Create pipeline.py

pipeline.py

@@ -0,0 +1,1299 @@
+# Copyright 2024 PixArt-Sigma Authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import html
+import inspect
+import re
+import urllib.parse as ul
+from typing import Callable, List, Optional, Tuple, Union
+
+import torch
+from transformers import T5EncoderModel, T5Tokenizer
+
+from diffusers.image_processor import PixArtImageProcessor, PipelineImageInput
+from diffusers.models import AutoencoderKL, PixArtTransformer2DModel
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    BACKENDS_MAPPING,
+    deprecate,
+    is_bs4_available,
+    is_ftfy_available,
+    logging,
+    replace_example_docstring,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha import (
+    ASPECT_RATIO_256_BIN,
+    ASPECT_RATIO_512_BIN,
+    ASPECT_RATIO_1024_BIN,
+)
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor,
+    generator: Optional[torch.Generator] = None,
+    sample_mode: str = "sample",
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_bs4_available():
+    from bs4 import BeautifulSoup
+
+if is_ftfy_available():
+    import ftfy
+
+def debug_print(message: str):
+    #print(message)
+    pass
+
+ASPECT_RATIO_2048_BIN = {
+    "0.25": [1024.0, 4096.0],
+    "0.26": [1024.0, 3968.0],
+    "0.27": [1024.0, 3840.0],
+    "0.28": [1024.0, 3712.0],
+    "0.32": [1152.0, 3584.0],
+    "0.33": [1152.0, 3456.0],
+    "0.35": [1152.0, 3328.0],
+    "0.4": [1280.0, 3200.0],
+    "0.42": [1280.0, 3072.0],
+    "0.48": [1408.0, 2944.0],
+    "0.5": [1408.0, 2816.0],
+    "0.52": [1408.0, 2688.0],
+    "0.57": [1536.0, 2688.0],
+    "0.6": [1536.0, 2560.0],
+    "0.68": [1664.0, 2432.0],
+    "0.72": [1664.0, 2304.0],
+    "0.78": [1792.0, 2304.0],
+    "0.82": [1792.0, 2176.0],
+    "0.88": [1920.0, 2176.0],
+    "0.94": [1920.0, 2048.0],
+    "1.0": [2048.0, 2048.0],
+    "1.07": [2048.0, 1920.0],
+    "1.13": [2176.0, 1920.0],
+    "1.21": [2176.0, 1792.0],
+    "1.29": [2304.0, 1792.0],
+    "1.38": [2304.0, 1664.0],
+    "1.46": [2432.0, 1664.0],
+    "1.67": [2560.0, 1536.0],
+    "1.75": [2688.0, 1536.0],
+    "2.0": [2816.0, 1408.0],
+    "2.09": [2944.0, 1408.0],
+    "2.4": [3072.0, 1280.0],
+    "2.5": [3200.0, 1280.0],
+    "2.89": [3328.0, 1152.0],
+    "3.0": [3456.0, 1152.0],
+    "3.11": [3584.0, 1152.0],
+    "3.62": [3712.0, 1024.0],
+    "3.75": [3840.0, 1024.0],
+    "3.88": [3968.0, 1024.0],
+    "4.0": [4096.0, 1024.0],
+}
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import PixArtSigmaPipeline
+
+        >>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-Sigma-XL-2-512-MS" too.
+        >>> pipe = PixArtSigmaPipeline.from_pretrained(
+        ...     "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", torch_dtype=torch.float16
+        ... )
+        >>> # Enable memory optimizations.
+        >>> # pipe.enable_model_cpu_offload()
+
+        >>> prompt = "A small cactus with a happy face in the Sahara desert."
+        >>> image = pipe(prompt).images[0]
+        ```
+"""
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class PixArtSigmaPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using PixArt-Sigma.
+    """
+
+    bad_punct_regex = re.compile(
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
+    )  # noqa
+
+    _optional_components = ["tokenizer", "text_encoder"]
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        vae: AutoencoderKL,
+        transformer: PixArtTransformer2DModel,
+        scheduler: KarrasDiffusionSchedulers,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = PixArtImageProcessor(
+            vae_scale_factor=self.vae_scale_factor
+        )
+
+    def get_timesteps(
+        self, num_inference_steps, strength, device, denoising_start=None
+    ):
+        # get the original timestep using init_timestep
+        if denoising_start is None and strength is not None:
+            init_timestep = min(
+                int(num_inference_steps * strength), num_inference_steps
+            )
+            debug_print(f"Init timestep: {init_timestep}")
+            t_start = max(num_inference_steps - init_timestep, 0)
+            debug_print(
+                f"t_start = max({num_inference_steps} - {init_timestep}, 0) = {t_start}"
+            )
+        else:
+            debug_print(f"denoising_start: {denoising_start}")
+            t_start = 0
+
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+        # Strength is irrelevant if we directly request a timestep to start at;
+        # that is, strength is determined by the denoising_start instead.
+        if denoising_start is not None:
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_start * self.scheduler.config.num_train_timesteps)
+                )
+            )
+
+            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
+                # if the scheduler is a 2nd order scheduler we might have to do +1
+                # because `num_inference_steps` might be even given that every timestep
+                # (except the highest one) is duplicated. If `num_inference_steps` is even it would
+                # mean that we cut the timesteps in the middle of the denoising step
+                # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1
+                # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
+                num_inference_steps = num_inference_steps + 1
+
+            # because t_n+1 >= t_n, we slice the timesteps starting from the end
+            timesteps = timesteps[-num_inference_steps:]
+            return timesteps, num_inference_steps
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.encode_prompt with 120->300
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: str = "",
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        clean_caption: bool = False,
+        max_sequence_length: int = 300,
+        **kwargs,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For
+                PixArt-Alpha, this should be "".
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                whether to use classifier free guidance or not
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                number of images that should be generated per prompt
+            device: (`torch.device`, *optional*):
+                torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the ""
+                string.
+            clean_caption (`bool`, defaults to `False`):
+                If `True`, the function will preprocess and clean the provided caption before encoding.
+            max_sequence_length (`int`, defaults to 300): Maximum sequence length to use for the prompt.
+        """
+
+        if "mask_feature" in kwargs:
+            deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
+            deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)
+
+        if device is None:
+            device = self._execution_device
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # See Section 3.1. of the paper.
+        max_length = max_sequence_length
+
+        if prompt_embeds is None:
+            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(
+                prompt, padding="longest", return_tensors="pt"
+            ).input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[
+                -1
+            ] and not torch.equal(text_input_ids, untruncated_ids):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because T5 can only handle sequences up to"
+                    f" {max_length} tokens: {removed_text}"
+                )
+
+            prompt_attention_mask = text_inputs.attention_mask
+            prompt_attention_mask = prompt_attention_mask.to(device)
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device), attention_mask=prompt_attention_mask
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        if self.text_encoder is not None:
+            dtype = self.text_encoder.dtype
+        elif self.transformer is not None:
+            dtype = self.transformer.dtype
+        else:
+            dtype = None
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(
+            bs_embed * num_images_per_prompt, seq_len, -1
+        )
+        prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1)
+        prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens = (
+                [negative_prompt] * batch_size
+                if isinstance(negative_prompt, str)
+                else negative_prompt
+            )
+            uncond_tokens = self._text_preprocessing(
+                uncond_tokens, clean_caption=clean_caption
+            )
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_attention_mask=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            negative_prompt_attention_mask = uncond_input.attention_mask
+            negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=negative_prompt_attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(
+                dtype=dtype, device=device
+            )
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(
+                1, num_images_per_prompt, 1
+            )
+            negative_prompt_embeds = negative_prompt_embeds.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+
+            negative_prompt_attention_mask = negative_prompt_attention_mask.view(
+                bs_embed, -1
+            )
+            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(
+                num_images_per_prompt, 1
+            )
+        else:
+            negative_prompt_embeds = None
+            negative_prompt_attention_mask = None
+
+        return (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        )
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        strength,
+        num_inference_steps,
+        negative_prompt,
+        callback_steps,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_attention_mask=None,
+        negative_prompt_attention_mask=None,
+    ):
+        if strength is None:
+            if height % 8 != 0 or width % 8 != 0:
+                raise ValueError(
+                    f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
+                )
+        else:
+            if strength < 0 or strength > 1:
+                raise ValueError(
+                    f"The value of strength should in [0.0, 1.0] but is {strength}"
+                )
+            if num_inference_steps is None:
+                raise ValueError("`num_inference_steps` cannot be None.")
+            elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
+                raise ValueError(
+                    f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
+                    f" {type(num_inference_steps)}."
+                )
+        if (callback_steps is None) or (
+            callback_steps is not None
+            and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            prompt = None
+
+        if prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (
+            not isinstance(prompt, str) and not isinstance(prompt, list)
+        ):
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+
+        if prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            negative_prompt = None
+
+        if prompt_embeds is not None and prompt_attention_mask is None:
+            raise ValueError(
+                "Must provide `prompt_attention_mask` when specifying `prompt_embeds`."
+            )
+
+        if (
+            negative_prompt_embeds is not None
+            and negative_prompt_attention_mask is None
+        ):
+            raise ValueError(
+                "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+            if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
+                raise ValueError(
+                    "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
+                    f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
+                    f" {negative_prompt_attention_mask.shape}."
+                )
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
+    def _text_preprocessing(self, text, clean_caption=False):
+        if clean_caption and not is_bs4_available():
+            logger.warning(
+                BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")
+            )
+            logger.warning("Setting `clean_caption` to False...")
+            clean_caption = False
+
+        if clean_caption and not is_ftfy_available():
+            logger.warning(
+                BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")
+            )
+            logger.warning("Setting `clean_caption` to False...")
+            clean_caption = False
+
+        if not isinstance(text, (tuple, list)):
+            text = [text]
+
+        def process(text: str):
+            if clean_caption:
+                text = self._clean_caption(text)
+                text = self._clean_caption(text)
+            else:
+                text = text.lower().strip()
+            return text
+
+        return [process(t) for t in text]
+
+    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
+    def _clean_caption(self, caption):
+        caption = str(caption)
+        caption = ul.unquote_plus(caption)
+        caption = caption.strip().lower()
+        caption = re.sub("<person>", "person", caption)
+        # urls:
+        caption = re.sub(
+            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+            "",
+            caption,
+        )  # regex for urls
+        caption = re.sub(
+            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+            "",
+            caption,
+        )  # regex for urls
+        # html:
+        caption = BeautifulSoup(caption, features="html.parser").text
+
+        # @<nickname>
+        caption = re.sub(r"@[\w\d]+\b", "", caption)
+
+        # 31C0—31EF CJK Strokes
+        # 31F0—31FF Katakana Phonetic Extensions
+        # 3200—32FF Enclosed CJK Letters and Months
+        # 3300—33FF CJK Compatibility
+        # 3400—4DBF CJK Unified Ideographs Extension A
+        # 4DC0—4DFF Yijing Hexagram Symbols
+        # 4E00—9FFF CJK Unified Ideographs
+        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
+        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
+        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
+        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
+        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
+        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
+        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
+        #######################################################
+
+        # все виды тире / all types of dash --> "-"
+        caption = re.sub(
+            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
+            "-",
+            caption,
+        )
+
+        # кавычки к одному стандарту
+        caption = re.sub(r"[`´«»“”¨]", '"', caption)
+        caption = re.sub(r"[‘’]", "'", caption)
+
+        # &quot;
+        caption = re.sub(r"&quot;?", "", caption)
+        # &amp
+        caption = re.sub(r"&amp", "", caption)
+
+        # ip adresses:
+        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
+
+        # article ids:
+        caption = re.sub(r"\d:\d\d\s+$", "", caption)
+
+        # \n
+        caption = re.sub(r"\\n", " ", caption)
+
+        # "#123"
+        caption = re.sub(r"#\d{1,3}\b", "", caption)
+        # "#12345.."
+        caption = re.sub(r"#\d{5,}\b", "", caption)
+        # "123456.."
+        caption = re.sub(r"\b\d{6,}\b", "", caption)
+        # filenames:
+        caption = re.sub(
+            r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption
+        )
+
+        #
+        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
+        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
+
+        caption = re.sub(
+            self.bad_punct_regex, r" ", caption
+        )  # ***AUSVERKAUFT***, #AUSVERKAUFT
+        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
+
+        # this-is-my-cute-cat / this_is_my_cute_cat
+        regex2 = re.compile(r"(?:\-|\_)")
+        if len(re.findall(regex2, caption)) > 3:
+            caption = re.sub(regex2, " ", caption)
+
+        caption = ftfy.fix_text(caption)
+        caption = html.unescape(html.unescape(caption))
+
+        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
+        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
+        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
+
+        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
+        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
+        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
+        caption = re.sub(
+            r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption
+        )
+        caption = re.sub(r"\bpage\s+\d+\b", "", caption)
+
+        caption = re.sub(
+            r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption
+        )  # j2d1a2a...
+
+        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
+
+        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
+        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
+        caption = re.sub(r"\s+", " ", caption)
+
+        caption.strip()
+
+        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
+        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
+        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
+        caption = re.sub(r"^\.\S+$", "", caption)
+
+        return caption.strip()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        _latents=None,
+        timestep=None,
+        add_noise=False,
+        image=None,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(width) // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if _latents is not None:
+            init_latents = _latents.to(device)
+        elif image is None and _latents is None:
+            debug_print("Make random latents tensor")
+            init_latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype
+            )
+
+        latents_mean = latents_std = None
+        if (
+            hasattr(self.vae.config, "latents_mean")
+            and self.vae.config.latents_mean is not None
+        ):
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if (
+            hasattr(self.vae.config, "latents_std")
+            and self.vae.config.latents_std is not None
+        ):
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+        if image is not None and hasattr(image, "shape") and image.shape[1] == 4:
+            debug_print("Received valid latent image input.")
+            init_latents = image
+
+        if init_latents is not None:
+            # scale the initial noise by the standard deviation required by the scheduler
+            debug_print(f"Scaling the initial noise by the std required by the scheduler.")
+            init_latents = init_latents * self.scheduler.init_noise_sigma
+
+        if image is not None and image.shape[1] < 4:
+            debug_print("Received RGB or similar image. Processing..")
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            if self.vae.config.force_upcast:
+                image = image.float()
+                self.vae.to(dtype=torch.float32)
+
+            if isinstance(generator, list) and len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+
+            elif isinstance(generator, list):
+                init_latents = [
+                    retrieve_latents(
+                        self.vae.encode(image[i : i + 1]), generator=generator[i]
+                    )
+                    for i in range(batch_size)
+                ]
+                init_latents = torch.cat(init_latents, dim=0)
+            else:
+                debug_print("Encode image to latents.")
+                init_latents = retrieve_latents(
+                    self.vae.encode(image), generator=generator
+                )
+
+            if self.vae.config.force_upcast:
+                self.vae.to(dtype)
+
+            debug_print("Set initial latents..")
+            init_latents = init_latents.to(dtype)
+            if latents_mean is not None and latents_std is not None:
+                debug_print("Scaling latents by mean/std")
+                latents_mean = latents_mean.to(device=device, dtype=dtype)
+                latents_std = latents_std.to(device=device, dtype=dtype)
+                init_latents = (
+                    (init_latents - latents_mean)
+                    * self.vae.config.scaling_factor
+                    / latents_std
+                )
+            else:
+                debug_print("Scaling latents only by scaling_factor")
+                init_latents = self.vae.config.scaling_factor * init_latents
+
+            if (
+                batch_size > init_latents.shape[0]
+                and batch_size % init_latents.shape[0] == 0
+            ):
+                # expand init_latents for batch_size
+                additional_image_per_prompt = batch_size // init_latents.shape[0]
+                init_latents = torch.cat(
+                    [init_latents] * additional_image_per_prompt, dim=0
+                )
+            elif (
+                batch_size > init_latents.shape[0]
+                and batch_size % init_latents.shape[0] != 0
+            ):
+                raise ValueError(
+                    f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+                )
+            else:
+                init_latents = torch.cat([init_latents], dim=0)
+
+        if (
+            add_noise
+            and timestep is not None
+            and (_latents is not None or image is not None)
+        ):
+            shape = init_latents.shape
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # get latents
+            debug_print(f"Adding noise to tensor for timestep: {timestep}")
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+        return init_latents
+
+    @property
+    def denoising_start(self):
+        return self._denoising_start
+
+    @property
+    def denoising_end(self):
+        return self._denoising_end
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: str = "",
+        strength: float = None,
+        num_inference_steps: int = 20,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_start: Optional[float] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 4.5,
+        num_images_per_prompt: Optional[int] = 1,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        image: Optional[PipelineImageInput] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+        callback_steps: int = 1,
+        clean_caption: bool = True,
+        use_resolution_binning: bool = True,
+        max_sequence_length: int = 300,
+        **kwargs,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            strength (`float`, *optional*, defaults to 0.3):
+                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
+                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
+                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
+                be maximum and the denoising process will run for the full number of iterations specified in
+                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
+                `denoising_start` being declared as an integer, the value of `strength` will be ignored.
+            num_inference_steps (`int`, *optional*, defaults to 100):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            denoising_start (`float`, *optional*):
+                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
+                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
+                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
+                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
+                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refine Image
+                Quality**](https://huggingface.co/docs/diffusers/using-diffusers/sdxl#refine-image-quality).
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 4.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size):
+                The width in pixels of the generated image.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
+                provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
+            negative_prompt_attention_mask (`torch.Tensor`, *optional*):
+                Pre-generated attention mask for negative text embeddings.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            clean_caption (`bool`, *optional*, defaults to `True`):
+                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
+                be installed. If the dependencies are not installed, the embeddings will be created from the raw
+                prompt.
+            use_resolution_binning (`bool` defaults to `True`):
+                If set to `True`, the requested height and width are first mapped to the closest resolutions using
+                `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to
+                the requested resolution. Useful for generating non-square images.
+            max_sequence_length (`int` defaults to 300): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.ImagePipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
+                returned where the first element is a list with the generated images
+        """
+        # 1. Check inputs. Raise error if not correct
+        height = height or self.transformer.config.sample_size * self.vae_scale_factor
+        width = width or self.transformer.config.sample_size * self.vae_scale_factor
+        if use_resolution_binning:
+            if self.transformer.config.sample_size == 256:
+                aspect_ratio_bin = ASPECT_RATIO_2048_BIN
+            elif self.transformer.config.sample_size == 128:
+                aspect_ratio_bin = ASPECT_RATIO_1024_BIN
+            elif self.transformer.config.sample_size == 64:
+                aspect_ratio_bin = ASPECT_RATIO_512_BIN
+            elif self.transformer.config.sample_size == 32:
+                aspect_ratio_bin = ASPECT_RATIO_256_BIN
+            else:
+                raise ValueError("Invalid sample size")
+            orig_height, orig_width = height, width
+            height, width = self.image_processor.classify_height_width_bin(
+                height, width, ratios=aspect_ratio_bin
+            )
+
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            strength,
+            num_inference_steps,
+            negative_prompt,
+            callback_steps,
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_attention_mask,
+        )
+
+        # 2. Default height and width to transformer
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        self._denoising_start = denoising_start
+        self._num_timesteps = num_inference_steps
+        self._denoising_end = denoising_end
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt,
+            do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            clean_caption=clean_caption,
+            max_sequence_length=max_sequence_length,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            prompt_attention_mask = torch.cat(
+                [negative_prompt_attention_mask, prompt_attention_mask], dim=0
+            )
+
+        # 4. Prepare timesteps
+        def denoising_value_valid(dnv):
+            return isinstance(dnv, float) and 0 < dnv < 1
+
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+
+        # 5. Prepare latents.
+        if image is not None:
+            image = self.image_processor.preprocess(image)
+            image = image.to(device=self.vae.device, dtype=self.vae.dtype)
+
+        latent_channels = self.transformer.config.in_channels
+        latent_timestep = None
+        if (
+            denoising_end is not None
+            or denoising_start is not None
+            or strength is not None
+        ):
+            timesteps, num_inference_steps = self.get_timesteps(
+                num_inference_steps,
+                strength,
+                device,
+                denoising_start=(
+                    self.denoising_start
+                    if denoising_value_valid(self.denoising_start)
+                    else None
+                ),
+            )
+            latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+            if latents is not None:
+                height, width = latents.shape[-2:]
+                height = height * self.vae_scale_factor
+                width = width * self.vae_scale_factor
+        add_noise = (
+            True
+            if (
+                self.denoising_start is None
+                and (image is not None or latents is not None)
+            )
+            else False
+        )
+        debug_print(f"Add_noise: {add_noise}")
+        if latents is None:
+            debug_print("Prepare latents..")
+            latents = self.prepare_latents(
+                batch_size * num_images_per_prompt,
+                latent_channels,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+                timestep=latent_timestep,
+                add_noise=add_noise,
+                image=image,
+            )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 6.1 Prepare micro-conditions.
+        added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
+
+        # 7. Denoising loop
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        if (
+            self.denoising_end is not None
+            and self.denoising_start is not None
+            and denoising_value_valid(self.denoising_end)
+            and denoising_value_valid(self.denoising_start)
+            and self.denoising_start >= self.denoising_end
+        ):
+            raise ValueError(
+                f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
+                + f" {self.denoising_end} when using type float."
+            )
+        if self.denoising_start is not None:
+            if denoising_value_valid(self.denoising_start):
+                discrete_timestep_cutoff = int(
+                    round(
+                        self.scheduler.config.num_train_timesteps
+                        - (denoising_start * self.scheduler.config.num_train_timesteps)
+                    )
+                )
+
+                num_inference_steps = (
+                    (timesteps < discrete_timestep_cutoff).sum().item()
+                )
+                debug_print(
+                    f"Beginning inference for stage2 with {num_inference_steps} steps."
+                )
+
+            else:
+                raise ValueError(
+                    f"`denoising_start` must be a float between 0 and 1: {denoising_start}"
+                )
+        if self.denoising_end is not None:
+            if denoising_value_valid(self.denoising_end):
+                discrete_timestep_cutoff = int(
+                    round(
+                        self.scheduler.config.num_train_timesteps
+                        - (
+                            self.denoising_end
+                            * self.scheduler.config.num_train_timesteps
+                        )
+                    )
+                )
+                num_inference_steps = len(
+                    list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))
+                )
+                debug_print(
+                    f"Beginning inference for stage1 with {num_inference_steps} steps."
+                )
+                timesteps = timesteps[:num_inference_steps]
+            else:
+                raise ValueError(
+                    f"`denoising_end` must be a float between 0 and 1: {denoising_end}"
+                )
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                latent_model_input = (
+                    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                )
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                current_timestep = t
+                if not torch.is_tensor(current_timestep):
+                    # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+                    # This would be a good case for the `match` statement (Python 3.10+)
+                    is_mps = latent_model_input.device.type == "mps"
+                    if isinstance(current_timestep, float):
+                        dtype = torch.float32 if is_mps else torch.float64
+                    else:
+                        dtype = torch.int32 if is_mps else torch.int64
+                    current_timestep = torch.tensor(
+                        [current_timestep],
+                        dtype=dtype,
+                        device=latent_model_input.device,
+                    )
+                elif len(current_timestep.shape) == 0:
+                    current_timestep = current_timestep[None].to(
+                        latent_model_input.device
+                    )
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                current_timestep = current_timestep.expand(latent_model_input.shape[0])
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    latent_model_input.to(
+                        device=self.transformer.device, dtype=self.transformer.dtype
+                    ),
+                    encoder_hidden_states=prompt_embeds,
+                    encoder_attention_mask=prompt_attention_mask,
+                    timestep=current_timestep,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                # learned sigma
+                if self.transformer.config.out_channels // 2 == latent_channels:
+                    noise_pred = noise_pred.chunk(2, dim=1)[0]
+                else:
+                    noise_pred = noise_pred
+
+                # compute previous image: x_t -> x_t-1
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs, return_dict=False
+                )[0]
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or (
+                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if not output_type == "latent":
+            image = self.vae.decode(
+                latents.to(device=self.vae.device, dtype=self.vae.dtype)
+                / self.vae.config.scaling_factor,
+                return_dict=False,
+            )[0]
+            if use_resolution_binning:
+                image = self.image_processor.resize_and_crop_tensor(
+                    image, orig_width, orig_height
+                )
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)