CREPE: Controlling Diffusion with Replica Exchange
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908860853256192 |
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| author | He, Jiajun Jeha, Paul Potaptchik, Peter Zhang, Leo Hernández-Lobato, José Miguel Du, Yuanqi Syed, Saifuddin Vargas, Francisco |
| author_facet | He, Jiajun Jeha, Paul Potaptchik, Peter Zhang, Leo Hernández-Lobato, José Miguel Du, Yuanqi Syed, Saifuddin Vargas, Francisco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CREPE: Controlling Diffusion with Replica Exchange He, Jiajun Jeha, Paul Potaptchik, Peter Zhang, Leo Hernández-Lobato, José Miguel Du, Yuanqi Syed, Saifuddin Vargas, Francisco Machine Learning 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. |
| title | CREPE: Controlling Diffusion with Replica Exchange |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.23265 |