提出可直接用于离散数据的扩散模型控制方法，提升生成质量与可控性。
Simple Guidance Mechanisms for Discrete Diffusion Models
- 基于无分类器和有分类器思路，推导出适用于离散数据的引导机制。
- 结合均匀噪声扩散与连续时间变分下界，在基因序列等任务上达最优性能。
- 适合需要高可控性生成的领域，如分子设计、图像离散生成。
连续数据的扩散模型因其高质量生成和良好控制能力而广泛应用。然而，连续引导方法无法直接用于离散数据，导致可控生成面临挑战。本文给出了无分类器和有分类器引导在离散扩散模型中的简洁推导，并提出一类采用均匀噪声的新扩散模型，其输出可连续编辑，更易控制。通过引入新型连续时间变分下界，显著提升模型性能，尤其在需引导或快速生成的场景中表现突出。实验表明，该方法在基因序列、小分子设计及离散化图像生成等多个离散数据领域，相比自回归和传统扩散基线，均实现了更强的可控生成能力。
原文摘要 · Abstract (English)
Diffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms. However, controllable diffusion on discrete data faces challenges given that continuous guidance methods do not directly apply to discrete diffusion. Here, we provide a straightforward derivation of classifier-free and classifier-based guidance for discrete diffusion, as well as a new class of diffusion models that leverage uniform noise and that are more guidable because they can continuously edit their outputs. We improve the quality of these models with a novel continuous-time variational lower bound that yields state-of-the-art performance, especially in settings involving guidance or fast generation. Empirically, we demonstrate that our guidance mechanisms combined with uniform noise diffusion improve controllable generation relative to autoregressive and diffusion baselines on several discrete data domains, including genomic sequences, small molecule design, and discretized image generation.
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