LunaSR General MC65-WW35 x2

Tiny x2 luminance super-resolution model specialized for captured game footage. The training patch mixture used approximately 65% Minecraft frames and 35% Wuthering Waves frames.

contains no source videos, extracted frames, faces, or training dataset.

Model

Architecture: Dense8-B6
Parameters: 3,876
Scale: x2
Signal: gamma-encoded BT.709 luma
Output: luma residual, not complete RGB
PyTorch checkpoint: pytorch_model.pt
Fixed-shape ONNX: model.onnx

The ONNX graph accepts [1, 1, 540, 960] float32 luma and returns [1, 1, 1080, 1920] float32 residual. Compose the final image as:

rgb_hr = clamp(bilinear_x2(rgb_lr) + luma_residual, 0, 1)

The same residual is added to R, G, and B to preserve the bilinearly upscaled chroma.

Quick start

python -m pip install -r requirements.txt

python -m scripts.upscale_images `
  --input "input.png" `
  --output "output" `
  --checkpoint pytorch_model.pt `
  --device cuda `
  --save-bilinear

Video upscaling requires FFmpeg and FFprobe on PATH:

python scripts/upscale_video.py `
  --input "input.mp4" `
  --output "output.mp4" `
  --checkpoint pytorch_model.pt `
  --device cuda `
  --batch-size 16

If the source container reports incorrect FPS metadata, normalize the input timestamps first or correct the output timestamps afterward.

External holdout results

BT.709 luma PSNR improvement over bilinear x2:

Domain	Baseline	Model	Delta
Minecraft	40.369 dB	42.566 dB	+2.197 dB
Wuthering Waves	46.005 dB	47.874 dB	+1.869 dB

The 65:35 weighted delta was +2.082 dB. See metrics/model_domain_eval_matrix.json for the reproducible evaluation record.

Validation

ONNX checker: passed
ONNX Runtime CPU execution: passed
PyTorch vs ONNX maximum absolute error: 3.8743019104e-7
ONNX operators: Add, Conv, DepthToSpace, Relu
Actual Intel NPU/OpenVINO runtime: not tested

RTX 4060 Ti FP32 measurements for the CUDA-resident RGB composition path:

Input -> output	Batch 1 throughput
256x256 -> 512x512	about 1,148 fps
512x512 -> 1024x1024	about 534 fps

These are implementation-specific local measurements, not guaranteed device performance.

Limitations

Single-frame model; no explicit temporal loss or recurrent state.
Trained primarily on two game domains and may oversharpen unrelated animation, text, faces, or photographic content.
The provided ONNX uses a fixed input shape for current NPU compiler compatibility.
RGB reconstruction preserves bilinear chroma and only predicts shared luma detail.

Files

model.onnx: fixed 960x540 residual-only ONNX
model.json: deployment contract
model.verification.json: numerical verification
pytorch_model.pt: original training checkpoint
lunasr/model.py: architecture definition
scripts/: image, video, export, and CUDA benchmark utilities
SHA256SUMS.txt: file integrity manifest

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