In-situ Autoguidance: Eliciting Self-Correction in Diffusion Models

Abstract:The generation of high-quality, diverse, and prompt-aligned images is a central goal in image-generating diffusion models. The popular classifier-free guidance (CFG) approach improves quality and alignment at the cost of reduced variation, creating an inherent entanglement of these effects. Recent work has successfully disentangled these properties by guiding a model with a separately trained, inferior counterpart; however, this solution introduces the considerable overhead of requiring an auxiliary model. We challenge this prerequisite by introducing In-situ Autoguidance, a method that elicits guidance from the model itself without any auxiliary components. Our approach dynamically generates an inferior prediction on the fly using a stochastic forward pass, reframing guidance as a form of inference-time self-correction. We demonstrate that this zero-cost approach is not only viable but also establishes a powerful new baseline for cost-efficient guidance, proving that the benefits of self-guidance can be achieved without external models.

Machine Learning (cs.LG)

I.2.6; I.5.1; I.5.4

arXiv:2510.17136 [cs.LG]

(or arXiv:2510.17136v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2510.17136 Focus to learn more arXiv-issued DOI via DataCite

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