Analyzing and Improving Fast Sampling of Text-to-Image Diffusion Models
Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed independently, leaving the overall performance and compatibility among these methods unexplored. In this paper, we bridge this gap by systematically elucidating the design space, and our comprehensive experiments identify the sampling time schedule as the most pivotal factor. Inspired by the geometric properties of diffusion models revealed through the Frenet-Serret formulas, we propose constant total rotation schedule (TORS), a scheduling strategy that ensures unif...
| Authors | Zhenyu Zhou, Defang Chen, Siwei Lyu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 February 2026 |
| Last updated at source | 30 August 2026 |
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