  Diffusion probabilistic models have recently achieved remarkable success in
generating high quality image and video data. In this work, we build on this
class of generative models and introduce a method for lossy compression of high
resolution images. The resulting codec, which we call DIffuson-based Residual
Augmentation Codec (DIRAC), is the first neural codec to allow smooth traversal
of the rate-distortion-perception tradeoff at test time, while obtaining
competitive performance with GAN-based methods in perceptual quality.
Furthermore, while sampling from diffusion probabilistic models is notoriously
expensive, we show that in the compression setting the number of steps can be
drastically reduced.
