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 logging
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
from PIL import Image, ImageDraw, ImageFont

class GenerationError(Exception):
    """Custom exception for generation errors"""
    pass

# Enhanced logging configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)

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


# -------------------- NSFW 检测模型加载 --------------------
try:
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    logger.info("Loading NSFW detector...")
    from transformers import AutoProcessor, AutoModelForImageClassification
    nsfw_processor = AutoProcessor.from_pretrained("Falconsai/nsfw_image_detection")
    nsfw_model = AutoModelForImageClassification.from_pretrained(
        "Falconsai/nsfw_image_detection"
    ).to(device)
    logger.info("NSFW detector loaded successfully.")
except Exception as e:
    logger.error(f"Failed to load NSFW detector: {e}")
    nsfw_model = None
    nsfw_processor = None
# -----------------------------------------------------------

def detect_nsfw(image: Image.Image, threshold: float = 0.5) -> bool:
    """Returns True if image is NSFW"""
    inputs = nsfw_processor(images=image, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = nsfw_model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        nsfw_score = probs[0][1].item()  # label 1 = NSFW
    return nsfw_score > threshold


progress=gr.Progress()

@spaces.GPU
def _infer(person,garment,denoise_steps,seed):
    progress(0,desc="Starting")
    device = "cuda"

    try:
        openpose_model.preprocessor.body_estimation.model.to(device)
        pipe.to(device)
        pipe.unet_encoder.to(device)

        personRGB =  person.convert("RGB")
        crop_size = personRGB.size

        human_img = personRGB.resize((768,1024))
        garm_img= garment.convert("RGB").resize((768,1024))
        
        progress(0.1,desc="Mask generating")
        
        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)

        progress(0.3,desc="DensePose processing")

        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))

        progress(0.5,desc="Image generating")

        def callback(pipe, step, timestep, callback_kwargs):
            progress_value = 0.5 + ((step+1.0)/denoise_steps)*(0.5/1.0)
            progress(progress_value, desc=f"Image generating, {step + 1}/{denoise_steps} steps")
            return callback_kwargs
        
        with torch.no_grad():
            # Extract the images
            with torch.cuda.amp.autocast():
                with torch.no_grad():
                    prompt = "model is wearing clothing"
                    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 clothing"
                    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,
                        callback_on_step_end=callback
                    )[0]
        out_img = images[0].resize(crop_size)       
    
        # NSFW 检测
        if nsfw_model and nsfw_processor:
            if detect_nsfw(out_img):
                msg = "Generated image contains NSFW content and cannot be displayed. Please provide a different image and try again."
                raise Exception(msg)

        info = {
            "status": "success"
        }  
        progress(1,desc="Complete")
        return out_img, info
    except GenerationError as e:
        error_info = {
            "error": str(e),
            "status": "failed",
        }
        return None, error_info
    except Exception as e:
        error_info = {
            "error": str(e),
            "status": "failed",
        }
        return None, error_info


def infer(person,garment,denoise_steps,seed):
    # 调用 GPU 函数
    out_img, info = _infer(person, garment,denoise_steps,seed)

    # 如果出错，抛出异常
    if info["status"] == "failed":
        raise gr.Error(info["error"])
    
    # 返回图片
    return out_img


title = "## AI Clothes Changer"
description = "Step into the world of AI clothes swap and unlock style possibilities with [AI Clothes Changer](https://www.aiclotheschanger.org)"

example_path = os.path.join(os.path.dirname(__file__), 'example')
person_list = os.listdir(os.path.join(example_path,"human"))
person_images = [os.path.join(example_path,"human",person) for person in person_list]

garment_list = os.listdir(os.path.join(example_path,"cloth"))
garment_images = [os.path.join(example_path,"cloth",garment) for garment in garment_list]


with gr.Blocks().queue() as demo:
    gr.Markdown(title)
    gr.Markdown(description)
    with gr.Row():
        with gr.Column():
            gr.Markdown("#### Person Image")
            person_image = gr.Image(
                sources=["upload"],
                type="pil",
                label="Person Image",
                width=512,
                height=512,
            )

            gr.Examples(
                inputs=person_image,
                examples_per_page=20,
                examples=person_images,
            )
        with gr.Column():   
            gr.Markdown("#### Garment Image")
            garment_image = gr.Image(
                sources=["upload"],
                type="pil",
                label="Garment Image",
                width=512,
                height=512,
            )

            gr.Examples(
                inputs=garment_image,
                examples_per_page=20,
                examples=garment_images,
            )
        with gr.Column():
            gr.Markdown("#### Generated Image")

            gen_image = gr.Image(
                label="Generated Image",
                width=512,
                height=512,
            )

            with gr.Row():
                gen_button = gr.Button("Generate")

            with gr.Accordion("Advanced Options", 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)
        
        gen_button.click(
            fn=infer,
            inputs=[person_image, garment_image, denoise_steps, seed], 
            outputs=[gen_image]
        )        

demo.launch()