LogoDiffuser: Training-Free Multilingual Logo Generation and Stylization via Letter-Aware Attention Control

TL;DR

LogoDiffuser is a training-free, multilingual logo generation method that uses attention control in diffusion transformers to produce visually appealing logos with accurate character structures across languages.

Contribution

It introduces a novel attention-based approach that integrates character structure and visual design without additional training, supporting multilingual logo synthesis.

Findings

Achieves state-of-the-art results in multilingual logo generation

Effectively controls character structure across languages

Demonstrates robustness without extra training data

Abstract

Recent advances in text-to-image generation have been remarkable, but generating multilingual design logos that harmoniously integrate visual and textual elements remains a challenging task. Existing methods often distort character geometry when applying creative styles and struggle to support multilingual text generation without additional training. To address these challenges, we propose LogoDiffuser, a training-free method that synthesizes multilingual logo designs using the multimodal diffusion transformer. Instead of using textual prompts, we input the target characters as images, enabling robust character structure control regardless of language. We first analyze the joint attention mechanism to identify core tokens, which are tokens that strongly respond to textual structures. With this observation, our method integrates character structure and visual design by injecting the most informative attention maps. Furthermore, we perform layer-wise aggregation of attention maps to mitigate attention shifts across layers and obtain consistent core tokens. Extensive experiments and user studies demonstrate that our method achieves state-of-the-art performance in multilingual logo generation.