EasyText: Controllable Diffusion Transformer for Multilingual Text
  Rendering
Authors: name: Runnan Lu, name: Yuxuan Zhang, name: Jailing Liu, name: Haifa Wang, name: Yiren Song

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.