FonTS: Text Rendering with Typography and Style Controls

TL;DR

This paper introduces a two-stage diffusion transformer pipeline that enhances word-level typographic and style control in text rendering, addressing inconsistencies and limited control in previous methods.

Contribution

It presents a novel typography control fine-tuning method and a style control adapter, along with a new dataset, for improved fine-grained text rendering control.

Findings

Achieves superior word-level typographic control

Enhances font and style consistency in text rendering

Demonstrates effectiveness through comprehensive experiments

Abstract

Visual text rendering are widespread in various real-world applications, requiring careful font selection and typographic choices. Recent progress in diffusion transformer (DiT)-based text-to-image (T2I) models show promise in automating these processes. However, these methods still encounter challenges like inconsistent fonts, style variation, and limited fine-grained control, particularly at the word-level. This paper proposes a two-stage DiT-based pipeline to address these problems by enhancing controllability over typography and style in text rendering. We introduce typography control fine-tuning (TC-FT), an parameter-efficient fine-tuning method (on $5\%$ key parameters) with enclosing typography control tokens (ETC-tokens), which enables precise word-level application of typographic features. To further address style inconsistency in text rendering, we propose a text-agnostic style control adapter (SCA) that prevents content leakage while enhancing style consistency. To implement TC-FT and SCA effectively, we incorporated HTML-render into the data synthesis pipeline and proposed the first word-level controllable dataset. Through comprehensive experiments, we demonstrate the effectiveness of our approach in achieving superior word-level typographic control, font consistency, and style consistency in text rendering tasks. The datasets and models will be available for academic use.