import glob -import struct -import ssl -import socket -import ipaddress -from PIL import Image, ImageOps -from PIL.PngImagePlugin import PngInfo -from io import BytesIO - -import aiohttp -from aiohttp import web -import logging - -import mimetypes -from comfy.cli_args import args -import comfy.utils -import comfy.model_management -from comfy_api import feature_flags -import node_helpers -from comfyui_version import __version__ -from app.frontend_management import FrontendManager -from comfy_api.internal import _ComfyNodeInternal - -from app.user_manager import UserManager -from app.model_manager import ModelFileManager -from app.custom_node_manager import CustomNodeManager -from typing import Optional, Union -from api_server.routes.internal.internal_routes import InternalRoutes -from protocol import BinaryEventTypes - -async def send_socket_catch_exception(function, message): - try: - await function(message) - except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err: - logging.warning("send error: {}".format(err)) - -@web.middleware -async def cache_control(request: web.Request, handler): - response: web.Response = await handler(request) - if request.path.endswith('.js') or request.path.endswith('.css') or request.path.endswith('index.json'): - response.headers.setdefault('Cache-Control', 'no-cache') - return response - - -@web.middleware -async def compress_body(request: web.Request, handler): - accept_encoding = request.headers.get("Accept-Encoding", "") - response: web.Response = await handler(request) - if not isinstance(response, web.Response): - return response - if response.content_type not in ["application/json", "text/plain"]: - return response - if response.body and "gzip" in accept_encoding: - response.enable_compression() - return response - - -def create_cors_middleware(allowed_origin: str): - @web.middleware - async def cors_middleware(request: web.Request, handler): - if request.method == "OPTIONS": - # Pre-flight request. Reply successfully: - response = web.Response() - else: - response = await handler(request) - - response.headers['Access-Control-Allow-Origin'] = allowed_origin - response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS' - response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' - response.headers['Access-Control-Allow-Credentials'] = 'true' - return response - - return cors_middleware - -def is_loopback(host): - if host is None: - return False - try: - if ipaddress.ip_address(host).is_loopback: - return True - else: - return False - except: - pass - - loopback = False - for family in (socket.AF_INET, socket.AF_INET6): - try: - r = socket.getaddrinfo(host, None, family, socket.SOCK_STREAM) - for family, _, _, _, sockaddr in r: - if not ipaddress.ip_address(sockaddr[0]).is_loopback: - return loopback - else: - loopback = True - except socket.gaierror: - pass - - return loopback - - -def create_origin_only_middleware(): - @web.middleware - async def origin_only_middleware(request: web.Request, handler): - #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason. - #in that case the Host and Origin hostnames won't match - #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime - if 'Host' in request.headers and 'Origin' in request.headers: - host = request.headers['Host'] - origin = request.headers['Origin'] - host_domain = host.lower() - parsed = urllib.parse.urlparse(origin) - origin_domain = parsed.netloc.lower() - host_domain_parsed = urllib.parse.urlsplit('//' + host_domain) - - #limit the check to when the host domain is localhost, this makes it slightly less safe but should still prevent the exploit - loopback = is_loopback(host_domain_parsed.hostname) - - if parsed.port is None: #if origin doesn't have a port strip it from the host to handle weird browsers, same for host - host_domain = host_domain_parsed.hostname - if host_domain_parsed.port is None: - origin_domain = parsed.hostname - - if loopback and host_domain is not None and origin_domain is not None and len(host_domain) > 0 and len(origin_domain) > 0: - if host_domain != origin_domain: - logging.warning("WARNING: request with non matching host and origin {} != {}, returning 403".format(host_domain, origin_domain)) - return web.Response(status=403) - - if request.method == "OPTIONS": - response = web.Response() - else: - response = await handler(request) - - return response - - return origin_only_middleware - -class PromptServer(): - def __init__(self, loop): - PromptServer.instance = self - - mimetypes.init() - mimetypes.add_type('application/javascript; charset=utf-8', '.js') - mimetypes.add_type('image/webp', '.webp') - - self.user_manager = UserManager() - self.model_file_manager = ModelFileManager() - self.custom_node_manager = CustomNodeManager() - self.internal_routes = InternalRoutes(self) - self.supports = ["custom_nodes_from_web"] - self.prompt_queue = execution.PromptQueue(self) - self.loop = loop - self.messages = asyncio.Queue() - self.client_session:Optional[aiohttp.ClientSession] = None - self.number = 0 - - middlewares = [cache_control] - if args.enable_compress_response_body: - middlewares.append(compress_body) - - if args.enable_cors_header: - middlewares.append(create_cors_middleware(args.enable_cors_header)) - else: - middlewares.append(create_origin_only_middleware()) - - max_upload_size = round(args.max_upload_size * 1024 * 1024) - self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) - self.sockets = dict() - self.sockets_metadata = dict() - self.web_root = ( - FrontendManager.init_frontend(args.front_end_version) - if args.front_end_root is None - else args.front_end_root - ) - logging.info(f"[Prompt Server] web root: {self.web_root}") - routes = web.RouteTableDef() - self.routes = routes - self.last_node_id = None - self.client_id = None - - self.on_prompt_handlers = [] - - @routes.get('/ws') - async def websocket_handler(request): - ws = web.WebSocketResponse() - await ws.prepare(request) - sid = request.rel_url.query.get('clientId', '') - if sid: - # Reusing existing session, remove old - self.sockets.pop(sid, None) - else: - sid = uuid.uuid4().hex - - # Store WebSocket for backward compatibility - self.sockets[sid] = ws - # Store metadata separately - self.sockets_metadata[sid] = {"feature_flags": {}} - - try: - # Send initial state to the new client - await self.send("status", {"status": self.get_queue_info(), "sid": sid}, sid) - # On reconnect if we are the currently executing client send the current node - if self.client_id == sid and self.last_node_id is not None: - await self.send("executing", { "node": self.last_node_id }, sid) - - # Flag to track if we've received the first message - first_message = True - - async for msg in ws: - if msg.type == aiohttp.WSMsgType.ERROR: - logging.warning('ws connection closed with exception %s' % ws.exception()) - elif msg.type == aiohttp.WSMsgType.TEXT: - try: - data = json.loads(msg.data) - # Check if first message is feature flags - if first_message and data.get("type") == "feature_flags": - # Store client feature flags - client_flags = data.get("data", {}) - self.sockets_metadata[sid]["feature_flags"] = client_flags - - # Send server feature flags in response - await self.send( - "feature_flags", - feature_flags.get_server_features(), - sid, - ) - - logging.debug( - f"Feature flags negotiated for client {sid}: {client_flags}" - ) - first_message = False - except json.JSONDecodeError: - logging.warning( - f"Invalid JSON received from client {sid}: {msg.data}" - ) - except Exception as e: - logging.error(f"Error processing WebSocket message: {e}") - finally: - self.sockets.pop(sid, None) - self.sockets_metadata.pop(sid, None) - return ws - - @routes.get("/") - async def get_root(request): - response = web.FileResponse(os.path.join(self.web_root, "index.html")) - response.headers['Cache-Control'] = 'no-cache' - response.headers["Pragma"] = "no-cache" - response.headers["Expires"] = "0" - return response - - @routes.get("/embeddings") - def get_embeddings(request): - embeddings = folder_paths.get_filename_list("embeddings") - return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) - - @routes.get("/models") - def list_model_types(request): - model_types = list(folder_paths.folder_names_and_paths.keys()) - - return web.json_response(model_types) - - @routes.get("/models/{folder}") - async def get_models(request): - folder = request.match_info.get("folder", None) - if not folder in folder_paths.folder_names_and_paths: - return web.Response(status=404) - files = folder_paths.get_filename_list(folder) - return web.json_response(files) - - @routes.get("/extensions") - async def get_extensions(request): - files = glob.glob(os.path.join( - glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True) - - extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)) - - for name, dir in nodes.EXTENSION_WEB_DIRS.items(): - files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True) - extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote( - name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files))) - - return web.json_response(extensions) - - def get_dir_by_type(dir_type): - if dir_type is None: - dir_type = "input" - - if dir_type == "input": - type_dir = folder_paths.get_input_directory() - elif dir_type == "temp": - type_dir = folder_paths.get_temp_directory() - elif dir_type == "output": - type_dir = folder_paths.get_output_directory() - - return type_dir, dir_type - - def compare_image_hash(filepath, image): - hasher = node_helpers.hasher() - - # function to compare hashes of two images to see if it already exists, fix to #3465 - if os.path.exists(filepath): - a = hasher() - b = hasher() - with open(filepath, "rb") as f: - a.update(f.read()) - b.update(image.file.read()) - image.file.seek(0) - return a.hexdigest() == b.hexdigest() - return False - - def image_upload(post, image_save_function=None): - image = post.get("image") - overwrite = post.get("overwrite") - image_is_duplicate = False - - image_upload_type = post.get("type") - upload_dir, image_upload_type = get_dir_by_type(image_upload_type) - - if image and image.file: - filename = image.filename - if not filename: - return web.Response(status=400) - - subfolder = post.get("subfolder", "") - full_output_folder = os.path.join(upload_dir, os.path.normpath(subfolder)) - filepath = os.path.abspath(os.path.join(full_output_folder, filename)) - - if os.path.commonpath((upload_dir, filepath)) != upload_dir: - return web.Response(status=400) - - if not os.path.exists(full_output_folder): - os.makedirs(full_output_folder) - - split = os.path.splitext(filename) - - if overwrite is not None and (overwrite == "true" or overwrite == "1"): - pass - else: - i = 1 - while os.path.exists(filepath): - if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465 - image_is_duplicate = True - break - filename = f"{split[0]} ({i}){split[1]}" - filepath = os.path.join(full_output_folder, filename) - i += 1 - - if not image_is_duplicate: - if image_save_function is not None: - image_save_function(image, post, filepath) - else: - with open(filepath, "wb") as f: - f.write(image.file.read()) - - return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type}) - else: - return web.Response(status=400) - - @routes.post("/upload/image") - async def upload_image(request): - post = await request.post() - return image_upload(post) - - - @routes.post("/upload/mask") - async def upload_mask(request): - post = await request.post() - - def image_save_function(image, post, filepath): - original_ref = json.loads(post.get("original_ref")) - filename, output_dir = folder_paths.annotated_filepath(original_ref['filename']) - - if not filename: - return web.Response(status=400) - - # validation for security: prevent accessing arbitrary path - if filename[0] == '/' or '..' in filename: - return web.Response(status=400) - - if output_dir is None: - type = original_ref.get("type", "output") - output_dir = folder_paths.get_directory_by_type(type) - - if output_dir is None: - return web.Response(status=400) - - if original_ref.get("subfolder", "") != "": - full_output_dir = os.path.join(output_dir, original_ref["subfolder"]) - if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: - return web.Response(status=403) - output_dir = full_output_dir - - file = os.path.join(output_dir, filename) - - if os.path.isfile(file): - with Image.open(file) as original_pil: - metadata = PngInfo() - if hasattr(original_pil,'text'): - for key in original_pil.text: - metadata.add_text(key, original_pil.text[key]) - original_pil = original_pil.convert('RGBA') - mask_pil = Image.open(image.file).convert('RGBA') - - # alpha copy - new_alpha = mask_pil.getchannel('A') - original_pil.putalpha(new_alpha) - original_pil.save(filepath, compress_level=4, pnginfo=metadata) - - return image_upload(post, image_save_function) - - @routes.get("/view") - async def view_image(request): - if "filename" in request.rel_url.query: - filename = request.rel_url.query["filename"] - filename, output_dir = folder_paths.annotated_filepath(filename) - - if not filename: - return web.Response(status=400) - - # validation for security: prevent accessing arbitrary path - if filename[0] == '/' or '..' in filename: - return web.Response(status=400) - - if output_dir is None: - type = request.rel_url.query.get("type", "output") - output_dir = folder_paths.get_directory_by_type(type) - - if output_dir is None: - return web.Response(status=400) - - if "subfolder" in request.rel_url.query: - full_output_dir = os.path.join(output_dir, request.rel_url.query["subfolder"]) - if os.path.commonpath((os.path.abspath(full_output_dir), output_dir)) != output_dir: - return web.Response(status=403) - output_dir = full_output_dir - - filename = os.path.basename(filename) - file = os.path.join(output_dir, filename) - - if os.path.isfile(file): - if 'preview' in request.rel_url.query: - with Image.open(file) as img: - preview_info = request.rel_url.query['preview'].split(';') - image_format = preview_info[0] - if image_format not in ['webp', 'jpeg'] or 'a' in request.rel_url.query.get('channel', ''): - image_format = 'webp' - - quality = 90 - if preview_info[-1].isdigit(): - quality = int(preview_info[-1]) - - buffer = BytesIO() - if image_format in ['jpeg'] or request.rel_url.query.get('channel', '') == 'rgb': - img = img.convert("RGB") - img.save(buffer, format=image_format, quality=quality) - buffer.seek(0) - - return web.Response(body=buffer.read(), content_type=f'image/{image_format}', - headers={"Content-Disposition": f"filename=\"{filename}\""}) - - if 'channel' not in request.rel_url.query: - channel = 'rgba' - else: - channel = request.rel_url.query["channel"] - - if channel == 'rgb': - with Image.open(file) as img: - if img.mode == "RGBA": - r, g, b, a = img.split() - new_img = Image.merge('RGB', (r, g, b)) - else: - new_img = img.convert("RGB") - - buffer = BytesIO() - new_img.save(buffer, format='PNG') - buffer.seek(0) - - return web.Response(body=buffer.read(), content_type='image/png', - headers={"Content-Disposition": f"filename=\"{filename}\""}) - - elif channel == 'a': - with Image.open(file) as img: - if img.mode == "RGBA": - _, _, _, a = img.split() - else: - a = Image.new('L', img.size, 255) - - # alpha img - alpha_img = Image.new('RGBA', img.size) - alpha_img.putalpha(a) - alpha_buffer = BytesIO() - alpha_img.save(alpha_buffer, format='PNG') - alpha_buffer.seek(0) - - return web.Response(body=alpha_buffer.read(), content_type='image/png', - headers={"Content-Disposition": f"filename=\"{filename}\""}) - else: - # Get content type from mimetype, defaulting to 'application/octet-stream' - content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream' - - # For security, force certain mimetypes to download instead of display - if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}: - content_type = 'application/octet-stream' # Forces download - - return web.FileResponse( - file, - headers={ - "Content-Disposition": f"filename=\"{filename}\"", - "Content-Type": content_type - } - ) - - return web.Response(status=404) - - @routes.get("/view_metadata/{folder_name}") - async def view_metadata(request): - folder_name = request.match_info.get("folder_name", None) - if folder_name is None: - return web.Response(status=404) - if not "filename" in request.rel_url.query: - return web.Response(status=404) - - filename = request.rel_url.query["filename"] - if not filename.endswith(".safetensors"): - return web.Response(status=404) - - safetensors_path = folder_paths.get_full_path(folder_name, filename) - if safetensors_path is None: - return web.Response(status=404) - out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024) - if out is None: - return web.Response(status=404) - dt = json.loads(out) - if not "__metadata__" in dt: - return web.Response(status=404) - return web.json_response(dt["__metadata__"]) - - @routes.get("/system_stats") - async def system_stats(request): - device = comfy.model_management.get_torch_device() - device_name = comfy.model_management.get_torch_device_name(device) - cpu_device = comfy.model_management.torch.device("cpu") - ram_total = comfy.model_management.get_total_memory(cpu_device) - ram_free = comfy.model_management.get_free_memory(cpu_device) - vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True) - vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True) - required_frontend_version = FrontendManager.get_required_frontend_version() - - system_stats = { - "system": { - "os": os.name, - "ram_total": ram_total, - "ram_free": ram_free, - "comfyui_version": __version__, - "required_frontend_version": required_frontend_version, - "python_version": sys.version, - "pytorch_version": comfy.model_management.torch_version, - "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded", - "argv": sys.argv - }, - "devices": [ - { - "name": device_name, - "type": device.type, - "index": device.index, - "vram_total": vram_total, - "vram_free": vram_free, - "torch_vram_total": torch_vram_total, - "torch_vram_free": torch_vram_free, - } - ] - } - return web.json_response(system_stats) - - @routes.get("/features") - async def get_features(request): - return web.json_response(feature_flags.get_server_features()) - - @routes.get("/prompt") - async def get_prompt(request): - return web.json_response(self.get_queue_info()) - - def node_info(node_class): - obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] - if issubclass(obj_class, _ComfyNodeInternal): - return obj_class.GET_NODE_INFO_V1() - info = {} - info['input'] = obj_class.INPUT_TYPES() - info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()} - info['output'] = obj_class.RETURN_TYPES - info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES) - info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output'] - info['name'] = node_class - info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class - info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else '' - info['python_module'] = getattr(obj_class, "RELATIVE_PYTHON_MODULE", "nodes") - info['category'] = 'sd' - if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True: - info['output_node'] = True - else: - info['output_node'] = False - - if hasattr(obj_class, 'CATEGORY'): - info['category'] = obj_class.CATEGORY - - if hasattr(obj_class, 'OUTPUT_TOOLTIPS'): - info['output_tooltips'] = obj_class.OUTPUT_TOOLTIPS - - if getattr(obj_class, "DEPRECATED", False): - info['deprecated'] = True - if getattr(obj_class, "EXPERIMENTAL", False): - info['experimental'] = True - - if hasattr(obj_class, 'API_NODE'): - info['api_node'] = obj_class.API_NODE - return info - - @routes.get("/object_info") - async def get_object_info(request): - with folder_paths.cache_helper: - out = {} - for x in nodes.NODE_CLASS_MAPPINGS: - try: - out[x] = node_info(x) - except Exception: - logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.") - logging.error(traceback.format_exc()) - return web.json_response(out) - - @routes.get("/object_info/{node_class}") - async def get_object_info_node(request): - node_class = request.match_info.get("node_class", None) - out = {} - if (node_class is not None) and (node_class in nodes.NODE_CLASS_MAPPINGS): - out[node_class] = node_info(node_class) - return web.json_response(out) - - @routes.get("/history") - async def get_history(request): - max_items = request.rel_url.query.get("max_items", None) - if max_items is not None: - max_items = int(max_items) - return web.json_response(self.prompt_queue.get_history(max_items=max_items)) - - @routes.get("/history/{prompt_id}") - async def get_history_prompt_id(request): - prompt_id = request.match_info.get("prompt_id", None) - return web.json_response(self.prompt_queue.get_history(prompt_id=prompt_id)) - - @routes.get("/queue") - async def get_queue(request): - queue_info = {} - current_queue = self.prompt_queue.get_current_queue_volatile() - queue_info['queue_running'] = current_queue[0] - queue_info['queue_pending'] = current_queue[1] - return web.json_response(queue_info) - - @routes.post("/prompt") - async def post_prompt(request): - logging.info("got prompt") - json_data = await request.json() - json_data = self.trigger_on_prompt(json_data) - - if "number" in json_data: - number = float(json_data['number']) - else: - number = self.number - if "front" in json_data: - if json_data['front']: - number = -number - - self.number += 1 - - if "prompt" in json_data: - prompt = json_data["prompt"] - prompt_id = str(json_data.get("prompt_id", uuid.uuid4())) - - partial_execution_targets = None - if "partial_execution_targets" in json_data: - partial_execution_targets = json_data["partial_execution_targets"] - - valid = await execution.validate_prompt(prompt_id, prompt, partial_execution_targets) - extra_data = {} - if "extra_data" in json_data: - extra_data = json_data["extra_data"] - - if "client_id" in json_data: - extra_data["client_id"] = json_data["client_id"] - if valid[0]: - outputs_to_execute = valid[2] - self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) - response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} - return web.json_response(response) - else: - logging.warning("invalid prompt: {}".format(valid[1])) - return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) - else: - error = { - "type": "no_prompt", - "message": "No prompt provided", - "details": "No prompt provided", - "extra_info": {} - } - return web.json_response({"error": error, "node_errors": {}}, status=400) - - @routes.post("/queue") - async def post_queue(request): - json_data = await request.json() - if "clear" in json_data: - if json_data["clear"]: - self.prompt_queue.wipe_queue() - if "delete" in json_data: - to_delete = json_data['delete'] - for id_to_delete in to_delete: - delete_func = lambda a: a[1] == id_to_delete - self.prompt_queue.delete_queue_item(delete_func) - - return web.Response(status=200) - - @routes.post("/interrupt") - async def post_interrupt(request): - nodes.interrupt_processing() - return web.Response(status=200) - - @routes.post("/free") - async def post_free(request): - json_data = await request.json() - unload_models = json_data.get("unload_models", False) - free_memory = json_data.get("free_memory", False) - if unload_models: - self.prompt_queue.set_flag("unload_models", unload_models) - if free_memory: - self.prompt_queue.set_flag("free_memory", free_memory) - return web.Response(status=200) - - @routes.post("/history") - async def post_history(request): - json_data = await request.json() - if "clear" in json_data: - if json_data["clear"]: - self.prompt_queue.wipe_history() - if "delete" in json_data: - to_delete = json_data['delete'] - for id_to_delete in to_delete: - self.prompt_queue.delete_history_item(id_to_delete) - - return web.Response(status=200) - - async def setup(self): - timeout = aiohttp.ClientTimeout(total=None) # no timeout - self.client_session = aiohttp.ClientSession(timeout=timeout) - - def add_routes(self): - self.user_manager.add_routes(self.routes) - self.model_file_manager.add_routes(self.routes) - self.custom_node_manager.add_routes(self.routes, self.app, nodes.LOADED_MODULE_DIRS.items()) - self.app.add_subapp('/internal', self.internal_routes.get_app()) - - # Prefix every route with /api for easier matching for delegation. - # This is very useful for frontend dev server, which need to forward - # everything except serving of static files. - # Currently both the old endpoints without prefix and new endpoints with - # prefix are supported. - api_routes = web.RouteTableDef() - for route in self.routes: - # Custom nodes might add extra static routes. Only process non-static - # routes to add /api prefix. - if isinstance(route, web.RouteDef): - api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs) - self.app.add_routes(api_routes) - self.app.add_routes(self.routes) - - # Add routes from web extensions. - for name, dir in nodes.EXTENSION_WEB_DIRS.items(): - self.app.add_routes([web.static('/extensions/' + name, dir)]) - - workflow_templates_path = FrontendManager.templates_path() - if workflow_templates_path: - self.app.add_routes([ - web.static('/templates', workflow_templates_path) - ]) - - # Serve embedded documentation from the package - embedded_docs_path = FrontendManager.embedded_docs_path() - if embedded_docs_path: - self.app.add_routes([ - web.static('/docs', embedded_docs_path) - ]) - - self.app.add_routes([ - web.static('/', self.web_root), - ]) - - def get_queue_info(self): - prompt_info = {} - exec_info = {} - exec_info['queue_remaining'] = self.prompt_queue.get_tasks_remaining() - prompt_info['exec_info'] = exec_info - return prompt_info - - async def send(self, event, data, sid=None): - if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE: - await self.send_image(data, sid=sid) - elif event == BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA: - # data is (preview_image, metadata) - preview_image, metadata = data - await self.send_image_with_metadata(preview_image, metadata, sid=sid) - elif isinstance(data, (bytes, bytearray)): - await self.send_bytes(event, data, sid) - else: - await self.send_json(event, data, sid) - - def encode_bytes(self, event, data): - if not isinstance(event, int): - raise RuntimeError(f"Binary event types must be integers, got {event}") - - packed = struct.pack(">I", event) - message = bytearray(packed) - message.extend(data) - return message - - async def send_image(self, image_data, sid=None): - image_type = image_data[0] - image = image_data[1] - max_size = image_data[2] - if max_size is not None: - if hasattr(Image, 'Resampling'): - resampling = Image.Resampling.BILINEAR - else: - resampling = Image.Resampling.LANCZOS - - image = ImageOps.contain(image, (max_size, max_size), resampling) - type_num = 1 - if image_type == "JPEG": - type_num = 1 - elif image_type == "PNG": - type_num = 2 - - bytesIO = BytesIO() - header = struct.pack(">I", type_num) - bytesIO.write(header) - image.save(bytesIO, format=image_type, quality=95, compress_level=1) - preview_bytes = bytesIO.getvalue() - await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid) - - async def send_image_with_metadata(self, image_data, metadata=None, sid=None): - image_type = image_data[0] - image = image_data[1] - max_size = image_data[2] - if max_size is not None: - if hasattr(Image, 'Resampling'): - resampling = Image.Resampling.BILINEAR - else: - resampling = Image.Resampling.LANCZOS - - image = ImageOps.contain(image, (max_size, max_size), resampling) - - mimetype = "image/png" if image_type == "PNG" else "image/jpeg" - - # Prepare metadata - if metadata is None: - metadata = {} - metadata["image_type"] = mimetype - - # Serialize metadata as JSON - import json - metadata_json = json.dumps(metadata).encode('utf-8') - metadata_length = len(metadata_json) - - # Prepare image data - bytesIO = BytesIO() - image.save(bytesIO, format=image_type, quality=95, compress_level=1) - image_bytes = bytesIO.getvalue() - - # Combine metadata and image - combined_data = bytearray() - combined_data.extend(struct.pack(">I", metadata_length)) - combined_data.extend(metadata_json) - combined_data.extend(image_bytes) - - await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA, combined_data, sid=sid) - - async def send_bytes(self, event, data, sid=None): - message = self.encode_bytes(event, data) - - if sid is None: - sockets = list(self.sockets.values()) - for ws in sockets: - await send_socket_catch_exception(ws.send_bytes, message) - elif sid in self.sockets: - await send_socket_catch_exception(self.sockets[sid].send_bytes, message) - - async def send_json(self, event, data, sid=None): - message = {"type": event, "data": data} - - if sid is None: - sockets = list(self.sockets.values()) - for ws in sockets: - await send_socket_catch_exception(ws.send_json, message) - elif sid in self.sockets: - await send_socket_catch_exception(self.sockets[sid].send_json, message) - - def send_sync(self, event, data, sid=None): - self.loop.call_soon_threadsafe( - self.messages.put_nowait, (event, data, sid)) - - def queue_updated(self): - self.send_sync("status", { "status": self.get_queue_info() }) - - async def publish_loop(self): - while True: - msg = await self.messages.get() - await self.send(*msg) - - async def start(self, address, port, verbose=True, call_on_start=None): - await self.start_multi_address([(address, port)], call_on_start=call_on_start) - - async def start_multi_address(self, addresses, call_on_start=None, verbose=True): - runner = web.AppRunner(self.app, access_log=None) - await runner.setup() - ssl_ctx = None - scheme = "http" - if args.tls_keyfile and args.tls_certfile: - ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) - ssl_ctx.load_cert_chain(certfile=args.tls_certfile, - keyfile=args.tls_keyfile) - scheme = "https" - - if verbose: - logging.info("Starting server\n") - for addr in addresses: - address = addr[0] - port = addr[1] - site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx) - await site.start() - - if not hasattr(self, 'address'): - self.address = address #TODO: remove this - self.port = port - - if ':' in address: - address_print = "[{}]".format(address) - else: - address_print = address - - if verbose: - logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address_print, port)) - - if call_on_start is not None: - call_on_start(scheme, self.address, self.port) - - def add_on_prompt_handler(self, handler): - self.on_prompt_handlers.append(handler) - - def trigger_on_prompt(self, json_data): - for handler in self.on_prompt_handlers: - try: - json_data = handler(json_data) - except Exception: - logging.warning("[ERROR] An error occurred during the on_prompt_handler processing") - logging.warning(traceback.format_exc()) - - return json_data - - def send_progress_text( - self, text: Union[bytes, bytearray, str], node_id: str, sid=None - ): - if isinstance(text, str): - text = text.encode("utf-8") - node_id_bytes = str(node_id).encode("utf-8") - - # Pack the node_id length as a 4-byte unsigned integer, followed by the node_id bytes - message = struct.pack(">I", len(node_id_bytes)) + node_id_bytes + text - - self.send_sync(BinaryEventTypes.TEXT, message, sid) diff --git a/tests-unit/README.md b/tests-unit/README.md deleted file mode 100644 index 81692b8f1de8c74fd058e627d10dbd0fc2821257..0000000000000000000000000000000000000000 --- a/tests-unit/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Pytest Unit Tests - -## Install test dependencies - -`pip install -r tests-unit/requirements.txt` - -## Run tests -`pytest tests-unit/` diff --git a/tests-unit/app_test/__init__.py b/tests-unit/app_test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/tests-unit/app_test/custom_node_manager_test.py b/tests-unit/app_test/custom_node_manager_test.py deleted file mode 100644 index b61e25e54e657609b3970358d6ff3354ff8f5a8e..0000000000000000000000000000000000000000 --- a/tests-unit/app_test/custom_node_manager_test.py +++ /dev/null @@ -1,147 +0,0 @@ -import pytest -from aiohttp import web -from unittest.mock import patch -from app.custom_node_manager import CustomNodeManager -import json - -pytestmark = ( - pytest.mark.asyncio -) # This applies the asyncio mark to all test functions in the module - - -@pytest.fixture -def custom_node_manager(): - return CustomNodeManager() - - -@pytest.fixture -def app(custom_node_manager): - app = web.Application() - routes = web.RouteTableDef() - custom_node_manager.add_routes( - routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")] - ) - app.add_routes(routes) - return app - - -async def test_get_workflow_templates(aiohttp_client, app, tmp_path): - client = await aiohttp_client(app) - # Setup temporary custom nodes file structure with 1 workflow file - custom_nodes_dir = tmp_path / "custom_nodes" - example_workflows_dir = ( - custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows" - ) - example_workflows_dir.mkdir(parents=True) - template_file = example_workflows_dir / "workflow1.json" - template_file.write_text("") - - with patch( - "folder_paths.folder_names_and_paths", - {"custom_nodes": ([str(custom_nodes_dir)], None)}, - ): - response = await client.get("/workflow_templates") - assert response.status == 200 - workflows_dict = await response.json() - assert isinstance(workflows_dict, dict) - assert "ComfyUI-TestExtension1" in workflows_dict - assert isinstance(workflows_dict["ComfyUI-TestExtension1"], list) - assert workflows_dict["ComfyUI-TestExtension1"][0] == "workflow1" - - -async def test_build_translations_empty_when_no_locales(custom_node_manager, tmp_path): - custom_nodes_dir = tmp_path / "custom_nodes" - custom_nodes_dir.mkdir(parents=True) - - with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]): - translations = custom_node_manager.build_translations() - assert translations == {} - - -async def test_build_translations_loads_all_files(custom_node_manager, tmp_path): - # Setup test directory structure - custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension" - locales_dir = custom_nodes_dir / "locales" / "en" - locales_dir.mkdir(parents=True) - - # Create test translation files - main_content = {"title": "Test Extension"} - (locales_dir / "main.json").write_text(json.dumps(main_content)) - - node_defs = {"node1": "Node 1"} - (locales_dir / "nodeDefs.json").write_text(json.dumps(node_defs)) - - commands = {"cmd1": "Command 1"} - (locales_dir / "commands.json").write_text(json.dumps(commands)) - - settings = {"setting1": "Setting 1"} - (locales_dir / "settings.json").write_text(json.dumps(settings)) - - with patch( - "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"] - ): - translations = custom_node_manager.build_translations() - - assert translations == { - "en": { - "title": "Test Extension", - "nodeDefs": {"node1": "Node 1"}, - "commands": {"cmd1": "Command 1"}, - "settings": {"setting1": "Setting 1"}, - } - } - - -async def test_build_translations_handles_invalid_json(custom_node_manager, tmp_path): - # Setup test directory structure - custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension" - locales_dir = custom_nodes_dir / "locales" / "en" - locales_dir.mkdir(parents=True) - - # Create valid main.json - main_content = {"title": "Test Extension"} - (locales_dir / "main.json").write_text(json.dumps(main_content)) - - # Create invalid JSON file - (locales_dir / "nodeDefs.json").write_text("invalid json{") - - with patch( - "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"] - ): - translations = custom_node_manager.build_translations() - - assert translations == { - "en": { - "title": "Test Extension", - } - } - - -async def test_build_translations_merges_multiple_extensions( - custom_node_manager, tmp_path -): - # Setup test directory structure for two extensions - custom_nodes_dir = tmp_path / "custom_nodes" - ext1_dir = custom_nodes_dir / "extension1" / "locales" / "en" - ext2_dir = custom_nodes_dir / "extension2" / "locales" / "en" - ext1_dir.mkdir(parents=True) - ext2_dir.mkdir(parents=True) - - # Create translation files for extension 1 - ext1_main = {"title": "Extension 1", "shared": "Original"} - (ext1_dir / "main.json").write_text(json.dumps(ext1_main)) - - # Create translation files for extension 2 - ext2_main = {"description": "Extension 2", "shared": "Override"} - (ext2_dir / "main.json").write_text(json.dumps(ext2_main)) - - with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]): - translations = custom_node_manager.build_translations() - - assert translations == { - "en": { - "title": "Extension 1", - "description": "Extension 2", - "shared": "Override", # Second extension should override first - } - } diff --git a/tests-unit/app_test/frontend_manager_test.py b/tests-unit/app_test/frontend_manager_test.py deleted file mode 100644 index ce43ac564e8a5431566cdaef270aff47f7b0d9ec..0000000000000000000000000000000000000000 --- a/tests-unit/app_test/frontend_manager_test.py +++ /dev/null @@ -1,207 +0,0 @@ -import argparse -import pytest -from requests.exceptions import HTTPError -from unittest.mock import patch, mock_open - -from app.frontend_management import ( - FrontendManager, - FrontEndProvider, - Release, -) -from comfy.cli_args import DEFAULT_VERSION_STRING - - -@pytest.fixture -def mock_releases(): - return [ - Release( - id=1, - tag_name="1.0.0", - name="Release 1.0.0", - prerelease=False, - created_at="2022-01-01T00:00:00Z", - published_at="2022-01-01T00:00:00Z", - body="Release notes for 1.0.0", - assets=[{"name": "dist.zip", "url": "https://example.com/dist.zip"}], - ), - Release( - id=2, - tag_name="2.0.0", - name="Release 2.0.0", - prerelease=False, - created_at="2022-02-01T00:00:00Z", - published_at="2022-02-01T00:00:00Z", - body="Release notes for 2.0.0", - assets=[{"name": "dist.zip", "url": "https://example.com/dist.zip"}], - ), - ] - - -@pytest.fixture -def mock_provider(mock_releases): - provider = FrontEndProvider( - owner="test-owner", - repo="test-repo", - ) - provider.all_releases = mock_releases - provider.latest_release = mock_releases[1] - FrontendManager.PROVIDERS = [provider] - return provider - - -def test_get_release(mock_provider, mock_releases): - version = "1.0.0" - release = mock_provider.get_release(version) - assert release == mock_releases[0] - - -def test_get_release_latest(mock_provider, mock_releases): - version = "latest" - release = mock_provider.get_release(version) - assert release == mock_releases[1] - - -def test_get_release_invalid_version(mock_provider): - version = "invalid" - with pytest.raises(ValueError): - mock_provider.get_release(version) - - -def test_init_frontend_default(): - version_string = DEFAULT_VERSION_STRING - frontend_path = FrontendManager.init_frontend(version_string) - assert frontend_path == FrontendManager.default_frontend_path() - - -def test_init_frontend_invalid_version(): - version_string = "test-owner/test-repo@1.100.99" - with pytest.raises(HTTPError): - FrontendManager.init_frontend_unsafe(version_string) - - -def test_init_frontend_invalid_provider(): - version_string = "invalid/invalid@latest" - with pytest.raises(HTTPError): - FrontendManager.init_frontend_unsafe(version_string) - - -@pytest.fixture -def mock_os_functions(): - with ( - patch("app.frontend_management.os.makedirs") as mock_makedirs, - patch("app.frontend_management.os.listdir") as mock_listdir, - patch("app.frontend_management.os.rmdir") as mock_rmdir, - ): - mock_listdir.return_value = [] # Simulate empty directory - yield mock_makedirs, mock_listdir, mock_rmdir - - -@pytest.fixture -def mock_download(): - with patch("app.frontend_management.download_release_asset_zip") as mock: - mock.side_effect = Exception("Download failed") # Simulate download failure - yield mock - - -def test_finally_block(mock_os_functions, mock_download, mock_provider): - # Arrange - mock_makedirs, mock_listdir, mock_rmdir = mock_os_functions - version_string = "test-owner/test-repo@1.0.0" - - # Act & Assert - with pytest.raises(Exception): - FrontendManager.init_frontend_unsafe(version_string, mock_provider) - - # Assert - mock_makedirs.assert_called_once() - mock_download.assert_called_once() - mock_listdir.assert_called_once() - mock_rmdir.assert_called_once() - - -def test_parse_version_string(): - version_string = "owner/repo@1.0.0" - repo_owner, repo_name, version = FrontendManager.parse_version_string( - version_string - ) - assert repo_owner == "owner" - assert repo_name == "repo" - assert version == "1.0.0" - - -def test_parse_version_string_invalid(): - version_string = "invalid" - with pytest.raises(argparse.ArgumentTypeError): - FrontendManager.parse_version_string(version_string) - - -def test_init_frontend_default_with_mocks(): - # Arrange - version_string = DEFAULT_VERSION_STRING - - # Act - with ( - patch("app.frontend_management.check_frontend_version") as mock_check, - patch.object( - FrontendManager, "default_frontend_path", return_value="/mocked/path" - ), - ): - frontend_path = FrontendManager.init_frontend(version_string) - - # Assert - assert frontend_path == "/mocked/path" - mock_check.assert_called_once() - - -def test_init_frontend_fallback_on_error(): - # Arrange - version_string = "test-owner/test-repo@1.0.0" - - # Act - with ( - patch.object( - FrontendManager, "init_frontend_unsafe", side_effect=Exception("Test error") - ), - patch("app.frontend_management.check_frontend_version") as mock_check, - patch.object( - FrontendManager, "default_frontend_path", return_value="/default/path" - ), - ): - frontend_path = FrontendManager.init_frontend(version_string) - - # Assert - assert frontend_path == "/default/path" - mock_check.assert_called_once() - - -def test_get_frontend_version(): - # Arrange - expected_version = "1.25.0" - mock_requirements_content = """torch -torchsde -comfyui-frontend-package==1.25.0 -other-package==1.0.0 -numpy""" - - # Act - with patch("builtins.open", mock_open(read_data=mock_requirements_content)): - version = FrontendManager.get_required_frontend_version() - - # Assert - assert version == expected_version - - -def test_get_frontend_version_invalid_semver(): - # Arrange - mock_requirements_content = """torch -torchsde -comfyui-frontend-package==1.29.3.75 -other-package==1.0.0 -numpy""" - - # Act - with patch("builtins.open", mock_open(read_data=mock_requirements_content)): - version = FrontendManager.get_required_frontend_version() - - # Assert - assert version is None diff --git a/tests-unit/app_test/model_manager_test.py b/tests-unit/app_test/model_manager_test.py deleted file mode 100644 index ae59206f65639ca1749b1cf7b6903ebab18fb298..0000000000000000000000000000000000000000 --- a/tests-unit/app_test/model_manager_test.py +++ /dev/null @@ -1,62 +0,0 @@ -import pytest -import base64 -import json -import struct -from io import BytesIO -from PIL import Image -from aiohttp import web -from unittest.mock import patch -from app.model_manager import ModelFileManager - -pytestmark = ( - pytest.mark.asyncio -) # This applies the asyncio mark to all test functions in the module - -@pytest.fixture -def model_manager(): - return ModelFileManager() - -@pytest.fixture -def app(model_manager): - app = web.Application() - routes = web.RouteTableDef() - model_manager.add_routes(routes) - app.add_routes(routes) - return app - -async def test_get_model_preview_safetensors(aiohttp_client, app, tmp_path): - img = Image.new('RGB', (100, 100), 'white') - img_byte_arr = BytesIO() - img.save(img_byte_arr, format='PNG') - img_byte_arr.seek(0) - img_b64 = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8') - - safetensors_file = tmp_path / "test_model.safetensors" - header_bytes = json.dumps({ - "__metadata__": { - "ssmd_cover_images": json.dumps([img_b64]) - } - }).encode('utf-8') - length_bytes = struct.pack('= 0.9) # Should be close to white - - # Test black spacing - result_black = node.stitch(image1, "right", False, 16, "black", image2) - spacing_region = result_black[0][:, :, 32:48, :] - assert torch.all(spacing_region <= 0.1) # Should be close to black - - def test_odd_spacing_width_made_even(self): - """Test that odd spacing widths are made even""" - node = ImageStitch() - image1 = self.create_test_image(height=32, width=32) - image2 = self.create_test_image(height=32, width=32) - - # Use odd spacing width - result = node.stitch(image1, "right", False, 15, "white", image2) - - # Should be made even (16), so total width = 32 + 16 + 32 = 80 - assert result[0].shape == (1, 32, 80, 3) - - def test_batch_size_matching(self): - """Test that different batch sizes are handled correctly""" - node = ImageStitch() - image1 = self.create_test_image(batch_size=2, height=32, width=32) - image2 = self.create_test_image(batch_size=1, height=32, width=32) - - result = node.stitch(image1, "right", False, 0, "white", image2) - - # Should match larger batch size - assert result[0].shape == (2, 32, 64, 3) - - def test_channel_matching_rgb_to_rgba(self): - """Test that channel differences are handled (RGB + alpha)""" - node = ImageStitch() - image1 = self.create_test_image(channels=3) # RGB - image2 = self.create_test_image(channels=4) # RGBA - - result = node.stitch(image1, "right", False, 0, "white", image2) - - # Should have 4 channels (RGBA) - assert result[0].shape[-1] == 4 - - def test_channel_matching_rgba_to_rgb(self): - """Test that channel differences are handled (RGBA + RGB)""" - node = ImageStitch() - image1 = self.create_test_image(channels=4) # RGBA - image2 = self.create_test_image(channels=3) # RGB - - result = node.stitch(image1, "right", False, 0, "white", image2) - - # Should have 4 channels (RGBA) - assert result[0].shape[-1] == 4 - - def test_all_color_options(self): - """Test all available color options""" - node = ImageStitch() - image1 = self.create_test_image(height=32, width=32) - image2 = self.create_test_image(height=32, width=32) - - colors = ["white", "black", "red", "green", "blue"] - - for color in colors: - result = node.stitch(image1, "right", False, 16, color, image2) - assert result[0].shape == (1, 32, 80, 3) # Basic shape check - - def test_all_directions(self): - """Test all direction options""" - node = ImageStitch() - image1 = self.create_test_image(height=32, width=32) - image2 = self.create_test_image(height=32, width=32) - - directions = ["right", "left", "up", "down"] - - for direction in directions: - result = node.stitch(image1, direction, False, 0, "white", image2) - assert result[0].shape == (1, 32, 64, 3) if direction in ["right", "left"] else (1, 64, 32, 3) - - def test_batch_size_channel_spacing_integration(self): - """Test integration of batch matching, channel matching, size matching, and spacings""" - node = ImageStitch() - image1 = self.create_test_image(batch_size=2, height=64, width=48, channels=3) - image2 = self.create_test_image(batch_size=1, height=32, width=32, channels=4) - - result = node.stitch(image1, "right", True, 8, "red", image2) - - # Should handle: batch matching, size matching, channel matching, spacing - assert result[0].shape[0] == 2 # Batch size matched - assert result[0].shape[-1] == 4 # Channels matched to max - assert result[0].shape[1] == 64 # Height from image1 (size matching) - # Width should be: 48 + 8 (spacing) + resized_image2_width - expected_image2_width = int(64 * (32/32)) # Resized to height 64 - expected_total_width = 48 + 8 + expected_image2_width - assert result[0].shape[2] == expected_total_width - diff --git a/tests-unit/comfy_test/folder_path_test.py b/tests-unit/comfy_test/folder_path_test.py deleted file mode 100644 index 775e15c36ba8a74657161b59ec98ebc62db52512..0000000000000000000000000000000000000000 --- a/tests-unit/comfy_test/folder_path_test.py +++ /dev/null @@ -1,162 +0,0 @@ -### 🗻 This file is created through the spirit of Mount Fuji at its peak -# TODO(yoland): clean up this after I get back down -import sys -import pytest -import os -import tempfile -from unittest.mock import patch -from importlib import reload - -import folder_paths -import comfy.cli_args -from comfy.options import enable_args_parsing -enable_args_parsing() - - -@pytest.fixture() -def clear_folder_paths(): - # Reload the module after each test to ensure isolation - yield - reload(folder_paths) - -@pytest.fixture -def temp_dir(): - with tempfile.TemporaryDirectory() as tmpdirname: - yield tmpdirname - - -@pytest.fixture -def set_base_dir(): - def _set_base_dir(base_dir): - # Mock CLI args - with patch.object(sys, 'argv', ["main.py", "--base-directory", base_dir]): - reload(comfy.cli_args) - reload(folder_paths) - yield _set_base_dir - # Reload the modules after each test to ensure isolation - with patch.object(sys, 'argv', ["main.py"]): - reload(comfy.cli_args) - reload(folder_paths) - - -def test_get_directory_by_type(clear_folder_paths): - test_dir = "/test/dir" - folder_paths.set_output_directory(test_dir) - assert folder_paths.get_directory_by_type("output") == test_dir - assert folder_paths.get_directory_by_type("invalid") is None - -def test_annotated_filepath(): - assert folder_paths.annotated_filepath("test.txt") == ("test.txt", None) - assert folder_paths.annotated_filepath("test.txt [output]") == ("test.txt", folder_paths.get_output_directory()) - assert folder_paths.annotated_filepath("test.txt [input]") == ("test.txt", folder_paths.get_input_directory()) - assert folder_paths.annotated_filepath("test.txt [temp]") == ("test.txt", folder_paths.get_temp_directory()) - -def test_get_annotated_filepath(): - default_dir = "/default/dir" - assert folder_paths.get_annotated_filepath("test.txt", default_dir) == os.path.join(default_dir, "test.txt") - assert folder_paths.get_annotated_filepath("test.txt [output]") == os.path.join(folder_paths.get_output_directory(), "test.txt") - -def test_add_model_folder_path_append(clear_folder_paths): - folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True) - folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False) - assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"] - - -def test_add_model_folder_path_insert(clear_folder_paths): - folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False) - folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True) - assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"] - - -def test_add_model_folder_path_re_add_existing_default(clear_folder_paths): - folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False) - folder_paths.add_model_folder_path("test_folder", "/old_default/path", is_default=True) - assert folder_paths.get_folder_paths("test_folder") == ["/old_default/path", "/test/path"] - folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=True) - assert folder_paths.get_folder_paths("test_folder") == ["/test/path", "/old_default/path"] - - -def test_add_model_folder_path_re_add_existing_non_default(clear_folder_paths): - folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False) - folder_paths.add_model_folder_path("test_folder", "/default/path", is_default=True) - assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"] - folder_paths.add_model_folder_path("test_folder", "/test/path", is_default=False) - assert folder_paths.get_folder_paths("test_folder") == ["/default/path", "/test/path"] - - -def test_recursive_search(temp_dir): - os.makedirs(os.path.join(temp_dir, "subdir")) - open(os.path.join(temp_dir, "file1.txt"), "w").close() - open(os.path.join(temp_dir, "subdir", "file2.txt"), "w").close() - - files, dirs = folder_paths.recursive_search(temp_dir) - assert set(files) == {"file1.txt", os.path.join("subdir", "file2.txt")} - assert len(dirs) == 2 # temp_dir and subdir - -def test_filter_files_extensions(): - files = ["file1.txt", "file2.jpg", "file3.png", "file4.txt"] - assert folder_paths.filter_files_extensions(files, [".txt"]) == ["file1.txt", "file4.txt"] - assert folder_paths.filter_files_extensions(files, [".jpg", ".png"]) == ["file2.jpg", "file3.png"] - assert folder_paths.filter_files_extensions(files, []) == files - -@patch("folder_paths.recursive_search") -@patch("folder_paths.folder_names_and_paths") -def test_get_filename_list(mock_folder_names_and_paths, mock_recursive_search): - mock_folder_names_and_paths.__getitem__.return_value = (["/test/path"], {".txt"}) - mock_recursive_search.return_value = (["file1.txt", "file2.jpg"], {}) - assert folder_paths.get_filename_list("test_folder") == ["file1.txt"] - -def test_get_save_image_path(temp_dir): - with patch("folder_paths.output_directory", temp_dir): - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path("test", temp_dir, 100, 100) - assert os.path.samefile(full_output_folder, temp_dir) - assert filename == "test" - assert counter == 1 - assert subfolder == "" - assert filename_prefix == "test" - - -def test_base_path_changes(set_base_dir): - test_dir = os.path.abspath("/test/dir") - set_base_dir(test_dir) - - assert folder_paths.base_path == test_dir - assert folder_paths.models_dir == os.path.join(test_dir, "models") - assert folder_paths.input_directory == os.path.join(test_dir, "input") - assert folder_paths.output_directory == os.path.join(test_dir, "output") - assert folder_paths.temp_directory == os.path.join(test_dir, "temp") - assert folder_paths.user_directory == os.path.join(test_dir, "user") - - assert os.path.join(test_dir, "custom_nodes") in folder_paths.get_folder_paths("custom_nodes") - - for name in ["checkpoints", "loras", "vae", "configs", "embeddings", "controlnet", "classifiers"]: - assert folder_paths.get_folder_paths(name)[0] == os.path.join(test_dir, "models", name) - - -def test_base_path_change_clears_old(set_base_dir): - test_dir = os.path.abspath("/test/dir") - set_base_dir(test_dir) - - assert len(folder_paths.get_folder_paths("custom_nodes")) == 1 - - single_model_paths = [ - "checkpoints", - "loras", - "vae", - "configs", - "clip_vision", - "style_models", - "diffusers", - "vae_approx", - "gligen", - "upscale_models", - "embeddings", - "hypernetworks", - "photomaker", - "classifiers", - ] - for name in single_model_paths: - assert len(folder_paths.get_folder_paths(name)) == 1 - - for name in ["controlnet", "diffusion_models", "text_encoders"]: - assert len(folder_paths.get_folder_paths(name)) == 2 diff --git a/tests-unit/execution_test/validate_node_input_test.py b/tests-unit/execution_test/validate_node_input_test.py deleted file mode 100644 index 85a0c9601fd859c9374bfc753bb3382194a709d3..0000000000000000000000000000000000000000 --- a/tests-unit/execution_test/validate_node_input_test.py +++ /dev/null @@ -1,119 +0,0 @@ -import pytest -from comfy_execution.validation import validate_node_input - - -def test_exact_match(): - """Test cases where types match exactly""" - assert validate_node_input("STRING", "STRING") - assert validate_node_input("STRING,INT", "STRING,INT") - assert validate_node_input("INT,STRING", "STRING,INT") # Order shouldn't matter - - -def test_strict_mode(): - """Test strict mode validation""" - # Should pass - received type is subset of input type - assert validate_node_input("STRING", "STRING,INT", strict=True) - assert validate_node_input("INT", "STRING,INT", strict=True) - assert validate_node_input("STRING,INT", "STRING,INT,BOOLEAN", strict=True) - - # Should fail - received type is not subset of input type - assert not validate_node_input("STRING,INT", "STRING", strict=True) - assert not validate_node_input("STRING,BOOLEAN", "STRING", strict=True) - assert not validate_node_input("INT,BOOLEAN", "STRING,INT", strict=True) - - -def test_non_strict_mode(): - """Test non-strict mode validation (default behavior)""" - # Should pass - types have overlap - assert validate_node_input("STRING,BOOLEAN", "STRING,INT") - assert validate_node_input("STRING,INT", "INT,BOOLEAN") - assert validate_node_input("STRING", "STRING,INT") - - # Should fail - no overlap in types - assert not validate_node_input("BOOLEAN", "STRING,INT") - assert not validate_node_input("FLOAT", "STRING,INT") - assert not validate_node_input("FLOAT,BOOLEAN", "STRING,INT") - - -def test_whitespace_handling(): - """Test that whitespace is handled correctly""" - assert validate_node_input("STRING, INT", "STRING,INT") - assert validate_node_input("STRING,INT", "STRING, INT") - assert validate_node_input(" STRING , INT ", "STRING,INT") - assert validate_node_input("STRING,INT", " STRING , INT ") - - -def test_empty_strings(): - """Test behavior with empty strings""" - assert validate_node_input("", "") - assert not validate_node_input("STRING", "") - assert not validate_node_input("", "STRING") - - -def test_single_vs_multiple(): - """Test single type against multiple types""" - assert validate_node_input("STRING", "STRING,INT,BOOLEAN") - assert validate_node_input("STRING,INT,BOOLEAN", "STRING", strict=False) - assert not validate_node_input("STRING,INT,BOOLEAN", "STRING", strict=True) - - -def test_non_string(): - """Test non-string types""" - obj1 = object() - obj2 = object() - assert validate_node_input(obj1, obj1) - assert not validate_node_input(obj1, obj2) - - -class NotEqualsOverrideTest(str): - """Test class for ``__ne__`` override.""" - - def __ne__(self, value: object) -> bool: - if self == "*" or value == "*": - return False - if self == "LONGER_THAN_2": - return not len(value) > 2 - raise TypeError("This is a class for unit tests only.") - - -def test_ne_override(): - """Test ``__ne__`` any override""" - any = NotEqualsOverrideTest("*") - invalid_type = "INVALID_TYPE" - obj = object() - assert validate_node_input(any, any) - assert validate_node_input(any, invalid_type) - assert validate_node_input(any, obj) - assert validate_node_input(any, {}) - assert validate_node_input(any, []) - assert validate_node_input(any, [1, 2, 3]) - - -def test_ne_custom_override(): - """Test ``__ne__`` custom override""" - special = NotEqualsOverrideTest("LONGER_THAN_2") - - assert validate_node_input(special, special) - assert validate_node_input(special, "*") - assert validate_node_input(special, "INVALID_TYPE") - assert validate_node_input(special, [1, 2, 3]) - - # Should fail - assert not validate_node_input(special, [1, 2]) - assert not validate_node_input(special, "TY") - - -@pytest.mark.parametrize( - "received,input_type,strict,expected", - [ - ("STRING", "STRING", False, True), - ("STRING,INT", "STRING,INT", False, True), - ("STRING", "STRING,INT", True, True), - ("STRING,INT", "STRING", True, False), - ("BOOLEAN", "STRING,INT", False, False), - ("STRING,BOOLEAN", "STRING,INT", False, True), - ], -) -def test_parametrized_cases(received, input_type, strict, expected): - """Parametrized test cases for various scenarios""" - assert validate_node_input(received, input_type, strict) == expected diff --git a/tests-unit/feature_flags_test.py b/tests-unit/feature_flags_test.py deleted file mode 100644 index f2702cfc84cdd427f82c3fe0a95148ee83c88f36..0000000000000000000000000000000000000000 --- a/tests-unit/feature_flags_test.py +++ /dev/null @@ -1,98 +0,0 @@ -"""Tests for feature flags functionality.""" - -from comfy_api.feature_flags import ( - get_connection_feature, - supports_feature, - get_server_features, - SERVER_FEATURE_FLAGS, -) - - -class TestFeatureFlags: - """Test suite for feature flags functions.""" - - def test_get_server_features_returns_copy(self): - """Test that get_server_features returns a copy of the server flags.""" - features = get_server_features() - # Verify it's a copy by modifying it - features["test_flag"] = True - # Original should be unchanged - assert "test_flag" not in SERVER_FEATURE_FLAGS - - def test_get_server_features_contains_expected_flags(self): - """Test that server features contain expected flags.""" - features = get_server_features() - assert "supports_preview_metadata" in features - assert features["supports_preview_metadata"] is True - assert "max_upload_size" in features - assert isinstance(features["max_upload_size"], (int, float)) - - def test_get_connection_feature_with_missing_sid(self): - """Test getting feature for non-existent session ID.""" - sockets_metadata = {} - result = get_connection_feature(sockets_metadata, "missing_sid", "some_feature") - assert result is False # Default value - - def test_get_connection_feature_with_custom_default(self): - """Test getting feature with custom default value.""" - sockets_metadata = {} - result = get_connection_feature( - sockets_metadata, "missing_sid", "some_feature", default="custom_default" - ) - assert result == "custom_default" - - def test_get_connection_feature_with_feature_flags(self): - """Test getting feature from connection with feature flags.""" - sockets_metadata = { - "sid1": { - "feature_flags": { - "supports_preview_metadata": True, - "custom_feature": "value", - }, - } - } - result = get_connection_feature(sockets_metadata, "sid1", "supports_preview_metadata") - assert result is True - - result = get_connection_feature(sockets_metadata, "sid1", "custom_feature") - assert result == "value" - - def test_get_connection_feature_missing_feature(self): - """Test getting non-existent feature from connection.""" - sockets_metadata = { - "sid1": {"feature_flags": {"existing_feature": True}} - } - result = get_connection_feature(sockets_metadata, "sid1", "missing_feature") - assert result is False - - def test_supports_feature_returns_boolean(self): - """Test that supports_feature always returns boolean.""" - sockets_metadata = { - "sid1": { - "feature_flags": { - "bool_feature": True, - "string_feature": "value", - "none_feature": None, - }, - } - } - - # True boolean feature - assert supports_feature(sockets_metadata, "sid1", "bool_feature") is True - - # Non-boolean values should return False - assert supports_feature(sockets_metadata, "sid1", "string_feature") is False - assert supports_feature(sockets_metadata, "sid1", "none_feature") is False - assert supports_feature(sockets_metadata, "sid1", "missing_feature") is False - - def test_supports_feature_with_missing_connection(self): - """Test supports_feature with missing connection.""" - sockets_metadata = {} - assert supports_feature(sockets_metadata, "missing_sid", "any_feature") is False - - def test_empty_feature_flags_dict(self): - """Test connection with empty feature flags dictionary.""" - sockets_metadata = {"sid1": {"feature_flags": {}}} - result = get_connection_feature(sockets_metadata, "sid1", "any_feature") - assert result is False - assert supports_feature(sockets_metadata, "sid1", "any_feature") is False diff --git a/tests-unit/folder_paths_test/__init__.py b/tests-unit/folder_paths_test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/tests-unit/folder_paths_test/filter_by_content_types_test.py b/tests-unit/folder_paths_test/filter_by_content_types_test.py deleted file mode 100644 index 683f9fc113a842cc3d36ff955aeb61d2ad6ff60b..0000000000000000000000000000000000000000 --- a/tests-unit/folder_paths_test/filter_by_content_types_test.py +++ /dev/null @@ -1,66 +0,0 @@ -import pytest -import os -import tempfile -from folder_paths import filter_files_content_types, extension_mimetypes_cache -from unittest.mock import patch - - -@pytest.fixture(scope="module") -def file_extensions(): - return { - 'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'], - 'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'], - 'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv'], - 'model': ['gltf', 'glb', 'obj', 'fbx', 'stl'] - } - - -@pytest.fixture(scope="module") -def mock_dir(file_extensions): - with tempfile.TemporaryDirectory() as directory: - for content_type, extensions in file_extensions.items(): - for extension in extensions: - with open(f"{directory}/sample_{content_type}.{extension}", "w") as f: - f.write(f"Sample {content_type} file in {extension} format") - yield directory - - -@pytest.fixture -def patched_mimetype_cache(file_extensions): - # Mock model file extensions since they may not be in the test-runner system's mimetype cache - new_cache = extension_mimetypes_cache.copy() - for extension in file_extensions["model"]: - new_cache[extension] = "model" - - with patch("folder_paths.extension_mimetypes_cache", new_cache): - yield - - -def test_categorizes_all_correctly(mock_dir, file_extensions, patched_mimetype_cache): - files = os.listdir(mock_dir) - for content_type, extensions in file_extensions.items(): - filtered_files = filter_files_content_types(files, [content_type]) - for extension in extensions: - assert f"sample_{content_type}.{extension}" in filtered_files - - -def test_categorizes_all_uniquely(mock_dir, file_extensions, patched_mimetype_cache): - files = os.listdir(mock_dir) - for content_type, extensions in file_extensions.items(): - filtered_files = filter_files_content_types(files, [content_type]) - assert len(filtered_files) == len(extensions) - - -def test_handles_bad_extensions(): - files = ["file1.txt", "file2.py", "file3.example", "file4.pdf", "file5.ini", "file6.doc", "file7.md"] - assert filter_files_content_types(files, ["image", "audio", "video"]) == [] - - -def test_handles_no_extension(): - files = ["file1", "file2", "file3", "file4", "file5", "file6", "file7"] - assert filter_files_content_types(files, ["image", "audio", "video"]) == [] - - -def test_handles_no_files(): - files = [] - assert filter_files_content_types(files, ["image", "audio", "video"]) == [] diff --git a/tests-unit/folder_paths_test/misc_test.py b/tests-unit/folder_paths_test/misc_test.py deleted file mode 100644 index fcf66745337a09d63e05ed8bbbf38aaccf5d4143..0000000000000000000000000000000000000000 --- a/tests-unit/folder_paths_test/misc_test.py +++ /dev/null @@ -1,51 +0,0 @@ -import pytest -import os -import tempfile -from folder_paths import get_input_subfolders, set_input_directory - -@pytest.fixture(scope="module") -def mock_folder_structure(): - with tempfile.TemporaryDirectory() as temp_dir: - # Create a nested folder structure - folders = [ - "folder1", - "folder1/subfolder1", - "folder1/subfolder2", - "folder2", - "folder2/deep", - "folder2/deep/nested", - "empty_folder" - ] - - # Create the folders - for folder in folders: - os.makedirs(os.path.join(temp_dir, folder)) - - # Add some files to test they're not included - with open(os.path.join(temp_dir, "root_file.txt"), "w") as f: - f.write("test") - with open(os.path.join(temp_dir, "folder1", "test.txt"), "w") as f: - f.write("test") - - set_input_directory(temp_dir) - yield temp_dir - - -def test_gets_all_folders(mock_folder_structure): - folders = get_input_subfolders() - expected = ["folder1", "folder1/subfolder1", "folder1/subfolder2", - "folder2", "folder2/deep", "folder2/deep/nested", "empty_folder"] - assert sorted(folders) == sorted(expected) - - -def test_handles_nonexistent_input_directory(): - with tempfile.TemporaryDirectory() as temp_dir: - nonexistent = os.path.join(temp_dir, "nonexistent") - set_input_directory(nonexistent) - assert get_input_subfolders() == [] - - -def test_empty_input_directory(): - with tempfile.TemporaryDirectory() as temp_dir: - set_input_directory(temp_dir) - assert get_input_subfolders() == [] # Empty since we don't include root diff --git a/tests-unit/prompt_server_test/__init__.py b/tests-unit/prompt_server_test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/tests-unit/prompt_server_test/user_manager_test.py b/tests-unit/prompt_server_test/user_manager_test.py deleted file mode 100644 index b939d8e68f821077ca2def1d630f5bd944db8293..0000000000000000000000000000000000000000 --- a/tests-unit/prompt_server_test/user_manager_test.py +++ /dev/null @@ -1,289 +0,0 @@ -import pytest -import os -from aiohttp import web -from app.user_manager import UserManager -from unittest.mock import patch - -pytestmark = ( - pytest.mark.asyncio -) # This applies the asyncio mark to all test functions in the module - - -@pytest.fixture -def user_manager(tmp_path): - um = UserManager() - um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join( - tmp_path, file - ) if file else tmp_path - return um - - -@pytest.fixture -def app(user_manager): - app = web.Application() - routes = web.RouteTableDef() - user_manager.add_routes(routes) - app.add_routes(routes) - return app - - -async def test_listuserdata_empty_directory(aiohttp_client, app, tmp_path): - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=test_dir") - assert resp.status == 404 - - -async def test_listuserdata_with_files(aiohttp_client, app, tmp_path): - os.makedirs(tmp_path / "test_dir") - with open(tmp_path / "test_dir" / "file1.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=test_dir") - assert resp.status == 200 - assert await resp.json() == ["file1.txt"] - - -async def test_listuserdata_recursive(aiohttp_client, app, tmp_path): - os.makedirs(tmp_path / "test_dir" / "subdir") - with open(tmp_path / "test_dir" / "file1.txt", "w") as f: - f.write("test content") - with open(tmp_path / "test_dir" / "subdir" / "file2.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=test_dir&recurse=true") - assert resp.status == 200 - assert set(await resp.json()) == {"file1.txt", "subdir/file2.txt"} - - -async def test_listuserdata_full_info(aiohttp_client, app, tmp_path): - os.makedirs(tmp_path / "test_dir") - with open(tmp_path / "test_dir" / "file1.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=test_dir&full_info=true") - assert resp.status == 200 - result = await resp.json() - assert len(result) == 1 - assert result[0]["path"] == "file1.txt" - assert "size" in result[0] - assert "modified" in result[0] - - -async def test_listuserdata_split_path(aiohttp_client, app, tmp_path): - os.makedirs(tmp_path / "test_dir" / "subdir") - with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=test_dir&recurse=true&split=true") - assert resp.status == 200 - assert await resp.json() == [["subdir/file1.txt", "subdir", "file1.txt"]] - - -async def test_listuserdata_invalid_directory(aiohttp_client, app): - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=") - assert resp.status == 400 - - -async def test_listuserdata_normalized_separator(aiohttp_client, app, tmp_path): - os_sep = "\\" - with patch("os.sep", os_sep): - with patch("os.path.sep", os_sep): - os.makedirs(tmp_path / "test_dir" / "subdir") - with open(tmp_path / "test_dir" / "subdir" / "file1.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.get("/userdata?dir=test_dir&recurse=true") - assert resp.status == 200 - result = await resp.json() - assert len(result) == 1 - assert "/" in result[0] # Ensure forward slash is used - assert "\\" not in result[0] # Ensure backslash is not present - assert result[0] == "subdir/file1.txt" - - # Test with full_info - resp = await client.get( - "/userdata?dir=test_dir&recurse=true&full_info=true" - ) - assert resp.status == 200 - result = await resp.json() - assert len(result) == 1 - assert "/" in result[0]["path"] # Ensure forward slash is used - assert "\\" not in result[0]["path"] # Ensure backslash is not present - assert result[0]["path"] == "subdir/file1.txt" - - -async def test_post_userdata_new_file(aiohttp_client, app, tmp_path): - client = await aiohttp_client(app) - content = b"test content" - resp = await client.post("/userdata/test.txt", data=content) - - assert resp.status == 200 - assert await resp.text() == '"test.txt"' - - # Verify file was created with correct content - with open(tmp_path / "test.txt", "rb") as f: - assert f.read() == content - - -async def test_post_userdata_overwrite_existing(aiohttp_client, app, tmp_path): - # Create initial file - with open(tmp_path / "test.txt", "w") as f: - f.write("initial content") - - client = await aiohttp_client(app) - new_content = b"updated content" - resp = await client.post("/userdata/test.txt", data=new_content) - - assert resp.status == 200 - assert await resp.text() == '"test.txt"' - - # Verify file was overwritten - with open(tmp_path / "test.txt", "rb") as f: - assert f.read() == new_content - - -async def test_post_userdata_no_overwrite(aiohttp_client, app, tmp_path): - # Create initial file - with open(tmp_path / "test.txt", "w") as f: - f.write("initial content") - - client = await aiohttp_client(app) - resp = await client.post("/userdata/test.txt?overwrite=false", data=b"new content") - - assert resp.status == 409 - - # Verify original content unchanged - with open(tmp_path / "test.txt", "r") as f: - assert f.read() == "initial content" - - -async def test_post_userdata_full_info(aiohttp_client, app, tmp_path): - client = await aiohttp_client(app) - content = b"test content" - resp = await client.post("/userdata/test.txt?full_info=true", data=content) - - assert resp.status == 200 - result = await resp.json() - assert result["path"] == "test.txt" - assert result["size"] == len(content) - assert "modified" in result - - -async def test_move_userdata(aiohttp_client, app, tmp_path): - # Create initial file - with open(tmp_path / "source.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.post("/userdata/source.txt/move/dest.txt") - - assert resp.status == 200 - assert await resp.text() == '"dest.txt"' - - # Verify file was moved - assert not os.path.exists(tmp_path / "source.txt") - with open(tmp_path / "dest.txt", "r") as f: - assert f.read() == "test content" - - -async def test_move_userdata_no_overwrite(aiohttp_client, app, tmp_path): - # Create source and destination files - with open(tmp_path / "source.txt", "w") as f: - f.write("source content") - with open(tmp_path / "dest.txt", "w") as f: - f.write("destination content") - - client = await aiohttp_client(app) - resp = await client.post("/userdata/source.txt/move/dest.txt?overwrite=false") - - assert resp.status == 409 - - # Verify files remain unchanged - with open(tmp_path / "source.txt", "r") as f: - assert f.read() == "source content" - with open(tmp_path / "dest.txt", "r") as f: - assert f.read() == "destination content" - - -async def test_move_userdata_full_info(aiohttp_client, app, tmp_path): - # Create initial file - with open(tmp_path / "source.txt", "w") as f: - f.write("test content") - - client = await aiohttp_client(app) - resp = await client.post("/userdata/source.txt/move/dest.txt?full_info=true") - - assert resp.status == 200 - result = await resp.json() - assert result["path"] == "dest.txt" - assert result["size"] == len("test content") - assert "modified" in result - - # Verify file was moved - assert not os.path.exists(tmp_path / "source.txt") - with open(tmp_path / "dest.txt", "r") as f: - assert f.read() == "test content" - - -async def test_listuserdata_v2_empty_root(aiohttp_client, app): - client = await aiohttp_client(app) - resp = await client.get("/v2/userdata") - assert resp.status == 200 - assert await resp.json() == [] - - -async def test_listuserdata_v2_nonexistent_subdirectory(aiohttp_client, app): - client = await aiohttp_client(app) - resp = await client.get("/v2/userdata?path=does_not_exist") - assert resp.status == 404 - - -async def test_listuserdata_v2_default(aiohttp_client, app, tmp_path): - os.makedirs(tmp_path / "test_dir" / "subdir") - (tmp_path / "test_dir" / "file1.txt").write_text("content") - (tmp_path / "test_dir" / "subdir" / "file2.txt").write_text("content") - - client = await aiohttp_client(app) - resp = await client.get("/v2/userdata?path=test_dir") - assert resp.status == 200 - data = await resp.json() - file_paths = {item["path"] for item in data if item["type"] == "file"} - assert file_paths == {"test_dir/file1.txt", "test_dir/subdir/file2.txt"} - - -async def test_listuserdata_v2_normalized_separators(aiohttp_client, app, tmp_path, monkeypatch): - # Force backslash as os separator - monkeypatch.setattr(os, 'sep', '\\') - monkeypatch.setattr(os.path, 'sep', '\\') - os.makedirs(tmp_path / "test_dir" / "subdir") - (tmp_path / "test_dir" / "subdir" / "file1.txt").write_text("x") - - client = await aiohttp_client(app) - resp = await client.get("/v2/userdata?path=test_dir") - assert resp.status == 200 - data = await resp.json() - for item in data: - assert "/" in item["path"] - assert "\\" not in item["path"]\ - -async def test_listuserdata_v2_url_encoded_path(aiohttp_client, app, tmp_path): - # Create a directory with a space in its name and a file inside - os.makedirs(tmp_path / "my dir") - (tmp_path / "my dir" / "file.txt").write_text("content") - - client = await aiohttp_client(app) - # Use URL-encoded space in path parameter - resp = await client.get("/v2/userdata?path=my%20dir&recurse=false") - assert resp.status == 200 - data = await resp.json() - assert len(data) == 1 - entry = data[0] - assert entry["name"] == "file.txt" - # Ensure the path is correctly decoded and uses forward slash - assert entry["path"] == "my dir/file.txt" diff --git a/tests-unit/requirements.txt b/tests-unit/requirements.txt deleted file mode 100644 index 3a6790ee07cdcc37515b9afbd649da0c99d837ca..0000000000000000000000000000000000000000 --- a/tests-unit/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -pytest>=7.8.0 -pytest-aiohttp -pytest-asyncio -websocket-client diff --git a/tests-unit/server/utils/file_operations_test.py b/tests-unit/server/utils/file_operations_test.py deleted file mode 100644 index 2a45cc47a1447e6c2ace4f40e6543b2b7c5e4fcf..0000000000000000000000000000000000000000 --- a/tests-unit/server/utils/file_operations_test.py +++ /dev/null @@ -1,42 +0,0 @@ -import pytest -from typing import List -from api_server.utils.file_operations import FileSystemOperations, FileSystemItem, is_file_info - -@pytest.fixture -def temp_directory(tmp_path): - # Create a temporary directory structure - dir1 = tmp_path / "dir1" - dir2 = tmp_path / "dir2" - dir1.mkdir() - dir2.mkdir() - (dir1 / "file1.txt").write_text("content1") - (dir2 / "file2.txt").write_text("content2") - (tmp_path / "file3.txt").write_text("content3") - return tmp_path - -def test_walk_directory(temp_directory): - result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory)) - - assert len(result) == 5 # 2 directories and 3 files - - files = [item for item in result if item['type'] == 'file'] - dirs = [item for item in result if item['type'] == 'directory'] - - assert len(files) == 3 - assert len(dirs) == 2 - - file_names = {file['name'] for file in files} - assert file_names == {'file1.txt', 'file2.txt', 'file3.txt'} - - dir_names = {dir['name'] for dir in dirs} - assert dir_names == {'dir1', 'dir2'} - -def test_walk_directory_empty(tmp_path): - result = FileSystemOperations.walk_directory(str(tmp_path)) - assert len(result) == 0 - -def test_walk_directory_file_size(temp_directory): - result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory)) - files = [item for item in result if is_file_info(item)] - for file in files: - assert file['size'] > 0 # Assuming all files have some content diff --git a/tests-unit/utils/extra_config_test.py b/tests-unit/utils/extra_config_test.py deleted file mode 100644 index eae1aa3d3ab8c57d668acf4971b4c886d7dd417f..0000000000000000000000000000000000000000 --- a/tests-unit/utils/extra_config_test.py +++ /dev/null @@ -1,303 +0,0 @@ -import pytest -import yaml -import os -import sys -from unittest.mock import Mock, patch, mock_open - -from utils.extra_config import load_extra_path_config -import folder_paths - - -@pytest.fixture() -def clear_folder_paths(): - # Clear the global dictionary before each test to ensure isolation - original = folder_paths.folder_names_and_paths.copy() - folder_paths.folder_names_and_paths.clear() - yield - folder_paths.folder_names_and_paths = original - - -@pytest.fixture -def mock_yaml_content(): - return { - 'test_config': { - 'base_path': '~/App/', - 'checkpoints': 'subfolder1', - } - } - - -@pytest.fixture -def mock_expanded_home(): - return '/home/user' - - -@pytest.fixture -def yaml_config_with_appdata(): - return """ - test_config: - base_path: '%APPDATA%/ComfyUI' - checkpoints: 'models/checkpoints' - """ - - -@pytest.fixture -def mock_yaml_content_appdata(yaml_config_with_appdata): - return yaml.safe_load(yaml_config_with_appdata) - - -@pytest.fixture -def mock_expandvars_appdata(): - mock = Mock() - - def expandvars(path): - if '%APPDATA%' in path: - if sys.platform == 'win32': - return path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming') - else: - return path.replace('%APPDATA%', '/Users/TestUser/AppData/Roaming') - return path - - mock.side_effect = expandvars - return mock - - -@pytest.fixture -def mock_add_model_folder_path(): - return Mock() - - -@pytest.fixture -def mock_expanduser(mock_expanded_home): - def _expanduser(path): - if path.startswith('~/'): - return os.path.join(mock_expanded_home, path[2:]) - return path - return _expanduser - - -@pytest.fixture -def mock_yaml_safe_load(mock_yaml_content): - return Mock(return_value=mock_yaml_content) - - -@patch('builtins.open', new_callable=mock_open, read_data="dummy file content") -def test_load_extra_model_paths_expands_userpath( - mock_file, - monkeypatch, - mock_add_model_folder_path, - mock_expanduser, - mock_yaml_safe_load, - mock_expanded_home -): - # Attach mocks used by load_extra_path_config - monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path) - monkeypatch.setattr(os.path, 'expanduser', mock_expanduser) - monkeypatch.setattr(yaml, 'safe_load', mock_yaml_safe_load) - - dummy_yaml_file_name = 'dummy_path.yaml' - load_extra_path_config(dummy_yaml_file_name) - - expected_calls = [ - ('checkpoints', os.path.join(mock_expanded_home, 'App', 'subfolder1'), False), - ] - - assert mock_add_model_folder_path.call_count == len(expected_calls) - - # Check if add_model_folder_path was called with the correct arguments - for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls): - assert actual_call.args[0] == expected_call[0] - assert os.path.normpath(actual_call.args[1]) == os.path.normpath(expected_call[1]) # Normalize and check the path to check on multiple OS. - assert actual_call.args[2] == expected_call[2] - - # Check if yaml.safe_load was called - mock_yaml_safe_load.assert_called_once() - - # Check if open was called with the correct file path - mock_file.assert_called_once_with(dummy_yaml_file_name, 'r', encoding='utf-8') - - -@patch('builtins.open', new_callable=mock_open) -def test_load_extra_model_paths_expands_appdata( - mock_file, - monkeypatch, - mock_add_model_folder_path, - mock_expandvars_appdata, - yaml_config_with_appdata, - mock_yaml_content_appdata -): - # Set the mock_file to return yaml with appdata as a variable - mock_file.return_value.read.return_value = yaml_config_with_appdata - - # Attach mocks - monkeypatch.setattr(folder_paths, 'add_model_folder_path', mock_add_model_folder_path) - monkeypatch.setattr(os.path, 'expandvars', mock_expandvars_appdata) - monkeypatch.setattr(yaml, 'safe_load', Mock(return_value=mock_yaml_content_appdata)) - - # Mock expanduser to do nothing (since we're not testing it here) - monkeypatch.setattr(os.path, 'expanduser', lambda x: x) - - dummy_yaml_file_name = 'dummy_path.yaml' - load_extra_path_config(dummy_yaml_file_name) - - if sys.platform == "win32": - expected_base_path = 'C:/Users/TestUser/AppData/Roaming/ComfyUI' - else: - expected_base_path = '/Users/TestUser/AppData/Roaming/ComfyUI' - expected_calls = [ - ('checkpoints', os.path.normpath(os.path.join(expected_base_path, 'models/checkpoints')), False), - ] - - assert mock_add_model_folder_path.call_count == len(expected_calls) - - # Check the base path variable was expanded - for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls): - assert actual_call.args == expected_call - - # Verify that expandvars was called - assert mock_expandvars_appdata.called - - -@patch("builtins.open", new_callable=mock_open, read_data="dummy yaml content") -@patch("yaml.safe_load") -def test_load_extra_path_config_relative_base_path( - mock_yaml_load, _mock_file, clear_folder_paths, monkeypatch, tmp_path -): - """ - Test that when 'base_path' is a relative path in the YAML, it is joined to the YAML file directory, and then - the items in the config are correctly converted to absolute paths. - """ - sub_folder = "./my_rel_base" - config_data = { - "some_model_folder": { - "base_path": sub_folder, - "is_default": True, - "checkpoints": "checkpoints", - "some_key": "some_value" - } - } - mock_yaml_load.return_value = config_data - - dummy_yaml_name = "dummy_file.yaml" - - def fake_abspath(path): - if path == dummy_yaml_name: - # If it's the YAML path, treat it like it lives in tmp_path - return os.path.join(str(tmp_path), dummy_yaml_name) - return os.path.join(str(tmp_path), path) # Otherwise, do a normal join relative to tmp_path - - def fake_dirname(path): - # We expect path to be the result of fake_abspath(dummy_yaml_name) - if path.endswith(dummy_yaml_name): - return str(tmp_path) - return os.path.dirname(path) - - monkeypatch.setattr(os.path, "abspath", fake_abspath) - monkeypatch.setattr(os.path, "dirname", fake_dirname) - - load_extra_path_config(dummy_yaml_name) - - expected_checkpoints = os.path.abspath(os.path.join(str(tmp_path), "my_rel_base", "checkpoints")) - expected_some_value = os.path.abspath(os.path.join(str(tmp_path), "my_rel_base", "some_value")) - - actual_paths = folder_paths.folder_names_and_paths["checkpoints"][0] - assert len(actual_paths) == 1, "Should have one path added for 'checkpoints'." - assert actual_paths[0] == expected_checkpoints - - actual_paths = folder_paths.folder_names_and_paths["some_key"][0] - assert len(actual_paths) == 1, "Should have one path added for 'some_key'." - assert actual_paths[0] == expected_some_value - - -@patch("builtins.open", new_callable=mock_open, read_data="dummy yaml content") -@patch("yaml.safe_load") -def test_load_extra_path_config_absolute_base_path( - mock_yaml_load, _mock_file, clear_folder_paths, monkeypatch, tmp_path -): - """ - Test that when 'base_path' is an absolute path, each subdirectory is joined with that absolute path, - rather than being relative to the YAML's directory. - """ - abs_base = os.path.join(str(tmp_path), "abs_base") - config_data = { - "some_absolute_folder": { - "base_path": abs_base, # <-- absolute - "is_default": True, - "loras": "loras_folder", - "embeddings": "embeddings_folder" - } - } - mock_yaml_load.return_value = config_data - - dummy_yaml_name = "dummy_abs.yaml" - - def fake_abspath(path): - if path == dummy_yaml_name: - # If it's the YAML path, treat it like it is in tmp_path - return os.path.join(str(tmp_path), dummy_yaml_name) - return path # For absolute base, we just return path directly - - def fake_dirname(path): - return str(tmp_path) if path.endswith(dummy_yaml_name) else os.path.dirname(path) - - monkeypatch.setattr(os.path, "abspath", fake_abspath) - monkeypatch.setattr(os.path, "dirname", fake_dirname) - - load_extra_path_config(dummy_yaml_name) - - # Expect the final paths to be /loras_folder and /embeddings_folder - expected_loras = os.path.join(abs_base, "loras_folder") - expected_embeddings = os.path.join(abs_base, "embeddings_folder") - - actual_loras = folder_paths.folder_names_and_paths["loras"][0] - assert len(actual_loras) == 1, "Should have one path for 'loras'." - assert actual_loras[0] == os.path.abspath(expected_loras) - - actual_embeddings = folder_paths.folder_names_and_paths["embeddings"][0] - assert len(actual_embeddings) == 1, "Should have one path for 'embeddings'." - assert actual_embeddings[0] == os.path.abspath(expected_embeddings) - - -@patch("builtins.open", new_callable=mock_open, read_data="dummy yaml content") -@patch("yaml.safe_load") -def test_load_extra_path_config_no_base_path( - mock_yaml_load, _mock_file, clear_folder_paths, monkeypatch, tmp_path -): - """ - Test that if 'base_path' is not present, each path is joined - with the directory of the YAML file (unless it's already absolute). - """ - config_data = { - "some_folder_without_base": { - "is_default": True, - "text_encoders": "clip", - "diffusion_models": "unet" - } - } - mock_yaml_load.return_value = config_data - - dummy_yaml_name = "dummy_no_base.yaml" - - def fake_abspath(path): - if path == dummy_yaml_name: - return os.path.join(str(tmp_path), dummy_yaml_name) - return os.path.join(str(tmp_path), path) - - def fake_dirname(path): - return str(tmp_path) if path.endswith(dummy_yaml_name) else os.path.dirname(path) - - monkeypatch.setattr(os.path, "abspath", fake_abspath) - monkeypatch.setattr(os.path, "dirname", fake_dirname) - - load_extra_path_config(dummy_yaml_name) - - expected_clip = os.path.join(str(tmp_path), "clip") - expected_unet = os.path.join(str(tmp_path), "unet") - - actual_text_encoders = folder_paths.folder_names_and_paths["text_encoders"][0] - assert len(actual_text_encoders) == 1, "Should have one path for 'text_encoders'." - assert actual_text_encoders[0] == os.path.abspath(expected_clip) - - actual_diffusion = folder_paths.folder_names_and_paths["diffusion_models"][0] - assert len(actual_diffusion) == 1, "Should have one path for 'diffusion_models'." - assert actual_diffusion[0] == os.path.abspath(expected_unet) diff --git a/tests-unit/utils/json_util_test.py b/tests-unit/utils/json_util_test.py deleted file mode 100644 index d3089d8d184533cf7510ed0db14cfaf0644c52cf..0000000000000000000000000000000000000000 --- a/tests-unit/utils/json_util_test.py +++ /dev/null @@ -1,71 +0,0 @@ -from utils.json_util import merge_json_recursive - - -def test_merge_simple_dicts(): - base = {"a": 1, "b": 2} - update = {"b": 3, "c": 4} - expected = {"a": 1, "b": 3, "c": 4} - assert merge_json_recursive(base, update) == expected - - -def test_merge_nested_dicts(): - base = {"a": {"x": 1, "y": 2}, "b": 3} - update = {"a": {"y": 4, "z": 5}} - expected = {"a": {"x": 1, "y": 4, "z": 5}, "b": 3} - assert merge_json_recursive(base, update) == expected - - -def test_merge_lists(): - base = {"a": [1, 2], "b": 3} - update = {"a": [3, 4]} - expected = {"a": [1, 2, 3, 4], "b": 3} - assert merge_json_recursive(base, update) == expected - - -def test_merge_nested_lists(): - base = {"a": {"x": [1, 2]}} - update = {"a": {"x": [3, 4]}} - expected = {"a": {"x": [1, 2, 3, 4]}} - assert merge_json_recursive(base, update) == expected - - -def test_merge_mixed_types(): - base = {"a": [1, 2], "b": {"x": 1}} - update = {"a": [3], "b": {"y": 2}} - expected = {"a": [1, 2, 3], "b": {"x": 1, "y": 2}} - assert merge_json_recursive(base, update) == expected - - -def test_merge_overwrite_non_dict(): - base = {"a": 1} - update = {"a": {"x": 2}} - expected = {"a": {"x": 2}} - assert merge_json_recursive(base, update) == expected - - -def test_merge_empty_dicts(): - base = {} - update = {"a": 1} - expected = {"a": 1} - assert merge_json_recursive(base, update) == expected - - -def test_merge_none_values(): - base = {"a": None} - update = {"a": {"x": 1}} - expected = {"a": {"x": 1}} - assert merge_json_recursive(base, update) == expected - - -def test_merge_different_types(): - base = {"a": [1, 2]} - update = {"a": "string"} - expected = {"a": "string"} - assert merge_json_recursive(base, update) == expected - - -def test_merge_complex_nested(): - base = {"a": [1, 2], "b": {"x": [3, 4], "y": {"p": 1}}} - update = {"a": [5], "b": {"x": [6], "y": {"q": 2}}} - expected = {"a": [1, 2, 5], "b": {"x": [3, 4, 6], "y": {"p": 1, "q": 2}}} - assert merge_json_recursive(base, update) == expected diff --git a/tests-unit/websocket_feature_flags_test.py b/tests-unit/websocket_feature_flags_test.py deleted file mode 100644 index e93b2e1dde81d9664ed9505ee14aa2d7d994a95d..0000000000000000000000000000000000000000 --- a/tests-unit/websocket_feature_flags_test.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Simplified tests for WebSocket feature flags functionality.""" -from comfy_api import feature_flags - - -class TestWebSocketFeatureFlags: - """Test suite for WebSocket feature flags integration.""" - - def test_server_feature_flags_response(self): - """Test server feature flags are properly formatted.""" - features = feature_flags.get_server_features() - - # Check expected server features - assert "supports_preview_metadata" in features - assert features["supports_preview_metadata"] is True - assert "max_upload_size" in features - assert isinstance(features["max_upload_size"], (int, float)) - - def test_progress_py_checks_feature_flags(self): - """Test that progress.py checks feature flags before sending metadata.""" - # This simulates the check in progress.py - client_id = "test_client" - sockets_metadata = {"test_client": {"feature_flags": {}}} - - # The actual check would be in progress.py - supports_metadata = feature_flags.supports_feature( - sockets_metadata, client_id, "supports_preview_metadata" - ) - - assert supports_metadata is False - - def test_multiple_clients_different_features(self): - """Test handling multiple clients with different feature support.""" - sockets_metadata = { - "modern_client": { - "feature_flags": {"supports_preview_metadata": True} - }, - "legacy_client": { - "feature_flags": {} - } - } - - # Check modern client - assert feature_flags.supports_feature( - sockets_metadata, "modern_client", "supports_preview_metadata" - ) is True - - # Check legacy client - assert feature_flags.supports_feature( - sockets_metadata, "legacy_client", "supports_preview_metadata" - ) is False - - def test_feature_negotiation_message_format(self): - """Test the format of feature negotiation messages.""" - # Client message format - client_message = { - "type": "feature_flags", - "data": { - "supports_preview_metadata": True, - "api_version": "1.0.0" - } - } - - # Verify structure - assert client_message["type"] == "feature_flags" - assert "supports_preview_metadata" in client_message["data"] - - # Server response format (what would be sent) - server_features = feature_flags.get_server_features() - server_message = { - "type": "feature_flags", - "data": server_features - } - - # Verify structure - assert server_message["type"] == "feature_flags" - assert "supports_preview_metadata" in server_message["data"] - assert server_message["data"]["supports_preview_metadata"] is True diff --git a/tests/README.md b/tests/README.md deleted file mode 100644 index 2005fd45b2bbd249fb7f1dfff789b2ae236568ba..0000000000000000000000000000000000000000 --- a/tests/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Automated Testing - -## Running tests locally - -Additional requirements for running tests: -``` -pip install pytest -pip install websocket-client==1.6.1 -opencv-python==4.6.0.66 -scikit-image==0.21.0 -``` -Run inference tests: -``` -pytest tests/inference -``` - -## Quality regression test -Compares images in 2 directories to ensure they are the same - -1) Run an inference test to save a directory of "ground truth" images -``` - pytest tests/inference --output_dir tests/inference/baseline -``` -2) Make code edits - -3) Run inference and quality comparison tests -``` -pytest -``` \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/tests/compare/conftest.py b/tests/compare/conftest.py deleted file mode 100644 index dd5078c9e6e432c7de2462c5641068bfa8e0aaee..0000000000000000000000000000000000000000 --- a/tests/compare/conftest.py +++ /dev/null @@ -1,41 +0,0 @@ -import os -import pytest - -# Command line arguments for pytest -def pytest_addoption(parser): - parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images') - parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test') - parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics') - parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images') - -# This initializes args at the beginning of the test session -@pytest.fixture(scope="session", autouse=True) -def args_pytest(pytestconfig): - args = {} - args['baseline_dir'] = pytestconfig.getoption('baseline_dir') - args['test_dir'] = pytestconfig.getoption('test_dir') - args['metrics_file'] = pytestconfig.getoption('metrics_file') - args['img_output_dir'] = pytestconfig.getoption('img_output_dir') - - # Initialize metrics file - with open(args['metrics_file'], 'a') as f: - # if file is empty, write header - if os.stat(args['metrics_file']).st_size == 0: - f.write("| date | run | file | status | value | \n") - f.write("| --- | --- | --- | --- | --- | \n") - - return args - - -def gather_file_basenames(directory: str): - files = [] - for file in os.listdir(directory): - if file.endswith(".png"): - files.append(file) - return files - -# Creates the list of baseline file names to use as a fixture -def pytest_generate_tests(metafunc): - if "baseline_fname" in metafunc.fixturenames: - baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir")) - metafunc.parametrize("baseline_fname", baseline_fnames) diff --git a/tests/compare/test_quality.py b/tests/compare/test_quality.py deleted file mode 100644 index 01c19054956dde3b611dafe97ad244a3579e0e85..0000000000000000000000000000000000000000 --- a/tests/compare/test_quality.py +++ /dev/null @@ -1,195 +0,0 @@ -import datetime -import numpy as np -import os -from PIL import Image -import pytest -from pytest import fixture -from typing import Tuple, List - -from cv2 import imread, cvtColor, COLOR_BGR2RGB -from skimage.metrics import structural_similarity as ssim - - -""" -This test suite compares images in 2 directories by file name -The directories are specified by the command line arguments --baseline_dir and --test_dir - -""" -# ssim: Structural Similarity Index -# Returns a tuple of (ssim, diff_image) -def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]: - score, diff = ssim(img0, img1, channel_axis=-1, full=True) - # rescale the difference image to 0-255 range - diff = (diff * 255).astype("uint8") - return score, diff - -# Metrics must return a tuple of (score, diff_image) -METRICS = {"ssim": ssim_score} -METRICS_PASS_THRESHOLD = {"ssim": 0.95} - - -class TestCompareImageMetrics: - @fixture(scope="class") - def test_file_names(self, args_pytest): - test_dir = args_pytest['test_dir'] - fnames = self.gather_file_basenames(test_dir) - yield fnames - del fnames - - @fixture(scope="class", autouse=True) - def teardown(self, args_pytest): - yield - # Runs after all tests are complete - # Aggregate output files into a grid of images - baseline_dir = args_pytest['baseline_dir'] - test_dir = args_pytest['test_dir'] - img_output_dir = args_pytest['img_output_dir'] - metrics_file = args_pytest['metrics_file'] - - grid_dir = os.path.join(img_output_dir, "grid") - os.makedirs(grid_dir, exist_ok=True) - - for metric_dir in METRICS.keys(): - metric_path = os.path.join(img_output_dir, metric_dir) - for file in os.listdir(metric_path): - if file.endswith(".png"): - score = self.lookup_score_from_fname(file, metrics_file) - image_file_list = [] - image_file_list.append([ - os.path.join(baseline_dir, file), - os.path.join(test_dir, file), - os.path.join(metric_path, file) - ]) - # Create grid - image_list = [[Image.open(file) for file in files] for files in image_file_list] - grid = self.image_grid(image_list) - grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}")) - - # Tests run for each baseline file name - @fixture() - def fname(self, baseline_fname): - yield baseline_fname - del baseline_fname - - def test_directories_not_empty(self, args_pytest): - baseline_dir = args_pytest['baseline_dir'] - test_dir = args_pytest['test_dir'] - assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty" - assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty" - - def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest): - # Check that all files in baseline_dir have a file in test_dir with matching metadata - baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname) - file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names] - file_match = self.find_file_match(baseline_file_path, file_paths) - assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}" - - # For a baseline image file, finds the corresponding file name in test_dir and - # compares the images using the metrics in METRICS - @pytest.mark.parametrize("metric", METRICS.keys()) - def test_pipeline_compare( - self, - args_pytest, - fname, - test_file_names, - metric, - ): - baseline_dir = args_pytest['baseline_dir'] - test_dir = args_pytest['test_dir'] - metrics_output_file = args_pytest['metrics_file'] - img_output_dir = args_pytest['img_output_dir'] - - baseline_file_path = os.path.join(baseline_dir, fname) - - # Find file match - file_paths = [os.path.join(test_dir, f) for f in test_file_names] - test_file = self.find_file_match(baseline_file_path, file_paths) - - # Run metrics - sample_baseline = self.read_img(baseline_file_path) - sample_secondary = self.read_img(test_file) - - score, metric_img = METRICS[metric](sample_baseline, sample_secondary) - metric_status = score > METRICS_PASS_THRESHOLD[metric] - - # Save metric values - with open(metrics_output_file, 'a') as f: - run_info = os.path.splitext(fname)[0] - metric_status_str = "PASS ✅" if metric_status else "FAIL ❌" - date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") - f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n") - - # Save metric image - metric_img_dir = os.path.join(img_output_dir, metric) - os.makedirs(metric_img_dir, exist_ok=True) - output_filename = f'{fname}' - Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename)) - - assert score > METRICS_PASS_THRESHOLD[metric] - - def read_img(self, filename: str) -> np.ndarray: - cvImg = imread(filename) - cvImg = cvtColor(cvImg, COLOR_BGR2RGB) - return cvImg - - def image_grid(self, img_list: list[list[Image.Image]]): - # imgs is a 2D list of images - # Assumes the input images are a rectangular grid of equal sized images - rows = len(img_list) - cols = len(img_list[0]) - - w, h = img_list[0][0].size - grid = Image.new('RGB', size=(cols*w, rows*h)) - - for i, row in enumerate(img_list): - for j, img in enumerate(row): - grid.paste(img, box=(j*w, i*h)) - return grid - - def lookup_score_from_fname(self, - fname: str, - metrics_output_file: str - ) -> float: - fname_basestr = os.path.splitext(fname)[0] - with open(metrics_output_file, 'r') as f: - for line in f: - if fname_basestr in line: - score = float(line.split('|')[5]) - return score - raise ValueError(f"Could not find score for {fname} in {metrics_output_file}") - - def gather_file_basenames(self, directory: str): - files = [] - for file in os.listdir(directory): - if file.endswith(".png"): - files.append(file) - return files - - def read_file_prompt(self, fname:str) -> str: - # Read prompt from image file metadata - img = Image.open(fname) - img.load() - return img.info['prompt'] - - def find_file_match(self, baseline_file: str, file_paths: List[str]): - # Find a file in file_paths with matching metadata to baseline_file - baseline_prompt = self.read_file_prompt(baseline_file) - - # Do not match empty prompts - if baseline_prompt is None or baseline_prompt == "": - return None - - # Find file match - # Reorder test_file_names so that the file with matching name is first - # This is an optimization because matching file names are more likely - # to have matching metadata if they were generated with the same script - basename = os.path.basename(baseline_file) - file_path_basenames = [os.path.basename(f) for f in file_paths] - if basename in file_path_basenames: - match_index = file_path_basenames.index(basename) - file_paths.insert(0, file_paths.pop(match_index)) - - for f in file_paths: - test_file_prompt = self.read_file_prompt(f) - if baseline_prompt == test_file_prompt: - return f diff --git a/tests/conftest.py b/tests/conftest.py deleted file mode 100644 index 4e30eb5813f44ba6c22027f5b0f3fc0023f81e6d..0000000000000000000000000000000000000000 --- a/tests/conftest.py +++ /dev/null @@ -1,36 +0,0 @@ -import os -import pytest - -# Command line arguments for pytest -def pytest_addoption(parser): - parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images') - parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") - parser.addoption("--port", type=int, default=8188, help="Set the listen port.") - -# This initializes args at the beginning of the test session -@pytest.fixture(scope="session", autouse=True) -def args_pytest(pytestconfig): - args = {} - args['output_dir'] = pytestconfig.getoption('output_dir') - args['listen'] = pytestconfig.getoption('listen') - args['port'] = pytestconfig.getoption('port') - - os.makedirs(args['output_dir'], exist_ok=True) - - return args - -def pytest_collection_modifyitems(items): - # Modifies items so tests run in the correct order - - LAST_TESTS = ['test_quality'] - - # Move the last items to the end - last_items = [] - for test_name in LAST_TESTS: - for item in items.copy(): - print(item.module.__name__, item) # noqa: T201 - if item.module.__name__ == test_name: - last_items.append(item) - items.remove(item) - - items.extend(last_items) diff --git a/tests/inference/__init__.py b/tests/inference/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/tests/inference/extra_model_paths.yaml b/tests/inference/extra_model_paths.yaml deleted file mode 100644 index 68e056564d515893a6860010e6b8c554829f6186..0000000000000000000000000000000000000000 --- a/tests/inference/extra_model_paths.yaml +++ /dev/null @@ -1,4 +0,0 @@ -# Config for testing nodes -testing: - custom_nodes: testing_nodes - diff --git a/tests/inference/graphs/default_graph_sdxl1_0.json b/tests/inference/graphs/default_graph_sdxl1_0.json deleted file mode 100644 index c06c6829c6253cc71982f3714620bd10dd41bd73..0000000000000000000000000000000000000000 --- a/tests/inference/graphs/default_graph_sdxl1_0.json +++ /dev/null @@ -1,144 +0,0 @@ -{ - "4": { - "inputs": { - "ckpt_name": "sd_xl_base_1.0.safetensors" - }, - "class_type": "CheckpointLoaderSimple" - }, - "5": { - "inputs": { - "width": 1024, - "height": 1024, - "batch_size": 1 - }, - "class_type": "EmptyLatentImage" - }, - "6": { - "inputs": { - "text": "a photo of a cat", - "clip": [ - "4", - 1 - ] - }, - "class_type": "CLIPTextEncode" - }, - "10": { - "inputs": { - "add_noise": "enable", - "noise_seed": 42, - "steps": 20, - "cfg": 7.5, - "sampler_name": "euler", - "scheduler": "normal", - "start_at_step": 0, - "end_at_step": 32, - "return_with_leftover_noise": "enable", - "model": [ - "4", - 0 - ], - "positive": [ - "6", - 0 - ], - "negative": [ - "15", - 0 - ], - "latent_image": [ - "5", - 0 - ] - }, - "class_type": "KSamplerAdvanced" - }, - "12": { - "inputs": { - "samples": [ - "14", - 0 - ], - "vae": [ - "4", - 2 - ] - }, - "class_type": "VAEDecode" - }, - "13": { - "inputs": { - "filename_prefix": "test_inference", - "images": [ - "12", - 0 - ] - }, - "class_type": "SaveImage" - }, - "14": { - "inputs": { - "add_noise": "disable", - "noise_seed": 42, - "steps": 20, - "cfg": 7.5, - "sampler_name": "euler", - "scheduler": "normal", - "start_at_step": 32, - "end_at_step": 10000, - "return_with_leftover_noise": "disable", - "model": [ - "16", - 0 - ], - "positive": [ - "17", - 0 - ], - "negative": [ - "20", - 0 - ], - "latent_image": [ - "10", - 0 - ] - }, - "class_type": "KSamplerAdvanced" - }, - "15": { - "inputs": { - "conditioning": [ - "6", - 0 - ] - }, - "class_type": "ConditioningZeroOut" - }, - "16": { - "inputs": { - "ckpt_name": "sd_xl_refiner_1.0.safetensors" - }, - "class_type": "CheckpointLoaderSimple" - }, - "17": { - "inputs": { - "text": "a photo of a cat", - "clip": [ - "16", - 1 - ] - }, - "class_type": "CLIPTextEncode" - }, - "20": { - "inputs": { - "text": "", - "clip": [ - "16", - 1 - ] - }, - "class_type": "CLIPTextEncode" - } - } \ No newline at end of file diff --git a/tests/inference/test_async_nodes.py b/tests/inference/test_async_nodes.py deleted file mode 100644 index f029953dd1eac9799e92a95457ec15a45bcfb126..0000000000000000000000000000000000000000 --- a/tests/inference/test_async_nodes.py +++ /dev/null @@ -1,423 +0,0 @@ -import pytest -import time -import torch -import urllib.error -import numpy as np -import subprocess - -from pytest import fixture -from comfy_execution.graph_utils import GraphBuilder -from tests.inference.test_execution import ComfyClient, run_warmup - - -@pytest.mark.execution -class TestAsyncNodes: - @fixture(scope="class", autouse=True, params=[ - (False, 0), - (True, 0), - (True, 100), - ]) - def _server(self, args_pytest, request): - pargs = [ - 'python','main.py', - '--output-directory', args_pytest["output_dir"], - '--listen', args_pytest["listen"], - '--port', str(args_pytest["port"]), - '--extra-model-paths-config', 'tests/inference/extra_model_paths.yaml', - '--cpu', - ] - use_lru, lru_size = request.param - if use_lru: - pargs += ['--cache-lru', str(lru_size)] - # Running server with args: pargs - p = subprocess.Popen(pargs) - yield - p.kill() - torch.cuda.empty_cache() - - @fixture(scope="class", autouse=True) - def shared_client(self, args_pytest, _server): - client = ComfyClient() - n_tries = 5 - for i in range(n_tries): - time.sleep(4) - try: - client.connect(listen=args_pytest["listen"], port=args_pytest["port"]) - except ConnectionRefusedError: - # Retrying... - pass - else: - break - yield client - del client - torch.cuda.empty_cache() - - @fixture - def client(self, shared_client, request): - shared_client.set_test_name(f"async_nodes[{request.node.name}]") - yield shared_client - - @fixture - def builder(self, request): - yield GraphBuilder(prefix=request.node.name) - - # Happy Path Tests - - def test_basic_async_execution(self, client: ComfyClient, builder: GraphBuilder): - """Test that a basic async node executes correctly.""" - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.1) - output = g.node("SaveImage", images=sleep_node.out(0)) - - result = client.run(g) - - # Verify execution completed - assert result.did_run(sleep_node), "Async sleep node should have executed" - assert result.did_run(output), "Output node should have executed" - - # Verify the image passed through correctly - result_images = result.get_images(output) - assert len(result_images) == 1, "Should have 1 image" - assert np.array(result_images[0]).min() == 0 and np.array(result_images[0]).max() == 0, "Image should be black" - - def test_multiple_async_parallel_execution(self, client: ComfyClient, builder: GraphBuilder): - """Test that multiple async nodes execute in parallel.""" - # Warmup execution to ensure server is fully initialized - run_warmup(client) - - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - # Create multiple async sleep nodes with different durations - sleep1 = g.node("TestSleep", value=image.out(0), seconds=0.3) - sleep2 = g.node("TestSleep", value=image.out(0), seconds=0.4) - sleep3 = g.node("TestSleep", value=image.out(0), seconds=0.5) - - # Add outputs for each - _output1 = g.node("PreviewImage", images=sleep1.out(0)) - _output2 = g.node("PreviewImage", images=sleep2.out(0)) - _output3 = g.node("PreviewImage", images=sleep3.out(0)) - - start_time = time.time() - result = client.run(g) - elapsed_time = time.time() - start_time - - # Should take ~0.5s (max duration) not 1.2s (sum of durations) - assert elapsed_time < 0.8, f"Parallel execution took {elapsed_time}s, expected < 0.8s" - - # Verify all nodes executed - assert result.did_run(sleep1) and result.did_run(sleep2) and result.did_run(sleep3) - - def test_async_with_dependencies(self, client: ComfyClient, builder: GraphBuilder): - """Test async nodes with proper dependency handling.""" - g = builder - image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - - # Chain of async operations - sleep1 = g.node("TestSleep", value=image1.out(0), seconds=0.2) - sleep2 = g.node("TestSleep", value=image2.out(0), seconds=0.2) - - # Average depends on both async results - average = g.node("TestVariadicAverage", input1=sleep1.out(0), input2=sleep2.out(0)) - output = g.node("SaveImage", images=average.out(0)) - - result = client.run(g) - - # Verify execution order - assert result.did_run(sleep1) and result.did_run(sleep2) - assert result.did_run(average) and result.did_run(output) - - # Verify averaged result - result_images = result.get_images(output) - avg_value = np.array(result_images[0]).mean() - assert abs(avg_value - 127.5) < 1, f"Average value {avg_value} should be ~127.5" - - def test_async_validate_inputs(self, client: ComfyClient, builder: GraphBuilder): - """Test async VALIDATE_INPUTS function.""" - g = builder - # Create a test node with async validation - validation_node = g.node("TestAsyncValidation", value=5.0, threshold=10.0) - g.node("SaveImage", images=validation_node.out(0)) - - # Should pass validation - result = client.run(g) - assert result.did_run(validation_node) - - # Test validation failure - validation_node.inputs['threshold'] = 3.0 # Will fail since value > threshold - with pytest.raises(urllib.error.HTTPError): - client.run(g) - - def test_async_lazy_evaluation(self, client: ComfyClient, builder: GraphBuilder): - """Test async nodes with lazy evaluation.""" - # Warmup execution to ensure server is fully initialized - run_warmup(client, prefix="warmup_lazy") - - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1) - - # Create async nodes that will be evaluated lazily - sleep1 = g.node("TestSleep", value=input1.out(0), seconds=0.3) - sleep2 = g.node("TestSleep", value=input2.out(0), seconds=0.3) - - # Use lazy mix that only needs sleep1 (mask=0.0) - lazy_mix = g.node("TestLazyMixImages", image1=sleep1.out(0), image2=sleep2.out(0), mask=mask.out(0)) - g.node("SaveImage", images=lazy_mix.out(0)) - - start_time = time.time() - result = client.run(g) - elapsed_time = time.time() - start_time - - # Should only execute sleep1, not sleep2 - assert elapsed_time < 0.5, f"Should skip sleep2, took {elapsed_time}s" - assert result.did_run(sleep1), "Sleep1 should have executed" - assert not result.did_run(sleep2), "Sleep2 should have been skipped" - - def test_async_check_lazy_status(self, client: ComfyClient, builder: GraphBuilder): - """Test async check_lazy_status function.""" - g = builder - # Create a node with async check_lazy_status - lazy_node = g.node("TestAsyncLazyCheck", - input1="value1", - input2="value2", - condition=True) - g.node("SaveImage", images=lazy_node.out(0)) - - result = client.run(g) - assert result.did_run(lazy_node) - - # Error Handling Tests - - def test_async_execution_error(self, client: ComfyClient, builder: GraphBuilder): - """Test that async execution errors are properly handled.""" - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - # Create an async node that will error - error_node = g.node("TestAsyncError", value=image.out(0), error_after=0.1) - g.node("SaveImage", images=error_node.out(0)) - - try: - client.run(g) - assert False, "Should have raised an error" - except Exception as e: - assert 'prompt_id' in e.args[0], f"Did not get proper error message: {e}" - assert e.args[0]['node_id'] == error_node.id, "Error should be from async error node" - - def test_async_validation_error(self, client: ComfyClient, builder: GraphBuilder): - """Test async validation error handling.""" - g = builder - # Node with async validation that will fail - validation_node = g.node("TestAsyncValidationError", value=15.0, max_value=10.0) - g.node("SaveImage", images=validation_node.out(0)) - - with pytest.raises(urllib.error.HTTPError) as exc_info: - client.run(g) - # Verify it's a validation error - assert exc_info.value.code == 400 - - def test_async_timeout_handling(self, client: ComfyClient, builder: GraphBuilder): - """Test handling of async operations that timeout.""" - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - # Very long sleep that would timeout - timeout_node = g.node("TestAsyncTimeout", value=image.out(0), timeout=0.5, operation_time=2.0) - g.node("SaveImage", images=timeout_node.out(0)) - - try: - client.run(g) - assert False, "Should have raised a timeout error" - except Exception as e: - assert 'timeout' in str(e).lower(), f"Expected timeout error, got: {e}" - - def test_concurrent_async_error_recovery(self, client: ComfyClient, builder: GraphBuilder): - """Test that workflow can recover after async errors.""" - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - # First run with error - error_node = g.node("TestAsyncError", value=image.out(0), error_after=0.1) - g.node("SaveImage", images=error_node.out(0)) - - try: - client.run(g) - except Exception: - pass # Expected - - # Second run should succeed - g2 = GraphBuilder(prefix="recovery_test") - image2 = g2.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - sleep_node = g2.node("TestSleep", value=image2.out(0), seconds=0.1) - g2.node("SaveImage", images=sleep_node.out(0)) - - result = client.run(g2) - assert result.did_run(sleep_node), "Should be able to run after error" - - def test_sync_error_during_async_execution(self, client: ComfyClient, builder: GraphBuilder): - """Test handling when sync node errors while async node is executing.""" - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - # Async node that takes time - sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.5) - - # Sync node that will error immediately - error_node = g.node("TestSyncError", value=image.out(0)) - - # Both feed into output - g.node("PreviewImage", images=sleep_node.out(0)) - g.node("PreviewImage", images=error_node.out(0)) - - try: - client.run(g) - assert False, "Should have raised an error" - except Exception as e: - # Verify the sync error was caught even though async was running - assert 'prompt_id' in e.args[0] - - # Edge Cases - - def test_async_with_execution_blocker(self, client: ComfyClient, builder: GraphBuilder): - """Test async nodes with execution blockers.""" - g = builder - image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - - # Async sleep nodes - sleep1 = g.node("TestSleep", value=image1.out(0), seconds=0.2) - sleep2 = g.node("TestSleep", value=image2.out(0), seconds=0.2) - - # Create list of images - image_list = g.node("TestMakeListNode", value1=sleep1.out(0), value2=sleep2.out(0)) - - # Create list of blocking conditions - [False, True] to block only the second item - int1 = g.node("StubInt", value=1) - int2 = g.node("StubInt", value=2) - block_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0)) - - # Compare each value against 2, so first is False (1 != 2) and second is True (2 == 2) - compare = g.node("TestIntConditions", a=block_list.out(0), b=2, operation="==") - - # Block based on the comparison results - blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False) - - output = g.node("PreviewImage", images=blocker.out(0)) - - result = client.run(g) - images = result.get_images(output) - assert len(images) == 1, "Should have blocked second image" - - def test_async_caching_behavior(self, client: ComfyClient, builder: GraphBuilder): - """Test that async nodes are properly cached.""" - # Warmup execution to ensure server is fully initialized - run_warmup(client, prefix="warmup_cache") - - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.2) - g.node("SaveImage", images=sleep_node.out(0)) - - # First run - result1 = client.run(g) - assert result1.did_run(sleep_node), "Should run first time" - - # Second run - should be cached - start_time = time.time() - result2 = client.run(g) - elapsed_time = time.time() - start_time - - assert not result2.did_run(sleep_node), "Should be cached" - assert elapsed_time < 0.1, f"Cached run took {elapsed_time}s, should be instant" - - def test_async_with_dynamic_prompts(self, client: ComfyClient, builder: GraphBuilder): - """Test async nodes within dynamically generated prompts.""" - # Warmup execution to ensure server is fully initialized - run_warmup(client, prefix="warmup_dynamic") - - g = builder - image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - - # Node that generates async nodes dynamically - dynamic_async = g.node("TestDynamicAsyncGeneration", - image1=image1.out(0), - image2=image2.out(0), - num_async_nodes=3, - sleep_duration=0.2) - g.node("SaveImage", images=dynamic_async.out(0)) - - start_time = time.time() - result = client.run(g) - elapsed_time = time.time() - start_time - - # Should execute async nodes in parallel within dynamic prompt - assert elapsed_time < 0.5, f"Dynamic async execution took {elapsed_time}s" - assert result.did_run(dynamic_async) - - def test_async_resource_cleanup(self, client: ComfyClient, builder: GraphBuilder): - """Test that async resources are properly cleaned up.""" - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - # Create multiple async nodes that use resources - resource_nodes = [] - for i in range(5): - node = g.node("TestAsyncResourceUser", - value=image.out(0), - resource_id=f"resource_{i}", - duration=0.1) - resource_nodes.append(node) - g.node("PreviewImage", images=node.out(0)) - - result = client.run(g) - - # Verify all nodes executed - for node in resource_nodes: - assert result.did_run(node) - - # Run again to ensure resources were cleaned up - result2 = client.run(g) - # Should be cached but not error due to resource conflicts - for node in resource_nodes: - assert not result2.did_run(node), "Should be cached" - - def test_async_cancellation(self, client: ComfyClient, builder: GraphBuilder): - """Test cancellation of async operations.""" - # This would require implementing cancellation in the client - # For now, we'll test that long-running async operations can be interrupted - pass # TODO: Implement when cancellation API is available - - def test_mixed_sync_async_execution(self, client: ComfyClient, builder: GraphBuilder): - """Test workflows with both sync and async nodes.""" - g = builder - image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - - # Mix of sync and async operations - # Sync: lazy mix images - sync_op1 = g.node("TestLazyMixImages", image1=image1.out(0), image2=image2.out(0), mask=mask.out(0)) - # Async: sleep - async_op1 = g.node("TestSleep", value=sync_op1.out(0), seconds=0.2) - # Sync: custom validation - sync_op2 = g.node("TestCustomValidation1", input1=async_op1.out(0), input2=0.5) - # Async: sleep again - async_op2 = g.node("TestSleep", value=sync_op2.out(0), seconds=0.2) - - output = g.node("SaveImage", images=async_op2.out(0)) - - result = client.run(g) - - # Verify all nodes executed in correct order - assert result.did_run(sync_op1) - assert result.did_run(async_op1) - assert result.did_run(sync_op2) - assert result.did_run(async_op2) - - # Image should be a mix of black and white (gray) - result_images = result.get_images(output) - avg_value = np.array(result_images[0]).mean() - assert abs(avg_value - 63.75) < 5, f"Average value {avg_value} should be ~63.75" diff --git a/tests/inference/test_execution.py b/tests/inference/test_execution.py deleted file mode 100644 index e7b29302e5769fe00c9bc10fda031a713413b078..0000000000000000000000000000000000000000 --- a/tests/inference/test_execution.py +++ /dev/null @@ -1,761 +0,0 @@ -from io import BytesIO -import numpy -from PIL import Image -import pytest -from pytest import fixture -import time -import torch -from typing import Union, Dict -import json -import subprocess -import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) -import uuid -import urllib.request -import urllib.parse -import urllib.error -from comfy_execution.graph_utils import GraphBuilder, Node - -def run_warmup(client, prefix="warmup"): - """Run a simple workflow to warm up the server.""" - warmup_g = GraphBuilder(prefix=prefix) - warmup_image = warmup_g.node("StubImage", content="BLACK", height=32, width=32, batch_size=1) - warmup_g.node("PreviewImage", images=warmup_image.out(0)) - client.run(warmup_g) - -class RunResult: - def __init__(self, prompt_id: str): - self.outputs: Dict[str,Dict] = {} - self.runs: Dict[str,bool] = {} - self.cached: Dict[str,bool] = {} - self.prompt_id: str = prompt_id - - def get_output(self, node: Node): - return self.outputs.get(node.id, None) - - def did_run(self, node: Node): - return self.runs.get(node.id, False) - - def was_cached(self, node: Node): - return self.cached.get(node.id, False) - - def was_executed(self, node: Node): - """Returns True if node was either run or cached""" - return self.did_run(node) or self.was_cached(node) - - def get_images(self, node: Node): - output = self.get_output(node) - if output is None: - return [] - return output.get('image_objects', []) - - def get_prompt_id(self): - return self.prompt_id - -class ComfyClient: - def __init__(self): - self.test_name = "" - - def connect(self, - listen:str = '127.0.0.1', - port:Union[str,int] = 8188, - client_id: str = str(uuid.uuid4()) - ): - self.client_id = client_id - self.server_address = f"{listen}:{port}" - ws = websocket.WebSocket() - ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id)) - self.ws = ws - - def queue_prompt(self, prompt, partial_execution_targets=None): - p = {"prompt": prompt, "client_id": self.client_id} - if partial_execution_targets is not None: - p["partial_execution_targets"] = partial_execution_targets - data = json.dumps(p).encode('utf-8') - req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data) - return json.loads(urllib.request.urlopen(req).read()) - - def get_image(self, filename, subfolder, folder_type): - data = {"filename": filename, "subfolder": subfolder, "type": folder_type} - url_values = urllib.parse.urlencode(data) - with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response: - return response.read() - - def get_history(self, prompt_id): - with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response: - return json.loads(response.read()) - - def set_test_name(self, name): - self.test_name = name - - def run(self, graph, partial_execution_targets=None): - prompt = graph.finalize() - for node in graph.nodes.values(): - if node.class_type == 'SaveImage': - node.inputs['filename_prefix'] = self.test_name - - prompt_id = self.queue_prompt(prompt, partial_execution_targets)['prompt_id'] - result = RunResult(prompt_id) - while True: - out = self.ws.recv() - if isinstance(out, str): - message = json.loads(out) - if message['type'] == 'executing': - data = message['data'] - if data['prompt_id'] != prompt_id: - continue - if data['node'] is None: - break - result.runs[data['node']] = True - elif message['type'] == 'execution_error': - raise Exception(message['data']) - elif message['type'] == 'execution_cached': - if message['data']['prompt_id'] == prompt_id: - cached_nodes = message['data'].get('nodes', []) - for node_id in cached_nodes: - result.cached[node_id] = True - - history = self.get_history(prompt_id)[prompt_id] - for node_id in history['outputs']: - node_output = history['outputs'][node_id] - result.outputs[node_id] = node_output - images_output = [] - if 'images' in node_output: - for image in node_output['images']: - image_data = self.get_image(image['filename'], image['subfolder'], image['type']) - image_obj = Image.open(BytesIO(image_data)) - images_output.append(image_obj) - node_output['image_objects'] = images_output - - return result - -# -# Loop through these variables -# -@pytest.mark.execution -class TestExecution: - # - # Initialize server and client - # - @fixture(scope="class", autouse=True, params=[ - # (use_lru, lru_size) - (False, 0), - (True, 0), - (True, 100), - ]) - def _server(self, args_pytest, request): - # Start server - pargs = [ - 'python','main.py', - '--output-directory', args_pytest["output_dir"], - '--listen', args_pytest["listen"], - '--port', str(args_pytest["port"]), - '--extra-model-paths-config', 'tests/inference/extra_model_paths.yaml', - '--cpu', - ] - use_lru, lru_size = request.param - if use_lru: - pargs += ['--cache-lru', str(lru_size)] - print("Running server with args:", pargs) # noqa: T201 - p = subprocess.Popen(pargs) - yield - p.kill() - torch.cuda.empty_cache() - - def start_client(self, listen:str, port:int): - # Start client - comfy_client = ComfyClient() - # Connect to server (with retries) - n_tries = 5 - for i in range(n_tries): - time.sleep(4) - try: - comfy_client.connect(listen=listen, port=port) - except ConnectionRefusedError as e: - print(e) # noqa: T201 - print(f"({i+1}/{n_tries}) Retrying...") # noqa: T201 - else: - break - return comfy_client - - @fixture(scope="class", autouse=True) - def shared_client(self, args_pytest, _server): - client = self.start_client(args_pytest["listen"], args_pytest["port"]) - yield client - del client - torch.cuda.empty_cache() - - @fixture - def client(self, shared_client, request): - shared_client.set_test_name(f"execution[{request.node.name}]") - yield shared_client - - @fixture - def builder(self, request): - yield GraphBuilder(prefix=request.node.name) - - def test_lazy_input(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1) - - lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) - output = g.node("SaveImage", images=lazy_mix.out(0)) - result = client.run(g) - - result_image = result.get_images(output)[0] - assert numpy.array(result_image).any() == 0, "Image should be black" - assert result.did_run(input1) - assert not result.did_run(input2) - assert result.did_run(mask) - assert result.did_run(lazy_mix) - - def test_full_cache(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - - lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) - g.node("SaveImage", images=lazy_mix.out(0)) - - client.run(g) - result2 = client.run(g) - for node_id, node in g.nodes.items(): - assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached" - - def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - - lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) - g.node("SaveImage", images=lazy_mix.out(0)) - - client.run(g) - mask.inputs['value'] = 0.4 - result2 = client.run(g) - assert not result2.did_run(input1), "Input1 should have been cached" - assert not result2.did_run(input2), "Input2 should have been cached" - - def test_error(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - # Different size of the two images - input2 = g.node("StubImage", content="NOISE", height=256, width=256, batch_size=1) - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - - lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) - g.node("SaveImage", images=lazy_mix.out(0)) - - try: - client.run(g) - assert False, "Should have raised an error" - except Exception as e: - assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}" - - @pytest.mark.parametrize("test_value, expect_error", [ - (5, True), - ("foo", True), - (5.0, False), - ]) - def test_validation_error_literal(self, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): - g = builder - validation1 = g.node("TestCustomValidation1", input1=test_value, input2=3.0) - g.node("SaveImage", images=validation1.out(0)) - - if expect_error: - with pytest.raises(urllib.error.HTTPError): - client.run(g) - else: - client.run(g) - - @pytest.mark.parametrize("test_type, test_value", [ - ("StubInt", 5), - ("StubMask", 5.0) - ]) - def test_validation_error_edge1(self, test_type, test_value, client: ComfyClient, builder: GraphBuilder): - g = builder - stub = g.node(test_type, value=test_value) - validation1 = g.node("TestCustomValidation1", input1=stub.out(0), input2=3.0) - g.node("SaveImage", images=validation1.out(0)) - - with pytest.raises(urllib.error.HTTPError): - client.run(g) - - @pytest.mark.parametrize("test_type, test_value, expect_error", [ - ("StubInt", 5, True), - ("StubFloat", 5.0, False) - ]) - def test_validation_error_edge2(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): - g = builder - stub = g.node(test_type, value=test_value) - validation2 = g.node("TestCustomValidation2", input1=stub.out(0), input2=3.0) - g.node("SaveImage", images=validation2.out(0)) - - if expect_error: - with pytest.raises(urllib.error.HTTPError): - client.run(g) - else: - client.run(g) - - @pytest.mark.parametrize("test_type, test_value, expect_error", [ - ("StubInt", 5, True), - ("StubFloat", 5.0, False) - ]) - def test_validation_error_edge3(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): - g = builder - stub = g.node(test_type, value=test_value) - validation3 = g.node("TestCustomValidation3", input1=stub.out(0), input2=3.0) - g.node("SaveImage", images=validation3.out(0)) - - if expect_error: - with pytest.raises(urllib.error.HTTPError): - client.run(g) - else: - client.run(g) - - @pytest.mark.parametrize("test_type, test_value, expect_error", [ - ("StubInt", 5, True), - ("StubFloat", 5.0, False) - ]) - def test_validation_error_edge4(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder): - g = builder - stub = g.node(test_type, value=test_value) - validation4 = g.node("TestCustomValidation4", input1=stub.out(0), input2=3.0) - g.node("SaveImage", images=validation4.out(0)) - - if expect_error: - with pytest.raises(urllib.error.HTTPError): - client.run(g) - else: - client.run(g) - - @pytest.mark.parametrize("test_value1, test_value2, expect_error", [ - (0.0, 0.5, False), - (0.0, 5.0, False), - (0.0, 7.0, True) - ]) - def test_validation_error_kwargs(self, test_value1, test_value2, expect_error, client: ComfyClient, builder: GraphBuilder): - g = builder - validation5 = g.node("TestCustomValidation5", input1=test_value1, input2=test_value2) - g.node("SaveImage", images=validation5.out(0)) - - if expect_error: - with pytest.raises(urllib.error.HTTPError): - client.run(g) - else: - client.run(g) - - def test_cycle_error(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - - lazy_mix1 = g.node("TestLazyMixImages", image1=input1.out(0), mask=mask.out(0)) - lazy_mix2 = g.node("TestLazyMixImages", image1=lazy_mix1.out(0), image2=input2.out(0), mask=mask.out(0)) - g.node("SaveImage", images=lazy_mix2.out(0)) - - # When the cycle exists on initial submission, it should raise a validation error - with pytest.raises(urllib.error.HTTPError): - client.run(g) - - def test_dynamic_cycle_error(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - generator = g.node("TestDynamicDependencyCycle", input1=input1.out(0), input2=input2.out(0)) - g.node("SaveImage", images=generator.out(0)) - - # When the cycle is in a graph that is generated dynamically, it should raise a runtime error - try: - client.run(g) - assert False, "Should have raised an error" - except Exception as e: - assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}" - assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node" - - def test_missing_node_error(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1) - input3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) - mix2 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input3.out(0), mask=mask.out(0)) - # We have multiple outputs. The first is invalid, but the second is valid - g.node("SaveImage", images=mix1.out(0)) - g.node("SaveImage", images=mix2.out(0)) - g.remove_node("removeme") - - client.run(g) - - # Add back in the missing node to make sure the error doesn't break the server - input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1) - client.run(g) - - def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder): - g = builder - # Creating the nodes in this specific order previously caused a bug - save = g.node("SaveImage") - is_changed = g.node("TestCustomIsChanged", should_change=False) - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - save.set_input('images', is_changed.out(0)) - is_changed.set_input('image', input1.out(0)) - - result1 = client.run(g) - result2 = client.run(g) - is_changed.set_input('should_change', True) - result3 = client.run(g) - result4 = client.run(g) - assert result1.did_run(is_changed), "is_changed should have been run" - assert not result2.did_run(is_changed), "is_changed should have been cached" - assert result3.did_run(is_changed), "is_changed should have been re-run" - assert result4.did_run(is_changed), "is_changed should not have been cached" - - def test_undeclared_inputs(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - input3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input4 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - average = g.node("TestVariadicAverage", input1=input1.out(0), input2=input2.out(0), input3=input3.out(0), input4=input4.out(0)) - output = g.node("SaveImage", images=average.out(0)) - - result = client.run(g) - result_image = result.get_images(output)[0] - expected = 255 // 4 - assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey" - - def test_for_loop(self, client: ComfyClient, builder: GraphBuilder): - g = builder - iterations = 4 - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - is_changed = g.node("TestCustomIsChanged", should_change=True, image=input2.out(0)) - for_open = g.node("TestForLoopOpen", remaining=iterations, initial_value1=is_changed.out(0)) - average = g.node("TestVariadicAverage", input1=input1.out(0), input2=for_open.out(2)) - for_close = g.node("TestForLoopClose", flow_control=for_open.out(0), initial_value1=average.out(0)) - output = g.node("SaveImage", images=for_close.out(0)) - - for iterations in range(1, 5): - for_open.set_input('remaining', iterations) - result = client.run(g) - result_image = result.get_images(output)[0] - expected = 255 // (2 ** iterations) - assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey" - assert result.did_run(is_changed) - - def test_mixed_expansion_returns(self, client: ComfyClient, builder: GraphBuilder): - g = builder - val_list = g.node("TestMakeListNode", value1=0.1, value2=0.2, value3=0.3) - mixed = g.node("TestMixedExpansionReturns", input1=val_list.out(0)) - output_dynamic = g.node("SaveImage", images=mixed.out(0)) - output_literal = g.node("SaveImage", images=mixed.out(1)) - - result = client.run(g) - images_dynamic = result.get_images(output_dynamic) - assert len(images_dynamic) == 3, "Should have 2 images" - assert numpy.array(images_dynamic[0]).min() == 25 and numpy.array(images_dynamic[0]).max() == 25, "First image should be 0.1" - assert numpy.array(images_dynamic[1]).min() == 51 and numpy.array(images_dynamic[1]).max() == 51, "Second image should be 0.2" - assert numpy.array(images_dynamic[2]).min() == 76 and numpy.array(images_dynamic[2]).max() == 76, "Third image should be 0.3" - - images_literal = result.get_images(output_literal) - assert len(images_literal) == 3, "Should have 2 images" - for i in range(3): - assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white" - - def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder): - g = builder - val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0) - mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1) - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0)) - rebatch = g.node("RebatchImages", images=mix.out(0), batch_size=3) - output = g.node("SaveImage", images=rebatch.out(0)) - - result = client.run(g) - images = result.get_images(output) - assert len(images) == 3, "Should have 3 image" - assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be 0.0" - assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5" - assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0" - - def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - output1 = g.node("SaveImage", images=input1.out(0)) - output2 = g.node("SaveImage", images=input1.out(0)) - - result = client.run(g) - images1 = result.get_images(output1) - images2 = result.get_images(output2) - assert len(images1) == 1, "Should have 1 image" - assert len(images2) == 1, "Should have 1 image" - - - # This tests that only constant outputs are used in the call to `IS_CHANGED` - def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1) - test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5) - - output = g.node("PreviewImage", images=test_node.out(0)) - - result = client.run(g) - images = result.get_images(output) - assert len(images) == 1, "Should have 1 image" - assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25" - - result = client.run(g) - images = result.get_images(output) - assert len(images) == 1, "Should have 1 image" - assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25" - assert not result.did_run(test_node), "The execution should have been cached" - - def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder): - # Warmup execution to ensure server is fully initialized - run_warmup(client) - - g = builder - image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - # Create sleep nodes for each duration - sleep_node1 = g.node("TestSleep", value=image.out(0), seconds=2.9) - sleep_node2 = g.node("TestSleep", value=image.out(0), seconds=3.1) - sleep_node3 = g.node("TestSleep", value=image.out(0), seconds=3.0) - - # Add outputs to verify the execution - _output1 = g.node("PreviewImage", images=sleep_node1.out(0)) - _output2 = g.node("PreviewImage", images=sleep_node2.out(0)) - _output3 = g.node("PreviewImage", images=sleep_node3.out(0)) - - start_time = time.time() - result = client.run(g) - elapsed_time = time.time() - start_time - - # The test should take around 3.0 seconds (the longest sleep duration) - # plus some overhead, but definitely less than the sum of all sleeps (9.0s) - assert elapsed_time < 8.9, f"Parallel execution took {elapsed_time}s, expected less than 8.9s" - - # Verify that all nodes executed - assert result.did_run(sleep_node1), "Sleep node 1 should have run" - assert result.did_run(sleep_node2), "Sleep node 2 should have run" - assert result.did_run(sleep_node3), "Sleep node 3 should have run" - - def test_parallel_sleep_expansion(self, client: ComfyClient, builder: GraphBuilder): - # Warmup execution to ensure server is fully initialized - run_warmup(client) - - g = builder - # Create input images with different values - image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - image3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - - # Create a TestParallelSleep node that expands into multiple TestSleep nodes - parallel_sleep = g.node("TestParallelSleep", - image1=image1.out(0), - image2=image2.out(0), - image3=image3.out(0), - sleep1=4.8, - sleep2=4.9, - sleep3=5.0) - output = g.node("SaveImage", images=parallel_sleep.out(0)) - - start_time = time.time() - result = client.run(g) - elapsed_time = time.time() - start_time - - # Similar to the previous test, expect parallel execution of the sleep nodes - # which should complete in less than the sum of all sleeps - assert elapsed_time < 10.0, f"Expansion execution took {elapsed_time}s, expected less than 5.5s" - - # Verify the parallel sleep node executed - assert result.did_run(parallel_sleep), "ParallelSleep node should have run" - - # Verify we get an image as output (blend of the three input images) - result_images = result.get_images(output) - assert len(result_images) == 1, "Should have 1 image" - # Average pixel value should be around 170 (255 * 2 // 3) - avg_value = numpy.array(result_images[0]).mean() - assert avg_value == 170, f"Image average value {avg_value} should be 170" - - # This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker - # as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node, - # only that one entry in the list is blocked. - def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder): - g = builder - image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0)) - int1 = g.node("StubInt", value=1) - int2 = g.node("StubInt", value=2) - int3 = g.node("StubInt", value=3) - int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0)) - compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==") - blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False) - - list_output = g.node("TestMakeListNode", value1=blocker.out(0)) - output = g.node("PreviewImage", images=list_output.out(0)) - - result = client.run(g) - assert result.did_run(output), "The execution should have run" - images = result.get_images(output) - assert len(images) == 2, "Should have 2 images" - assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black" - assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black" - - # Output nodes included in the partial execution list are executed - def test_partial_execution_included_outputs(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - - # Create two separate output nodes - output1 = g.node("SaveImage", images=input1.out(0)) - output2 = g.node("SaveImage", images=input2.out(0)) - - # Run with partial execution targeting only output1 - result = client.run(g, partial_execution_targets=[output1.id]) - - assert result.was_executed(input1), "Input1 should have been executed (run or cached)" - assert result.was_executed(output1), "Output1 should have been executed (run or cached)" - assert not result.did_run(input2), "Input2 should not have run" - assert not result.did_run(output2), "Output2 should not have run" - - # Verify only output1 produced results - assert len(result.get_images(output1)) == 1, "Output1 should have produced an image" - assert len(result.get_images(output2)) == 0, "Output2 should not have produced an image" - - # Output nodes NOT included in the partial execution list are NOT executed - def test_partial_execution_excluded_outputs(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - input3 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) - - # Create three output nodes - output1 = g.node("SaveImage", images=input1.out(0)) - output2 = g.node("SaveImage", images=input2.out(0)) - output3 = g.node("SaveImage", images=input3.out(0)) - - # Run with partial execution targeting only output1 and output3 - result = client.run(g, partial_execution_targets=[output1.id, output3.id]) - - assert result.was_executed(input1), "Input1 should have been executed" - assert result.was_executed(input3), "Input3 should have been executed" - assert result.was_executed(output1), "Output1 should have been executed" - assert result.was_executed(output3), "Output3 should have been executed" - assert not result.did_run(input2), "Input2 should not have run" - assert not result.did_run(output2), "Output2 should not have run" - - # Output nodes NOT in list ARE executed if necessary for nodes that are in the list - def test_partial_execution_dependencies(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - - # Create a processing chain with an OUTPUT_NODE that has socket outputs - output_with_socket = g.node("TestOutputNodeWithSocketOutput", image=input1.out(0), value=2.0) - - # Create another node that depends on the output_with_socket - dependent_node = g.node("TestLazyMixImages", - image1=output_with_socket.out(0), - image2=input1.out(0), - mask=g.node("StubMask", value=0.5, height=512, width=512, batch_size=1).out(0)) - - # Create the final output - final_output = g.node("SaveImage", images=dependent_node.out(0)) - - # Run with partial execution targeting only the final output - result = client.run(g, partial_execution_targets=[final_output.id]) - - # All nodes should have been executed because they're dependencies - assert result.was_executed(input1), "Input1 should have been executed" - assert result.was_executed(output_with_socket), "Output with socket should have been executed (dependency)" - assert result.was_executed(dependent_node), "Dependent node should have been executed" - assert result.was_executed(final_output), "Final output should have been executed" - - # Lazy execution works with partial execution - def test_partial_execution_with_lazy_nodes(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - input3 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) - - # Create masks that will trigger different lazy execution paths - mask1 = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1) # Will only need image1 - mask2 = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) # Will need both images - - # Create two lazy mix nodes - lazy_mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask1.out(0)) - lazy_mix2 = g.node("TestLazyMixImages", image1=input2.out(0), image2=input3.out(0), mask=mask2.out(0)) - - output1 = g.node("SaveImage", images=lazy_mix1.out(0)) - output2 = g.node("SaveImage", images=lazy_mix2.out(0)) - - # Run with partial execution targeting only output1 - result = client.run(g, partial_execution_targets=[output1.id]) - - # For output1 path - only input1 should run due to lazy evaluation (mask=0.0) - assert result.was_executed(input1), "Input1 should have been executed" - assert not result.did_run(input2), "Input2 should not have run (lazy evaluation)" - assert result.was_executed(mask1), "Mask1 should have been executed" - assert result.was_executed(lazy_mix1), "Lazy mix1 should have been executed" - assert result.was_executed(output1), "Output1 should have been executed" - - # Nothing from output2 path should run - assert not result.did_run(input3), "Input3 should not have run" - assert not result.did_run(mask2), "Mask2 should not have run" - assert not result.did_run(lazy_mix2), "Lazy mix2 should not have run" - assert not result.did_run(output2), "Output2 should not have run" - - # Multiple OUTPUT_NODEs with dependencies - def test_partial_execution_multiple_output_nodes(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - - # Create a chain of OUTPUT_NODEs - output_node1 = g.node("TestOutputNodeWithSocketOutput", image=input1.out(0), value=1.5) - output_node2 = g.node("TestOutputNodeWithSocketOutput", image=output_node1.out(0), value=2.0) - - # Create regular output nodes - save1 = g.node("SaveImage", images=output_node1.out(0)) - save2 = g.node("SaveImage", images=output_node2.out(0)) - save3 = g.node("SaveImage", images=input2.out(0)) - - # Run targeting only save2 - result = client.run(g, partial_execution_targets=[save2.id]) - - # Should run: input1, output_node1, output_node2, save2 - assert result.was_executed(input1), "Input1 should have been executed" - assert result.was_executed(output_node1), "Output node 1 should have been executed (dependency)" - assert result.was_executed(output_node2), "Output node 2 should have been executed (dependency)" - assert result.was_executed(save2), "Save2 should have been executed" - - # Should NOT run: input2, save1, save3 - assert not result.did_run(input2), "Input2 should not have run" - assert not result.did_run(save1), "Save1 should not have run" - assert not result.did_run(save3), "Save3 should not have run" - - # Empty partial execution list (should execute nothing) - def test_partial_execution_empty_list(self, client: ComfyClient, builder: GraphBuilder): - g = builder - input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - _output1 = g.node("SaveImage", images=input1.out(0)) - - # Run with empty partial execution list - try: - _result = client.run(g, partial_execution_targets=[]) - # Should get an error because no outputs are selected - assert False, "Should have raised an error for empty partial execution list" - except urllib.error.HTTPError: - pass # Expected behavior - diff --git a/tests/inference/test_inference.py b/tests/inference/test_inference.py deleted file mode 100644 index 7e4a206c41d95d5740c9c8b6e6c9071ad0ee86ce..0000000000000000000000000000000000000000 --- a/tests/inference/test_inference.py +++ /dev/null @@ -1,237 +0,0 @@ -from copy import deepcopy -from io import BytesIO -import numpy -import os -from PIL import Image -import pytest -from pytest import fixture -import time -import torch -from typing import Union -import json -import subprocess -import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) -import uuid -import urllib.request -import urllib.parse - - -from comfy.samplers import KSampler - -""" -These tests generate and save images through a range of parameters -""" - -class ComfyGraph: - def __init__(self, - graph: dict, - sampler_nodes: list[str], - ): - self.graph = graph - self.sampler_nodes = sampler_nodes - - def set_prompt(self, prompt, negative_prompt=None): - # Sets the prompt for the sampler nodes (eg. base and refiner) - for node in self.sampler_nodes: - prompt_node = self.graph[node]['inputs']['positive'][0] - self.graph[prompt_node]['inputs']['text'] = prompt - if negative_prompt: - negative_prompt_node = self.graph[node]['inputs']['negative'][0] - self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt - - def set_sampler_name(self, sampler_name:str, ): - # sets the sampler name for the sampler nodes (eg. base and refiner) - for node in self.sampler_nodes: - self.graph[node]['inputs']['sampler_name'] = sampler_name - - def set_scheduler(self, scheduler:str): - # sets the sampler name for the sampler nodes (eg. base and refiner) - for node in self.sampler_nodes: - self.graph[node]['inputs']['scheduler'] = scheduler - - def set_filename_prefix(self, prefix:str): - # sets the filename prefix for the save nodes - for node in self.graph: - if self.graph[node]['class_type'] == 'SaveImage': - self.graph[node]['inputs']['filename_prefix'] = prefix - - -class ComfyClient: - # From examples/websockets_api_example.py - - def connect(self, - listen:str = '127.0.0.1', - port:Union[str,int] = 8188, - client_id: str = str(uuid.uuid4()) - ): - self.client_id = client_id - self.server_address = f"{listen}:{port}" - ws = websocket.WebSocket() - ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id)) - self.ws = ws - - def queue_prompt(self, prompt): - p = {"prompt": prompt, "client_id": self.client_id} - data = json.dumps(p).encode('utf-8') - req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data) - return json.loads(urllib.request.urlopen(req).read()) - - def get_image(self, filename, subfolder, folder_type): - data = {"filename": filename, "subfolder": subfolder, "type": folder_type} - url_values = urllib.parse.urlencode(data) - with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response: - return response.read() - - def get_history(self, prompt_id): - with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response: - return json.loads(response.read()) - - def get_images(self, graph, save=True): - prompt = graph - if not save: - # Replace save nodes with preview nodes - prompt_str = json.dumps(prompt) - prompt_str = prompt_str.replace('SaveImage', 'PreviewImage') - prompt = json.loads(prompt_str) - - prompt_id = self.queue_prompt(prompt)['prompt_id'] - output_images = {} - while True: - out = self.ws.recv() - if isinstance(out, str): - message = json.loads(out) - if message['type'] == 'executing': - data = message['data'] - if data['node'] is None and data['prompt_id'] == prompt_id: - break #Execution is done - else: - continue #previews are binary data - - history = self.get_history(prompt_id)[prompt_id] - for node_id in history['outputs']: - node_output = history['outputs'][node_id] - images_output = [] - if 'images' in node_output: - for image in node_output['images']: - image_data = self.get_image(image['filename'], image['subfolder'], image['type']) - images_output.append(image_data) - output_images[node_id] = images_output - - return output_images - -# -# Initialize graphs -# -default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json' -with open(default_graph_file, 'r') as file: - default_graph = json.loads(file.read()) -DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10','14']) -DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0] - -# -# Loop through these variables -# -comfy_graph_list = [DEFAULT_COMFY_GRAPH] -comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID] -prompt_list = [ - 'a painting of a cat', -] - -sampler_list = KSampler.SAMPLERS -scheduler_list = KSampler.SCHEDULERS - -@pytest.mark.inference -@pytest.mark.parametrize("sampler", sampler_list) -@pytest.mark.parametrize("scheduler", scheduler_list) -@pytest.mark.parametrize("prompt", prompt_list) -class TestInference: - # - # Initialize server and client - # - @fixture(scope="class", autouse=True) - def _server(self, args_pytest): - # Start server - p = subprocess.Popen([ - 'python','main.py', - '--output-directory', args_pytest["output_dir"], - '--listen', args_pytest["listen"], - '--port', str(args_pytest["port"]), - ]) - yield - p.kill() - torch.cuda.empty_cache() - - def start_client(self, listen:str, port:int): - # Start client - comfy_client = ComfyClient() - # Connect to server (with retries) - n_tries = 5 - for i in range(n_tries): - time.sleep(4) - try: - comfy_client.connect(listen=listen, port=port) - except ConnectionRefusedError as e: - print(e) # noqa: T201 - print(f"({i+1}/{n_tries}) Retrying...") # noqa: T201 - else: - break - return comfy_client - - # - # Client and graph fixtures with server warmup - # - # Returns a "_client_graph", which is client-graph pair corresponding to an initialized server - # The "graph" is the default graph - @fixture(scope="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True) - def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph): - comfy_graph = request.param - - # Start client - comfy_client = self.start_client(args_pytest["listen"], args_pytest["port"]) - - # Warm up pipeline - comfy_client.get_images(graph=comfy_graph.graph, save=False) - - yield comfy_client, comfy_graph - del comfy_client - del comfy_graph - torch.cuda.empty_cache() - - @fixture - def client(self, _client_graph): - client = _client_graph[0] - yield client - - @fixture - def comfy_graph(self, _client_graph): - # avoid mutating the graph - graph = deepcopy(_client_graph[1]) - yield graph - - def test_comfy( - self, - client, - comfy_graph, - sampler, - scheduler, - prompt, - request - ): - test_info = request.node.name - comfy_graph.set_filename_prefix(test_info) - # Settings for comfy graph - comfy_graph.set_sampler_name(sampler) - comfy_graph.set_scheduler(scheduler) - comfy_graph.set_prompt(prompt) - - # Generate - images = client.get_images(comfy_graph.graph) - - assert len(images) != 0, "No images generated" - # assert all images are not blank - for images_output in images.values(): - for image_data in images_output: - pil_image = Image.open(BytesIO(image_data)) - assert numpy.array(pil_image).any() != 0, "Image is blank" - - diff --git a/tests/inference/testing_nodes/testing-pack/__init__.py b/tests/inference/testing_nodes/testing-pack/__init__.py deleted file mode 100644 index 3d5ac8a94febb0481c1cf52b7cd89436f0c5c9ce..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -from .specific_tests import TEST_NODE_CLASS_MAPPINGS, TEST_NODE_DISPLAY_NAME_MAPPINGS -from .flow_control import FLOW_CONTROL_NODE_CLASS_MAPPINGS, FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS -from .util import UTILITY_NODE_CLASS_MAPPINGS, UTILITY_NODE_DISPLAY_NAME_MAPPINGS -from .conditions import CONDITION_NODE_CLASS_MAPPINGS, CONDITION_NODE_DISPLAY_NAME_MAPPINGS -from .stubs import TEST_STUB_NODE_CLASS_MAPPINGS, TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS -from .async_test_nodes import ASYNC_TEST_NODE_CLASS_MAPPINGS, ASYNC_TEST_NODE_DISPLAY_NAME_MAPPINGS -from .api_test_nodes import API_TEST_NODE_CLASS_MAPPINGS, API_TEST_NODE_DISPLAY_NAME_MAPPINGS - -# NODE_CLASS_MAPPINGS = GENERAL_NODE_CLASS_MAPPINGS.update(COMPONENT_NODE_CLASS_MAPPINGS) -# NODE_DISPLAY_NAME_MAPPINGS = GENERAL_NODE_DISPLAY_NAME_MAPPINGS.update(COMPONENT_NODE_DISPLAY_NAME_MAPPINGS) - -NODE_CLASS_MAPPINGS = {} -NODE_CLASS_MAPPINGS.update(TEST_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(FLOW_CONTROL_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(UTILITY_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(CONDITION_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(TEST_STUB_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(ASYNC_TEST_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(API_TEST_NODE_CLASS_MAPPINGS) - -NODE_DISPLAY_NAME_MAPPINGS = {} -NODE_DISPLAY_NAME_MAPPINGS.update(TEST_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(UTILITY_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(CONDITION_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(ASYNC_TEST_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(API_TEST_NODE_DISPLAY_NAME_MAPPINGS) diff --git a/tests/inference/testing_nodes/testing-pack/api_test_nodes.py b/tests/inference/testing_nodes/testing-pack/api_test_nodes.py deleted file mode 100644 index b2eaae05ec985caeb313ce47f09ab2858782e043..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/api_test_nodes.py +++ /dev/null @@ -1,78 +0,0 @@ -import asyncio -import time -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict -from comfy_api.v0_0_2 import ComfyAPI, ComfyAPISync - -api = ComfyAPI() -api_sync = ComfyAPISync() - - -class TestAsyncProgressUpdate(ComfyNodeABC): - """Test node with async VALIDATE_INPUTS.""" - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "value": (IO.ANY, {}), - "sleep_seconds": (IO.FLOAT, {"default": 1.0}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "execute" - CATEGORY = "_for_testing/async" - - async def execute(self, value, sleep_seconds): - start = time.time() - expiration = start + sleep_seconds - now = start - while now < expiration: - now = time.time() - await api.execution.set_progress( - value=(now - start) / sleep_seconds, - max_value=1.0, - ) - await asyncio.sleep(0.01) - return (value,) - - -class TestSyncProgressUpdate(ComfyNodeABC): - """Test node with async VALIDATE_INPUTS.""" - - @classmethod - def INPUT_TYPES(cls) -> InputTypeDict: - return { - "required": { - "value": (IO.ANY, {}), - "sleep_seconds": (IO.FLOAT, {"default": 1.0}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "execute" - CATEGORY = "_for_testing/async" - - def execute(self, value, sleep_seconds): - start = time.time() - expiration = start + sleep_seconds - now = start - while now < expiration: - now = time.time() - api_sync.execution.set_progress( - value=(now - start) / sleep_seconds, - max_value=1.0, - ) - time.sleep(0.01) - return (value,) - - -API_TEST_NODE_CLASS_MAPPINGS = { - "TestAsyncProgressUpdate": TestAsyncProgressUpdate, - "TestSyncProgressUpdate": TestSyncProgressUpdate, -} - -API_TEST_NODE_DISPLAY_NAME_MAPPINGS = { - "TestAsyncProgressUpdate": "Async Progress Update Test Node", - "TestSyncProgressUpdate": "Sync Progress Update Test Node", -} diff --git a/tests/inference/testing_nodes/testing-pack/async_test_nodes.py b/tests/inference/testing_nodes/testing-pack/async_test_nodes.py deleted file mode 100644 index 547eea6f4117bee47539eacbbda2a54593cee538..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/async_test_nodes.py +++ /dev/null @@ -1,343 +0,0 @@ -import torch -import asyncio -from typing import Dict -from comfy.utils import ProgressBar -from comfy_execution.graph_utils import GraphBuilder -from comfy.comfy_types.node_typing import ComfyNodeABC -from comfy.comfy_types import IO - - -class TestAsyncValidation(ComfyNodeABC): - """Test node with async VALIDATE_INPUTS.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("FLOAT", {"default": 5.0}), - "threshold": ("FLOAT", {"default": 10.0}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process" - CATEGORY = "_for_testing/async" - - @classmethod - async def VALIDATE_INPUTS(cls, value, threshold): - # Simulate async validation (e.g., checking remote service) - await asyncio.sleep(0.05) - - if value > threshold: - return f"Value {value} exceeds threshold {threshold}" - return True - - def process(self, value, threshold): - # Create image based on value - intensity = value / 10.0 - image = torch.ones([1, 512, 512, 3]) * intensity - return (image,) - - -class TestAsyncError(ComfyNodeABC): - """Test node that errors during async execution.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": (IO.ANY, {}), - "error_after": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 10.0}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "error_execution" - CATEGORY = "_for_testing/async" - - async def error_execution(self, value, error_after): - await asyncio.sleep(error_after) - raise RuntimeError("Intentional async execution error for testing") - - -class TestAsyncValidationError(ComfyNodeABC): - """Test node with async validation that always fails.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("FLOAT", {"default": 5.0}), - "max_value": ("FLOAT", {"default": 10.0}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process" - CATEGORY = "_for_testing/async" - - @classmethod - async def VALIDATE_INPUTS(cls, value, max_value): - await asyncio.sleep(0.05) - # Always fail validation for values > max_value - if value > max_value: - return f"Async validation failed: {value} > {max_value}" - return True - - def process(self, value, max_value): - # This won't be reached if validation fails - image = torch.ones([1, 512, 512, 3]) * (value / max_value) - return (image,) - - -class TestAsyncTimeout(ComfyNodeABC): - """Test node that simulates timeout scenarios.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": (IO.ANY, {}), - "timeout": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0}), - "operation_time": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 10.0}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "timeout_execution" - CATEGORY = "_for_testing/async" - - async def timeout_execution(self, value, timeout, operation_time): - try: - # This will timeout if operation_time > timeout - await asyncio.wait_for(asyncio.sleep(operation_time), timeout=timeout) - return (value,) - except asyncio.TimeoutError: - raise RuntimeError(f"Operation timed out after {timeout} seconds") - - -class TestSyncError(ComfyNodeABC): - """Test node that errors synchronously (for mixed sync/async testing).""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": (IO.ANY, {}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "sync_error" - CATEGORY = "_for_testing/async" - - def sync_error(self, value): - raise RuntimeError("Intentional sync execution error for testing") - - -class TestAsyncLazyCheck(ComfyNodeABC): - """Test node with async check_lazy_status.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": (IO.ANY, {"lazy": True}), - "input2": (IO.ANY, {"lazy": True}), - "condition": ("BOOLEAN", {"default": True}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process" - CATEGORY = "_for_testing/async" - - async def check_lazy_status(self, condition, input1, input2): - # Simulate async checking (e.g., querying remote service) - await asyncio.sleep(0.05) - - needed = [] - if condition and input1 is None: - needed.append("input1") - if not condition and input2 is None: - needed.append("input2") - return needed - - def process(self, input1, input2, condition): - # Return a simple image - return (torch.ones([1, 512, 512, 3]),) - - -class TestDynamicAsyncGeneration(ComfyNodeABC): - """Test node that dynamically generates async nodes.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - "num_async_nodes": ("INT", {"default": 3, "min": 1, "max": 10}), - "sleep_duration": ("FLOAT", {"default": 0.2, "min": 0.1, "max": 1.0}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "generate_async_workflow" - CATEGORY = "_for_testing/async" - - def generate_async_workflow(self, image1, image2, num_async_nodes, sleep_duration): - g = GraphBuilder() - - # Create multiple async sleep nodes - sleep_nodes = [] - for i in range(num_async_nodes): - image = image1 if i % 2 == 0 else image2 - sleep_node = g.node("TestSleep", value=image, seconds=sleep_duration) - sleep_nodes.append(sleep_node) - - # Average all results - if len(sleep_nodes) == 1: - final_node = sleep_nodes[0] - else: - avg_inputs = {"input1": sleep_nodes[0].out(0)} - for i, node in enumerate(sleep_nodes[1:], 2): - avg_inputs[f"input{i}"] = node.out(0) - final_node = g.node("TestVariadicAverage", **avg_inputs) - - return { - "result": (final_node.out(0),), - "expand": g.finalize(), - } - - -class TestAsyncResourceUser(ComfyNodeABC): - """Test node that uses resources during async execution.""" - - # Class-level resource tracking for testing - _active_resources: Dict[str, bool] = {} - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": (IO.ANY, {}), - "resource_id": ("STRING", {"default": "resource_0"}), - "duration": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "use_resource" - CATEGORY = "_for_testing/async" - - async def use_resource(self, value, resource_id, duration): - # Check if resource is already in use - if self._active_resources.get(resource_id, False): - raise RuntimeError(f"Resource {resource_id} is already in use!") - - # Mark resource as in use - self._active_resources[resource_id] = True - - try: - # Simulate resource usage - await asyncio.sleep(duration) - return (value,) - finally: - # Always clean up resource - self._active_resources[resource_id] = False - - -class TestAsyncBatchProcessing(ComfyNodeABC): - """Test async processing of batched inputs.""" - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "images": ("IMAGE",), - "process_time_per_item": ("FLOAT", {"default": 0.1, "min": 0.01, "max": 1.0}), - }, - "hidden": { - "unique_id": "UNIQUE_ID", - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process_batch" - CATEGORY = "_for_testing/async" - - async def process_batch(self, images, process_time_per_item, unique_id): - batch_size = images.shape[0] - pbar = ProgressBar(batch_size, node_id=unique_id) - - # Process each image in the batch - processed = [] - for i in range(batch_size): - # Simulate async processing - await asyncio.sleep(process_time_per_item) - - # Simple processing: invert the image - processed_image = 1.0 - images[i:i+1] - processed.append(processed_image) - - pbar.update(1) - - # Stack processed images - result = torch.cat(processed, dim=0) - return (result,) - - -class TestAsyncConcurrentLimit(ComfyNodeABC): - """Test concurrent execution limits for async nodes.""" - - _semaphore = asyncio.Semaphore(2) # Only allow 2 concurrent executions - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": (IO.ANY, {}), - "duration": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 2.0}), - "node_id": ("INT", {"default": 0}), - }, - } - - RETURN_TYPES = (IO.ANY,) - FUNCTION = "limited_execution" - CATEGORY = "_for_testing/async" - - async def limited_execution(self, value, duration, node_id): - async with self._semaphore: - # Node {node_id} acquired semaphore - await asyncio.sleep(duration) - # Node {node_id} releasing semaphore - return (value,) - - -# Add node mappings -ASYNC_TEST_NODE_CLASS_MAPPINGS = { - "TestAsyncValidation": TestAsyncValidation, - "TestAsyncError": TestAsyncError, - "TestAsyncValidationError": TestAsyncValidationError, - "TestAsyncTimeout": TestAsyncTimeout, - "TestSyncError": TestSyncError, - "TestAsyncLazyCheck": TestAsyncLazyCheck, - "TestDynamicAsyncGeneration": TestDynamicAsyncGeneration, - "TestAsyncResourceUser": TestAsyncResourceUser, - "TestAsyncBatchProcessing": TestAsyncBatchProcessing, - "TestAsyncConcurrentLimit": TestAsyncConcurrentLimit, -} - -ASYNC_TEST_NODE_DISPLAY_NAME_MAPPINGS = { - "TestAsyncValidation": "Test Async Validation", - "TestAsyncError": "Test Async Error", - "TestAsyncValidationError": "Test Async Validation Error", - "TestAsyncTimeout": "Test Async Timeout", - "TestSyncError": "Test Sync Error", - "TestAsyncLazyCheck": "Test Async Lazy Check", - "TestDynamicAsyncGeneration": "Test Dynamic Async Generation", - "TestAsyncResourceUser": "Test Async Resource User", - "TestAsyncBatchProcessing": "Test Async Batch Processing", - "TestAsyncConcurrentLimit": "Test Async Concurrent Limit", -} diff --git a/tests/inference/testing_nodes/testing-pack/conditions.py b/tests/inference/testing_nodes/testing-pack/conditions.py deleted file mode 100644 index 0c200ee2892d4747d9ec6965b6d33f2223e3c08a..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/conditions.py +++ /dev/null @@ -1,194 +0,0 @@ -import re -import torch - -class TestIntConditions: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), - "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), - "operation": (["==", "!=", "<", ">", "<=", ">="],), - }, - } - - RETURN_TYPES = ("BOOLEAN",) - FUNCTION = "int_condition" - - CATEGORY = "Testing/Logic" - - def int_condition(self, a, b, operation): - if operation == "==": - return (a == b,) - elif operation == "!=": - return (a != b,) - elif operation == "<": - return (a < b,) - elif operation == ">": - return (a > b,) - elif operation == "<=": - return (a <= b,) - elif operation == ">=": - return (a >= b,) - - -class TestFloatConditions: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "a": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}), - "b": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}), - "operation": (["==", "!=", "<", ">", "<=", ">="],), - }, - } - - RETURN_TYPES = ("BOOLEAN",) - FUNCTION = "float_condition" - - CATEGORY = "Testing/Logic" - - def float_condition(self, a, b, operation): - if operation == "==": - return (a == b,) - elif operation == "!=": - return (a != b,) - elif operation == "<": - return (a < b,) - elif operation == ">": - return (a > b,) - elif operation == "<=": - return (a <= b,) - elif operation == ">=": - return (a >= b,) - -class TestStringConditions: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "a": ("STRING", {"multiline": False}), - "b": ("STRING", {"multiline": False}), - "operation": (["a == b", "a != b", "a IN b", "a MATCH REGEX(b)", "a BEGINSWITH b", "a ENDSWITH b"],), - "case_sensitive": ("BOOLEAN", {"default": True}), - }, - } - - RETURN_TYPES = ("BOOLEAN",) - FUNCTION = "string_condition" - - CATEGORY = "Testing/Logic" - - def string_condition(self, a, b, operation, case_sensitive): - if not case_sensitive: - a = a.lower() - b = b.lower() - - if operation == "a == b": - return (a == b,) - elif operation == "a != b": - return (a != b,) - elif operation == "a IN b": - return (a in b,) - elif operation == "a MATCH REGEX(b)": - try: - return (re.match(b, a) is not None,) - except: - return (False,) - elif operation == "a BEGINSWITH b": - return (a.startswith(b),) - elif operation == "a ENDSWITH b": - return (a.endswith(b),) - -class TestToBoolNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("*",), - }, - "optional": { - "invert": ("BOOLEAN", {"default": False}), - }, - } - - RETURN_TYPES = ("BOOLEAN",) - FUNCTION = "to_bool" - - CATEGORY = "Testing/Logic" - - def to_bool(self, value, invert = False): - if isinstance(value, torch.Tensor): - if value.max().item() == 0 and value.min().item() == 0: - result = False - else: - result = True - else: - try: - result = bool(value) - except: - # Can't convert it? Well then it's something or other. I dunno, I'm not a Python programmer. - result = True - - if invert: - result = not result - - return (result,) - -class TestBoolOperationNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "a": ("BOOLEAN",), - "b": ("BOOLEAN",), - "op": (["a AND b", "a OR b", "a XOR b", "NOT a"],), - }, - } - - RETURN_TYPES = ("BOOLEAN",) - FUNCTION = "bool_operation" - - CATEGORY = "Testing/Logic" - - def bool_operation(self, a, b, op): - if op == "a AND b": - return (a and b,) - elif op == "a OR b": - return (a or b,) - elif op == "a XOR b": - return (a ^ b,) - elif op == "NOT a": - return (not a,) - - -CONDITION_NODE_CLASS_MAPPINGS = { - "TestIntConditions": TestIntConditions, - "TestFloatConditions": TestFloatConditions, - "TestStringConditions": TestStringConditions, - "TestToBoolNode": TestToBoolNode, - "TestBoolOperationNode": TestBoolOperationNode, -} - -CONDITION_NODE_DISPLAY_NAME_MAPPINGS = { - "TestIntConditions": "Int Condition", - "TestFloatConditions": "Float Condition", - "TestStringConditions": "String Condition", - "TestToBoolNode": "To Bool", - "TestBoolOperationNode": "Bool Operation", -} diff --git a/tests/inference/testing_nodes/testing-pack/flow_control.py b/tests/inference/testing_nodes/testing-pack/flow_control.py deleted file mode 100644 index ba943be6072f2003ebe1e6aa996d2dd8370c8c09..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/flow_control.py +++ /dev/null @@ -1,173 +0,0 @@ -from comfy_execution.graph_utils import GraphBuilder, is_link -from comfy_execution.graph import ExecutionBlocker -from .tools import VariantSupport - -NUM_FLOW_SOCKETS = 5 -@VariantSupport() -class TestWhileLoopOpen: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - inputs = { - "required": { - "condition": ("BOOLEAN", {"default": True}), - }, - "optional": { - }, - } - for i in range(NUM_FLOW_SOCKETS): - inputs["optional"][f"initial_value{i}"] = ("*",) - return inputs - - RETURN_TYPES = tuple(["FLOW_CONTROL"] + ["*"] * NUM_FLOW_SOCKETS) - RETURN_NAMES = tuple(["FLOW_CONTROL"] + [f"value{i}" for i in range(NUM_FLOW_SOCKETS)]) - FUNCTION = "while_loop_open" - - CATEGORY = "Testing/Flow" - - def while_loop_open(self, condition, **kwargs): - values = [] - for i in range(NUM_FLOW_SOCKETS): - values.append(kwargs.get(f"initial_value{i}", None)) - return tuple(["stub"] + values) - -@VariantSupport() -class TestWhileLoopClose: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - inputs = { - "required": { - "flow_control": ("FLOW_CONTROL", {"rawLink": True}), - "condition": ("BOOLEAN", {"forceInput": True}), - }, - "optional": { - }, - "hidden": { - "dynprompt": "DYNPROMPT", - "unique_id": "UNIQUE_ID", - } - } - for i in range(NUM_FLOW_SOCKETS): - inputs["optional"][f"initial_value{i}"] = ("*",) - return inputs - - RETURN_TYPES = tuple(["*"] * NUM_FLOW_SOCKETS) - RETURN_NAMES = tuple([f"value{i}" for i in range(NUM_FLOW_SOCKETS)]) - FUNCTION = "while_loop_close" - - CATEGORY = "Testing/Flow" - - def explore_dependencies(self, node_id, dynprompt, upstream): - node_info = dynprompt.get_node(node_id) - if "inputs" not in node_info: - return - for k, v in node_info["inputs"].items(): - if is_link(v): - parent_id = v[0] - if parent_id not in upstream: - upstream[parent_id] = [] - self.explore_dependencies(parent_id, dynprompt, upstream) - upstream[parent_id].append(node_id) - - def collect_contained(self, node_id, upstream, contained): - if node_id not in upstream: - return - for child_id in upstream[node_id]: - if child_id not in contained: - contained[child_id] = True - self.collect_contained(child_id, upstream, contained) - - - def while_loop_close(self, flow_control, condition, dynprompt=None, unique_id=None, **kwargs): - assert dynprompt is not None - if not condition: - # We're done with the loop - values = [] - for i in range(NUM_FLOW_SOCKETS): - values.append(kwargs.get(f"initial_value{i}", None)) - return tuple(values) - - # We want to loop - upstream = {} - # Get the list of all nodes between the open and close nodes - self.explore_dependencies(unique_id, dynprompt, upstream) - - contained = {} - open_node = flow_control[0] - self.collect_contained(open_node, upstream, contained) - contained[unique_id] = True - contained[open_node] = True - - # We'll use the default prefix, but to avoid having node names grow exponentially in size, - # we'll use "Recurse" for the name of the recursively-generated copy of this node. - graph = GraphBuilder() - for node_id in contained: - original_node = dynprompt.get_node(node_id) - node = graph.node(original_node["class_type"], "Recurse" if node_id == unique_id else node_id) - node.set_override_display_id(node_id) - for node_id in contained: - original_node = dynprompt.get_node(node_id) - node = graph.lookup_node("Recurse" if node_id == unique_id else node_id) - assert node is not None - for k, v in original_node["inputs"].items(): - if is_link(v) and v[0] in contained: - parent = graph.lookup_node(v[0]) - assert parent is not None - node.set_input(k, parent.out(v[1])) - else: - node.set_input(k, v) - new_open = graph.lookup_node(open_node) - assert new_open is not None - for i in range(NUM_FLOW_SOCKETS): - key = f"initial_value{i}" - new_open.set_input(key, kwargs.get(key, None)) - my_clone = graph.lookup_node("Recurse") - assert my_clone is not None - result = map(lambda x: my_clone.out(x), range(NUM_FLOW_SOCKETS)) - return { - "result": tuple(result), - "expand": graph.finalize(), - } - -@VariantSupport() -class TestExecutionBlockerNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - inputs = { - "required": { - "input": ("*",), - "block": ("BOOLEAN",), - "verbose": ("BOOLEAN", {"default": False}), - }, - } - return inputs - - RETURN_TYPES = ("*",) - RETURN_NAMES = ("output",) - FUNCTION = "execution_blocker" - - CATEGORY = "Testing/Flow" - - def execution_blocker(self, input, block, verbose): - if block: - return (ExecutionBlocker("Blocked Execution" if verbose else None),) - return (input,) - -FLOW_CONTROL_NODE_CLASS_MAPPINGS = { - "TestWhileLoopOpen": TestWhileLoopOpen, - "TestWhileLoopClose": TestWhileLoopClose, - "TestExecutionBlocker": TestExecutionBlockerNode, -} -FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS = { - "TestWhileLoopOpen": "While Loop Open", - "TestWhileLoopClose": "While Loop Close", - "TestExecutionBlocker": "Execution Blocker", -} diff --git a/tests/inference/testing_nodes/testing-pack/specific_tests.py b/tests/inference/testing_nodes/testing-pack/specific_tests.py deleted file mode 100644 index 4f8f01ae4a48bc46791f4c20851f12adbc0e8beb..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/specific_tests.py +++ /dev/null @@ -1,519 +0,0 @@ -import torch -import time -import asyncio -from comfy.utils import ProgressBar -from .tools import VariantSupport -from comfy_execution.graph_utils import GraphBuilder -from comfy.comfy_types.node_typing import ComfyNodeABC -from comfy.comfy_types import IO - -class TestLazyMixImages: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image1": ("IMAGE",{"lazy": True}), - "image2": ("IMAGE",{"lazy": True}), - "mask": ("MASK",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "mix" - - CATEGORY = "Testing/Nodes" - - def check_lazy_status(self, mask, image1, image2): - mask_min = mask.min() - mask_max = mask.max() - needed = [] - if image1 is None and (mask_min != 1.0 or mask_max != 1.0): - needed.append("image1") - if image2 is None and (mask_min != 0.0 or mask_max != 0.0): - needed.append("image2") - return needed - - # Not trying to handle different batch sizes here just to keep the demo simple - def mix(self, mask, image1, image2): - mask_min = mask.min() - mask_max = mask.max() - if mask_min == 0.0 and mask_max == 0.0: - return (image1,) - elif mask_min == 1.0 and mask_max == 1.0: - return (image2,) - - if len(mask.shape) == 2: - mask = mask.unsqueeze(0) - if len(mask.shape) == 3: - mask = mask.unsqueeze(3) - if mask.shape[3] < image1.shape[3]: - mask = mask.repeat(1, 1, 1, image1.shape[3]) - - result = image1 * (1. - mask) + image2 * mask, - return (result[0],) - -class TestVariadicAverage: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("IMAGE",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "variadic_average" - - CATEGORY = "Testing/Nodes" - - def variadic_average(self, input1, **kwargs): - inputs = [input1] - while 'input' + str(len(inputs) + 1) in kwargs: - inputs.append(kwargs['input' + str(len(inputs) + 1)]) - return (torch.stack(inputs).mean(dim=0),) - - -class TestCustomIsChanged: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ("IMAGE",), - }, - "optional": { - "should_change": ("BOOL", {"default": False}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_is_changed" - - CATEGORY = "Testing/Nodes" - - def custom_is_changed(self, image, should_change=False): - return (image,) - - @classmethod - def IS_CHANGED(cls, should_change=False, *args, **kwargs): - if should_change: - return float("NaN") - else: - return False - -class TestIsChangedWithConstants: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ("IMAGE",), - "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_is_changed" - - CATEGORY = "Testing/Nodes" - - def custom_is_changed(self, image, value): - return (image * value,) - - @classmethod - def IS_CHANGED(cls, image, value): - if image is None: - return value - else: - return image.mean().item() * value - -class TestCustomValidation1: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("IMAGE,FLOAT",), - "input2": ("IMAGE,FLOAT",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_validation1" - - CATEGORY = "Testing/Nodes" - - def custom_validation1(self, input1, input2): - if isinstance(input1, float) and isinstance(input2, float): - result = torch.ones([1, 512, 512, 3]) * input1 * input2 - else: - result = input1 * input2 - return (result,) - - @classmethod - def VALIDATE_INPUTS(cls, input1=None, input2=None): - if input1 is not None: - if not isinstance(input1, (torch.Tensor, float)): - return f"Invalid type of input1: {type(input1)}" - if input2 is not None: - if not isinstance(input2, (torch.Tensor, float)): - return f"Invalid type of input2: {type(input2)}" - - return True - -class TestCustomValidation2: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("IMAGE,FLOAT",), - "input2": ("IMAGE,FLOAT",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_validation2" - - CATEGORY = "Testing/Nodes" - - def custom_validation2(self, input1, input2): - if isinstance(input1, float) and isinstance(input2, float): - result = torch.ones([1, 512, 512, 3]) * input1 * input2 - else: - result = input1 * input2 - return (result,) - - @classmethod - def VALIDATE_INPUTS(cls, input_types, input1=None, input2=None): - if input1 is not None: - if not isinstance(input1, (torch.Tensor, float)): - return f"Invalid type of input1: {type(input1)}" - if input2 is not None: - if not isinstance(input2, (torch.Tensor, float)): - return f"Invalid type of input2: {type(input2)}" - - if 'input1' in input_types: - if input_types['input1'] not in ["IMAGE", "FLOAT"]: - return f"Invalid type of input1: {input_types['input1']}" - if 'input2' in input_types: - if input_types['input2'] not in ["IMAGE", "FLOAT"]: - return f"Invalid type of input2: {input_types['input2']}" - - return True - -@VariantSupport() -class TestCustomValidation3: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("IMAGE,FLOAT",), - "input2": ("IMAGE,FLOAT",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_validation3" - - CATEGORY = "Testing/Nodes" - - def custom_validation3(self, input1, input2): - if isinstance(input1, float) and isinstance(input2, float): - result = torch.ones([1, 512, 512, 3]) * input1 * input2 - else: - result = input1 * input2 - return (result,) - -class TestCustomValidation4: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("FLOAT",), - "input2": ("FLOAT",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_validation4" - - CATEGORY = "Testing/Nodes" - - def custom_validation4(self, input1, input2): - result = torch.ones([1, 512, 512, 3]) * input1 * input2 - return (result,) - - @classmethod - def VALIDATE_INPUTS(cls, input1, input2): - if input1 is not None: - if not isinstance(input1, float): - return f"Invalid type of input1: {type(input1)}" - if input2 is not None: - if not isinstance(input2, float): - return f"Invalid type of input2: {type(input2)}" - - return True - -class TestCustomValidation5: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("FLOAT", {"min": 0.0, "max": 1.0}), - "input2": ("FLOAT", {"min": 0.0, "max": 1.0}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "custom_validation5" - - CATEGORY = "Testing/Nodes" - - def custom_validation5(self, input1, input2): - value = input1 * input2 - return (torch.ones([1, 512, 512, 3]) * value,) - - @classmethod - def VALIDATE_INPUTS(cls, **kwargs): - if kwargs['input2'] == 7.0: - return "7s are not allowed. I've never liked 7s." - return True - -class TestDynamicDependencyCycle: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("IMAGE",), - "input2": ("IMAGE",), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "dynamic_dependency_cycle" - - CATEGORY = "Testing/Nodes" - - def dynamic_dependency_cycle(self, input1, input2): - g = GraphBuilder() - mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) - mix1 = g.node("TestLazyMixImages", image1=input1, mask=mask.out(0)) - mix2 = g.node("TestLazyMixImages", image1=mix1.out(0), image2=input2, mask=mask.out(0)) - - # Create the cyle - mix1.set_input("image2", mix2.out(0)) - - return { - "result": (mix2.out(0),), - "expand": g.finalize(), - } - -class TestMixedExpansionReturns: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "input1": ("FLOAT",), - }, - } - - RETURN_TYPES = ("IMAGE","IMAGE") - FUNCTION = "mixed_expansion_returns" - - CATEGORY = "Testing/Nodes" - - def mixed_expansion_returns(self, input1): - white_image = torch.ones([1, 512, 512, 3]) - if input1 <= 0.1: - return (torch.ones([1, 512, 512, 3]) * 0.1, white_image) - elif input1 <= 0.2: - return { - "result": (torch.ones([1, 512, 512, 3]) * 0.2, white_image), - } - else: - g = GraphBuilder() - mask = g.node("StubMask", value=0.3, height=512, width=512, batch_size=1) - black = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) - white = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) - mix = g.node("TestLazyMixImages", image1=black.out(0), image2=white.out(0), mask=mask.out(0)) - return { - "result": (mix.out(0), white_image), - "expand": g.finalize(), - } - -class TestSamplingInExpansion: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "model": ("MODEL",), - "clip": ("CLIP",), - "vae": ("VAE",), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), - "steps": ("INT", {"default": 20, "min": 1, "max": 100}), - "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 30.0}), - "prompt": ("STRING", {"multiline": True, "default": "a beautiful landscape with mountains and trees"}), - "negative_prompt": ("STRING", {"multiline": True, "default": "blurry, bad quality, worst quality"}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "sampling_in_expansion" - - CATEGORY = "Testing/Nodes" - - def sampling_in_expansion(self, model, clip, vae, seed, steps, cfg, prompt, negative_prompt): - g = GraphBuilder() - - # Create a basic image generation workflow using the input model, clip and vae - # 1. Setup text prompts using the provided CLIP model - positive_prompt = g.node("CLIPTextEncode", - text=prompt, - clip=clip) - negative_prompt = g.node("CLIPTextEncode", - text=negative_prompt, - clip=clip) - - # 2. Create empty latent with specified size - empty_latent = g.node("EmptyLatentImage", width=512, height=512, batch_size=1) - - # 3. Setup sampler and generate image latent - sampler = g.node("KSampler", - model=model, - positive=positive_prompt.out(0), - negative=negative_prompt.out(0), - latent_image=empty_latent.out(0), - seed=seed, - steps=steps, - cfg=cfg, - sampler_name="euler_ancestral", - scheduler="normal") - - # 4. Decode latent to image using VAE - output = g.node("VAEDecode", samples=sampler.out(0), vae=vae) - - return { - "result": (output.out(0),), - "expand": g.finalize(), - } - -class TestSleep(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": (IO.ANY, {}), - "seconds": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 9999.0, "step": 0.01, "tooltip": "The amount of seconds to sleep."}), - }, - "hidden": { - "unique_id": "UNIQUE_ID", - }, - } - RETURN_TYPES = (IO.ANY,) - FUNCTION = "sleep" - - CATEGORY = "_for_testing" - - async def sleep(self, value, seconds, unique_id): - pbar = ProgressBar(seconds, node_id=unique_id) - start = time.time() - expiration = start + seconds - now = start - while now < expiration: - now = time.time() - pbar.update_absolute(now - start) - await asyncio.sleep(0.01) - return (value,) - -class TestParallelSleep(ComfyNodeABC): - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image1": ("IMAGE", ), - "image2": ("IMAGE", ), - "image3": ("IMAGE", ), - "sleep1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}), - "sleep2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}), - "sleep3": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}), - }, - "hidden": { - "unique_id": "UNIQUE_ID", - }, - } - RETURN_TYPES = ("IMAGE",) - FUNCTION = "parallel_sleep" - CATEGORY = "_for_testing" - OUTPUT_NODE = True - - def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id): - # Create a graph dynamically with three TestSleep nodes - g = GraphBuilder() - - # Create sleep nodes for each duration and image - sleep_node1 = g.node("TestSleep", value=image1, seconds=sleep1) - sleep_node2 = g.node("TestSleep", value=image2, seconds=sleep2) - sleep_node3 = g.node("TestSleep", value=image3, seconds=sleep3) - - # Blend the results using TestVariadicAverage - blend = g.node("TestVariadicAverage", - input1=sleep_node1.out(0), - input2=sleep_node2.out(0), - input3=sleep_node3.out(0)) - - return { - "result": (blend.out(0),), - "expand": g.finalize(), - } - -class TestOutputNodeWithSocketOutput: - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "image": ("IMAGE",), - "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), - }, - } - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process" - CATEGORY = "_for_testing" - OUTPUT_NODE = True - - def process(self, image, value): - # Apply value scaling and return both as output and socket - result = image * value - return (result,) - -TEST_NODE_CLASS_MAPPINGS = { - "TestLazyMixImages": TestLazyMixImages, - "TestVariadicAverage": TestVariadicAverage, - "TestCustomIsChanged": TestCustomIsChanged, - "TestIsChangedWithConstants": TestIsChangedWithConstants, - "TestCustomValidation1": TestCustomValidation1, - "TestCustomValidation2": TestCustomValidation2, - "TestCustomValidation3": TestCustomValidation3, - "TestCustomValidation4": TestCustomValidation4, - "TestCustomValidation5": TestCustomValidation5, - "TestDynamicDependencyCycle": TestDynamicDependencyCycle, - "TestMixedExpansionReturns": TestMixedExpansionReturns, - "TestSamplingInExpansion": TestSamplingInExpansion, - "TestSleep": TestSleep, - "TestParallelSleep": TestParallelSleep, - "TestOutputNodeWithSocketOutput": TestOutputNodeWithSocketOutput, -} - -TEST_NODE_DISPLAY_NAME_MAPPINGS = { - "TestLazyMixImages": "Lazy Mix Images", - "TestVariadicAverage": "Variadic Average", - "TestCustomIsChanged": "Custom IsChanged", - "TestIsChangedWithConstants": "IsChanged With Constants", - "TestCustomValidation1": "Custom Validation 1", - "TestCustomValidation2": "Custom Validation 2", - "TestCustomValidation3": "Custom Validation 3", - "TestCustomValidation4": "Custom Validation 4", - "TestCustomValidation5": "Custom Validation 5", - "TestDynamicDependencyCycle": "Dynamic Dependency Cycle", - "TestMixedExpansionReturns": "Mixed Expansion Returns", - "TestSamplingInExpansion": "Sampling In Expansion", - "TestSleep": "Test Sleep", - "TestParallelSleep": "Test Parallel Sleep", - "TestOutputNodeWithSocketOutput": "Test Output Node With Socket Output", -} diff --git a/tests/inference/testing_nodes/testing-pack/stubs.py b/tests/inference/testing_nodes/testing-pack/stubs.py deleted file mode 100644 index a1df87529c65ffa46db64cc197f54aef376ff8c0..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/stubs.py +++ /dev/null @@ -1,129 +0,0 @@ -import torch - -class StubImage: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "content": (['WHITE', 'BLACK', 'NOISE'],), - "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}), - "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "stub_image" - - CATEGORY = "Testing/Stub Nodes" - - def stub_image(self, content, height, width, batch_size): - if content == "WHITE": - return (torch.ones(batch_size, height, width, 3),) - elif content == "BLACK": - return (torch.zeros(batch_size, height, width, 3),) - elif content == "NOISE": - return (torch.rand(batch_size, height, width, 3),) - -class StubConstantImage: - def __init__(self): - pass - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}), - "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "stub_constant_image" - - CATEGORY = "Testing/Stub Nodes" - - def stub_constant_image(self, value, height, width, batch_size): - return (torch.ones(batch_size, height, width, 3) * value,) - -class StubMask: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), - "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}), - "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}), - }, - } - - RETURN_TYPES = ("MASK",) - FUNCTION = "stub_mask" - - CATEGORY = "Testing/Stub Nodes" - - def stub_mask(self, value, height, width, batch_size): - return (torch.ones(batch_size, height, width) * value,) - -class StubInt: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("INT", {"default": 0, "min": -0xffffffff, "max": 0xffffffff, "step": 1}), - }, - } - - RETURN_TYPES = ("INT",) - FUNCTION = "stub_int" - - CATEGORY = "Testing/Stub Nodes" - - def stub_int(self, value): - return (value,) - -class StubFloat: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value": ("FLOAT", {"default": 0.0, "min": -1.0e38, "max": 1.0e38, "step": 0.01}), - }, - } - - RETURN_TYPES = ("FLOAT",) - FUNCTION = "stub_float" - - CATEGORY = "Testing/Stub Nodes" - - def stub_float(self, value): - return (value,) - -TEST_STUB_NODE_CLASS_MAPPINGS = { - "StubImage": StubImage, - "StubConstantImage": StubConstantImage, - "StubMask": StubMask, - "StubInt": StubInt, - "StubFloat": StubFloat, -} -TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS = { - "StubImage": "Stub Image", - "StubConstantImage": "Stub Constant Image", - "StubMask": "Stub Mask", - "StubInt": "Stub Int", - "StubFloat": "Stub Float", -} diff --git a/tests/inference/testing_nodes/testing-pack/tools.py b/tests/inference/testing_nodes/testing-pack/tools.py deleted file mode 100644 index 34b28c0eb4892cdbc4e7575e651719f48ca917e9..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/tools.py +++ /dev/null @@ -1,53 +0,0 @@ - -def MakeSmartType(t): - if isinstance(t, str): - return SmartType(t) - return t - -class SmartType(str): - def __ne__(self, other): - if self == "*" or other == "*": - return False - selfset = set(self.split(',')) - otherset = set(other.split(',')) - return not selfset.issubset(otherset) - -def VariantSupport(): - def decorator(cls): - if hasattr(cls, "INPUT_TYPES"): - old_input_types = getattr(cls, "INPUT_TYPES") - def new_input_types(*args, **kwargs): - types = old_input_types(*args, **kwargs) - for category in ["required", "optional"]: - if category not in types: - continue - for key, value in types[category].items(): - if isinstance(value, tuple): - types[category][key] = (MakeSmartType(value[0]),) + value[1:] - return types - setattr(cls, "INPUT_TYPES", new_input_types) - if hasattr(cls, "RETURN_TYPES"): - old_return_types = cls.RETURN_TYPES - setattr(cls, "RETURN_TYPES", tuple(MakeSmartType(x) for x in old_return_types)) - if hasattr(cls, "VALIDATE_INPUTS"): - # Reflection is used to determine what the function signature is, so we can't just change the function signature - raise NotImplementedError("VariantSupport does not support VALIDATE_INPUTS yet") - else: - def validate_inputs(input_types): - inputs = cls.INPUT_TYPES() - for key, value in input_types.items(): - if isinstance(value, SmartType): - continue - if "required" in inputs and key in inputs["required"]: - expected_type = inputs["required"][key][0] - elif "optional" in inputs and key in inputs["optional"]: - expected_type = inputs["optional"][key][0] - else: - expected_type = None - if expected_type is not None and MakeSmartType(value) != expected_type: - return f"Invalid type of {key}: {value} (expected {expected_type})" - return True - setattr(cls, "VALIDATE_INPUTS", validate_inputs) - return cls - return decorator - diff --git a/tests/inference/testing_nodes/testing-pack/util.py b/tests/inference/testing_nodes/testing-pack/util.py deleted file mode 100644 index 17741c5f1dff2eeff86fec5173e675bf9ac6cf3b..0000000000000000000000000000000000000000 --- a/tests/inference/testing_nodes/testing-pack/util.py +++ /dev/null @@ -1,364 +0,0 @@ -from comfy_execution.graph_utils import GraphBuilder -from .tools import VariantSupport - -@VariantSupport() -class TestAccumulateNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "to_add": ("*",), - }, - "optional": { - "accumulation": ("ACCUMULATION",), - }, - } - - RETURN_TYPES = ("ACCUMULATION",) - FUNCTION = "accumulate" - - CATEGORY = "Testing/Lists" - - def accumulate(self, to_add, accumulation = None): - if accumulation is None: - value = [to_add] - else: - value = accumulation["accum"] + [to_add] - return ({"accum": value},) - -@VariantSupport() -class TestAccumulationHeadNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "accumulation": ("ACCUMULATION",), - }, - } - - RETURN_TYPES = ("ACCUMULATION", "*",) - FUNCTION = "accumulation_head" - - CATEGORY = "Testing/Lists" - - def accumulation_head(self, accumulation): - accum = accumulation["accum"] - if len(accum) == 0: - return (accumulation, None) - else: - return ({"accum": accum[1:]}, accum[0]) - -class TestAccumulationTailNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "accumulation": ("ACCUMULATION",), - }, - } - - RETURN_TYPES = ("ACCUMULATION", "*",) - FUNCTION = "accumulation_tail" - - CATEGORY = "Testing/Lists" - - def accumulation_tail(self, accumulation): - accum = accumulation["accum"] - if len(accum) == 0: - return (None, accumulation) - else: - return ({"accum": accum[:-1]}, accum[-1]) - -@VariantSupport() -class TestAccumulationToListNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "accumulation": ("ACCUMULATION",), - }, - } - - RETURN_TYPES = ("*",) - OUTPUT_IS_LIST = (True,) - - FUNCTION = "accumulation_to_list" - - CATEGORY = "Testing/Lists" - - def accumulation_to_list(self, accumulation): - return (accumulation["accum"],) - -@VariantSupport() -class TestListToAccumulationNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "list": ("*",), - }, - } - - RETURN_TYPES = ("ACCUMULATION",) - INPUT_IS_LIST = (True,) - - FUNCTION = "list_to_accumulation" - - CATEGORY = "Testing/Lists" - - def list_to_accumulation(self, list): - return ({"accum": list},) - -@VariantSupport() -class TestAccumulationGetLengthNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "accumulation": ("ACCUMULATION",), - }, - } - - RETURN_TYPES = ("INT",) - - FUNCTION = "accumlength" - - CATEGORY = "Testing/Lists" - - def accumlength(self, accumulation): - return (len(accumulation['accum']),) - -@VariantSupport() -class TestAccumulationGetItemNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "accumulation": ("ACCUMULATION",), - "index": ("INT", {"default":0, "step":1}) - }, - } - - RETURN_TYPES = ("*",) - - FUNCTION = "get_item" - - CATEGORY = "Testing/Lists" - - def get_item(self, accumulation, index): - return (accumulation['accum'][index],) - -@VariantSupport() -class TestAccumulationSetItemNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "accumulation": ("ACCUMULATION",), - "index": ("INT", {"default":0, "step":1}), - "value": ("*",), - }, - } - - RETURN_TYPES = ("ACCUMULATION",) - - FUNCTION = "set_item" - - CATEGORY = "Testing/Lists" - - def set_item(self, accumulation, index, value): - new_accum = accumulation['accum'][:] - new_accum[index] = value - return ({"accum": new_accum},) - -class TestIntMathOperation: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), - "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), - "operation": (["add", "subtract", "multiply", "divide", "modulo", "power"],), - }, - } - - RETURN_TYPES = ("INT",) - FUNCTION = "int_math_operation" - - CATEGORY = "Testing/Logic" - - def int_math_operation(self, a, b, operation): - if operation == "add": - return (a + b,) - elif operation == "subtract": - return (a - b,) - elif operation == "multiply": - return (a * b,) - elif operation == "divide": - return (a // b,) - elif operation == "modulo": - return (a % b,) - elif operation == "power": - return (a ** b,) - - -from .flow_control import NUM_FLOW_SOCKETS -@VariantSupport() -class TestForLoopOpen: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "remaining": ("INT", {"default": 1, "min": 0, "max": 100000, "step": 1}), - }, - "optional": { - f"initial_value{i}": ("*",) for i in range(1, NUM_FLOW_SOCKETS) - }, - "hidden": { - "initial_value0": ("*",) - } - } - - RETURN_TYPES = tuple(["FLOW_CONTROL", "INT",] + ["*"] * (NUM_FLOW_SOCKETS-1)) - RETURN_NAMES = tuple(["flow_control", "remaining"] + [f"value{i}" for i in range(1, NUM_FLOW_SOCKETS)]) - FUNCTION = "for_loop_open" - - CATEGORY = "Testing/Flow" - - def for_loop_open(self, remaining, **kwargs): - graph = GraphBuilder() - if "initial_value0" in kwargs: - remaining = kwargs["initial_value0"] - graph.node("TestWhileLoopOpen", condition=remaining, initial_value0=remaining, **{(f"initial_value{i}"): kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)}) - outputs = [kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)] - return { - "result": tuple(["stub", remaining] + outputs), - "expand": graph.finalize(), - } - -@VariantSupport() -class TestForLoopClose: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "flow_control": ("FLOW_CONTROL", {"rawLink": True}), - }, - "optional": { - f"initial_value{i}": ("*",{"rawLink": True}) for i in range(1, NUM_FLOW_SOCKETS) - }, - } - - RETURN_TYPES = tuple(["*"] * (NUM_FLOW_SOCKETS-1)) - RETURN_NAMES = tuple([f"value{i}" for i in range(1, NUM_FLOW_SOCKETS)]) - FUNCTION = "for_loop_close" - - CATEGORY = "Testing/Flow" - - def for_loop_close(self, flow_control, **kwargs): - graph = GraphBuilder() - while_open = flow_control[0] - sub = graph.node("TestIntMathOperation", operation="subtract", a=[while_open,1], b=1) - cond = graph.node("TestToBoolNode", value=sub.out(0)) - input_values = {f"initial_value{i}": kwargs.get(f"initial_value{i}", None) for i in range(1, NUM_FLOW_SOCKETS)} - while_close = graph.node("TestWhileLoopClose", - flow_control=flow_control, - condition=cond.out(0), - initial_value0=sub.out(0), - **input_values) - return { - "result": tuple([while_close.out(i) for i in range(1, NUM_FLOW_SOCKETS)]), - "expand": graph.finalize(), - } - -NUM_LIST_SOCKETS = 10 -@VariantSupport() -class TestMakeListNode: - def __init__(self): - pass - - @classmethod - def INPUT_TYPES(cls): - return { - "required": { - "value1": ("*",), - }, - "optional": { - f"value{i}": ("*",) for i in range(1, NUM_LIST_SOCKETS) - }, - } - - RETURN_TYPES = ("*",) - FUNCTION = "make_list" - OUTPUT_IS_LIST = (True,) - - CATEGORY = "Testing/Lists" - - def make_list(self, **kwargs): - result = [] - for i in range(NUM_LIST_SOCKETS): - if f"value{i}" in kwargs: - result.append(kwargs[f"value{i}"]) - return (result,) - -UTILITY_NODE_CLASS_MAPPINGS = { - "TestAccumulateNode": TestAccumulateNode, - "TestAccumulationHeadNode": TestAccumulationHeadNode, - "TestAccumulationTailNode": TestAccumulationTailNode, - "TestAccumulationToListNode": TestAccumulationToListNode, - "TestListToAccumulationNode": TestListToAccumulationNode, - "TestAccumulationGetLengthNode": TestAccumulationGetLengthNode, - "TestAccumulationGetItemNode": TestAccumulationGetItemNode, - "TestAccumulationSetItemNode": TestAccumulationSetItemNode, - "TestForLoopOpen": TestForLoopOpen, - "TestForLoopClose": TestForLoopClose, - "TestIntMathOperation": TestIntMathOperation, - "TestMakeListNode": TestMakeListNode, -} -UTILITY_NODE_DISPLAY_NAME_MAPPINGS = { - "TestAccumulateNode": "Accumulate", - "TestAccumulationHeadNode": "Accumulation Head", - "TestAccumulationTailNode": "Accumulation Tail", - "TestAccumulationToListNode": "Accumulation to List", - "TestListToAccumulationNode": "List to Accumulation", - "TestAccumulationGetLengthNode": "Accumulation Get Length", - "TestAccumulationGetItemNode": "Accumulation Get Item", - "TestAccumulationSetItemNode": "Accumulation Set Item", - "TestForLoopOpen": "For Loop Open", - "TestForLoopClose": "For Loop Close", - "TestIntMathOperation": "Int Math Operation", - "TestMakeListNode": "Make List", -} diff --git a/user/comfyui.log b/user/comfyui.log deleted file mode 100644 index ed553704cb2cd8c7e8afffc969dee160dc4f6ae0..0000000000000000000000000000000000000000 --- a/user/comfyui.log +++ /dev/null @@ -1,336 +0,0 @@ -## ComfyUI-Manager: installing dependencies done. -[2025-08-31 02:39:11.131] ** ComfyUI startup time: 2025-08-31 02:39:11.131 -[2025-08-31 02:39:11.131] ** Platform: Linux -[2025-08-31 02:39:11.132] ** Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] -[2025-08-31 02:39:11.132] ** Python executable: /usr/bin/python -[2025-08-31 02:39:11.132] ** ComfyUI Path: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:39:11.132] ** ComfyUI Base Folder Path: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:39:11.132] ** User directory: /home/ubuntu/comfy_wan2_2/ComfyUI/user -[2025-08-31 02:39:11.132] ** ComfyUI-Manager config path: /home/ubuntu/comfy_wan2_2/ComfyUI/user/default/ComfyUI-Manager/config.ini -[2025-08-31 02:39:11.132] ** Log path: /home/ubuntu/comfy_wan2_2/ComfyUI/user/comfyui.log - -Prestartup times for custom nodes: -[2025-08-31 02:39:12.839] 4.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-manager -[2025-08-31 02:39:12.839] -[2025-08-31 02:39:14.144] Checkpoint files will always be loaded safely. -[2025-08-31 02:39:14.379] Total VRAM 40442 MB, total RAM 201299 MB -[2025-08-31 02:39:14.379] pytorch version: 2.7.0+cu126 -[2025-08-31 02:39:16.006] xformers version: 0.0.30 -[2025-08-31 02:39:16.006] Set vram state to: NORMAL_VRAM -[2025-08-31 02:39:16.006] Device: cuda:0 NVIDIA A100-SXM4-40GB : cudaMallocAsync -[2025-08-31 02:39:16.254] Using xformers attention -[2025-08-31 02:39:16.264] torchaudio missing, ACE model will be broken -[2025-08-31 02:39:16.265] torchaudio missing, ACE model will be broken -[2025-08-31 02:39:17.232] Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] -[2025-08-31 02:39:17.232] ComfyUI version: 0.3.56 -[2025-08-31 02:39:17.235] ComfyUI frontend version: 1.25.11 -[2025-08-31 02:39:17.235] [Prompt Server] web root: /home/ubuntu/.local/lib/python3.10/site-packages/comfyui_frontend_package/static -[2025-08-31 02:39:17.237] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.238] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.239] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_upscale_model.py", line 2, in - from spandrel import ModelLoader, ImageModelDescriptor -ModuleNotFoundError: No module named 'spandrel' - -[2025-08-31 02:39:17.239] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_upscale_model.py module for custom nodes: No module named 'spandrel' -[2025-08-31 02:39:17.239] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.240] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.241] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.242] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.242] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.243] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.243] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.545] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.546] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.547] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.547] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.548] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.549] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.550] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.551] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.552] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.553] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.553] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.554] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.554] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.555] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.556] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.556] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.557] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.557] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.558] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.558] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.559] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.560] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.560] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.561] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.562] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.562] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.564] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio.py", line 4, in - import torchaudio - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/__init__.py", line 2, in - from . import _extension # noqa # usort: skip - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/__init__.py", line 42, in - _check_cuda_version() - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/utils.py", line 168, in _check_cuda_version - version = torchaudio.lib._torchaudio.cuda_version() -AttributeError: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) - -[2025-08-31 02:39:17.564] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio.py module for custom nodes: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) -[2025-08-31 02:39:17.564] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.566] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.566] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.567] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.568] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.568] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.569] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.570] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.570] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.571] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.571] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.572] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.573] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.574] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.574] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.575] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.576] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.577] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.578] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.579] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.579] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.580] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.580] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.581] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.581] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.582] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.583] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.583] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.585] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.585] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.586] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.586] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.587] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.588] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.588] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.590] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio_encoder.py", line 2, in - import comfy.audio_encoders.audio_encoders - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy/audio_encoders/audio_encoders.py", line 6, in - import torchaudio - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/__init__.py", line 2, in - from . import _extension # noqa # usort: skip - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/__init__.py", line 42, in - _check_cuda_version() - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/utils.py", line 168, in _check_cuda_version - version = torchaudio.lib._torchaudio.cuda_version() -AttributeError: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) - -[2025-08-31 02:39:17.590] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio_encoder.py module for custom nodes: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) -[2025-08-31 02:39:17.590] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_api_nodes/canary.py", line 5, in - raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") -Exception: INSTALL NEW VERSION OF PYAV TO USE API NODES. - -[2025-08-31 02:39:17.590] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_api_nodes/canary.py module for custom nodes: INSTALL NEW VERSION OF PYAV TO USE API NODES. -[2025-08-31 02:39:17.594] ### Loading: ComfyUI-Manager (V3.36) -[2025-08-31 02:39:17.594] [ComfyUI-Manager] network_mode: public -[2025-08-31 02:39:17.635] ### ComfyUI Version: v0.3.56 | Released on '2025-08-30' -[2025-08-31 02:39:17.644] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.700] ComfyUI found: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:39:17.700] '/home/ubuntu/comfy_wan2_2/ComfyUI' added to sys.path -[2025-08-31 02:39:17.702] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.729] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json -[2025-08-31 02:39:17.735] ComfyUI-GGUF: Partial torch compile only, consider updating pytorch -[2025-08-31 02:39:17.739] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.739] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.764] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json -[2025-08-31 02:39:17.778] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:39:17.779] -Import times for custom nodes: -[2025-08-31 02:39:17.785] 0.0 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/websocket_image_save.py -[2025-08-31 02:39:17.785] 0.0 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/ComfyUI-GGUF -[2025-08-31 02:39:17.787] 0.0 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-kjnodes -[2025-08-31 02:39:17.799] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-manager -[2025-08-31 02:39:17.823] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/ComfyUI-to-Python-Extension -[2025-08-31 02:39:17.823] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json -[2025-08-31 02:39:17.823] -[2025-08-31 02:39:17.823] WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies. - -[2025-08-31 02:39:17.823] IMPORT FAILED: nodes_ideogram.py -[2025-08-31 02:39:17.824] IMPORT FAILED: nodes_openai.py -[2025-08-31 02:39:17.824] IMPORT FAILED: nodes_minimax.py -[2025-08-31 02:39:17.826] IMPORT FAILED: nodes_veo2.py -[2025-08-31 02:39:17.835] IMPORT FAILED: nodes_kling.py -[2025-08-31 02:39:17.835] IMPORT FAILED: nodes_bfl.py -[2025-08-31 02:39:17.835] IMPORT FAILED: nodes_luma.py -[2025-08-31 02:39:17.835] IMPORT FAILED: nodes_recraft.py -[2025-08-31 02:39:17.877] IMPORT FAILED: nodes_pixverse.py -[2025-08-31 02:39:17.877] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json -[2025-08-31 02:39:17.877] IMPORT FAILED: nodes_stability.py -[2025-08-31 02:39:17.877] IMPORT FAILED: nodes_pika.py -[2025-08-31 02:39:17.877] IMPORT FAILED: nodes_runway.py -[2025-08-31 02:39:17.877] IMPORT FAILED: nodes_tripo.py -[2025-08-31 02:39:17.877] IMPORT FAILED: nodes_moonvalley.py -[2025-08-31 02:39:17.878] IMPORT FAILED: nodes_rodin.py -[2025-08-31 02:39:17.878] IMPORT FAILED: nodes_gemini.py -[2025-08-31 02:39:17.878] IMPORT FAILED: nodes_vidu.py -[2025-08-31 02:39:17.878] -This issue might be caused by new missing dependencies added the last time you updated ComfyUI. -[2025-08-31 02:39:17.878] Please do a: pip install -r requirements.txt -[2025-08-31 02:39:17.879] -[2025-08-31 02:39:17.892] WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies. - -[2025-08-31 02:39:17.903] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/github-stats.json -[2025-08-31 02:39:17.903] IMPORT FAILED: nodes_upscale_model.py -[2025-08-31 02:39:17.903] IMPORT FAILED: nodes_audio.py -[2025-08-31 02:39:17.904] IMPORT FAILED: nodes_audio_encoder.py -[2025-08-31 02:39:17.905] -This issue might be caused by new missing dependencies added the last time you updated ComfyUI. -[2025-08-31 02:39:17.905] Please do a: pip install -r requirements.txt -[2025-08-31 02:39:17.905] -[2025-08-31 02:39:17.906] ------------------------------------------------------------------------ -Error importing dependencies: No module named 'alembic' -Please install the updated requirements.txt file by running: -/usr/bin/python -m pip install -r /home/ubuntu/comfy_wan2_2/ComfyUI/requirements.txt -If you are on the portable package you can run: update\update_comfyui.bat to solve this problem. -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -[2025-08-31 02:39:17.918] ------------------------------------------------------------------------ -Error importing dependencies: No module named 'alembic' -Please install the updated requirements.txt file by running: -/usr/bin/python -m pip install -r /home/ubuntu/comfy_wan2_2/ComfyUI/requirements.txt -If you are on the portable package you can run: update\update_comfyui.bat to solve this problem. -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -[2025-08-31 02:39:17.919] Starting server - -[2025-08-31 02:39:17.919] To see the GUI go to: http://127.0.0.1:8188 -[2025-08-31 02:39:20.211] FETCH ComfyRegistry Data: 5/96 -[2025-08-31 02:39:22.996] FETCH ComfyRegistry Data: 10/96 -[2025-08-31 02:39:25.801] FETCH ComfyRegistry Data: 15/96 -[2025-08-31 02:39:28.587] FETCH ComfyRegistry Data: 20/96 -[2025-08-31 02:39:32.695] FETCH ComfyRegistry Data: 25/96 -[2025-08-31 02:39:35.538] FETCH ComfyRegistry Data: 30/96 -[2025-08-31 02:39:38.312] FETCH ComfyRegistry Data: 35/96 -[2025-08-31 02:39:41.106] FETCH ComfyRegistry Data: 40/96 -[2025-08-31 02:39:43.938] FETCH ComfyRegistry Data: 45/96 -[2025-08-31 02:39:46.794] FETCH ComfyRegistry Data: 50/96 -[2025-08-31 02:39:49.572] FETCH ComfyRegistry Data: 55/96 -[2025-08-31 02:39:52.421] FETCH ComfyRegistry Data: 60/96 -[2025-08-31 02:39:55.247] FETCH ComfyRegistry Data: 65/96 -[2025-08-31 02:39:58.095] FETCH ComfyRegistry Data: 70/96 -[2025-08-31 02:40:00.940] FETCH ComfyRegistry Data: 75/96 -[2025-08-31 02:40:04.510] FETCH ComfyRegistry Data: 80/96 -[2025-08-31 02:40:08.756] FETCH ComfyRegistry Data: 85/96 -[2025-08-31 02:40:12.940] FETCH ComfyRegistry Data: 90/96 -[2025-08-31 02:40:15.774] FETCH ComfyRegistry Data: 95/96 -[2025-08-31 02:40:16.827] FETCH ComfyRegistry Data [DONE] -[2025-08-31 02:40:17.001] [ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes -[2025-08-31 02:40:17.014] FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json [DONE] -[2025-08-31 02:40:17.110] [ComfyUI-Manager] All startup tasks have been completed. -[2025-08-31 02:40:26.250] got prompt -[2025-08-31 02:40:26.252] Failed to validate prompt for output 103: -[2025-08-31 02:40:26.252] * UNETLoader 37: -[2025-08-31 02:40:26.252] - Value not in list: unet_name: 'WAN 2.2\wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors' not in [] -[2025-08-31 02:40:26.252] * UNETLoader 91: -[2025-08-31 02:40:26.252] - Value not in list: unet_name: 'WAN 2.2\wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors' not in [] -[2025-08-31 02:40:26.253] Output will be ignored -[2025-08-31 02:40:26.253] invalid prompt: {'type': 'prompt_outputs_failed_validation', 'message': 'Prompt outputs failed validation', 'details': '', 'extra_info': {}} -[2025-08-31 02:46:24.847] got prompt -[2025-08-31 02:46:24.941] Using xformers attention in VAE -[2025-08-31 02:46:24.943] Using xformers attention in VAE -[2025-08-31 02:46:25.151] VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16 -[2025-08-31 02:46:25.502] Requested to load CLIPVisionModelProjection -[2025-08-31 02:46:25.622] loaded completely 38786.675 1208.09814453125 True -[2025-08-31 02:46:25.877] Using scaled fp8: fp8 matrix mult: False, scale input: False -[2025-08-31 02:46:26.595] Requested to load WanTEModel -[2025-08-31 02:46:26.608] loaded completely 9.5367431640625e+25 6419.477203369141 True -[2025-08-31 02:46:26.612] CLIP/text encoder model load device: cpu, offload device: cpu, current: cpu, dtype: torch.float16 -[2025-08-31 02:46:38.599] Requested to load WanVAE -[2025-08-31 02:46:38.629] loaded completely 33711.3369140625 242.02829551696777 True -[2025-08-31 02:46:39.697] Using scaled fp8: fp8 matrix mult: False, scale input: True -[2025-08-31 02:46:39.742] model weight dtype torch.float16, manual cast: None -[2025-08-31 02:46:39.742] model_type FLOW -[2025-08-31 02:46:40.661] Requested to load WAN21 -[2025-08-31 02:46:41.832] loaded completely 35156.22903843689 13629.075424194336 True -[2025-08-31 02:47:39.904] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:58<00:00, 5.94s/it] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:58<00:00, 5.80s/it] -[2025-08-31 02:47:41.203] Using scaled fp8: fp8 matrix mult: False, scale input: True -[2025-08-31 02:47:41.244] model weight dtype torch.float16, manual cast: None -[2025-08-31 02:47:41.244] model_type FLOW -[2025-08-31 02:47:42.245] Requested to load WAN21 -[2025-08-31 02:47:43.406] loaded completely 21516.78651829529 13629.075424194336 True -[2025-08-31 02:48:42.068] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:58<00:00, 5.98s/it] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:58<00:00, 5.86s/it] -[2025-08-31 02:48:45.513] Prompt executed in 140.60 seconds -[2025-08-31 02:55:38.828] got prompt -[2025-08-31 02:55:39.029] Requested to load WAN21 -[2025-08-31 02:55:44.834] loaded completely 21516.786579330445 13629.075424194336 True -[2025-08-31 02:56:07.104] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:22<00:00, 5.71s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:22<00:00, 5.56s/it] -[2025-08-31 02:56:10.479] Prompt executed in 31.55 seconds -[2025-08-31 02:56:38.426] got prompt -[2025-08-31 02:56:38.740] Requested to load WAN21 -[2025-08-31 02:56:42.151] loaded completely 21516.786579330445 13629.075424194336 True -[2025-08-31 02:57:04.537] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:22<00:00, 5.74s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:22<00:00, 5.59s/it] -[2025-08-31 02:57:07.794] Prompt executed in 29.13 seconds -[2025-08-31 02:58:25.547] got prompt -[2025-08-31 02:58:37.780] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:12<00:00, 3.05s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:12<00:00, 3.01s/it] -[2025-08-31 02:58:40.577] Prompt executed in 14.88 seconds -[2025-08-31 02:58:59.895] got prompt -[2025-08-31 02:59:18.631] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:18<00:00, 3.15s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:18<00:00, 3.08s/it] -[2025-08-31 02:59:21.391] Prompt executed in 21.29 seconds -[2025-08-31 02:59:36.883] got prompt -[2025-08-31 02:59:47.114] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:09<00:00, 1.61s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:09<00:00, 1.57s/it] -[2025-08-31 02:59:48.613] Prompt executed in 11.60 seconds -[2025-08-31 03:00:04.392] got prompt -[2025-08-31 03:00:04.685] Requested to load WAN21 -[2025-08-31 03:00:08.087] loaded completely 22733.298579330443 13629.075424194336 True -[2025-08-31 03:00:14.305] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.59s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.55s/it] -[2025-08-31 03:00:15.838] Prompt executed in 11.22 seconds -[2025-08-31 03:01:00.567] got prompt -[2025-08-31 03:01:00.610] Requested to load WAN21 -[2025-08-31 03:01:01.873] loaded completely 22733.298518295287 13629.075424194336 True -[2025-08-31 03:01:01.891] Patching comfy attention to use sageattn -[2025-08-31 03:01:38.386] 100%|███████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:36<00:00, 3.50s/it] 100%|███████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:36<00:00, 3.64s/it] -[2025-08-31 03:01:39.075] Restoring initial comfy attention -[2025-08-31 03:01:39.082] Requested to load WAN21 -[2025-08-31 03:01:39.092] Patching comfy attention to use sageattn -[2025-08-31 03:02:13.478] 100%|███████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:34<00:00, 3.50s/it] 100%|███████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:34<00:00, 3.44s/it] -[2025-08-31 03:02:14.171] Restoring initial comfy attention -[2025-08-31 03:02:15.389] Prompt executed in 74.82 seconds -[2025-08-31 03:03:27.633] got prompt -[2025-08-31 03:03:27.883] Requested to load WAN21 -[2025-08-31 03:03:35.527] loaded completely 22733.30237876892 13629.075424194336 True -[2025-08-31 03:03:35.544] Patching comfy attention to use sageattn -[2025-08-31 03:03:48.689] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.36s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.28s/it] -[2025-08-31 03:03:49.374] Restoring initial comfy attention -[2025-08-31 03:03:49.446] Requested to load WAN21 -[2025-08-31 03:03:57.476] loaded completely 22733.30237876892 13629.075424194336 True -[2025-08-31 03:03:57.493] Patching comfy attention to use sageattn -[2025-08-31 03:04:10.680] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.38s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:13<00:00, 3.30s/it] -[2025-08-31 03:04:11.367] Restoring initial comfy attention -[2025-08-31 03:04:12.585] Prompt executed in 44.80 seconds -[2025-08-31 03:04:40.122] got prompt -[2025-08-31 03:04:40.282] Patching comfy attention to use sageattn -[2025-08-31 03:04:46.701] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.64s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.60s/it] -[2025-08-31 03:04:47.034] Restoring initial comfy attention -[2025-08-31 03:04:47.037] Patching comfy attention to use sageattn -[2025-08-31 03:04:53.451] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.64s/it] 100%|█████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.60s/it] -[2025-08-31 03:04:53.785] Restoring initial comfy attention -[2025-08-31 03:04:54.938] Prompt executed in 14.68 seconds -[2025-08-31 03:07:41.074] Code successfully generated and written to <_io.StringIO object at 0x7ff0a46ab6d0> diff --git a/user/comfyui.prev.log b/user/comfyui.prev.log deleted file mode 100644 index c6c83f7675d08763982228d2524875b4d2689138..0000000000000000000000000000000000000000 --- a/user/comfyui.prev.log +++ /dev/null @@ -1,323 +0,0 @@ -## ComfyUI-Manager: installing dependencies done. -[2025-08-31 02:36:49.887] ** ComfyUI startup time: 2025-08-31 02:36:49.887 -[2025-08-31 02:36:49.887] ** Platform: Linux -[2025-08-31 02:36:49.887] ** Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] -[2025-08-31 02:36:49.887] ** Python executable: /usr/bin/python -[2025-08-31 02:36:49.888] ** ComfyUI Path: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:36:49.888] ** ComfyUI Base Folder Path: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:36:49.888] ** User directory: /home/ubuntu/comfy_wan2_2/ComfyUI/user -[2025-08-31 02:36:49.888] ** ComfyUI-Manager config path: /home/ubuntu/comfy_wan2_2/ComfyUI/user/default/ComfyUI-Manager/config.ini -[2025-08-31 02:36:49.888] ** Log path: /home/ubuntu/comfy_wan2_2/ComfyUI/user/comfyui.log - -Prestartup times for custom nodes: -[2025-08-31 02:36:51.581] 4.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-manager -[2025-08-31 02:36:51.581] -[2025-08-31 02:36:52.883] Checkpoint files will always be loaded safely. -[2025-08-31 02:36:53.116] Total VRAM 40442 MB, total RAM 201299 MB -[2025-08-31 02:36:53.116] pytorch version: 2.7.0+cu126 -[2025-08-31 02:36:54.808] xformers version: 0.0.30 -[2025-08-31 02:36:54.808] Set vram state to: NORMAL_VRAM -[2025-08-31 02:36:54.808] Device: cuda:0 NVIDIA A100-SXM4-40GB : cudaMallocAsync -[2025-08-31 02:36:55.060] Using xformers attention -[2025-08-31 02:36:55.070] torchaudio missing, ACE model will be broken -[2025-08-31 02:36:55.071] torchaudio missing, ACE model will be broken -[2025-08-31 02:36:56.036] Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] -[2025-08-31 02:36:56.037] ComfyUI version: 0.3.56 -[2025-08-31 02:36:56.039] ComfyUI frontend version: 1.25.11 -[2025-08-31 02:36:56.040] [Prompt Server] web root: /home/ubuntu/.local/lib/python3.10/site-packages/comfyui_frontend_package/static -[2025-08-31 02:36:56.042] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.043] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.044] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_upscale_model.py", line 2, in - from spandrel import ModelLoader, ImageModelDescriptor -ModuleNotFoundError: No module named 'spandrel' - -[2025-08-31 02:36:56.044] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_upscale_model.py module for custom nodes: No module named 'spandrel' -[2025-08-31 02:36:56.045] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.045] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.047] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.047] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.048] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.049] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.049] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.349] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.350] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.351] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.352] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.353] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.354] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.354] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.356] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.357] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.358] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.359] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.359] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.360] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.361] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.361] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.362] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.363] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.363] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.364] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.365] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.365] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.366] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.367] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.367] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.368] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.369] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.370] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio.py", line 4, in - import torchaudio - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/__init__.py", line 2, in - from . import _extension # noqa # usort: skip - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/__init__.py", line 42, in - _check_cuda_version() - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/utils.py", line 168, in _check_cuda_version - version = torchaudio.lib._torchaudio.cuda_version() -AttributeError: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) - -[2025-08-31 02:36:56.371] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio.py module for custom nodes: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) -[2025-08-31 02:36:56.371] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.373] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.373] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.374] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.375] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.375] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.376] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.377] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.377] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.378] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.378] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.379] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.380] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.381] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.381] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.382] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.383] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.384] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.385] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.386] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.386] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.387] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.387] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.388] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.388] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.389] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.389] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.390] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.391] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.392] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.392] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.393] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.394] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.394] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.395] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.397] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio_encoder.py", line 2, in - import comfy.audio_encoders.audio_encoders - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy/audio_encoders/audio_encoders.py", line 6, in - import torchaudio - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/__init__.py", line 2, in - from . import _extension # noqa # usort: skip - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/__init__.py", line 42, in - _check_cuda_version() - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/utils.py", line 168, in _check_cuda_version - version = torchaudio.lib._torchaudio.cuda_version() -AttributeError: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) - -[2025-08-31 02:36:56.397] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio_encoder.py module for custom nodes: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) -[2025-08-31 02:36:56.397] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_api_nodes/canary.py", line 5, in - raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") -Exception: INSTALL NEW VERSION OF PYAV TO USE API NODES. - -[2025-08-31 02:36:56.397] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_api_nodes/canary.py module for custom nodes: INSTALL NEW VERSION OF PYAV TO USE API NODES. -[2025-08-31 02:36:56.401] ### Loading: ComfyUI-Manager (V3.36) -[2025-08-31 02:36:56.401] [ComfyUI-Manager] network_mode: public -[2025-08-31 02:36:56.442] ### ComfyUI Version: v0.3.56 | Released on '2025-08-30' -[2025-08-31 02:36:56.450] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.506] ComfyUI found: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:36:56.506] '/home/ubuntu/comfy_wan2_2/ComfyUI' added to sys.path -[2025-08-31 02:36:56.507] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.508] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.601] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json -[2025-08-31 02:36:56.602] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json -[2025-08-31 02:36:56.635] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:36:56.647] -Import times for custom nodes: -[2025-08-31 02:36:56.649] 0.0 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/websocket_image_save.py -[2025-08-31 02:36:56.652] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-manager -[2025-08-31 02:36:56.657] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/ComfyUI-to-Python-Extension -[2025-08-31 02:36:56.662] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-kjnodes -[2025-08-31 02:36:56.668] -[2025-08-31 02:36:56.689] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json -[2025-08-31 02:36:56.689] WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies. - -[2025-08-31 02:36:56.689] IMPORT FAILED: nodes_ideogram.py -[2025-08-31 02:36:56.689] IMPORT FAILED: nodes_openai.py -[2025-08-31 02:36:56.690] IMPORT FAILED: nodes_minimax.py -[2025-08-31 02:36:56.695] IMPORT FAILED: nodes_veo2.py -[2025-08-31 02:36:56.695] IMPORT FAILED: nodes_kling.py -[2025-08-31 02:36:56.695] IMPORT FAILED: nodes_bfl.py -[2025-08-31 02:36:56.696] IMPORT FAILED: nodes_luma.py -[2025-08-31 02:36:56.709] IMPORT FAILED: nodes_recraft.py -[2025-08-31 02:36:56.732] IMPORT FAILED: nodes_pixverse.py -[2025-08-31 02:36:56.733] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json -[2025-08-31 02:36:56.733] IMPORT FAILED: nodes_stability.py -[2025-08-31 02:36:56.733] IMPORT FAILED: nodes_pika.py -[2025-08-31 02:36:56.734] IMPORT FAILED: nodes_runway.py -[2025-08-31 02:36:56.734] IMPORT FAILED: nodes_tripo.py -[2025-08-31 02:36:56.734] IMPORT FAILED: nodes_moonvalley.py -[2025-08-31 02:36:56.734] IMPORT FAILED: nodes_rodin.py -[2025-08-31 02:36:56.734] IMPORT FAILED: nodes_gemini.py -[2025-08-31 02:36:56.734] IMPORT FAILED: nodes_vidu.py -[2025-08-31 02:36:56.734] -This issue might be caused by new missing dependencies added the last time you updated ComfyUI. -[2025-08-31 02:36:56.735] Please do a: pip install -r requirements.txt -[2025-08-31 02:36:56.735] -[2025-08-31 02:36:56.735] WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies. - -[2025-08-31 02:36:56.735] IMPORT FAILED: nodes_upscale_model.py -[2025-08-31 02:36:56.735] IMPORT FAILED: nodes_audio.py -[2025-08-31 02:36:56.735] IMPORT FAILED: nodes_audio_encoder.py -[2025-08-31 02:36:56.735] -This issue might be caused by new missing dependencies added the last time you updated ComfyUI. -[2025-08-31 02:36:56.735] Please do a: pip install -r requirements.txt -[2025-08-31 02:36:56.735] -[2025-08-31 02:36:56.736] ------------------------------------------------------------------------ -Error importing dependencies: No module named 'alembic' -Please install the updated requirements.txt file by running: -/usr/bin/python -m pip install -r /home/ubuntu/comfy_wan2_2/ComfyUI/requirements.txt -If you are on the portable package you can run: update\update_comfyui.bat to solve this problem. -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -[2025-08-31 02:36:56.763] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/github-stats.json -[2025-08-31 02:36:56.776] ------------------------------------------------------------------------ -Error importing dependencies: No module named 'alembic' -Please install the updated requirements.txt file by running: -/usr/bin/python -m pip install -r /home/ubuntu/comfy_wan2_2/ComfyUI/requirements.txt -If you are on the portable package you can run: update\update_comfyui.bat to solve this problem. -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -[2025-08-31 02:36:56.777] Starting server - -[2025-08-31 02:36:56.777] To see the GUI go to: http://127.0.0.1:8188 -[2025-08-31 02:36:59.062] FETCH ComfyRegistry Data: 5/96 -[2025-08-31 02:37:01.931] FETCH ComfyRegistry Data: 10/96 -[2025-08-31 02:37:04.761] FETCH ComfyRegistry Data: 15/96 -[2025-08-31 02:37:07.577] FETCH ComfyRegistry Data: 20/96 -[2025-08-31 02:37:10.410] FETCH ComfyRegistry Data: 25/96 -[2025-08-31 02:37:13.390] FETCH ComfyRegistry Data: 30/96 -[2025-08-31 02:37:16.290] FETCH ComfyRegistry Data: 35/96 -[2025-08-31 02:37:19.108] FETCH ComfyRegistry Data: 40/96 -[2025-08-31 02:37:21.986] FETCH ComfyRegistry Data: 45/96 -[2025-08-31 02:37:25.459] FETCH ComfyRegistry Data: 50/96 -[2025-08-31 02:37:28.735] FETCH ComfyRegistry Data: 55/96 -[2025-08-31 02:37:31.593] FETCH ComfyRegistry Data: 60/96 -[2025-08-31 02:37:34.430] FETCH ComfyRegistry Data: 65/96 -[2025-08-31 02:37:37.261] FETCH ComfyRegistry Data: 70/96 -[2025-08-31 02:37:40.106] FETCH ComfyRegistry Data: 75/96 -[2025-08-31 02:37:42.669] 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 34.1k/34.1k [00:00<00:00, 56.7MB/s] -[2025-08-31 02:37:42.673] Extracted zip file to /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/ComfyUI-GGUF -[2025-08-31 02:37:42.703] Install: pip packages -[2025-08-31 02:37:42.954] FETCH ComfyRegistry Data: 80/96 -[2025-08-31 02:37:43.536] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'gguf>=0.13.0'] -[2025-08-31 02:37:43.798] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:37:43.799] [2025-08-31 02:37:44.034] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:44.034] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:44.035] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) - [2025-08-31 02:37:44.035] Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[!][2025-08-31 02:37:44.036] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:44.036] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:44.036] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:44.037] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:44.037] Collecting gguf>=0.13.0 -[2025-08-31 02:37:44.532] Downloading gguf-0.17.1-py3-none-any.whl.metadata (4.3 kB) -[2025-08-31 02:37:44.548] Downloading gguf-0.17.1-py3-none-any.whl (96 kB) -[2025-08-31 02:37:44.563] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:37:45.494] FETCH ComfyRegistry Data: 85/96 -[!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.580] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.581] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.581] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.581] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.582] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.582] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:46.582] [2025-08-31 02:37:46.585] Installing collected packages: gguf -[2025-08-31 02:37:46.586] Successfully installed gguf-0.17.1 -[2025-08-31 02:37:46.734] [!] -[2025-08-31 02:37:46.843] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:37:46.844] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:37:46.844] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'sentencepiece'] -[2025-08-31 02:37:47.235] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:37:47.235] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:47.469] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:47.469] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) - [2025-08-31 02:37:47.470] Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[!][2025-08-31 02:37:47.471] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:47.471] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:47.471] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) - [2025-08-31 02:37:47.472] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:47.472] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:37:48.406] FETCH ComfyRegistry Data: 90/96 -[!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.496] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.497] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.497] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.498] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.498] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.498] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:49.498] [!] -[2025-08-31 02:37:49.670] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:37:49.670] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:37:49.671] [2025-08-31 02:37:49.800] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'protobuf'] -[2025-08-31 02:37:50.062] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:37:50.063] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:50.297] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:50.298] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:50.298] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:50.299] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[2025-08-31 02:37:50.300] [2025-08-31 02:37:50.300] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:50.301] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:50.301] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:37:51.245] FETCH ComfyRegistry Data: 95/96 -[!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.343] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.343] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.344] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.344] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.344] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.345] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:37:52.345] [2025-08-31 02:37:52.502] FETCH ComfyRegistry Data [DONE] -[!] -[2025-08-31 02:37:52.591] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:37:52.637] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:37:52.640] [ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes -[2025-08-31 02:37:52.701] FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json [DONE] -[2025-08-31 02:37:52.799] [ComfyUI-Manager] All startup tasks have been completed. -[2025-08-31 02:37:53.543] -[ComfyUI-Manager] Queued works are completed. -{'install': 1} -[2025-08-31 02:37:53.543] -After restarting ComfyUI, please refresh the browser. diff --git a/user/comfyui.prev2.log b/user/comfyui.prev2.log deleted file mode 100644 index dd2d55757ba0c3dbc5cee24718108a3ebfe723bc..0000000000000000000000000000000000000000 --- a/user/comfyui.prev2.log +++ /dev/null @@ -1,424 +0,0 @@ -## ComfyUI-Manager: installing dependencies done. -[2025-08-31 02:33:27.122] ** ComfyUI startup time: 2025-08-31 02:33:27.122 -[2025-08-31 02:33:27.122] ** Platform: Linux -[2025-08-31 02:33:27.122] ** Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] -[2025-08-31 02:33:27.122] ** Python executable: /usr/bin/python -[2025-08-31 02:33:27.122] ** ComfyUI Path: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:33:27.122] ** ComfyUI Base Folder Path: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:33:27.122] ** User directory: /home/ubuntu/comfy_wan2_2/ComfyUI/user -[2025-08-31 02:33:27.123] ** ComfyUI-Manager config path: /home/ubuntu/comfy_wan2_2/ComfyUI/user/default/ComfyUI-Manager/config.ini -[2025-08-31 02:33:27.123] ** Log path: /home/ubuntu/comfy_wan2_2/ComfyUI/user/comfyui.log - -Prestartup times for custom nodes: -[2025-08-31 02:33:28.814] 4.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-manager -[2025-08-31 02:33:28.814] -[2025-08-31 02:33:30.117] Checkpoint files will always be loaded safely. -[2025-08-31 02:33:30.352] Total VRAM 40442 MB, total RAM 201299 MB -[2025-08-31 02:33:30.352] pytorch version: 2.7.0+cu126 -[2025-08-31 02:33:31.997] xformers version: 0.0.30 -[2025-08-31 02:33:31.997] Set vram state to: NORMAL_VRAM -[2025-08-31 02:33:31.997] Device: cuda:0 NVIDIA A100-SXM4-40GB : cudaMallocAsync -[2025-08-31 02:33:32.250] Using xformers attention -[2025-08-31 02:33:32.260] torchaudio missing, ACE model will be broken -[2025-08-31 02:33:32.261] torchaudio missing, ACE model will be broken -[2025-08-31 02:33:33.223] Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] -[2025-08-31 02:33:33.223] ComfyUI version: 0.3.56 -[2025-08-31 02:33:33.225] ComfyUI frontend version: 1.25.11 -[2025-08-31 02:33:33.226] [Prompt Server] web root: /home/ubuntu/.local/lib/python3.10/site-packages/comfyui_frontend_package/static -[2025-08-31 02:33:33.228] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.228] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.229] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_upscale_model.py", line 2, in - from spandrel import ModelLoader, ImageModelDescriptor -ModuleNotFoundError: No module named 'spandrel' - -[2025-08-31 02:33:33.229] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_upscale_model.py module for custom nodes: No module named 'spandrel' -[2025-08-31 02:33:33.230] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.231] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.232] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.232] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.233] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.234] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.234] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.534] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.535] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.536] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.537] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.538] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.538] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.539] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.540] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.542] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.542] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.543] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.543] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.544] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.545] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.545] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.546] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.546] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.547] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.547] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.548] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.548] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.549] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.550] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.550] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.551] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.552] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.553] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio.py", line 4, in - import torchaudio - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/__init__.py", line 2, in - from . import _extension # noqa # usort: skip - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/__init__.py", line 42, in - _check_cuda_version() - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/utils.py", line 168, in _check_cuda_version - version = torchaudio.lib._torchaudio.cuda_version() -AttributeError: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) - -[2025-08-31 02:33:33.553] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio.py module for custom nodes: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) -[2025-08-31 02:33:33.554] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.555] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.556] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.556] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.557] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.558] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.558] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.559] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.559] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.560] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.561] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.562] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.562] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.563] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.564] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.564] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.565] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.566] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.567] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.568] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.568] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.569] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.570] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.570] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.571] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.571] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.572] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.573] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.574] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.574] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.575] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.575] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.576] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.577] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.577] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.579] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio_encoder.py", line 2, in - import comfy.audio_encoders.audio_encoders - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy/audio_encoders/audio_encoders.py", line 6, in - import torchaudio - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/__init__.py", line 2, in - from . import _extension # noqa # usort: skip - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/__init__.py", line 42, in - _check_cuda_version() - File "/home/ubuntu/.local/lib/python3.10/site-packages/torchaudio/_extension/utils.py", line 168, in _check_cuda_version - version = torchaudio.lib._torchaudio.cuda_version() -AttributeError: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) - -[2025-08-31 02:33:33.579] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_extras/nodes_audio_encoder.py module for custom nodes: partially initialized module 'torchaudio' has no attribute 'lib' (most likely due to a circular import) -[2025-08-31 02:33:33.579] Traceback (most recent call last): - File "/home/ubuntu/comfy_wan2_2/ComfyUI/nodes.py", line 2129, in load_custom_node - module_spec.loader.exec_module(module) - File "", line 883, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/ubuntu/comfy_wan2_2/ComfyUI/comfy_api_nodes/canary.py", line 5, in - raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") -Exception: INSTALL NEW VERSION OF PYAV TO USE API NODES. - -[2025-08-31 02:33:33.579] Cannot import /home/ubuntu/comfy_wan2_2/ComfyUI/comfy_api_nodes/canary.py module for custom nodes: INSTALL NEW VERSION OF PYAV TO USE API NODES. -[2025-08-31 02:33:33.614] ### Loading: ComfyUI-Manager (V3.36) -[2025-08-31 02:33:33.615] [ComfyUI-Manager] network_mode: public -[2025-08-31 02:33:33.656] ### ComfyUI Version: v0.3.56 | Released on '2025-08-30' -[2025-08-31 02:33:33.667] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.724] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json -[2025-08-31 02:33:33.749] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json -[2025-08-31 02:33:33.754] ComfyUI found: /home/ubuntu/comfy_wan2_2/ComfyUI -[2025-08-31 02:33:33.754] '/home/ubuntu/comfy_wan2_2/ComfyUI' added to sys.path -[2025-08-31 02:33:33.755] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.756] Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': No module named 'pydantic_settings' -[2025-08-31 02:33:33.756] -Import times for custom nodes: -[2025-08-31 02:33:33.756] 0.0 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/websocket_image_save.py -[2025-08-31 02:33:33.756] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-manager -[2025-08-31 02:33:33.756] 0.1 seconds: /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/ComfyUI-to-Python-Extension -[2025-08-31 02:33:33.756] -[2025-08-31 02:33:33.756] WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies. - -[2025-08-31 02:33:33.756] IMPORT FAILED: nodes_ideogram.py -[2025-08-31 02:33:33.756] IMPORT FAILED: nodes_openai.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_minimax.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_veo2.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_kling.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_bfl.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_luma.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_recraft.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_pixverse.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_stability.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_pika.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_runway.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_tripo.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_moonvalley.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_rodin.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_gemini.py -[2025-08-31 02:33:33.757] IMPORT FAILED: nodes_vidu.py -[2025-08-31 02:33:33.757] -This issue might be caused by new missing dependencies added the last time you updated ComfyUI. -[2025-08-31 02:33:33.758] Please do a: pip install -r requirements.txt -[2025-08-31 02:33:33.758] -[2025-08-31 02:33:33.758] WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies. - -[2025-08-31 02:33:33.758] IMPORT FAILED: nodes_upscale_model.py -[2025-08-31 02:33:33.758] IMPORT FAILED: nodes_audio.py -[2025-08-31 02:33:33.758] IMPORT FAILED: nodes_audio_encoder.py -[2025-08-31 02:33:33.758] -This issue might be caused by new missing dependencies added the last time you updated ComfyUI. -[2025-08-31 02:33:33.758] Please do a: pip install -r requirements.txt -[2025-08-31 02:33:33.758] -[2025-08-31 02:33:33.759] ------------------------------------------------------------------------ -Error importing dependencies: No module named 'alembic' -Please install the updated requirements.txt file by running: -/usr/bin/python -m pip install -r /home/ubuntu/comfy_wan2_2/ComfyUI/requirements.txt -If you are on the portable package you can run: update\update_comfyui.bat to solve this problem. -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -[2025-08-31 02:33:33.772] ------------------------------------------------------------------------ -Error importing dependencies: No module named 'alembic' -Please install the updated requirements.txt file by running: -/usr/bin/python -m pip install -r /home/ubuntu/comfy_wan2_2/ComfyUI/requirements.txt -If you are on the portable package you can run: update\update_comfyui.bat to solve this problem. -This error is happening because ComfyUI now uses a local sqlite database. ------------------------------------------------------------------------- -[2025-08-31 02:33:33.772] Starting server - -[2025-08-31 02:33:33.772] To see the GUI go to: http://127.0.0.1:8188 -[2025-08-31 02:33:33.797] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/github-stats.json -[2025-08-31 02:33:33.838] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json -[2025-08-31 02:33:33.892] [ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json -[2025-08-31 02:33:36.258] FETCH ComfyRegistry Data: 5/96 -[2025-08-31 02:33:39.132] FETCH ComfyRegistry Data: 10/96 -[2025-08-31 02:33:42.362] FETCH ComfyRegistry Data: 15/96 -[2025-08-31 02:33:44.107] 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 24.7M/24.7M [00:00<00:00, 316MB/s] -[2025-08-31 02:33:44.268] Extracted zip file to /home/ubuntu/comfy_wan2_2/ComfyUI/custom_nodes/comfyui-kjnodes -[2025-08-31 02:33:44.299] Install: pip packages -[2025-08-31 02:33:45.144] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'pillow>=10.3.0'] -[2025-08-31 02:33:45.227] FETCH ComfyRegistry Data: 20/96 -[2025-08-31 02:33:45.406] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:33:45.407] [2025-08-31 02:33:45.638] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.639] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.640] [!]Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com - [2025-08-31 02:33:45.641] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.641] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.641] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.641] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.642] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:45.642] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:33:46.579] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.677] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.677] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.678] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.678] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.678] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.678] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:47.679] [!] -[2025-08-31 02:33:48.066] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:33:48.066] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:33:48.066] FETCH ComfyRegistry Data: 25/96 -[2025-08-31 02:33:48.236] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'scipy'] -[2025-08-31 02:33:48.496] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:33:48.496] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.730] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.730] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.730] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.731] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.731] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.731] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:48.731] Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[2025-08-31 02:33:48.731] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:33:49.667] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.747] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.747] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.747] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.748] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.748] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.749] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:50.750] [!] -[2025-08-31 02:33:50.917] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:33:50.917] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:33:50.918] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'color-matcher'] -[2025-08-31 02:33:51.306] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:33:51.307] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.543] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.543] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.543] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.543] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.544] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.544] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:51.544] Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[2025-08-31 02:33:51.545] FETCH ComfyRegistry Data: 30/96 -[2025-08-31 02:33:52.006] Collecting color-matcher -[2025-08-31 02:33:52.007] Downloading color_matcher-0.6.0-py3-none-any.whl.metadata (1.3 kB) -[2025-08-31 02:33:52.020] Collecting ddt (from color-matcher) -[2025-08-31 02:33:52.330] Downloading ddt-1.7.2-py2.py3-none-any.whl.metadata (832 bytes) -[2025-08-31 02:33:52.334] Collecting docutils (from color-matcher) -[2025-08-31 02:33:52.636] Downloading docutils-0.22-py3-none-any.whl.metadata (15 kB) -[2025-08-31 02:33:52.639] Downloading color_matcher-0.6.0-py3-none-any.whl (1.8 MB) -[2025-08-31 02:33:52.709] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 239.6 MB/s eta 0:00:00 -[2025-08-31 02:33:52.721] Downloading ddt-1.7.2-py2.py3-none-any.whl (7.1 kB) -[2025-08-31 02:33:52.725] Downloading docutils-0.22-py3-none-any.whl (630 kB) -[2025-08-31 02:33:52.728] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 630.7/630.7 kB 450.8 MB/s eta 0:00:00 -[2025-08-31 02:33:52.732] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:33:53.619] FETCH ComfyRegistry Data: 35/96 -[!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.708] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.708] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.709] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.709] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.709] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.709] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:54.709] [2025-08-31 02:33:54.716] Installing collected packages: ddt, docutils, color-matcher -[2025-08-31 02:33:54.716] Successfully installed color-matcher-0.6.0 ddt-1.7.2 docutils-0.22 -[2025-08-31 02:33:55.085] [!] -[2025-08-31 02:33:55.178] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:33:55.178] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:33:55.179] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'matplotlib'] -[2025-08-31 02:33:55.592] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:33:55.593] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.826] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.827] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.827] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.827] [!]Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com - [2025-08-31 02:33:55.828] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.828] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.829] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:55.829] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:33:56.769] FETCH ComfyRegistry Data: 40/96 -[!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.857] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.857] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.858] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.858] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.858] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.858] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:57.858] [!] -[2025-08-31 02:33:58.020] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:33:58.020] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:33:58.020] [2025-08-31 02:33:58.152] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'huggingface_hub'] -[2025-08-31 02:33:58.412] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:33:58.413] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.646] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.647] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.647] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.647] [!]Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com - [2025-08-31 02:33:58.648] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.648] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.649] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:33:58.649] [!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:33:59.599] FETCH ComfyRegistry Data: 45/96 -[!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.683] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.683] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.684] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.684] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.684] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.684] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:00.684] [!] -[2025-08-31 02:34:01.042] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:34:01.043] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:34:01.043] [2025-08-31 02:34:01.172] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'mss'] -[2025-08-31 02:34:01.441] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:34:01.442] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.676] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.676] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.677] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.678] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.678] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.679] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:01.679] Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[2025-08-31 02:34:01.680] Collecting mss -[2025-08-31 02:34:02.097] Downloading mss-10.1.0-py3-none-any.whl.metadata (6.7 kB) -[2025-08-31 02:34:02.105] Downloading mss-10.1.0-py3-none-any.whl (24 kB) -[2025-08-31 02:34:02.116] FETCH ComfyRegistry Data: 50/96 -[!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:34:03.051] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.145] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.145] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.146] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.146] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.146] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.146] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.146] [2025-08-31 02:34:04.153] Installing collected packages: mss -[2025-08-31 02:34:04.153] Successfully installed mss-10.1.0 -[2025-08-31 02:34:04.254] [!] -[2025-08-31 02:34:04.339] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:34:04.339] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:34:04.340] -## ComfyUI-Manager: EXECUTE => ['/usr/bin/python', '-m', 'pip', 'install', 'opencv-python'] -[2025-08-31 02:34:04.741] Defaulting to user installation because normal site-packages is not writeable -[2025-08-31 02:34:04.742] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.975] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.975] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.976] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.976] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) - [2025-08-31 02:34:04.977] Looking in indexes: https://pypi.org/simple/, https://pypi.ngc.nvidia.com -[!][2025-08-31 02:34:04.977] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) - [2025-08-31 02:34:04.977] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:04.978] FETCH ComfyRegistry Data: 55/96 -[!] WARNING: Error parsing dependencies of psutil: [Errno 2] No such file or directory: '/home/ubuntu/.local/lib/python3.10/site-packages/psutil-5.9.5.dist-info/METADATA' -[2025-08-31 02:34:05.923] [!] WARNING: Ignoring invalid distribution -5py (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.014] [!] WARNING: Ignoring invalid distribution -andas (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.015] [!] WARNING: Ignoring invalid distribution -atplotlib (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.016] [!] WARNING: Ignoring invalid distribution -etworkx (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.016] [!] WARNING: Ignoring invalid distribution -orch (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.016] [!] WARNING: Ignoring invalid distribution -orchvision (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.016] [!] WARNING: Ignoring invalid distribution -umpy (/usr/lib/python3/dist-packages) -[2025-08-31 02:34:07.017] [!] -[2025-08-31 02:34:07.178] [!] [notice] A new release of pip is available: 25.0 -> 25.2 -[2025-08-31 02:34:07.179] [!] [notice] To update, run: pip install --upgrade pip -[2025-08-31 02:34:07.179] -[ComfyUI-Manager] Queued works are completed. -{'install': 1} -[2025-08-31 02:34:08.146] -After restarting ComfyUI, please refresh the browser. -[2025-08-31 02:34:09.400] FETCH ComfyRegistry Data: 60/96 -[2025-08-31 02:34:12.272] FETCH ComfyRegistry Data: 65/96 -[2025-08-31 02:34:15.110] FETCH ComfyRegistry Data: 70/96 -[2025-08-31 02:34:17.902] FETCH ComfyRegistry Data: 75/96 -[2025-08-31 02:34:20.696] FETCH ComfyRegistry Data: 80/96 -[2025-08-31 02:34:23.569] FETCH ComfyRegistry Data: 85/96 -[2025-08-31 02:34:26.447] FETCH ComfyRegistry Data: 90/96 -[2025-08-31 02:34:30.198] FETCH ComfyRegistry Data: 95/96 -[2025-08-31 02:34:31.260] FETCH ComfyRegistry Data [DONE] -[2025-08-31 02:34:31.437] [ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes -[2025-08-31 02:34:31.450] FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json [DONE] -[2025-08-31 02:34:31.547] [ComfyUI-Manager] All startup tasks have been completed. -[2025-08-31 02:36:45.933] -Stopped server diff --git a/user/default/ComfyUI-Manager/cache/1514988643_custom-node-list.json b/user/default/ComfyUI-Manager/cache/1514988643_custom-node-list.json deleted file mode 100644 index 9707b3ce4544099b9d77deb01ce71d0e732467b1..0000000000000000000000000000000000000000 --- a/user/default/ComfyUI-Manager/cache/1514988643_custom-node-list.json +++ /dev/null @@ -1,32420 +0,0 @@ -{ - "custom_nodes": [ - { - "author": "Dr.Lt.Data", - "description": "ComfyUI-Manager itself is also a custom node.", - "files": [ - "https://github.com/ltdrdata/ComfyUI-Manager" - ], - "id": "manager", - "install_type": "git-clone", - "reference": "https://github.com/ltdrdata/ComfyUI-Manager", - "title": "ComfyUI-Manager" - }, - { - "author": "Dr.Lt.Data", - "description": "This node pack offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.\nNOTE: To use the UltralyticsDetectorProvider, you must install the 'ComfyUI Impact Subpack' separately.", - "files": [ - "https://github.com/ltdrdata/ComfyUI-Impact-Pack" - ], - "id": "comfyui-impact-pack", - "install_type": "git-clone", - "preemptions": [ - "SAMLoader" - ], - "reference": "https://github.com/ltdrdata/ComfyUI-Impact-Pack", - "title": "ComfyUI Impact Pack" - }, - { - "author": "Dr.Lt.Data", - "description": "This node pack provides nodes that complement the Impact Pack, such as the UltralyticsDetectorProvider.", - "files": [ - "https://github.com/ltdrdata/ComfyUI-Impact-Subpack" - ], - "id": "comfyui-impact-subpack", - "install_type": "git-clone", - "reference": "https://github.com/ltdrdata/ComfyUI-Impact-Subpack", - "title": "ComfyUI Impact Subpack" - }, - { - "author": "Dr.Lt.Data", - "description": "This extension provides various nodes to support Lora Block Weight and the Impact Pack. Provides many easily applicable regional features and applications for Variation Seed.", - "files": [ - "https://github.com/ltdrdata/ComfyUI-Inspire-Pack" - ], - "id": "inspire", - "install_type": "git-clone", - "nodename_pattern": "Inspire$", - "reference": "https://github.com/ltdrdata/ComfyUI-Inspire-Pack", - "title": "ComfyUI Inspire Pack" - }, - { - "author": "Dr.Lt.Data", - "description": "This is a helper extension for ComfyUI that assists with node connections.", - "files": [ - "https://github.com/ltdrdata/comfyui-connection-helper" - ], - "id": "connection-helper", - "install_type": "git-clone", - "nodename_pattern": "Inspire$", - "reference": "https://github.com/ltdrdata/comfyui-connection-helper", - "title": "ComfyUI Connection Helper" - }, - { - "author": "Dr.Lt.Data", - "description": "A massive node pack consisting of over 200 nodes, including image processing, masking, text handling, and arithmetic operations.\nNOTE: A replacement node pack provided for existing users following the retirement of the original author of the widely used WAS Node Suite.", - "files": [ - "https://github.com/ltdrdata/was-node-suite-comfyui" - ], - "id": "was-ns", - "install_type": "git-clone", - "reference": "https://github.com/ltdrdata/was-node-suite-comfyui", - "title": "WAS Node Suite (Revised)" - }, - { - "author": "comfyanonymous", - "description": "Nodes: ModelSamplerTonemapNoiseTest, TonemapNoiseWithRescaleCFG, ReferenceOnlySimple, RescaleClassifierFreeGuidanceTest, ModelMergeBlockNumber, ModelMergeSDXL, ModelMergeSDXLTransformers, ModelMergeSDXLDetailedTransformers.", - "files": [ - "https://github.com/comfyanonymous/ComfyUI_experiments" - ], - "id": "comfy-exp", - "install_type": "git-clone", - "reference": "https://github.com/comfyanonymous/ComfyUI_experiments", - "title": "ComfyUI_experiments" - }, - { - "author": "comfyanonymous", - "description": "This node enables the best performance on NVIDIA RTX\u2122 Graphics Cards (GPUs) for Stable Diffusion by leveraging NVIDIA TensorRT.", - "files": [ - "https://github.com/comfyanonymous/ComfyUI_TensorRT" - ], - "id": "tensorrt", - "install_type": "git-clone", - "reference": "https://github.com/comfyanonymous/ComfyUI_TensorRT", - "title": "TensorRT Node for ComfyUI" - }, - { - "author": "Comfy-Org", - "description": "Provides nodes to utilise NVIDIA NIM, a set of accelerated inference microservices that allow you to run AI models on NVIDIA GPUs anywhere.", - "files": [ - "https://github.com/Comfy-Org/NIMnodes" - ], - "id": "nimnodes", - "install_type": "git-clone", - "reference": "https://github.com/Comfy-Org/NIMnodes", - "title": "NVIDIA FLUX NIM" - }, - { - "author": "Stability-AI", - "description": "Nodes:Stability SD3, Stability Outpainting, Stability Search and Replace, Stability Image Core, Stability Inpainting, Stability Remove Background, Stability Creative Upscale.\nAdd API key to environment variable 'SAI_API_KEY'\nAlternatively you can write your API key to file 'sai_platform_key.txt'\nYou can also use and/or override the above by entering your API key in the 'api_key_override' field of each node.", - "files": [ - "https://github.com/Stability-AI/ComfyUI-SAI_API" - ], - "id": "sai-api", - "install_type": "git-clone", - "reference": "https://github.com/Stability-AI/ComfyUI-SAI_API", - "title": "Stability API nodes for ComfyUI" - }, - { - "author": "Stability-AI", - "description": "Nodes: ColorBlend, ControlLoraSave, GetImageSize. NOTE: Control-LoRA recolor example uses these nodes.", - "files": [ - "https://github.com/Stability-AI/stability-ComfyUI-nodes" - ], - "id": "sai-nodes", - "install_type": "git-clone", - "reference": "https://github.com/Stability-AI/stability-ComfyUI-nodes", - "title": "stability-ComfyUI-nodes" - }, - { - "author": "Fannovel16", - "description": "Plug-and-play ComfyUI node sets for making ControlNet hint images.", - "files": [ - "https://github.com/Fannovel16/comfyui_controlnet_aux" - ], - "id": "comfyui_controlnet_aux", - "install_type": "git-clone", - "preemptions": [ - "AIO_Preprocessor", - "AnimalPosePreprocessor", - "AnimeFace_SemSegPreprocessor", - "AnimeLineArtPreprocessor", - "BAE-NormalMapPreprocessor", - "BinaryPreprocessor", - "CannyEdgePreprocessor", - "ColorPreprocessor", - "DSINE-NormalMapPreprocessor", - "DWPreprocessor", - "DensePosePreprocessor", - "DepthAnythingPreprocessor", - "DiffusionEdge_Preprocessor", - "FacialPartColoringFromPoseKps", - "FakeScribblePreprocessor", - "HEDPreprocessor", - "HintImageEnchance", - "ImageGenResolutionFromImage", - "ImageGenResolutionFromLatent", - "ImageIntensityDetector", - "ImageLuminanceDetector", - "InpaintPreprocessor", - "LeReS-DepthMapPreprocessor", - "LineArtPreprocessor", - "LineartStandardPreprocessor", - "M-LSDPreprocessor", - "Manga2Anime_LineArt_Preprocessor", - "MaskOptFlow", - "MediaPipe-FaceMeshPreprocessor", - "MeshGraphormer-DepthMapPreprocessor", - "MiDaS-DepthMapPreprocessor", - "MiDaS-NormalMapPreprocessor", - "OneFormer-ADE20K-SemSegPreprocessor", - "OneFormer-COCO-SemSegPreprocessor", - "OpenposePreprocessor", - "PiDiNetPreprocessor", - "PixelPerfectResolution", - "SAMPreprocessor", - "SavePoseKpsAsJsonFile", - "ScribblePreprocessor", - "Scribble_XDoG_Preprocessor", - "SemSegPreprocessor", - "ShufflePreprocessor", - "TEEDPreprocessor", - "TilePreprocessor", - "UniFormer-SemSegPreprocessor", - "Unimatch_OptFlowPreprocessor", - "Zoe-DepthMapPreprocessor", - "Zoe_DepthAnythingPreprocessor" - ], - "reference": "https://github.com/Fannovel16/comfyui_controlnet_aux", - "title": "ComfyUI's ControlNet Auxiliary Preprocessors" - }, - { - "author": "Fannovel16", - "description": "A custom node suite for Video Frame Interpolation in ComfyUI", - "files": [ - "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation" - ], - "id": "frame-interpolation", - "install_type": "git-clone", - "reference": "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation", - "title": "ComfyUI Frame Interpolation" - }, - { - "author": "Fannovel16", - "description": "Implementation of MDM, MotionDiffuse and ReMoDiffuse into ComfyUI.", - "files": [ - "https://github.com/Fannovel16/ComfyUI-MotionDiff" - ], - "id": "motiondiff", - "install_type": "git-clone", - "reference": "https://github.com/Fannovel16/ComfyUI-MotionDiff", - "title": "ComfyUI MotionDiff" - }, - { - "author": "Fannovel16", - "description": "A minimalistic implementation of [a/Robust Video Matting (RVM)](https://github.com/PeterL1n/RobustVideoMatting/) in ComfyUI", - "files": [ - "https://github.com/Fannovel16/ComfyUI-Video-Matting" - ], - "id": "video-matting", - "install_type": "git-clone", - "reference": "https://github.com/Fannovel16/ComfyUI-Video-Matting", - "title": "ComfyUI-Video-Matting" - }, - { - "author": "Fannovel16", - "description": "Proper implementation of ImageMagick - the famous software suite for editing and manipulating digital images to ComfyUI using [a/wandpy](https://github.com/emcconville/wand).\nNOTE: You need to install ImageMagick, manually.", - "files": [ - "https://github.com/Fannovel16/ComfyUI-MagickWand" - ], - "id": "magicwand", - "install_type": "git-clone", - "reference": "https://github.com/Fannovel16/ComfyUI-MagickWand", - "title": "ComfyUI-MagickWand" - }, - { - "author": "time-river", - "description": "The CLIPSeg node generates a binary mask for a given input image and text prompt.\nNOTE:This custom node is a forked custom node with hotfixes applied from the [a/original repository](https://github.com/biegert/ComfyUI-CLIPSeg), which is no longer maintained.", - "files": [ - "https://raw.githubusercontent.com/time-river/ComfyUI-CLIPSeg/main/custom_nodes/clipseg.py" - ], - "id": "clipseg", - "install_type": "copy", - "reference": "https://github.com/time-river/ComfyUI-CLIPSeg", - "title": "CLIPSeg" - }, - { - "author": "BlenderNeko", - "description": "These custom nodes provides features that allow for better control over the effects of the text prompt.", - "files": [ - "https://github.com/BlenderNeko/ComfyUI_Cutoff" - ], - "id": "cutoff", - "install_type": "git-clone", - "reference": "https://github.com/BlenderNeko/ComfyUI_Cutoff", - "title": "ComfyUI Cutoff" - }, - { - "author": "BlenderNeko", - "description": "Advanced CLIP Text Encode (if you need A1111 like prompt. you need this. But Cutoff node includes this feature, already.)", - "files": [ - "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb" - ], - "id": "adv-encode", - "install_type": "git-clone", - "reference": "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb", - "title": "Advanced CLIP Text Encode" - }, - { - "author": "BlenderNeko", - "description": "This extension contains 6 nodes for ComfyUI that allows for more control and flexibility over the noise.", - "files": [ - "https://github.com/BlenderNeko/ComfyUI_Noise" - ], - "id": "comfy-noise", - "install_type": "git-clone", - "reference": "https://github.com/BlenderNeko/ComfyUI_Noise", - "title": "ComfyUI Noise" - }, - { - "author": "BlenderNeko", - "description": "This extension contains a tiled sampler for ComfyUI. It allows for denoising larger images by splitting it up into smaller tiles and denoising these. It tries to minimize any seams for showing up in the end result by gradually denoising all tiles one step at the time and randomizing tile positions for every step.", - "files": [ - "https://github.com/BlenderNeko/ComfyUI_TiledKSampler" - ], - "id": "tiled-sampling", - "install_type": "git-clone", - "reference": "https://github.com/BlenderNeko/ComfyUI_TiledKSampler", - "title": "Tiled sampling for ComfyUI" - }, - { - "author": "BlenderNeko", - "description": "It provides the capability to generate CLIP from an image input, unlike unCLIP, which works in all models. (To use this extension, you need to download the required model file from **Install Models**)", - "files": [ - "https://github.com/BlenderNeko/ComfyUI_SeeCoder" - ], - "id": "seecoder", - "install_type": "git-clone", - "reference": "https://github.com/BlenderNeko/ComfyUI_SeeCoder", - "title": "SeeCoder [WIP]" - }, - { - "author": "jags111", - "description": "A collection of ComfyUI custom nodes to help streamline workflows and reduce total node count.[w/NOTE: This node is originally created by LucianoCirino, but the [a/original repository](https://github.com/LucianoCirino/efficiency-nodes-comfyui) is no longer maintained and has been forked by a new maintainer. To use the forked version, you should uninstall the original version and **REINSTALL** this one.]", - "files": [ - "https://github.com/jags111/efficiency-nodes-comfyui" - ], - "id": "eff-nodes", - "install_type": "git-clone", - "reference": "https://github.com/jags111/efficiency-nodes-comfyui", - "title": "Efficiency Nodes for ComfyUI Version 2.0+" - }, - { - "author": "jags111", - "description": "a collection of nodes to explore Vector and image manipulation", - "files": [ - "https://github.com/jags111/ComfyUI_Jags_VectorMagic" - ], - "id": "vectormagic", - "install_type": "git-clone", - "reference": "https://github.com/jags111/ComfyUI_Jags_VectorMagic", - "title": "Jags_VectorMagic" - }, - { - "author": "jags111", - "description": "This extension offers various audio generation tools", - "files": [ - "https://github.com/jags111/ComfyUI_Jags_Audiotools" - ], - "id": "audiotools", - "install_type": "git-clone", - "reference": "https://github.com/jags111/ComfyUI_Jags_Audiotools", - "title": "Jags_Audiotools" - }, - { - "author": "Derfuu", - "description": "Automate calculation depending on image sizes or something you want.", - "files": [ - "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes" - ], - "id": "derfuu", - "install_type": "git-clone", - "nodename_pattern": "^DF_", - "reference": "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes", - "title": "Derfuu_ComfyUI_ModdedNodes" - }, - { - "apt_dependency": [ - "rustc", - "cargo" - ], - "author": "paulo-coronado", - "description": "CLIPTextEncodeBLIP: This custom node provides a CLIP Encoder that is capable of receiving images as input.", - "files": [ - "https://github.com/paulo-coronado/comfy_clip_blip_node" - ], - "id": "blip", - "install_type": "git-clone", - "reference": "https://github.com/paulo-coronado/comfy_clip_blip_node", - "title": "comfy_clip_blip_node" - }, - { - "author": "WASasquatch", - "description": "Nodes: ModelMergeByPreset. Merge checkpoint models by preset", - "files": [ - "https://github.com/WASasquatch/ComfyUI_Preset_Merger" - ], - "id": "preset-merger", - "install_type": "git-clone", - "reference": "https://github.com/WASasquatch/ComfyUI_Preset_Merger", - "title": "ComfyUI Preset Merger" - }, - { - "author": "WASasquatch", - "description": "Nodes: WAS_PFN_Latent. Perlin Power Fractal Noisey Latents", - "files": [ - "https://github.com/WASasquatch/PPF_Noise_ComfyUI" - ], - "id": "ppf", - "install_type": "git-clone", - "reference": "https://github.com/WASasquatch/PPF_Noise_ComfyUI", - "title": "PPF_Noise_ComfyUI" - }, - { - "author": "WASasquatch", - "description": "Power Noise Suite contains nodes centered around latent noise input, and diffusion, as well as latent adjustments.", - "files": [ - "https://github.com/WASasquatch/PowerNoiseSuite" - ], - "id": "power-noise", - "install_type": "git-clone", - "reference": "https://github.com/WASasquatch/PowerNoiseSuite", - "title": "Power Noise Suite for ComfyUI" - }, - { - "author": "WASasquatch", - "description": "This custom node provides advanced settings for FreeU.", - "files": [ - "https://github.com/WASasquatch/FreeU_Advanced" - ], - "id": "freeu-adv", - "install_type": "git-clone", - "reference": "https://github.com/WASasquatch/FreeU_Advanced", - "title": "FreeU_Advanced" - }, - { - "author": "WASasquatch", - "description": "Nodes:Conditioning (Blend), Inpainting VAE Encode (WAS), VividSharpen. Experimental nodes, or other random extra helper nodes.", - "files": [ - "https://github.com/WASasquatch/WAS_Extras" - ], - "id": "was-extras", - "install_type": "git-clone", - "reference": "https://github.com/WASasquatch/WAS_Extras", - "title": "WAS_Extras" - }, - { - "author": "WASasquatch", - "description": "All-in-One Face Fix KSampler for ComfyUI with YOLO detection and SAM segmentation", - "files": [ - "https://github.com/WASasquatch/face-upscaling-and-seamless-embedding" - ], - "install_type": "git-clone", - "reference": "https://github.com/WASasquatch/face-upscaling-and-seamless-embedding", - "title": "FUSE Face Enhancer" - }, - { - "author": "omar92", - "description": "openAI suite, String suite, Latent Tools, Image Tools: These custom nodes provide expanded functionality for image and string processing, latent processing, as well as the ability to interface with models such as ChatGPT/DallE-2.\nNOTE: Currently, this extension does not support the new OpenAI API, leading to compatibility issues.", - "files": [ - "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92" - ], - "id": "qol", - "install_type": "git-clone", - "reference": "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92", - "title": "Quality of life Suit:V2" - }, - { - "author": "lilly1987", - "description": "These custom nodes provides a feature to insert arbitrary inputs through wildcards in the prompt. Additionally, this tool provides features that help simplify workflows, such as VAELoaderDecoder and SimplerSample.", - "files": [ - "https://github.com/lilly1987/ComfyUI_node_Lilly" - ], - "id": "simle-wildcard", - "install_type": "git-clone", - "reference": "https://github.com/lilly1987/ComfyUI_node_Lilly", - "title": "simple wildcard for ComfyUI" - }, - { - "author": "sylym", - "description": "A node suite for ComfyUI that allows you to load image sequence and generate new image sequence with different styles or content.", - "files": [ - "https://github.com/sylym/comfy_vid2vid" - ], - "id": "vid2vid", - "install_type": "git-clone", - "reference": "https://github.com/sylym/comfy_vid2vid", - "title": "Vid2vid" - }, - { - "author": "EllangoK", - "description": "A collection of post processing nodes for ComfyUI, which enable a variety of visually striking image effects.", - "files": [ - "https://github.com/EllangoK/ComfyUI-post-processing-nodes" - ], - "id": "post-processing", - "install_type": "git-clone", - "reference": "https://github.com/EllangoK/ComfyUI-post-processing-nodes", - "title": "ComfyUI-post-processing-nodes" - }, - { - "author": "LEv145", - "description": "This tool provides a viewer node that allows for checking multiple outputs in a grid, similar to the X/Y Plot extension.", - "files": [ - "https://github.com/LEv145/images-grid-comfy-plugin" - ], - "id": "imagesgrid", - "install_type": "git-clone", - "reference": "https://github.com/LEv145/images-grid-comfy-plugin", - "title": "ImagesGrid" - }, - { - "author": "diontimmer", - "description": "Nodes: Pixel Sort, Swap Color Mode, Solid Color, Glitch This, Add Text To Image, Play Sound, Prettify Prompt, Generate Noise, Flatten Colors", - "files": [ - "https://github.com/diontimmer/ComfyUI-Vextra-Nodes" - ], - "id": "vextra", - "install_type": "git-clone", - "reference": "https://github.com/diontimmer/ComfyUI-Vextra-Nodes", - "title": "ComfyUI-Vextra-Nodes" - }, - { - "author": "CYBERLOOM-INC", - "description": "Provide various custom nodes for Latent, Sampling, Model, Loader, Image, Text. This is the fixed version of the original [a/ComfyUI-nodes-hnmr](https://github.com/hnmr293/ComfyUI-nodes-hnmr) by hnmr293.", - "files": [ - "https://github.com/CYBERLOOM-INC/ComfyUI-nodes-hnmr" - ], - "id": "hnmr", - "install_type": "git-clone", - "reference": "https://github.com/CYBERLOOM-INC/ComfyUI-nodes-hnmr", - "title": "ComfyUI-nodes-hnmr" - }, - { - "author": "BadCafeCode", - "description": "This is a low-dependency node pack primarily dealing with masks. The author recommends using Impact-Pack instead (unless you specifically have trouble installing dependencies).", - "files": [ - "https://github.com/BadCafeCode/masquerade-nodes-comfyui" - ], - "id": "masquerade", - "install_type": "git-clone", - "reference": "https://github.com/BadCafeCode/masquerade-nodes-comfyui", - "title": "Masquerade Nodes" - }, - { - "author": "Jcd1230", - "description": "Nodes: Image Remove Background (rembg)", - "files": [ - "https://github.com/Jcd1230/rembg-comfyui-node" - ], - "id": "rembg", - "install_type": "git-clone", - "reference": "https://github.com/Jcd1230/rembg-comfyui-node", - "title": "Rembg Background Removal Node for ComfyUI" - }, - { - "author": "YinBailiang", - "description": "Nodes: MergeBlockWeighted", - "files": [ - "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI" - ], - "id": "mergeblockweighted_fo_comfyui", - "install_type": "git-clone", - "reference": "https://github.com/YinBailiang/MergeBlockWeighted_fo_ComfyUI", - "title": "MergeBlockWeighted_fo_ComfyUI" - }, - { - "author": "trojblue", - "description": "Nodes: image_layering, color_correction, model_router", - "files": [ - "https://github.com/trojblue/trNodes" - ], - "id": "trnodes", - "install_type": "git-clone", - "reference": "https://github.com/trojblue/trNodes", - "title": "trNodes" - }, - { - "author": "szhublox", - "description": "Auto-MBW for ComfyUI loosely based on sdweb-auto-MBW. Nodes: auto merge block weighted", - "files": [ - "https://github.com/szhublox/ambw_comfyui" - ], - "id": "auto-mbw", - "install_type": "git-clone", - "reference": "https://github.com/szhublox/ambw_comfyui", - "title": "Auto-MBW" - }, - { - "author": "city96", - "description": "Run ComfyUI workflows on multiple local GPUs/networked machines. Nodes: Remote images, Local Remote control", - "files": [ - "https://github.com/city96/ComfyUI_NetDist" - ], - "id": "netdist", - "install_type": "git-clone", - "reference": "https://github.com/city96/ComfyUI_NetDist", - "title": "ComfyUI_NetDist" - }, - { - "author": "city96", - "description": "Custom node to convert the lantents between SDXL and SD v1.5 directly without the VAE decoding/encoding step.", - "files": [ - "https://github.com/city96/SD-Latent-Interposer" - ], - "id": "latent-interposer", - "install_type": "git-clone", - "reference": "https://github.com/city96/SD-Latent-Interposer", - "title": "Latent-Interposer" - }, - { - "author": "city96", - "description": "Upscaling stable diffusion latents using a small neural network.", - "files": [ - "https://github.com/city96/SD-Latent-Upscaler" - ], - "id": "latent-upscaler", - "install_type": "git-clone", - "pip": [ - "huggingface-hub" - ], - "reference": "https://github.com/city96/SD-Latent-Upscaler", - "title": "SD-Latent-Upscaler" - }, - { - "author": "city96", - "description": "Testbed for [a/DiT(Scalable Diffusion Models with Transformers)](https://github.com/facebookresearch/DiT). [w/None of this code is stable, expect breaking changes if for some reason you want to use this.]", - "files": [ - "https://github.com/city96/ComfyUI_DiT" - ], - "id": "dit", - "install_type": "git-clone", - "pip": [ - "huggingface-hub" - ], - "reference": "https://github.com/city96/ComfyUI_DiT", - "title": "ComfyUI_DiT [WIP]" - }, - { - "author": "city96", - "description": "This extension currently has two sets of nodes - one set for editing the contrast/color of images and another set for saving images as 16 bit PNG files.", - "files": [ - "https://github.com/city96/ComfyUI_ColorMod" - ], - "id": "colormod", - "install_type": "git-clone", - "reference": "https://github.com/city96/ComfyUI_ColorMod", - "title": "ComfyUI_ColorMod" - }, - { - "author": "city96", - "description": "This extension aims to add support for various random image diffusion models to ComfyUI.", - "files": [ - "https://github.com/city96/ComfyUI_ExtraModels" - ], - "id": "extramodels", - "install_type": "git-clone", - "reference": "https://github.com/city96/ComfyUI_ExtraModels", - "title": "Extra Models for ComfyUI" - }, - { - "author": "city96", - "description": "GGUF Quantization support for native ComfyUI models\nThis is currently very much WIP. These custom nodes provide support for model files stored in the GGUF format popularized by llama.cpp.\nWhile quantization wasn't feasible for regular UNET models (conv2d), transformer/DiT models such as flux seem less affected by quantization. This allows running it in much lower bits per weight variable bitrate quants on low-end GPUs.", - "files": [ - "https://github.com/city96/ComfyUI-GGUF" - ], - "id": "comfyui-gguf", - "install_type": "git-clone", - "preemptions": [ - "CLIPLoaderGGUF", - "DualCLIPLoaderGGUF", - "TripleCLIPLoaderGGUF", - "UnetLoaderGGUF", - "UnetLoaderGGUFAdvanced" - ], - "reference": "https://github.com/city96/ComfyUI-GGUF", - "title": "ComfyUI-GGUF" - }, - { - "author": "SLAPaper", - "description": "A custom node for ComfyUI, which can select one or some of images from a batch.", - "files": [ - "https://github.com/SLAPaper/ComfyUI-Image-Selector" - ], - "id": "image-selector", - "install_type": "git-clone", - "reference": "https://github.com/SLAPaper/ComfyUI-Image-Selector", - "title": "ComfyUI-Image-Selector" - }, - { - "author": "SLAPaper", - "description": "the sampler introduced by [a/hallatore](https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457)\ncode extracted from [a/smZNodes](https://github.com/shiimizu/ComfyUI_smZNodes).[w/NOTE:ComfyUI-dpmpp_2m_alt-Sampler is renamed to StableDiffusion-dpmpp_2m_alt-Sampler. Please reinstall.]", - "files": [ - "https://github.com/SLAPaper/StableDiffusion-dpmpp_2m_alt-Sampler" - ], - "id": "dpmpp2m-alt", - "install_type": "git-clone", - "reference": "https://github.com/SLAPaper/StableDiffusion-dpmpp_2m_alt-Sampler", - "title": "StableDiffusion-dpmpp_2m_alt-Sampler" - }, - { - "author": "flyingshutter", - "description": "Manipulation nodes for Image, Latent", - "files": [ - "https://github.com/flyingshutter/As_ComfyUI_CustomNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/flyingshutter/As_ComfyUI_CustomNodes", - "title": "As_ComfyUI_CustomNodes" - }, - { - "author": "Zuellni", - "description": "Nodes: DeepFloyd, Filter, Select, Save, Decode, Encode, Repeat, Noise, Noise", - "files": [ - "https://github.com/Zuellni/ComfyUI-Custom-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zuellni/ComfyUI-Custom-Nodes", - "title": "Zuellni/ComfyUI-Custom-Nodes" - }, - { - "author": "Zuellni", - "description": "A simple local text generator for ComfyUI utilizing [a/ExLlamaV2](https://github.com/turboderp/exllamav2).\n[w/NOTE:Manual package installation is required.]", - "files": [ - "https://github.com/Zuellni/ComfyUI-ExLlama-Nodes" - ], - "id": "exllamav2", - "install_type": "git-clone", - "reference": "https://github.com/Zuellni/ComfyUI-ExLlama-Nodes", - "title": "ComfyUI ExLlamaV2 Nodes" - }, - { - "author": "Zuellni", - "description": "Image scoring nodes for ComfyUI using PickScore with a batch of images to predict which ones fit a given prompt the best.", - "files": [ - "https://github.com/Zuellni/ComfyUI-PickScore-Nodes" - ], - "id": "pickscore", - "install_type": "git-clone", - "reference": "https://github.com/Zuellni/ComfyUI-PickScore-Nodes", - "title": "ComfyUI PickScore Nodes" - }, - { - "author": "AlekPet", - "description": "Nodes: PoseNode, PainterNode, TranslateTextNode, TranslateCLIPTextEncodeNode, DeepTranslatorTextNode, DeepTranslatorCLIPTextEncodeNode, ArgosTranslateTextNode, ArgosTranslateCLIPTextEncodeNode, PreviewTextNode, HexToHueNode, ColorsCorrectNode, IDENode.", - "files": [ - "https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet" - ], - "id": "alekpet", - "install_type": "git-clone", - "reference": "https://github.com/AlekPet/ComfyUI_Custom_Nodes_AlekPet", - "title": "AlekPet/ComfyUI_Custom_Nodes_AlekPet" - }, - { - "author": "pythongosssss", - "description": "A ComfyUI extension allowing the interrogation of booru tags from images.", - "files": [ - "https://github.com/pythongosssss/ComfyUI-WD14-Tagger" - ], - "id": "wd14", - "install_type": "git-clone", - "reference": "https://github.com/pythongosssss/ComfyUI-WD14-Tagger", - "title": "ComfyUI WD 1.4 Tagger" - }, - { - "author": "pythongosssss", - "description": "Enhancements & experiments for ComfyUI, mostly focusing on UI features", - "files": [ - "https://github.com/pythongosssss/ComfyUI-Custom-Scripts" - ], - "id": "comfyui-custom-scripts", - "install_type": "git-clone", - "reference": "https://github.com/pythongosssss/ComfyUI-Custom-Scripts", - "title": "ComfyUI-Custom-Scripts" - }, - { - "author": "strimmlarn", - "description": "Nodes: CalculateAestheticScore, LoadAesteticModel, AesthetlcScoreSorter, ScoreToNumber.\nAesthetic score for ComfyUI", - "files": [ - "https://github.com/strimmlarn/ComfyUI-Strimmlarns-Aesthetic-Score" - ], - "id": "aesthetic-score", - "install_type": "git-clone", - "js_path": "strimmlarn", - "reference": "https://github.com/strimmlarn/ComfyUI-Strimmlarns-Aesthetic-Score", - "title": "ComfyUI_Strimmlarns_aesthetic_score" - }, - { - "author": "TinyTerra", - "description": "This extension offers various pipe nodes, extensive XYZ plotting, fullscreen image viewer based on node history, dynamic widgets, interface customization, and more.", - "files": [ - "https://github.com/TinyTerra/ComfyUI_tinyterraNodes" - ], - "id": "ttn", - "install_type": "git-clone", - "nodename_pattern": "^ttN ", - "reference": "https://github.com/TinyTerra/ComfyUI_tinyterraNodes", - "title": "ComfyUI_tinyterraNodes" - }, - { - "author": "Jordach", - "description": "Nodes: Plasma Noise, Random Noise, Greyscale Noise, Pink Noise, Brown Noise, Plasma KSampler", - "files": [ - "https://github.com/Jordach/comfy-plasma" - ], - "id": "plasma", - "install_type": "git-clone", - "reference": "https://github.com/Jordach/comfy-plasma", - "title": "comfy-plasma" - }, - { - "author": "bvhari", - "description": "ComfyUI custom nodes to apply various image processing techniques.", - "files": [ - "https://github.com/bvhari/ComfyUI_ImageProcessing" - ], - "id": "imageprocessing", - "install_type": "git-clone", - "reference": "https://github.com/bvhari/ComfyUI_ImageProcessing", - "title": "ImageProcessing" - }, - { - "author": "bvhari", - "description": "A novel weighting scheme for token vectors from CLIP. Allows a wider range of values for the weight. Inspired by Perp-Neg.", - "files": [ - "https://github.com/bvhari/ComfyUI_PerpWeight" - ], - "id": "perpweight", - "install_type": "git-clone", - "reference": "https://github.com/bvhari/ComfyUI_PerpWeight", - "title": "ComfyUI_PerpWeight" - }, - { - "author": "bvhari", - "description": "Scaled Uniform Noise for Ancestral and Stochastic samplers", - "files": [ - "https://github.com/bvhari/ComfyUI_SUNoise" - ], - "id": "sunoise", - "install_type": "git-clone", - "reference": "https://github.com/bvhari/ComfyUI_SUNoise", - "title": "ComfyUI_SUNoise" - }, - { - "author": "bvhari", - "description": "Perpendicular CFG for reducing oversaturation issues with high guidance scale values.", - "files": [ - "https://github.com/bvhari/ComfyUI_PerpCFG" - ], - "install_type": "git-clone", - "reference": "https://github.com/bvhari/ComfyUI_PerpCFG", - "title": "ComfyUI_PerpCFG" - }, - { - "author": "bvhari", - "description": "A per channel implementation of the scaled CFG from this paper: [a/https://arxiv.org/abs/2503.18886](https://arxiv.org/abs/2503.18886)", - "files": [ - "https://github.com/bvhari/ComfyUI_CFGStar" - ], - "install_type": "git-clone", - "reference": "https://github.com/bvhari/ComfyUI_CFGStar", - "title": "ComfyUI_CFGStar" - }, - { - "author": "ssitu", - "description": "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A.", - "files": [ - "https://github.com/ssitu/ComfyUI_UltimateSDUpscale" - ], - "id": "usdu", - "install_type": "git-clone", - "reference": "https://github.com/ssitu/ComfyUI_UltimateSDUpscale", - "title": "UltimateSDUpscale" - }, - { - "author": "ssitu", - "description": "Unofficial ComfyUI nodes for restart sampling based on the paper 'Restart Sampling for Improving Generative Processes' ([a/paper](https://arxiv.org/abs/2306.14878), [a/repo](https://github.com/Newbeeer/diffusion_restart_sampling))", - "files": [ - "https://github.com/ssitu/ComfyUI_restart_sampling" - ], - "id": "restart-sampling", - "install_type": "git-clone", - "reference": "https://github.com/ssitu/ComfyUI_restart_sampling", - "title": "Restart Sampling" - }, - { - "author": "ssitu", - "description": "ComfyUI nodes for the roop A1111 webui script.", - "files": [ - "https://github.com/ssitu/ComfyUI_roop" - ], - "id": "roop", - "install_type": "git-clone", - "reference": "https://github.com/ssitu/ComfyUI_roop", - "title": "ComfyUI roop" - }, - { - "author": "ssitu", - "description": "ComfyUI nodes based on the paper [a/FABRIC: Personalizing Diffusion Models with Iterative Feedback](https://arxiv.org/abs/2307.10159) (Feedback via Attention-Based Reference Image Conditioning)", - "files": [ - "https://github.com/ssitu/ComfyUI_fabric" - ], - "id": "fabric", - "install_type": "git-clone", - "reference": "https://github.com/ssitu/ComfyUI_fabric", - "title": "ComfyUI fabric" - }, - { - "author": "space-nuko", - "description": "Modularized version of Disco Diffusion for use with ComfyUI.", - "files": [ - "https://github.com/space-nuko/ComfyUI-Disco-Diffusion" - ], - "id": "disco", - "install_type": "git-clone", - "reference": "https://github.com/space-nuko/ComfyUI-Disco-Diffusion", - "title": "Disco Diffusion" - }, - { - "author": "space-nuko", - "description": "A port of the openpose-editor extension for stable-diffusion-webui. NOTE: Requires [a/this ComfyUI patch](https://github.com/comfyanonymous/ComfyUI/pull/711) to work correctly", - "files": [ - "https://github.com/space-nuko/ComfyUI-OpenPose-Editor" - ], - "id": "openpose-editor", - "install_type": "git-clone", - "reference": "https://github.com/space-nuko/ComfyUI-OpenPose-Editor", - "title": "OpenPose Editor" - }, - { - "author": "space-nuko", - "description": "NODES: Dynamic Prompts Text Encode, Feeling Lucky Text Encode, Output String", - "files": [ - "https://github.com/space-nuko/nui-suite" - ], - "id": "nui", - "install_type": "git-clone", - "reference": "https://github.com/space-nuko/nui-suite", - "title": "nui suite" - }, - { - "author": "Nourepide", - "description": "Allor is a plugin for ComfyUI with an emphasis on transparency and performance.", - "files": [ - "https://github.com/Nourepide/ComfyUI-Allor" - ], - "id": "allor", - "install_type": "git-clone", - "reference": "https://github.com/Nourepide/ComfyUI-Allor", - "title": "Allor Plugin" - }, - { - "author": "melMass", - "description": "NODES: Face Swap, Film Interpolation, Latent Lerp, Int To Number, Bounding Box, Crop, Uncrop, ImageBlur, Denoise, ImageCompare, RGV to HSV, HSV to RGB, Color Correct, Modulo, Deglaze Image, Smart Step, ...", - "files": [ - "https://github.com/melMass/comfy_mtb" - ], - "id": "mtb", - "install_type": "git-clone", - "nodename_pattern": "\\(mtb\\)$", - "reference": "https://github.com/melMass/comfy_mtb", - "title": "MTB Nodes" - }, - { - "author": "melMass", - "description": "OpenImageIO plugin for ComfyUI", - "files": [ - "https://github.com/melMass/comfy_oiio" - ], - "install_type": "git-clone", - "reference": "https://github.com/melMass/comfy_oiio", - "title": "comfy-oiio" - }, - { - "author": "xXAdonesXx", - "description": "Implementation of AutoGen inside ComfyUI. This repository is under development, and not everything is functioning correctly yet.", - "files": [ - "https://github.com/xXAdonesXx/NodeGPT" - ], - "id": "nodegpt", - "install_type": "git-clone", - "reference": "https://github.com/xXAdonesXx/NodeGPT", - "reference2": "https://github.com/antonym-git/NodeGPT", - "title": "NodeGPT" - }, - { - "author": "ciri", - "description": "This node allows downloading models directly within ComfyUI for easier use and integration.", - "files": [ - "https://github.com/ciri/comfyui-model-downloader" - ], - "id": "model-downloader", - "install_type": "git-clone", - "reference": "https://github.com/ciri/comfyui-model-downloader", - "title": "ComfyUI Model Downloader" - }, - { - "author": "Suzie1", - "description": "Custom nodes for SDXL and SD1.5 including Multi-ControlNet, LoRA, Aspect Ratio, Process Switches, and many more nodes. NOTE: Maintainer is changed to Suzie1 from RockOfFire. [w/Using an outdated version has resulted in reported issues with updates not being applied. Trying to reinstall the software is advised.]", - "files": [ - "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes" - ], - "id": "comfyroll", - "install_type": "git-clone", - "reference": "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes", - "title": "Comfyroll Studio" - }, - { - "author": "bmad4ever", - "description": "ComfyUI extension that adds undo (and redo) functionality.", - "files": [ - "https://github.com/bmad4ever/ComfyUI-Bmad-DirtyUndoRedo" - ], - "install_type": "git-clone", - "reference": "https://github.com/bmad4ever/ComfyUI-Bmad-DirtyUndoRedo", - "title": "ComfyUI-Bmad-DirtyUndoRedo" - }, - { - "author": "bmad4ever", - "description": "Experimental sampler node. Sampling alternates between A and B inputs until only one remains, starting with A. B steps run over a 2x2 grid, where 3/4's of the grid are copies of the original input latent. When the optional mask is used, the region outside the defined roi is copied from the original latent at the end of every step.", - "files": [ - "https://github.com/bmad4ever/comfyui_ab_samplercustom" - ], - "id": "ab-sampler", - "install_type": "git-clone", - "reference": "https://github.com/bmad4ever/comfyui_ab_samplercustom", - "title": "comfyui_ab_sampler" - }, - { - "author": "bmad4ever", - "description": "Given a set of lists, the node adjusts them so that when used as input to another node all the possible argument permutations are computed.", - "files": [ - "https://github.com/bmad4ever/comfyui_lists_cartesian_product" - ], - "install_type": "git-clone", - "reference": "https://github.com/bmad4ever/comfyui_lists_cartesian_product", - "title": "Lists Cartesian Product" - }, - { - "author": "bmad4ever", - "description": "An 'opinionated' Wave Function Collapse implementation with a set of nodes for comfyui", - "files": [ - "https://github.com/bmad4ever/comfyui_wfc_like" - ], - "id": "wfc", - "install_type": "git-clone", - "reference": "https://github.com/bmad4ever/comfyui_wfc_like", - "title": "comfyui_wfc_like" - }, - { - "author": "bmad4ever", - "description": "image and latent quilting nodes for comfyui", - "files": [ - "https://github.com/bmad4ever/comfyui_quilting" - ], - "id": "quilting", - "install_type": "git-clone", - "reference": "https://github.com/bmad4ever/comfyui_quilting", - "title": "comfyui_quilting" - }, - { - "author": "FizzleDorf", - "description": "Scheduled prompts, scheduled float/int values and wave function nodes for animations and utility. compatable with [a/framesync](https://www.framesync.xyz/) and [a/keyframe-string-generator](https://www.chigozie.co.uk/keyframe-string-generator/) for audio synced animations in Comfyui.", - "files": [ - "https://github.com/FizzleDorf/ComfyUI_FizzNodes" - ], - "id": "fizz", - "install_type": "git-clone", - "reference": "https://github.com/FizzleDorf/ComfyUI_FizzNodes", - "title": "FizzNodes" - }, - { - "author": "FizzleDorf", - "description": "A ComfyUI implementation of Facebook Meta's [a/AITemplate](https://github.com/facebookincubator/AITemplate) repo for faster inference using cpp/cuda. This new repo is behind the old version but is a much more stable foundation to keep AIT online. Please be patient as the repo will eventually include the same features as before.\nNOTE: You can find the old AIT extension in the legacy channel.", - "files": [ - "https://github.com/FizzleDorf/ComfyUI-AIT" - ], - "id": "ait", - "install_type": "git-clone", - "reference": "https://github.com/FizzleDorf/ComfyUI-AIT", - "title": "ComfyUI-AIT" - }, - { - "author": "filipemeneses", - "description": "ComfyUI node that pixelizes images.", - "files": [ - "https://github.com/filipemeneses/comfy_pixelization" - ], - "id": "pixelization", - "install_type": "git-clone", - "reference": "https://github.com/filipemeneses/comfy_pixelization", - "title": "Pixelization" - }, - { - "author": "shiimizu", - "description": "Nodes such as CLIP Text Encode++ to achieve identical embeddings from stable-diffusion-webui for ComfyUI.", - "files": [ - "https://github.com/shiimizu/ComfyUI_smZNodes" - ], - "id": "smz", - "install_type": "git-clone", - "reference": "https://github.com/shiimizu/ComfyUI_smZNodes", - "title": "smZNodes" - }, - { - "author": "shiimizu", - "description": "The extension enables large image drawing & upscaling with limited VRAM via the following techniques:\n1.Two SOTA diffusion tiling algorithms: [a/Mixture of Diffusers](https://github.com/albarji/mixture-of-diffusers) and [a/MultiDiffusion](https://github.com/omerbt/MultiDiffusion)\n2.pkuliyi2015's Tiled VAE algorithm.", - "files": [ - "https://github.com/shiimizu/ComfyUI-TiledDiffusion" - ], - "id": "tiled-diffusion", - "install_type": "git-clone", - "reference": "https://github.com/shiimizu/ComfyUI-TiledDiffusion", - "title": "Tiled Diffusion & VAE for ComfyUI" - }, - { - "author": "shiimizu", - "description": "ComfyUI reference implementation for [a/PhotoMaker](https://github.com/TencentARC/PhotoMaker) models.\nNOTE: PhotoMaker V2 is supported.", - "files": [ - "https://github.com/shiimizu/ComfyUI-PhotoMaker-Plus" - ], - "id": "photomaker-plus", - "install_type": "git-clone", - "reference": "https://github.com/shiimizu/ComfyUI-PhotoMaker-Plus", - "title": "ComfyUI PhotoMaker Plus" - }, - { - "author": "shiimizu", - "description": "ComfyUI node for Semantic-aware Guidance based on the [a/paper](https://arxiv.org/abs/2404.05384) 'Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance'", - "files": [ - "https://github.com/shiimizu/ComfyUI-semantic-aware-guidance" - ], - "id": "s-cfg", - "install_type": "git-clone", - "reference": "https://github.com/shiimizu/ComfyUI-semantic-aware-guidance", - "title": "Semantic-aware Guidance (S-CFG)" - }, - { - "author": "ZaneA", - "description": "NODES: ImageRewardLoader, ImageRewardScore", - "files": [ - "https://github.com/ZaneA/ComfyUI-ImageReward" - ], - "install_type": "git-clone", - "reference": "https://github.com/ZaneA/ComfyUI-ImageReward", - "title": "ImageReward" - }, - { - "author": "SeargeDP", - "description": "Custom nodes for easier use of SDXL in ComfyUI including an img2img workflow that utilizes both the base and refiner checkpoints.", - "files": [ - "https://github.com/SeargeDP/SeargeSDXL" - ], - "id": "searge", - "install_type": "git-clone", - "reference": "https://github.com/SeargeDP/SeargeSDXL", - "title": "SeargeSDXL" - }, - { - "author": "SeargeDP", - "description": "A prompt-generator or prompt-improvement node for ComfyUI, utilizing the power of a language model to turn a provided text-to-image prompt into a more detailed and improved prompt.", - "files": [ - "https://github.com/SeargeDP/ComfyUI_Searge_LLM" - ], - "install_type": "git-clone", - "reference": "https://github.com/SeargeDP/ComfyUI_Searge_LLM", - "title": "Searge-LLM for ComfyUI v1.0" - }, - { - "author": "cubiq", - "description": "ComfyUI reference implementation for IPAdapter models. The code is mostly taken from the original IPAdapter repository and laksjdjf's implementation, all credit goes to them. I just made the extension closer to ComfyUI philosophy.", - "files": [ - "https://github.com/cubiq/ComfyUI_IPAdapter_plus" - ], - "id": "ipadapter", - "install_type": "git-clone", - "pip": [ - "insightface" - ], - "preemptions": [ - "IPAAdapterFaceIDBatch", - "IPAdapter", - "IPAdapterAdvanced", - "IPAdapterBatch", - "IPAdapterClipVisionEnhancer", - "IPAdapterClipVisionEnhancerBatch", - "IPAdapterCombineEmbeds", - "IPAdapterCombineParams", - "IPAdapterCombineWeights", - "IPAdapterEmbeds", - "IPAdapterEmbedsBatch", - "IPAdapterEncoder", - "IPAdapterFaceID", - "IPAdapterFromParams", - "IPAdapterInsightFaceLoader", - "IPAdapterLoadEmbeds", - "IPAdapterMS", - "IPAdapterModelLoader", - "IPAdapterNoise", - "IPAdapterPreciseComposition", - "IPAdapterPreciseCompositionBatch", - "IPAdapterPreciseStyleTransfer", - "IPAdapterPreciseStyleTransferBatch", - "IPAdapterPromptScheduleFromWeightsStrategy", - "IPAdapterRegionalConditioning", - "IPAdapterSaveEmbeds", - "IPAdapterStyleComposition", - "IPAdapterStyleCompositionBatch", - "IPAdapterTiled", - "IPAdapterTiledBatch", - "IPAdapterUnifiedLoader", - "IPAdapterUnifiedLoaderCommunity", - "IPAdapterUnifiedLoaderFaceID", - "IPAdapterWeights", - "IPAdapterWeightsFromStrategy", - "PrepImageForClipVision" - ], - "reference": "https://github.com/cubiq/ComfyUI_IPAdapter_plus", - "title": "ComfyUI_IPAdapter_plus" - }, - { - "author": "cubiq", - "description": "Native [a/InstantID](https://github.com/InstantID/InstantID) support for ComfyUI.\nThis extension differs from the many already available as it doesn't use diffusers but instead implements InstantID natively and it fully integrates with ComfyUI.\nPlease note this still could be considered beta stage, looking forward to your feedback.", - "files": [ - "https://github.com/cubiq/ComfyUI_InstantID" - ], - "id": "instantid", - "install_type": "git-clone", - "reference": "https://github.com/cubiq/ComfyUI_InstantID", - "title": "ComfyUI InstantID (Native Support)" - }, - { - "author": "cubiq", - "description": "This extension uses [a/DLib](http://dlib.net/) to calculate the Euclidean and Cosine distance between two faces.\nNOTE: Install the Shape Predictor, Face Recognition model from the Install models menu.", - "files": [ - "https://github.com/cubiq/ComfyUI_FaceAnalysis" - ], - "id": "faceanalysis", - "install_type": "git-clone", - "reference": "https://github.com/cubiq/ComfyUI_FaceAnalysis", - "title": "Face Analysis for ComfyUI" - }, - { - "author": "cubiq", - "description": "[a/PuLID](https://github.com/ToTheBeginning/PuLID) ComfyUI native implementation.", - "files": [ - "https://github.com/cubiq/PuLID_ComfyUI" - ], - "id": "pulid", - "install_type": "git-clone", - "reference": "https://github.com/cubiq/PuLID_ComfyUI", - "title": "PuLID_ComfyUI" - }, - { - "author": "cubiq", - "description": "This is an (very) advanced and (very) experimental custom node for the ComfyUI. It allows you to iteratively change the blocks weights of Flux models and check the difference each value makes.", - "files": [ - "https://github.com/cubiq/Block_Patcher_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/cubiq/Block_Patcher_ComfyUI", - "title": "Flux blocks patcher sampler" - }, - { - "author": "shockz0rz", - "description": "A set of custom nodes for creating image grids, sequences, and batches in ComfyUI.", - "files": [ - "https://github.com/shockz0rz/comfy-easy-grids" - ], - "id": "easy-grids", - "install_type": "git-clone", - "reference": "https://github.com/shockz0rz/comfy-easy-grids", - "title": "comfy-easy-grids" - }, - { - "author": "yolanother", - "description": "Nodes: Prompt Agent, Prompt Agent (String). This script provides a prompt agent node for the Comfy UI stable diffusion client.", - "files": [ - "https://github.com/yolanother/DTAIComfyPromptAgent" - ], - "id": "prompt-agent", - "install_type": "git-clone", - "reference": "https://github.com/yolanother/DTAIComfyPromptAgent", - "title": "Comfy UI Prompt Agent" - }, - { - "author": "yolanother", - "description": "Nodes: Image URL to Text, Image to Text.", - "files": [ - "https://github.com/yolanother/DTAIImageToTextNode" - ], - "id": "dta-img2txt", - "install_type": "git-clone", - "reference": "https://github.com/yolanother/DTAIImageToTextNode", - "title": "Image to Text Node" - }, - { - "author": "yolanother", - "description": "Nodes: Submit Image (Parameters), Submit Image. A collection of loaders that use a shared common online data source rather than relying on the files to be present locally.", - "files": [ - "https://github.com/yolanother/DTAIComfyLoaders" - ], - "id": "dta-loader", - "install_type": "git-clone", - "reference": "https://github.com/yolanother/DTAIComfyLoaders", - "title": "Comfy UI Online Loaders" - }, - { - "author": "yolanother", - "description": "A ComfyAI submit node to upload images to DoubTech.ai", - "files": [ - "https://github.com/yolanother/DTAIComfyImageSubmit" - ], - "id": "dta-submit", - "install_type": "git-clone", - "reference": "https://github.com/yolanother/DTAIComfyImageSubmit", - "title": "Comfy AI DoubTech.ai Image Sumission Node" - }, - { - "author": "yolanother", - "description": "This extension introduces QR code nodes for the Comfy UI stable diffusion client. NOTE: ComfyUI qrcode extension required.", - "files": [ - "https://github.com/yolanother/DTAIComfyQRCodes" - ], - "id": "dta-qr", - "install_type": "git-clone", - "reference": "https://github.com/yolanother/DTAIComfyQRCodes", - "title": "Comfy UI QR Codes" - }, - { - "author": "yolanother", - "description": "Nodes: String, Int, Float, Short String, CLIP Text Encode (With Variables), String Format, Short String Format. This extension introduces quality of life improvements by providing variable nodes and shared global variables.", - "files": [ - "https://github.com/yolanother/DTAIComfyVariables" - ], - "id": "dta-var", - "install_type": "git-clone", - "reference": "https://github.com/yolanother/DTAIComfyVariables", - "title": "Variables for Comfy UI" - }, - { - "author": "yolanother", - "description": "The SaveImageARGB16PNG node provides functionality for saving images as uncompressed PNG files with ARGB16 precision. This node is particularly useful for workflows that require high-quality image saving with metadata such as prompts and additional PNG info.", - "files": [ - "https://github.com/yolanother/ComfyUI-Save16bitPng" - ], - "install_type": "git-clone", - "reference": "https://github.com/yolanother/ComfyUI-Save16bitPng", - "title": "Save Uncompressed 16 Bit PNG" - }, - { - "author": "sipherxyz", - "description": "A comprehensive set of custom nodes for ComfyUI, focusing on utilities for image processing, JSON manipulation, model operations and working with object via URLs", - "files": [ - "https://github.com/sipherxyz/comfyui-art-venture" - ], - "id": "artventure", - "install_type": "git-clone", - "reference": "https://github.com/sipherxyz/comfyui-art-venture", - "title": "comfyui-art-venture" - }, - { - "author": "SOELexicon", - "description": "Nodes: MSSqlTableNode, MSSqlSelectNode. This extension provides custom nodes to interact with MSSQL.", - "files": [ - "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes" - ], - "id": "lexmsdb", - "install_type": "git-clone", - "reference": "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes", - "title": "LexMSDBNodes" - }, - { - "author": "pants007", - "description": "Nodes: Make Square Node, Interrogate Node, TextEncodeAIO", - "files": [ - "https://github.com/pants007/comfy-pants" - ], - "install_type": "git-clone", - "reference": "https://github.com/pants007/comfy-pants", - "title": "pants" - }, - { - "author": "evanspearman", - "description": "Provides Math Nodes for ComfyUI. Boolean Logic, Integer Arithmetic, Floating Point Arithmetic and Functions, Vec2, Vec3, and Vec4 Arithmetic and Functions", - "files": [ - "https://github.com/evanspearman/ComfyMath" - ], - "id": "comfymath", - "install_type": "git-clone", - "reference": "https://github.com/evanspearman/ComfyMath", - "title": "ComfyMath" - }, - { - "author": "civitai", - "description": "Tired of manually downloading and moving models, LoRAs, and more to the right places?\nSick of scouring Civitai for that one mystical LoRA someone was using to make that cool image?\nWant to be share a fully reproducable workflow?", - "files": [ - "https://github.com/civitai/civitai_comfy_nodes" - ], - "id": "civitai", - "install_type": "git-clone", - "reference": "https://github.com/civitai/civitai_comfy_nodes", - "title": "Civitai Comfy Nodes" - }, - { - "author": "andersxa", - "description": "Nodes: CLIP Directional Prompt Attention Encode. Direction prompt attention tries to solve the problem of contextual words (or parts of the prompt) having an effect on much later or irrelevant parts of the prompt.", - "files": [ - "https://github.com/andersxa/comfyui-PromptAttention" - ], - "id": "prompt-attention", - "install_type": "git-clone", - "pip": [ - "scikit-learn", - "matplotlib" - ], - "reference": "https://github.com/andersxa/comfyui-PromptAttention", - "title": "CLIP Directional Prompt Attention" - }, - { - "author": "ArtVentureX", - "description": "AnimateDiff integration for ComfyUI, adapts from sd-webui-animatediff.\n[w/You only need to download one of [a/mm_sd_v14.ckpt](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt) | [a/mm_sd_v15.ckpt](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt). Put the model weights under %%ComfyUI/custom_nodes/comfyui-animatediff/models%%. DO NOT change model filename.]", - "files": [ - "https://github.com/SipherAGI/comfyui-animatediff" - ], - "install_type": "git-clone", - "pip": [ - "flash_attn" - ], - "reference": "https://github.com/SipherAGI/comfyui-animatediff", - "title": "AnimateDiff" - }, - { - "author": "twri", - "description": "SDXL Prompt Styler is a node that enables you to style prompts based on predefined templates stored in a JSON file.", - "files": [ - "https://github.com/twri/sdxl_prompt_styler" - ], - "id": "twri-styler", - "install_type": "git-clone", - "reference": "https://github.com/twri/sdxl_prompt_styler", - "title": "SDXL Prompt Styler" - }, - { - "author": "wolfden", - "description": "These custom nodes provide a variety of customized prompt stylers based on [a/twri/SDXL Prompt Styler](https://github.com/twri/sdxl_prompt_styler).", - "files": [ - "https://github.com/wolfden/ComfyUi_PromptStylers" - ], - "id": "wolfden-styler", - "install_type": "git-clone", - "reference": "https://github.com/wolfden/ComfyUi_PromptStylers", - "title": "SDXL Prompt Styler (customized version by wolfden)" - }, - { - "author": "wolfden", - "description": "This custom node provides the capability to manipulate multiple string inputs.", - "files": [ - "https://github.com/wolfden/ComfyUi_String_Function_Tree" - ], - "id": "str-func-tree", - "install_type": "git-clone", - "reference": "https://github.com/wolfden/ComfyUi_String_Function_Tree", - "title": "ComfyUi_String_Function_Tree" - }, - { - "author": "daxthin", - "description": "Face Detailer is a custom node for the 'ComfyUI' framework inspired by !After Detailer extension from auto1111, it allows you to detect faces using Mediapipe and YOLOv8n to create masks for the detected faces.", - "files": [ - "https://github.com/nicofdga/DZ-FaceDetailer" - ], - "id": "dz-facedetailer", - "install_type": "git-clone", - "reference": "https://github.com/nicofdga/DZ-FaceDetailer", - "title": "DZ-FaceDetailer" - }, - { - "author": "asagi4", - "description": "Nodes for convenient prompt editing, making many common operations prompt-controllable", - "files": [ - "https://github.com/asagi4/comfyui-prompt-control" - ], - "id": "prompt-control", - "install_type": "git-clone", - "reference": "https://github.com/asagi4/comfyui-prompt-control", - "title": "ComfyUI Prompt Control" - }, - { - "author": "asagi4", - "description": "Attempts to implement [a/CADS](https://arxiv.org/abs/2310.17347) for ComfyUI. Credit also to the [a/A1111 implementation](https://github.com/v0xie/sd-webui-cads/tree/main) that I used as a reference.", - "files": [ - "https://github.com/asagi4/ComfyUI-CADS" - ], - "id": "cads", - "install_type": "git-clone", - "reference": "https://github.com/asagi4/ComfyUI-CADS", - "title": "ComfyUI-CADS" - }, - { - "author": "asagi4", - "description": "Nodes:MUJinjaRender, MUSimpleWildcard", - "files": [ - "https://github.com/asagi4/comfyui-utility-nodes" - ], - "id": "asagi-nodes", - "install_type": "git-clone", - "reference": "https://github.com/asagi4/comfyui-utility-nodes", - "title": "asagi4/comfyui-utility-nodes" - }, - { - "author": "asagi4", - "description": "An implementation of adaptive guidance for ComfyUI\nSee [a/https://bcv-uniandes.github.io/adaptiveguidance-wp](https://bcv-uniandes.github.io/adaptiveguidance-wp)", - "files": [ - "https://github.com/asagi4/ComfyUI-Adaptive-Guidance" - ], - "id": "comfyui-adaptive-guidance", - "install_type": "git-clone", - "reference": "https://github.com/asagi4/ComfyUI-Adaptive-Guidance", - "title": "Adaptive Guidance for ComfyUI" - }, - { - "author": "asagi4", - "description": "A very barebones mostly-copypaste implementation of [a/https://github.com/xie-lab-ml/Golden-Noise-for-Diffusion-Models](https://github.com/xie-lab-ml/Golden-Noise-for-Diffusion-Models)", - "files": [ - "https://github.com/asagi4/ComfyUI-NPNet" - ], - "id": "npnet", - "install_type": "git-clone", - "reference": "https://github.com/asagi4/ComfyUI-NPNet", - "title": "ComfyUI NPNet (Golden Noise)" - }, - { - "author": "jamesWalker55", - "description": "Nodes: P2LDGAN. This integrates P2LDGAN into ComfyUI. P2LDGAN extracts lineart from input images.\n[w/To use this extension, you need to download the [a/p2ldgan model](https://drive.google.com/file/d/1To4V_Btc3QhCLBWZ0PdSNgC1cbm3isHP) and save it in the %%ComfyUI/custom_nodes/comfyui-p2ldgan/checkpoints%% directory.]", - "files": [ - "https://github.com/jamesWalker55/comfyui-p2ldgan" - ], - "id": "p2ldgan", - "install_type": "git-clone", - "reference": "https://github.com/jamesWalker55/comfyui-p2ldgan", - "title": "ComfyUI - P2LDGAN Node" - }, - { - "author": "jamesWalker55", - "description": "Nodes: JWInteger, JWFloat, JWString, JWImageLoadRGB, JWImageResize, ...", - "files": [ - "https://github.com/jamesWalker55/comfyui-various" - ], - "id": "jameswalker-nodes", - "install_type": "git-clone", - "nodename_pattern": "^JW", - "reference": "https://github.com/jamesWalker55/comfyui-various", - "title": "Various ComfyUI Nodes by Type" - }, - { - "author": "adieyal", - "description": "Nodes: Random Prompts, Combinatorial Prompts, I'm Feeling Lucky, Magic Prompt, Jinja2 Templates. ComfyUI-DynamicPrompts is a custom nodes library that integrates into your existing ComfyUI Library. It provides nodes that enable the use of Dynamic Prompts in your ComfyUI.", - "files": [ - "https://github.com/adieyal/comfyui-dynamicprompts" - ], - "id": "dynamicprompt", - "install_type": "git-clone", - "reference": "https://github.com/adieyal/comfyui-dynamicprompts", - "title": "DynamicPrompts Custom Nodes" - }, - { - "author": "mihaiiancu", - "description": "Nodes: InpaintMediapipe. This node provides a simple interface to inpaint.", - "files": [ - "https://github.com/mihaiiancu/ComfyUI_Inpaint" - ], - "id": "inpaint", - "install_type": "git-clone", - "reference": "https://github.com/mihaiiancu/ComfyUI_Inpaint", - "title": "mihaiiancu/Inpaint" - }, - { - "author": "kwaroran", - "description": "Nodes: Remove Image Background (abg). A Anime Background Remover node for comfyui, based on this hf space, works same as AGB extention in automatic1111.", - "files": [ - "https://github.com/kwaroran/abg-comfyui" - ], - "id": "abg", - "install_type": "git-clone", - "reference": "https://github.com/kwaroran/abg-comfyui", - "title": "abg-comfyui" - }, - { - "author": "bash-j", - "description": "Nodes: Prompt With Style, Prompt With SDXL, Resize Image for SDXL, Save Image With Prompt Data, HaldCLUT, Empty Latent Ratio Select/Custom SDXL", - "files": [ - "https://github.com/bash-j/mikey_nodes" - ], - "id": "mikey", - "install_type": "git-clone", - "reference": "https://github.com/bash-j/mikey_nodes", - "title": "Mikey Nodes" - }, - { - "author": "blib-la", - "description": "node color customization, custom colors, dot reroutes, link rendering options, straight lines, group freezing, node pinning, automated arrangement of nodes, copy image\n[w/failfast-comfyui-extensions is renamed to blibla-comfyui-extensions. Please resintall to this.]", - "files": [ - "https://github.com/blib-la/blibla-comfyui-extensions" - ], - "id": "blibla-comfyui-extensions", - "install_type": "git-clone", - "reference": "https://github.com/blib-la/blibla-comfyui-extensions", - "title": "blibla-comfyui-extensions" - }, - { - "author": "Pfaeff", - "description": "Nodes: AstropulsePixelDetector, BackgroundRemover, ImagePadForBetterOutpaint, InpaintingPipelineLoader, Inpainting, ...", - "files": [ - "https://github.com/Pfaeff/pfaeff-comfyui" - ], - "id": "pfaeff", - "install_type": "git-clone", - "reference": "https://github.com/Pfaeff/pfaeff-comfyui", - "title": "pfaeff-comfyui" - }, - { - "author": "wallish77", - "description": "Nodes: Checkpoint Loader with Name, Save Prompt Info, Outpaint to Image, CLIP Positive-Negative, SDXL Quick Empty Latent, Empty Latent by Ratio, Time String, SDXL Steps, SDXL Resolutions ...", - "files": [ - "https://github.com/wallish77/wlsh_nodes" - ], - "id": "wlsh", - "install_type": "git-clone", - "reference": "https://github.com/wallish77/wlsh_nodes", - "title": "wlsh_nodes" - }, - { - "author": "Kosinkadink", - "description": "Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks.", - "files": [ - "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet" - ], - "id": "adv-cnet", - "install_type": "git-clone", - "reference": "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet", - "title": "ComfyUI-Advanced-ControlNet" - }, - { - "author": "Kosinkadink", - "description": "A forked repository that actively maintains [a/AnimateDiff](https://github.com/ArtVentureX/comfyui-animatediff), created by ArtVentureX.\n\nImproved AnimateDiff integration for ComfyUI, adapts from sd-webui-animatediff.\n[w/Download one or more motion models from [a/Original Models](https://huggingface.co/guoyww/animatediff/tree/main) | [a/Finetuned Models](https://huggingface.co/manshoety/AD_Stabilized_Motion/tree/main). See README for additional model links and usage. Put the model weights under %%ComfyUI/custom_nodes/ComfyUI-AnimateDiff-Evolved/models%%. You are free to rename the models, but keeping original names will ease use when sharing your workflow.]", - "files": [ - "https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved" - ], - "id": "ad-evolved", - "install_type": "git-clone", - "reference": "https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved", - "title": "AnimateDiff Evolved" - }, - { - "author": "Kosinkadink", - "description": "Nodes related to video workflows", - "files": [ - "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite" - ], - "id": "vhs", - "install_type": "git-clone", - "reference": "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite", - "title": "ComfyUI-VideoHelperSuite" - }, - { - "author": "Gourieff", - "description": "This extension collapses 'future warning' messages in your Console", - "files": [ - "https://raw.githubusercontent.com/Gourieff/ComfyUI-FutureWarningIgnore/main/0_FutureWarningIgnore.py" - ], - "id": "futureignore", - "install_type": "copy", - "reference": "https://github.com/Gourieff/ComfyUI-FutureWarningIgnore", - "title": "ComfyUI-FutureWarningIgnore" - }, - { - "author": "Gourieff", - "description": "The Fast and Simple Face Swap Extension Node for ComfyUI, based on ReActor SD-WebUI Face Swap Extension", - "files": [ - "https://github.com/Gourieff/ComfyUI-ReActor" - ], - "install_type": "git-clone", - "reference": "https://github.com/Gourieff/ComfyUI-ReActor", - "title": "comfyui-reactor-node" - }, - { - "author": "imb101", - "description": "Nodes:FaceSwapNode. Very basic custom node to enable face swapping in ComfyUI. (roop)", - "files": [ - "https://github.com/imb101/ComfyUI-FaceSwap" - ], - "id": "faceswap", - "install_type": "git-clone", - "reference": "https://github.com/imb101/ComfyUI-FaceSwap", - "title": "FaceSwap" - }, - { - "author": "Chaoses-Ib", - "description": "Nodes: LoadImageFromPath. Load Image From Path loads the image from the source path and does not have such problems.", - "files": [ - "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes" - ], - "id": "ib-nodes", - "install_type": "git-clone", - "reference": "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes", - "title": "ComfyUI_Ib_CustomNodes" - }, - { - "author": "AIrjen", - "description": "One Button Prompt has a prompt generation node for beginners who have problems writing a good prompt, or advanced users who want to get inspired. It generates an entire prompt from scratch. It is random, but controlled. You simply load up the script and press generate, and let it surprise you.", - "files": [ - "https://github.com/AIrjen/OneButtonPrompt" - ], - "id": "1button", - "install_type": "git-clone", - "reference": "https://github.com/AIrjen/OneButtonPrompt", - "title": "One Button Prompt" - }, - { - "author": "coreyryanhanson", - "description": "QR generation within ComfyUI. Contains nodes suitable for workflows from generating basic QR images to techniques with advanced QR masking.", - "files": [ - "https://github.com/coreyryanhanson/ComfyQR" - ], - "id": "comfyqr", - "install_type": "git-clone", - "reference": "https://github.com/coreyryanhanson/ComfyQR", - "title": "ComfyQR" - }, - { - "author": "coreyryanhanson", - "description": "A set of ComfyUI nodes to quickly test generated QR codes for scannability. A companion project to ComfyQR.", - "files": [ - "https://github.com/coreyryanhanson/ComfyQR-scanning-nodes" - ], - "id": "comfyqr-scanning", - "install_type": "git-clone", - "reference": "https://github.com/coreyryanhanson/ComfyQR-scanning-nodes", - "title": "ComfyQR-scanning-nodes" - }, - { - "author": "dimtoneff", - "description": "This node manipulates the pixel art image in ways that it should look pixel perfect (downscales, changes palette, upscales etc.).", - "files": [ - "https://github.com/dimtoneff/ComfyUI-PixelArt-Detector" - ], - "id": "pixelart-detector", - "install_type": "git-clone", - "reference": "https://github.com/dimtoneff/ComfyUI-PixelArt-Detector", - "title": "ComfyUI PixelArt Detector" - }, - { - "author": "theUpsider", - "description": "This extension allows users to load styles from a CSV file, primarily for migration purposes from the automatic1111 Stable Diffusion web UI.", - "files": [ - "https://github.com/theUpsider/ComfyUI-Styles_CSV_Loader" - ], - "id": "styles-csv-loader", - "install_type": "git-clone", - "reference": "https://github.com/theUpsider/ComfyUI-Styles_CSV_Loader", - "title": "Styles CSV Loader Extension for ComfyUI" - }, - { - "author": "M1kep", - "description": "Nodes: Range(Step), Range(Num Steps), List Length, Image Overlay, Stack Images, Empty Images, Join Image Lists, Join Float Lists. This extension provides various list manipulation nodes", - "files": [ - "https://github.com/M1kep/Comfy_KepListStuff" - ], - "id": "keplist", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/Comfy_KepListStuff", - "title": "Comfy_KepListStuff" - }, - { - "author": "M1kep", - "description": "Nodes: Int, Float, String, Operation, Checkpoint", - "files": [ - "https://github.com/M1kep/ComfyLiterals" - ], - "id": "comfyliterals", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/ComfyLiterals", - "title": "ComfyLiterals" - }, - { - "author": "M1kep", - "description": "Nodes: Build Gif, Special CLIP Loader. It offers various manipulation capabilities for the internal operations of the prompt.", - "files": [ - "https://github.com/M1kep/KepPromptLang" - ], - "id": "kepprompt", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/KepPromptLang", - "title": "KepPromptLang" - }, - { - "author": "M1kep", - "description": "This extension provides a custom node that allows the use of [a/Matte Anything](https://github.com/hustvl/Matte-Anything) in ComfyUI.", - "files": [ - "https://github.com/M1kep/Comfy_KepMatteAnything" - ], - "id": "kepmatte", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/Comfy_KepMatteAnything", - "title": "Comfy_KepMatteAnything" - }, - { - "author": "M1kep", - "description": "Nodes: KepRotateImage", - "files": [ - "https://github.com/M1kep/Comfy_KepKitchenSink" - ], - "id": "kepkitchen", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/Comfy_KepKitchenSink", - "title": "Comfy_KepKitchenSink" - }, - { - "author": "M1kep", - "description": "Nodes: TAESD VAE Decode", - "files": [ - "https://github.com/M1kep/ComfyUI-OtherVAEs" - ], - "id": "kep-othervae", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/ComfyUI-OtherVAEs", - "title": "ComfyUI-OtherVAEs" - }, - { - "author": "M1kep", - "description": "ComfyUI-KepOpenAI is a user-friendly node that serves as an interface to the GPT-4 with Vision (GPT-4V) API. This integration facilitates the processing of images coupled with text prompts, leveraging the capabilities of the OpenAI API to generate text completions that are contextually relevant to the provided inputs.", - "files": [ - "https://github.com/M1kep/ComfyUI-KepOpenAI" - ], - "id": "kep-openai", - "install_type": "git-clone", - "reference": "https://github.com/M1kep/ComfyUI-KepOpenAI", - "title": "ComfyUI-KepOpenAI" - }, - { - "author": "uarefans", - "description": "Nodes: Fans Styler (Max 10 Style), Fans Text Concat (Until 10 text), Fans Prompt Styler Postive (Can replace {prompt} word in your csv files), Fans Prompt Styler Negative (With sentence structure).", - "files": [ - "https://github.com/uarefans/ComfyUI-Fans" - ], - "id": "fans", - "install_type": "git-clone", - "reference": "https://github.com/uarefans/ComfyUI-Fans", - "title": "ComfyUI-Fans" - }, - { - "author": "NicholasMcCarthy", - "description": "ComfyUI custom nodes to apply various latent travel techniques.", - "files": [ - "https://github.com/NicholasMcCarthy/ComfyUI_TravelSuite" - ], - "id": "travel", - "install_type": "git-clone", - "reference": "https://github.com/NicholasMcCarthy/ComfyUI_TravelSuite", - "title": "ComfyUI_TravelSuite" - }, - { - "author": "ManglerFTW", - "description": "A set of custom nodes to perform image 2 image functions in ComfyUI.", - "files": [ - "https://github.com/ManglerFTW/ComfyI2I" - ], - "id": "comfyi2i", - "install_type": "git-clone", - "reference": "https://github.com/ManglerFTW/ComfyI2I", - "title": "ComfyI2I" - }, - { - "author": "Mike Sokol", - "description": "A small node suite for ComfyUI", - "files": [ - "https://github.com/m-sokes/ComfyUI-Sokes-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/m-sokes/ComfyUI-Sokes-Nodes", - "title": "ComfyUI Sokes Nodes \ud83e\uddac" - }, - { - "author": "Extraltodeus", - "description": "Nodes: NoisyLatentPerlin. This allows to create latent spaces filled with perlin-based noise that can actually be used by the samplers.", - "files": [ - "https://github.com/Extraltodeus/noise_latent_perlinpinpin" - ], - "id": "perlipinpin", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/noise_latent_perlinpinpin", - "title": "noise latent perlinpinpin" - }, - { - "author": "Extraltodeus", - "description": "Nodes:LoadLoraWithTags. Save/Load trigger words for loras from a json and auto fetch them on civitai if they are missing.", - "files": [ - "https://github.com/Extraltodeus/LoadLoraWithTags" - ], - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/LoadLoraWithTags", - "title": "LoadLoraWithTags" - }, - { - "author": "Extraltodeus", - "description": "A few nodes to mix sigmas and a custom scheduler that uses phi, then one using eval() to be able to schedule with custom formulas.", - "files": [ - "https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler" - ], - "id": "sigmas-tools", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler", - "title": "sigmas_tools_and_the_golden_scheduler" - }, - { - "author": "Extraltodeus", - "description": "My own version 'from scratch' of a self-rescaling CFG. It isn't much but it's honest work.\nTLDR: set your CFG at 8 to try it. No burned images and artifacts anymore. CFG is also a bit more sensitive because it's a proportion around 8. Low scale like 4 also gives really nice results since your CFG is not the CFG anymore. Also in general even with relatively low settings it seems to improve the quality.", - "files": [ - "https://github.com/Extraltodeus/ComfyUI-AutomaticCFG" - ], - "id": "autocfg", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/ComfyUI-AutomaticCFG", - "title": "ComfyUI-AutomaticCFG" - }, - { - "author": "Extraltodeus", - "description": "The main node makes your conditioning go towards similar concepts so to enrich your composition or further away so to make it more precise. It gathers similar pre-cond vectors for as long as the cosine similarity score diminishes. If it climbs back it stops. This allows to set a relative direction to similar concepts.\nThere are examples at the end but [a/you can also check this imgur album](https://imgur.com/a/WvPd81Y) which demonstrates the capability of improving variety.", - "files": [ - "https://github.com/Extraltodeus/Vector_Sculptor_ComfyUI" - ], - "id": "vector-sculptor", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/Vector_Sculptor_ComfyUI", - "title": "Vector_Sculptor_ComfyUI" - }, - { - "author": "Extraltodeus", - "description": "Provides the ability to set the temperature for both UNET and CLIP. For ComfyUI.", - "files": [ - "https://github.com/Extraltodeus/Stable-Diffusion-temperature-settings" - ], - "id": "sd-temperature", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/Stable-Diffusion-temperature-settings", - "title": "Stable-Diffusion-temperature-settings" - }, - { - "author": "Extraltodeus", - "description": "Allows to sample without generating any uncond with Stable Diffusion!", - "files": [ - "https://github.com/Extraltodeus/Uncond-Zero-for-ComfyUI" - ], - "id": "uncond-zero", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/Uncond-Zero-for-ComfyUI", - "title": "Uncond-Zero-for-ComfyUI" - }, - { - "author": "Extraltodeus", - "description": "A set of nodes to prepare the noise predictions before the CFG function", - "files": [ - "https://github.com/Extraltodeus/pre_cfg_comfy_nodes_for_ComfyUI" - ], - "id": "pre_cfg_comfy_nodes_for_comfyui", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/pre_cfg_comfy_nodes_for_ComfyUI", - "title": "pre_cfg_comfy_nodes_for_ComfyUI" - }, - { - "author": "Extraltodeus", - "description": "A powerful anti-burn allowing much higher CFG scales for latent diffusion models (for ComfyUI)", - "files": [ - "https://github.com/Extraltodeus/Skimmed_CFG" - ], - "id": "skimmed-cfg", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/Skimmed_CFG", - "title": "Skimmed_CFG" - }, - { - "author": "Extraltodeus", - "description": "Heuristic modification of the Heun sampler using a custom function based on normalized distances. For ComfyUI.", - "files": [ - "https://github.com/Extraltodeus/DistanceSampler" - ], - "id": "distancesampler", - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/DistanceSampler", - "title": "DistanceSampler" - }, - { - "author": "Extraltodeus", - "description": "Takes the difference in between the positive and negative conditioning at the attention.\nNOTE: Will not work with Flux", - "files": [ - "https://github.com/Extraltodeus/Negative-attention-for-ComfyUI-" - ], - "install_type": "git-clone", - "reference": "https://github.com/Extraltodeus/Negative-attention-for-ComfyUI-", - "title": "Negative-attention-for-ComfyUI-" - }, - { - "author": "JPS", - "description": "Nodes: Various nodes to handle SDXL Resolutions, SDXL Basic Settings, IP Adapter Settings, Revision Settings, SDXL Prompt Styler, Crop Image to Square, Crop Image to Target Size, Get Date-Time String, Resolution Multiply, Largest Integer, 5-to-1 Switches for Integer, Images, Latents, Conditioning, Model, VAE, ControlNet", - "files": [ - "https://github.com/JPS-GER/ComfyUI_JPS-Nodes" - ], - "id": "jps-nodes", - "install_type": "git-clone", - "reference": "https://github.com/JPS-GER/ComfyUI_JPS-Nodes", - "title": "JPS Custom Nodes for ComfyUI" - }, - { - "author": "hustille", - "description": "ComfyUI nodes primarily for seed and filename generation", - "files": [ - "https://github.com/hustille/ComfyUI_hus_utils" - ], - "id": "husutil", - "install_type": "git-clone", - "reference": "https://github.com/hustille/ComfyUI_hus_utils", - "title": "hus' utils for ComfyUI" - }, - { - "author": "hustille", - "description": "Nodes: KSampler With Refiner (Fooocus). The KSampler from [a/Fooocus](https://github.com/lllyasviel/Fooocus) as a ComfyUI node [w/NOTE: This patches basic ComfyUI behaviour - don't use together with other samplers. Or perhaps do? Other samplers might profit from those changes ... ymmv.]", - "files": [ - "https://github.com/hustille/ComfyUI_Fooocus_KSampler" - ], - "id": "fooocus-ksampler", - "install_type": "git-clone", - "reference": "https://github.com/hustille/ComfyUI_Fooocus_KSampler", - "title": "ComfyUI_Fooocus_KSampler" - }, - { - "author": "badjeff", - "description": "A ComfyUI custom node to read LoRA tag(s) from text and load it into checkpoint model.", - "files": [ - "https://github.com/badjeff/comfyui_lora_tag_loader" - ], - "id": "lora-tag-loader", - "install_type": "git-clone", - "reference": "https://github.com/badjeff/comfyui_lora_tag_loader", - "title": "badjeff/LoRA Tag Loader for ComfyUI" - }, - { - "author": "rgthree", - "description": "Nodes: Seed, Reroute, Context, Lora Loader Stack, Context Switch, Fast Muter. These custom nodes helps organize the building of complex workflows.", - "files": [ - "https://github.com/rgthree/rgthree-comfy" - ], - "id": "rgthree", - "install_type": "git-clone", - "nodename_pattern": " \\(rgthree\\)$", - "reference": "https://github.com/rgthree/rgthree-comfy", - "title": "rgthree's ComfyUI Nodes" - }, - { - "author": "AIGODLIKE", - "description": "It provides language settings. (Contribution from users of various languages is needed due to the support for each language.)", - "files": [ - "https://github.com/AIGODLIKE/AIGODLIKE-COMFYUI-TRANSLATION" - ], - "id": "translation", - "install_type": "git-clone", - "reference": "https://github.com/AIGODLIKE/AIGODLIKE-COMFYUI-TRANSLATION", - "title": "AIGODLIKE-COMFYUI-TRANSLATION" - }, - { - "author": "AIGODLIKE", - "description": "Improve the interactive experience of using ComfyUI, such as making the loading of ComfyUI models more intuitive and making it easier to create model thumbnails", - "files": [ - "https://github.com/AIGODLIKE/AIGODLIKE-ComfyUI-Studio" - ], - "id": "comfy-studio", - "install_type": "git-clone", - "reference": "https://github.com/AIGODLIKE/AIGODLIKE-ComfyUI-Studio", - "title": "AIGODLIKE-ComfyUI-Studio" - }, - { - "author": "AIGODLIKE", - "description": "Bridge between ComfyUI and blender's ComfyUI-BlenderAI-node addon.", - "files": [ - "https://github.com/AIGODLIKE/ComfyUI-CUP" - ], - "id": "comfycup", - "install_type": "git-clone", - "reference": "https://github.com/AIGODLIKE/ComfyUI-CUP", - "title": "ComfyUI-CUP" - }, - { - "author": "AIGODLIKE", - "description": "This project is used to enable [a/ToonCrafter](https://github.com/ToonCrafter/ToonCrafter) to be used in ComfyUI.\nYou can use it to achieve generative keyframe animation\nAnd use it in Blender for animation rendering and prediction", - "files": [ - "https://github.com/AIGODLIKE/ComfyUI-ToonCrafter" - ], - "id": "tooncrafter", - "install_type": "git-clone", - "reference": "https://github.com/AIGODLIKE/ComfyUI-ToonCrafter", - "title": "ComfyUI-ToonCrafter" - }, - { - "author": "syllebra", - "description": "Nodes: BilboX's PromptGeek Photo Prompt. This provides a convenient way to compose photorealistic prompts into ComfyUI. Post-Processing: adds various post processing effects. Bonus: Option to show a distant server shutdown menu.", - "files": [ - "https://github.com/syllebra/bilbox-comfyui" - ], - "id": "bilbox", - "install_type": "git-clone", - "reference": "https://github.com/syllebra/bilbox-comfyui", - "title": "BilboX's ComfyUI Custom Nodes" - }, - { - "author": "Girish Gopaul", - "description": "All the tools you need to save images with their generation metadata on ComfyUI. Compatible with Civitai & Prompthero geninfo auto-detection. Works with png, jpeg and webp.", - "files": [ - "https://github.com/giriss/comfy-image-saver" - ], - "id": "image-saver", - "install_type": "git-clone", - "reference": "https://github.com/giriss/comfy-image-saver", - "title": "Save Image with Generation Metadata" - }, - { - "author": "laksjdjf", - "description": "ComfyUI version of https://github.com/laksjdjf/pfg-webui. (To use this extension, you need to download the required model file from **Install Models**)", - "files": [ - "https://github.com/laksjdjf/pfg-ComfyUI" - ], - "id": "pfg", - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/pfg-ComfyUI", - "title": "pfg-ComfyUI" - }, - { - "author": "laksjdjf", - "description": "The custom nodes of laksjdjf have been integrated into the node pack of cgem156\ud83c\udf4c.\nNOTE:This includes the attention couple feature.", - "files": [ - "https://github.com/laksjdjf/cgem156-ComfyUI" - ], - "id": "cgem156", - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/cgem156-ComfyUI", - "title": "cgem156-ComfyUI\ud83c\udf4c" - }, - { - "author": "laksjdjf", - "description": "Nodes:Apply CDTuner, Apply Negapip. This extension provides the [a/CD(Color/Detail) Tuner](https://github.com/hako-mikan/sd-webui-cd-tuner) and the [a/Negative Prompt in the Prompt](https://github.com/hako-mikan/sd-webui-negpip) features.", - "files": [ - "https://github.com/laksjdjf/cd-tuner_negpip-ComfyUI" - ], - "id": "cdtuner", - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/cd-tuner_negpip-ComfyUI", - "title": "cd-tuner_negpip-ComfyUI" - }, - { - "author": "laksjdjf", - "description": "This extension node is intended for the use of LCM conversion for SSD-1B-anime. It does not guarantee operation with the original LCM (as it cannot load weights in the current version). To take advantage of fast generation with LCM, a node for using TAESD as a decoder is also provided. This is inspired by ComfyUI-OtherVAEs.", - "files": [ - "https://github.com/laksjdjf/LCMSampler-ComfyUI" - ], - "id": "lcm-sampler", - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/LCMSampler-ComfyUI", - "title": "LCMSampler-ComfyUI" - }, - { - "author": "laksjdjf", - "description": "This is a repository for using LoRTnoC (LoRA with hint block of ControlNet) on ComfyUI.\nNOTE:Please place the model file in the same location as controlnet. (Is this too arbitrary?)", - "files": [ - "https://github.com/laksjdjf/LoRTnoC-ComfyUI" - ], - "id": "lortnoc", - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/LoRTnoC-ComfyUI", - "title": "LoRTnoC-ComfyUI" - }, - { - "author": "laksjdjf", - "description": "Nodes:CLIP Text Encode (Batch), String Input, Batch String", - "files": [ - "https://github.com/laksjdjf/Batch-Condition-ComfyUI" - ], - "id": "batch-condition", - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/Batch-Condition-ComfyUI", - "title": "Batch-Condition-ComfyUI" - }, - { - "author": "laksjdjf", - "description": "This is an experimental node for generating an *imatrix* file to reduce quantization errors in GGUF files used with ComfyUI-GGUF.", - "files": [ - "https://github.com/laksjdjf/ComfyUI-Imatrix" - ], - "install_type": "git-clone", - "reference": "https://github.com/laksjdjf/ComfyUI-Imatrix", - "title": "ComfyUI-Imatrix" - }, - { - "author": "alsritter", - "description": "Nodes:Asymmetric_Tiling_KSampler. ", - "files": [ - "https://github.com/alsritter/asymmetric-tiling-comfyui" - ], - "id": "asymmetric", - "install_type": "git-clone", - "reference": "https://github.com/alsritter/asymmetric-tiling-comfyui", - "title": "asymmetric-tiling-comfyui" - }, - { - "author": "meap158", - "description": "Pause image generation when GPU temperature exceeds threshold.", - "files": [ - "https://github.com/meap158/ComfyUI-GPU-temperature-protection" - ], - "id": "gputemp", - "install_type": "git-clone", - "reference": "https://github.com/meap158/ComfyUI-GPU-temperature-protection", - "title": "GPU temperature protection" - }, - { - "author": "meap158", - "description": "Dynamic prompt expansion, powered by GPT-2 locally on your device.", - "files": [ - "https://github.com/meap158/ComfyUI-Prompt-Expansion" - ], - "id": "promtp-expansion", - "install_type": "git-clone", - "reference": "https://github.com/meap158/ComfyUI-Prompt-Expansion", - "title": "ComfyUI-Prompt-Expansion" - }, - { - "author": "meap158", - "description": "Instantly replace your image's background.", - "files": [ - "https://github.com/meap158/ComfyUI-Background-Replacement" - ], - "id": "bg-replacement", - "install_type": "git-clone", - "reference": "https://github.com/meap158/ComfyUI-Background-Replacement", - "title": "ComfyUI-Background-Replacement" - }, - { - "author": "TeaCrab", - "description": "Nodes:TC_EqualizeCLAHE, TC_SizeApproximation, TC_ImageResize, TC_ImageScale, TC_ColorFill.", - "files": [ - "https://github.com/TeaCrab/ComfyUI-TeaNodes" - ], - "id": "teanodes", - "install_type": "git-clone", - "reference": "https://github.com/TeaCrab/ComfyUI-TeaNodes", - "title": "ComfyUI-TeaNodes" - }, - { - "author": "nagolinc", - "description": "Based off of: [a/Birch-san/diffusers-play/approx_vae](https://github.com/Birch-san/diffusers-play/tree/main/approx_vae). This ComfyUI node allows you to quickly preview SDXL 1.0 latents.", - "files": [ - "https://github.com/nagolinc/ComfyUI_FastVAEDecorder_SDXL" - ], - "install_type": "git-clone", - "reference": "https://github.com/nagolinc/ComfyUI_FastVAEDecorder_SDXL", - "title": "ComfyUI_FastVAEDecorder_SDXL" - }, - { - "author": "nagolinc", - "description": "This provides a single node openai > Open AI query node\nthat takes a system prompt and user message and sends them to chatGPT 3.5\nNote, you MUST have an OPEN AI API key stored in the environment variable OPENAI_API_KEY in order for this to work.", - "files": [ - "https://github.com/nagolinc/comfyui_openai_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/nagolinc/comfyui_openai_node", - "title": "comfyui_openai_node" - }, - { - "author": "bradsec", - "description": "A custom node for Stable Diffusion ComfyUI to enable easy selection of image resolutions for SDXL SD15 SD21", - "files": [ - "https://github.com/bradsec/ComfyUI_ResolutionSelector" - ], - "id": "resolution-selector", - "install_type": "git-clone", - "reference": "https://github.com/bradsec/ComfyUI_ResolutionSelector", - "title": "ResolutionSelector for ComfyUI" - }, - { - "author": "kohya-ss", - "description": "Nodes: LLLiteLoader", - "files": [ - "https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI" - ], - "id": "lllite", - "install_type": "git-clone", - "reference": "https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI", - "title": "ControlNet-LLLite-ComfyUI" - }, - { - "author": "jjkramhoeft", - "description": "Nodes: SDXLRecommendedImageSize, JjkText, JjkShowText, JjkConcat. A set of custom nodes for ComfyUI - focused on text and parameter utility", - "files": [ - "https://github.com/jjkramhoeft/ComfyUI-Jjk-Nodes" - ], - "id": "jjk", - "install_type": "git-clone", - "reference": "https://github.com/jjkramhoeft/ComfyUI-Jjk-Nodes", - "title": "ComfyUI-Jjk-Nodes" - }, - { - "author": "dagthomas", - "description": "Easy prompting for generation of endless random art pieces and photographs!", - "files": [ - "https://github.com/dagthomas/comfyui_dagthomas" - ], - "id": "autoprompt", - "install_type": "git-clone", - "reference": "https://github.com/dagthomas/comfyui_dagthomas", - "title": "SDXL Auto Prompter" - }, - { - "author": "marhensa", - "description": "Input your desired output final resolution, it will automaticaly set the initial recommended SDXL ratio/size and its Upscale Factor to reach that output final resolution, also there's an option for 2x/4x reverse Upscale Factor. These all to avoid using bad/arbitary initial ratio/resolution.", - "files": [ - "https://github.com/marhensa/sdxl-recommended-res-calc" - ], - "id": "resoultion-calc", - "install_type": "git-clone", - "reference": "https://github.com/marhensa/sdxl-recommended-res-calc", - "title": "Recommended Resolution Calculator" - }, - { - "author": "Nuked", - "description": "A suite of custom nodes for ConfyUI that includes GPT text-prompt generation, LoadVideo,SaveVideo,LoadFramesFromFolder and FrameInterpolator", - "files": [ - "https://github.com/Nuked88/ComfyUI-N-Nodes" - ], - "id": "nnodes", - "install_type": "git-clone", - "reference": "https://github.com/Nuked88/ComfyUI-N-Nodes", - "title": "ComfyUI-N-Nodes" - }, - { - "author": "Nuked", - "description": "A simple sidebar for ComfyUI.", - "files": [ - "https://github.com/Nuked88/ComfyUI-N-Sidebar" - ], - "id": "nsidebar", - "install_type": "git-clone", - "reference": "https://github.com/Nuked88/ComfyUI-N-Sidebar", - "title": "ComfyUI-N-Sidebar" - }, - { - "author": "richinsley", - "description": "Nodes:LFO_Triangle, LFO_Sine, SawtoothNode, SquareNode, PulseNode. ComfyUI custom nodes to create Low Frequency Oscillators.", - "files": [ - "https://github.com/richinsley/Comfy-LFO" - ], - "id": "lfo", - "install_type": "git-clone", - "reference": "https://github.com/richinsley/Comfy-LFO", - "title": "Comfy-LFO" - }, - { - "author": "Beinsezii", - "description": "This contains all-in-one 'principled' nodes for T2I, I2I, refining, and scaling. Additionally it has many tools for directly manipulating the color of latents, high res fix math, and scripted image post-processing.", - "files": [ - "https://github.com/Beinsezii/bsz-cui-extras" - ], - "id": "bsz", - "install_type": "git-clone", - "reference": "https://github.com/Beinsezii/bsz-cui-extras", - "title": "bsz-cui-extras" - }, - { - "author": "youyegit", - "description": "Add Switch on nodes, Make nodes amount small! It helps conveniently to use less nodes for doing the same things.", - "files": [ - "https://github.com/youyegit/tdxh_node_comfyui" - ], - "id": "tdxh", - "install_type": "git-clone", - "reference": "https://github.com/youyegit/tdxh_node_comfyui", - "title": "tdxh_node_comfyui" - }, - { - "author": "Sxela", - "description": "WarpFusion workflow wrapper for ComfyUI", - "files": [ - "https://github.com/Sxela/ComfyWarp" - ], - "id": "comfywarp", - "install_type": "git-clone", - "reference": "https://github.com/Sxela/ComfyWarp", - "title": "ComfyWarp" - }, - { - "author": "skfoo", - "description": "Nodes:MultiLora Loader, Lora Text Extractor. Provides a node for assisting in loading loras through text.", - "files": [ - "https://github.com/skfoo/ComfyUI-Coziness" - ], - "id": "coziness", - "install_type": "git-clone", - "reference": "https://github.com/skfoo/ComfyUI-Coziness", - "title": "ComfyUI-Coziness" - }, - { - "author": "YOUR-WORST-TACO", - "description": "Nodes:TacoLatent, TacoAnimatedLoader, TacoImg2ImgAnimatedLoader, TacoGifMaker.", - "files": [ - "https://github.com/YOUR-WORST-TACO/ComfyUI-TacoNodes" - ], - "id": "taco", - "install_type": "git-clone", - "reference": "https://github.com/YOUR-WORST-TACO/ComfyUI-TacoNodes", - "title": "ComfyUI-TacoNodes" - }, - { - "author": "Lerc", - "description": "This extension provides a full page image editor with mask support. There are two nodes, one to receive images from the editor and one to send images to the editor.", - "files": [ - "https://github.com/Lerc/canvas_tab" - ], - "id": "canvastab", - "install_type": "git-clone", - "reference": "https://github.com/Lerc/canvas_tab", - "title": "Canvas Tab" - }, - { - "author": "Ttl", - "description": "Nodes:NNLatentUpscale, A custom ComfyUI node designed for rapid latent upscaling using a compact neural network, eliminating the need for VAE-based decoding and encoding.", - "files": [ - "https://github.com/Ttl/ComfyUi_NNLatentUpscale" - ], - "id": "nnlatent", - "install_type": "git-clone", - "preemptions": [ - "NNLatentUpscale" - ], - "reference": "https://github.com/Ttl/ComfyUi_NNLatentUpscale", - "title": "ComfyUI Neural Network Latent Upscale" - }, - { - "author": "spro", - "description": "Nodes: Latent Mirror. Node to mirror a latent along the Y (vertical / left to right) or X (horizontal / top to bottom) axis.", - "files": [ - "https://github.com/spro/comfyui-mirror" - ], - "id": "latentmirror", - "install_type": "git-clone", - "reference": "https://github.com/spro/comfyui-mirror", - "title": "Latent Mirror node for ComfyUI" - }, - { - "author": "Tropfchen", - "description": "Tired of forgetting and misspelling often weird names of embeddings you use? Or perhaps you use only one, cause you forgot you have tens of them installed?", - "files": [ - "https://github.com/Tropfchen/ComfyUI-Embedding_Picker" - ], - "id": "embedding-picker", - "install_type": "git-clone", - "reference": "https://github.com/Tropfchen/ComfyUI-Embedding_Picker", - "title": "Embedding Picker" - }, - { - "author": "Acly", - "description": "Provides nodes and server API extensions geared towards using ComfyUI as a backend for external tools.", - "files": [ - "https://github.com/Acly/comfyui-tooling-nodes" - ], - "id": "external-tooling", - "install_type": "git-clone", - "reference": "https://github.com/Acly/comfyui-tooling-nodes", - "title": "ComfyUI Nodes for External Tooling" - }, - { - "author": "Acly", - "description": "Nodes for better inpainting with ComfyUI. Adds various ways to pre-process inpaint areas. Supports the Fooocus inpaint model, a small and flexible patch which can be applied to any SDXL checkpoint and will improve consistency when generating masked areas.", - "files": [ - "https://github.com/Acly/comfyui-inpaint-nodes" - ], - "id": "inpaint-nodes", - "install_type": "git-clone", - "reference": "https://github.com/Acly/comfyui-inpaint-nodes", - "title": "ComfyUI Inpaint Nodes" - }, - { - "author": "picturesonpictures", - "description": "A collection of custom nodes for ComfyUI. Includes a quick canny edge detection node with unconventional settings, simple LoRA stack nodes for workflow efficiency, and a customizable aspect ratio node.", - "files": [ - "https://github.com/picturesonpictures/comfy_PoP" - ], - "id": "pop", - "install_type": "git-clone", - "reference": "https://github.com/picturesonpictures/comfy_PoP", - "title": "comfy_PoP" - }, - { - "author": "Dream Project", - "description": "This extension offers various nodes that are useful for Deforum-like animations in ComfyUI.", - "files": [ - "https://github.com/alt-key-project/comfyui-dream-project" - ], - "id": "dream-anime", - "install_type": "git-clone", - "reference": "https://github.com/alt-key-project/comfyui-dream-project", - "title": "Dream Project Animation Nodes" - }, - { - "author": "Dream Project", - "description": "Provide utilities for batch based video generation workflows (s.a. AnimateDiff and Stable Video Diffusion).", - "files": [ - "https://github.com/alt-key-project/comfyui-dream-video-batches" - ], - "id": "dream-video", - "install_type": "git-clone", - "reference": "https://github.com/alt-key-project/comfyui-dream-video-batches", - "title": "Dream Video Batches" - }, - { - "author": "seanlynch", - "description": "This package contains three nodes to help you compute optical flow between pairs of images, usually adjacent frames in a video, visualize the flow, and apply the flow to another image of the same dimensions. Most of the code is from Deforum, so this is released under the same license (MIT).", - "files": [ - "https://github.com/seanlynch/comfyui-optical-flow" - ], - "id": "optical-flow", - "install_type": "git-clone", - "reference": "https://github.com/seanlynch/comfyui-optical-flow", - "title": "ComfyUI Optical Flow" - }, - { - "author": "ealkanat", - "description": "ComfyUI Easy Padding is a simple custom ComfyUI node that helps you to add padding to images on ComfyUI.", - "files": [ - "https://github.com/ealkanat/comfyui-easy-padding" - ], - "id": "easy-padding", - "install_type": "git-clone", - "reference": "https://github.com/ealkanat/comfyui-easy-padding", - "title": "ComfyUI Easy Padding" - }, - { - "author": "ArtBot2023", - "description": "Character face swap with LoRA and embeddings.", - "files": [ - "https://github.com/ArtBot2023/CharacterFaceSwap" - ], - "id": "char-faceswap", - "install_type": "git-clone", - "reference": "https://github.com/ArtBot2023/CharacterFaceSwap", - "title": "Character Face Swap" - }, - { - "author": "mav-rik", - "description": "This is a copy of [a/facerestore custom node](https://civitai.com/models/24690/comfyui-facerestore-node) with a bit of a change to support CodeFormer Fidelity parameter. These ComfyUI nodes can be used to restore faces in images similar to the face restore option in AUTOMATIC1111 webui.\nNOTE: To use this node, you need to download the face restoration model and face detection model from the 'Install models' menu.", - "files": [ - "https://github.com/mav-rik/facerestore_cf" - ], - "id": "face-cf", - "install_type": "git-clone", - "reference": "https://github.com/mav-rik/facerestore_cf", - "title": "Facerestore CF (Code Former)" - }, - { - "author": "braintacles", - "description": "Nodes: CLIPTextEncodeSDXL-Multi-IO, CLIPTextEncodeSDXL-Pipe, Empty Latent Image from Aspect-Ratio, Random Find and Replace.", - "files": [ - "https://github.com/braintacles/braintacles-comfyui-nodes" - ], - "id": "braintacles", - "install_type": "git-clone", - "reference": "https://github.com/braintacles/braintacles-comfyui-nodes", - "title": "braintacles-nodes" - }, - { - "author": "hayden-fr", - "description": "Manage models: browsing, download and delete.", - "files": [ - "https://github.com/hayden-fr/ComfyUI-Model-Manager" - ], - "id": "modelmanager", - "install_type": "git-clone", - "reference": "https://github.com/hayden-fr/ComfyUI-Model-Manager", - "title": "ComfyUI-Model-Manager" - }, - { - "author": "ali1234", - "description": "Implements iteration over sequences within a single workflow run. [w/NOTE: This node replaces the execution of ComfyUI for iterative processing functionality.]", - "files": [ - "https://github.com/ali1234/comfyui-job-iterator" - ], - "id": "job-iterator", - "install_type": "git-clone", - "reference": "https://github.com/ali1234/comfyui-job-iterator", - "title": "comfyui-job-iterator" - }, - { - "author": "jmkl", - "description": "ComfyUI custom user.css and some script stuff. mainly for web interface.", - "files": [ - "https://github.com/jmkl/ComfyUI-ricing" - ], - "id": "ricing", - "install_type": "git-clone", - "reference": "https://github.com/jmkl/ComfyUI-ricing", - "title": "ComfyUI Ricing" - }, - { - "author": "budihartono", - "description": "Nodes: OTX Multiple Values, OTX KSampler Feeder. This extension provides custom nodes for ComfyUI created for personal projects. Made available for reference. Nodes may be updated or changed intermittently or not at all. Review & test before use.", - "files": [ - "https://github.com/budihartono/comfyui_otonx_nodes" - ], - "id": "otonx", - "install_type": "git-clone", - "reference": "https://github.com/budihartono/comfyui_otonx_nodes", - "title": "Otonx's Custom Nodes" - }, - { - "author": "budihartono", - "description": "Quickly create empty latents in common resolutions and aspect ratios for SD 1.5, SDXL, Flux, Chroma, and HiDream. Choose from curated presets or generate by axis and aspect ratio. Appears in the 'latent' node group.", - "files": [ - "https://github.com/budihartono/comfyui-aspect-ratio-presets" - ], - "id": "comfyui-aspect-ratio-presets", - "install_type": "git-clone", - "reference": "https://github.com/budihartono/comfyui-aspect-ratio-presets", - "title": "CAS Aspect Ratio Presets Node for ComfyUI" - }, - { - "author": "ramyma", - "description": "Nodes: Base64Image Input Node, Base64Image Output Node. [a/A8R8](https://github.com/ramyma/a8r8) supporting nodes to integrate with ComfyUI", - "files": [ - "https://github.com/ramyma/A8R8_ComfyUI_nodes" - ], - "id": "a8r8", - "install_type": "git-clone", - "reference": "https://github.com/ramyma/A8R8_ComfyUI_nodes", - "title": "A8R8 ComfyUI Nodes" - }, - { - "author": "spinagon", - "description": "Node for generating almost seamless textures, based on similar setting from A1111.", - "files": [ - "https://github.com/spinagon/ComfyUI-seamless-tiling" - ], - "id": "seamless", - "install_type": "git-clone", - "reference": "https://github.com/spinagon/ComfyUI-seamless-tiling", - "title": "Seamless tiling Node for ComfyUI" - }, - { - "author": "BiffMunky", - "description": "A small set of nodes I created for myself. Features multiple simultaneous prompts in batches, an image saver with ability to have JSON saved to separate folder, image analysis nodes, switches for text and numbers, and more.", - "files": [ - "https://github.com/tusharbhutt/Endless-Nodes" - ], - "id": "endless", - "install_type": "git-clone", - "reference": "https://github.com/tusharbhutt/Endless-Nodes", - "title": "Endless \ufe0f\ud83c\udf0a\u2728 Nodes" - }, - { - "author": "BiffMunky", - "description": "A small set of JavaScript files I created for myself. The scripts provide Quality of Life enhancements to the ComfyUI interface, such as changing fonts and font sizes.", - "files": [ - "https://github.com/tusharbhutt/Endless-Buttons" - ], - "install_type": "git-clone", - "reference": "https://github.com/tusharbhutt/Endless-Buttons", - "title": "Endless \ud83c\udf0a\u2728 Buttons" - }, - { - "author": "spacepxl", - "description": "Add Image Save nodes for TIFF 16 bit and EXR 32 bit formats. Probably only useful if you're applying a LUT or other color corrections, and care about preserving as much color accuracy as possible.", - "files": [ - "https://github.com/spacepxl/ComfyUI-HQ-Image-Save" - ], - "id": "hq-image-save", - "install_type": "git-clone", - "reference": "https://github.com/spacepxl/ComfyUI-HQ-Image-Save", - "title": "ComfyUI-HQ-Image-Save" - }, - { - "author": "spacepxl", - "description": "Image and matte filtering nodes for ComfyUI `image/filters/*`", - "files": [ - "https://github.com/spacepxl/ComfyUI-Image-Filters" - ], - "id": "image-fitlers", - "install_type": "git-clone", - "reference": "https://github.com/spacepxl/ComfyUI-Image-Filters", - "title": "ComfyUI-Image-Filters" - }, - { - "author": "spacepxl", - "description": "Unofficial ComfyUI implementation of [a/RAVE](https://rave-video.github.io/)", - "files": [ - "https://github.com/spacepxl/ComfyUI-RAVE" - ], - "id": "rave", - "install_type": "git-clone", - "reference": "https://github.com/spacepxl/ComfyUI-RAVE", - "title": "ComfyUI-RAVE" - }, - { - "author": "spacepxl", - "description": "Basic support for StyleGAN2 and StyleGAN3 models.", - "files": [ - "https://github.com/spacepxl/ComfyUI-StyleGan" - ], - "id": "stylegan", - "install_type": "git-clone", - "reference": "https://github.com/spacepxl/ComfyUI-StyleGan", - "title": "ComfyUI-StyleGan" - }, - { - "author": "spacepxl", - "description": "Based on [a/https://github.com/apple/ml-depth-pro](https://github.com/apple/ml-depth-pro)", - "files": [ - "https://github.com/spacepxl/ComfyUI-Depth-Pro" - ], - "install_type": "git-clone", - "reference": "https://github.com/spacepxl/ComfyUI-Depth-Pro", - "title": "ComfyUI-Depth-Pro" - }, - { - "author": "spacepxl", - "description": "Basic utility for testing diffusion model loss across the timestep schedule. Should work with any native models that use ksampler. This could be used for comparing models, testing captions on an image, etc.", - "files": [ - "https://github.com/spacepxl/ComfyUI-LossTesting" - ], - "install_type": "git-clone", - "reference": "https://github.com/spacepxl/ComfyUI-LossTesting", - "title": "ComfyUI-LossTesting" - }, - { - "author": "PTA", - "description": "A ComfyUI extension to apply better nodes layout algorithm to ComfyUI workflow (mostly for visualization purpose)", - "files": [ - "https://github.com/phineas-pta/comfyui-auto-nodes-layout" - ], - "id": "autolayout", - "install_type": "git-clone", - "reference": "https://github.com/phineas-pta/comfyui-auto-nodes-layout", - "title": "auto nodes layout" - }, - { - "author": "receyuki", - "description": "The ultimate solution for managing image metadata and multi-tool compatibility. ComfyUI node version of the SD Prompt Reader.", - "files": [ - "https://github.com/receyuki/comfyui-prompt-reader-node" - ], - "id": "sdpromptreader", - "install_type": "git-clone", - "reference": "https://github.com/receyuki/comfyui-prompt-reader-node", - "title": "SD Prompt Reader" - }, - { - "author": "cubiq", - "description": "Essential nodes that are weirdly missing from ComfyUI core. With few exceptions they are new features and not commodities. I hope this will be just a temporary repository until the nodes get included into ComfyUI.", - "files": [ - "https://github.com/cubiq/ComfyUI_essentials" - ], - "id": "essentials", - "install_type": "git-clone", - "reference": "https://github.com/cubiq/ComfyUI_essentials", - "title": "ComfyUI Essentials" - }, - { - "author": "Clybius", - "description": "Nodes: Latent Diffusion Mega Modifier. ComfyUI nodes which modify the latent during the diffusion process. (Sharpness, Tonemap, Rescale, Extra Noise)", - "files": [ - "https://github.com/Clybius/ComfyUI-Latent-Modifiers" - ], - "id": "latent-modifier", - "install_type": "git-clone", - "reference": "https://github.com/Clybius/ComfyUI-Latent-Modifiers", - "title": "ComfyUI-Latent-Modifiers" - }, - { - "author": "Clybius", - "description": "Nodes: SamplerCustomNoise, SamplerCustomNoiseDuo, SamplerCustomModelMixtureDuo, SamplerRES_Momentumized, SamplerDPMPP_DualSDE_Momentumized, SamplerCLYB_4M_SDE_Momentumized, SamplerTTM, SamplerLCMCustom\nThis extension provides various custom samplers not offered by the default nodes in ComfyUI.", - "files": [ - "https://github.com/Clybius/ComfyUI-Extra-Samplers" - ], - "id": "extra-samplers", - "install_type": "git-clone", - "reference": "https://github.com/Clybius/ComfyUI-Extra-Samplers", - "title": "ComfyUI Extra Samplers" - }, - { - "author": "Clybius", - "description": "A small collection of nodes intended for use with Lodestone Rock's Chroma model, for ComfyUI.", - "files": [ - "https://github.com/Clybius/ComfyUI-ClybsChromaNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Clybius/ComfyUI-ClybsChromaNodes", - "title": "ComfyUI-ClybsChromaNodes" - }, - { - "author": "mcmonkeyprojects", - "description": "Adds nodes for Dynamic Thresholding, CFG scheduling, and related techniques.", - "files": [ - "https://github.com/mcmonkeyprojects/sd-dynamic-thresholding" - ], - "id": "dynamic-thresholding", - "install_type": "git-clone", - "reference": "https://github.com/mcmonkeyprojects/sd-dynamic-thresholding", - "title": "Dynamic Thresholding" - }, - { - "author": "Tropfchen", - "description": "A slightly different Resolution Selector node, allowing to freely change base resolution and aspect ratio, with options to maintain the pixel count or use the base resolution as the highest or lowest dimension.", - "files": [ - "https://github.com/Tropfchen/ComfyUI-yaResolutionSelector" - ], - "id": "yars", - "install_type": "git-clone", - "reference": "https://github.com/Tropfchen/ComfyUI-yaResolutionSelector", - "title": "YARS: Yet Another Resolution Selector" - }, - { - "author": "chrisgoringe", - "description": "Nodes to create small variations on noise, to shape noise, and to control noise in batches. Replaces the old 'variation-seed' nodes.", - "files": [ - "https://github.com/chrisgoringe/cg-noisetools" - ], - "id": "cg-noisetools", - "install_type": "git-clone", - "reference": "https://github.com/chrisgoringe/cg-noisetools", - "title": "Noise variation and batch noise tools" - }, - { - "author": "chrisgoringe", - "description": "A set of custom nodes that pause the flow to allow you to pick images, edit parameters, set masks etc..", - "files": [ - "https://github.com/chrisgoringe/cg-image-filter" - ], - "id": "image-filter", - "install_type": "git-clone", - "reference": "https://github.com/chrisgoringe/cg-image-filter", - "title": "Image Filter" - }, - { - "author": "chrisgoringe", - "description": "A set of nodes that allow data to be 'broadcast' to some or all unconnected inputs. Greatly reduces link spaghetti.", - "files": [ - "https://github.com/chrisgoringe/cg-use-everywhere" - ], - "id": "ue", - "install_type": "git-clone", - "nodename_pattern": "(^(Prompts|Anything) Everywhere|Simple String)", - "reference": "https://github.com/chrisgoringe/cg-use-everywhere", - "title": "Use Everywhere (UE Nodes)" - }, - { - "author": "chrisgoringe", - "description": "Prompt Info", - "files": [ - "https://github.com/chrisgoringe/cg-prompt-info" - ], - "id": "promptinfo", - "install_type": "git-clone", - "reference": "https://github.com/chrisgoringe/cg-prompt-info", - "title": "Prompt Info" - }, - { - "author": "chrisgoringe", - "description": "Quickly and easily build a GUI on top of your workflow. Gather just the nodes that you want to see, with no spaghetti, onto controller panels, leaving your workflow untouched in the background.", - "files": [ - "https://github.com/chrisgoringe/cg-controller" - ], - "id": "cg-comfycontroller", - "install_type": "git-clone", - "reference": "https://github.com/chrisgoringe/cg-controller", - "title": "Comfy Controller" - }, - { - "author": "seanlynch", - "description": "Nodes: SRL Conditional Interrupt, SRL Format String, SRL Eval, SRL Filter Image List. This is a collection of nodes I find useful. Note that at least one module allows execution of arbitrary code. Do not use any of these nodes on a system that allow untrusted users to control workflows or inputs.[w/WARNING: The custom nodes in this extension are vulnerable to **security risks** because they allow the execution of arbitrary code through the workflow]", - "files": [ - "https://github.com/seanlynch/srl-nodes" - ], - "id": "srl", - "install_type": "git-clone", - "reference": "https://github.com/seanlynch/srl-nodes", - "title": "SRL's nodes" - }, - { - "author": "alpertunga-bile", - "description": "Custom AI prompt generator node for ComfyUI.", - "files": [ - "https://github.com/alpertunga-bile/prompt-generator-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/alpertunga-bile/prompt-generator-comfyui", - "title": "prompt-generator" - }, - { - "author": "kijai", - "description": "Various quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability.", - "files": [ - "https://github.com/kijai/ComfyUI-KJNodes" - ], - "id": "kjnodes", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-KJNodes", - "title": "KJNodes for ComfyUI" - }, - { - "author": "kijai", - "description": "ComfyUI- CCSR upscaler node", - "files": [ - "https://github.com/kijai/ComfyUI-CCSR" - ], - "id": "ccsr", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-CCSR", - "title": "ComfyUI-CCSR" - }, - { - "author": "kijai", - "description": "Preliminary use of SVD in ComfyUI.\nNOTE: Quick Implementation, Unstable. See details on repositories.", - "files": [ - "https://github.com/kijai/ComfyUI-SVD" - ], - "id": "kijai-svd", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-SVD", - "title": "ComfyUI-SVD" - }, - { - "author": "kijai", - "description": "This is a wrapper node for Marigold depth estimation: [https://github.com/prs-eth/Marigold](https://github.com/kijai/ComfyUI-Marigold). Currently using the same diffusers pipeline as in the original implementation, so in addition to the custom node, you need the model in diffusers format.\nNOTE: See details in repo to install.", - "files": [ - "https://github.com/kijai/ComfyUI-Marigold" - ], - "id": "marigold", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-Marigold", - "title": "Marigold depth estimation in ComfyUI" - }, - { - "author": "kijai", - "description": "This is a diffusers (0.27.2) wrapper node for Geowizard: [https://github.com/fuxiao0719/GeoWizard]. The model is autodownloaded from Hugginface to ComfyUI/models/diffusers/geowizard", - "files": [ - "https://github.com/kijai/ComfyUI-Geowizard" - ], - "id": "geowizard", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-Geowizard", - "title": "Geowizard depth and normal estimation in ComfyUI" - }, - { - "author": "kijai", - "description": "Fast and accurate monocular depth estimation.", - "files": [ - "https://github.com/kijai/ComfyUI-depth-fm" - ], - "id": "depth-fm", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-depth-fm", - "title": "ComfyUI-depth-fm" - }, - { - "author": "kijai", - "description": "Node to use [a/DDColor](https://github.com/piddnad/DDColor) in ComfyUI.", - "files": [ - "https://github.com/kijai/ComfyUI-DDColor" - ], - "id": "ddcolor-kijai", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-DDColor", - "title": "ComfyUI-DDColor" - }, - { - "author": "kijai", - "description": "This is a trainer for AnimateDiff MotionLoRAs, based on the implementation of MotionDirector by ExponentialML.\nNOTE:[a/ADMotionDirector](https://github.com/ExponentialML/AnimateDiff-MotionDirector)", - "files": [ - "https://github.com/kijai/ComfyUI-ADMotionDirector" - ], - "id": "motionlora-trainer", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-ADMotionDirector", - "title": "Animatediff MotionLoRA Trainer" - }, - { - "author": "kijai", - "description": "Moondream image to text query node with batch support", - "files": [ - "https://github.com/kijai/ComfyUI-moondream" - ], - "id": "moondream", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-moondream", - "title": "ComfyUI-moondream" - }, - { - "author": "kijai", - "description": "Wrapper nodes to use SUPIR upscaling process in ComfyUI", - "files": [ - "https://github.com/kijai/ComfyUI-SUPIR" - ], - "id": "supir", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-SUPIR", - "title": "ComfyUI-SUPIR" - }, - { - "author": "kijai", - "description": "Wrapper nodes to use DynamiCrafter image2video and frame interpolation models in ComfyUI\nAnd this extension supports ToonCrafter as well", - "files": [ - "https://github.com/kijai/ComfyUI-DynamiCrafterWrapper" - ], - "id": "dynamicrafter-kijai", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-DynamiCrafterWrapper", - "title": "ComfyUI-DynamiCrafterWrapper" - }, - { - "author": "kijai", - "description": "Node to use [a/APISR](https://github.com/Kiteretsu77/APISR) upscale models in ComfyUI.[w/NOTE: repo name is changed from ComfyUI-APISR -> ComfyUI-APISR-KJ]", - "files": [ - "https://github.com/kijai/ComfyUI-APISR-KJ" - ], - "id": "apisr", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-APISR-KJ", - "title": "ComfyUI-APISR" - }, - { - "author": "kijai", - "description": "This is simplified implementation of the [a/DiffusionLight](https://github.com/DiffusionLight/DiffusionLight) method of creating light probes. You will need the included LoRA, place it in ComfyUI/loras folder like usual, it's converted from the original diffusers one.", - "files": [ - "https://github.com/kijai/ComfyUI-DiffusionLight" - ], - "id": "diffusionlight", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-DiffusionLight", - "title": "DiffusionLight implementation for ComfyUI" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper nodes to use the Diffusers implementation of ELLA", - "files": [ - "https://github.com/kijai/ComfyUI-ELLA-wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-ELLA-wrapper", - "title": "ComfyUI-ELLA-wrapper" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper node to test LaVi-Bridge using Diffusers", - "files": [ - "https://github.com/kijai/ComfyUI-LaVi-Bridge-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-LaVi-Bridge-Wrapper", - "title": "ComfyUI-LaVi-Bridge-Wrapper" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper nodes to use the Diffusers implementation of BrushNet", - "files": [ - "https://github.com/kijai/ComfyUI-BrushNet-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-BrushNet-Wrapper", - "title": "ComfyUI-BrushNet-Wrapper" - }, - { - "author": "kijai", - "description": "ComfyUI native nodes for IC-Light", - "files": [ - "https://github.com/kijai/ComfyUI-IC-Light" - ], - "id": "ic-light-kijai", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-IC-Light", - "title": "ComfyUI-IC-Light" - }, - { - "author": "kijai", - "description": "ComfyUI nodes to use [a/DepthAnythingV2](https://depth-anything-v2.github.io/)\nNOTE:Models autodownload to ComfyUI/models/depthanything from [a/https://huggingface.co/Kijai/DepthAnythingV2-safetensors/tree/main](https://huggingface.co/Kijai/DepthAnythingV2-safetensors/tree/main)", - "files": [ - "https://github.com/kijai/ComfyUI-DepthAnythingV2" - ], - "id": "depth-anything-v2", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-DepthAnythingV2", - "title": "ComfyUI-DepthAnythingV2" - }, - { - "author": "kijai", - "description": "Nodes to use Florence2 VLM for image vision tasks: object detection, captioning, segmentation and ocr", - "files": [ - "https://github.com/kijai/ComfyUI-Florence2" - ], - "id": "florence2-kijai", - "install_type": "git-clone", - "preemptions": [ - "DownloadAndLoadFlorence2Lora", - "DownloadAndLoadFlorence2Model", - "Florence2ModelLoader", - "Florence2Run" - ], - "reference": "https://github.com/kijai/ComfyUI-Florence2", - "title": "ComfyUI-Florence2" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper nodes for Lumina models", - "files": [ - "https://github.com/kijai/ComfyUI-LuminaWrapper" - ], - "id": "lumina", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-LuminaWrapper", - "title": "ComfyUI-LuminaWrapper" - }, - { - "author": "kijai", - "description": "Optimized wrapper nodes for MimicMotion: [a/https://github.com/tencent/MimicMotion](https://github.com/tencent/MimicMotion)", - "files": [ - "https://github.com/kijai/ComfyUI-MimicMotionWrapper" - ], - "id": "mimicmotion-kijai", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-MimicMotionWrapper", - "title": "ComfyUI-MimicMotionWrapper" - }, - { - "author": "kijai", - "description": "Wrapper nodes for OpenDiT: [a/OpenDiT](https://github.com/NUS-HPC-AI-Lab/OpenDiT/), supports Open-Sora t2i and i2i", - "files": [ - "https://github.com/kijai/ComfyUI-OpenDiTWrapper" - ], - "id": "opendit-kijai", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-OpenDiTWrapper", - "title": "ComfyUI-OpenDiTWrapper" - }, - { - "author": "kijai", - "description": "Nodes for [a/LivePortrait](https://github.com/KwaiVGI/LivePortrait)", - "files": [ - "https://github.com/kijai/ComfyUI-LivePortraitKJ" - ], - "id": "liveportrait-kijai", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-LivePortraitKJ", - "title": "ComfyUI-LivePortraitKJ" - }, - { - "author": "kijai", - "description": "Rudimentary wrapper that runs [a/Kwai-Kolors](https://huggingface.co/Kwai-Kolors/Kolors) text2image pipeline using diffusers.", - "files": [ - "https://github.com/kijai/ComfyUI-KwaiKolorsWrapper" - ], - "id": "kwaikolors", - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-KwaiKolorsWrapper", - "title": "ComfyUI-KwaiKolorsWrapper" - }, - { - "author": "kijai", - "description": "Nodes to use [a/segment-anything-2](https://github.com/facebookresearch/segment-anything-2) for image or video segmentation.", - "files": [ - "https://github.com/kijai/ComfyUI-segment-anything-2" - ], - "id": "segment-anything-2", - "install_type": "git-clone", - "preemptions": [ - "DownloadAndLoadSAM2Model", - "Florence2toCoordinates", - "Sam2AutoSegmentation", - "Sam2Segmentation", - "Sam2VideoSegmentation", - "Sam2VideoSegmentationAddPoints" - ], - "reference": "https://github.com/kijai/ComfyUI-segment-anything-2", - "title": "ComfyUI-segment-anything-2" - }, - { - "author": "kijai", - "description": "These nodes include my wrapper for the original diffusers pipeline, as well as work in progress native ComfyUI implementation.\nFor the diffusers wrapper models should be downloaded automatically, for the native version you can get the unet [a/here](https://huggingface.co/Kijai/ControlNeXt-SVD-V2-Comfy/blob/main/controlnext-svd_v2-unet-fp16_converted.safetensors).", - "files": [ - "https://github.com/kijai/ComfyUI-ControlNeXt-SVD" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-ControlNeXt-SVD", - "title": "ComfyUI nodes for ControlNext-SVD v2" - }, - { - "author": "kijai", - "description": "Currently supports LoRA training, and untested full finetune with code from kohya's scripts: [a/https://github.com/kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts)", - "files": [ - "https://github.com/kijai/ComfyUI-FluxTrainer" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-FluxTrainer", - "title": "ComfyUI Flux Trainer" - }, - { - "author": "kijai", - "description": "Diffusers wrapper for CogVideoX -models: [a/https://github.com/THUDM/CogVideo](https://github.com/THUDM/CogVideo)", - "files": [ - "https://github.com/kijai/ComfyUI-CogVideoXWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-CogVideoXWrapper", - "title": "ComfyUI CogVideoX Wrapper" - }, - { - "author": "kijai", - "description": "Wrapper for PyramidFlow -models: [a/https://github.com/jy0205/Pyramid-Flow](https://github.com/jy0205/Pyramid-Flow)", - "files": [ - "https://github.com/kijai/ComfyUI-PyramidFlowWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-PyramidFlowWrapper", - "title": "ComfyUI PyramidFlow Wrapper" - }, - { - "author": "kijai", - "description": "Nodes to use the OneVision LLaVA models: [a/https://github.com/LLaVA-VL/LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT)", - "files": [ - "https://github.com/kijai/ComfyUI-LLaVA-OneVision" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-LLaVA-OneVision", - "title": "ComfyUI Llava-OneVision" - }, - { - "author": "kijai", - "description": "Original repo: [a/https://github.com/luckyhzt/LVCD](https://github.com/luckyhzt/LVCD)", - "files": [ - "https://github.com/kijai/ComfyUI-LVCDWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-LVCDWrapper", - "title": "ComfyUI wrapper nodes for LVCD" - }, - { - "author": "kijai", - "description": "ComfyUI nodes to use Lotus depth/normal prediction.\nNOTE:The necessary models can be downloaded from ComfyUI-Manager.", - "files": [ - "https://github.com/kijai/ComfyUI-Lotus" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-Lotus", - "title": "ComfyUI-Lotus" - }, - { - "author": "kijai", - "description": "NODES:(Down)load MoGe Model, MoGe Process", - "files": [ - "https://github.com/kijai/ComfyUI-MoGe" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-MoGe", - "title": "ComfyUI-MoGe" - }, - { - "author": "kijai", - "description": "ComfyUI nodes to use GIMM-VFI frame interpolation", - "files": [ - "https://github.com/kijai/ComfyUI-GIMM-VFI" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-GIMM-VFI", - "title": "ComfyUI-GIMM-VFI" - }, - { - "author": "kijai", - "description": "ComfyUI diffusers wrapper nodes for [a/HunyuanVideo](https://github.com/Tencent/HunyuanVideo)", - "files": [ - "https://github.com/kijai/ComfyUI-HunyuanVideoWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-HunyuanVideoWrapper", - "title": "ComfyUI-HunyuanVideoWrapper" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper for [a/StableX normal](https://github.com/Stable-X/StableNormal)/[a/delight](https://github.com/Stable-X/StableDelight) models", - "files": [ - "https://github.com/kijai/ComfyUI-StableXWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-StableXWrapper", - "title": "ComfyUI-StableXWrapper" - }, - { - "author": "kijai", - "description": "This node allows using Hugginface remote server for latent decoding. Currently supported models: SD, SDXL, Flux, HunyuanVideo", - "files": [ - "https://github.com/kijai/ComfyUI-HFRemoteVae" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-HFRemoteVae", - "title": "ComfyUI-HFRemoteVae" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper nodes for [a/Latent Bridge Matching (LBM)](https://github.com/gojasper/LBM)", - "files": [ - "https://github.com/kijai/ComfyUI-LBMWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-LBMWrapper", - "title": "ComfyUI-LBMWrapper" - }, - { - "author": "kijai", - "description": "ComfyUI wrapper nodes for [a/WanVideo](https://github.com/Wan-Video/Wan2.1) and related models.", - "files": [ - "https://github.com/kijai/ComfyUI-WanVideoWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-WanVideoWrapper", - "title": "ComfyUI-WanVideoWrapper" - }, - { - "author": "kijai", - "description": "Mel-Band RoFormer for Music Source Separation", - "files": [ - "https://github.com/kijai/ComfyUI-MelBandRoFormer" - ], - "install_type": "git-clone", - "reference": "https://github.com/kijai/ComfyUI-MelBandRoFormer", - "title": "ComfyUI-MelBandRoFormer" - }, - { - "author": "hhhzzyang", - "description": "Nodes: LamaaModelLoad, LamaApply, YamlConfigLoader. a costumer node is realized to remove anything/inpainting anything from a picture by mask inpainting.[w/WARN:This extension includes the entire model, which can result in a very long initial installation time, and there may be some compatibility issues with older dependencies and ComfyUI.]", - "files": [ - "https://github.com/hhhzzyang/Comfyui_Lama" - ], - "id": "lama", - "install_type": "git-clone", - "reference": "https://github.com/hhhzzyang/Comfyui_Lama", - "title": "Comfyui-Lama" - }, - { - "author": "audioscavenger", - "description": "Save as AVIF, WebP, JPEG, customize the folder, sub-folders, and filenames of your images!", - "files": [ - "https://github.com/audioscavenger/save-image-extended-comfyui" - ], - "id": "save-image-extended", - "install_type": "git-clone", - "reference": "https://github.com/audioscavenger/save-image-extended-comfyui", - "title": "Save Image Extended for ComfyUI" - }, - { - "author": "audioscavenger", - "description": "Load Image thumbnails, delete images, browse input subfolders.", - "files": [ - "https://github.com/audioscavenger/ComfyUI-Thumbnails" - ], - "id": "thumbnails", - "install_type": "git-clone", - "reference": "https://github.com/audioscavenger/ComfyUI-Thumbnails", - "title": "ComfyUI-Thumbnails" - }, - { - "author": "SOELexicon", - "description": "ComfyUI-LexTools is a Python-based image processing and analysis toolkit that uses machine learning models for semantic image segmentation, image scoring, and image captioning.", - "files": [ - "https://github.com/SOELexicon/ComfyUI-LexTools" - ], - "id": "lextools", - "install_type": "git-clone", - "reference": "https://github.com/SOELexicon/ComfyUI-LexTools", - "title": "ComfyUI-LexTools" - }, - { - "author": "mikkel", - "description": "The ComfyUI Text Overlay Plugin provides functionalities for superimposing text on images. Users can select different font types, set text size, choose color, and adjust the text's position on the image.", - "files": [ - "https://github.com/mikkel/ComfyUI-text-overlay" - ], - "id": "textoverlay-mikkel", - "install_type": "git-clone", - "reference": "https://github.com/mikkel/ComfyUI-text-overlay", - "title": "ComfyUI - Text Overlay Plugin" - }, - { - "author": "avatechai", - "description": "Include nodes for sam + bpy operation, that allows workflow creations for generative 2d character rig.", - "files": [ - "https://github.com/avatechai/avatar-graph-comfyui" - ], - "id": "avatar-graph", - "install_type": "git-clone", - "reference": "https://github.com/avatechai/avatar-graph-comfyui", - "title": "Avatar Graph" - }, - { - "author": "TRI3D-LC", - "description": "Nodes: tri3d-extract-hand, tri3d-fuzzification, tri3d-position-hands, tri3d-atr-parse.", - "files": [ - "https://github.com/TRI3D-LC/tri3d-comfyui-nodes" - ], - "id": "tri3d", - "install_type": "git-clone", - "reference": "https://github.com/TRI3D-LC/tri3d-comfyui-nodes", - "title": "tri3d-comfyui-nodes" - }, - { - "author": "TRI3D-LC", - "description": "Nodes: add-image-miro-board.", - "files": [ - "https://github.com/TRI3D-LC/ComfyUI-MiroBoard" - ], - "id": "miroboard", - "install_type": "git-clone", - "reference": "https://github.com/TRI3D-LC/ComfyUI-MiroBoard", - "title": "ComfyUI-MiroBoard" - }, - { - "author": "storyicon", - "description": "Based on GroundingDino and SAM, use semantic strings to segment any element in an image. The comfyui version of sd-webui-segment-anything.", - "files": [ - "https://github.com/storyicon/comfyui_segment_anything" - ], - "id": "sam", - "install_type": "git-clone", - "reference": "https://github.com/storyicon/comfyui_segment_anything", - "title": "segment anything" - }, - { - "author": "storyicon", - "description": "Nodes:MuseVImg2Vid (comfyui_musev_evolved)\nNOTE: Download [a/MuseV](https://huggingface.co/TMElyralab/MuseV) to ComfyUI/models/diffusers", - "files": [ - "https://github.com/storyicon/comfyui_musev_evolved" - ], - "id": "musev-evolved", - "install_type": "git-clone", - "reference": "https://github.com/storyicon/comfyui_musev_evolved", - "title": "ComfyUI MuseV Evolved" - }, - { - "author": "a1lazydog", - "description": "Load mp3 files and use the audio nodes to power animations and prompt scheduling. Use with FizzNodes.", - "files": [ - "https://github.com/a1lazydog/ComfyUI-AudioScheduler" - ], - "id": "audioscheduler", - "install_type": "git-clone", - "reference": "https://github.com/a1lazydog/ComfyUI-AudioScheduler", - "title": "ComfyUI-AudioScheduler" - }, - { - "author": "whatbirdisthat", - "description": "Cyberdolphin Suite of ComfyUI nodes for wiring up things.", - "files": [ - "https://github.com/whatbirdisthat/cyberdolphin" - ], - "install_type": "git-clone", - "reference": "https://github.com/whatbirdisthat/cyberdolphin", - "title": "cyberdolphin" - }, - { - "author": "chrish-slingshot", - "description": "A mixture of effects and quality of life nodes. Nodes: ImageGlitcher (gives an image a cool glitchy effect), ColorStylizer (highlights a single color in an image), QueryLocalLLM (queries a local LLM API though oobabooga), SDXLReslution (resolution picker for the standard SDXL resolutions, the complete list), SDXLResolutionSplit (splits the SDXL resolution into width and height). ", - "files": [ - "https://github.com/chrish-slingshot/CrasHUtils" - ], - "id": "crash", - "install_type": "git-clone", - "reference": "https://github.com/chrish-slingshot/CrasHUtils", - "title": "CrasH Utils" - }, - { - "author": "spinagon", - "description": "Nodes: Image Resize (seam carving). Seam carving (image resize) for ComfyUI. Based on [a/https://github.com/li-plus/seam-carving](https://github.com/li-plus/seam-carving). With seam carving algorithm, the image could be intelligently resized while keeping the important contents undistorted. The carving process could be further guided, so that an object could be removed from the image without apparent artifacts.", - "files": [ - "https://github.com/spinagon/ComfyUI-seam-carving" - ], - "id": "seamcarving", - "install_type": "git-clone", - "reference": "https://github.com/spinagon/ComfyUI-seam-carving", - "title": "ComfyUI-seam-carving" - }, - { - "author": "YMC", - "description": "ymc 's nodes for comfyui. This extension is composed of nodes that provide various utility features such as text, region, and I/O.", - "files": [ - "https://github.com/YMC-GitHub/ymc-node-suite-comfyui" - ], - "id": "ymc-suite", - "install_type": "git-clone", - "reference": "https://github.com/YMC-GitHub/ymc-node-suite-comfyui", - "title": "ymc-node-suite-comfyui" - }, - { - "author": "YMC", - "description": "comfyui custom nodes to caption image with joy", - "files": [ - "https://github.com/YMC-GitHub/ymc_node_joy" - ], - "install_type": "git-clone", - "reference": "https://github.com/YMC-GitHub/ymc_node_joy", - "title": "ymc_node_joy" - }, - { - "author": "YMC", - "description": "some comfyui custom nodes to set it as known type", - "files": [ - "https://github.com/YMC-GitHub/ymc-node-as-x-type" - ], - "install_type": "git-clone", - "reference": "https://github.com/YMC-GitHub/ymc-node-as-x-type", - "title": "ymc-node-as-x-type" - }, - { - "author": "YMC", - "description": "some comfyui custom nodes to make effect shatter", - "files": [ - "https://github.com/YMC-GitHub/comfyui_node_ymc_effect_shatter" - ], - "install_type": "git-clone", - "reference": "https://github.com/YMC-GitHub/comfyui_node_ymc_effect_shatter", - "title": "comfyui_node_ymc_effect_shatter" - }, - { - "author": "chibiace", - "description": "Nodes:Loader, Prompts, ImageTool, Wildcards, LoadEmbedding, ConditionText, SaveImages, ...", - "files": [ - "https://github.com/chibiace/ComfyUI-Chibi-Nodes" - ], - "id": "chibi", - "install_type": "git-clone", - "reference": "https://github.com/chibiace/ComfyUI-Chibi-Nodes", - "title": "ComfyUI-Chibi-Nodes" - }, - { - "author": "DigitalIO", - "description": "Wildcard implementation that can be reproduced with workflows.", - "files": [ - "https://github.com/DigitalIO/ComfyUI-stable-wildcards" - ], - "id": "stable-wildcards", - "install_type": "git-clone", - "reference": "https://github.com/DigitalIO/ComfyUI-stable-wildcards", - "title": "ComfyUI-stable-wildcards" - }, - { - "author": "THtianhao", - "description": "Nodes:RetainFace, FaceFusion, RatioMerge2Image, MaskMerge2Image, ReplaceBoxImg, ExpandMaskBox, FaceSkin, SkinRetouching, PortraitEnhancement, ...", - "files": [ - "https://github.com/THtianhao/ComfyUI-Portrait-Maker" - ], - "id": "portrait-maker", - "install_type": "git-clone", - "reference": "https://github.com/THtianhao/ComfyUI-Portrait-Maker", - "title": "ComfyUI-Portrait-Maker" - }, - { - "author": "THtianhao", - "description": "The official ComfyUI version of facechain greatly improves the speed of reasoning and has great custom process controls.", - "files": [ - "https://github.com/THtianhao/ComfyUI-FaceChain" - ], - "id": "facechain", - "install_type": "git-clone", - "reference": "https://github.com/THtianhao/ComfyUI-FaceChain", - "title": "ComfyUI-FaceChain" - }, - { - "author": "zer0TF", - "description": "Adds a configurable folder watcher that auto-converts Comfy metadata into a Civitai-friendly format for automatic resource tagging when you upload images. Oh, and it makes your UI awesome, too. \ud83d\udc9c", - "files": [ - "https://github.com/zer0TF/cute-comfy" - ], - "id": "cutecomfy", - "install_type": "git-clone", - "reference": "https://github.com/zer0TF/cute-comfy", - "title": "Cute Comfy" - }, - { - "author": "chflame163", - "description": "A text-to-speech plugin used under ComfyUI. It utilizes the Microsoft Speech TTS interface to convert text content into MP3 format audio files.", - "files": [ - "https://github.com/chflame163/ComfyUI_MSSpeech_TTS" - ], - "id": "msspeech", - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_MSSpeech_TTS", - "title": "ComfyUI_MSSpeech_TTS" - }, - { - "author": "chflame163", - "description": "Nodes:Word Cloud, Load Text File", - "files": [ - "https://github.com/chflame163/ComfyUI_WordCloud" - ], - "id": "wordcloud", - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_WordCloud", - "title": "ComfyUI_WordCloud" - }, - { - "author": "chflame163", - "description": "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress.", - "files": [ - "https://github.com/chflame163/ComfyUI_LayerStyle" - ], - "id": "layerstyle", - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_LayerStyle", - "title": "ComfyUI Layer Style" - }, - { - "author": "chflame163", - "description": "The nodes detached from ComfyUI Layer Style are mainly those with complex requirements for dependency packages.", - "files": [ - "https://github.com/chflame163/ComfyUI_LayerStyle_Advance" - ], - "id": "comfyui_layerstyle_advance", - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_LayerStyle_Advance", - "title": "ComfyUI_LayerStyle_Advance" - }, - { - "author": "chflame163", - "description": "A custom node for ComfyUI. It compare two images to rate facial similarity.", - "files": [ - "https://github.com/chflame163/ComfyUI_FaceSimilarity" - ], - "id": "face-similarity", - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_FaceSimilarity", - "title": "ComfyUI Face Similarity" - }, - { - "author": "chflame163", - "description": "[a/CatVTON](https://github.com/Zheng-Chong/CatVTON) warpper for ComfyUI", - "files": [ - "https://github.com/chflame163/ComfyUI_CatVTON_Wrapper" - ], - "id": "catvton-wrapper", - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_CatVTON_Wrapper", - "title": "ComfyUI_CatVTON_Wrapper" - }, - { - "author": "chflame163", - "description": "ComfyUI custom node of OmniGen project.", - "files": [ - "https://github.com/chflame163/ComfyUI_OmniGen_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_OmniGen_Wrapper", - "title": "ComfyUI_OmniGen_Wrapper" - }, - { - "author": "chflame163", - "description": "Unofficial implementation of [a/deepseek-ai/Janus](https://github.com/deepseek-ai/Janus) in ComfyUI.", - "files": [ - "https://github.com/chflame163/ComfyUI_Janus_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_Janus_Wrapper", - "title": "ComfyUI_Janus_Wrapper" - }, - { - "author": "chflame163", - "description": "The unofficial implementation of CogView4 project in ComfyUI.", - "files": [ - "https://github.com/chflame163/ComfyUI_CogView4_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/chflame163/ComfyUI_CogView4_Wrapper", - "title": "ComfyUI_CogView4_Wrapper" - }, - { - "author": "drustan-hawk", - "description": "Small collection of typed primitive nodes.", - "files": [ - "https://github.com/drustan-hawk/primitive-types" - ], - "install_type": "git-clone", - "reference": "https://github.com/drustan-hawk/primitive-types", - "title": "primitive-types" - }, - { - "author": "shadowcz007", - "description": "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ...", - "files": [ - "https://github.com/shadowcz007/comfyui-mixlab-nodes" - ], - "id": "mixlab", - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-mixlab-nodes", - "reference2": "https://github.com/MixLabPro/comfyui-mixlab-nodes", - "title": "comfyui-mixlab-nodes" - }, - { - "author": "shadowcz007", - "description": "Nodes:Detect By Label.", - "files": [ - "https://github.com/shadowcz007/comfyui-ultralytics-yolo" - ], - "id": "yolo", - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-ultralytics-yolo", - "title": "comfyui-ultralytics-yolo" - }, - { - "author": "shadowcz007", - "description": "[a/openai Consistency Decoder](https://github.com/openai/consistencydecoder). After downloading the [a/OpenAI VAE model](https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt), place it in the `model/vae` directory for use.", - "files": [ - "https://github.com/shadowcz007/comfyui-consistency-decoder" - ], - "id": "consistency-decoder", - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-consistency-decoder", - "title": "Consistency Decoder" - }, - { - "author": "shadowcz007", - "description": "[a/ImageReward](https://github.com/THUDM/ImageReward): Human preference learning in text-to-image generation. This is a [a/paper](https://arxiv.org/abs/2304.05977) from NeurIPS 2023", - "files": [ - "https://github.com/shadowcz007/comfyui-Image-reward" - ], - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-Image-reward", - "title": "comfyui-Image-reward" - }, - { - "author": "shadowcz007", - "description": "Nodes:Music Gen, Audio Play, Stable Audio", - "files": [ - "https://github.com/shadowcz007/comfyui-sound-lab" - ], - "id": "soundlab", - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-sound-lab", - "reference2": "https://github.com/MixLabPro/comfyui-sound-lab", - "title": "comfyui-sound-lab" - }, - { - "author": "shadowcz007", - "description": "Nodes:Edit Mask", - "files": [ - "https://github.com/shadowcz007/comfyui-edit-mask" - ], - "id": "edit-mask", - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-edit-mask", - "title": "comfyui-edit-mask" - }, - { - "author": "shadowcz007", - "description": "The ComfyUI version of [a/LivePortrait](https://github.com/KwaiVGI/LivePortrait).", - "files": [ - "https://github.com/shadowcz007/comfyui-liveportrait" - ], - "id": "liveportrait", - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-liveportrait", - "reference2": "https://github.com/MixLabPro/comfyui-liveportrait", - "title": "comfyui-liveportrait" - }, - { - "author": "shadowcz007", - "description": "Virtual try-on for creating a personal brand wardrobe collection.", - "files": [ - "https://github.com/shadowcz007/comfyui-try-on" - ], - "install_type": "git-clone", - "reference": "https://github.com/shadowcz007/comfyui-try-on", - "reference2": "https://github.com/MixLabPro/comfyui-try-on", - "title": "comfyui-try-on" - }, - { - "author": "ostris", - "description": "This is a collection of custom nodes for ComfyUI that I made for some QOL. I will be adding much more advanced ones in the future once I get more familiar with the API.", - "files": [ - "https://github.com/ostris/ostris_nodes_comfyui" - ], - "id": "ostris", - "install_type": "git-clone", - "nodename_pattern": "- Ostris$", - "reference": "https://github.com/ostris/ostris_nodes_comfyui", - "title": "Ostris Nodes ComfyUI" - }, - { - "author": "ostris", - "description": "Some tools to help with [a/Flex.1-alpha](https://huggingface.co/ostris/Flex.1-alpha) inference on Comfy UI.", - "files": [ - "https://github.com/ostris/ComfyUI-FlexTools" - ], - "install_type": "git-clone", - "nodename_pattern": "- Ostris$", - "reference": "https://github.com/ostris/ComfyUI-FlexTools", - "title": "Flex.1 tools" - }, - { - "author": "0xbitches", - "description": "This custom node implements a Latent Consistency Model sampler in ComfyUI. (LCM)", - "files": [ - "https://github.com/0xbitches/ComfyUI-LCM" - ], - "id": "lcm", - "install_type": "git-clone", - "reference": "https://github.com/0xbitches/ComfyUI-LCM", - "title": "Latent Consistency Model for ComfyUI" - }, - { - "author": "aszc-dev", - "description": "This extension contains a set of custom nodes for ComfyUI that allow you to use Core ML models in your ComfyUI workflows. The models can be obtained here, or you can convert your own models using coremltools. The main motivation behind using Core ML models in ComfyUI is to allow you to utilize the ANE (Apple Neural Engine) on Apple Silicon (M1/M2) machines to improve performance.", - "files": [ - "https://github.com/aszc-dev/ComfyUI-CoreMLSuite" - ], - "id": "coreml", - "install_type": "git-clone", - "reference": "https://github.com/aszc-dev/ComfyUI-CoreMLSuite", - "title": "Core ML Suite for ComfyUI" - }, - { - "author": "taabata", - "description": "Nodes:Prompt editing, Word as Image", - "files": [ - "https://raw.githubusercontent.com/taabata/Comfy_Syrian_Falcon_Nodes/main/SyrianFalconNodes.py" - ], - "id": "syrian", - "install_type": "copy", - "reference": "https://github.com/taabata/Comfy_Syrian_Falcon_Nodes", - "title": "Syrian Falcon Nodes" - }, - { - "author": "taabata", - "description": "ComfyUI custom nodes for inpainting/outpainting using the new latent consistency model (LCM)", - "files": [ - "https://github.com/taabata/LCM_Inpaint_Outpaint_Comfy" - ], - "id": "lcm-inpaint-outpaint", - "install_type": "git-clone", - "reference": "https://github.com/taabata/LCM_Inpaint_Outpaint_Comfy", - "title": "LCM_Inpaint-Outpaint_Comfy" - }, - { - "author": "taabata", - "description": "Canvas to use with ComfyUI", - "files": [ - "https://github.com/taabata/ComfyCanvas" - ], - "install_type": "git-clone", - "reference": "https://github.com/taabata/ComfyCanvas", - "title": "ComfyCanvas" - }, - { - "author": "taabata", - "description": "ComfyUI Diffusers wrapper nodes to run SANA models on low vram devices. Works on 2GB VRAM 12GB RAM laptop.", - "files": [ - "https://github.com/taabata/SANA_LOWVRAM" - ], - "install_type": "git-clone", - "reference": "https://github.com/taabata/SANA_LOWVRAM", - "title": "SANA_LOWVRAM" - }, - { - "author": "noxinias", - "description": "Nodes: Noxin Complete Chime, Noxin Scaled Resolutions, Load from Noxin Prompt Library, Save to Noxin Prompt Library", - "files": [ - "https://github.com/noxinias/ComfyUI_NoxinNodes" - ], - "id": "noxin", - "install_type": "git-clone", - "reference": "https://github.com/noxinias/ComfyUI_NoxinNodes", - "title": "ComfyUI_NoxinNodes" - }, - { - "author": "kinfolk0117", - "description": "Nodes:TileSplit, TileMerge.", - "files": [ - "https://github.com/kinfolk0117/ComfyUI_SimpleTiles" - ], - "id": "simpletiles", - "install_type": "git-clone", - "reference": "https://github.com/kinfolk0117/ComfyUI_SimpleTiles", - "title": "SimpleTiles" - }, - { - "author": "kinfolk0117", - "description": "Nodes:GradientPatchModelAddDownscale (Kohya Deep Shrink).", - "files": [ - "https://github.com/kinfolk0117/ComfyUI_GradientDeepShrink" - ], - "id": "deepshrink", - "install_type": "git-clone", - "reference": "https://github.com/kinfolk0117/ComfyUI_GradientDeepShrink", - "title": "ComfyUI_GradientDeepShrink" - }, - { - "author": "kinfolk0117", - "description": "Use [a/Pilgram2](https://github.com/mgineer85/pilgram2) filters in ComfyUI", - "files": [ - "https://github.com/kinfolk0117/ComfyUI_Pilgram" - ], - "id": "pilgram", - "install_type": "git-clone", - "reference": "https://github.com/kinfolk0117/ComfyUI_Pilgram", - "title": "ComfyUI_Pilgram" - }, - { - "author": "kinfolk0117", - "description": "Gridswapper takes a batch of latents and spreads them over the necessary amount of grids. It then automatically shuffles the images in the grids for each step. So, a batch of 12 latents for a 2x2 grid will generate 3 grid images in each step. It will then shuffle around the images for the next step. This makes it possible for all images to influence the others during the denoising process. This approach works well for generating 2-4 grids.", - "files": [ - "https://github.com/kinfolk0117/ComfyUI_GridSwapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/kinfolk0117/ComfyUI_GridSwapper", - "title": "Gridswapper" - }, - { - "author": "Fictiverse", - "description": "Nodes:Essential Params, Image Params, Video Params, Add Margin With Color, Resize to megapixels", - "files": [ - "https://github.com/Fictiverse/ComfyUI_Fictiverse" - ], - "id": "fictverse", - "install_type": "git-clone", - "reference": "https://github.com/Fictiverse/ComfyUI_Fictiverse", - "title": "ComfyUI Fictiverse Nodes" - }, - { - "author": "idrirap", - "description": "The aim of these custom nodes is to get an easy access to the tags used to trigger a lora / lycoris. Extract the tags from civitai or from the safetensors metadatas when available.", - "files": [ - "https://github.com/idrirap/ComfyUI-Lora-Auto-Trigger-Words" - ], - "id": "lora-auto-trigger", - "install_type": "git-clone", - "reference": "https://github.com/idrirap/ComfyUI-Lora-Auto-Trigger-Words", - "title": "ComfyUI-Lora-Auto-Trigger-Words" - }, - { - "author": "aianimation55", - "description": "It's a super simple custom node for Comfy UI, to generate text, with a font size option. Useful for bigger labelling of nodes, helpful for wider screen captures or tutorials. Plus you can of course use the text within your generations.", - "files": [ - "https://github.com/aianimation55/ComfyUI-FatLabels" - ], - "id": "fatlab", - "install_type": "git-clone", - "reference": "https://github.com/aianimation55/ComfyUI-FatLabels", - "title": "Comfy UI FatLabels" - }, - { - "author": "noEmbryo", - "description": "PromptTermList (1-6): are some nodes that help with the creation of Prompts inside ComfyUI. Resolution Scale outputs image dimensions using a scale factor. Regex Text Chopper outputs the chopped parts of a text using RegEx.", - "files": [ - "https://github.com/noembryo/ComfyUI-noEmbryo" - ], - "id": "noembryo", - "install_type": "git-clone", - "reference": "https://github.com/noembryo/ComfyUI-noEmbryo", - "title": "noEmbryo nodes" - }, - { - "author": "mikkel", - "description": "The ComfyUI Mask Bounding Box Plugin provides functionalities for selecting a specific size mask from an image. Can be combined with ClipSEG to replace any aspect of an SDXL image with an SD1.5 output.", - "files": [ - "https://github.com/mikkel/comfyui-mask-boundingbox" - ], - "id": "mask-bbox", - "install_type": "git-clone", - "reference": "https://github.com/mikkel/comfyui-mask-boundingbox", - "title": "ComfyUI - Mask Bounding Box" - }, - { - "author": "ParmanBabra", - "description": "Nodes:Multi Lora Loader, Random (Prompt), Combine (Prompt), CSV Prompts Loader", - "files": [ - "https://github.com/ParmanBabra/ComfyUI-Malefish-Custom-Scripts" - ], - "id": "malefish", - "install_type": "git-clone", - "reference": "https://github.com/ParmanBabra/ComfyUI-Malefish-Custom-Scripts", - "title": "ComfyUI-Malefish-Custom-Scripts" - }, - { - "author": "IAmMatan.com", - "description": "This extension adds nodes that allow you to easily serve your workflow (for example using a discord bot) ", - "files": [ - "https://github.com/matan1905/ComfyUI-Serving-Toolkit" - ], - "id": "serving-toolkit", - "install_type": "git-clone", - "reference": "https://github.com/matan1905/ComfyUI-Serving-Toolkit", - "title": "ComfyUI Serving toolkit" - }, - { - "author": "PCMonsterx", - "description": "CSV Loader for prompt building within ComfyUI interface. Allows access to positive/negative prompts associated with a name. Selections are being pulled from CSV files.", - "files": [ - "https://github.com/PCMonsterx/ComfyUI-CSV-Loader" - ], - "id": "csv-loader", - "install_type": "git-clone", - "reference": "https://github.com/PCMonsterx/ComfyUI-CSV-Loader", - "title": "ComfyUI-CSV-Loader" - }, - { - "author": "Trung0246", - "description": "Random nodes for ComfyUI I made to solve my struggle with ComfyUI (ex: pipe, process). Have varying quality.", - "files": [ - "https://github.com/Trung0246/ComfyUI-0246" - ], - "id": "0246", - "install_type": "git-clone", - "reference": "https://github.com/Trung0246/ComfyUI-0246", - "title": "ComfyUI-0246" - }, - { - "author": "fexli", - "description": "A set of Fe's Util nodes for ComfyUI", - "files": [ - "https://github.com/fexli/fexli-util-node-comfyui" - ], - "id": "fexli-util-node-comfyui", - "install_type": "git-clone", - "reference": "https://github.com/fexli/fexli-util-node-comfyui", - "title": "fexli-util-node-comfyui" - }, - { - "author": "AbyssBadger0", - "description": "Nodes:ImageOverlap-badger, FloatToInt-badger, IntToString-badger, FloatToString-badger, ImageNormalization-badger, ImageScaleToSide-badger, NovelToFizz-badger.", - "files": [ - "https://github.com/AbyssBadger0/ComfyUI_BadgerTools" - ], - "id": "badger", - "install_type": "git-clone", - "reference": "https://github.com/AbyssBadger0/ComfyUI_BadgerTools", - "title": "ComfyUI_BadgerTools" - }, - { - "author": "palant", - "description": "This custom node provides various tools for resizing images. The goal is resizing without distorting proportions, yet without having to perform any calculations with the size of the original image. If a mask is present, it is resized and modified along with the image.", - "files": [ - "https://github.com/palant/image-resize-comfyui" - ], - "id": "image-resize", - "install_type": "git-clone", - "reference": "https://github.com/palant/image-resize-comfyui", - "title": "Image Resize for ComfyUI" - }, - { - "author": "palant", - "description": "This tool will turn entire workflows or parts of them into single integrated nodes. In a way, it is similar to the Node Templates functionality but hides the inner structure. This is useful if all you want is to reuse and quickly configure a bunch of nodes without caring how they are interconnected.", - "files": [ - "https://github.com/palant/integrated-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/palant/integrated-nodes-comfyui", - "title": "Integrated Nodes for ComfyUI" - }, - { - "author": "whmc76", - "description": "Nodes:Openpose Editor Plus", - "files": [ - "https://github.com/whmc76/ComfyUI-Openpose-Editor-Plus" - ], - "id": "openpose-editor-plus", - "install_type": "git-clone", - "reference": "https://github.com/whmc76/ComfyUI-Openpose-Editor-Plus", - "title": "ComfyUI-Openpose-Editor-Plus" - }, - { - "author": "whmc76", - "description": "A matting toolkit based on ComfyUI, supporting multiple matting models and detail processing methods.", - "files": [ - "https://github.com/whmc76/ComfyUI-RemoveBackgroundSuite" - ], - "install_type": "git-clone", - "reference": "https://github.com/whmc76/ComfyUI-RemoveBackgroundSuite", - "title": "ComfyUI-RemoveBackgroundSuite" - }, - { - "author": "whmc76", - "description": "This plugin provides general-purpose utility nodes for ComfyUI. Currently, it implements a 'Blank Cell Generator' node, which can batch-generate images, masks, and latents with specified resolution and color.", - "files": [ - "https://github.com/whmc76/ComfyUI-UniversalToolkit" - ], - "install_type": "git-clone", - "reference": "https://github.com/whmc76/ComfyUI-UniversalToolkit", - "title": "ComfyUI-UniversalToolkit" - }, - { - "author": "martijnat", - "description": "a ComfyUI plugin for previewing latents without vae decoding. Useful for showing intermediate results and can be used a faster 'preview image' if you don't wan't to use vae decode.", - "files": [ - "https://github.com/martijnat/comfyui-previewlatent" - ], - "install_type": "git-clone", - "reference": "https://github.com/martijnat/comfyui-previewlatent", - "title": "comfyui-previewlatent" - }, - { - "author": "banodoco", - "description": "Steerable Motion is a ComfyUI node for batch creative interpolation. Our goal is to feature the best methods for steering motion with images as video models evolve.", - "files": [ - "https://github.com/banodoco/steerable-motion" - ], - "id": "steerable-motion", - "install_type": "git-clone", - "reference": "https://github.com/banodoco/steerable-motion", - "title": "Steerable Motion" - }, - { - "author": "gemell1", - "description": "Nodes:GMIC Image Processing.", - "files": [ - "https://github.com/gemell1/ComfyUI_GMIC" - ], - "id": "gmic", - "install_type": "git-clone", - "reference": "https://github.com/gemell1/ComfyUI_GMIC", - "title": "ComfyUI_GMIC" - }, - { - "author": "LonicaMewinsky", - "description": "Nodes:BreakFrames, GetKeyFrames, MakeGrid.", - "files": [ - "https://github.com/LonicaMewinsky/ComfyUI-MakeFrame" - ], - "id": "breakanim", - "install_type": "git-clone", - "reference": "https://github.com/LonicaMewinsky/ComfyUI-MakeFrame", - "title": "ComfyBreakAnim" - }, - { - "author": "TheBarret", - "description": "Nodes:Prompter, RF Noise, SeedMod.", - "files": [ - "https://github.com/TheBarret/ZSuite" - ], - "id": "zsuite", - "install_type": "git-clone", - "reference": "https://github.com/TheBarret/ZSuite", - "title": "ZSuite" - }, - { - "author": "romeobuilderotti", - "description": "Add custom Metadata fields to your saved PNG files.", - "files": [ - "https://github.com/romeobuilderotti/ComfyUI-PNG-Metadata" - ], - "id": "pngmeta", - "install_type": "git-clone", - "reference": "https://github.com/romeobuilderotti/ComfyUI-PNG-Metadata", - "title": "ComfyUI PNG Metadata" - }, - { - "author": "ka-puna", - "description": "NOTE: Concatenate Strings, Format Datetime String, Integer Caster, Multiline String, Truncate String. Yet Another Node Collection, a repository of simple nodes for ComfyUI. This repository eases the addition or removal of custom nodes to itself.", - "files": [ - "https://github.com/ka-puna/comfyui-yanc" - ], - "id": "yanc", - "install_type": "git-clone", - "reference": "https://github.com/ka-puna/comfyui-yanc", - "title": "comfyui-yanc" - }, - { - "author": "amorano", - "description": "Webcam, MIDI, Spout, and GLSL support with animation via tick. Features wave-based parameter modulation, math operations, universal value conversion, shape masking, image channel ops, batch processing, dynamic bus routing, GIPHY and SPOUT integration. Load images/videos from URLs, save output anywhere, and apply transformations like flattening, cropping, and color adjustments. Includes tools for color blindness simulation, stereograms, and stereoscopic imaging\u2014plus much more!", - "files": [ - "https://github.com/Amorano/Jovimetrix" - ], - "id": "jovimetrix", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovimetrix", - "title": "Jovimetrix" - }, - { - "author": "amorano", - "description": "Integrates GLSL shader support.", - "files": [ - "https://github.com/Amorano/Jovi_GLSL" - ], - "id": "jovi_glsl", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovi_GLSL", - "title": "Jovi_GLSL" - }, - { - "author": "amorano", - "description": "ComfyUI Nodes for using Spout streams.", - "files": [ - "https://github.com/Amorano/Jovi_Spout" - ], - "id": "jovi_spout", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovi_Spout", - "title": "Jovi_Spout" - }, - { - "author": "amorano", - "description": "Image metrics nodes for ComfyUI", - "files": [ - "https://github.com/Amorano/Jovi_Measure" - ], - "id": "jovi_measure", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovi_Measure", - "title": "Jovi_Measure" - }, - { - "author": "amorano", - "description": "Read and Process data from MIDI devices inside of ComfyUI.", - "files": [ - "https://github.com/Amorano/Jovi_MIDI" - ], - "id": "jovi_midi", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovi_MIDI", - "title": "Jovi_MIDI" - }, - { - "author": "amorano", - "description": "Capture Webcamera and URL media streams as ComfyUI images.", - "files": [ - "https://github.com/Amorano/Jovi_Capture" - ], - "id": "jovi_capture", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovi_Capture", - "title": "Jovi_Capture" - }, - { - "author": "amorano", - "description": "Colorize ComfyUI nodes with defaults per node, node category or via regex filtering.", - "files": [ - "https://github.com/Amorano/Jovi_Colorizer" - ], - "id": "jovijovi_colorizer_capture", - "install_type": "git-clone", - "reference": "https://github.com/Amorano/Jovi_Colorizer", - "title": "Jovi_Colorizer" - }, - { - "author": "Umikaze-job", - "description": "This extension simply connects the nodes and specifies the output path of the generated images to a manageable path.", - "files": [ - "https://github.com/Umikaze-job/select_folder_path_easy" - ], - "install_type": "git-clone", - "reference": "https://github.com/Umikaze-job/select_folder_path_easy", - "title": "select_folder_path_easy" - }, - { - "author": "Niutonian", - "description": "Nodes:Noodle webcam is a node that records frames and send them to your favourite node.", - "files": [ - "https://github.com/Niutonian/ComfyUi-NoodleWebcam" - ], - "id": "noodle-webcam", - "install_type": "git-clone", - "reference": "https://github.com/Niutonian/ComfyUi-NoodleWebcam", - "title": "ComfyUi-NoodleWebcam" - }, - { - "author": "Feidorian", - "description": "This extension provides various custom nodes. literals, loaders, logic, output, switches", - "files": [ - "https://github.com/Feidorian/feidorian-ComfyNodes" - ], - "id": "feidorian", - "install_type": "git-clone", - "nodename_pattern": "^Feidorian_", - "reference": "https://github.com/Feidorian/feidorian-ComfyNodes", - "title": "feidorian-ComfyNodes" - }, - { - "author": "wutipong", - "description": "Nodes:Create N-Token String", - "files": [ - "https://github.com/wutipong/ComfyUI-TextUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/wutipong/ComfyUI-TextUtils", - "title": "ComfyUI-TextUtils" - }, - { - "author": "natto-maki", - "description": "Nodes:OpenAI DALLe3, OpenAI Translate to English, String Function, Seed Generator", - "files": [ - "https://github.com/natto-maki/ComfyUI-NegiTools" - ], - "id": "negitools", - "install_type": "git-clone", - "reference": "https://github.com/natto-maki/ComfyUI-NegiTools", - "title": "ComfyUI-NegiTools" - }, - { - "author": "LonicaMewinsky", - "description": "Nodes:SaveTifImage. ComfyUI custom node for purpose of saving image as uint16 tif file.", - "files": [ - "https://github.com/LonicaMewinsky/ComfyUI-RawSaver" - ], - "id": "rawsaver", - "install_type": "git-clone", - "reference": "https://github.com/LonicaMewinsky/ComfyUI-RawSaver", - "title": "ComfyUI-RawSaver" - }, - { - "author": "jojkaart", - "description": "Nodes:LCMScheduler, SamplerLCMAlternative, SamplerLCMCycle. ComfyUI Custom Sampler nodes that add a new improved LCM sampler functions", - "files": [ - "https://github.com/jojkaart/ComfyUI-sampler-lcm-alternative" - ], - "id": "lmc-alt", - "install_type": "git-clone", - "reference": "https://github.com/jojkaart/ComfyUI-sampler-lcm-alternative", - "title": "ComfyUI-sampler-lcm-alternative" - }, - { - "author": "GTSuya-Studio", - "description": "ComfyUI-GTSuya-Nodes is a ComfyUI extension designed to add several wildcards supports into ComfyUI. Wildcards allow you to use __name__ syntax in your prompt to get a random line from a file named name.txt in a wildcards directory.", - "files": [ - "https://github.com/GTSuya-Studio/ComfyUI-Gtsuya-Nodes" - ], - "id": "gtsuya", - "install_type": "git-clone", - "reference": "https://github.com/GTSuya-Studio/ComfyUI-Gtsuya-Nodes", - "title": "ComfyUI-GTSuya-Nodes" - }, - { - "author": "oyvindg", - "description": "Nodes: BinaryImageMask, ImagePadding, LoadLastCreatedImage, RandomMask, TransparentImage.", - "files": [ - "https://github.com/oyvindg/ComfyUI-TrollSuite" - ], - "id": "troll", - "install_type": "git-clone", - "reference": "https://github.com/oyvindg/ComfyUI-TrollSuite", - "title": "ComfyUI-TrollSuite" - }, - { - "author": "drago87", - "description": "Nodes:File Padding, Image Info, VAE Loader With Name", - "files": [ - "https://github.com/drago87/ComfyUI_Dragos_Nodes" - ], - "id": "dragos", - "install_type": "git-clone", - "reference": "https://github.com/drago87/ComfyUI_Dragos_Nodes", - "title": "ComfyUI_Dragos_Nodes" - }, - { - "author": "bronkula", - "description": "Nodes:Fit Size From Int/Image/Resize, Load Image And Resize To Fit, Pick Image From Batch/List, Crop Image Into Even Pieces, Image Region To Mask... A simple set of nodes for making an image fit within a bounding box", - "files": [ - "https://github.com/bronkula/comfyui-fitsize" - ], - "id": "fitsize", - "install_type": "git-clone", - "reference": "https://github.com/bronkula/comfyui-fitsize", - "title": "comfyui-fitsize" - }, - { - "author": "toyxyz", - "description": "This node was created to send a webcam to ComfyUI in real time. This node is recommended for use with LCM.", - "files": [ - "https://github.com/toyxyz/ComfyUI_toyxyz_test_nodes" - ], - "id": "comfyui_toyxyz_test_nodes", - "install_type": "git-clone", - "reference": "https://github.com/toyxyz/ComfyUI_toyxyz_test_nodes", - "title": "ComfyUI_toyxyz_test_nodes" - }, - { - "author": "toyxyz", - "description": "This is the rgb2x wrapper node for ComfyUI. The required models are automatically downloaded on the first run.\noriginal project : [a/https://github.com/zheng95z/rgbx](original project : https://github.com/zheng95z/rgbx)", - "files": [ - "https://github.com/toyxyz/ComfyUI_rgbx_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/toyxyz/ComfyUI_rgbx_Wrapper", - "title": "ComfyUI_rgbx_Wrapper" - }, - { - "author": "thecooltechguy", - "description": "Easily use Stable Video Diffusion inside ComfyUI!", - "files": [ - "https://github.com/thecooltechguy/ComfyUI-Stable-Video-Diffusion" - ], - "install_type": "git-clone", - "reference": "https://github.com/thecooltechguy/ComfyUI-Stable-Video-Diffusion", - "title": "ComfyUI Stable Video Diffusion" - }, - { - "author": "thecooltechguy", - "description": "Easily use Magic Animate within ComfyUI!\n[w/WARN: This extension requires 15GB disk space.]", - "files": [ - "https://github.com/thecooltechguy/ComfyUI-MagicAnimate" - ], - "install_type": "git-clone", - "reference": "https://github.com/thecooltechguy/ComfyUI-MagicAnimate", - "title": "ComfyUI-MagicAnimate" - }, - { - "author": "thecooltechguy", - "description": "The best way to run, share, & discover thousands of ComfyUI workflows.", - "files": [ - "https://github.com/thecooltechguy/ComfyUI-ComfyWorkflows" - ], - "install_type": "git-clone", - "reference": "https://github.com/thecooltechguy/ComfyUI-ComfyWorkflows", - "title": "ComfyUI-ComfyWorkflows" - }, - { - "author": "Danand", - "description": " If you want to draw two different characters together without blending their features, so you could try to check out this custom node.", - "files": [ - "https://github.com/Danand/ComfyUI-ComfyCouple" - ], - "install_type": "git-clone", - "reference": "https://github.com/Danand/ComfyUI-ComfyCouple", - "title": "Comfy Couple" - }, - { - "author": "42lux", - "description": "A collection of custom nodes for ComfyUI focused on enhanced sampling, model optimization, and quality improvements.", - "files": [ - "https://github.com/42lux/ComfyUI-42lux" - ], - "install_type": "git-clone", - "reference": "https://github.com/42lux/ComfyUI-42lux", - "title": "ComfyUI-42lux" - }, - { - "author": "sergekatzmann", - "description": "Nodes:Image Square Adapter Node, Image Resize And Crop Node", - "files": [ - "https://github.com/sergekatzmann/ComfyUI_Nimbus-Pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/sergekatzmann/ComfyUI_Nimbus-Pack", - "title": "ComfyUI_Nimbus-Pack" - }, - { - "author": "komojini", - "description": "Nodes:XL DreamBooth LoRA, S3 Bucket LoRA", - "files": [ - "https://github.com/komojini/ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/komojini/ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes", - "title": "ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes" - }, - { - "author": "komojini", - "description": "Nodes:YouTube Video Loader. Custom ComfyUI Nodes for video generation", - "files": [ - "https://github.com/komojini/komojini-comfyui-nodes" - ], - "id": "komojini-nodes", - "install_type": "git-clone", - "reference": "https://github.com/komojini/komojini-comfyui-nodes", - "title": "komojini-comfyui-nodes" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of APISR for ComfyUI, both image and video", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-APISR" - ], - "id": "apisr-zho", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-APISR", - "title": "APISR IN COMFYUI" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Nodes:Text_Image_Zho, Text_Image_Multiline_Zho, RGB_Image_Zho, AlphaChanelAddByMask, ImageComposite_Zho, ...", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite" - ], - "id": "txtimg-composite", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite", - "title": "ComfyUI-Text_Image-Composite [WIP]" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "ComfyUI Portrait Master \u7b80\u4f53\u4e2d\u6587\u7248.", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/comfyui-portrait-master-zh-cn" - ], - "id": "portrait-master-zho", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/comfyui-portrait-master-zh-cn", - "title": "comfyui-portrait-master-zh-cn" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Nodes:Q-Align Scoring. Implementation of [a/Q-Align](https://arxiv.org/abs/2312.17090) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Q-Align" - ], - "id": "qalign-zho", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Q-Align", - "title": "ComfyUI-Q-Align" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/InstantID](https://github.com/InstantID/InstantID) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID" - ], - "id": "instantid-zho", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID", - "title": "ComfyUI-InstantID" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/PhotoMaker](https://github.com/TencentARC/PhotoMaker) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PhotoMaker-ZHO" - ], - "id": "photomaker-zho", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PhotoMaker-ZHO", - "title": "ComfyUI PhotoMaker (ZHO)" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "QWen-VL-Plus & QWen-VL-Max in ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Qwen-VL-API" - ], - "id": "qwen-vl-api", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Qwen-VL-API", - "title": "ComfyUI-Qwen-VL-API" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "My Workflows + Auxiliary nodes for Stable Video Diffusion (SVD)", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-SVD-ZHO" - ], - "id": "svd-zho", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-SVD-ZHO", - "title": "ComfyUI-SVD-ZHO (WIP)" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/SegMoE: Segmind Mixture of Diffusion Experts](https://github.com/segmind/segmoe) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-SegMoE" - ], - "id": "segmoe", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-SegMoE", - "title": "ComfyUI SegMoE" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/YOLO-World + EfficientSAM](https://huggingface.co/spaces/SkalskiP/YOLO-World) & [a/YOLO-World](https://github.com/AILab-CVC/YOLO-World) for ComfyUI\nNOTE: Install the efficient_sam model from the Install models menu.\n[w/When installing or updating this custom node, many installation packages may be downgraded due to the installation of requirements.\n!! python3.12 is incompatible.]", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-YoloWorld-EfficientSAM" - ], - "id": "yoloworld", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-YoloWorld-EfficientSAM", - "title": "ComfyUI YoloWorld-EfficientSAM" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/PixArt-alpha-Diffusers](https://github.com/PixArt-alpha/PixArt-alpha) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PixArt-alpha-Diffusers" - ], - "id": "pixart-alpha", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PixArt-alpha-Diffusers", - "title": "ComfyUI-PixArt-alpha-Diffusers" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of BRIA RMBG Model for ComfyUI.", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG" - ], - "id": "bria-ai-rmbg", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG", - "title": "ComfyUI-BRIA_AI-RMBG" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/DepthFM](https://github.com/CompVis/depth-fm) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-DepthFM" - ], - "id": "depthfm", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-DepthFM", - "title": "DepthFM IN COMFYUI" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Nodes:Phi3mini_4k_ModelLoader_Zho, Phi3mini_4k_Zho, Phi3mini_4k_Chat_Zho", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Phi-3-mini" - ], - "id": "phi3mini", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Phi-3-mini", - "title": "Phi-3-mini in ComfyUI" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Prompt Visualization | Art Gallery\n[w/WARN: Installation requires 2GB of space, and it will involve a long download time.]", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-ArtGallery" - ], - "id": "artgallery", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-ArtGallery", - "title": "ComfyUI-ArtGallery" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Animated optical illusions in ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Animated-optical-illusions" - ], - "id": "animated-optical-illusion", - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Animated-optical-illusions", - "title": "ComfyUI-Animated-optical-illusions" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "Unofficial implementation of [a/UltraEdit](https://github.com/HaozheZhao/UltraEdit) (Diffusers) for ComfyUI", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-UltraEdit-ZHO" - ], - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-UltraEdit-ZHO", - "title": "ComfyUI-UltraEdit-ZHO" - }, - { - "author": "ZHO-ZHO-ZHO", - "description": "ComfyUI-DeepSeek-JanusPro", - "files": [ - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-DeepSeek-JanusPro" - ], - "install_type": "git-clone", - "reference": "https://github.com/ZHO-ZHO-ZHO/ComfyUI-DeepSeek-JanusPro", - "title": "ComfyUI-DeepSeek-JanusPro" - }, - { - "author": "kenjiqq", - "description": "Nodes:Any List, Image Accumulator Start, Image Accumulator End, Load Lines From Text File, XY Grid Helper, Slice List, Axis To String/Int/Float/Model, ...", - "files": [ - "https://github.com/kenjiqq/qq-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/kenjiqq/qq-nodes-comfyui", - "title": "qq-nodes-comfyui" - }, - { - "author": "80sVectorz", - "description": "Adds Static Primitives to ComfyUI. Mostly to work with reroute nodes", - "files": [ - "https://github.com/80sVectorz/ComfyUI-Static-Primitives" - ], - "install_type": "git-clone", - "reference": "https://github.com/80sVectorz/ComfyUI-Static-Primitives", - "title": "ComfyUI-Static-Primitives" - }, - { - "author": "AbdullahAlfaraj", - "description": "Nodes: load Image with metadata, get config data, load image from base64 string, Load Loras From Prompt, Generate Latent Noise, Combine Two Latents Into Batch, General Purpose Controlnet Unit, ControlNet Script, Content Mask Latent, Auto-Photoshop-SD Seed, Expand and Blur the Mask", - "files": [ - "https://github.com/AbdullahAlfaraj/Comfy-Photoshop-SD" - ], - "install_type": "git-clone", - "reference": "https://github.com/AbdullahAlfaraj/Comfy-Photoshop-SD", - "title": "Comfy-Photoshop-SD" - }, - { - "author": "zhuanqianfish", - "description": "Capture window content from other programs, easyway combined with LCM for real-time painting", - "files": [ - "https://github.com/zhuanqianfish/ComfyUI-EasyNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/zhuanqianfish/ComfyUI-EasyNode", - "title": "EasyCaptureNode for ComfyUI" - }, - { - "author": "discopixel-studio", - "description": "A small collection of custom nodes for use with ComfyUI, by [a/Discopixel](https://discopixel.studio)", - "files": [ - "https://github.com/discopixel-studio/comfyui-discopixel" - ], - "install_type": "git-clone", - "reference": "https://github.com/discopixel-studio/comfyui-discopixel", - "title": "PhotoRoom Nodes by Discopixel" - }, - { - "author": "zcfrank1st", - "description": "Nodes: Yolov8Detection, Yolov8Segmentation. Deadly simple yolov8 comfyui plugin", - "files": [ - "https://github.com/zcfrank1st/Comfyui-Yolov8" - ], - "install_type": "git-clone", - "reference": "https://github.com/zcfrank1st/Comfyui-Yolov8", - "title": "ComfyUI Yolov8" - }, - { - "author": "SoftMeng", - "description": "Nodes: ComfyUI Mexx Styler, ComfyUI Mexx Styler Advanced", - "files": [ - "https://github.com/SoftMeng/ComfyUI_Mexx_Styler" - ], - "install_type": "git-clone", - "reference": "https://github.com/SoftMeng/ComfyUI_Mexx_Styler", - "title": "ComfyUI_Mexx_Styler" - }, - { - "author": "SoftMeng", - "description": "Nodes: ComfyUI_Mexx_Poster", - "files": [ - "https://github.com/SoftMeng/ComfyUI_Mexx_Poster" - ], - "install_type": "git-clone", - "reference": "https://github.com/SoftMeng/ComfyUI_Mexx_Poster", - "title": "ComfyUI_Mexx_Poster" - }, - { - "author": "SoftMeng", - "description": "Nodes: ComfyUI_ImageToText", - "files": [ - "https://github.com/SoftMeng/ComfyUI_ImageToText" - ], - "install_type": "git-clone", - "reference": "https://github.com/SoftMeng/ComfyUI_ImageToText", - "title": "ComfyUI_ImageToText" - }, - { - "author": "SoftMeng", - "description": "Accelerate ComfyUI Nodes for Faster Image Generation, Ensuring Consistency Pre and Post-Acceleration, Ideal for Bulk Image Production.", - "files": [ - "https://github.com/SoftMeng/ComfyUI-DeepCache-Fix" - ], - "install_type": "git-clone", - "reference": "https://github.com/SoftMeng/ComfyUI-DeepCache-Fix", - "title": "ComfyUI-DeepCache-Fix" - }, - { - "author": "SoftMeng", - "description": "ComfyUI is proud to present a new plugin designed to enhance user experience through seamless integration with Pillow, the powerful fork of Python Imaging Library (PIL). This plugin offers a suite of basic image manipulation tools that are easy to use and integrate directly into the ComfyUI framework.", - "files": [ - "https://github.com/SoftMeng/ComfyUI-PIL" - ], - "install_type": "git-clone", - "reference": "https://github.com/SoftMeng/ComfyUI-PIL", - "title": "ComfyUI-PIL" - }, - { - "author": "wmatson", - "description": "A collection of utility nodes primarily for interacting with comfy via automated systems", - "files": [ - "https://github.com/wmatson/easy-comfy-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/wmatson/easy-comfy-nodes", - "title": "easy-comfy-nodes" - }, - { - "author": "DrJKL", - "description": "A ComfyUI extension to add spatial anchors/waypoints to better navigate large workflows.", - "files": [ - "https://github.com/DrJKL/ComfyUI-Anchors" - ], - "install_type": "git-clone", - "reference": "https://github.com/DrJKL/ComfyUI-Anchors", - "title": "ComfyUI-Anchors" - }, - { - "author": "vanillacode314", - "description": "A simple wildcard node for ComfyUI. Can also be used a style prompt node.", - "files": [ - "https://github.com/vanillacode314/SimpleWildcardsComfyUI" - ], - "install_type": "git-clone", - "pip": [ - "pipe" - ], - "reference": "https://github.com/vanillacode314/SimpleWildcardsComfyUI", - "title": "Simple Wildcard" - }, - { - "author": "WebDev9000", - "description": "Nodes:Ignore Braces, Settings Switch.", - "files": [ - "https://github.com/WebDev9000/WebDev9000-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/WebDev9000/WebDev9000-Nodes", - "title": "WebDev9000-Nodes" - }, - { - "author": "Scholar01", - "description": "set denoise strength for keyframe", - "files": [ - "https://github.com/Scholar01/ComfyUI-Keyframe" - ], - "install_type": "git-clone", - "reference": "https://github.com/Scholar01/ComfyUI-Keyframe", - "title": "SComfyUI-Keyframe" - }, - { - "author": "Haoming02", - "description": "Perform Bringing-Old-Photos-Back-to-Life", - "files": [ - "https://github.com/Haoming02/comfyui-old-photo-restoration" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-old-photo-restoration", - "title": "ComfyUI Old Photo Restoration" - }, - { - "author": "Haoming02", - "description": "Color Grading for Stable Diffusion", - "files": [ - "https://github.com/Haoming02/comfyui-diffusion-cg" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-diffusion-cg", - "title": "Diffusion CG" - }, - { - "author": "Haoming02", - "description": "Add a button that formats the prompts in textfields", - "files": [ - "https://github.com/Haoming02/comfyui-prompt-format" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-prompt-format", - "title": "Prompt Format" - }, - { - "author": "Haoming02", - "description": "Add a button that clears the console", - "files": [ - "https://github.com/Haoming02/comfyui-clear-screen" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-clear-screen", - "title": "Clear Screen" - }, - { - "author": "Haoming02", - "description": "Snaps the menu to the corner automatically", - "files": [ - "https://github.com/Haoming02/comfyui-menu-anchor" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-menu-anchor", - "title": "Menu Anchor" - }, - { - "author": "Haoming02", - "description": "Use the Tab key to switch between textfields", - "files": [ - "https://github.com/Haoming02/comfyui-tab-handler" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-tab-handler", - "title": "Tab Handler" - }, - { - "author": "Haoming02", - "description": "A node that allows you to switch between execution flow", - "files": [ - "https://github.com/Haoming02/comfyui-floodgate" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-floodgate", - "title": "Floodgate" - }, - { - "author": "Haoming02", - "description": "Add a button that formats the workflow graph", - "files": [ - "https://github.com/Haoming02/comfyui-node-beautify" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-node-beautify", - "title": "Node Beautify" - }, - { - "author": "Haoming02", - "description": "Manipulate the details of generations.", - "files": [ - "https://github.com/Haoming02/comfyui-resharpen" - ], - "install_type": "git-clone", - "reference": "https://github.com/Haoming02/comfyui-resharpen", - "title": "ComfyUI ReSharpen" - }, - { - "author": "bedovyy", - "description": "This extension helps generate images through NAI.", - "files": [ - "https://github.com/bedovyy/ComfyUI_NAIDGenerator" - ], - "install_type": "git-clone", - "reference": "https://github.com/bedovyy/ComfyUI_NAIDGenerator", - "title": "ComfyUI_NAIDGenerator" - }, - { - "author": "Off-Live", - "description": "Nodes:Image Crop Fit, OFF SEGS to Image, Crop Center wigh SEGS, Watermarking, GW Number Formatting Node.", - "files": [ - "https://github.com/Off-Live/ComfyUI-off-suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/Off-Live/ComfyUI-off-suite", - "title": "ComfyUI-off-suite" - }, - { - "author": "ningxiaoxiao", - "description": "Real-time input output node for ComfyUI by NDI. Leveraging the powerful linking capabilities of NDI, you can access NDI video stream frames and send images generated by the model to NDI video streams.", - "files": [ - "https://github.com/ningxiaoxiao/comfyui-NDI" - ], - "install_type": "git-clone", - "pip": [ - "ndi-python" - ], - "reference": "https://github.com/ningxiaoxiao/comfyui-NDI", - "title": "comfyui-NDI" - }, - { - "author": "subtleGradient", - "description": "Two-finger scrolling (vertical and horizontal) to pan the canvas. Two-finger pinch to zoom in and out. Command-scroll up and down to zoom in and out. Fixes [a/comfyanonymous/ComfyUI#2059](https://github.com/comfyanonymous/ComfyUI/issues/2059).", - "files": [ - "https://github.com/subtleGradient/TinkerBot-tech-for-ComfyUI-Touchpad" - ], - "install_type": "git-clone", - "reference": "https://github.com/subtleGradient/TinkerBot-tech-for-ComfyUI-Touchpad", - "title": "Touchpad two-finger gesture support for macOS" - }, - { - "author": "zcfrank1st", - "description": "Nodes:visual_anagrams_sample, visual_anagrams_animate", - "files": [ - "https://github.com/zcfrank1st/comfyui_visual_anagrams" - ], - "install_type": "git-clone", - "reference": "https://github.com/zcfrank1st/comfyui_visual_anagrams", - "title": "comfyui_visual_anagram" - }, - { - "author": "Electrofried", - "description": "A simply node for hooking in to openAI API based servers via comfyUI", - "files": [ - "https://github.com/Electrofried/ComfyUI-OpenAINode" - ], - "install_type": "git-clone", - "reference": "https://github.com/Electrofried/ComfyUI-OpenAINode", - "title": "OpenAINode" - }, - { - "author": "AustinMroz", - "description": "Experimental utility nodes with a focus on manipulation of noised latents", - "files": [ - "https://github.com/AustinMroz/ComfyUI-SpliceTools" - ], - "id": "splicetools", - "install_type": "git-clone", - "reference": "https://github.com/AustinMroz/ComfyUI-SpliceTools", - "title": "SpliceTools" - }, - { - "author": "AustinMroz", - "description": "Nodes:DynamicSampler, MeasuredSampler, ResolveMaskPromise", - "files": [ - "https://github.com/AustinMroz/ComfyUI-DynamicOversampling" - ], - "id": "dynamic-oversampling", - "install_type": "git-clone", - "reference": "https://github.com/AustinMroz/ComfyUI-DynamicOversampling", - "title": "DynamicOversampling" - }, - { - "author": "AustinMroz", - "description": "Automatically creates checkpoints during workflow execution. If If an workflow is canceled or ComfyUI crashes mid-execution, then these checkpoints are used when the workflow is re-queued to resume execution with minimal progress loss.", - "files": [ - "https://github.com/AustinMroz/ComfyUI-WorkflowCheckpointing" - ], - "id": "workflowcheckpointing", - "install_type": "git-clone", - "reference": "https://github.com/AustinMroz/ComfyUI-WorkflowCheckpointing", - "title": "ComfyUI-WorkflowCheckpointing" - }, - { - "author": "AustinMroz", - "description": "Modifies execution to minimize RAM at the cost of performance", - "files": [ - "https://github.com/AustinMroz/ComfyUI-MinCache" - ], - "id": "comfyui-mincache", - "install_type": "git-clone", - "reference": "https://github.com/AustinMroz/ComfyUI-MinCache", - "title": "ComfyUI-MinCache" - }, - { - "author": "11cafe", - "description": "A ComfyUI custom node for project management to centralize the management of all your workflows in one place. Seamlessly switch between workflows, create and update them within a single workspace, like Google Docs.", - "files": [ - "https://github.com/11cafe/comfyui-workspace-manager" - ], - "install_type": "git-clone", - "reference": "https://github.com/11cafe/comfyui-workspace-manager", - "title": "ComfyUI Workspace Manager - Comfyspace" - }, - { - "author": "knuknX", - "description": "Nodes:BatchImageResizeProcessor, SingleImagePathLoader, SingleImageUrlLoader", - "files": [ - "https://github.com/knuknX/ComfyUI-Image-Tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/knuknX/ComfyUI-Image-Tools", - "title": "ComfyUI-Image-Tools" - }, - { - "author": "jtrue", - "description": "A collection of nodes powering a tensor oracle on a home network with automation", - "files": [ - "https://github.com/jtrue/ComfyUI-JaRue" - ], - "install_type": "git-clone", - "nodename_pattern": "_jru$", - "reference": "https://github.com/jtrue/ComfyUI-JaRue", - "title": "ComfyUI-JaRue" - }, - { - "author": "jtrue", - "description": "Word embedding utility nodes for ComfyUI. Load a pre-trained embedding model, explore neighbors, do analogies, and project any token/phrase onto 1D/2D/3D semantic axes with human\u2011readable summaries.", - "files": [ - "https://github.com/jtrue/ComfyUI-WordEmbeddings" - ], - "install_type": "git-clone", - "nodename_pattern": "_jru$", - "reference": "https://github.com/jtrue/ComfyUI-WordEmbeddings", - "title": "ComfyUI-WordEmbeddings" - }, - { - "author": "jtrue", - "description": "Interactive rectangle tools for ComfyUI: Select, Crop, Mask, Fill.", - "files": [ - "https://github.com/jtrue/ComfyUI-Rect" - ], - "install_type": "git-clone", - "reference": "https://github.com/jtrue/ComfyUI-Rect", - "title": "ComfyUI-Rect" - }, - { - "author": "filliptm", - "description": "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection.", - "files": [ - "https://github.com/filliptm/ComfyUI_Fill-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/filliptm/ComfyUI_Fill-Nodes", - "title": "ComfyUI_Fill-Nodes" - }, - { - "author": "filliptm", - "description": "Train Image Loras on both sd1.5 and SDXL. This repo git clones the pieces needed to train. It pops open a second terminal window do do the training. It will also display the inference samples in the node itself so you can track the results.", - "files": [ - "https://github.com/filliptm/ComfyUI_FL-Trainer" - ], - "install_type": "git-clone", - "reference": "https://github.com/filliptm/ComfyUI_FL-Trainer", - "title": "ComfyUI_FL-Trainer" - }, - { - "author": "filliptm", - "description": "Voice Clone and TTS model.", - "files": [ - "https://github.com/filliptm/ComfyUI_Fill-ChatterBox" - ], - "install_type": "git-clone", - "reference": "https://github.com/filliptm/ComfyUI_Fill-ChatterBox", - "title": "ComfyUI_Fill-ChatterBox" - }, - { - "author": "zfkun", - "description": "A collection of nodes for common tools, including text preview, text translation (multi-platform, multi-language), image loader, webcamera capture.", - "files": [ - "https://github.com/zfkun/ComfyUI_zfkun" - ], - "install_type": "git-clone", - "reference": "https://github.com/zfkun/ComfyUI_zfkun", - "title": "ComfyUI_zfkun" - }, - { - "author": "zcfrank1st", - "description": "A collection of utility nodes for ComfyUI, including audio/video processing, file uploads, and AI image generation.", - "files": [ - "https://github.com/zcfrank1st/Comfyui-Toolbox" - ], - "install_type": "git-clone", - "reference": "https://github.com/zcfrank1st/Comfyui-Toolbox", - "title": "Comfyui-Toolbox" - }, - { - "author": "talesofai", - "description": "This is an image/video/workflow browser and manager for ComfyUI. You could add image/video/workflow to collections and load it to ComfyUI. You will be able to use your collections everywhere.", - "files": [ - "https://github.com/talesofai/comfyui-browser" - ], - "install_type": "git-clone", - "reference": "https://github.com/talesofai/comfyui-browser", - "title": "ComfyUI Browser" - }, - { - "author": "yolain", - "description": "To enhance the usability of ComfyUI, optimizations and integrations have been implemented for several commonly used nodes.", - "files": [ - "https://github.com/yolain/ComfyUI-Easy-Use" - ], - "install_type": "git-clone", - "reference": "https://github.com/yolain/ComfyUI-Easy-Use", - "title": "ComfyUI Easy Use" - }, - { - "author": "bruefire", - "description": "This is an extension node for ComfyUI that allows you to load frames from a video in bulk and perform masking and sketching on each frame through a GUI.", - "files": [ - "https://github.com/bruefire/ComfyUI-SeqImageLoader" - ], - "install_type": "git-clone", - "reference": "https://github.com/bruefire/ComfyUI-SeqImageLoader", - "title": "ComfyUI Sequential Image Loader" - }, - { - "author": "mmaker", - "description": "Node: Color Enhance, Color Blend. This is the same algorithm GIMP/GEGL uses for color enhancement. The gist of this implementation is that it converts the color space to CIELCh(ab) and normalizes the chroma (or [colorfulness](https://en.wikipedia.org/wiki/Colorfulness)] component. Original source can be found in the link below.", - "files": [ - "https://git.mmaker.moe/mmaker/sd-webui-color-enhance" - ], - "install_type": "git-clone", - "reference": "https://git.mmaker.moe/mmaker/sd-webui-color-enhance", - "title": "mmaker/Color Enhance" - }, - { - "author": "modusCell", - "description": "Simple node for sharing latent image size between nodes. Preset dimensions for SD and XL.", - "files": [ - "https://github.com/modusCell/ComfyUI-dimension-node-modusCell" - ], - "install_type": "git-clone", - "reference": "https://github.com/modusCell/ComfyUI-dimension-node-modusCell", - "title": "Preset Dimensions" - }, - { - "author": "aria1th", - "description": "Logical Utils (compare, string, boolean operations) for ComfyUI", - "files": [ - "https://github.com/aria1th/ComfyUI-LogicUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/aria1th/ComfyUI-LogicUtils", - "title": "ComfyUI-LogicUtils" - }, - { - "author": "MitoshiroPJ", - "description": "This node splits its self-attention Q to focus on nearby samples.", - "files": [ - "https://github.com/MitoshiroPJ/comfyui_nearsighted_attention" - ], - "install_type": "git-clone", - "reference": "https://github.com/MitoshiroPJ/comfyui_nearsighted_attention", - "title": "ComfyUI Nearsighted Attention" - }, - { - "author": "MitoshiroPJ", - "description": "SDLI (Stable Diffusion Latents in Imagefile) is file format, that contains latents and lossy (decoded) image. Detailed format is written at [a/SDLI Tools](https://github.com/MitoshiroPJ/sdli_tools).", - "files": [ - "https://github.com/MitoshiroPJ/ComfyUI_save_image_sdli" - ], - "install_type": "git-clone", - "reference": "https://github.com/MitoshiroPJ/ComfyUI_save_image_sdli", - "title": "ComfyUI SaveImage SDLI" - }, - { - "author": "brianfitzgerald", - "description": "Implementation of the [a/StyleAligned](https://style-aligned-gen.github.io/) paper for ComfyUI. This node allows you to apply a consistent style to all images in a batch; by default it will use the first image in the batch as the style reference, forcing all other images to be consistent with it.", - "files": [ - "https://github.com/brianfitzgerald/style_aligned_comfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/brianfitzgerald/style_aligned_comfy", - "title": "StyleAligned for ComfyUI" - }, - { - "author": "deroberon", - "description": "The Demofusion Custom Node is a wrapper that adapts the work and implementation of the [a/DemoFusion](https://ruoyidu.github.io/demofusion/demofusion.html) technique created and implemented by Ruoyi Du to the Comfyui environment.", - "files": [ - "https://github.com/deroberon/demofusion-comfyui" - ], - "id": "demofusion", - "install_type": "git-clone", - "reference": "https://github.com/deroberon/demofusion-comfyui", - "title": "demofusion-comfyui" - }, - { - "author": "deroberon", - "description": "StableZero123 is a node wrapper that uses the model and technique provided [here](https://github.com/SUDO-AI-3D/zero123plus/). It uses the Zero123plus model to generate 3D views using just one image.", - "files": [ - "https://github.com/deroberon/StableZero123-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/deroberon/StableZero123-comfyui", - "title": "StableZero123-comfyui" - }, - { - "author": "glifxyz", - "description": "Custom set of nodes used by glif.app. With glif you can build mini apps that are powered by custom comfy workflows.", - "files": [ - "https://github.com/glifxyz/ComfyUI-GlifNodes" - ], - "id": "glif", - "install_type": "git-clone", - "reference": "https://github.com/glifxyz/ComfyUI-GlifNodes", - "title": "ComfyUI-GlifNodes" - }, - { - "author": "concarne000", - "description": "Nodes:Bing Image Grabber, Zephyr chat, Hermes Chat", - "files": [ - "https://github.com/concarne000/ConCarneNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/concarne000/ConCarneNode", - "title": "ConCarneNode" - }, - { - "author": "concarne000", - "description": "Simple stack push/pop style nodes for images, strings, integers and generic objects (image batches, latents, face models etc)", - "files": [ - "https://github.com/concarne000/ComfyUI-Stacker" - ], - "install_type": "git-clone", - "reference": "https://github.com/concarne000/ComfyUI-Stacker", - "title": "ComfyUI-Stacker" - }, - { - "author": "Aegis72", - "description": "These nodes will be placed in comfyui/custom_nodes/aegisflow and contains the image passer (accepts an image as either wired or wirelessly, input and passes it through. Latent passer does the same for latents, and the Preprocessor chooser allows a passthrough image and 10 controlnets to be passed in AegisFlow Shima. The inputs on the Preprocessor chooser should not be renamed if you intend to accept image inputs wirelessly through UE nodes. It can be done, but the send node input regex for each controlnet preprocessor column must also be changed.", - "files": [ - "https://github.com/aegis72/aegisflow_utility_nodes" - ], - "id": "aegis", - "install_type": "git-clone", - "reference": "https://github.com/aegis72/aegisflow_utility_nodes", - "title": "AegisFlow Utility Nodes" - }, - { - "author": "Aegis72", - "description": "This is a straight clone of Azazeal04's all-in-one styler menu, which was removed from gh on Jan 21, 2024. I have made no changes to the files at all.", - "files": [ - "https://github.com/aegis72/comfyui-styles-all" - ], - "id": "styles-all", - "install_type": "git-clone", - "reference": "https://github.com/aegis72/comfyui-styles-all", - "title": "ComfyUI-styles-all" - }, - { - "author": "glibsonoran", - "description": "A suite of nodes that includes: - Prompt enhancers/generators that employ remote AI services and local front-ends like: ChatGPT, Anthropic Claude, Groq, Gemini, LM Studio, Oobabooga, OpenRouter etc. - An Image Generator that uses Dall_e 3. - An image metadata extractor that extracts seed, prompt, cfg, size, denoise, etc from existing AI generated images and photo metadata (from exif data) from jpeg photographs. A tagger that appends text (tags) to the beginning, end and/or middle of a text block. Image and text multiplexer utilility. A text block remover that removes text between two named tags.", - "files": [ - "https://github.com/glibsonoran/Plush-for-ComfyUI" - ], - "id": "plush", - "install_type": "git-clone", - "reference": "https://github.com/glibsonoran/Plush-for-ComfyUI", - "title": "Plush-for-ComfyUI" - }, - { - "author": "vienteck", - "description": "This extension is a reimagined version based on the [a/ComfyUI-QualityOfLifeSuit_Omar92](https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92) extension, and it supports integration with ChatGPT through the new OpenAI API.\nNOTE: See detailed installation instructions on the [a/repository](https://github.com/vienteck/ComfyUI-Chat-GPT-Integration).", - "files": [ - "https://github.com/vienteck/ComfyUI-Chat-GPT-Integration" - ], - "install_type": "git-clone", - "reference": "https://github.com/vienteck/ComfyUI-Chat-GPT-Integration", - "title": "ComfyUI-Chat-GPT-Integration" - }, - { - "author": "MNeMoNiCuZ", - "description": "Added Lora Loader - Tag node, originally by badjeff", - "files": [ - "https://github.com/MNeMoNiCuZ/ComfyUI-mnemic-nodes" - ], - "id": "comfyui-mnemic-nodes", - "install_type": "git-clone", - "reference": "https://github.com/MNeMoNiCuZ/ComfyUI-mnemic-nodes", - "title": "ComfyUI-mnemic-nodes" - }, - { - "author": "AI2lab", - "description": "Unofficial implementation of siliconflow API for ComfyUI\nHow to use:apply api key in \uff1ahttps://cloud.siliconflow.cn/\nadd api key in config.json", - "files": [ - "https://github.com/AI2lab/comfyUI-siliconflow-api-2lab" - ], - "id": "siliconflow", - "install_type": "git-clone", - "reference": "https://github.com/AI2lab/comfyUI-siliconflow-api-2lab", - "title": "comfyUI-siliconflow-api-2lab" - }, - { - "author": "NimaNzrii", - "description": "popup preview for comfyui", - "files": [ - "https://github.com/NimaNzrii/comfyui-popup_preview" - ], - "id": "popup-preview", - "install_type": "git-clone", - "reference": "https://github.com/NimaNzrii/comfyui-popup_preview", - "title": "comfyui-popup_preview" - }, - { - "author": "NimaNzrii", - "description": "Powerfull bridge to Photoshop by NimaNzrii", - "files": [ - "https://github.com/NimaNzrii/comfyui-photoshop" - ], - "id": "comfy-photoshop", - "install_type": "git-clone", - "reference": "https://github.com/NimaNzrii/comfyui-photoshop", - "title": "comfyui-photoshop" - }, - { - "author": "Rui", - "description": "Rui's workflow-specific custom node, written using GPT.", - "files": [ - "https://github.com/rui40000/RUI-Nodes" - ], - "id": "rui-nodes", - "install_type": "git-clone", - "reference": "https://github.com/rui40000/RUI-Nodes", - "title": "RUI-Nodes" - }, - { - "author": "dmarx", - "description": "ComfyUI nodes to facilitate parameter/prompt keyframing using comfyui nodes for defining and manipulating parameter curves. Essentially provides a ComfyUI interface to the [a/keyframed](https://github.com/dmarx/keyframed) library.", - "files": [ - "https://github.com/dmarx/ComfyUI-Keyframed" - ], - "id": "keyframed", - "install_type": "git-clone", - "reference": "https://github.com/dmarx/ComfyUI-Keyframed", - "title": "ComfyUI-Keyframed" - }, - { - "author": "dmarx", - "description": "porting audioreactivity pipeline from vktrs to comfyui.", - "files": [ - "https://github.com/dmarx/ComfyUI-AudioReactive" - ], - "id": "audioreactive", - "install_type": "git-clone", - "reference": "https://github.com/dmarx/ComfyUI-AudioReactive", - "title": "ComfyUI-AudioReactive" - }, - { - "author": "TripleHeadedMonkey", - "description": "This extension provides various SDXL Prompt Stylers. See: [a/youtube](https://youtu.be/WBHI-2uww7o?si=dijvDaUI4nmx4VkF)", - "files": [ - "https://github.com/TripleHeadedMonkey/ComfyUI_MileHighStyler" - ], - "id": "milehighstyler", - "install_type": "git-clone", - "reference": "https://github.com/TripleHeadedMonkey/ComfyUI_MileHighStyler", - "title": "ComfyUI_MileHighStyler" - }, - { - "author": "BennyKok", - "description": "Open source comfyui deployment platform, a vercel for generative workflow infra.", - "files": [ - "https://github.com/BennyKok/comfyui-deploy" - ], - "id": "comfy-deploy", - "install_type": "git-clone", - "reference": "https://github.com/BennyKok/comfyui-deploy", - "title": "ComfyUI Deploy" - }, - { - "author": "florestefano1975", - "description": "ComfyUI Portrait Master. A node designed to help AI image creators to generate prompts for human portraits.", - "files": [ - "https://github.com/florestefano1975/comfyui-portrait-master" - ], - "id": "portrait-master", - "install_type": "git-clone", - "reference": "https://github.com/florestefano1975/comfyui-portrait-master", - "title": "comfyui-portrait-master" - }, - { - "author": "florestefano1975", - "description": "A suite of tools for prompt management. Combining nodes helps the user sequence strings for prompts, also creating logical groupings if necessary. Individual nodes can be chained together in any order.", - "files": [ - "https://github.com/florestefano1975/comfyui-prompt-composer" - ], - "id": "prompt-composer", - "install_type": "git-clone", - "reference": "https://github.com/florestefano1975/comfyui-prompt-composer", - "title": "comfyui-prompt-composer" - }, - { - "author": "florestefano1975", - "description": "This fork of the official StabilityAI repository contains a number of enhancements and implementations.", - "files": [ - "https://github.com/florestefano1975/ComfyUI-StabilityAI-Suite" - ], - "id": "sai-suite", - "install_type": "git-clone", - "reference": "https://github.com/florestefano1975/ComfyUI-StabilityAI-Suite", - "title": "ComfyUI StabilityAI Suite" - }, - { - "author": "florestefano1975", - "description": "Simple custom nodes for testing and use HiDiffusion technology: https://github.com/megvii-research/HiDiffusion/", - "files": [ - "https://github.com/florestefano1975/ComfyUI-HiDiffusion" - ], - "id": "hidiffusion", - "install_type": "git-clone", - "reference": "https://github.com/florestefano1975/ComfyUI-HiDiffusion", - "title": "ComfyUI HiDiffusion" - }, - { - "author": "florestefano1975", - "description": "A simple seed generator based on special number sequences: Fibonacci, Prime, Padovan, Triangular, Catalan, Pell, Lucas", - "files": [ - "https://github.com/florestefano1975/ComfyUI-Advanced-Sequence-Seed" - ], - "id": "adv-seq-seed-gen", - "install_type": "git-clone", - "reference": "https://github.com/florestefano1975/ComfyUI-Advanced-Sequence-Seed", - "title": "Advanced Sequence Seed Generator" - }, - { - "author": "florestefano1975", - "description": "Experience the CogVideoX model on ComfyUI", - "files": [ - "https://github.com/florestefano1975/ComfyUI-CogVideoX" - ], - "id": "sf-cog-video-x", - "install_type": "git-clone", - "reference": "https://github.com/florestefano1975/ComfyUI-CogVideoX", - "title": "ComfyUI-CogVideoX" - }, - { - "author": "mozman", - "description": "This extension provides styler nodes for SDXL.\n\nNOTE: Due to the dynamic nature of node name definitions, ComfyUI-Manager cannot recognize the node list from this extension. The Missing nodes and Badge features are not available for this extension.", - "files": [ - "https://github.com/mozman/ComfyUI_mozman_nodes" - ], - "id": "mozman-nodes", - "install_type": "git-clone", - "reference": "https://github.com/mozman/ComfyUI_mozman_nodes", - "title": "ComfyUI_mozman_nodes" - }, - { - "author": "rcsaquino", - "description": "Nodes: VAE Processor, VAE Loader, Background Remover", - "files": [ - "https://github.com/rcsaquino/comfyui-custom-nodes" - ], - "id": "rcsaquino-nodes", - "install_type": "git-clone", - "reference": "https://github.com/rcsaquino/comfyui-custom-nodes", - "title": "rcsaquino/comfyui-custom-nodes" - }, - { - "author": "rcfcu2000", - "description": "Nodes: Combine ZHGMasks, Cover ZHGMasks, ZHG FaceIndex, ZHG SaveImage, ZHG SmoothEdge, ZHG GetMaskArea, ...", - "files": [ - "https://github.com/rcfcu2000/zhihuige-nodes-comfyui" - ], - "id": "zhihuige-nodes", - "install_type": "git-clone", - "reference": "https://github.com/rcfcu2000/zhihuige-nodes-comfyui", - "title": "zhihuige-nodes-comfyui" - }, - { - "author": "IDGallagher", - "description": "Custom nodes to aid in the exploration of Latent Space", - "files": [ - "https://github.com/IDGallagher/ComfyUI-IG-Nodes" - ], - "id": "ig-nodes", - "install_type": "git-clone", - "reference": "https://github.com/IDGallagher/ComfyUI-IG-Nodes", - "title": "IG Interpolation Nodes" - }, - { - "author": "IDGallagher", - "description": "ComfyUI adaptation of https://github.com/G-U-N/Motion-I2V", - "files": [ - "https://github.com/IDGallagher/ComfyUI-IG-Motion-I2V" - ], - "id": "comfyui-ig-motion-i2v", - "install_type": "git-clone", - "reference": "https://github.com/IDGallagher/ComfyUI-IG-Motion-I2V", - "title": "ComfyUI-IG-Motion-I2V" - }, - { - "author": "IDGallagher", - "description": "Nodes for searching videos by motion", - "files": [ - "https://github.com/IDGallagher/MotionVideoSearch" - ], - "id": "motion-video-search", - "install_type": "git-clone", - "reference": "https://github.com/IDGallagher/MotionVideoSearch", - "title": "IG-Motion-Search" - }, - { - "author": "violet-chen", - "description": "Nodes: Psd2Png.", - "files": [ - "https://github.com/violet-chen/comfyui-psd2png" - ], - "id": "psd2png", - "install_type": "git-clone", - "reference": "https://github.com/violet-chen/comfyui-psd2png", - "title": "comfyui-psd2png" - }, - { - "author": "lldacing", - "description": "Provides some features and nodes related to API calls.", - "files": [ - "https://github.com/lldacing/comfyui-easyapi-nodes" - ], - "id": "easyapi", - "install_type": "git-clone", - "reference": "https://github.com/lldacing/comfyui-easyapi-nodes", - "title": "comfyui-easyapi-nodes" - }, - { - "author": "lldacing", - "description": "StableDelight: Revealing Hidden Textures by Removing Specular Reflections", - "files": [ - "https://github.com/lldacing/ComfyUI_StableDelight_ll" - ], - "id": "comfyui_stabledelight_ll", - "install_type": "git-clone", - "reference": "https://github.com/lldacing/ComfyUI_StableDelight_ll", - "title": "ComfyUI_StableDelight_ll" - }, - { - "author": "lldacing", - "description": "Hair transfer", - "files": [ - "https://github.com/lldacing/ComfyUI_StableHair_ll" - ], - "id": "comfyui_stablehair_ll", - "install_type": "git-clone", - "reference": "https://github.com/lldacing/ComfyUI_StableHair_ll", - "title": "ComfyUI_StableHair_ll" - }, - { - "author": "lldacing", - "description": "The implementation for PuLID-Flux, support TeaCache, no model pollution.", - "files": [ - "https://github.com/lldacing/ComfyUI_PuLID_Flux_ll" - ], - "id": "comfyui_pulid_flux_ll", - "install_type": "git-clone", - "reference": "https://github.com/lldacing/ComfyUI_PuLID_Flux_ll", - "title": "ComfyUI_PuLID_Flux_ll" - }, - { - "author": "lldacing", - "description": "Sync with version of BiRefNet. NODES:AutoDownloadBiRefNetModel, LoadRembgByBiRefNetModel, RembgByBiRefNet.", - "files": [ - "https://github.com/lldacing/ComfyUI_BiRefNet_ll" - ], - "install_type": "git-clone", - "reference": "https://github.com/lldacing/ComfyUI_BiRefNet_ll", - "title": "ComfyUI_BiRefNet_ll" - }, - { - "author": "lldacing", - "description": "Some patches for Flux|HunYuanVideo|LTXVideo etc, support TeaCache, PuLID, First Block Cache.", - "files": [ - "https://github.com/lldacing/ComfyUI_Patches_ll" - ], - "install_type": "git-clone", - "reference": "https://github.com/lldacing/ComfyUI_Patches_ll", - "title": "ComfyUI_Patches_ll" - }, - { - "author": "lldacing", - "description": "Background removal based on BEN. NODES:LoadRembgByBenModel, RembgByBen, GetMaskByBen, RembgByBenAdvanced, BlurFusionForegroundEstimation.", - "files": [ - "https://github.com/lldacing/ComfyUI_BEN_ll" - ], - "install_type": "git-clone", - "reference": "https://github.com/lldacing/ComfyUI_BEN_ll", - "title": "ComfyUI_BEN_ll" - }, - { - "author": "CosmicLaca", - "description": "This extension provides various utility nodes. Inputs(prompt, styles, dynamic, merger, ...), Outputs(style pile), Dashboard(selectors, loader, switch, ...), Networks(LORA, Embedding, Hypernetwork), Visuals(visual selectors, )", - "files": [ - "https://github.com/CosmicLaca/ComfyUI_Primere_Nodes" - ], - "id": "primere", - "install_type": "git-clone", - "reference": "https://github.com/CosmicLaca/ComfyUI_Primere_Nodes", - "title": "Primere nodes for ComfyUI" - }, - { - "author": "RenderRift", - "description": "Nodes:RR_Date_Folder_Format, RR_Image_Metadata_Overlay, RR_VideoPathMetaExtraction, RR_DisplayMetaOptions. This extension provides nodes designed to enhance the Animatediff workflow.", - "files": [ - "https://github.com/RenderRift/ComfyUI-RenderRiftNodes" - ], - "id": "renderrift", - "install_type": "git-clone", - "reference": "https://github.com/RenderRift/ComfyUI-RenderRiftNodes", - "title": "ComfyUI-RenderRiftNodes" - }, - { - "author": "OpenArt-AI", - "description": "ComfyUI Assistant is your one stop plugin for everything you need to get started with comfy-ui. Now it provides useful courses, tutorials, and basic templates.", - "files": [ - "https://github.com/OpenArt-AI/ComfyUI-Assistant" - ], - "id": "openart", - "install_type": "git-clone", - "reference": "https://github.com/OpenArt-AI/ComfyUI-Assistant", - "title": "ComfyUI Assistant" - }, - { - "author": "ttulttul", - "description": "Nodes to use Florence2 VLM for image vision tasks: object detection, captioning, segmentation and ocr", - "files": [ - "https://github.com/ttulttul/ComfyUI-Iterative-Mixer" - ], - "id": "itermix", - "install_type": "git-clone", - "reference": "https://github.com/ttulttul/ComfyUI-Iterative-Mixer", - "title": "ComfyUI Iterative Mixing Nodes" - }, - { - "author": "ttulttul", - "description": "This repo contains nodes for ComfyUI that implement some helpful operations on tensors, such as normalization.", - "files": [ - "https://github.com/ttulttul/ComfyUI-Tensor-Operations" - ], - "id": "tensorop", - "install_type": "git-clone", - "reference": "https://github.com/ttulttul/ComfyUI-Tensor-Operations", - "title": "ComfyUI-Tensor-Operations" - }, - { - "author": "jitcoder", - "description": "Shows Lora information from CivitAI and outputs trigger words and example prompt", - "files": [ - "https://github.com/jitcoder/lora-info" - ], - "id": "lorainfo", - "install_type": "git-clone", - "reference": "https://github.com/jitcoder/lora-info", - "title": "LoraInfo" - }, - { - "author": "ceruleandeep", - "description": "A ComfyUI extension for chatting with your images. Runs on your own system, no external services used, no filter. Uses the [a/LLaVA multimodal LLM](https://llava-vl.github.io/) so you can give instructions or ask questions in natural language. It's maybe as smart as GPT3.5, and it can see.", - "files": [ - "https://github.com/ceruleandeep/ComfyUI-LLaVA-Captioner" - ], - "id": "llava-captioner", - "install_type": "git-clone", - "reference": "https://github.com/ceruleandeep/ComfyUI-LLaVA-Captioner", - "title": "ComfyUI LLaVA Captioner" - }, - { - "author": "styler00dollar", - "description": "Directly upscaling inside the latent space. Model was trained for SD1.5 and drawn content. Might add new architectures or update models at some point. This took heavy inspriration from [city96/SD-Latent-Upscaler](https://github.com/city96/SD-Latent-Upscaler) and [Ttl/ComfyUi_NNLatentUpscale](https://github.com/Ttl/ComfyUi_NNLatentUpscale). ", - "files": [ - "https://github.com/styler00dollar/ComfyUI-sudo-latent-upscale" - ], - "id": "sudo-latent-upscale", - "install_type": "git-clone", - "reference": "https://github.com/styler00dollar/ComfyUI-sudo-latent-upscale", - "title": "ComfyUI-sudo-latent-upscale" - }, - { - "author": "styler00dollar", - "description": "This extension provides nodes for [a/DeepCache: Accelerating Diffusion Models for Free](https://arxiv.org/abs/2312.00858)\nNOTE:Original code can be found [a/here](https://gist.github.com/laksjdjf/435c512bc19636e9c9af4ee7bea9eb86). Full credit to laksjdjf for sharing the code. ", - "files": [ - "https://github.com/styler00dollar/ComfyUI-deepcache" - ], - "id": "deepcache", - "install_type": "git-clone", - "reference": "https://github.com/styler00dollar/ComfyUI-deepcache", - "title": "ComfyUI-deepcache" - }, - { - "author": "HarroweD and quadmoon", - "description": "Harronode is a custom node designed to build prompts easily for use with the Harrlogos SDXL LoRA. This Node simplifies the process of crafting prompts and makes all built in activation terms available at your fingertips.", - "files": [ - "https://github.com/NotHarroweD/Harronode" - ], - "id": "harrlogos-prompt-builder", - "install_type": "git-clone", - "nodename_pattern": "Harronode", - "reference": "https://github.com/NotHarroweD/Harronode", - "title": "Harrlogos Prompt Builder Node" - }, - { - "author": "Limitex", - "description": "Nodes: Center Calculation. Improved Numerical Calculation for ComfyUI", - "files": [ - "https://github.com/Limitex/ComfyUI-Calculation" - ], - "id": "calc", - "install_type": "git-clone", - "reference": "https://github.com/Limitex/ComfyUI-Calculation", - "title": "ComfyUI-Calculation" - }, - { - "author": "Limitex", - "description": "This extension enables the use of the diffuser pipeline in ComfyUI. It also includes nodes related to Stream Diffusion.", - "files": [ - "https://github.com/Limitex/ComfyUI-Diffusers" - ], - "id": "diffusers", - "install_type": "git-clone", - "reference": "https://github.com/Limitex/ComfyUI-Diffusers", - "title": "ComfyUI-Diffusers" - }, - { - "author": "aiXander", - "description": "Maintained by Eden.art, this is a growing suite of custom nodes for building advanced pipelines.", - "files": [ - "https://github.com/edenartlab/eden_comfy_pipelines" - ], - "id": "eden", - "install_type": "git-clone", - "reference": "https://github.com/edenartlab/eden_comfy_pipelines", - "title": "Eden.art nodesuite" - }, - { - "author": "aiXander", - "description": "Maintained by Eden.art, this is a very fast, well tuned trainer for SDXL and SD15", - "files": [ - "https://github.com/edenartlab/sd-lora-trainer" - ], - "id": "eden-lora-trainer", - "install_type": "git-clone", - "reference": "https://github.com/edenartlab/sd-lora-trainer", - "title": "Eden.art LoRa Trainer" - }, - { - "author": "pkpk", - "description": "A custom node on ComfyUI that saves images in AVIF format. Workflow can be loaded from images saved at this node.", - "files": [ - "https://github.com/pkpkTech/ComfyUI-SaveAVIF" - ], - "id": "saveavif", - "install_type": "git-clone", - "reference": "https://github.com/pkpkTech/ComfyUI-SaveAVIF", - "title": "ComfyUI-SaveAVIF" - }, - { - "author": "pkpkTech", - "description": "Use ngrok to allow external access to ComfyUI.\nNOTE: Need to manually modify a token inside the __init__.py file.", - "files": [ - "https://github.com/pkpkTech/ComfyUI-ngrok" - ], - "id": "ngrok", - "install_type": "git-clone", - "reference": "https://github.com/pkpkTech/ComfyUI-ngrok", - "title": "ComfyUI-ngrok" - }, - { - "author": "pkpk", - "description": "This is a custom node of ComfyUI that downloads and loads models from the input URL. The model is temporarily downloaded into memory and not saved to storage.\nThis could be useful when trying out models or when using various models on machines with limited storage. Since the model is downloaded into memory, expect higher memory usage than usual.", - "files": [ - "https://github.com/pkpkTech/ComfyUI-TemporaryLoader" - ], - "id": "temploader", - "install_type": "git-clone", - "reference": "https://github.com/pkpkTech/ComfyUI-TemporaryLoader", - "title": "ComfyUI-TemporaryLoader" - }, - { - "author": "pkpkTech", - "description": "Add a button to the menu to save and load the running queue and the pending queues.\nThis is intended to be used when you want to exit ComfyUI with queues still remaining.", - "files": [ - "https://github.com/pkpkTech/ComfyUI-SaveQueues" - ], - "id": "savequeues", - "install_type": "git-clone", - "reference": "https://github.com/pkpkTech/ComfyUI-SaveQueues", - "title": "ComfyUI-SaveQueues" - }, - { - "author": "Crystian", - "description": "With this suit, you can see the resources monitor, progress bar & time elapsed, metadata and compare between two images, compare between two JSONs, show any value to console/display, pipes, and more!\nThis provides better nodes to load/save images, previews, etc, and see \"hidden\" data without loading a new workflow.", - "files": [ - "https://github.com/crystian/ComfyUI-Crystools" - ], - "id": "crytools", - "install_type": "git-clone", - "nodename_pattern": " \\[Crystools\\]$", - "reference": "https://github.com/crystian/ComfyUI-Crystools", - "title": "Crystools" - }, - { - "author": "Crystian", - "description": "With this quality of life extension, you can save your workflow with a specific name and include additional details such as the author, a description, and the version (in metadata/json). Important: When you share your workflow (via png/json), others will be able to see your information!", - "files": [ - "https://github.com/crystian/ComfyUI-Crystools-save" - ], - "id": "crytools-save", - "install_type": "git-clone", - "reference": "https://github.com/crystian/ComfyUI-Crystools-save", - "title": "Crystools-save" - }, - { - "author": "Kangkang625", - "description": "This repo is a simple implementation of [a/Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example) based on its [a/huggingface pipeline](https://huggingface.co/Fantasy-Studio/Paint-by-Example).", - "files": [ - "https://github.com/Kangkang625/ComfyUI-paint-by-example" - ], - "id": "paint-by-example", - "install_type": "git-clone", - "pip": [ - "diffusers" - ], - "reference": "https://github.com/Kangkang625/ComfyUI-paint-by-example", - "title": "ComfyUI-Paint-by-Example" - }, - { - "author": "54rt1n", - "description": "ComfyUI powertools for SD1.5 and SDXL model merging.", - "files": [ - "https://github.com/54rt1n/ComfyUI-DareMerge" - ], - "id": "daremerge", - "install_type": "git-clone", - "reference": "https://github.com/54rt1n/ComfyUI-DareMerge", - "title": "ComfyUI-DareMerge" - }, - { - "author": "an90ray", - "description": "Nodes: RErouter, String (RE), Int (RE)", - "files": [ - "https://github.com/an90ray/ComfyUI_RErouter_CustomNodes" - ], - "id": "rerouter", - "install_type": "git-clone", - "reference": "https://github.com/an90ray/ComfyUI_RErouter_CustomNodes", - "title": "ComfyUI_RErouter_CustomNodes" - }, - { - "author": "jesenzhang", - "description": "This is a simple implementation StreamDiffusion(A Pipeline-Level Solution for Real-Time Interactive Generation) for ComfyUI", - "files": [ - "https://github.com/jesenzhang/ComfyUI_StreamDiffusion" - ], - "id": "streamdiffusion", - "install_type": "git-clone", - "reference": "https://github.com/jesenzhang/ComfyUI_StreamDiffusion", - "title": "ComfyUI_StreamDiffusion" - }, - { - "author": "ai-liam", - "description": "Nodes: LiamLoadImage. This node provides the capability to load images from a URL.", - "files": [ - "https://github.com/ai-liam/comfyui_liam_util" - ], - "id": "liam-util-single", - "install_type": "git-clone", - "reference": "https://github.com/ai-liam/comfyui_liam_util", - "title": "LiamUtil (single node)" - }, - { - "author": "ai-liam", - "description": "Nodes: LiamLibLoadImage, LiamLibImageToGray, LiamLibSaveImg, LiamLibFillImage, PreviewReliefImage, GetBetterDepthImage, LiamLibSaveText", - "files": [ - "https://github.com/ai-liam/comfyui-liam" - ], - "id": "liam-util", - "install_type": "git-clone", - "reference": "https://github.com/ai-liam/comfyui-liam", - "title": "LiamUtil" - }, - { - "author": "Ryuukeisyou", - "description": "This is a set of custom nodes for ComfyUI. The nodes utilize the [a/face parsing model](https://huggingface.co/jonathandinu/face-parsing) to provide detailed segmantation of face. To improve face segmantation accuracy, [a/yolov8 face model](https://huggingface.co/Bingsu/adetailer/) is used to first extract face from an image. There are also auxiliary nodes for image and mask processing. A guided filter is also provided for skin smoothing.", - "files": [ - "https://github.com/Ryuukeisyou/comfyui_face_parsing" - ], - "id": "face-parsing", - "install_type": "git-clone", - "reference": "https://github.com/Ryuukeisyou/comfyui_face_parsing", - "title": "comfyui_face_parsing" - }, - { - "author": "Ryuukeisyou", - "description": "ComfyUI implemntation for [a/SyncTalk](https://github.com/ZiqiaoPeng/SyncTalk)", - "files": [ - "https://github.com/Ryuukeisyou/ComfyUI-SyncTalk" - ], - "id": "synctalk", - "install_type": "git-clone", - "reference": "https://github.com/Ryuukeisyou/ComfyUI-SyncTalk", - "title": "ComfyUI-SyncTalk" - }, - { - "author": "tocubed", - "description": "Nodes: Shadertoy, Load Audio (from Path), Audio Frame Transform (Shadertoy), Audio Frame Transform (Beats)", - "files": [ - "https://github.com/tocubed/ComfyUI-AudioReactor" - ], - "id": "audioreactor", - "install_type": "git-clone", - "reference": "https://github.com/tocubed/ComfyUI-AudioReactor", - "title": "ComfyUI-AudioReactor" - }, - { - "author": "tocubed", - "description": "Wrapper for EvTexture Video Upscaler: [a/https://github.com/DachunKai/EvTexture](https://github.com/DachunKai/EvTexture)", - "files": [ - "https://github.com/tocubed/ComfyUI-EvTexture" - ], - "install_type": "git-clone", - "reference": "https://github.com/tocubed/ComfyUI-EvTexture", - "title": "ComfyUI-EvTexture" - }, - { - "author": "ntc-ai", - "description": "An experiment about combining multiple LoRAs with [a/DARE](https://arxiv.org/pdf/2311.03099.pdf)", - "files": [ - "https://github.com/ntc-ai/ComfyUI-DARE-LoRA-Merge" - ], - "install_type": "git-clone", - "reference": "https://github.com/ntc-ai/ComfyUI-DARE-LoRA-Merge", - "title": "ComfyUI - Apply LoRA Stacker with DARE" - }, - { - "author": "wwwins", - "description": "Nodes:SimpleAspectRatio", - "files": [ - "https://github.com/wwwins/ComfyUI-Simple-Aspect-Ratio" - ], - "install_type": "git-clone", - "reference": "https://github.com/wwwins/ComfyUI-Simple-Aspect-Ratio", - "title": "ComfyUI-Simple-Aspect-Ratio" - }, - { - "author": "ownimage", - "description": "Nodes:Caching Image Loader.", - "files": [ - "https://github.com/ownimage/ComfyUI-ownimage" - ], - "install_type": "git-clone", - "reference": "https://github.com/ownimage/ComfyUI-ownimage", - "title": "ComfyUI-ownimage" - }, - { - "author": "Ryuukeisyou", - "description": "Nodes:ImageLoadFromBase64, ImageLoadByPath, ImageLoadAsMaskByPath, ImageSaveToPath, ImageSaveAsBase64, VHSFileNamesToStrings(IOHelpers).", - "files": [ - "https://github.com/Ryuukeisyou/comfyui_io_helpers" - ], - "install_type": "git-clone", - "reference": "https://github.com/Ryuukeisyou/comfyui_io_helpers", - "title": "comfyui_io_helpers" - }, - { - "author": "flowtyone", - "description": "This is a custom node that lets you take advantage of Latent Diffusion Super Resolution (LDSR) models inside ComfyUI.", - "files": [ - "https://github.com/flowtyone/ComfyUI-Flowty-LDSR" - ], - "install_type": "git-clone", - "reference": "https://github.com/flowtyone/ComfyUI-Flowty-LDSR", - "title": "ComfyUI-Flowty-LDSR" - }, - { - "author": "flowtyone", - "description": "This is a custom node that lets you use TripoSR right from ComfyUI.\n[a/TripoSR](https://github.com/VAST-AI-Research/TripoSR) is a state-of-the-art open-source model for fast feedforward 3D reconstruction from a single image, collaboratively developed by Tripo AI and Stability AI. (TL;DR it creates a 3d model from an image.)", - "files": [ - "https://github.com/flowtyone/ComfyUI-Flowty-TripoSR" - ], - "install_type": "git-clone", - "reference": "https://github.com/flowtyone/ComfyUI-Flowty-TripoSR", - "title": "ComfyUI-Flowty-TripoSR" - }, - { - "author": "flowtyone", - "description": "This is a custom node that lets you use Convolutional Reconstruction Models right from ComfyUI.\n[a/CRM](https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/) is a high-fidelity feed-forward single image-to-3D generative model.", - "files": [ - "https://github.com/flowtyone/ComfyUI-Flowty-CRM" - ], - "install_type": "git-clone", - "reference": "https://github.com/flowtyone/ComfyUI-Flowty-CRM", - "title": "ComfyUI-Flowty-CRM" - }, - { - "author": "massao000", - "description": "Aspect ratio selector for ComfyUI based on [a/sd-webui-ar](https://github.com/alemelis/sd-webui-ar?tab=readme-ov-file).", - "files": [ - "https://github.com/massao000/ComfyUI_aspect_ratios" - ], - "install_type": "git-clone", - "reference": "https://github.com/massao000/ComfyUI_aspect_ratios", - "title": "ComfyUI_aspect_ratios" - }, - { - "author": "SiliconFlow", - "description": "[a/Onediff](https://github.com/siliconflow/onediff) ComfyUI Nodes.", - "files": [ - "https://github.com/siliconflow/onediff_comfy_nodes" - ], - "id": "onddiff", - "install_type": "git-clone", - "reference": "https://github.com/siliconflow/onediff_comfy_nodes", - "title": "OneDiff Nodes" - }, - { - "author": "hinablue", - "description": "Nodes:3D Pose Editor", - "files": [ - "https://github.com/hinablue/ComfyUI_3dPoseEditor" - ], - "id": "3d-pose-editor", - "install_type": "git-clone", - "reference": "https://github.com/hinablue/ComfyUI_3dPoseEditor", - "title": "ComfyUI 3D Pose Editor" - }, - { - "author": "chaojie", - "description": "ComfyUI-CameraCtrl-Wrapper", - "files": [ - "https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper" - ], - "id": "cameractrl-wrapper", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper", - "title": "ComfyUI-CameraCtrl-Wrapper" - }, - { - "author": "chaojie", - "description": "ComfyUI-EasyAnimate", - "files": [ - "https://github.com/chaojie/ComfyUI-EasyAnimate" - ], - "id": "easyanimate", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-EasyAnimate", - "title": "ComfyUI-EasyAnimate" - }, - { - "author": "chaojie", - "description": "ComfyUI_StreamingT2V", - "files": [ - "https://github.com/chaojie/ComfyUI_StreamingT2V" - ], - "id": "streamingt2v", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI_StreamingT2V", - "title": "ComfyUI_StreamingT2V" - }, - { - "author": "chaojie", - "description": "ComfyUI node for [a/Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan)", - "files": [ - "https://github.com/chaojie/ComfyUI-Open-Sora-Plan" - ], - "id": "opensora-plan", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Open-Sora-Plan", - "title": "ComfyUI-Open-Sora-Plan" - }, - { - "author": "chaojie", - "description": "ComfyUI MuseTalk", - "files": [ - "https://github.com/chaojie/ComfyUI-MuseTalk" - ], - "id": "musetalk-chaojie", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-MuseTalk", - "title": "ComfyUI-MuseTalk" - }, - { - "author": "chaojie", - "description": "ComfyUI MuseV", - "files": [ - "https://github.com/chaojie/ComfyUI-MuseV" - ], - "id": "musev", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-MuseV", - "title": "ComfyUI-MuseV" - }, - { - "author": "chaojie", - "description": "ComfyUI [a/AniPortrait](https://github.com/Zejun-Yang/AniPortrait)", - "files": [ - "https://github.com/chaojie/ComfyUI-AniPortrait" - ], - "id": "aniportrait", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-AniPortrait", - "title": "ComfyUI-AniPortrait" - }, - { - "author": "chaojie", - "description": "ComfyUI Img2Img-Turbo", - "files": [ - "https://github.com/chaojie/ComfyUI-Img2Img-Turbo" - ], - "id": "img2img-turbo", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Img2Img-Turbo", - "title": "ComfyUI-Img2Img-Turbo" - }, - { - "author": "chaojie", - "description": "ComfyUI Champ", - "files": [ - "https://github.com/chaojie/ComfyUI-Champ" - ], - "id": "champ", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Champ", - "title": "ComfyUI-Champ" - }, - { - "author": "chaojie", - "description": "ComfyUI Open Sora\nNOTE:only supports Linux now", - "files": [ - "https://github.com/chaojie/ComfyUI-Open-Sora" - ], - "id": "opensora", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Open-Sora", - "title": "ComfyUI-Open-Sora" - }, - { - "author": "chaojie", - "description": "ComfyUI Trajectory", - "files": [ - "https://github.com/chaojie/ComfyUI-Trajectory" - ], - "id": "trajectory", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Trajectory", - "title": "ComfyUI-Trajectory" - }, - { - "author": "chaojie", - "description": "ComfyUI dust3r", - "files": [ - "https://github.com/chaojie/ComfyUI-dust3r" - ], - "id": "dust3r", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-dust3r", - "title": "ComfyUI-dust3r" - }, - { - "author": "chaojie", - "description": "ComfyUI Gemma", - "files": [ - "https://github.com/chaojie/ComfyUI-Gemma" - ], - "id": "gamma", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Gemma", - "title": "ComfyUI-Gemma" - }, - { - "author": "chaojie", - "description": "Better Dynamic, Higher Resolution, and Stronger Coherence!", - "files": [ - "https://github.com/chaojie/ComfyUI-DynamiCrafter" - ], - "id": "dynamicrafter-chaojie", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-DynamiCrafter", - "title": "ComfyUI-DynamiCrafter" - }, - { - "author": "chaojie", - "description": "ComfyUI 3d engine", - "files": [ - "https://github.com/chaojie/ComfyUI-Panda3d" - ], - "id": "panda3d", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Panda3d", - "title": "ComfyUI-Panda3d" - }, - { - "author": "chaojie", - "description": "Pymunk is a easy-to-use pythonic 2d physics library that can be used whenever you need 2d rigid body physics from Python", - "files": [ - "https://github.com/chaojie/ComfyUI-Pymunk" - ], - "id": "pymunk", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Pymunk", - "title": "ComfyUI-Pymunk" - }, - { - "author": "chaojie", - "description": "Nodes: Download the weights of MotionCtrl [a/motionctrl.pth](https://huggingface.co/TencentARC/MotionCtrl/blob/main/motionctrl.pth) and put it to ComfyUI/models/checkpoints", - "files": [ - "https://github.com/chaojie/ComfyUI-MotionCtrl" - ], - "id": "motionctrl", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-MotionCtrl", - "title": "ComfyUI-MotionCtrl" - }, - { - "author": "chaojie", - "description": "Nodes: that we currently provide the package only for x86-64 linux, such as Ubuntu or Debian, and Python 3.8, 3.9, and 3.10.", - "files": [ - "https://github.com/chaojie/ComfyUI-Motion-Vector-Extractor" - ], - "id": "motion-vector-extractor", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Motion-Vector-Extractor", - "title": "ComfyUI-Motion-Vector-Extractor" - }, - { - "author": "chaojie", - "description": "Nodes: Download the weights of MotionCtrl-SVD [a/motionctrl_svd.ckpt](https://huggingface.co/TencentARC/MotionCtrl/blob/main/motionctrl_svd.ckpt) and put it to ComfyUI/models/checkpoints", - "files": [ - "https://github.com/chaojie/ComfyUI-MotionCtrl-SVD" - ], - "id": "motionctrl-svd", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-MotionCtrl-SVD", - "title": "ComfyUI-MotionCtrl-SVD" - }, - { - "author": "chaojie", - "description": "DragAnything", - "files": [ - "https://github.com/chaojie/ComfyUI-DragAnything" - ], - "id": "draganything", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-DragAnything", - "title": "ComfyUI-DragAnything" - }, - { - "author": "chaojie", - "description": "Nodes: Download the weights of DragNUWA [a/drag_nuwa_svd.pth](https://drive.google.com/file/d/1Z4JOley0SJCb35kFF4PCc6N6P1ftfX4i/view) and put it to ComfyUI/models/checkpoints/drag_nuwa_svd.pth\n[w/Due to changes in the torch package and versions of many other packages, it may disrupt your installation environment.]", - "files": [ - "https://github.com/chaojie/ComfyUI-DragNUWA" - ], - "id": "dragnuwa", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-DragNUWA", - "title": "ComfyUI-DragNUWA" - }, - { - "author": "chaojie", - "description": "Nodes: Run python tools/download_weights.py first to download weights automatically", - "files": [ - "https://github.com/chaojie/ComfyUI-Moore-AnimateAnyone" - ], - "id": "moore-animateanyone", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Moore-AnimateAnyone", - "title": "ComfyUI-Moore-AnimateAnyone" - }, - { - "author": "chaojie", - "description": "This is an implementation of [a/i2vgen-xl](https://github.com/ali-vilab/i2vgen-xl)", - "files": [ - "https://github.com/chaojie/ComfyUI-I2VGEN-XL" - ], - "id": "i2vgen-xl", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-I2VGEN-XL", - "title": "ComfyUI-I2VGEN-XL" - }, - { - "author": "chaojie", - "description": "This is an ComfyUI implementation of LightGlue to generate motion brush", - "files": [ - "https://github.com/chaojie/ComfyUI-LightGlue" - ], - "id": "lightglue", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-LightGlue", - "title": "ComfyUI-LightGlue" - }, - { - "author": "chaojie", - "description": "This is an ComfyUI implementation of RAFT to generate motion brush", - "files": [ - "https://github.com/chaojie/ComfyUI-RAFT" - ], - "id": "raft", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-RAFT", - "title": "ComfyUI-RAFT" - }, - { - "author": "chaojie", - "description": "Nodes:VideoLaVITLoader, VideoLaVITT2V, VideoLaVITI2V, VideoLaVITI2VLong, VideoLaVITT2VLong, VideoLaVITI2I", - "files": [ - "https://github.com/chaojie/ComfyUI-LaVIT" - ], - "id": "lavit", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-LaVIT", - "title": "ComfyUI-LaVIT" - }, - { - "author": "chaojie", - "description": "Nodes:SimDATrain, SimDALoader, SimDARun, VHS_FILENAMES_STRING_SimDA", - "files": [ - "https://github.com/chaojie/ComfyUI-SimDA" - ], - "id": "simda", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-SimDA", - "title": "ComfyUI-SimDA" - }, - { - "author": "chaojie", - "description": "Investigating the Effectiveness of Cross Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models", - "files": [ - "https://github.com/chaojie/ComfyUI-Video-Editing-X-Attention" - ], - "id": "video-editing-x-attention", - "install_type": "git-clone", - "reference": "https://github.com/chaojie/ComfyUI-Video-Editing-X-Attention", - "title": "ComfyUI-Video-Editing-X-Attention" - }, - { - "author": "alexopus", - "description": "Allows you to save images with their generation metadata compatible with Civitai. Works with png, jpeg and webp. Stores LoRAs, models and embeddings hashes for resource recognition.", - "files": [ - "https://github.com/alexopus/ComfyUI-Image-Saver" - ], - "id": "comfyui-image-saver", - "install_type": "git-clone", - "reference": "https://github.com/alexopus/ComfyUI-Image-Saver", - "title": "ComfyUI Image Saver" - }, - { - "author": "alexopus", - "description": "A ComfyUI extension that adds a notes sidebar for managing notes", - "files": [ - "https://github.com/alexopus/ComfyUI-Notes-Sidebar" - ], - "install_type": "git-clone", - "reference": "https://github.com/alexopus/ComfyUI-Notes-Sidebar", - "title": "ComfyUI Notes Sidebar" - }, - { - "author": "kft334", - "description": "Nodes: Image(s) To Websocket (Base64), Load Image (Base64),Load Images (Base64)", - "files": [ - "https://github.com/kft334/Knodes" - ], - "id": "knodes", - "install_type": "git-clone", - "reference": "https://github.com/kft334/Knodes", - "title": "Knodes" - }, - { - "author": "MrForExample", - "description": "Make 3D assets generation in ComfyUI good and convenient as it generates image/video!\nThis is an extensive node suite that enables ComfyUI to process 3D inputs (Mesh & UV Texture, etc.) using cutting edge algorithms (3DGS, NeRF, etc.) and models (InstantMesh, CRM, TripoSR, etc.)\nNOTE: Pre-built python wheels can manually download from [a/https://github.com/MrForExample/Comfy3D_Pre_Builds](https://github.com/MrForExample/Comfy3D_Pre_Builds) if automatic install failed", - "files": [ - "https://github.com/MrForExample/ComfyUI-3D-Pack" - ], - "id": "3dpack", - "install_type": "git-clone", - "nodename_pattern": "^\\[Comfy3D\\]", - "reference": "https://github.com/MrForExample/ComfyUI-3D-Pack", - "title": "ComfyUI-3D-Pack" - }, - { - "author": "Mr.ForExample", - "description": "Improved AnimateAnyone implementation that allows you to use the opse image sequence and reference image to generate stylized video.\nThe current goal of this project is to achieve desired pose2video result with 1+FPS on GPUs that are equal to or better than RTX 3080!\ud83d\ude80\n[w/The torch environment may be compromised due to version issues as some torch-related packages are being reinstalled.]", - "files": [ - "https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved" - ], - "id": "animateanyone-evolved", - "install_type": "git-clone", - "nodename_pattern": "^\\[AnimateAnyone\\]", - "reference": "https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved", - "title": "ComfyUI-AnimateAnyone-Evolved" - }, - { - "author": "tzwm", - "description": "Calculate the execution time of all nodes.", - "files": [ - "https://github.com/tzwm/comfyui-profiler" - ], - "install_type": "git-clone", - "reference": "https://github.com/tzwm/comfyui-profiler", - "title": "ComfyUI Profiler" - }, - { - "author": "Daniel Lewis", - "description": "This is a set of nodes to interact with llama-cpp-python", - "files": [ - "https://github.com/daniel-lewis-ab/ComfyUI-Llama" - ], - "install_type": "git-clone", - "reference": "https://github.com/daniel-lewis-ab/ComfyUI-Llama", - "title": "ComfyUI-Llama" - }, - { - "author": "Daniel Lewis", - "description": "Text To Speech (TTS) for ComfyUI", - "files": [ - "https://github.com/daniel-lewis-ab/ComfyUI-TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/daniel-lewis-ab/ComfyUI-TTS", - "title": "ComfyUI-TTS" - }, - { - "author": "djbielejeski", - "description": "Extension for Automatic1111 and ComfyUI to automatically create masks for Background/Hair/Body/Face/Clothes in Img2Img", - "files": [ - "https://github.com/djbielejeski/a-person-mask-generator" - ], - "install_type": "git-clone", - "reference": "https://github.com/djbielejeski/a-person-mask-generator", - "title": "a-person-mask-generator" - }, - { - "author": "smagnetize", - "description": "Nodes:SingleImageDataUrlLoader", - "files": [ - "https://github.com/smagnetize/kb-comfyui-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/smagnetize/kb-comfyui-nodes", - "title": "kb-comfyui-nodes" - }, - { - "author": "glowcone", - "description": "Nodes: LoadImageFromBase64. Loads an image and its transparency mask from a base64-encoded data URI for easy API connection.", - "files": [ - "https://github.com/glowcone/comfyui-base64-to-image" - ], - "install_type": "git-clone", - "reference": "https://github.com/glowcone/comfyui-base64-to-image", - "title": "Load Image From Base64 URI" - }, - { - "author": "glowcone", - "description": "Nodes: Convert String To Int, Convert String To Float", - "files": [ - "https://github.com/glowcone/comfyui-string-converter" - ], - "install_type": "git-clone", - "reference": "https://github.com/glowcone/comfyui-string-converter", - "title": "String Converter" - }, - { - "author": "AInseven", - "description": "fastblend for comfyui, and other nodes that I write for video2video. rebatch image, my openpose", - "files": [ - "https://github.com/AInseven/ComfyUI-fastblend" - ], - "install_type": "git-clone", - "reference": "https://github.com/AInseven/ComfyUI-fastblend", - "title": "ComfyUI-fastblend" - }, - { - "author": "HebelHuber", - "description": "Nodes:Enhanced Save Node", - "files": [ - "https://github.com/HebelHuber/comfyui-enhanced-save-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/HebelHuber/comfyui-enhanced-save-node", - "title": "comfyui-enhanced-save-node" - }, - { - "author": "LarryJane491", - "description": "If you see this message, your ComfyUI-Manager is outdated.\nRecent channel provides only the list of the latest nodes. If you want to find the complete node list, please go to the Default channel.\nMaking LoRA has never been easier!", - "files": [ - "https://github.com/LarryJane491/Lora-Training-in-Comfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/LarryJane491/Lora-Training-in-Comfy", - "title": "Lora-Training-in-Comfy" - }, - { - "author": "LarryJane491", - "description": "The LoRA Caption custom nodes, just like their name suggests, allow you to caption images so they are ready for LoRA training.", - "files": [ - "https://github.com/LarryJane491/Image-Captioning-in-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/LarryJane491/Image-Captioning-in-ComfyUI", - "title": "Image-Captioning-in-ComfyUI" - }, - { - "author": "Layer-norm", - "description": "A very simple ComfyUI node to remove item with mask.", - "files": [ - "https://github.com/Layer-norm/comfyui-lama-remover" - ], - "install_type": "git-clone", - "reference": "https://github.com/Layer-norm/comfyui-lama-remover", - "title": "Comfyui lama remover" - }, - { - "author": "Taremin", - "description": "Instead of LoraLoader or HypernetworkLoader, it receives a prompt and loads and applies LoRA or HN based on the specifications within the prompt. The main purpose of this custom node is to allow changes without reconnecting the LoraLoader node when the prompt is randomly altered, etc.", - "files": [ - "https://github.com/Taremin/comfyui-prompt-extranetworks" - ], - "install_type": "git-clone", - "reference": "https://github.com/Taremin/comfyui-prompt-extranetworks", - "title": "ComfyUI Prompt ExtraNetworks" - }, - { - "author": "Taremin", - "description": " This extension provides the StringToolsConcat node, which concatenates multiple texts, and the StringToolsRandomChoice node, which selects one randomly from multiple texts.", - "files": [ - "https://github.com/Taremin/comfyui-string-tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/Taremin/comfyui-string-tools", - "title": "ComfyUI String Tools" - }, - { - "author": "Taremin", - "description": "Make it possible to edit the prompt using the Monaco Editor, an editor implementation used in VSCode.\nNOTE: This extension supports both ComfyUI and A1111 simultaneously.", - "files": [ - "https://github.com/Taremin/webui-monaco-prompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/Taremin/webui-monaco-prompt", - "title": "WebUI Monaco Prompt" - }, - { - "author": "Taremin", - "description": "This is an extension for ComfyUI. It retains multiple workflow tabs so that they are not lost when reloading or restarting.", - "files": [ - "https://github.com/Taremin/comfyui-keep-multiple-tabs" - ], - "install_type": "git-clone", - "reference": "https://github.com/Taremin/comfyui-keep-multiple-tabs", - "title": "comfyui-keep-multiple-tabs" - }, - { - "author": "Taremin", - "description": "This is a custom node for ComfyUI.\nThe PromptGenerationConfig node allows users to configure settings such as image dimensions, step count, and CFGScale through prompts during image generation.\nThe PromptEdit node enables users to add text from the prompt to the negative prompt (or vice versa) and replace parts of the prompt using regular expressions.", - "files": [ - "https://github.com/Taremin/comfyui-prompt-config" - ], - "install_type": "git-clone", - "reference": "https://github.com/Taremin/comfyui-prompt-config", - "title": "comfyui-prompt-config" - }, - { - "author": "foxtrot-roger", - "description": "A bunch of nodes that can be useful to manipulate primitive types (numbers, text, ...) Also some helpers to generate text and timestamps.", - "files": [ - "https://github.com/foxtrot-roger/comfyui-rf-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/foxtrot-roger/comfyui-rf-nodes", - "title": "RF Nodes" - }, - { - "author": "abyz22", - "description": "Nodes:abyz22_Padding Image, abyz22_ImpactWildcardEncode, abyz22_setimageinfo, abyz22_SaveImage, abyz22_ImpactWildcardEncode_GetPrompt, abyz22_SetQueue, abyz22_drawmask, abyz22_FirstNonNull, abyz22_blendimages, abyz22_blend_onecolor. Please check workflow in [a/https://github.com/abyz22/image_control](https://github.com/abyz22/image_control)", - "files": [ - "https://github.com/abyz22/image_control" - ], - "install_type": "git-clone", - "reference": "https://github.com/abyz22/image_control", - "title": "image_control" - }, - { - "author": "HAL41", - "description": "Simple node to handle scaling of YOLOv8 segmentation masks", - "files": [ - "https://github.com/HAL41/ComfyUI-aichemy-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/HAL41/ComfyUI-aichemy-nodes", - "title": "ComfyUI aichemy nodes" - }, - { - "author": "nkchocoai", - "description": "Add a node that outputs width and height of the size selected from the preset (.csv).", - "files": [ - "https://github.com/nkchocoai/ComfyUI-SizeFromPresets" - ], - "install_type": "git-clone", - "reference": "https://github.com/nkchocoai/ComfyUI-SizeFromPresets", - "title": "ComfyUI-SizeFromPresets" - }, - { - "author": "nkchocoai", - "description": "Nodes: Format String, Join String List, Load Preset, Load Preset (Advanced), Const String, Const String (multi line). Add useful nodes related to prompt.", - "files": [ - "https://github.com/nkchocoai/ComfyUI-PromptUtilities" - ], - "install_type": "git-clone", - "reference": "https://github.com/nkchocoai/ComfyUI-PromptUtilities", - "title": "ComfyUI-PromptUtilities" - }, - { - "author": "nkchocoai", - "description": "Add a node for drawing text with CR Draw Text of ComfyUI_Comfyroll_CustomNodes to the area of SEGS detected by Ultralytics Detector of ComfyUI-Impact-Pack.", - "files": [ - "https://github.com/nkchocoai/ComfyUI-TextOnSegs" - ], - "install_type": "git-clone", - "reference": "https://github.com/nkchocoai/ComfyUI-TextOnSegs", - "title": "ComfyUI-TextOnSegs" - }, - { - "author": "nkchocoai", - "description": "Add a node to save images with metadata (PNGInfo) extracted from the input values of each node.\nSince the values are extracted dynamically, values output by various extension nodes can be added to metadata.", - "files": [ - "https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData" - ], - "install_type": "git-clone", - "reference": "https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData", - "title": "ComfyUI-SaveImageWithMetaData" - }, - { - "author": "nkchocoai", - "description": "Add nodes that generates danbooru tags by [a/Dart(Danbooru Tags Transformer)](https://huggingface.co/p1atdev/dart-v1-sft).", - "files": [ - "https://github.com/nkchocoai/ComfyUI-Dart" - ], - "id": "dart", - "install_type": "git-clone", - "reference": "https://github.com/nkchocoai/ComfyUI-Dart", - "title": "ComfyUI-Dart" - }, - { - "author": "nkchocoai", - "description": "This node is for playing the game of guessing prompts by looking at images generated from prompts output by TIPO, Tagger, etc..", - "files": [ - "https://github.com/nkchocoai/ComfyUI-DanbooruPromptQuiz" - ], - "install_type": "git-clone", - "reference": "https://github.com/nkchocoai/ComfyUI-DanbooruPromptQuiz", - "title": "ComfyUI-DanbooruPromptQuiz" - }, - { - "author": "JaredTherriault", - "description": "python and web UX improvements for ComfyUI: Lora/Embedding picker, web extension manager (enable/disable any extension without disabling python nodes), control any parameter with text prompts, image and video viewer, metadata viewer, token counter, comments in prompts, font control, and more! \n[w/'ImageFeed.js' from the custom scripts of pythongosssss is not compatible with this suite's ImageDrawer feature. Additionally, 'DynamicPrompts.js' and 'EditAttention.js' from the core, along with 'favicon.js' from the custom scripts of pythongosssss, are incompatible with advanced features of the suite. Please use the JNodes Extension Management setting in Settings > JNodes > Extension Management to disable these extensions by unchecking them to use the full functionality of the suite.]", - "files": [ - "https://github.com/JaredTherriault/ComfyUI-JNodes" - ], - "id": "jnodes", - "install_type": "git-clone", - "reference": "https://github.com/JaredTherriault/ComfyUI-JNodes", - "title": "ComfyUI-JNodes" - }, - { - "author": "prozacgod", - "description": "A simple, quick, and dirty implementation of multiple workspaces within ComfyUI.", - "files": [ - "https://github.com/prozacgod/comfyui-pzc-multiworkspace" - ], - "id": "multi-workspace", - "install_type": "git-clone", - "reference": "https://github.com/prozacgod/comfyui-pzc-multiworkspace", - "title": "ComfyUI Multi-Workspace" - }, - { - "author": "Siberpone", - "description": "A booru API powered prompt generator for A1111 and ComfyUI with flexible tag filtering system and customizable prompt templates.", - "files": [ - "https://github.com/Siberpone/lazy-pony-prompter" - ], - "id": "lazy-pony-prompter", - "install_type": "git-clone", - "reference": "https://github.com/Siberpone/lazy-pony-prompter", - "title": "Lazy Pony Prompter" - }, - { - "author": "dave-palt", - "description": "Nodes: DSP Image Concat", - "files": [ - "https://github.com/dave-palt/comfyui_DSP_imagehelpers" - ], - "id": "dsp-imagehelpers", - "install_type": "git-clone", - "reference": "https://github.com/dave-palt/comfyui_DSP_imagehelpers", - "title": "comfyui_DSP_imagehelpers" - }, - { - "author": "Inzaniak", - "description": "Ranbooru is an extension for the comfyUI. The purpose of this extension is to add a node that gets a random set of tags from boorus pictures. This is mostly being used to help me test my checkpoints on a large variety of", - "files": [ - "https://github.com/Inzaniak/comfyui-ranbooru" - ], - "id": "ranbooru", - "install_type": "git-clone", - "reference": "https://github.com/Inzaniak/comfyui-ranbooru", - "title": "Ranbooru for ComfyUI" - }, - { - "author": "miosp", - "description": "A node for JPEG de-artifacting using [a/FBCNN](https://github.com/jiaxi-jiang/FBCNN).", - "files": [ - "https://github.com/Miosp/ComfyUI-FBCNN" - ], - "id": "fbcnn", - "install_type": "git-clone", - "reference": "https://github.com/Miosp/ComfyUI-FBCNN", - "title": "ComfyUI-FBCNN" - }, - { - "author": "JcandZero", - "description": "GLM4 Vision Integration", - "files": [ - "https://github.com/JcandZero/ComfyUI_GLM4Node" - ], - "id": "glm4node", - "install_type": "git-clone", - "reference": "https://github.com/JcandZero/ComfyUI_GLM4Node", - "title": "ComfyUI_GLM4Node" - }, - { - "author": "darkpixel", - "description": "Slightly better random prompt generation tools that allow combining and picking prompts from both file and text input sources.", - "files": [ - "https://github.com/darkpixel/darkprompts" - ], - "id": "darkprompts", - "install_type": "git-clone", - "reference": "https://github.com/darkpixel/darkprompts", - "title": "DarkPrompts" - }, - { - "author": "yytdfc", - "description": "Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies. This repo is the ComfyUI nodes for Bedrock service. You could invoke the foundation model in your ComfyUI pipeline.", - "files": [ - "https://github.com/aws-samples/comfyui-llm-node-for-amazon-bedrock" - ], - "id": "bedrock", - "install_type": "git-clone", - "pip": [ - "boto3" - ], - "reference": "https://github.com/aws-samples/comfyui-llm-node-for-amazon-bedrock", - "title": "Amazon Bedrock nodes for ComfyUI" - }, - { - "author": "Qais Malkawi", - "description": "This Extension adds a few custom QOL nodes that ComfyUI lacks by default.", - "files": [ - "https://github.com/QaisMalkawi/ComfyUI-QaisHelper" - ], - "id": "qais-helper", - "install_type": "git-clone", - "reference": "https://github.com/QaisMalkawi/ComfyUI-QaisHelper", - "title": "ComfyUI-Qais-Helper" - }, - { - "author": "longgui0318", - "description": "Nodes:Split Masks, Mask Selection Of Masks, Mask Region Info", - "files": [ - "https://github.com/longgui0318/comfyui-mask-util" - ], - "id": "mask-util", - "install_type": "git-clone", - "reference": "https://github.com/longgui0318/comfyui-mask-util", - "title": "comfyui-mask-util" - }, - { - "author": "longgui0318", - "description": "Nodes:Generate Stable Diffsution Prompt With LLM, Translate Text With LLM, Chat With LLM", - "files": [ - "https://github.com/longgui0318/comfyui-llm-assistant" - ], - "id": "llm-assistant", - "install_type": "git-clone", - "reference": "https://github.com/longgui0318/comfyui-llm-assistant", - "title": "comfyui-llm-assistant" - }, - { - "author": "longgui0318", - "description": "The comfyui supported version of the [a/Magic Clothing](https://github.com/ShineChen1024/MagicClothing) project, not the diffusers version, allows direct integration with modules such as ipadapter.[w/comfyui-oms-diffusion is renamed to comfyui-magic-clothing. You may need to reinstall this.]", - "files": [ - "https://github.com/longgui0318/comfyui-magic-clothing" - ], - "id": "magic-clothing", - "install_type": "git-clone", - "reference": "https://github.com/longgui0318/comfyui-magic-clothing", - "title": "comfyui-magic-clothing" - }, - { - "author": "longgui0318", - "description": "Nodes:Init Layer Info Array, Added Layer Info To Array, Layer Info Array Fuse, Layer Image Seleted, Layer Images IPAdapter Advanced, Enhanced Random Light Source", - "files": [ - "https://github.com/longgui0318/comfyui-common-util" - ], - "id": "common-util", - "install_type": "git-clone", - "reference": "https://github.com/longgui0318/comfyui-common-util", - "title": "comfyui-common-util" - }, - { - "author": "DimaChaichan", - "description": "This exporter is a plugin for ComfyUI, which can export tasks for [a/LAizypainter](https://github.com/DimaChaichan/LAizypainter).\nLAizypainter is a Photoshop plugin with which you can send tasks directly to a Stable Diffusion server. More information about a [a/Task](https://github.com/DimaChaichan/LAizypainter?tab=readme-ov-file#task)", - "files": [ - "https://github.com/DimaChaichan/LAizypainter-Exporter-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/DimaChaichan/LAizypainter-Exporter-ComfyUI", - "title": "LAizypainter-Exporter-ComfyUI" - }, - { - "author": "adriflex", - "description": "Nodes:Blender viewport color, Blender Viewport depth", - "files": [ - "https://github.com/adriflex/ComfyUI_Blender_Texdiff" - ], - "id": "blender-texdiff", - "install_type": "git-clone", - "reference": "https://github.com/adriflex/ComfyUI_Blender_Texdiff", - "title": "ComfyUI_Blender_Texdiff" - }, - { - "author": "Shraknard", - "description": "Custom node for ComfyUI that makes parts of the image transparent (face, background...)", - "files": [ - "https://github.com/Shraknard/ComfyUI-Remover" - ], - "id": "remover", - "install_type": "git-clone", - "reference": "https://github.com/Shraknard/ComfyUI-Remover", - "title": "ComfyUI-Remover" - }, - { - "author": "FlyingFireCo", - "description": "Nodes:Tiled KSampler, Asymmetric Tiled KSampler, Circular VAEDecode.", - "files": [ - "https://github.com/FlyingFireCo/tiled_ksampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/FlyingFireCo/tiled_ksampler", - "title": "tiled_ksampler" - }, - { - "author": "Nlar", - "description": "Front end ComfyUI nodes for CartoonSegmentation Based upon the work of the CartoonSegmentation repository this project will provide a front end to some of the features.", - "files": [ - "https://github.com/Nlar/ComfyUI_CartoonSegmentation" - ], - "id": "cartoon-seg", - "install_type": "git-clone", - "reference": "https://github.com/Nlar/ComfyUI_CartoonSegmentation", - "title": "ComfyUI_CartoonSegmentation" - }, - { - "author": "godspede", - "description": "Just a simple substring node that takes text and length as input, and outputs the first length characters.", - "files": [ - "https://github.com/godspede/ComfyUI_Substring" - ], - "id": "substring", - "install_type": "git-clone", - "reference": "https://github.com/godspede/ComfyUI_Substring", - "title": "ComfyUI Substring" - }, - { - "author": "gokayfem", - "description": "Custom Nodes for Vision Language Models (VLM) , Large Language Models (LLM), Image Captioning, Automatic Prompt Generation, Creative and Consistent Prompt Suggestion, Keyword Extraction", - "files": [ - "https://github.com/gokayfem/ComfyUI_VLM_nodes" - ], - "id": "vlm", - "install_type": "git-clone", - "reference": "https://github.com/gokayfem/ComfyUI_VLM_nodes", - "title": "VLM_nodes" - }, - { - "author": "gokayfem", - "description": "Tell your dream and it interprets it and puts you inside your dream", - "files": [ - "https://github.com/gokayfem/ComfyUI-Dream-Interpreter" - ], - "id": "dream-interpreter", - "install_type": "git-clone", - "reference": "https://github.com/gokayfem/ComfyUI-Dream-Interpreter", - "title": "ComfyUI-Dream-Interpreter" - }, - { - "author": "gokayfem", - "description": "Works with any Depth Map and visualizes the applied version it inside ComfyUI", - "files": [ - "https://github.com/gokayfem/ComfyUI-Depth-Visualization" - ], - "id": "delpth-visualization", - "install_type": "git-clone", - "reference": "https://github.com/gokayfem/ComfyUI-Depth-Visualization", - "title": "ComfyUI-Depth-Visualization" - }, - { - "author": "gokayfem", - "description": "Visualize your textures inside ComfyUI", - "files": [ - "https://github.com/gokayfem/ComfyUI-Texture-Simple" - ], - "id": "texture-simple", - "install_type": "git-clone", - "reference": "https://github.com/gokayfem/ComfyUI-Texture-Simple", - "title": "ComfyUI-Texture-Simple" - }, - { - "author": "gokayfem", - "description": "Custom nodes for using fal API. Video generation with Kling, Runway, Luma. Image generation with Flux. LLMs and VLMs OpenAI, Claude, Llama and Gemini.", - "files": [ - "https://github.com/gokayfem/ComfyUI-fal-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/gokayfem/ComfyUI-fal-API", - "title": "ComfyUI-fal-API" - }, - { - "author": "Hiero207", - "description": "Nodes:Post to Discord w/ Webhook", - "files": [ - "https://github.com/Hiero207/ComfyUI-Hiero-Nodes" - ], - "id": "hiero", - "install_type": "git-clone", - "reference": "https://github.com/Hiero207/ComfyUI-Hiero-Nodes", - "title": "Hiero-Nodes" - }, - { - "author": "azure-dragon-ai", - "description": "Nodes:ImageScore, Loader, Image Processor, Real Image Processor, Fake Image Processor, Text Processor. ComfyUI Nodes for ClipScore", - "files": [ - "https://github.com/azure-dragon-ai/ComfyUI-ClipScore-Nodes" - ], - "id": "clipscore", - "install_type": "git-clone", - "reference": "https://github.com/azure-dragon-ai/ComfyUI-ClipScore-Nodes", - "title": "ComfyUI-ClipScore-Nodes" - }, - { - "author": "azure-dragon-ai", - "description": "ComfyUI Nodes for HPSv2, Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis", - "files": [ - "https://github.com/azure-dragon-ai/ComfyUI-HPSv2-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/azure-dragon-ai/ComfyUI-HPSv2-Nodes", - "title": "ComfyUI-HPSv2-Nodes" - }, - { - "author": "yuvraj108c", - "description": "Transcribe audio and add subtitles to videos using Whisper in ComfyUI", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Whisper" - ], - "id": "whisper", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Whisper", - "title": "ComfyUI Whisper" - }, - { - "author": "yuvraj108c", - "description": "A collection of nice utility nodes for ComfyUI", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Pronodes" - ], - "id": "pronodes", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Pronodes", - "title": "ComfyUI-Pronodes" - }, - { - "author": "yuvraj108c", - "description": "Nodes:Upscale Video Tensorrt", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Vsgan" - ], - "id": "vsgan", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Vsgan", - "title": "ComfyUI-Vsgan" - }, - { - "author": "yuvraj108c", - "description": "This extension provides a ComfyUI Custom Node implementation of the [a/Depth-Anything-Tensorrt](https://github.com/spacewalk01/depth-anything-tensorrt) in Python for ultra fast depth map generation", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Depth-Anything-Tensorrt" - ], - "id": "depth-anything-tensorrt", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Depth-Anything-Tensorrt", - "title": "ComfyUI Depth Anything TensorRT" - }, - { - "author": "yuvraj108c", - "description": "Convert Text-to-Speech inside ComfyUI using [a/Piper](https://github.com/rhasspy/piper)", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-PiperTTS" - ], - "id": "pipertts", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-PiperTTS", - "title": "ComfyUI PiperTTS" - }, - { - "author": "yuvraj108c", - "description": "This project provides a Tensorrt implementation for fast image upscaling inside ComfyUI (3-4x faster)", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Upscaler-Tensorrt" - ], - "id": "upscaler-tensorrt", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Upscaler-Tensorrt", - "title": "ComfyUI Upscaler TensorRT" - }, - { - "author": "yuvraj108c", - "description": "This repo provides a ComfyUI Custom Node implementation of [a/YOLO-NAS-POSE](https://github.com/Deci-AI/super-gradients), powered by TensorRT for ultra fast pose estimation. It has been adapted to work with openpose controlnet (experimental)", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-YoloNasPose-Tensorrt" - ], - "id": "yolonaspose-tensorrt", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-YoloNasPose-Tensorrt", - "title": "ComfyUI YoloNasPose Tensorrt" - }, - { - "author": "yuvraj108c", - "description": "This project provides a Tensorrt implementation of Dwpose for ultra fast pose estimation inside ComfyUI", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Dwpose-Tensorrt" - ], - "id": "dwpose-tensorrt", - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Dwpose-Tensorrt", - "title": "ComfyUI Dwpose TensorRT" - }, - { - "author": "yuvraj108c", - "description": "This project provides an experimental Tensorrt implementation for ultra fast face restoration inside ComfyUI.\nNote: This project doesn't do pre/post processing. It only works on cropped faces for now.", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Facerestore-Tensorrt" - ], - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Facerestore-Tensorrt", - "title": "ComfyUI Facerestore TensorRT" - }, - { - "author": "yuvraj108c", - "description": "This project provides a TensorRT implementation of [a/RIFE](https://github.com/hzwer/ECCV2022-RIFE) for ultra fast frame interpolation inside ComfyUI", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt" - ], - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Rife-Tensorrt", - "title": "ComfyUI Rife TensorRT" - }, - { - "author": "yuvraj108c", - "description": "This project is an unofficial ComfyUI implementation of [a/Video Depth Anything](https://github.com/DepthAnything/Video-Depth-Anything), for depth estimation on long videos without compromising quality, consistency, or generalization ability.", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Video-Depth-Anything" - ], - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Video-Depth-Anything", - "title": "ComfyUI Video Depth Anything" - }, - { - "author": "yuvraj108c", - "description": "This project is an unofficial ComfyUI implementation of [a/InvSR](https://github.com/zsyOAOA/InvSR) (Arbitrary-steps Image Super-resolution via Diffusion Inversion)", - "files": [ - "https://github.com/yuvraj108c/ComfyUI_InvSR" - ], - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI_InvSR", - "title": "ComfyUI InvSR" - }, - { - "author": "yuvraj108c", - "description": "This project is an unofficial ComfyUI implementation of [a/Thera](https://github.com/prs-eth/thera) (Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields)", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-Thera" - ], - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-Thera", - "title": "ComfyUI Thera" - }, - { - "author": "yuvraj108c", - "description": "This project provides an unofficial ComfyUI implementation of [a/FLOAT](https://github.com/deepbrainai-research/float) for Generative Motion Latent Flow Matching for Audio-driven Talking Portrait", - "files": [ - "https://github.com/yuvraj108c/ComfyUI-FLOAT" - ], - "install_type": "git-clone", - "reference": "https://github.com/yuvraj108c/ComfyUI-FLOAT", - "title": "ComfyUI FLOAT" - }, - { - "author": "blepping", - "description": "Better TAESD previews, BlehHyperTile.", - "files": [ - "https://github.com/blepping/ComfyUI-bleh" - ], - "id": "bleh", - "install_type": "git-clone", - "reference": "https://github.com/blepping/ComfyUI-bleh", - "title": "ComfyUI-bleh" - }, - { - "author": "blepping", - "description": "A janky implementation of Sonar sampling (momentum-based sampling) for ComfyUI.", - "files": [ - "https://github.com/blepping/ComfyUI-sonar" - ], - "id": "sonar", - "install_type": "git-clone", - "reference": "https://github.com/blepping/ComfyUI-sonar", - "title": "ComfyUI-sonar" - }, - { - "author": "blepping", - "description": "Janky implementation of [a/HiDiffusion](https://github.com/megvii-research/HiDiffusion) for ComfyUI. Enables generating at resolutions higher than what the model was trained for. Only supports SD 1.x (maybe 2.x) and SDXL.", - "files": [ - "https://github.com/blepping/comfyui_jankhidiffusion" - ], - "id": "jank-hidiffusion", - "install_type": "git-clone", - "reference": "https://github.com/blepping/comfyui_jankhidiffusion", - "title": "comfyui_jankhidiffusion" - }, - { - "author": "blepping", - "description": "Janky implementation of [a/DiffuseHigh](https://github.com/yhyun225/DiffuseHigh/) for ComfyUI. Enables generating at resolutions higher than what the model was trained for without requiring model patches.", - "files": [ - "https://github.com/blepping/comfyui_jankdiffusehigh" - ], - "id": "jank-diffusehigh", - "install_type": "git-clone", - "reference": "https://github.com/blepping/comfyui_jankdiffusehigh", - "title": "comfyui_jankdiffusehigh" - }, - { - "author": "blepping", - "description": "Experimental and mathematically unsound (but fun!) sampling for ComfyUI.\nFeel free create a question in Discussions for usage help: OCS Q&A Discussion[w/Status: In flux, may be useful but likely to change/break workflows frequently. Mainly for advanced users.]", - "files": [ - "https://github.com/blepping/comfyui_overly_complicated_sampling" - ], - "install_type": "git-clone", - "reference": "https://github.com/blepping/comfyui_overly_complicated_sampling", - "title": "comfyui_overly_complicated_sampling" - }, - { - "author": "blepping", - "description": "ComfyUI node to apply the ResAdapter Unet patch for SD1.5 models", - "files": [ - "https://github.com/blepping/ComfyUI-ApplyResAdapterUnet" - ], - "install_type": "git-clone", - "reference": "https://github.com/blepping/ComfyUI-ApplyResAdapterUnet", - "title": "ComfyUI-ApplyResAdapterUnet" - }, - { - "author": "JerryOrbachJr", - "description": "A ComfyUI custom node that randomly selects a height and width pair from a list in a config file", - "files": [ - "https://github.com/JerryOrbachJr/ComfyUI-RandomSize" - ], - "install_type": "git-clone", - "reference": "https://github.com/JerryOrbachJr/ComfyUI-RandomSize", - "title": "Random Size" - }, - { - "author": "jamal-alkharrat", - "description": "ComfyUI Custom Node to Rotate Images, Img2Img node.", - "files": [ - "https://github.com/jamal-alkharrat/ComfyUI_rotate_image" - ], - "install_type": "git-clone", - "reference": "https://github.com/jamal-alkharrat/ComfyUI_rotate_image", - "title": "ComfyUI_rotate_image" - }, - { - "author": "mape", - "description": "Multi-monitor image preview, Variable Assigment/Wireless Nodes, Prompt Tweaking, Command Palette, Pinned favourite nodes, Node navigation, Fuzzy search, Node time tracking, Organizing and Error management. For more info visit: [a/https://comfyui.ma.pe/](https://comfyui.ma.pe/)", - "files": [ - "https://github.com/mape/ComfyUI-mape-Helpers" - ], - "id": "mape-helpers", - "install_type": "git-clone", - "reference": "https://github.com/mape/ComfyUI-mape-Helpers", - "title": "mape's helpers" - }, - { - "author": "zhongpei", - "description": "Enhancing Image Restoration. (ref:[a/InstructIR](https://github.com/mv-lab/InstructIR))", - "files": [ - "https://github.com/zhongpei/ComfyUI-InstructIR" - ], - "id": "instructir", - "install_type": "git-clone", - "reference": "https://github.com/zhongpei/ComfyUI-InstructIR", - "title": "ComfyUI for InstructIR" - }, - { - "author": "Loewen-Hob", - "description": "This custom node is based on the [a/rembg-comfyui-node](https://github.com/Jcd1230/rembg-comfyui-node) but provides additional functionality to select ONNX models.", - "files": [ - "https://github.com/Loewen-Hob/rembg-comfyui-node-better" - ], - "id": "rembg-better", - "install_type": "git-clone", - "reference": "https://github.com/Loewen-Hob/rembg-comfyui-node-better", - "title": "Rembg Background Removal Node for ComfyUI (Better)" - }, - { - "author": "HaydenReeve", - "description": "Strings should be easy, and simple. This extension aims to provide a set of nodes that make working with strings in ComfyUI a little bit easier.", - "files": [ - "https://github.com/HaydenReeve/ComfyUI-Better-Strings" - ], - "id": "better-string", - "install_type": "git-clone", - "reference": "https://github.com/HaydenReeve/ComfyUI-Better-Strings", - "title": "ComfyUI Better Strings" - }, - { - "author": "StartHua", - "description": "Nodes:segformer_clothes, segformer_agnostic, segformer_remove_bg, stabel_vition. Nodes for model dress up.", - "files": [ - "https://github.com/StartHua/ComfyUI_Seg_VITON" - ], - "id": "seg-viton", - "install_type": "git-clone", - "reference": "https://github.com/StartHua/ComfyUI_Seg_VITON", - "title": "ComfyUI_Seg_VITON" - }, - { - "author": "StartHua", - "description": "JoyTag is a state of the art AI vision model for tagging images, with a focus on sex positivity and inclusivity. It uses the Danbooru tagging schema, but works across a wide range of images, from hand drawn to photographic.\nDownload the weight and put it under checkpoints: [a/https://huggingface.co/fancyfeast/joytag/tree/main](https://huggingface.co/fancyfeast/joytag/tree/main)", - "files": [ - "https://github.com/StartHua/Comfyui_joytag" - ], - "id": "joytag", - "install_type": "git-clone", - "reference": "https://github.com/StartHua/Comfyui_joytag", - "title": "Comfyui_joytag" - }, - { - "author": "StartHua", - "description": "SegFormer model fine-tuned on ATR dataset for clothes segmentation but can also be used for human segmentation!\nDownload the weight and put it under checkpoints: [a/https://huggingface.co/mattmdjaga/segformer_b2_clothes](https://huggingface.co/mattmdjaga/segformer_b2_clothes)", - "files": [ - "https://github.com/StartHua/Comfyui_segformer_b2_clothes" - ], - "id": "segformer-b2-clothes", - "install_type": "git-clone", - "reference": "https://github.com/StartHua/Comfyui_segformer_b2_clothes", - "title": "comfyui_segformer_b2_clothes" - }, - { - "author": "StartHua", - "description": "Nodes:Ood_hd_CXH, Ood_hd_CXH. [a/OOTDiffusion](https://github.com/levihsu/OOTDiffusion)", - "files": [ - "https://github.com/StartHua/ComfyUI_OOTDiffusion_CXH" - ], - "id": "ootdiffusion-cxh", - "install_type": "git-clone", - "reference": "https://github.com/StartHua/ComfyUI_OOTDiffusion_CXH", - "title": "ComfyUI_OOTDiffusion_CXH" - }, - { - "author": "StartHua", - "description": "Original project: [a/link](https://github.com/tencent-ailab/PCDMs)\nBased on testing, the author's original images work very well, but using my own images generally requires some luck!", - "files": [ - "https://github.com/StartHua/ComfyUI_PCDMs" - ], - "id": "pcdms", - "install_type": "git-clone", - "reference": "https://github.com/StartHua/ComfyUI_PCDMs", - "title": "ComfyUI_PCDMs" - }, - { - "author": "StartHua", - "description": "Phi-3.5-vision-instruct fast talk with image !\nFast , Fast ,Fast!\n1.Phi-3.5-vision-instruct", - "files": [ - "https://github.com/StartHua/Comfyui_CXH_Phi_3.5" - ], - "install_type": "git-clone", - "reference": "https://github.com/StartHua/Comfyui_CXH_Phi_3.5", - "title": "Comfyui_CXH_Phi_3.5" - }, - { - "author": "StartHua", - "description": "NODES:CXH_DeepLX_Free, CXH_DeepLX_translate", - "files": [ - "https://github.com/StartHua/Comfyui_CXH_DeepLX" - ], - "install_type": "git-clone", - "reference": "https://github.com/StartHua/Comfyui_CXH_DeepLX", - "title": "Comfyui_CXH_DeepLX" - }, - { - "author": "StartHua", - "description": "flux lora merge.\nadaptive Merge (uses tensor norms and weight), manual Merge (uses fixed weights you specify), additive Merge (uses 100% of the first and adds a percentage of the second)", - "files": [ - "https://github.com/StartHua/Comfyui_CXH_FluxLoraMerge" - ], - "install_type": "git-clone", - "reference": "https://github.com/StartHua/Comfyui_CXH_FluxLoraMerge", - "title": "Comfyui_CXH_FluxLoraMerge" - }, - { - "author": "StartHua", - "description": "NODES:CXH_Gemini2_TX, CXH_Gemini2_Vision, CXH_Local_Prompt", - "files": [ - "https://github.com/StartHua/Comfyui_Gemini2" - ], - "install_type": "git-clone", - "reference": "https://github.com/StartHua/Comfyui_Gemini2", - "title": "Comfyui_Gemini2" - }, - { - "author": "ricklove", - "description": "Nodes: Image Crop and Resize by Mask, Image Uncrop, Image Shadow, Optical Flow (Dip), Warp Image with Flow, Image Threshold (Channels), Finetune Variable, Finetune Analyze, Finetune Analyze Batch, ... Misc ComfyUI nodes by Rick Love", - "files": [ - "https://github.com/ricklove/comfyui-ricklove" - ], - "id": "ricklove", - "install_type": "git-clone", - "reference": "https://github.com/ricklove/comfyui-ricklove", - "title": "comfyui-ricklove" - }, - { - "author": "nosiu", - "description": "Implementation of [a/faceswap](https://github.com/nosiu/InstantID-faceswap/tree/main) based on [a/InstantID](https://github.com/InstantID/InstantID) for ComfyUI.", - "files": [ - "https://github.com/nosiu/comfyui-instantId-faceswap" - ], - "id": "comfyui-instantid-faceswap", - "install_type": "git-clone", - "reference": "https://github.com/nosiu/comfyui-instantId-faceswap", - "title": "comfyui-instantId-faceswap" - }, - { - "author": "nosiu", - "description": "A simple text randomizer for ComfyUI that can generate random and surprising results", - "files": [ - "https://github.com/nosiu/comfyui-text-randomizer" - ], - "id": "comfyui-text-randomizer", - "install_type": "git-clone", - "reference": "https://github.com/nosiu/comfyui-text-randomizer", - "title": "comfyui-text-randomizer" - }, - { - "author": "LyazS", - "description": "A Anime Character Segmentation node for comfyui, based on [this hf space](https://huggingface.co/spaces/skytnt/anime-remove-background).", - "files": [ - "https://github.com/LyazS/comfyui-anime-seg" - ], - "install_type": "git-clone", - "reference": "https://github.com/LyazS/comfyui-anime-seg", - "title": "Anime Character Segmentation node for comfyui" - }, - { - "author": "LyazS", - "description": "A net tool node for comfyui, rewrite from [comfyui-tooling-nodes](https://github.com/Acly/comfyui-tooling-nodes) but support more big data sending.", - "files": [ - "https://github.com/LyazS/comfyui-nettools" - ], - "install_type": "git-clone", - "reference": "https://github.com/LyazS/comfyui-nettools", - "title": "net tool node for comfyui" - }, - { - "author": "Chan-0312", - "description": "This is a project that generates videos frame by frame based on IPAdapter+ControlNet. Unlike [a/Steerable-motion](https://github.com/banodoco/Steerable-Motion), we do not rely on AnimateDiff. This decision is primarily due to the fact that the videos generated by AnimateDiff are often blurry. Through frame-by-frame control using IPAdapter+ControlNet, we can produce higher definition and more controllable videos.", - "files": [ - "https://github.com/Chan-0312/ComfyUI-IPAnimate" - ], - "install_type": "git-clone", - "reference": "https://github.com/Chan-0312/ComfyUI-IPAnimate", - "title": "ComfyUI-IPAnimate" - }, - { - "author": "Chan-0312", - "description": "Nodes:Easy2DDeforum (Chan)", - "files": [ - "https://github.com/Chan-0312/ComfyUI-EasyDeforum" - ], - "install_type": "git-clone", - "reference": "https://github.com/Chan-0312/ComfyUI-EasyDeforum", - "title": "ComfyUI-EasyDeforum" - }, - { - "author": "trumanwong", - "description": "An implementation of NSFW Detection for ComfyUI", - "files": [ - "https://github.com/trumanwong/ComfyUI-NSFW-Detection" - ], - "install_type": "git-clone", - "reference": "https://github.com/trumanwong/ComfyUI-NSFW-Detection", - "title": "ComfyUI-NSFW-Detection" - }, - { - "author": "TemryL", - "description": "ComfyS3 seamlessly integrates with [a/Amazon S3](https://aws.amazon.com/en/s3/) in ComfyUI. This open-source project provides custom nodes for effortless loading and saving of images, videos, and checkpoint models directly from S3 buckets within the ComfyUI graph interface.", - "files": [ - "https://github.com/TemryL/ComfyS3" - ], - "install_type": "git-clone", - "reference": "https://github.com/TemryL/ComfyS3", - "title": "ComfyS3" - }, - { - "author": "MaraScott", - "description": "A set of nodes including a universal bus, an Inpainting By Mask and a large Upscaler/Refiner\n[AnyBus,McInpainty,McBoaty]", - "files": [ - "https://github.com/MaraScott/ComfyUI_MaraScott_Nodes" - ], - "id": "marascott-nodes", - "install_type": "git-clone", - "reference": "https://github.com/MaraScott/ComfyUI_MaraScott_Nodes", - "title": "\ud83d\udc30 MaraScott Nodes" - }, - { - "author": "yffyhk", - "description": "Nodes: Get Danbooru, Tag Encode", - "files": [ - "https://github.com/yffyhk/comfyui_auto_danbooru" - ], - "install_type": "git-clone", - "reference": "https://github.com/yffyhk/comfyui_auto_danbooru", - "title": "comfyui_auto_danbooru" - }, - { - "author": "dfl", - "description": "CLIP text encoder with BREAK formatting like A1111 (uses chained ComfyUI conditioning concat).", - "files": [ - "https://github.com/dfl/comfyui-clip-with-break" - ], - "install_type": "git-clone", - "reference": "https://github.com/dfl/comfyui-clip-with-break", - "title": "comfyui-clip-with-break" - }, - { - "author": "dfl", - "description": "ComfyUI Custom Sampler nodes that implement Zheng et al.'s Trajectory Consistency Distillation based on [a/https://mhh0318.github.io/tcd](https://mhh0318.github.io/tcd)", - "files": [ - "https://github.com/dfl/comfyui-tcd-scheduler" - ], - "id": "dfl-tcd", - "install_type": "git-clone", - "reference": "https://github.com/dfl/comfyui-tcd-scheduler", - "title": "ComfyUI-TCD-scheduler" - }, - { - "author": "antrobot", - "description": "A small node pack containing various things I felt like ought to be in base comfy-UI. Currently includes Some image handling nodes to help with inpainting, a version of KSampler (advanced) that allows for denoise, and a node that can swap it's inputs. Remember to make an issue if you experience any bugs or errors!", - "files": [ - "https://github.com/antrobot1234/antrobots-comfyUI-nodepack" - ], - "install_type": "git-clone", - "reference": "https://github.com/antrobot1234/antrobots-comfyUI-nodepack", - "title": "antrobots ComfyUI Nodepack" - }, - { - "author": "bilal-arikan", - "description": "With this node you can upload text files to input folder from your local computer.", - "files": [ - "https://github.com/bilal-arikan/ComfyUI_TextAssets" - ], - "install_type": "git-clone", - "reference": "https://github.com/bilal-arikan/ComfyUI_TextAssets", - "title": "ComfyUI_TextAssets" - }, - { - "author": "kadirnar", - "description": "ComfyUI-Transformers is a cutting-edge project combining the power of computer vision and natural language processing to create intuitive and user-friendly interfaces. Our goal is to make technology more accessible and engaging.", - "files": [ - "https://github.com/kadirnar/ComfyUI-Transformers" - ], - "id": "comfy-transformers", - "install_type": "git-clone", - "reference": "https://github.com/kadirnar/ComfyUI-Transformers", - "title": "ComfyUI-Transformers" - }, - { - "author": "kadirnar", - "description": "Nodes:Load Ultralytics Model, Ultralytics Inference, Ultralytics Visualization, Convert to Dictionary, BBox to XYWH", - "files": [ - "https://github.com/kadirnar/ComfyUI-YOLO" - ], - "id": "comfy-yolo", - "install_type": "git-clone", - "reference": "https://github.com/kadirnar/ComfyUI-YOLO", - "title": "ComfyUI-YOLO" - }, - { - "author": "digitaljohn", - "description": "A set of custom ComfyUI nodes for performing basic post-processing effects including Film Grain and Vignette. These effects can help to take the edge off AI imagery and make them feel more natural.", - "files": [ - "https://github.com/digitaljohn/comfyui-propost" - ], - "install_type": "git-clone", - "reference": "https://github.com/digitaljohn/comfyui-propost", - "title": "ComfyUI-ProPost" - }, - { - "author": "deforum", - "description": "Official Deforum animation pipeline tools that provide a unique way to create frame-by-frame generative motion art.", - "files": [ - "https://github.com/XmYx/deforum-comfy-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/XmYx/deforum-comfy-nodes", - "title": "Deforum Nodes" - }, - { - "author": "XmYx", - "description": "Welcome to ComfyUI-SmolLM3, where we bring the magic of Hugging Face's SmolLM3 language models into your ComfyUI workflow! Whether you're crafting stories, generating ideas, or building AI-powered creativity tools, this node pack makes it delightfully simple.", - "files": [ - "https://github.com/XmYx/ComfyUI-SmolLM3" - ], - "install_type": "git-clone", - "reference": "https://github.com/XmYx/ComfyUI-SmolLM3", - "title": "ComfyUI-SmolLM3" - }, - { - "author": "adbrasi", - "description": "ComfyUI-TrashNodes-DownloadHuggingface is a ComfyUI node designed to facilitate the download of models you have just trained and uploaded to Hugging Face. This node is particularly useful for users who employ Google Colab for training and need to quickly download their models for deployment.", - "files": [ - "https://github.com/adbrasi/ComfyUI-TrashNodes-DownloadHuggingface" - ], - "install_type": "git-clone", - "reference": "https://github.com/adbrasi/ComfyUI-TrashNodes-DownloadHuggingface", - "title": "ComfyUI-TrashNodes-DownloadHuggingface" - }, - { - "author": "mbrostami", - "description": "ComfyUI Node to work with Hugging Face repositories", - "files": [ - "https://github.com/mbrostami/ComfyUI-HF" - ], - "install_type": "git-clone", - "reference": "https://github.com/mbrostami/ComfyUI-HF", - "title": "ComfyUI-HF" - }, - { - "author": "Billius-AI", - "description": "Nodes:Create Project Root, Add Folder, Add Folder Advanced, Add File Name Prefix, Add File Name Prefix Advanced, ShowPath", - "files": [ - "https://github.com/Billius-AI/ComfyUI-Path-Helper" - ], - "install_type": "git-clone", - "reference": "https://github.com/Billius-AI/ComfyUI-Path-Helper", - "title": "ComfyUI-Path-Helper" - }, - { - "author": "Franck-Demongin", - "description": "A custom node for ComfyUI to create a prompt based on a list of keywords saved in CSV files.", - "files": [ - "https://github.com/Franck-Demongin/NX_PromptStyler" - ], - "install_type": "git-clone", - "reference": "https://github.com/Franck-Demongin/NX_PromptStyler", - "title": "NX_PromptStyler" - }, - { - "author": "Franck-Demongin", - "description": "Nodes:Hugging Face Flux", - "files": [ - "https://github.com/Franck-Demongin/NX_HuggingFace_Flux" - ], - "install_type": "git-clone", - "reference": "https://github.com/Franck-Demongin/NX_HuggingFace_Flux", - "title": "NX_HuggingFace_Flux" - }, - { - "author": "Franck-Demongin", - "description": "A custom node for translating prompts with Google Translate or DeeplL directly in ComfyUI.", - "files": [ - "https://github.com/Franck-Demongin/NX_Translator" - ], - "install_type": "git-clone", - "reference": "https://github.com/Franck-Demongin/NX_Translator", - "title": "NX_Translator" - }, - { - "author": "xiaoxiaodesha", - "description": "Nodes:Combine HDMasks, Cover HDMasks, HD FaceIndex, HD SmoothEdge, HD GetMaskArea, HD Image Levels, HD Ultimate SD Upscale", - "files": [ - "https://github.com/xiaoxiaodesha/hd_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/xiaoxiaodesha/hd_node", - "title": "hd-nodes-comfyui" - }, - { - "author": "ShmuelRonen", - "description": "SVDResizer is a helper for resizing the source image, according to the sizes enabled in Stable Video Diffusion. The rationale behind the possibility of changing the size of the image in steps between the ranges of 576 and 1024, is the use of the greatest common denominator of these two numbers which is 64. SVD is lenient with resizing that adheres to this rule, so the chance of coherent video that is not the standard size of 576X1024 is greater. It is advisable to keep the value 1024 constant and play with the second size to maintain the stability of the result.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-SVDResizer" - ], - "id": "svdresizer", - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-SVDResizer", - "title": "ComfyUI-SVDResizer" - }, - { - "author": "ShmuelRonen", - "description": "The Wav2Lip node is a custom node for ComfyUI that allows you to perform lip-syncing on videos using the Wav2Lip model. It takes an input video and an audio file and generates a lip-synced output video.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_wav2lip" - ], - "id": "wav2lip", - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_wav2lip", - "title": "Wav2Lip Node for ComfyUI" - }, - { - "author": "ShmuelRonen", - "description": "ComfyUI_Gemini_Flash is a custom node for ComfyUI, integrating the capabilities of the Gemini 1.5 Flash model. This node supports text and vision-based prompts, allowing users to analyze and adapt images to text prompts for text2image tasks.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_Gemini_Flash" - ], - "id": "gemini-flash", - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_Gemini_Flash", - "title": "ComfyUI_Gemini_Flash" - }, - { - "author": "ShmuelRonen", - "description": "The ComfyUI_pixtral_vision is a powerful ComfyUI node designed to integrate seamlessly with the Mistral Pixtral API. It facilitates the analysis of images through deep learning models, interpreting and describing the visual content. Users can input an image directly and provide prompts for context, utilizing an API key for authentication.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_pixtral_vision" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_pixtral_vision", - "title": "ComfyUI_pixtral_vision" - }, - { - "author": "ShmuelRonen", - "description": "ComfyUI-FreeMemory is a custom node extension for ComfyUI that provides advanced memory management capabilities within your image generation workflows. It aims to help prevent out-of-memory errors and optimize resource usage during complex operations.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-FreeMemory" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-FreeMemory", - "title": "ComfyUI-FreeMemory" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node for Black Forest Labs' FLUX 1.1 [pro] API, supporting both regular and Ultra modes with optional Raw mode.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_Flux_1.1_RAW_API" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_Flux_1.1_RAW_API", - "title": "ComfyUI Flux 1.1 Ultra & Raw Node" - }, - { - "author": "ShmuelRonen", - "description": "A custom node for ComfyUI that enables smooth, keyframe-based animations for image generation. Create dynamic sequences with control over motion, zoom, rotation, and easing effects. Ideal for AI-assisted animation and video content creation.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-AstralAnimator" - ], - "id": "astralanimator", - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-AstralAnimator", - "title": "ComfyUI-AstralAnimator" - }, - { - "author": "ShmuelRonen", - "description": "A comprehensive ComfyUI wrapper for HiggsAudio v2, enabling high-quality text-to-speech generation with advanced voice cloning capabilities. Supports multiple voice presets and custom reference audio for voice cloning. Requires transformers==4.45.2 for compatibility.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-HiggsAudio_Wrapper" - ], - "id": "higgs-audio-wrapper", - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-HiggsAudio_Wrapper", - "title": "ComfyUI-HiggsAudio_Wrapper" - }, - { - "author": "ShmuelRonen", - "description": "A custom node that provides enhanced control over style transfer balance when using FLUX style models in ComfyUI. This node offers better control over the influence of text prompts versus style reference images.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Apply_Style_Model_Adjust" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Apply_Style_Model_Adjust", - "title": "Apply Style Model Adjust for ComfyUI" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node that integrates Mistral AI's Pixtral Large vision model, enabling powerful multimodal AI capabilities within ComfyUI. Pixtral Large is a 124B parameter model (123B decoder + 1B visual encoder)", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_pixtral_large" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_pixtral_large", - "title": "ComfyUI Pixtral Large Extension" - }, - { - "author": "ShmuelRonen", - "description": "A custom node for ComfyUI that enables coherent video generation while maintaining efficient memory usage, specifically optimized for heavy models like Flux.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-CohernetVideoSampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-CohernetVideoSampler", - "title": "ComfyUI Coherent Video Sampler Node" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Gemini_Flash_2.0_Exp" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Gemini_Flash_2.0_Exp", - "title": "ComfyUI-Gemini_Flash_2.0_Exp" - }, - { - "author": "ShmuelRonen", - "description": "A custom ComfyUI node designed to create seamless motion effects from single images by integrating with Hunyuan Video through latent space manipulation.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-ImageMotionGuider" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-ImageMotionGuider", - "title": "ComfyUI-ImageMotionGuider" - }, - { - "author": "ShmuelRonen", - "description": "Custom nodes for ComfyUI to generate empty latent space compatible with Hunyuan models for both image and video generation.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-EmptyHunyuanLatent" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-EmptyHunyuanLatent", - "title": "ComfyUI-EmptyHunyuanLatent" - }, - { - "author": "ShmuelRonen", - "description": "A custom node for ComfyUI that adds cinematic and movie scene styles to video generation prompts. This node helps create more dynamic and professional-looking video outputs by incorporating iconic movie scene aesthetics.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-HunyuanVideoStyler" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-HunyuanVideoStyler", - "title": "ComfyUI-HunyuanVideoStyler" - }, - { - "author": "ShmuelRonen", - "description": "This node provides lip-sync capabilities in ComfyUI using ByteDance's LatentSync model. It allows you to synchronize video lips with audio input.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-LatentSyncWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-LatentSyncWrapper", - "title": "ComfyUI-LatentSyncWrapper" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node implementation for optimized video generation and motion effects, designed to work with Hunyuan text-to-video models.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-HunyuanVideoSamplerSave" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-HunyuanVideoSamplerSave", - "title": "ComfyUI-HunyuanVideoSamplerSave" - }, - { - "author": "ShmuelRonen", - "description": "A custom node for ComfyUI that integrates DeepSeek's powerful chat and instruction API, enabling seamless AI interactions within your ComfyUI workflows.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-DeepSeek_R1-Chat" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-DeepSeek_R1-Chat", - "title": "ComfyUI DeepSeek_R1 Chat Node" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node extension that integrates the Janus-Pro-7B vision-language model from DeepSeek AI on your's local computer, enabling powerful image understanding and multi-turn conversation capabilities.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Janus_pro_vision" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Janus_pro_vision", - "title": "ComfyUI Janus Pro Vision" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node wrapper for JoyHallo - One-Shot Audio-Driven Talking Head Generation.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-JoyHallo_wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-JoyHallo_wrapper", - "title": "ComfyUI-JoyHallo_wrapper" - }, - { - "author": "ShmuelRonen", - "description": "A voice conversion extension node for ComfyUI based on [a/FreeVC](https://github.com/OlaWod/FreeVC), enabling high-quality voice conversion capabilities within the ComfyUI framework.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-FreeVC_wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-FreeVC_wrapper", - "title": "ComfyUI-FreeVC_wrapper" - }, - { - "author": "ShmuelRonen", - "description": "An advanced custom node for ComfyUI that provides optimized access to Wan2.1, a state-of-the-art video foundation model suite. The WanVideoKsampler node features intelligent memory management to enable higher resolution outputs and longer video sequences, even on consumer-grade hardware.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-WanVideoKsampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-WanVideoKsampler", - "title": "ComfyUI-WanVideoKsampler" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI extension that integrates PixArt-Alpha models directly into ComfyUI with advanced memory management.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-PixArt_XL" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-PixArt_XL", - "title": "ComfyUI-PixArt_XL" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node that simulates Photoshop's 'Flatten Image' functionality.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-PS_Flatten_Image" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-PS_Flatten_Image", - "title": "ComfyUI-PS_Flatten_Image" - }, - { - "author": "ShmuelRonen", - "description": "A Google Moogle is a Google Translator node for ComfyUI that provides easy-to-use text translation capabilities directly within your ComfyUI workflows.", - "files": [ - "https://github.com/ShmuelRonen/google_moogle" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/google_moogle", - "title": "Google Moogle" - }, - { - "author": "ShmuelRonen", - "description": "A custom node for ComfyUI that integrates OpenAI last T2S capabilities for free", - "files": [ - "https://github.com/ShmuelRonen/comfyui-openai_fm" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/comfyui-openai_fm", - "title": "comfyui-openai_fm" - }, - { - "author": "ShmuelRonen", - "description": "A powerful ComfyUI custom node for combining video clips with synchronized audio, background music, and advanced audio controls.", - "files": [ - "https://github.com/ShmuelRonen/DJ_VideoAudioMixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/DJ_VideoAudioMixer", - "title": "DJ_VideoAudioMixer" - }, - { - "author": "ShmuelRonen", - "description": "A custom node extension for ComfyUI that integrates Google's Veo 2 text-to-video generation capabilities.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Veo2-Experimental" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Veo2-Experimental", - "title": "ComfyUI-Veo2-Experimental" - }, - { - "author": "ShmuelRonen", - "description": "A memory-efficient implementation for upscaling videos in ComfyUI using non-diffusion upscaling models. This custom node is designed to handle large video frame sequences without memory bottlenecks.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-VideoUpscale_WithModel" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-VideoUpscale_WithModel", - "title": "ComfyUI-VideoUpscale_WithModel" - }, - { - "author": "ShmuelRonen", - "description": "This project adds high-quality Text-to-Speech capabilities to ComfyUI using the Orpheus TTS model. Create natural-sounding voices with emotional expressions, multilingual support, and audio effects.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Orpheus-TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Orpheus-TTS", - "title": "ComfyUI-Orpheus-TTS" - }, - { - "author": "ShmuelRonen", - "description": "A custom node for ComfyUI that integrates with [a/Hedra](https://www.hedra.com/)'s Character-3 API to generate talking avatar videos from images and audio.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_Hedra" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_Hedra", - "title": "ComfyUI Hedra Node" - }, - { - "author": "ShmuelRonen", - "description": "An extension that's adds advanced audio processing capabilities to ComfyUI with professional-grade audio effects and AI-powered audio enhancement.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Audio_Quality_Enhancer" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Audio_Quality_Enhancer", - "title": "ComfyUI-Audio_Quality_Enhancer" - }, - { - "author": "ShmuelRonen", - "description": "An extension that's adds advanced audio processing capabilities to ComfyUI with professional-grade audio effects and AI-powered audio enhancement.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-FramePackWrapper_Plus" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-FramePackWrapper_Plus", - "title": "ComfyUI-FramePackWrapper_Plus" - }, - { - "author": "ShmuelRonen", - "description": "A powerful ComfyUI custom node that brings Google's Gemini TTS capabilities directly to your workflow. Generate high-quality speech with 30+ voices supporting both free and paid tiers.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-Gemini_TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-Gemini_TTS", - "title": "ComfyUI-Gemini_TTS" - }, - { - "author": "ShmuelRonen", - "description": "An unofficial ComfyUI custom node integration for High-quality Text-to-Speech and Voice Conversion nodes for ComfyUI using ResembleAI's ChatterboxTTS with unlimited text length!!!.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI_ChatterBox_Voice" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI_ChatterBox_Voice", - "title": "ComfyUI_ChatterBox_Voice" - }, - { - "author": "ShmuelRonen", - "description": "A powerful ComfyUI custom node for text-based image editing using Black Forest Labs' Flux Kontext API. Transform your images with simple text instructions while maintaining character consistency and quality.", - "files": [ - "https://github.com/ShmuelRonen/FluxKontextCreator" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/FluxKontextCreator", - "title": "Flux Kontext Creator for ComfyUI" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI wrapper implementation of ThinkSound - an advanced AI model for generating high-quality audio from text descriptions and video content using Chain-of-Thought (CoT) reasoning.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-ThinkSound_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-ThinkSound_Wrapper", - "title": "ComfyUI-ThinkSound_Wrapper" - }, - { - "author": "ShmuelRonen", - "description": "A complete replacement for rgthree's Power Lora Loader with zero dependencies and two specialized versions to fit any workflow.", - "files": [ - "https://github.com/ShmuelRonen/multi-lora-stack" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/multi-lora-stack", - "title": "multi-lora-stack" - }, - { - "author": "ShmuelRonen", - "description": "A ComfyUI custom node for Google's Gemini 2.5 Flash Image (aka 'Nano Banana') model - the state-of-the-art image generation and editing AI.", - "files": [ - "https://github.com/ShmuelRonen/ComfyUI-NanoBanano" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShmuelRonen/ComfyUI-NanoBanano", - "title": "ComfyUI-NanoBanano" - }, - { - "author": "redhottensors", - "description": "Fully customizable Classifier Free Guidance for ComfyUI.", - "files": [ - "https://github.com/redhottensors/ComfyUI-Prediction" - ], - "id": "prediction", - "install_type": "git-clone", - "reference": "https://github.com/redhottensors/ComfyUI-Prediction", - "title": "ComfyUI-Prediction" - }, - { - "author": "Mamaaaamooooo", - "description": "Remove background of plural images.", - "files": [ - "https://github.com/Mamaaaamooooo/batchImg-rembg-ComfyUI-nodes" - ], - "id": "batch-rembg", - "install_type": "git-clone", - "reference": "https://github.com/Mamaaaamooooo/batchImg-rembg-ComfyUI-nodes", - "title": "Batch Rembg for ComfyUI" - }, - { - "author": "jordoh", - "description": "ComfyUI nodes wrapping the [a/deepface](https://github.com/serengil/deepface) library.", - "files": [ - "https://github.com/jordoh/ComfyUI-Deepface" - ], - "id": "deepface", - "install_type": "git-clone", - "reference": "https://github.com/jordoh/ComfyUI-Deepface", - "title": "ComfyUI Deepface" - }, - { - "author": "al-swaiti", - "description": "Nodes:Cascade Resolutions", - "files": [ - "https://github.com/al-swaiti/ComfyUI-CascadeResolutions" - ], - "id": "cascade-resolution", - "install_type": "git-clone", - "reference": "https://github.com/al-swaiti/ComfyUI-CascadeResolutions", - "title": "ComfyUI-CascadeResolutions" - }, - { - "author": "al-swaiti", - "description": "all art styles", - "files": [ - "https://github.com/al-swaiti/All-IN-ONE-style" - ], - "id": "all-in-one-style", - "install_type": "git-clone", - "reference": "https://github.com/al-swaiti/All-IN-ONE-style", - "title": "All-IN-ONE-style" - }, - { - "author": "al-swaiti", - "description": "ComfyUI extension for Ollama, Gemini, OpenAI, Claude, and Qwen with video and audio support", - "files": [ - "https://github.com/al-swaiti/ComfyUI-OllamaGemini" - ], - "install_type": "git-clone", - "reference": "https://github.com/al-swaiti/ComfyUI-OllamaGemini", - "title": "GeminiOllama ComfyUI Extension" - }, - { - "author": "mirabarukaso", - "description": "Slice regions of the canvas and convert them to masks for regional conditions widh PNG preview output. And a few support nodes.", - "files": [ - "https://github.com/mirabarukaso/ComfyUI_Mira" - ], - "id": "mira", - "install_type": "git-clone", - "reference": "https://github.com/mirabarukaso/ComfyUI_Mira", - "title": "ComfyUI_Mira" - }, - { - "author": "1038lab", - "description": "ComfyUI custom node implementation of OmniGen", - "files": [ - "https://github.com/1038lab/ComfyUI-OmniGen" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-OmniGen", - "title": "ComfyUI-OmniGen" - }, - { - "author": "1038lab", - "description": "A sophisticated ComfyUI custom node engineered for advanced image background removal and precise segmentation of objects, faces, clothing, and fashion elements. This tool leverages a diverse array of models, including RMBG-2.0, INSPYRENET, BEN, BEN2, BiRefNet models, SAM, and GroundingDINO, while also incorporating a new feature for real-time background replacement and enhanced edge detection for improved accuracy.", - "files": [ - "https://github.com/1038lab/ComfyUI-RMBG" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-RMBG", - "title": "ComfyUI-RMBG" - }, - { - "author": "1038lab", - "description": "WildPromptor simplifies prompt creation, organization, and customization in ComfyUI, turning chaotic workflows into an efficient, intuitive process.", - "files": [ - "https://github.com/1038lab/ComfyUI-WildPromptor" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-WildPromptor", - "title": "ComfyUI-WildPromptor" - }, - { - "author": "1038lab", - "description": "ComfyUI-EdgeTTS is a powerful text-to-speech node for ComfyUI, leveraging Microsoft's Edge TTS capabilities. It enables seamless conversion of text into natural-sounding speech, supporting multiple languages and voices. Ideal for enhancing user interactions, this node is easy to integrate and customize, making it perfect for various applications.", - "files": [ - "https://github.com/1038lab/ComfyUI-EdgeTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-EdgeTTS", - "title": "ComfyUI-EdgeTTS" - }, - { - "author": "1038lab", - "description": "ComfyUI integration for Pollinations API - Generate images and text based on user prompts", - "files": [ - "https://github.com/1038lab/ComfyUI-Pollinations" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-Pollinations", - "title": "ComfyUI-Pollinations" - }, - { - "author": "1038lab", - "description": "ComfyUI-SparkTTS is a custom ComfyUI node implementation of SparkTTS, an advanced text-to-speech system that harnesses the power of large language models (LLMs) to generate highly accurate and natural-sounding speech.", - "files": [ - "https://github.com/1038lab/ComfyUI-SparkTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-SparkTTS", - "title": "Comfyui-Spark-TTS" - }, - { - "author": "1038lab", - "description": "A ComfyUI custom node based on ByteDance MegaTTS3 MegaTTS3, enabling high-quality text-to-speech synthesis with voice cloning capabilities for both Chinese and English.", - "files": [ - "https://github.com/1038lab/ComfyUI-MegaTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-MegaTTS", - "title": "ComfyUI-MegaTTS" - }, - { - "author": "1038lab", - "description": "ComfyUI-ReduxFineTune is a custom node for ComfyUI that enables advanced style fine-tuning using the Flux Redux approach. It offers multiple unified fusion modes for precise and consistent control over style transfer, allowing users to fine-tune image styles with high flexibility and detail.", - "files": [ - "https://github.com/1038lab/ComfyUI-ReduxFineTune" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-ReduxFineTune", - "title": "ComfyUI-ReduxFineTune" - }, - { - "author": "1038lab", - "description": "A ComfyUI implementation of Latent Bridge Matching (LBM) for efficient image relighting. This node utilizes the LBM algorithm to perform single-step image-to-image translation specifically for relighting tasks.", - "files": [ - "https://github.com/1038lab/ComfyUI-LBM" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-LBM", - "title": "ComfyUI-LBM" - }, - { - "author": "1038lab", - "description": "Joy Caption is a ComfyUI custom node powered by the LLaVA model for efficient, stylized image captioning. Caption Tools nodes handle batch image processing and automatic separation of caption text.", - "files": [ - "https://github.com/1038lab/ComfyUI-JoyCaption" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-JoyCaption", - "title": "ComfyUI-JoyCaption" - }, - { - "author": "1038lab", - "description": "ComfyUI-MiniMax-Remover is a custom node for ComfyUI that enables fast and efficient object removal using minimax optimization. It works in two stages: first, it trains a remover with a simplified DiT model; then it distills a robust version using CFG guidance and fewer inference steps.", - "files": [ - "https://github.com/1038lab/ComfyUI-MiniMax-Remover" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-MiniMax-Remover", - "title": "ComfyUI-MiniMax-Remover" - }, - { - "author": "1038lab", - "description": "ComfyUI custom nodes for mosaic detection and creation.", - "files": [ - "https://github.com/1038lab/ComfyUI-Mosaic" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-Mosaic", - "title": "ComfyUI-Mosaic" - }, - { - "author": "1038lab", - "description": "A ComfyUI custom node for MiniCPM vision-language models, enabling high-quality image captioning and analysis.", - "files": [ - "https://github.com/1038lab/ComfyUI-MiniCPM" - ], - "install_type": "git-clone", - "reference": "https://github.com/1038lab/ComfyUI-MiniCPM", - "title": "ComfyUI-MiniCPM" - }, - { - "author": "Klinter", - "description": "Concat_strings atm - celebrating first_node", - "files": [ - "https://github.com/klinter007/klinter_nodes" - ], - "id": "klinter", - "install_type": "git-clone", - "reference": "https://github.com/klinter007/klinter_nodes", - "title": "Klinter_nodes" - }, - { - "author": "Ludobico", - "description": "ScenarioPrompt is a custom node that helps you understand what you're prompting for each property as you build your prompts", - "files": [ - "https://github.com/Ludobico/ComfyUI-ScenarioPrompt" - ], - "id": "scenarioprompt", - "install_type": "git-clone", - "reference": "https://github.com/Ludobico/ComfyUI-ScenarioPrompt", - "title": "ComfyUI-ScenarioPrompt" - }, - { - "author": "logtd", - "description": "A set of nodes to perform multi-object prompting with InstanceDiffusion", - "files": [ - "https://github.com/logtd/ComfyUI-InstanceDiffusion" - ], - "id": "instancediffusion", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-InstanceDiffusion", - "title": "InstanceDiffusion Nodes" - }, - { - "author": "logtd", - "description": "A set of nodes to track objects through videos using YOLO and other processors.", - "files": [ - "https://github.com/logtd/ComfyUI-TrackingNodes" - ], - "id": "tracking", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-TrackingNodes", - "title": "Tracking Nodes for Videos" - }, - { - "author": "logtd", - "description": "Nodes:Inversed Euler Sampler, Mix Noise with Latent, Combine Latent Noise", - "files": [ - "https://github.com/logtd/ComfyUI-InversedNoise" - ], - "id": "inversed-noise", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-InversedNoise", - "title": "ComfyUI-InversedNoise" - }, - { - "author": "logtd", - "description": "Nodes:Apply Ref UNet, Ref Sampler, Ref Sampler Custom", - "files": [ - "https://github.com/logtd/ComfyUI-RefSampling" - ], - "id": "refsampling", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-RefSampling", - "title": "ComfyUI-RefSampling" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use [a/FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing](https://github.com/yrcong/flatten).", - "files": [ - "https://github.com/logtd/ComfyUI-FLATTEN" - ], - "id": "flatten", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-FLATTEN", - "title": "ComfyUI-FLATTEN" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use RAVE attention as a temporal attention mechanism.\nThis differs from other implementations in that it does not concatenate the images together, but within the UNet's Self-Attention mechanism performs the RAVE technique. By not altering the images/latents throughout the UNet, this method does not affect other temporal techniques, style mechanisms, or other UNet modifications.\nFor example, it can be combined with AnimateDiff, ModelScope/ZeroScope, or FLATTEN.", - "files": [ - "https://github.com/logtd/ComfyUI-RAVE_ATTN" - ], - "id": "rave-attn", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-RAVE_ATTN", - "title": "ComfyUI-RAVE Attention" - }, - { - "author": "logtd", - "description": "A set of nodes to use Reference UNets", - "files": [ - "https://github.com/logtd/ComfyUI-RefUNet" - ], - "id": "refunet", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-RefUNet", - "title": "ComfyUI-RefUNet" - }, - { - "author": "logtd", - "description": "Nodes to use [a/Smoothed Energy Guidance](https://github.com/SusungHong/SEG-SDXL) for ComfyUI.", - "files": [ - "https://github.com/logtd/ComfyUI-SEGAttention" - ], - "id": "segattention", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-SEGAttention", - "title": "ComfyUI-SEGAttention" - }, - { - "author": "logtd", - "description": "ComfyUI Nodes to use [a/SSR Encoder:Encoding Selective Subject Representation for Subject-Driven Generation](https://github.com/Xiaojiu-z/SSR_Encoder).", - "files": [ - "https://github.com/logtd/ComfyUI-SSREncoder" - ], - "id": "ssrencoder", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-SSREncoder", - "title": "ComfyUI-SSREncoder" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use the SeeCoder from [a/Prompt-Free-Diffusion](https://github.com/SHI-Labs/Prompt-Free-Diffusion)", - "files": [ - "https://github.com/logtd/ComfyUI-SeeCoder" - ], - "id": "seecoder-logtd", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-SeeCoder", - "title": "ComfyUI-SeeCoder" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use [a/4D-Humans](ComfyUI nodes to use 4D-Humans)", - "files": [ - "https://github.com/logtd/ComfyUI-4DHumans" - ], - "id": "comfyui-4dhumans", - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-4DHumans", - "title": "ComfyUI-4DHumans" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use ReNoise", - "files": [ - "https://github.com/logtd/ComfyUI-ReNoise" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-ReNoise", - "title": "ComfyUI-ReNoise" - }, - { - "author": "logtd", - "description": "experimental node pack to test using reference videos for their motion.", - "files": [ - "https://github.com/logtd/ComfyUI-MotionThiefExperiment" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-MotionThiefExperiment", - "title": "ComfyUI-MotionThiefExperiment" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use [a/DiLightNet](https://github.com/iamNCJ/DiLightNet).\nThese nodes can run DiLightNet, but the Dust3r or BlenderPy implementations to create lighting are not included. Expect those to be added to seperate repos when time allows.", - "files": [ - "https://github.com/logtd/ComfyUI-DiLightNet" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-DiLightNet", - "title": "ComfyUI-DiLightNet" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use [a/ViewCrafter](https://github.com/Drexubery/ViewCrafter/tree/main) for novel view synthesis.", - "files": [ - "https://github.com/logtd/ComfyUI-ViewCrafter" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-ViewCrafter", - "title": "ComfyUI-ViewCrafter" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to use [a/APG scaling](https://huggingface.co/papers/2410.02416) for CFG, allowing for better image quality with higher CFG.", - "files": [ - "https://github.com/logtd/ComfyUI-APGScaling" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-APGScaling", - "title": "ComfyUI-APGScaling" - }, - { - "author": "logtd", - "description": "ComfyUI nodes for image editing with Flux, such as RF-Inversion and more", - "files": [ - "https://github.com/logtd/ComfyUI-Fluxtapoz" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-Fluxtapoz", - "title": "ComfyUI-Fluxtapoz" - }, - { - "author": "logtd", - "description": "ComfyUI nodes to edit videos using Genmo Mochi", - "files": [ - "https://github.com/logtd/ComfyUI-MochiEdit" - ], - "install_type": "git-clone", - "reference": "https://github.com/logtd/ComfyUI-MochiEdit", - "title": "ComfyUI-MochiEdit" - }, - { - "author": "Big-Idea-Technology", - "description": "ComfyUI-Book-Tools is a set o new nodes for ComfyUI that allows users to easily add text overlays to images within their ComfyUI projects. This Node leverages Python Imaging Library (PIL) and PyTorch to dynamically render text on images, supporting a wide range of customization options including font size, alignment, color, and padding. Loop with any parameters (*), prompt batch schedule with prompt selector, end queue for automatic ending current queue.", - "files": [ - "https://github.com/Big-Idea-Technology/ComfyUI-Book-Tools" - ], - "id": "booktool", - "install_type": "git-clone", - "reference": "https://github.com/Big-Idea-Technology/ComfyUI-Book-Tools", - "title": "ComfyUI-Book-Tools Nodes for ComfyUI" - }, - { - "author": "Big Idea Technology", - "description": "The LLM_Node enhances ComfyUI by integrating advanced language model capabilities, enabling a wide range of NLP tasks such as text generation, content summarization, question answering, and more. This flexibility is powered by various transformer model architectures from the transformers library, allowing for the deployment of models like T5, GPT-2, and others based on your project's needs.", - "files": [ - "https://github.com/Big-Idea-Technology/ComfyUI_LLM_Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Big-Idea-Technology/ComfyUI_LLM_Node", - "title": "LLM Node for ComfyUI" - }, - { - "author": "Guillaume-Fgt", - "description": "A custom node to create empty latents for Stable Cascade.\nfeatures: width and height incrementation of 64 by default, possibility to lock the aspect ratio, switch width/height at execution", - "files": [ - "https://github.com/Guillaume-Fgt/ComfyUI_StableCascadeLatentRatio" - ], - "id": "cascade-latent-ratio", - "install_type": "git-clone", - "reference": "https://github.com/Guillaume-Fgt/ComfyUI_StableCascadeLatentRatio", - "title": "ComfyUI_StableCascadeLatentRatio" - }, - { - "author": "AuroBit", - "description": "A ComfyUI custom node that simply integrates the [a/OOTDiffusion](https://github.com/levihsu/OOTDiffusion) functionality.", - "files": [ - "https://github.com/AuroBit/ComfyUI-OOTDiffusion" - ], - "id": "ootdiffusion", - "install_type": "git-clone", - "reference": "https://github.com/AuroBit/ComfyUI-OOTDiffusion", - "title": "ComfyUI OOTDiffusion" - }, - { - "author": "AuroBit", - "description": "A ComfyUI custom node that simply integrates the [a/animate-anyone-reproduction](https://github.com/bendanzzc/AnimateAnyone-reproduction) functionality.", - "files": [ - "https://github.com/AuroBit/ComfyUI-AnimateAnyone-reproduction" - ], - "id": "animateanyone-reproduction", - "install_type": "git-clone", - "reference": "https://github.com/AuroBit/ComfyUI-AnimateAnyone-reproduction", - "title": "ComfyUI-AnimateAnyone-reproduction" - }, - { - "author": "czcz1024", - "description": "Nodes:FaceCompare", - "files": [ - "https://github.com/czcz1024/Comfyui-FaceCompare" - ], - "id": "facecompare", - "install_type": "git-clone", - "reference": "https://github.com/czcz1024/Comfyui-FaceCompare", - "title": "Face Compare" - }, - { - "author": "TheBill2001", - "description": "This custom node allow upscaling an image by a factor using a model.", - "files": [ - "https://github.com/TheBill2001/comfyui-upscale-by-model" - ], - "install_type": "git-clone", - "reference": "https://github.com/TheBill2001/comfyui-upscale-by-model", - "title": "comfyui-upscale-by-model" - }, - { - "author": "TheBill2001", - "description": "Provide two custom nodes to load and save images with captions as separate files.", - "files": [ - "https://github.com/TheBill2001/ComfyUI-Save-Image-Caption" - ], - "install_type": "git-clone", - "reference": "https://github.com/TheBill2001/ComfyUI-Save-Image-Caption", - "title": "Save Images with Captions" - }, - { - "author": "leoleelxh", - "description": "A minimalist node that calls LLMs, combined with one API, can call all language models, including local models.", - "files": [ - "https://github.com/leoleelxh/ComfyUI-LLMs" - ], - "install_type": "git-clone", - "reference": "https://github.com/leoleelxh/ComfyUI-LLMs", - "title": "ComfyUI-LLMs" - }, - { - "author": "hughescr", - "description": "This is a single node which can take the POSE_KEYPOINT output from the OpenPose extractor node, parse it, and return x,y,width,height bounding boxes around any elements of the OpenPose skeleton", - "files": [ - "https://github.com/hughescr/ComfyUI-OpenPose-Keypoint-Extractor" - ], - "install_type": "git-clone", - "reference": "https://github.com/hughescr/ComfyUI-OpenPose-Keypoint-Extractor", - "title": "OpenPose Keypoint Extractor" - }, - { - "author": "jkrauss82", - "description": "Nodes:SaveImgAdv, CLIPTextEncodeWithStats. Collection of tools supporting txt2img generation in ComfyUI and other tasks.", - "files": [ - "https://github.com/jkrauss82/ultools-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/jkrauss82/ultools-comfyui", - "title": "ULTools for ComfyUI" - }, - { - "author": "hiforce", - "description": "Custom nodes pack provided by [a/HiFORCE](https://www.hiforce.net/) for ComfyUI. This custom node helps to conveniently enhance images through Sampler, Upscaler, Mask, and more.\nNOTE:You should install [a/ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack). Many optimizations are built upon the foundation of ComfyUI-Impact-Pack.", - "files": [ - "https://github.com/hiforce/comfyui-hiforce-plugin" - ], - "install_type": "git-clone", - "reference": "https://github.com/hiforce/comfyui-hiforce-plugin", - "title": "Comfyui HiFORCE Plugin" - }, - { - "author": "kuschanow", - "description": "This custom node helps to transform latent in different ways.", - "files": [ - "https://github.com/RomanKuschanow/ComfyUI-Advanced-Latent-Control" - ], - "install_type": "git-clone", - "reference": "https://github.com/RomanKuschanow/ComfyUI-Advanced-Latent-Control", - "reference2": "https://github.com/kuschanow/ComfyUI-Advanced-Latent-Control", - "title": "Advanced Latent Control" - }, - { - "author": "guill", - "description": "Nodes:Abracadabra Summary, Abracadabra", - "files": [ - "https://github.com/guill/abracadabra-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/guill/abracadabra-comfyui", - "title": "abracadabra-comfyui" - }, - { - "author": "guill", - "description": "A ComfyUI extension that makes your node connections delightfully droopy. (Disclaimer: despite what it may look like, this extension will not make your monitor taste like spaghetti.)", - "files": [ - "https://github.com/guill/comfyui-droopy-noodles" - ], - "install_type": "git-clone", - "reference": "https://github.com/guill/comfyui-droopy-noodles", - "title": "ComfyUI Droopy Noodles" - }, - { - "author": "cerspense", - "description": "Nodes:Image Dir Iterator, Modelscopet2v, Modelscopev2v, Vid Dir Iterator, Image Dir Iterator, Text File Line Iterator, Remap Range, Split Image Channels, Resize By Image, Increment Every N.", - "files": [ - "https://github.com/cerspense/ComfyUI_cspnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/cerspense/ComfyUI_cspnodes", - "title": "cspnodes" - }, - { - "author": "qwixiwp", - "description": "Nodes:load images (queue tools). tools made for queueing in comfyUI", - "files": [ - "https://github.com/qwixiwp/queuetools" - ], - "install_type": "git-clone", - "reference": "https://github.com/qwixiwp/queuetools", - "title": "queuetools" - }, - { - "author": "Chan-0312", - "description": "Welcome to ComfyUI Prompt Preview, where you can visualize the styles from [sdxl_prompt_styler](https://github.com/twri/sdxl_prompt_styler).", - "files": [ - "https://github.com/Chan-0312/ComfyUI-Prompt-Preview" - ], - "install_type": "git-clone", - "reference": "https://github.com/Chan-0312/ComfyUI-Prompt-Preview", - "title": "ComfyUI-Prompt-Preview" - }, - { - "author": "munkyfoot", - "description": "This extension provides a node that allows you to overlay text on an image or a batch of images with support for custom fonts and styles.", - "files": [ - "https://github.com/Munkyfoot/ComfyUI-TextOverlay" - ], - "id": "textoverlay-munkyfoot", - "install_type": "git-clone", - "reference": "https://github.com/Munkyfoot/ComfyUI-TextOverlay", - "title": "ComfyUI-TextOverlay" - }, - { - "author": "CC-BryanOttho", - "description": "This package provides three custom nodes designed to streamline workflows involving API requests, dynamic text manipulation based on API responses, and image posting to APIs. These nodes are particularly useful for automating interactions with APIs, enhancing text-based workflows with dynamic data, and facilitating image uploads.", - "files": [ - "https://github.com/CC-BryanOttho/ComfyUI_API_Manager" - ], - "install_type": "git-clone", - "reference": "https://github.com/CC-BryanOttho/ComfyUI_API_Manager", - "title": "ComfyUI_API_Manager" - }, - { - "author": "maracman", - "description": "Store a CSV of prompts where the style can change for each subject. The CSV node initialises with the column (style) and row (subject) names for easy interpretability.", - "files": [ - "https://github.com/maracman/ComfyUI-SubjectStyle-CSV" - ], - "install_type": "git-clone", - "reference": "https://github.com/maracman/ComfyUI-SubjectStyle-CSV", - "title": "ComfyUI-SubjectStyle-CSV" - }, - { - "author": "438443467", - "description": "Nodes:GPT4V-Image-Captioner", - "files": [ - "https://github.com/438443467/ComfyUI-GPT4V-Image-Captioner" - ], - "install_type": "git-clone", - "reference": "https://github.com/438443467/ComfyUI-GPT4V-Image-Captioner", - "title": "ComfyUI-GPT4V-Image-Captioner" - }, - { - "author": "uetuluk", - "description": "Nodes:Webcam Capture", - "files": [ - "https://github.com/uetuluk/comfyui-webcam-node" - ], - "id": "webcam", - "install_type": "git-clone", - "reference": "https://github.com/uetuluk/comfyui-webcam-node", - "title": "comfyui-webcam-node" - }, - { - "author": "huchenlei", - "description": "Transparent Image Layer Diffusion using Latent Transparency", - "files": [ - "https://github.com/huchenlei/ComfyUI-layerdiffuse" - ], - "id": "layerdiffuse", - "install_type": "git-clone", - "reference": "https://github.com/huchenlei/ComfyUI-layerdiffuse", - "title": "ComfyUI-layerdiffuse (layerdiffusion)" - }, - { - "author": "huchenlei", - "description": "ComfyUI node of [a/Kohaku's DanTagGen Demo](https://huggingface.co/KBlueLeaf/DanTagGen?not-for-all-audiences=true).", - "files": [ - "https://github.com/huchenlei/ComfyUI_DanTagGen" - ], - "id": "dantangen", - "install_type": "git-clone", - "reference": "https://github.com/huchenlei/ComfyUI_DanTagGen", - "title": "ComfyUI_DanTagGen" - }, - { - "author": "huchenlei", - "description": "Port of [a/https://github.com/huchenlei/sd-webui-openpose-editor](https://github.com/huchenlei/sd-webui-openpose-editor) in ComfyUI", - "files": [ - "https://github.com/huchenlei/ComfyUI-openpose-editor" - ], - "install_type": "git-clone", - "reference": "https://github.com/huchenlei/ComfyUI-openpose-editor", - "title": "ComfyUI-openpose-editor" - }, - { - "author": "huchenlei", - "description": "ComfyUI native implementation of [a/IC-Light](https://github.com/lllyasviel/IC-Light).", - "files": [ - "https://github.com/huchenlei/ComfyUI-IC-Light-Native" - ], - "id": "ic-light-native", - "install_type": "git-clone", - "reference": "https://github.com/huchenlei/ComfyUI-IC-Light-Native", - "title": "ComfyUI-IC-Light-Native" - }, - { - "author": "huchenlei", - "description": "[a/DenseDiffusion](https://github.com/naver-ai/DenseDiffusion) custom node for ComfyUI.", - "files": [ - "https://github.com/huchenlei/ComfyUI_densediffusion" - ], - "id": "densediffusion", - "install_type": "git-clone", - "reference": "https://github.com/huchenlei/ComfyUI_densediffusion", - "title": "ComfyUI DenseDiffusion" - }, - { - "author": "huchenlei", - "description": "ComfyUI implementation of [a/Omost](https://github.com/lllyasviel/Omost), and everything about regional prompt.\nNOTE: You need to install ComfyUI_densediffusion to use this node.", - "files": [ - "https://github.com/huchenlei/ComfyUI_omost" - ], - "id": "omost", - "install_type": "git-clone", - "reference": "https://github.com/huchenlei/ComfyUI_omost", - "title": "ComfyUI_omost" - }, - { - "author": "nathannlu", - "description": "Play with your pet while your workflow generates!", - "files": [ - "https://github.com/nathannlu/ComfyUI-Pets" - ], - "id": "pets", - "install_type": "git-clone", - "reference": "https://github.com/nathannlu/ComfyUI-Pets", - "title": "ComfyUI Pets" - }, - { - "author": "nathannlu", - "description": "Run your workflow using cloud GPU resources, from your local ComfyUI.\nNOTE:After you first install the plugin...\nThe first time you click 'generate', you will be prompted to log into your account.Subsequent generations after the first is faster (the first run it takes a while to process your workflow). Once those two steps have been completed, you will be able to seamlessly generate your workflow on the cloud!", - "files": [ - "https://github.com/nathannlu/ComfyUI-Cloud" - ], - "id": "cloud", - "install_type": "git-clone", - "reference": "https://github.com/nathannlu/ComfyUI-Cloud", - "title": "Comfy Cloud" - }, - { - "author": "11dogzi", - "description": "This is a node group kit that covers multiple nodes such as local refinement, tag management, random prompt words, text processing, image processing, mask processing, etc", - "files": [ - "https://github.com/11dogzi/Comfyui-ergouzi-Nodes" - ], - "id": "ergouzi-nodes", - "install_type": "git-clone", - "reference": "https://github.com/11dogzi/Comfyui-ergouzi-Nodes", - "title": "Comfyui-ergouzi-Nodes" - }, - { - "author": "11dogzi", - "description": "Partial redraw sampler and variant seed sampler", - "files": [ - "https://github.com/11dogzi/Comfyui-ergouzi-samplers" - ], - "id": "ergouzi-samplers", - "install_type": "git-clone", - "reference": "https://github.com/11dogzi/Comfyui-ergouzi-samplers", - "title": "Comfyui-ergouzi-samplers" - }, - { - "author": "11dogzi", - "description": "Group switching control, one click control to ignore and disable multiple groups, as well as wired switch combination nodes, allowing for arbitrary switching of annotation names", - "files": [ - "https://github.com/11dogzi/Comfyui-ergouzi-kaiguan" - ], - "id": "ergouzi-kaiguan", - "install_type": "git-clone", - "reference": "https://github.com/11dogzi/Comfyui-ergouzi-kaiguan", - "title": "Comfyui-ergouzi-kaiguan" - }, - { - "author": "11dogzi", - "description": "A comprehensive ComfyUI theme plugin with stunning cyberpunk aesthetics and powerful customization features", - "files": [ - "https://github.com/11dogzi/CYBERPUNK-STYLE-DIY" - ], - "id": "CYBERPUNK-STYLE-DIY", - "install_type": "git-clone", - "reference": "https://github.com/11dogzi/CYBERPUNK-STYLE-DIY", - "title": "CYBERPUNK-STYLE-DIY" - }, - { - "author": "11dogzi", - "description": "This is a hierarchical auxiliary project of the IPAdapter project, which uses a slider to quickly control the hierarchical weights and add fully random and semi random modes", - "files": [ - "https://github.com/11dogzi/ComfUI-EGAdapterMadAssistant" - ], - "id": "madassistant", - "install_type": "git-clone", - "reference": "https://github.com/11dogzi/ComfUI-EGAdapterMadAssistant", - "title": "ComfUI-EGAdapterMadAssistant" - }, - { - "author": "11dogzi", - "description": "This is a ComfyUI plugin for [a/HYPIR (Harnessing Diffusion-Yielded Score Priors for Image Restoration)](https://github.com/XPixelGroup/HYPIR), a state-of-the-art image restoration model based on Stable Diffusion 2.1.", - "files": [ - "https://github.com/11dogzi/Comfyui-HYPIR" - ], - "install_type": "git-clone", - "reference": "https://github.com/11dogzi/Comfyui-HYPIR", - "title": "HYPIR ComfyUI Plugin" - }, - { - "author": "BXYMartin", - "description": "Nodes:Multi-ControlNet Converter, List of Images, Convert PIL to Tensor (NHWC), Convert Tensor (NHWC) to (NCHW), Convert Tensor (NHWC) to PIL", - "files": [ - "https://github.com/BXYMartin/ComfyUI-InstantIDUtils" - ], - "id": "instantid-utils", - "install_type": "git-clone", - "reference": "https://github.com/BXYMartin/ComfyUI-InstantIDUtils", - "title": "ComfyUI-InstantIDUtils" - }, - { - "author": "cdb-boop", - "description": "A simple node to round an input image up (pad) or down (crop) to the nearest integer multiple. Padding offset from left/bottom and the padding value are adjustable.", - "files": [ - "https://github.com/cdb-boop/comfyui-image-round" - ], - "id": "image-round", - "install_type": "git-clone", - "reference": "https://github.com/cdb-boop/comfyui-image-round", - "title": "comfyui-image-round" - }, - { - "author": "cdb-boop", - "description": "Enhance old or low-quality images in ComfyUI. Optional features include automatic scratch removal and face enhancement. Based on Microsoft's Bringing-Old-Photos-Back-to-Life. Requires installing models, so see instructions here: https://github.com/cdb-boop/ComfyUI-Bringing-Old-Photos-Back-to-Life.", - "files": [ - "https://github.com/cdb-boop/ComfyUI-Bringing-Old-Photos-Back-to-Life" - ], - "install_type": "git-clone", - "reference": "https://github.com/cdb-boop/ComfyUI-Bringing-Old-Photos-Back-to-Life", - "title": "ComfyUI Bringing Old Photos Back to Life" - }, - { - "author": "atmaranto", - "description": "A version of ComfyUI-to-Python-Extension that works as a custom node. Adds a button in the UI that saves the current workflow as a Python file, a CLI for converting workflows, and slightly better custom node support.", - "files": [ - "https://github.com/atmaranto/ComfyUI-SaveAsScript" - ], - "id": "saveasscript", - "install_type": "git-clone", - "reference": "https://github.com/atmaranto/ComfyUI-SaveAsScript", - "title": "SaveAsScript" - }, - { - "author": "meshmesh-io", - "description": "Nodes:ColorListMaskToImage, FlattenAndCombineMaskImages", - "files": [ - "https://github.com/meshmesh-io/mm-comfyui-megamask" - ], - "id": "megamask", - "install_type": "git-clone", - "reference": "https://github.com/meshmesh-io/mm-comfyui-megamask", - "title": "mm-comfyui-megamask" - }, - { - "author": "meshmesh-io", - "description": "Nodes:Loop, LoopStart, LoopEnd, LoopStart_SEGIMAGE, LoopEnd_SEGIMAGE", - "files": [ - "https://github.com/meshmesh-io/mm-comfyui-loopback" - ], - "id": "mm-loopback", - "install_type": "git-clone", - "reference": "https://github.com/meshmesh-io/mm-comfyui-loopback", - "title": "mm-comfyui-loopback" - }, - { - "author": "meshmesh-io", - "description": "Nodes:Masks to Colored Masks, Color Picker", - "files": [ - "https://github.com/meshmesh-io/ComfyUI-MeshMesh" - ], - "id": "meshmesh", - "install_type": "git-clone", - "reference": "https://github.com/meshmesh-io/ComfyUI-MeshMesh", - "title": "ComfyUI-MeshMesh" - }, - { - "author": "CozyMantis", - "description": "A ComfyUI node to automatically extract masks for body regions and clothing/fashion items. Made with \ud83d\udc9a by the CozyMantis squad.", - "files": [ - "https://github.com/cozymantis/human-parser-comfyui-node" - ], - "id": "humanparser", - "install_type": "git-clone", - "reference": "https://github.com/cozymantis/human-parser-comfyui-node", - "title": "Cozy Human Parser" - }, - { - "author": "CozyMantis", - "description": "Generate OpenPose face/body reference poses in ComfyUI with ease. Made with \ud83d\udc9a by the CozyMantis squad.", - "files": [ - "https://github.com/cozymantis/pose-generator-comfyui-node" - ], - "id": "posegen", - "install_type": "git-clone", - "reference": "https://github.com/cozymantis/pose-generator-comfyui-node", - "title": "Cozy Reference Pose Generator" - }, - { - "author": "CozyMantis", - "description": "Various cozy nodes, made with \ud83d\udc9a by the CozyMantis squad.", - "files": [ - "https://github.com/cozymantis/cozy-utils-comfyui-nodes" - ], - "id": "cozy-utils", - "install_type": "git-clone", - "reference": "https://github.com/cozymantis/cozy-utils-comfyui-nodes", - "title": "Cozy Utils" - }, - { - "author": "vivax3794", - "description": "Nodes:Inspect, Any String, Model From URL", - "files": [ - "https://github.com/vivax3794/ComfyUI-Vivax-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/vivax3794/ComfyUI-Vivax-Nodes", - "title": "ComfyUI-Vivax-Nodes" - }, - { - "author": "vivax3794", - "description": "Creating subgraph and Calling subgraphs.", - "files": [ - "https://github.com/vivax3794/ComfyUI-Sub-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/vivax3794/ComfyUI-Sub-Nodes", - "title": "ComfyUI-Sub-Nodes" - }, - { - "author": "victorchall", - "description": "This node captures images one at a time from your webcam when you click generate.\nThis is particular useful for img2img or controlnet workflows.\nNOTE:This node will take over your webcam, so if you have another program using it, you may need to close that program first. Likewise, you may need to close Comfyui or close the workflow to release the webcam.", - "files": [ - "https://github.com/victorchall/comfyui_webcamcapture" - ], - "install_type": "git-clone", - "reference": "https://github.com/victorchall/comfyui_webcamcapture", - "title": "Comfyui Webcam capture node" - }, - { - "author": "ljleb", - "description": "model merging nodes powered by sd-mecha, a memory efficient state dict recipe merger.", - "files": [ - "https://github.com/ljleb/comfy-mecha" - ], - "id": "mecha", - "install_type": "git-clone", - "reference": "https://github.com/ljleb/comfy-mecha", - "title": "Mecha Merge Node Pack" - }, - { - "author": "diSty", - "description": "This node creates a sequence of frames by moving and scaling a subject image over a background image.", - "files": [ - "https://github.com/diStyApps/ComfyUI_FrameMaker" - ], - "id": "frame-maker", - "install_type": "git-clone", - "reference": "https://github.com/diStyApps/ComfyUI_FrameMaker", - "title": "ComfyUI Frame Maker" - }, - { - "author": "diSty", - "description": "Flow is a custom node designed to provide a more user-friendly interface for ComfyUI by acting as an alternative user interface for running workflows. It is not a replacement for workflow creation.\nFlow is currently in the early stages of development, so expect bugs and ongoing feature enhancements. With your support and feedback, Flow will settle into a steady stream.", - "files": [ - "https://github.com/diStyApps/ComfyUI-disty-Flow" - ], - "install_type": "git-clone", - "reference": "https://github.com/diStyApps/ComfyUI-disty-Flow", - "title": "Flow - Streamlined Way to ComfyUI" - }, - { - "author": "hackkhai", - "description": "This node improves the quality of the image mask. more suitable for image composite matting", - "files": [ - "https://github.com/hackkhai/ComfyUI-Image-Matting" - ], - "id": "image-matting", - "install_type": "git-clone", - "reference": "https://github.com/hackkhai/ComfyUI-Image-Matting", - "title": "ComfyUI-Image-Matting" - }, - { - "author": "ExponentialML", - "description": "Allows native usage of ModelScope based Text To Video Models in ComfyUI", - "files": [ - "https://github.com/ExponentialML/ComfyUI_ModelScopeT2V" - ], - "id": "modelscopet2v", - "install_type": "git-clone", - "reference": "https://github.com/ExponentialML/ComfyUI_ModelScopeT2V", - "title": "ComfyUI_ModelScopeT2V" - }, - { - "author": "ExponentialML", - "description": "DynamiCrafter that works natively with ComfyUI's nodes, optimizations, ControlNet, and more.", - "files": [ - "https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter" - ], - "id": "dynamicrafter", - "install_type": "git-clone", - "reference": "https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter", - "title": "ComfyUI - Native DynamiCrafter" - }, - { - "author": "ExponentialML", - "description": "ComfyUI Version of '[a/Visual Style Prompting with Swapping Self-Attention](https://github.com/naver-ai/Visual-Style-Prompting)'", - "files": [ - "https://github.com/ExponentialML/ComfyUI_VisualStylePrompting" - ], - "id": "visual-style-prompting", - "install_type": "git-clone", - "reference": "https://github.com/ExponentialML/ComfyUI_VisualStylePrompting", - "title": "ComfyUI_VisualStylePrompting" - }, - { - "author": "angeloshredder", - "description": "Nodes:Cascade_Resizer", - "files": [ - "https://github.com/angeloshredder/StableCascadeResizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/angeloshredder/StableCascadeResizer", - "title": "StableCascadeResizer" - }, - { - "author": "stavsap", - "description": "Custom ComfyUI Nodes for interacting with [a/Ollama](https://ollama.com/) using the [a/ollama python client](https://github.com/ollama/ollama-python).\nIntegrate the power of LLMs into CompfyUI workflows easily.", - "files": [ - "https://github.com/stavsap/comfyui-ollama" - ], - "id": "ollama", - "install_type": "git-clone", - "reference": "https://github.com/stavsap/comfyui-ollama", - "title": "ComfyUI Ollama" - }, - { - "author": "stavsap", - "description": "Custom ComfyUI Nodes for TTS with Kokoro, genenrate and merge speakers for new style generations.", - "files": [ - "https://github.com/stavsap/comfyui-kokoro" - ], - "install_type": "git-clone", - "reference": "https://github.com/stavsap/comfyui-kokoro", - "title": "comfyui-kokoro" - }, - { - "author": "cluny85", - "description": "Custom ComfyUI Nodes for verifing needed files/models are present per workflow, can download if missing.", - "files": [ - "https://github.com/stavsap/comfyui-downloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/stavsap/comfyui-downloader", - "title": "comfyui-downloader" - }, - { - "author": "dchatel", - "description": "Nodes:SmartMask, ResizeCropFit, Percent Padding, SoftErosion, StringScheduleHelper, DStack, DavchaConditioningConcat, DavchaModelMergeSimple, DavchaCLIPMergeSimple, DavchaModelMergeSD1, DavchaModelMergeSDXL, ConditioningCompress... Some personal QoL and experimental nodes", - "files": [ - "https://github.com/dchatel/comfyui_davcha" - ], - "install_type": "git-clone", - "reference": "https://github.com/dchatel/comfyui_davcha", - "title": "comfyui_davcha" - }, - { - "author": "dchatel", - "description": "These custom nodes provide a rotation aware face extraction, paste back, and various face related masking options.", - "files": [ - "https://github.com/dchatel/comfyui_facetools" - ], - "id": "facetools", - "install_type": "git-clone", - "reference": "https://github.com/dchatel/comfyui_facetools", - "title": "comfyui_facetools" - }, - { - "author": "prodogape", - "description": "This plugin is mainly based on Minio, implementing the ability to read images from Minio, save images, facilitating expansion and connection across multiple machines.", - "files": [ - "https://github.com/prodogape/ComfyUI-Minio" - ], - "id": "minio", - "install_type": "git-clone", - "reference": "https://github.com/prodogape/ComfyUI-Minio", - "title": "Comfyui-Minio" - }, - { - "author": "prodogape", - "description": "This node is primarily based on Easy-OCR to implement OCR text recognition functionality.", - "files": [ - "https://github.com/prodogape/ComfyUI-EasyOCR" - ], - "id": "easyocr", - "install_type": "git-clone", - "reference": "https://github.com/prodogape/ComfyUI-EasyOCR", - "title": "ComfyUI-EasyOCR" - }, - { - "author": "prodogape", - "description": "This node is mainly based on [a/OmDet](https://github.com/om-ai-lab/OmDet) for object detection, and it outputs related images, masks, and Labelme JSON information.", - "files": [ - "https://github.com/prodogape/ComfyUI-OmDet" - ], - "id": "omdet", - "install_type": "git-clone", - "reference": "https://github.com/prodogape/ComfyUI-OmDet", - "title": "ComfyUI-OmDet" - }, - { - "author": "prodogape", - "description": "This node is mainly based on the Yolov8 model for object detection, and it outputs related images, masks, and JSON information.[w/Repository url is changed. Please remove previous one and reinstall.]", - "files": [ - "https://github.com/prodogape/Comfyui-Yolov8-JSON" - ], - "install_type": "git-clone", - "reference": "https://github.com/prodogape/Comfyui-Yolov8-JSON", - "title": "Comfyui-Yolov8-JSON" - }, - { - "author": "kingzcheung", - "description": "These nodes are mainly used to translate prompt words from other languages into English. PromptTranslateToText implements prompt word translation based on Helsinki NLP translation model.It doesn't require internet connection\u3002", - "files": [ - "https://github.com/AIGCTeam/ComfyUI_kkTranslator_nodes" - ], - "id": "kktranslator", - "install_type": "git-clone", - "reference": "https://github.com/AIGCTeam/ComfyUI_kkTranslator_nodes", - "title": "ComfyUI_kkTranslator_nodes" - }, - { - "author": "vsevolod-oparin", - "description": "Nodes provide an options to combine prior and decoder models of Kandinsky 2.2.", - "files": [ - "https://github.com/vsevolod-oparin/comfyui-kandinsky22" - ], - "id": "kandinsky", - "install_type": "git-clone", - "reference": "https://github.com/vsevolod-oparin/comfyui-kandinsky22", - "title": "Kandinsky 2.2 ComfyUI Plugin" - }, - { - "author": "Xyem", - "description": "Xycuno Oobabooga provides custom nodes for ComfyUI, for sending requests to an [a/Oobabooga](https://github.com/oobabooga/text-generation-webui) instance to assist in creating prompt texts.", - "files": [ - "https://github.com/Xyem/Xycuno-Oobabooga" - ], - "id": "xycuno-oobabooga", - "install_type": "git-clone", - "reference": "https://github.com/Xyem/Xycuno-Oobabooga", - "title": "Xycuno Oobabooga" - }, - { - "author": "shi3z", - "description": "You can use memeplex and DALL-E thru ComfyUI. You need API keys.", - "files": [ - "https://github.com/shi3z/ComfyUI_Memeplex_DALLE" - ], - "id": "memeplex-dalle", - "install_type": "git-clone", - "reference": "https://github.com/shi3z/ComfyUI_Memeplex_DALLE", - "title": "ComfyUI_Memeplex_DALLE" - }, - { - "author": "impactframes", - "description": "Various AI tools to use in Comfy UI. Starting with VL and prompt making tools using Ollma as backend will evolve as I find time.", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_AI_tools" - ], - "id": "impactframes-tools", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_AI_tools", - "title": "IF_AI_tools" - }, - { - "author": "impactframes", - "description": "This repository hosts a Text-to-Speech (TTS) application that leverages Whisper Speech for voice synthesis, allowing users to train a voice model on-the-fly. It is built on ComfyUI and supports rapid training and inference processes.", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_AI_WishperSpeechNode" - ], - "id": "impactframes-whisper-speech", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_AI_WishperSpeechNode", - "title": "IF_AI_WishperSpeechNode" - }, - { - "author": "impactframes", - "description": "Talking avatars Heads for the IF_AI tools integrates dreamtalk in ComfyUI", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_AI_HFDownloaderNode" - ], - "id": "impactframes-hfdownloader", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_AI_HFDownloaderNode", - "title": "IF_AI_HFDownloaderNode" - }, - { - "author": "impactframes", - "description": "Talking avatars MemoAvatar Memory-Guided Diffusion for Expressive Talking Video Generation", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_MemoAvatar" - ], - "id": "impactframes-memoavatar", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_MemoAvatar", - "title": "IF_MemoAvatar" - }, - { - "author": "impactframes", - "description": "ComfyUI IF Trellis creates a 3d mesh from a single view or multi angle pictures", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_Trellis" - ], - "id": "impactframes-trellis", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_Trellis", - "title": "IF_Trellis" - }, - { - "author": "impactframes", - "description": "Create Video datasets straight from YT or a video file path", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_DatasetMkr" - ], - "id": "impactframes-datasetmkr", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_DatasetMkr", - "title": "IF_DatasetMkr" - }, - { - "author": "impactframes", - "description": "Parler TTS is a zeroshot voice synthesis with emotion and entonations, you can control the voice style via text prompt", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_AI_ParlerTTSNode" - ], - "id": "impactframes-parlertts", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_AI_ParlerTTSNode", - "title": "IF_ParlerTTSNode" - }, - { - "author": "impactframes", - "description": "Talking avatars Heads for the IF_AI tools integrates dreamtalk in ComfyUI", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_AI_Dreamtalk" - ], - "id": "impactframes-dreamtalk", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_AI_Dreamtalk", - "title": "IF_Dreamtalk" - }, - { - "author": "impactframes", - "description": "ComfyUI extension for video-based prompting and processing with support for various models and video processing capabilities", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_VideoPrompts" - ], - "id": "impactframes-videoprompts", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_VideoPrompts", - "title": "IF_VideoPrompts" - }, - { - "author": "impactframes", - "description": "Run Local and API LLMs, Features Conditioning manipulation via Omost, supports Ollama, LlamaCPP LMstudio, Koboldcpp, TextGen, Transformers or via APIs Anthropic, Groq, OpenAI, Google Gemini, Mistral, xAI and create your own charcters assistants (SystemPrompts) with custom presets and muchmore", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_LLM" - ], - "id": "impactframes-llm", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_LLM", - "title": "IF_LLM" - }, - { - "author": "impactframes", - "description": "It Load Images with subfolders form arbitrary folders previous on node outputs lists- convinient selection via file browser", - "files": [ - "https://github.com/if-ai/ComfyUI_IF_AI_LoadImages" - ], - "id": "impactframes-loadimages", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI_IF_AI_LoadImages", - "title": "IF_AI_LoadImages" - }, - { - "author": "impactframes", - "description": "Enjoy the latest GEMINI V2 API for ComfyUI - generate images, analyze content, and use multimodal capabilities with Google's Gemini models", - "files": [ - "https://github.com/if-ai/ComfyUI-IF_Gemini" - ], - "id": "impactframes-gemini", - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-IF_Gemini", - "title": "IF_Gemini" - }, - { - "author": "impactframes", - "description": "A ComfyUI custom node that automatically selects appropriate video resolution dimensions based on generation mode, aspect ratio, and quality settings. Designed to work seamlessly with video generation models and KJNodes image resize nodes.", - "files": [ - "https://github.com/if-ai/ComfyUI-WanResolutionSelector" - ], - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-WanResolutionSelector", - "title": "ComfyUI-WanResolutionSelector" - }, - { - "author": "impactframes", - "description": "This is a ComfyUI custom node wrapper for the HunyuanVideo-Foley model, which generates realistic audio from video and text descriptions.", - "files": [ - "https://github.com/if-ai/ComfyUI_HunyuanVideoFoley" - ], - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI_HunyuanVideoFoley", - "title": "ComfyUI HunyuanVideo-Foley" - }, - { - "author": "impactframes", - "description": "Download Youtube videos using ComfyUI", - "files": [ - "https://github.com/if-ai/ComfyUI-yt_dl" - ], - "install_type": "git-clone", - "reference": "https://github.com/if-ai/ComfyUI-yt_dl", - "title": "ComfyUI-yt_dl" - }, - { - "author": "dmMaze", - "description": "Apply screentone to line drawings or colored illustrations with diffusion models.", - "files": [ - "https://github.com/dmMaze/sketch2manga" - ], - "id": "sketch2manga", - "install_type": "git-clone", - "reference": "https://github.com/dmMaze/sketch2manga", - "title": "Sketch2Manga" - }, - { - "author": "olduvai-jp", - "description": "A simple and easy to use Hugging Face model loader.", - "files": [ - "https://github.com/olduvai-jp/ComfyUI-HfLoader" - ], - "id": "hfloader", - "install_type": "git-clone", - "reference": "https://github.com/olduvai-jp/ComfyUI-HfLoader", - "title": "ComfyUI-HfLoader" - }, - { - "author": "olduvai-jp", - "description": "Automatically archives generated files to Amazon S3 or S3-compatible storage services", - "files": [ - "https://github.com/olduvai-jp/ComfyUI-CloudArchive" - ], - "id": "comfyui-cloudarchive", - "install_type": "git-clone", - "reference": "https://github.com/olduvai-jp/ComfyUI-CloudArchive", - "title": "ComfyUI-CloudArchive" - }, - { - "author": "AiMiDi", - "description": "Nodes:Merge Tag, Clear Tag, Add Tag, Load Images Pair Batch, Save Images Pair", - "files": [ - "https://github.com/AiMiDi/ComfyUI-Aimidi-nodes" - ], - "id": "aimidi-nodes", - "install_type": "git-clone", - "reference": "https://github.com/AiMiDi/ComfyUI-Aimidi-nodes", - "title": "ComfyUI-Aimidi-nodes" - }, - { - "author": "ForeignGods", - "description": "Font Animation, Speech Recognition, Caption Generator, TTS", - "files": [ - "https://github.com/ForeignGods/ComfyUI-Mana-Nodes" - ], - "id": "mana-nodes", - "install_type": "git-clone", - "reference": "https://github.com/ForeignGods/ComfyUI-Mana-Nodes", - "title": "ComfyUI-Mana-Nodes" - }, - { - "author": "Cornea Valentin", - "description": "This ComfyUI custom node, named ControlNet Auxiliar, is designed to provide auxiliary functionalities for image processing tasks. It is particularly useful for various image manipulation and enhancement operations. The node is integrated with functionalities for converting images between different formats and applying various image processing techniques.", - "files": [ - "https://github.com/madtunebk/ComfyUI-ControlnetAux" - ], - "id": "controlnet-aux-valentin", - "install_type": "git-clone", - "reference": "https://github.com/madtunebk/ComfyUI-ControlnetAux", - "title": "ControlNet Auxiliar" - }, - { - "author": "MarkoCa1", - "description": "Why make this node? Because I only need simple text related operations and don't want to install anything extra.", - "files": [ - "https://github.com/MarkoCa1/ComfyUI-Text" - ], - "install_type": "git-clone", - "reference": "https://github.com/MarkoCa1/ComfyUI-Text", - "title": "ComfyUI-Text" - }, - { - "author": "MarkoCa1", - "description": "Mask cutout based on Segment Anything.", - "files": [ - "https://github.com/MarkoCa1/ComfyUI_Segment_Mask" - ], - "id": "seg-mask", - "install_type": "git-clone", - "reference": "https://github.com/MarkoCa1/ComfyUI_Segment_Mask", - "title": "ComfyUI_Segment_Mask" - }, - { - "author": "Shadetail", - "description": "Custom nodes for ComfyUI by Eagleshadow.", - "files": [ - "https://github.com/Shadetail/ComfyUI_Eagleshadow" - ], - "id": "eagleshadow", - "install_type": "git-clone", - "reference": "https://github.com/Shadetail/ComfyUI_Eagleshadow", - "title": "Eagleshadow Custom Nodes" - }, - { - "author": "Jannchie", - "description": "This is a completely different set of nodes than Comfy's own KSampler series. This set of nodes is based on Diffusers, which makes it easier to import models, apply prompts with weights, inpaint, reference only, controlnet, etc.", - "files": [ - "https://github.com/Jannchie/ComfyUI-J" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jannchie/ComfyUI-J", - "title": "ComfyUI-J" - }, - { - "author": "daxcay", - "description": "Jerry Davos Custom Nodes for Saving Latents in Directory (BatchLatentSave) , Importing Latent from directory (BatchLatentLoadFromDir) , List to string, string to list, get any file list from directory which give filepath, filename, move any files from any directory to any other directory, VHS Video combine file mover, rebatch list of strings, batch image load from any dir, load image batch from any directory and other custom nodes.", - "files": [ - "https://github.com/daxcay/ComfyUI-JDCN" - ], - "id": "jdcn", - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-JDCN", - "title": "ComfyUI-JDCN" - }, - { - "author": "daxcay", - "description": "Data research, preparation, and manipulation nodes for model trainers and artists.", - "files": [ - "https://github.com/daxcay/ComfyUI-DataSet" - ], - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-DataSet", - "title": "ComfyUI-DataSet" - }, - { - "author": "daxcay", - "description": "Node to enable seamless multiuser workflow collaboration, run on local and remote comfy servers.", - "files": [ - "https://github.com/daxcay/ComfyUI-Nexus" - ], - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-Nexus", - "title": "ComfyUI-Nexus" - }, - { - "author": "daxcay", - "description": "Node to enable WhatsApp in ComfyUI.", - "files": [ - "https://github.com/daxcay/ComfyUI-WA" - ], - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-WA", - "title": "ComfyUI-WA" - }, - { - "author": "daxcay", - "description": "Node to enable Telegram in ComfyUI.", - "files": [ - "https://github.com/daxcay/ComfyUI-TG" - ], - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-TG", - "title": "ComfyUI-TG" - }, - { - "author": "daxcay", - "description": "This node allows the execution of Node.js application within ComfyUI by leveraging the ComfyUI-NODEJS, which starts alongside ComfyUI and facilitates the installation of Node.js. The integration enables Python subprocesses to execute Node.js scripts.", - "files": [ - "https://github.com/daxcay/ComfyUI-NODEJS" - ], - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-NODEJS", - "title": "ComfyUI-NODEJS" - }, - { - "author": "daxcay", - "description": "Plays youtube videos in comfy. Use this node to share tutorials or renders. Youtube Playlists mode is also in Future Development in which you can add multiple youtube links and form a playlist which would be ideal for chained tutorials or lisitening and sharing songs playlists with others.", - "files": [ - "https://github.com/daxcay/ComfyUI-YouTubeVideoPlayer" - ], - "install_type": "git-clone", - "reference": "https://github.com/daxcay/ComfyUI-YouTubeVideoPlayer", - "title": "ComfyUI-YouTubeVideoPlayer" - }, - { - "author": "zhangp365", - "description": "Nodes:LoadImageWithSwitch, ImageBatchOneOrMore, ModifyTextGender, GenderControlOutput, ImageCompositeMaskedWithSwitch, ImageCompositeMaskedOneByOne, ColorCorrectOfUtils, SplitMask, MaskFastGrow, CheckpointLoaderSimpleWithSwitch, ImageResizeTo8x, MatchImageRatioToPreset, MaskFromFaceModel, MaskCoverFourCorners, DetectorForNSFW, DeepfaceAnalyzeFaceAttributes, VolcanoOutpainting, VolcanoImageEdit, etc.", - "files": [ - "https://github.com/zhangp365/ComfyUI-utils-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/zhangp365/ComfyUI-utils-nodes", - "title": "zhangp365/ComfyUI-utils-nodes" - }, - { - "author": "zhangp365", - "description": "Nodes: PhotoMakerLoaderV2,PhotoMakerEncodeV2", - "files": [ - "https://github.com/zhangp365/ComfyUI_photomakerV2_native" - ], - "id": "comfyui_photomakerV2_native", - "install_type": "git-clone", - "reference": "https://github.com/zhangp365/ComfyUI_photomakerV2_native", - "title": "ComfyUI_photomakerV2_native" - }, - { - "author": "ratulrafsan", - "description": "Dressup your models!\nThis is my quick implementation of the SAL-VTON node for ComfyUI.\nBased on the paper [a/Keyu Y. Tingwei G. et al. (2023). Linking Garment with Person via Semantically Associated Landmakrs for Virtual Try-On](https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.pdf)", - "files": [ - "https://github.com/ratulrafsan/Comfyui-SAL-VTON" - ], - "id": "sal-vton", - "install_type": "git-clone", - "reference": "https://github.com/ratulrafsan/Comfyui-SAL-VTON", - "title": "Comfyui-SAL-VTON" - }, - { - "author": "Nevysha", - "description": "A simple sidebar tweak to force fixe the ComfyUI menu to the top of the screen. Reaaally quick and dirty. May break with some ComfyUI setup.", - "files": [ - "https://github.com/Nevysha/ComfyUI-nevysha-top-menu" - ], - "id": "nevysha-top-menu", - "install_type": "git-clone", - "reference": "https://github.com/Nevysha/ComfyUI-nevysha-top-menu", - "title": "ComfyUI-nevysha-top-menu" - }, - { - "author": "alisson-anjos", - "description": "This is an extension for ComfyUI that makes it possible to use some LLM models provided by Ollama, such as Gemma, Llava (multimodal), Llama2, Llama3 or Mistral. Speaking specifically of the LLaVa - Large Language and Vision Assistant model, although trained on a relatively small dataset, it demonstrates exceptional capabilities in understanding images and answering questions about them. This model presents similar behaviors to multimodal models such as GPT-4, even when presented with invisible images and instructions.", - "files": [ - "https://github.com/alisson-anjos/ComfyUI-Ollama-Describer" - ], - "id": "ollama-describer", - "install_type": "git-clone", - "reference": "https://github.com/alisson-anjos/ComfyUI-Ollama-Describer", - "title": "ComfyUI-Ollama-Describer" - }, - { - "author": "chaosaiart", - "description": "LowVRAM Animation : txt2video - img2video - video2video , Frame by Frame, compatible with LowVRAM GPUs\nIncluded : Prompt Switch, Checkpoint Switch, Cache, Number Count by Frame, Ksampler txt2img & img2img ...", - "files": [ - "https://github.com/chaosaiart/Chaosaiart-Nodes" - ], - "id": "chaosaiart", - "install_type": "git-clone", - "reference": "https://github.com/chaosaiart/Chaosaiart-Nodes", - "title": "Chaosaiart-Nodes" - }, - { - "author": "viperyl", - "description": "Bilateral Reference Network achieves SOTA result in multi Salient Object Segmentation dataset, this repo pack BiRefNet as ComfyUI nodes, and make this SOTA model easier use for everyone.\nNOTE: The original node was replaced with a version maintained by hieuck because it is no longer maintained.", - "files": [ - "https://github.com/hieuck/ComfyUI-BiRefNet" - ], - "id": "comfyui-birefnet", - "install_type": "git-clone", - "reference": "https://github.com/hieuck/ComfyUI-BiRefNet", - "title": "ComfyUI-BiRefNet-Fix utils" - }, - { - "author": "viperyl", - "description": "This repo cast Recursive Generalization Transformer for Image Super-Resolution to ComfyUI, the original [a/paper link](https://arxiv.org/abs/2303.06373) and [a/github link](https://github.com/zhengchen1999/RGT)", - "files": [ - "https://github.com/viperyl/ComfyUI-RGT" - ], - "id": "rgt", - "install_type": "git-clone", - "pip": [ - "loguru" - ], - "reference": "https://github.com/viperyl/ComfyUI-RGT", - "title": "ComfyUI-RGT" - }, - { - "author": "SuperBeastsAI", - "description": "Nodes:HDR Effects (SuperBeasts.AI). This repository contains custom nodes for ComfyUI created and used by SuperBeasts.AI (@SuperBeasts.AI on Instagram)", - "files": [ - "https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts" - ], - "id": "superbeasts", - "install_type": "git-clone", - "reference": "https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts", - "title": "ComfyUI-SuperBeasts" - }, - { - "author": "hay86", - "description": "Unofficial implementation of [a/dreamtalk](https://github.com/ali-vilab/dreamtalk) for ComfyUI", - "files": [ - "https://github.com/hay86/ComfyUI_Dreamtalk" - ], - "id": "dreamtalk", - "install_type": "git-clone", - "reference": "https://github.com/hay86/ComfyUI_Dreamtalk", - "title": "ComfyUI Dreamtalk" - }, - { - "author": "hay86", - "description": "Unofficial implementation of [a/hallo](https://github.com/fudan-generative-vision/hallo) for ComfyUI", - "files": [ - "https://github.com/hay86/ComfyUI_Hallo" - ], - "id": "hallo-hay86", - "install_type": "git-clone", - "reference": "https://github.com/hay86/ComfyUI_Hallo", - "title": "ComfyUI Hallo" - }, - { - "author": "hay86", - "description": "Unofficial implementation of [a/OpenVoice](https://github.com/myshell-ai/OpenVoice) for ComfyUI", - "files": [ - "https://github.com/hay86/ComfyUI_OpenVoice" - ], - "id": "openvoice-hay86", - "install_type": "git-clone", - "reference": "https://github.com/hay86/ComfyUI_OpenVoice", - "title": "ComfyUI OpenVoice" - }, - { - "author": "hay86", - "description": "Unofficial implementation of [a/DDColor](https://github.com/piddnad/DDColor) for ComfyUI", - "files": [ - "https://github.com/hay86/ComfyUI_DDColor" - ], - "id": "ddcolor-hay86", - "install_type": "git-clone", - "reference": "https://github.com/hay86/ComfyUI_DDColor", - "title": "ComfyUI DDColor" - }, - { - "author": "hay86", - "description": "Unofficial implementation of [a/MiniCPM-V](https://github.com/OpenBMB/MiniCPM-V) for ComfyUI", - "files": [ - "https://github.com/hay86/ComfyUI_MiniCPM-V" - ], - "id": "minicpm-v", - "install_type": "git-clone", - "reference": "https://github.com/hay86/ComfyUI_MiniCPM-V", - "title": "ComfyUI MiniCPM-V" - }, - { - "author": "hay86", - "description": "Unofficial implementation of [a/LatentSync](https://github.com/bytedance/LatentSync) for ComfyUI", - "files": [ - "https://github.com/hay86/ComfyUI_LatentSync" - ], - "id": "latentsync", - "install_type": "git-clone", - "reference": "https://github.com/hay86/ComfyUI_LatentSync", - "title": "ComfyUI LatentSync" - }, - { - "author": "shinich39", - "description": "Get metadata from image.", - "files": [ - "https://github.com/shinich39/comfyui-get-meta" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-get-meta", - "title": "comfyui-get-meta" - }, - { - "author": "shinich39", - "description": "Load workflow from civitai image.", - "files": [ - "https://github.com/shinich39/comfyui-civitai-workflow" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-civitai-workflow", - "title": "comfyui-civitai-workflow" - }, - { - "author": "shinich39", - "description": "Prevent sleep while running ComfyUI.", - "files": [ - "https://github.com/shinich39/comfyui-prevent-sleep" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-prevent-sleep", - "title": "comfyui-prevent-sleep" - }, - { - "author": "shinich39", - "description": "Shuffle nodes after queue execution.", - "files": [ - "https://github.com/shinich39/comfyui-dynamic-routes" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-dynamic-routes", - "title": "comfyui-dynamic-routes" - }, - { - "author": "shinich39", - "description": "Load new workflow after mask editing.", - "files": [ - "https://github.com/shinich39/comfyui-innnnnpaint" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-innnnnpaint", - "title": "comfyui-innnnnpaint" - }, - { - "author": "shinich39", - "description": "Fix node to top.", - "files": [ - "https://github.com/shinich39/comfyui-no-one-above-me" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-no-one-above-me", - "title": "comfyui-no-one-above-me" - }, - { - "author": "shinich39", - "description": "Break the execution, save the incompleted image then continue later.", - "files": [ - "https://github.com/shinich39/comfyui-break-workflow" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-break-workflow", - "title": "comfyui-break-workflow" - }, - { - "author": "shinich39", - "description": "Set global prompts using note node.", - "files": [ - "https://github.com/shinich39/comfyui-global-prompts" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-global-prompts", - "title": "comfyui-global-prompts" - }, - { - "author": "shinich39", - "description": "Make Textarea Great Again", - "files": [ - "https://github.com/shinich39/comfyui-mtga" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinich39/comfyui-mtga", - "title": "comfyui-mtga" - }, - { - "author": "wei30172", - "description": "Setting Up a Web Interface Using ComfyUI.\nNOTE:When installed, you can access it via http://127.0.0.1:8188/comfygen.", - "files": [ - "https://github.com/wei30172/comfygen" - ], - "install_type": "git-clone", - "reference": "https://github.com/wei30172/comfygen", - "title": "comfygen" - }, - { - "author": "zombieyang", - "description": "Simplify ComfyUI to WebUI-liked interface and Connect with Photoshop.", - "files": [ - "https://github.com/zombieyang/sd-ppp" - ], - "install_type": "git-clone", - "reference": "https://github.com/zombieyang/sd-ppp", - "title": "SD-PPP" - }, - { - "author": "KytraScript", - "description": "A ComfyUI node that utilizes Moviepy to convert and send your images or videos to a webhook endpoint directly from ComfyUI.", - "files": [ - "https://github.com/KytraScript/ComfyUI_KytraWebhookHTTP" - ], - "install_type": "git-clone", - "reference": "https://github.com/KytraScript/ComfyUI_KytraWebhookHTTP", - "title": "ComfyUI_KytraWebhookHTTP" - }, - { - "author": "KytraScript", - "description": "Kytra's MatAnyone (Video Matting) implementation for ComfyUI - Based on pq-yang/MatAnyone", - "files": [ - "https://github.com/KytraScript/ComfyUI_MatAnyone_Kytra" - ], - "install_type": "git-clone", - "reference": "https://github.com/KytraScript/ComfyUI_MatAnyone_Kytra", - "title": "ComfyUI_MatAnyone_Kytra" - }, - { - "author": "1mckw", - "description": "Get random images from gelbooru or rule34.", - "files": [ - "https://github.com/1mckw/Comfyui-Gelbooru" - ], - "install_type": "git-clone", - "reference": "https://github.com/1mckw/Comfyui-Gelbooru", - "title": "Comfyui-Gelbooru" - }, - { - "author": "NeuralSamurAI", - "description": "The SuperPrompter node is a ComfyUI node that uses the SuperPrompt-v1 model from Hugging Face to generate text based on a given prompt. It provides various parameters to control the text generation process.", - "files": [ - "https://github.com/NeuralSamurAI/Comfyui-Superprompt-Unofficial" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeuralSamurAI/Comfyui-Superprompt-Unofficial", - "title": "SuperPrompter Node for ComfyUI" - }, - { - "author": "NeuralSamurAI", - "description": "Dimensional Latent Perlin is a custom node for ComfyUI that generates Perlin noise in the latent space. This node is designed to work seamlessly with various diffusion models and can be used as an alternative or complement to standard random noise generators in image generation pipelines.", - "files": [ - "https://github.com/NeuralSamurAI/ComfyUI-Dimensional-Latent-Perlin" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeuralSamurAI/ComfyUI-Dimensional-Latent-Perlin", - "title": "Dimensional Latent Perlin for ComfyUI" - }, - { - "author": "NeuralSamurAI", - "description": "PromptJSON is a custom node for ComfyUI that structures natural language prompts and generates prompts for external LLM nodes in image generation workflows. It aids in creating consistent, schema-based image descriptions.", - "files": [ - "https://github.com/NeuralSamurAI/ComfyUI-PromptJSON" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeuralSamurAI/ComfyUI-PromptJSON", - "title": "PromptJSON Node for ComfyUI" - }, - { - "author": "NeuralSamurAI", - "description": "FluxPseudoNegative is an advanced custom node for ComfyUI that converts negative prompts into positive ones. It's designed to enhance prompt engineering for image generation models that don't natively support negative prompts or where using negative prompts significantly increases generation time. So instead of hacking CFG we simply invert your negative words and find their antonyms!", - "files": [ - "https://github.com/NeuralSamurAI/ComfyUI-FluxPseudoNegativePrompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeuralSamurAI/ComfyUI-FluxPseudoNegativePrompt", - "title": "FluxPseudoNegative" - }, - { - "author": "MokkaBoss1", - "description": "Node Pack mostly for manipulating strings and integers", - "files": [ - "https://github.com/MokkaBoss1/ComfyUI_Mokkaboss1" - ], - "install_type": "git-clone", - "reference": "https://github.com/MokkaBoss1/ComfyUI_Mokkaboss1/wiki/Documentation-for-the-ComfyUI-Nodes-in-this-Node-Pack", - "title": "Node Pack mostly for manipulating strings and integers" - }, - { - "author": "jiaxiangc", - "description": "We provide ComfyUI-ResAdapter node to help users to use [a/ResAdapter](https://github.com/bytedance/res-adapter) in ComfyUI.", - "files": [ - "https://github.com/jiaxiangc/ComfyUI-ResAdapter" - ], - "install_type": "git-clone", - "reference": "https://github.com/jiaxiangc/ComfyUI-ResAdapter", - "title": "ResAdapter for ComfyUI" - }, - { - "author": "ParisNeo", - "description": "lollms_nodes_suite is a set of nodes for comfyui that harnesses the power of lollms, a state-of-the-art AI text generation tool, to improve the quality of image generation.", - "files": [ - "https://github.com/ParisNeo/lollms_nodes_suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/ParisNeo/lollms_nodes_suite", - "title": "lollms_nodes_suite" - }, - { - "author": "IsItDanOrAi", - "description": "This initiative represents a solo venture dedicated to integrating a stereopsis effect within ComfyUI (Stable Diffusion). Presently, the project is focused on the refinement of node categorization within a unified framework, as it is in the early stages of development. However, it has achieved functionality in a fundamental capacity. By processing a video through the Side-by-Side (SBS) node and applying Frame Delay to one of the inputs, it facilitates the creation of a stereopsis effect. This effect is compatible with any Virtual Reality headset that supports SBS video playback, offering a practical application in immersive media experiences.", - "files": [ - "https://github.com/IsItDanOrAi/ComfyUI-Stereopsis" - ], - "install_type": "git-clone", - "reference": "https://github.com/IsItDanOrAi/ComfyUI-Stereopsis", - "title": "ComfyUI-Stereopsis" - }, - { - "author": "nickve28", - "description": "Several utility nodes for use with ComfyUI.", - "files": [ - "https://github.com/nickve28/ComfyUI-Nich-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/nickve28/ComfyUI-Nich-Utils", - "title": "ComfyUI Nich Utils" - }, - { - "author": "FrankChieng", - "description": "implementation of [a/AniPortrait](https://github.com/Zejun-Yang/AniPortrait) generating of videos, includes self driven, face reenacment and audio driven with a reference image", - "files": [ - "https://github.com/frankchieng/ComfyUI_Aniportrait" - ], - "install_type": "git-clone", - "reference": "https://github.com/frankchieng/ComfyUI_Aniportrait", - "title": "ComfyUI_Aniportrait" - }, - { - "author": "FrankChieng", - "description": "implementation of MagicClothing with garment and prompt in ComfyUI", - "files": [ - "https://github.com/frankchieng/ComfyUI_MagicClothing" - ], - "install_type": "git-clone", - "reference": "https://github.com/frankchieng/ComfyUI_MagicClothing", - "title": "ComfyUI_MagicClothing" - }, - { - "author": "BlakeOne", - "description": "Create a custom scheduler from a weighted average of the built-in schedulers", - "files": [ - "https://github.com/BlakeOne/ComfyUI-SchedulerMixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/BlakeOne/ComfyUI-SchedulerMixer", - "title": "ComfyUI SchedulerMixer" - }, - { - "author": "BlakeOne", - "description": "Simple node for setting the sigma values directly. Note, for a full denoise the last sigma should be zero.", - "files": [ - "https://github.com/BlakeOne/ComfyUI-CustomScheduler" - ], - "install_type": "git-clone", - "reference": "https://github.com/BlakeOne/ComfyUI-CustomScheduler", - "title": "ComfyUI CustomScheduler" - }, - { - "author": "BlakeOne", - "description": "An extension for ComyUI that enables saving and loading node presets using the node's context menu.\nRight click a node and choose 'Presets' from its context menu to access the node's presets.", - "files": [ - "https://github.com/BlakeOne/ComfyUI-NodePresets" - ], - "id": "nodepresets", - "install_type": "git-clone", - "reference": "https://github.com/BlakeOne/ComfyUI-NodePresets", - "title": "ComfyUI NodePresets" - }, - { - "author": "BlakeOne", - "description": "An extension for ComyUI to allow resetting a node's inputs to their default values.\nNOTE:Right click any node and choose 'Reset' from the context menu.", - "files": [ - "https://github.com/BlakeOne/ComfyUI-NodeReset" - ], - "id": "nodereset", - "install_type": "git-clone", - "reference": "https://github.com/BlakeOne/ComfyUI-NodeReset", - "title": "ComfyUI NodeReset" - }, - { - "author": "kale4eat", - "description": "Path utility for ComfyUI", - "files": [ - "https://github.com/kale4eat/ComfyUI-path-util" - ], - "id": "demucus", - "install_type": "git-clone", - "reference": "https://github.com/kale4eat/ComfyUI-path-util", - "title": "ComfyUI_demucus" - }, - { - "author": "kale4eat", - "description": "String utility for ComfyUI", - "files": [ - "https://github.com/kale4eat/ComfyUI-string-util" - ], - "install_type": "git-clone", - "reference": "https://github.com/kale4eat/ComfyUI-string-util", - "title": "ComfyUI-string-util" - }, - { - "author": "kale4eat", - "description": "Text file utility for ComfyUI", - "files": [ - "https://github.com/kale4eat/ComfyUI-text-file-util" - ], - "install_type": "git-clone", - "reference": "https://github.com/kale4eat/ComfyUI-text-file-util", - "title": "ComfyUI-text-file-util" - }, - { - "author": "kale4eat", - "description": "Basic audio tools using torchaudio for ComfyUI. It is assumed to assist in the speech dataset creation for ASR, TTS, etc.", - "files": [ - "https://github.com/kale4eat/ComfyUI-speech-dataset-toolkit" - ], - "install_type": "git-clone", - "reference": "https://github.com/kale4eat/ComfyUI-speech-dataset-toolkit", - "title": "ComfyUI-speech-dataset-toolkit" - }, - { - "author": "DrMWeigand", - "description": "A collection of nodes for detecting color in images, leveraging RGB and LAB color spaces. These nodes aim to distinguish colored images from black and white, including those with color tints.", - "files": [ - "https://github.com/DrMWeigand/ComfyUI_ColorImageDetection" - ], - "install_type": "git-clone", - "reference": "https://github.com/DrMWeigand/ComfyUI_ColorImageDetection", - "title": "ComfyUI Color Detection Nodes" - }, - { - "author": "DrMWeigand", - "description": "A ComfyUI node for producing stereoscopic and autostereogram (magic eye) images and videos.", - "files": [ - "https://github.com/DrMWeigand/ComfyUI-StereoVision" - ], - "install_type": "git-clone", - "reference": "https://github.com/DrMWeigand/ComfyUI-StereoVision", - "title": "StereoVision Plugin for ComfyUI" - }, - { - "author": "bobmagicii", - "description": "Nodes:LoraWithMetadata, TypecasterImage.", - "files": [ - "https://github.com/bobmagicii/comfykit-custom-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/bobmagicii/comfykit-custom-nodes", - "title": "ComfyKit Custom Nodes" - }, - { - "author": "TJ16th", - "description": "Custom Node for comfyUI for virtual lighting based on normal map.\nYou can use normal maps to add virtual lighting effects to your images.", - "files": [ - "https://github.com/TJ16th/comfyUI_TJ_NormalLighting" - ], - "install_type": "git-clone", - "reference": "https://github.com/TJ16th/comfyUI_TJ_NormalLighting", - "title": "comfyUI_TJ_NormalLighting" - }, - { - "author": "A4P7J1N7M05OT", - "description": "A barebones ComfyUI wrapper for [a/PixelOE](https://github.com/KohakuBlueleaf/PixelOE).\nI cannot promise any support, if there is someone who wants to make a proper node, please do.", - "files": [ - "https://github.com/A4P7J1N7M05OT/ComfyUI-PixelOE-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/A4P7J1N7M05OT/ComfyUI-PixelOE-Wrapper", - "title": "ComfyUI-PixelOE-Wrapper" - }, - { - "author": "A4P7J1N7M05OT", - "description": "Shamelessly copied the code to auto color correct the image like in gimp from this answer: [a/https://stackoverflow.com/a/56365560/4561887](https://stackoverflow.com/a/56365560/4561887)", - "files": [ - "https://github.com/A4P7J1N7M05OT/ComfyUI-AutoColorGimp" - ], - "install_type": "git-clone", - "reference": "https://github.com/A4P7J1N7M05OT/ComfyUI-AutoColorGimp", - "title": "ComfyUI-AutoColorGimp" - }, - { - "author": "ronniebasak", - "description": "Tara is a powerful node for ComfyUI that integrates Large Language Models (LLMs) to enhance and automate workflow processes. With Tara, you can create complex, intelligent workflows that refine and generate content, manage API keys, and seamlessly integrate various LLMs into your projects.", - "files": [ - "https://github.com/ronniebasak/ComfyUI-Tara-LLM-Integration" - ], - "id": "tarallm", - "install_type": "git-clone", - "reference": "https://github.com/ronniebasak/ComfyUI-Tara-LLM-Integration", - "title": "ComfyUI-Tara-LLM-Integration" - }, - { - "author": "Sida Liu", - "description": "Attach a debug node to an output to obtain more detailed information. Uncover the details of your models in ComfyUI with ease.", - "files": [ - "https://github.com/liusida/ComfyUI-Debug" - ], - "id": "debug", - "install_type": "git-clone", - "reference": "https://github.com/liusida/ComfyUI-Debug", - "title": "ComfyUI-Debug" - }, - { - "author": "Sida Liu", - "description": "A simple password to protect ComfyUI.", - "files": [ - "https://github.com/liusida/ComfyUI-Login" - ], - "id": "login", - "install_type": "git-clone", - "reference": "https://github.com/liusida/ComfyUI-Login", - "title": "ComfyUI-Login" - }, - { - "author": "Sida Liu", - "description": "Use RetinaFace to detect and automatically crop faces.", - "files": [ - "https://github.com/liusida/ComfyUI-AutoCropFaces" - ], - "id": "autocropfaces", - "install_type": "git-clone", - "reference": "https://github.com/liusida/ComfyUI-AutoCropFaces", - "title": "ComfyUI-AutoCropFaces" - }, - { - "author": "Sida Liu", - "description": "Nodes that support Stable Diffusion 3 Medium better.", - "files": [ - "https://github.com/liusida/ComfyUI-SD3-nodes" - ], - "id": "sd3-nodes", - "install_type": "git-clone", - "reference": "https://github.com/liusida/ComfyUI-SD3-nodes", - "title": "ComfyUI-SD3-nodes" - }, - { - "author": "Sida Liu", - "description": "Load and apply B-LoRA models, currently B-LoRA models only works with SDXL (sdxl_base_1.0).", - "files": [ - "https://github.com/liusida/ComfyUI-B-LoRA" - ], - "id": "b-lora", - "install_type": "git-clone", - "reference": "https://github.com/liusida/ComfyUI-B-LoRA", - "title": "ComfyUI-B-LoRA" - }, - { - "author": "jtydhr88", - "description": "Encrypt your comfyui workflow, and share it with key", - "files": [ - "https://github.com/jtydhr88/ComfyUI-Workflow-Encrypt" - ], - "id": "workflow-encrypt", - "install_type": "git-clone", - "reference": "https://github.com/jtydhr88/ComfyUI-Workflow-Encrypt", - "title": "ComfyUI-Workflow-Encrypt" - }, - { - "author": "jtydhr88", - "description": "ComfyUI LayerDivider is custom nodes that generating layered psd files inside ComfyUI[w/Please follow readme and run install_windows_portable_win_py311_cu121 for ComfyUI embedded python.]", - "files": [ - "https://github.com/jtydhr88/ComfyUI-LayerDivider" - ], - "id": "comfyui-layerdivider", - "install_type": "git-clone", - "reference": "https://github.com/jtydhr88/ComfyUI-LayerDivider", - "title": "ComfyUI LayerDivider" - }, - { - "author": "jtydhr88", - "description": "ComfyUI Hunyuan3D-1-wrapper is a custom node that allows you to run [a/Tencent/Hunyuan3D-1](https://github.com/Tencent/Hunyuan3D-1) in ComfyUI as a wrapper.", - "files": [ - "https://github.com/jtydhr88/ComfyUI-Hunyuan3D-1-wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/jtydhr88/ComfyUI-Hunyuan3D-1-wrapper", - "title": "ComfyUI-Hunyuan3D-1-wrapper" - }, - { - "author": "jtydhr88", - "description": "This is a ComfyUI plugin that integrated OpenCut into ComfyUI, originally developed by [a/OpenCut](https://github.com/OpenCut-app/OpenCut)", - "files": [ - "https://github.com/jtydhr88/ComfyUI-OpenCut" - ], - "install_type": "git-clone", - "reference": "https://github.com/jtydhr88/ComfyUI-OpenCut", - "title": "ComfyUI-OpenCut" - }, - { - "author": "jtydhr88", - "description": "This is a ComfyUI plugin that provides a user interface of AudioMass, originally developed by [a/AudioMass](https://github.com/pkalogiros/audiomass)", - "files": [ - "https://github.com/jtydhr88/ComfyUI-AudioMass" - ], - "install_type": "git-clone", - "reference": "https://github.com/jtydhr88/ComfyUI-AudioMass", - "title": "ComfyUI-AudioMass" - }, - { - "author": "SeaArtLab", - "description": "This project implements the comfyui for long-clip, currently supporting the replacement of clip-l. For SD1.5, the SeaArtLongClip module can be used to replace the original clip in the model, expanding the token length from 77 to 248.", - "files": [ - "https://github.com/SeaArtLab/ComfyUI-Long-CLIP" - ], - "install_type": "git-clone", - "reference": "https://github.com/SeaArtLab/ComfyUI-Long-CLIP", - "title": "ComfyUI-Long-CLIP" - }, - { - "author": "tsogzark", - "description": "A simple node to load image from local path or http url.\nYou can find this node from 'image' category.", - "files": [ - "https://github.com/tsogzark/ComfyUI-load-image-from-url" - ], - "install_type": "git-clone", - "reference": "https://github.com/tsogzark/ComfyUI-load-image-from-url", - "title": "ComfyUI-load-image-from-url" - }, - { - "author": "discus0434", - "description": "This repository simply caches the CLIP embeddings and subtly accelerates the inference process by bypassing unnecessary computations.", - "files": [ - "https://github.com/discus0434/comfyui-caching-embeddings" - ], - "id": "caching-embeddings", - "install_type": "git-clone", - "reference": "https://github.com/discus0434/comfyui-caching-embeddings", - "title": "ComfyUI Caching Embeddings" - }, - { - "author": "discus0434", - "description": "Simple ComfyUI node that predicts the score of an aesthetic image with SigLIP-based predictor.", - "files": [ - "https://github.com/discus0434/comfyui-aesthetic-predictor-v2-5" - ], - "id": "aesthetic-predictor", - "install_type": "git-clone", - "reference": "https://github.com/discus0434/comfyui-aesthetic-predictor-v2-5", - "title": "ComfyUI Aesthetic Predictor V2.5" - }, - { - "author": "discus0434", - "description": "ComfyUI Flux Accelerator is a custom node for ComfyUI that accelerates Flux.1 image generation, just by using this node.", - "files": [ - "https://github.com/discus0434/comfyui-flux-accelerator" - ], - "install_type": "git-clone", - "reference": "https://github.com/discus0434/comfyui-flux-accelerator", - "title": "ComfyUI Flux Accelerator" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/StyleShot](https://github.com/open-mmlab/StyleShot.git)", - "files": [ - "https://github.com/AIFSH/StyleShot-ComfyUI" - ], - "id": "styleshot", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/StyleShot-ComfyUI", - "title": "StyleShot-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for separation vocals from music based on [a/ZFTurbo/Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training)", - "files": [ - "https://github.com/AIFSH/VocalSeparation-ComfyUI" - ], - "id": "vocalseparation", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/VocalSeparation-ComfyUI", - "title": "VocalSeparation-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/DiffMorpher](https://github.com/Kevin-thu/DiffMorpher),you can find base workflow in [a/doc](https://github.com/AIFSH/DiffMorpher-ComfyUI/blob/main/doc)", - "files": [ - "https://github.com/AIFSH/DiffMorpher-ComfyUI" - ], - "id": "diffmorpher", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/DiffMorpher-ComfyUI", - "title": "DiffMorpher-ComfyUI" - }, - { - "author": "AIFSH", - "description": "the custom code for [a/UVR5](https://github.com/Anjok07/ultimatevocalremovergui) to separate vocals and background music", - "files": [ - "https://github.com/AIFSH/ComfyUI-UVR5" - ], - "id": "uvr5", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-UVR5", - "title": "ComfyUI-UVR5" - }, - { - "author": "AIFSH", - "description": "Nodes:IP_LAP Node, Video Loader, PreView Video, Combine Audio Video. the comfyui custom node of [a/IP_LAP](https://github.com/Weizhi-Zhong/IP_LAP) to make audio driven videos!", - "files": [ - "https://github.com/AIFSH/ComfyUI-IP_LAP" - ], - "id": "iplap", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-IP_LAP", - "title": "ComfyUI-IP_LAP" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)! you can voice cloning and tts in comfyui now\n[w/NOTE:make sure ffmpeg is worked in your commandline]", - "files": [ - "https://github.com/AIFSH/ComfyUI-GPT_SoVITS" - ], - "id": "sovits", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-GPT_SoVITS", - "title": "ComfyUI-GPT_SoVITS" - }, - { - "author": "AIFSH", - "description": "the comfyui custom node of [a/MuseTalk](https://github.com/TMElyralab/MuseTalk) to make audio driven videos!", - "files": [ - "https://github.com/AIFSH/ComfyUI-MuseTalk_FSH" - ], - "id": "musetalk-fsh", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-MuseTalk_FSH", - "title": "ComfyUI-MuseTalk_FSH" - }, - { - "author": "AIFSH", - "description": "a comfyui cuatom node for audio subtitling based on [a/whisperX](https://github.com/m-bain/whisperX.git) and [a/translators](https://github.com/UlionTse/translators)", - "files": [ - "https://github.com/AIFSH/ComfyUI-WhisperX" - ], - "id": "whisperx", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-WhisperX", - "title": "ComfyUI-WhisperX" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git), you can Voice-Conversion in comfyui now!\nNOTE: make sure ffmpeg is worked in your commandline for Linux", - "files": [ - "https://github.com/AIFSH/ComfyUI-RVC" - ], - "id": "aifsh-rvc", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-RVC", - "title": "ComfyUI-RVC" - }, - { - "author": "AIFSH", - "description": "a custom comfyui node for [a/coqui-ai/TTS](https://github.com/coqui-ai/TTS.git)'s xtts module! support 17 languages voice cloning and tts", - "files": [ - "https://github.com/AIFSH/ComfyUI-XTTS" - ], - "id": "xtts", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-XTTS", - "title": "ComfyUI-XTTS" - }, - { - "author": "AIFSH", - "description": "a comfyui node for viewing Live2D model", - "files": [ - "https://github.com/AIFSH/ComfyUI-Live2DViewer" - ], - "id": "live2dviewer", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-Live2DViewer", - "title": "ComfyUI-Live2DViewer" - }, - { - "author": "AIFSH", - "description": "a custom comfyui node for [a/fish-speech](https://github.com/fishaudio/fish-speech.git)", - "files": [ - "https://github.com/AIFSH/ComfyUI-FishSpeech" - ], - "id": "fishspeech", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-FishSpeech", - "title": "ComfyUI-FishSpeech" - }, - { - "author": "AIFSH", - "description": "the comfyui custom node of [a/V-Express](https://github.com/tencent-ailab/V-Express) to make audio driven videos!", - "files": [ - "https://github.com/AIFSH/ComfyUI_V-Express" - ], - "id": "v-express-aifsh", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI_V-Express", - "title": "ComfyUI_V-Express" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/MimicBrush](https://github.com/ali-vilab/MimicBrush),then inpainting with reference image.", - "files": [ - "https://github.com/AIFSH/ComfyUI-MimicBrush" - ], - "id": "mimicbrush", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-MimicBrush", - "title": "ComfyUI-MimicBrush" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/hallo](https://github.com/fudan-generative-vision/hallo)", - "files": [ - "https://github.com/AIFSH/ComfyUI-Hallo" - ], - "id": "hallo", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-Hallo", - "title": "ComfyUI-Hallo" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/UniAnimate](https://github.com/ali-vilab/UniAnimate)", - "files": [ - "https://github.com/AIFSH/ComfyUI-UniAnimate" - ], - "id": "unianimate", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-UniAnimate", - "title": "ComfyUI-UniAnimate" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/3d-photo-inpainting](https://github.com/vt-vl-lab/3d-photo-inpainting),then you can render one image to zoom-in/dolly zoom/swing motion/circle motion video", - "files": [ - "https://github.com/AIFSH/ComfyUI-3d-photo-inpainting" - ], - "id": "3d-photo-inpainting", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-3d-photo-inpainting", - "title": "ComfyUI-3d-photo-inpainting" - }, - { - "author": "AIFSH", - "description": "a node for [a/AuraSR](https://github.com/fal-ai/aura-sr)", - "files": [ - "https://github.com/AIFSH/ComfyUI-AuraSR" - ], - "id": "aurasr-aifsh", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-AuraSR", - "title": "AIFSH/ComfyUI-AuraSR" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/MARS5-TTS](https://github.com/Camb-ai/MARS5-TTS)", - "files": [ - "https://github.com/AIFSH/ComfyUI-MARS5-TTS" - ], - "id": "mars5-tts", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-MARS5-TTS", - "title": "ComfyUI-MARS5-TTS" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/I2V-Adapter](https://github.com/KwaiVGI/I2V-Adapter)", - "files": [ - "https://github.com/AIFSH/ComfyUI-I2V-Adapter" - ], - "id": "i2v-adapter", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-I2V-Adapter", - "title": "ComfyUI-I2V-Adapter" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/MimicMotion](https://github.com/Tencent/MimicMotion)", - "files": [ - "https://github.com/AIFSH/ComfyUI-MimicMotion" - ], - "id": "mimicmotion-aifsh", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-MimicMotion", - "title": "ComfyUI-MimicMotion" - }, - { - "author": "AIFSH", - "description": "make [a/DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) available in ComfyUI", - "files": [ - "https://github.com/AIFSH/ComfyUI-DiffSynth-Studio" - ], - "id": "diffsynth-studio", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ComfyUI-DiffSynth-Studio", - "title": "ComfyUI-DiffSynth-Studio" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/CosyVoice](https://github.com/FunAudioLLM/CosyVoice)", - "files": [ - "https://github.com/AIFSH/CosyVoice-ComfyUI" - ], - "id": "cosyvoice", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/CosyVoice-ComfyUI", - "title": "CosyVoice-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/AniTalker](https://github.com/X-LANCE/AniTalker)", - "files": [ - "https://github.com/AIFSH/AniTalker-ComfyUI" - ], - "id": "anitalker", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/AniTalker-ComfyUI", - "title": "AniTalker-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/DH_live](https://github.com/kleinlee/DH_live)", - "files": [ - "https://github.com/AIFSH/DHLive-ComfyUI" - ], - "id": "dhlive", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/DHLive-ComfyUI", - "title": "DHLive-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a comfyui custom node for [a/GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)", - "files": [ - "https://github.com/AIFSH/GSTTS-ComfyUI" - ], - "id": "gstts", - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/GSTTS-ComfyUI", - "title": "GSTTS-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/FancyVideo](https://github.com/360CVGroup/FancyVideo)", - "files": [ - "https://github.com/AIFSH/FancyVideo-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/FancyVideo-ComfyUI", - "title": "FancyVideo-ComfyUI" - }, - { - "author": "AIFSH", - "description": "NODES:TextNode, PreViewVideo, VideoSysNode.", - "files": [ - "https://github.com/AIFSH/VideoSys-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/VideoSys-ComfyUI", - "title": "VideoSys-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/HivisionIDPhotos](https://github.com/Zeyi-Lin/HivisionIDPhotos)", - "files": [ - "https://github.com/AIFSH/HivisionIDPhotos-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/HivisionIDPhotos-ComfyUI", - "title": "HivisionIDPhotos-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio)", - "files": [ - "https://github.com/AIFSH/DiffSynth-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/DiffSynth-ComfyUI", - "title": "DiffSynth-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/RealisDance](https://github.com/damo-cv/RealisDance)", - "files": [ - "https://github.com/AIFSH/RealisDance-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/RealisDance-ComfyUI", - "title": "RealisDance-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/ViewCrafter](https://github.com/Drexubery/ViewCrafter)", - "files": [ - "https://github.com/AIFSH/ViewCrafter-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/ViewCrafter-ComfyUI", - "title": "ViewCrafter-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for SenseVoice", - "files": [ - "https://github.com/AIFSH/SenseVoice-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/SenseVoice-ComfyUI", - "title": "SenseVoice-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/EzAudio](https://github.com/haidog-yaqub/EzAudio)", - "files": [ - "https://github.com/AIFSH/EzAudio-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/EzAudio-ComfyUI", - "title": "EzAudio-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/Pyramid-Flow](https://github.com/jy0205/Pyramid-Flow)", - "files": [ - "https://github.com/AIFSH/PyramidFlow-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/PyramidFlow-ComfyUI", - "title": "PyramidFlow-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/JoyHallo](https://github.com/jdh-algo/JoyHallo)", - "files": [ - "https://github.com/AIFSH/JoyHallo-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/JoyHallo-ComfyUI", - "title": "JoyHallo-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/F5-TTS](https://github.com/SWivid/F5-TTS)", - "files": [ - "https://github.com/AIFSH/F5-TTS-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/F5-TTS-ComfyUI", - "title": "F5-TTS-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/FireRedTTS](https://github.com/FireRedTeam/FireRedTTS)", - "files": [ - "https://github.com/AIFSH/FireRedTTS-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/FireRedTTS-ComfyUI", - "title": "FireRedTTS-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom nodde for [a/IMAGDressing](https://github.com/muzishen/IMAGDressing)", - "files": [ - "https://github.com/AIFSH/IMAGDressing-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/IMAGDressing-ComfyUI", - "title": "IMAGDressing-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/OmniGen](https://github.com/VectorSpaceLab/OmniGen)", - "files": [ - "https://github.com/AIFSH/OmniGen-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/OmniGen-ComfyUI", - "title": "OmniGen-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/MaskGCT](https://github.com/open-mmlab/Amphion/blob/main/models/tts/maskgct/README.md) to Zero-Shot Text-to-Speech", - "files": [ - "https://github.com/AIFSH/MaskGCT-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/MaskGCT-ComfyUI", - "title": "MaskGCT-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/MiniMates](https://github.com/kleinlee/MiniMates)", - "files": [ - "https://github.com/AIFSH/MiniMates-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/MiniMates-ComfyUI", - "title": "MiniMates-ComfyUI" - }, - { - "author": "AIFSH", - "description": "a custom node for [a/echomimic_v2](https://github.com/antgroup/echomimic_v2)", - "files": [ - "https://github.com/AIFSH/EchoMimicV2-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/EchoMimicV2-ComfyUI", - "title": "EchoMimicV2-ComfyUI" - }, - { - "author": "AIFSH", - "description": "A ComfyUI chat node based on SemiUI.", - "files": [ - "https://github.com/AIFSH/SemiChat-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIFSH/SemiChat-ComfyUI", - "title": "SemiChat-ComfyUI" - }, - { - "author": "Koishi-Star", - "description": "\u0421omfyUI version of [a/Euler Smea Dyn Sampler](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler). It adds samplers directly to KSampler nodes.", - "files": [ - "https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler" - ], - "id": "smea", - "install_type": "git-clone", - "reference": "https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler", - "title": "Euler-Smea-Dyn-Sampler" - }, - { - "author": "Koishi-Star", - "description": "Using pyramid_noise instead of original noise in inference", - "files": [ - "https://github.com/Koishi-Star/Pyramid_Noise_For_Inference" - ], - "id": "Pyramid_Noise_For_Inference", - "install_type": "git-clone", - "reference": "https://github.com/Koishi-Star/Pyramid_Noise_For_Inference", - "title": "Pyramid_Noise_For_Inference" - }, - { - "author": "sdfxai", - "description": "SDFXBridgeForComfyUI is a custom node designed for seamless integration between ComfyUI and SDFX. This custom node allows users to make ComfyUI compatible with SDFX when running the ComfyUI instance on their local machines.", - "files": [ - "https://github.com/sdfxai/SDFXBridgeForComfyUI" - ], - "id": "sdfx", - "install_type": "git-clone", - "reference": "https://github.com/sdfxai/SDFXBridgeForComfyUI", - "title": "SDFXBridgeForComfyUI - ComfyUI Custom Node for SDFX Integration" - }, - { - "author": "smthemex", - "description": "FoleyCrafter is a video-to-audio generation framework which can produce realistic sound effects semantically relevant and synchronized with videos.", - "files": [ - "https://github.com/smthemex/ComfyUI_FoleyCrafter" - ], - "id": "comfyui_foleycrafter", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_FoleyCrafter", - "title": "ComfyUI_FoleyCrafter" - }, - { - "author": "smthemex", - "description": "using diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model", - "files": [ - "https://github.com/smthemex/ComfyUI_Diffree" - ], - "id": "comfyui_diffree", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Diffree", - "title": "ComfyUI_Diffree" - }, - { - "author": "smthemex", - "description": "you can using stable makeup when use comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_Stable_Makeup" - ], - "id": "Stable_Makeup", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Stable_Makeup", - "title": "ComfyUI_Stable_Makeup" - }, - { - "author": "smthemex", - "description": "You can make emoji from a video and a image in comfyui", - "files": [ - "https://github.com/smthemex/ComfyUI_FollowYourEmoji" - ], - "id": "FollowYourEmoji", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_FollowYourEmoji", - "title": "ComfyUI_FollowYourEmoji" - }, - { - "author": "smthemex", - "description": "You can using EchoMimic in comfyui,please using pip install install miss module", - "files": [ - "https://github.com/smthemex/ComfyUI_EchoMimic" - ], - "id": "EchoMimic", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_EchoMimic", - "title": "ComfyUI_EchoMimic" - }, - { - "author": "smthemex", - "description": "You can call Chatglm's API in comfyUI to translate and describe pictures, and the API similar to OpenAI.", - "files": [ - "https://github.com/smthemex/ComfyUI_ChatGLM_API" - ], - "id": "chatglm-api", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_ChatGLM_API", - "title": "ComfyUI_ChatGLM_API" - }, - { - "author": "smthemex", - "description": "You can use stable-audio-open-1.0 in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_StableAudio_Open" - ], - "id": "stable-audio-open-1.0", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_StableAudio_Open", - "title": "ComfyUI_StableAudio_Open" - }, - { - "author": "smthemex", - "description": "you can using anydoor ,change clothes,object", - "files": [ - "https://github.com/smthemex/ComfyUI_AnyDoor" - ], - "id": "ComfyUI_AnyDoor", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_AnyDoor", - "title": "ComfyUI_AnyDoor" - }, - { - "author": "smthemex", - "description": "A HiDiffusion node for ComfyUI.", - "files": [ - "https://github.com/smthemex/ComfyUI_HiDiffusion_Pro" - ], - "id": "hidiffusion-pro", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_HiDiffusion_Pro", - "title": "ComfyUI_HiDiffusion_Pro" - }, - { - "author": "smthemex", - "description": "you can using sotry-diffusion in comfyui", - "files": [ - "https://github.com/smthemex/ComfyUI_StoryDiffusion" - ], - "id": "StoryDiffusion", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_StoryDiffusion", - "title": "ComfyUI_StoryDiffusion" - }, - { - "author": "smthemex", - "description": "you can make story in comfyUI using MS-diffusion", - "files": [ - "https://github.com/smthemex/ComfyUI_MS_Diffusion" - ], - "id": "MS_Diffusion", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_MS_Diffusion", - "title": "ComfyUI_MS_Diffusion" - }, - { - "author": "smthemex", - "description": "you can using pic2story in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_Pic2Story" - ], - "id": "pic2story", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Pic2Story", - "title": "ComfyUI_Pic2Story" - }, - { - "author": "smthemex", - "description": "Llama3_8B for comfyUI\uff0c using pipeline workflow.", - "files": [ - "https://github.com/smthemex/ComfyUI_Llama3_8B" - ], - "id": "llama3-8b", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Llama3_8B", - "title": "ComfyUI_Llama3_8B" - }, - { - "author": "smthemex", - "description": "Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc)", - "files": [ - "https://github.com/smthemex/ComfyUI_ParlerTTS" - ], - "id": "parlertts", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_ParlerTTS", - "title": "ComfyUI_ParlerTTS" - }, - { - "author": "smthemex", - "description": "A tool for novice users in Chinese Mainland to call the huggingface hub and download the huggingface models.", - "files": [ - "https://github.com/smthemex/ComfyUI_Pipeline_Tool" - ], - "id": "pipeline-tool", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Pipeline_Tool", - "title": "ComfyUI_Pipeline_Tool" - }, - { - "author": "smthemex", - "description": "This node allows you to use ID_Animator, the zero shot video generation model", - "files": [ - "https://github.com/smthemex/ComfyUI_ID_Animator" - ], - "id": "id-animator", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_ID_Animator", - "title": "ComfyUI_ID_Animator" - }, - { - "author": "smthemex", - "description": "you can using customnet in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_CustomNet" - ], - "id": "customnet", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_CustomNet", - "title": "ComfyUI_CustomNet" - }, - { - "author": "smthemex", - "description": "You can use [a/Popspaper](https://popspaper.github.io/pOps/) method in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_Pops" - ], - "id": "pops", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Pops", - "title": "ComfyUI_Pops" - }, - { - "author": "smthemex", - "description": "using [a/StreamV2V](https://github.com/Jeff-LiangF/streamv2v) in ComfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_Streamv2v_Plus" - ], - "id": "streamv2v", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Streamv2v_Plus", - "title": "ComfyUI_Streamv2v_Plus" - }, - { - "author": "smthemex", - "description": "MooER is an LLM-based Speech Recognition and Translation Model from Moore Threads.You can use MooER when install ComfyUI_MooER node", - "files": [ - "https://github.com/smthemex/ComfyUI_MooER" - ], - "id": "comfyui_mooer", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_MooER", - "title": "ComfyUI_MooER" - }, - { - "author": "smthemex", - "description": "using InstantX's CSGO in comfyUI for style", - "files": [ - "https://github.com/smthemex/ComfyUI_CSGO_Wrapper" - ], - "id": "comfyui_csgo_wrapper", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_CSGO_Wrapper", - "title": "ComfyUI_CSGO_Wrapper" - }, - { - "author": "smthemex", - "description": "ou can using DeepFakeDefenders in comfyUI to Prediction image is a DeepFake img or not.", - "files": [ - "https://github.com/smthemex/ComfyUI_DeepFakeDefenders" - ], - "id": "comfyui_deepfakedefenders", - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_DeepFakeDefenders", - "title": "ComfyUI_DeepFakeDefenders" - }, - { - "author": "smthemex", - "description": "You can call Using Sapiens to get seg\uff0cnormal\uff0cpose\uff0cdepth\uff0cmask.", - "files": [ - "https://github.com/smthemex/ComfyUI_Sapiens" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Sapiens", - "title": "ComfyUI_Sapiens" - }, - { - "author": "smthemex", - "description": "Long-Duration and High-Resolution Audio-driven Portrait Image Animation,", - "files": [ - "https://github.com/smthemex/ComfyUI_Hallo2" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Hallo2", - "title": "ComfyUI_Hallo2" - }, - { - "author": "smthemex", - "description": "Try [a/OmniParser](https://github.com/microsoft/OmniParser) in ComfyUI which a simple screen parsing tool towards pure vision based GUI agent.", - "files": [ - "https://github.com/smthemex/ComfyUI_OmniParser" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_OmniParser", - "title": "ComfyUI_OmniParser" - }, - { - "author": "smthemex", - "description": "Using Demucs in comfyUI, make Music Source Separation", - "files": [ - "https://github.com/smthemex/ComfyUI_Demucs" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Demucs", - "title": "ComfyUI_Demucs" - }, - { - "author": "smthemex", - "description": "You can InstantIR to Fix blurry photos in ComfyUI \uff0c[a/InstantIR](https://github.com/instantX-research/InstantIR):Blind Image Restoration with Instant Generative Reference", - "files": [ - "https://github.com/smthemex/ComfyUI_InstantIR_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_InstantIR_Wrapper", - "title": "ComfyUI_InstantIR_Wrapper" - }, - { - "author": "smthemex", - "description": "Face Anonymization make simple and easy.", - "files": [ - "https://github.com/smthemex/ComfyUI_Face_Anon_Simple" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Face_Anon_Simple", - "title": "ComfyUI_Face_Anon_Simple" - }, - { - "author": "smthemex", - "description": "you can make PBR in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_PBR_Maker" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_PBR_Maker", - "title": "ComfyUI_PBR_Maker" - }, - { - "author": "smthemex", - "description": "You can use TRELLIS in comfyUI\n[a/TRELLIS](https://github.com/microsoft/TRELLIS/tree/main), Structured 3D Latents for Scalable and Versatile 3D Generation", - "files": [ - "https://github.com/smthemex/ComfyUI_TRELLIS" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_TRELLIS", - "title": "ComfyUI_TRELLIS" - }, - { - "author": "smthemex", - "description": "SVFR is a unified framework for face video restoration that supports tasks such as BFR, Colorization, Inpainting\uff0cyou can use it in ComfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_SVFR" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_SVFR", - "title": "ComfyUI_SVFR" - }, - { - "author": "smthemex", - "description": "ComfyUI_MangaNinjia is a ComfyUI node of MangaNinja which is a Line Art Colorization with Precise Reference Following method.", - "files": [ - "https://github.com/smthemex/ComfyUI_MangaNinjia" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_MangaNinjia", - "title": "ComfyUI_MangaNinjia" - }, - { - "author": "smthemex", - "description": "Sonic is a method about ' Shifting Focus to Global Audio Perception in Portrait Animation',you can use it in comfyUI.", - "files": [ - "https://github.com/smthemex/ComfyUI_Sonic" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Sonic", - "title": "ComfyUI_Sonic" - }, - { - "author": "smthemex", - "description": "DiffuEraser is a diffusion model for video Inpainting, you can use it in ComfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_DiffuEraser" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_DiffuEraser", - "title": "ComfyUI_DiffuEraser" - }, - { - "author": "smthemex", - "description": "[a/CSD_MT](https://github.com/Snowfallingplum/CSD-MT) is a method about 'Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth', you can use it in comfyUI.", - "files": [ - "https://github.com/smthemex/ComfyUI_CSD_MT" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_CSD_MT", - "title": "ComfyUI_CSD_MT" - }, - { - "author": "smthemex", - "description": "Light-A-Video: Training-free Video Relighting via Progressive Light Fusion,you can use it in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_Light_A_Video" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Light_A_Video", - "title": "ComfyUI_Light_A_Video" - }, - { - "author": "smthemex", - "description": "[a/YuE](https://github.com/multimodal-art-projection/YuE) is a groundbreaking series of open-source foundation models designed for music generation, specifically for transforming lyrics into full songs (lyrics2song). you can use it in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_YuE" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_YuE", - "title": "ComfyUI_YuE" - }, - { - "author": "smthemex", - "description": "PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data\uff0cyou can use it in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_PhotoDoodle" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_PhotoDoodle", - "title": "ComfyUI_PhotoDoodle" - }, - { - "author": "smthemex", - "description": "KV-Edit: Training-Free Image Editing for Precise Background Preservation,you can use it in comfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_KV_Edit" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_KV_Edit", - "title": "ComfyUI_KV_Edit" - }, - { - "author": "smthemex", - "description": "Personalize Anything for Free with Diffusion Transformer,use it in comfyUI with wrapper mode", - "files": [ - "https://github.com/smthemex/ComfyUI_Personalize_Anything" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_Personalize_Anything", - "title": "ComfyUI_Personalize_Anything" - }, - { - "author": "smthemex", - "description": "Use DICE-Talk in ComfyUI\uff0cwhich is a method about Correlation-Aware Emotional Talking Portrait Generation.", - "files": [ - "https://github.com/smthemex/ComfyUI_DICE_Talk" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_DICE_Talk", - "title": "ComfyUI_DICE_Talk" - }, - { - "author": "smthemex", - "description": "VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning,you can use it in ComfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_VisualCloze" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_VisualCloze", - "title": "ComfyUI_VisualCloze" - }, - { - "author": "smthemex", - "description": "HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters,try it in comfyUI ,if your VRAM >24G.", - "files": [ - "https://github.com/smthemex/ComfyUI_HunyuanAvatar_Sm" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_HunyuanAvatar_Sm", - "title": "ComfyUI_HunyuanAvatar_Sm" - }, - { - "author": "smthemex", - "description": "This is the comfyui implementation of [a/PartPacker](https://github.com/NVlabs/PartPacker): Efficient Part-level 3D Object Generation via Dual Volume Packing.Max varm12G", - "files": [ - "https://github.com/smthemex/ComfyUI_PartPacker" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_PartPacker", - "title": "ComfyUI_PartPacker" - }, - { - "author": "smthemex", - "description": "[a/SongGeneration](https://github.com/tencent-ailab/SongGeneration):High-Quality Song Generation with Multi-Preference Alignment (SOTA),you can try VRAM>12G", - "files": [ - "https://github.com/smthemex/ComfyUI_SongGeneration" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_SongGeneration", - "title": "ComfyUI_SongGeneration" - }, - { - "author": "smthemex", - "description": "[a/AniCrafter](https://github.com/MyNiuuu/AniCrafter): Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models, you can try this methods when use ComfyUI.", - "files": [ - "https://github.com/smthemex/ComfyUI_AniCrafter" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_AniCrafter", - "title": "ComfyUI_AniCrafter" - }, - { - "author": "smthemex", - "description": "ObjectClear:Complete Object Removal via Object-Effect Attention,you can try it in ComfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_ObjectClear" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_ObjectClear", - "title": "ComfyUI_ObjectClear" - }, - { - "author": "smthemex", - "description": "OmniSVG: A Unified Scalable Vector Graphics Generation Model,you can try it in ComfyUI.", - "files": [ - "https://github.com/smthemex/ComfyUI_OmniSVG" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_OmniSVG", - "title": "ComfyUI_OmniSVG" - }, - { - "author": "smthemex", - "description": "StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation,you can try it in ComfyUI", - "files": [ - "https://github.com/smthemex/ComfyUI_StableAvatar" - ], - "install_type": "git-clone", - "reference": "https://github.com/smthemex/ComfyUI_StableAvatar", - "title": "ComfyUI_StableAvatar" - }, - { - "author": "choey", - "description": "Comfy-Topaz is a custom node for ComfyUI, which integrates with Topaz Photo AI to enhance (upscale, sharpen, denoise, etc.) images, allowing this traditionally asynchronous step to become a part of ComfyUI workflows.\nNOTE:Licensed installation of Topaz Photo AI", - "files": [ - "https://github.com/choey/Comfy-Topaz" - ], - "id": "topaz", - "install_type": "git-clone", - "reference": "https://github.com/choey/Comfy-Topaz", - "title": "Comfy-Topaz" - }, - { - "author": "ALatentPlace", - "description": "Yet Another Node Collection. Adds some useful nodes, check out the GitHub page for more details.", - "files": [ - "https://github.com/ALatentPlace/ComfyUI_yanc" - ], - "id": "yanc-alatentplace", - "install_type": "git-clone", - "reference": "https://github.com/ALatentPlace/ComfyUI_yanc", - "title": "ComfyUI_yanc" - }, - { - "author": "ALatentPlace", - "description": "A custom node for a LMStudio integration into ComfyUI.", - "files": [ - "https://github.com/ALatentPlace/YANC_LMStudio" - ], - "install_type": "git-clone", - "reference": "https://github.com/ALatentPlace/YANC_LMStudio", - "title": "YANC_LMStudio" - }, - { - "author": "Wicloz", - "description": "Simple nodes to help clean up your workflow, mostly focussed on text operations.", - "files": [ - "https://github.com/Wicloz/ComfyUI-Simply-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Wicloz/ComfyUI-Simply-Nodes", - "title": "ComfyUI Simply Nodes" - }, - { - "author": "wandbrandon", - "description": "pixel art workshop nodes for comfyui.", - "files": [ - "https://github.com/wandbrandon/comfyui-pixel" - ], - "id": "pixel", - "install_type": "git-clone", - "reference": "https://github.com/wandbrandon/comfyui-pixel", - "title": "comfyui-pixel" - }, - { - "author": "nullquant", - "description": "These are custom nodes for ComfyUI native implementation of [a/BrushNet](https://arxiv.org/abs/2403.06976) (inpaint), PowerPaint (inpaint, object removal) and HiDiffusion (higher resolution for SD15 and SDXL)", - "files": [ - "https://github.com/nullquant/ComfyUI-BrushNet" - ], - "id": "brushnet", - "install_type": "git-clone", - "reference": "https://github.com/nullquant/ComfyUI-BrushNet", - "title": "BrushNet" - }, - { - "author": "pamparamm", - "description": "Perturbed-Attention Guidance, Smoothed Energy Guidance and Sliding Window Guidance for ComfyUI and SD Forge/reForge. (PAG)", - "files": [ - "https://github.com/pamparamm/sd-perturbed-attention" - ], - "id": "pag", - "install_type": "git-clone", - "reference": "https://github.com/pamparamm/sd-perturbed-attention", - "title": "sd-perturbed-attention" - }, - { - "author": "pamparamm", - "description": "ComfyUI port of Vectorscope CC and Diffusion Color Grading by Haoming02. Makes it possible to adjust Brightness/Contrast/Saturation/Hue during image generation.", - "files": [ - "https://github.com/pamparamm/ComfyUI-vectorscope-cc" - ], - "id": "vectorscope", - "install_type": "git-clone", - "reference": "https://github.com/pamparamm/ComfyUI-vectorscope-cc", - "title": "ComfyUI Vectorscope CC" - }, - { - "author": "pamparamm", - "description": "Fixed AttentionCouple, NegPip(negative weights in prompts) for SDXL and FLUX, more CFG++ and SMEA DY samplers, etc.", - "files": [ - "https://github.com/pamparamm/ComfyUI-ppm" - ], - "id": "comfyui-ppm", - "install_type": "git-clone", - "reference": "https://github.com/pamparamm/ComfyUI-ppm", - "title": "ComfyUI-ppm" - }, - { - "author": "unwdef", - "description": "Custom nodes for ComfyUI by unwdef.", - "files": [ - "https://github.com/unwdef/unwdef-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/unwdef/unwdef-nodes-comfyui", - "title": "unwdef-nodes" - }, - { - "author": "fevre27", - "description": "Unofficial ComfyUI implementation of Self-Guidance.", - "files": [ - "https://github.com/forever22777/comfyui-self-guidance" - ], - "id": "self-guidance", - "install_type": "git-clone", - "reference": "https://github.com/forever22777/comfyui-self-guidance", - "title": "Self-Guidance nodes" - }, - { - "author": "aburahamu", - "description": "This extension can send HTTP Requests. You can request image generation to StableDiffusion3 and post images to X (Twitter) and Discord.", - "files": [ - "https://github.com/aburahamu/ComfyUI-RequestsPoster" - ], - "id": "request-poster", - "install_type": "git-clone", - "reference": "https://github.com/aburahamu/ComfyUI-RequestsPoster", - "title": "ComfyUI-RequestPoster" - }, - { - "author": "aburahamu", - "description": "This custom node detects body parts (currently only hands) from the received image and outputs the image if the skeleton can be estimated.", - "files": [ - "https://github.com/aburahamu/ComfyUI-IsNiceParts" - ], - "id": "isniceparts", - "install_type": "git-clone", - "reference": "https://github.com/aburahamu/ComfyUI-IsNiceParts", - "title": "ComfyUI-IsNiceParts" - }, - { - "author": "Sorcerio", - "description": "An image generation based music visualizer integrated into comfyanonymous/ComfyUI as custom nodes.", - "files": [ - "https://github.com/Sorcerio/MBM-Music-Visualizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/Sorcerio/MBM-Music-Visualizer", - "title": "MBM's Music Visualizer" - }, - { - "author": "quadmoon", - "description": "These are just some nodes I wanted and couldn't find where anyone else had made them yet.", - "files": [ - "https://github.com/traugdor/ComfyUI-quadMoons-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/traugdor/ComfyUI-quadMoons-nodes", - "title": "quadmoon's ComfyUI nodes" - }, - { - "author": "quadmoon", - "description": "A ComfyUI extension for Riffusion audio generation.", - "files": [ - "https://github.com/traugdor/ComfyUI-Riffusion" - ], - "install_type": "git-clone", - "reference": "https://github.com/traugdor/ComfyUI-Riffusion", - "title": "ComfyUI-Riffusion" - }, - { - "author": "quadmoon", - "description": "GGUF implementation for the ComfyUI Ultimate SD Upscale node.", - "files": [ - "https://github.com/traugdor/ComfyUI-UltimateSDUpscale-GGUF" - ], - "install_type": "git-clone", - "reference": "https://github.com/traugdor/ComfyUI-UltimateSDUpscale-GGUF", - "title": "ComfyUI-UltimateSDUpscale-GGUF" - }, - { - "author": "quadme7macoon", - "description": "Nodes:Shadertoy, Shader, ColorChannelOffset.", - "files": [ - "https://github.com/e7mac/ComfyUI-ShadertoyGL" - ], - "install_type": "git-clone", - "reference": "https://github.com/e7mac/ComfyUI-ShadertoyGL", - "title": "ComfyUI-ShadertoyGL" - }, - { - "author": "royceschultz", - "description": "Transcribe audio and video files in ComfyUI.", - "files": [ - "https://github.com/royceschultz/ComfyUI-TranscriptionTools" - ], - "id": "transcription-tools", - "install_type": "git-clone", - "reference": "https://github.com/royceschultz/ComfyUI-TranscriptionTools", - "title": "ComfyUI-TranscriptionTools" - }, - { - "author": "kunieone", - "description": "Nodes:A_Face3DSwapper, A_FaceCrop, A_FacePaste, A_OpenPosePreprocessor, A_EmptyLatentImageLongside, A_GetImageSize, AlkaidLoader, AdapterFaceLoader, AdapterStyleLoader, ...", - "files": [ - "https://github.com/kunieone/ComfyUI_alkaid" - ], - "id": "alkadi", - "install_type": "git-clone", - "reference": "https://github.com/kunieone/ComfyUI_alkaid", - "title": "ComfyUI_alkaid" - }, - { - "author": "txt2any", - "description": "This is a custom node for ComfyUI that automatically saves your AI-generated images specifically to [a/www.txt2any.com](http://www.txt2any.com/).", - "files": [ - "https://github.com/txt2any/ComfyUI-PromptOrganizer" - ], - "id": "prompt-organizer", - "install_type": "git-clone", - "reference": "https://github.com/txt2any/ComfyUI-PromptOrganizer", - "title": "ComfyUI-PromptOrganizer" - }, - { - "author": "kealiu", - "description": "Nodes:Load From S3, Save To S3.", - "files": [ - "https://github.com/kealiu/ComfyUI-S3-Tools" - ], - "id": "savefile-to-s3", - "install_type": "git-clone", - "reference": "https://github.com/kealiu/ComfyUI-S3-Tools", - "title": "ComfyUI Load and Save file to S3" - }, - { - "author": "kealiu", - "description": "An unofficial ComfyUI custom node for [a/Zero-Shot Material Transfer from a Single Image](https://ttchengab.github.io/zest), Given an input image (e.g., a photo of an apple) and a single material exemplar image (e.g., a golden bowl), ZeST can transfer the gold material from the exemplar onto the apple with accurate lighting cues while making everything else consistent.", - "files": [ - "https://github.com/kealiu/ComfyUI-ZeroShot-MTrans" - ], - "id": "zeroshot-mtrans", - "install_type": "git-clone", - "reference": "https://github.com/kealiu/ComfyUI-ZeroShot-MTrans", - "title": "ComfyUI-ZeroShot-MTrans" - }, - { - "author": "kealiu", - "description": "Zero-1-to-3: Zero-shot One Image to 3D Object, unofficial porting of original [Zero123](https://github.com/cvlab-columbia/zero123)", - "files": [ - "https://github.com/kealiu/ComfyUI-Zero123-Porting" - ], - "id": "zero123-porting", - "install_type": "git-clone", - "reference": "https://github.com/kealiu/ComfyUI-Zero123-Porting", - "title": "ComfyUI-Zero123-Porting" - }, - { - "author": "Hopping-Mad-Games", - "description": "Nodes for calling LLMs, enabled by LiteLLM", - "files": [ - "https://github.com/Hopping-Mad-Games/ComfyUI_LiteLLM" - ], - "id": "litellm", - "install_type": "git-clone", - "reference": "https://github.com/Hopping-Mad-Games/ComfyUI_LiteLLM", - "title": "ComfyUI_LiteLLM" - }, - { - "author": "TashaSkyUp", - "description": "Comprehensive PyTorch nodes for ComfyUI - Neural network training, inference, and ML workflows", - "files": [ - "https://github.com/TashaSkyUp/EternalKernelPytorchNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/TashaSkyUp/EternalKernelPytorchNodes", - "title": "EternalKernel PyTorch Nodes" - }, - { - "author": "AonekoSS", - "description": "Node: utils/Simple Counter\nThis node is a simple counter, when pressing 'Queue Prompt' resets the count.", - "files": [ - "https://github.com/AonekoSS/ComfyUI-SimpleCounter" - ], - "id": "simplecounter", - "install_type": "git-clone", - "reference": "https://github.com/AonekoSS/ComfyUI-SimpleCounter", - "title": "ComfyUI-SimpleCounter" - }, - { - "author": "AonekoSS", - "description": "Nodes: LoRA-Tuner. For using multiple LoRA easily.", - "files": [ - "https://github.com/AonekoSS/ComfyUI-LoRA-Tuner" - ], - "id": "lora-tuner", - "install_type": "git-clone", - "reference": "https://github.com/AonekoSS/ComfyUI-LoRA-Tuner", - "title": "ComfyUI-LoRA-Tuner" - }, - { - "author": "heshengtao", - "description": "A set of block-based LLM agent node libraries designed for ComfyUI.This project aims to develop a complete set of nodes for LLM workflow construction based on comfyui. It allows users to quickly and conveniently build their own LLM workflows and easily integrate them into their existing SD workflows.", - "files": [ - "https://github.com/heshengtao/comfyui_LLM_party" - ], - "id": "llm-party", - "install_type": "git-clone", - "reference": "https://github.com/heshengtao/comfyui_LLM_party", - "title": "comfyui_LLM_party" - }, - { - "author": "heshengtao", - "description": "ComfyUI node library for fine-tuning LLMs", - "files": [ - "https://github.com/heshengtao/comfyui_LLM_schools" - ], - "install_type": "git-clone", - "reference": "https://github.com/heshengtao/comfyui_LLM_schools", - "title": "comfyui_LLM_schools" - }, - { - "author": "VAST-AI-Research", - "description": "Custom nodes for using [a/Tripo](https://www.tripo3d.ai/) in ComfyUI to create 3D from text and image prompts.", - "files": [ - "https://github.com/VAST-AI-Research/ComfyUI-Tripo" - ], - "id": "tripo", - "install_type": "git-clone", - "reference": "https://github.com/VAST-AI-Research/ComfyUI-Tripo", - "title": "Tripo for ComfyUI" - }, - { - "author": "JettHu", - "description": "ComfyUI reference implementation for [a/T-GATE](https://github.com/HaozheLiu-ST/T-GATE).", - "files": [ - "https://github.com/JettHu/ComfyUI_TGate" - ], - "id": "tgate", - "install_type": "git-clone", - "reference": "https://github.com/JettHu/ComfyUI_TGate", - "title": "ComfyUI_TGate" - }, - { - "author": "JettHu", - "description": "ComfyUI implementation for [a/TCD](https://github.com/jabir-zheng/TCD).", - "files": [ - "https://github.com/JettHu/ComfyUI-TCD" - ], - "id": "jetthu-tcd", - "install_type": "git-clone", - "reference": "https://github.com/JettHu/ComfyUI-TCD", - "title": "ComfyUI-TCD" - }, - { - "author": "sugarkwork", - "description": "This is a custom node of ComfyUI that categorizes tags outputted by tools like WD14Tagger, filters them by each category, and returns the filtered results.", - "files": [ - "https://github.com/sugarkwork/comfyui_tag_fillter" - ], - "id": "tag-filter", - "install_type": "git-clone", - "reference": "https://github.com/sugarkwork/comfyui_tag_fillter", - "reference2": "https://github.com/sugarkwork/comfyui_tag_filter", - "title": "comfyui_tag_filter" - }, - { - "author": "sugarkwork", - "description": "A ComfyUI custom node that calculates width and height while maintaining aspect ratio, making it easier to determine image resolutions with specified aspect ratios and longer side values.", - "files": [ - "https://github.com/sugarkwork/ComfyUI_AspectRatioToSize" - ], - "install_type": "git-clone", - "reference": "https://github.com/sugarkwork/ComfyUI_AspectRatioToSize", - "title": "ComfyUI_AspectRatioToSize" - }, - { - "author": "sugarkwork", - "description": "TensorRT Upscaler for ComfyUI", - "files": [ - "https://github.com/sugarkwork/comfyui-trtupscaler" - ], - "install_type": "git-clone", - "reference": "https://github.com/sugarkwork/comfyui-trtupscaler", - "title": "comfyui-trtupscaler" - }, - { - "author": "Intersection98", - "description": "A collection of post processing nodes for ComfyUI, dds image post-processing adjustment capabilities to the ComfyUI.", - "files": [ - "https://github.com/Intersection98/ComfyUI_MX_post_processing-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Intersection98/ComfyUI_MX_post_processing-nodes", - "title": "ComfyUI-MX-post-processing-nodes" - }, - { - "author": "TencentQQGYLab", - "description": "ComfyUI implementation for [a/ELLA](https://github.com/TencentQQGYLab/ELLA).", - "files": [ - "https://github.com/TencentQQGYLab/ComfyUI-ELLA" - ], - "id": "ella", - "install_type": "git-clone", - "reference": "https://github.com/TencentQQGYLab/ComfyUI-ELLA", - "title": "ComfyUI-ELLA" - }, - { - "author": "DarKDinDoN", - "description": "This node was designed to help with checkpoint configuration. Fee free to add new checkpoint configurations!", - "files": [ - "https://github.com/mech-tools/comfyui-checkpoint-automatic-config" - ], - "id": "checkpoint-autoconfig", - "install_type": "git-clone", - "reference": "https://github.com/mech-tools/comfyui-checkpoint-automatic-config", - "title": "ComfyUI Checkpoint Automatic Config" - }, - { - "author": "MinusZoneAI", - "description": "Use llama.cpp to help generate some nodes for prompt word related work", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-Prompt-MZ" - ], - "id": "prompt-mz", - "install_type": "git-clone", - "reference": "https://github.com/MinusZoneAI/ComfyUI-Prompt-MZ", - "title": "ComfyUI-Prompt-MZ" - }, - { - "author": "MinusZoneAI", - "description": "A stylized node with simple operation. The effect is achieved by I2I and lora. The clay style is currently implemented.Comes with watermark function.", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-StylizePhoto-MZ" - ], - "id": "stylizephoto", - "install_type": "git-clone", - "reference": "https://github.com/MinusZoneAI/ComfyUI-StylizePhoto-MZ", - "title": "ComfyUI-StylizePhoto-MZ" - }, - { - "author": "MinusZoneAI", - "description": "Nodes for fine-tuning lora in ComfyUI, dependent on training tools such as kohya-ss/sd-scripts", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-TrainTools-MZ" - ], - "id": "traintools", - "install_type": "git-clone", - "reference": "https://github.com/MinusZoneAI/ComfyUI-TrainTools-MZ", - "title": "ComfyUI-TrainTools-MZ" - }, - { - "author": "MinusZoneAI", - "description": "Implementation of Kolors on ComfyUI\nReference from [a/https://github.com/kijai/ComfyUI-KwaiKolorsWrapper](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper)\nUsing ComfyUI Native Sampling", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ" - ], - "id": "kolors-mz", - "install_type": "git-clone", - "reference": "https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ", - "title": "ComfyUI-Kolors-MZ" - }, - { - "author": "MinusZoneAI", - "description": "Quantization tools are from [a/https://github.com/casper-hansen/AutoAWQ](https://github.com/casper-hansen/AutoAWQ) and [a/https://github.com/IST-DASLab/marlin](https://github.com/IST-DASLab/marlin)\nOnly applicable to graphics cards with sm_80 and above (30 series and above)\nNeed to install marlin dependencies first", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-Flux1Quantize-MZ" - ], - "install_type": "git-clone", - "pip": [ - "git+https://github.com/IST-DASLab/marlin" - ], - "reference": "https://github.com/MinusZoneAI/ComfyUI-Flux1Quantize-MZ", - "title": "ComfyUI-Flux1Quantize-MZ" - }, - { - "author": "MinusZoneAI", - "description": "Nodes:MZ_Flux1PartialLoad_Patch. Tool nodes related to flux1", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-FluxExt-MZ" - ], - "install_type": "git-clone", - "reference": "https://github.com/MinusZoneAI/ComfyUI-FluxExt-MZ", - "title": "ComfyUI-FluxExt-MZ" - }, - { - "author": "MinusZoneAI", - "description": "Nodes:MZ_CogVideoXLoader", - "files": [ - "https://github.com/MinusZoneAI/ComfyUI-CogVideoX-MZ" - ], - "install_type": "git-clone", - "reference": "https://github.com/MinusZoneAI/ComfyUI-CogVideoX-MZ", - "title": "ComfyUI-CogVideoX-MZ" - }, - { - "author": "blueraincoatli", - "description": "Using rgthree's fast_group_muter and bookmark nodes, introduce the pyautogui library to simulate clicks and hotkeys, and run groups in sequence. screen manipulation is involved", - "files": [ - "https://github.com/blueraincoatli/comfyUI_SillyNodes" - ], - "id": "silly", - "install_type": "git-clone", - "reference": "https://github.com/blueraincoatli/comfyUI_SillyNodes", - "title": "comfyUI_SillyNodes" - }, - { - "author": "ty0x2333", - "description": "Execution Time Analysis, Reroute Enhancement, Node collection for developers.", - "files": [ - "https://github.com/ty0x2333/ComfyUI-Dev-Utils" - ], - "id": "dev-utils", - "install_type": "git-clone", - "reference": "https://github.com/ty0x2333/ComfyUI-Dev-Utils", - "title": "ComfyUI-Dev-Utils" - }, - { - "author": "lquesada", - "description": "'\ud83d\udd22 Prompt Combinator' is a node that generates all possible combinations of prompts from several lists of strings.\n'\ud83d\udd22 Prompt Combinator Merger' is a node that enables merging the output of two different '\ud83d\udd22 Prompt Combinator' nodes.", - "files": [ - "https://github.com/lquesada/ComfyUI-Prompt-Combinator" - ], - "id": "prompt-combinator", - "install_type": "git-clone", - "reference": "https://github.com/lquesada/ComfyUI-Prompt-Combinator", - "title": "ComfyUI-Prompt-Combinator" - }, - { - "author": "lquesada", - "description": "'\u2702\ufe0f Inpaint Crop' is a node that crops an image before sampling. The context area can be specified via the mask, expand pixels and expand factor or via a separate (optional) mask.\n'\u2702\ufe0f Inpaint Stitch' is a node that stitches the inpainted image back into the original image without altering unmasked areas.", - "files": [ - "https://github.com/lquesada/ComfyUI-Inpaint-CropAndStitch" - ], - "id": "crop-and-stitch", - "install_type": "git-clone", - "reference": "https://github.com/lquesada/ComfyUI-Inpaint-CropAndStitch", - "title": "ComfyUI-Inpaint-CropAndStitch" - }, - { - "author": "lquesada", - "description": "Nodes that allow making the UI interactive, with selectors and switches, etc.. Enables selecting across multiple options with the click of a button to move a workflow forward.", - "files": [ - "https://github.com/lquesada/ComfyUI-Interactive" - ], - "id": "comfyui-interactive", - "install_type": "git-clone", - "reference": "https://github.com/lquesada/ComfyUI-Interactive", - "title": "ComfyUI-Interactive" - }, - { - "author": "randjtw", - "description": "Nodes:Advance Aesthetic Score", - "files": [ - "https://github.com/randjtw/advance-aesthetic-score" - ], - "install_type": "git-clone", - "reference": "https://github.com/randjtw/advance-aesthetic-score", - "title": "advance-aesthetic-score" - }, - { - "author": "FredBill1", - "description": "Nodes:FBStringJoin, FBStringSplit, FBMultilineStringList, FBMultilineString", - "files": [ - "https://github.com/FredBill1/comfyui-fb-utils" - ], - "id": "fb-utils", - "install_type": "git-clone", - "reference": "https://github.com/FredBill1/comfyui-fb-utils", - "title": "comfyui-fb-utils" - }, - { - "author": "jeffy5", - "description": "A facefusion custom node for ComfyUI. Swap or restore faces for image or video", - "files": [ - "https://github.com/jeffy5/comfyui-faceless-node" - ], - "id": "faceless", - "install_type": "git-clone", - "reference": "https://github.com/jeffy5/comfyui-faceless-node", - "title": "Faceless Node for ComfyUI" - }, - { - "author": "TaiTair", - "description": "A hacky implementation of Simswap based on [a/Comfyui ReActor Node 0.5.1](https://github.com/Gourieff/comfyui-reactor-node) and [a/Simswap](https://github.com/neuralchen/SimSwap).", - "files": [ - "https://github.com/TaiTair/comfyui-simswap" - ], - "id": "simswap", - "install_type": "git-clone", - "reference": "https://github.com/TaiTair/comfyui-simswap", - "title": "Simswap Node for ComfyUI" - }, - { - "author": "fofr", - "description": "Original author is ByteDance.\nComfyUI sampler for HyperSDXL UNet\nPorted from: [a/https://huggingface.co/ByteDance/Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD)", - "files": [ - "https://github.com/fofr/ComfyUI-HyperSDXL1StepUnetScheduler" - ], - "id": "hypersdxl", - "install_type": "git-clone", - "reference": "https://github.com/fofr/ComfyUI-HyperSDXL1StepUnetScheduler", - "title": "ComfyUI-HyperSDXL1StepUnetScheduler (ByteDance)" - }, - { - "author": "fofr", - "description": "A prompt helper for ComfyUI, based on [a/prompter.fofr.ai](https://prompter.fofr.ai)", - "files": [ - "https://github.com/fofr/ComfyUI-Prompter-fofrAI" - ], - "id": "prompter-fofr", - "install_type": "git-clone", - "reference": "https://github.com/fofr/ComfyUI-Prompter-fofrAI", - "title": "ComfyUI-Prompter-fofrAI" - }, - { - "author": "fofr", - "description": "Nodes:Incrementer, Width and height from aspect ratio, Width and height for scaling image to ideal resolutio. A simple set of tooling nodes.", - "files": [ - "https://github.com/fofr/comfyui-fofr-toolkit" - ], - "id": "fofr-toolkit", - "install_type": "git-clone", - "reference": "https://github.com/fofr/comfyui-fofr-toolkit", - "title": "comfyui-fofr-toolkit" - }, - { - "author": "fofr", - "description": "Run [a/Replicate models](https://replicate.com/explore) in ComfyUI.", - "files": [ - "https://github.com/replicate/comfyui-replicate" - ], - "install_type": "git-clone", - "reference": "https://github.com/replicate/comfyui-replicate", - "title": "ComfyUI-Replicate" - }, - { - "author": "fofr", - "description": "A basic auth middleware for ComfyUI", - "files": [ - "https://github.com/fofr/comfyui-basic-auth" - ], - "install_type": "git-clone", - "reference": "https://github.com/fofr/comfyui-basic-auth", - "title": "ComfyUI-Basic-Auth" - }, - { - "author": "cfreilich", - "description": "Photoshop type functions and adjustment layers: 30 blend modes, Selective Color, Blend If, Color Balance, Solid Color Images, Black and White, Hue/Saturation, Levels, and RGB Splitting and Merging.", - "files": [ - "https://github.com/chrisfreilich/virtuoso-nodes" - ], - "id": "virtuoso", - "install_type": "git-clone", - "reference": "https://github.com/chrisfreilich/virtuoso-nodes", - "title": "Virtuoso Nodes for ComfyUI" - }, - { - "author": "da2el-ai", - "description": "This is a collection of custom nodes that make ComfyUI slightly more convenient.", - "files": [ - "https://github.com/da2el-ai/D2-nodes-ComfyUI" - ], - "id": "d2-nodes-comfyui", - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/D2-nodes-ComfyUI", - "title": "D2 Nodes ComfyUI" - }, - { - "author": "da2el-ai", - "description": "A handy custom node for using Refiner (switching to a different checkpoint midway) When you specify the end of the base checkpoint, you can extract refiner_start which is end + 1. The output is fixed as an INT, so it can be passed to the handy custom node, Anything Everywhere? Since it only outputs a numerical value, it can also be used for other purposes.", - "files": [ - "https://github.com/da2el-ai/ComfyUI-d2-steps" - ], - "id": "d2steps", - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/ComfyUI-d2-steps", - "title": "D2 Steps" - }, - { - "author": "da2el-ai", - "description": "This is a custom node that allows you to easily call up and set image size presets. Settings can be made by editing the included config.yaml. It is almost identical to Comfyroll Studio's CR AspectRatio. I created it because I wanted to easily edit the presets.", - "files": [ - "https://github.com/da2el-ai/ComfyUI-d2-size-selector" - ], - "id": "size-selector", - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/ComfyUI-d2-size-selector", - "title": "D2 Size Selector" - }, - { - "author": "da2el-ai", - "description": "Send images generated by ComfyUI to Eagle image management software", - "files": [ - "https://github.com/da2el-ai/ComfyUI-d2-send-eagle" - ], - "id": "d2-send-eagle", - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/ComfyUI-d2-send-eagle", - "title": "D2 Send Eagle" - }, - { - "author": "da2el-ai", - "description": "Custom node for using Prompt S/R in XY Plot\nAlso includes nodes for listing generic parameters like seed and cfg\nEasy to manipulate as elements are separated by line breaks\nDesigned for use with the XY Plot custom node qq-nodes-comfyui, but may work with other custom nodes as well", - "files": [ - "https://github.com/da2el-ai/ComfyUI-d2-xyplot-utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/ComfyUI-d2-xyplot-utils", - "title": "D2 XYPlot Utils" - }, - { - "author": "da2el-ai", - "description": "This is a version of [a/sd-d2-prompt-selector](https://github.com/da2el-ai/sd-d2-prompt-selector) reworked for ComfyUI. It's just a prototype that I've put together for now. The random syntax of sd-d2-prompt-selector cannot be used; instead, the DynamicPrompt syntax is used", - "files": [ - "https://github.com/da2el-ai/D2-PromptSelector-comfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/D2-PromptSelector-comfyUI", - "title": "D2-PromptSelector-comfyUI" - }, - { - "author": "da2el-ai", - "description": "Saves the image in Photoshop format (PSD)", - "files": [ - "https://github.com/da2el-ai/D2-SavePSD-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/da2el-ai/D2-SavePSD-ComfyUI", - "title": "D2-SavePSD-ComfyUI" - }, - { - "author": "nat-chan", - "description": "Transceiver is a python library that swiftly exchanges fundamental data structures, specifically numpy arrays, between processes, optimizing AI inference tasks that utilize ComfyUI.", - "files": [ - "https://github.com/nat-chan/comfyui-transceiver" - ], - "id": "transceiver", - "install_type": "git-clone", - "reference": "https://github.com/nat-chan/comfyui-transceiver", - "title": "ComfyUI-Transceiver\ud83d\udce1" - }, - { - "author": "nat-chan", - "description": "workflow.json -> workflow_api.json", - "files": [ - "https://github.com/nat-chan/ComfyUI-graphToPrompt" - ], - "id": "graph2prompt", - "install_type": "git-clone", - "reference": "https://github.com/nat-chan/ComfyUI-graphToPrompt", - "title": "ComfyUI-graphToPrompt" - }, - { - "author": "web3nomad", - "description": "Nodes: InvisibleWatermarkEncode", - "files": [ - "https://github.com/web3nomad/ComfyUI_Invisible_Watermark" - ], - "id": "invisible-watermark", - "install_type": "git-clone", - "reference": "https://github.com/web3nomad/ComfyUI_Invisible_Watermark", - "title": "ComfyUI Invisible Watermark" - }, - { - "author": "GentlemanHu", - "description": "An unofficial Python library for [a/Suno AI](https://www.suno.ai/) API", - "files": [ - "https://github.com/GentlemanHu/ComfyUI-SunoAI" - ], - "id": "suno-api", - "install_type": "git-clone", - "reference": "https://github.com/GentlemanHu/ComfyUI-SunoAI", - "title": "ComfyUI Suno API" - }, - { - "author": "TemryL", - "description": "ComfyUI adaptation of [a/IDM-VTON](https://github.com/yisol/IDM-VTON) for virtual try-on.", - "files": [ - "https://github.com/TemryL/ComfyUI-IDM-VTON" - ], - "id": "idm-vton", - "install_type": "git-clone", - "reference": "https://github.com/TemryL/ComfyUI-IDM-VTON", - "title": "ComfyUI-IDM-VTON [WIP]" - }, - { - "author": "NStor", - "description": "Russian localization of ComfyUI, ComafyUI-Manager & more...", - "files": [ - "https://github.com/Nestorchik/NStor-ComfyUI-Translation" - ], - "install_type": "git-clone", - "reference": "https://github.com/Nestorchik/NStor-ComfyUI-Translation", - "title": "ComfyUI-RUS localization" - }, - { - "author": "jax-explorer", - "description": "Nodes:FastImageListToImageBatch", - "files": [ - "https://github.com/jax-explorer/fast_video_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/jax-explorer/fast_video_comfyui", - "title": "fast_video_comfyui" - }, - { - "author": "jax-explorer", - "description": "for comfyonline dynamic loader\ncomfyonline is comfyui cloud website", - "files": [ - "https://github.com/jax-explorer/comfyui-model-dynamic-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/jax-explorer/comfyui-model-dynamic-loader", - "title": "comfyui-model-dynamic-loader" - }, - { - "author": "jax-explorer", - "description": "ComfyUI EasyControl Nodes is a collection of nodes for ComfyUI that allows you to load and use EasyControl models.", - "files": [ - "https://github.com/jax-explorer/ComfyUI-easycontrol" - ], - "install_type": "git-clone", - "reference": "https://github.com/jax-explorer/ComfyUI-easycontrol", - "title": "ComfyUI-easycontrol" - }, - { - "author": "jax-explorer", - "description": "Optimize OOM issues based on ComfyUI-LatentSyncWrapper. [a/ShmuelRonen/ComfyUI-LatentSyncWrapper](https://github.com/ShmuelRonen/ComfyUI-LatentSyncWrapper)\nVideBasic Optimize OOM Plan: [a/jax-explorer/ComfyUI-VideoBasic](https://github.com/jax-explorer/ComfyUI-VideoBasic)", - "files": [ - "https://github.com/jax-explorer/ComfyUI-VideoBasicLatentSync" - ], - "install_type": "git-clone", - "reference": "https://github.com/jax-explorer/ComfyUI-VideoBasicLatentSync", - "title": "ComfyUI-VideoBasicLatentSync" - }, - { - "author": "jax-explorer", - "description": "Used to solve the OOM (Out Of Memory) issue caused by loading all frames of a video at once in ComfyUI. All nodes use streamingly, and no longer load all frames of the video into memory at once.", - "files": [ - "https://github.com/jax-explorer/ComfyUI-VideoBasic" - ], - "install_type": "git-clone", - "reference": "https://github.com/jax-explorer/ComfyUI-VideoBasic", - "title": "ComfyUI-VideoBasic" - }, - { - "author": "jax-explorer", - "description": "[a/InstantCharacter](https://github.com/Tencent/InstantCharacter) ComfyUI Warpper", - "files": [ - "https://github.com/jax-explorer/ComfyUI-InstantCharacter" - ], - "install_type": "git-clone", - "reference": "https://github.com/jax-explorer/ComfyUI-InstantCharacter", - "title": "ComfyUI-InstantCharacter" - }, - { - "author": "sugarkwork", - "description": "This is a node for using cohere (Command R+) from ComfyUI. You need to edit the startup .bat file of ComfyUI and describe the API key obtained from Cohere as follows.", - "files": [ - "https://github.com/sugarkwork/comfyui_cohere" - ], - "id": "cohere", - "install_type": "git-clone", - "reference": "https://github.com/sugarkwork/comfyui_cohere", - "title": "comfyui_cohere" - }, - { - "author": "alessandrozonta", - "description": "This extension contains a custom node for ComfyUI. The node, called 'Bounding Box Crop', is designed to compute the top-left coordinates of a cropped bounding box based on input coordinates and dimensions of the final cropped image. It does so computing the center of the cropping area and then computing where the top-left coordinates would be.", - "files": [ - "https://github.com/alessandrozonta/ComfyUI-CenterNode" - ], - "id": "comfyui-centernode", - "install_type": "git-clone", - "reference": "https://github.com/alessandrozonta/ComfyUI-CenterNode", - "title": "ComfyUI-CenterNode" - }, - { - "author": "alessandrozonta", - "description": "This custom node for ComfyUI allows you to create layers of an image based on input masks and save them into a PSD file.", - "files": [ - "https://github.com/alessandrozonta/ComfyUI-Layers" - ], - "id": "layers", - "install_type": "git-clone", - "reference": "https://github.com/alessandrozonta/ComfyUI-Layers", - "title": "Save Layers Node for ComfyUI" - }, - { - "author": "alessandrozonta", - "description": "This extension contains a custom node for ComfyUI. The node, called 'Bounding Box Crop', is designed to compute the top-left coordinates of a cropped bounding box based on input coordinates and dimensions of the final cropped image. It does so computing the center of the cropping area and then computing where the top-left coordinates would be.", - "files": [ - "https://github.com/alessandrozonta/ComfyUI-OpenPose" - ], - "id": "openpose-alessandrozonta", - "install_type": "git-clone", - "reference": "https://github.com/alessandrozonta/ComfyUI-OpenPose", - "title": "OpenPose Node" - }, - { - "author": "alessandrozonta", - "description": "A ComfyUI custom node for loading images sequentially from a directory. Loops back to the first image when reaching the end", - "files": [ - "https://github.com/alessandrozonta/Comfyui-LoopLoader" - ], - "id": "Comfyui-LoopLoader", - "install_type": "git-clone", - "reference": "https://github.com/alessandrozonta/Comfyui-LoopLoader", - "title": "Comfyui-LoopLoader" - }, - { - "author": "alessandrozonta", - "description": "This custom node for ComfyUI analyzes OpenPose keypoints to determine if a person in an image is facing forward, showing their left side, or their right side.", - "files": [ - "https://github.com/alessandrozonta/ComfyUI-PoseDirection" - ], - "install_type": "git-clone", - "reference": "https://github.com/alessandrozonta/ComfyUI-PoseDirection", - "title": "ComfyUI-PoseDirection" - }, - { - "author": "curiousjp", - "description": "Permutes a mask batch to present possible additive combinations. Passing a mask batch (e.g. out of [a/SEGS to Mask Batch](https://github.com/ltdrdata/ComfyUI-Impact-Pack)) will return a new mask batch representing all the possible combinations of the included masks. So, a mask batch with two mask sections, 'A' and 'B', will return a batch containing an empty mask, an empty mask & A, an empty mask & B, and an empty mask & A & B.", - "files": [ - "https://github.com/curiousjp/ComfyUI-MaskBatchPermutations" - ], - "id": "maskbatch-permutations", - "install_type": "git-clone", - "reference": "https://github.com/curiousjp/ComfyUI-MaskBatchPermutations", - "title": "ComfyUI-MaskBatchPermutations" - }, - { - "author": "BAIS1C", - "description": "A Simple Python RSS Feed Reader to create Prompts in Comfy UI", - "files": [ - "https://github.com/BAIS1C/ComfyUI_RSS_Feed_Reader" - ], - "id": "rssfeed", - "install_type": "git-clone", - "reference": "https://github.com/BAIS1C/ComfyUI_RSS_Feed_Reader", - "title": "ComfyUI_RSS_Feed_Reader" - }, - { - "author": "runtime44", - "description": "Nodes: Runtime44Upscaler, Runtime44ColorMatch, Runtime44DynamicKSampler, Runtime44ImageOverlay, Runtime44ImageResizer, Runtime44ImageToNoise, Runtime44MaskSampler, Runtime44TiledMaskSampler, Runtime44IterativeUpscaleFactor, Runtime44ImageEnhance, Runtime44FilmGrain", - "files": [ - "https://github.com/runtime44/comfyui_r44_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/runtime44/comfyui_r44_nodes", - "title": "Runtime44 ComfyUI Nodes" - }, - { - "author": "osiworx", - "description": "Nodes for Comfyui to use Prompt Quill within complex workflows", - "files": [ - "https://github.com/osi1880vr/prompt_quill_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/osi1880vr/prompt_quill_comfyui", - "title": "ComfyUI_Prompt-Quill" - }, - { - "author": "philz1337x", - "description": "[a/Clarity AI](https://clarityai.cc) is a creative image enhancer and is able to upscale to high resolution. [w/NOTE: This is a Magnific AI alternative for ComfyUI.] \nCreate an API key on [a/ClarityAI.cc/api](https://clarityai.cc/api) and add to environment variable 'CAI_API_KEY'\nAlternatively you can write your API key to file 'cai_platform_key.txt'\nYou can also use and/or override the above by entering your API key in the 'api_key_override' field of the node.", - "files": [ - "https://github.com/philz1337x/ComfyUI-ClarityAI" - ], - "install_type": "git-clone", - "reference": "https://github.com/philz1337x/ComfyUI-ClarityAI", - "title": "\u2728 Clarity AI - Creative Image Upscaler and Enhancer for ComfyUI" - }, - { - "author": "KoreTeknology", - "description": "A research Node based project on Artificial Intelligence using ComfyUI visual editor with Stable diffusion Local processing focus in mind. This custom node is intended to serve the purpose to offer a large palette of prompting scenrarios, based on Public Checkpoint Models OR/AND Private custom Models and LoRas. It includes an integrated learning machine process as well as a set of workflows.", - "files": [ - "https://github.com/KoreTeknology/ComfyUI-Universal-Styler" - ], - "id": "universal-styler", - "install_type": "git-clone", - "reference": "https://github.com/KoreTeknology/ComfyUI-Universal-Styler", - "title": "ComfyUI Universal Styler" - }, - { - "author": "KoreTeknology", - "description": "This is set of custom nodes for your ComfyUI local installation. It offers the very basic nodes that are missing in the official 'Vanilla' package. It is a research Node based project on Artificial Intelligence using ComfyUI visual editor. This repository also includes a set of workflows to test the nodes.\nNOTE:Renamed from 'ComfyUI-Compositing-Nodes-Pack'", - "files": [ - "https://github.com/KoreTeknology/ComfyUI-Nai-Production-Nodes-Pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/KoreTeknology/ComfyUI-Nai-Production-Nodes-Pack", - "title": "ComfyUI Production Nodes Pack" - }, - { - "author": "ZeDarkAdam", - "description": "EmbeddingsNameLoader, EmbendingList", - "files": [ - "https://github.com/ZeDarkAdam/ComfyUI-Embeddings-Tools" - ], - "id": "embeddings-tools", - "install_type": "git-clone", - "reference": "https://github.com/ZeDarkAdam/ComfyUI-Embeddings-Tools", - "reference2": "https://github.com/ZDAVanO/ComfyUI-Embeddings-Tools", - "title": "ComfyUI-Embeddings-Tools" - }, - { - "author": "chenpx976", - "description": "add http api http://127.0.0.1:8188/comfyui-run/run use in other llm project.", - "files": [ - "https://github.com/chenpx976/ComfyUI-RunRunRun" - ], - "id": "runrunrun", - "install_type": "git-clone", - "reference": "https://github.com/chenpx976/ComfyUI-RunRunRun", - "title": "ComfyUI-RunRunRun" - }, - { - "author": "githubYiheng", - "description": "GetFileNameFromURL is a ComfyUI custom node that extracts the filename from a URL. It can handle various URLs and is capable of handling redirects.", - "files": [ - "https://github.com/githubYiheng/ComfyUI_GetFileNameFromURL" - ], - "id": "getfilename-from-url", - "install_type": "git-clone", - "reference": "https://github.com/githubYiheng/ComfyUI_GetFileNameFromURL", - "title": "ComfyUI_GetFileNameFromURL" - }, - { - "author": "githubYiheng", - "description": "Nodes:Apply Kmeans Filter", - "files": [ - "https://github.com/githubYiheng/comfyui_kmeans_filter" - ], - "id": "kmeans-filter", - "install_type": "git-clone", - "reference": "https://github.com/githubYiheng/comfyui_kmeans_filter", - "title": "comfyui_kmeans_filter" - }, - { - "author": "githubYiheng", - "description": "Nodes:Change Image Border", - "files": [ - "https://github.com/githubYiheng/ComfyUI_Change_IMAGE_BOREDER" - ], - "id": "change-image-border", - "install_type": "git-clone", - "reference": "https://github.com/githubYiheng/ComfyUI_Change_IMAGE_BOREDER", - "title": "ComfyUI_Change_IMAGE_BOREDER" - }, - { - "author": "githubYiheng", - "description": "Nodes:Apply Meanshift Filter", - "files": [ - "https://github.com/githubYiheng/comfyui_meanshift_filter" - ], - "id": "meanshift-filter", - "install_type": "git-clone", - "reference": "https://github.com/githubYiheng/comfyui_meanshift_filter", - "title": "comfyui_meanshift_filter" - }, - { - "author": "githubYiheng", - "description": "Nodes:Private ImageCPostprocessor", - "files": [ - "https://github.com/githubYiheng/comfyui_private_postprocessor" - ], - "id": "githubyiheng-private-postprocessor", - "install_type": "git-clone", - "reference": "https://github.com/githubYiheng/comfyui_private_postprocessor", - "title": "comfyui_private_postprocessor" - }, - { - "author": "Fihade", - "description": "Original repo: [a/https://github.com/lllyasviel/IC-Light](https://github.com/lllyasviel/IC-Light)\nModels: [a/https://huggingface.co/lllyasviel/ic-light/tree/main](https://huggingface.co/lllyasviel/ic-light/tree/main), [a/https://huggingface.co/digiplay/Photon_v1/tree/main](https://huggingface.co/digiplay/Photon_v1/tree/main)\nmodels go into ComfyUI/models/unet", - "files": [ - "https://github.com/Fihade/IC-Light-ComfyUI-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Fihade/IC-Light-ComfyUI-Node", - "title": "IC-Light-ComfyUI-Node" - }, - { - "author": "KewkLW", - "description": "text_append_node, vramdebugplus, tensordebugplus, animation_schedule_output", - "files": [ - "https://github.com/KewkLW/ComfyUI-kewky_tools" - ], - "id": "kewky-tools", - "install_type": "git-clone", - "reference": "https://github.com/KewkLW/ComfyUI-kewky_tools", - "title": "ComfyUI-kewky_tools" - }, - { - "author": "ITurchenko", - "description": "Nodes:SizeFromArray", - "files": [ - "https://github.com/ITurchenko/ComfyUI-SizeFromArray" - ], - "id": "sizefromarray", - "install_type": "git-clone", - "reference": "https://github.com/ITurchenko/ComfyUI-SizeFromArray", - "title": "ComfyUI-SizeFromArray" - }, - { - "author": "Suplex", - "description": "Misc Nodes: ControlNet Selector Node, Load Optional ControlNet Model, Diffusers Selector, Save Image JPG No Meta, Multi Input Variable Rewrite", - "files": [ - "https://github.com/saftle/uber_comfy_nodes" - ], - "id": "suplex", - "install_type": "git-clone", - "reference": "https://github.com/saftle/uber_comfy_nodes", - "title": "Suplex Misc ComfyUI Nodes" - }, - { - "author": "mephisto83", - "description": "An integration between comfy ui and petty paint", - "files": [ - "https://github.com/mephisto83/petty-paint-comfyui-node" - ], - "id": "petty-paint", - "install_type": "git-clone", - "reference": "https://github.com/mephisto83/petty-paint-comfyui-node", - "title": "petty-paint-comfyui-node" - }, - { - "author": "fsdymy1024", - "description": "Nodes:Save Image Without Metadata", - "files": [ - "https://github.com/fsdymy1024/ComfyUI_fsdymy" - ], - "id": "fsdymy", - "install_type": "git-clone", - "reference": "https://github.com/fsdymy1024/ComfyUI_fsdymy", - "title": "ComfyUI_fsdymy" - }, - { - "author": "ray", - "description": "Nodes:Image Gradient,Mask Gradient", - "files": [ - "https://github.com/huagetai/ComfyUI_LightGradient" - ], - "id": "light-gradient", - "install_type": "git-clone", - "reference": "https://github.com/huagetai/ComfyUI_LightGradient", - "title": "Light Gradient for ComfyUI" - }, - { - "author": "ray", - "description": "Nodes:Load ICLight Model,Apply ICLight,Simple Light Source,Calculate Normal Map", - "files": [ - "https://github.com/huagetai/ComfyUI-Gaffer" - ], - "id": "gaffer", - "install_type": "git-clone", - "reference": "https://github.com/huagetai/ComfyUI-Gaffer", - "title": "comfyui's gaffer(ComfyUI native implementation of IC-Light. )" - }, - { - "author": "YFG", - "description": "Utility custom nodes for special effects, image manipulation and quality of life tools.", - "files": [ - "https://github.com/gonzalu/ComfyUI_YFG_Comical" - ], - "id": "comical", - "install_type": "git-clone", - "reference": "https://github.com/gonzalu/ComfyUI_YFG_Comical", - "title": "\ud83d\ude38 YFG Comical Nodes" - }, - { - "author": "ruiqutech", - "description": "Nodes of EvaluateMultiple1, EvaluateMultiple3...\nSupport the execution of any fragment of Python code, generating multiple outputs from multiple inputs.", - "files": [ - "https://github.com/ruiqutech/ComfyUI-RuiquNodes" - ], - "id": "RuiquNodes", - "install_type": "git-clone", - "reference": "https://github.com/ruiqutech/ComfyUI-RuiquNodes", - "title": "RuiquNodes for ComfyUI" - }, - { - "author": "teward", - "description": "Nodes: HelperNodes_MultilineStringLiteral, HelperNodes_StringLiteral, HelperNodes_Steps, HelperNodes_CfgScale, HelperNodes_WidthHeight, HelperNodes_SchedulerSelector, HelperNodes_SamplerSelector, ...", - "files": [ - "https://github.com/teward/ComfyUI-Helper-Nodes" - ], - "id": "helper-nodes", - "install_type": "git-clone", - "reference": "https://github.com/teward/ComfyUI-Helper-Nodes", - "title": "ComfyUI-Helper-Nodes" - }, - { - "author": "fmatray", - "description": "Nodes for ComfyUI in order to generate battelmaps", - "files": [ - "https://github.com/fmatray/ComfyUI_BattlemapGrid" - ], - "id": "battlemap-grid", - "install_type": "git-clone", - "reference": "https://github.com/fmatray/ComfyUI_BattlemapGrid", - "title": "ComfyUI_BattlemapGrid" - }, - { - "author": "christian-byrne", - "description": "Get general description or specify questions to ask about images (medium, art style, background, etc.). Supports Chinese \ud83c\udde8\ud83c\uddf3 questions via MiniCPM model.", - "files": [ - "https://github.com/christian-byrne/img2txt-comfyui-nodes" - ], - "id": "img2txt-nodes", - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/img2txt-comfyui-nodes", - "title": "img2txt-comfyui-nodes" - }, - { - "author": "christian-byrne", - "description": "Extract the most common colors from an image, up to any number. Convert colors to plain English names using various color naming systems.", - "files": [ - "https://github.com/christian-byrne/img2colors-comfyui-node" - ], - "id": "img2colors-comfyui-node", - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/img2colors-comfyui-node", - "title": "Img2color - Extract Colors from Image" - }, - { - "author": "christian-byrne", - "description": "Match image/mask sizes", - "files": [ - "https://github.com/christian-byrne/size-match-compositing-nodes" - ], - "id": "sizematcher", - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/size-match-compositing-nodes", - "title": "Node - Size Matcher" - }, - { - "author": "christian-byrne", - "description": "Search navigation extension.", - "files": [ - "https://github.com/christian-byrne/comfyui-search-navigation" - ], - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/comfyui-search-navigation", - "title": "comfyui-search-navigation" - }, - { - "author": "christian-byrne", - "description": "Separate audio track into stems (vocals, bass, drums, other). Along with tools to recombine, tempo match, slice/crop audio.", - "files": [ - "https://github.com/christian-byrne/audio-separation-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/audio-separation-nodes-comfyui", - "title": "audio-separation-nodes-comfyui" - }, - { - "author": "christian-byrne", - "description": "comfyui-default-values-manager", - "files": [ - "https://github.com/christian-byrne/comfyui-default-values-manager" - ], - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/comfyui-default-values-manager", - "title": "comfyui-default-values-manager" - }, - { - "author": "christian-byrne", - "description": "Download youtube videos/playlists", - "files": [ - "https://github.com/christian-byrne/youtube-dl-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/youtube-dl-comfyui", - "title": "youtube-dl-comfyui" - }, - { - "author": "christian-byrne", - "description": "ComfyUI nodes for integrating Claude Code SDK - enables AI-powered code generation, analysis, and assistance within ComfyUI workflows", - "files": [ - "https://github.com/christian-byrne/claude-code-comfyui-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/christian-byrne/claude-code-comfyui-nodes", - "title": "Claude Code ComfyUI Nodes" - }, - { - "author": "oztrkoguz", - "description": "Nodes:story_sampler_simple, text2, kosmos2_sampler.\nI created a dataset for generating short stories [a/Short-Story](https://huggingface.co/datasets/oztrkoguz/Short-Story) and used it to fine-tune my own model using Phi-3.", - "files": [ - "https://github.com/oztrkoguz/ComfyUI_StoryCreator" - ], - "id": "storycreater", - "install_type": "git-clone", - "reference": "https://github.com/oztrkoguz/ComfyUI_StoryCreator", - "title": "ComfyUI StoryCreater" - }, - { - "author": "GraftingRayman", - "description": "Image Manipulation and Prompt Generation Nodes", - "files": [ - "https://github.com/GraftingRayman/ComfyUI_GraftingRayman" - ], - "id": "graftingrayman", - "install_type": "git-clone", - "reference": "https://github.com/GraftingRayman/ComfyUI_GraftingRayman", - "title": "GraftingRayman" - }, - { - "author": "GraftingRayman", - "description": "These nodes allow your YouTube LiveStream viewers to create on your local ComfyUI, you can make this a members only feature with a screen behind you displaying your members creations", - "files": [ - "https://github.com/GraftingRayman/ComfyUI_QueueTube" - ], - "install_type": "git-clone", - "reference": "https://github.com/GraftingRayman/ComfyUI_QueueTube", - "title": "ComfyUI QueueTube" - }, - { - "author": "GraftingRayman", - "description": "This is a PuLID node that has been extended with new features.", - "files": [ - "https://github.com/GraftingRayman/ComfyUI-PuLID-Flux-GR" - ], - "install_type": "git-clone", - "reference": "https://github.com/GraftingRayman/ComfyUI-PuLID-Flux-GR", - "title": "ComfyUI-PuLID-Flux-GR" - }, - { - "author": "royceschultz", - "description": "Send notifications when a workflow completes.", - "files": [ - "https://github.com/royceschultz/ComfyUI-Notifications" - ], - "install_type": "git-clone", - "reference": "https://github.com/royceschultz/ComfyUI-Notifications", - "title": "ComfyUI-Notifications" - }, - { - "author": "katalist-ai", - "description": "Nodes: NudenetDetector", - "files": [ - "https://github.com/katalist-ai/comfyUI-nsfw-detection" - ], - "id": "nsfw-detection", - "install_type": "git-clone", - "reference": "https://github.com/katalist-ai/comfyUI-nsfw-detection", - "title": "comfyUI-nsfw-detection" - }, - { - "author": "kaanyalova", - "description": "Adds a custom node for saving images in webp, jpeg, avif, jxl (no metadata) and supports loading workflows from saved images", - "files": [ - "https://github.com/kaanyalova/ComfyUI_ExtendedImageFormats" - ], - "id": "extended-image-format", - "install_type": "git-clone", - "reference": "https://github.com/kaanyalova/ComfyUI_ExtendedImageFormats", - "title": "Extended Image Formats for ComfyUI" - }, - { - "author": "badayvedat", - "description": "The ComfyUI-fal-Connector is a tool designed to provide an integration between ComfyUI and fal. This extension allows users to execute their ComfyUI workflows directly on [a/fal.ai](https://fal.ai/). This enables users to leverage the computational power and resources provided by fal.ai for running their ComfyUI workflows.", - "files": [ - "https://github.com/badayvedat/ComfyUI-fal-Connector" - ], - "id": "fal", - "install_type": "git-clone", - "reference": "https://github.com/badayvedat/ComfyUI-fal-Connector", - "title": "ComfyUI-fal-Connector" - }, - { - "author": "TheMistoAI", - "description": "A Fast, Accurate, and Detailed Line Detection Preprocessor.\nAnyline is a ControlNet line preprocessor that accurately extracts object edges, image details, and textual content from most images. Users can input any type of image to quickly obtain line drawings with clear edges, sufficient detail preservation, and high fidelity text, which are then used as input for conditional generation in Stable Diffusion.", - "files": [ - "https://github.com/TheMistoAI/ComfyUI-Anyline" - ], - "id": "anyline", - "install_type": "git-clone", - "reference": "https://github.com/TheMistoAI/ComfyUI-Anyline", - "title": "Anyline" - }, - { - "author": "mbrostami", - "description": "Nodes:TextualInversionTrainingSDXL, TextualInversionTraining", - "files": [ - "https://github.com/mbrostami/ComfyUI-TITrain" - ], - "id": "titrain", - "install_type": "git-clone", - "reference": "https://github.com/mbrostami/ComfyUI-TITrain", - "title": "ComfyUI-TITrain" - }, - { - "author": "ArcherFMY", - "description": "Generating seamless 360 degree panoramic image through text or perspective image.", - "files": [ - "https://github.com/ArcherFMY/Diffusion360_ComfyUI" - ], - "id": "diffusion360", - "install_type": "git-clone", - "reference": "https://github.com/ArcherFMY/Diffusion360_ComfyUI", - "title": "Diffusion360_ComfyUI" - }, - { - "author": "Makeezi", - "description": "connection nodes for api requests, fully supports promptLAB", - "files": [ - "https://github.com/Makeezi/ComfyUI-promptLAB" - ], - "id": "promptlab", - "install_type": "git-clone", - "reference": "https://github.com/Makeezi/ComfyUI-promptLAB", - "title": "ComfyUI-promptLAB" - }, - { - "author": "portu-sim", - "description": "BMAB for ComfyUI. BMAB is an custom nodes of ComfyUI and has the function of post-processing the generated image according to settings. If necessary, you can find and redraw people, faces, and hands, or perform functions such as resize, resample, and add noise. You can composite two images or perform the Upscale function.", - "files": [ - "https://github.com/portu-sim/comfyui_bmab" - ], - "id": "bmab", - "install_type": "git-clone", - "reference": "https://github.com/portu-sim/comfyui_bmab", - "title": "comfyui_bmab" - }, - { - "author": "griptape-ai", - "description": "This repo creates a series of nodes that enable you to utilize the [a/Griptape Python Framework](https://github.com/griptape-ai/griptape/) with ComfyUI, integrating AI into your workflow. This repo creates a series of nodes that enable you to utilize the Griptape Python Framework with ComfyUI, integrating AI into your workflow.", - "files": [ - "https://github.com/griptape-ai/ComfyUI-Griptape" - ], - "id": "griptape", - "install_type": "git-clone", - "reference": "https://github.com/griptape-ai/ComfyUI-Griptape", - "title": "ComfyUI Griptape Nodes" - }, - { - "author": "cavinHuang", - "description": "This is a plugin for displaying documentation for each comfyui node. ", - "files": [ - "https://github.com/CavinHuang/comfyui-nodes-docs" - ], - "id": "nodedocs", - "install_type": "git-clone", - "reference": "https://github.com/CavinHuang/comfyui-nodes-docs", - "title": "comfyui-nodes-docs" - }, - { - "author": "icesun963", - "description": "Download the model from huggingface and put it in any directory.", - "files": [ - "https://github.com/icesun963/ComfyUI_HFDownLoad" - ], - "id": "HFDownLoad-ic", - "install_type": "git-clone", - "reference": "https://github.com/icesun963/ComfyUI_HFDownLoad", - "title": "HFDownLoad Node for ComfyUI" - }, - { - "author": "conquestace", - "description": "Upload images automatically to image hosting sites.", - "files": [ - "https://github.com/conquestace/ComfyUI-ImageUploader" - ], - "id": "image-uploader", - "install_type": "git-clone", - "reference": "https://github.com/conquestace/ComfyUI-ImageUploader", - "title": "Image Uploader" - }, - { - "author": "chandlergis", - "description": "Nodes:ImageRequestNode", - "files": [ - "https://github.com/chandlergis/ComfyUI-IMG_Query" - ], - "id": "img-query", - "install_type": "git-clone", - "reference": "https://github.com/chandlergis/ComfyUI-IMG_Query", - "title": "ComfyUI-IMG_Query" - }, - { - "author": "Isaac Emesowum", - "description": "This extension offers automatic drums extraction from audio files, as well as a few helper nodes to support my audio synchronization AnimateDiff workflows.", - "files": [ - "https://github.com/iemesowum/ComfyUI_IsaacNodes" - ], - "id": "isaac", - "install_type": "git-clone", - "reference": "https://github.com/iemesowum/ComfyUI_IsaacNodes", - "title": "Isaac's Nodes" - }, - { - "author": "fexploit", - "description": "ComfyUI-AutoCropBgTrim is a powerful tool designed to automatically clean up the background of your images. This tool trims unnecessary spaces and pixels, leaving only the main subject of the image. It generates both a mask and an image output, making it easy to focus on the essential elements. Perfect for enhancing your photos and preparing them for professional use.", - "files": [ - "https://github.com/fexploit/ComfyUI-AutoTrimBG" - ], - "id": "autotrimbg", - "install_type": "git-clone", - "reference": "https://github.com/fexploit/ComfyUI-AutoTrimBG", - "title": "ComfyUI-AutoTrimBG" - }, - { - "author": "fexploit", - "description": "ComfyUI-AutoLabel is a custom node for ComfyUI that uses BLIP (Bootstrapping Language-Image Pre-training) to generate detailed descriptions of the main object in an image. This node leverages the power of BLIP to provide accurate and context-aware captions for images. by Fexploit.", - "files": [ - "https://github.com/fexploit/ComfyUI-AutoLabel" - ], - "id": "autolabel", - "install_type": "git-clone", - "reference": "https://github.com/fexploit/ComfyUI-AutoLabel", - "title": "ComfyUI-AutoLabel" - }, - { - "author": "fexploit", - "description": "ComfyUI-Classifier is a custom node for ComfyUI that uses a zero-shot classification model to classify text inputs based on a set of candidate labels. This node leverages the power of Hugging Face Transformers to provide accurate and flexible text classification.", - "files": [ - "https://github.com/fexploit/ComfyUI-Classifier" - ], - "id": "classifier", - "install_type": "git-clone", - "reference": "https://github.com/fexploit/ComfyUI-Classifier", - "title": "ComfyUI-Classifier" - }, - { - "author": "linshier", - "description": "Node:SendBase64ToRemote. To connect to another ComfyUI server.", - "files": [ - "https://github.com/linshier/comfyui-remote-tools" - ], - "id": "remote-tools", - "install_type": "git-clone", - "reference": "https://github.com/linshier/comfyui-remote-tools", - "title": "comfyui-remote-tools" - }, - { - "author": "Fantaxico", - "description": "Node:GCP Storage Node. Support google-cloud-storage.", - "files": [ - "https://github.com/Fantaxico/ComfyUI-GCP-Storage" - ], - "id": "gcp-storage", - "install_type": "git-clone", - "reference": "https://github.com/Fantaxico/ComfyUI-GCP-Storage", - "title": "ComfyUI-GCP-Storage" - }, - { - "author": "daniabib", - "description": "ComfyUI custom node implementation of [a/ProPainter](https://github.com/sczhou/ProPainter) framework for video inpainting.", - "files": [ - "https://github.com/daniabib/ComfyUI_ProPainter_Nodes" - ], - "id": "propainter", - "install_type": "git-clone", - "reference": "https://github.com/daniabib/ComfyUI_ProPainter_Nodes", - "title": "ComfyUI ProPainter Nodes" - }, - { - "author": "iFREEGROUP", - "description": "Node:Load Checkerboard Images for Calibrate Camera, Matrix and distortion coefficient to text, Undistort", - "files": [ - "https://github.com/iFREEGROUP/comfyui-undistort" - ], - "id": "undistort", - "install_type": "git-clone", - "reference": "https://github.com/iFREEGROUP/comfyui-undistort", - "title": "comfyui-undistort" - }, - { - "author": "Auttasak-L", - "description": "Nodes:Image cropping tool", - "files": [ - "https://github.com/Auttasak-L/ComfyUI-ImageCropper" - ], - "id": "imagecropper", - "install_type": "git-clone", - "reference": "https://github.com/Auttasak-L/ComfyUI-ImageCropper", - "title": "ComfyUI-ImageCropper" - }, - { - "author": "muzi12888", - "description": "Convert PoseKeypoint to mask, please refer to the example workflow for usage instructions.", - "files": [ - "https://github.com/muzi12888/ComfyUI-PoseKeypoint-Mask" - ], - "id": "posekeypoint-mask", - "install_type": "git-clone", - "reference": "https://github.com/muzi12888/ComfyUI-PoseKeypoint-Mask", - "title": "PoseKeypoint Mask" - }, - { - "author": "muzi12888", - "description": "Nodes for modifying a prompt to create prompt variations.\nScramblePrompts [m9]: Reorder prompts, remove prompts, modify weights\nTweakWeights [m9]: Modify the weights of prompts matching keywords", - "files": [ - "https://github.com/MarcusNyne/m9-prompts-comfyui" - ], - "id": "m9-prompts-comfyui", - "install_type": "git-clone", - "reference": "https://github.com/MarcusNyne/m9-prompts-comfyui", - "title": "m9-prompts-comfyui" - }, - { - "author": "xuhongming251", - "description": "Nodes:FaceEnhancement. Based on modelscope pipeline.", - "files": [ - "https://github.com/xuhongming251/ComfyUI-GPEN" - ], - "id": "gpen", - "install_type": "git-clone", - "reference": "https://github.com/xuhongming251/ComfyUI-GPEN", - "title": "ComfyUI-GPEN" - }, - { - "author": "xuhongming251", - "description": "MuseTalk ComfyUI Preprocess and Postprocess Nodes", - "files": [ - "https://github.com/xuhongming251/ComfyUI-MuseTalkUtils" - ], - "id": "musetalk-utils", - "install_type": "git-clone", - "reference": "https://github.com/xuhongming251/ComfyUI-MuseTalkUtils", - "title": "ComfyUI-MuseTalkUtils" - }, - { - "author": "xuhongming251", - "description": "ComfyUI processes local real-time camera feed and provides real-time preview of the result.", - "files": [ - "https://github.com/xuhongming251/ComfyUI_Camera" - ], - "install_type": "git-clone", - "reference": "https://github.com/xuhongming251/ComfyUI_Camera", - "title": "ComfyUI_Camera" - }, - { - "author": "Thomas Ward", - "description": "A collection of custom nodes to help with saving images, providing generation parameters, static literal nodes, and other useful nodes.", - "files": [ - "https://github.com/TW-CUI/TW-CUI-Util" - ], - "id": "tw-cui-util", - "install_type": "git-clone", - "reference": "https://github.com/TW-CUI/TW-CUI-Util", - "title": "TW-CUI-Util" - }, - { - "author": "lks-ai", - "description": "Nodes: AnyNode. Nodes that can be anything you ask. Auto-Generate functional nodes using LLMs. Create impossible workflows. API Compatibility: (OpenAI, LocalLLMs, Gemini).", - "files": [ - "https://github.com/lks-ai/anynode" - ], - "id": "anynode", - "install_type": "git-clone", - "reference": "https://github.com/lks-ai/anynode", - "title": "ComfyUI AnyNode: Any Node you ask for" - }, - { - "author": "lks-ai", - "description": "Nodes: StableAudioSampler. Wraps the new Stable Audio Open Model in the sampler that dropped Jun 5th. See Github for Features", - "files": [ - "https://github.com/lks-ai/ComfyUI-StableAudioSampler" - ], - "id": "stableaudiosampler", - "install_type": "git-clone", - "reference": "https://github.com/lks-ai/ComfyUI-StableAudioSampler", - "title": "ComfyUI Stable Audio Open 1.0 Sampler" - }, - { - "author": "SayanoAI", - "description": "ComfyUI custom nodes for RVC related inference and image generation", - "files": [ - "https://github.com/SayanoAI/Comfy-RVC" - ], - "id": "sayano-rvc", - "install_type": "git-clone", - "reference": "https://github.com/SayanoAI/Comfy-RVC", - "title": "Comfy-RVC" - }, - { - "author": "nirbhay-faaya", - "description": "Custom Image processing ComfyUI Nodes", - "files": [ - "https://github.com/nirbhay-faaya/ImgProcessing_ComfyUI" - ], - "id": "imgprocessing", - "install_type": "git-clone", - "reference": "https://github.com/nirbhay-faaya/ImgProcessing_ComfyUI", - "title": "ImgProcessing_ComfyUI" - }, - { - "author": "larsupb", - "description": "An extension for merging LoRAs. Offers a wide range of LoRA merge techniques (including dare) and XY plots. XY plots require efficiency nodes.", - "files": [ - "https://github.com/larsupb/LoRA-Merger-ComfyUI" - ], - "id": "lora-powermerger", - "install_type": "git-clone", - "reference": "https://github.com/larsupb/LoRA-Merger-ComfyUI", - "title": "LoRA Power-Merger ComfyUI" - }, - { - "author": "Mason-McGough", - "description": "Create colorful mosaic images in ComfyUI by computing label images and applying lookup tables.", - "files": [ - "https://github.com/Mason-McGough/ComfyUI-Mosaica" - ], - "id": "mosaica", - "install_type": "git-clone", - "reference": "https://github.com/Mason-McGough/ComfyUI-Mosaica", - "title": "Mosaica" - }, - { - "author": "cuongloveit", - "description": "Nodes:Send Http Request. You can use this node to save full size images through the websocket.", - "files": [ - "https://github.com/cuongloveit/comfy_http_request" - ], - "install_type": "git-clone", - "reference": "https://github.com/cuongloveit/comfy_http_request", - "title": "comfy_http_request" - }, - { - "author": "Ron-Digital", - "description": "ComfyUI-SceneGenerator is a ComfyUI plugin used to generate scene preview photos from JSON files. This plugin creates scenes based on the provided JSON configuration and produces two different image outputs: one containing only the products and the other containing both the products and the props.", - "files": [ - "https://github.com/Ron-Digital/ComfyUI-SceneGenerator" - ], - "id": "scenegenerator", - "install_type": "git-clone", - "reference": "https://github.com/Ron-Digital/ComfyUI-SceneGenerator", - "title": "ComfyUI-SceneGenerator" - }, - { - "author": "xliry", - "description": "Nodes:Send Video to Discord", - "files": [ - "https://github.com/xliry/ComfyUI_SendDiscord" - ], - "id": "senddiscord", - "install_type": "git-clone", - "reference": "https://github.com/xliry/ComfyUI_SendDiscord", - "title": "ComfyUI_SendDiscord" - }, - { - "author": "xliry", - "description": "Nodes:color2RGB", - "files": [ - "https://raw.githubusercontent.com/vxinhao/color2rgb/main/color2rgb.py" - ], - "install_type": "copy", - "reference": "https://github.com/vxinhao/color2rgb", - "title": "color2rgb" - }, - { - "author": "moyi7712", - "description": "It make any text2image create seamless patten", - "files": [ - "https://github.com/moyi7712/ComfyUI_Seamless_Patten" - ], - "id": "seamless-pattern", - "install_type": "git-clone", - "reference": "https://github.com/moyi7712/ComfyUI_Seamless_Patten", - "title": "ComfyUI_Seamless_Patten" - }, - { - "author": "nirex0", - "description": "All Credits go to the original Repo: [a/Hzzone/pytorch-openpose](https://github.com/Hzzone/pytorch-openpose).", - "files": [ - "https://github.com/nirex0/ComfyUI_pytorch_openpose" - ], - "id": "pytorch-openpose", - "install_type": "git-clone", - "reference": "https://github.com/nirex0/ComfyUI_pytorch_openpose", - "title": "ComfyUI_pytorch_openpose" - }, - { - "author": "AshMartian", - "description": "A collection of ComfyUI directory automation utility nodes. Directory Get-It-Right adds a GUI directory browser, and a smart directory loop/iteration node that supports regex + file extension filtering + sorting methods.", - "files": [ - "https://github.com/AshMartian/ComfyUI-DirGir" - ], - "id": "dir-gir", - "install_type": "git-clone", - "reference": "https://github.com/AshMartian/ComfyUI-DirGir", - "title": "Dir Gir" - }, - { - "author": "SozeInc", - "description": "Nodes: Ultimate Concat (Mobile), Send Notification (Mobile), Settings Launcher (Mobile), Settings Launcher Outputs (Mobile)", - "files": [ - "https://github.com/SozeInc/ComfyUI-Mobile" - ], - "id": "comfyui-mobile", - "install_type": "git-clone", - "reference": "https://github.com/SozeInc/ComfyUI-Mobile", - "title": "ComfyUI-Mobile" - }, - { - "author": "goktug", - "description": "Save Image Plus is a custom node for ComfyUI that allows you to save images in JPEG and WEBP formats with optional metadata embedding.", - "files": [ - "https://github.com/Goktug/comfyui-saveimage-plus" - ], - "id": "saveimage-plus", - "install_type": "git-clone", - "reference": "https://github.com/Goktug/comfyui-saveimage-plus", - "title": "Save Image Plus for ComfyUI" - }, - { - "author": "wujm424606", - "description": "Custom ComfyUI Nodes for interacting with [a/Ollama](https://ollama.com/) using the [a/ollama python client](https://github.com/ollama/ollama-python).\n Meanwhile it will provide better prompt descriptor for stable diffusion.", - "files": [ - "https://github.com/wujm424606/ComfyUi-Ollama-YN" - ], - "id": "ollama-YN", - "install_type": "git-clone", - "reference": "https://github.com/wujm424606/ComfyUi-Ollama-YN", - "title": "ComfyUi-Ollama-YN" - }, - { - "author": "tmagara", - "description": "prediction boost custom node for ComfyUI", - "files": [ - "https://github.com/tmagara/ComfyUI-Prediction-Boost" - ], - "id": "prediction-boost", - "install_type": "git-clone", - "reference": "https://github.com/tmagara/ComfyUI-Prediction-Boost", - "title": "ComfyUI-Prediction-Boost" - }, - { - "author": "chesnokovivan", - "description": "ComfyUI: Novakid. A node.", - "files": [ - "https://github.com/chesnokovivan/ComfyUI-Novakid" - ], - "id": "novakid", - "install_type": "git-clone", - "reference": "https://github.com/chesnokovivan/ComfyUI-Novakid", - "title": "ComfyUI-Novakid" - }, - { - "author": "Jin Liu", - "description": "Edit images in the Photopea editor directly within ComfyUI.", - "files": [ - "https://github.com/coolzilj/ComfyUI-Photopea" - ], - "id": "photopea", - "install_type": "git-clone", - "reference": "https://github.com/coolzilj/ComfyUI-Photopea", - "title": "ComfyUI-Photopea" - }, - { - "author": "bitaffinity", - "description": "Unofficial support for Hugging Face's hosted inference.", - "files": [ - "https://github.com/bitaffinity/ComfyUI_HF_Inference" - ], - "id": "hf-inference", - "install_type": "git-clone", - "reference": "https://github.com/bitaffinity/ComfyUI_HF_Inference", - "title": "ComfyUI_HF_Inference" - }, - { - "author": "claussteinmassl", - "description": "The CS Transform node is a custom node for ComfyUI that applies a series of transformations to an input image and mask. The transformations include scaling, rotation, and translation, all centered around a specified pivot point. The node ensures that the transformed image is properly accommodated within a canvas, which can be expanded if needed.", - "files": [ - "https://github.com/claussteinmassl/ComfyUI-CS-CustomNodes" - ], - "id": "cs-transform", - "install_type": "git-clone", - "reference": "https://github.com/claussteinmassl/ComfyUI-CS-CustomNodes", - "title": "CS Transform Node for ComfyUI" - }, - { - "author": "MariusKM", - "description": "Nodes:Badman_Blend, Badman_HexGenerator, Badman_String, Badman_Concat_String, Badman_Print, BadmanIO, BadmanIntUtil", - "files": [ - "https://github.com/MariusKM/ComfyUI-BadmanNodes" - ], - "id": "badman", - "install_type": "git-clone", - "reference": "https://github.com/MariusKM/ComfyUI-BadmanNodes", - "title": "ComfyUI-BadmanNodes" - }, - { - "author": "TMElyralab", - "description": "[a/MusePose](https://github.com/TMElyralab/MusePose) is an image-to-video generation framework for virtual human under control signal such as pose.\nNOTE: You need to download weigths manually from: [a/https://huggingface.co/TMElyralab/MusePose](https://huggingface.co/TMElyralab/MusePose).[w/The repository name has changed. If you are not receiving updates, please delete the existing node and reinstall it.]", - "files": [ - "https://github.com/TMElyralab/Comfyui-MusePose" - ], - "id": "musepose", - "install_type": "git-clone", - "reference": "https://github.com/TMElyralab/Comfyui-MusePose", - "title": "Comfyui-MusePose" - }, - { - "author": "PnthrLeo", - "description": "Image data check, filtering and augmentation tools for ComfyUI \ud83d\udd2c\nNOTE: Renamed from 'comfyUI-image-search'", - "files": [ - "https://github.com/PnthrLeo/comfyUI-PL-data-tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/PnthrLeo/comfyUI-PL-data-tools", - "title": "comfyUI-PL-data-tools" - }, - { - "author": "l20richo", - "description": "ComfyUI-Azure-Blob-Storage seamlessly integrates with [a/Azure Blob Storage](https://azure.microsoft.com/en-us/products/storage/blobs/) in ComfyUI. This open-source project provides custom nodes for effortless loading and saving of images, videos, and checkpoint models directly from Azure blob containers within the ComfyUI graph interface.", - "files": [ - "https://github.com/l20richo/ComfyUI-Azure-Blob-Storage" - ], - "id": "azure-blob-storage", - "install_type": "git-clone", - "reference": "https://github.com/l20richo/ComfyUI-Azure-Blob-Storage", - "title": "ComfyUI-Azure-Blob-Storage" - }, - { - "author": "AARG-FAN", - "description": "a wrap-up of ComfyUI nodes for converting pixels to raster, sent out to [a/Vtracer](https://github.com/visioncortex/vtracer)!", - "files": [ - "https://github.com/AARG-FAN/Image-Vector-for-ComfyUI" - ], - "id": "image-vector", - "install_type": "git-clone", - "reference": "https://github.com/AARG-FAN/Image-Vector-for-ComfyUI", - "title": "Image-vector-for-ComfyUI" - }, - { - "author": "Smirnov75", - "description": "A set of useful nodes for convenient use of ComfyUI, including: Seed randomization before the generation process starts, with saving of the last used values and the ability to automatically interrupt the current generation; A function to pause the generation process; Slider nodes for convenient control of input parameters; An alternative version of the standard Reroute node.", - "files": [ - "https://github.com/Smirnov75/ComfyUI-mxToolkit" - ], - "id": "mxtoolkit", - "install_type": "git-clone", - "reference": "https://github.com/Smirnov75/ComfyUI-mxToolkit", - "title": "ComfyUI-mxToolkit" - }, - { - "author": "humgate", - "description": "Simple JS application based on ComfyUI which takes prompt and style picture from user and runs hardcoded workflow inference returning generated image to user.", - "files": [ - "https://github.com/humgate/simplecomfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/humgate/simplecomfy", - "title": "simplecomfy" - }, - { - "author": "vanche1212", - "description": "Nodes:ApiRequestNode, LoadVideoNode, JsonParserNode, OllamaRequestNode, OldPhotoColorizationNode.", - "files": [ - "https://github.com/vanche1212/ComfyUI-ZMG-Nodes" - ], - "id": "zmg", - "install_type": "git-clone", - "reference": "https://github.com/vanche1212/ComfyUI-ZMG-Nodes", - "title": "ZMG PLUGIN" - }, - { - "author": "hben35096", - "description": "NODES:Batch Image Blend, Mask Levels Adjust, Get Batch Count, Load Lora Name, Load Sampler Name, Load Scheduler Name, Load Ckpt Name....\nThe nodes in this repository are only used as secondary nodes.", - "files": [ - "https://github.com/hben35096/ComfyUI-ReplenishNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/hben35096/ComfyUI-ReplenishNodes", - "title": "ComfyUI-ReplenishNodes" - }, - { - "author": "tiankuan93", - "description": "[Original] In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative models to enhance adapters for controlled generation. However, control signals can vary in strength, including text, audio, image reference, pose, depth map, etc. Among these, weaker conditions often struggle to be effective due to interference from stronger conditions, posing a challenge in balancing these conditions. In our work on portrait video generation, we identified audio signals as particularly weak, often overshadowed by stronger signals such as pose and original image. However, direct training with weak signals often leads to difficulties in convergence. To address this, we propose V-Express, a simple method that balances different control signals through a series of progressive drop operations. Our method gradually enables effective control by weak conditions, thereby achieving generation capabilities that simultaneously take into account pose, input image, and audio.\nNOTE: You need to downdload [a/model_ckpts](https://huggingface.co/tk93/V-Express/tree/main) manually.", - "files": [ - "https://github.com/tiankuan93/ComfyUI-V-Express" - ], - "id": "v-express", - "install_type": "git-clone", - "reference": "https://github.com/tiankuan93/ComfyUI-V-Express", - "title": "V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation" - }, - { - "author": "CMonk", - "description": "This custom nodes enables Stable Projectorz to work with ComfyUI Directly.", - "files": [ - "https://github.com/tianlang0704/ComfyUI-StableProjectorzBridge" - ], - "id": "projectorz", - "install_type": "git-clone", - "reference": "https://github.com/tianlang0704/ComfyUI-StableProjectorzBridge", - "title": "Stable Projectorz Bridge" - }, - { - "author": "Scorpinaus", - "description": "This node pack allows loading of SD checkpoints that uses diffusers format in comfyUI.", - "files": [ - "https://github.com/Scorpinaus/ComfyUI-DiffusersLoader" - ], - "id": "comfyui-diffusersloader", - "install_type": "git-clone", - "reference": "https://github.com/Scorpinaus/ComfyUI-DiffusersLoader", - "title": "ComfyUI-DiffusersLoader" - }, - { - "author": "chakib-belgaid", - "description": "This is a simple plugin for ComfyUI that allows you to import A1111 CSV styles into ComfyUI prompts.", - "files": [ - "https://github.com/chakib-belgaid/Comfyui_Prompt_styler" - ], - "id": "style-plugin", - "install_type": "git-clone", - "reference": "https://github.com/chakib-belgaid/Comfyui_Prompt_styler", - "title": "ComfyUI Style Plugin" - }, - { - "author": "chakib-belgaid", - "description": "A ComfyUI utility plugin designed to optimize the latent space for generating high-quality results. It approximates the closest size model for better generation results.", - "files": [ - "https://github.com/chakib-belgaid/ComfyUI-autosize" - ], - "id": "autosize", - "install_type": "git-clone", - "reference": "https://github.com/chakib-belgaid/ComfyUI-autosize", - "title": "ComfyUI-autosize" - }, - { - "author": "ThereforeGames", - "description": "A node that processes input text with the [a/Unprompted templating language](https://github.com/ThereforeGames/unprompted).", - "files": [ - "https://github.com/ThereforeGames/ComfyUI-Unprompted" - ], - "id": "unprompted", - "install_type": "git-clone", - "reference": "https://github.com/ThereforeGames/ComfyUI-Unprompted", - "title": "ComfyUI-Unprompted" - }, - { - "author": "Tool Of North america", - "description": "A very simple and easy to use node to automaticaaly create (square) image crops and masks using YoloV8. This can be very useful when using controlnet and ip adapters", - "files": [ - "https://github.com/tooldigital/ComfyUI-Yolo-Cropper" - ], - "id": "tooldigital", - "install_type": "git-clone", - "reference": "https://github.com/tooldigital/ComfyUI-Yolo-Cropper", - "title": "Easy automatic (square) image cropper using Yolo" - }, - { - "author": "luandev", - "description": "ComfyUI-CrewAI aims to integrate Crew AI's multi-agent collaboration framework into the ComfyUI environment. By combining the strengths of Crew AI's role-based, collaborative AI agent system with ComfyUI's intuitive interface, we will create a robust platform for managing and executing complex AI tasks seamlessly", - "files": [ - "https://github.com/luandev/ComfyUI-CrewAI" - ], - "id": "crewai", - "install_type": "git-clone", - "reference": "https://github.com/luandev/ComfyUI-CrewAI", - "title": "ComfyUI CrewAI" - }, - { - "author": "chandlergis", - "description": "Nodes:Image Emoji Overlay", - "files": [ - "https://github.com/chandlergis/ComfyUI_EmojiOverlay" - ], - "id": "emoji-overlay", - "install_type": "git-clone", - "reference": "https://github.com/chandlergis/ComfyUI_EmojiOverlay", - "title": "ComfyUI_EmojiOverlay" - }, - { - "author": "risunobushi", - "description": "A collection of simple nodes for Frequency Separation / Frequency Recombine with RGB and HSV methods", - "files": [ - "https://github.com/risunobushi/comfyUI_FrequencySeparation_RGB-HSV" - ], - "id": "freq-sep", - "install_type": "git-clone", - "reference": "https://github.com/risunobushi/comfyUI_FrequencySeparation_RGB-HSV", - "title": "comfyUI_FrequencySeparation_RGB-HSV" - }, - { - "author": "risunobushi", - "description": "A custom node for ComfyUI that calculates CLIP and LPIPS similarity scores between two images.", - "files": [ - "https://github.com/risunobushi/ComfyUI-Similarity-Score" - ], - "install_type": "git-clone", - "reference": "https://github.com/risunobushi/ComfyUI-Similarity-Score", - "title": "ComfyUI-Similarity-Score" - }, - { - "author": "risunobushi", - "description": "NODES: Extract Displacement Map Node, Displace Logo", - "files": [ - "https://github.com/risunobushi/ComfyUI_DisplacementMapTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/risunobushi/ComfyUI_DisplacementMapTools", - "title": "ComfyUI_DisplacementMapTools" - }, - { - "author": "risunobushi", - "description": "A ComfyUI custom node that integrates with the sm4ll-VTON API for virtual try-on functionality.", - "files": [ - "https://github.com/risunobushi/ComfyUI_sm4ll-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/risunobushi/ComfyUI_sm4ll-Wrapper", - "title": "ComfyUI_sm4ll-Wrapper" - }, - { - "author": "zohac", - "description": "Nodes:ZC DrawShape Node", - "files": [ - "https://github.com/zohac/ComfyUI_ZC_DrawShape" - ], - "id": "drawshape", - "install_type": "git-clone", - "reference": "https://github.com/zohac/ComfyUI_ZC_DrawShape", - "title": "ComfyUI_ZC_DrawShape" - }, - { - "author": "DataCTE", - "description": "This custom node for ComfyUI allows you to inject specific prompts at specific blocks of the Stable Diffusion UNet, providing fine-grained control over the generated image. It is based on the concept that the content/subject understanding of the model is primarily contained within the MID0 and MID1 blocks, as demonstrated in the B-Lora (Content Style implicit separation) paper. Features.\nInject different prompts into specific UNet blocks Three different node variations for flexible workflow integration Customize the learning rate of specific blocks to focus on content, lighting, style, or other aspects Potential for developing a 'Mix of Experts' approach by swapping blocks on-the-fly based on prompt content", - "files": [ - "https://github.com/DataCTE/prompt_injection" - ], - "id": "prompt-injection", - "install_type": "git-clone", - "reference": "https://github.com/DataCTE/prompt_injection", - "title": "Prompt Injection Node for ComfyUI" - }, - { - "author": "FrankChieng", - "description": "implementation easyanimate with llama3-8b-6bit instruction LLM generation prompt help", - "files": [ - "https://github.com/frankchieng/ComfyUI_llm_easyanimiate" - ], - "id": "llm-easyanimate", - "install_type": "git-clone", - "nodename_pattern": "^FrankChiengEasyAnimate", - "reference": "https://github.com/frankchieng/ComfyUI_llm_easyanimiate", - "title": "ComfyUI_llm_easyanimiate" - }, - { - "author": "nuanarchy", - "description": "ComfyUI implementation of [a/FlashFace: Human Image Personalization with High-fidelity Identity Preservation](https://github.com/ali-vilab/FlashFace)\nNOTE: You need to downalod models manually.", - "files": [ - "https://github.com/nuanarchy/ComfyUI-NuA-FlashFace" - ], - "id": "nua-flashface", - "install_type": "git-clone", - "reference": "https://github.com/nuanarchy/ComfyUI-NuA-FlashFace", - "title": "ComfyUI-NuA-FlashFace" - }, - { - "author": "nuanarchy", - "description": "ComfyUI implementation of '[a/Blind Image Restoration via Fast Diffusion Inversion](https://github.com/hamadichihaoui/BIRD)' Original [a/article](https://arxiv.org/abs/2405.19572)", - "files": [ - "https://github.com/nuanarchy/ComfyUI-NuA-BIRD" - ], - "id": "nua-bird", - "install_type": "git-clone", - "reference": "https://github.com/nuanarchy/ComfyUI-NuA-BIRD", - "title": "ComfyUI-NuA-BIRD" - }, - { - "author": "denfrost", - "description": "Custom nodes make easy Advanced Workflows. Focus on Image/Video and ControlNet efficiency and performances. Manipulation of Latent Space, Automatic pipeline with a bit efforts.", - "files": [ - "https://github.com/denfrost/Den_ComfyUI_Workflow" - ], - "id": "den", - "install_type": "git-clone", - "reference": "https://github.com/denfrost/Den_ComfyUI_Workflow", - "title": "Den_ComfyUI_Workflows" - }, - { - "author": "marduk191", - "description": "A node to set workflow settings.", - "files": [ - "https://github.com/marduk191/comfyui-marnodes" - ], - "id": "marnodes", - "install_type": "git-clone", - "reference": "https://github.com/marduk191/comfyui-marnodes", - "title": "marduk191 workflow settings" - }, - { - "author": "marduk191", - "description": "This custom node for ComfyUI integrates the Flux-Prompt-Enhance model, allowing you to enhance your prompts directly within your ComfyUI workflows.", - "files": [ - "https://github.com/marduk191/ComfyUI-Fluxpromptenhancer" - ], - "id": "fluxpromptenhancer", - "install_type": "git-clone", - "reference": "https://github.com/marduk191/ComfyUI-Fluxpromptenhancer", - "title": "Flux Prompt Enhance Node for ComfyUI" - }, - { - "author": "haohaocreates", - "description": "comfy ui custom node that returns an image from a batch based on selected criteria such as RGB value, brightness, etc (credits to chris goringe's custom nodes tutorial ).", - "files": [ - "https://github.com/haohaocreates/ComfyUI-HH-Image-Selector" - ], - "id": "hh-image-selector", - "install_type": "git-clone", - "reference": "https://github.com/haohaocreates/ComfyUI-HH-Image-Selector", - "title": "ComfyUI-HH-Image-Selector" - }, - { - "author": "exdysa", - "description": "Selector and Recourse. Presets & failsafes. Work flow. EXDYSA", - "files": [ - "https://github.com/exdysa/comfyui-selector" - ], - "install_type": "git-clone", - "reference": "https://github.com/exdysa/comfyui-selector", - "title": "comfyui-selector" - }, - { - "author": "Jin Liu", - "description": "A variety of custom nodes to enhance ComfyUI for a buttery smooth experience.", - "files": [ - "https://github.com/coolzilj/ComfyUI-LJNodes" - ], - "id": "ComfyUI-LJNodes", - "install_type": "git-clone", - "reference": "https://github.com/coolzilj/ComfyUI-LJNodes", - "title": "ComfyUI-LJNodes" - }, - { - "author": "GavChap", - "description": "You'll get a new node called SD3 Latent Select Resolution, you can pick the x and y sizes from a list.", - "files": [ - "https://github.com/GavChap/ComfyUI-SD3LatentSelectRes" - ], - "id": "sd3latent-select-res", - "install_type": "git-clone", - "reference": "https://github.com/GavChap/ComfyUI-SD3LatentSelectRes", - "title": "ComfyUI-SD3LatentSelectRes" - }, - { - "author": "BenNarum", - "description": "A set of tools for generating and altering sigmas in ComfyUI.", - "files": [ - "https://github.com/BenNarum/SigmaWaveFormNode" - ], - "id": "sigmawaveform", - "install_type": "git-clone", - "reference": "https://github.com/BenNarum/SigmaWaveFormNode", - "title": "SigmaWaveFormNodes" - }, - { - "author": "shobhitic", - "description": "This adds a node that has both the positive and negative prompts as input in one node. You can just add one node and be done with both Positive and Negative prompts, in place of adding two different nodes for them.", - "files": [ - "https://github.com/shobhitic/ComfyUI-PlusMinusTextClip" - ], - "id": "plusminustextclip", - "install_type": "git-clone", - "reference": "https://github.com/shobhitic/ComfyUI-PlusMinusTextClip", - "title": "PlusMinusTextClip - Single node for Positive and Negative Prompts" - }, - { - "author": "Late Night Labs", - "description": "Frame Selector & Sequence Selection Node for ComfyUI.", - "files": [ - "https://github.com/latenightlabs/ComfyUI-LNL" - ], - "id": "lnlframeselector", - "install_type": "git-clone", - "reference": "https://github.com/latenightlabs/ComfyUI-LNL", - "reference2": "https://github.com/asteriafilmco/ComfyUI-LNL", - "title": "LNL Frame Selector" - }, - { - "author": "Michael Standen", - "description": "A prompt generator and CLIP encoder using AI provided by Ollama.", - "files": [ - "https://github.com/ScreamingHawk/comfyui-ollama-prompt-encode" - ], - "id": "ollamapromptencode", - "install_type": "git-clone", - "reference": "https://github.com/ScreamingHawk/comfyui-ollama-prompt-encode", - "title": "Ollama Prompt Encode" - }, - { - "author": "NvidiaGameWorksAdmin", - "description": "Use ComfyUI with RTX Remix to remaster classic games [a/https://github.com/NVIDIAGameWorks/rtx-remix](https://github.com/NVIDIAGameWorks/rtx-remix)", - "files": [ - "https://github.com/NVIDIAGameWorks/ComfyUI-RTX-Remix" - ], - "id": "comfyui-rtx-remix", - "install_type": "git-clone", - "reference": "https://github.com/NVIDIAGameWorks/ComfyUI-RTX-Remix", - "title": "ComfyUI-RTX-Remix" - }, - { - "author": "toxicwind", - "description": "Text Randomization and Formatting, JSON Extraction and Processing, SD3 Resolution Solver", - "files": [ - "https://github.com/toxicwind/ComfyUI-TTools" - ], - "id": "ttools", - "install_type": "git-clone", - "reference": "https://github.com/toxicwind/ComfyUI-TTools", - "title": "TTools for ComfyUI" - }, - { - "author": "Yanick112", - "description": "This project converts raster images into SVG format using the [a/VTracer](https://github.com/visioncortex/vtracer) library. It's a handy tool for designers and developers who need to work with vector graphics programmatically.", - "files": [ - "https://github.com/Yanick112/ComfyUI-ToSVG" - ], - "id": "tosvg", - "install_type": "git-clone", - "reference": "https://github.com/Yanick112/ComfyUI-ToSVG", - "title": "ComfyUI-ToSVG" - }, - { - "author": "dicksondickson", - "description": "A set of custom nodes that I've either written myself or adapted from other authors", - "files": [ - "https://github.com/dicksondickson/ComfyUI-Dickson-Nodes" - ], - "id": "dicksonnodes", - "install_type": "git-clone", - "reference": "https://github.com/dicksondickson/ComfyUI-Dickson-Nodes", - "title": "ComfyUI-Dickson-Nodes" - }, - { - "author": "juehackr", - "description": "\ud83e\udd17\ud83e\udd17\ud83e\udd17Comfyui Universal Translation Plugin (no longer requires adding various nodes, directly add translation function on the existing nodes), allowing Comfyui to support Chinese input and automatic translation for any long text input box, while adding error translation function (calling Baidu Translate), achieving translation freedom!", - "files": [ - "https://github.com/juehackr/comfyui_fk_server" - ], - "id": "fk-server", - "install_type": "git-clone", - "reference": "https://github.com/juehackr/comfyui_fk_server", - "title": "comfyui_fk_server" - }, - { - "author": "G-370", - "description": "Nodes:Render SD3 Attention, SD3 Attention To Image, SD3 Image Into Attention.", - "files": [ - "https://github.com/G-370/ComfyUI-SD3-Powerlab" - ], - "id": "sd3-powerlab", - "install_type": "git-clone", - "reference": "https://github.com/G-370/ComfyUI-SD3-Powerlab", - "title": "ComfyUI-SD3-Powerlab" - }, - { - "author": "TylerZoro", - "description": "Tools for scaling images and latents appropriate to SD3 in ComfyUI.", - "files": [ - "https://github.com/TylerZoro/SD3-Scaling" - ], - "id": "sd3-scaling", - "install_type": "git-clone", - "reference": "https://github.com/TylerZoro/SD3-Scaling", - "title": "SD3-Scaling" - }, - { - "author": "baicai99", - "description": "Used to process video redrawing, frame skipping, frame ending early, etc.", - "files": [ - "https://github.com/baicai99/ComfyUI-FrameSkipping" - ], - "id": "FrameSkipping", - "install_type": "git-clone", - "reference": "https://github.com/baicai99/ComfyUI-FrameSkipping", - "title": "ComfyUI-FrameSkipping" - }, - { - "author": "SuperMasterBlasterLaser", - "description": "Nodes:YOLO Classifier Model Loader, YOLO Classify.", - "files": [ - "https://github.com/SuperMasterBlasterLaser/ComfyUI_YOLO_Classifiers" - ], - "id": "yolo-classifier", - "install_type": "git-clone", - "reference": "https://github.com/SuperMasterBlasterLaser/ComfyUI_YOLO_Classifiers", - "title": "ComfyUI_YOLO_Classifiers" - }, - { - "author": "SamKhoze", - "description": "DeepFuze is a state-of-the-art deep learning tool that seamlessly integrates with ComfyUI to revolutionize facial transformations, lipsyncing, video generation, voice cloning, face swapping, and lipsync translation. Leveraging advanced algorithms, DeepFuze enables users to combine audio and video with unparalleled realism, ensuring perfectly synchronized facial movements. This innovative solution is ideal for content creators, animators, developers, and anyone seeking to elevate their video editing projects with sophisticated AI-driven features.", - "files": [ - "https://github.com/SamKhoze/ComfyUI-DeepFuze" - ], - "id": "deepfuze", - "install_type": "git-clone", - "reference": "https://github.com/SamKhoze/ComfyUI-DeepFuze", - "title": "DeepFuze" - }, - { - "author": "superyoman", - "description": "Unofficial Luma API-ComfyUI version.[w/WARN: This project is for learning purpose only!]", - "files": [ - "https://github.com/superyoman/comfyui_lumaAPI" - ], - "id": "luma", - "install_type": "git-clone", - "reference": "https://github.com/superyoman/comfyui_lumaAPI", - "title": "comfyui_lumaAPI" - }, - { - "author": "chris-the-wiz", - "description": "Edit embeddings with a curve. Actually should work on any 1D input tensor. Tested with IPAdapter-Plus.", - "files": [ - "https://github.com/chris-the-wiz/EmbeddingsCurveEditor_ComfyUI" - ], - "id": "embeddings-curve-editor", - "install_type": "git-clone", - "reference": "https://github.com/chris-the-wiz/EmbeddingsCurveEditor_ComfyUI", - "title": "EmbeddingsCurveEditor_ComfyUI" - }, - { - "author": "zhulu111", - "description": "sdBxb, a tool that converts ComfyUI workflows into WeChat Mini Program, Douyin Mini Program, and H5 with one click, and supports payments.", - "files": [ - "https://github.com/zhulu111/ComfyUI_Bxb" - ], - "id": "ComfyUI_Bxb", - "install_type": "git-clone", - "reference": "https://github.com/zhulu111/ComfyUI_Bxb", - "title": "ComfyUI_Bxb" - }, - { - "author": "lordgasmic", - "description": "This is an attempt to recreate the wildcards plugin for Automatic1111 but for ComfyUI.", - "files": [ - "https://github.com/lordgasmic/comfyui_wildcards" - ], - "install_type": "git-clone", - "reference": "https://github.com/lordgasmic/comfyui_wildcards", - "title": "comfyui_wildcards" - }, - { - "author": "lordgasmic", - "description": "Nodes:Save Image with Options", - "files": [ - "https://github.com/lordgasmic/comfyui_save_image_with_options" - ], - "install_type": "git-clone", - "reference": "https://github.com/lordgasmic/comfyui_save_image_with_options", - "title": "comfyui_save_image_with_options" - }, - { - "author": "opvelll", - "description": "This is a custom node for Comfy UI. It mainly wraps itertools.product and can be used to create patterns by combining prompts. It is recommended to install this custom node in combination with the nodes from the WAS Node Suite.", - "files": [ - "https://github.com/opvelll/ComfyUI_TextListProduct" - ], - "id": "listproduct", - "install_type": "git-clone", - "reference": "https://github.com/opvelll/ComfyUI_TextListProduct", - "title": "Comfy UI Text List Product" - }, - { - "author": "jakechai", - "description": "A ComfyUI workflow customization by Jake.", - "files": [ - "https://github.com/jakechai/ComfyUI-JakeUpgrade" - ], - "id": "jkupgrade", - "install_type": "git-clone", - "reference": "https://github.com/jakechai/ComfyUI-JakeUpgrade", - "title": "ComfyUI-JakeUpgrade" - }, - { - "author": "celsojr2013", - "description": "Nodes:Simple Google Translator, Simple Resolution Solver.\nThis is a small set of simple nodes that help your workflow on ComfyUI.", - "files": [ - "https://github.com/celsojr2013/comfyui_simpletools" - ], - "install_type": "git-clone", - "reference": "https://github.com/celsojr2013/comfyui_simpletools", - "title": "ComfyUI SimpleTools Suit" - }, - { - "author": "celsojr2013", - "description": "Nodes:Jamworks_Login, Jamworks_Download, Shell_Command.\nA Simple Client for Jamworks Platform DAM Integration", - "files": [ - "https://github.com/celsojr2013/comfyui_jamworks_client" - ], - "install_type": "git-clone", - "reference": "https://github.com/celsojr2013/comfyui_jamworks_client", - "title": "comfyui_jamworks_client" - }, - { - "author": "MilitantHitchhiker", - "description": "Militant Hitchhiker's Switchblade Pack is a collection of custom nodes for ComfyUI that provide various multi-function capabilities.", - "files": [ - "https://github.com/MilitantHitchhiker/MilitantHitchhiker-SwitchbladePack" - ], - "id": "hitchhiker", - "install_type": "git-clone", - "reference": "https://github.com/MilitantHitchhiker/MilitantHitchhiker-SwitchbladePack", - "title": "MilitantHitchhiker-SwitchbladePack" - }, - { - "author": "slyt", - "description": "ComfyUI custom nodes for working with [a/Ollama](https://github.com/ollama/ollama).\nNOTE:Assumes that an Ollama server is running at http://127.0.0.1:11434 and accessible by the ComfyUI backend.", - "files": [ - "https://github.com/slyt/comfyui-ollama-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/slyt/comfyui-ollama-nodes", - "title": "comfyui-ollama-nodes" - }, - { - "author": "zwng", - "description": "Simple nodes for loading image files.Nodes that include a simple remote connection to Photoshop, a node that can overlay and preview an image with a mask, and a node that can load images directly from a file path.", - "files": [ - "https://github.com/za-wa-n-go/ComfyUI_Zwng_Nodes" - ], - "id": "zwng", - "install_type": "git-clone", - "reference": "https://github.com/za-wa-n-go/ComfyUI_Zwng_Nodes", - "title": "ComfyUI_Zwng_Nodes" - }, - { - "author": "Shibiko-AI", - "description": "This is a collection of tools that I use to make my life easier when developing ComfyUI applications. It is a collection of tools that I have created to help me with my development process. I have decided to share these tools with the community in the hopes that they will be useful to others as well. I use this tools to further develop features for [a/https://shibiko.ai](https://shibiko.ai)", - "files": [ - "https://github.com/Shibiko-AI/ShibikoAI-ComfyUI-Tools" - ], - "id": "shibiko-ai-tools", - "install_type": "git-clone", - "reference": "https://github.com/Shibiko-AI/ShibikoAI-ComfyUI-Tools", - "title": "Shibiko AI ComfyUI Tools" - }, - { - "author": "SherryXieYuchen", - "description": "Nodes:VAE Encode Inpaint, VAE Decode Inpaint, ColorCorrection Inpaint, ImagePreprocess Inpaint, ImagePostprocess Inpaint, Load Model Inpaint, Inpainting (using Model)", - "files": [ - "https://github.com/SherryXieYuchen/ComfyUI-Image-Inpainting" - ], - "id": "image-inpainting", - "install_type": "git-clone", - "reference": "https://github.com/SherryXieYuchen/ComfyUI-Image-Inpainting", - "title": "ComfyUI-Image-Inpainting" - }, - { - "author": "zeroxoxo", - "description": "ComfyUI node for fast neural style transfer. This is a simple conversion based on this: [a/https://github.com/rrmina/fast-neural-style-pytorch](https://github.com/rrmina/fast-neural-style-pytorch) Only basic inference functionality is ported for now.", - "files": [ - "https://github.com/zeroxoxo/ComfyUI-Fast-Style-Transfer" - ], - "id": "fast-style-transfer", - "install_type": "git-clone", - "reference": "https://github.com/zeroxoxo/ComfyUI-Fast-Style-Transfer", - "title": "ComfyUI-Fast-Style-Transfer" - }, - { - "author": "iwanders", - "description": "Nodes:IW SaveString, IW PrintString, IW ReplaceString, IW StringNode, IW StringConcat, IW TokenizerVocab, IW JsonPickItem.", - "files": [ - "https://github.com/iwanders/ComfyUI_nodes" - ], - "id": "iwanders-nodes", - "install_type": "git-clone", - "reference": "https://github.com/iwanders/ComfyUI_nodes", - "title": "iwanders/ComfyUI_nodes" - }, - { - "author": "rhdunn", - "description": "A collection of nodes for rerouting multiple I/O lines together in a bus.", - "files": [ - "https://github.com/rhdunn/comfyui-bus-plugin" - ], - "id": "bus", - "install_type": "git-clone", - "reference": "https://github.com/rhdunn/comfyui-bus-plugin", - "title": "comfyui-bus-plugin" - }, - { - "author": "rhdunn", - "description": "This plugin is compatible with the ComfyUI audio nodes.", - "files": [ - "https://github.com/rhdunn/comfyui-audio-processing" - ], - "install_type": "git-clone", - "reference": "https://github.com/rhdunn/comfyui-audio-processing", - "title": "comfyui-audio-processing" - }, - { - "author": "hyunamy", - "description": "A feature that sends an email via Gmail once image generation is completed in Comfy-ui.", - "files": [ - "https://github.com/hyunamy/comfy-ui-on-complete-email-me" - ], - "id": "hyunamy", - "install_type": "git-clone", - "reference": "https://github.com/hyunamy/comfy-ui-on-complete-email-me", - "title": "Comfy-UI on-complete-email-me" - }, - { - "author": "veighnsche", - "description": "Nodes:Image Dimension Resizer, Image Sizer, Random Ratio, Show Text, Random Title Character, Random Wildcard Tag Picker, Random Show Atm Loc Outfit, Contains Word, Elements Concatenator, ...", - "files": [ - "https://github.com/veighnsche/comfyui_gr85" - ], - "id": "gr85", - "install_type": "git-clone", - "reference": "https://github.com/veighnsche/comfyui_gr85", - "title": "comfyui_gr85" - }, - { - "author": "hwhaocool", - "description": "a comfyui custom node, which can select value from inputs", - "files": [ - "https://github.com/hwhaocool/ComfyUI-Select-Any" - ], - "id": "select-any", - "install_type": "git-clone", - "reference": "https://github.com/hwhaocool/ComfyUI-Select-Any", - "title": "ComfyUI-Select-Any" - }, - { - "author": "GreenLandisaLie", - "description": "ComfyUI implementation of [a/Aura-SR](https://github.com/fal-ai/aura-sr)", - "files": [ - "https://github.com/GreenLandisaLie/AuraSR-ComfyUI" - ], - "id": "aurasr-greenlandisalie", - "install_type": "git-clone", - "reference": "https://github.com/GreenLandisaLie/AuraSR-ComfyUI", - "title": "AuraSR-ComfyUI" - }, - { - "author": "licyk", - "description": "This extension is a node that directly expands the functionality of KSampler, rather than being in the form of a custom node. [w/Workflows created using this feature are not compatible with other users.]", - "files": [ - "https://github.com/licyk/ComfyUI-Restart-Sampler" - ], - "id": "restart-sampler-licyk", - "install_type": "git-clone", - "reference": "https://github.com/licyk/ComfyUI-Restart-Sampler", - "title": "ComfyUI-Restart-Sampler" - }, - { - "author": "licyk", - "description": "Image processing tool for ComfyUI", - "files": [ - "https://github.com/licyk/ComfyUI-HakuImg" - ], - "id": "HakuImg", - "install_type": "git-clone", - "reference": "https://github.com/licyk/ComfyUI-HakuImg", - "title": "ComfyUI-HakuImg" - }, - { - "author": "licyk", - "description": "Adding TCD sampling", - "files": [ - "https://github.com/licyk/ComfyUI-TCD-Sampler" - ], - "id": "TCD-Sampler", - "install_type": "git-clone", - "reference": "https://github.com/licyk/ComfyUI-TCD-Sampler", - "title": "ComfyUI-TCD-Sampler" - }, - { - "author": "my-opencode", - "description": "[a/ImageMagick](https://imagemagick.org/index.php) nodes for ComfyUI. Adds nodes to call ImageMagick subprocesses from ComfyUI.\nRequirements: [a/ImagMagick7](https://imagemagick.org/script/download.php), 'magick' command in your CLI environment.", - "files": [ - "https://github.com/my-opencode/ComfyUI_IndustrialMagick" - ], - "id": "industrialmagick", - "install_type": "git-clone", - "reference": "https://github.com/my-opencode/ComfyUI_IndustrialMagick", - "title": "ComfyUI_IndustrialMagick" - }, - { - "author": "my-opencode", - "description": "A custom node that returns the generation time of the KSampler. Intended for benchmarking or debugging.", - "files": [ - "https://github.com/my-opencode/ComfyUI_KSamplerTimer" - ], - "id": "ksamplertimer", - "install_type": "git-clone", - "reference": "https://github.com/my-opencode/ComfyUI_KSamplerTimer", - "title": "ComfyUI_KSamplerTimer" - }, - { - "author": "SEkINVR", - "description": "This custom node for ComfyUI allows you to save images in multiple formats, including PNG, JPG, WebP, and ICO.\n[w/ComfyUI-Save-Multi-Format is renamed to SaveAs. Remove previous one and reinstall to this.]", - "files": [ - "https://github.com/SEkINVR/ComfyUI-SaveAs" - ], - "id": "saveas", - "install_type": "git-clone", - "reference": "https://github.com/SEkINVR/ComfyUI-SaveAs", - "reference2": "https://github.com/AnimationOverhual/ComfyUI-SaveAs", - "title": "ComfyUI SaveAS" - }, - { - "author": "MrSamSeen", - "description": "Create immersive 3D stereoscopic images and videos! Transform your ComfyUI generations into stunning side-by-side 3D visuals for videos and image sequences. Powered by Depth-Anything-V2, no external depth maps needed. Perfect for VR, 3D displays, and cross-eyed viewing - no special glasses required!", - "files": [ - "https://github.com/MrSamSeen/ComfyUI_SSStereoscope" - ], - "id": "comfyui_ssstereoscope_bysamseen", - "install_type": "git-clone", - "reference": "https://github.com/MrSamSeen/ComfyUI_SSStereoscope", - "title": "SideBySide_Stereoscope" - }, - { - "author": "MrSamSeen", - "description": "Two powerful custom nodes for ComfyUI to create stunning before-and-after transition videos. These nodes are designed for visual comparisons, transformations, and creative effects, supporting both standard and depth map-based transitions.", - "files": [ - "https://github.com/MrSamSeen/ComfyUI_SSBeforeAfterNode" - ], - "id": "ComfyUI_SSBeforeAfterNode", - "install_type": "git-clone", - "reference": "https://github.com/MrSamSeen/ComfyUI_SSBeforeAfterNode", - "title": "ComfyUI_SSBeforeAfterNode" - }, - { - "author": "jroc22", - "description": "This is a simple node for creating prompts using a .csv file. I created this node as an easy way to output different prompts each time a workflow is run.", - "files": [ - "https://github.com/jroc22/ComfyUI-CSV-prompt-builder" - ], - "id": "csv-prompt-builder", - "install_type": "git-clone", - "reference": "https://github.com/jroc22/ComfyUI-CSV-prompt-builder", - "title": "ComfyUI-CSV-prompt-builder" - }, - { - "author": "DeJoker", - "description": "provide extra api to run prompt request with parallel execution of independent node", - "files": [ - "https://github.com/DeJoker/pipeline-parallel-comfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/DeJoker/pipeline-parallel-comfy", - "title": "Pipeline Parallel ComfyUI" - }, - { - "author": "yiwangsimple", - "description": "Content generation with open source models in comfyui via graq api implementation.\n[w/This repo is renamed from ComfyUI_GroqChat to ComfyUI_DW_CHAT. Please remove previous one and reinstall to this.]", - "files": [ - "https://github.com/yiwangsimple/ComfyUI_DW_Chat" - ], - "install_type": "git-clone", - "reference": "https://github.com/yiwangsimple/ComfyUI_DW_Chat", - "title": "ComfyUI_DW_Chat" - }, - { - "author": "yiwangsimple", - "description": "Based on the original repository [a/https://github.com/spacepxl/ComfyUI-Florence-2](https://github.com/spacepxl/ComfyUI-Florence-2), the model loading and storage methods have been improved, and sd3 has been newly added with enhanced speed and accuracy.", - "files": [ - "https://github.com/yiwangsimple/florence_dw" - ], - "install_type": "git-clone", - "reference": "https://github.com/yiwangsimple/florence_dw", - "title": "florence_dw" - }, - { - "author": "Tritant", - "description": "Generate random prompts easily.", - "files": [ - "https://github.com/tritant/ComfyUI_CreaPrompt" - ], - "id": "creaprompt", - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI_CreaPrompt", - "title": "ComfyUI-CreaPrompt" - }, - { - "author": "tritant", - "description": "Advanced LoRA merging node for ComfyUI (additive, average, sequential)", - "files": [ - "https://github.com/tritant/ComfyUI_Flux_Lora_Merger" - ], - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI_Flux_Lora_Merger", - "title": "Flux LoRA Merger" - }, - { - "author": "tritant", - "description": "Advanced Block LoRA merging node for ComfyUI (allows selective LoRA block merging)", - "files": [ - "https://github.com/tritant/ComfyUI_Flux_Block_Lora_Merger" - ], - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI_Flux_Block_Lora_Merger", - "title": "Flux Block LoRA Merger" - }, - { - "author": "tritant", - "description": "Adds realistic film grain, vignette and RGB aberration to photos", - "files": [ - "https://github.com/tritant/ComfyUI-Advanced-Photo-Grain" - ], - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI-Advanced-Photo-Grain", - "title": "Advanced Photo Grain" - }, - { - "author": "tritant", - "description": "Fix banding artifacts by re-sampling the latent with a low denoise strength.", - "files": [ - "https://github.com/tritant/ComfyUI_Remove_Banding_Artifacts" - ], - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI_Remove_Banding_Artifacts", - "title": "Remove Banding Artifacts" - }, - { - "author": "tritant", - "description": "This custom node for ComfyUI provides a powerful and flexible dynamic layering system, similar to what you would find in image editing software like Photoshop.", - "files": [ - "https://github.com/tritant/ComfyUI_Layers_Utility" - ], - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI_Layers_Utility", - "title": "Layers System" - }, - { - "author": "tritant", - "description": "A custom node for ComfyUI that provides advanced 2D relighting capabilities.", - "files": [ - "https://github.com/tritant/ComfyUI_Relight_Img" - ], - "install_type": "git-clone", - "reference": "https://github.com/tritant/ComfyUI_Relight_Img", - "title": "Advanced_Relight_Img" - }, - { - "author": "metncelik", - "description": "Nodes: Primitive BBOX, BBOX Padding, BBOX Resize, ImageResize KeepRatio.", - "files": [ - "https://github.com/metncelik/comfyui_met_suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/metncelik/comfyui_met_suite", - "title": "comfyui_met_suite" - }, - { - "author": "Smuzzies", - "description": "Meme Maker Node for ComfyUI.", - "files": [ - "https://github.com/Smuzzies/comfyui_meme_maker" - ], - "install_type": "git-clone", - "reference": "https://github.com/Smuzzies/comfyui_meme_maker", - "title": "comfyui_meme_maker" - }, - { - "author": "bluevisor", - "description": "This repository contains a simple custom node for ComfyUI that implements familiar PS-style blend modes using PyTorch. The PSBlendNode allows you to blend two images together using a variety of blend modes and an opacity parameter.", - "files": [ - "https://github.com/bluevisor/ComfyUI_PS_Blend_Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/bluevisor/ComfyUI_PS_Blend_Node", - "title": "ComfyUI_PS_Blend_Node" - }, - { - "author": "wTechArtist", - "description": "Nodes:Image Blending Mode Mask, Load Image With Bool, IPAdapter Mad Scientist Weight_Type, IPAdapter FaceID With Bool", - "files": [ - "https://github.com/wTechArtist/ComfyUI-CustomNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/wTechArtist/ComfyUI-CustomNodes", - "title": "ComfyUI-CustomNodes" - }, - { - "author": "wTechArtist", - "description": "NODES: WWL_StableDelight", - "files": [ - "https://github.com/wTechArtist/ComfyUI-StableDelight-weiweiliang" - ], - "install_type": "git-clone", - "reference": "https://github.com/wTechArtist/ComfyUI-StableDelight-weiweiliang", - "reference2": "https://github.com/wTechArtist/ComfyUI-WWL-StableDelight", - "title": "ComfyUI-StableDelight-weiweiliang" - }, - { - "author": "wTechArtist", - "description": "A professional video camera parameter estimation toolkit based on the VGGT model.", - "files": [ - "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera_Advanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera_Advanced", - "title": "ComfyUI VVL Video Camera Advanced" - }, - { - "author": "mullakhmetov", - "description": "ComfyS3 helpful util nodes for dynamic workflows", - "files": [ - "https://github.com/mullakhmetov/comfyui_dynamic_util_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/mullakhmetov/comfyui_dynamic_util_nodes", - "title": "comfyui_dynamic_util_nodes" - }, - { - "author": "HECer", - "description": "The ComfyUI-FilePathCreator is a custom node extension for ComfyUI designed to generate dynamic filenames based on user-defined parameters. This node helps streamline the process of creating organized and timestamped filenames, ideal for saving output files in a structured manner.", - "files": [ - "https://github.com/HECer/ComfyUI-FilePathCreator" - ], - "install_type": "git-clone", - "reference": "https://github.com/HECer/ComfyUI-FilePathCreator", - "title": "ComfyUI-FilePathCreator" - }, - { - "author": "adigayung", - "description": "Auto translate all languages \u200b\u200bto english", - "files": [ - "https://github.com/adigayung/ComfyUI-Translator" - ], - "install_type": "git-clone", - "reference": "https://github.com/adigayung/ComfyUI-Translator", - "title": "ComfyUI-Translator" - }, - { - "author": "ZZXYWQ", - "description": "Nodes: StreamRecorder, VideoFormatConverter, ZZX_PaintsUndo", - "files": [ - "https://github.com/ZZXYWQ/ComfyUI-ZZXYWQ" - ], - "id": "ZZXYWQ", - "install_type": "git-clone", - "reference": "https://github.com/ZZXYWQ/ComfyUI-ZZXYWQ", - "title": "ZZX Nodes" - }, - { - "author": "SiliconFlow", - "description": "[a/BizyAir](https://github.com/siliconflow/BizyAir) Comfy Nodes that can run in any environment.", - "files": [ - "https://github.com/siliconflow/BizyAir" - ], - "id": "bizyair", - "install_type": "git-clone", - "reference": "https://github.com/siliconflow/BizyAir", - "title": "\u2601\ufe0fBizyAir Nodes" - }, - { - "author": "BenNarum", - "description": "This extension provides nodes that allow experimentation with various elements (samplers, latent, activators, attenuator, scheulders, ...) of Stable Diffusion.", - "files": [ - "https://github.com/BenNarum/ComfyUI_CAS" - ], - "install_type": "git-clone", - "reference": "https://github.com/BenNarum/ComfyUI_CAS", - "title": "ComfyUI_CAS" - }, - { - "author": "Indra's Mirror", - "description": "ComfyUI-Documents is a powerful extension for ComfyUI that enhances workflows with advanced document processing capabilities. It includes nodes for loading and parsing various document types (PDF, TXT, DOC, DOCX), converting PDF pages to images, splitting PDFs into individual pages, and selecting specific images from batches. Features include text extraction, image conversion, and seamless integration with existing ComfyUI projects.", - "files": [ - "https://github.com/Excidos/ComfyUI-Documents" - ], - "install_type": "git-clone", - "reference": "https://github.com/Excidos/ComfyUI-Documents", - "title": "ComfyUI-Documents" - }, - { - "author": "Indra's Mirror", - "description": "ComfyUI-Lumina-Next-SFT-DiffusersWrapper is a custom node for ComfyUI that integrates the advanced Lumina-Next-SFT model. It offers high-quality image generation with features like time-aware scaling, optional ODE sampling, and support for high-resolution outputs. This node brings the power of the Lumina text-to-image pipeline directly into ComfyUI workflows, allowing for flexible and powerful image generation capabilities.", - "files": [ - "https://github.com/Excidos/ComfyUI-Lumina-Next-SFT-DiffusersWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/Excidos/ComfyUI-Lumina-Next-SFT-DiffusersWrapper", - "title": "ComfyUI-Lumina-Next-SFT-DiffusersWrapper" - }, - { - "author": "Expo", - "description": "A custom node for ComfyUI that integrates LM Studio's vision models to generate text descriptions of images. It provides a flexible and customizable way to add image-to-text capabilities to your ComfyUI workflows, working with LM Studio's local API.", - "files": [ - "https://github.com/mattjohnpowell/comfyui-lmstudio-image-to-text-node" - ], - "id": "comfyui-lmstudio-image-to-text-node", - "install_type": "git-clone", - "reference": "https://github.com/mattjohnpowell/comfyui-lmstudio-image-to-text-node", - "title": "LM Studio Image to Text Node for ComfyUI" - }, - { - "author": "injet-zhou", - "description": "Add more endpoints to make easy for utilizing ComfyUI API.", - "files": [ - "https://github.com/injet-zhou/comfyui_extra_api" - ], - "install_type": "git-clone", - "reference": "https://github.com/injet-zhou/comfyui_extra_api", - "title": "comfyui_extra_api" - }, - { - "author": "leestuartx", - "description": "ComfyUI-GG is a collection of ComfyUI nodes designed to enhance productivity in image processing workflows. This plugin provides a set of custom nodes that perform various image manipulations and metadata extractions to streamline your tasks.", - "files": [ - "https://github.com/leestuartx/ComfyUI-GG" - ], - "install_type": "git-clone", - "reference": "https://github.com/leestuartx/ComfyUI-GG", - "title": "ComfyUI-GG" - }, - { - "author": "mgfxer", - "description": "A set of custom nodes for frame interpolation and video processing in ComfyUI.", - "files": [ - "https://github.com/mgfxer/ComfyUI-FrameFX" - ], - "install_type": "git-clone", - "reference": "https://github.com/mgfxer/ComfyUI-FrameFX", - "title": "ComfyUI-FrameFX" - }, - { - "author": "Cyberschorsch", - "description": "Provides a custom node to load config for sampler nodes from a yaml file.", - "files": [ - "https://github.com/Cyberschorsch/ComfyUI-checkpoint-config-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/Cyberschorsch/ComfyUI-checkpoint-config-loader", - "title": "ComfyUI Checkpoint Loader Config" - }, - { - "author": "fearnworks", - "description": "This extension provides various nodes to support multimodal workflows.", - "files": [ - "https://github.com/fearnworks/ComfyUI_FearnworksNodes" - ], - "id": "fearnworks", - "install_type": "git-clone", - "reference": "https://github.com/fearnworks/ComfyUI_FearnworksNodes", - "title": "Fearnworks Nodes" - }, - { - "author": "807502278", - "description": "A simple 3D model processing tool within ComfyUI.", - "files": [ - "https://github.com/807502278/ComfyUI-3D-MeshTool" - ], - "id": "3D-MeshTool", - "install_type": "git-clone", - "reference": "https://github.com/807502278/ComfyUI-3D-MeshTool", - "title": "ComfyUI-3D-MeshTool" - }, - { - "author": "807502278", - "description": "Ready to use upon download. No need to install dependencies for the time being.\nIf there are new functions or suggestions, please provide feedback.\nAttention! The delfile node is not recommended for use on servers. I am not responsible for any losses incurred.", - "files": [ - "https://github.com/807502278/ComfyUI-WJNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/807502278/ComfyUI-WJNodes", - "title": "ComfyUI-WJNodes" - }, - { - "author": "807502278", - "description": "Suitable for Windows - MaskGCT ComfyUI Node Wrapping", - "files": [ - "https://github.com/807502278/ComfyUI_MaskGCT" - ], - "install_type": "git-clone", - "reference": "https://github.com/807502278/ComfyUI_MaskGCT", - "title": "ComfyUI_MaskGCT" - }, - { - "author": "JackEllie", - "description": "ComfyUI native implementation of [a/AI-Assistant](https://github.com/tori29umai0123/AI-Assistant).", - "files": [ - "https://github.com/JackEllie/ComfyUI_AI_Assistant" - ], - "id": "AI-Assistant", - "install_type": "git-clone", - "reference": "https://github.com/JackEllie/ComfyUI_AI_Assistant", - "title": "ComfyUI-AI-Assistant" - }, - { - "author": "APZmedia", - "description": "A comprehensive set of naming tools for ComfyUI to build, sanitize, and manage file names and paths.", - "files": [ - "https://github.com/APZmedia/ComfyUI-APZmedia-cleanName-from-string" - ], - "install_type": "git-clone", - "reference": "https://github.com/APZmedia/ComfyUI-APZmedia-cleanName-from-string", - "title": "APZmedia Naming Tools" - }, - { - "author": "APZmedia", - "description": "This node for ComfyUI allows saving images with an optional alpha channel (transparency). It supports saving images in formats like PNG, JPEG, and WebP.", - "files": [ - "https://github.com/APZmedia/APZmedia-comfyui-fast-image-save" - ], - "install_type": "git-clone", - "reference": "https://github.com/APZmedia/APZmedia-comfyui-fast-image-save", - "title": "APZmedia Fast Image Save Node" - }, - { - "author": "APZmedia", - "description": "A ComfyUI node to implement Together AI API image generation", - "files": [ - "https://github.com/APZmedia/APZmedia-comfy-together-lora" - ], - "install_type": "git-clone", - "reference": "https://github.com/APZmedia/APZmedia-comfy-together-lora", - "title": "APZmedia Together Image Generator for ComfyUI" - }, - { - "author": "N3rd00d", - "description": "Paint3D Nodes is a custom ComfyUI node for 3D model texture inpainting based on [a/Paint3D](https://arxiv.org/pdf/2312.13913).", - "files": [ - "https://github.com/N3rd00d/ComfyUI-Paint3D-Nodes" - ], - "id": "paint3d", - "install_type": "git-clone", - "reference": "https://github.com/N3rd00d/ComfyUI-Paint3D-Nodes", - "title": "ComfyUI-Paint3D-Nodes" - }, - { - "author": "sn0w12", - "description": "A collection of nodes and improvements created for general ease and lora management. These are just nodes I made and found useful, they should work with most other nodes. Most nodes that take in a prompt are made with booru tags in mind and might not work as expected with other prompts.", - "files": [ - "https://github.com/sn0w12/ComfyUI-Sn0w-Scripts" - ], - "install_type": "git-clone", - "reference": "https://github.com/sn0w12/ComfyUI-Sn0w-Scripts", - "title": "ComfyUI-Sn0w-Scripts" - }, - { - "author": "sn0w12", - "description": "Syntax highlighting and other quality of life improvements for ComfyUI.", - "files": [ - "https://github.com/sn0w12/ComfyUI-Syntax-Highlighting" - ], - "install_type": "git-clone", - "reference": "https://github.com/sn0w12/ComfyUI-Syntax-Highlighting", - "title": "ComfyUI-Syntax-Highlighting" - }, - { - "author": "MiaoshouAI", - "description": "Nodes to use Florence2 VLM for image tagging and captioning", - "files": [ - "https://github.com/miaoshouai/ComfyUI-Miaoshouai-Tagger" - ], - "id": "miaoshouai-tagger", - "install_type": "git-clone", - "reference": "https://github.com/miaoshouai/ComfyUI-Miaoshouai-Tagger", - "title": "ComfyUI-Miaoshouai-Tagger" - }, - { - "author": "MiaoshouAI", - "description": "A ComfyUI custom node for automatic video scene segmentation using TransNetV2.", - "files": [ - "https://github.com/miaoshouai/ComfyUI-Video-Segmentation" - ], - "install_type": "git-clone", - "reference": "https://github.com/miaoshouai/ComfyUI-Video-Segmentation", - "title": "ComfyUI Video Segmentation Node" - }, - { - "author": "Patricio Gonzalez Vivo", - "description": "A collections of nodes to support GLSL shaders inside a workflow. Provides nodes: glslViewer, glslEditor, glslEditorPro, int, float, vec2, vec3 and vec4.", - "files": [ - "https://github.com/patriciogonzalezvivo/comfyui_glslnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/patriciogonzalezvivo/comfyui_glslnodes", - "title": "GLSL Nodes" - }, - { - "author": "2kpr", - "description": "Implementation of UltraPixel on ComfyUI", - "files": [ - "https://github.com/2kpr/ComfyUI-UltraPixel" - ], - "id": "comfyui-ultrapixel", - "install_type": "git-clone", - "reference": "https://github.com/2kpr/ComfyUI-UltraPixel", - "title": "ComfyUI-UltraPixel" - }, - { - "author": "LightSketch-ai", - "description": "Two simple to install nodes to get Live Portrait working in ComfyUI without the need for a fancy GPU (Replicate account needed).", - "files": [ - "https://github.com/LightSketch-ai/ComfyUI-LivePortraitNode" - ], - "id": "lightsketchlp", - "install_type": "git-clone", - "reference": "https://github.com/LightSketch-ai/ComfyUI-LivePortraitNode", - "title": "ComfyUI-LivePortraitNode (Replicate API)" - }, - { - "author": "aaronchm", - "description": "Improved DanTagGen implementation that posesses all functionality of the A1111 webui extension.", - "files": [ - "https://github.com/Aaron-CHM/ComfyUI-z-a1111-sd-webui-DanTagGen" - ], - "id": "z-a1111-sd-webui-DanTagGen", - "install_type": "git-clone", - "reference": "https://github.com/Aaron-CHM/ComfyUI-z-a1111-sd-webui-DanTagGen", - "title": "z-a1111-sd-webui-DanTagGen" - }, - { - "author": "mikebilly", - "description": "Removes background using Transparent Background", - "files": [ - "https://github.com/mikebilly/Transparent-background-comfyUI" - ], - "id": "transparent-background-comfyui", - "install_type": "git-clone", - "reference": "https://github.com/mikebilly/Transparent-background-comfyUI", - "title": "Transparent-background-comfyUI" - }, - { - "author": "un-seen", - "description": "Nodes to perform tensor operations in ComfyUI", - "files": [ - "https://github.com/un-seen/comfyui-tensorops" - ], - "id": "comfyui-tensorop", - "install_type": "git-clone", - "reference": "https://github.com/un-seen/comfyui-tensorops", - "title": "comfyui-tensorop" - }, - { - "author": "un-seen", - "description": "This project is a ComfyUI version of [a/sd-webui-segment-anything](https://github.com/continue-revolution/sd-webui-segment-anything). At present, only the most core functionalities have been implemented. I would like to express my gratitude to [a/continue-revolution](https://github.com/continue-revolution) for their preceding work on which this is based.", - "files": [ - "https://github.com/un-seen/comfyui_segment_anything_plus" - ], - "install_type": "git-clone", - "reference": "https://github.com/un-seen/comfyui_segment_anything_plus", - "title": "ComfyUI Segment Anything" - }, - { - "author": "john-mnz", - "description": "ComfyUI node for background removal, implementing [a/InSPyReNet](https://github.com/plemeri/InSPyReNet)", - "files": [ - "https://github.com/john-mnz/ComfyUI-Inspyrenet-Rembg" - ], - "id": "inspyrenet", - "install_type": "git-clone", - "reference": "https://github.com/john-mnz/ComfyUI-Inspyrenet-Rembg", - "title": "ComfyUI-Inspyrenet-Rembg" - }, - { - "author": "Koushakur", - "description": "The latent gets passed straight through unaltered, if it's empty (i.e from a 'Empty Latent Image' node) FLOAT outputs the first value, otherwise it outputs the second value", - "files": [ - "https://github.com/Koushakur/ComfyUI-DenoiseChooser" - ], - "id": "denoise-chooser", - "install_type": "git-clone", - "reference": "https://github.com/Koushakur/ComfyUI-DenoiseChooser", - "title": "ComfyUI-DenoiseChooser" - }, - { - "author": "ycchanau", - "description": "Custom nodes that preview image with a magnifier.", - "files": [ - "https://github.com/ycchanau/ComfyUI_Preview_Magnifier" - ], - "id": "magnifier", - "install_type": "git-clone", - "reference": "https://github.com/ycchanau/ComfyUI_Preview_Magnifier", - "title": "ComfyUI Preview Magnifier" - }, - { - "author": "lrzjason", - "description": "NODES: Save Weight As Kolors Unet, Save Kolors", - "files": [ - "https://github.com/lrzjason/Comfyui-Kolors-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/lrzjason/Comfyui-Kolors-Utils", - "title": "Comfyui Kolors Utils" - }, - { - "author": "lrzjason", - "description": "NODES: Add Mask For IC Lora, Create Context Window, Concatenate Context Window, Auto Patch", - "files": [ - "https://github.com/lrzjason/Comfyui-In-Context-Lora-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/lrzjason/Comfyui-In-Context-Lora-Utils", - "title": "Comfyui-In-Context-Lora-Utils" - }, - { - "author": "lrzjason", - "description": "NODES: SDXLMixSampler, LatentByRatio", - "files": [ - "https://raw.githubusercontent.com/lrzjason/ComfyUIJasonNode/main/SDXLMixSampler.py", - "https://raw.githubusercontent.com/lrzjason/ComfyUIJasonNode/main/LatentByRatio.py" - ], - "install_type": "copy", - "reference": "https://github.com/lrzjason/ComfyUIJasonNode", - "title": "ComfyUIJasonNode" - }, - { - "author": "lrzjason", - "description": "Remove content inside 'think' tag from reasoning llm", - "files": [ - "https://github.com/lrzjason/Comfyui-ThinkRemover" - ], - "install_type": "git-clone", - "reference": "https://github.com/lrzjason/Comfyui-ThinkRemover", - "title": "Comfyui-ThinkRemover" - }, - { - "author": "lrzjason", - "description": "This custom node for ComfyUI provides watermark detection capabilities using a YOLO model trained by [a/fancyfeast](https://huggingface.co/fancyfeast), the creator of JoyCaption. The model is originally hosted at [a/Hugging Face Space](https://huggingface.co/spaces/fancyfeast/joycaption-watermark-detection).", - "files": [ - "https://github.com/lrzjason/ComfyUI-Watermark-Detection" - ], - "install_type": "git-clone", - "reference": "https://github.com/lrzjason/ComfyUI-Watermark-Detection", - "title": "ComfyUI Watermark Detection Node" - }, - { - "author": "cozy_comm", - "description": "Post images and video to Discord. Nodes to facilitate communication using REST.", - "files": [ - "https://github.com/cozy-comfyui/cozy_comm" - ], - "id": "cozy_comm", - "install_type": "git-clone", - "nodename_pattern": " \\(cozy\\)", - "reference": "https://github.com/cozy-comfyui/cozy_comm", - "title": "Cozy Communication" - }, - { - "author": "RhizoNymph", - "description": "Nodes to use [a/latte](https://github.com/Vchitect/Latte) for text to video generation", - "files": [ - "https://github.com/RhizoNymph/ComfyUI-Latte" - ], - "id": "latte", - "install_type": "git-clone", - "reference": "https://github.com/RhizoNymph/ComfyUI-Latte", - "title": "ComfyUI-Latte" - }, - { - "author": "RhizoNymph", - "description": "A node to replicate [a/https://huggingface.co/spaces/latentexplorers/latentnavigation-flux](A node to replicate https://huggingface.co/spaces/latentexplorers/latentnavigation-flux)", - "files": [ - "https://github.com/RhizoNymph/ComfyUI-CLIPSlider" - ], - "id": "clipslider", - "install_type": "git-clone", - "reference": "https://github.com/RhizoNymph/ComfyUI-CLIPSlider", - "title": "ComfyUI-CLIPSlider" - }, - { - "author": "RhizoNymph", - "description": "NODES:Color Wheel Generator", - "files": [ - "https://github.com/RhizoNymph/ComfyUI-ColorWheel" - ], - "install_type": "git-clone", - "reference": "https://github.com/RhizoNymph/ComfyUI-ColorWheel", - "title": "ComfyUI-ColorWheel" - }, - { - "author": "Marksusu", - "description": "MTCLIPEncode: An extension for ComfyUI's CLIPTextEncode node, offering multilingual translation (using MarianMT) and prompt enhancement (using Ollama). Seamlessly translate your native language prompts into English and further optimize them for generating your desired images with Stable Diffusion. Supports Krita AI Diffusion.", - "files": [ - "https://github.com/Marksusu/ComfyUI_MTCLIPEncode" - ], - "id": "mtclipencode", - "install_type": "git-clone", - "reference": "https://github.com/Marksusu/ComfyUI_MTCLIPEncode", - "title": "ComfyUI_MTCLIPEncode" - }, - { - "author": "fssorc", - "description": "Match two faces' shape before using other face swap nodes\nFace-swapping tools typically only replace facial features during the swap, without altering the facial shape. When there is a significant difference in facial shape between the target person and the person in the original photo, the result of the face swap is less satisfactory.\nThis project is a small script that can first liquefy and stretch the face in the original photo according to the horizontal and vertical proportions of the target person's facial contour. The resulting image can be used as input for other face-swapping nodes.", - "files": [ - "https://github.com/fssorc/ComfyUI_FaceShaper" - ], - "id": "facesharper", - "install_type": "git-clone", - "reference": "https://github.com/fssorc/ComfyUI_FaceShaper", - "title": "ComfyUI_FaceShaper" - }, - { - "author": "fssorc", - "description": "Generate transition frames between two character posture images. The prerequisite for running is to have installed comfyui_controlnet_aux, using its Open Pose or DWPose preprocessor", - "files": [ - "https://github.com/fssorc/ComfyUI_pose_inter" - ], - "install_type": "git-clone", - "reference": "https://github.com/fssorc/ComfyUI_pose_inter", - "title": "ComfyUI_pose_inter" - }, - { - "author": "fssorc", - "description": "Perform a Fast Fourier Transform on the image, and then users can freely select the filtering range to filter the image. The main function is to remove the grid patterns on the image, and it can also perform high-pass filtering and low-pass filtering. The detailed workflow is shown in the figure below. The PNG file contains the ComfyUI workflow.The working principle is similar to the FFT filter in Photoshop.", - "files": [ - "https://github.com/fssorc/ComfyUI_FFT" - ], - "install_type": "git-clone", - "reference": "https://github.com/fssorc/ComfyUI_FFT", - "title": "ComfyUI_FFT" - }, - { - "author": "fssorc", - "description": "Wrap Rope into ComfyUI, do a little change to use in ComfyUI. All credit goes to Hillobar and his ROPE [\u3141/https://github.com/Hillobar/Rope](https://github.com/Hillobar/Rope)", - "files": [ - "https://github.com/fssorc/ComfyUI_RopeWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/fssorc/ComfyUI_RopeWrapper", - "title": "ComfyUI_RopeWrapper" - }, - { - "author": "BetaDoggo", - "description": "Just a simple node to filter out NSFW outputs. This node utilizes [a/AdamCodd/vit-base-nsfw-detector](https://huggingface.co/AdamCodd/vit-base-nsfw-detector) to score the outputs. I chose this model because it's small, fast, and performed very well in my testing. Nudity tends to be scored in the 0.95+ range, but I've set the default to 0.8 as a safe baseline.", - "files": [ - "https://github.com/BetaDoggo/ComfyUI-YetAnotherSafetyChecker" - ], - "id": "yetanothersafetychecker", - "install_type": "git-clone", - "reference": "https://github.com/BetaDoggo/ComfyUI-YetAnotherSafetyChecker", - "title": "ComfyUI YetAnotherSafetyChecker" - }, - { - "author": "BetaDoggo", - "description": "100% of code taken from [a/https://gist.github.com/neggles/ecb6327251a9e274428d07636c727eb9](https://gist.github.com/neggles/ecb6327251a9e274428d07636c727eb9).", - "files": [ - "https://github.com/BetaDoggo/ComfyUI-WDV-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/BetaDoggo/ComfyUI-WDV-Nodes", - "title": "neggles/ComfyUI-WDV-Nodes [gist-wrapper]" - }, - { - "author": "BetaDoggo", - "description": "A silly POC Video Player for ComfyUI", - "files": [ - "https://github.com/BetaDoggo/ComfyUI-VideoPlayer" - ], - "id": "videoplayer", - "install_type": "git-clone", - "reference": "https://github.com/BetaDoggo/ComfyUI-VideoPlayer", - "title": "ComfyUI Video Player" - }, - { - "author": "BetaDoggo", - "description": "A revolutionary technique for increasing output variety.", - "files": [ - "https://github.com/BetaDoggo/ComfyUI-Gatcha-Embedding" - ], - "install_type": "git-clone", - "reference": "https://github.com/BetaDoggo/ComfyUI-Gatcha-Embedding", - "title": "Gatcha Embeddings" - }, - { - "author": "BetaDoggo", - "description": "A set of nodes for interfacing with the FastSDCPU webserver.", - "files": [ - "https://github.com/BetaDoggo/ComfyUI-FastSDCPU" - ], - "install_type": "git-clone", - "reference": "https://github.com/BetaDoggo/ComfyUI-FastSDCPU", - "title": "ComfyUI-FastSDCPU" - }, - { - "author": "BetaDoggo", - "description": "Nodes for using models from online providers. Flux, Auraflow, SoteDiffusion, etc.", - "files": [ - "https://github.com/BetaDoggo/ComfyUI-Cloud-APIs" - ], - "install_type": "git-clone", - "reference": "https://github.com/BetaDoggo/ComfyUI-Cloud-APIs", - "title": "ComfyUI-Cloud-APIs" - }, - { - "author": "WX-NPS1598", - "description": "A very useful automatic cropping tool! It can realize cropping, expansion and rotation functions in the form of a slider. ", - "files": [ - "https://github.com/WX-NPS1598/ComfyUI-Auto_Crop_By_NPS" - ], - "id": "autocrop-nps", - "install_type": "git-clone", - "reference": "https://github.com/WX-NPS1598/ComfyUI-Auto_Crop_By_NPS", - "title": "Auto Crop By NPS" - }, - { - "author": "googincheng", - "description": "Better user experience plugin for ComfyUI.", - "files": [ - "https://github.com/googincheng/ComfyUX" - ], - "id": "comfyux", - "install_type": "git-clone", - "reference": "https://github.com/googincheng/ComfyUX", - "title": "ComfyUX" - }, - { - "author": "wootwootwootwoot", - "description": "Batched Runge-Kutta Samplers for ComfyUI", - "files": [ - "https://github.com/wootwootwootwoot/ComfyUI-RK-Sampler" - ], - "id": "rk_sampler", - "install_type": "git-clone", - "reference": "https://github.com/wootwootwootwoot/ComfyUI-RK-Sampler", - "reference2": "https://github.com/memmaptensor/ComfyUI-RK-Sampler", - "title": "ComfyUI-RK-Sampler" - }, - { - "author": "Fantasy AI Studio", - "description": "Various custom nodes for ComfyUI", - "files": [ - "https://github.com/alanhuang67/ComfyUI-FAI-Node" - ], - "id": "FAI-Node", - "install_type": "git-clone", - "reference": "https://github.com/alanhuang67/ComfyUI-FAI-Node", - "title": "FAI-Node" - }, - { - "author": "MuziekMagie", - "description": "A [a/Matchering](https://github.com/sergree/matchering)-node for ComfyUI.\nNOTE: You take TWO audio files and feed them into Matchering", - "files": [ - "https://github.com/MuziekMagie/ComfyUI-Matchering" - ], - "id": "matchering", - "install_type": "git-clone", - "reference": "https://github.com/MuziekMagie/ComfyUI-Matchering", - "title": "ComfyUI-Matchering" - }, - { - "author": "Mintbeer96", - "description": "An OCR node for detect text in image and returns covering mask.", - "files": [ - "https://github.com/Mintbeer96/ComfyUI-KerasOCR" - ], - "install_type": "git-clone", - "reference": "https://github.com/Mintbeer96/ComfyUI-KerasOCR", - "title": "ComfyUI-KerasOCR" - }, - { - "author": "pikenrover", - "description": "Nodes:RandomPrompt, RandomPromptMixed, ImageScaleTo, EmptyLatentImageScaleBy, LoraLoaderExtended, Save Image w/Metadata, CheckpointLoaderSimpleExtended", - "files": [ - "https://github.com/pikenrover/ComfyUI_PRNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/pikenrover/ComfyUI_PRNodes", - "title": "ComfyUI_PRNodes" - }, - { - "author": "EnragedAntelope", - "description": "This custom node for ComfyUI allows you to use the Doubutsu small VLM model to describe images. Credit and further information on Doubutsu: [a/https://huggingface.co/qresearch/doubutsu-2b-pt-756](https://huggingface.co/qresearch/doubutsu-2b-pt-756)", - "files": [ - "https://github.com/EnragedAntelope/ComfyUI-Doubutsu-Describer" - ], - "install_type": "git-clone", - "reference": "https://github.com/EnragedAntelope/ComfyUI-Doubutsu-Describer", - "title": "ComfyUI-Doubutsu-Describer" - }, - { - "author": "EnragedAntelope", - "description": "Given min/max resolution constraints, this automatically suggests optimal dimensions while preserving aspect ratio. Ideal for Image to Image (I2I) and Image to Video (I2V) workflows!", - "files": [ - "https://github.com/EnragedAntelope/ComfyUI-ConstrainResolution" - ], - "install_type": "git-clone", - "reference": "https://github.com/EnragedAntelope/ComfyUI-ConstrainResolution", - "title": "ComfyUI-ConstrainResolution" - }, - { - "author": "EnragedAntelope", - "description": "A collection of ComfyUI custom nodes for interacting with various cloud services. These nodes are designed to work with any ComfyUI instance, including cloud-hosted environments (such as MimicPC) where users may have limited system access.", - "files": [ - "https://github.com/EnragedAntelope/ComfyUI-EACloudNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/EnragedAntelope/ComfyUI-EACloudNodes", - "title": "ComfyUI-EACloudNodes" - }, - { - "author": "EnragedAntelope", - "description": "Transform your images with cinematic lighting effects in a single click! ReLight is a powerful ComfyUI node that adds professional-grade lighting capabilities including multiple light sources, rim lighting, and 3D lighting simulation.", - "files": [ - "https://github.com/EnragedAntelope/comfyui-relight" - ], - "install_type": "git-clone", - "reference": "https://github.com/EnragedAntelope/comfyui-relight", - "title": "ComfyUI-ReLight" - }, - { - "author": "jn-jairo", - "description": "ComfyUI extension with patches and nodes.\nPatches:Preview device, Extension device, Temperature, Memory estimation, Optimizations, Easy generic inputs, Easy multiple inputs.\nNODES: Image nodes, Image/Area nodes, Image/Blip nodes, Image/Face nodes, Sampling nodes, Patch nodes, Primitive nodes, Primitive/Conversion nodes, Primitive/Process nodes, Workflow nodes, etc...", - "files": [ - "https://github.com/jn-jairo/jn_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/jn-jairo/jn_comfyui", - "title": "JNComfy" - }, - { - "author": "akierson", - "description": "Basic Color Nodes for ComfyUI", - "files": [ - "https://github.com/akierson/comfyui-colornodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/akierson/comfyui-colornodes", - "title": "comfyui-colornodes" - }, - { - "author": "akierson", - "description": "Misc Text Nodes for Comfy UI", - "files": [ - "https://github.com/akierson/ComfyUI-textnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/akierson/ComfyUI-textnodes", - "title": "ComfyUI-textnodes" - }, - { - "author": "ai-shizuka", - "description": "Nodes:ImageLoader, ImageSaver, ImagesSaver, ImageResize, ImageSize, GFPGANNode, MaskAddNode, Video Load, ...", - "files": [ - "https://github.com/ai-shizuka/ComfyUI-tbox" - ], - "install_type": "git-clone", - "reference": "https://github.com/ai-shizuka/ComfyUI-tbox", - "title": "ComfyUI-tbox" - }, - { - "author": "neverbiasu", - "description": "A ComfyUI extension for generating captions for your images. Runs on your own system, no external services used, no filter.\nUses various VLMs with APIs to generate captions for images. You can give instructions or ask questions in natural language.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-Image-Captioner" - ], - "id": "image-captioner", - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-Image-Captioner", - "title": "ComfyUI-Image-Captioner" - }, - { - "author": "neverbiasu", - "description": "This project adapts the SAM2 to incorporate functionalities from [a/comfyui_segment_anything](https://github.com/storyicon/comfyui_segment_anything). Many thanks to continue-revolution for their foundational work.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-SAM2" - ], - "id": "sam2", - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-SAM2", - "title": "ComfyUI SAM2(Segment Anything 2)" - }, - { - "author": "neverbiasu", - "description": "This project integrates [a/StyleShot](https://github.com/open-mmlab/StyleShot) functionality into ComfyUI, thanks to the foundational work by continue-revolution.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-StyleShot" - ], - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-StyleShot", - "title": "ComfyUI-StyleShot" - }, - { - "author": "neverbiasu", - "description": "This project adapts the dashscope([a/aliyun-bailian](https://bailian.console.aliyun.com)) api into ComfyUI.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-Dashscope" - ], - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-Dashscope", - "title": "ComfyUI-Dashscope" - }, - { - "author": "neverbiasu", - "description": "A ComfyUI integration for [a/ChatTTS](https://github.com/2noise/ChatTTS), enabling high-quality, controllable text-to-speech generation directly in your ComfyUI workflows.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-ChatTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-ChatTTS", - "title": "ComfyUI-ChatTTS" - }, - { - "author": "neverbiasu", - "description": "A ComfyUI custom node package based on the BAGEL-7B-MoT multimodal model.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-BAGEL" - ], - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-BAGEL", - "title": "ComfyUI-BAGEL" - }, - { - "author": "neverbiasu", - "description": "This repository adds ComfyUI custom nodes that wrap the Ovis-U1 multimodal model, exposing three primary workflows inside the ComfyUI editor.", - "files": [ - "https://github.com/neverbiasu/ComfyUI-Ovis-U1" - ], - "install_type": "git-clone", - "reference": "https://github.com/neverbiasu/ComfyUI-Ovis-U1", - "title": "ComfyUI-Ovis-U1" - }, - { - "author": "var1ableX", - "description": "Get Mask Dimensions", - "files": [ - "https://github.com/var1ableX/ComfyUI_Accessories" - ], - "install_type": "git-clone", - "reference": "https://github.com/var1ableX/ComfyUI_Accessories", - "title": "ComfyUI_Accessories" - }, - { - "author": "Makki_Shizu", - "description": "a simple resize image node(s) in comfyui. This repository is not specific to the length and width values of an image, but focuses more on adjusting the total pixel count, side length specifications, and size ratios of the image.", - "files": [ - "https://github.com/MakkiShizu/comfyui_reimgsize" - ], - "id": "reimgsize", - "install_type": "git-clone", - "reference": "https://github.com/MakkiShizu/comfyui_reimgsize", - "title": "comfyui_reimgsize" - }, - { - "author": "Makki_Shizu", - "description": "Optional wildcards in ComfyUI", - "files": [ - "https://github.com/MakkiShizu/ComfyUI-Prompt-Wildcards" - ], - "id": "Prompt-Wildcards", - "install_type": "git-clone", - "reference": "https://github.com/MakkiShizu/ComfyUI-Prompt-Wildcards", - "title": "ComfyUI-Prompt-Wildcards" - }, - { - "author": "Makki_Shizu", - "description": "Qwen2.5-VL in ComfyUI", - "files": [ - "https://github.com/MakkiShizu/ComfyUI-Qwen2_5-VL" - ], - "id": "Qwen2_5-VL", - "install_type": "git-clone", - "reference": "https://github.com/MakkiShizu/ComfyUI-Qwen2_5-VL", - "title": "ComfyUI-Qwen2_5-VL" - }, - { - "author": "Makki_Shizu", - "description": "Makki's self custom nodes for ComfyUI.", - "files": [ - "https://github.com/MakkiShizu/ComfyUI-MakkiTools" - ], - "id": "MakkiTools", - "install_type": "git-clone", - "reference": "https://github.com/MakkiShizu/ComfyUI-MakkiTools", - "title": "ComfyUI-MakkiTools" - }, - { - "author": "JosefKuchar", - "description": "Advanced tiling of various shapes for ComfyUI", - "files": [ - "https://github.com/JosefKuchar/ComfyUI-AdvancedTiling" - ], - "install_type": "git-clone", - "reference": "https://github.com/JosefKuchar/ComfyUI-AdvancedTiling", - "title": "ComfyUI-AdvancedTiling" - }, - { - "author": "Parameshvadivel", - "description": "Nodes:Preview SVG", - "files": [ - "https://github.com/Parameshvadivel/ComfyUI-SVGview" - ], - "id": "svgview", - "install_type": "git-clone", - "reference": "https://github.com/Parameshvadivel/ComfyUI-SVGview", - "title": "ComfyUI-SVGview" - }, - { - "author": "Visionatrix", - "description": "The ComfyUI-Visionatrix nodes are designed for convenient ComfyUI to [a/Visionatrix](https://github.com/Visionatrix/Visionatrix) workflow support migration, in particular to extract prompt input params (input, textarea, checkbox, select, range, file) to be used in simplified Visionatrix UI.", - "files": [ - "https://github.com/Visionatrix/ComfyUI-Visionatrix" - ], - "id": "visionatrix", - "install_type": "git-clone", - "reference": "https://github.com/Visionatrix/ComfyUI-Visionatrix", - "title": "ComfyUI-Visionatrix" - }, - { - "author": "Visionatrix", - "description": "ComfyUI Nodes for Remote VAE Decoding.", - "files": [ - "https://github.com/Visionatrix/ComfyUI-RemoteVAE" - ], - "install_type": "git-clone", - "reference": "https://github.com/Visionatrix/ComfyUI-RemoteVAE", - "title": "ComfyUI-RemoteVAE" - }, - { - "author": "Visionatrix", - "description": "Nodes for Google Gemini API, focusing on backward compatibility and stability within ComfyUI.", - "files": [ - "https://github.com/Visionatrix/ComfyUI-Gemini" - ], - "install_type": "git-clone", - "reference": "https://github.com/Visionatrix/ComfyUI-Gemini", - "title": "ComfyUI-Gemini" - }, - { - "author": "liangt", - "description": "Extend LoadImage node with subfolder support", - "files": [ - "https://github.com/liangt/comfyui-loadimagewithsubfolder" - ], - "install_type": "git-clone", - "reference": "https://github.com/liangt/comfyui-loadimagewithsubfolder", - "title": "comfyui-loadimagewithsubfolder" - }, - { - "author": "vault-developer", - "description": "ComfyuiImageBlender is a custom node for ComfyUI. It may be used to blend two images together using a specified blending mode.", - "files": [ - "https://github.com/vault-developer/comfyui-image-blender" - ], - "install_type": "git-clone", - "reference": "https://github.com/vault-developer/comfyui-image-blender", - "title": "ImageBlender" - }, - { - "author": "gisu", - "description": "Collection of nodes for the automation of workflows", - "files": [ - "https://github.com/gisu/comfyui-foxpack" - ], - "id": "foxp", - "install_type": "git-clone", - "reference": "https://github.com/gisu/comfyui-foxpack", - "title": "foxpack" - }, - { - "author": "webfiltered", - "description": "This node provides a simple way to view the output of many nodes, without leaving ComfyUI.", - "files": [ - "https://github.com/webfiltered/DebugNode-ComfyUI" - ], - "id": "debugnode", - "install_type": "git-clone", - "reference": "https://github.com/webfiltered/DebugNode-ComfyUI", - "title": "WTF? - a debug node for ComfyUI" - }, - { - "author": "pzc163", - "description": "Comfyui-CatVTON This repository is the modified official Comfyui node of CatVTON, which is a simple and efficient virtual try-on diffusion model with 1) Lightweight Network (899.06M parameters totally), 2) Parameter-Efficient Training (49.57M parameters trainable) 3) Simplified Inference (< 8G VRAM for 1024X768 resolution).\nThe original GitHub project is [a/https://github.com/Zheng-Chong/CatVTON](https://github.com/Zheng-Chong/CatVTON)", - "files": [ - "https://github.com/pzc163/Comfyui-CatVTON" - ], - "id": "comfyui-catvton", - "install_type": "git-clone", - "reference": "https://github.com/pzc163/Comfyui-CatVTON", - "title": "Comfyui-CatVTON" - }, - { - "author": "pzc163", - "description": "This is an implementation of [MiniCPMv2_6-prompt-generator](https://huggingface.co/pzc163/MiniCPMv2_6-prompt-generator) by [ComfyUI](https://github.com/comfyanonymous/ComfyUI), including support for single-image caption, generate prompt by upload image and batch-images Prompt generation.", - "files": [ - "https://github.com/pzc163/Comfyui_MiniCPMv2_6-prompt-generator" - ], - "id": "Comfyui_MiniCPMv2_6-prompt-generator", - "install_type": "git-clone", - "reference": "https://github.com/pzc163/Comfyui_MiniCPMv2_6-prompt-generator", - "title": "Comfyui_MiniCPMv2_6-prompt-generator" - }, - { - "author": "aisabervisionlab", - "description": "This is a simple node for connecting images. For pictures of the same size, users can choose to fill in vertical in the parameter to connect the pictures vertically or fill in horizontal to connect the pictures horizontally.", - "files": [ - "https://github.com/aisabervisionlab/ComfyUI_merge_ASVL" - ], - "id": "merge-asvl", - "install_type": "git-clone", - "reference": "https://github.com/aisabervisionlab/ComfyUI_merge_ASVL", - "title": "ComfyUI_merge_ASVL" - }, - { - "author": "akatz-ai", - "description": "Simple custom node pack for nodes I use in my workflows. Includes Dilate Mask Linear for animating masks.", - "files": [ - "https://github.com/akatz-ai/ComfyUI-AKatz-Nodes" - ], - "id": "akatz-ai", - "install_type": "git-clone", - "reference": "https://github.com/akatz-ai/ComfyUI-AKatz-Nodes", - "title": "Akatz Custom Nodes" - }, - { - "author": "akatz-ai", - "description": "Implementation of DepthFlow nodes for ComfyUI, adds a 2.5D parallax effect to images and videos. Compatible with Ryan's Flex system.", - "files": [ - "https://github.com/akatz-ai/ComfyUI-Depthflow-Nodes" - ], - "id": "depthflow-akatz-ai", - "install_type": "git-clone", - "reference": "https://github.com/akatz-ai/ComfyUI-Depthflow-Nodes", - "title": "\ud83c\udf0a Depthflow Nodes" - }, - { - "author": "akatz-ai", - "description": "Implementation of DepthCrafter nodes for ComfyUI, create consistent depth maps for your videos.", - "files": [ - "https://github.com/akatz-ai/ComfyUI-DepthCrafter-Nodes" - ], - "id": "depthcrafter-akatz-ai", - "install_type": "git-clone", - "reference": "https://github.com/akatz-ai/ComfyUI-DepthCrafter-Nodes", - "title": "DepthCrafter Nodes" - }, - { - "author": "akatz-ai", - "description": "Implementation of X-Portrait nodes for ComfyUI, animate portraits with an input video and a reference image.", - "files": [ - "https://github.com/akatz-ai/ComfyUI-X-Portrait-Nodes" - ], - "id": "comfyui-x-portrait-nodes", - "install_type": "git-clone", - "reference": "https://github.com/akatz-ai/ComfyUI-X-Portrait-Nodes", - "title": "ComfyUI-X-Portrait-Nodes" - }, - { - "author": "akatz-ai", - "description": "Custom nodes for performing basic math operations", - "files": [ - "https://github.com/akatz-ai/ComfyUI-Basic-Math" - ], - "install_type": "git-clone", - "reference": "https://github.com/akatz-ai/ComfyUI-Basic-Math", - "title": "ComfyUI-Basic-Math" - }, - { - "author": "teward", - "description": "ComfyUI custom node that activates integration with a Sentry instance for loading. Has no actual nodes.", - "files": [ - "https://github.com/teward/Comfy-Sentry" - ], - "install_type": "git-clone", - "reference": "https://github.com/teward/Comfy-Sentry", - "title": "Comfy-Sentry" - }, - { - "author": "Fuou Marinas", - "description": "A collection of ComfyUI nodes. Including: WFEN, RealViFormer, ProPIH", - "files": [ - "https://github.com/FuouM/FM_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/FuouM/FM_nodes", - "title": "FM_nodes" - }, - { - "author": "Fuou Marinas", - "description": "ComfyUI-native nodes to run First Order Motion Model for Image Animation and its non-diffusion-based successors. [a/https://github.com/AliaksandrSiarohin/first-order-model](https://github.com/AliaksandrSiarohin/first-order-model)", - "files": [ - "https://github.com/FuouM/ComfyUI-FirstOrderMM" - ], - "id": "fomm", - "install_type": "git-clone", - "reference": "https://github.com/FuouM/ComfyUI-FirstOrderMM", - "title": "ComfyUI-FirstOrderMM" - }, - { - "author": "Fuou Marinas", - "description": "Nodes:Neural Neighbor, CAST, EFDM, MicroAST, Coral Color Transfer.", - "files": [ - "https://github.com/FuouM/ComfyUI-StyleTransferPlus" - ], - "id": "styletransferplus", - "install_type": "git-clone", - "reference": "https://github.com/FuouM/ComfyUI-StyleTransferPlus", - "title": "ComfyUI-StyleTransferPlus" - }, - { - "author": "Fuou Marinas", - "description": "Run EbSynth, Fast Example-based Image Synthesis and Style Transfer, in ComfyUI.", - "files": [ - "https://github.com/FuouM/ComfyUI-EbSynth" - ], - "id": "comfyEbsynth", - "install_type": "git-clone", - "reference": "https://github.com/FuouM/ComfyUI-EbSynth", - "title": "ComfyUI-EbSynth" - }, - { - "author": "Fuou Marinas", - "description": "MatAnyone in ComfyUI (Remove background)", - "files": [ - "https://github.com/FuouM/ComfyUI-MatAnyone" - ], - "id": "ComfyUI-MatAnyone", - "install_type": "git-clone", - "reference": "https://github.com/FuouM/ComfyUI-MatAnyone", - "title": "ComfyUI-MatAnyone" - }, - { - "author": "MiddleKD", - "description": "ComfyUI's Smart Memory Management efficiently manages RAM, GPU memory, and garbage collection. This feature keeps frequently used models in memory to increase inference speed, and dynamically releases less important models when memory is low to optimize resources. However, not all ComfyUI custom node developers create nodes that are compatible with Smart memory management. This includes several impressive models. Mem-safe-wrapper is a custom node that wraps these model nodes to enable ComfyUI's Smart memory management capabilities.", - "files": [ - "https://github.com/MiddleKD/ComfyUI-mem-safe-wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/MiddleKD/ComfyUI-mem-safe-wrapper", - "title": "ComfyUI-mem-safe-wrapper" - }, - { - "author": "MiddleKD", - "description": "This is a ComfyUI custom node that helps generate images while preserving the text, logos, and details of e-commerce products.", - "files": [ - "https://github.com/MiddleKD/ComfyUI-productfix" - ], - "install_type": "git-clone", - "reference": "https://github.com/MiddleKD/ComfyUI-productfix", - "title": "ComfyUI-productfix" - }, - { - "author": "MiddleKD", - "description": "ComfyUI-denoise-mask-scheduler experimental approach involves selectively applying a denoise mask at each step during the inpainting inference process in diffusion models.", - "files": [ - "https://github.com/MiddleKD/ComfyUI-denoise-mask-scheduler" - ], - "install_type": "git-clone", - "reference": "https://github.com/MiddleKD/ComfyUI-denoise-mask-scheduler", - "title": "ComfyUI-denoise-mask-scheduler" - }, - { - "author": "PowerHouseMan", - "description": "AdvancedLivePortrait with Facial expression editor", - "files": [ - "https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait" - ], - "id": "advancedliveportrait", - "install_type": "git-clone", - "reference": "https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait", - "title": "ComfyUI-AdvancedLivePortrait" - }, - { - "author": "cdxOo", - "description": "multiline text node that strips c-style comments (i.e.'//' and '/* ... */') before passing output string downstream", - "files": [ - "https://github.com/cdxOo/comfyui-text-node-with-comments" - ], - "install_type": "git-clone", - "reference": "https://github.com/cdxOo/comfyui-text-node-with-comments", - "title": "Text Node With Comments (@cdxoo)" - }, - { - "author": "emojiiii", - "description": "Nodes:MultiTextEncode, KolorsMultiTextEncode, Caption, BatchImageProcessor", - "files": [ - "https://github.com/emojiiii/ComfyUI_Emojiiii_Custom_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/emojiiii/ComfyUI_Emojiiii_Custom_Nodes", - "title": "ComfyUI_Emojiiii_Custom_Nodes" - }, - { - "author": "aonekoss", - "description": "A simple counter, when pressing 'Queue Prompt' resets the count.", - "files": [ - "https://github.com/oleksandr612/ComfyUI-Counter" - ], - "install_type": "git-clone", - "reference": "https://github.com/oleksandr612/ComfyUI-Counter", - "title": "ComfyUI-Counter" - }, - { - "author": "alpertunga-bile", - "description": "Using image caption models to extract prompts in ComfyUI", - "files": [ - "https://github.com/alpertunga-bile/image-caption-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/alpertunga-bile/image-caption-comfyui", - "title": "image-caption-comfyui" - }, - { - "author": "Anibaaal", - "description": "Nodes: Easy Resolution Picker, Save Diffusion Model, Load Checkpoint BNB On the fly, Load UNET BNB On the fly", - "files": [ - "https://github.com/Anibaaal/ComfyUI-UX-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Anibaaal/ComfyUI-UX-Nodes", - "title": "ComfyUI UX Nodes" - }, - { - "author": "NMWave", - "description": "A small set of useful nodes which aid with the tagging process by splitting tags and strings, alternating tags from multiple sources and removing duplicates.", - "files": [ - "https://github.com/NMWave/ComfyUI-Nader-Tagging" - ], - "id": "naderimagecaptionandtag", - "install_type": "git-clone", - "reference": "https://github.com/NMWave/ComfyUI-Nader-Tagging", - "title": "Image Captioning and Tagging Assistor Nodes" - }, - { - "author": "caleboleary", - "description": "This ComfyUI node library builds upon the work done to train the [a/Arc2Face](https://github.com/foivospar/Arc2Face) model by foivospar. It provides a set of nodes for ComfyUI that allow users to extract face embeddings, generate images based on these embeddings, and perform image-to-image transformations.", - "files": [ - "https://github.com/caleboleary/ComfyUI-Arc2Face" - ], - "install_type": "git-clone", - "reference": "https://github.com/caleboleary/ComfyUI-Arc2Face", - "title": "Arc2Face ComfyUI Node Library" - }, - { - "author": "GeekyGhost", - "description": "GeekyRemB is a powerful suite of image processing nodes for ComfyUI, offering advanced background removal, animation, lighting effects, and keyframe-based positioning. Built on the rembg library with additional capabilities for chroma keying, mask refinement, realistic lighting, shadow generation, and dynamic animations.", - "files": [ - "https://github.com/GeekyGhost/ComfyUI-GeekyRemB" - ], - "install_type": "git-clone", - "reference": "https://github.com/GeekyGhost/ComfyUI-GeekyRemB", - "title": "ComfyUI-GeekyRemB" - }, - { - "author": "GeekyGhost", - "description": "A powerful and feature-rich custom node collection for ComfyUI that integrates the Kokoro TTS (Text-to-Speech) system with advanced voice modification capabilities. This package allows you to generate natural-sounding speech and apply various voice effects within ComfyUI workflows.", - "files": [ - "https://github.com/GeekyGhost/ComfyUI-Geeky-Kokoro-TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/GeekyGhost/ComfyUI-Geeky-Kokoro-TTS", - "title": "ComfyUI-Geeky-Kokoro-TTS" - }, - { - "author": "GeekyGhost", - "description": "Audio Mixing node for ComfyUI", - "files": [ - "https://github.com/GeekyGhost/ComfyUI_Geeky_AudioMixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/GeekyGhost/ComfyUI_Geeky_AudioMixer", - "title": "ComfyUI Geeky AudioMixer" - }, - { - "author": "GeekyGhost", - "description": "Unofficial optimized and enhanced fork of [a/LatentSync 1.5](https://github.com/bytedance/LatentSync) implementation for ComfyUI on Windows and WSL 2.0.", - "files": [ - "https://github.com/GeekyGhost/ComfyUI-Geeky-LatentSyncWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/GeekyGhost/ComfyUI-Geeky-LatentSyncWrapper", - "title": "ComfyUI-Geeky-LatentSyncWrapper 1.5" - }, - { - "author": "GeekyGhost", - "description": "Lets you send a section of an image to qwen edit or kontext to help isolate areas in need of change", - "files": [ - "https://github.com/GeekyGhost/ComfyUI-Image-Segmenting-Loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/GeekyGhost/ComfyUI-Image-Segmenting-Loader", - "title": "ComfyUI-Image-Segmenting-Loader" - }, - { - "author": "Dobidop", - "description": "Two simple nodes for stereoscopic image generation. Nodes: Stereo Image Node - a basic port from the Automatic1111 stereo script in thygate/stable-diffusion-webui-depthmap-script, LazyStereo - a na\u00efve stereo image generator", - "files": [ - "https://github.com/Dobidop/ComfyStereo" - ], - "id": "simple-stereoscopic", - "install_type": "git-clone", - "reference": "https://github.com/Dobidop/ComfyStereo", - "title": "Dobidop ComfyStereo" - }, - { - "author": "SeniorPioner", - "description": "Node Pack: PromptChecker for token toggling, KoboldCPP API, ModelMerging, Telegram-Bot-API, and more", - "files": [ - "https://github.com/bananasss00/ComfyUI-SP-Nodes" - ], - "id": "spnodes", - "install_type": "git-clone", - "reference": "https://github.com/bananasss00/ComfyUI-SP-Nodes", - "title": "SP-Nodes" - }, - { - "author": "leeguandong", - "description": "ComfyUI for [a/M3Net](https://github.com/I2-Multimedia-Lab/M3Net)", - "files": [ - "https://github.com/leeguandong/ComfyUI_M3Net" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_M3Net", - "title": "ComfyUI_M3Net" - }, - { - "author": "leeguandong", - "description": "ComfyUI for [a/InternVL](https://github.com/OpenGVLab/InternVL)", - "files": [ - "https://github.com/leeguandong/ComfyUI_InternVL2" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_InternVL2", - "title": "ComfyUI_InternVL2" - }, - { - "author": "leeguandong", - "description": "ComfyUI for [a/LLaSM](https://huggingface.co/spaces/LinkSoul/LLaSM)", - "files": [ - "https://github.com/leeguandong/ComfyUI_LLaSM" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_LLaSM", - "title": "ComfyUI_LLaSM" - }, - { - "author": "leeguandong", - "description": "Nodes:Load Video to Images, Image to Canny, ControlNet Model Loader, VEDit Model Loader, VEdit Sampler. [a/https://github.com/SingleZombie/DiffusersExample/tree/main/ReplaceAttn](https://github.com/SingleZombie/DiffusersExample/tree/main/ReplaceAttn)", - "files": [ - "https://github.com/leeguandong/ComfyUI_VideoEditing" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_VideoEditing", - "title": "ComfyUI nodes to use VideoEditing" - }, - { - "author": "leeguandong", - "description": "ComfyUI for [a/CrossImageAttention](https://github.com/garibida/cross-image-attention)", - "files": [ - "https://github.com/leeguandong/ComfyUI_CrossImageAttention" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_CrossImageAttention", - "title": "ComfyUI nodes to use CrossImageAttention" - }, - { - "author": "leeguandong", - "description": "ComfyUI for [a/style-aligned](https://github.com/google/style-aligned)", - "files": [ - "https://github.com/leeguandong/ComfyUI_Style_Aligned" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_Style_Aligned", - "title": "ComfyUI nodes to use Style-Aligned" - }, - { - "author": "leeguandong", - "description": "NODES:HF ModelLoader, Show Images, Text2Image Inference, Decode Latent, Show CrossAttn Map, Show SelfAttn Map", - "files": [ - "https://github.com/leeguandong/ComfyUI_VisualAttentionMap" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_VisualAttentionMap", - "title": "ComfyUI_VisualAttentionMap" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use MasaCtrl", - "files": [ - "https://github.com/leeguandong/ComfyUI_MasaCtrl" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_MasaCtrl", - "title": "ComfyUI nodes to use MasaCtrl" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use CompareModelWeights", - "files": [ - "https://github.com/leeguandong/ComfyUI_CompareModelWeights" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_CompareModelWeights", - "title": "ComfyUI_CompareModelWeights" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use FluxCustomId\nOriginal repo: [a/https://github.com/damo-cv/FLUX-customID](https://github.com/damo-cv/FLUX-customID)", - "files": [ - "https://github.com/leeguandong/ComfyUI_FluxCustomId" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_FluxCustomId", - "title": "ComfyUI_FluxCustomId" - }, - { - "author": "leeguandong", - "description": "In Flux, the T5 and CLIP in the text branch are weighted separately to regulate the strength of text-side embeddings.", - "files": [ - "https://github.com/leeguandong/ComfyUI_FluxClipWeight" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_FluxClipWeight", - "title": "ComfyUI nodes to use FluxClipWeight" - }, - { - "author": "leeguandong", - "description": "The attention mask in the T5 part of flux and SD3 utilizes the text-side attention mask to make the model focus more on text embeddings during image generation, thereby enhancing semantic alignment with the text.", - "files": [ - "https://github.com/leeguandong/ComfyUI_FluxAttentionMask" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_FluxAttentionMask", - "title": "ComfyUI nodes to use AttentionMask" - }, - { - "author": "leeguandong", - "description": "The latest DIT architecture-based image generation model from Zhipu that supports Chinese text generation.", - "files": [ - "https://github.com/leeguandong/ComfyUI_Cogview4" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_Cogview4", - "title": "ComfyUI_Cogview4" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use [a/1Prompt1Story](https://github.com/byliutao/1Prompt1Story)", - "files": [ - "https://github.com/leeguandong/ComfyUI_1Prompt1Story" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_1Prompt1Story", - "title": "ComfyUI_1Prompt1Story" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use [a/ChatGen](https://github.com/chengyou-jia/ChatGen)", - "files": [ - "https://github.com/leeguandong/ComfyUI_ChatGen" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_ChatGen", - "title": "ComfyUI_ChatGen" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use [a/DeepSeek-VL2](https://github.com/deepseek-ai/DeepSeek-VL2)", - "files": [ - "https://github.com/leeguandong/ComfyUI_DeepSeekVL2" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_DeepSeekVL2", - "title": "ComfyUI_DeepSeekVL2" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use [a/Flux-version-LayerDiffuse](https://github.com/RedAIGC/Flux-version-LayerDiffuse)", - "files": [ - "https://github.com/leeguandong/ComfyUI_FluxLayerDiffuse" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_FluxLayerDiffuse", - "title": "ComfyUI_FluxLayerDiffuse" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use [a/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it)", - "files": [ - "https://github.com/leeguandong/ComfyUI_Gemma3" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_Gemma3", - "title": "ComfyUI_Gemma3" - }, - { - "author": "leeguandong", - "description": "ComfyUI nodes to use [a/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)", - "files": [ - "https://github.com/leeguandong/ComfyUI_QWQ32B" - ], - "install_type": "git-clone", - "reference": "https://github.com/leeguandong/ComfyUI_QWQ32B", - "title": "ComfyUI_QWQ32B" - }, - { - "author": "lenskikh", - "description": "Node:Prompt Worker. A text manipulation node for postprocessing of prompt.", - "files": [ - "https://github.com/lenskikh/ComfyUI-Prompt-Worker" - ], - "install_type": "git-clone", - "reference": "https://github.com/lenskikh/ComfyUI-Prompt-Worker", - "title": "Propmt Worker" - }, - { - "author": "kappa54", - "description": "Custom nodes intended to improve usability.", - "files": [ - "https://github.com/kappa54m/ComfyUI_Usability" - ], - "id": "comfyui_usability", - "install_type": "git-clone", - "reference": "https://github.com/kappa54m/ComfyUI_Usability", - "title": "ComfyUI Usability" - }, - { - "author": "IuvenisSapiens", - "description": "This is an implementation of [MiniCPM-V-4](https://github.com/OpenBMB/MiniCPM-V) by [ComfyUI](https://github.com/comfyanonymous/ComfyUI), including support for text-based queries, video queries, single-image queries, and multi-image queries to generate captions or responses.", - "files": [ - "https://github.com/IuvenisSapiens/ComfyUI_MiniCPM-V-4" - ], - "id": "ComfyUI_MiniCPM-V-4", - "install_type": "git-clone", - "reference": "https://github.com/IuvenisSapiens/ComfyUI_MiniCPM-V-4", - "title": "ComfyUI_MiniCPM-V-4" - }, - { - "author": "IuvenisSapiens", - "description": "This is an implementation of [a/Qwen2-Audio-7B-Instruct-Int4](https://github.com/QwenLM/Qwen2-Audio) by [a/ComfyUI](https://github.com/comfyanonymous/ComfyUI), including support for text-based queries and audio queries to generate captions or responses.", - "files": [ - "https://github.com/IuvenisSapiens/ComfyUI_Qwen2-Audio-7B-Instruct-Int4" - ], - "id": "qwen2-audio-7b-instruct-int4", - "install_type": "git-clone", - "reference": "https://github.com/IuvenisSapiens/ComfyUI_Qwen2-Audio-7B-Instruct-Int4", - "title": "ComfyUI_Qwen2-Audio-7B-Instruct-Int4" - }, - { - "author": "IuvenisSapiens", - "description": "This is an implementation of [a/Qwen2.5-VL-Instruct](https://github.com/QwenLM/Qwen2.5-VL) by [a/ComfyUI](https://github.com/comfyanonymous/ComfyUI), which includes, but is not limited to, support for text-based queries, video queries, single-image queries, and multi-image queries to generate captions or responses.", - "files": [ - "https://github.com/IuvenisSapiens/ComfyUI_Qwen2_5-VL-Instruct" - ], - "id": "ComfyUI_Qwen2_5-VL-Instruct", - "install_type": "git-clone", - "reference": "https://github.com/IuvenisSapiens/ComfyUI_Qwen2_5-VL-Instruct", - "title": "ComfyUI_Qwen2-VL-Instruct" - }, - { - "author": "mltask", - "description": "a set of nodes to help u run ai code using MLTask", - "files": [ - "https://github.com/misterjoessef/MLTask_ComfyUI" - ], - "id": "mltask_comfyui", - "install_type": "git-clone", - "reference": "https://github.com/misterjoessef/MLTask_ComfyUI", - "title": "MLTask_ComfyUI" - }, - { - "author": "smlbiobot", - "description": "Flux Pro via Replicate API\nCreate API key at [a/https://replicate.com/account/api-tokens](https://replicate.com/account/api-tokens)\nCopy config.ini.example to config.ini and put the replicate key there.", - "files": [ - "https://github.com/smlbiobot/ComfyUI-Flux-Replicate-API" - ], - "id": "replicate-api", - "install_type": "git-clone", - "reference": "https://github.com/smlbiobot/ComfyUI-Flux-Replicate-API", - "title": "ComfyUI-Flux-Replicate-API" - }, - { - "author": "smlbiobot", - "description": "Prompt Expansion for Stable Diffusion, using Deepseek API.", - "files": [ - "https://github.com/smlbiobot/sml-comfyui-prompt-expansion" - ], - "id": "sml-comfyui-prompt-expansion", - "install_type": "git-clone", - "reference": "https://github.com/smlbiobot/sml-comfyui-prompt-expansion", - "title": "sml-comfyui-prompt-expansion" - }, - { - "author": "Jjulianadv", - "description": "This extension provides the ability to build prompts using wildcards for each region of a split image.", - "files": [ - "https://github.com/Julian-adv/WildDivide" - ], - "install_type": "git-clone", - "reference": "https://github.com/Julian-adv/WildDivide", - "title": "Wild Divide" - }, - { - "author": "goburiin", - "description": "Nodes:NSFW Detector", - "files": [ - "https://github.com/goburiin/nsfwrecog-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/goburiin/nsfwrecog-comfyui", - "title": "nsfwrecog-comfyui" - }, - { - "author": "eastoc", - "description": "Segment and Recognize Anything at Any Granularity.", - "files": [ - "https://github.com/eastoc/ComfyUI_SemanticSAM" - ], - "install_type": "git-clone", - "reference": "https://github.com/eastoc/ComfyUI_SemanticSAM", - "title": "Semantic-SAM" - }, - { - "author": "LING-APE", - "description": "Simple resuluition calculator to convert pixel resolution and aspect ratio to laten friendlt pixel width and height size.", - "files": [ - "https://github.com/Ling-APE/ComfyUI-PixelResolutionCalculator" - ], - "id": "PixelCalulator", - "install_type": "git-clone", - "reference": "https://github.com/Ling-APE/ComfyUI-PixelResolutionCalculator", - "title": "ComfyUI-PixelResolutionCalculator" - }, - { - "author": "Cyber-Blacat", - "description": "Some simple&practical ComfyUI image processing nodes.", - "files": [ - "https://github.com/Cyber-BlackCat/ComfyUI-MoneyMaker" - ], - "install_type": "git-clone", - "reference": "https://github.com/Cyber-BlackCat/ComfyUI-MoneyMaker", - "title": "ComfyUI-Yuan" - }, - { - "author": "blackcodetavern", - "description": "ComfyUI-Benripack is an extension for ComfyUI that provides a CharacterPipe node. This node allows for managing various elements such as images, prompts, and models in a single structure, simplifying the workflow for character-based image generation.", - "files": [ - "https://github.com/blackcodetavern/ComfyUI-Benripack" - ], - "install_type": "git-clone", - "reference": "https://github.com/blackcodetavern/ComfyUI-Benripack", - "title": "ComfyUI-Benripack" - }, - { - "author": "MohammadAboulEla", - "description": "The iTools are some quality of life nodes, like read a possible prompt used to create an image, save a prompt to file as a new line, read prompts from a multiline file.", - "files": [ - "https://github.com/MohammadAboulEla/ComfyUI-iTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/MohammadAboulEla/ComfyUI-iTools", - "title": "ComfyUI-iTools" - }, - { - "author": "Hellrunner2k", - "description": "Magical nodes that are meant for integration and science of course. ^^ Foundational Helpers and smart Containers that use automated functionalities to make room for creative use. A magical pack-synergy is at hand that does not require much extra clutter to make advanced techniques pop beautifully. The idea was to create universal artist's precision tools that do not care what you throw at them.", - "files": [ - "https://github.com/Hellrunner2k/ComfyUI-HellrunnersMagicalNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Hellrunner2k/ComfyUI-HellrunnersMagicalNodes", - "title": "Hellrunner's Magical Nodes" - }, - { - "author": "caleboleary", - "description": "Nodes:CharacterManagerNode, FilmGrain, FlipFlopperSameArch", - "files": [ - "https://github.com/caleboleary/Comfyui-calbenodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/caleboleary/Comfyui-calbenodes", - "title": "Comfyui-calbenodes" - }, - { - "author": "Raapys", - "description": "Simple latent-passthrough node for running a full VRAM cleanup between workflow stages.", - "files": [ - "https://github.com/Raapys/ComfyUI-LatentGC_Aggressive" - ], - "id": "latentgcaggressive", - "install_type": "git-clone", - "reference": "https://github.com/Raapys/ComfyUI-LatentGC_Aggressive", - "reference2": "https://github.com/0000111100001111/ComfyUI-LatentGC_Aggressive", - "title": "LatentGC Aggressive" - }, - { - "author": "Pheat-AI", - "description": "Nodes:Batch Image Blend by Mask, Batch Enlarged Overlay, Batch Image Overlay, Remove Black Pixels to Transparent, Canny Shrink and Recenter, ...", - "files": [ - "https://github.com/Pheat-AI/Remade_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pheat-AI/Remade_nodes", - "title": "Remade_nodes" - }, - { - "author": "OuticNZ", - "description": "Nodes:Text Switch 2 Way, Prompt Tidy, Text With Context.", - "files": [ - "https://github.com/OuticNZ/ComfyUI-Simple-Of-Complex" - ], - "install_type": "git-clone", - "reference": "https://github.com/OuticNZ/ComfyUI-Simple-Of-Complex", - "title": "ComfyUI-Simple-Of-Complex" - }, - { - "author": "justUmen", - "description": "Nodes: Ollama, Green Screen to Transparency, Save image for Bjornulf LobeChat, Text with random Seed, Random line from input, Combine images (Background+Overlay alpha), Image to grayscale (black & white), Remove image Transparency (alpha), Resize Image, ...", - "files": [ - "https://github.com/justUmen/Bjornulf_custom_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/justUmen/Bjornulf_custom_nodes", - "title": "Bjornulf_custom_nodes" - }, - { - "author": "jstit", - "description": "Nodes:ImageCropCircle.", - "files": [ - "https://github.com/jstit/comfyui_custom_node_image" - ], - "install_type": "git-clone", - "reference": "https://github.com/jstit/comfyui_custom_node_image", - "title": "comfyui_custom_node_image" - }, - { - "author": "jstit", - "description": "Nodes:Download Dreambooth Checkpoint, Get Random Value From List, Load Canny Pose Face, Transparent to White Background, Download Flux Lora.", - "files": [ - "https://github.com/HeadshotPro/ComfyUI-HeadshotPro" - ], - "install_type": "git-clone", - "reference": "https://github.com/HeadshotPro/ComfyUI-HeadshotPro", - "title": "ComfyUI-HeadshotPro" - }, - { - "author": "Isi-dev", - "description": "These are nodes to animate an image with a reference video using UniAnimate.", - "files": [ - "https://github.com/Isi-dev/ComfyUI-UniAnimate-W" - ], - "id": "comfyuiunianimatenodes", - "install_type": "git-clone", - "reference": "https://github.com/Isi-dev/ComfyUI-UniAnimate-W", - "title": "ComfyUI-UniAnimate-W" - }, - { - "author": "Isi-dev", - "description": "These are nodes and workflows that can facilitate the creation of animations and video compilations.", - "files": [ - "https://github.com/Isi-dev/ComfyUI-Animation_Nodes_and_Workflows" - ], - "id": "ComfyUI-Animation_Nodes_and_Workflows", - "install_type": "git-clone", - "reference": "https://github.com/Isi-dev/ComfyUI-Animation_Nodes_and_Workflows", - "title": "ComfyUI-Animation_Nodes_and_Workflows" - }, - { - "author": "Isi-dev", - "description": "These are ComfyUI nodes to assist in converting an image to sketches or lineArts.", - "files": [ - "https://github.com/Isi-dev/ComfyUI-Img2DrawingAssistants" - ], - "id": "Img2DrawingAssistants", - "install_type": "git-clone", - "reference": "https://github.com/Isi-dev/ComfyUI-Img2DrawingAssistants", - "title": "ComfyUI-Img2DrawingAssistants" - }, - { - "author": "Isi-dev", - "description": "This custom node provides a memory management utility for ComfyUI.\nIt allows you to delete a specific model (checkpoint, etc.) completely from VRAM and system RAM after use, while passing through any other input type unchanged (IMAGE, LATENT, CLIP, STRING, INT, CONDITIONING, VAE, etc.).\nThis is especially useful for low VRAM & low RAM environments, helping to reduce out-of-memory (OOM) errors in long workflows.", - "files": [ - "https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough" - ], - "install_type": "git-clone", - "reference": "https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough", - "title": "ComfyUI_DeleteModelPassthrough" - }, - { - "author": "XLabs-AI", - "description": "Nodes:Load Flux LoRA, Load Flux ControlNet, Apply Flux ControlNet, Xlabs Sampler", - "files": [ - "https://github.com/XLabs-AI/x-flux-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/XLabs-AI/x-flux-comfyui", - "title": "x-flux-comfyui" - }, - { - "author": "okgo4", - "description": "ComfyUI-Mosaic-Mask is an automatic tool designed to detect and mask mosaic areas in input images.", - "files": [ - "https://github.com/okgo4/ComfyUI-Mosaic-Mask" - ], - "install_type": "git-clone", - "reference": "https://github.com/okgo4/ComfyUI-Mosaic-Mask", - "title": "ComfyUI-Mosaic-Mask" - }, - { - "author": "ChrisColeTech", - "description": "This custom node package for ComfyUI is designed to streamline your workflow with powerful file-counting capabilities.", - "files": [ - "https://github.com/ChrisColeTech/ComfyUI-Line-counter" - ], - "install_type": "git-clone", - "reference": "https://github.com/ChrisColeTech/ComfyUI-Line-counter", - "title": "ComfyUI-Line-counter" - }, - { - "author": "ChrisColeTech", - "description": "This custom node for ComfyUI will add a simple and elegant resource monitor.", - "files": [ - "https://github.com/ChrisColeTech/ComfyUI-Elegant-Resource-Monitor" - ], - "install_type": "git-clone", - "reference": "https://github.com/ChrisColeTech/ComfyUI-Elegant-Resource-Monitor", - "title": "ComfyUI-Elegant-Resource-Monitor" - }, - { - "author": "dadoirie", - "description": "ComfyUI_Dados_Nodes is a collection of custom nodes for ComfyUI, designed to enhance functionality and provide integration with various services, including Pinterest. This privacy policy explains how these nodes handle user data.\nNOTE: [a/privacy_policy](https://github.com/dadoirie/ComfyUI_Dados_Nodes/blob/master/privacy_policy.md)", - "files": [ - "https://github.com/dadoirie/ComfyUI_Dados_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/dadoirie/ComfyUI_Dados_Nodes", - "title": "ComfyUI_Dados_Nodes" - }, - { - "author": "fanfanfan", - "description": "Support input of Chinese prompts.", - "files": [ - "https://github.com/yuan199696/chinese_clip_encode" - ], - "id": "chinese_clip_encode", - "install_type": "git-clone", - "reference": "https://github.com/yuan199696/chinese_clip_encode", - "title": "chinese_clip_encode" - }, - { - "author": "fanfanfan", - "description": "Support adding custom text to the generated images.", - "files": [ - "https://github.com/yuan199696/add_text_2_img" - ], - "id": "add_text_2_img", - "install_type": "git-clone", - "reference": "https://github.com/yuan199696/add_text_2_img", - "title": "add_text_2_img" - }, - { - "author": "fairy-root", - "description": "Ollama and Llava / vision integration for ComfyUI", - "files": [ - "https://github.com/fairy-root/comfyui-ollama-llms" - ], - "install_type": "git-clone", - "reference": "https://github.com/fairy-root/comfyui-ollama-llms", - "title": "Ollama and Llava Vision integration for ComfyUI" - }, - { - "author": "fairy-root", - "description": "A flexible and customizable prompt generator for generating detailed and creative prompts for image generation models for ComfyUI", - "files": [ - "https://github.com/fairy-root/Flux-Prompt-Generator" - ], - "install_type": "git-clone", - "reference": "https://github.com/fairy-root/Flux-Prompt-Generator", - "title": "Flux Prompt Generator for ComfyUI" - }, - { - "author": "fairy-root", - "description": "A simple but powerful node for ComfyUI that displays text input in a readable format. Perfect for viewing outputs from text generation nodes, prompt builders, interrogators, and more.", - "files": [ - "https://github.com/fairy-root/ComfyUI-Show-Text" - ], - "install_type": "git-clone", - "reference": "https://github.com/fairy-root/ComfyUI-Show-Text", - "title": "ComfyUI-Show-Text" - }, - { - "author": "fairy-root", - "description": "The OpenAI FM TTS node is a custom node for ComfyUI that seamlessly integrates the OpenAI FM Text-to-Speech service into your audio workflows. This node allows you to easily convert text to speech with a variety of voices and emotional styles directly within ComfyUI.", - "files": [ - "https://github.com/fairy-root/ComfyUI-OpenAI-FM" - ], - "install_type": "git-clone", - "reference": "https://github.com/fairy-root/ComfyUI-OpenAI-FM", - "title": "ComfyUI-OpenAI-FM" - }, - { - "author": "ryanontheinside", - "description": "Custom nodes introducing particle simulations, optical flow, audio manipulation & reactivity, and temporal masks", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_RyanOnTheInside" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_RyanOnTheInside", - "title": "RyanOnTheInside" - }, - { - "author": "RyanOnTheInside", - "description": "These nodes are for real-time applications of ComfyUI.", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_RealtimeNodes" - ], - "id": "comfyui_realtimenodes", - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_RealtimeNodes", - "title": "Nodes for use with real-time applications of ComfyUI" - }, - { - "author": "RyanOnTheInside", - "description": "A ComfyUI implementation of [a/EfficientTAM](https://github.com/yformer/EfficientTAM)", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_EfficientTAM" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_EfficientTAM", - "title": "ComfyUI-EfficientTAM" - }, - { - "author": "ryanontheinside", - "description": "Play Doom in ComfyUI.", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_Doom" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_Doom", - "title": "Doom" - }, - { - "author": "ryanontheinside", - "description": "Node and workflow profiling. Find bottlenecks in your workflows. See trends over time.", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_ProfilerX" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_ProfilerX", - "title": "ComfyUI_ProfilerX" - }, - { - "author": "ryanontheinside", - "description": "A collection of high-performance neural network-based Super Resolution models for ComfyUI.", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_SuperResolution" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_SuperResolution", - "title": "ComfyUI_SuperResolution" - }, - { - "author": "ryanontheinside", - "description": "Control Freak gives you physical control over your ComfyUI workflows by mapping MIDI controllers, gamepads, and other input devices to any node parameter or UI element. Transform your image generation experience with tactile, responsive control. Ever heard of 'flow state'?", - "files": [ - "https://github.com/ryanontheinside/ComfyUI_ControlFreak" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI_ControlFreak", - "title": "Control Freak for ComfyUI" - }, - { - "author": "ryanontheinside", - "description": "This node integrates the face-swapping capabilities from Deep Live Cam into ComfyUI, allowing you to perform real-time face swapping on images and video streams.", - "files": [ - "https://github.com/ryanontheinside/ComfyUI-DeepLiveCam" - ], - "install_type": "git-clone", - "reference": "https://github.com/ryanontheinside/ComfyUI-DeepLiveCam", - "title": "Deep Live Cam for ComfyUI" - }, - { - "author": "ControlAltAI", - "description": "Quality of Life Nodes from ControlAltAI. Flux Resolution Calculator, Flux Sampler, Flux Union ControlNet Apply, Noise Plus Blend, Boolean Logic, and Flux Region Nodes.", - "files": [ - "https://github.com/gseth/ControlAltAI-Nodes" - ], - "id": "controlaltai", - "install_type": "git-clone", - "reference": "https://github.com/gseth/ControlAltAI-Nodes", - "title": "ControlAltAI Nodes" - }, - { - "author": "OliverCrosby", - "description": "A simple minimap in the bottom-right of the window showing the full workflow, left click to navigate", - "files": [ - "https://github.com/OliverCrosby/Comfyui-Minimap" - ], - "id": "minimap", - "install_type": "git-clone", - "reference": "https://github.com/OliverCrosby/Comfyui-Minimap", - "title": "ComfyUI Minimap" - }, - { - "author": "Sieyalixnet", - "description": "An easy custom node that makes the some loaders' input as Text instead of file selector.\nFor example, there are many characters in different loras respectively. If you want to generate different characters' pictures, you have to select corresponding lora, and then edit the prompt. It may cost much time.\nTo solve this problem, You can use it with a chrome extension https://github.com/Sieyalixnet/ComfyUI-Prompt-Formatter-Extension that makes the queue prompt easier when you dealing with massive loras and prompt.", - "files": [ - "https://github.com/Sieyalixnet/ComfyUI_Textarea_Loaders" - ], - "install_type": "git-clone", - "reference": "https://github.com/Sieyalixnet/ComfyUI_Textarea_Loaders", - "title": "ComfyUI_Textarea_Loaders" - }, - { - "author": "markuryy", - "description": "The Flux Prompt Saver is set of simple nodes for saving images generated with Flux with A1111-style metadata.", - "files": [ - "https://github.com/markuryy/ComfyUI-Flux-Prompt-Saver" - ], - "install_type": "git-clone", - "reference": "https://github.com/markuryy/ComfyUI-Flux-Prompt-Saver", - "title": "ComfyUI Flux Prompt Saver" - }, - { - "author": "markuryy", - "description": "Metadata for loaded models", - "files": [ - "https://github.com/markuryy/ComfyUI-SuperLoader" - ], - "install_type": "git-clone", - "reference": "https://github.com/markuryy/ComfyUI-SuperLoader", - "title": "Super Loader" - }, - { - "author": "markuryy", - "description": "A collection of custom nodes for ComfyUI", - "files": [ - "https://github.com/markuryy/ComfyUI-Simple-Video-XY-Plot" - ], - "install_type": "git-clone", - "reference": "https://github.com/markuryy/ComfyUI-Simple-Video-XY-Plot", - "title": "Video XY Plot" - }, - { - "author": "eesahe", - "description": "InstantX's Flux union ControlNet loader and implementation", - "files": [ - "https://github.com/EeroHeikkinen/ComfyUI-eesahesNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/EeroHeikkinen/ComfyUI-eesahesNodes", - "title": "ComfyUI-eesahesNodes" - }, - { - "author": "anhkhoatranle30", - "description": "This is a pack with some handy nodes for ComfyUI.", - "files": [ - "https://github.com/anhkhoatranle30/Handy-Nodes-ComfyUI" - ], - "id": "handynode", - "install_type": "git-clone", - "reference": "https://github.com/anhkhoatranle30/Handy-Nodes-ComfyUI", - "title": "Handy Node ComfyUI" - }, - { - "author": "Artiprocher", - "description": "The FLUX model API from DashScope, developed by Black Forest Labs, offers superior image generation capabilities with optimized support for Chinese prompts, achieving a commendable tradeoff between performance and the quality of generated images compared to other open-source models.", - "files": [ - "https://github.com/modelscope/comfyscope" - ], - "id": "dashscope_api", - "install_type": "git-clone", - "reference": "https://github.com/modelscope/comfyscope", - "title": "Dashscope FLUX API for ComfyUI" - }, - { - "author": "JPrevots", - "description": "Nodes:PhyCV - Phase-Stretch Transform (PST), PhyCV - VEViD, PhyCV - Page.", - "files": [ - "https://github.com/JPrevots/ComfyUI-PhyCV" - ], - "install_type": "git-clone", - "reference": "https://github.com/JPrevots/ComfyUI-PhyCV", - "title": "ComfyUI-PhyCV" - }, - { - "author": "rnbwdsh", - "description": "Latent space walks for latents, conditionals and noise", - "files": [ - "https://github.com/rnbwdsh/ComfyUI-LatentWalk" - ], - "install_type": "git-clone", - "reference": "https://github.com/rnbwdsh/ComfyUI-LatentWalk", - "title": "Latent Walk" - }, - { - "author": "kudou-reira", - "description": "This is a simple ComfyUI node that will take in a string of 'color' (i.e. 'blue') and output a hex color format.", - "files": [ - "https://github.com/kasukanra/ComfyUI_StringToHex" - ], - "install_type": "git-clone", - "reference": "https://github.com/kasukanra/ComfyUI_StringToHex", - "title": "ComfyUI_StringToHex" - }, - { - "author": "phyblas", - "description": "Implementation of paint-by-example on ComfyUI", - "files": [ - "https://github.com/phyblas/paint-by-example_comfyui" - ], - "id": "paintbyexample", - "install_type": "git-clone", - "reference": "https://github.com/phyblas/paint-by-example_comfyui", - "title": "paint-by-example @ ComfyUI" - }, - { - "author": "aidenli", - "description": "A comfyui node that provides translation and image reverse push functions(JoyTag & JoyCaption).", - "files": [ - "https://github.com/aidenli/ComfyUI_NYJY" - ], - "id": "NYJY", - "install_type": "git-clone", - "reference": "https://github.com/aidenli/ComfyUI_NYJY", - "title": "ComfyUI_NYJY" - }, - { - "author": "Pseudotools", - "description": "A package designed to enable multi-regional prompting for architectural rendering, integrated with the Rhino Pseudorandom plugin.", - "files": [ - "https://github.com/Pseudotools/Pseudocomfy" - ], - "id": "pseudocomfy", - "install_type": "git-clone", - "reference": "https://github.com/Pseudotools/Pseudocomfy", - "title": "Pseudocomfy" - }, - { - "author": "TTPlanetPig", - "description": "This is a workflow for my simple logic amazing upscale node for DIT model. it can be common use for Flux,Hunyuan,SD3 It can simple tile the initial image into pieces and then use image-interrogator to get each tile prompts for more accurate upscale process. The condition will be properly handled and the hallucination will be significantly eliminated.", - "files": [ - "https://github.com/TTPlanetPig/Comfyui_TTP_Toolset" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/Comfyui_TTP_Toolset", - "title": "Comfyui_TTP_Toolset" - }, - { - "author": "TTPlanetPig", - "description": "Adapt for Hunyuan now\nNOTE: The files in the repo are not organized, which may lead to update issues.", - "files": [ - "https://github.com/TTPlanetPig/Comfyui_TTP_CN_Preprocessor" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/Comfyui_TTP_CN_Preprocessor", - "title": "for comfyui image proprocessor" - }, - { - "author": "TTPlanetPig", - "description": "Wrapped Joy Caption alpha 2 node for comfyui from [a/https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two](https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two) Easy use, for GPU with less 19G, please use nf4 for better balanced speed and result. This Node also took a reference from /chflame163/ComfyUI_LayerStyle and [a/https://huggingface.co/John6666/joy-caption-alpha-two-cli-mod](https://huggingface.co/John6666/joy-caption-alpha-two-cli-mod)", - "files": [ - "https://github.com/TTPlanetPig/Comfyui_JC2" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/Comfyui_JC2", - "title": "Comfyui_JC2" - }, - { - "author": "TTPlanetPig", - "description": "NODES:TTP_Hunyuan3DNode, TTP_SquareImage, TTP_GIFViewer", - "files": [ - "https://github.com/TTPlanetPig/Comfyui_Hunyuan3D" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/Comfyui_Hunyuan3D", - "title": "Comfyui_Hunyuan3D" - }, - { - "author": "TTPlanetPig", - "description": "This is an experimental project focused on Stable Diffusion (SD) models. In a single generated image, the same object or character consistently maintains a very high level of consistency. I had already attempted to address this issue in the SDXL model.", - "files": [ - "https://github.com/TTPlanetPig/Comfyui_Object_Migration" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/Comfyui_Object_Migration", - "title": "Clothing Migration Kit" - }, - { - "author": "TTPlanetPig", - "description": "Provide ComfyUI support for FramePack start-and-end image reference", - "files": [ - "https://github.com/TTPlanetPig/TTP_Comfyui_FramePack_SE" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/TTP_Comfyui_FramePack_SE", - "title": "TTP_Comfyui_FramePack_SE" - }, - { - "author": "TTPlanetPig", - "description": "This repository provides a custom ComfyUI node for running object detection with the [a/Qwen 2.5 VL](https://github.com/QwenLM/Qwen2.5-VL) model. The node downloads the selected model on demand, runs a detection prompt and outputs bounding boxes that can be used with segmentation nodes such as [a/SAM2](https://github.com/kijai/ComfyUI-segment-anything-2).", - "files": [ - "https://github.com/TTPlanetPig/Comfyui_Object_Detect_QWen_VL" - ], - "install_type": "git-clone", - "reference": "https://github.com/TTPlanetPig/Comfyui_Object_Detect_QWen_VL", - "title": "ComfyUI Qwen2.5-VL Object Detection Node" - }, - { - "author": "camenduru", - "description": "NODES: SendToTostAI", - "files": [ - "https://github.com/camenduru/ComfyUI-TostAI" - ], - "install_type": "git-clone", - "reference": "https://github.com/camenduru/ComfyUI-TostAI", - "title": "ComfyUI-TostAI" - }, - { - "author": "xlinx", - "description": "NODES: Auto-LLM-Text-Vision, Auto-LLM-Text, Auto-LLM-Vision", - "files": [ - "https://github.com/xlinx/ComfyUI-decadetw-auto-prompt-llm" - ], - "install_type": "git-clone", - "reference": "https://github.com/xlinx/ComfyUI-decadetw-auto-prompt-llm", - "title": "ComfyUI-decadetw-auto-prompt-llm" - }, - { - "author": "xlinx", - "description": "Auto messging sd-image and sd-info to mobile phone IM realtime. (LINE | Telegram | Discord)", - "files": [ - "https://github.com/xlinx/ComfyUI-decadetw-auto-messaging-realtime" - ], - "install_type": "git-clone", - "reference": "https://github.com/xlinx/ComfyUI-decadetw-auto-messaging-realtime", - "title": "ComfyUI-decadetw-auto-messaging-realtime" - }, - { - "author": "xlinx", - "description": "I'm SD-VJ. (share SD-generating-process in realtime by gpu)", - "files": [ - "https://github.com/xlinx/ComfyUI-decadetw-spout-syphon-im-vj" - ], - "install_type": "git-clone", - "reference": "https://github.com/xlinx/ComfyUI-decadetw-spout-syphon-im-vj", - "title": "ComfyUI-decadetw-spout-syphon-im-vj" - }, - { - "author": "wmpmiles", - "description": "Some ComfyUI nodes that provide some image-processing functionality. Resampling, Color Grading, Inpainting, ...", - "files": [ - "https://github.com/wmpmiles/comfyui-some-image-processing-stuff" - ], - "install_type": "git-clone", - "reference": "https://github.com/wmpmiles/comfyui-some-image-processing-stuff", - "title": "comfyui-some-image-processing-stuff" - }, - { - "author": "nonnonstop", - "description": "This extension applies a patch that limits the model loading speed when using an HDD in a Windows environment. See [a/comfyanonymous/ComfyUI#1992](https://github.com/comfyanonymous/ComfyUI/issues/1992). [w/As this patch is only useful in very limited environments, its installation is not recommended under normal circumstances. Memory usage may increase.]", - "files": [ - "https://github.com/nonnonstop/comfyui-faster-loading" - ], - "install_type": "git-clone", - "reference": "https://github.com/nonnonstop/comfyui-faster-loading", - "title": "comfyui-faster-loading" - }, - { - "author": "Dr.Jusseaux", - "description": "A collection of ComfyUI custom nodes that allow to use most Diffusers pipelines and components in Comfy(Txt2Img, Img2Img, Inpainting, LoRAS, B-LoRAS, ControlNet...)", - "files": [ - "https://github.com/maepopi/Diffusers-in-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/maepopi/Diffusers-in-ComfyUI", - "title": "Diffusers-in-ComfyUI" - }, - { - "author": "niknah", - "description": "Quick connections, Circuit board connections", - "files": [ - "https://github.com/niknah/quick-connections" - ], - "id": "quick-connections", - "install_type": "git-clone", - "reference": "https://github.com/niknah/quick-connections", - "title": "Quick Connections" - }, - { - "author": "niknah", - "description": "Text to speech with F5-TTS", - "files": [ - "https://github.com/niknah/ComfyUI-F5-TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/niknah/ComfyUI-F5-TTS", - "title": "ComfyUI F5-TTS" - }, - { - "author": "niknah", - "description": "Image to 3D using Hunyuan-3D-2", - "files": [ - "https://github.com/niknah/ComfyUI-Hunyuan-3D-2" - ], - "id": "comfyui-hunyuan-3d-2", - "install_type": "git-clone", - "reference": "https://github.com/niknah/ComfyUI-Hunyuan-3D-2", - "title": "ComfyUI Hunyuan-3D-2" - }, - { - "author": "niknah", - "description": "ComfyUI custom_node for ByteDance's InfiniteYou", - "files": [ - "https://github.com/niknah/ComfyUI-InfiniteYou" - ], - "install_type": "git-clone", - "reference": "https://github.com/niknah/ComfyUI-InfiniteYou", - "title": "ComfyUI-InfiniteYou" - }, - { - "author": "niknah", - "description": "General audio nodes. Mix, Bass/Treble, Concatenate, Pitch, Add/remove silence, Speed", - "files": [ - "https://github.com/niknah/audio-general-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/niknah/audio-general-ComfyUI", - "title": "Audio General" - }, - { - "author": "daryltucker", - "description": "The primary goal of these nodes is to provide a way to access files generated by ComfyUI workflows, local to the machine running ComfyUI. These nodes should always return an updated list of files when triggered.", - "files": [ - "https://github.com/daryltucker/ComfyUI-LoadFiles" - ], - "id": "LoadFiles", - "install_type": "git-clone", - "reference": "https://github.com/daryltucker/ComfyUI-LoadFiles", - "title": "ComfyUI-LoadFiles" - }, - { - "author": "X-T-E-R", - "description": "Load your model with image previews, or directly download and import Civitai models via URL. This custom ComfyUI node supports Checkpoint, LoRA, and LoRA Stack models, offering features like bypass options.", - "files": [ - "https://github.com/X-T-E-R/ComfyUI-EasyCivitai-XTNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/X-T-E-R/ComfyUI-EasyCivitai-XTNodes", - "title": "ComfyUI Easy Civitai (XTNodes)" - }, - { - "author": "hyejinlee12", - "description": "This node is to fill image for outpainting(inpainting)\nFill image using cv2 methods(cv2_ns, cv2_telea and edge_pad)", - "files": [ - "https://github.com/Lhyejin/ComfyUI-Fill-Image-for-Outpainting" - ], - "id": "fill-image-for-outpainting", - "install_type": "git-clone", - "reference": "https://github.com/Lhyejin/ComfyUI-Fill-Image-for-Outpainting", - "title": "ComfyUI-Fill-Image-for-Outpainting" - }, - { - "author": "yhayano-ponotech", - "description": "This repository contains custom nodes for ComfyUI that integrate the fal.ai FLUX.1 [dev] with LoRA API, specifically for text-to-image generation. These nodes allow you to use the FLUX.1 model directly within your ComfyUI workflows.", - "files": [ - "https://github.com/yhayano-ponotech/ComfyUI-Fal-API-Flux" - ], - "install_type": "git-clone", - "reference": "https://github.com/yhayano-ponotech/ComfyUI-Fal-API-Flux", - "title": "ComfyUI-Fal-API-Flux" - }, - { - "author": "yhayano-ponotech", - "description": "ComfyUI custom node for directly downloading generated images to your local PC with customizable filenames and formats (PNG/JPEG).", - "files": [ - "https://github.com/yhayano-ponotech/comfyui-save-image-local" - ], - "install_type": "git-clone", - "reference": "https://github.com/yhayano-ponotech/comfyui-save-image-local", - "title": "ComfyUI Local Save Node" - }, - { - "author": "erosDiffusion", - "description": "pass up to 8 images and visually place, rotate and scale them to build the perfect composition. group move and group rescale. remember their position and scaling value across generations to easy swap images. use the buffer zone to to park an asset you don't want to use or easily reach transformations controls", - "files": [ - "https://github.com/erosDiffusion/ComfyUI-enricos-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/erosDiffusion/ComfyUI-enricos-nodes", - "title": "ComfyUI-enricos-nodes" - }, - { - "author": "Steudio", - "description": "Divide and Conquer Node Suite: It calculates the optimal upscale resolution and seamlessly divides the image into tiles, ready for individual processing using your preferred workflow. After processing, the tiles are seamlessly merged into a larger image, offering sharper and more detailed visuals.", - "files": [ - "https://github.com/Steudio/ComfyUI_Steudio" - ], - "id": "Steudio", - "install_type": "git-clone", - "reference": "https://github.com/Steudio/ComfyUI_Steudio", - "title": "ComfyUI Steudio" - }, - { - "author": "Assistant", - "description": "Custom node to manage prompts in YAML format.", - "files": [ - "https://github.com/NakamuraShippo/ComfyUI-NS-PromptList" - ], - "install_type": "git-clone", - "reference": "https://github.com/NakamuraShippo/ComfyUI-NS-PromptList", - "title": "ComfyUI-PromptList" - }, - { - "author": "Assistant", - "description": "ComfyUI-NS-ManySliders is a custom node developed for ComfyUI that allows you to manipulate values using multiple sliders. With this node, you can easily adjust numerous numerical parameters intuitively, making it useful for various purposes.", - "files": [ - "https://github.com/NakamuraShippo/ComfyUI-NS-ManySliders" - ], - "install_type": "git-clone", - "reference": "https://github.com/NakamuraShippo/ComfyUI-NS-ManySliders", - "title": "ComfyUI-NS-ManySliders" - }, - { - "author": "Assistant", - "description": "A collection of nodes for ComfyUI. ex:A node for batch managing int, float, and string parameters with presets", - "files": [ - "https://github.com/NakamuraShippo/ComfyUI-NS-Util" - ], - "install_type": "git-clone", - "reference": "https://github.com/NakamuraShippo/ComfyUI-NS-Util", - "title": "ComfyUI-NS-Util" - }, - { - "author": "nux1111", - "description": "Run ComfyUI workflows on multiple local GPUs/networked machines with options to edit the json values within comfyui.\nOriginal repo: [a/city96/ComfyUI_NetDist](https://github.com/city96/ComfyUI_NetDist)", - "files": [ - "https://github.com/nux1111/ComfyUI_NetDist_Plus" - ], - "install_type": "git-clone", - "reference": "https://github.com/nux1111/ComfyUI_NetDist_Plus", - "title": "ComfyUI_NetDist_Plus" - }, - { - "author": "mittimi", - "description": "This node can easily switch between models and prompts by saving presets.", - "files": [ - "https://github.com/mittimi/ComfyUI_mittimiLoadPreset2" - ], - "id": "comfyui_mittimi_load_preset2", - "install_type": "git-clone", - "reference": "https://github.com/mittimi/ComfyUI_mittimiLoadPreset2", - "title": "ComfyUI_mittimiLoadPreset2" - }, - { - "author": "mittimi", - "description": "This is the node that performs the magnification calculation.", - "files": [ - "https://github.com/mittimi/ComfyUI_mittimiRecalculateSize" - ], - "id": "comfyui_mittimi_recalculate_size", - "install_type": "git-clone", - "reference": "https://github.com/mittimi/ComfyUI_mittimiRecalculateSize", - "title": "ComfyUI_mittimiRecalculateSize" - }, - { - "author": "mittimi", - "description": "This node can easily switch between vertical and horizontal values with a single button.", - "files": [ - "https://github.com/mittimi/ComfyUI_mittimiWidthHeight" - ], - "id": "comfyui_mittimi_width_height", - "install_type": "git-clone", - "reference": "https://github.com/mittimi/ComfyUI_mittimiWidthHeight", - "title": "ComfyUI_mittimiWidthHeight" - }, - { - "author": "mittimi", - "description": "It has the ability to concatenate text.", - "files": [ - "https://github.com/mittimi/ComfyUI_mittimiDaisyChainText" - ], - "install_type": "git-clone", - "reference": "https://github.com/mittimi/ComfyUI_mittimiDaisyChainText", - "title": "ComfyUI_mittimiDaisyChainText" - }, - { - "author": "RodrigoSKohl", - "description": "Simple Node to make panoramic images", - "files": [ - "https://github.com/RodrigoSKohl/ComfyUI-Panoramic-ImgStitcher" - ], - "install_type": "git-clone", - "reference": "https://github.com/RodrigoSKohl/ComfyUI-Panoramic-ImgStitcher", - "title": "Panoramic Image Stitcher" - }, - { - "author": "RodrigoSKohl", - "description": "This node is based on MykolaL/StableDesign", - "files": [ - "https://github.com/RodrigoSKohl/InteriorDesign-for-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/RodrigoSKohl/InteriorDesign-for-ComfyUI", - "title": "Interior Design for Comfyui" - }, - { - "author": "RodrigoSKohl", - "description": "Node to tryoff clothes", - "files": [ - "https://github.com/RodrigoSKohl/comfyui-tryoff-anyone" - ], - "install_type": "git-clone", - "reference": "https://github.com/RodrigoSKohl/comfyui-tryoff-anyone", - "title": "TryOff Anyone" - }, - { - "author": "nicehero", - "description": "SegGPT model for comfyui,segmentation everything with mask prompt. Download (https://huggingface.co/BAAI/SegGPT/blob/main/seggpt_vit_large.pth) in this node path.", - "files": [ - "https://github.com/nicehero/comfyui-SegGPT" - ], - "install_type": "git-clone", - "reference": "https://github.com/nicehero/comfyui-SegGPT", - "title": "comfyui-SegGPT" - }, - { - "author": "sakura1bgx", - "description": "ComfyUI_FlipStreamViewer is a tool that provides a customizable viewer interface for flipping images with frame interpolation.", - "files": [ - "https://github.com/sakura1bgx/ComfyUI_FlipStreamViewer" - ], - "install_type": "git-clone", - "reference": "https://github.com/sakura1bgx/ComfyUI_FlipStreamViewer", - "title": "ComfyUI_FlipStreamViewer" - }, - { - "author": "ducido", - "description": "This is a node to generate new image that combine 2 objects from different scene.", - "files": [ - "https://github.com/ducido/ObjectFusion_ComfyUI_nodes" - ], - "id": "objectfusion-nodes", - "install_type": "git-clone", - "reference": "https://github.com/ducido/ObjectFusion_ComfyUI_nodes", - "title": "ObjectFusion_ComfyUI_nodes" - }, - { - "author": "DanielHabib", - "description": "NODES:Mesh To Voxel, Voxel Block Saver, Voxel Viewer, Voxel Block Loader, Voxel Video Viewer, Voxel Blocks Into Voxel Video, Voxel Video Preview, Voxelize Mesh, ...", - "files": [ - "https://github.com/DanielHabib/ComfyUI-Voxels" - ], - "install_type": "git-clone", - "reference": "https://github.com/DanielHabib/ComfyUI-Voxels", - "title": "ComfyUI-Voxels" - }, - { - "author": "jsonL", - "description": "Nodes to use Florence2 VLM for image vision tasks: object detection, captioning, segmentation and ocr", - "files": [ - "https://github.com/StarMagicAI/comfyui_tagger" - ], - "id": "comfyui-tagger", - "install_type": "git-clone", - "reference": "https://github.com/StarMagicAI/comfyui_tagger", - "title": "ComfyUI-tagger" - }, - { - "author": "boredofnames", - "description": "NODES:Save Image and ntfy", - "files": [ - "https://github.com/boredofnames/ComfyUI-ntfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/boredofnames/ComfyUI-ntfy", - "title": "ComfyUI-ntfy" - }, - { - "author": "Xclbr7", - "description": "ComfyUI-Merlin is a custom node extension for ComfyUI, introducing the Magic Photo Prompter. This powerful tool enhances your prompt engineering process by allowing users to easily construct detailed, high-quality prompts for photo-realistic image generation.", - "files": [ - "https://github.com/Xclbr7/ComfyUI-Merlin" - ], - "install_type": "git-clone", - "reference": "https://github.com/Xclbr7/ComfyUI-Merlin", - "title": "ComfyUI-Merlin: Magic Photo Prompter" - }, - { - "author": "microbote", - "description": "StyledCLIPTextEncode is a node that enables you to build your prompts(both postive and negative) based on the selected style. It provides up-to 77 styles currently and has been tested on SDXL and SD1.5 checkpoints. It's ported from project [a/Style Selector for SDXL 1.0](https://github.com/ahgsql/StyleSelectorXL), which is only availabe on WebUI.", - "files": [ - "https://github.com/microbote/ComfyUI-StyledCLIPTextEncode" - ], - "install_type": "git-clone", - "reference": "https://github.com/microbote/ComfyUI-StyledCLIPTextEncode", - "title": "StyledCLIPTextEncode" - }, - { - "author": "tianguangliu", - "description": "Efficiency tools, Personalized style, Other Nodes, ...", - "files": [ - "https://github.com/tianguanggliu/Utools" - ], - "id": "utools", - "install_type": "git-clone", - "reference": "https://github.com/tianguanggliu/Utools", - "title": "comfyui-utools" - }, - { - "author": "celoron", - "description": "A ComfyUI node for transforming images into descriptive text using templated visual question answering. Leverages Hugging Face's VQA models with transformers", - "files": [ - "https://github.com/celoron/ComfyUI-VisualQueryTemplate" - ], - "install_type": "git-clone", - "reference": "https://github.com/celoron/ComfyUI-VisualQueryTemplate", - "title": "ComfyUI-VisualQueryTemplate" - }, - { - "author": "Alex Genovese", - "description": "Huggingface Api Serverless request", - "files": [ - "https://github.com/alexgenovese/ComfyUI_HF_Servelress_Inference" - ], - "install_type": "git-clone", - "reference": "https://github.com/alexgenovese/ComfyUI_HF_Servelress_Inference", - "title": "Huggingface Api Serverless" - }, - { - "author": "Alex Genovese", - "description": "ComfyUI UNO Nodes is a collection of nodes for ComfyUI that allows you to load and use UNO models.", - "files": [ - "https://github.com/alexgenovese/ComfyUI-UNO-Flux" - ], - "install_type": "git-clone", - "reference": "https://github.com/alexgenovese/ComfyUI-UNO-Flux", - "title": "ComfyUI UNO Nodes" - }, - { - "author": "freelifehacker", - "description": "NODES:ImageMask2PNG", - "files": [ - "https://github.com/freelifehacker/ComfyUI-ImgMask2PNG" - ], - "install_type": "git-clone", - "reference": "https://github.com/freelifehacker/ComfyUI-ImgMask2PNG", - "title": "ComfyUI-ImgMask2PNG" - }, - { - "author": "souki202", - "description": "This is a node that simply integrates LoadImage, Vae Encode, Upscale, Resolution factor correction, and Color Adjustment.", - "files": [ - "https://github.com/souki202/ComfyUI-LoadImage-Advanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/souki202/ComfyUI-LoadImage-Advanced", - "title": "ComfyUI-LoadImage-Advanced" - }, - { - "author": "drmbt", - "description": "A collection of forks, QoL nodes and utilities for ComfyUI", - "files": [ - "https://github.com/drmbt/comfyui-dreambait-nodes" - ], - "id": "drmbt", - "install_type": "git-clone", - "reference": "https://github.com/drmbt/comfyui-dreambait-nodes", - "title": "comfyui-dreambait-nodes" - }, - { - "author": "InstaSD", - "description": "A collection of nodes for use with InstaSD. These nodes will be transformed into app inputs when you deploy your ComfyUI workflow on InstaSD.", - "files": [ - "https://github.com/WaddingtonHoldings/ComfyUI-InstaSD" - ], - "install_type": "git-clone", - "reference": "https://github.com/WaddingtonHoldings/ComfyUI-InstaSD", - "title": "InstaSD nodes for ComfyUI" - }, - { - "author": "Shiba-2-shiba", - "description": "This is a custom node to convert png images into color ASCII art. As noted below, multiple font sizes are used in the specification. The resolution of the generated file is set to be the same as the input image.", - "files": [ - "https://github.com/Shiba-2-shiba/comfyui-color-ascii-art-node" - ], - "id": "comfyui-color-ascii-art-node", - "install_type": "git-clone", - "reference": "https://github.com/Shiba-2-shiba/comfyui-color-ascii-art-node", - "title": "ComfyUI-color-ascii-art-node" - }, - { - "author": "Shiba-2-shiba", - "description": "This is a custom node to convert only the Diffusion model part or CLIP model part to fp8 in ComfyUI.\nVAE fp8 conversion is not supported.\nThe advantage of this node is that you do not need to separate unet/clip/vae in advance when converting to fp8, but can use the safetenros files that ComfyUI provides.", - "files": [ - "https://github.com/Shiba-2-shiba/ComfyUI_DiffusionModel_fp8_converter" - ], - "id": "fp8-converter", - "install_type": "git-clone", - "reference": "https://github.com/Shiba-2-shiba/ComfyUI_DiffusionModel_fp8_converter", - "title": "ComfyUI_DiffusionModel_fp8_converter" - }, - { - "author": "Shiba-2-shiba", - "description": "This is a custom node to add timestep for FreeU V2.", - "files": [ - "https://github.com/Shiba-2-shiba/ComfyUI_FreeU_V2_timestepadd" - ], - "id": "ComfyUI_FreeU_V2_timestepadd", - "install_type": "git-clone", - "reference": "https://github.com/Shiba-2-shiba/ComfyUI_FreeU_V2_timestepadd", - "title": "ComfyUI_FreeU_V2_timestepadd" - }, - { - "author": "Shiba-2-shiba", - "description": "An experimental implementation of MagCache for SDXL", - "files": [ - "https://github.com/Shiba-2-shiba/ComfyUI-Magcache-for-SDXL" - ], - "install_type": "git-clone", - "reference": "https://github.com/Shiba-2-shiba/ComfyUI-Magcache-for-SDXL", - "title": "ComfyUI-Magcache-for-SDXL" - }, - { - "author": "Bao Pham", - "description": "This extension provides a set of nodes that can be used to mask multiple object at once", - "files": [ - "https://github.com/pbpbpb2705/ComfyUI-LyraVSIH" - ], - "id": "lyra-vsih", - "install_type": "git-clone", - "reference": "https://github.com/pbpbpb2705/ComfyUI-LyraVSIH", - "title": "ComfyUI-LyraVSIH" - }, - { - "author": "AbyssBadger0", - "description": "Nodes:KolorsAwesomePrompts", - "files": [ - "https://github.com/AbyssBadger0/ComfyUI_Kolors_awesome_prompts" - ], - "install_type": "git-clone", - "reference": "https://github.com/AbyssBadger0/ComfyUI_Kolors_awesome_prompts", - "title": "Kolors Awesome Prompts" - }, - { - "author": "Hmily", - "description": "An awesome light image processing tool nodes for ComfyUI.", - "files": [ - "https://github.com/ihmily/ComfyUI-Light-Tool" - ], - "id": "comfyui-light-tool", - "install_type": "git-clone", - "reference": "https://github.com/ihmily/ComfyUI-Light-Tool", - "title": "ComfyUI-Light-Tool" - }, - { - "author": "k-komarov", - "description": "Save Your Image to BunnyStorage", - "files": [ - "https://github.com/k-komarov/comfyui-bunny-cdn-storage" - ], - "install_type": "git-clone", - "reference": "https://github.com/k-komarov/comfyui-bunny-cdn-storage", - "title": "comfyui-bunny-cdn-storage" - }, - { - "author": "PabloGFX", - "description": "A custom node for ComfyUI that analyzes and sorts images based on head orientation using MediaPipe. It detects facial landmarks, calculates head pose, and intelligently sorts images for enhanced AI image processing workflows.", - "files": [ - "https://github.com/lazniak/Head-Orientation-Node-for-ComfyUI---by-PabloGFX" - ], - "id": "head-orientation-node", - "install_type": "git-clone", - "reference": "https://github.com/lazniak/Head-Orientation-Node-for-ComfyUI---by-PabloGFX", - "title": "Head-Orientation-Node - by PabloGFX" - }, - { - "author": "PabloGFX", - "description": "A ComfyUI custom node that integrates Google Photos into your workflows. List albums, load images from specific albums, and search photos directly within ComfyUI. Features customizable image loading options, sorting, and efficient caching for seamless integration of your Google Photos library into AI image processing pipelines.", - "files": [ - "https://github.com/lazniak/comfyui-google-photos-loader" - ], - "id": "google-photos-loader", - "install_type": "git-clone", - "reference": "https://github.com/lazniak/comfyui-google-photos-loader", - "title": "Google Photos Loader - by PabloGFX" - }, - { - "author": "PabloGFX", - "description": "LiquidTime is a simple yet powerful frame interpolation node for ComfyUI. Just input your sequence and desired frame count - the node handles all complex calculations and generates smooth in-between frames for you. A must-have tool for AI animation and video creation that lets you shape time like liquid.", - "files": [ - "https://github.com/lazniak/LiquidTime-Interpolation" - ], - "id": "liquid-time-interpolation", - "install_type": "git-clone", - "reference": "https://github.com/lazniak/LiquidTime-Interpolation", - "title": "LiquidTime - by PabloGFX" - }, - { - "author": "45uee", - "description": "Implementation of color transfer using KMeans algorithm", - "files": [ - "https://github.com/45uee/ComfyUI-Color_Transfer" - ], - "install_type": "git-clone", - "reference": "https://github.com/45uee/ComfyUI-Color_Transfer", - "title": "ComfyUI-Color_Transfer" - }, - { - "author": "Phando", - "description": "A collection of nodes to help streamline your ComfyUI workflows", - "files": [ - "https://github.com/Phando/ComfyUI-PhandoNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Phando/ComfyUI-PhandoNodes", - "title": "ComfyUI-PhandoNodes" - }, - { - "author": "geocine", - "description": "NODES:Image Selector (geocine), Image Scale (geocine)", - "files": [ - "https://github.com/geocine/geocine-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/geocine/geocine-comfyui", - "title": "geocine-comfyui" - }, - { - "author": "SeanScripts", - "description": "For unloading a model or all models, using the memory management that is already present in ComfyUI. Copied from [a/https://github.com/willblaschko/ComfyUI-Unload-Models](https://github.com/willblaschko/ComfyUI-Unload-Models) but without the unnecessary extra stuff.", - "files": [ - "https://github.com/SeanScripts/ComfyUI-Unload-Model" - ], - "install_type": "git-clone", - "reference": "https://github.com/SeanScripts/ComfyUI-Unload-Model", - "title": "ComfyUI-Unload-Model" - }, - { - "author": "SeanScripts", - "description": "For loading and running Pixtral, Llama 3.2 Vision, and Molmo models. Put models in the models/LLM folder.", - "files": [ - "https://github.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision" - ], - "install_type": "git-clone", - "reference": "https://github.com/SeanScripts/ComfyUI-PixtralLlamaMolmoVision", - "title": "ComfyUI-PixtralLlamaMolmoVision" - }, - { - "author": "ExterminanzHS", - "description": "Custom nodes for ComfyUI to automatically send generated images to Discord channels.", - "files": [ - "https://github.com/ExterminanzHS/Gecco-Discord-Autosend" - ], - "install_type": "git-clone", - "reference": "https://github.com/ExterminanzHS/Gecco-Discord-Autosend", - "title": "Gecco Discord Autosend" - }, - { - "author": "Hugo", - "description": "This repository wraps the latest BiRefNet model as ComfyUI nodes. Compared to the previous model, the latest model offers higher and better matting accuracy.", - "files": [ - "https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo" - ], - "id": "BiRefNet", - "install_type": "git-clone", - "reference": "https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo", - "title": "ComfyUI-BiRefNet-Hugo" - }, - { - "author": "MoonHugo", - "description": "Encapsulate the commonly used functions of FFmpeg into ComfyUI nodes, making it convenient for users to perform various video processing tasks within ComfyUI.", - "files": [ - "https://github.com/MoonHugo/ComfyUI-FFmpeg" - ], - "id": "FFmpeg", - "install_type": "git-clone", - "reference": "https://github.com/MoonHugo/ComfyUI-FFmpeg", - "title": "ComfyUI-FFmpeg" - }, - { - "author": "MoonHugo", - "description": "The implementation of the audio generation model stable-audio-open in ComfyUI enables ComfyUI to achieve text-to-audio functionality.", - "files": [ - "https://github.com/MoonHugo/ComfyUI-StableAudioOpen" - ], - "id": "stable-audio-open", - "install_type": "git-clone", - "reference": "https://github.com/MoonHugo/ComfyUI-StableAudioOpen", - "title": "ComfyUI-StableAudioOpen" - }, - { - "author": "MoonHugo", - "description": "This repository encapsulates the BAGEL model as ComfyUI nodes for use, including image editing and image inversion features, but it does not include text-to-image functionality.", - "files": [ - "https://github.com/MoonHugo/ComfyUI-BAGEL-Hugo" - ], - "install_type": "git-clone", - "reference": "https://github.com/MoonHugo/ComfyUI-BAGEL-Hugo", - "title": "ComfyUI-BAGEL-Hugo" - }, - { - "author": "GrenKain", - "description": "This repository provides custom nodes for ComfyUI that enable pixel art style image processing, including downscaling, upscaling, color quantization, and resolution adjustments.", - "files": [ - "https://github.com/GrenKain/PixelArt-Processing-Nodes-for-ComfyUI" - ], - "id": "gk_pixelart", - "install_type": "git-clone", - "reference": "https://github.com/GrenKain/PixelArt-Processing-Nodes-for-ComfyUI", - "title": "PixelArt Processing Nodes" - }, - { - "author": "Trgtuan10", - "description": "NODES:Object Mask.\nNOTE:push [a/yolov8x-seg.pt](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-seg.pt) in models/yolo", - "files": [ - "https://github.com/Trgtuan10/ComfyUI_YoloSegment_Mask" - ], - "install_type": "git-clone", - "reference": "https://github.com/Trgtuan10/ComfyUI_YoloSegment_Mask", - "title": "ComfyUI_YoloSegment_Mask" - }, - { - "author": "Tenney95", - "description": "ComfyUI-NodeAligner is a lightweight ComfyUI layout plugin that includes features such as node alignment, distribution, and resizing. This plugin is designed to simplify layout adjustments in visual node editors or custom UI components, making node arrangement more convenient and efficient.", - "files": [ - "https://github.com/Tenney95/ComfyUI-NodeAligner" - ], - "install_type": "git-clone", - "reference": "https://github.com/Tenney95/ComfyUI-NodeAligner", - "title": "ComfyUI-NodeAligner" - }, - { - "author": "VykosX", - "description": "Custom nodes to improve flow control and logic + several utilities to enhance capabilities", - "files": [ - "https://github.com/VykosX/ControlFlowUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/VykosX/ControlFlowUtils", - "title": "ControlFlowUtils" - }, - { - "author": "tachyon-beep", - "description": "A lightweight image tray forked from Comfy-UI-CustomScripts with simple sorting, positioning and filtering options.", - "files": [ - "https://github.com/tachyon-beep/comfyui-simplefeed" - ], - "id": "simplefeed", - "install_type": "git-clone", - "reference": "https://github.com/tachyon-beep/comfyui-simplefeed", - "title": "ComfyUI Simple Feed" - }, - { - "author": "alexisrolland", - "description": "Custom nodes to run microsoft/Phi models.", - "files": [ - "https://github.com/alexisrolland/ComfyUI-Phi" - ], - "install_type": "git-clone", - "reference": "https://github.com/alexisrolland/ComfyUI-Phi", - "title": "ComfyUI-Phi" - }, - { - "author": "alexisrolland", - "description": "Blender plugin to send requests to a ComfyUI server.", - "files": [ - "https://github.com/alexisrolland/ComfyUI-Blender" - ], - "install_type": "git-clone", - "reference": "https://github.com/alexisrolland/ComfyUI-Blender", - "title": "ComfyUI-Blender" - }, - { - "author": "LatentRat", - "description": "Nodes to run nodes on remote ComfyUI instances.", - "files": [ - "https://github.com/LatentRat/comfy_remote_run" - ], - "install_type": "git-clone", - "reference": "https://github.com/LatentRat/comfy_remote_run", - "title": "comfy_remote_run" - }, - { - "author": "kinglord", - "description": "New UI on the sidebar that allows for quick and easy navigation of images to help build styles, characters, backgrounds, etc. or even entire random prompts.", - "files": [ - "https://github.com/Kinglord/ComfyUI_Prompt_Gallery" - ], - "id": "promptGallery", - "install_type": "git-clone", - "reference": "https://github.com/Kinglord/ComfyUI_Prompt_Gallery", - "title": "Prompt Gallery" - }, - { - "author": "kinglord", - "description": "A custom front-end UX node that creates a visual library of all your LoRAs. It's designed to be fast, slim, and make using LoRAs in Comfy a lot more fun for visual users - especially if you have lots of LoRAs. Should make people used to A1111 and other UI heavy platforms feel more at home. If you've got lots of LoRAs, this sidebar could be your new best friend!", - "files": [ - "https://github.com/Kinglord/ComfyUI_LoRA_Sidebar" - ], - "install_type": "git-clone", - "reference": "https://github.com/Kinglord/ComfyUI_LoRA_Sidebar", - "title": "ComfyUI_LoRA_Sidebar" - }, - { - "author": "alexcong", - "description": "ComfyUI Qwen2-VL wrapper that supports text-based and single-image queries.", - "files": [ - "https://github.com/alexcong/ComfyUI_QwenVL" - ], - "id": "comfyui-qwen2-vl", - "install_type": "git-clone", - "reference": "https://github.com/alexcong/ComfyUI_QwenVL", - "title": "Qwen2-VL wrapper for ComfyUI" - }, - { - "author": "Bin-sam", - "description": "NODES:pose_extraction, Load_reference_unet, Load_denoising_unet, Load_Pose_Guider, Pose_Guider_Encode, DynamicPose_Sampler, load_pose_model, align", - "files": [ - "https://github.com/Bin-sam/DynamicPose-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/Bin-sam/DynamicPose-ComfyUI", - "title": "DynamicPose-ComfyUI" - }, - { - "author": "Metal3d", - "description": "Detect human parts using the DeepLabV3+ ResNet50 model from Keras-io. You can extract hair, arms, legs, and other parts with ease and with small memory usage.", - "files": [ - "https://github.com/metal3d/ComfyUI_Human_Parts" - ], - "id": "human-parts-detector", - "install_type": "git-clone", - "reference": "https://github.com/metal3d/ComfyUI_Human_Parts", - "title": "Human Parts Detector" - }, - { - "author": "Metal3d", - "description": "A set of photo effects for ComfyUI, for the moment, only Bleach Bypass effect is provided, but more to come!", - "files": [ - "https://github.com/metal3d/ComfyUI_M3D_photo_effects" - ], - "id": "ComfyUI_M3D_photo_effects", - "install_type": "git-clone", - "reference": "https://github.com/metal3d/ComfyUI_M3D_photo_effects", - "title": "M3D photo effects" - }, - { - "author": "Fuwuffy", - "description": "This is a collection of nodes created to aid when managing area conditionings.", - "files": [ - "https://github.com/Fuwuffyi/ComfyUI-VisualArea-Nodes" - ], - "id": "comfyui-visualarea-nodes", - "install_type": "git-clone", - "reference": "https://github.com/Fuwuffyi/ComfyUI-VisualArea-Nodes", - "title": "ComfyUI-VisualArea-Nodes" - }, - { - "author": "Cyber-BlackCat", - "description": "Using LLM and Joy tag pipeline to tag your image(s folder), it's suitable for train FLUX LoRA and also sdxl. Load images in order!", - "files": [ - "https://github.com/Cyber-BlackCat/ComfyUI_Auto_Caption" - ], - "install_type": "git-clone", - "reference": "https://github.com/Cyber-BlackCat/ComfyUI_Auto_Caption", - "title": "ComfyUI_Auto_Caption" - }, - { - "author": "Cyber-BlackCat", - "description": "modify the original node instruction of image vector, add \u2018imagemagick\u2019 which is the key base Python library of \u2018wand\u2019 library.", - "files": [ - "https://github.com/Cyber-BlackCat/ComfyUI-Image-Vector" - ], - "install_type": "git-clone", - "reference": "https://github.com/Cyber-BlackCat/ComfyUI-Image-Vector", - "title": "ComfyUI-Image-Vector" - }, - { - "author": "cr7Por", - "description": "comfyui custom node for depthflow\noriginal depthflow website: [a/https://github.com/BrokenSource/DepthFlow](https://github.com/BrokenSource/DepthFlow)\ncheck this for installation: [a/https://brokensrc.dev/get/](https://brokensrc.dev/get/)", - "files": [ - "https://github.com/cr7Por/ComfyUI_DepthFlow" - ], - "install_type": "git-clone", - "reference": "https://github.com/cr7Por/ComfyUI_DepthFlow", - "title": "ComfyUI_DepthFlow" - }, - { - "author": "aimerib", - "description": "A comfyui node that provides save image with higher bit depth.", - "files": [ - "https://github.com/aimerib/ComfyUI_HigherBitDepthSaveImage" - ], - "install_type": "git-clone", - "reference": "https://github.com/aimerib/ComfyUI_HigherBitDepthSaveImage", - "title": "ComfyUI-HigherBitDepthSaveImage" - }, - { - "author": "nchenevey1", - "description": "Provides nodes geared towards using GIMP as a frontend for ComfyUI.", - "files": [ - "https://github.com/nchenevey1/comfyui-gimp-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/nchenevey1/comfyui-gimp-nodes", - "title": "comfyui-gimp-nodes" - }, - { - "author": "MetaGLM", - "description": "This platform extension provides ZhipuAI nodes, enabling you to configure a workflow for online video generation.", - "files": [ - "https://github.com/MetaGLM/ComfyUI-ZhipuAI-Platform" - ], - "id": "zhipuai-platform", - "install_type": "git-clone", - "pip": [ - "zhipuai-platform-video" - ], - "reference": "https://github.com/MetaGLM/ComfyUI-ZhipuAI-Platform", - "title": "ComfyUI ZhipuAI Platform" - }, - { - "author": "zhiselfly", - "description": "Compatible with alimama's SD3-ControlNet Demo on ComfyUI.", - "files": [ - "https://github.com/zhiselfly/ComfyUI-Alimama-ControlNet-compatible" - ], - "install_type": "git-clone", - "reference": "https://github.com/zhiselfly/ComfyUI-Alimama-ControlNet-compatible", - "title": "ComfyUI-Alimama-ControlNet-compatible" - }, - { - "author": "pydn", - "description": "This custom node allows you to generate pure python code from your ComfyUI workflow with the click of a button. Great for rapid experimentation or production deployment.", - "files": [ - "https://github.com/pydn/ComfyUI-to-Python-Extension" - ], - "id": "comfyui-to-python-extension", - "install_type": "git-clone", - "reference": "https://github.com/pydn/ComfyUI-to-Python-Extension", - "title": "ComfyUI to Python Extension" - }, - { - "author": "Dayuppy", - "description": "A very simple Discord webhook integration node for ComfyUI that lets you post images and text.", - "files": [ - "https://github.com/Dayuppy/ComfyUI-DiscordWebhook" - ], - "id": "DiscordWebhook", - "install_type": "git-clone", - "reference": "https://github.com/Dayuppy/ComfyUI-DiscordWebhook", - "title": "Discord Webhook" - }, - { - "author": "NyaamZ", - "description": "Expansion of Efficiency Nodes for ComfyUI. Significant UX improvements.[w/NOTE: This node requires [a/efficiency-nodes-comfyui](https://github.com/jags111/efficiency-nodes-comfyui) and [a/ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts); it also requires start.bat to run.]", - "files": [ - "https://github.com/NyaamZ/efficiency-nodes-ED" - ], - "id": "efficiency-ed", - "install_type": "git-clone", - "reference": "https://github.com/NyaamZ/efficiency-nodes-ED", - "title": "Efficiency Nodes ExtendeD" - }, - { - "author": "NyaamZ", - "description": "Custom javascript extensions for better UX for ComfyUI. Double click on image to open. It's convenient for checking images.", - "files": [ - "https://github.com/NyaamZ/ComfyUI-ImageGallery-ED" - ], - "id": "image-gallery-ed", - "install_type": "git-clone", - "reference": "https://github.com/NyaamZ/ComfyUI-ImageGallery-ED", - "title": "ComfyUI ImageGallery ExtendeD" - }, - { - "author": "chrissy0", - "description": "This repository contains a custom node for ComfyUI that pads an image to be square, filling the new pixels black.", - "files": [ - "https://github.com/chrissy0/chris-comfyui-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/chrissy0/chris-comfyui-nodes", - "title": "chris-comfyui-nodes" - }, - { - "author": "revirevy", - "description": "This custom node allow you to upload result images to imgbb.", - "files": [ - "https://github.com/revirevy/Comfyui_saveimage_imgbb" - ], - "id": "Comfyui_saveimage_imgbb", - "install_type": "git-clone", - "reference": "https://github.com/revirevy/Comfyui_saveimage_imgbb", - "title": "Comfyui_saveimage_imgbb" - }, - { - "author": "Kinglord", - "description": "A custom node that adds a UI element to the sidebar allowing easy access, navigation, and use of a massive collection (100+) of LECO (Slider) LoRAs. LECOs are an amazing tool to generate variance in your output with a minimal impact to consistency, i.e. deviating form your prompt. They can also allow you access to control parts of your image without taking up CLIP space, saving your token weights for more valuable keywords. If you haven't used them, there's never been a better time to try!", - "files": [ - "https://github.com/Kinglord/ComfyUI_Slider_Sidebar" - ], - "install_type": "git-clone", - "reference": "https://github.com/Kinglord/ComfyUI_Slider_Sidebar", - "title": "ComfyUI_Slider_Sidebar" - }, - { - "author": "Isi-dev", - "description": "These are ComfyUI nodes to assist in converting images to paintings and to assist the Inspyrenet Rembg node to totally remove, or replace with a color, the original background from images so that the background does not reappear in videos or in nodes that do not retain the alpha channel in rgba images.", - "files": [ - "https://github.com/Isi-dev/ComfyUI-Img2PaintingAssistant" - ], - "id": "ComfyUI-Img2PaintingAssistant", - "install_type": "git-clone", - "reference": "https://github.com/Isi-dev/ComfyUI-Img2PaintingAssistant", - "reference2": "https://github.com/Isi-dev/ComfyUI_Img2PaintingAssistant", - "title": "Image to Painting and Inspyrenet Assistant Nodes" - }, - { - "author": "311-code", - "description": "This project allows you to adjust SDXL's two text encoder's strengths individually for clip_g (ViT-bigG) and clip_l (CLIP-ViT-L) within ComfyUI. (And other adjustments)", - "files": [ - "https://github.com/311-code/ComfyUI-MagicClip_Strength" - ], - "install_type": "git-clone", - "reference": "https://github.com/311-code/ComfyUI-MagicClip_Strength", - "title": "ComfyUI MagicClip_Strength for SDXL" - }, - { - "author": "godmt", - "description": "LIST and BATCH utilities which support: create, convert, get or slice items", - "files": [ - "https://github.com/godmt/ComfyUI-List-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/godmt/ComfyUI-List-Utils", - "title": "ComfyUI-List-Utils" - }, - { - "author": "godmt", - "description": "ComfyUI wrapper of IP-Composer", - "files": [ - "https://github.com/godmt/ComfyUI-IP-Composer" - ], - "install_type": "git-clone", - "reference": "https://github.com/godmt/ComfyUI-IP-Composer", - "title": "ComfyUI-IP-Composer" - }, - { - "author": "pedrogengo", - "description": "Luma Dream Machine API official ComfyUI custom node.", - "files": [ - "https://github.com/lumalabs/ComfyUI-LumaAI-API" - ], - "id": "lumaai-api", - "install_type": "git-clone", - "reference": "https://github.com/lumalabs/ComfyUI-LumaAI-API", - "title": "ComfyUI-LumaAI-API" - }, - { - "author": "mingsky", - "description": "Nodes: ConvertGrayChannelNode, AdjustBrightnessContrastSaturationNode, BaiduTranslateNode.", - "files": [ - "https://github.com/mingsky-ai/ComfyUI-MingNodes" - ], - "id": "ComfyUI_MingNodes_Mingsky", - "install_type": "git-clone", - "reference": "https://github.com/mingsky-ai/ComfyUI-MingNodes", - "title": "ComfyUI-MingNodes" - }, - { - "author": "blob8", - "description": "Using IPAdapter for style consistency, the node accepts a story structured as text {prompt} text {prompt} etc. and generates a comic, saving it to /output. It also adds LLM API Request node, providing an openai compatible LLM API for generating the stories.", - "files": [ - "https://github.com/blob8/ComfyUI_sloppy-comic" - ], - "install_type": "git-clone", - "reference": "https://github.com/blob8/ComfyUI_sloppy-comic", - "title": "ComfyUI_sloppy-comic" - }, - { - "author": "banqingyuan", - "description": "NODES: OCR Location Node, Image Erase Node, Chat Overlay Node, Extract JSON Node.", - "files": [ - "https://github.com/banqingyuan/ComfyUI-text-replace" - ], - "install_type": "git-clone", - "reference": "https://github.com/banqingyuan/ComfyUI-text-replace", - "title": "ComfyUI-text-replace" - }, - { - "author": "edelvarden", - "description": "Custom node for ComfyUI. It adds additional metadata for saved images, ensuring compatibility with the Civitai website.", - "files": [ - "https://github.com/edelvarden/comfyui_image_metadata_extension" - ], - "install_type": "git-clone", - "reference": "https://github.com/edelvarden/comfyui_image_metadata_extension", - "title": "comfyui_image_metadata_extension" - }, - { - "author": "edelvarden", - "description": "Custom node for ComfyUI to show values of primitives (str, float, int, or bool).", - "files": [ - "https://github.com/edelvarden/ComfyUI-Display-Value" - ], - "install_type": "git-clone", - "reference": "https://github.com/edelvarden/ComfyUI-Display-Value", - "title": "ComfyUI-Display-Value" - }, - { - "author": "dfghsdh", - "description": "Flux Prompt Generator is a custom node set for ComfyUI that enhances prompt generation and image captioning capabilities. It integrates advanced language models and image captioning techniques to provide versatile and powerful prompt manipulation tools for your AI image generation workflows.\nNOTE:PORT OF [a/https://huggingface.co/Aitrepreneur/FLUX-Prompt-Generator](https://huggingface.co/Aitrepreneur/FLUX-Prompt-Generator) for COMFYUI", - "files": [ - "https://github.com/dfghsdh/ComfyUI_FluxPromptGen" - ], - "install_type": "git-clone", - "reference": "https://github.com/dfghsdh/ComfyUI_FluxPromptGen", - "title": "ComfyUI_FluxPromptGen" - }, - { - "author": "liushuchun", - "description": "Nodes:ComfyUI_Lora_List_With_Url_Loader. Load loras from urls and auto fetch them on web if they are missing.", - "files": [ - "https://github.com/liushuchun/ComfyUI_Lora_List_With_Url_Loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/liushuchun/ComfyUI_Lora_List_With_Url_Loader", - "title": "ComfyUI_Lora_List_With_Url_Loader" - }, - { - "author": "silveroxides", - "description": "Nodes for loading both Checkpoints and UNET/Diffussion models quantized to bitsandbytes NF4 or FP4 format.\nStill under development and some limitations such as using LoRA might apply still.", - "files": [ - "https://github.com/silveroxides/ComfyUI_bnb_nf4_fp4_Loaders" - ], - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI_bnb_nf4_fp4_Loaders", - "title": "Model and Checkpoint Loaders for NF4 and FP4" - }, - { - "author": "silveroxides", - "description": "Tagger used to tag image of but not limited to furry art.", - "files": [ - "https://github.com/silveroxides/ComfyUI-RR-JointTagger" - ], - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI-RR-JointTagger", - "title": "ComfyUI-RR-JointTagger" - }, - { - "author": "silveroxides", - "description": "[WIP]Custom nodes for handling, inspecting, modifying and creating various model files.", - "files": [ - "https://github.com/silveroxides/ComfyUI-ModelUtils" - ], - "id": "comfyui_modelutils", - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI-ModelUtils", - "title": "Model Utility Toolkit" - }, - { - "author": "silveroxides", - "description": "Scheduler for ComfyUI and an attempt at optimized scheduler for the Chroma architecture.", - "files": [ - "https://github.com/silveroxides/ComfyUI_SigmoidOffsetScheduler" - ], - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI_SigmoidOffsetScheduler", - "title": "ComfyUI Sigmoid Offset Scheduler" - }, - { - "author": "silveroxides", - "description": "Toolkit for creating embeddings for various models in ComfyUI.", - "files": [ - "https://github.com/silveroxides/ComfyUI_EmbeddingToolkit" - ], - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI_EmbeddingToolkit", - "title": "ComfyUI_EmbeddingToolkit" - }, - { - "author": "silveroxides", - "description": "An implementation of Frequency-Decoupled Guidance (FDG) in pure Pytorch.", - "files": [ - "https://github.com/silveroxides/ComfyUI_FDGuidance" - ], - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI_FDGuidance", - "title": "ComfyUI_FDGuidance" - }, - { - "author": "turkyden", - "description": "a ComfyUI Custom Node for [a/smartcrop.py](https://github.com/smartcrop/smartcrop.py)", - "files": [ - "https://github.com/turkyden/ComfyUI-SmartCrop" - ], - "install_type": "git-clone", - "reference": "https://github.com/turkyden/ComfyUI-SmartCrop", - "title": "ComfyUI-SmartCrop" - }, - { - "author": "DareFail", - "description": "This is a ComfyUI node that connects with [a/Roboflow workflows](https://roboflow.com/workflows/build).\nRoboflow hosts hundreds of thousands of open source and custom object detection models.", - "files": [ - "https://github.com/DareFail/ComfyUI-Roboflow" - ], - "install_type": "git-clone", - "reference": "https://github.com/DareFail/ComfyUI-Roboflow", - "title": "ComfyUI-Roboflow" - }, - { - "author": "valofey", - "description": "This is a node to use OpenRouter API from within ComfyUI. It supports both prompt and image+prompt requests (for multimodal LLMs).", - "files": [ - "https://github.com/valofey/Openrouter-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/valofey/Openrouter-Node", - "title": "OpenRouter Node" - }, - { - "author": "Charlweed", - "description": "ImageTransceiver is a custom node that enables image generating clients to connect directly to ComfyUI, and send those images in near real-time. For example, an image manipulation program like GIMP can connect an image to a workflow in ComfyUI, and every time the image changes in GIMP, the changes are immediately made in the workflow. Cloning", - "files": [ - "https://github.com/Charlweed/image_transceiver" - ], - "install_type": "git-clone", - "reference": "https://github.com/Charlweed/image_transceiver", - "title": "ImageTransceiver - ComfyUI" - }, - { - "author": "tanglaoya321", - "description": "NODES:StoryMakerSinglePortraitNode, StoryMakerTwoPortraitNode, StoryMakerSwapClothNode.", - "files": [ - "https://github.com/tanglaoya321/ComfyUI-StoryMaker" - ], - "install_type": "git-clone", - "reference": "https://github.com/tanglaoya321/ComfyUI-StoryMaker", - "title": "ComfyUI-StoryMaker" - }, - { - "author": "CRT", - "description": "CRT-Nodes is a collection of custom nodes for ComfyUI", - "files": [ - "https://github.com/plugcrypt/CRT-Nodes" - ], - "id": "crt-nodes", - "install_type": "git-clone", - "reference": "https://github.com/plugcrypt/CRT-Nodes", - "reference2": "https://github.com/PGCRT/CRT-Nodes", - "title": "CRT-Nodes" - }, - { - "author": "GiusTex", - "description": "ComfyUI nodes for outpainting images with diffusers, based on [a/diffusers-image-outpaint](https://huggingface.co/spaces/fffiloni/diffusers-image-outpaint/tree/main) by fffiloni.", - "files": [ - "https://github.com/GiusTex/ComfyUI-DiffusersImageOutpaint" - ], - "install_type": "git-clone", - "reference": "https://github.com/GiusTex/ComfyUI-DiffusersImageOutpaint", - "title": "ComfyUI-DiffusersImageOutpaint" - }, - { - "author": "CY-CHENYUE", - "description": "Custom nodes for MiniCPM language models in ComfyUI. Provides advanced text generation and image understanding functions.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-MiniCPM-Plus" - ], - "id": "minicpm-plus", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-MiniCPM-Plus", - "title": "ComfyUI-MiniCPM-Plus" - }, - { - "author": "CY-CHENYUE", - "description": "Use of the molmo model.Generate detailed image descriptions and analysis using Molmo models in ComfyUI.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-Molmo" - ], - "id": "comfyui-molmo", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-Molmo", - "title": "ComfyUI-Molmo" - }, - { - "author": "CY-CHENYUE", - "description": "InpaintEasy is a set of optimized local repainting (Inpaint) nodes that provide a simpler and more powerful local repainting workflow. It makes local repainting work easier and more efficient with intelligent cropping and merging functions.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-InpaintEasy" - ], - "id": "ComfyUI-InpaintEasy", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-InpaintEasy", - "tags": [ - "inpaint", - "crop", - "image" - ], - "title": "ComfyUI-InpaintEasy" - }, - { - "author": "CY-CHENYUE", - "description": "OmniGen Unified Image Generation Model Integration.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-OmniGenX" - ], - "id": "ComfyUI-OmniGenX", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-OmniGenX", - "title": "ComfyUI-OmniGenX" - }, - { - "author": "CY-CHENYUE", - "description": "A ComfyUI custom node that provides fine-grained control over style transfer using Redux style models.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-Redux-Prompt" - ], - "id": "ComfyUI-Redux-Prompt", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-Redux-Prompt", - "tags": [ - "Flux", - "redux", - "prompt" - ], - "title": "ComfyUI-Redux-Prompt" - }, - { - "author": "CY-CHENYUE", - "description": "ComfyUI custom nodes for MiniCPM", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-MiniCPM-o" - ], - "id": "ComfyUI-MiniCPM-o", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-MiniCPM-o", - "title": "ComfyUI-MiniCPM-o" - }, - { - "author": "CY-CHENYUE", - "description": "ComfyUI nodes for Janus-Pro, a unified multimodal understanding and generation framework.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-Janus-Pro" - ], - "id": "ComfyUI-Janus-Pro", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-Janus-Pro", - "title": "ComfyUI-Janus-Pro" - }, - { - "author": "CY-CHENYUE", - "description": "ComfyUI-Free-GPU provides a node for releasing RAM and VRAM in ComfyUI.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-Free-GPU" - ], - "id": "ComfyUI-Free-GPU", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-Free-GPU", - "title": "ComfyUI-Free-GPU" - }, - { - "author": "CY-CHENYUE", - "description": "A custom node for ComfyUI to integrate Google Gemini API.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-Gemini-API" - ], - "id": "ComfyUI-Gemini-API", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-Gemini-API", - "title": "ComfyUI-Gemini-API" - }, - { - "author": "CY-CHENYUE", - "description": "A custom node for ComfyUI to integrate GPT API.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-GPT-API" - ], - "id": "ComfyUI-GPT-API", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-GPT-API", - "title": "ComfyUI-GPT-API" - }, - { - "author": "CY-CHENYUE", - "description": "A custom node for ComfyUI to FramePack.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-FramePack-HY" - ], - "id": "ComfyUI-FramePack-HY", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-FramePack-HY", - "title": "ComfyUI-FramePack-HY" - }, - { - "author": "CY-CHENYUE", - "description": "A powerful ComfyUI multi-image composition node that supports real-time adjustment of image position, size, and rotation angle on an interactive Canvas, with freehand drawing capabilities and real-time preview of composition effects.", - "files": [ - "https://github.com/CY-CHENYUE/ComfyUI-ImageCompositionCy" - ], - "id": "ComfyUI-ImageCompositionCy", - "install_type": "git-clone", - "reference": "https://github.com/CY-CHENYUE/ComfyUI-ImageCompositionCy", - "title": "ComfyUI-ImageCompositionCy" - }, - { - "author": "codecringebinge", - "description": "A ComfyUI Custom Node that enables arrow key canvas navigation with a pan speed setting.", - "files": [ - "https://github.com/codecringebinge/ComfyUI-Arrow-Key-Canvas-Navigation" - ], - "id": "codecringebinge.arrow.key.canvas.navigation", - "install_type": "git-clone", - "reference": "https://github.com/codecringebinge/ComfyUI-Arrow-Key-Canvas-Navigation", - "title": "ComfyUI-Arrow-Key-Canvas-Navigation" - }, - { - "author": "asaddi", - "description": "Yet another set of LLM nodes for ComfyUI (for local/remote OpenAI-like APIs, multi-modal models supported)", - "files": [ - "https://github.com/asaddi/ComfyUI-YALLM-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/asaddi/ComfyUI-YALLM-node", - "title": "ComfyUI-YALLM-node" - }, - { - "author": "asaddi", - "description": "A set of nodes for basic Llama 3.2 Vision support in ComfyUI. Give it an image and query and it will output a text response.", - "files": [ - "https://github.com/asaddi/YALLM-LlamaVision" - ], - "install_type": "git-clone", - "reference": "https://github.com/asaddi/YALLM-LlamaVision", - "title": "YALLM-LlamaVision" - }, - { - "author": "ycyy", - "description": "You can use this node to get information about lora. For example trigger words, description and example images.", - "files": [ - "https://github.com/ycyy/ComfyUI-YCYY-LoraInfo" - ], - "install_type": "git-clone", - "reference": "https://github.com/ycyy/ComfyUI-YCYY-LoraInfo", - "title": "ComfyUI-YCYY-LoraInfo" - }, - { - "author": "Darth-Veitcher", - "description": "Nodes: String Formatting (f-string and jinja2), Random Choice, Model Memory management, and other quality of life improvements.", - "files": [ - "https://github.com/darth-veitcher/comfydv" - ], - "id": "comfydv", - "install_type": "git-clone", - "reference": "https://github.com/darth-veitcher/comfydv", - "title": "Comfy DV" - }, - { - "author": "ez-af", - "description": "Conveniently control parts of text prompts with custom UI. Pack includes loaders from txt and csv files, dynamic text concatenation tool and easy-to-use input node", - "files": [ - "https://github.com/ez-af/ComfyUI-EZ-AF-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/ez-af/ComfyUI-EZ-AF-Nodes", - "title": "ComfyUI-EZ-AF-Nodes" - }, - { - "author": "danbochman", - "description": "Node for the FASHN Virtual Try-On API. Requires an API Key from fashn.ai", - "files": [ - "https://github.com/fashn-AI/ComfyUI-FASHN" - ], - "id": "fashn", - "install_type": "git-clone", - "reference": "https://github.com/fashn-AI/ComfyUI-FASHN", - "title": "FASHN Virtual Try-On" - }, - { - "author": "BRIA AI", - "description": "Custom nodes for ComfyUI using BRIA's API.", - "files": [ - "https://github.com/Bria-AI/ComfyUI-BRIA-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/Bria-AI/ComfyUI-BRIA-API", - "title": "BRIA AI API nodes" - }, - { - "author": "L.HC", - "description": "Two simple nodes: 1. Get the steps based on the model name, 2. Generate prompts using chatglm.", - "files": [ - "https://github.com/Mcmillian/ComfyUI-SimpleToolsNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Mcmillian/ComfyUI-SimpleToolsNodes", - "title": "SimpleToolsNodes" - }, - { - "author": "creeper", - "description": "A node that can use Nai in Comfyui", - "files": [ - "https://github.com/Creeper-MZ/comfyui_nai_api" - ], - "install_type": "git-clone", - "reference": "https://github.com/Creeper-MZ/comfyui_nai_api", - "title": "comfyui_nai_api" - }, - { - "author": "guyaton", - "description": "These are designed to be custom nodes i found usefulness to that hopefully others can share.", - "files": [ - "https://github.com/guyaton/guy-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/guyaton/guy-nodes-comfyui", - "title": "guy-nodes-comfyui" - }, - { - "author": "thoddnn", - "description": "Faster workflows for ComfyUI users on Mac with Apple silicon", - "files": [ - "https://github.com/thoddnn/ComfyUI-MLX" - ], - "install_type": "git-clone", - "reference": "https://github.com/thoddnn/ComfyUI-MLX", - "title": "ComfyUI MLX Nodes" - }, - { - "author": "acorderob", - "description": "Stable Diffusion WebUI & ComfyUI extension to post-process the prompt, including sending content from the prompt to the negative prompt and wildcards.", - "files": [ - "https://github.com/acorderob/sd-webui-prompt-postprocessor" - ], - "install_type": "git-clone", - "reference": "https://github.com/acorderob/sd-webui-prompt-postprocessor", - "title": "Prompt PostProcessor" - }, - { - "author": "Moooonet", - "description": "A powerful node alignment and color management plugin for ComfyUI, designed to enhance your workflow efficiency", - "files": [ - "https://github.com/Moooonet/ComfyUI-Align" - ], - "install_type": "git-clone", - "reference": "https://github.com/Moooonet/ComfyUI-Align", - "title": "ComfyUI-Align" - }, - { - "author": "Nojahhh", - "description": "ComfyUI GLM-4 Wrapper. This powerful tool enhances your prompt engineering process by allowing users to easily construct detailed, high-quality prompts for image/video generation based on user image and/or user prompts.", - "files": [ - "https://github.com/Nojahhh/ComfyUI_GLM4_Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/Nojahhh/ComfyUI_GLM4_Wrapper", - "title": "ComfyUI GLM-4 Wrapper" - }, - { - "author": "nilor-corp", - "description": "Custom utility nodes for ComfyUI by Nilor Corp. Probably not useful for most people, but contains stuff for working with lists, filenames, image batches, etc in a very specifc way.", - "files": [ - "https://github.com/nilor-corp/nilor-nodes" - ], - "id": "nilor-nodes", - "install_type": "git-clone", - "reference": "https://github.com/nilor-corp/nilor-nodes", - "title": "Nilor Nodes by Nilor Corp" - }, - { - "author": "willchil", - "description": "This ComfyUI node pack allows the user to take a panoramic image and a corresponding depth map, and turn them into a 3D environment, which they can view in an immersive WebXR environment.", - "files": [ - "https://github.com/willchil/ComfyUI-Environment-Visualizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/willchil/ComfyUI-Environment-Visualizer", - "title": "ComfyUI-Environment-Visualizer" - }, - { - "author": "YarvixPA", - "description": "A set of custom nodes that simplify things.", - "files": [ - "https://github.com/YarvixPA/ComfyUI-YarvixPA" - ], - "install_type": "git-clone", - "reference": "https://github.com/YarvixPA/ComfyUI-YarvixPA", - "title": "ComfyUI-YarvixPA" - }, - { - "author": "AcademiaSD", - "description": "Official set of custom nodes of AcademiaSD.", - "files": [ - "https://github.com/AcademiaSD/comfyui_AcademiaSD" - ], - "install_type": "git-clone", - "reference": "https://github.com/AcademiaSD/comfyui_AcademiaSD", - "title": "comfyui_AcademiaSD" - }, - { - "author": "SpenserCai", - "description": "Comfyui custom node for [a/FunAudioLLM](https://funaudiollm.github.io/) include [a/CosyVoice](https://github.com/FunAudioLLM/CosyVoice) and [a/SenseVoice](https://github.com/FunAudioLLM/SenseVoice).", - "files": [ - "https://github.com/SpenserCai/ComfyUI-FunAudioLLM" - ], - "id": "funaudiollm", - "install_type": "git-clone", - "reference": "https://github.com/SpenserCai/ComfyUI-FunAudioLLM", - "title": "ComfyUI-FunAudioLLM" - }, - { - "author": "GadzoinksOfficial", - "description": "Custom node for integrating with gadzoinks iPhone app", - "files": [ - "https://github.com/GadzoinksOfficial/gadzoinks_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/GadzoinksOfficial/gadzoinks_ComfyUI", - "title": "Gadzoinks" - }, - { - "author": "GadzoinksOfficial", - "description": "Another dynamic prompt node, designed to be easy to use and support wildcards", - "files": [ - "https://github.com/GadzoinksOfficial/comfyui_gprompts" - ], - "install_type": "git-clone", - "reference": "https://github.com/GadzoinksOfficial/comfyui_gprompts", - "title": "Gprompts" - }, - { - "author": "educator-art", - "description": "This node loads prompts (txt) and images (png) from a specified directory. By specifying an index, it outputs the selected file.", - "files": [ - "https://github.com/educator-art/ComfyUI-Load-DirectoryFiles" - ], - "install_type": "git-clone", - "reference": "https://github.com/educator-art/ComfyUI-Load-DirectoryFiles", - "title": "ComfyUI-Load-DirectoryFiles" - }, - { - "author": "educator-art", - "description": "Using the ollama gpt-oss:20b model, a prompt is generated. The user enters the desired theme into a custom node and runs it, and the model outputs a Stable Diffusion prompt. This is useful when you want the model to handle prompt creation for you.", - "files": [ - "https://github.com/educator-art/ComfyUI-gpt-oss-PromptDesigner" - ], - "install_type": "git-clone", - "reference": "https://github.com/educator-art/ComfyUI-gpt-oss-PromptDesigner", - "title": "ComfyUI-gpt-oss-PromptDesigner" - }, - { - "author": "raysers", - "description": "Simple use of [a/Mflux](https://github.com/filipstrand/mflux) in ComfyUI, suitable for users who are not familiar with terminal usage.\nNOTE: A MLX port of FLUX based on the Huggingface Diffusers implementation.", - "files": [ - "https://github.com/raysers/Mflux-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/raysers/Mflux-ComfyUI", - "title": "Mflux-ComfyUI" - }, - { - "author": "civen-cn", - "description": "Nodes related to [a/PaddleOCR](https://paddlepaddle.github.io/PaddleOCR) OCR.", - "files": [ - "https://github.com/civen-cn/ComfyUI-PaddleOcr" - ], - "install_type": "git-clone", - "reference": "https://github.com/civen-cn/ComfyUI-PaddleOcr", - "title": "ComfyUI-PaddleOcr" - }, - { - "author": "rdancer", - "description": "ComfyUI custom node implementing Florence 2 + Segment Anything Model 2, based on [a/SkalskiP's HuggingFace space](https://huggingface.co/spaces/SkalskiP/florence-sam)", - "files": [ - "https://github.com/rdancer/ComfyUI_Florence2SAM2" - ], - "install_type": "git-clone", - "reference": "https://github.com/rdancer/ComfyUI_Florence2SAM2", - "title": "ComfyUI_Florence2SAM2" - }, - { - "author": "gelasdev", - "description": "Custom nodes for integrating Flux models with the BFL API.", - "files": [ - "https://github.com/gelasdev/ComfyUI-FLUX-BFL-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/gelasdev/ComfyUI-FLUX-BFL-API", - "title": "ComfyUI-FLUX-BFL-API" - }, - { - "author": "ggarra13", - "description": "Nodes to interact with the mrv2 player", - "files": [ - "https://github.com/ggarra13/ComfyUI-mrv2" - ], - "install_type": "git-clone", - "reference": "https://github.com/ggarra13/ComfyUI-mrv2", - "title": "ComfyUI-mrv2" - }, - { - "author": "SSsnap", - "description": "The custom Snap processing node has been converted for ComfyUI production. It currently includes a simple PyQt5 interactive interface as well as practical nodes for basic operations like area calculation..", - "files": [ - "https://github.com/SS-snap/ComfyUI-Snap_Processing" - ], - "install_type": "git-clone", - "reference": "https://github.com/SS-snap/ComfyUI-Snap_Processing", - "title": "Snap Processing for Comfyui" - }, - { - "author": "SSsnap", - "description": "Through this node, you can more easily test the impact of different blocks in flux_lora on the final result.", - "files": [ - "https://github.com/SS-snap/ComfyUI-LBW_flux" - ], - "install_type": "git-clone", - "reference": "https://github.com/SS-snap/ComfyUI-LBW_flux", - "title": "ComfyUI-LBW_flux" - }, - { - "author": "SSsnap", - "description": "This node is used to enhance image details. We can add a latent space image and introduce any amount of noise. Then, we can start denoising at any timestep. This allows us to add more details to the image while maintaining overall consistency as much as possible.", - "files": [ - "https://github.com/SS-snap/ComfyUI-Ad_scheduler" - ], - "install_type": "git-clone", - "reference": "https://github.com/SS-snap/ComfyUI-Ad_scheduler", - "title": "ComfyUI-Ad-scheduler" - }, - { - "author": "SSsnap", - "description": "A pose remapping node with support for joint locking, motion-aware scaling, and optional easing - perfect for animation refinement and mech rig control.", - "files": [ - "https://github.com/SS-snap/Comfyui_SSsnap_pose-Remapping" - ], - "install_type": "git-clone", - "reference": "https://github.com/SS-snap/Comfyui_SSsnap_pose-Remapping", - "title": "Comfyui_SSsnap_pose-Remapping" - }, - { - "author": "RiceRound", - "description": "a lightweight open-source node for ComfyUI, designed to simplify workflows while providing encryption protection for them.", - "files": [ - "https://github.com/RiceRound/ComfyUI_CryptoCat" - ], - "id": "cryptocat", - "install_type": "git-clone", - "reference": "https://github.com/RiceRound/ComfyUI_CryptoCat", - "title": "ComfyUI Compression and Encryption Node" - }, - { - "author": "RiceRound", - "description": "This is an imaginative project that allows for one-click deployment, providing both an online page and a ComfyUI cloud node.[w/This custom node is vulnerable because it can dynamically download and execute nodes.]", - "files": [ - "https://github.com/RiceRound/ComfyUI_RiceRound" - ], - "id": "riceround", - "install_type": "git-clone", - "reference": "https://github.com/RiceRound/ComfyUI_RiceRound", - "title": "RiceRound Cloud Node" - }, - { - "author": "yvann-ba", - "description": "Audio Reactive nodes for AI animations \ud83d\udd0a Analyze audio, extract drums, bass, vocals. Compatible with IPAdapter, ControlNets, AnimateDiff... Generate reactive masks and weights. Create audio-driven visuals. Produce weight graphs and audio masks. Ideal for music videos and reactive animations. Features audio scheduling and waveform analysis", - "files": [ - "https://github.com/yvann-ba/ComfyUI_Yvann-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/yvann-ba/ComfyUI_Yvann-Nodes", - "title": "ComfyUI_Yvann-Nodes" - }, - { - "author": "Playbook", - "description": "Custom nodes for connecting 3D scenes and ComfyUI workflows.", - "files": [ - "https://github.com/playbook3d/playbook3d-comfyui-nodes" - ], - "id": "playbook-3d", - "install_type": "git-clone", - "reference": "https://github.com/playbook3d/playbook3d-comfyui-nodes", - "title": "Playbook Nodes" - }, - { - "author": "Elaine-chennn", - "description": "This repository contains a custom ComfyUI node for overlaying media using ffmpeg.", - "files": [ - "https://github.com/Elaine-chennn/comfyui-overlay-media" - ], - "install_type": "git-clone", - "reference": "https://github.com/Elaine-chennn/comfyui-overlay-media", - "title": "ComfyUI Overlay Media Node" - }, - { - "author": "LAOGOU-666", - "description": "Implementation of Fast Fourier Transform in COMFYUI", - "files": [ - "https://github.com/LAOGOU-666/ComfyUI_LG_FFT" - ], - "install_type": "git-clone", - "reference": "https://github.com/LAOGOU-666/ComfyUI_LG_FFT", - "title": "ComfyUI_LG_FFT" - }, - { - "author": "LAOGOU-666", - "description": "A simple implementation of real-time 3D lighting in ComfyUI. It's an open-source node, have fun playing around!", - "files": [ - "https://github.com/LAOGOU-666/Comfyui-LG_Relight" - ], - "install_type": "git-clone", - "reference": "https://github.com/LAOGOU-666/Comfyui-LG_Relight", - "title": "Comfyui-LG_Relight" - }, - { - "author": "LAOGOU-666", - "description": "An extension for ComfyUI that allows hot reloading. Once installed, you can preview changes in real-time while developing custom nodes or installing plugins without restarting ComfyUI.", - "files": [ - "https://github.com/LAOGOU-666/ComfyUI-LG_HotReload" - ], - "id": "ComfyUI-LG_HotReload", - "install_type": "git-clone", - "reference": "https://github.com/LAOGOU-666/ComfyUI-LG_HotReload", - "title": "ComfyUI-LG_HotReload" - }, - { - "author": "LAOGOU-666", - "description": "A ComfyUI extension that provides nodes for memory cleanup, including VRAM and RAM cleanup functions to optimize ComfyUI performance during long running workflows.", - "files": [ - "https://github.com/LAOGOU-666/Comfyui-Memory_Cleanup" - ], - "id": "comfyui_memory_cleanup", - "install_type": "git-clone", - "reference": "https://github.com/LAOGOU-666/Comfyui-Memory_Cleanup", - "title": "Comfyui-Memory_Cleanup" - }, - { - "author": "LAOGOU-666", - "description": "A ComfyUI extension for controlling and managing node group execution flow. Features include single/multiple group execution, delay control, signal chaining, and execution list repetition processing.", - "files": [ - "https://github.com/LAOGOU-666/Comfyui-LG_GroupExecutor" - ], - "id": "comfyui_lg_groupexecutor", - "install_type": "git-clone", - "reference": "https://github.com/LAOGOU-666/Comfyui-LG_GroupExecutor", - "title": "Comfyui-LG_GroupExecutor" - }, - { - "author": "LAOGOU-666", - "description": "This is a toolset designed for ComfyUI by LAOGOU-666, providing a series of practical image processing and operation nodes, making our operation more intuitive and convenient", - "files": [ - "https://github.com/LAOGOU-666/Comfyui_LG_Tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/LAOGOU-666/Comfyui_LG_Tools", - "title": "Comfyui_LG_Tools" - }, - { - "author": "VertexStudio", - "description": "NODES:Scale Image Node, Switch Image Node, Switch Text Node, First Number Node, Mirror Effect Node, Text To ImageNode, Flow Nodes, Simple Save Image Node", - "files": [ - "https://github.com/VertexStudio/roblox-comfyui-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/VertexStudio/roblox-comfyui-nodes", - "title": "roblox-comfyui-nodes" - }, - { - "author": "2kpr", - "description": "Implementation of PMRF on ComfyUI", - "files": [ - "https://github.com/2kpr/ComfyUI-PMRF" - ], - "id": "comfyui-pmrf", - "install_type": "git-clone", - "reference": "https://github.com/2kpr/ComfyUI-PMRF", - "title": "ComfyUI-PMRF" - }, - { - "author": "tkreuziger", - "description": "A set of custom nodes that are using Anthropic's Claude models for describing images and transforming texts.", - "files": [ - "https://github.com/tkreuziger/comfyui-claude" - ], - "install_type": "git-clone", - "reference": "https://github.com/tkreuziger/comfyui-claude", - "title": "ComfyUI and Claude" - }, - { - "author": "sipie800", - "description": "adapted from [a/https://github.com/balazik/ComfyUI-PuLID-Flux](https://github.com/balazik/ComfyUI-PuLID-Flux).\ncommon fusion methods for multi-image input, some further experimental fusion methods, switch between using gray image (official) and rgb.,", - "files": [ - "https://github.com/sipie800/ComfyUI-PuLID-Flux-Enhanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/sipie800/ComfyUI-PuLID-Flux-Enhanced", - "title": "ComfyUI-PuLID-Flux-Enhanced" - }, - { - "author": "EvilBT", - "description": "NODES:Joy Caption Two, Joy Caption Two Advanced, Joy Caption Two Load, Joy Caption Extra Options", - "files": [ - "https://github.com/EvilBT/ComfyUI_SLK_joy_caption_two" - ], - "install_type": "git-clone", - "reference": "https://github.com/EvilBT/ComfyUI_SLK_joy_caption_two", - "title": "JoyCaptionAlpha Two for ComfyUI" - }, - { - "author": "Q-Bug4", - "description": "A custom node for ComfyUI to parse and extract data from JSON strings.", - "files": [ - "https://github.com/Q-Bug4/Comfyui-Simple-Json-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Q-Bug4/Comfyui-Simple-Json-Node", - "title": "Simple JSON Parser Node for ComfyUI" - }, - { - "author": "Q-Bug4", - "description": "A custom node designed for ComfyUI, allowing users to format the current date and time based on a specified format.", - "files": [ - "https://github.com/Q-Bug4/Comfyui-Qb-DateNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Q-Bug4/Comfyui-Qb-DateNodes", - "title": "Comfyui-Qb-Date-Nodes" - }, - { - "author": "Q-Bug4", - "description": "ComfyUI Batch Toolkit: Custom nodes that simplify batch operations and improve efficiency.", - "files": [ - "https://github.com/Q-Bug4/comfyui-qbug-batch" - ], - "install_type": "git-clone", - "reference": "https://github.com/Q-Bug4/comfyui-qbug-batch", - "title": "comfyui-qbug-batch" - }, - { - "author": "bartly", - "description": "This is a node to remove background of human picture.", - "files": [ - "https://github.com/bartly/Comfyui_babel_removebg_api" - ], - "id": "BabelRemovebgApi", - "install_type": "git-clone", - "reference": "https://github.com/bartly/Comfyui_babel_removebg_api", - "title": "Babel Removebg Api Node for ComfyUI" - }, - { - "author": "NumZ", - "description": "Convert your workflows into node and chain them.", - "files": [ - "https://github.com/numz/ComfyUI-FlowChain" - ], - "id": "FlowChainNode", - "install_type": "git-clone", - "reference": "https://github.com/numz/Comfyui-FlowChain", - "title": "ComfyUI-FlowChain" - }, - { - "author": "NumZ", - "description": "TTS with emotional speech capabilities in 8 Languages 24 speakers.", - "files": [ - "https://github.com/numz/Comfyui-Orpheus" - ], - "id": "OrpheusNode", - "install_type": "git-clone", - "reference": "https://github.com/numz/Comfyui-Orpheus", - "title": "ComfyUI-Orpheus" - }, - { - "author": "NumZ", - "description": "Welcome to the ComfyUI-SeedVR2 Video Upscaler repository! This project offers a non-official video upscaling tool designed specifically for ComfyUI. With this tool, you can enhance your video quality, making your visual content more engaging and clearer.", - "files": [ - "https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler" - ], - "id": "SeedVR2_VideoUpscaler", - "install_type": "git-clone", - "reference": "https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler", - "title": "ComfyUI-SeedVR2_VideoUpscaler" - }, - { - "author": "SozeInc", - "description": "These nodes aid with batching image processing and maintaining input file names in output files and other quality of life nodes.", - "files": [ - "https://github.com/SozeInc/ComfyUI_Soze" - ], - "id": "ComfyUI_Soze", - "install_type": "git-clone", - "reference": "https://github.com/SozeInc/ComfyUI_Soze", - "title": "Quality of Life Nodes for ComfyUI" - }, - { - "author": "MzMaXaM", - "description": "A pack of nodes(only 2 for now) to make my life easier and hopefully yours ;)", - "files": [ - "https://github.com/MzMaXaM/ComfyUi-MzMaXaM" - ], - "install_type": "git-clone", - "reference": "https://github.com/MzMaXaM/ComfyUi-MzMaXaM", - "title": "ComfyUi-MzMaXaM" - }, - { - "author": "robertvoy", - "description": "Set of custom nodes to use with the ComfyUI Flux Continuum: Modular Interface. NODES: Text Versions, Image64 Display, Tabs, Step Slider, Denoise Slider, Guidance Slider, Batch Slider, Max Shift Slider, ControlNet Slider and more", - "files": [ - "https://github.com/robertvoy/ComfyUI-Flux-Continuum" - ], - "install_type": "git-clone", - "reference": "https://github.com/robertvoy/ComfyUI-Flux-Continuum", - "title": "ComfyUI Flux Continuum: Modular Interface" - }, - { - "author": "Lam Yan", - "description": "This extension has some useful nodes, loops, wechat public number +AI chat drawing, distributed cluster", - "files": [ - "https://github.com/yanlang0123/ComfyUI_Lam" - ], - "id": "ComfyUI_Lam", - "install_type": "git-clone", - "reference": "https://github.com/yanlang0123/ComfyUI_Lam", - "title": "ComfyUI_Lam" - }, - { - "author": "moustafa-nasr", - "description": "A simple node to save your history in html file. I saves the WorkFlow with all it's input values so you can duplicate it later.", - "files": [ - "https://github.com/moustafa-nasr/ComfyUI-SimpleLogger" - ], - "install_type": "git-clone", - "reference": "https://github.com/moustafa-nasr/ComfyUI-SimpleLogger", - "title": "ComfyUI-SimpleLogger" - }, - { - "author": "sweetndata", - "description": "NODES:Google Translate", - "files": [ - "https://github.com/sweetndata/ComfyUI-googletrans" - ], - "install_type": "git-clone", - "reference": "https://github.com/sweetndata/ComfyUI-googletrans", - "title": "ComfyUI-googletrans" - }, - { - "author": "sweetndata", - "description": "NODES:Image-Harmonizer", - "files": [ - "https://github.com/sweetndata/ComfyUI-Image-Harmonizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/sweetndata/ComfyUI-Image-Harmonizer", - "title": "ComfyUI-Image-Harmonizer" - }, - { - "author": "sweetndata", - "description": "NODES:Sticker Compositer.\nbackground frame + sticker", - "files": [ - "https://github.com/sweetndata/ComfyUI_Sticker_Compositer" - ], - "install_type": "git-clone", - "reference": "https://github.com/sweetndata/ComfyUI_Sticker_Compositer", - "title": "ComfyUI_Sticker_Compositer" - }, - { - "author": "BlackVortexAI", - "description": "This repository contains a user-defined node for ComfyUI, currently there are nodes for capturing captions. But will be expanded in the future.", - "files": [ - "https://github.com/BlackVortexAI/ComfyUI-BVortexNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/BlackVortexAI/ComfyUI-BVortexNodes", - "title": "BV Nodes" - }, - { - "author": "JosephThomasParker", - "description": "These nodes provide a wrapper for calling Draw Things image generations from ComfyUI.\nWait, why? The Draw Things app has been optimized for Apple hardware and runs roughly x3 faster than ComfyUI generations. But ComfyUI is a flexible and powerful tools, and has some features - like queuing and face swapping - that haven't been implemented in Draw Things.", - "files": [ - "https://github.com/JosephThomasParker/ComfyUI-DrawThingsWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/JosephThomasParker/ComfyUI-DrawThingsWrapper", - "title": "ComfyUI-DrawThingsWrapper" - }, - { - "author": "Kesin11", - "description": "Custom nodes for convenient filtering image or string lists in ComfyUI workflow.", - "files": [ - "https://github.com/Kesin11/ComfyUI-list-filter" - ], - "install_type": "git-clone", - "reference": "https://github.com/Kesin11/ComfyUI-list-filter", - "title": "ComfyUI-list-filter" - }, - { - "author": "taches-ai", - "description": "A collection of nodes to facilitate the creation of explicit NSFW scenes in ComfyUI.", - "files": [ - "https://github.com/taches-ai/comfyui-scene-composer" - ], - "install_type": "git-clone", - "reference": "https://github.com/taches-ai/comfyui-scene-composer", - "reference2": "https://github.com/mus-taches/comfyui-scene-composer", - "title": "ComfyUI Scene Composer" - }, - { - "author": "NguynHungNguyen", - "description": "Segment Any Bedroom Interior is a Python-based project designed to segment furniture and objects within a bedroom image. The segmentation process uses RGB codes to accurately differentiate between various pieces of furniture, providing a precise mask output for each segmented object. This project is integrated with ComfyUI to allow easy and intuitive usage.", - "files": [ - "https://github.com/NguynHungNguyen/Segment-Bedroom-Interior" - ], - "install_type": "git-clone", - "reference": "https://github.com/NguynHungNguyen/Segment-Bedroom-Interior", - "title": "Segment Any Bedroom Interior" - }, - { - "author": "MyShell", - "description": "This repository provides utility nodes for defining inputs and outputs in ComfyUI workflows. These nodes are essential for running ShellAgent apps with ComfyUI, but they can also be used independently to specify input/output variables and their requirements explicitly.", - "files": [ - "https://github.com/myshell-ai/ComfyUI-ShellAgent-Plugin" - ], - "id": "comfyui_shellagent_plugin", - "install_type": "git-clone", - "reference": "https://github.com/myshell-ai/ComfyUI-ShellAgent-Plugin", - "title": "ComfyUI-ShellAgent-Plugin" - }, - { - "author": "Vrch Studio (vrch.ai)", - "description": "The ComfyUI Web Viewer by [a/vrch.ai](https://vrch.ai) is a custom node collection offering a real-time AI-generated interactive art framework. This utility integrates realtime streaming into ComfyUI workflows, supporting keyboard control nodes, OSC control nodes, sound input nodes, and more. Accessible from any device with a web browser, it enables real time interaction with AI-generated content, making it ideal for interactive visual projects and enhancing ComfyUI workflows with efficient content management and display.", - "files": [ - "https://github.com/VrchStudio/comfyui-web-viewer" - ], - "install_type": "git-clone", - "reference": "https://github.com/VrchStudio/comfyui-web-viewer", - "title": "ComfyUI Web Viewer" - }, - { - "author": "kk8bit", - "description": "KayTool nodes is designed to enhance the efficiency of building ComfyUI workflows. It includes a variety of practical nodes: support for efficient models like BiRefNet and RemBG for background removal and mask post-processing, wireless data transfer (Set & Get ), AI translation (Tencent and Baidu), dynamic mathematical operations, image processing (size extraction, color adjustment, background removal, mask blurring and expansion), flexible text handling, precision sliders, advanced image saving with metadata support, and more.", - "files": [ - "https://github.com/kk8bit/KayTool" - ], - "install_type": "git-clone", - "reference": "https://github.com/kk8bit/KayTool", - "title": "KayTool" - }, - { - "author": "sousakujikken", - "description": "Pixydust Quantizer is a custom node extension for ComfyUI that allows for the simplified recreation of tile patterns used in 1990s 16-color PC graphics, offering advanced color quantization and palette optimization features.Additionally, it includes a CRT-like effect node that mimics the phosphor glow and slight color bleeding of old CRT displays, adding a nostalgic aesthetic.With version 2.0.0, it now supports batch image processing, enabling efficient handling of video frames and image sequences.", - "files": [ - "https://github.com/sousakujikken/ComfyUI-PixydustQuantizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/sousakujikken/ComfyUI-PixydustQuantizer", - "title": "ComfyUI-PixydustQuantizer" - }, - { - "author": "hoveychen", - "description": "MusePose Remaster is a remaster version of ComfyUI MusePose node.\nIt supports auto weights download, remove most necessary dependencies, etc.", - "files": [ - "https://github.com/hoveychen/ComfyUI-MusePose-Remaster" - ], - "id": "musepose-remaster", - "install_type": "git-clone", - "reference": "https://github.com/hoveychen/ComfyUI-MusePose-Remaster", - "title": "ComfyUI-MusePose-Remaster" - }, - { - "author": "AhBumm", - "description": "API call node for Third-party platforms both official and local. Support VLMs LLMs Dalle3 Flux-Pro SD3 etc. And some little tools: img to b64 url, b64 url to img, b64 url to b64 data, reg text to word and ',' only, etc.", - "files": [ - "https://github.com/AhBumm/ComfyUI_BillBum_APIset_Nodes" - ], - "id": "billbum", - "install_type": "git-clone", - "nodename_pattern": "\\(BillBum\\)$", - "reference": "https://github.com/AhBumm/ComfyUI_BillBum_APIset_Nodes", - "title": "Customizable API Call Nodes by BillBum" - }, - { - "author": "Scepter", - "description": "Custom nodes for various visual generation and editing tasks using Scepter.", - "files": [ - "https://github.com/modelscope/scepter" - ], - "id": "scepter", - "install_type": "git-clone", - "reference": "https://github.com/modelscope/scepter", - "title": "ComfyUI-Scepter" - }, - { - "author": "DeemosTech", - "description": "Comfyui-rodin is a 3D generation extension based on Rodin-API. It provides many of the functionality nodes currently available in RodinAPI and It provides a 3D preview node for ComfyUI.", - "files": [ - "https://github.com/DeemosTech/ComfyUI-Rodin" - ], - "id": "rodinHyperhuamn", - "install_type": "git-clone", - "reference": "https://github.com/DeemosTech/ComfyUI-Rodin", - "title": "ComfyUI-Rodin" - }, - { - "author": "Ardenius", - "description": "ARD ComfyUI Ardenius include ARD Control Box, ARD Math nodes and other helper nodes to be added in the future. for more info https://ko-fi.com/ardenius.", - "files": [ - "https://github.com/ArdeniusAI/ComfyUI-Ardenius" - ], - "id": "ARD", - "install_type": "git-clone", - "reference": "https://github.com/ArdeniusAI/ComfyUI-Ardenius", - "title": "ComfyUI-Ardenius" - }, - { - "author": "brayevalerien", - "description": "This repository is a quick port of [a/Resynthesizer](https://github.com/bootchk/resynthesizer) to ComfyUI.\nResynthesizer is the open-source implementation of a texture generation technique proposed by Paul Harrison in 2005, especially useful for removing an object from an image (inpainting), which is most likely close to what Photoshop uses to for the content aware fill feature. Note that this is not using a diffusion model to inpaint, as opposed to many techniques of today, which makes it very fast and predictable, but sometimes yields worse results.", - "files": [ - "https://github.com/brayevalerien/ComfyUI-resynthesizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/brayevalerien/ComfyUI-resynthesizer", - "title": "ComfyUI Resynthesizer" - }, - { - "author": "brayevalerien", - "description": "Very specific node for spliting a string with 12 lines into 12 individual strings.k", - "files": [ - "https://github.com/brayevalerien/ComfyUI-SplitString" - ], - "install_type": "git-clone", - "reference": "https://github.com/brayevalerien/ComfyUI-SplitString", - "title": "ComfyUI-splitstring" - }, - { - "author": "BZcreativ", - "description": "A custom node implementation for ComfyUI that integrates with Together.ai's FLUX image generation models. This project is inspired by and adapted from [a/ComfyUI-FLUX-BFL-API](https://github.com/gelasdev/ComfyUI-FLUX-BFL-API) to work with the Together.ai API.", - "files": [ - "https://github.com/BZcreativ/ComfyUI-FLUX-TOGETHER-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/BZcreativ/ComfyUI-FLUX-TOGETHER-API", - "title": "ComfyUI-FLUX-TOGETHER-API" - }, - { - "author": "stormcenter", - "description": "ComfyUI-AutoSplitGridImage is a custom node for ComfyUI that provides intelligent image splitting functionality. It combines edge detection for column splits and uniform division for row splits, offering a balanced approach to grid-based image segmentation.", - "files": [ - "https://github.com/stormcenter/ComfyUI-AutoSplitGridImage" - ], - "install_type": "git-clone", - "reference": "https://github.com/stormcenter/ComfyUI-AutoSplitGridImage", - "title": "ComfyUI-AutoSplitGridImage" - }, - { - "author": "stormcenter", - "description": "A custom node for ComfyUI that allows you to create iPhone-compatible Live Photos from videos. This node can convert video sequences into Live Photo format, with the ability to select key frames and customize the output.", - "files": [ - "https://github.com/stormcenter/ComfyUI-LivePhotoCreator" - ], - "install_type": "git-clone", - "reference": "https://github.com/stormcenter/ComfyUI-LivePhotoCreator", - "title": "ComfyUI LivePhoto Creator" - }, - { - "author": "stormcenter", - "description": "ComfyUI-SVGFullfill is a custom node for ComfyUI that handles SVG file processing. Key features: - SVG file upload and preview - Replace images (up to 3) and text elements (up to 10) in SVG - Chinese font support - Real-time canvas preview - PNG export", - "files": [ - "https://github.com/stormcenter/ComfyUI-SVGFullfill" - ], - "install_type": "git-clone", - "reference": "https://github.com/stormcenter/ComfyUI-SVGFullfill", - "title": "ComfyUI-SVGFullfill" - }, - { - "author": "AkashKarnatak", - "description": "ComfyUI nodes for our product Faishme", - "files": [ - "https://github.com/AkashKarnatak/ComfyUI_faishme" - ], - "install_type": "git-clone", - "reference": "https://github.com/AkashKarnatak/ComfyUI_faishme", - "title": "ComfyUI_faishme" - }, - { - "author": "ARZUMATA", - "description": "NODES:Caching CLIP Text Encode for FLUX.\nRandom nodes for ComfyUI for various purposes.", - "files": [ - "https://github.com/ARZUMATA/ComfyUI-ARZUMATA" - ], - "install_type": "git-clone", - "reference": "https://github.com/ARZUMATA/ComfyUI-ARZUMATA", - "title": "ComfyUI-ARZUMATA" - }, - { - "author": "ARZUMATA", - "description": "Qwen2 Nodes for ComfyUI.\nI needed to run Qwen2 on ComfyUI to use it in my workflow for batching images and captioning and none of the implementations I found on the web worked the way I wanted.[w/May contain bugs.]", - "files": [ - "https://github.com/ARZUMATA/ComfyUI-ARZUMATA-Qwen2" - ], - "install_type": "git-clone", - "reference": "https://github.com/ARZUMATA/ComfyUI-ARZUMATA-Qwen2", - "title": "ComfyUI-Qwen2" - }, - { - "author": "ARZUMATA", - "description": "A python port of pixelit by giventofly.", - "files": [ - "https://github.com/ARZUMATA/ComfyUI-ARZUMATA-PixelIt" - ], - "install_type": "git-clone", - "reference": "https://github.com/ARZUMATA/ComfyUI-ARZUMATA-PixelIt", - "title": "ComfyUI-ARZUMATA-PixelIt" - }, - { - "author": "Rinsanga1", - "description": "NODES:Florence2 Coordinates (XY Split), Phi-3.5 Vision Instruct.", - "files": [ - "https://github.com/Rinsanga1/comfyui-florence2xy" - ], - "install_type": "git-clone", - "reference": "https://github.com/Rinsanga1/comfyui-florence2xy", - "title": "comfyui-florence2xy" - }, - { - "author": "gt732", - "description": "This repository contains custom ComfyUI nodes designed to integrate with [a/DreamWaltz-G](https://github.com/Yukun-Huang/DreamWaltz-G), a cutting-edge model for generating expressive 3D Gaussian avatars using skeleton-guided 2D diffusion.", - "files": [ - "https://github.com/gt732/ComfyUI-DreamWaltz-G" - ], - "install_type": "git-clone", - "reference": "https://github.com/gt732/ComfyUI-DreamWaltz-G", - "title": "ComfyUI-DreamWaltz-G" - }, - { - "author": "clhui", - "description": "Some mathematical calculation nodes\uff0cfreedom And omnipotent, string calculation nodes, can customize the number of parameters and calculation formulas\uff08expression\uff09. The calculation content can also be displayed in places such as the label title of Comfy Node\uff0cString to Image Title Label", - "files": [ - "https://github.com/clhui/ComfyUi-clh-Tool" - ], - "id": "ComfyUi-clh-Tool", - "install_type": "git-clone", - "reference": "https://github.com/clhui/ComfyUi-clh-Tool", - "title": "Clh Tool for ComfyUI" - }, - { - "author": "ruucm", - "description": "Nodes: Load External LoRA Model Only", - "files": [ - "https://github.com/ruucm/ruucm-comfy" - ], - "id": "ruucm", - "install_type": "git-clone", - "nodename_pattern": " \\(ruucm\\)$", - "reference": "https://github.com/ruucm/ruucm-comfy", - "title": "Ruucm's ComfyUI Nodes" - }, - { - "author": "TZOOTZ", - "description": "The TZOOTZ VHS Effect Node is designed for multimedia creators who want to blend digital precision with analog imperfection \u2194\ufe0f. Inspired by retro VHS aesthetics, this node lets you apply grain, color bleeding, saturation adjustments, and more, giving any image a touch of analog warmth and noise.", - "files": [ - "https://github.com/TZOOTZ/ComfyUI-TZOOTZ_VHS" - ], - "install_type": "git-clone", - "pip": [ - "numpy<2" - ], - "reference": "https://github.com/TZOOTZ/ComfyUI-TZOOTZ_VHS", - "title": "TZOOTZ VHS Effect Node" - }, - { - "author": "jianzhichun", - "description": "ComfyUI-Easyai is a powerful extension for ComfyUI that enables users to share workflows and models to easyai.", - "files": [ - "https://github.com/jianzhichun/ComfyUI-Easyai" - ], - "id": "comfyui-easyai", - "install_type": "git-clone", - "reference": "https://github.com/jianzhichun/ComfyUI-Easyai", - "title": "ComfyUI-Easyai" - }, - { - "author": "Isulion", - "description": "ComfyUI Nodes that generate prompts and many more.", - "files": [ - "https://github.com/Isulion/ComfyUI_Isulion" - ], - "install_type": "git-clone", - "reference": "https://github.com/Isulion/ComfyUI_Isulion", - "title": "ComfyUI_Isulion Random Prompt Generator" - }, - { - "author": "sneccc", - "description": "NODES:Aesthetics, Aesthetics V2, Load AI Toolkit Latent Flux, Send_to_Eagle", - "files": [ - "https://github.com/sneccc/comfyui-snek-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/sneccc/comfyui-snek-nodes", - "title": "comfyui-snek-nodes" - }, - { - "author": "theshubzworld", - "description": "OllamaKiller is a cross-platform utility node for ComfyUI that automatically manages Ollama processes (`ollama.exe` on Windows, `ollama` on macOS/Linux). It helps free up VRAM by terminating Ollama processes after model execution, improving workflow performance and memory management. Features include graceful process termination, detailed status reporting, and seamless text passthrough.", - "files": [ - "https://github.com/theshubzworld/ComfyUI-ollama_killer" - ], - "install_type": "git-clone", - "reference": "https://github.com/theshubzworld/ComfyUI-ollama_killer", - "title": "ComfyUI-ollama_killer" - }, - { - "author": "theshubzworld", - "description": "A universal node for generating empty latent tensors with support for SD3.5, SDXL, and Flux models. Features extended aspect ratio support, batch processing, and flexible dimension overrides.", - "files": [ - "https://github.com/theshubzworld/ComfyUI-SD3.5-Latent-Size-Picker" - ], - "install_type": "git-clone", - "reference": "https://github.com/theshubzworld/ComfyUI-SD3.5-Latent-Size-Picker", - "title": "SD3.5 Empty Latent Size Picker" - }, - { - "author": "theshubzworld", - "description": "A custom ComfyUI node using Together AI's Vision models for free image descriptions, image generation, and image-to-image transformation. Features include customizable prompts, advanced parameters, and robust error handling.", - "files": [ - "https://github.com/theshubzworld/ComfyUI-TogetherVision" - ], - "id": "comfyui_together_vision", - "install_type": "git-clone", - "reference": "https://github.com/theshubzworld/ComfyUI-TogetherVision", - "title": "Together Vision Node" - }, - { - "author": "jeffrey2212", - "description": "The Pony Character Prompt Picker node reads an Excel file specified by the user, allows manual selection of a tab, and randomly picks a cell value from a specified column, starting from row 3 to the end. The selected value is output as a string to the next node in the ComfyUI workflow.", - "files": [ - "https://github.com/jeffrey2212/ComfyUI-PonyCharacterPrompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/jeffrey2212/ComfyUI-PonyCharacterPrompt", - "title": "Pony Character Prompt Picker for ComfyUI" - }, - { - "author": "theshubzworld", - "description": "A collection of custom nodes for ComfyUI that provide advanced face callout, annotation, and compositing effects using OpenCV and PIL. These nodes are designed for image processing workflows that require face detection, annotation, and creative compositing.", - "files": [ - "https://github.com/theshubzworld/ComfyUI-FaceCalloutNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/theshubzworld/ComfyUI-FaceCalloutNode", - "title": "ComfyUI-FaceCalloutNode" - }, - { - "author": "Jonseed", - "description": "A port of muerrilla's [a/sd-webui-Detail-Daemon](https://github.com/muerrilla/sd-webui-detail-daemon) as a node for ComfyUI, to adjust sigmas that control detail.", - "files": [ - "https://github.com/Jonseed/ComfyUI-Detail-Daemon" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jonseed/ComfyUI-Detail-Daemon", - "title": "ComfyUI-Detail-Daemon" - }, - { - "author": "chris-arsenault", - "description": "NODES:Frame Segmenter, Get Frame at Index, Repeat Sampler Config, Patch Repeat Sampler Config (Model), Patch Repeat Sampler Config (Latent), KSampler (Simple Input)", - "files": [ - "https://github.com/chris-arsenault/ComfyUI-AharaNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/chris-arsenault/ComfyUI-AharaNodes", - "title": "ComfyUI-AharaNodes" - }, - { - "author": "mfg637", - "description": "This extension contains various nodes for CFG scheduling and more. NODES:ScheduledCFGGuider, PerpNegScheduledCFGGuider, CosineScheduler, GaussianScheduler, LogNormalScheduler, InvertSigmas, ConcatSigmas, OffsetSigmas, SplitSigmasByValue", - "files": [ - "https://github.com/mfg637/ComfyUI-ScheduledGuider-Ext" - ], - "install_type": "git-clone", - "reference": "https://github.com/mfg637/ComfyUI-ScheduledGuider-Ext", - "title": "ComfyUI-ScheduledGuider-Ext" - }, - { - "author": "changwook987", - "description": "Context menu extension for CLIPTextEncode (sort prompt), EmptyLatentImage (sdxl size selector).", - "files": [ - "https://github.com/changwook987/ComfyUI-Small-Utility" - ], - "install_type": "git-clone", - "reference": "https://github.com/changwook987/ComfyUI-Small-Utility", - "title": "ComfyUI-Small-Utility" - }, - { - "author": "OpalSky", - "description": "A set of custom nodes for ComfyUI that provides enhanced string manipulation and prompt variant generation functionality for AI workflows.", - "files": [ - "https://github.com/OpalSky-AI/OpalSky_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/OpalSky-AI/OpalSky_Nodes", - "title": "OpalSky Nodes" - }, - { - "author": "JustinMatters", - "description": "Provides nodes to support generation of all possible combinations of a set of prompts via boolean logic", - "files": [ - "https://github.com/JustinMatters/comfyUI-JMNodes" - ], - "id": "JMNodes", - "install_type": "git-clone", - "reference": "https://github.com/JustinMatters/comfyUI-JMNodes", - "title": "ComfyUI JMNodes" - }, - { - "author": "lgldlk", - "description": "Just like when your pizza is ready and the oven goes 'Ding! \ud83c\udf55', this plugin lets your ComfyUI notify you when your AI creations are done baking!\nA ComfyUI custom node that sends you a friendly 'ding-dong' notification when your workflows are fully cooked and ready to serve. No more staring at the screen waiting - let the AI kitchen tell you when dinner's ready! \ud83d\udc68\u200d\ud83c\udf73", - "files": [ - "https://github.com/lgldlk/ComfyUI-PC-ding-dong" - ], - "install_type": "git-clone", - "reference": "https://github.com/lgldlk/ComfyUI-PC-ding-dong", - "title": "ComfyUI-PC-ding-dong" - }, - { - "author": "lgldlk", - "description": "One click replacement of smart objects or layers in PSD", - "files": [ - "https://github.com/lgldlk/ComfyUI-PSD-Replace" - ], - "install_type": "git-clone", - "reference": "https://github.com/lgldlk/ComfyUI-PSD-Replace", - "title": "ComfyUI-PSD-Replace" - }, - { - "author": "Wakfull33", - "description": "A custom node allowing to save images with CIVITAI readable datas", - "files": [ - "https://github.com/Wakfull33/ComfyUI-SaveImageCivitAI" - ], - "install_type": "git-clone", - "reference": "https://github.com/Wakfull33/ComfyUI-SaveImageCivitAI", - "title": "ComfyUI-SaveImageCivitAI" - }, - { - "author": "waterminer", - "description": "This extension provides tag completion feature in textbox.", - "files": [ - "https://github.com/waterminer/ComfyUI-tagcomplete" - ], - "install_type": "git-clone", - "reference": "https://github.com/waterminer/ComfyUI-tagcomplete", - "title": "ComfyUI-tagcomplete" - }, - { - "author": "grovebadger", - "description": "Node to process negative wildcard tokens () and move them from the positive prompt to the negative.", - "files": [ - "https://github.com/GrvBdgr/comfyui-negativewildcardsprocessor" - ], - "id": "neg_wildcard_processor", - "install_type": "git-clone", - "reference": "https://github.com/GrvBdgr/comfyui-negativewildcardsprocessor", - "title": "Negative Wildcard Processor Node for ComfyUI" - }, - { - "author": "catboxanon", - "description": "Fork of [a/sd_webui_stealth_pnginfo](https://github.com/ashen-sensored/sd_webui_stealth_pnginfo) with ComfyUI support.", - "files": [ - "https://github.com/catboxanon/comfyui_stealth_pnginfo" - ], - "install_type": "git-clone", - "reference": "https://github.com/catboxanon/comfyui_stealth_pnginfo", - "title": "comfyui_stealth_pnginfo" - }, - { - "author": "dafeng012", - "description": "This extension integrates ebsynth_utility into comfyui, and I've written some of my own nodes for secondary use.", - "files": [ - "https://github.com/dafeng012/comfyui-imgmake" - ], - "install_type": "git-clone", - "reference": "https://github.com/dafeng012/comfyui-imgmake", - "title": "comfyui-imgmake" - }, - { - "author": "zubenelakrab", - "description": "ComfyUI-ASV-Nodes make prompting easier.", - "files": [ - "https://github.com/zubenelakrab/ComfyUI-ASV-Nodes" - ], - "id": "ComfyUI-ASV-Nodes", - "install_type": "git-clone", - "reference": "https://github.com/zubenelakrab/ComfyUI-ASV-Nodes", - "title": "ComfyUI-ASV-Nodes Node" - }, - { - "author": "zubenelakrab", - "description": "An advanced ComfyUI extension that enables multi-agent LLM conversations using Ollama models.", - "files": [ - "https://github.com/xobiomesh/ComfyUI_xObiomesh" - ], - "install_type": "git-clone", - "reference": "https://github.com/xobiomesh/ComfyUI_xObiomesh", - "title": "ComfyUI Neural Nodes" - }, - { - "author": "KohakuBlueleaf", - "description": "A general extension to utilize TIPO or DanTagGen to do 'text-presampling' based on KGen library: [a/https://github.com/KohakuBlueleaf/KGen](https://github.com/KohakuBlueleaf/KGen)", - "files": [ - "https://github.com/KohakuBlueleaf/z-tipo-extension" - ], - "install_type": "git-clone", - "reference": "https://github.com/KohakuBlueleaf/z-tipo-extension", - "title": "TIPO-extension" - }, - { - "author": "KohakuBlueleaf", - "description": "HDM model loader for ComfyUI", - "files": [ - "https://github.com/KohakuBlueleaf/HDM-ext" - ], - "id": "HDM", - "install_type": "git-clone", - "reference": "https://github.com/KohakuBlueleaf/HDM-ext", - "title": "HDM-ext" - }, - { - "author": "hanoixan", - "description": "This extension provides convenience nodes for batch processing.", - "files": [ - "https://github.com/hanoixan/ComfyUI-DataBeast" - ], - "install_type": "git-clone", - "reference": "https://github.com/hanoixan/ComfyUI-DataBeast", - "title": "ComfyUI DataBeast" - }, - { - "author": "HelloVision", - "description": "This repository is the official implementation of the [a/HelloMeme](https://arxiv.org/pdf/2410.22901) ComfyUI interface, featuring both image and video generation functionalities. Example workflow files can be found in the ComfyUI_HelloMeme/workflows directory. Test images and videos are saved in the ComfyUI_HelloMeme/examples directory. Below are screenshots of the interfaces for image and video generation.\nNOTE: 'HelloMeme: Integrating Spatial Knitting Attentions to Embed High-Level and Fidelity-Rich Conditions in Diffusion Models'", - "files": [ - "https://github.com/HelloVision/ComfyUI_HelloMeme" - ], - "install_type": "git-clone", - "reference": "https://github.com/HelloVision/ComfyUI_HelloMeme", - "title": "ComfyUI_HelloMeme" - }, - { - "author": "recraftai", - "description": "Recraft AI API Custom Nodes", - "files": [ - "https://github.com/recraft-ai/ComfyUI-RecraftAI" - ], - "id": "comfyui-recraftai", - "install_type": "git-clone", - "reference": "https://github.com/recraft-ai/ComfyUI-RecraftAI", - "title": "ComfyUI-RecraftAI" - }, - { - "author": "basix", - "description": "A handful of image filters for ComfyUI (darken, lighten, levels, saturate, hue)", - "files": [ - "https://github.com/maludwig/basix_image_filters" - ], - "id": "basix_image_filters", - "install_type": "git-clone", - "reference": "https://github.com/maludwig/basix_image_filters", - "title": "Basix Image Filters" - }, - { - "author": "Frost Ming", - "description": "A comprehensive toolkit for standardizing, packaging and deploying ComfyUI workflows as reproducible environments and production-ready REST services", - "files": [ - "https://github.com/bentoml/comfy-pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/bentoml/comfy-pack", - "title": "Comfy-Pack" - }, - { - "author": "Poseidon-fan", - "description": "ComfyUI custom_node that publish output image to rabbit_mq", - "files": [ - "https://github.com/Poseidon-fan/ComfyUI-RabbitMQ-Publisher" - ], - "install_type": "git-clone", - "reference": "https://github.com/Poseidon-fan/ComfyUI-RabbitMQ-Publisher", - "title": "ComfyUI-RabbitMQ-Publisher" - }, - { - "author": "Blonicx", - "description": "This is a plugin for ComfyUI that adds new Util Nodes and Nodes for easier image creation and sharing.", - "files": [ - "https://github.com/Blonicx/ComfyUI-X-Rework" - ], - "id": "rework-x", - "install_type": "git-clone", - "reference": "https://github.com/Blonicx/ComfyUI-X-Rework", - "title": "ComfyUI-Rework-X" - }, - { - "author": "1zhangyy1", - "description": "This is a ComfyUI node package that integrates with VIDU API, supporting features such as text-to-video, image-to-video, character-to-video generation, and video super-resolution.", - "files": [ - "https://github.com/1zhangyy1/comfyui-vidu-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/1zhangyy1/comfyui-vidu-nodes", - "title": "ComfyUI VIDU" - }, - { - "author": "LevelPixel", - "description": "Main nodes of the Level Pixel company (aka levelpixel, LP). Includes convenient nodes for working with images from folders; counting files in a folder; cleaning memory; tag filters. Model Unloader, LLM Unloader, Free memory, Tag Filters, Tag Category Filters, Tag Choice Parser, File counter, Image Loader From Path (with counters), Image Remove Background based on RemBG, Autotagger.", - "files": [ - "https://github.com/LevelPixel/ComfyUI-LevelPixel" - ], - "install_type": "git-clone", - "reference": "https://github.com/LevelPixel/ComfyUI-LevelPixel", - "title": "ComfyUI Level Pixel" - }, - { - "author": "LevelPixel", - "description": "Advanced nodes of the Level Pixel company (levelpixel, LP). Includes convenient advanced nodes for working with LLM \u0438 VLM models (LLaVa) with GGUF format. Qwen2.5-VL and Qwen2.5 supported. Also included is a node for the RAM model. Nodes have the ability to automatically unload models from VRAM.", - "files": [ - "https://github.com/LevelPixel/ComfyUI-LevelPixel-Advanced" - ], - "id": "comfyui-levelpixel-advanced", - "install_type": "git-clone", - "reference": "https://github.com/LevelPixel/ComfyUI-LevelPixel-Advanced", - "title": "ComfyUI Level Pixel Advanced" - }, - { - "author": "morino-kumasan", - "description": "Encode Prompt in TOML for ComfyUI.", - "files": [ - "https://github.com/morino-kumasan/comfyui-toml-prompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/morino-kumasan/comfyui-toml-prompt", - "title": "comfyui-toml-prompt" - }, - { - "author": "wentao-uw", - "description": "This project is a ComfyUI version of [a/https://github.com/cozheyuanzhangde/Invariant-TemplateMatching](https://github.com/cozheyuanzhangde/Invariant-TemplateMatching).", - "files": [ - "https://github.com/wentao-uw/ComfyUI-template-matching" - ], - "install_type": "git-clone", - "reference": "https://github.com/wentao-uw/ComfyUI-template-matching", - "title": "ComfyUI template matching" - }, - { - "author": "w00dycomfyuirun", - "description": "ComfyUI_Appstore, a tool that converts ComfyUI workflows into web apps on huaxiaobao.net with one click, and supports payments, like ComfyUI_Bxb (Bxb) does. Providing a way for the comfyui authors to get profit from.", - "files": [ - "https://github.com/ronaldzgithub/ComfyUI_Appstore" - ], - "id": "ComfyUI_Appstore", - "install_type": "git-clone", - "reference": "https://github.com/ronaldzgithub/ComfyUI_Appstore", - "title": "ComfyUI_Appstore" - }, - { - "author": "kycg", - "description": "This tool allows you to download models from CivitAI based on a JSON configuration that defines LORA and checkpoint models. It uses token-based authentication to download files from specified URLs and saves them to specified directories. based on CivitAIDownloader", - "files": [ - "https://github.com/kycg/comfyui-Lora-auto-downloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/kycg/comfyui-Lora-auto-downloader", - "title": "Kw_Json_Lora_CivitAIDownloader" - }, - { - "author": "VangengLab", - "description": "We developed a custom_node for Liveportrait_v2 that enables flexible use on Comfyui to drive animal image-based emoji generation from videos.", - "files": [ - "https://github.com/VangengLab/ComfyUI-LivePortrait_v2" - ], - "install_type": "git-clone", - "reference": "https://github.com/VangengLab/ComfyUI-LivePortrait_v2", - "title": "ComfyUI-LivePortrait_v2" - }, - { - "author": "VangengLab", - "description": "We developed a custom_node for Liveportrait_v3 that enables flexible use on Comfyui to drive image-based emoji generation from photos.", - "files": [ - "https://github.com/VangengLab/ComfyUI-LivePortrait_v3" - ], - "install_type": "git-clone", - "reference": "https://github.com/VangengLab/ComfyUI-LivePortrait_v3", - "title": "ComfyUI-LivePortrait_v3" - }, - { - "author": "Comflowy", - "description": "Custom nodes for ComfyUI by Comflowy.", - "files": [ - "https://github.com/6174/comflowy-nodes" - ], - "id": "comflowy", - "install_type": "git-clone", - "reference": "https://github.com/6174/comflowy-nodes", - "title": "Comflowy's Custom Nodes" - }, - { - "author": "troyxmccall", - "description": "NODES:ScaleToTargetMegapixels.", - "files": [ - "https://github.com/troyxmccall/ComfyUI-ScaleToTargetMegapixels" - ], - "install_type": "git-clone", - "reference": "https://github.com/troyxmccall/ComfyUI-ScaleToTargetMegapixels", - "title": "ComfyUI-ScaleToTargetMegapixels" - }, - { - "author": "neph1", - "description": "This custom node provides a smooth step function that normalizes LoRA values by enhancing elements above the mean while reducing those below it. Users can independently control both the LoRA strength and smooth step intensity to fine-tune their results, though the effectiveness may vary between different seeds and LoRA types.", - "files": [ - "https://github.com/neph1/comfyui-smooth-step-lora-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/neph1/comfyui-smooth-step-lora-loader", - "title": "comfyui-smooth-step-lora-loader" - }, - { - "author": "ImmortalPie", - "description": "The PonySwitch node is a custom node for ComfyUI that modifies prompts based on a toggle switch and adds configurable pony tags.", - "files": [ - "https://github.com/ImmortalPie/ComfyUI-PonySwitch" - ], - "install_type": "git-clone", - "reference": "https://github.com/ImmortalPie/ComfyUI-PonySwitch", - "title": "PonySwitch Node" - }, - { - "author": "LatentSpaceDirective", - "description": "These are companion nodes for Texturaizer, a Blender plugin that connects complex 3D data to ComfyUI. https://texturaizer.com", - "files": [ - "https://github.com/LatentSpaceDirective/ComfyUI-Texturaizer" - ], - "id": "texturaizer", - "install_type": "git-clone", - "reference": "https://github.com/LatentSpaceDirective/ComfyUI-Texturaizer", - "title": "ComfyUI-Texturaizer" - }, - { - "author": "Lasse Lauwerys", - "description": "Implements proper multitouch zooming and panning into ComfyUI to make it more usable on mobile devices.", - "files": [ - "https://github.com/Iemand005/ComfyUI-Touch-Gestures" - ], - "install_type": "git-clone", - "reference": "https://github.com/Iemand005/ComfyUI-Touch-Gestures", - "title": "Touch screen gesture support" - }, - { - "author": "Lasse Lauwerys", - "description": "Implements proper touchpad/trackpad zooming and panning into ComfyUI to make it more usable on laptops.", - "files": [ - "https://github.com/Iemand005/ComfyUI-Touchpad-Gestures" - ], - "install_type": "git-clone", - "reference": "https://github.com/Iemand005/ComfyUI-Touchpad-Gestures", - "title": "Touchpad and trackpad gesture support" - }, - { - "author": "phazei", - "description": "Prompt Stash is a simple plugin for ComfyUI that lets you save your prompts and organize them into multiple lists. It also features a pass-through functionality, so you can hook it up to an LLM node (or any text outputting node) and capture its outputs directly.", - "files": [ - "https://github.com/phazei/ComfyUI-Prompt-Stash" - ], - "id": "ComfyUI-Prompt-Stash", - "install_type": "git-clone", - "reference": "https://github.com/phazei/ComfyUI-Prompt-Stash", - "title": "Prompt Stash" - }, - { - "author": "phazei", - "description": "Generate Orpheus TTS audio via LM Studio", - "files": [ - "https://github.com/phazei/ComfyUI-OrpheusTTS-LMStudio" - ], - "install_type": "git-clone", - "reference": "https://github.com/phazei/ComfyUI-OrpheusTTS-LMStudio", - "title": "ComfyUI-OrpheusTTS-LMStudio" - }, - { - "author": "Doctor Diffusion", - "description": "Nodes:Whisper Node, Prompt Schedule Converter. Convert song lyrics into a useable prompt travel schedule within comfyUI. Includes whisper large-v2.", - "files": [ - "https://github.com/DoctorDiffusion/ComfyUI-Schedulizer" - ], - "id": "schedulizer", - "install_type": "git-clone", - "reference": "https://github.com/DoctorDiffusion/ComfyUI-Schedulizer", - "title": "Schedulizer" - }, - { - "author": "Doctor Diffusion", - "description": "A node suite for downloading audio and video from youtube as we all sevral useful video utilits such as a final frame selector and a node that merges two videos into one.", - "files": [ - "https://github.com/DoctorDiffusion/ComfyUI-MediaMixer" - ], - "id": "mediamixer", - "install_type": "git-clone", - "reference": "https://github.com/DoctorDiffusion/ComfyUI-MediaMixer", - "title": "MediaMixer" - }, - { - "author": "Doctor Diffusion", - "description": "Use [a/Doctor Diffusion's snake oil nLoRAs](https://civitai.com/models/987843) as well as [a/other negative LoRAs](https://civitai.com/models/186617/doctor-diffusions-negative-xl-lora) easily within ComfyUI.", - "files": [ - "https://github.com/DoctorDiffusion/ComfyUI-SnakeOil" - ], - "install_type": "git-clone", - "reference": "https://github.com/DoctorDiffusion/ComfyUI-SnakeOil", - "title": "ComfyUI-SnakeOil" - }, - { - "author": "Doctor Diffusion", - "description": "Remove backgrounds from images with [a/BEN](https://huggingface.co/PramaLLC/BEN) in ComfyUI", - "files": [ - "https://github.com/DoctorDiffusion/ComfyUI-BEN" - ], - "install_type": "git-clone", - "reference": "https://github.com/DoctorDiffusion/ComfyUI-BEN", - "title": "ComfyUI BEN - Background Erase Network" - }, - { - "author": "Doctor Diffusion", - "description": "Audio to midi functionality within ComfyUI", - "files": [ - "https://github.com/DoctorDiffusion/ComfyUI-basic-pitch" - ], - "install_type": "git-clone", - "reference": "https://github.com/DoctorDiffusion/ComfyUI-basic-pitch", - "title": "ComfyUI-basic-pitch" - }, - { - "author": "robtl2", - "description": "A socket service that helps third-party DCC software maintain long-term image exchange with comfyUI.", - "files": [ - "https://github.com/robtl2/ComfyUI-ComfyBridge" - ], - "install_type": "git-clone", - "reference": "https://github.com/robtl2/ComfyUI-ComfyBridge", - "title": "ComfyUI-ComfyBridge" - }, - { - "author": "bombax-xiaoice", - "description": "ComfyUI supports over [a/Boese0601/MagicDance](https://github.com/Boese0601/MagicDance).", - "files": [ - "https://github.com/bombax-xiaoice/ComfyUI-MagicDance" - ], - "install_type": "git-clone", - "reference": "https://github.com/bombax-xiaoice/ComfyUI-MagicDance", - "title": "ComfyUI-MagicDance" - }, - { - "author": "bombax-xiaoice", - "description": "ComfyUI supports over [a/rhymes-ai/Allegro](https://huggingface.co/rhymes-ai/Allegro), which uses text prompt to generate short video in relatively high quality, especially comparing to other open source solutions available for now.", - "files": [ - "https://github.com/bombax-xiaoice/ComfyUI-Allegro" - ], - "install_type": "git-clone", - "reference": "https://github.com/bombax-xiaoice/ComfyUI-Allegro", - "title": "ComfyUI-Allegro" - }, - { - "author": "bombax-xiaoice", - "description": "Another comfy implementation for the short video generation project hpcaitech/Open-Sora, supporting latest V2 and V3 models as well as image to video functions, etc.", - "files": [ - "https://github.com/bombax-xiaoice/ComfyUI-Open-Sora-I2V" - ], - "install_type": "git-clone", - "reference": "https://github.com/bombax-xiaoice/ComfyUI-Open-Sora-I2V", - "title": "ComfyUI-Open-Sora-I2V" - }, - { - "author": "bombax-xiaoice", - "description": "Another comfy implementation for the short video generation project PKU-YuanGroup/Open-Sora-Plan, supporting latest 1.3.0 and 1.2.0 and image to video feature, etc.", - "files": [ - "https://github.com/bombax-xiaoice/ComfyUI-OpenSoraPlan" - ], - "install_type": "git-clone", - "reference": "https://github.com/bombax-xiaoice/ComfyUI-OpenSoraPlan", - "title": "ComfyUI-OpenSoraPlan" - }, - { - "author": "bombax-xiaoice", - "description": "ComfyUI supports over lihxxx/DisPose, which generates a new video with a reference video as poses and a reference image as everything else.", - "files": [ - "https://github.com/bombax-xiaoice/ComfyUI-DisPose" - ], - "install_type": "git-clone", - "reference": "https://github.com/bombax-xiaoice/ComfyUI-DisPose", - "title": "ComfyUI-DisPose" - }, - { - "author": "chenbaiyujason", - "description": "To use stepfun's library, you need an official api that supports multimodal inputs such as video and pictures [a/https://platform.stepfun.com/request-restriction](https://platform.stepfun.com/request-restriction)", - "files": [ - "https://github.com/chenbaiyujason/ComfyUI_StepFun" - ], - "install_type": "git-clone", - "reference": "https://github.com/chenbaiyujason/ComfyUI_StepFun", - "title": "ComfyUI-SCStepFun" - }, - { - "author": "yondonfu", - "description": "ComfyUI nodes for editing background of images/videos with CUDA acceleration support.", - "files": [ - "https://github.com/yondonfu/ComfyUI-Background-Edit" - ], - "id": "comfyui-background-edit", - "install_type": "git-clone", - "reference": "https://github.com/yondonfu/ComfyUI-Background-Edit", - "title": "ComfyUI-Background-Edit" - }, - { - "author": "yondonfu", - "description": "ComfyUI nodes for torch.compile.", - "files": [ - "https://github.com/yondonfu/ComfyUI-Torch-Compile" - ], - "id": "comfyui-torch-compile", - "install_type": "git-clone", - "reference": "https://github.com/yondonfu/ComfyUI-Torch-Compile", - "title": "ComfyUI-Torch-Compile" - }, - { - "author": "GorillaFrame", - "description": "GF Remove Background 2.0", - "files": [ - "https://github.com/gorillaframeai/GF_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/gorillaframeai/GF_nodes", - "title": "GFrbmg2" - }, - { - "author": "amaozhao", - "description": "DeployCash, a tool that converts workflows into WeChat and H5 with one click, and supports payments.", - "files": [ - "https://github.com/jacklukai/ComfyUI_DeployCash" - ], - "id": "ComfyUI_DeployCash", - "install_type": "git-clone", - "reference": "https://github.com/jacklukai/ComfyUI_DeployCash", - "title": "ComfyUI_DeployCash" - }, - { - "author": "zer0thgear", - "description": "Collection of personal nodes including tavern card-related nodes, prompt manipulation related utilities, and a node to combine a list of strings back into one string.", - "files": [ - "https://github.com/zer0thgear/zer0-comfy-utils" - ], - "id": "zer0-comfy-utils", - "install_type": "git-clone", - "reference": "https://github.com/zer0thgear/zer0-comfy-utils", - "title": "zer0 Comfy Utilities" - }, - { - "author": "fallingmeteorite", - "description": "NODES:Nsfw Image Check Node", - "files": [ - "https://github.com/fallingmeteorite/nsfw-image-check-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/fallingmeteorite/nsfw-image-check-comfyui", - "title": "nsfw-image-check-comfyui" - }, - { - "author": "VikramxD", - "description": "ComfyUI workflow for VEnhancer Inference", - "files": [ - "https://github.com/VikramxD/VEnhancer-ComfyUI-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/VikramxD/VEnhancer-ComfyUI-Wrapper", - "title": "VEnhancer ComfyUI Extension" - }, - { - "author": "BIMer-99", - "description": "This is a custom node designed to simplify the use of Hunyuan3D in ComfyUI", - "files": [ - "https://github.com/BIMer-99/Comfyui_Hunyuan3D_EX" - ], - "install_type": "git-clone", - "reference": "https://github.com/BIMer-99/Comfyui_Hunyuan3D_EX", - "title": "Comfyui_Hunyuan3D_EX" - }, - { - "author": "vuongminh1907", - "description": "Inspired by [a/InstantID](https://github.com/instantX-research/InstantID) and [a/InstantID Comfy](https://github.com/cubiq/ComfyUI_InstantID)\nThis ZenID Node has been refactored for specialized tasks like Face Swap", - "files": [ - "https://github.com/vuongminh1907/ComfyUI_ZenID" - ], - "install_type": "git-clone", - "reference": "https://github.com/vuongminh1907/ComfyUI_ZenID", - "title": "ComfyUI_ZenID" - }, - { - "author": "yorkane", - "description": "NODES:Advanced Lying Sigma Sampler, Save Image To target Path", - "files": [ - "https://github.com/yorkane/ComfyUI-KYNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/yorkane/ComfyUI-KYNode", - "title": "ComfyUI-KYNode" - }, - { - "author": "c0ffymachyne", - "description": "Audio processing nodes for comfyui.", - "files": [ - "https://github.com/c0ffymachyne/ComfyUI_SignalProcessing" - ], - "install_type": "git-clone", - "reference": "https://github.com/c0ffymachyne/ComfyUI_SignalProcessing", - "title": "ComfyUI Signal Processing" - }, - { - "author": "c0ffymachyne", - "description": "Bytebeat is like composing music with the tools of a programmer\u2019s toolkit. Instead of piano keys, you have operators like >>, |, and &. It\u2019s like giving your CPU a guitar and letting it shred! \ud83e\udd18", - "files": [ - "https://github.com/c0ffymachyne/ComfyUI_BeatByte" - ], - "install_type": "git-clone", - "reference": "https://github.com/c0ffymachyne/ComfyUI_BeatByte", - "title": "Bytebeat Synthesizer: Composing with Operators" - }, - { - "author": "liuqianhonga", - "description": "A ComfyUI custom node for image compression that supports multiple compression formats and parameter adjustments.", - "files": [ - "https://github.com/liuqianhonga/ComfyUI-Image-Compressor" - ], - "install_type": "git-clone", - "reference": "https://github.com/liuqianhonga/ComfyUI-Image-Compressor", - "title": "ComfyUI-Image-Compressor" - }, - { - "author": "liuqianhonga", - "description": "NODES: Webpage Screenshot, Camera Watermark, Template To Image", - "files": [ - "https://github.com/liuqianhonga/ComfyUI-Html2Image" - ], - "install_type": "git-clone", - "reference": "https://github.com/liuqianhonga/ComfyUI-Html2Image", - "title": "ComfyUI-Html2Image" - }, - { - "author": "liuqianhonga", - "description": "NODES: String Formatter, String List", - "files": [ - "https://github.com/liuqianhonga/ComfyUI-String-Helper" - ], - "install_type": "git-clone", - "reference": "https://github.com/liuqianhonga/ComfyUI-String-Helper", - "title": "ComfyUI-String-Helper" - }, - { - "author": "liuqianhonga", - "description": "A custom node collection developed for ComfyUI, offering preset dimensions for Latent, loading LoRA from folders, and integrating multiple commonly used custom nodes.", - "files": [ - "https://github.com/liuqianhonga/ComfyUI-QHNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/liuqianhonga/ComfyUI-QHNodes", - "title": "ComfyUI-QHNodes" - }, - { - "author": "duhaifeng", - "description": "This repository packages the latest BiRefNet model as a ComfyUI node for use, supporting chunked loading on both CPU and GPU, as well as model caching features.", - "files": [ - "https://github.com/rubi-du/ComfyUI-BiRefNet-Super" - ], - "install_type": "git-clone", - "reference": "https://github.com/rubi-du/ComfyUI-BiRefNet-Super", - "title": "ComfyUI-BiRefNet-lite" - }, - { - "author": "duhaifeng", - "description": "This node wraps the flux fill model as ComfyUI nodes. Use NF4 flux fill model, support for inpainting and outpainting image. Compared to the flux fill dev model, these nodes can use the flux fill model to perform inpainting and outpainting work under lower VRM conditions.", - "files": [ - "https://github.com/rubi-du/ComfyUI-Flux-Inpainting" - ], - "install_type": "git-clone", - "reference": "https://github.com/rubi-du/ComfyUI-Flux-Inpainting", - "title": "ComfyUI-Flux-Inpainting" - }, - { - "author": "duhaifeng", - "description": "This repository support processing Comfyui image nodes with ICC profile, load and save images with ICC profile", - "files": [ - "https://github.com/rubi-du/ComfyUI-ICC-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/rubi-du/ComfyUI-ICC-nodes", - "title": "ComfyUI-ICC-nodes" - }, - { - "author": "duhaifeng", - "description": "This repository packages the latest BiRefNet model as a ComfyUI node for use, supporting chunked loading on both CPU and GPU, as well as model caching features.", - "files": [ - "https://github.com/rubi-du/ComfyUI-MaskEditor-Extension" - ], - "install_type": "git-clone", - "reference": "https://github.com/rubi-du/ComfyUI-MaskEditor-Extension", - "title": "ComfyUI-MaskEditor-Extension" - }, - { - "author": "vkff5833", - "description": "A ComfyUI custom node that converts prompts between different AI image generation models.", - "files": [ - "https://github.com/vkff5833/ComfyUI-PromptConverter" - ], - "install_type": "git-clone", - "reference": "https://github.com/vkff5833/ComfyUI-PromptConverter", - "title": "ComfyUI-PromptConverter" - }, - { - "author": "yichengup", - "description": "NODES:Canvas View", - "files": [ - "https://github.com/yichengup/Comfyui-Ycanvas" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/Comfyui-Ycanvas", - "title": "Comfyui-Ycanvas" - }, - { - "author": "yichengup", - "description": "StyleModelApply adds more controls", - "files": [ - "https://github.com/yichengup/Comfyui_Flux_Style_Adjust" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/Comfyui_Flux_Style_Adjust", - "title": "Comfyui_Flux_Style_Adjust (Redux)" - }, - { - "author": "yichengup", - "description": "Redux style adds more controls", - "files": [ - "https://github.com/yichengup/Comfyui_Redux_Advanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/Comfyui_Redux_Advanced", - "title": "Comfyui_Redux_Advanced" - }, - { - "author": "yichengup", - "description": "About DeepSeek Chat API\nGo here to register and get the api-key [a/https://platform.deepseek.com/](https://platform.deepseek.com/) Then enter api_key in config.json", - "files": [ - "https://github.com/yichengup/Comfyui-Deepseek" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/Comfyui-Deepseek", - "title": "Comfyui-Deepseek" - }, - { - "author": "yichengup", - "description": "About DeepSeek Chat API\nGo here to register and get the api-key [a/https://platform.deepseek.com/](https://platform.deepseek.com/) Then enter api_key in config.json", - "files": [ - "https://github.com/yichengup/ComfyUI_Yc_JanusPro" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/ComfyUI_Yc_JanusPro", - "title": "ComfyUI_Yc_JanusPro" - }, - { - "author": "yichengup", - "description": "A collection of image processing extension nodes for ComfyUI.", - "files": [ - "https://github.com/yichengup/ComfyUI-YCNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/ComfyUI-YCNodes", - "title": "ComfyUI-YCNodes" - }, - { - "author": "yichengup", - "description": "video face liquefaction", - "files": [ - "https://github.com/yichengup/comfyui-face-liquify" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/comfyui-face-liquify", - "title": "comfyui-face-liquify" - }, - { - "author": "yichengup", - "description": "This is a custom node designed for ComfyUI to create transition effects between two images and generate a sequence of video frames.", - "files": [ - "https://github.com/yichengup/ComfyUI-LinearTransition" - ], - "install_type": "git-clone", - "reference": "https://github.com/yichengup/ComfyUI-LinearTransition", - "title": "ComfyUI-LinearTransition" - }, - { - "author": "Horizon Team", - "description": "Nodes for use of Chroma model and other prototype models", - "files": [ - "https://github.com/lodestone-rock/ComfyUI_FluxMod" - ], - "id": "fluxmod", - "install_type": "git-clone", - "reference": "https://github.com/lodestone-rock/ComfyUI_FluxMod", - "title": "ComfyUI_FluxMod" - }, - { - "author": "lth", - "description": "Use power of three.js in 3d view on comfyui.", - "files": [ - "https://github.com/lo-th/Comfyui_three_js" - ], - "id": "comfyui_three_js", - "install_type": "git-clone", - "reference": "https://github.com/lo-th/Comfyui_three_js", - "title": "Comfyui_three_js" - }, - { - "author": "AIPOQUE", - "description": "Without fine-tuning, FLUX.1 Dev model cannot understand exact color codes. However, it is known that FLUX.1 Dev can repeatedly produce certain colors with certain prompt(color name). Fortunately, on CIVITAI, [a/\u201cnovuschroma\u201d shared 155 pre-tested color names](https://civitai.com/models/879997/color-wildcards-for-flux-and-sdxl) that FLUX.1 Dev can handle. Thanks to his resource, color palette consists exclusively of 155 colors can be configured. \u2018ColorPalette\u2019 node from ComfyUI APQNodes converts input hex color code to the most similar color name(from pre-tested 155 color names) of which FLUX.1 Dev is aware.", - "files": [ - "https://github.com/AIPOQUE/ComfyUI-APQNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIPOQUE/ComfyUI-APQNodes", - "title": "ComfyUI-APQNodes" - }, - { - "author": "arcum42", - "description": "A collection of custom nodes by arcum42. Currently focused on saving metadata in images so that Civitai picks it up, pulling Civitai information, and misc helper nodes.", - "files": [ - "https://github.com/arcum42/ComfyUI_SageUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/arcum42/ComfyUI_SageUtils", - "title": "Sage Utils" - }, - { - "author": "Tomudo", - "description": "This is a custom node to convert image to ascii art string.", - "files": [ - "https://github.com/tomudo/ComfyUI-ascii-art" - ], - "id": "comfyui-ascii-art", - "install_type": "git-clone", - "reference": "https://github.com/tomudo/ComfyUI-ascii-art", - "title": "ComfyUI-ascii-art" - }, - { - "author": "tuohe", - "description": "Haiper API official ComfyUI custom node.", - "files": [ - "https://github.com/Haiper-ai/ComfyUI-HaiperAI-API" - ], - "id": "haiperai-api", - "install_type": "git-clone", - "reference": "https://github.com/Haiper-ai/ComfyUI-HaiperAI-API", - "title": "ComfyUI-HaiperAI-API" - }, - { - "author": "tungdop2", - "description": "The face restore node for ComfyUI, based on RestoreFormer", - "files": [ - "https://github.com/tungdop2/Comfyui_face_restorer" - ], - "id": "face_restorer", - "install_type": "git-clone", - "reference": "https://github.com/tungdop2/Comfyui_face_restorer", - "title": "Face Restorer for ComfyUI" - }, - { - "author": "tungdop2", - "description": "The Joy Caption Alpha Two node for ComfyUI, based on https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two", - "files": [ - "https://github.com/tungdop2/Comfyui_joy-caption-alpha-two" - ], - "id": "joy-captioner-alpha-two", - "install_type": "git-clone", - "reference": "https://github.com/tungdop2/Comfyui_joy-caption-alpha-two", - "title": "Joy Caption Alpha Two for ComfyUI" - }, - { - "author": "pschroedl", - "description": "NODES:(Down)Load SAM2-Realtime Model, Sam2RealtimeSegmentation", - "files": [ - "https://github.com/pschroedl/ComfyUI-SAM2-Realtime" - ], - "install_type": "git-clone", - "reference": "https://github.com/pschroedl/ComfyUI-SAM2-Realtime", - "title": "ComfyUI-SAM2-Realtime" - }, - { - "author": "Shakker-Labs", - "description": "nodes for flux ipadapter released by shakker-labs", - "files": [ - "https://github.com/Shakker-Labs/ComfyUI-IPAdapter-Flux" - ], - "install_type": "git-clone", - "reference": "https://github.com/Shakker-Labs/ComfyUI-IPAdapter-Flux", - "title": "ComfyUI-IPAdapter-Flux" - }, - { - "author": "iamandeepsandhu", - "description": "This project is designed to detect whether images generated by ComfyUI are Not Safe For Work (NSFW). It uses a machine learning model to classify images as either safe or not safe for work and returns a confidence score for the NSFW classification.", - "files": [ - "https://github.com/iamandeepsandhu/ComfyUI-NSFW-Check" - ], - "install_type": "git-clone", - "reference": "https://github.com/iamandeepsandhu/ComfyUI-NSFW-Check", - "title": "NSFW Check for ComfyUI" - }, - { - "author": "Black-Lioness", - "description": "A set of ComfyUI nodes designed to enhance your workflow with realistic filename generation and keyword generation.", - "files": [ - "https://github.com/Black-Lioness/ComfyUI-PromptUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/Black-Lioness/ComfyUI-PromptUtils", - "reference2": "https://github.com/RunningOverGlowies/ComfyUI-PromptUtils", - "title": "ComfyUI-PromptUtils" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that modifies the values in the samples input that fall outside of a min and max range using a multiplier.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-LatentClamp" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-LatentClamp", - "title": "ComfyUI-LatentClamp" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that takes a list of masks and returns a single mask based on your criteria.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-MaskArbiter" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-MaskArbiter", - "title": "ComfyUI-MaskArbiter" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that terminates the workflow processing if 'proceed' is set to False. More convenient than manually bypassing a bunch of nodes.\nThis is a restructured version of the 'SRL Conditional Interrupt' node from the [a/srl-nodes](https://github.com/seanlynch/srl-nodes) pack.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-ConditionalInterrupt" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-ConditionalInterrupt", - "title": "ComfyUI-ConditionalInterrupt" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that performs GPEN face restoration on the input image(s). Significantly faster than other implementations of GPEN.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-GPENO" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-GPENO", - "title": "ComfyUI-GPENO" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that takes an input image and clips the color channels independently to increase contrast and alter color cast. This is a reinterpretation of PhotoShop's 'Auto Tone' algorithm.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-ImageAutotone" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-ImageAutotone", - "title": "ComfyUI-ImageAutotone" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that picks from `input_a` and `input_b` based on the given `chance`.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-WeightedRandomChoice" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-WeightedRandomChoice", - "title": "ComfyUI-WeightedRandomChoice" - }, - { - "author": "SparknightLLC", - "description": "A node for ComfyUI that provides a convenient way of resizing or cropping an image for diffusion tasks.", - "files": [ - "https://github.com/SparknightLLC/ComfyUI-ImageAutosize" - ], - "install_type": "git-clone", - "reference": "https://github.com/SparknightLLC/ComfyUI-ImageAutosize", - "title": "ComfyUI-ImageAutosize" - }, - { - "author": "lightricks", - "description": "Custom nodes for LTX-Video support in ComfyUI", - "files": [ - "https://github.com/Lightricks/ComfyUI-LTXVideo" - ], - "id": "comfyui-ltxvideo", - "install_type": "git-clone", - "reference": "https://github.com/Lightricks/ComfyUI-LTXVideo", - "title": "ComfyUI-LTXVideo" - }, - { - "author": "Kai Duehrkop", - "description": "This extension offers a new Apply-Style node for Redux that allows for changing the influence of the conditioning image on the final outcome. This effectively allows for changing the style or content of an image using a prompt while using Redux.", - "files": [ - "https://github.com/kaibioinfo/ComfyUI_AdvancedRefluxControl" - ], - "id": "advancedRefluxControl", - "install_type": "git-clone", - "reference": "https://github.com/kaibioinfo/ComfyUI_AdvancedRefluxControl", - "title": "Advanced Reflux control" - }, - { - "author": "ramesh-x90", - "description": "This repository provides custom nodes for ComfyUI designed to process audio files, performing speaker diarization and integrating speaker data into whisper-transcribed segments. These nodes utilize the PyAnnote library for speaker identification and pandas for efficient data handling.", - "files": [ - "https://github.com/ramesh-x90/ComfyUI_pyannote" - ], - "install_type": "git-clone", - "reference": "https://github.com/ramesh-x90/ComfyUI_pyannote", - "title": "ComfyUI_pyannote" - }, - { - "author": "wu12023", - "description": "An extension to ComfyUI that evaluates images using multiple models.", - "files": [ - "https://github.com/wu12023/ComfyUI-Image-Evaluation" - ], - "install_type": "git-clone", - "reference": "https://github.com/wu12023/ComfyUI-Image-Evaluation", - "title": "ComfyUI-Image-Evaluation" - }, - { - "author": "windfancy", - "description": "NODES: PromptStyler, PromptLatent, PromptCLIPEncode, PromptSelector", - "files": [ - "https://github.com/windfancy/zsq_prompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/windfancy/zsq_prompt", - "title": "zsq_prompt" - }, - { - "author": "exectails", - "description": "Nodes that facilitate simpler information providing and gathering, such as Text Box, Show Data and Token Counter nodes.", - "files": [ - "https://github.com/exectails/comfyui-et_infoutils" - ], - "id": "et_infoutils", - "install_type": "git-clone", - "reference": "https://github.com/exectails/comfyui-et_infoutils", - "title": "Info Utils" - }, - { - "author": "exectails", - "description": "Nodes dedicated to the analysis and transformation of text strings, such as for formatting and conversions between types.", - "files": [ - "https://github.com/exectails/comfyui-et_stringutils" - ], - "id": "et_stringutils", - "install_type": "git-clone", - "reference": "https://github.com/exectails/comfyui-et_stringutils", - "title": "String Utils" - }, - { - "author": "exectails", - "description": "Nodes that implement functionality similar to the Dynamic Prompts extension for A1111.", - "files": [ - "https://github.com/exectails/comfyui-et_dynamicprompts" - ], - "id": "et_dynamicprompts", - "install_type": "git-clone", - "reference": "https://github.com/exectails/comfyui-et_dynamicprompts", - "title": "Dynamic Prompts" - }, - { - "author": "SleeeepyZhou", - "description": "A translation node for users in Chinese Mainland. (Because of the network firewall in Chinese Mainland, many translation APIs cannot be used normally.)", - "files": [ - "https://github.com/SleeeepyZhou/ComfyUI-CNtranslator" - ], - "id": "cn-translator", - "install_type": "git-clone", - "reference": "https://github.com/SleeeepyZhou/ComfyUI-CNtranslator", - "title": "CNtranslator" - }, - { - "author": "flycarl", - "description": "[a/sd-webui-pixelart](https://github.com/mrreplicart/sd-webui-pixelart) are referenced by many webui users, this node is mean to use it in ComfyUI.", - "files": [ - "https://github.com/flycarl/ComfyUI-Pixelate" - ], - "install_type": "git-clone", - "reference": "https://github.com/flycarl/ComfyUI-Pixelate", - "title": "ComfyUI-Pixelate" - }, - { - "author": "Alvaroeai", - "description": "This repository contains a custom node for ComfyUI that converts text into a JSON object. The node is designed to be user-friendly and supports multi-line JSON input.", - "files": [ - "https://github.com/Alvaroeai/ComfyUI-Text2Json" - ], - "install_type": "git-clone", - "reference": "https://github.com/Alvaroeai/ComfyUI-Text2Json", - "title": "ComfyUI-Text2Json" - }, - { - "author": "dymokomi", - "description": "NODES: DY Image Quantize, DY Image Cluster, DY Image Palette, DY Image Masks, Image List to Grid, DY Image Scaler, DY Random Lines, DY Adaptive Color Lines, DY Adaptive Color Circles, DY Adaptive Color Rectangles, DY Binary Pattern Stamper", - "files": [ - "https://github.com/dymokomi/comfyui_dygen" - ], - "install_type": "git-clone", - "reference": "https://github.com/dymokomi/comfyui_dygen", - "title": "comfyui_dygen" - }, - { - "author": "bananasss00", - "description": "flux patcher for Fill Flux.Dev lora [a/https://civitai.com/models/981615/fluxfill-inpaint-lora](https://civitai.com/models/981615/fluxfill-inpaint-lora)", - "files": [ - "https://github.com/bananasss00/ComfyUI-flux_fill_patcher" - ], - "install_type": "git-clone", - "reference": "https://github.com/bananasss00/ComfyUI-flux_fill_patcher", - "title": "ComfyUI-flux_fill_patcher" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-SoundHub is a collection of audio processing nodes designed for ComfyUI, enabling seamless audio processing and generation within your ComfyUI workflows.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-SoundHub" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-SoundHub", - "title": "ComfyUI-SoundHub" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for LLaMA-Mesh model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-LLaMA-Mesh" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-LLaMA-Mesh", - "title": "ComfyUI-LLaMA-Mesh" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for PhotoDoodle model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-PhotoDoodle" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-PhotoDoodle", - "title": "ComfyUI-PhotoDoodle" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for StyleStudio model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-StyleStudio" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-StyleStudio", - "title": "ComfyUI-StyleStudio" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for OrpheusTTS model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-OrpheusTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-OrpheusTTS", - "title": "ComfyUI-OrpheusTTS" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for LayerAnimate model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-LayerAnimate" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-LayerAnimate", - "title": "ComfyUI-LayerAnimate" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for SkyReels-A2 model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-SkyReels-A2" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-SkyReels-A2", - "title": "ComfyUI-SkyReels-A2" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for UNO model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-UNO" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-UNO", - "title": "ComfyUI-UNO" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for HiDream-I1 model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-HiDream-I1" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-HiDream-I1", - "title": "ComfyUI-HiDream-I1" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for Kimi-VL model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Kimi-VL" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Kimi-VL", - "title": "ComfyUI-Kimi-VL" - }, - { - "author": "Yuan-ManX", - "description": "Make Cobra avialbe in ComfyUI.\nCobra: Efficient Line Art COlorization with BRoAder References", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Cobra" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Cobra", - "title": "ComfyUI-Cobra" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI nodes for LiveCC model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-LiveCC" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-LiveCC", - "title": "ComfyUI-LiveCC" - }, - { - "author": "Yuan-ManX", - "description": "Make Dia avialbe in ComfyUI.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Dia" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Dia", - "title": "ComfyUI-Dia" - }, - { - "author": "Yuan-ManX", - "description": "Make AudioX avialbe in ComfyUI.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-AudioX" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-AudioX", - "title": "Yuan-ManX/ComfyUI-AudioX" - }, - { - "author": "Yuan-ManX", - "description": "Make Muyan-TTS avialbe in ComfyUI.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Muyan-TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Muyan-TTS", - "title": "ComfyUI-Muyan-TTS" - }, - { - "author": "Yuan-ManX", - "description": "Make Multiverse avialbe in ComfyUI.\nMultiverse: The First AI Multiplayer World Model. Two human players driving cars in Multiverse.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Multiverse" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Multiverse", - "title": "ComfyUI-Multiverse" - }, - { - "author": "Yuan-ManX", - "description": "Make Matrix-Game avialbe in ComfyUI.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Matrix-Game" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Matrix-Game", - "title": "ComfyUI-Matrix-Game" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-Step1X-3D is now available in ComfyUI, delivering high-fidelity 3D asset generation with consistent geometry-texture alignment. It supports multi-style outputs: cartoon, sketch, and photorealistic.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Step1X-3D" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Step1X-3D", - "title": "ComfyUI-Step1X-3D" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-MoviiGen is now available in ComfyUI, MoviiGen 1.1 is a cutting-edge video generation model that excels in cinematic aesthetics and visual quality.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-MoviiGen" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-MoviiGen", - "title": "ComfyUI-MoviiGen" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-AniSora is now available in ComfyUI, Index-AniSora is the most powerful open-source animated video generation model. It enables one-click creation of video shots across diverse anime styles including series episodes, Chinese original animations, manga adaptations, VTuber content, anime PVs, mad-style parodies, and more!", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-AniSora" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-AniSora", - "title": "ComfyUI-AniSora" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-Bagel is now available in ComfyUI, BAGEL is an open\u2011source multimodal foundation model with 7B active parameters (14B total) trained on large\u2011scale interleaved multimodal data. [w/Don't install together with neverbiasu/ComfyUI-BAGEL simultaneously.]", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Bagel" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Bagel", - "title": "ComfyUI-Bagel" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-HunyuanPortrait is now available in ComfyUI, HunyuanPortrait is a diffusion-based condition control method that employs implicit representations for highly controllable and lifelike portrait animation.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-HunyuanPortrait" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-HunyuanPortrait", - "title": "ComfyUI-HunyuanPortrait" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-HunyuanVideo-Avatar is now available in ComfyUI, HunyuanVideo-Avatar is a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-controllable, and multi-character dialogue videos.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-HunyuanVideo-Avatar" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-HunyuanVideo-Avatar", - "title": "ComfyUI-HunyuanVideo-Avatar" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-ChatterboxTTS is now available in ComfyUI, Chatterbox TTS is the first production-grade open-source TTS model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-ChatterboxTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-ChatterboxTTS", - "title": "ComfyUI-ChatterboxTTS" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-Direct3D\u2011S2 is now available in ComfyUI, Direct3D\u2011S2 - Gigascale 3D Generation Made Easy with Spatial Sparse Attention. Direct3D\u2011S2 is a scalable 3D generation framework based on sparse volumes that achieves superior output quality with dramatically reduced training costs.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Direct3D-S2" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Direct3D-S2", - "title": "ComfyUI-Direct3D-S2" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-Vui is now available in ComfyUI, Vui is a llama based transformer that predicts audio tokens.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Vui" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Vui", - "title": "ComfyUI-Vui" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-Hunyuan3D-2.1 is now available in ComfyUI, Hunyuan3D-2.1 is a scalable 3D asset creation system that advances state-of-the-art 3D generation through two pivotal innovations: Fully Open-Source Framework and Physically-Based Rendering (PBR) Texture Synthesis.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-Hunyuan3D-2.1" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-Hunyuan3D-2.1", - "title": "ComfyUI-Hunyuan3D-2.1" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-OmniGen2 is now available in ComfyUI, OmniGen2 is a powerful and efficient unified multimodal model. Its architecture is composed of two key components: a 3B Vision-Language Model (VLM) and a 4B diffusion model.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-OmniGen2" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-OmniGen2", - "title": "ComfyUI-OmniGen2" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-PosterCraft is now available in ComfyUI, PosterCraft is a unified framework for high-quality aesthetic poster generation that excels in precise text rendering, seamless integration of abstract art, striking layouts, and stylistic harmony.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-PosterCraft" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-PosterCraft", - "title": "ComfyUI-PosterCraft" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-ThinkSound is now available in ComfyUI, ThinkSound is a unified Any2Audio generation framework with flow matching guided by Chain-of-Thought (CoT) reasoning.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-ThinkSound" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-ThinkSound", - "title": "ComfyUI-ThinkSound" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-HiggsAudio is now available in ComfyUI, Higgs Audio v2 is a text-audio foundation model from Boson AI.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-HiggsAudio" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-HiggsAudio", - "title": "ComfyUI-HiggsAudio" - }, - { - "author": "Yuan-ManX", - "description": "ComfyUI-SkyworkUniPic is now available in ComfyUI, Skywork-UniPic is a unified autoregressive multimodal model with 1.5 billion parameters that natively integrates image understanding, text-to-image generation, and image editing capabilities within a single architecture.", - "files": [ - "https://github.com/Yuan-ManX/ComfyUI-SkyworkUniPic" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yuan-ManX/ComfyUI-SkyworkUniPic", - "title": "ComfyUI-SkyworkUniPic" - }, - { - "author": "Starnodes2024", - "description": "NODES: StarNode Startsettings for Flux and SD, Smplers for Flux and SD, Detail Deamon, Wildcards and more Helper Nodes", - "files": [ - "https://github.com/Starnodes2024/ComfyUI_StarNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Starnodes2024/ComfyUI_StarNodes", - "title": "ComfyUI_StarNodes" - }, - { - "author": "Starnodes2024", - "description": "Welcome to Star Beta Nodes - a collection of experimental custom nodes for ComfyUI designed for beta testing and feedback. These nodes provide enhanced functionality for image processing, video handling, and workflow automation.", - "files": [ - "https://github.com/Starnodes2024/ComfyUI_StarBetaNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Starnodes2024/ComfyUI_StarBetaNodes", - "title": "ComfyUI_StarBetaNodes" - }, - { - "author": "motivated3", - "description": "With the MiaoShua creator's end, you can use this plugin to upload debugged workflows for consumer end users to use.", - "files": [ - "https://github.com/motivated3/comfyui-shua-creator" - ], - "id": "comfyui-shua-creator", - "install_type": "git-clone", - "reference": "https://github.com/motivated3/comfyui-shua-creator", - "title": "ComfyUI MiaoShua Creator" - }, - { - "author": "takemetosiberia", - "description": "ComfyUI nodes for video object segmentation using [a/SAMURAI](https://github.com/yangchris11/samurai) model.", - "files": [ - "https://github.com/takemetosiberia/ComfyUI-SAMURAI--SAM2-" - ], - "install_type": "git-clone", - "reference": "https://github.com/takemetosiberia/ComfyUI-SAMURAI--SAM2-", - "title": "SAMURAI Nodes for ComfyUI" - }, - { - "author": "purpen", - "description": "NODES:AIRedoon Qwen Model Loader, AIRedoon Translator, AIRedoon Image Caption, AIRedoon LoRA Stack, AIRedoon Image RGBA2RGB, AIRedoon Preview Text, AIRedoon Save Text, ...\nRedoonAi Tool Kit", - "files": [ - "https://github.com/purpen/ComfyUI-AIRedoon" - ], - "install_type": "git-clone", - "reference": "https://github.com/purpen/ComfyUI-AIRedoon", - "title": "AIRedoon" - }, - { - "author": "purpen", - "description": "NODES:AIRedoon Image Caption.\nAnalyze image tagger", - "files": [ - "https://github.com/purpen/ComfyUI-ImageTagger" - ], - "install_type": "git-clone", - "reference": "https://github.com/purpen/ComfyUI-ImageTagger", - "title": "ComfyUI-ImageTagger" - }, - { - "author": "itsjustregi", - "description": "Easy Pony is a helper node that simplifies the process of adding scoring and other attributes to prompts when using Pony models.", - "files": [ - "https://github.com/regiellis/ComfyUI-EasyPony" - ], - "install_type": "git-clone", - "reference": "https://github.com/regiellis/ComfyUI-EasyPony", - "title": "ComfyUI-EasyPony" - }, - { - "author": "itsjustregi", - "description": "Simple set of nodes to assist with prompt generation for NOOBAI XL / Illustrious models", - "files": [ - "https://github.com/regiellis/ComfyUI-EasyNoobai" - ], - "install_type": "git-clone", - "reference": "https://github.com/regiellis/ComfyUI-EasyNoobai", - "title": "ComfyUI-EasyNoobai" - }, - { - "author": "itsjustregi", - "description": "ComfyUI custom node for flexible and efficient image color correction and post-processing.", - "files": [ - "https://github.com/regiellis/ComfyUI-EasyColorCorrector" - ], - "install_type": "git-clone", - "reference": "https://github.com/regiellis/ComfyUI-EasyColorCorrector", - "title": "Easy Color Correction" - }, - { - "author": "mrhan1993", - "description": "This extension provides image generation features based on Fooocus.", - "files": [ - "https://github.com/mrhan1993/ComfyUI-Fooocus" - ], - "install_type": "git-clone", - "reference": "https://github.com/mrhan1993/ComfyUI-Fooocus", - "title": "ComfyUI-Fooocus" - }, - { - "author": "Kling AI", - "description": "Provide high-quality video and image generation capabilities, meeting creators' needs for creative content production and management through more convenient operations, richer functionalities, professional parameters, and stunning effects.", - "files": [ - "https://github.com/KwaiVGI/ComfyUI-KLingAI-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/KwaiVGI/ComfyUI-KLingAI-API", - "title": "ComfyUI-KLingAI-API" - }, - { - "author": "lujiazho", - "description": "ComfyUI-CatvtonFluxWrapper provides ComfyUI nodes for diffusers implementation of Catvton-Flux.", - "files": [ - "https://github.com/lujiazho/ComfyUI-CatvtonFluxWrapper" - ], - "id": "comfyui-catvton-flux-wrapper", - "install_type": "git-clone", - "reference": "https://github.com/lujiazho/ComfyUI-CatvtonFluxWrapper", - "title": "ComfyUI-CatvtonFluxWrapper" - }, - { - "author": "Eugene (JEONG-JIWOO)", - "description": "A collection of utility nodes using Dictionary designed to optimize and manage workflows in ComfyUI.", - "files": [ - "https://github.com/JEONG-JIWOO/ComfyUI_Eugene_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/JEONG-JIWOO/ComfyUI_Eugene_Nodes", - "title": "ComfyUI_Eugene_Nodes" - }, - { - "author": "xs315431", - "description": "NODES: Get Prompt_Id, Success Callback\nget comfyui task id and Callback for successful image generation, in conjunction with the back-end", - "files": [ - "https://github.com/xs315431/Comfyui_Get_promptId" - ], - "install_type": "git-clone", - "reference": "https://github.com/xs315431/Comfyui_Get_promptId", - "title": "Comfyui_Get_promptId" - }, - { - "author": "5x00", - "description": "A simple ComfyUI node that let's you use Claude or ChatGPT 4o's VLM capabilities to generate captions/tags for images.", - "files": [ - "https://github.com/5x00/ComfyUI-VLM-Captions" - ], - "install_type": "git-clone", - "reference": "https://github.com/5x00/ComfyUI-VLM-Captions", - "title": "ComfyUI-VLM_Captions" - }, - { - "author": "5x00", - "description": "A simple ComfyUI nodes that integrates [a/PiAPI faceswap](https://piapi.ai/faceswap-api) service into ComfyUI. This can be helpful if you're trying to create a workflow that includes faceswap for commercial usage.", - "files": [ - "https://github.com/5x00/ComfyUI-PiAPI-Faceswap" - ], - "install_type": "git-clone", - "reference": "https://github.com/5x00/ComfyUI-PiAPI-Faceswap", - "title": "ComfyUI-PiAPI-Faceswap" - }, - { - "author": "ClownsharkBatwing", - "description": "Advanced samplers with new noise scaling math to enable SDE sampling with all publicly available native models; new unsampling/noise inversion methods and other advanced img2img techniques for inpainting and/or guiding the sampling process with guide images, with results superior to FlowEdit, RF Inversion, and other SOTA implementations. Also new style transfer methods unique to this node pack; regional conditioning for HiDream, Flux, AuraFlow, and WAN; methods for eliminating Flux blur; and temporal conditioning (shift gradually from one prompt to the next with video). 115 sampler types, 24 noise types, 11 noise scaling modes, in a single node. Also includes a wide variety of QoF and other utility nodes for boosting detail, manipulating sigmas, latents, images, and more.", - "files": [ - "https://github.com/ClownsharkBatwing/RES4LYF" - ], - "id": "res4lyf", - "install_type": "git-clone", - "reference": "https://github.com/ClownsharkBatwing/RES4LYF", - "title": "RES4LYF" - }, - { - "author": "NeoGriever", - "description": "NeoGriever's helper nodes. Better CLIP Text Encoder, Resolution Provider, Multimask Write/Read, TextBoxes Simple/Join/x2/x3, Sliders INT/FLOAT/STEPPER, String Tool/Squisher/Cutter, Create Solid Color, Fill with Color, Checkerboard Generator, Image Progress Bar", - "files": [ - "https://github.com/NeoGriever/ComfyUI-NeoGriever" - ], - "id": "neogrievernodes", - "install_type": "git-clone", - "reference": "https://github.com/NeoGriever/ComfyUI-NeoGriever", - "title": "ComfyUI - NeoGriever" - }, - { - "author": "PauldeLavallaz", - "description": "Node that generates prompts using Anthropic Claude API.", - "files": [ - "https://github.com/PauldeLavallaz/comfyui_claude_prompt_generator" - ], - "id": "claude_prompt_generator", - "install_type": "git-clone", - "reference": "https://github.com/PauldeLavallaz/comfyui_claude_prompt_generator", - "title": "Claude Prompt Generator" - }, - { - "author": "huanngzh", - "description": "This extension integrates [a/MV-Adapter](https://github.com/huanngzh/MV-Adapter) into ComfyUI, allowing users to generate multi-view consistent images from text prompts or single images directly within the ComfyUI interface.", - "files": [ - "https://github.com/huanngzh/ComfyUI-MVAdapter" - ], - "install_type": "git-clone", - "reference": "https://github.com/huanngzh/ComfyUI-MVAdapter", - "title": "ComfyUI-MVAdapter" - }, - { - "author": "Aerse", - "description": "ComfyUI-Seed-Nodes is a custom node library that extends the functionality of ComfyUI, offering advanced image loading and pixelation tools.", - "files": [ - "https://github.com/Aerse/ComfyUI-Seed-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Aerse/ComfyUI-Seed-Nodes", - "title": "ComfyUI-Seed-Nodes" - }, - { - "author": "Slickytail", - "description": "ComfyUI implementation of the [a/InstantX IP-Adapter for SD3.5 Large](https://huggingface.co/InstantX/SD3.5-Large-IP-Adapter).", - "files": [ - "https://github.com/Slickytail/ComfyUI-InstantX-IPAdapter-SD3" - ], - "install_type": "git-clone", - "reference": "https://github.com/Slickytail/ComfyUI-InstantX-IPAdapter-SD3", - "title": "ComfyUI-InstantX-IPAdapter-SD3" - }, - { - "author": "Slickytail", - "description": "ComfyUI implementation of Regional Adaptive Sampling, (original implementation at https://github.com/microsoft/RAS).", - "files": [ - "https://github.com/Slickytail/ComfyUI-RegionalAdaptiveSampling" - ], - "install_type": "git-clone", - "reference": "https://github.com/Slickytail/ComfyUI-RegionalAdaptiveSampling", - "title": "ComfyUI-RegionalAdaptiveSampling" - }, - { - "author": "sourceful-official", - "description": "ComfyUI-LoadLoraModelOnlyWithUrl", - "files": [ - "https://github.com/sourceful-official/LoadLoraModelOnlyWithUrl" - ], - "install_type": "git-clone", - "reference": "https://github.com/sourceful-official/LoadLoraModelOnlyWithUrl", - "reference2": "https://github.com/sourceful-official/ComfyUI_LoadLoraModelOnlyWithUrl", - "title": "LoadLoraModelOnlyWithUrl" - }, - { - "author": "kimara-ai", - "description": "The KimaraAIWatermarker custom node allows you to apply watermark text and logo overlays to images. Optionally, the watermark can be moved by the move_watermark_step amount of pixels after each generated image. To apply a moving watermark to a list of images, use the KimaraAIBatchImages node to concatenate the list into a single tensor, then use that as an input for the watermark node, as shown in the example image below.", - "files": [ - "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Advanced-Watermarks" - ], - "install_type": "git-clone", - "reference": "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Advanced-Watermarks", - "title": "Kimara.ai's Advanced Watermarking Tools" - }, - { - "author": "weilin9999", - "description": "quickly use the prompt word tool in ComfyUI", - "files": [ - "https://github.com/weilin9999/WeiLin-Comfyui-Tools" - ], - "id": "Comfyui-Tools", - "install_type": "git-clone", - "reference": "https://github.com/weilin9999/WeiLin-Comfyui-Tools", - "title": "WeiLin-Comfyui-Tools" - }, - { - "author": "LucipherDev", - "description": "ComfyUI Custom Node for 'Golden Noise for Diffusion Models: A Learning Framework'. This node refines the initial latent noise in the diffusion process, enhancing both image quality and semantic coherence.", - "files": [ - "https://github.com/LucipherDev/ComfyUI-Golden-Noise" - ], - "install_type": "git-clone", - "reference": "https://github.com/LucipherDev/ComfyUI-Golden-Noise", - "title": "ComfyUI-Golden-Noise" - }, - { - "author": "LucipherDev", - "description": "ComfyUI Custom Nodes for 'AniDoc: Animation Creation Made Easier'. This approach automates line art video colorization using a novel model that aligns color information from references, ensures temporal consistency, and reduces manual effort in animation production.", - "files": [ - "https://github.com/LucipherDev/ComfyUI-AniDoc" - ], - "install_type": "git-clone", - "reference": "https://github.com/LucipherDev/ComfyUI-AniDoc", - "title": "ComfyUI-AniDoc" - }, - { - "author": "LucipherDev", - "description": "ComfyUI Custom Nodes for 'TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching'. This generates high-quality 44.1kHz audio up to 30 seconds using just a text prompt.", - "files": [ - "https://github.com/LucipherDev/ComfyUI-TangoFlux" - ], - "install_type": "git-clone", - "reference": "https://github.com/LucipherDev/ComfyUI-TangoFlux", - "title": "ComfyUI-TangoFlux" - }, - { - "author": "envy-ai", - "description": "This extension extends ComfyUI's capabilities with respect to manipulating conditionings.", - "files": [ - "https://github.com/envy-ai/ComfyUI-ConDelta" - ], - "install_type": "git-clone", - "reference": "https://github.com/envy-ai/ComfyUI-ConDelta", - "title": "ComfyUI-ConDelta" - }, - { - "author": "kraglik", - "description": "A prompt generation system that manages relationships between prompt components to maintain logical consistency. Integrates with ComfyUI as a custom node.", - "files": [ - "https://github.com/kraglik/prompt_collapse" - ], - "install_type": "git-clone", - "reference": "https://github.com/kraglik/prompt_collapse", - "title": "PromptCollapse" - }, - { - "author": "abdozmantar", - "description": "DeepExtract is a powerful and efficient tool designed to separate vocals and sounds from audio files, providing an enhanced experience for musicians, producers, and audio engineers. With DeepExtract, you can quickly and effectively isolate vocals or instruments from mixed audio tracks, facilitating tasks like remixing, karaoke preparation, or audio analysis.", - "files": [ - "https://github.com/abdozmantar/ComfyUI-DeepExtract" - ], - "install_type": "git-clone", - "reference": "https://github.com/abdozmantar/ComfyUI-DeepExtract", - "title": "DeepExtract" - }, - { - "author": "ctefer", - "description": "This is a subset of nodes for ComfyUI that I made just for my own workflow. The nodes support Flux (single conditioning, no negatives) and are just a way of minimizing the noodles. There's no real journey to be made here, just anything that helps me get through the day.", - "files": [ - "https://github.com/CpreForEver/CFE_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/CpreForEver/CFE_comfyui", - "title": "CFE_comfyui" - }, - { - "author": "inflamously", - "description": "A crazy node that pragmatically just enhances a given prompt with various descriptions in the hope that the image quality just increase and prompting just gets easier.", - "files": [ - "https://github.com/inflamously/comfyui-prompt-enhancer" - ], - "install_type": "git-clone", - "reference": "https://github.com/inflamously/comfyui-prompt-enhancer", - "title": "comfyui-prompt-enhancer" - }, - { - "author": "111496583yzy", - "description": "The Jigsaw Puzzle Effect plugin provides a unique puzzle effect for image processing. By dividing an image into multiple puzzle pieces and optionally adding missing pieces, users can easily create artistic puzzle effects.", - "files": [ - "https://github.com/111496583yzy/comfyui-PuzzleCrack-Effect" - ], - "install_type": "git-clone", - "reference": "https://github.com/111496583yzy/comfyui-PuzzleCrack-Effect", - "title": "Jigsaw Puzzle Effect Plugin" - }, - { - "author": "nsdtcloud3d", - "description": "ComfyUI-3D-Covert is a 3D File Format Conversion Extension based on 3dconvert.nsdt.cloud-API. It provides a node ConvertTo3DFormat currently available, is a powerful tool designed to streamline the conversion of 3D models between a wide array of file formats.", - "files": [ - "https://github.com/nsdtcloud3d/ComfyUI-3D-Convert" - ], - "install_type": "git-clone", - "reference": "https://github.com/nsdtcloud3d/ComfyUI-3D-Convert", - "title": "ComfyUI-3D-Convert" - }, - { - "author": "Mr.Chip", - "description": "This extension offers a custom node to save image to S3-compatible oss.", - "files": [ - "https://github.com/mrchipset/ComfyUI-SaveImageS3" - ], - "id": "zouyuimages3", - "install_type": "git-clone", - "reference": "https://github.com/mrchipset/ComfyUI-SaveImageS3", - "title": "ComfyUI-SaveImageS3" - }, - { - "author": "DesertPixelAi", - "description": "A collection of custom nodes for ComfyUI focused on animation, image processing, and workflow optimization.", - "files": [ - "https://github.com/DesertPixelAi/ComfyUI-Desert-Pixel-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/DesertPixelAi/ComfyUI-Desert-Pixel-Nodes", - "title": "ComfyUI-Desert-Pixel-Nodes" - }, - { - "author": "DesertPixelAi", - "description": "A dynamic theme-based prompt generator for ComfyUI that creates versatile, random prompts optimized for face swap workflows.", - "files": [ - "https://github.com/DesertPixelAi/comfyui-dp-them-styler" - ], - "install_type": "git-clone", - "reference": "https://github.com/DesertPixelAi/comfyui-dp-them-styler", - "title": "ComfyUI DP Dynamic Random Styler" - }, - { - "author": "DesertPixelAi", - "description": "A custom ComfyUI node for generating consistent character images using Ideogram API v3's character reference feature. Part of the Desert Pixel (DP) node collection.", - "files": [ - "https://github.com/DesertPixelAi/ComfyUI-DP-Ideogram-Character" - ], - "install_type": "git-clone", - "reference": "https://github.com/DesertPixelAi/ComfyUI-DP-Ideogram-Character", - "title": "ComfyUI DP Ideogram Character Node" - }, - { - "author": "muhammederem", - "description": "A Python implementation for integrating the BLIP (Bootstrapping Language-Image Pre-training) model for visual question answering.", - "files": [ - "https://github.com/muhammederem/blip-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/muhammederem/blip-comfyui", - "title": "BLIP Vision-Language Model Integration" - }, - { - "author": "jiaqianjing", - "description": "A ComfyUI custom node for integrating with Midjourney API.", - "files": [ - "https://github.com/jiaqianjing/ComfyUI-MidjourneyHub" - ], - "install_type": "git-clone", - "reference": "https://github.com/jiaqianjing/ComfyUI-MidjourneyHub", - "title": "ComfyUI-MidjourneyHub" - }, - { - "author": "SlackinJack", - "description": "[a/Distrifuser](https://github.com/mit-han-lab/distrifuser) sampler node for ComfyUI\n", - "files": [ - "https://github.com/SlackinJack/distrifuser_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/SlackinJack/distrifuser_comfyui", - "title": "distrifuser_comfyui" - }, - { - "author": "Runware Inc.", - "description": "Runware Inference API Integration for ComfyUI (No GPU Required).", - "files": [ - "https://github.com/Runware/ComfyUI-Runware" - ], - "id": "runware", - "install_type": "git-clone", - "reference": "https://github.com/Runware/ComfyUI-Runware", - "title": "Runware.ai ComfyUI Inference API Integration" - }, - { - "author": "shannooty", - "description": "This project provides a set of custom timer nodes for ComfyUI. These nodes allow you to measure and append runtime information to strings or other data during your workflow.", - "files": [ - "https://github.com/Shannooty/ComfyUI-Timer-Nodes" - ], - "id": "comfyui-timer-nodes", - "install_type": "git-clone", - "reference": "https://github.com/Shannooty/ComfyUI-Timer-Nodes", - "title": "ComfyUI Timer Nodes" - }, - { - "author": "HM-RunningHub", - "description": "ComfyUI_RH_OminiControl is a ComfyUI plugin based on OminiControl By splitting the pipeline load, the plugin efficiently runs on NVIDIA RTX 4090 GPUs. Additionally, the spatial and fill functionalities are generated using the schnell model, reducing the number of sampling steps and improving overall efficiency.", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_OminiControl" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_OminiControl", - "title": "ComfyUI_RH_OminiControl" - }, - { - "author": "HM-RunningHub", - "description": "This is a UNO ComfyUI plugin implementation that can run the full version with 24GB VRAM, as well as quickly run the FP8 version.", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_UNO" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_UNO", - "title": "ComfyUI_RH_UNO" - }, - { - "author": "HM-RunningHub", - "description": "This is a ComfyUI plug-in for lllyasviel/FramePack, easy to use", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_FramePack" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_FramePack", - "title": "ComfyUI_RH_FramePack" - }, - { - "author": "HM-RunningHub", - "description": "This is a ComfyUI custom node implementation for image editing using the Step-1 model architecture, specifically adapted for reference-based image editing guided by text prompts.", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_Step1XEdit" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_Step1XEdit", - "title": "ComfyUI_RH_Step1XEdit" - }, - { - "author": "HM-RunningHub", - "description": "A custom node for ComfyUI that integrates Alibaba's Qwen-Image model for high-quality image generation with exceptional text rendering capabilities.", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_Qwen-Image" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_Qwen-Image", - "title": "ComfyUI Qwen-Image Node" - }, - { - "author": "sebord", - "description": "ComfyUI small node toolkit, this toolkit is mainly to update some practical small nodes, to make a contribution to the comfyui ecosystem, PS: 'LMCQ' is the abbreviation of the team name\nNOTE: The files in the repo are not organized, which may lead to update issues.", - "files": [ - "https://github.com/sebord/ComfyUI-LMCQ" - ], - "install_type": "git-clone", - "reference": "https://github.com/sebord/ComfyUI-LMCQ", - "title": "ComfyUI-LMCQ" - }, - { - "author": "InstantStudioAI", - "description": "A collection of nodes to enhance your experience with ComfyUI.", - "files": [ - "https://github.com/InstantStudioAI/ComfyUI-InstantStudio" - ], - "install_type": "git-clone", - "reference": "https://github.com/InstantStudioAI/ComfyUI-InstantStudio", - "title": "ComfyUI-InstantStudio" - }, - { - "author": "Tlant", - "description": "Use ollama to generate prompts based on reference text in comfyui.", - "files": [ - "https://github.com/Tlant/ComfyUI-OllamaPromptsGeneratorTlant" - ], - "install_type": "git-clone", - "reference": "https://github.com/Tlant/ComfyUI-OllamaPromptsGeneratorTlant", - "title": "ComfyUI-OllamaPromptsGeneratorTlant" - }, - { - "author": "DarioFT", - "description": "A custom node for ComfyUI that combines multiple videos from a directory with optional transitions and background music. Perfect for batch processing and creating seamless video compilations.", - "files": [ - "https://github.com/DarioFT/ComfyUI-VideoDirCombiner" - ], - "install_type": "git-clone", - "reference": "https://github.com/DarioFT/ComfyUI-VideoDirCombiner", - "title": "ComfyUI-VideoDirCombiner" - }, - { - "author": "Kim", - "description": "ComfyUI node collection: icon layout & processing, YOLO intelligent cropping, image filters & enhancement, text processing tools, metadata management, mask handling and image classification in one comprehensive toolbox.", - "files": [ - "https://github.com/wjl0313/ComfyUI_KimNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/wjl0313/ComfyUI_KimNodes", - "title": "ComfyUI_KimNodes" - }, - { - "author": "LiJT", - "description": "Custom node to use Gemini 1.5 and above for Comfyui to generates theme related prompts for image generators", - "files": [ - "https://github.com/LiJT/ComfyUI-Gemini-Prompt-Generator-JT" - ], - "install_type": "git-clone", - "reference": "https://github.com/LiJT/ComfyUI-Gemini-Prompt-Generator-JT", - "title": "Gemini prompt generator JT version" - }, - { - "author": "codeprimate", - "description": "A ComfyUI node that improves inpainting results by extending mask boundaries with geometric patterns, helping create smoother transitions and better context for AI-driven image completion.", - "files": [ - "https://github.com/codeprimate/ComfyUI-MaskContourProcessor" - ], - "install_type": "git-clone", - "reference": "https://github.com/codeprimate/ComfyUI-MaskContourProcessor", - "title": "ComfyUI Mask Contour Processor" - }, - { - "author": "Miyuutsu", - "description": "Simple ComfyUI Custom Node to enable saving checkpoints with V-Pred ZTSNR tensors and metadata.", - "files": [ - "https://github.com/Miyuutsu/comfyui-save-vpred" - ], - "install_type": "git-clone", - "reference": "https://github.com/Miyuutsu/comfyui-save-vpred", - "title": "comfyui-save-vpred" - }, - { - "author": "kostenickj", - "description": "NODES: EasyHRFix, EasyHRFix_Context, JKAnythingToString, JKBigContext, JKDynamicThresholdingMultiModel, JKEasyCheckpointLoader, JKEasyDetailer, JKEasyDetailer_Context, JKEasyKSampler_Context, JKEasyWatermark, JKInspireSchedulerAdapter, JKLilContext, JKMultiModelSamplerUnpatch, JKStringEmpty, JKStringEquals, JKStringNotEmpty, JKStringNotEquals, JKStringToSamplerAdapter", - "files": [ - "https://github.com/kostenickj/jk-comfyui-helpers" - ], - "install_type": "git-clone", - "reference": "https://github.com/kostenickj/jk-comfyui-helpers", - "title": "comfyui-jk-easy-nodes" - }, - { - "author": "IamCreateAI", - "description": "ComfyUI wrapper nodes for Ruyi, an image-to-video model by CreateAI.", - "files": [ - "https://github.com/IamCreateAI/Ruyi-Models" - ], - "install_type": "git-clone", - "reference": "https://github.com/IamCreateAI/Ruyi-Models", - "title": "ComfyUI-Ruyi" - }, - { - "author": "pollockjj", - "description": "This extension adds CUDA device selection to supported loader nodes in ComfyUI. By monkey-patching ComfyUI\u2019s memory management, each model component (like UNet, Clip, or VAE) can be loaded on a specific GPU. Examples included are multi-GPU workflows for SDXL, FLUX, LTXVideo, and Hunyuan Video for both standard and GGUF loader nodes.", - "files": [ - "https://github.com/pollockjj/ComfyUI-MultiGPU" - ], - "install_type": "git-clone", - "reference": "https://github.com/pollockjj/ComfyUI-MultiGPU", - "title": "ComfyUI-MultiGPU" - }, - { - "author": "PressWagon", - "description": "EA collection of ComfyUI custom nodes for formatting and debugging string data with the intention of collecting generation data to be processed by a custom node pack like comfy-image-saver, as well as miscellaneous extra nodes to experiment with.", - "files": [ - "https://github.com/PressWagon/ComfyUI-StringsAndThings" - ], - "install_type": "git-clone", - "reference": "https://github.com/PressWagon/ComfyUI-StringsAndThings", - "title": "ComfyUI-StringsAndThings" - }, - { - "author": "ADDOOR", - "description": "A collection of batch operation toolkits suitable for ComfyUI", - "files": [ - "https://github.com/Eagle-CN/ComfyUI-Addoor" - ], - "install_type": "git-clone", - "reference": "https://github.com/Eagle-CN/ComfyUI-Addoor", - "title": "ComfyUI-Addoor" - }, - { - "author": "CyanAutumn", - "description": "A random prompt node", - "files": [ - "https://github.com/CyanAutumn/ComfyUi_Random_Manage_Cyan" - ], - "id": "CyanAutumn", - "install_type": "git-clone", - "reference": "https://github.com/CyanAutumn/ComfyUi_Random_Manage_Cyan", - "title": "ComfyUi Random Manage Cyan" - }, - { - "author": "Black Forest Labs", - "description": "ComfyUI nodes for Black Forest Labs API Services", - "files": [ - "https://github.com/black-forest-labs/bfl-comfy-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/black-forest-labs/bfl-comfy-nodes", - "title": "Black Forest Labs API Nodes" - }, - { - "author": "kazeyori", - "description": "A ComfyUI plugin for quick image sequence processing. This plugin allows users to manipulate frame sequences with various operations including frame insertion, deletion, and duplication.", - "files": [ - "https://github.com/kazeyori/ComfyUI-QuickImageSequenceProcess" - ], - "install_type": "git-clone", - "reference": "https://github.com/kazeyori/ComfyUI-QuickImageSequenceProcess", - "title": "ComfyUI-QuickImageSequenceProcess" - }, - { - "author": "Light-x02", - "description": "Nodes for loading and saving images with metadata in ComfyUI.", - "files": [ - "https://github.com/Light-x02/ComfyUI-Image-Metadata-Nodes" - ], - "id": "image_metadata_nodes", - "install_type": "git-clone", - "reference": "https://github.com/Light-x02/ComfyUI-Image-Metadata-Nodes", - "title": "Image Metadata Nodes" - }, - { - "author": "Light-x02", - "description": "A combined node for ComfyUI with six configurable tabs for managing multiple parameter configurations, including guidance, sampler, scheduler, steps, and denoise.", - "files": [ - "https://github.com/Light-x02/ComfyUI-FluxSettingsNode" - ], - "id": "flux_settings_node", - "install_type": "git-clone", - "reference": "https://github.com/Light-x02/ComfyUI-FluxSettingsNode", - "title": "Flux Settings Node" - }, - { - "author": "Light-x02", - "description": "CropImageByLightx02 is a ComfyUI node that allows cropping an image (and optionally its mask) using pixel values.", - "files": [ - "https://github.com/Light-x02/ComfyUI_Crop_Image_By_Lightx02" - ], - "install_type": "git-clone", - "reference": "https://github.com/Light-x02/ComfyUI_Crop_Image_By_Lightx02", - "title": "Crop Image by Lightx02" - }, - { - "author": "marcoc2", - "description": "A collection of specialized image processing nodes for ComfyUI, focused on dataset preparation and pixel art manipulation.", - "files": [ - "https://github.com/marcoc2/ComfyUI-AnotherUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/marcoc2/ComfyUI-AnotherUtils", - "title": "Image Processing Suite for ComfyUI" - }, - { - "author": "marcoc2", - "description": "This is a custom node aiming to run CogView4 on diffusers while there is no official implementation on ComfyUI.\nNOTE: You will need a updated version of diffusers and I don't know if updating it my break other stuff, so I advise you to make in a new instance of ComfyUI", - "files": [ - "https://github.com/marcoc2/ComfyUI_CogView4-6B_diffusers" - ], - "install_type": "git-clone", - "reference": "https://github.com/marcoc2/ComfyUI_CogView4-6B_diffusers", - "title": "ComfyUI-Cog" - }, - { - "author": "BIMer-99", - "description": "This plugin is optimized for Fish-Speech-1.5 version and is only applicable to version 1.5", - "files": [ - "https://github.com/BIMer-99/ComfyUI_FishSpeech_EX" - ], - "install_type": "git-clone", - "reference": "https://github.com/BIMer-99/ComfyUI_FishSpeech_EX", - "title": "ComfyUI_FishSpeech_EX" - }, - { - "author": "AEmotionStudio", - "description": "A beautiful theme extension for ComfyUI that adds festive touches with dynamic backgrounds, snowfall effects, and animated node connections", - "files": [ - "https://github.com/AEmotionStudio/ComfyUI-ChristmasTheme" - ], - "install_type": "git-clone", - "reference": "https://github.com/AEmotionStudio/ComfyUI-ChristmasTheme", - "title": "ComfyUI Christmas Theme \ud83c\udf84\u2728" - }, - { - "author": "AEmotionStudio", - "description": "A visually stunning extension for ComfyUI that adds beautiful, customizable animations to both links and nodes in your workflow, with a focus on performance and customization. Includes an end-of-render animation and a text visibility tool for nodes. No extra packages are required, works with the latest version of ComfyUI, and should be compatible with most workflows. Larger workflows may experience performance issues, especially if you have a lot of nodes and are using a lower end system.", - "files": [ - "https://github.com/AEmotionStudio/ComfyUI-EnhancedLinksandNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/AEmotionStudio/ComfyUI-EnhancedLinksandNodes", - "reference2": "https://github.com/AEmotionStudio/ComfyUI-Enhanced", - "title": "ComfyUI-EnhancedLinksandNodes \ud83c\udfa8\u2728" - }, - { - "author": "AEmotionStudio", - "description": "ComfyUI-MagnifyGlass: A powerful & customizable magnifying glass for ComfyUI. Zoom into canvas details with smooth controls, configurable activation, custom styles (shape, size, border) & WebGL performance.", - "files": [ - "https://github.com/AEmotionStudio/ComfyUI-MagnifyGlass" - ], - "install_type": "git-clone", - "reference": "https://github.com/AEmotionStudio/ComfyUI-MagnifyGlass", - "title": "ComfyUI-MagnifyGlass" - }, - { - "author": "AEmotionStudio", - "description": "Transform AI image generation from random exploration into deliberate artistic navigation. This advanced KSampler replacement blends traditional noise with shader noise. Navigate latent space with intention using adjustable noise parameters, shape masks, and colors transformations.", - "files": [ - "https://github.com/AEmotionStudio/ComfyUI-ShaderNoiseKSampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/AEmotionStudio/ComfyUI-ShaderNoiseKSampler", - "title": "ComfyUI-ShaderNoiseKSampler" - }, - { - "author": "AEmotionStudio", - "description": "A ComfyUI extension that enables seamless sharing of AI-generated images and videos directly to Discord.", - "files": [ - "https://github.com/AEmotionStudio/ComfyUI-DiscordSend" - ], - "install_type": "git-clone", - "reference": "https://github.com/AEmotionStudio/ComfyUI-DiscordSend", - "title": "ComfyUI-DiscordSend" - }, - { - "author": "xfgexo", - "description": "A custom node pack made with efficiency and quality of life features in mind. Most notably is my Prompt Builder Deluxe Node. Unlike any other run-of-the-mill prompt builder or styler node out there. Mine allows you to create and design in a way no other node does.", - "files": [ - "https://github.com/xfgexo/EXO-Custom-ComfyUI-Nodes" - ], - "id": "exo-custom-nodes", - "install_type": "git-clone", - "reference": "https://github.com/xfgexo/EXO-Custom-ComfyUI-Nodes", - "title": "EXO Custom ComfyUI Nodes" - }, - { - "author": "jefferyharrell", - "description": "These are custom nodes for ComfyUI for the loading and saving of metadata in XMP format. XMP metadata is embedded in the images created by these nodes; it travels along wherever the image does. Both macOS and Windows index XMP metadata automatically, making it searchable from the Finder on the Mac or the File Explorer in Windows. Apps like Photoshop or Lightroom (and presumably many others) expose XMP metadata and allow it to be edited.", - "files": [ - "https://github.com/ComfyUI-JH/ComfyUI-JH-XMP-Metadata-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComfyUI-JH/ComfyUI-JH-XMP-Metadata-Nodes", - "title": "JH XMP Metadata Nodes" - }, - { - "author": "jefferyharrell", - "description": "NODES: Daisy-Chainable String Constant, Two-Way Switch, Three-Way Switch, Preview Imag\nMiscellaneous custom nodes for ComfyUI", - "files": [ - "https://github.com/ComfyUI-JH/ComfyUI-JH-Misc-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComfyUI-JH/ComfyUI-JH-Misc-Nodes", - "title": "JH Misc. Nodes" - }, - { - "author": "TKRLAB", - "description": "This repository provides a custom node for ComfyUI that allows managing positive and negative prompts in a structured JSON format. The node supports adding, updating, and logging prompts, ensuring seamless integration into your workflow.", - "files": [ - "https://github.com/TKRLAB/ComfyUI_Prompt_List_JSON" - ], - "install_type": "git-clone", - "reference": "https://github.com/TKRLAB/ComfyUI_Prompt_List_JSON", - "title": "Prompt List JSON" - }, - { - "author": "kevinmcmahondev", - "description": "A ComfyUI node that detects the skin tone of a person in an image and matches it to the standard emoji skin tone palette.", - "files": [ - "https://github.com/kevinmcmahondev/comfyui-skin-tone-detector" - ], - "install_type": "git-clone", - "reference": "https://github.com/kevinmcmahondev/comfyui-skin-tone-detector", - "title": "Skin Tone Detector for ComfyUI" - }, - { - "author": "kevinmcmahondev", - "description": "A ComfyUI node that provides advanced image adjustment filters and controls for image manipulation", - "files": [ - "https://github.com/kevinmcmahondev/comfyui-kmcdev-image-filter-adjustments" - ], - "install_type": "git-clone", - "reference": "https://github.com/kevinmcmahondev/comfyui-kmcdev-image-filter-adjustments", - "title": "KMCDev Nodes" - }, - { - "author": "mahdi", - "description": "Seamless Clone for ComfyUI", - "files": [ - "https://github.com/Aksaz/comfyui-seamless-clone" - ], - "install_type": "git-clone", - "reference": "https://github.com/Aksaz/comfyui-seamless-clone", - "title": "seamless-clone-comfyui" - }, - { - "author": "SlackinJack", - "description": "AsyncDiff node for ComfyUI", - "files": [ - "https://github.com/SlackinJack/asyncdiff_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/SlackinJack/asyncdiff_comfyui", - "title": "asyncdiff_comfyui" - }, - { - "author": "pharmapsychotic", - "description": "Image to caption with CLIP ViT-L/14. Small and fast addition to the CLIP-L model you already have loaded to generate captions for images within your workflow.", - "files": [ - "https://github.com/pharmapsychotic/comfy-cliption" - ], - "id": "comfy-cliption", - "install_type": "git-clone", - "reference": "https://github.com/pharmapsychotic/comfy-cliption", - "title": "comfy-cliption" - }, - { - "author": "York Xiang", - "description": "Helper nodes to display last seed and prompt.", - "files": [ - "https://github.com/bombless/comfyUI-RememberingUtils" - ], - "id": "comfyui-rememberingutils", - "install_type": "git-clone", - "reference": "https://github.com/bombless/comfyUI-RememberingUtils", - "title": "Remembering utils" - }, - { - "author": "shahkoorosh", - "description": "NODES:Custom Resolution Latent Node, Style Selector\nThis Custom node offers various experimental nodes to make it easier to use ComfyUI.", - "files": [ - "https://github.com/shahkoorosh/ComfyUI-KGnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/shahkoorosh/ComfyUI-KGnodes", - "title": "ComfyUI-KGnodes" - }, - { - "author": "shahkoorosh", - "description": "A powerful ComfyUI node for rendering text with advanced styling options, including full support for Persian/Farsi and Arabic scripts.", - "files": [ - "https://github.com/shahkoorosh/ComfyUI-PersianText" - ], - "install_type": "git-clone", - "reference": "https://github.com/shahkoorosh/ComfyUI-PersianText", - "title": "ComfyUI-PersianText" - }, - { - "author": "andygill", - "description": "ComfyUI nodes for 3D visualization.", - "files": [ - "https://github.com/andygill/comfyui-sunflower-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/andygill/comfyui-sunflower-nodes", - "title": "comfyui-sunflower-nodes" - }, - { - "author": "HM-RunningHub", - "description": "This is a ComfyUI plugin that makes it easier to call and run workflows from RunningHub in your local ComfyUI setup.", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_APICall" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_APICall", - "title": "ComfyUI_RH_APICall" - }, - { - "author": "HM-RunningHub", - "description": "This is a Seed-X-PPO-7B ComfyUI plugin. Easy to use", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_SeedXPro" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_SeedXPro", - "title": "ComfyUI SeedXPro Translation Node" - }, - { - "author": "HM-RunningHub", - "description": "This is a Seed-X-PPO-7B ComfyUI plugin. Easy to use", - "files": [ - "https://github.com/HM-RunningHub/ComfyUI_RH_DMOSpeech2" - ], - "install_type": "git-clone", - "reference": "https://github.com/HM-RunningHub/ComfyUI_RH_DMOSpeech2", - "title": "ComfyUI DMOSpeech2 Node" - }, - { - "author": "wqjuser", - "description": "Use an online large language model to describe images.", - "files": [ - "https://github.com/wqjuser/ComfyUI-Chat-Image" - ], - "install_type": "git-clone", - "reference": "https://github.com/wqjuser/ComfyUI-Chat-Image", - "title": "ComfyUI-Chat-Image" - }, - { - "author": "ronsantash", - "description": "FlexiLoRALoader - A ComfyUI custom node for dynamic LoRA weight management. Apply multiple LoRAs with flexible weight patterns and randomization features for creative AI image generation.\nFeatures: \u2022 Multiple LoRA handling (up to 3) \u2022 Weight pattern presets \u2022 Random/Sequential mode \u2022 Debug logging support", - "files": [ - "https://github.com/ronsantash/Comfyui-flexi-lora-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/ronsantash/Comfyui-flexi-lora-loader", - "title": "ComfyUIFlexiLoRALoader" - }, - { - "author": "cherninlab", - "description": "This custom node allows you to generate logo images using Google Fonts.", - "files": [ - "https://github.com/cherninlab/logo-generator-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/cherninlab/logo-generator-comfyui", - "title": "Logo Generator Node for ComfyUI" - }, - { - "author": "SongGuo11", - "description": "A ComfyUI custom node for saving images in various formats", - "files": [ - "https://github.com/SongGuo11/ComfyUI-SaveAnything-SG11" - ], - "install_type": "git-clone", - "reference": "https://github.com/SongGuo11/ComfyUI-SaveAnything-SG11", - "title": "ComfyUI SaveAnything Node (SG11)" - }, - { - "author": "ciga2011", - "description": "This node pack helps to convert various files to Markdown. It supports pdf, pptx, xlsx, docx, html and image files.", - "files": [ - "https://github.com/ciga2011/ComfyUI-MarkItDown" - ], - "id": "markitdown", - "install_type": "git-clone", - "pip": [ - "markitdown", - "openai" - ], - "reference": "https://github.com/ciga2011/ComfyUI-MarkItDown", - "title": "ComfyUI MarkItDown" - }, - { - "author": "ciga2011", - "description": "Generate images from text prompts using Pollinations' AI models for free.", - "files": [ - "https://github.com/ciga2011/ComfyUI-Pollinations" - ], - "id": "pollinations", - "install_type": "git-clone", - "reference": "https://github.com/ciga2011/ComfyUI-Pollinations", - "title": "ComfyUI Pollinations" - }, - { - "author": "ciga2011", - "description": "Optimize prompts for text-to-image models at no cost.", - "files": [ - "https://github.com/ciga2011/ComfyUI-PromptOptimizer" - ], - "id": "promptoptimizer", - "install_type": "git-clone", - "reference": "https://github.com/ciga2011/ComfyUI-PromptOptimizer", - "title": "ComfyUI Prompt Optimizer" - }, - { - "author": "IgalOgonov", - "description": "Custom node that allows storing and accessing strings, meant to be parts of a prompt, in a simplified manner. Partially supports dynamic prompt syntax.", - "files": [ - "https://github.com/IgalOgonov/ComfyUI_Simple_String_Repository" - ], - "install_type": "git-clone", - "reference": "https://github.com/IgalOgonov/ComfyUI_Simple_String_Repository", - "title": "Simple String Repository" - }, - { - "author": "fairy-root", - "description": "GLHF is a ComfyUI node that facilitates seamless interaction with the GLHF chat API. Designed to enhance user experience, it supports multiple language models, web search integration, and customizable instructions, making it a powerful extension for AI-driven workflows.", - "files": [ - "https://github.com/fairy-root/ComfyUI-GLHF" - ], - "id": "comfyui-glhf", - "install_type": "git-clone", - "reference": "https://github.com/fairy-root/ComfyUI-GLHF", - "title": "ComfyUI-GLHF" - }, - { - "author": "Jurdn", - "description": "This node utilizes the Groq.com API to enhance prompts. (Place API key and main system prompt in the groq_config.json)", - "files": [ - "https://github.com/jurdnisglobby/ComfyUI-Jurdns-Groq-Node" - ], - "id": "jurdnsgroqapinode", - "install_type": "git-clone", - "pip": [ - "groq" - ], - "reference": "https://github.com/jurdnisglobby/ComfyUI-Jurdns-Groq-Node", - "title": "Jurdns Groq API Node" - }, - { - "author": "randomnoner11", - "description": "Mistral AI API's chat completion endpoint in ComfyUI", - "files": [ - "https://github.com/randomnoner11/ComfyUI-MistralAI-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/randomnoner11/ComfyUI-MistralAI-API", - "title": "ComfyUI-MistralAI-API" - }, - { - "author": "ahernandezmiro", - "description": "A set of ComfyUI nodes for GPC Storage access", - "files": [ - "https://github.com/ahernandezmiro/ComfyUI-GCP_Storage_tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/ahernandezmiro/ComfyUI-GCP_Storage_tools", - "title": "ComfyUI-GCP_Storage_tools" - }, - { - "author": "rohitsainier", - "description": "A ComfyUI custom node package that allows downloading and organizing Instagram content directly in your ComfyUI Output folder", - "files": [ - "https://github.com/rohitsainier/ComfyUI-InstagramDownloader" - ], - "id": "comfyui-instagram-downloader", - "install_type": "git-clone", - "reference": "https://github.com/rohitsainier/ComfyUI-InstagramDownloader", - "title": "ComfyUI-InstagramDownloader" - }, - { - "author": "zmwv823", - "description": "Unofficial Simple And Rough Implementation Of [a/AnyText](https://github.com/tyxsspa/AnyText) and [a/Glyph-ByT5] (https://github.com/AIGText/Glyph-ByT5) and [a/JoyType](https://github.com/jdh-algo/JoyType)", - "files": [ - "https://github.com/zmwv823/ComfyUI_Anytext" - ], - "install_type": "git-clone", - "reference": "https://github.com/zmwv823/ComfyUI_Anytext", - "title": "ComfyUI_Anytext" - }, - { - "author": "SKBv0", - "description": "Nodes: MultiText, TextBox, TitlePlus, SeamlessTexture, AspectRatioPlus, DisplayEverything, ComparerPlus, AnySwitch, Node Design Tools...", - "files": [ - "https://github.com/SKBv0/ComfyUI_SKBundle" - ], - "install_type": "git-clone", - "reference": "https://github.com/SKBv0/ComfyUI_SKBundle", - "title": "ComfyUI SKBundle" - }, - { - "author": "civen-cn", - "description": "This is a ComfyUI node that allows you to translate subtitles using the Whisper. Now support for multiple languages: ['zh', 'en', 'ja', 'ko', 'ru', 'fr', 'de', 'es', 'pt', 'it', 'ar'] You may need to put fonts in the 'fonts' folder to support different languages.", - "files": [ - "https://github.com/civen-cn/ComfyUI-Whisper-Translator" - ], - "install_type": "git-clone", - "reference": "https://github.com/civen-cn/ComfyUI-Whisper-Translator", - "title": "ComfyUI Whisper Translator" - }, - { - "author": "WainWong", - "description": "ComfyUI Loop Image is a node package specifically designed for image loop processing. It provides two main processing modes: Batch Image Processing and Single Image Processing, along with supporting image segmentation and merging functions.", - "files": [ - "https://github.com/WainWong/ComfyUI-Loop-image" - ], - "install_type": "git-clone", - "reference": "https://github.com/WainWong/ComfyUI-Loop-image", - "title": "ComfyUI-Loop-image" - }, - { - "author": "Jash-Vora", - "description": "[a/FitDiT](https://arxiv.org/abs/2411.10499): Advancing the Authentic Garment Details for High-fidelity Virtual Try-onon", - "files": [ - "https://github.com/Jash-Vora/ComfyUI-GarmentDiT" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jash-Vora/ComfyUI-GarmentDiT", - "title": "FitDiT" - }, - { - "author": "rhplus0831", - "description": "Another mobile frontend for ComfyUI", - "files": [ - "https://github.com/rhplus0831/ComfyMepi" - ], - "install_type": "git-clone", - "reference": "https://github.com/rhplus0831/ComfyMepi", - "title": "ComfyMepi" - }, - { - "author": "0x-jerry", - "description": "Rembg Background Removal Node for ComfyUI", - "files": [ - "https://github.com/0x-jerry/comfyui-rembg" - ], - "install_type": "git-clone", - "reference": "https://github.com/0x-jerry/comfyui-rembg", - "title": "0x-jerry/Rembg Background Removal Node for ComfyUI" - }, - { - "author": "sanbuphy", - "description": "ComfyUI Workflow to run audioldm-l-full pipeline\n[a/https://huggingface.co/cvssp/audioldm-l-full](https://huggingface.co/cvssp/audioldm-l-full)", - "files": [ - "https://github.com/sanbuphy/ComfyUI-AudioLDM" - ], - "install_type": "git-clone", - "reference": "https://github.com/sanbuphy/ComfyUI-AudioLDM", - "title": "ComfyUI-AudioLDM" - }, - { - "author": "bear2b", - "description": "This node applies a custom 4x4 color matrix to an image using GPU acceleration via PyTorch.", - "files": [ - "https://github.com/bear2b/comfyui-argo-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/bear2b/comfyui-argo-nodes", - "title": "ColorMatrixGPU Node for ComfyUI" - }, - { - "author": "phuvinh010701", - "description": "Nodes for NSFW content filtering", - "files": [ - "https://github.com/phuvinh010701/ComfyUI-Nudenet" - ], - "install_type": "git-clone", - "reference": "https://github.com/phuvinh010701/ComfyUI-Nudenet", - "title": "ComfyUI-Nudenet" - }, - { - "author": "Vaibhavs10", - "description": "Run DDUF in ComfyUI - powered by Diffusers.", - "files": [ - "https://github.com/Vaibhavs10/ComfyUI-DDUF" - ], - "install_type": "git-clone", - "reference": "https://github.com/Vaibhavs10/ComfyUI-DDUF", - "title": "ComfyUI-DDUF" - }, - { - "author": "AconexOfficial", - "description": "Nodes to level up your workflows performance and streamline specific functions.", - "files": [ - "https://github.com/AconexOfficial/ComfyUI_GOAT_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/AconexOfficial/ComfyUI_GOAT_Nodes", - "title": "ComfyUI GOAT Nodes" - }, - { - "author": "Jaminanim", - "description": "A set of custom ComfyUI nodes for generating random integers within a range, adjusted to the nearest multiple of a user-defined divisor. Needlessly includes both an efficient and simple list implementation. Updates with each generation.", - "files": [ - "https://github.com/Jaminanim/ComfyUI-Random-Int-Divisor-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jaminanim/ComfyUI-Random-Int-Divisor-Node", - "title": "ComfyUI-Random-Int-Divisor-Node" - }, - { - "author": "cenzijing", - "description": "A ComfyUI custom node for creating mindmaps from markdown", - "files": [ - "https://github.com/cenzijing/ComfyUI-Markmap" - ], - "install_type": "git-clone", - "reference": "https://github.com/cenzijing/ComfyUI-Markmap", - "title": "ComfyUI-Markmap" - }, - { - "author": "bongsang", - "description": "The 'ComfyUI-Bongsang' is very useful tools for a diffusion model developer.", - "files": [ - "https://github.com/bongsang/ComfyUI-Bongsang" - ], - "install_type": "git-clone", - "reference": "https://github.com/bongsang/ComfyUI-Bongsang", - "title": "ComfyUI-Bongsang" - }, - { - "author": "muxueChen", - "description": "ComfyUI_NTCosyVoice is a plugin of ComfyUI for Cosysvoice2", - "files": [ - "https://github.com/muxueChen/ComfyUI_NTCosyVoice" - ], - "install_type": "git-clone", - "reference": "https://github.com/muxueChen/ComfyUI_NTCosyVoice", - "title": "CosyVoice2 for ComfyUI" - }, - { - "author": "inventorado", - "description": "Neural Network Toolkit (NNT) for ComfyUI is an extensive set of custom ComfyUI nodes for designing, training, and fine-tuning neural networks. This toolkit allows defining models, layers, training workflows, transformers, and tensor operations in a visual manner using nodes.", - "files": [ - "https://github.com/inventorado/ComfyUI_NNT" - ], - "id": "nnt", - "install_type": "git-clone", - "reference": "https://github.com/inventorado/ComfyUI_NNT", - "title": "ComfyUI Neural Network Toolkit NNT " - }, - { - "author": "Hullabalo", - "description": "A pair of nodes (Load Image and Save Image) to create a simple loop in your ComfyUI inpainting workflow, without the need of loading your last saved image, and a few others to cut and paste back the cutting into the source.", - "files": [ - "https://github.com/Hullabalo/ComfyUI-Loop" - ], - "install_type": "git-clone", - "reference": "https://github.com/Hullabalo/ComfyUI-Loop", - "title": "ComfyUI-Loop" - }, - { - "author": "hodanajan", - "description": "ComfyUI node to calculate optimal resolution to crop the image to (from a list of aspect ratios)", - "files": [ - "https://github.com/hodanajan/optimal-crop-resolution" - ], - "install_type": "git-clone", - "reference": "https://github.com/hodanajan/optimal-crop-resolution", - "title": "optimal-crop-resolution" - }, - { - "author": "JJ", - "description": "An extension for ComfyUI that adds utility functions and nodes not available in the default setup.", - "files": [ - "https://github.com/cnbjjj/ComfyUI-Jtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/cnbjjj/ComfyUI-Jtils", - "title": "ComfyUI-Jtils" - }, - { - "author": "mw", - "description": "A node that assists in one click generation of prompts (for image and video generation, etc.) in Comfyui.", - "files": [ - "https://github.com/billwuhao/ComfyUI_OneButtonPrompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_OneButtonPrompt", - "title": "MW-ComfyUI_OneButtonPrompt" - }, - { - "author": "mw", - "description": "A Text To Speech node using Step-Audio-TTS in ComfyUI. Can speak, rap, sing, or clone voice.", - "files": [ - "https://github.com/billwuhao/ComfyUI_StepAudioTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_StepAudioTTS", - "title": "ComfyUI_StepAudioTTS" - }, - { - "author": "mw", - "description": "A Text To Speech node using Kokoro TTS in ComfyUI. Supports 8 languages and 150 voices", - "files": [ - "https://github.com/billwuhao/ComfyUI_KokoroTTS_MW" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_KokoroTTS_MW", - "title": "ComfyUI_KokoroTTS_MW" - }, - { - "author": "mw", - "description": "Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation. A node for ComfyUI.", - "files": [ - "https://github.com/billwuhao/ComfyUI_DiffRhythm" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_DiffRhythm", - "title": "ComfyUI_DiffRhythm_MW" - }, - { - "author": "mw", - "description": "Portrait Tools: Facial detection cropping, alignment, ID photo, etc.", - "files": [ - "https://github.com/billwuhao/ComfyUI_PortraitTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_PortraitTools", - "title": "MW-ComfyUI_PortraitTools" - }, - { - "author": "mw", - "description": "Symbolic Music Generation, NotaGen node for ComfyUI.", - "files": [ - "https://github.com/billwuhao/ComfyUI_NotaGen" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_NotaGen", - "title": "ComfyUI_NotaGen" - }, - { - "author": "mw", - "description": "Super fast multilingual speech recognition model based on Whisper Large-v3 Turbo. A node for ComfyUI.", - "files": [ - "https://github.com/billwuhao/ComfyUI_EraX-WoW-Turbo" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_EraX-WoW-Turbo", - "title": "MW-ComfyUI_EraX-WoW-Turbo" - }, - { - "author": "mw", - "description": "XiaoMi GemmaX: Support 28 languages, Multilingual Translator based on Gemma. A node for ComfyUI.", - "files": [ - "https://github.com/billwuhao/ComfyUI_gemmax" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_gemmax", - "title": "MW-ComfyUI_gemmax" - }, - { - "author": "mw", - "description": "ComfyUI node of Conversational Speech Model (CSM).", - "files": [ - "https://github.com/billwuhao/ComfyUI_CSM" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_CSM", - "title": "ComfyUI_CSM" - }, - { - "author": "mw", - "description": "Using Spark-TTS in Comfyui. Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokenss", - "files": [ - "https://github.com/billwuhao/ComfyUI_SparkTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_SparkTTS", - "title": "ComfyUI_SparkTTS" - }, - { - "author": "mw", - "description": "This toolkit is designed for a wide range of audio tasks, from podcast enhancement and text-to-speech to creative music manipulation and fully automated, batch-processed audio-reactive visual generation.", - "files": [ - "https://github.com/billwuhao/ComfyUI_AudioTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_AudioTools", - "title": "ComfyUI_AudioTools" - }, - { - "author": "mw", - "description": "Lightweight and Efficient, \ud83c\udfa7Ultra High-Quality Voice Cloning, Chinese and English.", - "files": [ - "https://github.com/billwuhao/ComfyUI_MegaTTS3" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_MegaTTS3", - "title": "MW-ComfyUI_MegaTTS3" - }, - { - "author": "mw", - "description": "OuteTTS - Unified Text-To-Speech. A node for ComfyUI", - "files": [ - "https://github.com/billwuhao/ComfyUI_OuteTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_OuteTTS", - "title": "MW-ComfyUI_OuteTTS" - }, - { - "author": "mw", - "description": "IndexTTS Voice Cloning Nodes for ComfyUI. High-quality voice cloning, very fast, supports Chinese and English, and allows custom voice styles.", - "files": [ - "https://github.com/billwuhao/ComfyUI_IndexTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_IndexTTS", - "title": "ComfyUI_IndexTTS" - }, - { - "author": "mw", - "description": "ACE-Step: A Step Towards Music Generation Foundation Model", - "files": [ - "https://github.com/billwuhao/ComfyUI_ACE-Step" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_ACE-Step", - "title": "ComfyUI_ACE-Step" - }, - { - "author": "mw", - "description": "parakeet-tdt-0.6b-v2: Automatic speech recognition (ASR) model designed for high-quality English transcription, featuring support for punctuation, capitalization, and accurate timestamp prediction.", - "files": [ - "https://github.com/billwuhao/ComfyUI_parakeet-tdt" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_parakeet-tdt", - "title": "ComfyUI_parakeet-tdt" - }, - { - "author": "mw", - "description": "HeyGem AI avatar.", - "files": [ - "https://github.com/billwuhao/Comfyui_HeyGem" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/Comfyui_HeyGem", - "title": "Comfyui_HeyGem" - }, - { - "author": "mw", - "description": "Sing to Midi \ud83c\udfb6", - "files": [ - "https://github.com/billwuhao/ComfyUI_SOME" - ], - "install_type": "git-clone", - "reference": "https://github.com/billwuhao/ComfyUI_SOME", - "title": "ComfyUI_SOME" - }, - { - "author": "umiyuki", - "description": "A custom ComfyUI node that pads an image to a multiple of 8 width.", - "files": [ - "https://github.com/umiyuki/comfyui-pad-to-eight" - ], - "install_type": "git-clone", - "reference": "https://github.com/umiyuki/comfyui-pad-to-eight", - "title": "ComfyUI Pad To Eight" - }, - { - "author": "Meettya", - "description": "Node:Image Fit Calculator", - "files": [ - "https://github.com/Meettya/ComfyUI-OneForOne" - ], - "install_type": "git-clone", - "reference": "https://github.com/Meettya/ComfyUI-OneForOne", - "title": "ComfyUI-OneForOne" - }, - { - "author": "KunmyonChoi", - "description": "ComfyUI custom_node that load and save file directly from S3\nSimplified version of [a/https://github.com/kealiu/ComfyUI-S3-Tools](https://github.com/kealiu/ComfyUI-S3-Tools)", - "files": [ - "https://github.com/KunmyonChoi/ComfyUI_S3_direct" - ], - "install_type": "git-clone", - "reference": "https://github.com/KunmyonChoi/ComfyUI_S3_direct", - "title": "ComfyUI_S3_direct" - }, - { - "author": "ChenDarYen", - "description": "This is a ComfyUI implementation of the timestep shift technique used in [a/NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training.](https://arxiv.org/abs/2412.02030)\nFor more details, visit the official [a/NitroFusion GitHub repository](https://github.com/ChenDarYen/NitroFusion).", - "files": [ - "https://github.com/ChenDarYen/ComfyUI-TimestepShiftModel" - ], - "install_type": "git-clone", - "reference": "https://github.com/ChenDarYen/ComfyUI-TimestepShiftModel", - "title": "ComfyUI-TimestepShiftModel" - }, - { - "author": "ChenDarYen", - "description": "ComfyUI implemtation for NAG", - "files": [ - "https://github.com/ChenDarYen/ComfyUI-NAG" - ], - "install_type": "git-clone", - "reference": "https://github.com/ChenDarYen/ComfyUI-NAG", - "title": "ComfyUI-NAG" - }, - { - "author": "facok", - "description": "A custom LoRA-loading node designed to prevent issues such as blurriness and other artifacts when loading multiple LoRAs in HunYuan Video.\nUsage Instructions: The connection method remains unchanged from the original. The only difference is the additional blocks_type option. Please select double_blocks.", - "files": [ - "https://github.com/facok/ComfyUI-HunyuanVideoMultiLora" - ], - "install_type": "git-clone", - "reference": "https://github.com/facok/ComfyUI-HunyuanVideoMultiLora", - "title": "ComfyUI-HunyuanVideoMultiLora" - }, - { - "author": "facok", - "description": "This is a TeaCache acceleration node for HunYuan Video, supporting the native node workflow for seamless upgrades. Simply choose the acceleration multiplier you want\u2014currently, three levels are available.", - "files": [ - "https://github.com/facok/ComfyUI-TeaCacheHunyuanVideo" - ], - "install_type": "git-clone", - "reference": "https://github.com/facok/ComfyUI-TeaCacheHunyuanVideo", - "title": "ComfyUI-TeaCacheHunyuanVideo" - }, - { - "author": "FinetunersAI", - "description": "A suite of nodes for ComfyUI that helps making ComfyUI more accesible for artists", - "files": [ - "https://github.com/FinetunersAI/ComfyUI_Finetuners_Suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/FinetunersAI/ComfyUI_Finetuners_Suite", - "title": "ComfyUI_Finetuners_Suite" - }, - { - "author": "sh570655308", - "description": "Custom nodes use gigapixelai in comfyui.", - "files": [ - "https://github.com/sh570655308/ComfyUI-GigapixelAI" - ], - "id": "gigapixel", - "install_type": "git-clone", - "reference": "https://github.com/sh570655308/ComfyUI-GigapixelAI", - "title": "ComfyUI-GigapixelAI" - }, - { - "author": "sh570655308", - "description": "Custom nodes use topazvideoai in comfyui.", - "files": [ - "https://github.com/sh570655308/ComfyUI-TopazVideoAI" - ], - "id": "tvai", - "install_type": "git-clone", - "reference": "https://github.com/sh570655308/ComfyUI-TopazVideoAI", - "title": "ComfyUI-TopazVideoAI" - }, - { - "author": "jammyfu", - "description": "Powerful tools for image and text processing, including cross-platform workflow fixes, optimized resolution, dynamic text/image combos, and batch processing. Unlock seamless AI art creation and boost your productivity!", - "files": [ - "https://github.com/jammyfu/ComfyUI_PaintingCoderUtils" - ], - "id": "painting-coder-utils", - "install_type": "git-clone", - "reference": "https://github.com/jammyfu/ComfyUI_PaintingCoderUtils", - "title": "Painting Coder Utils" - }, - { - "author": "welltop-cn", - "description": "Unofficial implementation of [ali-vilab/TeaCache](https://github.com/ali-vilab/TeaCache) for ComfyUI", - "files": [ - "https://github.com/welltop-cn/ComfyUI-TeaCache" - ], - "id": "teacache", - "install_type": "git-clone", - "reference": "https://github.com/welltop-cn/ComfyUI-TeaCache", - "title": "ComfyUI-TeaCache" - }, - { - "author": "calcuis", - "description": "gguf node for comfyui", - "files": [ - "https://github.com/calcuis/gguf" - ], - "id": "gguf", - "install_type": "git-clone", - "preemptions": [ - "LoaderGGUF", - "ClipLoaderGGUF", - "DualClipLoaderGGUF", - "TripleClipLoaderGGUF", - "LoaderGGUFAdvanced", - "GGUFSave" - ], - "reference": "https://github.com/calcuis/gguf", - "title": "gguf" - }, - { - "author": "ainewsto", - "description": "NODES: ComfyUI-ImageFx, ComfyUI-Whisk, ComfyUI-Whisk-Prompts", - "files": [ - "https://github.com/ainewsto/comfyui-labs-google" - ], - "install_type": "git-clone", - "reference": "https://github.com/ainewsto/comfyui-labs-google", - "title": "comfyui-labs-google" - }, - { - "author": "ainewsto", - "description": "NODES: Comfly_Mj, Comfly_mjstyle, Comfly_upload, Comfly_Mju, Comfly_Mjv, Comfly_kling_text2video, Comfly_kling_image2video, Comfly_video_extend, Comfly_lip_sync, Comfly_kling_videoPreview, Comfly Gemini API, Comfly Doubao SeedEdit, Comfly ChatGPT Api,Comfly Jimeng API, Comfly_gpt_image_1_edit, Comfly_gpt_image_1", - "files": [ - "https://github.com/ainewsto/Comfyui_Comfly_v2" - ], - "install_type": "git-clone", - "reference": "https://github.com/ainewsto/Comfyui_Comfly_v2", - "title": "Comfyui_Comfly_v2" - }, - { - "author": "ainewsto", - "description": "A new ComfyUI node for OpenAI's ChatGPT API has been added. It currently supports single and multiple image inputs, text input, image generation, and image editing.", - "files": [ - "https://github.com/ainewsto/Comfyui-chatgpt-api" - ], - "install_type": "git-clone", - "reference": "https://github.com/ainewsto/Comfyui-chatgpt-api", - "title": "Comfyui-chatgpt-api" - }, - { - "author": "ainewsto", - "description": "NODES: Google Veo2 Video Generation", - "files": [ - "https://github.com/ainewsto/Comfyui-google-veo2-api" - ], - "install_type": "git-clone", - "reference": "https://github.com/ainewsto/Comfyui-google-veo2-api", - "title": "Comfyui-google-veo2-api" - }, - { - "author": "gremlation", - "description": "A ComfyUI node that displays the type and contents of whatever is connected to the input. In the case of a Tensor object, it shows the shape instead of its value.", - "files": [ - "https://github.com/gremlation/ComfyUI-ViewData" - ], - "install_type": "git-clone", - "reference": "https://github.com/gremlation/ComfyUI-ViewData", - "title": "ComfyUI-ViewData" - }, - { - "author": "gremlation", - "description": "A ComfyUI node that runs a [a/JMESPath](https://jmespath.org/) query against input JSON and outputs the result.", - "files": [ - "https://github.com/gremlation/ComfyUI-JMESPath" - ], - "install_type": "git-clone", - "reference": "https://github.com/gremlation/ComfyUI-JMESPath", - "title": "ComfyUI-JMESPath" - }, - { - "author": "gremlation", - "description": "A ComfyUI node that runs a [a/jq](https://jqlang.github.io/jq/) query against input JSON and outputs the result.", - "files": [ - "https://github.com/gremlation/ComfyUI-jq" - ], - "install_type": "git-clone", - "reference": "https://github.com/gremlation/ComfyUI-jq", - "title": "ComfyUI-jq" - }, - { - "author": "gremlation", - "description": "A ComfyUI node that extends an image vertically to add a label either above or below it.", - "files": [ - "https://github.com/gremlation/ComfyUI-ImageLabel" - ], - "install_type": "git-clone", - "reference": "https://github.com/gremlation/ComfyUI-ImageLabel", - "title": "ComfyUI-ImageLabel" - }, - { - "author": "gremlation", - "description": "A ComfyUI extension that improves panning and zooming on trackpads and with the mouse wheel.", - "files": [ - "https://github.com/gremlation/ComfyUI-TrackAndWheel" - ], - "install_type": "git-clone", - "reference": "https://github.com/gremlation/ComfyUI-TrackAndWheel", - "title": "ComfyUI-TrackAndWheel" - }, - { - "author": "fuselayer", - "description": "A simple mosaic blur node for ComfyUI that uses CV2 or Pillow", - "files": [ - "https://github.com/fuselayer/comfyui-mosaic-blur" - ], - "install_type": "git-clone", - "reference": "https://github.com/fuselayer/comfyui-mosaic-blur", - "title": "comfyui-mosaic-blur" - }, - { - "author": "jerrylongyan", - "description": "Some nodes for processing masks, currently including nodes that fill in the concave parts of existing masks with convex hulls.", - "files": [ - "https://github.com/jerrylongyan/ComfyUI-My-Mask" - ], - "install_type": "git-clone", - "reference": "https://github.com/jerrylongyan/ComfyUI-My-Mask", - "title": "ComfyUI-My-Mask" - }, - { - "author": "mira-6", - "description": "SASolver for Comfyui. Adapted from [a/comfyanonymous/ComfyUI#4454](https://github.com/comfyanonymous/ComfyUI/pull/4454) and [a/https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler](https://github.com/Koishi-Star/Euler-Smea-Dyn-Sampler)", - "files": [ - "https://github.com/mira-6/comfyui-sasolver" - ], - "install_type": "git-clone", - "reference": "https://github.com/mira-6/comfyui-sasolver", - "title": "comfyui-sasolver" - }, - { - "author": "dreamhartley", - "description": "A custom node that saves images while displaying the seed value used in generation", - "files": [ - "https://github.com/dreamhartley/ComfyUI_show_seed" - ], - "install_type": "git-clone", - "reference": "https://github.com/dreamhartley/ComfyUI_show_seed", - "title": "ComfyUI_show_seed" - }, - { - "author": "bubbliiiing", - "description": "Video Generation Nodes for EasyAnimate, which suppors text-to-video, image-to-video, video-to-video and different controls.", - "files": [ - "https://github.com/aigc-apps/EasyAnimate" - ], - "id": "easyanimatenodes", - "install_type": "git-clone", - "reference": "https://github.com/aigc-apps/EasyAnimate", - "title": "Video Generation Nodes for EasyAnimate" - }, - { - "author": "bubbliiiing", - "description": "VideoX-Fun is a video generation pipeline that can be used to generate AI images and videos, as well as to train baseline and Lora models for Diffusion Transformer. We support direct prediction from pre-trained baseline models to generate videos with different resolutions, durations, and FPS. Additionally, we also support users in training their own baseline and Lora models to perform specific style transformations.", - "files": [ - "https://github.com/aigc-apps/VideoX-Fun" - ], - "install_type": "git-clone", - "reference": "https://github.com/aigc-apps/VideoX-Fun", - "title": "VideoX-Fun" - }, - { - "author": "DraconicDragon", - "description": "A custom node implementation for ComfyUI that integrates with venice.ai's Flux and SDXL image generation models. This project is adapted from [a/ComfyUI-FLUX-TOGETHER-API](https://github.com/BZcreativ/ComfyUI-FLUX-TOGETHER-API) to work with the venice.ai API.", - "files": [ - "https://github.com/DraconicDragon/ComfyUI-Venice-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/DraconicDragon/ComfyUI-Venice-API", - "title": "ComfyUI-Venice-API" - }, - { - "author": "DraconicDragon", - "description": "Collection of one or more custom nodes for ComfyUI made mainly for personal use (GitHub README for more info). \nNodes: live Token Counter on any node, switch nodes with fallback functionality and FLOAT/INT nodes (and maybe more).", - "files": [ - "https://github.com/DraconicDragon/ComfyUI-RyuuNoodles" - ], - "install_type": "git-clone", - "reference": "https://github.com/DraconicDragon/ComfyUI-RyuuNoodles", - "title": "ComfyUI-RyuuNoodles" - }, - { - "author": "Wenaka2004", - "description": "ComfyUI custom node\uff0cuse Deepseek v3 to classify the input tags", - "files": [ - "https://github.com/Wenaka2004/ComfyUI-TagClassifier" - ], - "install_type": "git-clone", - "reference": "https://github.com/Wenaka2004/ComfyUI-TagClassifier", - "title": "ComfyUI-TagClassifier" - }, - { - "author": "westNeighbor", - "description": "Enhanced features with flexible choice of inputs and outputs, fine control for pose plotting, freedom to composite poses and fast local pose editting.", - "files": [ - "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-render" - ], - "install_type": "git-clone", - "reference": "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-render", - "title": "ComfyUI-ultimate-openpose-render" - }, - { - "author": "westNeighbor", - "description": "Super fast tensorrt performance with accuate pose estimation of dwpose model, giving the detecting threshold control, plus pose image render and pose json format output. Fine control for pose plotting.", - "files": [ - "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-estimator" - ], - "install_type": "git-clone", - "reference": "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-estimator", - "title": "ComfyUI-ultimate-openpose-estimator" - }, - { - "author": "westNeighbor", - "description": "Enhanced features with flexible choice of inputs and outputs, fine control for pose plotting, freedom to composite poses and fast local pose editting.", - "files": [ - "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-editor" - ], - "install_type": "git-clone", - "reference": "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-editor", - "title": "ComfyUI-ultimate-openpose-editor" - }, - { - "author": "a-und-b", - "description": "Simple custom node for ComfyUI to artificially delay a workflow at any point.", - "files": [ - "https://github.com/a-und-b/ComfyUI_Delay" - ], - "install_type": "git-clone", - "reference": "https://github.com/a-und-b/ComfyUI_Delay", - "title": "ComfyUI_Delay" - }, - { - "author": "a-und-b", - "description": "Simple custom node for ComfyUI that converts JSON strings to JSON objects.", - "files": [ - "https://github.com/a-und-b/ComfyUI_JSON_Helper" - ], - "install_type": "git-clone", - "reference": "https://github.com/a-und-b/ComfyUI_JSON_Helper", - "title": "ComfyUI_JSON_Helper" - }, - { - "author": "a-und-b", - "description": "A simple-as-possible custom node for ComfyUI to load LoRA models from a public URL.", - "files": [ - "https://github.com/a-und-b/ComfyUI_LoRA_from_URL" - ], - "install_type": "git-clone", - "reference": "https://github.com/a-und-b/ComfyUI_LoRA_from_URL", - "title": "ComfyUI_LoRA_from_URL" - }, - { - "author": "a-und-b", - "description": "ComfyUI custom node using the fal.ai API for the IC-Light V2 model", - "files": [ - "https://github.com/a-und-b/ComfyUI_IC-Light-v2_fal" - ], - "install_type": "git-clone", - "reference": "https://github.com/a-und-b/ComfyUI_IC-Light-v2_fal", - "title": "IC-Light V2 (fal.ai)" - }, - { - "author": "a-und-b", - "description": "Calculates the percentage of a mask area compared to the total image size and outputs a boolean based on a defined threshold.", - "files": [ - "https://github.com/a-und-b/ComfyUI_MaskAreaCondition" - ], - "install_type": "git-clone", - "reference": "https://github.com/a-und-b/ComfyUI_MaskAreaCondition", - "title": "ComfyUI Mask Area Condition" - }, - { - "author": "r3dial", - "description": "A custom node for ComfyUI that enables direct posting of images, videos, and messages to Discord channels. This node seamlessly integrates your ComfyUI workflows with Discord communication, allowing you to automatically share your generated content.", - "files": [ - "https://github.com/r3dial/redial-discomphy" - ], - "install_type": "git-clone", - "reference": "https://github.com/r3dial/redial-discomphy", - "title": "Redial Discomphy - Discord Integration for ComfyUI" - }, - { - "author": "r3dsd", - "description": "Easily Load Your Frequently Used Prompts in ComfyUI\nWith ComfyUI Template Loader, managing and reusing your favorite prompts has never been simpler. Save time and streamline your workflow by loading your go-to templates with just a few clicks!", - "files": [ - "https://github.com/r3dsd/comfyui-template-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/r3dsd/comfyui-template-loader", - "title": "Comfyui-Template-Loader" - }, - { - "author": "r3dsd", - "description": "Entry point for HommageTools node collection for ComfyUI. Handles node registration, imports, and logging configuration.", - "files": [ - "https://github.com/ArtHommage/HommageTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/ArtHommage/HommageTools", - "title": "HommageTools for ComfyUI" - }, - { - "author": "l-comm", - "description": "Watermark removal project", - "files": [ - "https://github.com/l-comm/WatermarkRemoval" - ], - "id": "watermark-removal", - "install_type": "git-clone", - "reference": "https://github.com/l-comm/WatermarkRemoval", - "title": "WatermarkRemoval" - }, - { - "author": "jhj0517", - "description": "Moondream's gaze detection feature wrapper node.", - "files": [ - "https://github.com/jhj0517/ComfyUI-Moondream-Gaze-Detection" - ], - "id": "comfyui-moondream-gaze-detection", - "install_type": "git-clone", - "reference": "https://github.com/jhj0517/ComfyUI-Moondream-Gaze-Detection", - "title": "ComfyUI-Moondream-Gaze-Detection" - }, - { - "author": "jhj0517", - "description": "kokoro-onnx (opensource TTS model) wrapper for ComfyUI.", - "files": [ - "https://github.com/jhj0517/ComfyUI-jhj-Kokoro-Onnx" - ], - "id": "comfyui-jhj-kokoro-onnx", - "install_type": "git-clone", - "reference": "https://github.com/jhj0517/ComfyUI-jhj-Kokoro-Onnx", - "title": "ComfyUI jhj Kokoro Onnx" - }, - { - "author": "jnxmx", - "description": "Nodes for auto download models from Hugging Face using their filenames as part of workflows", - "files": [ - "https://github.com/jnxmx/ComfyUI_HuggingFace_Downloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/jnxmx/ComfyUI_HuggingFace_Downloader", - "title": "ComfyUI_HuggingFace_Downloader" - }, - { - "author": "philiprodriguez", - "description": "A ComfyUI node which copies a given latent's samples tensor along the time axis ((length - 1) // 4) + 1 times to form a longer latent (see EmptyHunyuanLatentVideo's implementation for why this specific number of copies is used) and then prepares a noise_mask tensor of the same shape such that the value of the mask for a given time step is given by the function at https://www.desmos.com/calculator/vhw74mr1vh.", - "files": [ - "https://github.com/philiprodriguez/ComfyUI-HunyuanImageLatentToVideoLatent" - ], - "install_type": "git-clone", - "reference": "https://github.com/philiprodriguez/ComfyUI-HunyuanImageLatentToVideoLatent", - "title": "ComfyUI-HunyuanImageLatentToVideoLatent" - }, - { - "author": "benjiyaya", - "description": "A specialized node for ComfyUI that enable advanced motion and animation capabilities for image as guider for video processing In Hunyuan Video.", - "files": [ - "https://github.com/benjiyaya/ComfyUI-HunyuanVideoImagesGuider" - ], - "install_type": "git-clone", - "reference": "https://github.com/benjiyaya/ComfyUI-HunyuanVideoImagesGuider", - "title": "ComfyUI-HunyuanVideoImagesGuider" - }, - { - "author": "Zeks", - "description": "A set of nodes for rapidfiring the half backed latents, cleaning up obvious bad generations and automatically queueing the rest to fully generate.", - "files": [ - "https://github.com/Zeks/comfyui-rapidfire" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zeks/comfyui-rapidfire", - "title": "comfyui-rapidfire" - }, - { - "author": "meanin2", - "description": "Assorted custom nodes with a focus on simplicity and usability including watermark node and others focused on customizing my comfy experience.", - "files": [ - "https://github.com/meanin2/comfyui-MGnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/meanin2/comfyui-MGnodes", - "title": "comfyui-MGnodes" - }, - { - "author": "Kurdknight", - "description": "A comprehensive system information node for ComfyUI that provides detailed information about your system, GPU, CUDA, and AI libraries configuration. Works on both Windows and Linux systems.", - "files": [ - "https://github.com/Kurdknight/Kurdknight_comfycheck" - ], - "install_type": "git-clone", - "reference": "https://github.com/Kurdknight/Kurdknight_comfycheck", - "title": "KurdKnight ComfyUI System Check Node" - }, - { - "author": "ThepExcel", - "description": "This custom node for ComfyUI allows users to input multiline text and select a specific line by its number. The node processes the input and returns the selected line along with its index.", - "files": [ - "https://github.com/ThepExcel/aiangelgallery-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/ThepExcel/aiangelgallery-comfyui", - "title": "Multiline Text Choice Node for ComfyUI" - }, - { - "author": "BoyuanJiang", - "description": "FitDiT is designed for high-fidelity virtual try-on using Diffusion Transformers (DiT).", - "files": [ - "https://github.com/BoyuanJiang/FitDiT-ComfyUI" - ], - "id": "fitdit", - "install_type": "git-clone", - "reference": "https://github.com/BoyuanJiang/FitDiT/tree/FitDiT-ComfyUI", - "title": "FitDiT[official] - High-fidelity Virtual Try-on" - }, - { - "author": "nofunstudio", - "description": "ComfyUI Custom Node Pack Layered Infinite Zoom Node", - "files": [ - "https://github.com/nofunstudio/Node_Fun_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/nofunstudio/Node_Fun_ComfyUI", - "title": "Node_Fun_ComfyUI" - }, - { - "author": "PixelML", - "description": "A collection of custom nodes for ComfyUI focused on variable handling and workflow automation.", - "files": [ - "https://github.com/PixelML/ComfyUI-PixelML-CustomNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/PixelML/ComfyUI-PixelML-CustomNodes", - "title": "PixelML ComfyUI Nodes" - }, - { - "author": "shabri-arrahim", - "description": "This project provides custom safety checkers for image classification using Falcons AI and CompVis models. The safety checkers are designed to detect and filter out NSFW content from images.", - "files": [ - "https://github.com/shabri-arrahim/ComfyUI-Safety-Checker" - ], - "install_type": "git-clone", - "reference": "https://github.com/shabri-arrahim/ComfyUI-Safety-Checker", - "title": "ComfyUI Safety Checker" - }, - { - "author": "shenduldh", - "description": "Accelerate FLUX inferencing speed for ComfyUI.", - "files": [ - "https://github.com/shenduldh/ComfyUI-Lightning" - ], - "install_type": "git-clone", - "reference": "https://github.com/shenduldh/ComfyUI-Lightning", - "title": "ComfyUI-Lightning" - }, - { - "author": "theAdamColton", - "description": "This is the ComfyUI extension for use with texflow. It provides two new nodes, 'Load Texflow Depth Image' and 'Save Texflow Image'.\nFor more information see [a/the main texflow repo](https://github.com/theAdamColton/texflow/)", - "files": [ - "https://github.com/theAdamColton/ComfyUI-texflow-extension" - ], - "install_type": "git-clone", - "reference": "https://github.com/theAdamColton/ComfyUI-texflow-extension", - "title": "ComfyUI-texflow-extension" - }, - { - "author": "hmwl", - "description": "ComfyUI zip package image processing", - "files": [ - "https://github.com/hmwl/ComfyUI_zip" - ], - "install_type": "git-clone", - "reference": "https://github.com/hmwl/ComfyUI_zip", - "title": "ComfyUI_zip" - }, - { - "author": "hmwl", - "description": "A powerful task monitoring extension for ComfyUI that provides real-time progress tracking, workflow statistics, and execution monitoring.", - "files": [ - "https://github.com/hmwl/ComfyUI-TaskMonitor" - ], - "install_type": "git-clone", - "reference": "https://github.com/hmwl/ComfyUI-TaskMonitor", - "title": "ComfyUI-TaskMonitor" - }, - { - "author": "nisimjoseph", - "description": "A custom node for ComfyUI that generates creative and detailed prompts using OpenAI's GPT models.", - "files": [ - "https://github.com/nisimjoseph/ComfyUI_OpenAI-Prompter" - ], - "install_type": "git-clone", - "reference": "https://github.com/nisimjoseph/ComfyUI_OpenAI-Prompter", - "title": "ComfyUI OpenAI Prompter" - }, - { - "author": "ngosset", - "description": "Uses ResNet embeddings and cosine similarity to compare the likeness of two images.", - "files": [ - "https://github.com/ngosset/ComfyUI-ImageSimilarity" - ], - "id": "imageSimilarity", - "install_type": "git-clone", - "reference": "https://github.com/ngosset/ComfyUI-ImageSimilarity", - "title": "ImageSimilarity" - }, - { - "author": "Bellzs", - "description": "The plug-in is designed to automatically save the association between the LoRA model and Trigger words to a Local JSON file so that when the LoRA model is loaded, the associated trigger words can be automatically loaded via the node 'LoRA Trigger Local' without manual input.", - "files": [ - "https://github.com/Bellzs/ComfyUI-LoRA-Assistant" - ], - "install_type": "git-clone", - "reference": "https://github.com/Bellzs/ComfyUI-LoRA-Assistant", - "title": "ComfyUI-LoRA-Assistant" - }, - { - "author": "strand1", - "description": "A collection of nodes for using Autogen with ComfyUI\n[a/AutoGen](https://github.com/microsoft/AutoGen): assistant agents, group chats, code executor, etc.", - "files": [ - "https://github.com/strand1/ComfyUI-Autogen" - ], - "install_type": "git-clone", - "reference": "https://github.com/strand1/ComfyUI-Autogen", - "title": "ComfyUI-Autogen" - }, - { - "author": "hellercommaa", - "description": "A super simple node that outputs common video resolutions as 2 integers for Hunyuan and others!", - "files": [ - "https://github.com/HellerCommaA/ComfyUI-VideoResolutions" - ], - "id": "hunyuanvideoresolutions", - "install_type": "git-clone", - "reference": "https://github.com/HellerCommaA/ComfyUI-VideoResolutions", - "title": "Hunyuan Video Resolutions" - }, - { - "author": "benjiyaya", - "description": "A Text To Speech node using Kokoro TTS in ComfyUI.", - "files": [ - "https://github.com/benjiyaya/ComfyUI-KokoroTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/benjiyaya/ComfyUI-KokoroTTS", - "title": "ComfyUI-KokoroTTS" - }, - { - "author": "WangPengxing", - "description": "A custom node collection for ComfyUI, offering enhanced image processing features.", - "files": [ - "https://github.com/WangPengxing/ComfyUI_WPX_Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/WangPengxing/ComfyUI_WPX_Node", - "title": "ComfyUI WPX Nodes" - }, - { - "author": "PixelFunAI", - "description": "This collection provides four additional nodes for loading and managing Hunyuan Video LoRAs in ComfyUI", - "files": [ - "https://github.com/PixelFunAI/ComfyUI_PixelFun" - ], - "install_type": "git-clone", - "reference": "https://github.com/PixelFunAI/ComfyUI_PixelFun", - "title": "Hunyuan LoRA Loader Nodes" - }, - { - "author": "Burgstall-labs", - "description": "A ComfyUI wrapper for [a/Kokoro-onnx](https://github.com/thewh1teagle/kokoro-onnx)", - "files": [ - "https://github.com/Burgstall-labs/ComfyUI-BS_Kokoro-onnx" - ], - "install_type": "git-clone", - "reference": "https://github.com/Burgstall-labs/ComfyUI-BS_Kokoro-onnx", - "title": "ComfyUI-BS_Kokoro-onnx" - }, - { - "author": "Burgstall-labs", - "description": "A custom node for ComfyUI that extracts text segments based on specified start and/or end marker strings. You can define multiple marker pairs to extract different segments from the same input text.\nThe node intelligently adapts its extraction based on whether you provide a start marker, an end marker, or both for each pair.", - "files": [ - "https://github.com/Burgstall-labs/ComfyUI-BS-Textchop" - ], - "install_type": "git-clone", - "reference": "https://github.com/Burgstall-labs/ComfyUI-BS-Textchop", - "title": "ComfyUI-BS-Textchop" - }, - { - "author": "Burgstall-labs", - "description": "Custom nodes for ComfyUI designed for cropping and stitching video frames (image batches). Part of the 'Burgstall Enabling The Awesomeness' suite.", - "files": [ - "https://github.com/Burgstall-labs/ComfyUI-BETA-Cropnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Burgstall-labs/ComfyUI-BETA-Cropnodes", - "reference2": "https://github.com/Burgstall-labs/ComfyUI-BETA-Helpernodes", - "title": "ComfyUI-BETA-Cropnodes" - }, - { - "author": "Burgstall-labs", - "description": "Custom utility nodes for ComfyUI, providing helpers for tasks like video frame manipulation and advanced audio saving. Part of the 'Burgstall Enabling The Awesomeness' suite.", - "files": [ - "https://github.com/Burgstall-labs/ComfyUI-BETA-Helpernodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Burgstall-labs/ComfyUI-BETA-Helpernodes", - "title": "ComfyUI-BETA-Helpernodes" - }, - { - "author": "Kidev", - "description": "Provides tools for applying and removing fisheye lens effects from images.", - "files": [ - "https://github.com/Kidev/ComfyUI-Fisheye-effects" - ], - "install_type": "git-clone", - "reference": "https://github.com/Kidev/ComfyUI-Fisheye-effects", - "title": "ComfyUI Fisheye Effects Nodes" - }, - { - "author": "feixuetuba", - "description": "This is a ComfyUI plugin based on [a/Spleeter](https://github.com/deezer/spleete). The model files are available on BaiduNetDisk. After downloading the model, place it in the checkpoints directory.", - "files": [ - "https://github.com/feixuetuba/Spleeter" - ], - "install_type": "git-clone", - "reference": "https://github.com/feixuetuba/Spleeter", - "title": "Spleeter" - }, - { - "author": "aidec", - "description": "The Text Queue Processor can split text into groups line by line for batch processing (unfinished, with noticeable bugs still being fixed). Currently, on the first run, it may only process one item. Once that is completed, running it again usually works fine. After each run, the `start_index` needs to be manually reset. Occasionally, strange issues occur, such as multiple queues being added at once, while other times it works perfectly fine. The potential causes are still under investigation.", - "files": [ - "https://github.com/aidec/Comfyui_TextBatch_aidec" - ], - "install_type": "git-clone", - "reference": "https://github.com/aidec/Comfyui_TextBatch_aidec", - "title": "Comfyui_TextBatch_aidec" - }, - { - "author": "asutermo", - "description": "Try Off for ComfyUI using Flux and CatVTON.", - "files": [ - "https://github.com/asutermo/ComfyUI-Flux-TryOff" - ], - "id": "tryoffflux", - "install_type": "git-clone", - "reference": "https://github.com/asutermo/ComfyUI-Flux-TryOff", - "title": "ComfyUI-Flux-TryOff" - }, - { - "author": "bugltd", - "description": "Nodes: XY Plot with many options, Output Config (JSON / JSON5 / YAML), Queue, Format String, List utilities, Input nodes, ....", - "files": [ - "https://github.com/bugltd/ComfyLab-Pack" - ], - "id": "comfylab-pack", - "install_type": "git-clone", - "nodename_pattern": " \\(lab\\)$", - "reference": "https://github.com/bugltd/ComfyLab-Pack", - "title": "ComfyLab Pack" - }, - { - "author": "duchamps0305", - "description": "a simple white extractor node for comfyui.", - "files": [ - "https://github.com/duchamps0305/comfyui-white-extractor" - ], - "install_type": "git-clone", - "reference": "https://github.com/duchamps0305/comfyui-white-extractor", - "reference2": "https://github.com/alexpuliatti/comfyui-white-extractor", - "title": "comfyui-white-extractor" - }, - { - "author": "DJ-Tribefull", - "description": "A collection of nodes designed for efficiency and the reduction of screen-clutter. Includes a Global Seed controller with boolean toggles, SDXL All-in-One conditioner, a custom SDXL control module, Wildcard processor, Style Injector, and more. [w/WARNING: Updating this node-pack wil overwrite any changes you've made to the included wildcards and styles. Please backup your folders before updating.]", - "files": [ - "https://github.com/DJ-Tribefull/Comfyui_FOCUS_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/DJ-Tribefull/Comfyui_FOCUS_nodes", - "title": "Comfyui FOCUS nodes" - }, - { - "author": "KLL535", - "description": "Node to automate batch generation with randomize prompts from text files. It mimics Forge's functionality, allowing you to combine text elements and LoRA. The node supports writing LoRA in any order within a text file using formats like or , without needing separate nodes. The node understands LoRA names in Forge's style, when the name is not the filename, but the internal name from the metadata.", - "files": [ - "https://github.com/KLL535/ComfyUI_SimpleButcher" - ], - "install_type": "git-clone", - "reference": "https://github.com/KLL535/ComfyUI_SimpleButcher", - "title": "ComfyUI_SimpleButcher" - }, - { - "author": "KLL535", - "description": "Frontend extension that adds a sidebar for easy viewing of PNG file metadata.", - "files": [ - "https://github.com/KLL535/ComfyUI_PNGInfo_Sidebar" - ], - "install_type": "git-clone", - "reference": "https://github.com/KLL535/ComfyUI_PNGInfo_Sidebar", - "title": "ComfyUI_PNGInfo_Sidebar" - }, - { - "author": "mango125", - "description": "\ud83e\udd6d Mango Random Nodes - A collection of random file nodes for ComfyUI", - "files": [ - "https://github.com/mango-rgb/ComfyUI-Mango-Random-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/mango-rgb/ComfyUI-Mango-Random-node", - "title": "ComfyUI-Mango-Random" - }, - { - "author": "WUYUDING2583", - "description": "This node enables integration between ComfyUI and external services by adding callback capabilities to the image saving process. When an image is saved, the node automatically call your webhook with your specified URL with custom data.", - "files": [ - "https://github.com/WUYUDING2583/ComfyUI-Save-Image-Callback" - ], - "install_type": "git-clone", - "reference": "https://github.com/WUYUDING2583/ComfyUI-Save-Image-Callback", - "title": "Save Image With Callback" - }, - { - "author": "fblissjr", - "description": "this repo is to capture end-to-end data, metadata, and embeddings for ComfyUI workflows, specifically HunyuanVideo to start.", - "files": [ - "https://github.com/fblissjr/ComfyUI-EmbeddingPipelineAnalytics" - ], - "install_type": "git-clone", - "reference": "https://github.com/fblissjr/ComfyUI-EmbeddingPipelineAnalytics", - "title": "ComfyUI-EmbeddingPipelineAnalytics" - }, - { - "author": "fblissjr", - "description": "This custom node set for ComfyUI provides a DatasetBatchNode for automated, sequential processing of datasets, particularly useful for iterative training or batched image/video generation workflows.", - "files": [ - "https://github.com/fblissjr/ComfyUI-DatasetHelper" - ], - "install_type": "git-clone", - "reference": "https://github.com/fblissjr/ComfyUI-DatasetHelper", - "title": "ComfyUI Dataset Helper & Batch Node" - }, - { - "author": "fblissjr", - "description": "experimental wanvideo comfyui node with a singular goal - visually seamless transitions between context windows", - "files": [ - "https://github.com/fblissjr/ComfyUI-WanSeamlessFlow" - ], - "install_type": "git-clone", - "reference": "https://github.com/fblissjr/ComfyUI-WanSeamlessFlow", - "title": "wanvideo - seamless flow" - }, - { - "author": "fblissjr", - "description": "editing activations in wanvideo", - "files": [ - "https://github.com/fblissjr/ComfyUI-WanActivationEditor" - ], - "install_type": "git-clone", - "reference": "https://github.com/fblissjr/ComfyUI-WanActivationEditor", - "title": "ComfyUI-WanActivationEditor" - }, - { - "author": "fblissjr", - "description": "A comprehensive Vision-Language Model (VLM) integration system for ComfyUI with more intelligent prompt optimization, object detection, template support, and performance optimizations. Optimized for Wan2.1, Flux Kontext, and general purpose. Goes well with my other project, an MLX/llama.cpp server with hot swappable models and ollama api compatibility, (heylookitsanllm)[a/https://github.com/fblissjr/heylookitsanllm](https://github.com/fblissjr/heylookitsanllm)", - "files": [ - "https://github.com/fblissjr/shrug-prompter" - ], - "install_type": "git-clone", - "reference": "https://github.com/fblissjr/shrug-prompter", - "title": "Shrug-Prompter: Unified VLM Integration for ComfyUI" - }, - { - "author": "fblissjr", - "description": "Custom nodes for bridging Qwen-Image and WAN video models in ComfyUI.", - "files": [ - "https://github.com/fblissjr/ComfyUI-QwenImageWanBridge" - ], - "install_type": "git-clone", - "reference": "https://github.com/fblissjr/ComfyUI-QwenImageWanBridge", - "title": "ComfyUI-QwenImageWanBridge" - }, - { - "author": "vincentfs", - "description": "Implementation of architectural related graph algorithm in ComfyUI.", - "files": [ - "https://github.com/vincentfs/ComfyUI-ArchiGraph" - ], - "id": "archigraph", - "install_type": "git-clone", - "reference": "https://github.com/vincentfs/ComfyUI-ArchiGraph", - "title": "ComfyUI-ArchiGraph" - }, - { - "author": "ziwang-com", - "description": "Comfyui-deepseek-r1 Node Plugin", - "files": [ - "https://github.com/ziwang-com/comfyui-deepseek-r1" - ], - "install_type": "git-clone", - "reference": "https://github.com/ziwang-com/comfyui-deepseek-r1", - "title": "comfyui-deepseek-r1" - }, - { - "author": "davidgressett", - "description": "This custom node allows you to load data from one or more CSV files, then feed that data into subsequent nodes in a ComfyUI workflow.", - "files": [ - "https://github.com/davidgressett/comfyui-systemlevel" - ], - "install_type": "git-clone", - "reference": "https://github.com/davidgressett/comfyui-systemlevel", - "title": "CartesianCSVNode for ComfyUI" - }, - { - "author": "SshunWang", - "description": "Support both CosyVoice1.0 and CosyVoice2.0. Referenced [a/CosyVoice-ComfyUI](https://github.com/AIFSH/CosyVoice-ComfyUI), the following modifications have been made: Add support for CosyVoice2.0, Add whether to use stream processing options, Use speed control by CosyVoice, Add model path check to avoid duplicate downloads, Provide two ways of use", - "files": [ - "https://github.com/SshunWang/ComfyUI_CosyVoice" - ], - "install_type": "git-clone", - "reference": "https://github.com/SshunWang/ComfyUI_CosyVoice", - "title": "ComfyUI for CosyVoice" - }, - { - "author": "Kayarte", - "description": "This is a custom node for ComfyUI that analyzes audio files using Librosa, extracting tempo, beat times, energy levels, and timestamps. The analysis results can be displayed in a text box within ComfyUI.", - "files": [ - "https://github.com/Kayarte/AudioDriven-Latent-Space-Tools-for-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/Kayarte/AudioDriven-Latent-Space-Tools-for-ComfyUI", - "title": "AudioDriven-Latent-Space-Tools-for-ComfyUI" - }, - { - "author": "MontagenAI", - "description": "ComfyUI-Montagen is an extension designed to enhance the video editing ability of ComfyUI with custom nodes and Montagen. It offers basic video editing features and integrates media clips with the ComfyUI workflow. Furthermore, this extension unlocks the full potential of AI-based video generation and task automation.", - "files": [ - "https://github.com/MontagenAI/ComfyUI-Montagen" - ], - "install_type": "git-clone", - "reference": "https://github.com/MontagenAI/ComfyUI-Montagen", - "title": "ComfyUI-Montagen" - }, - { - "author": "Xiangyu-CAS", - "description": "This project addresses the issue of numerous hand defects in AI-generated portrait images by using a workflow based on FLUX/FLUX-Fill to correct hand deformities. It is effective for both real-life and anime images, including those generated by DiT/SDXL/Midjourney. It supports diffusers, gradio, ComfyUI, and more", - "files": [ - "https://github.com/Xiangyu-CAS/HandFixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/Xiangyu-CAS/HandFixer", - "title": "HandFixer" - }, - { - "author": "cake-ml", - "description": "TinySanaPreview is a custom ComfyUI node that implements real-time previews during generation for Sana diffusion models.", - "files": [ - "https://github.com/cake-ml/tiny-sana-preview" - ], - "install_type": "git-clone", - "reference": "https://github.com/cake-ml/tiny-sana-preview", - "title": "TinySanaPreview" - }, - { - "author": "huixingyun", - "description": "A ComfyUI plugin library based on [a/https://github.com/stavsap/comfyui-ollama](https://github.com/stavsap/comfyui-ollama), with the Ollama cluster provided by Huixingyun.", - "files": [ - "https://github.com/huixingyun/ComfyUI-HX-Captioner" - ], - "install_type": "git-clone", - "reference": "https://github.com/huixingyun/ComfyUI-HX-Captioner", - "title": "ComfyUI-HX-Captioner" - }, - { - "author": "huixingyun", - "description": "Some custom nodes used for pimg (a comfyui controller deployed in huixingyun).", - "files": [ - "https://github.com/huixingyun/ComfyUI-HX-Pimg" - ], - "install_type": "git-clone", - "reference": "https://github.com/huixingyun/ComfyUI-HX-Pimg", - "title": "ComfyUI-HX-Pimg" - }, - { - "author": "bradsec", - "description": "Essential string manipulation nodes for ComfyUI.", - "files": [ - "https://github.com/bradsec/ComfyUI_StringEssentials" - ], - "install_type": "git-clone", - "reference": "https://github.com/bradsec/ComfyUI_StringEssentials", - "title": "ComfyUI_StringEssentials" - }, - { - "author": "Dr.Positliver", - "description": "comfyui share models to oss conveniently.", - "files": [ - "https://github.com/Positliver/comfyui-zegr" - ], - "install_type": "git-clone", - "reference": "https://github.com/Positliver/comfyui-zegr", - "title": "comfyui-zegr" - }, - { - "author": "danielw", - "description": "A custom node for ComfyUI that enables Large Language Model (LLM) chat interactions with optional image input support.", - "files": [ - "https://github.com/tianyuw/ComfyUI-LLM-API" - ], - "id": "llm-api", - "install_type": "git-clone", - "reference": "https://github.com/tianyuw/ComfyUI-LLM-API", - "title": "Custom nodes for llm chat with optional image input" - }, - { - "author": "JohnDoeSmithee", - "description": "A custom node for SoX's mixdown function. Make sure that the sox command is installed and the path added before using this node.", - "files": [ - "https://github.com/JohnDoeSmithee/ComfyUI-SoX-Mixdown" - ], - "install_type": "git-clone", - "reference": "https://github.com/JohnDoeSmithee/ComfyUI-SoX-Mixdown", - "title": "ComfyUI-SoX-Mixdown" - }, - { - "author": "JTriggerFish", - "description": "A set of tools for manipulating latent tensors in ComfyUI", - "files": [ - "https://github.com/JTriggerFish/ComfyLatentTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/JTriggerFish/ComfyLatentTools", - "title": "Comfy Latent Tools" - }, - { - "author": "ProGamerGov", - "description": "A collection of custom nodes for working with and converting between and working with 360 degree equirectangular images, cubemap, and perspective images. Also includes Circular padding Conv2d options for models and VAEs, along with nodes to fix seams and polar artifacts. Panoramic 360 images are also sometimes known as VR photography (virtual reality), HDRI environments (ex: skyboxes), image spheres, spherical images, 360 pano.", - "files": [ - "https://github.com/ProGamerGov/ComfyUI_pytorch360convert" - ], - "id": "comfyui-pytorch360convert", - "install_type": "git-clone", - "reference": "https://github.com/ProGamerGov/ComfyUI_pytorch360convert", - "title": "ComfyUI_pytorch360convert" - }, - { - "author": "ProGamerGov", - "description": "A custom ComfyUI node for interactive 360\u00b0 panorama image previews. Panoramic 360 images are also sometimes known as VR photography (virtual reality), HDRI environments (ex: skyboxes), image spheres, spherical images, 360 pano, and 360 degree photos.", - "files": [ - "https://github.com/ProGamerGov/ComfyUI_preview360panorama" - ], - "id": "comfyui-preview360panorama", - "install_type": "git-clone", - "reference": "https://github.com/ProGamerGov/ComfyUI_preview360panorama", - "title": "Preview 360 Panorama for ComfyUI" - }, - { - "author": "burnsbert", - "description": "This ComfyUI extension provides custom nodes for integrating with LM Studio, allowing for loading, managing, and making requests of LLM models through the LMStudio local server and command-line interface.", - "files": [ - "https://github.com/burnsbert/ComfyUI-EBU-LMStudio" - ], - "id": "ebu-lmstudio", - "install_type": "git-clone", - "reference": "https://github.com/burnsbert/ComfyUI-EBU-LMStudio", - "title": "EBU LMStudio LLM Integration" - }, - { - "author": "burnsbert", - "description": "Custom nodes for general workflow quality of life including resolutions sorted by aspect ratio, upscaling helps, and unique file names", - "files": [ - "https://github.com/burnsbert/ComfyUI-EBU-Workflow" - ], - "id": "ebu-workflow", - "install_type": "git-clone", - "reference": "https://github.com/burnsbert/ComfyUI-EBU-Workflow", - "title": "EBU Workflow" - }, - { - "author": "burnsbert", - "description": "Custom nodes for enhancing and manipulating prompts in ComfyUI. Includes nodes for random color palette generation following different color theory methodologies, prompt text replacement and randomization, list sampling, loading files into strings, and season/weather/time-of-day generation.", - "files": [ - "https://github.com/burnsbert/ComfyUI-EBU-PromptHelper" - ], - "id": "ebu-prompthelper", - "install_type": "git-clone", - "reference": "https://github.com/burnsbert/ComfyUI-EBU-PromptHelper", - "title": "EBU PromptHelper" - }, - { - "author": "SykkoAtHome", - "description": "A custom node collection for ComfyUI that provides advanced face detection, alignment, and transformation capabilities using MediaPipe Face Mesh.", - "files": [ - "https://github.com/SykkoAtHome/ComfyUI_FaceProcessor" - ], - "install_type": "git-clone", - "reference": "https://github.com/SykkoAtHome/ComfyUI_FaceProcessor", - "title": "Face Processor for ComfyUI" - }, - { - "author": "Mattabyte", - "description": "This package provides custom nodes to ComfyUI to POST data to a secure API.", - "files": [ - "https://github.com/Mattabyte/ComfyUI-SecureApiCall" - ], - "install_type": "git-clone", - "reference": "https://github.com/Mattabyte/ComfyUI-SecureApiCall", - "title": "ComfyUI Secure API Call" - }, - { - "author": "oxysoft", - "description": "Implementation of GoWithTheFlow, original code at [a/https://github.com/Eyeline-Research/Go-with-the-Flow/](https://github.com/Eyeline-Research/Go-with-the-Flow/) and [a/https://github.com/RyannDaGreat/CommonSource/blob/master/noise_warp.py](https://github.com/RyannDaGreat/CommonSource/blob/master/noise_warp.py)", - "files": [ - "https://github.com/oxysoft/ComfyUI-gowiththeflow" - ], - "install_type": "git-clone", - "reference": "https://github.com/oxysoft/ComfyUI-gowiththeflow-loopback", - "reference2": "https://github.com/oxysoft/ComfyUI-gowiththeflow", - "title": "ComfyUI-gowiththeflow" - }, - { - "author": "willmiao", - "description": "Revolutionize your workflow with the ultimate LoRA companion for ComfyUI!", - "files": [ - "https://github.com/willmiao/ComfyUI-Lora-Manager" - ], - "install_type": "git-clone", - "reference": "https://github.com/willmiao/ComfyUI-Lora-Manager", - "title": "ComfyUI-Lora-Manager" - }, - { - "author": "tigeryy2", - "description": "ComfyUI nodes for LLM Structured Outputs with integration for prompting", - "files": [ - "https://github.com/tigeryy2/comfyui-structured-outputs" - ], - "install_type": "git-clone", - "reference": "https://github.com/tigeryy2/comfyui-structured-outputs", - "title": "ComfyUI Structured Outputs" - }, - { - "author": "Conor-Collins", - "description": "Advanced image input and output: EXR, 32 bit support and more", - "files": [ - "https://github.com/Conor-Collins/ComfyUI-CoCoTools_IO" - ], - "install_type": "git-clone", - "reference": "https://github.com/Conor-Collins/ComfyUI-CoCoTools_IO", - "title": "ComfyUI-CoCoTools_IO" - }, - { - "author": "852wa", - "description": "This is a custom node for ComfyUI.\nIt reduces colors based on a specified number and allows for adjustments to hue, saturation, and brightness.\nFeatures:Each parameter can be set to random, You can toggle masking (not changing colors) using color numbers, Mask inversion can also be toggled on or off.", - "files": [ - "https://github.com/852wa/ComfyUI-ColorshiftColor" - ], - "install_type": "git-clone", - "reference": "https://github.com/852wa/ComfyUI-ColorshiftColor", - "title": "ComfyUI-ColorshiftColor" - }, - { - "author": "852wa", - "description": "This is a custom node for ComfyUI.\nFeatures:Removes white areas in the input image by making them transparent based on brightness, Outputs in black and transparent, Outputs in gray and transparent.\nThis is a simple node with the above functionalities implemented. It also supports sequential processing.", - "files": [ - "https://github.com/852wa/ComfyUI-AAP" - ], - "install_type": "git-clone", - "reference": "https://github.com/852wa/ComfyUI-AAP", - "title": "ComfyUI-AdvancedAlphaProcessor" - }, - { - "author": "ReBeating", - "description": "A useful comfyui node named LoadArtistTag for selecting artist tags, including 1000+ single-artist tags and 300 mixed-artists tags.", - "files": [ - "https://github.com/ReBeating/ComfyUI-Artist-Selector" - ], - "install_type": "git-clone", - "reference": "https://github.com/ReBeating/ComfyUI-Artist-Selector", - "title": "ComfyUI-Artist-Selector" - }, - { - "author": "gmorks", - "description": "ComfyUI-SendToDiscord is a custom node for ComfyUI that simplifies sending preview images to Discord via webhooks. It supports both single-image uploads and batch mode, making it an efficient tool for sharing your generated images directly with your Discord server.", - "files": [ - "https://github.com/gmorks/ComfyUI-SendToDiscord" - ], - "install_type": "git-clone", - "reference": "https://github.com/gmorks/ComfyUI-SendToDiscord", - "title": "ComfyUI-SendToDiscord" - }, - { - "author": "gmorks", - "description": "Comfy UI node to prompt build for https://huggingface.co/cagliostrolab/animagine-xl-4.0 model", - "files": [ - "https://github.com/gmorks/ComfyUI-Animagine-Prompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/gmorks/ComfyUI-Animagine-Prompt", - "title": "ComfyUI-Animagine-Prompt" - }, - { - "author": "jinanlongen", - "description": "A custom node for ComfyUI that expands text prompts using the SuperPrompt-v1 T5 model. This node helps generate more detailed and descriptive prompts from simple input text, which can be particularly useful for image generation workflows.", - "files": [ - "https://github.com/jinanlongen/ComfyUI-Prompt-Expander" - ], - "install_type": "git-clone", - "reference": "https://github.com/jinanlongen/ComfyUI-Prompt-Expander", - "reference2": "https://github.com/derekluo/ComfyUI-Prompt-Expander", - "title": "ComfyUI Prompt Expander Node" - }, - { - "author": "Style-Mosaic", - "description": "A ComfyUI node that integrates DINO-X API for object detection and segmentation. This node allows you to detect and segment objects in images using text prompts.", - "files": [ - "https://github.com/Style-Mosaic/dino-x-comfyui-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Style-Mosaic/dino-x-comfyui-node", - "title": "ComfyUI DINO-X Detector Node" - }, - { - "author": "checkbins", - "description": "These nodes allow you to make Checkbin comparisons.", - "files": [ - "https://github.com/checkbins/checkbin-comfy" - ], - "id": "checkbin", - "install_type": "git-clone", - "reference": "https://github.com/checkbins/checkbin-comfy", - "title": "checkbin-comfy" - }, - { - "author": "GHOSTLXH", - "description": "This node group contains a series of ComfyUI nodes with built-in counters and specific output results based on the counter's output, aimed at implementing folder traversal functionality in the ComfyUI frontend. For specific examples, please refer to the sample workflow. Of course, you can also use your imagination to create other interesting things.", - "files": [ - "https://github.com/GHOSTLXH/ComfyUI-Counternodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/GHOSTLXH/ComfyUI-Counternodes", - "title": "ComfyUI-Counternodes" - }, - { - "author": "agilly1989", - "description": "CURRENTLY IN ACTIVE DEVELOPMENT (BETA)! IF THINGS BREAK ITS BECAUSE I BROKE IT.... This my implemenation of a `pipe` in ComfyUI. Is it better or worse than others? No idea. (also has some utility nodes)", - "files": [ - "https://github.com/agilly1989/ComfyUI_agilly1989_motorway" - ], - "install_type": "git-clone", - "reference": "https://github.com/agilly1989/ComfyUI_agilly1989_motorway", - "title": "ComfyUI_agilly1989_motorway" - }, - { - "author": "AiartvnTeam", - "description": "Node for compositing multiple images with interactive preview and layer management", - "files": [ - "https://github.com/aiartvn/A2V_Multi_Image_Composite" - ], - "id": "Aiartvn", - "install_type": "git-clone", - "reference": "https://github.com/aiartvn/A2V_Multi_Image_Composite", - "tags": [ - "image", - "composite", - "layer", - "blend", - "transform" - ], - "title": "A2V Multi Image Composite" - }, - { - "author": "zentrocdot", - "description": "Next to AI mathematical methods can be used for the detection of objects like a circle.", - "files": [ - "https://github.com/zentrocdot/ComfyUI_Circle_Detection" - ], - "install_type": "git-clone", - "reference": "https://github.com/zentrocdot/ComfyUI_Circle_Detection", - "title": "ComfyUI_Circle_Detection" - }, - { - "author": "zentrocdot", - "description": "This node uses the RealESRGAN model from [a/xinntao](https://github.com/xinntao/Real-ESRGAN).", - "files": [ - "https://github.com/zentrocdot/ComfyUI-RealESRGAN_Upscaler" - ], - "install_type": "git-clone", - "reference": "https://github.com/zentrocdot/ComfyUI-RealESRGAN_Upscaler", - "title": "ComfyUI-RealESRGAN_Upscaler" - }, - { - "author": "zentrocdot", - "description": "ComfyUI simple Image To Prompt node.", - "files": [ - "https://github.com/zentrocdot/ComfyUI-Simple_Image_To_Prompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/zentrocdot/ComfyUI-Simple_Image_To_Prompt", - "title": "ComfyUI-Simple_Image_To_Prompt" - }, - { - "author": "hgabha", - "description": "Custom Nodes by the team at WeirdWonderfulAI.Art. Line Count, Join String, Dither Image, Image Batch Loader, Prompt Writer", - "files": [ - "https://github.com/hgabha/WWAA-CustomNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/hgabha/WWAA-CustomNodes", - "title": "WWAA-CustomNodes" - }, - { - "author": "slvslvslv", - "description": "NODES: Smart HunyuanVideo Lora Select, Smart HunyuanVideo Lora StackSmart Format String, Smart Format String (10 params)", - "files": [ - "https://github.com/slvslvslv/ComfyUI-SmartHelperNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/slvslvslv/ComfyUI-SmartHelperNodes", - "title": "ComfyUI Smart Helper Nodes" - }, - { - "author": "slvslvslv", - "description": "NODES: Various nodes for image manipulation", - "files": [ - "https://github.com/slvslvslv/ComfyUI-SmartImageTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/slvslvslv/ComfyUI-SmartImageTools", - "title": "ComfyUI-SmartImageTools" - }, - { - "author": "Tr1dae", - "description": "Simple addition to add noise to an image. Found on reddit", - "files": [ - "https://github.com/Tr1dae/ComfyUI-Dequality" - ], - "install_type": "git-clone", - "reference": "https://github.com/Tr1dae/ComfyUI-Dequality", - "title": "ComfyUI-Dequality" - }, - { - "author": "greengerong", - "description": "This plugin integrates the Janus-Pro multi-modal model into ComfyUI, enabling advanced image understanding and text-to-image generation capabilities. It supports both image analysis and creative image generation workflows.", - "files": [ - "https://github.com/greengerong/ComfyUI-JanusPro-PL" - ], - "install_type": "git-clone", - "reference": "https://github.com/greengerong/ComfyUI-JanusPro-PL", - "title": "Janus-Pro ComfyUI Plugin" - }, - { - "author": "greengerong", - "description": "This is a video generation plugin implementation for ComfyUI based on the Lumina Video model.", - "files": [ - "https://github.com/greengerong/ComfyUI-Lumina-Video" - ], - "install_type": "git-clone", - "reference": "https://github.com/greengerong/ComfyUI-Lumina-Video", - "title": "ComfyUI-Lumina-Video" - }, - { - "author": "raindrop313", - "description": "ComfyUI nodes that support SD3/SD3.5 in FlowEdit", - "files": [ - "https://github.com/raindrop313/ComfyUI_SD3_Flowedit" - ], - "install_type": "git-clone", - "reference": "https://github.com/raindrop313/ComfyUI_SD3_Flowedit", - "title": "ComfyUI_SD3_Flowedit" - }, - { - "author": "raindrop313", - "description": "ComfyUI nodes that support video generation by start and end frames", - "files": [ - "https://github.com/raindrop313/ComfyUI-WanVideoStartEndFrames" - ], - "install_type": "git-clone", - "reference": "https://github.com/raindrop313/ComfyUI-WanVideoStartEndFrames", - "title": "ComfyUI-WanVideoStartEndFrames" - }, - { - "author": "martin-rizzo", - "description": "ComfyUI-TinyBreaker is a collection of custom nodes specifically designed to generate images using the TinyBreaker model. It's actively developed with ongoing improvements. Although still in progress, these nodes are functional and allow you to explore the potential of the model.", - "files": [ - "https://github.com/martin-rizzo/ComfyUI-TinyBreaker" - ], - "install_type": "git-clone", - "reference": "https://github.com/martin-rizzo/ComfyUI-TinyBreaker", - "title": "ComfyUI-TinyBreaker" - }, - { - "author": "Arkanun", - "description": "NODES: ReadCSVRowNode", - "files": [ - "https://github.com/Arkanun/ReadCSV_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/Arkanun/ReadCSV_ComfyUI", - "title": "ReadCSV_ComfyUI" - }, - { - "author": "gorillaframeai", - "description": "These custom nodes for ComfyUI provide advanced text translation capabilities using Google Translate. They are designed for seamless integration into the ComfyUI environment, offering users powerful tools for text and JSON file translation tasks.", - "files": [ - "https://github.com/gorillaframeai/GF_translate" - ], - "install_type": "git-clone", - "reference": "https://github.com/gorillaframeai/GF_translate", - "title": "GF_translate" - }, - { - "author": "DragonDiffusionbyBoyo", - "description": "The Vae node is a sneaky little node perfect for deployment in Schools or work environments where you do not want the kiddywinkles creating NSFW content. Just rename the node to VAE decode and it looks like a normal node but hidden inside is an NSFW detector. Once hidden in the workflow there are no settings to undo the NSFW detection so cannot be worked around unless you remove the node. The node looks innocent once renamed so is virtually undetectable. I have placed an example workflow for you to see how to connect it. Simple stuff really, but once connected just rename.", - "files": [ - "https://github.com/DragonDiffusionbyBoyo/Boyonodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/DragonDiffusionbyBoyo/Boyonodes", - "title": "Boyonodes" - }, - { - "author": "DragonDiffusionbyBoyo", - "description": "This is a ComfyUI wrapper for Andrew DalPino's SuperCool upscaler, enabling its use directly within ComfyUI's node workflow. No extra dependencies required\u2014just drop in the models and go.", - "files": [ - "https://github.com/DragonDiffusionbyBoyo/BoyoSupercoolWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/DragonDiffusionbyBoyo/BoyoSupercoolWrapper", - "title": "BoyoSupercoolWrapper" - }, - { - "author": "StarAsh042", - "description": "RollingArtist is a ComfyUI node designed to generate artist prompt texts with random weights, suitable for text-to-image generation models. The node reads an artist list from a CSV file and generates combined prompts based on the parameters.", - "files": [ - "https://github.com/StarAsh042/ComfyUI_RollingArtist" - ], - "install_type": "git-clone", - "reference": "https://github.com/StarAsh042/ComfyUI_RollingArtist", - "title": "ComfyUI_RollingArtist" - }, - { - "author": "magekinnarus", - "description": "Node to set v-prediction sampling when using SDXL and other models that may not have the necessary metadata to identify it as a v-prediction model. This node is useful for quantized models since they lack the necessary metadata.", - "files": [ - "https://github.com/magekinnarus/ComfyUI-V-Prediction-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/magekinnarus/ComfyUI-V-Prediction-Node", - "title": "ComfyUI-V-Prediction-Node" - }, - { - "author": "CC-SUN6", - "description": "About the comfyui image selector, image adjustment (panning, rotation, zoom), adjust image size to be a multiple of 8", - "files": [ - "https://github.com/CC-SUN6/ccsun_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/CC-SUN6/ccsun_node", - "title": "ccsun_node" - }, - { - "author": "DiaoDaiaChan", - "description": "A Novel AI / SD-WebUI request node, support nai3/nai4, use NovelAI model in Your Comfyui.", - "files": [ - "https://github.com/DiaoDaiaChan/ComfyUI_API_Request" - ], - "id": "diaodaiachan", - "install_type": "git-clone", - "reference": "https://github.com/DiaoDaiaChan/ComfyUI_API_Request", - "title": "Comfyui SDAPI Request / NovelAI" - }, - { - "author": "dorpxam", - "description": "A set of custom nodes enabling LoRA support for LTX Video", - "files": [ - "https://github.com/dorpxam/ComfyUI-LTXVideoLoRA" - ], - "install_type": "git-clone", - "reference": "https://github.com/dorpxam/ComfyUI-LTXVideoLoRA", - "title": "ComfyUI-LTXVideoLoRA" - }, - { - "author": "dorpxam", - "description": "A set of custom nodes enabling Text-to-Video support for FramePack-F1", - "files": [ - "https://github.com/dorpxam/ComfyUI-FramePack-F1-T2V" - ], - "install_type": "git-clone", - "reference": "https://github.com/dorpxam/ComfyUI-FramePack-F1-T2V", - "title": "ComfyUI-FramePack-F1-T2V" - }, - { - "author": "asdrabael", - "description": "ComfyUI Node for loading multiple Lora's [a/HunyuanVideo](https://github.com/Tencent/HunyuanVideo)", - "files": [ - "https://github.com/asdrabael/Hunyuan-Multi-Lora-Loader" - ], - "id": "Hunyuan Multi-Lora Loader", - "install_type": "git-clone", - "reference": "https://github.com/asdrabael/Hunyuan-Multi-Lora-Loader", - "title": "Hunyuan-Multi-Lora-Loader" - }, - { - "author": "lingha", - "description": "comfyui_kj, A tool that can package workflows into projects and publish them to a WeChat Mini Program named Kaji, allowing charges to be collected from users.", - "files": [ - "https://github.com/lingha0h/comfyui_kj" - ], - "id": "comfyui_kj", - "install_type": "git-clone", - "reference": "https://github.com/lingha0h/comfyui_kj", - "title": "comfyui_kj" - }, - { - "author": "vahlok-alunmid", - "description": "This extension provides two nodes to use with my experimental [a/ip-adapter finetune](https://civitai.com/models/1233692?modelVersionId=1390253) for NoobAI-XL style transfer. [a/Here](https://github.com/vahlok-alunmid/reForge-preprocessor_bigG_448) is the counterpart extension for Reforge WebUI.", - "files": [ - "https://github.com/vahlok-alunmid/ComfyUI-ExtendIPAdapterClipVision" - ], - "install_type": "git-clone", - "reference": "https://github.com/vahlok-alunmid/ComfyUI-ExtendIPAdapterClipVision", - "title": "ComfyUI-ExtendIPAdapterClipVision" - }, - { - "author": "guerreiro", - "description": "Comfyg Switch is a custom node that dynamically selects model configuration parameters based on the chosen checkpoint. It reads model-specific settings from a JSON file (model_configs.json).", - "files": [ - "https://github.com/guerreiro/comfyg-switch" - ], - "install_type": "git-clone", - "reference": "https://github.com/guerreiro/comfyg-switch", - "title": "Comfyg Switch" - }, - { - "author": "yanhuifair", - "description": "ComfyUI nodes for Janus", - "files": [ - "https://github.com/yanhuifair/comfyui-janus" - ], - "install_type": "git-clone", - "reference": "https://github.com/yanhuifair/comfyui-janus", - "title": "comfyui-janus" - }, - { - "author": "ShunL12324", - "description": "This is a ComfyUI extension that provides additional API endpoints functionality, primarily designed to support Comfy Portal - a modern iOS client application for ComfyUI.", - "files": [ - "https://github.com/ShunL12324/comfy-portal-endpoint" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShunL12324/comfy-portal-endpoint", - "title": "comfy-portal-endpoint" - }, - { - "author": "ShinChven", - "description": "This project contains custom nodes for ComfyUI, developed by ShinChven. The nodes in this package extend the functionality of ComfyUI by providing additional features and utilities.", - "files": [ - "https://github.com/ShinChven/sc-comfy-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/ShinChven/sc-comfy-nodes", - "title": "ShinChven's Custom Nodes Package" - }, - { - "author": "vkff5833", - "description": "Add a mobile-friendly web interface to ComfyUI.", - "files": [ - "https://github.com/vkff5833/ComfyUI-MobileClient" - ], - "install_type": "git-clone", - "reference": "https://github.com/vkff5833/ComfyUI-MobileClient", - "title": "ComfyUI-MobileClient" - }, - { - "author": "mediocreatmybest", - "description": "Additional ComfyUI nodes to utilise the Transformers pipeline in a simple and modular way.", - "files": [ - "https://github.com/mediocreatmybest/ComfyUI-Transformers-Pipeline" - ], - "install_type": "git-clone", - "reference": "https://github.com/mediocreatmybest/ComfyUI-Transformers-Pipeline", - "title": "ComfyUI-Transformers-Pipeline" - }, - { - "author": "IrisRainbowNeko", - "description": "ascii art preprocessors in ComfyUI", - "files": [ - "https://github.com/Deep-Neko/ComfyUI_ascii_art" - ], - "install_type": "git-clone", - "reference": "https://github.com/Deep-Neko/ComfyUI_ascii_art", - "title": "ascii-art-comfyui" - }, - { - "author": "mie", - "description": "Offering a series of utility nodes designed to simplify workflows and enhance efficiency", - "files": [ - "https://github.com/MieMieeeee/ComfyUI-MieNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/MieMieeeee/ComfyUI-MieNodes", - "title": "ComfyUI_MieNodes" - }, - { - "author": "mie", - "description": "Describe a single image or all images in a directory using models such as Janus Pro, Florence2, or JoyCaption (testing), with a particular focus on building datasets for training LoRA.", - "files": [ - "https://github.com/MieMieeeee/ComfyUI-CaptionThis" - ], - "install_type": "git-clone", - "reference": "https://github.com/MieMieeeee/ComfyUI-CaptionThis", - "title": "ComfyUI_CaptionThis" - }, - { - "author": "mie", - "description": "Provides a series of utility nodes designed for interacting with MinIO, including functionalities such as uploading and downloading files and folders, as well as creating buckets if they do not already exist.", - "files": [ - "https://github.com/MieMieeeee/ComfyUI-MinioConnector" - ], - "install_type": "git-clone", - "reference": "https://github.com/MieMieeeee/ComfyUI-MinioConnector", - "title": "ComfyUI-MinioConnector" - }, - { - "author": "lum3on", - "description": "An advanced chat node integrating LLMs, real-time web search, image handling, and image scraping. Supports APIs from OpenAI, Google, Anthropic, Grok, DeepSeek, and local Ollama. Includes custom node finder, smart assistant tools, and growing subnodes like text masking and concept eraser.", - "files": [ - "https://github.com/lum3on/comfyui_LLM_Polymath" - ], - "id": "llm_polymath", - "install_type": "git-clone", - "reference": "https://github.com/lum3on/comfyui_LLM_Polymath", - "title": "comfyui_LLM_Polymath" - }, - { - "author": "lum3on", - "description": "A custom ComfyUI node for generating images using the HiDream AI model. Uses quantization for lower memory usage.", - "files": [ - "https://github.com/lum3on/comfyui_HiDream-Sampler" - ], - "id": "hidream-sampler", - "install_type": "git-clone", - "reference": "https://github.com/lum3on/comfyui_HiDream-Sampler", - "title": "HiDream Sampler" - }, - { - "author": "lum3on", - "description": "This is a node to converts models into Fp8, bf16, fp16.", - "files": [ - "https://github.com/lum3on/ComfyUI-ModelQuantizer" - ], - "id": "ModelQuantizer", - "install_type": "git-clone", - "reference": "https://github.com/lum3on/ComfyUI-ModelQuantizer", - "title": "ComfyUI-ModelQuantizer" - }, - { - "author": "lum3on", - "description": "Professional-grade frame manipulation tools for ComfyUI, providing advanced video editing capabilities with native IMAGE tensor support.", - "files": [ - "https://github.com/lum3on/ComfyUI-FrameUtilitys" - ], - "install_type": "git-clone", - "reference": "https://github.com/lum3on/ComfyUI-FrameUtilitys", - "title": "ComfyUI-FrameUtilitys" - }, - { - "author": "lum3on", - "description": "A ComfyUI custom node implementation of EdgeTAM (On-Device Track Anything Model) for efficient, interactive video object tracking.", - "files": [ - "https://github.com/lum3on/comfyui_EdgeTAM" - ], - "install_type": "git-clone", - "reference": "https://github.com/lum3on/comfyui_EdgeTAM", - "title": "comfyui_EdgeTAM" - }, - { - "author": "lum3on", - "description": "A powerful audio generation extension for ComfyUI that integrates AudioX models for high-quality audio synthesis from text and video inputs.", - "files": [ - "https://github.com/lum3on/ComfyUI-StableAudioX" - ], - "install_type": "git-clone", - "reference": "https://github.com/lum3on/ComfyUI-StableAudioX", - "title": "ComfyUI-AudioX" - }, - { - "author": "lum3on", - "description": "This custom node for ComfyUI allows you to scrape and download images and videos from the Midjourney showcase pages. It uses undetected_chromedriver to bypass anti-scraping measures, but requires session cookies from a logged-in browser session to function correctly.", - "files": [ - "https://github.com/lum3on/ComfyUI_MJ-Scraper" - ], - "install_type": "git-clone", - "reference": "https://github.com/lum3on/ComfyUI_MJ-Scraper", - "title": "ComfyUI Midjourney Scraper Node" - }, - { - "author": "austinbrown34", - "description": "A custom nodes package for ComfyUI that enhances workflow flexibility by providing specialized nodes for saving and loading intermediate data (encoded prompts and sampled latents) in multiple formats. This package leverages helper classes for file I/O, supports gzip compression for efficient storage, and integrates progress feedback via a progress bar to improve user experience during long operations.", - "files": [ - "https://github.com/austinbrown34/ComfyUI-IO-Helpers" - ], - "install_type": "git-clone", - "reference": "https://github.com/austinbrown34/ComfyUI-IO-Helpers", - "title": "ComfyUI-IO-Helpers" - }, - { - "author": "HowToSD", - "description": "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib from within ComfyUI", - "files": [ - "https://github.com/HowToSD/ComfyUI-Data-Analysis" - ], - "install_type": "git-clone", - "reference": "https://github.com/HowToSD/ComfyUI-Data-Analysis", - "title": "ComfyUI-Data-Analysis" - }, - { - "author": "HowToSD", - "description": "PyTorch extension for ComfyUI featuring extensive PyTorch wrapper nodes for seamless tensor operations and PyTorch model training.", - "files": [ - "https://github.com/HowToSD/ComfyUI-Pt-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/HowToSD/ComfyUI-Pt-Wrapper", - "title": "ComfyUI-Pt-Wrapper" - }, - { - "author": "dasilva333", - "description": "This node calculates a contrasting complementary color based on an input RGB color. The goal is to ensure visibility and contrast when overlaying text, UI elements, or graphical components against a given background color.", - "files": [ - "https://github.com/dasilva333/ComfyUI_ContrastingColor" - ], - "install_type": "git-clone", - "reference": "https://github.com/dasilva333/ComfyUI_ContrastingColor", - "title": "ComfyUI_ContrastingColor" - }, - { - "author": "dasilva333", - "description": "This project generates an image from Markdown text using imgkit and wkhtmltoimage. It automatically scales the text to fit within the specified image dimensions.", - "files": [ - "https://github.com/dasilva333/ComfyUI_MarkdownImage" - ], - "install_type": "git-clone", - "reference": "https://github.com/dasilva333/ComfyUI_MarkdownImage", - "title": "ComfyUI_MarkdownImage" - }, - { - "author": "moon7star9", - "description": "A comprehensive node package that seamlessly integrates all BiRefNet series models into ComfyUI", - "files": [ - "https://github.com/moon7star9/ComfyUI_BiRefNet_Universal" - ], - "install_type": "git-clone", - "reference": "https://github.com/moon7star9/ComfyUI_BiRefNet_Universal", - "title": "ComfyUI_BiRefNet_Universal" - }, - { - "author": "wirytiox", - "description": "This node is a node made by GMapeSplat/ComfyUI_ezXY that i copied while his node doesn't work", - "files": [ - "https://github.com/wirytiox/ComfyUI-SelectStringFromListWithIndex" - ], - "id": "ComfyUI-SelectStringFromListWithIndex", - "install_type": "git-clone", - "reference": "https://github.com/wirytiox/ComfyUI-SelectStringFromListWithIndex", - "reference2": "https://github.com/mr-pepe69/ComfyUI-SelectStringFromListWithIndex", - "title": "ComfyUI-SelectStringFromListWithIndex" - }, - { - "author": "TheAIDoctor", - "description": "Nodes: Multi Int and Multi Text; allows for the creation of multiple int, floats and string storage and output from a single node.", - "files": [ - "https://github.com/BlueprintCoding/ComfyUI_AIDocsClinicalTools" - ], - "id": "The-AI-Doctors-Clinical-Tools", - "install_type": "git-clone", - "reference": "https://github.com/BlueprintCoding/ComfyUI_AIDocsClinicalTools", - "title": "The AI Doctors Clinical Tools" - }, - { - "author": "Mohammadreza Mohseni", - "description": "A collection of useful nodes for ComfyUI, including Float Preview for live image visualization.", - "files": [ - "https://github.com/mohseni-mr/ComfyUI-Mohseni-Kit" - ], - "id": "mohseni-kit", - "install_type": "git-clone", - "reference": "https://github.com/mohseni-mr/ComfyUI-Mohseni-Kit", - "title": "ComfyUI Mohseni Kit" - }, - { - "author": "BuffMcBigHuge", - "description": "TTS with Zyphra Zonos", - "files": [ - "https://github.com/BuffMcBigHuge/ComfyUI-Zonos" - ], - "install_type": "git-clone", - "reference": "https://github.com/BuffMcBigHuge/ComfyUI-Zonos", - "title": "ComfyUI-Zonos" - }, - { - "author": "BuffMcBigHuge", - "description": "Google AI Studio by BuffMcBigHuge", - "files": [ - "https://github.com/BuffMcBigHuge/ComfyUI-Google-AI-Studio" - ], - "install_type": "git-clone", - "reference": "https://github.com/BuffMcBigHuge/ComfyUI-Google-AI-Studio", - "title": "ComfyUI-Google-AI-Studio" - }, - { - "author": "BahaC", - "description": "A ComfyUI custom node that brings Zonos Text-to-Speech capabilities to your workflows, featuring high-quality speech synthesis and voice cloning.", - "files": [ - "https://github.com/BahaC/ComfyUI-ZonosTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/BahaC/ComfyUI-ZonosTTS", - "title": "ComfyUI Zonos TTS Node" - }, - { - "author": "dzqdzq", - "description": "Automatic cropping of transparent areas to prevent images from being too large, while also supporting resizing to prevent image dimensions from being too large.", - "files": [ - "https://github.com/dzqdzq/ComfyUI-crop-alpha" - ], - "install_type": "git-clone", - "reference": "https://github.com/dzqdzq/ComfyUI-crop-alpha", - "title": "ComfyUI-crop-alpha" - }, - { - "author": "bbtaivi", - "description": "Used to convert workflow node settings into AIV mini-program parameters.", - "files": [ - "https://github.com/bbtaivi/ComfyUI-Aiv-Param" - ], - "install_type": "git-clone", - "reference": "https://github.com/bbtaivi/ComfyUI-Aiv-Param", - "title": "AIV ComfyUI Node" - }, - { - "author": "PrunaAI", - "description": "This repository explains how to accelerate image generation in ComfyUI using Pruna, an inference optimization engine that makes AI models faster, smaller, cheaper, and greener. ComfyUI is a popular node-based GUI for image generation models, for which we provide a custom compilation node that accelerates Stable Diffusion (SD) and Flux inference, while preserving output quality.", - "files": [ - "https://github.com/PrunaAI/ComfyUI_pruna" - ], - "install_type": "git-clone", - "reference": "https://github.com/PrunaAI/ComfyUI_pruna", - "title": "Pruna nodes for ComfyUI" - }, - { - "author": "Hellfiredragon", - "description": "Custom nodes to manipulate images in ComfyUI", - "files": [ - "https://github.com/Hellfiredragon/comfyui-image-manipulation" - ], - "install_type": "git-clone", - "reference": "https://github.com/Hellfiredragon/comfyui-image-manipulation", - "title": "comfyui-image-manipulation" - }, - { - "author": "lunarring", - "description": "A package implementing a Bitalino device ComfyUI custom node.", - "files": [ - "https://github.com/lunarring/bitalino_comfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/lunarring/bitalino_comfy", - "title": "bitalino_comfy" - }, - { - "author": "AIDC-AI", - "description": "Your Intelligent Assistant for Comfy-UI.", - "files": [ - "https://github.com/AIDC-AI/ComfyUI-Copilot" - ], - "id": "ComfyUI-Copilot", - "install_type": "git-clone", - "reference": "https://github.com/AIDC-AI/ComfyUI-Copilot", - "title": "ComfyUI-Copilot" - }, - { - "author": "attashe", - "description": "Implement Region Attention for Flux model. Add node RegionAttention that takes a regions - mask + condition, mask could be set from comfyui masks or bbox in FluxRegionBBOX node.\nThis code is not optimized and has a memory leak. If you caught a OOM just try run a query againg - works on my RTX3080. For generation it uses a usual prompt that have influence to all picture and a regions that have their own prompts.\nBase prompt good for setup background and style of image. This is train-free technique and results not always stable - sometimes need to try several seeds or change prompt.", - "files": [ - "https://github.com/attashe/ComfyUI-FluxRegionAttention" - ], - "install_type": "git-clone", - "reference": "https://github.com/attashe/ComfyUI-FluxRegionAttention", - "title": "ComfyUI-FluxRegionAttention" - }, - { - "author": "yas-ponotech", - "description": "A collection of custom nodes for using the Stability AI API in ComfyUI.", - "files": [ - "https://github.com/yhayano-ponotech/comfyui-stability-ai-api" - ], - "install_type": "git-clone", - "reference": "https://github.com/yhayano-ponotech/comfyui-stability-ai-api", - "title": "ComfyUI-Stability-AI-API" - }, - { - "author": "HJH-AILab", - "description": "ComfyUI nodes for StableAnimator", - "files": [ - "https://github.com/HJH-AILab/ComfyUI_StableAnimator" - ], - "install_type": "git-clone", - "reference": "https://github.com/HJH-AILab/ComfyUI_StableAnimator", - "title": "ComfyUI_StableAnimator" - }, - { - "author": "HJH-AILab", - "description": "A wrapper of [a/CosyVoice2](https://github.com/FunAudioLLM/CosyVoice/)'s ComfyUI custom_nodes", - "files": [ - "https://github.com/HJH-AILab/ComfyUI_CosyVoice2" - ], - "install_type": "git-clone", - "reference": "https://github.com/HJH-AILab/ComfyUI_CosyVoice2", - "title": "ComfyUI_CosyVoice2" - }, - { - "author": "HJH-AILab", - "description": "a [a/Facefusion](https://github.com/facefusion/facefusion)'s wrapper for ComfyUI custom node.", - "files": [ - "https://github.com/HJH-AILab/ComfyUI_Facefusion" - ], - "install_type": "git-clone", - "reference": "https://github.com/HJH-AILab/ComfyUI_Facefusion", - "title": "ComfyUI_Facefusion" - }, - { - "author": "Easymode-ai", - "description": "ComfyUI [a/movingforward100/Shadow_R](https://github.com/movingforward100/Shadow_R) Wrapper", - "files": [ - "https://github.com/Easymode-ai/ComfyUI-ShadowR" - ], - "install_type": "git-clone", - "reference": "https://github.com/Easymode-ai/ComfyUI-ShadowR", - "title": "ComfyUI-ShadowR" - }, - { - "author": "Easymode-ai", - "description": "Comfyui [a/BPT](https://github.com/whaohan/bpt) Wrapper (Trimesh in/out connections)", - "files": [ - "https://github.com/Easymode-ai/ComfyUI-BPT" - ], - "install_type": "git-clone", - "reference": "https://github.com/Easymode-ai/ComfyUI-BPT", - "title": "ComfyUI-BPT" - }, - { - "author": "GamingDaveUk", - "description": "Nodes that I needed but couldnt find, so ended up making.", - "files": [ - "https://github.com/GamingDaveUk/daves_nodes" - ], - "id": "davesnodes", - "install_type": "git-clone", - "reference": "https://github.com/GamingDaveUk/daves_nodes", - "title": "Daves Nodes" - }, - { - "author": "chenlongming", - "description": "ComfyUI Spectral is a ComfyUI custom nodes library based on the spectral, mainly used for visual processing of spectral files", - "files": [ - "https://github.com/chenlongming/ComfyUI_Spectral" - ], - "install_type": "git-clone", - "reference": "https://github.com/chenlongming/ComfyUI_Spectral", - "title": "ComfyUI_Spectral" - }, - { - "author": "Chengym2023", - "description": "NODES: SiliconCloudReasoning, DeepSeekOnline", - "files": [ - "https://github.com/Chengym2023/ComfyUI-DeepSeek_Online" - ], - "install_type": "git-clone", - "reference": "https://github.com/Chengym2023/ComfyUI-DeepSeek_Online", - "title": "ComfyUI-DeepSeek_Online" - }, - { - "author": "gitmylo", - "description": "Various nodes related to audio.", - "files": [ - "https://github.com/gitmylo/ComfyUI-audio-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/gitmylo/ComfyUI-audio-nodes", - "title": "Audio nodes" - }, - { - "author": "aicuai", - "description": "This repository contains custom nodes for Stability AI API which supports SD3.0 and 3.5.", - "files": [ - "https://github.com/aicuai/aicu-comfyui-stability-ai-api" - ], - "install_type": "git-clone", - "reference": "https://github.com/aicuai/aicu-comfyui-stability-ai-api", - "title": "aicu-comfyui-stability-ai-api" - }, - { - "author": "benda1989", - "description": "a plugin of ComfyUI for CosyVoice2", - "files": [ - "https://github.com/benda1989/CosyVoice2_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/benda1989/CosyVoice2_ComfyUI", - "title": "GKK\u00b7CosyVoice" - }, - { - "author": "benda1989", - "description": "a plugin of ComfyUI for Long Sonic", - "files": [ - "https://github.com/benda1989/Sonic_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/benda1989/Sonic_ComfyUI", - "title": "GKK\u00b7Sonic" - }, - { - "author": "morgan55555", - "description": "Lock Mode feature for ComfyUI. Make simple no-code UI easily.", - "files": [ - "https://github.com/morgan55555/comfyui-lock-mode" - ], - "install_type": "git-clone", - "reference": "https://github.com/morgan55555/comfyui-lock-mode", - "title": "ComfyUI Lock Mode" - }, - { - "author": "pathway8-sudo", - "description": "Custom ComfyUI node that uses BRIA RMBG v1.4 for background removal and PNG cutting.", - "files": [ - "https://github.com/pathway8-sudo/ComfyUI-Pathway-CutPNG-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/pathway8-sudo/ComfyUI-Pathway-CutPNG-Node", - "title": "ComfyUI-Pathway-CutPNG-Node" - }, - { - "author": "crave33", - "description": "generate tags / prompt from danboru image_id input", - "files": [ - "https://github.com/crave33/RenesStuffDanbooruTagGet" - ], - "install_type": "git-clone", - "reference": "https://github.com/crave33/RenesStuffDanbooruTagGet", - "title": "RenesStuffDanboruTagGet" - }, - { - "author": "MeeeyoAI", - "description": "StringOps is a versatile text processing toolkit built for ComfyUI's node-based workflows", - "files": [ - "https://github.com/MeeeyoAI/ComfyUI_StringOps" - ], - "install_type": "git-clone", - "reference": "https://github.com/MeeeyoAI/ComfyUI_StringOps", - "title": "ComfyUI_StringOps" - }, - { - "author": "Pablerdo", - "description": "Cut and and drag that allows you to cut and drag multiple images on a path", - "files": [ - "https://github.com/Pablerdo/ComfyUI-MultiCutAndDrag" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pablerdo/ComfyUI-MultiCutAndDrag", - "title": "ComfyUI-MultiCutAndDrag" - }, - { - "author": "Pablerdo", - "description": "Custom node that merges general and subject-specific prompts", - "files": [ - "https://github.com/Pablerdo/ComfyUI-ZeptaframePromptMerger" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pablerdo/ComfyUI-ZeptaframePromptMerger", - "title": "ComfyUI-ZeptaframePromptMerger" - }, - { - "author": "Pablerdo", - "description": "Resize a batch of trajectories or images", - "files": [ - "https://github.com/Pablerdo/ComfyUI-ResizeZeptaPayload" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pablerdo/ComfyUI-ResizeZeptaPayload", - "title": "ComfyUI-ResizeZeptaPayload" - }, - { - "author": "Pablerdo", - "description": "Generative View Synthesis with Diffusion Models", - "files": [ - "https://github.com/Pablerdo/ComfyUI-StableVirtualCameraWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pablerdo/ComfyUI-StableVirtualCameraWrapper", - "title": "Stable Virtual Camera" - }, - { - "author": "orange90", - "description": "This is a node to run regex for strings.", - "files": [ - "https://github.com/orange90/ComfyUI-Regex-Runner" - ], - "id": "comfyui-regex-runner", - "install_type": "git-clone", - "reference": "https://github.com/orange90/ComfyUI-Regex-Runner", - "title": " ComfyUI-Regex-Runner" - }, - { - "author": "SirWillance", - "description": "A Beginner-friendly Node Suite for prompt refinement in ComfyUI, including custom nodes for weighting, splitting, combining, catalogues, and the PromptRefiner for a simple prompt interface. For more info, join me on https://www.twitch.tv/sirwillance. Be one of the first 50 followers to get a FREE upgrade to the Standard Tier!", - "files": [ - "https://github.com/SirWillance/FoW_Suite_LIGHT" - ], - "id": "fow-suite-light", - "install_type": "git-clone", - "reference": "https://github.com/SirWillance/FoW_Suite_LIGHT", - "title": "FoW - Light" - }, - { - "author": "KAVVATARE", - "description": "ComfyUI node that adds Brightness, RGB channels, and Depth of Field to AI-generated image", - "files": [ - "https://github.com/KAVVATARE/ComfyUI-Light-N-Color" - ], - "install_type": "git-clone", - "reference": "https://github.com/KAVVATARE/ComfyUI-Light-N-Color", - "title": " ComfyUI-Light-N-Color" - }, - { - "author": "KAVVATARE", - "description": "A simple ComfyUI node for generating right eye disparity for VR videos", - "files": [ - "https://github.com/KAVVATARE/ComfyUI_RightEyeDisparity" - ], - "install_type": "git-clone", - "reference": "https://github.com/KAVVATARE/ComfyUI_RightEyeDisparity", - "title": "RightEyeDisparity" - }, - { - "author": "fat-tire", - "description": "Lightweight [a/PyAV](https://pypi.org/project/av/)-powered ComfyUI nodes to load/save multimedia.", - "files": [ - "https://github.com/fat-tire/comfyui-unified-media-suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/fat-tire/comfyui-unified-media-suite", - "title": "ComfyUI Unified Media Suite" - }, - { - "author": "ajbergh", - "description": "This ComfyUI custom node enhances the standard CLIP text encoding functionality by integrating ethnicity and hairstyle selection into the positive prompt. Designed to work seamlessly with ComfyUI, this node allows you to influence the conditioning process by dynamically appending descriptive modifiers. Users can choose a specific ethnicity or hairstyle, or opt for a 'random' selection that picks an option from a predefined CSV list.", - "files": [ - "https://github.com/ajbergh/comfyui-ethnicity_hairstyle_clip_encoder" - ], - "install_type": "git-clone", - "reference": "https://github.com/ajbergh/comfyui-ethnicity_hairstyle_clip_encoder", - "title": "comfyui-ethnicity_hairstyle_clip_encoder" - }, - { - "author": "moose-lab", - "description": "Help comfy deisgner develope custom nodes by foreground GUI without any coding knowledge, complementing the workflow what you design with LLM automatically", - "files": [ - "https://github.com/moose-lab/ComfyUI-GPT" - ], - "install_type": "git-clone", - "reference": "https://github.com/moose-lab/ComfyUI-GPT", - "title": "ComfyUI-GPT" - }, - { - "author": "zichongc", - "description": "Non-native [a/AttentionDistillation](https://github.com/xugao97/AttentionDistillation) for ComfyUI.\nOfficial ComfyUI demo for the paper AttentionDistillation, implemented as an extension of ComfyUI. Note that this extension incorporates AttentionDistillation using diffusers.", - "files": [ - "https://github.com/zichongc/ComfyUI-Attention-Distillation" - ], - "install_type": "git-clone", - "reference": "https://github.com/zichongc/ComfyUI-Attention-Distillation", - "title": "ComfyUI-Attention-Distillation" - }, - { - "author": "PanicTitan", - "description": "Adaptation of Fooocus Prompt Expansion for ComfyUI\nForked from [a/ComfyUI-Prompt-Expansion](https://github.com/meap158/ComfyUI-Prompt-Expansion) with some updates and changes based on original Fooocus, to be more specific [a/expansion.py](https://github.com/lllyasviel/Fooocus/blob/main/extras/expansion.py) and [a/LykosAI - GPT-Prompt-Expansion-Fooocus-v2](https://huggingface.co/LykosAI/GPT-Prompt-Expansion-Fooocus-v2)", - "files": [ - "https://github.com/PanicTitan/ComfyUI-Fooocus-V2-Expansion" - ], - "install_type": "git-clone", - "reference": "https://github.com/PanicTitan/ComfyUI-Fooocus-V2-Expansion", - "title": "ComfyUI-Fooocus-V2-Expansion" - }, - { - "author": "panic-titan", - "description": "Real-time Gallery for ComfyUI with image metadata inspection. Support for images and video.", - "files": [ - "https://github.com/PanicTitan/ComfyUI-Gallery" - ], - "install_type": "git-clone", - "reference": "https://github.com/PanicTitan/ComfyUI-Gallery", - "title": "ComfyUI-Gallery" - }, - { - "author": "maximclouser", - "description": "Inference-time techniques to enhance diffusion-based image generation quality through random search and zero-order optimization algorithms", - "files": [ - "https://github.com/YRIKKA/ComfyUI-InferenceTimeScaling" - ], - "install_type": "git-clone", - "reference": "https://github.com/YRIKKA/ComfyUI-InferenceTimeScaling", - "title": "ComfyUI-InferenceTimeScaling" - }, - { - "author": "marawan206", - "description": "The Face Cropper Node (MarwanFaceCropping) is a custom image processing node designed for ComfyUI. It takes an input image and crops it to a 2:3 aspect ratio, ensuring that most of the subject remains in the frame while maintaining the correct proportions.", - "files": [ - "https://github.com/marawan206/ComfyUI-FaceCropper" - ], - "install_type": "git-clone", - "reference": "https://github.com/marawan206/ComfyUI-FaceCropper", - "title": "Face Cropper Node (2:3 Ratio)" - }, - { - "author": "JiSenHua", - "description": "A custom node for ComfyUI designed to facilitate the real-time transmission of rendered images, videos, or 3D models to TouchDesigner.", - "files": [ - "https://github.com/JiSenHua/ComfyUI-TD" - ], - "id": "touchdesigner", - "install_type": "git-clone", - "reference": "https://github.com/JiSenHua/ComfyUI-TD", - "title": "ComfyUI-TD" - }, - { - "author": "InceptionsAI", - "description": "Helper nodes for [a/RunComfy](https://www.runcomfy.com)", - "files": [ - "https://github.com/InceptionsAI/ComfyUI-RunComfy-Helper" - ], - "install_type": "git-clone", - "reference": "https://github.com/InceptionsAI/ComfyUI-RunComfy-Helper", - "title": "ComfyUI-RunComfy-Helper" - }, - { - "author": "fluffydiveX", - "description": "It is a simple HunyuanVideo block swap node for ComfyUI native nodes.", - "files": [ - "https://github.com/fluffydiveX/ComfyUI-hvBlockswap" - ], - "install_type": "git-clone", - "reference": "https://github.com/fluffydiveX/ComfyUI-hvBlockswap", - "title": "ComfyUI-hvBlockswap" - }, - { - "author": "keit", - "description": "ComfyUI nodes for image processing.", - "files": [ - "https://github.com/keit0728/ComfyUI-Image-Toolkit" - ], - "id": "comfyui-image-toolkit", - "install_type": "git-clone", - "reference": "https://github.com/keit0728/ComfyUI-Image-Toolkit", - "title": "ComfyUI-Image-Toolkit" - }, - { - "author": "keit", - "description": "This is a custom node that allows you to run musubi-tuner from ComfyUI.", - "files": [ - "https://github.com/keit0728/ComfyUI-musubi-tuner" - ], - "install_type": "git-clone", - "reference": "https://github.com/keit0728/ComfyUI-musubi-tuner", - "title": "ComfyUI-musubi-tuner" - }, - { - "author": "Mango1010", - "description": "Node pack designed to save images with metadata supported by Civitai.", - "files": [ - "https://github.com/mang01010/MangoNodePack" - ], - "id": "MangoNodePack", - "install_type": "git-clone", - "reference": "https://github.com/mang01010/MangoNodePack", - "title": "Mango Node Pack" - }, - { - "author": "0xRavenBlack", - "description": "ComfyUI Node to create Object-Oriented Prompts", - "files": [ - "https://github.com/0xRavenBlack/ComfyUI-OOP" - ], - "install_type": "git-clone", - "reference": "https://github.com/0xRavenBlack/ComfyUI-OOP", - "title": "ComfyUI-OOP" - }, - { - "author": "Legorobotdude", - "description": "Helps explore different parameters quickly", - "files": [ - "https://github.com/Legorobotdude/ComfyUI-VariationLab" - ], - "install_type": "git-clone", - "reference": "https://github.com/Legorobotdude/ComfyUI-VariationLab", - "title": "ComfyUI-VariationLab" - }, - { - "author": "lthero", - "description": "Add invisible watermark to images to protect your images", - "files": [ - "https://github.com/lthero-big/ComfyUI-GaussianShadingWatermark" - ], - "install_type": "git-clone", - "reference": "https://github.com/lthero-big/ComfyUI-GaussianShadingWatermark", - "title": "ComfyUI-GaussianShadingWatermark" - }, - { - "author": "JohanK66", - "description": "This package provides a custom node to ComfyUI to send a message and image by means of a webhook", - "files": [ - "https://github.com/JohanK66/ComfyUI-WebhookImage" - ], - "install_type": "git-clone", - "reference": "https://github.com/JohanK66/ComfyUI-WebhookImage", - "title": "ComfyUI WebhookImage" - }, - { - "author": "mr7thing", - "description": "This is a custom node for ComfyUI that can detect circular patterns in an image and generate a standardized circular output.", - "files": [ - "https://github.com/mr7thing/circle_pattern_processor" - ], - "install_type": "git-clone", - "reference": "https://github.com/mr7thing/circle_pattern_processor", - "title": "Circle Pattern Processor for ComfyUI" - }, - { - "author": "TheWhykiki", - "description": "A collection of useful nodes for ComfyUI that provide various workflow enhancements.", - "files": [ - "https://github.com/TheWhykiki/Whykiki-ComfyUIToolset" - ], - "install_type": "git-clone", - "reference": "https://github.com/TheWhykiki/Whykiki-ComfyUIToolset", - "title": "Whykiki ComfyUI Toolset" - }, - { - "author": "justin-vt", - "description": "A ComfyUI node that applies painterly/brush-stroke effects to images, using either ImageMagick (Wand) or G'MIC (gmic-py) under the hood.", - "files": [ - "https://github.com/justin-vt/ComfyUI-brushstrokes" - ], - "install_type": "git-clone", - "reference": "https://github.com/justin-vt/ComfyUI-brushstrokes", - "title": "ComfyUI-brushstrokes" - }, - { - "author": "pxl-pshr", - "description": "GlitchNodes is a collection of image processing nodes designed for ComfyUI that specializes in creating glitch art and retro effects.", - "files": [ - "https://github.com/pxl-pshr/GlitchNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/pxl-pshr/GlitchNodes", - "title": "GlitchNodes" - }, - { - "author": "ZYK-AI", - "description": "Plugin that implements world automatic typesetting and outputs only one paragraph of text", - "files": [ - "https://github.com/sittere/ComfyUI-YK_Line-loading" - ], - "id": "ComfyUI-YK_Line loading", - "install_type": "git-clone", - "reference": "https://github.com/sittere/ComfyUI-YK_Line-loading", - "title": "ComfyUI-YK Line loading" - }, - { - "author": "Jerome Bacquet", - "description": "Implementation of Instance nodes, Replicate nodes, and standard Save UI to improve the workflow into ComfyUI.", - "files": [ - "https://github.com/jerome7562/ComfyUI-XenoFlow" - ], - "id": "XenoFlow", - "install_type": "git-clone", - "reference": "https://github.com/jerome7562/ComfyUI-XenoFlow", - "title": "ComfyUI XenoFlow" - }, - { - "author": "chenpipi0807", - "description": "A powerful ComfyUI extension node that allows you to add various exquisite artistic text effects to your images, supporting a wide range of text styles and effects.", - "files": [ - "https://github.com/chenpipi0807/PIP_ArtisticWords" - ], - "install_type": "git-clone", - "reference": "https://github.com/chenpipi0807/PIP_ArtisticWords", - "title": "PIP Artistic Words for ComfyUI" - }, - { - "author": "chenpipi0807", - "description": "A simple and effective ComfyUI custom node for filtering inappropriate text content, automatically detecting and replacing prohibited words while preserving the original format.", - "files": [ - "https://github.com/chenpipi0807/ComfyUI_NSFW_Godie" - ], - "install_type": "git-clone", - "reference": "https://github.com/chenpipi0807/ComfyUI_NSFW_Godie", - "title": "ComfyUI NSFW Filter" - }, - { - "author": "chenpipi0807", - "description": "NODES: An industrial-grade zero-shot text-to-speech synthesis system with a ComfyUI interface.", - "files": [ - "https://github.com/chenpipi0807/ComfyUI-Index-TTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/chenpipi0807/ComfyUI-Index-TTS", - "title": "ComfyUI-Index-TTS" - }, - { - "author": "chenpipi0807", - "description": "The official ComfyUI TextEncodeQwenImageEdit node simplifies prompt engineering. This node supports custom system prompts to enhance flexibility.", - "files": [ - "https://github.com/chenpipi0807/Comfyui-Qwen-image-edit-CharacterConsistency" - ], - "install_type": "git-clone", - "reference": "https://github.com/chenpipi0807/Comfyui-Qwen-image-edit-CharacterConsistency", - "title": "Comfyui-Qwen-image-edit-CharacterConsistency" - }, - { - "author": "ifmylove2011", - "description": "NODES: TrimBG, TrimBG Advanced, Image Queue Loader, Load Image Alpha.\nA few tools for ComfyUI, perhaps it's exactly what you need.", - "files": [ - "https://github.com/ifmylove2011/comfyui-missed-tool" - ], - "install_type": "git-clone", - "reference": "https://github.com/ifmylove2011/comfyui-missed-tool", - "title": "comfyui-missed-tool" - }, - { - "author": "illuminatianon", - "description": "A ComfyUI custom node that provides dynamic text substitution using wildcards and CSV files. Perfect for creating varied prompts with consistent relationships between terms.", - "files": [ - "https://github.com/illuminatianon/comfyui-csvwildcards" - ], - "install_type": "git-clone", - "reference": "https://github.com/illuminatianon/comfyui-csvwildcards", - "title": "CSV Wildcard Node for ComfyUI" - }, - { - "author": "finegrain", - "description": "ComfyUI custom nodes to interact with the Finegrain API.", - "files": [ - "https://github.com/finegrain-ai/comfyui-finegrain" - ], - "install_type": "git-clone", - "reference": "https://github.com/finegrain-ai/comfyui-finegrain", - "title": "comfyui-finegrain" - }, - { - "author": "Diohim", - "description": "A collection of unusual but useful image processing nodes for ComfyUI.", - "files": [ - "https://github.com/Diohim/ComfyUI-Unusual-Tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/Diohim/ComfyUI-Unusual-Tools", - "title": "ComfyUI Unusual Tools" - }, - { - "author": "penposs", - "description": "This is a Google Gemini Pro API integration node for ComfyUI, supporting text, image, video, and audio inputs.", - "files": [ - "https://github.com/penposs/ComfyUI_Gemini_Pro" - ], - "install_type": "git-clone", - "reference": "https://github.com/penposs/ComfyUI_Gemini_Pro", - "title": "ComfyUI Gemini Pro Node" - }, - { - "author": "penposs", - "description": "Free trial of Tongyi Wanxiang wan2.1 model, this is a batch implementation of wan2.1 API, providing batch processing for your short video production.", - "files": [ - "https://github.com/penposs/Comfyui_wan_api" - ], - "install_type": "git-clone", - "reference": "https://github.com/penposs/Comfyui_wan_api", - "title": "Comfyui_wan_api" - }, - { - "author": "cardenluo", - "description": "ComfyUI Preset Manager, supporting various preset templates and workflow management", - "files": [ - "https://github.com/cardenluo/ComfyUI-Apt_Preset" - ], - "install_type": "git-clone", - "reference": "https://github.com/cardenluo/ComfyUI-Apt_Preset", - "title": "ComfyUI-Apt_Preset" - }, - { - "author": "Holasyb918", - "description": "ComfyUI adaptation of [a/GHOST 2.0](https://github.com/ai-forever/ghost-2.0).", - "files": [ - "https://github.com/Holasyb918/Ghost2_Comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/Holasyb918/Ghost2_Comfyui", - "title": "Ghost2_Comfyui" - }, - { - "author": "mit-han-lab", - "description": "Nunchaku ComfyUI Node. Nunchaku is the inference that supports SVDQuant. SVDQuant is a new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5\u00d7 memory and 8.7\u00d7 latency reduction on a 16GB laptop 4090 GPU. See more details: https://github.com/mit-han-lab/nunchaku", - "files": [ - "https://github.com/nunchaku-tech/ComfyUI-nunchaku" - ], - "install_type": "git-clone", - "reference": "https://github.com/nunchaku-tech/ComfyUI-nunchaku", - "title": "ComfyUI-nunchaku" - }, - { - "author": "Nikosis", - "description": "Nodes: Aspect Ratio, Prompt Multiple Styles Selector, Text Concatenate", - "files": [ - "https://github.com/Nikosis/ComfyUI-Nikosis-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Nikosis/ComfyUI-Nikosis-Nodes", - "title": "ComfyUI-Nikosis-Nodes" - }, - { - "author": "Nikosis", - "description": "Nodes: DepthAnything v2, LineArt, PyraCanny, Sketch", - "files": [ - "https://github.com/Nikosis/ComfyUI-Nikosis-Preprocessors" - ], - "install_type": "git-clone", - "reference": "https://github.com/Nikosis/ComfyUI-Nikosis-Preprocessors", - "title": "ComfyUI-Nikosis-Preprocessors" - }, - { - "author": "vadimcro", - "description": "A collection of advanced edge detection nodes for ComfyUI that generate high-quality edge maps / contours for usage with ControlNet Canny / Anyline guidance.", - "files": [ - "https://github.com/vadimcro/VKRiez-Edge" - ], - "install_type": "git-clone", - "reference": "https://github.com/vadimcro/VKRiez-Edge", - "title": "VKRiez-Edge" - }, - { - "author": "Duanyll", - "description": "A collection of custom nodes for ComfyUI", - "files": [ - "https://github.com/Duanyll/duanyll_nodepack" - ], - "install_type": "git-clone", - "reference": "https://github.com/Duanyll/duanyll_nodepack", - "title": "Duanyll Nodepack" - }, - { - "author": "irreveloper", - "description": "An Unofficial ComfyUI custom node package that integrates [a/Diffusion Self-Distillation (DSD)](https://github.com/primecai/diffusion-self-distillation) for zero-shot customized image generation.\nDSD is a model for subject-preserving image generation that allows you to create images of a specific subject in novel contexts without per-instance tuning.", - "files": [ - "https://github.com/irreveloper/ComfyUI-DSD" - ], - "install_type": "git-clone", - "reference": "https://github.com/irreveloper/ComfyUI-DSD", - "title": "ComfyUI-DSD" - }, - { - "author": "HannibalP", - "description": "This node improves the merging of LoRA for movements and physical resemblance when adding multiple LoRA to a model.", - "files": [ - "https://github.com/HannibalP/comfyui-HannibalPack" - ], - "install_type": "git-clone", - "reference": "https://github.com/HannibalP/comfyui-HannibalPack", - "title": "comfyui-HannibalPack" - }, - { - "author": "xingBaGan", - "description": "Real-time image transfer between client and server Base64 image encoding/decoding support Supports PNG image format Includes a floating preview window for received images Preview window has minimize/maximize functionality", - "files": [ - "https://github.com/xingBaGan/ComfyUI-connect-ui" - ], - "install_type": "git-clone", - "reference": "https://github.com/xingBaGan/ComfyUI-connect-ui", - "title": "ComfyUI-connect-ui" - }, - { - "author": "iDAPPA", - "description": "A simple, lightweight AMD GPU monitoring tool for ComfyUI that displays real-time information about your AMD GPU directly in the UI.", - "files": [ - "https://github.com/iDAPPA/ComfyUI-AMDGPUMonitor" - ], - "install_type": "git-clone", - "reference": "https://github.com/iDAPPA/ComfyUI-AMDGPUMonitor", - "title": "AMD GPU Monitor for ComfyUI" - }, - { - "author": "roundyyy", - "description": "A custom node for ComfyUI that implements mesh simplification with texture preservation using PyMeshLab. This node allows you to reduce the complexity of 3D meshes while preserving visual quality, and is compatible with ComfyUI-3D-Pack's mesh format.", - "files": [ - "https://github.com/roundyyy/ComfyUI-mesh-simplifier" - ], - "install_type": "git-clone", - "reference": "https://github.com/roundyyy/ComfyUI-mesh-simplifier", - "title": "Mesh Simplifier for ComfyUI" - }, - { - "author": "orssorbit", - "description": "This is a simple Wan block swap node for ComfyUI native nodes, works by swapping upto 40 blocks to the CPU to reduce VRAM.", - "files": [ - "https://github.com/orssorbit/ComfyUI-wanBlockswap" - ], - "install_type": "git-clone", - "reference": "https://github.com/orssorbit/ComfyUI-wanBlockswap", - "title": "ComfyUI-wanBlockswap" - }, - { - "author": "joreyaesh", - "description": "A ComfyUI extension to allow textarea elements to be scrolled over. Useful when using a trackpad in order to prevent accidental forward/back navigation (two fingers horizontally on a Mac) when scrolling around the UI.", - "files": [ - "https://github.com/joreyaesh/comfyui_scroll_over_textarea" - ], - "install_type": "git-clone", - "reference": "https://github.com/joreyaesh/comfyui_scroll_over_textarea", - "title": "ComfyUI Scroll Over Textarea" - }, - { - "author": "joreyaesh", - "description": "A ComfyUI extension that enhances touchpad navigation by redirecting two-finger scrolling over to the canvas, including over textareas. This can prevent accidental back/forward browser navigation when using horizontal touchpad gestures and provides smooth zooming and panning for Mac and other touchpad users.", - "files": [ - "https://github.com/joreyaesh/comfyui_touchpad_scroll_controller.enableTouchpadScroll" - ], - "install_type": "git-clone", - "reference": "https://github.com/joreyaesh/comfyui_touchpad_scroll_controller.enableTouchpadScroll", - "title": "ComfyUI Touchpad Scroll Controller" - }, - { - "author": "ali-vilab", - "description": "Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model.", - "files": [ - "https://github.com/ali-vilab/ACE_plus" - ], - "id": "ace_plus", - "install_type": "git-clone", - "reference": "https://github.com/ali-vilab/ACE_plus", - "title": "ComfyUI-ACE_Plus" - }, - { - "author": "chri002", - "description": "A simple set of nodes to generate a point cloud from an image and its depth map, perform transformations and some basic operations.", - "files": [ - "https://github.com/chri002/ComfyUI_depthMapOperation" - ], - "install_type": "git-clone", - "reference": "https://github.com/chri002/ComfyUI_depthMapOperation", - "title": "ComfyUI_depthMapOperation" - }, - { - "author": "Laurent2916", - "description": "PIQ ComfyUI custom nodes", - "files": [ - "https://github.com/Laurent2916/comfyui-piq" - ], - "install_type": "git-clone", - "reference": "https://github.com/Laurent2916/comfyui-piq", - "title": "comfyui-piq" - }, - { - "author": "thezveroboy", - "description": "Custom nodes for ComfyUI implementing the csm model for text-to-speech generation.", - "files": [ - "https://github.com/thezveroboy/ComfyUI-CSM-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/thezveroboy/ComfyUI-CSM-Nodes", - "title": "ComfyUI-CSM-Nodes" - }, - { - "author": "thezveroboy", - "description": "Custom nodes for ComfyUI implementing the csm model for text-to-speech generation.", - "files": [ - "https://github.com/thezveroboy/ComfyUI-WAN-ClipSkip" - ], - "install_type": "git-clone", - "reference": "https://github.com/thezveroboy/ComfyUI-WAN-ClipSkip", - "title": "ComfyUI-WAN-ClipSkip" - }, - { - "author": "thezveroboy", - "description": "I took the original source code from the repository [a/ComfyUI_ACE-Step](https://github.com/billwuhao/ComfyUI_ACE-Step) and modified it to make the model loading explicit instead of hidden.", - "files": [ - "https://github.com/thezveroboy/ComfyUI_ACE-Step-zveroboy" - ], - "install_type": "git-clone", - "reference": "https://github.com/thezveroboy/ComfyUI_ACE-Step-zveroboy", - "title": "ComfyUI_ACE-Step-zveroboy" - }, - { - "author": "thezveroboy", - "description": "A custom node for ComfyUI that loads a random image from a specified folder and outputs it in the standard ComfyUI IMAGE format, along with a MASK and the image path as STRING. Images are loaded in their original dimensions.", - "files": [ - "https://github.com/thezveroboy/comfyui-random-image-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/thezveroboy/comfyui-random-image-loader", - "title": "ComfyUI Random Image Loader" - }, - { - "author": "thezveroboy", - "description": "Custom nodes for ComfyUI for simple LUT file extraction from any image.", - "files": [ - "https://github.com/thezveroboy/ComfyUI-lut" - ], - "install_type": "git-clone", - "reference": "https://github.com/thezveroboy/ComfyUI-lut", - "title": "ComfyUI-LUT" - }, - { - "author": "thezveroboy", - "description": "Custom node for ComfyUI that fixes an existing node [a/comfyui-dynamicprompts](https://github.com/adieyal/comfyui-dynamicprompts).", - "files": [ - "https://github.com/thezveroboy/comfyui-RandomPromptsZveroboy" - ], - "install_type": "git-clone", - "reference": "https://github.com/thezveroboy/comfyui-RandomPromptsZveroboy", - "title": "comfyui-RandomPromptsZveroboy" - }, - { - "author": "tatookan", - "description": "Calling gemini2.0 at comfyui . The project will continue to organize good APIs!", - "files": [ - "https://github.com/tatookan/comfyui_ssl_gemini_EXP" - ], - "install_type": "git-clone", - "reference": "https://github.com/tatookan/comfyui_ssl_gemini_EXP", - "title": "comfyui_ssl_gemini_EXP" - }, - { - "author": "atluslin", - "description": "ComfyUI's Arcane stylization plugin", - "files": [ - "https://github.com/atluslin/comfyui_arcane_style_trans" - ], - "install_type": "git-clone", - "reference": "https://github.com/atluslin/comfyui_arcane_style_trans", - "title": "comfyui_arcane_style_trans" - }, - { - "author": "pixelworldai", - "description": "This node takes a batch of images with alpha channels (RGBA format) and combines them into a single image, respecting the transparency of each layer. It's particularly useful for compositing multiple masked elements (like faces) into a single image.", - "files": [ - "https://github.com/pixelworldai/ComfyUI-AlphaFlatten" - ], - "install_type": "git-clone", - "reference": "https://github.com/pixelworldai/ComfyUI-AlphaFlatten", - "title": "ComfyUI-AlphaFlatten" - }, - { - "author": "pixelworldai", - "description": "Embed images directly in your workflow JSONs", - "files": [ - "https://github.com/pixelworldai/ComfyUI-WorkflowGraphics" - ], - "install_type": "git-clone", - "reference": "https://github.com/pixelworldai/ComfyUI-WorkflowGraphics", - "title": "ComfyUI-WorkflowGraphics" - }, - { - "author": "CozyMantis (+ Curt-Park)", - "description": "It works the same as human-parser-comfyui-node but is implemented in pure Python so that it doesn't require a runtime build for InPlaceABNSync.", - "files": [ - "https://github.com/Curt-Park/human-parser-comfyui-node-in-pure-python" - ], - "id": "humanparser-pure-python", - "install_type": "git-clone", - "reference": "https://github.com/Curt-Park/human-parser-comfyui-node-in-pure-python", - "title": "Cozy Human Parser in pure Python" - }, - { - "author": "ComplexRobot", - "description": "Nodes for simple frame interpolation without the use of AI. Uses standard image operations to blend frames together.", - "files": [ - "https://github.com/ComplexRobot/ComfyUI-Simple-VFI" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComplexRobot/ComfyUI-Simple-VFI", - "title": "ComfyUI-Simple-VFI" - }, - { - "author": "Taithrah", - "description": "Simple nodes for ComfyUI - Token Counter - Optimal Empty Latent", - "files": [ - "https://github.com/Taithrah/ComfyUI_Fens_Simple_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Taithrah/ComfyUI_Fens_Simple_Nodes", - "title": "Fens-Simple-Nodes" - }, - { - "author": "Immac", - "description": "A mock of a possible implementation of for ComfyUI Core Video Nodes.", - "files": [ - "https://github.com/Immac/ComfyUI-CoreVideoMocks" - ], - "install_type": "git-clone", - "reference": "https://github.com/Immac/ComfyUI-CoreVideoMocks", - "title": "ComfyUI Core Video Nodes" - }, - { - "author": "kuo6", - "description": "Tools for processing equirectangular images, supporting conversion from equirectangular format to cubemap.", - "files": [ - "https://github.com/kukuo6666/ComfyUI-Equirect" - ], - "install_type": "git-clone", - "reference": "https://github.com/kukuo6666/ComfyUI-Equirect", - "title": "ComfyUI Equirectangular Tools" - }, - { - "author": "vahidzxc", - "description": "A collection of custom nodes for ComfyUI, focusing on improving workflow efficiency and adding new functionality.(work in progress!!!)", - "files": [ - "https://github.com/vahidzxc/va-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/vahidzxc/va-nodes", - "title": "va-nodes" - }, - { - "author": "blovett80", - "description": "A ComfyUI extension for using PixelDojo's Flux API to generate high-quality images directly within ComfyUI workflows.", - "files": [ - "https://github.com/blovett80/ComfyUI-PixelDojo" - ], - "install_type": "git-clone", - "reference": "https://github.com/blovett80/ComfyUI-PixelDojo", - "title": "ComfyUI-PixelDojo" - }, - { - "author": "yasser-baalla", - "description": "Create a custom node to select the closest images semantically to an input prompt", - "files": [ - "https://github.com/yasser-baalla/comfyUI-SemanticImageFetch" - ], - "install_type": "git-clone", - "reference": "https://github.com/yasser-baalla/comfyUI-SemanticImageFetch", - "title": "comfyUI-SemanticImageFetch" - }, - { - "author": "SijieMei", - "description": "Save prompt history and reselect", - "files": [ - "https://github.com/SijieMei/ComfyUI-promptHistory" - ], - "install_type": "git-clone", - "reference": "https://github.com/SijieMei/ComfyUI-promptHistory", - "title": "ComfyUI-Prompt-History" - }, - { - "author": "Tensor-Art", - "description": "This project implements a set of custom nodes for ComfyUI, integrating some of the API interfaces provided by [a/TAMS](https://tams.tensor.art/).", - "files": [ - "https://github.com/Tensor-Art/ComfyUI_TENSOR_ART" - ], - "install_type": "git-clone", - "reference": "https://github.com/Tensor-Art/ComfyUI_TENSOR_ART", - "title": "ComfyUI_TENSOR_ART" - }, - { - "author": "infinigence", - "description": "A model cached-loader custom node for ComfyUI.", - "files": [ - "https://github.com/infinigence/ComfyUI_Model_Cache" - ], - "install_type": "git-clone", - "reference": "https://github.com/infinigence/ComfyUI_Model_Cache", - "title": "ComfyUI_Model_Cache" - }, - { - "author": "infinigence", - "description": "NODES: DrawTextNode, Qwen2.5VL_api, ...", - "files": [ - "https://github.com/infinigence/ComfyUI-Infinigence-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/infinigence/ComfyUI-Infinigence-Nodes", - "title": "ComfyUI-Infinigence-Nodes" - }, - { - "author": "zaheenrahman", - "description": "A custom node for ComfyUI that performs color correction on clothing in face-swapped images. This node helps maintain the original clothing color when using face swap tools, addressing common color shifts that occur during the face swap process.", - "files": [ - "https://github.com/zaheenrahman/ComfyUI-ColorCorrection" - ], - "install_type": "git-clone", - "reference": "https://github.com/zaheenrahman/ComfyUI-ColorCorrection", - "title": "ComfyUI-ColorCorrection" - }, - { - "author": "CHAOSEA", - "description": "Smart Face Alignment and Pasting Node", - "files": [ - "https://github.com/CHAOSEA/ComfyUI_FaceAlignPaste" - ], - "install_type": "git-clone", - "reference": "https://github.com/CHAOSEA/ComfyUI_FaceAlignPaste", - "title": "ComfyUI_FaceAlignPaste" - }, - { - "author": "RaymondProduction", - "description": "A set of nodes for batch processing of text and images.", - "files": [ - "https://github.com/RaymondProduction/comfyui-zerna-pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/RaymondProduction/comfyui-zerna-pack", - "title": "Zerna Pack" - }, - { - "author": "svetozarov", - "description": "This ComfyUI extension provides custom nodes for working with Google Gemini and OpenAI ChatGPT.", - "files": [ - "https://github.com/svetozarov/AS_LLM_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/svetozarov/AS_LLM_nodes", - "title": "AS_LLM_nodes" - }, - { - "author": "Andro-Meta", - "description": "A ComfyUI custom node set for integrating [a/Ovis2](https://huggingface.co/AIDC-AI/Ovis2-34B), a powerful multimodal large language model designed to analyze images and videos.", - "files": [ - "https://github.com/Andro-Meta/ComfyUI-Ovis2" - ], - "install_type": "git-clone", - "reference": "https://github.com/Andro-Meta/ComfyUI-Ovis2", - "title": "ComfyUI-Ovis2" - }, - { - "author": "rainlizard", - "description": "A semi-random prompt generator for danbooru tags that works alongside your character prompt, allowing you to put your waifu in many scenarios. (for Illustrious/NoobAI/Pony)", - "files": [ - "https://github.com/rainlizard/ComfyUI-Raffle" - ], - "install_type": "git-clone", - "reference": "https://github.com/rainlizard/ComfyUI-Raffle", - "title": "Raffle" - }, - { - "author": "jupo-ai", - "description": "tag completer with csv file", - "files": [ - "https://github.com/jupo-ai/comfy-ex-tagcomplete" - ], - "id": "comfy-ex-tagcomplete", - "install_type": "git-clone", - "reference": "https://github.com/jupo-ai/comfy-ex-tagcomplete", - "title": "comfy-ex-tagcomplete" - }, - { - "author": "felixszeto", - "description": "This is a request node tool designed for making HTTP requests (GET/POST) to APIs and viewing the responses. It is useful for API testing and development.", - "files": [ - "https://github.com/felixszeto/ComfyUI-RequestNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/felixszeto/ComfyUI-RequestNodes", - "title": "ComfyUI-RequestNodes" - }, - { - "author": "AIExplorer25", - "description": "This custom node helps to auto download models from huggingface", - "files": [ - "https://github.com/AIExplorer25/ComfyUI_AutoDownloadModels" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIExplorer25/ComfyUI_AutoDownloadModels", - "title": "ComfyUI_AutoDownloadModels" - }, - { - "author": "AIExplorer25", - "description": "ComfyUI ChatGPT Helper ComfyUI ChatGPT Helper is a custom node extension for ComfyUI that integrates OpenAI's ChatGPT capabilities directly into your ComfyUI workflows. This tool allows for dynamic prompt generation, automated text manipulation, and enhanced interactivity within your AI image generation processes.", - "files": [ - "https://github.com/AIExplorer25/ComfyUI_ChatGptHelper" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIExplorer25/ComfyUI_ChatGptHelper", - "title": "ComfyUI_ChatGptHelper" - }, - { - "author": "AIExplorer25", - "description": "This custom node helps to generate cation for images for lora training.", - "files": [ - "https://github.com/AIExplorer25/ComfyUI_ImageCaptioner" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIExplorer25/ComfyUI_ImageCaptioner", - "title": "ComfyUI_ImageCaptioner" - }, - { - "author": "Altair200333", - "description": "This plugin adds a new node to ComfyUI. The node uses the FLUX.1 Fill API to fill in parts of an image.", - "files": [ - "https://github.com/Altair200333/ComfyUI_Flux_1.1_PRO" - ], - "install_type": "git-clone", - "reference": "https://github.com/Altair200333/ComfyUI_Flux_1.1_PRO", - "title": "Flux Pro Nodes for ComfyUI" - }, - { - "author": "PiggyDance", - "description": "OpenCV nodes for ComfyUI", - "files": [ - "https://github.com/PiggyDance/ComfyUI_OpenCV" - ], - "install_type": "git-clone", - "reference": "https://github.com/PiggyDance/ComfyUI_OpenCV", - "title": "ComfyUI_OpenCV" - }, - { - "author": "wywywywy", - "description": "Node to pause a workflow with a continue button", - "files": [ - "https://github.com/wywywywy/ComfyUI-pause" - ], - "install_type": "git-clone", - "reference": "https://github.com/wywywywy/ComfyUI-pause", - "title": "ComfyUI Pause Workflow Node" - }, - { - "author": "Semper-Sursum", - "description": "This custom node for ComfyUI enables direct integration with Hugging Face Flux models for image generation via API. Users can leverage the power of Flux models like FLUX.1 [schnell] and FLUX.1 [dev] without leaving the ComfyUI environment", - "files": [ - "https://github.com/Semper-Sursum/HF-Flux-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/Semper-Sursum/HF-Flux-ComfyUI", - "title": "HF-Flux-ComfyUI" - }, - { - "author": "Semper-Sursum", - "description": "ComfyUI nodes for PromptWrapper, Mainly for prompt word translation, Chinese translation into English or English translation into Chinese.", - "files": [ - "https://github.com/clouddreamfly/ComfyUI-PromptWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/clouddreamfly/ComfyUI-PromptWrapper", - "title": "ComfyUI-PromptWrapper" - }, - { - "author": "SYaroslavIv", - "description": "The SwD Preset Selector is a custom node for ComfyUI that provides a simple way to select predefined SwD configurations.", - "files": [ - "https://github.com/YaroslavIv/comfyui_swd" - ], - "install_type": "git-clone", - "reference": "https://github.com/YaroslavIv/comfyui_swd", - "title": "SwD Preset Selector for ComfyUI" - }, - { - "author": "game4d", - "description": "ComfyUI custom_node for [a/ByteDance's InfiniteYou](https://github.com/bytedance/InfiniteYou).", - "files": [ - "https://github.com/game4d/ComfyUI-BDsInfiniteYou" - ], - "install_type": "git-clone", - "reference": "https://github.com/game4d/ComfyUI-BDsInfiniteYou", - "title": "ComfyUI-BDsInfiniteYou" - }, - { - "author": "hayde0096", - "description": "Just a Sampler and another settings pipe for comfyui", - "files": [ - "https://github.com/hayde0096/Comfyui-EasySettingpipes" - ], - "install_type": "git-clone", - "reference": "https://github.com/hayde0096/Comfyui-EasySettingpipes", - "title": "EasySettingpipes" - }, - { - "author": "orex2121", - "description": "comfyui-OreX is a set of nodes for using LLM models via a free API.", - "files": [ - "https://github.com/orex2121/comfyui-OreX" - ], - "install_type": "git-clone", - "reference": "https://github.com/orex2121/comfyui-OreX", - "title": "comfyui-OreX" - }, - { - "author": "zakantonio", - "description": "A custom node for ComfyUI that transforms user photos into stylized character avatars.", - "files": [ - "https://github.com/zakantonio/AvatarGen-experience" - ], - "install_type": "git-clone", - "reference": "https://github.com/zakantonio/AvatarGen-experience", - "title": "Avatar Generation Experience" - }, - { - "author": "Gue-e", - "description": "A plugin application that utilizes ComfyUI to generate 360-degree panoramic images. It primarily works by converting between flat images and equidistant cylindrical projections, stretching flat images into a curved format, and setting regional conditions, while harnessing the power of large models to produce 360-degree panoramic views.", - "files": [ - "https://github.com/Gue-e/ComfyUI-PanoCard" - ], - "install_type": "git-clone", - "reference": "https://github.com/Gue-e/ComfyUI-PanoCard", - "title": "ComfyUI-PanoCard" - }, - { - "author": "stepfun-ai", - "description": "This repository contains ComfyUI custom nodes for StepVideo.", - "files": [ - "https://github.com/stepfun-ai/ComfyUI-StepVideo" - ], - "install_type": "git-clone", - "reference": "https://github.com/stepfun-ai/ComfyUI-StepVideo", - "title": "ComfyUI-StepVideo" - }, - { - "author": "LoveEatCandy", - "description": "Replace part of an image with another image", - "files": [ - "https://github.com/LoveEatCandy/COMFYUI-ReplacePartOfImage" - ], - "install_type": "git-clone", - "reference": "https://github.com/LoveEatCandy/COMFYUI-ReplacePartOfImage", - "title": "COMFYUI-ReplacePartOfImage" - }, - { - "author": "Flow-two", - "description": "Start and end frames video generation node that supports native ComfyUI.", - "files": [ - "https://github.com/Flow-two/ComfyUI-WanStartEndFramesNative" - ], - "install_type": "git-clone", - "reference": "https://github.com/Flow-two/ComfyUI-WanStartEndFramesNative", - "title": "ComfyUI-WanStartEndFramesNative" - }, - { - "author": "Creepybits", - "description": "A collection of switch nodes for ComfyUI", - "files": [ - "https://github.com/Creepybits/ComfyUI-Creepy_nodes" - ], - "id": "Creepy_nodes", - "install_type": "git-clone", - "reference": "https://github.com/Creepybits/ComfyUI-Creepy_nodes", - "title": "ComfyUI-Creepy_nodes" - }, - { - "author": "Creepybits", - "description": "Saves images directly to OneDrive using Microsoft's free API service.", - "files": [ - "https://github.com/Creepybits/ComfyUI-Save_To_OneDrive" - ], - "install_type": "git-clone", - "reference": "https://github.com/Creepybits/ComfyUI-Save_To_OneDrive", - "title": "Comfyui-Save_To_OneDrive" - }, - { - "author": "ImagineerNL", - "description": "ComfyUI node to vectorize 2 color images like logo or text calling the pure Python 'potracer' library for potrace. Requires LATEST VERSION of https://registry.comfy.org/nodes/ComfyUI-ToSVG to_SVG node to save as SVG.", - "files": [ - "https://github.com/ImagineerNL/ComfyUI-ToSVG-Potracer" - ], - "install_type": "git-clone", - "reference": "https://github.com/ImagineerNL/ComfyUI-ToSVG-Potracer", - "title": "ComfyUI-ToSVG-Potracer" - }, - { - "author": "ImagineerNL", - "description": "ComfyUI Utility Nodes by Imagineer. 1: Catch and Edit Text; useful for grabbing AI generated prompts which you edit by hand. Doing so mutes the upstream node, improving speed and saving external calls and budget.
    2. Preview Image - No Save: Previews as they should be", - "files": [ - "https://github.com/ImagineerNL/ComfyUI-IMGNR-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/ImagineerNL/ComfyUI-IMGNR-Utils", - "title": "ComfyUI-IMGNR-Utils" - }, - { - "author": "Yushan777", - "description": "Convert 2D images and videos to 3D SBS (side-by-side) format", - "files": [ - "https://github.com/yushan777/ComfyUI-Y7-SBS-2Dto3D" - ], - "install_type": "git-clone", - "reference": "https://github.com/yushan777/ComfyUI-Y7-SBS-2Dto3D", - "title": "ComfyUI-Y7-SBS-2Dto3D" - }, - { - "author": "Yushan777", - "description": "A collection of utility / quality-of-life nodes for ComfyUI. Probably only useful to me.", - "files": [ - "https://github.com/yushan777/ComfyUI-Y7Nodes" - ], - "id": "y7nodes", - "install_type": "git-clone", - "reference": "https://github.com/yushan777/ComfyUI-Y7Nodes", - "title": "Y7Nodes for ComfyUI" - }, - { - "author": "bemoregt", - "description": "ComfyUI Custom Node for converting images to frequency spectrum visualizations using FFT", - "files": [ - "https://github.com/bemoregt/ComfyUI_CustomNode_Image2Spectrum" - ], - "install_type": "git-clone", - "reference": "https://github.com/bemoregt/ComfyUI_CustomNode_Image2Spectrum", - "title": "ComfyUI_CustomNode_Image2Spectrum" - }, - { - "author": "pnikolic-amd", - "description": "This node enables better performance for Stable Diffusion models, by leveraging AMD MIGraphX, on Navi3 and Navi4 GPUs.", - "files": [ - "https://github.com/pnikolic-amd/ComfyUI_MIGraphX" - ], - "install_type": "git-clone", - "reference": "https://github.com/pnikolic-amd/ComfyUI_MIGraphX", - "title": "MIGraphX Node for ComfyUI" - }, - { - "author": "zzubnik", - "description": "Custom text tools for helping with multiple prompt generation in ComfyUI. These tools allow more variety than just relying on the randomness of the image generator. You can create related, themed prompts, random each time.", - "files": [ - "https://github.com/zzubnik/TT_TextTools" - ], - "id": "TT_TextTools", - "install_type": "git-clone", - "reference": "https://github.com/zzubnik/TT_TextTools", - "title": "TT_TextTools" - }, - { - "author": "dimtion", - "description": "Comfyui-raw-image provides the ability to load raw image files for ComfyUI", - "files": [ - "https://github.com/dimtion/comfyui-raw-image" - ], - "install_type": "git-clone", - "reference": "https://github.com/dimtion/comfyui-raw-image", - "title": "ComfyUI-Raw-Image" - }, - { - "author": "DiffusionWave", - "description": "A custom node for ComfyUI that allows selecting a base resolution, applying a custom scaling value based on FLOAT (up to 10 decimal places), and adding an extra integer value. Outputs include both INT and FLOAT resolutions, making it perfect for you to play around with.", - "files": [ - "https://github.com/DiffusionWave/PickResolution_DiffusionWave" - ], - "install_type": "git-clone", - "reference": "https://github.com/DiffusionWave/PickResolution_DiffusionWave", - "title": "PickResolution_DiffusionWave" - }, - { - "author": "Zar4X", - "description": "A collection of nodes for batch processing texts and images in ComfyUI", - "files": [ - "https://github.com/Zar4X/ComfyUI-Batch-Process" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zar4X/ComfyUI-Batch-Process", - "title": "ComfyUI-Batch-Process" - }, - { - "author": "Zar4X", - "description": "Precision dimension control nodes for aspect ratio adjustments and resizing workflows", - "files": [ - "https://github.com/Zar4X/ComfyUI-Image-Resizing" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zar4X/ComfyUI-Image-Resizing", - "title": "ComfyUI-Image-Resizing" - }, - { - "author": "WaveSpeedAI", - "description": "This is a custom node for ComfyUI that allows you to use the WaveSpeed AI API directly in ComfyUI. WaveSpeed AI is a high-performance AI image and video generation service platform offering industry-leading generation speeds. For more information, see [a/WaveSpeed AI Documentation](https://wavespeed.ai/docs).", - "files": [ - "https://github.com/WaveSpeedAI/wavespeed-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/WaveSpeedAI/wavespeed-comfyui", - "title": "wavespeed-comfyui" - }, - { - "author": "hekmon", - "description": "Extract CLIP and VAE models from a loaded checkpoint in ComfyUI.", - "files": [ - "https://github.com/hekmon/comfyui-checkpoint-extract" - ], - "install_type": "git-clone", - "reference": "https://github.com/hekmon/comfyui-checkpoint-extract", - "title": "comfyui-checkpoint-extract" - }, - { - "author": "hekmon", - "description": "Call LLM and VLM in a simple way using the OpenAI API standard from ComfyUI", - "files": [ - "https://github.com/hekmon/comfyui-openai-api" - ], - "install_type": "git-clone", - "reference": "https://github.com/hekmon/comfyui-openai-api", - "title": "ComfyUI OpenAI API" - }, - { - "author": "rookiepsi", - "description": "Nodes for ComfyUI that extend the core functionality without adding extra dependencies.", - "files": [ - "https://github.com/rookiepsi/comfyui-extended" - ], - "install_type": "git-clone", - "reference": "https://github.com/rookiepsi/comfyui-extended", - "title": "ComfyUI Extended" - }, - { - "author": "rookiepsi", - "description": "A custom node for ComfyUI that applies a Gaussian blur to a mask.", - "files": [ - "https://github.com/rookiepsi/comfypsi_blur_mask" - ], - "install_type": "git-clone", - "reference": "https://github.com/rookiepsi/comfypsi_blur_mask", - "title": "Blur Mask" - }, - { - "author": "younyokel", - "description": "This is a custom node for ComfyUI that provides tools to clean, optimize, and format text prompts. It includes features like converting tags, aligning brackets, and applying weights to prompts.", - "files": [ - "https://github.com/younyokel/comfyui_prompt_formatter" - ], - "install_type": "git-clone", - "reference": "https://github.com/younyokel/comfyui_prompt_formatter", - "title": "ComfyUI Prompt Formatter" - }, - { - "author": "MoonGoblinDev", - "description": "Civicomfy seamlessly integrates Civitai's vast model repository directly into ComfyUI, allowing you to search, download, and organize AI models without leaving your workflow.", - "files": [ - "https://github.com/MoonGoblinDev/Civicomfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/MoonGoblinDev/Civicomfy", - "title": "Civicomfy - Civitai Model Downloader for ComfyUI" - }, - { - "author": "hunzmusic", - "description": "This custom node package provides nodes specifically for using the mvadapter_ig2mv_sdxl.safetensors adapter within ComfyUI. This adapter is designed for image-guided multi-view generation, typically used for creating textures from 3D mesh renders (position and normal maps).", - "files": [ - "https://github.com/hunzmusic/ComfyUI-IG2MV" - ], - "install_type": "git-clone", - "reference": "https://github.com/hunzmusic/ComfyUI-IG2MV", - "title": "ComfyUI-IG2MV" - }, - { - "author": "LaVie024", - "description": "Utility nodes for some randomness in your workflows, like random latent sizes. A few modifications to a few existing nodes, includes nodes for sampler and model parameters. Also includes two schedulers and four samplers that can be used with any KSampler node.", - "files": [ - "https://github.com/LaVie024/comfyui-lopi999-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/LaVie024/comfyui-lopi999-nodes", - "title": "comfyui-lopi999-nodes" - }, - { - "author": "Ky11le", - "description": "A ComfyUI custom node for tiling images horizontally with configurable spacing", - "files": [ - "https://github.com/Ky11le/draw_tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/Ky11le/draw_tools", - "title": "draw_tools" - }, - { - "author": "cleanlii", - "description": "DalleImageNodes is a custom extension for ComfyUI that integrates OpenAI's DALL\u00b7E 3 API for: Image generation, Inpainting (image editing), Image variation.\nThis project supports the latest OpenAI Python SDK (v1.x) and automatically handles image resizing and format requirements (RGBA, fixed sizes) based on the examples from the offical Dall-E website.", - "files": [ - "https://github.com/cleanlii/comfyui-dalle-integration" - ], - "install_type": "git-clone", - "reference": "https://github.com/cleanlii/comfyui-dalle-integration", - "title": "DalleImageNodes - OpenAI DALL\u00b7E Nodes for ComfyUI" - }, - { - "author": "Sekiun", - "description": "Utility node for converting a .webp format image into sequential png images in ComfyUI", - "files": [ - "https://github.com/Sekiun/ComfyUI-WebpToPNGSequence" - ], - "install_type": "git-clone", - "reference": "https://github.com/Sekiun/ComfyUI-WebpToPNGSequence", - "title": "ComfyUI-WebpToPNGSequence" - }, - { - "author": "Michael Gold", - "description": "Easily download and install 2D to 3D and Flux models from Hugging Face.", - "files": [ - "https://github.com/michaelgold/ComfyUI-HF-Model-Downloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/michaelgold/ComfyUI-HF-Model-Downloader", - "title": "ComfyUI-HF-Model-Downloader" - }, - { - "author": "Siempreflaco", - "description": "NODES: Audio Recorder, Line Counter, Increment INT, Image Processor, Load 3D Mesh From Outputs", - "files": [ - "https://github.com/Siempreflaco/ComfyUI-NCNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Siempreflaco/ComfyUI-NCNodes", - "title": "ComfyUI-NCNodes" - }, - { - "author": "livepeer", - "description": "A suite of custom ComfyUI nodes for building real-time video and audio workflows using ComfyStream.", - "files": [ - "https://github.com/livepeer/ComfyUI-Stream-Pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/livepeer/ComfyUI-Stream-Pack", - "title": "ComfyUI-Stream-Pack" - }, - { - "author": "VertexAnomaly", - "description": "A ComfyUI node that provides an Image Loader that updates inside the workflow when the Image file is changed. This allows for a easy bridge between 3D software, and 2D art applications while retaining use of a full workflow rather than being restricted to a plugin.", - "files": [ - "https://github.com/VertexAnomaly/ComfyUI_ImageSentinel" - ], - "install_type": "git-clone", - "reference": "https://github.com/VertexAnomaly/ComfyUI_ImageSentinel", - "title": "ComfyUI_ImageSentinel" - }, - { - "author": "iSuneast", - "description": "The Webhook Notification plugin for ComfyUI, used to send webhook notifications when image generation is complete.", - "files": [ - "https://github.com/iSuneast/ComfyUI-WebhookNotifier" - ], - "install_type": "git-clone", - "reference": "https://github.com/iSuneast/ComfyUI-WebhookNotifier", - "title": "ComfyUI-WebhookNotifier" - }, - { - "author": "mobilehacker", - "description": "Simple node to convert lora_stack output into string to generate a1111-style lora strength output text, like . Used to include your Lora names from Lora Stack in text input-output nodes and so on.", - "files": [ - "https://github.com/mobilehacker/ComfyUI_format-lora-stack" - ], - "install_type": "git-clone", - "reference": "https://github.com/mobilehacker/ComfyUI_format-lora-stack", - "title": "ComfyUI_format-lora-stack" - }, - { - "author": "Jokimbe", - "description": "Connect to any Draw Things gRPC server", - "files": [ - "https://github.com/Jokimbe/ComfyUI-DrawThings-gRPC" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jokimbe/ComfyUI-DrawThings-gRPC", - "title": "ComfyUI-DrawThings-gRPC" - }, - { - "author": "Temult", - "description": "Interactive sigma schedule editor with graph and text input.", - "files": [ - "https://github.com/Temult/TWanSigmaGraph" - ], - "install_type": "git-clone", - "reference": "https://github.com/Temult/TWanSigmaGraph", - "title": "TWanSigmaGraph" - }, - { - "author": "Raykosan", - "description": "Professional image saturation control with artifact and highlight protection.", - "files": [ - "https://github.com/Raykosan/ComfyUI_RS-SaturationNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/Raykosan/ComfyUI_RS-SaturationNode", - "title": "ComfyUI_RS-SaturationNode" - }, - { - "author": "Raykosan", - "description": "A node for ComfyUI that allows you to overlay text on an image in an area defined by a mask, with support for text rotation, custom fonts, line breaks and transparency.", - "files": [ - "https://github.com/Raykosan/ComfyUI_RaykoStudio" - ], - "install_type": "git-clone", - "reference": "https://github.com/Raykosan/ComfyUI_RaykoStudio", - "title": "ComfyUI_RaykoStudio" - }, - { - "author": "MasterpieceX", - "description": "This Module provides nodes to allow the development of 3D Generative AI workflows that use the MasterpieceX Python SDK.", - "files": [ - "https://github.com/withmpx/mpx-comfyui-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/withmpx/mpx-comfyui-nodes", - "title": "mpx-comfyui-nodes" - }, - { - "author": "jerrywap", - "description": "A ComfyUI node that fetches an image from an HTTP URL and returns it as an image tensor. Useful for API-based workflows.", - "files": [ - "https://github.com/jerrywap/ComfyUI_LoadImageFromHttpURL" - ], - "id": "load-image-from-http-url", - "install_type": "git-clone", - "reference": "https://github.com/jerrywap/ComfyUI_LoadImageFromHttpURL", - "title": "ComfyUI_LoadImageFromHttpURL" - }, - { - "author": "jerrywap", - "description": "Send generated images or videos to any HTTP webhook with optional parameters such as prompt-id and metadata payload.", - "files": [ - "https://github.com/jerrywap/ComfyUI_UploadToWebhookHTTP" - ], - "id": "upload-to-webhook-http", - "install_type": "git-clone", - "reference": "https://github.com/jerrywap/ComfyUI_UploadToWebhookHTTP", - "title": "ComfyUI_UploadToWebhookHTTP" - }, - { - "author": "CGAnimitta", - "description": "A series of functional custom plugins, including Blender Bridge, Extract the value of any list type data, read text files, etc.", - "files": [ - "https://github.com/cganimitta/ComfyUI_CGAnimittaTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/cganimitta/ComfyUI_CGAnimittaTools", - "title": "ComfyUI_CGAnimittaTools" - }, - { - "author": "rickyars", - "description": "A ComfyUI node that generates tiled image compositions with overlapping regions. This approach creates coherent compositions by using the edges of each tile as seeds for neighboring tiles, resulting in seamless transitions.", - "files": [ - "https://github.com/rickyars/comfyui-llm-tile" - ], - "install_type": "git-clone", - "reference": "https://github.com/rickyars/comfyui-llm-tile", - "title": "Tiled Image Generator for ComfyUI" - }, - { - "author": "nako-nakoko", - "description": "Custom nodes with split, random, and select functions for easy visual and management of multiple cumbersome prompts", - "files": [ - "https://github.com/nako-nakoko/ComfyUI_Mel_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/nako-nakoko/ComfyUI_Mel_Nodes", - "title": "ComfyUI_Mel_Nodes" - }, - { - "author": "synthetai", - "description": "A ComfyUI custom node based on GFPGAN for face enhancement, capable of restoring and enhancing faces in images and videos", - "files": [ - "https://github.com/synthetai/ComfyUI_FaceEnhancer" - ], - "install_type": "git-clone", - "reference": "https://github.com/synthetai/ComfyUI_FaceEnhancer", - "title": "ComfyUI_FaceEnhancer" - }, - { - "author": "synthetai", - "description": "This is a custom node for ComfyUI that allows you to use the KLingAI API directly within the ComfyUI environment. It is developed based on the KLingAI API documentation. For more details, please refer to the official documentation. [a/KLingAI API Documentation](https://app.klingai.com/global/dev/document-api/quickStart/productIntroduction/overview).", - "files": [ - "https://github.com/synthetai/ComfyUI-JM-KLing-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/synthetai/ComfyUI-JM-KLing-API", - "title": "ComfyUI-JM-KLing-API" - }, - { - "author": "synthetai", - "description": "A custom node extension for ComfyUI that enables batch processing of prompts from text files to generate multiple images.", - "files": [ - "https://github.com/synthetai/ComfyUI_PromptBatcher" - ], - "install_type": "git-clone", - "reference": "https://github.com/synthetai/ComfyUI_PromptBatcher", - "title": "ComfyUI_PromptBatcher" - }, - { - "author": "synthetai", - "description": "A collection of utility nodes for ComfyUI, including audio/video processing, file uploads, and AI image generation.", - "files": [ - "https://github.com/synthetai/ComfyUI-ToolBox" - ], - "install_type": "git-clone", - "reference": "https://github.com/synthetai/ComfyUI-ToolBox", - "title": "ComfyUI-ToolBox" - }, - { - "author": "synthetai", - "description": "A collection of ComfyUI custom nodes that integrate with MiniMax API services.", - "files": [ - "https://github.com/synthetai/ComfyUI-JM-MiniMax-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/synthetai/ComfyUI-JM-MiniMax-API", - "title": "ComfyUI-JM-MiniMax-API" - }, - { - "author": "synthetai", - "description": "volcengine comfyui api", - "files": [ - "https://github.com/synthetai/ComfyUI-JM-Volcengine-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/synthetai/ComfyUI-JM-Volcengine-API", - "title": "ComfyUI-JM-Volcengine-API" - }, - { - "author": "chou18194766xx", - "description": "comfyui image encrypt and save.", - "files": [ - "https://github.com/chou18194766xx/comfyui-EncryptSave" - ], - "install_type": "git-clone", - "reference": "https://github.com/chou18194766xx/comfyui-EncryptSave", - "title": "comfyui-EncryptSave" - }, - { - "author": "chou18194766xx", - "description": "ComfyUI's non-persistent (in-memory) image preview feature", - "files": [ - "https://github.com/chou18194766xx/comfyui_EncryptPreview" - ], - "install_type": "git-clone", - "reference": "https://github.com/chou18194766xx/comfyui_EncryptPreview", - "title": "comfyui_EncryptPreview" - }, - { - "author": "KERRY-YUAN", - "description": "This node package contains automatic sampler setting according to model name in ComfyUI, adjusting image size according to specific constraints and some other nodes.", - "files": [ - "https://github.com/KERRY-YUAN/ComfyUI_Simple_Executor" - ], - "id": "NodeSimpleExecutor", - "install_type": "git-clone", - "reference": "https://github.com/KERRY-YUAN/ComfyUI_Simple_Executor", - "title": "NodeSimpleExecutor" - }, - { - "author": "KERRY-YUAN", - "description": "Spark-TTS controllable synthesis and voice cloning.", - "files": [ - "https://github.com/KERRY-YUAN/ComfyUI_Spark_TTS" - ], - "id": "ComfyUI_Spark_TTS", - "install_type": "git-clone", - "reference": "https://github.com/KERRY-YUAN/ComfyUI_Spark_TTS", - "title": "ComfyUI_Spark_TTS" - }, - { - "author": "KERRY-YUAN", - "description": "Float project applicable to ComfyUI.Generates speaking portrait video frames from an image and audio.", - "files": [ - "https://github.com/KERRY-YUAN/ComfyUI_Float_Animator" - ], - "id": "ComfyUI_Float_Animator", - "install_type": "git-clone", - "reference": "https://github.com/KERRY-YUAN/ComfyUI_Float_Animator", - "title": "ComfyUI_Float_Animator" - }, - { - "author": "brantje", - "description": "Adds extra API functionallity and prometheus endpoint", - "files": [ - "https://github.com/brantje/ComfyUI-api-tools" - ], - "id": "comfyui_api_tools", - "install_type": "git-clone", - "reference": "https://github.com/brantje/ComfyUI-api-tools", - "title": "ComfyUI-api-tools" - }, - { - "author": "brantje", - "description": "Fixed version of the original [a/MagicQuill](https://github.com/magic-quill/ComfyUI_MagicQuill) node. Required nodes: ComfyUI-Brushnet and ComfyUI Controlnet AUX", - "files": [ - "https://github.com/brantje/ComfyUI_MagicQuill" - ], - "id": "comfyui_magicquill_fixed", - "install_type": "git-clone", - "reference": "https://github.com/brantje/ComfyUI_MagicQuill", - "title": "ComfyUI-MagicQuill" - }, - { - "author": "oshtz", - "description": "Custom ComfyUI nodes including LLM integration, LoRA switchers, image tools, and more", - "files": [ - "https://github.com/oshtz/ComfyUI-oshtz-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/oshtz/ComfyUI-oshtz-nodes", - "title": "oshtz Nodes" - }, - { - "author": "HavocsCall", - "description": "NODES: Prompt Combiner, Float/Int Selector, Sampler Config, Text Box, Int to Float/String, Int to Float/String, Clip/Conditioning/Image/Latent/Model/String/VAE Switch", - "files": [ - "https://github.com/HavocsCall/comfyui_HavocsCall_Custom_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/HavocsCall/comfyui_HavocsCall_Custom_Nodes", - "title": "HavocsCall's Custom ComfyUI Nodes" - }, - { - "author": "danger-electrodes", - "description": "A set of node for ComfyUI to create an influencer", - "files": [ - "https://github.com/danger-electrodes/ComfyUI_Fawfluencer_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/danger-electrodes/ComfyUI_Fawfluencer_Nodes", - "title": "ComfyUI_Fawfluencer_Nodes" - }, - { - "author": "badxprogramm", - "description": "GradientBlurNode is a custom node for ComfyUI that allows for gradient-based image blurring. This tool provides precise control over the direction, intensity, and distribution of the blur, making it ideal for creating smooth transitions, focusing attention on specific parts of an image, or adding artistic effects.", - "files": [ - "https://github.com/badxprogramm/ComfyUI-GradientBlur" - ], - "install_type": "git-clone", - "reference": "https://github.com/badxprogramm/ComfyUI-GradientBlur", - "title": "GradientBlurNode for ComfyUI" - }, - { - "author": "linksluckytime", - "description": "A comprehensive collection of ComfyUI nodes designed to reduce reliance on multiple third-party node packages.", - "files": [ - "https://github.com/linksluckytime/comfyui_snacknodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/linksluckytime/comfyui_snacknodes", - "title": "comfyui_snacknodes" - }, - { - "author": "uihp", - "description": "String Chain: Reconnect your prompts", - "files": [ - "https://github.com/uihp/ComfyUI-String-Chain" - ], - "install_type": "git-clone", - "reference": "https://github.com/uihp/ComfyUI-String-Chain", - "title": "ComfyUI-String-Chain" - }, - { - "author": "leoleexh", - "description": "A new ComfyUI node for integrating Topaz Photo AI's powerful image enhancement capabilities.", - "files": [ - "https://github.com/leoleelxh/Comfy-Topaz-Photo" - ], - "id": "leoleexh's Custom-Nodes", - "install_type": "git-clone", - "reference": "https://github.com/leoleelxh/Comfy-Topaz-Photo", - "title": "Comfy-Topaz-Photo" - }, - { - "author": "big-mon", - "description": "Provides recommended resolution presets specifically. Select a preset from the dropdown to easily output the corresponding width and height values for use with nodes like Empty Latent Image.", - "files": [ - "https://github.com/big-mon/ComfyUI-ResolutionPresets" - ], - "id": "bigmonComfyuiResolutionPresets", - "install_type": "git-clone", - "reference": "https://github.com/big-mon/ComfyUI-ResolutionPresets", - "title": "ComfyUI-ResolutionPresets" - }, - { - "author": "hnmr293", - "description": "Save Image/Latent to Shared Memory", - "files": [ - "https://github.com/hnmr293/comfyui-savemem" - ], - "install_type": "git-clone", - "reference": "https://github.com/hnmr293/comfyui-savemem", - "title": "ComfyUI-SaveMem" - }, - { - "author": "hnmr293", - "description": "A collection of nodes for manipulating LATENT in ComfyUI.", - "files": [ - "https://github.com/hnmr293/ComfyUI-latent-ops" - ], - "install_type": "git-clone", - "reference": "https://github.com/hnmr293/ComfyUI-latent-ops", - "title": "ComfyUI-latent-ops" - }, - { - "author": "Reithan", - "description": "NRS seeks to replace the 'naive' linear interpolation of Classifier Free Guidance with a more nuanced and composable steering of the generation process with better mathematical basis.", - "files": [ - "https://github.com/Reithan/negative_rejection_steering" - ], - "install_type": "git-clone", - "reference": "https://github.com/Reithan/negative_rejection_steering", - "title": "Negative Rejection Steering" - }, - { - "author": "FunnyFinger", - "description": "A custom node for ComfyUI to have many sliders with interactive dynamic behavior. This repository includes all necessary code and assets for seamless integration.", - "files": [ - "https://github.com/FunnyFinger/Dynamic_Sliders_stack" - ], - "install_type": "git-clone", - "reference": "https://github.com/FunnyFinger/Dynamic_Sliders_stack", - "title": "Dynamic Sliders Stack" - }, - { - "author": "FunnyFinger", - "description": "A Custom Node for ComfyUi to add an interactive Radar graph to visually control weights.", - "files": [ - "https://github.com/FunnyFinger/ComfyUi-RadarWeightNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/FunnyFinger/ComfyUi-RadarWeightNode", - "title": "Radar Weights Node" - }, - { - "author": "xLegende", - "description": "This repository contains custom nodes for ComfyUI designed to help structure, filter, and generate text prompts using categorized tag definitions stored in a YAML file.", - "files": [ - "https://github.com/xLegende/ComfyUI-Prompt-Formatter" - ], - "id": "comfyui-prompt-formatter", - "install_type": "git-clone", - "reference": "https://github.com/xLegende/ComfyUI-Prompt-Formatter", - "title": "ComfyUI-Prompt-Formatter" - }, - { - "author": "QijiTec", - "description": "Default 16GB VRAM UNO in context generation ComfyUI-node, using RED-UNO FT model", - "files": [ - "https://github.com/QijiTec/ComfyUI-RED-UNO" - ], - "install_type": "git-clone", - "reference": "https://github.com/QijiTec/ComfyUI-RED-UNO", - "title": "ComfyUI-RED-UNO" - }, - { - "author": "Danteday", - "description": "A powerful extension for ComfyUI that enables adding notes to many node in your workflow. Keep track of important settings, reminders, and workflow documentation directly within your ComfyUI canvas.", - "files": [ - "https://github.com/Danteday/ComfyUI-NoteManager" - ], - "install_type": "git-clone", - "reference": "https://github.com/Danteday/ComfyUI-NoteManager", - "title": "NoteManager" - }, - { - "author": "zzw5516", - "description": "Automatic prompt translation to Chinese, custom prompt management, AI-based prompt expansion and translation, AI-generated image/video record management (file browser), and cloud storage upload management.", - "files": [ - "https://github.com/zzw5516/ComfyUI-zw-tools" - ], - "id": "zzw5516", - "install_type": "git-clone", - "reference": "https://github.com/zzw5516/ComfyUI-zw-tools", - "title": "ComfyUI-zw-tools" - }, - { - "author": "nisaruj", - "description": "ComfyUI custom nodes for Diffusion Attentive Attribution Maps (DAAM)", - "files": [ - "https://github.com/nisaruj/comfyui-daam" - ], - "id": "comfyui-daam", - "install_type": "git-clone", - "reference": "https://github.com/nisaruj/comfyui-daam", - "title": "ComfyUI-DAAM" - }, - { - "author": "bytedance", - "description": "Official ComfyUI Support - InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity", - "files": [ - "https://github.com/bytedance/ComfyUI_InfiniteYou" - ], - "install_type": "git-clone", - "reference": "https://github.com/bytedance/ComfyUI_InfiniteYou", - "title": "ComfyUI_InfiniteYou" - }, - { - "author": "bytedance", - "description": "Official implementation in ComfyUI of CVPR 2025 paper 'HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis'", - "files": [ - "https://github.com/bytedance/ComfyUI-HyperLoRA" - ], - "install_type": "git-clone", - "reference": "https://github.com/bytedance/ComfyUI-HyperLoRA", - "title": "ComfyUI-HyperLoRA" - }, - { - "author": "bytedance", - "description": "ComfyUI Lumi Batcher is a batch processing extension plugin designed for ComfyUI, aiming to improve workflow debugging efficiency. Traditional debugging methods require adjusting parameters one by one, while this tool significantly enhances work efficiency through batch processing capabilities.", - "files": [ - "https://github.com/bytedance/comfyui-lumi-batcher" - ], - "install_type": "git-clone", - "reference": "https://github.com/bytedance/comfyui-lumi-batcher", - "title": "comfyui-lumi-batcher" - }, - { - "author": "AstroCorp", - "description": "A collection of personal ComfyUI nodes designed to enhance and automate workflows.", - "files": [ - "https://github.com/AstroCorp/ComfyUI-AstroCorp-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/AstroCorp/ComfyUI-AstroCorp-Nodes", - "title": "ComfyUI AstroCorp Nodes" - }, - { - "author": "stevenwg", - "description": "Add vertical and horizontal video grid support", - "files": [ - "https://github.com/stevenwg/ComfyUI-VideoGrid" - ], - "install_type": "git-clone", - "reference": "https://github.com/stevenwg/ComfyUI-VideoGrid", - "title": "ComfyUI-VideoGrid" - }, - { - "author": "avenstack", - "description": "\ud83c\udfa7Ultra High-Quality Voice Cloning, Chinese and English.", - "files": [ - "https://github.com/avenstack/ComfyUI-AV-MegaTTS3" - ], - "install_type": "git-clone", - "reference": "https://github.com/avenstack/ComfyUI-AV-MegaTTS3", - "title": "ComfyUI-AV-MegaTTS3" - }, - { - "author": "avenstack", - "description": "LatentSync 1.5 wrapper for ComfyUI", - "files": [ - "https://github.com/avenstack/ComfyUI-AV-LatentSync" - ], - "install_type": "git-clone", - "reference": "https://github.com/avenstack/ComfyUI-AV-LatentSync", - "title": "ComfyUI-AV-LatentSync" - }, - { - "author": "avenstack", - "description": "FunASR wrapper for ComfyUI", - "files": [ - "https://github.com/avenstack/ComfyUI-AV-FunASR" - ], - "install_type": "git-clone", - "reference": "https://github.com/avenstack/ComfyUI-AV-FunASR", - "title": "ComfyUI-AV-FunASR" - }, - { - "author": "WarpedAnimation", - "description": "A toolset for Hunyuan Video (mainly), with some additional nodes applicable to Framepack Video and WAN Video", - "files": [ - "https://github.com/WarpedAnimation/ComfyUI-WarpedToolset" - ], - "install_type": "git-clone", - "reference": "https://github.com/WarpedAnimation/ComfyUI-WarpedToolset", - "title": "ComfyUI-WarpedToolset" - }, - { - "author": "Jint8888", - "description": "This project contains some custom ComfyUI nodes for image processing, AI conversation, and utility tasks.", - "files": [ - "https://github.com/Jint8888/Comfyui_JTnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jint8888/Comfyui_JTnodes", - "title": "Comfyui_JTnodes" - }, - { - "author": "ichabodcole", - "description": "A collection of ComfyUI Nodes, most to make dynamic prompting a bit easier.", - "files": [ - "https://github.com/ichabodcole/ComfyUI-Ichis-Pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/ichabodcole/ComfyUI-Ichis-Pack", - "title": "ComfyUI-Ichis-Pack" - }, - { - "author": "SignalCha1n", - "description": "A collection of custom nodes for ComfyUI designed to replicate certain visual elements and effects reminiscent of Snap and early digital aesthetics. These nodes are ideal for image processing tasks and are fully compatible with ComfyUI.", - "files": [ - "https://github.com/SignalCha1n/comfyui-ComfySnap" - ], - "install_type": "git-clone", - "reference": "https://github.com/SignalCha1n/comfyui-ComfySnap", - "title": "Snap Style Nodes for ComfyUI" - }, - { - "author": "judian17", - "description": "The unofficial implementation of ZIM in ComfyUI", - "files": [ - "https://github.com/judian17/ComfyUI_ZIM" - ], - "install_type": "git-clone", - "reference": "https://github.com/judian17/ComfyUI_ZIM", - "title": "ComfyUI_ZIM" - }, - { - "author": "judian17", - "description": "Extract LoRA from the original Fine-Tuned model.", - "files": [ - "https://github.com/judian17/ComfyUI-Extract_Flux_Lora" - ], - "install_type": "git-clone", - "reference": "https://github.com/judian17/ComfyUI-Extract_Flux_Lora", - "title": "ComfyUI-Extract_Flux_Lora" - }, - { - "author": "judian17", - "description": "This project provides a node for ComfyUI to use the JoyCaption-Beta model in GGUF format for image captioning.", - "files": [ - "https://github.com/judian17/ComfyUI-joycaption-beta-one-GGUF" - ], - "install_type": "git-clone", - "reference": "https://github.com/judian17/ComfyUI-joycaption-beta-one-GGUF", - "title": "ComfyUI JoyCaption-Beta-GGUF Node" - }, - { - "author": "judian17", - "description": "Unofficial ComfyUI implementation of [a/UniWorld-V1](https://github.com/PKU-YuanGroup/UniWorld-V1).", - "files": [ - "https://github.com/judian17/ComfyUI-UniWorld-jd17" - ], - "install_type": "git-clone", - "reference": "https://github.com/judian17/ComfyUI-UniWorld-jd17", - "title": "ComfyUI-UniWorld-jd17" - }, - { - "author": "AngelCookies", - "description": "A ComfyUI extension that tracks random seeds throughout your image generation workflows", - "files": [ - "https://github.com/AngelCookies/ComfyUI-Seed-Tracker" - ], - "install_type": "git-clone", - "reference": "https://github.com/AngelCookies/ComfyUI-Seed-Tracker", - "title": "ComfyUI-Seed-Tracker" - }, - { - "author": "TiamaTiramisu", - "description": "Nodes for integration with RisuAI", - "files": [ - "https://github.com/TiamaTiramisu/risutools" - ], - "install_type": "git-clone", - "reference": "https://github.com/TiamaTiramisu/risutools", - "title": "RisuTools" - }, - { - "author": "excelwong", - "description": "A ComfyUI custom node plugin for assembling prompts. It allows you to generate positive and negative prompts by selecting different options.", - "files": [ - "https://github.com/excelwong/ComfyUI-PromptComposer" - ], - "install_type": "git-clone", - "reference": "https://github.com/excelwong/ComfyUI-PromptComposer", - "title": "ComfyUI Prompt Composer" - }, - { - "author": "jida-ai", - "description": "Nodes related to video chat workflows", - "files": [ - "https://github.com/lebrosoft/ComfyUI-VideoChatWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/lebrosoft/ComfyUI-VideoChatWrapper", - "title": "ComfyUI-VideoChatWrapper" - }, - { - "author": "VK", - "description": "ComfyUI nodes to simplify my tiled render", - "files": [ - "https://github.com/VK/vk-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/VK/vk-nodes", - "title": "VK Nodes" - }, - { - "author": "MaruPelkar", - "description": "A custom node for ComfyUI that allows resizing of conditioning tensors, particularly useful for fixing size mismatches between CLIP Text Encode and CLIP Vision Encode outputs in SD3 workflows.", - "files": [ - "https://github.com/MaruPelkar/comfyui-conditioning-resizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/MaruPelkar/comfyui-conditioning-resizer", - "title": "ComfyUI Conditioning Resizer" - }, - { - "author": "bablueza", - "description": "NODES: Vaja Synthesis Api, ShowText", - "files": [ - "https://github.com/bablueza/ComfyUI-Vaja-Ai4thai" - ], - "install_type": "git-clone", - "reference": "https://github.com/bablueza/ComfyUI-Vaja-Ai4thai", - "title": "Vaja TextToSpeech Node for ComfyUI" - }, - { - "author": "GrailGreg", - "description": "Based on the original SaveImage node from ComfyUI This ComfyUI node provides functionality to save images in PNG format while simultaneously generating and returning their Base64 encoded strings. This is particularly useful for applications that require image data to be transmitted as strings, such as web applications.", - "files": [ - "https://github.com/GrailGreg/images_base64" - ], - "install_type": "git-clone", - "reference": "https://github.com/GrailGreg/images_base64", - "title": "Image Saving and Base64 Encoding Script" - }, - { - "author": "vekitan55", - "description": "A custom ComfyUI node set for merging Flux.1-based models with intuitive control. This extension provides both simplified group merging and expert per-layer control, including support for advanced difference-based merge modes. Basically the code was generated by ChatGPT.", - "files": [ - "https://github.com/vekitan55/SimpleFlux1Merger" - ], - "install_type": "git-clone", - "reference": "https://github.com/vekitan55/SimpleFlux1Merger", - "title": "Simple Flux.1 Merger for ComfyUI" - }, - { - "author": "kantsche", - "description": "A custom node extension for ComfyUI that allows mixing multiple models during the sampling process for enhanced image generation.", - "files": [ - "https://github.com/kantsche/ComfyUI-MixMod" - ], - "install_type": "git-clone", - "reference": "https://github.com/kantsche/ComfyUI-MixMod", - "title": "ComfyUI-MixMod" - }, - { - "author": "goldwins520", - "description": "A collection of custom nodes for ComfyUI", - "files": [ - "https://github.com/goldwins520/Comfyui_saveimg2webdav" - ], - "install_type": "git-clone", - "reference": "https://github.com/goldwins520/Comfyui_saveimg2webdav", - "title": "Save Image To Webdav" - }, - { - "author": "yogurt7771", - "description": "ComfyUI-YogurtNodes is a collection of custom nodes for ComfyUI, providing a series of practical image processing and workflow enhancement functionalities.", - "files": [ - "https://github.com/yogurt7771/ComfyUI-YogurtNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/yogurt7771/ComfyUI-YogurtNodes", - "title": "ComfyUI-YogurtNodes" - }, - { - "author": "comfy-deploy", - "description": "A custom node collection for integrating various LLM (Large Language Model) providers with ComfyUI.", - "files": [ - "https://github.com/comfy-deploy/comfyui-llm-toolkit" - ], - "install_type": "git-clone", - "reference": "https://github.com/comfy-deploy/comfyui-llm-toolkit", - "title": "ComfyUI LLM Toolkit" - }, - { - "author": "JustLateNightAI", - "description": "A ComfyUI node that will block images that flag custom set key words", - "files": [ - "https://github.com/JustLateNightAI/KeywordImageBlocker" - ], - "install_type": "git-clone", - "reference": "https://github.com/JustLateNightAI/KeywordImageBlocker", - "title": "KeywordImageBlocker" - }, - { - "author": "EmAySee", - "description": "Lots of randomizers, a simple oobabooga adapter with json options to pass, and other useful nodes.", - "files": [ - "https://github.com/EmAySee/ComfyUI_EmAySee_CustomNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/EmAySee/ComfyUI_EmAySee_CustomNodes", - "title": "ComfyUI_EmAySee_CustomNodes" - }, - { - "author": "pupba", - "description": "A collection of ComfyUI custom nodes designed for image batch processing, per-index image operations, and AWS integration using EventBridge.", - "files": [ - "https://github.com/pupba/Comfy_ForEach" - ], - "install_type": "git-clone", - "reference": "https://github.com/pupba/Comfy_ForEach", - "title": "ComfyForEach" - }, - { - "author": "n0neye", - "description": "[a/A3D](https://github.com/n0neye/A3D) is an AI x 3D hybrid tool that allows you to compose 3D scenes and render them with AI. This integration allows you to send the color & depth images to ComfyUI. You can use it as a pose controller, or scene composer for your ComfyUI workflows.", - "files": [ - "https://github.com/n0neye/A3D-comfyui-integration" - ], - "install_type": "git-clone", - "reference": "https://github.com/n0neye/A3D-comfyui-integration", - "title": "A3D ComfyUI Integration" - }, - { - "author": "perilli", - "description": "A custom node suite to augment the capabilities of the [a/AP Workflows for ComfyUI](https://perilli.com/ai/comfyui/)", - "files": [ - "https://github.com/alessandroperilli/APW_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/alessandroperilli/APW_Nodes", - "title": "apw_nodes" - }, - { - "author": "alessandroperilli", - "description": "A custom node suite to augment the capabilities of the [a/Open Creative Studio for ComfyUI](https://oc.studio/).", - "files": [ - "https://github.com/alessandroperilli/OCS_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/alessandroperilli/OCS_Nodes", - "title": "Open Creative Studio Nodes" - }, - { - "author": "rkfg", - "description": "It's a ComfyUI wrapper for [a/Dia TTS](https://github.com/nari-labs/dia) by Nari labs, includes a portion of their code used for inference.", - "files": [ - "https://github.com/rkfg/ComfyUI-Dia_tts" - ], - "install_type": "git-clone", - "reference": "https://github.com/rkfg/ComfyUI-Dia_tts", - "title": "Dia realistic TTS" - }, - { - "author": "hubentu", - "description": "A collection of custom nodes for ComfyUI that allow working with LoRAs and trigger words by index selection.", - "files": [ - "https://github.com/hubentu/ComfyUI-loras-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/hubentu/ComfyUI-loras-loader", - "title": "Multiple LoRA Loader for ComfyUI" - }, - { - "author": "BNP1111", - "description": "This model was fine-tuned on Flux.1-Dev to with reflection tuning to serve as a corrector for self-refinement framework introduced in From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning. ", - "files": [ - "https://github.com/BNP1111/comfyui_flux_corrector" - ], - "install_type": "git-clone", - "reference": "https://github.com/BNP1111/comfyui_flux_corrector", - "title": "comfyui_flux_corrector" - }, - { - "author": "Cryptyox", - "description": "This Comfyui node creates an anaglyph image from a color and depth map input. It achieves high speeds suitable for video to anaglyph conversion by using CUDA GPU acceleration.", - "files": [ - "https://github.com/Cryptyox/anaglyphTool-Comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/Cryptyox/anaglyphTool-Comfyui", - "title": "anaglyphTool-Comfyui" - }, - { - "author": "hugo", - "description": "A FastGAN Node for ComfyUI", - "files": [ - "https://github.com/hugobb/FastGAN-ComfyUI-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/hugobb/FastGAN-ComfyUI-Node", - "title": "fastgan-comfyui" - }, - { - "author": "marklieberman", - "description": "A node to pause execution of the workflow and select which input images should proceed to the output.", - "files": [ - "https://github.com/marklieberman/ComfyUI-Liebs-Picker" - ], - "install_type": "git-clone", - "reference": "https://github.com/marklieberman/ComfyUI-Liebs-Picker", - "title": "ComfyUI-Liebs-Picker" - }, - { - "author": "marklieberman", - "description": "A node to display a toast notification. Use it to send a toast when your prompt is complete. Also pairs well with [a/ComfyUI-Liebs_Picker](https://github.com/marklieberman/ComfyUI-Liebs-Picker) and [a/cg-image-filter](https://github.com/chrisgoringe/cg-image-filter) to be notified when the picker is waiting.", - "files": [ - "https://github.com/marklieberman/ComfyUI-Liebs-Toast" - ], - "install_type": "git-clone", - "reference": "https://github.com/marklieberman/ComfyUI-Liebs-Toast", - "title": "ComfyUI-Liebs-Toast" - }, - { - "author": "marklieberman", - "description": "An extension to modify the browser tab title when running ComfyUI workflows.", - "files": [ - "https://github.com/marklieberman/ComfyUI-Liebs-Title" - ], - "install_type": "git-clone", - "reference": "https://github.com/marklieberman/ComfyUI-Liebs-Title", - "title": "ComfyUI-Liebs-Title" - }, - { - "author": "SXQBW", - "description": "ComfyUI-Qwen-Omni is the first ComfyUI plugin that supports end-to-end multimodal interaction, enabling seamless joint generation and editing of text, images, and audio. Without intermediate steps, with just one operation, the model can simultaneously understand and process multiple input modalities, generating coherent text descriptions and voice outputs, providing an unprecedentedly smooth experience for AI creation.", - "files": [ - "https://github.com/SXQBW/ComfyUI-Qwen-Omni" - ], - "install_type": "git-clone", - "reference": "https://github.com/SXQBW/ComfyUI-Qwen-Omni", - "title": "ComfyUI-Qwen-Omni" - }, - { - "author": "SXQBW", - "description": "The Qwen3 ComfyUI Integration is a powerful tool designed specifically for ComfyUI workflows, aiming to seamlessly integrate Qwen series large language models (LLMs).", - "files": [ - "https://github.com/SXQBW/ComfyUI-Qwen" - ], - "install_type": "git-clone", - "reference": "https://github.com/SXQBW/ComfyUI-Qwen", - "title": "ComfyUI-Qwen" - }, - { - "author": "SXQBW", - "description": "A ComfyUI extension for Qwen-VL series large language models, supporting multi-modal functions such as text generation, image understanding, and video analysis.Support for Qwen2-VL, Qwen2.5-VL.", - "files": [ - "https://github.com/SXQBW/ComfyUI-Qwen-VL" - ], - "install_type": "git-clone", - "reference": "https://github.com/SXQBW/ComfyUI-Qwen-VL", - "title": "ComfyUI-Qwen-VL" - }, - { - "author": "nobrainX2", - "description": "This is a ComfyUI integration of the [a/Dia TTS model](https://github.com/nari-labs/dia/). Many thanks to nari-labs for their fantastic work.", - "files": [ - "https://github.com/nobrainX2/comfyUI-customDia" - ], - "install_type": "git-clone", - "reference": "https://github.com/nobrainX2/comfyUI-customDia", - "title": "ComfyUI Custom Dia" - }, - { - "author": "zygion", - "description": "NODES: Item List, Template Input, Template Processor, Scene Queue Node, Trigger Passthrough Node", - "files": [ - "https://github.com/zygion/comfyui-zygion-util-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/zygion/comfyui-zygion-util-nodes", - "title": "i-zygion-util-nodes" - }, - { - "author": "quank123wip", - "description": "ComfyUI Custom Node for [a/Step1X-Edit](https://github.com/stepfun-ai/Step1X-Edit/). Noted this node may consume large VRAMs!", - "files": [ - "https://github.com/quank123wip/ComfyUI-Step1X-Edit" - ], - "install_type": "git-clone", - "reference": "https://github.com/quank123wip/ComfyUI-Step1X-Edit", - "title": "ComfyUI-Step1X-Edit" - }, - { - "author": "Xkipper", - "description": "ComfyUI Skipper Custom Nodes", - "files": [ - "https://github.com/Xkipper/ComfyUI_SkipperNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Xkipper/ComfyUI_SkipperNodes", - "title": "ComfyUI_SkipperNodes" - }, - { - "author": "FewBox", - "description": "Comfy Custom Node for Try-on.", - "files": [ - "https://github.com/FewBox/fewbox-outfit-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/FewBox/fewbox-outfit-comfyui", - "title": "fewbox-outfit-comfyui" - }, - { - "author": "hua(Kungfu)", - "description": "Encapsulate the comfyui workflow as a gradio webui", - "files": [ - "https://github.com/kungful/ComfyUI_to_webui" - ], - "install_type": "git-clone", - "reference": "https://github.com/kungful/ComfyUI_to_webui", - "title": "ComfyUI_to_webui" - }, - { - "author": "Jacky-MYQ", - "description": "RGB to CMYK (save as tif)", - "files": [ - "https://github.com/Jacky-MYQ/comfyui-rgb2cmyk" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jacky-MYQ/comfyui-rgb2cmyk", - "title": "RGB to CMYK for ComfyUI (Save as tif)" - }, - { - "author": "Jacky-MYQ", - "description": "Image cropping and Image resizing", - "files": [ - "https://github.com/Jacky-MYQ/comfyui-DataCleaning" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jacky-MYQ/comfyui-DataCleaning", - "title": "comfyui-DataCleaning" - }, - { - "author": "lceric", - "description": "Ports the official ComfyUI GPT-API node, adding support for customizable api_base, auth_token, and model settings.", - "files": [ - "https://github.com/lceric/comfyui-gpt-image" - ], - "install_type": "git-clone", - "reference": "https://github.com/lceric/comfyui-gpt-image", - "title": "comfyui-gpt-image" - }, - { - "author": "Alexankharin", - "description": "ComfyUI nodes for the different projection models and camera movements", - "files": [ - "https://github.com/Alexankharin/camera-comfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/Alexankharin/camera-comfyUI", - "title": "camera-comfyUI" - }, - { - "author": "somesomebody", - "description": "Preview images of LoRA files and edit their associated JSON files.", - "files": [ - "https://github.com/somesomebody/lorainfo-sidebar" - ], - "install_type": "git-clone", - "reference": "https://github.com/somesomebody/lorainfo-sidebar", - "title": "lorainfo-sidebar" - }, - { - "author": "dezoomer", - "description": "A collection of custom nodes for ComfyUI.", - "files": [ - "https://github.com/De-Zoomer/ComfyUI-DeZoomer-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/De-Zoomer/ComfyUI-DeZoomer-Nodes", - "title": "ComfyUI-DeZoomer-Nodes" - }, - { - "author": "lisaks", - "description": "Pixstri is a custom plugin for ComfyUI designed to generate comic pages. It provides a hierarchical node system that allows you to create comic layouts with rows and frames, making it easy to design and preview comic pages within your ComfyUI workflows.", - "files": [ - "https://github.com/lisaks/comfyui-panelforge" - ], - "install_type": "git-clone", - "reference": "https://github.com/lisaks/comfyui-panelforge", - "title": "Pixstri ComfyUI Comics" - }, - { - "author": "BobRandomNumber", - "description": "An implementation of Nari-Labs Dia TTS", - "files": [ - "https://github.com/BobRandomNumber/ComfyUI-DiaTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/BobRandomNumber/ComfyUI-DiaTTS", - "title": "ComfyUI-DiaTTS" - }, - { - "author": "BobRandomNumber", - "description": "A non real-time ComfyUI implementation of Kyutai TTS", - "files": [ - "https://github.com/BobRandomNumber/ComfyUI-KyutaiTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/BobRandomNumber/ComfyUI-KyutaiTTS", - "title": "ComfyUI-KyutaiTTS" - }, - { - "author": "BobRandomNumber", - "description": "wrapper for the TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation project", - "files": [ - "https://github.com/BobRandomNumber/ComfyUI-TLBVFI" - ], - "install_type": "git-clone", - "reference": "https://github.com/BobRandomNumber/ComfyUI-TLBVFI", - "title": "ComfyUI-TLBVFI" - }, - { - "author": "santiagosamuel3455", - "description": "Imagen descripcion prompt system", - "files": [ - "https://github.com/santiagosamuel3455/ComfyUI-GeminiImageToPrompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/santiagosamuel3455/ComfyUI-GeminiImageToPrompt", - "title": "ComfyUI-GeminiImageToPrompt" - }, - { - "author": "philipy1219", - "description": "This project is the ComfyUI implementation of the TaylorSeer project [a/https://github.com/Shenyi-Z/TaylorSeer](https://github.com/Shenyi-Z/TaylorSeer).", - "files": [ - "https://github.com/philipy1219/ComfyUI-TaylorSeer" - ], - "install_type": "git-clone", - "reference": "https://github.com/philipy1219/ComfyUI-TaylorSeer", - "title": "ComfyUI-TaylorSeer" - }, - { - "author": "philipy1219", - "description": "This extension adds cloud storage support to ComfyUI, allowing you to save and load images, masks, and videos directly from cloud storage services. Currently supports Aliyun OSS and AWS S3.", - "files": [ - "https://github.com/philipy1219/ComfyUI-CloudStorage" - ], - "install_type": "git-clone", - "reference": "https://github.com/philipy1219/ComfyUI-CloudStorage", - "title": "ComfyUI-CloudStorage" - }, - { - "author": "FaberVS", - "description": "A collection of nodes and utilities to make working with multiple models, custom parameters, and prompt styles in ComfyUI easier, faster, and more flexible. You are welcome to use and adapt them for your own workflows!", - "files": [ - "https://github.com/FaberVS/MultiModel" - ], - "install_type": "git-clone", - "reference": "https://github.com/FaberVS/MultiModel", - "title": "MultiModel" - }, - { - "author": "ArtsticH", - "description": "ComfyUI_EasyKitHT_NodeAlignPro is a lightweight ComfyUI node alignment and node coloring tool for refactoring and rewriting the UI based on the open-source projects Comfyui-Align and Comfyui-Nodealigner.", - "files": [ - "https://github.com/ArtsticH/ComfyUI_EasyKitHT_NodeAlignPro" - ], - "install_type": "git-clone", - "reference": "https://github.com/ArtsticH/ComfyUI_EasyKitHT_NodeAlignPro", - "title": "ComfyUI_EasyKitHT_NodeAlignPro" - }, - { - "author": "matorzhin", - "description": "NODES: 'Load One Image with Name, Title, Description', 'Load Multiple Images with Name, Directory, Title, Description'", - "files": [ - "https://github.com/matorzhin/milan-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/matorzhin/milan-nodes-comfyui", - "title": "milan-nodes-comfyui" - }, - { - "author": "cathodeDreams", - "description": "NODES: Save Image (JPG), Combine Conditionings (Weighted)", - "files": [ - "https://github.com/cathodeDreams/comfyui-azul-scripts" - ], - "install_type": "git-clone", - "reference": "https://github.com/cathodeDreams/comfyui-azul-scripts", - "title": "comfyui-azul-scripts" - }, - { - "author": "unicough", - "description": "This custom node uses OpenAI Image API to generate image (if no input image is provided) or edit image (if input image is provided) with the latest gpt-image-1 model. To use it, you will need to provide your OpenAI API key. This makes the node to be friendly for situations where ComfyUi serves as API server, because you don't have to login like the official OpenAI GPT Image 1 node does.", - "files": [ - "https://github.com/unicough/comfy_openai_image_api" - ], - "id": "comfy_openai_image_api", - "install_type": "git-clone", - "reference": "https://github.com/unicough/comfy_openai_image_api", - "title": "OpenAI Image API with gpt-image-1" - }, - { - "author": "XieJunchen", - "description": "comfyUI_LLM is the integration of a large language model into ComfyUI", - "files": [ - "https://github.com/XieJunchen/comfyUI_LLM" - ], - "install_type": "git-clone", - "reference": "https://github.com/XieJunchen/comfyUI_LLM", - "title": "comfyUI_LLM" - }, - { - "author": "raykindle", - "description": "This custom node integrates the [a/Step1X-Edit](https://github.com/stepfun-ai/Step1X-Edit) image editing model into ComfyUI. Step1X-Edit is a state-of-the-art image editing model that processes a reference image and user's editing instruction to generate a new image.", - "files": [ - "https://github.com/raykindle/ComfyUI_Step1X-Edit" - ], - "install_type": "git-clone", - "reference": "https://github.com/raykindle/ComfyUI_Step1X-Edit", - "title": "ComfyUI_Step1X-Edit" - }, - { - "author": "Vaporbook", - "description": "A better SaveImage than SaveImage-Plus.", - "files": [ - "https://github.com/Vaporbook/ComfyUI-SaveImage-PP" - ], - "install_type": "git-clone", - "reference": "https://github.com/Vaporbook/ComfyUI-SaveImage-PP", - "title": "ComfyUI-SaveImage-PP" - }, - { - "author": "McKlinton2", - "description": "This custom node for ComfyUI enables detailed segmentation of colored mask images into specific anatomical regions for male and female bodies.", - "files": [ - "https://github.com/McKlinton2/comfyui-mcklinton-pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/McKlinton2/comfyui-mcklinton-pack", - "title": "ComfyUI McKlinton Pack \u2014 Mask Node" - }, - { - "author": "kambara", - "description": "A custom node that makes prompt editing easier by allowing phrase switching with just mouse operations.", - "files": [ - "https://github.com/kambara/ComfyUI-PromptPalette" - ], - "install_type": "git-clone", - "reference": "https://github.com/kambara/ComfyUI-PromptPalette", - "title": "ComfyUI-PromptPalette" - }, - { - "author": "MijnSpam", - "description": "Send generated image to PushOver API webhook with optional parameters such as prompt-id and metadata payload.", - "files": [ - "https://github.com/MijnSpam/UploadToPushOver" - ], - "install_type": "git-clone", - "reference": "https://github.com/MijnSpam/UploadToPushOver", - "title": "Upload to PushOver" - }, - { - "author": "MijnSpam", - "description": "Do you want to easily swap width and heigth? Than this is for you. From portrait to Landscape. Is you images model trained on 1MP pictures, then you can easily scale those down. For best pictures width and heigth should be a factor of 32, say no more...", - "files": [ - "https://github.com/MijnSpam/ComfyUI_SwapAndScale" - ], - "install_type": "git-clone", - "reference": "https://github.com/MijnSpam/ComfyUI_SwapAndScale", - "title": "Comfy swap and scale" - }, - { - "author": "wakattac", - "description": "ComfyUI node for [a/Abstract Image Generation](https://github.com/wakattac/ComfyUI-AbstractImaGen/). This node is designed to create unique abstract base images on the fly within your ComfyUI workflows, which can then be used as input for VAE encoding, image-to-image generation, or other creative processes.", - "files": [ - "https://github.com/wakattac/ComfyUI-AbstractImaGen" - ], - "id": "abstract-imagen", - "install_type": "git-clone", - "reference": "https://github.com/wakattac/ComfyUI-AbstractImaGen", - "title": "ComfyUI-AbstractImaGen" - }, - { - "author": "Irsalistic", - "description": "A ComfyUI node that uses NVIDIA's DAM model to identify objects in masked regions", - "files": [ - "https://github.com/Irsalistic/comfyui-dam-object-extractor" - ], - "install_type": "git-clone", - "reference": "https://github.com/Irsalistic/comfyui-dam-object-extractor", - "tags": [ - "object recognition", - "vision", - "image analysis" - ], - "title": "ComfyUI DAM Object Extractor" - }, - { - "author": "dicksensei69", - "description": "A custom node for ComfyUI that creates looping animations from image sequences.", - "files": [ - "https://github.com/dicksensei69/comfyui_loops" - ], - "install_type": "git-clone", - "reference": "https://github.com/dicksensei69/comfyui_loops", - "title": "ComfyUI Loops" - }, - { - "author": "tighug", - "description": "A custom node for ComfyUI that classifies images into NSFW (Not Safe For Work) categories.", - "files": [ - "https://github.com/tighug/comfyui-rating-checker" - ], - "install_type": "git-clone", - "reference": "https://github.com/tighug/comfyui-rating-checker", - "title": "ComfyUI Rating Checker" - }, - { - "author": "tighug", - "description": "A custom node for ComfyUI that allows you to send images directly to [a/Eagle](https://jp.eagle.cool/).", - "files": [ - "https://github.com/tighug/comfyui-eagle-feeder" - ], - "install_type": "git-clone", - "reference": "https://github.com/tighug/comfyui-eagle-feeder", - "title": "ComfyUI Eagle Feeder" - }, - { - "author": "BigStationW", - "description": "ReforgeCFG is a ComfyUI node designed to add details to your image. [a/While it already exists in Comfy Core](https://github.com/comfyanonymous/ComfyUI/blob/80a44b97f5cbcb890896e2b9e65d177f1ac6a588/comfy_extras/nodes_model_advanced.py#L258), it lacks timesteps for adjustment.", - "files": [ - "https://github.com/BigStationW/ComfyUi-RescaleCFGAdvanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/BigStationW/ComfyUi-RescaleCFGAdvanced", - "title": "ComfyUi-RescaleCFGAdvanced" - }, - { - "author": "BigStationW", - "description": "This node displays the positive and negative prompts of a loaded ComfyUi image.", - "files": [ - "https://github.com/BigStationW/ComfyUi-Load-Image-And-Display-Prompt-Metadata" - ], - "install_type": "git-clone", - "reference": "https://github.com/BigStationW/ComfyUi-Load-Image-And-Display-Prompt-Metadata", - "title": "ComfyUi-Load-Image-And-Display-Prompt-Metadata" - }, - { - "author": "BigStationW", - "description": "A more advanced version of the original ImageScaleToTotalPixels node", - "files": [ - "https://github.com/BigStationW/ComfyUi-Scale-Image-to-Total-Pixels-Advanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/BigStationW/ComfyUi-Scale-Image-to-Total-Pixels-Advanced", - "title": "ComfyUi-Scale-Image-to-Total-Pixels-Advanced" - }, - { - "author": "rakki194", - "description": "A simple custom node for ComfyUI that allows you to compare two images (or batches of images) side-by-side within the UI.", - "files": [ - "https://github.com/rakki194/ComfyUI-ImageCompare" - ], - "install_type": "git-clone", - "reference": "https://github.com/rakki194/ComfyUI-ImageCompare", - "title": "ComfyUI-ImageCompare" - }, - { - "author": "matoo", - "description": "A custom ComfyUI extension to compare two video/image sequences via wipe preview.", - "files": [ - "https://github.com/surinder83singh/ComfyUI-compare-videos" - ], - "install_type": "git-clone", - "reference": "https://github.com/surinder83singh/ComfyUI-compare-videos", - "title": "Compare Videos" - }, - { - "author": "JoeNavark", - "description": "Custom Graph Sigma is a ComfyUI custom node that provides an interactive spline-based curve editor for visually creating and exporting custom sigma schedules. This is especially useful for controlling the noise schedule or custom step values in diffusion models and other workflows that use a sequence of values over time or steps.", - "files": [ - "https://github.com/JoeNavark/comfyui_custom_sigma_editor" - ], - "install_type": "git-clone", - "reference": "https://github.com/JoeNavark/comfyui_custom_sigma_editor", - "title": "Custom Graph Sigma for ComfyUI" - }, - { - "author": "hybskgks28275", - "description": "Various custom nodes will be added.", - "files": [ - "https://github.com/hybskgks28275/ComfyUI-hybs-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/hybskgks28275/ComfyUI-hybs-nodes", - "title": "ComfyUI-hybs-nodes" - }, - { - "author": "mohsensd1373", - "description": "Upload image from comfyui to WordPress add your site setting in file wordpress_config.json", - "files": [ - "https://github.com/mohsensd1373/comfyui_wordpress" - ], - "install_type": "git-clone", - "reference": "https://github.com/mohsensd1373/comfyui_wordpress", - "title": "comfyui_wordpress" - }, - { - "author": "StableLlama", - "description": "Basic Python functions for manipulating data that every programmer is used to. Currently supported: BOOLEAN, FLOAT, INT, STRING and data lists.", - "files": [ - "https://github.com/StableLlama/ComfyUI-basic_data_handling" - ], - "install_type": "git-clone", - "reference": "https://github.com/StableLlama/ComfyUI-basic_data_handling", - "title": "Basic data handling" - }, - { - "author": "charlyad142", - "description": "This custom node for ComfyUI provides integration with the BFL (Black Forest Labs) API to enhance and optimize image processing. It allows the use of Flux Pro directly within ComfyUI, offering advanced image processing capabilities.", - "files": [ - "https://github.com/charlyad142/ComfyUI_bfl_api_pro_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/charlyad142/ComfyUI_bfl_api_pro_nodes", - "title": "ComfyUI BFL API Pro Nodes" - }, - { - "author": "ServiceStack", - "description": "This node allows downloading models and other resources used within a ComfyUI workflow making it easier to resolve referenced assets and share workflows", - "files": [ - "https://github.com/ServiceStack/comfy-asset-downloader" - ], - "id": "asset-downloader", - "install_type": "git-clone", - "reference": "https://github.com/ServiceStack/comfy-asset-downloader", - "title": "ComfyUI Asset Downloader" - }, - { - "author": "Njbx", - "description": "ComfyUI-LTX13B-Blockswap This is a simple LTX block swap node for ComfyUI native nodes for 13B model, works by swapping upto 47 blocks to the CPU to reduce VRAM.", - "files": [ - "https://github.com/Njbx/ComfyUI-LTX13B-Blockswap" - ], - "install_type": "git-clone", - "reference": "https://github.com/Njbx/ComfyUI-LTX13B-Blockswap", - "title": "ComfyUI-LTX13B-Blockswap" - }, - { - "author": "IIs-fanta", - "description": "Nodes for playing mini-games with ComfyUI.", - "files": [ - "https://github.com/IIs-fanta/ComfyUI-FANTA-GameBox" - ], - "install_type": "git-clone", - "reference": "https://github.com/IIs-fanta/ComfyUI-FANTA-GameBox", - "title": "ComfyUI-FANTA-GameBox" - }, - { - "author": "pixible", - "description": "Helps deciding different settings depending on the input string", - "files": [ - "https://github.com/gasparuff/CustomSelector" - ], - "install_type": "git-clone", - "reference": "https://github.com/gasparuff/CustomSelector", - "title": "comfyui-customselector" - }, - { - "author": "AIWarper", - "description": "ComfyUI diffusers wrapper nodes for [a/NormalCrafter](https://github.com/Binyr/NormalCrafter)", - "files": [ - "https://github.com/AIWarper/ComfyUI-NormalCrafterWrapper" - ], - "id": "normal-crafter-wrapper", - "install_type": "git-clone", - "reference": "https://github.com/AIWarper/ComfyUI-NormalCrafterWrapper", - "title": "NormalCrafterWrapper" - }, - { - "author": "AIWarper", - "description": "NODES: DWPose Scaler (Warper), Mouth Mask from Pose (Warper), Facial Part Mask from Pose (Warper)", - "files": [ - "https://github.com/AIWarper/ComfyUI-WarperNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIWarper/ComfyUI-WarperNodes", - "title": "ComfyUI-WarperNodes" - }, - { - "author": "Goshe-nite", - "description": "Nodes to make ComfyUI-Image-Saver and rgthree-comfy more compatible. Allowing Power Lora Loader node to be used with Image Saver node. Also adding nodes to extract Image Saver compatible strings to simplify workflows.", - "files": [ - "https://github.com/Goshe-nite/comfyui-gps-supplements" - ], - "id": "GPSupps", - "install_type": "git-clone", - "reference": "https://github.com/Goshe-nite/comfyui-gps-supplements", - "title": "GPS' Supplements for ComfyUI" - }, - { - "author": "fpgaminer", - "description": "Nodes for running the JoyCaption image captioner VLM.", - "files": [ - "https://github.com/fpgaminer/joycaption_comfyui" - ], - "id": "comfyui-joycaption", - "install_type": "git-clone", - "reference": "https://github.com/fpgaminer/joycaption_comfyui", - "title": "JoyCaption Nodes" - }, - { - "author": "1hew", - "description": "This is a custom node collection for ComfyUI that provides some utility nodes.", - "files": [ - "https://github.com/1hew/ComfyUI-1hewNodes" - ], - "id": "ComfyUI-1hewNodes", - "install_type": "git-clone", - "reference": "https://github.com/1hew/ComfyUI-1hewNodes", - "title": "ComfyUI 1hewNodes" - }, - { - "author": "cyberhirsch", - "description": "Save image node with dynamic paths and an 'Open Folder' button.", - "files": [ - "https://github.com/cyberhirsch/seb_nodes" - ], - "id": "seb_nodes", - "install_type": "git-clone", - "reference": "https://github.com/cyberhirsch/seb_nodes", - "title": "Seb Nodes" - }, - { - "author": "Alastor 666 1933", - "description": "This node allows you to cache/caching/store and reuse resized images, ControlNet images, masks, and texts. It avoids repeating heavy operations by loading previously saved files \u2014 saving time, memory, and processing power in future executions.", - "files": [ - "https://github.com/alastor-666-1933/caching_to_not_waste" - ], - "id": "caching_to_not_waste", - "install_type": "git-clone", - "reference": "https://github.com/alastor-666-1933/caching_to_not_waste", - "title": "Caching to not Waste" - }, - { - "author": "hayd-zju", - "description": "This node pack provides the official ComfyUI workflow for ICEdit.", - "files": [ - "https://github.com/hayd-zju/ICEdit-ComfyUI-official" - ], - "install_type": "git-clone", - "reference": "https://github.com/hayd-zju/ICEdit-ComfyUI-official", - "title": "ICEdit-ComfyUI-official" - }, - { - "author": "SanDiegoDude", - "description": "quick Comfy Node to convert input waveform audio to MP3", - "files": [ - "https://github.com/SanDiegoDude/ComfyUI-SaveAudioMP3" - ], - "install_type": "git-clone", - "reference": "https://github.com/SanDiegoDude/ComfyUI-SaveAudioMP3", - "title": "ComfyUI-SaveAudioMP3" - }, - { - "author": "SanDiegoDude", - "description": "ComfyUI nodes for DeepStereo, an auto-autostereogram generator", - "files": [ - "https://github.com/SanDiegoDude/ComfyUI-DeepStereo" - ], - "install_type": "git-clone", - "reference": "https://github.com/SanDiegoDude/ComfyUI-DeepStereo", - "title": "ComfyUI-DeepStereo" - }, - { - "author": "SanDiegoDude", - "description": "A custom ComfyUI node for integrating with the Fal Kontext API for advanced image editing and generation.", - "files": [ - "https://github.com/SanDiegoDude/ComfyUI-Kontext-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/SanDiegoDude/ComfyUI-Kontext-API", - "title": "ComfyUI-Kontext-API" - }, - { - "author": "tavyra", - "description": "Generate or draw FLOAT arrays within ComfyUI", - "files": [ - "https://github.com/tavyra/ComfyUI_Curves" - ], - "install_type": "git-clone", - "reference": "https://github.com/tavyra/ComfyUI_Curves", - "title": "ComfyUI_Curves" - }, - { - "author": "krmahil", - "description": "A ComfyUI node that breaks closed loops in masks to prevent inpainting models from modifying enclosed regions", - "files": [ - "https://github.com/krmahil/comfyui-hollow-preserve" - ], - "install_type": "git-clone", - "reference": "https://github.com/krmahil/comfyui-hollow-preserve", - "title": "Hollow Preserve" - }, - { - "author": "lihaoyun6", - "description": "String random picker for ComfyUI", - "files": [ - "https://github.com/lihaoyun6/ComfyUI-CSV-Random-Picker" - ], - "install_type": "git-clone", - "reference": "https://github.com/lihaoyun6/ComfyUI-CSV-Random-Picker", - "title": "ComfyUI-CSV-Random-Picker" - }, - { - "author": "lihaoyun6", - "description": "Embeds an invisible watermark in the input image", - "files": [ - "https://github.com/lihaoyun6/ComfyUI-BlindWatermark" - ], - "install_type": "git-clone", - "reference": "https://github.com/lihaoyun6/ComfyUI-BlindWatermark", - "title": "ComfyUI-BlindWatermark" - }, - { - "author": "lihaoyun6", - "description": "Enhance your prompts using the Qwen LLM to align the behavior and capabilities of the Qwen-Image/Edit online version.", - "files": [ - "https://github.com/lihaoyun6/ComfyUI-QwenPromptRewriter" - ], - "install_type": "git-clone", - "reference": "https://github.com/lihaoyun6/ComfyUI-QwenPromptRewriter", - "title": "Comfyui-QwenPromptRewriter" - }, - { - "author": "northumber", - "description": "Collection of nodes for ComfyUI for automation", - "files": [ - "https://github.com/northumber/ComfyUI-northTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/northumber/ComfyUI-northTools", - "title": "ComfyUI-northTools" - }, - { - "author": "neggo", - "description": "This node pack provides a Python node that uses the SambaNova API to send prompts to a chat AI model (e.g., DeepSeek-V3-0324) and retrieve responses, intended for integration into node-based workflows like ComfyUI.", - "files": [ - "https://github.com/neggo/comfyui-sambanova" - ], - "install_type": "git-clone", - "reference": "https://github.com/neggo/comfyui-sambanova", - "title": "comfyui-sambanova" - }, - { - "author": "Sinphaltimus", - "description": "Several nodes that attempt to extract metadata and raw text information from Gen AI models.", - "files": [ - "https://github.com/Sinphaltimus/comfyui_fedcoms_node_pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/Sinphaltimus/comfyui_fedcoms_node_pack", - "title": "comfyui_fedcoms_node_pack" - }, - { - "author": "XchanBik", - "description": "This node can store a route with a chosen ID then load it anywhere in the workflow. Goal it to make linking less messy in my taste.", - "files": [ - "https://github.com/XchanBik/ComfyUI_SimpleBridgeNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/XchanBik/ComfyUI_SimpleBridgeNode", - "title": "ComfyUI_SimpleBridgeNode" - }, - { - "author": "wings6407", - "description": "Use the point editor to perform image composition editing.", - "files": [ - "https://github.com/wings6407/ComfyUI_HBH-image_overlay" - ], - "install_type": "git-clone", - "reference": "https://github.com/wings6407/ComfyUI_HBH-image_overlay", - "title": "ComfyUI_HBH-image_overlay" - }, - { - "author": "monkeyWie", - "description": "This custom node for ComfyUI provides a set of input elements to create forms or interact with your workflows.", - "files": [ - "https://github.com/monkeyWie/ComfyUI-FormInput" - ], - "install_type": "git-clone", - "reference": "https://github.com/monkeyWie/ComfyUI-FormInput", - "title": "ComfyUI-FormInput" - }, - { - "author": "bollerdominik", - "description": "A simple node to load image from local path or http url.", - "files": [ - "https://github.com/bollerdominik/ComfyUI-load-lora-from-url" - ], - "install_type": "git-clone", - "reference": "https://github.com/bollerdominik/ComfyUI-load-lora-from-url", - "title": "ComfyUI-load-lora-from-url" - }, - { - "author": "newtextdoc1111", - "description": "Autocomplete and Related Tag display for ComfyUI", - "files": [ - "https://github.com/newtextdoc1111/ComfyUI-Autocomplete-Plus" - ], - "install_type": "git-clone", - "reference": "https://github.com/newtextdoc1111/ComfyUI-Autocomplete-Plus", - "title": "ComfyUI-Autocomplete-Plus" - }, - { - "author": "otacoo", - "description": "Extract generation info from PNG and JPEG images, supports both A1111 and (some) ComfyUI metadata", - "files": [ - "https://github.com/otacoo/comfyui_otacoo" - ], - "install_type": "git-clone", - "reference": "https://github.com/otacoo/comfyui_otacoo", - "title": "Metadata-Extractor" - }, - { - "author": "vladpro3", - "description": "Custom nodes for ComfyUI to improve promts and image settings", - "files": [ - "https://github.com/vladpro3/ComfyUI_BishaNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/vladpro3/ComfyUI_BishaNodes", - "title": "ComfyUI_BishaNodes" - }, - { - "author": "otacoo", - "description": "A ComfyUI node that waits for a GPU temp and/or a number of seconds.", - "files": [ - "https://github.com/usrname0/comfyui-holdup" - ], - "install_type": "git-clone", - "reference": "https://github.com/usrname0/comfyui-holdup", - "title": "comfyui-holdup" - }, - { - "author": "lerignoux", - "description": "Nodes to generate a pecha-kucha presentation in ComfyUI", - "files": [ - "https://github.com/lerignoux/ComfyUI-PechaKucha" - ], - "install_type": "git-clone", - "reference": "https://github.com/lerignoux/ComfyUI-PechaKucha", - "title": "ComfyUI-PechaKucha" - }, - { - "author": "lerignoux", - "description": "A ComfyUI custom node to generate 3D assets using [a/Stable3D](https://github.com/Stable-X/Stable3DGen)", - "files": [ - "https://github.com/lerignoux/ComfyUI-Stable3DGen" - ], - "install_type": "git-clone", - "reference": "https://github.com/lerignoux/ComfyUI-Stable3DGen", - "title": "ComfyUI Stable3DGen" - }, - { - "author": "GroxicTinch", - "description": "Allows making a mirror of options that are on a node, for use creating your own UI", - "files": [ - "https://github.com/GroxicTinch/EasyUI-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/GroxicTinch/EasyUI-ComfyUI", - "title": "EasyUI" - }, - { - "author": "Dontdrunk", - "description": "Provide powerful frontend and backend integration node packages for ComfyUI - this is an exceptionally robust integration extension.", - "files": [ - "https://github.com/Dontdrunk/ComfyUI-DD-Nodes" - ], - "id": "comfyui-dd-nodes", - "install_type": "git-clone", - "reference": "https://github.com/Dontdrunk/ComfyUI-DD-Nodes", - "title": "ComfyUI-DD-Nodes" - }, - { - "author": "Dontdrunk", - "description": "A plugin offering supplementary Chinese translations for ComfyUI custom nodes.", - "files": [ - "https://github.com/Dontdrunk/ComfyUI-DD-Translation" - ], - "id": "comfyui-dd-translation", - "install_type": "git-clone", - "reference": "https://github.com/Dontdrunk/ComfyUI-DD-Translation", - "title": "ComfyUI-DD-Translation" - }, - { - "author": "TrophiHunter", - "description": "I wanted a way to batch add effects to images inside Comfyui so I made these nodes. Some of the effects should be ordered specifically so they stack and are effecting the image emulating camera effectsI made some workflows to show you the correct order.", - "files": [ - "https://github.com/TrophiHunter/ComfyUI_Photography_Nodes" - ], - "id": "comfyui-photography-nodes", - "install_type": "git-clone", - "reference": "https://github.com/TrophiHunter/ComfyUI_Photography_Nodes", - "title": "Photography Nodes" - }, - { - "author": "magic-eraser-org", - "description": "ComfyUI-Unwatermark: A ComfyUI custom node to intelligently remove watermarks from images using the unwatermark.ai API.\nThis custom node for ComfyUI allows you to easily remove watermarks from your images by leveraging the power of the unwatermark.ai API.", - "files": [ - "https://github.com/magic-eraser-org/ComfyUI-Unwatermark" - ], - "install_type": "git-clone", - "reference": "https://github.com/magic-eraser-org/ComfyUI-Unwatermark", - "title": "ComfyUI-Unwatermark" - }, - { - "author": "Sayene", - "description": "Loads an image and its transparency mask from a base64-encoded data URI. This is useful for API connections as you can transfer data directly rather than specify a file location.", - "files": [ - "https://github.com/Sayene/comfyui-base64-to-image-size" - ], - "install_type": "git-clone", - "reference": "https://github.com/Sayene/comfyui-base64-to-image-size", - "title": "comfyui-base64-to-image-size" - }, - { - "author": "xuhongming251", - "description": "for use jimeng ai in comfyui", - "files": [ - "https://github.com/xuhongming251/ComfyUI-Jimeng" - ], - "install_type": "git-clone", - "reference": "https://github.com/xuhongming251/ComfyUI-Jimeng", - "title": "ComfyUI-Jimeng" - }, - { - "author": "Kyron Mahan", - "description": "A package for intelligent image scaling, aspect ratio adjustments, metadata extraction, and video frame processing for Wan 2.1 vid2vid/img2vid workflows with Pony/SDXL models.", - "files": [ - "https://github.com/babydjac/comfyui-smart-scaler" - ], - "id": "smart-scaler", - "install_type": "git-clone", - "reference": "https://github.com/babydjac/comfyui-smart-scaler", - "title": "ComfyUI Smart Scaler" - }, - { - "author": "purewater2011", - "description": "This plugin adds functionality to ComfyUI for detecting yellow tones in images, making it particularly useful for skin tone analysis and image color evaluation.", - "files": [ - "https://github.com/purewater2011/comfyui_color_detection" - ], - "install_type": "git-clone", - "reference": "https://github.com/purewater2011/comfyui_color_detection", - "title": "comfyui_color_detection" - }, - { - "author": "San4itos", - "description": "A custom node for ComfyUI to save image sequences as video files using FFmpeg. Supports various codecs, audio muxing, and in-node previews.", - "files": [ - "https://github.com/San4itos/ComfyUI-Save-Images-as-Video" - ], - "install_type": "git-clone", - "reference": "https://github.com/San4itos/ComfyUI-Save-Images-as-Video", - "title": "Save Images to Video (FFmpeg) for ComfyUI" - }, - { - "author": "X-School-Academy", - "description": "X-FluxAgent turns ComfyUI into a smart, AI-powered agent capable of building software, automating tasks, and even managing your daily workflows \u2014 all with natural language prompts, no coding experience needed.", - "files": [ - "https://github.com/X-School-Academy/X-FluxAgent" - ], - "install_type": "git-clone", - "reference": "https://github.com/X-School-Academy/X-FluxAgent", - "title": "X-FluxAgent" - }, - { - "author": "cluny85", - "description": "A set of utility nodes for ComfyUI focused on scripting. Includes an enhanced UUID generator node.", - "files": [ - "https://github.com/cluny85/ComfyUI-Scripting-Tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/cluny85/ComfyUI-Scripting-Tools", - "title": "ComfyUI-Scripting-Tools" - }, - { - "author": "LamEmil", - "description": "A collection of custom nodes for ComfyUI that enable the creation of various ASCII art effects, from static images to complex, colorized typing animations and video conversions.", - "files": [ - "https://github.com/LamEmil/ComfyUI_ASCIIArtNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/LamEmil/ComfyUI_ASCIIArtNode", - "title": "ComfyUI ASCII Art Nodes" - }, - { - "author": "jqy-yo", - "description": "Create a mask to slice the image at specific coordinates", - "files": [ - "https://github.com/jqy-yo/Comfyui-BBoxLowerMask2" - ], - "install_type": "git-clone", - "reference": "https://github.com/jqy-yo/Comfyui-BBoxLowerMask2", - "title": "BBoxLowerMask2" - }, - { - "author": "ICAI Icelandic Center for Artificial Intelligence", - "description": "This custom node for ComfyUI allows you to test combinations of samplers and schedulers. It generates a batch of generated images(latents), as well as RGB images, each one annotated with the specific combination used, performance timing, and several image quality metrics (Laplacian Variance, Gradient Mean, FFT-based sharpness) for our internal usage, but kept as they could may be useful for others to.", - "files": [ - "https://github.com/IcelandicCenterArtificialIntelligence/ComfyUI-SamplerSchedulerMetricsTester" - ], - "id": "SamplerSchedulerMetricsTester", - "install_type": "git-clone", - "reference": "https://github.com/IcelandicCenterArtificialIntelligence/ComfyUI-SamplerSchedulerMetricsTester", - "title": "Sampler Scheduler Metrics Tester for ComfyUI" - }, - { - "author": "2frames.app", - "description": "Nodes for ComfyUI, mostly for llm calling and automatizing pulid multiface.", - "files": [ - "https://github.com/2frames/ComfyUI-AQnodes" - ], - "id": "AQnodes", - "install_type": "git-clone", - "reference": "https://github.com/2frames/ComfyUI-AQnodes", - "title": "AQnodes for ComfyUI" - }, - { - "author": "BigWhiteFly", - "description": "concatenate all images in floders, concatenate caption txt files for trainning loras.", - "files": [ - "https://github.com/BigWhiteFly/ComfyUI-ImageConcat" - ], - "install_type": "git-clone", - "reference": "https://github.com/BigWhiteFly/ComfyUI-ImageConcat", - "title": "ComfyUI-ImageConcat" - }, - { - "author": "Jannled", - "description": "ComfyUI Nodes for OWL-ViT / OWLv2 using the HuggingFace Transformers implementation", - "files": [ - "https://github.com/Jannled/owl-vit-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jannled/owl-vit-comfyui", - "title": "OWL-ViT ComfyUI" - }, - { - "author": "thedivergentai", - "description": "This repository contains a collection of custom nodes for ComfyUI designed to integrate external AI models, provide utilities, and enable advanced workflows.", - "files": [ - "https://github.com/thedivergentai/divergent_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/thedivergentai/divergent_nodes", - "title": "Divergent Nodes" - }, - { - "author": "skycoder", - "description": "A utility node for extracting and trimming filenames from file paths", - "files": [ - "https://github.com/skycoder182/comfyui-filename-tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/skycoder182/comfyui-filename-tools", - "title": "Filename Tools" - }, - { - "author": "skycoder", - "description": "A collection of useful custom nodes for ComfyUI workflows", - "files": [ - "https://github.com/skycoder182/comfyui-skycoder-tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/skycoder182/comfyui-skycoder-tools", - "title": "Skycoder Tools" - }, - { - "author": "craig-tanaka", - "description": "This is a set of custom nodes for ComfyUI that provide anime-style image segmentation using efficient pre-trained models.", - "files": [ - "https://github.com/craig-tanaka/comfyui_animeseg" - ], - "install_type": "git-clone", - "reference": "https://github.com/craig-tanaka/comfyui_animeseg", - "title": "ComfyUI Anime Segmentation Nodes v1.1.0" - }, - { - "author": "lepiai", - "description": "ComfyUI Mini Toolkit \u2013 Helps reduce node redundancy. Let\u2019s see if there\u2019s time to keep it updated regularly.", - "files": [ - "https://github.com/lepiai/ComfyUI-Minitools" - ], - "install_type": "git-clone", - "reference": "https://github.com/lepiai/ComfyUI-Minitools", - "title": "ComfyUI-Minitools" - }, - { - "author": "wildminder", - "description": "Kalman-Inspired Feature Propagation for Video Face Super-Resolution", - "files": [ - "https://github.com/wildminder/ComfyUI-KEEP" - ], - "install_type": "git-clone", - "reference": "https://github.com/wildminder/ComfyUI-KEEP", - "title": "ComfyUI-KEEP" - }, - { - "author": "wildminder", - "description": "ComfyUI Chatterbox TTS & Voice Conversion Node", - "files": [ - "https://github.com/wildminder/ComfyUI-Chatterbox" - ], - "install_type": "git-clone", - "reference": "https://github.com/wildminder/ComfyUI-Chatterbox", - "title": "ComfyUI-Chatterbox" - }, - { - "author": "wildminder", - "description": "ComfyUI startup optimizer and patcher", - "files": [ - "https://github.com/wildminder/000_ComfyUI-Optim" - ], - "install_type": "git-clone", - "reference": "https://github.com/wildminder/000_ComfyUI-Optim", - "title": "ComfyUI-Optim" - }, - { - "author": "wildminder", - "description": "VibeVoice TTS. Expressive, long-form, multi-speaker conversational audio", - "files": [ - "https://github.com/wildminder/ComfyUI-VibeVoice" - ], - "install_type": "git-clone", - "reference": "https://github.com/wildminder/ComfyUI-VibeVoice", - "title": "ComfyUI-VibeVoice" - }, - { - "author": "cloudkoala", - "description": "A collection of custom nodes for ComfyUI focused on aspect ratio management and other utilities.", - "files": [ - "https://github.com/cloudkoala/comfyui-koala" - ], - "install_type": "git-clone", - "reference": "https://github.com/cloudkoala/comfyui-koala", - "title": "comfyui-koala" - }, - { - "author": "Limbicnation", - "description": "A robust custom depth estimation node for ComfyUI using Depth-Anything models. It integrates depth estimation with configurable post-processing options including blur, median filtering, contrast enhancement, and gamma correction.", - "files": [ - "https://github.com/Limbicnation/ComfyUIDepthEstimation" - ], - "install_type": "git-clone", - "reference": "https://github.com/Limbicnation/ComfyUIDepthEstimation", - "title": "Depth Estimation Node" - }, - { - "author": "Limbicnation", - "description": "A ComfyUI custom node for face detection and cropping using OpenCV Haar cascades, with full ComfyUI v3 schema support and backward compatibility.", - "files": [ - "https://github.com/Limbicnation/ComfyUI_FaceDetectionNode" - ], - "id": "comfyui-face-detection-node", - "install_type": "git-clone", - "nodename_pattern": "FaceDetectionNode", - "reference": "https://github.com/Limbicnation/ComfyUI_FaceDetectionNode", - "title": "ComfyUI Face Detection Node" - }, - { - "author": "Limbicnation", - "description": "Automatic background removal and transparency generation for ComfyUI", - "files": [ - "https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover" - ], - "install_type": "git-clone", - "reference": "https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover", - "title": "Transparency Background Remover" - }, - { - "author": "Limbicnation", - "description": "Advanced seed generator for ComfyUI with multiple modes, state persistence, and cross-library synchronization", - "files": [ - "https://github.com/Limbicnation/ComfyUI-RandomSeedGenerator" - ], - "install_type": "git-clone", - "reference": "https://github.com/Limbicnation/ComfyUI-RandomSeedGenerator", - "title": "ComfyUI-RandomSeedGenerator" - }, - { - "author": "hao-ai-lab", - "description": "A custom node suite for ComfyUI that provides accelerated multi-GPU video generation using [a/FastVideo](https://github.com/hao-ai-lab/FastVideo).", - "files": [ - "https://github.com/hao-ai-lab/FastVideo" - ], - "install_type": "git-clone", - "reference": "https://github.com/hao-ai-lab/FastVideo", - "title": "ComfyUI-FastVideo" - }, - { - "author": "TensorKaze", - "description": "Custom nodes for ComfyUI with advanced image scaling, latent manipulation, and Flux sampling", - "files": [ - "https://github.com/TensorKaze/ComfyUI-TkNodes" - ], - "id": "ComfyUI-TkNodes", - "install_type": "git-clone", - "reference": "https://github.com/TensorKaze/ComfyUI-TkNodes", - "title": "ComfyUI-TkNodes" - }, - { - "author": "angree", - "description": "Enhance GLB 3D models with realistic materials, smart emissive elements, and procedural normal maps", - "files": [ - "https://github.com/angree/ComfyUI-Q_GLB_Material_Modifier" - ], - "install_type": "git-clone", - "reference": "https://github.com/angree/ComfyUI-Q_GLB_Material_Modifier", - "title": "Q GLB Material Modifier" - }, - { - "author": "angree", - "description": "ComfyUI custom node for finding and analyzing mask sizes in images", - "files": [ - "https://github.com/angree/ComfyUI-Q_find-mask-size" - ], - "install_type": "git-clone", - "reference": "https://github.com/angree/ComfyUI-Q_find-mask-size", - "title": "Q Find Mask Size" - }, - { - "author": "babe-and-spencer-enterprises", - "description": "A custom ComfyUI node that lets you upload generated images directly to your [a/BASE](https://getbase.app/) account \u2014 no manual downloads or re-uploads needed.", - "files": [ - "https://github.com/babe-and-spencer-enterprises/base-comfyui-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/babe-and-spencer-enterprises/base-comfyui-node", - "title": "ComfyUI Upload to BASE Node" - }, - { - "author": "R5-Revo", - "description": "UniversalLLMNode is a custom node for ComfyUI that provides a unified interface to use multiple major LLM APIs, including OpenAI, Anthropic (Claude), Google Gemini, Groq, and Mistral. It is ideal for tasks such as automatically generating high-quality SDXL prompts for image generation.", - "files": [ - "https://github.com/R5-Revo/llm-node-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/R5-Revo/llm-node-comfyui", - "title": "Universal LLM Node for ComfyUI" - }, - { - "author": "jaimitoes", - "description": "Musubi Tuner by kohya_ss", - "files": [ - "https://github.com/jaimitoes/ComfyUI_Wan2_1_lora_trainer" - ], - "install_type": "git-clone", - "reference": "https://github.com/jaimitoes/ComfyUI_Wan2_1_lora_trainer", - "title": "ComfyUI_Wan2_1_lora_trainer" - }, - { - "author": "karthikg-09", - "description": "A lightweight ComfyUI custom node for generating high-quality masks and pose detection for virtual try-on applications. This node extracts only the essential masking functionality from FitDiT without requiring heavy diffusion models.", - "files": [ - "https://github.com/karthikg-09/ComfyUI-Vton-Mask" - ], - "install_type": "git-clone", - "reference": "https://github.com/karthikg-09/ComfyUI-Vton-Mask", - "title": "ComfyUI-Vton-Mask" - }, - { - "author": "kiko9", - "description": "A powerful ComfyUI custom node that extends the standard text encoder with persistent prompt storage, advanced search capabilities, and an automatic image gallery system using SQLite.", - "files": [ - "https://github.com/ComfyAssets/ComfyUI_PromptManager" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComfyAssets/ComfyUI_PromptManager", - "title": "ComfyUI_PromptManager" - }, - { - "author": "kiko9", - "description": "A modern ComfyUI custom node package that provides essential UI controls for image generation workflows. These nodes allow you to centralize commonly shared parameters (scheduler, sampler, dimensions, seeds) and link them to multiple nodes in your workflow, eliminating redundancy while maintaining JSON metadata compatibility.", - "files": [ - "https://github.com/ComfyAssets/ComfyUI_Selectors" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComfyAssets/ComfyUI_Selectors", - "title": "ComfyUI_Selectors" - }, - { - "author": "kiko9", - "description": "ComfyUI-KikoTools provides carefully crafted, production-ready nodes grouped under the 'ComfyAssets' category. Each tool is designed with clean interfaces, comprehensive testing, and optimized performance for SDXL and FLUX workflows.", - "files": [ - "https://github.com/ComfyAssets/ComfyUI-KikoTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComfyAssets/ComfyUI-KikoTools", - "title": "ComfyUI-KikoTools" - }, - { - "author": "kiko9", - "description": "Real-time monitoring and statistics for ComfyUI", - "files": [ - "https://github.com/ComfyAssets/ComfyUI-KikoStats" - ], - "install_type": "git-clone", - "reference": "https://github.com/ComfyAssets/ComfyUI-KikoStats", - "title": "ComfyUI-KikoStats" - }, - { - "author": "TFL-TFL", - "description": "Text translation node for ComfyUI: No need to apply for a translation API key, just use it. Currently supports more than thirty translation platforms.", - "files": [ - "https://github.com/TFL-TFL/ComfyUI_Text_Translation" - ], - "install_type": "git-clone", - "reference": "https://github.com/TFL-TFL/ComfyUI_Text_Translation", - "title": "ComfyUI_Text_Translation" - }, - { - "author": "Charonartist", - "description": "This is a ComfyUI custom node that automatically detects trigger words from text prompts and applies the corresponding LoRA models.", - "files": [ - "https://github.com/Charonartist/comfyui-auto-lora-v2" - ], - "install_type": "git-clone", - "reference": "https://github.com/Charonartist/comfyui-auto-lora-v2", - "title": "ComfyUI Auto LoRA" - }, - { - "author": "Charonartist", - "description": "Smart resize node for ComfyUI that handles portrait/landscape images with short/long side specification", - "files": [ - "https://github.com/Charonartist/comfyui-smart-resize-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Charonartist/comfyui-smart-resize-node", - "title": "ComfyUI Smart Resize Node" - }, - { - "author": "Charonartist", - "description": "ComfyUI custom node for removing specified tags and their content from text", - "files": [ - "https://github.com/Charonartist/comfyui-tag-remover" - ], - "install_type": "git-clone", - "reference": "https://github.com/Charonartist/comfyui-tag-remover", - "title": "ComfyUI Tag Remover" - }, - { - "author": "Charonartist", - "description": "This is a ComfyUI custom node that extracts the last frame (image) from an input image batch. It is particularly useful when you want to obtain the final scene or frame in a video generation workflow.", - "files": [ - "https://github.com/Charonartist/comfyui-last-frame-extractor" - ], - "install_type": "git-clone", - "reference": "https://github.com/Charonartist/comfyui-last-frame-extractor", - "title": "comfyui-last-frame-extractor" - }, - { - "author": "ptmaster", - "description": "NODES: OverrideLoadedDiffusionDevice.\nI happen to have two graphics cards, and I want to load models into another graphics card in Comfyui, so I designed this small node.", - "files": [ - "https://github.com/ptmaster/ComfyUI-Load-Diffusion-Model-to-Muti-GPUs/raw/refs/heads/main/Load%20Diffusion%20Model%20into%20Muti%20GPUs.py" - ], - "install_type": "copy", - "reference": "https://github.com/ptmaster/ComfyUI-Load-Diffusion-Model-to-Muti-GPUs", - "title": "ComfyUI-Load-Diffusion-Model-to-Muti-GPUs" - }, - { - "author": "ptmaster", - "description": "This node pack is designed to adjust audio playback speed within ComfyUI, particularly to sync audio with models like FantasyTalking (WAN) that require specific frame rates. It can also be used for general-purpose audio speed control.", - "files": [ - "https://github.com/ptmaster/comfyui-audio-speed" - ], - "install_type": "git-clone", - "reference": "https://github.com/ptmaster/comfyui-audio-speed", - "title": "ComfyUI-audio-speed" - }, - { - "author": "ptmaster", - "description": "The Comfyui-PT-Keyframe-Camera is a powerful tool designed to streamline the process of creating animations with keyframe cameras. It integrates seamlessly with existing workflows, allowing you to focus on your creativity without getting bogged down by technical details. This tool is perfect for animators, filmmakers, and anyone looking to add depth to their projects.", - "files": [ - "https://github.com/ptmaster/Comfyui-PT-Keyframe-Camera" - ], - "install_type": "git-clone", - "reference": "https://github.com/ptmaster/Comfyui-PT-Keyframe-Camera", - "title": "Comfyui-PT-Keyframe-Camera" - }, - { - "author": "doubletwisted", - "description": "ComfyUI plugin for submitting workflows to Thinkbox Deadline for distributed rendering. Enables render farm distribution with configurable job settings, batch processing, and automatic seed variation.", - "files": [ - "https://github.com/doubletwisted/ComfyUI-Deadline-Plugin" - ], - "install_type": "git-clone", - "nodename_pattern": "DeadlineSubmitNode", - "reference": "https://github.com/doubletwisted/ComfyUI-Deadline-Plugin", - "title": "ComfyUI Deadline Submission" - }, - { - "author": "openvino-dev-samples", - "description": "OpenVINO node is designed for optimizing the performance of model inference in ComfyUI by leveraging Intel OpenVINO toolkits. It can support running model on Intel CPU, GPU and NPU device.", - "files": [ - "https://github.com/openvino-dev-samples/comfyui_openvino" - ], - "id": "comfyui-openvino", - "install_type": "git-clone", - "reference": "https://github.com/openvino-dev-samples/comfyui_openvino", - "title": "ComfyUI-OpenVINO" - }, - { - "author": "coiichan", - "description": "Works with any Depth Map and visualizes the applied version it inside ComfyUI.", - "files": [ - "https://github.com/CoiiChan/ComfyUI-Depth-Visualization-Advanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/CoiiChan/ComfyUI-Depth-Visualization-Advanced", - "title": "ComfyUI-Depth-Visualization-advanced" - }, - { - "author": "coiichan", - "description": "A masking tool that provides the ability to break down the detailed contours of characters one by one for multi person use scenarios", - "files": [ - "https://github.com/CoiiChan/comfyui-every-person-seg-coii" - ], - "install_type": "git-clone", - "reference": "https://github.com/CoiiChan/comfyui-every-person-seg-coii", - "reference2": "https://github.com/CoiiChan/ComfyUI-Every-Person-Seg-CoiiNode", - "title": "comfyui-every-person-seg-coii" - }, - { - "author": "coiichan", - "description": "This allows for mathematical operations on input images and precise manipulation of channels through NumPy formulas, making it suitable for ComfyUI users with programming experience.", - "files": [ - "https://github.com/CoiiChan/ComfyUI-FuncAsTexture-CoiiNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/CoiiChan/ComfyUI-FuncAsTexture-CoiiNode", - "title": "ComfyUI-FuncAsTexture-CoiiNode" - }, - { - "author": "coulterj", - "description": "This ComfyUI custom node processes SVG (Scalable Vector Graphics) images to ensure the artwork consistently fills its canvas, is visually centered, and optionally has a margin applied. It's designed to provide more accurate results for complex vector graphics, such as those generated by vector tracing tools, where simple geometric bounding boxes may not align with perceived visual extents.", - "files": [ - "https://github.com/coulterj/comfyui-svg-visual-normalize" - ], - "install_type": "git-clone", - "reference": "https://github.com/coulterj/comfyui-svg-visual-normalize", - "title": "ComfyUI SVG Visual Normalize & Margin Node" - }, - { - "author": "papcorns", - "description": "A ComfyUI custom node that allows you to load images directly from URLs or local file paths. This node provides a convenient way to import images into your ComfyUI workflows without manually downloading them first.", - "files": [ - "https://github.com/papcorns/ComfyUI-Papcorns-Node-LoadImageFromUrl" - ], - "install_type": "git-clone", - "reference": "https://github.com/papcorns/ComfyUI-Papcorns-Node-LoadImageFromUrl", - "title": "ComfyUI Load Image From URL" - }, - { - "author": "papcorns", - "description": "A collection of custom nodes for ComfyUI that enhances image processing and cloud storage capabilities.", - "files": [ - "https://github.com/papcorns/Papcorns-Comfyui-Custom-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/papcorns/Papcorns-Comfyui-Custom-Nodes", - "title": "Papcorns ComfyUI Custom Nodes" - }, - { - "author": "gabe-init", - "description": "A custom node for ComfyUI that allows you to interact with OpenRouter's chat/completion API, providing access to a wide range of LLM models including GPT-4, Claude, Llama, Mistral, and more.", - "files": [ - "https://github.com/gabe-init/ComfyUI-Openrouter_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/gabe-init/ComfyUI-Openrouter_node", - "title": "ComfyUI OpenRouter Node" - }, - { - "author": "gabe-init", - "description": "ComfyUI ElevenLabs TTS Node - Text-to-speech integration with advanced voice controls", - "files": [ - "https://github.com/gabe-init/ComfyUI-11labs" - ], - "install_type": "git-clone", - "reference": "https://github.com/gabe-init/ComfyUI-11labs", - "title": "ComfyUI-11labs" - }, - { - "author": "gabe-init", - "description": "ComfyUI Google Image Search Node - Search and retrieve images from Google", - "files": [ - "https://github.com/gabe-init/ComfyUI-Google-Image-Search" - ], - "install_type": "git-clone", - "reference": "https://github.com/gabe-init/ComfyUI-Google-Image-Search", - "title": "ComfyUI-Google-Image-Search" - }, - { - "author": "gabe-init", - "description": "ComfyUI String Similarity Node - Advanced text comparison with multiple algorithms", - "files": [ - "https://github.com/gabe-init/ComfyUI-String-Similarity" - ], - "install_type": "git-clone", - "reference": "https://github.com/gabe-init/ComfyUI-String-Similarity", - "title": "ComfyUI-String-Similarity" - }, - { - "author": "GACLove", - "description": "ComfyUI-Lightx2vWrapper is an inference wrapper for Lightx2v designed for use with ComfyUI.", - "files": [ - "https://github.com/GACLove/ComfyUI-Lightx2vWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/GACLove/ComfyUI-Lightx2vWrapper", - "title": "ComfyUI-Lightx2vWrapper" - }, - { - "author": "GACLove", - "description": "ComfyUI-RIFE is an inference wrapper for RIFE designed for use with ComfyUI.", - "files": [ - "https://github.com/GACLove/ComfyUI-VFI" - ], - "install_type": "git-clone", - "reference": "https://github.com/GACLove/ComfyUI-VFI", - "title": "ComfyUI-VFI" - }, - { - "author": "Yahweasel", - "description": "This is a quick-and-filthy wrapper of [a/min-dalle](https://github.com/kuprel/min-dalle) for ComfyUI. min-dalle downloads and loads the actual model itself, making for a very simple, but very non-idiomatic, ComfyUI node.", - "files": [ - "https://github.com/Yahweasel/ComfyUI-MinDalle" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yahweasel/ComfyUI-MinDalle", - "title": "ComfyUI-MinDalle" - }, - { - "author": "AIToldMeTo", - "description": "A custom node for ComfyUI that provides the ability to clear the cache directly from your workflow.", - "files": [ - "https://github.com/AIToldMeTo/comfyui-cache-cleaner" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIToldMeTo/comfyui-cache-cleaner", - "title": "ComfyUI Cache Cleaner Node" - }, - { - "author": "SamTyurenkov", - "description": "Chat GPT Image Generation Chat Node for Comfy UI", - "files": [ - "https://github.com/SamTyurenkov/comfyui_chatgpt" - ], - "install_type": "git-clone", - "reference": "https://github.com/SamTyurenkov/comfyui_chatgpt", - "title": "comfyui_chatgpt" - }, - { - "author": "SamTyurenkov", - "description": "Some nodes to create a preprocessed videos", - "files": [ - "https://github.com/SamTyurenkov/comfyui-vace-preprocessors" - ], - "install_type": "git-clone", - "reference": "https://github.com/SamTyurenkov/comfyui-vace-preprocessors", - "title": "comfyui_vace_preprocessors" - }, - { - "author": "orion4d", - "description": "Complete collection of image effects for ComfyUI - 32 nodes across 6 categories", - "files": [ - "https://github.com/orion4d/ComfyUI-Image-Effects" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI-Image-Effects", - "title": "ComfyUI-Image-Effects" - }, - { - "author": "orion4d", - "description": "This repository contains a set of custom nodes for ComfyUI that allow you to load, manipulate, extract information from, and preview PDF files directly within your workflows.", - "files": [ - "https://github.com/orion4d/ComfyUI_pdf_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI_pdf_nodes", - "title": "ComfyUI PDF Nodes" - }, - { - "author": "orion4d", - "description": "This repository contains a collection of custom nodes for ComfyUI, designed for generating various patterns, optical illusions, and performing related image manipulations. All nodes are categorized under 'illusion' in the ComfyUI menu.", - "files": [ - "https://github.com/orion4d/illusion_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/illusion_node", - "title": "ComfyUI Illusion & Pattern Nodes" - }, - { - "author": "orion4d", - "description": "This ComfyUI node allows you to extract all images found in various types of documents and save them to disk. It also provides a preview of the first extracted image.", - "files": [ - "https://github.com/orion4d/ComfyUI_extract_imag" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI_extract_imag", - "title": "ComfyUI_extract_imag" - }, - { - "author": "orion4d", - "description": "utilitaires pour ComfyUI, con\u00e7ue pour effectuer des calculs et des conversions", - "files": [ - "https://github.com/orion4d/Calculator_Pro" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/Calculator_Pro", - "title": "CalculatorPro - Node Suite for ComfyUI" - }, - { - "author": "orion4d", - "description": "This project offers a collection of custom nodes for ComfyUI, dedicated to creating hexadecimal color palettes", - "files": [ - "https://github.com/orion4d/ComfyUI_colormaster" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI_colormaster", - "title": "ComfyUI Colormaster Nodes" - }, - { - "author": "orion4d", - "description": "This repository contains a versatile custom node for ComfyUI, Display Image with Mask, designed to offer advanced image viewing, masking, and saving capabilities directly within your workflow.", - "files": [ - "https://github.com/orion4d/ComfyUI_image-display" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI_image-display", - "title": "Display Image with Mask for ComfyUI" - }, - { - "author": "orion4d", - "description": "ComfyUI_DAO_master is a collection of custom nodes for ComfyUI. These nodes provide additional tools for image creation, manipulation, and visual experimentation, with a focus on vector workflows (DXF & SVG) and production utilities.", - "files": [ - "https://github.com/orion4d/ComfyUI_DAO_master" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI_DAO_master", - "title": "ComfyUI_DAO_master" - }, - { - "author": "aiaiaikkk", - "description": "Professional image adjustment tools for ComfyUI - Curves, Levels, HSL, and Camera Raw adjustments with real-time preview", - "files": [ - "https://github.com/aiaiaikkk/ComfyUI-Curve" - ], - "install_type": "git-clone", - "reference": "https://github.com/aiaiaikkk/ComfyUI-Curve", - "title": "ComfyUI-Curve" - }, - { - "author": "lxe", - "description": "A ComfyUI custom node that provides integration with OpenAI-compatible Large Language Model APIs, including OpenAI, local models, and other compatible endpoints. Supports both text-only and multimodal (text + image) interactions.", - "files": [ - "https://github.com/lxe/ComfyUI-OpenAI-Compat-LLM-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/lxe/ComfyUI-OpenAI-Compat-LLM-Node", - "title": "ComfyUI OpenAI Compatible LLM Node" - }, - { - "author": "narusas", - "description": "A collection of logic and utility nodes for ComfyUI to enhance workflow capabilities.", - "files": [ - "https://github.com/narusas/Comfyui-Logic-Support" - ], - "install_type": "git-clone", - "reference": "https://github.com/narusas/Comfyui-Logic-Support", - "title": "ComfyUI Logic Support" - }, - { - "author": "xhiroga", - "description": "ComfyUI custom node for FramePack, supporting 1-frame inferences.", - "files": [ - "https://github.com/xhiroga/ComfyUI-FramePackWrapper_PlusOne" - ], - "id": "comfyui-framepackwrapper-plusone", - "install_type": "git-clone", - "reference": "https://github.com/xhiroga/ComfyUI-FramePackWrapper_PlusOne", - "title": "ComfyUI-FramePackWrapper_PlusOne" - }, - { - "author": "babydjac", - "description": "Custom ComfyUI nodes for generating prompts using Grok AI, enhancing prompt creation for text-to-image workflows.", - "files": [ - "https://github.com/babydjac/comfyui-grok-prompts" - ], - "id": "comfyui-grok-prompts", - "install_type": "git-clone", - "reference": "https://github.com/babydjac/comfyui-grok-prompts", - "title": "ComfyUI Grok Prompts" - }, - { - "author": "LingSss9", - "description": "Merge up to 4 LoRA models with balanced, order-independent logic. Inspired by WebUI SuperMerger.", - "files": [ - "https://github.com/LingSss9/comfyui-merge" - ], - "id": "comfyui-merge", - "install_type": "git-clone", - "reference": "https://github.com/LingSss9/comfyui-merge", - "title": "comfyui-merge" - }, - { - "author": "p1atdev", - "description": "ComfyUI Timm Backbone Nodes is a custom node set that enables you to load and use pre-trained models from the [a/timm](https://github.com/huggingface/pytorch-image-models) library within ComfyUI workflows.", - "files": [ - "https://github.com/p1atdev/comfyui-timm-backbone" - ], - "install_type": "git-clone", - "reference": "https://github.com/p1atdev/comfyui-timm-backbone", - "title": "comfyui-timm-backbone" - }, - { - "author": "p1atdev", - "description": "This repository provides an unofficial ComfyUI custom node implementation of the paper TKG-DM: Training-free Chroma Key Content Generation Diffusion Model.", - "files": [ - "https://github.com/p1atdev/comfyui-tkg-chroma-key" - ], - "install_type": "git-clone", - "reference": "https://github.com/p1atdev/comfyui-tkg-chroma-key", - "title": "TKG-DM (Training-free Chroma Key Content Generation Diffusion Model) for ComfyUI" - }, - { - "author": "Zch6111", - "description": "AI_Text_Comfyui is a custom node for ComfyUI that connects to the OpenAI Chat API and automatically generates creative text prompts for AI workflows. This simplified version removes external dependencies like dotenv, requiring the OpenAI key to be set using a system environment variable.", - "files": [ - "https://github.com/Zch6111/AI_Text_Comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zch6111/AI_Text_Comfyui", - "title": "AI_Text_Comfyui" - }, - { - "author": "mrcuddle", - "description": "An implementation to detect underage subjects in images for ComfyUI.", - "files": [ - "https://github.com/T-Ph525/ComfyUI-Underage-Filter" - ], - "install_type": "git-clone", - "reference": "https://github.com/T-Ph525/ComfyUI-Underage-Filter", - "title": "Underage Filter" - }, - { - "author": "ToTheBeginning", - "description": "[a/DreamO](https://github.com/bytedance/DreamO) ComfyUI native implementation.", - "files": [ - "https://github.com/ToTheBeginning/ComfyUI-DreamO" - ], - "install_type": "git-clone", - "reference": "https://github.com/ToTheBeginning/ComfyUI-DreamO", - "title": "DreamO Comfyui" - }, - { - "author": "XWAVEart", - "description": "A collection of artistic glitch and image manipulation nodes for ComfyUI, featuring advanced noise effects, color manipulations, distortions, and more.", - "files": [ - "https://github.com/XWAVEart/comfyui-xwave-xlitch-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/XWAVEart/comfyui-xwave-xlitch-nodes", - "title": "ComfyUI XWAVE Nodes" - }, - { - "author": "vivi-gomez", - "description": "Addon for ComfyUI that adds 'Fix node (recreate + keep inputs)' context menu option", - "files": [ - "https://github.com/vivi-gomez/ComfyUI-fixnodetranslate" - ], - "install_type": "git-clone", - "reference": "https://github.com/vivi-gomez/ComfyUI-fixnodetranslate", - "title": "ComfyUI Fix Node Translate" - }, - { - "author": "Pigidiy", - "description": "A custom ComfyUI node that saves input AUDIO as .mp3 using ffmpeg.", - "files": [ - "https://github.com/Pigidiy/ComfyUI-LikeSpiderAI-SaveMP3" - ], - "id": "likeSpiderMP3", - "install_type": "git-clone", - "reference": "https://github.com/Pigidiy/ComfyUI-LikeSpiderAI-SaveMP3", - "title": "ComfyUI-LikeSpiderAI-SaveMP3" - }, - { - "author": "violet0927", - "description": "A ComfyUI custom node to upload LoRA models to Hugging Face Hub.", - "files": [ - "https://github.com/violet0927/ComfyUI-HuggingFaceLoraUploader" - ], - "id": "comfyui_huggingfacelorauploader", - "install_type": "git-clone", - "reference": "https://github.com/violet0927/ComfyUI-HuggingFaceLoraUploader", - "title": "Hugging Face LoRA Uploader" - }, - { - "author": "violet0927", - "description": "ComfyUI OmniConsistency Nodes is a collection of nodes for ComfyUI that allows you to load and use OmniConsistency models.", - "files": [ - "https://github.com/lc03lc/Comfyui_OmniConsistency" - ], - "install_type": "git-clone", - "reference": "https://github.com/lc03lc/Comfyui_OmniConsistency", - "title": "ComfyUI OmniConsistency Nodes" - }, - { - "author": "bikiam", - "description": "This is custom node for audio transcribe with SRT.", - "files": [ - "https://github.com/bikiam/ComfyUI_WhisperSRT" - ], - "install_type": "git-clone", - "reference": "https://github.com/bikiam/ComfyUI_WhisperSRT", - "title": "ComfyUI_WhisperSRT" - }, - { - "author": "thalismind", - "description": "This repository contains a ComfyUI node for blending images using various blending modes. Can be used to watermark images, create overlays, or apply effects to images in a ComfyUI workflow.", - "files": [ - "https://github.com/thalismind/ComfyUI-Blend-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/thalismind/ComfyUI-Blend-Nodes", - "title": "ComfyUI Blend Image Nodes" - }, - { - "author": "thalismind", - "description": "This custom node extends ComfyUI's image loading functionality with filename output and folder loading capabilities.", - "files": [ - "https://github.com/thalismind/ComfyUI-LoadImageWithFilename" - ], - "install_type": "git-clone", - "reference": "https://github.com/thalismind/ComfyUI-LoadImageWithFilename", - "title": "ComfyUI LoadImageWithFilename" - }, - { - "author": "boricuapab", - "description": "This is a custom node for ComfyUI that takes in a file path full of json's and finds the mape variable nodes in them and converts them to the kjnode set and get nodes.", - "files": [ - "https://github.com/boricuapab/ComfyUI-Bori-JsonSetGetConverter" - ], - "install_type": "git-clone", - "reference": "https://github.com/boricuapab/ComfyUI-Bori-JsonSetGetConverter", - "title": "ComfyUI-Bori-JsonSetGetConverter" - }, - { - "author": "boricuapab", - "description": "This is a custom node for ComfyUI that provides the user with a dropdown which contains the recommended resolutions for the Qwen Image Model.", - "files": [ - "https://github.com/boricuapab/ComfyUI-Bori-QwenImageResolutions" - ], - "install_type": "git-clone", - "reference": "https://github.com/boricuapab/ComfyUI-Bori-QwenImageResolutions", - "title": "ComfyUI-Bori-QwenImageResolutions" - }, - { - "author": "avocadori", - "description": "NODES: YAML Image Cycler (Full), YAML Image Cycler (Simple), YAML LoRA Extractor, YAML LoRA Loader, YAML LoRA Selector", - "files": [ - "https://github.com/avocadori/ComfyUI-load-image-prompt-lora" - ], - "install_type": "git-clone", - "reference": "https://github.com/avocadori/ComfyUI-load-image-prompt-lora", - "title": "ComfyUI-load-image-prompt-lora" - }, - { - "author": "Chrisvenator", - "description": "Create a painting by colors image from an image", - "files": [ - "https://github.com/Chrisvenator/ComfyUI-Painting-by-colors-generator" - ], - "install_type": "git-clone", - "reference": "https://github.com/Chrisvenator/ComfyUI-Painting-by-colors-generator", - "title": "painting-by-colors-generator" - }, - { - "author": "tetsuoo-online", - "description": "Custom nodes for ComfyUI that allow you to read and write XMP metadata to images", - "files": [ - "https://github.com/tetsuoo-online/comfyui-too-xmp-metadata" - ], - "install_type": "git-clone", - "reference": "https://github.com/tetsuoo-online/comfyui-too-xmp-metadata", - "title": "comfyui-too-xmp-metadata" - }, - { - "author": "e-tier-newbie", - "description": "A node for saving cleaned text outputs, useful for LoRA training. Removes unwanted tokens like and saves to .txt.", - "files": [ - "https://github.com/e-tier-newbie/ComfyUI-E-Tier-TextSaver" - ], - "id": "e-tier-text-saver", - "install_type": "git-clone", - "reference": "https://github.com/e-tier-newbie/ComfyUI-E-Tier-TextSaver", - "title": "ComfyUI-E-Tier-TextSaver" - }, - { - "author": "MDMAchine", - "description": "A wild collection of custom nodes for ComfyUI including noise schedulers, samplers, audio preview, latent visualizers, and more \u2014 built for maximal creative chaos.", - "files": [ - "https://github.com/MDMAchine/ComfyUI_MD_Nodes" - ], - "id": "comfyuimdnodes", - "install_type": "git-clone", - "reference": "https://github.com/MDMAchine/ComfyUI_MD_Nodes", - "title": "MD Nodes" - }, - { - "author": "shiertier", - "description": "ComfyUI Node Implementation: TeaCache Acceleration Specifically Designed for the Lumina Model", - "files": [ - "https://github.com/shiertier/ComfyUI-TeaCache-lumina2" - ], - "install_type": "git-clone", - "reference": "https://github.com/shiertier/ComfyUI-TeaCache-lumina2", - "title": "ComfyUI-TeaCache-Lumina" - }, - { - "author": "sjh00", - "description": "This is a custom node for ComfyUI that retrieves detailed information about an image, including its name, format (extension), DPI, dimensions, long side, short side, file size, and EXIF data. It also supports image saving ", - "files": [ - "https://github.com/sjh00/ComfyUI-LoadImageWithInfo" - ], - "install_type": "git-clone", - "reference": "https://github.com/sjh00/ComfyUI-LoadImageWithInfo", - "title": "ComfyUI LoadImageWithInfo" - }, - { - "author": "sm079", - "description": "face detection nodes for comfyui", - "files": [ - "https://github.com/sm079/ComfyUI-Face-Detection" - ], - "install_type": "git-clone", - "reference": "https://github.com/sm079/ComfyUI-Face-Detection", - "title": "ComfyUI-Face-Detection" - }, - { - "author": "r-vage", - "description": "this node contains a lot of small little helpers like switches, passers and selectors that i use a lot to build my workflows.", - "files": [ - "https://github.com/r-vage/ComfyUI-RvTools_v2" - ], - "install_type": "git-clone", - "reference": "https://github.com/r-vage/ComfyUI-RvTools_v2", - "title": "ComfyUI-RvTools_v2" - }, - { - "author": "Aljnk", - "description": "A collection of useful custom nodes for ComfyUI - image processing, text manipulation, and workflow automation.", - "files": [ - "https://github.com/Aljnk/ComfyUI-JNK-Tiny-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Aljnk/ComfyUI-JNK-Tiny-Nodes", - "title": "ComfyUI-JNK-Tiny-Nodes" - }, - { - "author": "Santodan", - "description": "Randomizes selected LoRAs and strengths. Includes trigger word output and support for exclusive/random selection.", - "files": [ - "https://github.com/Santodan/santodan-custom-nodes-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/Santodan/santodan-custom-nodes-comfyui", - "title": "Santodan Random LoRA Node" - }, - { - "author": "zccrs", - "description": "A comprehensive ComfyUI extension for creating, previewing, and analyzing DCI (DSG Combined Icons) format files. This extension fully implements the DCI specification, supporting multi-state icons, multiple color tones, scaling factors, and advanced metadata analysis.", - "files": [ - "https://github.com/zccrs/comfyui-dci" - ], - "install_type": "git-clone", - "reference": "https://github.com/zccrs/comfyui-dci", - "title": "ComfyUI DCI" - }, - { - "author": "s9roll7", - "description": "This is a node that outputs tracking results of a grid or specified points using CoTracker. It can be directly connected to the WanVideo ATI Tracks Node.", - "files": [ - "https://github.com/s9roll7/comfyui_cotracker_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/s9roll7/comfyui_cotracker_node", - "title": "Comfyui CoTracker Node" - }, - { - "author": "set-soft", - "description": "Audio batch creation, extraction, information, resample, mono and stereo conversion.\nAlso cut, concatenate, blend (mix) and de/normalize. Join/split channels (stereo).\nSignal generator (`sine`, `square`, `sawtooth`, `triangle`, `sweep`, `noise`).\nMusical note to frequency.\nAudio downloader for quick workflows which downloads its example data.", - "files": [ - "https://github.com/set-soft/ComfyUI-AudioBatch" - ], - "install_type": "git-clone", - "reference": "https://github.com/set-soft/ComfyUI-AudioBatch", - "title": "Audio Batch" - }, - { - "author": "set-soft", - "description": "Miscellaneous nodes for image manipulation.\nCurrently just download image with bypass, so you can create workflows including image examples.\nNo extra dependencies, just an internal module.", - "files": [ - "https://github.com/set-soft/ComfyUI-ImageMisc" - ], - "install_type": "git-clone", - "reference": "https://github.com/set-soft/ComfyUI-ImageMisc", - "title": "Image Misc" - }, - { - "author": "pictorialink", - "description": "This node uses the Translators library for translation.", - "files": [ - "https://github.com/pictorialink/ComfyUI-Text-Translation" - ], - "install_type": "git-clone", - "reference": "https://github.com/pictorialink/ComfyUI-Text-Translation", - "title": "ComfyUI-Text-Translation" - }, - { - "author": "pictorialink", - "description": "This project is a custom node plugin for ComfyUI that provides a form node for configuring and saving parameters related to LLMs (such as OpenAI, Kimi, DeepSeek). Users can input information such as API Key, Base, Version, and Model through the node. The node will automatically save the configuration to a local file and set it as environment variables, making it convenient for subsequent use.", - "files": [ - "https://github.com/pictorialink/ComfyUI-Custom-Node-Config" - ], - "install_type": "git-clone", - "reference": "https://github.com/pictorialink/ComfyUI-Custom-Node-Config", - "title": "ComfyUI-Custom-Node-Config" - }, - { - "author": "pictorialink", - "description": "Custom nodes for ComfyUI QWen3 8b running based on llama.cpp, which only support the CUDA framework and do not support MPS.", - "files": [ - "https://github.com/pictorialink/ComfyUI-Qwen3-llama.cpp" - ], - "install_type": "git-clone", - "reference": "https://github.com/pictorialink/ComfyUI-Qwen3-llama.cpp", - "title": "ComfyUI-Qwen3-llama.cpp" - }, - { - "author": "mo230761", - "description": "This repository provides the official ComfyUI workflow for [a/Insert Anything](https://github.com/song-wensong/insert-anything).", - "files": [ - "https://github.com/mo230761/InsertAnything-ComfyUI-official" - ], - "install_type": "git-clone", - "reference": "https://github.com/mo230761/InsertAnything-ComfyUI-official", - "title": "InsertAnything-ComfyUI-official" - }, - { - "author": "spawner", - "description": "comfy extension for lumina2 TeaCache", - "files": [ - "https://github.com/spawner1145/CUI-Lumina2-TeaCache" - ], - "install_type": "git-clone", - "reference": "https://github.com/spawner1145/CUI-Lumina2-TeaCache", - "title": "CUI-Lumina2-TeaCache" - }, - { - "author": "spawner", - "description": "gemini and openai in comfyui", - "files": [ - "https://github.com/spawner1145/comfyui-aichat" - ], - "install_type": "git-clone", - "reference": "https://github.com/spawner1145/comfyui-aichat", - "title": "comfyui-aichat" - }, - { - "author": "PenguinTeo", - "description": "A text overlay node for ComfyUI that supports rich effects such as gradients, outlines, and shadows. It is suitable for adding highly customizable text content to images.", - "files": [ - "https://github.com/PenguinTeo/Comfyui-TextEditor-Penguin" - ], - "install_type": "git-clone", - "reference": "https://github.com/PenguinTeo/Comfyui-TextEditor-Penguin", - "title": "Comfyui-TextEditor-Penguin" - }, - { - "author": "jack-liu", - "description": "Pillar is an extension plugin for ComfyUI, providing the ability to call local distributed services for ComfyUI. Currently, it integrates the llama-joycaption-beta-one-hf-llava model and offers a distributed deployment solution to solve the problem that the model occupies too many resources and affects the image generation efficiency.", - "files": [ - "https://github.com/aicoder-max/Pillar_For_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/aicoder-max/Pillar_For_ComfyUI", - "title": "Pillar_For_ComfyUI" - }, - { - "author": "scraed", - "description": "Achieve seamless inpainting results without needing a specialized inpainting model.", - "files": [ - "https://github.com/scraed/LanPaint" - ], - "install_type": "git-clone", - "reference": "https://github.com/scraed/LanPaint", - "title": "LanPaint" - }, - { - "author": "hvppycoding", - "description": "A ComfyUI custom node that randomly selects a (Sampler, Scheduler, Steps) combination from user-defined presets.", - "files": [ - "https://github.com/hvppycoding/comfyui-random-sampler-scheduler-steps" - ], - "install_type": "git-clone", - "reference": "https://github.com/hvppycoding/comfyui-random-sampler-scheduler-steps", - "title": "RandomSamplerSchedulerSteps for ComfyUI" - }, - { - "author": "hvppycoding", - "description": "NODES: Extract JSON, 'Prompt: Render Template from JSON'", - "files": [ - "https://github.com/hvppycoding/comfyui-json-prompt-renderer" - ], - "install_type": "git-clone", - "reference": "https://github.com/hvppycoding/comfyui-json-prompt-renderer", - "title": "json prompt renderer" - }, - { - "author": "o-l-l-i", - "description": "A smart, customizable resolution selector node with live aspect ratio preview, image overlays, and checkerboard support \u2014 cleanly parsed from a human-readable text file.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-Resolution-Picker" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-Resolution-Picker", - "title": "Olm Resolution Picker for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "The Olm LUT is a custom node for ComfyUI that allows you to apply .cube LUT (Look-Up Table) files to images within your generative workflows. It supports creative workflows including film emulation, color grading, and aesthetic stylization using LUTs.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-OlmLUT" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-OlmLUT", - "title": "Olm LUT Node for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "A single-purpose, multi-channel curve editor for ComfyUI, providing precise color control over R, G, B, and Luma channels directly within the node graph. It\u2019s a focused, lightweight, and standalone solution built specifically for one task: applying color curves cleanly and efficiently.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-CurveEditor" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-CurveEditor", - "title": "Olm Curve Editor for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "An interactive sketching and drawing node for ComfyUI with stylus/pen support \u2013 built for fast, intuitive scribbling directly inside your workflows, geared towards ControlNet-style workflows which utilize scribbles and line art.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-Sketch" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-Sketch", - "title": "Olm Sketch for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "An interactive image cropping node for ComfyUI, allowing precise visual selection of crop areas directly within your workflow. This node is designed to streamline the process of preparing images for various tasks, ensuring immediate visual feedback and control over your image dimensions.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-DragCrop" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-DragCrop", - "title": "Olm DragCrop for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "An interactive image adjustment node for ComfyUI, with an easy-to-use graphical interface and realtime preview.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-ImageAdjust" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-ImageAdjust", - "title": "Olm Image Adjust for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "An interactive, classic channel mixer color adjustment node for ComfyUI, with realtime preview and a responsive editing interface.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-ChannelMixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-ChannelMixer", - "title": "Olm Channel Mixer for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "An interactive color balance adjustment node for ComfyUI, featuring a clean interface and realtime preview.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-ColorBalance" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-ColorBalance", - "title": "Olm Color Balance for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "A compact, real-time histogram analysis node for ComfyUI \u2014 with easy-to-use interactive UI, smooth rendering, and accurate pixel sampling. Built for compositing-style workflows and color diagnostics.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-Histogram" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-Histogram", - "title": "Olm Histogram for ComfyUI" - }, - { - "author": "o-l-l-i", - "description": "A visual, interactive Lift, Gamma, Gain color correction node for ComfyUI, tailored for precise color grading workflows.", - "files": [ - "https://github.com/o-l-l-i/ComfyUI-Olm-LGG" - ], - "install_type": "git-clone", - "reference": "https://github.com/o-l-l-i/ComfyUI-Olm-LGG", - "title": "Olm LGG (Lift, Gamma, Gain) for ComfyUI" - }, - { - "author": "xiaogui8dangjia", - "description": "comfyui_imagetostl is a simple node for ComfyUI that converts grayscale images to STL.", - "files": [ - "https://github.com/xiaogui8dangjia/Comfyui-imagetoSTL" - ], - "install_type": "git-clone", - "reference": "https://github.com/xiaogui8dangjia/Comfyui-imagetoSTL", - "title": "Comfyui-imagetoSTL" - }, - { - "author": "NeonLightning", - "description": "This custom ComfyUI node transforms a core idea into a richly detailed positive prompt using a local [a/Ollama](https://ollama.com) LLM.", - "files": [ - "https://github.com/NeonLightning/neonllama" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeonLightning/neonllama", - "title": "neonllama" - }, - { - "author": "xmarre", - "description": "Drop-in TorchCompile node that preserves LoRA patches.", - "files": [ - "https://github.com/xmarre/TorchCompileModel_LoRASafe" - ], - "install_type": "git-clone", - "reference": "https://github.com/xmarre/TorchCompileModel_LoRASafe", - "title": "LoRA-Safe TorchCompile" - }, - { - "author": "Pigidiy", - "description": "Declarative UI Framework for ComfyUI Nodes. Minimalistic base class for creating UI-based audio/text/image nodes.", - "files": [ - "https://github.com/Pigidiy/ComfyUI-LikeSpiderAI-UI" - ], - "id": "like_spider_ui", - "install_type": "git-clone", - "reference": "https://github.com/Pigidiy/ComfyUI-LikeSpiderAI-UI", - "title": "ComfyUI-LikeSpiderAI-UI" - }, - { - "author": "hexxacubic", - "description": "A ComfyUI node pack for management of larger prompt amounts and prompt variations.", - "files": [ - "https://github.com/hexxacubic/ComfyUI-Prompt_Library" - ], - "install_type": "git-clone", - "reference": "https://github.com/hexxacubic/ComfyUI-Prompt_Library", - "title": "ComfyUI-Prompt_Library" - }, - { - "author": "MicheleGuidi", - "description": "Extension nodes for ComfyUI that improves automatic segmentation using bounding boxes generated by Florence 2 and segmentation from Segment Anything 2 (SAM2). Currently just an enhancement of nodes from [a/Kijai](https://github.com/kijai/ComfyUI-segment-anything-2).", - "files": [ - "https://github.com/MicheleGuidi/ComfyUI-Contextual-SAM2" - ], - "install_type": "git-clone", - "reference": "https://github.com/MicheleGuidi/ComfyUI-Contextual-SAM2", - "title": "ComfyUI-Computer-Vision" - }, - { - "author": "swhsiang", - "description": "ComfyUI custom node to support 3D GS rendering", - "files": [ - "https://github.com/swhsiang/comfyui-3d-gs-renderer" - ], - "install_type": "git-clone", - "reference": "https://github.com/swhsiang/comfyui-3d-gs-renderer", - "title": "comfyui-3d-gs-renderer" - }, - { - "author": "jasonjgardner", - "description": "A comprehensive ComfyUI plugin that enables seamless integration with Substance 3D Designer workflows through command line automation. This plugin provides custom nodes for cooking .sbs files, rendering .sbsar archives, controlling material parameters, and batch processing Substance materials within ComfyUI workflows.", - "files": [ - "https://github.com/jasonjgardner/comfui-substance-designer-integration" - ], - "install_type": "git-clone", - "reference": "https://github.com/jasonjgardner/comfui-substance-designer-integration", - "title": "ComfyUI Substance Designer Integration Plugin" - }, - { - "author": "sLKbabawhsiang", - "description": " Powerful Flux-Kontext image generation custom node for ComfyUI, using the official RabbitAI API. Supports text-to-image, image-to-image, and multi-image-to-image generation. Supports concurrent generation.", - "files": [ - "https://github.com/LKbaba/ComfyUI-TuZi-Flux-Kontext" - ], - "install_type": "git-clone", - "reference": "https://github.com/LKbaba/ComfyUI-TuZi-Flux-Kontext", - "title": "ComfyUI-TuZi-Flux-Kontext" - }, - { - "author": "INuBq8", - "description": "Bridge nodes for ComfyUI that send messages through WhatsApp (Twilio) and Discord when a workflow completes.", - "files": [ - "https://github.com/INuBq8/ComfyUI-NotificationBridge" - ], - "install_type": "git-clone", - "reference": "https://github.com/INuBq8/ComfyUI-NotificationBridge", - "title": "Notification Bridge" - }, - { - "author": "Erehr", - "description": "A collection of prompt managent nodes with advanced tag parsing. Prompt tag cloud, mutiselect, toggle list, randomizer, filter, autocompete.", - "files": [ - "https://github.com/Erehr/ComfyUI-EreNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Erehr/ComfyUI-EreNodes", - "title": "ComfyUI-EreNodes" - }, - { - "author": "Erehr", - "description": "A seamless, node-independent way to automatically send your ComfyUI generations to Eagle, complete with full metadata annotation and tags.", - "files": [ - "https://github.com/Erehr/ComfyUI-Eagle-Autosend" - ], - "install_type": "git-clone", - "reference": "https://github.com/Erehr/ComfyUI-Eagle-Autosend", - "title": "ComfyUI-Eagle-Autosend" - }, - { - "author": "xiaowc", - "description": "Comfyui custom nodes that support dynamic parameter addition and deletion.", - "files": [ - "https://github.com/xiaowc-lib/comfyui-dynamic-params" - ], - "install_type": "git-clone", - "reference": "https://github.com/xiaowc-lib/comfyui-dynamic-params", - "title": "Comfyui-Dynamic-Params" - }, - { - "author": "keit", - "description": "This is keit's utility nodes.", - "files": [ - "https://github.com/keit0728/ComfyUI-keitNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/keit0728/ComfyUI-keitNodes", - "title": "ComfyUI-keitNodes" - }, - { - "author": "fredconex", - "description": "This is a bunch of nodes for ComfyUI to help with sound work.", - "files": [ - "https://github.com/fredconex/ComfyUI-SoundFlow" - ], - "install_type": "git-clone", - "reference": "https://github.com/fredconex/ComfyUI-SoundFlow", - "title": "ComfyUI-SoundFlow" - }, - { - "author": "fredconex", - "description": "This node allow to intercept changes on the input string and choose between use the current one or sync with incoming new one.", - "files": [ - "https://github.com/fredconex/ComfyUI-SyncEdit" - ], - "install_type": "git-clone", - "reference": "https://github.com/fredconex/ComfyUI-SyncEdit", - "title": "Sync Edit" - }, - { - "author": "fredconex", - "description": "ComfyUI Nodes for SongBloom", - "files": [ - "https://github.com/fredconex/ComfyUI-SongBloom" - ], - "install_type": "git-clone", - "reference": "https://github.com/fredconex/ComfyUI-SongBloom", - "title": "SongBloom" - }, - { - "author": "A043-studios", - "description": "Professional 3D face reconstruction for ComfyUI using the Pixel3DMM method", - "files": [ - "https://github.com/A043-studios/comfyui-pixel3dmm" - ], - "install_type": "git-clone", - "reference": "https://github.com/A043-studios/comfyui-pixel3dmm", - "title": "Pixel3DMM ComfyUI Nodes" - }, - { - "author": "A043-studios", - "description": "Professional video animation nodes for ComfyUI based on Deforum-X-Flux research", - "files": [ - "https://github.com/A043-studios/comfyui-deforum-x-flux-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/A043-studios/comfyui-deforum-x-flux-nodes", - "title": "ComfyUI Deforum-X-Flux Nodes" - }, - { - "author": "A043-studios", - "description": "A ComfyUI integration of Kim Asendorf's iconic ASDFPixelSort algorithm, bringing classic pixel sorting effects directly into your ComfyUI workflows", - "files": [ - "https://github.com/A043-studios/ComfyUI-ASDF-Pixel-Sort-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/A043-studios/ComfyUI-ASDF-Pixel-Sort-Nodes", - "title": "ComfyUI ASDF Pixel Sort Nodes" - }, - { - "author": "A043-studios", - "description": "ASCII art generator for ComfyUI with multi-language character set support", - "files": [ - "https://github.com/A043-studios/Comfyui-ascii-generator" - ], - "install_type": "git-clone", - "reference": "https://github.com/A043-studios/Comfyui-ascii-generator", - "title": "ComfyUI ASCII Generator Node" - }, - { - "author": "A043-studios", - "description": "ComfyUI custom nodes for Hunyuan3D - Complete 3D generation suite", - "files": [ - "https://github.com/A043-studios/ComfyUI_HunyuanWorldnode" - ], - "install_type": "git-clone", - "reference": "https://github.com/A043-studios/ComfyUI_HunyuanWorldnode", - "title": "ComfyUI HunyuanWorld - Complete 3D Generation Suite" - }, - { - "author": "A043-studios", - "description": "Generate high-quality SVG graphics from text descriptions and images using OmniSVG in ComfyUI.", - "files": [ - "https://github.com/A043-studios/ComfyUI-OmniSVG" - ], - "install_type": "git-clone", - "reference": "https://github.com/A043-studios/ComfyUI-OmniSVG", - "title": "ComfyUI OmniSVG Nodes" - }, - { - "author": "Zachary116699", - "description": "Custom node for ComfyUI. It can read metadata from the image filepath, and filepath can be provided as a connected input, which allows it to batch read image metadata in a loop.", - "files": [ - "https://github.com/Zachary116699/ComfyUI-LoadImageWithMetaDataEx" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zachary116699/ComfyUI-LoadImageWithMetaDataEx", - "title": "ComfyUI_LoadImageWithMetaDataEx" - }, - { - "author": "AgencyMind", - "description": "A ComfyUI custom node extension that solves multi-GPU device conflicts for ControlNet preprocessors.", - "files": [ - "https://github.com/AgencyMind/ComfyUI-GPU-Preprocessor-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/AgencyMind/ComfyUI-GPU-Preprocessor-Wrapper", - "title": "ComfyUI-GPU-Preprocessor-Wrapper" - }, - { - "author": "AgencyMind", - "description": "When your workflow starts acting weird and you need to know what's actually happening to your data - not just guess from looking at the output.", - "files": [ - "https://github.com/AgencyMind/ComfyUI-Satori" - ], - "install_type": "git-clone", - "reference": "https://github.com/AgencyMind/ComfyUI-Satori", - "title": "ComfyUI-Satori" - }, - { - "author": "olivv-cs", - "description": "A set of custom nodes designed for experiments with video diffusion models.", - "files": [ - "https://github.com/olivv-cs/ComfyUI-FunPack" - ], - "install_type": "git-clone", - "reference": "https://github.com/olivv-cs/ComfyUI-FunPack", - "title": "ComfyUI-FunPack" - }, - { - "author": "xuhuan2048", - "description": "A tool for decomposing video storyboards, which can obtain storyboards and keyframes", - "files": [ - "https://github.com/gitadmini/comfyui_extractstoryboards" - ], - "id": "comfyui_extractstoryboards", - "install_type": "git-clone", - "reference": "https://github.com/gitadmini/comfyui_extractstoryboards", - "title": "ExtractStoryboards" - }, - { - "author": "pawelmal0101", - "description": "A simple ComfyUI custom node that sends webhook notifications when images are generated. Perfect for integrating your image generation workflow with external services or your own backend.", - "files": [ - "https://github.com/pawelmal0101/ComfyUI-Webhook" - ], - "install_type": "git-clone", - "reference": "https://github.com/pawelmal0101/ComfyUI-Webhook", - "title": "ComfyUI Webhook Notifier" - }, - { - "author": "NeoDroleDeGueule", - "description": "A ComfyUI custom node that blends two images in latent space using a mix factor slider.", - "files": [ - "https://github.com/NeoDroleDeGueule/comfyui-image-mixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeoDroleDeGueule/comfyui-image-mixer", - "title": "comfyui-image-mixer" - }, - { - "author": "hassan-sd", - "description": "Load images with automatic prompt extraction from Civitai URLs, caption files, or EXIF metadata. Features smart dataset detection and dynamic preview updates.", - "files": [ - "https://github.com/hassan-sd/comfyui-image-prompt-loader" - ], - "id": "hassanprompt", - "install_type": "git-clone", - "reference": "https://github.com/hassan-sd/comfyui-image-prompt-loader", - "title": "ComfyUI Image & Prompt Loader" - }, - { - "author": "LargeModGames", - "description": "Automatically download missing LoRAs from CivitAI and detect missing LoRAs in workflows. Features smart directory detection and easy installation.", - "files": [ - "https://github.com/LargeModGames/comfyui-smart-lora-downloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/LargeModGames/comfyui-smart-lora-downloader", - "title": "ComfyUI LoRA Auto Downloader" - }, - { - "author": "benjamin-bertram", - "description": "This custom node for ComfyUI provides a wrapper for Intel's Open Image Denoise (OIDN) library, allowing you to denoise images directly within your ComfyUI workflow.", - "files": [ - "https://github.com/benjamin-bertram/Comfyui_OIDN_Denoiser" - ], - "install_type": "git-clone", - "reference": "https://github.com/benjamin-bertram/Comfyui_OIDN_Denoiser", - "title": "ComfyUI OIDN Denoiser" - }, - { - "author": "Zehong-Ma", - "description": "official implementation of [zehong-ma/MagCache](https://github.com/zehong-ma/MagCache) for ComfyUI", - "files": [ - "https://github.com/Zehong-Ma/ComfyUI-MagCache" - ], - "install_type": "git-clone", - "reference": "https://github.com/Zehong-Ma/ComfyUI-MagCache", - "title": "ComfyUI-MagCache" - }, - { - "author": "without-ordinary", - "description": "An API interface for OpenOutpaint to work with ComfyUI workflow", - "files": [ - "https://github.com/without-ordinary/openoutpaint_comfyui_interface" - ], - "install_type": "git-clone", - "reference": "https://github.com/without-ordinary/openoutpaint_comfyui_interface", - "reference2": "https://github.com/without-ordinary/wo_openoutpaint_comfyui_interface", - "title": "OpenOutpaint ComfyUI Interface" - }, - { - "author": "adamreading", - "description": "A collection of custom nodes designed for ComfyUI from the AJO-reading organization. This repository currently includes the Audio Collect & Concat node, which collects multiple audio segments and concatenates them into a single audio stream.", - "files": [ - "https://github.com/AJO-reading/ComfyUI-AjoNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/AJO-reading/ComfyUI-AjoNodes", - "title": "ComfyUI-AjoNodes" - }, - { - "author": "neocrz", - "description": "A custom node set for ComfyUI that provides nodes for encoding and decoding images using Tiny AutoEncoders for Stable Diffusion (TAESD) models.", - "files": [ - "https://github.com/neocrz/comfyui-usetaesd" - ], - "install_type": "git-clone", - "reference": "https://github.com/neocrz/comfyui-usetaesd", - "title": "comfyui-usetaesd" - }, - { - "author": "havvk", - "description": "Tired of OOM errors and tedious file management? The AIIA Node Suite delivers the ultimate workflow overhaul, headlined by the AIIA Media Browser: a high-performance file hub fully integrated into ComfyUI. Instantly preview images, videos, and audio, and experience ultimate smoothness thanks to smart caching and virtual scrolling, even with tens of thousands of files. It also features a powerful fullscreen viewer, rich tooltip previews, and flexible sorting. Alongside the browser, our Video Combine node fundamentally solves memory bottlenecks in long video generation. By streaming frames from disk, you can create videos of virtually unlimited length, making OOM errors a thing of the past. The suite also includes OOM-safe FLOAT video generation, advanced audio processing, and other utilities, providing a complete end-to-end solution from content creation to asset management.", - "files": [ - "https://github.com/havvk/ComfyUI_AIIA" - ], - "install_type": "git-clone", - "reference": "https://github.com/havvk/ComfyUI_AIIA", - "title": "ComfyUI_AIIA" - }, - { - "author": "dseditor", - "description": "A ComfyUI custom node package for seamless integration with Threads (Meta's social platform). This package allows you to publish posts, manage images, and retrieve post history directly from your ComfyUI workflows.", - "files": [ - "https://github.com/dseditor/ComfyUI-Thread" - ], - "install_type": "git-clone", - "reference": "https://github.com/dseditor/ComfyUI-Thread", - "title": "ComfyUI-Thread" - }, - { - "author": "dseditor", - "description": "A powerful workflow scheduling extension for ComfyUI that enables automated daily execution of workflows with an intuitive web interface ,Adding shutdown computer after workflow node", - "files": [ - "https://github.com/dseditor/ComfyUI-ScheduledTask" - ], - "install_type": "git-clone", - "reference": "https://github.com/dseditor/ComfyUI-ScheduledTask", - "title": "ComfyUI-ScheduledTask" - }, - { - "author": "dseditor", - "description": "The ListHelper collection is a comprehensive set of custom nodes for ComfyUI that provides powerful list manipulation capabilities. This collection includes audio processing, text splitting, and number generation tools for enhanced workflow automation.", - "files": [ - "https://github.com/dseditor/ComfyUI-ListHelper" - ], - "install_type": "git-clone", - "reference": "https://github.com/dseditor/ComfyUI-ListHelper", - "title": "ComfyUI-ListHelper" - }, - { - "author": "Leon", - "description": "A comprehensive collection of utility and API integration nodes for ComfyUI. Includes image manipulation (4-grid split), string utilities, and multiple API integrations: ImgBB upload, HyprLab upload, Google Image API, Luma AI, FLUX Image API, FLUX Kontext API, and Midjourney proxy integration. Features include image/video hosting, AI image generation, and image description capabilities.", - "files": [ - "https://github.com/l3ony2k/comfyui-leon-nodes" - ], - "id": "leon", - "install_type": "git-clone", - "nodename_pattern": "^\ud83e\udd16 Leon", - "pip": [ - "Pillow", - "torch", - "numpy", - "requests", - "tenacity" - ], - "reference": "https://github.com/l3ony2k/comfyui-leon-nodes", - "tags": [ - "image", - "utility", - "api", - "upload", - "generation", - "split", - "string", - "imgbb", - "hypr", - "google", - "luma", - "flux", - "midjourney" - ], - "title": "Leon's Utility and API Integration Nodes" - }, - { - "author": "jurdnf", - "description": "A collection of ComfyUI nodes that sculpt diffusion models by applying gradient-based modifications to different layers and blocks.", - "files": [ - "https://github.com/jurdnf/ComfyUI-JurdnsModelSculptor" - ], - "install_type": "git-clone", - "reference": "https://github.com/jurdnf/ComfyUI-JurdnsModelSculptor", - "title": "ComfyUI-JurdnsModelSculptor" - }, - { - "author": "jurdnf", - "description": "A ComfyUI custom node that adds controlled noise injection during the sampling process for enhanced image generation quality and detail.", - "files": [ - "https://github.com/jurdnf/ComfyUI-JurdnsIterativeNoiseKSampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/jurdnf/ComfyUI-JurdnsIterativeNoiseKSampler", - "title": "ComfyUI-JurdnsIterativeNoiseKsampler" - }, - { - "author": "DrStone71", - "description": "This custom node for ComfyUI allows you to translate your prompt directly into the language used by your LLM", - "files": [ - "https://github.com/DrStone71/ComfyUI-Prompt-Translator" - ], - "install_type": "git-clone", - "reference": "https://github.com/DrStone71/ComfyUI-Prompt-Translator", - "title": "ComfyUI-Prompt-Translator" - }, - { - "author": "Phospholipids", - "description": "This extension offers wildcard prompting works solely in workflow.", - "files": [ - "https://github.com/kohs100/comfyui-ppwc" - ], - "install_type": "git-clone", - "reference": "https://github.com/kohs100/comfyui-ppwc", - "title": "PPWildCard" - }, - { - "author": "linjian-ufo", - "description": "This is a custom node for ComfyUI that calls the DeepSeek Chat API to process text input and return text output.", - "files": [ - "https://github.com/linjian-ufo/comfyui_deepseek_lj257_update" - ], - "install_type": "git-clone", - "reference": "https://github.com/linjian-ufo/comfyui_deepseek_lj257_update", - "title": "DeepSeek Chat Node for ComfyUI" - }, - { - "author": "linjian-ufo", - "description": "Professional AI Image Description Generator\nBased on Zhipu AI GLM-4V multimodal model, batch generate accurate and detailed descriptions for images in Chinese and English", - "files": [ - "https://github.com/linjian-ufo/ComfyUI_GLM4V_voltspark" - ], - "install_type": "git-clone", - "reference": "https://github.com/linjian-ufo/ComfyUI_GLM4V_voltspark", - "title": "GLM-4V Image Descriptor" - }, - { - "author": "jkhayiying", - "description": "This is a node to load an image from local path or url.", - "files": [ - "https://gitee.com/yyh915/jkha-load-img" - ], - "id": "JkhaImageLoaderPathOrUrl", - "install_type": "git-clone", - "reference": "https://gitee.com/yyh915/jkha-load-img", - "title": "ImageLoadFromLocalOrUrl Node for ComfyUI" - }, - { - "author": "jinchanz", - "description": "This is a set of custom nodes for calling an image translation API within ComfyUI.", - "files": [ - "https://github.com/jinchanz/ComfyUI-ADIC" - ], - "install_type": "git-clone", - "reference": "https://github.com/jinchanz/ComfyUI-ADIC", - "title": "ComfyUI-ADIC" - }, - { - "author": "Lord Lethris", - "description": "Stylized RPG character prompt generator for ComfyUI. Supports standard and Ollama-based prompts, works with SD, SDXL, Flux, and more.", - "files": [ - "https://github.com/lord-lethris/ComfyUI-RPG-Characters" - ], - "id": "rpg-characters", - "install_type": "git-clone", - "reference": "https://github.com/lord-lethris/ComfyUI-RPG-Characters", - "title": "ComfyUI-RPG-Characters" - }, - { - "author": "ialhabbal", - "description": "A powerful ComfyUI custom node for advanced face occlusion, segmentation, and masking, leveraging state-of-the-art face detection (insightface buffalo models) for robust and accurate results.", - "files": [ - "https://github.com/ialhabbal/OcclusionMask" - ], - "install_type": "git-clone", - "reference": "https://github.com/ialhabbal/OcclusionMask", - "title": "OcclusionMask" - }, - { - "author": "kael558", - "description": "GGUF Quantization support for native ComfyUI models with FantasyTalking.", - "files": [ - "https://github.com/kael558/ComfyUI-GGUF-FantasyTalking" - ], - "install_type": "git-clone", - "reference": "https://github.com/kael558/ComfyUI-GGUF-FantasyTalking", - "title": "ComfyUI-GGUF-FantasyTalking" - }, - { - "author": "\ud83d\ude08 CasterPollux", - "description": "Professional video object removal suite using MiniMax optimization. Includes BMO-enhanced nodes with VAE normalization, temporal preservation, and 6-step inference. Complete video inpainting solution for ComfyUI.", - "files": [ - "https://github.com/casterpollux/MiniMax-bmo" - ], - "install_type": "git-clone", - "nodename_pattern": "MiniMax.*BMO|BMO.*MiniMax", - "pip": [ - "segment-anything" - ], - "reference": "https://github.com/casterpollux/MiniMax-bmo", - "tags": [ - "video", - "inpainting", - "object-removal", - "suite", - "professional", - "BMO" - ], - "title": "MiniMax Video Object Remover Suite" - }, - { - "author": "drphero", - "description": "Automatically tests the impact of each phrase in a prompt by generating images with one phrase omitted at a time.", - "files": [ - "https://github.com/drphero/comfyui_prompttester" - ], - "install_type": "git-clone", - "reference": "https://github.com/drphero/comfyui_prompttester", - "title": "ComfyUI-PromptTester" - }, - { - "author": "azazeal04", - "description": "character nodes for characters from various anime shows and comics", - "files": [ - "https://github.com/azazeal04/Azazeal_Anime_Characters_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/azazeal04/Azazeal_Anime_Characters_ComfyUI", - "title": "anime_character_selector" - }, - { - "author": "flamacore", - "description": "A ComfyUI node for uploading generated content to YouTube. [a/Buy me a coffee](https://buymeacoffee.com/chao.k)", - "files": [ - "https://github.com/flamacore/ComfyUI-YouTubeUploader" - ], - "id": "comfyui-youtubeuploader", - "install_type": "git-clone", - "reference": "https://github.com/flamacore/ComfyUI-YouTubeUploader", - "title": "ComfyUI YouTube Uploader" - }, - { - "author": "robin-collins", - "description": "A modular collection of ComfyUI custom nodes with advanced dependency management and ComfyUI Manager integration.", - "files": [ - "https://github.com/robin-collins/ComfyUI-TechsToolz" - ], - "install_type": "git-clone", - "reference": "https://github.com/robin-collins/ComfyUI-TechsToolz", - "title": "ComfyUI-TechsToolz" - }, - { - "author": "highdoping", - "description": "Add ASS/SSA subtitle to video using ffmpeg.", - "files": [ - "https://github.com/HighDoping/ComfyUI_ASSSSA" - ], - "install_type": "git-clone", - "reference": "https://github.com/HighDoping/ComfyUI_ASSSSA", - "title": "ComfyUI-ASSSSA" - }, - { - "author": "highdoping", - "description": "Nodes for lama+refiner inpainting with ComfyUI.", - "files": [ - "https://github.com/fplu/comfyui_lama_with_refiner" - ], - "install_type": "git-clone", - "reference": "https://github.com/fplu/comfyui_lama_with_refiner", - "title": "lama_with_refiner" - }, - { - "author": "quasiblob", - "description": "Image Adjustments node for ComfyUI with minimal requirements, uses PyTorch for image manipulation operations.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageAdjustments" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageAdjustments", - "title": "ComfyUI-EsesImageAdjustments" - }, - { - "author": "quasiblob", - "description": "Non-destructive visual image composition helper tool node for ComfyUI with minimal requirements, works with larger images too.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesCompositionGuides" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesCompositionGuides", - "title": "ComfyUI-EsesCompositionGuides" - }, - { - "author": "quasiblob", - "description": "The 'Eses Image Offset' node offers basic image offsetting capabilities within ComfyUI. It allows shifting image and mask content horizontally and/or vertically, with an option to wrap content around the canvas edges for a tiling effect.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageOffset" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageOffset", - "title": "ComfyUI-EsesImageOffset" - }, - { - "author": "quasiblob", - "description": "The 'Eses Image Lens Effects' node is a multipurpose node for ComfyUI designed to simulate a variety of lens characteristics. It combines several typical effects into a single, convenient node, allowing to add realistic or stylistic lens distortion, chromatic aberration, post-process scaling, and a highly configurable vignette.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageLensEffects" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageLensEffects", - "title": "ComfyUI-EsesImageLensEffects" - }, - { - "author": "quasiblob", - "description": "'Eses Image Effect Bloom' image post processing effect for ComfyUI, it uses GPU and has optimized blur effect calculations. Minimal dependencies, simple and easy to use UI.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageEffectBloom" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageEffectBloom", - "title": "ComfyUI-EsesImageEffectBloom" - }, - { - "author": "quasiblob", - "description": "'Real-time golden ratio pattern composition evaluation tool node for ComfyUI. This does nothing else - but you can move, rotate and scale then pattern to find new things and structures in your images.", - "files": [ - "https://github.com/quasiblob/EsesCompositionGoldenRatio" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/EsesCompositionGoldenRatio", - "title": "EsesCompositionGoldenRatio" - }, - { - "author": "quasiblob", - "description": "The 'Eses Image Resize' node offers comprehensive image resizing capabilities within ComfyUI. It supports various scaling modes including scaling by a specific ratio, target megapixels, or directly to fixed dimensions. The node provides framing options to handle aspect ratio changes, allowing users to 'Crop to Fit' (fill) the target frame or 'Fit to Frame' (letterbox) the image with a customizable fill color. It also generates and outputs a corresponding mask, with control over the letterbox area's color (black or white) within the mask.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageResize" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageResize", - "title": "EsesImageResize" - }, - { - "author": "quasiblob", - "description": "Channel curves custom node for ComfyUI. RGB, R, G and B, luma and saturation curves adjustments. Save and load presets. Real-time curves adjustment tool directly within the user interface. Precise, interactive control over the tonal range of both image channels and masks, using a GPU-accelerated PyTorch backend for instant feedback.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageEffectCurves" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageEffectCurves", - "title": "ComfyUI-EsesImageEffectCurves" - }, - { - "author": "quasiblob", - "description": "The 'Eses Image Effect Levels' is a ComfyUI custom node that provides a real-time levels adjustment tool directly within the user interface. It allows for interactive control over the tonal range of both images and masks, using a GPU-accelerated PyTorch backend for near instant feedback.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageEffectLevels" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageEffectLevels", - "title": "ComfyUI-EsesImageEffectLevels" - }, - { - "author": "quasiblob", - "description": "Apply 2D transformations to images and masks within ComfyUI. Zoom, position, scale, flip, rotate, squash and stretch the input content. Tile images to create patterns (supports alpha channels). Fill options for managing canvas areas exposed by transformations. Apply masks to RGB images and invert mask inputs or outputs. No extra dependencies.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageTransform" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageTransform", - "title": "ComfyUI-EsesImageTransform" - }, - { - "author": "quasiblob", - "description": "Interactive A/B image comparison node with a draggable slider to reveal one image over another. Includes difference and other blend modes for more detailed analysis, allowing one to spot changes in similar images. Node also outputs a passthrough image of input A, and a grayscale difference mask.", - "files": [ - "https://github.com/quasiblob/ComfyUI-EsesImageCompare" - ], - "install_type": "git-clone", - "reference": "https://github.com/quasiblob/ComfyUI-EsesImageCompare", - "title": "ComfyUI-EsesImageCompare" - }, - { - "author": "TheLustriVA", - "description": "Resolution calculator nodes for ComfyUI with model-specific constraints and optimal bucket resolutions", - "files": [ - "https://github.com/TheLustriVA/ComfyUI-Image-Size-Tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/TheLustriVA/ComfyUI-Image-Size-Tools", - "title": "ComfyUI Image Size Tool" - }, - { - "author": "834t", - "description": "An intuitive, all-in-one node for ComfyUI that brings a powerful, layer-based regional prompting workflow directly into your graph. Say goodbye to managing countless Conditioning (Set Area) nodes and hello to drawing your creative vision.", - "files": [ - "https://github.com/834t/ComfyUI_834t_scene_composer" - ], - "install_type": "git-clone", - "reference": "https://github.com/834t/ComfyUI_834t_scene_composer", - "title": "Scene Composer for ComfyUI" - }, - { - "author": "Maxed-Out-99", - "description": "Custom ComfyUI nodes used in Maxed Out workflows (SDXL, Flux, etc.)", - "files": [ - "https://github.com/Maxed-Out-99/ComfyUI-MaxedOut" - ], - "install_type": "git-clone", - "reference": "https://github.com/Maxed-Out-99/ComfyUI-MaxedOut", - "title": "ComfyUI-MaxedOut" - }, - { - "author": "Maxed-Out-99", - "description": "Smart, unified model loaders for ComfyUI that support both standard .safetensors and quantized .gguf formats \u2014 no switching nodes required. Includes flexible UNET and CLIP loaders that work across models like SDXL, SD3, Flux, and more.", - "files": [ - "https://github.com/Maxed-Out-99/ComfyUI-SmartModelLoaders-MXD" - ], - "install_type": "git-clone", - "reference": "https://github.com/Maxed-Out-99/ComfyUI-SmartModelLoaders-MXD", - "title": "ComfyUI-SmartModelLoaders-MXD" - }, - { - "author": "lucak5s", - "description": "Face restoration with GFPGAN.", - "files": [ - "https://github.com/lucak5s/comfyui_gfpgan" - ], - "install_type": "git-clone", - "reference": "https://github.com/lucak5s/comfyui_gfpgan", - "title": "ComfyUI GFPGAN" - }, - { - "author": "joeriben", - "description": "ComfyUI nodes for the project AI for Arts Education", - "files": [ - "https://github.com/joeriben/ai4artsed_comfyui_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/joeriben/ai4artsed_comfyui_nodes", - "title": "AI4ArtsEd Nodes" - }, - { - "author": "DebugPadawan", - "description": "Essential custom nodes for ComfyUI workflows", - "files": [ - "https://github.com/DebugPadawan/DebugPadawans-ComfyUI-Essentials" - ], - "install_type": "git-clone", - "reference": "https://github.com/DebugPadawan/DebugPadawans-ComfyUI-Essentials", - "title": "DebugPadawan's ComfyUI Essentials" - }, - { - "author": "aleolidev", - "description": "A collection of custom image processing nodes for ComfyUI", - "files": [ - "https://github.com/aleolidev/comfy_kaizen_package" - ], - "id": "kaizen_package", - "install_type": "git-clone", - "reference": "https://github.com/aleolidev/comfy_kaizen_package", - "title": "Kaizen Package" - }, - { - "author": "cmdicely", - "description": "Custom node to extract the colors in an image as a palette for use with ComfyUI-PixelArt-Detector", - "files": [ - "https://github.com/cmdicely/simple_image_to_palette" - ], - "install_type": "git-clone", - "reference": "https://github.com/cmdicely/simple_image_to_palette", - "title": "Simple Image To Palette" - }, - { - "author": "cmdicely", - "description": "GrsAI API node supports models: Flux-Pro-1.1 (\u00a5 0.03), Flux-Ultra-1.1 (\u00a5 0.04), Flux Kontext Pro (\u00a5 0.035), Flux Kontext Max (\u00a5 0.07), GPT Image (\u00a5 0.02). Support text generated images, image generated images, and multi image fusion.", - "files": [ - "https://github.com/31702160136/ComfyUI-GrsAI" - ], - "install_type": "git-clone", - "reference": "https://github.com/31702160136/ComfyUI-GrsAI", - "title": "GrsAI api in ComfyUI" - }, - { - "author": "AKharytonchyk", - "description": "ComfyUI custom nodes for Telegram bot integration", - "files": [ - "https://github.com/AKharytonchyk/ComfyUI-telegram-bot-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/AKharytonchyk/ComfyUI-telegram-bot-node", - "title": "ComfyUI-telegram-bot-node" - }, - { - "author": "leonardomiramondi", - "description": "ComfyUI node for Flux Context (Kontext) image editing", - "files": [ - "https://github.com/leonardomiramondi/flux-context-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/leonardomiramondi/flux-context-comfyui", - "title": "Flux Context ComfyUI Node" - }, - { - "author": "kpsss34", - "description": "Diffusers custom_node", - "files": [ - "https://github.com/kpsss34/ComfyUI-kpsss34" - ], - "install_type": "git-clone", - "reference": "https://github.com/kpsss34/ComfyUI-kpsss34", - "title": "ComfyUI kpsss34 Custom Node" - }, - { - "author": "Gary-yeh", - "description": "A powerful multi-model image captioning node that supports both local BLIP models and the cloud-based Google Gemini API, specifically designed for ComfyUI.", - "files": [ - "https://github.com/Gary-yeh/comfyui-super-captioner" - ], - "install_type": "git-clone", - "reference": "https://github.com/Gary-yeh/comfyui-super-captioner", - "title": "comfyui-super-captioner" - }, - { - "author": "Gary-yeh", - "description": "This is a custom node suite for ComfyUI that automates the conversion of web content into an AI-refined news script. This simplified version focuses on the core 'fetch-and-process' workflow for a fast and direct automation experience.", - "files": [ - "https://github.com/Gary-yeh/ComfyUI-WebPrompter" - ], - "install_type": "git-clone", - "reference": "https://github.com/Gary-yeh/ComfyUI-WebPrompter", - "title": "ComfyUI-WebPrompter" - }, - { - "author": "fotobudka-team", - "description": "A ComfyUI custom node for automated face verification, designed to check if a person is clearly visible and suitable for passport-style photos. This node performs comprehensive facial analysis to ensure photo quality meets identification document standards.", - "files": [ - "https://github.com/fotobudka-team/comfyui-ai-faces" - ], - "install_type": "git-clone", - "reference": "https://github.com/fotobudka-team/comfyui-ai-faces", - "title": "ComfyUI AI Faces - Photo Verification Node" - }, - { - "author": "Ambrosinus", - "category": "Utils", - "description": "Ambrosinus ToolKit - Streamlined workflow export with transparent backgrounds, professional themes, and smart scaling. Perfect for creating clean, high-resolution workflow documentation and sharing.", - "files": [ - "https://github.com/lucianoambrosini/ComfyUI-ATk-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/lucianoambrosini/ComfyUI-ATk-Nodes", - "title": "ComfyUI-ATk-Nodes" - }, - { - "author": "wasilone11", - "description": "This custom node allows you to perform audio-video lip synchronization inside ComfyUI using a simple interface.", - "files": [ - "https://github.com/wasilone11/comfyui-sync-lipsync-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/wasilone11/comfyui-sync-lipsync-node", - "title": "ComfyUI Sync Lipsync Node" - }, - { - "author": "wasilone11", - "description": "This custom node allows you to generate personalized video messages (PVM) using audio, video, and multilingual text prompts inside ComfyUI.", - "files": [ - "https://github.com/wasilone11/comfyui-pvm-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/wasilone11/comfyui-pvm-node", - "title": "ComfyUI Sync PVM Node" - }, - { - "author": "uinodes", - "description": "This custom node is designed to provide graphical documentation for ComfyUI custom nodes.", - "files": [ - "https://github.com/uinodes/ComfyUI-uinodesDOC" - ], - "install_type": "git-clone", - "reference": "https://github.com/uinodes/ComfyUI-uinodesDOC", - "title": "ComfyUI-uinodesDOC" - }, - { - "author": "puke3615", - "description": "Simple REST API interfaces for ComfyUI with dynamic parameter replacement and output management", - "files": [ - "https://github.com/puke3615/ComfyUI-OneAPI" - ], - "install_type": "git-clone", - "reference": "https://github.com/puke3615/ComfyUI-OneAPI", - "title": "ComfyUI-OneAPI" - }, - { - "author": "bobsblazed", - "description": "This custom node for ComfyUI is designed to optimize latent generation for use with FLUX, SDXL and SD3. It provides flexible control over aspect ratios, megapixel sizes, and upscale factors, allowing users to dynamically create latents that fit specific tiling and resolution needs.", - "files": [ - "https://github.com/BobsBlazed/Bobs_Latent_Optimizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/BobsBlazed/Bobs_Latent_Optimizer", - "title": "Bobs_Latent_Optimizer" - }, - { - "author": "bobsblazed", - "description": "A custom LoRA loader node for ComfyUI with advanced block-weighting controls for both SDXL and FLUX models. Features presets for common use-cases like 'Character' and 'Style', and a 'Custom' mode for fine-grained control over individual model blocks.", - "files": [ - "https://github.com/BobsBlazed/Bobs-Lora-Loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/BobsBlazed/Bobs-Lora-Loader", - "title": "Bobs_LoRA_Loader" - }, - { - "author": "sdtana", - "description": "Implementation of [a/Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales](https://arxiv.org/abs/2506.19713) for ComfyUI.", - "files": [ - "https://github.com/sdtana/ComfyUI-FDG" - ], - "install_type": "git-clone", - "reference": "https://github.com/sdtana/ComfyUI-FDG", - "title": "ComfyUI-FDG" - }, - { - "author": "AbstractEyes", - "description": "A properly implemented lycoris loader for comfyui.", - "files": [ - "https://github.com/AbstractEyes/comfyui-lycoris" - ], - "install_type": "git-clone", - "reference": "https://github.com/AbstractEyes/comfyui-lycoris", - "title": "comfyui-lycoris" - }, - { - "author": "alchemine", - "description": "Custom nodes pack for ComfyUI", - "files": [ - "https://github.com/alchemine/comfyui-alchemine-pack" - ], - "install_type": "git-clone", - "reference": "https://github.com/alchemine/comfyui-alchemine-pack", - "title": "ComfyUI-Alchemine-Pack" - }, - { - "author": "HMG-Fiverr", - "description": "This node provides a button that, when clicked, triggers the execution of the node and generates a new random integer between 0 and 1000. The generated number is available as an output.", - "files": [ - "https://github.com/HMG-Fiverr/ComfyUI-RandomNumberButton" - ], - "install_type": "git-clone", - "reference": "https://github.com/HMG-Fiverr/ComfyUI-RandomNumberButton", - "title": "Random Number Button" - }, - { - "author": "Good-Dream-Studio", - "description": "Expose your workflows into HTTP endpoints directly from ComfyUI itself.", - "files": [ - "https://github.com/Good-Dream-Studio/ComfyUI-Connect" - ], - "install_type": "git-clone", - "reference": "https://github.com/Good-Dream-Studio/ComfyUI-Connect", - "title": "ComfyUI-Connect" - }, - { - "author": "eg0pr0xy", - "description": "Professional noise generation nodes for ComfyUI", - "files": [ - "https://github.com/eg0pr0xy/comfyui_noisegen" - ], - "install_type": "git-clone", - "reference": "https://github.com/eg0pr0xy/comfyui_noisegen", - "title": "ComfyUI-NoiseGen" - }, - { - "author": "aoliao", - "description": "This project integrates the ElevenLabs Text-to-Speech API as a custom node for ComfyUI. It enables seamless text-to-speech conversion directly within ComfyUI, providing the generated audio as a PyTorch tensor for immediate playback or further processing. Ideal for workflows requiring high-quality speech synthesis", - "files": [ - "https://github.com/sysL-padawan/comfyui-elevenlabs-integration" - ], - "install_type": "git-clone", - "reference": "https://github.com/sysL-padawan/comfyui-elevenlabs-integration", - "title": "ComfyUI ElevenLabs API integration" - }, - { - "author": "Windecay", - "description": "A simple node that can dynamically adjust the reserved memory of a workflow in real-time, used to avoid the utilization of shared memory.", - "files": [ - "https://github.com/Windecay/ComfyUI-ReservedVRAM" - ], - "install_type": "git-clone", - "reference": "https://github.com/Windecay/ComfyUI-ReservedVRAM", - "title": "ComfyUI-ReservedVRAM" - }, - { - "author": "Windecay", - "description": "Assist in determining reasonable input values for tiled_width/tiled_height in the UltimateSDUpscale node to improve efficiency.", - "files": [ - "https://github.com/Windecay/ComfyUI-SDupcaleTiledSize" - ], - "install_type": "git-clone", - "reference": "https://github.com/Windecay/ComfyUI-SDupcaleTiledSize", - "title": "ComfyUI-SDupcaleTiledSize" - }, - { - "author": "kaaskoek232", - "description": "Advanced memory management custom nodes for ComfyUI", - "files": [ - "https://github.com/kaaskoek232/ComfyUI-MemoryManagement" - ], - "install_type": "git-clone", - "reference": "https://github.com/kaaskoek232/ComfyUI-MemoryManagement", - "title": "ComfyUI-MemoryManagement" - }, - { - "author": "LK-168", - "description": "Image tool kit for comfyui with dghs-imgutils", - "files": [ - "https://github.com/LK-168/comfyui_imgutils" - ], - "install_type": "git-clone", - "reference": "https://github.com/LK-168/comfyui_imgutils", - "title": "comfyui_imgutils" - }, - { - "author": "Icyman86", - "description": "WAI's Character select ported to ComfyUI with a few tweaks", - "files": [ - "https://github.com/Icyman86/ComfyUI_AnimeCharacterSelect" - ], - "install_type": "git-clone", - "reference": "https://github.com/Icyman86/ComfyUI_AnimeCharacterSelect", - "title": "ComfyUI_AnimeCharacterSelect" - }, - { - "author": "Cyrus-Hei", - "description": "A prompt manager on the sidebar for ComfyUI, allowing easy saving and copying of prompts. ", - "files": [ - "https://github.com/Cyrus-Hei/comfyui-prompt-bank" - ], - "install_type": "git-clone", - "reference": "https://github.com/Cyrus-Hei/comfyui-prompt-bank", - "title": "comfyui-prompt-bank" - }, - { - "author": "KarmaSwint", - "description": "Custom cycling KSampler with progressive upscale and more. Professional post-processing nodes.", - "files": [ - "https://github.com/KarmaSwint/ComfyUI-KarmaNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/KarmaSwint/ComfyUI-KarmaNodes", - "title": "KarmaNodes" - }, - { - "author": "S4MUEL404", - "description": "A set of ComfyUI nodes for animating a foreground layer over a background, with unified timing and high-quality composition. It includes effectors for rotation, position, path-following position, scale, opacity, distortion, and masking, plus a core node that composes frames and exports APNG/WEBP and a frame sequence.", - "files": [ - "https://github.com/S4MUEL-404/ComfyUI-S4Motion" - ], - "id": "comfyui-s4motion", - "install_type": "git-clone", - "reference": "https://github.com/S4MUEL-404/ComfyUI-S4Motion", - "title": "ComfyUI S4Motion" - }, - { - "author": "S4MUEL404", - "description": "A custom node package for image processing in ComfyUI, for automating image processing within the ComfyUI workflow.", - "files": [ - "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image" - ], - "id": "comfyui-s4tool-image", - "install_type": "git-clone", - "reference": "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image", - "title": "ComfyUI S4Tool Image" - }, - { - "author": "S4MUEL404", - "description": "Text rendering and styling nodes for ComfyUI. This extension provides a basic text renderer, multiple font loaders, and a style node that adds stroke, shadow, gradient fill, and opacity control.", - "files": [ - "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Text" - ], - "id": "comfyui-s4tool-text", - "install_type": "git-clone", - "reference": "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Text", - "title": "ComfyUI S4Tool Text" - }, - { - "author": "fchangjun", - "description": "A custom node for saving multiple images simultaneously with batch processing and resizing capabilities.", - "files": [ - "https://github.com/fchangjun/Comfyui_MultiSaveImage" - ], - "install_type": "git-clone", - "reference": "https://github.com/fchangjun/Comfyui_MultiSaveImage", - "title": "MultiSaveImage Node" - }, - { - "author": "Yo1up", - "description": "ComfyUI nodes that allow the user to control the generation of diffusion models to increase and decrease level of detail. The model patch has no trainable parameters and can be applied to theoretically any diffusion model in existence. whether or not the implementation currently works for every diffusion model in existence is unknown.", - "files": [ - "https://github.com/Yo1up/Diffusion-Model-Detailer" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yo1up/Diffusion-Model-Detailer", - "title": "Diffusion-Model-Detailer" - }, - { - "author": "facefusion", - "description": "Industry leading face manipulation platform", - "files": [ - "https://github.com/facefusion/facefusion-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/facefusion/facefusion-comfyui", - "title": "FaceFusion ComfyUI" - }, - { - "author": "pmarmotte2", - "description": "A custom node for ComfyUI that performs speaker diarization to isolate individual speaker audio tracks from a single audio source.", - "files": [ - "https://github.com/pmarmotte2/ComfyUI-Speaker-Isolation" - ], - "install_type": "git-clone", - "reference": "https://github.com/pmarmotte2/ComfyUI-Speaker-Isolation", - "title": "ComfyUI-Speaker-Isolation" - }, - { - "author": "IIEleven11", - "description": "This package provides ComfyUI nodes for creating 3-scene storyboards from Ollama text output.", - "files": [ - "https://github.com/IIEleven11/ComfyUI-FairyTaler" - ], - "install_type": "git-clone", - "reference": "https://github.com/IIEleven11/ComfyUI-FairyTaler", - "title": "ComfyUI-FairyTaler" - }, - { - "author": "heheok", - "description": "This collection of custom nodes for ComfyUI is specifically designed to automate and streamline the process of creating infinite videos with WAN2.1 VACE.", - "files": [ - "https://github.com/heheok/comfyui_wan2.1_vace_infinite_helpers" - ], - "install_type": "git-clone", - "reference": "https://github.com/heheok/comfyui_wan2.1_vace_infinite_helpers", - "title": "comfyui_wan2.1_vace_infinite_helpers" - }, - { - "author": "Ltamann", - "description": "About TBG Enhanced Tiled Upscaler and Refiner Pro! We at TBG Think. Build. Generate. Pro AI upscaling & image enrichment are excited to make our TBG Enhanced Tiled Upscaler and Refiner Pro available to you for free non-commercial use. We believe in empowering creators and innovators, which is why anything you create or generate using our software", - "files": [ - "https://github.com/Ltamann/ComfyUI-TBG-ETUR" - ], - "install_type": "git-clone", - "reference": "https://github.com/Ltamann/ComfyUI-TBG-ETUR", - "title": "TBG_Enhanced Tiled Upscaler & Refiner FLUX PRO" - }, - { - "author": "Ltamann", - "description": "A curated collection of reusable ComfyUI nodes developed by TGB. These sidecodes encapsulate key breakthroughs in model sampling, noise scheduling, and image refinement for enhanced stable diffusion workflows.", - "files": [ - "https://github.com/Ltamann/ComfyUI-TBG-Takeaways" - ], - "install_type": "git-clone", - "reference": "https://github.com/Ltamann/ComfyUI-TBG-Takeaways", - "title": "TBG\u2019s ComfyUI Development Takeaways" - }, - { - "author": "DavidPiazza", - "description": "A custom node pack for ComfyUI that enables creative manipulation and 'bending' of neural network models. Perform various operations on loaded model checkpoints to create unique and experimental effects.", - "files": [ - "https://github.com/DavidPiazza/network_bending" - ], - "install_type": "git-clone", - "reference": "https://github.com/DavidPiazza/network_bending", - "title": "Network Bending for ComfyUI" - }, - { - "author": "DiffusionLight", - "description": "DiffusionLight (Turbo) implemented in ComfyUI", - "files": [ - "https://github.com/DiffusionLight/DiffusionLight-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/DiffusionLight/DiffusionLight-ComfyUI", - "title": "DiffusionLight-ComfyUI" - }, - { - "author": "sunx.ai", - "description": "A set of custom nodes developed by SunxAI for ComfyUI, including image loop processing and more. ", - "files": [ - "https://github.com/upseem/comfyui_sun_nodes" - ], - "id": "comfyui_sun_nodes", - "install_type": "git-clone", - "reference": "https://github.com/upseem/comfyui_sun_nodes", - "title": "SunxAI Custom Nodes for ComfyUI" - }, - { - "author": "sunx.ai", - "description": "Face detection & restoration tools for ComfyUI by Sunx.ai", - "files": [ - "https://github.com/upseem/comfyui_sunxAI_facetools" - ], - "install_type": "git-clone", - "reference": "https://github.com/upseem/comfyui_sunxAI_facetools", - "title": "comfyui_sunxAI_facetools" - }, - { - "author": "set-soft", - "description": "Audio separation (aka demixing) nodes, for Vocals, Instruments, Bass, Drums and Others (experimental Piano and Guitar).\nUsing MDX-Net and Demucs, no extra dependencies, support for batch and resample.\nChoose between High Quality and Speed. All safetensor models (No ONNX, No PyTorch)", - "files": [ - "https://github.com/set-soft/AudioSeparation" - ], - "install_type": "git-clone", - "reference": "https://github.com/set-soft/AudioSeparation", - "title": "Audio Separation (Demix)" - }, - { - "author": "creepybits", - "description": "This ComfyUI node will save images directly to Google Drive by using Google's free API service.", - "files": [ - "https://github.com/Creepybits/ComfyUI-Save_To_GDrive" - ], - "id": "creepybits", - "install_type": "git-clone", - "reference": "https://github.com/Creepybits/ComfyUI-Save_To_GDrive", - "title": "Save Image To Google Drive" - }, - { - "author": "Hazukiaoi", - "description": "This is a custom node pack designed for ComfyUI that seamlessly integrates the powerful LM Studio into your workflow, enabling you to perform a variety of tasks such as text generation and image understanding (Vision) using locally run LLMs.", - "files": [ - "https://github.com/Hazukiaoi/ComfyUI-LM_Studio_Tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/Hazukiaoi/ComfyUI-LM_Studio_Tools", - "title": "LM Studio Tools for ComfyUI" - }, - { - "author": "georgitsenov", - "description": "A custom ComfyUI node for saving generated images directly to Cloudflare R2 (or S3-compatible) buckets using boto3, with secure random filename generation and public URL return.", - "files": [ - "https://github.com/georgitsenov/ComfyUI-R2" - ], - "install_type": "git-clone", - "reference": "https://github.com/georgitsenov/ComfyUI-R2", - "title": "ComfyUI S3 Save Node" - }, - { - "author": "HappyXY", - "description": "use case of llm, image, video models on amazon bedrock", - "files": [ - "https://github.com/HappyXY/ComfyUI-AmazonBedrock" - ], - "install_type": "git-clone", - "reference": "https://github.com/HappyXY/ComfyUI-AmazonBedrock", - "title": "ComfyUI-AmazonBedrock" - }, - { - "author": "manifestations", - "description": "A collection of custom ComfyUI nodes and utilities for generating AI image prompts representing the diverse attire, cultures, regions, and appearances of the world. This project is designed for easy extension to new countries, cultures, and body parts, using a modular JSON-based data structure and dynamic node generation.", - "files": [ - "https://github.com/manifestations/comfyui-globetrotter" - ], - "install_type": "git-clone", - "reference": "https://github.com/manifestations/comfyui-globetrotter", - "title": "ComfyUI Globetrotter Nodes" - }, - { - "author": "manifestations", - "description": "Advanced, professional outfit and makeup generation nodes for ComfyUI, with dynamic UI and AI-powered prompt formatting.", - "files": [ - "https://github.com/manifestations/comfyui-outfit" - ], - "install_type": "git-clone", - "reference": "https://github.com/manifestations/comfyui-outfit", - "title": "ComfyUI Outfit Nodes" - }, - { - "author": "kaipard", - "description": "Add presets for latent and adjust the image size.", - "files": [ - "https://github.com/kaipard/comfyui-auto-latent-size" - ], - "install_type": "git-clone", - "reference": "https://github.com/kaipard/comfyui-auto-latent-size", - "title": "Auto Aspect Latent Generator" - }, - { - "author": "pvlprk", - "description": "A custom ComfyUI node that integrates with the OpenAI Assistants API.", - "files": [ - "https://github.com/pvlprk/comfyui-pvl-api-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/pvlprk/comfyui-pvl-api-nodes", - "title": "ComfyUI Assistant Node" - }, - { - "author": "danTheMonk", - "description": "A simple ComfyUI custom node extension that provides utility nodes for converting between integer and float values.", - "files": [ - "https://github.com/danTheMonk/comfyui-int-and-float" - ], - "install_type": "git-clone", - "reference": "https://github.com/danTheMonk/comfyui-int-and-float", - "title": "ComfyUI Int and Float Conversion Nodes" - }, - { - "author": "RamonGuthrie", - "description": "An advanced image stitching node for ComfyUI.", - "files": [ - "https://github.com/RamonGuthrie/ComfyUI-RBG-ImageStitchPlus" - ], - "install_type": "git-clone", - "reference": "https://github.com/RamonGuthrie/ComfyUI-RBG-ImageStitchPlus", - "title": "ComfyUI-RBG-ImageStitchPlus" - }, - { - "author": "vrgamegirl19", - "description": "Film grain and color match nodes designed for high-quality frame-by-frame video enhancement in ComfyUI.", - "files": [ - "https://github.com/vrgamegirl19/comfyui-vrgamedevgirl" - ], - "id": "vrgamedev_video_nodes", - "install_type": "git-clone", - "reference": "https://github.com/vrgamegirl19/comfyui-vrgamedevgirl", - "title": "VRGameDevGirl Video Enhancement Nodes" - }, - { - "author": "namtb96", - "description": "A ComfyUI custom node package for the OmniGen2 multimodal generation model.", - "files": [ - "https://github.com/namtb96/OmniGen2-Simple-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/namtb96/OmniGen2-Simple-Node", - "title": "OmniGen2 Simple Node" - }, - { - "author": "lonelyowl13", - "description": "A node for including random artists usernames to a prompt.", - "files": [ - "https://github.com/lonelyowl13/artist_randomizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/lonelyowl13/artist_randomizer", - "title": "Artist tag randomizer for comfyui" - }, - { - "author": "Aryan185", - "description": "ComfyUI node for Flux Kontext Pro and Max models from Replicate", - "files": [ - "https://github.com/Aryan185/ComfyUI-ExternalAPI-Helpers" - ], - "install_type": "git-clone", - "reference": "https://github.com/Aryan185/ComfyUI-ExternalAPI-Helpers", - "title": "ComfyUI-ExternalAPI-Helpers" - }, - { - "author": "iacoposk8", - "description": "A simple wrapper for Fooocus's inpainting code, designed to replicate its outstanding results. Future improvements for a more refined and lightweight version are planned.", - "files": [ - "https://github.com/iacoposk8/ComfyUI-Fooocus-Inpaint-Wrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/iacoposk8/ComfyUI-Fooocus-Inpaint-Wrapper", - "title": "ComfyUI Fooocus Inpaint Wrapper" - }, - { - "author": "iacoposk8", - "description": "Three custom nodes for ComfyUI that allow you to encrypt and decrypt Python objects or text using simple XOR encryption.", - "files": [ - "https://github.com/iacoposk8/xor_pickle_nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/iacoposk8/xor_pickle_nodes", - "title": "ComfyUI XOR Text & Pickle Nodes" - }, - { - "author": "FaraamFide", - "description": "ComfyUI-ParamNodes is a lightweight, dependency-free collection of custom nodes for ComfyUI, designed to parameterize your workflows for API-driven generation. It provides a clean set of input nodes to control strings, numbers, booleans, and model/LoRA selections, along with a simple logic switch for conditional execution.", - "files": [ - "https://github.com/FaraamFide/ComfyUI-ParamNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/FaraamFide/ComfyUI-ParamNodes", - "title": "ComfyUI-ParamNodes" - }, - { - "author": "chuchu114514", - "description": "This plugin includes two core nodes designed to handle proportion optimization tasks of varying complexity", - "files": [ - "https://github.com/chuchu114514/comfyui_proportion_solver" - ], - "install_type": "git-clone", - "reference": "https://github.com/chuchu114514/comfyui_proportion_solver", - "title": "comfyui_proportion_solver" - }, - { - "author": "yamanacn", - "description": "This is a custom node designed for ComfyUI that leverages the multimodal large model Doubao from Volcengine Ark to intelligently compare two input images. You can provide two images and a custom text prompt. The node will send this information to the large model and return a detailed textual description of the similarities and differences between the two images.", - "files": [ - "https://github.com/yamanacn/comfyui_kontext_Analyze" - ], - "install_type": "git-clone", - "reference": "https://github.com/yamanacn/comfyui_kontext_Analyze", - "title": "ComfyUI Kontext Duo Image Analyzer" - }, - { - "author": "weekii", - "description": "Professional-grade ComfyUI image saving plugin with support for multiple formats, custom naming, and advanced features.", - "files": [ - "https://github.com/weekii/comfyui-save-image-pro" - ], - "install_type": "git-clone", - "reference": "https://github.com/weekii/comfyui-save-image-pro", - "title": "comfyui-save-image-pro" - }, - { - "author": "bbaudio", - "description": "powerful nodes for wan2.1 vace", - "files": [ - "https://github.com/bbaudio-2025/ComfyUI-SuperUltimateVaceTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/bbaudio-2025/ComfyUI-SuperUltimateVaceTools", - "title": "ComfyUI-SuperUltimateVaceTools" - }, - { - "author": "robertvoy", - "description": "A custom node extension for ComfyUI that enables distributed image generation across multiple GPUs through a master-worker architecture.", - "files": [ - "https://github.com/robertvoy/ComfyUI-Distributed" - ], - "install_type": "git-clone", - "reference": "https://github.com/robertvoy/ComfyUI-Distributed", - "title": "ComfyUI-Distributed" - }, - { - "author": "FortunaCournot", - "description": "Contains ImageSBSConverter node to convert an image into a side-by-side image.", - "files": [ - "https://github.com/FortunaCournot/comfyui_stereoscopic" - ], - "id": "stereoscopic", - "install_type": "git-clone", - "reference": "https://github.com/FortunaCournot/comfyui_stereoscopic", - "title": "Stereoscopic" - }, - { - "author": "negaga53", - "description": "A powerful and versatile custom node for ComfyUI that provides multiple ways to load images into your workflows.", - "files": [ - "https://github.com/negaga53/comfyui-imgloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/negaga53/comfyui-imgloader", - "title": "ComfyUI Universal Image Loader" - }, - { - "author": "sunra-ai", - "description": "Professional ComfyUI plugin for Sunra.ai's FLUX.1 Kontext and Seedance models with enhanced UI", - "files": [ - "https://github.com/sunra-ai/comfyui-sunra" - ], - "install_type": "git-clone", - "reference": "https://github.com/sunra-ai/comfyui-sunra", - "title": "ComfyUI Sunra.ai Plugin" - }, - { - "author": "Ben Staniford", - "description": "A ComfyUI node designed to load the most recent image in a folder", - "files": [ - "https://github.com/benstaniford/comfy-load-last-image" - ], - "install_type": "git-clone", - "reference": "https://github.com/benstaniford/comfy-load-last-image", - "title": "ComfyUI Load Most Recent Image Node" - }, - { - "author": "Ben Staniford", - "description": "A ComfyUI custom node for loading images from a contact sheet of recent files", - "files": [ - "https://github.com/benstaniford/comfy-contact-sheet-image-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/benstaniford/comfy-contact-sheet-image-loader", - "title": "Comfy Contact Sheet Image Loader" - }, - { - "author": "Ben Staniford", - "description": "A ComfyUI custom node that provides a LoRa loader with persistent trigger word storage. Automatically saves and loads trigger words for each LoRa model, making your workflow more efficient.", - "files": [ - "https://github.com/benstaniford/comfy-lora-loader-with-triggerdb" - ], - "install_type": "git-clone", - "reference": "https://github.com/benstaniford/comfy-lora-loader-with-triggerdb", - "title": "LoRa Loader with Trigger Database" - }, - { - "author": "Ben Staniford", - "description": "A ComfyUI custom node that provides a database-driven prompt management system. Store, organize, and edit prompts in categories with persistent JSON storage.", - "files": [ - "https://github.com/benstaniford/comfy-prompt-db" - ], - "install_type": "git-clone", - "reference": "https://github.com/benstaniford/comfy-prompt-db", - "title": "Prompt Database for ComfyUI" - }, - { - "author": "Ben Staniford", - "description": "An image source switch node for ComfyUI", - "files": [ - "https://github.com/benstaniford/comfy-image-switch" - ], - "install_type": "git-clone", - "reference": "https://github.com/benstaniford/comfy-image-switch", - "title": "ComfyUI Image Switch Node" - }, - { - "author": "OneThingAI", - "description": "A custom node for ComfyUI that integrates with OneThing AI's image generation API.", - "files": [ - "https://github.com/OneThingAI/ComfyUI_Onething_Image" - ], - "install_type": "git-clone", - "reference": "https://github.com/OneThingAI/ComfyUI_Onething_Image", - "title": "ComfyUI OneThing AI Node" - }, - { - "author": "OneThingAI", - "description": "This custom node for ComfyUI allows you to get detailed text descriptions of images using the OneThing AI Vision API. The node integrates with OneThing AI's powerful vision models to provide detailed descriptions of image content.", - "files": [ - "https://github.com/OneThingAI/ComfyUI_Onething_CV" - ], - "install_type": "git-clone", - "reference": "https://github.com/OneThingAI/ComfyUI_Onething_CV", - "title": "ComfyUI OneThing CV Node" - }, - { - "author": "NeuroSenko", - "description": "A comprehensive set of ComfyUI nodes for using Large Language Models (LLM) as text encoders for SDXL image generation through a trained adapter.", - "files": [ - "https://github.com/NeuroSenko/ComfyUI_LLM_SDXL_Adapter" - ], - "install_type": "git-clone", - "reference": "https://github.com/NeuroSenko/ComfyUI_LLM_SDXL_Adapter", - "title": "ComfyUI LLM SDXL Adapter" - }, - { - "author": "MovieLabs", - "description": "An extension set of validation checks, automatic versioning numbering, automatic directory creation, and naming conventions are implemented to ensure that the file system is kept in sync with ShotGrid.", - "files": [ - "https://github.com/MovieLabs/comfyui-movielabs-util" - ], - "install_type": "git-clone", - "reference": "https://github.com/MovieLabs/comfyui-movielabs-util", - "title": "MovieLabs ComfyUI Nodes for Publishing Workflow" - }, - { - "author": "reallusion", - "description": "This nodepack contains custom nodes for ComfyUI designed specifically for handling Reallusion-related assets such as Character Creator and iClone image and video files. These nodes are intended to be used as backend components that communicate and operate through the AI Render Plugin interface of iClone or Character Creator, enabling a seamless integration between ComfyUI's powerful image/video generation capabilities and Reallusion\u2019s animation tools. By bridging ComfyUI with iClone/Character Creator\u2019s AI Render Plugin, these nodes facilitate workflows where AI-assisted content generation can be controlled, customized, and rendered directly from within Reallusion software environments.", - "files": [ - "https://github.com/reallusion/ComfyUI-Reallusion" - ], - "install_type": "git-clone", - "reference": "https://github.com/reallusion/ComfyUI-Reallusion", - "title": "Reallusion ComfyUI Custom Nodes" - }, - { - "author": "glitchinthemetrix16", - "description": "Custom nodes for ComfyUI to enable face swapping using the Roop library.", - "files": [ - "https://github.com/glitchinthemetrix16/ComfyUI-Roop" - ], - "install_type": "git-clone", - "reference": "https://github.com/glitchinthemetrix16/ComfyUI-Roop", - "title": "ComfyUI Roop Custom Nodes" - }, - { - "author": "IsItDanOrAi", - "description": "Excel spreadsheet-driven ComfyUI nodes that let you load models, values, and workflows based on saved rows in Excel. Great for organizing and switching between CLIPs, VAEs, LoRAs, and more.", - "files": [ - "https://github.com/IsItDanOrAi/ComfyUI-exLoadout" - ], - "id": "comfyui-exloadout", - "install_type": "git-clone", - "reference": "https://github.com/IsItDanOrAi/ComfyUI-exLoadout", - "title": "exLoadout: Excel-Based Model & Settings Loader" - }, - { - "author": "silveroxides", - "description": "Highly customizable Scheduler for ComfyUI.", - "files": [ - "https://github.com/silveroxides/ComfyUI_PowerShiftScheduler" - ], - "install_type": "git-clone", - "reference": "https://github.com/silveroxides/ComfyUI_PowerShiftScheduler", - "title": "ComfyUI Power Shift Scheduler" - }, - { - "author": "claptrap0", - "description": "Utilize the power of an LLM into ComfyUI to transform your text-to-image and text-to-video ideas into highly detailed prompts for generation while giving you full control.", - "files": [ - "https://github.com/claptrap0/ComfyUI_LLM_Hub" - ], - "install_type": "git-clone", - "reference": "https://github.com/claptrap0/ComfyUI_LLM_Hub", - "title": "ComfyUI_LLM_Hub" - }, - { - "author": "xChenNing", - "description": "Allows you to pin images to the JSON of your workflow, migrate with JSON, or embed in image's metadata. supports image compression.", - "files": [ - "https://github.com/CheNing233/ComfyUI_Image_Pin" - ], - "id": "comfyui-image-pin", - "install_type": "git-clone", - "reference": "https://github.com/CheNing233/ComfyUI_Image_Pin", - "title": "ComfyUI_Image_Pin" - }, - { - "author": "LaoMaoBoss", - "description": "ComfyUI custom node package. This custom node features multiple practical functions, including global variables, flow control, obtaining image or mask dimensions, and Dominant Axis Scale.", - "files": [ - "https://github.com/LaoMaoBoss/ComfyUI-WBLESS" - ], - "id": "LaoMaoBoss", - "install_type": "git-clone", - "reference": "https://github.com/LaoMaoBoss/ComfyUI-WBLESS", - "title": "ComfyUI-WBLESS" - }, - { - "author": "zeeoale", - "description": "A powerful custom node for ComfyUI that generates rich, dynamic prompts based on modular JSON worlds \u2014 with color realm control (RGB / CMYK), LoRA triggers, and optional AI-based prompt enhancement.", - "files": [ - "https://github.com/zeeoale/PromptCreatorNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/zeeoale/PromptCreatorNode", - "title": "PromptCreatorNodetraumakom Prompt Generator" - }, - { - "author": "Android zhang", - "description": "ComfyUI nodes to use Distill-Any-Depth prediction.", - "files": [ - "https://github.com/zade23/Comfyui-Distill-Any-Depth" - ], - "install_type": "git-clone", - "reference": "https://github.com/zade23/Comfyui-Distill-Any-Depth", - "title": "Comfyui-Distill-Any-Depth" - }, - { - "author": "Android zhang", - "description": "Runs the MoGe2 model on the input image. \n v1: Ruicheng/moge-vitl \n v2: Ruicheng/moge-2-vitl-normal", - "files": [ - "https://github.com/zade23/Comfyui-MoGe2" - ], - "install_type": "git-clone", - "reference": "https://github.com/zade23/Comfyui-MoGe2", - "title": "ComfyUI-MoGe2" - }, - { - "author": "swisscore-py", - "description": "Implement Telegram into your ComfyUI workflows.", - "files": [ - "https://github.com/SwissCore92/comfyui-telegram-suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/SwissCore92/comfyui-telegram-suite", - "title": "ComfyUI Telegram Suite" - }, - { - "author": "ZXL-Xinram", - "description": "A collection of utility nodes for ComfyUI focused on path processing and string operations.", - "files": [ - "https://github.com/ZXL-Xinram/ComfyUI-AutoFlow" - ], - "install_type": "git-clone", - "reference": "https://github.com/ZXL-Xinram/ComfyUI-AutoFlow", - "title": "ComfyUI-AutoFlow" - }, - { - "author": "lex-drl", - "description": "QoL nodes for semi-automatic calculation of the best (most optimal) sampling resolution \n\u2022 compatible with ANY model (from now or the future), \n\u2022 accounting for upscale... \n\u2022 ...and pixel-step.", - "files": [ - "https://github.com/Lex-DRL/ComfyUI-BestResolution" - ], - "install_type": "git-clone", - "reference": "https://github.com/Lex-DRL/ComfyUI-BestResolution", - "title": "Best Resolution" - }, - { - "author": "lex-drl", - "description": "Composing prompt variants from the same text pieces with ease:\n\u2022 Build your \"library\" (dictionary) of named text chunks (sub-strings) to use it across the entire workflow.\n\u2022 Compile these snippets into different prompts in-place - with just one string formatting node.\n\u2022 Freely update the dictionary down the line - get different prompts.\n\u2022 Reference text chunks within each other to build dependent hierarchies of less/more detailed descriptions.\n\u2022 A real life-saver for regional prompting (aka area composition).", - "files": [ - "https://github.com/Lex-DRL/ComfyUI-StringConstructor" - ], - "install_type": "git-clone", - "reference": "https://github.com/Lex-DRL/ComfyUI-StringConstructor", - "title": "String Constructor (Text-Formatting)" - }, - { - "author": "baikong", - "description": "Combine multiple 3D models into a single Blender file and render in ComfyUI.", - "files": [ - "https://github.com/JayLyu/blender-in-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/JayLyu/blender-in-comfyui", - "title": "blender-in-comfyui" - }, - { - "author": "MithrilMan", - "description": "Mithril-Nodes is a collection of custom nodes for ComfyUI that enhance workflow modularity, data routing, and configuration management. These nodes help you build more dynamic, organized, and reusable pipelines for generative AI workflows.", - "files": [ - "https://github.com/MithrilMan/ComfyUI-MithrilNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/MithrilMan/ComfyUI-MithrilNodes", - "title": "Mithril-Nodes for ComfyUI" - }, - { - "author": "Yukinoshita-Yukinoe", - "description": "Qwen3 api node", - "files": [ - "https://github.com/Yukinoshita-Yukinoe/ComfyUI-Qwen-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Yukinoshita-Yukinoe/ComfyUI-Qwen-Node", - "title": "ComfyUI-Qwen-Node" - }, - { - "author": "aiaiaikkk", - "description": "ComfyUI nodes for image editing prompt generation with visual canvas annotation and multi-model AI assistance", - "files": [ - "https://github.com/aiaiaikkk/super-prompt-canvas" - ], - "install_type": "git-clone", - "reference": "https://github.com/aiaiaikkk/super-prompt-canvas", - "title": "super-prompt-canvas" - }, - { - "author": "seanjang990", - "description": "This custom node for ComfyUI automatically crops a document by detecting edges, rotates it based on face orientation using MediaPipe, and adjusts it to a target aspect ratio (default 11:14).", - "files": [ - "https://github.com/seanjang990/comfyui-document-auto-crop" - ], - "install_type": "git-clone", - "reference": "https://github.com/seanjang990/comfyui-document-auto-crop", - "title": "ComfyUI Document Auto Crop Node" - }, - { - "author": "cjj198909", - "description": "A ComfyUI node that provides access to OpenAI's image generation and editing capabilities, including support for gpt-image-1 model with both OpenAI and Azure OpenAI providers.", - "files": [ - "https://github.com/cjj198909/comfy_openai_image_api_azure" - ], - "install_type": "git-clone", - "reference": "https://github.com/cjj198909/comfy_openai_image_api_azure", - "title": "OpenAI/Azure OpenAI Image API" - }, - { - "author": "paulh4x", - "description": "A Wrapper and a set of Custom Nodes for using RenderFormer as a 3d Environment in ComfyUI.", - "files": [ - "https://github.com/paulh4x/ComfyUI_PHRenderFormerWrapper" - ], - "install_type": "git-clone", - "reference": "https://github.com/paulh4x/ComfyUI_PHRenderFormerWrapper", - "title": "ComfyUI_PHRenderFormerWrapper" - }, - { - "author": "Aero-Ex", - "description": "This repository contains a powerful and versatile custom node for ComfyUI that seamlessly integrates with OpenAI-compatible Large Language Models (LLMs), including multimodal (vision-enabled) models like GPT-4o.\nThis single node allows you to perform both text generation and image analysis, making it an essential tool for advanced prompt engineering and creative automation.", - "files": [ - "https://github.com/Aero-Ex/ComfyUI-Vision-LLM-Analyzer" - ], - "install_type": "git-clone", - "reference": "https://github.com/Aero-Ex/ComfyUI-Vision-LLM-Analyzer", - "title": "ComfyUI Vision LLM Analyzer Node" - }, - { - "author": "StrawberryFist", - "description": "A comprehensive VRAM management tool for ComfyUI. Includes automatic cleanup and GPU monitoring.", - "files": [ - "https://github.com/strawberryPunch/vram_optimizer" - ], - "install_type": "git-clone", - "nodename_pattern": "StFist", - "pip": [ - "GPUtil>=1.4.0" - ], - "reference": "https://github.com/strawberryPunch/vram_optimizer", - "tags": [ - "vram", - "gpu", - "optimizer", - "monitoring" - ], - "title": "StrawberryFist VRAM Optimizer" - }, - { - "author": "blird", - "description": "This custom node for ComfyUI provides adaptive image resizing based on target pixel counts, maintaining aspect ratio and supporting different quality levels. It is useful for workflows that require images to fit within specific pixel budgets (e.g., for video, AI models, or memory constraints).", - "files": [ - "https://github.com/blird/ComfyUI-Wanify" - ], - "install_type": "git-clone", - "reference": "https://github.com/blird/ComfyUI-Wanify", - "title": "ComfyUI-Wanify: Adaptive Image Resize Node" - }, - { - "author": "hiderminer", - "description": "A collection of custom nodes for ComfyUI that provides useful image processing tools.", - "files": [ - "https://github.com/hiderminer/ComfyUI-HM-Utilities" - ], - "install_type": "git-clone", - "reference": "https://github.com/hiderminer/ComfyUI-HM-Utilities", - "title": "ComfyUI-HM-Tools" - }, - { - "author": "Dimona Patrick", - "description": "Professional audio processing nodes for ComfyUI", - "files": [ - "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Mixer" - ], - "install_type": "git-clone", - "reference": "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Mixer", - "title": "ComfyUI Mzikart Mixer" - }, - { - "author": "Edoardo Carmignani", - "description": "A one-click collection of alternate connection styles for ComfyUI.", - "files": [ - "https://github.com/edoardocarmignani/extralinks" - ], - "id": "extralinks", - "install_type": "git-clone", - "reference": "https://github.com/edoardocarmignani/extralinks", - "title": "ComfyUI-ExtraLinks" - }, - { - "author": "Edoardo Carmignani", - "description": "Node for ComfyUI designed for more neatly switching between tiled and default VAE Decode Nodes.", - "files": [ - "https://github.com/MasterDenis/VAE-Decode-Switch" - ], - "install_type": "git-clone", - "reference": "https://github.com/MasterDenis/VAE-Decode-Switch", - "title": "VAE Decode Switch for ComfyUI" - }, - { - "author": "webuilder", - "description": "A collection of utility nodes for ComfyUI, including useful functions such as audio processing and text manipulation.", - "files": [ - "https://github.com/webuilder/WB-ComfyUI-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/webuilder/WB-ComfyUI-Utils", - "title": "ComfyUI WB Utils" - }, - { - "author": "MartinDeanMoriarty", - "description": "Nodes to switch image input or output path with boolean conditions", - "files": [ - "https://github.com/MartinDeanMoriarty/ComfyUI-DeanLogic" - ], - "install_type": "git-clone", - "reference": "https://github.com/MartinDeanMoriarty/ComfyUI-DeanLogic", - "title": "ComfyUI-DeanLogic" - }, - { - "author": "rdomunky", - "description": "A ComfyUI custom node that enhances image loading with subfolder organization and dynamic filtering", - "files": [ - "https://github.com/rdomunky/comfyui-subfolderimageloader" - ], - "install_type": "git-clone", - "reference": "https://github.com/rdomunky/comfyui-subfolderimageloader", - "title": "comfyui-subfolderimageloader" - }, - { - "author": "NyaFuP", - "description": "A floating dialog-based image preview and selection system for ComfyUI.", - "files": [ - "https://github.com/NyaFuP/ComfyUI_Preview_Selector" - ], - "install_type": "git-clone", - "reference": "https://github.com/NyaFuP/ComfyUI_Preview_Selector", - "title": "NF Preview Selector" - }, - { - "author": "brucew4yn3rp", - "description": "This custom node allows users to selectively choose what to add to the generated image's metadata.", - "files": [ - "https://github.com/brucew4yn3rp/ComfyUI_SelectiveMetadata" - ], - "id": "SaveImageSelectiveMetadata", - "install_type": "git-clone", - "reference": "https://github.com/brucew4yn3rp/ComfyUI_SelectiveMetadata", - "title": "Save Image with Selective Metadata" - }, - { - "author": "cedarconnor", - "description": "Advanced equirectangular (360\u00b0) image processing nodes for ComfyUI, enabling precise rotation, horizon adjustment, and specialized cropping operations for panoramic images.", - "files": [ - "https://github.com/cedarconnor/comfyui-LatLong" - ], - "install_type": "git-clone", - "reference": "https://github.com/cedarconnor/comfyui-LatLong", - "title": "ComfyUI LatLong - Equirectangular Image Processing Nodes" - }, - { - "author": "cedarconnor", - "description": "ComfyUI custom nodes for integrating with the Upsampler API to enhance and upscale images using AI.", - "files": [ - "https://github.com/cedarconnor/upsampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/cedarconnor/upsampler", - "title": "ComfyUI Upsampler Nodes" - }, - { - "author": "cedarconnor", - "description": "A ComfyUI custom node package for batch image processing with filename preservation.", - "files": [ - "https://github.com/cedarconnor/comfyui-BatchNameLoop" - ], - "install_type": "git-clone", - "reference": "https://github.com/cedarconnor/comfyui-BatchNameLoop", - "title": "ComfyUI Batch Name Loop" - }, - { - "author": "cedarconnor", - "description": "Transform text and images into immersive 360\u00b0 3D worlds using Tencent's HunyuanWorld-1.0 in ComfyUI with native FLUX architecture integration.", - "files": [ - "https://github.com/cedarconnor/ComfyUI_HunyuanWorld" - ], - "install_type": "git-clone", - "reference": "https://github.com/cedarconnor/ComfyUI_HunyuanWorld", - "title": "ComfyUI HunyuanWorld - Professional 3D World Generation" - }, - { - "author": "vaishnav-vn", - "description": "repo has Custon node designed to expand, pad, and mask images to fixed or randomized aspect ratios with precise spatial and scale control \u2014 engineered for outpainting, compositional layout, and creative canvas expansion. ", - "files": [ - "https://github.com/vaishnav-vn/va1" - ], - "install_type": "git-clone", - "name": "Pad Image by Aspect", - "reference": "https://github.com/vaishnav-vn/va1", - "title": "va1" - }, - { - "author": "wawahuy", - "description": "Powerful REST API nodes for ComfyUI that enable seamless HTTP/REST integration into your workflows.", - "files": [ - "https://github.com/wawahuy/ComfyUI-HTTP" - ], - "install_type": "git-clone", - "reference": "https://github.com/wawahuy/ComfyUI-HTTP", - "title": "ComfyUI HTTP - REST API Nodes" - }, - { - "author": "watarika", - "description": "Sends images with metadata (PNGInfo) obtained from the input values of each node to Eagle. You can customize the tags to be registered in Eagle.", - "files": [ - "https://github.com/watarika/ComfyUI-SendToEagle-w-Metadata" - ], - "install_type": "git-clone", - "reference": "https://github.com/watarika/ComfyUI-SendToEagle-w-Metadata", - "title": "ComfyUI-SendToEagle-w-Metadata" - }, - { - "author": "Azornes", - "description": "Photoshop-like layered canvas editor to your ComfyUI workflow. This node is perfect for complex compositing, inpainting, and outpainting, featuring multi-layer support, masking, blend modes, and precise transformations. Includes optional AI-powered background removal for streamlined image editing.", - "files": [ - "https://github.com/Azornes/Comfyui-LayerForge" - ], - "id": "layerforge", - "install_type": "git-clone", - "reference": "https://github.com/Azornes/Comfyui-LayerForge", - "title": "Comfyui-LayerForge" - }, - { - "author": "Azornes", - "description": " Precise resolution and aspect ratio control for ComfyUI", - "files": [ - "https://github.com/Azornes/Comfyui-Resolution-Master" - ], - "install_type": "git-clone", - "reference": "https://github.com/Azornes/Comfyui-Resolution-Master", - "title": "Comfyui-Resolution-Master" - }, - { - "author": "einhorn13", - "description": "A collection of custom nodes for ComfyUI designed to enhance image processing workflows. Especially useful for high-resolution rendering, complex inpainting, tiling, and batch manipulation. This allows you to perform processing that would otherwise exceed your VRAM limits.", - "files": [ - "https://github.com/einhorn13/ComfyUI-ImageProcessUtilities" - ], - "install_type": "git-clone", - "reference": "https://github.com/einhorn13/ComfyUI-ImageProcessUtilities", - "title": "ComfyUI-ImageProcessUtilities" - }, - { - "author": "khanhlvg", - "description": "Vertex AI Custom Nodes for ComfyUI", - "files": [ - "https://github.com/khanhlvg/vertex-ai-comfyui-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/khanhlvg/vertex-ai-comfyui-nodes", - "title": "[Unofficial] Vertex AI Custom Nodes for ComfyUI" - }, - { - "author": "cuban044", - "description": "A custom node extension for ComfyUI that integrates Google's Veo 3 text-to-video generation capabilities.", - "files": [ - "https://github.com/cuban044/ComfyUI-Veo3-Experimental" - ], - "install_type": "git-clone", - "reference": "https://github.com/cuban044/ComfyUI-Veo3-Experimental", - "title": "[Unofficial] ComfyUI-Veo3-Experimental" - }, - { - "author": "builmenlabo", - "description": "Comprehensive collection of ComfyUI custom nodes: \ud83e\udd99 Advanced LLM text generation with Llama-CPP (CPU/GPU acceleration), \ud83c\udf10 Smart multi-language prompt translation (Google/DeepL/Yandex/Baidu), \ud83c\udf0d 20-language interface toggle, \ud83d\udcf8 AI-powered Gemini pose analysis, \ud83c\udf9b\ufe0f Smart ControlNet management. Perfect unified package for AI artists and creators. Blog: https://note.com/hirodream44", - "files": [ - "https://github.com/comnote-max/builmenlabo" - ], - "id": "builmenlabo", - "install_type": "git-clone", - "nodename_pattern": "builmenlabo", - "reference": "https://github.com/comnote-max/builmenlabo", - "tags": [ - "LLM", - "translation", - "multilingual", - "pose-analysis", - "controlnet", - "text-generation", - "gemini", - "llama-cpp", - "AI" - ], - "title": "ComfyUI builmenlabo - Unified Package" - }, - { - "author": "Cyrostar", - "description": "ComfyUI custom nodes for interacting with the Gemini api for image and video generation prompting.", - "files": [ - "https://github.com/Cyrostar/Artha-Gemini" - ], - "id": "Artha-Gemini", - "install_type": "git-clone", - "reference": "https://github.com/Cyrostar/Artha-Gemini", - "reference2": "https://github.com/Cyrostar/ComfyUI-Artha-Gemini", - "title": "Artha-Gemini" - }, - { - "author": "Cyrostar", - "description": "ComfyUI custom nodes for project management.", - "files": [ - "https://github.com/Cyrostar/Artha-Projekt" - ], - "id": "artha-projekt", - "install_type": "git-clone", - "reference": "https://github.com/Cyrostar/Artha-Projekt", - "title": "Artha-Projekt" - }, - { - "author": "GeraldWie", - "description": "A lightweight version of the custom nodes originally developed by [a/ManglerFTW](https://github.com/ManglerFTW/ComfyI2I) for performing image-to-image tasks in ComfyUI.", - "files": [ - "https://github.com/GeraldWie/ComfyUI-I2I-slim" - ], - "install_type": "git-clone", - "reference": "https://github.com/GeraldWie/ComfyUI-I2I-slim", - "title": "ComfyUI-I2I-slim" - }, - { - "author": "tauraloke", - "description": "A ComfyUI node for pixel art scaling. Automatically detects the pixel scale using an edge-aware method (Sobel filter + voting on tiles) and downscales the image to that pixel size, reducing color palette.", - "files": [ - "https://github.com/tauraloke/ComfyUI-Unfake-Pixels" - ], - "install_type": "git-clone", - "reference": "https://github.com/tauraloke/ComfyUI-Unfake-Pixels", - "title": "ComfyUI-Unfake-Pixels" - }, - { - "author": "adrianschubek", - "description": "comfyui-zeug (German for 'gear' or 'stuff') is a collection of custom nodes for ComfyUI, designed to enhance functionality and provide additional features.", - "files": [ - "https://github.com/adrianschubek/comfyui-zeug" - ], - "install_type": "git-clone", - "reference": "https://github.com/adrianschubek/comfyui-zeug", - "title": "comfyui-zeug" - }, - { - "author": "g0kuvonlange", - "description": "A simple custom node for ComfyUI to load LoRAs and videos directly from a URL. Ideal for users hosting files on a server with publicly accessible URLs.", - "files": [ - "https://github.com/g0kuvonlange/ComfyUI-Load-From-URL" - ], - "install_type": "git-clone", - "reference": "https://github.com/g0kuvonlange/ComfyUI-Load-From-URL", - "title": "ComfyUI Load From URL" - }, - { - "author": "visualbruno", - "description": "ComfyUI Wrapper for [a/Hunyuan3D v2.1](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1) - From Images to High-Fidelity 3D Assets with Production-Ready PBR Material", - "files": [ - "https://github.com/visualbruno/ComfyUI-Hunyuan3d-2-1" - ], - "install_type": "git-clone", - "reference": "https://github.com/visualbruno/ComfyUI-Hunyuan3d-2-1", - "title": "ComfyUI-Hunyuan3d-2-1" - }, - { - "author": "AIWarper", - "description": "An implementation of the DAViD tooling, a method for extracting depth, normals, and masks from an input image.", - "files": [ - "https://github.com/AIWarper/ComfyUI-DAViD" - ], - "install_type": "git-clone", - "reference": "https://github.com/AIWarper/ComfyUI-DAViD", - "title": "ComfyUI-DAViD" - }, - { - "author": "ComfyUI Studio", - "description": "\ud83e\udde9 Aspect Ratio Image Size Calculator, \ud83d\uddbc\ufe0f Aspect Ratio Resizer, and \ud83d\udcc4 Markdown Link Generator for ComfyUI.", - "files": [ - "https://github.com/comfyuistudio/ComfyUI-Studio-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/comfyuistudio/ComfyUI-Studio-nodes", - "tags": [ - "image", - "resize", - "aspect-ratio", - "markdown", - "utils" - ], - "title": "ComfyUI-Studio-nodes" - }, - { - "author": "jenn", - "description": "Book Cover Finder tool that wraps openlibrary.org", - "files": [ - "https://github.com/weberjc/book-cover-finder-comfy" - ], - "install_type": "git-clone", - "reference": "https://github.com/weberjc/book-cover-finder-comfy", - "title": "BookCoverFinder" - }, - { - "author": "RndNanthu", - "description": "Film Grain simulation, Log Color Conversions, Color Scopes (RGB Parade, Vectorscope, Gamut Warnings), False Color, and more.", - "files": [ - "https://github.com/rndnanthu/ComfyUI-RndNanthu" - ], - "id": "ComfyUI-RndNanthu", - "install_type": "git-clone", - "reference": "https://github.com/rndnanthu/ComfyUI-RndNanthu", - "title": "ComfyUI-RndNanthu" - }, - { - "author": "Pun0110", - "description": "Repository contains CSV Styler - custom node for ComfyUI. It loads styles from styles.csv file (Automatic1111 Web-Ui styles.csv format) and combine them with provided positive and negative prompt.\nNOTE: 'styles.csv' should be placed in ComfyUI root directory (near main.py).", - "files": [ - "https://github.com/Pun0110/ComfyUI-CSV-Styler" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pun0110/ComfyUI-CSV-Styler", - "title": "CSV Styler" - }, - { - "author": "cnnmmd", - "description": "This is a set of custom nodes for ComfyUI, designed for the following application: [a/https://github.com/cnnmmd/cnnmmd](https://github.com/cnnmmd/cnnmmd)", - "files": [ - "https://github.com/cnnmmd/comfyui_xoxxox_cnnmmd" - ], - "install_type": "git-clone", - "reference": "https://github.com/cnnmmd/comfyui_xoxxox_cnnmmd", - "title": "cnnmmd: comfyui_xoxxox_cnnmmd" - }, - { - "author": "CallMe1101", - "description": "A ComfyUI custom node developed based on OmniAvatar, capable of generating video sequences with synchronized lip movements and facial expressions by inputting a portrait image, audio, and text prompt. The node parameters and invocation method are fully consistent with the official OmniAvatar inference.", - "files": [ - "https://github.com/CallMe1101/ComfyUI_OmniAvatar" - ], - "install_type": "git-clone", - "reference": "https://github.com/CallMe1101/ComfyUI_OmniAvatar", - "title": "ComfyUI_OmniAvatar" - }, - { - "author": "ebrinz", - "description": "A standalone ComfyUI custom node package for Facebook's MusicGen using Hugging Face Transformers. Generate high-quality music from text prompts with full support for CUDA, MPS (Apple Silicon), and CPU.", - "files": [ - "https://github.com/ebrinz/ComfyUI-MusicGen-HF" - ], - "install_type": "git-clone", - "reference": "https://github.com/ebrinz/ComfyUI-MusicGen-HF", - "title": "ComfyUI-MusicGen-HF" - }, - { - "author": "mcDandy", - "description": "Adds math nodes for numbers and types which do not need it.", - "files": [ - "https://github.com/mcDandy/more_math" - ], - "install_type": "git-clone", - "reference": "https://github.com/mcDandy/more_math", - "title": "More Math" - }, - { - "author": "kmlbdh", - "description": "A custom node pack for ComfyUI that allows you to run Large Language Models (LLMs) locally and use them for prompt generation and other text tasks directly within your ComfyUI workflows.", - "files": [ - "https://github.com/kmlbdh/ComfyUI_LocalLLMNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/kmlbdh/ComfyUI_LocalLLMNodes", - "title": "ComfyUI_LocalLLMNodes" - }, - { - "author": "kmlbdh", - "description": "A custom ComfyUI node designed for stable, high-resolution video export \u2014 even on limited hardware.", - "files": [ - "https://github.com/kmlbdh/ComfyUI-kmlbdh-VideoCombine" - ], - "install_type": "git-clone", - "reference": "https://github.com/kmlbdh/ComfyUI-kmlbdh-VideoCombine", - "title": "kmlbdh Video Combine (Smart + Tiled)" - }, - { - "author": "joosthel", - "description": "TouchDesigner-style blob tracking and computer vision effects for ComfyUI. Simple nodes for bright spot detection, plexus connections, and technical aesthetics in video workflows.", - "files": [ - "https://github.com/joosthel/ComfyUI-CVOverlay" - ], - "install_type": "git-clone", - "reference": "https://github.com/joosthel/ComfyUI-CVOverlay", - "title": "ComfyUI-CVOverlay" - }, - { - "author": "fcanfora", - "description": "NODES: Load Camera From File, Load 3D, Load 3D - Animation, Preview 3D, Preview 3D - Animation", - "files": [ - "https://github.com/fcanfora/comfyui-camera-tools" - ], - "install_type": "git-clone", - "reference": "https://github.com/fcanfora/comfyui-camera-tools", - "title": "comfyui-camera-tools" - }, - { - "author": "lokinou", - "description": "Custom nodes to offload and rapatriate models from cpu.", - "files": [ - "https://github.com/lokinou/comfyui-offload-models" - ], - "install_type": "git-clone", - "reference": "https://github.com/lokinou/comfyui-offload-models", - "title": "ComfyUI-Offload-Models" - }, - { - "author": "rainlizard", - "description": "This is a modified implementation of impact-pack's iterative upscaler. It leans in on the idea that giving too much attention to computation at high resolutions isn't a good idea.", - "files": [ - "https://github.com/rainlizard/ComfyUI-WhirlpoolUpscaler" - ], - "install_type": "git-clone", - "reference": "https://github.com/rainlizard/ComfyUI-WhirlpoolUpscaler", - "title": "Whirlpool Upscaler" - }, - { - "author": "AlfredClark", - "description": "ComfyUI model metadata editing nodes.", - "files": [ - "https://github.com/AlfredClark/ComfyUI-ModelSpec" - ], - "install_type": "git-clone", - "reference": "https://github.com/AlfredClark/ComfyUI-ModelSpec", - "title": "ComfyUI-ModelSpec" - }, - { - "author": "zl9739379", - "description": "A custom node for ComfyUI that integrates ByteDance Volcano Engine's video generation AI model, supporting both text-to-video and image-to-video generation.", - "files": [ - "https://github.com/zl9739379/ComfyUI-ArkVideoGenerate" - ], - "install_type": "git-clone", - "reference": "https://github.com/zl9739379/ComfyUI-ArkVideoGenerate", - "title": "ComfyUI-ArkVideoGenerate" - }, - { - "author": "YaserJaradeh", - "description": "A collection of custom nodes for ComfyUI that provide dynamic input selection and intelligent upscaling functionality.", - "files": [ - "https://github.com/YaserJaradeh/comfyui-yaser-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/YaserJaradeh/comfyui-yaser-nodes", - "title": "Yaser-nodes for ComfyUI" - }, - { - "author": "gvfarns", - "description": "ComfyUI custom convenience nodes: Cropping images to a given aspect ratio, Cropping images to a max/min aspect ratio, If/else logic with provided float (rather than using a float node)", - "files": [ - "https://github.com/gvfarns/comfyui_gvf" - ], - "install_type": "git-clone", - "reference": "https://github.com/gvfarns/comfyui_gvf", - "title": "comfyui_gvf" - }, - { - "author": "mikeshuangyan", - "description": "MQ util nodes for ComfyUI", - "files": [ - "https://github.com/mikeshuangyan/ComfyUI_MqUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/mikeshuangyan/ComfyUI_MqUtils", - "title": "ComfyUI_MqUtils" - }, - { - "author": "Franklyc", - "description": "A simple but powerful custom node for ComfyUI that patches LoRA models by adding dummy adaLN_modulation_1 weights. This solves compatibility errors when using LoRAs with newer model architectures that expect these keys to be present in the final_layer.", - "files": [ - "https://github.com/Franklyc/comfyui-lora-adain-patcher-node" - ], - "install_type": "git-clone", - "reference": "https://github.com/Franklyc/comfyui-lora-adain-patcher-node", - "title": "ComfyUI LoRA adaLN Patcher Node" - }, - { - "author": "Simlym", - "description": "A simple and intuitive prompt management tool for ComfyUI.", - "files": [ - "https://github.com/Simlym/comfyui-prompt-helper" - ], - "id": "prompt-helper", - "install_type": "git-clone", - "reference": "https://github.com/Simlym/comfyui-prompt-helper", - "title": "ComfyUI Prompt Helper" - }, - { - "author": "woct0rdho", - "description": "RadialAttention in ComfyUI native workflow", - "files": [ - "https://github.com/woct0rdho/ComfyUI-RadialAttn" - ], - "install_type": "git-clone", - "reference": "https://github.com/woct0rdho/ComfyUI-RadialAttn", - "title": "ComfyUI-RadialAttn" - }, - { - "author": "jiafuzeng", - "description": "We present LatentSync, an end-to-end lip-sync method based on audio-conditioned latent diffusion models without any intermediate motion representation, diverging from previous diffusion-based lip-sync methods based on pixel-space diffusion or two-stage generation. Our framework can leverage the powerful capabilities of Stable Diffusion to directly model complex audio-visual correlations.", - "files": [ - "https://github.com/jiafuzeng/comfyui-LatentSync" - ], - "install_type": "git-clone", - "reference": "https://github.com/jiafuzeng/comfyui-LatentSync", - "title": "LatentSync" - }, - { - "author": "What-a-stupid-username", - "description": "A inversed euler sampler to revert image to noisy latent. Can be used to improve content consistency when perform I2I.", - "files": [ - "https://github.com/What-a-stupid-username/comfyui-InversedSampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/What-a-stupid-username/comfyui-InversedSampler", - "title": "comfyui_InversedSampler" - }, - { - "author": "rubenvillarreal", - "description": "A custom node for ComfyUI that provides pose alignment functionality.", - "files": [ - "https://github.com/rubenvillarreal/ComfyUI_PoseAlign" - ], - "install_type": "git-clone", - "reference": "https://github.com/rubenvillarreal/ComfyUI_PoseAlign", - "title": "ComfyUI_PoseAlign" - }, - { - "author": "charlyad142", - "description": "Un nodo personalizado para ComfyUI que ajusta im\u00e1genes a diferentes relaciones de aspecto manteniendo las proporciones originales.", - "files": [ - "https://github.com/charlyad142/ComfyUI_Charly_FitToAspectNode" - ], - "install_type": "git-clone", - "reference": "https://github.com/charlyad142/ComfyUI_Charly_FitToAspectNode", - "title": "ComfyUI Charly FitToAspectNode" - }, - { - "author": "Rizzlord", - "description": "RizzNodes for ComfyUI Welcome to RizzNodes, a collection of custom nodes for ComfyUI designed to streamline various workflows, from loading images and models in batches to dynamic prompt generation and memory management.", - "files": [ - "https://github.com/Rizzlord/ComfyUI-RizzNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Rizzlord/ComfyUI-RizzNodes", - "title": "ComfyUI-RizzNodes" - }, - { - "author": "jupo-ai", - "description": "generate empty latent with aspect ratios", - "files": [ - "https://github.com/jupo-ai/comfy-aspect-ratios" - ], - "id": "comfy-aspect-ratios", - "install_type": "git-clone", - "reference": "https://github.com/jupo-ai/comfy-aspect-ratios", - "title": "comfy-aspect-ratios" - }, - { - "author": "Brekel", - "description": "Nodes to enhance & streamline prompts. Enhance using local LLM within ComfyUI, generate using random lines from text files, or randomly select text file prompts", - "files": [ - "https://github.com/Brekel/ComfyUI-Brekel" - ], - "id": "brekel", - "install_type": "git-clone", - "reference": "https://github.com/Brekel/ComfyUI-Brekel", - "title": "ComfyUI-Brekel" - }, - { - "author": "judian17", - "description": "Prompt node for the [a/JoyCaption-beta-one-hf-llava](https://huggingface.co/mradermacher/llama-joycaption-beta-one-hf-llava-GGUF) model, to use JoyCaption-beta-one-hf-llava with Ollama", - "files": [ - "https://github.com/judian17/ComfyUI-JoyCaption-beta-one-hf-llava-Prompt_node" - ], - "install_type": "git-clone", - "reference": "https://github.com/judian17/ComfyUI-JoyCaption-beta-one-hf-llava-Prompt_node", - "title": "ComfyUI-JoyCaption-beta-one-hf-llava-Prompt_node" - }, - { - "author": "juddisjudd", - "description": "A complete collection of FLUX-optimized ComfyUI nodes for enhanced AI image generation workflows.", - "files": [ - "https://github.com/juddisjudd/ComfyUI-BawkNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/juddisjudd/ComfyUI-BawkNodes", - "title": "Bawk Nodes Collection" - }, - { - "author": "comfyui-wiki", - "description": "Workflow templates from ComfyUI Wiki - No custom nodes or dependencies included", - "files": [ - "https://github.com/comfyui-wiki/ComfyUI-Wiki-Workflows" - ], - "install_type": "git-clone", - "reference": "https://github.com/comfyui-wiki/ComfyUI-Wiki-Workflows", - "title": "ComfyUI Wiki Workflows" - }, - { - "author": "tercumantanumut", - "description": "Wrapper ComfyUI integration for the [a/Flux Omini Kontext](https://github.com/Saquib764/omini-kontext) pipeline, enabling seamless character/object insertion into scenes using FLUX.1-Kontext-dev with LoRA adaptation.", - "files": [ - "https://github.com/tercumantanumut/ComfyUI-Omini-Kontext" - ], - "install_type": "git-clone", - "reference": "https://github.com/tercumantanumut/ComfyUI-Omini-Kontext", - "title": "ComfyUI-Omini-Kontext" - }, - { - "author": "stduhpf", - "description": "This is a set of custom nodes for ComfyUI that replaces nodes like WanImageToVideo but using a Tiled VAE approach to reduce VRAM requirements.", - "files": [ - "https://github.com/stduhpf/ComfyUI--WanImageToVideoTiled" - ], - "install_type": "git-clone", - "reference": "https://github.com/stduhpf/ComfyUI--WanImageToVideoTiled", - "title": "WanImageToVideoTiledVAE for ComfyUI" - }, - { - "author": "stduhpf", - "description": "This is a custom node for ComfyUI that can be used to generate videos from either a starting frame, an end frame or both; with the Wan2.2 5B model (which uses the new Wan2.2 VAE, unlike Wan 2.2 A14B model wich uses the old Wan2.1 VAE).", - "files": [ - "https://github.com/stduhpf/ComfyUI--Wan22FirstLastFrameToVideoLatent" - ], - "install_type": "git-clone", - "reference": "https://github.com/stduhpf/ComfyUI--Wan22FirstLastFrameToVideoLatent", - "title": "Wan22FirstLastFrameToVideoLatent for ComfyUI" - }, - { - "author": "stduhpf", - "description": "These nodes are made to support 'Mixture of Expert' Flow models with the architecture of Wan2.2 A14B (With a high noise expert and low noise expert). Instead of guessing the denoising step at which to swap from tyhe high noise model to the low noise model, this node automatically chanage to the low noise model when we reach the diffusion timestep at which the signal to noise ratio is supposed to be 1:1.", - "files": [ - "https://github.com/stduhpf/ComfyUI-WanMoeKSampler" - ], - "install_type": "git-clone", - "reference": "https://github.com/stduhpf/ComfyUI-WanMoeKSampler", - "title": "KSampler for Wan 2.2 MoE for ComfyUI" - }, - { - "author": "kanibus", - "description": "Advanced Eye Tracking ControlNet System for ComfyUI - Professional eye-tracking with MediaPipe, 6-DOF Kalman filtering, and WAN 2.1/2.2 compatibility", - "files": [ - "https://github.com/kanibus/kanibus" - ], - "install_type": "git-clone", - "reference": "https://github.com/kanibus/kanibus", - "title": "KANIBUS - Advanced Eye Tracking ControlNet System" - }, - { - "author": "Novice_Chen", - "description": "A ComfyUI node that integrates LibLib's text-to-image and image-to-image generation capabilities, with customizable ControlNet and Lora support.", - "files": [ - "https://github.com/NewNoviceChen/ComfyUI-XingLiu" - ], - "install_type": "git-clone", - "reference": "https://github.com/NewNoviceChen/ComfyUI-XingLiu", - "title": "ComfyUI-XingLiu" - }, - { - "author": "chuchu114514", - "description": "Used for batch extraction of prompt words.", - "files": [ - "https://github.com/chuchu114514/comfyui_text_list_stepper" - ], - "install_type": "git-clone", - "reference": "https://github.com/chuchu114514/comfyui_text_list_stepper", - "title": "comfyui_text_list_stepper" - }, - { - "author": "ranska", - "description": "Comfyui custom node: Set of tools for pixel art palette.", - "files": [ - "https://github.com/ranska/pixel_palette_art" - ], - "install_type": "git-clone", - "reference": "https://github.com/ranska/pixel_palette_art", - "title": "Pixel Palette Art" - }, - { - "author": "SaturMars", - "description": "ComfyUI node for video super-resolution and frame interpolation using [a/NVEncC](https://github.com/rigaya/NVEnc).", - "files": [ - "https://github.com/SaturMars/ComfyUI-NVVFR" - ], - "install_type": "git-clone", - "reference": "https://github.com/SaturMars/ComfyUI-NVVFR", - "title": "ComfyUI-NVVFR" - }, - { - "author": "SaturMars", - "description": "This is a ComfyUI custom node used to convert Qwen-Image LoRA files trained on the ModelScope platform to a format that ComfyUI can recognize.", - "files": [ - "https://github.com/SaturMars/ComfyUI-QwenImageLoraConverter" - ], - "install_type": "git-clone", - "reference": "https://github.com/SaturMars/ComfyUI-QwenImageLoraConverter", - "title": "ComfyUI Qwen LoRA Converter Node" - }, - { - "author": "jqy-yo", - "description": "A collection of custom nodes for integrating Google Gemini API with ComfyUI, providing powerful AI capabilities for text generation, image generation, and video analysis. Nodes: Gemini Text API, Gemini Image Editor, Gemini Image Gen Advanced, Gemini Video Captioner.", - "files": [ - "https://github.com/jqy-yo/comfyui-gemini-nodes" - ], - "id": "gemini-nodes-jqy", - "install_type": "git-clone", - "reference": "https://github.com/jqy-yo/comfyui-gemini-nodes", - "title": "ComfyUI Gemini Nodes" - }, - { - "author": "fidecastro", - "description": "A comprehensive ComfyUI custom node that provides complete client functionality for llama-server from [a/llama.cpp](https://github.com/ggml-org/llama.cpp). This node acts as a bridge between ComfyUI workflows and llama-server instances, supporting every single parameter and endpoint that llama-server offers.", - "files": [ - "https://github.com/fidecastro/comfyui-llamacpp-client" - ], - "install_type": "git-clone", - "reference": "https://github.com/fidecastro/comfyui-llamacpp-client", - "title": "comfyui-llamacpp-client" - }, - { - "author": "PICOPON", - "description": "A custom ComfyUI node that integrates OpenAI-compatible APIs for prompt generation and enhancement, specifically designed for Stable Diffusion workflows.", - "files": [ - "https://github.com/PICOPON/ComfyUI-API-OpenAI-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/PICOPON/ComfyUI-API-OpenAI-Node", - "title": "ComfyUI OpenAI Node" - }, - { - "author": "verIdyia", - "description": "ComfyUI custom nodes for the DFloat11 compressed Qwen-Image model. This package provides efficient image generation with reduced memory usage through DFloat11 compression technology.", - "files": [ - "https://github.com/verIdyia/ComfyUI-Qwen-Image-DF11" - ], - "install_type": "git-clone", - "reference": "https://github.com/verIdyia/ComfyUI-Qwen-Image-DF11", - "title": "ComfyUI Qwen-Image DFloat11 Nodes" - }, - { - "author": "snomiao", - "description": "A ComfyUI custom node for cropping videos using FFmpeg with pixel-precise control.", - "files": [ - "https://github.com/snomiao/ComfyUI-Video-Crop" - ], - "install_type": "git-clone", - "reference": "https://github.com/snomiao/ComfyUI-Video-Crop", - "title": "ComfyUI Video Crop" - }, - { - "author": "max-dingsda", - "description": "This project makes local LLMs easy to use for prompt enhancement and image captioning \u2013No API keys. No external tools. No headache.", - "files": [ - "https://github.com/max-dingsda/OllamaTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/max-dingsda/OllamaTools", - "title": "OllamaTools for ComfyUI" - }, - { - "author": "HWDigi", - "description": "A comprehensive ComfyUI custom node suite for advanced Safe for Work prompt generation across all checkpoint types (Pony, SDXL, SD1.5, and more). This SFW edition is specifically designed for professional workflows, educational environments, and GitHub hosting compliance.", - "files": [ - "https://github.com/HWDigi/Factory-Prompts_comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/HWDigi/Factory-Prompts_comfyui", - "title": "Factory Prompt Generator" - }, - { - "author": "BitWalker", - "description": "Visual opencv node package based on comfy ui", - "files": [ - "https://github.com/Koren-cy/FlowCV" - ], - "install_type": "git-clone", - "reference": "https://github.com/Koren-cy/FlowCV", - "title": "FlowCV" - }, - { - "author": "switzerswish", - "description": "A ComfyUI custom node that parses prompt text for LoRA tags and visualizes their metadata, including trigger words, strength values, thumbnail previews, and example images.", - "files": [ - "https://github.com/oliverswitzer/ComfyUI-Lora-Visualizer" - ], - "install_type": "git-clone", - "reference": "https://github.com/oliverswitzer/ComfyUI-Lora-Visualizer", - "title": "LoRA Visualizer" - }, - { - "author": "diogod", - "description": "TTS Audio Suite - Universal multi-engine TTS extension for ComfyUI with unified architecture supporting ChatterBox, F5-TTS, and future engines like RVC. Features modular engine adapters, character voice management, comprehensive SRT subtitle support, and advanced audio processing capabilities.", - "files": [ - "https://github.com/diodiogod/TTS-Audio-Suite" - ], - "install_type": "git-clone", - "reference": "https://github.com/diodiogod/TTS-Audio-Suite", - "title": "TTS Audio Suite" - }, - { - "author": "Ferocit", - "description": "This repository contains custom nodes for ComfyUI, designed to enhance your workflow with text-based operations, particularly for managing and utilizing descriptive texts.", - "files": [ - "https://github.com/Ferocit/comfyui-feroccustomnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Ferocit/comfyui-feroccustomnodes", - "title": "comfyui-feroccustomnodes" - }, - { - "author": "drozbay", - "description": "Advanced/Experimental VACE nodes for WAN video models in ComfyUI.", - "files": [ - "https://github.com/drozbay/ComfyUI-WanVaceAdvanced" - ], - "install_type": "git-clone", - "reference": "https://github.com/drozbay/ComfyUI-WanVaceAdvanced", - "title": "ComfyUI-WanVaceAdvanced" - }, - { - "author": "Lovzu", - "description": "Ultra-lightweight text-to-speech model with just 15 million parameters", - "files": [ - "https://github.com/Lovzu/ComfyUI-KittenTTS" - ], - "install_type": "git-clone", - "reference": "https://github.com/Lovzu/ComfyUI-KittenTTS", - "title": "KittenTTS Node for Voice Generation" - }, - { - "author": "Pirog17000", - "description": "Pirog's Nodes - ComfyUI custom node pack providing enhanced sampling functionality", - "files": [ - "https://github.com/Pirog17000/Pirogs-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/Pirog17000/Pirogs-Nodes", - "title": "Pirog's Nodes for ComfyUI" - }, - { - "author": "vsaan212", - "description": "A custom ComfyUI node that splits text into multiple outputs for feeding complex multi-scene renders. This node allows you to dynamically control the number of splits and use custom separators.", - "files": [ - "https://github.com/vsaan212/Comfy-ui-textsplit" - ], - "install_type": "git-clone", - "reference": "https://github.com/vsaan212/Comfy-ui-textsplit", - "title": "ComfyUI Text Split Node" - }, - { - "author": "eric183", - "description": "A collection of custom nodes for ComfyUI, originally focused on workflow parsing, now expanded to provide advanced file loading features like 'Load Latent (Advanced)' for drag-and-drop latent file uploads and 'WorkflowImageFileLoader' for parsing prompts from image metadata. It also adds a js extension for a better UI experience.", - "files": [ - "https://github.com/eric183/ComfyUI-Only" - ], - "id": "comfyui-only", - "install_type": "git-clone", - "reference": "https://github.com/eric183/ComfyUI-Only", - "title": "ComfyUI-Only" - }, - { - "author": "squirrel765", - "description": "A unified ComfyUI node for both LLM (text-only) and VLM (image-to-text) generation using GGUF models..", - "files": [ - "https://github.com/squirrel765/ComfyUI-LLM-VLM-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/squirrel765/ComfyUI-LLM-VLM-Node", - "title": "ComfyUI-LLM-VLM-Node" - }, - { - "author": "squirrel765", - "description": "ExoticArts custom nodes for ComfyUI (EA Power LoRA, etc.)", - "files": [ - "https://github.com/ExoticArts/comfyui-ea-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/ExoticArts/comfyui-ea-nodes", - "title": "comfyui-ea-nodes" - }, - { - "author": "HSDHCdev", - "description": "Pixel art Enhancement Node for ComfyUI", - "files": [ - "https://github.com/HSDHCdev/ComfyUI-AI-Pixel-Art-Enhancer" - ], - "install_type": "git-clone", - "reference": "https://github.com/HSDHCdev/ComfyUI-AI-Pixel-Art-Enhancer", - "title": "AI Pixel Art Enhancer for ComfyUI" - }, - { - "author": "Rzgar Espo", - "description": "A universal node for generating empty latent tensors with support for Qwen Image, SDXL and Flux models. Features extended aspect ratio support, batch processing, and flexible dimension overrides.", - "files": [ - "https://github.com/rzgarespo/ComfyUI-qwen-image-size-picker" - ], - "install_type": "git-clone", - "reference": "https://github.com/rzgarespo/ComfyUI-qwen-image-size-picker", - "title": "ComfyUI-Qwen-Image-Size-Picker" - }, - { - "author": "luke-mino-altherr", - "description": "Creates spatial echo and ambient effects by applying reverb-like processing directly in latent space", - "files": [ - "https://github.com/luke-mino-altherr/ComfyUI-LatentReverb" - ], - "install_type": "git-clone", - "reference": "https://github.com/luke-mino-altherr/ComfyUI-LatentReverb", - "title": "ComfyUI-Latent-Reverb" - }, - { - "author": "Baverne", - "description": "Node set and workflow to run wan2.1 vace with tiled while keeping temporal and spatial consistency", - "files": [ - "https://github.com/Baverne/comfyUI-TiledWan" - ], - "install_type": "git-clone", - "reference": "https://github.com/Baverne/comfyUI-TiledWan", - "title": "comfyUI-TiledWan" - }, - { - "author": "teamalpha-ai", - "description": "ComfyUI wrapper nodes for Image Transformer", - "files": [ - "https://github.com/teamalpha-ai/comfyui-image-transformer" - ], - "install_type": "git-clone", - "reference": "https://github.com/teamalpha-ai/comfyui-image-transformer", - "title": "ComfyUI-ImageTransformer" - }, - { - "author": "karas17", - "description": "A versatile and highly customizable node for ComfyUI to add camera-style watermarks and frames to your images. Whether you want to emulate the classic Leica look, add EXIF data, or create professional-looking framed images, this plugin has you covered.", - "files": [ - "https://github.com/karas17/ComfyUI-Camera-Watermark" - ], - "install_type": "git-clone", - "reference": "https://github.com/karas17/ComfyUI-Camera-Watermark", - "title": "ComfyUI Camera Watermark" - }, - { - "author": "shinyakidoguchi301", - "description": "This custom node allows you to select a LoRA file from the models/loras directory (including subfolders), automatically load training tags from metadata or Eagle MEMO, set strength, and optionally display a preview image.", - "files": [ - "https://github.com/shinyakidoguchi301/comfyui-lora-tag-loader" - ], - "install_type": "git-clone", - "reference": "https://github.com/shinyakidoguchi301/comfyui-lora-tag-loader", - "title": "shinyakidoguchi301/LoRA Tag Loader for ComfyUI" - }, - { - "author": "njlent", - "description": "This is a standalone node pack that provides powerful color correction methods for ComfyUI, extracted from the ComfyUI-DLoRAL project. It allows you to transfer the color profile from a source image or video to a target image or video.\nThis is ideal for restoring the original color to AI-processed videos (like upscaling or frame interpolation) or for creative color grading.", - "files": [ - "https://github.com/njlent/ComfyUI_wavelet-colorfix" - ], - "install_type": "git-clone", - "reference": "https://github.com/njlent/ComfyUI_wavelet-colorfix", - "title": "ComfyUI Wavelet Color Fix" - }, - { - "author": "flybirdxx", - "description": "SDMatte is an interactive image matting method based on stable diffusion, which supports three types of visual prompts (points, boxes, and masks) for accurately extracting target objects from natural images.", - "files": [ - "https://github.com/flybirdxx/ComfyUI-SDMatte" - ], - "install_type": "git-clone", - "reference": "https://github.com/flybirdxx/ComfyUI-SDMatte", - "title": "ComfyUI-SDMatte" - }, - { - "author": "DenRakEiw", - "description": "Advanced color manipulation and image adjustments directly in ComfyUI's latent space", - "files": [ - "https://github.com/DenRakEiw/Latent_Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/DenRakEiw/Latent_Nodes", - "title": "ComfyUI Latent Color Tools" - }, - { - "author": "DenRakEiw", - "description": "A universal neural network latent upscaler that supports SD1.5, SDXL, Flux, and Wan2.2 models. Uses trained neural networks instead of simple interpolation for higher quality latent upscaling.\nBuilt upon the excellent foundation of [a/Ttl's ComfyUi_NNLatentUpscale](https://github.com/Ttl/ComfyUi_NNLatentUpscale) - this project extends the original work with universal model support and improved architectures.", - "files": [ - "https://github.com/DenRakEiw/WAN_NN_Latent_Upscale" - ], - "install_type": "git-clone", - "reference": "https://github.com/DenRakEiw/WAN_NN_Latent_Upscale", - "title": "Universal NN Latent Upscaler for ComfyUI" - }, - { - "author": "RainyN0077", - "description": "A powerful ComfyUI custom node for managing and combining multiple prompt entries. Supports weight adjustment, lexicon assistance, drag-and-drop sorting, and global settings to greatly enhance prompt engineering efficiency and flexibility.", - "files": [ - "https://github.com/RainyN0077/ComfyUI-PromptSE" - ], - "id": "comfyui-promptse", - "install_type": "git-clone", - "reference": "https://github.com/RainyN0077/ComfyUI-PromptSE", - "title": "ComfyUI-PromptSE" - }, - { - "author": "kusurin", - "description": "A ComfyUI custom node for creating chronophotography effects from video frames", - "files": [ - "https://github.com/kusurin/ComfyUI-chronophotography" - ], - "install_type": "git-clone", - "reference": "https://github.com/kusurin/ComfyUI-chronophotography", - "title": "ComfyUI-chronophotography" - }, - { - "author": "feffy380", - "description": "Caching optimization for Chroma", - "files": [ - "https://github.com/feffy380/comfyui-chroma-cache" - ], - "install_type": "git-clone", - "reference": "https://github.com/feffy380/comfyui-chroma-cache", - "title": "Chroma Cache" - }, - { - "author": "FearL0rd", - "description": "Attempt to create a ComfyUI node for masking AI-generated fingerprints.", - "files": [ - "https://github.com/FearL0rd/ComfyUI-MaskAIFingerprint" - ], - "install_type": "git-clone", - "reference": "https://github.com/FearL0rd/ComfyUI-MaskAIFingerprint", - "title": "ComfyUI MaskAIFingerprint" - }, - { - "author": "Alectriciti", - "description": "Adaptive Prompts is a modern reimagining of dynamic prompts for ComfyUI. It lets you randomize, restructure, and clean up prompts with powerful wildcard and string tools. For the sake of consistency, I will still refer to them as Dynamic Prompts.", - "files": [ - "https://github.com/Alectriciti/comfyui-adaptiveprompts" - ], - "install_type": "git-clone", - "reference": "https://github.com/Alectriciti/comfyui-adaptiveprompts", - "title": "comfyui-adaptiveprompts" - }, - { - "author": "VraethrDalkr", - "description": "A collection of custom nodes for ComfyUI that enable progressive blending and color matching effects across image batches/video frames.", - "files": [ - "https://github.com/VraethrDalkr/ComfyUI-ProgressiveBlend" - ], - "install_type": "git-clone", - "reference": "https://github.com/VraethrDalkr/ComfyUI-ProgressiveBlend", - "title": "ComfyUI-ProgressiveBlend" - }, - { - "author": "NHLStenden", - "description": "A custom node package for ComfyUI featuring advanced image processing tools.", - "files": [ - "https://github.com/NHLStenden/ComfyUI-ImageBag" - ], - "install_type": "git-clone", - "reference": "https://github.com/NHLStenden/ComfyUI-ImageBag", - "title": "ComfyUI-ImageBag" - }, - { - "author": "jupo-ai", - "description": "Preview media file in model's dir.", - "files": [ - "https://github.com/jupo-ai/comfy-preview-model" - ], - "id": "comfy-preview-model", - "install_type": "git-clone", - "reference": "https://github.com/jupo-ai/comfy-preview-model", - "title": "comfy-preview-model" - }, - { - "author": "jupo-ai", - "description": "Join Multiline text.", - "files": [ - "https://github.com/jupo-ai/comfy-join-prompt" - ], - "id": "comfy-join-prompt", - "install_type": "git-clone", - "reference": "https://github.com/jupo-ai/comfy-join-prompt", - "title": "comfy-join-prompt" - }, - { - "author": "apeirography", - "description": "Model Downloader + API-Friendly Wildcard Processor for ComfyUI", - "files": [ - "https://github.com/apeirography/DaimalyadNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/apeirography/DaimalyadNodes", - "title": "DaimalyadNodes" - }, - { - "author": "mamorett", - "description": "Modular ComfyUI nodes to run the vision-language model MiniCPM\u2011V\u20114 in GGUF format, powered by llama\u2011cpp\u2011python.", - "files": [ - "https://github.com/mamorett/ComfyUI_minicpmv4" - ], - "install_type": "git-clone", - "reference": "https://github.com/mamorett/ComfyUI_minicpmv4", - "title": "MiniCPM\u2011V\u20114 (GGUF) for ComfyUI" - }, - { - "author": "Verolelb", - "description": "A node to provide recommended aspect ratios for the Qwen model series.", - "files": [ - "https://github.com/Verolelb/ComfyUI-Qwen-Aspect-Ratio" - ], - "install_type": "git-clone", - "reference": "https://github.com/Verolelb/ComfyUI-Qwen-Aspect-Ratio", - "title": "ComfyUI-Qwen-Aspect-Ratio" - }, - { - "author": "jupo-ai", - "description": "Move nodes linearly.", - "files": [ - "https://github.com/jupo-ai/comfy-linear-move" - ], - "id": "comfy-linear-move", - "install_type": "git-clone", - "reference": "https://github.com/jupo-ai/comfy-linear-move", - "title": "comfy-linear-move" - }, - { - "author": "Marco Zanella", - "description": "A collection of logic and comparison nodes for ComfyUI. This package adds building blocks for boolean logic, arithmetic comparisons, and string comparisons, making it easier to design conditional workflows directly in ComfyUI.", - "files": [ - "https://github.com/marco-zanella/ComfyUI-BooleanExpression" - ], - "id": "ComfyUI-BooleanExpression", - "install_type": "git-clone", - "reference": "https://github.com/marco-zanella/ComfyUI-BooleanExpression", - "tags": [ - "boolean", - "logic", - "comparison" - ], - "title": "ComfyUI-BooleanExpression" - }, - { - "author": "WeChatCV", - "description": "This repository provides a temporary ComfyUI node implementation of the Stand-In preprocessor, aiming to correct the misunderstanding of Stand-In\u2019s preprocessing logic in [a/ComfyUI-WanVideoWrapper](https://github.com/kijai/ComfyUI-WanVideoWrapper).\n[w/We strongly recommend using the workflows provided in this repo to ensure proper compatibility and the best identity-preserving performance.]", - "files": [ - "https://github.com/WeChatCV/Stand-In_Preprocessor_ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/WeChatCV/Stand-In_Preprocessor_ComfyUI", - "title": "Stand-In Official Preprocessor ComfyUI Nodes" - }, - { - "author": "SilverAndJade", - "description": "A collection of utility nodes for ComfyUI that enhance workflow capabilities with advanced loaders and processing tools.", - "files": [ - "https://github.com/SilverAndJade/comfyui-silver-nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/SilverAndJade/comfyui-silver-nodes", - "title": "ComfyUI Silver Nodes" - }, - { - "author": "vsLinx", - "description": "Custom nodes that let you quickly load multiple images via a multi-select dialog with preview. The images are instantly uploaded to the input folder and can be output either as a list or a batch. ", - "files": [ - "https://github.com/vslinx/ComfyUI-vslinx-nodes" - ], - "id": "comfyui-vslinx-nodes", - "install_type": "git-clone", - "reference": "https://github.com/vslinx/ComfyUI-vslinx-nodes", - "title": "ComfyUI vsLinx Nodes" - }, - { - "author": "HoangYell", - "description": "A collection of custom ComfyUI nodes for advanced video processing and editing workflows.", - "files": [ - "https://github.com/HoangYell/comfyui-hoangyell-video" - ], - "install_type": "git-clone", - "reference": "https://github.com/HoangYell/comfyui-hoangyell-video", - "title": "comfyui-hoangyell-video-edit" - }, - { - "author": "krigeta", - "description": "creating a custom node to test how to use Blocknet controlnets in comfyUI using logic from Diffsynth Studio.", - "files": [ - "https://github.com/krigeta/qwen-image-controlnets-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/krigeta/qwen-image-controlnets-comfyui", - "title": "qwen-image-controlnets-comfyui" - }, - { - "author": "L33chKing", - "description": "A ComfyUI custom node that weights tags based on their frequency/rarity in danbooru", - "files": [ - "https://github.com/L33chKing/comfyui-tag-frequency-weighter" - ], - "install_type": "git-clone", - "reference": "https://github.com/L33chKing/comfyui-tag-frequency-weighter", - "title": "Tag Frequency Weighter for ComfyUI" - }, - { - "author": "routhakash", - "description": "A suite of advanced ComfyUI custom nodes for a complete 'Script-to-Screen' workflow, powered by local LLMs.", - "files": [ - "https://github.com/routhakash/AkkiNodes-LLM-Suite-for-ComfyUI" - ], - "install_type": "git-clone", - "reference": "https://github.com/routhakash/AkkiNodes-LLM-Suite-for-ComfyUI", - "title": "AkkiNodes LLM Suite: Your Personal AI Film Studio" - }, - { - "author": "moonwhaler", - "description": "A ComfyUI custom node for memory-efficient image upscaling using SeedVR2 models with advanced tiling and detail-preserving stitching.", - "files": [ - "https://github.com/moonwhaler/comfyui-seedvr2-tilingupscaler" - ], - "install_type": "git-clone", - "reference": "https://github.com/moonwhaler/comfyui-seedvr2-tilingupscaler", - "title": "SeedVR2 Tiling Upscaler" - }, - { - "author": "saquib764", - "description": "Nodes to use Omini Kontext framework for multi-image reference using Flux Kontext model. It has node to for Nunchaku compatibility. The is an editor node to place the character/product on the image for spatial control.", - "files": [ - "https://github.com/Saquib764/omini-kontext" - ], - "id": "omini_kontext_editor", - "install_type": "git-clone", - "reference": "https://github.com/Saquib764/omini-kontext", - "title": "Omini Kontext" - }, - { - "author": "obisin", - "description": "Smart dynamic layer swapping between GPU and CPU for optimal inference performance with comprehensive mixed precision handling and copy-compute overlap optimization. Enables running much larger models on limited VRAM setups.", - "files": [ - "https://github.com/obisin/ComfyUI-DGLS" - ], - "install_type": "git-clone", - "reference": "https://github.com/obisin/ComfyUI-DGLS", - "title": "ComfyUI - DGLS (Dynamic GPU Layer Swapping)" - }, - { - "author": "Dehypnotic", - "description": "Lightweight node to number text divisions (one per newline) and select single/multiple divisions by index with an optional separator", - "files": [ - "https://github.com/Dehypnotic/comfyui-numbered-text" - ], - "install_type": "git-clone", - "reference": "https://github.com/Dehypnotic/comfyui-numbered-text", - "title": "NumberedText" - }, - { - "author": "rickrender", - "description": "A collection of ComfyUI nodes for interfacing with the Vectorizer.ai API. Manipulate the results with options for scaling the SVG result back to raster image with increased resolution. Includes a background remover node to remove a prevalent background color from raster images. Enter credentials in config.json or directly within the node.", - "files": [ - "https://github.com/rickrender/ComfyUI-Vectorizer-API" - ], - "install_type": "git-clone", - "reference": "https://github.com/rickrender/ComfyUI-Vectorizer-API", - "title": "Vectorizer API" - }, - { - "author": "TinyBeeman", - "description": "A collection of custom nodes for ComfyUI, designed to provide utility functions for list processing, file management, and more.", - "files": [ - "https://github.com/TinyBeeman/ComfyUI-TinyBee" - ], - "install_type": "git-clone", - "reference": "https://github.com/TinyBeeman/ComfyUI-TinyBee", - "title": "ComfyUI-TinyBee" - }, - { - "author": "Hangover3832", - "description": "This repository fully replaces and extends the previous Hangover Nodes", - "files": [ - "https://github.com/Hangover3832/ComfyUI_Hangover-Utils" - ], - "install_type": "git-clone", - "reference": "https://github.com/Hangover3832/ComfyUI_Hangover-Utils", - "title": "ComfyUI_Hangover-Utils" - }, - { - "author": "Onionman61", - "description": "This is a custom node for ComfyUI that allows users to perform Image-to-Image generation by calling the FLUX.1-Kontext-Dev model via the official ModelScope API.", - "files": [ - "https://github.com/Onionman61/ComfyUI-ModelScope-Kontext" - ], - "install_type": "git-clone", - "reference": "https://github.com/Onionman61/ComfyUI-ModelScope-Kontext", - "title": "ComfyUI ModelScope Kontext API Node" - }, - { - "author": "svntax", - "description": "A ComfyUI custom node for the [a/Retro Diffusion API](https://retrodiffusion.ai/)", - "files": [ - "https://github.com/svntax/ComfyUI-RetroDiffusion-API-Node" - ], - "install_type": "git-clone", - "reference": "https://github.com/svntax/ComfyUI-RetroDiffusion-API-Node", - "title": "ComfyUI-RetroDiffusion-API-Node" - }, - { - "author": "chyer", - "description": "A comprehensive toolset of ComfyUI custom nodes for latent generation, image processing, and workflow utilities", - "files": [ - "https://github.com/chyer/Chye-ComfyUI-Toolset" - ], - "install_type": "git-clone", - "reference": "https://github.com/chyer/Chye-ComfyUI-Toolset", - "title": "Chye ComfyUI Toolset" - }, - { - "author": "bhvbhushan", - "description": "Advanced LoRA loader with per-block weight control for fine-grained influence over different model layers in ComfyUI", - "files": [ - "https://github.com/bhvbhushan/ComfyUI-LoRABlockWeight" - ], - "install_type": "git-clone", - "reference": "https://github.com/bhvbhushan/ComfyUI-LoRABlockWeight", - "title": "ComfyUI LoRA Block Weight Loader" - }, - { - "author": "BEIBEI-star661", - "description": "A high-quality sweep light effect node designed specifically for ComfyUI, supporting multiple parameter adjustments and anti-aliasing.", - "files": [ - "https://github.com/BEIBEI-star661/SJ_sweepEffect_Comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/BEIBEI-star661/SJ_sweepEffect_Comfyui", - "title": "SJ_sweepEffect_Comfyui" - }, - { - "author": "ReinerBforartists", - "description": "Auto Prompt Schedule is a ComfyUI helper node for the Prompt Schedule Node or the Batch Prompt Schedule node from Fizzledorf. You need this Prompt Schedule nodes to make use of the Auto Prompt Schedule Node.", - "files": [ - "https://github.com/ReinerBforartists/comfyui_auto_prompt_schedule" - ], - "install_type": "git-clone", - "reference": "https://github.com/ReinerBforartists/comfyui_auto_prompt_schedule", - "title": "Auto Prompt Schedule" - }, - { - "author": "ReinerBforartists", - "description": "Combines texts line by line to a single text", - "files": [ - "https://github.com/ReinerBforartists/comfyui_text_line_combine" - ], - "install_type": "git-clone", - "reference": "https://github.com/ReinerBforartists/comfyui_text_line_combine", - "title": "ComfyUI_Text_Line_Combine" - }, - { - "author": "dimtoneff", - "description": "A collection of custom nodes for ComfyUI that integrates various Vision-Language (VL) models, including Xiaomi MiMo-VL, LiquidAI LFM2-VL, Kwai Keye-VL, AIDC-AI Ovis2.5 and Ovis-U1. Tested with models: AIDC-AI/Ovis2.5-2B, AIDC-AI/Ovis2.5-9B, Kwai-Keye/Keye-VL-8B-Preview, MiMo-VL-7B-RL-GGUF, LiquidAI/LFM2-VL-450M, LiquidAI/LFM2-VL-1.6B, AIDC-AI/Ovis-U1-3B.", - "files": [ - "https://github.com/dimtoneff/ComfyUI-VL-Nodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/dimtoneff/ComfyUI-VL-Nodes", - "title": "ComfyUI-VL-Nodes" - }, - { - "author": "mangobyed", - "description": "A powerful ComfyUI custom node that performs object detection using YOLOv8 and outputs individual images for each detected object. Perfect for automatic mask generation, object isolation, and batch processing workflows.", - "files": [ - "https://github.com/mangobyed/ComfyUI_Detection_List" - ], - "install_type": "git-clone", - "reference": "https://github.com/mangobyed/ComfyUI_Detection_List", - "title": "ComfyUI YOLOv8 Object Detection Node" - }, - { - "author": "Daxamur", - "description": "ComfyUI video processing nodes with workflow metadata support", - "files": [ - "https://github.com/Daxamur/DaxNodes" - ], - "install_type": "git-clone", - "pip": [ - "mediapipe>=0.10.0", - "opencv-python>=4.8.0", - "scipy>=1.10.0", - "color-matcher>=0.3.0" - ], - "reference": "https://civitai.com/user/Daxamur", - "title": "DaxNodes" - }, - { - "author": "Dehypnotic", - "description": "Lightweight node that generates a string of numbers between a start and end value. Supports positive and negative steps, configurable separators, and inclusive/exclusive end values.", - "files": [ - "https://github.com/Dehypnotic/comfyui-range-to-string" - ], - "install_type": "git-clone", - "reference": "https://github.com/Dehypnotic/comfyui-range-to-string", - "title": "RangeToString" - }, - { - "author": "KY-2000", - "description": "Batch samplers, schedulers, cfg, shift and steps tester custom node, automatic looping functionality for Ksampler node", - "files": [ - "https://github.com/KY-2000/comfyui-ksampler-tester-loop" - ], - "install_type": "git-clone", - "reference": "https://github.com/KY-2000/comfyui-ksampler-tester-loop", - "title": "comfyui-ksampler-tester-loop" - }, - { - "author": "Firetheft", - "description": "ComfyUI Civitai Gallery is a powerful custom node for ComfyUI that integrates a seamless image browser for the Civitai website directly into your workflow. This node allows you to browse, search, and select images from Civitai and instantly import their prompts, negative prompts, and full-resolution images into your workflow. It is designed to significantly speed up your creative process by eliminating the need to switch between your browser and ComfyUI.", - "files": [ - "https://github.com/Firetheft/ComfyUI_Civitai_Gallery" - ], - "install_type": "git-clone", - "reference": "https://github.com/Firetheft/ComfyUI_Civitai_Gallery", - "title": "ComfyUI Civitai Gallery" - }, - { - "author": "RegulusAlpha", - "description": "A minimal dynamic prompting + mirrored wildcards node for ComfyUI.", - "files": [ - "https://github.com/RegulusAlpha/ComfyUI-DynPromptSimplified" - ], - "install_type": "git-clone", - "reference": "https://github.com/RegulusAlpha/ComfyUI-DynPromptSimplified", - "title": "ComfyUI Dynamic Prompting Simplified" - }, - { - "author": "Jelosus2", - "description": "A simple custom node to use reflect padding mode in the conv layers of VAEs.", - "files": [ - "https://github.com/Jelosus2/comfyui-vae-reflection" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jelosus2/comfyui-vae-reflection", - "title": "ComfyUI VAE Reflection" - }, - { - "author": "netroxin", - "description": "#Camera Movement Prompt Node for ComfyUI\nThis custom node script for ComfyUI generates descriptive camera movement prompts based on user-selected movement options for Wan2.2", - "files": [ - "https://github.com/netroxin/comfyui_netro" - ], - "install_type": "git-clone", - "reference": "https://github.com/netroxin/comfyui_netro", - "title": "comfyui_netro" - }, - { - "author": "alexds9", - "description": "Save checkpoint .safetensors with custom header JSON, optional prompt override, merge of EXTRA_PNGINFO, and smart/no-counter filename modes. Includes text outputs for path and final metadata.", - "files": [ - "https://github.com/a-l-e-x-d-s-9/ComfyUI-SaveCheckpointWithMetadata" - ], - "install_type": "git-clone", - "reference": "https://github.com/a-l-e-x-d-s-9/ComfyUI-SaveCheckpointWithMetadata", - "title": "Save Checkpoint with Metadata" - }, - { - "author": "jialuw0830", - "description": "Eigen AI FLUX API integration for ComfyUI with LoRA support and large font prompt inputs. Features high-quality image generation using FLUX.1-schnell model with multi-LoRA support, content upscaling, and optimized prompt input interface.", - "files": [ - "https://github.com/jialuw0830/flux_api_comfyui_plugin" - ], - "id": "eigen-ai-flux-api-plugin", - "install_type": "git-clone", - "reference": "https://github.com/jialuw0830/flux_api_comfyui_plugin", - "title": "Eigen AI FLUX API Plugin" - }, - { - "author": "LeanModels", - "description": "This repository provides the ComfyUI plugin for DFloat11 models. DFloat11 reduces model size by more than 30% while producing bit-for-bit identical outputs to the original. Unlike quantization techniques which trade quality for size, DFloat11 is a lossless compression method, preserving model output quality fully while supporting efficient inference.", - "files": [ - "https://github.com/LeanModels/ComfyUI-DFloat11" - ], - "install_type": "git-clone", - "reference": "https://github.com/LeanModels/ComfyUI-DFloat11", - "title": "ComfyUI-DFloat11" - }, - { - "author": "birdneststream", - "description": "ComfyUI node for converting images to IRC art blocks", - "files": [ - "https://github.com/birdneststream/ComfyUI-Mircify" - ], - "install_type": "git-clone", - "reference": "https://github.com/birdneststream/ComfyUI-Mircify", - "title": "ComfyUI-Mircify" - }, - { - "author": "RUiNtheExtinct", - "description": "A collection of custom nodes for ComfyUI that offers an extension to the existing Save and Preview Image/Video nodes allowing directly adding and previewing the files from your preferred cloud storage providers (S3/DropBox/Google Drive/...)", - "files": [ - "https://github.com/RUiNtheExtinct/comfyui-save-file-extended" - ], - "install_type": "git-clone", - "reference": "https://github.com/RUiNtheExtinct/comfyui-save-file-extended", - "title": "comfyui-save-file-extended" - }, - { - "author": "citronlegacy", - "description": "ComfyUI node: Get DateTime (outputs date, time, datetime as strings", - "files": [ - "https://github.com/citronlegacy/ComfyUI-CitronNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/citronlegacy/ComfyUI-CitronNodes", - "title": "ComfyUI-CitronNodes" - }, - { - "author": "ZeroSpaceStudios", - "description": "Custom nodes for ComfyUI with specialized image processing and saving functionality.", - "files": [ - "https://github.com/ZeroSpaceStudios/ComfyUI-ZSNodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/ZeroSpaceStudios/ComfyUI-ZSNodes", - "title": "ComfyUI-ZSNodes" - }, - { - "author": "thimpat", - "description": "A collection of essential utility nodes for handling image sequences, video, audio, and synchronized data within ComfyUI workflows.", - "files": [ - "https://github.com/thimpat/ThimPatUtils" - ], - "install_type": "git-clone", - "reference": "https://github.com/thimpat/ThimPatUtils", - "title": "ComfyUI Multimedia Utilities" - }, - { - "author": "thimpat", - "description": "High-utility nodes for ComfyUI with a focus on Flux 1 Dev workflows and Ultimate SD Upscale enhancement loops.", - "files": [ - "https://github.com/krakenunbound/ComfyUI-KrakenTools" - ], - "install_type": "git-clone", - "reference": "https://github.com/krakenunbound/ComfyUI-KrakenTools", - "title": "ComfyUI-KrakenTools" - }, - { - "author": "alFrame", - "description": "A ComfyUI custom node that allows you to pipe a generated prompt and either pass it as is, or copy and edit it manually. Or you can use the lower input field like any regular text input filed. The content of the lower text field will always dominate the output of the node.", - "files": [ - "https://github.com/alFrame/ComfyUI-AF-EditGeneratedPrompt" - ], - "install_type": "git-clone", - "reference": "https://github.com/alFrame/ComfyUI-AF-EditGeneratedPrompt", - "title": "AF - Edit Generated Prompt" - }, - { - "author": "joanna910225", - "description": "A tool that organizes workflow nodes into clean, user-friendly layouts", - "files": [ - "https://github.com/joanna910225/comfyui-housekeeper" - ], - "install_type": "git-clone", - "reference": "https://github.com/joanna910225/comfyui-housekeeper", - "title": "HouseKeeper" - }, - { - "author": "rslosch", - "description": "A ComfyUI custom node extension that provides easy-to-use prompt templates and wildcards for AI image generation.", - "files": [ - "https://github.com/rslosch/ComfyUI-EZ_Prompts" - ], - "install_type": "git-clone", - "reference": "https://github.com/rslosch/ComfyUI-EZ_Prompts", - "title": "ComfyUI-EZ_Prompts" - }, - { - "author": "isaac-mcfadyen", - "description": "A variety of random text encoder tools intended for use with ComfyUI and Qwen Image/Qwen Image Edit. More (may) be added as I try out various modifications to Qwen Image.", - "files": [ - "https://github.com/isaac-mcfadyen/ComfyUI-QwenClip" - ], - "install_type": "git-clone", - "reference": "https://github.com/isaac-mcfadyen/ComfyUI-QwenClip", - "title": "ComfyUI-QwenClip" - }, - { - "author": "isaac-mcfadyen", - "description": "High-Performance ComfyUI Custom Node Toolkit - Modular and Extensible Architecture\nA utility toolkit designed specifically for ComfyUI image processing, built with a modular architecture that supports fast extension and the addition of new nodes.", - "files": [ - "https://github.com/popoimm/comfyui-popo-utility" - ], - "install_type": "git-clone", - "reference": "https://github.com/popoimm/comfyui-popo-utility", - "title": "ComfyUI Popo Utility" - }, - { - "author": "orion4d", - "description": "A collection of high-quality nodes for ComfyUI, dedicated to improving the sharpness, clarity, and texture of your images", - "files": [ - "https://github.com/orion4d/ComfyUI_SharpnessPro" - ], - "install_type": "git-clone", - "reference": "https://github.com/orion4d/ComfyUI_SharpnessPro", - "title": "SharpnessPro pour ComfyUI" - }, - { - "author": "Fabio Sarracino", - "description": "ComfyUI wrapper for Microsoft VibeVoice TTS model. Supports single speaker, multi-speaker, and text file loading", - "files": [ - "https://github.com/Enemyx-net/VibeVoice-ComfyUI" - ], - "id": "vibevoice-comfyui", - "install_type": "git-clone", - "pip": [ - "torch>=2.0.0", - "torchaudio>=2.0.0", - "numpy>=1.20.0", - "transformers>=4.44.0", - "librosa>=0.9.0", - "soundfile>=0.12.0" - ], - "reference": "https://github.com/Enemyx-net/VibeVoice-ComfyUI", - "title": "VibeVoice ComfyUI" - }, - { - "author": "mikheys", - "description": "This repository contains a custom node for ComfyUI, 'Nano banana', designed for advanced image editing using the Google Gemini API. ", - "files": [ - "https://github.com/mikheys/comfyui-gemini-mikheys" - ], - "install_type": "git-clone", - "reference": "https://github.com/mikheys/comfyui-gemini-mikheys", - "title": "ComfyUI Nano Banana Node" - }, - { - "author": "Gipphe", - "description": "Gather statistics and information about your workflow.", - "files": [ - "https://github.com/Gipphe/comfyui-metadata-statistics" - ], - "install_type": "git-clone", - "reference": "https://github.com/Gipphe/comfyui-metadata-statistics", - "title": "ComfyUI Metadata Statistics" - }, - { - "author": "Saganaki22", - "description": "ComfyUI node for generating animated dotted waveform visualizations with multiple animation styles including teardrop bars", - "files": [ - "https://github.com/Saganaki22/ComfyUI-dotWaveform" - ], - "install_type": "git-clone", - "reference": "https://github.com/Saganaki22/ComfyUI-dotWaveform", - "title": "dotWaveform" - }, - { - "author": "sfinktah", - "description": "An assorted reliquary of nodes: mismatched, stubborn, and deliberately indistinct; they do what is required; do not ask for particulars.", - "files": [ - "https://github.com/sfinktah/comfy-ovum" - ], - "install_type": "git-clone", - "reference": "https://github.com/sfinktah/comfy-ovum", - "title": "comfy-ovum" - }, - { - "author": "fredhopp", - "description": "This project contains custom Flip-Flop nodes for ComfyUI.", - "files": [ - "https://github.com/fredhopp/comfyui-flipflopnodes" - ], - "install_type": "git-clone", - "reference": "https://github.com/fredhopp/comfyui-flipflopnodes", - "title": "comfyui-flipflopnodes" - }, - { - "author": "lucasgattas", - "description": "Egregora: Divide & Enhance is a small suite of custom nodes that help you split, enhance, and recombine images, plus a clean SDXL prompt mixer that keeps things simple while staying robust with lot\u00b4s of customization.", - "files": [ - "https://github.com/lucasgattas/comfyui-egregora-divide-and-enhance" - ], - "install_type": "git-clone", - "reference": "https://github.com/lucasgattas/comfyui-egregora-divide-and-enhance", - "title": "ComfyUI \u00b7 Egregora: Divide & Enhance" - }, - { - "author": "sweetndata", - "description": "NODES:Reflatent", - "files": [ - "https://github.com/sweetndata/ComfyUI-Reflatent" - ], - "install_type": "git-clone", - "reference": "https://github.com/sweetndata/ComfyUI-Reflatent", - "title": "ComfyUI-Reflatent" - }, - { - "author": "aesethtics", - "description": "A collection of utility nodes for ComfyUI to improve workflow efficiency.", - "files": [ - "https://github.com/aesethtics/ComfyUI-Utilitools" - ], - "install_type": "git-clone", - "reference": "https://github.com/aesethtics/ComfyUI-Utilitools", - "title": "ComfyUI Utilitools Nodes" - }, - { - "author": "aesethtics", - "description": "A collection of utility nodes for ComfyUI to improve workflow efficiency.", - "files": [ - "https://github.com/grmchn/ComfyUI-ProportionChanger" - ], - "install_type": "git-clone", - "reference": "https://github.com/grmchn/ComfyUI-ProportionChanger", - "title": "ComfyUI Utilitools Nodes" - }, - { - "author": "huwenkai26", - "description": "This is a ComfyUI custom node designed to automatically detect and remove text from images.", - "files": [ - "https://github.com/huwenkai26/comfyui-remove-text" - ], - "install_type": "git-clone", - "reference": "https://github.com/huwenkai26/comfyui-remove-text", - "title": "ComfyUI Text Remove Node" - }, - { - "author": "synchronicity-labs", - "description": "This custom node allows you to perform audio-video lip synchronization inside ComfyUI using a simple interface.", - "files": [ - "https://github.com/synchronicity-labs/sync-comfyui" - ], - "install_type": "git-clone", - "reference": "https://github.com/synchronicity-labs/sync-comfyui", - "title": "ComfyUI Sync Lipsync Node" - }, - { - "author": "BAIKEMARK", - "description": "A ComfyUI node that quickly fetches the most commonly used positive and negative prompts for Civitai models (Checkpoint / Lora) from the community, helping you effortlessly enhance your own creations!", - "files": [ - "https://github.com/BAIKEMARK/ComfyUI_Civitai_Prompt_Stats" - ], - "install_type": "git-clone", - "reference": "https://github.com/BAIKEMARK/ComfyUI_Civitai_Prompt_Stats", - "title": "Civitai Prompt Stats Node" - }, - { - "author": "BAIKEMARK", - "description": "A powerful node suite that finds the community's best 'recipes' for any Civitai model by analyzing top prompts, LoRA triggers, parameters, and combos.", - "files": [ - "https://github.com/BAIKEMARK/ComfyUI-Civitai-Recipe" - ], - "install_type": "git-clone", - "reference": "https://github.com/BAIKEMARK/ComfyUI-Civitai-Recipe", - "title": "Civitai Recipe Finder" - }, - { - "author": "Jarcis-cy", - "description": "ComfyUI wrapper nodes for HunyuanVideo-Foley: Generate audio from video + text prompts", - "files": [ - "https://github.com/Jarcis-cy/ComfyUI-HunyuanVideoFoley" - ], - "install_type": "git-clone", - "reference": "https://github.com/Jarcis-cy/ComfyUI-HunyuanVideoFoley", - "title": "HunyuanVideo-Foley Audio Generator" - }, - { - "author": "railep", - "description": "A ComfyUI custom node for generating synchronized audio for videos using the HunyuanVideo-Foley model.", - "files": [ - "https://github.com/railep/ComfyUI-HunyuanVideo-Foley" - ], - "install_type": "git-clone", - "reference": "https://github.com/railep/ComfyUI-HunyuanVideo-Foley", - "title": "HunyuanVideo-Foley Audio Generator" - }, - { - "author": "Dehypnotic", - "description": "An advanced aspect ratio calculator and image scaler with flexible scaling modes and intelligent image handling.", - "files": [ - "https://raw.githubusercontent.com/Dehypnotic/comfyui-aspect-ratio-advanced/main/aspect_ratio_advanced.py" - ], - "install_type": "copy", - "reference": "https://github.com/Dehypnotic/comfyui-aspect-ratio-advanced", - "title": "AspectRatioAdvanced" - }, - { - "author": "Ser-Hilary", - "description": "Nodes:sizing_node. Size calculation node related to image size in prompts supported by SDXL.", - "files": [ - "https://raw.githubusercontent.com/Ser-Hilary/SDXL_sizing/main/conditioning_sizing_for_SDXL.py" - ], - "install_type": "copy", - "reference": "https://github.com/Ser-Hilary/SDXL_sizing", - "title": "SDXL_sizing" - }, - { - "author": "ailex000", - "description": "Custom javascript extensions for better UX for ComfyUI. Supported nodes: PreviewImage, SaveImage. Double click on image to open.", - "files": [ - "https://raw.githubusercontent.com/ailex000/ComfyUI-Extensions/main/image-gallery/imageGallery.js" - ], - "install_type": "copy", - "js_path": "image-gallery", - "reference": "https://github.com/ailex000/ComfyUI-Extensions", - "title": "Image Gallery" - }, - { - "author": "rock-land", - "description": "ComfyUI Web Extension for saving views and navigating graphs.", - "files": [ - "https://raw.githubusercontent.com/rock-land/graphNavigator/main/graphNavigator/graphNavigator.js" - ], - "install_type": "copy", - "js_path": "graphNavigator", - "reference": "https://github.com/rock-land/graphNavigator", - "title": "graphNavigator" - }, - { - "author": "diffus3", - "description": "Extensions: subgraph, setget, multiReroute", - "files": [ - "https://raw.githubusercontent.com/diffus3/ComfyUI-extensions/main/multiReroute/multireroute.js", - "https://raw.githubusercontent.com/diffus3/ComfyUI-extensions/main/setget/setget.js" - ], - "install_type": "copy", - "js_path": "diffus3", - "reference": "https://github.com/diffus3/ComfyUI-extensions", - "title": "diffus3/ComfyUI-extensions" - }, - { - "author": "m957ymj75urz", - "description": "Nodes: RawText, RawTextCLIPEncode, RawTextCombine, RawTextReplace, Extension: m957ymj75urz.colors", - "files": [ - "https://raw.githubusercontent.com/m957ymj75urz/ComfyUI-Custom-Nodes/main/clip-text-encode-split/clip_text_encode_split.py", - "https://raw.githubusercontent.com/m957ymj75urz/ComfyUI-Custom-Nodes/main/colors/colors.js" - ], - "install_type": "copy", - "js_path": "m957ymj75urz", - "reference": "https://github.com/m957ymj75urz/ComfyUI-Custom-Nodes", - "title": "m957ymj75urz/ComfyUI-Custom-Nodes" - }, - { - "author": "Bikecicle", - "description": "Some additional audio utilites for use on top of Sample Diffusion ComfyUI Extension", - "files": [ - "https://raw.githubusercontent.com/NeuralNotW0rk/ComfyUI-Waveform-Extensions/main/EXT_AudioManipulation.py", - "https://raw.githubusercontent.com/NeuralNotW0rk/ComfyUI-Waveform-Extensions/main/EXT_VariationUtils.py" - ], - "install_type": "copy", - "reference": "https://github.com/Bikecicle/ComfyUI-Waveform-Extensions", - "title": "Waveform Extensions" - }, - { - "author": "dawangraoming", - "description": "KSampler is provided, based on GPU random noise", - "files": [ - "https://raw.githubusercontent.com/dawangraoming/ComfyUI_ksampler_gpu/main/ksampler_gpu.py" - ], - "install_type": "copy", - "reference": "https://github.com/dawangraoming/ComfyUI_ksampler_gpu", - "title": "KSampler GPU" - }, - { - "author": "fitCorder", - "description": "fcFloatMatic is a custom module, that when configured correctly will increment through the lines generating you loras at different strengths. The JSON file will load the config.", - "files": [ - "https://raw.githubusercontent.com/fitCorder/fcSuite/main/fcSuite.py" - ], - "install_type": "copy", - "reference": "https://github.com/fitCorder/fcSuite", - "title": "fcSuite" - }, - { - "author": "lordgasmic", - "description": "Nodes:CLIPTextEncodeWithWildcards. This wildcard node is a wildcard node that operates based on the seed.", - "files": [ - "https://raw.githubusercontent.com/lordgasmic/comfyui_wildcards/master/wildcards.py" - ], - "install_type": "copy", - "reference": "https://github.com/lordgasmic/ComfyUI-Wildcards", - "title": "Wildcards" - }, - { - "author": "throttlekitty", - "description": "A quick and easy ComfyUI custom node for setting SDXL-friendly aspect ratios.", - "files": [ - "https://raw.githubusercontent.com/throttlekitty/SDXLCustomAspectRatio/main/SDXLAspectRatio.py" - ], - "install_type": "copy", - "reference": "https://github.com/throttlekitty/SDXLCustomAspectRatio", - "title": "SDXLCustomAspectRatio" - }, - { - "author": "s1dlx", - "description": "Advanced merging methods.", - "files": [ - "https://raw.githubusercontent.com/s1dlx/comfy_meh/main/meh.py" - ], - "install_type": "copy", - "reference": "https://github.com/s1dlx/comfy_meh", - "title": "comfy_meh" - }, - { - "author": "tudal", - "description": "Mainly its prompt generating by custom syntax. Prompt Parser, Prompt tags, Random Line, Calculate Upscale, Image size to string, Type Converter, Image Resize To Height/Width, Load Random Image, Load Text", - "files": [ - "https://raw.githubusercontent.com/tudal/Hakkun-ComfyUI-nodes/main/hakkun_nodes.py" - ], - "install_type": "copy", - "reference": "https://github.com/tudal/Hakkun-ComfyUI-nodes", - "title": "Hakkun-ComfyUI-nodes" - }, - { - "author": "SadaleNet", - "description": "Nodes: CLIPTextEncodeA1111, RerouteTextForCLIPTextEncodeA1111.", - "files": [ - "https://raw.githubusercontent.com/SadaleNet/CLIPTextEncodeA1111-ComfyUI/master/custom_nodes/clip_text_encoder_a1111.py" - ], - "install_type": "copy", - "reference": "https://github.com/SadaleNet/CLIPTextEncodeA1111-ComfyUI", - "title": "ComfyUI A1111-like Prompt Custom Node Solution" - }, - { - "author": "wsippel", - "description": "Nodes: SDXLResolutionPresets. Easy access to the officially supported resolutions, in both horizontal and vertical formats: 1024x1024, 1152x896, 1216x832, 1344x768, 1536x640", - "files": [ - "https://raw.githubusercontent.com/wsippel/comfyui_ws/main/sdxl_utility.py" - ], - "install_type": "copy", - "reference": "https://github.com/wsippel/comfyui_ws", - "title": "SDXLResolutionPresets" - }, - { - "author": "nicolai256", - "description": "Nodes: yugioh_Presets. by Nicolai256 inspired by throttlekitty SDXLAspectRatio", - "files": [ - "https://raw.githubusercontent.com/nicolai256/comfyUI_Nodes_nicolai256/main/yugioh-presets.py" - ], - "id": "nicoali256", - "install_type": "copy", - "reference": "https://github.com/nicolai256/comfyUI_Nodes_nicolai256", - "title": "comfyUI_Nodes_nicolai256" - }, - { - "author": "Onierous", - "description": "Nodes: QRNG Node CSV. A node that takes in an array of random numbers from the ANU QRNG API and stores them locally for generating quantum random number noise_seeds in ComfyUI", - "files": [ - "https://raw.githubusercontent.com/Onierous/QRNG_Node_ComfyUI/main/qrng_node.py" - ], - "id": "qrng", - "install_type": "copy", - "reference": "https://github.com/Onierous/QRNG_Node_ComfyUI", - "title": "QRNG_Node_ComfyUI" - }, - { - "author": "ntdviet", - "description": "Nodes:LatentGarbageCollector. This ComfyUI custom node flushes the GPU cache and empty cuda interprocess memory. It's helpfull for low memory environment such as the free Google Colab, especially when the workflow VAE decode latents of the size above 1500x1500.", - "files": [ - "https://raw.githubusercontent.com/ntdviet/comfyui-ext/main/custom_nodes/gcLatentTunnel/gcLatentTunnel.py" - ], - "install_type": "copy", - "reference": "https://github.com/ntdviet/comfyui-ext", - "title": "ntdviet/comfyui-ext" - }, - { - "author": "alkemann", - "description": "Nodes:Int to Text, Seed With Text, Save A1 Image.", - "files": [ - "https://gist.githubusercontent.com/alkemann/7361b8eb966f29c8238fd323409efb68/raw/f9605be0b38d38d3e3a2988f89248ff557010076/alkemann.py" - ], - "id": "alkemann", - "install_type": "copy", - "reference": "https://gist.github.com/alkemann/7361b8eb966f29c8238fd323409efb68", - "title": "alkemann nodes" - }, - { - "author": "catscandrive", - "description": "Adds an Image Loader node that also shows images in subfolders of the default input directory", - "files": [ - "https://raw.githubusercontent.com/catscandrive/comfyui-imagesubfolders/main/loadImageWithSubfolders.py" - ], - "id": "imgsubfolders", - "install_type": "copy", - "reference": "https://github.com/catscandrive/comfyui-imagesubfolders", - "title": "Image loader with subfolders" - }, - { - "author": "Smuzzies", - "description": "Nodes: Chatbox Overlay. Custom node for ComfyUI to add a text box over a processed image before save node.", - "files": [ - "https://raw.githubusercontent.com/Smuzzies/comfyui_chatbox_overlay/main/chatbox_overlay.py" - ], - "id": "chatbox-overlay", - "install_type": "copy", - "reference": "https://github.com/Smuzzies/comfyui_chatbox_overlay", - "title": "Chatbox Overlay node for ComfyUI" - }, - { - "author": "CaptainGrock", - "description": "Nodes:Apply Invisible Watermark, Extract Watermark. Adds up to 12 characters encoded into an image that can be extracted.", - "files": [ - "https://raw.githubusercontent.com/CaptainGrock/ComfyUIInvisibleWatermark/main/Invisible%20Watermark.py" - ], - "id": "invisible-watermark-grock", - "install_type": "copy", - "reference": "https://github.com/CaptainGrock/ComfyUIInvisibleWatermark", - "title": "ComfyUIInvisibleWatermark" - }, - { - "author": "LZC", - "description": "Nodes:tensor_trans_pil, Make Transparent mask, MergeImages, words_generatee, load_PIL image", - "files": [ - "https://raw.githubusercontent.com/1shadow1/hayo_comfyui_nodes/main/LZCNodes.py" - ], - "id": "lzcnodes", - "install_type": "copy", - "reference": "https://github.com/1shadow1/hayo_comfyui_nodes", - "title": "Hayo comfyui nodes" - }, - { - "author": "underclockeddev", - "description": "A node which takes in x, y, width, height, total width, and total height, in order to accurately represent the area of an image which is covered by area-based conditioning.", - "files": [ - "https://raw.githubusercontent.com/underclockeddev/ComfyUI-PreviewSubselection-Node/master/preview_subselection.py" - ], - "id": "preview-subselection", - "install_type": "copy", - "reference": "https://github.com/underclockeddev/ComfyUI-PreviewSubselection-Node", - "title": "Preview Subselection Node for ComfyUI" - }, - { - "author": "underclockeddev", - "description": "Nodes:BrevImage. ComfyUI Load Image From URL", - "files": [ - "https://raw.githubusercontent.com/bkunbargi/BrevImage/main/BrevLoadImage.py" - ], - "id": "brevimage", - "install_type": "copy", - "reference": "https://github.com/bkunbargi/BrevImage", - "title": "BrevImage" - }, - { - "author": "jw782cn", - "description": "Extension to show random cat GIFs while queueing prompt.", - "files": [ - "https://github.com/jw782cn/ComfyUI-Catcat" - ], - "id": "catcat", - "install_type": "copy", - "reference": "https://github.com/jw782cn/ComfyUI-Catcat", - "title": "ComfyUI-Catcat" - }, - { - "author": "barckley75", - "description": "Nodes:TextToSpeech, phy_3_conditioning, SaveAudioToDaVinci, SaveImageToDaVinci.\nNOTE:In order to use DaVinci node you must have DaVinci Resolve Studio connected to the API. For more information check the help seciton in DaVinci Resolve Studio HELP>DOCUMENTATION>DEVELOPER. It will open a folder, search for scripting and the for README.txt file, the API documentation.", - "files": [ - "https://raw.githubusercontent.com/barckley75/comfyUI_DaVinciResolve/main/custom_nodes/node_text_to_speech.py", - "https://raw.githubusercontent.com/barckley75/comfyUI_DaVinciResolve/refs/heads/main/custom_nodes/nodes_phi_3_contitioning.py", - "https://raw.githubusercontent.com/barckley75/comfyUI_DaVinciResolve/main/custom_nodes/save_audio_to_davinci.py", - "https://raw.githubusercontent.com/barckley75/comfyUI_DaVinciResolve/main/custom_nodes/save_image_to_davinci.py" - ], - "install_type": "copy", - "reference": "https://github.com/barckley75/comfyUI_DaVinciResolve", - "title": "comfyUI_DaVinciResolve" - }, - { - "author": "seghier", - "description": "Use LibreTranslation in ComfyUI [a/https://github.com/LibreTranslate/LibreTranslate](https://github.com/LibreTranslate/LibreTranslate)", - "files": [ - "https://raw.githubusercontent.com/seghier/ComfyUI_LibreTranslate/main/translate_node.py" - ], - "install_type": "copy", - "reference": "https://github.com/seghier/ComfyUI_LibreTranslate", - "title": "ComfyUI_LibreTranslate" - }, - { - "author": "ultimatech-cn", - "description": "A ComfyUI custom node for face comparison. This node utilizes Face++'s facial recognition and comparison algorithms by directly calling the Face++ API. Its usage in the workflow is as follows:", - "files": [ - "https://raw.githubusercontent.com/ultimatech-cn/FaceSimilarity/main/faceSimilarity.py" - ], - "install_type": "copy", - "reference": "https://github.com/ultimatech-cn/FaceSimilarity", - "title": "FaceSimilarity" - }, - { - "author": "folkghost", - "description": "This repository contains a custom node for ComfyUI that allows searching for a keyword in the first column of a CSV file and returning a value from a specified column in that row. The node is designed to be modular and fit within the node-based workflow of ComfyUI.", - "files": [ - "https://raw.githubusercontent.com/folkghost/comfyui_search_csv/main/search_csv_node.py" - ], - "install_type": "copy", - "reference": "https://github.com/folkghost/comfyui_search_csv", - "title": "CSV Search Node" - }, - { - "author": "SimonHeese", - "description": "NODES:Animated Offset Padding, Animated Rotation & Zoom", - "files": [ - "https://github.com/SimonHeese/ComfyUI_AnimationNodes/raw/refs/heads/main/animated_offset_pad.py", - "https://github.com/SimonHeese/ComfyUI_AnimationNodes/raw/refs/heads/main/animated_rotation_zoom.py" - ], - "install_type": "copy", - "reference": "https://github.com/SimonHeese/ComfyUI_AnimationNodes", - "title": "ComfyUI_AnimationNodes" - }, - { - "author": "duskfallcrew", - "description": "Extremely inspired and forked from: [a/https://github.com/klimaleksus/stable-diffusion-webui-embedding-merge](https://github.com/klimaleksus/stable-diffusion-webui-embedding-merge)", - "files": [ - "https://github.com/duskfallcrew/Comfyui_EmbeddingMerge_Node/raw/refs/heads/main/merge_embed.py" - ], - "install_type": "copy", - "reference": "https://github.com/duskfallcrew/Comfyui_EmbeddingMerge_Node", - "title": "Embedding Merge for ComfyUI" - }, - { - "author": "Kayarte", - "description": "GIS Processing Nodes for ComfyUI", - "files": [ - "https://github.com/Kayarte/GeoNodes/raw/refs/heads/main/GISDetectionNode.py" - ], - "install_type": "copy", - "reference": "https://github.com/Kayarte/GeoNodes", - "title": "GeoNodes" - }, - { - "author": "huimengshiguang", - "description": "This is an extension script for Stable Diffusion WebUI, modified based on the original functionality. It now supports fixing FLUX panorama seams. It allows users to independently configure seamless image tiling for both the X and Y axes while also being capable of handling FLUX panorama seam issues.", - "files": [ - "https://raw.githubusercontent.com/huimengshiguang/AspectAwareTiling/refs/heads/main/hmsg-quanjing.py" - ], - "install_type": "copy", - "reference": "https://github.com/huimengshiguang/AspectAwareTiling", - "title": "AspectAwareTiling" - }, - { - "author": "theally", - "description": "Custom nodes for ComfyUI by TheAlly.", - "files": [ - "https://civitai.com/api/download/models/25114", - "https://civitai.com/api/download/models/24679", - "https://civitai.com/api/download/models/24154", - "https://civitai.com/api/download/models/23884", - "https://civitai.com/api/download/models/23649", - "https://civitai.com/api/download/models/23467", - "https://civitai.com/api/download/models/23296" - ], - "id": "ally", - "install_type": "unzip", - "reference": "https://civitai.com/models/19625?modelVersionId=23296", - "title": "TheAlly's Custom Nodes" - }, - { - "author": "xss", - "description": "Various image processing nodes.", - "files": [ - "https://civitai.com/api/download/models/32717", - "https://civitai.com/api/download/models/47776", - "https://civitai.com/api/download/models/29772", - "https://civitai.com/api/download/models/31618", - "https://civitai.com/api/download/models/31591", - "https://civitai.com/api/download/models/29773", - "https://civitai.com/api/download/models/29774", - "https://civitai.com/api/download/models/29755", - "https://civitai.com/api/download/models/29750" - ], - "id": "xss", - "install_type": "unzip", - "reference": "https://civitai.com/models/24869/comfyui-custom-nodes-by-xss", - "title": "Custom Nodes by xss" - }, - { - "author": "aimingfail", - "description": "This is a node to convert an image into a CMYK Halftone dot image.", - "files": [ - "https://civitai.com/api/download/models/158997" - ], - "id": "img2halftone", - "install_type": "unzip", - "reference": "https://civitai.com/models/143293/image2halftone-node-for-comfyui", - "title": "Image2Halftone Node for ComfyUI" - } - ] -} \ No newline at end of file diff --git a/user/default/ComfyUI-Manager/cache/1742899825_extension-node-map.json b/user/default/ComfyUI-Manager/cache/1742899825_extension-node-map.json deleted file mode 100644 index 2d113731f6bf040fe1fc425068ded5866018a3a7..0000000000000000000000000000000000000000 --- a/user/default/ComfyUI-Manager/cache/1742899825_extension-node-map.json +++ /dev/null @@ -1,43045 +0,0 @@ -{ - "https://gist.githubusercontent.com/alkemann/7361b8eb966f29c8238fd323409efb68/raw/f9605be0b38d38d3e3a2988f89248ff557010076/alkemann.py": [ - [ - "Int to Text", - "Save A1 Image", - "Seed With Text" - ], - { - "title_aux": "alkemann nodes" - } - ], - "https://git.mmaker.moe/mmaker/sd-webui-color-enhance": [ - [ - "MMakerColorBlend", - "MMakerColorEnhance" - ], - { - "title_aux": "mmaker/Color Enhance" - } - ], - "https://gitee.com/yyh915/jkha-load-img": [ - [ - "JkhaLoadImage" - ], - { - "title_aux": "ImageLoadFromLocalOrUrl Node for ComfyUI" - } - ], - "https://github.com/0x-jerry/comfyui-rembg": [ - [ - "Load Rembg Model", - "Rembg Remove background" - ], - { - "title_aux": "0x-jerry/Rembg Background Removal Node for ComfyUI" - } - ], - "https://github.com/0xRavenBlack/ComfyUI-OOP": [ - [ - "OOPAnimalNode", - "OOPClothingNode", - "OOPEnvironmentNode", - "OOPEyesNode", - "OOPHairNode", - "OOPLocationNode", - "OOPMouthNode", - "OOPNode", - "OOPPersonNode", - "OOPPoseNode", - "OOPStyleNode", - "OOPViewNode" - ], - { - "title_aux": "ComfyUI-OOP" - } - ], - "https://github.com/0xbitches/ComfyUI-LCM": [ - [ - "LCM_Sampler", - "LCM_Sampler_Advanced", - "LCM_img2img_Sampler", - "LCM_img2img_Sampler_Advanced" - ], - { - "title_aux": "Latent Consistency Model for ComfyUI" - } - ], - "https://github.com/1038lab/ComfyUI-EdgeTTS": [ - [ - "EdgeTTS", - "Save_Audio", - "WhisperSTT" - ], - { - "title_aux": "ComfyUI-EdgeTTS" - } - ], - "https://github.com/1038lab/ComfyUI-JoyCaption": [ - [ - "CaptionSaver", - "ImageBatchPath", - "JC", - "JC_ExtraOptions", - "JC_GGUF", - "JC_GGUF_adv", - "JC_adv" - ], - { - "title_aux": "ComfyUI-JoyCaption" - } - ], - "https://github.com/1038lab/ComfyUI-LBM": [ - [ - "LBM_DepthNormal", - "LBM_Relighting" - ], - { - "title_aux": "ComfyUI-LBM" - } - ], - "https://github.com/1038lab/ComfyUI-MegaTTS": [ - [ - "MegaTTS3", - "MegaTTS3S", - "MegaTTS_VoiceMaker" - ], - { - "title_aux": "ComfyUI-MegaTTS" - } - ], - "https://github.com/1038lab/ComfyUI-MiniCPM": [ - [ - "AILab_MiniCPM_V", - "AILab_MiniCPM_V_Advanced", - "AILab_MiniCPM_V_GGUF", - "AILab_MiniCPM_V_GGUF_Advanced" - ], - { - "title_aux": "ComfyUI-MiniCPM" - } - ], - "https://github.com/1038lab/ComfyUI-MiniMax-Remover": [ - [ - "ImageSizeAdjuster", - "MinimaxImageRemover", - "MinimaxModelLoader", - "MinimaxVideoLoader", - "MinimaxVideoRemover" - ], - { - "title_aux": "ComfyUI-MiniMax-Remover" - } - ], - "https://github.com/1038lab/ComfyUI-Mosaic": [ - [ - "MosaicCreator", - "MosaicDetector" - ], - { - "title_aux": "ComfyUI-Mosaic" - } - ], - "https://github.com/1038lab/ComfyUI-OmniGen": [ - [ - "ailab_OmniGen" - ], - { - "title_aux": "ComfyUI-OmniGen" - } - ], - "https://github.com/1038lab/ComfyUI-RMBG": [ - [ - "AILab_ColorInput", - "AILab_CropObject", - "AILab_ICLoRAConcat", - "AILab_ImageCombiner", - "AILab_ImageCompare", - "AILab_ImageCrop", - "AILab_ImageMaskConvert", - "AILab_ImageMaskResize", - "AILab_ImagePreview", - "AILab_ImageStitch", - "AILab_LamaRemover", - "AILab_LoadImage", - "AILab_LoadImageAdvanced", - "AILab_LoadImageSimple", - "AILab_MaskCombiner", - "AILab_MaskEnhancer", - "AILab_MaskExtractor", - "AILab_MaskOverlay", - "AILab_MaskPreview", - "AILab_Preview", - "AILab_ReferenceLatentMask", - "AILab_SDMatte", - "BiRefNetRMBG", - "BodySegment", - "ClothesSegment", - "FaceSegment", - "FashionSegmentAccessories", - "FashionSegmentClothing", - "RMBG", - "SAM2Segment", - "Segment", - "SegmentV2" - ], - { - "title_aux": "ComfyUI-RMBG" - } - ], - "https://github.com/1038lab/ComfyUI-ReduxFineTune": [ - [ - "ClipVisionStyleLoader", - "ReduxFineTune", - "ReduxFineTuneAdvanced" - ], - { - "title_aux": "ComfyUI-ReduxFineTune" - } - ], - "https://github.com/1038lab/ComfyUI-SparkTTS": [ - [ - "SparkTTS_AdvVoiceClone", - "SparkTTS_AudioRecorder", - "SparkTTS_VoiceClone", - "SparkTTS_VoiceCreator" - ], - { - "title_aux": "Comfyui-Spark-TTS" - } - ], - "https://github.com/1038lab/ComfyUI-WildPromptor": [ - [ - "AllInOneList", - "KeywordPicker", - "PromptBuilder", - "PromptConcat", - "WildPromptorAllInOne", - "WildPromptorGenerator", - "WildPromptor_DataToPromptList", - "WildPromptor_Enhancer" - ], - { - "title_aux": "ComfyUI-WildPromptor" - } - ], - "https://github.com/111496583yzy/comfyui-PuzzleCrack-Effect": [ - [ - "MyJigsawPuzzleEffect", - "MyRegionBoundaryEffect" - ], - { - "title_aux": "Jigsaw Puzzle Effect Plugin" - } - ], - "https://github.com/11dogzi/CYBERPUNK-STYLE-DIY": [ - [ - "CYBERPUNKHT" - ], - { - "title_aux": "CYBERPUNK-STYLE-DIY" - } - ], - "https://github.com/11dogzi/ComfUI-EGAdapterMadAssistant": [ - [ - "EGIPAdapter_Mad_Assistant", - "EGIPAdapter_Mad_AssistantV1", - "EGIPAdapter_Mad_AssistantV2", - "EGIPAdapter_Mad_AssistantV3", - "EGIPAdapter_Mad_AssistantV4", - "EGIPAdapter_Mad_AssistantV5", - "EGIPAdapter_Mad_AssistantV6" - ], - { - "title_aux": "ComfUI-EGAdapterMadAssistant" - } - ], - "https://github.com/11dogzi/Comfyui-HYPIR": [ - [ - "HYPIRAdvancedRestoration" - ], - { - "title_aux": "HYPIR ComfyUI Plugin" - } - ], - "https://github.com/11dogzi/Comfyui-ergouzi-Nodes": [ - [ - "EG-YSZT-ZT", - "EG_CPSYTJ", - "EG_FX_BDAPI", - "EG_HT_YSTZ", - "EG_JF_ZZSC", - "EG_JXFZ_node", - "EG_K_LATENT", - "EG_RY_HT", - "EG_SCQY_BHDQY", - "EG_SCQY_QBQY", - "EG_SCQY_SXQY", - "EG_SJ", - "EG_SJPJ_Node", - "EG_SS_RYZH", - "EG_SZ_JDYS", - "EG_TC_Node", - "EG_TSCDS_CJ", - "EG_TSCDS_DG", - "EG_TSCDS_FG", - "EG_TSCDS_JT", - "EG_TSCDS_QT", - "EG_TSCDS_RW", - "EG_TSCDS_WP", - "EG_TSCDS_ZL", - "EG_TSCMB_GL", - "EG_TXZZ_ZH", - "EG_TX_CCHQ", - "EG_TX_CJPJ", - "EG_TX_JZRY", - "EG_TX_LJ", - "EG_TX_LJBC", - "EG_TX_SFBLS", - "EG_TX_WHLJ", - "EG_WB_KSH", - "EG_WXZ_QH", - "EG_XZ_QH", - "EG_YSQY_BBLLD", - "EG_YSQY_BLLD", - "EG_ZY_WBK", - "EG_ZZHBCJ", - "EG_ZZKZ_HT_node", - "EG_ZZ_BSYH", - "EG_ZZ_BYYH", - "EG_ZZ_HSYH", - "EG_ZZ_MHHT", - "EG_ZZ_SSKZ", - "ER_JBCH", - "ER_TX_ZZCJ" - ], - { - "title_aux": "Comfyui-ergouzi-Nodes" - } - ], - "https://github.com/11dogzi/Comfyui-ergouzi-kaiguan": [ - [ - "ALLty", - "EGRWGL", - "EGRYDZQHNode", - "EGSEED", - "GroupSwitchNode", - "GroupSwitchNodee", - "GroupSwitchNodeee", - "GroupSwitchNodeeee", - "GroupSwitchNodi", - "hulue", - "jinyong" - ], - { - "title_aux": "Comfyui-ergouzi-kaiguan" - } - ], - "https://github.com/11dogzi/Comfyui-ergouzi-samplers": [ - [ - "EGBYZZCYQ", - "EGCYQJB", - "EGCYQJBCJ" - ], - { - "title_aux": "Comfyui-ergouzi-samplers" - } - ], - "https://github.com/1hew/ComfyUI-1hewNodes": [ - [ - "ImageAddLabel", - "ImageBBoxOverlayByMask", - "ImageBatchToList", - "ImageBlendModesByAlpha", - "ImageBlendModesByCSS", - "ImageCropByMaskAlpha", - "ImageCropSquare", - "ImageCropWithBBoxMask", - "ImageEdgeCropPad", - "ImageEditStitch", - "ImageGetSize", - "ImageHLFreqCombine", - "ImageHLFreqSeparate", - "ImageHLFreqTransform", - "ImageListAppend", - "ImageListToBatch", - "ImageLumaMatte", - "ImagePasteByBBoxMask", - "ImagePlot", - "ImageResizeFluxKontext", - "ImageResizeUniversal", - "ImageRotateWithMask", - "ImageSolid", - "ImageStrokeByMask", - "ImageTileMerge", - "ImageTileSplit", - "ImageTileSplitPreset", - "ListCustomFloat", - "ListCustomInt", - "ListCustomSeed", - "ListCustomString", - "MaskBatchMathOps", - "MaskBatchToList", - "MaskCropByBBoxMask", - "MaskFillHole", - "MaskListToBatch", - "MaskMathOps", - "MaskPasteByBBoxMask", - "PathBuild", - "RangeMapping", - "StepSplit", - "StringCoordinateToBBoxMask", - "StringCoordinateToBBoxes", - "TextCustomExtract", - "TextFilterComment", - "TextJoinByTextList", - "TextJoinMulti", - "TextLoadLocal", - "TextPrefixSuffix" - ], - { - "title_aux": "ComfyUI 1hewNodes" - } - ], - "https://github.com/1mckw/Comfyui-Gelbooru": [ - [ - "Gelbooru (ID)", - "Gelbooru (Random)", - "UrlsToImage" - ], - { - "title_aux": "Comfyui-Gelbooru" - } - ], - "https://github.com/1zhangyy1/comfyui-vidu-nodes": [ - [ - "Character2Video", - "Image2Video", - "StartEnd2Video", - "Text2Video", - "UpscaleVideo", - "VideoDownloader" - ], - { - "title_aux": "ComfyUI VIDU" - } - ], - "https://github.com/2frames/ComfyUI-AQnodes": [ - [ - "AQ_BatchAverageImage", - "AQ_BlendImages", - "AQ_CLIPSetLastLayer", - "AQ_ColorMatchImage", - "AQ_Gemini", - "AQ_ImageMaskSwitch", - "AQ_Image_DetailTransfer", - "AQ_Image_Pad", - "AQ_Increment", - "AQ_LoadImageBase64", - "AQ_MasksAndImagesAsList", - "AQ_Qwen", - "AQ_QwenLoader", - "AQ_Random", - "AQ_SaveImageWebpReturnBase64", - "AQ_SendImageToAPI", - "AQ_multiface_ApplyPulidFlux" - ], - { - "title_aux": "AQnodes for ComfyUI" - } - ], - "https://github.com/2kpr/ComfyUI-PMRF": [ - [ - "PMRF" - ], - { - "title_aux": "ComfyUI-PMRF" - } - ], - "https://github.com/2kpr/ComfyUI-UltraPixel": [ - [ - "UltraPixelLoad", - "UltraPixelProcess" - ], - { - "author": "italo", - "title_aux": "ComfyUI-UltraPixel" - } - ], - "https://github.com/311-code/ComfyUI-MagicClip_Strength": [ - [ - "CLIPTextEncodeSDXL_311_code" - ], - { - "title_aux": "ComfyUI MagicClip_Strength for SDXL" - } - ], - "https://github.com/31702160136/ComfyUI-GrsAI": [ - [ - "GPTImage_ImageToImage", - "GPTImage_TextToImage", - "GrsaiFluxKontext_ImageToImage", - "GrsaiFluxKontext_MultiImageToImage", - "GrsaiFluxKontext_TextToImage" - ], - { - "title_aux": "GrsAI api in ComfyUI" - } - ], - "https://github.com/42lux/ComfyUI-42lux": [ - [ - "FluxEmptyLatentSizePicker", - "HighResFixApply", - "HighResFixConditioningDuplicator", - "HighResFixModelInjection", - "ModelSamplingFluxNormalized", - "STORKSamplerSelect", - "SoulSampler", - "SoulSamplerAdvanced", - "SoulSamplerDPM", - "SoulSamplerDPMAdvanced", - "SoulSamplerHybrid", - "SoulSamplerHybridAdvanced" - ], - { - "title_aux": "ComfyUI-42lux" - } - ], - "https://github.com/438443467/ComfyUI-GPT4V-Image-Captioner": [ - [ - "GPT4VCaptioner" - ], - { - "title_aux": "ComfyUI-GPT4V-Image-Captioner" - 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[ - "ZonosEmotion", - "ZonosGenerate" - ], - { - "title_aux": "ComfyUI-Zonos" - } - ], - "https://github.com/Burgstall-labs/ComfyUI-BETA-Cropnodes": [ - [ - "BETACrop", - "BETAStitch", - "IndexedLoRALoader_BETA", - "LoadTextFromIndex", - "SaveAudioAdvanced_BETA", - "SelectSharpestFrames", - "SharpestFrameClipper", - "TextLineCount", - "WANResolutionCalculator" - ], - { - "title_aux": "ComfyUI-BETA-Cropnodes" - } - ], - "https://github.com/Burgstall-labs/ComfyUI-BETA-Helpernodes": [ - [ - "BETACrop", - "BETAStitch", - "IndexedLoRALoader_BETA", - "LoadTextFromIndex", - "SaveAudioAdvanced_BETA", - "SelectSharpestFrames", - "SharpestFrameClipper", - "TextLineCount", - "WANResolutionCalculator" - ], - { - "title_aux": "ComfyUI-BETA-Helpernodes" - } - ], - "https://github.com/Burgstall-labs/ComfyUI-BS-Textchop": [ - [ - "BSTextChop" - ], - { - "title_aux": "ComfyUI-BS-Textchop" - } - ], - "https://github.com/Burgstall-labs/ComfyUI-BS_Kokoro-onnx": [ - [ - "Kokoro TTS" - ], - { - "title_aux": "ComfyUI-BS_Kokoro-onnx" - } - ], - "https://github.com/CC-BryanOttho/ComfyUI_API_Manager": [ - [ - "APIRequestNode", - "PostImageToAPI", - "TextPromptCombinerNode" - ], - { - "title_aux": "ComfyUI_API_Manager" - } - ], - "https://github.com/CC-SUN6/ccsun_node": [ - [ - "Image Editing", - "Single Image", - "resize to 8", - "several images" - ], - { - "title_aux": "ccsun_node" - } - ], - "https://github.com/CHAOSEA/ComfyUI_FaceAlignPaste": [ - [ - "FaceAlignDouble", - "FaceAlignSingle", - "FaceAutoFitSingle" - ], - { - "title_aux": "ComfyUI_FaceAlignPaste" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-FramePack-HY": [ - [ - "CreateKeyframes_HY", - "FramePackBucketResize_HY", - "FramePackDiffusersSampler_HY", - "LoadFramePackDiffusersPipeline_HY" - ], - { - "title_aux": "ComfyUI-FramePack-HY" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-Free-GPU": [ - [ - "FreeGPUMemory" - ], - { - "title_aux": "ComfyUI-Free-GPU" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-GPT-API": [ - [ - "GPT-ImageGenerator" - ], - { - "title_aux": "ComfyUI-GPT-API" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-Gemini-API": [ - [ - "Google-Gemini" - ], - { - "title_aux": "ComfyUI-Gemini-API" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-ImageCompositionCy": [ - [ - "CombineImageAlpha", - "ImageCompositor", - "LoadImageAlpha" - ], - { - "title_aux": "ComfyUI-ImageCompositionCy" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-InpaintEasy": [ - [ - "CropByMask", - "ImageAndMaskResizeNode", - "ImageCropMerge", - "InpaintEasyModel" - ], - { - "title_aux": "ComfyUI-InpaintEasy" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-Janus-Pro": [ - [ - "JanusImageGeneration", - "JanusImageUnderstanding", - "JanusModelLoader" - ], - { - "title_aux": "ComfyUI-Janus-Pro" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-MiniCPM-Plus": [ - [ - "MiniCPM3_4B", - "MiniCPM3_4B_GPTQ_Int4", - "MiniCPM_V_2_6", - "MiniCPM_V_2_6_Int4", - "TextDisplay" - ], - { - "author": "CY-CHENYUE", - "description": "Custom nodes for MiniCPM language models in ComfyUI", - "nickname": "MiniCPM-Plus", - "title": "MiniCPM-Plus", - "title_aux": "ComfyUI-MiniCPM-Plus" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-MiniCPM-o": [ - [ - "Load MiniCPM Model", - "MiniCPM Image Chat", - "MiniCPMImageAnalyzer" - ], - { - "title_aux": "ComfyUI-MiniCPM-o" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-Molmo": [ - [ - "Molmo7BDbnb" - ], - { - "title_aux": "ComfyUI-Molmo" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-OmniGenX": [ - [ - "LoadOmniGen" - ], - { - "title_aux": "ComfyUI-OmniGenX" - } - ], - "https://github.com/CY-CHENYUE/ComfyUI-Redux-Prompt": [ - [ - "ReduxPromptStyler" - ], - { - "title_aux": "ComfyUI-Redux-Prompt" - } - ], - "https://github.com/CYBERLOOM-INC/ComfyUI-nodes-hnmr": [ - [ - "CLIPIter", - "Dict2Model", - "GridImage", - "ImageBlend2", - "KSamplerOverrided", - "KSamplerSetting", - "KSamplerXYZ", - "LatentToHist", - "LatentToImage", - "ModelIter", - "RandomLatentImage", - "SaveStateDict", - "SaveText", - "StateDictLoader", - "StateDictMerger", - "StateDictMergerBlockWeighted", - "StateDictMergerBlockWeightedMulti", - "VAEDecodeBatched", - "VAEEncodeBatched", - "VAEIter" - ], - { - "title_aux": "ComfyUI-nodes-hnmr" - } - ], - "https://github.com/CallMe1101/ComfyUI_OmniAvatar": [ - [ - "OmniAvatar All-in-One (14B)" - ], - { - "title_aux": "ComfyUI_OmniAvatar" - } - ], - "https://github.com/Chan-0312/ComfyUI-EasyDeforum": [ - [ - "Easy2DDeforum" - ], - { - "title_aux": "ComfyUI-EasyDeforum" - } - ], - "https://github.com/Chan-0312/ComfyUI-IPAnimate": [ - [ - "IPAdapterAnimate" - ], - { - "title_aux": "ComfyUI-IPAnimate" - } - ], - "https://github.com/Chan-0312/ComfyUI-Prompt-Preview": [ - [ - "SDXLPromptStylerAdvancedPreview", - "SDXLPromptStylerPreview" - ], - { - "title_aux": "ComfyUI-Prompt-Preview" - } - ], - "https://github.com/Chaoses-Ib/ComfyUI_Ib_CustomNodes": [ - [ - "ImageToPIL", - "LoadImageFromPath", - "PILToImage", - "PILToMask" - ], - { - "title_aux": "ComfyUI_Ib_CustomNodes" - } - ], - "https://github.com/Charlweed/image_transceiver": [ - [ - "ImageTransceiver" - ], - { - "title_aux": "ImageTransceiver - ComfyUI" - } - ], - "https://github.com/Charonartist/comfyui-auto-lora-v2": [ - [ - "AutoLoRANode", - "LoRABrowserNode", - "LoRAManagerNode" - ], - { - "title_aux": "ComfyUI Auto LoRA" - } - ], - "https://github.com/Charonartist/comfyui-last-frame-extractor": [ - [ - "LastFrameExtractorNode" - ], - { - "title_aux": "comfyui-last-frame-extractor" - } - ], - "https://github.com/Charonartist/comfyui-smart-resize-node": [ - [ - "SmartResizeNode" - ], - { - "title_aux": "ComfyUI Smart Resize Node" - } - ], - "https://github.com/Charonartist/comfyui-tag-remover": [ - [ - "TagRemoverNode" - ], - { - "title_aux": "ComfyUI Tag Remover" - } - ], - "https://github.com/CheNing233/ComfyUI_Image_Pin": [ - [ - "ImagePin" - ], - { - "title_aux": "ComfyUI_Image_Pin" - } - ], - "https://github.com/ChenDarYen/ComfyUI-NAG": [ - [ - "KSamplerWithNAG", - "KSamplerWithNAG (Advanced)", - "NAGCFGGuider", - "NAGGuider", - "SamplerCustomWithNAG" - ], - { - "title_aux": "ComfyUI-NAG" - } - ], - "https://github.com/ChenDarYen/ComfyUI-TimestepShiftModel": [ - [ - "Timestep Shift Model" - ], - { - "title_aux": "ComfyUI-TimestepShiftModel" - } - ], - "https://github.com/Chengym2023/ComfyUI-DeepSeek_Online": [ - [ - "DeepSeekOnline", - "SiliconCloud" - ], - { - "title_aux": "ComfyUI-DeepSeek_Online" - } - ], - "https://github.com/ChrisColeTech/ComfyUI-Elegant-Resource-Monitor": [ - [ - "Resource Monitor" - ], - { - "title_aux": "ComfyUI-Elegant-Resource-Monitor" - } - ], - "https://github.com/ChrisColeTech/ComfyUI-Line-counter": [ - [ - "Directory File Counter", - "Simple Number Counter", - "Text File Line Counter", - "Text File Line Reader" - ], - { - "title_aux": "ComfyUI-Line-counter" - } - ], - "https://github.com/Chrisvenator/ComfyUI-Painting-by-colors-generator": [ - [ - "EnhancedPaintByNumbersNode", - "HexStackNode", - "NumbersOverlayAdvancedNode", - "NumbersOverlayNode", - "PaintByNumbersNode", - "PaintByNumbersTemplateNode" - ], - { - "title_aux": "painting-by-colors-generator" - } - ], - "https://github.com/ClownsharkBatwing/RES4LYF": [ - [ - "AdvancedNoise", - "Base64ToConditioning", - "CLIPTextEncodeFluxUnguided", - "ClownModelLoader", - "ClownRegionalConditioning", - "ClownRegionalConditioning2", - "ClownRegionalConditioning3", - "ClownRegionalConditioning_AB", - "ClownRegionalConditioning_ABC", - "ClownRegionalConditionings", - "ClownScheduler", - "ClownpileModelWanVideo", - "Conditioning Recast FP64", - "ConditioningAdd", - "ConditioningAverageScheduler", - "ConditioningBatch4", - "ConditioningBatch8", - "ConditioningDownsample (T5)", - "ConditioningMultiply", - "ConditioningOrthoCollin", - "ConditioningToBase64", - "ConditioningTruncate", - "ConditioningZeroAndTruncate", - "Constant Scheduler", - "CrossAttn_EraseReplace_HiDream", - "EmptyLatentImage64", - "EmptyLatentImageCustom", - "Film Grain", - "FluxGuidanceDisable", - "FluxLoader", - "FluxOrthoCFGPatcher", - "Frame Select", - "Frame Select Latent", - "Frame Select Latent Raw", - "Frames Concat", - "Frames Concat Latent", - "Frames Concat Latent Raw", - "Frames Concat Masks", - "Frames Latent ReverseOrder", - "Frames Masks Uninterpolate", - "Frames Masks ZeroOut", - "Frames Slice", - "Frames Slice Latent", - "Frames Slice Latent Raw", - "Frequency Separation Hard Light", - "Frequency Separation Hard Light LAB", - "Frequency Separation Linear Light", - "Image Channels LAB", - "Image Crop Location Exact", - "Image Gaussian Blur", - "Image Get Color Swatches", - "Image Grain Add", - "Image Median Blur", - "Image Pair Split", - "Image Repeat Tile To Size", - "Image Sharpen FS", - "Latent Batcher", - "Latent Channels From To", - "Latent Clear State Info", - "Latent Display State Info", - "Latent Get Channel Means", - "Latent Match Channelwise", - "Latent Normalize Channels", - "Latent Replace State Info", - "Latent Transfer State Info", - "Latent TrimVideo State Info", - "Latent to Cuda", - "Latent to RawX", - "LatentBatch_channels", - "LatentBatch_channels_16", - "LatentNoiseBatch_fractal", - "LatentNoiseBatch_gaussian", - "LatentNoiseBatch_gaussian_channels", - "LatentNoiseBatch_perlin", - "LatentNoiseList", - "LatentNoised", - "LatentPhaseMagnitude", - "LatentPhaseMagnitudeMultiply", - "LatentPhaseMagnitudeOffset", - "LatentPhaseMagnitudePower", - "LatentUpscaleWithVAE", - "LayerPatcher", - "Linear Quadratic Advanced", - "Mask Bounding Box Aspect Ratio", - "Mask Sketch", - "MaskEdge", - "MaskEdgeRatio", - "MaskFloatToBoolean", - "MaskToggle", - "Masks From Color Swatches", - "Masks From Colors", - "Masks Unpack 16", - "Masks Unpack 4", - "Masks Unpack 8", - "ModelSamplingAdvanced", - "ModelSamplingAdvancedResolution", - "ModelTimestepPatcher", - "PrepForUnsampling", - "ReAuraPatcher", - "ReAuraPatcherAdvanced", - "ReChromaPatcher", - "ReChromaPatcherAdvanced", - "ReFluxPatcher", - "ReFluxPatcherAdvanced", - "ReHiDreamPatcher", - "ReHiDreamPatcherAdvanced", - "ReLTXVPatcher", - "ReLTXVPatcherAdvanced", - "ReReduxPatcher", - "ReSD35Patcher", - "ReSD35PatcherAdvanced", - "ReSDPatcher", - "ReWanPatcher", - "ReWanPatcherAdvanced", - "SD35Loader", - "SeedGenerator", - "Set Precision", - "Set Precision Advanced", - "Set Precision Universal", - "SetImageSize", - "SetImageSizeWithScale", - "Sigmas Abs", - "Sigmas AdaptiveNoiseFloor", - "Sigmas AdaptiveStep", - "Sigmas Add", - "Sigmas Append", - "Sigmas ArcCosine", - "Sigmas ArcSine", - "Sigmas ArcTangent", - "Sigmas Attractor", - "Sigmas CNFInverse", - "Sigmas CatmullRom", - "Sigmas Chaos", - "Sigmas Cleanup", - "Sigmas CollatzIteration", - "Sigmas Concat", - "Sigmas ConwaySequence", - "Sigmas Count", - "Sigmas CrossProduct", - "Sigmas DeleteBelowFloor", - "Sigmas DeleteDuplicates", - "Sigmas DotProduct", - "Sigmas Easing", - "Sigmas Fmod", - "Sigmas Frac", - "Sigmas From Text", - "Sigmas GammaBeta", - "Sigmas Gaussian", - "Sigmas GaussianCDF", - "Sigmas GilbreathSequence", - "Sigmas HarmonicDecay", - "Sigmas Hyperbolic", - "Sigmas If", - "Sigmas InvLerp", - "Sigmas Iteration Karras", - "Sigmas Iteration Polyexp", - "Sigmas KernelSmooth", - "Sigmas LambertW", - "Sigmas LangevinDynamics", - "Sigmas Lerp", - "Sigmas LinearSine", - "Sigmas Logarithm2", - "Sigmas Math1", - "Sigmas Math3", - "Sigmas Modulus", - "Sigmas Mult", - "Sigmas Noise Inversion", - "Sigmas NormalizingFlows", - "Sigmas Pad", - "Sigmas Percentile", - "Sigmas PersistentHomology", - "Sigmas Power", - "Sigmas QuantileNorm", - "Sigmas Quotient", - "Sigmas ReactionDiffusion", - "Sigmas Recast", - "Sigmas Resample", - "Sigmas Rescale", - "Sigmas RiemannianFlow", - "Sigmas SetFloor", - "Sigmas Sigmoid", - "Sigmas SmoothStep", - "Sigmas Split", - "Sigmas Split Value", - "Sigmas SquareRoot", - "Sigmas Start", - "Sigmas StepwiseMultirate", - "Sigmas TimeStep", - "Sigmas Truncate", - "Sigmas Unpad", - "Sigmas Variance Floor", - "Sigmas ZetaEta", - "Sigmas2 Add", - "Sigmas2 Mult", - "SigmasPreview", - "SigmasSchedulePreview", - "StableCascade_StageB_Conditioning64", - "StableCascade_StageC_VAEEncode_Exact", - "StyleModelApplyStyle", - "Tan Scheduler", - "Tan Scheduler 2", - "Tan Scheduler 2 Simple", - "TemporalCrossAttnMask", - "TemporalMaskGenerator", - "TemporalSplitAttnMask", - "TemporalSplitAttnMask (Midframe)", - "TextBox1", - "TextBox2", - "TextBox3", - "TextBoxConcatenate", - "TextConcatenate", - "TextLoadFile", - "TextShuffle", - "TextShuffleAndTruncate", - "TextTruncateTokens", - "TorchCompileModelAura", - "TorchCompileModelFluxAdv", - "TorchCompileModelSD35", - "TorchCompileModels", - "UNetSave", - "VAEEncodeAdvanced", - "VAEStyleTransferLatent" - ], - { - "title_aux": "RES4LYF" - } - ], - "https://github.com/Clybius/ComfyUI-ClybsChromaNodes": [ - [ - "ClybGuidance", - "InverseSquaredScheduler", - "PrintSigmas", - "SamplerClyb_BDF" - ], - { - "title_aux": "ComfyUI-ClybsChromaNodes" - } - ], - "https://github.com/Clybius/ComfyUI-Extra-Samplers": [ - [ - "GeometricCFGGuider", - "ImageAssistedCFGGuider", - "MegaCFGGuider", - "SamplerCLYB_4M_SDE_Momentumized", - "SamplerCustomModelMixtureDuo", - "SamplerCustomNoise", - "SamplerCustomNoiseDuo", - "SamplerDPMPP_3M_SDE_DynETA", - "SamplerDPMPP_DualSDE_Momentumized", - "SamplerEulerAncestralDancing_Experimental", - "SamplerLCMCustom", - "SamplerRES_Momentumized", - "SamplerSupreme", - "SamplerTTM", - "ScaledCFGGuider", - "SimpleExponentialScheduler", - "WarmupDecayCFGGuider" - ], - { - "title_aux": "ComfyUI Extra Samplers" - } - ], - "https://github.com/Clybius/ComfyUI-Latent-Modifiers": [ - [ - "Latent Diffusion Mega Modifier" - ], - { - "title_aux": "ComfyUI-Latent-Modifiers" - } - ], - "https://github.com/CoiiChan/ComfyUI-Depth-Visualization-Advanced": [ - [ - "DepthViewerAndQuilts" - ], - { - "title_aux": "ComfyUI-Depth-Visualization-advanced" - } - ], - "https://github.com/CoiiChan/ComfyUI-FuncAsTexture-CoiiNode": [ - [ - "Add", - "Ceil", - "Chroma_Key_Alpha", - "Clamp", - "Contant3Vector", - "CustomScriptNumpy", - "DDX", - "Desaturation", - "Distance", - "Divided", - "Dot", - "HueShift", - "InverseUVMapGenerator", - "Lerp", - "Max", - "Min", - "Multiply", - "Oneminus", - "Outline", - "Panner", - "Power", - "Rotator", - "Sine", - "Subtraction", - "TextureSampler", - "UVCoordinateGen", - "ifFunction" - ], - { - "title_aux": "ComfyUI-FuncAsTexture-CoiiNode" - } - ], - "https://github.com/Comfy-Org/NIMnodes": [ - [ - "Get_HFToken", - "InstallNIMNode", - "LoadNIMNode", - "NIMFLUXNode" - ], - { - "title_aux": "NVIDIA FLUX NIM" - } - ], - "https://github.com/ComfyAssets/ComfyUI-KikoStats": [ - [ - "ResourceMonitor" - ], - { - "title_aux": "ComfyUI-KikoStats" - } - ], - "https://github.com/ComfyAssets/ComfyUI-KikoTools": [ - [ - "BatchPrompts", - "DisplayAny", - "DisplayText", - "EmptyLatentBatch", - "FluxSamplerParams", - "GeminiPrompt", - "GlifConsistencyDecoder", - "GlifPatchConsistencyDecoderTiled", - "GlifVariable", - "HFHubEmbeddingLoader", - "HFHubLoraLoader", - "ImageScaleDownBy", - "ImageToMultipleOf", - "KikoFilmGrain", - "KikoLocalImageLoader", - "KikoPurgeVRAM", - "KikoSaveImage", - "LoRAFolderBatch", - "PlotParameters+", - "ResolutionCalculator", - "SDXLAspectRatio", - "SamplerCombo", - "SamplerComboCompact", - "SamplerSelectHelper", - "SchedulerSelectHelper", - "SeedHistory", - "TextEncodeSamplerParams", - "WidthHeightSelector" - ], - { - "title_aux": "ComfyUI-KikoTools" - } - ], - "https://github.com/ComfyAssets/ComfyUI_PromptManager": [ - [ - "PromptManager", - "PromptManagerText" - ], - { - "title_aux": "ComfyUI_PromptManager" - } - ], - "https://github.com/ComfyAssets/ComfyUI_Selectors": [ - [ - "HeightNode", - "SamplerSelector", - "SchedulerSelector", - "SeedHistory", - "WidthHeightNode", - "WidthNode" - ], - { - "title_aux": "ComfyUI_Selectors" - } - ], - "https://github.com/ComfyUI-JH/ComfyUI-JH-Misc-Nodes": [ - [ - "JHDaisyChainableStringConstantNode", - "JHPreviewImage", - "JHThreeWaySwitchNode", - "JHTwoWaySwitchNode" - ], - { - "title_aux": "JH Misc. Nodes" - } - ], - "https://github.com/ComplexRobot/ComfyUI-Simple-VFI": [ - [ - "Simple_Frame_Interpolation" - ], - { - "title_aux": "ComfyUI-Simple-VFI" - } - ], - "https://github.com/Conor-Collins/ComfyUI-CoCoTools_IO": [ - [ - "ColorspaceNode", - "CryptomatteLayer", - "ImageLoader", - "LoadExr", - "LoadExrLayerByName", - "LoadExrSequence", - "SaverNode", - "ZNormalizeNode" - ], - { - "title_aux": "ComfyUI-CoCoTools_IO" - } - ], - "https://github.com/CosmicLaca/ComfyUI_Primere_Nodes": [ - [ - "DebugToFile", - "PrimereAestheticCKPTScorer", - "PrimereAnyDetailer", - "PrimereAnyOutput", - "PrimereCKPT", - "PrimereCKPTLoader", - "PrimereCLIPEncoder", - "PrimereClearNetworkTagsPrompt", - "PrimereConceptDataTuple", - "PrimereDiTPurifyPrompt", - "PrimereDynamicParser", - "PrimereEmbedding", - "PrimereEmbeddingHandler", - "PrimereEmbeddingKeywordMerger", - "PrimereEmotionsStyles", - "PrimereFaceAnalyzer", - "PrimereFastSeed", - "PrimereHypernetwork", - "PrimereImageSegments", - "PrimereImgToPrompt", - "PrimereKSampler", - "PrimereLLMEnhancer", - "PrimereLLMEnhancerOptions", - "PrimereLORA", - "PrimereLYCORIS", - "PrimereLatentNoise", - "PrimereLensStyles", - "PrimereLoraKeywordMerger", - "PrimereLoraStackMerger", - "PrimereLycorisKeywordMerger", - "PrimereLycorisStackMerger", - "PrimereMetaCollector", - "PrimereMetaDistributor", - "PrimereMetaDistributorStage2", - "PrimereMetaHandler", - "PrimereMetaSave", - "PrimereMetaTupleCollector", - "PrimereMidjourneyStyles", - "PrimereModelConceptSelector", - "PrimereModelKeyword", - "PrimereNetworkDataCollector", - "PrimereNetworkTagLoader", - "PrimerePreviewImage", - "PrimerePrompt", - "PrimerePromptOrganizer", - "PrimerePromptOrganizerCSV", - "PrimerePromptSwitch", - "PrimereRefinerPrompt", - "PrimereResolution", - "PrimereResolutionCoordinatorMPX", - "PrimereResolutionMultiplierMPX", - "PrimereSamplersSteps", - "PrimereSeed", - "PrimereStyleLoader", - "PrimereStylePile", - "PrimereTextOutput", - "PrimereUpscaleModel", - "PrimereVAE", - "PrimereVAELoader", - "PrimereVisualCKPT", - "PrimereVisualEmbedding", - "PrimereVisualHypernetwork", - "PrimereVisualLORA", - "PrimereVisualLYCORIS", - "PrimereVisualPromptOrganizerCSV", - "PrimereVisualStyle" - ], - { - "title_aux": "Primere nodes for ComfyUI" - } - ], - "https://github.com/CpreForEver/CFE_comfyui": [ - [ - "CFE Aspect Ratio", - "CFE FLUX Guidance", - "CFE FLUX Sampler", - "CFE FLUX Sampler (Pipe)", - "CFE Flux In Pipe", - "CFE Flux Out Pipe", - "CFE Lora Params", - "CFE Scheduler", - "CFE Sigma Sampler", - "CFE Sigma Sampler Strings" - ], - { - "title_aux": "CFE_comfyui" - } - ], - "https://github.com/Creeper-MZ/comfyui_nai_api": [ - [ - "NovelAI", - "NovelAI_Declutter_Preprocessor", - "NovelAI_Lineart_Processor", - "NovelAI_Prompt", - "NovelAI_Sketch_Processor", - "NovelAI_VIBE" - ], - { - "title_aux": "comfyui_nai_api" - } - ], - "https://github.com/Creepybits/ComfyUI-Creepy_nodes": [ - [ - "AudioKeywordExtractor", - "CLIPSwitch", - "Categorizer", - "CollectAndDistributeText", - "Coloring", - "ConditionalLoRAApplier", - "CustomNodeManager", - "DelayNode", - "DelayTextNode", - "DynamicClipswitch", - "DynamicConditioning", - "DynamicDelayText", - "DynamicImageSwitch", - "DynamicLatentSwitch", - "DynamicModelswitch", - "DynamicVAESwitch", - "EvaluaterNode", - "FilterImages", - "GeminiAPI", - "GeminiAudioAnalyzer", - "GeminiTokenCounter", - "IMGToIMGConditioning", - "KeywordExtractor", - "LoadBatchImagesDir", - "MasterKey", - "Modelswitch", - "PeopleEvaluationNode", - "PromptGenerator", - "RandomAudioSegment", - "SanitizeFilename", - "SummaryWriter", - "SystemPromp", - "Textswitch", - "VAESwitch" - ], - { - "title_aux": "ComfyUI-Creepy_nodes" - } - ], - "https://github.com/Creepybits/ComfyUI-Save_To_GDrive": [ - [ - "SaveImageToGoogleDrive" - ], - { - "title_aux": "Save Image To Google Drive" - } - ], - "https://github.com/Creepybits/ComfyUI-Save_To_OneDrive": [ - [ - "SaveImageToOneDrive_CreepyBits" - ], - { - "title_aux": "Comfyui-Save_To_OneDrive" - } - ], - "https://github.com/Cryptyox/anaglyphTool-Comfyui": [ - [ - "AnaglyphTool", - "CrossEyeTool", - "StereogramTool" - ], - { - "author": "Timon", - "description": "Provides CUDA GPU accelerated nodes for creating 3D images (Anaglyph, Cross-Eye, Stereogram).", - "nickname": "StereoTools", - "title": "Stereo Tools (CUDA)", - "title_aux": "anaglyphTool-Comfyui" - } - ], - "https://github.com/Curt-Park/human-parser-comfyui-node-in-pure-python": [ - [ - "Cozy Human Parser ATR", - "Cozy Human Parser LIP", - "Cozy Human Parser Pascal" - ], - { - "title_aux": "Cozy Human Parser in pure Python" - } - ], - "https://github.com/CyanAutumn/ComfyUi_Random_Manage_Cyan": [ - [ - "Random Prompt Cyan", - "Remove Prompt Cyan" - ], - { - "title_aux": "ComfyUi Random Manage Cyan" - } - ], - "https://github.com/Cyber-BlackCat/ComfyUI-Image-Vector": [ - [ - "Vector" - ], - { - "title_aux": "ComfyUI-Image-Vector" - } - ], - "https://github.com/Cyber-BlackCat/ComfyUI-MoneyMaker": [ - [ - ", and the value is the function name in the right of the", - "Black and white", - "Image Judgment", - "Image Resize MM", - "ImageMinusMask", - "Light or Dark", - "Load Random Images", - "Mask Preprocess Morphology", - "Mask To Gray", - "Number", - "PhotoShop Transfer", - "SomethingShow", - "TensorShow", - "a fake Nod" - ], - { - "title_aux": "ComfyUI-Yuan" - } - ], - "https://github.com/Cyber-BlackCat/ComfyUI_Auto_Caption": [ - [ - "Auto Caption", - "Auto_Caption2", - "ExtraOptionsSet", - "Joy Model load", - "Joy_Model2_load", - "LoadManyImages" - ], - { - "title_aux": "ComfyUI_Auto_Caption" - } - ], - "https://github.com/Cyberschorsch/ComfyUI-checkpoint-config-loader": [ - [ - "Checkpoint Loader Config" - ], - { - "title_aux": "ComfyUI Checkpoint Loader Config" - } - ], - "https://github.com/Cyrostar/Artha-Gemini": [ - [ - "Gemini Backdrop", - "Gemini Body", - "Gemini Camera", - "Gemini Cloth", - "Gemini Compose", - "Gemini Condense", - "Gemini Face", - "Gemini Form", - "Gemini Imagen", - "Gemini Instruct", - "Gemini Light", - "Gemini Makeup", - "Gemini Markdown", - "Gemini Motion", - "Gemini Operation", - "Gemini Portrait", - "Gemini Prompter", - "Gemini Question", - "Gemini Response", - "Gemini Scenery", - "Gemini Speech", - "Gemini Style", - "Gemini Subject", - "Gemini Translate", - "Gemini Vision" - ], - { - "title_aux": "Artha-Gemini" - } - ], - "https://github.com/Cyrostar/Artha-Projekt": [ - [ - "Project Pause", - "Project Prefix", - "Project Seed", - "Project Setup" - ], - { - "title_aux": "Artha-Projekt" - } - ], - "https://github.com/DJ-Tribefull/Comfyui_FOCUS_nodes": [ - [ - "Control Pipe (Focus Nodes)", - "FOCUS Upscale (Focus Nodes)", - "Global Seed Controller (Focus Nodes)", - "KSampler Settings (Focus Nodes)", - "Model Unloader (Focus Nodes)", - "Prompt Box (Focus Nodes)", - "SDXL All-In-One (Focus Nodes)", - "SDXL Control Module (Focus Nodes)", - "SDXL Preprocess (Focus Nodes)", - "Style Injector (Focus Nodes)", - "Style Selector (Focus Nodes)", - "Text Display (Focus Nodes)", - "Wildcard Processor (Focus Nodes)" - ], - { - "title_aux": "Comfyui FOCUS nodes" - } - ], - "https://github.com/Danand/ComfyUI-ComfyCouple": [ - [ - "Attention couple", - "Comfy Couple" - ], - { - "author": "Rei D.", - "description": "If you want to draw two different characters together without blending their features, so you could try to check out this custom node.", - "nickname": "Danand", - "title": "Comfy Couple", - "title_aux": "Comfy Couple" - } - ], - "https://github.com/DanielHabib/ComfyUI-Voxels": [ - [ - "ImageBatchToImageList", - "MaskBatchToMaskList", - "MeshToVoxel", - "VoxelBlockLoader", - "VoxelBlockSaver", - "VoxelBlocksIntoVoxelVideo", - "VoxelVideoAPIInputNode", - "VoxelVideoLoader", - "VoxelVideoPreview", - "VoxelVideoViewer", - "VoxelViewer", - "VoxelizeMesh" - ], - { - "title_aux": "ComfyUI-Voxels" - } - ], - "https://github.com/DareFail/ComfyUI-Roboflow": [ - [ - "CustomWorkflow_1image", - "LabelEmotions", - "RemoveBackground" - ], - { - "title_aux": "ComfyUI-Roboflow" - } - ], - "https://github.com/DarioFT/ComfyUI-VideoDirCombiner": [ - [ - "VideoDirCombiner" - ], - { - "title_aux": "ComfyUI-VideoDirCombiner" - } - ], - "https://github.com/DataCTE/prompt_injection": [ - [ - "AdvancedPromptInjection", - "AdvancedSD15PromptInjection", - "PromptInjection", - "SD15PromptInjection", - "SimplePromptInjection", - "SimpleSD15PromptInjection" - ], - { - "title_aux": "Prompt Injection Node for ComfyUI" - } - ], - "https://github.com/DavidPiazza/network_bending": [ - [ - "AudioFeatureExtractor", - "AudioLatentBlend", - "AudioLatentGuidance", - "AudioLatentInterpolate", - "AudioLatentManipulator", - "AudioReferenceEncoder", - "AudioStyleTransfer", - "AudioVAEDecode", - "AudioVAEEncode", - "LatentFormatConverter", - "ModelMixer", - "NetworkBending", - "NetworkBendingAdvanced", - "VAEChannelManipulator", - "VAELatentBending", - "VAEMixer", - "VAENetworkBending" - ], - { - "title_aux": "Network Bending for ComfyUI" - } - ], - "https://github.com/Daxamur/DaxNodes": [ - [ - "DaxGetStringByIndex", - "DaxStringSplitter", - "FaceFrameDetector", - "RuntimeGenerationLengthSet", - "TrimBatch", - "VideoColorCorrectV3", - "VideoPreview", - "VideoSave", - "VideoSegmentCombinerV2", - "VideoSegmentSaverV2", - "VideoSegmentStateLoader", - "VideoStreamRIFEVFI", - "VideoStreamUpscaler", - "WANResolutionPicker" - ], - { - "title_aux": "DaxNodes" - } - ], - "https://github.com/Dayuppy/ComfyUI-DiscordWebhook": [ - [ - "DiscordPostViaWebhook", - "DiscordSetWebhook", - "Set Discord Webhook", - "Use Discord Webhook" - ], - { - "author": "Dayuppy", - "description": "A very simple Discord webhook integration node for ComfyUI that lets you post images and text.", - "nickname": "DiscordWebhook", - "title": "Discord Webhook", - "title_aux": "Discord Webhook" - } - ], - "https://github.com/De-Zoomer/ComfyUI-DeZoomer-Nodes": [ - [ - "CaptionRefinement", - "VideoCaptioning" - ], - { - "title_aux": "ComfyUI-DeZoomer-Nodes" - } - ], - "https://github.com/DeJoker/pipeline-parallel-comfy": [ - [ - "PipelineParallel" - ], - { - "title_aux": "Pipeline Parallel ComfyUI" - } - ], - "https://github.com/DebugPadawan/DebugPadawans-ComfyUI-Essentials": [ - [ - "DebugPadawan_ConditionalString", - "DebugPadawan_DebugPrint", - "DebugPadawan_ListInfo", - "DebugPadawan_TextJoiner", - "DebugPadawan_TextSplitter", - "DebugPadawan_TextToJSON", - "DebugPadawan_WaitNode" - ], - { - "title_aux": "DebugPadawan's ComfyUI Essentials" - } - ], - "https://github.com/DeemosTech/ComfyUI-Rodin": [ - [ - "LoadRodinAPIKEY", - "Preview_3DMesh", - "PromptForRodin", - "RodinImage3D", - "RodinMultipleImage3D", - "RodinText3D" - ], - { - "title_aux": "ComfyUI-Rodin" - } - ], - "https://github.com/Deep-Neko/ComfyUI_ascii_art": [ - [ - "AsciiGenerator" - ], - { - "author": "DeepNeko ", - "title_aux": "ascii-art-comfyui" - } - ], - "https://github.com/Dehypnotic/comfyui-numbered-text": [ - [ - "NumberedText" - ], - { - "title_aux": "NumberedText" - } - ], - "https://github.com/Dehypnotic/comfyui-range-to-string": [ - [ - "RangeToString" - ], - { - "title_aux": "RangeToString" - } - ], - "https://github.com/DenRakEiw/Latent_Nodes": [ - [ - "LatentColorMatch", - "LatentColorMatchSimple", - "LatentImageAdjust" - ], - { - "title_aux": "ComfyUI Latent Color Tools" - } - ], - "https://github.com/DenRakEiw/WAN_NN_Latent_Upscale": [ - [ - "UniversalNNLatentUpscale" - ], - { - "title_aux": "Universal NN Latent Upscaler for ComfyUI" - } - ], - "https://github.com/Derfuu/Derfuu_ComfyUI_ModdedNodes": [ - [], - { - "author": "Derfuu", - "description": "Pack of simple (or not) and modded nodes for scaling images/latents, editing numbers or text. Automate calculations depending on image sizes or any other thing you want. Or randomize any number in your workflow. Debug node included.", - "nickname": "Derfuu simple/modded Nodes", - "nodename_pattern": "^DF_", - "title": "Derfuu simple/modded Nodes", - "title_aux": "Derfuu_ComfyUI_ModdedNodes" - } - ], - "https://github.com/DesertPixelAi/ComfyUI-DP-Ideogram-Character": [ - [ - "DP_IdeogramCharacter" - ], - { - "title_aux": "ComfyUI DP Ideogram Character Node" - } - ], - "https://github.com/DesertPixelAi/ComfyUI-Desert-Pixel-Nodes": [ - [ - "DP 10 Images Switch Or Batch", - "DP 10 String Switch Or Connect", - "DP 2 String Switch", - "DP 3 Images Switch Or Batch", - "DP 3 String Switch Or Connect", - "DP 5 Find And Replace", - "DP 5 Image And Mask Switch", - "DP 5 Images Switch Or Batch", - "DP 5 String Switch Or Connect", - "DP Add Background To Png", - "DP Add Weight To String Sdxl", - "DP Advanced Sampler", - "DP Advanced Weight String Sdxl", - "DP Animation Calculator 10 Inputs", - "DP Animation Calculator 5 Inputs", - "DP Art Style Generator", - "DP Aspect Ratio Picker", - "DP Big Letters", - "DP Broken Token", - "DP Clean Prompt", - "DP Clean Prompt Travel", - "DP Condition Switch", - "DP ControlNet Apply Advanced", - "DP Crazy Prompt Mixer", - "DP Create Json File", - "DP Custom Aspect Ratio", - "DP Diff Int 8step Selector", - "DP Draggable Floats 1", - "DP Draggable Floats 2", - "DP Draggable Floats 3", - "DP Draggable Int 1step", - "DP Draggable Int 4step", - "DP Draggable Int 8step", - "DP Extract Mask", - "DP Fast Slow Motion", - "DP Five Lora", - "DP Five Lora Random", - "DP Float Stepper", - "DP Get Seed From Image", - "DP IF Int Condition", - "DP Image And String Pairs Switch", - "DP Image Color Analyzer", - "DP Image Color Analyzer Small", - "DP Image Color Effect", - "DP Image Effect Processor", - "DP Image Effect Processor Small", - "DP Image Empty Latent Switch Flux", - "DP Image Empty Latent Switch SDXL", - "DP Image Grid To Image", - "DP Image Slice To Grid", - "DP Image Slide Show", - "DP Image Strip", - "DP Image To Pixelgrid", - "DP Int 0 1000", - "DP Latent Split", - "DP Line Cycler", - "DP Load Checkpoint With Info", - "DP Load Controlnet Model With Name", - "DP Load Dual CLIP With Info", - "DP Load Image Effects", - "DP Load Image Effects Small", - "DP Load Image Folder", - "DP Load Image Minimal", - "DP Load Image V2", - "DP Load Image With Seed", - "DP Load UNET With Info", - "DP Logo Animator", - "DP Lora Random Strength Controller", - "DP Lora Strength Controller", - "DP Mask Settings", - "DP Place Image", - "DP Prompt Inverter", - "DP Prompt Manager Small", - "DP Prompt Mode Controller", - "DP Prompt Styler", - "DP Prompt Token Compressor", - "DP Prompt Travel Prompt", - "DP Quick Model Link", - "DP Random Character", - "DP Random Crazy Prompt Generator", - "DP Random Logo Style Generator", - "DP Random Min Max", - "DP Random Mode Controller", - "DP Random Mode Switch", - "DP Random Psychedelic Punk Generator", - "DP Random Superhero Prompt Generator", - "DP Random Vehicle Generator", - "DP Resize Image And Mask", - "DP Sampler With Info", - "DP Save Image V2", - "DP Save Preview Image", - "DP Stitch 2 Images", - "DP String Text", - "DP String Text With Sdxl Weight", - "DP Strip Edge Masks", - "DP Switch Controller", - "DP Text Preview", - "DP Transition Frames Selector", - "DP Versatile Prompt Subjects Generator", - "DP Video Effect Receiver", - "DP Video Effect Sender", - "DP Video Flicker", - "DP Video Looper", - "DP Video Transition", - "DP Words", - "DP_Crazy_Prompt_Mixer", - "DP_Float_Stepper", - "DP_Image_To_Pixelgrid", - "DP_Prompt_Inverter" - ], - { - "title_aux": "ComfyUI-Desert-Pixel-Nodes" - } - ], - "https://github.com/DesertPixelAi/comfyui-dp-them-styler": [ - [ - "DP_Add_Logo_Banner", - "DP_Advanced_Sampler_Modified", - "DP_Dynamic_Random_Styler", - "DP_Gender_Age_Detector" - ], - { - "title_aux": "ComfyUI DP Dynamic Random Styler" - } - ], - "https://github.com/DiaoDaiaChan/ComfyUI_API_Request": [ - [ - "Character_Prompt_Select", - "NovelAI_Request", - "NovelAI_Request_Payload", - "SDWebUI_Request", - "SDWebUI_Request_Payload", - "SDWebUI_Request_PayloadExtend" - ], - { - "title_aux": "Comfyui SDAPI Request / NovelAI" - } - ], - "https://github.com/DiffusionLight/DiffusionLight-ComfyUI": [ - [ - "DiffusionLightBall2Envmap", - "DiffusionLightChromeballMask", - "DiffusionLightExposure2HDR", - "DiffusionLightExposureBracket", - "DiffusionLightPadBlackBorder", - "DiffusionLightPercentileToPixelValueTonemap", - "DiffusionLightSaveHDR" - ], - { - "title_aux": "DiffusionLight-ComfyUI" - } - ], - "https://github.com/Diohim/ComfyUI-Unusual-Tools": [ - [ - "AdjustCrop", - "AutoImageResize", - "BatchLoadLatentImage", - "BatchSaveLatentImage", - "FillMaskWithColor" - ], - { - "title_aux": "ComfyUI Unusual Tools" - } - ], - "https://github.com/Dobidop/ComfyStereo": [ - [ - "DeoVRViewNode", - "StereoImageNode" - ], - { - "title_aux": "Dobidop ComfyStereo" - } - ], - "https://github.com/DoctorDiffusion/ComfyUI-BEN": [ - [ - "BackgroundEraseNetwork" - ], - { - "title_aux": "ComfyUI BEN - Background Erase Network" - } - ], - "https://github.com/DoctorDiffusion/ComfyUI-MediaMixer": [ - [ - "FinalFrameSelector", - "FirstFrameSelector", - "PromptJournal", - "ReverseFrameSequence", - "VideoMerge", - "YouTubeVideoDownloader" - ], - { - "title_aux": "MediaMixer" - } - ], - "https://github.com/DoctorDiffusion/ComfyUI-Schedulizer": [ - [ - "prompt_schedule_converter", - "whisper_node" - ], - { - "title_aux": "Schedulizer" - } - ], - "https://github.com/DoctorDiffusion/ComfyUI-SnakeOil": [ - [ - "NegativeLoRALoader" - ], - { - "title_aux": "ComfyUI-SnakeOil" - } - ], - "https://github.com/DoctorDiffusion/ComfyUI-basic-pitch": [ - [ - "AudioToMidi", - "SaveMidi" - ], - { - "title_aux": "ComfyUI-basic-pitch" - } - ], - "https://github.com/Dontdrunk/ComfyUI-DD-Nodes": [ - [ - "DD-AdvancedFusion", - "DD-ConditionSwitcher", - "DD-DimensionCalculator", - "DD-ImageSizeLimiter", - "DD-ImageStroke", - "DD-ImageToVideo", - "DD-ImageUniformSize", - "DD-LatentSwitcher", - "DD-MaskUniformSize", - "DD-ModelOptimizer", - "DD-ModelSwitcher", - "DD-QwenMTTranslator", - "DD-SamplingOptimizer", - "DD-SimpleLatent", - "DD-VideoFrameExtractor" - ], - { - "title_aux": "ComfyUI-DD-Nodes" - } - ], - "https://github.com/DrMWeigand/ComfyUI-StereoVision": [ - [ - "AutostereogramGenerator", - "StereoscopicGenerator" - ], - { - "title_aux": "StereoVision Plugin for ComfyUI" - } - ], - "https://github.com/DrMWeigand/ComfyUI_ColorImageDetection": [ - [ - "LABColorDetection", - "RGBColorDetection" - ], - { - "title_aux": "ComfyUI Color Detection Nodes" - } - ], - "https://github.com/DrStone71/ComfyUI-Prompt-Translator": [ - [ - "CLIP Text Encode (Translate)", - "CLIP Text Translate Advanced", - "Combine Conditioning", - "Conditional Translate", - "Language Package Manager", - "Prompt Text (Translate)", - "Text Translate", - "Universal Text Translate" - ], - { - "title_aux": "ComfyUI-Prompt-Translator" - } - ], - "https://github.com/DraconicDragon/ComfyUI-RyuuNoodles": [ - [ - "Ryuu_CleanStringAdvanced", - "Ryuu_ColorMatch", - "Ryuu_ExtractAndSaveLora", - "Ryuu_FallbackPassthrough", - "Ryuu_FallbackSwitchAny", - "Ryuu_FallbackSwitchImage", - "Ryuu_FallbackSwitchLatent", - "Ryuu_FloatPlain", - "Ryuu_FloatPlainLarger", - "Ryuu_FloatSlider", - "Ryuu_IntSlider", - "Ryuu_IsMultipleOf", - "Ryuu_ScaleToMultiple", - "Ryuu_ScaleToMultipleAdvanced", - "Ryuu_ScaleToMultipleLatentSizePicker", - "Ryuu_TestNode", - "Ryuu_TextEncoderDiffCheck", - "Ryuu_TokenCountTextBox" - ], - { - "title_aux": "ComfyUI-RyuuNoodles" - } - ], - "https://github.com/DraconicDragon/ComfyUI-Venice-API": [ - [ - "CharCountTextBox", - "GenerateImage_VENICE", - "GenerateSpeech_VENICE", - "GenerateTextAdvanced_VENICE", - "GenerateTextVeniceParameters_VENICE", - "GenerateText_VENICE", - "I2IEnhanceUpscale_VENICE" - ], - { - "title_aux": "ComfyUI-Venice-API" - } - ], - "https://github.com/DragonDiffusionbyBoyo/BoyoSupercoolWrapper": [ - [ - "BoyoSuperCoolWrapper" - ], - { - "title_aux": "BoyoSupercoolWrapper" - } - ], - "https://github.com/DragonDiffusionbyBoyo/Boyonodes": [ - [ - "BoyoAudioEval", - "BoyoChainBastardLoops", - "BoyoFramePackLoRA", - "BoyoLoadImageList", - "BoyoLoopCollector", - "BoyoLoopImageSaver", - "BoyoPairedSaver", - "BoyoPromptInjector", - "BoyoPromptLoop", - "BoyoSaver", - "BoyoTiledVAEDecode", - "BoyoVAEDecode", - "BoyoWanFunEmptyLatent", - "BoyoWanFunImageSampler", - "Boyolatent", - "MandelbrotVideo" - ], - { - "title_aux": "Boyonodes" - } - ], - "https://github.com/Dream-Pixels-Forge/ComfyUI-Mzikart-Mixer": [ - [ - "ArrangementEnforcer", - "AudioPostProcessor", - "CompressorNode", - "LimiterNode", - "MasteringEffects" - ], - { - "title_aux": "ComfyUI Mzikart Mixer" - } - ], - "https://github.com/Duanyll/duanyll_nodepack": [ - [ - "AdvancedMorphology", - "AsAny", - "BBoxCrop", - "BBoxImageStitcher", - "CoverWordsWithRectangles", - "CreateArkClient", - "CreateBoundingBoxesMaskQwen", - "CreateS3Client", - "DownloadImageFromUrl", - "DrawBBox", - "DrawBBoxMask", - "DrawBoundingBoxesQwen", - "DrawTextInBBox", - "DumpJson", - "ExpandBBoxByRatio", - "FillBBoxWithImage", - "FluxKontextTrue3DPE", - "FluxTextLoraLoader", - "GetTextBBoxWithAnchor", - "HfCheckpointLoader", - "HfClipLoader", - "HfDiffusionModelLoader", - "HfDualClipLoader", - "HfLoraLoader", - "HfLoraLoaderModelOnly", - "HfQuadrupleClipLoader", - "HfTripleClipLoader", - "HfVaeLoader", - "HttpPostForJson", - "ImageCropFromPadded", - "ImageDifferenceCmap", - "ImagePadToResolution", - "InsightFaceSimilarity", - "JsonPathQuery", - "JsonPathQuerySingle", - "JsonPathUpdate", - "LaplacianVariance", - "LogicAnd", - "LogicOr", - "MaskToBBox", - "MergeBBoxes", - "ParseBBoxQwenVL", - "ParseJson5", - "ParseLlmJsonOutput", - "PhotoDoddleConditioning", - "ReadTextFile", - "SeedEditNode", - "UploadImageToS3" - ], - { - "title_aux": "Duanyll Nodepack" - } - ], - "https://github.com/Eagle-CN/ComfyUI-Addoor": [ - [ - "AD_AnyFileList", - "AD_BatchImageLoadFromDir", - "AD_CSVPromptStyler", - "AD_CSVReader", - "AD_CSVTranslator", - "AD_DeleteLocalAny", - "AD_FluxTrainStepMath", - "AD_HFDownload", - "AD_ImageDrawRectangleSimple", - "AD_ImageIndexer", - "AD_ImageSaver", - "AD_LoadImageAdvanced", - "AD_PromptReplace", - "AD_TextIndexer", - "AD_TextListToString", - "AD_TextSaver", - "AD_TxtToCSVCombiner", - "AD_ZipSave", - "AD_advanced-padding", - "AD_color-image", - "AD_image-concat", - "AD_image-resize", - "AD_mockup-maker", - "AD_poster-maker", - "AD_prompt-saver", - "ImageCaptioner", - "ImageResize", - "Incrementer \ud83e\udeb4", - "TextAppendNode", - "Width and height for scaling image to ideal resolution \ud83e\udeb4", - "Width and height from aspect ratio \ud83e\udeb4", - "YANC.MultilineString", - "comfyui-easy-padding", - "image concat mask" - ], - { - "author": "ComfyUI Addoor", - "description": "Save prompts to CSV file with customizable naming pattern", - "title": "ComfyUI-PromptSaver", - "title_aux": "ComfyUI-Addoor" - } - ], - "https://github.com/Easymode-ai/ComfyUI-BPT": [ - [ - "TrimeshBPT", - "TrimeshLoad", - "TrimeshPreview", - "TrimeshSave" - ], - { - "title_aux": "ComfyUI-BPT" - } - ], - "https://github.com/Easymode-ai/ComfyUI-ShadowR": [ - [ - "ShadowRModelLoader", - "ShadowRShadowRemover" - ], - { - "title_aux": "ComfyUI-ShadowR" - } - ], - "https://github.com/EeroHeikkinen/ComfyUI-eesahesNodes": [ - [ - "InstantX Flux Union ControlNet Loader" - ], - { - "author": "eesahe", - "description": "InstantX's Flux union ControlNet loader and implementation", - "nickname": "eesahesNodes", - "title": "eesahe's Nodes", - "title_aux": "ComfyUI-eesahesNodes" - } - ], - "https://github.com/Elaine-chennn/comfyui-overlay-media": [ - [ - "OverlayMediaNode", - "VideoUpload" - ], - { - "title_aux": "ComfyUI Overlay Media Node" - } - ], - "https://github.com/Electrofried/ComfyUI-OpenAINode": [ - [ - "OpenAINode" - ], - { - "title_aux": "OpenAINode" - } - ], - "https://github.com/EllangoK/ComfyUI-post-processing-nodes": [ - [ - "ArithmeticBlend", - "AsciiArt", - "Blend", - "Blur", - "CannyEdgeMask", - "ChromaticAberration", - "ColorCorrect", - "ColorTint", - "Dissolve", - "Dither", - "DodgeAndBurn", - "FilmGrain", - "Glow", - "HSVThresholdMask", - "KMeansQuantize", - "KuwaharaBlur", - "Parabolize", - "PencilSketch", - "PixelSort", - "Pixelize", - "Quantize", - "Sharpen", - "SineWave", - "Solarize", - "Vignette" - ], - { - "title_aux": "ComfyUI-post-processing-nodes" - } - ], - "https://github.com/EmAySee/ComfyUI_EmAySee_CustomNodes": [ - [ - "EmAySee_AnyPassthrough", - "EmAySee_CheckboxFloatNode", - "EmAySee_DateTimeStringNode", - "EmAySee_DynamicStringSelectorNode", - "EmAySee_GreaterThanNode", - "EmAySee_HostPinger", - "EmAySee_ImagePassthrough", - "EmAySee_IntegerStringSelectorNode", - "EmAySee_IntegerStringSelectorNodeDynamic", - "EmAySee_MultiplierNode", - "EmAySee_ProbabilityStringSelectorNode", - "EmAySee_RandomIntFromList", - "EmAySee_RandomIntegerFromListNode", - "EmAySee_RandomIntegerFromTogglesNode_PremadeLabels", - "EmAySee_RandomStringSelectorNode", - "EmAySee_RandomStringSelectorNodeFourChoice", - "EmAySee_RandomStringSelectorNodeThreeChoice", - "EmAySee_RemoveDuplicateCSV", - "EmAySee_RepaintKSampler", - "EmAySee_SaveImage", - "EmAySee_SaveTextToFile", - "EmAySee_StringPoseSelectorNode", - "EmAySee_StringTupleInputNode", - "EmAySee_SubmitToOobaboogaAPI", - "EmAySee_SubmitToOobaboogaAPIWithKey", - "EmAySee_ToggleIntNode", - "EmAySee_VarTextReplacer", - "EmAySee_VeryUniqueStringSelectorNode" - ], - { - "title_aux": "ComfyUI_EmAySee_CustomNodes" - } - ], - "https://github.com/EnragedAntelope/ComfyUI-ConstrainResolution": [ - [ - "ConstrainResolution" - ], - { - "title_aux": "ComfyUI-ConstrainResolution" - } - ], - "https://github.com/EnragedAntelope/ComfyUI-Doubutsu-Describer": [ - [ - "DoubutsuDescriber" - ], - { - "title_aux": "ComfyUI-Doubutsu-Describer" - } - ], - "https://github.com/EnragedAntelope/ComfyUI-EACloudNodes": [ - [ - "GroqNode", - "OpenRouterModels", - "OpenrouterNode" - ], - { - "title_aux": "ComfyUI-EACloudNodes" - } - ], - "https://github.com/EnragedAntelope/comfyui-relight": [ - [ - "ReLight" - ], - { - "title_aux": "ComfyUI-ReLight" - } - ], - "https://github.com/Erehr/ComfyUI-EreNodes": [ - [ - "ErePromptCloud", - "ErePromptFilter", - "ErePromptGallery", - "ErePromptLoraStack", - "ErePromptMultiSelect", - "ErePromptMultiline", - "ErePromptRandomizer", - "ErePromptToggle" - ], - { - "title_aux": "ComfyUI-EreNodes" - } - ], - "https://github.com/EvilBT/ComfyUI_SLK_joy_caption_two": [ - [ - "Batch_joy_caption_two", - "Batch_joy_caption_two_advanced", - "Joy_caption_two", - "Joy_caption_two_advanced", - "Joy_caption_two_load", - "Joy_extra_options" - ], - { - "title_aux": "JoyCaptionAlpha Two for ComfyUI" - } - ], - "https://github.com/Excidos/ComfyUI-Documents": [ - [ - "ChunkRouter", - "DocumentLoader", - "ImageSelector", - "PDFPageSplitter", - "PDFToImage", - "TextChunker" - ], - { - "title_aux": "ComfyUI-Documents" - } - ], - "https://github.com/Excidos/ComfyUI-Lumina-Next-SFT-DiffusersWrapper": [ - [ - "LuminaDiffusersNode" - ], - { - "title_aux": "ComfyUI-Lumina-Next-SFT-DiffusersWrapper" - } - ], - "https://github.com/ExoticArts/comfyui-ea-nodes": [ - [ - "EA_AutoTrimPingPong", - "EA_FilenameCombine", - "EA_LightningMotionBias", - "EA_ListVideos", - "EA_ManifestIndex", - "EA_PingPong", - "EA_PowerLora", - "EA_PowerLora_CLIP", - "EA_PowerLora_WanVideo", - "EA_TrimFrames", - "EA_VideoLoad" - ], - { - "title_aux": "comfyui-ea-nodes" - } - ], - "https://github.com/ExponentialML/ComfyUI_ModelScopeT2V": [ - [ - "ModelScopeT2VLoader" - ], - { - "title_aux": "ComfyUI_ModelScopeT2V" - } - ], - "https://github.com/ExponentialML/ComfyUI_Native_DynamiCrafter": [ - [ - "DynamiCrafterLoader", - "DynamiCrafterProcessor" - ], - { - "title_aux": "ComfyUI - Native DynamiCrafter" - } - ], - "https://github.com/ExponentialML/ComfyUI_VisualStylePrompting": [ - [ - "ApplyVisualStyle" - ], - { - "title_aux": "ComfyUI_VisualStylePrompting" - } - ], - "https://github.com/ExterminanzHS/Gecco-Discord-Autosend": [ - [ - "GeccoAutosend", - "GeccoImageSave", - "GeccoSelectchannel" - ], - { - "title_aux": "Gecco Discord Autosend" - } - ], - "https://github.com/Extraltodeus/ComfyUI-AutomaticCFG": [ - [ - "Automatic CFG", - "Automatic CFG - Advanced", - "Automatic CFG - Attention modifiers", - "Automatic CFG - Attention modifiers tester", - "Automatic CFG - Custom attentions", - "Automatic CFG - Excellent attention", - "Automatic CFG - Negative", - "Automatic CFG - Post rescale only", - "Automatic CFG - Preset Loader", - "Automatic CFG - Unpatch function", - "Automatic CFG - Warp Drive", - "SAG delayed activation", - "Temperature separate settings CLIP SDXL", - "Temperature settings CLIP", - "Temperature settings SD 1.5", - "Temperature settings SDXL", - "Zero Uncond CFG - standalone patch (incompatible with the others)" - ], - { - "title_aux": "ComfyUI-AutomaticCFG" - } - ], - "https://github.com/Extraltodeus/DistanceSampler": [ - [ - "SamplerDistance", - "SamplerDistanceAdvanced" - ], - { - "title_aux": "DistanceSampler" - } - ], - "https://github.com/Extraltodeus/LoadLoraWithTags": [ - [ - "LoraLoaderTagsQuery" - ], - { - "title_aux": "LoadLoraWithTags" - } - ], - "https://github.com/Extraltodeus/Negative-attention-for-ComfyUI-": [ - [ - "Negative cross attention", - "Negative cross attention concatenate" - ], - { - "title_aux": "Negative-attention-for-ComfyUI-" - } - ], - "https://github.com/Extraltodeus/Skimmed_CFG": [ - [ - "Skimmed CFG", - "Skimmed CFG - Clean Skim", - "Skimmed CFG - Difference CFG", - "Skimmed CFG - Timed flip", - "Skimmed CFG - linear interpolation", - "Skimmed CFG - linear interpolation dual scales", - "Skimmed CFG - replace" - ], - { - "title_aux": "Skimmed_CFG" - } - ], - "https://github.com/Extraltodeus/Stable-Diffusion-temperature-settings": [ - [ - "CLIP Temperature", - "Unet Temperature" - ], - { - "title_aux": "Stable-Diffusion-temperature-settings" - } - ], - "https://github.com/Extraltodeus/Uncond-Zero-for-ComfyUI": [ - [ - "Conditioning combine positive and negative", - "Conditioning crop or fill", - "Uncond Zero", - "interrupt on NaN" - ], - { - "title_aux": "Uncond-Zero-for-ComfyUI" - } - ], - "https://github.com/Extraltodeus/Vector_Sculptor_ComfyUI": [ - [ - "CLIP Vector Sculptor text encode", - "Conditioning (Average keep magnitude)", - "Conditioning (Slerp)", - "Conditioning SDXL merge clip_g / clip_l", - "Conditioning normalize magnitude to empty" - ], - { - "title_aux": "Vector_Sculptor_ComfyUI" - } - ], - "https://github.com/Extraltodeus/noise_latent_perlinpinpin": [ - [ - "NoisyLatentPerlin", - "NoisyLatentPerlin16ch" - ], - { - "title_aux": "noise latent perlinpinpin" - } - ], - "https://github.com/Extraltodeus/sigmas_tools_and_the_golden_scheduler": [ - [ - "Aligned Scheduler", - "Gaussian Tail Scheduler", - "Get sigmas as float", - "Graph sigmas", - "Manual scheduler", - "Merge many sigmas by average", - "Merge sigmas by average", - "Merge sigmas gradually", - "Multiply sigmas", - "Output min/max sigmas", - "Split and concatenate sigmas", - "The Golden Scheduler" - ], - { - "title_aux": "sigmas_tools_and_the_golden_scheduler" - } - ], - "https://github.com/FaberVS/MultiModel": [ - [ - "ActiveModel", - "DenoiseSelector", - "KSamplerPipe", - "ListSelector", - "ModelParamsPipe", - "MySwitchIndex", - "ParamsPipeUnpack", - "PromptBuilder" - ], - { - "title_aux": "MultiModel" - } - ], - "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation": [ - [ - "AMT VFI", - "CAIN VFI", - "FILM VFI", - "FLAVR VFI", - "GMFSS Fortuna VFI", - "IFRNet VFI", - "IFUnet VFI", - "KSampler Gradually Adding More Denoise (efficient)", - "M2M VFI", - "Make Interpolation State List", - "RIFE VFI", - "STMFNet VFI", - "Sepconv VFI", - "VFI FloatToInt" - ], - { - "title_aux": "ComfyUI Frame Interpolation" - } - ], - "https://github.com/Fannovel16/ComfyUI-MagickWand": [ - [ - "ImageMagick Adaptive Blur", - "ImageMagick Adaptive Resize", - "ImageMagick Adaptive Sharpen", - "ImageMagick Adaptive Threshold", - "ImageMagick Auto Gamma", - "ImageMagick Auto Level", - "ImageMagick Auto Orient", - "ImageMagick Auto Threshold", - "ImageMagick Blue Shift", - "ImageMagick Blur", - "ImageMagick Brightness Contrast", - "ImageMagick Canny", - "ImageMagick Charcoal", - "ImageMagick Chop", - "ImageMagick Clahe", - "ImageMagick Clamp", - "ImageMagick Coalesce", - "ImageMagick Color Decision List", - "ImageMagick Color Matrix", - "ImageMagick Combine", - "ImageMagick Concat", - "ImageMagick Contrast", - "ImageMagick Contrast Stretch", - "ImageMagick Crop", - "ImageMagick Cycle Color Map", - "ImageMagick Decipher", - "ImageMagick Despeckle", - "ImageMagick Distort", - "ImageMagick Edge", - "ImageMagick Emboss", - "ImageMagick Encipher", - "ImageMagick Enhance", - "ImageMagick Equalize", - "ImageMagick Evaluate", - "ImageMagick Extent", - "ImageMagick Flip", - "ImageMagick Flop", - "ImageMagick Forward Fourier Transform", - "ImageMagick Function", - "ImageMagick Gamma", - "ImageMagick Gaussian Blur", - "ImageMagick Hough Lines", - "ImageMagick Implode", - "ImageMagick Kmeans", - "ImageMagick Kuwahara", - "ImageMagick Level", - "ImageMagick Levelize", - "ImageMagick Linear Stretch", - "ImageMagick Liquid Rescale", - "ImageMagick Local Contrast", - "ImageMagick Magnify", - "ImageMagick Mean Shift", - "ImageMagick Merge Layers", - "ImageMagick Mode", - "ImageMagick Modulate", - "ImageMagick Morphology", - "ImageMagick Motion Blur", - "ImageMagick Negate", - "ImageMagick Noise", - "ImageMagick Normalize", - "ImageMagick Oil Paint", - "ImageMagick Ordered Dither", - "ImageMagick Polynomial", - "ImageMagick Posterize", - "ImageMagick Quantize", - "ImageMagick Random Threshold", - "ImageMagick Range Threshold", - "ImageMagick Resample", - "ImageMagick Resize", - "ImageMagick Roll", - "ImageMagick Rotational Blur", - "ImageMagick Sample", - "ImageMagick Scale", - "ImageMagick Selective Blur", - "ImageMagick Sepia Tone", - "ImageMagick Shade", - "ImageMagick Shadow", - "ImageMagick Sharpen", - "ImageMagick Shave", - "ImageMagick Sigmoidal Contrast", - "ImageMagick Sketch", - "ImageMagick Smush", - "ImageMagick Solarize", - "ImageMagick Splice", - "ImageMagick Spread", - "ImageMagick Statistic", - "ImageMagick Swirl", - "ImageMagick Threshold", - "ImageMagick Thumbnail", - "ImageMagick Transform", - "ImageMagick Transform Colorspace", - "ImageMagick Transparentize", - "ImageMagick Transpose", - "ImageMagick Transverse", - "ImageMagick Unsharp Mask", - "ImageMagick Vignette", - "ImageMagick Wave", - "ImageMagick Wavelet Denoise", - "ImageMagick White Balance" - ], - { - "title_aux": "ComfyUI-MagickWand" - } - ], - "https://github.com/Fannovel16/ComfyUI-MotionDiff": [ - [ - "EmptyMotionData", - "ExportSMPLTo3DSoftware", - "Export_SMPLMultipleSubjects_To_3DSoftware", - "Human4D_Img2SMPL", - "Humans4DLoader", - "MotionCLIPTextEncode", - "MotionDataVisualizer", - "MotionDiffLoader", - "MotionDiffSimpleSampler", - "RenderMultipleSubjectsSMPLMesh", - "RenderSMPLMesh", - "Render_OpenPose_From_SMPL_Mesh_Multiple_Subjects", - "SMPLLoader", - "SMPLShapeParameters", - "SaveSMPL", - "SmplifyMotionData", - "SpectreFaceReconLoader", - "SpectreImg2SMPL", - "mgpt_model_loader", - "mgpt_t2m" - ], - { - "title_aux": "ComfyUI MotionDiff" - } - ], - "https://github.com/Fannovel16/ComfyUI-Video-Matting": [ - [ - "BRIAAI Matting", - "Robust Video Matting" - ], - { - "title_aux": "ComfyUI-Video-Matting" - } - ], - "https://github.com/Fannovel16/comfyui_controlnet_aux": [ - [ - "AIO_Preprocessor", - "AnimalPosePreprocessor", - "AnimeFace_SemSegPreprocessor", - "AnimeLineArtPreprocessor", - "AnyLineArtPreprocessor_aux", - "BAE-NormalMapPreprocessor", - "BinaryPreprocessor", - "CannyEdgePreprocessor", - "ColorPreprocessor", - "ControlNetAuxSimpleAddText", - "ControlNetPreprocessorSelector", - "DSINE-NormalMapPreprocessor", - "DWPreprocessor", - "DensePosePreprocessor", - "DepthAnythingPreprocessor", - "DepthAnythingV2Preprocessor", - "DiffusionEdge_Preprocessor", - "ExecuteAllControlNetPreprocessors", - "FacialPartColoringFromPoseKps", - "FakeScribblePreprocessor", - "HEDPreprocessor", - "HintImageEnchance", - "ImageGenResolutionFromImage", - "ImageGenResolutionFromLatent", - "ImageIntensityDetector", - "ImageLuminanceDetector", - "InpaintPreprocessor", - "LeReS-DepthMapPreprocessor", - "LineArtPreprocessor", - "LineartStandardPreprocessor", - "M-LSDPreprocessor", - "Manga2Anime_LineArt_Preprocessor", - "MaskOptFlow", - "MediaPipe-FaceMeshPreprocessor", - "MeshGraphormer+ImpactDetector-DepthMapPreprocessor", - "MeshGraphormer-DepthMapPreprocessor", - "Metric3D-DepthMapPreprocessor", - "Metric3D-NormalMapPreprocessor", - "Metric_DepthAnythingV2Preprocessor", - "MiDaS-DepthMapPreprocessor", - "MiDaS-NormalMapPreprocessor", - "OneFormer-ADE20K-SemSegPreprocessor", - "OneFormer-COCO-SemSegPreprocessor", - "OpenposePreprocessor", - "PiDiNetPreprocessor", - "PixelPerfectResolution", - "PyraCannyPreprocessor", - "RenderAnimalKps", - "RenderPeopleKps", - "SAMPreprocessor", - "SavePoseKpsAsJsonFile", - "ScribblePreprocessor", - "Scribble_PiDiNet_Preprocessor", - "Scribble_XDoG_Preprocessor", - "SemSegPreprocessor", - "ShufflePreprocessor", - "TEEDPreprocessor", - "TTPlanet_TileGF_Preprocessor", - "TTPlanet_TileSimple_Preprocessor", - "TilePreprocessor", - "UniFormer-SemSegPreprocessor", - "Unimatch_OptFlowPreprocessor", - "UpperBodyTrackingFromPoseKps", - "Zoe-DepthMapPreprocessor", - "Zoe_DepthAnythingPreprocessor" - ], - { - "preemptions": [ - "AIO_Preprocessor", - "AnimalPosePreprocessor", - "AnimeFace_SemSegPreprocessor", - "AnimeLineArtPreprocessor", - "BAE-NormalMapPreprocessor", - "BinaryPreprocessor", - "CannyEdgePreprocessor", - "ColorPreprocessor", - "DSINE-NormalMapPreprocessor", - "DWPreprocessor", - "DensePosePreprocessor", - "DepthAnythingPreprocessor", - "DiffusionEdge_Preprocessor", - "FacialPartColoringFromPoseKps", - "FakeScribblePreprocessor", - "HEDPreprocessor", - "HintImageEnchance", - "ImageGenResolutionFromImage", - "ImageGenResolutionFromLatent", - "ImageIntensityDetector", - "ImageLuminanceDetector", - "InpaintPreprocessor", - "LeReS-DepthMapPreprocessor", - "LineArtPreprocessor", - "LineartStandardPreprocessor", - "M-LSDPreprocessor", - "Manga2Anime_LineArt_Preprocessor", - "MaskOptFlow", - "MediaPipe-FaceMeshPreprocessor", - "MeshGraphormer-DepthMapPreprocessor", - "MiDaS-DepthMapPreprocessor", - "MiDaS-NormalMapPreprocessor", - "OneFormer-ADE20K-SemSegPreprocessor", - "OneFormer-COCO-SemSegPreprocessor", - "OpenposePreprocessor", - "PiDiNetPreprocessor", - "PixelPerfectResolution", - "SAMPreprocessor", - "SavePoseKpsAsJsonFile", - "ScribblePreprocessor", - "Scribble_XDoG_Preprocessor", - "SemSegPreprocessor", - "ShufflePreprocessor", - "TEEDPreprocessor", - "TilePreprocessor", - "UniFormer-SemSegPreprocessor", - "Unimatch_OptFlowPreprocessor", - "Zoe-DepthMapPreprocessor", - "Zoe_DepthAnythingPreprocessor" - ], - "title_aux": "ComfyUI's ControlNet Auxiliary Preprocessors" - } - ], - "https://github.com/Fantaxico/ComfyUI-GCP-Storage": [ - [ - "GCPStorageNode" - ], - { - "title_aux": "ComfyUI-GCP-Storage" - } - ], - "https://github.com/FaraamFide/ComfyUI-ParamNodes": [ - [ - "HelperModelSwitch", - "ParamBoolean", - "ParamFloat", - "ParamImage", - "ParamInt", - "ParamString", - "ParamUniversal" - ], - { - "title_aux": "ComfyUI-ParamNodes" - } - ], - "https://github.com/FearL0rd/ComfyUI-MaskAIFingerprint": [ - [ - "MaskAIFingerprint" - ], - { - "title_aux": "ComfyUI MaskAIFingerprint" - } - ], - "https://github.com/Feidorian/feidorian-ComfyNodes": [ - [], - { - "nodename_pattern": "^Feidorian_", - "title_aux": "feidorian-ComfyNodes" - } - ], - "https://github.com/Ferocit/comfyui-feroccustomnodes": [ - [ - "LoadDescriptionNode", - "RandomLineFromText" - ], - { - "title_aux": "comfyui-feroccustomnodes" - } - ], - "https://github.com/FewBox/fewbox-outfit-comfyui": [ - [ - "FewBoxInContextLora", - "FewBoxLab", - "FewBoxSaveImage", - "FewBoxWatermark", - "FewBoxWebDAV" - ], - { - "title_aux": "fewbox-outfit-comfyui" - } - ], - "https://github.com/Fictiverse/ComfyUI_Fictiverse": [ - [ - "Add Margin With Color", - "Any to Int/Float/String", - "Audio Duration", - "Essential Params", - "Get Last Output Video Path", - "If Image Valid", - "Image Params", - "Is Image Valid ?", - "Math Operation", - "None if same Image", - "Prompt Assembler", - "Resize Images To Megapixels", - "Resize To Megapixels", - "Video Params" - ], - { - "title_aux": "ComfyUI Fictiverse Nodes" - } - ], - "https://github.com/Fihade/IC-Light-ComfyUI-Node": [ - [ - "LoadICLightUnetDiffusers", - "diffusers_model_loader", - "iclight_diffusers_sampler" - ], - { - "title_aux": "IC-Light-ComfyUI-Node" - } - ], - "https://github.com/FinetunersAI/ComfyUI_Finetuners_Suite": [ - [ - "AutoImageResize", - "GroupLink", - "ModelListNode", - "VariablesInjector", - "VariablesLogicNode" - ], - { - "title_aux": "ComfyUI_Finetuners_Suite" - } - ], - "https://github.com/Firetheft/ComfyUI_Civitai_Gallery": [ - [ - "CivitaiGalleryNode" - ], - { - "title_aux": "ComfyUI Civitai Gallery" - } - ], - "https://github.com/FizzleDorf/ComfyUI-AIT": [ - [ - "AIT_Unet_Loader", - "AIT_VAE_Encode_Loader" - ], - { - "title_aux": "ComfyUI-AIT" - } - ], - "https://github.com/FizzleDorf/ComfyUI_FizzNodes": [ - [ - "AbsCosWave", - "AbsSinWave", - "BatchGLIGENSchedule", - "BatchPromptSchedule", - "BatchPromptScheduleEncodeSDXL", - "BatchPromptScheduleLatentInput", - "BatchPromptScheduleNodeFlowEnd", - "BatchPromptScheduleSDXLLatentInput", - "BatchStringSchedule", - "BatchValueSchedule", - "BatchValueScheduleLatentInput", - "CalculateFrameOffset", - "ConcatStringSingle", - "CosWave", - "FizzFrame", - "FizzFrameConcatenate", - "ImagesFromBatchSchedule", - "Init FizzFrame", - "InvCosWave", - "InvSinWave", - "Lerp", - "PromptSchedule", - "PromptScheduleEncodeSDXL", - "PromptScheduleNodeFlow", - "PromptScheduleNodeFlowEnd", - "SawtoothWave", - "SinWave", - "SquareWave", - "StringConcatenate", - "StringSchedule", - "TriangleWave", - "ValueSchedule", - "convertKeyframeKeysToBatchKeys" - ], - { - "title_aux": "FizzNodes" - } - ], - "https://github.com/Flow-two/ComfyUI-WanStartEndFramesNative": [ - [ - "GetImagesFromBatchRanged_F2", - "WanImageToVideo_F2", - "WanSkipEndFrameImages_F2" - ], - { - "title_aux": "ComfyUI-WanStartEndFramesNative" - } - ], - "https://github.com/FlyingFireCo/tiled_ksampler": [ - [ - "Asymmetric Tiled KSampler", - "Circular VAEDecode", - "Tiled KSampler" - ], - { - "title_aux": "tiled_ksampler" - } - ], - "https://github.com/ForeignGods/ComfyUI-Mana-Nodes": [ - [ - "Canvas Properties", - "Combine Video", - "Font Properties", - "Generate Audio", - "Preset Color Animations", - "Save/Preview Text", - "Scheduled Values", - "Speech Recognition", - "Split Video", - "Text to Image Generator" - ], - { - "title_aux": "ComfyUI-Mana-Nodes" - } - ], - "https://github.com/FortunaCournot/comfyui_stereoscopic": [ - [ - "DecryptWatermark", - "EncryptWatermark", - "GetResolutionForVR", - "ImageVRConverter", - "SaveStrippedUTF8File", - "ScaleByFactor", - "StripXML" - ], - { - "title_aux": "Stereoscopic" - } - ], - "https://github.com/Franck-Demongin/NX_HuggingFace_Flux": [ - [ - "HFFlux" - ], - { - "title_aux": "NX_HuggingFace_Flux" - } - ], - "https://github.com/Franck-Demongin/NX_PromptStyler": [ - [ - "NX_PromptStyler" - ], - { - "title_aux": "NX_PromptStyler" - } - ], - "https://github.com/Franck-Demongin/NX_Translator": [ - [ - "Nx_Translator" - ], - { - "title_aux": "NX_Translator" - } - ], - "https://github.com/Franklyc/comfyui-lora-adain-patcher-node": [ - [ - "LoraAdaLNPatcher" - ], - { - "title_aux": "ComfyUI LoRA adaLN Patcher Node" - } - ], - "https://github.com/FunnyFinger/ComfyUi-RadarWeightNode": [ - [ - "RadarWeightsNode" - ], - { - "title_aux": "Radar Weights Node" - } - ], - "https://github.com/FuouM/ComfyUI-EbSynth": [ - [ - "ES_Guides7", - "ES_Translate", - "ES_VideoTransfer", - "ES_VideoTransferExtra" - ], - { - "author": "Fuou Marinas", - "description": "Run EbSynth in ComfyUI.", - "nickname": "EbSynth", - "title": "ComfyUI-EbSynth", - "title_aux": "ComfyUI-EbSynth" - } - ], - "https://github.com/FuouM/ComfyUI-FirstOrderMM": [ - [ - "Articulate_Runner", - "FOMM_Partswap", - "FOMM_Runner", - "FOMM_Seg10Chooser", - "FOMM_Seg15Chooser", - "FOMM_Seg5Chooser", - "FSRT_Runner", - "MRFA_Runner", - "Spline_Runner" - ], - { - "author": "Fuou Marinas", - "description": "ComfyUI-native nodes to run First Order Motion Model for Image Animation and its non-diffusion-based successors.", - "nickname": "FOMM", - "title": "ComfyUI-FirstOrderMM", - "title_aux": "ComfyUI-FirstOrderMM" - } - ], - "https://github.com/FuouM/ComfyUI-MatAnyone": [ - [ - "MatAnyone", - "SolidColorBatched" - ], - { - "author": "Fuou Marinas", - "description": "A collection of nodes.", - "nickname": "FM_nodes", - "title": "FM Nodes", - "title_aux": "ComfyUI-MatAnyone" - } - ], - "https://github.com/FuouM/ComfyUI-StyleTransferPlus": [ - [ - "AESFA", - "AesFAStyleBlend", - "AesPA", - "CAST", - "CoralColorTransfer", - "EFDM", - "MicroAST", - "NeuralNeighbor", - "TSSAT", - "UniST", - "UniST_Video" - ], - { - "author": "ZJU", - "description": "A collection of style transfer nodes.", - "nickname": "StyleTransferPlus", - "title": "ComfyUI-StyleTransferPlus", - "title_aux": "ComfyUI-StyleTransferPlus" - } - ], - "https://github.com/FuouM/FM_nodes": [ - [ - "CoLIE_LowLight_Enhance", - "ConvIR_DeHaze", - "ConvIR_DeRain", - "ConvIR_DeSnow", - "ConvIR_DefocusDeblur", - "ConvIR_MotionDeBlur", - "ProPIH_Harmonizer", - "RealViFormerSR", - "StabStitch", - "StabStitch_Crop_Resize", - "StabStitch_Stabilize", - "WFEN" - ], - { - "author": "Fuou Marinas", - "description": "A collection of nodes.", - "nickname": "FM_nodes", - "title": "FM Nodes", - "title_aux": "FM_nodes" - } - ], - "https://github.com/Fuwuffyi/ComfyUI-VisualArea-Nodes": [ - [ - "VisualAreaPrompt", - "VisualAreaPromptAdvanced" - ], - { - "author": "Fuwuffy", - "description": "This is a collection of nodes created to aid when managing area conditionings.", - "nickname": "comfy-visual-area", - "title": "ComfyUI Visual Area Nodes", - "title_aux": "ComfyUI-VisualArea-Nodes" - } - ], - "https://github.com/G-370/ComfyUI-SD3-Powerlab": [ - [ - "G370SD3PowerLab_AttentionToImage", - "G370SD3PowerLab_ImageIntoAttention", - "G370SD3PowerLab_ImageIntoLayer", - "G370SD3PowerLab_LayerToImage", - "G370SD3PowerLab_RenderAttention" - ], - { - "title_aux": "ComfyUI-SD3-Powerlab" - } - ], - "https://github.com/GACLove/ComfyUI-Lightx2vWrapper": [ - [ - "LightX2VConfigCombiner", - "LightX2VInferenceConfig", - "LightX2VLightweightVAE", - "LightX2VLoRALoader", - "LightX2VMemoryOptimization", - "LightX2VModularInference", - "LightX2VQuantization", - "LightX2VTeaCache" - ], - { - "title_aux": "ComfyUI-Lightx2vWrapper" - } - ], - "https://github.com/GACLove/ComfyUI-VFI": [ - [ - "RIFEInterpolation" - ], - { - "title_aux": "ComfyUI-VFI" - } - ], - "https://github.com/GHOSTLXH/ComfyUI-Counternodes": [ - [ - "AlternatingOutput", - "AlternatingOutputB", - "ImageCounter", - "IntervalCounter", - "IntervalCounterB", - "LoadPromptFromTXT" - ], - { - "title_aux": "ComfyUI-Counternodes" - } - ], - "https://github.com/GTSuya-Studio/ComfyUI-Gtsuya-Nodes": [ - [ - "Danbooru (ID)", - "Danbooru (Random)", - "Random File From Path", - "Replace Strings", - "Simple Wildcards", - "Simple Wildcards (Dir.)", - "Wildcards Nodes" - ], - { - "title_aux": "ComfyUI-GTSuya-Nodes" - } - ], - "https://github.com/GadzoinksOfficial/comfyui_gprompts": [ - [ - "GPrompts" - ], - { - "author": "gadzoinksofficial", - "description": "Another dynamic prompt node, designed to be easy to use and support wildcards", - "nickname": "Gprompts", - "title": "Gprompts", - "title_aux": "Gprompts" - } - ], - "https://github.com/GadzoinksOfficial/gadzoinks_ComfyUI": [ - [ - "Gadzoinks" - ], - { - "author": "gadzoinksofficial", - "description": "Custom node for integrating with gadzoinks iPhone app", - "nickname": "Gadzoinks", - "title": "Gadzoinks", - "title_aux": "Gadzoinks" - } - ], - "https://github.com/GamingDaveUk/daves_nodes": [ - [ - "davesTextToList" - ], - { - "title_aux": "Daves Nodes" - } - ], - "https://github.com/Gary-yeh/ComfyUI-WebPrompter": [ - [ - "ContentFetcher (WebPrompter)", - "LLMNewsScriptGenerator (WebPrompter)" - ], - { - "title_aux": "ComfyUI-WebPrompter" - } - ], - "https://github.com/Gary-yeh/comfyui-super-captioner": [ - [ - "SuperCaptioner" - ], - { - "title_aux": "comfyui-super-captioner" - } - ], - "https://github.com/GavChap/ComfyUI-SD3LatentSelectRes": [ - [ - "SD3LatentSelectRes" - ], - { - "title_aux": "ComfyUI-SD3LatentSelectRes" - } - ], - "https://github.com/GeekyGhost/ComfyUI-Geeky-Kokoro-TTS": [ - [ - "GeekyKokoroAdvancedVoice", - "GeekyKokoroTTS" - ], - { - "title_aux": "ComfyUI-Geeky-Kokoro-TTS" - } - ], - "https://github.com/GeekyGhost/ComfyUI-Geeky-LatentSyncWrapper": [ - [ - "GeekyLatentSyncNode", - "GeekyVideoLengthAdjuster" - ], - { - "title_aux": "ComfyUI-Geeky-LatentSyncWrapper 1.5" - } - ], - "https://github.com/GeekyGhost/ComfyUI-GeekyRemB": [ - [ - "GeekyRemB" - ], - { - "title_aux": "ComfyUI-GeekyRemB" - } - ], - "https://github.com/GeekyGhost/ComfyUI-Image-Segmenting-Loader": [ - [ - "GeekyQwenCompositor", - "GeekyQwenEffects", - "GeekyQwenSegmentLoader" - ], - { - "title_aux": "ComfyUI-Image-Segmenting-Loader" - } - ], - "https://github.com/GeekyGhost/ComfyUI_Geeky_AudioMixer": [ - [ - "GeekyAudioMixer" - ], - { - "title_aux": "ComfyUI Geeky AudioMixer" - } - ], - "https://github.com/GentlemanHu/ComfyUI-SunoAI": [ - [ - "GentlemanHu_SunoAI", - "GentlemanHu_SunoAI_NotSafe" - ], - { - "title_aux": "ComfyUI Suno API" - } - ], - "https://github.com/GeraldWie/ComfyUI-I2I-slim": [ - [ - "Color Transfer Slim", - "Combine and Paste Slim", - "Inpaint Segments Slim", - "Mask Ops Slim" - ], - { - "author": "GeraldWie", - "title": "ComfyI2I-lite", - "title_aux": "ComfyUI-I2I-slim" - } - ], - "https://github.com/Gipphe/comfyui-metadata-statistics": [ - [ - "RecordModels" - ], - { - "title_aux": "ComfyUI Metadata Statistics" - } - ], - "https://github.com/GiusTex/ComfyUI-DiffusersImageOutpaint": [ - [ - "DiffusersImageOutpaint", - "EncodeDiffusersOutpaintPrompt", - "LoadDiffuserControlnet", - "LoadDiffuserModel", - "PadImageForDiffusersOutpaint" - ], - { - "title_aux": "ComfyUI-DiffusersImageOutpaint" - } - ], - "https://github.com/Goktug/comfyui-saveimage-plus": [ - [ - "SaveImagePlus" - ], - { - "title_aux": "Save Image Plus for ComfyUI" - } - ], - "https://github.com/Goshe-nite/comfyui-gps-supplements": [ - [ - "KSampler to Image Saver", - "Lora Prompt Concatenation", - "Lora to String", - "Model to String", - "gpsdebugger" - ], - { - "title_aux": "GPS' Supplements for ComfyUI" - } - ], - "https://github.com/Gourieff/ComfyUI-ReActor": [ - [ - "ImageRGBA2RGB", - "ReActorBuildFaceModel", - "ReActorFaceBoost", - "ReActorFaceSwap", - "ReActorFaceSwapOpt", - "ReActorImageDublicator", - "ReActorLoadFaceModel", - "ReActorMakeFaceModelBatch", - "ReActorMaskHelper", - "ReActorOptions", - "ReActorRestoreFace", - "ReActorSaveFaceModel", - "ReActorSetWeight", - "ReActorUnload" - ], - { - "title_aux": "comfyui-reactor-node" - } - ], - "https://github.com/GraftingRayman/ComfyUI-PuLID-Flux-GR": [ - [ - "GRApplyPulidFlux", - "GRPulidFluxEvaClipLoader", - "GRPulidFluxInsightFaceLoader", - "GRPulidFluxModelLoader" - ], - { - "title_aux": "ComfyUI-PuLID-Flux-GR" - } - ], - "https://github.com/GraftingRayman/ComfyUI_GraftingRayman": [ - [ - "GR BLIP 2 Caption Generator", - "GR BLIP 2 Text Expander", - "GR Background Remover REMBG", - "GR Checkered Board", - "GR Counter", - "GR Flip Tile Random Inverted", - "GR Flip Tile Random Red Ring", - "GR Florence 2 Caption Generator", - "GR INT Incremetor", - "GR Image Details Displayer", - "GR Image Details Saver", - "GR Image Multiplier", - "GR Image Paste", - "GR Image Paste With Mask", - "GR Image Resize", - "GR Image Resize Methods", - "GR Image Size", - "GR Image/Depth Mask", - "GR Lora Randomizer", - "GR Mask", - "GR Mask Create", - "GR Mask Create Random", - "GR Mask Create Random Multi", - "GR Mask Resize", - "GR Multi Mask Create", - "GR Onomatopoeia", - "GR Pan Or Zoom", - "GR Prompt Generator", - "GR Prompt Generator Extended", - "GR Prompt HUB", - "GR Prompt Selector", - "GR Prompt Selector Multi", - "GR Prompty", - "GR Scroller", - "GR Sigmas", - "GR Stack Image", - "GR Text Overlay", - "GR Tile and Border Image", - "GR Tile and Border Image Random Flip" - ], - { - "title_aux": "GraftingRayman" - } - ], - "https://github.com/GraftingRayman/ComfyUI_QueueTube": [ - [ - "GR QueueTube" - ], - { - "title_aux": "ComfyUI QueueTube" - } - ], - "https://github.com/GrailGreg/images_base64": [ - [ - "SaveImage64", - "ShowText64" - ], - { - "title_aux": "Image Saving and Base64 Encoding Script" - } - ], - "https://github.com/GreenLandisaLie/AuraSR-ComfyUI": [ - [ - "AuraSR.AuraSRUpscaler" - ], - { - "title_aux": "AuraSR-ComfyUI" - } - ], - "https://github.com/GrenKain/PixelArt-Processing-Nodes-for-ComfyUI": [ - [ - "PixelArtDownscaleNode", - "PixelArtNode" - ], - { - "title_aux": "PixelArt Processing Nodes" - } - ], - "https://github.com/GroxicTinch/EasyUI-ComfyUI": [ - [ - "UINode" - ], - { - "title_aux": "EasyUI" - } - ], - "https://github.com/GrvBdgr/comfyui-negativewildcardsprocessor": [ - [ - "custom_token_processor", - "neg_wildcard_processor" - ], - { - "title_aux": "Negative Wildcard Processor Node for ComfyUI" - } - ], - "https://github.com/Gue-e/ComfyUI-PanoCard": [ - [ - "PanoCardViewer", - "PanoCondAllBatch", - "PanoCondFaceBatch", - "PanoCondFaceClamp", - "PanoCondFaceDetailerHook", - "PanoCondFaceUnPack", - "PanoImage2FaceSplit", - "PanoImageAdjust", - "PanoImageEqu2Equ", - "PanoImageEqu2Face", - "PanoImageEqu2Pic", - "PanoImageFace2Equ", - "PanoImageFaceClamp", - "PanoImageFaceToLong", - "PanoImageHeightPad", - "PanoImagePad", - "PanoImagePic2Equ", - "PanoImageRoll", - "PanoImageUnPack", - "PanoImageWidthPad", - "PanoLongMaskSplit", - "PanoMaskUnPack", - "PanoPipe", - "PanoPromptSplit", - "PanoRegionalPrompt" - ], - { - "title_aux": "ComfyUI-PanoCard" - } - ], - "https://github.com/Guillaume-Fgt/ComfyUI_StableCascadeLatentRatio": [ - [ - "StableCascadeLatentRatio" - ], - { - "title_aux": "ComfyUI_StableCascadeLatentRatio" - } - ], - "https://github.com/HAL41/ComfyUI-aichemy-nodes": [ - [ - "aichemyYOLOv8Segmentation" - ], - { - "title_aux": "ComfyUI aichemy nodes" - } - ], - "https://github.com/HECer/ComfyUI-FilePathCreator": [ - [ - "FilePathCreator", - "FilePathExtractor" - ], - { - "title_aux": "ComfyUI-FilePathCreator" - } - ], - "https://github.com/HJH-AILab/ComfyUI_CosyVoice2": [ - [ - "CosyVoiceModel", - "CosyVoiceNode", - "HJHCosyVoiceSaveAudio" - ], - { - "title_aux": "ComfyUI_CosyVoice2" - } - ], - "https://github.com/HJH-AILab/ComfyUI_Facefusion": [ - [ - "FacefusionFaceEnhancerProcessor", - "FacefusionFaceSwapperProcessor", - "FacefusionFrameEnhancerProcessor", - "FacefusionLipSyncerProcessor", - "FacefusionOptionsNode", - "FacefusionProcesserOptionsNode", - "FacefusionProcessingNode" - ], - { - "title_aux": "ComfyUI_Facefusion" - } - ], - "https://github.com/HJH-AILab/ComfyUI_StableAnimator": [ - [ - "StableAnimatorDWPoseDetectorAlignedModels", - "StableAnimatorLoadFramesFromFolderNode", - "StableAnimatorModels", - "StableAnimatorNode", - "StableAnimatorSkeletonNode" - ], - { - "title_aux": "ComfyUI_StableAnimator" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_APICall": [ - [ - "RH_AudioUploader", - "RH_BatchImages", - "RH_ExecuteNode", - "RH_ExtractImage", - "RH_ImageUploaderNode", - "RH_NodeInfoListNode", - "RH_SettingsNode", - "RH_Utils", - "RH_VideoUploader" - ], - { - "title_aux": "ComfyUI_RH_APICall" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_DMOSpeech2": [ - [ - "RunningHub DMOSpeech2" - ], - { - "title_aux": "ComfyUI DMOSpeech2 Node" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_FramePack": [ - [ - "RunningHub_FramePack", - "RunningHub_FramePack_F1" - ], - { - "title_aux": "ComfyUI_RH_FramePack" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_OminiControl": [ - [ - "RunningHub_Omini_Fill", - "RunningHub_Omini_Spatial", - "RunningHub_Omini_Subject" - ], - { - "title_aux": "ComfyUI_RH_OminiControl" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_Qwen-Image": [ - [ - "QwenImageModelLoader", - "RH_QwenImageGenerator", - "RH_QwenImagePromptEnhancer" - ], - { - "title_aux": "ComfyUI Qwen-Image Node" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_SeedXPro": [ - [ - "RunningHub SeedXPro Translator" - ], - { - "title_aux": "ComfyUI SeedXPro Translation Node" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_Step1XEdit": [ - [ - "RunningHub_Step1XEdit" - ], - { - "title_aux": "ComfyUI_RH_Step1XEdit" - } - ], - "https://github.com/HM-RunningHub/ComfyUI_RH_UNO": [ - [ - "RunningHub_UNO_Loadmodel", - "RunningHub_UNO_Sampler" - ], - { - "title_aux": "ComfyUI_RH_UNO" - } - ], - "https://github.com/HMG-Fiverr/ComfyUI-RandomNumberButton": [ - [ - "RandomNumberButton" - ], - { - "title_aux": "Random Number Button" - } - ], - "https://github.com/HSDHCdev/ComfyUI-AI-Pixel-Art-Enhancer": [ - [ - "AIPixelArtEnhancer" - ], - { - "title_aux": "AI Pixel Art Enhancer for ComfyUI" - } - ], - "https://github.com/HWDigi/Factory-Prompts_comfyui": [ - [ - "FactoryPromptsNegative", - "FactoryPromptsNegativeCategorized", - "FactoryPromptsNegativeToggle", - "FactoryPromptsPositive" - ], - { - "title_aux": "Factory Prompt Generator" - } - ], - "https://github.com/Haiper-ai/ComfyUI-HaiperAI-API": [ - [ - "HaiperImage2Video", - "HaiperKeyframeConditioning", - "HaiperText2Image", - "HaiperText2Video" - ], - { - "title_aux": "ComfyUI-HaiperAI-API" - } - ], - "https://github.com/Hangover3832/ComfyUI_Hangover-Utils": [ - [ - "Image Clipboard Paster", - "Image Scale Bounding Box", - "Make Inpaint Model", - "Save Image w/o Metadata", - "Sympy Math Interpreter" - ], - { - "author": "AlexL", - "description": "Scales an input image into a given box size, whereby the aspect ratio keeps retained.", - "nickname": "Hangover-Image_Scale_Bouning_Box", - "title": "ComfyUI-Hangover-Image_Scale_Bouning_Box", - "title_aux": "ComfyUI_Hangover-Utils" - } - ], - "https://github.com/HannibalP/comfyui-HannibalPack": [ - [ - "HannibalLoraLoader" - ], - { - "title_aux": "comfyui-HannibalPack" - } - ], - "https://github.com/Haoming02/comfyui-diffusion-cg": [ - [ - "Normalization", - "Recenter", - "Recenter XL" - ], - { - "title_aux": "Diffusion CG" - } - ], - "https://github.com/Haoming02/comfyui-floodgate": [ - [ - "FloodGate" - ], - { - "title_aux": "Floodgate" - } - ], - "https://github.com/Haoming02/comfyui-resharpen": [ - [ - "Resharpen" - ], - { - "title_aux": "ComfyUI ReSharpen" - } - ], - "https://github.com/HappyXY/ComfyUI-AmazonBedrock": [ - [ - "Amazon Bedrock - Nova Canvas Background Prompt Replace", - "Amazon Bedrock - Nova Canvas Generate Image", - "Amazon Bedrock - Nova Canvas Generate Variations", - "Amazon Bedrock - Nova Canvas Remove Object", - "Amazon Bedrock - Nova Reel Video", - "Bedrock - Claude", - "Bedrock - Claude Multimodal", - "Bedrock - Nova", - "Bedrock - SDXL" - ], - { - "title_aux": "ComfyUI-AmazonBedrock" - } - ], - "https://github.com/HavocsCall/comfyui_HavocsCall_Custom_Nodes": [ - [ - "Clip Switch", - "Combine String", - "Conditioning Switch", - "Float Selector", - "Float to Integer", - "Float to String", - "Image Switch", - "Integer Selector", - "Integer to Float", - "Integer to String", - "Latent Switch", - "Load Image", - "Logic Compare", - "Math Operation", - "Model Switch", - "Prompt Combiner", - "Prompt Styler", - "Sampler Config", - "Save Image", - "String Switch", - "String to Float", - "String to Integer", - "Text Box", - "VAE Switch" - ], - { - "title_aux": "HavocsCall's Custom ComfyUI Nodes" - } - ], - "https://github.com/HaydenReeve/ComfyUI-Better-Strings": [ - [ - "BetterString" - ], - { - "title_aux": "ComfyUI Better Strings" - } - ], - "https://github.com/Hazukiaoi/ComfyUI-LM_Studio_Tools": [ - [ - "LMS_APIConfig", - "LMS_GetAssistantMessage", - "LMS_Request", - "LMS_SelectModel", - "LMS_SystemPrompt", - "LMS_UnloadModel", - "LMS_UserPrompt" - ], - { - "title_aux": "LM Studio Tools for ComfyUI" - } - ], - "https://github.com/HeadshotPro/ComfyUI-HeadshotPro": [ - [ - "[HSP] Download Dreambooth Checkpoint", - "[HSP] Download Flux Lora", - "[HSP] Get Random Value From List", - "[HSP] Load Canny Pose Face", - "[HSP] Transparent to White Background" - ], - { - "title_aux": "ComfyUI-HeadshotPro" - } - ], - "https://github.com/HebelHuber/comfyui-enhanced-save-node": [ - [ - "EnhancedSaveNode" - ], - { - "title_aux": "comfyui-enhanced-save-node" - } - ], - "https://github.com/HellerCommaA/ComfyUI-VideoResolutions": [ - [ - "HunyuanResolutions" - ], - { - "title_aux": "Hunyuan Video Resolutions" - } - ], - "https://github.com/Hellfiredragon/comfyui-image-manipulation": [ - [ - "AlphaApplyMaskToImage", - "CreateMaskFromColorsNode" - ], - { - "title_aux": "comfyui-image-manipulation" - } - ], - "https://github.com/HelloVision/ComfyUI_HelloMeme": [ - [ - "GetExpression", - "GetExpression2", - "GetFaceLandmarks", - "GetHeadPose", - "HMFaceToolkitsLoader", - "HMImagePipelineLoader", - "HMPipelineImage", - "HMPipelineVideo", - "HMVideoPipelineLoader" - ], - { - "title_aux": "ComfyUI_HelloMeme" - } - ], - "https://github.com/Hellrunner2k/ComfyUI-HellrunnersMagicalNodes": [ - [ - "AdjustMojo", - "BufferedEncoder", - "LoRABox", - "LoadMaskMap", - "MagicalSaveNode", - "MaskMapPrompt", - "MaskMapPromptMix", - "MojoLoader", - "MojoMaker", - "SaveMojo", - "TEAce", - "ThermalLatenator" - ], - { - "title_aux": "Hellrunner's Magical Nodes" - } - ], - "https://github.com/Hiero207/ComfyUI-Hiero-Nodes": [ - [ - "Load Prompt Travel file", - "Post to Discord w/ Webhook", - "Save Prompt Travel file" - ], - { - "author": "Hiero", - "description": "Just some nodes that I wanted/needed, so I made them.", - "nickname": "HNodes", - "title": "Hiero-Nodes", - "title_aux": "Hiero-Nodes" - } - ], - "https://github.com/HighDoping/ComfyUI_ASSSSA": [ - [ - "ASSSubtitleReader", - "ASSSubtitleSave", - "FFMpegSettings", - "MultilineTextInput", - "SubtitleEmbedding", - "SubtitleExtraction", - "VideoTranscoding" - ], - { - "title_aux": "ComfyUI-ASSSSA" - } - ], - "https://github.com/HoangYell/comfyui-hoangyell-video": [ - [ - "AddIntroImage" - ], - { - "title_aux": "comfyui-hoangyell-video-edit" - } - ], - "https://github.com/Holasyb918/Ghost2_Comfyui": [ - [ - "AlignPipeline", - "BlenderPipeline", - "FaceAnalysisePipeline", - "FaceParsingPipeline", - "LoadAlignerModel", - "LoadBlenderModel", - "LoadFaceAnalysisModel", - "LoadFaceParsingModel", - "LoadInpainterModel", - "LoadStyleMatteModel" - ], - { - "title_aux": "Ghost2_Comfyui" - } - ], - "https://github.com/Hopping-Mad-Games/ComfyUI_LiteLLM": [ - [ - "AgentMemoryProvider", - "AgentNode", - "BasicRecursionFilterNode", - "DocumentChunkRecursionFilterNode", - "DocumentProcessor", - "LinuxMemoryDirectory", - "QueryNode" - ], - { - "description": "Nodes for interfacing with LiteLLM", - "nickname": "Tasha", - "title": "ComfyUI_LiteLLM", - "title_aux": "ComfyUI_LiteLLM" - } - ], - "https://github.com/Hullabalo/ComfyUI-Loop": [ - [ - "ImageCropLoop", - "ImageCutLoop", - "ImagePasteLoop", - "LoadImageSimple", - "LoopImageSimple", - "SaveImageSimple" - ], - { - "title_aux": "ComfyUI-Loop" - } - ], - "https://github.com/IDGallagher/ComfyUI-IG-Motion-I2V": [ - [ - "MI2V Flow Animator", - "MI2V Flow Predictor", - "MI2V PauseNode", - "MotionPainter" - ], - { - "author": "IDGallagher", - "description": "Custom nodes to aid in the exploration of Latent Space", - "nickname": "IG Interpolation Nodes", - "title": "IG Interpolation Nodes", - "title_aux": "ComfyUI-IG-Motion-I2V" - } - ], - "https://github.com/IDGallagher/ComfyUI-IG-Nodes": [ - [ - "IG Analyze SSIM", - "IG Cross Fade Images", - "IG Explorer", - "IG Float", - "IG Float List", - "IG Folder", - "IG Image Crop", - "IG Int", - "IG Interpolate", - "IG Load Image", - "IG Load Images", - "IG MotionPredictor", - "IG Multiply", - "IG Path Join", - "IG PointCloud From Cylindrical", - "IG PointCloud From Depth", - "IG Save PLY PointCloud", - "IG Simple Translate Stitcher", - "IG Stitch Depth Tiles", - "IG Stitch Images CV2", - "IG String", - "IG String List", - "IG Tile Image", - "IG ZFill", - "SM Video Base", - "SM Video Base Control" - ], - { - "author": "IDGallagher", - "description": "Custom nodes to aid in the exploration of Latent Space", - "nickname": "IG Interpolation Nodes", - "title": "IG Interpolation Nodes", - "title_aux": "IG Interpolation Nodes" - } - ], - "https://github.com/IDGallagher/MotionVideoSearch": [ - [ - "IG Motion Video Frame", - "IG Motion Video Search" - ], - { - "author": "IDGallagher", - "description": "Search an index of videos by motion image", - "nickname": "IG Motion Video Search", - "title": "IG Motion Video Search", - "title_aux": "IG-Motion-Search" - } - ], - "https://github.com/IIEleven11/ComfyUI-FairyTaler": [ - [ - "FairyTalerStoryboard", - "SceneParser", - "SceneToConditioning", - "StoryboardCompositor", - "ThreeSceneGenerator" - ], - { - "author": "Eleven", - "description": "Turn your AI roleplay into AI generated scenes from every response. Visualize what you read!", - "nickname": "ComfyUI FairyTaler", - "title": "ComfyUI FairyTaler Storyboard Nodes", - "title_aux": "ComfyUI-FairyTaler" - } - ], - "https://github.com/IIs-fanta/ComfyUI-FANTA-GameBox": [ - [ - "BilliardsGameNode", - "BrickBreakerNode", - "FlappyBirdNode", - "SnakeGameNode" - ], - { - "title_aux": "ComfyUI-FANTA-GameBox" - } - ], - "https://github.com/INuBq8/ComfyUI-NotificationBridge": [ - [ - "DiscordNotifyNode", - "WhatsAppNotifyNodeTwilio" - ], - { - "title_aux": "Notification Bridge" - } - ], - "https://github.com/ITurchenko/ComfyUI-SizeFromArray": [ - [ - "SizeFromArray" - ], - { - "title_aux": "ComfyUI-SizeFromArray" - } - ], - "https://github.com/IamCreateAI/Ruyi-Models": [ - [ - "Ruyi_EnhanceAVideo", - "Ruyi_I2VSampler", - "Ruyi_LoadLora", - "Ruyi_LoadModel", - "Ruyi_TeaCache" - ], - { - "title_aux": "ComfyUI-Ruyi" - } - ], - "https://github.com/IcelandicCenterArtificialIntelligence/ComfyUI-SamplerSchedulerMetricsTester": [ - [ - "SamplerSchedulerMetricsTester" - ], - { - "title_aux": "Sampler Scheduler Metrics Tester for ComfyUI" - } - ], - "https://github.com/Icyman86/ComfyUI_AnimeCharacterSelect": [ - [ - "ActionPromptNode", - "CharacterPromptNode", - "CombinePromptStringsNode", - "EnhancedCharacterPromptNode", - "MinimalCharacterActionPrompt" - ], - { - "title_aux": "ComfyUI_AnimeCharacterSelect" - } - ], - "https://github.com/IgalOgonov/ComfyUI_Simple_String_Repository": [ - [ - "SimpleStringRepository", - "SimpleStringRepositoryCompact", - "SimpleStringRepositoryLarge", - "SimpleStringRepositoryLargeCompact", - "SimpleStringRepositorySmall", - "SimpleStringRepositorySmallCompact" - ], - { - "title_aux": "Simple String Repository" - } - ], - "https://github.com/ImagineerNL/ComfyUI-IMGNR-Utils": [ - [ - "CatchEditTextNode", - "PreviewImageBase64Node" - ], - { - "title_aux": "ComfyUI-IMGNR-Utils" - } - ], - "https://github.com/ImagineerNL/ComfyUI-ToSVG-Potracer": [ - [ - "PotracerVectorize", - "SaveAsSVG Potracer (Temporary Fix)" - ], - { - "title_aux": "ComfyUI-ToSVG-Potracer" - } - ], - "https://github.com/Immac/ComfyUI-CoreVideoMocks": [ - [ - "CoreVideoMocks:AV1Codec", - "CoreVideoMocks:BatchAudioStreams", - "CoreVideoMocks:BatchSubtitlesStreams", - "CoreVideoMocks:BatchVideoStreams", - "CoreVideoMocks:CodecFromVideoStream", - "CoreVideoMocks:CombineVideo", - "CoreVideoMocks:DecodeVideoStream", - "CoreVideoMocks:EncodeVideoStream", - "CoreVideoMocks:GetAudioStream", - "CoreVideoMocks:GetSubtitleStream", - "CoreVideoMocks:GetVideoStream", - "CoreVideoMocks:LoadVideo", - "CoreVideoMocks:PreviewVideo", - "CoreVideoMocks:SaveVideo", - "CoreVideoMocks:SplitVideo", - "CoreVideoMocks:VP9Codec", - "CoreVideoMocks:VideoStreamData" - ], - { - "title_aux": "ComfyUI Core Video Nodes" - } - ], - "https://github.com/ImmortalPie/ComfyUI-PonySwitch": [ - [ - "PonySwitch" - ], - { - "title_aux": "PonySwitch Node" - } - ], - "https://github.com/InstantStudioAI/ComfyUI-InstantStudio": [ - [ - "HuggingFace Classify", - "Moondream", - "UploadImagesToInstantStudio" - ], - { - "title_aux": "ComfyUI-InstantStudio" - } - ], - "https://github.com/Intersection98/ComfyUI_MX_post_processing-nodes": [ - [ - "MX_AlphaBlend", - "MX_Blend", - "MX_Blur", - "MX_Canny", - "MX_ChromaticAberration", - "MX_ColorCorrect", - "MX_ColorMatch", - "MX_ColorTint", - "MX_Glow", - "MX_HSVThresholdMask", - "MX_KuwaharaBlur(Cartoon)", - "MX_LUT", - "MX_LensBokeh", - "MX_LensOpticAxis", - "MX_LensZoomBurst", - "MX_Mosaic", - "MX_Noise", - "MX_Posterize", - "MX_SineWave", - "MX_Solarize", - "MX_Vignette" - ], - { - "title_aux": "ComfyUI-MX-post-processing-nodes" - } - ], - "https://github.com/Inzaniak/comfyui-ranbooru": [ - [ - "LockSeed", - "PromptBackground", - "PromptLimit", - "PromptMix", - "PromptRandomWeight", - "PromptRemove", - "Ranbooru", - "RanbooruURL", - "RandomPicturePath", - "TimestampFileName" - ], - { - "title_aux": "Ranbooru for ComfyUI" - } - ], - "https://github.com/Irsalistic/comfyui-dam-object-extractor": [ - [ - "DAMObjectNameNode", - "DAMVisualizeNode" - ], - { - "title_aux": "ComfyUI DAM Object Extractor" - } - ], - "https://github.com/IsItDanOrAi/ComfyUI-Stereopsis": [ - [ - "Dan_FrameDelay", - "Dan_Stereopsis" - ], - { - "title_aux": "ComfyUI-Stereopsis" - } - ], - "https://github.com/IsItDanOrAi/ComfyUI-exLoadout": [ - [ - "dropdowns", - "exCheckpointLoader", - "exLoadoutCheckpointLoader", - "exLoadoutEditCell", - "exLoadoutReadColumn", - "exLoadoutSeg", - "exLoadoutSeg2", - "exLoadoutSelector", - "exSeg", - "exSeg2" - ], - { - "title_aux": "exLoadout: Excel-Based Model & Settings Loader" - } - ], - "https://github.com/Isi-dev/ComfyUI-Animation_Nodes_and_Workflows": [ - [ - "CLIPTextEncodeStyles", - "JoinVideos", - "MakeDrivingVideoForLivePortrait", - "MakePortraitWalk", - "MoveInOrOut", - "MoveLeftOrRight", - "MoveUpOrDown", - "Replace_Img_or_Vid_Bg_Assistant" - ], - { - "title_aux": "ComfyUI-Animation_Nodes_and_Workflows" - } - ], - "https://github.com/Isi-dev/ComfyUI-Img2DrawingAssistants": [ - [ - "LineArt_Assistant", - "LineArt_Assistant_2", - "Sketch_Assistant", - "Sketch_Assistant_grayScale" - ], - { - "title_aux": "ComfyUI-Img2DrawingAssistants" - } - ], - "https://github.com/Isi-dev/ComfyUI-Img2PaintingAssistant": [ - [ - "Painting", - "ProcessInspyrenetRembg" - ], - { - "title_aux": "Image to Painting and Inspyrenet Assistant Nodes" - } - ], - "https://github.com/Isi-dev/ComfyUI-UniAnimate-W": [ - [ - "Animate_X_Image", - "Animate_X_Image_Long", - "Animate_X_Image_v2", - "Animate_X_ReposeImage", - "Animate_X_ReposeImage_v2", - "Gen_align_pose", - "Gen_align_pose2", - "ReposeImage", - "UniAnimateImage", - "UniAnimateImageLong" - ], - { - "title_aux": "ComfyUI-UniAnimate-W" - } - ], - "https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough": [ - [ - "ControlledControlNetLoader", - "ControlledUnetLoaderGGUF", - "ControlledVAELoader", - "DeleteModelPassthrough" - ], - { - "title_aux": "ComfyUI_DeleteModelPassthrough" - } - ], - "https://github.com/Isulion/ComfyUI_Isulion": [ - [ - "CustomTextNode", - "CustomTextNode \u2328\ufe0f", - "DisplayImageFromURL", - "IsuCollage_Node", - "Isulion Civitai Image Display", - "Isulion Civitai Model Explorer", - "Isulion Civitai Trending", - "IsulionActionGenerator", - "IsulionAlienWorldGenerator", - "IsulionAnimalBehaviorGenerator", - "IsulionAnimalRandom", - "IsulionArtStyleGenerator", - "IsulionArtifactGenerator", - "IsulionCivitaiImageDisplay", - "IsulionCivitaiModelExplorer", - "IsulionCivitaiTrending", - "IsulionClothingGenerator", - "IsulionCuteAnimalRandom", - "IsulionEmotionGenerator", - "IsulionEpochGenerator", - "IsulionFantasyRaceGenerator", - "IsulionHabitatGenerator", - "IsulionLoadImagesNode", - "IsulionMagicalEffectGenerator", - "IsulionMegaPromptV3", - "IsulionMultiplePromptGenerator", - "IsulionMythicalLocationGenerator", - "IsulionNegativePromptGenerator", - "IsulionProfessionGenerator", - "IsulionPromptEnhancer", - "IsulionSceneComposition", - "IsulionSpacecraftGenerator", - "IsulionStyleMixer", - "IsulionTechGenerator", - "IsulionTimeOfDayGenerator", - "IsulionVideoPromptGenerator", - "IsulionWeatherGenerator", - "\u29c9 IsulionOverlay", - "\ud83d\udca4 IsulionShutdown", - "\ud83e\udde9 IsulionQRCode" - ], - { - "title_aux": "ComfyUI_Isulion Random Prompt Generator" - } - ], - "https://github.com/IuvenisSapiens/ComfyUI_MiniCPM-V-4": [ - [ - "DisplayText", - "MiniCPM_VQA", - "MiniCPM_VQA_Polished", - "MultipleImagesInput" - ], - { - "title_aux": "ComfyUI_MiniCPM-V-4" - } - ], - "https://github.com/IuvenisSapiens/ComfyUI_Qwen2-Audio-7B-Instruct-Int4": [ - [ - "AudioLoader", - "AudioPreviewer", - "Qwen2_AQA" - ], - { - "title_aux": "ComfyUI_Qwen2-Audio-7B-Instruct-Int4" - } - ], - "https://github.com/IuvenisSapiens/ComfyUI_Qwen2_5-VL-Instruct": [ - [ - "ImageLoader", - "MultiplePathsInput", - "Qwen2_VQA", - "VideoLoader" - ], - { - "title_aux": "ComfyUI_Qwen2-VL-Instruct" - } - ], - "https://github.com/JEONG-JIWOO/ComfyUI_Eugene_Nodes": [ - [ - "DictBus", - "DictBusEdit", - "DictBusUnpack", - "DictMultilineSelect", - "DictTemplate", - "DictUpdate1", - "DictUpdate10", - "DictUpdate5", - "LoraPresetListLoader", - "LoraPresetLoader", - "LoraPresetSaver", - "LoraPresetSelector" - ], - { - "title_aux": "ComfyUI_Eugene_Nodes" - } - ], - "https://github.com/JPS-GER/ComfyUI_JPS-Nodes": [ - [ - "CLIPTextEncode SDXL Plus (JPS)", - "Conditioning Switch (JPS)", - "ControlNet Switch (JPS)", - "Crop Image Pipe (JPS)", - "Crop Image Settings (JPS)", - "Crop Image Square (JPS)", - "Crop Image TargetSize (JPS)", - "CtrlNet CannyEdge Pipe (JPS)", - "CtrlNet CannyEdge Settings (JPS)", - "CtrlNet MiDaS Pipe (JPS)", - "CtrlNet MiDaS Settings (JPS)", - "CtrlNet OpenPose Pipe (JPS)", - "CtrlNet OpenPose Settings (JPS)", - "CtrlNet ZoeDepth Pipe (JPS)", - "CtrlNet ZoeDepth Settings (JPS)", - "Disable Enable Switch (JPS)", - "Enable Disable Switch (JPS)", - "Generation TXT IMG Settings (JPS)", - "Get Date Time String (JPS)", - "Get Image Size (JPS)", - "IP Adapter Settings (JPS)", - "IP Adapter Settings Pipe (JPS)", - "IP Adapter Tiled Settings (JPS)", - "IP Adapter Tiled Settings Pipe (JPS)", - "IPA Switch (JPS)", - "Image Prepare Pipe (JPS)", - "Image Prepare Settings (JPS)", - "Image Switch (JPS)", - "ImageToImage Pipe (JPS)", - "ImageToImage Settings (JPS)", - "Images Masks MultiPipe (JPS)", - "InstantID Mask Prepare Pipe (JPS)", - "InstantID Mask Prepare Settings (JPS)", - "InstantID Pipe (JPS)", - "InstantID Pose Prepare Pipe (JPS)", - "InstantID Pose Prepare Settings (JPS)", - "InstantID Settings (JPS)", - "InstantID Source Prepare Pipe (JPS)", - "InstantID Source Prepare Settings (JPS)", - "Integer Switch (JPS)", - "Largest Int (JPS)", - "Latent Switch (JPS)", - "Lora Loader (JPS)", - "Mask Switch (JPS)", - "Model Switch (JPS)", - "Multiply Float Float (JPS)", - "Multiply Int Float (JPS)", - "Multiply Int Int (JPS)", - "Prepare Image (JPS)", - "Prepare Image Plus (JPS)", - "Prepare Image Tiled IPA (JPS)", - "Resolution Multiply (JPS)", - "Revision Settings (JPS)", - "Revision Settings Pipe (JPS)", - "SDXL Basic Settings (JPS)", - "SDXL Basic Settings Pipe (JPS)", - "SDXL Fundamentals MultiPipe (JPS)", - "SDXL Prompt Handling (JPS)", - "SDXL Prompt Handling Plus (JPS)", - "SDXL Prompt Styler (JPS)", - "SDXL Recommended Resolution Calc (JPS)", - "SDXL Resolutions (JPS)", - "SDXL Settings (JPS)", - "SDXL Settings Pipe (JPS)", - "Sampler Scheduler Settings (JPS)", - "Save Images Plus (JPS)", - "Substract Int Int (JPS)", - "Text Concatenate (JPS)", - "Text Prompt (JPS)", - "Text Prompt Combo (JPS)", - "Time Seed (JPS)", - "VAE Switch (JPS)" - ], - { - "author": "JPS", - "description": "Various nodes to handle SDXL Resolutions, SDXL Basic Settings, IP Adapter Settings, Revision Settings, SDXL Prompt Styler, Crop Image to Square, Crop Image to Target Size, Get Date-Time String, Resolution Multiply, Largest Integer, 5-to-1 Switches for Integer, Images, Latents, Conditioning, Model, VAE, ControlNet", - "nickname": "JPS Custom Nodes", - "title": "JPS Custom Nodes for ComfyUI", - "title_aux": "JPS Custom Nodes for ComfyUI" - } - ], - "https://github.com/JPrevots/ComfyUI-PhyCV": [ - [ - "PAGE", - "PST", - "VEVID" - ], - { - "title_aux": "ComfyUI-PhyCV" - } - ], - "https://github.com/JTriggerFish/ComfyLatentTools": [ - [ - "DownsampledAttentionGuidance", - "DownsampledLatentGuidance", - "GenericAttentionGuidance", - "LatentNormalizedLanczosResize" - ], - { - "title_aux": "Comfy Latent Tools" - } - ], - "https://github.com/JackEllie/ComfyUI_AI_Assistant": [ - [ - "apply_lighting_effects", - "clean_prompt_tags", - "noline_process", - "prompt_blacklist", - "prompt_sorting", - "resize_image_sdxl_ratio" - ], - { - "title_aux": "ComfyUI-AI-Assistant" - } - ], - "https://github.com/Jacky-MYQ/comfyui-DataCleaning": [ - [ - "CleanData" - ], - { - "title_aux": "comfyui-DataCleaning" - } - ], - "https://github.com/Jacky-MYQ/comfyui-rgb2cmyk": [ - [ - "RGB2CMYK" - ], - { - "title_aux": "RGB to CMYK for ComfyUI (Save as tif)" - } - ], - "https://github.com/Jaminanim/ComfyUI-Random-Int-Divisor-Node": [ - [ - "RandomIntegerNodeEfficient", - "RandomIntegerNodeEfficientAdvanced", - "RandomIntegerNodeList" - ], - { - "title_aux": "ComfyUI-Random-Int-Divisor-Node" - } - ], - "https://github.com/Jannchie/ComfyUI-J": [ - [ - "DiffusersCompelPromptEmbedding", - "DiffusersControlnetLoader", - "DiffusersControlnetUnit", - "DiffusersControlnetUnitStack", - "DiffusersDecoder", - "DiffusersGenerator", - "DiffusersPipeline", - "DiffusersPrepareLatents", - "DiffusersTextureInversionLoader", - "DiffusersXLPipeline", - "GetAverageColorFromImage", - "GetFilledColorImage" - ], - { - "title_aux": "ComfyUI-J" - } - ], - "https://github.com/Jannled/owl-vit-comfyui": [ - [ - "OWL_BBox_Visualizer", - "OWL_Load_Model", - "OWL_Objectness_Inference" - ], - { - "title_aux": "OWL-ViT ComfyUI" - } - ], - "https://github.com/Jarcis-cy/ComfyUI-HunyuanVideoFoley": [ - [ - "HunyuanVideoFoleyGenerateAudio", - "VideoAudioMerger" - ], - { - "title_aux": "HunyuanVideo-Foley Audio Generator" - } - ], - "https://github.com/JaredTherriault/ComfyUI-JNodes": [ - [ - "JNodes_AddOrSetMetaDataKey", - "JNodes_AnyToString", - "JNodes_AppendReversedFrames", - "JNodes_AudioInputOptions", - "JNodes_BooleanSelectorWithString", - "JNodes_BreakMediaInfo", - "JNodes_CheckpointSelectorWithString", - "JNodes_ConditioningInOut", - "JNodes_CreateStereoscopicImageFromDepth", - "JNodes_DiffusionModelSelector", - "JNodes_FloatLiteral", - "JNodes_GetCleanFilename", - "JNodes_GetComfyDirectory", - "JNodes_GetLeafDirectory", - "JNodes_GetOutputDirectory", - "JNodes_GetParameterFromList", - "JNodes_GetParameterGlobal", - "JNodes_GetTempDirectory", - "JNodes_ImageFormatSelector", - "JNodes_ImageSizeSelector", - "JNodes_IntLiteral", - "JNodes_JoinVideosInDirectory", - "JNodes_LoadVideo", - "JNodes_LoadVisualMediaFromPath", - "JNodes_LoadVisualMediaFromPath_Batch", - "JNodes_LoadVisualMediaFromPath_List", - "JNodes_LoraExtractor", - "JNodes_MediaInfoToString", - "JNodes_ModelInOut", - "JNodes_OutVideoInfo", - "JNodes_ParseDynamicPrompts", - "JNodes_ParseParametersToGlobalList", - "JNodes_ParseWildcards", - "JNodes_PromptBuilderSingleSubject", - "JNodes_RemoveCommentedText", - "JNodes_RemoveMetaDataKey", - "JNodes_RemoveParseableDataForInference", - "JNodes_SamplerSelectorWithString", - "JNodes_SaveImageWithOutput", - "JNodes_SaveVideo", - "JNodes_SaveVideoWithOptions", - "JNodes_SchedulerSelectorWithString", - "JNodes_SearchAndReplace", - "JNodes_SearchAndReplaceFromFile", - "JNodes_SearchAndReplaceFromList", - "JNodes_SelectRandomFileFromDirectory", - "JNodes_SeparateStringByDelimiters", - "JNodes_SetMetadataA1111", - "JNodes_SetNegativePromptInMetaData", - "JNodes_SetPositivePromptInMetaData", - "JNodes_SplitAndJoin", - "JNodes_StringLiteral", - "JNodes_SubdirectorySelector", - "JNodes_SyncedStringLiteral", - "JNodes_TokenCounter", - "JNodes_TrimAndStrip", - "JNodes_UploadVideo", - "JNodes_UploadVisualMedia", - "JNodes_VaeSelectorWithString" - ], - { - "title_aux": "ComfyUI-JNodes" - } - ], - "https://github.com/Jash-Vora/ComfyUI-GarmentDiT": [ - [ - "GarmentEnhancementNode" - ], - { - "title_aux": "FitDiT" - } - ], - "https://github.com/JcandZero/ComfyUI_GLM4Node": [ - [ - "GLM3_turbo_CHAT", - "GLM4_CHAT", - "GLM4_Vsion_IMGURL" - ], - { - "title_aux": "ComfyUI_GLM4Node" - } - ], - "https://github.com/Jcd1230/rembg-comfyui-node": [ - [ - "Image Remove Background (rembg)" - ], - { - "title_aux": "Rembg Background Removal Node for ComfyUI" - } - ], - "https://github.com/Jelosus2/comfyui-vae-reflection": [ - [ - "AddReflectionToVAE" - ], - { - "title_aux": "ComfyUI VAE Reflection" - } - ], - "https://github.com/JerryOrbachJr/ComfyUI-RandomSize": [ - [ - "JOJR_RandomSize" - ], - { - "author": "JerryOrbachJr", - "description": "A ComfyUI custom node that randomly selects a height and width pair from a list in a config file", - "nickname": "Random Size", - "title": "Random Size", - "title_aux": "Random Size" - } - ], - "https://github.com/JettHu/ComfyUI-TCD": [ - [ - "TCDModelSamplingDiscrete" - ], - { - "title_aux": "ComfyUI-TCD" - } - ], - "https://github.com/JettHu/ComfyUI_TGate": [ - [ - "TGateApply", - "TGateApplyAdvanced", - "TGateApplySimple" - ], - { - "title_aux": "ComfyUI_TGate" - } - ], - "https://github.com/JiSenHua/ComfyUI-TD": [ - [ - "Comfy3DPacktoTD", - "Hy3DtoTD", - "ImagetoTD", - "ImagetoTD(JPEG)", - "LoadTDImage", - "Tripo3DtoTD", - "TripoSRtoTD", - "VideotoTD" - ], - { - "title_aux": "ComfyUI-TD" - } - ], - "https://github.com/Jint8888/Comfyui_JTnodes": [ - [ - "JT Find Text From Excel", - "JT Read From Excel", - "JTBrightness", - "JTImagesavetopath", - "JTSaveTextToExcel", - "JTSaveTextToFile", - "JTcounter", - "SiliconflowFree" - ], - { - "title_aux": "Comfyui_JTnodes" - } - ], - "https://github.com/JoeNavark/comfyui_custom_sigma_editor": [ - [ - "CustomSplineSigma", - "SigmaJoiner" - ], - { - "title_aux": "Custom Graph Sigma for ComfyUI" - } - ], - "https://github.com/JohanK66/ComfyUI-WebhookImage": [ - [ - "Notif-Webhook" - ], - { - "title_aux": "ComfyUI WebhookImage" - } - ], - "https://github.com/JohnDoeSmithee/ComfyUI-SoX-Mixdown": [ - [ - "SoxMixNode" - ], - { - "title_aux": "ComfyUI-SoX-Mixdown" - } - ], - "https://github.com/Jokimbe/ComfyUI-DrawThings-gRPC": [ - [ - "DrawThingsControlNet", - "DrawThingsHints", - "DrawThingsLoRA", - "DrawThingsNegative", - "DrawThingsPositive", - "DrawThingsPrompt", - "DrawThingsRefiner", - "DrawThingsSampler", - "DrawThingsUpscaler" - ], - { - "title_aux": "ComfyUI-DrawThings-gRPC" - } - ], - "https://github.com/Jonseed/ComfyUI-Detail-Daemon": [ - [ - "DetailDaemonGraphSigmasNode", - "DetailDaemonSamplerNode", - "LyingSigmaSampler", - "MultiplySigmas" - ], - { - "title_aux": "ComfyUI-Detail-Daemon" - } - ], - "https://github.com/Jordach/comfy-plasma": [ - [ - "JDC_AutoContrast", - "JDC_BlendImages", - "JDC_BrownNoise", - "JDC_Contrast", - "JDC_EqualizeGrey", - "JDC_GaussianBlur", - "JDC_GreyNoise", - "JDC_Greyscale", - "JDC_ImageLoader", - "JDC_ImageLoaderMeta", - "JDC_PinkNoise", - "JDC_Plasma", - "JDC_PlasmaSampler", - "JDC_PowerImage", - "JDC_RandNoise", - "JDC_ResizeFactor" - ], - { - "title_aux": "comfy-plasma" - } - ], - "https://github.com/JosefKuchar/ComfyUI-AdvancedTiling": [ - [ - "AdvancedTiling", - "AdvancedTilingSettings", - "AdvancedTilingVAEDecode" - ], - { - "title_aux": "ComfyUI-AdvancedTiling" - } - ], - "https://github.com/JosephThomasParker/ComfyUI-DrawThingsWrapper": [ - [ - "DrawThingsGenerateFromPipeline", - "DrawThingsImg2Img", - "DrawThingsImg2ImgPipeline", - "DrawThingsPipelineAddControl", - "DrawThingsPipelineAddCustom", - "DrawThingsPipelineAddLora", - "DrawThingsTxt2Img", - "DrawThingsTxt2ImgPipeline" - ], - { - "title_aux": "ComfyUI-DrawThingsWrapper" - } - ], - "https://github.com/Julian-adv/WildDivide": [ - [ - "Attention couple wild divide", - "Comfy Divide", - "WildPromptGenerator", - "WildcardDivide", - "WildcardEncode" - ], - { - "author": "Julian Adventurer.", - "description": "This node is used to encode a wildcard string.", - "nickname": "WildDivide", - "title": "Wild Divide", - "title_aux": "Wild Divide" - } - ], - "https://github.com/JustLateNightAI/KeywordImageBlocker": [ - [ - "TagKeywordBlocker" - ], - { - "title_aux": "KeywordImageBlocker" - } - ], - "https://github.com/JustinMatters/comfyUI-JMNodes": [ - [ - "JMBinaryNot", - "JMIntegerToBooleans", - "JMNumberList", - "JMSWitchablePrompt" - ], - { - "title_aux": "ComfyUI JMNodes" - } - ], - "https://github.com/KAVVATARE/ComfyUI-Light-N-Color": [ - [ - "ControlNetSwitch", - "FluxLightingAndColor", - "FluxSamplerPuLID", - "ImageSwitch", - "LatentSwitch", - "LoadInputOutputImage" - ], - { - "title_aux": " ComfyUI-Light-N-Color" - } - ], - "https://github.com/KAVVATARE/ComfyUI_RightEyeDisparity": [ - [ - "RightEyeImageNode", - "VideoRightEyeNode" - ], - { - "title_aux": "RightEyeDisparity" - } - ], - "https://github.com/KERRY-YUAN/ComfyUI_Float_Animator": [ - [ - "Float_Animator" - ], - { - "title_aux": "ComfyUI_Float_Animator" - } - ], - "https://github.com/KERRY-YUAN/ComfyUI_Simple_Executor": [ - [ - "NodeAutoSampler", - "NodeImagePre", - "NodeImageResize" - ], - { - "title_aux": "NodeSimpleExecutor" - } - ], - "https://github.com/KERRY-YUAN/ComfyUI_Spark_TTS": [ - [ - "Spark_TTS_Clone", - "Spark_TTS_Creation" - ], - { - "title_aux": "ComfyUI_Spark_TTS" - } - ], - "https://github.com/KLL535/ComfyUI_SimpleButcher": [ - [ - "Simple Auto Bypass", - "Simple Extract Lora From Text", - "Simple Image Saver (as Forge)", - "Simple Load Image With Metadata", - "Simple Load Images from Dir", - "Simple Load Line From Text File", - "Simple Lora Loader", - "Simple Remove Think" - ], - { - "title_aux": "ComfyUI_SimpleButcher" - } - ], - "https://github.com/KY-2000/comfyui-ksampler-tester-loop": [ - [ - "AllParametersLoop", - "AllParametersLoopAdvanced", - "FloatRangeLoop", - "ParametersRangeLoop", - "SamplerLoop", - "SamplerLoopAdvanced", - "SamplerSchedulerLoop", - "SamplerSchedulerLoopAdvanced", - "SchedulerLoop" - ], - { - "title_aux": "comfyui-ksampler-tester-loop" - } - ], - "https://github.com/Kangkang625/ComfyUI-paint-by-example": [ - [ - "PaintbyExamplePipeLoader", - "PaintbyExampleSampler" - ], - { - "title_aux": "ComfyUI-Paint-by-Example" - } - ], - "https://github.com/KarmaSwint/ComfyUI-KarmaNodes": [ - [ - "Karma-Film-Grain", - "Karma-KSampler-Cycle", - "Karma-Kolors", - "Karma_Film_Grain", - "Karma_Kolors" - ], - { - "title_aux": "KarmaNodes" - } - ], - "https://github.com/Kayarte/AudioDriven-Latent-Space-Tools-for-ComfyUI": [ - [ - "AdvancedNoisePatterns", - "AudioNoiseMapper", - "LibrosaAnalysisNode", - "NoiseToLatentConverter" - ], - { - "title_aux": "AudioDriven-Latent-Space-Tools-for-ComfyUI" - } - ], - "https://github.com/Kayarte/GeoNodes/raw/refs/heads/main/GISDetectionNode.py": [ - [ - "GISDetectionNode" - ], - { - "title_aux": "GeoNodes" - } - ], - "https://github.com/Kesin11/ComfyUI-list-filter": [ - [ - "list_filter_FilterImageListByIndexList", - "list_filter_FilterStringListByIndexList", - "list_filter_FindAnyStrings", - "list_filter_FindNotAnyStrings", - "list_filter_StringToIndex", - "random_normal_dist" - ], - { - "title_aux": "ComfyUI-list-filter" - } - ], - "https://github.com/KewkLW/ComfyUI-kewky_tools": [ - [ - "CLIPInterrogator", - "FormattedPromptNode", - "ImageBatcher", - "LoadImagePlus", - "LoadVideoPlus", - "TensorDebugPlus", - "TextAppendNode", - "TextSearchNode", - "VRAM_Debug_Plus" - ], - { - "title_aux": "ComfyUI-kewky_tools" - } - ], - "https://github.com/Kidev/ComfyUI-Fisheye-effects": [ - [ - "Defisheye", - "Fisheye" - ], - { - "title_aux": "ComfyUI Fisheye Effects Nodes" - } - ], - "https://github.com/KohakuBlueleaf/HDM-ext": [ - [ - "HDMCameraParam", - "HDMLoader", - "HDMTreadGamma" - ], - { - "title_aux": "HDM-ext" - } - ], - "https://github.com/KohakuBlueleaf/z-tipo-extension": [ - [ - "TIPO", - "TIPOFormat", - "TIPOOperation" - ], - { - "title_aux": "TIPO-extension" - } - ], - "https://github.com/KoreTeknology/ComfyUI-Nai-Production-Nodes-Pack": [ - [ - "Brightness Image", - "ColorMatch2", - "Contrast Image", - "Get Text", - "Image Difference", - "ImageConcatenate", - "ImageDesaturate", - "ImageExtend", - "ImageFlip", - "ImageRotate", - "LoadImageNai", - "Math Operation", - "NoteAdvanced", - "Set Text" - ], - { - "title_aux": "ComfyUI Production Nodes Pack" - } - ], - "https://github.com/KoreTeknology/ComfyUI-Universal-Styler": [ - [ - "\ud83d\udee1\ufe0f Load Scripts from Database", - "\ud83d\udee1\ufe0f Save Script to Database (In progress)", - "\ud83d\udee1\ufe0f Set Main Channel" - ], - { - "title_aux": "ComfyUI Universal Styler" - } - ], - "https://github.com/Koren-cy/FlowCV": [ - [ - "Example" - ], - { - "title_aux": "FlowCV" - } - ], - "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet": [ - [ - "ACN_AdvancedControlNetApply", - "ACN_AdvancedControlNetApplySingle", - "ACN_AdvancedControlNetApplySingle_v2", - "ACN_AdvancedControlNetApply_v2", - "ACN_ControlNet++InputNode", - "ACN_ControlNet++LoaderAdvanced", - "ACN_ControlNet++LoaderSingle", - "ACN_ControlNetLoaderAdvanced", - "ACN_ControlNetLoaderWithLoraAdvanced", - "ACN_CtrLoRALoader", - "ACN_CustomControlNetWeightsFlux", - "ACN_CustomControlNetWeightsSD15", - "ACN_CustomT2IAdapterWeights", - "ACN_DefaultUniversalWeights", - "ACN_DiffControlNetLoaderAdvanced", - "ACN_ExtrasMiddleMult", - "ACN_ReferenceControlNet", - "ACN_ReferenceControlNetFinetune", - "ACN_ReferencePreprocessor", - "ACN_ScaledSoftControlNetWeights", - "ACN_SoftControlNetWeightsSD15", - "ACN_SoftT2IAdapterWeights", - "ACN_SparseCtrlIndexMethodNode", - "ACN_SparseCtrlLoaderAdvanced", - "ACN_SparseCtrlMergedLoaderAdvanced", - "ACN_SparseCtrlRGBPreprocessor", - "ACN_SparseCtrlSpreadMethodNode", - "ACN_SparseCtrlWeightExtras", - "ACN_TimestepKeyframeFromStrengthList", - "ACN_TimestepKeyframeInterpolation", - "ControlNetLoaderAdvanced", - "CustomControlNetWeights", - "CustomT2IAdapterWeights", - "DiffControlNetLoaderAdvanced", - "LatentKeyframe", - "LatentKeyframeBatchedGroup", - "LatentKeyframeGroup", - "LatentKeyframeTiming", - "LoadImagesFromDirectory", - "ScaledSoftControlNetWeights", - "ScaledSoftMaskedUniversalWeights", - "SoftControlNetWeights", - "SoftT2IAdapterWeights", - "TimestepKeyframe" - ], - { - "title_aux": "ComfyUI-Advanced-ControlNet" - } - ], - "https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved": [ - [ - "ADE_AdjustPEFullStretch", - "ADE_AdjustPEManual", - "ADE_AdjustPESweetspotStretch", - "ADE_AdjustWeightAllAdd", - "ADE_AdjustWeightAllMult", - "ADE_AdjustWeightIndivAdd", - "ADE_AdjustWeightIndivAttnAdd", - "ADE_AdjustWeightIndivAttnMult", - "ADE_AdjustWeightIndivMult", - "ADE_AncestralOptions", - "ADE_AnimateDiffCombine", - "ADE_AnimateDiffKeyframe", - "ADE_AnimateDiffLoRALoader", - "ADE_AnimateDiffLoaderGen1", - "ADE_AnimateDiffLoaderV1Advanced", - "ADE_AnimateDiffLoaderWithContext", - "ADE_AnimateDiffModelSettings", - "ADE_AnimateDiffModelSettingsAdvancedAttnStrengths", - "ADE_AnimateDiffModelSettingsSimple", - "ADE_AnimateDiffModelSettings_Release", - "ADE_AnimateDiffSamplingSettings", - "ADE_AnimateDiffSettings", - "ADE_AnimateDiffUniformContextOptions", - "ADE_AnimateDiffUnload", - "ADE_ApplyAnimateDiffModel", - "ADE_ApplyAnimateDiffModelSimple", - "ADE_ApplyAnimateDiffModelWithCameraCtrl", - "ADE_ApplyAnimateDiffModelWithPIA", - "ADE_ApplyAnimateLCMI2VModel", - "ADE_AttachLoraHookToCLIP", - "ADE_AttachLoraHookToConditioning", - "ADE_BatchedContextOptions", - "ADE_CFGExtrasPAG", - "ADE_CFGExtrasPAGSimple", - "ADE_CFGExtrasRescaleCFG", - "ADE_CFGExtrasRescaleCFGSimple", - "ADE_CameraCtrlAnimateDiffKeyframe", - "ADE_CameraManualPoseAppend", - "ADE_CameraPoseAdvanced", - "ADE_CameraPoseBasic", - "ADE_CameraPoseCombo", - "ADE_CombineLoraHooks", - "ADE_CombineLoraHooksEight", - "ADE_CombineLoraHooksFour", - "ADE_ConditioningCombine", - "ADE_ConditioningSetMask", - "ADE_ConditioningSetMaskAndCombine", - "ADE_ConditioningSetUnmaskedAndCombine", - "ADE_ContextExtras_ContextRef", - "ADE_ContextExtras_ContextRef_Keyframe", - "ADE_ContextExtras_ContextRef_KeyframeFromList", - "ADE_ContextExtras_ContextRef_KeyframeInterpolation", - "ADE_ContextExtras_ContextRef_ModeFirst", - "ADE_ContextExtras_ContextRef_ModeIndexes", - "ADE_ContextExtras_ContextRef_ModeSliding", - "ADE_ContextExtras_ContextRef_TuneAttn", - "ADE_ContextExtras_ContextRef_TuneAttnAdain", - "ADE_ContextExtras_NaiveReuse", - "ADE_ContextExtras_NaiveReuse_Keyframe", - "ADE_ContextExtras_NaiveReuse_KeyframeFromList", - "ADE_ContextExtras_NaiveReuse_KeyframeInterpolation", - "ADE_ContextExtras_Set", - "ADE_CustomCFG", - "ADE_CustomCFGKeyframe", - "ADE_CustomCFGKeyframeFromList", - "ADE_CustomCFGKeyframeInterpolation", - "ADE_CustomCFGKeyframeSimple", - "ADE_CustomCFGSimple", - "ADE_EmptyLatentImageLarge", - "ADE_InjectI2VIntoAnimateDiffModel", - "ADE_InjectPIAIntoAnimateDiffModel", - "ADE_InputPIA_Multival", - "ADE_InputPIA_PaperPresets", - "ADE_IterationOptsDefault", - "ADE_IterationOptsFreeInit", - "ADE_LoadAnimateDiffModel", - "ADE_LoadAnimateDiffModelWithCameraCtrl", - "ADE_LoadAnimateLCMI2VModel", - "ADE_LoadCameraPoses", - "ADE_LoadCameraPosesFromPath", - "ADE_LoopedUniformContextOptions", - "ADE_LoopedUniformViewOptions", - "ADE_LoraHookKeyframe", - "ADE_LoraHookKeyframeFromStrengthList", - "ADE_LoraHookKeyframeInterpolation", - "ADE_MultivalConvertToMask", - "ADE_MultivalDynamic", - "ADE_MultivalDynamicFloatInput", - "ADE_MultivalDynamicFloats", - "ADE_MultivalScaledMask", - "ADE_NoiseCalibration", - "ADE_NoiseLayerAdd", - "ADE_NoiseLayerAddWeighted", - "ADE_NoiseLayerNormalizedSum", - "ADE_NoiseLayerReplace", - "ADE_NoisedImageInjectOptions", - "ADE_NoisedImageInjection", - "ADE_PIA_AnimateDiffKeyframe", - "ADE_PairedConditioningCombine", - "ADE_PairedConditioningSetMask", - "ADE_PairedConditioningSetMaskAndCombine", - "ADE_PairedConditioningSetUnmaskedAndCombine", - "ADE_PerturbedAttentionGuidanceMultival", - "ADE_RawSigmaSchedule", - "ADE_RegisterLoraHook", - "ADE_RegisterLoraHookModelOnly", - "ADE_RegisterModelAsLoraHook", - "ADE_RegisterModelAsLoraHookModelOnly", - "ADE_ReplaceCameraParameters", - "ADE_ReplaceOriginalPoseAspectRatio", - "ADE_RescaleCFGMultival", - "ADE_SetLoraHookKeyframe", - "ADE_SigmaSchedule", - "ADE_SigmaScheduleSplitAndCombine", - "ADE_SigmaScheduleToSigmas", - "ADE_SigmaScheduleWeightedAverage", - "ADE_SigmaScheduleWeightedAverageInterp", - "ADE_StandardStaticContextOptions", - "ADE_StandardStaticViewOptions", - "ADE_StandardUniformContextOptions", - "ADE_StandardUniformViewOptions", - "ADE_TimestepsConditioning", - "ADE_UpscaleAndVAEEncode", - "ADE_UseEvolvedSampling", - "ADE_ViewsOnlyContextOptions", - "ADE_VisualizeContextOptionsK", - "ADE_VisualizeContextOptionsKAdv", - "ADE_VisualizeContextOptionsSCustom", - "AnimateDiffLoaderV1", - "CheckpointLoaderSimpleWithNoiseSelect" - ], - { - "title_aux": "AnimateDiff Evolved" - } - ], - "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite": [ - [ - "VHS_AudioToVHSAudio", - "VHS_BatchManager", - "VHS_DuplicateImages", - "VHS_DuplicateLatents", - "VHS_DuplicateMasks", - "VHS_GetImageCount", - "VHS_GetLatentCount", - "VHS_GetMaskCount", - "VHS_LoadAudio", - "VHS_LoadAudioUpload", - "VHS_LoadImagePath", - "VHS_LoadImages", - "VHS_LoadImagesPath", - "VHS_LoadVideo", - "VHS_LoadVideoFFmpeg", - "VHS_LoadVideoFFmpegPath", - "VHS_LoadVideoPath", - "VHS_MergeImages", - "VHS_MergeLatents", - "VHS_MergeMasks", - "VHS_PruneOutputs", - "VHS_SelectEveryNthImage", - "VHS_SelectEveryNthLatent", - "VHS_SelectEveryNthMask", - "VHS_SelectFilename", - "VHS_SelectImages", - "VHS_SelectLatents", - "VHS_SelectLatest", - "VHS_SelectMasks", - "VHS_SplitImages", - "VHS_SplitLatents", - "VHS_SplitMasks", - "VHS_Unbatch", - "VHS_VAEDecodeBatched", - "VHS_VAEEncodeBatched", - "VHS_VHSAudioToAudio", - "VHS_VideoCombine", - "VHS_VideoInfo", - "VHS_VideoInfoLoaded", - "VHS_VideoInfoSource" - ], - { - "title_aux": "ComfyUI-VideoHelperSuite" - } - ], - "https://github.com/Koushakur/ComfyUI-DenoiseChooser": [ - [ - "DenoiseChooser|Koushakur" - ], - { - "title_aux": "ComfyUI-DenoiseChooser" - } - ], - "https://github.com/KunmyonChoi/ComfyUI_S3_direct": [ - [ - "Direct Load Image From S3", - "Direct Save Image To S3", - "Save VHS Video to S3" - ], - { - "title_aux": "ComfyUI_S3_direct" - } - ], - "https://github.com/Kurdknight/Kurdknight_comfycheck": [ - [ - "SystemCheck", - "SystemViz" - ], - { - "title_aux": "KurdKnight ComfyUI System Check Node" - } - ], - "https://github.com/KwaiVGI/ComfyUI-KLingAI-API": [ - [ - "Client", - "Effects", - "Image Generator", - "Image2Video", - "KLingAI Preview Video", - "Lip Sync", - "Lip Sync Audio Input", - "Lip Sync Text Input", - "Text2Video", - "Video Extend", - "Virtual Try On" - ], - { - "title_aux": "ComfyUI-KLingAI-API" - } - ], - "https://github.com/Ky11le/draw_tools": [ - [ - "DetectInnerBox", - "PasteIntoFrame", - "TextBoxAutoWrap" - ], - { - "title_aux": "draw_tools" - } - ], - "https://github.com/KytraScript/ComfyUI_KytraWebhookHTTP": [ - [ - "SendToDiscordWebhook" - ], - { - "title_aux": "ComfyUI_KytraWebhookHTTP" - } - ], - "https://github.com/KytraScript/ComfyUI_MatAnyone_Kytra": [ - [ - "Kytra_Images_To_RGB", - "MatAnyoneVideoMatting" - ], - { - "title_aux": "ComfyUI_MatAnyone_Kytra" - } - ], - "https://github.com/L33chKing/comfyui-tag-frequency-weighter": [ - [ - "TagFrequencyWeighter" - ], - { - "title_aux": "Tag Frequency Weighter for ComfyUI" - } - ], - "https://github.com/LAOGOU-666/ComfyUI-LG_HotReload": [ - [ - "HotReload_Terminal" - ], - { - "title_aux": "ComfyUI-LG_HotReload" - } - ], - "https://github.com/LAOGOU-666/ComfyUI_LG_FFT": [ - [ - "LG_FFTNode", - "LG_IFFTNode" - ], - { - "title_aux": "ComfyUI_LG_FFT" - } - ], - "https://github.com/LAOGOU-666/Comfyui-LG_GroupExecutor": [ - [ - "GroupExecutorRepeater", - "GroupExecutorSender", - "GroupExecutorSingle", - "ImageListRepeater", - "ImageListSplitter", - "LG_AccumulatePreview", - "LG_FastPreview", - "LG_ImageReceiver", - "LG_ImageSender", - "MaskListRepeater", - "MaskListSplitter" - ], - { - "title_aux": "Comfyui-LG_GroupExecutor" - } - ], - "https://github.com/LAOGOU-666/Comfyui-LG_Relight": [ - [ - "LG_Relight", - "LG_Relight_Basic", - "LG_Relight_Ultra" - ], - { - "title_aux": "Comfyui-LG_Relight" - } - ], - "https://github.com/LAOGOU-666/Comfyui-Memory_Cleanup": [ - [ - "RAMCleanup", - "VRAMCleanup" - ], - { - "title_aux": "Comfyui-Memory_Cleanup" - } - ], - "https://github.com/LAOGOU-666/Comfyui_LG_Tools": [ - [ - "BridgePreviewNode", - "CachePreviewBridge", - "ColorAdjustment", - "FastCanvas", - "FastCanvasComposite", - "FastCanvasTool", - "GroupSwitcher", - "IPAdapterWeightTypes", - "ImageCropper", - "ImageSelector", - "ImageSizeAdjustment", - "InspyrenetRembgLoader", - "InspyrenetRembgProcess", - "LG_FloatRange", - "LG_InstallDependencies", - "LG_LatentBatchToList", - "LG_LoadImage", - "LG_Noise", - "LG_PipManager", - "LG_SaveImage", - "LazySwitch1way", - "LazySwitch2way", - "MuterSwitcher" - ], - { - "title_aux": "Comfyui_LG_Tools" - } - ], - "https://github.com/LEv145/images-grid-comfy-plugin": [ - [ - "GridAnnotation", - "ImageCombine", - "ImagesGridByColumns", - "ImagesGridByRows", - "LatentCombine" - ], - { - "title_aux": "ImagesGrid" - } - ], - "https://github.com/LK-168/comfyui_imgutils": [ - [ - "BBoxFilter", - "BBoxToMaskNode", - "CensorWithMask", - "ImgutilsAutoSegmenter", - "ImgutilsBBoxSegmenter", - "ImgutilsGenericDetector", - "MaskAttributeNodeLK", - "MaskCombineNodeLK", - "MaskEdgeNodeLK", - "MaskHelperLK", - "MaskInfoNodeLK", - "MaskMorphologyNodeLK", - "MaskToBBoxNode", - "SAMLoaderLK", - "SAMPredictorNode" - ], - { - "title_aux": "comfyui_imgutils" - } - ], - "https://github.com/LKbaba/ComfyUI-TuZi-Flux-Kontext": [ - [ - "FluxKontext_ImageToImage", - "FluxKontext_MultiImageToImage", - "FluxKontext_TextToImage" - ], - { - "title_aux": "ComfyUI-TuZi-Flux-Kontext" - } - ], - "https://github.com/LamEmil/ComfyUI_ASCIIArtNode": [ - [ - "ASCIIAnimationGenerator", - "ASCIIArtGenerator", - "ColorASCIIAnimationGenerator", - "RealisticColorASCIIAnimationGenerator", - "SequentialTwoPassTypingColorASCIIAnimation", - "TwoPassTypingColorASCIIAnimation", - "VideoToColorStaticASCIIArt", - "VideoToDynamicColorASCIIArt" - ], - { - "title_aux": "ComfyUI ASCII Art Nodes" - } - ], - "https://github.com/LaoMaoBoss/ComfyUI-WBLESS": [ - [ - "Inversed Switch", - "Switch" - ], - { - "title_aux": "ComfyUI-WBLESS" - } - ], - "https://github.com/LargeModGames/comfyui-smart-lora-downloader": [ - [ - "LoRAAutoDownloader", - "WorkflowLoRAScanner" - ], - { - "title_aux": "ComfyUI LoRA Auto Downloader" - } - ], - "https://github.com/LarryJane491/Image-Captioning-in-ComfyUI": [ - [ - "LoRA Caption Load", - "LoRA Caption Save" - ], - { - "title_aux": "Image-Captioning-in-ComfyUI" - } - ], - "https://github.com/LarryJane491/Lora-Training-in-Comfy": [ - [ - "Lora Training in Comfy (Advanced)", - "Lora Training in ComfyUI", - "Tensorboard Access" - ], - { - "title_aux": "Lora-Training-in-Comfy" - } - ], - "https://github.com/LatentSpaceDirective/ComfyUI-Texturaizer": [ - [ - "Texturaizer_ApplyControlNets", - "Texturaizer_ApplyStyleToPrompt", - "Texturaizer_CachedCNLoader", - "Texturaizer_CachedCheckpointLoader", - "Texturaizer_ClipEncodeSwitchVersion", - "Texturaizer_CombinedConditioningFromColors", - "Texturaizer_ExtractCNData", - "Texturaizer_GenerateNoise", - "Texturaizer_GetCNData", - "Texturaizer_GetClipModelName", - "Texturaizer_GetFluxGuidance", - "Texturaizer_GetIPAdapterData", - "Texturaizer_GetImageData", - "Texturaizer_GetJsonData", - "Texturaizer_GetLoraData", - "Texturaizer_GetMaterialTileData", - "Texturaizer_GetModelName", - "Texturaizer_GetPromptData", - "Texturaizer_GetRenderData", - "Texturaizer_GetSamplerData", - "Texturaizer_GetSegData", - "Texturaizer_GetStyleData", - "Texturaizer_GetVAEName", - "Texturaizer_IPAdapterEmbeds", - "Texturaizer_KSamplerAdvanced", - "Texturaizer_Placeholder", - "Texturaizer_PowerLoraLoader", - "Texturaizer_SendImage", - "Texturaizer_SetGlobalDir", - "Texturaizer_SigmasSelector", - "Texturaizer_SwitchAny", - "Texturaizer_SwitchLazy", - "Texturaizer_UseSDXL" - ], - { - "title_aux": "ComfyUI-Texturaizer" - } - ], - "https://github.com/Layer-norm/comfyui-lama-remover": [ - [ - "LamaRemover", - "LamaRemoverIMG" - ], - { - "title_aux": "Comfyui lama remover" - } - ], - "https://github.com/LeanModels/ComfyUI-DFloat11": [ - [ - "DFloat11ModelLoader" - ], - { - "title_aux": "ComfyUI-DFloat11" - } - ], - "https://github.com/Legorobotdude/ComfyUI-VariationLab": [ - [ - "CFGExplorer", - "CheckpointExplorer", - "StepExplorer" - ], - { - "title_aux": "ComfyUI-VariationLab" - } - ], - "https://github.com/Lerc/canvas_tab": [ - [ - "Canvas_Tab", - "Send_To_Editor" - ], - { - "author": "Lerc", - "description": "This extension provides a full page image editor with mask support. There are two nodes, one to receive images from the editor and one to send images to the editor.", - "nickname": "Canvas Tab", - "title": "Canvas Tab", - "title_aux": "Canvas Tab" - } - ], - "https://github.com/LevelPixel/ComfyUI-LevelPixel": [ - [ - "AnyToText|LP", - "BoolToInt|LP", - "BoolToString|LP", - "CLIP Text Encode Translate [LP]", - "CLIPTextEncodeTranslate|LP", - "Calculate Target Size By Mask [LP]", - "CalculateTargetSizeByMask|LP", - "ComboToText|LP", - "Convert Any To Text [LP]", - "Convert Bool To Int [LP]", - "Convert Bool To String [LP]", - "Convert Combo To Text [LP]", - "Convert Float To Int [LP]", - "Convert Float To String [LP]", - "Convert Int To Bool [LP]", - "Convert Int To Float [LP]", - "Convert Int To String [LP]", - "Convert String To Bool [LP]", - "Convert String To Combo [LP]", - "Convert String To Float [LP]", - "Convert String To Int [LP]", - "Convert String To Number [LP]", - "Count Objects [LP]", - "CountObjects|LP", - "Cropped Aspect Size Parameters [LP]", - "Cropped Forsed Size Parameters [LP]", - "Cropped Free Size Parameters [LP]", - "Cropped Ranged Size Parameters [LP]", - "CroppedAspectSizeParameters|LP", - "CroppedForsedSizeParameters|LP", - "CroppedFreeSizeParameters|LP", - "CroppedRangedSizeParameters|LP", - "Delay [LP]", - "Delay|LP", - "Extend Factor Parameters [LP]", - "ExtendFactorParameters|LP", - "Fast Checker Pattern [LP]", - "FastCheckerPattern|LP", - "File Counter [LP]", - "FileCounter|LP", - "Find Value From File [LP]", - "FindValueFromFile|LP", - "FloatToInt|LP", - "FloatToString|LP", - "Get ComfyUI Folder Path [LP]", - "Get ComfyUI HTTP Folder Path [LP]", - "Get Filename By Index In Folder [LP]", - "Get Iterator Data From Image Folders [LP]", - "Get Iterator Data From Videos [LP]", - "GetComfyUIFolderPath|LP", - "GetComfyUIHttpFolderPath|LP", - "GetFilenameByIndexInFolder|LP", - "GetIteratorDataImageFolders|LP", - "GetIteratorDataVideos|LP", - "Hard Model Unloader [LP]", - "HardModelUnloader|LP", - "HundredthsSimpleFloatSlider|LP", - "Image Data Iterator [LP]", - "Image Loader From Path [LP]", - "Image Overlay [LP]", - "ImageDataIterator|LP", - "ImageLoaderFromPath|LP", - "ImageOverlay|LP", - "Inpaint Crop [LP]", - "Inpaint Stitch [LP]", - "InpaintCrop|LP", - "InpaintStitch|LP", - "IntToBool|LP", - "IntToFloat|LP", - "IntToString|LP", - "Iterator [LP]", - "Iterator|LP", - "Keep Only English Tags [LP]", - "Keep Only English Words [LP]", - "KeepOnlyEnglishTags|LP", - "KeepOnlyEnglishWords|LP", - "Load Image [LP]", - "Load LoRA Tag [LP]", - "LoadImage|LP", - "LoraTagLoader|LP", - "Model Unloader [LP]", - "ModelUnloader|LP", - "Override CLIP Device [LP]", - "Override VAE Device [LP]", - "OverrideCLIPDevice|LP", - "OverrideVAEDevice|LP", - "Pipe In [LP]", - "Pipe Out [LP]", - "Pipe [LP]", - "PipeIn|LP", - "PipeOut|LP", - "Pipe|LP", - "Preview Image Bridge [LP]", - "PreviewImageForConditions|LP", - "Remove Banned Tags From Tags [LP]", - "Remove Banned Tags From Text [LP]", - "Remove Duplicate Tags [LP]", - "RemoveBannedTagsFromTags|LP", - "RemoveBannedTagsFromText|LP", - "RemoveDuplicateTags|LP", - "Resize Image To Target Size [LP]", - "Resize Image and Masks [LP]", - "ResizeImageAndMasks|LP", - "ResizeImageToTargetSize|LP", - "Resorting Tags [LP]", - "ResortingTags|LP", - "Seed [LP]", - "Seed|LP", - "Show Text Bridge [LP]", - "Show Text [LP]", - "ShowTextBridge|LP", - "ShowText|LP", - "Simple Float Slider - Hundredths Step [LP]", - "Simple Float Slider - Tenths Step [LP]", - "Simple Float Slider [LP]", - "SimpleFloatSlider|LP", - "Soft Model Unloader [LP]", - "SoftModelUnloader|LP", - "Split Compound Text [LP]", - "SplitCompoundText|LP", - "String Cycler [LP]", - "String [LP]", - "StringCycler|LP", - "StringToBool|LP", - "StringToCombo|LP", - "StringToFloat|LP", - "StringToInt|LP", - "StringToNumber|LP", - "String|LP", - "Tag Category Filter [LP]", - "Tag Category Keeper [LP]", - "Tag Category Remover [LP]", - "Tag Category [LP]", - "Tag Merger [LP]", - "Tag Remover [LP]", - "Tag Replace [LP]", - "Tag Switcher [LP]", - "TagCategoryFilter|LP", - "TagCategoryKeeper|LP", - "TagCategoryRemover|LP", - "TagCategory|LP", - "TagMerger|LP", - "TagRemover|LP", - "TagReplace|LP", - "TagSwitcher|LP", - "TenthsSimpleFloatSlider|LP", - "Text Choice Parser [LP]", - "Text Replace [LP]", - "Text To List [LP]", - "Text Translate [LP]", - "Text [LP]", - "TextChoiceParser|LP", - "TextReplace|LP", - "TextToList|LP", - "TextTranslateManualAll|LP", - "TextTranslateManual|LP", - "TextTranslate|LP", - "Text|LP" - ], - { - "title_aux": "ComfyUI Level Pixel" - } - ], - "https://github.com/LevelPixel/ComfyUI-LevelPixel-Advanced": [ - [ - "Autotagger [LP]", - "Autotagger|LP", - "Color Input [LP]", - "ColorInput|LP", - "Image Remove Background (BiRefNet) [LP]", - "Image Remove Background (RMBG) [LP]", - "Image Remove Background (rembg) [LP]", - "ImageRemoveBackgroundBiRefNet|LP", - "ImageRemoveBackgroundRMBG|LP", - "ImageRemoveBackground|LP", - "LLM Advanced [LP]", - "LLM Loader [LP]", - "LLM Sampler [LP]", - "LLMAdvanced|LP", - "LLMLoader|LP", - "LLMSampler|LP", - "LLava Advanced [LP]", - "LLava Clip Loader [LP]", - "LLava Loader [LP]", - "LLava Sampler Advanced [LP]", - "LLava Sampler Simple [LP]", - "LLava Simple [LP]", - "LLavaAdvanced|LP", - "LLavaClipLoader|LP", - "LLavaLoader|LP", - "LLavaSamplerAdvanced|LP", - "LLavaSamplerSimple|LP", - "LLavaSimple|LP", - "Multimodal Generator Advanced [LP]", - "MultimodalGeneratorAdvanced|LP", - "Recognize Anything (RAM) [LP]", - "RecognizeAnything(RAM)|LP" - ], - { - "title_aux": "ComfyUI Level Pixel Advanced" - } - ], - "https://github.com/Lhyejin/ComfyUI-Fill-Image-for-Outpainting": [ - [ - "FillImageForOutpainting" - ], - { - "title_aux": "ComfyUI-Fill-Image-for-Outpainting" - } - ], - "https://github.com/LiJT/ComfyUI-Gemini-Prompt-Generator-JT": [ - [ - "GeminiPromptGeneratorJT" - ], - { - "title_aux": "Gemini prompt generator JT version" - } - ], - "https://github.com/Light-x02/ComfyUI-FluxSettingsNode": [ - [ - "DisableNoise", - "FluxSettingsNode" - ], - { - "title_aux": "Flux Settings Node" - } - ], - "https://github.com/Light-x02/ComfyUI-Image-Metadata-Nodes": [ - [ - "ImageMetadataLoader", - "ImageMetadataSaver" - ], - { - "title_aux": "Image Metadata Nodes" - } - ], - "https://github.com/Light-x02/ComfyUI_Crop_Image_By_Lightx02": [ - [ - "CropImageByLightx02" - ], - { - "title_aux": "Crop Image by Lightx02" - } - ], - "https://github.com/LightSketch-ai/ComfyUI-LivePortraitNode": [ - [ - "LightSketch Live Portrait", - "Preview Video" - ], - { - "title_aux": "ComfyUI-LivePortraitNode (Replicate API)" - } - ], - "https://github.com/Lightricks/ComfyUI-LTXVideo": [ - [ - "AddLatentGuide", - "ImageToCPU", - "LTXAttentioOverride", - "LTXAttentionBank", - "LTXAttnOverride", - "LTXFetaEnhance", - "LTXFlowEditCFGGuider", - "LTXFlowEditSampler", - "LTXForwardModelSamplingPred", - "LTXPerturbedAttention", - "LTXPrepareAttnInjections", - "LTXQ8Patch", - "LTXRFForwardODESampler", - "LTXRFReverseODESampler", - "LTXReverseModelSamplingPred", - "LTXVAdainLatent", - "LTXVAddGuideAdvanced", - "LTXVApplySTG", - "LTXVBaseSampler", - "LTXVExtendSampler", - "LTXVFilmGrain", - "LTXVInContextSampler", - "LTXVLatentUpsampler", - "LTXVLinearOverlapLatentTransition", - "LTXVLoopingSampler", - "LTXVMultiPromptProvider", - "LTXVPatcherVAE", - "LTXVPreprocessMasks", - "LTXVPromptEnhancer", - "LTXVPromptEnhancerLoader", - "LTXVQ8LoraModelLoader", - "LTXVSelectLatents", - "LTXVSetVideoLatentNoiseMasks", - "LTXVTiledSampler", - "LTXVTiledVAEDecode", - "ModifyLTXModel", - "STGAdvancedPresets", - "STGGuiderAdvanced", - "STGGuiderNode", - "Set VAE Decoder Noise" - ], - { - "title_aux": "ComfyUI-LTXVideo" - } - ], - "https://github.com/Limbicnation/ComfyUI-RandomSeedGenerator": [ - [ - "AdvancedSeedGenerator" - ], - { - "title_aux": "ComfyUI-RandomSeedGenerator" - } - ], - "https://github.com/Limbicnation/ComfyUI-TransparencyBackgroundRemover": [ - [ - "AutoGrabCutRemover", - "GrabCutRefinement", - "TransparencyBackgroundRemover", - "TransparencyBackgroundRemoverBatch" - ], - { - "title_aux": "Transparency Background Remover" - } - ], - "https://github.com/Limbicnation/ComfyUIDepthEstimation": [ - [ - "DepthEstimationNode" - ], - { - "title_aux": "Depth Estimation Node" - } - ], - "https://github.com/Limbicnation/ComfyUI_FaceDetectionNode": [ - [ - "FaceDetectionNode", - "custom_nodes" - ], - { - "nodename_pattern": "FaceDetectionNode", - "title_aux": "ComfyUI Face Detection Node" - } - ], - "https://github.com/Limitex/ComfyUI-Calculation": [ - [ - "CenterCalculation", - "CreateQRCode" - ], - { - "title_aux": "ComfyUI-Calculation" - } - ], - "https://github.com/Limitex/ComfyUI-Diffusers": [ - [ - "CreateIntListNode", - "DiffusersClipTextEncode", - "DiffusersModelMakeup", - "DiffusersPipelineLoader", - "DiffusersSampler", - "DiffusersSchedulerLoader", - "DiffusersVaeLoader", - "LcmLoraLoader", - "StreamDiffusionCreateStream", - "StreamDiffusionFastSampler", - "StreamDiffusionSampler", - "StreamDiffusionWarmup" - ], - { - "title_aux": "ComfyUI-Diffusers" - } - ], - "https://github.com/Ling-APE/ComfyUI-PixelResolutionCalculator": [ - [ - "LatentSizeToPixelSize", - "PixelResolutionCalculator" - ], - { - "title_aux": "ComfyUI-PixelResolutionCalculator" - } - ], - "https://github.com/LingSss9/comfyui-merge": [ - [ - "LoadLoRAMenu", - "MergeLoRAsKohyaSSLike", - "OnlyLoadLoRAsModel", - "SaveLoRAModels" - ], - { - "author": "cyberblackcat", - "description": "This extension provides some nodes to support merge lora, adjust Lora Block Weight.", - "nickname": "CBC", - "title": "merge", - "title_aux": "comfyui-merge" - } - ], - "https://github.com/Loewen-Hob/rembg-comfyui-node-better": [ - [ - "Image Remove Background (rembg)" - ], - { - "title_aux": "Rembg Background Removal Node for ComfyUI (Better)" - } - ], - "https://github.com/LonicaMewinsky/ComfyUI-MakeFrame": [ - [ - "BreakFrames", - "BreakGrid", - "GetKeyFrames", - "MakeGrid", - "RandomImageFromDir" - ], - { - "title_aux": "ComfyBreakAnim" - } - ], - "https://github.com/LonicaMewinsky/ComfyUI-RawSaver": [ - [ - "SaveTifImage" - ], - { - "title_aux": "ComfyUI-RawSaver" - } - ], - "https://github.com/LoveEatCandy/COMFYUI-ReplacePartOfImage": [ - [ - "ReplacePartOfImage" - ], - { - "title_aux": "COMFYUI-ReplacePartOfImage" - } - ], - "https://github.com/Lovzu/ComfyUI-KittenTTS": [ - [ - "KittenTTS" - ], - { - "title_aux": "KittenTTS Node for Voice Generation" - } - ], - "https://github.com/Ltamann/ComfyUI-TBG-ETUR": [ - [ - "EdgePadNode", - "TBG_masked_attention" - ], - { - "title_aux": "TBG_Enhanced Tiled Upscaler & Refiner FLUX PRO" - } - ], - "https://github.com/Ltamann/ComfyUI-TBG-Takeaways": [ - [ - "BasicSchedulerNormalized", - "LogSigmaSamplerNode", - "LogSigmaStepSamplerNode", - "ModelSamplingFluxGradual", - "PolyExponentialSigmaAdder", - "TBG_FluxKontextStabilizer" - ], - { - "title_aux": "TBG\u2019s ComfyUI Development Takeaways" - } - ], - "https://github.com/LucipherDev/ComfyUI-AniDoc": [ - [ - "AniDocLoader", - "AniDocSampler", - "GetAniDocControlnetImages", - "LoadCoTracker" - ], - { - "title_aux": "ComfyUI-AniDoc" - } - ], - "https://github.com/LucipherDev/ComfyUI-Golden-Noise": [ - [ - "GoldenNoise" - ], - { - "title_aux": "ComfyUI-Golden-Noise" - } - ], - "https://github.com/LucipherDev/ComfyUI-TangoFlux": [ - [ - "TangoFluxLoader", - "TangoFluxSampler", - "TangoFluxVAEDecodeAndPlay" - ], - { - "title_aux": "ComfyUI-TangoFlux" - } - ], - "https://github.com/Ludobico/ComfyUI-ScenarioPrompt": [ - [ - "ScenarioPrompt" - ], - { - "title_aux": "ComfyUI-ScenarioPrompt" - } - ], - "https://github.com/LyazS/comfyui-anime-seg": [ - [ - "Anime Character Seg" - ], - { - "title_aux": "Anime Character Segmentation node for comfyui" - } - ], - "https://github.com/LyazS/comfyui-nettools": [ - [ - "NTL_LoadImagesBase64", - "NTL_SendImagesWebSocket" - ], - { - "title_aux": "net tool node for comfyui" - } - ], - "https://github.com/M1kep/ComfyLiterals": [ - [ - "Checkpoint", - "Float", - "Int", - "KepStringLiteral", - "Lora", - "Operation", - "String" - ], - { - "title_aux": "ComfyLiterals" - } - ], - "https://github.com/M1kep/ComfyUI-KepOpenAI": [ - [ - "KepOpenAI_ImageWithPrompt" - ], - { - "title_aux": "ComfyUI-KepOpenAI" - } - ], - "https://github.com/M1kep/ComfyUI-OtherVAEs": [ - [ - "OtherVAE_Taesd" - ], - { - "title_aux": "ComfyUI-OtherVAEs" - } - ], - "https://github.com/M1kep/Comfy_KepKitchenSink": [ - [ - "KepRotateImage" - ], - { - "title_aux": "Comfy_KepKitchenSink" - } - ], - "https://github.com/M1kep/Comfy_KepListStuff": [ - [ - "Empty Images", - "Image Overlay", - "ImageListLoader", - "Join Float Lists", - "Join Image Lists", - "KepStringList", - "KepStringListFromNewline", - "Kep_JoinListAny", - "Kep_RepeatList", - "Kep_ReverseList", - "Kep_VariableImageBuilder", - "List Length", - "Range(Num Steps) - Float", - "Range(Num Steps) - Int", - "Range(Step) - Float", - "Range(Step) - Int", - "Stack Images", - "XYAny", - "XYImage" - ], - { - "title_aux": "Comfy_KepListStuff" - } - ], - "https://github.com/M1kep/Comfy_KepMatteAnything": [ - [ - "MatteAnything_DinoBoxes", - "MatteAnything_GenerateVITMatte", - "MatteAnything_InitSamPredictor", - "MatteAnything_LoadDINO", - "MatteAnything_LoadVITMatteModel", - "MatteAnything_SAMLoader", - "MatteAnything_SAMMaskFromBoxes", - "MatteAnything_ToTrimap" - ], - { - "title_aux": "Comfy_KepMatteAnything" - } - ], - "https://github.com/M1kep/KepPromptLang": [ - [ - "Build Gif", - "Special CLIP Loader" - ], - { - "title_aux": "KepPromptLang" - } - ], - "https://github.com/MDMAchine/ComfyUI_MD_Nodes": [ - [ - "ACE_LatentVisualizer", - "APGGuiderForked", - "AdvancedAudioPreviewAndSave", - "AdvancedMediaSave", - "HybridAdaptiveSigmas", - "MasteringChainNode", - "NoiseDecayScheduler_Custom", - "PingPongSampler_Custom_FBG", - "PingPongSampler_Custom_Lite", - "SceneGeniusAutocreator", - "SeedSaver", - "UniversalGuardian" - ], - { - "title_aux": "MD Nodes" - } - ], - "https://github.com/MNeMoNiCuZ/ComfyUI-mnemic-nodes": [ - [ - "AudioVisualizer", - "LoraTagLoader", - "ResolutionSelector", - "StringCleaning", - "StringTextExtractor", - "StringTextSplitter", - "TiktokenTokenizer", - "WildcardProcessor", - "\u26d4 Generate Negative Prompt", - "\u2702\ufe0f String Text Extractor", - "\u2702\ufe0f String Text Splitter", - "\u2728\ud83c\udf10 Groq ALM API - Translate [EN only]", - "\u2728\ud83d\udcac Groq LLM API", - "\u2728\ud83d\udcdd Groq ALM API - Transcribe", - "\u2728\ud83d\udcf7 Groq VLM API", - "\ud83c\udfb5\ud83d\udcca Audio Visualizer", - "\ud83c\udff7\ufe0f LoRA Loader Prompt Tags", - "\ud83d\udcbe Save Text File With Path", - "\ud83d\udcc1 Get File Path", - "\ud83d\udcc5 Format Date Time", - "\ud83d\udcd0 Resolution Image Size Selector", - "\ud83d\udcdd Wildcard Processor", - "\ud83d\udd20 Tiktoken Tokenizer Info", - "\ud83d\uddbc\ufe0f Download Image from URL", - "\ud83d\uddbc\ufe0f+\ud83d\udcdd Load Text-Image Pair (Single)", - "\ud83d\uddbc\ufe0f+\ud83d\udcdd Load Text-Image Pairs (List)", - "\ud83d\uddbc\ufe0f\ud83d\udcca Metadata Extractor", - "\ud83e\uddf9 String Cleaning" - ], - { - "title_aux": "ComfyUI-mnemic-nodes" - } - ], - "https://github.com/Makeezi/ComfyUI-promptLAB": [ - [ - "PromptLAB" - ], - { - "title_aux": "ComfyUI-promptLAB" - } - ], - "https://github.com/MakkiShizu/ComfyUI-MakkiTools": [ - [ - "AnyImageStitch_makki", - "AnyImagetoConditioning_flux_kontext_makki", - "AutoLoop_create_pseudo_loop_video_makki", - "BatchLoraLoader_makki", - "Environment_INFO_makki", - "GetImageNthCount_makki", - "ImageChannelSeparate_makki", - "ImageCountConcatenate_makki", - "ImageHeigthStitch_makki", - "ImageWidthStitch_makki", - "Image_Resize_makki", - "MergeImageChannels_makki", - "Prism_Mirage_makki", - "int_calculate_statistics_makki", - "random_any_makki", - "show_type_makki", - "timer_makki", - "translator_m2m100_makki", - "translators_makki" - ], - { - "title_aux": "ComfyUI-MakkiTools" - } - ], - "https://github.com/MakkiShizu/ComfyUI-Prompt-Wildcards": [ - [ - "makitextwildcards", - "makiwildcards", - "makiwildcards_Advanced", - "textconcatenate", - "textconcatenate_v2" - ], - { - "title_aux": "ComfyUI-Prompt-Wildcards" - } - ], - "https://github.com/MakkiShizu/ComfyUI-Qwen2_5-VL": [ - [ - "BatchImageLoaderToLocalFiles", - "DownloadAndLoadQwen2_5_VLModel", - "Qwen2_5_VL_Run", - "Qwen2_5_VL_Run_Advanced" - ], - { - "title_aux": "ComfyUI-Qwen2_5-VL" - } - ], - "https://github.com/MakkiShizu/comfyui_reimgsize": [ - [ - "Cropimg", - "Reimgsize", - "Resizebyratio" - ], - { - "title_aux": "comfyui_reimgsize" - } - ], - "https://github.com/Mamaaaamooooo/batchImg-rembg-ComfyUI-nodes": [ - [ - "Image Remove Background (rembg)" - ], - { - "title_aux": "Batch Rembg for ComfyUI" - } - ], - "https://github.com/ManglerFTW/ComfyI2I": [ - [ - "Color Transfer", - "Combine and Paste", - "Inpaint Segments", - "Mask Ops" - ], - { - "author": "ManglerFTW", - "title": "ComfyI2I", - "title_aux": "ComfyI2I" - } - ], - "https://github.com/MarcusNyne/m9-prompts-comfyui": [ - [ - "ScramblePrompts_m9", - "TweakWeights_m9" - ], - { - "title_aux": "m9-prompts-comfyui" - } - ], - "https://github.com/MariusKM/ComfyUI-BadmanNodes": [ - [ - "BadmanBrightness", - "BadmanCLIPTextEncodeSDXLRegion", - "BadmanDesaturate", - "BadmanDilateErodeMask", - "BadmanIO", - "BadmanIntUtil", - "BadmanMaskBlur", - "BadmanStringSelect", - "BadmanStringToInteger", - "BadmanWildCardProcessor", - "Badman_Blend", - "Badman_ColorTransferLab", - "Badman_Concat_String", - "Badman_HexGenerator", - "Badman_PalletteGenerator", - "Badman_Print", - "Badman_String" - ], - { - "title_aux": "ComfyUI-BadmanNodes" - } - ], - "https://github.com/MarkoCa1/ComfyUI-Text": [ - [ - "CombinationText", - "PlaceholderText", - "ReplaceText", - "ShowText" - ], - { - "title_aux": "ComfyUI-Text" - } - ], - "https://github.com/MarkoCa1/ComfyUI_Segment_Mask": [ - [ - "AutomaticMask(segment anything)" - ], - { - "title_aux": "ComfyUI_Segment_Mask" - } - ], - "https://github.com/Marksusu/ComfyUI_MTCLIPEncode": [ - [ - "MTCLIPEncode" - ], - { - "title_aux": "ComfyUI_MTCLIPEncode" - } - ], - "https://github.com/MartinDeanMoriarty/ComfyUI-DeanLogic": [ - [ - "ImageCount", - "ImageInputSwitch", - "ImageOutputSwitch", - "Int Compare" - ], - { - "title_aux": "ComfyUI-DeanLogic" - } - ], - "https://github.com/MaruPelkar/comfyui-conditioning-resizer": [ - [ - "ConditioningResizer" - ], - { - "title_aux": "ComfyUI Conditioning Resizer" - } - ], - "https://github.com/Mason-McGough/ComfyUI-Mosaica": [ - [ - "ApplyLUTToLabelImage", - "KMeans", - "LoadLUTFromMatplotlib", - "MeanShift", - "RandomLUT", - "Watershed" - ], - { - "title_aux": "Mosaica" - } - ], - "https://github.com/MasterDenis/VAE-Decode-Switch": [ - [ - "VAEDecodeSwitcher" - ], - { - "title_aux": "VAE Decode Switch for ComfyUI" - } - ], - "https://github.com/Mattabyte/ComfyUI-SecureApiCall": [ - [ - "SaveLatentToS3", - "SaveVideoFilesS3", - "SecureAPI-SecureAPI", - "SecureAPI-SecureAPI-AWS" - ], - { - "title_aux": "ComfyUI Secure API Call" - } - ], - "https://github.com/Maxed-Out-99/ComfyUI-MaxedOut": [ - [ - "Crop Image By Mask", - "Flux Empty Latent Image", - "Flux Image Scale To Total Pixels (Flux Safe)", - "Flux Resolution Selector", - "FluxResolutionMatcher", - "Image Scale To Total Pixels (SDXL Safe)", - "LatentHalfMasks", - "Load Image Batch MXD", - "LoadImageWithPromptsMXD", - "LoadLatent_WithParams", - "LoadLatents_FromFolder_WithParams", - "Place Image By Mask", - "Prompt With Guidance (Flux)", - "SaveLatentMXD", - "Sdxl Empty Latent Image", - "Sdxl Resolution Selector", - "Wan2_2EmptyLatentImageMXD" - ], - { - "title_aux": "ComfyUI-MaxedOut" - } - ], - "https://github.com/Maxed-Out-99/ComfyUI-SmartModelLoaders-MXD": [ - [ - "CLIPLoaderUnified", - "DualCLIPLoaderUnified", - "QuadrupleCLIPLoaderUnified", - "TripleCLIPLoaderUnified", - "UNETLoaderUnified" - ], - { - "title_aux": "ComfyUI-SmartModelLoaders-MXD" - } - ], - "https://github.com/McKlinton2/comfyui-mcklinton-pack": [ - [ - "ColormaskNode", - "LoadFilteredImageBatch", - "MultiLayerComposeNode", - "SaveTextArrayToFiles" - ], - { - "title_aux": "ComfyUI McKlinton Pack \u2014 Mask Node" - } - ], - "https://github.com/Mcmillian/ComfyUI-SimpleToolsNodes": [ - [ - "GetModelStep", - "GlmPromptNode" - ], - { - "title_aux": "SimpleToolsNodes" - } - ], - "https://github.com/MeeeyoAI/ComfyUI_StringOps": [ - [ - "AddPrefixSuffix", - "AddPrefixSuffixToLines", - "BatchReplaceStrings", - "CheckSubstringPresence", - "CompareInt", - "ConditionalTextOutput", - "CountOccurrences", - "CustomCrop", - "DecodePreview", - "ExtractAndCombineLines", - "ExtractBeforeAfter", - "ExtractLinesByIndex", - "ExtractSpecificData", - "ExtractSpecificLines", - "ExtractSubstring", - "ExtractSubstringByIndices", - "FileCopyCutNode", - "FileDeleteNode", - "FileListAndSuffix", - "FileNameReplacer", - "FilterLinesBySubstrings", - "FilterLinesByWordCount", - "FindExcelData", - "FindFirstLineContent", - "FloatToInteger", - "GenerateNumbers", - "GenerateVideoPrompt", - "GenericImageLoader", - "GetCurrentTime", - "GetFloatParam", - "GetIntParam", - "GetRandomIntegerInRange", - "ImageAdjuster", - "ImageOverlayAlignment", - "LoadAndAdjustImage", - "MultiParamInputNode", - "NumberExtractor", - "ProcessString", - "RandomLineFromText", - "ReadExcelData", - "ReadExcelRowOrColumnDiff", - "ReadWebNode", - "RemoveContentBetweenChars", - "ReplaceMultiple", - "ReplaceNthOccurrence", - "SaveImagEX", - "SelectionParameter", - "ShuffleTextLines", - "SimpleRandomSeed", - "SimpleTextReplacer", - "SingleTextInput", - "SplitAndExtractText", - "SplitStringByDelimiter", - "TextConcatenation", - "TextConcatenator", - "TextConditionCheck", - "TextToImage", - "TextToList", - "WriteExcelData", - "WriteExcelImage", - "WriteToTxtFile" - ], - { - "title_aux": "ComfyUI_StringOps" - } - ], - "https://github.com/Meettya/ComfyUI-OneForOne": [ - [ - "OFO Image Fit" - ], - { - "title_aux": "ComfyUI-OneForOne" - } - ], - "https://github.com/MetaGLM/ComfyUI-ZhipuAI-Platform": [ - [ - "VideoReportData", - "VideoReportGenerate", - "VideoReportPull" - ], - { - "title_aux": "ComfyUI ZhipuAI Platform" - } - ], - "https://github.com/MicheleGuidi/ComfyUI-Contextual-SAM2": [ - [ - "Sam2ContextSegmentation", - "Sam2TiledSegmentation" - ], - { - "title_aux": "ComfyUI-Computer-Vision" - } - ], - "https://github.com/MiddleKD/ComfyUI-denoise-mask-scheduler": [ - [ - "ApplyDenoiseMaskSchedulerWithSigma", - "ApplyDenoiseMaskSchedulerWithStep", - "DynamicImageResize" - ], - { - "title_aux": "ComfyUI-denoise-mask-scheduler" - } - ], - "https://github.com/MiddleKD/ComfyUI-mem-safe-wrapper": [ - [ - "MakeModelMemorySafe-safewrapper", - "ResetModelPatcher-safewrapper", - "SimpleDummyModel-safewrapper", - "SimpleDummyRun-safewrapper" - ], - { - "title_aux": "ComfyUI-mem-safe-wrapper" - } - ], - "https://github.com/MiddleKD/ComfyUI-productfix": [ - [ - "ApplyLatentInjection", - "DetailTransferAdd", - "DetailTransferLatentAdd", - "DynamicImageResize", - "GetTextMask", - "ResetModelPatcherCalculateWeight", - "VQDecoder", - "VQEncoder", - "VQLoader" - ], - { - "title_aux": "ComfyUI-productfix" - } - ], - "https://github.com/MijnSpam/ComfyUI_SwapAndScale": [ - [ - "SwapAndScale" - ], - { - "title_aux": "Comfy swap and scale" - } - ], - "https://github.com/MijnSpam/UploadToPushOver": [ - [ - "UploadToPushOver" - ], - { - "title_aux": "Upload to PushOver" - } - ], - "https://github.com/MilitantHitchhiker/MilitantHitchhiker-SwitchbladePack": [ - [ - "FluxModelSave_v2", - "FluxQuantNode", - "GODARCScheduler", - "GroqAPIPromptEnhancer", - "IntegratedRandomPromptGenerator", - "ModelAnalyserNode", - "TextAppender_v2" - ], - { - "author": "Militant Hitchhiker", - "description": "Militant Hitchhiker's multi-function nodes.", - "nickname": "Switchblade", - "title": "Switchblade Pack", - "title_aux": "MilitantHitchhiker-SwitchbladePack" - } - ], - "https://github.com/Mintbeer96/ComfyUI-KerasOCR": [ - [ - "KerasOCR" - ], - { - "title_aux": "ComfyUI-KerasOCR" - } - ], - "https://github.com/Miosp/ComfyUI-FBCNN": [ - [ - "JPEG artifacts removal FBCNN" - ], - { - "title_aux": "ComfyUI-FBCNN" - } - ], - "https://github.com/MitoshiroPJ/ComfyUI_save_image_sdli": [ - [ - "PreviewSdlImage", - "SaveSdlImage" - ], - { - "title_aux": "ComfyUI SaveImage SDLI" - } - ], - "https://github.com/MitoshiroPJ/comfyui_nearsighted_attention": [ - [ - "NearSightedAttention", - "NearSightedAttentionSimple", - "NearSightedTile", - "SlothfulAttention" - ], - { - "title_aux": "ComfyUI Nearsighted Attention" - } - ], - "https://github.com/Miyuutsu/comfyui-save-vpred": [ - [ - "CheckpointSaveVpred" - ], - { - "author": "miyuu", - "description": "Used to save SDXL V-Prediction models directly with correct tensors.", - "nickname": "vpred-save", - "title": "vpred-save", - "title_aux": "comfyui-save-vpred" - } - ], - "https://github.com/MohammadAboulEla/ComfyUI-iTools": [ - [ - "iToolsAddOverlay", - "iToolsCheckerBoard", - "iToolsCompareImage", - "iToolsGridFiller", - "iToolsKSampler", - "iToolsLineLoader", - "iToolsLoadImagePlus", - "iToolsLoadImages", - "iToolsLoadRandomImage", - "iToolsPreviewImage", - "iToolsPreviewText", - "iToolsPromptLoader", - "iToolsPromptRecord", - "iToolsPromptSaver", - "iToolsPromptStyler", - "iToolsPromptStylerExtra", - "iToolsRegexNode", - "iToolsTextReplacer", - "iToolsVaePreview" - ], - { - "title_aux": "ComfyUI-iTools" - } - ], - "https://github.com/MokkaBoss1/ComfyUI_Mokkaboss1": [ - [ - "AnimeCosplayDir", - "AspectRatioCondition", - "ChooseImage", - "Colors", - "CombinedCrop", - "ConnectFloat", - "ConnectImage", - "ConnectInteger", - "ConnectInteger2", - "ConnectLatent", - "ConnectString", - "CycleInteger", - "DirSelector", - "DoubleClipTextEncode", - "DoubleConditioningMixer", - "EmbeddingLoader", - "FilmCharDir", - "FlexEmptyLatent", - "FloatEvaluate", - "FuseImages", - "FuseImages2", - "HashText", - "HueSatLum", - "HueShift", - "ImageDimensions", - "ImageDimensionsBatch", - "ImageOverlayResized", - "ImageResizeLong", - "ImageZigzag", - "IndoorBackgrounds", - "IndoorDir", - "IntEvaluate", - "IntFloatDict", - "IntStringDict", - "JsonSearch", - "KillWorkflow", - "LandscapeBackgrounds", - "LandscapeDir", - "LinEqEval", - "MakeupStylesDir", - "Mbsampler", - "OptimalCrop", - "Overlay", - "PhotomontageA", - "PhotomontageB", - "PhotomontageC", - "PostSamplerCrop", - "PresetLoad", - "PresetRemove", - "PresetSave", - "PromptSwitcher", - "QuadClipTextEncode", - "RandomString", - "SDXLEmptyLatent", - "SavePrompt", - "SaveWithMetaData", - "SaveWithMetaData2", - "SearchReplace", - "SimplePrompts", - "SpecificStylesDir", - "SplitImages", - "StringJoin", - "TimeStamp", - "TintnShift", - "TricolorComposition", - "WorkflowSettings", - "WrapText", - "X_In_a_Dress", - "X_In_a_Suit", - "X_In_a_Suit)", - "ZoomCrop", - "imageborder" - ], - { - "title_aux": "Node Pack mostly for manipulating strings and integers" - } - ], - "https://github.com/MontagenAI/ComfyUI-Montagen": [ - [ - "MontagenAudioAdapter", - "MontagenAudioConvertResourceAdapter", - "MontagenAudioListAdapter", - "MontagenCreateTimeline", - "MontagenEdgeTTSNode", - "MontagenFishAudioCloneNode", - "MontagenFishAudioTTSNode", - "MontagenImageAdapter", - "MontagenImageListAdapter", - "MontagenRenderTimeline", - "MontagenResourceConvertAudioAdapter", - "MontagenSRTListParser", - "MontagenStickerAdapter", - "MontagenStickerListAdapter", - "MontagenTextAdapter", - "MontagenTextListAdapter", - "MontagenVideoAdapter", - "MontagenVideoListAdapter" - ], - { - "title_aux": "ComfyUI-Montagen" - } - ], - "https://github.com/MoonHugo/ComfyUI-BAGEL-Hugo": [ - [ - "BagelByHugo" - ], - { - "title_aux": "ComfyUI-BAGEL-Hugo" - } - ], - "https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo": [ - [ - "BiRefNet_Hugo" - ], - { - "title_aux": "ComfyUI-BiRefNet-Hugo" - } - ], - "https://github.com/MoonHugo/ComfyUI-FFmpeg": [ - [ - "AddAudio", - "AddImgWatermark", - "AddTextWatermark", - "ExtractAudio", - "Frames2Video", - "ImageCopy", - "ImagePath2Tensor", - "ImagesSave", - "LoadImageFromDir", - "MergingVideoByPlenty", - "MergingVideoByTwo", - "MultiCuttingVideo", - "PipVideo", - "SingleCuttingVideo", - "StitchingVideo", - "Video2Frames", - "VideoFlip", - "VideoPlayback", - "VideoTransition" - ], - { - "title_aux": "ComfyUI-FFmpeg" - } - ], - "https://github.com/MoonHugo/ComfyUI-StableAudioOpen": [ - [ - "Text2Audio" - ], - { - "title_aux": "ComfyUI-StableAudioOpen" - } - ], - "https://github.com/MovieLabs/comfyui-movielabs-util": [ - [ - "PublishAsset", - "PublishBlender" - ], - { - "title_aux": "MovieLabs ComfyUI Nodes for Publishing Workflow" - } - ], - "https://github.com/MrForExample/ComfyUI-3D-Pack": [ - [], - { - "nodename_pattern": "^\\[Comfy3D\\]", - "title_aux": "ComfyUI-3D-Pack" - } - ], - "https://github.com/MrForExample/ComfyUI-AnimateAnyone-Evolved": [ - [], - { - "nodename_pattern": "^\\[AnimateAnyone\\]", - "title_aux": "ComfyUI-AnimateAnyone-Evolved" - } - ], - "https://github.com/MrSamSeen/ComfyUI_SSBeforeAfterNode": [ - [ - "SSBeforeAndAfterVideo", - "SSBeforeAndAfterVideoWithDepthMap" - ], - { - "title_aux": "ComfyUI_SSBeforeAfterNode" - } - ], - "https://github.com/MrSamSeen/ComfyUI_SSStereoscope": [ - [ - "SBS_External_Depthmap_by_SamSeen", - "SBS_Image_Uploader", - "SBS_V2_by_SamSeen", - "SBS_Video_Combiner", - "SBS_Video_Uploader" - ], - { - "title_aux": "SideBySide_Stereoscope" - } - ], - "https://github.com/Munkyfoot/ComfyUI-TextOverlay": [ - [ - "Text Overlay" - ], - { - "title_aux": "ComfyUI-TextOverlay" - } - ], - "https://github.com/MuziekMagie/ComfyUI-Matchering": [ - [ - "Matchering", - "MatcheringAdvanced", - "MatcheringLimiterConfig" - ], - { - "title_aux": "ComfyUI-Matchering" - } - ], - "https://github.com/MzMaXaM/ComfyUi-MzMaXaM": [ - [ - "KSamplerWithVAE", - "SelectLatentSize1MP", - "SelectLatentSize2MP", - "TextEncode3in1", - "UpscaleImageBy1_5x", - "UpscaleLatentBy1_5x", - "selectLatentSizePlus" - ], - { - "title_aux": "ComfyUi-MzMaXaM" - } - ], - "https://github.com/N3rd00d/ComfyUI-Paint3D-Nodes": [ - [ - "3D_GenerateDepthImage", - "3D_GenerateInpaintMask", - "3D_GenerateInpaintUVMapMask", - "3D_GeneratePreviewVideo", - "3D_LoadMeshModel", - "3D_Projection", - "3D_SaveUVMapImage", - "3D_TrainConfig", - "3D_TrainConfigPipe" - ], - { - "title_aux": "ComfyUI-Paint3D-Nodes" - } - ], - "https://github.com/NHLStenden/ComfyUI-ImageBag": [ - [ - "EnhancedImageColourTransferNode" - ], - { - "title_aux": "ComfyUI-ImageBag" - } - ], - "https://github.com/NMWave/ComfyUI-Nader-Tagging": [ - [ - "Load Text List", - "Split Sentences", - "Split Tags", - "Tag Alternating Combiner", - "Tag Duplicate Remover", - "Token Counter" - ], - { - "title_aux": "Image Captioning and Tagging Assistor Nodes" - } - ], - "https://github.com/NVIDIAGameWorks/ComfyUI-RTX-Remix": [ - [ - "RTXRemixCloseProject", - "RTXRemixCreateLayer", - "RTXRemixDefineLayerId", - "RTXRemixDeleteFile", - "RTXRemixEndContext", - "RTXRemixGetDefaultDirectory", - "RTXRemixGetEditTarget", - "RTXRemixGetLayers", - "RTXRemixGetLoadedProject", - "RTXRemixGetTextures", - "RTXRemixIngestTexture", - "RTXRemixInvertBool", - "RTXRemixLayerType", - "RTXRemixLayerTypes", - "RTXRemixMuteLayer", - "RTXRemixOpenProject", - "RTXRemixRemoveLayer", - "RTXRemixRestAPIDetails", - "RTXRemixSaveLayer", - "RTXRemixSetEditTarget", - "RTXRemixSetTexture", - "RTXRemixStartContext", - "RTXRemixStrToList", - "RTXRemixStringConcatenate", - "RTXRemixStringConstant", - "RTXRemixSwitch", - "RTXRemixTextureTypeToUSDAttribute", - "RTXRemixTexturesType", - "RTXRemixTexturesTypes" - ], - { - "title_aux": "ComfyUI-RTX-Remix" - } - ], - "https://github.com/NakamuraShippo/ComfyUI-NS-ManySliders": [ - [ - "NS_ManySliders" - ], - { - "title_aux": "ComfyUI-NS-ManySliders" - } - ], - "https://github.com/NakamuraShippo/ComfyUI-NS-PromptList": [ - [ - "NS-PromptList" - ], - { - "title_aux": "ComfyUI-PromptList" - } - ], - "https://github.com/NakamuraShippo/ComfyUI-NS-Util": [ - [ - "AlbedoMapGenerator", - "NS-FlexPreset", - "NS-ManySliders", - "NS-PromptList", - "NS-ToonFilter", - "SimpleLLMAddDocument", - "SimpleLLMAddMemory", - "SimpleLLMAgent", - "SimpleLLMAgentToTool", - "SimpleLLMAgentWithRules", - "SimpleLLMChainOfThought", - "SimpleLLMConfigClaude", - "SimpleLLMConfigGemini", - "SimpleLLMConfigOllama", - "SimpleLLMConfigOpenAI", - "SimpleLLMDisplayText", - "SimpleLLMLoadWorkflow", - "SimpleLLMMemoryBank", - "SimpleLLMMergeText", - "SimpleLLMRAGQuery", - "SimpleLLMRunPrompt", - "SimpleLLMStringViewer", - "SimpleLLMTextInput", - "SimpleLLMTextOutput", - "SimpleLLMVectorStore" - ], - { - "title_aux": "ComfyUI-NS-Util" - } - ], - "https://github.com/NeoDroleDeGueule/comfyui-image-mixer": [ - [ - "ImageLatentMixer" - ], - { - "title_aux": "comfyui-image-mixer" - } - ], - "https://github.com/NeoGriever/ComfyUI-NeoGriever": [ - [ - "NGs_BetterCLIPTextEncode", - "NGs_Checkerboard_Generator", - "NGs_Create_Solid_Color", - "NGs_Discord_Webhook", - "NGs_Fill_with_Color", - "NGs_Image_Progress_Bar", - "NGs_Multimask_Read", - "NGs_Multimask_Write", - "NGs_ResolutionProvider", - "NGs_Sliders_FLOAT", - "NGs_Sliders_INT", - "NGs_Sliders_PERCENTAGECUT", - "NGs_String_Operator", - "NGs_String_Squisher", - "NGs_Tag_Source", - "NGs_TextBox_JOIN", - "NGs_TextBox_SIMPLE", - "NGs_TextBox_x2", - "NGs_TextBox_x3", - "NGs_Text_Cut_String" - ], - { - "title_aux": "ComfyUI - NeoGriever" - } - ], - "https://github.com/NeonLightning/neonllama": [ - [ - "OllamaPromptFromIdea" - ], - { - "title_aux": "neonllama" - } - ], - "https://github.com/NeuralSamurAI/ComfyUI-Dimensional-Latent-Perlin": [ - [ - "NoisyLatentPerlinD" - ], - { - "title_aux": "Dimensional Latent Perlin for ComfyUI" - } - ], - "https://github.com/NeuralSamurAI/ComfyUI-FluxPseudoNegativePrompt": [ - [ - "FluxPseudoNegativeNode" - ], - { - "title_aux": "FluxPseudoNegative" - } - ], - "https://github.com/NeuralSamurAI/ComfyUI-PromptJSON": [ - [ - "PromptJSON" - ], - { - "title_aux": "PromptJSON Node for ComfyUI" - } - ], - "https://github.com/NeuralSamurAI/Comfyui-Superprompt-Unofficial": [ - [ - "SuperPrompterNode" - ], - { - "title_aux": "SuperPrompter Node for ComfyUI" - } - ], - "https://github.com/NeuroSenko/ComfyUI_LLM_SDXL_Adapter": [ - [ - "ApplyLLMToSDXLAdapter", - "LLMAdapterLoader", - "LLMAdapterLoaderCustom", - "LLMGGUFModelLoader", - "LLMModelLoader", - "LLMTextEncoder", - "T5GEMMALoader", - "T5GEMMATextEncoder" - ], - { - "title_aux": "ComfyUI LLM SDXL Adapter" - } - ], - "https://github.com/NewNoviceChen/ComfyUI-XingLiu": [ - [ - "Image2ImageByAlpha", - "Image2ImageCustom", - "Image2ImageCustomAlpha", - "MakeAuth", - "MakeControlNet", - "MakeHiResFix", - "MakeLora", - "MergeControlNet", - "MergeLora", - "Text2ImageByAlpha", - "Text2ImageCustom", - "Text2ImageCustomAlpha", - "UploadLibLib" - ], - { - "title_aux": "ComfyUI-XingLiu" - } - ], - "https://github.com/NguynHungNguyen/Segment-Bedroom-Interior": [ - [ - "BedroomFurnitureMask" - ], - { - "title_aux": "Segment Any Bedroom Interior" - } - ], - "https://github.com/NicholasMcCarthy/ComfyUI_TravelSuite": [ - [ - "LatentTravel" - ], - { - "title_aux": "ComfyUI_TravelSuite" - } - ], - "https://github.com/Nikosis/ComfyUI-Nikosis-Nodes": [ - [ - "AspectRatioNikosis", - "PromptCameraAngleSelectorNikosis", - "PromptMultipleStylesSelectorNikosis", - "TextConcatenateNikosis" - ], - { - "title_aux": "ComfyUI-Nikosis-Nodes" - } - ], - "https://github.com/Nikosis/ComfyUI-Nikosis-Preprocessors": [ - [ - "DepthAnythingV2Nikosis", - "EdgePreprocessorNikosis", - "LaplacianPreprocessorNikosis", - "LineArtPreprocessorNikosis", - "LineArtSketchPreprocessorNikosis" - ], - { - "title_aux": "ComfyUI-Nikosis-Preprocessors" - } - ], - "https://github.com/NimaNzrii/comfyui-photoshop": [ - [ - "\ud83d\udd39 Photoshop RemoteConnection", - "\ud83d\udd39ClipPass", - "\ud83d\udd39Photoshop ComfyUI Plugin", - "\ud83d\udd39SendTo Photoshop Plugin", - "\ud83d\udd39modelPass" - ], - { - "title_aux": "comfyui-photoshop" - } - ], - "https://github.com/NimaNzrii/comfyui-popup_preview": [ - [ - "PreviewPopup" - ], - { - "title_aux": "comfyui-popup_preview" - } - ], - "https://github.com/Niutonian/ComfyUi-NoodleWebcam": [ - [ - "WebcamNode" - ], - { - "title_aux": "ComfyUi-NoodleWebcam" - } - ], - "https://github.com/Njbx/ComfyUI-LTX13B-Blockswap": [ - [ - "LTXBlockswap" - ], - { - "title_aux": "ComfyUI-LTX13B-Blockswap" - } - ], - "https://github.com/Nlar/ComfyUI_CartoonSegmentation": [ - [ - "AnimeSegmentation", - "KenBurnsConfigLoader", - "KenBurns_Processor", - "LoadImageFilename" - ], - { - "author": "Nels Larsen", - "description": "This extension offers a front end to the Cartoon Segmentation Project (https://github.com/CartoonSegmentation/CartoonSegmentation)", - "nickname": "CfyCS", - "title": "ComfyUI_CartoonSegmentation", - "title_aux": "ComfyUI_CartoonSegmentation" - } - ], - "https://github.com/Nojahhh/ComfyUI_GLM4_Wrapper": [ - [ - "GLM-4 Inferencing", - "GLM-4 Model Loader", - "GLM-4 Prompt Enhancer" - ], - { - "title_aux": "ComfyUI GLM-4 Wrapper" - } - ], - "https://github.com/NotHarroweD/Harronode": [ - [ - "Harronode" - ], - { - "author": "HarroweD and quadmoon (https://github.com/traugdor)", - "description": "This extension to ComfyUI will build a prompt for the Harrlogos LoRA for SDXL.", - "nickname": "Harronode", - "nodename_pattern": "Harronode", - "title": "Harrlogos Prompt Builder Node", - "title_aux": "Harrlogos Prompt Builder Node" - } - ], - "https://github.com/Nourepide/ComfyUI-Allor": [ - [ - "AlphaChanelAdd", - "AlphaChanelAddByMask", - "AlphaChanelAsMask", - "AlphaChanelRemove", - "AlphaChanelRestore", - "ClipClamp", - "ClipVisionClamp", - "ClipVisionOutputClamp", - "ConditioningClamp", - "ControlNetClamp", - "GligenClamp", - "ImageBatchCopy", - "ImageBatchFork", - "ImageBatchGet", - "ImageBatchJoin", - "ImageBatchPermute", - "ImageBatchRemove", - "ImageClamp", - "ImageCompositeAbsolute", - "ImageCompositeAbsoluteByContainer", - "ImageCompositeRelative", - "ImageCompositeRelativeByContainer", - "ImageContainer", - "ImageContainerInheritanceAdd", - "ImageContainerInheritanceMax", - "ImageContainerInheritanceScale", - "ImageContainerInheritanceSum", - "ImageDrawArc", - "ImageDrawArcByContainer", - "ImageDrawChord", - "ImageDrawChordByContainer", - "ImageDrawEllipse", - "ImageDrawEllipseByContainer", - "ImageDrawLine", - "ImageDrawLineByContainer", - "ImageDrawPieslice", - "ImageDrawPiesliceByContainer", - "ImageDrawPolygon", - "ImageDrawRectangle", - "ImageDrawRectangleByContainer", - "ImageDrawRectangleRounded", - "ImageDrawRectangleRoundedByContainer", - "ImageEffectsAdjustment", - "ImageEffectsGrayscale", - "ImageEffectsLensBokeh", - "ImageEffectsLensChromaticAberration", - "ImageEffectsLensOpticAxis", - "ImageEffectsLensVignette", - "ImageEffectsLensZoomBurst", - "ImageEffectsNegative", - "ImageEffectsSepia", - "ImageFilterBilateralBlur", - "ImageFilterBlur", - "ImageFilterBoxBlur", - "ImageFilterContour", - "ImageFilterDetail", - "ImageFilterEdgeEnhance", - "ImageFilterEdgeEnhanceMore", - "ImageFilterEmboss", - "ImageFilterFindEdges", - "ImageFilterGaussianBlur", - "ImageFilterGaussianBlurAdvanced", - "ImageFilterMax", - "ImageFilterMedianBlur", - "ImageFilterMin", - "ImageFilterMode", - "ImageFilterRank", - "ImageFilterSharpen", - "ImageFilterSmooth", - "ImageFilterSmoothMore", - "ImageFilterStackBlur", - "ImageNoiseBeta", - "ImageNoiseBinomial", - "ImageNoiseBytes", - "ImageNoiseGaussian", - "ImageSegmentation", - "ImageSegmentationCustom", - "ImageSegmentationCustomAdvanced", - "ImageText", - "ImageTextMultiline", - "ImageTextMultilineOutlined", - "ImageTextOutlined", - "ImageTransformCropAbsolute", - "ImageTransformCropCorners", - "ImageTransformCropRelative", - "ImageTransformPaddingAbsolute", - "ImageTransformPaddingRelative", - "ImageTransformResizeAbsolute", - "ImageTransformResizeClip", - "ImageTransformResizeRelative", - "ImageTransformRotate", - "ImageTransformTranspose", - "LatentClamp", - "MaskClamp", - "ModelClamp", - "StyleModelClamp", - "UpscaleModelClamp", - "VaeClamp" - ], - { - "title_aux": "Allor Plugin" - } - ], - "https://github.com/Nuked88/ComfyUI-N-Nodes": [ - [ - "CLIPTextEncodeAdvancedNSuite [n-suite]", - "DynamicPrompt [n-suite]", - "Float Variable [n-suite]", - "FrameInterpolator [n-suite]", - "GPT Loader Simple [n-suite]", - "GPT Sampler [n-suite]", - "ImagePadForOutpaintAdvanced [n-suite]", - "Integer Variable [n-suite]", - "Llava Clip Loader [n-suite]", - "LoadFramesFromFolder [n-suite]", - "LoadImageFromFolder [n-suite]", - "LoadVideo [n-suite]", - "SaveVideo [n-suite]", - "SetMetadataForSaveVideo [n-suite]", - "String Variable [n-suite]" - ], - { - "title_aux": "ComfyUI-N-Nodes" - } - ], - "https://github.com/NyaFuP/ComfyUI_Preview_Selector": [ - [ - "NFPreviewSelector" - ], - { - "title_aux": "NF Preview Selector" - } - ], - "https://github.com/NyaamZ/efficiency-nodes-ED": [ - [ - "Context To BasicPipe", - "Context To DetailerPipe", - "Control Net Script \ud83d\udcacED", - "Detailer (SEGS) \ud83d\udcacED", - "Efficient Loader \ud83d\udcacED", - "Embedding Stacker \ud83d\udcacED", - "FaceDetailer \ud83d\udcacED", - "Get Booru Tag \ud83d\udcacED", - "Int Holder \ud83d\udcacED", - "KSampler (Efficient) \ud83d\udcacED", - "KSampler Text \ud83d\udcacED", - "LoRA Stacker \ud83d\udcacED", - "Load Image \ud83d\udcacED", - "MaskDetailer \ud83d\udcacED", - "Refiner Script \ud83d\udcacED", - "Regional Processor \ud83d\udcacED", - "Regional Script \ud83d\udcacED", - "Regional Stacker \ud83d\udcacED", - "SUPIR Model Loader \ud83d\udcacED", - "SUPIR Sampler \ud83d\udcacED", - "Save Image \ud83d\udd14ED", - "Simple Text \ud83d\udcacED", - "TIPO Script \ud83d\udcacED", - "Ultimate SD Upscale \ud83d\udcacED", - "Wildcard Encode \ud83d\udcacED" - ], - { - "author": "NyaamZ", - "description": "Expansion of Efficiency Nodes for ComfyUI. Significant UX improvements.", - "nickname": "Efficiency Nodes ED", - "title": "Efficiency Nodes ExtendeD", - "title_aux": "Efficiency Nodes ExtendeD" - } - ], - "https://github.com/Off-Live/ComfyUI-off-suite": [ - [ - "Apply CLAHE", - "Cached Image Load From URL", - "CalcMaskBound", - "Crop Center wigh SEGS", - "Crop Center with SEGS", - "Dilate Mask for Each Face", - "GW Number Formatting", - "Grid Image from batch (OFF)", - "Image Crop Fit", - "Image Resize Fit", - "OFF SEGS to Image", - "Paste Face Segment to Image", - "Query Gender and Age", - "RandomSeedfromList", - "SEGS to Face Crop Data", - "Safe Mask to Image", - "VAE Encode For Inpaint V2", - "Watermarking" - ], - { - "title_aux": "ComfyUI-off-suite" - } - ], - "https://github.com/OneThingAI/ComfyUI_Onething_CV": [ - [ - "OneThingAI ImageToText" - ], - { - "title_aux": "ComfyUI OneThing CV Node" - } - ], - "https://github.com/OneThingAI/ComfyUI_Onething_Image": [ - [ - "OneThingAILoader" - ], - { - "title_aux": "ComfyUI OneThing AI Node" - } - ], - "https://github.com/Onionman61/ComfyUI-ModelScope-Kontext": [ - [ - "ModelScopeKontextAPI" - ], - { - "title_aux": "ComfyUI ModelScope Kontext API Node" - } - ], - "https://github.com/OpalSky-AI/OpalSky_Nodes": [ - [ - "PromptAssistantOpalSky", - "StringSwitchOpalSky", - "string_switch_opalsky" - ], - { - "title_aux": "OpalSky Nodes" - } - ], - "https://github.com/OuticNZ/ComfyUI-Simple-Of-Complex": [ - [ - "Pipe From Parameters", - "Pipe To Parameters", - "Prompt Tidy", - "Text Switch 2 Way", - "Text With Context" - ], - { - "title_aux": "ComfyUI-Simple-Of-Complex" - } - ], - "https://github.com/PCMonsterx/ComfyUI-CSV-Loader": [ - [ - "Load Artists CSV", - "Load Artmovements CSV", - "Load Characters CSV", - "Load Colors CSV", - "Load Composition CSV", - "Load Lighting CSV", - "Load Negative CSV", - "Load Positive CSV", - "Load Settings CSV", - "Load Styles CSV" - ], - { - "title_aux": "ComfyUI-CSV-Loader" - } - ], - "https://github.com/PICOPON/ComfyUI-API-OpenAI-Node": [ - [ - "OpenAINode" - ], - { - "title_aux": "ComfyUI OpenAI Node" - } - ], - "https://github.com/Pablerdo/ComfyUI-MultiCutAndDrag": [ - [ - "BatchImageToMask", - "LoadImageFromBase64", - "LoadImagesFromBase64Array", - "MapTrajectoriesToSegmentedMasks", - "MultiCutAndDragOnPath" - ], - { - "title_aux": "ComfyUI-MultiCutAndDrag" - } - ], - "https://github.com/Pablerdo/ComfyUI-ResizeZeptaPayload": [ - [ - "ResizeImageBatch", - "ResizeTrajectories" - ], - { - "title_aux": "ComfyUI-ResizeZeptaPayload" - } - ], - "https://github.com/Pablerdo/ComfyUI-StableVirtualCameraWrapper": [ - [ - "SVCFly", - "SVCFly_Bash" - ], - { - "title_aux": "Stable Virtual Camera" - } - ], - "https://github.com/Pablerdo/ComfyUI-ZeptaframePromptMerger": [ - [ - "MergePrompts" - ], - { - "title_aux": "ComfyUI-ZeptaframePromptMerger" - } - ], - "https://github.com/PanicTitan/ComfyUI-Fooocus-V2-Expansion": [ - [ - "FooocusV2Expansion" - ], - { - "title_aux": "ComfyUI-Fooocus-V2-Expansion" - } - ], - "https://github.com/PanicTitan/ComfyUI-Gallery": [ - 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{ - "title_aux": "ComfyUI-LikeSpiderAI-SaveMP3" - } - ], - "https://github.com/Pigidiy/ComfyUI-LikeSpiderAI-UI": [ - [ - "AudioExport" - ], - { - "title_aux": "ComfyUI-LikeSpiderAI-UI" - } - ], - "https://github.com/Pirog17000/Pirogs-Nodes": [ - [ - "BatchCropFromMaskSimple", - "BatchUncropSimple", - "BlurByMask", - "BlurMask", - "CropImage", - "CropMaskByBBox", - "DSLRNoise", - "GradientMaskGenerator", - "ImageBlendByMask", - "ImageScalePro", - "InvertMask", - "KSamplerMultiSeed", - "KSamplerMultiSeedPlus", - "LensSimulatedBloom", - "PreviewImageQueue", - "PromptRandomizer", - "StringCombine", - "TestResetButton", - "Watermark" - ], - { - "title_aux": "Pirog's Nodes for ComfyUI" - } - ], - "https://github.com/PixelFunAI/ComfyUI_PixelFun": [ - [ - "HunyuanLoadAndEditLoraBlocks", - "HunyuanLoadFromBlockCache", - "HunyuanLoraFromJson", - "HunyuanLoraFromPrompt" - ], - { - "title_aux": "Hunyuan LoRA Loader Nodes" - } - ], - "https://github.com/PixelML/ComfyUI-PixelML-CustomNodes": [ - [ - "AgenticflowAIVariable", - "BooleanInput_PixelML", - "FloatInput_PixelML", - "IntegerInput_PixelML", - "LoadImageFromURL_PixelML", - "SaveImage_PixelML", - "StringInput_PixelML" - ], - { - "title_aux": "PixelML ComfyUI Nodes" - } - ], - "https://github.com/PnthrLeo/comfyUI-PL-data-tools": [ - [ - "AreasGenerator", - "BatchImageGetter", - "CloseImagesSearcher" - ], - { - "title_aux": "comfyUI-PL-data-tools" - } - ], - "https://github.com/Poseidon-fan/ComfyUI-RabbitMQ-Publisher": [ - [ - "Publish Image To RabbitMQ" - ], - { - "title_aux": "ComfyUI-RabbitMQ-Publisher" - } - ], - "https://github.com/Positliver/comfyui-zegr": [ - [ - "ZEGR_ALI_UF", - "ZEGR_LF", - "ZEGR_WD" - ], - { - "title_aux": "comfyui-zegr" - } - ], - "https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait": [ - [ - "AdvancedLivePortrait", - "ExpData", - "ExpressionEditor", - "LoadExpData", - "PrintExpData:", - "SaveExpData" - ], - { - "title_aux": "ComfyUI-AdvancedLivePortrait" - } - ], - "https://github.com/PressWagon/ComfyUI-StringsAndThings": [ - [ - "DebugString", - "FormatConcatStrings", - "FormattingSingle", - "FourierAnalysisNode", - "ImageDifference", - "MosaicEffectNode", - "PWLoraNameCollector", - "PWLoraSelector", - "TextEmbeddingsInterrogator" - ], - { - "title_aux": "ComfyUI-StringsAndThings" - } - ], - "https://github.com/ProGamerGov/ComfyUI_preview360panorama": [ - [ - "PanoramaVideoViewerNode", - "PanoramaViewerNode" - ], - { - "title_aux": "Preview 360 Panorama for ComfyUI" - } - ], - "https://github.com/ProGamerGov/ComfyUI_pytorch360convert": [ - [ - "Apply Circular Padding Model", - "Apply Circular Padding VAE", - "Create 180 To 360 Mask", - "Create Pole Mask", - "Create Seam Mask", - "Crop 360 to 180 Equirectangular", - "Crop Image with Coords", - "Crop Stereo to Monoscopic", - "Cubemap to Equirectangular", - "Equirectangular Mask to Face", - "Equirectangular Rotation", - "Equirectangular to Cubemap", - "Equirectangular to Face", - "Equirectangular to Perspective", - "Face Mask to Equirectangular", - "Face to Equirectangular", - "Mask Equirectangular Rotation", - "Masked Diff C2E", - "Merge Monoscopic into Stereo", - "Pad 180 to 360 Equirectangular", - "Paste Image with Coords", - "Roll Image Axes", - "Roll Mask Axes", - "Split Cubemap Faces", - "Stack Cubemap Faces" - ], - { - "title_aux": "ComfyUI_pytorch360convert" - } - ], - "https://github.com/PrunaAI/ComfyUI_pruna": [ - [ - "CacheModelAdaptive", - "CacheModelAuto", - "CacheModelPeriodic", - "PrunaCompileModel" - ], - { - "title_aux": "Pruna nodes for ComfyUI" - } - ], - "https://github.com/Pseudotools/Pseudocomfy": [ - [ - "PseudoApplyDenseDiffusionSDXL", - "PseudoApplyIPAdaperSDXL", - "PseudoConcatStrings", - "PseudoFloatToInt", - "PseudoIPAdapterUnifiedLoaderClone", - "PseudoLoadModelSnapshot", - "PseudoMaskAggregate", - "PseudoMaskBlur", - "PseudoMaskClamp", - "PseudoMaskInvert", - "PseudoMaskRemap", - "PseudoMaskReshape", - "PseudoPreviewStrings", - "PseudoProcessEnvironmentalPrompts", - "PseudoProcessImagePrompt", - "PseudoProcessMaterialPrompts", - "PseudoRemapNormalizedFloat", - "PseudoSaveImageWithEmbeddedMasks", - "PseudoUnpackModelSnapshot" - ], - { - "title_aux": "Pseudocomfy" - } - ], - "https://github.com/Pun0110/ComfyUI-CSV-Styler": [ - [ - "PT.CSV Styler" - ], - { - "title_aux": "CSV Styler" - } - ], - "https://github.com/Q-Bug4/Comfyui-Qb-DateNodes": [ - [ - "DateTimeFormatterNode" - ], - { - "title_aux": "Comfyui-Qb-Date-Nodes" - } - ], - "https://github.com/Q-Bug4/Comfyui-Simple-Json-Node": [ - [ - "JSONArrayIteratorNode", - "JSONGeneratorNode", - "JSONKeyCheckerNode", - "JSONLengthNode", - "JSONMergeNode", - "JSONModifierNode", - "JSONObjectIteratorNode", - "JSONParserNode", - "JSONStringifierNode", - "RandomJSONValueNode" - ], - { - "title_aux": "Simple JSON Parser Node for ComfyUI" - } - ], - "https://github.com/Q-Bug4/comfyui-qbug-batch": [ - [ - "CrossJoinSelector", - "ListFiles", - "NoPreviewSaveImage" - ], - { - "title_aux": "comfyui-qbug-batch" - } - ], - "https://github.com/QaisMalkawi/ComfyUI-QaisHelper": [ - [ - "Bool Binary Operation", - "Bool Unary Operation", - "Item Debugger", - "Item Switch", - "Nearest SDXL Resolution", - "SDXL Resolution", - "Size Swapper" - ], - { - "title_aux": "ComfyUI-Qais-Helper" - } - ], - "https://github.com/QijiTec/ComfyUI-RED-UNO": [ - [ - "REDUNOGenerate", - "REDUNOModelLoader" - ], - { - "title_aux": "ComfyUI-RED-UNO" - } - ], - "https://github.com/R5-Revo/llm-node-comfyui": [ - [ - "UniversalLLMNode" - ], - { - "title_aux": "Universal LLM Node for ComfyUI" - } - ], - "https://github.com/RUiNtheExtinct/comfyui-save-file-extended": [ - [ - "LoadAudioExtended", - "LoadImageExtended", - "LoadVideoExtended", - "SaveAudioExtended", - "SaveAudioMP3Extended", - "SaveAudioOpusExtended", - "SaveImageExtended", - "SaveVideoExtended", - "SaveWEBMExtended" - ], - { - "title_aux": "comfyui-save-file-extended" - } - ], - "https://github.com/Raapys/ComfyUI-LatentGC_Aggressive": [ - [ - "LatentGC" - ], - { - "title_aux": "LatentGC Aggressive" - } - ], - "https://github.com/RainyN0077/ComfyUI-PromptSE": [ - [ - "PromptSE" - ], - { - "title_aux": "ComfyUI-PromptSE" - } - ], - "https://github.com/RamonGuthrie/ComfyUI-RBG-ImageStitchPlus": [ - [ - "RBGImageStitchPlus", - "RBGPadPro" - ], - { - "title_aux": "ComfyUI-RBG-ImageStitchPlus" - } - ], - "https://github.com/Raykosan/ComfyUI_RS-SaturationNode": [ - [ - "RS_SaturationSwitch" - ], - { - "title_aux": "ComfyUI_RS-SaturationNode" - } - ], - "https://github.com/Raykosan/ComfyUI_RaykoStudio": [ - [ - "RS_RusTextOverlay" - ], - { - "title_aux": "ComfyUI_RaykoStudio" - } - ], - "https://github.com/RaymondProduction/comfyui-zerna-pack": [ - [ - "CLIPDynamicPromptEncoder //Zerna Pack", - "DynamicPromptInjector //Zerna Pack", - "LastImage //Zerna Pack", - "UnzipPrompt //Zerna Pack" - ], - { - "author": "Raymond", - "description": "A set of nodes for batch processing of text and images.", - "nickname": "Zerna Pack", - "title": "Zerna Pack", - "title_aux": "Zerna Pack" - } - ], - "https://github.com/ReBeating/ComfyUI-Artist-Selector": [ - [ - "LoadArtistTag" - ], - { - "title_aux": "ComfyUI-Artist-Selector" - } - ], - "https://github.com/RegulusAlpha/ComfyUI-DynPromptSimplified": [ - [ - "DynPromptExpand" - ], - { - "title_aux": "ComfyUI Dynamic Prompting Simplified" - } - ], - "https://github.com/ReinerBforartists/comfyui_auto_prompt_schedule": [ - [ - "AutoPromptSchedule" - ], - { - "title_aux": "Auto Prompt Schedule" - } - ], - "https://github.com/ReinerBforartists/comfyui_text_line_combine": [ - [ - "CombineTextLines" - ], - { - "title_aux": "ComfyUI_Text_Line_Combine" - } - ], - "https://github.com/Reithan/negative_rejection_steering": [ - [ - "NRS" - ], - { - "title_aux": "Negative Rejection Steering" - } - ], - "https://github.com/RenderRift/ComfyUI-RenderRiftNodes": [ - [ - "AnalyseMetadata", - "DateIntegerNode", - "DisplayMetaOptions", - "LoadImageWithMeta", - "MetadataOverlayNode", - "VideoPathMetaExtraction" - ], - { - "title_aux": "ComfyUI-RenderRiftNodes" - } - ], - "https://github.com/RhizoNymph/ComfyUI-CLIPSlider": [ - [ - "CLIPSlider" - ], - { - "title_aux": "ComfyUI-CLIPSlider" - } - ], - "https://github.com/RhizoNymph/ComfyUI-ColorWheel": [ - [ - "AccurateColorWheelNode" - ], - { - "title_aux": "ComfyUI-ColorWheel" - } - ], - "https://github.com/RhizoNymph/ComfyUI-Latte": [ - [ - "LatteVideoGenerator" - ], - { - "title_aux": "ComfyUI-Latte" - } - ], - "https://github.com/RiceRound/ComfyUI_CryptoCat": [ - [ - "CryptoCatImage", - "DecodeCryptoNode", - "ExcuteCryptoNode", - "RandomSeedNode", - "SaveCryptoBridgeNode", - "SaveCryptoNode" - ], - { - "title_aux": "ComfyUI Compression and Encryption Node" - } - ], - "https://github.com/RiceRound/ComfyUI_RiceRound": [ - [ - "RiceRoundAdvancedChoiceNode", - "RiceRoundBooleanNode", - "RiceRoundDecryptNode", - "RiceRoundDownloadImageAndMaskNode", - "RiceRoundDownloadImageNode", - "RiceRoundDownloadMaskNode", - "RiceRoundEncryptNode", - "RiceRoundFloatNode", - "RiceRoundImageBridgeNode", - "RiceRoundImageNode", - "RiceRoundImageUrlNode", - "RiceRoundInputTextNode", - "RiceRoundIntNode", - "RiceRoundMaskBridgeNode", - "RiceRoundOutputBooleanNode", - "RiceRoundOutputFloatNode", - "RiceRoundOutputImageBridgeNode", - "RiceRoundOutputImageNode", - "RiceRoundOutputIntNode", - "RiceRoundOutputMaskBridgeNode", - "RiceRoundOutputTextNode", - "RiceRoundRandomSeedNode", - "RiceRoundSimpleChoiceNode", - "RiceRoundSimpleImageNode", - "RiceRoundStrToBooleanNode", - "RiceRoundStrToFloatNode", - "RiceRoundStrToIntNode", - "RiceRoundUploadImageNode" - ], - { - "title_aux": "RiceRound Cloud Node" - } - ], - "https://github.com/Rinsanga1/comfyui-florence2xy": [ - [ - "Florence2toCoordinatesButxy", - "LoadImageWithName" - ], - { - "title_aux": "comfyui-florence2xy" - } - ], - "https://github.com/Rizzlord/ComfyUI-RizzNodes": [ - [ - "RizzAnything", - "RizzBatchImageLoader", - "RizzBlur", - "RizzClean", - "RizzCropAndScaleFromMask", - "RizzDynamicPromptGenerator", - "RizzEditImage", - "RizzLoadLatestImage", - "RizzLoadLatestMesh", - "RizzModelBatchLoader", - "RizzPasteAndUnscale", - "RizzUpscaleImageBatch" - ], - { - "title_aux": "ComfyUI-RizzNodes" - } - ], - "https://github.com/RodrigoSKohl/ComfyUI-Panoramic-ImgStitcher": [ - [ - "Image Stitching Node" - ], - { - "title_aux": "Panoramic Image Stitcher" - } - ], - "https://github.com/RodrigoSKohl/InteriorDesign-for-ComfyUI": [ - [ - "Control Items", - "Image Normalize", - "Interior Design Segmentator" - ], - { - "title_aux": "Interior Design for Comfyui" - } - ], - "https://github.com/RodrigoSKohl/comfyui-tryoff-anyone": [ - [ - "TryOffAnyoneNode" - ], - { - "title_aux": "TryOff Anyone" - } - ], - "https://github.com/RomanKuschanow/ComfyUI-Advanced-Latent-Control": [ - [ - "LatentAddTransform", - "LatentInterpolateTransform", - "LatentMirror", - "LatentNormalize", - "LatentShift", - "MirrorTransform", - "MultiplyTransform", - "OffsetCombine", - "OneTimeLatentAddTransform", - "OneTimeLatentInterpolateTransform", - "OneTimeMirrorTransform", - "OneTimeMultiplyTransform", - "OneTimeShiftTransform", - "ShiftTransform", - "TransformHijack", - "TransformOffset", - "TransformSampler", - "TransformSamplerAdvanced", - "TransformsCombine" - ], - { - "title_aux": "Advanced Latent Control" - } - ], - "https://github.com/Ron-Digital/ComfyUI-SceneGenerator": [ - [ - "Scene Generator" - ], - { - "title_aux": "ComfyUI-SceneGenerator" - } - ], - "https://github.com/Runware/ComfyUI-Runware": [ - [ - "Runware API Manager", - "Runware Background Removal", - "Runware ControlNet", - "Runware ControlNet Combine", - "Runware ControlNet PreProcessor", - "Runware DeepCache", - "Runware Embedding Search", - "Runware Embeddings Combine", - "Runware IPAdapter", - "Runware IPAdapters Combine", - "Runware Image Caption", - "Runware Image Inference", - "Runware Image Masking", - "Runware Image Upscaler", - "Runware Imagen Inference", - "Runware Kontext Inference", - "Runware Lora Combine", - "Runware Lora Search", - "Runware Model Search", - "Runware Multi Inference", - "Runware Outpaint", - "Runware PhotoMaker V2", - "Runware Reference Images", - "Runware Refiner", - "Runware TeaCache", - "Runware VAE Search" - ], - { - "title_aux": "Runware.ai ComfyUI Inference API Integration" - } - ], - "https://github.com/Ryuukeisyou/comfyui_face_parsing": [ - [ - "BBoxDecompose(FaceParsing)", - "BBoxDetect(FaceParsing)", - "BBoxDetectorLoader(FaceParsing)", - "BBoxListItemSelect(FaceParsing)", - "BBoxResize(FaceParsing)", - "ColorAdjust(FaceParsing)", - "FaceParse(FaceParsing)", - "FaceParsingModelLoader(FaceParsing)", - "FaceParsingProcessorLoader(FaceParsing)", - "FaceParsingResultsParser(FaceParsing)", - "GuidedFilter(FaceParsing)", - "ImageCropWithBBox(FaceParsing)", - "ImageCropWithBBoxList(FaceParsing)", - "ImageInsertWithBBox(FaceParsing)", - "ImageListSelect(FaceParsing)", - "ImagePadWithBBox(FaceParsing)", - "ImageResizeCalculator(FaceParsing)", - "ImageResizeWithBBox(FaceParsing)", - "ImageSize(FaceParsing)", - "LatentCropWithBBox(FaceParsing)", - "LatentInsertWithBBox(FaceParsing)", - "LatentSize(FaceParsing)", - "MaskBatchComposite(FaceParsing)", - "MaskBlackOut(FaceParsing)", - "MaskBorderDissolve(FaceParsing)", - "MaskBorderDissolveAdvanced(FaceParsing)", - "MaskComposite(FaceParsing)", - "MaskCropWithBBox(FaceParsing)", - "MaskInsertWithBBox(FaceParsing)", - "MaskListSelect(FaceParsing)", - "MaskToBBoxList(FaceParsing)", - "SkinDetectTraditional(FaceParsing)" - ], - { - "title_aux": "comfyui_face_parsing" - } - ], - "https://github.com/Ryuukeisyou/comfyui_io_helpers": [ - [ - "ImageLoadAsMaskByPath(IOHelpers)", - "ImageLoadByPath(IOHelpers)", - "ImageLoadFromBase64(IOHelpers)", - "ImageSaveAsBase64(IOHelpers)", - "ImageSaveToPath(IOHelpers)", - "TypeConversion(IOHelpers)", - "VHSFileNamesToStrings(IOHelpers)" - ], - { - "title_aux": "comfyui_io_helpers" - } - ], - "https://github.com/S4MUEL-404/ComfyUI-S4Motion": [ - [ - "\ud83d\udc80Motion Config", - "\ud83d\udc80Motion Distortion", - "\ud83d\udc80Motion Mask", - "\ud83d\udc80Motion Opacity", - "\ud83d\udc80Motion Position", - "\ud83d\udc80Motion Position On Path", - "\ud83d\udc80Motion Rotation", - "\ud83d\udc80Motion Scale", - "\ud83d\udc80Motion Shake" - ], - { - "title_aux": "ComfyUI S4Motion" - } - ], - "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image": [ - [ - "CombineImageBatch", - "GetImageBatch", - "ImageAdjustment", - "ImageBlendWithAlpha", - "ImageBoard", - "ImageColor", - "ImageCombine", - "ImageCropToFit", - "ImageFromBase64", - "ImageGetColor", - "ImageMaskExpand", - "ImageOverlay", - "ImagePalette", - "ImagePalette631", - "ImagePrimaryColor", - "ImageResize", - "ImageTilingPattern", - "ImageToBase64", - "SetImageBatch" - ], - { - "title_aux": "ComfyUI S4Tool Image" - } - ], - "https://github.com/S4MUEL-404/ComfyUI-S4Tool-Text": [ - [ - "S4Tools Text Basic", - "S4Tools Text Font Base64", - "S4Tools Text Font URL", - "S4Tools Text Font file", - "S4Tools Text Style" - ], - { - "title_aux": "ComfyUI S4Tool Text" - } - ], - "https://github.com/SEkINVR/ComfyUI-SaveAs": [ - [ - "ComfyUISaveAs" - ], - { - "title_aux": "ComfyUI SaveAS" - } - ], - "https://github.com/SKBv0/ComfyUI_SKBundle": [ - [ - "AspectRatioAdvanced", - "DisplayEverything", - "ImageComparer", - "LensFlare", - "MultiFloat", - "MultiTextNode", - "PaintPro", - "SKB_AnySwitch", - "SeamlessTexture", - "TextBox", - "TitlePlus" - ], - { - "title_aux": "ComfyUI SKBundle" - } - ], - "https://github.com/SLAPaper/ComfyUI-Image-Selector": [ - [ - "ImageDuplicator", - "ImageSelector", - "LatentDuplicator", - "LatentSelector" - ], - { - "title_aux": "ComfyUI-Image-Selector" - } - ], - "https://github.com/SOELexicon/ComfyUI-LexMSDBNodes": [ - [ - "MSSqlSelectNode", - "MSSqlTableNode" - ], - { - "title_aux": "LexMSDBNodes" - } - ], - "https://github.com/SOELexicon/ComfyUI-LexTools": [ - [ - "AesthetlcScoreSorter", - "AgeClassifierNode", - "ArtOrHumanClassifierNode", - "CalculateAestheticScore", - "DocumentClassificationNode", - "FoodCategoryClassifierNode", - "ImageAspectPadNode", - "ImageCaptioning", - "ImageFilterByFloatScoreNode", - "ImageFilterByIntScoreNode", - "ImageQualityScoreNode", - "ImageRankingNode", - "ImageScaleToMin", - "LoadAesteticModel", - 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[ - "ChatGPTImageEditNode", - "ChatGPTImageGenerationNode", - "ImageToBase64" - ], - { - "title_aux": "comfyui_chatgpt" - } - ], - "https://github.com/San4itos/ComfyUI-Save-Images-as-Video": [ - [ - "SaveFramesToVideoFFmpeg_san4itos" - ], - { - "title_aux": "Save Images to Video (FFmpeg) for ComfyUI" - } - ], - "https://github.com/SanDiegoDude/ComfyUI-DeepStereo": [ - [ - "ColorPickerNode", - "DepthMapProcessor", - "ImageEffectsTransformer", - "ImageResizeAndTransform", - "MiDaSDepthEstimator", - "ProceduralTextureGenerator", - "RandomDotStereogramGenerator", - "RandomNoiseGenerator", - "StereogramGenerator", - "TextureTransformer" - ], - { - "title_aux": "ComfyUI-DeepStereo" - } - ], - "https://github.com/SanDiegoDude/ComfyUI-Kontext-API": [ - [ - "FalKontextMaxMultiImageNode", - "KontextAPINode" - ], - { - "title_aux": "ComfyUI-Kontext-API" - } - ], - "https://github.com/SanDiegoDude/ComfyUI-SaveAudioMP3": [ - [ - "SaveAudioMP3" - ], - { - "title_aux": "ComfyUI-SaveAudioMP3" - } - ], - 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], - { - "title_aux": "ComfyUI-PixArt_XL" - } - ], - "https://github.com/ShmuelRonen/ComfyUI-SVDResizer": [ - [ - "SVDRsizer" - ], - { - "title_aux": "ComfyUI-SVDResizer" - } - ], - "https://github.com/ShmuelRonen/ComfyUI-ThinkSound_Wrapper": [ - [ - "ThinkSoundFeatureUtilsLoader", - "ThinkSoundModelLoader", - "ThinkSoundSampler" - ], - { - "title_aux": "ComfyUI-ThinkSound_Wrapper" - } - ], - "https://github.com/ShmuelRonen/ComfyUI-Veo2-Experimental": [ - [ - "VeoTextToVideo", - "VeoToVHS", - "VeoVideoPreview" - ], - { - "title_aux": "ComfyUI-Veo2-Experimental" - } - ], - "https://github.com/ShmuelRonen/ComfyUI-VideoUpscale_WithModel": [ - [ - "Free_Video_Memory", - "Video_Upscale_With_Model" - ], - { - "title_aux": "ComfyUI-VideoUpscale_WithModel" - } - ], - "https://github.com/ShmuelRonen/ComfyUI-WanVideoKsampler": [ - [ - "WanVideoKsampler" - ], - { - "title_aux": "ComfyUI-WanVideoKsampler" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_ChatterBox_Voice": [ - [ - "ChatterBoxVoiceCapture", - "ChatterBoxVoiceTTS", - "ChatterBoxVoiceVC" - ], - { - "title_aux": "ComfyUI_ChatterBox_Voice" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_Flux_1.1_RAW_API": [ - [ - "FluxPro11WithFinetune" - ], - { - "title_aux": "ComfyUI Flux 1.1 Ultra & Raw Node" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_Gemini_Flash": [ - [ - "Gemini_Flash_002" - ], - { - "title_aux": "ComfyUI_Gemini_Flash" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_Hedra": [ - [ - "HedraImageToVideo" - ], - { - "title_aux": "ComfyUI Hedra Node" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_pixtral_large": [ - [ - "ComfyUIPixtralLarge", - "MultiImagesInput", - "preview_text" - ], - { - "title_aux": "ComfyUI Pixtral Large Extension" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_pixtral_vision": [ - [ - "ComfyUIPixtralVision", - "MultiImagesInput", - "preview_text" - ], - { - "title_aux": "ComfyUI_pixtral_vision" - } - ], - "https://github.com/ShmuelRonen/ComfyUI_wav2lip": [ - [ - "LoadAudio", - "Wav2Lip" - ], - { - "title_aux": "Wav2Lip Node for ComfyUI" - } - ], - "https://github.com/ShmuelRonen/DJ_VideoAudioMixer": [ - [ - "DJ_VideoAudioMixer" - ], - { - "title_aux": "DJ_VideoAudioMixer" - } - ], - "https://github.com/ShmuelRonen/FluxKontextCreator": [ - [ - "FluxKontextCreator", - "FluxKontextCreatorExperimental" - ], - { - "title_aux": "Flux Kontext Creator for ComfyUI" - } - ], - "https://github.com/ShmuelRonen/comfyui-openai_fm": [ - [ - "OpenAIFMNode" - ], - { - "title_aux": "comfyui-openai_fm" - } - ], - "https://github.com/ShmuelRonen/google_moogle": [ - [ - "googletrans" - ], - { - "title_aux": "Google Moogle" - } - ], - "https://github.com/ShmuelRonen/multi-lora-stack": [ - [ - "MultiLoRAStack", - "MultiLoRAStackModelOnly" - ], - { - "title_aux": "multi-lora-stack" - } - ], - "https://github.com/Shraknard/ComfyUI-Remover": [ - [ - "Remover" - ], - { - "title_aux": "ComfyUI-Remover" - } - ], - "https://github.com/Siberpone/lazy-pony-prompter": [ - [ - "LPP_Danbooru", - "LPP_Deleter", - "LPP_Derpibooru", - "LPP_E621", - "LPP_Loader_Danbooru", - "LPP_Loader_Derpibooru", - "LPP_Loader_E621", - "LPP_Saver" - ], - { - "title_aux": "Lazy Pony Prompter" - } - ], - "https://github.com/Siempreflaco/ComfyUI-NCNodes": [ - [ - "Load3DMesh", - "NCAudioRecorderNode", - "NCImageProcessor", - "NCIncrementINT", - "NCLineCounter" - ], - { - "title_aux": "ComfyUI-NCNodes" - } - ], - "https://github.com/Sieyalixnet/ComfyUI_Textarea_Loaders": [ - [ - "CheckPointLoader_Text", - "EmptyLatentImage_Text", - "LoRALoader_Text", - "LoadImage_Text" - ], - { - "title_aux": "ComfyUI_Textarea_Loaders" - } - ], - "https://github.com/SignalCha1n/comfyui-ComfySnap": [ - [ - "FaceAvoidRandomY", - "LowQualityDigitalLook", - "SnapBasicFilters", - "SnapTextOverlay" - ], - { - "title_aux": "Snap Style Nodes for ComfyUI" - } - ], - "https://github.com/SijieMei/ComfyUI-promptHistory": [ - [ - "PromptHistory" - ], - { - "title_aux": "ComfyUI-Prompt-History" - } - ], - "https://github.com/SilverAndJade/comfyui-silver-nodes": [ - [ - "SilverFileTextLoader", - "SilverFlickrRandomImage", - "SilverFolderFilePathLoader", - "SilverFolderImageLoader", - "SilverFolderVideoLoader", - "SilverLoraModelLoader", - "SilverRandomFromList", - "SilverStringReplacer", - "SilverUrlImageLoader", - "SilverWebImageLoader" - ], - { - "title_aux": "ComfyUI Silver Nodes" - } - ], - "https://github.com/Simlym/comfyui-prompt-helper": [ - [ - "PromptProcessor" - ], - { - "title_aux": "ComfyUI Prompt Helper" - } - ], - "https://github.com/SimonHeese/ComfyUI_AnimationNodes/raw/refs/heads/main/animated_offset_pad.py": [ - [ - "AnimatedOffsetPadding" - ], - { - "title_aux": "ComfyUI_AnimationNodes" - } - ], - "https://github.com/Sinphaltimus/comfyui_fedcoms_node_pack": [ - [ - "EnhancedModelMetadataReader", - "ModelDataExtractor", - "ModelMetadataReader" - ], - { - "title_aux": "comfyui_fedcoms_node_pack" - } - ], - "https://github.com/SipherAGI/comfyui-animatediff": [ - [ - "AnimateDiffCombine", - "AnimateDiffLoraLoader", - "AnimateDiffModuleLoader", - "AnimateDiffSampler", - "AnimateDiffSlidingWindowOptions", - "ImageSizeAndBatchSize", - "LoadVideo" - ], - { - "title_aux": "AnimateDiff" - } - ], - "https://github.com/SlackinJack/asyncdiff_comfyui": [ - [ - "ADADSampler", - "ADControlNetLoader", - "ADIPAdapterLoader", - "ADLoraLoader", - "ADModelLoader", - "ADMultiLoraCombiner", - "ADPipelineConfig", - "ADSDSampler", - "ADSDUpscaleSampler", - "ADSVDSampler", - "ADSchedulerSelector" - ], - { - "title_aux": "asyncdiff_comfyui" - } - ], - "https://github.com/SlackinJack/distrifuser_comfyui": [ - [ - "DFPipelineConfig", - "DFSampler" - ], - { - "title_aux": "distrifuser_comfyui" - } - ], - "https://github.com/SleeeepyZhou/ComfyUI-CNtranslator": [ - [ - "CNtranslator", - "TextShow" - ], - { - "title_aux": "CNtranslator" - } - ], - "https://github.com/Slickytail/ComfyUI-InstantX-IPAdapter-SD3": [ - [ - "ApplyIPAdapterSD3", - "IPAdapterSD3Loader" - ], - { - "title_aux": "ComfyUI-InstantX-IPAdapter-SD3" - } - ], - "https://github.com/Slickytail/ComfyUI-RegionalAdaptiveSampling": [ - [ - "RegionalAdaptiveSampling" - ], - { - "title_aux": "ComfyUI-RegionalAdaptiveSampling" - } - ], - "https://github.com/Smirnov75/ComfyUI-mxToolkit": [ - [ - "mxSeed", - "mxSlider", - "mxSlider2D", - "mxStop" - ], - { - "title_aux": "ComfyUI-mxToolkit" - } - ], - "https://github.com/Smuzzies/comfyui_meme_maker": [ - [ - "MemeMaker" - ], - { - "title_aux": "comfyui_meme_maker" - } - ], - "https://github.com/SoftMeng/ComfyUI-DeepCache-Fix": [ - [ - "DeepCache_Fix" - ], - { - "title_aux": "ComfyUI-DeepCache-Fix" - } - ], - "https://github.com/SoftMeng/ComfyUI-PIL": [ - [ - "PIL Effects (Mexx)", - "PIL Merge Image (Mexx)", - "PIL Remove Black Dots (Mexx)", - "PIL TITLE (Mexx)" - ], - { - "title_aux": "ComfyUI-PIL" - } - ], - "https://github.com/SoftMeng/ComfyUI_ImageToText": [ - [ - "ComfyUI_ImageToText" - ], - { - "title_aux": "ComfyUI_ImageToText" - } - ], - "https://github.com/SoftMeng/ComfyUI_Mexx_Poster": [ - [ - "ComfyUI_Mexx_Poster" - ], - { - "title_aux": "ComfyUI_Mexx_Poster" - } - ], - "https://github.com/SoftMeng/ComfyUI_Mexx_Styler": [ - [ - "MexxSDXLPromptStyler", - "MexxSDXLPromptStylerAdvanced" - ], - { - "title_aux": "ComfyUI_Mexx_Styler" - } - ], - "https://github.com/SongGuo11/ComfyUI-SaveAnything-SG11": [ - [ - "SG11_SaveAnything" - ], - { - "title_aux": "ComfyUI SaveAnything Node (SG11)" - } - ], - "https://github.com/Sorcerio/MBM-Music-Visualizer": [ - [ - "id", - "mbmAudioFeatureCalculator", - "mbmAudioLoader", - "mbmImageConcat", - "mbmPromptSequenceBuilder", - "mbmPromptSequenceBuilderAdv", - "mbmPromptSequenceInterpolator", - "mbmPromptSequenceLoader", - "mbmPromptSequenceRenderer" - ], - { - "title_aux": "MBM's Music Visualizer" - } - ], - "https://github.com/SozeInc/ComfyUI-Mobile": [ - [ - "Mobile_Settings_Launcher_Data", - "Send Notification (Mobile)", - "Settings Launcher (Mobile)", - "Ultimate Concat (Mobile)" - ], - { - "title_aux": "ComfyUI-Mobile" - } - ], - "https://github.com/SozeInc/ComfyUI_Soze": [ - [ - "Alpha Crop and Position Image", - "CSV Reader", - "CSV Reader X Checkpoint", - "CSV Reader X Lora", - "CSV Writer", - "Checkpoint File Loader", - "ComfyDeploy API Boolean Parameters", - "ComfyDeploy API Float Parameters", - "ComfyDeploy API Image Parameters", - "ComfyDeploy API Int Parameters", - "ComfyDeploy API Mixed Parameters", - "ComfyDeploy API Node", - "ComfyDeploy API String Parameters", - "ElevenLabs Voice Retriever", - "Empty Images", - "Get Most Common Image Colors", - "Image Batch Process Switch", - "Image List Loader", - "Image Overlay", - "Is Input In List", - "Is String Empty", - "Load Image", - "Load Image From URL", - "Load Images From Folder", - "Lora File Loader", - "Multiline Concatenate Strings", - "Output Filename", - "Pad Mask", - "Prompt Cache", - "Range(Num Steps) - Float", - "Range(Num Steps) - Int", - "Range(Step) - Float", - "Range(Step) - Int", - "Shrink Image", - "Special Character Replacer", - "Text Contains (Return Bool)", - "Text Contains (Return String)", - "Variable Image Builder", - "XY Any", - "XY Image" - ], - { - "title_aux": "Quality of Life Nodes for ComfyUI" - } - ], - "https://github.com/SparknightLLC/ComfyUI-ConditionalInterrupt": [ - [ - "Conditional Interrupt" - ], - { - "title_aux": "ComfyUI-ConditionalInterrupt" - } - ], - "https://github.com/SparknightLLC/ComfyUI-GPENO": [ - [ - "GPENO Face Restoration" - ], - { - "author": "yangxy (yangtao9009@gmail.com)", - "title_aux": "ComfyUI-GPENO" - } - ], - "https://github.com/SparknightLLC/ComfyUI-ImageAutosize": [ - [ - "ImageAutosize" - ], - { - "title_aux": "ComfyUI-ImageAutosize" - } - ], - "https://github.com/SparknightLLC/ComfyUI-ImageAutotone": [ - [ - "ImageAutotone" - ], - { - "title_aux": "ComfyUI-ImageAutotone" - } - ], - "https://github.com/SparknightLLC/ComfyUI-LatentClamp": [ - [ - "LatentClamp" - ], - { - "title_aux": "ComfyUI-LatentClamp" - } - ], - "https://github.com/SparknightLLC/ComfyUI-MaskArbiter": [ - [ - "GroundingDinoSAM2SegmentList", - "MaskArbiter" - ], - { - "title_aux": "ComfyUI-MaskArbiter" - } - ], - "https://github.com/SparknightLLC/ComfyUI-WeightedRandomChoice": [ - [ - "WeightedRandomChoice" - ], - { - "title_aux": "ComfyUI-WeightedRandomChoice" - } - ], - "https://github.com/SpenserCai/ComfyUI-FunAudioLLM": [ - [ - "CosyVoiceCrossLingualNode", - "CosyVoiceInstructNode", - "CosyVoiceLoadSpeakerModelFromUrlNode", - "CosyVoiceLoadSpeakerModelNode", - "CosyVoiceSFTNode", - "CosyVoiceSaveSpeakerModelNode", - "CosyVoiceZeroShotNode", - "SenseVoiceNode" - ], - { - "title_aux": "ComfyUI-FunAudioLLM" - } - ], - "https://github.com/Stability-AI/ComfyUI-SAI_API": [ - [ - "Stability Conservative Upscale", - "Stability Control Sketch", - "Stability Control Structure", - "Stability Control Style", - "Stability Creative Upscale", - "Stability Erase", - "Stability Fast Upscale", - "Stability Image Core", - "Stability Image Ultra", - "Stability Inpainting", - "Stability Outpainting", - "Stability Remove Background", - "Stability Replace Background and Relight", - "Stability SD3", - "Stability Search And Recolor", - "Stability Search and Replace" - ], - { - "title_aux": "Stability API nodes for ComfyUI" - } - ], - "https://github.com/Stability-AI/stability-ComfyUI-nodes": [ - [ - "ColorBlend", - "ControlLoraSave", - "GetImageSize" - ], - { - "title_aux": "stability-ComfyUI-nodes" - } - ], - "https://github.com/StableLlama/ComfyUI-basic_data_handling": [ - [ - "Basic data handling: Boolean And", - "Basic data handling: Boolean Nand", - "Basic data handling: Boolean Nor", - "Basic data handling: Boolean Not", - "Basic data handling: Boolean Or", - "Basic data handling: Boolean Xor", - "Basic data handling: CastToBoolean", - "Basic data handling: CastToDict", - "Basic data handling: CastToFloat", - "Basic data handling: CastToInt", - "Basic data handling: CastToList", - "Basic data handling: CastToSet", - "Basic data handling: CastToString", - "Basic data handling: CompareLength", - "Basic data handling: ContinueFlow", - "Basic data handling: DataListAll", - "Basic data handling: DataListAny", - "Basic data handling: DataListAppend", - "Basic data handling: DataListContains", - "Basic data handling: DataListCount", - "Basic data handling: DataListCreate", - "Basic data handling: DataListCreateFromBoolean", - "Basic data handling: DataListCreateFromFloat", - "Basic data handling: DataListCreateFromInt", - "Basic data handling: DataListCreateFromString", - "Basic data handling: DataListEnumerate", - "Basic data handling: DataListExtend", - "Basic data handling: DataListFilter", - "Basic data handling: DataListFilterSelect", - "Basic data handling: DataListFirst", - "Basic data handling: DataListGetItem", - "Basic data handling: DataListIndex", - "Basic data handling: DataListInsert", - "Basic data handling: DataListLast", - "Basic data handling: DataListLength", - "Basic data handling: DataListMax", - "Basic data handling: DataListMin", - "Basic data handling: DataListPop", - "Basic data handling: DataListPopRandom", - "Basic data handling: DataListRange", - "Basic data handling: DataListRemove", - "Basic data handling: DataListReverse", - "Basic data handling: DataListSetItem", - "Basic data handling: DataListSlice", - "Basic data handling: DataListSort", - "Basic data handling: DataListSum", - "Basic data handling: DataListToList", - "Basic data handling: DataListToSet", - "Basic data handling: DataListZip", - "Basic data handling: DictCompare", - "Basic data handling: DictContainsKey", - "Basic data handling: DictCreate", - "Basic data handling: DictCreateFromBoolean", - "Basic data handling: DictCreateFromFloat", - "Basic data handling: DictCreateFromInt", - "Basic data handling: DictCreateFromItemsDataList", - "Basic data handling: DictCreateFromItemsList", - "Basic data handling: DictCreateFromLists", - "Basic data handling: DictCreateFromString", - "Basic data handling: DictExcludeKeys", - "Basic data handling: DictFilterByKeys", - "Basic data handling: DictFromKeys", - "Basic data handling: DictGet", - "Basic data handling: DictGetKeysValues", - "Basic data handling: DictGetMultiple", - "Basic data handling: DictInvert", - "Basic data handling: DictItems", - "Basic data handling: DictKeys", - "Basic data handling: DictLength", - "Basic data handling: DictMerge", - "Basic data handling: DictPop", - "Basic data handling: DictPopItem", - "Basic data handling: DictPopRandom", - "Basic data handling: DictRemove", - "Basic data handling: DictSet", - "Basic data handling: DictSetDefault", - "Basic data handling: DictUpdate", - "Basic data handling: DictValues", - "Basic data handling: Equal", - "Basic data handling: ExecutionOrder", - "Basic data handling: FloatAdd", - "Basic data handling: FloatAsIntegerRatio", - "Basic data handling: FloatCreate", - "Basic data handling: FloatDivide", - "Basic data handling: FloatDivideSafe", - "Basic data handling: FloatFromHex", - "Basic data handling: FloatHex", - "Basic data handling: FloatIsInteger", - "Basic data handling: FloatMultiply", - "Basic data handling: FloatPower", - "Basic data handling: FloatRound", - "Basic data handling: FloatSubtract", - "Basic data handling: FlowSelect", - "Basic data handling: ForceCalculation", - "Basic data handling: GreaterThan", - "Basic data handling: GreaterThanOrEqual", - "Basic data handling: IfElifElse", - "Basic data handling: IfElse", - "Basic data handling: IntAdd", - "Basic data handling: IntBitCount", - "Basic data handling: IntBitLength", - "Basic data handling: IntCreate", - "Basic data handling: IntCreateWithBase", - "Basic data handling: IntDivide", - "Basic data handling: IntDivideSafe", - "Basic data handling: IntFromBytes", - "Basic data handling: IntModulus", - "Basic data handling: IntMultiply", - "Basic data handling: IntPower", - "Basic data handling: IntSubtract", - "Basic data handling: IntToBytes", - "Basic data handling: IsNull", - "Basic data handling: LessThan", - "Basic data handling: LessThanOrEqual", - "Basic data handling: ListAll", - "Basic data handling: ListAny", - "Basic data handling: ListAppend", - "Basic data handling: ListContains", - "Basic data handling: ListCount", - "Basic data handling: ListCreate", - "Basic data handling: ListCreateFromBoolean", - "Basic data handling: ListCreateFromFloat", - "Basic data handling: ListCreateFromInt", - "Basic data handling: ListCreateFromString", - "Basic data handling: ListEnumerate", - "Basic data handling: ListExtend", - "Basic data handling: ListFirst", - "Basic data handling: ListGetItem", - "Basic data handling: ListIndex", - "Basic data handling: ListInsert", - "Basic data handling: ListLast", - "Basic data handling: ListLength", - "Basic data handling: ListMax", - "Basic data handling: ListMin", - "Basic data handling: ListPop", - "Basic data handling: ListPopRandom", - "Basic data handling: ListRange", - "Basic data handling: ListRemove", - "Basic data handling: ListReverse", - "Basic data handling: ListSetItem", - "Basic data handling: ListSlice", - "Basic data handling: ListSort", - "Basic data handling: ListSum", - "Basic data handling: ListToDataList", - "Basic data handling: ListToSet", - "Basic data handling: MathAbs", - "Basic data handling: MathAcos", - "Basic data handling: MathAsin", - "Basic data handling: MathAtan", - "Basic data handling: MathAtan2", - "Basic data handling: MathCeil", - "Basic data handling: MathCos", - "Basic data handling: MathDegrees", - "Basic data handling: MathE", - "Basic data handling: MathExp", - "Basic data handling: MathFloor", - "Basic data handling: MathFormula", - "Basic data handling: MathLog", - "Basic data handling: MathLog10", - "Basic data handling: MathMax", - "Basic data handling: MathMin", - "Basic data handling: MathPi", - "Basic data handling: MathRadians", - "Basic data handling: MathSin", - "Basic data handling: MathSqrt", - "Basic data handling: MathTan", - "Basic data handling: NotEqual", - "Basic data handling: NumberInRange", - "Basic data handling: PathAbspath", - "Basic data handling: PathBasename", - "Basic data handling: PathCommonPrefix", - "Basic data handling: PathDirname", - "Basic data handling: PathExists", - "Basic data handling: PathExpandVars", - "Basic data handling: PathGetCwd", - "Basic data handling: PathGetExtension", - "Basic data handling: PathGetSize", - "Basic data handling: PathGlob", - "Basic data handling: PathIsAbsolute", - "Basic data handling: PathIsDir", - "Basic data handling: PathIsFile", - "Basic data handling: PathJoin", - "Basic data handling: PathListDir", - "Basic data handling: PathLoadImageRGB", - "Basic data handling: PathLoadImageRGBA", - "Basic data handling: PathLoadMaskFromAlpha", - "Basic data handling: PathLoadMaskFromGreyscale", - "Basic data handling: PathLoadStringFile", - "Basic data handling: PathNormalize", - "Basic data handling: PathRelative", - "Basic data handling: PathSaveImageRGB", - "Basic data handling: PathSaveImageRGBA", - "Basic data handling: PathSaveStringFile", - "Basic data handling: PathSetExtension", - "Basic data handling: PathSplit", - "Basic data handling: PathSplitExt", - "Basic data handling: RegexFindallDataList", - "Basic data handling: RegexFindallList", - "Basic data handling: RegexGroupDict", - "Basic data handling: RegexSearchGroupsDataList", - "Basic data handling: RegexSearchGroupsList", - "Basic data handling: RegexSplitDataList", - "Basic data handling: RegexSplitList", - "Basic data handling: RegexSub", - "Basic data handling: RegexTest", - "Basic data handling: SetAdd", - "Basic data handling: SetAll", - "Basic data handling: SetAny", - "Basic data handling: SetContains", - "Basic data handling: SetCreate", - "Basic data handling: SetCreateFromBoolean", - "Basic data handling: SetCreateFromFloat", - "Basic data handling: SetCreateFromInt", - "Basic data handling: SetCreateFromString", - "Basic data handling: SetDifference", - "Basic data handling: SetDiscard", - "Basic data handling: SetEnumerate", - "Basic data handling: SetIntersection", - "Basic data handling: SetIsDisjoint", - "Basic data handling: SetIsSubset", - "Basic data handling: SetIsSuperset", - "Basic data handling: SetLength", - "Basic data handling: SetPop", - "Basic data handling: SetPopRandom", - "Basic data handling: SetRemove", - "Basic data handling: SetSum", - "Basic data handling: SetSymmetricDifference", - "Basic data handling: SetToDataList", - "Basic data handling: SetToList", - "Basic data handling: SetUnion", - "Basic data handling: StringCapitalize", - "Basic data handling: StringCasefold", - "Basic data handling: StringCenter", - "Basic data handling: StringComparison", - "Basic data handling: StringConcat", - "Basic data handling: StringCount", - "Basic data handling: StringDataListJoin", - "Basic data handling: StringDecode", - "Basic data handling: StringEncode", - "Basic data handling: StringEndswith", - "Basic data handling: StringEscape", - "Basic data handling: StringExpandtabs", - "Basic data handling: StringFind", - "Basic data handling: StringFormatMap", - "Basic data handling: StringIn", - "Basic data handling: StringIsAlnum", - "Basic data handling: StringIsAlpha", - "Basic data handling: StringIsAscii", - "Basic data handling: StringIsDecimal", - "Basic data handling: StringIsDigit", - "Basic data handling: StringIsIdentifier", - "Basic data handling: StringIsLower", - "Basic data handling: StringIsNumeric", - "Basic data handling: StringIsPrintable", - "Basic data handling: StringIsSpace", - "Basic data handling: StringIsTitle", - "Basic data handling: StringIsUpper", - "Basic data handling: StringLength", - "Basic data handling: StringListJoin", - "Basic data handling: StringLjust", - "Basic data handling: StringLower", - "Basic data handling: StringLstrip", - "Basic data handling: StringRemoveprefix", - "Basic data handling: StringRemovesuffix", - "Basic data handling: StringReplace", - "Basic data handling: StringRfind", - "Basic data handling: StringRjust", - "Basic data handling: StringRsplitDataList", - "Basic data handling: StringRsplitList", - "Basic data handling: StringRstrip", - "Basic data handling: StringSplitDataList", - "Basic data handling: StringSplitList", - "Basic data handling: StringSplitlinesDataList", - "Basic data handling: StringSplitlinesList", - "Basic data handling: StringStartswith", - "Basic data handling: StringStrip", - "Basic data handling: StringSwapcase", - "Basic data handling: StringTitle", - "Basic data handling: StringUnescape", - "Basic data handling: StringUpper", - "Basic data handling: StringZfill", - "Basic data handling: SwitchCase", - "Basic data handling: TimeAddDelta", - "Basic data handling: TimeDelta", - "Basic data handling: TimeDifference", - "Basic data handling: TimeExtract", - "Basic data handling: TimeFormat", - "Basic data handling: TimeNow", - "Basic data handling: TimeParse", - "Basic data handling: TimeSubtractDelta", - "Basic data handling: TimeToUnix", - "Basic data handling: UnixToTime" - ], - { - "title_aux": "Basic data handling" - } - ], - "https://github.com/StarAsh042/ComfyUI_RollingArtist": [ - [ - "RollingArtist" - ], - { - "title_aux": "ComfyUI_RollingArtist" - } - ], - "https://github.com/StarMagicAI/comfyui_tagger": [ - [ - "DownloadAndLoadFlorence2Lora_jsonL", - "DownloadAndLoadFlorence2Model_jsonL", - "Florence2ModelLoader_jsonL", - "Florence2Run_jsonL", - "batch_text_save_jsonL" - ], - { - "title_aux": "ComfyUI-tagger" - } - ], - "https://github.com/Starnodes2024/ComfyUI_StarBetaNodes": [ - [ - "StarApplyOverlayDepth", - "StarOllamaSysprompterJC", - "StarQwenEditEncoder", - "StarQwenImageEditInputs", - "StarQwenImageRatio", - "StarQwenWanRatio", - "StarSaveFolderString" - ], - { - "title_aux": "ComfyUI_StarBetaNodes" - } - ], - "https://github.com/Starnodes2024/ComfyUI_StarNodes": [ - [ - "AdaptiveDetailEnhancement", - "DetailStarDaemon", - "FluxFillSampler", - "FluxStartSettings", - "Fluxstarsampler", - "OllamaModelChooser", - "SD35StartSettings", - "SDXLStartSettings", - "SDstarsampler", - "Star Face Loader", - "Star3LoRAs", - "StarConditioningLoader", - "StarConditioningSaver", - "StarDeleteSamplerSettings", - "StarDenoiseSlider", - "StarDivisibleDimension", - "StarEasyTextStorage", - "StarFiveWildcards", - "StarFrameFromVideo", - "StarGridCaptionsBatcher", - "StarGridComposer", - "StarGridImageBatcher", - "StarIconExporter", - "StarImageLoader1by1", - "StarImageSwitch", - "StarImageSwitch2", - "StarInfiniteYouAdvancedPatchMaker", - "StarInfiniteYouApply", - "StarInfiniteYouFaceSwapMod", - "StarInfiniteYouPatch", - "StarInfiniteYouPatchCombine", - "StarInfiniteYouSaver", - "StarLatentSwitch", - "StarLoadSamplerSettings", - "StarNewsScraper", - "StarPSDSaver", - "StarPSDSaver2", - "StarPaletteExtractor", - "StarRandomImageLoader", - "StarSavePanoramaJPEG", - "StarSaveSamplerSettings", - "StarTextFilter", - "StarTextInput", - "StarWildcardsAdvanced", - "Star_Image2Latent", - "Star_Show_Last_Frame", - "Starnodes_Aspect_Ratio", - "Starnodes_Aspect_Ratio_Advanced", - "Starnodes_Aspect_Video_Ratio", - "Starupscale" - ], - { - "title_aux": "ComfyUI_StarNodes" - } - ], - "https://github.com/StartHua/ComfyUI_OOTDiffusion_CXH": [ - [ - "Ood_CXH" - ], - { - "title_aux": "ComfyUI_OOTDiffusion_CXH" - } - ], - "https://github.com/StartHua/ComfyUI_PCDMs": [ - [ - "PCDMS_CXH" - ], - { - "title_aux": "ComfyUI_PCDMs" - } - ], - "https://github.com/StartHua/ComfyUI_Seg_VITON": [ - [ - "segformer_agnostic", - "segformer_clothes", - "segformer_remove_bg", - "stabel_vition" - ], - { - "title_aux": "ComfyUI_Seg_VITON" - } - ], - "https://github.com/StartHua/Comfyui_CXH_DeepLX": [ - [ - "CXH_DeepLX_Free", - "CXH_DeepLX_translate" - ], - { - "title_aux": "Comfyui_CXH_DeepLX" - } - ], - "https://github.com/StartHua/Comfyui_CXH_FluxLoraMerge": [ - [ - "CXH_Lora_Merge" - ], - { - "title_aux": "Comfyui_CXH_FluxLoraMerge" - } - ], - "https://github.com/StartHua/Comfyui_CXH_Phi_3.5": [ - [ - "CXH_Phi_Run", - "CXH_Phi_chat_load", - "CXH_Phi_chat_min", - "CXH_Phi_load" - ], - { - "title_aux": "Comfyui_CXH_Phi_3.5" - } - ], - "https://github.com/StartHua/Comfyui_Gemini2": [ - [ - "CXH_Gemini2_TX", - "CXH_Gemini2_Vision", - "CXH_Local_Prompt" - ], - { - "title_aux": "Comfyui_Gemini2" - } - ], - "https://github.com/StartHua/Comfyui_joytag": [ - [ - "CXH_JoyTag" - ], - { - "title_aux": "Comfyui_joytag" - } - ], - "https://github.com/StartHua/Comfyui_segformer_b2_clothes": [ - [ - "segformer_b2_clothes", - "segformer_b3_fashion" - ], - { - "title_aux": "comfyui_segformer_b2_clothes" - } - ], - "https://github.com/Steudio/ComfyUI_Steudio": [ - [ - "Combine Tiles", - "Display UI", - "Divide Image and Select Tile", - "Divide and Conquer Algorithm", - "Load Images into List", - "Ratio Calculator", - "Ratio to Size", - "Seed Shifter", - "Sequence Generator", - "Simple Config" - ], - { - "title_aux": "ComfyUI Steudio" - } - ], - "https://github.com/Style-Mosaic/dino-x-comfyui-node": [ - [ - "DinoxDetector" - ], - { - "title_aux": "ComfyUI DINO-X Detector Node" - } - ], - "https://github.com/SuperBeastsAI/ComfyUI-SuperBeasts": [ - [ - "Deflicker - Experimental (SuperBeasts.AI)", - "HDR Effects (SuperBeasts.AI)", - "Image Batch Manager (SuperBeasts.AI)", - "Make Resized Mask Batch (SuperBeasts.AI)", - "Mask Batch Manager (SuperBeasts.AI)", - "Pixel Deflicker - Experimental (SuperBeasts.AI)", - "SB Load Model (SuperBeasts.AI)", - "String List Manager (SuperBeasts.AI)", - "Super Pop Color Adjustment (SuperBeasts.AI)", - "Super Pop Residual Blend (SuperBeasts.AI)" - ], - { - "title_aux": "ComfyUI-SuperBeasts" - } - ], - "https://github.com/SuperMasterBlasterLaser/ComfyUI_YOLO_Classifiers": [ - [ - "YOLO Classifier Model Loader", - "YOLO Classify" - ], - { - "title_aux": "ComfyUI_YOLO_Classifiers" - } - ], - "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes": [ - [ - "CR 8 Channel In", - "CR 8 Channel Out", - "CR Apply ControlNet", - "CR Apply LoRA Stack", - "CR Apply Model Merge", - "CR Apply Multi Upscale", - "CR Apply Multi-ControlNet", - "CR Arabic Text RTL", - "CR Aspect Ratio", - "CR Aspect Ratio Banners", - "CR Aspect Ratio SDXL", - "CR Aspect Ratio Social Media", - "CR Batch Images From List", - "CR Batch Process Switch", - "CR Binary Pattern", - "CR Binary To Bit List", - "CR Bit Schedule", - "CR Central Schedule", - "CR Checker Pattern", - "CR Clamp Value", - "CR Clip Input Switch", - "CR Color Bars", - "CR Color Gradient", - "CR Color Panel", - "CR Color Tint", - "CR Combine Prompt", - "CR Combine Schedules", - "CR Comic Panel Templates", - "CR Composite Text", - "CR Conditioning Input Switch", - "CR Conditioning Mixer", - "CR ControlNet Input Switch", - "CR Current Frame", - "CR Cycle Images", - "CR Cycle Images Simple", - "CR Cycle LoRAs", - "CR Cycle Models", - "CR Cycle Text", - "CR Cycle Text Simple", - "CR Data Bus In", - "CR Data Bus Out", - "CR Debatch Frames", - "CR Diamond Panel", - "CR Draw Perspective Text", - "CR Draw Pie", - "CR Draw Shape", - "CR Draw Text", - "CR Encode Scheduled Prompts", - "CR Feathered Border", - "CR Float Range List", - "CR Float To Integer", - "CR Float To String", - "CR Font File List", - "CR Get Parameter From Prompt", - "CR Gradient Float", - "CR Gradient Integer", - "CR Half Drop Panel", - "CR Halftone Filter", - "CR Halftone Grid", - "CR Hires Fix Process Switch", - "CR Image Border", - "CR Image Grid Panel", - "CR Image Input Switch", - "CR Image Input Switch (4 way)", - "CR Image List", - "CR Image List Simple", - "CR Image Output", - "CR Image Panel", - "CR Image Pipe Edit", - "CR Image Pipe In", - "CR Image Pipe Out", - "CR Image Size", - "CR Img2Img Process Switch", - "CR Increment Float", - "CR Increment Integer", - "CR Index", - "CR Index Increment", - "CR Index Multiply", - "CR Index Reset", - "CR Input Text List", - "CR Integer Multiple", - "CR Integer Range List", - "CR Integer To String", - "CR Interpolate Latents", - "CR Intertwine Lists", - "CR Keyframe List", - "CR Latent Batch Size", - "CR Latent Input Switch", - "CR LoRA List", - "CR LoRA Stack", - "CR Load Animation Frames", - "CR Load Flow Frames", - "CR Load GIF As List", - "CR Load Image List", - "CR Load Image List Plus", - "CR Load LoRA", - "CR Load Prompt Style", - "CR Load Schedule From File", - "CR Load Scheduled ControlNets", - "CR Load Scheduled LoRAs", - "CR Load Scheduled Models", - "CR Load Text List", - "CR Mask Text", - "CR Math Operation", - "CR Model Input Switch", - "CR Model List", - "CR Model Merge Stack", - "CR Module Input", - "CR Module Output", - "CR Module Pipe Loader", - "CR Multi Upscale Stack", - "CR Multi-ControlNet Stack", - "CR Multiline Text", - "CR Output Flow Frames", - "CR Output Schedule To File", - "CR Overlay Text", - "CR Overlay Transparent Image", - "CR Page Layout", - "CR Pipe Switch", - "CR Polygons", - "CR Prompt List", - "CR Prompt List Keyframes", - "CR Prompt Scheduler", - "CR Prompt Text", - "CR Radial Gradient", - "CR Random Hex Color", - "CR Random LoRA Stack", - "CR Random Multiline Colors", - "CR Random Multiline Values", - "CR Random Panel Codes", - "CR Random RGB", - "CR Random RGB Gradient", - "CR Random Shape Pattern", - "CR Random Weight LoRA", - "CR Repeater", - "CR SD1.5 Aspect Ratio", - "CR SDXL Aspect Ratio", - "CR SDXL Base Prompt Encoder", - "CR SDXL Prompt Mix Presets", - "CR SDXL Prompt Mixer", - "CR SDXL Style Text", - "CR Save Text To File", - "CR Schedule Input Switch", - "CR Schedule To ScheduleList", - "CR Seamless Checker", - "CR Seed", - "CR Seed to Int", - "CR Select Font", - "CR Select ISO Size", - "CR Select Model", - "CR Select Resize Method", - "CR Set Switch From String", - "CR Set Value On Binary", - "CR Set Value On Boolean", - "CR Set Value on String", - "CR Simple Banner", - "CR Simple Binary Pattern", - "CR Simple Binary Pattern Simple", - "CR Simple Image Compare", - "CR Simple List", - "CR Simple Meme Template", - "CR Simple Prompt List", - "CR Simple Prompt List Keyframes", - "CR Simple Prompt Scheduler", - "CR Simple Schedule", - "CR Simple Text Panel", - "CR Simple Text Scheduler", - "CR Simple Text Watermark", - "CR Simple Titles", - "CR Simple Value Scheduler", - "CR Split String", - "CR Starburst Colors", - "CR Starburst Lines", - "CR String To Boolean", - "CR String To Combo", - "CR String To Number", - "CR Style Bars", - "CR Switch Model and CLIP", - "CR Text", - "CR Text Blacklist", - "CR Text Concatenate", - "CR Text Cycler", - "CR Text Input Switch", - "CR Text Input Switch (4 way)", - "CR Text Length", - "CR Text List", - "CR Text List Simple", - "CR Text List To String", - "CR Text Operation", - "CR Text Replace", - "CR Text Scheduler", - "CR Thumbnail Preview", - "CR Trigger", - "CR Upscale Image", - "CR VAE Decode", - "CR VAE Input Switch", - "CR Value", - "CR Value Cycler", - "CR Value Scheduler", - "CR Vignette Filter", - "CR XY From Folder", - "CR XY Index", - "CR XY Interpolate", - "CR XY List", - "CR XY Product", - "CR XY Save Grid Image", - "CR XYZ Index", - "CR_Aspect Ratio For Print" - ], - { - "author": "Suzie1", - "description": "175 custom nodes for artists, designers and animators.", - "nickname": "Comfyroll Studio", - "title": "Comfyroll Studio", - "title_aux": "Comfyroll Studio" - } - ], - "https://github.com/Sxela/ComfyWarp": [ - [ - "ApplyMask", - "ApplyMaskConditional", - "ApplyMaskLatent", - "ApplyMaskLatentConditional", - "ExtractFlowAndMixConsistencyMaps", - "ExtractOpticalFlow", - "FixedQueue", - "KeyframedFlowApplication", - "LoadFrame", - "LoadFrameFromDataset", - "LoadFrameFromFolder", - "LoadFramePairFromDataset", - "LoadFrameSequence", - "MakeFrameDataset", - "MakePaths", - "MixConsistencyMaps", - "OffsetNumber", - "RenderVideo", - "ResizeToFit", - "SaveFrame", - "SchedulerFloat", - "SchedulerInt", - "SchedulerString", - "WarpFrame" - ], - { - "title_aux": "ComfyWarp" - } - ], - "https://github.com/SykkoAtHome/ComfyUI_FaceProcessor": [ - [ - "FaceFitAndRestore", - "FaceTracker", - "FaceWrapper", - "HighPassFilter", - "ImageFeeder" - ], - { - "title_aux": "Face Processor for ComfyUI" - } - ], - "https://github.com/T-Ph525/ComfyUI-Underage-Filter": [ - [ - "AgeCheckerNode", - "MultiTypeGateNode", - "UnderageFilterNode" - ], - { - "title_aux": "Underage Filter" - } - ], - "https://github.com/TFL-TFL/ComfyUI_Text_Translation": [ - [ - "Get_Translator", - "Text", - "Text_Concatenate", - "Text_Switch", - "Text_Translation", - "Text_Translation_V2", - "Text_Translation_V2_Full" - ], - { - "title_aux": "ComfyUI_Text_Translation" - } - ], - "https://github.com/THtianhao/ComfyUI-FaceChain": [ - [ - "FC CropAndPaste", - "FC CropBottom", - "FC CropToOrigin", - "FC FaceDetectCrop", - "FC FaceFusion", - "FC FaceSegAndReplace", - "FC FaceSegment", - "FC MaskOP", - "FC RemoveCannyFace", - "FC ReplaceByMask", - "FC StyleLoraLoad" - ], - { - "title_aux": "ComfyUI-FaceChain" - } - ], - "https://github.com/THtianhao/ComfyUI-Portrait-Maker": [ - [ - "PM_BoxCropImage", - "PM_ColorTransfer", - "PM_ExpandMaskBox", - "PM_FaceFusion", - "PM_FaceShapMatch", - "PM_FaceSkin", - "PM_GetImageInfo", - "PM_ImageResizeTarget", - "PM_ImageScaleShort", - "PM_MakeUpTransfer", - "PM_MaskDilateErode", - "PM_MaskMerge2Image", - "PM_PortraitEnhancement", - "PM_RatioMerge2Image", - "PM_ReplaceBoxImg", - "PM_RetinaFace", - "PM_Similarity", - "PM_SkinRetouching", - "PM_SuperColorTransfer", - "PM_SuperMakeUpTransfer" - ], - { - "title_aux": "ComfyUI-Portrait-Maker" - } - ], - "https://github.com/TJ16th/comfyUI_TJ_NormalLighting": [ - [ - "EulerLightingNode" - ], - { - "title_aux": "comfyUI_TJ_NormalLighting" - } - ], - "https://github.com/TKRLAB/ComfyUI_Prompt_List_JSON": [ - [ - "ComfyUI_Prompt_JSON" - ], - { - "author": "TKRLAB", - "description": "ComfyUI JSON-based prompt management tool.", - "title": "ComfyUI_Prompt_List_JSON", - "title_aux": "Prompt List JSON" - } - ], - "https://github.com/TMElyralab/Comfyui-MusePose": [ - [ - "filenamestring", - "musepose", - "museposealign" - ], - { - "title_aux": "Comfyui-MusePose" - } - ], - "https://github.com/TRI3D-LC/ComfyUI-MiroBoard": [ - [ - "add-image-miro-board" - ], - { - "title_aux": "ComfyUI-MiroBoard" - } - ], - "https://github.com/TRI3D-LC/tri3d-comfyui-nodes": [ - [ - "TRI3D_CutByMaskAspectRatio", - "get_histogram_limits", - "main_light_layer", - "main_scaled_paste", - "simple_rescale_histogram", - "tri3d-HistogramEqualization", - "tri3d-LAB_2_RGB", - "tri3d-RGB_2_LAB", - "tri3d-adjust-neck", - "tri3d-atr-parse", - "tri3d-atr-parse-batch", - "tri3d-bgremove-mega", - "tri3d-clean_mask", - "tri3d-clear-memory", - "tri3d-clipdrop-bgremove-api", - "tri3d-clipdrop-bgreplace-api", - "tri3d-composite-image-splitter", - "tri3d-dwpose", - "tri3d-extract-hand", - "tri3d-extract-masks-batch", - "tri3d-extract-parts-batch", - "tri3d-extract-parts-batch2", - "tri3d-extract-parts-mask-batch", - "tri3d-extract-pascal-parts-batch", - "tri3d-extract_pose_part", - "tri3d-face-recognise", - "tri3d-flexible_color_extract", - "tri3d-float-to-image", - "tri3d-fuzzification", - "tri3d-get_histogram_limits", - "tri3d-get_mean_and_standard_deviation", - "tri3d-get_threshold_for_bg_swap", - "tri3d-image-mask-2-box", - "tri3d-image-mask-box-2-image", - "tri3d-interaction-canny", - "tri3d-levindabhi-cloth-seg", - "tri3d-load-pose-json", - "tri3d-load_AEMatter_Model", - "tri3d-luminosity-match", - "tri3d-main_transparent_background", - "tri3d-photoroom-bgremove-api", - "tri3d-pose-adaption", - "tri3d-pose-to-image", - "tri3d-position-hands", - "tri3d-position-parts-batch", - "tri3d-position-pascal-parts-batch", - "tri3d-recolor-mask", - "tri3d-recolor-mask-LAB_space", - "tri3d-recolor-mask-LAB_space_manual", - "tri3d-recolor-mask-RGB_space", - "tri3d-renormalize_array", - "tri3d-run_AEMatter_inference", - "tri3d-scaled-paste", - "tri3d-scaled-paste_unsafe", - "tri3d-simple_bg_swap", - "tri3d-simple_rescale_histogram", - "tri3d-skin-feathered-padded-mask", - "tri3d-swap-pixels", - "tri3d_CutByMaskAspectRatio", - "tri3d_H_Stack_Images", - "tri3d_Image_extend", - "tri3d_MaskAreaPercentage", - "tri3d_NSFWFilter", - "tri3d_NarrowfyImage", - "tri3d_Remove_Small_Mask_Islands", - "tri3d_SaveFlattenedPoseKpsAsJsonFile", - "tri3d_SaveImage_absolute", - "tri3d_SaveText_absolute", - "tri3d_Skip_HeadMask", - "tri3d_Skip_HeadMask_AddNeck", - "tri3d_Skip_LipMask", - "tri3d_SmartBox", - "tri3d_Smart_Depth", - "tri3d_StringContains", - "tri3d_Wait_And_Read_File", - "tri3d_extract_facer_mask", - "tri3d_fill_mask", - "tri3d_is_only_trouser", - "tri3d_position_pose_part" - ], - { - "title_aux": "tri3d-comfyui-nodes" - } - ], - "https://github.com/TTPlanetPig/Comfyui_Hunyuan3D": [ - [ - "GifImageViewerNode", - "Hunyuan3DNode", - "SquareImage" - ], - { - "title_aux": "Comfyui_Hunyuan3D" - } - ], - "https://github.com/TTPlanetPig/Comfyui_JC2": [ - [ - "ExtraOptionsNode", - "ExtraOptionsNode_Beta", - "JoyCaption2", - "JoyCaption2_simple", - "JoyCaptionBetaOne_Full", - "JoyCaptionBetaOne_Simple" - ], - { - "title_aux": "Comfyui_JC2" - } - ], - "https://github.com/TTPlanetPig/Comfyui_Object_Detect_QWen_VL": [ - [ - "BBoxesToSAM2", - "DownloadAndLoadQwenModel", - "QwenVLDetection" - ], - { - "title_aux": "ComfyUI Qwen2.5-VL Object Detection Node" - } - ], - "https://github.com/TTPlanetPig/Comfyui_Object_Migration": [ - [ - "TTP_Expand_And_Mask", - "TTP_text_mix" - ], - { - "title_aux": "Clothing Migration Kit" - } - ], - "https://github.com/TTPlanetPig/Comfyui_TTP_CN_Preprocessor": [ - [ - "TTPlanet_Tile_Preprocessor_GF", - "TTPlanet_Tile_Preprocessor_Simple", - "TTPlanet_Tile_Preprocessor_cufoff", - "TTPlanet_inpainting_Preprecessor" - ], - { - "title_aux": "for comfyui image proprocessor" - } - ], - "https://github.com/TTPlanetPig/Comfyui_TTP_Toolset": [ - [ - "TTP_CoordinateSplitter", - "TTP_Expand_And_Mask", - "TTP_Image_Assy", - "TTP_Image_Tile_Batch", - "TTP_Tile_image_size", - "TTP_condsetarea_merge", - "TTP_condsetarea_merge_test", - "TTP_condtobatch", - "TTP_text_mix", - "TTPlanet_Tile_Preprocessor_Simple", - "TeaCacheHunyuanVideoSampler" - ], - { - "title_aux": "Comfyui_TTP_Toolset" - } - ], - "https://github.com/TTPlanetPig/TTP_Comfyui_FramePack_SE": [ - [ - "TTPlanet_FramePack" - ], - { - "title_aux": "TTP_Comfyui_FramePack_SE" - } - ], - "https://github.com/TW-CUI/TW-CUI-Util": [ - [ - "TWCUI_Util_CommonSDXLResolutions", - "TWCUI_Util_FloatLiteral", - "TWCUI_Util_GenerationParameters", - "TWCUI_Util_GenerationPrompts", - "TWCUI_Util_IntLiteral", - "TWCUI_Util_ModelVAELORALoader", - "TWCUI_Util_ModelVAELoader", - "TWCUI_Util_MultilineStringLiteral", - "TWCUI_Util_SaveImage", - "TWCUI_Util_SaveImageAdvanced", - "TWCUI_Util_StringLiteral" - ], - { - "title_aux": "TW-CUI-Util" - } - ], - "https://github.com/TZOOTZ/ComfyUI-TZOOTZ_VHS": [ - [ - "TZOOTZ_VHSNode" - ], - { - "title_aux": "TZOOTZ VHS Effect Node" - } - ], - "https://github.com/TaiTair/comfyui-simswap": [ - [ - "Simswap", - "SimswapBuildFaceModel", - "SimswapFaceSwapOpt", - "SimswapImageDublicator", - "SimswapLoadFaceModel", - "SimswapMaskHelper", - "SimswapOptions", - "SimswapRestoreFace", - "SimswapSaveFaceModel" - ], - { - "title_aux": "Simswap Node for ComfyUI" - } - ], - "https://github.com/Taithrah/ComfyUI_Fens_Simple_Nodes": [ - [ - "FensTokenCounter", - "OptiEmptyLatent" - ], - { - "title_aux": "Fens-Simple-Nodes" - } - ], - "https://github.com/Taremin/comfyui-prompt-config": [ - [ - "PromptEdit", - "PromptGenerationConfig" - ], - { - "title_aux": "comfyui-prompt-config" - } - ], - "https://github.com/Taremin/comfyui-prompt-extranetworks": [ - [ - "PromptControlNetApply", - "PromptControlNetPrepare", - "PromptExtraNetworks" - ], - { - "title_aux": "ComfyUI Prompt ExtraNetworks" - } - ], - "https://github.com/Taremin/comfyui-string-tools": [ - [ - "StringToolsBalancedChoice", - "StringToolsConcat", - "StringToolsRandomChoice", - "StringToolsString", - "StringToolsText" - ], - { - "title_aux": "ComfyUI String Tools" - } - ], - "https://github.com/Taremin/webui-monaco-prompt": [ - [ - "WebuiMonacoPromptFind", - "WebuiMonacoPromptReplace" - ], - { - "title_aux": "WebUI Monaco Prompt" - } - ], - "https://github.com/TeaCrab/ComfyUI-TeaNodes": [ - [ - "TC_ColorFill", - "TC_CropTo", - "TC_EqualizeCLAHE", - "TC_ImageResize", - "TC_ImageScale", - "TC_KorniaGamma", - "TC_RandomColorFill", - "TC_SizeApproximation" - ], - { - "title_aux": "ComfyUI-TeaNodes" - } - ], - "https://github.com/TemryL/ComfyS3": [ - [ - "DownloadFileS3", - "LoadImageS3", - "SaveImageS3", - "SaveVideoFilesS3", - "UploadFileS3" - ], - { - "title_aux": "ComfyS3" - } - ], - "https://github.com/TemryL/ComfyUI-IDM-VTON": [ - [ - "IDM-VTON", - "PipelineLoader" - ], - { - "title_aux": "ComfyUI-IDM-VTON [WIP]" - } - ], - "https://github.com/Temult/TWanSigmaGraph": [ - [ - "TWanSigmaGraph" - ], - { - "title_aux": "TWanSigmaGraph" - } - ], - "https://github.com/TencentQQGYLab/ComfyUI-ELLA": [ - [ - "CombineClipEllaEmbeds", - "ConcatConditionEllaEmbeds", - "ConditionToEllaEmbeds", - "ELLALoader", - "EllaApply", - "EllaCombineEmbeds", - "EllaEncode", - "EllaTextEncode", - "SetEllaTimesteps", - "T5TextEncode #ELLA", - "T5TextEncoderLoader #ELLA" - ], - { - "title_aux": "ComfyUI-ELLA" - } - ], - "https://github.com/Tensor-Art/ComfyUI_TENSOR_ART": [ - [ - "TA_AIToolsNode", - "TA_ExecuteNode", - "TA_UploadImageNode" - ], - { - "title_aux": "ComfyUI_TENSOR_ART" - } - ], - "https://github.com/TensorKaze/ComfyUI-TkNodes": [ - [ - "FluxAdvancedSampler", - "FluxLatentSampler", - "LoadImageAndScaleToTotalPixels", - "LoadModelAndUpscaleImage", - "MultiLatentSelector", - "MultiModelLoader", - "RepeatLatentBatchOptional", - "VAEEncodeOptional" - ], - { - "title_aux": "ComfyUI-TkNodes" - } - ], - "https://github.com/TheBarret/ZSuite": [ - [ - "ZSuite: Prompter", - "ZSuite: RF Noise", - "ZSuite: SeedMod" - ], - { - "title_aux": "ZSuite" - } - ], - "https://github.com/TheBill2001/comfyui-upscale-by-model": [ - [ - "UpscaleImageByUsingModel" - ], - { - "author": "Tr\u1ea7n Nam Tu\u1ea5n", - "description": "This custom node allow upscaling an image by a factor using a model.", - "nickname": "Upscale Image By (Using Model)", - "title": "Upscale Image By (Using Model)", - "title_aux": "comfyui-upscale-by-model" - } - ], - "https://github.com/TheLustriVA/ComfyUI-Image-Size-Tools": [ - [ - "FluxResolutionNode", - "ImageSizeDetectorNode", - "SD15ResolutionNode", - "SDXLResolutionNode", - "WAN21AdvancedResolutionNode", - "WAN21ResolutionNode" - ], - { - "title_aux": "ComfyUI Image Size Tool" - } - ], - "https://github.com/TheMistoAI/ComfyUI-Anyline": [ - [ - "AnyLinePreprocessor" - ], - { - "title_aux": "Anyline" - } - ], - "https://github.com/TheWhykiki/Whykiki-ComfyUIToolset": [ - [ - "SequentialImageLoaderV8" - ], - { - "title_aux": "Whykiki ComfyUI Toolset" - } - ], - "https://github.com/ThepExcel/aiangelgallery-comfyui": [ - [ - "ThepExcel_AiAngel_MultilineTextChoiceNode" - ], - { - "title_aux": "Multiline Text Choice Node for ComfyUI" - } - ], - "https://github.com/ThereforeGames/ComfyUI-Unprompted": [ - [ - "Unprompted", - "UnpromptedSetRack" - ], - { - "title_aux": "ComfyUI-Unprompted" - } - ], - "https://github.com/TiamaTiramisu/risutools": [ - [ - "CheckFileNamePrefixExists", - "LoadImageFromText", - "LoadLastFileNamePrefix", - "UUIDGenerator" - ], - { - "title_aux": "RisuTools" - } - ], - "https://github.com/TinyBeeman/ComfyUI-TinyBee": [ - [ - "Filter Existing Files", - "Filter List", - "Filter Words", - "Get File List", - "Get List From File", - "Incrementer", - "Indexed Entry", - "List Count", - "Process Path Name", - "Random Entry", - "Randomize List", - "Replace List", - "Sort List" - ], - { - "title_aux": "ComfyUI-TinyBee" - } - ], - "https://github.com/TinyTerra/ComfyUI_tinyterraNodes": [ - [ - "ttN KSampler_v2", - "ttN advPlot combo", - "ttN advPlot images", - "ttN advPlot merge", - "ttN advPlot range", - "ttN advPlot string", - "ttN advanced xyPlot", - "ttN compareInput", - "ttN concat", - "ttN conditioning", - "ttN debugInput", - "ttN float", - "ttN hiresfixScale", - "ttN imageOutput", - "ttN imageREMBG", - "ttN int", - "ttN multiModelMerge", - "ttN pipe2BASIC", - "ttN pipe2DETAILER", - "ttN pipeEDIT", - "ttN pipeEncodeConcat", - "ttN pipeIN", - "ttN pipeKSampler", - "ttN pipeKSamplerAdvanced", - "ttN pipeKSamplerAdvanced_v2", - "ttN pipeKSamplerSDXL", - "ttN pipeKSamplerSDXL_v2", - "ttN pipeKSampler_v2", - "ttN pipeLoader", - "ttN pipeLoaderSDXL", - "ttN pipeLoaderSDXL_v2", - "ttN pipeLoader_v2", - "ttN pipeLoraStack", - "ttN pipeOUT", - "ttN seed", - "ttN text", - "ttN text3BOX_3WAYconcat", - "ttN text7BOX_concat", - "ttN textCycleLine", - "ttN textDebug", - "ttN textOutput", - "ttN tinyLoader", - "ttN xyPlot" - ], - { - "author": "tinyterra", - "description": "This extension offers extensive xyPlot, various pipe nodes, fullscreen image viewer based on node history, dynamic widgets, interface customization, and more.", - "nickname": "\ud83c\udf0f", - "nodename_pattern": "^ttN ", - "title": "tinyterraNodes", - "title_aux": "ComfyUI_tinyterraNodes" - } - ], - "https://github.com/Tlant/ComfyUI-OllamaPromptsGeneratorTlant": [ - [ - "LoadImageAndExtractMetadataTlant", - "LoadRandomTxtFileTlant", - "LoadRandomTxtFileTlantV2", - "LoadRandomTxtFileTlantV3", - "LoadSequencedTxtFileTlant", - "LoadSpecificTxtFileTlant", - "OllamaPromptsGeneratorTlant", - "OllamaSimpleTextGeneratorTlant", - "RandomImageLoaderTlant", - "ReasoningLLMOutputCleaner", - "SaveImagePairForKontext", - "StringFormatterTlant" - ], - { - "title_aux": "ComfyUI-OllamaPromptsGeneratorTlant" - } - ], - "https://github.com/ToTheBeginning/ComfyUI-DreamO": [ - [ - "ApplyDreamO", - "DreamOProcessorLoader", - "DreamORefEncode" - ], - { - "title_aux": "DreamO Comfyui" - } - ], - "https://github.com/Tr1dae/ComfyUI-Dequality": [ - [ - "Dequality" - ], - { - "title_aux": "ComfyUI-Dequality" - } - ], - "https://github.com/Trgtuan10/ComfyUI_YoloSegment_Mask": [ - [ - "Object Mask" - ], - { - "title_aux": "ComfyUI_YoloSegment_Mask" - } - ], - "https://github.com/Tropfchen/ComfyUI-Embedding_Picker": [ - [ - "EmbeddingPicker" - ], - { - "title_aux": "Embedding Picker" - } - ], - "https://github.com/Tropfchen/ComfyUI-yaResolutionSelector": [ - [ - "YARS", - "YARSAdv" - ], - { - "title_aux": "YARS: Yet Another Resolution Selector" - } - ], - "https://github.com/TrophiHunter/ComfyUI_Photography_Nodes": [ - [ - "Bloom", - "Bloom Lens Flares", - "Chromatic Aberration", - "Contrast Adaptive Sharpening", - "Contrast Brightness", - "Depth of Field", - "Get Watermark", - "Halation", - "Lens Dirt", - "Lens Distortion", - "Levels Adjustment", - "Lut", - "Manga Toner", - "Monitor Filter", - "Multi Scale Contrast", - "NTSC Filter", - "Noise", - "Physically Accurate Lens Dirt", - "Pixel Art", - "Saturation Vibrance", - "Sensor Dust", - "Sharpen Simple", - "Sharpen Unsharp Mask", - "Tint", - "VHS Chroma Smear", - "VHS Degrade", - "Vignette Effect", - "Watermark" - ], - { - "title_aux": "Photography Nodes" - } - ], - "https://github.com/Trung0246/ComfyUI-0246": [ - [ - "0246.Beautify", - "0246.BoxRange", - "0246.CastReroute", - "0246.Cloud", - "0246.Count", - "0246.Highway", - "0246.HighwayBatch", - "0246.Hold", - "0246.Hub", - "0246.Junction", - "0246.JunctionBatch", - "0246.Loop", - "0246.Merge", - "0246.Meta", - "0246.RandomInt", - "0246.Script", - "0246.ScriptNode", - "0246.ScriptPile", - "0246.ScriptRule", - "0246.Stringify", - "0246.Switch", - "0246.Tag" - ], - { - "author": "Trung0246", - "description": "Random nodes for ComfyUI I made to solve my struggle with ComfyUI (ex: pipe, process). Have varying quality.", - "nickname": "ComfyUI-0246", - "title": "ComfyUI-0246", - "title_aux": "ComfyUI-0246" - } - ], - "https://github.com/Ttl/ComfyUi_NNLatentUpscale": [ - [ - "NNLatentUpscale" - ], - { - "preemptions": [ - "NNLatentUpscale" - ], - "title_aux": "ComfyUI Neural Network Latent Upscale" - } - ], - "https://github.com/TylerZoro/SD3-Scaling": [ - [ - "SD3ImageScaleToTotalPixels" - ], - { - "title_aux": "SD3-Scaling" - } - ], - "https://github.com/Umikaze-job/select_folder_path_easy": [ - [ - "SelectFolderPathEasy" - ], - { - "title_aux": "select_folder_path_easy" - } - ], - "https://github.com/VAST-AI-Research/ComfyUI-Tripo": [ - [ - "TripoAPIDraft", - "TripoAnimateRetargetNode", - "TripoAnimateRigNode", - "TripoConvertNode", - "TripoMeshCompletion", - "TripoMeshSegmentation", - "TripoRefineModel", - "TripoSmartLowPoly", - "TripoStylizeModel", - "TripoTextureModel" - ], - { - "title_aux": "Tripo for ComfyUI" - } - ], - "https://github.com/VK/vk-nodes": [ - [ - "PrepareJobs", - "SketchyText", - "SketchyThumbnail", - "TiledConfigNode", - "TiledCropNode", - "TiledRenderNode", - "TiledSetupNode", - "VKLoadAudio" - ], - { - "title_aux": "VK Nodes" - } - ], - "https://github.com/Vaibhavs10/ComfyUI-DDUF": [ - [ - "DDUFLoader", - "DiffusersModelMakeup", - "DiffusersPipelineLoader", - "DiffusersSchedulerLoader", - "DiffusersSimpleSampler" - ], - { - "title_aux": "ComfyUI-DDUF" - } - ], - "https://github.com/VangengLab/ComfyUI-LivePortrait_v2": [ - [ - "LivePortraitProcess_animal" - ], - { - "title_aux": "ComfyUI-LivePortrait_v2" - } - ], - "https://github.com/VangengLab/ComfyUI-LivePortrait_v3": [ - [ - "LivePortraitp2p" - ], - { - "title_aux": "ComfyUI-LivePortrait_v3" - } - ], - "https://github.com/Vaporbook/ComfyUI-SaveImage-PP": [ - [ - "SaveImagePP" - ], - { - "title_aux": "ComfyUI-SaveImage-PP" - } - ], - "https://github.com/Verolelb/ComfyUI-Qwen-Aspect-Ratio": [ - [ - "QwenAspectRatioSelectorLatent" - ], - { - "title_aux": "ComfyUI-Qwen-Aspect-Ratio" - } - ], - "https://github.com/VertexAnomaly/ComfyUI_ImageSentinel": [ - [ - "ImageSentinel" - ], - { - "title_aux": "ComfyUI_ImageSentinel" - } - ], - "https://github.com/VertexStudio/roblox-comfyui-nodes": [ - [ - "FirstLetterNode", - "FlowNodes", - "MirrorEffectNode", - "SaveImageNode", - "ScaleImageNode", - "SwitchImageNode", - "SwitchTextNode", - "TextToImageNode" - ], - { - "title_aux": "roblox-comfyui-nodes" - } - ], - "https://github.com/VikramxD/VEnhancer-ComfyUI-Wrapper": [ - [ - "MultiGPUInference", - "MultiGPUVEnhancerLoader", - "SingleGPUInference", - "SingleGPUVEnhancerLoader", - "VideoLoader", - "VideoSaver" - ], - { - "title_aux": "VEnhancer ComfyUI Extension" - } - ], - "https://github.com/Visionatrix/ComfyUI-Gemini": [ - [ - "ConcatText_Zho", - "DisplayText_Zho", - "Gemini_15P_API_S_Advance_Zho", - "Gemini_15P_API_S_Chat_Advance_Zho", - "Gemini_API_Chat_Zho", - "Gemini_API_S_Chat_Zho", - "Gemini_API_S_Vsion_ImgURL_Zho", - "Gemini_API_S_Zho", - "Gemini_API_Vsion_ImgURL_Zho", - "Gemini_API_Zho", - "Gemini_FileUpload_API_S_Zho", - "Gemini_File_API_S_Zho" - ], - { - "title_aux": "ComfyUI-Gemini" - } - ], - "https://github.com/Visionatrix/ComfyUI-RemoteVAE": [ - [ - "RemoteVAEDecode" - ], - { - "title_aux": "ComfyUI-RemoteVAE" - } - ], - "https://github.com/Visionatrix/ComfyUI-Visionatrix": [ - [ - "StyleAlignedBatchAlign", - "VixCheckboxLogic", - "VixDictionaryConvert", - "VixDictionaryGet", - "VixDictionaryNew", - "VixDictionaryUpdate", - "VixDynamicLoraDefinition", - "VixImageFilters", - "VixMultilineText", - "VixTextConcatenate", - "VixTextReplace", - "VixUiAspectRatioSelector", - "VixUiCheckbox", - "VixUiCheckboxLogic", - "VixUiList", - "VixUiListLogic", - "VixUiPrompt", - "VixUiRangeFloat", - "VixUiRangeInt", - "VixUiRangeScaleFloat", - "VixUiWorkflowMetadata" - ], - { - "title_aux": "ComfyUI-Visionatrix" - } - ], - "https://github.com/VraethrDalkr/ComfyUI-ProgressiveBlend": [ - [ - "ProgressiveColorMatchBlend", - "ProgressiveImageBatchBlend" - ], - { - "title_aux": "ComfyUI-ProgressiveBlend" - } - ], - "https://github.com/VrchStudio/comfyui-web-viewer": [ - [ - "VrchAnyOSCControlNode", - "VrchAudioChannelLoaderNode", - "VrchAudioConcatNode", - "VrchAudioEmotionVisualizerNode", - "VrchAudioFrequencyBandAnalyzerNode", - "VrchAudioGenresNode", - "VrchAudioMusic2EmotionNode", - "VrchAudioRecorderNode", - "VrchAudioSaverNode", - "VrchAudioVisualizerNode", - "VrchAudioWebViewerNode", - "VrchBPMDetectorNode", - "VrchBooleanKeyControlNode", - "VrchChannelOSCControlNode", - "VrchChannelX4OSCControlNode", - "VrchDelayNode", - "VrchDelayOSCControlNode", - "VrchFloatKeyControlNode", - "VrchFloatOSCControlNode", - "VrchFloatRemapNode", - "VrchGamepadLoaderNode", - "VrchImageChannelLoaderNode", - "VrchImageFlipBookWebViewerNode", - "VrchImagePreviewBackgroundNewNode", - "VrchImagePreviewBackgroundNode", - "VrchImageSaverNode", - "VrchImageSwitchOSCControlNode", - "VrchImageWebSocketChannelLoaderNode", - "VrchImageWebSocketFilterSettingsNode", - "VrchImageWebSocketSettingsNode", - "VrchImageWebSocketSimpleWebViewerNode", - "VrchImageWebSocketWebViewerNode", - "VrchImageWebViewerNode", - "VrchInstantQueueKeyControlNode", - "VrchIntKeyControlNode", - "VrchIntOSCControlNode", - "VrchIntRemapNode", - "VrchJsonUrlLoaderNode", - "VrchJsonWebSocketChannelLoaderNode", - "VrchJsonWebSocketSenderNode", - "VrchLatentWebSocketChannelLoaderNode", - "VrchLatentWebSocketSenderNode", - "VrchMicLoaderNode", - "VrchMidiDeviceLoaderNode", - "VrchModelWebViewerNode", - "VrchOSCControlSettingsNode", - "VrchQRCodeNode", - "VrchSwitchOSCControlNode", - "VrchTextConcatOSCControlNode", - "VrchTextKeyControlNode", - "VrchTextSrtPlayerNode", - "VrchTextSwitchOSCControlNode", - "VrchTriggerToggleNode", - "VrchTriggerToggleX4Node", - "VrchTriggerToggleX8Node", - "VrchVideoWebViewerNode", - "VrchWebSocketServerNode", - "VrchWebViewerNode", - "VrchXYOSCControlNode", - "VrchXYZOSCControlNode", - "VrchXboxControllerNode" - ], - { - "title_aux": "ComfyUI Web Viewer" - } - ], - "https://github.com/VykosX/ControlFlowUtils": [ - [ - "Cycle", - "CycleContinue", - "CycleEnd", - "DataMonitor", - "FallbackAnyBatch", - "FallbackImagePreviewer", - "FolderSearch", - "GarbageCollector", - "HaltExecution", - "IfConditionSelector", - "ImageResolutionAdjust", - "InvertCondition", - "LoopClose", - "LoopOpen", - "LoraSelector", - "MemoryStorage", - "ModelSelector", - "NullInput", - "NullOutput", - "ReadTextFile", - "SaveTextFile", - "SimpleToggle", - "StringOperation", - "UniversalSwitch", - "UnloadModels", - "VAESelector", - "Wait" - ], - { - "title_aux": "ControlFlowUtils" - } - ], - "https://github.com/WASasquatch/ComfyUI_Preset_Merger": [ - [ - "Preset_Model_Merge" - ], - { - "title_aux": "ComfyUI Preset Merger" - } - ], - "https://github.com/WASasquatch/FreeU_Advanced": [ - [ - "FreeU (Advanced)", - "FreeU_V2 (Advanced)" - ], - { - "title_aux": "FreeU_Advanced" - } - ], - "https://github.com/WASasquatch/PPF_Noise_ComfyUI": [ - [ - "Blend Latents (PPF Noise)", - "Cross-Hatch Power Fractal (PPF Noise)", - "Images as Latents (PPF Noise)", - "Perlin Power Fractal Latent (PPF Noise)" - ], - { - "title_aux": "PPF_Noise_ComfyUI" - } - ], - "https://github.com/WASasquatch/PowerNoiseSuite": [ - [ - "Blend Latents (PPF Noise)", - "Cross-Hatch Power Fractal (PPF Noise)", - "Cross-Hatch Power Fractal Settings (PPF Noise)", - "Images as Latents (PPF Noise)", - "Latent Adjustment (PPF Noise)", - "Latents to CPU (PPF Noise)", - "Linear Cross-Hatch Power Fractal (PPF Noise)", - "Perlin Power Fractal Latent (PPF Noise)", - "Perlin Power Fractal Settings (PPF Noise)", - "Power KSampler Advanced (PPF Noise)", - "Power-Law Noise (PPF Noise)" - ], - { - "title_aux": "Power Noise Suite for ComfyUI" - } - ], - "https://github.com/WASasquatch/WAS_Extras": [ - [ - "BLVAEEncode", - "CLIPTextEncodeList", - "CLIPTextEncodeSequence2", - "ConditioningBlend", - "DebugInput", - "KSamplerSeq", - "KSamplerSeq2", - "VAEEncodeForInpaint (WAS)", - "VividSharpen", - "VividSharpenV2" - ], - { - "title_aux": "WAS_Extras" - } - ], - "https://github.com/WASasquatch/face-upscaling-and-seamless-embedding": [ - [ - "FUSEGenericKSampler", - "FUSEKSampler", - "FUSESamplerMaskOptions", - "FUSEYOLOSettings" - ], - { - "title_aux": "FUSE Face Enhancer" - } - ], - "https://github.com/WUYUDING2583/ComfyUI-Save-Image-Callback": [ - [ - "Save Image With Callback" - ], - { - "title_aux": "Save Image With Callback" - } - ], - "https://github.com/WX-NPS1598/ComfyUI-Auto_Crop_By_NPS": [ - [ - "AutoCropByNPS" - ], - { - "title_aux": "Auto Crop By NPS" - } - ], - "https://github.com/WaddingtonHoldings/ComfyUI-InstaSD": [ - [ - "GPTImage1Generate", - "InstaCBoolean", - "InstaCFloat", - "InstaCInteger", - "InstaCLoadImageFromS3", - "InstaCLoraLoader", - "InstaCSaveImageToS3", - "InstaCSeed", - "InstaCText", - "InstaCTextML", - "InstaLoadImageLocal", - "InstaLoadImageWithMask", - "InstaPromptMultipleStyleSelector", - "InstaPromptStyleSelector", - "LoadVideo", - "PreViewVideo" - ], - { - "title_aux": "InstaSD nodes for ComfyUI" - } - ], - "https://github.com/WainWong/ComfyUI-Loop-image": [ - [ - "CyberEve_BatchImageLoopClose", - "CyberEve_BatchImageLoopOpen", - "CyberEve_LoopIndexSwitch", - "CyberEve_MaskMerge", - "CyberEve_MaskSegmentation", - "CyberEve_SingleImageLoopClose", - "CyberEve_SingleImageLoopOpen" - ], - { - "title_aux": "ComfyUI-Loop-image" - } - ], - "https://github.com/Wakfull33/ComfyUI-SaveImageCivitAI": [ - [ - "SaveCivitai" - ], - { - "title_aux": "ComfyUI-SaveImageCivitAI" - } - ], - "https://github.com/WangPengxing/ComfyUI_WPX_Node": [ - [ - "AnimalContour", - "AnimalContourSilhouette", - "CircleContour", - "DetermineRowsAndCols", - "PenetrateStyle", - "RectangleContour", - "SplitMaskElements", - "SplitStickers" - ], - { - "title_aux": "ComfyUI WPX Nodes" - } - ], - "https://github.com/WarpedAnimation/ComfyUI-WarpedToolset": [ - [ - "ClipLoaderGGUF", - "DualClipLoaderGGUF", - "GGUFRun", - "GGUFSave", - "GGUFUndo", - "LoaderGGUF", - "LoaderGGUFAdvanced", - "QuadrupleClipLoaderGGUF", - "TENSORBoost", - "TENSORCut", - "TripleClipLoaderGGUF", - "VaeGGUF", - "WarpedBasicGuider", - "WarpedBundleAllVideoImages", - "WarpedBundleVideoImages", - "WarpedCLIPLoader", - "WarpedCLIPVisionLoader", - "WarpedCheckpointLoader", - "WarpedClipLoaderGGUF", - "WarpedCreateEmptyImageBatch", - "WarpedCreateEmptyLatentBatch", - "WarpedCreateSpecialImageBatch", - "WarpedDualCLIPLoader", - "WarpedDualClipLoaderGGUF", - "WarpedDualEncoder", - "WarpedDualGuider", - "WarpedFramepackLoraSelectBatch", - "WarpedFramepackMultiLoraSelect", - "WarpedFramepackMultiLoraSelectExt", - "WarpedFramepackSampler", - "WarpedGetImageFromVideo", - "WarpedGetTwoImagesFromVideo", - "WarpedHunyuanImageToVideo", - "WarpedHunyuanLoraAvgMerge", - "WarpedHunyuanLoraBatchMerge", - "WarpedHunyuanLoraConvert", - "WarpedHunyuanLoraConvertKeys", - "WarpedHunyuanLoraMerge", - "WarpedHunyuanMultiLoraAvgMerge", - "WarpedHunyuanMultiLoraLoader", - "WarpedHunyuanMultiLoraMerge", - "WarpedHunyuanMultiLoraMixer", - "WarpedHunyuanMultiLoraMixerExt", - "WarpedHunyuanVideoLoraLoader", - "WarpedImageNoiseAugmentation", - "WarpedImageScaleToSide", - "WarpedLeapfusionHunyuanI2V", - "WarpedLoadFramePackModel", - "WarpedLoadLorasBatchByPrefix", - "WarpedLoadVideosBatch", - "WarpedLoaderGGUF", - "WarpedLoraKeysAndMetadataReader", - "WarpedLoraReSave", - "WarpedMultiLoraLoader", - "WarpedNumericalConversion", - "WarpedReverseImageBatch", - "WarpedSamplerCustomAdv", - "WarpedSamplerCustomAdvLatent", - "WarpedSamplerCustomBatch", - "WarpedSamplerCustomScripted", - "WarpedSaveAnimatedPng", - "WarpedUpscaleWithModel", - "WarpedVAELoader", - "WarpedWanImageToVideo", - "WarpedWanLoadAndEditLoraBlocks", - "WarpedWanLoraMerge" - ], - { - "title_aux": "ComfyUI-WarpedToolset" - } - ], - "https://github.com/WaveSpeedAI/wavespeed-comfyui": [ - [ - "WaveSpeedAI BytedanceSeedanceLiteI2VNode", - "WaveSpeedAI BytedanceSeedanceLiteT2VNode", - "WaveSpeedAI BytedanceSeedanceProI2VNode", - "WaveSpeedAI BytedanceSeedanceProT2VNode", - "WaveSpeedAI Client", - "WaveSpeedAI DiaTTSNode", - "WaveSpeedAI Flux Image2Image", - "WaveSpeedAI Flux Loras", - "WaveSpeedAI Flux Text2Image", - "WaveSpeedAI FluxControlLoraCannyNode", - "WaveSpeedAI FluxControlLoraDepthNode", - "WaveSpeedAI FluxControlnetUnionPro2_0Node", - "WaveSpeedAI FluxDevFillNode", - "WaveSpeedAI FluxDevLoraNode", - "WaveSpeedAI FluxDevLoraUltraFastNode", - "WaveSpeedAI FluxDevNode", - "WaveSpeedAI FluxDevUltraFastNode", - "WaveSpeedAI FluxProReduxNode", - "WaveSpeedAI FluxReduxDevNode", - "WaveSpeedAI FluxSchnellLoraNode", - "WaveSpeedAI FluxSchnellNode", - "WaveSpeedAI FramepackNode", - "WaveSpeedAI GhibliNode", - "WaveSpeedAI GoogleVeo3FastNode", - "WaveSpeedAI GoogleVeo3Node", - "WaveSpeedAI HidreamE1FullNode", - "WaveSpeedAI HidreamI1DevNode", - "WaveSpeedAI HidreamI1FullNode", - "WaveSpeedAI Hunyuan3DV2MultiViewNode", - "WaveSpeedAI HunyuanCustomRef2V480pNode", - "WaveSpeedAI HunyuanCustomRef2V720pNode", - "WaveSpeedAI HunyuanVideoI2VNode", - "WaveSpeedAI HunyuanVideoT2VNode", - "WaveSpeedAI InstantCharacterNode", - "WaveSpeedAI KwaivgiKlingV16I2VProNode", - "WaveSpeedAI KwaivgiKlingV16I2VStandardNode", - "WaveSpeedAI KwaivgiKlingV16T2VStandardNode", - "WaveSpeedAI KwaivgiKlingV21I2vMasterNode", - "WaveSpeedAI KwaivgiKlingV21I2vProNode", - "WaveSpeedAI KwaivgiKlingV21I2vStandardNode", - "WaveSpeedAI KwaivgiKlingV21T2vMasterNode", - "WaveSpeedAI LtxVideoV097I2V480pNode", - "WaveSpeedAI LtxVideoV097I2V720pNode", - "WaveSpeedAI MMAudioV2Node", - "WaveSpeedAI Magi124bNode", - "WaveSpeedAI Minimax Image2Video", - "WaveSpeedAI MinimaxVideo01Node", - "WaveSpeedAI NightmareAIRealESRGANNode", - "WaveSpeedAI Preview Video", - "WaveSpeedAI SDXLLoraNode", - "WaveSpeedAI SDXLNode", - "WaveSpeedAI Save Audio", - "WaveSpeedAI SeedEditV3Node", - "WaveSpeedAI SeedreamV3Node", - "WaveSpeedAI SkyReelsV1Node", - "WaveSpeedAI Step1xEditNode", - "WaveSpeedAI UnoNode", - "WaveSpeedAI Upload Audio", - "WaveSpeedAI Upload Image", - "WaveSpeedAI Upload Video", - "WaveSpeedAI Veo2I2vNode", - "WaveSpeedAI Veo2T2vNode", - "WaveSpeedAI ViduImageToVideo20Node", - "WaveSpeedAI ViduReferenceToVideo20Node", - "WaveSpeedAI ViduStartEndToVideo20Node", - "WaveSpeedAI Wan Image2Video", - "WaveSpeedAI Wan Loras", - "WaveSpeedAI Wan Text2Video", - "WaveSpeedAI Wan2114BVaceNode", - "WaveSpeedAI Wan21I2V480pLoraNode", - "WaveSpeedAI Wan21I2V480pLoraUltraFastNode", - "WaveSpeedAI Wan21I2V480pNode", - "WaveSpeedAI Wan21I2V480pUltraFastNode", - "WaveSpeedAI Wan21I2V720pLoraNode", - "WaveSpeedAI Wan21I2V720pLoraUltraFastNode", - "WaveSpeedAI Wan21I2V720pNode", - "WaveSpeedAI Wan21I2V720pUltraFastNode", - "WaveSpeedAI Wan21T2V480pLoraNode", - "WaveSpeedAI Wan21T2V480pLoraUltraFastNode", - "WaveSpeedAI Wan21T2V480pUltraFastNode", - "WaveSpeedAI Wan21T2V720pLoraNode", - "WaveSpeedAI Wan21T2V720pLoraUltraFastNode", - "WaveSpeedAI Wan21T2V720pNode", - "WaveSpeedAI Wan21T2V720pUltraFastNode" - ], - { - "title_aux": "wavespeed-comfyui" - } - ], - "https://github.com/WeChatCV/Stand-In_Preprocessor_ComfyUI": [ - [ - "ApplyFaceProcessor", - "FaceOnlyModeSwitch", - "FaceProcessorLoader", - "VideoFramePreprocessor" - ], - { - "title_aux": "Stand-In Official Preprocessor ComfyUI Nodes" - } - 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"XWAVEPixelate", - "XWaveChromaticAberration", - "XWaveColorChannelManipulation", - "XWaveColorFilter", - "XWaveColorShiftExpansion", - "XWaveCurvedHueShift", - "XWaveGaussianBlur", - "XWaveHistogramGlitch", - "XWaveJPEGArtifacts", - "XWaveNoiseEffect", - "XWavePosterize", - "XWaveRGBChannelShift", - "XWaveSharpen" - ], - { - "title_aux": "ComfyUI XWAVE Nodes" - } - ], - "https://github.com/XchanBik/ComfyUI_SimpleBridgeNode": [ - [ - "LoraTextLoader", - "SimpleBridgeLoadNode", - "SimpleBridgeStoreNode" - ], - { - "description": "A custom node for ComfyUI to store and retrieve data dynamically.", - "nickname": "SimpleBridgeNode", - "title": "SimpleBridgeNode", - "title_aux": "ComfyUI_SimpleBridgeNode" - } - ], - "https://github.com/Xclbr7/ComfyUI-Merlin": [ - [ - "GeminiPromptExpander", - "Magic Photo Prompter \ud83e\ude84" - ], - { - "title_aux": "ComfyUI-Merlin: Magic Photo Prompter" - } - ], - "https://github.com/Xiangyu-CAS/HandFixer": [ - [ - "MediapipeHandNode" - ], - { - "title_aux": "HandFixer" - } - ], - "https://github.com/XieJunchen/comfyUI_LLM": [ - [ - "AppendImagesToBatch", - "CloudImageUploadNode", - "CloudImagesToVideoAndUpload", - "CloudVideoUploadNode", - "ComfyUI_LLM_Ollama", - "CreateEmptyImageBatch", - "DeepSeek_Online", - "GetFirstImageFromBatch", - "GetVideoClipByIndex", - "LoadGifFromLocal", - "LoadImgFromUrl", - "RemoveFirstOrLastImageFromBatch", - "SplitVideoByFrames", - "StringArrayFormatter", - "StringArrayIndexer" - ], - { - "title_aux": "comfyUI_LLM" - } - ], - "https://github.com/Xkipper/ComfyUI_SkipperNodes": [ - [ - "Embedding Stack", - "Simple Box" - ], - { - "title_aux": "ComfyUI_SkipperNodes" - } - ], - "https://github.com/XmYx/ComfyUI-SmolLM3": [ - [ - "SmolLM3ModelLoader", - "SmolLM3Sampler", - "SmolLM3SimpleGenerate" - ], - { - "title_aux": "ComfyUI-SmolLM3" - } - ], - "https://github.com/XmYx/deforum-comfy-nodes": [ - [ - "DeforumAddNoiseNode", - "DeforumAnimParamsNode", - "DeforumAreaPromptNode", - "DeforumBaseParamsNode", - "DeforumCacheLatentNode", - 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[ - "AniSora", - "AniSoraPrompt", - "LoadAniSoraModel", - "SaveAniSora" - ], - { - "title_aux": "ComfyUI-AniSora" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-AudioX": [ - [ - "AudioXPrompt", - "Condition", - "Generate", - "LoadAudioXAudio", - "LoadAudioXModel", - "LoadAudioXVideo", - "SaveAudioXAudio" - ], - { - "title_aux": "Yuan-ManX/ComfyUI-AudioX" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Bagel": [ - [ - "BagelPrompt", - "ImageEditing", - "ImageGeneration", - "ImageThinkEditing", - "ImageThinkGeneration", - "ImageUnderstanding", - "LoadBAGELModel", - "LoadEditImage" - ], - { - "title_aux": "ComfyUI-Bagel" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-ChatterboxTTS": [ - [ - "ChatterboxAudioPrompt", - "ChatterboxPrompt", - "ChatterboxTTS", - "ChatterboxVC", - "LoadChatterboxAudio", - "LoadChatterboxTTSModel", - "LoadChatterboxTargetAudio", - "LoadChatterboxVCModel", - "SaveChatterboxAudio" - ], - { - "title_aux": "ComfyUI-ChatterboxTTS" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Cobra": [ - [ - "ColorizeImage", - "DrawColorHint", - "ExtractLineArt", - "GetColorValue", - "LoadCobraModel" - ], - { - "title_aux": "ComfyUI-Cobra" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Dia": [ - [ - "DiaTTS", - "InputDiaText", - "LoadDiaAudio", - "LoadDiaModel", - "SaveDiaAudio" - ], - { - "title_aux": "ComfyUI-Dia" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Direct3D-S2": [ - [ - "Direct3DS2", - "LoadDirect3DS2Image", - "LoadDirect3DS2Model", - "SaveDirect3DS2Mesh" - ], - { - "title_aux": "ComfyUI-Direct3D-S2" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-HiDream-I1": [ - [ - "GenerateHiDreamImage", - "LoadHiDreamModel", - "SaveHiDreamImage" - ], - { - "title_aux": "ComfyUI-HiDream-I1" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-HiggsAudio": [ - [ - "HiggsAudio", - "LoadHiggsAudioModel", - "LoadHiggsAudioPrompt", - "LoadHiggsAudioSystemPrompt", - "LoadHiggsAudioTokenizer", - "SaveHiggsAudio" - ], - { - "title_aux": "ComfyUI-HiggsAudio" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Hunyuan3D-2.1": [ - [ - "Hunyuan3DShapeGeneration", - "Hunyuan3DTexureSynthsis", - "LoadHunyuan3DImage", - "LoadHunyuan3DModel" - ], - { - "title_aux": "ComfyUI-Hunyuan3D-2.1" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-HunyuanPortrait": [ - [ - "HunyuanPortrait", - "LoadHunyuanPortraitConfig", - "LoadHunyuanPortraitImage", - "LoadHunyuanPortraitVideo" - ], - { - "title_aux": "ComfyUI-HunyuanPortrait" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Kimi-VL": [ - [ - "KimiVL", - "LoadKimiVLImage", - "LoadKimiVLModel", - "SaveKimiVLText" - ], - { - "title_aux": "ComfyUI-Kimi-VL" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-LLaMA-Mesh": [ - [ - "Apply Gradient Color", - "Chat LLaMa Mesh", - "Visualize Mesh" - ], - { - "title_aux": "ComfyUI-LLaMA-Mesh" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-LayerAnimate": [ - [ - "LayerAnimateNode", - "LoadImages", - "LoadPretrainedModel" - ], - { - "title_aux": "ComfyUI-LayerAnimate" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-LiveCC": [ - [ - "LiveCC", - "LiveCCPrompt", - "LoadLiveCCModel", - "LoadLiveCCVideo", - "SaveLiveCCText" - ], - { - "title_aux": "ComfyUI-LiveCC" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Matrix-Game": [ - [ - "GameVideoGenerator", - "LoadDiTModel", - "LoadGameImage", - "LoadMouseIcon", - "LoadTextEncoderModel", - "LoadVAEModel", - "MatrixGameOutput" - ], - { - "title_aux": "ComfyUI-Matrix-Game" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-MoviiGen": [ - [ - "LoadMoviiGenModel", - "MoviiGen", - "MoviiGenPrompt", - "SaveMoviiGen" - ], - { - "title_aux": "ComfyUI-MoviiGen" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Multiverse": [ - [ - "PlayGame" - ], - { - "title_aux": "ComfyUI-Multiverse" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-Muyan-TTS": [ - [ - "Generate", - "InputText", - "LoadMuyanTTSModel", - "LoadRefAudio", - "PromptText", - "SaveMuyanTTSAudio" - ], - { - "title_aux": "ComfyUI-Muyan-TTS" - } - ], - "https://github.com/Yuan-ManX/ComfyUI-OmniGen2": [ - [ - "LoadOmniGen2Image", - "LoadOmniGen2Model", - 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"FilmsImage_Zho", - "MovementsImage_Zho", - "StylesImage_Zho" - ], - { - "title_aux": "ComfyUI-ArtGallery" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG": [ - [ - "BRIA_RMBG_ModelLoader_Zho", - "BRIA_RMBG_Zho" - ], - { - "title_aux": "ComfyUI-BRIA_AI-RMBG" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-DeepSeek-JanusPro": [ - [ - "Janus_ImageGeneration", - "Janus_ModelLoader", - "Janus_MultimodalUnderstanding" - ], - { - "title_aux": "ComfyUI-DeepSeek-JanusPro" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-DepthFM": [ - [ - "DepthFM_Literative_Zho", - "DepthFM_ModelLoader_Zho", - "DepthFM_Zho" - ], - { - "title_aux": "DepthFM IN COMFYUI" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID": [ - [ - "IDBaseModelLoader_fromhub", - "IDBaseModelLoader_local", - "IDControlNetLoader", - "IDGenerationNode", - "ID_Prompt_Styler", - "InsightFaceLoader_Zho", - "Ipadapter_instantidLoader" - ], - { - "title_aux": "ComfyUI-InstantID" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Phi-3-mini": [ - [ - "Phi3mini_4k_Chat_Zho", - "Phi3mini_4k_ModelLoader_Zho", - "Phi3mini_4k_Zho" - ], - { - "title_aux": "Phi-3-mini in ComfyUI" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PhotoMaker-ZHO": [ - [ - "BaseModel_Loader_fromhub", - "BaseModel_Loader_local", - "LoRALoader", - "NEW_PhotoMaker_Generation", - "PhotoMakerAdapter_Loader_fromhub", - "PhotoMakerAdapter_Loader_local", - "PhotoMaker_Generation", - "Prompt_Styler", - "Ref_Image_Preprocessing" - ], - { - "title_aux": "ComfyUI PhotoMaker (ZHO)" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-PixArt-alpha-Diffusers": [ - [ - "PA_BaseModelLoader_fromhub_Zho", - "PA_Generation_Zho", - "PA_Styler_Zho" - ], - { - "title_aux": "ComfyUI-PixArt-alpha-Diffusers" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Q-Align": [ - [ - "QAlign_Zho" - ], - { - "title_aux": "ComfyUI-Q-Align" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Qwen-VL-API": [ - [ - "QWenVL_API_S_Multi_Zho", - "QWenVL_API_S_Zho" - ], - { - "title_aux": "ComfyUI-Qwen-VL-API" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-SVD-ZHO": [ - [ - "SVD_Aspect_Ratio_Zho", - "SVD_Steps_MotionStrength_Seed_Zho", - "SVD_Styler_Zho" - ], - { - "title_aux": "ComfyUI-SVD-ZHO (WIP)" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-SegMoE": [ - [ - "SMoE_Generation_Zho", - "SMoE_ModelLoader_Zho" - ], - { - "title_aux": "ComfyUI SegMoE" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite": [ - [ - "AlphaChanelAddByMask", - "ImageCompositeBy_BG_Zho", - "ImageCompositeBy_Zho", - "ImageComposite_BG_Zho", - "ImageComposite_Zho", - "RGB_Image_Zho", - "Text_Image_Frame_Zho", - "Text_Image_Multiline_Zho", - "Text_Image_Zho" - ], - { - "title_aux": "ComfyUI-Text_Image-Composite [WIP]" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-UltraEdit-ZHO": [ - [ - "UltraEdit_Generation_Zho", - "UltraEdit_ModelLoader_Zho", - "UltraEdit_ModelLoader_local_Zho" - ], - { - "title_aux": "ComfyUI-UltraEdit-ZHO" - } - ], - "https://github.com/ZHO-ZHO-ZHO/ComfyUI-YoloWorld-EfficientSAM": [ - [ - "ESAM_ModelLoader_Zho", - "Yoloworld_ESAM_DetectorProvider_Zho", - "Yoloworld_ESAM_Zho", - "Yoloworld_ModelLoader_Zho" - ], - { - "title_aux": "ComfyUI YoloWorld-EfficientSAM" - } - ], - "https://github.com/ZHO-ZHO-ZHO/comfyui-portrait-master-zh-cn": [ - [ - "PortraitMaster_\u4e2d\u6587\u7248" - ], - { - "title_aux": "comfyui-portrait-master-zh-cn" - } - ], - "https://github.com/ZXL-Xinram/ComfyUI-AutoFlow": [ - [ - "AutoFlowPathJoiner", - "AutoFlowPathParser", - "AutoFlowPathValidator", - "AutoFlowStringCase", - "AutoFlowStringConcat", - "AutoFlowStringFormat", - "AutoFlowStringMultiConcat", - "AutoFlowStringReplace", - "AutoFlowStringSplit", - "AutoFlowTimestampFormatter", - "AutoFlowTimestampGenerator" - ], - { - "title_aux": "ComfyUI-AutoFlow" - } - ], - "https://github.com/ZZXYWQ/ComfyUI-ZZXYWQ": [ - [ - "StreamRecorder", - "VideoFormatConverter", - "ZZX_PaintsUndo" - ], - { - "title_aux": "ZZX Nodes" - } - ], - "https://github.com/Zachary116699/ComfyUI-LoadImageWithMetaDataEx": [ - [ - "ZLoadImageWithMetaDataFP" - ], - { - "title_aux": "ComfyUI_LoadImageWithMetaDataEx" - } - ], - "https://github.com/ZaneA/ComfyUI-ImageReward": [ - [ - "ImageRewardLoader", - "ImageRewardScore" - ], - { - "title_aux": "ImageReward" - } - ], - "https://github.com/Zar4X/ComfyUI-Batch-Process": [ - [ - "ImageBatchLoader", - "ImageBatchSaver", - "LoraBatchLoader", - "SimpleImageTagger", - "TXTBatchLoader", - "TextModifyTool" - ], - { - "title_aux": "ComfyUI-Batch-Process" - } - ], - "https://github.com/Zar4X/ComfyUI-Image-Resizing": [ - [ - "CalculateAspectRatioExtension", - "CalculateUpscaleFactor", - "CalculateUpscaleRounds", - "ExtendCanvasByPercentage", - "ExtendCanvasByPercentage (ZX)", - "ImageAspectRatioExtractor", - "ImageCropByPercentage", - "ImageResolutionExtractor", - "MaskCropByPercentage", - "ResizeToMultiple" - ], - { - "title_aux": "ComfyUI-Image-Resizing" - } - ], - "https://github.com/Zch6111/AI_Text_Comfyui": [ - [ - "AutoPromptGeneratorNode", - "GeminiImageToPrompt", - "SmartAutoPromptNode" - ], - { - "title_aux": "AI_Text_Comfyui" - } - ], - "https://github.com/ZeDarkAdam/ComfyUI-Embeddings-Tools": [ - [ - "EmbeddingsNameLoader", - "EmbendingList" - ], - { - "title_aux": "ComfyUI-Embeddings-Tools" - } - ], - "https://github.com/Zehong-Ma/ComfyUI-MagCache": [ - [ - "CompileModel", - "MagCache", - "MagCacheCalibration" - ], - { - "title_aux": "ComfyUI-MagCache" - } - ], - "https://github.com/Zeks/comfyui-rapidfire": [ - [ - "BracketEscaper", - "CachedCheckpoint", - "CsvWriterNode", - "HyperTile //Inspire", - "ImmatureImageCounter", - "ImmatureImageDataLoader", - "KSampler //Inspire", - "KSamplerAdvanced //Inspire", - "KSamplerAdvancedPipe //Inspire", - "KSamplerAdvancedProgress //Inspire", - "KSamplerPipe //Inspire", - "KSamplerProgress //Inspire", - "MultiModelAdvancedKsampler", - "MultiModelCheckpointIterator", - "MultiModelPromptSaver", - "MultiModelPromptSaverIterative", - "MultiModelPromptSaverIterativeFirst", - "Ranbooru", - "RandomCharacterSelector", - "RandomNoise //Inspire", - "RapidSchedulerCombo", - "RapidSchedulerSelector", - "ScheduledCFGGuider //Inspire", - "ScheduledPerpNegCFGGuider //Inspire", - "StringHasher" - ], - { - "title_aux": "comfyui-rapidfire" - } - ], - "https://github.com/ZeroSpaceStudios/ComfyUI-ZSNodes": [ - [ - "ZS_BoundingBoxCrop", - "ZS_SaveImage" - ], - { - "title_aux": "ComfyUI-ZSNodes" - } - ], - "https://github.com/a-l-e-x-d-s-9/ComfyUI-SaveCheckpointWithMetadata": [ - [ - "SaveCheckpointWithMetadata" - ], - { - "title_aux": "Save Checkpoint with Metadata" - } - ], - "https://github.com/a-und-b/ComfyUI_Delay": [ - [ - "Add Delay" - ], - { - "title_aux": "ComfyUI_Delay" - } - ], - "https://github.com/a-und-b/ComfyUI_IC-Light-v2_fal": [ - [ - "ICLightV2" - ], - { - "title_aux": "IC-Light V2 (fal.ai)" - } - ], - "https://github.com/a-und-b/ComfyUI_JSON_Helper": [ - [ - "JSONStringToObjectNode" - ], - { - "title_aux": "ComfyUI_JSON_Helper" - } - ], - "https://github.com/a-und-b/ComfyUI_LoRA_from_URL": [ - [ - "Load LoRA From URL" - ], - { - "title_aux": "ComfyUI_LoRA_from_URL" - } - ], - "https://github.com/a-und-b/ComfyUI_MaskAreaCondition": [ - [ - "MaskAreaCondition", - "SelectData" - ], - { - "title_aux": "ComfyUI Mask Area Condition" - } - ], - "https://github.com/a1lazydog/ComfyUI-AudioScheduler": [ - [ - "AmplitudeToGraph", - "AmplitudeToNumber", - "AudioToAmplitudeGraph", - "AudioToAudioData", - "AudioToFFTs", - "BatchAmplitudeSchedule", - "ClipAmplitude", - "FloatArrayToGraph", - "GateNormalizedAmplitude", - "NormalizeAmplitude", - "NormalizedAmplitudeDrivenString", - "NormalizedAmplitudeToGraph", - "NormalizedAmplitudeToNumber", - "TransientAmplitudeBasic" - ], - { - "title_aux": "ComfyUI-AudioScheduler" - } - ], - "https://github.com/abdozmantar/ComfyUI-DeepExtract": [ - [ - "VocalAndSoundRemoverNode" - ], - { - "title_aux": "DeepExtract" - } - ], - "https://github.com/aburahamu/ComfyUI-IsNiceParts": [ - [ - "NiceHand" - ], - { - "title_aux": "ComfyUI-IsNiceParts" - } - ], - "https://github.com/aburahamu/ComfyUI-RequestsPoster": [ - [ - "GetImageFromSD3byI2I", - "GetImageFromSD3byT2I", - "PostImage2Discord", - "PostImage2X", - "PostText" - ], - { - "title_aux": "ComfyUI-RequestPoster" - } - ], - "https://github.com/abyz22/image_control": [ - [ - "abyz22_AddPrompt", - "abyz22_Convertpipe", - "abyz22_Editpipe", - "abyz22_FirstNonNull", - "abyz22_FromBasicPipe_v2", - "abyz22_Frompipe", - "abyz22_ImpactWildcardEncode", - "abyz22_ImpactWildcardEncode_GetPrompt", - "abyz22_Ksampler", - "abyz22_Padding Image", - "abyz22_RandomMask", - "abyz22_RemoveControlnet", - "abyz22_ResizeOpenpose", - "abyz22_SaveImage", - "abyz22_SetQueue", - "abyz22_ToBasicPipe", - "abyz22_Topipe", - "abyz22_blend_onecolor", - "abyz22_blendimages", - "abyz22_bypass", - "abyz22_censoring", - "abyz22_drawmask", - "abyz22_lamaInpaint", - "abyz22_lamaPreprocessor", - "abyz22_makecircles", - "abyz22_path_generator", - "abyz22_setimageinfo", - "abyz22_smallhead" - ], - { - "title_aux": "image_control" - } - ], - "https://github.com/acorderob/sd-webui-prompt-postprocessor": [ - [ - "ACBPPPSelectVariable", - "ACBPromptPostProcessor" - ], - { - "author": "ACB", - "description": "Node for processing prompts. Includes the following options: send to negative prompt, set variables, if/elif/else command for conditional content, wildcards and choices.", - "nickname": "ACB PPP", - "title": "Prompt Post Processor", - "title_aux": "Prompt PostProcessor" - } - ], - "https://github.com/adbrasi/ComfyUI-TrashNodes-DownloadHuggingface": [ - [ - "DownloadLinkChecker", - "ShowFileNames" - ], - { - "title_aux": "ComfyUI-TrashNodes-DownloadHuggingface" - } - ], - "https://github.com/adieyal/comfyui-dynamicprompts": [ - [ - "DPCombinatorialGenerator", - "DPFeelingLucky", - "DPJinja", - "DPMagicPrompt", - "DPOutput", - "DPRandomGenerator" - ], - { - "title_aux": "DynamicPrompts Custom Nodes" - } - ], - "https://github.com/adigayung/ComfyUI-Translator": [ - [ - "CLIP Text Encode (Auto Translate)", - "Prompt Text (Auto Translate)" - ], - { - "title_aux": "ComfyUI-Translator" - } - ], - "https://github.com/adrianschubek/comfyui-zeug": [ - [ - "ZeugBool", - "ZeugCleanGpuPass", - "ZeugFloat", - "ZeugFloatToStr", - "ZeugInt", - "ZeugIntToStr", - "ZeugIntToWxH", - "ZeugJoinStr", - "ZeugJoinStrList", - "ZeugPrintPass", - "ZeugSplitStrList", - "ZeugStr", - "ZeugStrToFloat", - "ZeugStrToInt", - "ZeugWxHToInt" - ], - { - "title_aux": "comfyui-zeug" - } - ], - "https://github.com/adriflex/ComfyUI_Blender_Texdiff": [ - [ - "ViewportColor", - "ViewportDepth" - ], - { - "title_aux": "ComfyUI_Blender_Texdiff" - } - ], - "https://github.com/aegis72/aegisflow_utility_nodes": [ - [ - "Add Text To Image", - "Aegisflow CLIP Pass", - "Aegisflow Conditioning Pass", - "Aegisflow Image Pass", - "Aegisflow Latent Pass", - "Aegisflow Mask Pass", - "Aegisflow Model Pass", - "Aegisflow Pos/Neg Pass", - "Aegisflow SDXL Tuple Pass", - "Aegisflow VAE Pass", - "Aegisflow controlnet preprocessor bus", - "Apply Instagram Filter", - "Binary INT Switch", - "Brightness_Contrast_Ally", - "Flatten Colors", - "Gaussian Blur_Ally", - "GlitchThis Effect", - "Hue Rotation", - "Image Flip_ally", - "Placeholder Tuple", - "Swap Color Mode", - "aegisflow Multi_Pass", - "aegisflow Multi_Pass XL", - "af_pipe_in_15", - "af_pipe_in_xl", - "af_pipe_out_15", - "af_pipe_out_xl" - ], - { - "title_aux": "AegisFlow Utility Nodes" - } - ], - "https://github.com/aegis72/comfyui-styles-all": [ - [ - "menus" - ], - { - "title_aux": "ComfyUI-styles-all" - } - ], - "https://github.com/aesethtics/ComfyUI-Utilitools": [ - [ - "UtilAdd", - "UtilAspectRatio", - "UtilBatchController", - "UtilBooleanAND", - "UtilBooleanNOT", - "UtilBooleanOR", - "UtilCalculator", - "UtilConstantFloat", - "UtilConstantInt", - "UtilConstantString", - "UtilCounter", - "UtilDateTimestamp", - "UtilDivide", - "UtilFloatToInt", - "UtilIfThenElse", - "UtilImageDimensions", - "UtilIntToFloat", - "UtilListCreate", - "UtilListIndex", - "UtilMultiply", - "UtilPassthrough", - "UtilStringReplace", - "UtilSubtract", - "UtilSwitch", - "UtilTextConcat", - "UtilWhateverToString" - ], - { - "title_aux": "ComfyUI Utilitools Nodes" - } - ], - "https://github.com/agilly1989/ComfyUI_agilly1989_motorway": [ - [ - "MotorwayFloat", - "MotorwayInt", - "MotorwaySeed", - "MotorwayStr", - "MotorwayStrMulti" - ], - { - "title_aux": "ComfyUI_agilly1989_motorway" - } - ], - "https://github.com/ahernandezmiro/ComfyUI-GCP_Storage_tools": [ - [ - "GCPReadImageNode", - "GCPWriteImageNode" - ], - { - "title_aux": "ComfyUI-GCP_Storage_tools" - } - ], - "https://github.com/ai-liam/comfyui-liam": [ - [ - "AiStoreAzureGPTLiam", - "GetBetterDepthImage", - "LiamLibDisplayText", - "LiamLibFillImage", - "LiamLibImageToGray", - "LiamLibLoadImage", - "LiamLibMergeText", - "LiamLibSaveImg", - "LiamLibSaveText", - "OllamaApiTNodeLiam", - "PreviewReliefImage", - "SpeechRecognitionLiam", - "SpeechSynthesisLiam" - ], - { - "title_aux": "LiamUtil" - } - ], - "https://github.com/ai-liam/comfyui_liam_util": [ - [ - "LiamLoadImage" - ], - { - "title_aux": "LiamUtil (single node)" - } - ], - "https://github.com/ai-shizuka/ComfyUI-tbox": [ - [ - "AnimalPosePreprocessor", - "BatchManager", - "CannyPreprocessor", - "ConstrainImageNode", - "DWPosePreprocessor", - "DWPreprocessor", - "DensePosePreprocessor", - "GFPGANNode", - "ImageLoader", - "ImageResize", - "ImageSaver", - "ImageSize", - "ImagesSaver", - "LineArtPreprocessor", - "LineartStandardPreprocessor", - "MaskAddNode", - "MiDaSDepthPreprocessor", - "PurgeVRAMNode", - "VideoInfo", - "VideoLoader", - "VideoSaver", - "WatermarkNode" - ], - { - "author": "tstandley", - "title_aux": "ComfyUI-tbox" - } - ], - "https://github.com/aiaiaikkk/ComfyUI-Curve": [ - [ - "CameraRawEnhanceNode", - "CameraRawToneCurveNode", - "ColorGradingNode", - "CurvePresetNode", - "GaussianBlurNode", - "HistogramAnalysisNode", - "PhotoshopCurveNode", - "PhotoshopHSLNode", - "PhotoshopLevelsNode" - ], - { - "title_aux": "ComfyUI-Curve" - } - ], - "https://github.com/aiaiaikkk/super-prompt-canvas": [ - [ - "AdvancedBackgroundRemoval", - "BackgroundRemovalSettings", - "CustomModelPromptGenerator", - "KontextSuperPrompt", - "LRPGCanvas", - "OllamaKontextPromptGenerator", - "OllamaServiceManager", - "TextGenWebUIFluxKontextEnhancer" - ], - { - "title_aux": "super-prompt-canvas" - } - ], - "https://github.com/aianimation55/ComfyUI-FatLabels": [ - [ - "FatLabels" - ], - { - "title_aux": "Comfy UI FatLabels" - } - ], - "https://github.com/aiartvn/A2V_Multi_Image_Composite": [ - [ - "A2V_Multi_Image_Composite" - ], - { - "title_aux": "A2V Multi Image Composite" - } - ], - "https://github.com/aicuai/aicu-comfyui-stability-ai-api": [ - [ - "Preview3DModel", - "Save3DModel", - "StabilityControlSketch", - "StabilityControlStructure", - "StabilityControlStyle", - "StabilityEdit", - "StabilityImageCore", - "StabilityImageSD3", - "StabilityImageToVideo", - "StabilityImageUltra", - "StabilityUpscaleConservative", - "StabilityUpscaleCreative", - "StabilityUpscaleFast", - "StableFast3D", - "StablePointAware3D" - ], - { - "title_aux": "aicu-comfyui-stability-ai-api" - } - ], - "https://github.com/aidec/Comfyui_TextBatch_aidec": [ - [ - "ImageFilenameProcessor", - "ImageInfoExtractor", - "ImageQueueProcessor", - "LoadImagesFromDirBatch", - "PathParser", - "TextBatch", - "TextQueueProcessor", - "TextSplitCounter" - ], - { - "title_aux": "Comfyui_TextBatch_aidec" - } - ], - "https://github.com/aidenli/ComfyUI_NYJY": [ - [ - "BailianChat", - "BailianChatOption", - "BailianVL", - "BailianVLOption", - "CivitaiPrompt", - "CommonLLMChat", - "ConvertAnyToString", - "ConvertStringToNumber", - "CustomLatentImage-NYJY", - "CustomLatentImageSimple", - "FloatSlider-NYJY", - "FluxProOnline", - "GetItemFromList", - "JoyCaption", - "JoyCaptionAlpha1Online", - "JoyCaptionAlpha2Online", - "JoyTag", - "JsonDumps", - "JsonGetValueByKeys", - "JsonLoads", - "ReadFileToString", - "SplitString", - "Translate" - ], - { - "title_aux": "ComfyUI_NYJY" - } - ], - "https://github.com/aigc-apps/EasyAnimate": [ - [ - "CameraBasicFromChaoJie", - "CameraCombineFromChaoJie", - "CameraJoinFromChaoJie", - "CameraTrajectoryFromChaoJie", - "CreateTrajectoryBasedOnKJNodes", - "EasyAnimateI2VSampler", - "EasyAnimateT2VSampler", - "EasyAnimateV2VSampler", - "EasyAnimateV5_I2VSampler", - "EasyAnimateV5_T2VSampler", - "EasyAnimateV5_V2VSampler", - "EasyAnimate_TextBox", - "ImageMaximumNode", - "LoadEasyAnimateLora", - "LoadEasyAnimateModel", - "TextBox" - ], - { - "title_aux": "Video Generation Nodes for EasyAnimate" - } - ], - "https://github.com/aigc-apps/VideoX-Fun": [ - [ - "CameraBasicFromChaoJie", - "CameraCombineFromChaoJie", - "CameraJoinFromChaoJie", - "CameraTrajectoryFromChaoJie", - "CogVideoXFunInpaintSampler", - "CogVideoXFunT2VSampler", - "CogVideoXFunV2VSampler", - "CreateTrajectoryBasedOnKJNodes", - "FunCompile", - "FunRiflex", - "FunTextBox", - "ImageMaximumNode", - "LoadCogVideoXFunLora", - "LoadCogVideoXFunModel", - "LoadWan2_2FunLora", - "LoadWan2_2FunModel", - "LoadWan2_2Lora", - "LoadWan2_2Model", - "LoadWanFunLora", - "LoadWanFunModel", - "LoadWanLora", - "LoadWanModel", - "VideoToCanny", - "VideoToDepth", - "VideoToOpenpose", - "Wan2_2FunInpaintSampler", - "Wan2_2FunT2VSampler", - "Wan2_2FunV2VSampler", - "Wan2_2I2VSampler", - "Wan2_2T2VSampler", - "WanFunInpaintSampler", - "WanFunT2VSampler", - "WanFunV2VSampler", - "WanI2VSampler", - "WanT2VSampler" - ], - { - "title_aux": "VideoX-Fun" - } - ], - "https://github.com/aimerib/ComfyUI_HigherBitDepthSaveImage": [ - [ - "SaveImageHigherBitDepth" - ], - { - "title_aux": "ComfyUI-HigherBitDepthSaveImage" - } - ], - "https://github.com/ainewsto/Comfyui-chatgpt-api": [ - [ - "ComfyuiChatGPTApi", - "Comfyui_gpt_image_1", - "Comfyui_gpt_image_1_edit" - ], - { - "title_aux": "Comfyui-chatgpt-api" - } - ], - "https://github.com/ainewsto/Comfyui-google-veo2-api": [ - [ - "ComfyuiGoogleVeo2" - ], - { - "title_aux": "Comfyui-google-veo2-api" - } - ], - "https://github.com/ainewsto/Comfyui_Comfly_v2": [ - [ - "ComflyChatGPTApi", - "ComflyGeminiAPI", - "ComflyJimengApi", - "ComflyJimengVideoApi", - "ComflySeededit", - "Comfly_Doubao_Seededit", - "Comfly_Doubao_Seedream", - "Comfly_Flux_Kontext", - "Comfly_Flux_Kontext_Edit", - "Comfly_Flux_Kontext_bfl", - "Comfly_Googel_Veo3", - "Comfly_MiniMax_video", - "Comfly_Mj", - "Comfly_Mj_swap_face", - "Comfly_Mju", - "Comfly_Mjv", - "Comfly_gpt_image_1", - "Comfly_gpt_image_1_edit", - "Comfly_kling_image2video", - "Comfly_kling_multi_image2video", - "Comfly_kling_text2video", - "Comfly_lip_sync", - "Comfly_mj_video", - "Comfly_mj_video_extend", - "Comfly_mjstyle", - "Comfly_nano_banana", - "Comfly_nano_banana_fal", - "Comfly_qwen_image", - "Comfly_qwen_image_edit", - "Comfly_upload", - "Comfly_video_extend" - ], - { - "title_aux": "Comfyui_Comfly_v2" - } - ], - "https://github.com/ainewsto/comfyui-labs-google": [ - [ - "ComfyUI-ImageFx", - "ComfyUI-Whisk", - "ComfyUI-Whisk-Prompts" - ], - { - "title_aux": "comfyui-labs-google" - } - ], - "https://github.com/aisabervisionlab/ComfyUI_merge_ASVL": [ - [ - "ASVL" - ], - { - "title_aux": "ComfyUI_merge_ASVL" - } - ], - "https://github.com/ajbergh/comfyui-ethnicity_hairstyle_clip_encoder": [ - [ - "CLIPTextEncodeWithExtras" - ], - { - "title_aux": "comfyui-ethnicity_hairstyle_clip_encoder" - } - ], - "https://github.com/akatz-ai/ComfyUI-AKatz-Nodes": [ - [ - "AK_AdjustDepthmapBrightness", - "AK_AdjustListSize", - "AK_AnimatedDilationMaskLinear", - "AK_AudioFramesyncSchedule", - "AK_AudioreactiveDilateMaskInfinite", - "AK_AudioreactiveDilationMask", - "AK_AudioreactiveDynamicDilationMask", - "AK_BinaryAmplitudeGate", - "AK_BlobTrack", - "AK_BrightnessToFloatList", - "AK_ConvertListToFloatList", - "AK_DilateMaskLinearInfinite", - "AK_FadeBetweenBatches", - "AK_FlexFeatureToFloatList", - "AK_FloatListToDilateMaskSchedule", - "AK_FloatListToFlexFeature", - "AK_IPAdapterCustomWeights", - "AK_KeyframeScheduler", - "AK_LagChop", - "AK_ListToNumpyFloatArray", - "AK_MakeDepthmapSeamless", - "AK_NormalizeMaskImage", - "AK_RescaleFloatList", - "AK_ScaleMask", - "AK_ScheduledBinaryComparison", - "AK_ShrinkNumSequence", - "AK_SplitImageBatch", - "AK_VideoSpeedAdjust", - "Scale Mask Node" - ], - { - "author": "akatz", - "description": "Custom node pack for nodes I use in my workflows.", - "nickname": "Akatz Custom Nodes", - "title": "Akatz Custom Nodes", - "title_aux": "Akatz Custom Nodes" - } - ], - "https://github.com/akatz-ai/ComfyUI-Basic-Math": [ - [ - "BasicMath", - "BooleanInput", - "BooleanLogic", - "BooleanUnary", - "FloatComparison", - "FloatInput", - "IntMath", - "IntegerComparison", - "IntegerInput", - "MathConstants", - "NumberClamp", - "NumberComparison", - "NumberInRange", - "NumberLerp", - "NumberRound", - "PreciseFloatInput", - "StringComparison", - "StringInput", - "ToBool", - "ToFloat", - "ToInt", - "ToString", - "UnaryMath" - ], - { - "title_aux": "ComfyUI-Basic-Math" - } - ], - "https://github.com/akatz-ai/ComfyUI-DepthCrafter-Nodes": [ - [ - "DepthCrafter", - "DownloadAndLoadDepthCrafterModel" - ], - { - "author": "akatz", - "description": "Custom nodes for use with DepthCrafter. Create consistent depth maps for your videos.", - "nickname": "DepthCrafter Nodes", - "title": "DepthCrafter Nodes", - "title_aux": "DepthCrafter Nodes" - } - ], - "https://github.com/akatz-ai/ComfyUI-Depthflow-Nodes": [ - [ - "Depthflow", - "DepthflowEffectColor", - "DepthflowEffectDOF", - "DepthflowEffectInpaint", - "DepthflowEffectVignette", - "DepthflowMotionArc", - "DepthflowMotionCosine", - "DepthflowMotionLinear", - "DepthflowMotionPresetCircle", - "DepthflowMotionPresetDolly", - "DepthflowMotionPresetHorizontal", - "DepthflowMotionPresetOrbital", - "DepthflowMotionPresetVertical", - "DepthflowMotionPresetZoom", - "DepthflowMotionSetTarget", - "DepthflowMotionSine", - "DepthflowMotionTriangle" - ], - { - "author": "akatz", - "description": "Custom nodes for use with Tremeschin's Depthflow library.", - "nickname": "Depthflow Nodes", - "title": "Depthflow Nodes", - "title_aux": "\ud83c\udf0a Depthflow Nodes" - } - ], - "https://github.com/akatz-ai/ComfyUI-X-Portrait-Nodes": [ - [ - "DownloadXPortraitModel", - "XPortrait" - ], - { - "author": "akatz", - "description": "Custom nodes for use with X-Portrait. Animate portraits with an input video and a reference image.", - "nickname": "X-Portrait Nodes", - "title": "X-Portrait Nodes", - "title_aux": "ComfyUI-X-Portrait-Nodes" - } - ], - "https://github.com/akierson/ComfyUI-textnodes": [ - [ - "Prompt Truncate", - "Tidy Tags" - ], - { - "title_aux": "ComfyUI-textnodes" - } - ], - "https://github.com/akierson/comfyui-colornodes": [ - [ - "Color Picker", - "Color to Hex", - "Color to RGB", - "Image Replace Color", - "Invert Color" - ], - { - "title_aux": "comfyui-colornodes" - } - ], - "https://github.com/al-swaiti/All-IN-ONE-style": [ - [ - "ComfyUIStyler", - "menus" - ], - { - "title_aux": "All-IN-ONE-style" - } - ], - "https://github.com/al-swaiti/ComfyUI-CascadeResolutions": [ - [ - "CascadeResolutions" - ], - { - "title_aux": "ComfyUI-CascadeResolutions" - } - ], - "https://github.com/al-swaiti/ComfyUI-OllamaGemini": [ - [ - "ClaudeAPI", - "GeminiAPI", - "GeminiBRIA_RMBG", - "GeminiCLIPSeg", - "GeminiCombineSegMasks", - "GeminiComfyUIStyler", - "GeminiConvertRasterToVector", - "GeminiFLUXResolutions", - "GeminiImageGenerator", - "GeminiSVGPreview", - "GeminiSaveSVG", - "GeminiSaveText", - "GeminiSmartPromptGenerator", - "GeminiTextSplitter", - "ListAvailableModels", - "OllamaAPI", - "OpenAIAPI", - "QwenAPI", - "style_menus" - ], - { - "title_aux": "GeminiOllama ComfyUI Extension" - } - ], - "https://github.com/alFrame/ComfyUI-AF-EditGeneratedPrompt": [ - [ - "AF_Edit_Generated_Prompt" - ], - { - "title_aux": "AF - Edit Generated Prompt" - } - ], - "https://github.com/alanhuang67/ComfyUI-FAI-Node": [ - [ - "FAIDynamicMask", - "FAIScaleScheduler", - "FAI_Voronoi_Generator" - ], - { - "title_aux": "FAI-Node" - } - ], - "https://github.com/alastor-666-1933/caching_to_not_waste": [ - [ - "caching_condition", - "caching_controlnet", - "caching_from_combined_images", - "caching_image", - "caching_mask", - "caching_text", - "caching_wildcard_list" - ], - { - "title_aux": "Caching to not Waste" - } - ], - "https://github.com/alchemine/comfyui-alchemine-pack": [ - [ - "DanbooruPopularPostsTagsRetriever", - "DanbooruPostTagsRetriever", - "DanbooruRelatedTagsRetriever", - "FilterSubtags", - "FilterTags", - "FixBreakAfterTIPO", - "GeminiInference", - "OllamaInference", - "ProcessTags", - "ReplaceUnderscores", - "SignalSwitch", - "TextEditingInference", - "TokenAnalyzer", - "WidthHeight" - ], - { - "title_aux": "ComfyUI-Alchemine-Pack" - } - ], - "https://github.com/aleolidev/comfy_kaizen_package": [ - [ - "KaizenImageComposite" - ], - { - "title_aux": "Kaizen Package" - } - ], - "https://github.com/alessandroperilli/APW_Nodes": [ - [ - "APW_CloudImageSize", - "APW_ImageListFilter", - "APW_ImageSaver", - "APW_LocalImageSize", - "APW_LocalVideoSize" - ], - { - "title_aux": "apw_nodes" - } - ], - "https://github.com/alessandroperilli/OCS_Nodes": [ - [ - "OCS_CloudImageSize", - "OCS_ImageListFilter", - "OCS_ImageSaver", - "OCS_LocalImageSize", - "OCS_LocalVideoSize" - ], - { - "title_aux": "Open Creative Studio Nodes" - } - ], - "https://github.com/alessandrozonta/ComfyUI-CenterNode": [ - [ - "BBoxCrop" - ], - { - "title_aux": "ComfyUI-CenterNode" - } - ], - "https://github.com/alessandrozonta/ComfyUI-Layers": [ - [ - "LayersSaver - Save Layer", - "LayersSaver - Save Layer From Images" - ], - { - "title_aux": "Save Layers Node for ComfyUI" - } - ], - "https://github.com/alessandrozonta/ComfyUI-OpenPose": [ - [ - "OpenPose - Get poses" - ], - { - "author": "joe", - "title_aux": "OpenPose Node" - } - ], - "https://github.com/alessandrozonta/ComfyUI-PoseDirection": [ - [ - "OpenPose - Get direction" - ], - { - "title_aux": "ComfyUI-PoseDirection" - } - ], - "https://github.com/alessandrozonta/Comfyui-LoopLoader": [ - [ - "LoadLoopImagesFromDir" - ], - { - "title_aux": "Comfyui-LoopLoader" - } - ], - "https://github.com/alexcong/ComfyUI_QwenVL": [ - [ - "Qwen2.5", - "Qwen2.5VL" - ], - { - "title_aux": "Qwen2-VL wrapper for ComfyUI" - } - ], - "https://github.com/alexgenovese/ComfyUI-UNO-Flux": [ - [ - "UNOGenerate", - "UNOModelLoader" - ], - { - "title_aux": "ComfyUI UNO Nodes" - } - ], - "https://github.com/alexgenovese/ComfyUI_HF_Servelress_Inference": [ - [ - "HF_QuestionAnswer", - "Job_Caption", - "Joy_caption", - "Joy_caption_load" - ], - { - "author": "Alex Genovese", - "description": "Huggingface Api Serverless request", - "nickname": "alexgenovese", - "title": "Huggingface Api Serverless", - "title_aux": "Huggingface Api Serverless" - } - ], - "https://github.com/alexisrolland/ComfyUI-Blender": [ - [ - "BlenderInputBoolean", - "BlenderInputCombo", - "BlenderInputFloat", - "BlenderInputGroup", - "BlenderInputInt", - "BlenderInputLoad3D", - "BlenderInputLoadCheckpoint", - "BlenderInputLoadDiffusionModel", - "BlenderInputLoadImage", - "BlenderInputLoadLora", - "BlenderInputSeed", - "BlenderInputString", - "BlenderInputStringMultiline", - "BlenderOutputDownload3D", - "BlenderOutputSaveGlb", - "BlenderOutputSaveImage" - ], - { - "title_aux": "ComfyUI-Blender" - } - ], - "https://github.com/alexisrolland/ComfyUI-Phi": [ - [ - "LoadPhi", - "LoadPhiMultimodal", - "LoadPhiVision", - "RunPhi", - "RunPhiMultimodal", - "RunPhiVision" - ], - { - "title_aux": "ComfyUI-Phi" - } - ], - "https://github.com/alisson-anjos/ComfyUI-Ollama-Describer": [ - [ - "InputText", - "JsonPropertyExtractorNode", - "OllamaCaptionerExtraOptions", - "OllamaImageCaptioner", - "OllamaImageDescriber", - "OllamaTextDescriber", - "ShowText", - "TextTransformer" - ], - { - "title_aux": "ComfyUI-Ollama-Describer" - } - ], - "https://github.com/alpertunga-bile/image-caption-comfyui": [ - [ - "Image Caption Node", - "Insert Prompt Node" - ], - { - "title_aux": "image-caption-comfyui" - } - ], - "https://github.com/alpertunga-bile/prompt-generator-comfyui": [ - [ - "Prompt Generator" - ], - { - "title_aux": "prompt-generator" - } - ], - "https://github.com/alsritter/asymmetric-tiling-comfyui": [ - [ - "Asymmetric_Tiling_KSampler" - ], - { - "title_aux": "asymmetric-tiling-comfyui" - } - ], - "https://github.com/alt-key-project/comfyui-dream-project": [ - [ - "Analyze Palette [Dream]", - "Beat Curve [Dream]", - "Big Float Switch [Dream]", - "Big Image Switch [Dream]", - "Big Int Switch [Dream]", - "Big Latent Switch [Dream]", - "Big Palette Switch [Dream]", - "Big Text Switch [Dream]", - "Boolean To Float [Dream]", - "Boolean To Int [Dream]", - "Build Prompt [Dream]", - "CSV Curve [Dream]", - "CSV Generator [Dream]", - "Calculation [Dream]", - "Common Frame Dimensions [Dream]", - "Compare Palettes [Dream]", - "FFMPEG Video Encoder [Dream]", - "File Count [Dream]", - "Finalize Prompt [Dream]", - "Float Input [Dream]", - "Float to Log Entry [Dream]", - "Frame Count Calculator [Dream]", - "Frame Counter (Directory) [Dream]", - "Frame Counter (Simple) [Dream]", - "Frame Counter Info [Dream]", - "Frame Counter Offset [Dream]", - "Frame Counter Time Offset [Dream]", - "Image Brightness Adjustment [Dream]", - "Image Color Shift [Dream]", - "Image Contrast Adjustment [Dream]", - "Image Motion [Dream]", - "Image Sequence Blend [Dream]", - "Image Sequence Loader [Dream]", - "Image Sequence Saver [Dream]", - "Image Sequence Tweening [Dream]", - "Int Input [Dream]", - "Int to Log Entry [Dream]", - "Laboratory [Dream]", - "Linear Curve [Dream]", - "Log Entry Joiner [Dream]", - "Log File [Dream]", - "Noise from Area Palettes [Dream]", - "Noise from Palette [Dream]", - "Palette Color Align [Dream]", - "Palette Color Shift [Dream]", - "Random Prompt Words [Dream]", - "Sample Image Area as Palette [Dream]", - "Sample Image as Palette [Dream]", - "Saw Curve [Dream]", - "Sine Curve [Dream]", - "Smooth Event Curve [Dream]", - "String Input [Dream]", - "String Tokenizer [Dream]", - "String to Log Entry [Dream]", - "Text Input [Dream]", - "Triangle Curve [Dream]", - "Triangle Event Curve [Dream]", - "WAV Curve [Dream]" - ], - { - "title_aux": "Dream Project Animation Nodes" - } - ], - "https://github.com/alt-key-project/comfyui-dream-video-batches": [ - [ - "Blended Transition [DVB]", - "Calculation [DVB]", - "Create Frame Set [DVB]", - "Divide [DVB]", - "Fade From Black [DVB]", - "Fade To Black [DVB]", - "Float Input [DVB]", - "For Each Done [DVB]", - "For Each Filename [DVB]", - "Frame Set Append [DVB]", - "Frame Set Frame Dimensions Scaled [DVB]", - "Frame Set Index Offset [DVB]", - "Frame Set Merger [DVB]", - "Frame Set Reindex [DVB]", - "Frame Set Repeat [DVB]", - "Frame Set Reverse [DVB]", - "Frame Set Split Beginning [DVB]", - "Frame Set Split End [DVB]", - "Frame Set Splitter [DVB]", - "Generate Inbetween Frames [DVB]", - "Int Input [DVB]", - "Linear Camera Pan [DVB]", - "Linear Camera Roll [DVB]", - "Linear Camera Zoom [DVB]", - "Load Image From Path [DVB]", - "Multiply [DVB]", - "Sine Camera Pan [DVB]", - "Sine Camera Roll [DVB]", - "Sine Camera Zoom [DVB]", - "String Input [DVB]", - "Text Input [DVB]", - "Trace Memory Allocation [DVB]", - "Unwrap Frame Set [DVB]" - ], - { - "title_aux": "Dream Video Batches" - } - ], - "https://github.com/an90ray/ComfyUI_RErouter_CustomNodes": [ - [ - "CLIPTextEncode (RE)", - "CLIPTextEncodeSDXL (RE)", - "CLIPTextEncodeSDXLRefiner (RE)", - "Int (RE)", - "RErouter <=", - "RErouter =>", - "String (RE)" - ], - { - "title_aux": "ComfyUI_RErouter_CustomNodes" - } - ], - "https://github.com/andersxa/comfyui-PromptAttention": [ - [ - "CLIPAttentionMaskEncode" - ], - { - "title_aux": "CLIP Directional Prompt Attention" - } - ], - "https://github.com/andygill/comfyui-sunflower-nodes": [ - [ - "DepthViewToIsometric", - "DisparityToDepthView", - "EquirectangularToRectilinear", - "ImageChannelSelect", - "MaskChannelSelect", - "ResizeDown" - ], - { - "title_aux": "comfyui-sunflower-nodes" - } - ], - "https://github.com/angeloshredder/StableCascadeResizer": [ - [ - "CascadeResize" - ], - { - "title_aux": "StableCascadeResizer" - } - ], - "https://github.com/angree/ComfyUI-Q_GLB_Material_Modifier": [ - [ - "QManualGLBMaterialModifier", - "QPresetGLBMaterialModifier" - ], - { - "title_aux": "Q GLB Material Modifier" - } - ], - "https://github.com/angree/ComfyUI-Q_find-mask-size": [ - [ - "QImageCropCalculator" - ], - { - "title_aux": "Q Find Mask Size" - } - ], - "https://github.com/anhkhoatranle30/Handy-Nodes-ComfyUI": [ - [ - "Custom Save Image //Handy" - ], - { - "author": "Khoa Tran", - "description": "This extension offers various handy nodes.", - "nickname": "Handy-Nodes-ComfyUI", - "title": "Handy-Nodes-ComfyUI", - "title_aux": "Handy Node ComfyUI" - } - ], - "https://github.com/apeirography/DaimalyadNodes": [ - [ - "DaimalyadModelDownloader", - "DaimalyadWildcardProcessor" - ], - { - "title_aux": "DaimalyadNodes" - } - ], - "https://github.com/arcum42/ComfyUI_SageUtils": [ - [ - "SageSetWildcardText", - "Sage_AdvSamplerInfo", - "Sage_CLIPLoaderFromInfo", - "Sage_CLIPSelector", - "Sage_CacheMaintenance", - "Sage_CheckLorasForUpdates", - "Sage_CheckpointSelector", - "Sage_ChromaCLIPLoaderFromInfo", - "Sage_CleanText", - "Sage_CollectKeywordsFromLoraStack", - "Sage_ConditioningZeroOut", - "Sage_ConstructLLMPrompt", - "Sage_ConstructLLMPromptExtra", - "Sage_ConstructMetadata", - "Sage_ConstructMetadataFlexible", - "Sage_ConstructMetadataLite", - "Sage_CropImage", - "Sage_CubiqImageResize", - "Sage_DualCLIPSelector", - "Sage_DualCLIPTextEncode", - "Sage_DualCLIPTextEncodeLumina2", - "Sage_EmptyLatentImagePassthrough", - "Sage_FloatToStr", - "Sage_FreeMemory", - "Sage_FreeU2", - "Sage_GetFileHash", - "Sage_GuessResolutionByRatio", - "Sage_Halt", - "Sage_HiDreamE1_Instruction", - "Sage_IntToStr", - "Sage_JoinText", - "Sage_KSampler", - "Sage_KSamplerAudioDecoder", - "Sage_KSamplerTiledDecoder", - "Sage_LMStudioLLMPromptText", - "Sage_LMStudioLLMPromptVision", - "Sage_LMStudioLLMPromptVisionRefine", - "Sage_LastLoraInfo", - "Sage_LoadImage", - "Sage_LoadImageTextSetFromFolderNode", - "Sage_LoadModelFromInfo", - "Sage_Load_Dataset_From_Folder", - "Sage_LogicalSwitch", - "Sage_LoraStack", - "Sage_LoraStackInfoDisplay", - "Sage_LoraStackLoader", - "Sage_ModelInfo", - "Sage_ModelInfoDisplay", - "Sage_ModelLoraStackLoader", - "Sage_ModelReport", - "Sage_ModelShiftOnly", - "Sage_ModelShifts", - "Sage_MultiModelPicker", - "Sage_OllamaLLMPromptText", - "Sage_OllamaLLMPromptVision", - "Sage_OllamaLLMPromptVisionRefine", - "Sage_PonyPrefix", - "Sage_PonyStyle", - "Sage_QuadCLIPSelector", - "Sage_QuickLoraStack", - "Sage_QuickNineLoraStack", - "Sage_QuickResPicker", - "Sage_QuickSixLoraStack", - "Sage_ReferenceImage", - "Sage_SamplerInfo", - "Sage_SamplerSelector", - "Sage_SaveImageWithMetadata", - "Sage_SaveText", - "Sage_SchedulerSelector", - "Sage_SetText", - "Sage_SetTextWithInt", - "Sage_SixLoraStack", - "Sage_TextRandomLine", - "Sage_TextSelectLine", - "Sage_TextSubstitution", - "Sage_TextSwitch", - "Sage_TextWeight", - "Sage_TilingInfo", - "Sage_TrainingCaptionsToConditioning", - "Sage_TripleCLIPSelector", - "Sage_TripleJoinText", - "Sage_TripleLoraStack", - "Sage_TripleQuickLoraStack", - "Sage_UNETLoRALoader", - "Sage_UNETLoaderFromInfo", - "Sage_UNETSelector", - "Sage_UnetClipVaeToModelInfo", - "Sage_VAELoaderFromInfo", - "Sage_VAESelector", - "Sage_ViewAnything", - "Sage_ViewNotes" - ], - { - "title_aux": "Sage Utils" - } - ], - "https://github.com/asaddi/ComfyUI-YALLM-node": [ - [ - "LLMChat", - "LLMMinP", - "LLMModel", - "LLMPrependAppend", - "LLMProvider", - "LLMTemperature", - "LLMTextLatch", - "LLMTopK", - "LLMTopP" - ], - { - "title_aux": "ComfyUI-YALLM-node" - } - ], - "https://github.com/asaddi/YALLM-LlamaVision": [ - [ - "LLMSamplerSettings", - "LlamaVisionChat", - "LlamaVisionModel" - ], - { - "title_aux": "YALLM-LlamaVision" - } - ], - "https://github.com/asagi4/ComfyUI-Adaptive-Guidance": [ - [ - "AdaptiveGuidance", - "AdaptiveProjectedGuidance", - "PerpNegAdaptiveGuidanceGuider" - ], - { - "title_aux": "Adaptive Guidance for ComfyUI" - } - ], - "https://github.com/asagi4/ComfyUI-CADS": [ - [ - "CADS" - ], - { - "title_aux": "ComfyUI-CADS" - } - ], - "https://github.com/asagi4/ComfyUI-NPNet": [ - [ - "NPNetGoldenNoise" - ], - { - "title_aux": "ComfyUI NPNet (Golden Noise)" - } - ], - "https://github.com/asagi4/comfyui-prompt-control": [ - [ - "PCAddMaskToCLIP", - "PCAddMaskToCLIPMany", - "PCAttentionCoupleBatchNegative", - "PCExtractScheduledPrompt", - "PCLazyLoraLoader", - "PCLazyLoraLoaderAdvanced", - "PCLazyTextEncode", - "PCLazyTextEncodeAdvanced", - "PCLoraHooksFromText", - "PCMacroExpand", - "PCSaveExpandedWorkflow", - "PCSetLogLevel", - "PCSetPCTextEncodeSettings", - "PCTextEncode", - "PCTextEncodeWithRange" - ], - { - "author": "asagi4", - "description": "Control LoRA and prompt scheduling, advanced text encoding, regional prompting, and much more, through your text prompt. Generates dynamic graphs that are literally identical to handcrafted noodle soup.", - "nickname": "ComfyUI Prompt Control", - "title": "ComfyUI Prompt Control", - "title_aux": "ComfyUI Prompt Control" - } - ], - "https://github.com/asagi4/comfyui-utility-nodes": [ - [ - "MUConditioningCutoff", - "MUForceCacheClear", - "MUJinjaRender", - "MURemoveControlNet", - "MUReplaceModelWeights", - "MUSimpleWildcard" - ], - { - "title_aux": "asagi4/comfyui-utility-nodes" - } - ], - "https://github.com/asdrabael/Hunyuan-Multi-Lora-Loader": [ - [ - "HunyuanMultiLoraLoader", - "HunyuanMultiLoraLoaderWrapper" - ], - { - "title_aux": "Hunyuan-Multi-Lora-Loader" - } - ], - "https://github.com/asutermo/ComfyUI-Flux-TryOff": [ - [ - "TryOffFluxFillModelNode", - "TryOffFluxFillPipelineNode", - "TryOffModelNode", - "TryOffQuantizerNode", - "TryOffRunNode", - "TryOnOffModelNode", - "TryOnOffRunNode", - "TryOnRunNode" - ], - { - "title_aux": "ComfyUI-Flux-TryOff" - } - ], - "https://github.com/aszc-dev/ComfyUI-CoreMLSuite": [ - [ - "Core ML Converter", - "Core ML LCM Converter", - "Core ML LoRA Loader", - "CoreMLModelAdapter", - "CoreMLSampler", - "CoreMLSamplerAdvanced", - "CoreMLUNetLoader" - ], - { - "title_aux": "Core ML Suite for ComfyUI" - } - ], - "https://github.com/atluslin/comfyui_arcane_style_trans": [ - [ - "Arcane_style_trans" - ], - { - "title_aux": "comfyui_arcane_style_trans" - } - ], - "https://github.com/attashe/ComfyUI-FluxRegionAttention": [ - [ - "BBoxToMaskNode", - "BoundingBoxNode", - "CLIPDebug", - "FluxRegionBBOX", - "FluxRegionMask", - "RegionAttention", - "VisualizeBBoxesNode" - ], - { - "title_aux": "ComfyUI-FluxRegionAttention" - } - ], - "https://github.com/audioscavenger/ComfyUI-Thumbnails": [ - [ - "LoadImage" - ], - { - "author": "AudioscavengeR", - "description": "Load Image thumbnails and show input subfolders.", - "nickname": "LoadImageThumbnails", - "title": "LoadImageThumbnails", - "title_aux": "ComfyUI-Thumbnails" - } - ], - "https://github.com/audioscavenger/save-image-extended-comfyui": [ - [ - "SaveImageExtended" - ], - { - "author": "AudioscavengeR", - "description": "1 custom node to save your pictures in various folders and formats.", - "nickname": "Save Image Extended", - "title": "Save Image Extended", - "title_aux": "Save Image Extended for ComfyUI" - } - ], - "https://github.com/austinbrown34/ComfyUI-IO-Helpers": [ - [ - "EncodedPromptFromFile", - "EncodedPromptToFile", - "IO_LoadImage", - "SampledLatentsFromFile", - "SampledLatentsToFile" - ], - { - "title_aux": "ComfyUI-IO-Helpers" - } - ], - "https://github.com/avatechai/avatar-graph-comfyui": [ - [ - "ApplyMeshTransformAsShapeKey", - "B_ENUM", - "B_VECTOR3", - "B_VECTOR4", - "Combine Points", - "CreateShapeFlow", - "ExportBlendshapes", - "ExportGLTF", - "Extract Boundary Points", - "Image Alpha Mask Merge", - "ImageBridge", - "LoadImageFromRequest", - "LoadImageWithAlpha", - "LoadValueFromRequest", - "SAM MultiLayer", - "Save Image With Workflow" - ], - { - "author": "Avatech Limited", - "description": "Include nodes for sam + bpy operation, that allows workflow creations for generative 2d character rig.", - "nickname": "Avatar Graph", - "title": "Avatar Graph", - "title_aux": "Avatar Graph" - } - ], - "https://github.com/avenstack/ComfyUI-AV-FunASR": [ - [ - "AVASRTimestamp", - "AVFormat2Subtitle", - "AVSaveSubtitles", - "AVSpeechTimestamp" - ], - { - "title_aux": "ComfyUI-AV-FunASR" - } - ], - "https://github.com/avenstack/ComfyUI-AV-LatentSync": [ - [ - "AVLatentSync", - "AVVideoLengthAdjuster" - ], - { - "title_aux": "ComfyUI-AV-LatentSync" - } - ], - "https://github.com/avenstack/ComfyUI-AV-MegaTTS3": [ - [ - "AVMegaTTS3", - "AVPromptInit" - ], - { - "title_aux": "ComfyUI-AV-MegaTTS3" - } - ], - "https://github.com/avocadori/ComfyUI-load-image-prompt-lora": [ - [ - "YAMLImageCycler", - "YAMLImageCyclerSimple", - "YAMLLoRAExtractor", - "YAMLLoRALoader", - "YAMLLoRASelector" - ], - { - "title_aux": "ComfyUI-load-image-prompt-lora" - } - ], - "https://github.com/aws-samples/comfyui-llm-node-for-amazon-bedrock": [ - [ - "Amazon Bedrock - Luma AI Ray Video", - "Amazon Bedrock - Nova Canvas Background Prompt Replace", - "Amazon Bedrock - Nova Canvas Generate Image", - "Amazon Bedrock - Nova Canvas Generate Variations", - "Amazon Bedrock - Nova Reel Video", - "Amazon Bedrock - SD3 & SD3.5 Large | Image to Image", - "Amazon Bedrock - Stability AI Models | Text to Image", - "Bedrock - Claude", - "Bedrock - Claude Multimodal", - "Bedrock - Nova", - "Bedrock - SDXL", - "Bedrock - Titan Inpainting", - "Bedrock - Titan Outpainting", - "Bedrock - Titan Text to Image", - "Bedrock - Titan Variation", - "Image From S3", - "Image From URL", - "Image OCR By Textract", - "Image OCR By Textract V2", - "Image OCR By Textract V3", - "Image OCR by PaddleOCR", - "Image To S3", - "JSON Text Extraction", - "Prompt Regex Remove", - "Prompt Template", - "Prompt Template with Two Inputs" - ], - { - "title_aux": "Amazon Bedrock nodes for ComfyUI" - } - ], - "https://github.com/azure-dragon-ai/ComfyUI-ClipScore-Nodes": [ - [ - "HaojihuiClipScoreFakeImageProcessor", - "HaojihuiClipScoreImageProcessor", - "HaojihuiClipScoreImageScore", - "HaojihuiClipScoreLoader", - "HaojihuiClipScoreRealImageProcessor", - "HaojihuiClipScoreTextProcessor" - ], - { - "title_aux": "ComfyUI-ClipScore-Nodes" - } - ], - "https://github.com/azure-dragon-ai/ComfyUI-HPSv2-Nodes": [ - [ - "GetImageSize", - "HaojihuiHPSv2ImageProcessor", - "HaojihuiHPSv2ImageScore", - "HaojihuiHPSv2ImageScores", - "HaojihuiHPSv2Loader", - "HaojihuiHPSv2SaveAnimatedWEBP", - "HaojihuiHPSv2SaveImage", - "HaojihuiHPSv2SaveWEBP", - "HaojihuiHPSv2SaveWebpImage", - "HaojihuiHPSv2TextProcessor", - "SaveImageWebp", - "ScaleShort" - ], - { - "title_aux": "ComfyUI-HPSv2-Nodes" - } - ], - "https://github.com/babe-and-spencer-enterprises/base-comfyui-node": [ - [ - "UploadToBaseNode" - ], - { - "title_aux": "ComfyUI Upload to BASE Node" - } - ], - "https://github.com/bablueza/ComfyUI-Vaja-Ai4thai": [ - [ - "ShowText", - "Vaja Synthesis Api" - ], - { - "title_aux": "Vaja TextToSpeech Node for ComfyUI" - } - ], - "https://github.com/babydjac/comfyui-grok-prompts": [ - [ - "Flux", - "PonyXL" - ], - { - "title_aux": "ComfyUI Grok Prompts" - } - ], - "https://github.com/babydjac/comfyui-smart-scaler": [ - [ - "AspectRatioAdjuster", - "BatchFrameProcessor", - "DynamicResolutionSelector", - "ImageMetadataExtractor", - "SizeParser", - "SmartAspectScaler", - "WanVideoFrameScaler" - ], - { - "title_aux": "ComfyUI Smart Scaler" - } - ], - "https://github.com/badayvedat/ComfyUI-fal-Connector": [ - [ - "RemoteCheckpointLoader_fal", - "RemoteLoraLoader_fal" - ], - { - "title_aux": "ComfyUI-fal-Connector" - } - ], - "https://github.com/badjeff/comfyui_lora_tag_loader": [ - [ - "LoraTagLoader" - ], - { - "title_aux": "badjeff/LoRA Tag Loader for ComfyUI" - } - ], - "https://github.com/badxprogramm/ComfyUI-GradientBlur": [ - [ - "GradientBlur" - ], - { - "title_aux": "GradientBlurNode for ComfyUI" - } - ], - "https://github.com/baicai99/ComfyUI-FrameSkipping": [ - [ - "FrameSelector", - "FrameSkipping", - "FrameTruncating", - "IntOperationsNode", - "MaskFrameSkipping", - "MaskGenerator", - "MaskSelector" - ], - { - "title_aux": "ComfyUI-FrameSkipping" - } - ], - "https://github.com/bananasss00/ComfyUI-SP-Nodes": [ - [ - "BoolSwitchOutStr", - "CivitaiPrompts", - "ComfyuiRuntimeArgs", - "FaceScatter", - "FaceScatter2", - "FluxInspireLbw_Batch", - "FluxInspireLbw_BlockVectorPreset", - "GodnessMerger_Apply", - "GodnessMerger_InputBlocks", - "GodnessMerger_InputBlocksExperimental", - "GodnessMerger_LabelEmb", - "GodnessMerger_MiddleBlock", - "GodnessMerger_MiddleBlockExperimental", - "GodnessMerger_NoiseInjection", - "GodnessMerger_Out", - "GodnessMerger_OutputBlocks", - "GodnessMerger_OutputBlocksExperimental", - "GodnessMerger_RAW_Apply", - "GodnessMerger_TimeEmbed", - "ImageMonitor", - "ImgMetaValueExtractor", - "LoraLoaderByPath", - "LoraLoaderFromFolder", - "LoraLoaderOnlyModelByPath", - "NoiseInjectionEssentialsHookProvider", - "PreviewImageWEBP", - "PromptChecker", - "RandomPromptFromBook", - "Random_Model_Merge", - "SD3BlocksMultiply", - "SD3Multiply", - "SP-CheckpointSave", - "SP-UnetSave", - "SP_AnyPipe10", - "SP_AnyPipe15", - "SP_AnyPipe20", - "SP_AnyPipe30", - "SP_AnyPipe40", - "SP_AnyPipe5", - "SP_AnyPipe50", - "SP_DDInpaint_Pipe", - "SP_DictValue", - "SP_DynamicCombo", - "SP_FlorenceCaption", - "SP_FluxFastMergePatchFP8 [Experimental]", - "SP_FluxLoader", - "SP_FluxUnsampler", - "SP_FluxUnsampler_ForwardODESampler", - "SP_FluxUnsampler_InverseSampler", - "SP_HiresGen", - "SP_HiresGen_Dynamic", - "SP_HiresGen_HiresCfg", - "SP_HiresGen_Sharpen", - "SP_HunyuanLoader", - "SP_ImpactSwitchCombo", - "SP_KSampler", - "SP_KSamplerSelect", - "SP_KoboldCpp", - "SP_KoboldCppWithContext", - "SP_KoboldCpp_BannedTokens", - "SP_KoboldCpp_OverrideCfg", - "SP_ListAny", - "SP_ModelLoader", - "SP_Name_Checkpoint", - "SP_Name_Clip", - "SP_Name_ClipVision", - "SP_Name_ControlNet", - "SP_Name_StyleModel", - "SP_Name_Unet", - "SP_Name_UpscaleModel", - "SP_Name_Vae", - "SP_Pass", - "SP_Pipe", - "SP_Pipe_ToBasicPipe", - "SP_SD3Loader", - "SP_SDLoader", - "SP_SetPipeModelType", - "SP_Supir", - "SP_SupirSampler", - "SP_SupirSampler_DPMPP2M", - "SP_SupirSampler_EDM", - "SP_SwitchBooleanAny", - "SP_UnlistValues", - "SP_WebsocketSendImage", - "SP_XYGrid", - "SP_XYValues", - "ScatterParams", - "ScatterParamsBatch", - "SendTelegramChatBot", - "StrToCombo", - "TextSplitJoinByDelimiter" - ], - { - "author": "SeniorPioner", - "description": "Node Pack: PromptChecker for token toggling, KoboldCPP API, ModelMerging, Telegram-Bot-API, and more", - "nickname": "SP-Nodes", - "title": "SP-Nodes", - "title_aux": "SP-Nodes" - } - ], - "https://github.com/bananasss00/ComfyUI-flux_fill_patcher": [ - [ - "ApplyFluxFillPatch" - ], - { - "title_aux": "ComfyUI-flux_fill_patcher" - } - ], - "https://github.com/banodoco/steerable-motion": [ - [ - "BatchCreativeInterpolation", - "IpaConfiguration", - "RemoveAndInterpolateFrames", - "VideoContinuationGenerator", - "VideoFrameExtractorAndMaskGenerator", - "WanInputFrameNumber", - "WanVideoBlender" - ], - { - "title_aux": "Steerable Motion" - } - ], - "https://github.com/banqingyuan/ComfyUI-text-replace": [ - [ - "ChatOverlayNode", - "ExtractJsonNode", - "ImageEraseNode", - "OCRLocNode" - ], - { - "title_aux": "ComfyUI-text-replace" - } - ], - "https://github.com/bartly/Comfyui_babel_removebg_api": [ - [ - "BabelRemovebg" - ], - { - "title_aux": "Babel Removebg Api Node for ComfyUI" - } - ], - "https://github.com/bash-j/mikey_nodes": [ - [ - "AddMetaData", - "Batch Crop Image", - "Batch Crop Resize Inplace", - "Batch Load Images", - "Batch Resize Image for SDXL", - "Checkpoint Loader Simple Mikey", - "CheckpointHash", - "CheckpointSaveModelOnly", - "CinematicLook", - "Empty Latent Ratio Custom SDXL", - "Empty Latent Ratio Select SDXL", - "EvalFloats", - "FaceFixerOpenCV", - "FileNamePrefix", - "FileNamePrefixDateDirFirst", - "Float to String", - "GetSubdirectories", - "HaldCLUT", - "Image Caption", - "ImageBorder", - "ImageOverlay", - "ImagePaste", - "Int to String", - "LMStudioPrompt", - "Load Image Based on Number", - "LoraSyntaxProcessor", - "Mikey Sampler", - "Mikey Sampler Base Only", - "Mikey Sampler Base Only Advanced", - "Mikey Sampler Tiled", - "Mikey Sampler Tiled Base Only", - "MikeyLatentTileSampler", - "MikeyLatentTileSamplerCustom", - "MikeySamplerTiledAdvanced", - "MikeySamplerTiledAdvancedBaseOnly", - "ModelMergePixArtSigmaXL2_1024MS", - "ModelMergeTrainDiff", - "ModelMergeTrainDiffPixartSigmaXL2_1024MS", - "MosaicExpandImage", - "OobaPrompt", - "PresetRatioSelector", - "Prompt With SDXL", - "Prompt With Style", - "Prompt With Style V2", - "Prompt With Style V3", - "Range Float", - "Range Integer", - "Ratio Advanced", - "RemoveTextBetween", - "Resize Image for SDXL", - "SD3TextConditioningWithOptionsOnePrompt", - "SRFloatPromptInput", - "SRIntPromptInput", - "SRStringPromptInput", - "Save Image If True", - "Save Image With Prompt Data", - "Save Images Mikey", - "Save Images No Display", - "SaveMetaData", - "SearchAndReplace", - "Seed String", - "Style Conditioner", - "Style Conditioner Base Only", - "Text2InputOr3rdOption", - "TextCombinations", - "TextCombinations3", - "TextConcat", - "TextPadderMikey", - "TextPreserve", - "Upscale Tile Calculator", - "Wildcard Processor", - "WildcardAndLoraSyntaxProcessor", - "WildcardOobaPrompt" - ], - { - "title_aux": "Mikey Nodes" - } - ], - "https://github.com/bbaudio-2025/ComfyUI-SuperUltimateVaceTools": [ - [ - "CustomCropArea", - "CustomRefineOption", - "NAGParamtersSetting", - "RefineTest", - "RegionalBatchPrompt", - "SuperUltimateVACEUpscale", - "VACEControlImageCombine", - "VACEPromptCheckTotalFrame", - "VACEPromptCombine", - "VaceLongVideo" - ], - { - "title_aux": "ComfyUI-SuperUltimateVaceTools" - } - ], - "https://github.com/bbtaivi/ComfyUI-Aiv-Param": [ - [ - "AivParam" - ], - { - "title_aux": "AIV ComfyUI Node" - } - ], - "https://github.com/bear2b/comfyui-argo-nodes": [ - [ - "ColorMatrixGPU", - "LoadGridFromURL", - "SaveGridToS3" - ], - { - "title_aux": "ColorMatrixGPU Node for ComfyUI" - } - ], - "https://github.com/bedovyy/ComfyUI_NAIDGenerator": [ - [ - "ColorizeNAID", - "DeclutterNAID", - "EmotionNAID", - "GenerateNAID", - "Img2ImgOptionNAID", - "InpaintingOptionNAID", - "LineArtNAID", - "MaskImageToNAID", - "ModelOptionNAID", - "NetworkOptionNAID", - "PromptToNAID", - "RemoveBGNAID", - "SketchNAID", - "V4BasePrompt", - "V4NegativePrompt", - "VibeTransferOptionNAID" - ], - { - "title_aux": "ComfyUI_NAIDGenerator" - } - ], - "https://github.com/bemoregt/ComfyUI_CustomNode_Image2Spectrum": [ - [ - "Image_Spectrum" - ], - { - "title_aux": "ComfyUI_CustomNode_Image2Spectrum" - } - ], - "https://github.com/benda1989/CosyVoice2_ComfyUI": [ - [ - "CosyVoice3s", - "CosyVoiceCrossLingual", - "CosyVoiceLoader", - "CosyVoiceNLControl", - "CosyVoiceSonic", - "Text2" - ], - { - "title_aux": "GKK\u00b7CosyVoice" - } - ], - "https://github.com/benda1989/Sonic_ComfyUI": [ - [ - "SonicLoader", - "SonicSimper", - "SonicSpeechs" - ], - { - "title_aux": "GKK\u00b7Sonic" - } - ], - "https://github.com/benjamin-bertram/Comfyui_OIDN_Denoiser": [ - [ - "OIDNDenoiser" - ], - { - "title_aux": "ComfyUI OIDN Denoiser" - } - ], - "https://github.com/benjiyaya/ComfyUI-HunyuanVideoImagesGuider": [ - [ - "Hunyuan Video Image To Guider" - ], - { - "title_aux": "ComfyUI-HunyuanVideoImagesGuider" - } - ], - "https://github.com/benjiyaya/ComfyUI-KokoroTTS": [ - [ - "Kokoro TextToSpeech" - ], - { - "title_aux": "ComfyUI-KokoroTTS" - } - ], - "https://github.com/benstaniford/comfy-contact-sheet-image-loader": [ - [ - "ContactSheetImageLoader" - ], - { - "title_aux": "Comfy Contact Sheet Image Loader" - } - ], - "https://github.com/benstaniford/comfy-image-switch": [ - [ - "ImageSwitchNode", - "SwitchAnyValid" - ], - { - "title_aux": "ComfyUI Image Switch Node" - } - ], - "https://github.com/benstaniford/comfy-load-last-image": [ - [ - "LoadMostRecentImage" - ], - { - "title_aux": "ComfyUI Load Most Recent Image Node" - } - ], - "https://github.com/benstaniford/comfy-lora-loader-with-triggerdb": [ - [ - "LoRaLoaderWithTriggerDB" - ], - { - "title_aux": "LoRa Loader with Trigger Database" - } - ], - "https://github.com/benstaniford/comfy-prompt-db": [ - [ - "PromptDB", - "PromptStack" - ], - { - "title_aux": "Prompt Database for ComfyUI" - } - ], - "https://github.com/bentoml/comfy-pack": [ - [ - "CPackInputAny", - "CPackInputFile", - "CPackInputImage", - "CPackInputInt", - "CPackInputString", - "CPackOutputAudio", - "CPackOutputFile", - "CPackOutputImage", - "CPackOutputTextFile", - "CPackOutputVideo", - "CPackOutputZip", - "CPackOutputZipSwitch" - ], - { - "title_aux": "Comfy-Pack" - } - ], - "https://github.com/bhvbhushan/ComfyUI-LoRABlockWeight": [ - [ - "HierarchicalLoRAWeightEditor", - "NunchakuHierarchicalLoRALoader" - ], - { - "title_aux": "ComfyUI LoRA Block Weight Loader" - } - ], - "https://github.com/big-mon/ComfyUI-ResolutionPresets": [ - [ - "ResolutionPresetsSDXL" - ], - { - "title_aux": "ComfyUI-ResolutionPresets" - } - ], - "https://github.com/bikiam/ComfyUI_WhisperSRT": [ - [ - "WhisperAudioToSRTText" - ], - { - "title_aux": "ComfyUI_WhisperSRT" - } - ], - "https://github.com/bilal-arikan/ComfyUI_TextAssets": [ - [ - "LoadTextAsset" - ], - { - "title_aux": "ComfyUI_TextAssets" - } - ], - "https://github.com/billwuhao/ComfyUI_ACE-Step": [ - [ - "ACELoRALoader", - "ACEModelLoader", - "ACEStepEdit", - "ACEStepExtend", - "ACEStepGen", - "ACEStepRepainting", - "GenerationParameters", - "LyricsLangSwitch", - "MultiLineLyrics", - "MultiLinePromptACES" - ], - { - "title_aux": "ComfyUI_ACE-Step" - } - ], - "https://github.com/billwuhao/ComfyUI_AudioTools": [ - [ - "AddSubtitlesToVideo", - "AdjustAudio", - "AudioAddWatermark", - "AudioConcatenate", - "AudioDenoising", - "AudioRecorderAT", - "ClearVoiceRun", - "LoadAudioMW", - "MergeAudioMW", - "MinimalPauseNode", - "MultiLinePromptAT", - "MusicSeparation", - "RemoveSilence", - "SpeechSeparation", - "StringEditNode", - "TrimAudio" - ], - { - "title_aux": "ComfyUI_AudioTools" - } - ], - "https://github.com/billwuhao/ComfyUI_CSM": [ - [ - "CSMDialogRun", - "CSMSpeakersPreview", - "MultiLineText" - ], - { - "title_aux": "ComfyUI_CSM" - } - ], - "https://github.com/billwuhao/ComfyUI_DiffRhythm": [ - [ - "DiffRhythmRun", - "MultiLineLyricsDR" - ], - { - "title_aux": "ComfyUI_DiffRhythm_MW" - } - ], - "https://github.com/billwuhao/ComfyUI_EraX-WoW-Turbo": [ - [ - "EraXWoWRUN", - "WhisperTurboRun" - ], - { - "title_aux": "MW-ComfyUI_EraX-WoW-Turbo" - } - ], - "https://github.com/billwuhao/ComfyUI_IndexTTS": [ - [ - "IndexSpeakersPreview", - "IndexTTSRun", - "MultiLinePromptIndex" - ], - { - "title_aux": "ComfyUI_IndexTTS" - } - ], - "https://github.com/billwuhao/ComfyUI_KokoroTTS_MW": [ - [ - "KokoroRun", - "KokoroZHRun", - "MultiLinePromptKK" - ], - { - "title_aux": "ComfyUI_KokoroTTS_MW" - } - ], - "https://github.com/billwuhao/ComfyUI_MegaTTS3": [ - [ - "MegaTTS3Run", - "MegaTTS3SpeakersPreview", - "MultiLinePromptMG" - ], - { - "title_aux": "MW-ComfyUI_MegaTTS3" - } - ], - "https://github.com/billwuhao/ComfyUI_NotaGen": [ - [ - "NotaGenRun" - ], - { - "title_aux": "ComfyUI_NotaGen" - } - ], - "https://github.com/billwuhao/ComfyUI_OneButtonPrompt": [ - [ - "LoadImageAndPromptFromURL", - "LoadImageFromURL", - "LoadPrompt", - "StringEditNodeOBP" - ], - { - "title_aux": "MW-ComfyUI_OneButtonPrompt" - } - ], - "https://github.com/billwuhao/ComfyUI_OuteTTS": [ - [ - "OuteTTSRun" - ], - { - "title_aux": "MW-ComfyUI_OuteTTS" - } - ], - "https://github.com/billwuhao/ComfyUI_PortraitTools": [ - [ - "AlignFace", - "BeautifyPhoto", - "DetectCropFace", - "IDPhotos", - "ImageWatermark", - "LoadImageMW" - ], - { - "title_aux": "MW-ComfyUI_PortraitTools" - } - ], - "https://github.com/billwuhao/ComfyUI_SOME": [ - [ - "SomeSing2Midi" - ], - { - "title_aux": "ComfyUI_SOME" - } - ], - "https://github.com/billwuhao/ComfyUI_SparkTTS": [ - [ - "AudioRecorderSpark", - "SparkTTSClone", - "SparkTTSRun" - ], - { - "title_aux": "ComfyUI_SparkTTS" - } - ], - "https://github.com/billwuhao/ComfyUI_StepAudioTTS": [ - [ - "AudioRecorder", - "StepAudioClone", - "StepAudioRun" - ], - { - "title_aux": "ComfyUI_StepAudioTTS" - } - ], - "https://github.com/billwuhao/ComfyUI_gemmax": [ - [ - "GemmaxRun", - "QuickMTRun" - ], - { - "title_aux": "MW-ComfyUI_gemmax" - } - ], - "https://github.com/billwuhao/ComfyUI_parakeet-tdt": [ - [ - "ParakeetASRRun" - ], - { - "title_aux": "ComfyUI_parakeet-tdt" - } - ], - "https://github.com/billwuhao/Comfyui_HeyGem": [ - [ - "HeyGemRun" - ], - { - "title_aux": "Comfyui_HeyGem" - } - ], - "https://github.com/birdneststream/ComfyUI-Mircify": [ - [ - "IRC Art Converter", - "IRC PNG Exporter", - "IRC Text Saver" - ], - { - "title_aux": "ComfyUI-Mircify" - } - ], - "https://github.com/bitaffinity/ComfyUI_HF_Inference": [ - [ - "Classification", - "FeatureExtraction", - "Generation", - "ObjectDetection", - "QuestionAnswering", - "Segmentation", - "TextToImage", - "Translation" - ], - { - "title_aux": "ComfyUI_HF_Inference" - } - ], - "https://github.com/black-forest-labs/bfl-comfy-nodes": [ - [ - "FLUX 1.0 [canny]", - "FLUX 1.0 [canny] Finetuned", - "FLUX 1.0 [depth]", - "FLUX 1.0 [depth] Finetuned", - "FLUX 1.0 [dev]", - "FLUX 1.0 [fill]", - "FLUX 1.0 [fill] Finetuned", - "FLUX 1.0 [pro]", - "FLUX 1.0 [pro] Finetuned", - "FLUX 1.1 [pro]", - "FLUX 1.1 [ultra]", - "FLUX 1.1 [ultra] Finetuned" - ], - { - "title_aux": "Black Forest Labs API Nodes" - } - ], - "https://github.com/blackcodetavern/ComfyUI-Benripack": [ - [ - "AnimationExtractor", - "CharacterPipe", - "Load3DModel" - ], - { - "title_aux": "ComfyUI-Benripack" - } - ], - "https://github.com/blepping/ComfyUI-ApplyResAdapterUnet": [ - [ - "ApplyResAdapterUnet" - ], - { - "title_aux": "ComfyUI-ApplyResAdapterUnet" - } - ], - "https://github.com/blepping/ComfyUI-bleh": [ - [ - "BlehBlockCFG", - "BlehBlockOps", - "BlehCFGInitSampler", - "BlehCast", - "BlehDeepShrink", - "BlehDisableNoise", - "BlehDiscardPenultimateSigma", - "BlehEnsurePreviewer", - "BlehForceSeedSampler", - "BlehGlobalSageAttention", - "BlehHyperTile", - "BlehImageAsLatent", - "BlehInsaneChainSampler", - "BlehLatentAsImage", - "BlehLatentBlend", - "BlehLatentOps", - "BlehLatentScaleBy", - "BlehModelPatchConditional", - "BlehModelPatchFastTerminate", - "BlehPlug", - "BlehRefinerAfter", - "BlehSageAttentionSampler", - "BlehSetSamplerPreset", - "BlehSetSigmas", - "BlehTAEVideoDecode", - "BlehTAEVideoEncode" - ], - { - "title_aux": "ComfyUI-bleh" - } - ], - "https://github.com/blepping/ComfyUI-sonar": [ - [ - "FreeUExtreme", - "FreeUExtremeConfig", - "NoisyLatentLike", - "SONAR_CUSTOM_NOISE to NOISE", - "SamplerConfigOverride", - "SamplerSonarDPMPPSDE", - "SamplerSonarEuler", - "SamplerSonarEulerA", - "SonarAdvanced1fNoise", - "SonarAdvancedCollatzNoise", - "SonarAdvancedDistroNoise", - "SonarAdvancedPowerLawNoise", - "SonarAdvancedPyramidNoise", - "SonarAdvancedVoronoiNoise", - "SonarApplyLatentOperationCFG", - "SonarBlendedNoise", - "SonarChannelNoise", - "SonarCompositeNoise", - "SonarCustomNoise", - "SonarCustomNoiseAdv", - "SonarCustomNoiseParameters", - "SonarGuidanceConfig", - "SonarGuidedNoise", - "SonarLatentOperationAdvanced", - "SonarLatentOperationFilteredNoise", - "SonarLatentOperationNoise", - "SonarLatentOperationQuantileFilter", - "SonarLatentOperationSetSeed", - "SonarModulatedNoise", - "SonarNoiseImage", - "SonarNormalizeNoiseToScale", - "SonarPatternBreakNoise", - "SonarPerDimNoise", - "SonarPowerFilter", - "SonarPowerFilterNoise", - "SonarPowerNoise", - "SonarPreviewFilter", - "SonarQuantileFilteredNoise", - "SonarRandomNoise", - "SonarRepeatedNoise", - "SonarResizedNoise", - "SonarResizedNoiseAdv", - "SonarRippleFilteredNoise", - "SonarScatternetFilteredNoise", - "SonarScheduledNoise", - "SonarShuffledNoise", - "SonarSplitNoiseChain", - "SonarWaveletCFG", - "SonarWaveletFilteredNoise", - "SonarWaveletNoise" - ], - { - "title_aux": "ComfyUI-sonar" - } - ], - "https://github.com/blepping/comfyui_jankdiffusehigh": [ - [ - "DiffuseHighParam", - "DiffuseHighSampler" - ], - { - "title_aux": "comfyui_jankdiffusehigh" - } - ], - "https://github.com/blepping/comfyui_jankhidiffusion": [ - [ - "ApplyMSWMSAAttention", - "ApplyMSWMSAAttentionSimple", - "ApplyRAUNet", - "ApplyRAUNetSimple" - ], - { - "title_aux": "comfyui_jankhidiffusion" - } - ], - "https://github.com/blepping/comfyui_overly_complicated_sampling": [ - [ - "OCS ApplyFilterImage", - "OCS ApplyFilterLatent", - "OCS Group", - "OCS ModelSetMaxSigma", - "OCS MultiParam", - "OCS Param", - "OCS Sampler", - "OCS SimpleRestartSchedule", - "OCS Substeps", - "OCSNoise Conditioning", - "OCSNoise ExpressionFilteredNoise", - "OCSNoise ImmiscibleReference", - "OCSNoise OverrideSamplerNoise", - "OCSNoise PerlinAdvanced", - "OCSNoise PerlinSimple", - "OCSNoise to SONAR_CUSTOM_NOISE" - ], - { - "title_aux": "comfyui_overly_complicated_sampling" - } - ], - "https://github.com/blird/ComfyUI-Wanify": [ - [ - "AdaptiveImageResize" - ], - { - "title_aux": "ComfyUI-Wanify: Adaptive Image Resize Node" - } - ], - "https://github.com/blob8/ComfyUI_sloppy-comic": [ - [ - "Generate Comic", - "LLM API Request" - ], - { - "title_aux": "ComfyUI_sloppy-comic" - } - ], - "https://github.com/blovett80/ComfyUI-PixelDojo": [ - [ - "PixelDojoAPI" - ], - { - "title_aux": "ComfyUI-PixelDojo" - } - ], - "https://github.com/blueraincoatli/comfyUI_SillyNodes": [ - [ - "BooleanJumper|SillyNode", - "CloseErrorWindowNode|SillyNode", - "QueueSequence|SillyNode", - "Screenshots|SillyNode", - "dummyInput|SillyNode", - "dummyInput|blueraincoat" - ], - { - "title_aux": "comfyUI_SillyNodes" - } - ], - "https://github.com/bluevisor/ComfyUI_PS_Blend_Node": [ - [ - "PSBlendNode" - ], - { - "title_aux": "ComfyUI_PS_Blend_Node" - } - ], - "https://github.com/bmad4ever/comfyui_ab_samplercustom": [ - [ - "AB SamplerCustom (experimental)" - ], - { - "title_aux": "comfyui_ab_sampler" - } - ], - "https://github.com/bmad4ever/comfyui_lists_cartesian_product": [ - [ - "AnyListCartesianProduct" - ], - { - "title_aux": "Lists Cartesian Product" - } - ], - "https://github.com/bmad4ever/comfyui_quilting": [ - [ - "GuessQuiltingBlockSize_Bmad", - "ImageQuiltingSeamlessMB_Bmad", - "ImageQuiltingSeamlessSB_Bmad", - "ImageQuilting_Bmad", - "LatentQuiltingSeamlessMB_Bmad", - "LatentQuiltingSeamlessSB_Bmad", - "LatentQuilting_Bmad" - ], - { - "title_aux": "comfyui_quilting" - } - ], - "https://github.com/bmad4ever/comfyui_wfc_like": [ - [ - "WFC_CustomTemperature_Bmad", - "WFC_CustomValueWeights_Bmad", - "WFC_Decode_BMad", - "WFC_EmptyState_Bmad", - "WFC_Encode_BMad", - "WFC_Filter_Bmad", - "WFC_GenParallel_Bmad", - "WFC_Generate_BMad", - "WFC_SampleNode_BMad" - ], - { - "title_aux": "comfyui_wfc_like" - } - ], - "https://github.com/bobmagicii/comfykit-custom-nodes": [ - [ - "LoraStackFiveSimple", - "LoraThree", - "LoraWithMeta", - "TypecasterClip", - "TypecasterCond", - "TypecasterImage", - "TypecasterLatent", - "TypecasterModel", - "TypecasterVae" - ], - { - "title_aux": "ComfyKit Custom Nodes" - } - ], - "https://github.com/bollerdominik/ComfyUI-load-lora-from-url": [ - [ - "LoadLoraFromUrlOrPath", - "LoadVideoLoraFromUrlOrPath", - "LoadVideoLoraFromUrlOrPathSelect" - ], - { - "title_aux": "ComfyUI-load-lora-from-url" - } - ], - "https://github.com/bombax-xiaoice/ComfyUI-Allegro": [ - [ - "AllegroDecoder", - "AllegroEncoder", - "AllegroSampler", - "AllegroTI2VEncoder", - "AllegroTI2VSampler", - "AllegroTextEncoder", - "LoadAllegroModel", - "LoadAllegroTI2VModel" - ], - { - "title_aux": "ComfyUI-Allegro" - } - ], - "https://github.com/bombax-xiaoice/ComfyUI-DisPose": [ - [ - "DisPoseDecoder", - "DisPoseLoader", - "DisPoseSampler" - ], - { - "title_aux": "ComfyUI-DisPose" - } - ], - "https://github.com/bombax-xiaoice/ComfyUI-MagicDance": [ - [ - "LoadMagicDanceModel", - "MagicDanceDecoder", - "MagicDanceEncoder", - "MagicDanceSampler" - ], - { - "title_aux": "ComfyUI-MagicDance" - } - ], - "https://github.com/bombax-xiaoice/ComfyUI-Open-Sora-I2V": [ - [ - "OpenSoraDecoder", - "OpenSoraEncoder", - "OpenSoraLoader", - "OpenSoraSampler", - "OpenSoraTextEncoder" - ], - { - "title_aux": "ComfyUI-Open-Sora-I2V" - } - ], - "https://github.com/bombax-xiaoice/ComfyUI-OpenSoraPlan": [ - [ - "OpenSoraPlan0LoaderT2V", - "OpenSoraPlan1LoaderT2V", - "OpenSoraPlan2LoaderI2V", - "OpenSoraPlan2LoaderT2V", - "OpenSoraPlan2SamplerI2V", - "OpenSoraPlan3LoaderI2V", - "OpenSoraPlan3LoaderT2V", - "OpenSoraPlan3SamplerI2V", - "OpenSoraPlanDecoder", - "OpenSoraPlanPromptRefiner", - "OpenSoraPlanSamplerT2V" - ], - { - "title_aux": "ComfyUI-OpenSoraPlan" - } - ], - "https://github.com/bombless/comfyUI-RememberingUtils": [ - [ - "RememberLastSeed", - "ShowLastSeed", - "ShowLastText" - ], - { - "title_aux": "Remembering utils" - } - ], - "https://github.com/bongsang/ComfyUI-Bongsang": [ - [ - "AnyInfo", - "RgbChannel" - ], - { - "title_aux": "ComfyUI-Bongsang" - } - ], - "https://github.com/boredofnames/ComfyUI-ntfy": [ - [ - "Ntfy", - "SaveImageAndNtfy" - ], - { - "title_aux": "ComfyUI-ntfy" - } - ], - "https://github.com/boricuapab/ComfyUI-Bori-JsonSetGetConverter": [ - [ - "Bori Json Get Set Convert" - ], - { - "title_aux": "ComfyUI-Bori-JsonSetGetConverter" - } - ], - "https://github.com/boricuapab/ComfyUI-Bori-QwenImageResolutions": [ - [ - "Bori Qwen Image Resolution" - ], - { - "title_aux": "ComfyUI-Bori-QwenImageResolutions" - } - ], - "https://github.com/bradsec/ComfyUI_ResolutionSelector": [ - [ - "ResolutionSelector" - ], - { - "title_aux": "ResolutionSelector for ComfyUI" - } - ], - "https://github.com/bradsec/ComfyUI_StringEssentials": [ - [ - "StringMultiReplace", - "StringPreview", - "StringStrip", - "StringTextbox" - ], - { - "title_aux": "ComfyUI_StringEssentials" - } - ], - "https://github.com/braintacles/braintacles-comfyui-nodes": [ - [ - "CLIPTextEncodeSDXL-Multi-IO", - "CLIPTextEncodeSDXL-Pipe", - "Empty Latent Image from Aspect-Ratio", - "Interval Sampler", - "Random Find and Replace" - ], - { - "title_aux": "braintacles-nodes" - } - ], - "https://github.com/brantje/ComfyUI-api-tools": [ - [ - "SimpleGenImageInterface" - ], - { - "title_aux": "ComfyUI-api-tools" - } - ], - "https://github.com/brantje/ComfyUI_MagicQuill": [ - [ - "MagicQuill" - ], - { - "author": "Zichen LIU (https://zliucz.github.io/) and Yue YU (https://bruceyyu.github.io/)", - "description": "Official ComfyUI Implementations for Paper - MagicQuill: An Intelligent Interactive Image Editing System", - "nickname": "MagicQuill nodes", - "title": "MagicQuill", - "title_aux": "ComfyUI-MagicQuill" - } - ], - "https://github.com/brayevalerien/ComfyUI-SplitString": [ - [ - "Split String" - ], - { - "title_aux": "ComfyUI-splitstring" - } - ], - "https://github.com/brayevalerien/ComfyUI-resynthesizer": [ - [ - "Resynthesize" - ], - { - "title_aux": "ComfyUI Resynthesizer" - } - ], - "https://github.com/brianfitzgerald/style_aligned_comfy": [ - [ - "StyleAlignedBatchAlign", - "StyleAlignedReferenceSampler", - "StyleAlignedSampleReferenceLatents" - ], - { - "title_aux": "StyleAligned for ComfyUI" - } - ], - "https://github.com/bronkula/comfyui-fitsize": [ - [ - "FS: Crop Image Into Even Pieces", - "FS: Fit Image And Resize", - "FS: Fit Size From Image", - "FS: Fit Size From Int", - "FS: Image Region To Mask", - "FS: Load Image And Resize To Fit", - "FS: Pick Image From Batch", - "FS: Pick Image From Batches", - "FS: Pick Image From List" - ], - { - "title_aux": "comfyui-fitsize" - } - ], - "https://github.com/brucew4yn3rp/ComfyUI_SelectiveMetadata": [ - [ - "Multiline String", - "Save Image (Selective Metadata)", - "SaveImage" - ], - { - "title_aux": "Save Image with Selective Metadata" - } - ], - "https://github.com/bruefire/ComfyUI-SeqImageLoader": [ - [ - "VFrame Loader With Mask Editor", - "Video Loader With Mask Editor" - ], - { - "title_aux": "ComfyUI Sequential Image Loader" - } - ], - "https://github.com/budihartono/comfyui-aspect-ratio-presets": [ - [ - "CAS Empty Latent Aspect Ratio Axis", - "CAS Empty Latent Aspect Ratio Preset" - ], - { - "title_aux": "CAS Aspect Ratio Presets Node for ComfyUI" - } - ], - "https://github.com/budihartono/comfyui_otonx_nodes": [ - [ - "OTX Integer Multiple Inputs 4", - "OTX Integer Multiple Inputs 5", - "OTX Integer Multiple Inputs 6", - "OTX KSampler Feeder", - "OTX Versatile Multiple Inputs 4", - "OTX Versatile Multiple Inputs 5", - "OTX Versatile Multiple Inputs 6" - ], - { - "title_aux": "Otonx's Custom Nodes" - } - ], - "https://github.com/bugltd/ComfyLab-Pack": [ - [ - "Convert to Any (lab)", - "File Queue (lab)", - "Format: Multiline (lab)", - "Format: String (lab)", - "Generic Queue (lab)", - "Image Queue (lab)", - "Image: Downscale to Total Pixels (lab)", - "Input: Boolean (lab)", - "Input: Float (lab)", - "Input: Folder (lab)", - "Input: Integer (lab)", - "Input: Multiline (lab)", - "Input: String (lab)", - "List: Checkpoints (lab)", - "List: Limit (lab)", - "List: LoRAs (lab)", - "List: Merge (lab)", - "List: Random Seeds (lab)", - "List: Samplers (lab)", - "List: Schedulers (lab)", - "List: from Elements (lab)", - "List: from File (backend) (lab)", - "List: from Multiline (lab)", - "List: from String (lab)", - "Load Image (RGBA) (lab)", - "Output Config: Load (lab)", - "Output Config: Retrieve (backend) (lab)", - "Plot Config: Grid (lab)", - "Plot Config: Header/Footer (lab)", - "Resolution to Dimensions (lab)", - "Save Text File (lab)", - "Sleep (lab)", - "XY Plot: Queue (lab)", - "XY Plot: Render (lab)", - "XY Plot: Split Data (lab)" - ], - { - "nodename_pattern": " \\(lab\\)$", - "title_aux": "ComfyLab Pack" - } - ], - "https://github.com/burnsbert/ComfyUI-EBU-LMStudio": [ - [ - "EbuLMStudioBrainstormer", - "EbuLMStudioLoadModel", - "EbuLMStudioMakeRequest", - "EbuLMStudioUnload", - "EbuLMStudioUnloadGuider" - ], - { - "title_aux": "EBU LMStudio LLM Integration" - } - ], - "https://github.com/burnsbert/ComfyUI-EBU-PromptHelper": [ - [ - "EbuPromptHelperCharacterDescriberFemale", - "EbuPromptHelperCharacterDescriberMale", - "EbuPromptHelperCombineTwoStrings", - "EbuPromptHelperConsumeListItem", - "EbuPromptHelperCurrentDateTime", - "EbuPromptHelperListSampler", - "EbuPromptHelperLoadFileAsString", - "EbuPromptHelperRandomColorPalette", - "EbuPromptHelperRandomize", - "EbuPromptHelperReplace", - "EbuPromptHelperSeasonWeatherTimeOfDay", - "EbuPromptHelperTruncate" - ], - { - "title_aux": "EBU PromptHelper" - } - ], - "https://github.com/burnsbert/ComfyUI-EBU-Workflow": [ - [ - "EbuAppendToFile", - "EbuDecodeNewLines", - "EbuEncodeNewLines", - "EbuFileListCache", - "EbuGetImageAspectRatio", - "EbuReadFromFile", - "EbuScalingResolution", - "EbuScalingTile", - "EbuUniqueFileName" - ], - { - "title_aux": "EBU Workflow" - } - ], - "https://github.com/bvhari/ComfyUI_CFGStar": [ - [ - "CFGStar" - ], - { - "title_aux": "ComfyUI_CFGStar" - } - ], - "https://github.com/bvhari/ComfyUI_ImageProcessing": [ - [ - "BilateralFilter", - "Brightness", - "Gamma", - "Hue", - "Saturation", - "SigmoidCorrection", - "UnsharpMask" - ], - { - "title_aux": "ImageProcessing" - } - ], - "https://github.com/bvhari/ComfyUI_PerpCFG": [ - [ - "PerpCFG" - ], - { - "title_aux": "ComfyUI_PerpCFG" - } - ], - "https://github.com/bvhari/ComfyUI_PerpWeight": [ - [ - "CLIPTextEncodePerpWeight" - ], - { - "title_aux": "ComfyUI_PerpWeight" - } - ], - "https://github.com/bvhari/ComfyUI_SUNoise": [ - [ - "SUNoiseLatent", - "SamplersSUNoise", - "SamplersSUNoiseAdvanced" - ], - { - "title_aux": "ComfyUI_SUNoise" - } - ], - "https://github.com/bytedance/ComfyUI-HyperLoRA": [ - [ - "HyperLoRAApplyLoRA", - "HyperLoRABaseCond", - "HyperLoRAConfig", - "HyperLoRAFaceAttr", - "HyperLoRAGenerateBaseLoRA", - "HyperLoRAGenerateIDLoRA", - "HyperLoRAIDCond", - "HyperLoRALoader", - "HyperLoRASaveLoRA", - "HyperLoRAUniGenerateIDLoRA", - "HyperLoRAUniLoader" - ], - { - "title_aux": "ComfyUI-HyperLoRA" - } - ], - "https://github.com/bytedance/ComfyUI_InfiniteYou": [ - [ - "FaceCombine", - "FaceSwap_InfiniteYou", - "InfiniteYouApply" - ], - { - "title_aux": "ComfyUI_InfiniteYou" - } - ], - "https://github.com/c0ffymachyne/ComfyUI_BeatByte": [ - [ - "BytebeatSynth" - ], - { - "title_aux": "Bytebeat Synthesizer: Composing with Operators" - } - ], - "https://github.com/c0ffymachyne/ComfyUI_SignalProcessing": [ - [ - "SignalProcessingBaxandall3BandEQ", - "SignalProcessingBaxandallEQ", - "SignalProcessingCompressor", - "SignalProcessingConvolutionReverb", - "SignalProcessingFilter", - "SignalProcessingHarmonicsEnhancer", - "SignalProcessingLimiter", - "SignalProcessingLoadAudio", - "SignalProcessingLoudness", - "SignalProcessingMixdown", - "SignalProcessingNormalizer", - "SignalProcessingPadSynth", - "SignalProcessingPadSynthChoir", - "SignalProcessingPaulStretch", - "SignalProcessingPitchShifter", - "SignalProcessingSaturation", - "SignalProcessingSpectrogram", - "SignalProcessingStereoWidening", - "SignalProcessingWaveform" - ], - { - "title_aux": "ComfyUI Signal Processing" - } - ], - "https://github.com/cake-ml/tiny-sana-preview": [ - [ - "TinySanaPreview" - ], - { - "title_aux": "TinySanaPreview" - } - ], - "https://github.com/calcuis/gguf": [ - [ - "AudioEncoderLoaderGGUF", - "ClipLoaderGGUF", - "DualClipLoaderGGUF", - "GGUFRun", - "GGUFSave", - "GGUFUndo", - "LoaderGGUF", - "LoaderGGUFAdvanced", - "QuadrupleClipLoaderGGUF", - "TENSORBoost", - "TENSORCut", - "TripleClipLoaderGGUF", - "VaeGGUF" - ], - { - "preemptions": [ - "LoaderGGUF", - "ClipLoaderGGUF", - "DualClipLoaderGGUF", - "TripleClipLoaderGGUF", - "LoaderGGUFAdvanced", - "GGUFSave" - ], - "title_aux": "gguf" - } - ], - "https://github.com/caleboleary/ComfyUI-Arc2Face": [ - [ - "Arc2FaceEncoderLoader", - "Arc2FaceFaceExtractor", - "Arc2FaceGenerator", - "Arc2FaceImageGridGenerator", - "Arc2FaceImg2ImgGenerator", - "Arc2FaceUNetLoader" - ], - { - "title_aux": "Arc2Face ComfyUI Node Library" - } - ], - "https://github.com/camenduru/ComfyUI-TostAI": [ - [ - "SendToTostAI" - ], - { - "title_aux": "ComfyUI-TostAI" - } - ], - "https://github.com/cardenluo/ComfyUI-Apt_Preset": [ - [ - "AD_DrawSchedule", - "AD_ImageExpandBatch", - "AD_MaskExpandBatch", - "AD_batch_replace", - "AD_font2img", - "AD_pingpong_vedio", - "AD_sch_IPA", - "AD_sch_image_merge", - "AD_sch_latent", - "AD_sch_mask", - "AD_sch_prompt_adv", - "AD_sch_prompt_basic", - "AD_sch_prompt_stack", - "AD_sch_value", - "AD_slice_Condi", - "AI_GLM4", - "AI_Ollama", - "Amp_audio_Normalized", - "Amp_drive_String", - "Amp_drive_mask", - "Amp_drive_value", - "Apply_CN_union", - "Apply_ControlNetStack", - "Apply_IPA", - "Apply_IPA_SD3", - "Apply_LoRAStack", - "Apply_Redux", - "Apply_adv_CN", - "Apply_condiStack", - "Apply_latent", - "Apply_textStack", - "CN_preset1_Unpack", - "CN_preset1_pack", - "Data_Highway", - "Data_basic", - "Data_bus_chx", - "Data_chx_Merge", - "Data_presetData", - "Data_preset_save", - "Data_sampleData", - "Data_select", - "IO_adjust_image", - "IO_clear_cache", - "IO_input_any", - "IO_inputbasic", - "IO_load_anyimage", - "IO_save_image", - "IO_video_encode", - "IPA_XL_PromptInjection", - "IPA_clip_vision", - "IPA_dapterSD3LOAD", - "Image_Channel_Apply", - "Image_Channel_Extract", - "Image_Channel_RemoveAlpha", - "Image_Pair_Merge", - "Image_Pair_crop", - "Image_Resize2", - "Image_Resize_sum", - "Image_Upscaletile", - "Image_batch_composite", - "Image_batch_select", - "Image_pad_outfill", - "Image_solo_crop", - "Image_solo_stitch", - "Image_transform_layer", - "Image_transform_solo", - "Mask_Detect_label", - "Mask_Remove_bg", - "Mask_face_detect", - "Mask_image2mask", - "Mask_math", - "Mask_splitMask", - "Mask_splitMask_by_color", - "Mask_split_mulMask", - "Mask_transform_sum", - "Model_Preset_Unpack", - "Model_Preset_pack", - "Stack_CN_union", - "Stack_ControlNet", - "Stack_ControlNet1", - "Stack_IPA", - "Stack_IPA_SD3", - "Stack_LoRA", - "Stack_Redux", - "Stack_WanCameralToVideo", - "Stack_WanFirstLastFrameToVideo", - "Stack_WanFunControlToVideo", - "Stack_WanFunInpaintToVideo", - "Stack_WanImageToVideo", - "Stack_WanVaceToVideo", - "Stack_adv_CN", - "Stack_condi", - "Stack_latent", - "Stack_pre_Mark", - "Stack_sample_data", - "Stack_text", - "basicIn_Sampler", - "basicIn_Scheduler", - "basicIn_Seed", - "basicIn_color", - "basicIn_float", - "basicIn_int", - "basicIn_string", - "basic_Ksampler_adv", - "basic_Ksampler_custom", - "basic_Ksampler_full", - "basic_Ksampler_mid", - "basic_Ksampler_simple", - "batch_BatchGetByIndex", - "batch_BatchSlice", - "batch_MergeBatch", - "chx_IPA_XL", - "chx_IPA_adv", - "chx_IPA_apply_combine", - "chx_IPA_basic", - "chx_IPA_faceID", - "chx_IPA_faceID_adv", - "chx_IPA_region_combine", - "chx_Ksampler_Kontext", - "chx_Ksampler_Kontext_adv", - "chx_Ksampler_Kontext_inpaint", - "chx_Ksampler_VisualStyle", - "chx_Ksampler_dual_area", - "chx_Ksampler_dual_paint", - "chx_Ksampler_inpaint", - "chx_Ksampler_mix", - "chx_Ksampler_refine", - "chx_Ksampler_texture", - "chx_StyleModelApply", - "chx_Style_Redux", - "chx_YC_LG_Redux", - "chx_ksampler_Deforum_sch", - "chx_ksampler_tile", - "chx_latent_adjust", - "color_Local_Gray", - "color_OneColor_keep", - "color_OneColor_replace", - "color_adjust_HDR", - "color_adjust_HSL", - "color_adjust_WB_balance", - "color_adjust_light", - "color_match_adv", - "color_selector", - "color_tool", - "creat_any_List", - "creat_any_batch", - "creat_image_batch", - "creat_image_batch_input", - "creat_mask_batch", - "creat_mask_batch_input", - "create_AD_mask", - "create_Mask_match_shape", - "create_Mask_visual_tag", - "create_RadialGradient", - "create_lineGradient", - "create_mask_array", - "create_mask_solo", - "create_mulcolor_img", - "excel_Prompter", - "excel_column_diff", - "excel_imgEditor_helper", - "excel_insert_image", - "excel_qwen_artistic", - "excel_qwen_font", - "excel_read", - "excel_row_diff", - "excel_search_data", - "excel_write_data", - "img_effect_CircleWarp", - "img_effect_Liquify", - "img_effect_Load", - "img_effect_Stretch", - "img_effect_WaveWarp", - "latent_Image2Noise", - "latent_chx_noise", - "latent_ratio", - "lay_ImageGrid", - "lay_MaskGrid", - "lay_compare_img", - "lay_edge_cut", - "lay_fill_inpaint", - "lay_image_grid_note", - "lay_images_free_layout", - "lay_imgCanvas", - "lay_text_sum", - "lay_texture_Offset", - "list_ListGetByIndex", - "list_ListSlice", - "list_MergeList", - "list_num_range", - "list_sch_Value", - "load_FLUX", - "load_GGUF", - "load_SD35", - "load_basic", - "math_Remap_data", - "math_calculate", - "model_Regional", - "model_Style_Align", - "model_adjust_color", - "model_diff_inpaint", - "pack_Pack", - "pack_Unpack", - "param_preset_Unpack", - "param_preset_pack", - "photoshop_preset_Unpack", - "photoshop_preset_pack", - "pre_Flex2", - "pre_Kontext", - "pre_Kontext_mul", - "pre_QwenEdit", - "pre_controlnet", - "pre_controlnet_union", - "pre_ic_light_sd15", - "pre_latent_light", - "pre_mul_Mulcondi", - "pre_qwen_controlnet", - "pre_sample_data", - "sampler_DynamicTileMerge", - "sampler_DynamicTileSplit", - "sampler_enhance", - "sch_Prompt", - "sch_Value", - "sch_image", - "sch_mask", - "sch_split_text", - "sch_text", - "stack_Mask2color", - "stack_sum_pack", - "sum_create_chx", - "sum_editor", - "sum_latent", - "sum_load_adv", - "sum_lora", - "sum_stack_AD", - "sum_stack_Wan", - "sum_stack_all", - "sum_stack_image", - "text_SuperPrompter", - "text_free_wildcards", - "text_mul_Join", - "text_mul_Split", - "text_mul_remove", - "text_mul_replace", - "text_stack_wildcards", - "text_sum", - "type_AnyCast", - "type_Anyswitch", - "type_BasiPIPE", - "type_BatchToList", - "type_Image_Batch2List", - "type_Image_List2Batch", - "type_ListToBatch", - "type_Mask_Batch2List", - "type_Mask_List2Batch", - "type_text_list2batch", - "unpack_box2", - "view_Data", - "view_GetLength", - "view_GetShape", - "view_GetWidgetsValues", - "view_Mask_And_Img", - "view_bridge_Text", - "view_bridge_image", - "view_combo", - "view_latent", - "view_mask", - "view_node_Script" - ], - { - "title_aux": "ComfyUI-Apt_Preset" - } - ], - "https://github.com/casterpollux/MiniMax-bmo": [ - [ - "MinimaxRemoverBMO" - ], - { - "nodename_pattern": "MiniMax.*BMO|BMO.*MiniMax", - "title_aux": "MiniMax Video Object Remover Suite" - } - ], - "https://github.com/catboxanon/comfyui_stealth_pnginfo": [ - [ - "CatboxAnonSaveImageStealth" - ], - { - "title_aux": "comfyui_stealth_pnginfo" - } - ], - "https://github.com/cdb-boop/ComfyUI-Bringing-Old-Photos-Back-to-Life": [ - [ - "BOPBTL_BlendFaces", - "BOPBTL_DetectEnhanceBlendFaces", - "BOPBTL_DetectFaces", - "BOPBTL_EnhanceFaces", - "BOPBTL_EnhanceFacesAdvanced", - "BOPBTL_LoadFaceDetectorModel", - "BOPBTL_LoadFaceEnhancerModel", - "BOPBTL_LoadRestoreOldPhotosModel", - "BOPBTL_LoadScratchMaskModel", - "BOPBTL_RestoreOldPhotos", - "BOPBTL_ScratchMask" - ], - { - "title_aux": "ComfyUI Bringing Old Photos Back to Life" - } - ], - "https://github.com/cdb-boop/comfyui-image-round": [ - [ - "ComfyUI_Image_Round__CircularCrop", - "ComfyUI_Image_Round__ImageCropAdvanced", - "ComfyUI_Image_Round__ImageRound", - "ComfyUI_Image_Round__ImageRoundAdvanced" - ], - { - "title_aux": "comfyui-image-round" - } - ], - "https://github.com/cdxOo/comfyui-text-node-with-comments": [ - [ - "text-node-with-comments" - ], - { - "title_aux": "Text Node With Comments (@cdxoo)" - } - ], - "https://github.com/cedarconnor/ComfyUI_HunyuanWorld": [ - [ - "HYW_Config", - "HYW_MeshAnalyzer", - "HYW_MeshExport", - "HYW_MeshProcessor", - "HYW_MetadataManager", - "HYW_ModelLoader", - "HYW_PanoGen", - "HYW_PanoGenBatch", - "HYW_PanoInpaint_Advanced", - "HYW_PanoInpaint_Scene", - "HYW_PanoInpaint_Sky", - "HYW_PanoramaValidator", - "HYW_PerspectiveToPanoramaMask", - "HYW_RuntimeFromStock", - "HYW_SeamlessWrap360", - "HYW_SettingsLoader", - "HYW_ShiftPanorama", - "HYW_SkyMaskGenerator", - "HYW_TextureBaker", - "HYW_Thumbnailer", - "HYW_WorldReconstructor" - ], - { - "title_aux": "ComfyUI HunyuanWorld - Professional 3D World Generation" - } - ], - "https://github.com/cedarconnor/comfyui-BatchNameLoop": [ - [ - "Batch Image Iterator", - "Batch Image Loader", - "Batch Image Saver", - "Batch Image Single Saver" - ], - { - "title_aux": "ComfyUI Batch Name Loop" - } - ], - "https://github.com/cedarconnor/comfyui-LatLong": [ - [ - "Equirectangular Crop 180", - "Equirectangular Crop Square", - "Equirectangular Perspective Extract", - "Equirectangular Processor", - "Equirectangular Rotate", - "Equirectangular To Cubemap" - ], - { - "title_aux": "ComfyUI LatLong - Equirectangular Image Processing Nodes" - } - ], - "https://github.com/cedarconnor/upsampler": [ - [ - "Upsampler Dynamic Upscale", - "Upsampler Precise Upscale", - "Upsampler Smart Upscale" - ], - { - "title_aux": "ComfyUI Upsampler Nodes" - } - ], - "https://github.com/celoron/ComfyUI-VisualQueryTemplate": [ - [ - "VisualQueryTemplateNode" - ], - { - "title_aux": "ComfyUI-VisualQueryTemplate" - } - ], - "https://github.com/celsojr2013/comfyui_jamworks_client": [ - [ - "Jamworks_Download", - "Jamworks_Login", - "Shell_Command" - ], - { - "title_aux": "comfyui_jamworks_client" - } - ], - "https://github.com/celsojr2013/comfyui_simpletools": [ - [ - "GoogleTranslator", - "Parameters", - "ResolutionSolver" - ], - { - "title_aux": "ComfyUI SimpleTools Suit" - } - ], - "https://github.com/cenzijing/ComfyUI-Markmap": [ - [ - "MarkmapNode", - "ReadHtmlNode" - ], - { - "title_aux": "ComfyUI-Markmap" - } - ], - "https://github.com/cerspense/ComfyUI_cspnodes": [ - [ - "DepthToNormalMap", - "GetMP4Prompt", - "ImageDirIterator", - "IncrementEveryN", - "Modelscopet2v", - "Modelscopev2v", - "RemapRange", - "ResizeByImage", - "SplitImageChannels", - "VidDirIterator" - ], - { - "title_aux": "cspnodes" - } - ], - "https://github.com/ceruleandeep/ComfyUI-LLaVA-Captioner": [ - [ - "LlavaCaptioner" - ], - { - "title_aux": "ComfyUI LLaVA Captioner" - } - ], - "https://github.com/cganimitta/ComfyUI_CGAnimittaTools": [ - [ - "CGA_BlackBorderCrop", - "CGA_BlenderBridge", - "CGA_ColorToGrayscale", - "CGA_ExtractFromList", - "CGA_FrameExtraction\ud83c\udf9e\ufe0f", - "CGA_ListSubfolders", - "CGA_NegativeSelector", - "CGA_TxtReaderNode" - ], - { - "title_aux": "ComfyUI_CGAnimittaTools" - } - ], - "https://github.com/chakib-belgaid/ComfyUI-autosize": [ - [ - "CustomAutoSize", - "SDXLAutoSize" - ], - { - "title_aux": "ComfyUI-autosize" - } - ], - "https://github.com/chakib-belgaid/Comfyui_Prompt_styler": [ - [ - "Prompt_Styler" - ], - { - "title_aux": "ComfyUI Style Plugin" - } - ], - "https://github.com/chandlergis/ComfyUI-IMG_Query": [ - [ - "ImageRequestNode" - ], - { - "title_aux": "ComfyUI-IMG_Query" - } - ], - "https://github.com/chandlergis/ComfyUI_EmojiOverlay": [ - [ - "Image Emoji Overlay" - ], - { - "title_aux": "ComfyUI_EmojiOverlay" - } - ], - "https://github.com/changwook987/ComfyUI-Small-Utility": [ - [ - "Eval", - "RandomEmptyLatent" - ], - { - "title_aux": "ComfyUI-Small-Utility" - } - ], - "https://github.com/chaojie/ComfyUI-AniPortrait": [ - [ - "AniPortraitLoader", - "AniPortraitRun", - "Box2Video", - "CoverVideo", - "MaskList2Video" - ], - { - "title_aux": "ComfyUI-AniPortrait" - } - ], - "https://github.com/chaojie/ComfyUI-CameraCtrl-Wrapper": [ - [ - "CameraBasic", - "CameraCombine", - "CameraCtrlLoader", - "CameraCtrlRun", - "CameraJoin", - "CameraTrajectory" - ], - { - "title_aux": "ComfyUI-CameraCtrl-Wrapper" - } - ], - "https://github.com/chaojie/ComfyUI-Champ": [ - [ - "ChampLoader", - "ChampRun", - "ImageCombineOneColumn", - "ImageCombineOneRow" - ], - { - "title_aux": "ComfyUI-Champ" - } - ], - "https://github.com/chaojie/ComfyUI-DragAnything": [ - [ - "DragAnythingLoader", - "DragAnythingPipelineRun", - "DragAnythingPipelineRunRandom", - "DragAnythingRun", - "DragAnythingRunRandom", - "LoadText", - "SaveText", - "VHS_FILENAMES_STRING" - ], - { - "title_aux": "ComfyUI-DragAnything" - } - ], - "https://github.com/chaojie/ComfyUI-DragNUWA": [ - [ - "BrushMotion", - "CompositeMotionBrush", - "CompositeMotionBrushWithoutModel", - "DragNUWA Run", - "DragNUWA Run MotionBrush", - "Get First Image", - "Get Last Image", - "InstantCameraMotionBrush", - "InstantObjectMotionBrush", - "Load CheckPoint DragNUWA", - "Load MotionBrush From Optical Flow", - "Load MotionBrush From Optical Flow Directory", - "Load MotionBrush From Optical Flow Without Model", - "Load MotionBrush From Tracking Points", - "Load MotionBrush From Tracking Points Without Model", - "Load Pose KeyPoints", - "Loop", - "LoopEnd_IMAGE", - "LoopStart_IMAGE", - "Split Tracking Points" - ], - { - "title_aux": "ComfyUI-DragNUWA" - } - ], - "https://github.com/chaojie/ComfyUI-DynamiCrafter": [ - [ - "DynamiCrafter Simple", - "DynamiCrafterInterp Simple", - "DynamiCrafterInterpLoader", - "DynamiCrafterLoader" - ], - { - "title_aux": "ComfyUI-DynamiCrafter" - } - ], - "https://github.com/chaojie/ComfyUI-EasyAnimate": [ - [ - "EasyAnimateLoader", - "EasyAnimateRun" - ], - { - "title_aux": "ComfyUI-EasyAnimate" - } - ], - "https://github.com/chaojie/ComfyUI-Gemma": [ - [ - "GemmaLoader", - "GemmaRun" - ], - { - "title_aux": "ComfyUI-Gemma" - } - ], - "https://github.com/chaojie/ComfyUI-I2VGEN-XL": [ - [ - "I2VGEN-XL Simple", - "Modelscope Pipeline Loader" - ], - { - "title_aux": "ComfyUI-I2VGEN-XL" - } - ], - "https://github.com/chaojie/ComfyUI-Img2Img-Turbo": [ - [ - "Img2ImgTurboEdgeLoader", - "Img2ImgTurboEdgeRun", - "Img2ImgTurboSketchLoader", - "Img2ImgTurboSketchRun" - ], - { - "title_aux": "ComfyUI-Img2Img-Turbo" - } - ], - "https://github.com/chaojie/ComfyUI-LaVIT": [ - [ - "VHS_FILENAMES_STRING_LaVIT", - "VideoLaVITI2I", - "VideoLaVITI2V", - "VideoLaVITI2VLong", - "VideoLaVITLoader", - "VideoLaVITT2V", - "VideoLaVITT2VLong", - "VideoLaVITUnderstandingImage", - "VideoLaVITUnderstandingLoader", - "VideoLaVITUnderstandingVideo", - "VideoLaVITVideoDetokenizerLoader", - "VideoLaVITVideoReconstruction" - ], - { - "title_aux": "ComfyUI-LaVIT" - } - ], - "https://github.com/chaojie/ComfyUI-LightGlue": [ - [ - "LightGlue Loader", - "LightGlue Simple", - "LightGlue Simple Multi" - ], - { - "title_aux": "ComfyUI-LightGlue" - } - ], - "https://github.com/chaojie/ComfyUI-Moore-AnimateAnyone": [ - [ - "Moore-AnimateAnyone Denoising Unet", - "Moore-AnimateAnyone Image Encoder", - "Moore-AnimateAnyone Pipeline Loader", - "Moore-AnimateAnyone Pose Guider", - "Moore-AnimateAnyone Reference Unet", - "Moore-AnimateAnyone Simple", - "Moore-AnimateAnyone VAE" - ], - { - "title_aux": "ComfyUI-Moore-AnimateAnyone" - } - ], - "https://github.com/chaojie/ComfyUI-Motion-Vector-Extractor": [ - [ - "Motion Vector Extractor", - "VideoCombineThenPath" - ], - { - "title_aux": "ComfyUI-Motion-Vector-Extractor" - } - ], - "https://github.com/chaojie/ComfyUI-MotionCtrl": [ - [ - "Load Motion Camera Preset", - "Load Motion Traj Preset", - "Load Motionctrl Checkpoint", - "Motionctrl Cond", - "Motionctrl Sample", - "Motionctrl Sample Simple", - "Select Image Indices" - ], - { - "title_aux": "ComfyUI-MotionCtrl" - } - ], - "https://github.com/chaojie/ComfyUI-MotionCtrl-SVD": [ - [ - "Load Motionctrl-SVD Camera Preset", - "Load Motionctrl-SVD Checkpoint", - "Motionctrl-SVD Sample Simple" - ], - { - "title_aux": "ComfyUI-MotionCtrl-SVD" - } - ], - "https://github.com/chaojie/ComfyUI-MuseTalk": [ - [ - "MuseTalkCupAudio", - "MuseTalkRun", - "VHS_FILENAMES_STRING_MuseTalk" - ], - { - "title_aux": "ComfyUI-MuseTalk" - } - ], - "https://github.com/chaojie/ComfyUI-MuseV": [ - [ - "MuseVRun", - "MuseVRunVid2Vid", - "VHS_FILENAMES_STRING_MuseV" - ], - { - "author": "infguo", - "title_aux": "ComfyUI-MuseV" - } - ], - "https://github.com/chaojie/ComfyUI-Open-Sora": [ - [ - "OpenSoraLoader", - "OpenSoraRun", - "OpenSoraSampler" - ], - { - "title_aux": "ComfyUI-Open-Sora" - } - ], - "https://github.com/chaojie/ComfyUI-Open-Sora-Plan": [ - [ - "OpenSoraPlanDecode", - "OpenSoraPlanLoader", - "OpenSoraPlanRun", - "OpenSoraPlanSample" - ], - { - "title_aux": "ComfyUI-Open-Sora-Plan" - } - ], - "https://github.com/chaojie/ComfyUI-Panda3d": [ - [ - "Panda3dAmbientLight", - "Panda3dAttachNewNode", - "Panda3dBase", - "Panda3dDirectionalLight", - "Panda3dLoadDepthModel", - "Panda3dLoadModel", - "Panda3dLoadTexture", - "Panda3dModelMerge", - "Panda3dTest", - "Panda3dTextureMerge" - ], - { - "title_aux": "ComfyUI-Panda3d" - } - ], - "https://github.com/chaojie/ComfyUI-Pymunk": [ - [ - "PygameRun", - "PygameSurface", - "PymunkDynamicBox", - "PymunkDynamicCircle", - "PymunkRun", - "PymunkShapeMerge", - "PymunkSpace", - "PymunkStaticLine" - ], - { - "title_aux": "ComfyUI-Pymunk" - } - ], - "https://github.com/chaojie/ComfyUI-RAFT": [ - [ - "Load MotionBrush", - "RAFT Run", - "Save MotionBrush", - "VizMotionBrush" - ], - { - "title_aux": "ComfyUI-RAFT" - } - ], - "https://github.com/chaojie/ComfyUI-SimDA": [ - [ - "SimDALoader", - "SimDARun", - "SimDATrain", - "VHS_FILENAMES_STRING_SimDA" - ], - { - "title_aux": "ComfyUI-SimDA" - } - ], - "https://github.com/chaojie/ComfyUI-Trajectory": [ - [ - "Trajectory_Canvas_Tab" - ], - { - "author": "Lerc", - "description": "This extension provides a full page image editor with mask support. There are two nodes, one to receive images from the editor and one to send images to the editor.", - "nickname": "Canvas Tab", - "title": "Canvas Tab", - "title_aux": "ComfyUI-Trajectory" - } - ], - "https://github.com/chaojie/ComfyUI-Video-Editing-X-Attention": [ - [ - "StringList", - "VEXAGuidance", - "VEXALoader", - "VEXARun" - ], - { - "title_aux": "ComfyUI-Video-Editing-X-Attention" - } - ], - "https://github.com/chaojie/ComfyUI-dust3r": [ - [ - "CameraPoseVideo", - "Dust3rLoader", - "Dust3rRun" - ], - { - "title_aux": "ComfyUI-dust3r" - } - ], - "https://github.com/chaojie/ComfyUI_StreamingT2V": [ - [ - "LoadText_StreamingT2V", - "PromptTravelIndex", - "SaveText_StreamingT2V", - "StreamingT2VLoaderAnimateDiff", - "StreamingT2VLoaderAnimateDiffModel", - "StreamingT2VLoaderEnhanceModel", - "StreamingT2VLoaderModelscopeModel", - "StreamingT2VLoaderModelscopeT2V", - "StreamingT2VLoaderSVD", - "StreamingT2VLoaderSVDModel", - "StreamingT2VLoaderStreamModel", - "StreamingT2VLoaderVidXTendModel", - "StreamingT2VRunEnhanceStep", - "StreamingT2VRunI2V", - "StreamingT2VRunLongStep", - "StreamingT2VRunLongStepVidXTendPipeline", - "StreamingT2VRunLongStepVidXTendPipelineCustomRef", - "StreamingT2VRunLongStepVidXTendPipelineCustomRefOutExtendOnly", - "StreamingT2VRunLongStepVidXTendPipelinePromptTravel", - "StreamingT2VRunShortStepAnimateDiff", - "StreamingT2VRunShortStepModelscopeT2V", - "StreamingT2VRunShortStepSVD", - "StreamingT2VRunT2V", - "VHS_FILENAMES_STRING_StreamingT2V" - ], - { - "title_aux": "ComfyUI_StreamingT2V" - } - ], - "https://github.com/chaosaiart/Chaosaiart-Nodes": [ - [ - "chaosaiart_Any_Switch", - "chaosaiart_Any_Switch_Big_Number", - "chaosaiart_Any_Switch_small", - "chaosaiart_AutoNone_Switch_small", - "chaosaiart_CheckpointLoader", - "chaosaiart_CheckpointPrompt", - "chaosaiart_CheckpointPrompt2", - "chaosaiart_CheckpointPrompt_Frame", - "chaosaiart_CheckpointPrompt_FrameMixer", - "chaosaiart_ControlNetApply", - "chaosaiart_ControlNetApply2", - "chaosaiart_ControlNetApply3", - "chaosaiart_Denoising_Switch", - "chaosaiart_EmptyLatentImage", - "chaosaiart_FramePromptCLIPEncode", - "chaosaiart_Frame_Switch", - "chaosaiart_KSampler1", - "chaosaiart_KSampler2", - "chaosaiart_KSampler3", - "chaosaiart_KSampler4", - "chaosaiart_KSampler5", - "chaosaiart_KSampler7", - "chaosaiart_KSampler_a1", - "chaosaiart_KSampler_a1a", - "chaosaiart_KSampler_a2", - "chaosaiart_KSampler_expert_0", - "chaosaiart_KSampler_expert_1", - "chaosaiart_Ksampler_attribut", - "chaosaiart_Load_Image_Batch", - "chaosaiart_Load_Image_Batch_2img", - "chaosaiart_MainPromptCLIPEncode", - "chaosaiart_Number", - "chaosaiart_Number2", - "chaosaiart_Number_Counter", - "chaosaiart_Number_Switch", - "chaosaiart_Prompt", - "chaosaiart_Prompt_Frame", - "chaosaiart_Prompt_mixer_byFrame", - "chaosaiart_SaveImage", - "chaosaiart_Show_Info", - "chaosaiart_Simple_Prompt", - "chaosaiart_Style_Node", - "chaosaiart_TextCLIPEncode", - "chaosaiart_TextCLIPEncode_lora", - "chaosaiart_adjust_color", - "chaosaiart_any_array2input_1Input", - "chaosaiart_any_array2input_all_big", - "chaosaiart_any_array2input_all_small", - "chaosaiart_any_input2array_big", - "chaosaiart_any_input2array_small", - "chaosaiart_controlnet_weidgth", - "chaosaiart_convert", - "chaosaiart_convert_Prompt", - "chaosaiart_deepseek_fix", - "chaosaiart_forPreview", - "chaosaiart_image_loop", - "chaosaiart_img2gif", - "chaosaiart_img2video", - "chaosaiart_lora", - "chaosaiart_lora_advanced", - "chaosaiart_merge_Folders", - "chaosaiart_oneNode", - "chaosaiart_reloadAny_Load", - "chaosaiart_reloadAny_Save", - "chaosaiart_reloadIMG_Load", - "chaosaiart_reloadIMG_Save", - "chaosaiart_reloadLatent_Load", - "chaosaiart_reloadLatent_Save", - "chaosaiart_restarter", - "chaosaiart_restarter_advanced", - "chaosaiart_video2img1", - "chaosaiart_zoom_frame" - ], - { - "title_aux": "Chaosaiart-Nodes" - } - ], - "https://github.com/charlyad142/ComfyUI_Charly_FitToAspectNode": [ - [ - "Charly FitToAspectNode" - ], - { - "title_aux": "ComfyUI Charly FitToAspectNode" - } - ], - "https://github.com/charlyad142/ComfyUI_bfl_api_pro_nodes": [ - [ - "BFL Canny Control", - "BFL Depth Control", - "BFL Flux Kontext", - "BFL Flux Ultra", - "BFL Image Expander", - "BFL Image Generator", - "BFL Inpainting" - ], - { - "title_aux": "ComfyUI BFL API Pro Nodes" - } - ], - "https://github.com/checkbins/checkbin-comfy": [ - [ - "Checkbin Get Image Bin", - "Checkbin Get String Bin", - "Checkbin Save Image Bin", - "Checkbin Save String Bin", - "Checkbin Start Run", - "Checkbin Submit Bin" - ], - { - "title_aux": "checkbin-comfy" - } - ], - "https://github.com/chenbaiyujason/ComfyUI_StepFun": [ - [ - "CombineStrings", - "JSONParser", - "StepFunClient", - "TextImageChat", - "VideoChat", - "VideoFileUploader" - ], - { - "title_aux": "ComfyUI-SCStepFun" - } - ], - "https://github.com/chenlongming/ComfyUI_Spectral": [ - [ - "Calculate", - "KMeans", - "LoadEnvi", - "LoadSpectral", - "Plot" - ], - { - "title_aux": "ComfyUI_Spectral" - } - ], - "https://github.com/chenpipi0807/ComfyUI-Index-TTS": [ - [ - "AudioCleanupNode", - "IndexTTSNode", - "IndexTTSProNode", - "NovelTextStructureNode", - "TimbreAudioLoader" - ], - { - "author": "ComfyUI-Index-TTS", - "description": "ComfyUI\u63a5\u53e3\u7684\u5de5\u4e1a\u7ea7\u96f6\u6837\u672c\u6587\u672c\u5230\u8bed\u97f3\u5408\u6210\u7cfb\u7edf", - "title": "IndexTTS for ComfyUI", - "title_aux": "ComfyUI-Index-TTS" - } - ], - "https://github.com/chenpipi0807/ComfyUI_NSFW_Godie": [ - [ - "NSFWFilterNode" - ], - { - "title_aux": "ComfyUI NSFW Filter" - } - ], - "https://github.com/chenpipi0807/Comfyui-Qwen-image-edit-CharacterConsistency": [ - [ - "TextEncodeQwenImageEditEnhanced" - ], - { - "title_aux": "Comfyui-Qwen-image-edit-CharacterConsistency" - } - ], - "https://github.com/chenpipi0807/PIP_ArtisticWords": [ - [ - "PIP Artistic Text Generator", - "PIP ArtisticWords Fusion", - "PIP ColorPicker", - "PIP SVG Recorder", - "PIP Text Preview", - "PIPAdvancedColorAnalyzer", - "PIPColorPicker", - "PIPColorWheel" - ], - { - "title_aux": "PIP Artistic Words for ComfyUI" - } - ], - "https://github.com/cherninlab/logo-generator-comfyui": [ - [ - "GoogleFontsLogo" - ], - { - "title_aux": "Logo Generator Node for ComfyUI" - } - ], - "https://github.com/chesnokovivan/ComfyUI-Novakid": [ - [ - "Novakid Styler" - ], - { - "title_aux": "ComfyUI-Novakid" - } - ], - "https://github.com/chflame163/ComfyUI_CatVTON_Wrapper": [ - [ - "CatVTONWrapper" - ], - { - "author": "chflame", - "description": "CatVTON warpper for ComfyUI", - "nickname": "CatVTON_Wrapper", - "title": "CatVTON_Wrapper", - "title_aux": "ComfyUI_CatVTON_Wrapper" - } - ], - "https://github.com/chflame163/ComfyUI_CogView4_Wrapper": [ - [ - "CogView4" - ], - { - "title_aux": "ComfyUI_CogView4_Wrapper" - } - ], - "https://github.com/chflame163/ComfyUI_FaceSimilarity": [ - [ - "Face Similarity" - ], - { - "title_aux": "ComfyUI Face Similarity" - } - ], - "https://github.com/chflame163/ComfyUI_Janus_Wrapper": [ - [ - "JanusImage2Text", - "JanusTextToImage", - "LoadJanusModel" - ], - { - "title_aux": "ComfyUI_Janus_Wrapper" - } - ], - "https://github.com/chflame163/ComfyUI_LayerStyle": [ - [ - "LayerColor: AutoAdjust", - "LayerColor: AutoAdjustV2", - "LayerColor: AutoBrightness", - "LayerColor: Brightness & Contrast", - "LayerColor: BrightnessContrastV2", - "LayerColor: Color of Shadow & Highlight", - "LayerColor: ColorAdapter", - "LayerColor: ColorBalance", - "LayerColor: ColorTemperature", - "LayerColor: ColorofShadowHighlightV2", - "LayerColor: Exposure", - "LayerColor: Gamma", - "LayerColor: HSV", - "LayerColor: LAB", - "LayerColor: LUT Apply", - "LayerColor: Levels", - "LayerColor: Negative", - "LayerColor: RGB", - "LayerColor: YUV", - "LayerFilter: AddGrain", - "LayerFilter: ChannelShake", - "LayerFilter: ColorMap", - "LayerFilter: Film", - "LayerFilter: FilmV2", - "LayerFilter: GaussianBlur", - "LayerFilter: GaussianBlurV2", - "LayerFilter: HDREffects", - "LayerFilter: HalfTone", - "LayerFilter: LightLeak", - "LayerFilter: MotionBlur", - "LayerFilter: Sharp & Soft", - "LayerFilter: SkinBeauty", - "LayerFilter: SoftLight", - "LayerFilter: WaterColor", - "LayerMask: BlendIf Mask", - "LayerMask: CreateGradientMask", - "LayerMask: ImageToMask", - "LayerMask: LoadSegformerModel", - "LayerMask: MaskBoxDetect", - "LayerMask: MaskBoxExtend", - "LayerMask: MaskByColor", - "LayerMask: MaskEdgeShrink", - "LayerMask: MaskEdgeUltraDetail", - "LayerMask: MaskEdgeUltraDetail V2", - "LayerMask: MaskGradient", - "LayerMask: MaskGrain", - "LayerMask: MaskGrow", - "LayerMask: MaskInvert", - "LayerMask: MaskMotionBlur", - "LayerMask: MaskPreview", - "LayerMask: MaskStroke", - "LayerMask: PixelSpread", - "LayerMask: RemBgUltra", - "LayerMask: RmBgUltra V2", - "LayerMask: SegformerB2ClothesUltra", - "LayerMask: SegformerClothesPipelineLoader", - "LayerMask: SegformerClothesSetting", - "LayerMask: SegformerFashionPipelineLoader", - "LayerMask: SegformerFashionSetting", - "LayerMask: SegformerUltraV2", - "LayerMask: SegformerUltraV3", - "LayerMask: Shadow & Highlight Mask", - "LayerMask: ShadowHighlightMaskV2", - "LayerStyle: ColorOverlay", - "LayerStyle: ColorOverlay V2", - "LayerStyle: DropShadow", - "LayerStyle: DropShadow V2", - "LayerStyle: DropShadow V3", - "LayerStyle: Gradient Map", - "LayerStyle: GradientOverlay", - "LayerStyle: GradientOverlay V2", - "LayerStyle: InnerGlow", - "LayerStyle: InnerGlow V2", - "LayerStyle: InnerShadow", - "LayerStyle: InnerShadow V2", - "LayerStyle: OuterGlow", - "LayerStyle: OuterGlow V2", - "LayerStyle: Stroke", - "LayerStyle: Stroke V2", - "LayerUtility: AnyRerouter", - "LayerUtility: BatchSelector", - "LayerUtility: Boolean", - "LayerUtility: BooleanOperator", - "LayerUtility: BooleanOperatorV2", - "LayerUtility: CheckMask", - "LayerUtility: CheckMaskV2", - "LayerUtility: ChoiceTextPreset", - "LayerUtility: ColorImage", - "LayerUtility: ColorImage V2", - "LayerUtility: ColorName", - "LayerUtility: ColorPicker", - "LayerUtility: CropBoxResolve", - "LayerUtility: CropByMask", - "LayerUtility: CropByMask V2", - "LayerUtility: CropByMask V3", - "LayerUtility: ExtendCanvas", - "LayerUtility: ExtendCanvasV2", - "LayerUtility: Float", - "LayerUtility: FluxKontextImageScale", - "LayerUtility: GetImageSize", - "LayerUtility: GetMainColors", - "LayerUtility: GetMainColorsV2", - "LayerUtility: GradientImage", - "LayerUtility: GradientImage V2", - "LayerUtility: GrayValue", - "LayerUtility: HLFrequencyDetailRestore", - "LayerUtility: HSV Value", - "LayerUtility: ICMask", - "LayerUtility: ICMaskCropBack", - "LayerUtility: If", - "LayerUtility: ImageBlend", - "LayerUtility: ImageBlend V2", - "LayerUtility: ImageBlendAdvance", - "LayerUtility: ImageBlendAdvance V2", - "LayerUtility: ImageBlendAdvance V3", - "LayerUtility: ImageChannelMerge", - "LayerUtility: ImageChannelSplit", - "LayerUtility: ImageCombineAlpha", - "LayerUtility: ImageHub", - "LayerUtility: ImageMaskScaleAs", - "LayerUtility: ImageMaskScaleAsV2", - "LayerUtility: ImageOpacity", - "LayerUtility: ImageReel", - "LayerUtility: ImageReelComposit", - "LayerUtility: ImageRemoveAlpha", - "LayerUtility: ImageScaleByAspectRatio", - "LayerUtility: ImageScaleByAspectRatio V2", - "LayerUtility: ImageScaleRestore", - "LayerUtility: ImageScaleRestore V2", - "LayerUtility: ImageShift", - "LayerUtility: ImageTaggerSave", - "LayerUtility: ImageTaggerSaveV2", - "LayerUtility: Integer", - "LayerUtility: LayerImageTransform", - "LayerUtility: LayerMaskTransform", - "LayerUtility: LoadImagesFromPath", - "LayerUtility: LoadVQAModel", - "LayerUtility: NameToColor", - "LayerUtility: NumberCalculator", - "LayerUtility: NumberCalculatorV2", - "LayerUtility: PrintInfo", - "LayerUtility: PurgeVRAM", - "LayerUtility: PurgeVRAM V2", - "LayerUtility: QueueStop", - "LayerUtility: RGB Value", - "LayerUtility: RandomGenerator", - "LayerUtility: RandomGeneratorV2", - "LayerUtility: RestoreCropBox", - "LayerUtility: RoundedRectangle", - "LayerUtility: Seed", - "LayerUtility: SimpleTextImage", - "LayerUtility: String", - "LayerUtility: StringCondition", - "LayerUtility: SwitchCase", - "LayerUtility: TextBox", - "LayerUtility: TextImage", - "LayerUtility: TextImage V2", - "LayerUtility: TextJoin", - "LayerUtility: TextJoinV2", - "LayerUtility: TextPreseter", - "LayerUtility: VQAPrompt", - "LayerUtility: XY to Percent" - ], - { - "author": "chflame", - "description": "A set of nodes for ComfyUI that can composite layer and mask to achieve Photoshop like functionality.", - "nickname": "LayerStyle", - "title": "LayerStyle", - "title_aux": "ComfyUI Layer Style" - } - ], - "https://github.com/chflame163/ComfyUI_LayerStyle_Advance": [ - [ - "LayerMask: BBoxJoin", - "LayerMask: BenUltra", - "LayerMask: BiRefNetUltra", - "LayerMask: BiRefNetUltraV2", - "LayerMask: DrawBBoxMask", - "LayerMask: DrawBBoxMaskV2", - "LayerMask: EVFSAMUltra", - "LayerMask: Florence2Ultra", - "LayerMask: HumanPartsUltra", - "LayerMask: LoadBenModel", - "LayerMask: LoadBiRefNetModel", - "LayerMask: LoadBiRefNetModelV2", - "LayerMask: LoadFlorence2Model", - "LayerMask: LoadSAM2Model", - "LayerMask: LoadSegmentAnythingModels", - "LayerMask: MaskByDifferent", - "LayerMask: MediapipeFacialSegment", - "LayerMask: ObjectDetectorFL2", - "LayerMask: ObjectDetectorGemini", - "LayerMask: ObjectDetectorGeminiV2", - "LayerMask: ObjectDetectorMask", - "LayerMask: ObjectDetectorYOLO8", - "LayerMask: ObjectDetectorYOLOWorld", - "LayerMask: PersonMaskUltra", - "LayerMask: PersonMaskUltra V2", - "LayerMask: SAM2Ultra", - "LayerMask: SAM2UltraV2", - "LayerMask: SAM2VideoUltra", - "LayerMask: SegmentAnythingUltra", - "LayerMask: SegmentAnythingUltra V2", - "LayerMask: SegmentAnythingUltra V3", - "LayerMask: TransparentBackgroundUltra", - "LayerMask: YoloV8Detect", - "LayerUtility: AddBlindWaterMark", - "LayerUtility: Collage", - "LayerUtility: CreateQRCode", - "LayerUtility: DecodeQRCode", - "LayerUtility: DeepSeekAPI", - "LayerUtility: DeepSeekAPIV2", - "LayerUtility: Florence2Image2Prompt", - "LayerUtility: Gemini", - "LayerUtility: GeminiImageEdit", - "LayerUtility: GeminiV2", - "LayerUtility: GetColorTone", - "LayerUtility: GetColorToneV2", - "LayerUtility: ImageAutoCrop", - "LayerUtility: ImageAutoCrop V2", - "LayerUtility: ImageAutoCrop V3", - "LayerUtility: ImageRewardFilter", - "LayerUtility: JimengI2IAPI", - "LayerUtility: JoyCaption2", - "LayerUtility: JoyCaption2ExtraOptions", - "LayerUtility: JoyCaption2Split", - "LayerUtility: JoyCaptionBeta1", - "LayerUtility: JoyCaptionBeta1ExtraOptions", - "LayerUtility: LaMa", - "LayerUtility: LlamaVision", - "LayerUtility: LoadJoyCaption2Model", - "LayerUtility: LoadJoyCaptionBeta1Model", - "LayerUtility: LoadPSD", - "LayerUtility: LoadSmolLM2Model", - "LayerUtility: LoadSmolVLMModel", - "LayerUtility: PhiPrompt", - "LayerUtility: PromptEmbellish", - "LayerUtility: PromptTagger", - "LayerUtility: QWenImage2Prompt", - "LayerUtility: SD3NegativeConditioning", - "LayerUtility: SaveImagePlus", - "LayerUtility: SaveImagePlusV2", - "LayerUtility: ShowBlindWaterMark", - "LayerUtility: SmolLM2", - "LayerUtility: SmolVLM", - "LayerUtility: UserPromptGeneratorReplaceWord", - "LayerUtility: UserPromptGeneratorTxt2ImgPrompt", - "LayerUtility: UserPromptGeneratorTxt2ImgPromptWithReference", - "LayerUtility: ZhipuGLM4", - "LayerUtility: ZhipuGLM4V" - ], - { - "author": "chflame", - "description": "A set of nodes for ComfyUI that can composite layer and mask to achieve Photoshop like functionality.", - "nickname": "LayerStyle", - "title": "LayerStyle", - "title_aux": "ComfyUI_LayerStyle_Advance" - } - ], - "https://github.com/chflame163/ComfyUI_MSSpeech_TTS": [ - [ - "Input Trigger", - "MicrosoftSpeech_TTS", - "Play Sound", - "Play Sound (loop)" - ], - { - "title_aux": "ComfyUI_MSSpeech_TTS" - } - ], - "https://github.com/chflame163/ComfyUI_OmniGen_Wrapper": [ - [ - "dzOmniGenWrapper" - ], - { - "title_aux": "ComfyUI_OmniGen_Wrapper" - } - ], - "https://github.com/chflame163/ComfyUI_WordCloud": [ - [ - "ComfyWordCloud", - "LoadTextFile", - "RGB_Picker" - ], - { - "title_aux": "ComfyUI_WordCloud" - } - ], - "https://github.com/chibiace/ComfyUI-Chibi-Nodes": [ - [ - "ConditionText", - "ConditionTextMulti", - "ConditionTextPrompts", - "ImageAddText", - "ImageSimpleResize", - "ImageSizeInfo", - "ImageTool", - "Int2String", - "LoadEmbedding", - "LoadImageExtended", - "Loader", - "Prompts", - "RandomResolutionLatent", - "SaveImages", - "SeedGenerator", - "SimpleSampler", - "TextSplit", - "Textbox", - "Wildcards" - ], - { - "title_aux": "ComfyUI-Chibi-Nodes" - } - ], - "https://github.com/choey/Comfy-Topaz": [ - [ - "TopazPhotoAI", - "TopazSharpenSettings", - "TopazUpscaleSettings" - ], - { - "title_aux": "Comfy-Topaz" - } - ], - "https://github.com/chou18194766xx/comfyui-EncryptSave": [ - [ - "EncryptSaveAES" - ], - { - "title_aux": "comfyui-EncryptSave" - } - ], - "https://github.com/chou18194766xx/comfyui_EncryptPreview": [ - [ - "EncryptPreviewImage" - ], - { - "title_aux": "comfyui_EncryptPreview" - } - ], - "https://github.com/chri002/ComfyUI_depthMapOperation": [ - [ - "CleanPoints (KDTree)", - "CloudPointsInfo", - "CubeLimit", - "Export to PLY", - "ImageToPoints", - "ImageToPoints (Legacy)", - "ImageToPoints (Torch)", - "Import PLY", - "InterpolatePoints (KDTree)", - "PointsToImage (Orthographic)", - "PointsToImage (Projection)", - "PointsToImage advance (DEBUG)", - "PointsToImage advance (Orthographic)", - "PointsToImage advance (Projection)", - "TransformPoints" - ], - { - "title_aux": "ComfyUI_depthMapOperation" - } - ], - "https://github.com/chris-arsenault/ComfyUI-AharaNodes": [ - [ - "FrameSegmenter", - "FrameSegmenterIndexer", - "RepeatSampler", - "RepeatSamplerConfigNode", - "RepeatSamplerConfigPatchLatent", - "RepeatSamplerConfigPatchModel" - ], - { - "title_aux": "ComfyUI-AharaNodes" - } - ], - "https://github.com/chris-the-wiz/EmbeddingsCurveEditor_ComfyUI": [ - [ - "Embeddings Curve Editor" - ], - { - "title_aux": "EmbeddingsCurveEditor_ComfyUI" - } - ], - "https://github.com/chrisfreilich/virtuoso-nodes": [ - [ - "BlackAndWhite", - "BlendIf", - "BlendModes", - "ColorBalance", - "ColorBalanceAdvanced", - "GaussianBlur", - "GaussianBlurDepth", - "HueSat", - "HueSatAdvanced", - "LensBlur", - "LensBlurDepth", - "Levels", - "MergeRGB", - "MotionBlur", - "MotionBlurDepth", - "SelectiveColor", - "SolidColor", - "SolidColorHSV", - "SolidColorRGB", - "SplitRGB" - ], - { - "author": "Chris Freilich", - "description": "This extension provides a \"Levels\" node.", - "nickname": "Virtuoso Pack - Contrast", - "title": "Virtuoso Pack - Contrast", - "title_aux": "Virtuoso Nodes for ComfyUI" - } - ], - "https://github.com/chrisgoringe/cg-image-filter": [ - [ - "Any List to String", - "Batch from Image List", - "Image Filter", - "Image List From Batch", - "Mask Image Filter", - "Masked Section", - "Pick from List", - "Split String by Commas", - "String List from Strings", - "String to Float", - "String to Int", - "Text Image Filter", - "Text Image Filter with Extras" - ], - { - "author": "chrisgoringe", - "description": "A custom node that pauses the flow while you choose which image or images to pass on to the rest of the workflow. Simplified and improved version of cg-image-picker.", - "nickname": "Image Filter", - "title": "Image Filter", - "title_aux": "Image Filter" - } - ], - "https://github.com/chrisgoringe/cg-noisetools": [ - [ - "Batch Noise Simulate", - "Mix Noise", - "Seperable Batch Noise", - "Shape Noise", - "Split Sigmas with Rewind" - ], - { - "title_aux": "Noise variation and batch noise tools" - } - ], - "https://github.com/chrisgoringe/cg-use-everywhere": [ - [ - "Seed Everywhere" - ], - { - "nodename_pattern": "(^(Prompts|Anything) Everywhere|Simple String)", - "title_aux": "Use Everywhere (UE Nodes)" - } - ], - "https://github.com/chrissy0/chris-comfyui-nodes": [ - [ - "PadImageSquare" - ], - { - "title_aux": "chris-comfyui-nodes" - } - ], - "https://github.com/christian-byrne/audio-separation-nodes-comfyui": [ - [ - "AudioCombine", - "AudioCrop", - "AudioGetTempo", - "AudioSeparation", - "AudioSpeedShift", - "AudioTempoMatch", - "AudioVideoCombine" - ], - { - "title_aux": "audio-separation-nodes-comfyui" - } - ], - "https://github.com/christian-byrne/claude-code-comfyui-nodes": [ - [ - "ClaudeCodeArguments", - "ClaudeCodeContext", - "ClaudeCodeExecute", - "ClaudeCodeMCP", - "ClaudeCodeMemory", - "ClaudeCodeReader", - "ClaudeCodeTools", - "ClaudeRedditScraper" - ], - { - "title_aux": "Claude Code ComfyUI Nodes" - } - ], - "https://github.com/christian-byrne/img2colors-comfyui-node": [ - [ - "bmy_Img2ColorNode" - ], - { - "author": "christian-byrne", - "description": "", - "nickname": "img2color", - "title": "Img2Color Node - Detect and describe color palettes in images", - "title_aux": "Img2color - Extract Colors from Image" - } - ], - "https://github.com/christian-byrne/img2txt-comfyui-nodes": [ - [ - "img2txt BLIP/Llava Multimodel Tagger" - ], - { - "author": "christian-byrne", - "title": "Img2Txt auto captioning", - "title_aux": "img2txt-comfyui-nodes" - } - ], - "https://github.com/christian-byrne/size-match-compositing-nodes": [ - [ - "Composite Alpha Layer", - "Size Match Images/Masks" - ], - { - "title_aux": "Node - Size Matcher" - } - ], - "https://github.com/christian-byrne/youtube-dl-comfyui": [ - [ - "YoutubeDL" - ], - { - "title_aux": "youtube-dl-comfyui" - } - ], - "https://github.com/chuchu114514/comfyui_proportion_solver": [ - [ - "ProportionSolver", - "ProportionSolverAdvanced" - ], - { - "title_aux": "comfyui_proportion_solver" - } - ], - "https://github.com/chuchu114514/comfyui_text_list_stepper": [ - [ - "TextListProcessor_Gemini" - ], - { - "title_aux": "comfyui_text_list_stepper" - } - ], - "https://github.com/chyer/Chye-ComfyUI-Toolset": [ - [ - "CYHARRHalationNode", - "CYHChromaticAberrationNode", - "CYHFilmGrainNode", - "CYHFolderFilenameBuilderNode", - "CYHGlobalColorGradingNode", - "CYHLatentFluxAspectRatio", - "CYHLatentPhoneAspectRatio", - "CYHLatentQwenAspectRatio", - "CYHLatentSDXLAspectRatio", - "CYHLatentSocialAspectRatio", - "CYHLatentVideoAspectRatio", - "CYHResolutionMultiplierNode", - "PromptEnhancer", - "PromptEnhancerEditable", - "PromptToolsSetup" - ], - { - "title_aux": "Chye ComfyUI Toolset" - } - ], - "https://github.com/ciga2011/ComfyUI-MarkItDown": [ - [ - "WIZ_AUDIO2MARKDOWN", - "WIZ_EXCEL2MARKDOWN", - "WIZ_HTML2MARKDOWN", - "WIZ_IMAGE2MARKDOWN", - "WIZ_IPYNB2MARKDOWN", - "WIZ_LLM_CLIENT", - "WIZ_MARKITDOWN", - "WIZ_PDF2MARKDOWN", - "WIZ_POWERPOINT2MARKDOWN", - "WIZ_WORD2MARKDOWN" - ], - { - "title_aux": "ComfyUI MarkItDown" - } - ], - "https://github.com/ciga2011/ComfyUI-Pollinations": [ - [ - "PollinationsNode" - ], - { - "title_aux": "ComfyUI Pollinations" - } - ], - "https://github.com/ciga2011/ComfyUI-PromptOptimizer": [ - [ - "PromptOptimizer" - ], - { - "title_aux": "ComfyUI Prompt Optimizer" - } - ], - "https://github.com/ciri/comfyui-model-downloader": [ - [ - "Auto Model Downloader", - "CivitAI Downloader", - "HF Downloader" - ], - { - "title_aux": "ComfyUI Model Downloader" - } - ], - "https://github.com/citronlegacy/ComfyUI-CitronNodes": [ - [ - "GetDateTime", - "nodes", - "project" - ], - { - "title_aux": "ComfyUI-CitronNodes" - } - ], - "https://github.com/city96/ComfyUI-GGUF": [ - [ - "CLIPLoaderGGUF", - "DualCLIPLoaderGGUF", - "QuadrupleCLIPLoaderGGUF", - "TripleCLIPLoaderGGUF", - "UnetLoaderGGUF", - "UnetLoaderGGUFAdvanced" - ], - { - "preemptions": [ - "CLIPLoaderGGUF", - "DualCLIPLoaderGGUF", - "TripleCLIPLoaderGGUF", - "UnetLoaderGGUF", - "UnetLoaderGGUFAdvanced" - ], - "title_aux": "ComfyUI-GGUF" - } - ], - "https://github.com/city96/ComfyUI_ColorMod": [ - [ - "CV2Tonemap", - "CV2TonemapDrago", - "CV2TonemapDurand", - "CV2TonemapMantiuk", - "CV2TonemapReinhard", - "ColorModCompress", - "ColorModEdges", - "ColorModMove", - "ColorModPivot", - "ColorspaceConvert", - "HDRCreate", - "HDRExposureFusion", - "LoadImageHDR", - "LoadImageHighPrec", - "PreviewImageHighPrec", - "SaveImageHDR", - "SaveImageHighPrec" - ], - { - "title_aux": "ComfyUI_ColorMod" - } - ], - "https://github.com/city96/ComfyUI_DiT": [ - [ - "DiTCheckpointLoader", - "DiTCheckpointLoaderSimple", - "DiTLabelCombine", - "DiTLabelSelect", - "DiTSampler" - ], - { - "title_aux": "ComfyUI_DiT [WIP]" - } - ], - "https://github.com/city96/ComfyUI_ExtraModels": [ - [ - "DiTCondLabelEmpty", - "DiTCondLabelSelect", - "DitCheckpointLoader", - "EmptyDCAELatentImage", - "EmptySanaLatentImage", - "ExtraVAELoader", - "GemmaLoader", - "GemmaTextEncode", - "HYDiTCheckpointLoader", - "HYDiTSrcSizeCond", - "HYDiTTextEncode", - "HYDiTTextEncodeSimple", - "HYDiTTextEncoderLoader", - "MiaoBiCLIPLoader", - "MiaoBiDiffusersLoader", - "OverrideCLIPDevice", - "OverrideVAEDevice", - "PixArtCheckpointLoader", - "PixArtCheckpointLoaderSimple", - "PixArtControlNetCond", - "PixArtLoraLoader", - "PixArtResolutionCond", - "PixArtResolutionSelect", - "PixArtT5FromSD3CLIP", - "PixArtT5TextEncode", - "SanaCheckpointLoader", - "SanaResolutionCond", - "SanaResolutionSelect", - "SanaTextEncode", - "T5TextEncode", - "T5v11Loader" - ], - { - "title_aux": "Extra Models for ComfyUI" - } - ], - "https://github.com/city96/ComfyUI_NetDist": [ - [ - "CombineImageBatch", - "FetchRemote", - "LoadCurrentWorkflowJSON", - "LoadDiskWorkflowJSON", - "LoadImageUrl", - "LoadLatentNumpy", - "LoadLatentUrl", - "RemoteChainEnd", - "RemoteChainStart", - "RemoteQueueSimple", - "RemoteQueueWorker", - "SaveDiskWorkflowJSON", - "SaveImageUrl", - "SaveLatentNumpy" - ], - { - "title_aux": "ComfyUI_NetDist" - } - ], - "https://github.com/city96/SD-Latent-Interposer": [ - [ - "LatentInterposer" - ], - { - "title_aux": "Latent-Interposer" - } - ], - "https://github.com/city96/SD-Latent-Upscaler": [ - [ - "LatentUpscaler" - ], - { - "title_aux": "SD-Latent-Upscaler" - } - ], - "https://github.com/civen-cn/ComfyUI-PaddleOcr": [ - [ - "OcrBlur", - "OcrBoxMask", - "OcrImageText" - ], - { - "title_aux": "ComfyUI-PaddleOcr" - } - ], - "https://github.com/civen-cn/ComfyUI-Whisper-Translator": [ - [ - "Add Subtitles To FramesX", - "Apply WhisperX" - ], - { - "title_aux": "ComfyUI Whisper Translator" - } - ], - "https://github.com/civitai/civitai_comfy_nodes": [ - [ - "CivitAI_Checkpoint_Loader", - "CivitAI_Lora_Loader" - ], - { - "title_aux": "Civitai Comfy Nodes" - } - ], - "https://github.com/cjj198909/comfy_openai_image_api_azure": [ - [ - "OpenAI Image API" - ], - { - "title_aux": "OpenAI/Azure OpenAI Image API" - } - ], - "https://github.com/claptrap0/ComfyUI_LLM_Hub": [ - [ - "Generated_Output", - "LLM_Hub", - "LLM_Settings" - ], - { - "title_aux": "ComfyUI_LLM_Hub" - } - ], - "https://github.com/claussteinmassl/ComfyUI-CS-CustomNodes": [ - [ - "CS Transform" - ], - { - "title_aux": "CS Transform Node for ComfyUI" - } - ], - "https://github.com/cleanlii/comfyui-dalle-integration": [ - [ - "DalleImageEdit", - "DalleImageGeneration", - "DalleImageVariation" - ], - { - "title_aux": "DalleImageNodes - OpenAI DALL\u00b7E Nodes for ComfyUI" - } - ], - "https://github.com/clhui/ComfyUi-clh-Tool": [ - [ - "EchartGraph_clh", - "EchartOptionByPath_clh", - "EchartOption_clh", - "INTConstant_clh", - "JavaScript_clh", - "JoinStringMulti_clh", - "MathExpression_clh", - "SetRedis|clh", - "ShowText_clh", - "SomethingToString_clh", - "String2FatLabels_clh", - "String2Image_clh", - "StringConstant_clh" - ], - { - "author": "Dr.Lt.Data", - "description": "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.", - "nickname": "CLH Simple Tool", - "title": "CLH simple Tool", - "title_aux": "Clh Tool for ComfyUI" - } - ], - "https://github.com/clouddreamfly/ComfyUI-PromptWrapper": [ - [ - "CombinePrompt", - "CustomPrompt", - "DrawStylePrompt", - "GeneratePrompt", - "InputPrompt", - "LightPrompt", - "MultiCombinePrompt", - "MultiReplacePrompt", - "NegativePrompt", - "PortraitCosmeticPrompt", - "PortraitFashionPrompt", - "PortraitPosePrompt", - "PortraitPrompt", - "PortraitSkinPrompt", - "PreviewPrompt", - "PromptTranslation", - "RandomLinePrompt", - "RandomsPrompt", - "RandomsWeightPrompt", - "ReplacePrompt", - "SavePrompt", - "SceneryPrompt" - ], - { - "title_aux": "ComfyUI-PromptWrapper" - } - ], - "https://github.com/cloudkoala/comfyui-koala": [ - [ - "AspectRatioLatentNode", - "SaveImageAnywhere", - "SaveMeshAnywhere" - ], - { - "title_aux": "comfyui-koala" - } - ], - "https://github.com/cluny85/ComfyUI-Scripting-Tools": [ - [ - "EnhancedUUIDGeneratorNode", - "UUIDGeneratorNode" - ], - { - "title_aux": "ComfyUI-Scripting-Tools" - } - ], - "https://github.com/cmdicely/simple_image_to_palette": [ - [ - "Example" - ], - { - "title_aux": "Simple Image To Palette" - } - ], - "https://github.com/cnnmmd/comfyui_xoxxox_cnnmmd": [ - [ - "Xoxxox_CnvDat", - "Xoxxox_CnvSen", - "Xoxxox_CnvTxt", - "Xoxxox_CnvVce", - "Xoxxox_DlyGet", - "Xoxxox_DlySet", - "Xoxxox_GenImg", - "Xoxxox_GenTxt", - "Xoxxox_GetAud", - "Xoxxox_GetDir", - "Xoxxox_GetDis", - "Xoxxox_GetImg", - "Xoxxox_GetMem", - "Xoxxox_GetTxt", - "Xoxxox_IniFlw", - "Xoxxox_LogNum", - "Xoxxox_LogTxt", - "Xoxxox_PutTxt", - "Xoxxox_RcvVce", - "Xoxxox_RepTxt", - "Xoxxox_RunFlw", - "Xoxxox_SenSlc", - "Xoxxox_SenTxt", - "Xoxxox_SetAud", - "Xoxxox_SetDir", - "Xoxxox_SetDis", - "Xoxxox_SetImg", - "Xoxxox_SetMem", - "Xoxxox_SetNil", - "Xoxxox_SetTxt", - "Xoxxox_SndVce", - "Xoxxox_SwtImg", - "Xoxxox_TrnBak" - ], - { - "title_aux": "cnnmmd: comfyui_xoxxox_cnnmmd" - } - ], - "https://github.com/codeprimate/ComfyUI-MaskContourProcessor": [ - [ - "MaskContourProcessor" - ], - { - "title_aux": "ComfyUI Mask Contour Processor" - } - ], - "https://github.com/comfy-deploy/comfyui-llm-toolkit": [ - [ - "APIKeyInput", - "AudioDurationFrames", - "BFLProviderNode", - "BananaTaskGenerator", - "BlankImage", - "CheckImageEmpty", - "ConfigGenerateImage", - "ConfigGenerateImageBFL", - "ConfigGenerateImageFluxDev", - "ConfigGenerateImageGemini", - "ConfigGenerateImageOpenAI", - "ConfigGenerateImagePortrait", - "ConfigGenerateImageSeedanceEditV3", - "ConfigGenerateMusic", - "ConfigGenerateSpeech", - "ConfigGenerateVideo", - "ConfigGenerateVideoHailuoI2VPro", - "ConfigGenerateVideoHailuoI2VStandard", - "ConfigGenerateVideoHailuoT2VPro", - "ConfigGenerateVideoHailuoT2VStandard", - "ConfigGenerateVideoKlingI2VMaster", - "ConfigGenerateVideoKlingI2VPro", - "ConfigGenerateVideoKlingI2VStandard", - "ConfigGenerateVideoSeedanceProI2V", - "ConfigGenerateVideoSeedanceProT2V", - "ConfigGenerateVideoVeo2I2V", - "ConfigGenerateVideoVeo2T2V", - "ConfigGenerateVideoVeo3", - "ConfigGenerateVideoVeo3Fast", - "Display_Text", - "FramesToSeconds", - "GeminiProviderNode", - "GenerateImage", - "GenerateLyrics", - "GenerateMusic", - "GenerateSpeech", - "GenerateVideo", - "GroqProviderNode", - "HighLowSNR", - "ImageComparer", - "JoinStringsMulti", - "LLMPromptManager", - "LLMToolkitProviderSelector", - "LLMToolkitTextGenerator", - "LLMToolkitTextGeneratorStream", - "LoadAudioFromPath", - "LoadVideoFromPath", - "LocalTransformersProviderNode", - "LocalVLLMProviderNode", - "OpenAIProviderNode", - "PlayRandomSound", - "PreviewImageLogic", - "PreviewOutputs", - "PreviewVideo", - "PromptManager", - "ResolutionSelector", - "StylePromptGenerator", - "SunoProviderSelector", - "SwitchAny", - "SwitchAnyRoute", - "SwitchAnyRoute_wANY", - "SwitchAny_wANY", - "SystemPromptTaskGenerator", - "TestAPIKeyContext", - "UpscaleVideo", - "WaveSpeedProviderNode" - ], - { - "author": "ComfyDeploy", - "description": "llm toolkit", - "nickname": "llm_toolkit", - "title": "llm toolkit", - "title_aux": "ComfyUI LLM Toolkit" - } - ], - "https://github.com/comfyanonymous/ComfyUI": [ - [ - "AddNoise", - "AlignYourStepsScheduler", - "AudioEncoderEncode", - "AudioEncoderLoader", - "BasicGuider", - "BasicScheduler", - "BetaSamplingScheduler", - "CFGGuider", - "CFGNorm", - "CFGZeroStar", - "CLIPLoader", - "CLIPMergeAdd", - "CLIPMergeSimple", - "CLIPMergeSubtract", - "CLIPSave", - "CLIPSetLastLayer", - "CLIPTextEncode", - "CLIPTextEncodeControlnet", - "CLIPTextEncodeFlux", - "CLIPTextEncodeHiDream", - "CLIPTextEncodeHunyuanDiT", - "CLIPTextEncodeLumina2", - "CLIPTextEncodePixArtAlpha", - "CLIPTextEncodeSD3", - "CLIPTextEncodeSDXL", - "CLIPTextEncodeSDXLRefiner", - "CLIPVisionEncode", - "CLIPVisionLoader", - "Canny", - "CheckpointLoader", - "CheckpointLoaderSimple", - "CheckpointSave", - "ConditioningAverage", - "ConditioningCombine", - "ConditioningConcat", - "ConditioningSetArea", - "ConditioningSetAreaPercentage", - "ConditioningSetAreaPercentageVideo", - "ConditioningSetAreaStrength", - "ConditioningSetMask", - "ConditioningSetTimestepRange", - "ConditioningStableAudio", - "ConditioningZeroOut", - "ControlNetApply", - "ControlNetApplyAdvanced", - "ControlNetApplySD3", - "ControlNetInpaintingAliMamaApply", - "ControlNetLoader", - "CosmosImageToVideoLatent", - "CosmosPredict2ImageToVideoLatent", - "CreateVideo", - "CropMask", - "DiffControlNetLoader", - "DifferentialDiffusion", - "DiffusersLoader", - "DisableNoise", - "DualCFGGuider", - "DualCLIPLoader", - "EmptyCosmosLatentVideo", - "EmptyHunyuanLatentVideo", - "EmptyImage", - "EmptyLTXVLatentVideo", - "EmptyLatentAudio", - "EmptyLatentHunyuan3Dv2", - "EmptyLatentImage", - "EmptyMochiLatentVideo", - "EmptySD3LatentImage", - "ExponentialScheduler", - "ExtendIntermediateSigmas", - "FeatherMask", - "FlipSigmas", - "FluxDisableGuidance", - "FluxGuidance", - "FluxKontextImageScale", - "FluxKontextMaxImageNode", - "FluxKontextMultiReferenceLatentMethod", - "FluxKontextProImageNode", - "FluxProCannyNode", - "FluxProDepthNode", - "FluxProExpandNode", - "FluxProFillNode", - "FluxProImageNode", - "FluxProUltraImageNode", - "FreSca", - "FreeU", - "FreeU_V2", - "GITSScheduler", - "GLIGENLoader", - "GLIGENTextBoxApply", - "GeminiImageNode", - "GeminiInputFiles", - "GeminiNode", - "GetImageSize", - "GetVideoComponents", - "GrowMask", - "Hunyuan3Dv2Conditioning", - "Hunyuan3Dv2ConditioningMultiView", - "HunyuanImageToVideo", - "HyperTile", - "HypernetworkLoader", - "ImageAddNoise", - "ImageBatch", - "ImageBlend", - "ImageBlur", - "ImageColorToMask", - "ImageCompositeMasked", - "ImageCrop", - "ImageFlip", - "ImageFromBatch", - "ImageInvert", - "ImageOnlyCheckpointLoader", - "ImageOnlyCheckpointSave", - "ImagePadForOutpaint", - "ImageQuantize", - "ImageRGBToYUV", - "ImageRotate", - "ImageScale", - "ImageScaleBy", - "ImageScaleToTotalPixels", - "ImageSharpen", - "ImageStitch", - "ImageToMask", - "ImageUpscaleWithModel", - "ImageYUVToRGB", - "InpaintModelConditioning", - "InstructPixToPixConditioning", - "InvertMask", - "JoinImageWithAlpha", - "KSampler", - "KSamplerAdvanced", - "KSamplerSelect", - "KarrasScheduler", - "KlingCameraControlI2VNode", - "KlingCameraControlT2VNode", - "KlingCameraControls", - "KlingDualCharacterVideoEffectNode", - "KlingImage2VideoNode", - "KlingImageGenerationNode", - "KlingLipSyncAudioToVideoNode", - "KlingLipSyncTextToVideoNode", - "KlingSingleImageVideoEffectNode", - "KlingStartEndFrameNode", - "KlingTextToVideoNode", - "KlingVideoExtendNode", - "KlingVirtualTryOnNode", - "LTXVAddGuide", - "LTXVConditioning", - "LTXVCropGuides", - "LTXVImgToVideo", - "LTXVPreprocess", - "LTXVScheduler", - "LaplaceScheduler", - "LatentAdd", - "LatentApplyOperation", - "LatentApplyOperationCFG", - "LatentBatch", - "LatentBatchSeedBehavior", - "LatentBlend", - "LatentComposite", - "LatentCompositeMasked", - "LatentConcat", - "LatentCrop", - "LatentCut", - "LatentFlip", - "LatentFromBatch", - "LatentInterpolate", - "LatentMultiply", - "LatentOperationSharpen", - "LatentOperationTonemapReinhard", - "LatentRotate", - "LatentSubtract", - "LatentUpscale", - "LatentUpscaleBy", - "Load3D", - "Load3DAnimation", - "LoadAudio", - "LoadImage", - "LoadImageMask", - "LoadImageOutput", - "LoadImageSetFromFolderNode", - "LoadImageTextSetFromFolderNode", - "LoadLatent", - "LoadVideo", - "LoraLoader", - "LoraLoaderModelOnly", - "LoraModelLoader", - "LoraSave", - "LossGraphNode", - "LotusConditioning", - "LumaConceptsNode", - "LumaImageModifyNode", - "LumaImageNode", - "LumaImageToVideoNode", - "LumaReferenceNode", - "LumaVideoNode", - "Mahiro", - "MaskComposite", - "MaskPreview", - "MaskToImage", - "MinimaxHailuoVideoNode", - "MinimaxImageToVideoNode", - "MinimaxSubjectToVideoNode", - "MinimaxTextToVideoNode", - "ModelComputeDtype", - "ModelMergeAdd", - "ModelMergeAuraflow", - "ModelMergeBlocks", - "ModelMergeCosmos14B", - "ModelMergeCosmos7B", - "ModelMergeCosmosPredict2_14B", - "ModelMergeCosmosPredict2_2B", - "ModelMergeFlux1", - "ModelMergeLTXV", - "ModelMergeMochiPreview", - "ModelMergeQwenImage", - "ModelMergeSD1", - "ModelMergeSD2", - "ModelMergeSD35_Large", - "ModelMergeSD3_2B", - "ModelMergeSDXL", - "ModelMergeSimple", - "ModelMergeSubtract", - "ModelMergeWAN2_1", - "ModelPatchLoader", - "ModelSamplingAuraFlow", - "ModelSamplingContinuousEDM", - "ModelSamplingContinuousV", - "ModelSamplingDiscrete", - "ModelSamplingFlux", - "ModelSamplingLTXV", - "ModelSamplingSD3", - "ModelSamplingStableCascade", - "ModelSave", - "MoonvalleyImg2VideoNode", - "MoonvalleyTxt2VideoNode", - "MoonvalleyVideo2VideoNode", - "Morphology", - "OpenAIChatConfig", - "OpenAIChatNode", - "OpenAIDalle2", - "OpenAIDalle3", - "OpenAIGPTImage1", - "OpenAIInputFiles", - "OptimalStepsScheduler", - "PatchModelAddDownscale", - "PerpNeg", - "PerpNegGuider", - "PerturbedAttentionGuidance", - "PhotoMakerEncode", - "PhotoMakerLoader", - "PikaImageToVideoNode2_2", - "PikaScenesV2_2", - "PikaStartEndFrameNode2_2", - "PikaTextToVideoNode2_2", - "Pikadditions", - "Pikaffects", - "Pikaswaps", - "PixverseImageToVideoNode", - "PixverseTemplateNode", - "PixverseTextToVideoNode", - "PixverseTransitionVideoNode", - "PolyexponentialScheduler", - "PorterDuffImageComposite", - "Preview3D", - "Preview3DAnimation", - "PreviewAny", - "PreviewAudio", - "PreviewImage", - "PrimitiveBoolean", - "PrimitiveFloat", - "PrimitiveInt", - "PrimitiveString", - "PrimitiveStringMultiline", - "QuadrupleCLIPLoader", - "QwenImageDiffsynthControlnet", - "RandomNoise", - "RebatchImages", - "RebatchLatents", - "RecordAudio", - "RecraftColorRGB", - "RecraftControls", - "RecraftCreativeUpscaleNode", - "RecraftCrispUpscaleNode", - "RecraftImageInpaintingNode", - "RecraftImageToImageNode", - "RecraftRemoveBackgroundNode", - "RecraftReplaceBackgroundNode", - "RecraftStyleV3DigitalIllustration", - "RecraftStyleV3InfiniteStyleLibrary", - "RecraftStyleV3LogoRaster", - "RecraftStyleV3RealisticImage", - "RecraftTextToImageNode", - "RecraftTextToVectorNode", - "RecraftVectorizeImageNode", - "ReferenceLatent", - "RenormCFG", - "RepeatImageBatch", - "RepeatLatentBatch", - "RescaleCFG", - "ResizeAndPadImage", - "Rodin3D_Detail", - "Rodin3D_Regular", - "Rodin3D_Sketch", - "Rodin3D_Smooth", - "RunwayFirstLastFrameNode", - "RunwayImageToVideoNodeGen3a", - "RunwayImageToVideoNodeGen4", - "RunwayTextToImageNode", - "SDTurboScheduler", - "SD_4XUpscale_Conditioning", - "SV3D_Conditioning", - "SVD_img2vid_Conditioning", - "SamplerCustom", - "SamplerCustomAdvanced", - "SamplerDPMAdaptative", - "SamplerDPMPP_2M_SDE", - "SamplerDPMPP_2S_Ancestral", - "SamplerDPMPP_3M_SDE", - "SamplerDPMPP_SDE", - "SamplerER_SDE", - "SamplerEulerAncestral", - "SamplerEulerAncestralCFGPP", - "SamplerLMS", - "SamplerSASolver", - "SamplingPercentToSigma", - "SaveAnimatedPNG", - "SaveAnimatedWEBP", - "SaveAudio", - "SaveAudioMP3", - "SaveAudioOpus", - "SaveGLB", - "SaveImage", - "SaveImageWebsocket", - "SaveLatent", - "SaveLoRANode", - "SaveSVGNode", - "SaveVideo", - "SaveWEBM", - "SelfAttentionGuidance", - "SetFirstSigma", - "SetLatentNoiseMask", - "SetUnionControlNetType", - "SkipLayerGuidanceDiT", - "SkipLayerGuidanceDiTSimple", - "SkipLayerGuidanceSD3", - "SolidMask", - "SplitImageWithAlpha", - "SplitSigmas", - "SplitSigmasDenoise", - "StabilityStableImageSD_3_5Node", - "StabilityStableImageUltraNode", - "StabilityUpscaleConservativeNode", - "StabilityUpscaleCreativeNode", - "StabilityUpscaleFastNode", - "StableCascade_EmptyLatentImage", - "StableCascade_StageB_Conditioning", - "StableCascade_StageC_VAEEncode", - "StableCascade_SuperResolutionControlnet", - "StableZero123_Conditioning", - "StableZero123_Conditioning_Batched", - "StubConstantImage", - "StubFloat", - "StubImage", - "StubInt", - "StubMask", - "StyleModelApply", - "StyleModelLoader", - "T5TokenizerOptions", - "TCFG", - "TestAccumulateNode", - "TestAccumulationGetItemNode", - "TestAccumulationGetLengthNode", - "TestAccumulationHeadNode", - "TestAccumulationSetItemNode", - "TestAccumulationTailNode", - "TestAccumulationToListNode", - "TestAsyncBatchProcessing", - "TestAsyncConcurrentLimit", - "TestAsyncError", - "TestAsyncLazyCheck", - "TestAsyncProgressUpdate", - "TestAsyncResourceUser", - "TestAsyncTimeout", - "TestAsyncValidation", - "TestAsyncValidationError", - "TestBoolOperationNode", - "TestCustomIsChanged", - "TestCustomValidation1", - "TestCustomValidation2", - "TestCustomValidation3", - "TestCustomValidation4", - "TestCustomValidation5", - "TestDynamicAsyncGeneration", - "TestDynamicDependencyCycle", - "TestExecutionBlocker", - "TestFloatConditions", - "TestForLoopClose", - "TestForLoopOpen", - "TestIntConditions", - "TestIntMathOperation", - "TestIsChangedWithConstants", - "TestLazyMixImages", - "TestListToAccumulationNode", - "TestMakeListNode", - "TestMixedExpansionReturns", - "TestOutputNodeWithSocketOutput", - "TestParallelSleep", - "TestSamplingInExpansion", - "TestSleep", - "TestStringConditions", - "TestSyncError", - "TestSyncProgressUpdate", - "TestToBoolNode", - "TestVariadicAverage", - "TestWhileLoopClose", - "TestWhileLoopOpen", - "TextEncodeHunyuanVideo_ImageToVideo", - "TextEncodeQwenImageEdit", - "ThresholdMask", - "TomePatchModel", - "TorchCompileModel", - "TrainLoraNode", - "TripleCLIPLoader", - "TripoConversionNode", - "TripoImageToModelNode", - "TripoMultiviewToModelNode", - "TripoRefineNode", - "TripoRetargetNode", - "TripoRigNode", - "TripoTextToModelNode", - "TripoTextureNode", - "UNETLoader", - "UpscaleModelLoader", - "VAEDecode", - "VAEDecodeAudio", - "VAEDecodeHunyuan3D", - "VAEDecodeTiled", - "VAEEncode", - "VAEEncodeAudio", - "VAEEncodeForInpaint", - "VAEEncodeTiled", - "VAELoader", - "VAESave", - "VPScheduler", - "VideoLinearCFGGuidance", - "VideoTriangleCFGGuidance", - "VoxelToMesh", - "VoxelToMeshBasic", - "WanCameraEmbedding", - "WebcamCapture", - "unCLIPCheckpointLoader", - "unCLIPConditioning" - ], - { - "title_aux": "ComfyUI" - } - ], - "https://github.com/comfyanonymous/ComfyUI_TensorRT": [ - [ - "DYNAMIC_TRT_MODEL_CONVERSION", - "STATIC_TRT_MODEL_CONVERSION", - "TensorRTLoader" - ], - { - "title_aux": "TensorRT Node for ComfyUI" - } - ], - "https://github.com/comfyanonymous/ComfyUI_experiments": [ - [ - "ModelMergeBlockNumber", - "ModelMergeSDXL", - "ModelMergeSDXLDetailedTransformers", - "ModelMergeSDXLTransformers", - "ModelSamplerTonemapNoiseTest", - "ReferenceOnlySimple", - "RescaleClassifierFreeGuidanceTest", - "TonemapNoiseWithRescaleCFG" - ], - { - "title_aux": "ComfyUI_experiments" - } - ], - "https://github.com/comfyuistudio/ComfyUI-Studio-nodes": [ - [ - "AspectRatioImageSize", - "AspectRatioResizeImage", - "MarkdownModelNote" - ], - { - "title_aux": "ComfyUI-Studio-nodes" - } - ], - "https://github.com/comnote-max/builmenlabo": [ - [ - "GeminiPoseAnalyzer", - "LlamaCppAIO", - "LlamaCppCompleteUnload", - "LlamaCppGenerate", - "LlamaCppLoader", - "LlamaCppMemoryInfo", - "LlamaCppSafeUnload", - "LlamaCppUnload", - "MultiControlNetLoader", - "PromptTranslator" - ], - { - "nodename_pattern": "builmenlabo", - "title_aux": "ComfyUI builmenlabo - Unified Package" - } - ], - "https://github.com/concarne000/ComfyUI-Stacker": [ - [ - "StackPopFloat", - "StackPopImage", - "StackPopInt", - "StackPopObject", - "StackPopString", - "StackPushFloat", - "StackPushImage", - "StackPushInt", - "StackPushObject", - "StackPushString" - ], - { - "title_aux": "ComfyUI-Stacker" - } - ], - "https://github.com/concarne000/ConCarneNode": [ - [ - "BingImageGrabber", - "Hermes", - "Zephyr" - ], - { - "title_aux": "ConCarneNode" - } - ], - "https://github.com/conquestace/ComfyUI-ImageUploader": [ - [ - "ImageUploader" - ], - { - "author": "ConquestAce", - "description": "Upload to temporary websites with API.", - "nickname": "Image Uploader", - "title": "Image Uploader", - "title_aux": "Image Uploader" - } - ], - "https://github.com/coreyryanhanson/ComfyQR": [ - [ - "comfy-qr-by-image-size", - "comfy-qr-by-module-size", - "comfy-qr-by-module-split", - "comfy-qr-mask_errors" - ], - { - "title_aux": "ComfyQR" - } - ], - "https://github.com/coreyryanhanson/ComfyQR-scanning-nodes": [ - [ - "comfy-qr-read", - "comfy-qr-validate" - ], - { - "title_aux": "ComfyQR-scanning-nodes" - } - ], - "https://github.com/coulterj/comfyui-svg-visual-normalize": [ - [ - "SVGVisualBoundsNormalize" - ], - { - "title_aux": "ComfyUI SVG Visual Normalize & Margin Node" - } - ], - "https://github.com/cozy-comfyui/cozy_comm": [ - [ - "CozyDiscordPost" - ], - { - "nodename_pattern": " \\(cozy\\)", - "title_aux": "Cozy Communication" - } - ], - "https://github.com/cozymantis/cozy-utils-comfyui-nodes": [ - [ - "Cozy Sampler Options" - ], - { - "title_aux": "Cozy Utils" - } - ], - "https://github.com/cozymantis/human-parser-comfyui-node": [ - [ - "Cozy Human Parser ATR", - "Cozy Human Parser LIP", - "Cozy Human Parser Pascal" - ], - { - "title_aux": "Cozy Human Parser" - } - ], - "https://github.com/cozymantis/pose-generator-comfyui-node": [ - [ - "Cozy Pose Body Reference", - "Cozy Pose Face Reference" - ], - { - "title_aux": "Cozy Reference Pose Generator" - } - ], - "https://github.com/cr7Por/ComfyUI_DepthFlow": [ - [ - "DepthFlowSimple" - ], - { - "title_aux": "ComfyUI_DepthFlow" - } - ], - "https://github.com/craig-tanaka/comfyui_animeseg": [ - [ - "AdvancedAnimeSeg", - "SimpleAnimeSeg" - ], - { - "title_aux": "ComfyUI Anime Segmentation Nodes v1.1.0" - } - ], - "https://github.com/crave33/RenesStuffDanbooruTagGet": [ - [ - "DanbooruTagFetcher" - ], - { - "title_aux": "RenesStuffDanboruTagGet" - } - ], - "https://github.com/crystian/ComfyUI-Crystools": [ - [], - { - "author": "Crystian", - "description": "Plugins for multiples uses, mainly for debugging, you need them! IG: https://www.instagram.com/crystian.ia", - "nickname": "Crystools", - "nodename_pattern": " \\[Crystools\\]$", - "title": "Crystools", - "title_aux": "Crystools" - } - ], - "https://github.com/cuban044/ComfyUI-Veo3-Experimental": [ - [ - "Veo3TextToVideo", - "Veo3ToVHS", - "Veo3VideoPreview" - ], - { - "title_aux": "[Unofficial] ComfyUI-Veo3-Experimental" - } - ], - "https://github.com/cubiq/Block_Patcher_ComfyUI": [ - [ - "FluxBlockPatcherSampler", - "FluxBlockShareKV", - "PlotBlockParams" - ], - { - "title_aux": "Flux blocks patcher sampler" - } - ], - "https://github.com/cubiq/ComfyUI_FaceAnalysis": [ - [ - "FaceAlign", - "FaceAnalysisModels", - "FaceBoundingBox", - "FaceEmbedDistance", - "FaceSegmentation", - "FaceWarp" - ], - { - "title_aux": "Face Analysis for ComfyUI" - } - ], - "https://github.com/cubiq/ComfyUI_IPAdapter_plus": [ - [ - "IPAAdapterFaceIDBatch", - "IPAdapter", - "IPAdapterAdvanced", - "IPAdapterBatch", - "IPAdapterClipVisionEnhancer", - "IPAdapterClipVisionEnhancerBatch", - "IPAdapterCombineEmbeds", - "IPAdapterCombineParams", - "IPAdapterCombineWeights", - "IPAdapterEmbeds", - "IPAdapterEmbedsBatch", - "IPAdapterEncoder", - "IPAdapterFaceID", - "IPAdapterFaceIDKolors", - "IPAdapterFromParams", - "IPAdapterInsightFaceLoader", - "IPAdapterLoadEmbeds", - "IPAdapterMS", - "IPAdapterModelLoader", - "IPAdapterNoise", - "IPAdapterPreciseComposition", - "IPAdapterPreciseCompositionBatch", - "IPAdapterPreciseStyleTransfer", - "IPAdapterPreciseStyleTransferBatch", - "IPAdapterPromptScheduleFromWeightsStrategy", - "IPAdapterRegionalConditioning", - "IPAdapterSaveEmbeds", - "IPAdapterStyleComposition", - "IPAdapterStyleCompositionBatch", - "IPAdapterTiled", - "IPAdapterTiledBatch", - "IPAdapterUnifiedLoader", - "IPAdapterUnifiedLoaderCommunity", - "IPAdapterUnifiedLoaderFaceID", - "IPAdapterWeights", - "IPAdapterWeightsFromStrategy", - "PrepImageForClipVision" - ], - { - "preemptions": [ - "IPAAdapterFaceIDBatch", - "IPAdapter", - "IPAdapterAdvanced", - "IPAdapterBatch", - "IPAdapterClipVisionEnhancer", - "IPAdapterClipVisionEnhancerBatch", - "IPAdapterCombineEmbeds", - "IPAdapterCombineParams", - "IPAdapterCombineWeights", - "IPAdapterEmbeds", - "IPAdapterEmbedsBatch", - "IPAdapterEncoder", - "IPAdapterFaceID", - "IPAdapterFromParams", - "IPAdapterInsightFaceLoader", - "IPAdapterLoadEmbeds", - "IPAdapterMS", - "IPAdapterModelLoader", - "IPAdapterNoise", - "IPAdapterPreciseComposition", - "IPAdapterPreciseCompositionBatch", - "IPAdapterPreciseStyleTransfer", - "IPAdapterPreciseStyleTransferBatch", - "IPAdapterPromptScheduleFromWeightsStrategy", - "IPAdapterRegionalConditioning", - "IPAdapterSaveEmbeds", - "IPAdapterStyleComposition", - "IPAdapterStyleCompositionBatch", - "IPAdapterTiled", - "IPAdapterTiledBatch", - "IPAdapterUnifiedLoader", - "IPAdapterUnifiedLoaderCommunity", - "IPAdapterUnifiedLoaderFaceID", - "IPAdapterWeights", - "IPAdapterWeightsFromStrategy", - "PrepImageForClipVision" - ], - "title_aux": "ComfyUI_IPAdapter_plus" - } - ], - "https://github.com/cubiq/ComfyUI_InstantID": [ - [ - "ApplyInstantID", - "ApplyInstantIDAdvanced", - "ApplyInstantIDControlNet", - "FaceKeypointsPreprocessor", - "InstantIDAttentionPatch", - "InstantIDFaceAnalysis", - "InstantIDModelLoader" - ], - { - "title_aux": "ComfyUI InstantID (Native Support)" - } - ], - "https://github.com/cubiq/ComfyUI_essentials": [ - [ - "ApplyCLIPSeg+", - "BatchCount+", - "CLIPTextEncodeSDXL+", - "ConditioningCombineMultiple+", - "ConsoleDebug+", - "DebugTensorShape+", - "DisplayAny", - "DrawText+", - "ExtractKeyframes+", - "FluxAttentionSeeker+", - "FluxBlocksBuster+", - "FluxSamplerParams+", - "GetImageSize+", - "GuidanceTimestepping+", - "ImageApplyLUT+", - "ImageBatchMultiple+", - "ImageBatchToList+", - "ImageCASharpening+", - "ImageColorMatch+", - "ImageColorMatchAdobe+", - "ImageComposite+", - "ImageCompositeFromMaskBatch+", - "ImageCrop+", - "ImageDesaturate+", - "ImageEnhanceDifference+", - "ImageExpandBatch+", - "ImageFlip+", - "ImageFromBatch+", - "ImageHistogramMatch+", - "ImageListToBatch+", - "ImagePosterize+", - "ImagePreviewFromLatent+", - "ImageRandomTransform+", - "ImageRemoveAlpha+", - "ImageRemoveBackground+", - "ImageResize+", - "ImageSeamCarving+", - "ImageSmartSharpen+", - "ImageTile+", - "ImageToDevice+", - "ImageUntile+", - "InjectLatentNoise+", - "KSamplerVariationsStochastic+", - "KSamplerVariationsWithNoise+", - "LoadCLIPSegModels+", - "LorasForFluxParams+", - "MaskBatch+", - "MaskBlur+", - "MaskBoundingBox+", - "MaskExpandBatch+", - "MaskFix+", - "MaskFlip+", - "MaskFromBatch+", - "MaskFromColor+", - "MaskFromList+", - "MaskFromRGBCMYBW+", - "MaskFromSegmentation+", - "MaskPreview+", - "MaskSmooth+", - "ModelCompile+", - "ModelSamplingSD3Advanced+", - "NoiseFromImage+", - "PixelOEPixelize+", - "PlotParameters+", - "RemBGSession+", - "RemoveLatentMask+", - "SD3AttentionSeekerLG+", - "SD3AttentionSeekerT5+", - "SD3NegativeConditioning+", - "SDXLEmptyLatentSizePicker+", - "SamplerSelectHelper+", - "SchedulerSelectHelper+", - "SimpleComparison+", - "SimpleCondition+", - "SimpleMath+", - "SimpleMathBoolean+", - "SimpleMathCondition+", - "SimpleMathDual+", - "SimpleMathFloat+", - "SimpleMathInt+", - "SimpleMathPercent+", - "SimpleMathSlider+", - "SimpleMathSliderLowRes+", - "TextEncodeForSamplerParams+", - "TransitionMask+", - "TransparentBGSession+" - ], - { - "title_aux": "ComfyUI Essentials" - } - ], - "https://github.com/cubiq/PuLID_ComfyUI": [ - [ - "ApplyPulid", - "ApplyPulidAdvanced", - "PulidEvaClipLoader", - "PulidInsightFaceLoader", - "PulidModelLoader" - ], - { - "title_aux": "PuLID_ComfyUI" - } - ], - "https://github.com/cuongloveit/comfy_http_request": [ - [ - "Send Http Request", - "Send Http request" - ], - { - "title_aux": "comfy_http_request" - } - ], - "https://github.com/curiousjp/ComfyUI-MaskBatchPermutations": [ - [ - "CombinatorialDetailer", - "FlattenAgainstOriginal", - "PermuteMaskBatch" - ], - { - "title_aux": "ComfyUI-MaskBatchPermutations" - } - ], - "https://github.com/cyberhirsch/seb_nodes": [ - [ - "AspectRatioSeb", - "DepthInpaintSeb", - "SaveImageSeb", - "SwitchMasksSeb", - "SwitchSeb", - "UnifiedPrompterSeb" - ], - { - "title_aux": "Seb Nodes" - } - ], - "https://github.com/czcz1024/Comfyui-FaceCompare": [ - [ - "FaceCompare" - ], - { - "author": "czcz1024", - "description": "Face Compare", - "nickname": "Face Compare", - "title": "Face Compare", - "title_aux": "Face Compare" - } - ], - "https://github.com/da2el-ai/ComfyUI-d2-send-eagle": [ - [ - "D2 Send Eagle" - ], - { - "author": "da2el", - "description": "Send images to Eagle, an image management application", - "title": "D2 Send Eagle", - "title_aux": "D2 Send Eagle" - } - ], - "https://github.com/da2el-ai/ComfyUI-d2-size-selector": [ - [ - "D2_SizeSelector" - ], - { - "author": "da2el", - "description": "Easy select image size", - "title": "D2 Size Selector", - "title_aux": "D2 Size Selector" - } - ], - "https://github.com/da2el-ai/ComfyUI-d2-steps": [ - [ - "D2 Refiner Steps", - "D2 Refiner Steps A1111", - "D2 Refiner Steps Tester" - ], - { - "author": "da2el", - "description": "Calculate the steps for the refiner", - "title": "D2 Steps", - "title_aux": "D2 Steps" - } - ], - "https://github.com/da2el-ai/ComfyUI-d2-xyplot-utils": [ - [ - "D2 Checkpoint List", - "D2 Checkpoint Loader", - "D2 Multi Output", - "D2 Prompt SR", - "D2 Regex Switcher" - ], - { - "author": "da2el", - "description": "A parameter output node compatible with qq-nodes-comfyui. It outputs parameters such as Prompt S/R and seed.", - "title": "D2 XYPlot Utils", - "title_aux": "D2 XYPlot Utils" - } - ], - "https://github.com/da2el-ai/D2-SavePSD-ComfyUI": [ - [ - "D2 Apply Alpha Channel", - "D2 Extract Alpha", - "D2 Save PSD" - ], - { - "author": "da2el", - "description": "", - "title": "D2 Save PSD", - "title_aux": "D2-SavePSD-ComfyUI" - } - ], - "https://github.com/da2el-ai/D2-nodes-ComfyUI": [ - [ - "D2 Any Delivery", - "D2 Checkpoint Loader", - "D2 Controlnet Loader", - "D2 Cut By Mask", - "D2 EmptyImage Alpha", - "D2 Filename Template", - "D2 Filename Template2", - "D2 Folder Image Queue", - "D2 Get Image Size", - "D2 Grid Image", - "D2 Image Mask Stack", - "D2 Image Resize", - "D2 Image Stack", - "D2 KSampler", - "D2 KSampler(Advanced)", - "D2 List To String", - "D2 Load Folder Images", - "D2 Load Image", - "D2 Load Lora", - "D2 Model and CLIP Merge SDXL", - "D2 Mosaic Filter", - "D2 Multi Output", - "D2 Paste By Mask", - "D2 Pipe", - "D2 Preview Image", - "D2 Prompt", - "D2 Refiner Steps", - "D2 Refiner Steps A1111", - "D2 Refiner Steps Tester", - "D2 Regex Replace", - "D2 Regex Switcher", - "D2 Resize Calculator", - "D2 Save Image", - "D2 Save Image Eagle", - "D2 Send File Eagle", - "D2 Size Selector", - "D2 Token Counter", - "D2 XY Annotation", - "D2 XY Folder Images", - "D2 XY Grid Image", - "D2 XY List To Plot", - "D2 XY Model List", - "D2 XY Plot", - "D2 XY Plot Easy", - "D2 XY Plot Easy Mini", - "D2 XY Prompt SR", - "D2 XY Prompt SR2", - "D2 XY Seed", - "D2 XY Seed2", - "D2 XY String To Plot", - "D2 XY Upload Image" - ], - { - "author": "da2el", - "description": "A Collection of Handy Custom Nodes for ComfyUI", - "title": "D2 Nodes", - "title_aux": "D2 Nodes ComfyUI" - } - ], - "https://github.com/dadoirie/ComfyUI_Dados_Nodes": [ - [ - "DN_CSVMultiDropDownNode", - "DN_JoyTaggerNode", - "DN_MiaoshouAITaggerNode", - "DN_MultilineString", - "DN_SmolVLMNode", - "DN_TextConcatenateNode", - "DN_TextDropDownNode", - "DN_WildcardPromptEditorNode", - "DN_WildcardsProcessor", - "PinterestFetch", - "inactivePinterestImageNode" - ], - { - "author": "Dado", - "description": "Node with dynamic text inputs for concatenation", - "title": "Text Concatenator", - "title_aux": "ComfyUI_Dados_Nodes" - } - ], - "https://github.com/dafeng012/comfyui-imgmake": [ - [ - "LoadImageListPlus", - "LoadImagesFromPath_lp", - "SaveImage_lp", - "SelectImageName", - "VideoKeyFramesExtractor", - "ebsynth_hecheng", - "ebsynth_main", - "ebsynth_process", - "image2mask", - "video2image" - ], - { - "title_aux": "comfyui-imgmake" - } - ], - "https://github.com/dagthomas/comfyui_dagthomas": [ - [ - "APNLatent", - "CustomPromptLoader", - "DynamicStringCombinerNode", - "FileReaderNode", - "FlexibleStringMergerNode", - "GPT4MiniNode", - "GPT4VisionNode", - "GeminiCustomVision", - "GeminiTextOnly", - "Gpt4CustomVision", - "Gpt4VisionCloner", - "OllamaNode", - "OllamaVisionNode", - "PGSD3LatentGenerator", - "PhiCustomModelInference", - "PhiModelInference", - "PhiModelLoader", - "PromptGenerator", - "RandomIntegerNode", - "SentenceMixerNode", - "StringMergerNode" - ], - { - "title_aux": "SDXL Auto Prompter" - } - ], - "https://github.com/danTheMonk/comfyui-int-and-float": [ - [ - "FloatToInt", - "IntToFloat" - ], - { - "title_aux": "ComfyUI Int and Float Conversion Nodes" - } - ], - "https://github.com/danger-electrodes/ComfyUI_Fawfluencer_Nodes": [ - [ - "FawfaceModelSpreadsheetRealismNode", - "FawfakeAuthenticImageSaveNode", - "FawfluxencerNode", - "FawfulizedAddImagesToImageList", - "FawfulizedEmptyImageList", - "FawfulizedHunyuanAddNoise", - "FawfulizedHunyuanBasicGuider", - "FawfulizedHunyuanBasicScheduler", - "FawfulizedHunyuanBetaSamplingScheduler", - "FawfulizedHunyuanCFGGuider", - "FawfulizedHunyuanControlNetApply", - "FawfulizedHunyuanControlNetApplyAdvanced", - "FawfulizedHunyuanControlNetLoader", - "FawfulizedHunyuanDiffControlNetLoader", - "FawfulizedHunyuanDisableNoise", - "FawfulizedHunyuanDualCFGGuider", - "FawfulizedHunyuanExponentialScheduler", - "FawfulizedHunyuanFlipSigmas", - "FawfulizedHunyuanKSamplerSelect", - "FawfulizedHunyuanKarrasScheduler", - "FawfulizedHunyuanLaplaceScheduler", - "FawfulizedHunyuanLatentVideo", - "FawfulizedHunyuanPolyexponentialScheduler", - "FawfulizedHunyuanRandomNoise", - "FawfulizedHunyuanSDTurboScheduler", - "FawfulizedHunyuanSamplerCustom", - "FawfulizedHunyuanSamplerCustomAdvanced", - "FawfulizedHunyuanSamplerDPMAdaptative", - "FawfulizedHunyuanSamplerDPMPP_2M_SDE", - "FawfulizedHunyuanSamplerDPMPP_2S_Ancestral", - "FawfulizedHunyuanSamplerDPMPP_3M_SDE", - "FawfulizedHunyuanSamplerDPMPP_SDE", - "FawfulizedHunyuanSamplerEulerAncestral", - "FawfulizedHunyuanSamplerEulerAncestralCFGPP", - "FawfulizedHunyuanSamplerLMS", - "FawfulizedHunyuanSetFirstSigma", - "FawfulizedHunyuanSetLatentNoiseMask", - "FawfulizedHunyuanSplitSigmas", - "FawfulizedHunyuanSplitSigmasDenoise", - "FawfulizedHunyuanVPScheduler", - "Img2ImgFawfluencerNodeSDXL" - ], - { - "title_aux": "ComfyUI_Fawfluencer_Nodes" - } - ], - "https://github.com/daniabib/ComfyUI_ProPainter_Nodes": [ - [ - "ProPainterInpaint", - "ProPainterOutpaint" - ], - { - "title_aux": "ComfyUI ProPainter Nodes" - } - ], - "https://github.com/daniel-lewis-ab/ComfyUI-Llama": [ - [ - "Call LLM Advanced", - "Call LLM Basic", - "LLM_Create_Completion Advanced", - "LLM_Detokenize", - "LLM_Embed", - "LLM_Eval", - "LLM_Load_State", - "LLM_Reset", - "LLM_Sample", - "LLM_Save_State", - "LLM_Token_BOS", - "LLM_Token_EOS", - "LLM_Tokenize", - "Load LLM Model Advanced", - "Load LLM Model Basic" - ], - { - "title_aux": "ComfyUI-Llama" - } - ], - "https://github.com/daniel-lewis-ab/ComfyUI-TTS": [ - [ - "Load_Piper_Model", - "Piper_Speak_Text" - ], - { - "title_aux": "ComfyUI-TTS" - } - ], - "https://github.com/darkpixel/darkprompts": [ - [ - "DarkAnyToString", - "DarkCheckpointRandomizer", - "DarkCheckpointSwitcher", - "DarkCombine", - "DarkFaceIndexGenerator", - "DarkFaceIndexShuffle", - "DarkFolders", - "DarkLoRALoader", - "DarkLoraStackFromString", - "DarkPopLoraFromStack", - "DarkPrompt" - ], - { - "title_aux": "DarkPrompts" - } - ], - "https://github.com/darth-veitcher/comfydv": [ - [ - "CircuitBreaker", - "FormatString", - "ModelUnloader", - "RandomChoice" - ], - { - "author": "Darth Veitcher", - "description": "This collection of nodes provides string formatting, random choices, model memory management, and other quality of life improvements.", - "nickname": "DV Nodes", - "title": "Comfy DV Nodes", - "title_aux": "Comfy DV" - } - ], - "https://github.com/daryltucker/ComfyUI-LoadFiles": [ - [ - "CountLines", - "ListFilenames", - "LoadImages" - ], - { - "title_aux": "ComfyUI-LoadFiles" - } - ], - "https://github.com/dasilva333/ComfyUI_ContrastingColor": [ - [ - "ContrastingComplementaryColor|pysssss" - ], - { - "title_aux": "ComfyUI_ContrastingColor" - } - ], - "https://github.com/dasilva333/ComfyUI_MarkdownImage": [ - [ - "CreateDialogImage", - "CreateDialogImageV2", - "CreateMarkdownImage", - "CreateMarkdownImageV2" - ], - { - "title_aux": "ComfyUI_MarkdownImage" - } - ], - "https://github.com/dave-palt/comfyui_DSP_imagehelpers": [ - [ - "dsp-imagehelpers-concat" - ], - { - "title_aux": "comfyui_DSP_imagehelpers" - } - ], - "https://github.com/davidgressett/comfyui-systemlevel": [ - [ - "CartesianCSVNode" - ], - { - "title_aux": "CartesianCSVNode for ComfyUI" - } - ], - "https://github.com/daxcay/ComfyUI-DataSet": [ - [ - "DataSet_ClaudeAIChat", - "DataSet_ClaudeAIChatImage", - "DataSet_ConceptManager", - "DataSet_CopyFiles", - "DataSet_FindAndReplace", - "DataSet_GroqChat", - "DataSet_GroqChatImage", - "DataSet_LoadImage", - "DataSet_OpenAIChat", - "DataSet_OpenAIChatImage", - "DataSet_OpenAIChatImageBatch", - "DataSet_PathSelector", - "DataSet_SaveImage", - "DataSet_SaveImagePro", - "DataSet_TextFilesLoad", - "DataSet_TextFilesLoadFromList", - "DataSet_TextFilesSave", - "DataSet_TriggerWords", - "DataSet_Visualizer" - ], - { - "author": "Daxton Caylor", - "description": "Data Research, Preparation, and Manipulation Nodes for Model Trainers, Artists, Designers, and Animators.", - "nickname": "ComfyUI-DataSet", - "title": "ComfyUI-DataSet", - "title_aux": "ComfyUI-DataSet" - } - ], - "https://github.com/daxcay/ComfyUI-JDCN": [ - [ - "JDCN_AnyCheckpointLoader", - "JDCN_AnyFileList", - "JDCN_AnyFileListHelper", - "JDCN_AnyFileListRandom", - "JDCN_AnyFileSelector", - "JDCN_BatchCounter", - "JDCN_BatchCounterAdvance", - "JDCN_BatchImageLoadFromDir", - "JDCN_BatchImageLoadFromList", - "JDCN_BatchLatentLoadFromDir", - "JDCN_BatchLatentLoadFromList", - "JDCN_BatchSaveLatent", - "JDCN_BoolInt", - "JDCN_EnableDisable", - "JDCN_FileMover", - "JDCN_ImageSaver", - "JDCN_ListToString", - "JDCN_LoadImage", - "JDCN_ReBatch", - "JDCN_SeamlessExperience", - "JDCN_ShowAny", - "JDCN_SplitString", - "JDCN_StringManipulator", - "JDCN_StringToList", - "JDCN_SwapInputs", - "JDCN_TXTFileSaver", - "JDCN_VHSFileMover" - ], - { - "author": "Daxton Caylor & Jerry Davos", - "description": "Custom Utility Nodes for Artists, Designers and Animators.", - "nickname": "ComfyUI-JDCN", - "title": "ComfyUI-JDCN", - "title_aux": "ComfyUI-JDCN" - } - ], - "https://github.com/daxcay/ComfyUI-TG": [ - [ - "TG_ImageSaver" - ], - { - "author": "Daxton Caylor", - "description": "This node enables someone to run comfyui in telegram.", - "nickname": "ComfyUI-TG", - "title": "ComfyUI-TG", - "title_aux": "ComfyUI-TG" - } - ], - "https://github.com/daxcay/ComfyUI-WA": [ - [ - "WA_ImageSaver" - ], - { - "author": "Daxton Caylor", - "description": "This node enables someone to run comfyui in whatsapp.", - "nickname": "ComfyUI-WA", - "title": "ComfyUI-WA", - "title_aux": "ComfyUI-WA" - } - ], - "https://github.com/daxcay/ComfyUI-YouTubeVideoPlayer": [ - [ - "YouTubeVideoPlayer" - ], - { - "author": "Daxton Caylor & Jerry Davos", - "description": "YouTube Video Player in Comfy.", - "nickname": "ComfyUI-YouTubeVideoPlayer", - "title": "ComfyUI-YouTubeVideoPlayer", - "title_aux": "ComfyUI-YouTubeVideoPlayer" - } - ], - "https://github.com/dchatel/comfyui_davcha": [ - [ - "ApplyMask", - "ConditioningCompress", - "DStack", - "DavchaCLIPMergeSimple", - "DavchaCLIPTextEncode", - "DavchaConditioningConcat", - "DavchaEmptyLatentImage", - "DavchaLLM", - "DavchaLLMAdvanced", - "DavchaLoadLLM", - "DavchaLoadVideo", - "DavchaMaskImage", - "DavchaModelMergeSD1", - "DavchaModelMergeSDXL", - "DavchaModelMergeSimple", - "DavchaPop", - "PadAndResize", - "PercentPadding", - "ResizeCropFit", - "SmartMask", - "SoftErosion", - "StringScheduleHelper" - ], - { - "title_aux": "comfyui_davcha" - } - ], - "https://github.com/dchatel/comfyui_facetools": [ - [ - "BiSeNetMask", - "CropFaces", - "DetectFaces", - "GenderFaceFilter", - "JonathandinuMask", - "MergeWarps", - "OrderedFaceFilter", - "WarpFacesBack" - ], - { - "title_aux": "comfyui_facetools" - } - ], - "https://github.com/denfrost/Den_ComfyUI_Workflow": [ - [ - "Den_BatchIndex_AS", - "Den_CropImage_AS", - "Den_Eval_AS", - "Den_FaceRestoreCFWithModel", - "Den_GPTLoaderSimple_llama", - "Den_GPTSampler_llama", - "Den_ImageMixMasked_As", - "Den_ImageToLatentSpace", - "Den_ImageToMask_AS", - "Den_Int2Any_AS", - "Den_LatentAdd_AS", - "Den_LatentMixMasked_As", - "Den_LatentMix_AS", - "Den_LatentToImages_AS", - "Den_LoadLatent_AS", - "Den_MapRange_AS", - "Den_MaskToImage_AS", - "Den_Math_AS", - "Den_NoiseImage_AS", - "Den_Number2Float_AS", - "Den_Number2Int_AS", - "Den_Number_AS", - "Den_SVD_img2vid", - "Den_SaveLatent_AS", - "Den_TextToImage_AS", - "Den_TextWildcardList_AS", - "Increment_AS" - ], - { - "title_aux": "Den_ComfyUI_Workflows" - } - ], - "https://github.com/deroberon/StableZero123-comfyui": [ - [ - "SDZero ImageSplit", - "Stablezero123", - "Stablezero123WithDepth" - ], - { - "title_aux": "StableZero123-comfyui" - } - ], - "https://github.com/deroberon/demofusion-comfyui": [ - [ - "Batch Unsampler", - "Demofusion", - "Demofusion From Single File", - "Iterative Mixing KSampler" - ], - { - "title_aux": "demofusion-comfyui" - } - ], - "https://github.com/dfghsdh/ComfyUI_FluxPromptGen": [ - [ - "FluxImageCaptionNode", - "FluxPromptGeneratorNode" - ], - { - "title_aux": "ComfyUI_FluxPromptGen" - } - ], - "https://github.com/dfl/comfyui-clip-with-break": [ - [ - "AdvancedCLIPTextEncodeWithBreak", - "CLIPTextEncodeWithBreak" - ], - { - "author": "dfl", - "description": "CLIP text encoder that does BREAK prompting like A1111", - "nickname": "CLIP with BREAK", - "title": "CLIP with BREAK syntax", - "title_aux": "comfyui-clip-with-break" - } - ], - "https://github.com/dfl/comfyui-tcd-scheduler": [ - [ - "SamplerTCD", - "SamplerTCD EulerA", - "TCDScheduler" - ], - { - "title_aux": "ComfyUI-TCD-scheduler" - } - ], - "https://github.com/diStyApps/ComfyUI-disty-Flow": [ - [ - "Flow" - ], - { - "title_aux": "Flow - Streamlined Way to ComfyUI" - } - ], - "https://github.com/diStyApps/ComfyUI_FrameMaker": [ - [ - "FrameMaker", - "FrameMakerBatch" - ], - { - "title_aux": "ComfyUI Frame Maker" - } - ], - "https://github.com/dicksensei69/comfyui_loops": [ - [ - "LoopImageNode" - ], - { - "title_aux": "ComfyUI Loops" - } - ], - "https://github.com/dicksondickson/ComfyUI-Dickson-Nodes": [ - [ - "DicksonColorMatch", - "DicksonLoadImage", - "Dickson_TTP_Preprocessor_Simple", - "Dickson_TTP_Preprocessor_cufoff", - "Dickson_TTP_Tile_Preprocessor_GF" - ], - { - "description": "This is a set of custom nodes that I've either written myself or adapted from other authors for my own convenience. Currently includes color matching node forked from StableSR and TTPlanet's controlnet preprocessor. https://github.com/dicksondickson", - "nickname": "Dickson Nodes", - "title": "Dickson Nodes", - "title_aux": "ComfyUI-Dickson-Nodes" - } - ], - "https://github.com/digitaljohn/comfyui-propost": [ - [ - "ProPostApplyLUT", - "ProPostDepthMapBlur", - "ProPostFilmGrain", - "ProPostRadialBlur", - "ProPostVignette" - ], - { - "title_aux": "ComfyUI-ProPost" - } - ], - "https://github.com/dimtion/comfyui-raw-image": [ - [ - "Load Raw Image" - ], - { - "title_aux": "ComfyUI-Raw-Image" - } - ], - "https://github.com/dimtoneff/ComfyUI-PixelArt-Detector": [ - [ - "PixelArtAddDitherPattern", - "PixelArtDetectorConverter", - "PixelArtDetectorSave", - "PixelArtDetectorToImage", - "PixelArtLoadPalettes", - "PixelArtPaletteGenerator" - ], - { - "title_aux": "ComfyUI PixelArt Detector" - } - ], - "https://github.com/dimtoneff/ComfyUI-VL-Nodes": [ - [ - "GGUF_VLM_ImageToText", - "GGUF_VLM_ModelLoader", - "InternVL3_5_ImageToText", - "InternVL3_5_ModelLoader", - "KeyeModelLoader", - "KeyeNode", - "LFM2TransformerImageToText", - "LFM2TransformerModelLoader", - "LoadImagesFromDirBatch_VL", - "LoadImagesFromDirList_VL", - "Ovis25ImageToText", - "Ovis25ModelLoader", - "OvisU1ImageCaption", - "OvisU1VLModelLoader", - "TextSave_VL", - "VLNodesFreeMemoryAPI" - ], - { - "title_aux": "ComfyUI-VL-Nodes" - } - ], - "https://github.com/diodiogod/TTS-Audio-Suite": [ - [ - "AudioAnalyzerNode", - "AudioAnalyzerOptionsNode", - "CharacterVoicesNode", - "ChatterBoxEngineNode", - "ChatterBoxF5TTSEditOptions", - "F5TTSEngineNode", - "HiggsAudioEngineNode", - "MouthMovementAnalyzer", - "UnifiedTTSSRTNode", - "UnifiedTTSTextNode", - "UnifiedVoiceChangerNode", - "VibeVoiceEngineNode", - "VisemeDetectionOptionsNode" - ], - { - "title_aux": "TTS Audio Suite" - } - ], - "https://github.com/diontimmer/ComfyUI-Vextra-Nodes": [ - [ - "Add Text To Image", - "Apply Instagram Filter", - "Create Solid Color", - "Flatten Colors", - "Generate Noise Image", - "GlitchThis Effect", - "Hue Rotation", - "Load Picture Index", - "Pixel Sort", - "Play Sound At Execution", - "Prettify Prompt Using distilgpt2", - "Swap Color Mode" - ], - { - "title_aux": "ComfyUI-Vextra-Nodes" - } - ], - "https://github.com/discopixel-studio/comfyui-discopixel": [ - [ - "PhotoroomRemoveBG" - ], - { - "author": "Anson Kao", - "description": "A small collection of custom nodes for use with ComfyUI, by Discopixel", - "nickname": "ComfyUI Discopixel", - "title": "ComfyUI Discopixel", - "title_aux": "PhotoRoom Nodes by Discopixel" - } - ], - "https://github.com/discus0434/comfyui-caching-embeddings": [ - [ - "CachingCLIPTextEncode" - ], - { - "title_aux": "ComfyUI Caching Embeddings" - } - ], - "https://github.com/discus0434/comfyui-flux-accelerator": [ - [ - "\ud83c\udf6dFluxAccelerator" - ], - { - "title_aux": "ComfyUI Flux Accelerator" - } - ], - "https://github.com/djbielejeski/a-person-mask-generator": [ - [ - "APersonFaceLandmarkMaskGenerator", - "APersonMaskGenerator" - ], - { - "title_aux": "a-person-mask-generator" - } - ], - "https://github.com/dmMaze/sketch2manga": [ - [ - "BlendScreentone", - "EmptyLatentImageAdvanced" - ], - { - "title_aux": "Sketch2Manga" - } - ], - "https://github.com/dmarx/ComfyUI-AudioReactive": [ - [ - "OpAbs", - "OpBandpass", - "OpClamp", - "OpHarmonic", - "OpModulo", - "OpNormalize", - "OpNovelty", - "OpPercussive", - "OpPow", - "OpPow2", - "OpPredominant_pulse", - "OpQuantize", - "OpRms", - "OpSmoosh", - "OpSmooth", - "OpSqrt", - "OpStretch", - "OpSustain", - "OpThreshold" - ], - { - "title_aux": "ComfyUI-AudioReactive" - } - ], - "https://github.com/dmarx/ComfyUI-Keyframed": [ - [ - "Example", - "KfAddCurveToPGroup", - "KfAddCurveToPGroupx10", - "KfApplyCurveToCond", - "KfConditioningAdd", - "KfConditioningAddx10", - "KfCurveConstant", - "KfCurveDraw", - "KfCurveFromString", - "KfCurveFromYAML", - "KfCurveInverse", - "KfCurveToAcnLatentKeyframe", - "KfCurvesAdd", - "KfCurvesAddx10", - "KfCurvesDivide", - "KfCurvesMultiply", - "KfCurvesMultiplyx10", - "KfCurvesSubtract", - "KfDebug_Clip", - "KfDebug_Cond", - "KfDebug_Curve", - "KfDebug_Float", - "KfDebug_Image", - "KfDebug_Int", - "KfDebug_Latent", - "KfDebug_Model", - "KfDebug_Passthrough", - "KfDebug_Segs", - "KfDebug_String", - "KfDebug_Vae", - "KfDrawSchedule", - "KfEvaluateCurveAtT", - "KfGetCurveFromPGroup", - "KfGetScheduleConditionAtTime", - "KfGetScheduleConditionSlice", - "KfKeyframedCondition", - "KfKeyframedConditionWithText", - "KfPGroupCurveAdd", - "KfPGroupCurveMultiply", - "KfPGroupDraw", - "KfPGroupProd", - "KfPGroupSum", - "KfSetCurveLabel", - "KfSetKeyframe", - "KfSinusoidalAdjustAmplitude", - "KfSinusoidalAdjustFrequency", - "KfSinusoidalAdjustPhase", - "KfSinusoidalAdjustWavelength", - "KfSinusoidalEntangledZeroOneFromFrequencyx2", - "KfSinusoidalEntangledZeroOneFromFrequencyx3", - "KfSinusoidalEntangledZeroOneFromFrequencyx4", - "KfSinusoidalEntangledZeroOneFromFrequencyx5", - "KfSinusoidalEntangledZeroOneFromFrequencyx6", - "KfSinusoidalEntangledZeroOneFromFrequencyx7", - "KfSinusoidalEntangledZeroOneFromFrequencyx8", - "KfSinusoidalEntangledZeroOneFromFrequencyx9", - "KfSinusoidalEntangledZeroOneFromWavelengthx2", - "KfSinusoidalEntangledZeroOneFromWavelengthx3", - "KfSinusoidalEntangledZeroOneFromWavelengthx4", - "KfSinusoidalEntangledZeroOneFromWavelengthx5", - "KfSinusoidalEntangledZeroOneFromWavelengthx6", - "KfSinusoidalEntangledZeroOneFromWavelengthx7", - "KfSinusoidalEntangledZeroOneFromWavelengthx8", - "KfSinusoidalEntangledZeroOneFromWavelengthx9", - "KfSinusoidalGetAmplitude", - "KfSinusoidalGetFrequency", - "KfSinusoidalGetPhase", - "KfSinusoidalGetWavelength", - "KfSinusoidalWithFrequency", - "KfSinusoidalWithWavelength" - ], - { - "title_aux": "ComfyUI-Keyframed" - } - ], - "https://github.com/dorpxam/ComfyUI-FramePack-F1-T2V": [ - [ - "FramePackF1T2VLoraStack", - "FramePackF1T2VSampler", - "FramePackF1T2VSamplerSettings", - "FramePackF1T2VTextEncode", - "FramePackF1T2VUserSettings" - ], - { - "title_aux": "ComfyUI-FramePack-F1-T2V" - } - ], - "https://github.com/dorpxam/ComfyUI-LTXVideoLoRA": [ - [ - "LTXVLoRABlockEdit", - "LTXVLoRALoader", - "LTXVLoRASelector" - ], - { - "title_aux": "ComfyUI-LTXVideoLoRA" - } - ], - "https://github.com/doubletwisted/ComfyUI-Deadline-Plugin": [ - [ - "DeadlineSeed", - "DeadlineSubmit" - ], - { - "nodename_pattern": "DeadlineSubmitNode", - "title_aux": "ComfyUI Deadline Submission" - } - ], - "https://github.com/drago87/ComfyUI_Dragos_Nodes": [ - [ - "file_padding", - "image_info", - "lora_loader", - "vae_loader" - ], - { - "title_aux": "ComfyUI_Dragos_Nodes" - } - ], - "https://github.com/dreamhartley/ComfyUI_show_seed": [ - [ - "Show Seed" - ], - { - "title_aux": "ComfyUI_show_seed" - } - ], - "https://github.com/drmbt/comfyui-dreambait-nodes": [ - [ - "AudioInfoPlus", - "BoolPlusPlus", - "CompareImageSimilarity", - "DRMBT_AspectPadImageForOutpainting", - "DRMBT_LoadMedia", - "DRMBT_MultiMinMax", - "DRMBT_String_Item_Menu", - "DictToOutputs", - "DownloadAndLoadMiniCPMV", - "DrawMana", - "DrawText", - "DreambaitFolderOpener", - "DynamicDictionary", - "DynamicStringConcatenate", - "ImageFrameBlend", - "ImageResizeFaceAware", - "ListItemExtract", - "ListItemSelector", - "LoadAudioPlus", - "MiniCPMVNode", - "MusicGen", - "NormalizeAudio", - "NumberPlusPlus", - "NumberRemap", - "Qwen2AudioInstruct", - "ShotHistory", - "StringToDict", - "SwitchDuo", - "TextBoxStyle", - "TextLineSelect", - "TextLinesToList", - "TextMargins", - "TextPlusPlus", - "TextShadow" - ], - { - "title_aux": "comfyui-dreambait-nodes" - } - ], - "https://github.com/drozbay/ComfyUI-WanVaceAdvanced": [ - [ - "VaceAdvancedModelPatch", - "VaceStrengthTester", - "WanVacePhantomDual", - "WanVacePhantomDualV2", - "WanVacePhantomExperimental", - "WanVacePhantomExperimentalV2", - "WanVacePhantomSimple", - "WanVacePhantomSimpleV2", - "WanVaceToVideoLatent" - ], - { - "title_aux": "ComfyUI-WanVaceAdvanced" - } - ], - "https://github.com/drphero/comfyui_prompttester": [ - [ - "PromptTester" - ], - { - "title_aux": "ComfyUI-PromptTester" - } - ], - "https://github.com/drustan-hawk/primitive-types": [ - [ - "float", - "int", - "string", - "string_multiline" - ], - { - "title_aux": "primitive-types" - } - ], - "https://github.com/dseditor/ComfyUI-ListHelper": [ - [ - "AudioListCombine", - "AudioListGenerator", - "AudioToFrameCount", - "CeilDivide", - "FrameMatch", - "LoadVideoPath", - "MergeVideoFilename", - "NumberListGenerator", - "PromptListGenerator", - "SaveVideoPath" - ], - { - "title_aux": "ComfyUI-ListHelper" - } - ], - "https://github.com/dseditor/ComfyUI-ScheduledTask": [ - [ - "DailyPromptScheduler", - "ShutdownNode", - "TimeToSeedList" - ], - { - "title_aux": "ComfyUI-ScheduledTask" - } - ], - "https://github.com/dseditor/ComfyUI-Thread": [ - [ - "PublishThread", - "StartWithLongLiveToken", - "ThreadPublishVideo", - "ThreadsHistory" - ], - { - "title_aux": "ComfyUI-Thread" - } - ], - "https://github.com/duchamps0305/comfyui-white-extractor": [ - [ - "WhitePercentage" - ], - { - "title_aux": "comfyui-white-extractor" - } - ], - "https://github.com/ducido/ObjectFusion_ComfyUI_nodes": [ - [ - "Custom ESAM_ModelLoader_Zho", - "Custom Generate Stable Diffsution Prompt With LLM", - "Custom Yoloworld_ESAM_Zho", - "Custom Yoloworld_ModelLoader_Zho", - "ObjectCrop" - ], - { - "title_aux": "ObjectFusion_ComfyUI_nodes" - } - ], - "https://github.com/duskfallcrew/Comfyui_EmbeddingMerge_Node/raw/refs/heads/main/merge_embed.py": [ - [ - "EmbeddingMerger" - ], - { - "title_aux": "Embedding Merge for ComfyUI" - } - ], - "https://github.com/dymokomi/comfyui_dygen": [ - [ - "AdaptiveColorCircles", - "AdaptiveColorLines", - "AdaptiveColorRectangles", - "BinaryPatternStamper", - "DYImageCluster", - "DYImageMasks", - "DYImagePalette", - "DYImageQuantize", - "ImageListToGrid", - "ImageScaler", - "RandomLines" - ], - { - "title_aux": "comfyui_dygen" - } - ], - "https://github.com/dzqdzq/ComfyUI-crop-alpha": [ - [ - "FastAlphaCropper", - "ShrinkImage" - ], - { - "title_aux": "ComfyUI-crop-alpha" - } - ], - "https://github.com/e-tier-newbie/ComfyUI-E-Tier-TextSaver": [ - [ - "E_TierTextSaver" - ], - { - "title_aux": "ComfyUI-E-Tier-TextSaver" - } - ], - "https://github.com/e7mac/ComfyUI-ShadertoyGL": [ - [ - "ColorChannelOffset", - "Shader", - "Shadertoy" - ], - { - "title_aux": "ComfyUI-ShadertoyGL" - } - ], - "https://github.com/ealkanat/comfyui-easy-padding": [ - [ - "comfyui-easy-padding" - ], - { - "title_aux": "ComfyUI Easy Padding" - } - ], - "https://github.com/eastoc/ComfyUI_SemanticSAM": [ - [ - "PointPrompt", - "SemanticSAMLoader", - "SemanticSAMSegment" - ], - { - "title_aux": "Semantic-SAM" - } - ], - "https://github.com/ebrinz/ComfyUI-MusicGen-HF": [ - [ - "AudioOutputToConditioningQueue", - "BPMDurationInput", - "ConditioningQueueManager", - "HuggingFaceMusicGen", - "LoadAudioStandalone", - "LoopingAudioPreview", - "MusicGenAudioToFile", - "ProfessionalLoopTransition", - "SaveAudioStandalone", - "SmoothAudioQueue", - "custom_nodes" - ], - { - "title_aux": "ComfyUI-MusicGen-HF" - } - ], - "https://github.com/edelvarden/ComfyUI-Display-Value": [ - [ - "DisplayValue" - ], - { - "title_aux": "ComfyUI-Display-Value" - } - ], - "https://github.com/edenartlab/eden_comfy_pipelines": [ - [ - "AnimatedShapeMaskNode", - "Animation_RGB_Mask", - "AspectPadImageForOutpainting", - "CLIP_Interrogator", - "ConvertToGrayscale", - "DepthSlicer", - "Eden_AllMediaLoader", - "Eden_Bool", - "Eden_BoolBinaryOperation", - "Eden_Compare", - "Eden_Debug_Anything", - "Eden_DepthSlice_MaskVideo", - "Eden_DetermineFrameCount", - "Eden_FaceToMask", - "Eden_Face_Crop", - "Eden_Float", - "Eden_FloatToInt", - "Eden_GPTPromptEnhancer", - "Eden_GPTStructuredOutput", - "Eden_IMG_padder", - "Eden_IMG_unpadder", - "Eden_ImageMaskComposite", - "Eden_Image_Math", - "Eden_Int", - "Eden_IntToFloat", - "Eden_MaskBoundingBox", - "Eden_MaskCombiner", - "Eden_Math", - "Eden_RGBA_to_RGB", - "Eden_RandomFilepathSampler", - "Eden_RandomNumberSampler", - "Eden_RandomPromptFromFile", - "Eden_Regex_Replace", - "Eden_RepeatLatentBatch", - "Eden_Save_Param_Dict", - "Eden_Seed", - "Eden_String", - "Eden_StringHash", - "Eden_StringReplace", - "Eden_gpt4_node", - "Eden_randbool", - "Extend_Sequence", - "FolderScanner", - "GetRandomFile", - "Get_Prefixed_Imgs", - "HistogramMatching", - "IMG_blender", - "IMG_resolution_multiple_of", - "IMG_scaler", - "IP_Adapter_Settings_Distribution", - "If ANY execute A else B", - "ImageDescriptionNode", - "ImageFolderIterator", - "KeyframeBlender", - "LatentTypeConversion", - "Linear_Combine_IP_Embeds", - "LoadImagesByFilename", - "LoadRandomImage", - "Load_Embeddings_From_Folder", - "MaskFromRGB_KMeans", - "MaskedRegionVideoExport", - "OrganicFillNode", - "ParallaxZoom", - "Random_Style_Mixture", - "SDAnyConverter", - "SDTypeConverter", - "SaveImageAdvanced", - "SavePosEmbeds", - "VAEDecode_to_folder", - "VideoFrameSelector", - "WidthHeightPicker" - ], - { - "title_aux": "Eden.art nodesuite" - } - ], - "https://github.com/edenartlab/sd-lora-trainer": [ - [ - "Eden_LoRa_trainer" - ], - { - "title_aux": "Eden.art LoRa Trainer" - } - ], - "https://github.com/educator-art/ComfyUI-Load-DirectoryFiles": [ - [ - "Load Images and Prompts from Directory", - "Load Images and Prompts from Directory(Advanced)" - ], - { - "title_aux": "ComfyUI-Load-DirectoryFiles" - } - ], - "https://github.com/educator-art/ComfyUI-gpt-oss-PromptDesigner": [ - [ - "Load gpt-oss Prompt Designer" - ], - { - "title_aux": "ComfyUI-gpt-oss-PromptDesigner" - } - ], - "https://github.com/eg0pr0xy/comfyui_noisegen": [ - [ - "AudioAnalyzer", - "AudioMixer", - "AudioSave", - "BandLimitedNoise", - "ChaosNoiseMix", - "ConvolutionReverb", - "FeedbackProcessor", - "GranularProcessor", - "GranularSequencer", - "HarshFilter", - "MicrosoundSculptor", - "ModulationMatrix", - "MultiDistortion", - "NoiseGenerator", - "PerlinNoise", - "SpectralProcessor", - "SpectrumAnalyzer", - "TrueChaos" - ], - { - "title_aux": "ComfyUI-NoiseGen" - } - ], - "https://github.com/einhorn13/ComfyUI-ImageProcessUtilities": [ - [ - "CombineCoords", - "CropByCoords", - "ImageTiler", - "ImageUntiler", - "PasteByCoords", - "ReorderBatch", - "SplitCoords", - "StringToIntegers" - ], - { - "title_aux": "ComfyUI-ImageProcessUtilities" - } - ], - "https://github.com/emojiiii/ComfyUI_Emojiiii_Custom_Nodes": [ - [ - "BatchImageProcessor", - "Caption", - "CaptionDownload", - "KolorsMultiTextEncode", - "MultiTextEncode" - ], - { - "title_aux": "ComfyUI_Emojiiii_Custom_Nodes" - } - ], - "https://github.com/envy-ai/ComfyUI-ConDelta": [ - [ - "ApplyConDelta", - "ApplyConDeltaAutoScale", - "CFGlessNegativePrompt", - "ClampConDelta", - "ConditioningAddConDelta", - "ConditioningAddConDeltaAutoScale", - "ConditioningAverageMultiple", - "ConditioningGetNoise", - "ConditioningGetRandom", - "ConditioningScale", - "ConditioningSubtract", - "ExtendedConditioningAverage", - "GetConDeltaFromPrompt", - "HardClampConDelta", - "LoadConditioningDelta", - "MaskConDelta", - "QuickConDelta", - "SaveConditioningDelta", - "ThresholdConditioning" - ], - { - "title_aux": "ComfyUI-ConDelta" - } - ], - "https://github.com/eric183/ComfyUI-Only": [ - [ - "ArchiveImageLoader", - "LatentLoaderAdvanced", - "WorkflowImageFileLoader", - "WorkflowJSONParser" - ], - { - "title_aux": "ComfyUI-Only" - } - ], - "https://github.com/erosDiffusion/ComfyUI-enricos-nodes": [ - [ - "Compositor3", - "CompositorColorPicker", - "CompositorConfig3", - "CompositorMasksOutputV3", - "CompositorTools3", - "CompositorTransformsOutV3", - "ImageColorSampler" - ], - { - "title_aux": "ComfyUI-enricos-nodes" - } - ], - "https://github.com/evanspearman/ComfyMath": [ - [ - "CM_BoolBinaryOperation", - "CM_BoolToInt", - "CM_BoolUnaryOperation", - "CM_BreakoutVec2", - "CM_BreakoutVec3", - "CM_BreakoutVec4", - "CM_ComposeVec2", - "CM_ComposeVec3", - "CM_ComposeVec4", - "CM_FloatBinaryCondition", - "CM_FloatBinaryOperation", - "CM_FloatToInt", - "CM_FloatToNumber", - "CM_FloatUnaryCondition", - "CM_FloatUnaryOperation", - "CM_IntBinaryCondition", - "CM_IntBinaryOperation", - "CM_IntToBool", - "CM_IntToFloat", - "CM_IntToNumber", - "CM_IntUnaryCondition", - "CM_IntUnaryOperation", - "CM_NearestSDXLExtendedResolution", - "CM_NearestSDXLResolution", - "CM_NumberBinaryCondition", - "CM_NumberBinaryOperation", - "CM_NumberToFloat", - "CM_NumberToInt", - "CM_NumberUnaryCondition", - "CM_NumberUnaryOperation", - "CM_SDXLExtendedResolution", - "CM_SDXLResolution", - "CM_Vec2BinaryCondition", - "CM_Vec2BinaryOperation", - "CM_Vec2ScalarOperation", - "CM_Vec2ToScalarBinaryOperation", - "CM_Vec2ToScalarUnaryOperation", - "CM_Vec2UnaryCondition", - "CM_Vec2UnaryOperation", - "CM_Vec3BinaryCondition", - "CM_Vec3BinaryOperation", - "CM_Vec3ScalarOperation", - "CM_Vec3ToScalarBinaryOperation", - "CM_Vec3ToScalarUnaryOperation", - "CM_Vec3UnaryCondition", - "CM_Vec3UnaryOperation", - "CM_Vec4BinaryCondition", - "CM_Vec4BinaryOperation", - "CM_Vec4ScalarOperation", - "CM_Vec4ToScalarBinaryOperation", - "CM_Vec4ToScalarUnaryOperation", - "CM_Vec4UnaryCondition", - "CM_Vec4UnaryOperation" - ], - { - "title_aux": "ComfyMath" - } - ], - "https://github.com/excelwong/ComfyUI-PromptComposer": [ - [ - "PromptComposer" - ], - { - "title_aux": "ComfyUI Prompt Composer" - } - ], - "https://github.com/exdysa/comfyui-selector": [ - [ - "RecourseAny", - "RecourseCkpt", - "RecourseImage", - "RecoursePolar", - "RecourseStrings", - "SelInClip", - "SelInFloat", - "SelInGuider", - "SelInInt", - "SelInLatent", - "SelInModel", - "SelInPolar", - "SelInSampler", - "SelInSigmas", - "SelInVae", - "SelOutCLIP", - "SelOutModel", - "SelOutPolar", - "Selector", - "Selector Advanced", - "Selector Hub" - ], - { - "author": "\"\u02f6\ud835\udfa2\u292c\u2ad2\u2d56s\u143c\u02f6\"", - "description": "\"EXDYSA. Selector and Recourse. Presets & failsafes. Work flow.\"", - "nickname": "\"Selector\"", - "title": "\"Selector\"", - "title_aux": "comfyui-selector" - } - ], - "https://github.com/exectails/comfyui-et_dynamicprompts": [ - [ - "ETDynamicPrompt" - ], - { - "title_aux": "Dynamic Prompts" - } - ], - "https://github.com/exectails/comfyui-et_infoutils": [ - [ - "ETInspectTextNode", - "ETIntBoxNode", - "ETPresentImageNode", - "ETShowDataNode", - "ETStringBoxNode", - "ETTextBoxNode", - "ETTokenCountNode" - ], - { - "title_aux": "Info Utils" - } - ], - "https://github.com/exectails/comfyui-et_stringutils": [ - [ - "ETATOI", - "ETITOA", - "ETJoinTextNode", - "ETReplaceTextNode", - "ETSplitTextNode", - "ETSwitchTextNode", - "ETTextFormatter10Node", - "ETTextFormatter2Node", - "ETTextFormatter5Node" - ], - { - "title_aux": "String Utils" - } - ], - "https://github.com/ez-af/ComfyUI-EZ-AF-Nodes": [ - [ - "EZ_CSV_Loader", - "EZ_Extract_Prompt", - "EZ_Find_Replace", - "EZ_Input", - "EZ_Prompt_Loader", - "EZ_Switch", - "EZ_Tag_Loader", - "EZ_Test", - 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"QwenVLLoader", - "QwenVLTextEncoder", - "QwenWANCompareLatents", - "QwenWANConditioningDebug", - "QwenWANKeyframeEditor", - "QwenWANKeyframeExtractor", - "QwenWANLatentDebug" - ], - { - "title_aux": "ComfyUI-QwenImageWanBridge" - } - ], - "https://github.com/fblissjr/ComfyUI-WanActivationEditor": [ - [ - "WanVideoActivationEditor", - "WanVideoAdvancedActivationEditor", - "WanVideoBlockActivationBuilder", - "WanVideoBlockActivationViewer", - "WanVideoBlockStrengthBuilder", - "WanVideoDirectInjector", - "WanVideoEmbeddingAmplifier", - "WanVideoEmbeddingAnalyzer", - "WanVideoEmbeddingDatabase", - "WanVideoGuidanceController", - "WanVideoInjectionTester", - "WanVideoLatentEncoder", - "WanVideoLatentInjector", - "WanVideoNoiseController", - "WanVideoProjectionBooster", - "WanVideoSequentialMixer", - "WanVideoStrengthVisualizer", - "WanVideoVectorArithmetic", - "WanVideoVectorDifference", - "WanVideoVectorInterpolation" - ], - { - "title_aux": "ComfyUI-WanActivationEditor" - } - ], - "https://github.com/fblissjr/ComfyUI-WanSeamlessFlow": [ - [ - "WanAdaptiveFlow", - "WanBlendVisualize", - "WanEmbeddingPrevizCanvas", - "WanMinimalCanvasTest", - "WanSmartBlend" - ], - { - "title_aux": "wanvideo - seamless flow" - } - ], - "https://github.com/fblissjr/shrug-prompter": [ - [ - "AccumulationNodeCompat", - "AdvancedVLMSampler", - "AnyTypePassthrough", - "AutoMemoryManager", - "DualProviderConfig", - "GlobalMemoryCleanup", - "ImageToAny", - "LoopAwareResponseIterator", - "LoopAwareVLMAccumulator", - "LoopSafeAccumulator", - "PromptTemplateLoader", - "RemoteTextEncoder", - "RobustImageRangeExtractor", - "SeedPromptGenerator", - "ShrugPrompter", - "SmartImageRangeExtractor", - "TextCleanup", - "TextListCleanup", - "TextListIndexer", - "TextListToString", - "TwoRoundVLMPrompter", - "VLMImagePassthrough", - "VLMImageProcessor", - "VLMImageResizer", - "VLMPrompterFast", - "VLMProviderConfig", - "VLMResponseExtractor", - "VLMResultCollector", - "VLMResultIterator", - "VLMResultsToGeneric", - "VLMStyleRewriter", - "VideoFramePairExtractor", - 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"SaveWebPImage" - ], - { - "title_aux": "ComfyUI_Fill-Nodes" - } - ], - "https://github.com/flamacore/ComfyUI-YouTubeUploader": [ - [ - "YouTubeAuthNode", - "YouTubeUploaderNode" - ], - { - "title_aux": "ComfyUI YouTube Uploader" - } - ], - "https://github.com/florestefano1975/ComfyUI-Advanced-Sequence-Seed": [ - [ - "AdvancedSequenceSeedNode" - ], - { - "title_aux": "Advanced Sequence Seed Generator" - } - ], - "https://github.com/florestefano1975/ComfyUI-CogVideoX": [ - [ - "CogVideoX Image-2-Video Extended", - "CogVideoX Save Video" - ], - { - "title_aux": "ComfyUI-CogVideoX" - } - ], - "https://github.com/florestefano1975/ComfyUI-HiDiffusion": [ - [ - "HiDiffusionSD15", - "HiDiffusionSD21", - "HiDiffusionSDXL", - "HiDiffusionSDXLTurbo" - ], - { - "title_aux": "ComfyUI HiDiffusion" - } - ], - "https://github.com/florestefano1975/ComfyUI-StabilityAI-Suite": [ - [ - "StabilityAI Suite - Creative Upscale", - "StabilityAI Suite - Creative Upscale Recover File", - "StabilityAI Suite - Image Core + Style Preset", - "StabilityAI Suite - Inpainting", - "StabilityAI Suite - Outpainting", - "StabilityAI Suite - Remove Background", - "StabilityAI Suite - SD3", - "StabilityAI Suite - Search and Replace" - ], - { - "title_aux": "ComfyUI StabilityAI Suite" - } - ], - "https://github.com/florestefano1975/comfyui-portrait-master": [ - [ - "PortraitMaster", - "PortraitMasterBaseCharacter", - "PortraitMasterMakeup", - "PortraitMasterPromptStyler", - "PortraitMasterSkinDetails", - "PortraitMasterStylePose" - ], - { - "title_aux": "comfyui-portrait-master" - } - ], - "https://github.com/florestefano1975/comfyui-prompt-composer": [ - [ - "PromptComposerCustomLists", - "PromptComposerEffect", - "PromptComposerGrouping", - "PromptComposerMerge", - "PromptComposerStyler", - "PromptComposerTextSingle", - "promptComposerTextMultiple" - ], - { - "title_aux": "comfyui-prompt-composer" - } - ], - "https://github.com/flowtyone/ComfyUI-Flowty-CRM": [ - [ - "CCMSampler", - "CRMModelLoader", - "CRMModeler", - "CRMModelerCuda", - "CRMPoseSampler", - "CRMPoserConfig", - "CRMPreprocessForPoser", - "CRMViewer" - ], - { - "title_aux": "ComfyUI-Flowty-CRM" - } - ], - "https://github.com/flowtyone/ComfyUI-Flowty-LDSR": [ - [ - "LDSRModelLoader", - "LDSRUpscale", - "LDSRUpscaler" - ], - { - "title_aux": "ComfyUI-Flowty-LDSR" - } - ], - "https://github.com/flowtyone/ComfyUI-Flowty-TripoSR": [ - [ - "TripoSRModelLoader", - "TripoSRSampler", - "TripoSRViewer" - ], - { - "title_aux": "ComfyUI-Flowty-TripoSR" - } - ], - "https://github.com/fluffydiveX/ComfyUI-hvBlockswap": [ - [ - "hvBlockSwap" - ], - { - "title_aux": "ComfyUI-hvBlockswap" - } - ], - "https://github.com/flybirdxx/ComfyUI-SDMatte": [ - [ - "SDMatteApply" - ], - { - "title_aux": "ComfyUI-SDMatte" - } - ], - "https://github.com/flycarl/ComfyUI-Pixelate": [ - [ - "ComfyUIPixelate" - ], - { - "title_aux": "ComfyUI-Pixelate" - } - ], - "https://github.com/flyingshutter/As_ComfyUI_CustomNodes": [ - [ - "BatchIndex_AS", - "CropImage_AS", - "Eval_AS", - "ImageMixMasked_As", - 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[ - "BasicAuthSetup" - ], - { - "title_aux": "ComfyUI-Basic-Auth" - } - ], - "https://github.com/fofr/comfyui-fofr-toolkit": [ - [ - "Incrementer \ud83e\udeb4", - "Width and height for scaling image to ideal resolution \ud83e\udeb4", - "Width and height from aspect ratio \ud83e\udeb4" - ], - { - "title_aux": "comfyui-fofr-toolkit" - } - ], - "https://github.com/forever22777/comfyui-self-guidance": [ - [ - "CLIPConditioning", - "CheckpointLoaderMixWithDiffusers", - "SelfGuidanceSampler" - ], - { - "title_aux": "Self-Guidance nodes" - } - ], - "https://github.com/fotobudka-team/comfyui-ai-faces": [ - [ - "PhotoVerification" - ], - { - "title_aux": "ComfyUI AI Faces - Photo Verification Node" - } - ], - "https://github.com/foxtrot-roger/comfyui-rf-nodes": [ - [ - "LogBool", - "LogFloat", - "LogInt", - "LogNumber", - "LogString", - "LogVec2", - "LogVec3", - "RF_AtIndexString", - "RF_BoolToString", - "RF_FloatToString", - "RF_IntToString", - "RF_JsonStyleLoader", - "RF_MergeLines", - "RF_NumberToString", - "RF_OptionsString", - "RF_RangeFloat", - "RF_RangeInt", - "RF_RangeNumber", - "RF_SavePromptInfo", - "RF_SplitLines", - "RF_TextConcatenate", - "RF_TextInput", - "RF_TextReplace", - "RF_Timestamp", - "RF_ToString", - "RF_Vec2ToString", - "RF_Vec3ToString", - "TextLine" - ], - { - "title_aux": "RF Nodes" - } - ], - "https://github.com/fpgaminer/joycaption_comfyui": [ - [ - "JJC_JoyCaption", - "JJC_JoyCaption_Custom" - ], - { - "title_aux": "JoyCaption Nodes" - } - ], - "https://github.com/fplu/comfyui_lama_with_refiner": [ - [ - "INPAINT_InpaintWithLaMaRefinerModel", - "INPAINT_LoadInpaintLaMaModel" - ], - { - "title_aux": "lama_with_refiner" - } - ], - "https://github.com/frankchieng/ComfyUI_Aniportrait": [ - [ - "AniPortrait_Audio2Video", - "AniPortrait_Audio_Path", - "AniPortrait_LoadVideoPath", - "AniPortrait_Pose_Gen_Video", - "AniPortrait_Ref_Image_Path", - "AniPortrait_Video_Gen_Pose" - ], - { - "title_aux": "ComfyUI_Aniportrait" - } - ], - "https://github.com/frankchieng/ComfyUI_MagicClothing": [ - [ - "MagicClothing_Animatediff", - "MagicClothing_Generate", - "MagicClothing_Inpainting" - ], - { - "title_aux": "ComfyUI_MagicClothing" - } - ], - "https://github.com/frankchieng/ComfyUI_llm_easyanimiate": [ - [], - { - "nodename_pattern": "^FrankChiengEasyAnimate", - "title_aux": "ComfyUI_llm_easyanimiate" - } - ], - "https://github.com/fredconex/ComfyUI-SongBloom": [ - [ - "SongBloomGenerate", - "SongBloomModelLoader" - ], - { - "title_aux": "SongBloom" - } - ], - "https://github.com/fredconex/ComfyUI-SoundFlow": [ - [ - "SoundFlow_Concatenator", - "SoundFlow_DuckCompressor", - "SoundFlow_Equalizer", - "SoundFlow_Fade", - "SoundFlow_GainPitchControl", - "SoundFlow_GetLength", - "SoundFlow_Mixer", - "SoundFlow_PreviewAudio", - "SoundFlow_SetLength", - "SoundFlow_SilenceTrimmer", - "SoundFlow_SimpleCompressor", - "SoundFlow_TrimAudio" - ], - { - "title_aux": "ComfyUI-SoundFlow" - } - ], - "https://github.com/fredconex/ComfyUI-SyncEdit": [ - [ - "SyncTextEditor" - ], - { - "title_aux": "Sync Edit" - } - ], - "https://github.com/fredhopp/comfyui-flipflopnodes": [ - [ - "FF Group Positioner", - "FF Load Image with Metadata", - "FF Text" - ], - { - "title_aux": "comfyui-flipflopnodes" - } - ], - "https://github.com/freelifehacker/ComfyUI-ImgMask2PNG": [ - [ - "ImageMask2PNG" - ], - { - "title_aux": "ComfyUI-ImgMask2PNG" - } - ], - "https://github.com/fsdymy1024/ComfyUI_fsdymy": [ - [ - "IPAdapterLayerWeight", - "Preview Image Without Metadata", - "PreviewImageWithoutMetadata", - "Save Image Without Metadata", - "SaveImageWithoutMetadata", - "ShowText", - "ZhiPuAiNode" - ], - { - "title_aux": "ComfyUI_fsdymy" - } - ], - "https://github.com/fssorc/ComfyUI_FFT": [ - [ - "FFTNode", - "FindFFTSpot", - "InvertFFTNode", - "InvertFFTWithMask" - ], - { - "title_aux": "ComfyUI_FFT" - } - ], - "https://github.com/fssorc/ComfyUI_FaceShaper": [ - [ - "FaceAlignmentCropper", - "FaceShaper", - "FaceShaperComposite", - "FaceShaperCropper", - "FaceShaperFaceMask", - "FaceShaperLoadInsightFaceCropper", - "FaceShaperLoadMediaPipeCropper", - "FaceShaperMatchV2", - "FaceShaperModels", - "FaceShaperShowLandMarks" - ], - { - "title_aux": "ComfyUI_FaceShaper" - } - ], - "https://github.com/fssorc/ComfyUI_RopeWrapper": [ - [ - "RopeVideoCombine", - "RopeWrapper_DetectNode", - "RopeWrapper_FaceRestore", - "RopeWrapper_LoadModels", - "RopeWrapper_LoadSwapInfo", - "RopeWrapper_OptionNode", - "RopeWrapper_SaveSwapInfo", - "RopeWrapper_SwapNode", - "RopeWrapper_SwapNodeTEST" - ], - { - "title_aux": "ComfyUI_RopeWrapper" - } - ], - "https://github.com/fssorc/ComfyUI_pose_inter": [ - [ - "GenTPose", - "PoseModify", - "Pose_Inter", - "Pose_Inter_V2" - ], - { - "title_aux": "ComfyUI_pose_inter" - } - ], - "https://github.com/fuselayer/comfyui-mosaic-blur": [ - [ - "ImageMosaic" - ], - { - "title_aux": "comfyui-mosaic-blur" - } - ], - "https://github.com/g0kuvonlange/ComfyUI-Load-From-URL": [ - [ - "Load LoRA From URL", - "Load LoRAs from JSON", - "Load Video From URL" - ], - { - "title_aux": "ComfyUI Load From URL" - } - ], - "https://github.com/gabe-init/ComfyUI-11labs": [ - [ - "ElevenLabsNode" - ], - { - "title_aux": "ComfyUI-11labs" - } - ], - "https://github.com/gabe-init/ComfyUI-Google-Image-Search": [ - [ - "GoogleImageSearchNode" - ], - { - "title_aux": "ComfyUI-Google-Image-Search" - } - ], - "https://github.com/gabe-init/ComfyUI-Openrouter_node": [ - [ - "OpenRouterNode" - ], - { - "title_aux": "ComfyUI OpenRouter Node" - } - ], - "https://github.com/gabe-init/ComfyUI-String-Similarity": [ - [ - "StringSimilarity" - ], - { - "title_aux": "ComfyUI-String-Similarity" - } - ], - "https://github.com/game4d/ComfyUI-BDsInfiniteYou": [ - [ - "InfiniteYou_Image", - "InfiniteYou_Load" - ], - { - "title_aux": "ComfyUI-BDsInfiniteYou" - } - ], - "https://github.com/gasparuff/CustomSelector": [ - [ - "CustomSelector" - ], - { - "title_aux": "comfyui-customselector" - } - ], - "https://github.com/gelasdev/ComfyUI-FLUX-BFL-API": [ - [ - "FluxConfig_BFL", - "FluxDeleteFinetune_BFL", - "FluxDevRedux_BFL", - "FluxDev_BFL", - "FluxFinetuneDetails_BFL", - "FluxFinetuneStatus_BFL", - "FluxFinetune_BFL", - "FluxKontextMax_BFL", - "FluxKontextPro_BFL", - "FluxMyFinetunes_BFL", - "FluxPro11Redux_BFL", - "FluxPro11UltraFinetune_BFL", - "FluxPro11UltraRedux_BFL", - "FluxPro11Ultra_BFL", - "FluxPro11_BFL", - "FluxProCannyFinetune_BFL", - "FluxProCanny_BFL", - "FluxProDepthFinetune_BFL", - "FluxProDepth_BFL", - "FluxProFillFinetune_BFL", - "FluxProFill_BFL", - "FluxProFinetune_BFL", - "FluxPro_BFL" - ], - { - "title_aux": "ComfyUI-FLUX-BFL-API" - } - ], - "https://github.com/gemell1/ComfyUI_GMIC": [ - [ - "GmicCliWrapper", - "GmicQtWrapper" - ], - { - "title_aux": "ComfyUI_GMIC" - } - ], - "https://github.com/geocine/geocine-comfyui": [ - [ - "Image Scale", - "Image Selector", - "LoRA Name List", - "Prompt Text", - "Seed to Noise", - "ShowTextNode", - "Text Replace" - ], - { - "title_aux": "geocine-comfyui" - } - ], - "https://github.com/georgitsenov/ComfyUI-R2": [ - [ - "S3SaveNode" - ], - { - "title_aux": "ComfyUI S3 Save Node" - } - ], - "https://github.com/ggarra13/ComfyUI-mrv2": [ - [ - "mrv2AnnotationsImageNode", - "mrv2SaveEXRImage" - ], - { - "title_aux": "ComfyUI-mrv2" - } - ], - "https://github.com/giriss/comfy-image-saver": [ - [ - "Cfg Literal", - "Checkpoint Selector", - "Int Literal", - "Sampler Selector", - "Save Image w/Metadata", - "Scheduler Selector", - "Seed Generator", - "String Literal", - "Width/Height Literal" - ], - { - "title_aux": "Save Image with Generation Metadata" - } - ], - "https://github.com/gisu/comfyui-foxpack": [ - [ - "Add_To_List", - "BaseSamplerSetup", - "Big_Prompter", - "Change_Entries_In_A_List", - "Change_Entry_From_List", - "CheckpointMetaExtractor", - "CheckpointSelector", - "Complete_Setup", - "Convert_Into", - "Negate_Boolean", - "Optional_Value_Override", - "OverrideSamplerSetup", - "Override_Value_If_Unset", - "Pick_Value_From_Dict", - "Pick_Values_From_List", - "Refine_Prompt", - "Refine_Setup", - "Remap_Values", - "Remove_Values_From_List", - "Select_By_Index", - "Select_Line_By_Index", - "Select_String_By_Index", - "SetupSelector", - "Show_Type", - "Split_Entry_In_2Chunks", - "Split_Entry_In_4Chunks", - "Split_Entry_In_6Chunks", - "Split_Entry_In_8Chunks", - "Step_Denoise", - "UniversalLatentHelper", - "Universal_VAE_Loader" - ], - { - "title_aux": "foxpack" - } - ], - "https://github.com/gitadmini/comfyui_extractstoryboards": [ - [ - "Example", - "ExtractStoryboards_xuhuan1024", - "IntBatchSize_xuhuan1024", - "IntBatch_xuhuan1024" - ], - { - "title_aux": "ExtractStoryboards" - } - ], - "https://github.com/githubYiheng/ComfyUI_Change_IMAGE_BOREDER": [ - [ - "ChangeImageBorder" - ], - { - "title_aux": "ComfyUI_Change_IMAGE_BOREDER" - } - ], - "https://github.com/githubYiheng/ComfyUI_GetFileNameFromURL": [ - [ - "GetFileNameFromURL" - ], - { - "title_aux": "ComfyUI_GetFileNameFromURL" - } - ], - "https://github.com/githubYiheng/comfyui_kmeans_filter": [ - [ - "ImageKmeansFilter" - ], - { - "title_aux": "comfyui_kmeans_filter" - } - ], - "https://github.com/githubYiheng/comfyui_meanshift_filter": [ - [ - "ImageMeanshiftFilter" - ], - { - "title_aux": "comfyui_meanshift_filter" - } - ], - "https://github.com/githubYiheng/comfyui_private_postprocessor": [ - [ - "ImageCPostprocessor", - "PrivateImageMask" - ], - { - "title_aux": "comfyui_private_postprocessor" - } - ], - "https://github.com/glibsonoran/Plush-for-ComfyUI": [ - [ - "AI Chooser", - "Add Parameters", - "AdvPromptEnhancer", - "Custom API Key", - "DalleImage", - "Enhancer", - "GPT Image", - "Gemini Image", - "Image Mixer", - "Imagen Image", - "ImgTextSwitch", - "Load Remote Models", - "LoadText|plush", - "Model-CLIP Output Switch", - "ParseJSON", - "Plush-Exif Wrangler", - "Random Image Output", - "Random Mixer", - "Random Output", - "Remove Text", - "SaveText|plush", - "Tagger", - "Text (Any)", - "Type Converter", - "mulTextSwitch" - ], - { - "title_aux": "Plush-for-ComfyUI" - } - ], - "https://github.com/glifxyz/ComfyUI-GlifNodes": [ - [ - "FilmGrain", - "FluxReduxFloatRamp", - "GlifConsistencyDecoder", - "GlifPatchConsistencyDecoderTiled", - "GlifVariable", - "HFHubEmbeddingLoader", - "HFHubLoraLoader", - "ImagePaddingAdvanced", - "ImageToMultipleOf", - "LoraLoaderFromURL", - "SDXLAspectRatio" - ], - { - "title_aux": "ComfyUI-GlifNodes" - } - ], - "https://github.com/glitchinthemetrix16/ComfyUI-Roop": [ - [ - "RoopBatchFaceSwap", - "RoopFaceSwap", - "RoopFaceSwapVideo", - "RoopFaceSwapWithEnhancer", - "RoopSendWebhookFile", - "RoopSendWebhookImage" - ], - { - "title_aux": "ComfyUI Roop Custom Nodes" - } - ], - "https://github.com/glowcone/comfyui-base64-to-image": [ - [ - "LoadImageFromBase64" - ], - { - "title_aux": "Load Image From Base64 URI" - } - ], - "https://github.com/glowcone/comfyui-string-converter": [ - [ - "StringToFloat", - "StringToInt" - ], - { - "title_aux": "String Converter" - } - ], - "https://github.com/gmorks/ComfyUI-Animagine-Prompt": [ - [ - "AnimaginePrompt", - "MultiWildcardLoader", - "MultilineTextInput", - "TextFileLoader" - ], - { - "title_aux": "ComfyUI-Animagine-Prompt" - } - ], - "https://github.com/gmorks/ComfyUI-SendToDiscord": [ - [ - "PreviewImageWithDiscord" - ], - { - "title_aux": "ComfyUI-SendToDiscord" - } - ], - "https://github.com/goburiin/nsfwrecog-comfyui": [ - [ - "NSFWDetectorNode" - ], - { - "title_aux": "nsfwrecog-comfyui" - } - ], - "https://github.com/godmt/ComfyUI-IP-Composer": [ - [ - "IPCompConceptMerge", - "IPCompConceptSubspace", - "IPCompLoadOpenCLIP", - "IPLoadConceptSubspace", - "IPSaveConceptSubspace" - ], - { - "title_aux": "ComfyUI-IP-Composer" - } - ], - "https://github.com/godmt/ComfyUI-List-Utils": [ - [ - "GODMT_AnyCast", - "GODMT_AnyToDict", - "GODMT_BatchGetByIndex", - "GODMT_BatchItemCast", - "GODMT_BatchSlice", - "GODMT_BatchToList", - "GODMT_CreateArange", - "GODMT_CreateBatch", - "GODMT_CreateLinspace", - "GODMT_CreateList", - "GODMT_CreateRange", - "GODMT_Exec", - "GODMT_GetLength", - "GODMT_GetShape", - "GODMT_GetWidgetsValues", - "GODMT_ListDir", - "GODMT_ListGetByIndex", - "GODMT_ListSlice", - "GODMT_ListToBatch", - "GODMT_MergeBatch", - "GODMT_MergeList", - "GODMT_Pack", - "GODMT_SplitString", - "GODMT_Unpack" - ], - { - "title_aux": "ComfyUI-List-Utils" - } - ], - "https://github.com/godspede/ComfyUI_Substring": [ - [ - "SubstringTheory" - ], - { - "title_aux": "ComfyUI Substring" - } - ], - "https://github.com/gokayfem/ComfyUI-Depth-Visualization": [ - [ - "DepthViewer" - ], - { - "title_aux": "ComfyUI-Depth-Visualization" - } - ], - "https://github.com/gokayfem/ComfyUI-Dream-Interpreter": [ - [ - "DreamViewer" - ], - { - "title_aux": "ComfyUI-Dream-Interpreter" - } - ], - "https://github.com/gokayfem/ComfyUI-Texture-Simple": [ - [ - "TextureViewer" - ], - { - "title_aux": "ComfyUI-Texture-Simple" - } - ], - "https://github.com/gokayfem/ComfyUI-fal-API": [ - [ - "CombinedVideoGeneration_fal", - "FluxDev_fal", - "FluxGeneral_fal", - "FluxLoraTrainer_fal", - "FluxLora_fal", - "FluxPro11_fal", - "FluxProKontextMulti_fal", - "FluxProKontextTextToImage_fal", - "FluxProKontext_fal", - "FluxPro_fal", - "FluxSchnell_fal", - "FluxUltra_fal", - "Hidreamfull_fal", - "HunyuanVideoLoraTrainer_fal", - "Ideogramv3_fal", - "Imagen4Preview_fal", - "KlingMaster_fal", - "KlingPro10_fal", - "KlingPro16_fal", - "Kling_fal", - "LLM_fal", - "LoadVideoURL", - "LtxVideoTrainer_fal", - "LumaDreamMachine_fal", - "MiniMaxSubjectReference_fal", - "MiniMaxTextToVideo_fal", - "MiniMax_fal", - "Recraft_fal", - "RunwayGen3_fal", - "Sana_fal", - "SeedEditV3_fal", - "SeedanceImageToVideo_fal", - "SeedanceTextToVideo_fal", - "Upscaler_fal", - "VLM_fal", - "Veo2ImageToVideo_fal", - "Veo3_fal", - "VideoUpscaler_fal", - "WanLoraTrainer_fal", - "WanPro_fal" - ], - { - "title_aux": "ComfyUI-fal-API" - } - ], - "https://github.com/gokayfem/ComfyUI_VLM_nodes": [ - [ - "AudioLDM2Node", - "ChatMusician", - "CreativeArtPromptGenerator", - "Joytag", - "JsonToText", - "KeywordExtraction", - "Kosmos2model", - "LLMLoader", - "LLMOptionalMemoryFreeAdvanced", - "LLMOptionalMemoryFreeSimple", - "LLMPromptGenerator", - "LLMSampler", - "LLava Loader Simple", - "LLavaOptionalMemoryFreeAdvanced", - "LLavaOptionalMemoryFreeSimple", - "LLavaPromptGenerator", - "LLavaSamplerAdvanced", - "LLavaSamplerSimple", - "LlavaClipLoader", - "MCLLaVAModel", - "MiniCPMNode", - "MolmoNode", - "MoonDream", - "Moondream2model", - "Paligemma", - "PlayMusic", - "PromptGenerateAPI", - "Qwen2VLNode", - "SaveAudioNode", - "SimpleText", - "StructuredOutput", - "Suggester", - "UformGen2QwenNode", - "ViewText" - ], - { - "title_aux": "VLM_nodes" - } - ], - "https://github.com/goldwins520/Comfyui_saveimg2webdav": [ - [ - "SaveFileToWebDAV", - "SaveImageToWebDAV" - ], - { - "title_aux": "Save Image To Webdav" - } - ], - "https://github.com/gonzalu/ComfyUI_YFG_Comical": [ - [ - "Image10Switcher_node", - "Image15Switcher_node", - "Image20Switcher_node", - "Image3Switcher_node", - "Image5Switcher_node", - "MonoClip_node", - "PixelArt_node", - "RandomOrgTrueRandomNumber_node", - "VAEDecodePreview_node", - "image2contrastMask_node", - "image2imbgg_node", - "image_halftone", - "image_histograms_node", - "image_histograms_node_compact", - "images_side_by_side", - "imgbbLoader_node", - "smartCheckpointLoader_node", - "storeURL_node", - "textMaskOverlay_node" - ], - { - "author": "Manny Gonzalez", - "description": "Utility custom nodes for special effects, image manipulation and quality of life tools.", - "nickname": "\ud83d\udc2f YFG Comical Nodes", - "title": "\ud83d\udc2f YFG Comical Nodes", - "title_aux": "\ud83d\ude38 YFG Comical Nodes" - } - ], - "https://github.com/gorillaframeai/GF_nodes": [ - [ - "GFrbmg2", - "GFrbmg2Plus" - ], - { - "title_aux": "GFrbmg2" - } - ], - "https://github.com/gorillaframeai/GF_translate": [ - [ - "GFDeepTranslate", - "GFJsonTranslate" - ], - { - "title_aux": "GF_translate" - } - ], - "https://github.com/greengerong/ComfyUI-JanusPro-PL": [ - [ - "JanusProImageGenerator", - "JanusProImageUnderstanding", - "JanusProModelLoader" - ], - { - "title_aux": "Janus-Pro ComfyUI Plugin" - } - ], - "https://github.com/greengerong/ComfyUI-Lumina-Video": [ - [ - "LuminaVideoModelLoader", - "LuminaVideoSampler", - "LuminaVideoVAEDecode" - ], - { - "title_aux": "ComfyUI-Lumina-Video" - } - ], - "https://github.com/gremlation/ComfyUI-ImageLabel": [ - [ - "gremlation:ComfyUI-ImageLabel:ImageLabel" - ], - { - "title_aux": "ComfyUI-ImageLabel" - } - ], - "https://github.com/gremlation/ComfyUI-JMESPath": [ - [ - "gremlation:ComfyUI-JMESPath" - ], - { - "title_aux": "ComfyUI-JMESPath" - } - ], - "https://github.com/gremlation/ComfyUI-ViewData": [ - [ - "gremlation:ComfyUI-ViewData:ViewData" - ], - { - "title_aux": "ComfyUI-ViewData" - } - ], - "https://github.com/gremlation/ComfyUI-jq": [ - [ - "gremlation:ComfyUI-jq" - ], - { - "title_aux": "ComfyUI-jq" - } - ], - "https://github.com/griptape-ai/ComfyUI-Griptape": [ - [ - "Griptape Agent Config: Amazon Bedrock Drivers", - "Griptape Agent Config: Amazon Bedrock [DEPRECATED]", - "Griptape Agent Config: Anthropic Drivers", - "Griptape Agent Config: Anthropic [DEPRECATED]", - "Griptape Agent Config: Azure OpenAI Drivers", - "Griptape Agent Config: Azure OpenAI [DEPRECATED]", - "Griptape Agent Config: Cohere Drivers", - "Griptape Agent Config: Custom Structure", - "Griptape Agent Config: Environment Variables", - "Griptape Agent Config: Expand", - "Griptape Agent Config: Google Drivers", - "Griptape Agent Config: Google [DEPRECATED]", - "Griptape Agent Config: Griptape Cloud", - "Griptape Agent Config: Grok Drivers", - "Griptape Agent Config: Groq Drivers", - "Griptape Agent Config: HuggingFace Drivers", - "Griptape Agent Config: HuggingFace [DEPRECATED]", - "Griptape Agent Config: LM Studio Drivers", - "Griptape Agent Config: LM Studio [DEPRECATED]", - "Griptape Agent Config: Ollama Drivers", - "Griptape Agent Config: Ollama [DEPRECATED]", - "Griptape Agent Config: OpenAI Compatible Drivers", - "Griptape Agent Config: OpenAI Compatible [DEPRECATED]", - "Griptape Agent Config: OpenAI Drivers", - "Griptape Agent Config: OpenAI [DEPRECATED]", - "Griptape Audio Transcription Driver: Groq", - "Griptape Audio Transcription Driver: OpenAI", - "Griptape Code: Run Griptape Cloud Structure", - "Griptape Code: Run Python [DEPRECATED]", - "Griptape Combine: Merge Dictionary", - "Griptape Combine: Merge Inputs", - "Griptape Combine: Merge Texts", - "Griptape Combine: RAG Module List", - "Griptape Combine: Rules List", - "Griptape Combine: String List", - "Griptape Combine: Tool List", - "Griptape Config: Environment Variables", - "Griptape Convert: Agent to Tool", - "Griptape Convert: Text to CLIP Encode", - "Griptape Convert: Text to Combo", - "Griptape Create: Agent", - "Griptape Create: Agent from Config", - "Griptape Create: CLIP Text Encode", - "Griptape Create: Image Inpainting Variation", - "Griptape Create: Image Variation", - "Griptape Create: Image from Text", - "Griptape Create: Key Value Pair", - "Griptape Create: Rules", - "Griptape Create: Text", - "Griptape Display: Artifact", - "Griptape Display: Data as Text", - "Griptape Display: Dictionary", - "Griptape Display: Image", - "Griptape Display: Text", - "Griptape Display: Text as Markdown", - "Griptape Driver: Amazon Bedrock Stable Diffusion", - "Griptape Driver: Amazon Bedrock Titan", - "Griptape Driver: Azure OpenAI Image Generation", - "Griptape Driver: Black Forest Labs Image Generation", - "Griptape Driver: Leonardo.AI", - "Griptape Driver: OpenAI Compatible Image Generation", - "Griptape Driver: OpenAI Image Generation", - "Griptape Embedding Driver: Amazon Bedrock Titan", - "Griptape Embedding Driver: Amazon SageMaker Jumpstart", - "Griptape Embedding Driver: Azure OpenAI", - "Griptape Embedding Driver: Cohere", - "Griptape Embedding Driver: Google", - "Griptape Embedding Driver: HuggingFace", - "Griptape Embedding Driver: LM Studio", - "Griptape Embedding Driver: Ollama", - "Griptape Embedding Driver: OpenAI", - "Griptape Embedding Driver: OpenAI Compatible", - "Griptape Embedding Driver: Voyage AI", - "Griptape End Workflow", - "Griptape Expand: Agent Nodes", - "Griptape Load: Audio", - "Griptape Load: Image From URL", - "Griptape Load: Text", - "Griptape Prompt Driver: Amazon Bedrock", - "Griptape Prompt Driver: Amazon SageMaker Jumpstart", - "Griptape Prompt Driver: Anthropic", - "Griptape Prompt Driver: Azure OpenAI", - "Griptape Prompt Driver: Cohere", - "Griptape Prompt Driver: Google", - "Griptape Prompt Driver: Griptape Cloud", - "Griptape Prompt Driver: Grok", - "Griptape Prompt Driver: Groq", - "Griptape Prompt Driver: HuggingFace", - "Griptape Prompt Driver: LM Studio", - "Griptape Prompt Driver: Ollama", - "Griptape Prompt Driver: OpenAI", - "Griptape Prompt Driver: OpenAI Compatible", - "Griptape RAG Query: Translate Module", - "Griptape RAG Rerank: Text Chunks Module", - "Griptape RAG Response: Footnote Prompt Module", - "Griptape RAG Response: Prompt Module", - "Griptape RAG Response: Text Chunks Module", - "Griptape RAG Retrieve: Text Loader Module", - "Griptape RAG Retrieve: Vector Store Module", - "Griptape RAG: Engine", - "Griptape Replace: Rulesets on Agent", - "Griptape Replace: Tools on Agent", - "Griptape Rerank Driver: Cohere", - "Griptape Rerank Driver: Local", - "Griptape Retrieve: Cloud Ruleset", - "Griptape Run: Agent", - "Griptape Run: Audio Transcription", - "Griptape Run: Cloud Assistant", - "Griptape Run: Image Description", - "Griptape Run: Parallel Image Description", - "Griptape Run: Parallel Prompt Task", - "Griptape Run: Prompt Task", - "Griptape Run: Task", - "Griptape Run: Text Extraction", - "Griptape Run: Text Summary", - "Griptape Run: Text to Speech", - "Griptape Run: Tool Task", - "Griptape Run: Toolkit Task", - "Griptape Save: Text", - "Griptape Set: Default Agent", - "Griptape Start Workflow", - "Griptape Text To Speech Driver: ElevenLabs", - "Griptape Text To Speech Driver: OpenAI", - "Griptape Tool: Audio Transcription", - "Griptape Tool: Calculator", - "Griptape Tool: DateTime", - "Griptape Tool: Extraction", - "Griptape Tool: FileManager", - "Griptape Tool: Griptape Cloud KnowledgeBase", - "Griptape Tool: Prompt Summary", - "Griptape Tool: Query", - "Griptape Tool: RAG", - "Griptape Tool: Text to Speech", - "Griptape Tool: VectorStore", - "Griptape Tool: WebScraper", - "Griptape Tool: WebSearch", - "Griptape Util: Create Agent Modelfile", - "Griptape Util: Create Model from Modelfile", - "Griptape Util: Remove Ollama Model", - "Griptape Util: Switch Node", - "Griptape Vector Store Driver: Amazon OpenSearch", - "Griptape Vector Store Driver: Azure MongoDB", - "Griptape Vector Store Driver: Griptape Cloud", - "Griptape Vector Store Driver: Local", - "Griptape Vector Store Driver: Marqo", - "Griptape Vector Store Driver: MongoDB Atlas", - "Griptape Vector Store Driver: PGVector", - "Griptape Vector Store Driver: Pinecone", - "Griptape Vector Store Driver: Qdrant", - "Griptape Vector Store Driver: Redis", - "Griptape Vector Store: Add Text", - "Griptape Vector Store: Query", - "Griptape WebSearch Driver: DuckDuckGo", - "Griptape WebSearch Driver: Exa", - "Griptape WebSearch Driver: Google", - "Griptape WebSearch Driver: Serper", - "Griptape WebSearch Driver: Tavily" - ], - { - "author": "Jason Schleifer", - "description": "This extension offers various nodes that allow you to work with LLMs using the Griptape Python Framework (https://griptape.ai)", - "nickname": "ComfyUI-Griptape", - "title": "ComfyUI Griptape Nodes", - "title_aux": "ComfyUI Griptape Nodes" - } - ], - "https://github.com/grmchn/ComfyUI-ProportionChanger": [ - [ - "PoseJSONToPoseKeypoint", - "PoseKeypointPreview", - "ProportionChangerDWPoseDetector", - "ProportionChangerDWPoseRender", - "ProportionChangerInterpolator", - "ProportionChangerKeypointDenoiser", - "ProportionChangerKeypointDenoiserAdvanced", - "ProportionChangerParams", - "ProportionChangerReference" - ], - { - "title_aux": "ComfyUI Utilitools Nodes" - } - ], - "https://github.com/gseth/ControlAltAI-Nodes": [ - [ - "BooleanBasic", - "BooleanReverse", - "ChooseUpscaleModel", - "FluxAttentionCleanup", - "FluxAttentionControl", - "FluxControlNetApply", - "FluxResolutionNode", - "FluxSampler", - "FluxUnionControlNetApply", - "GetImageSizeRatio", - "HiDreamResolutionNode", - "IntegerSettings", - "IntegerSettingsAdvanced", - "NoisePlusBlend", - "PerturbationTexture", - "RegionMaskConditioning", - "RegionMaskGenerator", - "RegionMaskProcessor", - "RegionMaskValidator", - "RegionOverlayVisualizer", - "TextBridge", - "ThreeWaySwitch", - "TwoWaySwitch" - ], - { - "title_aux": "ControlAltAI Nodes" - } - ], - "https://github.com/gt732/ComfyUI-DreamWaltz-G": [ - [ - "DreamWaltzGStageOneTrainer", - "DreamWaltzGStageTwoTrainer" - ], - { - "title_aux": "ComfyUI-DreamWaltz-G" - } - ], - "https://github.com/guerreiro/comfyg-switch": [ - [ - "ComfygSwitch" - ], - { - "title_aux": "Comfyg Switch" - } - ], - "https://github.com/guill/abracadabra-comfyui": [ - [ - "AbracadabraNode", - "AbracadabraNodeDefSummary" - ], - { - "title_aux": "abracadabra-comfyui" - } - ], - "https://github.com/guyaton/guy-nodes-comfyui": [ - [ - "GuyRecommendedLatentResCalc" - ], - { - "title_aux": "guy-nodes-comfyui" - } - ], - "https://github.com/gvfarns/comfyui_gvf": [ - [ - "CheckpointLoaderWithName", - "CropToAspectRatio", - "CropToAspectRatioMinMax", - "IfElseFloat", - "IfElseInt", - "StringContains" - ], - { - "title_aux": "comfyui_gvf" - } - ], - "https://github.com/hackkhai/ComfyUI-Image-Matting": [ - [ - "ApplyMatting", - "CreateTrimap", - "MattingModelLoader" - ], - { - "title_aux": "ComfyUI-Image-Matting" - } - ], - "https://github.com/hanoixan/ComfyUI-DataBeast": [ - [ - "DBConvertToBoolean //DataBeast", - "DBConvertToFloat //DataBeast", - "DBConvertToInt //DataBeast", - "DBConvertToString //DataBeast", - "DBFloatExpression //DataBeast", - "DBGetBatchList //DataBeast", - "DBGetItem //DataBeast", - "DBLoadData //DataBeast", - "DBStringExpression //DataBeast" - ], - { - "author": "hanoixan", - "description": "This extension provides nodes for controlling data-driven processing in Comfy-UI", - "nickname": "DataBeast", - "title": "DataBeast", - "title_aux": "ComfyUI DataBeast" - } - ], - "https://github.com/hao-ai-lab/FastVideo": [ - [ - "DITConfig", - "InferenceArgs", - "LoadImagePath", - "TextEncoderConfig", - "VAEConfig", - "VideoGenerator" - ], - { - "title_aux": "ComfyUI-FastVideo" - } - ], - "https://github.com/haohaocreates/ComfyUI-HH-Image-Selector": [ - [ - "Image Selector" - ], - { - "title_aux": "ComfyUI-HH-Image-Selector" - } - ], - "https://github.com/hassan-sd/comfyui-image-prompt-loader": [ - [ - "ImagePromptLoader", - "apt", - "author", - "category", - "description", - "files", - "install_type", - "js_path", - "license", - "name", - "nodename_pattern", - "pip", - "preemptions", - "reference", - "repository", - "tags", - "title_aux", - "version" - ], - { - "title_aux": "ComfyUI Image & Prompt Loader" - } - ], - "https://github.com/havvk/ComfyUI_AIIA": [ - [ - "AIIA_E2E_Speaker_Diarization", - "AIIA_FloatProcess_InMemory", - "AIIA_FloatProcess_ToDisk", - "AIIA_GenerateSpeakerSegments", - "AIIA_Utils_Image_Concanate", - "AIIA_VideoCombine" - ], - { - "title_aux": "ComfyUI_AIIA" - } - ], - "https://github.com/hay86/ComfyUI_DDColor": [ - [ - "D_DDColor" - ], - { - "title_aux": "ComfyUI DDColor" - } - ], - "https://github.com/hay86/ComfyUI_Dreamtalk": [ - [ - "D_DreamTalk" - ], - { - "title_aux": "ComfyUI Dreamtalk" - } - ], - "https://github.com/hay86/ComfyUI_Hallo": [ - [ - "D_HalloNode" - ], - { - "title_aux": "ComfyUI Hallo" - } - ], - "https://github.com/hay86/ComfyUI_LatentSync": [ - [ - "D_LatentSyncNode" - ], - { - "title_aux": "ComfyUI LatentSync" - } - ], - "https://github.com/hay86/ComfyUI_MiniCPM-V": [ - [ - "D_MiniCPM_VQA" - ], - { - "title_aux": "ComfyUI MiniCPM-V" - } - ], - "https://github.com/hay86/ComfyUI_OpenVoice": [ - [ - "D_OpenVoice_STS", - "D_OpenVoice_TTS", - "D_OpenVoice_TTS_V2" - ], - { - "title_aux": "ComfyUI OpenVoice" - } - ], - "https://github.com/hayd-zju/ICEdit-ComfyUI-official": [ - [ - "SaveImageWebsocket" - ], - { - "title_aux": "ICEdit-ComfyUI-official" - } - ], - "https://github.com/hayde0096/Comfyui-EasySettingpipes": [ - [ - "ConvertAny", - "SamplerSetup", - "SamplerSetupUnpack" - ], - { - "title_aux": "EasySettingpipes" - } - ], - "https://github.com/hben35096/ComfyUI-ReplenishNodes": [ - [ - "Batch Image Blend", - "FLOAT Output", - "Fill Alpha", - "Get Batch Count", - "Image Align", - "Image Blend BG", - "Integer Output", - "Load CLIP Name", - "Load Ckpt Name", - "Load Lora Name", - "Load Sampler Name", - "Load Scheduler Name", - "Load UNET Name", - "Mask Levels Adjust", - "Multi Line Text", - "Multiple Image Blend", - "Multiple Image Blend 2", - "Preview Image-JPEG", - "Reference Resize", - "Seed Output", - "To JPEG", - "To RGB" - ], - { - "title_aux": "ComfyUI-ReplenishNodes" - } - ], - "https://github.com/heheok/comfyui_wan2.1_vace_infinite_helpers": [ - [ - "CyclicCharacterAndBackgroundPrompt", - "LatestVideoFromFolder", - "PrepareControlVideo" - ], - { - "title_aux": "comfyui_wan2.1_vace_infinite_helpers" - } - ], - "https://github.com/hekmon/comfyui-checkpoint-extract": [ - [ - "CLIPModelSaver", - "VAEModelSaver" - ], - { - "title_aux": "comfyui-checkpoint-extract" - } - ], - "https://github.com/hekmon/comfyui-openai-api": [ - [ - "OAIAPIChatCompletion", - "OAIAPIClient", - "OAIAPIDebug", - "OAIAPIDeveloperRole", - "OAIAPIExtraBody", - "OAIAPIFrequencyPenalty", - "OAIAPIMaxTokens", - "OAIAPIPresencePenalty", - "OAIAPISeed", - "OAIAPITemperature", - "OAIAPITopP" - ], - { - "title_aux": "ComfyUI OpenAI API" - } - ], - "https://github.com/heshengtao/comfyui_LLM_party": [ - [ - "About_us", - "AmapRegeoTool", - "AmapWeatherTool", - "Browser_display", - "CLIPTextEncode_party", - "Combine_Videos_party", - "Dingding", - "Dingding_tool", - "EasyOCR_advance", - "EasyOCR_choose", - "FeishuDownloadAudio", - "FeishuDownloadImage", - "FeishuGetHistory", - "FeishuSendMsg", - "FileOnlineDelete_gitee", - "FileOnlineStorage_gitee", - "FilePathExists", - "FolderCleaner", - "GGUFLoader", - "GeocodeTool", - "Image2Video_party", - "Images2Image", - "KG_csv_toolkit_developer", - "KG_csv_toolkit_user", - "KG_json_toolkit_developer", - "KG_json_toolkit_user", - "KG_neo_toolkit_developer", - "KG_neo_toolkit_user", - "KSampler_party", - "LLM", - "LLM_api_loader", - "LLM_local", - "LLM_local_loader", - "LLavaLoader", - "LorapathLoader", - "Lorebook", - "Mcp_tool", - "RSS_loader", - "RSS_tool", - "SpeedChange", - "URL2IMG", - "VAEDecode_party", - "accuweather_tool", - "advance_ebd_tool", - "aisuite_loader", - "any2str", - "any_switcher", - "api_function", - "api_tool", - "arxiv_tool", - "bing_loader", - "bing_tool", - "bool_logic", - "browser_use_tool", - "check_text", - "check_web_tool", - "classify_function", - "classify_function_plus", - "classify_persona", - "classify_persona_plus", - "clear_file", - "clear_model", - "custom_persona", - "custom_string_format", - "dall_e_tool", - "discord_bot", - "discord_file_monitor", - "discord_send", - "duckduckgo_loader", - "duckduckgo_tool", - "easy_GGUFLoader", - "easy_LLM_api_loader", - "easy_LLM_local_loader", - "easy_LLavaLoader", - "easy_load_llm_lora", - "easy_vlmLoader", - "ebd_tool", - "embeddings_function", - "end_anything", - "end_dialog", - "end_workflow", - "extra_parameters", - "feishu", - "feishu_tool", - "file_combine", - "file_combine_plus", - "file_path_iterator", - "files_read_tool", - "fish_tts", - "fish_whisper", - "flux_persona", - "genai_api_loader", - "get_string", - "github_tool", - "google_loader", - "google_tool", - "got_ocr", - "gpt_sovits", - "graph_md_to_html", - "html2img_function", - "ic_lora_persona", - "image_iterator", - "img2path", - "img_hosting", - "interpreter_function", - "interpreter_tool", - "interrupt_loop", - "json2text", - "json_extractor", - "json_get_value", - "json_iterator", - "json_parser", - "json_writing", - "keyword_tool", - "list_append", - "list_append_plus", - "list_extend", - "list_extend_plus", - "listen_audio", - "load_SQL_memo", - "load_bool", - "load_ebd", - "load_excel", - "load_file", - "load_file_folder", - "load_float", - "load_img_path", - "load_int", - "load_keyword", - "load_llm_lora", - "load_memo", - "load_name", - "load_openai_ebd", - "load_persona", - "load_redis_memo", - "load_url", - "load_wikipedia", - "md_to_excel", - "md_to_html", - "mini_error_correction", - "mini_flux_prompt", - "mini_flux_tag", - "mini_intent_recognition", - "mini_ocr", - "mini_party", - "mini_sd_prompt", - "mini_sd_tag", - "mini_story", - "mini_summary", - "mini_translate", - "none2false", - "omost_decode", - "omost_json2py", - "omost_setting", - "open_url_function", - "open_url_tool", - "openai_dall_e", - "openai_ebd_tool", - "openai_tts", - "openai_whisper", - "parameter_combine", - "parameter_combine_plus", - "parameter_function", - "path2img_tool", - "red_book_text_persona", - "replace_string", - "save_SQL_memo", - "save_ebd_database", - "save_memo", - "save_openai_ebd", - "save_redis_memo", - "savepersona", - "searxng_tool", - "send_to_wechat_official", - "show_text_party", - "sql_tool", - "srt2txt", - "start_anything", - "start_dialog", - "start_workflow", - "story_json_tool", - "str2float", - "str2int", - "string_combine", - "string_combine_plus", - "string_logic", - "substring", - "svg2html", - "svg2img_function", - "text2json", - "text2parameters", - "text_iterator", - "text_writing", - "time_sleep", - "time_tool", - "tool_combine", - "tool_combine_plus", - "translate_persona", - "txt2srt", - "url2img_tool", - "vlmLoader", - "weekday_tool", - "whisper_local", - "wikipedia_tool", - "work_wechat", - "work_wechat_tool", - "workflow_tool", - "workflow_transfer", - "workflow_transfer_v2" - ], - { - "title_aux": "comfyui_LLM_party" - } - ], - "https://github.com/heshengtao/comfyui_LLM_schools": [ - [ - "CausalLM_trainer", - "IA3_Arguments", - "LLM_Arguments", - "Lora_or_adapter_Arguments", - "P_or_Prompt_Arguments", - "Prefix_Arguments", - "download_dataset", - "get_dataset_name", - "split_dataset" - ], - { - "title_aux": "comfyui_LLM_schools" - } - ], - "https://github.com/hexxacubic/ComfyUI-Prompt_Library": [ - [ - "Double_Prompt_Encode", - "Multi_Wildcard_Loader", - "Prompt_Extender", - "Prompt_Library", - "Simple_Prompt_Library" - ], - { - "title_aux": "ComfyUI-Prompt_Library" - } - ], - "https://github.com/hgabha/WWAA-CustomNodes": [ - [ - "WWAA-BuildString", - "WWAA-LineCount", - "WWAA_AdvancedGridLayoutNode", - "WWAA_AdvancedTextFileReader", - "WWAA_DitherNode", - "WWAA_GBCamera", - "WWAA_GridLayoutNode", - "WWAA_ImageLoader", - "WWAA_ImageToTextFile", - "WWAA_IndexGridLayoutNode", - "WWAA_NestedLoopCounter", - "WWAA_PromptWriter", - "WWAA_SearchReplaceText", - "WWAA_Switch_Int" - ], - { - "title_aux": "WWAA-CustomNodes" - } - ], - "https://github.com/hhhzzyang/Comfyui_Lama": [ - [ - "LamaApply", - "LamaModelLoader", - "YamlConfigLoader" - ], - { - "title_aux": "Comfyui-Lama" - } - ], - "https://github.com/hiderminer/ComfyUI-HM-Utilities": [ - [ - "AutoCropImage", - "NormalizeImageWithRectangle" - ], - { - "title_aux": "ComfyUI-HM-Tools" - } - ], - "https://github.com/hieuck/ComfyUI-BiRefNet": [ - [ - "BiRefNet" - ], - { - "title_aux": "ComfyUI-BiRefNet-Fix utils" - } - ], - "https://github.com/hiforce/comfyui-hiforce-plugin": [ - [ - "HfBoolSwitchKSampleStatus", - "HfImageAutoExpansionSquare", - "HfImageToRGB", - "HfImageToRGBA", - "HfInitImageWithMaxSize", - "HfIterativeLatentUpscale", - "HfLoadImageWithCropper", - "HfLookbackSamplerLoader", - "HfLoopback", - "HfResizeImage", - "HfSampler", - "HfSamplerLoader", - "HfSamplerLoopback", - "HfSaveImage", - "HfSwitchKSampleStatus", - "HfTwoSamplersForMask", - "HfTwoStepSamplers", - "LoadImageFromURL" - ], - { - "title_aux": "Comfyui HiFORCE Plugin" - } - ], - "https://github.com/hinablue/ComfyUI_3dPoseEditor": [ - [ - "Hina.PoseEditor3D" - ], - { - "title_aux": "ComfyUI 3D Pose Editor" - } - ], - "https://github.com/hmwl/ComfyUI_zip": [ - [ - "CleanFolders", - "CompressImages", - "UnzipToInput" - ], - { - "title_aux": "ComfyUI_zip" - } - ], - "https://github.com/hnmr293/comfyui-savemem": [ - [ - "SaveImagesMemory", - "SaveLatentsMemory" - ], - { - "title_aux": "ComfyUI-SaveMem" - } - ], - "https://github.com/hodanajan/optimal-crop-resolution": [ - [ - "AspectRatioCalculator", - "ResolutionMatcher" - ], - { - "title_aux": "optimal-crop-resolution" - } - ], - "https://github.com/hoveychen/ComfyUI-MusePose-Remaster": [ - [ - "musepose_getposes", - "musepose_inference" - ], - { - "title_aux": "ComfyUI-MusePose-Remaster" - } - ], - "https://github.com/huagetai/ComfyUI-Gaffer": [ - [ - "ApplyICLight", - "CalculateNormalMap", - "GrayScaler", - "ICLightModelLoader", - "LightSource" - ], - { - "title_aux": "comfyui's gaffer(ComfyUI native implementation of IC-Light. )" - } - ], - "https://github.com/huagetai/ComfyUI_LightGradient": [ - [ - "ImageGradient", - "MaskGradient" - ], - { - "title_aux": "Light Gradient for ComfyUI" - } - ], - "https://github.com/huanngzh/ComfyUI-MVAdapter": [ - [ - "BiRefNet", - "ControlImagePreprocessor", - "ControlNetModelLoader", - "CustomLoraModelLoader", - "DiffusersMVModelMakeup", - "DiffusersMVPipelineLoader", - "DiffusersMVSampler", - "DiffusersMVSchedulerLoader", - "DiffusersMVVaeLoader", - "ImagePreprocessor", - "LdmPipelineLoader", - "LdmVaeLoader", - "ViewSelector" - ], - { - "title_aux": "ComfyUI-MVAdapter" - } - ], - "https://github.com/hubentu/ComfyUI-loras-loader": [ - [ - "DynamicLoRALoader", - "LoRAStringAdapter", - "MultiLoRAnameLoader", - "MultiLoraLoader", - "MultiTriggerLoader" - ], - { - "title_aux": "Multiple LoRA Loader for ComfyUI" - } - ], - "https://github.com/huchenlei/ComfyUI-IC-Light-Native": [ - [ - "ICLightApplyMaskGrey", - "ICLightAppply", - "VAEEncodeArgMax" - ], - { - "title_aux": "ComfyUI-IC-Light-Native" - } - ], - "https://github.com/huchenlei/ComfyUI-layerdiffuse": [ - [ - "LayeredDiffusionApply", - "LayeredDiffusionCondApply", - "LayeredDiffusionCondJointApply", - "LayeredDiffusionDecode", - "LayeredDiffusionDecodeRGBA", - "LayeredDiffusionDecodeSplit", - "LayeredDiffusionDiffApply", - "LayeredDiffusionJointApply" - ], - { - "title_aux": "ComfyUI-layerdiffuse (layerdiffusion)" - } - ], - "https://github.com/huchenlei/ComfyUI-openpose-editor": [ - [ - "huchenlei.LoadOpenposeJSON" - ], - { - "title_aux": "ComfyUI-openpose-editor" - } - ], - "https://github.com/huchenlei/ComfyUI_DanTagGen": [ - [ - "PromptDanTagGen" - ], - { - "title_aux": "ComfyUI_DanTagGen" - } - ], - "https://github.com/huchenlei/ComfyUI_densediffusion": [ - [ - "DenseDiffusionAddCondNode", - "DenseDiffusionApplyNode" - ], - { - "title_aux": "ComfyUI DenseDiffusion" - } - ], - "https://github.com/huchenlei/ComfyUI_omost": [ - [ - "OmostDenseDiffusionLayoutNode", - "OmostGreedyBagsTextEmbeddingNode", - "OmostLLMChatNode", - "OmostLLMHTTPServerNode", - "OmostLLMLoaderNode", - "OmostLayoutCondNode", - "OmostLoadCanvasConditioningNode", - "OmostLoadCanvasPythonCodeNode", - "OmostRenderCanvasConditioningNode" - ], - { - "title_aux": "ComfyUI_omost" - } - ], - "https://github.com/hughescr/ComfyUI-OpenPose-Keypoint-Extractor": [ - [ - "Openpose Keypoint Extractor" - ], - { - "title_aux": "OpenPose Keypoint Extractor" - } - ], - "https://github.com/hugobb/FastGAN-ComfyUI-Node": [ - [ - "GenerateImages", - "LoadFastGAN", - "LoadLatent", - "SampleLatent", - "SaveLatent" - ], - { - "title_aux": "fastgan-comfyui" - } - ], - "https://github.com/huixingyun/ComfyUI-HX-Captioner": [ - [ - "HXOllamaCaptioner" - ], - { - "title_aux": "ComfyUI-HX-Captioner" - } - ], - "https://github.com/huixingyun/ComfyUI-HX-Pimg": [ - [ - "SaveImageWithPromptsWebsocket" - ], - { - "title_aux": "ComfyUI-HX-Pimg" - } - ], - "https://github.com/hunzmusic/ComfyUI-IG2MV": [ - [ - "DiffusersIGMVModelMakeup", - "DiffusersIGMVSampler" - ], - { - "title_aux": "ComfyUI-IG2MV" - } - ], - "https://github.com/hustille/ComfyUI_Fooocus_KSampler": [ - [ - "KSampler With Refiner (Fooocus)" - ], - { - "title_aux": "ComfyUI_Fooocus_KSampler" - } - ], - "https://github.com/hustille/ComfyUI_hus_utils": [ - [ - "3way Prompt Styler", - "Batch State", - "Date Time Format", - "Debug Extra", - "Fetch widget value", - "Text Hash" - ], - { - "title_aux": "hus' utils for ComfyUI" - } - ], - "https://github.com/huwenkai26/comfyui-remove-text": [ - [ - "ImageRemoveText" - ], - { - "title_aux": "ComfyUI Text Remove Node" - } - ], - "https://github.com/hvppycoding/comfyui-json-prompt-renderer": [ - [ - "ExtractJSON", - "TemplateRenderFromJSON" - ], - { - "title_aux": "json prompt renderer" - } - ], - "https://github.com/hvppycoding/comfyui-random-sampler-scheduler-steps": [ - [ - "RandomSamplerSchedulerSteps" - ], - { - "title_aux": "RandomSamplerSchedulerSteps for ComfyUI" - } - ], - "https://github.com/hwhaocool/ComfyUI-Select-Any": [ - [ - "SelectAnyValues" - ], - { - "title_aux": "ComfyUI-Select-Any" - } - ], - "https://github.com/hybskgks28275/ComfyUI-hybs-nodes": [ - [ - "Random Resolution Selector", - "Resolution Selector", - "Seed List Generator" - ], - { - "title_aux": "ComfyUI-hybs-nodes" - } - ], - "https://github.com/hyunamy/comfy-ui-on-complete-email-me": [ - [ - "OnCompleteEmailMe", - "OnCompletePlaySound", - "OnCompleteWebhook" - ], - { - "title_aux": "Comfy-UI on-complete-email-me" - } - ], - "https://github.com/iDAPPA/ComfyUI-AMDGPUMonitor": [ - [ - "AMDGPUMonitor" - ], - { - "title_aux": "AMD GPU Monitor for ComfyUI" - } - ], - "https://github.com/iFREEGROUP/comfyui-undistort": [ - [ - "IG_LoadCheckerboardImageForCalibrateCamera", - "IG_MatrixAndDistCoefToText", - "IG_Undistort" - ], - { - "title_aux": "comfyui-undistort" - } - ], - "https://github.com/iacoposk8/ComfyUI-Fooocus-Inpaint-Wrapper": [ - [ - "AlignYourStepsScheduler", - "BasicScheduler", - "CLIPLoader", - "CLIPMergeSimple", - "CLIPSave", - "CLIPSetLastLayer", - "CLIPTextEncode", - "CLIPTextEncodeSDXL", - "CLIPTextEncodeSDXLRefiner", - "CLIPVisionEncode", - "CLIPVisionLoader", - "Canny", - "CheckpointLoader", - "CheckpointLoaderSimple", - "CheckpointSave", - "ConditioningAverage", - "ConditioningCombine", - "ConditioningConcat", - "ConditioningSetArea", - "ConditioningSetAreaPercentage", - "ConditioningSetMask", - "ConditioningSetTimestepRange", - "ConditioningZeroOut", - "ControlNetApply", - "ControlNetApplyAdvanced", - "ControlNetLoader", - "CropMask", - "DiffControlNetLoader", - "DiffusersLoader", - "DualCLIPLoader", - "EmptyImage", - "EmptyLatentImage", - "ExponentialScheduler", - "FeatherMask", - "FlipSigmas", - "FooocusInpaintWrapper", - "FreeU", - "FreeU_V2", - "GLIGENLoader", - "GLIGENTextBoxApply", - "GrowMask", - "HyperTile", - "HypernetworkLoader", - "ImageBatch", - "ImageBlend", - "ImageBlur", - "ImageColorToMask", - "ImageCompositeMasked", - "ImageCrop", - "ImageInvert", - "ImageOnlyCheckpointLoader", - "ImageOnlyCheckpointSave", - "ImagePadForOutpaint", - "ImageQuantize", - "ImageScale", - "ImageScaleBy", - "ImageScaleToTotalPixels", - "ImageSharpen", - "ImageToMask", - "ImageUpscaleWithModel", - "InpaintModelConditioning", - "InvertMask", - "JoinImageWithAlpha", - "KSampler", - "KSamplerAdvanced", - "KSamplerSelect", - "KarrasScheduler", - "LatentAdd", - "LatentBatch", - "LatentBatchSeedBehavior", - "LatentBlend", - "LatentComposite", - "LatentCompositeMasked", - "LatentCrop", - "LatentFlip", - "LatentFromBatch", - "LatentInterpolate", - "LatentMultiply", - "LatentRotate", - "LatentSubtract", - "LatentUpscale", - "LatentUpscaleBy", - "LoadImage", - "LoadImageMask", - "LoadLatent", - "LoraLoader", - "LoraLoaderModelOnly", - "MaskComposite", - "MaskToImage", - "ModelMergeAdd", - "ModelMergeBlocks", - "ModelMergeSimple", - "ModelMergeSubtract", - "ModelSamplingContinuousEDM", - "ModelSamplingDiscrete", - "PatchModelAddDownscale", - "PerpNeg", - "PhotoMakerEncode", - "PhotoMakerLoader", - "PolyexponentialScheduler", - "PorterDuffImageComposite", - "PreviewImage", - "RebatchImages", - "RebatchLatents", - "RepeatImageBatch", - "RepeatLatentBatch", - "RescaleCFG", - "SDTurboScheduler", - "SD_4XUpscale_Conditioning", - "SVD_img2vid_Conditioning", - "SamplerCustom", - "SamplerDPMPP_2M_SDE", - "SamplerDPMPP_SDE", - "SamplerTCD", - "SaveAnimatedPNG", - "SaveAnimatedWEBP", - "SaveImage", - "SaveLatent", - "SelfAttentionGuidance", - "SetLatentNoiseMask", - "SolidMask", - "SplitImageWithAlpha", - "SplitSigmas", - "StableZero123_Conditioning", - "StableZero123_Conditioning_Batched", - "StyleModelApply", - "StyleModelLoader", - "TomePatchModel", - "UNETLoader", - "UpscaleModelLoader", - "VAEDecode", - "VAEDecodeTiled", - "VAEEncode", - "VAEEncodeForInpaint", - "VAEEncodeTiled", - "VAELoader", - "VAESave", - "VPScheduler", - "VideoLinearCFGGuidance", - "unCLIPCheckpointLoader", - "unCLIPConditioning" - ], - { - "title_aux": "ComfyUI Fooocus Inpaint Wrapper" - } - ], - "https://github.com/iacoposk8/xor_pickle_nodes": [ - [ - "DecryptXORText", - "Load XOR Pickle From File", - "Save XOR Pickle To File" - ], - { - "title_aux": "ComfyUI XOR Text & Pickle Nodes" - } - ], - "https://github.com/ialhabbal/OcclusionMask": [ - [ - "BatchLoadImages", - "ImageOcclusion" - ], - { - "title_aux": "OcclusionMask" - } - ], - "https://github.com/iamandeepsandhu/ComfyUI-NSFW-Check": [ - [ - "NSFWScore" - ], - { - "title_aux": "NSFW Check for ComfyUI" - } - ], - "https://github.com/icesun963/ComfyUI_HFDownLoad": [ - [ - "Apply EasyOCR V2", - "HFDownLoad_Tool", - "LayerMask: SegmentAnythingUltra V2.1", - "LayerUtility: LaMa V2" - ], - { - "author": "chflame", - "description": "A set of nodes for ComfyUI that can composite layer and mask to achieve Photoshop like functionality.", - "nickname": "LayerStyle", - "title": "LayerStyle", - "title_aux": "HFDownLoad Node for ComfyUI" - } - ], - "https://github.com/ichabodcole/ComfyUI-Ichis-Pack": [ - [ - "ICHIS_Aspect_Ratio_Plus", - "ICHIS_Extract_Tags", - "ICHIS_Text_Selector" - ], - { - "title_aux": "ComfyUI-Ichis-Pack" - } - ], - "https://github.com/idrirap/ComfyUI-Lora-Auto-Trigger-Words": [ - [ - "FusionText", - "LoraListNames", - "LoraLoaderAdvanced", - "LoraLoaderStackedAdvanced", - "LoraLoaderStackedVanilla", - "LoraLoaderVanilla", - "LoraTagsOnly", - "Randomizer", - "TagsFormater", - "TagsSelector", - "TextInputBasic" - ], - { - "title_aux": "ComfyUI-Lora-Auto-Trigger-Words" - } - ], - "https://github.com/iemesowum/ComfyUI_IsaacNodes": [ - [ - "I_AmplitudeToWeights", - "I_BinaryAmplitudeGate", - "I_UnmixAudio", - "I_WeightsListToWeights" - ], - { - "author": "Isaac Emesowum", - "description": "This extension offers automatic drums extraction from audio files, as well as a few helper nodes to support my audio synchronization AnimateDiff workflows.", - "nickname": "Isaac's Nodes", - "title": "Isaac's Nodes", - "title_aux": "Isaac's Nodes" - } - ], - "https://github.com/if-ai/ComfyUI-IF_AI_Dreamtalk": [ - [ - "IF_DreamTalk" - ], - { - "title_aux": "IF_Dreamtalk" - } - ], - "https://github.com/if-ai/ComfyUI-IF_AI_HFDownloaderNode": [ - [ - "IF_HFDownload", - "IF_HFDownloadNode" - ], - { - "title_aux": "IF_AI_HFDownloaderNode" - } - ], - "https://github.com/if-ai/ComfyUI-IF_AI_ParlerTTSNode": [ - [ - "IF_ParlerTTS" - ], - { - "title_aux": "IF_ParlerTTSNode" - } - ], - "https://github.com/if-ai/ComfyUI-IF_AI_WishperSpeechNode": [ - [ - "IF_WhisperSpeech" - ], - { - "title_aux": "IF_AI_WishperSpeechNode" - } - ], - "https://github.com/if-ai/ComfyUI-IF_AI_tools": [ - [ - "IF_ChatPrompt", - "IF_DisplayOmni", - "IF_DisplayText", - "IF_DisplayTextWildcard", - "IF_ImagePrompt", - "IF_JoinText", - "IF_LoadImagesS", - "IF_PromptMkr", - "IF_SaveText", - "IF_StepCounter", - "IF_TextTyper", - "IF_VisualizeGraph", - "IF_tools_LoadImagesS" - ], - { - "title_aux": "IF_AI_tools" - } - ], - "https://github.com/if-ai/ComfyUI-IF_DatasetMkr": [ - [ - "IF_DatasetMkr", - "IF_HyDatasetMkr" - ], - { - "title_aux": "IF_DatasetMkr" - } - ], - "https://github.com/if-ai/ComfyUI-IF_Gemini": [ - [ - "IFGeminiNode", - "IFPromptCombiner", - "IFTaskPromptManager" - ], - { - "title_aux": "IF_Gemini" - } - ], - "https://github.com/if-ai/ComfyUI-IF_LLM": [ - [ - "IF_DisplayText", - "IF_JoinText", - "IF_LLM", - "IF_LLM_DisplayOmni", - "IF_LLM_DisplayText", - "IF_LLM_DisplayTextWildcard", - "IF_LLM_JoinText", - "IF_LLM_ListModels", - "IF_LLM_LoadImagesS", - "IF_LLM_SaveText", - "IF_LLM_TextTyper", - "IF_LoadImagesS", - "IF_TextTyper", - "IF_saveText", - "ListModelsNode" - ], - { - "title_aux": "IF_LLM" - } - ], - "https://github.com/if-ai/ComfyUI-IF_MemoAvatar": [ - [ - "IF_MemoAvatar", - "IF_MemoCheckpointLoader" - ], - { - "title_aux": "IF_MemoAvatar" - } - ], - "https://github.com/if-ai/ComfyUI-IF_Trellis": [ - [ - "IF_TrellisCheckpointLoader", - "IF_TrellisImageTo3D" - ], - { - "title_aux": "IF_Trellis" - } - ], - "https://github.com/if-ai/ComfyUI-IF_VideoPrompts": [ - [ - "VideoPromptNode" - ], - { - "title_aux": "IF_VideoPrompts" - } - ], - "https://github.com/if-ai/ComfyUI-WanResolutionSelector": [ - [ - "VideoResolutionSelector" - ], - { - "title_aux": "ComfyUI-WanResolutionSelector" - } - ], - "https://github.com/if-ai/ComfyUI-yt_dl": [ - [ - "YouTubeDownloader" - ], - { - "title_aux": "ComfyUI-yt_dl" - } - ], - "https://github.com/if-ai/ComfyUI_HunyuanVideoFoley": [ - [ - "HunyuanVideoFoley" - ], - { - "title_aux": "ComfyUI HunyuanVideo-Foley" - } - ], - "https://github.com/if-ai/ComfyUI_IF_AI_LoadImages": [ - [ - "IF_LoadImagesS" - ], - { - "title_aux": "IF_AI_LoadImages" - } - ], - "https://github.com/ifmylove2011/comfyui-missed-tool": [ - [ - "ImageQueueLoader", - "LoadImageA", - "LoraLoad", - "LoraMerge", - "LoraSaver", - "ScaleMultilplePixels", - "TrimBG", - "TrimBGAdvanced", - "TxtSave" - ], - { - "title_aux": "comfyui-missed-tool" - } - ], - "https://github.com/ihmily/ComfyUI-Light-Tool": [ - [ - "Light-Tool: AddBackground", - "Light-Tool: AddBackgroundV2", - "Light-Tool: BoundingBoxCropping", - "Light-Tool: Calculate", - "Light-Tool: ConvertNumType", - "Light-Tool: CropImage", - "Light-Tool: DeserializeJsonString", - "Light-Tool: GetImageSize", - "Light-Tool: GetImagesCount", - "Light-Tool: Hex2RGB", - "Light-Tool: ImageConcat", - "Light-Tool: ImageMaskApply", - "Light-Tool: ImageOverlay", - "Light-Tool: ImageToMask", - "Light-Tool: InputText", - "Light-Tool: InputTextList", - "Light-Tool: InvertMask", - "Light-Tool: IsTransparent", - "Light-Tool: KeyValue", - "Light-Tool: LoadImage", - "Light-Tool: LoadImageFromURL", - "Light-Tool: LoadImagesFromDir", - "Light-Tool: LoadMetadataFromURL", - "Light-Tool: LoadVideo", - "Light-Tool: MaskBoundingBoxCropping", - "Light-Tool: MaskContourExtractor", - "Light-Tool: MaskImageToTransparent", - "Light-Tool: MaskToImage", - "Light-Tool: MorphologicalTF", - "Light-Tool: PhantomTankEffect", - "Light-Tool: PreviewVideo", - "Light-Tool: RGB2Hex", - "Light-Tool: RGB2RGBA", - "Light-Tool: RGBA2RGB", - "Light-Tool: ResizeImage", - "Light-Tool: ResizeImageByMaxSize", - "Light-Tool: ResizeImageByRatio", - "Light-Tool: ResizeImageV2", - "Light-Tool: SaveMetadata", - "Light-Tool: SaveToAliyunOSS", - "Light-Tool: SaveVideo", - "Light-Tool: ScaleImage", - "Light-Tool: SerializeJsonObject", - "Light-Tool: ShowText", - "Light-Tool: SimpleImageOverlay", - "Light-Tool: SimpleTextConnect", - "Light-Tool: SolidColorBackground", - "Light-Tool: TextConnect", - "Light-Tool: UpscaleImage" - ], - { - "author": "Hmily", - "description": "An awesome light tool nodes for ComfyUI.", - "nickname": "ComfyUI-Light-Tool", - "title": "ComfyUI-Light-Tool", - "title_aux": "ComfyUI-Light-Tool" - } - ], - "https://github.com/illuminatianon/comfyui-csvwildcards": [ - [ - "CSVWildcardNode", - "DisplayTextNode" - ], - { - "title_aux": "CSV Wildcard Node for ComfyUI" - } - ], - "https://github.com/imb101/ComfyUI-FaceSwap": [ - [ - "FaceSwapNode" - ], - { - "title_aux": "FaceSwap" - } - ], - "https://github.com/infinigence/ComfyUI-Infinigence-Nodes": [ - [ - "DrawTextNode", - "Qwen2.5VL_api" - ], - { - "title_aux": "ComfyUI-Infinigence-Nodes" - } - ], - "https://github.com/inflamously/comfyui-prompt-enhancer": [ - [ - "PROMPT_ENHANCER", - "PROMPT_ENHANCER_CHAIN_CONTROL", - "PROMPT_ENHANCER_CHAIN_RANDOM", - "PROMPT_ENHANCER_REPROMPT" - ], - { - "title_aux": "comfyui-prompt-enhancer" - } - ], - "https://github.com/injet-zhou/comfyui_extra_api": [ - [ - "SimpleGenImageInterface" - ], - { - "title_aux": "comfyui_extra_api" - } - ], - "https://github.com/inventorado/ComfyUI_NNT": [ - [ - "NntAnalyzeInferenceMetrics", - "NntAnalyzeModel", - "NntCompileModel", - "NntDatasetToImageTensor", - "NntDatasetToTargetTensor", - "NntDatasetToTensor", - "NntDatasetToTextTensor", - "NntDefineActivationLayer", - "NntDefineAlibiPositionalBias", - "NntDefineConvLayer", - "NntDefineDenseLayer", - "NntDefineFlattenLayer", - "NntDefineGRULayer", - "NntDefineLSTMLayer", - "NntDefineLinearAttention", - "NntDefineLocalAttention", - "NntDefineMultiheadAttention", - "NntDefineNormLayer", - "NntDefinePoolingLayer", - "NntDefinePositionalEncoding", - "NntDefineRNNLayer", - "NntDefineReformerAttention", - "NntDefineRelativePositionBias", - "NntDefineReshapeLayer", - "NntDefineRotaryPositionalEmbedding", - "NntDefineTransformerEncoderLayer", - "NntDefineTransformerXLAttention", - "NntDefineVanillaAttention", - "NntEditModelLayers", - "NntEvaluatePredictions", - "NntFileLoader", - "NntFineTuneModel", - "NntHuggingFaceDataLoader", - "NntImageToTensor", - "NntInference", - "NntInputLayer", - "NntLoadModel", - "NntMergeExtendModel", - "NntPlotTensors", - "NntRandomTensorGenerator", - "NntSHAPSummaryNode", - "NntSaveModel", - "NntShowLayerStack", - "NntShowModelInfo", - "NntTensorElementToImage", - "NntTensorOperations", - "NntTensorSlice", - "NntTensorToText", - "NntTextBatchProcessor", - "NntTextToTensor", - "NntTimeSeriesDataLoader", - "NntTorchvisionDataLoader", - "NntTorchvisionDatasets", - "NntTrainModel", - "NntTrainingHyperparameters", - "NntVisualizeConfidenceScores", - "NntVisualizeGraph", - "NntVisualizePredictionMetrics", - "NntVisualizeTrainingMetrics" - ], - { - "title_aux": "ComfyUI Neural Network Toolkit NNT " - } - ], - "https://github.com/irreveloper/ComfyUI-DSD": [ - [ - "DSDGeminiPromptEnhancer", - "DSDImageGenerator", - "DSDModelDownloader", - "DSDModelLoader", - "DSDModelSelector", - "DSDResizeSelector" - ], - { - "title_aux": "ComfyUI-DSD" - } - ], - "https://github.com/isaac-mcfadyen/ComfyUI-QwenClip": [ - [ - "CLIPSetQwenImageEditPrompt", - "CLIPSetQwenImagePrompt" - ], - { - "title_aux": "ComfyUI-QwenClip" - } - ], - "https://github.com/iwanders/ComfyUI_nodes": [ - [ - "IW_JsonPickItem", - "IW_ModelHook", - "IW_StringConcat", - "IW_StringFromInt", - "IW_StringNode", - "IW_StringPrint", - "IW_StringReplace", - "IW_StringSave", - "IW_TokenizerVocab" - ], - { - "title_aux": "iwanders/ComfyUI_nodes" - } - ], - "https://github.com/jacklukai/ComfyUI_DeployCash": [ - [ - "DeployCash", - "DeployCash_saveImage", - "DeployCash_textInput" - ], - { - "title_aux": "ComfyUI_DeployCash" - } - ], - "https://github.com/jags111/ComfyUI_Jags_Audiotools": [ - [ - "BatchJoinAudio", - "BatchToList", - "BitCrushAudioFX", - "BulkVariation", - "ChorusAudioFX", - "ClippingAudioFX", - "CompressorAudioFX", - "ConcatAudioList", - "ConvolutionAudioFX", - "CutAudio", - "DelayAudioFX", - "DistortionAudioFX", - "DuplicateAudio", - "GainAudioFX", - "GenerateAudioSample", - "GenerateAudioWave", - "GetAudioFromFolderIndex", - "GetSingle", - "GetStringByIndex", - "HighShelfFilter", - "HighpassFilter", - "ImageToSpectral", - "InvertAudioFX", - "JoinAudio", - "LadderFilter", - "LimiterAudioFX", - "ListToBatch", - "LoadAudioDir", - "LoadAudioFile", - "LoadAudioModel (DD)", - "LoadVST3", - "LowShelfFilter", - "LowpassFilter", - "MP3CompressorAudioFX", - "MixAudioTensors", - "NoiseGateAudioFX", - "OTTAudioFX", - "PeakFilter", - "PhaserEffectAudioFX", - "PitchShiftAudioFX", - "PlotSpectrogram", - "PreviewAudioFile", - "PreviewAudioTensor", - "ResampleAudio", - "ReverbAudioFX", - "ReverseAudio", - "SaveAudioTensor", - "SequenceVariation", - "SliceAudio", - "SoundPlayer", - "StretchAudio", - "samplerate" - ], - { - "author": "jags111", - "description": "This extension offers various audio generation tools", - "nickname": "Audiotools", - "title": "Jags_Audiotools", - "title_aux": "Jags_Audiotools" - } - ], - "https://github.com/jags111/ComfyUI_Jags_VectorMagic": [ - [ - "CircularVAEDecode", - "JagsCLIPSeg", - "JagsClipseg", - "JagsCombineMasks", - "SVG", - "YoloSEGdetectionNode", - "YoloSegNode", - "color_drop", - "xy_Tiling_KSampler" - ], - { - "author": "jags111", - "description": "This extension offers various vector manipulation and generation tools", - "nickname": "Jags_VectorMagic", - "title": "Jags_VectorMagic", - "title_aux": "Jags_VectorMagic" - } - ], - "https://github.com/jags111/efficiency-nodes-comfyui": [ - [ - "AnimateDiff Script", - "Apply ControlNet Stack", - "Control Net Stacker", - "Eff. Loader SDXL", - "Efficient Loader", - "HighRes-Fix Script", - "Image Overlay", - "Join XY Inputs of Same Type", - "KSampler (Efficient)", - "KSampler Adv. (Efficient)", - "KSampler SDXL (Eff.)", - "LatentUpscaler", - "LoRA Stack to String converter", - "LoRA Stacker", - "Manual XY Entry Info", - "NNLatentUpscale", - "Noise Control Script", - "Pack SDXL Tuple", - "Tiled Upscaler Script", - "Unpack SDXL Tuple", - "XY Input: Add/Return Noise", - "XY Input: Aesthetic Score", - "XY Input: CFG Scale", - "XY Input: Checkpoint", - "XY Input: Clip Skip", - "XY Input: Control Net", - "XY Input: Control Net Plot", - "XY Input: Denoise", - "XY Input: LoRA", - "XY Input: LoRA Plot", - "XY Input: LoRA Stacks", - "XY Input: Manual XY Entry", - "XY Input: Prompt S/R", - "XY Input: Refiner On/Off", - "XY Input: Sampler/Scheduler", - "XY Input: Seeds++ Batch", - "XY Input: Steps", - "XY Input: VAE", - "XY Plot" - ], - { - "title_aux": "Efficiency Nodes for ComfyUI Version 2.0+" - } - ], - "https://github.com/jaimitoes/ComfyUI_Wan2_1_lora_trainer": [ - [ - "MusubiCompileSettings", - "MusubiMemorySettings", - "MusubiSamplingSettings", - "WanCacheLatents", - "WanCacheTextEncoder", - "WanDatasetConfig", - "WanLoRATrainer" - ], - { - "title_aux": "ComfyUI_Wan2_1_lora_trainer" - } - ], - "https://github.com/jakechai/ComfyUI-JakeUpgrade": [ - [ - "Animation Prompt JK", - "Animation Value JK", - "Base Image Parameters Extract JK", - "Base Model Parameters Extract JK", - "Base Model Parameters JK", - "Base Model Parameters SD3API JK", - "Base Model Pipe Extract JK", - "Base Model Pipe JK", - "Bool Binary And JK", - "Bool Binary OR JK", - "CM_BoolBinaryOperation JK", - "CM_BoolToInt JK", - "CM_BoolUnaryOperation JK", - "CM_BreakoutVec2 JK", - "CM_BreakoutVec3 JK", - "CM_BreakoutVec4 JK", - "CM_ComposeVec2 JK", - "CM_ComposeVec3 JK", - "CM_ComposeVec4 JK", - "CM_FillVec2 JK", - "CM_FillVec3 JK", - "CM_FillVec4 JK", - "CM_FloatBinaryCondition JK", - "CM_FloatBinaryOperation JK", - "CM_FloatToInt JK", - "CM_FloatToNumber JK", - "CM_FloatUnaryCondition JK", - "CM_FloatUnaryOperation JK", - "CM_IntBinaryCondition JK", - "CM_IntBinaryOperation JK", - "CM_IntToBool JK", - "CM_IntToFloat JK", - "CM_IntToNumber JK", - "CM_IntUnaryCondition JK", - "CM_IntUnaryOperation JK", - "CM_NumberBinaryCondition JK", - "CM_NumberBinaryOperation JK", - "CM_NumberToFloat JK", - "CM_NumberToInt JK", - "CM_NumberUnaryCondition JK", - "CM_NumberUnaryOperation JK", - "CM_PromptCombine_JK", - "CM_StringBinaryCondition_JK", - "CM_Vec2BinaryCondition JK", - "CM_Vec2BinaryOperation JK", - "CM_Vec2FloatOperation_JK", - "CM_Vec2ToFloatBinaryOperation JK", - "CM_Vec2ToFloatUnaryOperation JK", - "CM_Vec2UnaryCondition JK", - "CM_Vec2UnaryOperation JK", - "CM_Vec3BinaryCondition JK", - "CM_Vec3BinaryOperation JK", - "CM_Vec3FloatOperation_JK", - "CM_Vec3ToFloatBinaryOperation JK", - "CM_Vec3ToFloatUnaryOperation JK", - "CM_Vec3UnaryCondition JK", - "CM_Vec3UnaryOperation JK", - "CM_Vec4BinaryCondition JK", - "CM_Vec4BinaryOperation JK", - "CM_Vec4FloatOperation_JK", - "CM_Vec4ToFloatBinaryOperation JK", - "CM_Vec4ToFloatUnaryOperation JK", - "CM_Vec4UnaryCondition JK", - "CM_Vec4UnaryOperation JK", - "CR Apply ControlNet JK", - "CR Apply LoRA Stack JK", - "CR Apply LoRA Stack Model Only JK", - "CR Apply Multi-ControlNet Adv JK", - "CR Apply Multi-ControlNet JK", - "CR Aspect Ratio JK", - "CR Boolean JK", - "CR Clip Input Switch JK", - "CR Conditioning Input Switch JK", - "CR ControlNet Input Switch JK", - "CR ControlNet Loader JK", - "CR ControlNet Stack Input Switch JK", - "CR Float Input Switch JK", - "CR Guider Input Switch JK", - "CR Image Input Switch JK", - "CR Impact Pipe Input Switch JK", - "CR Int Input Switch JK", - "CR Latent Input Switch JK", - "CR LoRA Stack JK", - "CR LoRA Stack Model Only JK", - "CR Load LoRA JK", - "CR Mask Input Switch JK", - "CR Mesh Input Switch JK", - "CR Model Input Switch JK", - "CR Multi-ControlNet Param Stack JK", - "CR Multi-ControlNet Stack JK", - "CR Noise Input Switch JK", - "CR Orbit Pose Input Switch JK", - "CR Pipe Input Switch JK", - "CR Ply Input Switch JK", - "CR SD1.5 Aspect Ratio JK", - "CR SD3 Aspect Ratio JK", - "CR SDXL Aspect Ratio JK", - "CR Sampler Input Switch JK", - "CR Sigmas Input Switch JK", - "CR Text Input Switch JK", - "CR TriMesh Input Switch JK", - "CR VAE Input Switch JK", - "Ckpt Loader JK", - "Color Grading JK", - "Create Loop Schedule List", - "Detailer Parameters JK", - "Embedding Picker JK", - "Embedding Picker Multi JK", - "Empty Latent Color JK", - "Evaluate Examples JK", - "Evaluate Floats JK", - "Evaluate Ints JK", - "Evaluate Strings JK", - "Get OrbitPoses From List JK", - "Get Size JK", - "Guidance Default JK", - "HintImageEnchance JK", - "Hy3D Cam Config 20to21 JK", - "IPAAdapterFaceIDBatch", - "IPAdapter", - "IPAdapterAdvanced", - "IPAdapterBatch", - "IPAdapterClipVisionEnhancer", - "IPAdapterClipVisionEnhancerBatch", - "IPAdapterCombineEmbeds", - "IPAdapterCombineParams", - "IPAdapterCombineWeights", - "IPAdapterEmbeds", - "IPAdapterEmbedsBatch", - "IPAdapterEncoder", - "IPAdapterFaceID", - "IPAdapterFaceIDKolors", - "IPAdapterFromParams", - "IPAdapterInsightFaceLoader", - "IPAdapterLoadEmbeds", - "IPAdapterMS", - "IPAdapterModelLoader", - "IPAdapterNoise", - "IPAdapterPreciseComposition", - "IPAdapterPreciseCompositionBatch", - "IPAdapterPreciseStyleTransfer", - "IPAdapterPreciseStyleTransferBatch", - "IPAdapterPromptScheduleFromWeightsStrategy", - "IPAdapterRegionalConditioning", - "IPAdapterSaveEmbeds", - "IPAdapterStyleComposition", - "IPAdapterStyleCompositionBatch", - "IPAdapterTiled", - "IPAdapterTiledBatch", - "IPAdapterUnifiedLoader", - "IPAdapterUnifiedLoaderCommunity", - "IPAdapterUnifiedLoaderFaceID", - "IPAdapterWeights", - "IPAdapterWeightsFromStrategy", - "Image Crop By Mask Resolution Grp JK", - "Image Crop by Mask Params JK", - "Image Crop by Mask Resolution JK", - "Image Remove Alpha JK", - "Image Resize Mode JK", - "Image Upscale Parameters Extract JK", - "Inject Noise Params JK", - "Is Mask Empty JK", - "Ksampler Adv Parameters Default JK", - "Ksampler Parameters Default JK", - "Ksampler Parameters JK", - "Latent Crop Offset JK", - "Latent Upscale Parameters Extract JK", - "Load Image With Alpha JK", - "Load Image With Metadata JK", - "Load String List From JSON JK", - "Make Image Grid JK", - "Metadata Pipe Extract JK", - "Metadata Pipe JK", - "NodesState JK", - "Noise Injection Parameters JK", - "Noise Injection Pipe Extract JK", - "OpenDWPose_JK", - "Orbit Poses JK", - "OrbitLists to OrbitPoses JK", - "OrbitPoses to OrbitLists JK", - "Pipe End JK", - "PrepImageForClipVision", - "Project Setting JK", - "Random Beats JK", - "Refine 1 Parameters Extract JK", - "Refine 2 Parameters Extract JK", - "Refine Model Parameters JK", - "Refine Pipe Extract JK", - "Refine Pipe JK", - "Remove Input JK", - "Reroute Ckpt JK", - "Reroute List JK", - "Reroute Resize JK", - "Reroute Sampler JK", - "Reroute String JK", - "Reroute Upscale JK", - "Reroute Vae JK", - "Rough Outline JK", - "SD3 Prompts Switch JK", - "SDXL Target Res JK", - "SDXLPromptStylerAll", - "SDXLPromptStylerHorror", - "SDXLPromptStylerMisc", - "SDXLPromptStylerbyArtist", - "SDXLPromptStylerbyCamera", - "SDXLPromptStylerbyComposition", - "SDXLPromptStylerbyCyberpunkSurrealism", - "SDXLPromptStylerbyDepth", - "SDXLPromptStylerbyDiva", - "SDXLPromptStylerbyEnvironment", - "SDXLPromptStylerbyFantasySetting", - "SDXLPromptStylerbyFilter", - "SDXLPromptStylerbyFocus", - "SDXLPromptStylerbyFooocus", - "SDXLPromptStylerbyImpressionism", - "SDXLPromptStylerbyLighting", - "SDXLPromptStylerbyMarc", - "SDXLPromptStylerbyMileHigh", - "SDXLPromptStylerbyMood", - "SDXLPromptStylerbyMre", - "SDXLPromptStylerbyMythicalCreature", - "SDXLPromptStylerbyOriginal", - "SDXLPromptStylerbyQuantumRealism", - "SDXLPromptStylerbySai", - "SDXLPromptStylerbySteamPunkRealism", - "SDXLPromptStylerbySubject", - "SDXLPromptStylerbySurrealism", - "SDXLPromptStylerbyTheme", - "SDXLPromptStylerbyTimeofDay", - "SDXLPromptStylerbyTwri", - "SDXLPromptStylerbyWyvern", - "SDXLPromptbyCelticArt", - "SDXLPromptbyContemporaryNordicArt", - "SDXLPromptbyFashionArt", - "SDXLPromptbyGothicRevival", - "SDXLPromptbyIrishFolkArt", - "SDXLPromptbyRomanticNationalismArt", - "SDXLPromptbySportsArt", - "SDXLPromptbyStreetArt", - "SDXLPromptbyVikingArt", - "SDXLPromptbyWildlifeArt", - "Sampler Loader JK", - "Save Image with Metadata Flow JK", - "Save Image with Metadata JK", - "Save String List To JSON JK", - "Scale To Resolution JK", - "Split Image Grid JK", - "String To Combo JK", - "Tiling Mode JK", - "Upscale Method JK", - "Upscale Model Loader JK", - "Upscale Model Parameters Extract JK", - "Upscale Model Parameters JK", - "Vae Loader JK" - ], - { - "title_aux": "ComfyUI-JakeUpgrade" - } - ], - "https://github.com/jamal-alkharrat/ComfyUI_rotate_image": [ - [ - "RotateImage" - ], - { - "title_aux": "ComfyUI_rotate_image" - } - ], - "https://github.com/jamesWalker55/comfyui-p2ldgan": [ - [ - "P2LDGAN" - ], - { - "title_aux": "ComfyUI - P2LDGAN Node" - } - ], - "https://github.com/jamesWalker55/comfyui-various": [ - [ - "BatchLoadImage", - "BatchSaveImage", - "GroupInfoExtractFloat", - "GroupInfoExtractInt", - "GroupLoadBatchImages", - "GroupLoadImage", - "JWAudioBlend", - "JWAudioSaveToPath", - "JWDatetimeString", - "JWImageBatchCount", - "JWImageContrast", - "JWImageExtractFromBatch", - "JWImageFlip", - "JWImageLevels", - "JWImageLoadRGB", - "JWImageLoadRGBA", - "JWImageLoadRGBA From Clipboard", - "JWImageLoadRGBFromClipboard", - "JWImageLoadRGBIfExists", - "JWImageMix", - "JWImageResize", - "JWImageResizeByFactor", - "JWImageResizeByLongerSide", - "JWImageResizeByShorterSide", - "JWImageResizeToSquare", - "JWImageSaturation", - "JWImageSaveToPath", - "JWImageSequenceExtractFromBatch", - "JWImageStackChannels", - "JWInfoHashExtractFloat", - "JWInfoHashExtractInteger", - "JWInfoHashExtractString", - "JWInfoHashFromInfoHashList", - "JWInfoHashFromRangedInfo", - "JWInfoHashListExtractStringList", - "JWInfoHashListFromRangedInfo", - "JWInfoHashPrint", - "JWLoadAudio", - "JWLoadImageSequence", - "JWLoadImagesFromString", - "JWLoopImageSequence", - "JWMaskLikeImageSize", - "JWMaskResize", - "JWMaskSequenceApplyToLatent", - "JWMaskSequenceFromMask", - "JWMaskSequenceJoin", - "JWPrintFloat", - "JWPrintImage", - "JWPrintInteger", - "JWPrintLatent", - "JWPrintMask", - "JWPrintString", - "JWRangedInfoCalculateSubBatch", - "JWReferenceOnly", - "JWSaveImageSequence", - "JWStringListCLIPEncode", - "JWStringListFromString", - "JWStringListFromStrings", - "JWStringListJoin", - "JWStringListRepeat", - "JWStringListToFormatedString", - "JWStringListToString", - "JWUncropCrop", - "JWUncropNewRect", - "JWUncropUncrop", - "JamesLoadImageGroup", - "RAFTEstimate", - "RAFTFlowToImage", - "RAFTLoadFlowFromEXRChannels", - "RCReceiveFloat", - "RCReceiveFloatList", - "RCReceiveInt", - "RCReceiveIntList", - "RCReceiveLatent", - "RCSendLatent" - ], - { - "nodename_pattern": "^JW", - "title_aux": "Various ComfyUI Nodes by Type" - } - ], - "https://github.com/jammyfu/ComfyUI_PaintingCoderUtils": [ - [ - "PaintingCoder::DynamicImageCombiner", - "PaintingCoder::DynamicMaskCombiner", - "PaintingCoder::ImageLatentCreator", - "PaintingCoder::ImageLatentCreatorPlus", - "PaintingCoder::ImageResolutionAdjuster", - "PaintingCoder::ImageSizeCreator", - "PaintingCoder::ImageSizeCreatorPlus", - "PaintingCoder::ImageSwitch", - "PaintingCoder::ImageToBase64", - "PaintingCoder::LatentSwitch", - "PaintingCoder::MaskPreview", - "PaintingCoder::MaskSwitch", - "PaintingCoder::MultilineTextInput", - "PaintingCoder::OutputToTextConverter", - "PaintingCoder::RemoveEmptyLinesAndLeadingSpaces", - "PaintingCoder::ShowTextPlus", - "PaintingCoder::SimpleTextInput", - "PaintingCoder::TextCombiner", - "PaintingCoder::TextSwitch", - "PaintingCoder::WebImageLoader" - ], - { - "title_aux": "Painting Coder Utils" - } - ], - "https://github.com/jasonjgardner/comfui-substance-designer-integration": [ - [ - "SubstanceBatchProcessor", - "SubstanceCooker", - "SubstanceInfoExtractor", - "SubstanceParameterController", - "SubstanceRenderer" - ], - { - "title_aux": "ComfyUI Substance Designer Integration Plugin" - } - ], - "https://github.com/jax-explorer/ComfyUI-InstantCharacter": [ - [ - "InstantCharacterGenerate", - "InstantCharacterLoadModel", - "InstantCharacterLoadModelFromLocal" - ], - { - "title_aux": "ComfyUI-InstantCharacter" - } - ], - "https://github.com/jax-explorer/ComfyUI-VideoBasic": [ - [ - "VideoBasicLoadVideo", - "VideoBasicMergeVideo", - "VideoBasicVideoSave", - "VideoBasicVideoUpscaleWithModel" - ], - { - "title_aux": "ComfyUI-VideoBasic" - } - ], - "https://github.com/jax-explorer/ComfyUI-VideoBasicLatentSync": [ - [ - "VideoBasicLatentSyncLengthAdjuster", - "VideoBasicLatentSyncNode" - ], - { - "title_aux": "ComfyUI-VideoBasicLatentSync" - } - ], - "https://github.com/jax-explorer/ComfyUI-easycontrol": [ - [ - "EasyControlGenerate", - "EasyControlLoadFlux", - "EasyControlLoadLora", - "EasyControlLoadMultiLora", - "EasyControlLoadStyleLora", - "EasyControlLoadStyleLoraFromCivitai" - ], - { - "title_aux": "ComfyUI-easycontrol" - } - ], - "https://github.com/jax-explorer/comfyui-model-dynamic-loader": [ - [ - "ComfyOnlineSaveFile", - "ComfyOnlineUploadAnything", - "EmbeddingLoader", - "LoadCheckpointFromCivitAI", - "LoadHunyuanLoraFromCivitAI", - "LoadHunyuanLoraFromComfyOnline", - "LoadHunyuanLoraFromHF", - "LoadImageFromURL", - "LoadLoraFromCivitAI", - "LoadLoraFromComfyOnline", - "LoadLoraFromHF", - "LoadLoraFromHFWithToken", - "LoadWanVideoLoraFromCivitAI", - "LoadWanVideoLoraFromComfyOnline", - "LoadWanVideoLoraFromHF", - "SaveAudioAsWav", - "SaveText" - ], - { - "title_aux": "comfyui-model-dynamic-loader" - } - ], - "https://github.com/jax-explorer/fast_video_comfyui": [ - [ - "FastImageListToImageBatch" - ], - { - "title_aux": "fast_video_comfyui" - } - ], - "https://github.com/jeffrey2212/ComfyUI-PonyCharacterPrompt": [ - [ - "Pony Character Prompt Picker" - ], - { - "title_aux": "Pony Character Prompt Picker for ComfyUI" - } - ], - "https://github.com/jeffy5/comfyui-faceless-node": [ - [ - "FacelessFaceRestore", - "FacelessFaceSwap", - "FacelessLoadImageUrl", - "FacelessLoadVideo", - "FacelessLoadVideoImages", - "FacelessLoadVideoUrl", - "FacelessMergeVideos", - "FacelessRemoveBackground", - "FacelessSaveVideo", - "FacelessUploadVideo", - "FacelessVideoFaceRestore", - "FacelessVideoFaceSwap", - "FacelessVideoRemoveBackground" - ], - { - "title_aux": "Faceless Node for ComfyUI" - } - ], - "https://github.com/jerrylongyan/ComfyUI-My-Mask": [ - [ - "MaskToBottonHalfConvexMask", - "MaskToConvexMask" - ], - { - "title_aux": "ComfyUI-My-Mask" - } - ], - "https://github.com/jerrywap/ComfyUI_LoadImageFromHttpURL": [ - [ - "LoadImageFromHttpURL" - ], - { - "title_aux": "ComfyUI_LoadImageFromHttpURL" - } - ], - "https://github.com/jerrywap/ComfyUI_UploadToWebhookHTTP": [ - [ - "UploadToWebHookHTTP" - ], - { - "title_aux": "ComfyUI_UploadToWebhookHTTP" - } - ], - "https://github.com/jesenzhang/ComfyUI_StreamDiffusion": [ - [ - "StreamDiffusion_Loader", - "StreamDiffusion_Sampler" - ], - { - "title_aux": "ComfyUI_StreamDiffusion" - } - ], - "https://github.com/jhj0517/ComfyUI-Moondream-Gaze-Detection": [ - [ - "(Down)Load Moondream Model", - "Gaze Detection", - "Gaze Detection Video" - ], - { - "title_aux": "ComfyUI-Moondream-Gaze-Detection" - } - ], - "https://github.com/jhj0517/ComfyUI-jhj-Kokoro-Onnx": [ - [ - "(Down)Load Kokoro Model", - "Kokoro Audio Generator" - ], - { - "title_aux": "ComfyUI jhj Kokoro Onnx" - } - ], - "https://github.com/jiafuzeng/comfyui-LatentSync": [ - [ - "LatentSyncNode" - ], - { - "title_aux": "LatentSync" - } - ], - "https://github.com/jialuw0830/flux_api_comfyui_plugin": [ - [ - "FluxAPINode", - "KontextAPINode", - "QwenAPINode", - "nodes" - ], - { - "title_aux": "Eigen AI FLUX API Plugin" - } - ], - "https://github.com/jiaqianjing/ComfyUI-MidjourneyHub": [ - [ - "GPTImageEditNode", - "GPTImageGenerateNode", - "MidjourneyActionNode", - "MidjourneyBatchActionNode", - "MidjourneyBlendNode", - "MidjourneyImagineNode" - ], - { - "title_aux": "ComfyUI-MidjourneyHub" - } - ], - "https://github.com/jiaxiangc/ComfyUI-ResAdapter": [ - [ - "ResAdapterLoader" - ], - { - "title_aux": "ResAdapter for ComfyUI" - } - ], - "https://github.com/jinanlongen/ComfyUI-Prompt-Expander": [ - [ - "PromptExpanderNode" - ], - { - "title_aux": "ComfyUI Prompt Expander Node" - } - ], - "https://github.com/jinchanz/ComfyUI-ADIC": [ - [ - "ADIC_COMMON_API", - "AliCloudOSSUpload", - "ImageTranslateAPI", - "ImageTranslateParamsBuilder", - "ImageTranslateResultExtractor", - "LoadImagesFromUrls", - "MaletteFluxKontextImageScale", - "MaletteImageConcatFromBatch", - "MaletteImageStitch", - "MaletteReferenceLatent", - "MarketImageGenerateWithPolling", - "PythonCodeExecutor", - "StringToJsonArray" - ], - { - "title_aux": "ComfyUI-ADIC" - } - ], - "https://github.com/jitcoder/lora-info": [ - [ - "ImageFromURL", - "LoraInfo" - ], - { - "title_aux": "LoraInfo" - } - ], - "https://github.com/jjkramhoeft/ComfyUI-Jjk-Nodes": [ - [ - "JjkConcat", - "JjkShowText", - "JjkText", - "SDXLRecommendedImageSize" - ], - { - "title_aux": "ComfyUI-Jjk-Nodes" - } - ], - "https://github.com/jkrauss82/ultools-comfyui": [ - [ - "CLIPTextEncodeWithStats", - "OpenPoseEditorAdv", - "SaveImgAdv", - "SolidMaskAdv" - ], - { - "title_aux": "ULTools for ComfyUI" - } - ], - "https://github.com/jn-jairo/jn_comfyui": [ - [ - "JN_AreaAround", - "JN_AreaInfo", - "JN_AreaNormalize", - "JN_AreaToMask", - "JN_AreaWidthHeight", - "JN_AreaXY", - "JN_AudioArrayToBatch", - "JN_AudioAutoTune", - "JN_AudioBatchToArray", - "JN_AudioCompare", - "JN_AudioConcatenation", - "JN_AudioGetChannels", - "JN_AudioInfo", - "JN_AudioNoiseReduction", - "JN_AudioNormalize", - "JN_AudioPitch", - "JN_AudioPlot", - "JN_AudioReverberation", - "JN_AudioSampleRate", - "JN_AudioSetChannels", - "JN_AudioSlice", - "JN_AudioSpeed", - "JN_AudioSplitChannels", - "JN_AudioStackChannels", - "JN_AudioTempo", - "JN_AudioTrimSilence", - "JN_AudioVolume", - "JN_Blip", - "JN_BlipLoader", - "JN_BooleanOperation", - "JN_Condition", - "JN_CoolDown", - "JN_CoolDownOutput", - "JN_DatetimeFormat", - "JN_DatetimeInfo", - "JN_DatetimeNow", - "JN_Dump", - "JN_DumpOutput", - "JN_Exec", - "JN_ExecOutput", - "JN_FaceCrop", - "JN_FaceRestoreModelLoader", - "JN_FaceRestoreWithModel", - "JN_FirstActive", - "JN_Flow", - "JN_FlowOutput", - "JN_ImageAddBackground", - "JN_ImageAddMask", - "JN_ImageBatch", - "JN_ImageCenterArea", - "JN_ImageCrop", - "JN_ImageGrid", - "JN_ImageInfo", - "JN_ImageRemoveBackground", - "JN_ImageSharpness", - "JN_ImageSquare", - "JN_ImageToMask", - "JN_ImageUncrop", - "JN_KSampler", - "JN_KSamplerAdvancedParams", - "JN_KSamplerFaceRestoreParams", - "JN_KSamplerResizeInputParams", - "JN_KSamplerResizeMaskAreaParams", - "JN_KSamplerResizeOutputParams", - "JN_KSamplerSeamlessParams", - "JN_KSamplerTileParams", - "JN_KeyValue", - "JN_LoadAudioDirectory", - "JN_LoadImageDirectory", - "JN_LogicOperation", - "JN_MaskBatch", - "JN_MaskInfo", - "JN_MaskToArea", - "JN_MaskToImage", - "JN_MathOperation", - "JN_MathOperationArray", - "JN_MeowHrtfAudio3d", - "JN_MeowHrtfModel", - "JN_MeowHrtfPosition", - "JN_MeowLoadVoice", - "JN_MeowSaveVoice", - "JN_MeowSentenceSplit", - "JN_MeowTts", - "JN_MeowTtsAudioToContext", - "JN_MeowTtsCoarse", - "JN_MeowTtsDecode", - "JN_MeowTtsFine", - "JN_MeowTtsLoadContext", - "JN_MeowTtsModel", - "JN_MeowTtsModelCoarse", - "JN_MeowTtsModelEncodec", - "JN_MeowTtsModelFine", - "JN_MeowTtsModelHubert", - "JN_MeowTtsModelSemantic", - "JN_MeowTtsSaveContext", - "JN_MeowTtsSemantic", - "JN_MeowTtsTokenizerHubert", - "JN_MeowVc", - "JN_MeowVcConvertVoice", - "JN_MeowVcEncodeSource", - "JN_MeowVcEncodeTarget", - "JN_MeowVcLoadSpeaker", - "JN_MeowVcModelFreeVC", - "JN_MeowVcModelWavLM", - "JN_MeowVcSaveSpeaker", - "JN_PreviewAudio", - "JN_PreviewImage", - "JN_PreviewMask", - "JN_PrimitiveArrayInfo", - "JN_PrimitiveBatchToArray", - "JN_PrimitiveBoolean", - "JN_PrimitiveFloat", - "JN_PrimitiveInt", - "JN_PrimitivePrompt", - "JN_PrimitiveString", - "JN_PrimitiveStringMultiline", - "JN_PrimitiveStringToArray", - "JN_PrimitiveToArray", - "JN_PrimitiveToBoolean", - "JN_PrimitiveToFloat", - "JN_PrimitiveToInt", - "JN_PrimitiveToString", - "JN_RemBGSession", - "JN_SaveAudio", - "JN_SaveImage", - "JN_Seamless", - "JN_SeamlessBorder", - "JN_SeamlessBorderCrop", - "JN_SelectItem", - "JN_Sleep", - "JN_SleepOutput", - "JN_SliceOperation", - "JN_StopIf", - "JN_StopIfOutput", - "JN_TensorInfo", - "JN_TextConcatenation", - "JN_TextReplace", - "JN_TimedeltaFormat", - "JN_TimedeltaInfo" - ], - { - "title_aux": "JNComfy" - } - ], - "https://github.com/jnxmx/ComfyUI_HuggingFace_Downloader": [ - [ - "Hugging Face Download Folder", - "Hugging Face Download Model" - ], - { - "title_aux": "ComfyUI_HuggingFace_Downloader" - } - ], - "https://github.com/joanna910225/comfyui-housekeeper": [ - [ - "housekeeper-alignment", - "housekeeper-alignment-cmd", - "vue-basic" - ], - { - "title_aux": "HouseKeeper" - } - ], - "https://github.com/joeriben/ai4artsed_comfyui_nodes": [ - [ - "ai4artsed_conditioning_fusion", - "ai4artsed_image_analysis", - "ai4artsed_openrouter_key", - "ai4artsed_prompt_interception", - "ai4artsed_random_artform_generator", - "ai4artsed_random_instruction_generator", - "ai4artsed_random_language_selector", - "ai4artsed_stabilitai_key", - "ai4artsed_switch_promptsafety", - "ai4artsed_t5_clip_fusion", - "ai4artsed_text_remix", - "ai4artsed_vector_dimension_eliminator" - ], - { - "title_aux": "AI4ArtsEd Nodes" - } - ], - "https://github.com/john-mnz/ComfyUI-Inspyrenet-Rembg": [ - [ - "InspyrenetRembg", - "InspyrenetRembgAdvanced" - ], - { - "title_aux": "ComfyUI-Inspyrenet-Rembg" - } - ], - "https://github.com/jojkaart/ComfyUI-sampler-lcm-alternative": [ - [ - "LCMScheduler", - "SamplerLCMAlternative", - "SamplerLCMCycle", - "SamplerLCMDualNoise", - "SamplerLCMDuoFusion", - "SamplerLCMParallel" - ], - { - "title_aux": "ComfyUI-sampler-lcm-alternative" - } - ], - "https://github.com/joosthel/ComfyUI-CVOverlay": [ - [ - "CV_AestheticOverlay", - "CV_BlobTracker", - "CV_ModelLoader", - "CV_ObjectDetector" - ], - { - "title_aux": "ComfyUI-CVOverlay" - } - ], - "https://github.com/jordoh/ComfyUI-Deepface": [ - [ - "DeepfaceAnalyze", - "DeepfaceExtractFaces", - "DeepfaceVerify" - ], - { - "title_aux": "ComfyUI Deepface" - } - ], - "https://github.com/joreyaesh/comfyui_scroll_over_textarea": [ - [ - "ScrollOverTextareaDummyNode" - ], - { - "title_aux": "ComfyUI Scroll Over Textarea" - } - ], - "https://github.com/joreyaesh/comfyui_touchpad_scroll_controller.enableTouchpadScroll": [ - [ - "TouchpadScrollControllerDummyNode" - ], - { - "title_aux": "ComfyUI Touchpad Scroll Controller" - } - ], - "https://github.com/jqy-yo/Comfyui-BBoxLowerMask2": [ - [ - "BBoxLowerMask2" - ], - { - "title_aux": "BBoxLowerMask2" - } - ], - "https://github.com/jqy-yo/comfyui-gemini-nodes": [ - [ - "GeminiFieldExtractor", - "GeminiImageEditor", - "GeminiImageGenADV", - "GeminiJSONExtractor", - "GeminiJSONParser", - "GeminiStructuredOutput", - "GeminiTextAPI", - "GeminiVideoCaptioner", - "GeminiVideoGenerator", - "UnofficialGeminiAPI", - "UnofficialGeminiStreamAPI" - ], - { - "title_aux": "ComfyUI Gemini Nodes" - } - ], - "https://github.com/jroc22/ComfyUI-CSV-prompt-builder": [ - [ - "BuildPromptFromCSV" - ], - { - "title_aux": "ComfyUI-CSV-prompt-builder" - } - ], - "https://github.com/jstit/comfyui_custom_node_image": [ - [ - "ImageCropCircle" - ], - { - "title_aux": "comfyui_custom_node_image" - } - ], - "https://github.com/jtrue/ComfyUI-JaRue": [ - [ - "Text2Image_jru", - "YouTube2Prompt_jru" - ], - { - "nodename_pattern": "_jru$", - "title_aux": "ComfyUI-JaRue" - } - ], - "https://github.com/jtrue/ComfyUI-Rect": [ - [ - "RectCrop", - "RectFill", - "RectMask", - "RectSelect" - ], - { - "description": "Rectangle selection and utilities for ComfyUI (modular).", - "nickname": "Rect", - "title": "Rect", - "title_aux": "ComfyUI-Rect" - } - ], - "https://github.com/jtrue/ComfyUI-WordEmbeddings": [ - [ - "WordEmbeddingsEquation", - "WordEmbeddingsExplorer", - "WordEmbeddingsInterpolator", - "WordEmbeddingsLoader", - "WordEmbeddingsLocalModelLoader", - "WordEmbeddingsTokenAxis", - "WordEmbeddingsTokenAxis2D", - "WordEmbeddingsTokenAxis3D", - "WordEmbeddingsTokenCentrality", - "WordEmbeddingsTokenNeighbors" - ], - { - "nodename_pattern": "_jru$", - "title_aux": "ComfyUI-WordEmbeddings" - } - ], - "https://github.com/jtydhr88/ComfyUI-AudioMass": [ - [ - "ComfyUIAudioMass" - ], - { - "title_aux": "ComfyUI-AudioMass" - } - ], - "https://github.com/jtydhr88/ComfyUI-Hunyuan3D-1-wrapper": [ - [ - "Hunyuan3D V1 - Image Loader", - "Hunyuan3D V1 - Image2Views", - "Hunyuan3D V1 - Image2Views Pipeline Load", - "Hunyuan3D V1 - Text2Image", - "Hunyuan3D V1 - Text2Image Pipeline Load", - "Hunyuan3D V1 - Views2Mesh", - "Hunyuan3D V1 - Views2Mesh Pipeline Load" - ], - { - "title_aux": "ComfyUI-Hunyuan3D-1-wrapper" - } - ], - "https://github.com/jtydhr88/ComfyUI-LayerDivider": [ - [ - "LayerDivider - Color Base", - "LayerDivider - Divide Layer", - "LayerDivider - Load SAM Mask Generator", - "LayerDivider - Segment Mask" - ], - { - "title_aux": "ComfyUI LayerDivider" - } - ], - "https://github.com/jtydhr88/ComfyUI-OpenCut": [ - [ - "ComfyUIOpenCut" - ], - { - "title_aux": "ComfyUI-OpenCut" - } - ], - "https://github.com/juddisjudd/ComfyUI-BawkNodes": [ - [ - "BawkSampler", - "DiffusionModelLoader", - "FluxImageSaver", - "FluxWildcardEncode" - ], - { - "title_aux": "Bawk Nodes Collection" - } - ], - "https://github.com/judian17/ComfyUI-Extract_Flux_Lora": [ - [ - "ExtractFluxLoRA" - ], - { - "title_aux": "ComfyUI-Extract_Flux_Lora" - } - ], - "https://github.com/judian17/ComfyUI-JoyCaption-beta-one-hf-llava-Prompt_node": [ - [ - "JoyCaptionOllamaExtraOptions", - "JoyCaptionOllamaPrompter" - ], - { - "title_aux": "ComfyUI-JoyCaption-beta-one-hf-llava-Prompt_node" - } - ], - "https://github.com/judian17/ComfyUI-UniWorld-jd17": [ - [ - "UniWorldEncoderNode", - "UniWorldScheduler", - "UniWorldSiglipEncoder", - "UniWorld_T5_CLIP_Encoder" - ], - { - "title_aux": "ComfyUI-UniWorld-jd17" - } - ], - "https://github.com/judian17/ComfyUI-joycaption-beta-one-GGUF": [ - [ - "JJC_JoyCaption_Custom_GGUF", - "JJC_JoyCaption_GGUF", - "JJC_JoyCaption_GGUF_ExtraOptions" - ], - { - "title_aux": "ComfyUI JoyCaption-Beta-GGUF Node" - } - ], - "https://github.com/judian17/ComfyUI_ZIM": [ - [ - "MaskToBbox_ZIM", - "MaskToPoints_ZIM", - "ZimSegment" - ], - { - "title_aux": "ComfyUI_ZIM" - } - ], - "https://github.com/juehackr/comfyui_fk_server": [ - [ - "FK_3dpose", - "FK_Node", - "FK_ShowBaseNode" - ], - { - "title_aux": "comfyui_fk_server" - } - ], - "https://github.com/jurdnf/ComfyUI-JurdnsIterativeNoiseKSampler": [ - [ - "KSamplerIterativeNoise" - ], - { - "title_aux": "ComfyUI-JurdnsIterativeNoiseKsampler" - } - ], - "https://github.com/jurdnf/ComfyUI-JurdnsModelSculptor": [ - [ - "ModelSculptorFlux", - "ModelSculptorSD3", - "ModelSculptorSDXL" - ], - { - "title_aux": "ComfyUI-JurdnsModelSculptor" - } - ], - "https://github.com/jurdnisglobby/ComfyUI-Jurdns-Groq-Node": [ - [ - "JurdnsGroqAPIPromptEnhancer" - ], - { - "title_aux": "Jurdns Groq API Node" - } - ], - "https://github.com/justUmen/Bjornulf_custom_nodes": [ - [ - "Bjornulf_APIGenerateCivitAI", - "Bjornulf_APIGenerateCivitAIAddLORA", - "Bjornulf_APIGenerateFalAI", - "Bjornulf_APIGenerateFlux", - "Bjornulf_APIGenerateGPT4o", - "Bjornulf_APIGenerateStability", - "Bjornulf_AddLineNumbers", - "Bjornulf_AllLoraSelector", - "Bjornulf_AnythingToFloat", - "Bjornulf_AnythingToInt", - "Bjornulf_AnythingToText", - "Bjornulf_ApiDynamicTextInputs", - "Bjornulf_AudioPreview", - "Bjornulf_AudioVideoSync", - "Bjornulf_BoundingRectangleMask", - "Bjornulf_BoundingRectangleMaskBlur", - "Bjornulf_CharacterDescriptionGenerator", - "Bjornulf_CivitAILoraSelector", - "Bjornulf_CivitAILoraSelectorHunyuan", - "Bjornulf_CivitAILoraSelectorPONY", - "Bjornulf_CivitAILoraSelectorSD15", - "Bjornulf_CivitAILoraSelectorSDXL", - "Bjornulf_CivitAIModelSelectorFLUX_D", - "Bjornulf_CivitAIModelSelectorFLUX_S", - "Bjornulf_CivitAIModelSelectorPony", - "Bjornulf_CivitAIModelSelectorSD15", - "Bjornulf_CivitAIModelSelectorSDXL", - "Bjornulf_CombineBackgroundOverlay", - "Bjornulf_CombineImages", - "Bjornulf_CombineTexts", - "Bjornulf_CombineTextsByLines", - "Bjornulf_CombineVideoAudio", - "Bjornulf_ConcatVideos", - "Bjornulf_ConcatVideosFromList", - "Bjornulf_ConditionalSwitch", - "Bjornulf_ConvertVideo", - "Bjornulf_DisplayNote", - "Bjornulf_EmptyVideoLatentWithSingle", - "Bjornulf_ExecuteWorkflowNode", - "Bjornulf_FFmpegConfig", - "Bjornulf_FaceSettings", - "Bjornulf_FixFace", - "Bjornulf_FourImageViewer", - "Bjornulf_FreeVRAM", - "Bjornulf_GlobalSeedManager", - "Bjornulf_GrayscaleTransform", - "Bjornulf_GreenScreenToTransparency", - "Bjornulf_HiResFix", - "Bjornulf_HorizontalCutAndShift", - "Bjornulf_HuggingFaceDownloader", - "Bjornulf_IfElse", - "Bjornulf_ImageBlend", - "Bjornulf_ImageDetails", - "Bjornulf_ImageMaskCutter", - "Bjornulf_ImageNote", - "Bjornulf_ImageNoteLoadImage", - "Bjornulf_ImageUpscaleWithModelTransparency", - "Bjornulf_ImagesListToVideo", - "Bjornulf_JSONImagePromptExtractor", - "Bjornulf_KokoroTTS", - "Bjornulf_LargestMaskOnly", - "Bjornulf_LatentResolutionSelector", - "Bjornulf_LineSelector", - "Bjornulf_ListLooper", - "Bjornulf_ListLooperCharacter", - "Bjornulf_ListLooperOutfitFemale", - "Bjornulf_ListLooperOutfitMale", - "Bjornulf_ListLooperScene", - "Bjornulf_ListLooperStyle", - "Bjornulf_ListSelector", - "Bjornulf_LoadCivitAILinks", - "Bjornulf_LoadFromBase64", - "Bjornulf_LoadGlobalVariables", - "Bjornulf_LoadImageWithTransparency", - "Bjornulf_LoadImageWithTransparencyFromPath", - "Bjornulf_LoadImagesFromSelectedFolder", - "Bjornulf_LoadTensor", - "Bjornulf_LoadTextFromFolder", - "Bjornulf_LoadTextFromPath", - "Bjornulf_LoadTextPickMeGlobal", - "Bjornulf_LoaderLoraWithPath", - "Bjornulf_LoopAllLines", - "Bjornulf_LoopBasicBatch", - "Bjornulf_LoopCombosSamplersSchedulers", - "Bjornulf_LoopFloat", - "Bjornulf_LoopImages", - "Bjornulf_LoopInteger", - "Bjornulf_LoopIntegerSequential", - "Bjornulf_LoopLinesSequential", - "Bjornulf_LoopLoraSelector", - "Bjornulf_LoopModelClipVae", - "Bjornulf_LoopModelSelector", - "Bjornulf_LoopRandomSeed", - "Bjornulf_LoopSamplers", - "Bjornulf_LoopSchedulers", - "Bjornulf_LoopTexts", - "Bjornulf_LoopWriteText", - "Bjornulf_LoraSelectorHunyuan", - "Bjornulf_LoraSelectorWanVideo", - "Bjornulf_MatchTextToInput", - "Bjornulf_MathNode", - "Bjornulf_MergeImagesHorizontally", - "Bjornulf_MergeImagesVertically", - "Bjornulf_ModelClipVaeSelector", - "Bjornulf_MultiOpenAIVisionNode", - "Bjornulf_OllamaConfig", - "Bjornulf_OllamaImageVision", - "Bjornulf_OllamaSystemJobSelector", - "Bjornulf_OllamaSystemPersonaSelector", - "Bjornulf_OllamaTalk", - "Bjornulf_OllamaVisionPromptSelector", - "Bjornulf_OpenAIVisionNode", - "Bjornulf_PassPreviewImage", - "Bjornulf_PauseResume", - "Bjornulf_PickInput", - "Bjornulf_PickMe", - "Bjornulf_PlayAudio", - "Bjornulf_PreviewFirstImage", - "Bjornulf_PurgeCLIPNode", - "Bjornulf_RandomFloatNode", - "Bjornulf_RandomImage", - "Bjornulf_RandomIntNode", - "Bjornulf_RandomLineFromInput", - "Bjornulf_RandomLoraSelector", - "Bjornulf_RandomModelClipVae", - "Bjornulf_RandomModelSelector", - "Bjornulf_RandomTexts", - "Bjornulf_ReassembleImageGrid", - "Bjornulf_RemoteTextEncodingWithCLIPs", - "Bjornulf_RemoteVAEDecoderNode", - "Bjornulf_RemoteVAEDecoderNodeTiled", - "Bjornulf_RemoveTransparency", - "Bjornulf_ResizeImage", - "Bjornulf_ResizeImagePercentage", - "Bjornulf_SaveBjornulfLobeChat", - "Bjornulf_SaveGlobalVariables", - "Bjornulf_SaveImagePath", - "Bjornulf_SaveImageToFolder", - "Bjornulf_SaveTensors", - "Bjornulf_SaveText", - "Bjornulf_SaveTmpAudio", - "Bjornulf_SaveTmpImage", - "Bjornulf_SaveTmpVideo", - "Bjornulf_ScramblerCharacter", - "Bjornulf_SelectImageFromList", - "Bjornulf_ShowFloat", - "Bjornulf_ShowInt", - "Bjornulf_ShowJson", - "Bjornulf_ShowStringText", - "Bjornulf_ShowText", - "Bjornulf_SpeechToText", - "Bjornulf_SplitImageGrid", - "Bjornulf_StyleSelector", - "Bjornulf_SwitchAnything", - "Bjornulf_SwitchText", - "Bjornulf_TextAnalyzer", - "Bjornulf_TextGenerator", - "Bjornulf_TextGeneratorCharacterCreature", - "Bjornulf_TextGeneratorCharacterFemale", - "Bjornulf_TextGeneratorCharacterMale", - "Bjornulf_TextGeneratorCharacterObject", - "Bjornulf_TextGeneratorCharacterPose", - "Bjornulf_TextGeneratorOutfitFemale", - "Bjornulf_TextGeneratorOutfitMale", - "Bjornulf_TextGeneratorScene", - "Bjornulf_TextGeneratorStyle", - "Bjornulf_TextGeneratorText2Video", - "Bjornulf_TextReplace", - "Bjornulf_TextSplitin10", - "Bjornulf_TextSplitin5", - "Bjornulf_TextToAnything", - "Bjornulf_TextToSpeech", - "Bjornulf_TextToStringAndSeed", - "Bjornulf_TextToVariable", - "Bjornulf_ToDoList", - "Bjornulf_VideoDetails", - "Bjornulf_VideoLatentResolutionSelector", - "Bjornulf_VideoPingPong", - "Bjornulf_VideoPreview", - "Bjornulf_VideoTextGenerator", - "Bjornulf_VideoToImagesList", - "Bjornulf_WriteText", - "Bjornulf_WriteTextAdvanced", - "Bjornulf_WriteTextPickMe", - "Bjornulf_WriteTextPickMeChain", - "Bjornulf_WriteTextPickMeGlobal", - "Bjornulf_XTTSConfig", - "Bjornulf_imagesToVideo", - "Bjornulf_loadImageBase64Transparency", - "Bjornulf_ollamaLoader" - ], - { - "title_aux": "Bjornulf_custom_nodes" - } - ], - "https://github.com/justin-vt/ComfyUI-brushstrokes": [ - [ - "OpenCVBrushStrokesNode", - "PILBrushStrokesNode", - "WandBrushStrokesNode" - ], - { - "title_aux": "ComfyUI-brushstrokes" - } - ], - "https://github.com/k-komarov/comfyui-bunny-cdn-storage": [ - [ - "Save Image to BunnyStorage" - ], - { - "title_aux": "comfyui-bunny-cdn-storage" - } - ], - "https://github.com/ka-puna/comfyui-yanc": [ - [ - "YANC.ConcatStrings", - "YANC.FormatDatetimeString", - "YANC.GetWidgetValueString", - "YANC.IntegerCaster", - "YANC.MultilineString", - "YANC.SaveImageWEBP", - "YANC.TruncateString" - ], - { - "title_aux": "comfyui-yanc" - } - ], - "https://github.com/kaanyalova/ComfyUI_ExtendedImageFormats": [ - [ - "DDSSaveImage", - "ExtendedSaveImage" - ], - { - "title_aux": "Extended Image Formats for ComfyUI" - } - ], - "https://github.com/kaaskoek232/ComfyUI-MemoryManagement": [ - [ - "AutoMemoryCleanup", - "MemoryCleanup", - "MemoryLeakDetector", - "MemoryMonitor", - "SmartMemoryManager", - "VRAMOptimizer", - "VRAMUnload" - ], - { - "title_aux": "ComfyUI-MemoryManagement" - } - ], - "https://github.com/kadirnar/ComfyUI-Transformers": [ - [ - "DepthEstimationInference", - "ImageClassificationPipeline", - "ImageSegmentationPipeline", - "LoadDepthModel", - "ObjectDetectionPipeline" - ], - { - "title_aux": "ComfyUI-Transformers" - } - ], - "https://github.com/kadirnar/ComfyUI-YOLO": [ - [ - "BBoxToCoco", - "BBoxToXYWH", - "BBoxVisNode", - "CocoToNumber", - "ConvertToDict", - "CustomUltralyticsModelLoader", - "GetImageSize", - "ImageResizeAdvanced", - "UltralyticsInference", - "UltralyticsModelLoader", - "UltralyticsVisualization", - "ViewText" - ], - { - "title_aux": "ComfyUI-YOLO" - } - ], - "https://github.com/kael558/ComfyUI-GGUF-FantasyTalking": [ - [ - "CLIPLoaderGGUF", - "DownloadAndLoadWav2VecModel", - "FantasyTalkingModelLoader", - "FantasyTalkingWav2VecEmbeds", - "LoadWanVideoT5TextEncoderGGUF", - "ReCamMasterPoseVisualizer", - "UnetLoaderGGUF", - "UnetLoaderGGUF_LowVRAM", - "WanVideoATITracks", - "WanVideoATITracksVisualize", - "WanVideoATI_comfy", - "WanVideoControlnet", - "WanVideoControlnetLoader", - "WanVideoDiffusionForcingSampler", - "WanVideoFunCameraEmbeds", - "WanVideoReCamMasterCameraEmbed", - "WanVideoReCamMasterDefaultCamera", - "WanVideoReCamMasterGenerateOrbitCamera", - "WanVideoUni3C_ControlnetLoader", - "WanVideoUni3C_embeds", - "WanVideoUniAnimateDWPoseDetector", - "WanVideoUniAnimatePoseInput" - ], - { - "title_aux": "ComfyUI-GGUF-FantasyTalking" - } - ], - "https://github.com/kaibioinfo/ComfyUI_AdvancedRefluxControl": [ - [ - "ReduxAdvanced", - "StyleModelApplySimple" - ], - { - "title_aux": "Advanced Reflux control" - } - ], - "https://github.com/kaipard/comfyui-auto-latent-size": [ - [ - "AutoAspectLatent" - ], - { - "title_aux": "Auto Aspect Latent Generator" - } - ], - "https://github.com/kale4eat/ComfyUI-path-util": [ - [ - "path_util_PathAbspath", - "path_util_PathBasename", - "path_util_PathDirname", - "path_util_PathExists", - "path_util_PathIsdir", - "path_util_PathIsfile", - "path_util_PathJoin", - "path_util_PathRelpath", - "path_util_PathSplitext" - ], - { - "title_aux": "ComfyUI_demucus" - } - ], - "https://github.com/kale4eat/ComfyUI-speech-dataset-toolkit": [ - [ - "SDT_AudioProperty", - "SDT_BSRoFormerApply", - "SDT_BSRoFormerLoader", - "SDT_ConcatAudio", - "SDT_CutAudio", - "SDT_DemucsApply", - "SDT_DemucsLoader", - "SDT_FasterWhisperListSegments", - "SDT_FasterWhisperLoader", - "SDT_FasterWhisperSegmentProperty", - "SDT_FasterWhisperTextFromSegments", - "SDT_FasterWhisperTranscribe", - "SDT_GriffinLim", - "SDT_HighpassBiquad", - "SDT_JoinAudio", - "SDT_KotobaWhisperListSegments", - "SDT_KotobaWhisperLoaderLong", - "SDT_KotobaWhisperLoaderShort", - "SDT_KotobaWhisperSegmentProperty", - "SDT_KotobaWhisperTranscribeLong", - "SDT_KotobaWhisperTranscribeShort", - "SDT_LFCC", - "SDT_LoadAudio", - "SDT_LoadAudios", - "SDT_LowpassBiquad", - "SDT_MFCC", - "SDT_MakeSilenceAudio", - "SDT_MelBandRoformerLoader", - "SDT_MelSpectrogram", - "SDT_NemoAsrLoader", - "SDT_NemoAsrTranscribe", - "SDT_NueAsrLoader", - "SDT_NueAsrTranscribe", - "SDT_PlotMelFilterBank", - "SDT_PlotPitch", - "SDT_PlotSpecgram", - "SDT_PlotSpectrogram", - "SDT_PlotWaveForm", - "SDT_ResampleAudio", - "SDT_SaveAudio", - "SDT_SilenceAudio", - "SDT_SileroVADApply", - "SDT_SileroVADCollectChunks", - "SDT_SileroVADListTimestamps", - "SDT_SileroVADLoader", - "SDT_SileroVADTimestampProperty", - "SDT_Spectrogram", - "SDT_SpeechMOSLoader", - "SDT_SpeechMOSScore", - "SDT_SplitAudio", - "SDT_TrimAudio", - "SDT_TrimAudioBySample" - ], - { - "title_aux": "ComfyUI-speech-dataset-toolkit" - } - ], - "https://github.com/kale4eat/ComfyUI-string-util": [ - [ - "string_util_Str", - "string_util_StrConcat", - "string_util_StrCount", - "string_util_StrEndsWith", - "string_util_StrEqual", - "string_util_StrFind", - "string_util_StrFormat", - "string_util_StrJoin", - "string_util_StrLen", - "string_util_StrLower", - "string_util_StrLstrip", - "string_util_StrNotEqual", - "string_util_StrReplace", - "string_util_StrRstrip", - "string_util_StrSlice", - "string_util_StrSplit", - "string_util_StrStartsWith", - "string_util_StrStrip", - "string_util_StrUpper" - ], - { - "title_aux": "ComfyUI-string-util" - } - ], - "https://github.com/kale4eat/ComfyUI-text-file-util": [ - [ - "text_file_util_ReadAllLines", - "text_file_util_ReadAllText", - "text_file_util_WriteText", - "text_file_util_WriteTextLines", - "text_file_util_WriteTextWithSequentialNumbering" - ], - { - "title_aux": "ComfyUI-text-file-util" - } - ], - "https://github.com/kambara/ComfyUI-PromptPalette": [ - [ - "PromptPalette" - ], - { - "title_aux": "ComfyUI-PromptPalette" - } - ], - "https://github.com/kanibus/kanibus": [ - [ - "AIDepthControl", - "AdvancedTrackingPro", - "BodyPoseEstimator", - "EmotionAnalyzer", - "HandTracking", - "KanibusMaster", - "LandmarkPro468", - "MultiControlNetApply", - "NeuralPupilTracker", - "NormalMapGenerator", - "ObjectSegmentation", - "SmartFacialMasking", - "TemporalSmoother", - "VideoFrameLoader" - ], - { - "title_aux": "KANIBUS - Advanced Eye Tracking ControlNet System" - } - ], - "https://github.com/kantsche/ComfyUI-MixMod": [ - [ - "MixModBandFFTGuiderNode", - "MixModDepthGuiderNode", - "MixModDynamicMaskAlternativeGuiderNode", - "MixModDynamicMaskGuiderNode", - "MixModFFTGuiderNode", - "MixModGuiderComponentNode", - "MixModGuiderComponentPipelineNode", - "MixModGuiderNode", - "MixModHighResGuiderNode", - "MixModOptionsMaskNode", - "MixModOptionsScaleNode", - "MixModOptionsSchedulerNode", - "MixModPipelineNode" - ], - { - "author": "Kantsche", - "description": "Model Mixture Guider", - "nickname": "MixMod", - "title": "ComfyUI-MixMod", - "title_aux": "ComfyUI-MixMod" - } - ], - "https://github.com/kappa54m/ComfyUI_Usability": [ - [ - "KLoadImageByPath", - "KLoadImageByPathAdvanced", - "KLoadImageDedup" - ], - { - "title_aux": "ComfyUI Usability" - } - ], - "https://github.com/karas17/ComfyUI-Camera-Watermark": [ - [ - "CameraWatermarkNode", - "ImageLoaderWithEXIF" - ], - { - "title_aux": "ComfyUI Camera Watermark" - } - ], - "https://github.com/karthikg-09/ComfyUI-Vton-Mask": [ - [ - "ComfyUIVtonMaskGenerator", - "ComfyUIVtonMaskLoader" - ], - { - "title_aux": "ComfyUI-Vton-Mask" - } - ], - "https://github.com/kasukanra/ComfyUI_StringToHex": [ - [ - "ColorNameToHex" - ], - { - "title_aux": "ComfyUI_StringToHex" - } - ], - "https://github.com/katalist-ai/comfyUI-nsfw-detection": [ - [ - "NudenetDetector" - ], - { - "title_aux": "comfyUI-nsfw-detection" - } - ], - "https://github.com/kazeyori/ComfyUI-QuickImageSequenceProcess": [ - [ - "QuickImageSequenceProcess" - ], - { - "author": "kazeyori", - "description": "A ComfyUI plugin for efficient image sequence processing. Features frame insertion, duplication, and removal with intuitive controls.", - "nickname": "QuickSeq", - "title": "Quick Image Sequence Process", - "title_aux": "ComfyUI-QuickImageSequenceProcess" - } - ], - "https://github.com/kealiu/ComfyUI-S3-Tools": [ - [ - "Load Image From S3", - "Save Image To S3" - ], - { - "title_aux": "ComfyUI Load and Save file to S3" - } - ], - "https://github.com/kealiu/ComfyUI-Zero123-Porting": [ - [ - "Zero123: Image Preprocess", - "Zero123: Image Rotate in 3D" - ], - { - "title_aux": "ComfyUI-Zero123-Porting" - } - ], - "https://github.com/kealiu/ComfyUI-ZeroShot-MTrans": [ - [ - "ZeST: Grayout Subject" - ], - { - "title_aux": "ComfyUI-ZeroShot-MTrans" - } - ], - "https://github.com/keit0728/ComfyUI-Image-Toolkit": [ - [ - "AlphaFlatten", - "AlphaToGrayscale", - "AntialiasingImage", - "BinarizeImage", - "BinarizeImageUsingOtsu", - "BrightnessTransparency", - "GrayscaleImage", - "RemoveWhiteBackgroundNoise" - ], - { - "title_aux": "ComfyUI-Image-Toolkit" - } - ], - "https://github.com/keit0728/ComfyUI-keitNodes": [ - [ - "AspectRatioResolutionFinder", - "M2MTranslator", - "PixelLimitResizer", - "WanVideoOptimalResizer", - "WanVideoResolutionFinder" - ], - { - "title_aux": "ComfyUI-keitNodes" - } - ], - "https://github.com/keit0728/ComfyUI-musubi-tuner": [ - [ - "MusubiTunerWanGenerateVideo" - ], - { - "title_aux": "ComfyUI-musubi-tuner" - } - ], - "https://github.com/kenjiqq/qq-nodes-comfyui": [ - [ - "Any List", - "Any List Iterator", - "Any To Any", - "Axis Pack", - "Axis To Any", - "Axis Unpack", - "Load Lines From Text File", - "Slice List", - "Text Splitter", - "XY Grid Accumulator", - "XY Grid Helper" - ], - { - "title_aux": "qq-nodes-comfyui" - } - ], - "https://github.com/kevinmcmahondev/comfyui-kmcdev-image-filter-adjustments": [ - [ - "ImageBlankAlpha", - "ImageBlendMask", - "ImageFilterAdjustments", - "ImageMixColorByMask" - ], - { - "title_aux": "KMCDev Nodes" - } - ], - "https://github.com/kevinmcmahondev/comfyui-skin-tone-detector": [ - [ - "SkinToneDetector" - ], - { - "title_aux": "Skin Tone Detector for ComfyUI" - } - ], - "https://github.com/kft334/Knodes": [ - [ - "Image(s) To Websocket (Base64)", - "ImageOutput", - "Load Image (Base64)", - "Load Images (Base64)" - ], - { - "title_aux": "Knodes" - } - ], - "https://github.com/khanhlvg/vertex-ai-comfyui-nodes": [ - [ - "Chirp", - "Gemini", - "ImagenComputedMaskConfig", - "ImagenMaskEditing", - "Imagen_Product_Recontext", - "Imagen_T2I", - "Lyria", - "PreviewVideo", - "Veo2", - "Veo2Extend", - "Veo3", - "Veo_Prompt_Writer", - "Virtual_Try_On" - ], - { - "title_aux": "[Unofficial] Vertex AI Custom Nodes for ComfyUI" - } - ], - "https://github.com/kijai/ComfyUI-ADMotionDirector": [ - [ - "ADMD_AdditionalModelSelect", - "ADMD_CheckpointLoader", - "ADMD_ComfyModelLoader", - "ADMD_DiffusersLoader", - "ADMD_InitializeTraining", - "ADMD_LoadLora", - "ADMD_MakeBatchList", - "ADMD_SaveLora", - "ADMD_TrainLora", - "ADMD_ValidationSampler", - "ADMD_ValidationSettings" - ], - { - "title_aux": "Animatediff MotionLoRA Trainer" - } - ], - "https://github.com/kijai/ComfyUI-APISR-KJ": [ - [ - "APISR_upscale" - ], - { - "title_aux": "ComfyUI-APISR" - } - ], - "https://github.com/kijai/ComfyUI-BrushNet-Wrapper": [ - [ - "brushnet_ella_loader", - "brushnet_ipadapter_matteo", - "brushnet_model_loader", - "brushnet_sampler", - "brushnet_sampler_ella", - "powerpaint_brushnet_sampler" - ], - { - "title_aux": "ComfyUI-BrushNet-Wrapper" - } - ], - "https://github.com/kijai/ComfyUI-CCSR": [ - [ - "CCSR_Model_Select", - "CCSR_Upscale", - "DownloadAndLoadCCSRModel" - ], - { - "title_aux": "ComfyUI-CCSR" - } - ], - "https://github.com/kijai/ComfyUI-CogVideoXWrapper": [ - [ - "CogVideoContextOptions", - "CogVideoControlNet", - "CogVideoDecode", - "CogVideoEnhanceAVideo", - "CogVideoImageEncode", - "CogVideoImageEncodeFunInP", - "CogVideoLatentPreview", - "CogVideoLoraSelect", - "CogVideoLoraSelectComfy", - "CogVideoSampler", - "CogVideoTextEncode", - "CogVideoTextEncodeCombine", - "CogVideoTransformerEdit", - "CogVideoXFasterCache", - "CogVideoXFunResizeToClosestBucket", - "CogVideoXModelLoader", - "CogVideoXTeaCache", - "CogVideoXTorchCompileSettings", - "CogVideoXVAELoader", - "DownloadAndLoadCogVideoControlNet", - "DownloadAndLoadCogVideoGGUFModel", - "DownloadAndLoadCogVideoModel", - "DownloadAndLoadToraModel", - "ToraEncodeOpticalFlow", - "ToraEncodeTrajectory" - ], - { - "title_aux": "ComfyUI CogVideoX Wrapper" - } - ], - "https://github.com/kijai/ComfyUI-ControlNeXt-SVD": [ - [ - "ControlNextDecode", - "ControlNextDiffusersScheduler", - "ControlNextGetPoses", - "ControlNextSVDApply", - "ControlNextSampler", - "DownloadAndLoadControlNeXt" - ], - { - "title_aux": "ComfyUI nodes for ControlNext-SVD v2" - } - ], - "https://github.com/kijai/ComfyUI-DDColor": [ - [ - "DDColor_Colorize" - ], - { - "title_aux": "ComfyUI-DDColor" - } - ], - "https://github.com/kijai/ComfyUI-DepthAnythingV2": [ - [ - "DepthAnything_V2", - "DownloadAndLoadDepthAnythingV2Model" - ], - { - "title_aux": "ComfyUI-DepthAnythingV2" - } - ], - "https://github.com/kijai/ComfyUI-DiffusionLight": [ - [ - "chrome_ball_to_envmap", - "exposure_to_hdr" - ], - { - "title_aux": "DiffusionLight implementation for ComfyUI" - } - ], - "https://github.com/kijai/ComfyUI-DynamiCrafterWrapper": [ - [ - "DownloadAndLoadCLIPModel", - "DownloadAndLoadCLIPVisionModel", - "DownloadAndLoadDynamiCrafterCNModel", - "DownloadAndLoadDynamiCrafterModel", - "DynamiCrafterBatchInterpolation", - "DynamiCrafterCNLoader", - "DynamiCrafterControlnetApply", - "DynamiCrafterI2V", - "DynamiCrafterLoadInitNoise", - "DynamiCrafterModelLoader", - "ToonCrafterDecode", - "ToonCrafterInterpolation" - ], - { - "title_aux": "ComfyUI-DynamiCrafterWrapper" - } - ], - "https://github.com/kijai/ComfyUI-ELLA-wrapper": [ - [ - "diffusers_model_loader", - "diffusers_sampler", - "ella_model_loader", - "ella_sampler", - "ella_t5_embeds" - ], - { - "title_aux": "ComfyUI-ELLA-wrapper" - } - ], - "https://github.com/kijai/ComfyUI-Florence2": [ - [ - "DownloadAndLoadFlorence2Lora", - "DownloadAndLoadFlorence2Model", - "Florence2ModelLoader", - "Florence2Run" - ], - { - "preemptions": [ - "DownloadAndLoadFlorence2Lora", - "DownloadAndLoadFlorence2Model", - "Florence2ModelLoader", - "Florence2Run" - ], - "title_aux": "ComfyUI-Florence2" - } - ], - "https://github.com/kijai/ComfyUI-FluxTrainer": [ - [ - "ExtractFluxLoRA", - "FluxKohyaInferenceSampler", - "FluxTrainAndValidateLoop", - "FluxTrainBlockSelect", - "FluxTrainEnd", - "FluxTrainLoop", - "FluxTrainModelSelect", - "FluxTrainResume", - "FluxTrainSave", - "FluxTrainSaveModel", - "FluxTrainValidate", - "FluxTrainValidationSettings", - "FluxTrainerLossConfig", - "InitFluxLoRATraining", - "InitFluxTraining", - "InitSD3LoRATraining", - "InitSDXLLoRATraining", - "OptimizerConfig", - "OptimizerConfigAdafactor", - "OptimizerConfigProdigy", - "OptimizerConfigProdigyPlusScheduleFree", - "SD3ModelSelect", - "SD3TrainValidationSettings", - "SDXLModelSelect", - "SDXLTrainValidate", - "SDXLTrainValidationSettings", - "TrainDatasetAdd", - "TrainDatasetGeneralConfig", - "TrainDatasetRegularization", - "TrainNetworkConfig", - "UploadToHuggingFace", - "VisualizeLoss" - ], - { - "title_aux": "ComfyUI Flux Trainer" - } - ], - "https://github.com/kijai/ComfyUI-GIMM-VFI": [ - [ - "DownloadAndLoadGIMMVFIModel", - "GIMMVFI_interpolate" - ], - { - "title_aux": "ComfyUI-GIMM-VFI" - } - ], - "https://github.com/kijai/ComfyUI-Geowizard": [ - [ - "geowizard_model_loader", - "geowizard_sampler" - ], - { - "title_aux": "Geowizard depth and normal estimation in ComfyUI" - } - ], - "https://github.com/kijai/ComfyUI-HFRemoteVae": [ - [ - "HFRemoteVAE", - "HFRemoteVAEDecode" - ], - { - "title_aux": "ComfyUI-HFRemoteVae" - } - ], - "https://github.com/kijai/ComfyUI-HunyuanVideoWrapper": [ - [ - "DownloadAndLoadHyVideoTextEncoder", - "HunyuanVideoFresca", - "HunyuanVideoSLG", - "HyVideoBlockSwap", - "HyVideoCFG", - "HyVideoContextOptions", - "HyVideoCustomPromptTemplate", - "HyVideoDecode", - "HyVideoEmptyTextEmbeds", - "HyVideoEncode", - "HyVideoEncodeKeyframes", - "HyVideoEnhanceAVideo", - "HyVideoGetClosestBucketSize", - "HyVideoI2VEncode", - "HyVideoInverseSampler", - "HyVideoLatentPreview", - "HyVideoLoopArgs", - "HyVideoLoraBlockEdit", - "HyVideoLoraSelect", - "HyVideoModelLoader", - "HyVideoPromptMixSampler", - "HyVideoReSampler", - "HyVideoSTG", - "HyVideoSampler", - "HyVideoTeaCache", - "HyVideoTextEmbedBridge", - "HyVideoTextEmbedsLoad", - "HyVideoTextEmbedsSave", - "HyVideoTextEncode", - "HyVideoTextImageEncode", - "HyVideoTorchCompileSettings", - "HyVideoVAELoader" - ], - { - "title_aux": "ComfyUI-HunyuanVideoWrapper" - } - ], - "https://github.com/kijai/ComfyUI-IC-Light": [ - [ - "BackgroundScaler", - "CalculateNormalsFromImages", - "DetailTransfer", - "ICLightConditioning", - "LightSource", - "LoadAndApplyICLightUnet", - "LoadHDRImage" - ], - { - "title_aux": "ComfyUI-IC-Light" - } - ], - "https://github.com/kijai/ComfyUI-KJNodes": [ - [ - "AddLabel", - "AppendInstanceDiffusionTracking", - "AppendStringsToList", - "ApplyRifleXRoPE_HunuyanVideo", - "ApplyRifleXRoPE_WanVideo", - "AudioConcatenate", - "BOOLConstant", - "BatchCLIPSeg", - "BatchCropFromMask", - "BatchCropFromMaskAdvanced", - "BatchUncrop", - "BatchUncropAdvanced", - "BboxToInt", - "BboxVisualize", - "CFGZeroStarAndInit", - "CameraPoseVisualizer", - "CheckpointLoaderKJ", - "CheckpointPerturbWeights", - "ColorMatch", - "ColorToMask", - "CondPassThrough", - "ConditioningMultiCombine", - "ConditioningSetMaskAndCombine", - "ConditioningSetMaskAndCombine3", - "ConditioningSetMaskAndCombine4", - "ConditioningSetMaskAndCombine5", - "CreateAudioMask", - "CreateFadeMask", - "CreateFadeMaskAdvanced", - "CreateFluidMask", - "CreateGradientFromCoords", - "CreateGradientMask", - "CreateInstanceDiffusionTracking", - "CreateMagicMask", - "CreateShapeImageOnPath", - "CreateShapeMask", - "CreateShapeMaskOnPath", - "CreateTextMask", - "CreateTextOnPath", - "CreateVoronoiMask", - "CrossFadeImages", - "CrossFadeImagesMulti", - "CustomControlNetWeightsFluxFromList", - "CustomSigmas", - "CutAndDragOnPath", - "DiTBlockLoraLoader", - "DifferentialDiffusionAdvanced", - "DiffusionModelLoaderKJ", - "DiffusionModelSelector", - "DownloadAndLoadCLIPSeg", - "DrawInstanceDiffusionTracking", - "DummyOut", - "EmptyLatentImageCustomPresets", - "EmptyLatentImagePresets", - "FastPreview", - "FilterZeroMasksAndCorrespondingImages", - "FlipSigmasAdjusted", - "FloatConstant", - "FloatToMask", - "FloatToSigmas", - "FluxBlockLoraSelect", - "GLIGENTextBoxApplyBatchCoords", - "GenerateNoise", - "GetImageRangeFromBatch", - "GetImageSizeAndCount", - "GetImagesFromBatchIndexed", - "GetLatentRangeFromBatch", - "GetLatentSizeAndCount", - "GetLatentsFromBatchIndexed", - "GetMaskSizeAndCount", - "GradientToFloat", - "GrowMaskWithBlur", - "HunyuanVideoBlockLoraSelect", - "HunyuanVideoEncodeKeyframesToCond", - "INTConstant", - "ImageAddMulti", - "ImageAndMaskPreview", - "ImageBatchFilter", - "ImageBatchJoinWithTransition", - "ImageBatchMulti", - "ImageBatchRepeatInterleaving", - "ImageBatchTestPattern", - "ImageConcanate", - "ImageConcatFromBatch", - "ImageConcatMulti", - "ImageCropByMask", - "ImageCropByMaskAndResize", - "ImageCropByMaskBatch", - "ImageGrabPIL", - "ImageGridComposite2x2", - "ImageGridComposite3x3", - "ImageGridtoBatch", - "ImageNoiseAugmentation", - "ImageNormalize_Neg1_To_1", - "ImagePadForOutpaintMasked", - "ImagePadForOutpaintTargetSize", - "ImagePadKJ", - "ImagePass", - "ImagePrepForICLora", - "ImageResizeKJ", - "ImageResizeKJv2", - "ImageTensorList", - "ImageTransformByNormalizedAmplitude", - "ImageUncropByMask", - "ImageUpscaleWithModelBatched", - "InjectNoiseToLatent", - "InsertImageBatchByIndexes", - "InsertImagesToBatchIndexed", - "InsertLatentToIndexed", - "InterpolateCoords", - "Intrinsic_lora_sampling", - "JoinStringMulti", - "JoinStrings", - "LazySwitchKJ", - "LeapfusionHunyuanI2VPatcher", - "LoadAndResizeImage", - "LoadImagesFromFolderKJ", - "LoadResAdapterNormalization", - "LoadVideosFromFolder", - "LoraExtractKJ", - "MaskBatchMulti", - "MaskOrImageToWeight", - "MergeImageChannels", - "ModelPassThrough", - "ModelPatchTorchSettings", - "ModelSaveKJ", - "NormalizedAmplitudeToFloatList", - "NormalizedAmplitudeToMask", - "OffsetMask", - "OffsetMaskByNormalizedAmplitude", - "PadImageBatchInterleaved", - "PatchModelPatcherOrder", - "PathchSageAttentionKJ", - "PlotCoordinates", - "PointsEditor", - "PreviewAnimation", - "RemapImageRange", - "RemapMaskRange", - "ReplaceImagesInBatch", - "ResizeMask", - "ReverseImageBatch", - "RoundMask", - "SV3D_BatchSchedule", - "SaveImageKJ", - "SaveImageWithAlpha", - "SaveStringKJ", - "ScaleBatchPromptSchedule", - "ScheduledCFGGuidance", - "Screencap_mss", - "SeparateMasks", - "SetShakkerLabsUnionControlNetType", - "ShuffleImageBatch", - "SigmasToFloat", - "SkipLayerGuidanceWanVideo", - "Sleep", - "SomethingToString", - "SoundReactive", - "SplineEditor", - "SplitBboxes", - "SplitImageChannels", - "StableZero123_BatchSchedule", - "StringConstant", - "StringConstantMultiline", - "StringToFloatList", - "StyleModelApplyAdvanced", - "Superprompt", - "TimerNodeKJ", - "TorchCompileControlNet", - "TorchCompileCosmosModel", - "TorchCompileLTXModel", - "TorchCompileModelFluxAdvanced", - "TorchCompileModelFluxAdvancedV2", - "TorchCompileModelHyVideo", - "TorchCompileModelQwenImage", - "TorchCompileModelWanVideo", - "TorchCompileModelWanVideoV2", - "TorchCompileVAE", - "TransitionImagesInBatch", - "TransitionImagesMulti", - "VAELoaderKJ", - "VRAM_Debug", - "Wan21BlockLoraSelect", - "WanVideoEnhanceAVideoKJ", - "WanVideoNAG", - "WanVideoTeaCacheKJ", - "WebcamCaptureCV2", - "WeightScheduleConvert", - "WeightScheduleExtend", - "WidgetToString" - ], - { - "title_aux": "KJNodes for ComfyUI" - } - ], - "https://github.com/kijai/ComfyUI-KwaiKolorsWrapper": [ - [ - "DownloadAndLoadChatGLM3", - "DownloadAndLoadKolorsModel", - "KolorsSampler", - "KolorsTextEncode", - "LoadChatGLM3" - ], - { - "title_aux": "ComfyUI-KwaiKolorsWrapper" - } - ], - "https://github.com/kijai/ComfyUI-LBMWrapper": [ - [ - "LBMSampler", - "LoadLBMModel" - ], - { - "title_aux": "ComfyUI-LBMWrapper" - } - ], - "https://github.com/kijai/ComfyUI-LLaVA-OneVision": [ - [ - "DownloadAndLoadLLaVAOneVisionModel", - "LLaVA_OneVision_Run", - "OneVisionCaptionFolder", - "SaveCaptionToTextFile" - ], - { - "title_aux": "ComfyUI Llava-OneVision" - } - ], - "https://github.com/kijai/ComfyUI-LVCDWrapper": [ - [ - "LVCDDecoder", - "LVCDSampler", - "LoadLVCDModel" - ], - { - "title_aux": "ComfyUI wrapper nodes for LVCD" - } - ], - "https://github.com/kijai/ComfyUI-LaVi-Bridge-Wrapper": [ - [ - "lavi_bridge_llama_encoder", - "lavi_bridge_t5_encoder", - "lavibridge_model_loader", - "lavibridge_sampler" - ], - { - "title_aux": "ComfyUI-LaVi-Bridge-Wrapper" - } - ], - "https://github.com/kijai/ComfyUI-LivePortraitKJ": [ - [ - "DownloadAndLoadLivePortraitModels", - "KeypointScaler", - "KeypointsToImage", - "LivePortraitComposite", - "LivePortraitCropper", - "LivePortraitLoadCropper", - "LivePortraitLoadFaceAlignmentCropper", - "LivePortraitLoadMediaPipeCropper", - "LivePortraitProcess", - "LivePortraitRetargeting" - ], - { - "title_aux": "ComfyUI-LivePortraitKJ" - } - ], - "https://github.com/kijai/ComfyUI-Lotus": [ - [ - "LoadLotusModel", - "LotusSampler" - ], - { - "title_aux": "ComfyUI-Lotus" - } - ], - "https://github.com/kijai/ComfyUI-LuminaWrapper": [ - [ - "DownloadAndLoadGemmaModel", - "DownloadAndLoadLuminaModel", - "GemmaSampler", - "LuminaGemmaTextEncode", - "LuminaGemmaTextEncodeArea", - "LuminaT2ISampler", - "LuminaTextAreaAppend" - ], - { - "title_aux": "ComfyUI-LuminaWrapper" - } - ], - "https://github.com/kijai/ComfyUI-Marigold": [ - [ - "ColorizeDepthmap", - "MarigoldDepthEstimation", - "MarigoldDepthEstimationVideo", - "MarigoldDepthEstimation_v2", - "MarigoldDepthEstimation_v2_video", - "MarigoldModelLoader", - "RemapDepth", - "SaveImageOpenEXR" - ], - { - "title_aux": "Marigold depth estimation in ComfyUI" - } - ], - "https://github.com/kijai/ComfyUI-MelBandRoFormer": [ - [ - "MelBandRoFormerModelLoader", - "MelBandRoFormerSampler" - ], - { - "title_aux": "ComfyUI-MelBandRoFormer" - } - ], - "https://github.com/kijai/ComfyUI-MimicMotionWrapper": [ - [ - "DiffusersScheduler", - "DownloadAndLoadMimicMotionModel", - "MimicMotionDecode", - "MimicMotionGetPoses", - "MimicMotionSampler" - ], - { - "title_aux": "ComfyUI-MimicMotionWrapper" - } - ], - "https://github.com/kijai/ComfyUI-MoGe": [ - [ - "DownloadAndLoadMoGeModel", - "MoGeProcess" - ], - { - "title_aux": "ComfyUI-MoGe" - } - ], - "https://github.com/kijai/ComfyUI-OpenDiTWrapper": [ - [ - "DownloadAndLoadOpenDiTT5Model", - "DownloadAndLoadOpenSoraModel", - "DownloadAndLoadOpenSoraVAE", - "OpenDiTConditioning", - "OpenDiTSampler", - "OpenSoraDecode", - "OpenSoraEncodeReference" - ], - { - "title_aux": "ComfyUI-OpenDiTWrapper" - } - ], - "https://github.com/kijai/ComfyUI-PyramidFlowWrapper": [ - [ - "PyramidFlowLatentPreview", - "PyramidFlowSampler", - "PyramidFlowTextEncode", - "PyramidFlowTorchCompileSettings", - "PyramidFlowTransformerLoader", - "PyramidFlowVAEDecode", - "PyramidFlowVAEEncode", - "PyramidFlowVAELoader" - ], - { - "title_aux": "ComfyUI PyramidFlow Wrapper" - } - ], - "https://github.com/kijai/ComfyUI-SUPIR": [ - [ - "SUPIR_Upscale", - "SUPIR_conditioner", - "SUPIR_decode", - "SUPIR_encode", - "SUPIR_first_stage", - "SUPIR_model_loader", - "SUPIR_model_loader_v2", - "SUPIR_model_loader_v2_clip", - "SUPIR_sample", - "SUPIR_tiles" - ], - { - "title_aux": "ComfyUI-SUPIR" - } - ], - "https://github.com/kijai/ComfyUI-StableXWrapper": [ - [ - "DifferenceExtractorNode", - "DownloadAndLoadStableXModel", - "StableXProcessImage" - ], - { - "title_aux": "ComfyUI-StableXWrapper" - } - ], - "https://github.com/kijai/ComfyUI-WanVideoWrapper": [ - [ - "CreateCFGScheduleFloatList", - "CreateScheduleFloatList", - "DownloadAndLoadNLFModel", - "DownloadAndLoadWav2VecModel", - "DrawNLFPoses", - "DummyComfyWanModelObject", - "ExtractStartFramesForContinuations", - "FantasyPortraitFaceDetector", - "FantasyPortraitModelLoader", - "FantasyTalkingModelLoader", - "FantasyTalkingWav2VecEmbeds", - "LandmarksToImage", - "LoadVQVAE", - "LoadWanVideoClipTextEncoder", - "LoadWanVideoT5TextEncoder", - "MTVCrafterEncodePoses", - "MultiTalkModelLoader", - "MultiTalkWav2VecEmbeds", - "NLFPredict", - "QwenLoader", - "ReCamMasterPoseVisualizer", - "WanVideoATITracks", - "WanVideoATITracksVisualize", - "WanVideoATI_comfy", - "WanVideoAddControlEmbeds", - "WanVideoAddExtraLatent", - "WanVideoAddFantasyPortrait", - "WanVideoAddMTVMotion", - "WanVideoAddStandInLatent", - "WanVideoApplyNAG", - "WanVideoBlockList", - "WanVideoBlockSwap", - "WanVideoClipVisionEncode", - "WanVideoContextOptions", - "WanVideoControlEmbeds", - "WanVideoControlnet", - "WanVideoControlnetLoader", - "WanVideoDecode", - "WanVideoDiffusionForcingSampler", - "WanVideoEasyCache", - "WanVideoEmptyEmbeds", - "WanVideoEncode", - "WanVideoEnhanceAVideo", - "WanVideoExperimentalArgs", - "WanVideoExtraModelSelect", - "WanVideoFlowEdit", - "WanVideoFreeInitArgs", - "WanVideoFunCameraEmbeds", - "WanVideoImageClipEncode", - "WanVideoImageResizeToClosest", - "WanVideoImageToVideoEncode", - "WanVideoImageToVideoMultiTalk", - "WanVideoLatentReScale", - "WanVideoLoopArgs", - "WanVideoLoraBlockEdit", - "WanVideoLoraSelect", - "WanVideoLoraSelectMulti", - "WanVideoMagCache", - "WanVideoMiniMaxRemoverEmbeds", - "WanVideoModelLoader", - "WanVideoPhantomEmbeds", - "WanVideoPromptExtender", - "WanVideoPromptExtenderSelect", - "WanVideoReCamMasterCameraEmbed", - "WanVideoReCamMasterDefaultCamera", - "WanVideoReCamMasterGenerateOrbitCamera", - "WanVideoRealisDanceLatents", - "WanVideoRoPEFunction", - "WanVideoSLG", - "WanVideoSampler", - "WanVideoScheduler", - "WanVideoSetBlockSwap", - "WanVideoSetLoRAs", - "WanVideoSetRadialAttention", - "WanVideoSigmaToStep", - "WanVideoTeaCache", - "WanVideoTextEmbedBridge", - "WanVideoTextEncode", - "WanVideoTextEncodeCached", - "WanVideoTextEncodeSingle", - "WanVideoTinyVAELoader", - "WanVideoTorchCompileSettings", - "WanVideoUni3C_ControlnetLoader", - "WanVideoUni3C_embeds", - "WanVideoUniAnimateDWPoseDetector", - "WanVideoUniAnimatePoseInput", - "WanVideoVACEEncode", - "WanVideoVACEModelSelect", - "WanVideoVACEStartToEndFrame", - "WanVideoVAELoader", - "WanVideoVRAMManagement", - "Wav2VecModelLoader" - ], - { - "title_aux": "ComfyUI-WanVideoWrapper" - } - ], - "https://github.com/kijai/ComfyUI-depth-fm": [ - [ - "Depth_fm" - ], - { - "title_aux": "ComfyUI-depth-fm" - } - ], - "https://github.com/kijai/ComfyUI-moondream": [ - [ - "MoondreamQuery", - "MoondreamQueryCaptions" - ], - { - "title_aux": "ComfyUI-moondream" - } - ], - "https://github.com/kijai/ComfyUI-segment-anything-2": [ - [ - "DownloadAndLoadSAM2Model", - "Florence2toCoordinates", - "Sam2AutoSegmentation", - "Sam2Segmentation", - "Sam2VideoSegmentation", - "Sam2VideoSegmentationAddPoints" - ], - { - "preemptions": [ - "DownloadAndLoadSAM2Model", - "Florence2toCoordinates", - "Sam2AutoSegmentation", - "Sam2Segmentation", - "Sam2VideoSegmentation", - "Sam2VideoSegmentationAddPoints" - ], - "title_aux": "ComfyUI-segment-anything-2" - } - ], - "https://github.com/kimara-ai/ComfyUI-Kimara-AI-Advanced-Watermarks": [ - [ - "KimaraAIBatchImages", - "KimaraAIWatermarker" - ], - { - "title_aux": "Kimara.ai's Advanced Watermarking Tools" - } - ], - "https://github.com/kinfolk0117/ComfyUI_GradientDeepShrink": [ - [ - "GradientPatchModelAddDownscale", - "GradientPatchModelAddDownscaleAdvanced" - ], - { - "title_aux": "ComfyUI_GradientDeepShrink" - } - ], - "https://github.com/kinfolk0117/ComfyUI_GridSwapper": [ - [ - "GridSwapper" - ], - { - "title_aux": "Gridswapper" - } - ], - "https://github.com/kinfolk0117/ComfyUI_Pilgram": [ - [ - "Pilgram" - ], - { - "title_aux": "ComfyUI_Pilgram" - } - ], - "https://github.com/kinfolk0117/ComfyUI_SimpleTiles": [ - [ - "DynamicTileMerge", - "DynamicTileSplit", - "TileCalc", - "TileMerge", - "TileSplit" - ], - { - "title_aux": "SimpleTiles" - } - ], - "https://github.com/kk8bit/KayTool": [ - [ - "AB_Images", - "AIO_Translater", - "Abc_Math", - "Baidu_Translater", - "Color_Adjustment", - "Custom_Save_Image", - "Display_Any", - "Image_Composer", - "Image_Cropper", - "Image_Mask_Composer", - "Image_Resizer", - "Image_Size_Extractor", - "Kay_BiRefNet_Loader", - "Load_Image_Folder", - "Mask_Blur_Plus", - "Mask_Filler", - "Preview_Mask", - "Preview_Mask_Plus", - "RemBG_Loader", - "Remove_BG", - "Slider_10", - "Slider_100", - "Slider_1000", - "Strong_Prompt", - "Tencent_Translater", - "Text", - "To_Int" - ], - { - "title_aux": "KayTool" - } - ], - "https://github.com/klinter007/klinter_nodes": [ - [ - "AspectSelector", - "BBoxCropper", - "FolderLoader", - "Json Extractor - klinter", - "LoadImagePlus", - "LoadVideoForExtendingKlinter", - "OutpaintPadding", - "PrepVideoForExtendKlinter", - "SaveAudioPlus", - "SizeSelector", - "SpeedRamp", - "YellowBus", - "ZoomOutComposer", - "concat", - "filter", - "nodevalue2stringmulti", - "string_contact_multi" - ], - { - "title_aux": "Klinter_nodes" - } - ], - "https://github.com/kmlbdh/ComfyUI-kmlbdh-VideoCombine": [ - [ - "DeleteFolderAny", - "KMLBDH_RAMCleaner", - "KMLBDH_VideoCombine" - ], - { - "title_aux": "kmlbdh Video Combine (Smart + Tiled)" - } - ], - "https://github.com/kmlbdh/ComfyUI_LocalLLMNodes": [ - [ - "AddUserLocalKontextPreset", - "ArabicProductDescriptionGenerator", - "LocalKontextPromptGenerator", - "RemoveUserLocalKontextPreset", - "SetLocalLLMServiceConnector" - ], - { - "title_aux": "ComfyUI_LocalLLMNodes" - } - ], - "https://github.com/knuknX/ComfyUI-Image-Tools": [ - [ - "BatchImagePathLoader", - "ImageBgRemoveProcessor", - "ImageCheveretoUploader", - "ImageStandardResizeProcessor", - "JSONMessageNotifyTool", - "PreviewJSONNode", - "SingleImagePathLoader", - "SingleImageUrlLoader" - ], - { - "title_aux": "ComfyUI-Image-Tools" - } - ], - "https://github.com/kohs100/comfyui-ppwc": [ - [ - "PPWCReplace" - ], - { - "author": "Phospholipids", - "description": "This extension offers wildcard prompting works solely in workflow.", - "nickname": "PPWC", - "title": "PPWildCard", - "title_aux": "PPWildCard" - } - ], - "https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI": [ - [ - "LLLiteLoader" - ], - { - "title_aux": "ControlNet-LLLite-ComfyUI" - } - ], - "https://github.com/komojini/ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes": [ - [ - "S3 Bucket LoRA", - "S3Bucket_Load_LoRA", - "XL DreamBooth LoRA", - "XLDB_LoRA" - ], - { - "title_aux": "ComfyUI_SDXL_DreamBooth_LoRA_CustomNodes" - } - ], - "https://github.com/komojini/komojini-comfyui-nodes": [ - [ - "BatchCreativeInterpolationNodeDynamicSettings", - "CachedGetter", - "DragNUWAImageCanvas", - "FlowBuilder", - "FlowBuilder (adv)", - "FlowBuilder (advanced)", - "FlowBuilder (advanced) Setter", - "FlowBuilderSetter", - "FlowBuilderSetter (adv)", - "Getter", - "ImageCropByRatio", - "ImageCropByRatioAndResize", - "ImageGetter", - "ImageMerger", - "ImagesCropByRatioAndResizeBatch", - "KSamplerAdvancedCacheable", - "KSamplerCacheable", - "Setter", - "UltimateVideoLoader", - "UltimateVideoLoader (simple)", - "YouTubeVideoLoader" - ], - { - "title_aux": "komojini-comfyui-nodes" - } - ], - "https://github.com/kostenickj/jk-comfyui-helpers": [ - [ - "EasyHRFix", - "EasyHRFix_Context", - "JKAnythingToString", - "JKBigContext", - "JKDynamicThresholdingMultiModel", - "JKEasyCheckpointLoader", - "JKEasyDetailer", - "JKEasyDetailer_Context", - "JKEasyKSampler_Context", - "JKEasyUpscaleImage", - "JKEasyWatermark", - "JKInspireSchedulerAdapter", - "JKLilContext", - "JKMultiModelSamplerUnpatch", - "JKStringEmpty", - "JKStringEquals", - "JKStringNotEmpty", - "JKStringNotEquals", - "JKStringToSamplerAdapter" - ], - { - "title_aux": "comfyui-jk-easy-nodes" - } - ], - "https://github.com/kpsss34/ComfyUI-kpsss34": [ - [ - "SD35sLoaderSampler" - ], - { - "title_aux": "ComfyUI kpsss34 Custom Node" - } - ], - "https://github.com/krakenunbound/ComfyUI-KrakenTools": [ - [ - "KrakenFluxEmptyLatentImage", - "KrakenResolutionHelper", - "KrakenUpscaleTileCalc" - ], - { - "title_aux": "ComfyUI-KrakenTools" - } - ], - "https://github.com/krigeta/qwen-image-controlnets-comfyui": [ - [ - "QwenImageBlockwiseControlNetApply", - "QwenImageBlockwiseControlNetLoader", - "QwenImageCannyPreprocessor", - "QwenImageDepthPreprocessor" - ], - { - "title_aux": "qwen-image-controlnets-comfyui" - } - ], - "https://github.com/krmahil/comfyui-hollow-preserve": [ - [ - "RemoveEnclosedMaskedAreas" - ], - { - "title_aux": "Hollow Preserve" - } - ], - "https://github.com/kukuo6666/ComfyUI-Equirect": [ - [ - "CubemapToEquirect", - "EquirectToCubemap" - ], - { - "title_aux": "ComfyUI Equirectangular Tools" - } - ], - "https://github.com/kungful/ComfyUI_to_webui": [ - [ - "BarcodeGeneratorNode", - "Barcode_seed", - "DeepseekNode", - "Go_to_image", - "GradioInputImage", - "GradioTextBad", - "GradioTextOk", - "HuaFloatNode", - "HuaIntNode", - "Hua_CheckpointLoaderSimple", - "Hua_LoraLoader", - "Hua_LoraLoaderModelOnly", - "Hua_Output", - "Hua_UNETLoader", - "Hua_Video_Output", - "Hua_gradio_Seed", - "Hua_gradio_jsonsave", - "Hua_gradio_resolution", - "Huaword", - "Modelhua", - "brucelee", - "\u5c0f\u5b57\u4f53\u8bf4\u660e\uff1a\u6211\u662fcomfyui_hua_boy\u7684model" - ], - { - "title_aux": "ComfyUI_to_webui" - } - ], - "https://github.com/kunieone/ComfyUI_alkaid": [ - [ - "A_EmptyLatentImageLongside", - "A_Face3DSwapper", - "A_FaceCrop", - "A_FacePaste", - "A_GetImageSize", - "A_OpenPosePreprocessor", - "AdapterFace", - "AdapterFaceLoader", - "AdapterStyle", - "AdapterStyleLoader", - "AlkaidLoader", - "ApplyAdapter", - "ApplyControlNet_KPS", - "CombineAdapterPatch", - "KSamplerHires" - ], - { - "title_aux": "ComfyUI_alkaid" - } - ], - "https://github.com/kusurin/ComfyUI-chronophotography": [ - [ - "CreateChronophotography" - ], - { - "title_aux": "ComfyUI-chronophotography" - } - ], - "https://github.com/kwaroran/abg-comfyui": [ - [ - "Remove Image Background (abg)" - ], - { - "title_aux": "abg-comfyui" - } - ], - "https://github.com/kycg/comfyui-Lora-auto-downloader": [ - [ - "Kw_JsonLoraLoader", - "Kw_Json_Lora_CivitAIDownloader" - ], - { - "title_aux": "Kw_Json_Lora_CivitAIDownloader" - } - ], - "https://github.com/l-comm/WatermarkRemoval": [ - [ - "FindWatermarkNode", - "RemoveWatermarkNode" - ], - { - "author": "l-comm", - "description": "Remove watermark", - "nickname": "Watermark Removal", - "title": "Watermark Removal", - "title_aux": "WatermarkRemoval" - } - ], - "https://github.com/l20richo/ComfyUI-Azure-Blob-Storage": [ - [ - "DownloadFileBLOB", - "LoadImageBLOB", - "SaveImageBLOB", - "SaveVideoFilesBLOB", - "UploadFileBLOB" - ], - { - "title_aux": "ComfyUI-Azure-Blob-Storage" - } - ], - "https://github.com/l3ony2k/comfyui-leon-nodes": [ - [ - "Leon_ByteDance_Image_API_Node", - "Leon_DALLE_Image_API_Node", - "Leon_Flux_Image_API_Node", - "Leon_Flux_Kontext_API_Node", - "Leon_GPT_Image_API_Node", - "Leon_GPT_OSS_API_Node", - "Leon_Google_Image_API_Node", - "Leon_Hypr_Upload_Node", - "Leon_Ideogram_Image_API_Node", - "Leon_Image_Split_4Grid_Node", - "Leon_ImgBB_Upload_Node", - "Leon_LLM_Chat_API_Node", - "Leon_LLM_JSON_API_Node", - "Leon_Luma_AI_Image_API_Node", - "Leon_Midjourney_Describe_API_Node", - "Leon_Midjourney_Proxy_API_Node", - "Leon_Midjourney_Upload_API_Node", - "Leon_Model_Selector_Node", - "Leon_Nano_Banana_API_Node", - "Leon_Qwen_Image_API_Node", - "Leon_Qwen_Image_Edit_API_Node", - "Leon_Recraft_Image_API_Node", - "Leon_StableDiffusion_35_API_Node", - "Leon_StableDiffusion_3_Ultra_API_Node", - "Leon_StableDiffusion_XL_API_Node", - "Leon_String_Combine_Node" - ], - { - "nodename_pattern": "^\ud83e\udd16 Leon", - "title_aux": "Leon's Utility and API Integration Nodes" - } - ], - "https://github.com/laksjdjf/Batch-Condition-ComfyUI": [ - [ - "Batch String", - "CLIP Text Encode (Batch)", - "String Input" - ], - { - "title_aux": "Batch-Condition-ComfyUI" - } - ], - "https://github.com/laksjdjf/ComfyUI-Imatrix": [ - [ - "ImatrixUNETLoader", - "LoRAdiff", - "SaveImatrix" - ], - { - "title_aux": "ComfyUI-Imatrix" - } - ], - "https://github.com/laksjdjf/LCMSampler-ComfyUI": [ - [ - "SamplerLCM", - "TAESDLoader" - ], - { - "title_aux": "LCMSampler-ComfyUI" - } - ], - "https://github.com/laksjdjf/LoRTnoC-ComfyUI": [ - [ - "LortnocLoader" - ], - { - "title_aux": "LoRTnoC-ComfyUI" - } - ], - "https://github.com/laksjdjf/cd-tuner_negpip-ComfyUI": [ - [ - "CDTuner", - "Negapip", - "Negpip" - ], - { - "title_aux": "cd-tuner_negpip-ComfyUI" - } - ], - "https://github.com/laksjdjf/cgem156-ComfyUI": [ - [ - "GradualLatentSampler", - "LCMSamplerRCFG", - "LoadAestheticShadow", - "PredictAesthetic", - "TCDSampler", - "TextScheduler" - ], - { - "title_aux": "cgem156-ComfyUI\ud83c\udf4c" - } - ], - "https://github.com/laksjdjf/pfg-ComfyUI": [ - [ - "PFG" - ], - { - "title_aux": "pfg-ComfyUI" - } - ], - "https://github.com/larsupb/LoRA-Merger-ComfyUI": [ - [ - "PM LoRA Apply", - "PM LoRA Loader", - "PM LoRA Merger", - "PM LoRA Resizer", - "PM LoRA SVD Merger", - "PM LoRA Save", - "XY: PM LoRA Modes", - "XY: PM LoRA SVD Rank", - "XY: PM LoRA Strengths" - ], - { - "title_aux": "LoRA Power-Merger ComfyUI" - } - ], - "https://github.com/latenightlabs/ComfyUI-LNL": [ - [ - "LNL_FrameSelectorV3", - "LNL_FrameSelectorV4" - ], - { - "title_aux": "LNL Frame Selector" - } - ], - "https://github.com/lazniak/Head-Orientation-Node-for-ComfyUI---by-PabloGFX": [ - [ - "HeadOrientationNode" - ], - { - "title_aux": "Head-Orientation-Node - by PabloGFX" - } - ], - "https://github.com/lazniak/LiquidTime-Interpolation": [ - [ - "LiquidTime" - ], - { - "title_aux": "LiquidTime - by PabloGFX" - } - ], - "https://github.com/lazniak/comfyui-google-photos-loader": [ - [ - "ContentFilter", - "DatePicker", - "Google Photos Album Lister", - "Google Photos Album Loader", - "Google Photos Album Selector", - "Google Photos Cache Manager", - "Google Photos Clear Cache", - "Google Photos Images Loader", - "Google Photos Login/Logout" - ], - { - "title_aux": "Google Photos Loader - by PabloGFX" - } - ], - "https://github.com/lc03lc/Comfyui_OmniConsistency": [ - [ - "Comfyui_OmniConsistency" - ], - { - "title_aux": "ComfyUI OmniConsistency Nodes" - } - ], - "https://github.com/lceric/comfyui-gpt-image": [ - [ - "GPTImage1Generate" - ], - { - "title_aux": "comfyui-gpt-image" - } - ], - "https://github.com/lebrosoft/ComfyUI-VideoChatWrapper": [ - [ - "AudioCombine", - "ConcatAudio", - "ConvertAudioChannels", - "JoinAudio", - "ResampleAudio", - "SplitAudio", - "VCW_LoadVideo", - "VCW_ModelLoader", - "VCW_VideoSummary" - ], - { - "title_aux": "ComfyUI-VideoChatWrapper" - } - ], - "https://github.com/leeguandong/ComfyUI_1Prompt1Story": [ - [ - "GenerateStoryImage", - "PromptStoryModelLoader" - ], - { - "title_aux": "ComfyUI_1Prompt1Story" - } - ], - "https://github.com/leeguandong/ComfyUI_ChatGen": [ - [ - "ChatGenGenerate", - "ChatGenImageProcessor", - "ChatGenModelLoader" - ], - { - "title_aux": "ComfyUI_ChatGen" - } - ], - "https://github.com/leeguandong/ComfyUI_Cogview4": [ - [ - "CogView4ImageGenerator", - "CogView4ModelLoader" - ], - { - "title_aux": "ComfyUI_Cogview4" - } - ], - "https://github.com/leeguandong/ComfyUI_CompareModelWeights": [ - [ - "CheckPointLoader_Compare", - "CompareModelWeightsDiff", - "CompareModelWeightsDiffNormalized", - "PreviewImageCompareModelWeights" - ], - { - "title_aux": "ComfyUI_CompareModelWeights" - } - ], - "https://github.com/leeguandong/ComfyUI_CrossImageAttention": [ - [ - "AppearanceTransferInference", - "AppearanceTransferModelModelLoader", - "CIAConfig", - "LoadImagePath", - "LoadLatents" - ], - { - "title_aux": "ComfyUI nodes to use CrossImageAttention" - } - ], - "https://github.com/leeguandong/ComfyUI_DeepSeekVL2": [ - [ - "deepseek_vl2_inference", - "deepseek_vl2_model_loader" - ], - { - "title_aux": "ComfyUI_DeepSeekVL2" - } - ], - "https://github.com/leeguandong/ComfyUI_FluxAttentionMask": [ - [ - "AMModelLoader", - "AMSample", - "AttentionMask" - ], - { - "title_aux": "ComfyUI nodes to use AttentionMask" - } - ], - "https://github.com/leeguandong/ComfyUI_FluxClipWeight": [ - [ - "CLIPTextEncodeFluxWeight" - ], - { - "title_aux": "ComfyUI nodes to use FluxClipWeight" - } - ], - "https://github.com/leeguandong/ComfyUI_FluxCustomId": [ - [ - "ApplyCustomIDFlux", - "CustomIDModelLoader" - ], - { - "title_aux": "ComfyUI_FluxCustomId" - } - ], - "https://github.com/leeguandong/ComfyUI_FluxLayerDiffuse": [ - [ - "FluxTransparentI2I", - "FluxTransparentModelLoader", - "FluxTransparentT2I" - ], - { - "title_aux": "ComfyUI_FluxLayerDiffuse" - } - ], - "https://github.com/leeguandong/ComfyUI_Gemma3": [ - [ - "ApplyGemma3", - "Gemma3ModelLoader" - ], - { - "title_aux": "ComfyUI_Gemma3" - } - ], - "https://github.com/leeguandong/ComfyUI_InternVL2": [ - [ - "DynamicPreprocess", - "InternVLHFInference", - "InternVLLMDEPLOYInference", - "InternVLModelLoader" - ], - { - "title_aux": "ComfyUI_InternVL2" - } - ], - "https://github.com/leeguandong/ComfyUI_LLaSM": [ - [ - "LLaSM2Interface", - "LLaSM2ModelLoader", - "LLaSMLoadAudio" - ], - { - "title_aux": "ComfyUI_LLaSM" - } - ], - "https://github.com/leeguandong/ComfyUI_M3Net": [ - [ - "M3Net_Interface", - "M3Net_ModelLoader" - ], - { - "title_aux": "ComfyUI_M3Net" - } - ], - "https://github.com/leeguandong/ComfyUI_MasaCtrl": [ - [ - "DirectSampler", - "MasaCtrlConcatImage", - "MasaCtrlInversion", - "MasaCtrlLoadImage", - "MasaCtrlModelLoader", - "MutualSelfAttentionControlMaskAutoSampler", - "MutualSelfAttentionControlSampler" - ], - { - "title_aux": "ComfyUI nodes to use MasaCtrl" - } - ], - "https://github.com/leeguandong/ComfyUI_QWQ32B": [ - [ - "QwQModelLoader", - "QwQTextGenerator" - ], - { - "title_aux": "ComfyUI_QWQ32B" - } - ], - "https://github.com/leeguandong/ComfyUI_Style_Aligned": [ - [ - "SAControlnet_ModelLoader", - "SADepth", - "SAHandler", - "SAInversion", - "SASDXLControlnetKsampler", - "SASDXLKampler", - "SASDXLTransferKsampler", - "SASDXL_ModelLoader", - "SchedulerLoader" - ], - { - "title_aux": "ComfyUI nodes to use Style-Aligned" - } - ], - "https://github.com/leeguandong/ComfyUI_VideoEditing": [ - [ - "LoadVideo2Images", - "VEdit_ControlNet_ModelLoader", - "VEdit_ModelLoader", - "VEdit_Sampler", - "VEdit_image2canny" - ], - { - "title_aux": "ComfyUI nodes to use VideoEditing" - } - ], - "https://github.com/leeguandong/ComfyUI_VisualAttentionMap": [ - [ - "DecodeLatent", - "HFModelLoader", - "ShowCrossAttn", - "ShowImages", - "ShowSelfAttn", - "Text2ImageInference" - ], - { - "title_aux": "ComfyUI_VisualAttentionMap" - } - ], - "https://github.com/leestuartx/ComfyUI-GG": [ - [ - "AddPaddingToImage", - "ForLoopNode", - "ImageAndTextDescriptionBySeed", - "ImageMetadataExtractor", - "InputNode", - "MetadataExtractBySeed", - "MetadataExtractorBySeed", - "OutputNode", - "ResizeImageProportionally", - "WorkspaceNode" - ], - { - "title_aux": "ComfyUI-GG" - } - ], - "https://github.com/lenskikh/ComfyUI-Prompt-Worker": [ - [ - "Clip and Text -> Encode", - "Prompt Body", - "Prompt Clothes", - "Prompt Merger", - "Prompt Worker", - "Prompt \u0421onstructor" - ], - { - "title_aux": "Propmt Worker" - } - ], - "https://github.com/leoleelxh/Comfy-Topaz-Photo": [ - [ - "ComfyTopazPhoto", - "ComfyTopazPhotoTestAndClean" - ], - { - "title_aux": "Comfy-Topaz-Photo" - } - ], - "https://github.com/leoleelxh/ComfyUI-LLMs": [ - [ - "LLMs Chat", - "LLMs Vision Unified", - "LLMs_Vision_Unified" - ], - { - "title_aux": "ComfyUI-LLMs" - } - ], - "https://github.com/leonardomiramondi/flux-context-comfyui": [ - [ - "FluxKontextNode" - ], - { - "title_aux": "Flux Context ComfyUI Node" - } - ], - "https://github.com/lepiai/ComfyUI-Minitools": [ - [ - "LP-CropTransparentEdges", - "LP-ImageToMaskWithAlpha", - "LP-TranslateToEN", - "LP-color2RGB", - "LP-hex2dec", - "NumericSlider" - ], - { - "title_aux": "ComfyUI-Minitools" - } - ], - "https://github.com/lerignoux/ComfyUI-PechaKucha": [ - [ - "GeneratePowerpoint", - "SplitPrompt" - ], - { - "title_aux": "ComfyUI-PechaKucha" - } - ], - "https://github.com/lerignoux/ComfyUI-Stable3DGen": [ - [ - "Stable3DGenerate3D", - "Stable3DLoadModels", - "Stable3DPreprocessImage" - ], - { - "title_aux": "ComfyUI Stable3DGen" - } - ], - "https://github.com/lgldlk/ComfyUI-PC-ding-dong": [ - [ - "pc ding dong", - "pc ding dong text", - "pc time sleep" - ], - { - "title_aux": "ComfyUI-PC-ding-dong" - } - ], - "https://github.com/lgldlk/ComfyUI-PSD-Replace": [ - [ - "psd replace" - ], - { - "title_aux": "ComfyUI-PSD-Replace" - } - ], - "https://github.com/liangt/comfyui-loadimagewithsubfolder": [ - [ - "LoadImageWithSubfolder" - ], - { - "title_aux": "comfyui-loadimagewithsubfolder" - } - ], - "https://github.com/licyk/ComfyUI-HakuImg": [ - [ - "BlendImage", - "Blur", - "Chromatic", - "Color", - "Curve", - "CustomExif", - "Flip", - "Glow", - "InOutPaint", - "LenDistortion", - "OutlineExpansion", - "PixelOE", - "Pixelize", - "PreResize", - "SaveImageWithCustomExif", - "Sketch", - "TiltShift" - ], - { - "title_aux": "ComfyUI-HakuImg" - } - ], - "https://github.com/licyk/ComfyUI-TCD-Sampler": [ - [ - "TCDScheduler" - ], - { - "title_aux": "ComfyUI-TCD-Sampler" - } - ], - "https://github.com/lihaoyun6/ComfyUI-BlindWatermark": [ - [ - "ApplyBlindWatermark", - "ApplyBlindWatermarkAdvanced", - "DecodeBlindWatermark", - "DecodeBlindWatermarkAdvanced" - ], - { - "title_aux": "ComfyUI-BlindWatermark" - } - ], - "https://github.com/lihaoyun6/ComfyUI-CSV-Random-Picker": [ - [ - "CSVRandomPicker" - ], - { - "title_aux": "ComfyUI-CSV-Random-Picker" - } - ], - "https://github.com/lihaoyun6/ComfyUI-QwenPromptRewriter": [ - [ - "QwenPromptRewriter" - ], - { - "title_aux": "Comfyui-QwenPromptRewriter" - } - ], - "https://github.com/lingha0h/comfyui_kj": [ - [ - "cpm_textInput" - ], - { - "title_aux": "comfyui_kj" - } - ], - "https://github.com/linjian-ufo/ComfyUI_GLM4V_voltspark": [ - [ - "Glm4vBatchNode", - "Glm4vNode" - ], - { - "title_aux": "GLM-4V Image Descriptor" - } - ], - "https://github.com/linjian-ufo/comfyui_deepseek_lj257_update": [ - [ - "DeepSeekChatNode" - ], - { - "title_aux": "DeepSeek Chat Node for ComfyUI" - } - ], - "https://github.com/linksluckytime/comfyui_snacknodes": [ - [ - "ImageInfo", - "ImageScaler", - "TextBox", - "TextProcessor" - ], - { - "title_aux": "comfyui_snacknodes" - } - ], - "https://github.com/linshier/comfyui-remote-tools": [ - [ - "LoadBase64(js)", - "LoadBase64FromRemote", - "SendBase64ToRemote" - ], - { - "title_aux": "comfyui-remote-tools" - } - ], - "https://github.com/lisaks/comfyui-panelforge": [ - [ - "FrameNode", - "PageNode", - "RowNode" - ], - { - "title_aux": "Pixstri ComfyUI Comics" - } - ], - "https://github.com/liuqianhonga/ComfyUI-Html2Image": [ - [ - "CameraWatermark", - "TemplateToImage", - "WebpageScreenshot" - ], - { - "title_aux": "ComfyUI-Html2Image" - } - ], - "https://github.com/liuqianhonga/ComfyUI-Image-Compressor": [ - [ - "BatchImageCompressor", - "ImageCompressor" - ], - { - "title_aux": "ComfyUI-Image-Compressor" - } - ], - "https://github.com/liuqianhonga/ComfyUI-QHNodes": [ - [ - "BatchImageCompressor", - "CameraWatermark", - "DownloadCheckpoint", - "DownloadControlNet", - "DownloadLora", - "DownloadUNET", - "DownloadVAE", - "DynamicExpression", - "FileSave", - "Gemini", - "ImageCompressor", - "ImageCountFromFolder", - "JsonToCSV", - "JsonUnpack", - "LoadImageFromFolder", - "LoadLoraFromFolder", - "LoadPromptsFromFolder", - "PresetSizeLatent", - "SamplerSettings", - "ShowTranslateString", - "StringConverter", - "StringFormatter", - "StringList", - "StringListFromCSV", - "StringListToCSV", - "StringMatcher", - "StringTranslate", - "TemplateToImage", - "TimeFormatter", - "UnifiedPromptGenerator", - "WebpageScreenshot" - ], - { - "title_aux": "ComfyUI-QHNodes" - } - ], - "https://github.com/liuqianhonga/ComfyUI-String-Helper": [ - [ - "JsonToCSV", - "ShowTranslateString", - "StringConverter", - "StringFormatter", - "StringList", - "StringListFromCSV", - "StringListToCSV", - "StringMatcher", - "StringTranslate", - "TimeFormatter" - ], - { - "title_aux": "ComfyUI-String-Helper" - } - ], - "https://github.com/liushuchun/ComfyUI_Lora_List_With_Url_Loader": [ - [ - "LoraListUrlLoader" - ], - { - "title_aux": "ComfyUI_Lora_List_With_Url_Loader" - } - ], - "https://github.com/liusida/ComfyUI-AutoCropFaces": [ - [ - "AutoCropFaces" - ], - { - "title_aux": "ComfyUI-AutoCropFaces" - } - ], - "https://github.com/liusida/ComfyUI-B-LoRA": [ - [ - "LoadBLoRA" - ], - { - "title_aux": "ComfyUI-B-LoRA" - } - ], - "https://github.com/liusida/ComfyUI-Debug": [ - [ - "DebugInspectorNode", - "DebugModelInspectorNode", - "DebugModelPrintOutNode" - ], - { - "title_aux": "ComfyUI-Debug" - } - ], - "https://github.com/liusida/ComfyUI-Login": [ - [ - "LoadImageIncognito" - ], - { - "title_aux": "ComfyUI-Login" - } - ], - "https://github.com/liusida/ComfyUI-SD3-nodes": [ - [ - "SD3EmptyLatent", - "SD3LoadCLIPs", - "SD3LoadCheckpoint" - ], - { - "title_aux": "ComfyUI-SD3-nodes" - } - ], - "https://github.com/livepeer/ComfyUI-Stream-Pack": [ - [ - "FaceMeshDrawNode", - "FaceMeshMaskNode", - "FaceMeshNode", - "FeatureBankAttentionProcessor", - "SuperResolutionModelLoader", - "SuperResolutionUpscale" - ], - { - "title_aux": "ComfyUI-Stream-Pack" - } - ], - "https://github.com/ljleb/comfy-mecha": [ - [ - "Already Loaded Model Mecha Recipe", - "Any Model Mecha Recipe", - "Blocks Mecha Hyper", - "Bool Mecha Hyper", - "Float Mecha Hyper", - "Int Mecha Hyper", - "Lora Mecha Recipe", - "Mecha Converter", - "Mecha Deserializer", - "Mecha Merge Method Cache Unit", - "Mecha Merger", - "Mecha Recipe List", - "Mecha Regex Weights", - "Mecha Serializer", - "Mecha Subtract Recipe List", - "Model Mecha Recipe", - "SD1-LDM Mecha Blocks Parameters", - "SDXL-SGM Mecha Blocks Parameters", - "String Mecha Hyper" - ], - { - "title_aux": "Mecha Merge Node Pack" - } - ], - "https://github.com/lks-ai/ComfyUI-StableAudioSampler": [ - [ - "StableAudioConditioning", - "StableAudioLoadModel", - "StableAudioPrompt", - "StableAudioSampler" - ], - { - "author": "lks-ai", - "description": "A Simple integration of Stable Audio Diffusion with knobs and stuff!", - "nickname": "stableaudio", - "title": "StableAudioSampler", - "title_aux": "ComfyUI Stable Audio Open 1.0 Sampler" - } - ], - "https://github.com/lks-ai/anynode": [ - [ - "AnyNode", - "AnyNodeAnthropic", - "AnyNodeCodeViewer", - "AnyNodeExport", - "AnyNodeGemini", - "AnyNodeLocal" - ], - { - "author": "newsbubbles", - "description": "This single node uses an LLM to generate a functionality based on your request. You can make the node do anything.", - "nickname": "AnyNode", - "title": "AnyNode v0.1.1", - "title_aux": "ComfyUI AnyNode: Any Node you ask for" - } - ], - "https://github.com/lldacing/ComfyUI_BEN_ll": [ - [ - "BlurFusionForegroundEstimationForBen", - "GetMaskByBen", - "LoadRembgByBenModel", - "RembgByBen", - "RembgByBenAdvanced" - ], - { - "title_aux": "ComfyUI_BEN_ll" - } - ], - "https://github.com/lldacing/ComfyUI_BiRefNet_ll": [ - [ - "AutoDownloadBiRefNetModel", - "BlurFusionForegroundEstimation", - "GetMaskByBiRefNet", - "LoadRembgByBiRefNetModel", - "RembgByBiRefNet", - "RembgByBiRefNetAdvanced" - ], - { - "title_aux": "ComfyUI_BiRefNet_ll" - } - ], - "https://github.com/lldacing/ComfyUI_Patches_ll": [ - [ - "ApplyFirstBlockCachePatch", - "ApplyFirstBlockCachePatchAdvanced", - "ApplyTeaCachePatch", - "ApplyTeaCachePatchAdvanced", - "DitForwardOverrider", - "FluxForwardOverrider", - "VideoForwardOverrider" - ], - { - "title_aux": "ComfyUI_Patches_ll" - } - ], - "https://github.com/lldacing/ComfyUI_PuLID_Flux_ll": [ - [ - "ApplyPulidFlux", - "FixPulidFluxPatch", - "PulidFluxEvaClipLoader", - "PulidFluxFaceDetector", - "PulidFluxInsightFaceLoader", - "PulidFluxModelLoader", - "PulidFluxOptions" - ], - { - "title_aux": "ComfyUI_PuLID_Flux_ll" - } - ], - "https://github.com/lldacing/ComfyUI_StableDelight_ll": [ - [ - "ApplyStableDelight", - "LoadStableDelightModel" - ], - { - "title_aux": "ComfyUI_StableDelight_ll" - } - ], - "https://github.com/lldacing/ComfyUI_StableHair_ll": [ - [ - "ApplyHairRemover", - "ApplyHairTransfer", - "LoadStableHairRemoverModel", - "LoadStableHairTransferModel" - ], - { - "title_aux": "ComfyUI_StableHair_ll" - } - ], - "https://github.com/lldacing/comfyui-easyapi-nodes": [ - [ - "Base64ToImage", - "Base64ToMask", - "BboxToBbox", - "BboxToCropData", - "BboxesToBboxes", - "ColorPicker", - "ConvertToJsonStr", - "ConvertTypeToAny", - "CopyAndRenameFiles", - "CropImageByBbox", - "CropTargetSizeImageByBbox", - "EmptyOutputNode", - "FilterSortDependSubGraphs", - "FilterValueForList", - "ForEachClose", - "ForEachOpen", - "GetImageBatchSize", - "GetValueFromJsonObj", - "IfElseForEmptyObject", - "ImageEqual", - "ImageSizeGetter", - "ImageToBase64", - "ImageToBase64Advanced", - "IndexOfList", - "IndexesOfList", - "InnerIntCompare", - "InnerIntMathOperation", - "InnerLoopClose", - "InsightFaceBBOXDetect", - "IntToList", - "IntToNumber", - "IsNoneOrEmpty", - "IsNoneOrEmptyOptional", - "JoinList", - "ListMerge", - "ListUnWrapper", - "ListWrapper", - "LoadImageFromLocalPath", - "LoadImageFromURL", - "LoadImageToBase64", - "LoadJsonStrToList", - "LoadLocalFilePath", - "LoadMaskFromLocalPath", - "LoadMaskFromURL", - "MaskByBboxes", - "MaskImageToBase64", - "MaskToBase64", - "MaskToBase64Image", - "MaskToRle", - "NodeListMerge", - "NodeListToList", - "NodeListToListMerge", - "NoneNode", - "ReadTextFromLocalFile", - "RleToMask", - "SDBaseVerNumber", - "SamAutoMaskSEGS", - "SamAutoMaskSEGSAdvanced", - "SaveImagesWithoutOutput", - "SaveSingleImageWithoutOutput", - "SaveTextToFileByImagePath", - "SaveTextToLocalFile", - "SelectBbox", - "SelectBboxes", - "ShowBoolean", - "ShowFloat", - "ShowInt", - "ShowNumber", - "ShowString", - "SliceList", - "SortDependSubGraphs", - "SplitStringToList", - "StringArea", - "StringToList", - "TryFreeMemory" - ], - { - "title_aux": "comfyui-easyapi-nodes" - } - ], - "https://github.com/lo-th/Comfyui_three_js": [ - [ - "ThreeView" - ], - { - "title_aux": "Comfyui_three_js" - } - ], - "https://github.com/logtd/ComfyUI-4DHumans": [ - [ - "LoadDetectron", - "LoadHMR", - "ProcessHumans", - "SelectHuman" - ], - { - "title_aux": "ComfyUI-4DHumans" - } - ], - "https://github.com/logtd/ComfyUI-APGScaling": [ - [ - "APGFunction" - ], - { - "title_aux": "ComfyUI-APGScaling" - } - ], - "https://github.com/logtd/ComfyUI-DiLightNet": [ - [ - "LoadDiLightControlNet", - "PrepareDiLightCond" - ], - { - "title_aux": "ComfyUI-DiLightNet" - } - ], - "https://github.com/logtd/ComfyUI-FLATTEN": [ - [ - "ApplyFlattenAttentionNode", - "CreateFlowNoiseNode", - "FlattenCheckpointLoaderNode", - "KSamplerFlattenNode", - "TrajectoryNode", - "UnsamplerFlattenNode" - ], - { - "title_aux": "ComfyUI-FLATTEN" - } - ], - "https://github.com/logtd/ComfyUI-Fluxtapoz": [ - [ - "AddFluxFlow", - "ApplyFluxRaveAttention", - "ApplyRefFlux", - "ApplyRegionalConds", - "ConfigureModifiedFlux", - "CreateRegionalCond", - "FlowEditForwardSampler", - "FlowEditGuider", - "FlowEditReverseSampler", - "FlowEditSampler", - "FluxAttnOverride", - "FluxDeGuidance", - "FluxForwardODESampler", - "FluxInverseSampler", - "FluxNoiseMixer", - "FluxReverseODESampler", - "InFluxFlipSigmas", - "InFluxModelSamplingPred", - "OutFluxModelSamplingPred", - "PAGAttention", - "PrepareAttnBank", - "RFDoubleBlocksOverride", - "RFSingleBlocksOverride", - "RegionalStyleModelApply", - "SEGAttention" - ], - { - "title_aux": "ComfyUI-Fluxtapoz" - } - ], - "https://github.com/logtd/ComfyUI-InstanceDiffusion": [ - [ - "ApplyScaleUModelNode", - "DownloadInstanceDiffusionModels", - "InstanceDiffusionTrackingPrompt", - "LoadInstanceFusersNode", - "LoadInstancePositionNetModel", - "LoadInstanceScaleUNode" - ], - { - "title_aux": "InstanceDiffusion Nodes" - } - ], - "https://github.com/logtd/ComfyUI-InversedNoise": [ - [ - "CombineNoiseLatentNode", - "MixNoiseNode", - "SamplerInversedEulerNode" - ], - { - "title_aux": "ComfyUI-InversedNoise" - } - ], - "https://github.com/logtd/ComfyUI-MochiEdit": [ - [ - "MochiPrepareSigmas", - "MochiResampler", - "MochiUnsampler", - "MochiWrapperResampler", - "MochiWrapperSamplerCustom", - "MochiWrapperUnsampler" - ], - { - "title_aux": "ComfyUI-MochiEdit" - } - ], - "https://github.com/logtd/ComfyUI-MotionThiefExperiment": [ - [ - "ApplyRefMotionNode", - "MotionRefSettingsCustomNode", - "MotionRefSettingsDefaultNode" - ], - { - "title_aux": "ComfyUI-MotionThiefExperiment" - } - ], - "https://github.com/logtd/ComfyUI-RAVE_ATTN": [ - [ - "ApplyRaveAttentionNode", - "AttentionOverrideSD15Node", - "AttentionOverrideSDXLNode" - ], - { - "title_aux": "ComfyUI-RAVE Attention" - } - ], - "https://github.com/logtd/ComfyUI-ReNoise": [ - [ - "ReNoiseModelSamplingPred", - "ReNoiseSampler" - ], - { - "title_aux": "ComfyUI-ReNoise" - } - ], - "https://github.com/logtd/ComfyUI-RefSampling": [ - [ - "ApplyRefContentNode", - "ApplyRefStyleNode", - "ApplyRefUNetNode" - ], - { - "title_aux": "ComfyUI-RefSampling" - } - ], - "https://github.com/logtd/ComfyUI-RefUNet": [ - [ - "ConfigRefMapAdv", - "ConfigureRefNet", - "CreateRefBank", - "CustomRefMapSD1", - "PrepareRefLatents", - "ReadSampler", - "RefModelSamplingPred", - "VisionClipEncode", - "WriteSampler" - ], - { - "title_aux": "ComfyUI-RefUNet" - } - ], - "https://github.com/logtd/ComfyUI-SEGAttention": [ - [ - "SEGAttention" - ], - { - "title_aux": "ComfyUI-SEGAttention" - } - ], - "https://github.com/logtd/ComfyUI-SSREncoder": [ - [ - "ApplySSR", - "EncodeSSRQuery", - "LoadSSRAligner", - "LoadSSRAttention" - ], - { - "title_aux": "ComfyUI-SSREncoder" - } - ], - "https://github.com/logtd/ComfyUI-SeeCoder": [ - [ - "LoadSeeCoder", - "LoadSeeCoderUncond", - "SeecoderEncode" - ], - { - "title_aux": "ComfyUI-SeeCoder" - } - ], - "https://github.com/logtd/ComfyUI-TrackingNodes": [ - [ - "OpenPoseTrackerNode", - "YOLOTrackerNode" - ], - { - "title_aux": "Tracking Nodes for Videos" - } - ], - "https://github.com/logtd/ComfyUI-ViewCrafter": [ - [ - "ApplyViewCrafter", - "LoadViewCrafter", - "ScaleImages" - ], - { - "title_aux": "ComfyUI-ViewCrafter" - } - ], - "https://github.com/lokinou/comfyui-offload-models": [ - [ - "OffloadModel", - "RecallModel" - ], - { - "title_aux": "ComfyUI-Offload-Models" - } - ], - "https://github.com/lonelyowl13/artist_randomizer": [ - [ - "AddRandomArtists", - "TextInput" - ], - { - "title_aux": "Artist tag randomizer for comfyui" - } - ], - "https://github.com/longgui0318/comfyui-common-util": [ - [ - "Added Layer Info To Array", - "Enhanced Random Light Source", - "Float Relay", - "HLFrequencyDetailRestore", - "Hex to Color", - "Image Add Alpha", - "Image Frequency Analyzer", - "Image Relay", - "Image Remove Alpha", - "Image Resize With Padding", - "Init Layer Info Array", - "Int Relay", - "Layer Image Seleted", - "Layer Images IPAdapter Advanced", - "Layer Info Array Fuse", - "Mask Relay", - "String Relay" - ], - { - "title_aux": "comfyui-common-util" - } - ], - "https://github.com/longgui0318/comfyui-llm-assistant": [ - [ - "Chat With LLM", - "Generate Stable Diffsution Prompt With LLM", - "Translate Text With LLM" - ], - { - "title_aux": "comfyui-llm-assistant" - } - ], - "https://github.com/longgui0318/comfyui-magic-clothing": [ - [ - "Add Magic Clothing Attention", - "Change Pipeline Dtype And Device", - "Change Pixel Value Normalization", - "Diffusers Model Makeup &MC", - "Diffusers Scheduler Loader &MC", - "Load Magic Clothing Adapter", - "Load Magic Clothing Model", - "Load Magic Clothing Pipeline", - "Load Magic Clothing Pipeline With Path", - "RUN Magic Clothing Diffusers Model" - ], - { - "title_aux": "comfyui-magic-clothing" - } - ], - "https://github.com/longgui0318/comfyui-mask-util": [ - [ - "Image Adaptive Crop M&R", - "Image Adaptive Crop With Mask", - "Image Change DType", - "Image Change Device", - "Image Resolution Adaptive With X", - "Image Resolution Limit With 8K", - "Load Image With Name", - "Mask Change DType", - "Mask Change Device", - "Mask Selection Of Masks", - "Model Change Device", - "Model Change Device Repeaters", - "Output Image To Input", - "Split Masks" - ], - { - "title_aux": "comfyui-mask-util" - } - ], - "https://github.com/lord-lethris/ComfyUI-RPG-Characters": [ - [ - "ModelLikenessSwitch", - "PromptConcatenatorNode", - "PromptConditioningConverter", - "PromptSelectorNode", - "RPGArtStyleSelector", - "RPGCharacterSelector" - ], - { - "title_aux": "ComfyUI-RPG-Characters" - } - ], - "https://github.com/lordgasmic/comfyui_save_image_with_options": [ - [ - "SaveImageWithOptions" - ], - { - "title_aux": "comfyui_save_image_with_options" - } - ], - "https://github.com/lordgasmic/comfyui_wildcards": [ - [ - "CLIPTextEncodeWithWildcards" - ], - { - "title_aux": "comfyui_wildcards" - } - ], - "https://github.com/lquesada/ComfyUI-Inpaint-CropAndStitch": [ - [ - "InpaintCrop", - "InpaintCropImproved", - "InpaintExtendOutpaint", - "InpaintResize", - "InpaintStitch", - "InpaintStitchImproved" - ], - { - "title_aux": "ComfyUI-Inpaint-CropAndStitch" - } - ], - "https://github.com/lquesada/ComfyUI-Interactive": [ - [ - "InteractiveFloat", - "InteractiveInteger", - "InteractiveReset", - "InteractiveSave", - "InteractiveSeed", - "InteractiveSelector", - "InteractiveSelectorWithParameters", - "InteractiveString", - "InteractiveStringAppend", - "InteractiveStringMultiline", - "InteractiveSwitch", - "InteractiveSwitchWithParameters" - ], - { - "title_aux": "ComfyUI-Interactive" - } - ], - "https://github.com/lquesada/ComfyUI-Prompt-Combinator": [ - [ - "PromptCombinator", - "PromptCombinatorExportGallery", - "PromptCombinatorMerger", - "PromptCombinatorRandomPrompt" - ], - { - "title_aux": "ComfyUI-Prompt-Combinator" - } - ], - "https://github.com/lrzjason/ComfyUI-Watermark-Detection": [ - [ - "WatermarkDetector", - "WatermarkDetectorLoader" - ], - { - "title_aux": "ComfyUI Watermark Detection Node" - } - ], - "https://github.com/lrzjason/Comfyui-In-Context-Lora-Utils": [ - [ - "AddMaskForICLora", - "AutoPatch", - "ConcatContextWindow", - "CreateContextWindow" - ], - { - "title_aux": "Comfyui-In-Context-Lora-Utils" - } - ], - "https://github.com/lrzjason/Comfyui-Kolors-Utils": [ - [ - "SaveKolors", - "SaveWeightAsKolorsUnet" - ], - { - "title_aux": "Comfyui Kolors Utils" - } - ], - "https://github.com/lrzjason/Comfyui-ThinkRemover": [ - [ - "ThinkRemover" - ], - { - "title_aux": "Comfyui-ThinkRemover" - } - ], - "https://github.com/ltdrdata/ComfyUI-Impact-Pack": [ - [ - "AddMask", - "AnyPipeToBasic", - "BasicPipeToDetailerPipe", - "BasicPipeToDetailerPipeSDXL", - "BboxDetectorCombined_v2", - "BboxDetectorSEGS", - "BitwiseAndMask", - "BitwiseAndMaskForEach", - "BlackPatchRetryHookProvider", - "CLIPSegDetectorProvider", - "CfgScheduleHookProvider", - "CombineRegionalPrompts", - "CoreMLDetailerHookProvider", - "CustomNoiseDetailerHookProvider", - "CustomSamplerDetailerHookProvider", - "DenoiseScheduleHookProvider", - "DenoiseSchedulerDetailerHookProvider", - "DetailerForEach", - "DetailerForEachAutoRetry", - "DetailerForEachDebug", - "DetailerForEachDebugPipe", - "DetailerForEachPipe", - "DetailerForEachPipeForAnimateDiff", - "DetailerHookCombine", - "DetailerPipeToBasicPipe", - "EditBasicPipe", - "EditDetailerPipe", - "EditDetailerPipeSDXL", - "EmptySegs", - "FaceDetailer", - "FaceDetailerPipe", - "FromBasicPipe", - "FromBasicPipe_v2", - "FromDetailerPipe", - "FromDetailerPipeSDXL", - "FromDetailerPipe_v2", - "GITSSchedulerFuncProvider", - "ImageListToImageBatch", - "ImageMaskSwitch", - "ImageReceiver", - "ImageSender", - "ImpactAssembleSEGS", - "ImpactBoolean", - "ImpactCombineConditionings", - "ImpactCompare", - "ImpactConcatConditionings", - "ImpactConditionalBranch", - "ImpactConditionalBranchSelMode", - "ImpactConditionalStopIteration", - "ImpactControlBridge", - "ImpactControlNetApplyAdvancedSEGS", - "ImpactControlNetApplySEGS", - "ImpactControlNetClearSEGS", - "ImpactConvertDataType", - "ImpactCount_Elts_in_SEGS", - "ImpactDecomposeSEGS", - "ImpactDilateMask", - "ImpactDilateMaskInSEGS", - "ImpactDilate_Mask_SEG_ELT", - "ImpactDummyInput", - "ImpactEdit_SEG_ELT", - "ImpactExecutionOrderController", - "ImpactFlattenMask", - "ImpactFloat", - "ImpactFrom_SEG_ELT", - "ImpactFrom_SEG_ELT_bbox", - "ImpactFrom_SEG_ELT_crop_region", - "ImpactGaussianBlurMask", - "ImpactGaussianBlurMaskInSEGS", - "ImpactHFTransformersClassifierProvider", - "ImpactIPAdapterApplySEGS", - "ImpactIfNone", - "ImpactImageBatchToImageList", - "ImpactImageInfo", - "ImpactInt", - "ImpactInversedSwitch", - "ImpactIsNotEmptySEGS", - "ImpactKSamplerAdvancedBasicPipe", - "ImpactKSamplerBasicPipe", - "ImpactLatentInfo", - "ImpactListBridge", - "ImpactLogger", - "ImpactLogicalOperators", - "ImpactMakeAnyList", - "ImpactMakeImageBatch", - "ImpactMakeImageList", - "ImpactMakeMaskBatch", - "ImpactMakeMaskList", - "ImpactMakeTileSEGS", - "ImpactMinMax", - "ImpactNeg", - "ImpactNegativeConditioningPlaceholder", - "ImpactNodeSetMuteState", - "ImpactQueueTrigger", - "ImpactQueueTriggerCountdown", - "ImpactRemoteBoolean", - "ImpactRemoteInt", - "ImpactSAM2VideoDetectorSEGS", - "ImpactSEGSClassify", - "ImpactSEGSConcat", - "ImpactSEGSIntersectionFilter", - "ImpactSEGSLabelAssign", - "ImpactSEGSLabelFilter", - "ImpactSEGSMerge", - "ImpactSEGSNMSFilter", - "ImpactSEGSOrderedFilter", - "ImpactSEGSPicker", - "ImpactSEGSRangeFilter", - "ImpactSEGSToMaskBatch", - "ImpactSEGSToMaskList", - "ImpactScaleBy_BBOX_SEG_ELT", - "ImpactSchedulerAdapter", - "ImpactSegsAndMask", - "ImpactSegsAndMaskForEach", - "ImpactSelectNthItemOfAnyList", - "ImpactSetWidgetValue", - "ImpactSimpleDetectorSEGS", - "ImpactSimpleDetectorSEGSPipe", - "ImpactSimpleDetectorSEGS_for_AD", - "ImpactSleep", - "ImpactStringSelector", - "ImpactSwitch", - "ImpactValueReceiver", - "ImpactValueSender", - "ImpactWildcardEncode", - "ImpactWildcardProcessor", - "IterativeImageUpscale", - "IterativeLatentUpscale", - "KSamplerAdvancedProvider", - "KSamplerProvider", - "LamaRemoverDetailerHookProvider", - "LatentPixelScale", - "LatentReceiver", - "LatentSender", - "LatentSwitch", - "MaskDetailerPipe", - "MaskListToMaskBatch", - "MaskRectArea", - "MaskRectAreaAdvanced", - "MaskToSEGS", - "MaskToSEGS_for_AnimateDiff", - "MasksToMaskList", - "MediaPipeFaceMeshToSEGS", - "NoiseInjectionDetailerHookProvider", - "NoiseInjectionHookProvider", - "ONNXDetectorProvider", - "ONNXDetectorSEGS", - "PixelKSampleHookCombine", - "PixelKSampleUpscalerProvider", - "PixelKSampleUpscalerProviderPipe", - "PixelTiledKSampleUpscalerProvider", - "PixelTiledKSampleUpscalerProviderPipe", - "PreviewBridge", - "PreviewBridgeLatent", - "PreviewDetailerHookProvider", - "ReencodeLatent", - "ReencodeLatentPipe", - "RegionalPrompt", - "RegionalSampler", - "RegionalSamplerAdvanced", - "RemoveImageFromSEGS", - "RemoveNoiseMask", - "SAMDetectorCombined", - "SAMDetectorSegmented", - "SAMLoader", - "SEGSDetailer", - "SEGSDetailerForAnimateDiff", - "SEGSLabelFilterDetailerHookProvider", - "SEGSOrderedFilterDetailerHookProvider", - "SEGSPaste", - "SEGSPreview", - "SEGSPreviewCNet", - "SEGSRangeFilterDetailerHookProvider", - "SEGSSwitch", - "SEGSToImageList", - "SEGSUpscaler", - "SEGSUpscalerPipe", - "SegmDetectorCombined_v2", - "SegmDetectorSEGS", - "Segs Mask", - "Segs Mask ForEach", - "SegsToCombinedMask", - "SetDefaultImageForSEGS", - "StepsScheduleHookProvider", - "StringListToString", - "SubtractMask", - "SubtractMaskForEach", - "TiledKSamplerProvider", - "ToBasicPipe", - "ToBinaryMask", - "ToDetailerPipe", - "ToDetailerPipeSDXL", - "TwoAdvancedSamplersForMask", - "TwoSamplersForMask", - "TwoSamplersForMaskUpscalerProvider", - "TwoSamplersForMaskUpscalerProviderPipe", - "UnsamplerDetailerHookProvider", - "UnsamplerHookProvider", - "VariationNoiseDetailerHookProvider", - "WildcardPromptFromString" - ], - { - "author": "Dr.Lt.Data", - "description": "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler.", - "nickname": "Impact Pack", - "preemptions": [ - "SAMLoader" - ], - "title": "Impact Pack", - "title_aux": "ComfyUI Impact Pack" - } - ], - "https://github.com/ltdrdata/ComfyUI-Impact-Subpack": [ - [ - "UltralyticsDetectorProvider" - ], - { - "author": "Dr.Lt.Data", - "description": "This extension provides UltralyticsDetectorProvider node", - "nickname": "Impact Subpack", - "title": "Impact Subpack", - "title_aux": "ComfyUI Impact Subpack" - } - ], - "https://github.com/ltdrdata/ComfyUI-Inspire-Pack": [ - [ - "AnimeLineArt_Preprocessor_Provider_for_SEGS //Inspire", - "ApplyLBW //Inspire", - "ApplyRegionalIPAdapters //Inspire", - "BindImageListPromptList //Inspire", - "CLIPTextEncodeWithWeight //Inspire", - "CacheBackendData //Inspire", - "CacheBackendDataList //Inspire", - "CacheBackendDataNumberKey //Inspire", - "CacheBackendDataNumberKeyList //Inspire", - "CacheBridge //Inspire", - "Canny_Preprocessor_Provider_for_SEGS //Inspire", - "ChangeImageBatchSize //Inspire", - "ChangeLatentBatchSize //Inspire", - "CheckpointLoaderSimpleShared //Inspire", - "ColorMapToMasks //Inspire", - "ColorMaskToDepthMask //Inspire", - "Color_Preprocessor_Provider_for_SEGS //Inspire", - "CompositeNoise //Inspire", - "ConcatConditioningsWithMultiplier //Inspire", - "ConditioningStretch //Inspire", - "ConditioningUpscale //Inspire", - "DWPreprocessor_Provider_for_SEGS //Inspire", - "DropItems //Inspire", - "FakeScribblePreprocessor_Provider_for_SEGS //Inspire", - "FloatRange //Inspire", - "ForeachListBegin //Inspire", - "ForeachListEnd //Inspire", - "FromIPAdapterPipe //Inspire", - "GlobalSampler //Inspire", - "GlobalSeed //Inspire", - "HEDPreprocessor_Provider_for_SEGS //Inspire", - "HyperTile //Inspire", - "IPAdapterModelHelper //Inspire", - "ImageBatchSplitter //Inspire", - "InpaintPreprocessor_Provider_for_SEGS //Inspire", - "IsCached //Inspire", - "KSampler //Inspire", - "KSamplerAdvanced //Inspire", - "KSamplerAdvancedPipe //Inspire", - "KSamplerAdvancedProgress //Inspire", - "KSamplerPipe //Inspire", - "KSamplerProgress //Inspire", - "LatentBatchSplitter //Inspire", - "LeRes_DepthMap_Preprocessor_Provider_for_SEGS //Inspire", - "LineArt_Preprocessor_Provider_for_SEGS //Inspire", - "ListCounter //Inspire", - "LoadDiffusionModelShared //Inspire", - "LoadImage //Inspire", - "LoadImageListFromDir //Inspire", - "LoadImagesFromDir //Inspire", - "LoadLBW //Inspire", - "LoadPromptsFromDir //Inspire", - "LoadPromptsFromFile //Inspire", - "LoadSinglePromptFromFile //Inspire", - "LoadTextEncoderShared //Inspire", - "LoraBlockInfo //Inspire", - "LoraLoaderBlockWeight //Inspire", - "MakeBasicPipe //Inspire", - "MakeLBW //Inspire", - "Manga2Anime_LineArt_Preprocessor_Provider_for_SEGS //Inspire", - "MediaPipeFaceMeshDetectorProvider //Inspire", - "MediaPipe_FaceMesh_Preprocessor_Provider_for_SEGS //Inspire", - "MeshGraphormerDepthMapPreprocessorProvider_for_SEGS //Inspire", - "MiDaS_DepthMap_Preprocessor_Provider_for_SEGS //Inspire", - "OpenPose_Preprocessor_Provider_for_SEGS //Inspire", - "PromptBuilder //Inspire", - "PromptExtractor //Inspire", - "RGB_HexToHSV //Inspire", - "RandomGeneratorForList //Inspire", - "RandomNoise //Inspire", - "RegionalCFG //Inspire", - "RegionalConditioningColorMask //Inspire", - "RegionalConditioningSimple //Inspire", - "RegionalIPAdapterColorMask //Inspire", - "RegionalIPAdapterEncodedColorMask //Inspire", - "RegionalIPAdapterEncodedMask //Inspire", - "RegionalIPAdapterMask //Inspire", - "RegionalPromptColorMask //Inspire", - "RegionalPromptSimple //Inspire", - "RegionalSeedExplorerColorMask //Inspire", - "RegionalSeedExplorerMask //Inspire", - "RemoveBackendData //Inspire", - "RemoveBackendDataNumberKey //Inspire", - "RemoveControlNet //Inspire", - "RemoveControlNetFromRegionalPrompts //Inspire", - "RetrieveBackendData //Inspire", - "RetrieveBackendDataNumberKey //Inspire", - "SaveLBW //Inspire", - "ScheduledCFGGuider //Inspire", - "ScheduledPerpNegCFGGuider //Inspire", - "SeedExplorer //Inspire", - "SeedLogger //Inspire", - "SelectNthMask //Inspire", - "ShowCachedInfo //Inspire", - "StableCascade_CheckpointLoader //Inspire", - "TilePreprocessor_Provider_for_SEGS //Inspire", - "ToIPAdapterPipe //Inspire", - "UnzipPrompt //Inspire", - "WildcardEncode //Inspire", - "WorklistToItemList //Inspire", - "XY Input: Lora Block Weight //Inspire", - "ZipPrompt //Inspire", - "Zoe_DepthMap_Preprocessor_Provider_for_SEGS //Inspire" - ], - { - "author": "Dr.Lt.Data", - "description": "This extension provides various nodes to support Lora Block Weight, Regional Nodes, Backend Cache, Prompt Utils, List Utils and the Impact Pack.", - "nickname": "Inspire Pack", - "nodename_pattern": "Inspire$", - "title": "Inspire Pack", - "title_aux": "ComfyUI Inspire Pack" - } - ], - "https://github.com/ltdrdata/comfyui-connection-helper": [ - [], - { - "author": "Dr.Lt.Data", - "description": "Helper", - "nickname": "Connection Helper", - "nodename_pattern": "Inspire$", - "title": "ComfyUI Connection Helper", - "title_aux": "ComfyUI Connection Helper" - } - ], - "https://github.com/ltdrdata/was-node-suite-comfyui": [ - [ - "BLIP Analyze Image", - "BLIP Model Loader", - "Blend Latents", - "Boolean To Text", - "Bounded Image Blend", - "Bounded Image Blend with Mask", - "Bounded Image Crop", - "Bounded Image Crop with Mask", - "Bus Node", - "CLIP Input Switch", - "CLIP Vision Input Switch", - "CLIPSEG2", - "CLIPSeg Batch Masking", - "CLIPSeg Masking", - "CLIPSeg Model Loader", - "CLIPTextEncode (BlenderNeko Advanced + NSP)", - "CLIPTextEncode (NSP)", - "Cache Node", - "Checkpoint Loader", - "Checkpoint Loader (Simple)", - "Conditioning Input Switch", - "Constant Number", - "Control Net Model Input Switch", - "Convert Masks to Images", - "Create Grid Image", - "Create Grid Image from Batch", - "Create Morph Image", - "Create Morph Image from Path", - "Create Video from Path", - "Debug Number to Console", - "Dictionary to Console", - "Diffusers Hub Model Down-Loader", - "Diffusers Model Loader", - "Export API", - "HSL to Hex", - "Hex to HSL", - "Image Analyze", - "Image Aspect Ratio", - "Image Batch", - "Image Blank", - "Image Blend", - "Image Blend by Mask", - "Image Blending Mode", - "Image Bloom Filter", - "Image Bounds", - "Image Bounds to Console", - "Image Canny Filter", - "Image Chromatic Aberration", - "Image Color Palette", - "Image Crop Face", - "Image Crop Location", - "Image Crop Square Location", - "Image Displacement Warp", - "Image Dragan Photography Filter", - "Image Edge Detection Filter", - "Image Film Grain", - "Image Filter Adjustments", - "Image Flip", - "Image Generate Gradient", - "Image Gradient Map", - "Image High Pass Filter", - "Image History Loader", - "Image Input Switch", - "Image Levels Adjustment", - "Image Load", - "Image Lucy Sharpen", - "Image Median Filter", - "Image Mix RGB Channels", - "Image Monitor Effects Filter", - "Image Nova Filter", - "Image Padding", - "Image Paste Crop", - "Image Paste Crop by Location", - "Image Paste Face", - "Image Perlin Noise", - "Image Perlin Power Fractal", - "Image Pixelate", - "Image Power Noise", - "Image Rembg (Remove Background)", - "Image Remove Background (Alpha)", - "Image Remove Color", - "Image Resize", - "Image Rotate", - "Image Rotate Hue", - "Image SSAO (Ambient Occlusion)", - "Image SSDO (Direct Occlusion)", - "Image Save", - "Image Seamless Texture", - "Image Select Channel", - "Image Select Color", - "Image Send HTTP", - "Image Shadows and Highlights", - "Image Size to Number", - "Image Stitch", - "Image Style Filter", - "Image Threshold", - "Image Tiled", - "Image Transpose", - "Image Voronoi Noise Filter", - "Image fDOF Filter", - "Image to Latent Mask", - "Image to Noise", - "Image to Seed", - "Images to Linear", - "Images to RGB", - "Inset Image Bounds", - "Integer place counter", - "KSampler (WAS)", - "KSampler Cycle", - "Latent Batch", - "Latent Input Switch", - "Latent Noise Injection", - "Latent Size to Number", - "Latent Upscale by Factor (WAS)", - "Load Cache", - "Load Image Batch", - "Load Lora", - "Load Text File", - "Logic Boolean", - "Logic Boolean Primitive", - "Logic Comparison AND", - "Logic Comparison OR", - "Logic Comparison XOR", - "Logic NOT", - "Lora Input Switch", - "Lora Loader", - "Mask Arbitrary Region", - "Mask Batch", - "Mask Batch to Mask", - "Mask Ceiling Region", - "Mask Crop Dominant Region", - "Mask Crop Minority Region", - "Mask Crop Region", - "Mask Dilate Region", - "Mask Dominant Region", - "Mask Erode Region", - "Mask Fill Holes", - "Mask Floor Region", - "Mask Gaussian Region", - "Mask Invert", - "Mask Minority Region", - "Mask Paste Region", - "Mask Rect Area", - "Mask Rect Area (Advanced)", - "Mask Smooth Region", - "Mask Threshold Region", - "Masks Add", - "Masks Combine Batch", - "Masks Combine Regions", - "Masks Subtract", - "MiDaS Depth Approximation", - "MiDaS Mask Image", - "MiDaS Model Loader", - "Model Input Switch", - "Number Counter", - "Number Input Condition", - "Number Input Switch", - "Number Multiple Of", - "Number Operation", - "Number PI", - "Number to Float", - "Number to Int", - "Number to Seed", - "Number to String", - "Number to Text", - "Prompt Multiple Styles Selector", - "Prompt Styles Selector", - "Random Number", - "SAM Image Mask", - "SAM Model Loader", - "SAM Parameters", - "SAM Parameters Combine", - "Samples Passthrough (Stat System)", - "Save Text File", - "Seed", - "String to Text", - "Tensor Batch to Image", - "Text Add Token by Input", - "Text Add Tokens", - "Text Compare", - "Text Concatenate", - "Text Contains", - "Text Dictionary Convert", - "Text Dictionary Get", - "Text Dictionary Keys", - "Text Dictionary New", - "Text Dictionary To Text", - "Text Dictionary Update", - "Text File History Loader", - "Text Find", - "Text Find and Replace", - "Text Find and Replace Input", - "Text Find and Replace by Dictionary", - "Text Input Switch", - "Text List", - "Text List Concatenate", - "Text List to Text", - "Text Load Line From File", - "Text Multiline", - "Text Multiline (Code Compatible)", - "Text Parse A1111 Embeddings", - "Text Parse Noodle Soup Prompts", - "Text Parse Tokens", - "Text Random Line", - "Text Random Prompt", - "Text Shuffle", - "Text Sort", - "Text String", - "Text String Truncate", - "Text to Conditioning", - "Text to Console", - "Text to Number", - "Text to String", - "True Random.org Number Generator", - "Upscale Model Loader", - "Upscale Model Switch", - "VAE Input Switch", - "Video Dump Frames", - "Write to GIF", - "Write to Video", - "unCLIP Checkpoint Loader" - ], - { - "title_aux": "WAS Node Suite (Revised)" - } - ], - "https://github.com/lthero-big/ComfyUI-GaussianShadingWatermark": [ - [ - "DPR_Extractor", - "DPR_GS_Latent", - "DPR_KSamplerAdvanced" - ], - { - "title_aux": "ComfyUI-GaussianShadingWatermark" - } - ], - "https://github.com/luandev/ComfyUI-CrewAI": [ - [ - "DisplayText", - "\ud83d\udcceCrewAI Agent", - "\ud83d\udcceCrewAI Agent List", - "\ud83d\udcceCrewAI Crew", - "\ud83d\udcceCrewAI LLM Chat GPT", - "\ud83d\udcceCrewAI LLM Hugging Face", - "\ud83d\udcceCrewAI LLM Ollama", - "\ud83d\udcceCrewAI LLM OpenAI", - "\ud83d\udcceCrewAI Task", - "\ud83d\udcceCrewAI Task List", - "\ud83d\udcceCrewAI Text" - ], - { - "title_aux": "ComfyUI CrewAI" - } - ], - "https://github.com/lucak5s/comfyui_gfpgan": [ - [ - "GFPGANRestorer" - ], - { - "title_aux": "ComfyUI GFPGAN" - } - ], - "https://github.com/lucasgattas/comfyui-egregora-divide-and-enhance": [ - [ - "Egregora Algorithm", - "Egregora Analyze Content", - "Egregora Combine", - "Egregora Divide and Select", - "Egregora Preview", - "Egregora Turbo Prompt" - ], - { - "title_aux": "ComfyUI \u00b7 Egregora: Divide & Enhance" - } - ], - "https://github.com/lujiazho/ComfyUI-CatvtonFluxWrapper": [ - [ - "CatvtonFluxSampler", - "LoadCatvtonFlux", - "LoadCatvtonFluxLoRA", - "ModelPrinter" - ], - { - "title_aux": "ComfyUI-CatvtonFluxWrapper" - } - ], - "https://github.com/luke-mino-altherr/ComfyUI-LatentReverb": [ - [ - "LatentReverb" - ], - { - "title_aux": "ComfyUI-Latent-Reverb" - } - ], - "https://github.com/lum3on/ComfyUI-FrameUtilitys": [ - [ - "FrameExtender", - "FrameExtenderAdvanced", - "FrameReplacer", - "GitInstaller" - ], - { - "title_aux": "ComfyUI-FrameUtilitys" - } - ], - "https://github.com/lum3on/ComfyUI-ModelQuantizer": [ - [ - "ControlNetFP8QuantizeNode", - "ControlNetMetadataViewerNode", - "ModelToStateDict", - "QuantizeFP8Format", - "QuantizeModel", - "SaveAsSafeTensor" - ], - { - "title_aux": "ComfyUI-ModelQuantizer" - } - ], - "https://github.com/lum3on/ComfyUI-StableAudioX": [ - [ - "AudioXAdvancedVolumeControl", - "AudioXAudioProcessor", - "AudioXEnhancedTextToAudio", - "AudioXEnhancedTextToMusic", - "AudioXEnhancedVideoToAudio", - "AudioXModelLoader", - "AudioXMultiModalGeneration", - "AudioXPromptHelper", - "AudioXTextToAudio", - "AudioXTextToMusic", - "AudioXVideoAudioCombiner", - "AudioXVideoMuter", - "AudioXVideoToAudio", - "AudioXVideoToMusic", - "AudioXVolumeControl" - ], - { - "title_aux": "ComfyUI-AudioX" - } - ], - "https://github.com/lum3on/ComfyUI_MJ-Scraper": [ - [ - "MJScraper" - ], - { - "title_aux": "ComfyUI Midjourney Scraper Node" - } - ], - "https://github.com/lum3on/comfyui_EdgeTAM": [ - [ - "EdgeTAMVideoTracker", - "InteractiveMaskEditor" - ], - { - "title_aux": "comfyui_EdgeTAM" - } - ], - "https://github.com/lum3on/comfyui_HiDream-Sampler": [ - [ - "HiDreamImg2Img", - "HiDreamSampler", - "HiDreamSamplerAdvanced" - ], - { - "title_aux": "HiDream Sampler" - } - ], - "https://github.com/lum3on/comfyui_LLM_Polymath": [ - [ - "ConceptEraserNode", - "flux_context_preset", - "polymath_SaveAbsolute", - "polymath_StringListPicker", - "polymath_TextSplitter", - "polymath_chat", - "polymath_helper", - "polymath_scraper", - "polymath_settings", - "polymath_text_mask" - ], - { - "title_aux": "comfyui_LLM_Polymath" - } - ], - "https://github.com/lumalabs/ComfyUI-LumaAI-API": [ - [ - "CharacterReference", - "ConcatReferences", - "ImgBBUpload", - "LumaAIClient", - "LumaAddAudio2Video", - "LumaExtendGeneration", - "LumaImage2Video", - "LumaImageGeneration", - "LumaInterpolateGenerations", - "LumaModifyImage", - "LumaPreviewVideo", - "LumaText2Video", - "LumaUpscaleGeneration", - "Reference" - ], - { - "title_aux": "ComfyUI-LumaAI-API" - } - ], - "https://github.com/lxe/ComfyUI-OpenAI-Compat-LLM-Node": [ - [ - "OpenAILLMNode" - ], - { - "title_aux": "ComfyUI OpenAI Compatible LLM Node" - } - ], - "https://github.com/m-sokes/ComfyUI-Sokes-Nodes": [ - [ - "ComfyUI Folder Paths | sokes \ud83e\uddac", - "Current Date & Time | sokes \ud83e\uddac", - "Generate Random Background | sokes \ud83e\uddac", - "Hex Color Swatch | sokes \ud83e\uddac", - "Hex to Color Name | sokes \ud83e\uddac", - "Image Picker | sokes \ud83e\uddac", - "Latent Switch x9 | sokes \ud83e\uddac", - "Load Random Image | sokes \ud83e\uddac", - "Random Hex Color | sokes \ud83e\uddac", - "Random Number | sokes \ud83e\uddac", - "Replace Text with RegEx | sokes \ud83e\uddac", - "Runpod Serverless | sokes \ud83e\uddac", - "Street View Loader | sokes \ud83e\uddac" - ], - { - "title_aux": "ComfyUI Sokes Nodes \ud83e\uddac" - } - ], - "https://github.com/maepopi/Diffusers-in-ComfyUI": [ - [ - "BLoRALoader", - "GenerateImg2Image", - "GenerateInpaintImage", - "GenerateTxt2Image", - "Img2ImgStableDiffusionPipeline", - "InpaintingStableDiffusionPipeline", - "LoRALoader", - "MakeCanny", - "Text2ImgStableDiffusionPipeline" - ], - { - "title_aux": "Diffusers-in-ComfyUI" - } - ], - "https://github.com/magekinnarus/ComfyUI-V-Prediction-Node": [ - [ - "AddParam" - ], - { - "title_aux": "ComfyUI-V-Prediction-Node" - } - ], - "https://github.com/magic-eraser-org/ComfyUI-Unwatermark": [ - [ - "Remove Watermark" - ], - { - "title_aux": "ComfyUI-Unwatermark" - } - ], - "https://github.com/mamorett/ComfyUI_minicpmv4": [ - [ - "GenCheckerImage", - "MiniCPMV4GGUFLoader", - "MiniCPMV4VisionInfer", - "VisionPromptBuilder" - ], - { - "title_aux": "MiniCPM\u2011V\u20114 (GGUF) for ComfyUI" - } - ], - "https://github.com/mang01010/MangoNodePack": [ - [ - "CompositeMangoLoader", - "FluxGuidanceMango", - "FluxSamplerMango", - "ImageSaverMango", - "KSamplerMango", - "LatentImageMango", - "LoraStackMango", - "MangoImageLoader", - "MangoLoader", - "MangoLoader10Loras", - "MangoModelData", - "MangoPromptLoad", - "MangoTriggerExporter", - "PromptEmbedMango", - "PromptMango", - "PromptSave" - ], - { - "title_aux": "Mango Node Pack" - } - ], - "https://github.com/mango-rgb/ComfyUI-Mango-Random-node": [ - [ - "RandomFilePathNode", - "RandomImageNode", - "RandomImagePathNode", - "RandomTextNode", - "RandomVideoPathNode" - ], - { - "title_aux": "ComfyUI-Mango-Random" - } - ], - "https://github.com/mangobyed/ComfyUI_Detection_List": [ - [ - "YOLOv8ObjectDetectionNode" - ], - { - "title_aux": "ComfyUI YOLOv8 Object Detection Node" - } - ], - "https://github.com/manifestations/comfyui-globetrotter": [ - [ - "LoRATrainerNode", - "OllamaLLMNode", - "OllamaVisionNode", - "TextCombinerNode" - ], - { - "title_aux": "ComfyUI Globetrotter Nodes" - } - ], - "https://github.com/manifestations/comfyui-outfit": [ - [ - "OllamaLLMNode", - "SimpleOllamaNode" - ], - { - "title_aux": "ComfyUI Outfit Nodes" - } - ], - "https://github.com/mape/ComfyUI-mape-Helpers": [ - [ - "mape Variable" - ], - { - "author": "mape", - "description": "Various QoL improvements like prompt tweaking, variable assignment, image preview, fuzzy search, error reporting, organizing and node navigation.", - "nickname": "\ud83d\udfe1 mape's helpers", - "title": "mape's helpers", - "title_aux": "mape's helpers" - } - ], - "https://github.com/maracman/ComfyUI-SubjectStyle-CSV": [ - [ - "CSVPromptProcessor" - ], - { - "title_aux": "ComfyUI-SubjectStyle-CSV" - } - ], - "https://github.com/marawan206/ComfyUI-FaceCropper": [ - [ - "NodoFaceCropping" - ], - { - "title_aux": "Face Cropper Node (2:3 Ratio)" - } - ], - "https://github.com/marco-zanella/ComfyUI-BooleanExpression": [ - [ - "BooleanExpression.And", - "BooleanExpression.ArithmenticComparison.BinaryComparison", - "BooleanExpression.ArithmenticComparison.EqualTo", - "BooleanExpression.ArithmenticComparison.GreaterThan", - "BooleanExpression.ArithmenticComparison.GreaterThanOrEqualTo", - "BooleanExpression.ArithmenticComparison.LessThan", - "BooleanExpression.ArithmenticComparison.LessThanOrEqualTo", - "BooleanExpression.ArithmenticComparison.NotEqualTo", - "BooleanExpression.BinaryExpression", - "BooleanExpression.ConditionalBranch", - "BooleanExpression.False", - "BooleanExpression.Nand", - "BooleanExpression.Nor", - "BooleanExpression.Not", - "BooleanExpression.Or", - "BooleanExpression.StringComparison.AlphabeticalEqualTo", - "BooleanExpression.StringComparison.AlphabeticalGreaterThan", - "BooleanExpression.StringComparison.AlphabeticalGreaterThanOrEqualTo", - "BooleanExpression.StringComparison.AlphabeticalLessThan", - "BooleanExpression.StringComparison.AlphabeticalLessThanOrEqualTo", - "BooleanExpression.StringComparison.AlphabeticalNotEqualTo", - "BooleanExpression.StringComparison.Contains", - "BooleanExpression.StringComparison.EndsWith", - "BooleanExpression.StringComparison.NotContains", - "BooleanExpression.StringComparison.NotEndsWith", - "BooleanExpression.StringComparison.NotStartsWith", - "BooleanExpression.StringComparison.StartsWith", - "BooleanExpression.StringComparison.StringComparison", - "BooleanExpression.True", - "BooleanExpression.Xor" - ], - { - "title_aux": "ComfyUI-BooleanExpression" - } - ], - "https://github.com/marcoc2/ComfyUI-AnotherUtils": [ - [ - "AdaptiveNoise", - "CIELChNoiseGEGLLike", - "CharacterConstructor", - "CharacterRandomizer", - "CustomCrop", - "FightingGameCharacter", - "ImageTypeDetector", - "LastImage", - "LoadImageRemoveAlpha", - "LoadImagesOriginal", - "MeanCurvatureBlurGEGLLike", - "NearestUpscale", - "PixelArtConverter", - "PixelArtConverterParallel", - "PixelArtNormalizer", - "RGBNoiseGEGLLike", - "RemoveAlpha", - "SmartResize", - "WalkingPoseGenerator" - ], - { - "title_aux": "Image Processing Suite for ComfyUI" - } - ], - "https://github.com/marcoc2/ComfyUI_CogView4-6B_diffusers": [ - [ - "CogView4Generator" - ], - { - "title_aux": "ComfyUI-Cog" - } - ], - "https://github.com/marduk191/ComfyUI-Fluxpromptenhancer": [ - [ - "FluxPromptEnhance" - ], - { - "title_aux": "Flux Prompt Enhance Node for ComfyUI" - } - ], - "https://github.com/marduk191/comfyui-marnodes": [ - [ - "ImageToDevice", - "marduk191_5_text_string", - "marduk191_5way_text_switch", - "marduk191_s_random_latent", - "marduk191_workflow_settings" - ], - { - "author": "\u02f6marduk191", - "description": "marduk191s nodes.", - "nickname": "marduk191 workflow settings", - "title": "marduk191 workflow settings", - "title_aux": "marduk191 workflow settings" - } - ], - "https://github.com/marhensa/sdxl-recommended-res-calc": [ - [ - "RecommendedResCalc" - ], - { - "title_aux": "Recommended Resolution Calculator" - } - ], - "https://github.com/marklieberman/ComfyUI-Liebs-Picker": [ - [ - "LiebsPicker", - "LiebsPickerSEGS" - ], - { - "title_aux": "ComfyUI-Liebs-Picker" - } - ], - "https://github.com/marklieberman/ComfyUI-Liebs-Title": [ - [ - "LiebsTitleVar" - ], - { - "title_aux": "ComfyUI-Liebs-Title" - } - ], - "https://github.com/marklieberman/ComfyUI-Liebs-Toast": [ - [ - "LiebsToast" - ], - { - "title_aux": "ComfyUI-Liebs-Toast" - } - ], - "https://github.com/markuryy/ComfyUI-Flux-Prompt-Saver": [ - [ - "FluxPromptSaver", - "FluxTextSampler", - "ModelName" - ], - { - "title_aux": "ComfyUI Flux Prompt Saver" - } - ], - "https://github.com/markuryy/ComfyUI-Simple-Video-XY-Plot": [ - [ - "VideoXYPlotSampler" - ], - { - "title_aux": "Video XY Plot" - } - ], - "https://github.com/markuryy/ComfyUI-SuperLoader": [ - [ - "Display String", - "Display String Multiline", - "LoRA Metadata" - ], - { - "title_aux": "Super Loader" - } - ], - "https://github.com/martijnat/comfyui-previewlatent": [ - [ - "PreviewLatent", - "PreviewLatentAdvanced", - "PreviewLatentFlux", - "PreviewLatentXL" - ], - { - "title_aux": "comfyui-previewlatent" - } - ], - "https://github.com/massao000/ComfyUI_aspect_ratios": [ - [ - "Aspect Ratios Node" - ], - { - "title_aux": "ComfyUI_aspect_ratios" - } - ], - "https://github.com/matan1905/ComfyUI-Serving-Toolkit": [ - [ - "AlwaysExecute", - "CommandPickerServing", - "DiscordServing", - "ServingInputImage", - "ServingInputImageAsLatent", - "ServingInputNumber", - "ServingInputText", - "ServingInputTextImage", - "ServingMultiImageOutput", - "ServingOutput", - "ServingTextOutput", - "TelegramServing", - "WebSocketServing" - ], - { - "title_aux": "ComfyUI Serving toolkit" - } - ], - "https://github.com/matorzhin/milan-nodes-comfyui": [ - [ - "LoadMultipleImagesExtended", - "LoadOneImageExtended" - ], - { - "title_aux": "milan-nodes-comfyui" - } - ], - "https://github.com/mattjohnpowell/comfyui-lmstudio-image-to-text-node": [ - [ - "Expo Lmstudio Image To Text", - "Expo Lmstudio Text Generation", - "Expo Lmstudio Unified", - "ExpoLmstudioImageToText", - "ExpoLmstudioTextGeneration", - "ExpoLmstudioUnified" - ], - { - "author": "Matt John Powell", - "description": "This extension provides three custom nodes for ComfyUI that integrate LM Studio's capabilities:", - "nickname": "LM Studio Nodes", - "title": "LM Studio Nodes for ComfyUI", - "title_aux": "LM Studio Image to Text Node for ComfyUI" - } - ], - "https://github.com/mav-rik/facerestore_cf": [ - [ - "CropFace", - "FaceRestoreCFWithModel", - "FaceRestoreModelLoader" - ], - { - "title_aux": "Facerestore CF (Code Former)" - } - ], - "https://github.com/max-dingsda/OllamaTools": [ - [ - "OllamaPicDescriber", - "OllamaPromptBooster", - "PromptStylist" - ], - { - "title_aux": "OllamaTools for ComfyUI" - } - ], - "https://github.com/mbrostami/ComfyUI-HF": [ - [ - "GPT2Node" - ], - { - "title_aux": "ComfyUI-HF" - } - ], - "https://github.com/mbrostami/ComfyUI-TITrain": [ - [ - "TextualInversionTraining", - "TextualInversionTrainingSDXL" - ], - { - "title_aux": "ComfyUI-TITrain" - } - ], - "https://github.com/mcDandy/more_math": [ - [ - "mrmth_AudioMathNode", - "mrmth_ConditioningMathNode", - "mrmth_FloatMathNode", - "mrmth_FloatToInt", - "mrmth_ImageMathNode", - "mrmth_IntToFloat", - "mrmth_LatentMathNode", - "mrmth_NoiseMathNode" - ], - { - "title_aux": "More Math" - } - ], - "https://github.com/mcmonkeyprojects/sd-dynamic-thresholding": [ - [ - "DynamicThresholdingFull", - "DynamicThresholdingSimple" - ], - { - "title_aux": "Dynamic Thresholding" - } - ], - "https://github.com/meanin2/comfyui-MGnodes": [ - [ - "ImageWatermarkNode", - "TextExtractorNode" - ], - { - "title_aux": "comfyui-MGnodes" - } - ], - "https://github.com/meap158/ComfyUI-Background-Replacement": [ - [ - "BackgroundReplacement", - "ImageComposite" - ], - { - "title_aux": "ComfyUI-Background-Replacement" - } - ], - "https://github.com/meap158/ComfyUI-GPU-temperature-protection": [ - [ - "GPUTemperatureProtection" - ], - { - "title_aux": "GPU temperature protection" - } - ], - "https://github.com/meap158/ComfyUI-Prompt-Expansion": [ - [ - "PromptExpansion" - ], - { - "title_aux": "ComfyUI-Prompt-Expansion" - } - ], - "https://github.com/mech-tools/comfyui-checkpoint-automatic-config": [ - [ - "CheckpointAutomaticConfig", - "ConfigPipe" - ], - { - "title_aux": "ComfyUI Checkpoint Automatic Config" - } - ], - "https://github.com/mediocreatmybest/ComfyUI-Transformers-Pipeline": [ - [ - "BatchProcessorTpl", - "CaptionExportTpl", - "CaptionGeneratorTpl", - "DebugModelNodeTpl", - "DebugNodeTpl", - "ExifMetadataExtractorTpl", - "Florence2NodeTpl", - "ImageLoaderTpl", - "ModelLoaderTpl", - "PresetModelListTpl", - "TaskListTpl" - ], - { - "title_aux": "ComfyUI-Transformers-Pipeline" - } - ], - "https://github.com/melMass/comfy_mtb": [ - [ - "Animation Builder (mtb)", - "Any To String (mtb)", - "Batch Float (mtb)", - "Batch Float Assemble (mtb)", - "Batch Float Fill (mtb)", - "Batch Make (mtb)", - "Batch Merge (mtb)", - "Batch Shake (mtb)", - "Batch Shape (mtb)", - "Batch Transform (mtb)", - "Bbox (mtb)", - "Bbox From Mask (mtb)", - "Blur (mtb)", - "Color Correct (mtb)", - "Colored Image (mtb)", - "Concat Images (mtb)", - "Crop (mtb)", - "Debug (mtb)", - "Deep Bump (mtb)", - "Export With Ffmpeg (mtb)", - "Face Swap (mtb)", - "Film Interpolation (mtb)", - "Fit Number (mtb)", - "Float To Number (mtb)", - "Get Batch From History (mtb)", - "Image Compare (mtb)", - "Image Premultiply (mtb)", - "Image Remove Background Rembg (mtb)", - "Image Resize Factor (mtb)", - "Image Tile Offset (mtb)", - "Int To Bool (mtb)", - "Int To Number (mtb)", - "Interpolate Clip Sequential (mtb)", - "Latent Lerp (mtb)", - "Load Face Analysis Model (mtb)", - "Load Face Enhance Model (mtb)", - "Load Face Swap Model (mtb)", - "Load Film Model (mtb)", - "Load Image From Url (mtb)", - "Load Image Sequence (mtb)", - "Mask To Image (mtb)", - "Math Expression (mtb)", - "Model Patch Seamless (mtb)", - "Pick From Batch (mtb)", - "Qr Code (mtb)", - "Restore Face (mtb)", - "Save Gif (mtb)", - "Save Image Grid (mtb)", - "Save Image Sequence (mtb)", - "Save Tensors (mtb)", - "Sharpen (mtb)", - "Smart Step (mtb)", - "Stack Images (mtb)", - "String Replace (mtb)", - "Styles Loader (mtb)", - "Text To Image (mtb)", - "Transform Image (mtb)", - "Uncrop (mtb)", - "Unsplash Image (mtb)", - "Vae Decode (mtb)" - ], - { - "nodename_pattern": "\\(mtb\\)$", - "title_aux": "MTB Nodes" - } - ], - "https://github.com/melMass/comfy_oiio": [ - [ - "OIIO_ColorspaceConvert", - "OIIO_ColorspaceMatchFinder", - "OIIO_LoadImage", - "OIIO_SaveImage" - ], - { - "title_aux": "comfy-oiio" - } - ], - "https://github.com/mephisto83/petty-paint-comfyui-node": [ - [ - "ConvertWhiteToAlpha", - "PPGenerateRandomFloat", - "PPGenerateRandomNumber", - "PPKSamplerAdvanced", - "PPSelectRandomValue", - "PettyImageImageColorToMask", - "PettyPaintAppend", - "PettyPaintApplyLoRAStack", - "PettyPaintArguments", - "PettyPaintBlurs", - "PettyPaintCheckpointLoaderSimple", - "PettyPaintComponent", - "PettyPaintConditioningSetMaskAndCombine", - "PettyPaintControlNetToMasking", - "PettyPaintConvert", - "PettyPaintCountFiles", - "PettyPaintEnsureDirectory", - "PettyPaintExec", - "PettyPaintFakeConvert", - "PettyPaintFileExists", - "PettyPaintImageColorsToMasks", - "PettyPaintImageCompositeMasked", - "PettyPaintImageDims", - "PettyPaintImageMaskCropper", - "PettyPaintImagePlacement", - "PettyPaintImageSave", - "PettyPaintImageStore", - "PettyPaintImageToMask", - "PettyPaintImagesToMasks", - "PettyPaintJsonMap", - "PettyPaintJsonRead", - "PettyPaintJsonReadArray", - "PettyPaintKSampler", - "PettyPaintKSamplerAdvanced", - "PettyPaintLoRAStack", - "PettyPaintLoadImage", - "PettyPaintLoadImageMasks", - "PettyPaintLoadImages", - "PettyPaintMap", - "PettyPaintMasksToImages", - "PettyPaintNot", - "PettyPaintPassThroughNode", - "PettyPaintProcessor", - "PettyPaintRemoveAddText", - "PettyPaintSDTurboScheduler", - "PettyPaintStoryImage", - "PettyPaintText", - "PettyPaintTexts_to_Conditioning", - "PettyPaintToJson", - "PettyPaintVAEDecode", - "SkippableVAEEncode" - ], - { - "title_aux": "petty-paint-comfyui-node" - } - ], - "https://github.com/meshmesh-io/ComfyUI-MeshMesh": [ - [ - "ColorPicker", - "MasksToColoredMasks" - ], - { - "title_aux": "ComfyUI-MeshMesh" - } - ], - "https://github.com/meshmesh-io/mm-comfyui-loopback": [ - [ - "Loop", - "LoopEnd", - "LoopEnd_SEGIMAGE", - "LoopStart", - "LoopStart_SEGIMAGE" - ], - { - "title_aux": "mm-comfyui-loopback" - } - ], - "https://github.com/meshmesh-io/mm-comfyui-megamask": [ - [ - "ColorListMaskToImage", - "FlattenAndCombineMaskImages" - ], - { - "title_aux": "mm-comfyui-megamask" - } - ], - "https://github.com/metal3d/ComfyUI_Human_Parts": [ - [ - "HumanParts" - ], - { - "title_aux": "Human Parts Detector" - } - ], - "https://github.com/metal3d/ComfyUI_M3D_photo_effects": [ - [ - "Bleach Bypass", - "RGB Curve" - ], - { - "title_aux": "M3D photo effects" - } - ], - "https://github.com/metncelik/comfyui_met_suite": [ - [ - "BBOXPadding", - "BBOXResize", - "ImageResizeKeepRatio", - "PrimitiveBBOX", - "RaiseError" - ], - { - "title_aux": "comfyui_met_suite" - } - ], - "https://github.com/mfg637/ComfyUI-ScheduledGuider-Ext": [ - [ - "ArctanScheduler", - "ConcatSigmas", - "CosineScheduler", - "CustomBaseLogarithm", - "CustomExponent", - "GaussianScheduler", - "InvertSigmas", - "LogNormal Scheduler", - "OffsetSigmas", - "Parametric Peak #1", - "PerpNegScheduledCFGGuider", - "PredefinedExponent", - "PredefinedLogarithm", - "ScaleToRange", - "ScheduledCFGGuider", - "SigmasToPower", - "SplitSigmasByValue", - "k/x scheduler" - ], - { - "title_aux": "ComfyUI-ScheduledGuider-Ext" - } - ], - "https://github.com/mgfxer/ComfyUI-FrameFX": [ - [ - "DynamicAnimatedWeightsHelper", - "EdgeFXSourceImages", - "FlorencePromptTravelHelper", - "LivePromptInterpolation", - "MaskSequenceHelper", - "PromptStackManager", - "PromptTravelHelper" - ], - { - "author": "mgfxer", - "description": "This extension provides various frame and mask sequence manipulation tools for animation workflows.", - "nickname": "FrameFX \ud83d\udcab", - "title": "FrameFX", - "title_aux": "ComfyUI-FrameFX" - } - ], - "https://github.com/miaoshouai/ComfyUI-Miaoshouai-Tagger": [ - [ - "Miaoshouai_Caption_Analyzer", - "Miaoshouai_Flux_CLIPTextEncode", - "Miaoshouai_SaveTags", - "Miaoshouai_Tagger" - ], - { - "title_aux": "ComfyUI-Miaoshouai-Tagger" - } - ], - "https://github.com/miaoshouai/ComfyUI-Video-Segmentation": [ - [ - "DownloadAndLoadTransNetModel", - "SelectVideo", - "TransNetV2_Run", - "ZipCompress" - ], - { - "title_aux": "ComfyUI Video Segmentation Node" - } - ], - "https://github.com/michaelgold/ComfyUI-HF-Model-Downloader": [ - [ - "DownloadModel", - "ModelDownloader" - ], - { - "title_aux": "ComfyUI-HF-Model-Downloader" - } - ], - "https://github.com/microbote/ComfyUI-StyledCLIPTextEncode": [ - [ - "StyledCLIPTextEncode" - ], - { - "title_aux": "StyledCLIPTextEncode" - } - ], - "https://github.com/mihaiiancu/ComfyUI_Inpaint": [ - [ - "InpaintMediapipe" - ], - { - "title_aux": "mihaiiancu/Inpaint" - } - ], - "https://github.com/mikebilly/Transparent-background-comfyUI": [ - [ - "Transparentbackground RemBg" - ], - { - "title_aux": "Transparent-background-comfyUI" - } - ], - "https://github.com/mikeshuangyan/ComfyUI_MqUtils": [ - [ - "MqCheckFP4Support", - "MqIntSwitch", - "MqIntToString", - "MqTextSplitter" - ], - { - "title_aux": "ComfyUI_MqUtils" - } - ], - "https://github.com/mikheys/comfyui-gemini-mikheys": [ - [ - "Nano_Banana" - ], - { - "title_aux": "ComfyUI Nano Banana Node" - } - ], - "https://github.com/mikkel/ComfyUI-text-overlay": [ - [ - "Image Text Overlay" - ], - { - "title_aux": "ComfyUI - Text Overlay Plugin" - } - ], - "https://github.com/mikkel/comfyui-mask-boundingbox": [ - [ - "Mask Bounding Box" - ], - { - "title_aux": "ComfyUI - Mask Bounding Box" - } - ], - "https://github.com/mingsky-ai/ComfyUI-MingNodes": [ - [ - "AddWaterMarkNode", - "AdjustBrightnessContrastSaturationNode", - "BaiduTranslateNode", - "ColorBalanceNode", - "ConvertGrayChannelNode", - "HSLColorNode", - "HighlightShadowBrightnessNode", - "ImitationHueNode", - "LightShapeNode", - "RemoveWatermarkNode" - ], - { - "title_aux": "ComfyUI-MingNodes" - } - ], - "https://github.com/mira-6/comfyui-sasolver": [ - [ - "SamplerSASolver", - "SamplerSASolverExperimental" - ], - { - "title_aux": "comfyui-sasolver" - } - ], - "https://github.com/mirabarukaso/ComfyUI_Mira": [ - [ - "BooleanListInterpreter1", - "BooleanListInterpreter4", - "BooleanListInterpreter8", - "CanvasCreatorAdvanced", - "CanvasCreatorBasic", - "CanvasCreatorSimple", - "CheckpointLoaderSimpleMira", - "CreateMaskWithCanvas", - "CreateNestedPNGMask", - "CreateSimpleMask", - "CreateTillingPNGMask", - "CreateWatermarkRemovalMask", - "EightBooleanTrigger", - "EightFloats", - "EvenOrOdd", - "EvenOrOddList", - "FlatColorQuantization", - "FloatListInterpreter1", - "FloatListInterpreter4", - "FloatListInterpreter8", - "FloatMultiplication", - "FourBooleanTrigger", - "FourFloats", - "FunctionSelectAuto", - "FunctionSwap", - "GzippedBase64ToImage", - "ImageBrightness", - "ImageColorTransferMira", - "ImageContrast", - "ImageGamma", - "ImageGrayscale", - "ImageHUE", - "ImageRGBChannel", - "ImageSaturation", - "ImageSaverMira", - "ImageSharpness", - "ImageToGzippedBase64", - "ImageToneCurve", - "IntMultiplication", - "IntSubtraction", - "IntToFloatMultiplication", - "LoRALoaderWithNameStacker", - "LoRAfromText", - "LogicNot", - "NoneToZero", - "NumeralToString", - "OneFloat", - "PngColorMasksToMaskList", - "PngColorMasksToRGB", - "PngColorMasksToString", - "PngColorMasksToStringList", - "PngRectanglesToMask", - "PngRectanglesToMaskList", - "RandomNestedLayouts", - "RandomTillingLayouts", - "ReverseImageAndAllImages", - "SN74HC1G86", - "SN74HC86", - "SN74LVC1G125", - "SeedGeneratorMira", - "SingleBooleanTrigger", - "SixBooleanTrigger", - "StackImages", - "StepsAndCfg", - "TextBoxMira", - "TextCombinerSix", - "TextCombinerTwo", - "TextLoopCombiner", - "TextSwitcherThreeWays", - "TextSwitcherTwoWays", - "TextWildcardSeprator", - "TextWithBooleanSwitchAndCommonTextInput", - "TwoBooleanTrigger", - "TwoFloats", - "UpscaleImageByModelThenResize", - "illustrious_character_select", - "illustrious_character_select_en", - "llm_prompt_gen_node", - "local_llm_prompt_gen" - ], - { - "title_aux": "ComfyUI_Mira" - } - ], - "https://github.com/misterjoessef/MLTask_ComfyUI": [ - [ - "FacebookPosterData", - "InstagramPosterData", - "LinkedinPosterData", - "MLTaskUtilsTextImageGenerator", - "PinterestPosterData", - "SocialManMediaToPoster", - "SocialManPostData", - "SocialManPoster", - "TiktokPosterData", - "TwitterPosterData", - "YoutubePosterData" - ], - { - "title_aux": "MLTask_ComfyUI" - } - ], - "https://github.com/mittimi/ComfyUI_mittimiDaisyChainText": [ - [ - "DaisyChainTextMittimi" - ], - { - "author": "mittimi", - "description": "It has the ability to concatenate text.", - "nickname": "mittimiDaisyChainText", - "title": "mittimiDaisyChainText", - "title_aux": "ComfyUI_mittimiDaisyChainText" - } - ], - "https://github.com/mittimi/ComfyUI_mittimiLoadPreset2": [ - [ - "CombineParamDataMittimi", - "LoadImageParamMittimi", - "LoadSetParamMittimi", - "SaveImageParamMittimi", - "SaveParamToPresetMittimi" - ], - { - "author": "mittimi", - "description": "This node can easily switch between models and prompts by saving presets.", - "nickname": "mittimiLoadPreset2", - "title": "mittimiLoadPreset2", - "title_aux": "ComfyUI_mittimiLoadPreset2" - } - ], - "https://github.com/mittimi/ComfyUI_mittimiRecalculateSize": [ - [ - "RecalculateSizeMittimi01" - ], - { - "author": "mittimi", - "description": "Switch between vertical and horizontal values with a single button.", - "nickname": "mittimiWidthHeight", - "title": "mittimiWidthHeight", - "title_aux": "ComfyUI_mittimiRecalculateSize" - } - ], - "https://github.com/mittimi/ComfyUI_mittimiWidthHeight": [ - [ - "WidthHeightMittimi01" - ], - { - "author": "mittimi", - "description": "Switch between vertical and horizontal values with a single button.", - "nickname": "mittimiWidthHeight", - "title": "mittimiWidthHeight", - "title_aux": "ComfyUI_mittimiWidthHeight" - } - ], - "https://github.com/mo230761/InsertAnything-ComfyUI-official": [ - [ - "CropBack", - "CropBackNoScaling", - "FillProcess", - "FillProcessNoScaling", - "MaskOption", - "ReduxProcess" - ], - { - "title_aux": "InsertAnything-ComfyUI-official" - } - ], - "https://github.com/mobilehacker/ComfyUI_format-lora-stack": [ - [ - "FormatLoraStack" - ], - { - "title_aux": "ComfyUI_format-lora-stack" - } - ], - "https://github.com/modelscope/comfyscope": [ - [ - "DashScopeFLUXAPI" - ], - { - "title_aux": "Dashscope FLUX API for ComfyUI" - } - ], - "https://github.com/modusCell/ComfyUI-dimension-node-modusCell": [ - [ - "DimensionProviderFree modusCell", - "DimensionProviderRatio modusCell", - "String Concat modusCell" - ], - { - "title_aux": "Preset Dimensions" - } - ], - "https://github.com/mohseni-mr/ComfyUI-Mohseni-Kit": [ - [ - "FloatPreview" - ], - { - "title_aux": "ComfyUI Mohseni Kit" - } - ], - "https://github.com/mohsensd1373/comfyui_wordpress": [ - [ - "SaveToWordPressNode" - ], - { - "title_aux": "comfyui_wordpress" - } - ], - "https://github.com/monkeyWie/ComfyUI-FormInput": [ - [ - "BooleanInput_FormInput", - "DisplayText_FormInput", - "TextInput_FormInput" - ], - { - "title_aux": "ComfyUI-FormInput" - } - ], - "https://github.com/moon7star9/ComfyUI_BiRefNet_Universal": [ - [ - "BiRefNet_Loader", - "BiRefNet_Remove_Background" - ], - { - "title_aux": "ComfyUI_BiRefNet_Universal" - } - ], - "https://github.com/moonwhaler/comfyui-seedvr2-tilingupscaler": [ - [ - "SeedVR2TilingUpscaler" - ], - { - "title_aux": "SeedVR2 Tiling Upscaler" - } - ], - "https://github.com/morino-kumasan/comfyui-toml-prompt": [ - [ - "CheckPointLoaderSimpleFromString", - "IntSelector", - "JsonExtractFloat", - "JsonExtractInt", - "JsonExtractString", - "KSamplerFromJsonInfo", - "LatentSelector", - "MultipartCLIPTextEncode", - "MultipleLoraTagLoader", - "PromptLoader", - "SeedGenerator", - "StringConcat", - "StringConcatInt", - "StringPicker", - "StringSelector", - "StringViewer", - "SummaryReader", - "TomlPromptDecode" - ], - { - "title_aux": "comfyui-toml-prompt" - } - ], - "https://github.com/moustafa-nasr/ComfyUI-SimpleLogger": [ - [ - "Log Image", - "LogImageNode" - ], - { - "title_aux": "ComfyUI-SimpleLogger" - } - ], - "https://github.com/moyi7712/ComfyUI_Seamless_Patten": [ - [ - "SeamlessApply", - "SeamlessKSampler", - "SeamlessKSamplerAdvanced", - "SeamlessVae" - ], - { - "title_aux": "ComfyUI_Seamless_Patten" - } - ], - "https://github.com/mr7thing/circle_pattern_processor": [ - [ - "CirclePatternProcessor", - "CirclePatternSVGExporter", - "ImageBinarizer" - ], - { - "title_aux": "Circle Pattern Processor for ComfyUI" - } - ], - "https://github.com/mrchipset/ComfyUI-SaveImageS3": [ - [ - "SaveImageS3" - ], - { - "author": "Mr.Chip", - "description": "This extension offers a custom node to save image to S3-compatible oss.", - "nickname": "SaveImageS3", - "title": "SaveImageS3", - "title_aux": "ComfyUI-SaveImageS3" - } - ], - "https://github.com/mrhan1993/ComfyUI-Fooocus": [ - [ - "AlignYourStepsScheduler", - "BasicScheduler", - "CLIPLoader", - "CLIPMergeSimple", - "CLIPSave", - "CLIPSetLastLayer", - "CLIPTextEncode", - "CLIPTextEncodeSDXL", - "CLIPTextEncodeSDXLRefiner", - "CLIPVisionEncode", - "CLIPVisionLoader", - "Canny", - "CheckpointLoader", - "CheckpointLoaderSimple", - "CheckpointSave", - "ClearVram", - "ConditioningAverage", - "ConditioningCombine", - "ConditioningConcat", - "ConditioningSetArea", - "ConditioningSetAreaPercentage", - "ConditioningSetMask", - "ConditioningSetTimestepRange", - "ConditioningZeroOut", - "ControlNetApply", - "ControlNetApplyAdvanced", - "ControlNetLoader", - "CropMask", - "DiffControlNetLoader", - "DiffusersLoader", - "DualCLIPLoader", - "EmptyImage", - "EmptyLatentImage", - "EnhanceControl", - "EnhanceControls", - "ExponentialScheduler", - "FeatherMask", - "FlipSigmas", - "FooocusSampler", - "FooocusSettings", - "FreeU", - "FreeU_V2", - "GLIGENLoader", - "GLIGENTextBoxApply", - "GrowMask", - "HyperTile", - "HypernetworkLoader", - "ImageBatch", - "ImageBlend", - "ImageBlur", - "ImageColorToMask", - "ImageCompositeMasked", - "ImageCrop", - "ImageInvert", - "ImageOnlyCheckpointLoader", - "ImageOnlyCheckpointSave", - "ImagePadForOutpaint", - "ImagePrompts", - "ImageQuantize", - "ImageScale", - "ImageScaleBy", - "ImageScaleToTotalPixels", - "ImageSharpen", - "ImageToMask", - "ImageUpscaleWithModel", - "InpaintModelConditioning", - "InpaintOutpaint", - "InvertMask", - "JoinImageWithAlpha", - "KSampler", - "KSamplerAdvanced", - "KSamplerSelect", - "KarrasScheduler", - "LatentAdd", - "LatentBatch", - "LatentBatchSeedBehavior", - "LatentBlend", - "LatentComposite", - "LatentCompositeMasked", - "LatentCrop", - "LatentFlip", - "LatentFromBatch", - "LatentInterpolate", - "LatentMultiply", - "LatentRotate", - "LatentSubtract", - "LatentUpscale", - "LatentUpscaleBy", - "LoadImage", - "LoadImageMask", - "LoadLatent", - "LoraLoader", - "LoraLoaderModelOnly", - "LoraStacks", - "MaskComposite", - "MaskToImage", - "ModelMergeAdd", - "ModelMergeBlocks", - "ModelMergeSimple", - "ModelMergeSubtract", - "ModelSamplingContinuousEDM", - "ModelSamplingDiscrete", - "PatchModelAddDownscale", - "PerpNeg", - "PhotoMakerEncode", - "PhotoMakerLoader", - "PolyexponentialScheduler", - "PorterDuffImageComposite", - "PreviewImage", - "RebatchImages", - "RebatchLatents", - "RepeatImageBatch", - "RepeatLatentBatch", - "RescaleCFG", - "SDTurboScheduler", - "SD_4XUpscale_Conditioning", - "SVD_img2vid_Conditioning", - "SamplerCustom", - "SamplerDPMPP_2M_SDE", - "SamplerDPMPP_SDE", - "SamplerTCD", - "SaveAnimatedPNG", - "SaveAnimatedWEBP", - "SaveImage", - "SaveLatent", - "SelfAttentionGuidance", - "SetLatentNoiseMask", - "SolidMask", - "SplitImageWithAlpha", - "SplitSigmas", - "StableZero123_Conditioning", - "StableZero123_Conditioning_Batched", - "StyleModelApply", - "StyleModelLoader", - "TomePatchModel", - "UNETLoader", - "UpscaleModelLoader", - "UpscaleVary", - "VAEDecode", - "VAEDecodeTiled", - "VAEEncode", - "VAEEncodeForInpaint", - "VAEEncodeTiled", - "VAELoader", - "VAESave", - "VPScheduler", - "VideoLinearCFGGuidance", - "unCLIPCheckpointLoader", - "unCLIPConditioning" - ], - { - "author": "Konie", - "title_aux": "ComfyUI-Fooocus" - } - ], - "https://github.com/muhammederem/blip-comfyui": [ - [ - "Blip Processor Node", - "List to Text Node" - ], - { - "title_aux": "BLIP Vision-Language Model Integration" - } - ], - "https://github.com/mullakhmetov/comfyui_dynamic_util_nodes": [ - [ - "ConcatStrings", - "FormatString", - "GetFiles", - "LoadImageByPath", - "StringOutput" - ], - { - "title_aux": "comfyui_dynamic_util_nodes" - } - ], - "https://github.com/muxueChen/ComfyUI_NTCosyVoice": [ - [ - "NTCosyVoiceCrossLingualSampler", - "NTCosyVoiceInstruct2Sampler", - "NTCosyVoiceZeroShotSampler" - ], - { - "title_aux": "CosyVoice2 for ComfyUI" - } - ], - "https://github.com/muzi12888/ComfyUI-PoseKeypoint-Mask": [ - [ - "Image Brightness", - "Openpose Keypoint Mask" - ], - { - "title_aux": "PoseKeypoint Mask" - } - ], - "https://github.com/my-opencode/ComfyUI_IndustrialMagick": [ - [ - "IndustrialMagick", - "IndustrialMagickImageIngest" - ], - { - "title_aux": "ComfyUI_IndustrialMagick" - } - ], - "https://github.com/my-opencode/ComfyUI_KSamplerTimer": [ - [ - "KSamplerTimer" - ], - { - "author": "Ludovic Anterieur", - "description": "This extension provides a wrapper of the native KSampler which outputs generation time.", - "nickname": "\u23f1", - "title": "KSampler (timer)", - "title_aux": "ComfyUI_KSamplerTimer" - } - ], - "https://github.com/myshell-ai/ComfyUI-ShellAgent-Plugin": [ - [ - "ShellAgentPluginInputAudio", - "ShellAgentPluginInputBoolean", - "ShellAgentPluginInputFloat", - "ShellAgentPluginInputImage", - "ShellAgentPluginInputInteger", - "ShellAgentPluginInputText", - "ShellAgentPluginInputVideo", - "ShellAgentPluginOutputBoolean", - "ShellAgentPluginOutputFloat", - "ShellAgentPluginOutputInteger", - "ShellAgentPluginOutputText", - "ShellAgentPluginSaveAudio", - "ShellAgentPluginSaveAudios", - "ShellAgentPluginSaveImage", - "ShellAgentPluginSaveImages", - "ShellAgentPluginSaveVideoVHS" - ], - { - "author": "MyShell", - "description": "", - "title": "comfyui-shellagent-plugin", - "title_aux": "ComfyUI-ShellAgent-Plugin" - } - ], - "https://github.com/n0neye/A3D-comfyui-integration": [ - [ - "A3DListener", - "UniqueNodeName" - ], - { - "title_aux": "A3D ComfyUI Integration" - } - ], - "https://github.com/nagolinc/ComfyUI_FastVAEDecorder_SDXL": [ - [ - "FastLatentToImage" - ], - { - "title_aux": "ComfyUI_FastVAEDecorder_SDXL" - } - ], - "https://github.com/nagolinc/comfyui_openai_node": [ - [ - "OpenAINode" - ], - { - "title_aux": "comfyui_openai_node" - } - ], - "https://github.com/nako-nakoko/ComfyUI_Mel_Nodes": [ - [ - "AddFileNameonly", - "ResolutionSwitcher", - "Split Image Batch", - "Unet Selector_gguf", - "mel_RandomIntNode", - "mel_TextFilterNode", - "mel_TextSplitNode", - "mel_TextSplitNode2" - ], - { - "title_aux": "ComfyUI_Mel_Nodes" - } - ], - "https://github.com/namtb96/OmniGen2-Simple-Node": [ - [ - "OmniGen2ModelLoader", - "OmniGen2Sampler" - ], - { - "title_aux": "OmniGen2 Simple Node" - } - ], - "https://github.com/narusas/Comfyui-Logic-Support": [ - [ - "BooleanIndexAdder", - "NumberConditionChecker", - "NumberRangeIndex", - "NumberSequenceGenerator", - "StringConcatenator", - "StringSwitchByNumber" - ], - { - "title_aux": "ComfyUI Logic Support" - } - ], - "https://github.com/natto-maki/ComfyUI-NegiTools": [ - [ - "NegiTools_CompositeImages", - "NegiTools_DepthEstimationByMarigold", - "NegiTools_DetectFaceRotationForInpainting", - "NegiTools_ImageProperties", - "NegiTools_LatentProperties", - "NegiTools_NoiseImageGenerator", - "NegiTools_OpenAiDalle3", - "NegiTools_OpenAiGpt", - "NegiTools_OpenAiGpt4v", - "NegiTools_OpenAiTranslate", - "NegiTools_OpenPoseToPointList", - "NegiTools_PointListToMask", - "NegiTools_RandomImageLoader", - "NegiTools_SaveImageToDirectory", - "NegiTools_SeedGenerator", - "NegiTools_StereoImageGenerator", - "NegiTools_StringFunction" - ], - { - "title_aux": "ComfyUI-NegiTools" - } - ], - "https://github.com/nchenevey1/comfyui-gimp-nodes": [ - [ - "NC_LoadImageGIMP", - "NC_LoadMaskGIMP", - "NC_SendImageDimsWebSocketGIMP", - "NC_SendImageWebSocketGIMP" - ], - { - "title_aux": "comfyui-gimp-nodes" - } - ], - "https://github.com/negaga53/comfyui-imgloader": [ - [ - "ImageLoader" - ], - { - "title_aux": "ComfyUI Universal Image Loader" - } - ], - "https://github.com/neggo/comfyui-sambanova": [ - [ - "SambaNova API Node", - "SambaNovaNode" - ], - { - "title_aux": "comfyui-sambanova" - } - ], - "https://github.com/neocrz/comfyui-usetaesd": [ - [ - "DecodeTAESD", - "DecodeTAESDTiled", - "EncodeTAESD", - "EncodeTAESDTiled" - ], - { - "title_aux": "comfyui-usetaesd" - } - ], - "https://github.com/neph1/comfyui-smooth-step-lora-loader": [ - [ - "Smooth_Step_Lora_Loader" - ], - { - "title_aux": "comfyui-smooth-step-lora-loader" - } - ], - "https://github.com/netroxin/comfyui_netro": [ - [ - "CamPromptNode" - ], - { - "title_aux": "comfyui_netro" - } - ], - "https://github.com/neverbiasu/ComfyUI-BAGEL": [ - [ - "BagelImageEdit", - "BagelImageUnderstanding", - "BagelModelLoader", - "BagelTextToImage" - ], - { - "title_aux": "ComfyUI-BAGEL" - } - ], - "https://github.com/neverbiasu/ComfyUI-ChatTTS": [ - [ - "ChatTTSLoader", - "ChatTTS_ExtractSpeaker", - "ChatTTS_LoadSpeakerProfile", - "ChatTTS_Sampler", - "ChatTTS_SaveSpeakerProfile", - "ChatTTS_SeedBasedSpeaker", - "ChatTTS_TextNormalizer", - "ChatTTS_TextSplitter" - ], - { - "title_aux": "ComfyUI-ChatTTS" - } - ], - "https://github.com/neverbiasu/ComfyUI-Dashscope": [ - [ - "DashscopeEmoCaller", - "DashscopeLLMLoader", - "DashscopeModelCaller", - "DashscopeOCRCaller", - "DashscopeVLMLoader" - ], - { - "title_aux": "ComfyUI-Dashscope" - } - ], - "https://github.com/neverbiasu/ComfyUI-Image-Captioner": [ - [ - "ImageCaptioner" - ], - { - "title_aux": "ComfyUI-Image-Captioner" - } - ], - "https://github.com/neverbiasu/ComfyUI-Ovis-U1": [ - [ - "OvisU1ImageEdit", - "OvisU1ImageToText", - "OvisU1ModelLoader", - "OvisU1TextToImage" - ], - { - "title_aux": "ComfyUI-Ovis-U1" - } - ], - "https://github.com/neverbiasu/ComfyUI-SAM2": [ - [ - "GroundingDinoModelLoader (segment anything2)", - "GroundingDinoSAM2Segment (segment anything2)", - "InvertMask (segment anything)", - "IsMaskEmpty", - "SAM2ModelLoader (segment anything2)" - ], - { - "title_aux": "ComfyUI SAM2(Segment Anything 2)" - } - ], - "https://github.com/neverbiasu/ComfyUI-StyleShot": [ - [ - "StyleShotApply" - ], - { - "title_aux": "ComfyUI-StyleShot" - } - ], - "https://github.com/ngosset/ComfyUI-ImageSimilarity": [ - [ - "Image Similarity" - ], - { - "title_aux": "ImageSimilarity" - } - ], - "https://github.com/nicehero/comfyui-SegGPT": [ - [ - "SegGPT" - ], - { - "title_aux": "comfyui-SegGPT" - } - ], - "https://github.com/nickve28/ComfyUI-Nich-Utils": [ - [ - "Image from Dir Selector (Nich)", - "Select Text with Regular Expression (Nich)" - ], - { - "title_aux": "ComfyUI Nich Utils" - } - ], - "https://github.com/nicofdga/DZ-FaceDetailer": [ - [ - "DZ_Face_Detailer" - ], - { - "title_aux": "DZ-FaceDetailer" - } - ], - "https://github.com/niknah/ComfyUI-F5-TTS": [ - [ - "F5TTSAudio", - "F5TTSAudioAdvanced", - "F5TTSAudioInputs" - ], - { - "title_aux": "ComfyUI F5-TTS" - } - ], - "https://github.com/niknah/ComfyUI-Hunyuan-3D-2": [ - [ - "Hunyuan3D2ImageTo3D" - ], - { - "title_aux": "ComfyUI Hunyuan-3D-2" - } - ], - "https://github.com/niknah/ComfyUI-InfiniteYou": [ - [ - "InfiniteYouSampler" - ], - { - "title_aux": "ComfyUI-InfiniteYou" - } - ], - "https://github.com/niknah/audio-general-ComfyUI": [ - [ - "AudioBassTreble", - "AudioConcat", - "AudioInfo", - "AudioMix", - "AudioPitch", - "AudioSampleRate", - "AudioSpeed", - "AudioTrimSilenceRosa", - "AudioTrimSilenceVAD" - ], - { - "title_aux": "Audio General" - } - ], - "https://github.com/nilor-corp/nilor-nodes": [ - [ - "Nilor Blur Analysis", - "Nilor Categorize String", - "Nilor Count Images In Directory", - "Nilor Extract Filename from Path", - "Nilor Int To List Of Bools", - "Nilor Interpolated Float List", - "Nilor Inverse Map Float List", - "Nilor List of Ints", - "Nilor Load Image By Index", - "Nilor One Minus Float List", - "Nilor Output Filename String", - "Nilor Random String", - "Nilor Remap Float List", - "Nilor Remap Float List Auto Input", - "Nilor Repeat & Trim Image Batch", - "Nilor Repeat, Shuffle, & Trim Image Batch", - "Nilor Save EXR Arbitrary", - "Nilor Save Image To HF Dataset", - "Nilor Save Video To HF Dataset", - "Nilor Select Index From List", - "Nilor Shuffle Image Batch", - "Nilor To Sparse Index Method", - "Nilor n Fractions of Int" - ], - { - "title_aux": "Nilor Nodes by Nilor Corp" - } - ], - "https://github.com/ningxiaoxiao/comfyui-NDI": [ - [ - "NDI_LoadImage", - "NDI_SendImage" - ], - { - "title_aux": "comfyui-NDI" - } - ], - "https://github.com/nirbhay-faaya/ImgProcessing_ComfyUI": [ - [ - "ImageConcat", - "ImageCropMultEight", - "ImageCut", - "LightingPreprocessor" - ], - { - "title_aux": "ImgProcessing_ComfyUI" - } - ], - "https://github.com/nirex0/ComfyUI_pytorch_openpose": [ - [ - "pytorch_openpose" - ], - { - "title_aux": "ComfyUI_pytorch_openpose" - } - ], - "https://github.com/nisaruj/comfyui-daam": [ - [ - "CLIPTextEncodeWithTokens", - "DAAMAnalyzer", - "KSamplerDAAM" - ], - { - "title_aux": "ComfyUI-DAAM" - } - ], - "https://github.com/nisimjoseph/ComfyUI_OpenAI-Prompter": [ - [ - "OpenAI Prompt Generator" - ], - { - "title_aux": "ComfyUI OpenAI Prompter" - } - ], - "https://github.com/njlent/ComfyUI_wavelet-colorfix": [ - [ - "WaveletColorFix" - ], - { - "title_aux": "ComfyUI Wavelet Color Fix" - } - ], - "https://github.com/nkchocoai/ComfyUI-DanbooruPromptQuiz": [ - [ - "DanbooruPromptComparison", - "DanbooruPromptQuiz" - ], - { - "title_aux": "ComfyUI-DanbooruPromptQuiz" - } - ], - "https://github.com/nkchocoai/ComfyUI-Dart": [ - [ - "DanbooruTagsTransformerBanTagsFromRegex", - "DanbooruTagsTransformerComposePrompt", - "DanbooruTagsTransformerComposePromptV2", - "DanbooruTagsTransformerDecode", - "DanbooruTagsTransformerDecodeBySplitedParts", - "DanbooruTagsTransformerGenerate", - "DanbooruTagsTransformerGenerateAdvanced", - "DanbooruTagsTransformerGenerationConfig", - "DanbooruTagsTransformerGetAspectRatio", - "DanbooruTagsTransformerLoader", - "DanbooruTagsTransformerRearrangedByAnimagine", - "DanbooruTagsTransformerRemoveTagToken" - ], - { - "title_aux": "ComfyUI-Dart" - } - ], - "https://github.com/nkchocoai/ComfyUI-PromptUtilities": [ - [ - "PromptUtilitiesConstString", - "PromptUtilitiesConstStringMultiLine", - "PromptUtilitiesFormatString", - "PromptUtilitiesJoinStringList", - "PromptUtilitiesLoadPreset", - "PromptUtilitiesLoadPresetAdvanced", - "PromptUtilitiesPromptWeight", - "PromptUtilitiesRandomPreset", - "PromptUtilitiesRandomPresetAdvanced", - "PromptUtilitiesReplaceOrInsertTag", - "PromptUtilitiesRoundPromptWeight", - "PromptUtilitiesSampleTags", - "PromptUtilitiesSampleTagsWithWeight" - ], - { - "title_aux": "ComfyUI-PromptUtilities" - } - ], - "https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData": [ - [ - "CreateExtraMetaData", - "SaveImageWithMetaData" - ], - { - "title_aux": "ComfyUI-SaveImageWithMetaData" - } - ], - "https://github.com/nkchocoai/ComfyUI-SizeFromPresets": [ - [ - "EmptyLatentImageFromPresetsSD15", - "EmptyLatentImageFromPresetsSDXL", - "GetSimilarResolution", - "GetSimilarResolutionEmptyLatent", - "RandomEmptyLatentImageFromPresetsSD15", - "RandomEmptyLatentImageFromPresetsSDXL", - "RandomSizeFromPresetsSD15", - "RandomSizeFromPresetsSDXL", - "SizeFromPresetsSD15", - "SizeFromPresetsSDXL" - ], - { - "title_aux": "ComfyUI-SizeFromPresets" - } - ], - "https://github.com/nkchocoai/ComfyUI-TextOnSegs": [ - [ - "CalcMaxFontSize", - "ExtractDominantColor", - "GetComplementaryColor", - "SegsToRegion", - "TextOnSegsFloodFill" - ], - { - "title_aux": "ComfyUI-TextOnSegs" - } - ], - "https://github.com/nobrainX2/comfyUI-customDia": [ - [ - "Audio retimer", - "Dia text to speech" - ], - { - "title_aux": "ComfyUI Custom Dia" - } - ], - "https://github.com/noembryo/ComfyUI-noEmbryo": [ - [ - "PromptTermList1", - "PromptTermList2", - "PromptTermList3", - "PromptTermList4", - "PromptTermList5", - "PromptTermList6" - ], - { - "author": "noEmbryo", - "description": "Some useful nodes for ComfyUI", - "nickname": "noEmbryo", - "title": "noEmbryo nodes", - "title_aux": "noEmbryo nodes" - } - ], - "https://github.com/nofunstudio/Node_Fun_ComfyUI": [ - [ - "DynamicQueueCounter", - "FalAPI_kling_video", - "FalAPI_recraft_upscale", - "FluxKontextInpaintingConditioning", - "Fun KSampler", - "IframeView", - "IndexedStringSelector", - "Kontext Inpainting Conditioning", - "LayeredInfiniteZoom", - "MultiAlphaComposite", - "Replicate flux 1.1 pro ultra", - "ReplicateAPI_flux_1_1_pro_ultra", - "ReplicateAPI_flux_fill_pro", - "StringLower" - ], - { - "title_aux": "Node_Fun_ComfyUI" - } - ], - "https://github.com/northumber/ComfyUI-northTools": [ - [ - "BooleanIndexesToString", - "ConcatHistoryString", - "ExtractMetadataByKey", - "ImageToTrue", - "LoadImagesFromDirByIndexBatch", - "LoadImagesFromDirByIndexList", - "LoadImagesFromDirList", - "SumIntegers" - ], - { - "title_aux": "ComfyUI-northTools" - } - ], - "https://github.com/nosiu/comfyui-instantId-faceswap": [ - [ - "AngleFromFace", - "AngleFromKps", - "ComposeRotated", - "ControlNetInstantIdApply", - "FaceEmbed", - "FaceEmbedCombine", - "InstantIdAdapterApply", - "InstantIdAndControlnetApply", - "Kps2dRandomizer", - "Kps3dFromImage", - "Kps3dRandomizer", - "KpsCrop", - "KpsDraw", - "KpsMaker", - "KpsRotate", - "KpsScale", - "KpsScaleBy", - "LoadInsightface", - "LoadInstantIdAdapter", - "MaskFromKps", - "PreprocessImage", - "PreprocessImageAdvanced", - "RotateImage" - ], - { - "title_aux": "comfyui-instantId-faceswap" - } - ], - "https://github.com/nosiu/comfyui-text-randomizer": [ - [ - "ConcatText", - "RandomTextChoice", - "RandomizeText", - "RandomizeTextWithCheck", - "ShowText" - ], - { - "title_aux": "comfyui-text-randomizer" - } - ], - "https://github.com/noxinias/ComfyUI_NoxinNodes": [ - [ - "NoxinChime", - "NoxinPromptLoad", - "NoxinPromptSave", - "NoxinScaledResolution", - "NoxinSimpleMath", - "NoxinSplitPrompt" - ], - { - "title_aux": "ComfyUI_NoxinNodes" - } - ], - "https://github.com/nsdtcloud3d/ComfyUI-3D-Convert": [ - [ - "ConvertTo3DFormat", - "Load3DConvertAPIKEY", - "Load3DFile" - ], - { - "title_aux": "ComfyUI-3D-Convert" - } - ], - "https://github.com/ntc-ai/ComfyUI-DARE-LoRA-Merge": [ - [ - "Apply LoRA", - "DARE Merge LoRA Stack", - "Save LoRA" - ], - { - "title_aux": "ComfyUI - Apply LoRA Stacker with DARE" - } - ], - "https://github.com/nuanarchy/ComfyUI-NuA-BIRD": [ - [ - "Bird_Deblurring_NuA", - "Bird_Denoising_NuA", - "Bird_Inpainting_NuA", - "Bird_Loader_NuA", - "Bird_Non_Uniform_Deblurring_NuA", - "Bird_Super_Resolution_NuA" - ], - { - "title_aux": "ComfyUI-NuA-BIRD" - } - ], - "https://github.com/nuanarchy/ComfyUI-NuA-FlashFace": [ - [ - "FlashFace_Loader_NuA", - "FlashFace_Sampler_NuA" - ], - { - "title_aux": "ComfyUI-NuA-FlashFace" - } - ], - "https://github.com/nullquant/ComfyUI-BrushNet": [ - [ - "BlendInpaint", - "BrushNet", - "BrushNetLoader", - "CutForInpaint", - "PowerPaint", - "PowerPaintCLIPLoader", - "RAUNet", - "Terminal" - ], - { - "author": "nullquant", - "description": "These are custom nodes for ComfyUI native implementation of BrushNet, PowerPaint and RAUNet models", - "nickname": "BrushName nodes", - "title": "BrushNet", - "title_aux": "BrushNet" - } - ], - "https://github.com/numz/ComfyUI-FlowChain": [ - [ - "WorkflowLipSync" - ], - { - "title_aux": "ComfyUI-FlowChain" - } - ], - "https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler": [ - [ - "SeedVR2", - "SeedVR2BlockSwap" - ], - { - "title_aux": "ComfyUI-SeedVR2_VideoUpscaler" - } - ], - "https://github.com/numz/Comfyui-Orpheus": [ - [ - "orpheus", - "orpheusAdvanced" - ], - { - "title_aux": "ComfyUI-Orpheus" - } - ], - "https://github.com/nunchaku-tech/ComfyUI-nunchaku": [ - [ - "NunchakuWheelInstaller" - ], - { - "title_aux": "ComfyUI-nunchaku" - } - ], - "https://github.com/nux1111/ComfyUI_NetDist_Plus": [ - [ - "CombineImageBatch", - "ConditioningFromBase64(Nux)", - "ConditioningToBase64(Nux)", - "ExtractBase64FromImage(Nux)", - "ExtractBase64FromImageUpload(Nux)", - "FetchRemote", - "FetchRemoteWithExtras(Nux)", - "LatentToBase64(Nux)", - "LoadCurrentWorkflowJSON", - "LoadDiskWorkflowJSON", - "LoadImageUrl", - "LoadLatentFromBase64(Nux)", - "LoadLatentNumpy", - "LoadLatentUrl", - "LoadWorkflowJSON", - "RemoteApplyValues(Nux)", - "RemoteApplyValuesMulti(Nux)", - "RemoteChainEnd", - "RemoteChainStart", - "RemoteChainStart(Nux)", - "RemoteQueueSimple", - "RemoteQueueSimple(Nux)", - "RemoteQueueWorker", - "SaveDiskWorkflowJSON", - "SaveImageUrl", - "SaveImageWithBase64(Nux)", - "SaveLatentNumpy" - ], - { - "title_aux": "ComfyUI_NetDist_Plus" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-ChannelMixer": [ - [ - "OlmChannelMixer" - ], - { - "title_aux": "Olm Channel Mixer for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-ColorBalance": [ - [ - "OlmColorBalance" - ], - { - "title_aux": "Olm Color Balance for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-CurveEditor": [ - [ - "OlmCurveEditor" - ], - { - "title_aux": "Olm Curve Editor for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-DragCrop": [ - [ - "OlmDragCrop" - ], - { - "title_aux": "Olm DragCrop for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-Histogram": [ - [ - "OlmHistogram" - ], - { - "title_aux": "Olm Histogram for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-ImageAdjust": [ - [ - "OlmImageAdjust" - ], - { - "title_aux": "Olm Image Adjust for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-LGG": [ - [ - "OlmLGG" - ], - { - "title_aux": "Olm LGG (Lift, Gamma, Gain) for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-Resolution-Picker": [ - [ - "OlmResolutionPicker" - ], - { - "title_aux": "Olm Resolution Picker for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-Olm-Sketch": [ - [ - "OlmSketch" - ], - { - "title_aux": "Olm Sketch for ComfyUI" - } - ], - "https://github.com/o-l-l-i/ComfyUI-OlmLUT": [ - [ - "OlmLUT" - ], - { - "title_aux": "Olm LUT Node for ComfyUI" - } - ], - "https://github.com/obisin/ComfyUI-DGLS": [ - [ - "DGLSCleanup", - "DGLSModelLoader", - "DynamicSwappingLoader" - ], - { - "title_aux": "ComfyUI - DGLS (Dynamic GPU Layer Swapping)" - } - ], - "https://github.com/okgo4/ComfyUI-Mosaic-Mask": [ - [ - "MosaicMask" - ], - { - "title_aux": "ComfyUI-Mosaic-Mask" - } - ], - "https://github.com/olduvai-jp/ComfyUI-HfLoader": [ - [ - "ControlNet Loader From HF", - "Lora Loader From HF" - ], - { - "title_aux": "ComfyUI-HfLoader" - } - ], - "https://github.com/oleksandr612/ComfyUI-Counter": [ - [ - "Simple Counter" - ], - { - "title_aux": "ComfyUI-Counter" - } - ], - "https://github.com/oliverswitzer/ComfyUI-Lora-Visualizer": [ - [ - "LoRAVisualizer", - "PromptComposer", - "PromptSplitter" - ], - { - "title_aux": "LoRA Visualizer" - } - ], - "https://github.com/olivv-cs/ComfyUI-FunPack": [ - [ - "FunPackCLIPLoader", - "FunPackContinueVideo", - "FunPackImg2LatentInterpolation", - "FunPackPromptEnhancer", - "FunPackVideoStitch" - ], - { - "title_aux": "ComfyUI-FunPack" - } - ], - "https://github.com/omar92/ComfyUI-QualityOfLifeSuit_Omar92": [ - [ - "CLIPStringEncode _O", - "Chat completion _O", - "ChatGPT Simple _O", - "ChatGPT _O", - "ChatGPT compact _O", - "Chat_Completion _O", - "Chat_Message _O", - "Chat_Message_fromString _O", - "Concat Text _O", - "ConcatRandomNSP_O", - "Debug String _O", - "Debug Text _O", - "Debug Text route _O", - "Edit_image _O", - "Equation1param _O", - "Equation2params _O", - "GetImage_(Width&Height) _O", - "GetLatent_(Width&Height) _O", - "ImageScaleFactor _O", - "ImageScaleFactorSimple _O", - "LatentUpscaleFactor _O", - "LatentUpscaleFactorSimple _O", - "LatentUpscaleMultiply", - "Note _O", - "QOL Split String", - "RandomNSP _O", - "Replace Text _O", - "String _O", - "Text _O", - "Text2Image _O", - "Trim Text _O", - "VAEDecodeParallel _O", - "combine_chat_messages _O", - "compine_chat_messages _O", - "concat Strings _O", - "create image _O", - "create_image _O", - "debug Completeion _O", - "debug messages_O", - "float _O", - "floatToInt _O", - "floatToText _O", - "int _O", - "intToFloat _O", - "load_openAI _O", - "replace String _O", - "replace String advanced _O", - "saveTextToFile _O", - "seed _O", - "selectLatentFromBatch _O", - "string2Image _O", - "trim String _O", - "variation_image _O" - ], - { - "title_aux": "Quality of life Suit:V2" - } - ], - "https://github.com/openvino-dev-samples/comfyui_openvino": [ - [ - "OpenVINO_TorchCompileModel" - ], - { - "title_aux": "ComfyUI-OpenVINO" - } - ], - "https://github.com/opvelll/ComfyUI_TextListProduct": [ - [ - "ProductedString", - "PromptPairConcat", - "TextListProduct", - "TextListProductWithSingleA", - "TextListProductWithSingleB", - "TextListProductWithSingleBoth" - ], - { - "title_aux": "Comfy UI Text List Product" - } - ], - "https://github.com/orange90/ComfyUI-Regex-Runner": [ - [ - "RegexNode" - ], - { - "title_aux": " ComfyUI-Regex-Runner" - } - ], - "https://github.com/orex2121/comfyui-OreX": [ - [ - "IoNetVision", - "KontextPresetsOrex", - "OreX Image Save", - "flux-kontext-orexnodes", - "orex IoNet Chat", - "orex IoNet Vision", - "orex IoNet Vision Url", - "orex Kontext Presets", - "orex Load Image", - "orex Load Image Batch", - "orex Load Image Batch Size", - "orex Save Image" - ], - { - "title_aux": "comfyui-OreX" - } - ], - "https://github.com/orion4d/Calculator_Pro": [ - [ - "DataConverter", - "LengthConverter", - "ManualRateConverter", - "MassConverter", - "ScientificCalculatorTri", - "TimeConverter", - "UniversalConverter", - "VolumeConverter" - ], - { - "title_aux": "CalculatorPro - Node Suite for ComfyUI" - } - ], - "https://github.com/orion4d/ComfyUI-Image-Effects": [ - [ - "AsciiArtNode", - "AsciiTextNode", - "AuroraNode", - "BarrelDistortionNode", - "CSSFiltersNode", - "ChannelMixerNode", - "ColorBalanceNode", - "CrystallizeNode", - "CurvesNode", - "FilmGrainNode", - "FisheyeNode", - "GodRaysNode", - "HexagonalPixelateNode", - "HolographicNode", - "KaleidoscopeAdvancedNode", - "KaleidoscopeNode", - "LensFlareNode", - "LevelsNode", - "LightLeaksNode", - "NeonGlowNode", - "PinchNode", - "PolaroidNode", - "PolygonNode", - "RippleNode", - "SaverPlusNode", - "ShadowHighlightNode", - "SpherizeNode", - "TriangulateNode", - "VHSGlitchNode", - "VibranceNode", - "VintageTVNode", - "VoronoiNode" - ], - { - "title_aux": "ComfyUI-Image-Effects" - } - ], - "https://github.com/orion4d/ComfyUI_DAO_master": [ - [ - "ConvertIMGtoSVG", - "ConvertSVGtoIMG", - "DAO Blur", - "DAO Move", - "DAO RVB Color Picker", - "DAO Text Maker", - "DXF Add Circle", - "DXF Add Ellipse", - "DXF Add Line", - "DXF Add Polygon", - "DXF Add Rectangle", - "DXF Add Rounded Rectangle", - "DXF Add Star", - "DXF Add Triangle", - "DXF Import", - "DXF New", - "DXF Preview", - "DXF Save", - "DXF Stats", - "DXF Transform", - "DXF to SVG", - "Folder File Pro", - "Load Image Pro", - "Path To Image", - "SVG Boolean", - "SVG Load", - "SVG Passthrough", - "SVG Preview", - "SVG Save", - "SVG Style" - ], - { - "title_aux": "ComfyUI_DAO_master" - } - ], - "https://github.com/orion4d/ComfyUI_SharpnessPro": [ - [ - "Clarity", - "HighPassSharpen", - "SmartSharpen", - "Texture", - "UnsharpMaskSharpen" - ], - { - "title_aux": "SharpnessPro pour ComfyUI" - } - ], - "https://github.com/orion4d/ComfyUI_colormaster": [ - [ - "AnnotateHexLines", - "ColorPaletteExtractor", - "HexColorToImage", - "ImageCollageNode", - "SelectHexLine" - ], - { - "title_aux": "ComfyUI Colormaster Nodes" - } - ], - "https://github.com/orion4d/ComfyUI_extract_imag": [ - [ - "ExtractAndSaveImagesFromDocument" - ], - { - "title_aux": "ComfyUI_extract_imag" - } - ], - "https://github.com/orion4d/ComfyUI_image-display": [ - [ - "DisplayImageWithMask" - ], - { - "title_aux": "Display Image with Mask for ComfyUI" - } - ], - "https://github.com/orion4d/ComfyUI_pdf_nodes": [ - [ - "PDFExtractTextFromPages", - "PDFGetPageCount", - "PDFLoad", - "PDFMerge", - "PDFRotatePages", - "PDFSave", - "PDFSelectPageAndExtractText" - ], - { - "title_aux": "ComfyUI PDF Nodes" - } - ], - "https://github.com/orion4d/illusion_node": [ - [ - "AdvancedAutostereogramNode", - "AutostereogramNode", - "CheckerboardNode", - "ColorImageNode", - "PatternGeneratorNode", - "TessellationNode", - "TileImageRepeaterNode" - ], - { - "title_aux": "ComfyUI Illusion & Pattern Nodes" - } - ], - "https://github.com/orssorbit/ComfyUI-wanBlockswap": [ - [ - "wanBlockSwap" - ], - { - "title_aux": "ComfyUI-wanBlockswap" - } - ], - "https://github.com/oshtz/ComfyUI-oshtz-nodes": [ - [ - "EasyAspectRatioNode", - "GPTImage1", - "LLMAIONode", - "LoRASwitcherNode", - "LoRASwitcherNode20", - "LoRASwitcherNode40", - "LoraSwitcherDynamic", - "StringSplitterNode" - ], - { - "title_aux": "oshtz Nodes" - } - ], - "https://github.com/osi1880vr/prompt_quill_comfyui": [ - [ - "PromptQuillGenerate", - "PromptQuillGenerateConditioning", - "PromptQuillSail", - "PromptQuillSailConditioning" - ], - { - "title_aux": "ComfyUI_Prompt-Quill" - } - ], - "https://github.com/ostris/ComfyUI-FlexTools": [ - [ - "Flex2Conditioner", - "FlexGuidance", - "FlexLoraLoader", - "FlexLoraLoaderModelOnly" - ], - { - "nodename_pattern": "- Ostris$", - "title_aux": "Flex.1 tools" - } - ], - "https://github.com/ostris/ostris_nodes_comfyui": [ - [ - "Batch Image Loader - Ostris", - "LLM Pipe Loader - Ostris", - "LLM Prompt Upsampling - Ostris", - "One Seed - Ostris", - "Save Image Direct - Ostris", - "Text Box - Ostris" - ], - { - "nodename_pattern": "- Ostris$", - "title_aux": "Ostris Nodes ComfyUI" - } - ], - "https://github.com/ownimage/ComfyUI-ownimage": [ - [ - "Caching Image Loader" - ], - { - "title_aux": "ComfyUI-ownimage" - } - ], - "https://github.com/oxysoft/ComfyUI-gowiththeflow": [ - [ - "KSamplerNoiseless", - "NoiseWarperNode" - ], - { - "title_aux": "ComfyUI-gowiththeflow" - } - ], - "https://github.com/oyvindg/ComfyUI-TrollSuite": [ - [ - "BinaryImageMask", - "ImagePadding", - "LoadLastImage", - "RandomMask", - "TransparentImage" - ], - { - "title_aux": "ComfyUI-TrollSuite" - } - ], - "https://github.com/oztrkoguz/ComfyUI_StoryCreator": [ - [ - "Kosmos2SamplerSimple2", - "KosmosLoader2", - "StoryLoader", - "StorySamplerSimple", - "Write2" - ], - { - "title_aux": "ComfyUI StoryCreater" - } - ], - "https://github.com/p1atdev/comfyui-timm-backbone": [ - [ - "TimmBackboneImageEncode", - "TimmBackboneImageNormalize", - "TimmBackboneLoader", - "TimmBackboneRGB2BGR", - "TimmEmbedsPrint" - ], - { - "title_aux": "comfyui-timm-backbone" - } - ], - "https://github.com/p1atdev/comfyui-tkg-chroma-key": [ - [ - "ApplyTKGChromaKeyAdvanced", - "ApplyTKGChromaKeySDXL" - ], - { - "title_aux": "TKG-DM (Training-free Chroma Key Content Generation Diffusion Model) for ComfyUI" - } - ], - "https://github.com/palant/image-resize-comfyui": [ - [ - "ImageResize" - ], - { - "title_aux": "Image Resize for ComfyUI" - } - ], - "https://github.com/pamparamm/ComfyUI-ppm": [ - [ - "AttentionCouplePPM", - "CFGLimiterGuider", - "CFGPPSamplerSelect", - "CLIPMicroConditioning", - "CLIPNegPip", - "CLIPTextEncodeBREAK", - "CLIPTextEncodeInvertWeights", - "CLIPTokenCounter", - "ConditioningZeroOutCombine", - "ConvertTimestepToSigma", - "DynSamplerSelect", - "DynamicThresholdingPost", - "DynamicThresholdingSimplePost", - "EmptyLatentImageAR", - "FreeU2PPM", - "Guidance Limiter", - "LatentOperationTonemapLuminance", - "LatentToMaskBB", - "LatentToWidthHeight", - "MaskCompositePPM", - "PPMSamplerSelect", - "RenormCFGPost", - "RescaleCFGPost", - "SamplerGradientEstimation" - ], - { - "title_aux": "ComfyUI-ppm" - } - ], - "https://github.com/pamparamm/ComfyUI-vectorscope-cc": [ - [ - "DiffusionCG", - "NormalizeLatent", - "VectorscopeCC" - ], - { - "title_aux": "ComfyUI Vectorscope CC" - } - ], - "https://github.com/pamparamm/sd-perturbed-attention": [ - [ - "NormalizedAttentionGuidance", - "PerturbedAttention", - "Pladis", - "SlidingWindowGuidanceAdvanced", - "SmoothedEnergyGuidanceAdvanced", - "TRTAttachPag", - "TRTPerturbedAttention", - "TokenPerturbationGuidance" - ], - { - "title_aux": "sd-perturbed-attention" - } - ], - "https://github.com/pants007/comfy-pants": [ - [ - "CLIPTextEncodeAIO", - "Image Make Square" - ], - { - "title_aux": "pants" - } - ], - "https://github.com/papcorns/ComfyUI-Papcorns-Node-LoadImageFromUrl": [ - [ - "LoadImageFromUrlOrPath" - ], - { - "title_aux": "ComfyUI Load Image From URL" - } - ], - "https://github.com/papcorns/Papcorns-Comfyui-Custom-Nodes": [ - [ - "PapcornsAspectResize", - "PapcornsAudioTrimAndSave", - "PapcornsAudioTrimmer", - "PapcornsFpsCalculator", - "PapcornsMemoryManager", - "PapcornsSimpleMemoryManager", - "UploadImageToGCS" - ], - { - "title_aux": "Papcorns ComfyUI Custom Nodes" - } - ], - "https://github.com/pathway8-sudo/ComfyUI-Pathway-CutPNG-Node": [ - [ - "CutPNGNode" - ], - { - "title_aux": "ComfyUI-Pathway-CutPNG-Node" - } - ], - "https://github.com/patriciogonzalezvivo/comfyui_glslnodes": [ - [ - "float", - "glslBuffers", - "glslEditor", - "glslEditorPro", - "glslUniforms", - "glslViewer", - "int", - "vec2", - "vec2 (pos)", - "vec3", - "vec3 (pos)", - "vec4", - "vec4 (color)" - ], - { - "author": "Patricio Gonzalez Vivo", - "description": "A set of nodes to work with GLSL shaders", - "nickname": "GLSL Nodes", - "title": "GLSL Nodes", - "title_aux": "GLSL Nodes" - } - ], - "https://github.com/paulh4x/ComfyUI_PHRenderFormerWrapper": [ - [ - "RenderFormerCamera", - "RenderFormerCameraTarget", - "RenderFormerExampleScene", - "RenderFormerFromJSON", - "RenderFormerGenerator", - "RenderFormerLighting", - "RenderFormerLightingCombine", - "RenderFormerLightingTarget", - "RenderFormerLoadMesh", - "RenderFormerMeshCombine", - "RenderFormerMeshTarget", - "RenderFormerModelLoader", - "RenderFormerRandomizeColors", - "RenderFormerRemeshMesh", - "RenderFormerSceneBuilder" - ], - { - "title_aux": "ComfyUI_PHRenderFormerWrapper" - } - ], - "https://github.com/paulo-coronado/comfy_clip_blip_node": [ - [ - "CLIPTextEncodeBLIP", - "CLIPTextEncodeBLIP-2", - "Example" - ], - { - "title_aux": "comfy_clip_blip_node" - } - ], - "https://github.com/pawelmal0101/ComfyUI-Webhook": [ - [ - "Webhook" - ], - { - "title_aux": "ComfyUI Webhook Notifier" - } - ], - "https://github.com/pbpbpb2705/ComfyUI-LyraVSIH": [ - [ - "MultiObjectMask" - ], - { - "title_aux": "ComfyUI-LyraVSIH" - } - ], - "https://github.com/penposs/ComfyUI_Gemini_Pro": [ - [ - "Gemini File Processing", - "Gemini File Upload", - "Gemini Pro", - "Gemini-Pro-Chat", - "Gemini-Pro-Editimage" - ], - { - "title_aux": "ComfyUI Gemini Pro Node" - } - ], - "https://github.com/penposs/Comfyui_wan_api": [ - [ - "WanAPIImageToVideo", - "WanAPIImageUploader", - "WanAPI_Image2Video", - "WanAPI_ImageUploader" - ], - { - "title_aux": "Comfyui_wan_api" - } - ], - "https://github.com/pharmapsychotic/comfy-cliption": [ - [ - "CLIPtionBeamSearch", - "CLIPtionGenerate", - "CLIPtionLoader" - ], - { - "title_aux": "comfy-cliption" - } - ], - "https://github.com/phazei/ComfyUI-OrpheusTTS-LMStudio": [ - [ - "OrpheusLMStudioTTS" - ], - { - "title_aux": "ComfyUI-OrpheusTTS-LMStudio" - } - ], - "https://github.com/phazei/ComfyUI-Prompt-Stash": [ - [ - "PromptStashManager", - "PromptStashPassthrough", - "PromptStashSaver" - ], - { - "title_aux": "Prompt Stash" - } - ], - "https://github.com/philiprodriguez/ComfyUI-HunyuanImageLatentToVideoLatent": [ - [ - "HunyuanImageLatentToVideoLatent" - ], - { - "title_aux": "ComfyUI-HunyuanImageLatentToVideoLatent" - } - ], - "https://github.com/philipy1219/ComfyUI-CloudStorage": [ - [ - "LoadImageFromCloud", - "LoadMaskFromCloud", - "LoadVideoFromCloud", - "SaveImageToCloud", - "UploadFileToCloud" - ], - { - "title_aux": "ComfyUI-CloudStorage" - } - ], - "https://github.com/philipy1219/ComfyUI-TaylorSeer": [ - [ - "FluxBlockSwap", - "HidreamBlockSwap", - "TaylorSeer" - ], - { - "title_aux": "ComfyUI-TaylorSeer" - } - ], - "https://github.com/philz1337x/ComfyUI-ClarityAI": [ - [ - "Clarity AI Upscaler" - ], - { - "title_aux": "\u2728 Clarity AI - Creative Image Upscaler and Enhancer for ComfyUI" - } - ], - "https://github.com/phuvinh010701/ComfyUI-Nudenet": [ - [ - "ApplyNudenet", - "FilterdLabel", - "NudenetModelLoader" - ], - { - "title_aux": "ComfyUI-Nudenet" - } - ], - "https://github.com/phyblas/paint-by-example_comfyui": [ - [ - "PaintbyExampleAdvanced", - "PaintbyExampleGen", - "PaintbyExampleSimple", - "PaintbyIchimatsu", - "PaintbySingleColor" - ], - { - "title_aux": "paint-by-example @ ComfyUI" - } - ], - "https://github.com/pictorialink/ComfyUI-Custom-Node-Config": [ - [ - "FormSubmitNode" - ], - { - "title_aux": "ComfyUI-Custom-Node-Config" - } - ], - "https://github.com/pictorialink/ComfyUI-Qwen3-llama.cpp": [ - [ - "Qwen25_VL", - "Qwen3" - ], - { - "title_aux": "ComfyUI-Qwen3-llama.cpp" - } - ], - "https://github.com/pictorialink/ComfyUI-Text-Translation": [ - [ - "Get_Translator", - "Text", - "Text_Concatenate", - "Text_Switch", - "Text_Translation", - "Text_Translation_V2", - "Text_Translation_V2_Full" - ], - { - "title_aux": "ComfyUI-Text-Translation" - } - ], - "https://github.com/picturesonpictures/comfy_PoP": [ - [ - "AdaptiveCannyDetector_PoP", - "AnyAspectRatio", - "ConditioningMultiplier_PoP", - "ConditioningNormalizer_PoP", - "DallE3_PoP", - "EfficientAttention", - "LoadImageResizer_PoP", - "LoraStackLoader10_PoP", - "LoraStackLoader_PoP", - "VAEDecoderPoP", - "VAEEncoderPoP" - ], - { - "title_aux": "comfy_PoP" - } - ], - "https://github.com/pikenrover/ComfyUI_PRNodes": [ - [ - "CheckpointLoaderSimpleExtended", - "EmptyLatentImageScaleBy", - "ImageScaleTo", - "LoadRandomImage", - "LoraLoaderExtended", - "RandomPrompt", - "RandomPromptMixed", - "Save Image w/Metadata" - ], - { - "title_aux": "ComfyUI_PRNodes" - } - ], - "https://github.com/pixelworldai/ComfyUI-AlphaFlatten": [ - [ - "FlattenByAlpha" - ], - { - "title_aux": "ComfyUI-AlphaFlatten" - } - ], - "https://github.com/pkpkTech/ComfyUI-SaveAVIF": [ - [ - "SaveAvif" - ], - { - "title_aux": "ComfyUI-SaveAVIF" - } - ], - "https://github.com/pkpkTech/ComfyUI-TemporaryLoader": [ - [ - "LoadTempCheckpoint", - "LoadTempLoRA", - "LoadTempMultiLoRA" - ], - { - "title_aux": "ComfyUI-TemporaryLoader" - } - ], - "https://github.com/playbook3d/playbook3d-comfyui-nodes": [ - [ - "Beauty Pass Sequence", - "Depth Pass Sequence", - "Mask Pass Sequence", - "Outline Pass Sequence", - "Playbook Aspect Ratio Select", - "Playbook Beauty", - "Playbook Beauty Sequence", - "Playbook Boolean", - "Playbook Depth", - "Playbook Depth Sequence", - "Playbook Float", - "Playbook Image", - "Playbook LoRA Select", - "Playbook LoRA Selection", - "Playbook Mask", - "Playbook Mask Sequence", - "Playbook Number", - "Playbook Outline", - "Playbook Outline Sequence", - "Playbook Render Result", - "Playbook Seed", - "Playbook Text", - "Playbook Video" - ], - { - "title_aux": "Playbook Nodes" - } - ], - "https://github.com/plugcrypt/CRT-Nodes": [ - [ - "AdvancedBloomFX", - "AdvancedStringReplace", - "ArcaneBloomFX", - "AudioCompressor", - "AudioLoaderCrawl", - "AudioOrManualFrameCount", - "AudioPreviewer", - "AutopromptProcessor", - "Boolean Transform", - "BooleanInvert", - "CLIPTextEncodeFluxMerged", - "CRT Post-Process Suite", - "CRTChromaKeyOverlay", - "CRTFirstLastFrameSelector", - "CRTLoadLastMedia", - "CRTLoadLastVideo", - "CRTPctCropCalculator", - "CRTPostProcess", - "CRT_AddSettingsAndPrompt", - "CRT_DynamicPromptScheduler", - "CRT_FileBatchPromptScheduler", - "CRT_UpscaleModelAdv", - "CRT_WAN_BatchSampler", - "ClarityFX", - "ClearStyleModelDualCache", - "ColorIsolationFX", - "ColourfulnessFX", - "ContourFX", - "EnableLatent", - "FaceEnhancementPipeline", - "FaceEnhancementPipelineWithInjection", - "FancyNoteNode", - "FancyTimerNode", - "FileLoaderCrawl", - "FileLoaderCrawlBatch", - "FilmGrainFX", - "FluxAIO_CRT", - "FluxControlnetSampler", - "FluxControlnetSamplerWithInjection", - "FluxLoraBlocksPatcher", - "FluxSemanticEncoder", - "FluxTiledSamplerCustomAdvanced", - "ImageLoaderCrawl", - "LatentNoiseInjectionSampler", - "LensDistortFX", - "LensFX", - "LoadImageResize", - "LoadLastLatent", - "LoadVideoForVCaptioning", - "Lora Loader Str", - "MaskEmptyFloatNode", - "MaskPassOrPlaceholder", - "ParametricEQNode", - "PonyFaceEnhancementPipelineWithInjection", - "PonyUpscaleSamplerWithInjection", - "Remove Trailing Comma", - "Resolution", - "SamplerSchedulerSelector", - "SaveAudioWithPath", - "SaveImageWithPath", - "SaveLatentWithPath", - "SaveTextWithPath", - "SaveVideoWithPath", - "SeamlessLoopBlender", - "SimpleFluxShiftNode", - "SimpleKnobNode", - "SimpleToggleNode", - "SmartControlNetApply", - "SmartDeNoiseFX", - "SmartPreprocessor", - "SmartStyleModelApplyDual", - "Strength To Steps", - "Technicolor2FX", - "Toggle Lora Unet Blocks L1", - "Toggle Lora Unet Blocks L2", - "Video Duration Calculator", - "VideoLoaderCrawl", - "WAN2.2 LoRA Compare Sampler" - ], - { - "author": "chflame", - "description": "A set of nodes for ComfyUI that can composite layer and mask to achieve Photoshop like functionality.", - "nickname": "LayerStyle", - "title": "LayerStyle", - "title_aux": "CRT-Nodes" - } - ], - "https://github.com/pmarmotte2/ComfyUI-Speaker-Isolation": [ - [ - "SpeakerDiarizer" - ], - { - "title_aux": "ComfyUI-Speaker-Isolation" - } - ], - "https://github.com/pnikolic-amd/ComfyUI_MIGraphX": [ - [ - "CompileDiffusersMIGraphX" - ], - { - "title_aux": "MIGraphX Node for ComfyUI" - } - ], - "https://github.com/pollockjj/ComfyUI-MultiGPU": [ - [ - "DeviceSelectorMultiGPU", - "HunyuanVideoEmbeddingsAdapter" - ], - { - "title_aux": "ComfyUI-MultiGPU" - } - ], - "https://github.com/popoimm/comfyui-popo-utility": [ - [ - "PopoImageAspectRatioNode", - "PopoImageDimensionsNode", - "PopoImageSizeNode", - "PopoMathExpressionNode" - ], - { - "title_aux": "ComfyUI Popo Utility" - } - ], - "https://github.com/portu-sim/comfyui_bmab": [ - [ - "BMAB Alpha Composit", - "BMAB Base64 Image", - "BMAB Basic", - "BMAB Black And White", - "BMAB Blend", - "BMAB Clip Text Encoder SDXL", - "BMAB Conditioning To Bind", - "BMAB Context", - "BMAB ControlNet", - "BMAB ControlNet IPAdapter", - "BMAB ControlNet Openpose", - "BMAB Crop", - "BMAB Detail Anything", - "BMAB Detect And Mask", - "BMAB Detect And Paste", - "BMAB Detection Crop", - "BMAB Detector", - "BMAB Dummy", - "BMAB Edge", - "BMAB Extractor", - "BMAB Face Detailer", - "BMAB Flux ControlNet", - "BMAB Flux Integrator", - "BMAB Google Gemini Prompt", - "BMAB Image Storage", - "BMAB Import Integrator", - "BMAB Inpaint", - "BMAB Integrator", - "BMAB KSampler", - "BMAB KSamplerHiresFix", - "BMAB KSamplerHiresFixWithUpscaler", - "BMAB KSamplerKohyaDeepShrink", - "BMAB Lama Inpaint", - "BMAB LoRA Loader", - "BMAB Load Image", - "BMAB Load Output Image", - "BMAB Masks To Images", - "BMAB Model To Bind", - "BMAB Noise Generator", - "BMAB Normalize Size", - "BMAB Openpose Hand Detailer", - "BMAB Outpaint By Ratio", - "BMAB Person Detailer", - "BMAB Preview Text", - "BMAB Prompt", - "BMAB Reframe", - "BMAB Remote Access And Save", - "BMAB Remove Background", - "BMAB Resize By Person", - "BMAB Resize By Ratio", - "BMAB Resize and Fill", - "BMAB SD-WebUI API BMAB Extension", - "BMAB SD-WebUI API ControlNet", - "BMAB SD-WebUI API I2I", - "BMAB SD-WebUI API Server", - "BMAB SD-WebUI API T2I", - "BMAB SD-WebUI API T2I Hires.Fix", - "BMAB Save Image", - "BMAB SeedGenerator", - "BMAB Segment Anything", - "BMAB Simple Hand Detailer", - "BMAB Square", - "BMAB Subframe Hand Detailer", - "BMAB Text", - "BMAB ToBind", - "BMAB Upscale With Model", - "BMAB Upscaler", - "BMAB Watermark", - "BMAB Zoom Out" - ], - { - "title_aux": "comfyui_bmab" - } - ], - "https://github.com/prodogape/ComfyUI-EasyOCR": [ - [ - "Apply EasyOCR" - ], - { - "title_aux": "ComfyUI-EasyOCR" - } - ], - "https://github.com/prodogape/ComfyUI-Minio": [ - [ - "Load Image From Minio", - "Save Image To Minio", - "Set Minio Config" - ], - { - "title_aux": "Comfyui-Minio" - } - ], - "https://github.com/prodogape/ComfyUI-OmDet": [ - [ - "Apply OmDet" - ], - { - "title_aux": "ComfyUI-OmDet" - } - ], - "https://github.com/prodogape/Comfyui-Yolov8-JSON": [ - [ - "Apply Yolov8 Model", - "Apply Yolov8 Model Seg", - "Draw Labelme Json", - "Load Yolov8 Model", - "Load Yolov8 Model From Path", - "Save Labelme Json" - ], - { - "title_aux": "Comfyui-Yolov8-JSON" - } - ], - "https://github.com/pschroedl/ComfyUI-SAM2-Realtime": [ - [ - "DownloadAndLoadSAM2RealtimeModel", - "Sam2RealtimeSegmentation" - ], - { - "title_aux": "ComfyUI-SAM2-Realtime" - } - ], - "https://github.com/ptmaster/ComfyUI-Load-Diffusion-Model-to-Muti-GPUs/raw/refs/heads/main/Load%20Diffusion%20Model%20into%20Muti%20GPUs.py": [ - [ - "OverrideLoadedDiffusionDevice" - ], - { - "title_aux": "ComfyUI-Load-Diffusion-Model-to-Muti-GPUs" - } - ], - "https://github.com/ptmaster/Comfyui-PT-Keyframe-Camera": [ - [ - "PT_KeyframeCamera" - ], - { - "title_aux": "Comfyui-PT-Keyframe-Camera" - } - ], - "https://github.com/ptmaster/comfyui-audio-speed": [ - [ - "PT48KHZ", - "PTAudioSpeed", - "PTEnsureStereo" - ], - { - "title_aux": "ComfyUI-audio-speed" - } - ], - "https://github.com/pupba/Comfy_ForEach": [ - [ - "EventBridgeTriggerNode", - "FolderImageLoaderNode", - "IndexedImageSelectorNode", - "IndexedNameSelectorNode", - "IsLastIndexNode", - "LoadPreCheckpointModel", - "LoadPreControlNetModel", - "SaveExactNameImageNode", - "StringViewer", - "TaskIDStorageNode" - ], - { - "title_aux": "ComfyForEach" - } - ], - "https://github.com/purewater2011/comfyui_color_detection": [ - [ - "IsYellowish", - "YellowHeatmap" - ], - { - "title_aux": "comfyui_color_detection" - } - ], - "https://github.com/purpen/ComfyUI-AIRedoon": [ - [ - "AIRedoonApplyLoRAStack", - "AIRedoonCheckLoraFile", - "AIRedoonCheckModelFile", - "AIRedoonConcatText", - "AIRedoonImageCaptioning", - "AIRedoonImageRGBA2RGB", - "AIRedoonLoRAStack", - "AIRedoonPreviewText", - "AIRedoonQwenModelLoader", - "AIRedoonSaveText", - "AIRedoonTranslator" - ], - { - "title_aux": "AIRedoon" - } - ], - "https://github.com/purpen/ComfyUI-ImageTagger": [ - [ - "AIRedoonImageCaptioning" - ], - { - "title_aux": "ComfyUI-ImageTagger" - } - ], - "https://github.com/pvlprk/comfyui-pvl-api-nodes": [ - [ - "PVL Call OpenAI Assistant", - "PVL ComfyDeploy API Caller", - "PVL KONTEXT MAX", - "PVLCheckIfConnected", - "PVL_ImageResize", - "PVL_ImageStitch", - "PVL_NoneOutputNode", - "PVL_SaveOrNot", - "PVL_Switch_Huge", - "PVL_fal_FluxDev_API", - "PVL_fal_FluxGeneral_API", - "PVL_fal_FluxPro_Fill_API", - "PVL_fal_FluxPro_v1_1_Ultra_API", - "PVL_fal_FluxWithLora_API", - "PVL_fal_KontextDevInpaint_API", - "PVL_fal_KontextDevLora_API", - "PVL_fal_KontextMaxMulti_API", - "PVL_fal_KontextMaxSingle_API", - "PVL_fal_KontextPro_API", - "PVL_fal_Kontext_Dev_API", - "PVL_fal_LumaPhoton_FlashReframe_API", - "PVL_fal_LumaPhoton_Reframe_API", - "PvlKontextMax" - ], - { - "title_aux": "ComfyUI Assistant Node" - } - ], - "https://github.com/pxl-pshr/GlitchNodes": [ - [ - "ASCII", - "Corruptor", - "DataBend", - "DitherMe", - "FrequencyModulation", - "GlitchIT", - "LineScreen", - "LuminousFlow", - "OrderedDithering", - "Pixel8Bit", - "PixelFloat", - "PixelRedistribution", - "Rekked", - "Scanz", - "TvGlitch", - "VHSonAcid", - "VaporWave", - "VideoModulation", - "interference" - ], - { - "title_aux": "GlitchNodes" - } - ], - "https://github.com/pythongosssss/ComfyUI-Custom-Scripts": [ - [ - "CheckpointLoader|pysssss", - "ConstrainImageforVideo|pysssss", - "ConstrainImage|pysssss", - "LoadText|pysssss", - "LoraLoader|pysssss", - "MathExpression|pysssss", - "MultiPrimitive|pysssss", - "PlaySound|pysssss", - "Repeater|pysssss", - "ReroutePrimitive|pysssss", - "SaveText|pysssss", - "ShowText|pysssss", - "StringFunction|pysssss", - "SystemNotification|pysssss" - ], - { - "title_aux": "ComfyUI-Custom-Scripts" - } - ], - "https://github.com/pythongosssss/ComfyUI-WD14-Tagger": [ - [ - "WD14Tagger|pysssss" - ], - { - "title_aux": "ComfyUI WD 1.4 Tagger" - } - ], - "https://github.com/pzc163/Comfyui_MiniCPMv2_6-prompt-generator": [ - [ - "Prompt_Generator", - "Save_Prompts" - ], - { - "title_aux": "Comfyui_MiniCPMv2_6-prompt-generator" - } - ], - "https://github.com/quank123wip/ComfyUI-Step1X-Edit": [ - [ - "Step-1XEditNode" - ], - { - "title_aux": "ComfyUI-Step1X-Edit" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesCompositionGuides": [ - [ - "EsesCompositionGuides" - ], - { - "title_aux": "ComfyUI-EsesCompositionGuides" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageAdjustments": [ - [ - "EsesImageAdjustments2" - ], - { - "title_aux": "ComfyUI-EsesImageAdjustments" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageCompare": [ - [ - "EsesImageCompare" - ], - { - "title_aux": "ComfyUI-EsesImageCompare" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageEffectBloom": [ - [ - "EsesImageEffectBloom" - ], - { - "title_aux": "ComfyUI-EsesImageEffectBloom" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageEffectCurves": [ - [ - "EsesImageEffectCurves" - ], - { - "title_aux": "ComfyUI-EsesImageEffectCurves" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageEffectLevels": [ - [ - "EsesImageEffectLevels" - ], - { - "title_aux": "ComfyUI-EsesImageEffectLevels" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageLensEffects": [ - [ - "EsesImageLensEffects" - ], - { - "title_aux": "ComfyUI-EsesImageLensEffects" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageOffset": [ - [ - "EsesImageOffset" - ], - { - "title_aux": "ComfyUI-EsesImageOffset" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageResize": [ - [ - "EsesImageResize" - ], - { - "title_aux": "EsesImageResize" - } - ], - "https://github.com/quasiblob/ComfyUI-EsesImageTransform": [ - [ - "EsesImageTransform" - ], - { - "title_aux": "ComfyUI-EsesImageTransform" - } - ], - "https://github.com/quasiblob/EsesCompositionGoldenRatio": [ - [ - "EsesCompositionGoldenRatio" - ], - { - "title_aux": "EsesCompositionGoldenRatio" - } - ], - "https://github.com/qwixiwp/queuetools": [ - [ - "load images (queue tools)" - ], - { - "title_aux": "queuetools" - } - ], - "https://github.com/r3dial/redial-discomphy": [ - [ - "DiscordMessage" - ], - { - "title_aux": "Redial Discomphy - Discord Integration for ComfyUI" - } - ], - "https://github.com/r3dsd/comfyui-template-loader": [ - [ - "TemplateLoader" - ], - { - "title_aux": "Comfyui-Template-Loader" - } - ], - "https://github.com/railep/ComfyUI-HunyuanVideo-Foley": [ - [ - "HunyuanFoleyNode" - ], - { - "title_aux": "HunyuanVideo-Foley Audio Generator" - } - ], - "https://github.com/raindrop313/ComfyUI-WanVideoStartEndFrames": [ - [ - "WanVideoSEDecode", - "WanVideoSEImageClipEncode", - "WanVideoSEModelLoader", - "WanVideoSESampler", - "WanVideoSEVAELoader" - ], - { - "title_aux": "ComfyUI-WanVideoStartEndFrames" - } - ], - "https://github.com/raindrop313/ComfyUI_SD3_Flowedit": [ - [ - "FlowEditCFGGuider", - "FlowEditSampler", - "OutSD3ModelSamplingPred" - ], - { - "title_aux": "ComfyUI_SD3_Flowedit" - } - ], - "https://github.com/rainlizard/ComfyUI-Raffle": [ - [ - "PreviewHistory", - "Raffle", - "TagCategoryStrength" - ], - { - "title_aux": "Raffle" - } - ], - "https://github.com/rainlizard/ComfyUI-WhirlpoolUpscaler": [ - [ - "WhirlpoolUpscaler" - ], - { - "title_aux": "Whirlpool Upscaler" - } - ], - "https://github.com/ramesh-x90/ComfyUI_pyannote": [ - [ - "Speaker Diarization", - "Whisper Segments to Speaker" - ], - { - "title_aux": "ComfyUI_pyannote" - } - ], - "https://github.com/ramyma/A8R8_ComfyUI_nodes": [ - [ - "AttentionCouple", - "AttentionCoupleRegion", - "AttentionCoupleRegions", - "Base64ImageInput", - "Base64ImageOutput" - ], - { - "title_aux": "A8R8 ComfyUI Nodes" - } - ], - "https://github.com/randjtw/advance-aesthetic-score": [ - [ - "Adv_Scoring" - ], - { - "title_aux": "advance-aesthetic-score" - } - ], - "https://github.com/randomnoner11/ComfyUI-MistralAI-API": [ - [ - "InvokeMistralEndpoint", - "LoadFewShotPrompt" - ], - { - "title_aux": "ComfyUI-MistralAI-API" - } - ], - "https://github.com/ranska/pixel_palette_art": [ - [ - "ColorFormatterNode", - "ColorPreviewNode", - "CreateColorFromRGBNode", - "GimpPaletteLoader", - "PaletteFormatter", - "PixelPaletteExtractor" - ], - { - "title_aux": "Pixel Palette Art" - } - ], - "https://github.com/ratulrafsan/Comfyui-SAL-VTON": [ - [ - "SALVTON_Apply", - "SV_random" - ], - { - "title_aux": "Comfyui-SAL-VTON" - } - ], - "https://github.com/raykindle/ComfyUI_Step1X-Edit": [ - [ - "Step1XEditGenerate", - "Step1XEditModelLoader", - "Step1XEditTeaCacheGenerate", - "Step1XEditTeaCacheModelLoader" - ], - { - "title_aux": "ComfyUI_Step1X-Edit" - } - ], - "https://github.com/raysers/Mflux-ComfyUI": [ - [ - "MfluxControlNetLoader", - "MfluxCustomModels", - "MfluxImg2Img", - "MfluxLorasLoader", - "MfluxModelsDownloader", - "MfluxModelsLoader", - "QuickMfluxNode" - ], - { - "title_aux": "Mflux-ComfyUI" - } - ], - "https://github.com/rcfcu2000/zhihuige-nodes-comfyui": [ - [ - "Combine ZHGMasks", - "Cover ZHGMasks", - "From ZHG pip", - "GroundingDinoModelLoader (zhihuige)", - "GroundingDinoPIPESegment (zhihuige)", - "GroundingDinoSAMSegment (zhihuige)", - "InvertMask (zhihuige)", - "SAMModelLoader (zhihuige)", - "To ZHG pip", - "ZHG FaceIndex", - "ZHG GetMaskArea", - "ZHG Image Levels", - "ZHG SaveImage", - "ZHG SmoothEdge", - "ZHG UltimateSDUpscale" - ], - { - "title_aux": "zhihuige-nodes-comfyui" - } - ], - "https://github.com/rcsaquino/comfyui-custom-nodes": [ - [ - "BackgroundRemover | rcsaquino", - "VAELoader | rcsaquino", - "VAEProcessor | rcsaquino" - ], - { - "title_aux": "rcsaquino/comfyui-custom-nodes" - } - ], - "https://github.com/rdancer/ComfyUI_Florence2SAM2": [ - [ - "RdancerFlorence2SAM2GenerateMask" - ], - { - "title_aux": "ComfyUI_Florence2SAM2" - } - ], - "https://github.com/rdomunky/comfyui-subfolderimageloader": [ - [ - "SubfolderImageLoader" - ], - { - "title_aux": "comfyui-subfolderimageloader" - } - ], - "https://github.com/reallusion/ComfyUI-Reallusion": [ - [ - "additional_image", - "control_net", - "core", - "upscale_data" - ], - { - "title_aux": "Reallusion ComfyUI Custom Nodes" - } - ], - "https://github.com/receyuki/comfyui-prompt-reader-node": [ - [ - "SDAnyConverter", - "SDBatchLoader", - "SDLoraLoader", - "SDLoraSelector", - "SDParameterExtractor", - "SDParameterGenerator", - "SDPromptMerger", - "SDPromptReader", - "SDPromptSaver", - "SDTypeConverter" - ], - { - "author": "receyuki", - "description": "The ultimate solution for managing image metadata and multi-tool compatibility. ComfyUI node version of the SD Prompt Reader", - "nickname": "SD Prompt Reader", - "title": "SD Prompt Reader", - "title_aux": "SD Prompt Reader" - } - ], - "https://github.com/recraft-ai/ComfyUI-RecraftAI": [ - [ - "RecraftBackgroundRemover", - "RecraftBackgroundReplacer", - "RecraftClarityUpscaler", - "RecraftClient", - "RecraftGenerativeUpscaler", - "RecraftImageGenerator", - "RecraftImageToImageTransformer", - "RecraftInpainter" - ], - { - "title_aux": "ComfyUI-RecraftAI" - } - ], - "https://github.com/redhottensors/ComfyUI-Prediction": [ - [ - "AvoidErasePrediction", - "CFGPrediction", - "CharacteristicGuidancePrediction", - "CombinePredictions", - "ConditionedPrediction", - "EarlyMiddleLatePrediction", - "InterpolatePredictions", - "LogSigmas", - "PerpNegPrediction", - "SamplerCustomPrediction", - "ScalePrediction", - "ScaledGuidancePrediction", - "SelectSigmas", - "SplitAtSigma", - "SwitchPredictions" - ], - { - "author": "RedHotTensors", - "description": "Fully customizable Classifer Free Guidance for ComfyUI", - "nickname": "ComfyUI-Prediction", - "title": "ComfyUI-Prediction", - "title_aux": "ComfyUI-Prediction" - } - ], - "https://github.com/regiellis/ComfyUI-EasyColorCorrector": [ - [ - "BatchColorCorrection", - "ColorCorrectionViewer", - "ColorPaletteExtractor", - "EasyColorCorrection", - "FilmEmulation", - "RawImageProcessor", - "VAEColorCorrector" - ], - { - "title_aux": "Easy Color Correction" - } - ], - "https://github.com/regiellis/ComfyUI-EasyNoobai": [ - [ - "EasyNoobai", - "EasyNoobaiMasterModel", - "NoobaiArtists", - "NoobaiCharacters", - "NoobaiClothing", - "NoobaiE621Artists", - "NoobaiE621Characters", - "NoobaiHairstyles", - "NoobaiPony", - "NoobaiPoses" - ], - { - "title_aux": "ComfyUI-EasyNoobai" - } - ], - "https://github.com/regiellis/ComfyUI-EasyPony": [ - [ - "EasyPony" - ], - { - "title_aux": "ComfyUI-EasyPony" - } - ], - "https://github.com/revirevy/Comfyui_saveimage_imgbb": [ - [ - "ImgBBUploader", - "LLM_prompt_generator" - ], - { - "author": "N.RHEVI", - "description": "This custom node allow save image to imgbb.", - "nickname": "save image to imgbb", - "title": "save image to imgbb", - "title_aux": "Comfyui_saveimage_imgbb" - } - ], - "https://github.com/rgthree/rgthree-comfy": [ - [], - { - "author": "rgthree", - "description": "A bunch of nodes I created that I also find useful.", - "nickname": "rgthree", - "nodename_pattern": " \\(rgthree\\)$", - "title": "Comfy Nodes", - "title_aux": "rgthree's ComfyUI Nodes" - } - ], - "https://github.com/rhdunn/comfyui-audio-processing": [ - [ - "ComfyAudio.ApplyFilterBank", - "ComfyAudio.GriffinLim", - "ComfyAudio.InverseSpectrogram", - "ComfyAudio.LinearFilterBank", - "ComfyAudio.LoadAudio", - "ComfyAudio.MelScaleFilterBank", - "ComfyAudio.PlotFilterBank", - "ComfyAudio.PlotSpectrogram", - "ComfyAudio.PlotWaveform", - "ComfyAudio.Spectrogram" - ], - { - "title_aux": "comfyui-audio-processing" - } - ], - "https://github.com/rhdunn/comfyui-bus-plugin": [ - [ - "ComfyBus.CLIPConditioningBusNode", - "ComfyBus.CLIPEncodedPromptBusNode", - "ComfyBus.CheckpointBusNode", - "ComfyBus.ImageBusNode", - "ComfyBus.ImageParameterBusNode", - "ComfyBus.ImageScaleToSideParameterBusNode", - "ComfyBus.ImageSizeBusNode", - "ComfyBus.LatentImageBusNode", - "ComfyBus.LatentImageParameterBusNode", - "ComfyBus.PromptBusNode", - "ComfyBus.PromptSDXLBusNode" - ], - { - "title_aux": "comfyui-bus-plugin" - } - ], - "https://github.com/rhplus0831/ComfyMepi": [ - [ - "MepiCheckpoint", - "MepiImageSize", - "MepiNegativePrompt", - "MepiPositivePrompt", - "MepiSaveImage", - "MepiStepsAndCfg" - ], - { - "title_aux": "ComfyMepi" - } - ], - "https://github.com/richinsley/Comfy-LFO": [ - [ - "LFO_Pulse", - "LFO_Sawtooth", - "LFO_Sine", - "LFO_Square", - "LFO_Triangle" - ], - { - "title_aux": "Comfy-LFO" - } - ], - "https://github.com/ricklove/comfyui-ricklove": [ - [ - "RL_BBox", - "RL_CacheImageSequence", - "RL_CacheMaskSequence", - "RL_CivitaiTopImagePrompts", - "RL_Crop_Resize", - "RL_Crop_Resize_Batch", - "RL_Finetune_Analyze", - "RL_Finetune_Analyze_Batch", - "RL_Finetune_Variable", - "RL_ForceDependencyOrder", - "RL_ForceDependencyOrder_ImageString", - "RL_ForceDependencyOrder_Images", - "RL_ForceDependencyOrder_Latents", - "RL_ForceDependencyOrder_String", - "RL_ForceDependencyOrder_Strings", - "RL_IfFileExists", - "RL_Image_Shadow", - "RL_Image_Threshold_Channels", - "RL_Internet_Search", - "RL_LoadImageSequence", - "RL_Load_Flow", - "RL_LoraTextExtractTags", - "RL_Optical_Flow_Dip", - "RL_RebootComfyIfLeaky", - "RL_SaveImageSequence", - "RL_Save_Flow", - "RL_SequenceContext", - "RL_Sequence_ToFilePathList", - "RL_StopIfBlack", - "RL_Uncrop", - "RL_Warp_Image", - "RL_Zoe_Depth_Map_Preprocessor", - "RL_Zoe_Depth_Map_Preprocessor_Raw_Infer", - "RL_Zoe_Depth_Map_Preprocessor_Raw_Process" - ], - { - "title_aux": "comfyui-ricklove" - } - ], - "https://github.com/rickrender/ComfyUI-Vectorizer-API": [ - [ - "BackgroundRemoverNode", - "BackgroundRemoverSVGNode", - "VectorizerAINode" - ], - { - "title_aux": "Vectorizer API" - } - ], - "https://github.com/rickyars/comfyui-llm-tile": [ - [ - "TiledImageGenerator", - "TiledImageGeneratorAdvanced" - ], - { - "title_aux": "Tiled Image Generator for ComfyUI" - } - ], - "https://github.com/risunobushi/ComfyUI-Similarity-Score": [ - [ - "ImageSimilarityScores" - ], - { - "title_aux": "ComfyUI-Similarity-Score" - } - ], - "https://github.com/risunobushi/ComfyUI_DisplacementMapTools": [ - [ - "DisplaceLogo", - "ExtractDisplacementMap" - ], - { - "title_aux": "ComfyUI_DisplacementMapTools" - } - ], - "https://github.com/risunobushi/ComfyUI_sm4ll-Wrapper": [ - [ - "VTONAPINode", - "VTONAPIPaidNode" - ], - { - "title_aux": "ComfyUI_sm4ll-Wrapper" - } - ], - "https://github.com/risunobushi/comfyUI_FrequencySeparation_RGB-HSV": [ - [ - "FrequencyCombination", - "FrequencyCombinationHSV", - "FrequencySeparation", - "FrequencySeparationHSV" - ], - { - "title_aux": "comfyUI_FrequencySeparation_RGB-HSV" - } - ], - "https://github.com/rkfg/ComfyUI-Dia_tts": [ - [ - "DiaModelLoader", - "DiaSampler" - ], - { - "title_aux": "Dia realistic TTS" - } - ], - "https://github.com/rndnanthu/ComfyUI-RndNanthu": [ - [ - "AutoGradePro", - "ColorAnalysisPlotNode", - "ColorSpaceSim", - "ConvertToLogImage", - "FilmGrain", - "ProColorGrading", - "PromptGenerator" - ], - { - "title_aux": "ComfyUI-RndNanthu" - } - ], - "https://github.com/robertvoy/ComfyUI-Distributed": [ - [ - "DistributedCollector", - "DistributedSeed", - "ImageBatchDivider", - "UltimateSDUpscaleDistributed" - ], - { - "title_aux": "ComfyUI-Distributed" - } - ], - "https://github.com/robertvoy/ComfyUI-Flux-Continuum": [ - [ - "BatchSlider", - "BooleanToEnabled", - "CannySlider", - "ConfigurableDrawText", - "ConfigurableModelRouter", - "ControlNetSlider", - "DenoiseSlider", - "DrawTextConfig", - "FluxContinuumModelRouter", - "GPUSlider", - "GuidanceSlider", - "IPAdapterSlider", - "ImageBatchBoolean", - "ImpactControlBridgeFix", - "IntPass", - "LatentPass", - "MaxShiftSlider", - "OutputGetString", - "PipePass", - "ResolutionMultiplySlider", - "ResolutionPicker", - "SEGSPass", - "SamplerParameterPacker", - "SamplerParameterUnpacker", - "SelectFromBatch", - "SimpleTextTruncate", - "SplitVec2", - "SplitVec3", - "StepSlider", - "TextVersions" - ], - { - "title_aux": "ComfyUI Flux Continuum: Modular Interface" - } - ], - "https://github.com/robtl2/ComfyUI-ComfyBridge": [ - [ - "CB_ImageReceiver", - "CB_ImageSender" - ], - { - "title_aux": "ComfyUI-ComfyBridge" - } - ], - "https://github.com/rohitsainier/ComfyUI-InstagramDownloader": [ - [ - "InstagramDownloader", - "MediaOrganizer" - ], - { - "title_aux": "ComfyUI-InstagramDownloader" - } - ], - "https://github.com/romeobuilderotti/ComfyUI-PNG-Metadata": [ - [ - "SetMetadataAll", - "SetMetadataString" - ], - { - "title_aux": "ComfyUI PNG Metadata" - } - ], - "https://github.com/ronaldzgithub/ComfyUI_Appstore": [ - [ - "ComfyUIAppstoreHost", - "ComfyUIAppstoreParam", - "sdAppstore_saveImage" - ], - { - "title_aux": "ComfyUI_Appstore" - } - ], - "https://github.com/ronniebasak/ComfyUI-Tara-LLM-Integration": [ - [ - "TaraAdvancedComposition", - "TaraApiKeyLoader", - "TaraApiKeySaver", - "TaraDaisyChainNode", - "TaraLLMConfig", - "TaraPresetLLMConfig", - "TaraPrompter", - "TaraPrompterAdvanced" - ], - { - "title_aux": "ComfyUI-Tara-LLM-Integration" - } - ], - "https://github.com/ronsantash/Comfyui-flexi-lora-loader": [ - [ - "ComfyUIFlexiLoRALoader" - ], - { - "title_aux": "ComfyUIFlexiLoRALoader" - } - ], - "https://github.com/rookiepsi/comfypsi_blur_mask": [ - [ - "comfypsi_blur_mask" - ], - { - "title_aux": "Blur Mask" - } - ], - "https://github.com/rookiepsi/comfyui-extended": [ - [ - "ImageLiquify", - "ImageSwitch", - "MaskSwitch", - "PreviewBoolean", - "PreviewFloat", - "PreviewInteger", - "PreviewMask", - "PreviewText", - "PrimitiveBoolean", - "PrimitiveDimensions", - "PrimitiveFloat", - "PrimitiveInteger", - "PrimitiveText", - "UtilityExpression", - "UtilityImageDimensions", - "UtilitySwitch", - "rookiepsi_BlurMask", - "rookiepsi_ConstructMask", - "rookiepsi_CropImageToMask", - "rookiepsi_ResizeMask" - ], - { - "title_aux": "ComfyUI Extended" - } - ], - "https://github.com/roundyyy/ComfyUI-mesh-simplifier": [ - [ - "MeshSimplifierNode" - ], - { - "title_aux": "Mesh Simplifier for ComfyUI" - } - ], - "https://github.com/routhakash/AkkiNodes-LLM-Suite-for-ComfyUI": [ - [ - "AICharacterLookdevBible-Akki", - "AICinematographer_Akki", - "AIQCSupervisor-Akki", - "AISceneChoreographerBible-Akki", - "AIScriptCrafter01FoundationBible-Akki", - "AIScriptCrafter02BeatSheetBible-Akki", - "AIScriptCrafter03ScreenplayBible-Akki", - "AISetLookdevBible-Akki", - "AIShotDurationCalculator-Akki", - "AIVideoPromptEngineerPro-Akki", - "AssetSelector-Akki", - "GenericFileLoader-Akki", - "GenericFileSaver-Akki", - "GenericImageLoader-Akki", - "GenericImageNamer-Akki", - "KeywordLoader-Akki", - "LLMLoader-Akki", - "LLMLoaderLMStudio-Akki", - "LoadTextFileAdvanced-Akki", - "LoadTextFileSimple-Akki", - "LookdevBibleLoader-Akki", - "ProShotListParser-Akki", - "ProjectDirector-Akki", - "SaveTextFile-Akki", - "SceneChoreographyLoader-Akki", - "ShotAssetLoader-Akki", - "ShotSelector-Akki", - "StoryWriter-Akki", - "VideoPromptLoader-Akki" - ], - { - "title_aux": "AkkiNodes LLM Suite: Your Personal AI Film Studio" - } - ], - "https://github.com/royceschultz/ComfyUI-Notifications": [ - [ - "Notif-PlaySound", - "Notif-SystemNotification", - "Notif-UnifiedNotification", - "Notif-Webhook" - ], - { - "title_aux": "ComfyUI-Notifications" - } - ], - "https://github.com/royceschultz/ComfyUI-TranscriptionTools": [ - [ - "TT-AudioSink", - "TT-ConvertVhsAudioToAudio", - "TT-LoadAudio", - "TT-LoadBatch", - "TT-LoadVideoAudio", - "TT-LoadWhisperModel", - "TT-WhisperTranscription", - "TT-WhisperTranscriptionBatch" - ], - { - "title_aux": "ComfyUI-TranscriptionTools" - } - ], - "https://github.com/rslosch/ComfyUI-EZ_Prompts": [ - [ - "EZPromptsNode", - "LoadImageSetFromFolderSortedNode", - "PadImageForOutpaintByAspectRatio" - ], - { - "title_aux": "ComfyUI-EZ_Prompts" - } - ], - "https://github.com/rubenvillarreal/ComfyUI_PoseAlign": [ - [ - "PoseAlignTwoToOne", - "PoseViewer" - ], - { - "title_aux": "ComfyUI_PoseAlign" - } - ], - "https://github.com/rubi-du/ComfyUI-BiRefNet-Super": [ - [ - "BiRefNet_Lite", - "BiRefNet_Super", - "BiRefNet_onnx" - ], - { - "title_aux": "ComfyUI-BiRefNet-lite" - } - ], - "https://github.com/rubi-du/ComfyUI-Flux-Inpainting": [ - [ - "Flux Inpainting", - "FluxGuffInpainting", - "FluxInpainting", - "FluxSimpleInpainting", - "FluxTransformerInpainting", - "FluxVAELoader" - ], - { - "title_aux": "ComfyUI-Flux-Inpainting" - } - ], - "https://github.com/rubi-du/ComfyUI-ICC-nodes": [ - [ - "LoadImageICC", - "PreviewImageICC", - "SaveImageICC" - ], - { - "title_aux": "ComfyUI-ICC-nodes" - } - ], - "https://github.com/rui40000/RUI-Nodes": [ - [ - "ABCondition", - "CharacterCount" - ], - { - "title_aux": "RUI-Nodes" - } - ], - "https://github.com/ruiqutech/ComfyUI-RuiquNodes": [ - [ - "EvaluateListMultiple1", - "EvaluateListMultiple3", - "EvaluateListMultiple6", - "EvaluateListMultiple9", - "EvaluateMultiple1", - "EvaluateMultiple3", - "EvaluateMultiple6", - "EvaluateMultiple9", - "ImageDilate", - "ImageErode", - "ListPath", - "MaskDilate", - "MaskErode", - "PreviewMask", - "RangeSplit", - "SaveMask", - "StringAsAny", - "StringConcat1", - "StringConcat3", - "StringConcat6", - "StringConcat9", - "StringPathStem", - "TermsToList", - "VAEDecodeSave" - ], - { - "title_aux": "RuiquNodes for ComfyUI" - } - ], - "https://github.com/runtime44/comfyui_r44_nodes": [ - [ - "Runtime44ColorMatch", - "Runtime44DynamicKSampler", - "Runtime44FilmGrain", - "Runtime44ImageEnhance", - "Runtime44ImageOverlay", - "Runtime44ImageResizer", - "Runtime44ImageToNoise", - "Runtime44IterativeUpscaleFactor", - "Runtime44MaskSampler", - "Runtime44TiledMaskSampler", - "Runtime44Upscaler" - ], - { - "title_aux": "Runtime44 ComfyUI Nodes" - } - ], - "https://github.com/ruucm/ruucm-comfy": [ - [ - "BatchAverageImage", - "LoadExternalLoraModelOnly", - "RuucmShareScreen" - ], - { - "nodename_pattern": " \\(ruucm\\)$", - "title_aux": "Ruucm's ComfyUI Nodes" - } - ], - "https://github.com/ryanontheinside/ComfyUI-DeepLiveCam": [ - [ - "DeepLiveCamNode" - ], - { - "title_aux": "Deep Live Cam for ComfyUI" - } - ], - "https://github.com/ryanontheinside/ComfyUI_ControlFreak": [ - [ - "ControlFreak" - ], - { - "title_aux": "Control Freak for ComfyUI" - } - ], - "https://github.com/ryanontheinside/ComfyUI_Doom": [ - [ - "Doom" - ], - { - "title_aux": "Doom" - } - ], - "https://github.com/ryanontheinside/ComfyUI_EfficientTAM": [ - [ - "EfficientTAMLoader", - "EfficientTAMPredictor" - ], - { - "title_aux": "ComfyUI-EfficientTAM" - } - ], - "https://github.com/ryanontheinside/ComfyUI_ProfilerX": [ - [ - "CATEGORY", - "ExecutionTracker", - "FUNCTION", - "INPUT_TYPES", - "OUTPUT_NODE", - "ProfilerX", - "RETURN_TYPES" - ], - { - "title_aux": "ComfyUI_ProfilerX" - } - ], - "https://github.com/ryanontheinside/ComfyUI_RealtimeNodes": [ - [ - "BlendshapeControlFloat", - "BlendshapeControlInt", - "BlendshapeTrigger", - "CompareMediaPipeEmbeddings", - "CoordinateConverter", - "FaceDetectionToMask", - "FaceLandmarkPosition", - "FaceTextureWarp", - "HandLandmarkPosition", - "HeadPoseControlFloat", - "HeadPoseControlInt", - "HeadPoseTrigger", - "MaskFromFaceLandmarks", - "MaskFromHandLandmarks", - "MaskFromPoseLandmarks", - "MediaPipeFaceDetector", - "MediaPipeFaceDetectorModelLoader", - "MediaPipeFaceLandmarker", - "MediaPipeFaceLandmarkerModelLoader", - "MediaPipeFaceStylizer", - "MediaPipeFaceStylizerModelLoader", - "MediaPipeGestureRecognizer", - "MediaPipeGestureRecognizerModelLoader", - "MediaPipeHandLandmarker", - "MediaPipeHandLandmarkerModelLoader", - "MediaPipeHolisticLandmarker", - "MediaPipeHolisticVisualizer", - "MediaPipeImageEmbedder", - "MediaPipeImageEmbedderModelLoader", - "MediaPipeImageSegmenter", - "MediaPipeImageSegmenterModelLoader", - "MediaPipeInteractiveSegmenter", - "MediaPipeInteractiveSegmenterModelLoader", - "MediaPipeObjectDetector", - "MediaPipeObjectDetectorModelLoader", - "MediaPipePoseLandmarker", - "MediaPipePoseLandmarkerModelLoader", - "Point2D", - "PointList", - "PoseLandmarkPosition", - "RTCoordinateConverter", - "RTDrawLines", - "RTDrawPoints", - "RTDrawPolygon", - "ReshapeMediaPipeEmbedding", - "SelectMediaPipeSegment", - "VisualizeFaceDetections", - "VisualizeFaceLandmarks", - "VisualizeGestureRecognitions", - "VisualizeHandLandmarks", - "VisualizeObjectDetections", - "VisualizePoseLandmarks" - ], - { - "title_aux": "Nodes for use with real-time applications of ComfyUI" - } - ], - "https://github.com/ryanontheinside/ComfyUI_RyanOnTheInside": [ - [ - "ACEStepAnalyzeLatent", - "ACEStepAudioPostProcessor", - "ACEStepExtendGuider", - "ACEStepHybridGuider", - "ACEStepMaskVisualizer", - "ACEStepRepaintGuider", - "ACEStepTimeRange", - "AdvancedLuminanceMask", - "AnimatedFeaturePreview", - "AreaFeatureNode", - "AudioChannelMerge", - "AudioChannelSplit", - "AudioDither", - "AudioFade", - "AudioFeatureExtractor", - "AudioFeatureVisualizer", - "AudioFilter", - "AudioGain", - "AudioInfo", - "AudioLatentBlend", - "AudioLatentInfo", - "AudioMaskAnalyzer", - "AudioPad", - "AudioPitchShift", - "AudioRegionMask", - "AudioResample", - "AudioSeparatorSimple", - "AudioSubtract", - "AudioTemporalMask", - "AudioTimeStretch", - "AudioTrim", - "AudioVolumeNormalization", - "Audio_Combine", - "Audio_Concatenate", - "BrightnessFeatureNode", - "ColorFeatureNode", - "Color_Picker", - "ContextModifier", - "DepthBlender", - "DepthFeatureNode", - "DepthInjection", - "DepthMapProtrusion", - "DepthRippleEffect", - "DepthShapeModifier", - "DepthShapeModifierPrecise", - "Doom_", - "DownloadCREPEModel", - "DownloadOpenUnmixModel", - "DrawableFeatureNode", - "DyeImage", - "EffectVisualizer", - "EmbeddingGuidedLatentInterpolate", - "EmitterEmissionRateModulation", - "EmitterMovement", - "EmptyImageAndMaskFromAudio", - "EmptyImageFromAudio", - "EmptyMaskFromAudio", - "FeatureAccumulate", - "FeatureCombine", - "FeatureContiguousInterpolate", - "FeatureFade", - "FeatureInfoNode", - "FeatureInterpolateMulti", - "FeatureInterpolator", - "FeatureMath", - "FeatureMixer", - "FeatureOscillator", - "FeaturePeakDetector", - "FeatureRebase", - "FeatureRenormalize", - "FeatureScaler", - "FeatureSmoothing", - "FeatureToFilteredList", - "FeatureToFlexFloatParam", - "FeatureToFlexIntParam", - "FeatureToFloat", - "FeatureToLatentKeyframe", - "FeatureToMask", - "FeatureToSplineData", - "FeatureToTimestepKeyframe", - "FeatureToWeightsStrategy", - "FeatureTruncateOrExtend", - "FlexAudioPitchShift", - "FlexAudioTimeStretch", - "FlexAudioVisualizerCircular", - "FlexAudioVisualizerContour", - "FlexAudioVisualizerLine", - "FlexFeatureAttentionControl", - "FlexImageBloom", - "FlexImageChromaticAberration", - "FlexImageColorGrade", - "FlexImageContrast", - "FlexImageDepthWarp", - "FlexImageEdgeDetect", - "FlexImageGlitch", - "FlexImageHorizontalToVertical", - "FlexImageHueShift", - "FlexImageKaleidoscope", - "FlexImageParallax", - "FlexImagePixelate", - "FlexImagePosterize", - "FlexImageTiltShift", - "FlexImageTransform", - "FlexImageVignette", - "FlexImageWarp", - "FlexLatentBlend", - "FlexLatentInterpolate", - "FlexLatentNoise", - "FlexMaskBinary", - "FlexMaskDepthChamber", - "FlexMaskEmanatingRings", - "FlexMaskInterpolate", - "FlexMaskMath", - "FlexMaskMorph", - "FlexMaskOpacity", - "FlexMaskRandomShapes", - "FlexMaskTransform", - "FlexMaskVoronoiScheduled", - "FlexMaskWarp", - "FlexMaskWavePropagation", - "FlexVideoDirection", - "FlexVideoFrameBlend", - "FlexVideoSeek", - "FlexVideoSpeed", - "FlexlatentAudioBlend", - "FloatFeatureNode", - "FrequencyFilterCustom", - "FrequencyFilterPreset", - "FrequencyRange", - "GravityWell", - "ImageCASBatch", - "ImageChunk", - "ImageDifference", - "ImageIndexSelect", - "ImageInterval", - "ImageIntervalSelectPercentage", - "ImageScaleToTarget", - "Image_Shuffle", - "Knob", - "LatentFrequencyBlender", - "LocationFromMask", - "LocationFromPoint", - "LocationTransform", - "MIDIFeatureExtractor", - "MIDILoader", - "MIDIToAudio", - "ManualFeatureFromPipe", - "ManualFeatureNode", - "ManualFeaturePipe", - "ManualWhisperAlignmentData", - "MaskCompositePlus", - "MaskMath", - "MaskMorph", - "MaskRings", - "MaskToAudioMask", - "MaskTransform", - "MaskWarp", - "MotionFeatureNode", - "MovingShape", - "OpticalFlowDirectionMask", - "OpticalFlowMaskModulation", - "OpticalFlowParticleSystem", - "ParticleColorModulation", - "ParticleEmissionMask", - "ParticleEmitter", - "ParticleSizeModulation", - "ParticleSpeedModulation", - "PitchFeatureExtractor", - "PitchRange", - "PitchRangeByNoteNode", - "PitchRangePreset", - "PitchVisualizer", - "PoseInterpolator", - "PreviewFeature", - "ProximityFeatureNode", - "ProximityVisualizer", - "RhythmFeatureExtractor", - "SplineFeatureModulator", - "SplineRhythmModulator", - "SpringJointSetting", - "StaticBody", - "SwapDevice", - "TextMaskNode", - "TimeFeatureNode", - "TranslucentComposite", - "TriggerBuilder", - "VideoChunk", - "Vortex", - "WhisperAutoAdjust", - "WhisperFeature", - "WhisperTextRenderer", - "WhisperTimeAdjuster", - "WhisperToPromptTravel", - "_mfc" - ], - { - "title_aux": "RyanOnTheInside" - } - ], - "https://github.com/ryanontheinside/ComfyUI_SuperResolution": [ - [ - "SuperResolutionModelLoader", - "SuperResolutionUpscale" - ], - { - "title_aux": "ComfyUI_SuperResolution" - } - ], - "https://github.com/rzgarespo/ComfyUI-qwen-image-size-picker": [ - [ - "QwenImageSize" - ], - { - "title_aux": "ComfyUI-Qwen-Image-Size-Picker" - } - ], - "https://github.com/s9roll7/comfyui_cotracker_node": [ - [ - "CoTrackerNode", - "GridPointGeneratorNode", - "PerlinCoordinateRandomizerNode", - "XYMotionAmplifierNode" - ], - { - "title_aux": "Comfyui CoTracker Node" - } - ], - "https://github.com/saftle/uber_comfy_nodes": [ - [ - "ControlNet Selector", - "ControlNetOptionalLoader", - "DiffusersSelector", - "ModelSimilarityNode", - "MultiInputVariableRewrite", - "SaveImageJPGNoMeta", - "TextRegexOperations", - "VideoSegmentCalculator" - ], - { - "title_aux": "Suplex Misc ComfyUI Nodes" - } - ], - "https://github.com/sakura1bgx/ComfyUI_FlipStreamViewer": [ - [ - "FlipStreamBatchPrompt", - "FlipStreamChat", - "FlipStreamFileSelect_AnimateDiffModel", - "FlipStreamFileSelect_Checkpoints", - "FlipStreamFileSelect_ControlNetModel", - "FlipStreamFileSelect_Input", - "FlipStreamFileSelect_Output", - "FlipStreamFileSelect_TensorRT", - "FlipStreamFileSelect_VAE", - "FlipStreamFilmVfi", - "FlipStreamGate", - "FlipStreamGetParam", - "FlipStreamGetPreviewRoi", - "FlipStreamImageSize", - "FlipStreamInputBox", - "FlipStreamLogBox", - "FlipStreamParseJson", - "FlipStreamPreviewBox", - "FlipStreamRembg", - "FlipStreamScreenGrabber", - "FlipStreamSection", - "FlipStreamSegMask", - "FlipStreamSelectBox_Samplers", - "FlipStreamSelectBox_Scheduler", - "FlipStreamSetMessage", - "FlipStreamSetParam", - "FlipStreamSetUpdateAndReload", - "FlipStreamSlider", - "FlipStreamSource", - "FlipStreamSwitch", - "FlipStreamSwitchImage", - "FlipStreamSwitchLatent", - "FlipStreamTextBox", - "FlipStreamTextReplace", - "FlipStreamVideoInput", - "FlipStreamViewer" - ], - { - "title_aux": "ComfyUI_FlipStreamViewer" - } - ], - "https://github.com/sanbuphy/ComfyUI-AudioLDM": [ - [ - "AudioLDM", - "PreviewAudioLDM", - "SaveAudioLDM" - ], - { - "title_aux": "ComfyUI-AudioLDM" - } - ], - "https://github.com/santiagosamuel3455/ComfyUI-GeminiImageToPrompt": [ - [ - "DeepseekR1KlingAINode", - "GeminiImageToPromptNode", - "GeminiTextToCinematicPromptNode", - "ShowGeneratedText", - "ShowTextNode" - ], - { - "title_aux": "ComfyUI-GeminiImageToPrompt" - } - ], - "https://github.com/scraed/LanPaint": [ - [ - "LanPaint_KSampler", - "LanPaint_KSamplerAdvanced", - "LanPaint_MaskBlend", - "LanPaint_SamplerCustom", - "LanPaint_SamplerCustomAdvanced" - ], - { - "title_aux": "LanPaint" - } - ], - "https://github.com/sdfxai/SDFXBridgeForComfyUI": [ - [ - "SDFXClipTextEncode" - ], - { - "title_aux": "SDFXBridgeForComfyUI - ComfyUI Custom Node for SDFX Integration" - } - ], - "https://github.com/sdtana/ComfyUI-FDG": [ - [ - "FDGNode" - ], - { - "title_aux": "ComfyUI-FDG" - } - ], - "https://github.com/seanjang990/comfyui-document-auto-crop": [ - [ - "CropRotateNode" - ], - { - "title_aux": "ComfyUI Document Auto Crop Node" - } - ], - "https://github.com/seanlynch/comfyui-optical-flow": [ - [ - "Apply optical flow", - "Compute optical flow", - "Visualize optical flow" - ], - { - "title_aux": "ComfyUI Optical Flow" - } - ], - "https://github.com/seanlynch/srl-nodes": [ - [ - "SRL Conditional Interrrupt", - "SRL Eval", - "SRL Filter Image List", - "SRL Format String" - ], - { - "title_aux": "SRL's nodes" - } - ], - "https://github.com/sebord/ComfyUI-LMCQ": [ - [ - "LmcqCodeDecryptionLoader", - "LmcqCodeEncryption", - "LmcqDeepGen", - "LmcqDeepLoader", - "LmcqGetMachineCode", - "LmcqImageSaver", - "LmcqImageSaverTransit", - "LmcqImageSaverWeb", - "LmcqInputValidator", - "LmcqLoadFluxNF4Checkpoint", - "LmcqRuntimeLoraDecryption", - "LmcqRuntimeLoraEncryption", - "LmcqRuntimeModelDecryption", - "LmcqRuntimeModelEncryption", - "LmcqRuntimeWorkflowDecryption", - "LmcqRuntimeWorkflowEncryption" - ], - { - "title_aux": "ComfyUI-LMCQ" - } - ], - "https://github.com/sergekatzmann/ComfyUI_Nimbus-Pack": [ - [ - "AdjustAndRoundDimensions", - "AspectRatioMobileDevices", - "ImageResizeAndCropNode", - "ImageSquareAdapterNode", - "PopularScreenResolutions" - ], - { - "title_aux": "ComfyUI_Nimbus-Pack" - } - ], - "https://github.com/sh570655308/ComfyUI-GigapixelAI": [ - [ - "GigapixelAI", - "GigapixelModelSettings", - "GigapixelUpscaleSettings" - ], - { - "title_aux": "ComfyUI-GigapixelAI" - } - ], - "https://github.com/sh570655308/ComfyUI-TopazVideoAI": [ - [ - "TopazUpscaleParams", - "TopazVideoAI" - ], - { - "title_aux": "ComfyUI-TopazVideoAI" - } - ], - "https://github.com/shabri-arrahim/ComfyUI-Safety-Checker": [ - [ - "CompVisSafetyChecker", - "FalconsAISafetyChecker", - "loadImageBase64" - ], - { - "title_aux": "ComfyUI Safety Checker" - } - ], - "https://github.com/shadowcz007/comfyui-Image-reward": [ - [ - "ImageBatchToList_", - "ImageRewardScore_" - ], - { - "title_aux": "comfyui-Image-reward" - } - ], - "https://github.com/shadowcz007/comfyui-consistency-decoder": [ - [ - "VAEDecodeConsistencyDecoder", - "VAELoaderConsistencyDecoder" - ], - { - "title_aux": "Consistency Decoder" - } - ], - "https://github.com/shadowcz007/comfyui-edit-mask": [ - [ - "EditMask" - ], - { - "title_aux": "comfyui-edit-mask" - } - ], - "https://github.com/shadowcz007/comfyui-liveportrait": [ - [ - "ExpressionEditor_", - "ExpressionVideo2VideoNode", - "ExpressionVideoNode", - "FaceCropInfo", - "LivePortraitNode", - "LivePortraitVideoNode", - "Retargeting" - ], - { - "title_aux": "comfyui-liveportrait" - } - ], - "https://github.com/shadowcz007/comfyui-mixlab-nodes": [ - [ - "3DImage", - "AnalyzeAudio", - "AppInfo", - "ApplyVisualStylePrompting_", - "AreaToMask", - "AudioPlay", - "CenterImage", - "CkptNames_", - "Color", - "ComparingTwoFrames_", - "CompositeImages_", - "CreateJsonNode", - "DepthViewer", - "DynamicDelayProcessor", - "EmbeddingPrompt", - "EnhanceImage", - "FaceToMask", - "FeatheredMask", - "FloatSlider", - "FloatingVideo", - "Font", - "GLIGENTextBoxApply_Advanced", - "GetImageSize_", - "GradientImage", - "GridDisplayAndSave", - "GridInput", - "GridOutput", - "ImageBatchToList_", - "ImageColorTransfer", - "ImageCropByAlpha", - "ImageListToBatch_", - "ImagesPrompt_", - "IncrementingListNode_", - "IntNumber", - "JoinWithDelimiter", - "KeyInput", - "LimitNumber", - "ListSplit_", - "LoadImagesFromPath", - "LoadImagesFromURL", - "LoadImagesToBatch", - "LoraNames_", - "LoraPrompt", - "MaskListMerge_", - "MaskListReplace_", - "MergeLayers", - "MirroredImage", - "MultiplicationNode", - "NewLayer", - "NoiseImage", - "OutlineMask", - "P5Input", - "PreviewMask_", - "PromptImage", - "PromptSimplification", - "PromptSlide", - "RandomPrompt", - "ResizeImageMixlab", - "SamplerNames_", - "SaveImageAndMetadata_", - "SaveImageToLocal", - "ScreenShare", - "Seed_", - "ShowLayer", - "SmoothMask", - "SpeechRecognition", - "SpeechSynthesis", - "SplitImage", - "SplitLongMask", - "StyleAlignedBatchAlign_", - "StyleAlignedReferenceSampler_", - "StyleAlignedSampleReferenceLatents_", - "SvgImage", - "SwitchByIndex", - "TESTNODE_", - "TESTNODE_TOKEN", - "TextImage", - "TextInput_", - "TextToNumber", - "TransparentImage", - "VAEDecodeConsistencyDecoder", - "VAELoaderConsistencyDecoder" - ], - { - "title_aux": "comfyui-mixlab-nodes" - } - ], - "https://github.com/shadowcz007/comfyui-sound-lab": [ - [ - "AudioPlay", - "Musicgen_", - "StableAudio_" - ], - { - "title_aux": "comfyui-sound-lab" - } - ], - "https://github.com/shadowcz007/comfyui-try-on": [ - [ - "CatVTONNode", - "FashionClothMask", - "FashionClothMask2" - ], - { - "author": "chflame", - "description": "CatVTON warpper for ComfyUI", - "nickname": "CatVTON_Wrapper", - "title": "CatVTON_Wrapper", - "title_aux": "comfyui-try-on" - } - ], - "https://github.com/shadowcz007/comfyui-ultralytics-yolo": [ - [ - "DetectByLabel" - ], - { - "title_aux": "comfyui-ultralytics-yolo" - } - ], - "https://github.com/shahkoorosh/ComfyUI-KGnodes": [ - [ - "CustomResolutionLatentNode", - "FaceDetectorAndCropper", - "ImageScaleToSide", - "OverlayRGBAonRGB", - "StyleSelector", - "TextBehindImage" - ], - { - "author": "ShahKoorosh", - "description": "This Custom node pack offers various nodes to make it easier to use ComfyUI.", - "nickname": "KGnodes", - "title": "ComfyUI-KGnodes", - "title_aux": "ComfyUI-KGnodes" - } - ], - "https://github.com/shahkoorosh/ComfyUI-PersianText": [ - [ - "PersianText" - ], - { - "author": "shahkoorosh", - "description": "A powerful ComfyUI node for rendering text with advanced styling options, including full support for Persian/Farsi and Arabic scripts.", - "nickname": "PersianText", - "title": "ComfyUI-PersianText", - "title_aux": "ComfyUI-PersianText" - } - ], - "https://github.com/shenduldh/ComfyUI-Lightning": [ - [ - "ApplyFBCacheAndSkipBlocks", - "ApplyMBCacheAndSkipBlocks", - "ApplyMBCacheAndSkipBlocksForSana", - "ApplySageAttention", - "ApplySpargeAttn", - "ApplyTeaCacheAndSkipBlocks", - "ApplyToCa", - "ApplyTokenMerging", - "CompileAndQuantizeModel", - "SanaCLIPLoader", - "SanaDiffusionLoader", - "SanaEmptyLatentImage", - "SanaTextEncode", - "SanaVAELoader", - "SaveSpargeAttnHyperparams" - ], - { - "title_aux": "ComfyUI-Lightning" - } - ], - "https://github.com/shi3z/ComfyUI_Memeplex_DALLE": [ - [ - "DallERender", - "GPT", - "MemeplexCustomSDXLRender", - "MemeplexRender", - "TextInput", - "TextSend" - ], - { - "title_aux": "ComfyUI_Memeplex_DALLE" - } - ], - "https://github.com/shiertier/ComfyUI-TeaCache-lumina2": [ - [ - "TeaCacheForLumina2", - "TeaCacheForLuminaAuto", - "TeaCacheForLuminaNext" - ], - { - "title_aux": "ComfyUI-TeaCache-Lumina" - } - ], - "https://github.com/shiimizu/ComfyUI-PhotoMaker-Plus": [ - [ - "PhotoMakerEncodePlus", - "PhotoMakerInsightFaceLoader", - "PhotoMakerLoaderPlus", - "PhotoMakerLoraLoaderPlus", - "PhotoMakerStyles", - "PrepImagesForClipVisionFromPath" - ], - { - "title_aux": "ComfyUI PhotoMaker Plus" - } - ], - "https://github.com/shiimizu/ComfyUI-TiledDiffusion": [ - [ - "NoiseInversion", - "SpotDiffusionParams_TiledDiffusion", - "TiledDiffusion", - "VAEDecodeTiled_TiledDiffusion", - "VAEEncodeTiled_TiledDiffusion" - ], - { - "title_aux": "Tiled Diffusion & VAE for ComfyUI" - } - ], - "https://github.com/shiimizu/ComfyUI-semantic-aware-guidance": [ - [ - "SemanticAwareGuidance" - ], - { - "title_aux": "Semantic-aware Guidance (S-CFG)" - } - ], - "https://github.com/shiimizu/ComfyUI_smZNodes": [ - [ - "smZ CLIPTextEncode", - "smZ Settings" - ], - { - "title_aux": "smZNodes" - } - ], - "https://github.com/shinich39/comfyui-break-workflow": [ - [ - "BreakWorkflow" - ], - { - "author": "shinich39", - "description": "Break the execution, save the incompleted image then continue later.", - "nickname": "comfyui-break-workflow", - "title": "comfyui-break-workflow", - "title_aux": "comfyui-break-workflow" - } - ], - "https://github.com/shinich39/comfyui-dynamic-routes": [ - [ - "DynamicRoutes" - ], - { - "author": "shinich39", - "description": "Shuffle nodes after queue execution.", - "nickname": "comfyui-dynamic-routes", - "title": "comfyui-dynamic-routes", - "title_aux": "comfyui-dynamic-routes" - } - ], - "https://github.com/shinich39/comfyui-get-meta": [ - [ - "GetBooleanFromImage", - "GetComboFromImage", - "GetFloatFromImage", - "GetIntFromImage", - "GetNodesFromImage", - "GetPromptFromImage", - "GetStringFromImage", - "GetWorkflowFromImage" - ], - { - "author": "shinich39", - "description": "Get metadata from image.", - "nickname": "comfyui-get-meta", - "title": "comfyui-get-meta", - "title_aux": "comfyui-get-meta" - } - ], - "https://github.com/shinich39/comfyui-no-one-above-me": [ - [ - "NoOneAboveMe" - ], - { - "author": "shinich39", - "description": "Fix node to top.", - "nickname": "comfyui-no-one-above-me", - "title": "comfyui-no-one-above-me", - "title_aux": "comfyui-no-one-above-me" - } - ], - "https://github.com/shinyakidoguchi301/comfyui-lora-tag-loader": [ - [ - "LoRA_TagLoader" - ], - { - "title_aux": "shinyakidoguchi301/LoRA Tag Loader for ComfyUI" - } - ], - "https://github.com/shobhitic/ComfyUI-PlusMinusTextClip": [ - [ - "PlusMinusTextClip" - ], - { - "title_aux": "PlusMinusTextClip - Single node for Positive and Negative Prompts" - } - ], - "https://github.com/shockz0rz/comfy-easy-grids": [ - [ - "FloatToText", - "GridFloatList", - "GridFloats", - "GridIntList", - "GridInts", - "GridLoras", - "GridStringList", - "GridStrings", - "ImageGridCommander", - "IntToText", - "SaveImageGrid", - "TextConcatenator" - ], - { - "title_aux": "comfy-easy-grids" - } - ], - "https://github.com/silveroxides/ComfyUI-ModelUtils": [ - [ - "CLIPMetaKeys", - "CheckpointMetaKeys", - "LoRAMetaKeys", - "UNetMetaKeys" - ], - { - "title_aux": "Model Utility Toolkit" - } - ], - "https://github.com/silveroxides/ComfyUI_EmbeddingToolkit": [ - [ - "SaveA1111WeightedEmbeddings", - "SaveTokenEmbeddings", - "SaveWeightedEmbeddings", - "SliceExistingEmbedding" - ], - { - "title_aux": "ComfyUI_EmbeddingToolkit" - } - ], - "https://github.com/silveroxides/ComfyUI_FDGuidance": [ - [ - "FDG_APG_Patcher", - "FrequencyDecoupledGuidance" - ], - { - "title_aux": "ComfyUI_FDGuidance" - } - ], - "https://github.com/silveroxides/ComfyUI_PowerShiftScheduler": [ - [ - "PowerShiftScheduler" - ], - { - "title_aux": "ComfyUI Power Shift Scheduler" - } - ], - "https://github.com/silveroxides/ComfyUI_SigmoidOffsetScheduler": [ - [ - "SigmoidOffsetScheduler" - ], - { - "title_aux": "ComfyUI Sigmoid Offset Scheduler" - } - ], - "https://github.com/sipherxyz/comfyui-art-venture": [ - [ - "AV_AwsBedrockClaudeApi", - "AV_AwsBedrockMistralApi", - "AV_CheckpointMerge", - "AV_CheckpointModelsToParametersPipe", - "AV_CheckpointSave", - "AV_ClaudeApi", - "AV_ControlNetEfficientLoader", - "AV_ControlNetEfficientLoaderAdvanced", - "AV_ControlNetEfficientStacker", - "AV_ControlNetEfficientStackerSimple", - "AV_ControlNetLoader", - "AV_ControlNetPreprocessor", - "AV_LLMApiConfig", - "AV_LLMChat", - "AV_LLMCompletion", - "AV_LLMMessage", - "AV_LoraListLoader", - "AV_LoraListStacker", - "AV_LoraLoader", - "AV_OpenAIApi", - "AV_ParametersPipeToCheckpointModels", - "AV_ParametersPipeToPrompts", - "AV_PromptsToParametersPipe", - "AV_SAMLoader", - "AV_VAELoader", - "AspectRatioSelector", - "BLIPCaption", - "BLIPLoader", - "BooleanPrimitive", - "CheckpointNameSelector", - "ColorBlend", - "ColorCorrect", - "DeepDanbooruCaption", - "DependenciesEdit", - "DownloadAndLoadBlip", - "DownloadISNetModel", - "Fooocus_KSampler", - "Fooocus_KSamplerAdvanced", - "GetBoolFromJson", - "GetFloatFromJson", - "GetIntFromJson", - "GetObjectFromJson", - "GetSAMEmbedding", - "GetTextFromJson", - "ISNetLoader", - "ISNetSegment", - "ImageAlphaComposite", - "ImageApplyChannel", - "ImageExtractChannel", - "ImageGaussianBlur", - "ImageMuxer", - "ImageRepeat", - "ImageScaleDown", - "ImageScaleDownBy", - "ImageScaleDownToSize", - "ImageScaleToMegapixels", - "LaMaInpaint", - "LoadImageAsMaskFromUrl", - "LoadImageFromUrl", - "LoadJsonFromText", - "LoadJsonFromUrl", - "LoadLaMaModel", - "MergeModels", - "NumberScaler", - "OverlayInpaintedImage", - "OverlayInpaintedLatent", - "PrepareImageAndMaskForInpaint", - "QRCodeGenerator", - "RandomFloat", - "RandomInt", - "SAMEmbeddingToImage", - "SDXLAspectRatioSelector", - "SDXLPromptStyler", - "SeedSelector", - "StringToInt", - "StringToNumber", - "TextRandomMultiline", - "TextSwitchCase" - ], - { - "title_aux": "comfyui-art-venture" - } - ], - "https://github.com/sipie800/ComfyUI-PuLID-Flux-Enhanced": [ - [ - "ApplyPulidFlux", - "PulidFluxEvaClipLoader", - "PulidFluxInsightFaceLoader", - "PulidFluxModelLoader" - ], - { - "title_aux": "ComfyUI-PuLID-Flux-Enhanced" - } - ], - "https://github.com/sittere/ComfyUI-YK_Line-loading": [ - [ - "MultiTextLoader" - ], - { - "title_aux": "ComfyUI-YK Line loading" - } - ], - "https://github.com/sjh00/ComfyUI-LoadImageWithInfo": [ - [ - "LoadImageWithInfo", - "SaveImageWithInfo" - ], - { - "title_aux": "ComfyUI LoadImageWithInfo" - } - ], - "https://github.com/skfoo/ComfyUI-Coziness": [ - [ - "LoraTextExtractor-b1f83aa2", - "MultiLoraLoader-70bf3d77" - ], - { - "title_aux": "ComfyUI-Coziness" - } - ], - "https://github.com/skycoder182/comfyui-filename-tools": [ - [ - "ExtractAndTrimFilename", - "LoadImageWithFilename" - ], - { - "title_aux": "Filename Tools" - } - ], - "https://github.com/skycoder182/comfyui-skycoder-tools": [ - [ - "Aspect_Ratio_and_Tile_size_calculator", - "BLIP2Captioning", - "BooleanToggle", - "ConcatenateAndTestIfEmpty", - "DirectoryImageInfo", - "DirectoryImageLoader", - "ImageBasicNode" - ], - { - "title_aux": "Skycoder Tools" - } - ], - "https://github.com/slvslvslv/ComfyUI-SmartHelperNodes": [ - [ - "SmartFormatString", - "SmartFormatString10", - "SmartHVLoraSelect", - "SmartHVLoraStack", - "SmartLoadLoRA", - "SmartModelOrLoraToString", - "SmartPrompt", - "SmartRemoveComments", - "SmartSaveText", - "SmartShowAnything" - ], - { - "title_aux": "ComfyUI Smart Helper Nodes" - } - ], - "https://github.com/slvslvslv/ComfyUI-SmartImageTools": [ - [ - "SmartBackgroundRemove", - "SmartDrawPoints", - "SmartGenerateImage", - "SmartImagePaletteConvert", - "SmartImagePaletteExtract", - "SmartImagePoint", - "SmartImagePreviewScaled", - "SmartImageRegion", - "SmartImagesProcessor", - "SmartPoint", - "SmartPointSet", - "SmartPointSetMerge", - "SmartPreviewPalette", - "SmartSaveAnimatedPNG", - "SmartSavePNG", - "SmartSemiTransparenceRemove", - "SmartVideoPreviewScaled" - ], - { - "title_aux": "ComfyUI-SmartImageTools" - } - ], - "https://github.com/slyt/comfyui-ollama-nodes": [ - [ - "BooleanToString", - "DownloadHuggingfaceModel", - "FloatToString", - "GenerateOllama", - "IntToString", - "ListModels", - "ListToString", - "PullModel" - ], - { - "title_aux": "comfyui-ollama-nodes" - } - ], - "https://github.com/sm079/ComfyUI-Face-Detection": [ - [ - "FaceCombine", - "FaceDetection" - ], - { - "title_aux": "ComfyUI-Face-Detection" - } - ], - "https://github.com/smagnetize/kb-comfyui-nodes": [ - [ - "SingleImageDataUrlLoader" - ], - { - "title_aux": "kb-comfyui-nodes" - } - ], - "https://github.com/smlbiobot/ComfyUI-Flux-Replicate-API": [ - [ - "SML_FluxProUltra_Replicate_Standalone", - "SML_FluxPro_Replicate_Standalone" - ], - { - "title_aux": "ComfyUI-Flux-Replicate-API" - } - ], - "https://github.com/smlbiobot/sml-comfyui-prompt-expansion": [ - [ - "SML_Prompt_Generator" - ], - { - "title_aux": "sml-comfyui-prompt-expansion" - } - ], - "https://github.com/smthemex/ComfyUI_AniCrafter": [ - [ - "AniCrafterLoader", - "AniCrafterPreImage", - "AniCrafterPreText", - "AniCrafterPreVideo", - "AniCrafterSampler" - ], - { - "title_aux": "ComfyUI_AniCrafter" - } - ], - "https://github.com/smthemex/ComfyUI_AnyDoor": [ - [ - "AnyDoor_LoadModel", - "AnyDoor_img2img" - ], - { - "title_aux": "ComfyUI_AnyDoor" - } - ], - "https://github.com/smthemex/ComfyUI_CSD_MT": [ - [ - "CSDMTLoader", - "CSDMTSampler" - ], - { - "title_aux": "ComfyUI_CSD_MT" - } - ], - "https://github.com/smthemex/ComfyUI_CSGO_Wrapper": [ - [ - "Blip_Loader", - "CSGO_Loader", - "CSGO_Sampler" - ], - { - "title_aux": "ComfyUI_CSGO_Wrapper" - } - ], - "https://github.com/smthemex/ComfyUI_ChatGLM_API": [ - [ - "Glm_4_9b_Chat", - "Glm_4v_9b", - "Glm_Lcoal_Or_Repo", - "ZhipuaiApi_Character", - "ZhipuaiApi_Txt", - "ZhipuaiApi_img" - ], - { - "title_aux": "ComfyUI_ChatGLM_API" - } - ], - "https://github.com/smthemex/ComfyUI_CustomNet": [ - [ - "CustomNet_LoadModel", - "CustomNet_Sampler" - ], - { - "title_aux": "ComfyUI_CustomNet" - } - ], - "https://github.com/smthemex/ComfyUI_DICE_Talk": [ - [ - "Dice_Talk_Loader", - "Dice_Talk_PreData", - "Dice_Talk_Sampler" - ], - { - "title_aux": "ComfyUI_DICE_Talk" - } - ], - "https://github.com/smthemex/ComfyUI_DeepFakeDefenders": [ - [ - "DeepFakeDefender_Loader", - "DeepFakeDefender_Sampler" - ], - { - "title_aux": "ComfyUI_DeepFakeDefenders" - } - ], - "https://github.com/smthemex/ComfyUI_Demucs": [ - [ - "Demucs_Loader", - "Demucs_Sampler" - ], - { - "title_aux": "ComfyUI_Demucs" - } - ], - "https://github.com/smthemex/ComfyUI_Diffree": [ - [ - "Diffree_Model_Loader", - "Diffree_Sampler" - ], - { - "title_aux": "ComfyUI_Diffree" - } - ], - "https://github.com/smthemex/ComfyUI_DiffuEraser": [ - [ - "DiffuEraserLoader", - "DiffuEraserSampler" - ], - { - "title_aux": "ComfyUI_DiffuEraser" - } - ], - "https://github.com/smthemex/ComfyUI_EchoMimic": [ - [ - "Echo_LoadModel", - "Echo_Predata", - "Echo_Sampler" - ], - { - "title_aux": "ComfyUI_EchoMimic" - } - ], - "https://github.com/smthemex/ComfyUI_Face_Anon_Simple": [ - [ - "Face_Anon_Simple_Align", - "Face_Anon_Simple_LoadModel", - "Face_Anon_Simple_Sampler" - ], - { - "title_aux": "ComfyUI_Face_Anon_Simple" - } - ], - "https://github.com/smthemex/ComfyUI_FoleyCrafter": [ - [ - "FoleyCrafter_LoadModel", - "FoleyCrafter_Sampler" - ], - { - "title_aux": "ComfyUI_FoleyCrafter" - } - ], - "https://github.com/smthemex/ComfyUI_FollowYourEmoji": [ - [ - "Emoji_Make_Temple", - "FollowYouEmoji_LoadModel", - "FollowYouEmoji_Sampler" - ], - { - "title_aux": "ComfyUI_FollowYourEmoji" - } - ], - "https://github.com/smthemex/ComfyUI_Hallo2": [ - [ - "HalloLoader", - "HalloPreImgAndAudio", - "HallosSampler", - "HallosUpscaleloader", - "HallosVideoUpscale" - ], - { - "title_aux": "ComfyUI_Hallo2" - } - ], - "https://github.com/smthemex/ComfyUI_HiDiffusion_Pro": [ - [ - "HI_Diffusers_Model_Loader", - "Hi_Sampler" - ], - { - "title_aux": "ComfyUI_HiDiffusion_Pro" - } - ], - "https://github.com/smthemex/ComfyUI_HunyuanAvatar_Sm": [ - [ - "HY_Avatar_Loader", - "HY_Avatar_PreData", - "HY_Avatar_Sampler" - ], - { - "title_aux": "ComfyUI_HunyuanAvatar_Sm" - } - ], - "https://github.com/smthemex/ComfyUI_ID_Animator": [ - [ - "ID_Animator", - "ID_Repo_Choice" - ], - { - "title_aux": "ComfyUI_ID_Animator" - } - ], - "https://github.com/smthemex/ComfyUI_InstantIR_Wrapper": [ - [ - "InstantIR_Loader", - "InstantIR_Sampler" - ], - { - "author": "zhaoyafei", - "title_aux": "ComfyUI_InstantIR_Wrapper" - } - ], - "https://github.com/smthemex/ComfyUI_KV_Edit": [ - [ - "KV_Edit_Load", - "KV_Edit_PreData", - "KV_Edit_Sampler" - ], - { - "title_aux": "ComfyUI_KV_Edit" - } - ], - "https://github.com/smthemex/ComfyUI_Light_A_Video": [ - [ - "Light_A_Video_Loader", - "Light_A_Video_Sampler" - ], - { - "title_aux": "ComfyUI_Light_A_Video" - } - ], - "https://github.com/smthemex/ComfyUI_Llama3_8B": [ - [ - "ChatQA_1p5_8b", - "Local_Or_Repo_Choice", - "Meta_Llama3_8B", - "MiniCPM_Llama3_V25" - ], - { - "title_aux": "ComfyUI_Llama3_8B" - } - ], - "https://github.com/smthemex/ComfyUI_MS_Diffusion": [ - [ - "MS_Object_img_Batch", - "MSdiffusion_Model_Loader", - "MSdiffusion_Sampler" - ], - { - "title_aux": "ComfyUI_MS_Diffusion" - } - ], - "https://github.com/smthemex/ComfyUI_MangaNinjia": [ - [ - "MangaNinjiaLoader", - "MangaNinjiaSampler", - "MarkImageNode" - ], - { - "title_aux": "ComfyUI_MangaNinjia" - } - ], - "https://github.com/smthemex/ComfyUI_MooER": [ - [ - "MooER_LoadModel", - "MooER_Sampler" - ], - { - "title_aux": "ComfyUI_MooER" - } - ], - "https://github.com/smthemex/ComfyUI_ObjectClear": [ - [ - "ObjectClearBatch", - "ObjectClearLoader", - "ObjectClearSampler", - "ObjectClearVision" - ], - { - "title_aux": "ComfyUI_ObjectClear" - } - ], - "https://github.com/smthemex/ComfyUI_OmniParser": [ - [ - "OmniParser_Loader", - "OmniParser_Sampler" - ], - { - "title_aux": "ComfyUI_OmniParser" - } - ], - "https://github.com/smthemex/ComfyUI_OmniSVG": [ - [ - "OmniSVGLoader", - "OmniSVGSampler" - ], - { - "title_aux": "ComfyUI_OmniSVG" - } - ], - "https://github.com/smthemex/ComfyUI_PBR_Maker": [ - [ - "Load_MatForger", - "MatForger_Sampler" - ], - { - "title_aux": "ComfyUI_PBR_Maker" - } - ], - "https://github.com/smthemex/ComfyUI_ParlerTTS": [ - [ - "ParlerTTS_LoadModel", - "ParlerTTS_Sampler" - ], - { - "title_aux": "ComfyUI_ParlerTTS" - } - ], - "https://github.com/smthemex/ComfyUI_PartPacker": [ - [ - "PartPacker_Loader", - "PartPacker_Sampler" - ], - { - "title_aux": "ComfyUI_PartPacker" - } - ], - "https://github.com/smthemex/ComfyUI_Personalize_Anything": [ - [ - "Personalize_Anything_Load", - "Personalize_Anything_Sampler" - ], - { - "title_aux": "ComfyUI_Personalize_Anything" - } - ], - "https://github.com/smthemex/ComfyUI_PhotoDoodle": [ - [ - "PhotoDoodle_Loader", - "PhotoDoodle_Sampler" - ], - { - "title_aux": "ComfyUI_PhotoDoodle" - } - ], - "https://github.com/smthemex/ComfyUI_Pic2Story": [ - [ - "Pic2Story_Loader", - "Pic2Story_Sampler" - ], - { - "title_aux": "ComfyUI_Pic2Story" - } - ], - "https://github.com/smthemex/ComfyUI_Pipeline_Tool": [ - [ - "Pipeline_Tool" - ], - { - "title_aux": "ComfyUI_Pipeline_Tool" - } - ], - "https://github.com/smthemex/ComfyUI_Pops": [ - [ - "Pops_Decoder", - "Pops_Repo_Loader", - "Pops_Sampler" - ], - { - "title_aux": "ComfyUI_Pops" - } - ], - "https://github.com/smthemex/ComfyUI_SVFR": [ - [ - "SVFR_LoadModel", - "SVFR_Sampler", - "SVFR_img2mask" - ], - { - "title_aux": "ComfyUI_SVFR" - } - ], - "https://github.com/smthemex/ComfyUI_Sapiens": [ - [ - "SapiensLoader", - "SapiensSampler" - ], - { - "title_aux": "ComfyUI_Sapiens" - } - ], - "https://github.com/smthemex/ComfyUI_SongGeneration": [ - [ - "SongGeneration_Sampler", - "SongGeneration_Stage1", - "SongGeneration_Stage2" - ], - { - "title_aux": "ComfyUI_SongGeneration" - } - ], - "https://github.com/smthemex/ComfyUI_Sonic": [ - [ - "SONICSampler", - "SONICTLoader", - "SONIC_PreData" - 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} - ], - "https://github.com/space-nuko/ComfyUI-Disco-Diffusion": [ - [ - "DiscoDiffusion_DiscoDiffusion", - "DiscoDiffusion_DiscoDiffusionExtraSettings", - "DiscoDiffusion_GuidedDiffusionLoader", - "DiscoDiffusion_OpenAICLIPLoader" - ], - { - "title_aux": "Disco Diffusion" - } - ], - "https://github.com/space-nuko/ComfyUI-OpenPose-Editor": [ - [ - "Nui.OpenPoseEditor" - ], - { - "title_aux": "OpenPose Editor" - } - ], - "https://github.com/space-nuko/nui-suite": [ - [ - "Nui.DynamicPromptsTextGen", - "Nui.FeelingLuckyTextGen", - "Nui.OutputString" - ], - { - "title_aux": "nui suite" - } - ], - "https://github.com/spacepxl/ComfyUI-Depth-Pro": [ - [ - "DepthPro", - "FocalFromList", - "FocalMMtoPX", - "FocalPXtoMM", - "LoadDepthPro", - "MetricDepthToInverse", - "MetricDepthToRelative" - ], - { - "title_aux": "ComfyUI-Depth-Pro" - } - ], - "https://github.com/spacepxl/ComfyUI-HQ-Image-Save": [ - [ - "LoadEXR", - "LoadEXRFrames", - "LoadImageAndPrompt", - "LoadLatentEXR", - "SaveEXR", - "SaveEXRFrames", - 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} - ], - "https://github.com/stduhpf/ComfyUI-WanMoeKSampler": [ - [ - "SplitSigmasAtT", - "WanMoeKSampler", - "WanMoeKSamplerAdvanced" - ], - { - "title_aux": "KSampler for Wan 2.2 MoE for ComfyUI" - } - ], - "https://github.com/stepfun-ai/ComfyUI-StepVideo": [ - [ - "TI2V", - "TI2V_API" - ], - { - "title_aux": "ComfyUI-StepVideo" - } - ], - "https://github.com/stevenwg/ComfyUI-VideoGrid": [ - [ - "VideosConcateHorizontal:", - "VideosConcateVertical" - ], - { - "title_aux": "ComfyUI-VideoGrid" - } - ], - "https://github.com/stormcenter/ComfyUI-AutoSplitGridImage": [ - [ - "EvenImageResizer", - "GridImageSplitter" - ], - { - "title_aux": "ComfyUI-AutoSplitGridImage" - } - ], - "https://github.com/stormcenter/ComfyUI-LivePhotoCreator": [ - [ - "ImageCompareTransition", - "LivePhotoCreator", - "LivePhotoPreview" - ], - { - "title_aux": "ComfyUI LivePhoto Creator" - } - ], - "https://github.com/stormcenter/ComfyUI-SVGFullfill": [ - [ - "SVGEditor", - "SVGUploader" - ], - { - "title_aux": "ComfyUI-SVGFullfill" - } - ], - "https://github.com/storyicon/comfyui_musev_evolved": [ - [ - "AnimationZoom (comfyui_musev_evolved)", - "ImageSelector (comfyui_musev_evolved)", - "MuseVImg2Vid V1 (comfyui_musev_evolved)", - "MuseVPredictor V1 (comfyui_musev_evolved)" - ], - { - "author": "infguo", - "title_aux": "ComfyUI MuseV Evolved" - } - ], - "https://github.com/storyicon/comfyui_segment_anything": [ - [ - "GroundingDinoModelLoader (segment anything)", - "GroundingDinoSAMSegment (segment anything)", - "InvertMask (segment anything)", - "IsMaskEmpty", - "SAMModelLoader (segment anything)" - ], - { - "title_aux": "segment anything" - } - ], - "https://github.com/strand1/ComfyUI-Autogen": [ - [ - "AutogenAssistantAgent", - "AutogenCodeExecutor", - "AutogenGroupChat", - "AutogenModel" - ], - { - "title_aux": "ComfyUI-Autogen" - } - ], - "https://github.com/strawberryPunch/vram_optimizer": [ - [ - "StrawberryGPUMonitor", - "StrawberryVramOptimizer", - "custom_nodes" - ], - { - "nodename_pattern": "StFist", - 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], - { - "title_aux": "ComfyUI-RetroDiffusion-API-Node" - } - ], - "https://github.com/sweetndata/ComfyUI-Image-Harmonizer": [ - [ - "harmonizer" - ], - { - "title_aux": "ComfyUI-Image-Harmonizer" - } - ], - "https://github.com/sweetndata/ComfyUI-Reflatent": [ - [ - "RefLatent" - ], - { - "title_aux": "ComfyUI-Reflatent" - } - ], - "https://github.com/sweetndata/ComfyUI-googletrans": [ - [ - "googletrans" - ], - { - "title_aux": "ComfyUI-googletrans" - } - ], - "https://github.com/sweetndata/ComfyUI_Sticker_Compositer": [ - [ - "Sticker_Compositer" - ], - { - "title_aux": "ComfyUI_Sticker_Compositer" - } - ], - "https://github.com/syllebra/bilbox-comfyui": [ - [ - "BilboXLut", - "BilboXPhotoPrompt", - "BilboXVignette" - ], - { - "title_aux": "BilboX's ComfyUI Custom Nodes" - } - ], - "https://github.com/sylym/comfy_vid2vid": [ - [ - "CheckpointLoaderSimpleSequence", - "DdimInversionSequence", - "KSamplerSequence", - "LoadImageMaskSequence", - "LoadImageSequence", - "LoraLoaderSequence", - 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"JM-MiniMax-API/text-to-speech", - "JM-MiniMax-API/video-generation", - "JM-MiniMax-API/voice-cloning", - "JM-MiniMax-API/voice-design" - ], - { - "title_aux": "ComfyUI-JM-MiniMax-API" - } - ], - "https://github.com/synthetai/ComfyUI-JM-Volcengine-API": [ - [ - "VolcengineI2VS2Pro", - "VolcengineImgEditV3", - "volcengine-doubao-seedance", - "volcengine-i2v-s2pro", - "volcengine-img-edit-v3", - "volcengine-seedream-v3" - ], - { - "title_aux": "ComfyUI-JM-Volcengine-API" - } - ], - "https://github.com/synthetai/ComfyUI-ToolBox": [ - [ - "AutoDLDownload", - "CreatePaths", - "FolderDeleter", - "FolderViewe", - "PathOutput" - ], - { - "title_aux": "ComfyUI-ToolBox" - } - ], - "https://github.com/synthetai/ComfyUI_FaceEnhancer": [ - [ - "GFPGANFaceEnhancer", - "GFPGANFolderProcessor" - ], - { - "title_aux": "ComfyUI_FaceEnhancer" - } - ], - "https://github.com/synthetai/ComfyUI_PromptBatcher": [ - [ - "LoadPromptsFromDir", - "SaveTextToFiles" - ], - { - "title_aux": "ComfyUI_PromptBatcher" - } - ], - "https://github.com/sysL-padawan/comfyui-elevenlabs-integration": [ - [ - "ElevenlabsTextToEffect", - "ElevenlabsTextToSpeech" - ], - { - "title_aux": "ComfyUI ElevenLabs API integration" - } - ], - "https://github.com/szhublox/ambw_comfyui": [ - [ - "Auto Merge Block Weighted", - "CLIPMergeSimple", - "CheckpointSave", - "ModelMergeBlocks", - "ModelMergeSimple" - ], - { - "title_aux": "Auto-MBW" - } - ], - "https://github.com/taabata/LCM_Inpaint_Outpaint_Comfy": [ - [ - "ComfyNodesToSaveCanvas", - "FloatNumber", - "FreeU_LCM", - "ImageDims", - "ImageOutputToComfyNodes", - "ImageResize", - "ImageShuffle", - "ImageSwitch", - "LCMGenerate", - "LCMGenerate_ReferenceOnly", - "LCMGenerate_SDTurbo", - "LCMGenerate_img2img", - "LCMGenerate_img2img_IPAdapter", - "LCMGenerate_img2img_controlnet", - "LCMGenerate_inpaintv2", - "LCMGenerate_inpaintv3", - "LCMLoader", - "LCMLoader_RefInpaint", - "LCMLoader_ReferenceOnly", - "LCMLoader_SDTurbo", - "LCMLoader_controlnet", - "LCMLoader_controlnet_inpaint", - "LCMLoader_img2img", - "LCMLoraLoader_inpaint", - "LCMLoraLoader_ipadapter", - "LCMLora_inpaint", - "LCMLora_inpaintV2", - "LCMLora_ipadapter", - "LCMT2IAdapter", - "LCM_IPAdapter", - "LCM_IPAdapter_inpaint", - "LCM_outpaint_prep", - "LoadImageNode_LCM", - "Loader_SegmindVega", - "OutpaintCanvasTool", - "SaveImage_Canvas", - "SaveImage_LCM", - "SaveImage_Puzzle", - "SaveImage_PuzzleV2", - "SegmindVega", - "SettingsSwitch", - "stitch" - ], - { - "title_aux": "LCM_Inpaint-Outpaint_Comfy" - } - ], - "https://github.com/taabata/SANA_LOWVRAM": [ - [ - "SANADiffuse", - "SANATextEncode" - ], - { - "title_aux": "SANA_LOWVRAM" - } - ], - "https://github.com/takemetosiberia/ComfyUI-SAMURAI--SAM2-": [ - [ - "SAMURAIBoxInputNode", - "SAMURAIPointsInputNode", - "SAMURAIRefineNode" - ], - { - "title_aux": "SAMURAI Nodes for ComfyUI" - } - ], - "https://github.com/talesofai/comfyui-browser": [ - [ - "DifyTextGenerator //Browser", - "LoadImageByUrl //Browser", - "SelectInputs //Browser", - "UploadToRemote //Browser", - "XyzPlot //Browser" - ], - { - "title_aux": "ComfyUI Browser" - } - ], - "https://github.com/tanglaoya321/ComfyUI-StoryMaker": [ - [ - "StoryMakerSinglePortraitNode", - "StoryMakerSwapClothNode", - "StoryMakerTwoPortraitNode" - ], - { - "title_aux": "ComfyUI-StoryMaker" - } - ], - "https://github.com/tatookan/comfyui_ssl_gemini_EXP": [ - [ - "SSL_GeminiAPIKeyConfig", - "SSL_GeminiTextPrompt" - ], - { - "title_aux": "comfyui_ssl_gemini_EXP" - } - ], - "https://github.com/tauraloke/ComfyUI-Unfake-Pixels": [ - [ - "PixelArtScaler" - ], - { - "title_aux": "ComfyUI-Unfake-Pixels" - } - ], - "https://github.com/tavyra/ComfyUI_Curves": [ - [ - "Curve Visualizer", - "RGB Curve Editor", - "RGBCurvesAdvanced" - ], - { - "title_aux": "ComfyUI_Curves" - } - ], - "https://github.com/teamalpha-ai/comfyui-image-transformer": [ - [ - "ImageTransformerResizeToMaxPixels" - ], - { - "title_aux": "ComfyUI-ImageTransformer" - } - ], - "https://github.com/tercumantanumut/ComfyUI-Omini-Kontext": [ - [ - "OminiKontextImageEncoder", - "OminiKontextImageScale", - "OminiKontextLatentCombiner", - "OminiKontextLatentDecoder", - "OminiKontextLatentVisualizer", - "OminiKontextLoRALoader", - "OminiKontextLoRAMerge", - "OminiKontextLoRAUnload", - "OminiKontextPipeline", - "OminiKontextPipelineLoader", - "OminiKontextReferenceEncoder", - "OminiKontextSplitPipelineLoader", - "OminiKontextTextEncoder" - ], - { - "title_aux": "ComfyUI-Omini-Kontext" - } - ], - "https://github.com/tetsuoo-online/comfyui-too-xmp-metadata": [ - [ - "ReadXMPMetadata", - "WriteXMPMetadataLossless", - "WriteXMPMetadataTensor" - ], - { - "title_aux": "comfyui-too-xmp-metadata" - } - ], - "https://github.com/teward/ComfyUI-Helper-Nodes": [ - [ - "HelperNodes_CfgScale", - "HelperNodes_CheckpointSelector", - "HelperNodes_MultilineStringLiteral", - "HelperNodes_Prompt", - "HelperNodes_SDXLCommonResolutions", - "HelperNodes_SamplerSelector", - "HelperNodes_SaveImage", - "HelperNodes_SchedulerSelector", - "HelperNodes_SeedSelector", - "HelperNodes_Steps", - "HelperNodes_StringLiteral", - "HelperNodes_VAESelector", - "HelperNodes_WidthHeight" - ], - { - "title_aux": "ComfyUI-Helper-Nodes" - } - ], - "https://github.com/thalismind/ComfyUI-Blend-Nodes": [ - [ - "BlendImageNode" - ], - { - "title_aux": "ComfyUI Blend Image Nodes" - } - ], - "https://github.com/thalismind/ComfyUI-LoadImageWithFilename": [ - [ - "CropImageByMask", - "LoadImageFolder", - "LoadImageWithFilename", - "SaveImageWithFilename" - ], - { - "title_aux": "ComfyUI LoadImageWithFilename" - } - ], - "https://github.com/theAdamColton/ComfyUI-texflow-extension": [ - [ - "Load Texflow Depth Image", - "Save Texflow Image" - ], - { - "title_aux": "ComfyUI-texflow-extension" - } - ], - "https://github.com/theUpsider/ComfyUI-Styles_CSV_Loader": [ - [ - "Load Styles CSV" - ], - { - "title_aux": "Styles CSV Loader Extension for ComfyUI" - } - ], - "https://github.com/thecooltechguy/ComfyUI-MagicAnimate": [ - [ - "MagicAnimate", - "MagicAnimateModelLoader" - ], - { - "title_aux": "ComfyUI-MagicAnimate" - } - ], - "https://github.com/thecooltechguy/ComfyUI-Stable-Video-Diffusion": [ - [ - "SVDDecoder", - "SVDModelLoader", - "SVDSampler", - "SVDSimpleImg2Vid" - ], - { - "title_aux": "ComfyUI Stable Video Diffusion" - } - ], - "https://github.com/thedivergentai/divergent_nodes": [ - [ - "CLIPTokenCounter", - "DivergentGeminiNode", - "KoboldCppApiNode", - "LoraStrengthXYPlot", - "MusiQNode", - "SaveImageEnhancedNode" - ], - { - "title_aux": "Divergent Nodes" - } - ], - "https://github.com/theshubzworld/ComfyUI-FaceCalloutNode": [ - [ - "FaceCalloutEffect", - "IntegratedFaceComposite", - "IsolatedFaceCallout" - ], - { - "title_aux": "ComfyUI-FaceCalloutNode" - } - ], - "https://github.com/theshubzworld/ComfyUI-SD3.5-Latent-Size-Picker": [ - [ - "FluxEmptyLatent", - "SD3_5EmptyLatent" - ], - { - "title_aux": "SD3.5 Empty Latent Size Picker" - } - ], - "https://github.com/theshubzworld/ComfyUI-TogetherVision": [ - [ - "Together Image \ud83c\udfa8", - "TogetherVisionBatchNode", - "TogetherVisionNode", - "TogetherVisionNode \ud83d\udd0d (Enhanced)" - ], - { - "title_aux": "Together Vision Node" - } - ], - "https://github.com/theshubzworld/ComfyUI-ollama_killer": [ - [ - "OllamaKiller" - ], - { - "title_aux": "ComfyUI-ollama_killer" - } - ], - "https://github.com/thezveroboy/ComfyUI-CSM-Nodes": [ - [ - "CSMTextToSpeech", - "LoadCSMCheckpoint", - "LoadCSMTokenizer" - ], - { - "title_aux": "ComfyUI-CSM-Nodes" - } - ], - "https://github.com/thezveroboy/ComfyUI-WAN-ClipSkip": [ - [ - "CLIPSkip" - ], - { - "title_aux": "ComfyUI-WAN-ClipSkip" - } - ], - "https://github.com/thezveroboy/ComfyUI-lut": [ - [ - "ImageToLUT" - ], - { - "title_aux": "ComfyUI-LUT" - } - ], - "https://github.com/thezveroboy/ComfyUI_ACE-Step-zveroboy": [ - [ - "ACEModelLoaderZveroboy", - "ACEStepEditZveroboy", - "ACEStepExtendZveroboy", - "ACEStepGenerateZveroboy", - "ACEStepRepaintZveroboy" - ], - { - "title_aux": "ComfyUI_ACE-Step-zveroboy" - } - ], - "https://github.com/thezveroboy/comfyui-RandomPromptsZveroboy": [ - [ - "RandomPromptsZveroboy" - ], - { - "title_aux": "comfyui-RandomPromptsZveroboy" - } - ], - "https://github.com/thezveroboy/comfyui-random-image-loader": [ - [ - "LoadRandomImage" - ], - { - "title_aux": "ComfyUI Random Image Loader" - } - ], - "https://github.com/thimpat/ThimPatUtils": [ - [ - "CalculateAndDisplay", - "CalculateVideoFrameCount", - "ExtractAudioInfo", - "IntToFloatConverter", - "LoadPathToAudioInfo", - "ResizeVideoFrames" - ], - { - "title_aux": "ComfyUI Multimedia Utilities" - } - ], - "https://github.com/thoddnn/ComfyUI-MLX": [ - [ - "MLXClipTextEncoder", - "MLXDecoder", - "MLXLoadFlux", - "MLXSampler" - ], - { - "title_aux": "ComfyUI MLX Nodes" - } - ], - "https://github.com/tianguanggliu/Utools": [ - [ - "UTools" - ], - { - "title_aux": "comfyui-utools" - } - ], - "https://github.com/tiankuan93/ComfyUI-V-Express": [ - [ - "Load_Audio_Path", - "Load_Audio_Path_From_Video", - "Load_Image_Path", - "Load_Kps_Path", - "Load_Kps_Path_From_Video", - "Load_Video_Path", - "VEINTConstant", - "VEPreview_Video", - "VEStringConstant", - "V_Express_Loader", - "V_Express_Sampler" - ], - { - "title_aux": "V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation" - } - ], - "https://github.com/tianlang0704/ComfyUI-StableProjectorzBridge": [ - [ - "ProjectorzControlnetInput", - "ProjectorzControlnetParameter", - "ProjectorzInitInput", - "ProjectorzOutput", - "ProjectorzParameter", - "ProjectorzStringToFloat", - "ProjectorzStringToInt" - ], - { - "title_aux": "Stable Projectorz Bridge" - } - ], - "https://github.com/tianyuw/ComfyUI-LLM-API": [ - [ - "PromptWithImage" - ], - { - "title_aux": "Custom nodes for llm chat with optional image input" - } - ], - "https://github.com/tighug/comfyui-eagle-feeder": [ - [ - "EagleFeederAnimatedWebp", - "EagleFeederMp4", - "EagleFeederPng" - ], - { - "title_aux": "ComfyUI Eagle Feeder" - } - ], - "https://github.com/tighug/comfyui-rating-checker": [ - [ - "RatingCheckerGantMan", - "RatingCheckerMarqo", - "RatingCheckerNudeNet" - ], - { - "title_aux": "ComfyUI Rating Checker" - } - ], - "https://github.com/tkreuziger/comfyui-claude": [ - [ - "Combine Texts", - "Describe Image", - "Transform Text" - ], - { - "title_aux": "ComfyUI and Claude" - } - ], - "https://github.com/tmagara/ComfyUI-Prediction-Boost": [ - [ - "PredictionBoost" - ], - { - "title_aux": "ComfyUI-Prediction-Boost" - } - ], - "https://github.com/tocubed/ComfyUI-AudioReactor": [ - [ - "AudioFrameTransformBeats", - "AudioFrameTransformShadertoy", - "AudioLoadPath", - "Shadertoy" - ], - { - "title_aux": "ComfyUI-AudioReactor" - } - ], - "https://github.com/tocubed/ComfyUI-EvTexture": [ - [ - "EVTEventsToImage", - "EVTLoadEvTextureModel", - "EVTTextureUpscaleVideo", - "EVTVideoToEvents" - ], - { - "title_aux": "ComfyUI-EvTexture" - } - ], - "https://github.com/tomudo/ComfyUI-ascii-art": [ - [ - "ImageToAscii" - ], - { - "author": "dfl", - "description": "CLIP text encoder that does BREAK prompting like A1111", - "nickname": "CLIP with BREAK", - "title": "CLIP with BREAK syntax", - "title_aux": "ComfyUI-ascii-art" - } - ], - "https://github.com/tooldigital/ComfyUI-Yolo-Cropper": [ - [ - "ToolYoloCropper" - ], - { - "title_aux": "Easy automatic (square) image cropper using Yolo" - } - ], - "https://github.com/toxicwind/ComfyUI-TTools": [ - [ - "TTools Extract JSON", - "TTools SD3 Resolution Solver" - ], - { - "title_aux": "TTools for ComfyUI" - } - ], - "https://github.com/toyxyz/ComfyUI_rgbx_Wrapper": [ - [ - "rgb2x" - ], - { - "title_aux": "ComfyUI_rgbx_Wrapper" - } - ], - "https://github.com/toyxyz/ComfyUI_toyxyz_test_nodes": [ - [ - "CaptureWebcam", - "Depth to normal", - "Direct Screen Capture", - "Export glb", - "ImageResize_Padding", - "LatentDelay", - "Load Random Text From File", - "LoadWebcamImage", - "Remove noise", - "SaveImagetoPath", - "VisualAreaMask" - ], - { - "title_aux": "ComfyUI_toyxyz_test_nodes" - } - ], - "https://github.com/traugdor/ComfyUI-Riffusion": [ - [ - "RiffusionNode", - "RiffusionToBatchNode" - ], - { - "title_aux": "ComfyUI-Riffusion" - } - ], - "https://github.com/traugdor/ComfyUI-UltimateSDUpscale-GGUF": [ - [ - "UltimateSDUpscaleGGUF" - ], - { - "title_aux": "ComfyUI-UltimateSDUpscale-GGUF" - } - ], - "https://github.com/traugdor/ComfyUI-quadMoons-nodes": [ - [ - "AnimateDiff Script", - "Apply ControlNet Stack", - "Control Net Stacker", - "Eff. Loader SDXL", - "Efficient Loader", - "HighRes-Fix Script", - "Image Overlay", - "Join XY Inputs of Same Type", - "KSampler (Efficient)", - "KSampler Adv. (Efficient)", - "KSampler SDXL (Eff.)", - "LatentUpscaler", - "LoRA Stack to String converter", - "LoRA Stacker", - "Manual XY Entry Info", - "NNLatentUpscale", - "Noise Control Script", - "Pack SDXL Tuple", - "Tiled Upscaler Script", - "Unpack SDXL Tuple", - "XY Input: Add/Return Noise", - "XY Input: Aesthetic Score", - "XY Input: CFG Scale", - "XY Input: Checkpoint", - "XY Input: Clip Skip", - "XY Input: Control Net", - "XY Input: Control Net Plot", - "XY Input: Denoise", - "XY Input: LoRA", - "XY Input: LoRA Plot", - "XY Input: LoRA Stacks", - "XY Input: Manual XY Entry", - "XY Input: Prompt S/R", - "XY Input: Refiner On/Off", - "XY Input: Sampler/Scheduler", - "XY Input: Seeds++ Batch", - "XY Input: Steps", - "XY Input: VAE", - "XY Plot", - "quadmoonBatchFromLatent", - "quadmoonCLIPTextEncode", - "quadmoonCLIPTextEncode2", - "quadmoonChangeBackground", - "quadmoonConvertBoolToString", - "quadmoonConvertFloatToString", - "quadmoonConvertImageToPrompt", - "quadmoonConvertIntToString", - "quadmoonConvertNormalizeHW", - "quadmoonConvertNumberToString", - "quadmoonINTConditionalOperation", - "quadmoonKSampler", - "quadmoonKSamplerAdvanced", - "quadmoonKSamplerBatched", - "quadmoonLatentImage", - "quadmoonLoadConfigs", - "quadmoonModelLoader", - "quadmoonRotationalSampler", - "quadmoonSaveNeg", - "quadmoonSavePrompt", - "quadmoonSmartNeg", - "quadmoonSmartPrompt", - "quadmoonThebutton" - ], - { - "author": "quadmoon (https://github.com/traugdor)", - "description": "These are just some nodes I wanted and couldn't find where anyone else had made them yet.", - "nickname": "quadmoon's Nodes", - "title": "quadmoon's ComfyUI nodes", - "title_aux": "quadmoon's ComfyUI nodes" - } - ], - "https://github.com/tritant/ComfyUI-Advanced-Photo-Grain": [ - [ - "PhotoFilmGrain" - ], - { - "title_aux": "Advanced Photo Grain" - } - ], - "https://github.com/tritant/ComfyUI_CreaPrompt": [ - [ - "CreaPrompt", - "CreaPrompt List", - "CreaPrompt_0", - "CreaPrompt_1", - "CreaPrompt_2", - "CreaPrompt_3", - "CreaPrompt_4" - ], - { - "title_aux": "ComfyUI-CreaPrompt" - } - ], - "https://github.com/tritant/ComfyUI_Flux_Block_Lora_Merger": [ - [ - "FluxBlockLoraMerger" - ], - { - "title_aux": "Flux Block LoRA Merger" - } - ], - "https://github.com/tritant/ComfyUI_Flux_Lora_Merger": [ - [ - "FluxLoraMerger" - ], - { - "title_aux": "Flux LoRA Merger" - } - ], - "https://github.com/tritant/ComfyUI_Layers_Utility": [ - [ - "LayerSystem" - ], - { - "title_aux": "Layers System" - } - ], - "https://github.com/tritant/ComfyUI_Relight_Img": [ - [ - "RelightNode" - ], - { - "title_aux": "Advanced_Relight_Img" - } - ], - "https://github.com/tritant/ComfyUI_Remove_Banding_Artifacts": [ - [ - "ResampleBandingFix" - ], - { - "title_aux": "Remove Banding Artifacts" - } - ], - "https://github.com/trojblue/trNodes": [ - [ - "trColorCorrection", - "trLayering", - "trRouter", - "trRouterLonger" - ], - { - "title_aux": "trNodes" - } - ], - "https://github.com/troyxmccall/ComfyUI-ScaleToTargetMegapixels": [ - [ - "ScaleToTargetMegapixels" - ], - { - "title_aux": "ComfyUI-ScaleToTargetMegapixels" - } - ], - "https://github.com/trumanwong/ComfyUI-NSFW-Detection": [ - [ - "NSFWDetection" - ], - { - "title_aux": "ComfyUI-NSFW-Detection" - } - ], - "https://github.com/tsogzark/ComfyUI-load-image-from-url": [ - [ - "LoadImageFromUrlOrPath" - ], - { - "title_aux": "ComfyUI-load-image-from-url" - } - ], - "https://github.com/ttulttul/ComfyUI-Iterative-Mixer": [ - [ - "Batch Unsampler", - "Iterative Mixing KSampler", - "Iterative Mixing KSampler Advanced", - "IterativeMixingSampler", - "IterativeMixingScheduler", - "IterativeMixingSchedulerAdvanced", - "Latent Batch Comparison Plot", - "Latent Batch Statistics Plot", - "MixingMaskGenerator" - ], - { - "title_aux": "ComfyUI Iterative Mixing Nodes" - } - ], - "https://github.com/ttulttul/ComfyUI-Tensor-Operations": [ - [ - "Fast Image to Noise", - "Image Match Normalize", - "Latent Match Normalize" - ], - { - "title_aux": "ComfyUI-Tensor-Operations" - } - ], - "https://github.com/tungdop2/Comfyui_face_restorer": [ - [ - "FaceRestorer", - "FaceRestorerLoader" - ], - { - "title_aux": "Face Restorer for ComfyUI" - } - ], - "https://github.com/tungdop2/Comfyui_joy-caption-alpha-two": [ - [ - "JoyCaptioner" - ], - { - "title_aux": "Joy Caption Alpha Two for ComfyUI" - } - ], - "https://github.com/turkyden/ComfyUI-SmartCrop": [ - [ - "ImageSmartCrop" - ], - { - "title_aux": "ComfyUI-SmartCrop" - } - ], - "https://github.com/tusharbhutt/Endless-Nodes": [ - [ - "BatchNegativePrompts", - "Eight_Input_Int_Switch", - "Eight_Input_Int_Switch_Widget", - "Eight_Input_Text_Switch", - "EndlessReplicateLatents", - "FluxBatchPrompts", - "FluxKontextBatchPrompts", - "Four_Input_Int_Switch", - "Four_Input_Int_Switch_Widget", - "Four_Input_Text_Switch", - "ImageComplexityScorer", - "ImageNoveltyScorer", - "Image_saver", - "LatentReplicator", - "LatentReplicatorPrompts", - "PromptCounter", - "Random_Prompt_Multipicker", - "Random_Prompt_Selector", - "Randomizer_Chaos", - "Randomizer_Mayhem", - "Randomizer_Pandemonium", - "SDXLBatchPrompts", - "SimpleBatchPrompts", - "Six_Input_Int_Switch", - "Six_Input_Int_Switch_Widget", - "Six_Input_Text_Switch" - ], - { - "title_aux": "Endless \ufe0f\ud83c\udf0a\u2728 Nodes" - } - ], - "https://github.com/twri/sdxl_prompt_styler": [ - [ - "SDXLPromptStyler", - "SDXLPromptStylerAdvanced" - ], - { - "title_aux": "SDXL Prompt Styler" - } - ], - "https://github.com/ty0x2333/ComfyUI-Dev-Utils": [ - [ - "TY_ExecutionTime", - "TY_UploadAnything", - "TY_UrlDownload" - ], - { - "title_aux": "ComfyUI-Dev-Utils" - } - ], - "https://github.com/uarefans/ComfyUI-Fans": [ - [ - "Fans Prompt Styler Negative", - "Fans Prompt Styler Positive", - "Fans Styler", - "Fans Text Concatenate" - ], - { - "title_aux": "ComfyUI-Fans" - } - ], - "https://github.com/uetuluk/comfyui-webcam-node": [ - [ - "webcam_capture_node" - ], - { - "title_aux": "comfyui-webcam-node" - } - ], - "https://github.com/uihp/ComfyUI-String-Chain": [ - [ - "String Chain", - "String Concat", - "String Toggle", - "String Toggle (Multiline)" - ], - { - "title_aux": "ComfyUI-String-Chain" - } - ], - "https://github.com/umiyuki/comfyui-pad-to-eight": [ - [ - "Pad To Eight" - ], - { - "title_aux": "ComfyUI Pad To Eight" - } - ], - "https://github.com/un-seen/comfyui-tensorops": [ - [ - "BackgroundSelect", - "ChannelSelector", - "DownloadAndLoadFlorence2Model", - "DownloadAndLoadSAM2Model", - "FaceSwap", - "FalDifferentialDiffusion", - "FalDiffusion", - "FetchFromRedis", - "FetchJsonFromSurreal", - "Florence2Run", - "Florence2toCoordinates", - "ForegroundMask", - "GetLayerMask", - "MaskImage", - "Sam2AutoSegmentation", - "Sam2Segmentation", - "Sam2VideoSegmentation", - "Sam2VideoSegmentationAddPoints", - "SaveImageToS3", - "SaveJsonToSurreal", - "SaveTextToSurreal", - "SaveToRedis", - "SendImageOnWebSocket", - "SendJsonOnWebSocket", - "SeparateMask" - ], - { - "title_aux": "comfyui-tensorop" - } - ], - "https://github.com/un-seen/comfyui_segment_anything_plus": [ - [ - "GroundingDinoModelLoader (segment anything plus)", - "GroundingDinoSAMSegment (segment anything plus)", - "InvertMask (segment anything plus)", - "IsMaskEmpty (segment anything plus)", - "SAMModelLoader (segment anything plus)" - ], - { - "title_aux": "ComfyUI Segment Anything" - } - ], - "https://github.com/unicough/comfy_openai_image_api": [ - [ - "OpenAI Image API" - ], - { - "title_aux": "OpenAI Image API with gpt-image-1" - } - ], - "https://github.com/unwdef/unwdef-nodes-comfyui": [ - [ - "RandomTextFromMultiline", - "RandomizeLoras", - "RandomizeLorasStack", - "TextMultilineWithVariables" - ], - { - "title_aux": "unwdef-nodes" - } - ], - "https://github.com/upseem/comfyui_sun_nodes": [ - [ - "SunxAI_BatchImageLoopCloseChen", - "SunxAI_BatchImageLoopOpenChen" - ], - { - "title_aux": "SunxAI Custom Nodes for ComfyUI" - } - ], - "https://github.com/upseem/comfyui_sunxAI_facetools": [ - [ - "ApplyInstantID", - "ColorAdjustNew(FaceParsing)", - "CropFaces", - "DetectFaces", - "Example", - "InstantIDFaceAnalysis", - "InstantIDModelLoader", - "LoadFaceEmbeds", - "SaveFaceEmbeds", - "SaveImageWebsocketNew", - "SelectFloatByBool", - "VAEDecodeNew", - "VAEEncodeNew", - "WarpFacesBack" - ], - { - "title_aux": "comfyui_sunxAI_facetools" - } - ], - "https://github.com/usrname0/comfyui-holdup": [ - [ - "HoldUp" - ], - { - "title_aux": "comfyui-holdup" - } - ], - "https://github.com/vadimcro/VKRiez-Edge": [ - [ - "VKriezEnhancedEdgePreprocessor", - "VKriezHybridEdgePreprocessor" - ], - { - "title_aux": "VKRiez-Edge" - } - ], - "https://github.com/vahidzxc/va-nodes": [ - [ - "VA_Seed" - ], - { - "title_aux": "va-nodes" - } - ], - "https://github.com/vahlok-alunmid/ComfyUI-ExtendIPAdapterClipVision": [ - [ - "EXTEND_CLIP_VISION_INPUT_SIZE", - "IPAdapterAdvancedSizeAware" - ], - { - "title_aux": "ComfyUI-ExtendIPAdapterClipVision" - } - ], - "https://github.com/vaishnav-vn/va1": [ - [ - "RandomAspectRatioMask" - ], - { - "title_aux": "va1" - } - ], - "https://github.com/valofey/Openrouter-Node": [ - [ - "OpenrouterNode" - ], - { - "title_aux": "OpenRouter Node" - } - ], - "https://github.com/vanche1212/ComfyUI-ZMG-Nodes": [ - [ - "VC_Load_Video_Path_Unified_Output", - "VC_Load_Video_Upload_Unified_Output", - "VC_Video_Combine_Unified_Output", - "Waveform2Audio", - "\ud83d\ude0bAPI Request Node", - "\ud83d\ude0bJSON Parser Node", - "\ud83d\ude0bOld Photo Colorization Node", - "\ud83d\ude0bOllama Request Node", - "\ud83d\ude0bSave Image Unified Output" - ], - { - "title_aux": "ZMG PLUGIN" - } - ], - "https://github.com/vanillacode314/SimpleWildcardsComfyUI": [ - [ - "SimpleConcat", - "SimpleWildcard" - ], - { - "author": "VanillaCode314", - "description": "A simple wildcard node for ComfyUI. Can also be used a style prompt node.", - "nickname": "Simple Wildcard", - "title": "Simple Wildcard", - "title_aux": "Simple Wildcard" - } - ], - "https://github.com/var1ableX/ComfyUI_Accessories": [ - [ - "ACC_AnyCast", - "AccMakeListNode", - "GetMaskDimensions", - "GetRandomDimensions", - "isImageEmpty", - "isMaskEmpty" - ], - { - "title_aux": "ComfyUI_Accessories" - } - ], - "https://github.com/vault-developer/comfyui-image-blender": [ - [ - "ImageBlender" - ], - { - "title_aux": "ImageBlender" - } - ], - "https://github.com/veighnsche/comfyui_gr85": [ - [ - "GR85_CTGPhrases", - "GR85_CTGPhrasesSimple", - "GR85_ContainsWord", - "GR85_FilterAndCombineMasks", - "GR85_Florence2RunCTPG", - "GR85_Florence2toCoordinatesGR85", - "GR85_FluxAttentionSeeker2", - "GR85_FluxAttentionSeeker3", - "GR85_FluxAttentionSeekerGenerator", - "GR85_FluxModelMergeParameters", - "GR85_ImageDimensionResizer", - "GR85_ImageSizer", - "GR85_ImageSizerAll", - "GR85_IntToString", - "GR85_IntegerSequenceModifier", - "GR85_IslandMaskGenerator", - "GR85_MaskBatchToSEGS", - "GR85_MaskConnectMST", - "GR85_MaskSplitter", - "GR85_NextSeed", - "GR85_PasteByMaskGr85", - "GR85_RandomFloat", - "GR85_RandomInt", - "GR85_RandomRatio", - "GR85_RandomizedMaskTransform", - "GR85_Sam2Segmentation", - "GR85_SaveImageFile", - "GR85_SaveTextFile", - "GR85_SeedBasedOutputSelector", - "GR85_ShowText", - "GR85_SimpleWildcardPicker", - "GR85_StrSafe", - "GR85_TagInjector", - "GR85_TagInjectorDuo", - "GR85_TagInjectorLarge", - "GR85_TagInjectorSingle", - "GR85_VerticalWildcardPicker" - ], - { - "title_aux": "comfyui_gr85" - } - ], - "https://github.com/vekitan55/SimpleFlux1Merger": [ - [ - "ExpertFlux1Merge", - "SimplifiedFlux1Merge" - ], - { - "title_aux": "Simple Flux.1 Merger for ComfyUI" - } - ], - "https://github.com/verIdyia/ComfyUI-Qwen-Image-DF11": [ - [ - "DFloat11QwenImageLoader", - "QwenImageAspectRatio", - "QwenImageDecode", - "QwenImagePresetSampler", - "QwenImageSampler", - "QwenImageTextEncode" - ], - { - "title_aux": "ComfyUI Qwen-Image DFloat11 Nodes" - } - ], - "https://github.com/victorchall/comfyui_webcamcapture": [ - [ - "WebcamCapture" - ], - { - "title_aux": "Comfyui Webcam capture node" - } - ], - "https://github.com/vienteck/ComfyUI-Chat-GPT-Integration": [ - [ - "ChatGptPrompt" - ], - { - "title_aux": "ComfyUI-Chat-GPT-Integration" - } - ], - "https://github.com/violet-chen/comfyui-psd2png": [ - [ - "Psd2Png", - "StringInsert" - ], - { - "title_aux": "comfyui-psd2png" - } - ], - "https://github.com/violet0927/ComfyUI-HuggingFaceLoraUploader": [ - [ - "HuggingFaceLoraUploader", - "ModelScopeLoraUploader" - ], - { - "title_aux": "Hugging Face LoRA Uploader" - } - ], - "https://github.com/viperyl/ComfyUI-RGT": [ - [ - "RGT_Upscale" - ], - { - "title_aux": "ComfyUI-RGT" - } - ], - "https://github.com/visualbruno/ComfyUI-Hunyuan3d-2-1": [ - [ - "Hy3D21CameraConfig", - "Hy3D21ExportMesh", - "Hy3D21GenerateMultiViewsBatch", - "Hy3D21IMRemesh", - "Hy3D21LoadImageWithTransparency", - "Hy3D21LoadMesh", - "Hy3D21MeshGenerationBatch", - "Hy3D21MeshUVWrap", - "Hy3D21MeshlibDecimate", - "Hy3D21MultiViewsGeneratorWithMetaData", - "Hy3D21MultiViewsMeshGenerator", - "Hy3D21PostprocessMesh", - "Hy3D21ResizeImages", - "Hy3D21SimpleMeshlibDecimate", - "Hy3D21UseMultiViews", - "Hy3D21UseMultiViewsFromMetaData", - "Hy3D21VAEConfig", - "Hy3D21VAEDecode", - "Hy3D21VAELoader", - "Hy3DBakeMultiViews", - "Hy3DBakeMultiViewsWithMetaData", - "Hy3DHighPolyToLowPolyBakeMultiViewsWithMetaData", - "Hy3DInPaint", - "Hy3DMeshGenerator", - "Hy3DMultiViewsGenerator" - ], - { - "title_aux": "ComfyUI-Hunyuan3d-2-1" - } - ], - "https://github.com/vivax3794/ComfyUI-Sub-Nodes": [ - [ - "VIV_Default", - "VIV_Subgraph", - "VIV_Subgraph_Inputs", - "VIV_Subgraph_Outputs" - ], - { - "title_aux": "ComfyUI-Sub-Nodes" - } - ], - "https://github.com/vivax3794/ComfyUI-Vivax-Nodes": [ - [ - "Any String", - "Chunk Up", - "Get Chunk", - "Inspect", - "Join Chunks", - "Model From URL" - ], - { - "title_aux": "ComfyUI-Vivax-Nodes" - } - ], - "https://github.com/vkff5833/ComfyUI-MobileClient": [ - [ - "MobileClient" - ], - { - "title_aux": "ComfyUI-MobileClient" - } - ], - "https://github.com/vkff5833/ComfyUI-PromptConverter": [ - [ - "PromptConverter", - "PromptConverterWithFilter" - ], - { - "title_aux": "ComfyUI-PromptConverter" - } - ], - "https://github.com/vladpro3/ComfyUI_BishaNodes": [ - [ - "CreatePromptsWithTextFromFile", - "EmptyLatentSizePicker", - "LoadDataFromFiles", - "SimpleSizePicker", - "WildcardReplace", - "WildcardReplaceFromFile" - ], - { - "title_aux": "ComfyUI_BishaNodes" - } - ], - "https://github.com/vrgamegirl19/comfyui-vrgamedevgirl": [ - [ - "ColorMatchToReference", - "FastFilmGrain", - "FastLaplacianSharpen", - "FastSobelSharpen", - "FastUnsharpSharpen" - ], - { - "title_aux": "VRGameDevGirl Video Enhancement Nodes" - } - ], - "https://github.com/vsaan212/Comfy-ui-textsplit": [ - [ - "TextSplit" - ], - { - "title_aux": "ComfyUI Text Split Node" - } - ], - "https://github.com/vsevolod-oparin/comfyui-kandinsky22": [ - [ - "comfy-kandinsky22-decoder-loader", - "comfy-kandinsky22-hint-combiner", - "comfy-kandinsky22-image-encoder", - "comfy-kandinsky22-img-latents", - "comfy-kandinsky22-latents", - "comfy-kandinsky22-movq-decoder", - "comfy-kandinsky22-positive-text-encoder", - "comfy-kandinsky22-prior-averaging-2", - "comfy-kandinsky22-prior-averaging-3", - "comfy-kandinsky22-prior-averaging-4", - "comfy-kandinsky22-prior-loader", - "comfy-kandinsky22-text-encoder", - "comfy-kandinsky22-unet-decoder" - ], - { - "title_aux": "Kandinsky 2.2 ComfyUI Plugin" - } - ], - "https://github.com/vslinx/ComfyUI-vslinx-nodes": [ - [ - "vsLinx_LoadSelectedImagesBatch", - "vsLinx_LoadSelectedImagesList" - ], - { - "title_aux": "ComfyUI vsLinx Nodes" - } - ], - "https://github.com/vuongminh1907/ComfyUI_ZenID": [ - [ - "ApplyZenID", - "InstantIDFaceAnalysis", - "InstantIDModelLoader", - "ZenIDCombineFace" - ], - { - "title_aux": "ComfyUI_ZenID" - } - ], - "https://github.com/wTechArtist/ComfyUI-CustomNodes": [ - [ - "GPT4 WWL", - "IPAdapter FaceID With Bool", - "IPAdapter Mad Scientist Weight_Type", - "Image Blending Mode Mask", - "Load Image With Bool", - "Load Lora With Shared" - ], - { - "title_aux": "ComfyUI-CustomNodes" - } - ], - "https://github.com/wTechArtist/ComfyUI-StableDelight-weiweiliang": [ - [ - "WWL_StableDelight" - ], - { - "title_aux": "ComfyUI-StableDelight-weiweiliang" - } - ], - "https://github.com/wTechArtist/ComfyUI_VVL_VideoCamera_Advanced": [ - [ - "VGGTVideoCameraNode" - ], - { - "title_aux": "ComfyUI VVL Video Camera Advanced" - } - ], - "https://github.com/wakattac/ComfyUI-AbstractImaGen": [ - [ - "AbstractImageBackground", - "AbstractImageFilledShapes", - "AbstractImageGenerator", - "AbstractImageLines", - "AbstractImageNoise", - "AbstractImagePattern", - "AbstractImagePostprocessing" - ], - { - "title_aux": "ComfyUI-AbstractImaGen" - } - ], - "https://github.com/wallish77/wlsh_nodes": [ - [ - "Alternating KSampler (WLSH)", - "Build Filename String (WLSH)", - "CLIP +/- w/Text Unified (WLSH)", - "CLIP Positive-Negative (WLSH)", - "CLIP Positive-Negative XL (WLSH)", - "CLIP Positive-Negative XL w/Text (WLSH)", - "CLIP Positive-Negative w/Text (WLSH)", - "Checkpoint Loader w/Name (WLSH)", - "Empty Latent by Pixels (WLSH)", - "Empty Latent by Ratio (WLSH)", - "Empty Latent by Size (WLSH)", - "Generate Border Mask (WLSH)", - "Grayscale Image (WLSH)", - "Image Load with Metadata (WLSH)", - "Image Save with Prompt (WLSH)", - "Image Save with Prompt File (WLSH)", - "Image Save with Prompt/Info (WLSH)", - "Image Save with Prompt/Info File (WLSH)", - "Image Scale By Factor (WLSH)", - "Image Scale by Shortside (WLSH)", - "KSamplerAdvanced (WLSH)", - "Multiply Integer (WLSH)", - "Outpaint to Image (WLSH)", - "Prompt Weight (WLSH)", - "Quick Resolution Multiply (WLSH)", - "Resolutions by Ratio (WLSH)", - "SDXL Quick Empty Latent (WLSH)", - "SDXL Quick Image Scale (WLSH)", - "SDXL Resolutions (WLSH)", - "SDXL Steps (WLSH)", - "Save Positive Prompt(WLSH)", - "Save Prompt (WLSH)", - "Save Prompt/Info (WLSH)", - "Seed and Int (WLSH)", - "Seed to Number (WLSH)", - "Simple Pattern Replace (WLSH)", - "Simple String Combine (WLSH)", - "Time String (WLSH)", - "Upscale by Factor with Model (WLSH)", - "VAE Encode for Inpaint w/Padding (WLSH)" - ], - { - "title_aux": "wlsh_nodes" - } - ], - "https://github.com/wasilone11/comfyui-pvm-node": [ - [ - "SyncPVMessengerNode" - ], - { - "title_aux": "ComfyUI Sync PVM Node" - } - ], - "https://github.com/wasilone11/comfyui-sync-lipsync-node": [ - [ - "SyncLipsyncInputNode", - "SyncLipsyncMainNode", - "SyncLipsyncOutputNode" - ], - { - "title_aux": "ComfyUI Sync Lipsync Node" - } - ], - "https://github.com/watarika/ComfyUI-SendToEagle-w-Metadata": [ - [ - "CreateExtraMetadata", - "SendToEagleWithMetadata", - "SendToEagleWithMetadataSimple" - ], - { - "title_aux": "ComfyUI-SendToEagle-w-Metadata" - } - ], - "https://github.com/wawahuy/ComfyUI-HTTP": [ - [ - "Base64ToImageNode", - "HTTPFormDataConcatNode", - "HTTPFormDataNode", - "HTTPFormFileItemNode", - "HTTPFormImageItemNode", - "HTTPFormTextItemNode", - "HTTPGetJSONFieldNode", - "HTTPGetNode", - "HTTPPostFormDataNode", - "HTTPPostJSONNode", - "HTTPPostRawNode", - "ImageToBase64Node" - ], - { - "title_aux": "ComfyUI HTTP - REST API Nodes" - } - ], - "https://github.com/web3nomad/ComfyUI_Invisible_Watermark": [ - [ - "InvisibleWatermarkEncode" - ], - { - "title_aux": "ComfyUI Invisible Watermark" - } - ], - "https://github.com/weberjc/book-cover-finder-comfy": [ - [ - "BookCoverFinder" - ], - { - "title_aux": "BookCoverFinder" - } - ], - "https://github.com/webfiltered/DebugNode-ComfyUI": [ - [ - "WTFDebugNode" - ], - { - "title_aux": "WTF? - a debug node for ComfyUI" - } - ], - "https://github.com/webuilder/WB-ComfyUI-Utils": [ - [ - "WB_AudioDuration" - ], - { - "title_aux": "ComfyUI WB Utils" - } - ], - "https://github.com/weekii/comfyui-save-image-pro": [ - [ - "SaveImageAdvanced", - "SaveImageSimple" - ], - { - "author": "weekii", - "description": "\u4e13\u4e1a\u7ea7\u56fe\u50cf\u4fdd\u5b58\u63d2\u4ef6\uff0c\u652f\u6301\u591a\u683c\u5f0f\u3001\u81ea\u5b9a\u4e49\u547d\u540d\u548c\u9ad8\u7ea7\u529f\u80fd", - "nickname": "Save Image Pro", - "title": "ComfyUI Save Image Pro", - "title_aux": "comfyui-save-image-pro" - } - ], - "https://github.com/weilin9999/WeiLin-Comfyui-Tools": [ - [ - "WeiLinPromptUI", - "WeiLinPromptUIOnlyLoraStack", - "WeiLinPromptUIWithoutLora" - ], - { - "title_aux": "WeiLin-Comfyui-Tools" - } - ], - "https://github.com/welltop-cn/ComfyUI-TeaCache": [ - [ - "CompileModel", - "TeaCache", - "TeaCacheForCogVideoX" - ], - { - "title_aux": "ComfyUI-TeaCache" - } - ], - "https://github.com/wentao-uw/ComfyUI-template-matching": [ - [ - "IsMaskEmptyNode (template matching)", - "TemplateMatching (template matching)" - ], - { - "title_aux": "ComfyUI template matching" - } - ], - "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-editor": [ - [ - "AppendageEditorNode", - "OpenposeEditorNode" - ], - { - "title_aux": "ComfyUI-ultimate-openpose-editor" - } - ], - "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-estimator": [ - [ - "OpenposeEstimatorNode" - ], - { - "title_aux": "ComfyUI-ultimate-openpose-estimator" - } - ], - "https://github.com/westNeighbor/ComfyUI-ultimate-openpose-render": [ - [ - "OpenposeRenderNode" - ], - { - "title_aux": "ComfyUI-ultimate-openpose-render" - } - ], - "https://github.com/whatbirdisthat/cyberdolphin": [ - [ - "\ud83d\udc2c Gradio ChatInterface", - "\ud83d\udc2c OpenAI Advanced", - "\ud83d\udc2c OpenAI Compatible", - "\ud83d\udc2c OpenAI DALL\u00b7E", - "\ud83d\udc2c OpenAI Simple" - ], - { - "title_aux": "cyberdolphin" - } - ], - "https://github.com/whmc76/ComfyUI-Openpose-Editor-Plus": [ - [ - "CDL.OpenPoseEditorPlus" - ], - { - "title_aux": "ComfyUI-Openpose-Editor-Plus" - } - ], - "https://github.com/whmc76/ComfyUI-RemoveBackgroundSuite": [ - [ - "BiRefNetUltra_RBS", - "MaskProcessDetails_RBS", - "TransparentBackgroundUltra_RBS" - ], - { - "title_aux": "ComfyUI-RemoveBackgroundSuite" - } - ], - "https://github.com/whmc76/ComfyUI-UniversalToolkit": [ - [ - "AudioCropProcessUTK", - "CheckMask_UTK", - "CropByMask_UTK", - "DepthMapBlur_UTK", - "EmptyUnitGenerator_UTK", - "FillMaskedArea_UTK", - "ImageAndMaskPreview_UTK", - "ImageCombineAlpha_UTK", - "ImageConcatenateMulti_UTK", - "ImageConcatenate_UTK", - "ImageMaskScaleAs_UTK", - "ImagePadForOutpaintMasked_UTK", - "ImageRatioDetector_UTK", - "ImageRemoveAlpha_UTK", - "ImageScaleByAspectRatio_UTK", - "ImageScaleRestore_UTK", - "ImitationHueNode_UTK", - "LoadAudioPlusFromPath_UTK", - "LoadKontextPresets_UTK", - "LoraInfo_UTK", - "MaskAdd_UTK", - "MaskAnd_UTK", - "MaskSub_UTK", - "MathExpression_UTK", - "PurgeVRAM_UTK", - "RestoreCropBox_UTK", - "TextBoxNode_UTK", - "TextConcatenate_UTK", - "ThinkRemover_UTK", - "Video_Prompt_Helper" - ], - { - "title_aux": "ComfyUI-UniversalToolkit" - } - ], - "https://github.com/wildminder/ComfyUI-Chatterbox": [ - [ - "ChatterboxTTS", - "ChatterboxVC" - ], - { - "title_aux": "ComfyUI-Chatterbox" - } - ], - "https://github.com/wildminder/ComfyUI-KEEP": [ - [ - "KEEP_FaceUpscaleImage", - "KEEP_ModelLoader", - "KEEP_ProcessImageSequence" - ], - { - "title_aux": "ComfyUI-KEEP" - } - ], - "https://github.com/wildminder/ComfyUI-VibeVoice": [ - [ - "VibeVoiceTTS" - ], - { - "title_aux": "ComfyUI-VibeVoice" - } - ], - "https://github.com/willchil/ComfyUI-Environment-Visualizer": [ - [ - "EnvironmentVisualizer", - "InterpolateEdges", - "MapEquirectangular" - ], - { - "title_aux": "ComfyUI-Environment-Visualizer" - } - ], - "https://github.com/windfancy/zsq_prompt": [ - [ - "BatchPromptJson", - "BatchPromptSelector", - "ConnectionString", - "DoubleCLIPEncode", - "FloatMathOperation", - "ImageAddText", - "ImageEmpty", - "IndexString", - "IntMathOperation", - "JoinImageBatch", - "LLMImage", - "LLMText", - "OptionString", - "PortraitStyler", - "SaveJpgImage", - "StringInput", - "ZSQPixelLatent", - "ZSQRatioLatent", - "ZSQShowINT", - "ZSQShowText", - "checkpoint_sampler", - "controlnetStack", - "controlnetStack_2", - "imageConcat", - "imageCount", - "imageCrop", - "imageDetailTransfer", - "imageFilter", - "imageFlip", - "imageGaussianBlur", - "imageHug", - "imageRGB", - "imageRatio", - "imageResize", - "imageRotate", - "imageSaveSimple", - "imageScaleDown", - "imageScaleDownBy", - "imageSharpen", - "imageSize", - "imageTilesFromBatch", - "imagesSplitImage", - "loraStack", - "loraStack_2", - "stylesSelector", - "zsqcheckpoint", - "zsqcontrolnet", - "zsqsampler" - ], - { - "title_aux": "zsq_prompt" - } - ], - "https://github.com/wings6407/ComfyUI_HBH-image_overlay": [ - [ - "HBH_ImageCoordinatePicker", - "HBH_ImageCoordinatePreview", - "HBH_ImageInteractivePicker", - "HBH_ImageOverlay", - "HBH_ImageOverlayPreview", - "HBH_ImagePreview" - ], - { - "title_aux": "ComfyUI_HBH-image_overlay" - } - ], - "https://github.com/wirytiox/ComfyUI-SelectStringFromListWithIndex": [ - [ - "StringFromList" - ], - { - "title_aux": "ComfyUI-SelectStringFromListWithIndex" - } - ], - "https://github.com/withmpx/mpx-comfyui-nodes": [ - [ - "Agent_PickBestImageFromList", - "Agent_ReflectionOnImageList", - "ImagesTo3DModels", - "LoadImageData", - "ObjectListToImageList", - "PickFromList", - "SaveModelsToDisk", - "ShowList", - "ShowString", - "StringListToStringList", - "StringListToText", - "TextToImage", - "TextToList", - "TextToObjectList", - "TextToScriptBreakdown", - "TextToStory", - "TextToText", - "TransformObjectList", - "TwoTextToText" - ], - { - "title_aux": "mpx-comfyui-nodes" - } - ], - "https://github.com/wjl0313/ComfyUI_KimNodes": [ - [ - "Add_ImageMetadata", - "BoundingBox_Cropper", - "Crop_Paste", - "Distribute_Icons", - "Edge_Element_Cropper", - "ExtractDifferenceLora", - "IconDistributeByGrid", - "Icon_Position_Cropper", - "Image_Classification", - "Image_PixelFilter", - "Image_Resize", - "KimFilter", - "KimHDR", - "LoRA_Metadata_Reader", - "LoadImage_Metadata", - "Manual_MetadataInput", - "Mask_Noise_Cleaner", - "Mask_White_Area_Ratio", - "MaxLength_ImageListSelector", - "Percentage_Cropper", - "Pixelate_Filter", - "Prompt_Text", - "Save_Image", - "Seamless_Icon_Generator", - "Split_Mask", - "Text_Match", - "Text_Processor", - "Transparent_Area_Cropper", - "Transparent_Image_Filter", - "Whitening_Node", - "YOLOWorld_Match", - "YOLO_Crop", - "YOLO_Multi_Crop" - ], - { - "title_aux": "ComfyUI_KimNodes" - } - ], - "https://github.com/wmatson/easy-comfy-nodes": [ - [ - "EZAssocDictNode", - "EZAssocImgNode", - "EZAssocStrNode", - "EZEmptyDictNode", - "EZHttpPostNode", - "EZLoadImgBatchFromUrlsNode", - "EZLoadImgFromUrlNode", - "EZRemoveImgBackground", - "EZS3Uploader" - ], - { - "title_aux": "easy-comfy-nodes" - } - ], - "https://github.com/wmpmiles/comfyui-some-image-processing-stuff": [ - [ - "Blur Mask", - "Color Grading", - "Latent Zero Mask", - "Mask-Crop Inpaint | Post", - "Mask-Crop Inpaint | Pre", - "Mask-Crop | Post", - "Mask-Crop | Pre", - "Resample Image", - "Resample Latent", - "Resample Mask", - "Resampler | Area", - "Resampler | Jinc-Lanczos", - "Resampler | Lanczos", - "Resampler | Mitchell-Netravali", - "Resampler | Nearest-Neighbor", - "Resampler | Triangle", - "Scaler | Area", - "Scaler | Fixed", - "Scaler | Megapixels", - "Scaler | Pixel Deltas", - "Scaler | Side", - "Scaler | Sides Unlinked" - ], - { - "title_aux": "comfyui-some-image-processing-stuff" - } - ], - "https://github.com/woct0rdho/ComfyUI-RadialAttn": [ - [ - "PatchRadialAttn" - ], - { - "title_aux": "ComfyUI-RadialAttn" - } - ], - "https://github.com/wolfden/ComfyUi_PromptStylers": [ - [ - "SDXLPromptStylerAll", - "SDXLPromptStylerHorror", - "SDXLPromptStylerMisc", - "SDXLPromptStylerbyArtist", - "SDXLPromptStylerbyCamera", - "SDXLPromptStylerbyComposition", - "SDXLPromptStylerbyCyberpunkSurrealism", - "SDXLPromptStylerbyDepth", - "SDXLPromptStylerbyEnvironment", - "SDXLPromptStylerbyFantasySetting", - "SDXLPromptStylerbyFilter", - "SDXLPromptStylerbyFocus", - "SDXLPromptStylerbyImpressionism", - "SDXLPromptStylerbyLighting", - "SDXLPromptStylerbyMileHigh", - "SDXLPromptStylerbyMood", - "SDXLPromptStylerbyMythicalCreature", - "SDXLPromptStylerbyOriginal", - "SDXLPromptStylerbyQuantumRealism", - "SDXLPromptStylerbySteamPunkRealism", - "SDXLPromptStylerbySubject", - "SDXLPromptStylerbySurrealism", - 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], - { - "title_aux": "ComfyUI Pause Workflow Node" - } - ], - "https://github.com/xXAdonesXx/NodeGPT": [ - [ - "AppendAgent", - "Assistant", - "Chat", - "ChatGPT", - "CombineInput", - "Conditioning", - "CostumeAgent_1", - "CostumeAgent_2", - "CostumeMaster_1", - "Critic", - "DisplayString", - "DisplayTextAsImage", - "EVAL", - "Engineer", - "Executor", - "GroupChat", - "Image_generation_Conditioning", - "LM_Studio", - "LoadAPIconfig", - "LoadTXT", - "MemGPT", - "Memory_Excel", - "Model_1", - "Ollama", - "Output2String", - "Planner", - "Scientist", - "TextCombine", - "TextGeneration", - "TextGenerator", - "TextInput", - "TextOutput", - "UserProxy", - "llama-cpp", - "llava", - "oobaboogaOpenAI" - ], - { - "title_aux": "NodeGPT" - } - ], - "https://github.com/xfgexo/EXO-Custom-ComfyUI-Nodes": [ - [ - "ComfyUI_EXO_Clip_Text_Encode", - "ComfyUI_EXO_DisplayText", - "ComfyUI_EXO_FluxSampler", - "ComfyUI_EXO_FluxSamplerMini", - "ComfyUI_EXO_ImageRescale", - "ComfyUI_EXO_LatentImageSize", - "ComfyUI_EXO_LatentImageSizeX", - 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"https://github.com/xiaoxiaodesha/hd_node": [ - [ - "Combine HDMasks", - "Cover HDMasks", - "HD FaceIndex", - "HD GetMaskArea", - "HD Image Levels", - "HD SmoothEdge", - "HD UltimateSDUpscale" - ], - { - "title_aux": "hd-nodes-comfyui" - } - ], - "https://github.com/xingBaGan/ComfyUI-connect-ui": [ - [ - "SaveImageByWebsocket", - "reciveImageByWebsocket" - ], - { - "title_aux": "ComfyUI-connect-ui" - } - ], - "https://github.com/xlinx/ComfyUI-decadetw-auto-messaging-realtime": [ - [ - "Auto-MSG-ALL", - "Auto-MSG-Discord-Bot", - "Auto-MSG-Line-Notify", - "Auto-MSG-Telegram-Bot" - ], - { - "title_aux": "ComfyUI-decadetw-auto-messaging-realtime" - } - ], - "https://github.com/xlinx/ComfyUI-decadetw-auto-prompt-llm": [ - [ - "Auto-LLM-Chat", - "Auto-LLM-Text", - "Auto-LLM-Text-Vision", - "Auto-LLM-Vision" - ], - { - "title_aux": "ComfyUI-decadetw-auto-prompt-llm" - } - ], - "https://github.com/xlinx/ComfyUI-decadetw-spout-syphon-im-vj": [ - [ - "Im-SD-VJ-SPOUT", - "Im-SD-VJ-SYPHON" - ], - { - "title_aux": "ComfyUI-decadetw-spout-syphon-im-vj" - } - ], - "https://github.com/xliry/ComfyUI_SendDiscord": [ - [ - "SendDiscord" - ], - { - "title_aux": "ComfyUI_SendDiscord" - } - ], - "https://github.com/xmarre/TorchCompileModel_LoRASafe": [ - [ - "TorchCompileModel_LoRASafe" - ], - { - "title_aux": "LoRA-Safe TorchCompile" - } - ], - "https://github.com/xobiomesh/ComfyUI_xObiomesh": [ - [ - "OllamaModelSelect", - "OllamaTextGen", - "ShowText_xO", - "xO_ComfyUIPortRunner", - "xO_GetImageFilename", - "xO_LoadRecentFile", - "xO_TestScriptRunner", - "xO_WorkflowRunner" - ], - { - "title_aux": "ComfyUI Neural Nodes" - } - ], - "https://github.com/xs315431/Comfyui_Get_promptId": [ - [ - "GetPromptId", - "SuccessCallback", - "UploadVideo" - ], - { - "title_aux": "Comfyui_Get_promptId" - } - ], - "https://github.com/xuhongming251/ComfyUI-GPEN": [ - [ - "FaceEnhancement" - ], - { - "title_aux": "ComfyUI-GPEN" - } - ], - "https://github.com/xuhongming251/ComfyUI-Jimeng": [ - [ - "JimengAPIClient", - "JimengFirstLastFrame2Video", - "JimengImage2Video", - "PreviewVideoFromUrl" - ], - { - "title_aux": "ComfyUI-Jimeng" - } - ], - "https://github.com/xuhongming251/ComfyUI-MuseTalkUtils": [ - [ - "MuseTalkPostprocess", - "MuseTalkPreprocess", - "MuseTalkTrain", - "MuseTalkTrainPreprocess", - "MuseTalkUncropMask" - ], - { - "title_aux": "ComfyUI-MuseTalkUtils" - } - ], - "https://github.com/xuhongming251/ComfyUI_Camera": [ - [ - "Load Image From Local Camera", - "Save Image To Local Camera" - ], - { - "title_aux": "ComfyUI_Camera" - } - ], - "https://github.com/yamanacn/comfyui_kontext_Analyze": [ - [ - "KontextDuoImageAnalyzer" - ], - { - "title_aux": "ComfyUI Kontext Duo Image Analyzer" - } - ], - "https://github.com/yanhuifair/comfyui-janus": [ - [ - "JanusProImageGenerationNode", - "JanusProModelLoaderNode", - "JanusProMultimodalUnderstandingNode" - ], - { - "title_aux": "comfyui-janus" - } - ], - "https://github.com/yanlang0123/ComfyUI_Lam": [ - [ - "AppParams", - "AspectRatio", - "AudioBeforeAfterSilence", - "AutioInfo", - "AutioPath", - "DoWhileEnd", - "DoWhileStart", - "EasyPromptSelecto", - "FaceFusion", - "ForEnd", - "ForInnerEnd", - "ForInnerStart", - "ForStart", - "GLM3Prompt", - "IdentifyingQR", - "IfInnerExecute", - "Image2Video", - "ImageAddMask", - "ImageBlank", - "ImageClone", - "ImageCropFaces", - "ImageLama", - "ImageToMasks", - "JyAnimationGroup", - "JyAnimationIn", - "JyAnimationOut", - "JyAudio2CaptionsGroup", - "JyAudioNative", - "JyAudioTrack", - "JyCaptionsNative", - "JyCaptionsTrack", - "JyEffectNative", - "JyEffectTrack", - "JyMediaAnimation", - "JyMediaNative", - "JyMediaTrack", - "JyMultiAudioGroup", - "JyMultiCaptionsGroup", - "JyMultiEffectGroup", - "JyMultiMediaGroup", - "JySaveDraft", - "JySaveNoOutDraft", - "JySaveNotOutDraft", - "JySaveOutDraft", - "JyTransition", - "LAM.OpenPoseEditorPlus", - "LamCommonHidden", - "LamCommonNames", - "LamCommonPrint", - "LamCommonPrintNoOutput", - "LamFaceAnalysisModels", - "LamGetPngInfo", - "LamHeyGemNode", - "LamHeyGemQueryNode", - 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], - { - "title_aux": "ComfyUI_Lam" - } - ], - "https://github.com/yasser-baalla/comfyUI-SemanticImageFetch": [ - [ - "ColorGradeSampler", - "ColorGrading", - "SemanticImageFetch" - ], - { - "title_aux": "comfyUI-SemanticImageFetch" - } - ], - "https://github.com/ycchanau/ComfyUI_Preview_Magnifier": [ - [ - "YC.ImageComparerMagnifier", - "YC.PreviewImageMagnifier", - "YC.XYPreviewImageMagnifier" - ], - { - "title_aux": "ComfyUI Preview Magnifier" - } - ], - "https://github.com/ycyy/ComfyUI-YCYY-LoraInfo": [ - [ - "LoraInfo" - ], - { - "title_aux": "ComfyUI-YCYY-LoraInfo" - } - ], - "https://github.com/yffyhk/comfyui_auto_danbooru": [ - [ - "GetDanbooru", - "TagEncode" - ], - { - "title_aux": "comfyui_auto_danbooru" - } - ], - "https://github.com/yhayano-ponotech/ComfyUI-Fal-API-Flux": [ - [ - "FalAPIFluxControlNetConfigNode", - "FalAPIFluxControlNetUnionConfigNode", - "FalAPIFluxDevCannyWithLoraNode", - "FalAPIFluxDevImageToImageNode", - "FalAPIFluxDevNode", - "FalAPIFluxDevWithLoraAndControlNetImageToImageNode", - "FalAPIFluxDevWithLoraAndControlNetInpaintNode", - "FalAPIFluxDevWithLoraAndControlNetNode", - "FalAPIFluxDevWithLoraImageToImageNode", - "FalAPIFluxDevWithLoraInpaintNode", - "FalAPIFluxDevWithLoraNode", - "FalAPIFluxLoraConfigNode", - "FalAPIFluxProCannyNode", - "FalAPIFluxProDepthNode", - "FalAPIFluxProFillNode", - "FalAPIFluxProNode", - "FalAPIFluxProReduxNode", - "FalAPIFluxProV11Node", - "FalAPIFluxProV11UltraNode" - ], - { - "title_aux": "ComfyUI-Fal-API-Flux" - } - ], - "https://github.com/yhayano-ponotech/comfyui-save-image-local": [ - [ - "Local Save" - ], - { - "title_aux": "ComfyUI Local Save Node" - } - ], - "https://github.com/yhayano-ponotech/comfyui-stability-ai-api": [ - [ - "Preview3DModel", - "Save3DModel", - "StabilityControlSketch", - "StabilityControlStructure", - "StabilityControlStyle", - "StabilityEdit", - "StabilityImageCore", - "StabilityImageSD3", - "StabilityImageToVideo", - "StabilityImageUltra", - "StabilityUpscaleConservative", - "StabilityUpscaleCreative", - "StabilityUpscaleFast", - "StableFast3D", - "StablePointAware3D" - ], - { - "title_aux": "ComfyUI-Stability-AI-API" - } - ], - "https://github.com/yichengup/ComfyUI-LinearTransition": [ - [ - "GradientTransition", - "LinearTransition" - ], - { - "title_aux": "ComfyUI-LinearTransition" - } - ], - "https://github.com/yichengup/ComfyUI-YCNodes": [ - [ - "AdvancedImageSelector", - "DynamicThreshold", - "ImageBatchSelector", - "ImageBlendResize", - "ImageIC", - "ImageICAdvanced", - "ImageLoaderAdvanced", - "ImageMirror", - "ImageMosaic", - "ImageRotate", - "ImageSelector", - "ImageUpscaleTiled", - "IrregularToEllipseMask", - "LoadImagesFromFolder", - "MaskBatchComposite", - "MaskBatchCopy", - "MaskContourFillNode", - "MaskCropRestore_YC", - "MaskCrop_YC", - "MaskFilterBySolidity", - "MaskFromBatch", - "MaskPreviewNode", - "MaskRepeatBatch", - "MaskSmartValleySplit", - "MaskSplitFilter", - "MaskTopNFilter", - "TextKeyword", - "YC Extract Number", - "YC Mask Condition Switch", - "YC Seed List", - "YC Super Selector", - "YC Text Condition Switch", - "YC Text Index Switch", - "YC Universal Gate", - "YCImageSmartCrop", - "YCImageSmartPad", - "YCImageTile", - "YCImageUntile", - "YCMaskComposite", - "YCRemapMaskRange", - "YCTextImageGenerator", - "YC_FiveTextCombineNode", - "YC_Image_Save", - "YC_SingleTextNode", - "YC_textReplaceNode" - ], - { - "title_aux": "ComfyUI-YCNodes" - } - ], - "https://github.com/yichengup/ComfyUI_Yc_JanusPro": [ - [ - "ImageAnalyzer", - "JanusChatAnalyzer", - "JanusImageGenerator", - "JanusProLoader" - ], - { - "title_aux": "ComfyUI_Yc_JanusPro" - } - ], - "https://github.com/yichengup/Comfyui-Deepseek": [ - [ - "DeepseekAdvancedNode", - "DeepseekNode", - "DeepseekReasonerNode", - "SiliconDeepseekChat", - "SiliconDeepseekReasoner" - ], - { - "title_aux": "Comfyui-Deepseek" - } - ], - "https://github.com/yichengup/Comfyui-Ycanvas": [ - [ - "CanvasNode" - ], - { - "title_aux": "Comfyui-Ycanvas" - } - ], - "https://github.com/yichengup/Comfyui_Flux_Style_Adjust": [ - [ - "StyleModelAdvancedApply" - ], - { - "title_aux": "Comfyui_Flux_Style_Adjust (Redux)" - } - ], - "https://github.com/yichengup/Comfyui_Redux_Advanced": [ - [ - "StyleAdvancedApply", - "YC_LG_Redux" - ], - { - "title_aux": "Comfyui_Redux_Advanced" - } - ], - "https://github.com/yichengup/comfyui-face-liquify": [ - [ - "FaceLiquifyNode" - ], - { - "title_aux": "comfyui-face-liquify" - } - ], - "https://github.com/yiwangsimple/ComfyUI_DW_Chat": [ - [ - "DeepSeekChatNode", - "DeepSeekTranslator", - "ErrorLogNode", - "ExecutionTime", - "FileBasedChatNode", - "FluxPromptEngineeringNode", - "Gemini1_5Text", - "Gemini1_5Vision", - "GeminiFluxPrompt", - "Gemma2PromptNode", - "GemmaDialogueNode", - "GithubLinkNode", - "GroqChatNode", - "MoonshotMultiChatNode", - "MoonshotSingleChatNode", - "OllamaImageToText", - "OllamaPromptExtractor", - "OllamaTextToText", - "PaliGemma3bCaptioner", - "PromptEngineeringNode", - "PromptExtractorNode", - "Qwen2VLCaption", - "Qwen2VLLocalCaption", - "SD3LongCaptionerV2", - "SDPromptAgent", - "dwimage2" - ], - { - "title_aux": "ComfyUI_DW_Chat" - } - ], - "https://github.com/yiwangsimple/florence_dw": [ - [ - "Florence2", - "Florence2Postprocess", - "Florence2PostprocessAll", - "LoadFlorence2Model" - ], - { - "title_aux": "florence_dw" - } - ], - "https://github.com/yolain/ComfyUI-Easy-Use": [ - [ - "dynamicThresholdingFull", - "easy LLLiteLoader", - "easy XYInputs: CFG Scale", - "easy XYInputs: Checkpoint", - "easy XYInputs: ControlNet", - "easy XYInputs: Denoise", - "easy XYInputs: FluxGuidance", - "easy XYInputs: Lora", - "easy XYInputs: ModelMergeBlocks", - "easy XYInputs: NegativeCond", - "easy XYInputs: NegativeCondList", - "easy XYInputs: PositiveCond", - "easy XYInputs: PositiveCondList", - "easy XYInputs: PromptSR", - "easy XYInputs: Sampler/Scheduler", - "easy XYInputs: Seeds++ Batch", - "easy XYInputs: Steps", - "easy XYPlot", - "easy XYPlotAdvanced", - "easy a1111Loader", - "easy ab", - "easy anythingIndexSwitch", - "easy anythingInversedSwitch", - "easy applyBrushNet", - "easy applyFooocusInpaint", - "easy applyInpaint", - "easy applyPowerPaint", - "easy batchAnything", - "easy blocker", - "easy boolean", - "easy cascadeKSampler", - "easy cascadeLoader", - "easy ckptNames", - "easy cleanGpuUsed", - "easy clearCacheAll", - "easy clearCacheKey", - "easy comfyLoader", - "easy compare", - "easy conditioningIndexSwitch", - "easy controlnetLoader", - "easy controlnetLoader++", - "easy controlnetLoaderADV", - "easy controlnetNames", - "easy controlnetStack", - "easy controlnetStackApply", - "easy convertAnything", - "easy detailerFix", - "easy float", - "easy fluxLoader", - "easy forLoopEnd", - "easy forLoopStart", - "easy fullCascadeKSampler", - "easy fullLoader", - "easy fullkSampler", - "easy globalSeed", - "easy hiresFix", - "easy humanSegmentation", - "easy hunyuanDiTLoader", - "easy icLightApply", - "easy if", - "easy ifElse", - "easy imageBatchToImageList", - "easy imageChooser", - "easy imageColorMatch", - "easy imageConcat", - "easy imageCount", - "easy imageCropFromMask", - "easy imageDetailTransfer", - "easy imageIndexSwitch", - "easy imageInsetCrop", - "easy imageInterrogator", - "easy imageListToImageBatch", - "easy imagePixelPerfect", - "easy imageRatio", - "easy imageRemBg", - "easy imageSave", - "easy imageScaleDown", - "easy imageScaleDownBy", - "easy imageScaleDownToSize", - "easy imageScaleToNormPixels", - "easy imageSize", - "easy imageSizeByLongerSide", - "easy imageSizeBySide", - "easy imageSplitGrid", - "easy imageSplitList", - "easy imageSplitTiles", - "easy imageSwitch", - "easy imageTilesFromBatch", - "easy imageToBase64", - "easy imageToMask", - "easy imageUncropFromBBOX", - "easy imagesCountInDirectory", - "easy imagesSplitImage", - "easy indexAnything", - "easy injectNoiseToLatent", - "easy instantIDApply", - "easy instantIDApplyADV", - "easy int", - "easy ipadapterApply", - "easy ipadapterApplyADV", - "easy ipadapterApplyEmbeds", - "easy ipadapterApplyEncoder", - "easy ipadapterApplyFaceIDKolors", - 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"id": "https://github.com/comfyanonymous/ComfyUI_experiments", - "tags": "Dynamic Thresholding, DT, CFG, controlnet, reference only" - }, - { - "description": "To implement the feature of automatically detecting faces and enhancing details, various detection nodes and detailers provided by the Impact Pack can be applied. Similarly to Loopback Scaler, it also provides various custom workflows that can apply Ksampler while gradually scaling up.", - "id": "https://github.com/ltdrdata/ComfyUI-Impact-Pack", - "tags": "ddetailer, adetailer, ddsd, DD, loopback scaler, prompt, wildcard, dynamic prompt" - }, - { - "description": "The Inspire Pack provides the functionality of Lora Block Weight, Variation Seed.", - "id": "https://github.com/ltdrdata/ComfyUI-Inspire-Pack", - "tags": "lora block weight, effective block analyzer, lbw, variation seed" - }, - { - "description": "This extension provides a feature that generates segment masks on an image using a text prompt. When used in conjunction with Impact Pack, it enables applications such as DDSD.", - "id": "https://github.com/biegert/ComfyUI-CLIPSeg/raw/main/custom_nodes/clipseg.py", - "tags": "ddsd" - }, - { - "description": "This extension is a less feature-rich and well-maintained alternative to Impact Pack, but it has fewer dependencies and may be easier to install on abnormal configurations. The author recommends trying Impact Pack first.", - "id": "https://github.com/BadCafeCode/masquerade-nodes-comfyui", - "tags": "ddetailer" - }, - { - "description": "By using this extension, prompts like 'blue hair' can be prevented from interfering with other prompts by blocking the attribute 'blue' from being used in prompts other than 'hair'.", - "id": "https://github.com/BlenderNeko/ComfyUI_Cutoff", - "tags": "cutoff" - }, - { - "description": "There are differences in the processing methods of prompts, such as weighting and scheduling, between A1111 and ComfyUI. With this extension, various settings can be used to implement prompt processing methods similar to A1111. As this feature is also integrated into ComfyUI Cutoff, please download the Cutoff extension if you plan to use it in conjunction with Cutoff.", - "id": "https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb", - "tags": "prompt, weight" - }, - { - "description": "There are differences in the processing methods of prompts, such as weighting and scheduling, between A1111 and ComfyUI. This extension helps to reproduce the same embedding as A1111.", - "id": "https://github.com/shiimizu/ComfyUI_smZNodes", - "tags": "prompt, weight" - }, - { - "description": "The extension provides an unsampler that reverses the sampling process, allowing for a function similar to img2img alt to be implemented. Furthermore, ComfyUI uses CPU's Random instead of GPU's Random for better reproducibility compared to A1111. This extension provides the ability to use GPU's Random for Latent Noise. However, since GPU's Random may vary depending on the GPU model, reproducibility on different devices cannot be guaranteed.", - "id": "https://github.com/BlenderNeko/ComfyUI_Noise", - "tags": "img2img alt, random" - }, - { - "description": "The extension provides seecoder feature.", - "id": "https://github.com/BlenderNeko/ComfyUI_SeeCoder", - "tags": "seecoder, prompt-free-diffusion" - }, - { - "description": "This extension provides features such as a wildcard function that randomly selects prompts belonging to a category and the ability to directly load lora from prompts.", - "id": "https://github.com/lilly1987/ComfyUI_node_Lilly", - "tags": "prompt, wildcard" - }, - { - "description": "ComfyUI already provides the ability to composite latents by default. However, this extension makes it more convenient to use by visualizing the composite area.", - "id": "https://github.com/Davemane42/ComfyUI_Dave_CustomNode", - "tags": "latent couple" - }, - { - "description": "This tool provides a viewer node that allows for checking multiple outputs in a grid, similar to the X/Y Plot extension.", - "id": "https://github.com/LEv145/images-grid-comfy-plugin", - "tags": "X/Y Plot" - }, - { - "description": "This extension generates clip text by taking an image as input and using the Deepbooru model.", - "id": "https://github.com/pythongosssss/ComfyUI-WD14-Tagger", - "tags": "deepbooru, clip interrogation" - }, - { - "description": "This node takes two models, merges individual blocks together at various ratios, and automatically rates each merge, keeping the ratio with the highest score. ", - "id": "https://github.com/szhublox/ambw_comfyui", - "tags": "supermerger" - }, - { - "description": "ComfyUI nodes for the Ultimate Stable Diffusion Upscale script by Coyote-A. Uses the same script used in the A1111 extension to hopefully replicate images generated using the A1111 webui.", - "id": "https://github.com/ssitu/ComfyUI_UltimateSDUpscale", - "tags": "upscaler, Ultimate SD Upscale" - }, - { - "description": "A1111 provides KSampler that uses GPU-based random noise. This extension offers KSampler utilizing GPU-based random noise.", - "id": "https://github.com/dawangraoming/ComfyUI_ksampler_gpu/raw/main/ksampler_gpu.py", - "tags": "random, noise" - }, - { - "description": "This extension provides nodes with the functionality of dynamic prompts.", - "id": "https://github.com/space-nuko/nui-suite", - "tags": "prompt, dynamic prompt" - }, - { - "description": "This extension provides bunch of nodes including roop", - "id": "https://github.com/melMass/comfy_mtb", - "tags": "roop" - }, - { - "description": "This extension provides nodes for the roop A1111 webui script.", - "id": "https://github.com/ssitu/ComfyUI_roop", - "tags": "roop" - }, - { - "description": "This extension provides the ability to use prompts like \n\n**a [large::0.1] [cat|dog:0.05] [::0.5] [in a park:in space:0.4]**\n\n", - "id": "https://github.com/asagi4/comfyui-prompt-control", - "tags": "prompt, prompt editing" - }, - { - "description": "This extension is a port of sd-dynamic-prompt to ComfyUI.", - "id": "https://github.com/adieyal/comfyui-dynamicprompts", - "tags": "prompt, dynamic prompt" - }, - { - "description": "A Anime Background Remover node for comfyui, based on this hf space, works same as AGB extention in automatic1111.", - "id": "https://github.com/kwaroran/abg-comfyui", - "tags": "abg, background remover" - }, - { - "description": "This is a ported version of ComfyUI for the sd-webui-roop-nsfw extension.", - "id": "https://github.com/Gourieff/comfyui-reactor-node", - "tags": "reactor, sd-webui-roop-nsfw" - }, - { - "description": "This custom nodes provide a functionality similar to regional prompts, offering couple features at the attention level.", - "id": "https://github.com/laksjdjf/cgem156-ComfyUI", - "tags": "regional prompt, latent couple, prompt" - }, - { - "description": "This custom nodes provide functionality that assists in animation creation, similar to deforum.", - "id": "https://github.com/FizzleDorf/ComfyUI_FizzNodes", - "tags": "deforum" - }, - { - "description": "This custom nodes provide functionality that assists in animation creation, similar to deforum.", - "id": "https://github.com/seanlynch/comfyui-optical-flow", - "tags": "deforum, vid2vid" - }, - { - "description": "Similar to sd-webui-fabric, this custom nodes provide the functionality of [a/FABRIC](https://github.com/sd-fabric/fabric).", - "id": "https://github.com/ssitu/ComfyUI_fabric", - "tags": "fabric" - }, - { - "description": "Similar to text-generation-webui, this custom nodes provide the functionality of [a/exllama](https://github.com/turboderp/exllama).", - "id": "https://github.com/Zuellni/ComfyUI-ExLlama", - "tags": "ExLlama, prompt, language model" - }, - { - "description": "ComfyUI node for generating seamless textures Replicates 'Tiling' option from A1111", - "id": "https://github.com/spinagon/ComfyUI-seamless-tiling", - "tags": "tiling" - }, - { - "description": "This extension is a port of the [a/sd-webui-cd-tuner](https://github.com/hako-mikan/sd-webui-cd-tuner)(a.k.a. CD(color/Detail) Tuner )and [a/sd-webui-negpip](https://github.com/hako-mikan/sd-webui-negpip)(a.k.a. NegPiP) extensions of A1111 to ComfyUI.", - "id": "https://github.com/laksjdjf/cd-tuner_negpip-ComfyUI", - "tags": "cd-tuner, negpip" - }, - { - "description": "This custom node is a port of the Dynamic Thresholding extension from A1111 to make it available for use in ComfyUI.", - "id": "https://github.com/mcmonkeyprojects/sd-dynamic-thresholding", - "tags": "DT, dynamic thresholding" - }, - { - "description": "This extension provides custom nodes developed based on [a/LaMa](https://github.com/advimman/lama) and [a/Inpainting anything](https://github.com/geekyutao/Inpaint-Anything).", - "id": "https://github.com/hhhzzyang/Comfyui_Lama", - "tags": "lama, inpainting anything" - }, - { - "description": "This extension provides custom nodes for [a/LaMa](https://github.com/advimman/lama) functionality.", - "id": "https://github.com/mlinmg/ComfyUI-LaMA-Preprocessor", - "tags": "lama" - }, - { - "description": "This extension provides custom nodes for [a/SD Webui Diffusion Color Grading](https://github.com/Haoming02/sd-webui-diffusion-cg) functionality.", - "id": "https://github.com/Haoming02/comfyui-diffusion-cg", - "tags": "diffusion-cg" - }, - { - "description": "This extension provides custom nodes for [a/sd-webui-cads](https://github.com/v0xie/sd-webui-cads) functionality.", - "id": "https://github.com/asagi4/ComfyUI-CADS", - "tags": "diffusion-cg" - }, - { - "description": "This extension supports both A1111 and ComfyUI simultaneously.", - "id": "https://git.mmaker.moe/mmaker/sd-webui-color-enhance", - "tags": "color-enhance" - }, - { - "description": "This extension provides custom nodes for [a/Mixture of Diffusers](https://github.com/albarji/mixture-of-diffusers) and [a/MultiDiffusion](https://github.com/omerbt/MultiDiffusion)", - "id": "https://github.com/shiimizu/ComfyUI-TiledDiffusion", - "tags": "multidiffusion" - }, - { - "description": "This extension provides some alternative functionalities of the [a/sd-webui-bmab](https://github.com/portu-sim/sd-webui-bmab) extension.", - "id": "https://github.com/abyz22/image_control", - "tags": "BMAB" - }, - { - "description": "This extension provides some alternative functionalities of the [a/stable-diffusion-webui-sonar](https://github.com/Kahsolt/stable-diffusion-webui-sonar) extension.", - "id": "https://github.com/blepping/ComfyUI-sonar", - "tags": "sonar" - }, - { - "description": "a comfyui custom node for [a/Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git), you can Voice-Conversion in comfyui now!", - "id": "https://github.com/AIFSH/ComfyUI-RVC", - "tags": "sonar" - }, - { - "description": "a comfyui custom node for [a/sd-webui-bmab](https://github.com/portu-sim/sd-webui-bmab)", - "id": "https://github.com/portu-sim/comfyui-bmab", - "tags": "bmab" - }, - { - "description": "This extension is a port of [a/unprompted](https://github.com/ThereforeGames/unprompted)", - "id": "https://github.com/ThereforeGames/ComfyUI-Unprompted", - "tags": "unprompted" - } - ] -} \ No newline at end of file diff --git a/user/default/ComfyUI-Manager/cache/4245046894_model-list.json b/user/default/ComfyUI-Manager/cache/4245046894_model-list.json deleted file mode 100644 index b52e764efa0c9769cb7b1ff46232729d2b9c29bd..0000000000000000000000000000000000000000 --- a/user/default/ComfyUI-Manager/cache/4245046894_model-list.json +++ /dev/null @@ -1,5295 +0,0 @@ -{ - "models": [ - { - "base": "FLUX.1", - "description": "(FLUX.1 Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taef1_decoder.pth", - "name": "TAEF1 Decoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.71MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taef1_decoder.pth" - }, - { - "base": "FLUX.1", - "description": "(FLUX.1 Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taef1_encoder.pth", - "name": "TAEF1 Encoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.71MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taef1_encoder.pth" - }, - { - "base": "SD3", - "description": "(SD3 Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taesd3_decoder.pth", - "name": "TAESD3 Decoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.94MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taesd3_decoder.pth" - }, - { - "base": "SD3", - "description": "(SD3 Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taesd3_encoder.pth", - "name": "TAESD3 Encoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.94MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taesd3_encoder.pth" - }, - { - "base": "SDXL", - "description": "(SDXL Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taesdxl_decoder.pth", - "name": "TAESDXL Decoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.91MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth" - }, - { - "base": "SDXL", - "description": "(SDXL Verison) To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taesdxl_encoder.pth", - "name": "TAESDXL Encoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.91MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taesdxl_encoder.pth" - }, - { - "base": "SD1.x", - "description": "To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taesd_decoder.pth", - "name": "TAESD Decoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.91MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth" - }, - { - "base": "SD1.x", - "description": "To view the preview in high quality while running samples in ComfyUI, you will need this model.", - "filename": "taesd_encoder.pth", - "name": "TAESD Encoder", - "reference": "https://github.com/madebyollin/taesd", - "save_path": "vae_approx", - "size": "4.91MB", - "type": "TAESD", - "url": "https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth" - }, - { - "base": "upscale", - "description": "RealESRGAN x2 upscaler model", - "filename": "RealESRGAN_x2.pth", - "name": "RealESRGAN x2", - "reference": "https://huggingface.co/ai-forever/Real-ESRGAN", - "save_path": "default", - "size": "67.1MB", - "type": "upscale", - "url": "https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth" - }, - { - "base": "upscale", - "description": "RealESRGAN x4 upscaler model", - "filename": "RealESRGAN_x4.pth", - "name": "RealESRGAN x4", - "reference": "https://huggingface.co/ai-forever/Real-ESRGAN", - "save_path": "default", - "size": "67.0MB", - "type": "upscale", - "url": "https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth" - }, - { - "base": "upscale", - "description": "ESRGAN x4 upscaler model", - "filename": "ESRGAN_4x.pth", - "name": "ESRGAN x4", - "reference": "https://huggingface.co/Afizi/ESRGAN_4x.pth", - "save_path": "default", - "size": "66.9MB", - "type": "upscale", - "url": "https://huggingface.co/Afizi/ESRGAN_4x.pth/resolve/main/ESRGAN_4x.pth" - }, - { - "base": "upscale", - "description": "4x_foolhardy_Remacri upscaler model", - "filename": "4x_foolhardy_Remacri.pth", - "name": "4x_foolhardy_Remacri", - "reference": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri", - "save_path": "default", - "size": "67.0MB", - "type": "upscale", - "url": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth" - }, - { - "base": "upscale", - "description": "4x-AnimeSharp upscaler model", - "filename": "4x-AnimeSharp.pth", - "name": "4x-AnimeSharp", - "reference": "https://huggingface.co/Kim2091/AnimeSharp/", - "save_path": "default", - "size": "67.0MB", - "type": "upscale", - "url": "https://huggingface.co/Kim2091/AnimeSharp/resolve/main/4x-AnimeSharp.pth" - }, - { - "base": "upscale", - "description": "4x-UltraSharp upscaler model", - "filename": "4x-UltraSharp.pth", - "name": "4x-UltraSharp", - "reference": "https://huggingface.co/Kim2091/UltraSharp/", - "save_path": "default", - "size": "67.0MB", - "type": "upscale", - "url": "https://huggingface.co/Kim2091/UltraSharp/resolve/main/4x-UltraSharp.pth" - }, - { - "base": "upscale", - "description": "4x_NMKD-Siax_200k upscaler model", - "filename": "4x_NMKD-Siax_200k.pth", - "name": "4x_NMKD-Siax_200k", - "reference": "https://huggingface.co/gemasai/4x_NMKD-Siax_200k", - "save_path": "default", - "size": "67.0MB", - "type": "upscale", - "url": "https://huggingface.co/gemasai/4x_NMKD-Siax_200k/resolve/main/4x_NMKD-Siax_200k.pth" - }, - { - "base": "upscale", - "description": "8x_NMKD-Superscale_150000_G upscaler model", - "filename": "8x_NMKD-Superscale_150000_G.pth", - "name": "8x_NMKD-Superscale_150000_G", - 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"url": "https://github.com/hben35096/assets/releases/download/yolo8/face_yolov8n-seg2_60.pt" - }, - { - "base": "Ultralytics", - "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", - "filename": "hair_yolov8n-seg_60.pt", - "name": "hair_yolov8n-seg_60.pt (segm)", - "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", - "save_path": "ultralytics/segm", - "size": "6.77MB", - "type": "Ultralytics", - "url": "https://github.com/hben35096/assets/releases/download/yolo8/hair_yolov8n-seg_60.pt" - }, - { - "base": "Ultralytics", - "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", - "filename": "skin_yolov8m-seg_400.pt", - "name": "skin_yolov8m-seg_400.pt (segm)", - "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", - "save_path": "ultralytics/segm", - "size": "54.9MB", - "type": "Ultralytics", - "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8m-seg_400.pt" - }, - { - "base": "Ultralytics", - "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", - "filename": "skin_yolov8n-seg_400.pt", - "name": "skin_yolov8n-seg_400.pt (segm)", - "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", - "save_path": "ultralytics/segm", - "size": "6.83MB", - "type": "Ultralytics", - "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_400.pt" - }, - { - "base": "Ultralytics", - "description": "These are the available models in the UltralyticsDetectorProvider of Impact Pack.", - "filename": "skin_yolov8n-seg_800.pt", - "name": "skin_yolov8n-seg_800.pt (segm)", - "reference": "https://github.com/hben35096/assets/releases/tag/yolo8", - "save_path": "ultralytics/segm", - "size": "6.84MB", - "type": "Ultralytics", - "url": "https://github.com/hben35096/assets/releases/download/yolo8/skin_yolov8n-seg_800.pt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the ArtVentureX/AnimateDiff extension node.", - "filename": "mm_sd_v14.ckpt", - "name": "animatediff/mmd_sd_v14.ckpt (comfyui-animatediff) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "AnimateDiff", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the ArtVentureX/AnimateDiff extension node.", - "filename": "mm_sd_v15.ckpt", - "name": "animatediff/mm_sd_v15.ckpt (comfyui-animatediff) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "AnimateDiff", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "mm_sd_v14.ckpt", - "name": "animatediff/mmd_sd_v14.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v14.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "mm_sd_v15.ckpt", - "name": "animatediff/mm_sd_v15.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "mm_sd_v15_v2.ckpt", - "name": "animatediff/mm_sd_v15_v2.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_models", - "size": "1.82GB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v3_sd15_mm.ckpt", - "name": "animatediff/v3_sd15_mm.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_mm.ckpt" - }, - { - "base": "SDXL", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "mm_sdxl_v10_beta.ckpt", - "name": "animatediff/mm_sdxl_v10_beta.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_models", - "size": "950.1MB", - "type": "animatediff", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/mm_sdxl_v10_beta.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "mm-Stabilized_high.pth", - "name": "AD_Stabilized_Motion/mm-Stabilized_high.pth (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_high.pth" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "mm-Stabilized_mid.pth", - "name": "AD_Stabilized_Motion/mm-Stabilized_mid.pth (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/manshoety/AD_Stabilized_Motion", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/manshoety/AD_Stabilized_Motion/resolve/main/mm-Stabilized_mid.pth" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "temporaldiff-v1-animatediff.ckpt", - "name": "CiaraRowles/temporaldiff-v1-animatediff.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/CiaraRowles/TemporalDiff", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff", - "url": "https://huggingface.co/CiaraRowles/TemporalDiff/resolve/main/temporaldiff-v1-animatediff.ckpt" - }, - { - "base": "SD1.x", - "description": "AnimateDiff-PIA Model", - "filename": "pia.ckpt", - "name": "Leoxing/pia.ckpt", - "reference": "https://huggingface.co/Leoxing/PIA/tree/main", - "save_path": "animatediff_models", - "size": "1.67GB", - "type": "animatediff-pia", - "url": "https://huggingface.co/Leoxing/PIA/resolve/main/pia.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_PanLeft.ckpt", - "name": "animatediff/v2_lora_PanLeft.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanLeft.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_PanRight.ckpt", - "name": "animatediff/v2_lora_PanRight.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_PanRight.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_RollingAnticlockwise.ckpt", - "name": "animatediff/v2_lora_RollingAnticlockwise.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingAnticlockwise.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_RollingClockwise.ckpt", - "name": "animatediff/v2_lora_RollingClockwise.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_RollingClockwise.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_TiltDown.ckpt", - "name": "animatediff/v2_lora_TiltDown.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltDown.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_TiltUp.ckpt", - "name": "animatediff/v2_lora_TiltUp.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_TiltUp.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_ZoomIn.ckpt", - "name": "animatediff/v2_lora_ZoomIn.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomIn.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "v2_lora_ZoomOut.ckpt", - "name": "animatediff/v2_lora_ZoomOut.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "animatediff_motion_lora", - "size": "77.5MB", - "type": "motion lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v2_lora_ZoomOut.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "lt_long_mm_32_frames.ckpt", - "name": "LongAnimatediff/lt_long_mm_32_frames.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", - "save_path": "animatediff_models", - "size": "1.82GB", - "type": "animatediff", - "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_32_frames.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "lt_long_mm_16_64_frames.ckpt", - "name": "LongAnimatediff/lt_long_mm_16_64_frames.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", - "save_path": "animatediff_models", - "size": "1.83GB", - "type": "animatediff", - "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames.ckpt" - }, - { - "base": "SD1.x", - "description": "Pressing 'install' directly downloads the model from the Kosinkadink/ComfyUI-AnimateDiff-Evolved extension node.", - "filename": "lt_long_mm_16_64_frames_v1.1.ckpt", - "name": "LongAnimatediff/lt_long_mm_16_64_frames_v1.1.ckpt (ComfyUI-AnimateDiff-Evolved) (Updated path)", - "reference": "https://huggingface.co/Lightricks/LongAnimateDiff", - "save_path": "animatediff_models", - "size": "1.83GB", - "type": "animatediff", - "url": "https://huggingface.co/Lightricks/LongAnimateDiff/resolve/main/lt_long_mm_16_64_frames_v1.1.ckpt" - }, - { - "base": "SD1.x", - "description": "AnimateDiff SparseCtrl RGB ControlNet model", - "filename": "v3_sd15_sparsectrl_rgb.ckpt", - "name": "animatediff/v3_sd15_sparsectrl_rgb.ckpt (ComfyUI-AnimateDiff-Evolved)", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "controlnet/SD1.5/animatediff", - "size": "1.99GB", - "type": "controlnet", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_rgb.ckpt" - }, - { - "base": "SD1.x", - "description": "AnimateDiff SparseCtrl Scribble ControlNet model", - "filename": "v3_sd15_sparsectrl_scribble.ckpt", - "name": "animatediff/v3_sd15_sparsectrl_scribble.ckpt", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "controlnet/SD1.5/animatediff", - "size": "1.99GB", - "type": "controlnet", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_sparsectrl_scribble.ckpt" - }, - { - "base": "SD1.x", - "description": "AnimateDiff Adapter LoRA (SD1.5)", - "filename": "v3_sd15_adapter.ckpt", - "name": "animatediff/v3_sd15_adapter.ckpt", - "reference": "https://huggingface.co/guoyww/animatediff", - "save_path": "loras/SD1.5/animatediff", - "size": "102.1MB", - "type": "lora", - "url": "https://huggingface.co/guoyww/animatediff/resolve/main/v3_sd15_adapter.ckpt" - }, - { - "base": "MotionCtrl", - "description": "To use the ComfyUI-MotionCtrl extension, downloading this model is required.", - "filename": "motionctrl.pth", - "name": "TencentARC/motionctrl.pth", - "reference": "https://huggingface.co/TencentARC/MotionCtrl", - "save_path": "checkpoints/motionctrl", - "size": "4.02GB", - "type": "checkpoint", - "url": "https://huggingface.co/TencentARC/MotionCtrl/resolve/main/motionctrl.pth" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter_sd15.safetensors", - "name": "ip-adapter_sd15.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "44.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.safetensors" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter_sd15_light_v11.bin", - "name": "ip-adapter_sd15_light_v11.bin", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "44.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light_v11.bin" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter_sd15_light.safetensors", - "name": "ip-adapter_sd15_light.safetensors [DEPRECATED]", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "44.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_light.safetensors" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter-plus_sd15.safetensors", - "name": "ip-adapter-plus_sd15.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "98.2MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.safetensors" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter-plus-face_sd15.safetensors", - "name": "ip-adapter-plus-face_sd15.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "98.2MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus-face_sd15.safetensors" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter-full-face_sd15.safetensors", - "name": "ip-adapter-full-face_sd15.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "43.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-full-face_sd15.safetensors" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter_sd15_vit-G.safetensors", - "name": "ip-adapter_sd15_vit-G.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "46.2MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15_vit-G.safetensors" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid_sd15.bin", - "name": "ip-adapter-faceid_sd15.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "96.7MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15.bin" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID Plus V2 Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid-plusv2_sd15.bin", - "name": "ip-adapter-faceid-plusv2_sd15.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "156.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15.bin" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID Plus Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid-plus_sd15.bin", - "name": "ip-adapter-faceid-plus_sd15.bin [DEPRECATED]", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "156.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15.bin" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID Portrait V11 Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid-portrait-v11_sd15.bin", - "name": "ip-adapter-faceid-portrait-v11_sd15.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "64.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait-v11_sd15.bin" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID Portrait Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid-portrait_sd15.bin", - "name": "ip-adapter-faceid-portrait_sd15.bin [DEPRECATED]", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "64.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sd15.bin" - }, - { - "base": "SDXL", - "description": "IP-Adapter-FaceID Model (SDXL) [ipadapter]", - "filename": "ip-adapter-faceid_sdxl.bin", - "name": "ip-adapter-faceid_sdxl.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "1.07GB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl.bin" - }, - { - "base": "SDXL", - "description": "IP-Adapter-FaceID Plus Model (SDXL) [ipadapter]", - "filename": "ip-adapter-faceid-plusv2_sdxl.bin", - "name": "ip-adapter-faceid-plusv2_sdxl.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "1.49GB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl.bin" - }, - { - "base": "SDXL", - "description": "IP-Adapter-FaceID Portrait Model (SDXL) [ipadapter]", - "filename": "ip-adapter-faceid-portrait_sdxl.bin", - "name": "ip-adapter-faceid-portrait_sdxl.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "749.8MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl.bin" - }, - { - "base": "SDXL", - "description": "IP-Adapter-FaceID Portrait Model (SDXL/unnorm) [ipadapter]", - "filename": "ip-adapter-faceid-portrait_sdxl_unnorm.bin", - "name": "ip-adapter-faceid-portrait_sdxl_unnorm.bin", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "ipadapter", - "size": "1.01GB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-portrait_sdxl_unnorm.bin" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID LoRA Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid_sd15_lora.safetensors", - "name": "ip-adapter-faceid_sd15_lora.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "loras/ipadapter", - "size": "51.1MB", - "type": "lora", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sd15_lora.safetensors" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID Plus LoRA Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid-plus_sd15_lora.safetensors", - "name": "ip-adapter-faceid-plus_sd15_lora.safetensors [DEPRECATED]", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "loras/ipadapter", - "size": "51.1MB", - "type": "lora", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plus_sd15_lora.safetensors" - }, - { - "base": "SD1.5", - "description": "IP-Adapter-FaceID-Plus V2 LoRA Model (SD1.5) [ipadapter]", - "filename": "ip-adapter-faceid-plusv2_sd15_lora.safetensors", - "name": "ip-adapter-faceid-plusv2_sd15_lora.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "loras/ipadapter", - "size": "51.1MB", - "type": "lora", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sd15_lora.safetensors" - }, - { - "base": "SDXL", - "description": "IP-Adapter-FaceID LoRA Model (SDXL) [ipadapter]", - "filename": "ip-adapter-faceid_sdxl_lora.safetensors", - "name": "ip-adapter-faceid_sdxl_lora.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "loras/ipadapter", - "size": "371.8MB", - "type": "lora", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid_sdxl_lora.safetensors" - }, - { - "base": "SDXL", - "description": "IP-Adapter-FaceID-Plus V2 LoRA Model (SDXL) [ipadapter]", - "filename": "ip-adapter-faceid-plusv2_sdxl_lora.safetensors", - "name": "ip-adapter-faceid-plusv2_sdxl_lora.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter-FaceID", - "save_path": "loras/ipadapter", - "size": "371.8MB", - "type": "lora", - "url": "https://huggingface.co/h94/IP-Adapter-FaceID/resolve/main/ip-adapter-faceid-plusv2_sdxl_lora.safetensors" - }, - { - "base": "SDXL", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip-adapter_sdxl.safetensors", - "name": "ip-adapter_sdxl.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "702.6MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors" - }, - { - "base": "SDXL", - "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints [ipadapter]", - "filename": "ip-adapter_sdxl_vit-h.safetensors", - "name": "ip-adapter_sdxl_vit-h.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "698.4MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.safetensors" - }, - { - "base": "SDXL", - "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints [ipadapter]", - "filename": "ip-adapter-plus_sdxl_vit-h.safetensors", - "name": "ip-adapter-plus_sdxl_vit-h.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "847.5MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.safetensors" - }, - { - "base": "SDXL", - "description": "This model requires the use of the SD1.5 encoder despite being for SDXL checkpoints [ipadapter]", - "filename": "ip-adapter-plus-face_sdxl_vit-h.safetensors", - "name": "ip-adapter-plus-face_sdxl_vit-h.safetensors", - "reference": "https://huggingface.co/h94/IP-Adapter", - "save_path": "ipadapter", - "size": "847.5MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus-face_sdxl_vit-h.safetensors" - }, - { - "base": "SD1.5", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip_plus_composition_sd15.safetensors", - "name": "ip_plus_composition_sd15.safetensors", - "reference": "https://huggingface.co/ostris/ip-composition-adapter", - "save_path": "ipadapter", - "size": "98.2MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sd15.safetensors" - }, - { - "base": "SDXL", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "ip_plus_composition_sdxl.safetensors", - "name": "ip_plus_composition_sdxl.safetensors", - "reference": "https://huggingface.co/ostris/ip-composition-adapter", - "save_path": "ipadapter", - "size": "847.5MB", - "type": "IP-Adapter", - "url": "https://huggingface.co/ostris/ip-composition-adapter/resolve/main/ip_plus_composition_sdxl.safetensors" - }, - { - "base": "Kolors", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "Kolors-IP-Adapter-Plus.bin", - "name": "Kolors-IP-Adapter-Plus.bin (Kwai-Kolors/Kolors-IP-Adapter-Plus)", - "reference": "https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus", - "save_path": "ipadapter", - "size": "1.01GB", - "type": "IP-Adapter", - "url": "https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-Plus/resolve/main/ip_adapter_plus_general.bin" - }, - { - "base": "Kolors", - "description": "You can use this model in the [a/ComfyUI IPAdapter plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus) extension.", - "filename": "Kolors-IP-Adapter-FaceID-Plus.bin", - "name": "Kolors-IP-Adapter-FaceID-Plus.bin (Kwai-Kolors/Kolors-IP-Adapter-Plus)", - "reference": "https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus", - "save_path": "ipadapter", - "size": "2.39GB", - "type": "IP-Adapter", - "url": "https://huggingface.co/Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus/resolve/main/ipa-faceid-plus.bin" - }, - { - "base": "GFPGAN", - "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", - "filename": "GFPGANv1.4.pth", - "name": "GFPGANv1.4.pth", - "reference": "https://github.com/TencentARC/GFPGAN/releases", - "save_path": "facerestore_models", - "size": "348.6MB", - "type": "GFPGAN", - "url": "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth" - }, - { - "base": "CodeFormer", - "description": "Face Restoration Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", - "filename": "codeformer.pth", - "name": "codeformer.pth", - "reference": "https://github.com/sczhou/CodeFormer/releases", - "save_path": "facerestore_models", - "size": "376.6MB", - "type": "CodeFormer", - "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth" - }, - { - "base": "facexlib", - "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", - "filename": "detection_Resnet50_Final.pth", - "name": "detection_Resnet50_Final.pth", - "reference": "https://github.com/xinntao/facexlib", - "save_path": "facerestore_models", - "size": "109.5MB", - "type": "facexlib", - "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" - }, - { - "base": "facexlib", - "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", - "filename": "detection_mobilenet0.25_Final.pth", - "name": "detection_mobilenet0.25_Final.pth", - "reference": "https://github.com/xinntao/facexlib", - "save_path": "facerestore_models", - "size": "1.79MB", - "type": "facexlib", - "url": "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth" - }, - { - "base": "facexlib", - "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", - "filename": "yolov5l-face.pth", - "name": "yolov5l-face.pth", - "reference": "https://github.com/xinntao/facexlib", - "save_path": "facedetection", - "size": "187.0MB", - "type": "facexlib", - "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth" - }, - { - "base": "facexlib", - "description": "Face Detection Models. Download the model required for using the 'Facerestore CF (Code Former)' custom node.", - "filename": "yolov5n-face.pth", - "name": "yolov5n-face.pth", - "reference": "https://github.com/xinntao/facexlib", - "save_path": "facedetection", - "size": "7.15MB", - "type": "facexlib", - "url": "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth" - }, - { - "base": "SDXL", - "description": "PhotoMaker model. This model is compatible with SDXL.", - "filename": "photomaker-v1.bin", - "name": "photomaker-v1.bin", - "reference": "https://huggingface.co/TencentARC/PhotoMaker", - "save_path": "photomaker", - "size": "934.1MB", - "type": "photomaker", - "url": "https://huggingface.co/TencentARC/PhotoMaker/resolve/main/photomaker-v1.bin" - }, - { - "base": "SDXL", - "description": "PhotoMaker model. This model is compatible with SDXL.", - "filename": "photomaker-v2.bin", - "name": "photomaker-v2.bin", - "reference": "https://huggingface.co/TencentARC/PhotoMaker-V2", - "save_path": "photomaker", - "size": "1.8GB", - "type": "photomaker", - "url": "https://huggingface.co/TencentARC/PhotoMaker-V2/resolve/main/photomaker-v2.bin" - }, - { - "base": "inswapper", - "description": "Antelopev2 1k3d68.onnx model for InstantId. (InstantId needs all Antelopev2 models)", - "filename": "1k3d68.onnx", - "name": "1k3d68.onnx", - "reference": "https://github.com/cubiq/ComfyUI_InstantID#installation", - "save_path": "insightface/models/antelopev2", - "size": "143.6MB", - "type": "insightface", - "url": "https://huggingface.co/MonsterMMORPG/tools/resolve/main/1k3d68.onnx" - }, - { - "base": "inswapper", - "description": "Antelopev2 2d106det.onnx model for InstantId. (InstantId needs all Antelopev2 models)", - "filename": "2d106det.onnx", - "name": "2d106det.onnx", - "reference": "https://github.com/cubiq/ComfyUI_InstantID#installation", - "save_path": "insightface/models/antelopev2", - "size": "5.03MB", - "type": "insightface", - "url": "https://huggingface.co/MonsterMMORPG/tools/resolve/main/2d106det.onnx" - }, - { - "base": "inswapper", - "description": "Antelopev2 genderage.onnx model for InstantId. (InstantId needs all Antelopev2 models)", - "filename": "genderage.onnx", - "name": "genderage.onnx", - "reference": "https://github.com/cubiq/ComfyUI_InstantID#installation", - "save_path": "insightface/models/antelopev2", - "size": "1.32MB", - "type": "insightface", - "url": "https://huggingface.co/MonsterMMORPG/tools/resolve/main/genderage.onnx" - }, - { - "base": "inswapper", - "description": "Antelopev2 glintr100.onnx model for InstantId. (InstantId needs all Antelopev2 models)", - "filename": "glintr100.onnx", - "name": "glintr100.onnx", - "reference": "https://github.com/cubiq/ComfyUI_InstantID#installation", - "save_path": "insightface/models/antelopev2", - "size": "260.7MB", - "type": "insightface", - "url": "https://huggingface.co/MonsterMMORPG/tools/resolve/main/glintr100.onnx" - }, - { - "base": "inswapper", - "description": "Antelopev2 scrfd_10g_bnkps.onnx model for InstantId. 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}, - { - "base": "pixart-sigma", - "description": "PixArt-Sigma Diffusion model", - "filename": "PixArt-Sigma-XL-2-1024-MS.safetensors", - "name": "PixArt-Sigma-XL-2-1024-MS.safetensors (diffusion)", - "reference": "https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", - "save_path": "diffusion_models/PixArt-Sigma", - "size": "2.44GB", - "type": "diffusion_model", - "url": "https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/resolve/main/transformer/diffusion_pytorch_model.safetensors" - }, - { - "base": "pixart-alpha", - "description": "PixArt-Alpha Diffusion model", - "filename": "PixArt-XL-2-1024-MS.safetensors", - "name": "PixArt-XL-2-1024-MS.safetensors (diffusion)", - "reference": "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS", - "save_path": "diffusion_models/PixArt-Alpha", - "size": "2.45GB", - "type": "diffusion_model", - "url": "https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/resolve/main/transformer/diffusion_pytorch_model.safetensors" - }, - { - "base": "Hunyuan-DiT", - "description": "Different versions of HunyuanDIT packaged for ComfyUI use.", - "filename": "hunyuan_dit_1.2.safetensors", - "name": "hunyuan_dit_1.2.safetensors", - "reference": "https://huggingface.co/comfyanonymous/hunyuan_dit_comfyui", - "save_path": "checkpoints/hunyuan_dit_comfyui", - "size": "8.24GB", - "type": "checkpoint", - "url": "https://huggingface.co/comfyanonymous/hunyuan_dit_comfyui/resolve/main/hunyuan_dit_1.2.safetensors" - }, - { - "base": "Hunyuan-DiT", - "description": "Different versions of HunyuanDIT packaged for ComfyUI use.", - "filename": "hunyuan_dit_1.1.safetensors", - "name": "hunyuan_dit_1.1.safetensors", - "reference": "https://huggingface.co/comfyanonymous/hunyuan_dit_comfyui", - "save_path": "checkpoints/hunyuan_dit_comfyui", - "size": "8.24GB", - "type": "checkpoint", - "url": "https://huggingface.co/comfyanonymous/hunyuan_dit_comfyui/resolve/main/hunyuan_dit_1.1.safetensors" - }, - { - "base": "Hunyuan-DiT", - 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}, - { - "base": "Hunyuan Video", - "description": "Huyuan Video VAE model. repackaged version.", - "filename": "hunyuan_video_vae_bf16.safetensors", - "name": "Comfy-Org/hunyuan_video_vae_bf16.safetensors", - "reference": "https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged", - "save_path": "default", - "size": "493MB", - "type": "VAE", - "url": "https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged/resolve/main/split_files/vae/hunyuan_video_vae_bf16.safetensors" - }, - { - "base": "Hunyuan Video", - "description": "Huyuan Video Image2Video diffusion model. repackaged version.", - "filename": "hunyuan_video_image_to_video_720p_bf16.safetensors", - "name": "Comfy-Org/hunyuan_video_image_to_video_720p_bf16.safetensors", - "reference": "https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged", - "save_path": "diffusion_models/hunyuan_video", - "size": "25.6GB", - "type": "diffusion_model", - "url": "https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged/resolve/main/split_files/diffusion_models/hunyuan_video_image_to_video_720p_bf16.safetensors" - }, - { - "base": "LLaVA-Llama-3", - "description": "llava_llama3_fp8_scaled text encoder model. 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}, - { - "base": "FLUX.1", - "description": "FLUX.1 [Dev] Diffusion model (Q3_K_S/.gguf)", - "filename": "flux1-dev-Q3_K_S.gguf", - "name": "city96/flux1-dev-Q3_K_S.gguf", - "reference": "https://huggingface.co/city96/FLUX.1-dev-gguf", - "save_path": "diffusion_models/FLUX1", - "size": "5.23GB", - "type": "diffusion_model", - "url": "https://huggingface.co/city96/FLUX.1-dev-gguf/resolve/main/flux1-dev-Q3_K_S.gguf" - }, - { - "base": "FLUX.1", - "description": "FLUX.1 [Dev] Diffusion model (Q4_0/.gguf)", - "filename": "flux1-dev-Q4_0.gguf", - "name": "city96/flux1-dev-Q4_0.gguf", - "reference": "https://huggingface.co/city96/FLUX.1-dev-gguf", - "save_path": "diffusion_models/FLUX1", - "size": "6.79GB", - "type": "diffusion_model", - "url": "https://huggingface.co/city96/FLUX.1-dev-gguf/resolve/main/flux1-dev-Q4_0.gguf" - }, - { - "base": "FLUX.1", - "description": "FLUX.1 [Dev] Diffusion model (Q4_1/.gguf)", - "filename": "flux1-dev-Q4_1.gguf", - "name": "city96/flux1-dev-Q4_1.gguf", - 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--git a/user_manager.py b/user_manager.py deleted file mode 100644 index a2d376c0c4766b750fe6a1fd1558da4739f364dd..0000000000000000000000000000000000000000 --- a/user_manager.py +++ /dev/null @@ -1,445 +0,0 @@ -from __future__ import annotations -import json -import os -import re -import uuid -import glob -import shutil -import logging -from aiohttp import web -from urllib import parse -from comfy.cli_args import args -import folder_paths -from .app_settings import AppSettings -from typing import TypedDict - -default_user = "default" - - -class FileInfo(TypedDict): - path: str - size: int - modified: int - created: int - - -def get_file_info(path: str, relative_to: str) -> FileInfo: - return { - "path": os.path.relpath(path, relative_to).replace(os.sep, '/'), - "size": os.path.getsize(path), - "modified": os.path.getmtime(path), - "created": os.path.getctime(path) - } - - -class UserManager(): - def __init__(self): - user_directory = folder_paths.get_user_directory() - - self.settings = AppSettings(self) - if not os.path.exists(user_directory): - os.makedirs(user_directory, exist_ok=True) - if not args.multi_user: - logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******") - logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******") - - if args.multi_user: - if os.path.isfile(self.get_users_file()): - with open(self.get_users_file()) as f: - self.users = json.load(f) - else: - self.users = {} - else: - self.users = {"default": "default"} - - def get_users_file(self): - return os.path.join(folder_paths.get_user_directory(), "users.json") - - def get_request_user_id(self, request): - user = "default" - if args.multi_user and "comfy-user" in request.headers: - user = request.headers["comfy-user"] - - if user not in self.users: - raise KeyError("Unknown user: " + user) - - return user - - def get_request_user_filepath(self, request, file, type="userdata", create_dir=True): - user_directory = folder_paths.get_user_directory() - - if type == "userdata": - root_dir = user_directory - else: - raise KeyError("Unknown filepath type:" + type) - - user = self.get_request_user_id(request) - path = user_root = os.path.abspath(os.path.join(root_dir, user)) - - # prevent leaving /{type} - if os.path.commonpath((root_dir, user_root)) != root_dir: - return None - - if file is not None: - # Check if filename is url encoded - if "%" in file: - file = parse.unquote(file) - - # prevent leaving /{type}/{user} - path = os.path.abspath(os.path.join(user_root, file)) - if os.path.commonpath((user_root, path)) != user_root: - return None - - parent = os.path.split(path)[0] - - if create_dir and not os.path.exists(parent): - os.makedirs(parent, exist_ok=True) - - return path - - def add_user(self, name): - name = name.strip() - if not name: - raise ValueError("username not provided") - user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name) - user_id = user_id + "_" + str(uuid.uuid4()) - - self.users[user_id] = name - - with open(self.get_users_file(), "w") as f: - json.dump(self.users, f) - - return user_id - - def add_routes(self, routes): - self.settings.add_routes(routes) - - @routes.get("/users") - async def get_users(request): - if args.multi_user: - return web.json_response({"storage": "server", "users": self.users}) - else: - user_dir = self.get_request_user_filepath(request, None, create_dir=False) - return web.json_response({ - "storage": "server", - "migrated": os.path.exists(user_dir) - }) - - @routes.post("/users") - async def post_users(request): - body = await request.json() - username = body["username"] - if username in self.users.values(): - return web.json_response({"error": "Duplicate username."}, status=400) - - user_id = self.add_user(username) - return web.json_response(user_id) - - @routes.get("/userdata") - async def listuserdata(request): - """ - List user data files in a specified directory. - - This endpoint allows listing files in a user's data directory, with options for recursion, - full file information, and path splitting. - - Query Parameters: - - dir (required): The directory to list files from. - - recurse (optional): If "true", recursively list files in subdirectories. - - full_info (optional): If "true", return detailed file information (path, size, modified time). - - split (optional): If "true", split file paths into components (only applies when full_info is false). - - Returns: - - 400: If 'dir' parameter is missing. - - 403: If the requested path is not allowed. - - 404: If the requested directory does not exist. - - 200: JSON response with the list of files or file information. - - The response format depends on the query parameters: - - Default: List of relative file paths. - - full_info=true: List of dictionaries with file details. - - split=true (and full_info=false): List of lists, each containing path components. - """ - directory = request.rel_url.query.get('dir', '') - if not directory: - return web.Response(status=400, text="Directory not provided") - - path = self.get_request_user_filepath(request, directory) - if not path: - return web.Response(status=403, text="Invalid directory") - - if not os.path.exists(path): - return web.Response(status=404, text="Directory not found") - - recurse = request.rel_url.query.get('recurse', '').lower() == "true" - full_info = request.rel_url.query.get('full_info', '').lower() == "true" - split_path = request.rel_url.query.get('split', '').lower() == "true" - - # Use different patterns based on whether we're recursing or not - if recurse: - pattern = os.path.join(glob.escape(path), '**', '*') - else: - pattern = os.path.join(glob.escape(path), '*') - - def process_full_path(full_path: str) -> FileInfo | str | list[str]: - if full_info: - return get_file_info(full_path, path) - - rel_path = os.path.relpath(full_path, path).replace(os.sep, '/') - if split_path: - return [rel_path] + rel_path.split('/') - - return rel_path - - results = [ - process_full_path(full_path) - for full_path in glob.glob(pattern, recursive=recurse) - if os.path.isfile(full_path) - ] - - return web.json_response(results) - - @routes.get("/v2/userdata") - async def list_userdata_v2(request): - """ - List files and directories in a user's data directory. - - This endpoint provides a structured listing of contents within a specified - subdirectory of the user's data storage. - - Query Parameters: - - path (optional): The relative path within the user's data directory - to list. Defaults to the root (''). - - Returns: - - 400: If the requested path is invalid, outside the user's data directory, or is not a directory. - - 404: If the requested path does not exist. - - 403: If the user is invalid. - - 500: If there is an error reading the directory contents. - - 200: JSON response containing a list of file and directory objects. - Each object includes: - - name: The name of the file or directory. - - type: 'file' or 'directory'. - - path: The relative path from the user's data root. - - size (for files): The size in bytes. - - modified (for files): The last modified timestamp (Unix epoch). - """ - requested_rel_path = request.rel_url.query.get('path', '') - - # URL-decode the path parameter - try: - requested_rel_path = parse.unquote(requested_rel_path) - except Exception as e: - logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}") - return web.Response(status=400, text="Invalid characters in path parameter") - - - # Check user validity and get the absolute path for the requested directory - try: - base_user_path = self.get_request_user_filepath(request, None, create_dir=False) - - if requested_rel_path: - target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False) - else: - target_abs_path = base_user_path - - except KeyError as e: - # Invalid user detected by get_request_user_id inside get_request_user_filepath - logging.warning(f"Access denied for user: {e}") - return web.Response(status=403, text="Invalid user specified in request") - - - if not target_abs_path: - # Path traversal or other issue detected by get_request_user_filepath - return web.Response(status=400, text="Invalid path requested") - - # Handle cases where the user directory or target path doesn't exist - if not os.path.exists(target_abs_path): - # Check if it's the base user directory that's missing (new user case) - if target_abs_path == base_user_path: - # It's okay if the base user directory doesn't exist yet, return empty list - return web.json_response([]) - else: - # A specific subdirectory was requested but doesn't exist - return web.Response(status=404, text="Requested path not found") - - if not os.path.isdir(target_abs_path): - return web.Response(status=400, text="Requested path is not a directory") - - results = [] - try: - for root, dirs, files in os.walk(target_abs_path, topdown=True): - # Process directories - for dir_name in dirs: - dir_path = os.path.join(root, dir_name) - rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/') - results.append({ - "name": dir_name, - "path": rel_path, - "type": "directory" - }) - - # Process files - for file_name in files: - file_path = os.path.join(root, file_name) - rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/') - entry_info = { - "name": file_name, - "path": rel_path, - "type": "file" - } - try: - stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk - entry_info["size"] = stats.st_size - entry_info["modified"] = stats.st_mtime - except OSError as stat_error: - logging.warning(f"Could not stat file {file_path}: {stat_error}") - pass # Include file with available info - results.append(entry_info) - except OSError as e: - logging.error(f"Error listing directory {target_abs_path}: {e}") - return web.Response(status=500, text="Error reading directory contents") - - # Sort results alphabetically, directories first then files - results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower())) - - return web.json_response(results) - - def get_user_data_path(request, check_exists = False, param = "file"): - file = request.match_info.get(param, None) - if not file: - return web.Response(status=400) - - path = self.get_request_user_filepath(request, file) - if not path: - return web.Response(status=403) - - if check_exists and not os.path.exists(path): - return web.Response(status=404) - - return path - - @routes.get("/userdata/{file}") - async def getuserdata(request): - path = get_user_data_path(request, check_exists=True) - if not isinstance(path, str): - return path - - return web.FileResponse(path) - - @routes.post("/userdata/{file}") - async def post_userdata(request): - """ - Upload or update a user data file. - - This endpoint handles file uploads to a user's data directory, with options for - controlling overwrite behavior and response format. - - Query Parameters: - - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". - - full_info (optional): If "true", returns detailed file information (path, size, modified time). - If "false", returns only the relative file path. - - Path Parameters: - - file: The target file path (URL encoded if necessary). - - Returns: - - 400: If 'file' parameter is missing. - - 403: If the requested path is not allowed. - - 409: If overwrite=false and the file already exists. - - 200: JSON response with either: - - Full file information (if full_info=true) - - Relative file path (if full_info=false) - - The request body should contain the raw file content to be written. - """ - path = get_user_data_path(request) - if not isinstance(path, str): - return path - - overwrite = request.query.get("overwrite", 'true') != "false" - full_info = request.query.get('full_info', 'false').lower() == "true" - - if not overwrite and os.path.exists(path): - return web.Response(status=409, text="File already exists") - - try: - body = await request.read() - - with open(path, "wb") as f: - f.write(body) - except OSError as e: - logging.warning(f"Error saving file '{path}': {e}") - return web.Response( - status=400, - reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|" - ) - - user_path = self.get_request_user_filepath(request, None) - if full_info: - resp = get_file_info(path, user_path) - else: - resp = os.path.relpath(path, user_path) - - return web.json_response(resp) - - @routes.delete("/userdata/{file}") - async def delete_userdata(request): - path = get_user_data_path(request, check_exists=True) - if not isinstance(path, str): - return path - - os.remove(path) - - return web.Response(status=204) - - @routes.post("/userdata/{file}/move/{dest}") - async def move_userdata(request): - """ - Move or rename a user data file. - - This endpoint handles moving or renaming files within a user's data directory, with options for - controlling overwrite behavior and response format. - - Path Parameters: - - file: The source file path (URL encoded if necessary) - - dest: The destination file path (URL encoded if necessary) - - Query Parameters: - - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true". - - full_info (optional): If "true", returns detailed file information (path, size, modified time). - If "false", returns only the relative file path. - - Returns: - - 400: If either 'file' or 'dest' parameter is missing - - 403: If either requested path is not allowed - - 404: If the source file does not exist - - 409: If overwrite=false and the destination file already exists - - 200: JSON response with either: - - Full file information (if full_info=true) - - Relative file path (if full_info=false) - """ - source = get_user_data_path(request, check_exists=True) - if not isinstance(source, str): - return source - - dest = get_user_data_path(request, check_exists=False, param="dest") - if not isinstance(source, str): - return dest - - overwrite = request.query.get("overwrite", 'true') != "false" - full_info = request.query.get('full_info', 'false').lower() == "true" - - if not overwrite and os.path.exists(dest): - return web.Response(status=409, text="File already exists") - - logging.info(f"moving '{source}' -> '{dest}'") - shutil.move(source, dest) - - user_path = self.get_request_user_filepath(request, None) - if full_info: - resp = get_file_info(dest, user_path) - else: - resp = os.path.relpath(dest, user_path) - - return web.json_response(resp) diff --git a/utils/.DS_Store b/utils/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 Binary files a/utils/.DS_Store and /dev/null differ diff --git a/utils/__init__.py b/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/utils/extra_config.py b/utils/extra_config.py deleted file mode 100644 index a0fcda9e8900ee2c67d79a1e588ff973f9ea7ea6..0000000000000000000000000000000000000000 --- a/utils/extra_config.py +++ /dev/null @@ -1,34 +0,0 @@ -import os -import yaml -import folder_paths -import logging - -def load_extra_path_config(yaml_path): - with open(yaml_path, 'r', encoding='utf-8') as stream: - config = yaml.safe_load(stream) - yaml_dir = os.path.dirname(os.path.abspath(yaml_path)) - for c in config: - conf = config[c] - if conf is None: - continue - base_path = None - if "base_path" in conf: - base_path = conf.pop("base_path") - base_path = os.path.expandvars(os.path.expanduser(base_path)) - if not os.path.isabs(base_path): - base_path = os.path.abspath(os.path.join(yaml_dir, base_path)) - is_default = False - if "is_default" in conf: - is_default = conf.pop("is_default") - for x in conf: - for y in conf[x].split("\n"): - if len(y) == 0: - continue - full_path = y - if base_path: - full_path = os.path.join(base_path, full_path) - elif not os.path.isabs(full_path): - full_path = os.path.abspath(os.path.join(yaml_dir, y)) - normalized_path = os.path.normpath(full_path) - logging.info("Adding extra search path {} {}".format(x, normalized_path)) - folder_paths.add_model_folder_path(x, normalized_path, is_default) diff --git a/utils/install_util.py b/utils/install_util.py deleted file mode 100644 index 0f59bcf9193570a36641bd4e0c70c80d05be1a0f..0000000000000000000000000000000000000000 --- a/utils/install_util.py +++ /dev/null @@ -1,18 +0,0 @@ -from pathlib import Path -import sys - -# The path to the requirements.txt file -requirements_path = Path(__file__).parents[1] / "requirements.txt" - - -def get_missing_requirements_message(): - """The warning message to display when a package is missing.""" - - extra = "" - if sys.flags.no_user_site: - extra = "-s " - return f""" -Please install the updated requirements.txt file by running: -{sys.executable} {extra}-m pip install -r {requirements_path} -If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem. -""".strip() diff --git a/utils/json_util.py b/utils/json_util.py deleted file mode 100644 index da45af4f74f386973d3f1968d88b083ca5906d81..0000000000000000000000000000000000000000 --- a/utils/json_util.py +++ /dev/null @@ -1,26 +0,0 @@ -def merge_json_recursive(base, update): - """Recursively merge two JSON-like objects. - - Dictionaries are merged recursively - - Lists are concatenated - - Other types are overwritten by the update value - - Args: - base: Base JSON-like object - update: Update JSON-like object to merge into base - - Returns: - Merged JSON-like object - """ - if not isinstance(base, dict) or not isinstance(update, dict): - if isinstance(base, list) and isinstance(update, list): - return base + update - return update - - merged = base.copy() - for key, value in update.items(): - if key in merged: - merged[key] = merge_json_recursive(merged[key], value) - else: - merged[key] = value - - return merged