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2025-12-06 18:07:15
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Cascaded Diffusion Models for High Fidelity Image Generation Cascaded Diffusion Models for High Fidelity Image Generation Jonathan Ho*, Chitwan Saharia*, William Chan, David J. Fleet, Mohammad Norouzi, Tim Salimans Google Research (* denotes equal contribution) Paper Selected generated images from our 256x256 class-conditional ImageNet model. Summary Cascaded Diffusion Models (CDM) are pipelines of diffusion models that generate images of increasing resolution. CDMs yield high fidelity samples superior to BigGAN-deep and VQ-VAE-2 in terms of both FID score and classification accuracy score on class-conditional ImageNet generation. These results are achieved with pure generative models without any classifier. We introduce conditioning augmentation, our data augmentation technique that we find critical towards achieving high sample fidelity. A cascaded diffusion model comprising a base model and two super-resolution models. Abstract We show that cascaded dif...
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