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

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.