The idea is simple.
For generative machine learning. I wonder if you can use a classifier and evaluate the probabilities that the sample belongs to different categories of noise levels.
That is. You precompute samples with different levels of noise and label them accordingly. So you have samples with 0% noise 10%noise 20%noise … 100%noise labels.
A fast way to precompute is just to add random noise of a certain % amount to the image or sample.
The reason for this approach is that I think getting to a generated sample of 0% noise gets faster if the algorithm can ”think” or recognize a little improvement in the noise level.