# coding: utf-8 """ The entrance of the gradio """ import os import sys # КРИТИЧНИЙ ФІКС 1: Запобігаємо шторму потоків (Thread Thrashing) os.environ["OMP_NUM_THREADS"] = "2" os.environ["MKL_NUM_THREADS"] = "2" os.environ["OPENBLAS_NUM_THREADS"] = "2" os.environ["VECLIB_MAXIMUM_THREADS"] = "2" os.environ["NUMEXPR_NUM_THREADS"] = "2" # ============================================================================== # КРИТИЧНИЙ ФІКС 2: Патч для 5D GridSample на CUDA. # ============================================================================== import onnxruntime as ort _orig_InferenceSession = ort.InferenceSession class PatchedInferenceSession(_orig_InferenceSession): def __init__(self, path_or_bytes, *args, **kwargs): if isinstance(path_or_bytes, str) and "warping_spade" in path_or_bytes: print(f"🎯 [MONKEYPATCH] Forcing {path_or_bytes} to run strictly on CPUExecutionProvider!") kwargs["providers"] = ["CPUExecutionProvider"] super().__init__(path_or_bytes, *args, **kwargs) ort.InferenceSession = PatchedInferenceSession if hasattr(ort, 'capi') and hasattr(ort.capi, 'onnxruntime_inference_collection'): ort.capi.onnxruntime_inference_collection.InferenceSession = PatchedInferenceSession # ============================================================================== import pdb import gradio as gr # ============================================================================== # КРИТИЧНИЙ ФІКС 3 (MONKEYPATCH FOR GR.INFO): # Вирішуємо конфлікт версій Gradio. Вирізаємо 'duration', якого немає в Gradio 4.36.1, # щоб уникнути TypeError на самому фініші генерації відео. # ============================================================================== _orig_Info = gr.Info def patched_Info(message, *args, **kwargs): kwargs.pop('duration', None) # Видаляємо duration, якщо він переданий автором return _orig_Info(message, *args, **kwargs) gr.Info = patched_Info # ============================================================================== import os.path as osp from omegaconf import OmegaConf from src.pipelines.gradio_live_portrait_pipeline import GradioLivePortraitPipeline from huggingface_hub import snapshot_download # Спочатку скачуємо ВСІ необхідні ONNX ваги та компоненти Kokoro checkpoint_dir = "./checkpoints" if not os.path.exists(os.path.join(checkpoint_dir, "liveportrait_onnx")): print("Завантаження повного пакету моделей з Hugging Face Hub...") snapshot_download( repo_id="warmshao/FasterLivePortrait", local_dir=checkpoint_dir ) print("Всі特色 моделі успішно завантажено!") def load_description(fp): if os.path.exists(fp): with open(fp, 'r', encoding='utf-8') as f: content = f.read() return content return "" import argparse parser = argparse.ArgumentParser(description='Faster Live Portrait Pipeline') parser.add_argument('--mode', required=False, type=str, default="onnx") parser.add_argument('--use_mp', action='store_true', help='use mediapipe or not') args, unknown = parser.parse_known_args() # Налаштовуємо конфіги if args.mode == "onnx": cfg_path = "configs/onnx_mp_infer.yaml" if args.use_mp else "configs/onnx_infer.yaml" else: cfg_path = "configs/trt_mp_infer.yaml" if args.use_mp else "configs/trt_infer.yaml" infer_cfg = OmegaConf.load(cfg_path) gradio_pipeline = GradioLivePortraitPipeline(infer_cfg) 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 change_animal_model(is_animal): global gradio_pipeline gradio_pipeline.clean_models() gradio_pipeline.init_models(is_animal=is_animal) # assets title_md = "assets/gradio/gradio_title.md" example_portrait_dir = "assets/examples/source" example_video_dir = "assets/examples/driving" #################### interface logic #################### 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(format="png", type="numpy") output_image_paste_back = gr.Image(format="png", type="numpy") with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Plus Jakarta Sans")])) as demo: gr.HTML(load_description(title_md)) gr.Markdown(load_description("assets/gradio/gradio_description_upload.md")) with gr.Row(): with gr.Column(): with gr.Tabs(): with gr.TabItem("🖼️ Source Image") as tab_image: with gr.Accordion(open=True, label="Source Image"): source_image_input = gr.Image(type="filepath") with gr.TabItem("🎞️ Source Video") as tab_video: with gr.Accordion(open=True, label="Source Video"): source_video_input = gr.Video() tab_selection = gr.Textbox(value="Image", visible=False) tab_image.select(lambda: "Image", None, tab_selection) tab_video.select(lambda: "Video", None, tab_selection) with gr.Accordion(open=True, label="Cropping Options for Source Image or Video"): with gr.Row(): flag_do_crop_input = gr.Checkbox(value=True, label="do crop (source)") scale = gr.Number(value=2.3, label="source crop scale", minimum=1.8, maximum=3.2, step=0.05) vx_ratio = gr.Number(value=0.0, label="source crop x", minimum=-0.5, maximum=0.5, step=0.01) vy_ratio = gr.Number(value=-0.125, label="source crop y", minimum=-0.5, maximum=0.5, step=0.01) with gr.Column(): with gr.Tabs(): with gr.TabItem("🎞️ Driving Video") as v_tab_video: with gr.Accordion(open=True, label="Driving Video"): driving_video_input = gr.Video() with gr.TabItem("🖼️ Driving Image") as v_tab_image: with gr.Accordion(open=True, label="Driving Image"): driving_image_input = gr.Image(type="filepath") with gr.TabItem("📁 Driving Pickle") as v_tab_pickle: with gr.Accordion(open=True, label="Driving Pickle"): driving_pickle_input = gr.File(type="filepath", file_types=[".pkl"]) with gr.TabItem("🎵 Driving Audio") as v_tab_audio: with gr.Accordion(open=True, label="Driving Audio"): driving_audio_input = gr.Audio( value=None, type="filepath", interactive=True, show_label=False, waveform_options=gr.WaveformOptions( sample_rate=24000, ), ) with gr.TabItem("📄Driving Text") as v_tab_text: with gr.Accordion(open=True, label="Driving Text"): driving_text_input = gr.Textbox(value="Hi, I am created by Faster LivePortrait!", label="Driving Text") voice_dir = "checkpoints/Kokoro-82M/voices/" voice_names = [] if os.path.exists(voice_dir): voice_names = [os.path.splitext(vname)[0] for vname in os.listdir(voice_dir) if vname.endswith(".pt")] if not voice_names: voice_names = ['af_heart'] voice_name = gr.Dropdown( choices=voice_names, value='af_heart', label="Voice Name") v_tab_selection = gr.Textbox(value="Video", visible=False) v_tab_video.select(lambda: "Video", None, v_tab_selection) v_tab_image.select(lambda: "Image", None, v_tab_selection) v_tab_pickle.select(lambda: "Pickle", None, v_tab_selection) v_tab_audio.select(lambda: "Audio", None, v_tab_selection) v_tab_text.select(lambda: "Text", None, v_tab_selection) with gr.Accordion(open=True, label="Cropping Options for Driving Video"): with gr.Row(): flag_crop_driving_video_input = gr.Checkbox(value=False, label="do crop (driving)") scale_crop_driving_video = gr.Number(value=2.2, label="driving crop scale", minimum=1.8, maximum=3.2, step=0.05) vx_ratio_crop_driving_video = gr.Number(value=0.0, label="driving crop x", minimum=-0.5, maximum=0.5, step=0.01) vy_ratio_crop_driving_video = gr.Number(value=-0.1, label="driving crop y", minimum=-0.5, maximum=0.5, step=0.01) with gr.Row(): with gr.Accordion(open=True, label="Animation Options"): with gr.Row(): flag_relative_input = gr.Checkbox(value=False, label="relative motion") flag_stitching = gr.Checkbox(value=True, label="stitching") driving_multiplier = gr.Number(value=1.0, label="driving multiplier", minimum=0.0, maximum=2.0, step=0.02) cfg_scale = gr.Number(value=4.0, label="cfg_scale", minimum=0.0, maximum=10.0, step=0.5) flag_remap_input = gr.Checkbox(value=True, label="paste-back") animation_region = gr.Radio(["all", "exp", "pose", "lip", "eyes"], value="all", label="animation region") flag_video_editing_head_rotation = gr.Checkbox(value=False, label="relative head rotation (v2v)") driving_smooth_observation_variance = gr.Number(value=1e-7, label="motion smooth strength (v2v)", minimum=1e-11, maximum=1e-2, step=1e-8) flag_is_animal = gr.Checkbox(value=False, label="is_animal") gr.Markdown(load_description("assets/gradio/gradio_description_animate_clear.md")) with gr.Row(): process_button_animation = gr.Button("🚀 Animate", variant="primary") with gr.Column(): with gr.Row(): with gr.Column(): output_video_i2v = gr.Video(autoplay=False, label="The animated video in the original image space") with gr.Column(): output_video_concat_i2v = gr.Video(autoplay=False, label="The animated video") with gr.Row(): with gr.Column(): output_image_i2i = gr.Image(format="png", type="numpy", label="The animated image in the original image space", visible=False) with gr.Column(): output_image_concat_i2i = gr.Image(format="png", type="numpy", label="The animated image", visible=False) with gr.Row(): process_button_reset = gr.ClearButton( [source_image_input, source_video_input, driving_pickle_input, driving_video_input, driving_image_input, output_video_i2v, output_video_concat_i2v, output_image_i2i, output_image_concat_i2i], value="🧹 Clear") # Retargeting gr.Markdown(load_description("assets/gradio/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() 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() flag_is_animal.change(change_animal_model, inputs=[flag_is_animal]) process_button_retargeting.click( 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=[ source_image_input, source_video_input, driving_video_input, driving_image_input, driving_pickle_input, driving_audio_input, driving_text_input, flag_relative_input, flag_do_crop_input, flag_remap_input, driving_multiplier, flag_stitching, flag_crop_driving_video_input, flag_video_editing_head_rotation, flag_is_animal, animation_region, scale, vx_ratio, vy_ratio, scale_crop_driving_video, vx_ratio_crop_driving_video, vy_ratio_crop_driving_video, driving_smooth_observation_variance, tab_selection, v_tab_selection, cfg_scale, voice_name ], outputs=[output_video_i2v, output_video_i2v, output_video_concat_i2v, output_video_concat_i2v, output_image_i2i, output_image_i2i, output_image_concat_i2i, output_image_concat_i2i], show_progress=True ) if __name__ == '__main__': demo.queue() demo.launch( server_port=7860, share=False, server_name="0.0.0.0" )