  Parametric generative deep models are state-of-the-art for photo and
non-photo realistic image stylization. However, learning complicated image
representations requires compute-intense models parametrized by a huge number
of weights, which in turn requires large datasets to make learning successful.
Non-parametric exemplar-based generation is a technique that works well to
reproduce style from small datasets, but is also compute-intensive. These
aspects are a drawback for the practice of digital AI artists: typically one
wants to use a small set of stylization images, and needs a fast flexible model
in order to experiment with it. With this motivation, our work has these
contributions: (i) a novel stylization method called Fully Adversarial Mosaics
(FAMOS) that combines the strengths of both parametric and non-parametric
approaches; (ii) multiple ablations and image examples that analyze the method
and show its capabilities; (iii) source code that will empower artists and
machine learning researchers to use and modify FAMOS.
