Combining discrete and continuous data is an important capability for generative models. We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that provides the missing link in enabling flow-based generative models to be applied to multimodal continuous and discrete data problems. Our key insight is that the discrete equivalent of continuous space flow matching can be realized using Continuous Time Markov Chains. DFMs benefit from a simple derivation that includes discrete diffusion models as a specific instance while allowing improved performance over existing diffusion-based approaches. We utilize our DFMs method to build a multimodal flow-based modeling framework. We apply this capability to the task of protein co-design, wherein we learn a model for jointly generating protein structure and sequence. Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure.

通过离散流模型（DFMs）实现了流模型在多模态连续与离散数据问题上的应用，利用连续时间马尔可夫链实现了离散空间流匹配，进一步改进了基于扩散的方法，成功构建了多模态流建模框架，并应用于蛋白质共设计任务，取得了最先进的共设计性能，同时能灵活生成序列或结构。

离散状态空间上的生成流: 实现多模态流与蛋白质协同设计应用