This project implements a photo-to-cartoon translation system using deep learning and GAN architectures. Based on the U-GAT-IT approach with AdaLIN normalization and attention mechanisms, it performs unpaired image translation without requiring paired training data. The model learns the mapping between real photos and cartoon-style images from unaligned datasets, targeting a realistic cartoon style that balances simplicity with clear identity preservation.