I’ve been playing with diffusion a ton for the past few months, writing a new sampler that implements an iterative blending technique described in a recent paper. The latent space is rich in semantic information, so it can be a great place to apply various transformations rather than operating on the image directly. Yet it still has a significant spatial component, so things you do in one spatial area will affect that same area of the image.
Stable Diffusion 1.5 may be quite old now, but it is an incredibly rich model that still yields shockingly good results. SDXL is newer and more high tech, but it’s not a revolutionary improvement. It can be less malleable than the older model and harder to work with to achieve a given desired result.
Stable Diffusion 1.5 may be quite old now, but it is an incredibly rich model that still yields shockingly good results. SDXL is newer and more high tech, but it’s not a revolutionary improvement. It can be less malleable than the older model and harder to work with to achieve a given desired result.