The approach is different, it can use pretrained models, i.e. stable diffusion, which is a pretty exciting research development. This means that it only requires 'fine-tuning' existing models to get this result.
I agree with that, but it's hard for me to get excited with the knowledge that it'll almost certainly be discarded and forgotten. I've seen too many papers that looked interesting from a theoretical perspective, but were simply never brought to the public because of the barrier of dev+training.
In this case, you need someone that can implement the method as described (hard!), and then you need someone with a setup better than a rented 8xA100 (expensive and not available on many cloud providers) to actually reproduce the model.
To put it in context, in almost all areas of research (physics, biology, chemistry, electronics, etc), running experiments is expensive. ML is in the category that there can still be advances done by amateurs at home. I don't think it's worth writing off everything that requires more resources than a hobbyist.
