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About this paper
Alessio Spagnoletti, Abdul-Lateef Haji-Ali, Andrés Almansa, Alain Oliviero Durmus, Eric Moulines, Marcelo Pereyra
The authors analyze repeated denoising with consistency models (CMs). They derive a finite-horizon Wasserstein bound: each step contracts inherited error and adds approximation error, given stability and accuracy assumptions. In a constructed uniform-target example, their certificate is 0.1746 versus 0.7659 for a prior bound; these are upper bounds, not measured errors or evidence of general dominance.
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