Fundamentally, Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. After training, we can use the Diffusion Model to generate data by simply passing randomly sampled noise through the learned denoising process.

How much of that is the actual photo and how much you might consider to be synthesized is a line I think each person draws for themselves. I think it depends on the context; Moon photography makes for a neat demo but it is rarely relevant. A better question is whether these kinds of software enhancements hallucinate errors along the same lines of what happened in Xerox copiers for years.