OpenDLSS-NR README

The project's README from github.com/maanHimself/OpenDLSS-NR. As one markdown file: README.md.

OpenDLSS-NR

A Vulkan reimplementation of NVIDIA's DLSS 5 Neural Rendering network, bit-exact against the original.

The same 71-block Swin / ViT network as DLSS-NR build 310.8.0, running FP8 on the tensor cores. The intermediates match too, not just the final image: all 75 block boundaries, byte for byte.

ports/browser-webgpu/ is a second, independent implementation: the same bytes in a browser, with no tensor cores and no FP8.

You supply the weights, as a model directory in the layout described below.

The network

A U-net of shifted-window transformer blocks with a global ViT at the bottom: 71 blocks over six pooling levels, FP8 (E4M3) activations with FP16 accumulation, 141 MiB of weights. It is a generative neural rendering network (NVIDIA's term): it re-renders the frame the engine already drew, generating detail from injected noise and adjusting tone, structure and skin under a style setting. Input and output are the same resolution; it is not an upscaler.

The same frame with neural rendering off (left) and on (right)

The WebGPU port at 2048x1152, NR off on the left and on on the right. Scene: Cowboy Gramps by Muhammed Ismayil, CC0.

It takes one rendered frame (a low dynamic range proxy of it, three lanes of Gaussian noise, the previous frame's output reprojected, and five conditioning scalars) and produces four f32 channels per pixel: an RGB residual and one temporal-blend logit. docs/network.md is the graph in full. NVIDIA describes the model in its report, DLSS 5: Generative Neural Rendering (project page).

Build and run

powershell -File scripts\fetch_tools.ps1 [-Npm]     # once: tools\ (glslang, Vulkan-Headers, volk, CMake, Ninja)
powershell -File scripts\build.ps1                  # shaders, PTX, build\dlss5vk.exe
powershell -File scripts\fetch_filament.ps1         # once, for the demo: third_party\filament (+ the patch)
powershell -File scripts\build_filament.ps1         # once, for the demo: third_party\filament-install
powershell -File scripts\build_demo.ps1             # build\demo\dlss5-demo.exe
build\dlss5vk.exe bench   --model <dir> --width 768 --height 768
build\dlss5vk.exe profile --model <dir> --width 768 --height 768   # per-dispatch timings
build\dlss5vk.exe parity  --model <dir> --fixture <dir>            # bit-exactness against a fixture
build\dlss5vk.exe verify  --model <dir> --fixture <dir>            # block-0 kernel-by-kernel bisect
python scripts\ptx\test_fast_divmod.py                             # the PTX divider, over every n < 2^24 (numpy)

The demo can be double-clicked. It lists every scene under build\scenes in the Demo scene dropdown and starts on the first one, or loads the glTF given on the command line. The model directory is --model <dir>, else DLSS5VK_MODEL, else models\nr next to this README. See demo/README.md for the renderer, the keys, the scenes and view.json.

Performance

RTX 4070 SUPER, whole network per frame, minimum over 40 frames. 241 dispatches at every resolution.

resolution time
768x768 2.8 ms
1920x1080 7.8 ms
2560x1440 12.6 ms
3840x2160 29.3 ms

The GPU alternates between two clock states under sustained load, so medians run a few percent higher. Compare minima.

What is here

Part Files Notes
Host src/ (C++20) Vulkan context, model loading and weight re-layout, kernel wrappers, the network graph, a CPU reference of the arithmetic, the dlss5vk tool
GLSL kernels shaders/ The reference route: cooperative-matrix FP8 GEMMs, fused 32-channel block, fused QKV + window attention, expert MLP, global attention, elementwise ops. Exact and complete on their own.
PTX kernels scripts/ptx/ Python generators emitting PTX for the fast route: mma.sync E4M3 with f16 accumulation, cp.async rings, barrier-free chaining through device counters, split-K GEMMs, streamed global attention. Generated into build/ptx by the build.
Demo demo/, third_party/filament.patch The network inside a Filament (Apache-2.0) frame: Filament patched for per-object motion vectors and a Vulkan interop hook, glTF scenes through gltfio, ImGui controls.
WebGPU port ports/browser-webgpu/ The same network in a browser, bit-exact against the same captures, with no tensor core, no FP8, no fusion between blocks and no chaining: the exactness is in the specification, not in the hardware. 72 ms at 512x512 against 2.7 ms here.

Not implemented: DLSS-SR, which is a different network. The temporal path is implemented, but in the demo: the network's history input lanes and its per-pixel blend logit drive a reprojected feedback loop (docs/frame.md). The dlss5vk tool runs single frames with no history, which is what the reference captures were made with.

Requirements

Model directory

nr::Model reads manifest.json: a stages array (each entry: id, file relative to the directory, packedByteLength, sha256) and a tensors array (each entry: name, block, layer, parameter, stage, stageOffset, byteLength). Stage files hold the E4M3 weights as packed bytes; the host re-lays them out into the matrix forms the kernels consume (src/nr_model.cpp). Nothing in this repository produces such a directory.

The graph is the 71-block network of 310.8.0 and nothing else: a model with a different block count is refused at load.

Fixtures

parity compares against recorded captures of the original, which are not part of this repository. A fixture is a directory with a manifest.json:

key
sourceDimensions, fullDimensions the valid size and the padded field
proxy or inputFeatures the input: an RGBA f32 image (with conditioning, seed, autoMask), or the f32 features themselves
checks what the fixture gates, any of "boundaries", "head", "output"; required and never empty
blocks, transitions "boundaries": E4M3 references (block / id, width, height, channels, file)
omittedBoundaries "boundaries": {name: reason} for each comparable boundary the fixture has no reference for
referenceHead "head": the f32 RGBA head
nativeOutput "output": the composed image, dtype "f32" (RGBA halves, needs proxy) or "u8" (an 8-bit capture)

Everything is validated before the GPU runs, and a fixture that fails any of it is refused: a declared check without its reference, a reference that is missing, short or names nothing in the graph, a reference no declared check uses, or a comparable boundary (blocks 0-69, the five encoder transitions) with neither a reference nor a reason. Verdicts are bit-exact (the pass), equal only up to the sign of zero (a failure), within one code (the 8-bit capture only, reported apart) or a mismatch; see docs/numerics.md. The head and the output are compared on the production schedule, resubmitted --repeat times (default 3), which must also agree with the same graph under barriers; the boundaries come from an instrumented run, whose head must agree with production's. verify additionally needs inputFeatures and a block-0 reference.

Tuning switches

All default to the fast, exact route. Every switch keeps the output byte-identical, and parity under each of them is part of the gate. Any switch that sends a kernel back to GLSL also turns counter chaining off, because only the PTX kernels take part in it.

Documentation

docs/README.md is the index: network.md is the graph, numerics.md the exactness contract, weights.md the layouts, execution.md the scheduling, and frame.md the demo's frame.

Not affiliated with NVIDIA

This project is not affiliated with, endorsed by, or supported by NVIDIA. It contains no NVIDIA software, weights, headers, or instructions for obtaining them. No rights under any NVIDIA intellectual property are granted or implied by this repository or its license, and you are responsible for the licenses that apply to whatever model data you use with it.

License

MIT for everything in this repository (LICENSE). Third-party components are listed in NOTICE.

OpenDLSS-NR (maanHimself/OpenDLSS-NR)

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What is this?OpenDLSS-NR is a from-scratch Vulkan reimplementation of NVIDIA's DLSS 5 Neural Rendering network, matching the original bit-exactly down to every internal block boundary, not just the final image.
Is this an upscaler like other DLSS modes?No. It re-renders the frame at the same resolution it was given, generating detail from injected noise and adjusting tone, structure and skin under a style setting; it does not change resolution.
How do I run the demo?After building, the demo executable can just be double-clicked; it lists every scene found under build\scenes in a dropdown, starts on the first one, or you can pass a glTF file on the command line.
What do I need to build and run this?A Windows machine with an NVIDIA Ada-or-newer GPU and a driver exposing specific cooperative-matrix and FP8 Vulkan extensions, plus Visual Studio 2022's C++ x64 toolset, git, Python 3, and Node.js + npm for the scene converter.
Does this come with the actual DLSS weights?No. You must supply your own model directory of weights in the documented layout; the repository contains no NVIDIA weights, software, or instructions for obtaining them, and nothing in it generates such a directory.
Does the WebGPU port work without an NVIDIA GPU?Yes, the browser port in ports/browser-webgpu/ runs the same bytes with no tensor cores and no FP8, bit-exact against the same captures, but much slower (72 ms vs 2.7 ms at 512x512 on native Vulkan).
What is Filament used for in the demo?Filament (Apache-2.0) provides the demo's rendering frame; it's patched for per-object motion vectors and a Vulkan interop hook, loads glTF scenes through gltfio, and exposes ImGui controls.
How is bit-exactness actually verified?Fixtures are recorded captures of the original network, and a 'parity' check compares the Vulkan implementation against them at every block boundary, the network head, and the final output, reporting results as bit-exact, sign-of-zero-only, within-one-code, or mismatch.
How fast does the network run?On an RTX 4070 SUPER the full 71-block network takes about 7.8 ms at 1920x1080 and 29.3 ms at 3840x2160, measured as the minimum over 40 frames at 241 GPU dispatches per frame.
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