DiT Scalable Diffusion Models with Transformers (DiT) is by William Peebles and Saining Xie. The abstract from the paper is: We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass compl