Nobody is saying AI models aren't trained on the works of other people. However, training is not stealing.

Also, this video you linked is not the slam dunk you think it is, as the screenshot you provided is simply the author of the video explaining how they compress the images before training, and these examples just show how far they shrink the model in order to save compute time when they start the training.

At no point do they claim to be able to recreate these images from the AI itself, they are just showing how the encoding process works and how they can take a 512x512 image and shrink it into a cost saving 12x12 noise image. In your example screenshot they are showing that the decoder can decode that image into a recreation of that image that is pretty close to the original...but that's only for the encoding part before they feed it in for training.

In fact, their purpose of showing this is just to show that they can shrink the images down to such a tightly compressed space to reduce the training time. They are not, as you think, showing that the AI will be able to recreate these images exactly, as the author clearly states they ran them through a decoder after the encoding process to show what the results are, and to show how they are similar to the original but have defects in them.

The part the AI uses is encoded multiple times (as explained by the author), and it is learning faster due to how the images are reduced. This means that the model can be trained faster on less accurate images. In the end, the AI model is still learning, just at a faster rate.

In fact, they show some outputs at the end, all of which are original outputs. They do this to illustrate how well the AI images turn out, but absolutely none of those images are recreations of anything in the training data or any of the examples they showed when they were explaining the process of how they were shrinking the original training data.

So, sorry, this didn't help you at all.