Spaces:
Running
on
A10G
Running
on
A10G
File size: 3,074 Bytes
63074f5 714bf26 63074f5 714bf26 b1373ae 75453c0 b1373ae 63074f5 714bf26 b1373ae 63074f5 b1373ae 63074f5 b1373ae 63074f5 b1373ae 75453c0 63074f5 b1373ae 63074f5 b1373ae 75453c0 b1373ae 63074f5 b1373ae 714bf26 b1373ae 75453c0 b1373ae 63074f5 |
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 |
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}"
|