DreamO: A Unified Framework for Image Customization
Authors: name: Chong Mou, name: Yanze Wu, name: Wenxu Wu, name: Zinan Guo, name: Pengze Zhang, name: Yufeng Cheng, name: Yiming Luo, name: Fei Ding, name: Shiwen Zhang, name: Xinghui Li, name: Mengtian Li, name: Songtao Zhao, name: Jian Zhang, name: Qian He, name: Xinglong Wu

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.