| Installation 🤗 Diffusers is tested on Python 3.8+, PyTorch 1.7.0+, and Flax. Follow the installation instructions below for the deep learning library you are using: PyTorch installation instructions Flax 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: Copied python -m venv .env Activate the virtual environment: Copied source .env/bin/activate You should also install 🤗 Transformers because 🤗 Diffusers relies on its models: Pytorch Hide Pytorch content Note - PyTorch only supports Python 3.8 - 3.11 on Windows. Copied pip install diffusers["torch"] transformers JAX Hide JAX content Copied pip install diffusers["flax"] transformers Install with conda After activating your virtual environment, with conda (maintained by the community): Copied conda install -c conda-forge diffusers Install from source Before installing 🤗 Diffusers from source, make sure you have PyTorch and 🤗 Accelerate installed. To install 🤗 Accelerate: Copied pip install accelerate Then install 🤗 Diffusers from source: Copied pip install git+https://github.com/huggingface/diffusers 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: Copied git clone https://github.com/huggingface/diffusers.git | |
| cd diffusers Pytorch Hide Pytorch content Copied pip install -e ".[torch]" JAX Hide JAX content Copied pip install -e ".[flax]" 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 ~/diffusers/ folder you cloned to. You must keep the diffusers 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: Copied cd ~/diffusers/ | |
| 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. Copied 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: Copied export DISABLE_TELEMETRY=YES On Windows: Copied set DISABLE_TELEMETRY=YES | |