Multi-attribute fusion generation via text-to-image customization

Text-to-Image methods have shown impressive results, allowing casual users to generate high quality images by providing textual descriptions. Main text-to-image methods base on Generative Adversarial Network and diffusion models, show better results in terms of fidelity and novelty, but lack customization and multi-attribute fusion. This paper presents a text-to-image customization method that generate customized images fusing multiple attributes based on textual descriptions. Users guide image-generation with natural language without changing the properties of the attribute. New images change the appearance, pose, and context drastically. In this paper, we use the cross-attention mechanism to effectively capture the hierarchical relationships between text and images, guide image-generation through diffusion model in the process of multi-attribute fusion and present the effect of customized images with multi-attribute fusion. Experiment shows that, generated images exhibit good visual fidelity, novelty, and text alignment, and meet the customized needs of users.