  Diffusion models (DMs) have revolutionized generative learning. They utilize
a diffusion process to encode data into a simple Gaussian distribution.
However, encoding a complex, potentially multimodal data distribution into a
single continuous Gaussian distribution arguably represents an unnecessarily
challenging learning problem. We propose Discrete-Continuous Latent Variable
Diffusion Models (DisCo-Diff) to simplify this task by introducing
complementary discrete latent variables. We augment DMs with learnable discrete
latents, inferred with an encoder, and train DM and encoder end-to-end.
DisCo-Diff does not rely on pre-trained networks, making the framework
universally applicable. The discrete latents significantly simplify learning
the DM's complex noise-to-data mapping by reducing the curvature of the DM's
generative ODE. An additional autoregressive transformer models the
distribution of the discrete latents, a simple step because DisCo-Diff requires
only few discrete variables with small codebooks. We validate DisCo-Diff on toy
data, several image synthesis tasks as well as molecular docking, and find that
introducing discrete latents consistently improves model performance. For
example, DisCo-Diff achieves state-of-the-art FID scores on class-conditioned
ImageNet-64/128 datasets with ODE sampler.
