StyleGAN Tour

Key Insight

Standard GANs feed the noise vector in only at the input and let the layers do the rest. StyleGAN instead first maps the noise into an intermediate W latent space and then injects that code into every layer through adaptive instance normalization (AdaIN) — a step that rescales each layer's feature maps using shift-and-scale numbers derived from the style code, so the one code steers features at every scale from pose down to skin texture. Because of this design the latent space becomes "disentangled": moving along one direction tends to change a single attribute (hair, smile, lighting) while leaving the rest alone. This project runs inference on a pretrained StyleGAN2 face model — the kind behind sites like thispersondoesnotexist.com — and compares editing in the shared W code versus the roomier W+ space, which gives each layer its own code for finer, more local control.