Update app.py

app.py

@@ -1,95 +1,246 @@
+import os
 from flask import Flask, request, jsonify
+from PIL import Image
+from io import BytesIO
+import base64
 import torch
-from diffusers import UNet2DConditionModel  # Modification ici
+import requests
+import numpy as np
+import uuid
 from transformers import (
-    AutoTokenizer, 
-    CLIPTextModel, 
-    CLIPTextModelWithProjection, 
-    CLIPVisionModelWithProjection
+    CLIPImageProcessor,
+    CLIPVisionModelWithProjection,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    AutoTokenizer
 )
-from PIL import Image
-import base64
-from io import BytesIO
+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__)
 
-# Global variables for models to load them once at startup
-unet = None
-tokenizer_one = None
-tokenizer_two = None
-noise_scheduler = None
-text_encoder_one = None
-text_encoder_two = None
-image_encoder = None
-vae = None
-UNet_Encoder = None
-
-# Load models once at startup
-def load_models():
-    global unet, tokenizer_one, tokenizer_two, noise_scheduler
-    global text_encoder_one, text_encoder_two, image_encoder, vae, UNet_Encoder
+# Variables globales pour stocker les modèles
+models_loaded = False
 
-    if unet is None:
-        # Load models only when required to reduce memory usage
-        unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-v1-4")
-    
-    if tokenizer_one is None:
-        tokenizer_one = AutoTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-    
-    if tokenizer_two is None:
-        tokenizer_two = AutoTokenizer.from_pretrained("openai/clip-vit-large-patch14-336")
+def load_models():
+    global unet, tokenizer_one, tokenizer_two, noise_scheduler, text_encoder_one, text_encoder_two
+    global image_encoder, vae, UNet_Encoder, parsing_model, openpose_model, pipe
     
-    if noise_scheduler is None:
-        noise_scheduler = DDPMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4")
-
-    if text_encoder_one is None:
-        text_encoder_one = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
-
-    if text_encoder_two is None:
-        text_encoder_two = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14-336")
+    if not models_loaded:
+        base_path = 'yisol/IDM-VTON'
+        unet = UNet2DConditionModel.from_pretrained(base_path, subfolder="unet", torch_dtype=torch.float16, force_download=False)
+        unet.requires_grad_(False)
+        
+        tokenizer_one = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer", use_fast=False, force_download=False)
+        tokenizer_two = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer_2", use_fast=False, force_download=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, force_download=False)
+        text_encoder_two = CLIPTextModelWithProjection.from_pretrained(base_path, subfolder="text_encoder_2", torch_dtype=torch.float16, force_download=False)
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(base_path, subfolder="image_encoder", torch_dtype=torch.float16, force_download=False)
+        vae = AutoencoderKL.from_pretrained(base_path, subfolder="vae", torch_dtype=torch.float16, force_download=False)
+        UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(base_path, subfolder="unet_encoder", torch_dtype=torch.float16, force_download=False)
+        
+        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,
+            force_download=False
+        )
+        pipe.unet_encoder = UNet_Encoder
+        
+        global models_loaded
+        models_loaded = True
+
+def pil_to_binary_mask(pil_image, threshold=0):
+    np_image = np.array(pil_image.convert("L"))  # Convert to grayscale directly
+    binary_mask = np_image > threshold
+    mask = np.uint8(binary_mask * 255)
+    return Image.fromarray(mask)
+
+def get_image_from_url(url):
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        return Image.open(BytesIO(response.content))
+    except Exception as e:
+        logging.error(f"Error fetching image from URL: {e}")
+        raise
+
+def decode_image_from_base64(base64_str):
+    try:
+        img_data = base64.b64decode(base64_str)
+        return Image.open(BytesIO(img_data))
+    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")
+        return base64.b64encode(buffered.getvalue()).decode("utf-8")
+    except Exception as e:
+        logging.error(f"Error encoding image: {e}")
+        raise
+
+def save_image(img):
+    unique_name = f"{uuid.uuid4()}.webp"
+    img.save(unique_name, format="WEBP", lossless=True)
+    return unique_name
+
+def clear_gpu_memory():
+    torch.cuda.empty_cache()
+    torch.cuda.ipc_collect()
+
+@spaces.GPU
+def start_tryon(human_dict, garment_image, garment_description, use_auto_mask, use_auto_crop, denoise_steps, seed, category='upper_body'):
+    device = "cuda"
+    openpose_model.preprocessor.body_estimation.model.to(device)
+    pipe.to(device)
+    pipe.unet_encoder.to(device)
+
+    garment_image = garment_image.convert("RGB").resize((768, 1024))
+    human_image_orig = human_dict["background"].convert("RGB")
+
+    if use_auto_crop:
+        width, height = human_image_orig.size
+        target_width = int(min(width, height * (3 / 4)))
+        target_height = int(min(height, width * (4 / 3)))
+        left, top = (width - target_width) / 2, (height - target_height) / 2
+        right, bottom = (width + target_width) / 2, (height + target_height) / 2
+        cropped_img = human_image_orig.crop((left, top, right, bottom)).resize((768, 1024))
+    else:
+        cropped_img = human_image_orig.resize((768, 1024))
+
+    if use_auto_mask:
+        keypoints = openpose_model(cropped_img.resize((384, 512)))
+        model_parse, _ = parsing_model(cropped_img.resize((384, 512)))
+        mask, mask_gray = get_mask_location('hd', category, model_parse, keypoints)
+        mask = mask.resize((768, 1024))
+    else:
+        mask = pil_to_binary_mask(human_dict['layers'][0].convert("RGB").resize((768, 1024)))
     
-    if image_encoder is None:
-        image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
-
-    if vae is None:
-        vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-v1-4")
-
-    if UNet_Encoder is None:
-        UNet_Encoder = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-v1-4")
-
-# Helper function to process base64 image
-def decode_image(image_base64):
-    image_data = base64.b64decode(image_base64)
-    image = Image.open(BytesIO(image_data)).convert("RGB")
-    return image
-
-# Helper function to encode image to base64
-def encode_image(image):
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-# Route for image processing
-@app.route('/process_image', methods=['POST'])
-def process_image():
-    data = request.json
-
-    # Load the models (this will only happen once)
-    load_models()
-
-    # Extract the image from the request
-    image_base64 = data.get('image_base64')
-    if not image_base64:
-        return jsonify({"error": "No image provided"}), 400
-
-    image = decode_image(image_base64)
-
-    # Perform inference with the models (example, modify as needed)
-    processed_image = image  # Placeholder for actual image processing
-
-    # Return the processed image as base64
-    processed_image_base64 = encode_image(processed_image)
-    return jsonify({"processed_image": processed_image_base64})
-
-if __name__ == '__main__':
+    mask_gray = (1 - transforms.ToTensor()(mask)) * transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])(cropped_img)
+    mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
+
+    human_image_arg = _apply_exif_orientation(cropped_img.resize((384, 512)))
+    human_image_arg = convert_PIL_to_numpy(human_image_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_image = args.func(args, human_image_arg)
+    pose_image = Image.fromarray(pose_image[:, :, ::-1]).resize((768, 1024))
+
+    with torch.no_grad(), torch.cuda.amp.autocast():
+        prompt = "model is wearing " + garment_description
+        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_description
+        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_image = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])(pose_image).unsqueeze(0).to(device, torch.float16)
+        garment_tensor = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])(garment_image).unsqueeze(0).to(device, torch.float16)
+
+        images = pipe(
+            prompt_embeds=prompt_embeds.to(device, torch.float16),
+            negative_prompt_embeds=negative_prompt_embeds.to(device, torch.float16),
+            pose_image=pose_image,
+            garment_image=garment_tensor,
+            mask_image=mask_gray.to(device, torch.float16),
+            generator=torch.Generator(device).manual_seed(seed),
+            num_inference_steps=denoise_steps
+        ).images
+
+    if images:
+        output_image = images[0]
+        output_base64 = encode_image_to_base64(output_image)
+        mask_image = mask
+        mask_base64 = encode_image_to_base64(mask_image)
+        return output_image, mask_image
+    else:
+        raise ValueError("Failed to generate image")
+
+
+# Route pour récupérer l'image générée
+@app.route('/api/get_image/<image_id>', methods=['GET'])
+def get_image(image_id):
+    # Construire le chemin complet de l'image
+    image_path = image_id  # Assurez-vous que le nom de fichier correspond à celui que vous avez utilisé lors de la sauvegarde
+
+    # Renvoyer l'image
+    try:
+        return send_file(image_path, mimetype='image/webp')
+    except FileNotFoundError:
+        return jsonify({'error': 'Image not found'}), 404
+
+@app.route('/tryon', methods=['POST'])
+def tryon_handler():
+    try:
+        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))
+        category = data.get('category', '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, category
+        )
+
+        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
+        })
+    except Exception as e:
+        logging.error(f"Error in tryon_handler: {e}")
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == "__main__":
+    load_models()  # Charge les modèles au démarrage
     app.run(host='0.0.0.0', port=7860)