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Running
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Running
on
Zero
Commit
•
e276a90
1
Parent(s):
992a99c
Also return the frames as images (#8)
Browse files- Also return the frames as images (4764ded05b15440d0b438d387e412bf9cd885914)
Co-authored-by: Fabrice TIERCELIN <[email protected]>
app.py
CHANGED
@@ -53,7 +53,7 @@ def sample(
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export_to_video(frames, video_path, fps=fps_id)
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torch.manual_seed(seed)
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return video_path, seed
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def resize_image(image, output_size=(1024, 576)):
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# Calculate aspect ratios
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@@ -91,18 +91,20 @@ with gr.Blocks() as demo:
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#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
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''')
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with gr.Row():
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image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
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generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
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if __name__ == "__main__":
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#demo.queue(max_size=20, api_open=False)
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export_to_video(frames, video_path, fps=fps_id)
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torch.manual_seed(seed)
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return video_path, frames, seed
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def resize_image(image, output_size=(1024, 576)):
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# Calculate aspect ratios
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#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
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''')
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with gr.Row():
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with gr.Column():
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image = gr.Image(label="Upload your image", type="pil")
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with gr.Accordion("Advanced options", open=False):
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seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int, step=1)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)
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fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
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generate_btn = gr.Button(value="Animate", variant="primary")
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with gr.Column():
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video = gr.Video(label="Generated video")
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gallery = gr.Gallery(label="Generated frames")
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image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
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generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, gallery, seed], api_name="video")
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if __name__ == "__main__":
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#demo.queue(max_size=20, api_open=False)
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