chaiNNer-org/chaiNNer
ChaiNNer
chaiNNer is a GPU-accelerated AI image upscaling application that uses a visual node-based interface for constructing image processing pipelines. At its core, it provides a node-based visual programming environment where users connect processing nodes in a directed acyclic graph, with a graph execution scheduler that traverses the pipeline in topological order. The application includes an iterator-based batch processing system that automatically applies the same pipeline to multiple files, and a model format conversion pipeline that transforms neural network models between PyTorch, ONNX, and NCNN formats.
The application distinguishes itself through its plugin-based node architecture, where each image operation is encapsulated as a self-contained node with typed inputs and outputs that can be dynamically loaded. It supports cross-framework model conversion for use on different GPU hardware, and includes a neural network dependency manager that installs and configures runtimes without requiring system-level Python modifications. Custom AI model integration allows users to parse and incorporate various upscaling network architectures directly into the processing pipeline.
Beyond its core upscaling capabilities, chaiNNer provides image property adjustment, background removal using pre-trained neural network models, and the ability to chain multiple image operations in a flexible sequence. The visual node pipeline editor enables users to build reusable processing workflows by dragging handles between nodes, with pipeline execution showing animated progress and providing pause or stop controls.