Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction
This work addresses the need for more integrated AI systems that combine visual and language capabilities, though it appears incremental as it builds on existing frameworks like MetaQueries and M2-omni.
The authors tackled the problem of creating a unified multimodal framework for vision and language tasks by introducing Ming-Lite-Uni, which enables text-to-image generation and instruction-based image editing, with experimental results showing strong performance and fluid interaction.
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.