  Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in
various image generation and synthesis tasks, yet their notoriously high
computational cost hinders their efficient deployment on edge devices. Directly
applying generic compression approaches yields poor results on GANs, which
motivates a number of recent GAN compression works. While prior works mainly
accelerate conditional GANs, e.g., pix2pix and CycleGAN, compressing
state-of-the-art unconditional GANs has rarely been explored and is more
challenging. In this paper, we propose novel approaches for unconditional GAN
compression. We first introduce effective channel pruning and knowledge
distillation schemes specialized for unconditional GANs. We then propose a
novel content-aware method to guide the processes of both pruning and
distillation. With content-awareness, we can effectively prune channels that
are unimportant to the contents of interest, e.g., human faces, and focus our
distillation on these regions, which significantly enhances the distillation
quality. On StyleGAN2 and SN-GAN, we achieve a substantial improvement over the
state-of-the-art compression method. Notably, we reduce the FLOPs of StyleGAN2
by 11x with visually negligible image quality loss compared to the full-size
model. More interestingly, when applied to various image manipulation tasks,
our compressed model forms a smoother and better disentangled latent manifold,
making it more effective for image editing.
