app.py

import spaces
import gradio as gr
from PIL import Image
from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
from src.unet_hacked_tryon import UNet2DConditionModel
from transformers import (
    CLIPImageProcessor,
    CLIPVisionModelWithProjection,
    CLIPTextModel,
    CLIPTextModelWithProjection,
)
from diffusers import DDPMScheduler,AutoencoderKL
from typing import List

import torch
import os
import io
import warnings
import requests
from transformers import AutoTokenizer
import numpy as np
from utils_mask import get_mask_location
from torchvision import transforms
import apply_net
from preprocess.humanparsing.run_parsing import Parsing
from preprocess.openpose.run_openpose import OpenPose
from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
from torchvision.transforms.functional import to_pil_image
# import pillow_heif  # HEIC 이미지 처리용 (아이폰 촬영 사진 포맷)
from urllib.parse import urlparse


# zeroGPU 환경에서 compile 사용 여부
is_compile_for_zeroGPU = False # True: compile 사용, False: compile 사용 안 함

# SSL 경고 억제
warnings.filterwarnings("ignore", message=".*OpenSSL.*")
warnings.filterwarnings("ignore", category=UserWarning, module="urllib3")

# requests 세션 설정
session = requests.Session()
session.verify = False  # SSL 검증 비활성화 (개발 환경용)

def pil_to_binary_mask(pil_image, threshold=0):
    np_image = np.array(pil_image)
    grayscale_image = Image.fromarray(np_image).convert("L")
    binary_mask = np.array(grayscale_image) > threshold
    mask = np.zeros(binary_mask.shape, dtype=np.uint8)
    for i in range(binary_mask.shape[0]):
        for j in range(binary_mask.shape[1]):
            if binary_mask[i,j] == True :
                mask[i,j] = 1
    mask = (mask*255).astype(np.uint8)
    output_mask = Image.fromarray(mask)
    return output_mask


print("=" * 60)
print("Starting GENAI-VTON Application Initialization")
print("=" * 60)

base_path = 'yisol/IDM-VTON'
example_path = os.path.join(os.path.dirname(__file__), 'example')

print("\n[1/10] Loading UNet model...")
unet = UNet2DConditionModel.from_pretrained(
    base_path,
    subfolder="unet",
    torch_dtype=torch.float16,
)
unet.requires_grad_(False)
# torch.compile() 적용 - 추론 속도 20-40% 향상 (PyTorch 2.0+)
# 주의: 첫 번째 추론은 컴파일로 인해 느릴 수 있음
if is_compile_for_zeroGPU == True:
    print("✓ UNet model loaded successfully")
else:
    if hasattr(torch, 'compile'):
        try:
            unet = torch.compile(unet, mode="reduce-overhead")
            print("✓ UNet model loaded and compiled successfully")
        except Exception as e:
            print(f"✓ UNet model loaded (compile skipped: {e})")
    else:
        print("✓ UNet model loaded successfully")

print("\n[2/10] Loading tokenizers...")
tokenizer_one = AutoTokenizer.from_pretrained(
    base_path,
    subfolder="tokenizer",
    revision=None,
    use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
    base_path,
    subfolder="tokenizer_2",
    revision=None,
    use_fast=False,
)
print("✓ Tokenizers loaded successfully")

print("\n[3/10] Loading noise scheduler...")
noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
print("✓ Noise scheduler loaded successfully")

print("\n[4/10] Loading text encoders...")
text_encoder_one = CLIPTextModel.from_pretrained(
    base_path,
    subfolder="text_encoder",
    torch_dtype=torch.float16,
)
text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
    base_path,
    subfolder="text_encoder_2",
    torch_dtype=torch.float16,
)
print("✓ Text encoders loaded successfully")

print("\n[5/10] Loading image encoder...")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
    base_path,
    subfolder="image_encoder",
    torch_dtype=torch.float16,
    )
print("✓ Image encoder loaded successfully")

print("\n[6/10] Loading VAE...")
vae = AutoencoderKL.from_pretrained(base_path,
                                    subfolder="vae",
                                    torch_dtype=torch.float16,
)
# torch.compile() 적용 - VAE 인코딩/디코딩 속도 향상
if is_compile_for_zeroGPU == True:
    print("✓ VAE loaded successfully")
else:
    if hasattr(torch, 'compile'):
        try:
            vae = torch.compile(vae, mode="reduce-overhead")
            print("✓ VAE loaded and compiled successfully")
        except Exception as e:
            print(f"✓ VAE loaded (compile skipped: {e})")
    else:
        print("✓ VAE loaded successfully")

print("\n[7/10] Loading UNet Encoder...")
UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
    base_path,
    subfolder="unet_encoder",
    torch_dtype=torch.float16,
)
# torch.compile() 적용 - UNet Encoder 속도 향상
if is_compile_for_zeroGPU == True:
    print("✓ UNet Encoder loaded successfully")
else:
    if hasattr(torch, 'compile'):
        try:
            UNet_Encoder = torch.compile(UNet_Encoder, mode="reduce-overhead")
            print("✓ UNet Encoder loaded and compiled successfully")
        except Exception as e:
            print(f"✓ UNet Encoder loaded (compile skipped: {e})")
    else:
        print("✓ UNet Encoder loaded successfully")

print("\n[8/10] Initializing parsing and openpose models...")
parsing_model = Parsing(0)
openpose_model = OpenPose(0)
print("✓ Parsing and OpenPose models initialized")

print("\n[9/10] Configuring model parameters...")
UNet_Encoder.requires_grad_(False)
image_encoder.requires_grad_(False)
vae.requires_grad_(False)
unet.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
tensor_transfrom = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
    )
print("✓ Model parameters configured")

print("\n[10/10] Initializing TryonPipeline...")
pipe = TryonPipeline.from_pretrained(
        base_path,
        unet=unet,
        vae=vae,
        feature_extractor= CLIPImageProcessor(),
        text_encoder = text_encoder_one,
        text_encoder_2 = text_encoder_two,
        tokenizer = tokenizer_one,
        tokenizer_2 = tokenizer_two,
        scheduler = noise_scheduler,
        image_encoder=image_encoder,
        torch_dtype=torch.float16,
)
pipe.unet_encoder = UNet_Encoder
print("✓ TryonPipeline initialized successfully")

# torch, diffusers 등 버전 정리 후 적용 가능. 
# # xFormers 메모리 효율적 어텐션 활성화 (메모리 20-30% 감소, 속도 10-20% 향상)
# print("\n[Optimization] Enabling xFormers memory efficient attention...")
# try:
#     pipe.enable_xformers_memory_efficient_attention()
#     print("✓ xFormers memory efficient attention enabled")
# except Exception as e:
#     print(f"⚠ xFormers not available, using default attention: {e}")

print("\n" + "=" * 60)
print("All models loaded successfully!")
print("=" * 60 + "\n")

# Warm-up: 첫 번째 추론 지연 감소를 위한 모델 초기화
# JIT 컴파일, CUDA 커널 로딩 등을 미리 수행
print("=" * 60)
print("Warming up models (CPU)...")
print("=" * 60)

def warmup_models_cpu():
    """앱 시작 시 CPU 모델 초기화를 위한 Warm-up 함수"""
    try:
        # CPU에서 텍스트 임베딩 Warm-up (Tokenizer + Text Encoder 초기화)
        print("[CPU Warm-up 1/2] Text Encoder warm-up...")
        with torch.no_grad():
            dummy_prompt = "a photo of clothing"
            dummy_tokens = tokenizer_one(
                dummy_prompt,
                padding="max_length",
                max_length=tokenizer_one.model_max_length,
                truncation=True,
                return_tensors="pt"
            )
            # CPU에서 실행 가능한 초기화
            _ = text_encoder_one(dummy_tokens.input_ids, output_hidden_states=True)
        print("✓ Text Encoder warmed up")
        
        # Tensor 변환 Warm-up
        print("[CPU Warm-up 2/2] Tensor transform warm-up...")
        dummy_img = Image.new('RGB', (768, 1024), color='white')
        _ = tensor_transfrom(dummy_img)
        print("✓ Tensor transform warmed up")
        
        return True
    except Exception as e:
        print(f"⚠ CPU Warm-up partially completed: {e}")
        return False

# CPU Warm-up 실행
warmup_success = warmup_models_cpu()
if warmup_success:
    print("\n✓ CPU warm-up completed successfully")
else:
    print("\n⚠ CPU warm-up completed with warnings")
print("=" * 60 + "\n")

# torch.compile 오류 시 eager 모드로 폴백 설정
# 커스텀 UNet forward 메서드 호환성 문제 대응
if is_compile_for_zeroGPU == True:
    print("✓ torch.compile is disabled for ZeroGPU")
else:
    try:
        import torch._dynamo
        torch._dynamo.config.suppress_errors = True
        print("✓ torch._dynamo.config.suppress_errors enabled (fallback to eager mode on error)")
    except Exception as e:
        print(f"⚠ torch._dynamo config not available: {e}")

# GPU Warm-up 함수 (앱 로드 시 자동 실행)
# Text Encoder, VAE GPU 로딩 및 CUDA 커널 초기화
@spaces.GPU
def warmup_gpu():
    """앱 로드 시 GPU 모델 초기화를 위한 Warm-up 함수"""
    try:
        device = "cuda"
        print("=" * 60)
        print("GPU Warm-up: Loading models to GPU and initializing CUDA kernels...")
        print("=" * 60)
        
        # 모델을 GPU로 이동
        print("[GPU Warm-up 1/4] Moving models to GPU...")
        pipe.to(device)
        pipe.unet_encoder.to(device)
        print("✓ Models moved to GPU")
        
        # 더미 텐서 생성
        with torch.no_grad():
            with torch.cuda.amp.autocast():
                # 1. 더미 프롬프트 임베딩 생성 (Text Encoder GPU warm-up)
                print("[GPU Warm-up 2/4] Text Encoder GPU warm-up...")
                dummy_prompt = "a photo of white t-shirt"
                _ = pipe.encode_prompt(
                    dummy_prompt,
                    num_images_per_prompt=1,
                    do_classifier_free_guidance=True,
                    negative_prompt="low quality",
                )
                print("✓ Text Encoder GPU warmed up")
                
                # 2. 더미 이미지로 VAE 인코딩/디코딩 (VAE GPU warm-up)
                print("[GPU Warm-up 3/4] VAE GPU warm-up...")
                dummy_img = torch.randn(1, 3, 1024, 768).to(device, torch.float16)
                latents = pipe.vae.encode(dummy_img).latent_dist.sample()
                _ = pipe.vae.decode(latents)
                print("✓ VAE GPU warmed up (encode + decode)")
                
                # 3. CUDA 동기화 (커널 로딩 완료 대기)
                print("[GPU Warm-up 4/4] CUDA synchronization...")
                torch.cuda.synchronize()
                print("✓ CUDA kernels initialized")
        
        # GPU 메모리 정리
        torch.cuda.empty_cache()
        
        print("\n" + "=" * 60)
        print("✓ GPU Warm-up completed!")
        print("  Text Encoder, VAE ready. UNet will compile on first request.")
        print("  (torch.compile errors will fallback to eager mode)")
        print("=" * 60 + "\n")
        
        return "GPU Warm-up completed successfully!"
    except Exception as e:
        print(f"\n⚠ GPU Warm-up failed: {e}")
        print("  Models will be loaded on first user request.")
        return f"GPU Warm-up skipped: {e}"


# 이미지 전처리 함수
def preprocess_image(image):
    # HEIC 이미지 처리
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    
    # HEIC 이미지를 JPEG로 변환 - 이거 안 먹히는 거 같은데.... 
    try:
        output = io.BytesIO()
        image.convert("RGB").save(output, format="JPEG", quality=95)
        output.seek(0)
        image = Image.open(output)
    except Exception as e:
        print(f"Error converting image: {e}")
        # 변환 실패 시 원본 이미지 사용
        image = image.convert("RGB")

    # 이미지 크기 가져오기
    width, height = image.size
    
    # 3:4 비율로 중앙 자르기
    target_width = int(min(width, height * (3 / 4)))
    target_height = int(min(height, width * (4 / 3)))
    left = (width - target_width) / 2
    top = (height - target_height) / 2
    right = (width + target_width) / 2
    bottom = (height + target_height) / 2
    
    # 이미지 자르기
    cropped_img = image.crop((left, top, right, bottom))
    
    # 768x1024로 리사이징
    resized_img = cropped_img.resize((768, 1024), resample=Image.Resampling.LANCZOS)
    
    return resized_img


# URL에서 이미지 가져오기 함수
def load_image_from_url(url):
    try:
        response = session.get(url, stream=True, timeout=10)
        response.raise_for_status()  # HTTP 오류 확인
        
        # 이미지 다운로드
        img = Image.open(response.raw).convert("RGB")
        
        # JPEG로 변환
        output = io.BytesIO()
        img.save(output, format="JPEG", quality=95)
        output.seek(0)
        
        # 변환된 JPEG 이미지 반환
        jpeg_img = Image.open(output)
        return jpeg_img
        
    except requests.exceptions.RequestException as e:
        print(f"Error downloading image from URL: {e}")
        return None
    except Exception as e:
        print(f"Error processing image from URL: {e}")
        return None
    

def process_url_image(url):
    """Process image from URL and return PIL Image"""
    if not url or not url.strip():
        return None
    
    # URL 유효성 검사
    try:
        result = urlparse(url)
        if not all([result.scheme, result.netloc]):
            print("Invalid URL format")
            return None
    except Exception as e:
        print(f"Error parsing URL: {e}")
        return None

    img = load_image_from_url(url)
    if img is None:
        print("Failed to load image from URL")
        return None
    
    return preprocess_image(img)

def load_example_for_editor(image_path):
    """Load example image for ImageEditor component"""
    if image_path is None:
        return None
    
    # ImageEditor는 특정 형식을 기대하므로 딕셔너리 형태로 반환
    return {
        "background": image_path,
        "layers": None,
        "composite": None
    }

def download_model_file(model_path, urls):
    """Download model file from multiple URLs if it doesn't exist"""
    if os.path.exists(model_path):
        print(f"Model file already exists: {model_path}")
        return True
    
    os.makedirs(os.path.dirname(model_path), exist_ok=True)
    
    for url in urls:
        try:
            print(f"Downloading from: {url}")
            response = requests.get(url, stream=True)
            response.raise_for_status()
            
            total_size = int(response.headers.get('content-length', 0))
            block_size = 8192
            
            with open(model_path, 'wb') as f:
                downloaded = 0
                for chunk in response.iter_content(chunk_size=block_size):
                    if chunk:
                        f.write(chunk)
                        downloaded += len(chunk)
                        if total_size > 0:
                            percent = (downloaded / total_size) * 100
                            if(percent % 10 == 0):
                                print(f"\rDownload progress: {percent:.1f}%", end='', flush=True)                            
                            
            
            print(f"\nSuccessfully downloaded: {model_path}")
            return True
            
        except Exception as e:
            print(f"Failed to download from {url}: {e}")
            continue
    
    print(f"Failed to download model file from all URLs: {model_path}")
    return False

def download_densepose_model():
    """Download DensePose model file"""
    model_path = "ckpt/densepose/model_final_162be9.pkl"
    urls = [
        "https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl",
        "https://github.com/facebookresearch/densepose/releases/download/v1.0/model_final_162be9.pkl"
    ]
    return download_model_file(model_path, urls)

def download_openpose_model():
    """Download OpenPose model file"""
    model_path = "ckpt/openpose/ckpts/body_pose_model.pth"
    urls = [
        "https://huggingface.co/lllyasviel/Annotators/resolve/main/body_pose_model.pth"
    ]
    return download_model_file(model_path, urls)

def download_humanparsing_models():
    """Download Human Parsing model files"""
    base_url = "https://huggingface.co/Longcat2957/humanparsing-onnx/resolve/main"
    
    models = [
        ("ckpt/humanparsing/parsing_atr.onnx", f"{base_url}/parsing_atr.onnx"),
        ("ckpt/humanparsing/parsing_lip.onnx", f"{base_url}/parsing_lip.onnx")
    ]
    
    success = True
    for model_path, url in models:
        if os.path.exists(model_path):
            print(f"Human parsing model already exists: {model_path}")
            continue
            
        print(f"Downloading {model_path} from {url}")
        if download_model_file(model_path, [url]):
            print(f"Successfully downloaded: {model_path}")
        else:
            print(f"Failed to download: {model_path}")
            success = False
    
    return success

def download_all_models():
    """Download all required model files"""
    print("Checking and downloading required model files...")
    
    # Download DensePose model
    print("\n[1/3] Downloading DensePose model...")
    densepose_success = download_densepose_model()
    if densepose_success:
        print("✓ DensePose model ready")
    else:
        print("⚠ DensePose model download failed (will download on demand)")
    
    # Download OpenPose model
    print("\n[2/3] Downloading OpenPose model...")
    openpose_success = download_openpose_model()
    if openpose_success:
        print("✓ OpenPose model ready")
    else:
        print("⚠ OpenPose model download failed (will download on demand)")
    
    # Download Human Parsing models
    print("\n[3/3] Downloading Human Parsing models...")
    parsing_success = download_humanparsing_models()
    if parsing_success:
        print("✓ Human Parsing models ready")
    else:
        print("⚠ Human Parsing models download failed (will download on demand)")
    
    return densepose_success and openpose_success and parsing_success

@spaces.GPU
def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop, denoise_steps,seed):
    device = "cuda"
    
    openpose_model.preprocessor.body_estimation.model.to(device)
    pipe.to(device)
    pipe.unet_encoder.to(device)

    garm_img= garm_img.convert("RGB").resize((768,1024))
    human_img_orig = dict["background"].convert("RGB")    
    
    if is_checked_crop:
        width, height = human_img_orig.size
        target_width = int(min(width, height * (3 / 4)))
        target_height = int(min(height, width * (4 / 3)))
        left = (width - target_width) / 2
        top = (height - target_height) / 2
        right = (width + target_width) / 2
        bottom = (height + target_height) / 2
        cropped_img = human_img_orig.crop((left, top, right, bottom))
        crop_size = cropped_img.size
        human_img = cropped_img.resize((768,1024))
    else:
        human_img = human_img_orig.resize((768,1024))


    if is_checked:
        keypoints = openpose_model(human_img.resize((384,512)))
        model_parse, _ = parsing_model(human_img.resize((384,512)))
        mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
        mask = mask.resize((768,1024))
    else:
        mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
        # mask = transforms.ToTensor()(mask)
        # mask = mask.unsqueeze(0)
    mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
    mask_gray = to_pil_image((mask_gray+1.0)/2.0)


    human_img_arg = _apply_exif_orientation(human_img.resize((384,512)))
    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
     
    

    # DensePose 모델 다운로드 및 경로 설정
    densepose_model_path = './ckpt/densepose/model_final_162be9.pkl'
    
    # 모델 파일이 없으면 다운로드 시도
    if not os.path.exists(densepose_model_path):
        print("DensePose model not found, attempting to download...")
        download_success = download_densepose_model()
        if not download_success:
            print("Failed to download DensePose model")
            return None, None
    
    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', densepose_model_path, 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
    # verbosity = getattr(args, "verbosity", None)
    pose_img = args.func(args,human_img_arg)    
    pose_img = pose_img[:,:,::-1]    
    pose_img = Image.fromarray(pose_img).resize((768,1024))
    
    with torch.no_grad():
        # Extract the images
        with torch.cuda.amp.autocast():
            with torch.no_grad():
                prompt = "model is wearing " + garment_des
                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
                with torch.inference_mode():
                    (
                        prompt_embeds,
                        negative_prompt_embeds,
                        pooled_prompt_embeds,
                        negative_pooled_prompt_embeds,
                    ) = pipe.encode_prompt(
                        prompt,
                        num_images_per_prompt=1,
                        do_classifier_free_guidance=True,
                        negative_prompt=negative_prompt,
                    )
                                    
                    prompt = "a photo of " + garment_des
                    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
                    if not isinstance(prompt, List):
                        prompt = [prompt] * 1
                    if not isinstance(negative_prompt, List):
                        negative_prompt = [negative_prompt] * 1
                    with torch.inference_mode():
                        (
                            prompt_embeds_c,
                            _,
                            _,
                            _,
                        ) = pipe.encode_prompt(
                            prompt,
                            num_images_per_prompt=1,
                            do_classifier_free_guidance=False,
                            negative_prompt=negative_prompt,
                        )



                    pose_img =  tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16)
                    garm_tensor =  tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16)
                    generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
                    images = pipe(
                        prompt_embeds=prompt_embeds.to(device,torch.float16),
                        negative_prompt_embeds=negative_prompt_embeds.to(device,torch.float16),
                        pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.float16),
                        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,torch.float16),
                        num_inference_steps=denoise_steps,
                        generator=generator,
                        strength = 1.0,
                        pose_img = pose_img.to(device,torch.float16),
                        text_embeds_cloth=prompt_embeds_c.to(device,torch.float16),
                        cloth = garm_tensor.to(device,torch.float16),
                        mask_image=mask,
                        image=human_img, 
                        height=1024,
                        width=768,
                        ip_adapter_image = garm_img.resize((768,1024)),
                        guidance_scale=2.0,
                    )[0]

    if is_checked_crop:
        out_img = images[0].resize(crop_size)        
        human_img_orig.paste(out_img, (int(left), int(top)))    
        return human_img_orig, mask_gray
    else:
        return images[0], mask_gray
    # return images[0], mask_gray

print("\n" + "=" * 60)
print("Loading Example Images...")
print("=" * 60)

garm_list = os.listdir(os.path.join(example_path,"cloth"))
garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
print(f"✓ Found {len(garm_list_path)} garment example images")

human_list = os.listdir(os.path.join(example_path,"human"))
human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
print(f"✓ Found {len(human_list_path)} human example images")

# human_ex_list를 단순한 이미지 경로 리스트로 변경 (그리드 표시를 위해)
human_ex_list = human_list_path

##default human

print("\n" + "=" * 60)
print("Creating Gradio Application Interface...")
print("=" * 60)

image_blocks = gr.Blocks().queue()
with image_blocks as demo:
    print("✓ Gradio Blocks created")
    
    gr.Markdown("## DXCO : GENAI-VTON")
    gr.Markdown("임성남, 윤지영, 조민주 based on IDM-VTON")    
    gr.Markdown("* 맨 처음 추론 시 [5분] 걸림 - compile과 GPU warm-up *")
    gr.Markdown("권장 이미지 사이즈 - 3:4비율(384x512,768x1024)")
    
    with gr.Row():
        with gr.Column():
            imgs = gr.ImageEditor(sources='upload', type="pil", label='대상 이미지', interactive=True)
            with gr.Row():
                img_url_input = gr.Textbox(label="대상 이미지 URL", placeholder="예) https://example.com/human_image.jpg")
            with gr.Row():
                is_checked = gr.Checkbox(label="Yes", info="자동 마스킹",value=True)
            with gr.Row():
                is_checked_crop = gr.Checkbox(label="Yes", info="자동 크롭 및 리사이징",value=True)
            example = gr.Examples(
                inputs=imgs,
                examples_per_page=10,
                examples=human_ex_list
            )

        with gr.Column():
            garm_img = gr.Image(label="의상 이미지", sources='upload', type="pil")
            with gr.Row():
                garm_url_input = gr.Textbox(label="의상 이미지 URL", placeholder="예) https://example.com/garment.jpg")
            with gr.Row(elem_id="prompt-container"):
                with gr.Row():
                    prompt = gr.Textbox(placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", show_label=False, elem_id="prompt")
            example = gr.Examples(
                inputs=garm_img,
                examples_per_page=8,
                examples=garm_list_path)
        
        with gr.Column():
            masked_img = gr.Image(label="Masked image output", elem_id="masked-img", show_share_button=False)
        
        with gr.Column():
            image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False)

    with gr.Column():
        try_button = gr.Button(value="Try-on")
        with gr.Accordion(label="Advanced Settings", open=False):
            with gr.Row():
                denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1)
                seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)

    # is_checked = gr.Number(value=True)

    # 이미지 업로드 시 전처리
    # imgs.upload(
    #     fn=preprocess_image,
    #     inputs=imgs,
    #     outputs=imgs,  # 전처리된 이미지를 ImageEditor에 다시 표시
    # )


    # 대상 이미지: URL 입력 처리
    img_url_input.change(
        fn=lambda url: process_url_image(url),
        inputs=img_url_input,
        outputs=imgs,
    )

    # 의상 이미지: URL 입력 처리
    garm_url_input.change(
        fn=lambda url: process_url_image(url),
        inputs=garm_url_input,
        outputs=garm_img,
    )

    try_button.click(
        fn=start_tryon,
        inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed],
        outputs=[image_out, masked_img],
        api_name='tryon'
    )
    
    # GPU Warm-up 상태 표시용 (숨김)
    warmup_status = gr.Textbox(visible=False)
    
    # 앱 로드 시 GPU Warm-up 자동 실행 (torch.compile 첫 컴파일)
    if is_compile_for_zeroGPU == True:
        print("✓ GPU warm-up is disabled for ZeroGPU")
    else:
        demo.load(
            fn=warmup_gpu,
            inputs=None,
            outputs=warmup_status,
        )
    
    print("✓ Gradio interface components created")
    print("✓ Event handlers configured")
    print("✓ GPU warm-up scheduled on app load")

print("\n" + "=" * 60)
print("Gradio Application Interface Created Successfully!")
print("=" * 60)

# DensePose 모델 다운로드
print("\n" + "=" * 60)
print("Checking and Downloading Additional Models...")
print("=" * 60)
try:
    download_all_models()
    print("\n✓ All model files downloaded successfully.")
except Exception as e:
    print(f"\n⚠ Warning: Could not download all model files: {e}")
    print("The models will be downloaded when needed during inference.")

# 앱 실행
print("\n" + "=" * 60)
print("Launching Application Server...")
print("=" * 60)
if __name__ == "__main__":
    try:
        print("Starting GENAI-VTON application on http://0.0.0.0:7860")
        print("Please wait while the server starts...")
        image_blocks.launch(server_name="0.0.0.0", server_port=7860, share=False)
    except Exception as e:
        print(f"\n❌ Error starting the application: {e}")
        print("Please check if all required dependencies are installed.")
        import traceback
        traceback.print_exc()