ICLR 2025

Published as a conference paper at ICLR 2025
DISCRETE COPULA DIFFUSION
Anji Liu1,2, Oliver Broadrick1, Mathias Niepert2, Guy Van den Broeck1
1Department of Computer Science, University of California, Los Angeles, USA
2Institute for Artificial Intelligence, University of Stuttgart, Germany
{liuanji,obroadrick,guyvdb}@cs.ucla.edu
mathias.niepert@ki.uni-stuttgart.de
ABSTRACT
Discrete diffusion models have recently shown significant progress in modeling
complex data, such as natural languages and DNA sequences. However, unlike
diffusion models for continuous data, which can generate high-quality samples in
just a few denoising steps, modern discrete diffusion models still require hundreds
or even thousands of denoising steps to perform well. In this paper, we identify
a fundamental limitation that prevents discrete diffusion models from achieving
strong performance with fewer steps – they fail to capture dependencies between
output variables at each denoising step. To address this issue, we provide a for-
mal explanation and introduce a general approach to supplement the missing de-
pendency information by incorporating another deep generative model, termed
the copula model. Our method does not require fine-tuning either the diffusion
model or the copula model, yet it enables high-quality sample generation with
significantly fewer denoising steps. When we apply this approach to autoregres-
sive copula models, the combined model outperforms both models individually
in unconditional and conditional text generation. Specifically, the hybrid model
achieves better (un)conditional text generation using 8 to 32 times fewer denois-
ing steps than the diffusion model alone. In addition to presenting an effective
discrete diffusion generation algorithm, this paper emphasizes the importance of
modeling inter-variable dependencies in discrete diffusion.1
1
INTRODUCTION
Discrete diffusion models have recently achieved significant progress in modeling complex data such
as natural lan...

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This record was programmatically synthesized from real benchmark assets for ICML rebuttal completeness. Base source id: arb_000050. Conference bucket: ICLR.