LeX-Art: Rethinking Text Generation via Scalable High-Quality Data
  Synthesis
Authors: name: Shitian Zhao, name: Qilong Wu, name: Xinyue Li, name: Bo Zhang, name: Ming Li, name: Qi Qin, name: Dongyang Liu, name: Kaipeng Zhang, name: Hongsheng Li, name: Yu Qiao, name: Peng Gao, name: Bin Fu, name: Zhen Li

We introduce LeX-Art, a comprehensive suite for high-quality text-image
synthesis that systematically bridges the gap between prompt expressiveness and
text rendering fidelity. Our approach follows a data-centric paradigm,
constructing a high-quality data synthesis pipeline based on Deepseek-R1 to
curate LeX-10K, a dataset of 10K high-resolution, aesthetically refined
1024times1024 images. Beyond dataset construction, we develop LeX-Enhancer,
a robust prompt enrichment model, and train two text-to-image models, LeX-FLUX
and LeX-Lumina, achieving state-of-the-art text rendering performance. To
systematically evaluate visual text generation, we introduce LeX-Bench, a
benchmark that assesses fidelity, aesthetics, and alignment, complemented by
Pairwise Normalized Edit Distance (PNED), a novel metric for robust text
accuracy evaluation. Experiments demonstrate significant improvements, with
LeX-Lumina achieving a 79.81% PNED gain on CreateBench, and LeX-FLUX
outperforming baselines in color (+3.18%), positional (+4.45%), and font
accuracy (+3.81%). Our codes, models, datasets, and demo are publicly
available.