  The high-quality images yielded by generative adversarial networks (GANs)
have motivated investigations into their application for image editing.
However, GANs are often limited in the control they provide for performing
specific edits. One of the principal challenges is the entangled latent space
of GANs, which is not directly suitable for performing independent and detailed
edits. Recent editing methods allow for either controlled style edits or
controlled semantic edits. In addition, methods that use semantic masks to edit
images have difficulty preserving the identity and are unable to perform
controlled style edits. We propose a method to disentangle a GAN$\text{'}$s
latent space into semantic and style spaces, enabling controlled semantic and
style edits for face images independently within the same framework. To achieve
this, we design an encoder-decoder based network architecture ($S^2$-Flow),
which incorporates two proposed inductive biases. We show the suitability of
$S^2$-Flow quantitatively and qualitatively by performing various semantic and
style edits.
