mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-09-21 03:35:28 +00:00
96 lines
5.4 KiB
Markdown
96 lines
5.4 KiB
Markdown
<div align="center">
|
|
|
|
<picture>
|
|
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/finegrain-ai/refiners/main/assets/logo_dark.png">
|
|
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/finegrain-ai/refiners/main/assets/logo_light.png">
|
|
<img alt="Finegrain Refiners Library" width="352" height="128" style="max-width: 100%;">
|
|
</picture>
|
|
|
|
**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)
|
|
|
|
______________________________________________________________________
|
|
|
|
[](https://github.com/astral-sh/rye)
|
|
[](https://github.com/astral-sh/ruff)
|
|
[](https://github.com/pypa/hatch)
|
|
[](https://pypi.org/project/refiners/)
|
|
[](https://pypi.org/project/refiners/)
|
|
[](/LICENSE) \
|
|
[](https://finegrain.ai/bounties)
|
|
[](https://discord.gg/mCmjNUVV7d)
|
|
[](https://huggingface.co/refiners)
|
|
[](https://huggingface.co/finegrain)
|
|
[](https://registry.comfy.org/publishers/finegrain/nodes/comfyui-refiners)
|
|
|
|
</div>
|
|
|
|
## Latest News 🔥
|
|
|
|
- Added [ELLA](https://arxiv.org/abs/2403.05135) for better prompts handling (contributed by [@ily-R](https://github.com/ily-R))
|
|
- Added the Box Segmenter all-in-one solution ([model](https://huggingface.co/finegrain/finegrain-box-segmenter), [HF Space](https://huggingface.co/spaces/finegrain/finegrain-object-cutter))
|
|
- Added [MVANet](https://arxiv.org/abs/2404.07445) for high resolution segmentation
|
|
- Added [IC-Light](https://github.com/lllyasviel/IC-Light) to manipulate the illumination of images
|
|
- 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
|
|
- ...see past [releases](https://github.com/finegrain-ai/refiners/releases)
|
|
|
|
## 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/).
|
|
|
|
## Projects using Refiners
|
|
|
|
- [Finegrain Editor](https://editor.finegrain.ai/signup?utm_source=github&utm_campaign=refiners): use state-of-the-art visual AI skills to edit product photos
|
|
- [Visoid](https://www.visoid.com/): AI-powered architectural visualization
|
|
- [imaginAIry](https://github.com/brycedrennan/imaginAIry): Pythonic AI generation of images and videos
|
|
|
|
## 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)
|
|
|
|
## 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}}
|
|
}
|
|
```
|