Diffusion models exhibit robust generative properties by approximating the underlying distribution of a dataset and synthesizing data by sampling from the approximated distribution. In this work, we explore how the generative performance may be be modulated if noise sources with temporal correlations -- akin to those used in the field of active matter -- are used for the destruction of the data in the forward process. Our numerical and analytical experiments suggest that the corresponding reverse process may exhibit improved generative properties.

翻译：扩散模型通过近似数据集的底层分布并从近似分布中采样合成数据，展现出强大的生成特性。在本研究中，我们探讨了若在正向过程中使用具有时间相关性的噪声源（类似于活性物质领域所采用的噪声）来破坏数据，其生成性能可能如何被调控。我们的数值与解析实验表明，相应的逆向过程可能展现出更优的生成特性。