Finegrain Refiners Library **The simplest way to train and run adapters on top of foundation models** [**Manifesto**](https://refine.rs/home/why/) | [**Docs**](https://refine.rs) | [**Guides**](https://refine.rs/guides/adapting_sdxl/) | [**Discussions**](https://github.com/finegrain-ai/refiners/discussions) | [**Discord**](https://discord.gg/mCmjNUVV7d) ______________________________________________________________________ [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/refiners)](https://pypi.org/project/refiners/) [![PyPI Status](https://badge.fury.io/py/refiners.svg)](https://badge.fury.io/py/refiners) [![license](https://img.shields.io/badge/license-MIT-blue)](/LICENSE) [![code bounties](https://img.shields.io/badge/code-bounties-blue)](https://finegrain.ai/bounties) [![chat](https://img.shields.io/discord/1179456777406922913?logo=discord&logoColor=white&color=%235765F2)](https://discord.gg/mCmjNUVV7d)
## Latest News 🔥 - Added Multi Upscaler for high-resolution image generation, inspired from [Clarity Upscaler](https://github.com/philz1337x/clarity-upscaler) ([HF Space](https://huggingface.co/spaces/finegrain/enhancer)) - Added [HQ-SAM](https://arxiv.org/abs/2306.01567) for high quality mask prediction with Segment Anything - Added [SDXL-Lightning](https://arxiv.org/abs/2402.13929) - Added [Latent Consistency Models](https://arxiv.org/abs/2310.04378) and [LCM-LoRA](https://arxiv.org/abs/2311.05556) for Stable Diffusion XL - Added [Style Aligned adapter](https://arxiv.org/abs/2312.02133) to Stable Diffusion models - Added [ControlLoRA (v2) adapter](https://github.com/HighCWu/control-lora-v2) to Stable Diffusion XL - Added [Euler's method](https://arxiv.org/abs/2206.00364) to solvers (contributed by [@israfelsr](https://github.com/israfelsr)) - Added [DINOv2](https://github.com/facebookresearch/dinov2) for high-performance visual features (contributed by [@Laurent2916](https://github.com/Laurent2916)) - Added [FreeU](https://github.com/ChenyangSi/FreeU) for improved quality at no cost (contributed by [@isamu-isozaki](https://github.com/isamu-isozaki)) - Added [Restart Sampling](https://github.com/Newbeeer/diffusion_restart_sampling) for improved image generation ([example](https://github.com/Newbeeer/diffusion_restart_sampling/issues/4)) - Added [Self-Attention Guidance](https://github.com/KU-CVLAB/Self-Attention-Guidance/) to avoid e.g. too smooth images ([example](https://github.com/SusungHong/Self-Attention-Guidance/issues/4)) - Added [T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) for extra guidance ([example](https://github.com/TencentARC/T2I-Adapter/discussions/93)) - Added [MultiDiffusion](https://github.com/omerbt/MultiDiffusion) for e.g. panorama images - Added [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), aka image prompt ([example](https://github.com/tencent-ailab/IP-Adapter/issues/92)) - Added [Segment Anything](https://github.com/facebookresearch/segment-anything) to foundation models - Added [SDXL 1.0](https://github.com/Stability-AI/generative-models) to foundation models - Made possible to add new concepts to the CLIP text encoder, e.g. via [Textual Inversion](https://arxiv.org/abs/2208.01618) ## Installation The current recommended way to install Refiners is from source using [Rye](https://rye-up.com/): ```bash 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](https://refine.rs/getting-started/recommended/), a description of the [key concepts](https://refine.rs/concepts/chain/), as well as in-depth foundation model adaptation [guides](https://refine.rs/guides/adapting_sdxl/). ## 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](https://arxiv.org/abs/2302.05543) - [T2I-Adapter](https://arxiv.org/abs/2302.08453) - [IP-Adapter](https://arxiv.org/abs/2308.06721) - [Medical SAM Adapter](https://arxiv.org/abs/2304.12620) - [3DSAM-adapter](https://arxiv.org/abs/2306.13465) - [SAM-adapter](https://arxiv.org/abs/2304.09148) - [Cross Modality Attention Adapter](https://arxiv.org/abs/2307.01124) - [UniAdapter](https://arxiv.org/abs/2302.06605) ## Projects using Refiners - https://github.com/brycedrennan/imaginAIry ## Credits We took inspiration from these great projects: - [tinygrad](https://github.com/tinygrad/tinygrad) - For something between PyTorch and [karpathy/micrograd](https://github.com/karpathy/micrograd) - [Composer](https://github.com/mosaicml/composer) - A PyTorch Library for Efficient Neural Network Training - [Keras](https://github.com/keras-team/keras) - Deep Learning for humans ## Citation ```bibtex @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}} } ```