Towards More General Control of Diffusion Models Using Jeffrey Guidance
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso, Jes Frellsen, Pierre-Alexandre Mattei
Abstract
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.
AI Impact Assessments
(1 models)Scientific Impact Assessment: "Towards More General Control of Diffusion Models Using Jeffrey Guidance"
1. Core Contribution
The paper proposes Jeffrey guidance, a framework that leverages Jeffrey's rule of conditioning to control diffusion model outputs by updating marginal distributions toward prescribed targets while preserving conditional structure. The key insight is that Jeffrey's rule generalizes Bayes' rule: instead of conditioning on a specific class (as in classifier guidance), one can target an entire distribution over some variable space. The updated joint distribution minimizes KL divergence to the original while satisfying the marginal constraint — an information projection.
The paper demonstrates two applications: (1) matching Inception embedding distributions to training data (reducing FID), and (2) fairness objectives on CelebA-HQ, including gender parity and attribute decorrelation. Importantly, standard classifier guidance is shown to be a special case when the target marginal is a point mass.
2. Methodological Rigor
The theoretical grounding is sound. Jeffrey's rule is well-established in epistemology and probability theory, and the connection to diffusion guidance is natural through the density ratio formulation (Equations 11-15). The paper correctly identifies that the resulting guidance term takes the form of a log-density-ratio correction, fitting neatly into existing energy-based guidance frameworks.
However, there are notable approximation gaps. The use of Tweedie's formula to estimate clean samples from noisy intermediates introduces bias, particularly at early timesteps. The authors acknowledge this (Appendix C, Proposition 1) and note that exact sampling would require knowledge of the reverse transition kernel , which is intractable. The practical reliance on means the method doesn't truly follow the Jeffrey-updated diffusion path — a limitation shared with universal guidance approaches but worth emphasizing given the paper's claims of principled foundations.
The density ratio estimation via logistic regression is simple and appropriate for low-dimensional embeddings (Inception features) but raises scalability questions for high-dimensional or complex attribute spaces. The fairness experiments use discrete attributes predicted by classifiers, introducing additional noise through classifier accuracy.
The experimental evaluation, while demonstrating the concept, is limited in scope. Only three datasets are used (CIFAR-10, FFHQ, CelebA-HQ), and comparisons are primarily against a single "standard guidance" baseline (ancestral sampling + class-conditional guidance). The absence of comparisons with methods like Parihar et al. (2024) or Tiwary et al. (2026) on fairness metrics weakens the empirical claims. Error bars are only provided for one experiment (Figure 6).
3. Potential Impact
The framework has several promising directions:
Embedding distribution matching is conceptually interesting but the FID reduction application is somewhat circular — optimizing toward training Inception statistics naturally lowers FID, which measures exactly this. The authors commendably acknowledge this limitation, noting that large FID improvements can occur with minimal perceptual changes (Appendix C.1), which they frame as evidence against FID's reliability as a perceptual metric.
Fairness applications are more compelling. The ability to decorrelate attributes (Table 2, achieving near-zero Pearson correlation between Male and Young with only ~1 FID point degradation) addresses a genuinely difficult problem that classifier guidance cannot naturally express. Decorrelation requires targeting a product of marginals rather than a single class, which is a clean demonstration of Jeffrey guidance's generality.
Future directions mentioned (memorization mitigation, domain adaptation, drug design) are speculative but plausible extensions. The framework's generality could influence how researchers think about distributional control beyond point conditioning.
4. Timeliness & Relevance
The paper addresses a real gap in diffusion model control. Current guidance methods are largely designed for conditional sampling (class or text conditioning), and extending them to distributional objectives typically requires ad-hoc modifications. Providing a principled framework with a clear target distribution is valuable for interpretability and reproducibility of guidance methods.
The fairness application is timely given increasing scrutiny of generative model biases. However, the paper operates on relatively small-scale models (unconditional DDPMs on 256×256 images), while the field has moved toward large-scale text-to-image models. Demonstrating Jeffrey guidance on models like Stable Diffusion would significantly strengthen the relevance.
5. Strengths & Limitations
Strengths:
Limitations:
Overall Assessment: This is a well-motivated conceptual contribution that introduces a principled framework generalizing classifier guidance. The theoretical connection between Jeffrey's rule and diffusion guidance is elegant and opens new possibilities for distributional control. However, the practical impact is somewhat limited by the approximations required, modest experimental scale, and limited baselines. It reads more as a promising proof-of-concept than a fully developed method. The decorrelation application is the most convincing demonstration of the framework's unique capabilities.
Generated Jun 12, 2026
Comparison History (15)
Paper 1 offers a fundamental methodological advancement for diffusion models, a highly influential and widely used class of generative AI. By introducing a principled framework (Jeffrey guidance) that improves sample quality and enables fairness interventions, its algorithmic contributions are highly likely to see broad adoption across diverse domains including computer vision, audio, and even scientific generation, yielding a wider and more immediate scientific impact than the domain-specific benchmark presented in Paper 2.
Paper 1 introduces a fundamental, principled mathematical framework for controlling diffusion models, a highly prominent area in modern AI. By replacing heuristic methods with Jeffrey's rule, it offers broad foundational advancements for generative modeling, including fairness and quality improvements. In contrast, Paper 2 presents a domain-specific architectural plugin for spatio-temporal forecasting; while useful, its contribution is more incremental compared to the theoretical and widespread potential impact of Paper 1.
Paper 1 introduces Jeffrey guidance, a principled probabilistic framework grounded in Jeffrey's rule of conditioning that generalizes diffusion model control beyond standard guidance. Its theoretical rigor, broad applicability (FID improvement, fairness enforcement), and novel formulation that addresses a fundamental limitation of existing guidance methods give it higher impact potential. Paper 2 proposes a useful but more incremental sampling-time modification for tail coverage that is narrower in scope, lacks the same theoretical depth, and addresses a less broadly impactful problem.
Paper 1 introduces a broadly applicable, principled mathematical framework for controlling diffusion models, a highly active and widely impactful area of AI research. Its ability to improve sample quality and enforce fairness constraints gives it immense cross-disciplinary potential. While Paper 2 provides a highly valuable medical benchmark, its impact is constrained to a specific subfield of oncology, whereas Paper 1's methodological innovation will likely influence a wider array of domains and generate broader scientific interest.
Paper 1 offers a principled, general framework (Jeffrey guidance) that broadens diffusion-model control beyond standard conditioning, with clear demonstrations (FID improvements; fairness via attribute independence). This combination of theoretical novelty and broad applicability to controllable generative modeling, evaluation metrics, and responsible AI suggests wide uptake. Paper 2 provides insightful empirical findings for module-specific manifold constraints in transformer optimization, but its scope is narrower (specific to a particular geometry method and GPT-2 setting) and may translate less directly into widely adopted practice than a general diffusion guidance framework.
Paper 1 addresses the highly active field of diffusion models, introducing a principled framework for better control and fairness. Generative AI control has broad applicability across multiple modalities. While Paper 2 presents a novel quantization metric using dynamical systems, it targets a more specialized domain. The broader applications, relevance to AI fairness, and significant improvements in generative modeling give Paper 1 a higher potential for widespread scientific impact.
Paper 1 provides a foundational theoretical framework for mechanistic interpretability, specifically addressing Sparse Autoencoders (SAEs), which are currently at the forefront of AI safety and understanding. By formalizing concept learning and explaining empirical phenomena, it offers fundamental insights that could shape future research directions across LLM interpretability. Paper 2 is strong methodologically, but its impact is more confined to generative modeling techniques, whereas Paper 1 addresses a critical bottleneck in understanding complex AI systems.
While Paper 1 offers a strong methodological advance for diffusion models, Paper 2 addresses a highly critical and timely bottleneck in AI safety: the tension between helpfulness and harmlessness in LLM alignment. By applying mechanistic interpretability to understand RLHF reward models, Paper 2 provides insights that could broadly impact the development of safer, more reliable AI systems, giving it a higher potential for immediate real-world application and widespread scientific impact across the rapidly growing field of AI alignment.
Paper 2 likely has higher impact: it introduces a principled, general framework (Jeffrey guidance) that broadens diffusion-model control beyond standard guidance, with demonstrated gains (FID improvements) and applications to fairness constraints—highly timely and broadly relevant across generative modeling, controllable synthesis, and responsible AI. Paper 1 identifies an important failure mode of ICL for structured data and a privacy–adaptability trade-off, but its scope is narrower (tabular structured generation) and primarily diagnostic rather than enabling new capabilities, with evidence limited to two 7B models.
Paper 2 likely has higher impact due to timeliness and broad real-world relevance: it targets citation-augmented LLM deployments in high-stakes domains and provides a large, public, rigorously controlled benchmark (balanced 2x2 factorial design) enabling reproducible study across many models and settings. Its findings generalize across domains and inform evaluation, safety, and product design. Paper 1 is methodologically interesting and novel for diffusion control, but its applications (FID improvement, fairness constraints) are narrower and mainly within generative vision, with less immediate cross-field adoption potential.