  Generative DNNs are a powerful tool for image synthesis, but they are limited
by their computational load. On the other hand, given a trained model and a
task, e.g. faces generation within a range of characteristics, the output image
quality will be unevenly distributed among images with different
characteristics. It follows, that we might restrain the models complexity on
some instances, maintaining a high quality. We propose a method for diminishing
computations by adding so-called early exit branches to the original
architecture, and dynamically switching the computational path depending on how
difficult it will be to render the output. We apply our method on two different
SOTA models performing generative tasks: generation from a semantic map, and
cross-reenactment of face expressions; showing it is able to output images with
custom lower-quality thresholds. For a threshold of LPIPS <=0.1, we diminish
their computations by up to a half. This is especially relevant for real-time
applications such as synthesis of faces, when quality loss needs to be
contained, but most of the inputs need fewer computations than the complex
instances.
