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IDM-VTON_DailyTestFORPROD

Paused

with Download Button

beaf101 verified over 1 year ago

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import os

file_path = "/home/user/.pyenv/versions/3.10.16/lib/python3.10/site-packages/diffusers/utils/dynamic_modules_utils.py"
if os.path.exists(file_path):
    with open(file_path, "r") as f:
        lines = f.readlines()

    # Modify the import line
    new_lines = []
    for line in lines:
        if "from huggingface_hub import cached_download" in line:
            new_lines.append("from huggingface_hub import hf_hub_download, model_info\n")  # Remove cached_download
        else:
            new_lines.append(line)

    # Write back the changes
    with open(file_path, "w") as f:
        f.writelines(new_lines)

    print("Patched diffusers successfully!")
else:
    print("File not found:", file_path)

import gradio as gr
from diffusers.utils import load_image
import tempfile
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
from transformers import AutoTokenizer
import spaces
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

HF_TEST_USER_NAME = os.environ["HF_TEST_USER_NAME"]
HF_PASS = os.environ["HF_PASS"]

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

@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")
     
    

    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:
        return images[0], mask_gray
    # return images[0], mask_gray


@spaces.GPU
def start_tryon2(main_img_path, garm_img_path,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)

    main_img = load_image(main_img_path)
    garm_img = load_image(garm_img_path)

    garm_img= garm_img.convert("RGB").resize((768,1024))
    human_img_orig = main_img.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))
    
    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:
        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

css = '''
/*.gradio-container {width: 85% !important; */
.gradio-container {width: 100% !important; 
background: linear-gradient(135deg, #CCFED9, #95BAFF);
}
#huggingface-space-header {
    display: none !important;
}
#masked-img {
    display: none !important;
    }
#generate_btn {
    background: #553566 !important;
    color: white !important;
    }
*/
html, body {
    height: 100%;
    margin: 0;
    background: linear-gradient(135deg, #CCFED9, #95BAFF) !important;
}
.gradio-container {
    
    background: transparent !important;
}
*/
html body h1#app-title {
    text-align: center;
    font-size: 40px;
    font-weight: bold;
    color: #D46BCA !important;
    }
#reupload_wrapper button {
    background: #553566 !important;
    color: white !important;
    }
#usage_tips_section {
    display: none !important;
}
#doodle_section, #advanced_section {
    display: none !important;
    }
#enable_doodle_container {
    display: none !important;
}
footer, [class*="footer"], [class*="api"], [class*="settings"] {
    display: none !important;
}
a[href*="api"], .api-button, #api-button {
    display: none !important;   
}
#prompt_suggestions_box {
    background: white;
    border: 2px solid #D46BCA;
    padding: 15px;
    border-radius: 10px;
    box-shadow: 0 4px 15px rgba(0,0,0,0.2);
    position: fixed;
    top: 20%;
    left: 50%;
    transform: translate(-50%, 0);
    width: 40%;
    z-index: 9999;
}
.scrollable-prompts {
    flex: 1;
    overflow-y: auto;
    padding: 10px;
    max-height: 300px; /* or any height you prefer */
}
#suggested_prompts_btn {
    background: #D46BCA !important;
    color: white !important;
}
#close_prompt_box {
    background: #553566 !important;
    color: white !important;
    width: 100%;
}
#download_btn {
background: #553566 !important;
color: white !important;
}
input[type="radio"]:checked + span {
    border-color: #D46BCA !important; 
    }
        
'''

def start_tryon_with_download(img_dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
    # Call your existing start_tryon
    output_img, mask_img = start_tryon(img_dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed)

    # Save output image to temp file
    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
    output_img.save(tmp_file.name)

    # Return images for display plus file path for download
    return output_img, mask_img, tmp_file.name

image_blocks = gr.Blocks(css=css).queue()
with image_blocks as demo:
    gr.Markdown("## <span id='app-title'>Virtual Try-on</span>")
    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)


        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")

        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, show_download_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, show_download_button=False)
            # download_btn = gr.DownloadButton(label="Download Generated Image", elem_id="download_btn")


    with gr.Column():
        try_button = gr.Button(value="Try-on",  elem_id="generate_btn")
        download_btn = gr.DownloadButton(label="Download Generated Image", elem_id="download_btn")
        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)



    # 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')
    try_button.click(
        fn=start_tryon_with_download,
        inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed],
        outputs=[image_out, masked_img, download_btn],
        api_name='tryon'
    )

image_blocks.launch(auth=(HF_TEST_USER_NAME, HF_PASS), show_error=True)
# image_blocks.launch(show_error=True)

	import os

	file_path = "/home/user/.pyenv/versions/3.10.16/lib/python3.10/site-packages/diffusers/utils/dynamic_modules_utils.py"
	if os.path.exists(file_path):
	with open(file_path, "r") as f:
	lines = f.readlines()

	# Modify the import line
	new_lines = []
	for line in lines:
	if "from huggingface_hub import cached_download" in line:
	new_lines.append("from huggingface_hub import hf_hub_download, model_info\n") # Remove cached_download
	else:
	new_lines.append(line)

	# Write back the changes
	with open(file_path, "w") as f:
	f.writelines(new_lines)

	print("Patched diffusers successfully!")
	else:
	print("File not found:", file_path)

	import gradio as gr
	from diffusers.utils import load_image
	import tempfile
	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
	from transformers import AutoTokenizer
	import spaces
	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

	HF_TEST_USER_NAME = os.environ["HF_TEST_USER_NAME"]
	HF_PASS = os.environ["HF_PASS"]

	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

	@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")



	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:
	return images[0], mask_gray
	# return images[0], mask_gray


	@spaces.GPU
	def start_tryon2(main_img_path, garm_img_path,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)

	main_img = load_image(main_img_path)
	garm_img = load_image(garm_img_path)

	garm_img= garm_img.convert("RGB").resize((768,1024))
	human_img_orig = main_img.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))

	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:
	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

	css = '''
	/.gradio-container {width: 85% !important; /
	.gradio-container {width: 100% !important;
	background: linear-gradient(135deg, #CCFED9, #95BAFF);
	}
	#huggingface-space-header {
	display: none !important;
	}
	#masked-img {
	display: none !important;
	}
	#generate_btn {
	background: #553566 !important;
	color: white !important;
	}

	*/
	html, body {
	height: 100%;
	margin: 0;
	background: linear-gradient(135deg, #CCFED9, #95BAFF) !important;
	}

	.gradio-container {

	background: transparent !important;
	}
	*/

	html body h1#app-title {
	text-align: center;
	font-size: 40px;
	font-weight: bold;
	color: #D46BCA !important;
	}
	#reupload_wrapper button {
	background: #553566 !important;
	color: white !important;
	}
	#usage_tips_section {
	display: none !important;
	}
	#doodle_section, #advanced_section {
	display: none !important;
	}
	#enable_doodle_container {
	display: none !important;
	}
	footer, [class="footer"], [class="api"], [class*="settings"] {
	display: none !important;
	}
	a[href*="api"], .api-button, #api-button {
	display: none !important;
	}
	#prompt_suggestions_box {
	background: white;
	border: 2px solid #D46BCA;
	padding: 15px;
	border-radius: 10px;
	box-shadow: 0 4px 15px rgba(0,0,0,0.2);
	position: fixed;
	top: 20%;
	left: 50%;
	transform: translate(-50%, 0);
	width: 40%;
	z-index: 9999;
	}
	.scrollable-prompts {
	flex: 1;
	overflow-y: auto;
	padding: 10px;
	max-height: 300px; /* or any height you prefer */
	}
	#suggested_prompts_btn {
	background: #D46BCA !important;
	color: white !important;
	}
	#close_prompt_box {
	background: #553566 !important;
	color: white !important;
	width: 100%;
	}
	#download_btn {
	background: #553566 !important;
	color: white !important;
	}
	input[type="radio"]:checked + span {
	border-color: #D46BCA !important;
	}

	'''

	def start_tryon_with_download(img_dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
	# Call your existing start_tryon
	output_img, mask_img = start_tryon(img_dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed)

	# Save output image to temp file
	tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
	output_img.save(tmp_file.name)

	# Return images for display plus file path for download
	return output_img, mask_img, tmp_file.name

	image_blocks = gr.Blocks(css=css).queue()
	with image_blocks as demo:
	gr.Markdown("## <span id='app-title'>Virtual Try-on</span>")
	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)


	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")

	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, show_download_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, show_download_button=False)
	# download_btn = gr.DownloadButton(label="Download Generated Image", elem_id="download_btn")


	with gr.Column():
	try_button = gr.Button(value="Try-on", elem_id="generate_btn")
	download_btn = gr.DownloadButton(label="Download Generated Image", elem_id="download_btn")
	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)



	# 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')
	try_button.click(
	fn=start_tryon_with_download,
	inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed],
	outputs=[image_out, masked_img, download_btn],
	api_name='tryon'
	)

	image_blocks.launch(auth=(HF_TEST_USER_NAME, HF_PASS), show_error=True)
	# image_blocks.launch(show_error=True)