  An unpaired image-to-image (I2I) translation technique seeks to find a
mapping between two domains of data in a fully unsupervised manner. While
initial solutions to the I2I problem were provided by generative adversarial
neural networks (GANs), diffusion models (DMs) currently hold the
state-of-the-art status on the I2I translation benchmarks in terms of Frechet
inception distance (FID). Yet, DMs suffer from limitations, such as not using
data from the source domain during the training or maintaining consistency of
the source and translated images only via simple pixel-wise errors. This work
improves a recent UVCGAN model and equips it with modern advancements in model
architectures and training procedures. The resulting revised model
significantly outperforms other advanced GAN- and DM-based competitors on a
variety of benchmarks. In the case of Male-to-Female translation of CelebA, the
model achieves more than 40% improvement in FID score compared to the
state-of-the-art results. This work also demonstrates the ineffectiveness of
the pixel-wise I2I translation faithfulness metrics and suggests their
revision. The code and trained models are available at
https://github.com/LS4GAN/uvcgan2
