  The style-based GAN architecture (StyleGAN) yields state-of-the-art results
in data-driven unconditional generative image modeling. We expose and analyze
several of its characteristic artifacts, and propose changes in both model
architecture and training methods to address them. In particular, we redesign
the generator normalization, revisit progressive growing, and regularize the
generator to encourage good conditioning in the mapping from latent codes to
images. In addition to improving image quality, this path length regularizer
yields the additional benefit that the generator becomes significantly easier
to invert. This makes it possible to reliably attribute a generated image to a
particular network. We furthermore visualize how well the generator utilizes
its output resolution, and identify a capacity problem, motivating us to train
larger models for additional quality improvements. Overall, our improved model
redefines the state of the art in unconditional image modeling, both in terms
of existing distribution quality metrics as well as perceived image quality.
