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This research proposes stroke-size control as a method to simplify prediction targets in diffusion models, specifically addressing challenges in the low signal-to-noise regime. By varying the roughness of supervised targets across timesteps, the approach aims to improve pixel-level predictions without relying on fine-grained details when noise is high. The study offers a novel geometric intuition for enhancing diffusion model robustness through controlled intervention rather than standard architectural changes.
Can We Change the Stroke Size for Easier Diffusion?
arXiv:2603.26783v3 Announce Type: replace-cross Abstract: Diffusion models can be challenged in the low signal-to-noise regime, where they have to make pixel-level predictions despite the presence of high noise. The geometric intuition is akin to using the finest stroke for oil painting throughout, which may be ineffective. We therefore study \emph{stroke-size control} as a controlled intervention that changes the roughness of the supervised target, predictions and perturbations across timesteps, in an attempt to ease the low signal-to-noise challenge via prediction target simplification.
Title:Can We Change the Stroke Size for Easier Diffusion?
Abstract:Diffusion models can be challenged in the low signal-to-noise regime, where they have to make pixel-level predictions despite the presence of high noise. The geometric intuition is akin to using the finest stroke for oil painting throughout, which may be ineffective. We therefore study \emph{stroke-size control} as a controlled intervention that changes the roughness of the supervised target, predictions and perturbations across timesteps, in an attempt to ease the low signal-to-noise challenge via prediction target simplification.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2603.26783 [cs.CV]
(or arXiv:2603.26783v3 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2603.26783 Focus to learn more arXiv-issued DOI via DataCite
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