title: Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass
    Diffusion Transformers
summary: 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.
paper_authors._id: 65af4ff77af20f8e92a512bd
paper_authors.name: Katherine Crowson
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1665633390910-610bf970bdc6d6300330609a.png
paper_authors.fullname: Katherine Crowson
paper_authors.type: user
paper_authors.user: RiversHaveWings
paper_authors._id: 65af4ff77af20f8e92a512be
paper_authors.name: Stefan Andreas Baumann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-23T11:03:48.977Z
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paper_authors.fullname: Stefan Baumann
paper_authors.type: user
paper_authors.user: stefan-baumann
paper_authors._id: 65af4ff77af20f8e92a512bf
paper_authors.name: Alex Birch
paper_authors._id: 65af4ff77af20f8e92a512c0
paper_authors.name: Tanishq Mathew Abraham
paper_authors._id: 65af4ff77af20f8e92a512c1
paper_authors.name: Daniel Z. Kaplan
paper_authors._id: 65af4ff77af20f8e92a512c2
paper_authors.name: Enrico Shippole
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-29T08:26:20.731Z
paper_authors._id: 6276ba3c2d26ac639e5a2b01
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paper_authors.fullname: Enrico Shippole
paper_authors.type: user
paper_authors.user: conceptofmind
paper_title: Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass
    Diffusion Transformers
paper_summary: 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.
paper_ai_keywords: Hourglass Diffusion Transformer (HDiT) image generAR generative model pixel-scaling high-resolution pixel-space Transformer architecture convolutional U-Nets scalability Transformers self-conditioning ImageNet FFHQ-$1024^2$ state-of-the-art