We introduce CAFLOW, a new diverse image-to-image translation model that simultaneously leverages the power of autoregressive modeling and the modeling efficiency of conditional normalizing flows. We transform the conditioning image into a sequence of latent encodings using a multiscale normalizing flow and repeat the process for the conditioned image. We model the conditional distribution of the latent encodings by modeling the autoregressive 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 autoregressive structure.</p
