A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation https://refine.rs/
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rename test_unet.py to test_sd15_unet.py + use test_device fixture
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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 🔥

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.

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:

Projects using Refiners

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}}
}