[2401.11605] Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers

Computer Science > Computer Vision and Pattern Recognition arXiv:2401.11605 (cs) [Submitted on 21 Jan 2024 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers Authors:Katherine Crowson, Stefan Andreas Baumann, Alex Birch, Tanishq Mathew Abraham, Daniel Z. Kaplan, Enrico Shippole View a PDF of the paper titled Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers, by Katherine Crowson and Stefan Andreas Baumann and Alex Birch and Tanishq Mathew Abraham and Daniel Z. Kaplan and Enrico Shippole View PDF HTML (experimental) Abstract: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-$1024^2$. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI);...