StyleGAN-T provides training code for a Generative Adversarial Network designed for text-conditioned image synthesis at large scale. The model enables fast text-to-image generation using a modified StyleGAN architecture with CLIP-based text conditioning. The project includes training utilities for both zip and webdataset formats, custom CUDA kernels for performance, and supports unconditional and conditional training on custom datasets.