  We extensively study how to combine Generative Adversarial Networks and
learned compression to obtain a state-of-the-art generative lossy compression
system. In particular, we investigate normalization layers, generator and
discriminator architectures, training strategies, as well as perceptual losses.
In contrast to previous work, i) we obtain visually pleasing reconstructions
that are perceptually similar to the input, ii) we operate in a broad range of
bitrates, and iii) our approach can be applied to high-resolution images. We
bridge the gap between rate-distortion-perception theory and practice by
evaluating our approach both quantitatively with various perceptual metrics,
and with a user study. The study shows that our method is preferred to previous
approaches even if they use more than 2x the bitrate.
