  StyleGAN2 is a state-of-the-art network in generating realistic images.
Besides, it was explicitly trained to have disentangled directions in latent
space, which allows efficient image manipulation by varying latent factors.
Editing existing images requires embedding a given image into the latent space
of StyleGAN2. Latent code optimization via backpropagation is commonly used for
qualitative embedding of real world images, although it is prohibitively slow
for many applications. We propose a way to distill a particular image
manipulation of StyleGAN2 into image-to-image network trained in paired way.
The resulting pipeline is an alternative to existing GANs, trained on unpaired
data. We provide results of human faces' transformation: gender swap,
aging/rejuvenation, style transfer and image morphing. We show that the quality
of generation using our method is comparable to StyleGAN2 backpropagation and
current state-of-the-art methods in these particular tasks.
