Add log for show start up steps

app.py

@@ -27,7 +27,7 @@ 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)
+# import pillow_heif  # HEIC 이미지 처리용 (아이폰 촬영 사진 포맷)
 from urllib.parse import urlparse
 
 # SSL 경고 억제
@@ -52,15 +52,23 @@ def pil_to_binary_mask(pil_image, threshold=0):
     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)
+print("✓ UNet model loaded successfully")
+
+print("\n[2/10] Loading tokenizers...")
 tokenizer_one = AutoTokenizer.from_pretrained(
     base_path,
     subfolder="tokenizer",
@@ -73,8 +81,13 @@ tokenizer_two = AutoTokenizer.from_pretrained(
     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",
@@ -85,26 +98,37 @@ text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
     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,
 )
+print("✓ VAE loaded successfully")
 
-# "stabilityai/stable-diffusion-xl-base-1.0",
+print("\n[7/10] Loading UNet Encoder...")
 UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
     base_path,
     subfolder="unet_encoder",
     torch_dtype=torch.float16,
 )
+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)
@@ -117,7 +141,9 @@ tensor_transfrom = transforms.Compose(
                 transforms.Normalize([0.5], [0.5]),
             ]
     )
+print("✓ Model parameters configured")
 
+print("\n[10/10] Initializing TryonPipeline...")
 pipe = TryonPipeline.from_pretrained(
         base_path,
         unet=unet,
@@ -132,6 +158,11 @@ pipe = TryonPipeline.from_pretrained(
         torch_dtype=torch.float16,
 )
 pipe.unet_encoder = UNet_Encoder
+print("✓ TryonPipeline initialized successfully")
+
+print("\n" + "=" * 60)
+print("All models loaded successfully!")
+print("=" * 60 + "\n")
 
 
 # 이미지 전처리 함수
@@ -316,16 +347,28 @@ def download_all_models():
     print("Checking and downloading required model files...")
     
     # Download DensePose model
-    print("\n=== Downloading 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=== Downloading 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=== Downloading 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
 
@@ -460,20 +503,30 @@ def start_tryon(dict,garm_img,garment_des,is_checked,denoise_steps,seed, is_chec
         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")
@@ -549,21 +602,36 @@ with image_blocks as demo:
         outputs=[image_out, masked_img],
         api_name='tryon'
     )
+    
+    print("✓ Gradio interface components created")
+    print("✓ Event handlers configured")
+
+print("\n" + "=" * 60)
+print("Gradio Application Interface Created Successfully!")
+print("=" * 60)
 
 # DensePose 모델 다운로드
-print("Initializing DensePose model...")
+print("\n" + "=" * 60)
+print("Checking and Downloading Additional Models...")
+print("=" * 60)
 try:
     download_all_models()
-    print("All model files downloaded successfully.")
+    print("\n✓ All model files downloaded successfully.")
 except Exception as e:
-    print(f"Warning: Could not download all model files: {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...")
+        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"Error starting the application: {e}")
+        print(f"\n❌ Error starting the application: {e}")
         print("Please check if all required dependencies are installed.")
+        import traceback
+        traceback.print_exc()