Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
Diffusion/score-based models have emerged as powerful generative models, capable of generating high-quality samples that mimic the training data distribution. However, it has been observed that they are prone to reproducing training samples-known as "memorization"-potentially violating copyright and privacy. In this paper, we study the effect of Higher-Order Langevin Dynamics (HOLD) on this phenomenon. HOLD diffusion processes introduce auxiliary variables; if the data variable is interpreted as "position," then the auxiliary variables can be interpreted as "velocity" and "acceleration," depending on the chosen order of the model. They were originally proposed based on the intuition that ...
| Authors | Benjamin Sterling, Mónica F. Bugallo, Tom Tirer |
|---|---|
| Primary category | stat.ML |
| Published | 18 May 2026 |
| Last updated at source | 23 May 2026 |
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