  StyleGAN is arguably one of the most intriguing and well-studied generative
models, demonstrating impressive performance in image generation, inversion,
and manipulation. In this work, we explore the recent StyleGAN3 architecture,
compare it to its predecessor, and investigate its unique advantages, as well
as drawbacks. In particular, we demonstrate that while StyleGAN3 can be trained
on unaligned data, one can still use aligned data for training, without
hindering the ability to generate unaligned imagery. Next, our analysis of the
disentanglement of the different latent spaces of StyleGAN3 indicates that the
commonly used W/W+ spaces are more entangled than their StyleGAN2 counterparts,
underscoring the benefits of using the StyleSpace for fine-grained editing.
Considering image inversion, we observe that existing encoder-based techniques
struggle when trained on unaligned data. We therefore propose an encoding
scheme trained solely on aligned data, yet can still invert unaligned images.
Finally, we introduce a novel video inversion and editing workflow that
leverages the capabilities of a fine-tuned StyleGAN3 generator to reduce
texture sticking and expand the field of view of the edited video.
