> With the image generators, they are able to take a relatively small text prompt and create an image that is many bytes larger than the input. Yes, but that's

> With the image generators, they are able to take a relatively small text prompt and create an image that is many bytes larger than the input. Yes, but that's not the hard part. The diffusion models in question work fine without any kind of text supervision, modeling purely the image. We also have plenty of text embeddings, so also not the hard part. The hard part is, how do you have a model which takes a large amount of 'random noise' (what even is 'random noise' in text?) and then make a tiny update which makes it more sensible such that it can diffuse towards high-quality text? Once that's solved, then control or conditioning is relatively easy.