Finite-Sample Guarantees for Fully Discrete Conditional Diffusion Models
The approach aligns the score-training objective with the sampling grid, allowing statistical estimation and numerical discretization errors to be analyzed together, and establishes finite-sample guarantees for conditional diffusion models learned from data and implemented with finitely many denoising steps.
Qiu-Yun Zhu
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