Our work on diffusion models takes on a similar scope as existing work on other types of deep
generative models, such as efforts to improve the sample quality of GANs, ﬂows, autoregressive
models, and so forth. Our paper represents progress in making diffusion models a generally useful
tool in this family of techniques, so it may serve to amplify any impacts that generative models have
had (and will have) on the broader world.
Unfortunately, there are numerous well-known malicious uses of generative models. Sample gen-
eration techniques can be employed to produce fake images and videos of high proﬁle ﬁgures for
political purposes. While fake images were manually created long before software tools were avail-
able, generative models such as ours make the process easier. Fortunately, CNN-generated images
currently have subtle ﬂaws that allow detection [62], but improvements in generative models may
Denoising Diffusion Probabilistic Models