Given its success in various image generation applications, the usage of diffusion models has expanded to other applications including material discovery. This extension involves mapping the conventional image generation tasks based on provided text to the material generation tasks guided by specified chemical properties. As such, various classes of materials including small molecules , proteins , and crystalline materials have been explored within this context. However, despite these strides, the generation of porous materials using diffusion models remains unexplored due to several challenges arising from the relatively large number of atoms, and the difficulty in capturing the essential topological features for generating porous materials.