We have recently released new point release versions of NNFlowVector and NNCleanup. One important feature for both of these releases is the support for Nuke15.1, which is the latest Nuke release from Foundry that was made public in June earlier this year. Nuke15.1 has updated the bundled version of PyTorch from v1.12.1 to v2.1.1, which is a rather huge step in development for PyTorch. This means faster and better execution for machine learning models in general, but the biggest difference is for the MacOS platform. The old version (PyTorch v1.12.1) was the first that supported MPS acceleration, but there was quite a lot of instructions that wasn’t ready and automatically was executed on the CPU instead. With the new release (PyTorch v2.1.1) the MPS support is way more mature and the speed has improved significantly. Here is a speed test we ran for NNFlowVector on a Full HD clip (and process scale set to 0.5) on a MacBook M3 Pro, 36Gb unified RAM:
Nuke15.0: Variant “A” – 4.3 sec/frame (MPS)
Nuke15.0: Variant “BB” – 33.9 sec/frame (CPU fallback)
Nuke15.1: Variant “A” – 2.1 sec/frame (MPS)
Nuke15.1: Variant “BB” – 3.0 sec/frame (MPS)
As you can see above, the speed has more than doubled between the Nuke15.0 build and the Nuke15.1 build (using the same version of NNFlowVector, v2.3.0).
NNFlowVector v.2.3.0 also features a new transformer variant called “BB”. This variant is similar to the “AA” variant, but it has been trained on larger image areas (using a much larger GPU) which makes it perform even better in most scenarios. Our recommendation is to at least try the default “A” model variant, and also the “AA” and “BB” variants when testing which variant is best for your specific material. NNFlowVector v2.3.0 also features an important fix for anamorphic material.
NNCleanup v1.5.0 also features a whole new suite of model variants, called “AAA“, “BBB“, “CCC” and “DDD“. These have been trained using a much larger batch size on a much larger dataset, which makes them perform quite a lot better in general. (Because of this, we have deprecated the original “A”, “B”, “C” and “D” model variants.).
We hope you find these updates as useful as we do!
Cheers, David
