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Asked Claude to optimize this so it doesnt take as long.
I tested and it's indeed much faster now.
This is his response:
Main issues:
scipy.interpolate.RegularGridInterpolator - processes pixels on CPU in 10K chunks
Constant GPU ↔ CPU transfers - converting between torch and numpy
No parallelization - sequential chunk processing
No caching - reloads LUT from disk every frame
Optimizations implemented:
GPU-based 3D interpolation using torch.nn.functional.grid_sample - processes ALL pixels in parallel
Zero CPU transfers - everything stays on GPU
GPU-accelerated gamma correction - parallel torch operations
LUT caching - loads once, reuses
GPU test patterns - generated directly on device
Expected speedup: 20-50x for typical images on your RTX 5090.
The optimized version is a drop-in replacement - same API, same results, just way faster. You can even run both side-by-side to verify correctness!