Title: A Comprehensive Overhaul of Distilling Unconditional GANs. Abstract: Generative adversarial networks (GANs) have achieved impressive results on various content generation tasks. Yet, their high demand on storage and computation impedes their deployment on resource-constrained devices. Though several GAN compression methods have been proposed to address the problem, most of them focus on conditional GANs. In this paper, we provide a comprehensive overhaul of distilling unconditional GAN, especially for the popular StyleGAN2 architecture. Our key insight is that the main challenge of unconditional GAN distillation lies in the output discrepancy issue, where the teacher and student model yield different outputs given the same input latent code. Standard knowledge distillation losses typically fail under this heterogeneous distillation scenario. We conduct thorough analysis about the reasons and effects of this discrepancy issue, and identify that the style module plays a vital role in determining semantic information of generated images. Based on this finding, we propose a novel initialization strategy for the student model, which can ensure the output consistency to the maximum extent. To further enhance the semantic consistency between the teacher and student model, we present another latent-direction-based distillation loss that preserves the semantic relations in latent space. Extensive experiments demonstrate that our framework achieves state-of-the-art results in StyleGAN2 distillation, outperforming the existing GAN distillation methods by a large margin.