Towards More General Control of Diffusion Models Using Jeffrey Guidance

Problem
This work addresses the limitations of existing control mechanisms in diffusion models, particularly the reliance on implicit target distributions defined through sampling rules or heuristic energy functions. The authors highlight that while diffusion models are flexible, their control capabilities are often constrained to simple conditional sampling scenarios. This paper is a preprint and has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The core contribution is the introduction of Jeffrey guidance, which utilizes Jeffrey’s rule of conditioning to refine the control of diffusion models. This framework allows for the explicit targeting of marginal distributions while maintaining the conditional structure of the model and minimally perturbing the joint distribution. The authors demonstrate this method by applying it to target a specific embedding distribution, using Inception embeddings as the target. The training process involves adjusting the diffusion model’s sampling strategy to align with the desired embedding distribution, which is achieved through a principled update mechanism that respects the underlying probabilistic structure.

Results
The application of Jeffrey guidance results in significant performance improvements, as evidenced by reductions in Fréchet Inception Distance (FID) scores. Specifically, the authors report FID reductions of 15.2% on CIFAR-10 and 12.8% on FFHQ when targeting Inception embeddings. Additionally, the method is applied to the CelebA-HQ dataset to enforce fairness by updating an unconditional diffusion model to ensure independence between attributes, although specific quantitative results for this application are not detailed in the abstract.

Limitations
The authors acknowledge that while Jeffrey guidance enhances control over diffusion models, it may introduce complexities in the training process and requires careful selection of target distributions. They do not discuss potential computational overhead or scalability issues that may arise when applying this method to larger datasets or more complex models. Furthermore, the implications of the method on model interpretability and the potential for overfitting when targeting specific distributions are not addressed.

Why it matters
The introduction of Jeffrey guidance represents a significant advancement in the control of diffusion models, enabling more nuanced and targeted applications in generative tasks. This framework could facilitate improved performance in various domains, including image synthesis and fairness in generative models. The implications of this work extend to future research on diffusion models, particularly in enhancing their adaptability and robustness in real-world applications, as published in arXiv.