diffusers

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

https://github.com/huggingface/diffusers

Science Score: 54.0%

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CITATION.cff file
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Low similarity (15.5%) to scientific vocabulary

Keywords

deep-learning diffusion flux image-generation image2image image2video latent-diffusion-models pytorch qwen-image score-based-generative-modeling stable-diffusion stable-diffusion-diffusers text2image text2video video2video

Keywords from Contributors

transformers jax pretrained-models audio deepseek gemma qwen vlm glm model-hub

Last synced: 6 months ago · JSON representation ·

Repository

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

Basic Info

Host: GitHub
Owner: huggingface
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://huggingface.co/docs/diffusers
Size: 76 MB

Statistics

Stars: 30,596
Watchers: 220
Forks: 6,284
Open Issues: 798
Releases: 86

Topics

Created over 3 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Code of conduct Citation

README.md

🤗 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, simple over easy, and customizability over abstractions.

🤗 Diffusers offers three core components:

State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code.
Interchangeable noise schedulers for different diffusion speeds and output quality.
Pretrained models 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, 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

Apple Silicon (M1/M2) support

Please refer to the How to use Stable Diffusion in Apple Silicon 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 for 30,000+ checkpoints):

```python from diffusers import DiffusionPipeline import torch

pipeline = DiffusionPipeline.frompretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torchdtype=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.frompretrained("google/ddpm-cat-256") model = UNet2DModel.frompretrained("google/ddpm-cat-256").to("cuda") scheduler.set_timesteps(50)

samplesize = model.config.samplesize noise = torch.randn((1, 3, samplesize, samplesize), device="cuda") input = noise

for t in scheduler.timesteps: with torch.nograd(): noisyresidual = model(input, t).sample prevnoisysample = scheduler.step(noisyresidual, t, input).prevsample input = prevnoisysample

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 to launch your diffusion journey today!

How to navigate the documentation

| Documentation | What can I learn? | |---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Tutorial | 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 | 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 | 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 | Guides for how to optimize your diffusion model to run faster and consume less memory. | | Training | 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. You can look out for issues you'd like to tackle to contribute to the library. - See Good first issues for general opportunities to contribute - See New model/pipeline to contribute exciting new diffusion models / diffusion pipelines - See New scheduler

Also, say 👋 in our public Discord channel . We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕.

Popular Tasks & Pipelines

Task	Pipeline	🤗 Hub
Unconditional Image Generation	DDPM	google/ddpm-ema-church-256
Text-to-Image	Stable Diffusion Text-to-Image	stable-diffusion-v1-5/stable-diffusion-v1-5
Text-to-Image	unCLIP	kakaobrain/karlo-v1-alpha
Text-to-Image	DeepFloyd IF	DeepFloyd/IF-I-XL-v1.0
Text-to-Image	Kandinsky	kandinsky-community/kandinsky-2-2-decoder
Text-guided Image-to-Image	ControlNet	lllyasviel/sd-controlnet-canny
Text-guided Image-to-Image	InstructPix2Pix	timbrooks/instruct-pix2pix
Text-guided Image-to-Image	Stable Diffusion Image-to-Image	stable-diffusion-v1-5/stable-diffusion-v1-5
Text-guided Image Inpainting	Stable Diffusion Inpainting	runwayml/stable-diffusion-inpainting
Image Variation	Stable Diffusion Image Variation	lambdalabs/sd-image-variations-diffusers
Super Resolution	Stable Diffusion Upscale	stabilityai/stable-diffusion-x4-upscaler
Super Resolution	Stable Diffusion Latent Upscale	stabilityai/sd-x2-latent-upscaler

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
@hojonathanho original DDPM implementation, available here as well as the extremely useful translation into PyTorch by @pesser, available here
@ermongroup's DDIM implementation, available here
@yang-song's Score-VE and Score-VP implementations, available here

We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available here 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}} }

Owner

Name: Hugging Face
Login: huggingface
Kind: organization
Location: NYC + Paris

Website: https://huggingface.co/
Twitter: huggingface
Repositories: 344
Profile: https://github.com/huggingface

The AI community building the future.

Citation (CITATION.cff)

cff-version: 1.2.0
title: 'Diffusers: State-of-the-art diffusion models'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Patrick
    family-names: von Platen
  - given-names: Suraj
    family-names: Patil
  - given-names: Anton
    family-names: Lozhkov
  - given-names: Pedro
    family-names: Cuenca
  - given-names: Nathan
    family-names: Lambert
  - given-names: Kashif
    family-names: Rasul
  - given-names: Mishig
    family-names: Davaadorj
  - given-names: Dhruv
    family-names: Nair
  - given-names: Sayak
    family-names: Paul
  - given-names: Steven
    family-names: Liu
  - given-names: William
    family-names: Berman
  - given-names: Yiyi
    family-names: Xu
  - given-names: Thomas
    family-names: Wolf
repository-code: 'https://github.com/huggingface/diffusers'
abstract: >-
  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.
keywords:
  - deep-learning
  - pytorch
  - image-generation
  - hacktoberfest
  - diffusion
  - text2image
  - image2image
  - score-based-generative-modeling
  - stable-diffusion
  - stable-diffusion-diffusers
license: Apache-2.0
version: 0.12.1

Committers

Last synced: 9 months ago

All Time

Total Commits: 5,334
Total Committers: 922
Avg Commits per committer: 5.785
Development Distribution Score (DDS): 0.826

Past Year

Commits: 1,377
Committers: 316
Avg Commits per committer: 4.358
Development Distribution Score (DDS): 0.794

Top Committers

Name	Email	Commits
Patrick von Platen	p**n@g**m	928
Sayak Paul	s**l@g**m	647
Suraj Patil	s**5@g**m	276
Dhruv Nair	d**r@g**m	259
Anton Lozhkov	a**n@h**o	211
YiYi Xu	y**0@g**m	188
Aryan	a**n@h**o	154
Pedro Cuenca	p**o@h**o	151
Steven Liu	5****u	149
Will Berman	w**n@g**m	123
hlky	h**y@h**c	111
M. Tolga Cangöz	4****I	76
Linoy Tsaban	5****n	55
Kashif Rasul	k**l@g**m	50
dg845	5****5	30
Álvaro Somoza	a****a	29
Yao Matrix	m**o@i**m	27
apolinário	j**s@g**m	26
Nathan Lambert	n**n@h**o	26
Lucain	l**p@g**m	24
Younes Belkada	4****a	23
Tolga Cangöz	4****z	23
Haofan Wang	h**i@g**m	21
Vinh H. Pham	p**7@g**m	20
SahilCarterr	1****r	19
Parag Ekbote	t**9@g**m	18
Junsong Chen	c**7@i**m	18
Bagheera	5****a	18
Mishig Davaadorj	m**j@c**u	17
Beinsezii	3****i	15
and 892 more...

Committer Domains (Top 20 + Academic)

qq.com: 24 intel.com: 13 huggingface.co: 8 163.com: 7 nyu.edu: 5 google.com: 4 canva.com: 4 yandex.ru: 4 microsoft.com: 3 naver.com: 3 126.com: 2 gatech.edu: 2 berkeley.edu: 2 foxmail.com: 2 bk.ru: 2 gmx.de: 2 zju.edu.cn: 2 usc.edu: 2 metaloft.com: 1 resium.com: 1 weizmann.ac.il: 1 pilani.bits-pilani.ac.in: 1 post.bgu.ac.il: 1 hit.edu.cn: 1 g.rit.edu: 1 mail.ustc.edu.cn: 1 anu.edu.au: 1 mails.tsinghua.edu.cn: 1 coloradocollege.edu: 1 cs.tu-darmstadt.de: 1 stanford.edu: 1 duke.edu: 1 cmu.edu: 1 stu.xmu.edu.cn: 1 seas.harvard.edu: 1 ntu.edu.tw: 1 uibk.ac.at: 1 mail.nwpu.edu.cn: 1 pcampus.edu.np: 1 lamda.nju.edu.cn: 1 imperial.ac.uk: 1 mails.ucas.edu.cn: 1 alum.mit.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 3,279
Total pull requests: 5,867
Average time to close issues: about 2 months
Average time to close pull requests: 13 days
Total issue authors: 1,720
Total pull request authors: 894
Average comments per issue: 4.34
Average comments per pull request: 3.74
Merged pull requests: 3,838
Bot issues: 0
Bot pull requests: 26

Past Year

Issues: 941
Pull requests: 2,516
Average time to close issues: 13 days
Average time to close pull requests: 8 days
Issue authors: 563
Pull request authors: 364
Average comments per issue: 2.38
Average comments per pull request: 3.14
Merged pull requests: 1,573
Bot issues: 0
Bot pull requests: 18

View more stats

Top Authors

Issue Authors

ghost (149)
sayakpaul (93)
vladmandic (53)
nitinmukesh (48)
tin2tin (41)
patrickvonplaten (37)
bghira (32)
yiyixuxu (29)
alexblattner (28)
a-r-r-o-w (26)
clarencechen (18)
christopher5106 (18)
apolinario (18)
AmericanPresidentJimmyCarter (16)
SDD445 (15)

Pull Request Authors

sayakpaul (1,066)
DN6 (419)
a-r-r-o-w (399)
yiyixuxu (282)
hlky (229)
stevhliu (181)
patrickvonplaten (111)
linoytsaban (102)
standardAI (74)
tolgacangoz (65)
yao-matrix (62)
asomoza (57)
dg845 (52)
lawrence-cj (48)
bghira (37)

Top Labels

Issue Labels

bug (1,643) stale (850) contributions-welcome (107) Good second issue (47) good first issue (43) help wanted (39) New pipeline/model (34) community-examples (32) wip (28) hacktoberfest (17) enhancement (15) scheduler (15) training (13) lora (8) needs-code-example (7) single_file (7) roadmap (7) question (6) advanced (6) SD.Next (5) quantization (4) New scheduler (4) IPAdapter (4) peft (3) consider-for-modular-diffusers (3) inpainting (3) Good Example PR (2) performance (2) should-move-to-discussion (2) close-to-merge (1)

Pull Request Labels

stale (364) close-to-merge (145) roadmap (130) refactor (42) dependencies (26) torch.compile (25) scheduler (19) performance (19) wip (18) Good Example PR (17) lora (17) PAG (16) python (14) training (13) quantization (13) bug (10) contributions-welcome (7) video (5) consider-for-modular-diffusers (4) single_file (4) help wanted (3) ONNX (3) IPAdapter (2) documentation (2) peft (2) need-test (2) advanced (2) New pipeline/model (1) observability (1) jax/flax (1)

Packages

Total packages: 8
Total downloads:
- pypi 4,245,540 last-month
Total docker downloads: 312,393

Total dependent packages: 189
(may contain duplicates)
Total dependent repositories: 5,177
(may contain duplicates)
Total versions: 213
Total maintainers: 6

pypi.org: diffusers

State-of-the-art diffusion in PyTorch and JAX.

Homepage: https://github.com/huggingface/diffusers
Documentation: https://diffusers.readthedocs.io/
License: Apache 2.0 License
Latest release: 0.35.1
published 6 months ago

Versions: 93
Dependent Packages: 187
Dependent Repositories: 5,162
Downloads: 4,245,498 Last month
Docker Downloads: 312,393

Rankings

Forks count: 0.1%

Stargazers count: 0.1%

Dependent repos count: 0.1%

Dependent packages count: 0.2%

Downloads: 0.2%

Average: 0.4%

Docker downloads count: 1.4%

Maintainers (4)

lysandre spsayakpaul yiyixu DN6

Last synced: 6 months ago

proxy.golang.org: github.com/huggingface/diffusers

Documentation: https://pkg.go.dev/github.com/huggingface/diffusers#section-documentation
License: apache-2.0
Latest release: v0.35.1
published 6 months ago

Versions: 87
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Forks count: 0.1%

Stargazers count: 0.5%

Average: 3.7%

Dependent repos count: 4.7%

Dependent packages count: 9.6%

Last synced: 6 months ago

pypi.org: diffusers-qaic

State-of-the-art diffusion in PyTorch and JAX.

Homepage: https://github.com/huggingface/diffusers
Documentation: https://diffusers-qaic.readthedocs.io/
License: Apache
Latest release: 0.24.0
published about 1 year ago

Versions: 2
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 14 Last month

Rankings

Forks count: 0.2%

Stargazers count: 0.2%

Dependent packages count: 9.9%

Average: 16.6%

Dependent repos count: 55.9%

Maintainers (1)

xihajun

Last synced: 6 months ago

conda-forge.org: diffusers

:hugs: 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. PyPI: [https://pypi.org/project/diffusers/](https://pypi.org/project/diffusers/)

Homepage: https://github.com/huggingface/diffusers
License: Apache-2.0
Latest release: 0.7.2
published over 3 years ago

Versions: 13
Dependent Packages: 0
Dependent Repositories: 7

Rankings

Forks count: 2.2%

Stargazers count: 2.4%

Dependent repos count: 12.9%

Average: 17.3%

Dependent packages count: 51.6%

Last synced: 6 months ago

pypi.org: soujpg-diffusers

State-of-the-art diffusion in PyTorch and JAX.

Homepage: https://github.com/huggingface/diffusers
Documentation: https://soujpg-diffusers.readthedocs.io/
License: Apache 2.0 License
Latest release: 0.27.2
published almost 2 years ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 28 Last month

Rankings

Forks count: 0.1%

Stargazers count: 0.2%

Dependent packages count: 10.0%

Average: 19.0%

Dependent repos count: 65.8%

Maintainers (1)

jasstionzyf

Last synced: 6 months ago

anaconda.org: diffusers-torch

Homepage: https://github.com/huggingface/diffusers
License: Apache-2.0
Latest release: 0.32.2
published 12 months ago

Versions: 4
Dependent Packages: 1
Dependent Repositories: 0

Rankings

Stargazers count: 8.3%

Forks count: 8.6%

Dependent packages count: 20.5%

Average: 23.7%

Dependent repos count: 57.7%

Last synced: 6 months ago

anaconda.org: diffusers-base

Homepage: https://github.com/huggingface/diffusers
License: Apache-2.0
Latest release: 0.32.2
published 12 months ago

Versions: 4
Dependent Packages: 1
Dependent Repositories: 0

Rankings

Stargazers count: 8.3%

Forks count: 8.6%

Dependent packages count: 20.5%

Average: 23.7%

Dependent repos count: 57.7%

Last synced: 6 months ago

anaconda.org: diffusers

Homepage: https://github.com/huggingface/diffusers
License: Apache-2.0
Latest release: 0.32.2
published 12 months ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 7

Rankings

Forks count: 6.3%

Stargazers count: 6.7%

Average: 26.2%

Dependent repos count: 40.7%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

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examples/controlnet/requirements.txt pypi

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datasets *
ftfy *
modelcards *
tensorboard *
torchvision *
transformers >=4.25.1

examples/research_projects/onnxruntime/textual_inversion/requirements.txt pypi

accelerate >=0.16.0
ftfy *
modelcards *
tensorboard *
torchvision *
transformers >=4.25.1

examples/research_projects/onnxruntime/unconditional_image_generation/requirements.txt pypi

accelerate >=0.16.0
datasets *
tensorboard *
torchvision *

examples/t2i_adapter/requirements.txt pypi

accelerate >=0.16.0
datasets *
ftfy *
safetensors *
tensorboard *
torchvision *
transformers >=4.25.1
wandb *

examples/text_to_image/requirements.txt pypi

Jinja2 *
accelerate >=0.16.0
datasets *
ftfy *
tensorboard *
torchvision *
transformers >=4.25.1

examples/text_to_image/requirements_flax.txt pypi

Jinja2 *
datasets *
flax *
ftfy *
optax *
tensorboard *
torch *
torchvision *
transformers >=4.25.1

examples/text_to_image/requirements_sdxl.txt pypi

Jinja2 *
accelerate >=0.22.0
ftfy *
tensorboard *
torchvision *
transformers >=4.25.1

examples/textual_inversion/requirements.txt pypi

Jinja2 *
accelerate >=0.16.0
ftfy *
tensorboard *
torchvision *
transformers >=4.25.1

examples/textual_inversion/requirements_flax.txt pypi

Jinja2 *
flax *
ftfy *
optax *
tensorboard *
torch *
torchvision *
transformers >=4.25.1

examples/unconditional_image_generation/requirements.txt pypi

accelerate >=0.16.0
datasets *
torchvision *

pyproject.toml pypi

setup.py pypi

deps *