This repository provides PyTorch implementations of a wide range of GAN architectures suggested in research papers. The collection includes models for image generation, image-to-image translation, super-resolution, and semi-supervised learning. Each implementation focuses on capturing the core ideas of the original papers rather than exact layer configurations. Models covered include AC-GAN, Conditional GAN, InfoGAN, Wasserstein GAN, and architectural variants like BigGAN, DiscoGAN, and DualGAN.