Update workflow.py

workflow.py

@@ -1,686 +1,686 @@
-import os
-import random
-import sys
-from typing import Sequence, Mapping, Any, Union
-import torch
-import time
-from PIL import Image
-import numpy as np
-
-
-def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
-    """Returns the value at the given index of a sequence or 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 to find the given name."""
-    if path is None:
-        path = os.getcwd()
-    if name in os.listdir(path):
-        path_name = os.path.join(path, name)
-        print(f"{name} found: {path_name}")
-        return path_name
-    parent_directory = os.path.dirname(path)
-    if parent_directory == path:
-        return None
-    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 and initialize them"""
-    import asyncio
-    import execution
-    from nodes import init_extra_nodes
-    import server
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    server_instance = server.PromptServer(loop)
-    execution.PromptQueue(server_instance)
-    init_extra_nodes()
-
-
-from nodes import NODE_CLASS_MAPPINGS
-
-
-class FitCheckWorkflow:
-    def __init__(self):
-        import_custom_nodes()
-        with torch.inference_mode():
-            # Initialize all node classes
-            self.loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
-            self.comfyuivtonmaskloader = NODE_CLASS_MAPPINGS["ComfyUIVtonMaskLoader"]()
-            self.emptyimage = NODE_CLASS_MAPPINGS["EmptyImage"]()
-            self.rmbg = NODE_CLASS_MAPPINGS["RMBG"]()
-            self.layerutility_imageremovealpha = NODE_CLASS_MAPPINGS["LayerUtility: ImageRemoveAlpha"]()
-            self.inpaintcropimproved = NODE_CLASS_MAPPINGS["InpaintCropImproved"]()
-            self.geminiflash = NODE_CLASS_MAPPINGS["GeminiFlash"]()
-            self.stringfunctionpysssss = NODE_CLASS_MAPPINGS["StringFunction|pysssss"]()
-            self.cr_text_replace = NODE_CLASS_MAPPINGS["CR Text Replace"]()
-            self.dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()
-            self.cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
-            self.vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
-            self.unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
-            self.stylemodelloader = NODE_CLASS_MAPPINGS["StyleModelLoader"]()
-            self.clipvisionloader = NODE_CLASS_MAPPINGS["CLIPVisionLoader"]()
-            self.clipvisionencode = NODE_CLASS_MAPPINGS["CLIPVisionEncode"]()
-            self.loraloadermodelonly = NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]()
-            self.fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
-            self.stylemodelapply = NODE_CLASS_MAPPINGS["StyleModelApply"]()
-            self.conditioningzeroout = NODE_CLASS_MAPPINGS["ConditioningZeroOut"]()
-            self.controlnetloader = NODE_CLASS_MAPPINGS["ControlNetLoader"]()
-            self.setunioncontrolnettype = NODE_CLASS_MAPPINGS["SetUnionControlNetType"]()
-            self.upscalemodelloader = NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
-            self.imageupscalewithmodel = NODE_CLASS_MAPPINGS["ImageUpscaleWithModel"]()
-            self.imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
-            self.comfyuivtonmaskgenerator = NODE_CLASS_MAPPINGS["ComfyUIVtonMaskGenerator"]()
-            self.imagetomask = NODE_CLASS_MAPPINGS["ImageToMask"]()
-            self.layermask_maskgrow = NODE_CLASS_MAPPINGS["LayerMask: MaskGrow"]()
-            self.loadimagemask = NODE_CLASS_MAPPINGS["LoadImageMask"]()
-            self.mask_fill_holes = NODE_CLASS_MAPPINGS["Mask Fill Holes"]()
-            self.resizemask = NODE_CLASS_MAPPINGS["ResizeMask"]()
-            self.imageconcanate = NODE_CLASS_MAPPINGS["ImageConcanate"]()
-            self.getimagesize = NODE_CLASS_MAPPINGS["GetImageSize+"]()
-            self.pixelperfectresolution = NODE_CLASS_MAPPINGS["PixelPerfectResolution"]()
-            self.aio_preprocessor = NODE_CLASS_MAPPINGS["AIO_Preprocessor"]()
-            self.layerutility_purgevram_v2 = NODE_CLASS_MAPPINGS["LayerUtility: PurgeVRAM V2"]()
-            self.controlnetapplyadvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()
-            self.getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
-            self.sammodelloader_segment_anything = NODE_CLASS_MAPPINGS["SAMModelLoader (segment anything)"]()
-            self.groundingdinomodelloader_segment_anything = NODE_CLASS_MAPPINGS["GroundingDinoModelLoader (segment anything)"]()
-            self.groundingdinosamsegment_segment_anything = NODE_CLASS_MAPPINGS["GroundingDinoSAMSegment (segment anything)"]()
-            self.maskcomposite = NODE_CLASS_MAPPINGS["MaskComposite"]()
-            self.apersonmaskgenerator = NODE_CLASS_MAPPINGS["APersonMaskGenerator"]()
-            self.masktoimage = NODE_CLASS_MAPPINGS["MaskToImage"]()
-            self.inpaintmodelconditioning = NODE_CLASS_MAPPINGS["InpaintModelConditioning"]()
-            self.differentialdiffusion = NODE_CLASS_MAPPINGS["DifferentialDiffusion"]()
-            self.ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
-            self.vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
-            self.imagecrop = NODE_CLASS_MAPPINGS["ImageCrop+"]()
-            self.inpaintstitchimproved = NODE_CLASS_MAPPINGS["InpaintStitchImproved"]()
-            self.showtextpysssss = NODE_CLASS_MAPPINGS["ShowText|pysssss"]()
-            
-            # Initialize commonly used nodes
-            self.comfyuivtonmaskloader_983 = self.comfyuivtonmaskloader.load_mask_model(device="cpu")
-            self.emptyimage_1015 = self.emptyimage.generate(width=768, height=1024, batch_size=1, color=0)
-            self.dualcliploader_1024 = self.dualcliploader.load_clip(
-                clip_name1="clip_l.safetensors",
-                clip_name2="t5xxl_fp8_e4m3fn.safetensors",
-                type="flux",
-                device="default",
-            )
-            self.vaeloader_1023 = self.vaeloader.load_vae(vae_name="ae.safetensors")
-            self.unetloader_1025 = self.unetloader.load_unet(
-                unet_name="flux1-fill-dev.safetensors", weight_dtype="fp8_e4m3fn"
-            )
-            self.stylemodelloader_1026 = self.stylemodelloader.load_style_model(
-                style_model_name="flux1-redux-dev.safetensors"
-            )
-            self.clipvisionloader_1151 = self.clipvisionloader.load_clip(
-                clip_name="sigclip_vision_patch14_384.safetensors"
-            )
-            self.controlnetloader_1042 = self.controlnetloader.load_controlnet(
-                control_net_name="flux-union-pro-v2.safetensors"
-            )
-            self.setunioncontrolnettype_1041 = self.setunioncontrolnettype.set_controlnet_type(
-                type="depth", control_net=get_value_at_index(self.controlnetloader_1042, 0)
-            )
-            self.upscalemodelloader_1155 = self.upscalemodelloader.load_model(
-                model_name="RealESRGAN_x2.pth"
-            )
-            # self.upscalemodelloader_1189 = self.upscalemodelloader.load_model(
-            #     model_name="Phips/1xDeNoise_realplksr_otf.safetensors"
-            # )
-            self.comfyuivtonmaskloader_1173 = self.comfyuivtonmaskloader.load_mask_model(device="cpu")
-            self.sammodelloader_segment_anything_1167 = self.sammodelloader_segment_anything.main(
-                model_name="sam_vit_h (2.56GB)"
-            )
-            self.groundingdinomodelloader_segment_anything_1168 = self.groundingdinomodelloader_segment_anything.main(
-                model_name="GroundingDINO_SwinT_OGC (694MB)"
-            )
-
-    @torch.inference_mode()
-    def __call__(self, *args, **kwargs):
-        start = time.time()
-        
-        # Extract parameters from kwargs with defaults
-        api_key = kwargs.get("api_key", "AIzaSyA2XScgkb65IaskjGK6EkUb7HKGjl9cKNw")
-        swap_type = kwargs.get("swap_type", "Dresses")
-        mode = kwargs.get("mode", "balanced")
-        seed = kwargs.get("seed", random.randint(1, 2**64))
-        
-        # Validate parameters
-        valid_swap_types = ["Upper-body", "Lower-body", "Dresses", "Manual"]
-        valid_modes = ["speed", "balanced", "quality"]
-        
-        if swap_type not in valid_swap_types:
-            raise ValueError(f"swap_type must be one of {valid_swap_types}")
-        if mode not in valid_modes:
-            raise ValueError(f"mode must be one of {valid_modes}")
-            
-        print(f"Running FitCheck with swap_type: {swap_type}, mode: {mode}")
-        
-        # Load images
-        loadimage_904 = self.loadimage.load_image(image="model_img.png")
-        loadimage_909 = self.loadimage.load_image(image="cloth_img.png")
-
-        # RMBG processing
-        rmbg_1160 = self.rmbg.process_image(
-            model="RMBG-2.0",
-            sensitivity=1,
-            process_res=1024,
-            mask_blur=0,
-            mask_offset=0,
-            invert_output=False,
-            refine_foreground=True,
-            background="Alpha",
-            background_color="#000000",
-            image=get_value_at_index(loadimage_909, 0),
-        )
-
-        layerutility_imageremovealpha_1158 = self.layerutility_imageremovealpha.image_remove_alpha(
-            fill_background=True,
-            background_color="#000000",
-            RGBA_image=get_value_at_index(loadimage_909, 0),
-            mask=get_value_at_index(rmbg_1160, 1),
-        )
-
-        inpaintcropimproved_1003 = self.inpaintcropimproved.inpaint_crop(
-            downscale_algorithm="bilinear",
-            upscale_algorithm="bicubic",
-            preresize=False,
-            preresize_mode="ensure minimum resolution",
-            preresize_min_width=1024,
-            preresize_min_height=1024,
-            preresize_max_width=16384,
-            preresize_max_height=16384,
-            mask_fill_holes=True,
-            mask_expand_pixels=0,
-            mask_invert=False,
-            mask_blend_pixels=0,
-            mask_hipass_filter=0.1,
-            extend_for_outpainting=False,
-            extend_up_factor=1,
-            extend_down_factor=1,
-            extend_left_factor=1,
-            extend_right_factor=1,
-            context_from_mask_extend_factor=1.1500000000000001,
-            output_resize_to_target_size=True,
-            output_target_width=768,
-            output_target_height=1024,
-            output_padding="0",
-            image=get_value_at_index(layerutility_imageremovealpha_1158, 0),
-            mask=get_value_at_index(rmbg_1160, 1),
-        )
-
-        # Gemini processing with configurable API key
-        geminiflash_1120 = self.geminiflash.generate_content(
-            prompt="What kind of outfit is this,models size like slim,plus size etc,and describe it clearly in short, return to the point combined prompt in plain text",
-            input_type="image",
-            model_version="gemini-2.0-flash",
-            operation_mode="analysis",
-            chat_mode=False,
-            clear_history=True,
-            Additional_Context="",
-            api_key=api_key,
-            max_output_tokens=8192,
-            temperature=0.4,
-            structured_output=False,
-            max_images=6,
-            batch_count=1,
-            seed=random.randint(1, 2**64),
-            images=get_value_at_index(inpaintcropimproved_1003, 1),
-        )
-
-        stringfunctionpysssss_1110 = self.stringfunctionpysssss.exec(
-            action="append",
-            tidy_tags="no",
-            text_a="The fashion model wearing the [outfit]\n",
-            text_b="The 2 shirts on both sides are exactly the same, same color, same logo, same text, same features",
-            text_c="",
-        )
-
-        cr_text_replace_1119 = self.cr_text_replace.replace_text(
-            find1="[outfit]",
-            replace1=get_value_at_index(geminiflash_1120, 0),
-            find2="",
-            replace2="",
-            find3="",
-            replace3="",
-            text=get_value_at_index(stringfunctionpysssss_1110, 0),
-        )
-        
-        print("\n=================\n\n\n")
-        print("Generated prompt:\n", get_value_at_index(cr_text_replace_1119, 0))
-        print("\n\n\n=================\n")
-
-        cliptextencode_1022 = self.cliptextencode.encode(
-            text=get_value_at_index(cr_text_replace_1119, 0),
-            clip=get_value_at_index(self.dualcliploader_1024, 0),
-        )
-
-        clipvisionencode_1027 = self.clipvisionencode.encode(
-            crop="none",
-            clip_vision=get_value_at_index(self.clipvisionloader_1151, 0),
-            image=get_value_at_index(inpaintcropimproved_1003, 1),
-        )
-
-        # Always load cat-vton LoRA first
-        loraloadermodelonly_1032 = self.loraloadermodelonly.load_lora_model_only(
-            lora_name="cat-vton.safetensors",
-            strength_model=1,
-            model=get_value_at_index(self.unetloader_1025, 0),
-        )
-
-        # Mode-based LoRA loading and configuration
-        if mode == "speed":
-            loraloadermodelonly_1031 = self.loraloadermodelonly.load_lora_model_only(
-                lora_name="turbo.safetensors",
-                strength_model=1.0,
-                model=get_value_at_index(loraloadermodelonly_1032, 0),
-            )
-            current_model = get_value_at_index(loraloadermodelonly_1031, 0)
-            steps = 11
-        elif mode == "balanced":
-            loraloadermodelonly_1031 = self.loraloadermodelonly.load_lora_model_only(
-                lora_name="turbo.safetensors",
-                strength_model=0.5,
-                model=get_value_at_index(loraloadermodelonly_1032, 0),
-            )
-            current_model = get_value_at_index(loraloadermodelonly_1031, 0)
-            steps = 17
-        else:  # quality
-            current_model = get_value_at_index(loraloadermodelonly_1032, 0)
-            steps = 34
-
-        fluxguidance_1020 = self.fluxguidance.append(
-            guidance=50, conditioning=get_value_at_index(cliptextencode_1022, 0)
-        )
-
-        stylemodelapply_1019 = self.stylemodelapply.apply_stylemodel(
-            strength=1,
-            strength_type="multiply",
-            conditioning=get_value_at_index(fluxguidance_1020, 0),
-            style_model=get_value_at_index(self.stylemodelloader_1026, 0),
-            clip_vision_output=get_value_at_index(clipvisionencode_1027, 0),
-        )
-
-        conditioningzeroout_1021 = self.conditioningzeroout.zero_out(
-            conditioning=get_value_at_index(fluxguidance_1020, 0)
-        )
-
-        imageupscalewithmodel_1156 = self.imageupscalewithmodel.upscale(
-            upscale_model=get_value_at_index(self.upscalemodelloader_1155, 0),
-            image=get_value_at_index(loadimage_904, 0),
-        )
-
-        imageresize_1058 = self.imageresize.execute(
-            width=1536,
-            height=1536,
-            interpolation="nearest",
-            method="keep proportion",
-            condition="always",
-            multiple_of=0,
-            image=get_value_at_index(imageupscalewithmodel_1156, 0),
-        )
-
-        # Conditional logic based on swap_type
-        if swap_type != "Manual":
-            # Generate masks automatically for Upper-body, Lower-body, Dresses
-            comfyuivtonmaskgenerator_982 = self.comfyuivtonmaskgenerator.generate_mask(
-                category=swap_type,
-                offset_top=0,
-                offset_bottom=0,
-                offset_left=0,
-                offset_right=0,
-                mask_model=get_value_at_index(self.comfyuivtonmaskloader_983, 0),
-                vton_image=get_value_at_index(imageresize_1058, 0),
-            )
-
-            imagetomask_990 = self.imagetomask.image_to_mask(
-                channel="red", image=get_value_at_index(comfyuivtonmaskgenerator_982, 1)
-            )
-
-            layermask_maskgrow_891 = self.layermask_maskgrow.mask_grow(
-                invert_mask=False,
-                grow=0,
-                blur=3,
-                mask=get_value_at_index(imagetomask_990, 0),
-            )
-            
-            # Use automatically generated mask
-            resize_mask_source = get_value_at_index(layermask_maskgrow_891, 0)
-        else:
-            # Manual mode - load user provided mask
-            loadimage_manual_mask = self.loadimage.load_image(image="mask_img.png")
-            
-            # Convert image to mask (same as automatic mode)
-            imagetomask_manual = self.imagetomask.image_to_mask(
-                channel="red", image=get_value_at_index(loadimage_manual_mask, 0)
-            )
-            mask_fill_holes_1147 = self.mask_fill_holes.fill_region(
-                masks=get_value_at_index(imagetomask_manual, 0),
-            )
-            # Use user provided mask
-            resize_mask_source = get_value_at_index(mask_fill_holes_1147, 0)
-
-        resizemask_1059 = self.resizemask.resize(
-            width=get_value_at_index(imageresize_1058, 1),
-            height=get_value_at_index(imageresize_1058, 2),
-            keep_proportions=False,
-            upscale_method="nearest-exact",
-            crop="disabled",
-            mask=resize_mask_source,
-        )
-
-        inpaintcropimproved_999 = self.inpaintcropimproved.inpaint_crop(
-            downscale_algorithm="nearest",
-            upscale_algorithm="nearest",
-            preresize=False,
-            preresize_mode="ensure minimum resolution",
-            preresize_min_width=1024,
-            preresize_min_height=1024,
-            preresize_max_width=16384,
-            preresize_max_height=16384,
-            mask_fill_holes=True,
-            mask_expand_pixels=8,
-            mask_invert=False,
-            mask_blend_pixels=20,
-            mask_hipass_filter=0.1,
-            extend_for_outpainting=False,
-            extend_up_factor=1,
-            extend_down_factor=1,
-            extend_left_factor=1,
-            extend_right_factor=1,
-            context_from_mask_extend_factor=1.0500000000000003,
-            output_resize_to_target_size=True,
-            output_target_width=768,
-            output_target_height=1024,
-            output_padding="64",
-            image=get_value_at_index(imageresize_1058, 0),
-            mask=get_value_at_index(resizemask_1059, 0),
-        )
-
-        imageconcanate_1044 = self.imageconcanate.concatenate(
-            direction="left",
-            match_image_size=True,
-            image1=get_value_at_index(inpaintcropimproved_999, 1),
-            image2=get_value_at_index(self.emptyimage_1015, 0),
-        )
-
-        getimagesize_1047 = self.getimagesize.execute(
-            image=get_value_at_index(imageconcanate_1044, 0)
-        )
-
-        pixelperfectresolution_1049 = self.pixelperfectresolution.execute(
-            image_gen_width=get_value_at_index(getimagesize_1047, 0),
-            image_gen_height=get_value_at_index(getimagesize_1047, 1),
-            resize_mode="Just Resize",
-            original_image=get_value_at_index(imageconcanate_1044, 0),
-        )
-
-        aio_preprocessor_1046 = self.aio_preprocessor.execute(
-            preprocessor="Zoe_DepthAnythingPreprocessor",
-            resolution=get_value_at_index(pixelperfectresolution_1049, 0),
-            image=get_value_at_index(imageconcanate_1044, 0),
-        )
-
-        layerutility_purgevram_v2_1191 = self.layerutility_purgevram_v2.purge_vram_v2(
-            purge_cache=True,
-            purge_models=True,
-            anything=get_value_at_index(aio_preprocessor_1046, 0),
-        )
-
-        controlnetapplyadvanced_1043 = self.controlnetapplyadvanced.apply_controlnet(
-            strength=0.7000000000000002,
-            start_percent=0,
-            end_percent=0.5000000000000001,
-            positive=get_value_at_index(stylemodelapply_1019, 0),
-            negative=get_value_at_index(conditioningzeroout_1021, 0),
-            control_net=get_value_at_index(self.setunioncontrolnettype_1041, 0),
-            image=get_value_at_index(layerutility_purgevram_v2_1191, 0),
-            vae=get_value_at_index(self.vaeloader_1023, 0),
-        )
-
-        imageconcanate_1013 = self.imageconcanate.concatenate(
-            direction="left",
-            match_image_size=True,
-            image1=get_value_at_index(inpaintcropimproved_999, 1),
-            image2=get_value_at_index(inpaintcropimproved_1003, 1),
-        )
-
-        # Second mask generation logic (only if not Manual)
-        if swap_type != "Manual":
-            getimagesizeandcount_1165 = self.getimagesizeandcount.getsize(
-                image=get_value_at_index(inpaintcropimproved_999, 1)
-            )
-
-            comfyuivtonmaskgenerator_1179 = self.comfyuivtonmaskgenerator.generate_mask(
-                category=swap_type,
-                offset_top=0,
-                offset_bottom=0,
-                offset_left=0,
-                offset_right=0,
-                mask_model=get_value_at_index(self.comfyuivtonmaskloader_1173, 0),
-                vton_image=get_value_at_index(getimagesizeandcount_1165, 0),
-            )
-
-            imagetomask_1175 = self.imagetomask.image_to_mask(
-                channel="red", image=get_value_at_index(comfyuivtonmaskgenerator_1179, 1)
-            )
-
-            groundingdinosamsegment_segment_anything_1176 = self.groundingdinosamsegment_segment_anything.main(
-                prompt="hand",
-                threshold=0.28,
-                sam_model=get_value_at_index(self.sammodelloader_segment_anything_1167, 0),
-                grounding_dino_model=get_value_at_index(self.groundingdinomodelloader_segment_anything_1168, 0),
-                image=get_value_at_index(getimagesizeandcount_1165, 0),
-            )
-
-            layerutility_purgevram_v2_1192 = self.layerutility_purgevram_v2.purge_vram_v2(
-                purge_cache=True,
-                purge_models=True,
-                anything=get_value_at_index(groundingdinosamsegment_segment_anything_1176, 1),
-            )
-
-            maskcomposite_1174 = self.maskcomposite.combine(
-                x=0,
-                y=0,
-                operation="subtract",
-                destination=get_value_at_index(imagetomask_1175, 0),
-                source=get_value_at_index(layerutility_purgevram_v2_1192, 0),
-            )
-
-            apersonmaskgenerator_1181 = self.apersonmaskgenerator.generate_mask(
-                face_mask=True,
-                background_mask=False,
-                hair_mask=False,
-                body_mask=False,
-                clothes_mask=False,
-                confidence=0.4,
-                refine_mask=True,
-                images=get_value_at_index(getimagesizeandcount_1165, 0),
-            )
-
-            apersonmaskgenerator_1177 = self.apersonmaskgenerator.generate_mask(
-                face_mask=False,
-                background_mask=False,
-                hair_mask=True,
-                body_mask=False,
-                clothes_mask=False,
-                confidence=0.4,
-                refine_mask=True,
-                images=get_value_at_index(getimagesizeandcount_1165, 0),
-            )
-
-            maskcomposite_1171 = self.maskcomposite.combine(
-                x=0,
-                y=0,
-                operation="add",
-                destination=get_value_at_index(apersonmaskgenerator_1181, 0),
-                source=get_value_at_index(apersonmaskgenerator_1177, 0),
-            )
-
-            maskcomposite_1169 = self.maskcomposite.combine(
-                x=0,
-                y=0,
-                operation="subtract",
-                destination=get_value_at_index(maskcomposite_1174, 0),
-                source=get_value_at_index(maskcomposite_1171, 0),
-            )
-
-            layermask_maskgrow_1178 = self.layermask_maskgrow.mask_grow(
-                invert_mask=False,
-                grow=0,
-                blur=3,
-                mask=get_value_at_index(maskcomposite_1169, 0),
-            )
-            
-            # Use processed mask for automatic modes
-            masktoimage_mask_source = get_value_at_index(layermask_maskgrow_1178, 0)
-        else:
-            # Use cropped mask for Manual mode
-            masktoimage_mask_source = get_value_at_index(inpaintcropimproved_999, 2)
-
-        masktoimage_1017 = self.masktoimage.mask_to_image(
-            mask=masktoimage_mask_source
-        )
-
-        imageconcanate_1016 = self.imageconcanate.concatenate(
-            direction="left",
-            match_image_size=True,
-            image1=get_value_at_index(masktoimage_1017, 0),
-            image2=get_value_at_index(self.emptyimage_1015, 0),
-        )
-
-        imagetomask_1035 = self.imagetomask.image_to_mask(
-            channel="red", image=get_value_at_index(imageconcanate_1016, 0)
-        )
-
-        inpaintmodelconditioning_1033 = self.inpaintmodelconditioning.encode(
-            noise_mask=True,
-            positive=get_value_at_index(controlnetapplyadvanced_1043, 0),
-            negative=get_value_at_index(controlnetapplyadvanced_1043, 1),
-            vae=get_value_at_index(self.vaeloader_1023, 0),
-            pixels=get_value_at_index(imageconcanate_1013, 0),
-            mask=get_value_at_index(imagetomask_1035, 0),
-        )
-
-        differentialdiffusion_1040 = self.differentialdiffusion.apply(
-            model=current_model
-        )
-
-        ksampler_1030 = self.ksampler.sample(
-            seed=seed,
-            steps=steps,
-            cfg=1,
-            sampler_name="euler",
-            scheduler="simple",
-            denoise=1,
-            model=get_value_at_index(differentialdiffusion_1040, 0),
-            positive=get_value_at_index(inpaintmodelconditioning_1033, 0),
-            negative=get_value_at_index(inpaintmodelconditioning_1033, 1),
-            latent_image=get_value_at_index(inpaintmodelconditioning_1033, 2),
-        )
-
-        vaedecode_1036 = self.vaedecode.decode(
-            samples=get_value_at_index(ksampler_1030, 0),
-            vae=get_value_at_index(self.vaeloader_1023, 0),
-        )
-
-        imagecrop_1055 = self.imagecrop.execute(
-            width=768,
-            height=1024,
-            position="top-right",
-            x_offset=0,
-            y_offset=0,
-            image=get_value_at_index(vaedecode_1036, 0),
-        )
-        
-
-        imageupscalewithmodel_1188 = self.imageupscalewithmodel.upscale(
-            upscale_model=get_value_at_index(self.upscalemodelloader_1155, 0),
-            image=get_value_at_index(imagecrop_1055, 0),
-        )
-        layerutility_purgevram_v2_1187 = self.layerutility_purgevram_v2.purge_vram_v2(
-            purge_cache=True,
-            purge_models=True,
-            anything=get_value_at_index(imageupscalewithmodel_1188, 0),
-        )
-
-        inpaintstitchimproved_1054 = self.inpaintstitchimproved.inpaint_stitch(
-            stitcher=get_value_at_index(inpaintcropimproved_999, 0),
-            inpainted_image=get_value_at_index(layerutility_purgevram_v2_1187, 0),
-        )
-
-        showtextpysssss_1111 = self.showtextpysssss.notify(
-            text=get_value_at_index(cr_text_replace_1119, 0),
-            unique_id=16351491204491641391,
-        )
-
-        # layerutility_purgevram_v2_1187 = self.layerutility_purgevram_v2.purge_vram_v2(
-        #     purge_cache=True,
-        #     purge_models=True,
-        #     anything=get_value_at_index(inpaintstitchimproved_1054, 0),
-        # )
-
-        # imageupscalewithmodel_1188 = self.imageupscalewithmodel.upscale(
-        #     upscale_model=get_value_at_index(self.upscalemodelloader_1189, 0),
-        #     image=get_value_at_index(layerutility_purgevram_v2_1187, 0),
-        # )
-
-        # Convert output to image and save
-        imgs = []
-        for res in inpaintstitchimproved_1054[0]:
-            img = Image.fromarray(np.clip(255. * res.detach().cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-            img.save("fitcheck_output.png")
-            imgs.append(img)
-            
-        stop = time.time()
-        print(f"Total time: {stop - start:.2f} seconds")
-        return imgs
-
-    def cleanup(self):
-        """Clean up VRAM and cache after inference"""
-        try:
-            import torch
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-                torch.cuda.synchronize()
-            print("VRAM cleanup completed")
-        except Exception as e:
-            print(f"Cleanup warning: {e}")
-
-
-# Example usage:
-# generator = FitCheckWorkflow()
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+import torch
+import time
+from PIL import Image
+import numpy as np
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or 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 to find the given name."""
+    if path is None:
+        path = os.getcwd()
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+    parent_directory = os.path.dirname(path)
+    if parent_directory == path:
+        return None
+    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 and initialize them"""
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+    init_extra_nodes()
+
+
+from nodes import NODE_CLASS_MAPPINGS
+
+
+class FitCheckWorkflow:
+    def __init__(self):
+        import_custom_nodes()
+        with torch.inference_mode():
+            # Initialize all node classes
+            self.loadimage = NODE_CLASS_MAPPINGS["LoadImage"]()
+            self.comfyuivtonmaskloader = NODE_CLASS_MAPPINGS["ComfyUIVtonMaskLoader"]()
+            self.emptyimage = NODE_CLASS_MAPPINGS["EmptyImage"]()
+            self.rmbg = NODE_CLASS_MAPPINGS["RMBG"]()
+            self.layerutility_imageremovealpha = NODE_CLASS_MAPPINGS["LayerUtility: ImageRemoveAlpha"]()
+            self.inpaintcropimproved = NODE_CLASS_MAPPINGS["InpaintCropImproved"]()
+            self.geminiflash = NODE_CLASS_MAPPINGS["GeminiFlash"]()
+            self.stringfunctionpysssss = NODE_CLASS_MAPPINGS["StringFunction|pysssss"]()
+            self.cr_text_replace = NODE_CLASS_MAPPINGS["CR Text Replace"]()
+            self.dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()
+            self.cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
+            self.vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
+            self.unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
+            self.stylemodelloader = NODE_CLASS_MAPPINGS["StyleModelLoader"]()
+            self.clipvisionloader = NODE_CLASS_MAPPINGS["CLIPVisionLoader"]()
+            self.clipvisionencode = NODE_CLASS_MAPPINGS["CLIPVisionEncode"]()
+            self.loraloadermodelonly = NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]()
+            self.fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
+            self.stylemodelapply = NODE_CLASS_MAPPINGS["StyleModelApply"]()
+            self.conditioningzeroout = NODE_CLASS_MAPPINGS["ConditioningZeroOut"]()
+            self.controlnetloader = NODE_CLASS_MAPPINGS["ControlNetLoader"]()
+            self.setunioncontrolnettype = NODE_CLASS_MAPPINGS["SetUnionControlNetType"]()
+            self.upscalemodelloader = NODE_CLASS_MAPPINGS["UpscaleModelLoader"]()
+            self.imageupscalewithmodel = NODE_CLASS_MAPPINGS["ImageUpscaleWithModel"]()
+            self.imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
+            self.comfyuivtonmaskgenerator = NODE_CLASS_MAPPINGS["ComfyUIVtonMaskGenerator"]()
+            self.imagetomask = NODE_CLASS_MAPPINGS["ImageToMask"]()
+            self.layermask_maskgrow = NODE_CLASS_MAPPINGS["LayerMask: MaskGrow"]()
+            self.loadimagemask = NODE_CLASS_MAPPINGS["LoadImageMask"]()
+            self.mask_fill_holes = NODE_CLASS_MAPPINGS["Mask Fill Holes"]()
+            self.resizemask = NODE_CLASS_MAPPINGS["ResizeMask"]()
+            self.imageconcanate = NODE_CLASS_MAPPINGS["ImageConcanate"]()
+            self.getimagesize = NODE_CLASS_MAPPINGS["GetImageSize+"]()
+            self.pixelperfectresolution = NODE_CLASS_MAPPINGS["PixelPerfectResolution"]()
+            self.aio_preprocessor = NODE_CLASS_MAPPINGS["AIO_Preprocessor"]()
+            self.layerutility_purgevram_v2 = NODE_CLASS_MAPPINGS["LayerUtility: PurgeVRAM V2"]()
+            self.controlnetapplyadvanced = NODE_CLASS_MAPPINGS["ControlNetApplyAdvanced"]()
+            self.getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
+            self.sammodelloader_segment_anything = NODE_CLASS_MAPPINGS["SAMModelLoader (segment anything)"]()
+            self.groundingdinomodelloader_segment_anything = NODE_CLASS_MAPPINGS["GroundingDinoModelLoader (segment anything)"]()
+            self.groundingdinosamsegment_segment_anything = NODE_CLASS_MAPPINGS["GroundingDinoSAMSegment (segment anything)"]()
+            self.maskcomposite = NODE_CLASS_MAPPINGS["MaskComposite"]()
+            self.apersonmaskgenerator = NODE_CLASS_MAPPINGS["APersonMaskGenerator"]()
+            self.masktoimage = NODE_CLASS_MAPPINGS["MaskToImage"]()
+            self.inpaintmodelconditioning = NODE_CLASS_MAPPINGS["InpaintModelConditioning"]()
+            self.differentialdiffusion = NODE_CLASS_MAPPINGS["DifferentialDiffusion"]()
+            self.ksampler = NODE_CLASS_MAPPINGS["KSampler"]()
+            self.vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
+            self.imagecrop = NODE_CLASS_MAPPINGS["ImageCrop+"]()
+            self.inpaintstitchimproved = NODE_CLASS_MAPPINGS["InpaintStitchImproved"]()
+            self.showtextpysssss = NODE_CLASS_MAPPINGS["ShowText|pysssss"]()
+            
+            # Initialize commonly used nodes
+            self.comfyuivtonmaskloader_983 = self.comfyuivtonmaskloader.load_mask_model(device="cpu")
+            self.emptyimage_1015 = self.emptyimage.generate(width=768, height=1024, batch_size=1, color=0)
+            self.dualcliploader_1024 = self.dualcliploader.load_clip(
+                clip_name1="clip_l.safetensors",
+                clip_name2="t5xxl_fp8_e4m3fn.safetensors",
+                type="flux",
+                device="default",
+            )
+            self.vaeloader_1023 = self.vaeloader.load_vae(vae_name="ae.safetensors")
+            self.unetloader_1025 = self.unetloader.load_unet(
+                unet_name="flux1-fill-dev.safetensors", weight_dtype="fp8_e4m3fn"
+            )
+            self.stylemodelloader_1026 = self.stylemodelloader.load_style_model(
+                style_model_name="flux1-redux-dev.safetensors"
+            )
+            self.clipvisionloader_1151 = self.clipvisionloader.load_clip(
+                clip_name="sigclip_vision_patch14_384.safetensors"
+            )
+            self.controlnetloader_1042 = self.controlnetloader.load_controlnet(
+                control_net_name="flux-union-pro-v2.safetensors"
+            )
+            self.setunioncontrolnettype_1041 = self.setunioncontrolnettype.set_controlnet_type(
+                type="depth", control_net=get_value_at_index(self.controlnetloader_1042, 0)
+            )
+            self.upscalemodelloader_1155 = self.upscalemodelloader.load_model(
+                model_name="RealESRGAN_x2.pth"
+            )
+            # self.upscalemodelloader_1189 = self.upscalemodelloader.load_model(
+            #     model_name="Phips/1xDeNoise_realplksr_otf.safetensors"
+            # )
+            self.comfyuivtonmaskloader_1173 = self.comfyuivtonmaskloader.load_mask_model(device="cpu")
+            self.sammodelloader_segment_anything_1167 = self.sammodelloader_segment_anything.main(
+                model_name="sam_vit_h (2.56GB)"
+            )
+            self.groundingdinomodelloader_segment_anything_1168 = self.groundingdinomodelloader_segment_anything.main(
+                model_name="GroundingDINO_SwinT_OGC (694MB)"
+            )
+
+    @torch.inference_mode()
+    def __call__(self, *args, **kwargs):
+        start = time.time()
+        
+        # Extract parameters from kwargs with defaults
+        api_key = kwargs.get("api_key", "AIzaSyA2XScgkb65IaskjGK6EkUb7HKGjl9cKNw")
+        swap_type = kwargs.get("swap_type", "Dresses")
+        mode = kwargs.get("mode", "balanced")
+        seed = kwargs.get("seed", random.randint(1, 2**64))
+        
+        # Validate parameters
+        valid_swap_types = ["Upper-body", "Lower-body", "Dresses", "Manual"]
+        valid_modes = ["speed", "balanced", "quality"]
+        
+        if swap_type not in valid_swap_types:
+            raise ValueError(f"swap_type must be one of {valid_swap_types}")
+        if mode not in valid_modes:
+            raise ValueError(f"mode must be one of {valid_modes}")
+            
+        print(f"Running FitCheck with swap_type: {swap_type}, mode: {mode}")
+        
+        # Load images
+        loadimage_904 = self.loadimage.load_image(image="model_img.png")
+        loadimage_909 = self.loadimage.load_image(image="cloth_img.png")
+
+        # RMBG processing
+        rmbg_1160 = self.rmbg.process_image(
+            model="RMBG-2.0",
+            sensitivity=1,
+            process_res=1024,
+            mask_blur=0,
+            mask_offset=0,
+            invert_output=False,
+            refine_foreground=True,
+            background="Alpha",
+            background_color="#000000",
+            image=get_value_at_index(loadimage_909, 0),
+        )
+
+        layerutility_imageremovealpha_1158 = self.layerutility_imageremovealpha.image_remove_alpha(
+            fill_background=True,
+            background_color="#000000",
+            RGBA_image=get_value_at_index(loadimage_909, 0),
+            mask=get_value_at_index(rmbg_1160, 1),
+        )
+
+        inpaintcropimproved_1003 = self.inpaintcropimproved.inpaint_crop(
+            downscale_algorithm="bilinear",
+            upscale_algorithm="bicubic",
+            preresize=False,
+            preresize_mode="ensure minimum resolution",
+            preresize_min_width=1024,
+            preresize_min_height=1024,
+            preresize_max_width=16384,
+            preresize_max_height=16384,
+            mask_fill_holes=True,
+            mask_expand_pixels=0,
+            mask_invert=False,
+            mask_blend_pixels=0,
+            mask_hipass_filter=0.1,
+            extend_for_outpainting=False,
+            extend_up_factor=1,
+            extend_down_factor=1,
+            extend_left_factor=1,
+            extend_right_factor=1,
+            context_from_mask_extend_factor=1.1500000000000001,
+            output_resize_to_target_size=True,
+            output_target_width=768,
+            output_target_height=1024,
+            output_padding="0",
+            image=get_value_at_index(layerutility_imageremovealpha_1158, 0),
+            mask=get_value_at_index(rmbg_1160, 1),
+        )
+
+        # Gemini processing with configurable API key
+        geminiflash_1120 = self.geminiflash.generate_content(
+            prompt="What kind of outfit is this,models size like slim,plus size etc,and describe it clearly in short, return to the point combined prompt in plain text",
+            input_type="image",
+            model_version="gemini-2.0-flash",
+            operation_mode="analysis",
+            chat_mode=False,
+            clear_history=True,
+            Additional_Context="",
+            api_key=api_key,
+            max_output_tokens=8192,
+            temperature=0.4,
+            structured_output=False,
+            max_images=6,
+            batch_count=1,
+            seed=random.randint(1, 2**64),
+            images=get_value_at_index(inpaintcropimproved_1003, 1),
+        )
+
+        stringfunctionpysssss_1110 = self.stringfunctionpysssss.exec(
+            action="append",
+            tidy_tags="no",
+            text_a="The fashion model wearing the [outfit]\n",
+            text_b="The 2 shirts on both sides are exactly the same, same color, same logo, same text, same features",
+            text_c="",
+        )
+
+        cr_text_replace_1119 = self.cr_text_replace.replace_text(
+            find1="[outfit]",
+            replace1=get_value_at_index(geminiflash_1120, 0),
+            find2="",
+            replace2="",
+            find3="",
+            replace3="",
+            text=get_value_at_index(stringfunctionpysssss_1110, 0),
+        )
+        
+        print("\n=================\n\n\n")
+        print("Generated prompt:\n", get_value_at_index(cr_text_replace_1119, 0))
+        print("\n\n\n=================\n")
+
+        cliptextencode_1022 = self.cliptextencode.encode(
+            text=get_value_at_index(cr_text_replace_1119, 0),
+            clip=get_value_at_index(self.dualcliploader_1024, 0),
+        )
+
+        clipvisionencode_1027 = self.clipvisionencode.encode(
+            crop="none",
+            clip_vision=get_value_at_index(self.clipvisionloader_1151, 0),
+            image=get_value_at_index(inpaintcropimproved_1003, 1),
+        )
+
+        # Always load cat-vton LoRA first
+        loraloadermodelonly_1032 = self.loraloadermodelonly.load_lora_model_only(
+            lora_name="cat-vton.safetensors",
+            strength_model=1,
+            model=get_value_at_index(self.unetloader_1025, 0),
+        )
+
+        # Mode-based LoRA loading and configuration
+        if mode == "speed":
+            loraloadermodelonly_1031 = self.loraloadermodelonly.load_lora_model_only(
+                lora_name="turbo.safetensors",
+                strength_model=1.0,
+                model=get_value_at_index(loraloadermodelonly_1032, 0),
+            )
+            current_model = get_value_at_index(loraloadermodelonly_1031, 0)
+            steps = 11
+        elif mode == "balanced":
+            loraloadermodelonly_1031 = self.loraloadermodelonly.load_lora_model_only(
+                lora_name="turbo.safetensors",
+                strength_model=0.5,
+                model=get_value_at_index(loraloadermodelonly_1032, 0),
+            )
+            current_model = get_value_at_index(loraloadermodelonly_1031, 0)
+            steps = 17
+        else:  # quality
+            current_model = get_value_at_index(loraloadermodelonly_1032, 0)
+            steps = 34
+
+        fluxguidance_1020 = self.fluxguidance.append(
+            guidance=50, conditioning=get_value_at_index(cliptextencode_1022, 0)
+        )
+
+        stylemodelapply_1019 = self.stylemodelapply.apply_stylemodel(
+            strength=1,
+            strength_type="multiply",
+            conditioning=get_value_at_index(fluxguidance_1020, 0),
+            style_model=get_value_at_index(self.stylemodelloader_1026, 0),
+            clip_vision_output=get_value_at_index(clipvisionencode_1027, 0),
+        )
+
+        conditioningzeroout_1021 = self.conditioningzeroout.zero_out(
+            conditioning=get_value_at_index(fluxguidance_1020, 0)
+        )
+
+        imageupscalewithmodel_1156 = self.imageupscalewithmodel.upscale(
+            upscale_model=get_value_at_index(self.upscalemodelloader_1155, 0),
+            image=get_value_at_index(loadimage_904, 0),
+        )
+
+        imageresize_1058 = self.imageresize.execute(
+            width=1536,
+            height=1536,
+            interpolation="nearest",
+            method="keep proportion",
+            condition="always",
+            multiple_of=0,
+            image=get_value_at_index(imageupscalewithmodel_1156, 0),
+        )
+
+        # Conditional logic based on swap_type
+        if swap_type != "Manual":
+            # Generate masks automatically for Upper-body, Lower-body, Dresses
+            comfyuivtonmaskgenerator_982 = self.comfyuivtonmaskgenerator.generate_mask(
+                category=swap_type,
+                offset_top=0,
+                offset_bottom=0,
+                offset_left=0,
+                offset_right=0,
+                mask_model=get_value_at_index(self.comfyuivtonmaskloader_983, 0),
+                vton_image=get_value_at_index(imageresize_1058, 0),
+            )
+
+            imagetomask_990 = self.imagetomask.image_to_mask(
+                channel="red", image=get_value_at_index(comfyuivtonmaskgenerator_982, 1)
+            )
+
+            layermask_maskgrow_891 = self.layermask_maskgrow.mask_grow(
+                invert_mask=False,
+                grow=0,
+                blur=3,
+                mask=get_value_at_index(imagetomask_990, 0),
+            )
+            
+            # Use automatically generated mask
+            resize_mask_source = get_value_at_index(layermask_maskgrow_891, 0)
+        else:
+            # Manual mode - load user provided mask
+            loadimage_manual_mask = self.loadimage.load_image(image="mask_img.png")
+            
+            # Convert image to mask (same as automatic mode)
+            imagetomask_manual = self.imagetomask.image_to_mask(
+                channel="red", image=get_value_at_index(loadimage_manual_mask, 0)
+            )
+            # mask_fill_holes_1147 = self.mask_fill_holes.fill_region(
+            #     masks=get_value_at_index(imagetomask_manual, 0),
+            # )
+            # Use user provided mask
+            resize_mask_source = get_value_at_index(imagetomask_manual, 0)
+
+        resizemask_1059 = self.resizemask.resize(
+            width=get_value_at_index(imageresize_1058, 1),
+            height=get_value_at_index(imageresize_1058, 2),
+            keep_proportions=False,
+            upscale_method="nearest-exact",
+            crop="disabled",
+            mask=resize_mask_source,
+        )
+
+        inpaintcropimproved_999 = self.inpaintcropimproved.inpaint_crop(
+            downscale_algorithm="nearest",
+            upscale_algorithm="nearest",
+            preresize=False,
+            preresize_mode="ensure minimum resolution",
+            preresize_min_width=1024,
+            preresize_min_height=1024,
+            preresize_max_width=16384,
+            preresize_max_height=16384,
+            mask_fill_holes=True,
+            mask_expand_pixels=8,
+            mask_invert=False,
+            mask_blend_pixels=20,
+            mask_hipass_filter=0.1,
+            extend_for_outpainting=False,
+            extend_up_factor=1,
+            extend_down_factor=1,
+            extend_left_factor=1,
+            extend_right_factor=1,
+            context_from_mask_extend_factor=1.0500000000000003,
+            output_resize_to_target_size=True,
+            output_target_width=768,
+            output_target_height=1024,
+            output_padding="64",
+            image=get_value_at_index(imageresize_1058, 0),
+            mask=get_value_at_index(resizemask_1059, 0),
+        )
+
+        imageconcanate_1044 = self.imageconcanate.concatenate(
+            direction="left",
+            match_image_size=True,
+            image1=get_value_at_index(inpaintcropimproved_999, 1),
+            image2=get_value_at_index(self.emptyimage_1015, 0),
+        )
+
+        getimagesize_1047 = self.getimagesize.execute(
+            image=get_value_at_index(imageconcanate_1044, 0)
+        )
+
+        pixelperfectresolution_1049 = self.pixelperfectresolution.execute(
+            image_gen_width=get_value_at_index(getimagesize_1047, 0),
+            image_gen_height=get_value_at_index(getimagesize_1047, 1),
+            resize_mode="Just Resize",
+            original_image=get_value_at_index(imageconcanate_1044, 0),
+        )
+
+        aio_preprocessor_1046 = self.aio_preprocessor.execute(
+            preprocessor="Zoe_DepthAnythingPreprocessor",
+            resolution=get_value_at_index(pixelperfectresolution_1049, 0),
+            image=get_value_at_index(imageconcanate_1044, 0),
+        )
+
+        layerutility_purgevram_v2_1191 = self.layerutility_purgevram_v2.purge_vram_v2(
+            purge_cache=True,
+            purge_models=True,
+            anything=get_value_at_index(aio_preprocessor_1046, 0),
+        )
+
+        controlnetapplyadvanced_1043 = self.controlnetapplyadvanced.apply_controlnet(
+            strength=0.7000000000000002,
+            start_percent=0,
+            end_percent=0.5000000000000001,
+            positive=get_value_at_index(stylemodelapply_1019, 0),
+            negative=get_value_at_index(conditioningzeroout_1021, 0),
+            control_net=get_value_at_index(self.setunioncontrolnettype_1041, 0),
+            image=get_value_at_index(layerutility_purgevram_v2_1191, 0),
+            vae=get_value_at_index(self.vaeloader_1023, 0),
+        )
+
+        imageconcanate_1013 = self.imageconcanate.concatenate(
+            direction="left",
+            match_image_size=True,
+            image1=get_value_at_index(inpaintcropimproved_999, 1),
+            image2=get_value_at_index(inpaintcropimproved_1003, 1),
+        )
+
+        # Second mask generation logic (only if not Manual)
+        if swap_type != "Manual":
+            getimagesizeandcount_1165 = self.getimagesizeandcount.getsize(
+                image=get_value_at_index(inpaintcropimproved_999, 1)
+            )
+
+            comfyuivtonmaskgenerator_1179 = self.comfyuivtonmaskgenerator.generate_mask(
+                category=swap_type,
+                offset_top=0,
+                offset_bottom=0,
+                offset_left=0,
+                offset_right=0,
+                mask_model=get_value_at_index(self.comfyuivtonmaskloader_1173, 0),
+                vton_image=get_value_at_index(getimagesizeandcount_1165, 0),
+            )
+
+            imagetomask_1175 = self.imagetomask.image_to_mask(
+                channel="red", image=get_value_at_index(comfyuivtonmaskgenerator_1179, 1)
+            )
+
+            groundingdinosamsegment_segment_anything_1176 = self.groundingdinosamsegment_segment_anything.main(
+                prompt="hand",
+                threshold=0.28,
+                sam_model=get_value_at_index(self.sammodelloader_segment_anything_1167, 0),
+                grounding_dino_model=get_value_at_index(self.groundingdinomodelloader_segment_anything_1168, 0),
+                image=get_value_at_index(getimagesizeandcount_1165, 0),
+            )
+
+            layerutility_purgevram_v2_1192 = self.layerutility_purgevram_v2.purge_vram_v2(
+                purge_cache=True,
+                purge_models=True,
+                anything=get_value_at_index(groundingdinosamsegment_segment_anything_1176, 1),
+            )
+
+            maskcomposite_1174 = self.maskcomposite.combine(
+                x=0,
+                y=0,
+                operation="subtract",
+                destination=get_value_at_index(imagetomask_1175, 0),
+                source=get_value_at_index(layerutility_purgevram_v2_1192, 0),
+            )
+
+            apersonmaskgenerator_1181 = self.apersonmaskgenerator.generate_mask(
+                face_mask=True,
+                background_mask=False,
+                hair_mask=False,
+                body_mask=False,
+                clothes_mask=False,
+                confidence=0.4,
+                refine_mask=True,
+                images=get_value_at_index(getimagesizeandcount_1165, 0),
+            )
+
+            apersonmaskgenerator_1177 = self.apersonmaskgenerator.generate_mask(
+                face_mask=False,
+                background_mask=False,
+                hair_mask=True,
+                body_mask=False,
+                clothes_mask=False,
+                confidence=0.4,
+                refine_mask=True,
+                images=get_value_at_index(getimagesizeandcount_1165, 0),
+            )
+
+            maskcomposite_1171 = self.maskcomposite.combine(
+                x=0,
+                y=0,
+                operation="add",
+                destination=get_value_at_index(apersonmaskgenerator_1181, 0),
+                source=get_value_at_index(apersonmaskgenerator_1177, 0),
+            )
+
+            maskcomposite_1169 = self.maskcomposite.combine(
+                x=0,
+                y=0,
+                operation="subtract",
+                destination=get_value_at_index(maskcomposite_1174, 0),
+                source=get_value_at_index(maskcomposite_1171, 0),
+            )
+
+            layermask_maskgrow_1178 = self.layermask_maskgrow.mask_grow(
+                invert_mask=False,
+                grow=0,
+                blur=3,
+                mask=get_value_at_index(maskcomposite_1169, 0),
+            )
+            
+            # Use processed mask for automatic modes
+            masktoimage_mask_source = get_value_at_index(layermask_maskgrow_1178, 0)
+        else:
+            # Use cropped mask for Manual mode
+            masktoimage_mask_source = get_value_at_index(inpaintcropimproved_999, 2)
+
+        masktoimage_1017 = self.masktoimage.mask_to_image(
+            mask=masktoimage_mask_source
+        )
+
+        imageconcanate_1016 = self.imageconcanate.concatenate(
+            direction="left",
+            match_image_size=True,
+            image1=get_value_at_index(masktoimage_1017, 0),
+            image2=get_value_at_index(self.emptyimage_1015, 0),
+        )
+
+        imagetomask_1035 = self.imagetomask.image_to_mask(
+            channel="red", image=get_value_at_index(imageconcanate_1016, 0)
+        )
+
+        inpaintmodelconditioning_1033 = self.inpaintmodelconditioning.encode(
+            noise_mask=True,
+            positive=get_value_at_index(controlnetapplyadvanced_1043, 0),
+            negative=get_value_at_index(controlnetapplyadvanced_1043, 1),
+            vae=get_value_at_index(self.vaeloader_1023, 0),
+            pixels=get_value_at_index(imageconcanate_1013, 0),
+            mask=get_value_at_index(imagetomask_1035, 0),
+        )
+
+        differentialdiffusion_1040 = self.differentialdiffusion.apply(
+            model=current_model
+        )
+
+        ksampler_1030 = self.ksampler.sample(
+            seed=seed,
+            steps=steps,
+            cfg=1,
+            sampler_name="euler",
+            scheduler="simple",
+            denoise=1,
+            model=get_value_at_index(differentialdiffusion_1040, 0),
+            positive=get_value_at_index(inpaintmodelconditioning_1033, 0),
+            negative=get_value_at_index(inpaintmodelconditioning_1033, 1),
+            latent_image=get_value_at_index(inpaintmodelconditioning_1033, 2),
+        )
+
+        vaedecode_1036 = self.vaedecode.decode(
+            samples=get_value_at_index(ksampler_1030, 0),
+            vae=get_value_at_index(self.vaeloader_1023, 0),
+        )
+
+        imagecrop_1055 = self.imagecrop.execute(
+            width=768,
+            height=1024,
+            position="top-right",
+            x_offset=0,
+            y_offset=0,
+            image=get_value_at_index(vaedecode_1036, 0),
+        )
+        
+
+        imageupscalewithmodel_1188 = self.imageupscalewithmodel.upscale(
+            upscale_model=get_value_at_index(self.upscalemodelloader_1155, 0),
+            image=get_value_at_index(imagecrop_1055, 0),
+        )
+        layerutility_purgevram_v2_1187 = self.layerutility_purgevram_v2.purge_vram_v2(
+            purge_cache=True,
+            purge_models=True,
+            anything=get_value_at_index(imageupscalewithmodel_1188, 0),
+        )
+
+        inpaintstitchimproved_1054 = self.inpaintstitchimproved.inpaint_stitch(
+            stitcher=get_value_at_index(inpaintcropimproved_999, 0),
+            inpainted_image=get_value_at_index(layerutility_purgevram_v2_1187, 0),
+        )
+
+        showtextpysssss_1111 = self.showtextpysssss.notify(
+            text=get_value_at_index(cr_text_replace_1119, 0),
+            unique_id=16351491204491641391,
+        )
+
+        # layerutility_purgevram_v2_1187 = self.layerutility_purgevram_v2.purge_vram_v2(
+        #     purge_cache=True,
+        #     purge_models=True,
+        #     anything=get_value_at_index(inpaintstitchimproved_1054, 0),
+        # )
+
+        # imageupscalewithmodel_1188 = self.imageupscalewithmodel.upscale(
+        #     upscale_model=get_value_at_index(self.upscalemodelloader_1189, 0),
+        #     image=get_value_at_index(layerutility_purgevram_v2_1187, 0),
+        # )
+
+        # Convert output to image and save
+        imgs = []
+        for res in inpaintstitchimproved_1054[0]:
+            img = Image.fromarray(np.clip(255. * res.detach().cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+            img.save("fitcheck_output.png")
+            imgs.append(img)
+            
+        stop = time.time()
+        print(f"Total time: {stop - start:.2f} seconds")
+        return imgs
+
+    def cleanup(self):
+        """Clean up VRAM and cache after inference"""
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+                torch.cuda.synchronize()
+            print("VRAM cleanup completed")
+        except Exception as e:
+            print(f"Cleanup warning: {e}")
+
+
+# Example usage:
+# generator = FitCheckWorkflow()
 # imgs = generator(api_key="your_api_key", swap_type="Dresses", mode="balanced")