We made discrete diffusion model inference faster with high-order methods, both theoretically and empirically.

We propose Hierarchical Diffusion Language Models, which is a discrete diffusion with a general time-varying next semantic scale prediction process for language modeling.

Masked Diffusion Models can generate low-perplexity text efficiently, but can not handle tasks requiring high accuracy efficiently.