app.py

import os
import random
import sys
from typing import Sequence, Mapping, Any, Union
import torch
import gradio as gr
import spaces
from comfy import model_management
import subprocess

from huggingface_hub import hf_hub_download
from huggingface_hub import snapshot_download
#hf_hub_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", filename="flux1-redux-dev.safetensors", local_dir="models/style_models")

#https://huggingface.co/autismanon/modeldump/blob/main/dreamshaper_8.safetensors
print("dreamshaper_8.safetensors")
hf_hub_download(repo_id="autismanon/modeldump", filename="dreamshaper_8.safetensors", local_dir="models/checkpoints/SD1.5/")

#https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_openpose.pth
print("control_v11p_sd15_openpose.pth")
hf_hub_download(repo_id="lllyasviel/ControlNet-v1-1", filename="control_v11p_sd15_openpose.pth", local_dir="models/controlnet/SD1.5/")

#https://huggingface.co/microsoft/Florence-2-base
print("microsoft/Florence-2-base")
snapshot_download(repo_id="microsoft/Florence-2-base", local_dir="models/LLM/Florence-2-base/")

#https://huggingface.co/ahtoshkaa/Dreamshaper/blob/d4415d1a2644f08ab34bd7adabfbbb70571a782a/dreamshaper_8Inpainting.safetensors
print("dreamshaper_8Inpainting.safetensors")
hf_hub_download(repo_id="ahtoshkaa/Dreamshaper", filename="dreamshaper_8Inpainting.safetensors", local_dir="models/checkpoints/SD1.5/")

#https://huggingface.co/naonovn/Lora/blob/main/add_detail.safetensors
print("add_detail.safetensors")
hf_hub_download(repo_id="naonovn/Lora", filename="add_detail.safetensors", local_dir="models/loras/")

#https://huggingface.co/Dreamspire/BaldifierW2/blob/main/BaldifierW2.safetensors
print("BaldifierW2.safetensors")
hf_hub_download(repo_id="Dreamspire/BaldifierW2", filename="BaldifierW2.safetensors", local_dir="models/loras/")

#https://huggingface.co/lokCX/4x-Ultrasharp/blob/main/4x-UltraSharp.pth
print("UltraSharp.pth")
hf_hub_download(repo_id="lokCX/4x-Ultrasharp", filename="4x-UltraSharp.pth", local_dir="models/upscale_models/")

#./clip_vision/CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors
#https://huggingface.co/h94/IP-Adapter/blob/main/models/image_encoder/model.safetensors
print("CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/image_encoder/model.safetensors", local_dir="models/clip_vision/")
# rename
try:
    source_file = "models/clip_vision/models/image_encoder/model.safetensors"
    destination_file = "models/clip_vision/CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors"

    result = subprocess.run(["mv", source_file, destination_file], check=True, capture_output=True, text=True)

    # check=True raises a CalledProcessError if the command fails (returns a non-zero exit code)
    # capture_output=True captures stdout and stderr.  text=True decodes to string
    print(f"Command executed successfully. Return code: {result.returncode}")
    print(f"Standard output: {result.stdout}")
    print(f"Standard error: {result.stderr}")

except subprocess.CalledProcessError as e:
    print(f"Command failed with error code: {e.returncode}")
    print(f"Standard output: {e.stdout}")
    print(f"Standard error: {e.stderr}")
except FileNotFoundError:
    print("Error: The 'mv' command was not found in your system's PATH.")

print("CLIP-ViT-H-14-laion2B-s32B-b79K.safetensors done")


#./ipadapter/ip-adapter_sd15_light_v11.bin
#./ipadapter/ip-adapter_sd15.safetensors
#./ipadapter/ip-adapter-plus-face_sd15.safetensors
#https://huggingface.co/h94/IP-Adapter/blob/main/models/
print("ipadapter")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/ip-adapter_sd15.safetensors", local_dir="models/ipadapter/")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/ip-adapter_sd15_light_v11.bin", local_dir="models/ipadapter/")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/ip-adapter-plus_sd15.safetensors", local_dir="models/ipadapter/")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/ip-adapter-plus-face_sd15.safetensors", local_dir="models/ipadapter/")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/ip-adapter-full-face_sd15.safetensors", local_dir="models/ipadapter/")
hf_hub_download(repo_id="h94/IP-Adapter", filename="models/ip-adapter_sd15_vit-G.safetensors", local_dir="models/ipadapter/")
# rename
try:
    source_file = "models/ipadapter/models/*"
    destination_file = "models/ipadapter/"

    result = subprocess.run(["mv", source_file, destination_file], check=True, capture_output=True, text=True)

    # check=True raises a CalledProcessError if the command fails (returns a non-zero exit code)
    # capture_output=True captures stdout and stderr.  text=True decodes to string
    print(f"Command executed successfully. Return code: {result.returncode}")
    print(f"Standard output: {result.stdout}")
    print(f"Standard error: {result.stderr}")

except subprocess.CalledProcessError as e:
    print(f"Command failed with error code: {e.returncode}")
    print(f"Standard output: {e.stdout}")
    print(f"Standard error: {e.stderr}")
except FileNotFoundError:
    print("Error: The 'mv' command was not found in your system's PATH.")
print("ipadapter done")


#download auto when startup
#./insightface/models/buffalo_l/w600k_r50.onnx
#./insightface/models/buffalo_l/det_10g.onnx
#./insightface/models/buffalo_l/2d106det.onnx
#./insightface/models/buffalo_l/1k3d68.onnx
#./insightface/models/buffalo_l/genderage.onnx

#./annotator/yzd-v/DWPose/yolox_l.onnx

print("UltraSharp.pth")

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()


# MODIFIED FUNCTION - THE CORE FIX IS HERE
def import_custom_nodes() -> None:
    """
    This function now correctly mimics the necessary parts of ComfyUI's startup
    to ensure all paths and nodes are initialized.
    """
    import asyncio
    import execution
    from nodes import init_extra_nodes
    import server

    # 2. Initialize the server and queue (needed as a dependency for some nodes)
    # We create a new loop each time, as per the original request to keep logic inside the function.
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    server_instance = server.PromptServer(loop)
    execution.PromptQueue(server_instance)

    # 3. Initialize the custom nodes. This will now work because paths are set.
    init_extra_nodes()
    print("Custom nodes initialized.")


from nodes import NODE_CLASS_MAPPINGS

print("import_custom_nodes()")
import_custom_nodes()
print("import_custom_nodes() done")


if "Florence2ModelLoader" in NODE_CLASS_MAPPINGS:
    print("Manually initializing Florence2ModelLoader.INPUT_TYPES() to populate model paths.")
    florence_class = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]
    florence_class.INPUT_TYPES()
# =========================================================================

florence2modelloader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
florence2run = NODE_CLASS_MAPPINGS["Florence2Run"]()
florence2modelloader_204 = florence2modelloader.loadmodel(
    model="Florence-2-base",
    precision="fp16",
    attention="sdpa",
    convert_to_safetensors=False,
)
print("load done: Florence-2-base")


checkpointloadersimple = NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]()
checkpointloadersimple_50 = checkpointloadersimple.load_checkpoint(
    ckpt_name="SD1.5/dreamshaper_8.safetensors"
)
print("load done: dreamshaper_8.safetensors")

cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()

controlnetloader = NODE_CLASS_MAPPINGS["ControlNetLoader"]()
controlnetloader_73 = controlnetloader.load_controlnet(
    control_net_name="SD1.5/control_v11p_sd15_openpose.pth"
)
print("load done: control_v11p_sd15_openpose.pth")

loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()







checkpointloadersimple_319 = checkpointloadersimple.load_checkpoint(
    ckpt_name="SD1.5/dreamshaper_8Inpainting.safetensors"
)
print("load done: dreamshaper_8Inpainting.safetensors")

loraloader = NODE_CLASS_MAPPINGS["LoraLoader"]()
loraloader_338 = loraloader.load_lora(
    lora_name="add_detail.safetensors",
    strength_model=1,
    strength_clip=1,
    model=get_value_at_index(checkpointloadersimple_319, 0),
    clip=get_value_at_index(checkpointloadersimple_319, 1),
)
print("load done: add_detail.safetensors")



loraloader_353 = loraloader.load_lora(
    lora_name="BaldifierW2.safetensors",
    strength_model=2,
    strength_clip=1,
    model=get_value_at_index(loraloader_338, 0),
    clip=get_value_at_index(loraloader_338, 1),
)
print("load done: BaldifierW2.safetensors")

controlnetloader_389 = controlnetloader.load_controlnet(
    control_net_name="SD1.5/control_v11p_sd15_openpose.pth"
)
print("load done: control_v11p_sd15_openpose.pth")

dwpreprocessor = NODE_CLASS_MAPPINGS["DWPreprocessor"]()
controlnetapplyadvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()

layerutility_imagescalebyaspectratio_v2 = NODE_CLASS_MAPPINGS[
    "LayerUtility: ImageScaleByAspectRatio V2"
]()


layermask_personmaskultra_v2 = NODE_CLASS_MAPPINGS[
    "LayerMask: PersonMaskUltra V2"
]()

growmask = NODE_CLASS_MAPPINGS["GrowMask"]()

inpaintmodelconditioning = NODE_CLASS_MAPPINGS["InpaintModelConditioning"]()
ksampler = NODE_CLASS_MAPPINGS["KSampler"]()


vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
vaeencode = NODE_CLASS_MAPPINGS["VAEEncode"]()
faceanalysismodels = NODE_CLASS_MAPPINGS["FaceAnalysisModels"]()
faceanalysismodels_506 = faceanalysismodels.load_models(
    library="insightface", provider="CPU"
)
upscalemodelloader = NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
upscalemodelloader_835 = upscalemodelloader.load_model(
    model_name="4x-UltraSharp.pth"
)
print("load done: UltraSharp.pth")
ipadapterunifiedloader = NODE_CLASS_MAPPINGS["IPAdapterUnifiedLoader"]()
ipadapteradvanced = NODE_CLASS_MAPPINGS["IPAdapterAdvanced"]()
facesegmentation = NODE_CLASS_MAPPINGS["FaceSegmentation"]()
layerutility_imageblend_v2 = NODE_CLASS_MAPPINGS[
    "LayerUtility: ImageBlend V2"
]()
image_comparer_rgthree = NODE_CLASS_MAPPINGS["Image Comparer (rgthree)"]()
saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()
imageupscalewithmodel = NODE_CLASS_MAPPINGS["ImageUpscaleWithModel"]()

print("load done")

model_loaders = [
    checkpointloadersimple_50,
    checkpointloadersimple_319,
    # controlnetloader_73,
    # florence2modelloader_204,
    # loraloader_338,
    # loraloader_353,
    # controlnetloader_389,
    # upscalemodelloader_835
]
print("model_management.load_models_gpu(model_loaders)")
model_management.load_models_gpu([
    loader[0].patcher if hasattr(loader[0], 'patcher') else loader[0] for loader in model_loaders
])
print("model_management.load_models_gpu(model_loaders) done")



@spaces.GPU(duration=60)
def generate_image(model_image, hairstyle_template_image):
    with torch.inference_mode():
        cliptextencode_52 = cliptextencode.encode(
            text="multiple_hands, multiple_legs, multiple_girls\nlow quality, blurry, out of focus, distorted, unrealistic, extra limbs, missing limbs, deformed hands, deformed fingers, extra fingers, long neck, unnatural face, bad anatomy, bad proportions, poorly drawn face, poorly drawn eyes, asymmetrical eyes, extra eyes, extra head, floating objects, watermark, text, logo, jpeg artifacts, overexposed, underexposed, harsh lighting, bad posture, strange angles, unnatural expressions, oversaturated colors, messy hair, unrealistic skin texture, wrinkles inappropriately placed, incorrect shading, pixelation, complex background, busy background, detailed background, crowded scene, clutter, messy elements, unnecessary objects, overlapping objects, intricate patterns, vibrant colors, high contrast, graffiti, shadows, reflections, multiple layers, unrealistic lighting, overexposed areas.",
            clip=get_value_at_index(checkpointloadersimple_50, 1),
        )




        loadimage_144 = loadimage.load_image(image=hairstyle_template_image)



        florence2run_203 = florence2run.encode(
            text_input="",
            task="more_detailed_caption",
            fill_mask=True,
            keep_model_loaded=False,
            max_new_tokens=1024,
            num_beams=3,
            do_sample=True,
            output_mask_select="",
            seed=random.randint(1, 2**64),
            image=get_value_at_index(loadimage_144, 0),
            florence2_model=get_value_at_index(florence2modelloader_204, 0),
        )

        cliptextencode_188 = cliptextencode.encode(
            text=get_value_at_index(florence2run_203, 2),
            clip=get_value_at_index(checkpointloadersimple_50, 1),
        )







        cliptextencode_836 = cliptextencode.encode(
            text=" Bald, no hair, small head, small head, nothing around, no light, no highlights, no sunlight，Smooth forehead，No wrinkles",
            clip=get_value_at_index(loraloader_353, 1),
        )

        cliptextencode_321 = cliptextencode.encode(
            text="wrinkles,Big forehead, big head, big back of the head，multiple_hands, multiple_legs, multiple_girls\nlow quality, blurry, out of focus, distorted, unrealistic, extra limbs, missing limbs, deformed hands, deformed fingers, extra fingers, long neck, unnatural face, bad anatomy, bad proportions, poorly drawn face, poorly drawn eyes, asymmetrical eyes, extra eyes, extra head, floating objects, watermark, text, logo, jpeg artifacts, overexposed, underexposed, harsh lighting, bad posture, strange angles, unnatural expressions, oversaturated colors, messy hair, unrealistic skin texture, wrinkles inappropriately placed, incorrect shading, pixelation, complex background, busy background, detailed background, crowded scene, clutter, messy elements, unnecessary objects, overlapping objects, intricate patterns, vibrant colors, high contrast, graffiti, shadows, reflections, multiple layers, unrealistic lighting, overexposed areas.",
            clip=get_value_at_index(loraloader_353, 1),
        )


        loadimage_317 = loadimage.load_image(image=model_image)


        dwpreprocessor_390 = dwpreprocessor.estimate_pose(
            detect_hand="enable",
            detect_body="enable",
            detect_face="enable",
            resolution=768,
            bbox_detector="yolox_l.onnx",
            pose_estimator="dw-ll_ucoco_384_bs5.torchscript.pt",
            scale_stick_for_xinsr_cn="disable",
            image=get_value_at_index(loadimage_317, 0),
        )


        controlnetapplyadvanced_388 = controlnetapplyadvanced.apply_controlnet(
            strength=1,
            start_percent=0,
            end_percent=1,
            positive=get_value_at_index(cliptextencode_836, 0),
            negative=get_value_at_index(cliptextencode_321, 0),
            control_net=get_value_at_index(controlnetloader_389, 0),
            image=get_value_at_index(dwpreprocessor_390, 0),
            vae=get_value_at_index(checkpointloadersimple_319, 2),
        )


        layerutility_imagescalebyaspectratio_v2_331 = (
            layerutility_imagescalebyaspectratio_v2.image_scale_by_aspect_ratio(
                aspect_ratio="original",
                proportional_width=1,
                proportional_height=1,
                fit="letterbox",
                method="lanczos",
                round_to_multiple="8",
                scale_to_side="longest",
                scale_to_length=768,
                background_color="#000000",
                image=get_value_at_index(loadimage_317, 0),
                mask=get_value_at_index(loadimage_317, 1),
            )
        )


        layermask_personmaskultra_v2_327 = (
            layermask_personmaskultra_v2.person_mask_ultra_v2(
                face=False,
                hair=True,
                body=False,
                clothes=False,
                accessories=False,
                background=False,
                confidence=0.4,
                detail_method="VITMatte",
                detail_erode=6,
                detail_dilate=6,
                black_point=0.01,
                white_point=0.99,
                process_detail=True,
                device="cuda",
                max_megapixels=2,
                images=get_value_at_index(
                    layerutility_imagescalebyaspectratio_v2_331, 0
                ),
            )
        )


        growmask_502 = growmask.expand_mask(
            expand=20,
            tapered_corners=True,
            mask=get_value_at_index(layermask_personmaskultra_v2_327, 1),
        )


        inpaintmodelconditioning_330 = inpaintmodelconditioning.encode(
            noise_mask=True,
            positive=get_value_at_index(controlnetapplyadvanced_388, 0),
            negative=get_value_at_index(controlnetapplyadvanced_388, 1),
            vae=get_value_at_index(checkpointloadersimple_319, 2),
            pixels=get_value_at_index(layerutility_imagescalebyaspectratio_v2_331, 0),
            mask=get_value_at_index(growmask_502, 0),
        )


        ksampler_318 = ksampler.sample(
            seed=random.randint(1, 2**64),
            steps=10,
            cfg=2.5,
            sampler_name="euler_ancestral",
            scheduler="normal",
            denoise=1,
            model=get_value_at_index(loraloader_353, 0),
            positive=get_value_at_index(inpaintmodelconditioning_330, 0),
            negative=get_value_at_index(inpaintmodelconditioning_330, 1),
            latent_image=get_value_at_index(inpaintmodelconditioning_330, 2),
        )


        vaedecode_322 = vaedecode.decode(
            samples=get_value_at_index(ksampler_318, 0),
            vae=get_value_at_index(checkpointloadersimple_319, 2),
        )


        vaeencode_191 = vaeencode.encode(
            pixels=get_value_at_index(vaedecode_322, 0),
            vae=get_value_at_index(checkpointloadersimple_50, 2),
        )




        faceanalysismodels_840 = faceanalysismodels.load_models(
            library="insightface", provider="CUDA"
        )



        # for q in range(1):
        ipadapterunifiedloader_90 = ipadapterunifiedloader.load_models(
            preset="PLUS (high strength)",
            model=get_value_at_index(checkpointloadersimple_50, 0),
        )

        layerutility_imagescalebyaspectratio_v2_187 = (
            layerutility_imagescalebyaspectratio_v2.image_scale_by_aspect_ratio(
                aspect_ratio="original",
                proportional_width=132,
                proportional_height=1,
                fit="letterbox",
                method="lanczos",
                round_to_multiple="8",
                scale_to_side="longest",
                scale_to_length=768,
                background_color="#000000",
                image=get_value_at_index(loadimage_144, 0),
            )
        )

        ipadapteradvanced_85 = ipadapteradvanced.apply_ipadapter(
            weight=1,
            weight_type="strong style transfer",
            combine_embeds="concat",
            start_at=0,
            end_at=1,
            embeds_scaling="V only",
            model=get_value_at_index(ipadapterunifiedloader_90, 0),
            ipadapter=get_value_at_index(ipadapterunifiedloader_90, 1),
            image=get_value_at_index(
                layerutility_imagescalebyaspectratio_v2_187, 0
            ),
        )

        dwpreprocessor_72 = dwpreprocessor.estimate_pose(
            detect_hand="enable",
            detect_body="enable",
            detect_face="enable",
            resolution=1024,
            bbox_detector="yolox_l.onnx",
            pose_estimator="dw-ll_ucoco_384_bs5.torchscript.pt",
            scale_stick_for_xinsr_cn="disable",
            image=get_value_at_index(vaedecode_322, 0),
        )

        controlnetapplyadvanced_189 = controlnetapplyadvanced.apply_controlnet(
            strength=1,
            start_percent=0,
            end_percent=1,
            positive=get_value_at_index(cliptextencode_188, 0),
            negative=get_value_at_index(cliptextencode_52, 0),
            control_net=get_value_at_index(controlnetloader_73, 0),
            image=get_value_at_index(dwpreprocessor_72, 0),
            vae=get_value_at_index(checkpointloadersimple_50, 2),
        )

        ksampler_45 = ksampler.sample(
            seed=random.randint(1, 2**64),
            steps=15,
            cfg=1,
            sampler_name="dpmpp_2m",
            scheduler="karras",
            denoise=1,
            model=get_value_at_index(ipadapteradvanced_85, 0),
            positive=get_value_at_index(controlnetapplyadvanced_189, 0),
            negative=get_value_at_index(controlnetapplyadvanced_189, 1),
            latent_image=get_value_at_index(vaeencode_191, 0),
        )

        vaedecode_67 = vaedecode.decode(
            samples=get_value_at_index(ksampler_45, 0),
            vae=get_value_at_index(checkpointloadersimple_50, 2),
        )

        layermask_personmaskultra_v2_192 = (
            layermask_personmaskultra_v2.person_mask_ultra_v2(
                face=False,
                hair=True,
                body=False,
                clothes=False,
                accessories=False,
                background=False,
                confidence=0.4,
                detail_method="VITMatte",
                detail_erode=6,
                detail_dilate=6,
                black_point=0.01,
                white_point=0.99,
                process_detail=True,
                device="cuda",
                max_megapixels=2,
                images=get_value_at_index(vaedecode_67, 0),
            )
        )

        facesegmentation_505 = facesegmentation.segment(
            area="face+forehead (if available)",
            grow=-5,
            grow_tapered=False,
            blur=41,
            analysis_models=get_value_at_index(faceanalysismodels_506, 0),
            image=get_value_at_index(
                layerutility_imagescalebyaspectratio_v2_331, 0
            ),
        )

        growmask_396 = growmask.expand_mask(
            expand=0,
            tapered_corners=True,
            mask=get_value_at_index(facesegmentation_505, 0),
        )

        layerutility_imageblend_v2_399 = layerutility_imageblend_v2.image_blend_v2(
            invert_mask=True,
            blend_mode="normal",
            opacity=100,
            background_image=get_value_at_index(
                layerutility_imagescalebyaspectratio_v2_331, 0
            ),
            layer_image=get_value_at_index(vaedecode_322, 0),
            layer_mask=get_value_at_index(growmask_396, 0),
        )

        layerutility_imageblend_v2_314 = layerutility_imageblend_v2.image_blend_v2(
            invert_mask=True,
            blend_mode="normal",
            opacity=100,
            background_image=get_value_at_index(layerutility_imageblend_v2_399, 0),
            layer_image=get_value_at_index(layermask_personmaskultra_v2_192, 0),
        )

        image_comparer_rgthree_486 = image_comparer_rgthree.compare_images(
            image_a=get_value_at_index(layerutility_imageblend_v2_314, 0),
            image_b=get_value_at_index(
                layerutility_imagescalebyaspectratio_v2_331, 0
            ),
        )

        saveimage_680 = saveimage.save_images(
            filename_prefix="ComfyUI",
            images=get_value_at_index(layerutility_imageblend_v2_314, 0),
        )

        saved_path = f"output/{saveimage_680['ui']['images'][0]['filename']}"


        facesegmentation_838 = facesegmentation.segment(
            area="face+forehead (if available)",
            grow=0,
            grow_tapered=False,
            blur=13,
            analysis_models=get_value_at_index(faceanalysismodels_840, 0),
            image=get_value_at_index(layerutility_imageblend_v2_399, 0),
        )

        growmask_839 = growmask.expand_mask(
            expand=0,
            tapered_corners=True,
            mask=get_value_at_index(facesegmentation_838, 0),
        )

        layerutility_imageblend_v2_686 = layerutility_imageblend_v2.image_blend_v2(
            invert_mask=False,
            blend_mode="normal",
            opacity=100,
            background_image=get_value_at_index(layerutility_imageblend_v2_314, 0),
            layer_image=get_value_at_index(layerutility_imageblend_v2_399, 0),
            layer_mask=get_value_at_index(growmask_839, 0),
        )

        image_comparer_rgthree_820 = image_comparer_rgthree.compare_images(
            image_a=get_value_at_index(layerutility_imageblend_v2_399, 0),
            image_b=get_value_at_index(
                layerutility_imagescalebyaspectratio_v2_331, 0
            ),
        )

        imageupscalewithmodel_831 = imageupscalewithmodel.upscale(
            upscale_model=get_value_at_index(upscalemodelloader_835, 0),
            image=get_value_at_index(layerutility_imageblend_v2_686, 0),
        )

        return saved_path


if __name__ == "__main__":
    # main()

    with gr.Blocks() as app:
        gr.Markdown("# Swap Hairstyle")

        with gr.Row():
            # 添加输入
            with gr.Column():
                with gr.Row():
                    # 第一组包括结构图像和深度强度
                    with gr.Group():
                        model_image = gr.Image(label="Model Image", type="filepath")
                    # 第二组包括风格图像和风格强度
                    with gr.Group():
                        hairstyle_template_image = gr.Image(label="Hairstyle Template Image", type="filepath")
                
            with gr.Column():
                # 输出图像
                output_image = gr.Image(label="Generated Image")
        
        with gr.Row():
            generate_btn = gr.Button("Generate")

        generate_btn.click(
            fn=generate_image,
            inputs=[model_image, hairstyle_template_image],
            outputs=[output_image]
        )
        
        app.launch(share=True)