CREPE: Controlling Diffusion with Replica Exchange

• URL: http://arxiv.org/abs/2509.23265v1
• Date: Sat, 27 Sep 2025 11:45:37 GMT
• 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,
• Abstract summary: Inference-time control of diffusion models aims to steer model outputs to satisfy new constraints without retraining.<n>We propose a flexible alternative based on replica exchange, an algorithm designed initially for sampling problems.<n> 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.
• Score: 32.38925001748167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 the 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.

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