As an alternative to generative modeling approaches such as denoising diffusion, energy-based models (EBMs), and generative adversarial networks (GANs), we explore discriminator-only generation (DOG). DOG obtains samples by direct gradient descent on the input of a discriminator. DOG is conceptually simple, generally applicable to many domains, and even trains faster than GANs on the QM9 molecule dataset. While DOG does not (yet?) reach state-of-the-art quality on image generation tasks, it outperforms recent GAN approaches on several graph generation benchmarks, using only their discriminators.