# huggingface/diffusers

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

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/huggingface/diffusers
- **GitHub Source**: https://github.com/huggingface/diffusers
- **Global Rank**: #420 on GitHubRepo
- **Stars**: 34,753
- **Forks**: 7,403
- **Open Issues**: 1,506
- **Primary Language**: Python
- **Software License**: Apache-2.0
- **Last Commit / Push**: Oct 18, 2026
- **Topics**: 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

## Project Overview
diffusers is an open-source project written in Python: 🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.

Engineered for speed, consistency, and developer ease, it solves common hurdles in deep-learning, diffusion, flux. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- 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 & Quick Start
### Linux
```bash
pip install --upgrade diffusers[torch]
```

### macOS
```bash
pip install diffusers # or: brew install diffusers
```

### Windows
```bash
pip install diffusers
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Python >= 3.9, pip, virtualenv
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
diffusers coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### Production System Integration
Embed diffusers into Python backend services to handle core application logic.

### CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.

### Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.

### Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.

## Target Audience
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 34,753 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Python for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with Python and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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