Can We Change the Stroke Size for Easier Diffusion?
Yunwei Bai
Ying Kiat Tan
Yao Shu
Tsuhan Chen
Sep 2026
Artificial Intelligence
Computer Vision
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.
