[jsheard]

To be clear DLSS is a very different beast than your typical AI upscaler, it uses the principle of temporal reuse where real samples from previous frames are combined with samples from the current frame in order to converge towards a higher resolution over time. It's not guessing new samples out of thin air, just guessing whether old samples are still usable, which is why DLSS is so fast and accurate compared to general purpose AI upscalers and why you can't use DLSS on images or videos.

[jms55]

To add to this, DLSS 2 functions exactly the same as a non-ML temporal upscaler does: it blends pixels from the previous frame with pixels from the current frame.

The ML part of DLSS is that the blend weights are determined by a neural net, rather than handwritten heuristics.

DLSS 1 _did_ try and and use neural networks to predict the new (upscaled) pixels outright, which went really poorly for a variety of reasons I don't feel like getting into, hence why they abandoned that approach.