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| # 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 torch | |
| #추가 | |
| from elevenlabs_utils import ElevenLabsPipeline | |
| from setup_environment import initialize_environment | |
| from src.utils.video import extract_audio | |
| #from flux_dev import create_flux_tab | |
| from flux_schnell import create_flux_tab | |
| # from diffusers import FluxPipeline | |
| # 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) | |
| # #========================= # FLUX 모델 로드 설정 | |
| # flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) | |
| # flux_pipe.enable_sequential_cpu_offload() | |
| # flux_pipe.vae.enable_slicing() | |
| # flux_pipe.vae.enable_tiling() | |
| # flux_pipe.to(torch.float16) | |
| # @spaces.GPU(duration=120) | |
| # def generate_image(prompt, guidance_scale, width, height): | |
| # # 이미지를 생성하는 함수 | |
| # output_image = flux_pipe( | |
| # prompt=prompt, | |
| # guidance_scale=guidance_scale, | |
| # height=height, | |
| # width=width, | |
| # num_inference_steps=4, | |
| # max_sequence_length=256, | |
| # ).images[0] | |
| # # 결과 폴더 생성 | |
| # result_folder = "/tmp/flux/" | |
| # os.makedirs(result_folder, exist_ok=True) | |
| # # 파일 이름 생성 | |
| # timestamp = datetime.now().strftime("%Y%m%d%H%M%S") | |
| # #filename = f"{prompt.replace(' ', '_')}_{timestamp}.png" | |
| # filename = f"{'_'.join(prompt.split()[:3])}_{timestamp}.png" | |
| # output_path = os.path.join(result_folder, filename) | |
| # # # 이미지를 저장 | |
| # # output_image.save(output_path) | |
| # return output_image, output_path # 두 개의 출력 반환 | |
| # def flux_tab(): #image_input): # image_input을 인자로 받습니다. | |
| # with gr.Tab("FLUX 이미지 생성"): | |
| # with gr.Row(): | |
| # with gr.Column(): | |
| # # 사용자 입력 설정 | |
| # prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world") | |
| # guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=20.0, value=3.5, step=0.1) | |
| # width = gr.Slider(label="Width", minimum=256, maximum=2048, value=512, step=64) | |
| # height = gr.Slider(label="Height", minimum=256, maximum=2048, value=512, step=64) | |
| # with gr.Column(): | |
| # # 출력 이미지와 다운로드 버튼 | |
| # output_image = gr.Image(type="pil", label="Output") | |
| # download_button = gr.File(label="Download") | |
| # generate_button = gr.Button("이미지 생성") | |
| # #use_in_text2lipsync_button = gr.Button("이 이미지를 Text2Lipsync에서 사용하기") # 새로운 버튼 추가 | |
| # # 클릭 이벤트를 정의 | |
| # generate_button.click( | |
| # fn=generate_image, | |
| # inputs=[prompt, guidance_scale, width, height], | |
| # outputs=[output_image, download_button] | |
| # ) | |
| # # # 새로운 버튼 클릭 이벤트 정의 | |
| # # use_in_text2lipsync_button.click( | |
| # # fn=lambda img: img, # 간단한 람다 함수를 사용하여 이미지를 그대로 전달 | |
| # # inputs=[output_image], # 생성된 이미지를 입력으로 사용 | |
| # # outputs=[image_input] # Text to LipSync 탭의 image_input을 업데이트 | |
| # # ) | |
| # #========================= # FLUX 모델 로드 설정 | |
| initialize_environment() | |
| import sys | |
| 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') | |
| import os | |
| # 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 | |
| # audio_path="assets/examples/driving/test_aud.mp3" | |
| #audio_path_component = gr.Textbox(label="Input", value="assets/examples/driving/test_aud.mp3") | |
| # @spaces.GPU(duration=120) | |
| # def gpu_wrapped_stf_pipeline_execute(audio_path): | |
| # return stf_pipeline.execute(audio_path) | |
| # ###### 테스트중 ###### | |
| # stf_pipeline = STFPipeline() | |
| # driving_video_path=gr.Video() | |
| # # set tyro theme | |
| # tyro.extras.set_accent_color("bright_cyan") | |
| # args = tyro.cli(ArgumentConfig) | |
| # with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| # with gr.Row(): | |
| # audio_path_component = gr.Textbox(label="Input", value="assets/examples/driving/test_aud.mp3") | |
| # stf_button = gr.Button("stf test", variant="primary") | |
| # stf_button.click( | |
| # fn=gpu_wrapped_stf_pipeline_execute, | |
| # inputs=[ | |
| # audio_path_component | |
| # ], | |
| # outputs=[driving_video_path] | |
| # ) | |
| # with gr.Row(): | |
| # driving_video_path.render() | |
| # with gr.Row(): | |
| # create_flux_tab() # image_input을 flux_tab에 전달합니다. | |
| # ###### 테스트중 ###### | |
| 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 | |
| ) | |
| # 추가 정의 | |
| elevenlabs_pipeline = ElevenLabsPipeline() | |
| stf_pipeline = STFPipeline() | |
| driving_video_path=gr.Video() | |
| def gpu_wrapped_stf_pipeline_execute(audio_path): | |
| return stf_pipeline.execute(audio_path) | |
| def gpu_wrapped_elevenlabs_pipeline_generate_voice(text, voice): | |
| return elevenlabs_pipeline.generate_voice(text, voice) | |
| def gpu_wrapped_execute_video(*args, **kwargs): | |
| return gradio_pipeline.execute_video(*args, **kwargs) | |
| def gpu_wrapped_execute_image(*args, **kwargs): | |
| return gradio_pipeline.execute_image(*args, **kwargs) | |
| 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) | |
| def txt_to_driving_video(text): | |
| audio_path = gpu_wrapped_elevenlabs_pipeline_generate_voice(text) | |
| driving_video_path = gpu_wrapped_stf_pipeline_execute(audio_path) | |
| return driving_video_path | |
| # assets | |
| title_md = "assets/gradio_title.md" | |
| example_portrait_dir = "assets/examples/source" | |
| example_portrait_dir_custom = "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() | |
| video_input = gr.Video() | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| #gr.HTML(load_description(title_md)) | |
| with gr.Tabs(): | |
| with gr.Tab("Text to LipSync"): | |
| gr.Markdown("# Text to LipSync") | |
| with gr.Row(): | |
| with gr.Column(): | |
| script_txt = gr.Text() | |
| # with gr.Column(): | |
| # txt2video_gen_button = gr.Button("txt2video generation", variant="primary") | |
| with gr.Column(): | |
| audio_gen_button = gr.Button("Audio generation", variant="primary") | |
| with gr.Row(): | |
| output_audio = gr.Audio(label="Generated audio", type="filepath") | |
| with gr.Row(): | |
| video_gen_button = gr.Button("Audio to Video generation", variant="primary") | |
| 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, "01.webp")], | |
| [osp.join(example_portrait_dir, "02.webp")], | |
| [osp.join(example_portrait_dir, "03.jpg")], | |
| [osp.join(example_portrait_dir, "04.jpg")], | |
| [osp.join(example_portrait_dir, "05.jpg")], | |
| [osp.join(example_portrait_dir, "06.jpg")], | |
| [osp.join(example_portrait_dir, "07.jpg")], | |
| [osp.join(example_portrait_dir, "08.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, | |
| ) | |
| 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 | |
| ) | |
| # txt2video_gen_button.click( | |
| # fn=txt_to_driving_video, | |
| # inputs=[ | |
| # script_txt | |
| # ], | |
| # outputs=[video_input], | |
| # show_progress=True | |
| # ) | |
| audio_gen_button.click( | |
| fn=gpu_wrapped_elevenlabs_pipeline_generate_voice, | |
| inputs=[ | |
| script_txt | |
| ], | |
| outputs=[output_audio], | |
| show_progress=True | |
| ) | |
| video_gen_button.click( | |
| fn=gpu_wrapped_stf_pipeline_execute, | |
| inputs=[ | |
| output_audio | |
| ], | |
| outputs=[video_input], | |
| 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 | |
| ) | |
| # 세 번째 탭: Flux 개발용 탭 | |
| with gr.Tab("FLUX Image"): | |
| flux_demo = create_flux_tab(image_input) # Flux 개발용 탭 생성 | |
| demo.launch( | |
| server_port=args.server_port, | |
| share=args.share, | |
| server_name=args.server_name | |
| ) | |