Comment by yorwba

Normalizing flows generate samples by starting from Gaussian noise and passing it through a series of invertible transformations. Diffusion models generate samples by starting from Gaussian noise and running it through an inverse diffusion process.

To get deterministic results, you fix the seed for your pseudorandom number generator and make sure not to execute any operations that produce different results on different hardware. There's no difference between the approaches in that respect.