AI-assisted graphic design has emerged as a powerful tool for automating the creation and editing of design elements such as posters, banners, and advertisements. While diffusion-based text-to-image models have demonstrated strong capabilities in visual content generation, their text rendering performance, particularly for small-scale typography and non-Latin scripts, remains limited. In this paper, we propose UTDesign, a unified framework for high-precision stylized text editing and conditional text generation in design images, supporting both English and Chinese scripts. Our framework introduces a novel DiT-based text style transfer model trained from scratch on a synthetic dataset, capable of generating transparent RGBA text foregrounds that preserve the style of reference glyphs. We further extend this model into a conditional text generation framework by training a multi-modal condition encoder on a curated dataset with detailed text annotations, enabling accurate, style-consistent text synthesis conditioned on background images, prompts, and layout specifications. Finally, we integrate our approach into a fully automated text-to-design (T2D) pipeline by incorporating pre-trained text-to-image (T2I) models and an MLLM-based layout planner. Extensive experiments demonstrate that UTDesign achieves state-of-the-art performance among open-source methods in terms of stylistic consistency and text accuracy, and also exhibits unique advantages compared to proprietary commercial approaches. Code and data for this paper are available at https://github.com/ZYM-PKU/UTDesign.

Scene text image super-resolution (STISR) has obtained widespread attention in recent years due to its ability to enhance text recognition performance. Many previous methods proposed to incorporate text prior knowledge into the super-resolution architecture for reconstructing high-quality text images. However, these text priors are typically derived from pretrained text recognition models, and the inaccurate recognition feedback will hinder overall performance. In this paper, we propose a novel model, QT-TextSR, which promotes scene text image super-resolution by introducing efficient interaction with text recognition to release the inaccurate text feedback through a Query-aware Transformer. Specifically, QT-TextSR decomposes scene text image super-resolution and scene text recognition into different sets of queries within a Vision-Language Cooperation Module, explicitly modeling discriminative and interactive features between text recognition and text image super-resolution tasks. By employing two separate yet simultaneous projection heads on the corresponding features, QT-TextSR can recover the low-quality text image meanwhile obtain the recognition results. Additionally, to mitigate the limitations caused by recognition errors and enhance text structure preservation, we introduce a strong texture prior through self-supervised pre-training, leveraging visual cues more effectively. Experiments on public dataset, TextZoom demonstrate that our QT-TextSR significantly outperforms previous state-of-the-art methods in the metrics of Recognition Accuracy (68% v s . 65.5%), PSNR (22.51 v s . 22.10), and SSIM (0.7960 v s . 7930). The code for QT-TextSR is available at https://github.com/lcy0604/QT-TextSR .