ganspace

Why we included this project

GANSpace is the reference implementation of a NeurIPS 2020 paper that shows how to pull interpretable editing controls out of already-trained GANs. Instead of retraining a model, it runs principal component analysis on the activations of a pretrained StyleGAN, StyleGAN2, or BigGAN, and the resulting latent directions map to edits people can actually recognize, like shifting the viewpoint or aging a face. The repo ships modified versions of those three generators with per-layer latent support, plus an interactive explorer that lets you sample directions, apply saved edits, and preview the result on a chosen class. It is a good starting point for researchers and ML engineers who want to steer image synthesis without starting from scratch, and the Colab notebook makes it easy to try the workflow before investing in a local GPU setup. Treat it as a research and experimentation toolkit rather than a turnkey product, which suits teams exploring controllable generation instead of shipping it as an endpoint.