StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

Understand

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets.

• We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works.
• Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner.
• Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images.