Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers

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 × 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 . Code is available at github.com/crowsonkb/k-diffusion.