BigGAN-PyTorch

Why we included this project

BigGAN-PyTorch is the reference implementation of the BigGAN architecture, the model that first pushed class-conditional generation to 512x512 resolution on ImageNet. If you are reproducing the Large Scale GAN Training paper or building a codebase that needs to synthesize high-fidelity natural images from scratch, this is where you start. The repo ships complete training scripts for 4 to 8 GPU setups, using gradient accumulation to fake the mega-batches the original work ran on TPUs, along with utilities for caching datasets as HDF5 for faster I/O and for computing the Inception statistics behind FID. A bundled converter also ports the official pre-trained TFHub generator weights into PyTorch, so you can sample from a ready model without retraining. Just be aware of the hardware appetite: full-size training assumes multi-GPU rigs and a lot of RAM, though the code is clean enough to adapt down for smaller experiments.