  We present the Hourglass Diffusion Transformer (HDiT), an image generative
model that exhibits linear scaling with pixel count, supporting training at
high-resolution (e.g. $1024 \times 1024$) directly in pixel-space. Building on
the Transformer architecture, which is known to scale to billions of
parameters, it bridges the gap between the efficiency of convolutional U-Nets
and the scalability of Transformers. HDiT trains successfully without typical
high-resolution training techniques such as multiscale architectures, latent
autoencoders or self-conditioning. We demonstrate that HDiT performs
competitively with existing models on ImageNet $256^2$, and sets a new
state-of-the-art for diffusion models on FFHQ-$1024^2$.
