Towards More General Control of Diffusion Models Using Jeffrey Guidance

2026-06-12 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Jeffrey guidance is a novel framework extending diffusion model control beyond standard guidance methods by leveraging Jeffrey's rule of conditioning. Proposed by Raphaël Razafindralambo et al., this principled approach updates marginal distributions towards a prescribed target while minimally perturbing the joint distribution. It enables more general and complex objectives, such as matching embedding distributions and enforcing fairness. Experiments on CIFAR-10 and FFHQ datasets demonstrated substantial Fréchet Inception Distance (FID) reductions, with FID decreasing from approximately 5.88 to 2.55 on CIFAR-10 training set and from 20.13 to 12.91 on FFHQ test set. Additionally, Jeffrey guidance successfully decorrelated "Male" and "Young" attributes on CelebA-HQ, achieving near-zero correlation and balancing gender distribution, tasks difficult for traditional guidance. The method operates as a plug-and-play implementation, adding a single correction term during sampling without retraining the neural network.

Key takeaway

For AI Scientists and Machine Learning Engineers working on generative models, Jeffrey guidance offers a principled way to achieve precise control over diffusion model outputs. You should consider integrating this plug-and-play method to address complex objectives like reducing FID or enforcing fairness constraints, especially when standard guidance falls short. This approach allows for fine-tuning generated distributions without costly model retraining, enabling more robust and ethically aligned generative AI applications.

Key insights

Jeffrey guidance uses Jeffrey's rule to precisely control diffusion model outputs by updating marginal distributions to explicit targets.

Principles

• Preserve conditional structure.
• Minimally perturb joint distribution.
• Jeffrey's rule generalizes Bayes's.

Method

Jeffrey guidance adds a correction term to the score during diffusion model sampling. It estimates density ratios via probabilistic classification to steer generated outputs towards a target marginal distribution.

In practice

• Reduce FID by matching Inception embeddings.
• Enforce attribute independence for fairness.
• Balance attribute distributions.

Topics

• Diffusion Models
• Jeffrey Guidance
• Generative AI Control
• Fréchet Inception Distance
• Algorithmic Fairness
• Density Ratio Estimation

Code references

• ermongroup/ddim
• Kaggle/kagglehub
• openai/guided-diffusion
• mseitzer/pytorch-fid
• openai/improved-diffusion

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.