MSE Loss simply tells the squared difference between two images in your case. Suppose there is some idea point, which has the minimum MSE Loss. As SGD would try to move towards this point when using MSE Loss, you would get samples resembling the ideal point.
Now the ideal point can be anything. It can be a random matrix.