| 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=8, 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, |
| 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}" |
|
|
