DreamO: A Unified Framework for Image Customization
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.
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Wenxu Wu
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Guo
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Fei Ding
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Xinghui Li
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Jian Zhang
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Xinglong Wu
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Xinglong Wu
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Xingzhe-xlwu
DreamO: A Unified Framework for Image Customization
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.
DreamO, an image customization framework using diffusion transformers, supports various tasks and integrates multiple conditions through a unified approach.
diffusion transformer DiT feature routing constraint placeholder strategy progressive training strategy baseline consistency full-scale training quality alignment stage quality biases