SiT
Scalable Interpolant Transformers (SiT) introduce advancements in flow and diffusion-based generative modeling. Built on Diffusion Transformers (DiT), SiT connects distributions with flexible design choices. This repository includes PyTorch models, pre-trained weights, and a sampling script, designed to perform well on the ImageNet 256x256 benchmark. It is suitable for professionals exploring generative model technologies.
