The field of text generation and editing is rapidly advancing with the introduction of diffusion models. Researchers are exploring new ways to improve the accuracy and fidelity of text editing, particularly in scenes with complex backgrounds and non-Latin characters. One of the key directions is the use of latent diffusion models, which have shown promising results in generating high-quality text. However, these models still face challenges in maintaining the coherence and consistency of the generated text with the surrounding environment. To address these issues, researchers are proposing innovative solutions, such as the use of glyph encoders, aspect-aware diffusion transformers, and counting-guidance diffusion. These approaches have achieved state-of-the-art results in various benchmarks, demonstrating the potential of diffusion models in advancing the field of text generation and editing. Noteworthy papers in this area include FLUX-Text, which presents a simple and advanced multilingual scene text editing framework, and GlyphMastero, which introduces a specialized glyph encoder for high-fidelity scene text editing.