ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with
  Reward Feedback Learning
Authors: name: Weifeng Chen, name: Jiacheng Zhang, name: Jie Wu, name: Hefeng Wu, name: Xuefeng Xiao, name: Liang Lin

The rapid development of diffusion models has triggered diverse applications.
Identity-preserving text-to-image generation (ID-T2I) particularly has received
significant attention due to its wide range of application scenarios like AI
portrait and advertising. While existing ID-T2I methods have demonstrated
impressive results, several key challenges remain: (1) It is hard to maintain
the identity characteristics of reference portraits accurately, (2) The
generated images lack aesthetic appeal especially while enforcing identity
retention, and (3) There is a limitation that cannot be compatible with
LoRA-based and Adapter-based methods simultaneously. To address these issues,
we present ID-Aligner, a general feedback learning framework to
enhance ID-T2I performance. To resolve identity features lost, we introduce
identity consistency reward fine-tuning to utilize the feedback from face
detection and recognition models to improve generated identity preservation.
Furthermore, we propose identity aesthetic reward fine-tuning leveraging
rewards from human-annotated preference data and automatically constructed
feedback on character structure generation to provide aesthetic tuning signals.
Thanks to its universal feedback fine-tuning framework, our method can be
readily applied to both LoRA and Adapter models, achieving consistent
performance gains. Extensive experiments on SD1.5 and SDXL diffusion models
validate the effectiveness of our approach. Project Page:
\url{https://idaligner.github.io/}