Subject-Diffusion:Open Domain Personalized Text-to-Image Generation
  without Test-time Fine-tuning
Jian Ma Junhao Liang Chen Chen Haonan Lu
Recent progress in personalized image generation using diffusion models has
been significant. However, development in the area of open-domain and
non-fine-tuning personalized image generation is proceeding rather slowly. In
this paper, we propose Subject-Diffusion, a novel open-domain personalized
image generation model that, in addition to not requiring test-time
fine-tuning, also only requires a single reference image to support
personalized generation of single- or multi-subject in any domain. Firstly, we
construct an automatic data labeling tool and use the LAION-Aesthetics dataset
to construct a large-scale dataset consisting of 76M images and their
corresponding subject detection bounding boxes, segmentation masks and text
descriptions. Secondly, we design a new unified framework that combines text
and image semantics by incorporating coarse location and fine-grained reference
image control to maximize subject fidelity and generalization. Furthermore, we
also adopt an attention control mechanism to support multi-subject generation.
Extensive qualitative and quantitative results demonstrate that our method
outperforms other SOTA frameworks in single, multiple, and human customized
image generation. Please refer to our
\href{https://oppo-mente-lab.github.io/subject_diffusion/}{project page}