🧨 Diffusers Training Examples

Diffusers training examples are a collection of scripts to demonstrate how to effectively use the diffusers library for a variety of use cases.

Note: If you are looking for official examples on how to use diffusers for inference, please have a look at src/diffusers/pipelines

Our examples aspire to be self-contained, easy-to-tweak, beginner-friendly and for one-purpose-only. More specifically, this means:

We provide official examples that cover the most popular tasks of diffusion models. Official examples are actively maintained by the diffusers maintainers and we try to rigorously follow our example philosophy as defined above. If you feel like another important example should exist, we are more than happy to welcome a Feature Request or directly a Pull Request from you!

Training examples show how to pretrain or fine-tune diffusion models for a variety of tasks. Currently we support:

If possible, please install xFormers for memory efficient attention. This could help make your training faster and less memory intensive.

Task πŸ€— Accelerate πŸ€— Datasets Colab
Unconditional Image Generation βœ… βœ… Open In Colab
Text-to-Image fine-tuning βœ… βœ…
Textual Inversion βœ… - Open In Colab
Dreambooth βœ… - Open In Colab
Training with LoRA βœ… - -
ControlNet βœ… βœ… -

Community

In addition, we provide community examples, which are examples added and maintained by our community. Community examples can consist of both training examples or inference pipelines. For such examples, we are more lenient regarding the philosophy defined above and also cannot guarantee to provide maintenance for every issue. Examples that are useful for the community, but are either not yet deemed popular or not yet following our above philosophy should go into the community examples folder. The community folder therefore includes training examples and inference pipelines. Note: Community examples can be a great first contribution to show to the community how you like to use diffusers πŸͺ„.

Important note

To make sure you can successfully run the latest versions of the example scripts, you have to install the library from source and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .

Then cd in the example folder of your choice and run

pip install -r requirements.txt