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

• URL: http://arxiv.org/abs/2401.11605v1
• Date: Sun, 21 Jan 2024 21:49:49 GMT
• Title: Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
• Authors: Katherine Crowson and Stefan Andreas Baumann and Alex Birch and Tanishq Mathew Abraham and Daniel Z. Kaplan and Enrico Shippole
• Abstract summary: We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution. 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.
• Score: 2.078423403798577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-$1024^2$.

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