I think the claims here are kinda weak, not gonna lie. It's unclear to me that this means of adding case-by-case additional conditioning data to improve results

I think the claims here are kinda weak, not gonna lie. It's unclear to me that this means of adding case-by-case additional conditioning data to improve results is particularly novel or effective compared to the popular StyleGAN-based attribute walk the overall approach is based on (Though as a side note it's definitely fun to see this kind of thing again after it's fallen out of favor in recent years with all the attention large foundation models have been getting in the generative space), and as a machine learning result, as far as I can tell this is kind of just saying "if you have additional highly-relevant data you can slightly overfit your model to cover a specific case better" Is there something I'm missing here?