Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction

TL;DR

Ming-Lite-Uni introduces a unified multimodal framework that combines vision and language models for versatile tasks like image generation and editing, with open-source code and promising experimental results.

Contribution

The paper presents a novel unified architecture with multi-scale tokens and alignment strategies, enabling advanced multimodal capabilities beyond existing models.

Findings

Strong performance demonstrated in experiments

Fluid interactive process observed

Open-source implementation available

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.