Spaces:
Running
on
A10G
Running
on
A10G
| from model import Model | |
| import gradio as gr | |
| import os | |
| on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR" | |
| examples = [ | |
| ['Motion 1', "An astronaut dancing in the outer space"], | |
| ['Motion 2', "An astronaut dancing in the outer space"], | |
| ['Motion 3', "An astronaut dancing in the outer space"], | |
| ['Motion 4', "An astronaut dancing in the outer space"], | |
| ['Motion 5', "An astronaut dancing in the outer space"], | |
| ] | |
| def create_demo(model: Model): | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown('## Text and Pose Conditional Video Generation') | |
| with gr.Row(): | |
| gr.Markdown( | |
| 'Selection: **one motion** and a **prompt**, or use the examples below.') | |
| with gr.Column(): | |
| gallery_pose_sequence = gr.Gallery(label="Pose Sequence", value=[('__assets__/poses_skeleton_gifs/dance1.gif', "Motion 1"), ('__assets__/poses_skeleton_gifs/dance2.gif', "Motion 2"), ( | |
| '__assets__/poses_skeleton_gifs/dance3.gif', "Motion 3"), ('__assets__/poses_skeleton_gifs/dance4.gif', "Motion 4"), ('__assets__/poses_skeleton_gifs/dance5.gif', "Motion 5")]).style(grid=[2], height="auto") | |
| input_video_path = gr.Textbox( | |
| label="Pose Sequence", visible=False, value="Motion 1") | |
| gr.Markdown("## Selection") | |
| pose_sequence_selector = gr.Markdown( | |
| 'Pose Sequence: **Motion 1**') | |
| 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.Image(label="Generated Video") | |
| input_video_path.change(on_video_path_update, | |
| None, pose_sequence_selector) | |
| gallery_pose_sequence.select( | |
| pose_gallery_callback, None, input_video_path) | |
| inputs = [ | |
| input_video_path, | |
| prompt, | |
| chunk_size, | |
| watermark, | |
| merging_ratio, | |
| ] | |
| gr.Examples(examples=examples, | |
| inputs=inputs, | |
| outputs=result, | |
| fn=model.process_controlnet_pose, | |
| # cache_examples=on_huggingspace, | |
| cache_examples=False, | |
| run_on_click=False, | |
| ) | |
| run_button.click(fn=model.process_controlnet_pose, | |
| inputs=inputs, | |
| outputs=result,) | |
| return demo | |
| def on_video_path_update(evt: gr.EventData): | |
| return f'Selection: **{evt._data}**' | |
| def pose_gallery_callback(evt: gr.SelectData): | |
| return f"Motion {evt.index+1}" | |