EasyText: Controllable Diffusion Transformer for Multilingual Text
  Rendering
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.
683d0b6c5810d395f0a9a49e
Runnan Lu
683d0b6c5810d395f0a9a49f
Yuxuan Zhang
683d0b6c5810d395f0a9a4a0
Jailing Liu
683d0b6c5810d395f0a9a4a1
Haifa Wang
claimed_verified
2025-06-04T15:03:39.215Z
637745113a63a2983ffbde13
https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
user
wanghaofan
683d0b6c5810d395f0a9a4a2
Yiren Song
EasyText: Controllable Diffusion Transformer for Multilingual Text
  Rendering
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.
The paper presents EasyText, a multilingual text rendering framework using DiT that enhances rendering precision and visual quality with large datasets.
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