When I was taking Jeremy Howard’s course last fall, the breakthrough in SD was going from 1000 steps to 50 steps via classifier-free guidance, which is this nea

When I was taking Jeremy Howard’s course last fall, the breakthrough in SD was going from 1000 steps to 50 steps via classifier-free guidance, which is this neat hack where you run inference with your conditioning and without and then mix the result. To this day I still don’t get it. But it works. Now we find this way to skip to the end by building a model that learns the high dimensional curvature of the path that a diffusion process takes through space on its way to an acceptable image, and we just basically move the model along that path. That’s my naive understanding of LCM. Seems to good to be true, but it does work and it has a good theoretical basis too. Makes you wonder what is next? Will there be a single step network that can train on LCM to predict the final destination? LoL that would be pushing things too far..