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=12 if on_huggingspace else 8, step=1, visible=not on_huggingspace) 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, ] 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}"