RTXVideoSuperResolution always returns an all-zero (black) tensor on Linux, no exception raised

## Summary
`RTXVideoSuperResolution` (and the underlying `nvvfx.VideoSuperRes` from the `nvidia-vfx` pip package) runs without raising any exception, but always returns an all-zero tensor (black output) on Linux, for every `quality` level (LOW/MEDIUM/HIGH/ULTRA) and both `resize_type` options tested.

## Reproduced outside ComfyUI (standalone Python)
```python
import torch, nvvfx
frame = torch.rand(3, 704, 1280, device='cuda')
with nvvfx.VideoSuperRes(nvvfx.effects.QualityLevel.MEDIUM) as sr:
sr.output_width = 2560
sr.output_height = 1408
sr.load()
out = sr.run(frame).image
t = torch.from_dlpack(out)
print(t.shape, t.dtype)
print(t.min().item(), t.max().item(), t.mean().item())
```
Output:
```
torch.Size([3, 1408, 2560]) torch.float32
0.0 0.0 0.0
```

No exception anywhere in the call chain (`sr.load()`, `sr.run()`, DLPack conversion all "succeed"), just an all-zero result.

## Environment
- OS: Linux (Ubuntu-based), driver 595.84, CUDA 13.2
- GPU: NVIDIA GeForce RTX 5060 Ti (16GB), confirmed as the default CUDA device (`torch.cuda.current_device()` → RTX 5060 Ti). A second GPU (GTX 1660 Super) is present in the system but not selected by default.
- `nvidia-vfx` package: initially got a broken metadata-only install from PyPI (`import nvidia_vfx` → `ModuleNotFoundError`, despite `pip show` reporting it as installed). Reinstalled via `pip install nvidia-vfx --extra-index-url https://pypi.nvidia.com`, which pulled the real ~597MB wheel. The real module name is `nvvfx` (not `nvidia_vfx`), which is not obvious from the pip package name — might be worth a note in the README/install docs.
- After the reinstall, `import nvvfx` works and the module loads fine (no import errors), but inference output is all zeros as shown above.
- SageAttention was ruled out: same black-output result with `--use-sage-attention` on and off, and with the node isolated in a minimal workflow (LoadImage → RTXVideoSuperResolution → PreviewImage, no other nodes).

## Suspected root cause
`libnvidia-ngx-vsr.so` (bundled inside the `nvvfx` wheel) appears to depend on the NGX model store (the same system used by DLSS) being initialized on the machine. Checked for it under typical Linux paths (`~/.local/share`, `/usr/share/nvidia`, etc.) — nothing found. `nvidia-ngx-updater` (present at `/usr/bin/nvidia-ngx-updater`) runs with exit code 0 but produces no visible output and does not appear to create any NGX model tree on this Linux install. No `.trt`/`.engine`/`.onnx` files ship inside the `nvvfx` wheel itself, so the actual super-resolution model seems to need to come from NGX at runtime — and on this system that step silently produces nothing usable, with the SDK still reporting success.

## Question
Is there a documented way to manually initialize/download the NGX model store for `VideoSuperRes` on Linux, or is Linux NGX support for this specific effect still incomplete? Happy to test further if there's a diagnostic step to check.

No repository files or tests are named. Start by reproducing the standalone Python example with nvvfx and checking the documented Linux NGX model-store setup; done means identifying a valid initialization or download procedure, or confirming that Linux support for VideoSuperRes is incomplete.

Written by the indexing model from the issue text.