提出可插拔的条件扩散机制，清晰分离条件与生成过程。
A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models
- 用联合扩散学习无条件动态，推理时加修正项实现条件控制。
- 推导出显式条件反向SDE和近似概率流ODE，支持可比采样。
- 引入对数福克-普兰克残差正则化，提升ODE采样质量。
我们提出一种基于目标与条件多速联合扩散的扩散模型条件机制。该机制学习一个无条件联合得分网络，并在推理时通过可插拔的修正项实现条件约束。该修正项将条件贡献与已学习的无条件动态分离，提供对条件如何引导目标分布生成的透明视角。基于此，我们推导出显式的条件反向时间SDE及近似概率流ODE，实现原理严谨且可直接比较的条件采样器。为降低诱导的ODE-SDE差异，我们引入对数福克-普兰克残差正则化，显著提升ODE采样质量。在条件图像生成任务上的实验表明性能具有竞争力，验证了可插拔条件视角的有效性。额外的ODE-SDE对比实验显示，该正则化改进了确定性ODE采样效果。
原文摘要 · Abstract (English)
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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