English

Multi-Subject Personalization

Computer Vision and Pattern Recognition 2024-05-22 v1

Abstract

Creative story illustration requires a consistent interplay of multiple characters or objects. However, conventional text-to-image models face significant challenges while producing images featuring multiple personalized subjects. For example, they distort the subject rendering, or the text descriptions fail to render coherent subject interactions. We present Multi-Subject Personalization (MSP) to alleviate some of these challenges. We implement MSP using Stable Diffusion and assess our approach against other text-to-image models, showcasing its consistent generation of good-quality images representing intended subjects and interactions.

Keywords

Cite

@article{arxiv.2405.12742,
  title  = {Multi-Subject Personalization},
  author = {Arushi Jain and Shubham Paliwal and Monika Sharma and Vikram Jamwal and Lovekesh Vig},
  journal= {arXiv preprint arXiv:2405.12742},
  year   = {2024}
}

Comments

2023 Conference on Neural Information Processing Systems

R2 v1 2026-06-28T16:34:14.441Z