In this work, we developed a diffusion model for the generation of pure silica zeolite, marking the first application of diffusion models to porous materials. Our model successfully generated new zeolite structures unseen during the training phase, and demonstrates over 2,000-fold improvement over the GAN model under the same condition. Moreover, inverse design of samples with user-desired properties was also achieved through conditional generation and clearly shows vast improvement compared to the unconditional generation case.