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import gradio as gr |
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from model import Model |
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import os |
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on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR" |
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def create_demo(model: Model): |
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examples = [ |
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["__assets__/canny_videos_edge/butterfly.mp4", |
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"white butterfly, a high-quality, detailed, and professional photo"], |
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["__assets__/canny_videos_edge/deer.mp4", |
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"oil painting of a deer, a high-quality, detailed, and professional photo"], |
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["__assets__/canny_videos_edge/fox.mp4", |
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"wild red fox is walking on the grass, a high-quality, detailed, and professional photo"], |
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["__assets__/canny_videos_edge/girl_dancing.mp4", |
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"oil painting of a girl dancing close-up, masterpiece, a high-quality, detailed, and professional photo"], |
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["__assets__/canny_videos_edge/girl_turning.mp4", |
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"oil painting of a beautiful girl, a high-quality, detailed, and professional photo"], |
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["__assets__/canny_videos_edge/halloween.mp4", |
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"beautiful girl halloween style, a high-quality, detailed, and professional photo"], |
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["__assets__/canny_videos_edge/santa.mp4", |
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"a santa claus, a high-quality, detailed, and professional photo"], |
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] |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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gr.Markdown('## Text and Canny-Edge Conditional Video Generation') |
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with gr.Row(): |
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gr.HTML( |
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""" |
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<div style="text-align: left; auto;"> |
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<h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> |
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Description: For performance purposes, our current preview release supports any input videos but caps output videos after 80 frames and the input videos are scaled down before processing. |
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</h3> |
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</div> |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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input_video = gr.Video( |
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label="Input Video", source='upload', format="mp4", visible=True).style(height="auto") |
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with gr.Column(): |
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prompt = gr.Textbox(label='Prompt') |
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run_button = gr.Button(label='Run') |
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with gr.Accordion('Advanced options', open=False): |
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watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero", |
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"None"], label="Watermark", value='Picsart AI Research') |
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chunk_size = gr.Slider( |
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label="Chunk size", minimum=2, maximum=16, value=2, step=1, visible=not on_huggingspace, |
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info="Number of frames processed at once. Reduce for lower memory usage.") |
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merging_ratio = gr.Slider( |
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label="Merging ratio", minimum=0.0, maximum=0.9, step=0.1, value=0.0, visible=not on_huggingspace, |
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info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference).") |
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with gr.Column(): |
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result = gr.Video(label="Generated Video").style(height="auto") |
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inputs = [ |
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input_video, |
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prompt, |
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chunk_size, |
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watermark, |
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merging_ratio, |
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] |
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gr.Examples(examples=examples, |
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inputs=inputs, |
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outputs=result, |
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fn=model.process_controlnet_canny, |
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cache_examples=False, |
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run_on_click=False, |
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) |
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run_button.click(fn=model.process_controlnet_canny, |
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inputs=inputs, |
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outputs=result,) |
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return demo |
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