In this work, for the first time, porous materials were successfully generated using a diffusion model. Specifically, pure silica zeolites that consist of silicon and oxygen atoms were targeted for the generation. Among the previous attempts made with regard to the design of zeolites and other porous materials , it must be stated that Deem and coworkers have employed Monte Carlo approach, and Kim et al. used GAN architecture to generate new zeolite structures. However, Deem's approach lacked the ability to generate structures with user-desired properties, while the structures generated using Kim's GAN architecture exhibited poor structural validity. In contrast, our diffusion model has successfully generated structures with high validity while exhibiting exceptional capabilities in inverse design at the same time. Specifically, quantitative comparisons with GAN demonstrated that the diffusion model outperforms GAN by more than 2,000 times in terms of structure validity, revealing that diffusion models outperform GANs not only in image processing but also in materials generation. Furthermore, the ability of our diffusion model to extend to inverse design for various properties such as void fraction, Henry coefficient and heat of adsorption demonstrates the potential of this model to significantly reduce time in user-desired materials discovery.