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.

本文提出了一种利用早期退出支路来降低计算量的方法，并在两种最先进的生成任务模型上应用，即从语义映射中生成和面部表情的交叉重演，可以输出具有自定义较低质量阈值的图像，尤其适用于实时应用场景。