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TextDiffuser: Diffusion Models as Text Painters

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arxiv 2305.10855 v5 pith:JOEZNDQ6 submitted 2023-05-18 cs.CV

Jingye Chen , Yupan Huang , Tengchao Lv , Lei Cui , Qifeng Chen , Furu Wei This is my paper
classification cs.CV
keywords textimagestextdiffuserdiffusionmodelsannotationscoherentdataset
verification ladder T0 review T1 audit T2 compute T3 formal
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Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout. Additionally, we contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations. We further collect the MARIO-Eval benchmark to serve as a comprehensive tool for evaluating text rendering quality. Through experiments and user studies, we show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text. The code, model, and dataset will be available at \url{https://aka.ms/textdiffuser}.

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Cited by 5 Pith papers

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  4. TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision

    cs.CV 2025-07 reject novelty 5.0 of 10

    The GCDA framework claims state-of-the-art text rendering in diffusion images via dual-stream encoding, attention segregation, and OCR supervision, but the paper lacks verifiable artifacts and contains internal incons...

  5. Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences

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