The current stable build. chaiNNer sets up its own Python environment the first time you open it, so there is nothing to install beforehand.
Alpha v0.26.1
2026-10-27
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All platforms
2026-10-27
Full Changelog: v0.26.0...v0.26.1
2026-10-18
This is a long overdue release. I had implemented a bunch of things earlier this year and then subsequently forgot to release them after they had been tested out by people, so this update is releasing all that to stable.
Part of why I hadn't released these changes is because I have been privately working on a Rust rewrite of chaiNNer's backend. I've been working on and off on that since around March. I will discuss that more soon, so stay tuned for updates and announcements on that. If you don't know what that entails, basically that just means everything will be significantly faster, and some things (like dependency management) a bit less jank.
This is the last release (minus any patches for necessary bug fixes) with the Python backend. It is also the last release that will be marked Alpha, and going forward I will use true semantic versioning starting from v27.0.0.
Full Changelog: v0.26.0...v0.26.0
2025-10-23
This is a small update to address a few issues that have been resolved since the last release. If the current version of chaiNNer is working fine for you, you can ignore this update.
If you have an older (1000 series or lower) Nvidia GPU, please reinstall PyTorch from the dependency manager.
NOTE: I had to disable code signing for MacOS due to it causing build issues. I do not know how exactly that will manifest when trying to install this update, but just a heads up that you might get some kind of warning now that you did not used to.
Full Changelog: v0.25.0...v0.25.1
2025-10-19
After more than a year without a stable release, chaiNNer development has resumed at reduced capacity. This release brings the features from the nightly version to stable (with many of the reported bugs from the nightly version fixed).
The main new feature is support for using multiple iterators of equal length together. Nested or combined iteration is still not supported. UX for this feature remains limited but stable based on user feedback.
Users with RTX 5000-series GPUs should install this update and update PyTorch through the dependency manager.
Full Changelog: v0.24.1...v0.25.0
2024-06-07
This update contains a few bug fixes and some QOL improvements.
Full Changelog: v0.24.0...v0.24.1
2024-06-01
This update brings many new major features to chaiNNer, including but not limited to conditional branching (via two new nodes: Conditional and Compare), node passthrough/skipping, new themes, PyTorch optimizations, and many more fixes and quality of life improvements all around.
The conditional and compare nodes bring a long-awaited feature: branching support. Well, mostly. These nodes allow you to switch between inputs based on a condition created by comparing two values in various ways. This means users can finally set up chains that do one thing under a certain condition and another thing under a different one (e.g. if an image is over a certain size vs under a certain size). The thing to keep in mind is how you have to set this up might be opposite of what's intuitive. Since you're switching between values, it's what comes before the condition that is decided between, not what comes after.
The disable toggle has also been replaced with a button that allows you to swap between three states: "enabled", "disabled", and "skipped". In skipped mode, the node will not be run, but it also will not disable downstream nodes like disabling it would. This is another heavily-requested feature that we are happy is finally in chaiNNer.
New themes can be selected in settings. This is semi-experimental as we plan on adding support for fully custom themes in the future, as well as improving the existing default selection. The current selection is mainly just a proof of concept to show that we can support themes of some kind, and also to give users some options for customization. We'd love to hear feedback on the current selection and if you have any suggestions on themes to add.
PyTorch upscaling has been heavily optimized. I was able to using a visual profiling tool (called VizTracer, which I highly recommend for anyone wanting to optimize a python project) to see what was slowing down upscaling. Upscaling has been optimized to a point where it's roughly 1.25x faster than it was before (though YMMV depending on what model you use, as well as other factors). While not perfect, it's still a large improvement and should help your PyTorch upscaling chains run faster.
atexit to always kill worker by @RunDevelopment in #2822Full Changelog: v0.23.2...v0.24.0
2024-04-24
This is a hotfix release to fix a regression introduced in the v0.23.2 which caused iterators to error when running. Apologies to anyone who was affected by this issue.
2024-04-23
This update fixes a few bugs and adds some new features.
Please note: If you install or update chaiNNer and get a critical error upon setup completion, please attempt restarting chaiNNer before reporting.
goIntoDirectory by @RunDevelopment in #2801Full Changelog: v0.23.1...v0.23.2