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ayo 0e851b7f90 release: prepare 0.1.5 with RDNA2 kernels and in-game DLSS/FSR switching
- Add native preset K kernels for gfx1030–gfx1036 using software matrix
  products, with fast and accuracy builds and RDNA2 motion kernels.
- Follow OptiScaler's DLSS and RR preset overrides, sanitize unsupported
  values and select the preset matching the feature's quality mode.
- Enable FSR 4 under NVIDIA GPU spoofing by exposing real AMD PCI IDs
  to OptiScaler and FidelityFX modules; bundle AMD's loader and upscaler.
- Keep K on native kernels in engine auto mode unless HIP is selected,
  and fall back to CUDA for M/L when shader_float8 is unavailable.
- Add native-engine K GPU timings, preset recreation harness controls
  and regression coverage; fix the input-wait test's glibc name clash.
- Update configuration, installer guidance and release documentation,
  simplify the README and move gameplay videos to docs/videos.md.

RDNA2 kernels remain untested on RDNA2 hardware; software matrix
arithmetic was validated against WMMA on RDNA3.
2026-10-17 16:05:09 -04:00

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Modified in this fork for CUDA Ray Reconstruction support and validation (2026).
d4r @VERSION@: where every file in this release comes from
============================================================
Built from source by d4r (GPL-3.0-only, d4r/licenses/d4r-LICENSE.txt):
d4r/nvngx.dll NGX shim tools/d4r_nvngx_shim.cpp in the d4r repository
d4r/nvcuda.dll Wine CUDA bridge tools/wine_nvcuda_bridge.c
d4r/kernels/ native RDNA3/RDNA4 kernels for the DLSS 4 and 4.5 network layers, kernels/k and kernels/m
(d4r-kernels.txt lists hashes of the DLSS code each kernel replaces; it holds no NVIDIA code)
d4r/d4r.ini, d4r/d4r-check.sh, D4R_README.txt
@installer install.sh downloads and verifies NVIDIA DLLs; no driver installation. Then builds the engine's
@installer models from them with d4r/tools/build-engine-models.sh
@installer d4r/tools/models/ d4r's engine-model compilers and shader sources (engine/, kernels/tools, kernels/rr,
@installer vulkan/ptx2glsl.py) and the D3D12 test app (tools/d3d12_dlss_harness.cpp)
@installer d4r/tools/models/bin/glslangValidator
@installer Khronos glslang 16.6.0 (unmodified release build), https://github.com/KhronosGroup/glslang
@installer License: d4r/licenses/glslang-LICENSE.txt
Third-party software, unmodified or patched by d4r:
d4r/zluda/libcuda.so ZLUDA (Apache License 2.0 or MIT), https://github.com/vosen/ZLUDA
commit @ZLUDA_COMMIT@ with d4r/source/patches/zluda/0002 to 0013 applied.
It contains LLVM (Apache License 2.0 with LLVM exceptions).
Licenses: d4r/licenses/ZLUDA-LICENSE-*.txt, d4r/licenses/LLVM-LICENSE.txt
d3d12.dll, d3d12core.dll
vkd3d-proton (GNU LGPL 2.1), https://github.com/HansKristian-Work/vkd3d-proton
commit @VKD3D_COMMIT@ with d4r/source/patches/vkd3d-proton/0001 to 0003 applied,
built with its build-win64.txt cross file (meson --buildtype release).
Licenses: d4r/licenses/vkd3d-proton-*.txt
d4r/rocm/lib/ ROCm 7.2.4 runtime, unmodified, from AMD's Ubuntu 22.04 packages
(https://repo.radeon.com/rocm/apt/7.2.4): HIP (hip-runtime-amd, MIT),
comgr (NCSA; contains LLVM, Apache License 2.0 with LLVM exceptions),
ROCr (hsa-rocr, NCSA) and rocprofiler-register (MIT).
Sources: https://github.com/ROCm/rocm-systems and https://github.com/ROCm/llvm-project,
tag rocm-7.2.4. Licenses: d4r/licenses/ROCm-*
libelf.so.1 (elfutils 0.186, GNU LGPL 3.0 or GPL 2.0) and libnuma.so.1 (numactl 2.0.14,
GNU LGPL 2.1), unmodified, from Ubuntu 22.04's libelf1 and libnuma1 packages; their
sources are Ubuntu's elfutils 0.186-1ubuntu0.1 and numactl 2.0.14-3ubuntu2 source packages.
Licenses: d4r/licenses/ROCm-lib*-copyright.txt
dxgi.dll OptiScaler 0.9.4 (GNU GPL 3.0), unmodified OptiScaler.dll renamed to dxgi.dll.
Source: https://github.com/optiscaler/OptiScaler/tree/v0.9.4
License: d4r/licenses/OptiScaler-LICENSE-GPL-3.0.txt
OptiScaler.ini OptiScaler 0.9.4's OptiScaler.ini with the values d4r needs set
(packaging/optiscaler.settings in the d4r repository)
amd_fidelityfx_dx12.dll, amd_fidelityfx_upscaler_dx12.dll
AMD FidelityFX SDK 2.3 loader and FSR 4.1.1 upscaler (MIT), unmodified, as shipped in
the OptiScaler 0.9.4 release. https://github.com/GPUOpen-LibrariesAndSDKs/FidelityFX-SDK
License: d4r/licenses/FidelityFX-LICENSE.txt
@clean Not included: NVIDIA's DLSS libraries (nvngx_dlss.dll, nvngx_dlssd.dll) and NGX runtime (_nvngx.dll). You supply them
@clean and are responsible for complying with NVIDIA's license terms.
@full NVIDIA's software, not covered by any license in d4r/licenses and included by whoever built this
@full zip, not by NVIDIA. NVIDIA's license does not permit redistributing it; do not share this zip:
@full d4r/nvngx_dlss.dll NVIDIA DLSS Super Resolution library @DLSS_VERSION@
@full d4r/ngx/_nvngx.dll NVIDIA NGX runtime from NVIDIA's Windows display driver, @NGX_VERSION@
@installer NVIDIA DLLs are not bundled. install.sh downloads DLSS 310.7.0 from NVIDIA/DLSS
@installer commit a291cc7d2cc642a51566f3dfd5376f635cd1b284, DLSS Ray Reconstruction 310.9.1 from commit
@installer 374959484e79a640feaba44c93ac8cfb0a03f5b5 and _nvngx.dll from NVIDIA driver 596.36.
@installer URLs and SHA-256 hashes are pinned in install.sh. NVIDIA license terms apply. The engine models
@installer install.sh builds in d4r/engine hold values read from those DLLs: they are not part of this zip.
@installer Included NVIDIA PTX-derived kernels (not covered by d4r licenses):
@installer d4r/kernels/*/hiluma_engine_*, rrlite_enc0_*, rrlite_dec0_*, rrlite_post_*, rrlite_downsample_*
@installer NVIDIA's PTX with parts replaced by d4r code (kernels/tex) or translated to
@installer HIP (kernels/native), compiled by ZLUDA or ROCm's clang.
@full Optional d4r/nvngx_dlssd.dll: NVIDIA DLSS Ray Reconstruction library, when supplied through D4R_BUNDLE_DLSSD
@full d4r/kernels/*/hiluma_engine_*, rrlite_enc0_*, rrlite_dec0_*, rrlite_post_*, rrlite_downsample_*
@full DLSS kernels: NVIDIA's PTX from nvngx_dlss.dll, with parts replaced by d4r code
@full (kernels/tex in the d4r repository) or translated to HIP (kernels/native),
@full compiled by ZLUDA or ROCm's clang
d4r is not affiliated with NVIDIA, AMD or the OptiScaler project.