In this work, we developed a text-guided denoising diffusion model for crystal structure generation. By aligning embeddings from both a text encoder and graph neural networks through contrastive learning, we improved the model's ability to generate crystal structures guided by textual inputs. Furthermore, contrastive learning proved more effective when the textual descriptions had diverse styles, rather than relying on strictly formatted text. This approach enabled the successful generation of crystal structures for well-known systems such as the binary Ti-O and ternary Zn-Ti-O chemical spaces, offering insights into the generative capabilities of AI models. In addition, the model explored less-characterized systems like Li-P-S-Cl, important for solid-state batteries, where both stable and metastable structures were identified, and a revised phase diagram was proposed. While further developments are required to expand towards more complex structures and properties, these methods have already made large-scale computational searches of chemical space more accessible.