用确定性算法替代随机去噪，提升离散扩散模型生成效率与质量。
Deterministic Discrete Denoising
- 引入改进的herding算法实现确定性状态转移。
- 在文本与图像生成任务中同时提升效率与样本质量。
- 适合追求高效稳定生成的开发者与研究者使用。
我们提出一种针对离散状态扩散模型的确定性去噪算法。核心思想是通过引入herding算法的变体，对生成反向马尔可夫链进行去随机化，从而实现由弱混沌动力驱动的确定性状态转移。该方法可直接替代原有的随机去噪过程，无需重新训练或连续状态嵌入。我们在文本和图像生成任务上均实现了效率与样本质量的一致提升。此外，该算法在基于扩散的组合优化问题中也取得了更优解。结果表明，虽在连续扩散中已有成熟应用，但确定性反向过程在离散状态空间同样有效。此方法简单且前景广阔，可有效增强离散扩散模型的生成能力。
原文摘要 · Abstract (English)
We propose a deterministic denoising algorithm for discrete-state diffusion models. The key idea is to derandomize the generative reverse Markov chain by introducing a variant of the herding algorithm, which induces deterministic state transitions driven by weakly chaotic dynamics. It serves as a direct replacement for the stochastic denoising process, without requiring retraining or continuous state embeddings. We demonstrate consistent improvements in both efficiency and sample quality on text and image generation tasks. In addition, the proposed algorithm yields improved solutions for diffusion-based combinatorial optimization. Thus, herding-based denoising is a simple yet promising approach for enhancing the generative process of discrete diffusion models. Furthermore, our results reveal that deterministic reverse processes, well established in continuous diffusion, can also be effective in discrete state spaces.
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