Ming-Lite-Uni: Advancements in Unified Architecture for Natural
  Multimodal Interaction
Authors: name: Biao Gong; name: Cheng Zou; name: Dandan Zheng; name: Hu Yu; name: Jingdong Chen; name: Jianxin Sun; name: Junbo Zhao; name: Jun Zhou; name: Kaixiang Ji; name: Lixiang Ru; name: Libin Wang; name: Qingpei Guo; name: Rui Liu; name: Weilong Chai; name: Xinyu Xiao; name: Ziyuan Huang
Published: 2025-05-05 08:56:12

Abstract: We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a
newly designed unified visual generator and a native multimodal autoregressive
model tailored for unifying vision and language. Specifically, this project
provides an open-source implementation of the integrated MetaQueries and
M2-omni framework, while introducing the novel multi-scale learnable tokens and
multi-scale representation alignment strategy. By leveraging a fixed MLLM and a
learnable diffusion model, Ming-Lite-Uni enables native multimodal AR models to
perform both text-to-image generation and instruction based image editing
tasks, expanding their capabilities beyond pure visual understanding. Our
experimental results demonstrate the strong performance of Ming-Lite-Uni and
illustrate the impressive fluid nature of its interactive process. All code and
model weights are open-sourced to foster further exploration within the
community. Notably, this work aligns with concurrent multimodal AI milestones -
such as ChatGPT-4o with native image generation updated in March 25, 2025 -
underscoring the broader significance of unified models like Ming-Lite-Uni on
the path toward AGI. Ming-Lite-Uni is in alpha stage and will soon be further
refined.