usion models usually simply define the forward pass from noise to a less noisy sample,
schedulers define the whole denoising process, i.e.: How many denoising steps? Stochastic or deterministic? What algorithm to use to find the denoised sample? They can be quite complex and often define a trade-off between denoising speed and denoising quality.
It is extremely difficult to measure quantitatively which scheduler works best for a given diffusion