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,
    AutoTokenizer,
)
from diffusers import DDPMScheduler, AutoencoderKL
from typing import List

import torch
import os
import io
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

# ------------------------------------------------------------------------------------
# Helpers
# ------------------------------------------------------------------------------------
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
    mask = (mask * 255).astype(np.uint8)
    return Image.fromarray(mask)

# ------------------------------------------------------------------------------------
# Load models / pipeline
# ------------------------------------------------------------------------------------
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
)

UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
    base_path,
    subfolder="unet_encoder",
    torch_dtype=torch.float16,
)

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

for m in (UNet_Encoder, image_encoder, vae, unet, text_encoder_one, text_encoder_two):
    m.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

# ------------------------------------------------------------------------------------
# Core try-on function
# ------------------------------------------------------------------------------------
def _tryon_core(
    human_img: Image.Image,
    garm_img: Image.Image,
    garment_des: str = "",
    auto_mask: bool = True,
    auto_crop: bool = False,
    denoise_steps: int = 30,
    seed: int | None = 42,
) -> Image.Image:
    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 = human_img.convert("RGB")

    if auto_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_used = cropped_img.resize((768, 1024))
    else:
        human_img_used = human_img_orig.resize((768, 1024))

    # Mask
    if auto_mask:
        keypoints = openpose_model(human_img_used.resize((384, 512)))
        model_parse, _ = parsing_model(human_img_used.resize((384, 512)))
        mask, _ = get_mask_location("hd", "upper_body", model_parse, keypoints)
        mask = mask.resize((768, 1024))
    else:
        mask = pil_to_binary_mask(Image.new("L", (768, 1024), 255))

    # DensePose
    human_img_arg = _apply_exif_orientation(human_img_used.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 = pose_img[:, :, ::-1]
    pose_img = Image.fromarray(pose_img).resize((768, 1024))

    # Run pipeline
    with torch.no_grad(), torch.cuda.amp.autocast():
        prompt = "model is wearing " + (garment_des or "a garment")
        negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
        (
            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_c = "a photo of " + (garment_des or "a garment")
        if not isinstance(prompt_c, List):
            prompt_c = [prompt_c]
        (prompt_embeds_c, _, _, _,) = pipe.encode_prompt(
            prompt_c,
            num_images_per_prompt=1,
            do_classifier_free_guidance=False,
            negative_prompt=negative_prompt,
        )

        pose_tensor = 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=int(denoise_steps),
            generator=generator,
            strength=1.0,
            pose_img=pose_tensor,
            text_embeds_cloth=prompt_embeds_c.to(device, torch.float16),
            cloth=garm_tensor,
            mask_image=mask,
            image=human_img_used,
            height=1024,
            width=768,
            ip_adapter_image=garm_img.resize((768, 1024)),
            guidance_scale=2.0,
        )[0]

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

# ------------------------------------------------------------------------------------
# Gradio UI (and HTTP function endpoint via /run/tryon)
# ------------------------------------------------------------------------------------
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 = {"background": ex_human, "layers": None, "composite": None}
    human_ex_list.append(ex_dict)

@spaces.GPU
def start_tryon(dict_img, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
    human_img = dict_img["background"].convert("RGB")
    out_img = _tryon_core(
        human_img=human_img,
        garm_img=garm_img,
        garment_des=garment_des,
        auto_mask=bool(is_checked),
        auto_crop=bool(is_checked_crop),
        denoise_steps=int(denoise_steps),
        seed=int(seed) if seed is not None else None,
    )
    mask_gray = pil_to_binary_mask(out_img.convert("L"))
    return out_img, mask_gray

with gr.Blocks() as image_blocks:
    gr.Markdown("## IDM-VTON 👕👔👚")
    gr.Markdown(
        "Virtual Try-on with your image and garment image. Check out the "
        "[source codes](https://github.com/yisol/IDM-VTON) and the "
        "[model](https://huggingface.co/yisol/IDM-VTON)"
    )
    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)
            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",
                    )
            gr.Examples(inputs=garm_img, examples_per_page=8, examples=garm_list_path)

        with gr.Column():
            masked_img = gr.Image(label="Masked image output", elem_id="masked-img", show_share_button=False)
        with gr.Column():
            image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False)

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

    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",   # <-- HTTP: POST /run/tryon
    )

# IMPORTANT: expose a top-level `demo` for Gradio Spaces
demo = image_blocks