  StyleGAN has shown remarkable performance in unconditional image generation.
However, its high computational cost poses a significant challenge for
practical applications. Although recent efforts have been made to compress
StyleGAN while preserving its performance, existing compressed models still lag
behind the original model, particularly in terms of sample diversity. To
overcome this, we propose a novel channel pruning method that leverages varying
sensitivities of channels to latent vectors, which is a key factor in sample
diversity. Specifically, by assessing channel importance based on their
sensitivities to latent vector perturbations, our method enhances the diversity
of samples in the compressed model. Since our method solely focuses on the
channel pruning stage, it has complementary benefits with prior training
schemes without additional training cost. Extensive experiments demonstrate
that our method significantly enhances sample diversity across various
datasets. Moreover, in terms of FID scores, our method not only surpasses
state-of-the-art by a large margin but also achieves comparable scores with
only half training iterations.
