  There has been remarkable recent work in unpaired image-to-image translation.
However, they're restricted to translation on single pairs of distributions,
with some exceptions. In this study, we extend one of these works to a scalable
multidistribution translation mechanism. Our translation models not only
converts from one distribution to another but can be stacked to create
composite translation functions. We show that this composite property makes it
possible to generate images with characteristics not seen in the training set.
We also propose a decoupled training mechanism to train multiple distributions
separately, which we show, generates better samples than isolated joint
training. Further, we do a qualitative and quantitative analysis to assess the
plausibility of the samples. The code is made available at
https://github.com/lgraesser/im2im2im.
