The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks. Though the generative process is traditionally understood as an "iterative denoiser", there is no universally accepted language to describe it. We introduce a novel perspective to describe DMs using the mathematical language of memory retrieval from the field of energy-based Associative Memories (AMs), making efforts to keep our presentation approachable to newcomers to both of these fields. Unifying these two fields provides insight that DMs can be seen as a particular kind of AM where Lyapunov stability guarantees are bypassed by intelligently engineering the dynamics (i.e., the noise and step size schedules) of the denoising process. Finally, we present a growing body of evidence that records DMs exhibiting empirical behavior we would expect from AMs, and conclude by discussing research opportunities that are revealed by understanding DMs as a form of energy-based memory.

翻译：扩散模型（DMs）的生成过程最近在许多人工智能生成基准测试中取得了最先进的性能。尽管生成过程传统上被理解为“迭代去噪器”，但目前尚无普遍接受的描述语言。我们引入了一种新颖视角，使用基于能量的关联记忆（AMs）领域中记忆检索的数学语言来描述DMs，并努力使我们的表述对这两个领域的新手都易于理解。统一这两个领域提供了以下洞见：DMs可被视为一种特殊的AM，其通过智能设计去噪过程的动态特性（即噪声和步长调度）绕过了李雅普诺夫稳定性保证。最后，我们呈现了越来越多的证据，记录了DMs表现出我们期望从AMs中观察到的经验行为，并通过将DMs理解为一种基于能量的记忆形式，讨论了由此揭示的研究机遇。