This repository provides the official implementation of a CVPR 2020 research paper introducing a U-Net based discriminator for Generative Adversarial Networks. The discriminator uses skip connections to preserve spatial information, improving gradient flow during training. The project includes training scripts with options for CutMix augmentation, warmup scheduling, and supports both conditional and unconditional image generation on datasets like CelebA and FFHQ.