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Can We Change the Stroke Size for Easier Diffusion?

arXiv cs.CV / 3/31/2026

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Key Points

  • The paper explores whether diffusion models can be improved in low signal-to-noise (SNR) settings by introducing “stroke-size control” to adjust how the model targets and perturbs data across diffusion timesteps.
  • The proposed intervention is motivated by geometric intuition: using an overly fine “stroke” (i.e., overly granular pixel-level prediction) may be ineffective when noise dominates.
  • It studies the theoretical and empirical benefits and trade-offs of changing the effective roughness of the supervised target, aiming to ease the pixel-level prediction burden under high noise.
  • The authors indicate that code will be released, enabling replication and further experimentation with the stroke-size approach.

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 stroke-size control as a controlled intervention that changes the effective roughness of the supervised target, predictions and perturbations across timesteps, in an attempt to ease the low signal-to-noise challenge. We analyze the advantages and trade-offs of the intervention both theoretically and empirically. Code will be released.

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Can We Change the Stroke Size for Easier Diffusion? | AI Navigate