Blue noise for diffusion models

Most of the existing diffusion models use Gaussian noise for training and sampling across all time steps, which may not optimally account for the frequency contents reconstructed by the denoising network. Despite the diverse applications of correlated noise in computer graphics, its potential for improving the training process has been underexplored. In this paper, we introduce a novel and general class of diffusion models taking correlated noise within and across images into account. More specifically, we propose a time-varying noise …

applications computer computer graphics contents cs.cv cs.gr cs.lg denoising diffusion diffusion models diverse diverse applications graphics network noise paper process sampling training