Iterating Consistency Models: Stability, Error Bounds and Noise Schedules
Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade sample quality in ways that are highly sensitive to the schedule and that existing theory does not fully explain. To provide accuracy guarantees and guide CM sampler design, we analyze multistep CM sampling as a composition of noising and approximate denoising operators. Under explicit, verifiable stability assumptions, we derive a non-asymptotic error bound that separates contraction of the initialization error from accumulation of approximation error. The bound assigns distinct roles to the schedule: large early noise levels drive contrac...
| Authors | Alessio Spagnoletti, Abdul-Lateef Haji-Ali, Andrés Almansa et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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