Ming-UniVision: Joint Image Understanding and Generation with a Unified
  Continuous Tokenizer
Visual tokenization remains a core challenge in unifying visual understanding
and generation within the autoregressive paradigm. Existing methods typically
employ tokenizers in discrete latent spaces to align with the tokens from large
language models, where the quantization errors can limit semantic
expressiveness and degrade the capability of vision-language understanding. To
address this, we introduce MingTok, a new family of visual tokenizers with a
continuous latent space, for unified autoregressive generation and
understanding. While understanding tasks favor discriminative high-dimensional
features, generation tasks prefer compact low-level codes. Thus, to reconcile
these competing demands, MingTok adopts a three-stage sequential architecture
involving low-level encoding, semantic expansion, and visual reconstruction.
Built on top of it, Ming-UniVision eliminates the need for task-specific visual
representations, and unifies diverse vision-language tasks under a single
autoregrsssive prediction paradigm. By formulating both understanding and
generation as next-token prediction in a shared continuous space, it seamlessly
supports multi-round, in-context tasks such as iterative understanding,
generation and editing. Empirically, we find that using a unified continuous
visual representation reconciles the competing requirements on the tokenizers
by the understanding and generation tasks, thereby leading to state-of-the-art
level performance across both domains. We hope our findings will facilitate
unified visual tokenization in the continuous domain. Inference code and model
weights are released to benefit community.
68e743637ae125f9582e6a5d
Ziyuan Huang
claimed_verified
2025-10-09T07:57:48.507Z
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Ziyuan
user
zyhuangnus
68e743637ae125f9582e6a5e
DanDan Zheng
claimed_verified
2025-10-09T07:57:46.443Z
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zheng
user
forde450
68e743637ae125f9582e6a5f
Cheng Zou
claimed_verified
2025-10-11T13:48:49.933Z
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Cheng Zou
user
wuyouant
68e743637ae125f9582e6a60
Rui Liu
68e743637ae125f9582e6a61
Xiaolong Wang
claimed_verified
2025-10-09T07:57:38.365Z
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Xiaolong Wang
user
Xiaolong-Wang
68e743637ae125f9582e6a62
Kaixiang Ji
claimed_verified
2025-10-09T12:48:31.833Z
68e7773faf3a162f9301a117
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Kaixiang Ji
user
TorryJ
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Weilong Chai
claimed_verified
2025-10-09T12:48:26.937Z
64d2fe8ed8b712baf1948c03
/avatars/e8f5f368632065e16bd758a7a2cf182e.svg
Willie Chai
user
Williechai
68e743637ae125f9582e6a64
Jianxin Sun
68e743637ae125f9582e6a65
Libin Wang
claimed_verified
2025-10-09T12:48:29.030Z
66b180741fa16824b629943b
/avatars/42d1076da1ae16ce2277f254416ff839.svg
wang
user
lbwang
68e743637ae125f9582e6a66
Yongjie Lv
68e743637ae125f9582e6a67
Taozhi Huang
68e743637ae125f9582e6a68
Jiajia Liu
68e743637ae125f9582e6a69
Qingpei Guo
claimed_verified
2025-10-09T07:57:42.652Z
6482dd5ec2ec7df31fd5cfd5
/avatars/c74fb04b2b4488a9f3f7872b2f91cd7b.svg
qingpei.gqp
user
qingpei
68e743637ae125f9582e6a6a
Ming Yang
68e743637ae125f9582e6a6b
Jingdong Chen
68e743637ae125f9582e6a6c
Jun Zhou
Ming-UniVision: Joint Image Understanding and Generation with a Unified
  Continuous Tokenizer
Visual tokenization remains a core challenge in unifying visual understanding
and generation within the autoregressive paradigm. Existing methods typically
employ tokenizers in discrete latent spaces to align with the tokens from large
language models, where the quantization errors can limit semantic
expressiveness and degrade the capability of vision-language understanding. To
address this, we introduce MingTok, a new family of visual tokenizers with a
continuous latent space, for unified autoregressive generation and
understanding. While understanding tasks favor discriminative high-dimensional
features, generation tasks prefer compact low-level codes. Thus, to reconcile
these competing demands, MingTok adopts a three-stage sequential architecture
involving low-level encoding, semantic expansion, and visual reconstruction.
Built on top of it, Ming-UniVision eliminates the need for task-specific visual
representations, and unifies diverse vision-language tasks under a single
autoregrsssive prediction paradigm. By formulating both understanding and
generation as next-token prediction in a shared continuous space, it seamlessly
supports multi-round, in-context tasks such as iterative understanding,
generation and editing. Empirically, we find that using a unified continuous
visual representation reconciles the competing requirements on the tokenizers
by the understanding and generation tasks, thereby leading to state-of-the-art
level performance across both domains. We hope our findings will facilitate
unified visual tokenization in the continuous domain. Inference code and model
weights are released to benefit community.
MingTok, a continuous latent space visual tokenizer, unifies vision-language understanding and generation within an autoregressive framework, achieving state-of-the-art performance across both domains.
visual tokenization autoregressive paradigm discrete latent spaces large language models quantization errors continuous latent space three-stage sequential architecture low-level encoding semantic expansion visual reconstruction Ming-UniVision task-specific visual representations next-token prediction iterative understanding generation and editing