Open Image Denoise v2.3.0

• Significantly improved image quality of the RT filter in high quality
  mode for HDR denoising with prefiltering, i.e., the following combinations
  of input features and parameters:
  - HDR color + albedo + normal + cleanAux
  - albedo
  - normal
  In these cases a much more complex filter is used, which results in lower
  performance than before (about 2x). To revert to the previous performance
  behavior, please switch to the balanced quality mode.
• Added fast quality mode (OIDN_QUALITY_FAST) for even higher performance
  (about 1.5-2x) interactive/real-time previews and lower default memory usage
  at the cost of somewhat lower image quality. Currently this is implemented
  for the RT filter except prefiltering (albedo, normal). In other cases
  denoising implicitly falls back to balanced mode.
• Added Intel Arrow Lake, Lunar Lake, and Battlemage GPU support
• Execute Async functions asynchronously on CPU devices as well
• Load/initialize device modules lazily (improves stability)
• Added oidnIsCPUDeviceSupported, oidnIsSYCLDeviceSupported,
  oidnIsCUDADeviceSupported, oidnIsHIPDeviceSupported,
  and oidnIsMetalDeviceSupported API functions for checking whether a
  physical device of a particular type is supported
• Release the CUDA primary context when destroying the device object if using
  the CUDA driver API
• Added OIDN_LIBRARY_NAME CMake option for setting the base name of the Open
  Image Denoise library files
• Fixed device creation error with oidnNewDevice when the default device of
  the specified type (e.g. CUDA) is not supported but there are other
  supported non-default devices of that type in the system
• Fixed CMake error when building with Metal support using non-Apple Clang
• Fixed iOS build errors
• Added support for building with ROCm 6.x
• oidnNewCUDADevice and oidnNewHIPDevice no longer accept negative device
  IDs. If the goal is to use the current device, its actual ID needs to be
  passed.
• Upgraded to oneTBB 2021.12.0 in the official binaries
• Training:
  • Improved training performance on CUDA and MPS devices, added --compile
    option
  • Added --quality option (high, balanced, fast) for selecting the
    size of the model to train, changed the default from balanced to high
  • Added new models to the --model option (unet_small, unet_large,
    unet_xl)
  • Added support for training with prefiltered auxiliary features by
    passing --aux_results to preprocess.py and train.py
  • Added experimental support for depth (z)