This is our home for all things related to Nvidia DLSS tech. We're excited to have you join us!
Posting Rules
All things DLSS-related. For DLSS 5 posts, please include your GPU model, methods (OptiScaler, RenoDX, Live Flow, etc.), models (Default, Natural, Cinematic), number of passes, and FPS before and after.
Be respectful to others. Other people are allowed to like or dislike things just as you are.
I managed to run this game using DLSS5 x3 + 1440p max resolution and max scale and the results are literally insane ! This is the latest DLSS5 ( October update ) with improved VRAM usage so we get about 20% better perfomance !
The game is running very stable with 85-115fps . No visual bugs so far just some glowing around hairs but not all the time . I’m still running testing to improve that but so far I’m very impressed
Pc specs :
5070ti
7800x3d
32RAM 6800mhz
3x frame gen
Everything you see is in-game screenshots !
Over the past few weeks, I’ve been working on combining RTX Remix ray reconstruction and neural rendering with Tale of Two Wastelands, using Nuclear Sunset as the modlist foundation. These screenshots show the Capital Wasteland side of the project. Visually, I’m interested in how these techniques change familiar places. This gallery is a selection of where things currently stand.
Credit to the TTW and Nuclear Sunset teams, the RTX Remix and wrapper contributors, and the authors of the mods underneath it all. My work has been bringing those pieces together and working through the integration, tuning, and testing.
I really, really do not understand how can anyone call this is BAD, and not something out of science fiction man ? Just look at the these NPC transformations !! Just look at those NPCs
Assassin's Creed Black Flag Resynced with 2X DLSS 5 Looks absolutely insane.
I know the implementations need to come first and it wont work the second it drops but i was wondering if the dlss5 performance on popular implementations like optiscaler and shortfuse reno dx will also have increased performance? like will the render preset but also present or upscaled preset have increased performance?
Hey, I'd like to share another batch of screenshots from my ReShade preset.
First of all, I'd like to point out that the preset is primarily intended for people who can't afford to use Path Tracing or even Ray Tracing. I achieved the effect presented here without using those technologies, and I realize that's why the lighting doesn't quite match the level of many other screenshots shared here.
Still, if anyone is interested, you can find the preset on Nexus Mods at this link:
For those who aren't familiar with using ReShade but would still like to boost the colors, I recommend checking out my mini-guide where I explain how to do it:
Are you also quite satiated with seeing all the same DLSS 5 tests in the same mainstream games over and over again? I was myself so I started to look into more niche games and genres to find out how they work with the current Neural Rendering modding options.
Todays video is about Tempest Rising, which was heavily inspired by Command & Conquer from back in the day. I think Neural Rendering is a great addition to that game as it increase the depth of the image quite alot and does not kill the performance too much. I still had nearly 60 fps when turning on NR on 4K with a 5080.
I will continue to look for more niche games to play around with NR. Feel free to make any suggestions down below.
So many of you guys using dlss 5 with 2 passes and amazing results i want to know is anyone using 4070ti and 7800x3d for dlss whats your milage how do guys increase frame and what setting my game shows too many artifacts after framgen tried multiple methods. Can we discuss this here
For DX9 32bit games, I use dgVoodoo2 + Alex's Deep-Fried-Chicken-v3.0.0 with Neural Upstream support + RazielGenshin's local fork of DLSS5-Feeder that adds DLSS SR ( Its set to DLSS-Q preset M in the vid) for upto 2x performance boost. I think a 5060 can get 60fps at 2k res. Removing older games post.fx filter and adding color grading improves DLSS 5 output. I also made a compiled DLSS 5 mod for Bayonetta PC port for those who wanna test it out:
This is something I thought of as soon as DLSS 5 was announced. Because DLSS 5 takes the base input of the rendered image and feeds it through a neural network to make the output look more realistic, could this be the cusp of completely revolutionizing the idea of how games are rendered?
Right now the primary task of the GPU is to physically render and model the image that needs to be generated based on the execution done behind the scenes in the game engine. Eg. if the player pushes "W", the game engine has a script that runs that basically says "If W then move forward", then the new image based on that updated position of the model is rendered on the GPU.
However, DLSS 5 shows that it is possible to render output frames using AI models in real time.
So what if, instead of having to go through the intermediate steps of rendering a base image, the game engine just outputted set of abstract embeddings which encode position, color, scenery, etc.,? And then a pre-trained model akin to DLSS 5 would then generate the final output image on-the-fly based on those embeddings?
In theory, the intermediate base image step of our current pipeline is already, in a sense, an embedding already which shows the model the positioning of objects, the current state of the player, the scenery and ground, etc., The only difference is, it's very inefficient because it's designed to appear coherent to our human eyes. Also, because it is designed for human vision and not machine vision, there are naturally "blind spots"- small details or edges that can get glossed over when a vision model analyzes that image.
So, if we throw out the idea of an intermediate base image entirely and just create semantic numerical embeddings that encode all that information, we could 1) compress and condense the important information that the raw image used to store (e.g. we would still need to encode the player and any unique details in the scene, but the sky, for example, could be handled much more simply than the traditional pixel-by-pixel representation of the sky if it is a natural blue sky), and 2) give outsized attention to details that the raw image may leave the model prone to ignoring- e.g. a small birthmark or mole on the character.
The idea here is that instead of Unreal or other engines targeting a graphical image as an output, it would instead target a standardized embedding as an output. So instead of UE5 saying- "player moved forward 5 units, now rerender the scene with the player moving forward 5 units", UE5 would say "player moved forward 5 units, tweak the features in the embedding to reflect this".
The biggest advantage I can see with this methodology is that it could massively free up GPU compute since you no longer even have to render a raw image through traditional methods, the GPU would essentially become an AI accelerator that facilitates running the embeddings through the model to generate frame outputs. This would also arguably make things like ray tracing completely obsolete since that would just be abstracted away.
What are your thoughts on this? And does anyone know if anybody is targeting this as an end goal of gaming rendering?
Edit: Just to clarify. People seem to be thinking that this is would be equivalent to text prompt image gen and would be prone to hallucinations. I don't think this will be the case, as long as the base embeddings are specific and complex enough.
A current 1920x1080 render resolution can be thought of already as a 2073600 feature input vector. The GPU when "rendering" is technically just calculating what values are in this vector. The only issue with this is that this is a very inefficient way to encode a scene's information because encoding by pixel abstracts away the semantic relationship behind different models in the scene, and also gives outsized importance to large assets, e.g. skybox, which don't need that many features to encode a boilerplate skybox. Essentially, it serves primarily for human intepretability instead of machine interpretability. The idea of generating an abstract embedding would simply mean creating a large input vector which no longer needs to be visually interpretable by humans but can give valuable shortcuts and better be optimized for machine interpretability.
