README.md

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🤗 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)).
- Various noise schedulers that can be used interchangeably for the prefered 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, that 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 models (see [examples](https://github.com/huggingface/diffusers/tree/main/examples)).

## Quickstart

In order to get started, we recommend taking a look at two notebooks:

- The [Diffusers](https://github.com/patrickvonplaten/notebooks/blob/master/Diffusers.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, but also to get an understanding of each independent building blocks in the library.
- The [Training diffusers](https://colab.research.google.com/gist/anton-l/cde0c3643e991ad7dbc01939865acaf4/diffusers_training_example.ipynb) notebook, which summarizes diffuser model training methods. This notebook takes a step-by-step approach to training your
  diffuser model on an image dataset, with explanatory graphics.

## Definitions

**Models**: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to *denoise* a noisy input to an image.
*Examples*: UNet, Conditioned UNet, 3D UNet, Transformer UNet

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    <img src="https://user-images.githubusercontent.com/10695622/174349667-04e9e485-793b-429a-affe-096e8199ad5b.png" width="800"/>
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    <em> Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
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**Schedulers**: Algorithm class for both **inference** and **training**.
The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training.
*Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902)

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    <img src="https://user-images.githubusercontent.com/10695622/174349706-53d58acc-a4d1-4cda-b3e8-432d9dc7ad38.png" width="800"/>
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    <em> Sampling and training algorithms. Figure from DDPM paper (https://arxiv.org/abs/2006.11239). </em>
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**Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ...
*Examples*: Glide, Latent-Diffusion, Imagen, DALL-E 2

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    <img src="https://user-images.githubusercontent.com/10695622/174348898-481bd7c2-5457-4830-89bc-f0907756f64c.jpeg" width="550"/>
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    <em> Figure from ImageGen (https://imagen.research.google/). </em>
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## Philosophy

- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
- Diffusers is **modality independent** and focusses on providing pretrained models and tools to build systems that generate **continous outputs**, *e.g.* vision and audio.
- Diffusion models and schedulers are provided as consise, elementary building blocks whereas diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of other library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).

## Installation

```
pip install diffusers  # should install diffusers 0.1.2
```

## Examples

If you want to run the code yourself 💻, you can try out:
- [Text-to-Image Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256#usage)
- [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256#inference-with-an-unrolled-loop)
- [Unconditional Diffusion with discrete scheduler](https://huggingface.co/google/ddpm-celebahq-256)
- [Unconditional Diffusion with continous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)

If you just want to play around with some models, you can try out the following 🚀 spaces:
- [Text-to-Image Latent Diffusion](https://huggingface.co/spaces/CompVis/text2img-latent-diffusion)
- [Faces generator](https://huggingface.co/spaces/CompVis/celeba-latent-diffusion)
- [DDPM with different schedulers](https://huggingface.co/spaces/fusing/celeba-diffusion)

## In the works

For the first release, 🤗 Diffusers focuses on text-to-image diffusion techniques. However, diffusers can be used for much more than that! Over the upcoming releases, we'll be focusing on:

- Diffusers for audio
- Diffusers for reinforcement learning (initial work happening in https://github.com/huggingface/diffusers/pull/105).
- Diffusers for video generation
- Diffusers for molecule generation (initial work happening in https://github.com/huggingface/diffusers/pull/54)

A few pipeline components are already being worked on, namely:

- BDDMPipeline for spectrogram-to-sound vocoding
- GLIDEPipeline to support OpenAI's GLIDE model
- Grad-TTS for text to audio generation / conditional audio generation

We want diffusers to be a toolbox useful for diffusers models in general; if you find yourself limited in any way by the current API, or would like to see additional models, schedulers, or techniques, please open a [GitHub issue](https://github.com/huggingface/diffusers/issues) mentioning what you would like to see.

## Credits

This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:

- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)
- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion)
- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim).
- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)

We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights.