Forward distribution process. We have an initial, iterative, process where the structure of the image is “destroyed” in a data distribution. In simple words, is like we iteratively add noise to the image, until all the pixels become pure noise and the image is not recognizable (by the human eye).
Reverse diffusion process. Then, there is a reverse diffusion process which is the actual learning process: this restores the structure of the data. It is like our model learns how to “de-noise” the pixels to recreate the image.