Spaces:
Runtime error
Runtime error
File size: 3,524 Bytes
2b86658 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
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}"
|