MagicTailor: Component-Controllable Personalization in Text-to-Image
  Diffusion Models
Authors: name: Donghao Zhou, name: Jiancheng Huang, name: Jinbin Bai, name: Jiaze Wang, name: Hao Chen, name: Guangyong Chen, name: Xiaowei Hu, name: Pheng-Ann Heng

Recent advancements in text-to-image (T2I) diffusion models have enabled the
creation of high-quality images from text prompts, but they still struggle to
generate images with precise control over specific visual concepts. Existing
approaches can replicate a given concept by learning from reference images, yet
they lack the flexibility for fine-grained customization of the individual
component within the concept. In this paper, we introduce
component-controllable personalization, a novel task that pushes the boundaries
of T2I models by allowing users to reconfigure specific components when
personalizing visual concepts. This task is particularly challenging due to two
primary obstacles: semantic pollution, where unwanted visual elements corrupt
the personalized concept, and semantic imbalance, which causes disproportionate
learning of the concept and component. To overcome these challenges, we design
MagicTailor, an innovative framework that leverages Dynamic Masked Degradation
(DM-Deg) to dynamically perturb undesired visual semantics and Dual-Stream
Balancing (DS-Bal) to establish a balanced learning paradigm for desired visual
semantics. Extensive comparisons, ablations, and analyses demonstrate that
MagicTailor not only excels in this challenging task but also holds significant
promise for practical applications, paving the way for more nuanced and
creative image generation.