# TextMastero: Mastering High-Quality Scene Text Editing in Diverse Languages and Styles

arXiv: https://arxiv.org/abs/2408.10623 · HTML: https://mlsanity.com/paper/2408.10623/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- First posted on arXiv 20 Aug 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Built on by (most cited first, 5 of 5)

- [PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering](https://mlsanity.com/paper/2504.06632/) (2025)
- [DCText: Scheduled Attention Masking for Visual Text Generation via Divide-and-Conquer Strategy](https://mlsanity.com/paper/2512.01302/) (2025)
- [TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment](https://mlsanity.com/paper/2512.09350/) (2025)
- [MULTITEXTEDIT: Benchmarking Cross-Lingual Degradation in Text-in-Image Editing](https://mlsanity.com/paper/2605.08163/) (2026)
- [ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing](https://mlsanity.com/paper/2609.40356/) (2026)

## Abstract (arXiv)

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 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.