Scene text editing aims to modify texts on images while maintaining the style of newly generated text similar to the original. Given an image, a target area, and target text, the task produces an output image with the target text in the selected area, replacing the original. This task has been studied extensively, with initial success using Generative Adversarial Networks (GANs) to balance text fidelity and style similarity. However, GAN-based methods struggled with complex backgrounds or text styles. Recent works leverage diffusion models, showing improved results, yet still face challenges, especially with non-Latin languages like CJK characters (Chinese, Japanese, Korean) that have complex glyphs, often producing inaccurate or unrecognizable characters. To address these issues, we present \emph{TextMastero} - a carefully designed multilingual scene text editing architecture based on latent diffusion models (LDMs). TextMastero introduces two key modules: a glyph conditioning module for fine-grained content control in generating accurate texts, and a latent guidance module for providing comprehensive style information to ensure similarity before and after editing. Both qualitative and quantitative experiments demonstrate that our method surpasses all known existing works in text fidelity and style similarity.

本研究解决了现有场景文本编辑技术在复杂背景和非拉丁字符文本样式处理中的不足，尤其是在中文、日文和韩文字符的准确生成上。提出的TextMastero架构基于潜在扩散模型，引入了精细内容控制的字形条件模块和全面风格信息的潜在引导模块，显著提升了文本忠实度和风格相似性，是对现有技术的一次重要突破。

TextMastero：掌握多语言和多样式的高质量场景文本编辑