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

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Latest News 🔥

• Added Latent Consistency Models and LCM-LoRA for Stable Diffusion XL
• Added Style Aligned adapter to Stable Diffusion models
• Added ControlLoRA (v2) adapter to Stable Diffusion XL
• Added Euler's method to solvers (contributed by @israfelsr)
• Added DINOv2 for high-performance visual features (contributed by @Laurent2916)
• Added FreeU for improved quality at no cost (contributed by @isamu-isozaki)
• Added Restart Sampling for improved image generation (example)
• Added Self-Attention Guidance to avoid e.g. too smooth images (example)
• Added T2I-Adapter for extra guidance (example)
• Added MultiDiffusion for e.g. panorama images
• Added IP-Adapter, aka image prompt (example)
• Added Segment Anything to foundation models
• Added SDXL 1.0 to foundation models
• Made possible to add new concepts to the CLIP text encoder, e.g. via Textual Inversion

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:

• ControlNet
• T2I-Adapter
• IP-Adapter
• Medical SAM Adapter
• 3DSAM-adapter
• SAM-adapter
• Cross Modality Attention Adapter
• UniAdapter

Projects using Refiners

• https://github.com/brycedrennan/imaginAIry

Credits

We took inspiration from these great projects:

• tinygrad - For something between PyTorch and karpathy/micrograd
• Composer - A PyTorch Library for Efficient Neural Network Training
• Keras - Deep Learning for humans

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}}
}