Personalizing Text-to-Image Generation with Context-Independent Rewriting

The article discusses the development of a personalized prompt rewriting method for text-to-image generation, which overcomes the limitations of existing studies. The proposed method utilizes a retriever to source relevant user histories and a rewriter to carry out personalized prompt rewriting. The approach is demonstrated to be effective in generating images that cater to users’ preferences based on their historical data.
The article highlights three primary ethical considerations regarding the use of the proposed method: copyright, privacy, and informed consent. To address these concerns, the authors ensure that all user data is anonymized and obtain proper consent from users before utilizing their data.
Existing studies on text-to-image generation have limitations, such as requiring extra images and fine-tuning of text-to-image models, lacking the ability to learn from user interaction history, and lacking public, personalized text-to-image datasets that truly reflect user preferences. The proposed method aims to overcome these limitations by leveraging user histories and providing a more comprehensive understanding of user preferences.
In conclusion, the article presents a novel approach to personalized prompt rewriting for text-to-image generation, which has the potential to revolutionize the field of computer graphics. By utilizing user histories and incorporating ethical considerations, the proposed method offers a more accurate and personalized way of generating images that cater to users’ preferences.