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:
requirements.txt
file. Example scripts shall not depend on any local files. This means that one can simply download an example script, e.g. train_unconditional.py, install the required dependencies, e.g. requirements.txt and execute the example script.diffusers
library. We often purposefully leave out certain state-of-the-art methods if we consider them too complex for beginners.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 | π€ Accelerate | π€ Datasets | Colab |
---|---|---|---|
Unconditional Image Generation | β | β | |
Text-to-Image fine-tuning | β | β | |
Textual Inversion | β | - | |
Dreambooth | β | - | |
Training with LoRA | β | - | - |
ControlNet | β | β | - |
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
πͺ.
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