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Added --static flag to force static engine generation for Upscale TensorRT.
Temp state for downloaded models will now ensure file integrity in case of internet loss.
Added --depth_quality flag with options low, high ( default high ). For the users with lower end system to benefit of higher inference speeds. ( Not reliable for TensorRT )
Added testing builds for linux-full and linux-lite ( Full is WIP but should be functional with the build-linux.py script )
Improvements
Upgraded base polygraphy from 0.49.12 -> 0.49.13
Removed unnecessary clamps.
The metadata, progress bar and everything else related to video details should be MUCH more accurate now, including logging.
Log.txt should now be more compact with Arguments no longer having unused arguments.
Rife TRT will only build Static Engines in order to improve stability and versatility.
Rife CUDA is now up to 15% faster than before.
Rife TRT is now up to 15% faster than before.
Chained Processes with Upscaling and Interpolation TRT / CUDA will now be more memory efficient and performant.
Benchmarks are now going to be more accurate for interpolation.
Upgraded ONNXRuntime-DirectML to 1.19.1
Improved the progress bar with more information and less unnecessary eye candy.
Improved stability of image inputs.
More video metadata requests.
The download progress bar will now also tell the download speed in MB/s
Some slight adjustments to the building methodology of TAS.
Fixes:
Some CUDA Race conditions could be met in extremely High FPS workflows ( 500+ FPS ).
Fix some oversights in Rife 4.22 and Rife 4.22-lite that were reducing the output quality.
Adobe Edition
NEW
Added Rife4.22-Lite and Rife4.22-Lite-Tensorrt options to the dropdowns.