  We introduce CAFLOW, a new diverse image-to-image translation model that
simultaneously leverages the power of auto-regressive modeling and the modeling
efficiency of conditional normalizing flows. We transform the conditioning
image into a sequence of latent encodings using a multi-scale normalizing flow
and repeat the process for the conditioned image. We model the conditional
distribution of the latent encodings by modeling the auto-regressive
distributions with an efficient multi-scale normalizing flow, where each
conditioning factor affects image synthesis at its respective resolution scale.
Our proposed framework performs well on a range of image-to-image translation
tasks. It outperforms former designs of conditional flows because of its
expressive auto-regressive structure.
