ID-Aligner is a feedback learning framework designed to enhance identity retention in text-to-image generation, addressing issues such as identity feature maintenance, aesthetic appeal of generated images, and compatibility with LoRA and Adapter methods. It utilizes feedback from face detection and recognition models to improve the retention of identity features and provides aesthetic adjustment signals through human-annotated preference data and automatically constructed feedback. ID-Aligner is compatible with LoRA and Adapter models and has been widely validated through extensive experiments demonstrating its effectiveness.