LogoDiffuser: Training-Free Multilingual Logo Generation and Stylization via Letter-Aware Attention Control
This addresses the challenge of harmonizing visual and textual elements in logo design for multilingual applications, representing an incremental improvement over existing methods.
The paper tackled the problem of generating multilingual logos that preserve character geometry under creative styles without requiring additional training, achieving state-of-the-art performance as demonstrated by experiments and user studies.
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.