Diffusion models feature high sample quality, but are not effective at learning semantically meaningful latent representations. Here, we propose InfoDiffusion, an algorithm that enables diffusion models to perform representation learning using low-dimensional latent variables. We introduce auxiliary-variable diffusion models—a model family that contains an additional set of semantically meaningful latents—and we derive new variational inference algorithms that optimize a learning objective regul…