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import gradio as gr
from musepose_inference import MusePoseInference
from pose_align import PoseAlignmentInference
class App:
def __init__(self):
self.pose_alignment_infer = PoseAlignmentInference()
self.musepose_infer = MusePoseInference()
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()
if __name__ == "__main__":
app = App()
app.launch() |