Evaluating the design space of diffusion-based generative models
<|reference_start|>Evaluating the design space of diffusion-based generative models: Most existing theoretical investigations of the accuracy of diffusion models, albeit significant, assume the score function has been approximated to a certain accuracy, and then use this a priori bound to control the error of generation. This article instead provides a first quantitative understanding of the whole generation process, i.e., both training and sampling. More precisely, it conducts a non-asymptotic convergence analysis of denoising score matching under gradient descent. In addition, a refined sampling error analysis for variance exploding models is also provided. The combination of these two results yields a full error analysis, which elucidates (again, but this time theoretically) how to design the training and sampling processes for effective generation. For instance, our theory implies a preference toward noise distribution and loss weighting in training that qualitatively agree with the ones used in [Karras et al. 2022]. It also provides perspectives on the choices of time and variance schedules in sampling: when the score is well trained, the design in [Song et al. 2020] is more preferable, but when it is less trained, the design in [Karras et al. 2022] becomes more preferable.<|reference_end|>
@article{wang2024evaluating,
    title={Evaluating the design space of diffusion-based generative models},
    author={Yuqing Wang, Ye He, Molei Tao},
    journal={arXiv preprint arXiv:2406.12839},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12839},
    primaryClass={cs.LG math.DS math.OC math.PR stat.ML}
}