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A10G
Running
on
A10G
| import gradio as gr | |
| from model import Model | |
| import os | |
| on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR" | |
| def create_demo(model: Model): | |
| examples = [ | |
| ["__assets__/canny_videos_edge/butterfly.mp4", | |
| "white butterfly, a high-quality, detailed, and professional photo"], | |
| ["__assets__/canny_videos_edge/deer.mp4", | |
| "oil painting of a deer, a high-quality, detailed, and professional photo"], | |
| ["__assets__/canny_videos_edge/fox.mp4", | |
| "wild red fox is walking on the grass, a high-quality, detailed, and professional photo"], | |
| ["__assets__/canny_videos_edge/girl_dancing.mp4", | |
| "oil painting of a girl dancing close-up, masterpiece, a high-quality, detailed, and professional photo"], | |
| ["__assets__/canny_videos_edge/girl_turning.mp4", | |
| "oil painting of a beautiful girl, a high-quality, detailed, and professional photo"], | |
| ["__assets__/canny_videos_edge/halloween.mp4", | |
| "beautiful girl halloween style, a high-quality, detailed, and professional photo"], | |
| ["__assets__/canny_videos_edge/santa.mp4", | |
| "a santa claus, a high-quality, detailed, and professional photo"], | |
| ] | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown('## Text and Canny-Edge Conditional Video Generation') | |
| with gr.Row(): | |
| gr.HTML( | |
| """ | |
| <div style="text-align: left; auto;"> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| 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. | |
| </h3> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_video = gr.Video( | |
| label="Input Video", source='upload', format="mp4", visible=True).style(height="auto") | |
| with gr.Column(): | |
| prompt = gr.Textbox(label='Prompt') | |
| run_button = gr.Button(label='Run') | |
| with gr.Accordion('Advanced options', open=False): | |
| watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero", | |
| "None"], label="Watermark", value='Picsart AI Research') | |
| chunk_size = gr.Slider( | |
| label="Chunk size", minimum=2, maximum=16, value=2, step=1, visible=not on_huggingspace, | |
| info="Number of frames processed at once. Reduce for lower memory usage.") | |
| merging_ratio = gr.Slider( | |
| label="Merging ratio", minimum=0.0, maximum=0.9, step=0.1, value=0.0, visible=not on_huggingspace, | |
| info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference).") | |
| with gr.Column(): | |
| result = gr.Video(label="Generated Video").style(height="auto") | |
| inputs = [ | |
| input_video, | |
| prompt, | |
| chunk_size, | |
| watermark, | |
| merging_ratio, | |
| ] | |
| gr.Examples(examples=examples, | |
| inputs=inputs, | |
| outputs=result, | |
| fn=model.process_controlnet_canny, | |
| # cache_examples=on_huggingspace, | |
| cache_examples=False, | |
| run_on_click=False, | |
| ) | |
| run_button.click(fn=model.process_controlnet_canny, | |
| inputs=inputs, | |
| outputs=result,) | |
| return demo | |