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
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


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]:
                mask[i, j] = 1
    return Image.fromarray((mask * 255).astype(np.uint8))


def add_watermark(main_image, logo_path='logo.png', position='bottom-left', size_percentage=10):
    logo = Image.open(logo_path).convert('RGBA')
    main_width, main_height = main_image.size
    logo_width = int(main_width * size_percentage / 100)
    logo_height = int(logo.size[1] * (logo_width / logo.size[0]))
    logo = logo.resize((logo_width, logo_height), Image.Resampling.LANCZOS)

    if main_image.mode != 'RGBA':
        main_image = main_image.convert('RGBA')

    watermarked = Image.new('RGBA', main_image.size, (0, 0, 0, 0))
    watermarked.paste(main_image, (0, 0))

    if position == 'bottom-left':
        pos = (10, main_height - logo_height - 10)
    elif position == 'bottom-right':
        pos = (main_width - logo_width - 10, main_height - logo_height - 10)
    elif position == 'top-right':
        pos = (main_width - logo_width - 10, 10)
    elif position == 'top-left':
        pos = (10, 10)

    watermarked.paste(logo, pos, logo)
    return watermarked.convert('RGB')


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)
tokenizer_one = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer", use_fast=False)
tokenizer_two = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer_2", 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)
UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(base_path, subfolder="unet_encoder", torch_dtype=torch.float16)

parsing_model = Parsing(0)
openpose_model = OpenPose(0)

for model in [unet, text_encoder_one, text_encoder_two, image_encoder, vae, UNet_Encoder]:
    model.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
        cropped_img = human_img_orig.crop((left, top, left + target_width, top + target_height))
        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, _ = 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_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'))
    pose_img = args.func(args, human_img_arg)
    pose_img = Image.fromarray(pose_img[:, :, ::-1]).resize((768, 1024))

    with torch.no_grad():
        with torch.cuda.amp.autocast():
            if not garment_des or not isinstance(garment_des, str):
                garment_des = "a garment"
    
            prompt = "model is wearing " + garment_des
            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"

            if not garment_des or not isinstance(garment_des, str):
                garment_des = "a garment"
                
            prompt_embeds, neg_embeds, pooled_prompt_embeds, neg_pooled_prompt_embeds = pipe.encode_prompt([prompt], 1, True, [negative_prompt])

            prompt_c = "a photo of " + garment_des
            prompt_embeds_c, _, _, _ = pipe.encode_prompt(
                [prompt_c], 1, False, [negative_prompt])

            pose_tensor = tensor_transfrom(pose_img).unsqueeze(0).to(device)
            garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device)
            generator = torch.Generator(device).manual_seed(seed) if seed is not None else None

            output = pipe(
                prompt_embeds=prompt_embeds.to(device),
                negative_prompt_embeds=neg_embeds.to(device),
                pooled_prompt_embeds=pooled_prompt_embeds.to(device),
                negative_pooled_prompt_embeds=neg_pooled_prompt_embeds.to(device),
                num_inference_steps=denoise_steps,
                generator=generator,
                strength=1.0,
                pose_img=pose_tensor,
                text_embeds_cloth=prompt_embeds_c.to(device),
                cloth=garm_tensor,
                mask_image=mask,
                image=human_img,
                height=1024,
                width=768,
                ip_adapter_image=garm_img,
                guidance_scale=2.0,
            )[0]

    result_img = output[0].resize(crop_size) if is_checked_crop else output[0]
    if is_checked_crop:
        human_img_orig.paste(result_img, (left, top))
        result_img = human_img_orig

    return add_watermark(result_img), None

# --- Gradio UI setup ---
garm_list = os.listdir(os.path.join(example_path, "cloth"))
garm_list_path = [os.path.join(example_path, "cloth", g) for g in garm_list]
human_list = os.listdir(os.path.join(example_path, "human"))
human_list_path = [os.path.join(example_path, "human", h) for h in human_list]
human_ex_list = [{'background': h, 'layers': None, 'composite': None} for h in human_list_path]

image_blocks = gr.Blocks().queue()
with image_blocks as demo:
    gr.Markdown(
        """
        <div style="text-align: center; background: linear-gradient(135deg, #2541b2 0%, #1a237e 100%); padding: 2.5rem; color: white; border-radius: 0 0 20px 20px; margin-bottom: 2rem; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
            <h1 style="color: white; font-size: 2.5rem; font-weight: 600; margin-bottom: 1rem;">Deradh Virtual Try-On Experience</h1>
            <div style="margin: 1rem 0;">
                <a href="https://deradh.com" style="color: white; text-decoration: none; padding: 0.5rem 1rem; border: 2px solid white; border-radius: 25px; transition: all 0.3s ease;">
                    Visit Deradh.com
                </a>
            </div>
        </div>
        <div style="text-align: center; padding: 1rem; color: #6ed7fe; font-size: 1.2rem; font-weight: 500; margin-bottom: 2rem;">
            Experience the future of fashion with our AI-powered virtual try-on technology. Every user gets 2-3 free trials per day.
        </div>
        """)

    with gr.Row():
        with gr.Column():
            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
            is_checked = gr.Checkbox(label="Use auto mask", value=True)
            is_checked_crop = gr.Checkbox(label="Auto-crop image", value=False)
            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")
            prompt = gr.Textbox(placeholder="Garment description e.g., Blue Hoodie", show_label=False)
            gr.Examples(inputs=garm_img, examples_per_page=8, examples=garm_list_path)

        with gr.Column():
            image_out = gr.Image(label="Try-On Output", elem_id="output-img", show_share_button=False)

    try_button = gr.Button(value="Try-on")
    with gr.Accordion(label="Advanced Settings", open=False):
        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, gr.Image(visible=False)],
        api_name='tryon'
    )

image_blocks.launch()