VisualCloze: A Universal Image Generation Framework via Visual
  In-Context Learning
Recent progress in diffusion models significantly advances various image
generation tasks. However, the current mainstream approach remains focused on
building task-specific models, which have limited efficiency when supporting a
wide range of different needs. While universal models attempt to address this
limitation, they face critical challenges, including generalizable task
instruction, appropriate task distributions, and unified architectural design.
To tackle these challenges, we propose VisualCloze, a universal image
generation framework, which supports a wide range of in-domain tasks,
generalization to unseen ones, unseen unification of multiple tasks, and
reverse generation. Unlike existing methods that rely on language-based task
instruction, leading to task ambiguity and weak generalization, we integrate
visual in-context learning, allowing models to identify tasks from visual
demonstrations. Meanwhile, the inherent sparsity of visual task distributions
hampers the learning of transferable knowledge across tasks. To this end, we
introduce Graph200K, a graph-structured dataset that establishes various
interrelated tasks, enhancing task density and transferable knowledge.
Furthermore, we uncover that our unified image generation formulation shared a
consistent objective with image infilling, enabling us to leverage the strong
generative priors of pre-trained infilling models without modifying the
architectures.
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Zhong-Yu Li
claimed_verified
2025-04-11T07:25:42.946Z
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Zhong-Yu Li
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lzyhha
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Ruoyi Du
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2025-04-11T08:27:33.519Z
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Ruoyi Du
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RuoyiDu
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Juncheng Yan
admin_assigned
2025-04-11T08:27:41.530Z
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Juncheng Yan
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JonsonYan
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Le Zhuo
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2025-09-13T15:05:18.924Z
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Le Zhuo
user
JackyZhuo
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Zhen Li
claimed_verified
2025-04-13T19:25:40.503Z
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Zhen Li
user
Paper99
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Peng Gao
admin_assigned
2025-04-11T08:28:33.570Z
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Peng Gao
user
cosumosu25
67f86e6884277ab58c40ce90
Zhanyu Ma
67f86e6884277ab58c40ce91
Ming-Ming Cheng
admin_assigned
2025-04-11T08:28:11.683Z
64e496ae0195913c7fa91c66
/avatars/23f274f0a3b4ef6ded35205df9bfb564.svg
chengmingming
user
mingming8688
VisualCloze: A Universal Image Generation Framework via Visual
  In-Context Learning
Recent progress in diffusion models significantly advances various image
generation tasks. However, the current mainstream approach remains focused on
building task-specific models, which have limited efficiency when supporting a
wide range of different needs. While universal models attempt to address this
limitation, they face critical challenges, including generalizable task
instruction, appropriate task distributions, and unified architectural design.
To tackle these challenges, we propose VisualCloze, a universal image
generation framework, which supports a wide range of in-domain tasks,
generalization to unseen ones, unseen unification of multiple tasks, and
reverse generation. Unlike existing methods that rely on language-based task
instruction, leading to task ambiguity and weak generalization, we integrate
visual in-context learning, allowing models to identify tasks from visual
demonstrations. Meanwhile, the inherent sparsity of visual task distributions
hampers the learning of transferable knowledge across tasks. To this end, we
introduce Graph200K, a graph-structured dataset that establishes various
interrelated tasks, enhancing task density and transferable knowledge.
Furthermore, we uncover that our unified image generation formulation shared a
consistent objective with image infilling, enabling us to leverage the strong
generative priors of pre-trained infilling models without modifying the
architectures.
VisualCloze framework enhances universal image generation through visual in-context learning, Graph200K dataset, and leveraging pre-trained infilling models.
diffusion models VisualCloze universal image generation visual in-context learning Graph200K image infilling pre-trained infilling models