  Generative adversarial networks (GANs) have an enormous potential impact on
digital content creation, e.g., photo-realistic digital avatars, semantic
content editing, and quality enhancement of speech and images. However, the
performance of modern GANs comes together with massive amounts of computations
performed during the inference and high energy consumption. That complicates,
or even makes impossible, their deployment on edge devices. The problem can be
reduced with quantization -- a neural network compression technique that
facilitates hardware-friendly inference by replacing floating-point
computations with low-bit integer ones. While quantization is well established
for discriminative models, the performance of modern quantization techniques in
application to GANs remains unclear. GANs generate content of a more complex
structure than discriminative models, and thus quantization of GANs is
significantly more challenging. To tackle this problem, we perform an extensive
experimental study of state-of-art quantization techniques on three diverse GAN
architectures, namely StyleGAN, Self-Attention GAN, and CycleGAN. As a result,
we discovered practical recipes that allowed us to successfully quantize these
models for inference with 4/8-bit weights and 8-bit activations while
preserving the quality of the original full-precision models.
