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Browse files- README.md +1 -1
- app.py +32 -43
- requirements.txt +76 -25
README.md
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@@ -4,7 +4,7 @@ emoji: π
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colorFrom: indigo
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.0
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app_file: app.py
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pinned: false
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---
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colorFrom: indigo
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.48.0
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app_file: app.py
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pinned: false
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---
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app.py
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import gradio as gr
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import os
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import
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import torch
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from PIL import Image
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import argparse
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import pathlib
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os.system("
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os.
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@@ -40,24 +43,21 @@ def update_style_image(style_name: str) -> dict:
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return gr.Markdown.update(value=text)
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def
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def inference(img,vid):
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if not os.path.exists('temp'):
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os.system('mkdir temp')
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img.save("temp/image.jpg", "JPEG")
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os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4' --cpu")
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return './temp/result.mp4'
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def main():
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with gr.Blocks(
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gr.Markdown(title)
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gr.Markdown(DESCRIPTION)
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@@ -71,12 +71,11 @@ def main():
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with gr.Row():
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input_image = gr.Image(label='Input Image',
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type="pil")
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with gr.Row():
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paths = sorted(pathlib.Path('assets').glob('*.png'))
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for path in paths])
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with gr.Box():
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gr.Markdown('''## Step 2 (Select Driving Video)
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with gr.Column():
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with gr.Row():
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driving_video = gr.Video(label='Driving Video',
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with gr.Row():
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paths = sorted(pathlib.Path('assets').glob('*.mp4'))
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for path in paths])
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with gr.Box():
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gr.Markdown('''## Step 3 (Generate Animated Image based on the Video)
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- Hit the **Generate** button. (Note:
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''')
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with gr.Row():
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with gr.Column():
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generate_button = gr.Button('Generate')
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with gr.Column():
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result = gr.Video(
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gr.Markdown(FOOTER)
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generate_button.click(fn=inference,
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inputs=[
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driving_video
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],
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outputs=result)
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outputs=example_images.components)
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example_video.click(fn=set_example_video,
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inputs=example_video,
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outputs=example_video.components)
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demo.launch(
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enable_queue=True,
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debug=True
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)
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if __name__ == '__main__':
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main()
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import os
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import pathlib
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import gradio as gr
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import torch
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from PIL import Image
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repo_dir = pathlib.Path("Thin-Plate-Spline-Motion-Model").absolute()
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if not repo_dir.exists():
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os.system("git clone https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model")
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os.chdir(repo_dir.name)
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if not (repo_dir / "checkpoints").exists():
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os.system("mkdir checkpoints")
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if not (repo_dir / "checkpoints/vox.pth.tar").exists():
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os.system("gdown 1-CKOjv_y_TzNe-dwQsjjeVxJUuyBAb5X -O checkpoints/vox.pth.tar")
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return gr.Markdown.update(value=text)
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def inference(img, vid):
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if not os.path.exists('temp'):
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os.system('mkdir temp')
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img.save("temp/image.jpg", "JPEG")
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if torch.cuda.is_available():
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os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4'")
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else:
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os.system(f"python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image 'temp/image.jpg' --driving_video {vid} --result_video './temp/result.mp4' --cpu")
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return './temp/result.mp4'
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def main():
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with gr.Blocks(css='style.css') as demo:
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gr.Markdown(title)
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gr.Markdown(DESCRIPTION)
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with gr.Row():
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input_image = gr.Image(label='Input Image',
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type="pil")
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with gr.Row():
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paths = sorted(pathlib.Path('assets').glob('*.png'))
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gr.Examples(inputs=[input_image],
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examples=[[path.as_posix()] for path in paths])
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with gr.Box():
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gr.Markdown('''## Step 2 (Select Driving Video)
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with gr.Column():
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with gr.Row():
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driving_video = gr.Video(label='Driving Video',
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format="mp4")
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with gr.Row():
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paths = sorted(pathlib.Path('assets').glob('*.mp4'))
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gr.Examples(inputs=[driving_video],
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examples=[[path.as_posix()] for path in paths])
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with gr.Box():
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gr.Markdown('''## Step 3 (Generate Animated Image based on the Video)
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- Hit the **Generate** button. (Note: On cpu-basic, it takes ~ 10 minutes to generate final results.)
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''')
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with gr.Row():
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with gr.Column():
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generate_button = gr.Button('Generate')
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with gr.Column():
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result = gr.Video(label="Output")
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gr.Markdown(FOOTER)
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generate_button.click(fn=inference,
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inputs=[
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driving_video
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],
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outputs=result)
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demo.queue(max_size=10).launch()
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if __name__ == '__main__':
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main()
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requirements.txt
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@@ -1,27 +1,78 @@
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python-dateutil==2.8.2
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PyYAML
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six==1.16.0
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aiofiles==23.2.1
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altair==5.1.2
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annotated-types==0.6.0
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anyio==3.7.1
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attrs==23.1.0
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beautifulsoup4==4.12.2
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certifi==2023.7.22
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charset-normalizer==3.3.0
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click==8.1.7
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cmake==3.27.7
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contourpy==1.1.1
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cycler==0.12.1
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exceptiongroup==1.1.3
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fastapi==0.103.2
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ffmpy==0.3.1
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filelock==3.12.4
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fonttools==4.43.1
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fsspec==2023.9.2
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gdown==4.7.1
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gradio==3.48.0
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gradio_client==0.6.1
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h11==0.14.0
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httpcore==0.18.0
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httpx==0.25.0
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huggingface-hub==0.18.0
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idna==3.4
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imageio==2.31.5
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imageio-ffmpeg==0.4.9
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importlib-resources==6.1.0
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Jinja2==3.1.2
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jsonschema==4.19.1
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jsonschema-specifications==2023.7.1
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kiwisolver==1.4.5
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lazy_loader==0.3
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lit==17.0.3
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MarkupSafe==2.1.3
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matplotlib==3.8.0
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mpmath==1.3.0
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networkx==3.1
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numpy==1.25.2
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orjson==3.9.9
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packaging==23.2
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pandas==2.1.1
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Pillow==10.1.0
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psutil==5.9.6
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pydantic==2.4.2
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pydantic_core==2.10.1
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pydub==0.25.1
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pyparsing==3.1.1
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PySocks==1.7.1
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python-dateutil==2.8.2
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python-multipart==0.0.6
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pytz==2023.3.post1
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PyYAML==6.0.1
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referencing==0.30.2
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requests==2.31.0
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rpds-py==0.10.6
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scikit-image==0.22.0
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scipy==1.11.3
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semantic-version==2.10.0
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setuptools-scm==8.0.4
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six==1.16.0
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sniffio==1.3.0
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soupsieve==2.5
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starlette==0.27.0
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sympy==1.12
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tifffile==2023.9.26
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tomli==2.0.1
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toolz==0.12.0
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torch==2.0.0
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torchvision==0.15.1
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tqdm==4.66.1
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triton==2.0.0
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typing_extensions==4.8.0
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tzdata==2023.3
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urllib3==2.0.7
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uvicorn==0.23.2
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websockets==11.0.3
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