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<p align="center">
<br>
<img src="https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/en/imgs/diffusers_library.jpg" width="400"/>
<br>
<p>
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<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="https://pepy.tech/project/diffusers"><img alt="GitHub release" src="https://static.pepy.tech/badge/diffusers/month"></a>
<a href="CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg"></a>
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🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
🤗 Diffusers offers three core components:
- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.
- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.
- Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
## Installation
We recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
### PyTorch
With `pip` (official package):
```bash
pip install --upgrade diffusers[torch]
```
With `conda` (maintained by the community):
```sh
conda install -c conda-forge diffusers
```
### Flax
With `pip` (official package):
```bash
pip install --upgrade diffusers[flax]
```
### Apple Silicon (M1/M2) support
Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
## Quickstart
Generating outputs is super easy with 🤗 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 30,000+ checkpoints):
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipeline.to("cuda")
pipeline("An image of a squirrel in Picasso style").images[0]
```
You can also dig into the models and schedulers toolbox to build your own diffusion system:
```python
from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import torch
scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
scheduler.set_timesteps(50)
sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")
input = noise
for t in scheduler.timesteps:
with torch.no_grad():
noisy_residual = model(input, t).sample
prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
input = prev_noisy_sample
image = (input / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = Image.fromarray((image * 255).round().astype("uint8"))
image
```
Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today!
## How to navigate the documentation
| **Documentation** | **What can I learn?** |
|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |
| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |
| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |
| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. |
| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. |
## Contribution
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 ☕.
## Popular Tasks & Pipelines
<table>
<tr>
<th>Task</th>
<th>Pipeline</th>
<th>🤗 Hub</th>
</tr>
<tr style="border-top: 2px solid black">
<td>Unconditional Image Generation</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/ddpm"> DDPM </a></td>
<td><a href="https://huggingface.co/google/ddpm-ema-church-256"> google/ddpm-ema-church-256 </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img">Stable Diffusion Text-to-Image</a></td>
<td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/unclip">unCLIP</a></td>
<td><a href="https://huggingface.co/kakaobrain/karlo-v1-alpha"> kakaobrain/karlo-v1-alpha </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/deepfloyd_if">DeepFloyd IF</a></td>
<td><a href="https://huggingface.co/DeepFloyd/IF-I-XL-v1.0"> DeepFloyd/IF-I-XL-v1.0 </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/kandinsky">Kandinsky</a></td>
<td><a href="https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder"> kandinsky-community/kandinsky-2-2-decoder </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/controlnet">ControlNet</a></td>
<td><a href="https://huggingface.co/lllyasviel/sd-controlnet-canny"> lllyasviel/sd-controlnet-canny </a></td>
</tr>
<tr>
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/pix2pix">InstructPix2Pix</a></td>
<td><a href="https://huggingface.co/timbrooks/instruct-pix2pix"> timbrooks/instruct-pix2pix </a></td>
</tr>
<tr>
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img">Stable Diffusion Image-to-Image</a></td>
<td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-guided Image Inpainting</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Image Variation</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/image_variation">Stable Diffusion Image Variation</a></td>
<td><a href="https://huggingface.co/lambdalabs/sd-image-variations-diffusers"> lambdalabs/sd-image-variations-diffusers </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Super Resolution</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale">Stable Diffusion Upscale</a></td>
<td><a href="https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler"> stabilityai/stable-diffusion-x4-upscaler </a></td>
</tr>
<tr>
<td>Super Resolution</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_upscale">Stable Diffusion Latent Upscale</a></td>
<td><a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler"> stabilityai/sd-x2-latent-upscaler </a></td>
</tr>
</table>
## Popular libraries using 🧨 Diffusers
- https://github.com/microsoft/TaskMatrix
- https://github.com/invoke-ai/InvokeAI
- https://github.com/InstantID/InstantID
- https://github.com/apple/ml-stable-diffusion
- https://github.com/Sanster/lama-cleaner
- https://github.com/IDEA-Research/Grounded-Segment-Anything
- https://github.com/ashawkey/stable-dreamfusion
- https://github.com/deep-floyd/IF
- https://github.com/bentoml/BentoML
- https://github.com/bmaltais/kohya_ss
- +14,000 other amazing GitHub repositories 💪
Thank you for using us ❤️.
## 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.
## Citation
```bibtex
@misc{von-platen-etal-2022-diffusers,
author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Dhruv Nair and Sayak Paul and William Berman and Yiyi Xu and Steven Liu and Thomas Wolf},
title = {Diffusers: State-of-the-art diffusion models},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/diffusers}}
}
```
Raw data
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"requires_python": ">=3.8.0",
"maintainer_email": null,
"keywords": "deep learning diffusion jax pytorch stable diffusion audioldm",
"author": "The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/diffusers/graphs/contributors)",
"author_email": "diffusers@huggingface.co",
"download_url": "https://files.pythonhosted.org/packages/78/5d/156acb741303abbee214926804c5f0d09eacd35d05ad942577e996acdac3/diffusers-0.31.0.tar.gz",
"platform": null,
"description": "<!---\nCopyright 2022 - The HuggingFace Team. All rights reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n-->\n\n<p align=\"center\">\n <br>\n <img src=\"https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/en/imgs/diffusers_library.jpg\" width=\"400\"/>\n <br>\n<p>\n<p align=\"center\">\n <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>\n <a href=\"https://github.com/huggingface/diffusers/releases\"><img alt=\"GitHub release\" src=\"https://img.shields.io/github/release/huggingface/diffusers.svg\"></a>\n <a href=\"https://pepy.tech/project/diffusers\"><img alt=\"GitHub release\" src=\"https://static.pepy.tech/badge/diffusers/month\"></a>\n <a href=\"CODE_OF_CONDUCT.md\"><img alt=\"Contributor Covenant\" src=\"https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg\"></a>\n <a href=\"https://twitter.com/diffuserslib\"><img alt=\"X account\" src=\"https://img.shields.io/twitter/url/https/twitter.com/diffuserslib.svg?style=social&label=Follow%20%40diffuserslib\"></a>\n</p>\n\n\ud83e\udd17 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, \ud83e\udd17 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).\n\n\ud83e\udd17 Diffusers offers three core components:\n\n- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.\n- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.\n- Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.\n\n## Installation\n\nWe recommend installing \ud83e\udd17 Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.\n\n### PyTorch\n\nWith `pip` (official package):\n\n```bash\npip install --upgrade diffusers[torch]\n```\n\nWith `conda` (maintained by the community):\n\n```sh\nconda install -c conda-forge diffusers\n```\n\n### Flax\n\nWith `pip` (official package):\n\n```bash\npip install --upgrade diffusers[flax]\n```\n\n### Apple Silicon (M1/M2) support\n\nPlease refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.\n\n## Quickstart\n\nGenerating outputs is super easy with \ud83e\udd17 Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 30,000+ checkpoints):\n\n```python\nfrom diffusers import DiffusionPipeline\nimport torch\n\npipeline = DiffusionPipeline.from_pretrained(\"stable-diffusion-v1-5/stable-diffusion-v1-5\", torch_dtype=torch.float16)\npipeline.to(\"cuda\")\npipeline(\"An image of a squirrel in Picasso style\").images[0]\n```\n\nYou can also dig into the models and schedulers toolbox to build your own diffusion system:\n\n```python\nfrom diffusers import DDPMScheduler, UNet2DModel\nfrom PIL import Image\nimport torch\n\nscheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cat-256\")\nmodel = UNet2DModel.from_pretrained(\"google/ddpm-cat-256\").to(\"cuda\")\nscheduler.set_timesteps(50)\n\nsample_size = model.config.sample_size\nnoise = torch.randn((1, 3, sample_size, sample_size), device=\"cuda\")\ninput = noise\n\nfor t in scheduler.timesteps:\n with torch.no_grad():\n noisy_residual = model(input, t).sample\n prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample\n input = prev_noisy_sample\n\nimage = (input / 2 + 0.5).clamp(0, 1)\nimage = image.cpu().permute(0, 2, 3, 1).numpy()[0]\nimage = Image.fromarray((image * 255).round().astype(\"uint8\"))\nimage\n```\n\nCheck out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today!\n\n## How to navigate the documentation\n\n| **Documentation** | **What can I learn?** |\n|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |\n| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |\n| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |\n| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. |\n| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. |\n## Contribution\n\nWe \u2764\ufe0f contributions from the open-source community!\nIf you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).\nYou can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.\n- 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\n- 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\n- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)\n\nAlso, say \ud83d\udc4b 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 \u2615.\n\n\n## Popular Tasks & Pipelines\n\n<table>\n <tr>\n <th>Task</th>\n <th>Pipeline</th>\n <th>\ud83e\udd17 Hub</th>\n </tr>\n <tr style=\"border-top: 2px solid black\">\n <td>Unconditional Image Generation</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/ddpm\"> DDPM </a></td>\n <td><a href=\"https://huggingface.co/google/ddpm-ema-church-256\"> google/ddpm-ema-church-256 </a></td>\n </tr>\n <tr style=\"border-top: 2px solid black\">\n <td>Text-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img\">Stable Diffusion Text-to-Image</a></td>\n <td><a href=\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5\"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>\n </tr>\n <tr>\n <td>Text-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/unclip\">unCLIP</a></td>\n <td><a href=\"https://huggingface.co/kakaobrain/karlo-v1-alpha\"> kakaobrain/karlo-v1-alpha </a></td>\n </tr>\n <tr>\n <td>Text-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/deepfloyd_if\">DeepFloyd IF</a></td>\n <td><a href=\"https://huggingface.co/DeepFloyd/IF-I-XL-v1.0\"> DeepFloyd/IF-I-XL-v1.0 </a></td>\n </tr>\n <tr>\n <td>Text-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/kandinsky\">Kandinsky</a></td>\n <td><a href=\"https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder\"> kandinsky-community/kandinsky-2-2-decoder </a></td>\n </tr>\n <tr style=\"border-top: 2px solid black\">\n <td>Text-guided Image-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/controlnet\">ControlNet</a></td>\n <td><a href=\"https://huggingface.co/lllyasviel/sd-controlnet-canny\"> lllyasviel/sd-controlnet-canny </a></td>\n </tr>\n <tr>\n <td>Text-guided Image-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/pix2pix\">InstructPix2Pix</a></td>\n <td><a href=\"https://huggingface.co/timbrooks/instruct-pix2pix\"> timbrooks/instruct-pix2pix </a></td>\n </tr>\n <tr>\n <td>Text-guided Image-to-Image</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img\">Stable Diffusion Image-to-Image</a></td>\n <td><a href=\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5\"> stable-diffusion-v1-5/stable-diffusion-v1-5 </a></td>\n </tr>\n <tr style=\"border-top: 2px solid black\">\n <td>Text-guided Image Inpainting</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint\">Stable Diffusion Inpainting</a></td>\n <td><a href=\"https://huggingface.co/runwayml/stable-diffusion-inpainting\"> runwayml/stable-diffusion-inpainting </a></td>\n </tr>\n <tr style=\"border-top: 2px solid black\">\n <td>Image Variation</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/image_variation\">Stable Diffusion Image Variation</a></td>\n <td><a href=\"https://huggingface.co/lambdalabs/sd-image-variations-diffusers\"> lambdalabs/sd-image-variations-diffusers </a></td>\n </tr>\n <tr style=\"border-top: 2px solid black\">\n <td>Super Resolution</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale\">Stable Diffusion Upscale</a></td>\n <td><a href=\"https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler\"> stabilityai/stable-diffusion-x4-upscaler </a></td>\n </tr>\n <tr>\n <td>Super Resolution</td>\n <td><a href=\"https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_upscale\">Stable Diffusion Latent Upscale</a></td>\n <td><a href=\"https://huggingface.co/stabilityai/sd-x2-latent-upscaler\"> stabilityai/sd-x2-latent-upscaler </a></td>\n </tr>\n</table>\n\n## Popular libraries using \ud83e\udde8 Diffusers\n\n- https://github.com/microsoft/TaskMatrix\n- https://github.com/invoke-ai/InvokeAI\n- https://github.com/InstantID/InstantID\n- https://github.com/apple/ml-stable-diffusion\n- https://github.com/Sanster/lama-cleaner\n- https://github.com/IDEA-Research/Grounded-Segment-Anything\n- https://github.com/ashawkey/stable-dreamfusion\n- https://github.com/deep-floyd/IF\n- https://github.com/bentoml/BentoML\n- https://github.com/bmaltais/kohya_ss\n- +14,000 other amazing GitHub repositories \ud83d\udcaa\n\nThank you for using us \u2764\ufe0f.\n\n## Credits\n\nThis 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:\n\n- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)\n- @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)\n- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim)\n- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)\n\nWe 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.\n\n## Citation\n\n```bibtex\n@misc{von-platen-etal-2022-diffusers,\n author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Dhruv Nair and Sayak Paul and William Berman and Yiyi Xu and Steven Liu and Thomas Wolf},\n title = {Diffusers: State-of-the-art diffusion models},\n year = {2022},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/huggingface/diffusers}}\n}\n```\n",
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