This comment in another thread suggests why that's the case: > In other text-to-image algorithms I'm familiar with (the ones you'll typically see passed around

This comment in another thread suggests why that's the case: > In other text-to-image algorithms I'm familiar with (the ones you'll typically see passed around as colab notebooks that people post outputs from on Twitter), the basic idea is to encode the text, and then try to make an image that maximally matches that text encoding. But this maximization often leads to artifacts - if you ask for an image of a sunset, you'll often get multiple suns, because that's even more sunset-like. There's a lot of tricks and hacks to regularize the process so that it's not so aggressive, but it's always an uphill battle. > Here, they instead take the text embedding, use a trained model (what they call the 'prior') to predict the corresponding image embedding - this removes the dangerous maximization. Then, another trained model (the 'decoder') produces images from the predicted embedding. https://news.ycombinator.com/item?id=30933091 https://news.ycombinator.com/item?id=30933091