LeX-Art: Rethinking Text Generation via Scalable High-Quality Data
  Synthesis
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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Shitian Zhao
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Qilong Wu
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Jerry Wu
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Xinyue Li
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Bo Zhang
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Bo Zhang
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Ming Li
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Qi Qin
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qinqi
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Dakerqi
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Dongyang Liu
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Dongyang Liu (Chris Liu)
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Kaipeng Zhang
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KAIPENG ZHANG
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Hongsheng Li
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Hongsheng LI
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Yu Qiao
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Peng Gao
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Peng Gao
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cosumosu25
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Bin Fu
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Zhen Li
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Zhen Li
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Paper99
LeX-Art: Rethinking Text Generation via Scalable High-Quality Data
  Synthesis
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.
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.
Deepseek-R1 LeX-10K LeX-Enhancer LeX-FLUX LeX-Lumina LeX-Bench Pairwise Normalized Edit Distance (PNED) CreateBench