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 \textbf{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. \textbf{Project Page:
https://idaligner.github.io/}

通过引入通用反馈学习框架，本研究提出了 ID-Aligner 方法用于改进 ID-T2I 性能，解决参考肖像的身份特征保留、生成图像的美学吸引力缺失以及与 LoRA 和 Adapter 方法的兼容性等关键问题。大量的实验证明了这一方法的有效性。

ID-Aligner: 用奖励反馈学习增强保持身份的图文生成

ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with  Reward Feedback Learning

Recent advancements in diffusion-based generative image editing have sparked
a profound revolution, reshaping the landscape of image outpainting and
inpainting tasks. Despite these strides, the field grapples with inherent
challenges, including: i) inferior quality; ii) poor consistency; iii)
insufficient instrcution adherence; iv) suboptimal generation efficiency. To
address these obstacles, we present ByteEdit, an innovative feedback learning
framework meticulously designed to Boost, Comply, and Accelerate Generative
Image Editing tasks. ByteEdit seamlessly integrates image reward models
dedicated to enhancing aesthetics and image-text alignment, while also
introducing a dense, pixel-level reward model tailored to foster coherence in
the output. Furthermore, we propose a pioneering adversarial and progressive
feedback learning strategy to expedite the model's inference speed. Through
extensive large-scale user evaluations, we demonstrate that ByteEdit surpasses
leading generative image editing products, including Adobe, Canva, and MeiTu,
in both generation quality and consistency. ByteEdit-Outpainting exhibits a
remarkable enhancement of 388% and 135% in quality and consistency,
respectively, when compared to the baseline model. Experiments also verfied
that our acceleration models maintains excellent performance results in terms
of quality and consistency.

通过引入 ByteEdit 模型的创新反馈学习框架，我们能够改善扩展式图像编辑任务的生成质量和一致性，同时提供了增强美学和图像 - 文本对齐的图像奖励模型以及促进输出一致性的像素级奖励模型。