the first image in the first post is the model output “supposed SR image” before applying Gaussian kernel.
the second image is the blurred image after applying Gaussian kernel, and it doesn’t have the artifact because of the kernel and because the model is learnt to produce images, which after applying the kernel they match the original blurred image.
I’m aware of the checkerbord problem, but I think the reason here is beside this problem is the Gaussian kernel.
The only reason or interpretation I see, is that the model learned the gaussian kernel and it is producing output with similar pattern “noise” that is erased by the kernel.