We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.

本研究针对图像理解与生成任务中的模型整合问题，提出了JanusFlow框架，结合了自回归语言模型与修正流。研究发现，修正流可以在大型语言模型框架内简单训练，从而实现高效统一的多模态处理，实验结果显示JanusFlow在标准基准上表现优于现有的统一模型。

JanusFlow：调和自回归与修正流以实现统一的多模态理解与生成