I suppose it also doesn't really matter what kinds of resources the game originally requires. The diffusion model isn't going to require twice as much memory just because the game does. Presumably you wouldn't even necessarily need to be able to render the original game in real time - I would imagine the basic technique would work even if you used a state of the Hollywood-quality offline renderer to render each input frame, and that the performance of the diffusion model would be similar?
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Well the majority of ML systems are compression machines (entropy minimizers), so ideally you'd want to see if you can learn the assets and game mechanics through play alone (what this paper shows). Better would be to do so more efficiently than that devs themselves, finding better compression. Certainly the game is not perfectly optimized. But still, this is a step in that direction. I mean no one has accomplished this before so even with a model with far higher capacity it's progress. (I think people are interpreting my comment as dismissive. I'm critiquing but the key point I was making was about how there's likely better architectures, training methods, and all sorts of stuff to still research. Personally I'm glad there's still more to research. That's the fun part)