High-resolution cropping in post-production. Gigapixel performs around 2 million operations per pixel to get you results with as much detail and sharpness as possible.īreathtakingly sharp prints. Or transform your DSLR photos into sharp and ultra-detailed 100MP images.Īfter being trained with millions of sample images, Gigapixel can now enlarge your images with a surprising level of quality. Upscale your photos by up to 600% while perfectly preserving image quality.Ĭreate a beautiful 80 x 60 inch print from a simple iPhone photo. Enlarging your image without losing detail has always been impossible. It’ll help you make up you mind on weather the boost in image quality is worth the speed reduction or not.Beautiful photo enlargements using machine learning. I suggest you take the time to prepare some upscales with the fp16 models beforehand for comparison’s sake. ![]() Make sure to backup your stuff first, then extract in “ C:\ProgramData\Topaz Labs LLC\Topaz Gigapixel AI\tgrc” and replace everything when prompted. ![]() json files if you decide to give it a whirl (w/ fp32 models included, it’s on me ): /. But for the most part, the difference is barely noticeable (you need to REALLY zoom-in to catch it) and sometimes even, inexistant. I gave it a go and noticed that in some instances, the fp32’s produced a fairly crispier and more detailed image, especially around the eyes and hair, while also handling noise and compression artifacts more smoothly. ![]() Yes, you can actually trick gigapixel into using the fp32 models instead if you edit the. These are the only fp16 models present in the server atm, and that’s what prompted me to think that the others were missing: I actually found them in the json files originally, otherwise I wouldn’t have guessed both openvino and coreml have fp16 & fp32 models. I believe these FP16 OpenVINO models were accidentally left out from being uploaded to the server:
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