{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mcmc-correction-of-score-based-diffusion","title":"MCMC-Correction of Score-Based Diffusion Models for Model Composition","arxiv_id":"2307.14012","date":"2023-07-26","proceeding":null,"authors":["Anders Sjöberg","Jakob Lindqvist","Magnus Önnheim","Mats Jirstrand","Lennart Svensson"],"abstract":"Diffusion models can be parameterised in terms of either a score or an energy function. An energy parameterisation is appealing since it enables an extended sampling procedure with a Metropolis--Hastings (MH) correction step, based on the change in total energy in the proposed samples. Improved sampling is important for model compositions, where off-the-shelf models are combined with each other, in order to sample from new distributions. For model composition, score-based diffusions have the advantages that they are popular and that many pre-trained models are readily available. However, this parameterisation does not, in general, define an energy, and the MH acceptance probability is therefore unavailable, and generally ill-defined. We propose keeping the score parameterisation and computing an acceptance probability inspired by energy-based models through line integration of the score function. This allows us to reuse existing diffusion models and still combine the reverse process with various Markov-Chain Monte Carlo (MCMC) methods. We evaluate our method using numerical experiments and find that score-parameterised versions of the MCMC samplers can achieve similar improvements to the corresponding energy parameterisation.","url_abs":"https://arxiv.org/abs/2307.14012v2","url_pdf":"https://arxiv.org/pdf/2307.14012v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mcmc-correction-of-score-based-diffusion","repo_url":"https://github.com/FraunhoferChalmersCentre/mcmc_corr_score_diffusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"mcmc-correction-of-score-based-diffusion","repo_url":"https://github.com/jackonelli/mcmc_corr_score_diffusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}