Conditional diffusion models achieve optimal sample complexity rates (up to log factors) for learning target distributions, with explicit bounds on how many denoising steps and training samples are needed.
This paper proves that conditional diffusion models trained on finite data can generate high-quality samples with guaranteed accuracy. The authors show how to align the training objective with the sampling process to control both statistical and numerical errors, deriving rates that match classical distribution estimation theory.