EasyText: Controllable Diffusion Transformer for Multilingual Text Rendering
This work addresses the problem of rendering arbitrary languages in text generation for applications in multilingual content creation, representing a novel method for a known bottleneck rather than a foundational advancement.
The paper tackles the challenge of generating accurate multilingual text with diffusion models by introducing EasyText, a framework based on Diffusion Transformers that uses character positioning encoding and interpolation for controllable rendering, achieving effective results in multilingual text rendering, visual quality, and layout-aware integration.
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.