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=4, 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)
with gr.Column(scale=3):
with gr.Column():
nb_detect_resolution = gr.Number(label="Detect Resolution", value=512, precision=0)
nb_image_resolution = gr.Number(label="Image Resolution.", value=720, precision=0)
nb_align_frame = gr.Number(label="Align Frame", value=0, precision=0)
nb_max_frame = gr.Number(label="Max Frame", value=300, precision=0)
with gr.Row():
btn_align_pose = gr.Button("ALIGN POSE", variant="primary")
with gr.Column():
examples = [
[os.path.join("examples", "dance.mp4"), os.path.join("examples", "ref.png"),
512, 720, 0, 300]]
ex_step1 = gr.Examples(examples=examples,
inputs=[vid_dance_input, img_pose_input, nb_detect_resolution,
nb_image_resolution, nb_align_frame, nb_max_frame],
outputs=[vid_dance_output, vid_dance_output_demo],
fn=self.pose_alignment_infer.align_pose,
cache_examples="lazy")
btn_align_pose.click(fn=self.pose_alignment_infer.align_pose,
inputs=[vid_dance_input, img_pose_input, nb_detect_resolution, nb_image_resolution,
nb_align_frame, nb_max_frame],
outputs=[vid_dance_output, vid_dance_output_demo])
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=4, 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)
with gr.Column(scale=3):
with gr.Column():
weight_dtype = gr.Dropdown(label="Compute Type", choices=["fp16", "fp32"],
value="fp16")
nb_width = gr.Number(label="Width.", value=512, precision=0)
nb_height = gr.Number(label="Height.", value=512, precision=0)
nb_video_frame_length = gr.Number(label="Video Frame Length", value=300, precision=0)
nb_video_slice_frame_length = gr.Number(label="Video Slice Frame Number ", value=48,
precision=0)
nb_video_slice_overlap_frame_number = gr.Number(
label="Video Slice Overlap Frame Number", value=4, precision=0)
nb_cfg = gr.Number(label="CFG (Classifier Free Guidance)", value=3.5, precision=0)
nb_seed = gr.Number(label="Seed", value=99, precision=0)
nb_steps = gr.Number(label="DDIM Sampling Steps", value=20, precision=0)
nb_fps = gr.Number(label="FPS (Frames Per Second) ", value=-1, precision=0,
info="Set to '-1' to use same FPS with pose's")
nb_skip = gr.Number(label="SKIP (Frame Sample Rate = SKIP+1)", value=1, precision=0)
with gr.Row():
btn_generate = gr.Button("GENERATE", variant="primary")
btn_generate.click(fn=self.musepose_infer.infer_musepose,
inputs=[img_musepose_input, vid_pose_input, weight_dtype, nb_width, nb_height,
nb_video_frame_length, nb_video_slice_frame_length,
nb_video_slice_overlap_frame_number, nb_cfg, nb_seed, nb_steps, nb_fps,
nb_skip],
outputs=[vid_output, vid_output_demo])
vid_dance_output.change(fn=self.on_step1_complete,
inputs=[img_pose_input, vid_dance_output],
outputs=[img_musepose_input, vid_pose_input])
return demo
@staticmethod
def header():
header = gr.HTML(
"""
Note: This space only allows video input up to 3 seconds because ZeroGPU limits the function runtime to 2 minutes.
If you want longer video inputs, you have to run it locally. Click the link above and follow the README to try it locally.
When you have completed the 1: Pose Alignment process, go to 2: MusePose Inference and click the "GENERATE" button.
"""
)
return header
def launch(self):
demo = self.musepose_demo()
demo.queue().launch(
share=True
)
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()