  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 complexity as measured by Gflops. We find that
DiTs with higher Gflops -- through increased transformer depth/width or
increased number of input tokens -- consistently have lower FID. In addition to
possessing good scalability properties, our largest DiT-XL/2 models outperform
all prior diffusion models on the class-conditional ImageNet 512x512 and
256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.
