import gradio as gr from musepose_inference import MusePoseInference from pose_align import PoseAlignmentInference class App: def __init__(self): self.pose_alignment_infer = MusePoseInference() self.musepose_infer = PoseAlignmentInference() def musepose_demo(self): with gr.Blocks() as demo: 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_algin_pose = gr.Button("ALIGN POSE", variant="primary") btn_algin_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") 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 launch(self): demo = self.musepose_demo() demo.queue().launch()