chaiNNer – node-based image processing workflows

chaiNNer is a node-based image processing application that lets users build workflows by connecting individual processing operations. Rather than presenting a fixed set of editing tools, it provides a visual environment in which the output of one operation becomes the input to another.

Originally developed for AI image upscaling, chaiNNer has grown into a general-purpose image processing environment. Its approach is particularly useful for repetitive jobs, where loading, resizing, processing and saving images can be assembled into a reusable pipeline.

The application combines an Electron-based interface with a Python processing backend. It supports several neural network inference frameworks, allowing users to select a suitable backend for their hardware and models.

This is free and open source software.

Key Features

• Build image processing workflows using a graphical node editor.
• Connect processing nodes through colour-coded handles that indicate compatible data types.
• Drag nodes from the selection panel or create them through the context menu.
• Construct workflows for AI image upscaling, resizing, conversion and other image processing operations.
• Combine several processing stages into a single automated pipeline.
• Run, pause and stop workflows through the graphical interface.
• Monitor the progress of individual nodes during execution.
• Batch process directories of images using iteration nodes.
• Read and save video using dedicated input and output nodes.
• Support compatible equal-length iterators in more advanced workflows.
• Use AI models through PyTorch, NCNN, ONNX and TensorRT.
• Support different super-resolution model architectures through the Spandrel model loader.
• Use TensorRT acceleration for supported models on NVIDIA GPUs.
• Support AMD graphics through NCNN and, on suitable Linux systems, PyTorch with ROCm.
• Use NCNN inference on compatible Intel graphics hardware.
• Offer CPU processing where supported by the selected framework.
• Cross-platform support – runs on Linux, Windows and macOS.

Website: github.com/chaiNNer-org/chaiNNer
Support:
Developer: chaiNNer-org
License: GNU General Public License v3.0

chaiNNer is written in Python. Learn Python with our recommended free books and free tutorials.

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