  Recent thrilling progress in large-scale text-to-image (T2I) models has
unlocked unprecedented synthesis quality of AI-generated content (AIGC)
including image generation, 3D and video composition. Further, personalized
techniques enable appealing customized production of a novel concept given only
several images as reference. However, an intriguing problem persists: Is it
possible to capture multiple, novel concepts from one single reference image?
In this paper, we identify that existing approaches fail to preserve visual
consistency with the reference image and eliminate cross-influence from
concepts. To alleviate this, we propose an attention calibration mechanism to
improve the concept-level understanding of the T2I model. Specifically, we
first introduce new learnable modifiers bound with classes to capture
attributes of multiple concepts. Then, the classes are separated and
strengthened following the activation of the cross-attention operation,
ensuring comprehensive and self-contained concepts. Additionally, we suppress
the attention activation of different classes to mitigate mutual influence
among concepts. Together, our proposed method, dubbed DisenDiff, can learn
disentangled multiple concepts from one single image and produce novel
customized images with learned concepts. We demonstrate that our method
outperforms the current state of the art in both qualitative and quantitative
evaluations. More importantly, our proposed techniques are compatible with LoRA
and inpainting pipelines, enabling more interactive experiences.
