Hello everyone, I have a visual convolutional model and a video that has been decoded into many frames. When I perform inference on each frame in a loop, the speed is a bit slow. So, I started 4 threads, each running inference simultaneously, but I found that the speed is the same as serial inference, every single forward inference is slower. I used the mactop tool to check the GPU utilization, and it was only around 20%. Is this normal? How can I accelerate it?

Hello, I'm using videotoolbox superresolution API in MACOS 26: https://developer.apple.com/documentation/videotoolbox/vtsuperresolutionscalerconfiguration/downloadconfigurationmodel(completionhandler:)?language=objc, when using swift, it's ok, when using objective-c, I get error when downloading model with downloadConfigurationModelWithCompletionHandler: [Auto] MA-auto{_failedLockContent} | failure reported by server | error:[com.apple.MobileAssetError.AutoAsset:MissingReference(6111)] [Auto] MA-auto{_failedLockContent} | failure reported by server | error:[com.apple.MobileAssetError.AutoAsset:UnderlyingError(6107)_1_com.apple.MobileAssetError.Download:47] Download completion handler called with error: The operation couldnxe2x80x99t be completed. (VTFrameProcessorErrorDomain error -19743.)

hello, I'm using VideoTololbox VTFrameRateConversionConfiguration to perform frame interpolation: https://developer.apple.com/documentation/videotoolbox/vtframerateconversionconfiguration?language=objc ,when using 640x480 vidoe input, I got error: Error ! Invalid configuration [VEEspressoModel] build failure : flow_adaptation_feature_extractor_rev2.espresso.net. Configuration: landscape640x480 [EpsressoModel] Cannot load Net file flow_adaptation_feature_extractor_rev2.espresso.net. Configuration: landscape640x480 Error: failed to create FRCFlowAdaptationFeatureExtractor for usage 8 Failed to switch (0x12c40e140) [usage:8, 1/4 flow:0, adaptation layer:1, twoStage:0, revision:2, flow size (320x240)]. Could not init FlowAdaptation initFlowAdaptationWithError fail tried 2048x1080 is ok.