![]() Each row contains examples from a particular class. Selected example generations of class conditional 256×256 natural images. Selected example generations of unconditional 1024×1024 faces. Cascaded generation allows training different models in parallel and inference is also efficient as lower resolution models can use more iterations, while higher resolution models use fewer iterations.Ĭascaded generation of unconditional 1024×1024 faces. Due to the user requirement, the developed. The vast majority of techniques in the literature require parameters that the user must determine according to the noise intensity. Image denoising is a preliminary step for many studies in the field of image processing. We also generate 256×256 class conditional natural images by using a cascade of a class conditional diffusion model at 64×64 resolution followed by a 4x super-resolution model. Image distortion effects, called noise, may occur due to various reasons such as image acquisition, transfer, and duplication. We generate unconditional 1024×1024 unconditional face images using a cascade of an unconditional diffusion model at 64×64 resolution followed by two 4× super-resolution models. (Below) We also achieve 40% confusion rate on the much difficult task of 64圆4 -> 256x256 natural images outperforming regression baseline by a large margin. We measure the performance of the model through confusion rates (% of time, raters choose model output over reference images.) (Above) We achieve close to 50% confusion rate on the task of 16×16 -> 128×128 faces outperforming state of the art face super-resolution methods. Subjects are asked to choose between reference high resolution image, and the model output. We conduct 2-Alternatative Forced Choice Experiment human evaluation experiment. Super Resolution results: (Above) 64×64 → 512×512 face super-resolution, (Below) 64×64 -> 256×256 natural image super-resolution. We also explore 64×64 → 256×256 super-resolution on natural images. We also train face super-resolution model for 64×64 → 256×256 and 256×256 → 1024×1024 effectively allowing us to do 16× super-resolution through cascading. 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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