mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-21 21:58:47 +00:00
92 lines
5.4 KiB
Markdown
92 lines
5.4 KiB
Markdown
<div align="center">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/finegrain-ai/refiners/main/assets/logo_dark.png">
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<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/finegrain-ai/refiners/main/assets/logo_light.png">
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<img alt="Finegrain Refiners Library" width="352" height="128" style="max-width: 100%;">
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</picture>
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**The simplest way to train and run adapters on top of foundation models**
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[**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)
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______________________________________________________________________
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[](https://pypi.org/project/refiners/)
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[](https://badge.fury.io/py/refiners)
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[](/LICENSE)
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[](https://finegrain.ai/bounties)
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[](https://discord.gg/mCmjNUVV7d)
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</div>
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## Latest News 🔥
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- Added [HQ-SAM](https://arxiv.org/abs/2306.01567) for high quality mask prediction with Segment Anything
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- Added [SDXL-Lightning](https://arxiv.org/abs/2402.13929)
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- Added [Latent Consistency Models](https://arxiv.org/abs/2310.04378) and [LCM-LoRA](https://arxiv.org/abs/2311.05556) for Stable Diffusion XL
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- Added [Style Aligned adapter](https://arxiv.org/abs/2312.02133) to Stable Diffusion models
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- Added [ControlLoRA (v2) adapter](https://github.com/HighCWu/control-lora-v2) to Stable Diffusion XL
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- Added [Euler's method](https://arxiv.org/abs/2206.00364) to solvers (contributed by [@israfelsr](https://github.com/israfelsr))
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- Added [DINOv2](https://github.com/facebookresearch/dinov2) for high-performance visual features (contributed by [@Laurent2916](https://github.com/Laurent2916))
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- Added [FreeU](https://github.com/ChenyangSi/FreeU) for improved quality at no cost (contributed by [@isamu-isozaki](https://github.com/isamu-isozaki))
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- Added [Restart Sampling](https://github.com/Newbeeer/diffusion_restart_sampling) for improved image generation ([example](https://github.com/Newbeeer/diffusion_restart_sampling/issues/4))
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- 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))
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- Added [T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) for extra guidance ([example](https://github.com/TencentARC/T2I-Adapter/discussions/93))
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- Added [MultiDiffusion](https://github.com/omerbt/MultiDiffusion) for e.g. panorama images
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- Added [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), aka image prompt ([example](https://github.com/tencent-ailab/IP-Adapter/issues/92))
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- Added [Segment Anything](https://github.com/facebookresearch/segment-anything) to foundation models
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- Added [SDXL 1.0](https://github.com/Stability-AI/generative-models) to foundation models
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- Made possible to add new concepts to the CLIP text encoder, e.g. via [Textual Inversion](https://arxiv.org/abs/2208.01618)
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## Installation
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The current recommended way to install Refiners is from source using [Rye](https://rye-up.com/):
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```bash
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git clone "git@github.com:finegrain-ai/refiners.git"
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cd refiners
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rye sync --all-features
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```
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## Documentation
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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/).
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## Awesome Adaptation Papers
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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:
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- [ControlNet](https://arxiv.org/abs/2302.05543)
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- [T2I-Adapter](https://arxiv.org/abs/2302.08453)
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- [IP-Adapter](https://arxiv.org/abs/2308.06721)
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- [Medical SAM Adapter](https://arxiv.org/abs/2304.12620)
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- [3DSAM-adapter](https://arxiv.org/abs/2306.13465)
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- [SAM-adapter](https://arxiv.org/abs/2304.09148)
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- [Cross Modality Attention Adapter](https://arxiv.org/abs/2307.01124)
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- [UniAdapter](https://arxiv.org/abs/2302.06605)
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## Projects using Refiners
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- https://github.com/brycedrennan/imaginAIry
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## Credits
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We took inspiration from these great projects:
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- [tinygrad](https://github.com/tinygrad/tinygrad) - For something between PyTorch and [karpathy/micrograd](https://github.com/karpathy/micrograd)
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- [Composer](https://github.com/mosaicml/composer) - A PyTorch Library for Efficient Neural Network Training
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- [Keras](https://github.com/keras-team/keras) - Deep Learning for humans
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## Citation
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```bibtex
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@misc{the-finegrain-team-2023-refiners,
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author = {Benjamin Trom and Pierre Chapuis and Cédric Deltheil},
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title = {Refiners: The simplest way to train and run adapters on top of foundation models},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/finegrain-ai/refiners}}
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}
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```
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