{"slug": "a-plug-in-interpretation-of-conditioning-in-score-based-diffusion-models", "title": "A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models", "summary": "Researchers propose a conditioning mechanism for score-based diffusion models that uses multi-speed joint diffusion of target and condition, enforcing conditioning at inference via a plug-in correction term. The method, detailed in arXiv:2608.19504v1, derives explicit conditional reverse-time SDEs and approximate probability-flow ODEs, and introduces log-Fokker–Planck residual regularization to improve deterministic ODE sampling. Experiments on conditional image generation show competitive performance and support the effectiveness of the plug-in conditioning view.", "body_md": "arXiv:2608.19504v1 Announce Type: new\nAbstract: We propose a conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition. The mechanism learns an unconditional joint score network and enforces conditioning at inference via a plug-in correction term. The plug-in term separates the conditioning contribution from the learned unconditional dynamics, offering a transparent view of how the condition steers generation of the target distribution. Building on this, we derive explicit conditional reverse-time SDEs and approximate probability-flow ODEs, enabling principled and directly comparable conditional samplers. To reduce the induced ODE--SDE discrepancy, we introduce a log-Fokker--Planck residual regularization that improves ODE sampling quality. Experiments on conditional image generation tasks demonstrate competitive performance and support the effectiveness of the plug-in conditioning view. Additional ODE--SDE comparison experiments show that the log-Fokker--Planck residual regularization improves deterministic ODE sampling.", "url": "https://wpnews.pro/news/a-plug-in-interpretation-of-conditioning-in-score-based-diffusion-models", "canonical_source": "https://arxiv.org/abs/2608.19504", "published_at": "2026-08-21 04:00:00+00:00", "updated_at": "2026-08-21 04:16:43.589588+00:00", "lang": "en", "topics": ["machine-learning", "generative-ai"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/a-plug-in-interpretation-of-conditioning-in-score-based-diffusion-models", "markdown": "https://wpnews.pro/news/a-plug-in-interpretation-of-conditioning-in-score-based-diffusion-models.md", "text": "https://wpnews.pro/news/a-plug-in-interpretation-of-conditioning-in-score-based-diffusion-models.txt", "jsonld": "https://wpnews.pro/news/a-plug-in-interpretation-of-conditioning-in-score-based-diffusion-models.jsonld"}}