# coding: utf-8 """ The entrance of the gradio """ import tyro import gradio as gr import os.path as osp from src.utils.helper import load_description from src.gradio_pipeline import GradioPipeline from src.config.crop_config import CropConfig from src.config.argument_config import ArgumentConfig from src.config.inference_config import InferenceConfig import spaces import cv2 # import gdown # folder_url = f"https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib" # gdown.download_folder(url=folder_url, output="pretrained_weights", quiet=False) import sys from src.utils.video import extract_audio from elevenlabs_utils import ElevenLabsPipeline from setup_environment import initialize_environment initialize_environment() sys.path.append('/home/user/.local/lib/python3.10/site-packages') sys.path.append('/home/user/.local/lib/python3.10/site-packages/stf_alternative/src/stf_alternative') sys.path.append('/home/user/.local/lib/python3.10/site-packages/stf_tools/src/stf_tools') sys.path.append('/home/user/app/') sys.path.append('/home/user/app/stf/') sys.path.append('/home/user/app/stf/stf_alternative/') sys.path.append('/home/user/app/stf/stf_alternative/src/stf_alternative') sys.path.append('/home/user/app/stf/stf_tools') sys.path.append('/home/user/app/stf/stf_tools/src/stf_tools') # CUDA 경로를 환경 변수로 설정 os.environ['PATH'] = '/usr/local/cuda/bin:' + os.environ.get('PATH', '') os.environ['LD_LIBRARY_PATH'] = '/usr/local/cuda/lib64:' + os.environ.get('LD_LIBRARY_PATH', '') # 확인용 출력 print("PATH:", os.environ['PATH']) print("LD_LIBRARY_PATH:", os.environ['LD_LIBRARY_PATH']) from stf_utils import STFPipeline def partial_fields(target_class, kwargs): return target_class(**{k: v for k, v in kwargs.items() if hasattr(target_class, k)}) # set tyro theme tyro.extras.set_accent_color("bright_cyan") args = tyro.cli(ArgumentConfig) # specify configs for inference inference_cfg = partial_fields(InferenceConfig, args.__dict__) # use attribute of args to initial InferenceConfig crop_cfg = partial_fields(CropConfig, args.__dict__) # use attribute of args to initial CropConfig gradio_pipeline = GradioPipeline( inference_cfg=inference_cfg, crop_cfg=crop_cfg, args=args ) @spaces.GPU(duration=240) def gpu_wrapped_execute_video(*args, **kwargs): return gradio_pipeline.execute_video(*args, **kwargs) @spaces.GPU(duration=240) def gpu_wrapped_execute_image(*args, **kwargs): return gradio_pipeline.execute_image(*args, **kwargs) @spaces.GPU(duration=240) def gpu_wrapped_stf_pipeline_execute(audio_path): return stf_pipeline.execute(audio_path) @spaces.GPU(duration=240) def gpu_wrapped_elevenlabs_pipeline_generate_voice(text, voice): return elevenlabs_pipeline.generate_voice(text, voice) def is_square_video(video_path): video = cv2.VideoCapture(video_path) width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) video.release() if width != height: raise gr.Error("Error: the video does not have a square aspect ratio. We currently only support square videos") return gr.update(visible=True) # assets title_md = "assets/gradio_title.md" example_portrait_dir = "assets/examples/source" example_video_dir = "assets/examples/driving" data_examples = [ [osp.join(example_portrait_dir, "s9.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True], [osp.join(example_portrait_dir, "s6.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True], [osp.join(example_portrait_dir, "s10.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True], [osp.join(example_portrait_dir, "s5.jpg"), osp.join(example_video_dir, "d18.mp4"), True, True, True, True], [osp.join(example_portrait_dir, "s7.jpg"), osp.join(example_video_dir, "d19.mp4"), True, True, True, True], [osp.join(example_portrait_dir, "s22.jpg"), osp.join(example_video_dir, "d0.mp4"), True, True, True, True], ] #################### interface logic #################### # Define components first eye_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target eyes-open ratio") lip_retargeting_slider = gr.Slider(minimum=0, maximum=0.8, step=0.01, label="target lip-open ratio") retargeting_input_image = gr.Image(type="filepath") output_image = gr.Image(type="numpy") output_image_paste_back = gr.Image(type="numpy") output_video = gr.Video() output_video_concat = gr.Video() def run_end_to_end(image_path, text, voice, input_video, flag_relative, flag_do_crop, flag_remap, flag_crop_driving_video, male, animal): # # animal 체크 여부에 따라 다른 pipeline 사용 # if animal: # gradio_pipeline = GradioPipelineAnimal( # inference_cfg=inference_cfg, # crop_cfg=crop_cfg, # args=args # ) # else: # gradio_pipeline = GradioPipeline( # inference_cfg=inference_cfg, # crop_cfg=crop_cfg, # args=args # ) if not male: stf_pipeline = STFPipeline() else: stf_pipeline = STFPipeline(template_video_path="/home/user/app/stf/TEMP/Cam2_2309071202_0012_Natural_Looped.mp4", config_path="/home/user/app/stf/TEMP/front_config_v3.json", checkpoint_path="/home/user/app/stf/TEMP/0157.pth", ) if input_video is None: #audio_path = elevenlabs_pipeline.generate_voice(text, voice) audio_path = gpu_wrapped_elevenlabs_pipeline_generate_voice(text, voice) #driving_video_path = stf_pipeline.execute(audio_path) driving_video_path = gpu_wrapped_stf_pipeline_execute(audio_path) else: driving_video_path = input_video audio_path = osp.join("animations", Path(input_video).stem+".wav") extract_audio(driving_video_path, audio_path) #output_path, crop_output_path = gradio_pipeline.execute_video( output_path, crop_output_path = gpu_wrapped_execute_video( input_source_image_path=image_path, input_driving_video_path=driving_video_path, # input_driving_video_pickle_path=None, flag_do_crop_input=flag_do_crop, # flag_remap_input=flag_remap, # driving_multiplier=1.0, # flag_stitching=False, flag_crop_driving_video_input=flag_crop_driving_video, # scale=2.3, # vx_ratio=0.0, # vy_ratio=-0.125, # scale_crop_driving_video=2.2, # vx_ratio_crop_driving_video=0.0, # vy_ratio_crop_driving_video=-0.1, # tab_selection=None, audio_path=audio_path ) return output_path, crop_output_path with gr.Blocks(theme=gr.themes.Soft()) as demo: with gr.Tabs(): # 첫 번째 탭: Text to LipSync with gr.Tab("Text to LipSync"): gr.Markdown("# Text to LipSync") with gr.Row(): script_txt = gr.Text() voice = gr.Audio(label="사용자 음성", type="filepath") input_video = gr.Video() with gr.Row(): image_input = gr.Image(type="filepath") # 여기서 image_input을 정의합니다. output_video.render() crop_output_video.render() with gr.Row(): flag_relative_input = gr.Checkbox(value=True, label="relative motion") flag_do_crop_input = gr.Checkbox(value=True, label="do crop") flag_remap_input = gr.Checkbox(value=True, label="paste-back") flag_crop_driving_video_input = gr.Checkbox(value=False, label="do crop (driving video)") male = gr.Checkbox(value=False, label="male") animal = gr.Checkbox(value=False, label="animal") # animal 체크박스 추가 with gr.Row(): generate_speech = gr.Button("🚀 Generate Speech", variant="primary") generate_speech.click( fn=run_end_to_end, inputs=[ image_input, script_txt, voice, input_video, flag_relative_input, flag_do_crop_input, flag_remap_input, flag_crop_driving_video_input, male, animal # 추가된 animal 입력 ], outputs=[output_video, crop_output_video] ) # # 두 번째 탭: FLUX 이미지 생성 # with gr.Tab("FLUX 이미지 생성"): # flux_tab(image_input) # FLUX 이미지 생성을 위한 별도의 탭 # # 세 번째 탭: Flux 개발용 탭 # with gr.Tab("FLUX Dev"): # flux_demo = create_flux_tab() # Flux 개발용 탭 생성 # #flux_demo.render() # 해당 UI를 별도의 탭에만 렌더링 # with gr.Blocks(theme=gr.themes.Soft()) as demo: # gr.HTML(load_description(title_md)) # gr.Markdown(load_description("assets/gradio_description_upload.md")) # with gr.Row(): # with gr.Accordion(open=True, label="Source Portrait"): # image_input = gr.Image(type="filepath") # gr.Examples( # examples=[ # [osp.join(example_portrait_dir, "s9.jpg")], # [osp.join(example_portrait_dir, "s6.jpg")], # [osp.join(example_portrait_dir, "s10.jpg")], # [osp.join(example_portrait_dir, "s5.jpg")], # [osp.join(example_portrait_dir, "s7.jpg")], # [osp.join(example_portrait_dir, "s12.jpg")], # [osp.join(example_portrait_dir, "s22.jpg")], # ], # inputs=[image_input], # cache_examples=False, # ) # with gr.Accordion(open=True, label="Driving Video"): # video_input = gr.Video() # gr.Examples( # examples=[ # [osp.join(example_video_dir, "d0.mp4")], # [osp.join(example_video_dir, "d18.mp4")], # [osp.join(example_video_dir, "d19.mp4")], # [osp.join(example_video_dir, "d14_trim.mp4")], # [osp.join(example_video_dir, "d6_trim.mp4")], # ], # inputs=[video_input], # cache_examples=False, # ) # with gr.Row(): # with gr.Accordion(open=False, label="Animation Instructions and Options"): # gr.Markdown(load_description("assets/gradio_description_animation.md")) # with gr.Row(): # flag_relative_input = gr.Checkbox(value=True, label="relative motion") # flag_do_crop_input = gr.Checkbox(value=True, label="do crop") # flag_remap_input = gr.Checkbox(value=True, label="paste-back") # gr.Markdown(load_description("assets/gradio_description_animate_clear.md")) # with gr.Row(): # with gr.Column(): # process_button_animation = gr.Button("🚀 Animate", variant="primary") # with gr.Column(): # process_button_reset = gr.ClearButton([image_input, video_input, output_video, output_video_concat], value="🧹 Clear") # with gr.Row(): # with gr.Column(): # with gr.Accordion(open=True, label="The animated video in the original image space"): # output_video.render() # with gr.Column(): # with gr.Accordion(open=True, label="The animated video"): # output_video_concat.render() # with gr.Row(): # # Examples # gr.Markdown("## You could also choose the examples below by one click ⬇️") # with gr.Row(): # gr.Examples( # examples=data_examples, # fn=gpu_wrapped_execute_video, # inputs=[ # image_input, # video_input, # flag_relative_input, # flag_do_crop_input, # flag_remap_input # ], # outputs=[output_image, output_image_paste_back], # examples_per_page=6, # cache_examples=False, # ) # gr.Markdown(load_description("assets/gradio_description_retargeting.md"), visible=True) # with gr.Row(visible=True): # eye_retargeting_slider.render() # lip_retargeting_slider.render() # with gr.Row(visible=True): # process_button_retargeting = gr.Button("🚗 Retargeting", variant="primary") # process_button_reset_retargeting = gr.ClearButton( # [ # eye_retargeting_slider, # lip_retargeting_slider, # retargeting_input_image, # output_image, # output_image_paste_back # ], # value="🧹 Clear" # ) # with gr.Row(visible=True): # with gr.Column(): # with gr.Accordion(open=True, label="Retargeting Input"): # retargeting_input_image.render() # gr.Examples( # examples=[ # [osp.join(example_portrait_dir, "s9.jpg")], # [osp.join(example_portrait_dir, "s6.jpg")], # [osp.join(example_portrait_dir, "s10.jpg")], # [osp.join(example_portrait_dir, "s5.jpg")], # [osp.join(example_portrait_dir, "s7.jpg")], # [osp.join(example_portrait_dir, "s12.jpg")], # [osp.join(example_portrait_dir, "s22.jpg")], # ], # inputs=[retargeting_input_image], # cache_examples=False, # ) # with gr.Column(): # with gr.Accordion(open=True, label="Retargeting Result"): # output_image.render() # with gr.Column(): # with gr.Accordion(open=True, label="Paste-back Result"): # output_image_paste_back.render() # # binding functions for buttons # process_button_retargeting.click( # # fn=gradio_pipeline.execute_image, # fn=gpu_wrapped_execute_image, # inputs=[eye_retargeting_slider, lip_retargeting_slider, retargeting_input_image, flag_do_crop_input], # outputs=[output_image, output_image_paste_back], # show_progress=True # ) # process_button_animation.click( # fn=gpu_wrapped_execute_video, # inputs=[ # image_input, # video_input, # flag_relative_input, # flag_do_crop_input, # flag_remap_input # ], # outputs=[output_video, output_video_concat], # show_progress=True # ) # # image_input.change( # # fn=gradio_pipeline.prepare_retargeting, # # inputs=image_input, # # outputs=[eye_retargeting_slider, lip_retargeting_slider, retargeting_input_image] # # ) # video_input.upload( # fn=is_square_video, # inputs=video_input, # outputs=video_input # ) demo.launch( server_port=args.server_port, share=args.share, server_name=args.server_name )