title: LeX-Art: Rethinking Text Generation via Scalable High-Quality Data
    Synthesis
summary: 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.
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paper_authors.fullname: Ming Li
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paper_authors.name: Qi Qin
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paper_authors.name: Dongyang Liu
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paper_authors.fullname: Dongyang Liu (Chris Liu)
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paper_authors.name: Kaipeng Zhang
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paper_authors.name: Bin Fu
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paper_title: LeX-Art: Rethinking Text Generation via Scalable High-Quality Data
    Synthesis
paper_summary: 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.
paper_ai_summary: A suite called LeX-Art for high-quality text-image synthesis includes data-centric pipeline, prompt enrichment, and text-to-image models, achieving state-of-the-art performance with a new benchmark and metric.
paper_ai_keywords: Deepseek-R1 LeX-10K LeX-Enhancer LeX-FLUX LeX-Lumina LeX-Bench Pairwise Normalized Edit Distance (PNED) CreateBench