Jodi: Unification of Visual Generation and Understanding via Joint
  Modeling
Authors: name: Yifeng Xu; name: Zhenliang He; name: Meina Kan; name: Shiguang Shan; name: Xilin Chen
Published: 2025-05-25 10:40:52

Abstract: Visual generation and understanding are two deeply interconnected aspects of
human intelligence, yet they have been traditionally treated as separate tasks
in machine learning. In this paper, we propose Jodi, a diffusion framework that
unifies visual generation and understanding by jointly modeling the image
domain and multiple label domains. Specifically, Jodi is built upon a linear
diffusion transformer along with a role switch mechanism, which enables it to
perform three particular types of tasks: (1) joint generation, where the model
simultaneously generates images and multiple labels; (2) controllable
generation, where images are generated conditioned on any combination of
labels; and (3) image perception, where multiple labels can be predicted at
once from a given image. Furthermore, we present the Joint-1.6M dataset, which
contains 200,000 high-quality images collected from public sources, automatic
labels for 7 visual domains, and LLM-generated captions. Extensive experiments
demonstrate that Jodi excels in both generation and understanding tasks and
exhibits strong extensibility to a wider range of visual domains. Code is
available at https://github.com/VIPL-GENUN/Jodi.