A new analysis shows O~(d/epsilon) steps suffice for KL-close diffusion sampling under only L2 score error and finite second moment assumptions, improving the known O~(d/epsilon^2).
Improved analysis for a proximal algorithm for sampling
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A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions
A new analysis shows O~(d/epsilon) steps suffice for KL-close diffusion sampling under only L2 score error and finite second moment assumptions, improving the known O~(d/epsilon^2).