It is capable of filling in the missing information. A neural network model is then trained to recover the original images.Ī massive amount of prior knowledge is embedded into the model. The degraded images are then reduced to a smaller size. Good-quality images are first artificially corrupted to emulate real-world degradation. In contrast, AI upscalers are models trained with massive amounts of data. However, if the image itself is corrupted or distorted, there’s no way for these algorithms to fill in missing information accurately. They enlarge the canvas and fill in the new pixels by performing mathematical operations using only the image’s pixel values. Traditional algorithms for resizing images, such as the nearest neighbor interpolation and Lanczos interpolation, have been criticized for using only pixel values of the image. You can, but the result won’t be as good. To complicate the matter, a complex scene generated by Stable Diffusion is often not as sharp as it should be. Its screen displays 2,532 x 1,170 pixels so an unscaled Stable Diffusion image would need to be enlarged and look low quality. It’s camera produces 12 MP images – that is 4,032 × 3,024 pixels. The default image size of Stable Diffusion v1 is 512×512 pixels. How to use AI upscaler for Stable Diffusion?.Why can’t we use a traditional upscaler?.
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