For deep-learning image modeling, the goal is to learn a density that assigns high likelihood to real images and to images matching a prompt.

“because it really depends on what you end up using your density for. Right? If you like let's say you are like a completely deep learning build, you want to have just a large model which kind of describes the density of images. Right? You do diffusion, we have kind of get to that, but we are doing diffusion for image modelling. Then what you ultimately interested in is you want a density”

“see, which explains images the best, right? So you want to have them as high of a likelihood as possible for like real images, or if you have like a a prompt that given the prompt the high likelihood for things that are actually of that uh kind of prompt description.”