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.
View on arXiv@article{nisonoff2025_2406.01572,
title={ Unlocking Guidance for Discrete State-Space Diffusion and Flow Models },
author={ Hunter Nisonoff and Junhao Xiong and Stephan Allenspach and Jennifer Listgarten },
journal={arXiv preprint arXiv:2406.01572},
year={ 2025 }
}