Can We Change "Stroke Size" to Simplify Regression Learning?
Deep-learning-based image generation is increasingly useful but expensive. Across the different timesteps in the denoising chain, high-noise timesteps present distinct optimization and prediction challenges, which can increase the cost of real-world applications. Inspired by the varying stroke sizes used in human painting, in this work, we ask whether a similar stroke-size variation (i.e., rough stroke applied early) in diffusion can simplify existing image-generation algorithms. We subsequently develop the MultiStroke operator, which linearly interpolates coarse and fine strokes in a coarse-to-fine manner to potentially simplify the prediction target, especially at high-noise timesteps. ...
| Authors | Yunwei Bai, Ying Kiat Tan, Tsuhan Chen |
|---|---|
| Primary category | cs.CV |
| Published | 25 March 2026 |
| Last updated at source | 14 October 2026 |
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