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 model to incorporate correlated noise into the training process, as well as a method for fast generation of correlated noise mask. Our model is built upon deterministic diffusion models and utilizes blue noise to help improve the generation quality compared to using Gaussian white (random) noise only. Further, our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method, achieving improvements on different tasks over existing deterministic diffusion models in terms of FID metric.

在本文中，我们提出了一种新颖且通用的扩散模型，它考虑了图像内部和图像之间存在的相关噪声，通过引入时间变化的噪声模型和快速生成相关噪声掩模的方法，结合确定性扩散模型和蓝噪声，改进了训练过程和生成质量，并在不同数据集上进行了定性和定量评估，以FID度量标准比现有的确定性扩散模型取得了改进。

扩散模型的蓝噪声