readme: <p align="center">
      <br>
      <img src="https://github.com/huggingface/diffusers/raw/main/docs/source/imgs/diffusers_library.jpg" width="400"/>
      <br>
  <p>
  <p align="center">
      <a href="https://github.com/huggingface/diffusers/blob/main/LICENSE">
          <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue">
      </a>
      <a href="https://github.com/huggingface/diffusers/releases">
          <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg">
      </a>
      <a href="CODE_OF_CONDUCT.md">
          <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg">
      </a>
  </p>
  
  🤗 Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves
  as a modular toolbox for inference and training of diffusion models.
  
  More precisely, 🤗 Diffusers offers:
  
  - State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)). Check [this overview](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/README.md#pipelines-summary) to see all supported pipelines and their corresponding official papers.
  - Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
  - Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
  - Training examples to show how to train the most popular diffusion model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)).
  
  ## Installation
  
  ### For PyTorch
  
  **With `pip`**
      
  ```bash
  pip install --upgrade diffusers[torch]
  ```
  
  **With `conda`**
  
  ```sh
  conda install -c conda-forge diffusers
  ```
  
  ### For Flax
  
  **With `pip`**
  
  ```bash
  pip install --upgrade diffusers[flax]
  ```
  
  **Apple Silicon (M1/M2) support**
  
  Please, refer to [the documentation](https://huggingface.co/docs/diffusers/optimization/mps).
  
  ## Contributing
  
  We ❤️  contributions from the open-source community! 
  If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).
  You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.
  - See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute
  - See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
  - See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
  
  Also, say 👋 in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or
  just hang out ☕.
  
  ## Quickstart
  
  In order to get started, we recommend taking a look at two notebooks:
  
  - The [Getting started with Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines.
    Take a look at this notebook to learn how to use the pipeline abstraction, which takes care of everything (model, scheduler, noise handling) for you, and also to understand each independent building block in the library.
  - The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffusion models training methods. This notebook takes a step-by-step approach to training your
    diffusion models on an image dataset, with explanatory graphics. 
    
  ## Stable Diffusion is fully compatible with `diffusers`!  
  
  Stable Diffusion is a text-to-im