RepText: Rendering Visual Text via Replicating

2025-04-28

AI Summary

This work addresses the low fidelity and poor controllability of pretrained text-to-image models in multilingual (especially non-Latin-script) text rendering. We propose a semantics-agnostic, “copy-style” text rendering paradigm. Our method extends ControlNet by integrating glyph encoding, spatial condition modeling, and diffusion process optimization. Key contributions include: (1) a language-agnostic joint control mechanism for glyph appearance and spatial positioning; (2) a text-aware loss function coupled with noise-initialized glyph latent variables; and (3) background preservation via region masking. Experiments demonstrate that our approach significantly outperforms existing open-source methods on multilingual text rendering, achieving visual quality comparable to proprietary commercial models. Comprehensive ablation studies validate the effectiveness and robustness of each component across diverse scripts and layout configurations.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Although contemporary text-to-image generation models have achieved remarkable breakthroughs in producing visually appealing images, their capacity to generate precise and flexible typographic elements, especially non-Latin alphabets, remains constrained. To address these limitations, we start from an naive assumption that text understanding is only a sufficient condition for text rendering, but not a necessary condition. Based on this, we present RepText, which aims to empower pre-trained monolingual text-to-image generation models with the ability to accurately render, or more precisely, replicate, multilingual visual text in user-specified fonts, without the need to really understand them. Specifically, we adopt the setting from ControlNet and additionally integrate language agnostic glyph and position of rendered text to enable generating harmonized visual text, allowing users to customize text content, font and position on their needs. To improve accuracy, a text perceptual loss is employed along with the diffusion loss. Furthermore, to stabilize rendering process, at the inference phase, we directly initialize with noisy glyph latent instead of random initialization, and adopt region masks to restrict the feature injection to only the text region to avoid distortion of the background. We conducted extensive experiments to verify the effectiveness of our RepText relative to existing works, our approach outperforms existing open-source methods and achieves comparable results to native multi-language closed-source models. To be more fair, we also exhaustively discuss its limitations in the end.

Problem

• Enhancing multilingual visual text rendering in text-to-image models
• Improving typographic accuracy without requiring text understanding
• Enabling customizable text content, font, and position in generated images

Innovation

• Uses ControlNet with glyph and position integration
• Employs text perceptual loss for accuracy
• Initializes with noisy glyph latent for stability