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import gradio as gr |
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import random |
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def infer(input_path: str, resize_image: bool, n_frames: int, n_steps: int, seed: str, decoding_t: int, fps_id: int, motion_bucket_id: int, cond_aug: float, skip_filter: bool = False) -> str: |
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if seed == "random": |
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seed = random.randint(0, 2**32) |
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seed = int(seed) |
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output_paths = sample( |
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input_path=input_path, |
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resize_image=resize_image, |
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num_frames=n_frames, |
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num_steps=n_steps, |
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fps_id=fps_id, |
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motion_bucket_id=motion_bucket_id, |
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cond_aug=cond_aug, |
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seed=seed, |
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decoding_t=decoding_t, |
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device=device, |
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skip_filter=skip_filter, |
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) |
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return output_paths[0] |
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with gr.Blocks() as demo: |
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with gr.Column(): |
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image = gr.Image(label="input image", type="filepath") |
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resize_image = gr.Checkbox(label="resize to optimal size", value=True) |
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btn = gr.Button("Run") |
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with gr.Accordion(label="Advanced options", open=False): |
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n_frames = gr.Number(precision=0, label="number of frames", value=num_frames) |
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n_steps = gr.Number(precision=0, label="number of steps", value=num_steps) |
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seed = gr.Text(value="random", label="seed (integer or 'random')",) |
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decoding_t = gr.Number(precision=0, label="number of frames decoded at a time", value=2) |
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fps_id = gr.Number(precision=0, label="frames per second", value=6) |
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motion_bucket_id = gr.Number(precision=0, value=127, label="motion bucket id") |
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cond_aug = gr.Number(label="condition augmentation factor", value=0.02) |
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skip_filter = gr.Checkbox(value=False, label="skip nsfw/watermark filter") |
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with gr.Column(): |
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video_out = gr.Video(label="generated video") |
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examples = [ |
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["https://user-images.githubusercontent.com/33302880/284758167-367a25d8-8d7b-42d3-8391-6d82813c7b0f.png"] |
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] |
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inputs = [image, resize_image, n_frames, n_steps, seed, decoding_t, fps_id, motion_bucket_id, cond_aug, skip_filter] |
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outputs = [video_out] |
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btn.click(infer, inputs=inputs, outputs=outputs) |
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gr.Examples(examples=examples, inputs=inputs, outputs=outputs, fn=infer) |
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demo.queue().launch(debug=True, share=True, show_error=True) |
