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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.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) | |
| def musepose_demo(self): | |
| with gr.Blocks() as demo: | |
| md_header = self.header() | |
| with gr.Tabs(): | |
| with gr.TabItem('Step1: Pose Alignment'): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| img_input = gr.Image(label="Input Image here", type="filepath", scale=5) | |
| vid_dance_input = gr.Video(label="Input Dance Video", scale=5) | |
| with gr.Column(scale=3): | |
| vid_dance_output = gr.Video(label="Aligned pose output will be displayed here", scale=5) | |
| vid_dance_output_demo = gr.Video(label="Output demo video will be displayed here", 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("assets", "videos", "dance.mp4"), os.path.join("assets", "images", "ref.png"), | |
| 512, 720, 0, 300]] | |
| ex_step1 = gr.Examples(examples=examples, | |
| inputs=[vid_dance_input, img_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_input, nb_detect_resolution, nb_image_resolution, | |
| nb_align_frame, nb_max_frame], | |
| outputs=[vid_dance_output, vid_dance_output_demo]) | |
| with gr.TabItem('Step2: MusePose Inference'): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| img_input = gr.Image(label="Input Image here", type="filepath", scale=5) | |
| vid_pose_input = gr.Video(label="Input Aligned Pose Video here", scale=5) | |
| with gr.Column(scale=3): | |
| vid_output = gr.Video(label="Output Video will be displayed here", scale=5) | |
| vid_output_demo = gr.Video(label="Output demo video will be displayed here", 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") | |
| with gr.Column(): | |
| examples = [ | |
| [os.path.join("assets", "images", "ref.png"), os.path.join("assets", "videos", "pose.mp4"), | |
| "fp16", 512, 512, 300, 48, 4, 3.5, 99, 20, -1, 1]] | |
| ex_step2 = gr.Examples(examples=examples, | |
| inputs=[img_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], | |
| fn=self.musepose_infer.infer_musepose, | |
| cache_examples="lazy") | |
| btn_generate.click(fn=self.musepose_infer.infer_musepose, | |
| inputs=[img_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]) | |
| return demo | |
| def header(): | |
| header = gr.HTML( | |
| """ | |
| <style> | |
| p, li { | |
| font-size: 16px; | |
| } | |
| </style> | |
| <h2>Gradio demo for <a href="https://github.com/TMElyralab/MusePose">MusePose</a></h2> | |
| <p>Demo list you can try in other environment:</p> | |
| <ul> | |
| <li><a href="https://github.com/jhj0517/MusePose-WebUI"><strong>MusePose WebUI</strong></a> (This repository, you can try in local)</li> | |
| <li><a href="https://github.com/jhj0517/stable-diffusion-webui-MusePose.git"><strong>stable-diffusion-webui-MusePose</strong></a> (SD WebUI extension)</li> | |
| <li><a href="https://github.com/TMElyralab/Comfyui-MusePose"><strong>Comfyui-MusePose</strong></a> (ComfyUI custom node)</li> | |
| </ul> | |
| """ | |
| ) | |
| return header | |
| def launch(self): | |
| demo = self.musepose_demo() | |
| demo.queue().launch() | |
| 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("assets", "videos"), 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') | |
| args = parser.parse_args() | |
| app = App(args=args) | |
| app.launch() |