A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models
arXiv:2608.19504v1 Announce Type: new Abstract: 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.
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[Submitted on 19 Aug 2026]
Title:A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models
View a PDF of the paper titled A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models, by Libo Chen and 4 other authors
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Abstract: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.
Comments: Accepted at BMVC 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.19504 [cs.CV]
(or arXiv:2608.19504v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.19504
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Libo Chen [view email] [v1] Wed, 19 Aug 2026 23:45:05 UTC (5,225 KB)
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