Bibliographic Details
Title: |
Cascaded Diffusion Models for High Fidelity Image Generation |
Authors: |
Ho, Jonathan, Saharia, Chitwan, Chan, William, Fleet, David J., Norouzi, Mohammad, Salimans, Tim |
Publication Year: |
2021 |
Collection: |
Computer Science |
Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
More Details: |
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. |
Document Type: |
Working Paper |
Access URL: |
http://arxiv.org/abs/2106.15282 |
Accession Number: |
edsarx.2106.15282 |
Database: |
arXiv |