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README.md
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<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 foundational models** ([dive in!](https://blog.finegrain.ai/posts/simplifying-ai-code/))
**The simplest way to train and run adapters on top of foundation models** ([dive in!](https://blog.finegrain.ai/posts/simplifying-ai-code/))
______________________________________________________________________
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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))
- 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 foundational models
- Added [SDXL 1.0](https://github.com/Stability-AI/generative-models) to foundational models
- 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)
## Getting Started
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That's why we're building Refiners.
It's a framework to easily bridge the last mile quality gap of foundational models like Stable Diffusion or Segment Anything Model (SAM), by adapting them to specific tasks with lightweight trainable and composable patches.
It's a framework to easily bridge the last mile quality gap of foundation models like Stable Diffusion or Segment Anything Model (SAM), by adapting them to specific tasks with lightweight trainable and composable patches.
We decided to build Refiners in the open.
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```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 foundational models},
title = {Refiners: The simplest way to train and run adapters on top of foundation models},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},

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docs/reference/SUMMARY.md
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* [<code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> Layers](fluxion/layers.md)
* [<code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> Model Converter](fluxion/model_converter.md)
* [<code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> Utils](fluxion/utils.md)
* <code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> Foundational Models
* <code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> Foundation Models
* [<code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> CLIP](foundationals/clip.md)
* [<code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> DINOv2](foundationals/dinov2.md)
* [<code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> Latent Diffusion](foundationals/latent_diffusion.md)

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pyproject.toml
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[project]
name = "refiners"
version = "0.2.0"
description = "The simplest way to train and run adapters on top of foundational models"
description = "The simplest way to train and run adapters on top of foundation models"
authors = [{ name = "The Finegrain Team", email = "bonjour@lagon.tech" }]
license = "MIT"
dependencies = [