This is a challenging question to answer because different upscaling algorithms may have different performance depending on the input image, the output resolution, the hardware and software environment, and other factors. However, based on the information I found online, here is a possible ranking of the three FOSS upscaling algorithms based on speed:

Upscayl: This is a free and open source desktop application that uses Real-ESRGAN to upscale and enhance images. It claims to be fast and easy to use, with processing times of less than 10 seconds on some machines. It supports multiple upscaling options, image formats, and platforms (Windows, Mac, Linux).
Upscale.media by PixelBin: This is a web-based upscaler that allows you to upscale an individual image up to 400% for free. It uses AI technology to improve and increase the resolution of an image without losing quality. It also has a mobile app for Android devices. The speed of this service may depend on your internet connection and server load.
image-upscaling: This is a GitHub topic that contains various algorithmic solutions to optimize inference for convolution-based image upsampling. These are coded for clarity, not speed, so they may not be suitable for practical use. However, they can be useful for learning purposes or for experimenting with different techniques. The speed of these algorithms may vary depending on the implementation, the input image, and the hardware and software environment.
I hope this helps you compare the three FOSS upscaling algorithms based on speed. Please note that this ranking is not definitive or authoritative, and it may change over time as new updates or benchmarks are available. If you want more accurate or reliable results, you may want to test the algorithms yourself with your own images and settings.
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