stylegan2-pytorch

Why we included this project

Training a photorealistic image generator usually means wrestling with a research codebase or paying for a hosted API, but this project reduces that to a single pip install and one command that points at a folder of examples. It wraps StyleGAN2 to generate convincing faces, flowers, cityscapes, and similar texture-heavy subjects, with checkpointing, resume-after-crash, latent-space interpolation videos, and low-data augmentation all handled through command-line flags. You can scale from a single consumer GPU to larger setups by raising the network capacity or enabling multi-GPU training, though you will still need a CUDA-capable GPU and some tolerance for GAN instability. For teams prototyping synthetic data or concept imagery, it is a practical way to explore StyleGAN2 without writing training loops.