Unlocking Guidance for Discrete State-Space Diffusion and Flow Models

Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, Jennifer Listgarten

Keywords: discrete state-space generative models, diffusion, flow-matching, flow models, guidance, protein design

Abstract:

Generative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences. In continuous state-spaces, controllable and flexible generation of samples with desired properties has been realized using guidance on diffusion and flow models. However, these guidance approaches are not readily amenable to discrete state-space models. Consequently, we introduce a general and principled method for applying guidance on such models. Our method depends on leveraging continuous-time Markov processes on discrete state-spaces, which unlocks computational tractability for sampling from a desired guided distribution. We demonstrate the utility of our approach, Discrete Guidance, on a range of applications including guided generation of small-molecules, DNA sequences and protein sequences.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 8041