Artistic typography is a technique that enables one to visualize the meaning of an input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods directly design the overall geometry and texture of input character, making it challenging to ensure both creativity and legibility. In this paper, we introduce a dual-branch, training-free method called VitaGlyph, enabling flexible artistic typography with controllable geometry changes while maintaining legibility. The key insight of VitaGlyph is to treat the input character as a scene composed of a Subject and its Surrounding, which are rendered with varying degrees of geometric transformation. To enhance the visual appeal and creativity of the generated artistic typography, the Subject flexibly expresses the essential concept of the input character, while the Surrounding enriches relevant background without altering the shape. Specifically, we implement VitaGlyph through a three-phase framework: (i) Knowledge Acquisition leverages large language models to design text descriptions for the Subject and Surrounding. (ii) Regional Interpretation detects the part that matches the subject description most closely and refines the structure using Semantic Typography. (iii) Attentional Compositional Generation separately renders the textures of the Subject and Surrounding and blends them in an attention-based manner. Experiments demonstrate that VitaGlyph not only achieves better artistry and legibility, but also manages to depict multiple customized concepts, facilitating more creative and pleasing artistic typography generation. Our code is available at https://github.com/Carlofkl/VitaGlyph.

Customized text-to-image generation, which synthesizes images based on user-specified concepts, has made significant progress in handling individual concepts. However, when extended to multiple concepts, existing methods often struggle with properly integrating different models and avoiding the unintended blending of characteristics from distinct concepts. In this paper, we propose MC2, a novel approach for multi-concept customization that enhances flexibility and fidelity through inference-time optimization. MC2enables the integration of multiple single-concept models with heterogeneous architectures. By adaptively refining attention weights between visual and textual tokens, our method ensures that image regions accurately correspond to their associated concepts while minimizing interference between concepts. Extensive experiments demonstrate that MC2outperforms training-based methods in terms of prompt-reference alignment. Furthermore, MC2can be seamlessly applied to text-to-image generation, providing robust compositional capabilities. To facilitate the evaluation of multi-concept customization, we also introduce a new benchmark, MC++. The code is available at https://github.com/jiangJiaxiu/MC-2.

Text-to-image diffusion models have demonstrated rich visual-linguistic capability. However, existing image classification methods based on diffusion models simply choose the best-predicted noise, not exploiting the relationships between visual elements and text adequately. To this end, we propose a novel Multi-attentional Distance Classifier (MDC) by exploring some beneficial information in diffusion models. Specifically, MDC joints self- and cross-attention maps to model the semantic and structural distances of the latent variables of images under different category conditions, measuring the relevance between images and categories. With two types of distances integrated, we can classify image's category with minimum distance. We evaluate MDC on CIFAR-10, STL-10, and CIFAR-100 datasets under the zero-shot setting and it shows MDC achieves superior performance to prior works. Further experiments prove that by introducing attention in the diffusion process, MDC can discover key semantic and structure information of categories among images. Codes are publicly available at https://github.com/Carlofkl/MDC.