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import gradio as gr
import argparse
import os
from musepose_inference import MusePoseInference
from pose_align import PoseAlignmentInference
from downloading_weights import download_models
class App:
def __init__(self, args):
self.args = args
self.pose_alignment_infer = PoseAlignmentInference(
model_dir=args.model_dir,
output_dir=args.output_dir
)
self.musepose_infer = MusePoseInference(
model_dir=args.model_dir,
output_dir=args.output_dir
)
if not args.disable_model_download_at_start:
download_models(model_dir=args.model_dir)
@staticmethod
def on_step1_complete(input_img: str, input_pose_vid: str):
return [gr.Image(label="Input Image", value=input_img, type="filepath", scale=5),
gr.Video(label="Input Aligned Pose Video", value=input_pose_vid, scale=5)]
def musepose_demo(self):
with gr.Blocks() as demo:
md_header = self.header()
with gr.Tabs():
with gr.TabItem('1: Pose Alignment'):
with gr.Row():
with gr.Column(scale=3):
img_pose_input = gr.Image(label="Input Image", type="filepath", scale=5)
vid_dance_input = gr.Video(label="Input Dance Video", max_length=10, scale=5)
with gr.Column(scale=3):
vid_dance_output = gr.Video(label="Aligned Pose Output", scale=5, interactive=False)
vid_dance_output_demo = gr.Video(label="Aligned Pose Output Demo", scale=5)
# Rest of the column setup remains the same
with gr.Column(scale=3):
# Column settings remain the same
with gr.TabItem('2: MusePose Inference'):
with gr.Row():
with gr.Column(scale=3):
img_musepose_input = gr.Image(label="Input Image", type="filepath", scale=5)
vid_pose_input = gr.Video(label="Input Aligned Pose Video", max_length=10, scale=5)
with gr.Column(scale=3):
vid_output = gr.Video(label="MusePose Output", scale=5)
vid_output_demo = gr.Video(label="MusePose Output Demo", scale=5)
# Rest of the settings remains the same
return demo
@staticmethod
def header():
header = gr.HTML(
"""
<h1 style="font-size: 23px;">
<a href="https://github.com/jhj0517/MusePose-WebUI" target="_blank">MusePose WebUI</a>
</h1>
<p style="font-size: 18px;">
<strong>Note</strong>: This space now allows video input up to <strong>10 seconds</strong> because ZeroGPU limits the function runtime to 2 minutes. <br>
If you want longer video inputs, you have to run it locally. Click the link above and follow the README to try it locally.<br><br>
When you have completed the <strong>1: Pose Alignment</strong> process, go to <strong>2: MusePose Inference</strong> and click the "GENERATE" button.
</p>
"""
)
return header
def launch(self):
demo = self.musepose_demo()
demo.queue().launch(
share=self.args.share
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', type=str, default=os.path.join("pretrained_weights"), help='Pretrained models directory for MusePose')
parser.add_argument('--output_dir', type=str, default=os.path.join("outputs"), help='Output directory for the result')
parser.add_argument('--disable_model_download_at_start', type=bool, default=False, nargs='?', const=True, help='Disable model download at start or not')
parser.add_argument('--share', type=bool, default=False, nargs='?', const=True, help='Gradio makes sharable link if it is true')
args = parser.parse_args()
app = App(args=args)
app.launch()
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