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

import numpy as np
from PIL import Image


def get_mask_location(mode, category, parsing, keypoints):
    parsing = np.array(parsing)
    mask = np.zeros_like(parsing)
    
    print(f"Selected category: {category}")
    print(f"Parsing shape: {parsing.shape}")
    print(f"Unique values in parsing: {np.unique(parsing)}")
    
    if category == "상의":
        # 상의에 해당하는 부분만 마스킹 (상체, 팔)
        upper_body = [5, 6, 7]
        mask[np.isin(parsing, upper_body)] = 255
        print(f"Masking upper body parts: {upper_body}")
    elif category == "하의":
        # 하의에 해당하는 부분만 마스킹 (하체)
        lower_body = [9, 12, 13, 14, 15, 16, 17, 18, 19]
        mask[np.isin(parsing, lower_body)] = 255
        print(f"Masking lower body parts: {lower_body}")
    elif category == "드레스":
        # 드레스에 해당하는 부분 마스킹 (상체와 하체)
        full_body = [5, 6, 7, 9, 12, 13, 14, 15, 16, 17, 18, 19]
        mask[np.isin(parsing, full_body)] = 255
        print(f"Masking full body parts: {full_body}")
    else:
        raise ValueError(f"Unknown category: {category}")
    
    print(f"Mask shape: {mask.shape}, Unique values in mask: {np.unique(mask)}")
    print(f"Number of masked pixels: {np.sum(mask == 255)}")
    
    # 마스크 시각화를 위한 코드 추가
    import matplotlib.pyplot as plt
    plt.figure(figsize=(10, 10))
    plt.imshow(mask, cmap='gray')
    plt.title(f"Mask for {category}")
    plt.savefig(f"mask_{category}.png")
    plt.close()
    
    mask_gray = Image.fromarray(mask.astype(np.uint8))
    return mask_gray, mask_gray
    


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
def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed, category):
    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))


    status_message = ""
    if is_checked:
        try:
            print(f"Processing category: {category}")
            keypoints = openpose_model(human_img.resize((384,512)))
            model_parse, _ = parsing_model(human_img.resize((384,512)))
            
            # 파싱 모델의 출력 확인
            print(f"Parsing model output shape: {model_parse.shape}")
            print(f"Unique values in parsing model output: {np.unique(model_parse)}")
            
            mask, mask_gray = get_mask_location('hd', category, model_parse, keypoints)
            
            # 마스크 확인 및 시각화
            mask_array = np.array(mask)
            print(f"Mask shape after get_mask_location: {mask_array.shape}")
            print(f"Unique values in mask after get_mask_location: {np.unique(mask_array)}")
            print(f"Number of masked pixels after get_mask_location: {np.sum(mask_array == 255)}")
            
            plt.figure(figsize=(10, 10))
            plt.imshow(mask_array, cmap='gray')
            plt.title(f"Mask after get_mask_location for {category}")
            plt.savefig(f"mask_after_get_mask_location_{category}.png")
            plt.close()
            
            mask = mask.resize((768,1024))
            print(f"Mask created for category {category}")
            
            # 최종 마스크 확인
            mask_array_final = np.array(mask)
            print(f"Final mask shape: {mask_array_final.shape}")
            print(f"Unique values in final mask: {np.unique(mask_array_final)}")
            print(f"Number of masked pixels in final mask: {np.sum(mask_array_final == 255)}")
            
            plt.figure(figsize=(10, 10))
            plt.imshow(mask_array_final, cmap='gray')
            plt.title(f"Final Mask for {category}")
            plt.savefig(f"final_mask_{category}.png")
            plt.close()
            
        except Exception as e:
            status_message = f"자동 마스크 생성 중 오류가 발생했습니다: {str(e)}. 기본 마스크를 사용합니다."
            print(f"Error in mask creation: {str(e)}")
            mask = Image.new('L', (768, 1024), 255)
    else:
        if dict['layers'] and dict['layers'][0]:
            mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
        else:
            mask = Image.new('L', (768, 1024), 255)
    
    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))

    with torch.no_grad():
        with torch.cuda.amp.autocast():
            with torch.no_grad():
                prompt = "((best quality, masterpiece, ultra-detailed, high quality photography, photo realistic)), the model is wearing " + garment_des
                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, normal quality, low quality, blurry, jpeg artifacts, sketch"
                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 = "((best quality, masterpiece, ultra-detailed, high quality photography, photo realistic)), a photo of " + garment_des
                    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, normal quality, low quality, blurry, jpeg artifacts, sketch"
                    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
                    result = 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,
                    )
                    
                    # 결과 형태 확인 및 처리
                    if isinstance(result, tuple):
                        images = result[0]
                    elif hasattr(result, 'images'):
                        images = result.images
                    else:
                        raise ValueError(f"Unexpected result type: {type(result)}")

    print(f"Result type: {type(result)}")
    print(f"Result content: {result}")
    print(f"Mask shape: {mask.size}")
    print(f"Human image shape: {human_img.size}")
    print(f"Garment image shape: {garm_img.size}")
    print(f"Output image shape: {images[0].size}")

    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, status_message
    else:
        return images[0], mask_gray, status_message

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(theme="Nymbo/Nymbo_Theme").queue(max_size=12)
with image_blocks as demo:
    with gr.Column():
        try_button = gr.Button(value="가상 피팅 시작")
        with gr.Accordion(label="고급 설정", open=False):
            with gr.Row():
                denoise_steps = gr.Number(label="디노이징 단계", minimum=20, maximum=40, value=30, step=1)
                seed = gr.Number(label="시드", minimum=-1, maximum=2147483647, step=1, value=-1)

    with gr.Row():
        with gr.Column():
            imgs = gr.ImageEditor(sources='upload', type="pil", label='인물 사진. 펜으로 마스크 또는 자동 마스킹 사용', interactive=True)
            with gr.Row():
                is_checked = gr.Checkbox(label="예", info="자동 생성 마스크 사용 (5초 소요)",value=True)
            with gr.Row():
                category = gr.Dropdown(
                    choices=["상의", "하의", "드레스"],
                    label="카테고리",
                    value="상의"
                )
            with gr.Row():
                is_checked_crop = gr.Checkbox(label="예", info="자동 자르기 및 크기 조정 사용",value=False)

            example = gr.Examples(
                inputs=imgs,
                examples_per_page=15,
                examples=human_ex_list
            )

        with gr.Column():
            garm_img = gr.Image(label="의류", sources='upload', type="pil")
            with gr.Row(elem_id="prompt-container"):
                with gr.Row():
                    prompt = gr.Textbox(label="의류 설명", placeholder="반소매 라운드넥 티셔츠", show_label=True, elem_id="prompt")
            example = gr.Examples(
                inputs=garm_img,
                examples_per_page=16,
                examples=garm_list_path)
        with gr.Column():
            masked_img = gr.Image(label="마스크 적용 이미지", elem_id="masked-img",show_share_button=False)
        with gr.Column():
            image_out = gr.Image(label="결과", elem_id="output-img",show_share_button=False)
        
    with gr.Column():
        status_message = gr.Textbox(label="상태", interactive=False)

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

image_blocks.launch(auth=("gini","pick"))