[Paluth]

I haven't read the paper on Policy Diffusion yet so I don't know what they do differently. But I can ELI5 image diffusion models, like stable diffusion. Essentially you add random noise to an image, and the ask the model to predict the noise, such that if you remove that noise detected by the model, you obtain the original image. After the model has been trained enough, int the noise removal task, you can pass just random noise, ask the model to remove noise from the noise only image, then remove a little bit of the noise the model suggested, and do it again. And again, for multiple steps, eventually all the noise is removed and you end up with an image "dreamed" by the model from random noise. You can also condition the noise removal with things like text or other images to guide the noise removal process toward a certain target image.