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Installation

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Installation

Diffusers is tested on Python 3.8+ and PyTorch 1.4+. Install PyTorch according to your system and setup.

Create a virtual environment for easier management of separate projects and to avoid compatibility issues between dependencies. Use uv, a Rust-based Python package and project manager, to create a virtual environment and install Diffusers.

bash
uv venv my-env
source my-env/bin/activate

Install Diffusers with one of the following methods.

<hfoptions id="install"> <hfoption id="pip">

PyTorch only supports Python 3.8 - 3.11 on Windows.

bash
uv pip install diffusers["torch"] transformers
</hfoption> <hfoption id="conda">
bash
conda install -c conda-forge diffusers
</hfoption> <hfoption id="source">

A source install installs the main version instead of the latest stable version. The main version is useful for staying updated with the latest changes but it may not always be stable. If you run into a problem, open an Issue and we will try to resolve it as soon as possible.

Make sure Accelerate is installed.

bash
uv pip install accelerate

Install Diffusers from source with the command below.

bash
uv pip install git+https://github.com/huggingface/diffusers
</hfoption> </hfoptions>

Editable install

An editable install is recommended for development workflows or if you're using the main version of the source code. A special link is created between the cloned repository and the Python library paths. This avoids reinstalling a package after every change.

Clone the repository and install Diffusers with the following commands.

bash
git clone https://github.com/huggingface/diffusers.git
cd diffusers
uv pip install -e ".[torch]"

[!WARNING] You must keep the diffusers folder if you want to keep using the library with the editable install.

Update your cloned repository to the latest version of Diffusers with the command below.

bash
cd ~/diffusers/
git pull

Cache

Model weights and files are downloaded from the Hub to a cache, which is usually your home directory. Change the cache location with the HF_HOME or HF_HUB_CACHE environment variables or configuring the cache_dir parameter in methods like [~DiffusionPipeline.from_pretrained].

<hfoptions id="cache"> <hfoption id="env variable">
bash
export HF_HOME="/path/to/your/cache"
export HF_HUB_CACHE="/path/to/your/hub/cache"
</hfoption> <hfoption id="from_pretrained">
py
from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    cache_dir="/path/to/your/cache"
)
</hfoption> </hfoptions>

Cached files allow you to use Diffusers offline. Set the HF_HUB_OFFLINE environment variable to 1 to prevent Diffusers from connecting to the internet.

shell
export HF_HUB_OFFLINE=1

For more details about managing and cleaning the cache, take a look at the Understand caching guide.

Telemetry logging

Diffusers gathers telemetry information during [~DiffusionPipeline.from_pretrained] requests. The data gathered includes the Diffusers and PyTorch version, the requested model or pipeline class, and the path to a pretrained checkpoint if it is hosted on the Hub.

This usage data helps us debug issues and prioritize new features. Telemetry is only sent when loading models and pipelines from the Hub, and it is not collected if you're loading local files.

Opt-out and disable telemetry collection with the HF_HUB_DISABLE_TELEMETRY environment variable.

<hfoptions id="telemetry"> <hfoption id="Linux/macOS">
bash
export HF_HUB_DISABLE_TELEMETRY=1
</hfoption> <hfoption id="Windows">
bash
set HF_HUB_DISABLE_TELEMETRY=1
</hfoption> </hfoptions>