Latent-Diffusion

diffusion models generate by learning to undo noise: destroy an image with a fixed gaussian corruption process, train a network to reverse one small step of the destruction, then chain the reversals from pure noise back to data (ho et al. 2020, denoising diffusion probabilistic models). 𐃏 stable diffusion (rombach et al. 2022, high-resolution image synthesis with latent diffusion models) runs this machinery not on pixels but in the latent space of an autoencoder, with a text-conditioned u-net doing the denoising. first the maths, then the architecture.