Text2Video-Zero / app_pose.py
lev1's picture
Enable caching
c068cee
raw
history blame
2.75 kB
import gradio as gr
import os
from model import Model
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=8, value=8, step=1)
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 = True,
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}"