Diffusion Rejection Sampling
<|reference_start|>Diffusion Rejection Sampling: 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.<|reference_end|>
@article{na2024diffusion,
    title={Diffusion Rejection Sampling},
    author={Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang,
  Il-Chul Moon},
    journal={arXiv preprint arXiv:2405.17880},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17880},
    primaryClass={cs.LG}
}