StarNet Software

AI-powered software for star removal and noise reduction in astrophotography.

Command-line Tools

This page describes the standalone StarNet and DeepSNR command-line packages. Use these files for command-line workflows, automation, integration with other image processing software, and standalone processing outside PixInsight.

Command-line tool releases

Command-line archives are for standalone processing, automation, and applications that call StarNet or DeepSNR executables directly. For example, Siril's StarNet integration uses the command-line StarNet installation path.

Release matrix

Current command-line packages are published for Linux x64, Windows x64, macOS x64, and macOS ARM64. StarNet2 and DeepSNR follow the same target lanes as the PixInsight modules, but command-line releases can move on their own schedule and are packaged as separate files for terminal use, scripts, and host applications that call external executables.

Current ORT/CoreML command-line packages are self-contained. Users do not install a separate TensorFlow, Torch, or backend-runtime package.

Current StarNet2 command-line release matrix

Operating system Architecture Version Backend Release date
Linux x64 2.5.2 ONNX/ORT 2026-06-06
Windows x64 2.5.2 ONNX/ORT 2026-06-06
macOS x64 2.5.2 ONNX/ORT 2026-06-06
macOS ARM64 2.5.2 CoreML 2026-06-06

Current DeepSNR command-line release matrix

Operating system Architecture Version Backend Release date
Linux x64 1.2.1 ONNX/ORT 2026-05-30
Windows x64 1.2.1 ONNX/ORT 2026-05-30
macOS x64 1.2.0 ONNX/ORT 2026-05-27
macOS ARM64 1.2.0 CoreML 2026-05-27

System and build information

Current command-line packages are self-contained. The table below separates what users need to run the package from the systems used to build and validate the release.

Current command-line system information

Target Runtime requirement Built and validated on
Linux x64 x86_64 Linux on a compatible glibc-based Linux distribution. Ubuntu 24.04.2 LTS / WSL2.
Windows x64 64-bit Windows 10 or 11. Windows 11 x64.
macOS x64 macOS 13.1 or newer. Intel Mac, or Apple Silicon through Rosetta 2. macOS 26.5 Tahoe on Apple Silicon.
macOS ARM64 Apple Silicon Mac with macOS 13.1 or newer. macOS 26.5 Tahoe on Apple Silicon.

Current command-line packages include their required runtime files. They do not require separate TensorFlow, Torch, CUDA, Python, Visual Studio, ONNX Runtime, or CoreML runtime installs.

Updates in the latest releases

Recent command-line releases focus on current runtime backends, explicitly documented input/output behavior, and focused StarNet2 and DeepSNR updates:

  • StarNet2 2.5.2 improves highlight protection in very bright image regions and fixes unscreen star-layer artifacts in saturated or near-saturated regions.
  • DeepSNR 1.2.1 adds clearer backend/provider console reporting and an automatic CUDA-provider attempt with CPU fallback for ORT packages when advanced users supply compatible GPU runtime files.
  • TIFF/TIF and PNG are the recommended tested input formats; JPEG/JPG and BMP might work through OpenCV but were not tested for this refresh.
  • Supported inputs are 8-bit or 16-bit integer grayscale/RGB images. Unsupported inputs, including 32-bit floating-point images, alpha channels, and unsupported channel/depth layouts, are rejected.
  • TIFF and PNG outputs are written as 16-bit images by default; use --eight only when an 8-bit output file is intentionally needed. TIFF outputs are saved with LZW compression.
  • StarNet2 includes --unscreen for an optional star-layer output alongside the existing starless output and optional subtractive mask.
  • macOS ARM64 command-line packages use native CoreML and are the recommended Apple Silicon lane for best performance.

Command-line download pages