IMO, forward diffusion process isn't really a "process" -- it's need not be sequential, it's parallelizable. The sole purpose of forward process is simulating noisy data from a set of "noisy data distributions" crafted with a known set of noise-scales -- that's it. SBM and DDPM both have this process. For SBMs, it is again a heuristic HP to choose the correct largest scale so that it can overpower the data variance and reach an uninformative prior. For DDPM, it always reaches the prior due to the way noise-scales and attenuation coefficients are computed from \beta_t.

Agree with your second part. SDE formulation is good -- it basically brings SBMs into a more stronger theoretical framework. SDEs offer a reverse process which is analytic where the score naturally appears -- i.e. again not much HP.