I will discuss two recent applications of reversed diffusions to sampling: the proximal sampler and score-based generative models (SGMs). First, the proximal sampler, which is the sampling analogue of the proximal point method from optimization, is an attractive alternative to the well-studied Langevin algorithm. Second, SGMs such as DALL-E 2 have led to spectacular empirical successes for audio and image generation, and they can provably sample from arbitrarily non-log-concave data distributions given an accurate approximation of the score function. This is based on joint work with Sitan Chen, Yongxin Chen, Jerry Li, Yuanzhi Li, Adil Salim, Andre Wibisono, and Anru Zhang.