Yeah, I think it is.  Super simple architecture, no adversarial losses, works with a simple reconstruction loss.
There were really 2 tricks to make it work.

The data augmentation requires some tuning.  Too much distortion and you don’t learn, not enough and there is no contrast or prediction gradient to learn.
For still images, After distorting the input, you have to “loss mask” the parts of the image that fall outside the boundaries of the original image.  Otherwise the algorithm “cheats” by using the boundary to learn the parameters of your distortion!