this post was submitted on 20 Aug 2026
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…Perhaps. But I’m not sure what any of this has to do with the paper. It doesn’t claim anything like that, and the term “Artificial Intelligence” does not appear in anywhere in the work.
It does illustrate that a toy diffusion model can approximate an image outside its dataset. In fact, the target image being present in the dataset has a remarkably small effect as training scale goes up.
I don't think this is really all that surprising if you understand how diffusers actually work. They don't just draw contours and shit. They apply sequential transformations across a matrix of pixels in an iterative fashion, the transformation varies each iteration based on prompt embeddings and the surrounding pixels of each respective transformation, typically starting from a seed of arbitrary static. A lot of images share overwhelmingly identical techniques, with a tiny sliver being responsible for the truly creative deltas. Diffusers effectively learn techniques so it tracks that dropping singular training data here and there doesn't change much.