I still would not call the diffusion process a form of compression. The reason why is because as a whole these models don’t aim to exactly replicate their dataset. If they did, that’s considered overfitting which is a failure of the model (as another commenter said). Generally, these models can almost never be coaxed to give their original data back. To really be considered a form of compression, you’d have to make it easier to do that. Technically, you can do it (e.g. describing a very specific scene in a very specific style), but at that point you’re basically just giving detailed instructions on what to do. If I told a human to paint a very picture and gave them extremely specific steps, that would not be considered compression. That would just be them knowing how existing art patterns work and using that knowledge to follow my instructions. In general, I don’t think it should be considered compression because the results are almost always novel and it’s extremely hard to get anything even close to the original dataset.