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TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering

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arxiv 2311.16465 v1 pith:IVMEPYL4 submitted 2023-11-28 cs.CV

Jingye Chen , Yupan Huang , Tengchao Lv , Lei Cui , Qifeng Chen , Furu Wei This is my paper
classification cs.CV
keywords textmodellanguagelayouttextdiffuser-2methodsrenderingdiffusion
verification ladder T0 review T1 audit T2 compute T3 formal
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The diffusion model has been proven a powerful generative model in recent years, yet remains a challenge in generating visual text. Several methods alleviated this issue by incorporating explicit text position and content as guidance on where and what text to render. However, these methods still suffer from several drawbacks, such as limited flexibility and automation, constrained capability of layout prediction, and restricted style diversity. In this paper, we present TextDiffuser-2, aiming to unleash the power of language models for text rendering. Firstly, we fine-tune a large language model for layout planning. The large language model is capable of automatically generating keywords for text rendering and also supports layout modification through chatting. Secondly, we utilize the language model within the diffusion model to encode the position and texts at the line level. Unlike previous methods that employed tight character-level guidance, this approach generates more diverse text images. We conduct extensive experiments and incorporate user studies involving human participants as well as GPT-4V, validating TextDiffuser-2's capacity to achieve a more rational text layout and generation with enhanced diversity. The code and model will be available at \url{https://aka.ms/textdiffuser-2}.

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Cited by 5 Pith papers

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  1. ArtChart: Faithful Artistic Chart Generation with Integrated Text Rendering

    cs.CV 2026-07 conditional novelty 6.0 of 10

    ArtChart, a ControlNet + GRPO + multi-expert distillation system, achieves about 9.1/10 math, 9.5/10 text, and 7.7/10 layout on a new 2K bilingual artistic-chart benchmark, well above open baselines.

  2. UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    UniGlyph replaces pre-rendered glyph conditions with segmentation-derived masks in a ControlNet diffusion model, reporting gains on visual text rendering benchmarks.

  3. 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...

  4. PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

    cs.CV 2025-06 conditional novelty 5.0 of 10

    PosterCraft improves text-to-poster generation by cascading four stages of training (text rendering, region-weighted fine-tuning, preference optimization, and vision-language feedback), outperforming open-source basel...

  5. MixDiffusion: Mixing Diffusion-based Uni-condition Text-to-Image Generation Models for Multi-condition Image Synthesis

    cs.CV 2026-07 conditional novelty 4.0 of 10

    MixDiffusion derives a joint noise prediction as the sum of per-condition noise estimates minus the base model, enabling multi-condition control without training.

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