title: Ming-Lite-Uni: Advancements in Unified Architecture for Natural
    Multimodal Interaction
summary: 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.
paper_authors._id: 681973cfa70a4728958323aa
paper_authors.name: Biao Gong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:10:35.774Z
paper_authors._id: 644fcbea4f7316588267dc80
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644fcbea4f7316588267dc80/w8-2Gkaw9BN9VzppNXrTP.jpeg
paper_authors.fullname: Biao Gong
paper_authors.type: user
paper_authors.user: BiaoGong
paper_authors._id: 681973cfa70a4728958323ab
paper_authors.name: Cheng Zou
paper_authors._id: 681973cfa70a4728958323ac
paper_authors.name: Dandan Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:10:11.829Z
paper_authors._id: 65dd699a89a2a760d15f7d35
paper_authors.avatarUrl: /avatars/e098b56c413d147d1f38cf33a4b0ecde.svg
paper_authors.fullname: Dandan Zheng
paper_authors.type: user
paper_authors.user: zhengdd0422
paper_authors._id: 681973cfa70a4728958323ad
paper_authors.name: Hu Yu
paper_authors._id: 681973cfa70a4728958323ae
paper_authors.name: Jingdong Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:10:51.440Z
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paper_authors.avatarUrl: /avatars/5d211e2c13d6c4e011e5e58b738413f7.svg
paper_authors.fullname: chenjingdong 
paper_authors.type: user
paper_authors.user: chenjingdong
paper_authors._id: 681973cfa70a4728958323af
paper_authors.name: Jianxin Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:10:02.728Z
paper_authors._id: 6417cd278f689506e71439ac
paper_authors.avatarUrl: /avatars/0993d834c6c3bbc53081aa139ee14a12.svg
paper_authors.fullname: jianxinsun
paper_authors.type: user
paper_authors.user: jianxinsun
paper_authors._id: 681973cfa70a4728958323b0
paper_authors.name: Junbo Zhao
paper_authors._id: 681973cfa70a4728958323b1
paper_authors.name: Jun Zhou
paper_authors._id: 681973cfa70a4728958323b2
paper_authors.name: Kaixiang Ji
paper_authors._id: 681973cfa70a4728958323b3
paper_authors.name: Lixiang Ru
paper_authors._id: 681973cfa70a4728958323b4
paper_authors.name: Libin Wang
paper_authors._id: 681973cfa70a4728958323b5
paper_authors.name: Qingpei Guo
paper_authors._id: 681973cfa70a4728958323b6
paper_authors.name: Rui Liu
paper_authors._id: 681973cfa70a4728958323b7
paper_authors.name: Weilong Chai
paper_authors._id: 681973cfa70a4728958323b8
paper_authors.name: Xinyu Xiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:11:49.316Z
paper_authors._id: 67cc852d2cfa481bce2dd07e
paper_authors.avatarUrl: /avatars/0c1c32ec066a8de9148b083b39d1fab8.svg
paper_authors.fullname: xinyu xiao
paper_authors.type: user
paper_authors.user: bear-xxy
paper_authors._id: 681973cfa70a4728958323b9
paper_authors.name: Ziyuan Huang
paper_title: Ming-Lite-Uni: Advancements in Unified Architecture for Natural
    Multimodal Interaction
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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