update app.py

app.py

@@ -1,472 +1,476 @@
-import sys
-sys.path.append('./')
-from PIL import Image
-import cv2
-import gradio as gr
-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
-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
-
-device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-
-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
-
-
-base_path = 'yisol/IDM-VTON'
-example_path = os.path.join(os.path.dirname(__file__), 'example')
-
-unet = UNet2DConditionModel.from_pretrained(
-    base_path,
-    subfolder="unet",
-    torch_dtype=torch.float16,
-)
-unet.requires_grad_(False)
-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,
-)
-noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
-
-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,
-)
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-    base_path,
-    subfolder="image_encoder",
-    torch_dtype=torch.float16,
-    )
-vae = AutoencoderKL.from_pretrained(base_path,
-                                    subfolder="vae",
-                                    torch_dtype=torch.float16,
-)
-
-# "stabilityai/stable-diffusion-xl-base-1.0",
-UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
-    base_path,
-    subfolder="unet_encoder",
-    torch_dtype=torch.float16,
-)
-
-parsing_model = Parsing(0)
-openpose_model = OpenPose(0)
-
-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]),
-            ]
-    )
-
-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
-
-# Function to visualize parsing
-def visualize_parsing(image, mask):
-    """
-    Visualize the parsing by applying a color map to the segmentation mask.
-    """
-    # Ensure image is in RGB format and convert to numpy array
-    image_array = np.array(image.convert('RGB'), dtype=np.uint8)
-
-    # Create a color map
-    num_classes = np.max(mask) + 1
-    colors = np.random.randint(0, 255, size=(num_classes, 3), dtype=np.uint8)
-    
-    # Apply color map to the mask
-    color_mask = colors[mask.astype(int)]
-
-    # Ensure color_mask is correctly shaped and typed
-    color_mask = np.array(color_mask, dtype=np.uint8)
-
-    # Combine the original image and the color mask
-    combined_image = cv2.addWeighted(image_array, 0.5, color_mask, 0.5, 0)
-
-    return Image.fromarray(combined_image)
-
-def process_densepose(human_img):
-    """
-    Processes the human image using DensePose and returns the DensePose image.
-    Assumes human_img is a dictionary with a 'background' key pointing to the image path.
-    """
-    # Load image from path
-    image_path = human_img['background']  # Assuming 'background' is the correct key
-    if isinstance(image_path, Image.Image):
-        image = image_path
-    else:
-        image = Image.open(image_path)  # Only call Image.open if it's not already an Image object
-
-    # Apply EXIF orientation and resize
-    human_img_arg = _apply_exif_orientation(image.resize((384, 512)))
-    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
-
-    # Setup DensePose arguments
-    args = apply_net.create_argument_parser().parse_args(
-        ('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda')
-    )
-    pose_img = args.func(args, human_img_arg)
-    pose_img = pose_img[:, :, ::-1]  # Convert from BGR to RGB
-    pose_img = Image.fromarray(pose_img).resize((768, 1024))
-
-    return pose_img, pose_img
-
-def process_human_parsing(human_img):
-    """
-    Processes the human image to perform segmentation using a human parsing model.
-    """
-    
-    image_path = human_img['background']  # Assuming 'background' is the correct key
-    if isinstance(image_path, Image.Image):
-        image = image_path
-    else:
-        image = Image.open(image_path)  # Only call Image.open if it's not already an Image object
-
-    image = image.resize((384, 512))
-    model_parse, _ = parsing_model(image)
-    # parsing_image = visualize_parsing(human_img, model_parse)  # Visualization function needed
-    # vis_image = visualize_parsing(image, model_parse)
-    # state_message = "Human parsing processing completed"
-    return model_parse
-
-def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
-    """
-    Preprocesses images and generates outputs using various models.
-    
-    Parameters:
-    - human_img: PIL image of the human.
-    - garm_img: PIL image of the garment.
-    - garment_des: Description of the garment.
-    - is_checked: Boolean flag indicating whether to use auto-generated mask.
-    - is_checked_crop: Boolean flag indicating whether to use auto-crop & resizing.
-    - denoise_steps: Number of denoising steps.
-    - seed: Seed for random generator.
-    - pose_img: DensePose image generated in the previous step.
-
-    Returns:
-    - Processed images: Depending on the conditions, it returns human_img_orig, mask_gray, and final output images.
-    """
-    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)))
-        print(keypoints)
-        model_parse, _ = parsing_model(human_img.resize((384,512)))
-        print(model_parse)
-        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")
-     
-    
-
-    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', '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:
-        # out_img = images[0].resize(crop_size)
-        return images[0], mask_gray
-    
-    
-
-
-    
-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]
-
-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]
-
-human_ex_list = []
-for ex_human in human_list_path:
-    ex_dict= {}
-    ex_dict['background'] = ex_human
-    ex_dict['layers'] = None
-    ex_dict['composite'] = None
-    human_ex_list.append(ex_dict)
-
-##default human
-
-
-image_blocks = gr.Blocks().queue()
-with image_blocks as demo:    
-    with gr.Row():
-        with gr.Column():
-            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
-            with gr.Row():
-                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
-            with gr.Row():
-                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
-
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=10,
-                examples=human_ex_list
-            )
-
-        with gr.Column():
-            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
-            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():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            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", height=400)
-            image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False)
-            
-        with gr.Column():
-            densepose_img_out = gr.Image(label="Output", elem_id="densepose-img",show_share_button=False)
-        # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto")
-
-
-
-    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)
-
-    densepose_state = gr.State(None)
-
-    # Define the steps in sequence
-    image_blocks = gr.Blocks().queue()
-with image_blocks as demo:    
-    with gr.Row():
-        with gr.Column():
-            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
-            with gr.Row():
-                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
-            with gr.Row():
-                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
-
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=10,
-                examples=human_ex_list
-            )
-
-        with gr.Column():
-            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
-            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():
-            densepose_img_out = gr.Image(label="Dense-pose", elem_id="densepose-img", show_share_button=False)
-        # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto")
-        
-        with gr.Column():
-            human_parse_img_out = gr.Image(label="Human-Parse", elem_id="humanparse-img", show_share_button=False)
-        # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto")
-
-    with gr.Column():
-        try_button = gr.Button(value="Try-on")
-        get_denspose =gr.Button(value="Get-DensePose")
-        get_humanparse =gr.Button(value="Get-HumanParse")
-        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)
-
-    densepose_state = gr.State(None)
-
-    # Define the steps in sequence
-    get_denspose.click(
-        fn=process_densepose,
-        inputs=[imgs],
-        outputs=[densepose_img_out, densepose_state],
-        api_name='process_densepose'
-    )
-    get_humanparse.click(
-        fn=process_human_parsing,
-        inputs=[imgs],
-        outputs=[human_parse_img_out],
-        api_name='process_humanparse'
-    )
-    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='start_tryon'
-    )
-
-image_blocks.launch(server_name="0.0.0.0", server_port=3000)
-
-
-
+import sys
+sys.path.append('./')
+from PIL import Image
+try:
+    import cv2
+    print("OpenCV is installed correctly.")
+except ImportError:
+    print("OpenCV is not installed.")
+import gradio as gr
+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
+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
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
+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
+
+
+base_path = 'yisol/IDM-VTON'
+example_path = os.path.join(os.path.dirname(__file__), 'example')
+
+unet = UNet2DConditionModel.from_pretrained(
+    base_path,
+    subfolder="unet",
+    torch_dtype=torch.float16,
+)
+unet.requires_grad_(False)
+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,
+)
+noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
+
+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,
+)
+image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+    base_path,
+    subfolder="image_encoder",
+    torch_dtype=torch.float16,
+    )
+vae = AutoencoderKL.from_pretrained(base_path,
+                                    subfolder="vae",
+                                    torch_dtype=torch.float16,
+)
+
+# "stabilityai/stable-diffusion-xl-base-1.0",
+UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
+    base_path,
+    subfolder="unet_encoder",
+    torch_dtype=torch.float16,
+)
+
+parsing_model = Parsing(0)
+openpose_model = OpenPose(0)
+
+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]),
+            ]
+    )
+
+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
+
+# Function to visualize parsing
+def visualize_parsing(image, mask):
+    """
+    Visualize the parsing by applying a color map to the segmentation mask.
+    """
+    # Ensure image is in RGB format and convert to numpy array
+    image_array = np.array(image.convert('RGB'), dtype=np.uint8)
+
+    # Create a color map
+    num_classes = np.max(mask) + 1
+    colors = np.random.randint(0, 255, size=(num_classes, 3), dtype=np.uint8)
+    
+    # Apply color map to the mask
+    color_mask = colors[mask.astype(int)]
+
+    # Ensure color_mask is correctly shaped and typed
+    color_mask = np.array(color_mask, dtype=np.uint8)
+
+    # Combine the original image and the color mask
+    combined_image = cv2.addWeighted(image_array, 0.5, color_mask, 0.5, 0)
+
+    return Image.fromarray(combined_image)
+
+def process_densepose(human_img):
+    """
+    Processes the human image using DensePose and returns the DensePose image.
+    Assumes human_img is a dictionary with a 'background' key pointing to the image path.
+    """
+    # Load image from path
+    image_path = human_img['background']  # Assuming 'background' is the correct key
+    if isinstance(image_path, Image.Image):
+        image = image_path
+    else:
+        image = Image.open(image_path)  # Only call Image.open if it's not already an Image object
+
+    # Apply EXIF orientation and resize
+    human_img_arg = _apply_exif_orientation(image.resize((384, 512)))
+    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
+
+    # Setup DensePose arguments
+    args = apply_net.create_argument_parser().parse_args(
+        ('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda')
+    )
+    pose_img = args.func(args, human_img_arg)
+    pose_img = pose_img[:, :, ::-1]  # Convert from BGR to RGB
+    pose_img = Image.fromarray(pose_img).resize((768, 1024))
+
+    return pose_img, pose_img
+
+def process_human_parsing(human_img):
+    """
+    Processes the human image to perform segmentation using a human parsing model.
+    """
+    
+    image_path = human_img['background']  # Assuming 'background' is the correct key
+    if isinstance(image_path, Image.Image):
+        image = image_path
+    else:
+        image = Image.open(image_path)  # Only call Image.open if it's not already an Image object
+
+    image = image.resize((384, 512))
+    model_parse, _ = parsing_model(image)
+    # parsing_image = visualize_parsing(human_img, model_parse)  # Visualization function needed
+    # vis_image = visualize_parsing(image, model_parse)
+    # state_message = "Human parsing processing completed"
+    return model_parse
+
+def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
+    """
+    Preprocesses images and generates outputs using various models.
+    
+    Parameters:
+    - human_img: PIL image of the human.
+    - garm_img: PIL image of the garment.
+    - garment_des: Description of the garment.
+    - is_checked: Boolean flag indicating whether to use auto-generated mask.
+    - is_checked_crop: Boolean flag indicating whether to use auto-crop & resizing.
+    - denoise_steps: Number of denoising steps.
+    - seed: Seed for random generator.
+    - pose_img: DensePose image generated in the previous step.
+
+    Returns:
+    - Processed images: Depending on the conditions, it returns human_img_orig, mask_gray, and final output images.
+    """
+    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)))
+        print(keypoints)
+        model_parse, _ = parsing_model(human_img.resize((384,512)))
+        print(model_parse)
+        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")
+     
+    
+
+    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', '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:
+        # out_img = images[0].resize(crop_size)
+        return images[0], mask_gray
+    
+    
+
+
+    
+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]
+
+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]
+
+human_ex_list = []
+for ex_human in human_list_path:
+    ex_dict= {}
+    ex_dict['background'] = ex_human
+    ex_dict['layers'] = None
+    ex_dict['composite'] = None
+    human_ex_list.append(ex_dict)
+
+##default human
+
+
+image_blocks = gr.Blocks().queue()
+with image_blocks as demo:    
+    with gr.Row():
+        with gr.Column():
+            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
+            with gr.Row():
+                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
+            with gr.Row():
+                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
+
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=10,
+                examples=human_ex_list
+            )
+
+        with gr.Column():
+            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
+            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():
+            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
+            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", height=400)
+            image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False)
+            
+        with gr.Column():
+            densepose_img_out = gr.Image(label="Output", elem_id="densepose-img",show_share_button=False)
+        # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto")
+
+
+
+    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)
+
+    densepose_state = gr.State(None)
+
+    # Define the steps in sequence
+    image_blocks = gr.Blocks().queue()
+with image_blocks as demo:    
+    with gr.Row():
+        with gr.Column():
+            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
+            with gr.Row():
+                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
+            with gr.Row():
+                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
+
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=10,
+                examples=human_ex_list
+            )
+
+        with gr.Column():
+            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
+            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():
+            densepose_img_out = gr.Image(label="Dense-pose", elem_id="densepose-img", show_share_button=False)
+        # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto")
+        
+        with gr.Column():
+            human_parse_img_out = gr.Image(label="Human-Parse", elem_id="humanparse-img", show_share_button=False)
+        # densepose_img = gr.Gallery(label="All images", show_label=False, elem_id="all-images", columns=[3], rows=[1], object_fit="contain", height="auto")
+
+    with gr.Column():
+        try_button = gr.Button(value="Try-on")
+        get_denspose =gr.Button(value="Get-DensePose")
+        get_humanparse =gr.Button(value="Get-HumanParse")
+        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)
+
+    densepose_state = gr.State(None)
+
+    # Define the steps in sequence
+    get_denspose.click(
+        fn=process_densepose,
+        inputs=[imgs],
+        outputs=[densepose_img_out, densepose_state],
+        api_name='process_densepose'
+    )
+    get_humanparse.click(
+        fn=process_human_parsing,
+        inputs=[imgs],
+        outputs=[human_parse_img_out],
+        api_name='process_humanparse'
+    )
+    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='start_tryon'
+    )
+
+image_blocks.launch(server_name="0.0.0.0", server_port=3000)
+
+
+