A key strength of diffusion models lies in their flexibility, since their outputs can be controlled at sampling time through guidance. However, beyond simple cases such as conditional sampling, the target distribution is often left implicit, defined only through a sampling rule or a heuristic energy function. To address this, we propose Jeffrey guidance, a principled framework that extends diffusion-model control to applications beyond what standard guidance can express. It leverages Jeffrey's rule of conditioning to update marginal distributions towards a prescribed target, preserving the conditional structure and minimally perturbing the joint distribution. We first demonstrate Jeffrey guidance by targeting a prescribed embedding distribution. With Inception embeddings as the target, this leads to substantial reductions in FID on both CIFAR-10 and FFHQ. We further apply Jeffrey guidance to fairness on CelebA-HQ, updating an unconditional diffusion model to enforce independence between attributes.

翻译：扩散模型的核心优势在于其灵活性，因为其输出可通过采样过程中的引导（guidance）进行控制。然而，除条件采样等简单情形外，目标分布往往仅通过采样规则或启发式能量函数隐式定义。为此，我们提出杰弗里引导（Jeffrey guidance）——一种将扩散模型控制扩展至标准引导方法无法企及应用的通用框架。该方法利用杰弗里条件化规则（Jeffrey's rule of conditioning）将边际分布更新至预设目标，在保持条件结构的前提下最小化对联合分布的扰动。我们首先通过预设嵌入分布验证杰弗里引导的有效性：以Inception嵌入为目标，该方法在CIFAR-10和FFHQ数据集上均显著降低了FID指标。进一步地，我们将杰弗里引导应用于CelebA-HQ数据集的公平性任务，通过更新无条件扩散模型实现属性间的独立性约束。