Various things which need to be fixed outside of the CUDA 13 PR:

• patchelf breaking Jeston binaries because of invalid binary modifications (there’s an open issue for this on Nixpkgs IIRC)
• packages with stubs should provide a setup hook which replaces the stub in the run path with wherever the library will be found at runtime (without duplicating run path entries — this is part of what started me making arrayUtilities setup hooks)
• cuda compat library precedence in run path entries is or can be lower than /run/opengl-driver/lib so it won’t be used; need to update hook to fix that (the other driving force behind arrayUtilities)
• Support for Clang as a host compiler for backendStdenv
• investigate cc-wrapper scripts to see if there’s anything relevant for NVCC (like random seed, or anything which enables deterministic compilation and linking)

There are others but I can’t remember them :/

And if anyone wanted to run the demo, I've packaged it with a flake: https://github.com/ConnorBaker/ContinuousSR

Still need to download the model from their google drive (https://github.com/ConnorBaker/ContinuousSR?tab=readme-ov-file#pretrained-model)

$ nix develop --command -- python demo.py --input butterflyx4.png --model ContinuousSR.pth --scale 4,4 --output output.png
/nix/store/i8vsz78lc405s5rifmz3p1lpvzhh1x74-python3-3.13.7-env/lib/python3.13/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
  warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
/nix/store/i8vsz78lc405s5rifmz3p1lpvzhh1x74-python3-3.13.7-env/lib/python3.13/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /build/pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/home/connorbaker/ContinuousSR/models/gaussian.py:245: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor).
  get_xyz = torch.tensor(get_coord(lr_h * 2, lr_w * 2)).reshape(lr_h * 2, lr_w * 2, 2).cuda()
finished!