title: EasyText: Controllable Diffusion Transformer for Multilingual Text
    Rendering
summary: Generating accurate multilingual text with diffusion models has long been
  desired but remains challenging. Recent methods have made progress in rendering
  text in a single language, but rendering arbitrary languages is still an
  unexplored area. This paper introduces EasyText, a text rendering framework
  based on DiT (Diffusion Transformer), which connects denoising latents with
  multilingual character tokens encoded as character tokens. We propose character
  positioning encoding and position encoding interpolation techniques to achieve
  controllable and precise text rendering. Additionally, we construct a
  large-scale synthetic text image dataset with 1 million multilingual image-text
  annotations as well as a high-quality dataset of 20K annotated images, which
  are used for pretraining and fine-tuning respectively. Extensive experiments
  and evaluations demonstrate the effectiveness and advancement of our approach
  in multilingual text rendering, visual quality, and layout-aware text
  integration.
paper_authors._id: 683d0b6c5810d395f0a9a49e
paper_authors.name: Runnan Lu
paper_authors._id: 683d0b6c5810d395f0a9a49f
paper_authors.name: Yuxuan Zhang
paper_authors._id: 683d0b6c5810d395f0a9a4a0
paper_authors.name: Jailing Liu
paper_authors._id: 683d0b6c5810d395f0a9a4a1
paper_authors.name: Haifa Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T15:03:39.215Z
paper_authors._id: 637745113a63a2983ffbde13
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 683d0b6c5810d395f0a9a4a2
paper_authors.name: Yiren Song
paper_title: EasyText: Controllable Diffusion Transformer for Multilingual Text
    Rendering
paper_summary: Generating accurate multilingual text with diffusion models has long been
  desired but remains challenging. Recent methods have made progress in rendering
  text in a single language, but rendering arbitrary languages is still an
  unexplored area. This paper introduces EasyText, a text rendering framework
  based on DiT (Diffusion Transformer), which connects denoising latents with
  multilingual character tokens encoded as character tokens. We propose character
  positioning encoding and position encoding interpolation techniques to achieve
  controllable and precise text rendering. Additionally, we construct a
  large-scale synthetic text image dataset with 1 million multilingual image-text
  annotations as well as a high-quality dataset of 20K annotated images, which
  are used for pretraining and fine-tuning respectively. Extensive experiments
  and evaluations demonstrate the effectiveness and advancement of our approach
  in multilingual text rendering, visual quality, and layout-aware text
  integration.
paper_ai_summary: The paper presents EasyText, a multilingual text rendering framework using DiT that enhances rendering precision and visual quality with large datasets.
paper_ai_keywords: DiT (Diffusion Transformer) denoising latents multilingual character tokens character positioning encoding position encoding interpolation synthetic text image dataset pretraining fine-tuning multilingual text rendering visual quality layout-aware text integration