AMO Sampler: Enhancing Text Rendering with Overshooting

2024-11-28

arXiv.org

AI Summary

Current text-to-image diffusion models (e.g., SD3, Flux, AuraFlow) suffer from pervasive spelling errors and textual inconsistency when rendering in-image text. To address this, we propose a training-free attention-modulated oversampling method grounded in the rectified flow (RF) ODE solver. Our approach integrates Langevin dynamics principles with a text-attention-guided noise re-injection mechanism, enabling adaptive oversampling intensity per image patch to jointly enhance textual accuracy and preserve fine-grained visual fidelity. Evaluated on SD3 and Flux, our method improves text rendering accuracy by 32.3% and 35.9%, respectively—without any additional training cost or inference latency overhead. To our knowledge, this is the first efficient, lightweight, plug-and-play solution for precise in-image text synthesis in diffusion-based generative modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Improving text-image alignment in text-to-image generation
• Reducing misspelled text in state-of-the-art diffusion models
• Enhancing text rendering without extra training or computational cost

Innovation

• Training-free overshooting sampler enhances text rendering
• Attention Modulated Overshooting adapts strength per patch
• Improves text accuracy without extra inference cost