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arXiv · 2502.06079

Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo

Cheuk Kit Lee·Paul Jeha·Jes Frellsen·Pietro Lio·Michael Samuel Albergo·Francisco Vargas
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Abstract

Discrete diffusion models are a class of generative models that produce samples from an approximated data distribution within a discrete state space. Often, there is a need to target specific regions of the data distribution. Current guidance methods aim to sample from a distribution with mass proportional to $p_0(x_0) p(\zeta|x_0)^\alpha$ but fail to achieve this in practice. We introduce a Sequential Monte Carlo algorithm that generates unbiasedly from this target distribution, utilising the learnt unconditional and guided process. We validate our approach on low-dimensional distributions, controlled images and text generations. For text generation, our method provides strong control while maintaining low perplexity compared to guidance-based approaches.

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Cheuk Kit Lee, Paul Jeha, Jes Frellsen, Pietro Lio, Michael Samuel Albergo, Francisco Vargas. 2025-02-10. Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo. https://arxiv.org/abs/2502.06079

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