Enhanced convergence guarantees of score-based generative models in $\mathcal{W}_2$-distance beyond log-concavity

Although score-based generative models (SGMs) have achieved remarkable success in real-world sample generation tasks, it is still far from sufficient to understand them from a mathematical perspective. In this paper, we aim to provide enhanced convergence guarantees for two existing SGMs in $\mathca