  In recent years, the use of Generative Adversarial Networks (GANs) has become
very popular in generative image modeling. While style-based GAN architectures
yield state-of-the-art results in high-fidelity image synthesis,
computationally, they are highly complex. In our work, we focus on the
performance optimization of style-based generative models. We analyze the most
computationally hard parts of StyleGAN2, and propose changes in the generator
network to make it possible to deploy style-based generative networks in the
edge devices. We introduce MobileStyleGAN architecture, which has x3.5 fewer
parameters and is x9.5 less computationally complex than StyleGAN2, while
providing comparable quality.
