Can We Change the Stroke Size for Easier Diffusion?
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910171758854144 |
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| author | Bai, Yunwei Tan, Ying Kiat Shu, Yao Chen, Tsuhan |
| author_facet | Bai, Yunwei Tan, Ying Kiat Shu, Yao Chen, Tsuhan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26783 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Can We Change the Stroke Size for Easier Diffusion? Bai, Yunwei Tan, Ying Kiat Shu, Yao Chen, Tsuhan Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Can We Change the Stroke Size for Easier Diffusion? |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.26783 |