AMO Sampler: Enhancing Text Rendering with Overshooting
Xixi Hu, Keyang Xu, Bo Liu, Qiang Liu, Hongliang Fei
2024-12-03
Summary
This paper discusses the AMO Sampler, a new method that improves how text is rendered in images generated by AI, making the text clearer and more accurate.
What's the problem?
When AI models generate images from text descriptions, they often struggle to accurately depict the text. This can result in misspelled words or inconsistent lettering, which makes the images look unprofessional or confusing. Current models like Stable Diffusion 3 and others still have trouble with this task, which limits their effectiveness in applications where clear text is important.
What's the solution?
The researchers introduced a method called the Attention Modulated Overshooting (AMO) sampler, which enhances text rendering without needing extra training. This method works by overshooting during the image generation process, which helps correct errors that can happen when generating text. The AMO sampler adjusts how much it overshoots based on the importance of different parts of the image related to the text, ensuring that areas with text get more attention for better accuracy. The results showed significant improvements in text rendering accuracy without sacrificing overall image quality.
Why it matters?
This research is important because it helps improve the quality of AI-generated images that include text. By making it easier for models to render clear and accurate text, the AMO Sampler can enhance various applications such as graphic design, advertising, and social media content creation, where high-quality visuals are essential.
Abstract
Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Sate-of-the-art models like Stable Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text depiction, resulting in misspelled or inconsistent text. We introduce a training-free method with minimal computational overhead that significantly enhances text rendering quality. Specifically, we introduce an overshooting sampler for pretrained rectified flow (RF) models, by alternating between over-simulating the learned ordinary differential equation (ODE) and reintroducing noise. Compared to the Euler sampler, the overshooting sampler effectively introduces an extra Langevin dynamics term that can help correct the compounding error from successive Euler steps and therefore improve the text rendering. However, when the overshooting strength is high, we observe over-smoothing artifacts on the generated images. To address this issue, we propose an Attention Modulated Overshooting sampler (AMO), which adaptively controls the strength of overshooting for each image patch according to their attention score with the text content. AMO demonstrates a 32.3% and 35.9% improvement in text rendering accuracy on SD3 and Flux without compromising overall image quality or increasing inference cost.