![]() ![]() We demonstrate the performance of SR3 on the tasks of face and natural image super-resolution. Yielding a competitive FID score of 11.3 on ImageNet. Generative models are chained with super-resolution models, We further show theĮffectiveness of SR3 in cascaded image generation, where GANs do not exceed a confusion rate of 34%. Rate close to 50%, suggesting photo-realistic outputs, while We conduct humanĮvaluation on a standard 8× face super-resolution task onĬelebA-HQ, comparing with SOTA GAN methods. Performance on super-resolution tasks at different magnificationįactors, on faces and natural images. ![]() Iteratively refines the noisy output using a U-Net model trained Inference starts with pure Gaussian noise and Performs super-resolution through a stochastic denoising Probabilistic models to conditional image generation and We present SR3, an approach to image Super- Resolution via ![]()
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