  We propose an algorithm for taming Normalizing Flow models - changing the
probability that the model will produce a specific image or image category. We
focus on Normalizing Flows because they can calculate the exact generation
probability likelihood for a given image. We demonstrate taming using models
that generate human faces, a subdomain with many interesting privacy and bias
considerations. Our method can be used in the context of privacy, e.g.,
removing a specific person from the output of a model, and also in the context
of debiasing by forcing a model to output specific image categories according
to a given target distribution. Taming is achieved with a fast fine-tuning
process without retraining the model from scratch, achieving the goal in a
matter of minutes. We evaluate our method qualitatively and quantitatively,
showing that the generation quality remains intact, while the desired changes
are applied.
