Summary of Id-aligner: Enhancing Identity-preserving Text-to-image Generation with Reward Feedback Learning, by Weifeng Chen et al.
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
by Weifeng Chen, Jiacheng Zhang, Jie Wu, Hefeng Wu, Xuefeng Xiao, Liang Lin
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary 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. Existing ID-T2I methods have demonstrated impressive results, but several key challenges remain: maintaining the identity characteristics of reference portraits accurately, generating images with aesthetic appeal while enforcing identity retention, and limitations 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. ID-Aligner resolves identity features lost by introducing identity consistency reward fine-tuning, utilizing feedback from face detection and recognition models to improve generated identity preservation. Additionally, it proposes 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ID-Aligner is a new way to make computer-generated portraits that look like real people. It helps keep the identity of the person in the portrait accurate while also making it look good aesthetically. This is important because it can be used for things like AI portraits and advertising. Right now, there are some problems with this technology, such as it being hard to make sure the portrait looks like the real person and that it doesn’t just become a generic image. The new method uses feedback from other computer programs to help make sure the portrait is accurate and looks good. This means it can be used with different types of computers and software, which makes it more useful. The people who made this technology tested it and found that it works well for making portraits that are both accurate and visually appealing. |
Keywords
» Artificial intelligence » Attention » Diffusion » Fine tuning » Image generation » Lora
