![]() Why even bother making memories at all when you can simply tweak a basic photo and make it look to the rest of the world like you’re living your life when in actuality, you’re on the couch, wasting away watching videos on your phone? Why learn how to frame a photo properly before clicking that shutter button when we can simply ask the Magic Editor to fix it later? Why spend all that effort working out and taking care of ourselves when the Magic Editor can simply make us look as attractive as the rest of the Kardashian doppelgängers on TikTok? (Note that Google hasn’t confirmed if the tool can actually tweak people’s faces I’m just imagining Magic Editor to come with similar features as the Facetune app). And body positivity influencers on the Gram have long exposed influencer-favorite mobile apps that are so easy to use, even the Photoshop-uninitiated can make themselves look runway-ready in minutes.īut creating such apps can have consequences as well, especially when they're made in such a way where users barely need to lift a finger. Skylum's Luminar, which integrates AI for photo editing, has been doing this for years and doing a stellar job of it. I am fully aware that we already have impressive photo editing apps out there that do something similar. And I’m in no way suggesting that this feature is going to be inherently problematic.īut, it could also have problematic applications, especially in this modern age where we all expect everything to just come easily and where most of what we perceive to be real online actually isn't. Each row contains examples from a particular class.Make no mistake the Magic Editor sounds like it’s going to be an amazing tool, and it’s going to help everyone – from photographers and influencers to regular folks – salvage images with potential, but aren't quite right, in the first place. 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. 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. 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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