Omni Zero Couples

.dockerignore

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+models
+venv

.gitignore

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+__pycache__
+models
+.cog/*
+.cog
+venv

LICENSE

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+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,42 @@
+---
+title: Omni-Zero-Couples
+emoji: 🧛🏻‍♂️
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+pinned: false
+license: gpl-3.0
+---
+
+[![Buy me a coffee](https://img.buymeacoffee.com/button-api/?text=Buy%20me%20a%20coffee&emoji=&slug=vk654cf2pv8&button_colour=BD5FFF&font_colour=ffffff&font_family=Bree&outline_colour=000000&coffee_colour=FFDD00)](https://www.buymeacoffee.com/vk654cf2pv8)
+
+# Omni-Zero-Couples: A diffusion pipeline for zero-shot stylized couples portrait creation. 
+
+## Use Omni-Zero in HuggingFace Spaces ZeroGPU [https://huggingface.co/spaces/okaris/omni-zero-couples](https://huggingface.co/spaces/okaris/omni-zero-couples)
+![Omni-Zero-Couples-Huggingface](https://github.com/user-attachments/assets/1f4b272b-db36-4355-91f0-b2c1ca310680)
+
+## Run on Replicate [https://replicate.com/okaris/omni-zero-couples](https://replicate.com/okaris/omni-zero-couples)
+![Omni-Zero-Couples-Replicate](https://github.com/user-attachments/assets/aeee3626-c343-4441-8e36-89896096910b)
+<img width="1799" alt="Screenshot 2024-09-25 at 17 18 45" src="https://github.com/user-attachments/assets/aeee3626-c343-4441-8e36-89896096910b">
+
+### Multiple Identities and Styles
+![Omni-Zero-Couples](https://github.com/user-attachments/assets/87218819-5114-49d8-a0f2-eadf4201736e)
+
+### Single Identity and Style [https://github.com/okaris/omni-zero](https://github.com/okaris/omni-zero)
+![Omni-Zero](https://github.com/okaris/omni-zero/assets/1448702/2c51fb77-a810-4c0a-9555-791a294455ca)
+
+### How to run
+```
+git clone https://github.com/okaris/omni-zero-couples.git
+cd omni-zero-couples
+pip install -r requirements.txt
+python demo.py
+```
+
+### Credits
+- Special thanks to my friend Misch Strotz, Co-Founder of [letz.ai](https://letz.ai) for providing compute for the research
+- This project wouldn't be possible without the great work of the [InstantX Team](https://github.com/InstantID)
+- Thanks to [@fofrAI](http://twitter.com/fofrAI) for inspiring me with his [face-to-many workflow](https://github.com/fofr/cog-face-to-many)
+- Thanks to Matteo ([@cubiq](https://twitter.com/cubiq])) for creating the ComfyUI nodes for IP-Adapter which inspired the quality improvements for diffusers

app.py

@@ -0,0 +1,317 @@
+import os
+
+import gradio as gr
+import spaces
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+import torch
+
+#Hack for ZeroGPU
+torch.jit.script = lambda f: f
+####
+
+import cv2
+import numpy as np
+import PIL
+from controlnet_aux import ZoeDetector
+from diffusers import DPMSolverMultistepScheduler
+from diffusers.image_processor import IPAdapterMaskProcessor
+from diffusers.models import ControlNetModel
+from huggingface_hub import snapshot_download
+from insightface.app import FaceAnalysis
+from pipeline import OmniZeroPipeline
+from transformers import CLIPVisionModelWithProjection
+from utils import align_images, draw_kps, load_and_resize_image
+
+
+def patch_onnx_runtime(
+    inter_op_num_threads: int = 16,
+    intra_op_num_threads: int = 16,
+    omp_num_threads: int = 16,
+):
+    import os
+
+    import onnxruntime as ort
+
+    os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
+
+    _default_session_options = ort.capi._pybind_state.get_default_session_options()
+
+    def get_default_session_options_new():
+        _default_session_options.inter_op_num_threads = inter_op_num_threads
+        _default_session_options.intra_op_num_threads = intra_op_num_threads
+        return _default_session_options
+
+    ort.capi._pybind_state.get_default_session_options = get_default_session_options_new
+    
+
+base_model = "frankjoshua/albedobaseXL_v13"
+
+patch_onnx_runtime()
+
+snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2")
+face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider'])
+face_analysis.prepare(ctx_id=0, det_size=(640, 640))
+
+dtype = torch.float16
+
+ip_adapter_plus_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    "h94/IP-Adapter", 
+    subfolder="models/image_encoder",
+    torch_dtype=dtype,
+).to("cuda")
+
+zoedepthnet_path = "okaris/zoe-depth-controlnet-xl"
+zoedepthnet = ControlNetModel.from_pretrained(zoedepthnet_path,torch_dtype=dtype).to("cuda")
+
+identitiynet_path = "okaris/face-controlnet-xl"
+identitynet = ControlNetModel.from_pretrained(identitiynet_path, torch_dtype=dtype).to("cuda")
+
+zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
+ip_adapter_mask_processor = IPAdapterMaskProcessor()
+
+pipeline = OmniZeroPipeline.from_pretrained(
+    base_model,
+    controlnet=[identitynet, identitynet, zoedepthnet],
+    torch_dtype=dtype,
+    image_encoder=ip_adapter_plus_image_encoder,
+).to("cuda")
+
+config = pipeline.scheduler.config
+config["timestep_spacing"] = "trailing"
+pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero")
+
+pipeline.load_ip_adapter(["okaris/ip-adapter-instantid", "okaris/ip-adapter-instantid", "h94/IP-Adapter"], subfolder=[None, None, "sdxl_models"], weight_name=["ip-adapter-instantid.bin", "ip-adapter-instantid.bin", "ip-adapter-plus_sdxl_vit-h.safetensors"])
+
+@spaces.GPU()
+def generate(
+    base_image="https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg",
+    style_image="https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg",
+    identity_image_1="https://cdn-prod.styleof.com/inferences/cm1hp4lea14oz14jeoghnex7g/dlgc5xwo0qzey7qaixy45i1o-medium.jpeg",
+    identity_image_2="https://cdn-prod.styleof.com/inferences/cm1ho69ha14np14jesnusqiep/mp3aaktzqz20ujco5i3bi5s1-medium.jpeg",
+    seed=42,
+    prompt="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy",
+    negative_prompt="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    guidance_scale=3.0,
+    number_of_images=1,
+    number_of_steps=10,
+    base_image_strength=0.3,
+    style_image_strength=1.0,
+    identity_image_strength_1=1.0,
+    identity_image_strength_2=1.0,
+    depth_image=None,
+    depth_image_strength=0.2,
+    mask_guidance_start=0.0,
+    mask_guidance_end=1.0,
+    progress=gr.Progress(track_tqdm=True)
+):
+    resolution = 1024
+
+    if base_image is not None:
+        base_image = load_and_resize_image(base_image, resolution, resolution)
+
+    if depth_image is None:
+        depth_image = zoe_depth_detector(base_image, detect_resolution=resolution, image_resolution=resolution)
+    else:
+        depth_image = load_and_resize_image(depth_image, resolution, resolution)
+
+    base_image, depth_image = align_images(base_image, depth_image)
+
+    if style_image is not None:
+        style_image = load_and_resize_image(style_image, resolution, resolution)
+    else:
+        raise ValueError("You must provide a style image")
+    
+    if identity_image_1 is not None:
+        identity_image_1 = load_and_resize_image(identity_image_1, resolution, resolution)
+    else:
+        raise ValueError("You must provide an identity image")
+    
+    if identity_image_2 is not None:
+        identity_image_2 = load_and_resize_image(identity_image_2, resolution, resolution)
+    else:
+        raise ValueError("You must provide an identity image 2")
+
+    height, width = base_image.size
+
+    face_info_1 = face_analysis.get(cv2.cvtColor(np.array(identity_image_1), cv2.COLOR_RGB2BGR))
+    for i, face in enumerate(face_info_1):
+        print(f"Face 1 -{i}: Age: {face['age']}, Gender: {face['gender']}")
+    face_info_1 = sorted(face_info_1, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
+    face_emb_1 = torch.tensor(face_info_1['embedding']).to("cuda", dtype=dtype)
+
+    face_info_2 = face_analysis.get(cv2.cvtColor(np.array(identity_image_2), cv2.COLOR_RGB2BGR))
+    for i, face in enumerate(face_info_2):
+        print(f"Face 2 -{i}: Age: {face['age']}, Gender: {face['gender']}")
+    face_info_2 = sorted(face_info_2, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
+    face_emb_2 = torch.tensor(face_info_2['embedding']).to("cuda", dtype=dtype)
+
+    zero = np.zeros((width, height, 3), dtype=np.uint8)
+    # face_kps_identity_image_1 = draw_kps(zero, face_info_1['kps'])
+    # face_kps_identity_image_2 = draw_kps(zero, face_info_2['kps'])
+
+    face_info_img2img = face_analysis.get(cv2.cvtColor(np.array(base_image), cv2.COLOR_RGB2BGR))
+    faces_info_img2img = sorted(face_info_img2img, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])
+    face_info_a = faces_info_img2img[-1]
+    face_info_b = faces_info_img2img[-2]
+    # face_emb_a = torch.tensor(face_info_a['embedding']).to("cuda", dtype=dtype)
+    # face_emb_b = torch.tensor(face_info_b['embedding']).to("cuda", dtype=dtype)
+    face_kps_identity_image_a = draw_kps(zero, face_info_a['kps'])
+    face_kps_identity_image_b = draw_kps(zero, face_info_b['kps'])
+
+    general_mask = PIL.Image.fromarray(np.ones((width, height, 3), dtype=np.uint8))
+
+    control_mask_1 = zero.copy()
+    x1, y1, x2, y2 = face_info_a["bbox"]
+    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+    control_mask_1[y1:y2, x1:x2] = 255
+    control_mask_1 = PIL.Image.fromarray(control_mask_1.astype(np.uint8))
+
+    control_mask_2 = zero.copy()
+    x1, y1, x2, y2 = face_info_b["bbox"]
+    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+    control_mask_2[y1:y2, x1:x2] = 255
+    control_mask_2 = PIL.Image.fromarray(control_mask_2.astype(np.uint8))
+
+    controlnet_masks = [control_mask_1, control_mask_2, general_mask]
+    ip_adapter_images = [face_emb_1, face_emb_2, style_image, ]
+
+    masks = ip_adapter_mask_processor.preprocess([control_mask_1, control_mask_2, general_mask], height=height, width=width)
+    ip_adapter_masks = [mask.unsqueeze(0) for mask in masks]
+
+    inpaint_mask = torch.logical_or(torch.tensor(np.array(control_mask_1)), torch.tensor(np.array(control_mask_2))).float()
+    inpaint_mask = PIL.Image.fromarray((inpaint_mask.numpy() * 255).astype(np.uint8)).convert("RGB")
+
+    new_ip_adapter_masks = []
+    for ip_img, mask in zip(ip_adapter_images, controlnet_masks):
+        if isinstance(ip_img, list):
+            num_images = len(ip_img)
+            mask = mask.repeat(1, num_images, 1, 1)
+
+        new_ip_adapter_masks.append(mask)
+        
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+
+    pipeline.set_ip_adapter_scale([identity_image_strength_1, identity_image_strength_2,
+        {
+            "down": { "block_2": [0.0, 0.0] }, #Composition
+            "up": { "block_0": [0.0, style_image_strength, 0.0] } #Style
+        }
+    ])
+
+    images = pipeline(
+        prompt=prompt,
+        negative_prompt=negative_prompt, 
+        guidance_scale=guidance_scale,
+        num_inference_steps=number_of_steps,
+        num_images_per_prompt=number_of_images,
+        ip_adapter_image=ip_adapter_images,
+        cross_attention_kwargs={"ip_adapter_masks": ip_adapter_masks},
+        image=base_image,
+        mask_image=inpaint_mask,
+        i2i_mask_guidance_start=mask_guidance_start,
+        i2i_mask_guidance_end=mask_guidance_end,
+        control_image=[face_kps_identity_image_a, face_kps_identity_image_b, depth_image],
+        control_mask=controlnet_masks,
+        identity_control_indices=[(0,0), (1,1)],
+        controlnet_conditioning_scale=[identity_image_strength_1, identity_image_strength_2, depth_image_strength],
+        strength=1-base_image_strength,
+        generator=generator,
+        seed=seed,
+    ).images
+
+    return images
+
+#Move the components in the example fields outside so they are available when gr.Examples is instantiated
+buy_me_a_coffee_button = """
+[![Buy me a coffee](https://img.buymeacoffee.com/button-api/?text=Buy%20me%20a%20coffee&emoji=&slug=vk654cf2pv8&button_colour=BD5FFF&font_colour=ffffff&font_family=Bree&outline_colour=000000&coffee_colour=FFDD00)](https://www.buymeacoffee.com/vk654cf2pv8)
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center'>Omni Zero Couples</h1>")
+    gr.Markdown("<h4 style='text-align: center'>A diffusion pipeline for zero-shot stylized portrait creation [<a href='https://github.com/okaris/omni-zero-couples' target='_blank'>GitHub</a>]")#, [<a href='https://styleof.com/s/remix-yourself' target='_blank'>StyleOf Remix Yourself</a>]</h4>")
+    gr.Markdown(buy_me_a_coffee_button)
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                prompt = gr.Textbox(label="Prompt", value="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy")
+            with gr.Row():
+                negative_prompt = gr.Textbox(label="Negative Prompt", value="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured")
+            with gr.Row():
+                with gr.Column(min_width=140):
+                    with gr.Row():
+                        base_image = gr.Image(label="Base Image")
+                    with gr.Row():
+                        base_image_strength = gr.Slider(label="Strength",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
+                with gr.Column(min_width=140):
+                    with gr.Row():
+                        identity_image = gr.Image(label="Identity Image")
+                    with gr.Row():
+                        identity_image_strength = gr.Slider(label="Strength",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
+                with gr.Column(min_width=140):
+                    with gr.Row():
+                        identity_image_2 = gr.Image(label="Identity Image 2")
+                    with gr.Row():
+                        identity_image_strength_2 = gr.Slider(label="Strength",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
+            with gr.Accordion("Advanced options", open=False):    
+                with gr.Row():
+                    style_image = gr.Image(label="Style Image")
+                    style_image_strength = gr.Slider(label="Style Strength",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
+                with gr.Row():
+                    seed = gr.Slider(label="Seed",step=1, minimum=0, maximum=10000000, value=42)
+                    number_of_images = gr.Slider(label="Number of Outputs",step=1, minimum=1, maximum=4, value=1)
+                with gr.Row():
+                    guidance_scale = gr.Slider(label="Guidance Scale",step=0.1, minimum=0.0, maximum=14.0, value=3.0)
+                    number_of_steps = gr.Slider(label="Number of Steps",step=1, minimum=1, maximum=50, value=10)
+                with gr.Row():
+                    mask_guidance_start = gr.Slider(label="Mask Guidance Start",step=0.01, minimum=0.0, maximum=1.0, value=0.0)
+                    mask_guidance_end = gr.Slider(label="Mask Guidance End",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
+            
+        with gr.Column():
+            with gr.Row():
+                out = gr.Gallery(label="Output(s)")
+            with gr.Row():
+                # clear = gr.Button("Clear")
+                submit = gr.Button("Generate")
+        
+                submit.click(generate, inputs=[
+                    base_image,
+                    style_image if style_image is not None else bas,
+                    identity_image,
+                    identity_image_2,
+                    seed,
+                    prompt,
+                    negative_prompt,
+                    guidance_scale,
+                    number_of_images,
+                    number_of_steps,
+                    base_image_strength,
+                    style_image_strength,
+                    identity_image_strength,
+                    identity_image_strength_2,
+                    mask_guidance_start,
+                    mask_guidance_end,
+                    ],
+                    outputs=[out]
+                )
+        # clear.click(lambda: None, None, chatbot, queue=False)
+    gr.Examples(
+        examples=[
+            [
+                "https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg",
+                "https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg",
+                "https://cdn-prod.styleof.com/inferences/cm1hp4lea14oz14jeoghnex7g/dlgc5xwo0qzey7qaixy45i1o-medium.jpeg",
+                "https://cdn-prod.styleof.com/inferences/cm1ho69ha14np14jesnusqiep/mp3aaktzqz20ujco5i3bi5s1-medium.jpeg",
+                42,
+                "Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy",
+            ]
+        ],
+        inputs=[base_image, style_image, identity_image, identity_image_2, seed, prompt],
+        outputs=[out],
+        fn=generate,
+        cache_examples="lazy",
+    )
+if __name__ == "__main__":
+    demo.launch()

cog.yaml

@@ -0,0 +1,29 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  # set to true if your model requires a GPU
+  gpu: true
+
+  # a list of ubuntu apt packages to install
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+
+  # python version in the form '3.8' or '3.8.12'
+  python_version: "3.11"
+  python_requirements: "requirements.txt"
+
+  # a list of packages in the format <package-name>==<version>
+  # python_packages:
+    # - "numpy==1.19.4"
+    # - "torch==1.8.0"
+    # - "torchvision==0.9.0"
+  
+  # commands run after the environment is setup
+  # run:
+    # - "echo env is ready!"
+    # - "echo another command if needed"
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

demo.py

@@ -0,0 +1,39 @@
+from omni_zero import OmniZeroCouple
+
+def demo():
+    omni_zero = OmniZeroCouple(
+        base_model="frankjoshua/albedobaseXL_v13",
+        device="cuda",
+    )
+        
+    base_image="https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg"
+    style_image="https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg"
+    identity_image_1="https://ichef.bbci.co.uk/images/ic/1040x1040/p0f5vv8q.jpg"#"https://cdn-prod.styleof.com/inferences/cm1hp4lea14oz14jeoghnex7g/dlgc5xwo0qzey7qaixy45i1o-medium.jpeg"
+    identity_image_2="https://www.judentum-projekt.de/images/meitner22-2_640.jpg"#"https://cdn-prod.styleof.com/inferences/cm1ho69ha14np14jesnusqiep/mp3aaktzqz20ujco5i3bi5s1-medium.jpeg"
+
+    images = omni_zero.generate(
+        seed=42,
+        prompt="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        negative_prompt="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+        guidance_scale=3.0,
+        number_of_images=1,
+        number_of_steps=10,
+        base_image=base_image,
+        base_image_strength=0.3,
+        style_image=style_image,
+        style_image_strength=1.0,
+        identity_image_1=identity_image_1,
+        identity_image_strength_1=1.0,
+        identity_image_2=identity_image_2,
+        identity_image_strength_2=1.0,
+        depth_image=None,
+        depth_image_strength=0.2, 
+        mask_guidance_start=0.0,
+        mask_guidance_end=1.0,
+    )
+
+    for i, image in enumerate(images):
+        image.save(f"oz_output_{i}.jpg")
+
+if __name__ == "__main__":
+    demo()

omni_zero.py

@@ -0,0 +1,366 @@
+import os
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+import sys
+
+sys.path.insert(0, './diffusers/src')
+
+import cv2
+import numpy as np
+import PIL
+import torch
+from controlnet_aux import ZoeDetector
+from diffusers import DPMSolverMultistepScheduler
+from diffusers.image_processor import IPAdapterMaskProcessor
+from diffusers.models import ControlNetModel
+from huggingface_hub import snapshot_download
+from insightface.app import FaceAnalysis
+from pipeline import OmniZeroPipeline
+from transformers import CLIPVisionModelWithProjection
+from utils import align_images, draw_kps, load_and_resize_image
+import random
+
+class OmniZeroSingle():
+    def __init__(self,
+        base_model="stabilityai/stable-diffusion-xl-base-1.0",
+        device="cuda",
+    ):
+        snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2")
+        self.face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
+
+        dtype = torch.float16
+
+        ip_adapter_plus_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            "h94/IP-Adapter", 
+            subfolder="models/image_encoder",
+            torch_dtype=dtype,
+        ).to(device)
+
+        zoedepthnet_path = "okaris/zoe-depth-controlnet-xl"
+        zoedepthnet = ControlNetModel.from_pretrained(zoedepthnet_path,torch_dtype=dtype).to(device)
+
+        identitiynet_path = "okaris/face-controlnet-xl"
+        identitynet = ControlNetModel.from_pretrained(identitiynet_path, torch_dtype=dtype).to(device)
+
+        self.zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to(device)
+
+        self.pipeline = OmniZeroPipeline.from_pretrained(
+            base_model,
+            controlnet=[identitynet, zoedepthnet],
+            torch_dtype=dtype,
+            image_encoder=ip_adapter_plus_image_encoder,
+        ).to(device)
+
+        config = self.pipeline.scheduler.config
+        config["timestep_spacing"] = "trailing"
+        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero")
+
+        self.pipeline.load_ip_adapter(["okaris/ip-adapter-instantid", "h94/IP-Adapter", "h94/IP-Adapter"], subfolder=[None, "sdxl_models", "sdxl_models"], weight_name=["ip-adapter-instantid.bin", "ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus_sdxl_vit-h.safetensors"])
+   
+    def get_largest_face_embedding_and_kps(self, image, target_image=None):
+        face_info = self.face_analysis.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))
+        if len(face_info) == 0:
+            return None, None
+        largest_face = sorted(face_info, key=lambda x: x['bbox'][2] * x['bbox'][3], reverse=True)[0]
+        face_embedding = torch.tensor(largest_face['embedding']).to("cuda")
+        if target_image is None:
+            target_image = image
+        zeros = np.zeros((target_image.size[1], target_image.size[0], 3), dtype=np.uint8)
+        face_kps_image = draw_kps(zeros, largest_face['kps'])
+        return face_embedding, face_kps_image
+    
+    def generate(self,
+        seed=42,
+        prompt="A person",
+        negative_prompt="blurry, out of focus",
+        guidance_scale=3.0,
+        number_of_images=1,
+        number_of_steps=10,
+        base_image=None,
+        base_image_strength=0.15,
+        composition_image=None,
+        composition_image_strength=1.0,
+        style_image=None,
+        style_image_strength=1.0,
+        identity_image=None,
+        identity_image_strength=1.0,
+        depth_image=None,
+        depth_image_strength=0.5,        
+    ):
+        resolution = 1024
+
+        if base_image is not None:
+            base_image = load_and_resize_image(base_image, resolution, resolution)
+        else:
+            if composition_image is not None:
+                base_image = load_and_resize_image(composition_image, resolution, resolution)
+            else:
+                raise ValueError("You must provide a base image or a composition image")
+
+        if depth_image is None:
+            depth_image = self.zoe_depth_detector(base_image, detect_resolution=resolution, image_resolution=resolution)
+        else:
+            depth_image = load_and_resize_image(depth_image, resolution, resolution)
+
+        base_image, depth_image = align_images(base_image, depth_image)
+
+        if composition_image is not None:
+            composition_image = load_and_resize_image(composition_image, resolution, resolution)
+        else: 
+            composition_image = base_image
+
+        if style_image is not None:
+            style_image = load_and_resize_image(style_image, resolution, resolution)
+        else:
+            raise ValueError("You must provide a style image")
+        
+        if identity_image is not None:
+            identity_image = load_and_resize_image(identity_image, resolution, resolution)
+        else:
+            raise ValueError("You must provide an identity image")
+        
+        face_embedding_identity_image, target_kps = self.get_largest_face_embedding_and_kps(identity_image, base_image)
+        if face_embedding_identity_image is None:
+            raise ValueError("No face found in the identity image, the image might be cropped too tightly or the face is too small")
+        
+        face_embedding_base_image, face_kps_base_image = self.get_largest_face_embedding_and_kps(base_image)
+        if face_embedding_base_image is not None:
+            target_kps = face_kps_base_image
+
+        self.pipeline.set_ip_adapter_scale([identity_image_strength,
+            {
+                "down": { "block_2": [0.0, 0.0] },
+                "up": { "block_0": [0.0, style_image_strength, 0.0] }
+            },
+            {
+                "down": { "block_2": [0.0, composition_image_strength] },
+                "up": { "block_0": [0.0, 0.0, 0.0] }
+            }
+        ])
+
+        generator = torch.Generator(device="cpu").manual_seed(seed)
+
+        images = self.pipeline(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            ip_adapter_image=[face_embedding_identity_image, style_image, composition_image],
+            image=base_image,
+            control_image=[target_kps, depth_image],
+            controlnet_conditioning_scale=[identity_image_strength, depth_image_strength],
+            identity_control_indices=[(0,0)],
+            num_inference_steps=number_of_steps, 
+            num_images_per_prompt=number_of_images,
+            strength=(1-base_image_strength),
+            generator=generator,
+            seed=seed,
+        ).images
+
+        return images
+    
+class OmniZeroCouple():
+    def __init__(self,
+        base_model="stabilityai/stable-diffusion-xl-base-1.0",
+        device="cuda",
+    ):
+        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+        self.patch_onnx_runtime()
+
+        snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2")
+        self.face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
+
+        self.dtype = dtype = torch.float16
+
+        ip_adapter_plus_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            "h94/IP-Adapter", 
+            subfolder="models/image_encoder",
+            torch_dtype=dtype,
+        ).to(device)
+
+        zoedepthnet_path = "okaris/zoe-depth-controlnet-xl"
+        zoedepthnet = ControlNetModel.from_pretrained(zoedepthnet_path,torch_dtype=dtype).to(device)
+
+        identitiynet_path = "okaris/face-controlnet-xl"
+        identitynet = ControlNetModel.from_pretrained(identitiynet_path, torch_dtype=dtype).to(device)
+
+        self.zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to(device)
+        self.ip_adapter_mask_processor = IPAdapterMaskProcessor()
+
+        self.pipeline = OmniZeroPipeline.from_pretrained(
+            base_model,
+            controlnet=[identitynet, identitynet, zoedepthnet],
+            torch_dtype=dtype,
+            image_encoder=ip_adapter_plus_image_encoder,
+        ).to(device)
+
+        config = self.pipeline.scheduler.config
+        config["timestep_spacing"] = "trailing"
+        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero")
+
+        self.pipeline.load_ip_adapter(["okaris/ip-adapter-instantid", "okaris/ip-adapter-instantid", "h94/IP-Adapter"], subfolder=[None, None, "sdxl_models"], weight_name=["ip-adapter-instantid.bin", "ip-adapter-instantid.bin", "ip-adapter-plus_sdxl_vit-h.safetensors"])
+   
+    def generate(self,
+        seed=42,
+        prompt="A person",
+        negative_prompt="blurry, out of focus",
+        guidance_scale=3.0,
+        number_of_images=1,
+        number_of_steps=10,
+        base_image=None,
+        base_image_strength=0.2,
+        style_image=None,
+        style_image_strength=1.0,
+        identity_image_1=None,
+        identity_image_strength_1=1.0,
+        identity_image_2=None,
+        identity_image_strength_2=1.0,
+        depth_image=None,
+        depth_image_strength=0.5,
+        mask_guidance_start=0.0,
+        mask_guidance_end=1.0,      
+    ):
+
+        if seed == -1:
+            seed = random.randint(0, 1000000)
+
+        resolution = 1024
+
+        if base_image is not None:
+            base_image = load_and_resize_image(base_image, resolution, resolution)
+
+        if depth_image is None:
+            depth_image = self.zoe_depth_detector(base_image, detect_resolution=resolution, image_resolution=resolution)
+        else:
+            depth_image = load_and_resize_image(depth_image, resolution, resolution)
+
+        base_image, depth_image = align_images(base_image, depth_image)
+
+        if style_image is not None:
+            style_image = load_and_resize_image(style_image, resolution, resolution)
+        else:
+            raise ValueError("You must provide a style image")
+        
+        if identity_image_1 is not None:
+            identity_image_1 = load_and_resize_image(identity_image_1, resolution, resolution)
+        else:
+            raise ValueError("You must provide an identity image")
+        
+        if identity_image_2 is not None:
+            identity_image_2 = load_and_resize_image(identity_image_2, resolution, resolution)
+        else:
+            raise ValueError("You must provide an identity image 2")
+
+        height, width = base_image.size
+
+        face_info_1 = self.face_analysis.get(cv2.cvtColor(np.array(identity_image_1), cv2.COLOR_RGB2BGR))
+        for i, face in enumerate(face_info_1):
+            print(f"Face 1 -{i}: Age: {face['age']}, Gender: {face['gender']}")
+        face_info_1 = sorted(face_info_1, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
+        face_emb_1 = torch.tensor(face_info_1['embedding']).to("cuda", dtype=self.dtype)
+
+        face_info_2 = self.face_analysis.get(cv2.cvtColor(np.array(identity_image_2), cv2.COLOR_RGB2BGR))
+        for i, face in enumerate(face_info_2):
+            print(f"Face 2 -{i}: Age: {face['age']}, Gender: {face['gender']}")
+        face_info_2 = sorted(face_info_2, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
+        face_emb_2 = torch.tensor(face_info_2['embedding']).to("cuda", dtype=self.dtype)
+
+        zero = np.zeros((width, height, 3), dtype=np.uint8)
+        # face_kps_identity_image_1 = self.draw_kps(zero, face_info_1['kps'])
+        # face_kps_identity_image_2 = self.draw_kps(zero, face_info_2['kps'])
+
+        face_info_img2img = self.face_analysis.get(cv2.cvtColor(np.array(base_image), cv2.COLOR_RGB2BGR))
+        faces_info_img2img = sorted(face_info_img2img, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])
+        face_info_a = faces_info_img2img[-1]
+        face_info_b = faces_info_img2img[-2]
+        # face_emb_a = torch.tensor(face_info_a['embedding']).to("cuda", dtype=self.dtype)
+        # face_emb_b = torch.tensor(face_info_b['embedding']).to("cuda", dtype=self.dtype)
+        face_kps_identity_image_a = draw_kps(zero, face_info_a['kps'])
+        face_kps_identity_image_b = draw_kps(zero, face_info_b['kps'])
+
+        general_mask = PIL.Image.fromarray(np.ones((width, height, 3), dtype=np.uint8))
+
+        control_mask_1 = zero.copy()
+        x1, y1, x2, y2 = face_info_a["bbox"]
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        control_mask_1[y1:y2, x1:x2] = 255
+        control_mask_1 = PIL.Image.fromarray(control_mask_1.astype(np.uint8))
+
+        control_mask_2 = zero.copy()
+        x1, y1, x2, y2 = face_info_b["bbox"]
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        control_mask_2[y1:y2, x1:x2] = 255
+        control_mask_2 = PIL.Image.fromarray(control_mask_2.astype(np.uint8))
+
+        controlnet_masks = [control_mask_1, control_mask_2, general_mask]
+        ip_adapter_images = [face_emb_1, face_emb_2, style_image, ]
+
+        masks = self.ip_adapter_mask_processor.preprocess([control_mask_1, control_mask_2, general_mask], height=height, width=width)
+        ip_adapter_masks = [mask.unsqueeze(0) for mask in masks]
+
+        inpaint_mask = torch.logical_or(torch.tensor(np.array(control_mask_1)), torch.tensor(np.array(control_mask_2))).float()
+        inpaint_mask = PIL.Image.fromarray((inpaint_mask.numpy() * 255).astype(np.uint8)).convert("RGB")
+
+        new_ip_adapter_masks = []
+        for ip_img, mask in zip(ip_adapter_images, controlnet_masks):
+            if isinstance(ip_img, list):
+                num_images = len(ip_img)
+                mask = mask.repeat(1, num_images, 1, 1)
+
+            new_ip_adapter_masks.append(mask)
+            
+        generator = torch.Generator(device="cpu").manual_seed(seed)
+
+        self.pipeline.set_ip_adapter_scale([identity_image_strength_1, identity_image_strength_2,
+            {
+                "down": { "block_2": [0.0, 0.0] }, #Composition
+                "up": { "block_0": [0.0, style_image_strength, 0.0] } #Style
+            }
+        ])
+
+        images = self.pipeline(
+            prompt=prompt,
+            negative_prompt=negative_prompt, 
+            guidance_scale=guidance_scale,
+            num_inference_steps=number_of_steps,
+            num_images_per_prompt=number_of_images,
+            ip_adapter_image=ip_adapter_images,
+            cross_attention_kwargs={"ip_adapter_masks": ip_adapter_masks},
+            image=base_image,
+            mask_image=inpaint_mask,
+            i2i_mask_guidance_start=mask_guidance_start,
+            i2i_mask_guidance_end=mask_guidance_end,
+            control_image=[face_kps_identity_image_a, face_kps_identity_image_b, depth_image],
+            control_mask=controlnet_masks,
+            identity_control_indices=[(0,0), (1,1)],
+            controlnet_conditioning_scale=[identity_image_strength_1, identity_image_strength_2, depth_image_strength],
+            strength=1-base_image_strength,
+            generator=generator,
+            seed=seed,
+        ).images
+
+        return images
+
+    def patch_onnx_runtime(
+        self,
+        inter_op_num_threads: int = 16,
+        intra_op_num_threads: int = 16,
+        omp_num_threads: int = 16,
+    ):
+        import os
+
+        import onnxruntime as ort
+
+        os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
+
+        _default_session_options = ort.capi._pybind_state.get_default_session_options()
+
+        def get_default_session_options_new():
+            _default_session_options.inter_op_num_threads = inter_op_num_threads
+            _default_session_options.intra_op_num_threads = intra_op_num_threads
+            return _default_session_options
+
+        ort.capi._pybind_state.get_default_session_options = get_default_session_options_new
+        

predict.py

@@ -0,0 +1,77 @@
+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+from cog import BasePredictor, Input, Path
+from typing import List
+from omni_zero import OmniZeroCouple
+from PIL import Image
+
+class Predictor(BasePredictor):
+    def setup(self):
+        """Load the model into memory to make running multiple predictions efficient"""
+        self.omni_zero = OmniZeroCouple(
+            base_model="frankjoshua/albedobaseXL_v13",
+        )
+    def predict(
+        self,
+        base_image: Path = Input(description="Base image for the model", default=None),
+        base_image_strength: float = Input(description="Base image strength for the model", default=0.2, ge=0.0, le=1.0),
+        style_image: Path = Input(description="Style image for the model", default=None),
+        style_image_strength: float = Input(description="Style image strength for the model", default=1.0, ge=0.0, le=1.0),
+        identity_image_1: Path = Input(description="First identity image for the model", default=None),
+        identity_image_strength_1: float = Input(description="First identity image strength for the model", default=1.0, ge=0.0, le=1.0),
+        identity_image_2: Path = Input(description="Second identity image for the model", default=None),
+        identity_image_strength_2: float = Input(description="Second identity image strength for the model", default=1.0, ge=0.0, le=1.0),
+        seed: int = Input(description="Random seed for the model. Use -1 for random", default=-1),
+        prompt: str = Input(description="Prompt for the model", default="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy"),
+        negative_prompt: str = Input(description="Negative prompt for the model", default="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"),
+        guidance_scale: float = Input(description="Guidance scale for the model", default=3.0, ge=0.0, le=14.0),
+        number_of_images: int = Input(description="Number of images to generate", default=1, ge=1, le=4),
+        number_of_steps: int = Input(description="Number of steps for the model", default=10, ge=1, le=50),
+        depth_image: Path = Input(description="Depth image for the model", default=None),
+        depth_image_strength: float = Input(description="Depth image strength for the model", default=0.2, ge=0.0, le=1.0),
+        mask_guidance_start: float = Input(description="Mask guidance start value", default=0.0, ge=0.0, le=1.0),
+        mask_guidance_end: float = Input(description="Mask guidance end value", default=1.0, ge=0.0, le=1.0),
+    ) -> List[Path]:
+        """Run a single prediction on the model"""
+
+        base_image = Image.open(base_image) if base_image else None
+        style_image = Image.open(style_image) if style_image else None
+        identity_image_1 = Image.open(identity_image_1) if identity_image_1 else None
+        identity_image_2 = Image.open(identity_image_2) if identity_image_2 else None
+        depth_image = Image.open(depth_image) if depth_image else None
+
+        print("base_image", base_image)
+        print("style_image", style_image)
+        print("identity_image_1", identity_image_1)
+        print("identity_image_2", identity_image_2)
+        print("depth_image", depth_image)
+
+        images = self.omni_zero.generate(
+            seed=seed,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            number_of_images=number_of_images,
+            number_of_steps=number_of_steps,
+            base_image=base_image,
+            base_image_strength=base_image_strength,
+            style_image=style_image,
+            style_image_strength=style_image_strength,
+            identity_image_1=identity_image_1,
+            identity_image_strength_1=identity_image_strength_1,
+            identity_image_2=identity_image_2,
+            identity_image_strength_2=identity_image_strength_2,
+            depth_image=depth_image,
+            depth_image_strength=depth_image_strength,
+            mask_guidance_start=mask_guidance_start,
+            mask_guidance_end=mask_guidance_end,
+        )
+        
+        outputs = []
+        for i, image in enumerate(images):
+            output_path = f"oz_output_{i}.jpg"
+            image.save(output_path)
+            outputs.append(Path(output_path))
+        
+        return outputs

requirements.txt

@@ -0,0 +1,22 @@
+--extra-index-url https://download.pytorch.org/whl/cu124
+accelerate
+diffusers==0.30.3
+controlnet_aux==0.0.9
+huggingface_hub==0.25.1
+# insightface==0.7.3
+git+https://github.com/badayvedat/insightface.git@1ffa3405eedcfe4193c3113affcbfc294d0e684f#subdirectory=python-package
+numpy==1.26.2
+opencv_contrib_python==4.9.0.80
+opencv_python==4.9.0.80
+opencv_python_headless==4.7.0.72
+Pillow==10.1.0
+pydantic<2.0.0
+torch==2.4.0
+torchvision==0.19.0
+torchaudio==2.4.0
+torchsde==0.2.6
+transformers==4.44.2
+onnxruntime-gpu
+hf_transfer
+gradio
+spaces

utils.py

@@ -0,0 +1,168 @@
+import math
+import PIL
+from PIL import Image
+import cv2
+import numpy as np
+
+from diffusers.utils import load_image
+
+def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]):
+    """
+    Draw keypoints on an image.
+
+    Args:
+    image_pil (PIL.Image): Image on which to draw the keypoints.
+    kps (list): List of keypoints to draw.
+    color_list (list): List of colors to use for drawing the keypoints.
+
+    Returns:
+    PIL.Image: Image with keypoints drawn on it.
+    """
+    
+    stickwidth = 4
+    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
+    kps = np.array(kps)
+
+    # w, h = image_pil.size
+    # out_img = np.zeros([h, w, 3])
+    if type(image_pil) == PIL.Image.Image:
+        out_img = np.array(image_pil)
+    else:
+        out_img = image_pil
+
+    for i in range(len(limbSeq)):
+        index = limbSeq[i]
+        color = color_list[index[0]]
+
+        x = kps[index][:, 0]
+        y = kps[index][:, 1]
+        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
+        polygon = cv2.ellipse2Poly(
+            (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1
+        )
+        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
+    out_img = (out_img * 0.6).astype(np.uint8)
+
+    for idx_kp, kp in enumerate(kps):
+        color = color_list[idx_kp]
+        x, y = kp
+        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
+
+    out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
+    return out_img_pil
+
+
+def load_and_resize_image(image_path, max_width, max_height, maintain_aspect_ratio=True):
+    """
+    Load and resize an image to the specified dimensions.
+    
+    Args:
+    image_path (str): Path to the image file.
+    max_width (int): Maximum width of the resized image.
+    max_height (int): Maximum height of the resized image.
+    maintain_aspect_ratio (bool): Whether to maintain the aspect ratio of the image.
+    
+    Returns:
+    PIL.Image: Resized image.
+    """
+
+    # Open the image
+    if isinstance(image_path, np.ndarray):
+        image_path = Image.fromarray(image_path)
+
+    image = load_image(image_path)
+
+    # Get the current width and height of the image
+    current_width, current_height = image.size
+
+    if maintain_aspect_ratio:
+        # Calculate the aspect ratio of the image
+        aspect_ratio = current_width / current_height
+
+        # Calculate the new dimensions based on the max width and height
+        if current_width / max_width > current_height / max_height:
+            new_width = max_width
+            new_height = int(new_width / aspect_ratio)
+        else:
+            new_height = max_height
+            new_width = int(new_height * aspect_ratio)
+    else:
+        # Use the max width and height as the new dimensions
+        new_width = max_width
+        new_height = max_height
+
+    # Ensure the new dimensions are divisible by 8
+    new_width = (new_width // 8) * 8
+    new_height = (new_height // 8) * 8
+
+    # Resize the image
+    resized_image = image.resize((new_width, new_height))
+
+    return resized_image
+
+
+def align_images(image1, image2):
+    """
+    Resize two images to the same dimensions by cropping the larger image(s) to match the smaller one.
+
+    Args:
+    image1 (PIL.Image): First image to be aligned.
+    image2 (PIL.Image): Second image to be aligned.
+
+    Returns:
+    tuple: A tuple containing two images with the same dimensions.
+    """
+    # Determine the new size by taking the smaller width and height from both images
+    new_width = min(image1.size[0], image2.size[0])
+    new_height = min(image1.size[1], image2.size[1])
+
+    # Crop both images if necessary
+    if image1.size != (new_width, new_height):
+        image1 = image1.crop((0, 0, new_width, new_height))
+    if image2.size != (new_width, new_height):
+        image2 = image2.crop((0, 0, new_width, new_height))
+
+    return image1, image2
+
+def align_images_2(image1, image2):
+    """
+    Resize and crop the second image to match the dimensions of the first image by
+    scaling to aspect fill and then center cropping the extra parts.
+
+    Args:
+    image1 (PIL.Image): First image which will act as the reference for alignment.
+    image2 (PIL.Image): Second image to be aligned to the first image's dimensions.
+
+    Returns:
+    tuple: A tuple containing the first image and the aligned second image.
+    """
+    # Get dimensions of the first image
+    target_width, target_height = image1.size
+
+    # Calculate the aspect ratio of the second image
+    aspect_ratio = image2.width / image2.height
+
+    # Calculate dimensions to aspect fill
+    if target_width / target_height > aspect_ratio:
+        # The first image is wider relative to its height than the second image
+        fill_height = target_height
+        fill_width = int(fill_height * aspect_ratio)
+    else:
+        # The first image is taller relative to its width than the second image
+        fill_width = target_width
+        fill_height = int(fill_width / aspect_ratio)
+
+    # Resize the second image to fill dimensions
+    filled_image = image2.resize((fill_width, fill_height), Image.Resampling.LANCZOS)
+
+    # Calculate top-left corner of crop box to center crop
+    left = (fill_width - target_width) / 2
+    top = (fill_height - target_height) / 2
+    right = left + target_width
+    bottom = top + target_height
+
+    # Crop the filled image to match the size of the first image
+    cropped_image = filled_image.crop((int(left), int(top), int(right), int(bottom)))
+
+    return image1, cropped_image