GAN Inversion

Key Insight

A trained GAN only runs one direction: noise in, image out. GAN inversion solves the reverse problem — given a real photo, find the latent code that makes the generator reproduce it. Once you have that code you can edit the real image by nudging it in latent space, which is why inversion is the bridge between "generate random faces" and "edit this particular face." This project finds the code two ways and compares the trade-off: first by directly optimizing the latent to minimize reconstruction error (slow but accurate), then by training an encoder that predicts it in a single forward pass (fast but approximate) — often inverting into the more expressive W+ space because its per-layer codes can match a real photo more closely.