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info.json
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{
"abstract": "We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality. A cascaded diffusion model comprises a pipeline of multiple diffusion models that generate images of increasing resolution, beginning with a standard diffusion model at the lowest resolution, followed by one or more super-resolution diffusion models that successively upsample the image and add higher resolution details. We find that the sample quality of a cascading pipeline relies crucially on conditioning augmentation, our proposed method of data augmentation of the lower resolution conditioning inputs to the super-resolution models. Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256, outperforming VQ-VAE-2.",
"authors": [
"Jonathan Ho",
"Chitwan Saharia",
"William Chan",
"David J. Fleet",
"Mohammad Norouzi",
"Tim Salimans"
],
"emails": [
"jonathanho@google.com",
"sahariac@google.com",
"williamchan@google.com",
"davidfleet@google.com",
"mnorouzi@google.com",
"salimans@google.com"
],
"extra_links": [
[
"code",
"https://cascaded-diffusion.github.io/"
]
],
"id": "21-0635",
"issue": 47,
"pages": [
1,
33
],
"title": "Cascaded Diffusion Models for High Fidelity Image Generation",
"volume": 23,
"year": 2022
}