The result is impressive, but the moment you started to use it on someone you're actually familiar, it becomes weird very quickly for the obvious reasons. The teech, for example, are never right.

This kind of "believable but not truthful" results are rampant in all these machine-learning based tools. It's not very harmful in case of upscaling a few photos I guess, but I've been bitten by it in an acclaimed translation service called DeepL. I use it to translate Japanese to English frequently, and have found that it often (nontrivially) made up sentences that don't exist in the original paragraphs, sometimes have the opposite meanings, or totally ignore part of the text to make the result "more fluent". And unlike traditional translation tools, they are very hard to notice if you know nothing about the original language. I have to from time to time use some more "primitive" translation tools, and compare the results side by side, to avoid such issues. It's frustrating.

I can recall seeing a conspiracy get traction on Twitter a few months back, where it was claimed that a photograph of a famous person was actually a body double. Someone used an ML upscaler to "enhance" the image, and their followers began scrutinizing the result: "The teeth are different!", "The nose shape is wrong!", "It's not the same person!"

Not to mention what happens when some good-intending but ill-informed agency release an enhanced photo of a suspect on the run.