  The discovery of interpretable directions in the latent spaces of pre-trained
GAN models has recently become a popular topic. In particular, StyleGAN2 has
enabled various image generation and manipulation tasks due to its rich and
disentangled latent spaces. The discovery of such directions is typically done
either in a supervised manner, which requires annotated data for each desired
manipulation or in an unsupervised manner, which requires a manual effort to
identify the directions. As a result, existing work typically finds only a
handful of directions in which controllable edits can be made. In this study,
we design a novel submodular framework that finds the most representative and
diverse subset of directions in the latent space of StyleGAN2. Our approach
takes advantage of the latent space of channel-wise style parameters, so-called
style space, in which we cluster channels that perform similar manipulations
into groups. Our framework promotes diversity by using the notion of clusters
and can be efficiently solved with a greedy optimization scheme. We evaluate
our framework with qualitative and quantitative experiments and show that our
method finds more diverse and disentangled directions. Our project page can be
found at http://catlab-team.github.io/fantasticstyles.
