A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models
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...
| Authors | Libo Chen, Souvik Ghosh, Teo Deveney et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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