Diffusion models have emerged as powerful tools for high-quality data generation, such as image generation. Despite its success in continuous spaces, discrete diffusion models, which apply to domains such as texts and natural languages, remain under-studied and often suffer from slow generation speed. In this paper, we propose a novel de-randomized diffusion process, which leads to an accelerated algorithm for discrete diffusion models. Our technique significantly reduces the number of function evaluations (i.e., calls to the neural network), making the sampling process much faster. Furthermore, we introduce a continuous-time (i.e., infinite-step) sampling algorithm that can provide even better sample qualities than its discrete-time (finite-step) counterpart. Extensive experiments on natural language generation and machine translation tasks demonstrate the superior performance of our method in terms of both generation speed and sample quality over existing methods for discrete diffusion models.

翻译：扩散模型已成为高质量数据生成的强大工具，例如图像生成。尽管扩散模型在连续空间中取得了成功，但应用于文本和自然语言等领域的离散扩散模型仍研究不足，且常受生成速度缓慢的困扰。本文提出了一种新的去随机化扩散过程，从而得到一种加速离散扩散模型的算法。我们的技术显著减少了函数评估次数（即对神经网络的调用次数），使采样过程更加快速。此外，我们引入了一种连续时间（即无限步）采样算法，该算法相比离散时间（有限步）对应算法，能提供更优的样本质量。在自然语言生成和机器翻译任务上的大量实验表明，与现有离散扩散模型方法相比，我们的方法在生成速度和样本质量方面均表现出优越性能。