Previous neural sampling methods, primarily using analytical lobe mixtures and normalizing flows, often struggle with specular materials, particularly at grazing angles. Furthermore, they are limited to reflection, and do not handle transmission. Our key observation is that previous normalizing flows impose significant restriction in their network architecture for easy computation of the Jacobian. However, for low-dimensional BSDF sampling, the Jacobian computation is not the bottleneck. Therefore, we propose to use diffusion models to importance sample full BSDFs. Our method has two variants, one for most reflective materials that learns a distribution on a disk, and the other for extremely specular reflective materials and full BSDFs, which learns a distribution on a sphere. Our equal-time evaluations show that our method outperforms normalizing flows and significantly surpasses them in certain specular materials.