this post was submitted on 16 Mar 2026
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DLSS is short for Deep Learning Super Sampling, it does the upscaling using deep learning, what people also call AI. The upscaler has to be trained on images. Depending on how you train it you either get something that looks almost exactly the same as the game at a higher resolution or you get AI slop.
deep learning isn't really the same thing as a large language model. People call LLMs AI.
I'm aware of how it works, but the results aren't bad. Worst case scenario is you get some ghosting with DLSS, but it's far from what I'd call AI slop.
But it literally follows the same process. Why is one slop, but not the other? You’re being hypocrital.
One is upscaling the image while preserving it as much as possible, the other is applying a filter to try and "enhance" it by drastically changing the image and ignoring artist's intent. What's hard to get?
This isn't applying a filter, it's ~~applying~~ running the image through a transformer network trained on advanced lighting methods like subsurface scattering to make materials more lifelike. It seems to change artistic intent quite a lot on these existing games, but frankly I'm excited to see what creators do with a game designed from the ground up to utilize AI-enhanced lighting. The DF video also states that this is an early preview (hence the dual 5090s) that is expected to change over time.
It is not. It is approximating the results of training data consisting of output images that have been rendered with subsurface scattering. It isn't actually running the subsurface scattering algorithm.
Well that just sounds like subsurface scattering with extra steps!
.... this is AI we're talkin about, literally everything is trained. I thought that would be assumed, sorry for not being clear enough.
It's not. it's a completely different set of steps (at least at runtime). The Venn diagram circles don't touch.
It's a meme my bro
How is "upscaling while preserving it" not the exact same philosophy as "enhance by applying a filter?"
You just don't like the specific filter, it's very literally the same process.
Because a pixelated circle being upscaled is a circle, but a pixelated circle being turned into a high definition pie is no longer a circle, and that's especially problematic if the circle was just a cross hair or some other random circle like thing the AI thought was meant to be a pie.
Yes, both things are the same, but that's like saying you had a tiny spider in your house and you were okay because it killed mosquitoes in your house, so you should be okay with having a colony of bats since they are also animals and eat mosquitoes. Yes, both are the same, but the scales and the amount of intrusion are completely different.
If your training data has a pixelated circle as an input and a circle as output, your neural network will "upscale" your pixelated circle to a circle. If your training data has a pixelated circle as input and a high definition pie as output, your neural network will "upscale" your pixelated circle to a high definition pie. Even if it's the same algorithm in both cases.
... How if flying a spaceship different from driving a car? They're both controlled applications of kinetic energy to move people or objects.
At the end of the day, it's all a pile of transistors and the only thing that is of import is the intent behind usage.
In one case it's saying you can use a neural net to take something rendered at resolution A/4 and make it visually indistinguishable from the same render at resolution A.
The other is rendering something and radically changing the artistic or visual style.
Upsampling can be replicated within some margin by lowering framerate and letting the GPU work longer on each frame. It strives to restore detail left out from working quicker by guessing.
You cannot turn this feature off and get similar results by lowering the frame rate. It aims to add detail that was never present by guessing.
Upsampling methods have been produced that don't use neural networks. The differences in behavior are in the realm of efficiency, and in many cases you would be hard pressed to tell which is which. The neural network is an implementation detail.
In the other case, the changes are more broad than can be captured by non AI techniques easily. The generative capabilities are central to the feature.
Process matters, but zooming out too far makes everything identical, and the intent matters too. "I want to see your art better" as opposed to "I want to make your art better".
Are you really asking why compressing and uncompressing art made by a human being is different from slop produced by the slop machine?
One exists to reconstruct an image as closely to the original as possible while saving space, the other is meant to insert arbitrary changes to the initial image and produce something else.
Not all answers are easy. This new dlss looks like it was trained on stolen work. Old dlss had a neutral network that was tuned before the plagiarism machine became popular.
Piracy is not stealing.
It is when it's used by corporations for profit, IMO. Not for individual private enjoyment.
Oh yeah? Well vegatables are both in pig troughs and on dinner plates. Why's one slop and not the other? They were grown with the same process!
Because one is shitty and the other isn't.
I don't like AI but christ Lemmy is getting annoying lately with kneejerk "slop" claims for anything with the letters AI in it. A lot of this stuff has been used for ages and yeah, they're leaning into the current hype but the over reaction is just ridiculous (see: the "open slop" list of open source projects that includes those that have the audacity to allow developers the ability to use AI line completion)
It genuinely diminishes actual concerns with AI tech when people are losing it over things that have existed long before the current bubble but just have AI™️ on the package now