AMO Sampler: Enhancing Text Rendering with Overshooting

TL;DR

The paper introduces AMO Sampler, a training-free method that enhances text rendering in text-to-image models by adaptively controlling overshooting, significantly improving accuracy without extra inference cost.

Contribution

It proposes an Attention Modulated Overshooting sampler (AMO) that adaptively adjusts overshooting strength based on attention, improving text rendering in pretrained models.

Findings

32.3% improvement in SD3 text accuracy

35.9% improvement in Flux text accuracy

No increase in inference cost or image quality degradation

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. Code available at: https://github.com/hxixixh/amo-release.