diffusers is by huggingface — not by us.
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Indexed Sep 19, 2026 · 34,547 stars at index time. Maintainers: claiming verifies your identity and unlocks a higher assurance tier. Removal requests are honored.
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diffusers
unclaimed listingactively maintainedFreeApache-2.0
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
by Open Source Community · New publisher
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huggingface/diffusers is an open-source project by huggingface: 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.. Indexed here so it can be found — not resold.
It is free. Get it from the upstream repository: https://github.com/huggingface/diffusers
From the project's own README (excerpt, reproduced for discovery under its Apache-2.0 license):
🤗 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):
With conda (maintained by the community):
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 frompretrained method to load any pretrained diffusion model (browse the Hub for 30,000+ checkpoints):
You can also dig into the models and schedulers toolbox to build your own diffusion system:
Check out the Quickstart to launch your diffusion journey today!
How to navigate the documentation
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01Capabilities
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02Requirements & stack
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03Community
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04Trust Passport
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05Versions
Full history →| Version | Channel | Released | Notes |
|---|---|---|---|
| 0.0.0 | stable | Sep 19, 2026 | Indexed listing — see the upstream repository for real release history. |