Russell Croman Astrophotography  

 

 

StarXTerminator

Support

StarXTerminator is a new tool, so some glitches are to be expected. As usual with software development, it works great on all of my test systems, but there are many scenarios in the wild. Please take advantage of the trial period to make sure it works for you before purchasing. The following is a list of known issues and to-do items. If you're seeing any other issues, feel free to fill out the support form below to send me a note.

– Updated 16 September 2021 –

  • Adding AI download for the Mac version, and download progress reporting on the Windows version
    • This is just a few days out
  • New AI version forthcoming in a few days
    • This should handle those pesky faint, small stars that it currently misses in some images, and have better performance on very crowded star fields.
  • Some users are seeing Photoshop throw a "disk error" when trying to run the plugin
    • Working on it, but so far I haven't been able to replicate it
  • Photoshop versions earlier than CC are untested
    • Some folks are having luck, others not so much. Working on it
  • Currently not working in Affinity – to be addressed.
  • More AI training to see if I can get it to not zap those faint background galaxies in wide-field photos
  • Notes on supported GPUs:
    • The plug-in uses the machine learning libraries provided by Apple and Microsoft. If those libraries support a particular GPU, the plugin should take advantage of them.
    • I've tested on an M1 Mac and it's quite zippy. Intel Macs use the integrated GPU, but it's much slower than Apple silicon or a discrete GPU.
    • I develop on a Mac Pro with recent AMD GPUs, so I know those work
    • GPU support in Windows seems to be more spotty at the moment... I'll be looking into that more as well.
    • Older machines will always struggle with neural networks. AI takes a tremendous amount of computational power and memory. The plug-in will run on the CPU, but it will be a good time for a coffee break. You can also probably just skip the calibration and use a batch size of 1.

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