import os import random import sys from typing import Sequence, Mapping, Any, Union import torch from PIL import Image from huggingface_hub import hf_hub_download import spaces import subprocess, sys # --------------------------------------------------------------------------------- # 🛠️ Monkey-patch для gradio_client: игнорируем булевы схемы и не падаем на TypeError # --------------------------------------------------------------------------------- import gradio_client.utils as _gc_utils # Сохраняем оригинальные функции _orig_js2pt = _gc_utils._json_schema_to_python_type _orig_get_type = _gc_utils.get_type def _safe_json_schema_to_python_type(schema, defs=None): """ Если schema — bool (True/False), возвращаем 'Any', иначе — вызываем оригинальный код. """ if isinstance(schema, bool): return "Any" return _orig_js2pt(schema, defs) def _safe_get_type(schema): """ Если schema — bool, возвращаем 'Any', иначе — вызываем оригинальную функцию get_type. """ if isinstance(schema, bool): return "Any" return _orig_get_type(schema) # Заменяем в модуле _gc_utils._json_schema_to_python_type = _safe_json_schema_to_python_type _gc_utils.get_type = _safe_get_type # --------------------------------------------------------------------------------- # Дальше уже можно безопасно импортировать Gradio import gradio import gradio_client import gradio as gr print("gradio version:", gradio.__version__) print("gradio_client version:", gradio_client.__version__) hf_hub_download(repo_id="facefusion/models-3.3.0", filename="hyperswap_1a_256.onnx", local_dir="models/hyperswap") hf_hub_download(repo_id="facefusion/models-3.3.0", filename="hyperswap_1b_256.onnx", local_dir="models/hyperswap") hf_hub_download(repo_id="facefusion/models-3.3.0", filename="hyperswap_1c_256.onnx", local_dir="models/hyperswap") hf_hub_download(repo_id="martintomov/comfy", filename="facedetection/yolov5l-face.pth", local_dir="models") ###hf_hub_download(repo_id="darkeril/collection", filename="detection_Resnet50_Final.pth", local_dir="models/facedetection") hf_hub_download(repo_id="gmk123/GFPGAN", filename="parsing_parsenet.pth", local_dir="models/facedetection") hf_hub_download(repo_id="MonsterMMORPG/tools", filename="1k3d68.onnx", local_dir="models/insightface/models/buffalo_l") hf_hub_download(repo_id="MonsterMMORPG/tools", filename="2d106det.onnx", local_dir="models/insightface/models/buffalo_l") hf_hub_download(repo_id="maze/faceX", filename="det_10g.onnx", local_dir="models/insightface/models/buffalo_l") hf_hub_download(repo_id="typhoon01/aux_models", filename="genderage.onnx", local_dir="models/insightface/models/buffalo_l") hf_hub_download(repo_id="maze/faceX", filename="w600k_r50.onnx", local_dir="models/insightface/models/buffalo_l") hf_hub_download(repo_id="vladmandic/insightface-faceanalysis", filename="buffalo_l.zip", local_dir="models/insightface/models/buffalo_l") def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any: """Returns the value at the given index of a sequence or mapping. If the object is a sequence (like list or string), returns the value at the given index. If the object is a mapping (like a dictionary), returns the value at the index-th key. Some return a dictionary, in these cases, we look for the "results" key Args: obj (Union[Sequence, Mapping]): The object to retrieve the value from. index (int): The index of the value to retrieve. Returns: Any: The value at the given index. Raises: IndexError: If the index is out of bounds for the object and the object is not a mapping. """ try: return obj[index] except KeyError: return obj["result"][index] def find_path(name: str, path: str = None) -> str: """ Recursively looks at parent folders starting from the given path until it finds the given name. Returns the path as a Path object if found, or None otherwise. """ # If no path is given, use the current working directory if path is None: path = os.getcwd() # Check if the current directory contains the name if name in os.listdir(path): path_name = os.path.join(path, name) print(f"{name} found: {path_name}") return path_name # Get the parent directory parent_directory = os.path.dirname(path) # If the parent directory is the same as the current directory, we've reached the root and stop the search if parent_directory == path: return None # Recursively call the function with the parent directory return find_path(name, parent_directory) def add_comfyui_directory_to_sys_path() -> None: """ Add 'ComfyUI' to the sys.path """ comfyui_path = find_path("ComfyUI") if comfyui_path is not None and os.path.isdir(comfyui_path): sys.path.append(comfyui_path) print(f"'{comfyui_path}' added to sys.path") def add_extra_model_paths() -> None: """ Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path. """ try: from main import load_extra_path_config except ImportError: print( "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead." ) from utils.extra_config import load_extra_path_config extra_model_paths = find_path("extra_model_paths.yaml") if extra_model_paths is not None: load_extra_path_config(extra_model_paths) else: print("Could not find the extra_model_paths config file.") add_comfyui_directory_to_sys_path() add_extra_model_paths() def import_custom_nodes() -> None: """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS This function sets up a new asyncio event loop, initializes the PromptServer, creates a PromptQueue, and initializes the custom nodes. """ import asyncio import execution from nodes import init_extra_nodes import server # Creating a new event loop and setting it as the default loop loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # Creating an instance of PromptServer with the loop server_instance = server.PromptServer(loop) execution.PromptQueue(server_instance) # Initializing custom nodes # Запускаем корутину и ждём её завершения loop.run_until_complete(init_extra_nodes()) import_custom_nodes() from nodes import NODE_CLASS_MAPPINGS # --- Глобальная загрузка моделей (один раз при старте) --- loadimage = NODE_CLASS_MAPPINGS["LoadImage"]() vhs_loadvideo = NODE_CLASS_MAPPINGS["VHS_LoadVideo"]() reactoroptions = NODE_CLASS_MAPPINGS["ReActorOptions"]() vhs_videoinfoloaded = NODE_CLASS_MAPPINGS["VHS_VideoInfoLoaded"]() reactorfaceswapopt = NODE_CLASS_MAPPINGS["ReActorFaceSwapOpt"]() vhs_videocombine = NODE_CLASS_MAPPINGS["VHS_VideoCombine"]() @spaces.GPU(duration=60) def generate_image(source_image, input_video, input_index, input_faces_order, swap_model, pingpong, loop_count, select_every_nth, use_audio): with torch.inference_mode(): loadimage_29 = loadimage.load_image(image=source_image) vhs_loadvideo_51 = vhs_loadvideo.load_video( video=input_video, force_rate=0, custom_width=0, custom_height=0, frame_load_cap=0, skip_first_frames=0, select_every_nth=select_every_nth, format="AnimateDiff", unique_id=17765013700631265033, ) reactoroptions_107 = reactoroptions.execute( input_faces_order=input_faces_order, input_faces_index=str(input_index), # Преобразуем в строку detect_gender_input="no", source_faces_order="large-small", source_faces_index="0", detect_gender_source="no", console_log_level=1, ) for q in range(1): vhs_videoinfoloaded_105 = vhs_videoinfoloaded.get_video_info( video_info=get_value_at_index(vhs_loadvideo_51, 3) ) reactorfaceswapopt_106 = reactorfaceswapopt.execute( enabled=True, swap_model=swap_model, # Используем выбранную модель facedetection="YOLOv5l", face_restore_model="none", face_restore_visibility=1, codeformer_weight=0.5, input_image=get_value_at_index(vhs_loadvideo_51, 0), source_image=get_value_at_index(loadimage_29, 0), options=get_value_at_index(reactoroptions_107, 0), ) # Формируем аргументы для combine_video combine_kwargs = dict( frame_rate=get_value_at_index(vhs_videoinfoloaded_105, 0), loop_count=loop_count, filename_prefix="vidswap", format="video/h264-mp4", pix_fmt="yuv420p", crf=20, save_metadata=False, trim_to_audio=False, pingpong=pingpong, save_output=True, images=get_value_at_index(reactorfaceswapopt_106, 0), unique_id=17889577966051683261, ) if use_audio: combine_kwargs["audio"] = get_value_at_index(vhs_loadvideo_51, 2) vhs_videocombine_28 = vhs_videocombine.combine_video(**combine_kwargs) saved_path = f"output/{vhs_videocombine_28['ui']['gifs'][0]['filename']}" return saved_path if __name__ == "__main__": with gr.Blocks() as app: # Заголовок gr.Markdown("# ComfyUI Reactor Video Face Swap Hyperswap") gr.Markdown( "ComfyUI Reactor Video Face Swap Hyperswap running directly on Gradio. - " "[How to convert your any ComfyUI workflow to Gradio]" "(https://huggingface.co/blog/run-comfyui-workflows-on-spaces)" ) with gr.Row(): with gr.Column(): # Вложенный Row для групп with gr.Row(): # Первая группа with gr.Group(): source_image = gr.Image(label="Source Image (Face)", type="filepath") swap_model = gr.Dropdown( choices=["hyperswap_1a_256.onnx", "hyperswap_1b_256.onnx", "hyperswap_1c_256.onnx"], value="hyperswap_1b_256.onnx", label="Swap Model" ) input_index = gr.Dropdown(choices=[0, 1, 2, 3, 4], value=0, label="Target Face Index") input_faces_order_dropdown = gr.Dropdown( choices=[ "left-right", "right-left", "top-bottom", "bottom-top", "large-small", "small-large" ], value="large-small", # значение по умолчанию label="Target Faces Order") # Вторая группа (обратите внимание — она должна быть на том же уровне, что и первая) with gr.Group(): input_video = gr.Video(label="Target Video (Body)") select_every_nth = gr.Dropdown(choices=[1, 2], value=1, label='"1" = choose every frame, "2" - every second frame') loop_count = gr.Dropdown(choices=[0, 1, 2, 3, 4], value=0, label='"Loop_Count" = repeat loop append to your video') pingpong_checkbox = gr.Checkbox(label='"Pingpong" = reverse append to your video', value=False) audio_checkbox = gr.Checkbox(label='"Audio" = enable audio', value=False) # Кнопка генерации generate_btn = gr.Button("Generate") with gr.Column(): # Вывод результата output_video = gr.Video(label="Generated Video") # with gr.Accordion("Notes (click to open)", open=False): # gr.Markdown("Added text here") text = """ ***Note: Hyperswap_1b_256.onnx is the best (in most cases) - but model has inner bug - sometimes they produce "FAIL" swap (working, but do not do any swapping). ***Target_Face_Index: Index_0 = Largest Face. To switch for another target face - switch to Index_1, Index_2, e.t.c. ***Note: If you forget to add "Loop_Count" or "Pingpong" options to your video - after finish you can enable this options and press "Generate" button - which leads to quickly re-save new result video (without re-generating). ***Note: "1" or "2" - if you have video 60fps or 48 fps - you can choose "2" to select every 2nd frame - for two time reduce total number of frames in video (got 30 fps and 24 fps video, accordingly). ***Note: If needed, use AdvancedLivePortrait to correct faces on video before swapping. Here is [workflow](https://openart.ai/workflows/ocelot_vibrant_0/advanced-liveportrait-for-video-as-source/hV07PExjpK3JEd6kNnkr) for ComfyUI. ***Note: Use Avidemux - simple but powerful freeware video editor. [Download](https://www.avidemux.org/nightly/) - choose win64 v2.8.2 for Windows 10 """ gr.Markdown(text) # Связываем клик кнопки с функцией generate_btn.click( fn=generate_image, inputs=[source_image, input_video, input_index, input_faces_order_dropdown, swap_model, pingpong_checkbox, loop_count, select_every_nth, audio_checkbox], outputs=[output_video] ) app.launch(share=True)