Installation

🤗 Diffusers uses the pyproject.toml file for building and packaging, as introduced in PEP 517. View this configuration file file for more details on the specific build configuration of this project.

🤗 Diffusers is tested on Python 3.8+, MindSpore 2.3.1. Follow the installation instructions below for the deep learning library you are using:

• MindSpore installation instructions

Install with pip

You should install 🤗 Diffusers in a virtual environment. If you're unfamiliar with Python virtual environments, take a look at this guide. A virtual environment makes it easier to manage different projects and avoid compatibility issues between dependencies.

Start by creating a virtual environment in your project directory:

python -m venv .env

Activate the virtual environment:

source .env/bin/activate

You should also install 🤗 Transformers because 🤗 Diffusers relies on its models:

pip install mindone transformers

Tip

We define both required and optional dependencies in the pyproject.toml file. If you need to install optional dependencies, you can specify them. For example, if you want to install the 'training' optional dependency, you would run:

pip install mindone[training]

Install from source

Before installing 🤗 Diffusers from source, make sure you have MindSpore installed.

Then install 🤗 Diffusers from source:

pip install git+https://github.com/mindspore-lab/mindone

This command installs the bleeding edge main version rather than the latest stable version. The main version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the main version may not always be stable. We strive to keep the main version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an Issue so we can fix it even sooner!

Editable install

You will need an editable install if you'd like to:

• Use the main version of the source code.
• Contribute to 🤗 Diffusers and need to test changes in the code.

Clone the repository and install 🤗 Diffusers with the following commands:

git clone https://github.com/mindspore-lab/mindone.git
cd mindone

Warning

⚠️ PEP 660 – Editable installs for pyproject.toml based builds defines how to build projects that only use pyproject.toml. Build tools must implement PEP 660 for editable installs to work. You need a front-end (such as pip ≥ 21.3) and a backend. The statuses of some popular backends are:

• Setuptools implements PEP 660 as of version 64.
• Flit implements PEP 660 as of version 3.4.
• Poetry implements PEP 660 as of version 1.0.8.

(from: https://stackoverflow.com/a/69711730)

pip install -e .

These commands will link the folder you cloned the repository to and your Python library paths. Python will now look inside the folder you cloned to in addition to the normal library paths. For example, if your Python packages are typically installed in ~/anaconda3/envs/main/lib/python3.8/site-packages/, Python will also search the ~/mindone/ folder you cloned to.

Warning

You must keep the mindone folder if you want to keep using the library.

Now you can easily update your clone to the latest version of 🤗 Diffusers with the following command:

cd ~/mindone/
git pull

Your Python environment will find the main version of 🤗 Diffusers on the next run.

Cache

Model weights and files are downloaded from the Hub to a cache which is usually your home directory. You can change the cache location by specifying the HF_HOME or HUGGINFACE_HUB_CACHE environment variables or configuring the cache_dir parameter in methods like from_pretrained.

Cached files allow you to run 🤗 Diffusers offline. To prevent 🤗 Diffusers from connecting to the internet, set the HF_HUB_OFFLINE environment variable to True and 🤗 Diffusers will only load previously downloaded files in the cache.

export HF_HUB_OFFLINE=True

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

Telemetry logging

Our library gathers telemetry information during from_pretrained requests. The data gathered includes the version of 🤗 Diffusers and PyTorch/Flax, the requested model or pipeline class, and the path to a pretrained checkpoint if it is hosted on the Hugging Face 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.

We understand that not everyone wants to share additional information,and we respect your privacy. You can disable telemetry collection by setting the DISABLE_TELEMETRY environment variable from your terminal:

On Linux/MacOS:

export DISABLE_TELEMETRY=YES

On Windows:

set DISABLE_TELEMETRY=YES