InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing
Unified multimodal models (UMMs) that integrate understanding, reasoning, generation, and editing face inherent trade-offs between maintaining strong semantic comprehension and acquiring powerful generation capabilities. In this report, we present InternVL-U, a lightweight 4B-parameter UMM that democratizes these capabilities within a unified framework. Guided by the principles of unified contextual modeling and modality-specific modular design with decoupled visual representations, InternVL-U integrates a state-of-the-art Multimodal Large Language Model (MLLM) with a specialized MMDiT-based visual generation head. To further bridge the gap between aesthetic generation and high-level intelligence, we construct a comprehensive data synthesis pipeline targeting high-semantic-density tasks, such as text rendering and scientific reasoning, under a reasoning-centric paradigm that leverages Chain-of-Thought (CoT) to better align abstract user intent with fine-grained visual generation details. Extensive experiments demonstrate that InternVL-U achieves a superior performance - efficiency balance. Despite using only 4B parameters, it consistently outperforms unified baseline models with over 3x larger scales such as BAGEL (14B) on various generation and editing tasks, while retaining strong multimodal understanding and reasoning capabilities.
69b0dab91f7902d47f6df9f4
Changyao Tian
admin_assigned
2026-03-11T08:58:46.935Z
64b7475efa7eabaae5f7ba94
https://cdn-avatars.huggingface.co/v1/production/uploads/64b7475efa7eabaae5f7ba94/YLv44PZM6tw1sxACY1-U_.png
Changyao Tian
Changyao
user
Changyao
69b0dab91f7902d47f6df9f5
Danni Yang
69b0dab91f7902d47f6df9f6
Guanzhou Chen
admin_assigned
2026-03-11T09:02:47.520Z
6624ba6d79d897d7ddee24b5
https://cdn-avatars.huggingface.co/v1/production/uploads/6624ba6d79d897d7ddee24b5/eHkAquXvHBlCNNgaRDYgG.jpeg
Guanzhou Chen
Rayment
user
Rayment
69b0dab91f7902d47f6df9f7
Erfei Cui
admin_assigned
2026-03-11T09:03:00.093Z
6579b818563044badca392fc
https://cdn-avatars.huggingface.co/v1/production/uploads/6579b818563044badca392fc/XTKQ9Lhceibp9dnQADPQF.jpeg
cuierfei
cuierfei
user
cuierfei
69b0dab91f7902d47f6df9f8
Zhaokai Wang
admin_assigned
2026-03-11T09:03:05.782Z
665d4b515fdfe8f923e347a7
/avatars/d114b24c02dadfca0a8aee104755a8ec.svg
Zhaokai Wang
wzk1015
user
wzk1015
69b0dab91f7902d47f6df9f9
Yuchen Duan
69b0dab91f7902d47f6df9fa
Penghao Yin
claimed_verified
2026-03-24T13:20:50.966Z
674919baf73677ad66df861e
/avatars/f50544f6196977e37e830d93c378efb5.svg
Penghao
pH202411
user
pH202411
69b0dab91f7902d47f6df9fb
Sitao Chen
69b0dab91f7902d47f6df9fc
Ganlin Yang
claimed_verified
2026-03-11T08:28:44.761Z
6565d7149afd51867e55520b
/avatars/027b17651e61df598af53f69b92e7771.svg
Ganlin Yang
ganlinyang
user
ganlinyang
69b0dab91f7902d47f6df9fd
Mingxin Liu
69b0dab91f7902d47f6df9fe
Zirun Zhu
69b0dab91f7902d47f6df9ff
Ziqian Fan
69b0dab91f7902d47f6dfa00
Leyao Gu
claimed_verified
2026-03-11T08:28:40.045Z
6856267ef4f1a0d4a229fc01
https://cdn-avatars.huggingface.co/v1/production/uploads/6856267ef4f1a0d4a229fc01/EfNHfWfSMOT5y9_oY5Fz5.jpeg
Gu LY
Glllllly
user
Glllllly
69b0dab91f7902d47f6dfa01
Haomin Wang
claimed_verified
2026-03-12T09:07:50.660Z
64d83ee0763279bb4ddd5ba8
https://cdn-avatars.huggingface.co/v1/production/uploads/64d83ee0763279bb4ddd5ba8/exbvc1hhIXXKPVmJpxRtP.jpeg
Haomin Wang
KiyotakaWang
user
KiyotakaWang
69b0dab91f7902d47f6dfa02
Qi Wei
69b0dab91f7902d47f6dfa03
Jinhui Yin
69b0dab91f7902d47f6dfa04
Xue Yang
69b0dab91f7902d47f6dfa05
Zhihang Zhong
claimed_verified
2026-03-11T08:28:42.146Z
6938f4de790b5cd0f6df6462
/avatars/4f22f0499d96bb749af7e8dba2b0b533.svg
Zhihang Zhong
Zuica96
user
Zuica96
69b0dab91f7902d47f6dfa06
Qi Qin
69b0dab91f7902d47f6dfa07
Yi Xin
69b0dab91f7902d47f6dfa08
Bin Fu
69b0dab91f7902d47f6dfa09
Yihao Liu
69b0dab91f7902d47f6dfa0a
Jiaye Ge
69b0dab91f7902d47f6dfa0b
Qipeng Guo
69b0dab91f7902d47f6dfa0c
Gen Luo
69b0dab91f7902d47f6dfa0d
Hongsheng Li
69b0dab91f7902d47f6dfa0e
Yu Qiao
69b0dab91f7902d47f6dfa0f
Kai Chen
69b0dab91f7902d47f6dfa10
Hongjie Zhang
admin_assigned
2026-03-11T09:43:51.298Z
68ad21659f53995426c4b77e
/avatars/ae39fafe87bc622b807b6318ef777e82.svg
hongjie zhang
redscoll
user
redscoll
InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing
Unified multimodal models (UMMs) that integrate understanding, reasoning, generation, and editing face inherent trade-offs between maintaining strong semantic comprehension and acquiring powerful generation capabilities. In this report, we present InternVL-U, a lightweight 4B-parameter UMM that democratizes these capabilities within a unified framework. Guided by the principles of unified contextual modeling and modality-specific modular design with decoupled visual representations, InternVL-U integrates a state-of-the-art Multimodal Large Language Model (MLLM) with a specialized MMDiT-based visual generation head. To further bridge the gap between aesthetic generation and high-level intelligence, we construct a comprehensive data synthesis pipeline targeting high-semantic-density tasks, such as text rendering and scientific reasoning, under a reasoning-centric paradigm that leverages Chain-of-Thought (CoT) to better align abstract user intent with fine-grained visual generation details. Extensive experiments demonstrate that InternVL-U achieves a superior performance - efficiency balance. Despite using only 4B parameters, it consistently outperforms unified baseline models with over 3x larger scales such as BAGEL (14B) on various generation and editing tasks, while retaining strong multimodal understanding and reasoning capabilities.
InternVL-U is a 4-billion parameter unified multimodal model that combines advanced visual generation with robust semantic understanding through specialized modular design and reasoning-centric data synthesis.
Unified multimodal models Multimodal Large Language Model MMDiT-based visual generation head Chain-of-Thought visual representations modality-specific modular design unified contextual modeling text rendering scientific reasoning high-semantic-density tasks