![]() Sudo xattr -c /Applications/PixInsight/lib/*. DirectX 10-compatible graphics cards and above. Apple says it recorded up to 5.6x faster combined vector and raster GPU performance in Affinity Photo with the 16-core M1 Pro, and up to 8.5x faster with the 32-core M1 Max. Windows-based PC (64 bit) with mouse or equivalent input device. Sudo cp ~/Downloads/StarNet_MacOS/*.pb /Applications/PixInsight/lib. Our changes have also improved performance on the previous M1 chip, which is now roughly 10 faster in our benchmark in version 1.10.3. Sudo xattr -c /Applications/PixInsight/bin/StarNet-pxm.dylib /Applications/PixInsight/bin/libtensorflow* Sudo cp ~/Downloads/StarNet_MacOS/libtensorflow_framework.so /Applications/PixInsight/bin/libtensorflow_framework.2.dylib Sudo cp ~/Downloads/StarNet_MacOS/libtensorflow.so /Applications/PixInsight/bin/libtensorflow.2.dylib Update (March 31, 2017): The latest Affinity Photo update has resolved many bugs and is significantly more stable. ![]() Sudo mv /Applications/PixInsight/bin/libtensorflow* ~/someplace-else Affinity Photo does auto-save your documents and keep in mind that this is for their first non-beta release for Windows so I think we have to cut them some slack at least until their next update. Sudo cp StarNet-pxm.dylib /Applications/PixInsight/binÄownload & uncompress standalone StarNet++ from Use Pacifist to extract StarNet-pxm.dylib from the PI installer pkg In case anybody else is looking for this, here's the entire step-by-step: I've now got the StarNet process working perfectly in PI 1.8.8-11 on my M1 MacBook Air (8 GB RAM) and it feels faster than my 2017 Intel Mac mini (6 cores/12 threads with 64 GB RAM). Thank you VERY MUCH for your help getting the StarNet process running in PixInsight on the M1 Mac.
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