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
    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,
)

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(duration=100)  # 実行時間を100秒に設定
def start_tryon(
    dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed, num_images
):
    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_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 = pose_img[:, :, ::-1]
    pose_img = Image.fromarray(pose_img).resize((768, 1024))

    # テキストエンコーディングは一度だけ行う
    with torch.no_grad():
        with torch.cuda.amp.autocast():
            prompt = "model is wearing " + garment_des
            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
            negative_prompt_c = "monochrome, lowres, bad anatomy, worst quality, low quality"
            (
                prompt_embeds_c,
                _,
                _,
                _,
            ) = pipe.encode_prompt(
                prompt_c,
                num_images_per_prompt=1,
                do_classifier_free_guidance=False,
                negative_prompt=negative_prompt_c,
            )

    pose_img_tensor = tensor_transfrom(pose_img).unsqueeze(0).to(device, torch.float16)
    garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device, torch.float16)

    # 初期化
    output_images = gr.State([])

    # 最初にマスク画像を一度だけ出力
    yield output_images.value, mask_gray

    for i in range(int(num_images)):
        current_seed = seed + i if seed is not None and seed != -1 else None
        generator = (
            torch.Generator(device).manual_seed(int(current_seed)) if current_seed is not None else None
        )

        with torch.no_grad():
            with torch.cuda.amp.autocast():
                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_tensor.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_copy = human_img_orig.copy()
            human_img_copy.paste(out_img, (int(left), int(top)))
            new_image = human_img_copy
        else:
            new_image = images[0]

        # 画像を追加
        output_images.value.append(new_image)

        # ギャラリーを更新しつつmasked_imgを保持
        yield output_images.value, mask_gray  # mask_grayを再度渡す

    # 最終的な結果を返す
    return output_images.value, 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)

image_blocks = gr.Blocks().queue()
with image_blocks as demo:
    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)

            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_gallery = gr.Gallery(
                label="Generated Images", elem_id="output-gallery", show_label=True
            )

    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)
                num_images = gr.Slider(
                    label="Number of Images", minimum=1, maximum=10, step=1, value=1  # 最大値を10に変更
                )

    try_button.click(
        fn=start_tryon,
        inputs=[
            imgs,
            garm_img,
            prompt,
            is_checked,
            is_checked_crop,
            denoise_steps,
            seed,
            num_images,
        ],
        outputs=[image_gallery, masked_img],
        api_name='tryon',
    )

image_blocks.launch(show_error=True)