Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints

• URL: http://arxiv.org/abs/2502.13450v2
• Date: Thu, 03 Jul 2025 12:02:01 GMT
• Title: Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
• Authors: Gautham Govind Anil, Sachin Yadav, Dheeraj Nagaraj, Karthikeyan Shanmugam, Prateek Jain,
• Abstract summary: Interleaved Gibbs Diffusion (IGD) is a novel generative modeling framework for discrete-continuous data.<n>IGD generalizes discrete time Gibbs sampling type Markov chain for the case of discrete-continuous generation.<n>It achieves state-of-the-art results without relying on domain-specific inductive biases.
• Score: 30.624303845550575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Interleaved Gibbs Diffusion (IGD), a novel generative modeling framework for discrete-continuous data, focusing on problems with important, implicit and unspecified constraints in the data. Most prior works on discrete and discrete-continuous diffusion assume a factorized denoising distribution, which can hinder the modeling of strong dependencies between random variables in such problems. We empirically demonstrate a significant improvement in 3-SAT performance out of the box by switching to a Gibbs-sampling style discrete diffusion model which does not assume factorizability. Motivated by this, we introduce IGD which generalizes discrete time Gibbs sampling type Markov chain for the case of discrete-continuous generation. IGD allows for seamless integration between discrete and continuous denoisers while theoretically guaranteeing exact reversal of a suitable forward process. Further, it provides flexibility in the choice of denoisers, allows conditional generation via state-space doubling and inference time refinement. Empirical evaluations on three challenging generation tasks - molecule structures, layouts and tabular data - demonstrate state-of-the-art performance. Notably, IGD achieves state-of-the-art results without relying on domain-specific inductive biases like equivariant diffusion or auxiliary losses. We explore a wide range of modeling, and interleaving strategies along with hyperparameters in each of these problems.

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