TransGAN replaces convolutional layers in traditional GANs with pure Transformer architectures. The model generates images through an adversarial training process where a generator creates samples and a discriminator evaluates them, both implemented using Transformer encoders. It supports distributed training, gradient accumulation, and includes IS/FID evaluation metrics for benchmarking generated image quality.