Ming-Lite-Uni: Advancements in Unified Architecture for Natural
  Multimodal 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.
681973cfa70a4728958323aa
Biao Gong
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2025-05-06T09:10:35.774Z
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Biao Gong
user
BiaoGong
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Cheng Zou
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Dandan Zheng
admin_assigned
2025-05-06T09:10:11.829Z
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Dandan Zheng
user
zhengdd0422
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Hu Yu
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Jingdong Chen
admin_assigned
2025-05-06T09:10:51.440Z
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chenjingdong 
user
chenjingdong
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Jianxin Sun
admin_assigned
2025-05-06T09:10:02.728Z
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jianxinsun
user
jianxinsun
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Junbo Zhao
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Jun Zhou
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Kaixiang Ji
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Lixiang Ru
681973cfa70a4728958323b4
Libin Wang
681973cfa70a4728958323b5
Qingpei Guo
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Rui Liu
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Weilong Chai
681973cfa70a4728958323b8
Xinyu Xiao
admin_assigned
2025-05-06T09:11:49.316Z
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xinyu xiao
user
bear-xxy
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Ziyuan Huang
Ming-Lite-Uni: Advancements in Unified Architecture for Natural
  Multimodal 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.
Ming-Lite-Uni, an open-source multimodal framework, integrates vision and language using unified visual generators and autoregressive models, demonstrating strong performance in text-to-image generation and image editing.
Multimodal framework unified visual generator multimodal autoregressive model MetaQueries M2-omni multi-scale learnable tokens multi-scale representation alignment MLLM diffusion model text-to-image generation instruction-based image editing