← Back to Discover
jamesb-io03i8

jamesb-io03i8/DLSS5-Universal

PythonMITactive
75Health

Enable DLSS 5 Neural Rendering on NVIDIA RTX 20-50, AMD RDNA 3-4, and Intel Arc GPUs. One-click setup for any DX11/DX12 game. Auto-detection, Feeder mode for non-DLSS games, emulator support, and ReShade integration.

Stars19
Forks16
Open Issues13
Contributors16
Last Push1d ago

Health Breakdown

Activity
25
Community
25
Maintenance
10
Popularity
15
#dlss5-2026#dlss5-amd#dlss5-any-gpu#dlss5-emulator-support#dlss5-feeder#dlss5-game-mod#dlss5-installer#dlss5-mod#dlss5-neural-rendering#dlss5-one-click#dlss5-oneclick#dlss5-optiscaler#dlss5-reshade#dlss5-rtx#dlss5-rtx-20#dlss5-rtx-30#dlss5-rtx-40#dlss5-swapper#dlss5-tool#dlss5-universal
View on GitHub ↗Issues (13) ↗Pull Requests ↗Wiki ↗

Should you contribute to jamesb-io03i8/DLSS5-Universal?

jamesb-io03i8/DLSS5-Universal has a FoundDev health score of 75/100, which puts it in the active-and-maintained tier. The maintainer team is shipping recently, issues are being closed, and a PR you open this week has a realistic chance of being reviewed.

Last push was 1 days ago — that signals an actively maintained project. New issues are likely to get a maintainer response within days. The project is written primarily in Python, so prior Python experience will shorten ramp-up.

Licensed under MIT, a standard OSI-approved license — safe to contribute to under normal employer IP policies.

Community

jamesb-io03i875

Enable DLSS 5 Neural Rendering on NVIDIA RTX 20-50, AMD RDNA 3-4, and Intel Arc GPUs. One-click setup for any DX11/DX12 game. Auto-detection, Feeder mode for non-DLSS games, emulator support, and ReShade integration.

active
1916 contributors13 issues
1d ago

More Python repos

runpod-workers
runpod-workers/worker-vllm
The Runpod worker template for serving our large language model endpoints. Powered by vLLM.
46697
openstack
openstack/nova
OpenStack Compute (Nova). Mirror of code maintained at opendev.org.
3.2k92
josharsh
josharsh/100LinesOfCode
🚀 100+ mini-projects demonstrating the power of concise code. Perfect for learning, portfolio building, and first-time open source contributors. Under 100 lines each!
84092