title: JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified
    Multimodal Understanding and Generation
summary: 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.
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paper_title: JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified
    Multimodal Understanding and Generation
paper_summary: 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.
paper_ai_keywords: autoregressive language models rectified flow generative modeling decoupling encoder aligned representations unified training vision-language models