Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this paper, we propose TextFlow, a training-free scene text editing framework that integrates the strengths of Attention Boost (AttnBoost) and Flow Manifold Steering (FMS) to enable flexible, high-fidelity text manipulation without additional training. Specifically, FMS preserves the structural and style consistency by modeling the visual flow of characters and background regions, while AttnBoost enhances the rendering of textual content through attention-based guidance. By jointly leveraging these complementary modules, our approach performs end-to-end text editing through semantic alignment and spatial refinement in a plug-and-play manner. Extensive experiments demonstrate that our framework achieves visual quality and text accuracy comparable to or superior to those of training-based counterparts, generalizing well across diverse scenes and languages. This study advances scene text editing toward a more efficient, generalizable, and training-free paradigm. Code is available at https://github.com/lyb18758/TextFlow

翻译：场景文本编辑旨在修改自然图像中的文本内容，同时保持视觉真实性和语义一致性。现有方法通常需要特定任务的训练或配对数据，限制了其可扩展性和适应性。本文提出TextFlow——一个免训练的场景文本编辑框架，该框架整合了Attention Boost (AttnBoost) 和Flow Manifold Steering (FMS) 的优势，无需额外训练即可实现灵活、高保真的文本操作。具体而言，FMS通过建模字符与背景区域的视觉流来保持结构与风格一致性，而AttnBoost则通过基于注意力的引导增强文本内容的渲染。通过联合利用这些互补模块，我们的方法以即插即用的方式，通过语义对齐和空间精炼实现端到端文本编辑。大量实验表明，本框架在视觉质量和文本准确度上达到甚至超越基于训练的方法，并能很好地泛化至多种场景和语言。本研究推动了场景文本编辑向更高效、更可泛化且免训练范式的演进。代码已在https://github.com/lyb18758/TextFlow开源。