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 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.
65af4ff77af20f8e92a512bd
Katherine Crowson
extracted_confirmed
2024-01-23T12:43:49.887Z
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Katherine Crowson
user
RiversHaveWings
65af4ff77af20f8e92a512be
Stefan Andreas Baumann
admin_assigned
2024-01-23T11:03:48.977Z
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Stefan Baumann
user
stefan-baumann
65af4ff77af20f8e92a512bf
Alex Birch
65af4ff77af20f8e92a512c0
Tanishq Mathew Abraham
65af4ff77af20f8e92a512c1
Daniel Z. Kaplan
65af4ff77af20f8e92a512c2
Enrico Shippole
claimed_verified
2024-01-29T08:26:20.731Z
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Enrico Shippole
user
conceptofmind
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 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.
The Hourglass Diffusion Transformer achieves state-of-the-art performance on high-resolution image generation by combining the scalability of Transformers with the efficiency of U-Nets, operating directly in pixel space without additional high-resolution training techniques.
Hourglass Diffusion Transformer HDiT image generative model pixel-space Transformer architecture convolutional U-Nets multiscale architectures latent autoencoders self-conditioning ImageNet FFHQ