  Generative Adversarial Networks (GANs) are currently an indispensable tool
for visual editing, being a standard component of image-to-image translation
and image restoration pipelines. Furthermore, GANs are especially useful for
controllable generation since their latent spaces contain a wide range of
interpretable directions, well suited for semantic editing operations. By
gradually changing latent codes along these directions, one can produce
impressive visual effects, unattainable without GANs.
  In this paper, we significantly expand the range of visual effects achievable
with the state-of-the-art models, like StyleGAN2. In contrast to existing
works, which mostly operate by latent codes, we discover interpretable
directions in the space of the generator parameters. By several simple methods,
we explore this space and demonstrate that it also contains a plethora of
interpretable directions, which are an excellent source of non-trivial semantic
manipulations. The discovered manipulations cannot be achieved by transforming
the latent codes and can be used to edit both synthetic and real images. We
release our code and models and hope they will serve as a handy tool for
further efforts on GAN-based image editing.
