We propose an approach for the generation of topology-optimized structures with text-guided appearance stylization. This methodology aims to enrich the concurrent design of a structure’s physical functionality and aesthetic appearance. Users can effortlessly input descriptive text to govern the style of the structure. Our system employs a hash-encoded neural network as the implicit structure representation backbone, which serves as the foundation for the co-optimization of structural mechanical performance, style, and connectivity, to ensure full-color, high-quality 3D-printable solutions. We substantiate the effectiveness of our system through extensive comparisons, demonstrations, and a 3D-printing test.