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.
Abstract (translated)
近期在文本到图像生成领域取得了显著的进展，但在创建多语言设计标志时，如何和谐地整合视觉和文字元素仍然是一项挑战。现有的方法往往会在应用创意风格时扭曲字符几何形状，并且难以支持无需额外训练就能进行多种语言的文字生成。为了解决这些挑战，我们提出了一种名为LogoDiffuser的方法，这是一种无需训练的合成多语言标志设计的方法，使用的是多模态扩散变换器。不同于使用文本提示，我们输入目标文字作为图像，从而可以对任何语言的文字结构进行全面控制。 首先，我们通过分析联合注意力机制来识别核心令牌（即强烈响应于文本结构的令牌）。基于这一观察，我们的方法通过注入最具有信息量的注意力图谱将字符结构和视觉设计结合在一起。此外，我们执行层次化的注意力图谱聚合以减轻不同层之间的注意力偏移，并获取一致的核心令牌。 广泛的实验和用户研究证明了我们的方法在多语言标志生成方面达到了最先进的性能水平。
URL
https://arxiv.org/abs/2603.09759
