In this paper, the authors introduce TextDiffuser-2, a method that utilizes language models to improve text rendering. The previous methods in this field had limitations such as limited flexibility and automation, constrained layout prediction, and restricted style diversity. TextDiffuser-2 addresses these challenges by fine-tuning a large language model for layout planning and incorporating it within the diffusion model. This approach allows for more diverse text images and enhances rational text layout and generation. The authors conducted extensive experiments and user studies to validate the effectiveness of TextDiffuser-2. The results demonstrate its superiority in terms of accuracy and diversity compared to existing methods.
https://jingyechen.github.io/textdiffuser2/