Learning to diffuse from original image to an isotropic Gaussian, with an invertible transformation, means you are able to un-diffuse isotropic Gaussians back i

Learning to diffuse from original image to an isotropic Gaussian, with an invertible transformation, means you are able to un-diffuse isotropic Gaussians back into images once training is complete. The idea is for the transformation to be generalized enough that this process returns images that are feasibly part of the dataset from which the training data was sampled.