Refiners is a PyTorch microframework designed for simplifying the training and deployment of adapters for foundation models. It targets researchers and developers working with large AI models, offering a streamlined API for adapting these models to specific tasks and datasets, thereby enhancing efficiency and customization.

How It Works

Refiners provides a modular and composable architecture, treating adapters as first-class citizens. It abstracts away the complexities of model loading, adapter integration, and training loops, allowing users to focus on the adaptation process. This approach facilitates experimentation with various adapter types and configurations, promoting efficient fine-tuning of large foundation models.

Quick Start & Requirements

• Install from source using Rye: git clone "git@github.com:finegrain-ai/refiners.git" && cd refiners && rye sync --all-features
• Requires Python 3.x.
• Official documentation and guides are available at refine.rs.

Highlighted Details

• Supports a growing list of adapters, including ELLA, Box Segmenter, MVANet, IC-Light, Multi Upscaler, and HQ-SAM.
• Integrates with ComfyUI via the comfyui-refiners node.
• Inspired by projects like tinygrad, Composer, and Keras.
• Actively developed with contributions from the community.

Maintenance & Community

• Active development with recent additions of new models and features.
• Community engagement via Discord and GitHub Discussions.
• Projects like Finegrain Editor, Visoid, and imaginAIry utilize Refiners.

Licensing & Compatibility

• Licensed under the MIT license, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The framework is still evolving, with new features and adapters being added regularly. While the documentation is comprehensive, users might encounter evolving APIs as the project matures.