ID-Aligner enhances identity-preserving text-to-image generation through reward feedback learning. The paper introduces two key rewards: identity consistency reward and identity aesthetic reward, which improve identity preservation and visual appeal. The method is compatible with both LoRA-based and Adapter-based models, achieving superior performance compared to existing methods. The framework uses face detection and recognition models to measure identity consistency and provides aesthetic tuning signals through human-annotated preference data and automatically constructed feedback. Extensive experiments on SD1.5 and SDXL diffusion models validate the effectiveness of the approach. The method is flexible and can be applied to both LoRA and Adapter models, achieving consistent performance gains in identity preservation and aesthetic quality. The results show that ID-Aligner outperforms existing methods in identity consistency and aesthetic quality, demonstrating its effectiveness in identity-preserving generation.ID-Aligner enhances identity-preserving text-to-image generation through reward feedback learning. The paper introduces two key rewards: identity consistency reward and identity aesthetic reward, which improve identity preservation and visual appeal. The method is compatible with both LoRA-based and Adapter-based models, achieving superior performance compared to existing methods. The framework uses face detection and recognition models to measure identity consistency and provides aesthetic tuning signals through human-annotated preference data and automatically constructed feedback. Extensive experiments on SD1.5 and SDXL diffusion models validate the effectiveness of the approach. The method is flexible and can be applied to both LoRA and Adapter models, achieving consistent performance gains in identity preservation and aesthetic quality. The results show that ID-Aligner outperforms existing methods in identity consistency and aesthetic quality, demonstrating its effectiveness in identity-preserving generation.