EasyText: Controllable Diffusion Transformer for Multilingual Text Rendering

TL;DR

EasyText introduces a diffusion transformer-based framework for controllable, high-quality multilingual text rendering, leveraging large-scale synthetic datasets and novel encoding techniques to improve accuracy and layout control.

Contribution

The paper presents EasyText, a novel diffusion transformer framework with character positioning encoding and interpolation for precise multilingual text rendering.

Findings

Effective multilingual text rendering demonstrated

High visual quality and layout control achieved

Large-scale synthetic datasets enhance training

Abstract

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.