consistency models

“One thing that they said in the paper, it's not here, but that that it, they, it takes about two X to compute to train the This consistency model from as a as a distillation of whatever they distilled from. So approximately twice the compute.”

“The locations that you're learning are not basically on the same in the, in this latent space. They're not in the same trajectory As each other, right? So they like and this causes a lot of inefficiency, and that's sort of the whole point to this, the, these …”

“With the regular diffusion models that we all know and love, they have to iterate multiple times generally to generate a good image. Whereas The, these consistency models are designed so they can generate a good image with only one pass through the network.”

“None of this technology that we're discussing today is in any of really in any of the big models that we know and love with maybe of maybe with the exception of flux.”

“And so like when you stack all of these things together, then you're able to train much more effectively and continuous time does much better. Then these discrete, this n is the number of discrete steps that your model is taking, and, you know, maybe one inter…”

“What a consistency model does is it says that everything should be on the same trajectory, right? So I'm gonna, if I estimate it, I can I'm gonna learn how to map from any point on this trajectory to the to this point in the data.”