app.py

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)