[2509.23265] CREPE: Controlling Diffusion with Replica Exchange

Computer Science > Machine Learning arXiv:2509.23265 (cs) [Submitted on 27 Sep 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:CREPE: Controlling Diffusion with Replica Exchange Authors:Jiajun He, Paul Jeha, Peter Potaptchik, Leo Zhang, José Miguel Hernández-Lobato, Yuanqi Du, Saifuddin Syed, Francisco Vargas View a PDF of the paper titled CREPE: Controlling Diffusion with Replica Exchange, by Jiajun He and 7 other authors View PDF HTML (experimental) Abstract:Inference-time control of diffusion models aims to steer model outputs to satisfy new constraints without retraining. Previous approaches have mostly relied on heuristic guidance or have been coupled with Sequential Monte Carlo (SMC) for bias correction. In this paper, we propose a flexible alternative based on replica exchange, an algorithm designed initially for sampling problems. We refer to this method as CREPE (Controlling with REPlica Exchange). Unlike SMC, CREPE: (1) generates particles sequentially, (2) maintains high diversity in the generated samples after a burn-in period, and (3) enables online refinement or early termination. We demonstrate its versatility across various tasks, including temperature annealing, reward-tilting, model composition and classifier-free guidance debiasing, with competitive performance compared to prior SMC methods. Comments: Subjects: Machine Learning (cs.LG) Cite as: arXiv:2509.23265 [cs.LG]   (or arXiv:2509.23265v2 [cs.LG] for this version)   https://doi.org/10.4...