HOLD method reduces diffusion model memorization by smoothing score functions
New theoretical work shows higher-order dynamics prevent AI from copying training data.
Diffusion models are known to occasionally reproduce exact training samples—a memorization issue that raises copyright and privacy red flags. In a new arXiv paper, Benjamin Sterling, Mónica F. Bugallo, and Tom Tirer propose Higher-Order Langevin Dynamics (HOLD) as a principled fix. HOLD extends standard diffusion by introducing auxiliary variables analogous to 'velocity' and 'acceleration' (depending on order). These extra degrees of freedom impose dynamical constraints that regularize the data variable's trajectory, effectively smoothing out the learned score function.
This paper provides the first theoretical analysis of HOLD's regularization effect. The authors show that in HOLD, the data variable's dynamics are governed by a low-pass-filtered version of the score function, with smoothness increasing alongside model order. They also analyze the optimal empirical score and demonstrate that distribution collapse is less likely as order rises. Empirical validation on real-world data confirms that HOLD reduces memorization while maintaining generation quality. For practitioners, this offers a drop-in method to make diffusion models safer for deployment without sacrificing performance.
- HOLD introduces auxiliary 'velocity' and 'acceleration' variables to regularize diffusion trajectories.
- The dynamics are governed by a low-pass-filtered score function, with smoothness increasing with model order.
- First theoretical proof that higher-order Langevin dynamics reduces memorization and prevents distribution collapse.
Why It Matters
This technique could help AI companies avoid copyright lawsuits by preventing model regurgitation of training data.