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| from text_to_animation.model import ControlAnimationModel | |
| import gradio as gr | |
| import os | |
| huggingspace_name = os.environ.get("SPACE_AUTHOR_NAME") | |
| on_huggingspace = huggingspace_name if huggingspace_name is not None else False | |
| 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: ControlAnimationModel): | |
| 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__/dance1.gif", "Motion 1"), | |
| ("__assets__/dance2.gif", "Motion 2"), | |
| ("__assets__/dance3.gif", "Motion 3"), | |
| ("__assets__/dance4.gif", "Motion 4"), | |
| ("__assets__/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}" | |