Diffusion models naturally fit continuous data such as images, where pixel colors can be interpolated meaningfully; discrete words do not necessarily have meaningful intermediate values.

“Yeah, so there was a lot of research that went into figuring out how to apply a technology that was inherently very tied to kind of like continuous structure in the data.”

“So if you think about a diffusion model, it's learning how to denoise images and it kind of like makes sense for continuous data because if you think about...”

“even two pixel colors you can kind of like interpolate between them and it will still make sense but if you think about two words there is not necessarily something in between them right it's all discrete and so it required a lot of R&D and a new science that had to be developed to figure out how to how to extend those kind of ideas to discrete spaces.”