Related papers: Diffusion Models Beat GANs on Image Synthesis

Diffusion Models Beat GANs on Image Synthesis

URL: http://arxiv.org/abs/2105.05233v3
Date: Thu, 13 May 2021 17:57:08 GMT
Title: Diffusion Models Beat GANs on Image Synthesis
Authors: Prafulla Dhariwal, Alex Nichol
Abstract summary: We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. For conditional image synthesis, we further improve sample quality with classifier guidance. We achieve an FID of 2.97 on ImageNet 128$times$128, 4.59 on ImageNet 256$times$256, and 7.72 on ImageNet 512$times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample.
Score: 4.919647298882951
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\times$128, 4.59 on ImageNet 256$\times$256, and 7.72 on ImageNet 512$\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.85 on ImageNet 512$\times$512. We release our code at https://github.com/openai/guided-diffusion

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