LibreAI / Image / ChaiNNer
// image · chaiNNer-org/chaiNNer

ChaiNNer

A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling…

★ 6.1k stars⑂ 374 forksGPL-3.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

ChaiNNer is an open-source image project by chaiNNer-org (chaiNNer-org/chaiNNer on GitHub). It has 6,057 GitHub stars and 374 forks, is released under the GPL-3.0 license, and was last updated on GitHub on 1 October 2026.

GitHub stars
6,057
Forks
374
License
GPL-3.0
Last updated on GitHub
1 October 2026
Category
Image
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Choose the right tool

    Generation studio, upscaler, background remover, or photo library — match the tool to the task.

  2. Install it your way

    Desktop apps, Docker images, and portable builds cover most platforms; pick the simplest for your OS.

  3. Add models if needed

    Generation tools need checkpoint files; utilities like upscalers usually bundle everything.

  4. Create or organize

    Generate, enhance, or import — then export, share, or keep building your library.

// features

Why people love it

No subscriptions

The defining trait of this whole category: professional results without monthly fees.

Runs locally

Your images stay on your machine — important for client work and privacy.

Model freedom

Bring any compatible open checkpoint; you are never locked to one vendor's style.

Purpose-built utilities

Single-job tools (upscale, de-background, restore) that do one thing excellently.

Smart photo libraries

Self-hosted managers with AI tagging, face recognition, and deduplication.

Extensible ecosystems

Extensions, scripts, and community presets extend the popular studios far beyond stock.

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// run it

Run it yourself

  1. Step 1 — Clone the repo

    Download the source and move into the project folder.

    git clone https://github.com/chaiNNer-org/chaiNNer.git
    cd chaiNNer
  2. Step 2 — Create an environment

    Isolate dependencies so nothing clashes with your other projects.

    python -m venv .venv
    source .venv/bin/activate  # Windows: .venv\Scripts\activate
  3. Step 3 — Install and launch

    Install the requirements, then follow the README's launch command (it varies per project).

    pip install -r requirements.txt
// use cases

What you can build

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// faq

Questions, answered

Which should I start with?

For generation: a polished studio interface. For enhancement: a dedicated upscaler. For libraries: a photo manager. The guides above narrow it down.

Do I need a powerful GPU?

Generation benefits hugely from one; upscalers and photo managers are far lighter.

Who maintains ChaiNNer?

It is an open-source project at chaiNNer-org/chaiNNer with 374 forks. Check the repo's contributors and recent commits to see how active maintenance is.

Are they free for commercial work?

Mostly yes under MIT/Apache/GPL — but verify each license, especially for client deliverables.

Can they match Midjourney quality?

With the right open checkpoints, absolutely — the interface matters less than the model you load.

Where do models come from?

Community hubs like CivitAI and Hugging Face host thousands of open checkpoints.

// keep exploring

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// alternatives

Alternatives in Image

The most-starred other Image projects in the LibreAI catalogue — the realistic shortlist if ChaiNNer is not the right fit.

Get ChaiNNer

Free and open — grab the source, star the repo, and start creating.