arXiv:2606.13240v1 Announce Type: cross 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 distributio
The continuous advancements in diffusion models necessitate more sophisticated and principled control mechanisms to unlock their full potential across diverse applications.
This development enhances the controllability and flexibility of diffusion models, expanding their utility beyond simple conditional generation to more complex, application-specific requirements.
Diffusion models can now be guided with a more principled and explicit framework, allowing for finer-grained control over their outputs and opening doors for new capabilities beyond standard guidance methods.
- · AI researchers
- · Generative AI developers
- · Creative industries
- · Autonomous systems
- · Developers relying on heuristic guidance
- · Less flexible generative AI approaches
More robust and tailored generative AI applications will emerge.
The improved control will accelerate the adoption of diffusion models in specialized and sensitive domains.
Heightened ethical considerations may arise as generative AI becomes more controllable and adaptable to specific, potentially harmful, objectives.
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Read at arXiv cs.AI