  Recent advances in powerful pre-trained diffusion models encourage the
development of methods to improve the sampling performance under well-trained
diffusion models. This paper introduces Diffusion Rejection Sampling (DiffRS),
which uses a rejection sampling scheme that aligns the sampling transition
kernels with the true ones at each timestep. The proposed method can be viewed
as a mechanism that evaluates the quality of samples at each intermediate
timestep and refines them with varying effort depending on the sample.
Theoretical analysis shows that DiffRS can achieve a tighter bound on sampling
error compared to pre-trained models. Empirical results demonstrate the
state-of-the-art performance of DiffRS on the benchmark datasets and the
effectiveness of DiffRS for fast diffusion samplers and large-scale
text-to-image diffusion models. Our code is available at
https://github.com/aailabkaist/DiffRS.
