  High-quality, diverse, and photorealistic images can now be generated by
unconditional GANs (e.g., StyleGAN). However, limited options exist to control
the generation process using (semantic) attributes, while still preserving the
quality of the output. Further, due to the entangled nature of the GAN latent
space, performing edits along one attribute can easily result in unwanted
changes along other attributes. In this paper, in the context of conditional
exploration of entangled latent spaces, we investigate the two sub-problems of
attribute-conditioned sampling and attribute-controlled editing. We present
StyleFlow as a simple, effective, and robust solution to both the sub-problems
by formulating conditional exploration as an instance of conditional continuous
normalizing flows in the GAN latent space conditioned by attribute features. We
evaluate our method using the face and the car latent space of StyleGAN, and
demonstrate fine-grained disentangled edits along various attributes on both
real photographs and StyleGAN generated images. For example, for faces, we vary
camera pose, illumination variation, expression, facial hair, gender, and age.
Finally, via extensive qualitative and quantitative comparisons, we demonstrate
the superiority of StyleFlow to other concurrent works.
