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A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation https://refine.rs/
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README.md

Finegrain Refiners Library

The simplest way to train and run adapters on top of foundation models

Manifesto | Docs | Guides | Discussions | Discord

dependencies - Rye linting - Ruff packaging - Hatch PyPI - Python Version PyPI - Status license
code bounties Discord HuggingFace - Refiners HuggingFace - Finegrain ComfyUI Registry

Latest News 🔥

  • Added ELLA for better prompts handling (contributed by @ily-R)
  • Added the Box Segmenter all-in-one solution (model, HF Space)
  • Added MVANet for high resolution segmentation
  • Added IC-Light to manipulate the illumination of images
  • Added Multi Upscaler for high-resolution image generation, inspired from Clarity Upscaler (HF Space)
  • Added HQ-SAM for high quality mask prediction with Segment Anything
  • ...see past releases

Installation

The current recommended way to install Refiners is from source using Rye:

git clone "git@github.com:finegrain-ai/refiners.git"
cd refiners
rye sync --all-features

Documentation

Refiners comes with a MkDocs-based documentation website available at https://refine.rs. You will find there a quick start guide, a description of the key concepts, as well as in-depth foundation model adaptation guides.

Projects using Refiners

Awesome Adaptation Papers

If you're interested in understanding the diversity of use cases for foundation model adaptation (potentially beyond the specific adapters supported by Refiners), we suggest you take a look at these outstanding papers:

Credits

We took inspiration from these great projects:

Citation

@misc{the-finegrain-team-2023-refiners,
  author = {Benjamin Trom and Pierre Chapuis and Cédric Deltheil},
  title = {Refiners: The simplest way to train and run adapters on top of foundation models},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/finegrain-ai/refiners}}
}