Okay so the paper is actually interesting, and they have a explainer website: https://zheng-dai.github.io/AblationBasedCounterfactuals/
https://www.nature.com/articles/s41467-026-75667-5
https://github.com/zheng-dai/counterfactualuniverses
Amongst other things, they experimented with "what happens to a generation if you remove one image from the training dataset," particularly the "source" image for the target generation. What makes it interesting is they tested this at scale. There's (expectedly, somewhat janky) demo code to illustrate the spread. One example I exported myself:

Where each generation (the "counterfactual") in the top row corresponds to the image that was removed from the dataset in the bottom.
...Personally, I don't find the conclusion surprising.
These are models.
Lets say you make a model to predict hurricanes. With a small dataset/model, exclude the hurricane most similar to the one you're trying to predict, and it won't do a good job. But do the same with a model based on a huge dataset, and it should still model the novel hurricane reasonably well.
Diffusion models are no different.
I think AI Bros have overly anthropomorphized them with terms like "creativity" and such; they don't have this. But at the end of the day, they can model things that aren't strictly in their dataset. That's kind of the basic premise.


