app.py

import os
from flask import Flask, request, jsonify
from PIL import Image
from io import BytesIO
import torch
import base64
import io 
import logging
import gradio as gr
import numpy as np
import spaces
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 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

app = Flask(__name__)

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

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


def decode_image_from_base64(base64_str):
    try:
        img_data = base64.b64decode(base64_str)
        img = Image.open(BytesIO(img_data))
        return img
    except Exception as e:
        logging.error(f"Error decoding image: {e}")
        raise

def encode_image_to_base64(img):
    try:
        buffered = BytesIO()
        img.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
        return img_str
    except Exception as e:
        logging.error(f"Error encoding image: {e}")
        raise

@spaces.GPU
def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed, categorie = 'upper_body'):
    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', categorie , 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))

    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"
            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 " + garment_des
                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,
                )[0]

    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
    else:
        return images[0], mask_gray


def clear_gpu_memory():
    torch.cuda.empty_cache()
    torch.cuda.synchronize()

@app.route('/tryon', methods=['POST'])
def tryon():
    data = request.json
    human_image = decode_image_from_base64(data['human_image'])
    garment_image = decode_image_from_base64(data['garment_image'])
    description = data.get('description')
    use_auto_mask = data.get('use_auto_mask', True)
    use_auto_crop = data.get('use_auto_crop', False)
    denoise_steps = int(data.get('denoise_steps', 30))
    seed = int(data.get('seed', 42))
    categorie = data.get('categorie' , 'upper_body')
    human_dict = {
        'background': human_image,
        'layers': [human_image] if not use_auto_mask else None,
        'composite': None
    }
    #clear_gpu_memory()

    output_image, mask_image = start_tryon(human_dict, garment_image, description, use_auto_mask, use_auto_crop, denoise_steps, seed , categorie)

    output_base64 = encode_image_to_base64(output_image)
    mask_base64 = encode_image_to_base64(mask_image)

    return jsonify({
        'output_image': output_base64,
        'mask_image': mask_base64
    })

@spaces.GPU 
def generate_mask(human_img, categorie='upper_body'):
    device = "cuda"
    openpose_model.preprocessor.body_estimation.model.to(device)
    pipe.to(device)

    try:
        # Redimensionner l'image pour le modèle
        human_img_resized = human_img.convert("RGB").resize((384, 512))

        # Générer les points clés et le masque
        keypoints = openpose_model(human_img_resized)
        model_parse, _ = parsing_model(human_img_resized)
        mask, _ = get_mask_location('hd', categorie, model_parse, keypoints)

        # Redimensionner le masque à la taille d'origine de l'image
        mask_resized = mask.resize(human_img.size)
        
        return mask_resized
    except Exception as e:
        logging.error(f"Error generating mask: {e}")
        raise e

   
@app.route('/generate_mask', methods=['POST'])
def generate_mask_api():
    try:
        # Récupérer les données de l'image à partir de la requête
        data = request.json
        base64_image = data.get('human_image')
        categorie = data.get('categorie', 'upper_body')
        
        # Décodage de l'image à partir de base64
        human_img = decode_image_from_base64(base64_image)
        
        # Appeler la fonction pour générer le masque
        mask_resized = generate_mask(human_img, categorie)
        
        # Encodage du masque en base64 pour la réponse
        mask_base64 = encode_image_to_base64(mask_resized)
        
        return jsonify({
            'mask_image': mask_base64
        }), 200
    except Exception as e:
        logging.error(f"Error generating mask: {e}")
        return jsonify({'error': str(e)}), 500


if __name__ == "__main__":
    app.run(debug=True, host="0.0.0.0", port=7860)