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="ezioruan/inswapper_128.onnx", filename="inswapper_128.onnx", local_dir="models/insightface") hf_hub_download(repo_id="martintomov/comfy", filename="facerestore_models/GPEN-BFR-512.onnx", local_dir="models/facerestore_models") # hf_hub_download(repo_id="Gourieff/ReActor", filename="models/facerestore_models/GPEN-BFR-512.onnx", local_dir="models/facerestore_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") 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 init_extra_nodes() import_custom_nodes() from nodes import NODE_CLASS_MAPPINGS @spaces.GPU(duration=20) def generate_image(source_image, target_image, restore_strength, target_index): with torch.inference_mode(): loadimage = NODE_CLASS_MAPPINGS["LoadImage"]() loadimage_1 = loadimage.load_image(image=target_image) loadimage_3 = loadimage.load_image(image=source_image) reactorfaceswap = NODE_CLASS_MAPPINGS["ReActorFaceSwap"]() saveimage = NODE_CLASS_MAPPINGS["SaveImage"]() reactorfaceswap_2 = reactorfaceswap.execute( enabled=True, swap_model="inswapper_128.onnx", facedetection="retinaface_resnet50", face_restore_model="GPEN-BFR-512.onnx", face_restore_visibility=restore_strength, codeformer_weight=0.5, detect_gender_input="no", detect_gender_source="no", input_faces_index=str(target_index), # Преобразуем в строку source_faces_index="0", console_log_level=1, input_image=get_value_at_index(loadimage_1, 0), source_image=get_value_at_index(loadimage_3, 0), ) saveimage_4 = saveimage.save_images( filename_prefix="ComfyUI", images=get_value_at_index(reactorfaceswap_2, 0), ) saved_path = f"output/{saveimage_4['ui']['images'][0]['filename']}" return saved_path if __name__ == "__main__": with gr.Blocks() as app: # Add a title gr.Markdown("# ComfyUI Reactor Fast Face Swap") gr.Markdown("ComfyUI Reactor Fast Face Swap 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(): # Add an input # prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...") # Add a `Row` to include the groups side by side with gr.Row(): # First group includes structure image and depth strength with gr.Group(): source_image = gr.Image(label="Source Image", type="filepath") # depth_strength = gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength") # Second group includes style image and style strength with gr.Group(): target_image = gr.Image(label="Target Image", type="filepath") restore_strength = gr.Slider(minimum=0, maximum=1, step=0.05, value=0.7, label="Face Restore Strength") target_index = gr.Dropdown(choices=[0, 1, 2, 3, 4], value=0, label="Target Face Index") gr.Markdown("Index_0 = Largest Face. To switch for another target face - switch to Index_1, e.t.c") # The generate button generate_btn = gr.Button("Generate") with gr.Column(): # The output image output_image = gr.Image(label="Generated Image") # When clicking the button, it will trigger the `generate_image` function, with the respective inputs # and the output an image generate_btn.click( fn=generate_image, inputs=[source_image, target_image, restore_strength, target_index], outputs=[output_image] ) app.launch(share=True)