title: DreamO: A Unified Framework for Image Customization
summary: Recently, extensive research on image customization (e.g., identity, subject,
  style, background, etc.) demonstrates strong customization capabilities in
  large-scale generative models. However, most approaches are designed for
  specific tasks, restricting their generalizability to combine different types
  of condition. Developing a unified framework for image customization remains an
  open challenge. In this paper, we present DreamO, an image customization
  framework designed to support a wide range of tasks while facilitating seamless
  integration of multiple conditions. Specifically, DreamO utilizes a diffusion
  transformer (DiT) framework to uniformly process input of different types.
  During training, we construct a large-scale training dataset that includes
  various customization tasks, and we introduce a feature routing constraint to
  facilitate the precise querying of relevant information from reference images.
  Additionally, we design a placeholder strategy that associates specific
  placeholders with conditions at particular positions, enabling control over the
  placement of conditions in the generated results. Moreover, we employ a
  progressive training strategy consisting of three stages: an initial stage
  focused on simple tasks with limited data to establish baseline consistency, a
  full-scale training stage to comprehensively enhance the customization
  capabilities, and a final quality alignment stage to correct quality biases
  introduced by low-quality data. Extensive experiments demonstrate that the
  proposed DreamO can effectively perform various image customization tasks with
  high quality and flexibly integrate different types of control conditions.
paper_authors._id: 6809b14111003e54bd204d99
paper_authors.name: Chong Mou
paper_authors._id: 6809b14111003e54bd204d9a
paper_authors.name: Yanze Wu
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paper_authors.fullname: Yanze Wu
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paper_authors.name: Wenxu Wu
paper_authors._id: 6809b14111003e54bd204d9c
paper_authors.name: Zinan Guo
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paper_authors.fullname: Guo
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paper_authors.user: Zinan123212
paper_authors._id: 6809b14111003e54bd204d9d
paper_authors.name: Pengze Zhang
paper_authors._id: 6809b14111003e54bd204d9e
paper_authors.name: Yufeng Cheng
paper_authors.status: claimed_verified
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paper_authors.fullname: Yufeng Cheng
paper_authors.type: user
paper_authors.user: cb1cyf
paper_authors._id: 6809b14111003e54bd204d9f
paper_authors.name: Yiming Luo
paper_authors._id: 6809b14111003e54bd204da0
paper_authors.name: Fei Ding
paper_authors._id: 6809b14111003e54bd204da1
paper_authors.name: Shiwen Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:32:47.326Z
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paper_authors.fullname: Shiwen Zhang
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paper_authors.user: shiwenzh
paper_authors._id: 6809b14111003e54bd204da2
paper_authors.name: Xinghui Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:32:57.170Z
paper_authors._id: 6752cd83ffaeeb979db974ae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ci915Tdmv3uWbCqGy7LqL.png
paper_authors.fullname: Xinghui Li
paper_authors.type: user
paper_authors.user: Crayon-Shinchan
paper_authors._id: 6809b14111003e54bd204da3
paper_authors.name: Mengtian Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:33:22.554Z
paper_authors._id: 6805bdfb344d6d8a8fd5b07a
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paper_authors.fullname: Mengtian Li
paper_authors.type: user
paper_authors.user: LemonSky1995
paper_authors._id: 6809b14111003e54bd204da4
paper_authors.name: Songtao Zhao
paper_authors._id: 6809b14111003e54bd204da5
paper_authors.name: Jian Zhang
paper_authors._id: 6809b14111003e54bd204da6
paper_authors.name: Qian He
paper_authors._id: 6809b14111003e54bd204da7
paper_authors.name: Xinglong Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:29:20.661Z
paper_authors._id: 67bc6b515d9470ec64bdcc33
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dttL8Fb3bKCVG5zjg_02q.png
paper_authors.fullname: Xinglong Wu
paper_authors.type: user
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paper_title: DreamO: A Unified Framework for Image Customization
paper_summary: Recently, extensive research on image customization (e.g., identity, subject,
  style, background, etc.) demonstrates strong customization capabilities in
  large-scale generative models. However, most approaches are designed for
  specific tasks, restricting their generalizability to combine different types
  of condition. Developing a unified framework for image customization remains an
  open challenge. In this paper, we present DreamO, an image customization
  framework designed to support a wide range of tasks while facilitating seamless
  integration of multiple conditions. Specifically, DreamO utilizes a diffusion
  transformer (DiT) framework to uniformly process input of different types.
  During training, we construct a large-scale training dataset that includes
  various customization tasks, and we introduce a feature routing constraint to
  facilitate the precise querying of relevant information from reference images.
  Additionally, we design a placeholder strategy that associates specific
  placeholders with conditions at particular positions, enabling control over the
  placement of conditions in the generated results. Moreover, we employ a
  progressive training strategy consisting of three stages: an initial stage
  focused on simple tasks with limited data to establish baseline consistency, a
  full-scale training stage to comprehensively enhance the customization
  capabilities, and a final quality alignment stage to correct quality biases
  introduced by low-quality data. Extensive experiments demonstrate that the
  proposed DreamO can effectively perform various image customization tasks with
  high quality and flexibly integrate different types of control conditions.
paper_ai_summary: DreamO, an image customization framework using diffusion transformers, supports various tasks and integrates multiple conditions through a unified approach.
paper_ai_keywords: diffusion transformer DiT feature routing constraint placeholder strategy progressive training strategy baseline consistency full-scale training quality alignment stage quality biases