demo_generate/animegan_method.py

import torch
from PIL import Image
import torchvision.transforms as transforms
import logging
from datetime import datetime
import io

logger = logging.getLogger(__name__)

class AnimeGANProcessor:
    def __init__(self, device):
        self.device = device
        self.model = None
        self.load_model()
    
    def load_model(self):
        try:
            logger.info("Loading AnimeGAN model...")
            self.model = torch.hub.load(
                'bryandlee/animegan2-pytorch:main', 
                'generator',
                pretrained='face_paint_512_v2',
                trust_repo=True
            ).to(self.device)
            self.model.eval()
            logger.info("Model loaded successfully")
        except Exception as e:
            logger.error(f"Error loading model: {str(e)}")
            raise
    
    def process_image(self, image):
        """Convert image to anime style"""
        try:
            transform = transforms.Compose([
                transforms.Resize((512, 512)),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
            ])
            with torch.no_grad():
                output = self.model(transform(image).unsqueeze(0).to(self.device))
            return transforms.ToPILImage()((output * 0.5 + 0.5).squeeze().cpu())
        except Exception as e:
            logger.error(f"Error processing image: {str(e)}")
            raise

def generate_anime(image_data):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    processor = AnimeGANProcessor(device)
    
    start_time = datetime.now()
    logger.info(f"Generating anime image - {start_time}")
    
    try:
        image = Image.open(io.BytesIO(image_data)).convert("RGB")
        processed_img = processor.process_image(image)
        
        img_io = io.BytesIO()
        processed_img.save(img_io, 'PNG')
        img_io.seek(0)
        
        duration = (datetime.now() - start_time).total_seconds()
        logger.info(f"Successfully processed. Duration: {duration} seconds")
        return img_io
    except Exception as e:
        logger.error(f"Processing error: {str(e)}", exc_info=True)
        raise