Yeah, the neural network "diffusion models" are not very well named. If you have background in natural sciences, you would understand diffusion to mean, well, diffusion. Whereas there generative neural networks are about (1) blurring data by Gaussian noise, (2) teaching a NN to denoise the noised data, and finally (3) with Gaussian noise as input, let the denoiser NN to generate new data. So it's not so much about diffusion as it's about reversing the diffusion. And it's not really (smooth) diffusion, but Gaussian noise.
"Denoising autoencoder" is already used for processes that reconstruct partially corrupted input. So what name to suggest for a process that reconstructs data from nothing but noise?
https://distill.pub/2020/growing-ca