GlyphBanana: Advancing Precise Text Rendering Through Agentic Workflows
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911509271019520 |
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| author | Yan, Zexuan Jin, Jiarui Ma, Yue Wang, Shijian Hu, Jiahui Jiao, Wenxiang Lu, Yuan Zhang, Linfeng |
| author_facet | Yan, Zexuan Jin, Jiarui Ma, Yue Wang, Shijian Hu, Jiahui Jiao, Wenxiang Lu, Yuan Zhang, Linfeng |
| contents | Despite recent advances in generative models driving significant progress in text rendering, accurately generating complex text and mathematical formulas remains a formidable challenge. This difficulty primarily stems from the limited instruction-following capabilities of current models when encountering out-of-distribution prompts. To address this, we introduce GlyphBanana, alongside a corresponding benchmark specifically designed for rendering complex characters and formulas. GlyphBanana employs an agentic workflow that integrates auxiliary tools to inject glyph templates into both the latent space and attention maps, facilitating the iterative refinement of generated images. Notably, our training-free approach can be seamlessly applied to various Text-to-Image (T2I) models, achieving superior precision compared to existing baselines. Extensive experiments demonstrate the effectiveness of our proposed workflow. Associated code is publicly available at https://github.com/yuriYanZeXuan/GlyphBanana. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12155 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GlyphBanana: Advancing Precise Text Rendering Through Agentic Workflows Yan, Zexuan Jin, Jiarui Ma, Yue Wang, Shijian Hu, Jiahui Jiao, Wenxiang Lu, Yuan Zhang, Linfeng Computer Vision and Pattern Recognition Artificial Intelligence Despite recent advances in generative models driving significant progress in text rendering, accurately generating complex text and mathematical formulas remains a formidable challenge. This difficulty primarily stems from the limited instruction-following capabilities of current models when encountering out-of-distribution prompts. To address this, we introduce GlyphBanana, alongside a corresponding benchmark specifically designed for rendering complex characters and formulas. GlyphBanana employs an agentic workflow that integrates auxiliary tools to inject glyph templates into both the latent space and attention maps, facilitating the iterative refinement of generated images. Notably, our training-free approach can be seamlessly applied to various Text-to-Image (T2I) models, achieving superior precision compared to existing baselines. Extensive experiments demonstrate the effectiveness of our proposed workflow. Associated code is publicly available at https://github.com/yuriYanZeXuan/GlyphBanana. |
| title | GlyphBanana: Advancing Precise Text Rendering Through Agentic Workflows |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2603.12155 |